diff --git a/.github/workflows/node.js.yml b/.github/workflows/node.js.yml index ca29e7cf..4df5ff65 100644 --- a/.github/workflows/node.js.yml +++ b/.github/workflows/node.js.yml @@ -5,9 +5,9 @@ name: Node.js CI on: push: - branches: [ master ] + branches: [ master, dev, danfo/typescript ] pull_request: - branches: [ master ] + branches: [ master, dev ] jobs: test: @@ -16,7 +16,7 @@ jobs: strategy: matrix: - node-version: [10.x, 12.x, 14.x] + node-version: [12.x, 14.x] steps: - uses: actions/checkout@v2 @@ -24,7 +24,4 @@ jobs: uses: actions/setup-node@v1 with: node-version: ${{ matrix.node-version }} - - run: cd danfojs-node && yarn - - run: cd danfojs-node && yarn test - - run: cd danfojs-browser && yarn - - run: cd danfojs-browser && yarn test + - run: yarn test diff --git a/.gitignore b/.gitignore index 211887f2..1313f538 100644 --- a/.gitignore +++ b/.gitignore @@ -1,2 +1,5 @@ .editorconfig -node_modules \ No newline at end of file +node_modules +test/fixtures/* +test/samples/* +*.xlsx diff --git a/README.md b/README.md index 656a905d..8f880afe 100644 --- a/README.md +++ b/README.md @@ -47,145 +47,168 @@ easy and intuitive. It is heavily inspired by [Pandas](https://pandas.pydata.org - Robust data preprocessing functions like [OneHotEncoders](https://danfo.jsdata.org/api-reference/general-functions/danfo.onehotencoder), [LabelEncoders](https://danfo.jsdata.org/api-reference/general-functions/danfo.labelencoder), and scalers like [StandardScaler](https://danfo.jsdata.org/api-reference/general-functions/danfo.standardscaler) and [MinMaxScaler](https://danfo.jsdata.org/api-reference/general-functions/danfo.minmaxscaler) are supported on DataFrame and Series +## Installation +There are three ways to install and use Danfo.js in your application +* For Nodejs applications, you can install the [__danfojs-node__]() version via package managers like yarn and/or npm: -To use Danfo.js via script tags, copy and paste the CDN below to the body of your HTML file +```bash +npm install danfojs-node + +or + +yarn add danfojs-node +``` +For client-side applications built with frameworks like React, Vue, Next.js, etc, you can install the [__danfojs__]() version: + +```bash +npm install danfojs + +or + +yarn add danfojs +``` + +For use directly in HTML files, you can add the latest script tag from [JsDelivr](https://www.jsdelivr.com/package/npm/danfojs) to your HTML file: + ```html - + ``` See all available versions [here](https://www.jsdelivr.com/package/npm/danfojs) ### Example Usage in the Browser -> See the example below in [Code Sandbox](https://codepen.io/risingodegua/pen/bGwPGMG) +> Run in [Code Sandbox](https://codepen.io/risingodegua/pen/bGwPGMG) ```html - - - - - + + + + Document - - - + +
- - - + + ``` Output in Browser: ![](assets/browser-out.gif) -## How to install -Danfo.js is hosted on NPM, and can installed via package managers like npm and yarn - -```sh -npm install danfojs-node -``` - ### Example usage in Nodejs ```javascript const dfd = require("danfojs-node") +const file_url = "https://web.stanford.edu/class/archive/cs/cs109/cs109.1166/stuff/titanic.csv" +dfd.readCSV(file_url) + .then(df => { + //prints the first five columns + df.head().print() + + // Calculate descriptive statistics for all numerical columns + df.describe().print() -dfd.read_csv("https://web.stanford.edu/class/archive/cs/cs109/cs109.1166/stuff/titanic.csv") - .then(df => { - //prints the first five columns - df.head().print() + //prints the shape of the data + console.log(df.shape); - //Calculate descriptive statistics for all numerical columns - df.describe().print() + //prints all column names + console.log(df.columns); - //prints the shape of the data - console.log(df.shape); + // //prints the inferred dtypes of each column + df.ctypes.print() - //prints all column names - console.log(df.column_names); + //selecting a column by subsetting + df['Name'].print() - //prints the inferred dtypes of each column - df.ctypes.print() + //drop columns by names + cols_2_remove = ['Age', 'Pclass'] + df_drop = df.drop({ columns: cols_2_remove, axis: 1 }) + df_drop.print() - //selecting a column by subsetting - df['Name'].print() - //drop columns by names - cols_2_remove = ['Age', 'Pclass'] - df_drop = df.drop({ columns: cols_2_remove, axis: 1 }) - df_drop.print() + //select columns by dtypes + let str_cols = df_drop.selectDtypes(["string"]) + let num_cols = df_drop.selectDtypes(["int32", "float32"]) + str_cols.print() + num_cols.print() - //select columns by dtypes - let str_cols = df_drop.select_dtypes(["string"]) - let num_cols = df_drop.select_dtypes(["int32", "float32"]) - str_cols.print() - num_cols.print() + //add new column to Dataframe + let new_vals = df['Fare'].round(1) + df_drop.addColumn("fare_round", new_vals, { inplace: true }) + df_drop.print() - //add new column to Dataframe - let new_vals = df['Fare'].round().values - df_drop.addColumn({ column: "fare_round", value: new_vals}) - df_drop.print() + df_drop['fare_round'].round(2).print(5) - df_drop['fare_round'].print(5) + //prints the number of occurence each value in the column + df_drop['Survived'].valueCounts().print() - //prints the number of occurence each value in the column - df_drop['Survived'].value_counts().print() + //print the last ten elementa of a DataFrame + df_drop.tail(10).print() - //print the last ten elementa of a DataFrame - df_drop.tail(10).print() + //prints the number of missing values in a DataFrame + df_drop.isNa().sum().print() - //prints the number of missing values in a DataFrame - df_drop.isna().sum().print() + }).catch(err => { + console.log(err); + }) - }).catch(err => { - console.log(err); - }) ``` Output in Node Console: ![](assets/node-rec.gif) - -> If you want to use Danfo in frontend frameworks like React/Vue, read this [guide](https://danfo.jsdata.org/examples/using-danfojs-in-react) - -#### You can play with Danfo.js on Dnotebooks playground [here](https://playnotebook.jsdata.org/demo) +## Notebook support +* You can use Danfo.js on Dnotebooks playground [here](https://playnotebook.jsdata.org/demo) +* VsCode nodejs notebook extension now supports Danfo.js. See guide [here](https://marketplace.visualstudio.com/items?itemName=donjayamanne.typescript-notebook) #### [See the Official Getting Started Guide](https://danfo.jsdata.org/getting-started) ## Documentation The official documentation can be found [here](https://danfo.jsdata.org) +## Danfo.js Official Book + +![image](https://user-images.githubusercontent.com/29900845/134811659-25ff6b05-8e0d-415f-a60c-03ab1d33fd71.jpeg) + + +We recently published a book titled "Building Data Driven Applications with Danfo.js". Read more about it [here](https://danfo.jsdata.org/building-data-driven-applications-with-danfo.js-book) + ## Discussion and Development Development discussions take place [here](https://github.com/opensource9ja/danfojs/discussions). @@ -196,4 +219,4 @@ All contributions, bug reports, bug fixes, documentation improvements, enhanceme #### Created by [Rising Odegua](https://github.com/risenW) and [Stephen Oni](https://github.com/steveoni) -Danfo.js - Open Source JavaScript library for manipulating data. | Product Hunt Embed +Danfo.js - Open Source JavaScript library for manipulating data. | Product Hunt Embed \ No newline at end of file diff --git a/danfojs-browser/lib/bundle.js b/danfojs-browser/lib/bundle.js deleted file mode 100644 index 2daa2df0..00000000 --- a/danfojs-browser/lib/bundle.js +++ /dev/null @@ -1,3 +0,0 @@ -/*! 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this.scopedRun((()=>this.state.kernelDepth++),(()=>this.state.kernelDepth--),(()=>{this.ENV.getBool("DEBUG")||this.state.profiling?(f=this.profiler.profileKernel(l,u,(()=>i())),this.ENV.getBool("DEBUG")&&this.profiler.logKernelProfile(f),t=f.outputs):t=i()})),r&&this.addTapeNode(l,u,t,h,n,c),this.state.profiling&&this.state.activeProfile.kernels.push({name:l,bytesAdded:this.state.numBytes-a,totalBytesSnapshot:this.state.numBytes,tensorsAdded:this.state.numTensors-s,totalTensorsSnapshot:this.state.numTensors,inputShapes:Object.keys(u).map((e=>null!=u[e]?u[e].shape:null)),outputShapes:t.map((e=>e.shape)),kernelTimeMs:f.timeMs,extraInfo:f.extraInfo}),Array.isArray(o)?t:t[0]}saveTensorsForBackwardMode(e){return e.map((e=>this.keep(this.clone(e))))}getTensorsForGradient(e,t,n){const r=qr(e);if(null!=r){const e=r.inputsToSave||[],a=r.outputsToSave||[];let s;r.saveAllInputs?(Q(Array.isArray(t),(()=>"saveAllInputs is true, expected inputs to be an 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this.state.registeredVariables[a.name]=a,this.incRef(a,this.backend),a}trackTensor(e,t){this.state.numTensors++,"string"===e.dtype&&this.state.numStringTensors++;let n=0;"complex64"!==e.dtype&&"string"!==e.dtype&&(n=e.size*_e(e.dtype)),this.state.numBytes+=n,this.state.tensorInfo.has(e.dataId)||(this.state.numDataBuffers++,this.state.tensorInfo.set(e.dataId,{backend:t||this.backend,dtype:e.dtype,shape:e.shape,bytes:n})),e instanceof ka||this.track(e)}incRef(e,t){this.trackTensor(e,t),this.backend.incRef(e.dataId)}removeDataId(e,t){this.state.tensorInfo.has(e)&&this.state.tensorInfo.get(e).backend===t&&(this.state.tensorInfo.delete(e),this.state.numDataBuffers--)}disposeTensor(e){if(!this.state.tensorInfo.has(e.dataId))return;const t=this.state.tensorInfo.get(e.dataId);if(this.state.numTensors--,"string"===e.dtype&&(this.state.numStringTensors--,this.state.numBytes-=t.bytes),"complex64"!==e.dtype&&"string"!==e.dtype){const 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e=1024;e<2048;e++)t[e]=939524096+(e-1024<<13);return t}(),t=function(){const e=new Uint32Array(64);e[0]=0,e[31]=1199570944,e[32]=2147483648,e[63]=3347054592;for(let t=1;t<31;t++)e[t]=t<<23;for(let t=33;t<63;t++)e[t]=2147483648+(t-32<<23);return e}(),n=function(){const e=new Uint32Array(64);for(let t=0;t<64;t++)e[t]=1024;return e[0]=e[32]=0,e}();return r=>{const a=new ArrayBuffer(4*r.length),s=new Uint32Array(a);for(let a=0;a>10]+(1023&i)]+t[i>>10];s[a]=o}return new Float32Array(a)}}class fs{constructor(){this.saveRouters=[],this.loadRouters=[]}static getInstance(){return null==fs.instance&&(fs.instance=new fs),fs.instance}static registerSaveRouter(e){fs.getInstance().saveRouters.push(e)}static registerLoadRouter(e){fs.getInstance().loadRouters.push(e)}static getSaveHandlers(e){return fs.getHandlers(e,"save")}static getLoadHandlers(e,t){return fs.getHandlers(e,"load",t)}static getHandlers(e,t,n){const r=[];return("load"===t?fs.getInstance().loadRouters:fs.getInstance().saveRouters).forEach((t=>{const a=t(e,n);null!==a&&r.push(a)})),r}}const ds=e=>fs.registerSaveRouter(e),ps=e=>fs.registerLoadRouter(e),ms=e=>fs.getSaveHandlers(e),gs=(e,t)=>fs.getLoadHandlers(e,t),ys="tensorflowjs",bs="models_store",vs="model_info_store";function ws(){if(!We().getBool("IS_BROWSER"))throw new Error("Failed to obtain IndexedDB factory because the current environmentis not a web browser.");const e="undefined"==typeof window?self:window,t=e.indexedDB||e.mozIndexedDB||e.webkitIndexedDB||e.msIndexedDB||e.shimIndexedDB;if(null==t)throw new Error("The current browser does not appear to support IndexedDB.");return t}function _s(e){const t=e.result;t.createObjectStore(bs,{keyPath:"modelPath"}),t.createObjectStore(vs,{keyPath:"modelPath"})}class xs{constructor(e){if(this.indexedDB=ws(),null==e||!e)throw new Error("For IndexedDB, modelPath must not be null, undefined or empty.");this.modelPath=e}async 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d=No(Do(Ao(h,No(To(f),this.epsilon)),-this.learningRate),s);s.assign(d)})),this.accBeta1.assign(Do(this.accBeta1,this.beta1)),this.accBeta2.assign(Do(this.accBeta2,this.beta2))})),this.incrementIterations()}dispose(){this.accBeta1.dispose(),this.accBeta2.dispose(),null!=this.accumulatedFirstMoment&&mo(this.accumulatedFirstMoment.map((e=>e.variable))),null!=this.accumulatedSecondMoment&&mo(this.accumulatedSecondMoment.map((e=>e.variable)))}async getWeights(){const e=[...this.accumulatedFirstMoment,...this.accumulatedSecondMoment];return[await this.saveIterations()].concat(e.map((e=>({name:e.originalName,tensor:e.variable}))))}async setWeights(e){e=await this.extractIterations(e),po((()=>{this.accBeta1.assign(Go(this.beta1,this.iterations_+1)),this.accBeta2.assign(Go(this.beta2,this.iterations_+1))}));const t=e.length/2;this.accumulatedFirstMoment=e.slice(0,t).map((e=>({originalName:e.name,variable:e.tensor.variable(!1)}))),this.accumulatedSecondMoment=e.slice(t,2*t).map((e=>({originalName:e.name,variable:e.tensor.variable(!1)})))}getConfig(){return{learningRate:this.learningRate,beta1:this.beta1,beta2:this.beta2,epsilon:this.epsilon}}static fromConfig(e,t){return new e(t.learningRate,t.beta1,t.beta2,t.epsilon)}}Ko.className="Adam",Gi(Ko);const qo=Za({abs_:function(e){const t=qa(e,"x","abs");if("complex64"===t.dtype){const e={x:t};return Wa.runKernel(bt,e)}{const e={x:t};return Wa.runKernel(Ge,e)}}});function Yo(e,t){const n=e.length,r=[];for(let a=0;a1&&1===i&&r.unshift(s)}return r}function Jo(e,t){const n=[];for(let r=0;r1)&&n.unshift(s)}return n}function Zo(e,t){const n=[],r=Math.max(e.length,t.length);for(let a=0;a{this.iteration=Wo(0).variable(),this.accBeta1=Wo(t).variable()})),null==r&&(this.epsilon=Wa.backend.epsilon())}applyGradients(e){const t=Array.isArray(e)?e.map((e=>e.name)):Object.keys(e);po((()=>{const n=Xo(1,this.accBeta1),r=Ao(-this.learningRate,No(Do(this.iteration,this.decay),1));t.forEach(((t,a)=>{const s=Wa.registeredVariables[t];null==this.accumulatedFirstMoment[a]&&(this.accumulatedFirstMoment[a]={originalName:`${t}/m`,variable:Ro(s).variable(!1)}),null==this.accumulatedWeightedInfNorm[a]&&(this.accumulatedWeightedInfNorm[a]={originalName:`${t}/v`,variable:Ro(s).variable(!1)});const i=Array.isArray(e)?e[a].tensor:e[t];if(null==i)return;const o=this.accumulatedFirstMoment[a].variable,l=this.accumulatedWeightedInfNorm[a].variable,u=No(Do(o,this.beta1),Do(i,1-this.beta1)),c=Do(l,this.beta2),h=qo(i),f=Qo(c,h);o.assign(u),l.assign(f);const d=No(Do(Ao(r,n),Ao(u,No(f,this.epsilon))),s);s.assign(d)})),this.iteration.assign(No(this.iteration,1)),this.accBeta1.assign(Do(this.accBeta1,this.beta1))})),this.incrementIterations()}dispose(){this.accBeta1.dispose(),this.iteration.dispose(),null!=this.accumulatedFirstMoment&&mo(this.accumulatedFirstMoment.map((e=>e.variable))),null!=this.accumulatedWeightedInfNorm&&mo(this.accumulatedWeightedInfNorm.map((e=>e.variable)))}async getWeights(){throw new Error("getWeights() is not implemented for Adamax yet.")}async setWeights(e){throw new Error("setWeights() is not implemented for Adamax yet.")}getConfig(){return{learningRate:this.learningRate,beta1:this.beta1,beta2:this.beta2,epsilon:this.epsilon,decay:this.decay}}static fromConfig(e,t){return new e(t.learningRate,t.beta1,t.beta2,t.epsilon,t.decay)}}el.className="Adamax",Gi(el);class tl extends Uo{constructor(e){super(),this.learningRate=e,this.setLearningRate(e)}applyGradients(e){(Array.isArray(e)?e.map((e=>e.name)):Object.keys(e)).forEach(((t,n)=>{const r=Array.isArray(e)?e[n].tensor:e[t];if(null==r)return;const a=Wa.registeredVariables[t];po((()=>{const e=No(Do(this.c,r),a);a.assign(e)}))})),this.incrementIterations()}setLearningRate(e){this.learningRate=e,null!=this.c&&this.c.dispose(),this.c=go(Wo(-e))}dispose(){this.c.dispose()}async getWeights(){return[await this.saveIterations()]}async setWeights(e){if(0!==(e=await this.extractIterations(e)).length)throw new Error("SGD optimizer does not have settable weights.")}getConfig(){return{learningRate:this.learningRate}}static fromConfig(e,t){return new e(t.learningRate)}}tl.className="SGD",Gi(tl);class nl extends tl{constructor(e,t,n=!1){super(e),this.learningRate=e,this.momentum=t,this.useNesterov=n,this.accumulations=[],this.m=Wo(this.momentum)}applyGradients(e){(Array.isArray(e)?e.map((e=>e.name)):Object.keys(e)).forEach(((t,n)=>{const r=Wa.registeredVariables[t];if(null==this.accumulations[n]){const e=!1;this.accumulations[n]={originalName:`${t}/momentum`,variable:po((()=>Ro(r).variable(e)))}}const a=this.accumulations[n].variable,s=Array.isArray(e)?e[n].tensor:e[t];null!=s&&po((()=>{let e;const t=No(Do(this.m,a),s);e=this.useNesterov?No(Do(this.c,No(s,Do(t,this.m))),r):No(Do(this.c,t),r),a.assign(t),r.assign(e)}))})),this.incrementIterations()}dispose(){this.m.dispose(),null!=this.accumulations&&mo(this.accumulations.map((e=>e.variable)))}setMomentum(e){this.momentum=e}async getWeights(){return[await this.saveIterations()].concat(this.accumulations.map((e=>({name:e.originalName,tensor:e.variable}))))}async setWeights(e){e=await this.extractIterations(e),this.accumulations=e.map((e=>({originalName:e.name,variable:e.tensor.variable(!1)})))}getConfig(){return{learningRate:this.learningRate,momentum:this.momentum,useNesterov:this.useNesterov}}static fromConfig(e,t){return new e(t.learningRate,t.momentum,t.useNesterov)}}nl.className="Momentum",Gi(nl);class rl extends Uo{constructor(e,t=.9,n=0,r=null,a=!1){if(super(),this.learningRate=e,this.decay=t,this.momentum=n,this.epsilon=r,this.accumulatedMeanSquares=[],this.accumulatedMoments=[],this.accumulatedMeanGrads=[],this.centered=a,null==r&&(this.epsilon=Wa.backend.epsilon()),null==e)throw new Error("learningRate for RMSPropOptimizer must be defined.")}applyGradients(e){(Array.isArray(e)?e.map((e=>e.name)):Object.keys(e)).forEach(((t,n)=>{const r=Wa.registeredVariables[t],a=!1;null==this.accumulatedMeanSquares[n]&&(this.accumulatedMeanSquares[n]={originalName:`${t}/rms`,variable:po((()=>Ro(r).variable(a)))}),null==this.accumulatedMoments[n]&&(this.accumulatedMoments[n]={originalName:`${t}/momentum`,variable:po((()=>Ro(r).variable(a)))}),null==this.accumulatedMeanGrads[n]&&this.centered&&(this.accumulatedMeanGrads[n]={originalName:`${t}/mg`,variable:po((()=>Ro(r).variable(a)))});const s=Array.isArray(e)?e[n].tensor:e[t];if(null==s)return;const i=this.accumulatedMeanSquares[n].variable,o=this.accumulatedMoments[n].variable;po((()=>{const e=No(Do(i,this.decay),Do(Fo(s),1-this.decay));if(this.centered){const t=this.accumulatedMeanGrads[n].variable,a=No(Do(t,this.decay),Do(s,1-this.decay)),l=Ao(Do(s,this.learningRate),To(Xo(e,No(Fo(a),this.epsilon)))),u=No(Do(o,this.momentum),l);i.assign(e),t.assign(a),o.assign(u);const c=Xo(r,u);r.assign(c)}else{const e=No(Do(i,this.decay),Do(Fo(s),1-this.decay)),t=No(Do(o,this.momentum),Ao(Do(s,this.learningRate),To(No(e,this.epsilon))));i.assign(e),o.assign(t);const n=Xo(r,t);r.assign(n)}}))})),this.incrementIterations()}dispose(){null!=this.accumulatedMeanSquares&&mo(this.accumulatedMeanSquares.map((e=>e.variable))),null!=this.accumulatedMeanGrads&&this.centered&&mo(this.accumulatedMeanGrads.map((e=>e.variable))),null!=this.accumulatedMoments&&mo(this.accumulatedMoments.map((e=>e.variable)))}async getWeights(){const e=[...this.accumulatedMeanSquares,...this.accumulatedMoments];return this.centered&&e.push(...this.accumulatedMeanGrads),[await this.saveIterations()].concat(e.map((e=>({name:e.originalName,tensor:e.variable}))))}async setWeights(e){e=await this.extractIterations(e);const t=this.centered?e.length/3:e.length/2,n=!1;this.accumulatedMeanSquares=e.slice(0,t).map((e=>({originalName:e.name,variable:e.tensor.variable(n)}))),this.accumulatedMoments=e.slice(t,2*t).map((e=>({originalName:e.name,variable:e.tensor.variable(n)}))),this.centered&&(this.accumulatedMeanGrads=e.slice(2*t,3*t).map((e=>({originalName:e.name,variable:e.tensor.variable(n)}))))}getConfig(){return{learningRate:this.learningRate,decay:this.decay,momentum:this.momentum,epsilon:this.epsilon,centered:this.centered}}static fromConfig(e,t){return new e(t.learningRate,t.decay,t.momentum,t.epsilon,t.centered)}}rl.className="RMSProp",Gi(rl);const al=Za({acos_:function(e){const t={x:qa(e,"x","acos")};return Wa.runKernel(Xe,t)}}),sl=Za({acosh_:function(e){const t={x:qa(e,"x","acosh")};return Wa.runKernel(Ke,t)}}),il=Za({addN_:function(e){Q(Array.isArray(e),(()=>"The argument passed to tf.addN() must be a list of tensors")),Q(e.length>=1,(()=>`Must pass at least one tensor to tf.addN(), but got 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t={x:qa(e,"x","atan")};return Wa.runKernel(rt,t)}}),pl=Za({atan2_:function(e,t){let n=qa(e,"a","atan2"),r=qa(t,"b","atan2");[n,r]=Fa(n,r);const a={a:n,b:r};return Wa.runKernel(st,a)}}),ml=Za({atanh_:function(e){const t={x:qa(e,"x","atanh")};return Wa.runKernel(at,t)}});function gl(e,t,n,r,a="NHWC",s){return vl(e,[...t,e[3]],n,s,r,null,null,Il(a))}function yl(e,t,n,r,a,s,i="channelsLast"){const[o,l]=xl(t);let u;if("channelsLast"===i)u=[o,l,e[3],e[3]];else{if("channelsFirst"!==i)throw new Error(`Unknown dataFormat ${i}`);u=[o,l,e[1],e[1]]}return vl(e,u,n,r,a,s,!1,i)}function bl(e,t,n,r,a,s,i="NDHWC"){const[o,l,u]=kl(t);let c,h;if("NDHWC"===i)h="channelsLast",c=[o,l,u,e[4],e[4]];else{if("NCDHW"!==i)throw new Error(`Unknown dataFormat ${i}`);h="channelsFirst",c=[o,l,u,e[1],e[1]]}return wl(e,c,n,r,a,!1,h,s)}function vl(e,t,n,r,a,s,i=!1,o="channelsLast"){let[l,u,c,h]=[-1,-1,-1,-1];if("channelsLast"===o)[l,u,c,h]=e;else{if("channelsFirst"!==o)throw new Error(`Unknown dataFormat 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u={x:o},c={filterSize:t,strides:n,pad:r,dimRoundingMode:a,dataFormat:s};let h=Wa.runKernel(lt,u,c);return h=Ks(h,o.dtype),l?Al(h,[h.shape[1],h.shape[2],h.shape[3],h.shape[4]]):h}}),Fl=Za({concat_:function(e,t=0){Q(e.length>=1,(()=>"Pass at least one tensor to concat"));const n=Ya(e,"tensors","concat","string_or_numeric");if("complex64"===n[0].dtype&&n.forEach((e=>{if("complex64"!==e.dtype)throw new Error(`Cannot concatenate complex64 tensors with a tensor\n with dtype ${e.dtype}. `)})),1===n.length)return qs(n[0]);const r=n,a={axis:t};return Wa.runKernel(vt,r,a)}}),Rl=Za({sigmoid_:function(e){const t={x:qa(e,"x","sigmoid")};return Wa.runKernel(gr,t)}}),Ol=Za({slice_:function(e,t,n){const r=qa(e,"x","slice","string_or_numeric");if(0===r.rank)throw new Error("Slicing scalar is not possible");const a={x:r},s={begin:t,size:n};return Wa.runKernel(fr,a,s)}}),Ml=Za({tanh_:function(e){const t={x:qa(e,"x","tanh")};return Wa.runKernel(Tr,t)}}),Bl=Za({basicLSTMCell_:function(e,t,n,r,a,s){const 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Do(e,Ao(n,No(Xp(),t)))}))}getConfig(){return{maxValue:this.maxValue,axis:this.axis}}}wm.className="MaxNorm",Gi(wm);class _m extends vm{constructor(e){super(),this.defaultAxis=0,this.axis=null!=e.axis?e.axis:this.defaultAxis}apply(e){return po((()=>Ao(e,No(Xp(),bm(e,this.axis)))))}getConfig(){return{axis:this.axis}}}_m.className="UnitNorm",Gi(_m);class xm extends vm{apply(e){return zc(e)}}xm.className="NonNeg",Gi(xm);class km extends vm{constructor(e){super(),this.defaultMinValue=0,this.defaultMaxValue=1,this.defaultRate=1,this.defaultAxis=0,this.minValue=null!=e.minValue?e.minValue:this.defaultMinValue,this.maxValue=null!=e.maxValue?e.maxValue:this.defaultMaxValue,this.rate=null!=e.rate?e.rate:this.defaultRate,this.axis=null!=e.axis?e.axis:this.defaultAxis}apply(e){return po((()=>{const t=bm(e,this.axis),n=No(Do(this.rate,Hl(t,this.minValue,this.maxValue)),Do(1-this.rate,t));return 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Error("Not a valid tensor name: '"+e+"'");return(0===Wm.length?"":Wm.join("/")+"/")+e}function jm(e){if(!Gm(e))throw new Error("Not a valid tensor name: '"+e+"'");$m.has(e)||$m.set(e,0);const t=$m.get(e);if($m.set(e,$m.get(e)+1),t>0){const n=`${e}_${t}`;return $m.set(n,1),n}return e}const Hm=new RegExp(/^[A-Za-z0-9][-A-Za-z0-9\._\/]*$/);function Gm(e){return!!e.match(Hm)}function Xm(e,t,n){null==t&&(t=0),null==n&&(n=e.length);let r=1;for(let a=t;a{switch(e.rank){case 1:return th(e,t,n);case 2:return nh(e,[t,0],[n,e.shape[1]]);case 3:return rh(e,[t,0,0],[n,e.shape[1],e.shape[2]]);case 4:return ah(e,[t,0,0,0],[n,e.shape[1],e.shape[2],e.shape[3]]);case 5:return Ol(e,[t,0,0,0,0],[n,e.shape[1],e.shape[2],e.shape[3],e.shape[4]]);case 6:return Ol(e,[t,0,0,0,0,0],[n,e.shape[1],e.shape[2],e.shape[3],e.shape[4],e.shape[5]]);default:throw new Yp(`sliceAlongFirstAxis() received an unsupported tensor rank: ${e.rank}`)}}))}function tg(e,t,n){return po((()=>{switch(e.rank){case 1:return th(e,t,n);case 2:return nh(e,[0,t],[e.shape[0],n]);case 3:return rh(e,[0,0,t],[e.shape[0],e.shape[1],n]);case 4:return ah(e,[0,0,0,t],[e.shape[0],e.shape[1],e.shape[2],n]);default:throw new Yp(`sliceAlongLastAxis() received an unsupported tensor rank: ${e.rank}`)}}))}function ng(e,t,n,r){return po((()=>{switch(e.rank){case 1:return th(e,t,n);case 2:switch(r){case 1:return eg(e,t,n);case 2:return tg(e,t,n);default:throw new Yp(`The axis is not within the rank of the tensor ${r}`)}case 3:switch(r){case 1:return eg(e,t,n);case 2:return rh(e,[0,t,0],[e.shape[0],n,e.shape[2]]);case 3:return tg(e,t,n);default:throw new Yp(`The axis is not within the rank of the tensor ${r}`)}case 4:switch(r){case 1:return eg(e,t,n);case 2:return ah(e,[0,t,0,0],[e.shape[0],n,e.shape[2],e.shape[3]]);case 3:return ah(e,[0,0,t,0],[e.shape[0],e.shape[1],n,e.shape[3]]);case 4:return tg(e,t,n);default:throw new Yp(`The axis is not within the rank of the tensor ${r}`)}default:throw new Yp(`sliceAlongLastAxis() received an unsupported tensor rank: ${e.rank}`)}}))}function rg(e,t=-1){let n;return t<0&&(n=e[0].rank,t=0!==n?n:0),t===e[0].rank&&(t=-1),Fl(e,t)}function ag(e,t){switch(e.rank){case 1:return Gl([e,t]);case 2:return Xl([e,t],0);case 3:return Kl([e,t],0);case 4:return ql([e,t],0);default:throw new Yp(`concatAlongFirstAxis() received an unsupported tensor rank: ${e.rank}`)}}function sg(e,t){if(Array.isArray(t)||(t=[t]),e.rank!==t.length)throw new Yp(`The length of input n (${t.length}) does not match the number of dimensions in input x (${e.rank})`);return _u(e,t)}function ig(e,t=0,n=1,r,a){return Mc(e,t,n,r,a)}function og(e,t,n,r){if(e.rank<2||t.rank<2)throw new Jp(`dot requires both inputs to be rank >= 2 but got x shape = ${e.shape} and y shape = ${t.shape}`);if(t.rank>=3&&e.shape.slice(-1)[0]!==t.shape.slice(-2)[0])throw new Jp(`If rank y >= 3, then the second last dim of y must equal the last dim of x but got x shape = ${e.shape} and y shape = ${t.shape}`);if(2===e.rank&&2===t.rank)return Yh({a:e,b:t,transposeA:!1,transposeB:!1,bias:r?cg(e.rank,r,"channelsLast"):null,activation:n});{const a=e.shape.slice(),s=a.pop();e=e.reshape([-1,s]);const i=t.shape.slice(),o=i.pop(),l=i.pop(),u=[...i,o],c=Array.from({length:t.rank},((e,n)=>0===n?t.rank-2:n<=t.rank-2?n-1:n));t=t.transpose(c).reshape([l,-1]);const h=[...a,...u];return Yh({a:e,b:t,transposeA:!1,transposeB:!1,bias:r?cg(e.rank,r,"channelsLast"):null,activation:n}).reshape(h)}}function lg(e,t,n){return po((()=>(t=Array.isArray(t)?yh(t,"int32"):t.toInt(),Eu(e,t,n))))}function ug(e){return Do(e,e)}function cg(e,t,n){const r=t.shape;if(1!==t.rank&&t.rank!==e)throw new Yp(`Unexpected bias dimensions: ${t.rank}; expected it to be 1 or ${e}`);if(5===e){if("channelsFirst"===n)return 1===r.length?t.reshape([1,r[0],1,1,1]):t.reshape([1,r[3],r[0],r[1],r[2]]);if("channelsLast"===n)return 1===r.length?t.reshape([1,1,1,1,r[0]]):t.reshape([1].concat(r))}else if(4===e){if("channelsFirst"===n)return 1===r.length?t.reshape([1,r[0],1,1]):t.reshape([1,r[2],r[0],r[1]]);if("channelsLast"===n)return 1===r.length?t.reshape([1,1,1,r[0]]):t.reshape([1].concat(r))}else if(3===e){if("channelsFirst"===n)return 1===r.length?t.reshape([1,r[0],1]):t.reshape([1,r[1],r[0]]);if("channelsLast"===n)return 1===r.length?t.reshape([1,1,r[0]]):t.reshape([1].concat(r))}else if(e<3)return t;throw new Yp(`Unsupported input rank by biasAdd: ${t.rank}`)}function hg(e,t,n){return po((()=>(null==n&&(n="channelsLast"),Pm(n),e.add(cg(e.rank,t,n)))))}function fg(e,t,n,r){return po((()=>$h(e,t,n,r)))}function dg(e,t,n=!1){return n?e():t()}const pg=["fanIn","fanOut","fanAvg"],mg=["normal","uniform","truncatedNormal"];class gg extends ji{fromConfigUsesCustomObjects(){return!1}getConfig(){return{}}}class yg extends gg{apply(e,t){return oc(e,t)}}yg.className="Zeros",Gi(yg);class bg extends gg{apply(e,t){return lc(e,t)}}bg.className="Ones",Gi(bg);class vg extends gg{constructor(e){if(super(),"object"!=typeof e)throw new Yp(`Expected argument of type ConstantConfig but got ${e}`);if(void 0===e.value)throw new Yp(`config must have value set but got ${e}`);this.value=e.value}apply(e,t){return po((()=>Do(Wo(this.value),lc(e,t))))}getConfig(){return{value:this.value}}}vg.className="Constant",Gi(vg);class wg extends gg{constructor(e){super(),this.DEFAULT_MINVAL=-.05,this.DEFAULT_MAXVAL=.05,this.minval=e.minval||this.DEFAULT_MINVAL,this.maxval=e.maxval||this.DEFAULT_MAXVAL,this.seed=e.seed}apply(e,t){return Bc(e,this.minval,this.maxval,t)}getConfig(){return{minval:this.minval,maxval:this.maxval,seed:this.seed}}}wg.className="RandomUniform",Gi(wg);class _g extends gg{constructor(e){super(),this.DEFAULT_MEAN=0,this.DEFAULT_STDDEV=.05,this.mean=e.mean||this.DEFAULT_MEAN,this.stddev=e.stddev||this.DEFAULT_STDDEV,this.seed=e.seed}apply(e,t){if("float32"!==(t=t||"float32")&&"int32"!==t)throw new Jp(`randomNormal does not support dType ${t}.`);return ig(e,this.mean,this.stddev,t,this.seed)}getConfig(){return{mean:this.mean,stddev:this.stddev,seed:this.seed}}}_g.className="RandomNormal",Gi(_g);class xg extends gg{constructor(e){super(),this.DEFAULT_MEAN=0,this.DEFAULT_STDDEV=.05,this.mean=e.mean||this.DEFAULT_MEAN,this.stddev=e.stddev||this.DEFAULT_STDDEV,this.seed=e.seed}apply(e,t){if("float32"!==(t=t||"float32")&&"int32"!==t)throw new Jp(`truncatedNormal does not support dType ${t}.`);return kh(e,this.mean,this.stddev,t,this.seed)}getConfig(){return{mean:this.mean,stddev:this.stddev,seed:this.seed}}}xg.className="TruncatedNormal",Gi(xg);class kg extends gg{constructor(e){super(),this.gain=null!=e.gain?e.gain:1}apply(e,t){return po((()=>{if(2!==e.length||e[0]!==e[1])throw new Yp("Identity matrix initializer can only be used for 2D square matrices.");return Do(this.gain,xu(e[0]))}))}getConfig(){return{gain:this.gain}}}kg.className="Identity",Gi(kg);class Eg extends gg{constructor(e){if(super(),e.scale<0)throw new Yp(`scale must be a positive float. Got: ${e.scale}`);var t;this.scale=null==e.scale?1:e.scale,this.mode=null==e.mode?"fanIn":e.mode,t=this.mode,dm(pg,"FanMode",t),this.distribution=null==e.distribution?"normal":e.distribution,function(e){dm(mg,"Distribution",e)}(this.distribution),this.seed=e.seed}apply(e,t){const n=function(e,t="channelsLast"){let n,r;if(Pm(t),2===e.length)n=e[0],r=e[1];else if(-1!==[3,4,5].indexOf(e.length)){if("channelsFirst"===t){const t=Xm(e,2);n=e[1]*t,r=e[0]*t}else if("channelsLast"===t){const t=Xm(e,0,e.length-2);n=e[e.length-2]*t,r=e[e.length-1]*t}}else{const t=Xm(e);n=Math.sqrt(t),r=Math.sqrt(t)}return[n,r]}(e),r=n[0],a=n[1];let s=this.scale;if("fanIn"===this.mode?s/=Math.max(1,r):"fanOut"===this.mode?s/=Math.max(1,a):s/=Math.max(1,(r+a)/2),"normal"===this.distribution){const n=Math.sqrt(s);if("float32"!==(t=t||"float32")&&"int32"!==t)throw new Jp(`${this.getClassName()} does not support dType ${t}.`);return kh(e,0,n,t,this.seed)}{const n=Math.sqrt(3*s);return Bc(e,-n,n,t)}}getConfig(){return{scale:this.scale,mode:this.mode,distribution:this.distribution,seed:this.seed}}}Eg.className="VarianceScaling",Gi(Eg);class Cg extends Eg{constructor(e){super({scale:1,mode:"fanAvg",distribution:"uniform",seed:null==e?null:e.seed})}getClassName(){return Eg.className}}Cg.className="GlorotUniform",Gi(Cg);class Sg extends Eg{constructor(e){super({scale:1,mode:"fanAvg",distribution:"normal",seed:null==e?null:e.seed})}getClassName(){return Eg.className}}Sg.className="GlorotNormal",Gi(Sg);class Ng extends Eg{constructor(e){super({scale:2,mode:"fanIn",distribution:"normal",seed:null==e?null:e.seed})}getClassName(){return Eg.className}}Ng.className="HeNormal",Gi(Ng);class Ig extends Eg{constructor(e){super({scale:2,mode:"fanIn",distribution:"uniform",seed:null==e?null:e.seed})}getClassName(){return Eg.className}}Ig.className="HeUniform",Gi(Ig);class Ag extends Eg{constructor(e){super({scale:1,mode:"fanIn",distribution:"normal",seed:null==e?null:e.seed})}getClassName(){return Eg.className}}Ag.className="LeCunNormal",Gi(Ag);class Dg extends Eg{constructor(e){super({scale:1,mode:"fanIn",distribution:"uniform",seed:null==e?null:e.seed})}getClassName(){return Eg.className}}Dg.className="LeCunNormal",Gi(Dg);class Tg extends gg{constructor(e){if(super(),this.DEFAULT_GAIN=1,this.gain=null==e.gain?this.DEFAULT_GAIN:e.gain,this.seed=e.seed,null!=this.seed)throw new Jp("Random seed is not implemented for Orthogonal Initializer yet.")}apply(e,t){return po((()=>{if(e.length<2)throw new Jp("Shape must be at least 2D.");e[0]*e[1]>2e3&&console.warn(`Orthogonal initializer is being called on a matrix with more than 2000 (${e[0]*e[1]}) elements: Slowness may result.`);const t=ig(e[0]>e[1]?[e[1],e[0]]:e,0,1,"float32");let n=Wf.gramSchmidt(t);return e[0]>e[1]&&(n=n.transpose()),Do(this.gain,n)}))}getConfig(){return{gain:this.gain,seed:this.seed}}}Tg.className="Orthogonal",Gi(Tg);const Fg={constant:"Constant",glorotNormal:"GlorotNormal",glorotUniform:"GlorotUniform",heNormal:"HeNormal",heUniform:"HeUniform",identity:"Identity",leCunNormal:"LeCunNormal",leCunUniform:"LeCunUniform",ones:"Ones",orthogonal:"Orthogonal",randomNormal:"RandomNormal",randomUniform:"RandomUniform",truncatedNormal:"TruncatedNormal",varianceScaling:"VarianceScaling",zeros:"Zeros"};function Rg(e,t={}){return um(e,Hi.getMap().classNameMap,t,"initializer")}function Og(e){return om(e)}function Mg(e){if("string"==typeof e){const t=e in Fg?Fg[e]:e;if("GlorotNormal"===t)return new Sg;if("GlorotUniform"===t)return new Cg;if("HeNormal"===t)return new Ng;if("HeUniform"===t)return new Ig;if("LeCunNormal"===t)return new Ag;if("LeCunUniform"===t)return new Dg;{const e={};return e.className=t,e.config={},Rg(e)}}return e instanceof gg?e:Rg(e)}function Bg(){return new yg}function $g(){return new bg}function Pg(e){return new vg(e)}function Lg(e){return new wg(e)}function zg(e){return new _g(e)}function Wg(e){return new xg(e)}function Ug(e){return new kg(e)}function Vg(e){return new Eg(e)}function jg(e){return new Cg(e)}function Hg(e){return new Sg(e)}function Gg(e){return new Ng(e)}function Xg(e){return new Ig(e)}function Kg(e){return new Ag(e)}function qg(e){return new Dg(e)}function Yg(e){return new Tg(e)}let Jg=0;function Zg(){return Jg++}const Qg={};function ey(e=""){return e in Qg||(Qg[e]=0),Qg[e]+=1,e+Qg[e].toString()}function ty(e){return Array.isArray(e)&&Array.isArray(e[0])}function ny(e){return 0===e.length?[]:Array.isArray(e[0])?e:[e]}function ry(e){let t;if(Array.isArray(e)){if(1!==e.length)throw new Yp(`Expected Tensor length to be 1; got ${e.length}`);t=e[0]}else t=e;return t}function ay(e){if(Array.isArray(e)&&Array.isArray(e[0])){if(1===e.length)return(e=e)[0];throw new Yp(`Expected exactly 1 Shape; got ${e.length}`)}return e}function sy(e){let t=0;for(const n of e)0===n.shape.length?t+=1:t+=n.shape.reduce(((e,t)=>e*t));return t}class iy{constructor(e,t="float32",n="Variable",r=!0,a=null){this.dtype=null==t?"float32":t,this.shape=e.shape,this.id=Zg(),n=null==n?"Variable":n,this.originalName=Vm(n),this.name=jm(this.originalName),this.trainable_=r,this.constraint=a,this.val=Nh(e,this.trainable_,this.name,this.dtype)}read(){return this.assertNotDisposed(),this.val}write(e){return this.assertNotDisposed(),function(e,t){if(e.shape.toString()!==t.shape.toString())throw new Error("Shape mismatch: "+JSON.stringify(e.shape)+" vs. "+JSON.stringify(t.shape))}(this.val,e),this.val.id!==e.id&&(this.val.assign(e),null!=this.constraint&&this.val.assign(this.constraint.apply(this.val))),this}dispose(){this.assertNotDisposed(),this.val.dispose()}assertNotDisposed(){if(this.val.isDisposed)throw new Error(`LayersVariable ${this.name} is already disposed.`)}get trainable(){return this.trainable_}set trainable(e){this.trainable_=e,this.val.trainable=e}}function oy(e){return e.map((e=>e.read()))}function ly(e){e.forEach((e=>{e[0].write(e[1])}))}class uy{constructor(e){this.dtype=e.dtype,this.shape=e.shape,null!=e.shape?this.ndim=e.shape.length:this.ndim=e.ndim,this.maxNDim=e.maxNDim,this.minNDim=e.minNDim,this.axes=e.axes||{}}}class cy{constructor(e,t,n,r,a,s,i){this.dtype=e,this.shape=t,this.sourceLayer=n,this.inputs=r,this.callArgs=a,this.outputTensorIndex=i,this.id=Zg(),null!=s&&(this.originalName=Vm(s),this.name=jm(this.originalName)),this.rank=t.length}}let hy=0;class fy{constructor(e,t){this.callArgs=t,this.id=hy++,this.outboundLayer=e.outboundLayer,this.inboundLayers=e.inboundLayers,this.nodeIndices=e.nodeIndices,this.tensorIndices=e.tensorIndices,this.inputTensors=e.inputTensors,this.outputTensors=e.outputTensors,this.inputMasks=e.inputMasks,this.outputMasks=e.outputMasks,this.inputShapes=e.inputShapes,this.outputShapes=e.outputShapes;for(const t of e.inboundLayers)null!=t&&t.outboundNodes.push(this);e.outboundLayer.inboundNodes.push(this)}getConfig(){const e=[];for(const t of this.inboundLayers)null!=t?e.push(t.name):e.push(null);return{outboundLayer:this.outboundLayer?this.outboundLayer.name:null,inboundLayers:e,nodeIndices:this.nodeIndices,tensorIndices:this.tensorIndices}}}let dy=0;class py extends ji{constructor(e={}){super(),this._callHook=null,this._addedWeightNames=[],this._stateful=!1,this.id=dy++,this.activityRegularizer=null,this.inputSpec=null,this.supportsMasking=!1,this._trainableWeights=[],this._nonTrainableWeights=[],this._losses=[],this._updates=[],this._built=!1,this.inboundNodes=[],this.outboundNodes=[];let t=e.name;if(!t){const e=this.getClassName();t=am(e)+"_"+ey(e)}if(this.name=t,this.trainable_=null==e.trainable||e.trainable,null!=e.inputShape||null!=e.batchInputShape){let t;if(null!=e.batchInputShape)t=e.batchInputShape;else if(null!=e.inputShape){let n=null;null!=e.batchSize&&(n=e.batchSize),t=[n].concat(e.inputShape)}this.batchInputShape=t;let n=e.dtype;null==n&&(n=e.inputDType),null==n&&(n="float32"),this.dtype=n}null!=e.weights?this.initialWeights=e.weights:this.initialWeights=null,this._refCount=null,this.fastWeightInitDuringBuild=!1}static nodeKey(e,t){return e.name+"_ib-"+t.toString()}getNodeAtIndex(e,t){if(0===this.inboundNodes.length)throw new qp(`The layer has never been called and thus has no defined ${t}.`);if(this.inboundNodes.length<=e)throw new Yp(`Asked to get ${t} at node ${e}, but the layer has only ${this.inboundNodes.length} inbound nodes.`);return this.inboundNodes[e]}getInputAt(e){return nm(this.getNodeAtIndex(e,"input").inputTensors)}getOutputAt(e){return nm(this.getNodeAtIndex(e,"output").outputTensors)}get input(){if(this.inboundNodes.length>1)throw new Kp(`Layer ${this.name} has multiple inbound nodes, hence the notion of "layer input" is ill-defined. 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Input received: ${e}`);for(let n=0;na.maxNDim)throw new Yp(`Input ${n} is incompatible with layer ${this.name}: expected max_ndim=${a.maxNDim}, found ndim=${s}`);if(null!=a.minNDim&&s=0?e[r]:e[e.length+r];if(null!=s&&-1===[s,null].indexOf(i))throw new Yp(`Input ${n} is incompatible with layer ${this.name}: expected axis ${r} of input shape to have value ${s} but got shape ${e}.`)}}if(null!=a.shape)for(let e=0;e{if(!this.built){this.assertInputCompatibility(e);const t=[];for(const n of rm(e))t.push(n.shape);this.build(nm(t)),this.built=!0,this.initialWeights&&this.setWeights(this.initialWeights),null===this._refCount&&a&&(this._refCount=1)}if(this.assertInputCompatibility(e),a){let r=this.call(e,t);const a=rm(r),s=[];for(let e of a)-1!==n.indexOf(e)&&(e=e.clone()),s.push(e);if(r=nm(s),null!=this.activityRegularizer)throw new Jp("Layer invocation in the presence of activity regularizer(s) is not supported yet.");return r}{const n=function(e){e=rm(e);const t=[];for(const n of e)t.push(n.shape);return nm(t)}(e),r=this.computeOutputShape(n);let a;const s="float32";if(this.warnOnIncompatibleInputShape(Array.isArray(e)?n[0]:n),a=null!=r&&r.length>0&&Array.isArray(r[0])?r.map(((n,r)=>new cy(s,n,this,rm(e),t,this.name,r))):new cy(s,r,this,rm(e),t,this.name),this.addInboundNode(e,a,null,null,n,r,t),this._refCount++,null!=this.activityRegularizer)throw new Jp("Layer invocation in the presence of activity regularizer(s) is not supported yet.");return a}}))}warnOnIncompatibleInputShape(e){if(null!=this.batchInputShape)if(e.length!==this.batchInputShape.length)console.warn(`The rank of the input tensor provided (shape: ${JSON.stringify(e)}) does not match that of the batchInputShape (${JSON.stringify(this.batchInputShape)}) of the layer ${this.name}`);else{let t=!1;this.batchInputShape.forEach(((n,r)=>{null!=n&&null!=e[r]&&e[r]!==n&&(t=!0)})),t&&console.warn(`The shape of the input tensor (${JSON.stringify(e)}) does not match the expectation of layer ${this.name}: ${JSON.stringify(this.batchInputShape)}`)}}get outputShape(){if(null==this.inboundNodes||0===this.inboundNodes.length)throw new Kp(`The layer ${this.name} has never been called and thus has no defined output shape.`);const e=[];for(const t of this.inboundNodes){const n=JSON.stringify(t.outputShapes);-1===e.indexOf(n)&&e.push(n)}if(1===e.length){const e=this.inboundNodes[0].outputShapes;return Array.isArray(e)&&Array.isArray(e[0])&&1===e.length?e[0]:e}throw new Kp(`The layer ${this.name} has multiple inbound nodes with different output shapes. Hence the notion of "output shape" is ill-defined for the layer.`)}countParams(){if(!this.built)throw new qp(`You tried to call countParams() on ${this.name}, but the layer is not built yet. Build it first by calling build(batchInputShape).`);return sy(this.weights)}build(e){this.built=!0}getWeights(e=!1){return oy(e?this.trainableWeights:this.weights)}setWeights(e){po((()=>{const t=this.weights;if(t.length!==e.length)throw new Yp(`You called setWeights(weights) on layer "${this.name}" with a weight list of length ${e.length}, but the layer was expecting ${t.length} weights. 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fy({outboundLayer:this,inboundLayers:l,nodeIndices:u,tensorIndices:c,inputTensors:o,outputTensors:t,inputMasks:n,outputMasks:r,inputShapes:a,outputShapes:s},i);for(let e=0;ee.dispose())),this.weights.length}assertNotDisposed(){if(0===this._refCount)throw new Error(`Layer '${this.name}' is already disposed.`)}dispose(){if(!this.built)throw new Error(`Cannot dispose Layer ${this.name} because it has not been built yet.`);if(null===this._refCount)throw new Error(`Cannot dispose Layer ${this.name} because it has not been used yet.`);this.assertNotDisposed();let e=0;return 0==--this._refCount&&(e=this.disposeWeights()),{refCountAfterDispose:this._refCount,numDisposedVariables:e}}}function my(e,t,n){if((null==t||null!=n&&n>0)&&(t=e.sourceLayer,n=e.nodeIndex),0===t.inboundNodes.length)return[e];{const e=t.inboundNodes[n];if(0===e.inboundLayers.length)return e.inputTensors;{const t=[];for(let n=0;n0){const a=await Promise.all(t);for(let t=0;tNo(this.totals[e],Do(r,n))));this.totals[e]=a,null!=t&&t.dispose()}}}async onEpochEnd(e,t){if(null!=t)for(const e of this.params.metrics)null!=this.totals[e]&&("number"==typeof this.totals[e]?t[e]=this.totals[e]/this.seen:po((()=>{const n=Do(Ao(1,this.seen),this.totals[e]);t[e]=n,this.totals[e].dispose(),go(t[e])})))}}class Ey extends _y{async onTrainBegin(e){this.epoch=[],this.history={}}async onEpochEnd(e,t){null==t&&(t={}),this.epoch.push(e);for(const e in t)null==this.history[e]&&(this.history[e]=[]),this.history[e].push(t[e])}async syncData(){const e=[],t=[],n=[];for(const r in this.history){const a=this.history[r];for(let s=0;s{const s=sa();return s-rnew Cy(e,t)))}class Ny{constructor(){}static registerCallbackConstructor(e,t){Q(e>=0&&Number.isInteger(e),(()=>`Verbosity level is expected to be an integer >= 0, but got ${e}`)),Ny.checkForDuplicate(t),null==Ny.constructors[e]&&(Ny.constructors[e]=[]),Ny.constructors[e].push(t)}static checkForDuplicate(e){for(const t in 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po((()=>hc(6,zc(e))))}}Vb.className="relu6",Gi(Vb);class jb extends Lb{apply(e){return e}}jb.className="linear",Gi(jb);class Hb extends Lb{apply(e){return Rl(e)}}Hb.className="sigmoid",Gi(Hb);class Gb extends Lb{apply(e){return function(e){return po((()=>{const t=No(.5,Do(.2,e));return Hl(t,0,1)}))}(e)}}Gb.className="hardSigmoid",Gi(Gb);class Xb extends Lb{apply(e){return Lu(e)}}Xb.className="softplus",Gi(Xb);class Kb extends Lb{apply(e){return function(e){return po((()=>Ao(e,qo(e).add(1))))}(e)}}Kb.className="softsign",Gi(Kb);class qb extends Lb{apply(e){return Ml(e)}}qb.className="tanh",Gi(qb);class Yb extends Lb{apply(e,t=-1){return sh(e,t)}}Yb.className="softmax",Gi(Yb);class Jb extends Lb{apply(e,t=-1){return Vu(e,t)}}Jb.className="logSoftmax",Gi(Jb);class Zb extends Lb{apply(e,t=1){return po((()=>Rl(e.mul(t)).mul(e)))}}Zb.className="swish",Gi(Zb);class Qb extends Lb{apply(e){return po((()=>Do(e,Ml(Lu(e)))))}}function ev(e){return e.getClassName()}function tv(e,t={}){return um(e,Hi.getMap().classNameMap,t,"activation")}function nv(e){if(null==e){return tv({className:"linear",config:{}})}if("string"==typeof e){const t={};return t.className=e,t.config={},tv(t)}return e instanceof Lb?e:tv(e)}function rv(e){if(null!=e&&"object"!=typeof e)throw new Error(`Argument to L1L2 regularizer's constructor is expected to be an object, but received: ${e}`)}Qb.className="mish",Gi(Qb);class av extends ji{}class sv extends av{constructor(e){super(),rv(e),this.l1=null==e||null==e.l1?.01:e.l1,this.l2=null==e||null==e.l2?.01:e.l2,this.hasL1=0!==this.l1,this.hasL2=0!==this.l2}apply(e){return po((()=>{let t=oc([1]);return this.hasL1&&(t=No(t,Uu(Do(this.l1,qo(e))))),this.hasL2&&(t=No(t,Uu(Do(this.l2,ug(e))))),t.asScalar()}))}getConfig(){return{l1:this.l1,l2:this.l2}}static fromConfig(e,t){return new e({l1:t.l1,l2:t.l2})}}sv.className="L1L2",Gi(sv);const iv={l1l2:"L1L2"};function ov(e){return om(e)}function lv(e,t={}){return um(e,Hi.getMap().classNameMap,t,"regularizer")}function uv(e){return null==e?null:"string"==typeof e?lv({className:e in iv?iv[e]:e,config:{}}):e instanceof av?e:lv(e)}class cv extends py{constructor(e){super(null==e?{}:e),this.supportsMasking=!0,null!=e&&(this.maxValue=e.maxValue)}call(e,t){e=ry(e);let n=zc(e);return null!=this.maxValue&&(n=Hl(n,0,this.maxValue)),n}computeOutputShape(e){return e}getConfig(){const e={maxValue:this.maxValue},t=super.getConfig();return Object.assign(e,t),e}}cv.className="ReLU",Gi(cv);class hv extends py{constructor(e){super(null==e?{}:e),this.DEFAULT_ALPHA=.3,null==e&&(e={}),this.alpha=null==e.alpha?this.DEFAULT_ALPHA:e.alpha}call(e,t){const n=ry(e);return Tu(n,this.alpha)}computeOutputShape(e){return e}getConfig(){const e={alpha:this.alpha},t=super.getConfig();return Object.assign(e,t),e}}hv.className="LeakyReLU",Gi(hv);class fv extends py{constructor(e){if(super(null==e?{}:e),this.DEFAULT_ALPHA_INITIALIZER="zeros",null==e&&(e={}),this.supportsMasking=!0,this.alphaInitializer=Mg(e.alphaInitializer||this.DEFAULT_ALPHA_INITIALIZER),this.alphaRegularizer=uv(e.alphaRegularizer),this.alphaConstraint=Nm(e.alphaConstraint),null==e.sharedAxes)this.sharedAxes=null;else if(Array.isArray(e.sharedAxes))this.sharedAxes=e.sharedAxes;else{if("number"!=typeof e.sharedAxes)throw new Yp(`Expected sharedAxes to be a number or an array of numbers, but got ${e.sharedAxes}`);this.sharedAxes=[e.sharedAxes]}}build(e){const t=(e=ay(e)).slice(1);if(null!=this.sharedAxes)for(const e of this.sharedAxes)t[e-1]=1;this.alpha=this.addWeight("alpha",t,"float32",this.alphaInitializer,this.alphaRegularizer,!0,this.alphaConstraint);const n={};if(null!=this.sharedAxes)for(let t=1;t(Pm(t),"channelsFirst"===t?gi(e,[0,2,3,1]):e)))}function wv(e,t){return po((()=>(Pm(t),"channelsFirst"===t?gi(e,[0,2,3,4,1]):e)))}function _v(e,t,n,r=[1,1],a="valid",s,i,o=null){return po((()=>{if(null==s&&(s="channelsLast"),Pm(s),3!==e.rank&&4!==e.rank)throw new Yp(`conv2dWithBiasActivation expects input to be of rank 3 or 4, but received ${e.rank}.`);if(3!==t.rank&&4!==t.rank)throw new Yp(`conv2dWithBiasActivation expects kernel to be of rank 3 or 4, but received ${e.rank}.`);let l=vv(e,s);if("causal"===a)throw new Jp("The support for CAUSAL padding mode in conv1dWithBias is not implemented yet.");return l=Gh({x:l,filter:t,strides:r,pad:"same"===a?"same":"valid",dilations:i,dataFormat:"NHWC",bias:n,activation:o}),"channelsFirst"===s&&(l=gi(l,[0,3,1,2])),l}))}mv.className="Softmax",Gi(mv);class xv extends py{constructor(e,t){if(super(t),this.bias=null,this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_BIAS_INITIALIZER="zeros",xv.verifyArgs(t),this.rank=e,mm(this.rank,"rank"),1!==this.rank&&2!==this.rank&&3!==this.rank)throw new Jp(`Convolution layer for rank other than 1, 2, or 3 (${this.rank}) is not implemented yet.`);if(this.kernelSize=gv(t.kernelSize,e,"kernelSize"),this.strides=gv(null==t.strides?1:t.strides,e,"strides"),this.padding=null==t.padding?"valid":t.padding,Lm(this.padding),this.dataFormat=null==t.dataFormat?"channelsLast":t.dataFormat,Pm(this.dataFormat),this.activation=nv(t.activation),this.useBias=null==t.useBias||t.useBias,this.biasInitializer=Mg(t.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.biasConstraint=Nm(t.biasConstraint),this.biasRegularizer=uv(t.biasRegularizer),this.activityRegularizer=uv(t.activityRegularizer),this.dilationRate=gv(null==t.dilationRate?1:t.dilationRate,e,"dilationRate"),1===this.rank&&Array.isArray(this.dilationRate)&&1!==this.dilationRate.length)throw new Yp(`dilationRate must be a number or an array of a single number for 1D convolution, but received ${JSON.stringify(this.dilationRate)}`);if(2===this.rank){if("number"==typeof this.dilationRate)this.dilationRate=[this.dilationRate,this.dilationRate];else if(2!==this.dilationRate.length)throw new Yp(`dilationRate must be a number or array of two numbers for 2D convolution, but received ${JSON.stringify(this.dilationRate)}`)}else if(3===this.rank)if("number"==typeof this.dilationRate)this.dilationRate=[this.dilationRate,this.dilationRate,this.dilationRate];else if(3!==this.dilationRate.length)throw new Yp(`dilationRate must be a number or array of three numbers for 3D convolution, but received ${JSON.stringify(this.dilationRate)}`)}static verifyArgs(e){if(em("kernelSize"in e,"required key 'kernelSize' not in config"),"number"!=typeof e.kernelSize&&!pm(e.kernelSize,"number",1,3))throw new Yp(`BaseConv expects config.kernelSize to be number or number[] with length 1, 2, or 3, but received ${JSON.stringify(e.kernelSize)}.`)}getConfig(){const e={kernelSize:this.kernelSize,strides:this.strides,padding:this.padding,dataFormat:this.dataFormat,dilationRate:this.dilationRate,activation:ev(this.activation),useBias:this.useBias,biasInitializer:Og(this.biasInitializer),biasRegularizer:ov(this.biasRegularizer),activityRegularizer:ov(this.activityRegularizer),biasConstraint:Cm(this.biasConstraint)},t=super.getConfig();return Object.assign(e,t),e}}class kv extends xv{constructor(e,t){super(e,t),this.kernel=null,kv.verifyArgs(t),this.filters=t.filters,mm(this.filters,"filters"),this.kernelInitializer=Mg(t.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.kernelConstraint=Nm(t.kernelConstraint),this.kernelRegularizer=uv(t.kernelRegularizer)}build(e){e=ay(e);const t="channelsFirst"===this.dataFormat?1:e.length-1;if(null==e[t])throw new Yp(`The channel dimension of the input should be defined. Found ${e[t]}`);const n=e[t],r=this.kernelSize.concat([n,this.filters]);this.kernel=this.addWeight("kernel",r,null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.filters],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint)),this.inputSpec=[{ndim:this.rank+2,axes:{[t]:n}}],this.built=!0}call(e,t){return po((()=>{let t;e=ry(e);const n=null==this.bias?null:this.bias.read(),r=ym(this.activation.getClassName());if(null!=r&&2===this.rank)t=_v(e,this.kernel.read(),n,this.strides,this.padding,this.dataFormat,this.dilationRate,r);else{if(1===this.rank)t=function(e,t,n,r=1,a="valid",s,i=1){return po((()=>{if(null==s&&(s="channelsLast"),Pm(s),3!==e.shape.length)throw new Yp(`The input of a conv1dWithBias operation should be 3, but is ${e.shape.length} instead.`);if(3!==t.shape.length)throw new Yp(`The kernel for a conv1dWithBias operation should be 3, but is ${t.shape.length} instead`);if(null!=n&&1!==n.shape.length)throw new Yp(`The bias for a conv1dWithBias operation should be 1, but is ${t.shape.length} instead`);if("channelsFirst"===s&&(e=gi(e,[0,2,1])),"causal"===a)throw new Jp("The support for CAUSAL padding mode in conv1dWithBias is not implemented yet.");let o=Jl(e,t,r,"same"===a?"same":"valid","NWC",i);return null!=n&&(o=hg(o,n)),o}))}(e,this.kernel.read(),n,this.strides[0],this.padding,this.dataFormat,this.dilationRate[0]);else if(2===this.rank)t=_v(e,this.kernel.read(),n,this.strides,this.padding,this.dataFormat,this.dilationRate);else{if(3!==this.rank)throw new Jp("convolutions greater than 3D are not implemented yet.");t=function(e,t,n,r=[1,1,1],a="valid",s,i){return po((()=>{if(null==s&&(s="channelsLast"),Pm(s),4!==e.rank&&5!==e.rank)throw new Yp(`conv3dWithBias expects input to be of rank 4 or 5, but received ${e.rank}.`);if(4!==t.rank&&5!==t.rank)throw new Yp(`conv3dWithBias expects kernel to be of rank 4 or 5, but received ${e.rank}.`);let o=wv(e,s);if("causal"===a)throw new Jp("The support for CAUSAL padding mode in conv3dWithBias is not implemented yet.");return o=eu(o,t,r,"same"===a?"same":"valid","NDHWC",i),null!=n&&(o=hg(o,n)),"channelsFirst"===s&&(o=gi(o,[0,4,1,2,3])),o}))}(e,this.kernel.read(),n,this.strides,this.padding,this.dataFormat,this.dilationRate)}null!=this.activation&&(t=this.activation.apply(t))}return t}))}computeOutputShape(e){e=ay(e);const t=[],n="channelsLast"===this.dataFormat?e.slice(1,e.length-1):e.slice(2);for(let e=0;e 0 but got ${JSON.stringify(e.filters)}`)}}class Ev extends kv{constructor(e){super(2,e),Ev.verifyArgs(e)}getConfig(){const e=super.getConfig();return delete e.rank,e}static verifyArgs(e){if("number"!=typeof e.kernelSize&&!pm(e.kernelSize,"number",1,2))throw new Yp(`Conv2D expects config.kernelSize to be number or number[] with length 1 or 2, but received ${JSON.stringify(e.kernelSize)}.`)}}Ev.className="Conv2D",Gi(Ev);class Cv extends kv{constructor(e){super(3,e),Cv.verifyArgs(e)}getConfig(){const e=super.getConfig();return delete e.rank,e}static verifyArgs(e){if("number"!=typeof e.kernelSize&&(!Array.isArray(e.kernelSize)||1!==e.kernelSize.length&&3!==e.kernelSize.length))throw new Yp(`Conv3D expects config.kernelSize to be number or [number, number, number], but received ${JSON.stringify(e.kernelSize)}.`)}}Cv.className="Conv3D",Gi(Cv);class Sv extends Ev{constructor(e){if(super(e),this.inputSpec=[new uy({ndim:4})],"same"!==this.padding&&"valid"!==this.padding)throw new Yp(`Conv2DTranspose currently supports only padding modes 'same' and 'valid', but received padding mode ${this.padding}`)}build(e){if(4!==(e=ay(e)).length)throw new Yp("Input should have rank 4; Received input shape: "+JSON.stringify(e));const t="channelsFirst"===this.dataFormat?1:e.length-1;if(null==e[t])throw new Yp("The channel dimension of the inputs should be defined. Found `None`.");const n=e[t],r=this.kernelSize.concat([this.filters,n]);this.kernel=this.addWeight("kernel",r,"float32",this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.filters],"float32",this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint)),this.inputSpec=[new uy({ndim:4,axes:{[t]:n}})],this.built=!0}call(e,t){return po((()=>{let t=ry(e);if(4!==t.shape.length)throw new Yp(`Conv2DTranspose.call() expects input tensor to be rank-4, but received a tensor of rank-${t.shape.length}`);const n=t.shape,r=n[0];let a,s;"channelsFirst"===this.dataFormat?(a=2,s=3):(a=1,s=2);const i=n[a],o=n[s],l=this.kernelSize[0],u=this.kernelSize[1],c=this.strides[0],h=this.strides[1],f=[r,bv(i,c,l,this.padding),bv(o,h,u,this.padding),this.filters];"channelsLast"!==this.dataFormat&&(t=gi(t,[0,2,3,1]));let d=Ql(t,this.kernel.read(),f,this.strides,this.padding);return"channelsLast"!==this.dataFormat&&(d=gi(d,[0,3,1,2])),null!=this.bias&&(d=hg(d,this.bias.read(),this.dataFormat)),null!=this.activation&&(d=this.activation.apply(d)),d}))}computeOutputShape(e){const t=(e=ay(e)).slice();let n,r,a;"channelsFirst"===this.dataFormat?(n=1,r=2,a=3):(n=3,r=1,a=2);const s=this.kernelSize[0],i=this.kernelSize[1],o=this.strides[0],l=this.strides[1];return t[n]=this.filters,t[r]=bv(t[r],o,s,this.padding),t[a]=bv(t[a],l,i,this.padding),t}getConfig(){const e=super.getConfig();return delete e.dilationRate,e}}Sv.className="Conv2DTranspose",Gi(Sv);class Nv extends Cv{constructor(e){if(super(e),this.inputSpec=[new uy({ndim:5})],"same"!==this.padding&&"valid"!==this.padding)throw new Yp(`Conv3DTranspose currently supports only padding modes 'same' and 'valid', but received padding mode ${this.padding}`)}build(e){if(5!==(e=ay(e)).length)throw new Yp("Input should have rank 5; Received input shape: "+JSON.stringify(e));const t="channelsFirst"===this.dataFormat?1:e.length-1;if(null==e[t])throw new Yp("The channel dimension of the inputs should be defined. Found `None`.");const n=e[t],r=this.kernelSize.concat([this.filters,n]);this.kernel=this.addWeight("kernel",r,"float32",this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.filters],"float32",this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint)),this.inputSpec=[new uy({ndim:5,axes:{[t]:n}})],this.built=!0}call(e,t){return po((()=>{let t=ry(e);if(5!==t.shape.length)throw new Yp(`Conv3DTranspose.call() expects input tensor to be rank-4, but received a tensor of rank-${t.shape.length}`);const n=t.shape,r=n[0];let a,s,i;"channelsFirst"===this.dataFormat?(i=2,a=3,s=4):(i=1,a=2,s=3);const o=n[i],l=n[a],u=n[s],c=this.kernelSize[0],h=this.kernelSize[1],f=this.kernelSize[2],d=this.strides[0],p=this.strides[1],m=this.strides[2],g=[r,bv(o,d,c,this.padding),bv(l,p,h,this.padding),bv(u,m,f,this.padding),this.filters];"channelsLast"!==this.dataFormat&&(t=gi(t,[0,2,3,4,1]));let y=nu(t,this.kernel.read(),g,this.strides,this.padding);return"channelsLast"!==this.dataFormat&&(y=gi(y,[0,4,1,2,3])),null!==this.bias&&(y=hg(y,this.bias.read(),this.dataFormat)),null!==this.activation&&(y=this.activation.apply(y)),y}))}computeOutputShape(e){const t=(e=ay(e)).slice();let n,r,a,s;"channelsFirst"===this.dataFormat?(n=1,r=2,a=3,s=4):(n=4,r=1,a=2,s=3);const i=this.kernelSize[0],o=this.kernelSize[1],l=this.kernelSize[2],u=this.strides[0],c=this.strides[1],h=this.strides[2];return t[n]=this.filters,t[r]=bv(t[r],u,i,this.padding),t[a]=bv(t[a],c,o,this.padding),t[s]=bv(t[s],h,l,this.padding),t}getConfig(){const e=super.getConfig();return delete e.dilationRate,e}}Nv.className="Conv3DTranspose",Gi(Nv);class Iv extends kv{constructor(e,t){if(super(e,t),this.DEFAULT_DEPTHWISE_INITIALIZER="glorotUniform",this.DEFAULT_POINTWISE_INITIALIZER="glorotUniform",this.depthwiseKernel=null,this.pointwiseKernel=null,null==t.filters)throw new Yp("The `filters` configuration field is required by SeparableConv, but is unspecified.");if(null!=t.kernelInitializer||null!=t.kernelRegularizer||null!=t.kernelConstraint)throw new Yp("Fields kernelInitializer, kernelRegularizer and kernelConstraint are invalid for SeparableConv2D. Use depthwiseInitializer, depthwiseRegularizer, depthwiseConstraint, pointwiseInitializer, pointwiseRegularizer and pointwiseConstraint instead.");if(null!=t.padding&&"same"!==t.padding&&"valid"!==t.padding)throw new Yp(`SeparableConv${this.rank}D supports only padding modes: 'same' and 'valid', but received ${JSON.stringify(t.padding)}`);this.depthMultiplier=null==t.depthMultiplier?1:t.depthMultiplier,this.depthwiseInitializer=Mg(t.depthwiseInitializer||this.DEFAULT_DEPTHWISE_INITIALIZER),this.depthwiseRegularizer=uv(t.depthwiseRegularizer),this.depthwiseConstraint=Nm(t.depthwiseConstraint),this.pointwiseInitializer=Mg(t.depthwiseInitializer||this.DEFAULT_POINTWISE_INITIALIZER),this.pointwiseRegularizer=uv(t.pointwiseRegularizer),this.pointwiseConstraint=Nm(t.pointwiseConstraint)}build(e){if((e=ay(e)).length{let t;if(e=ry(e),1===this.rank)throw new Jp("1D separable convolution is not implemented yet.");return 2===this.rank&&("channelsFirst"===this.dataFormat&&(e=gi(e,[0,2,3,1])),t=Yc(e,this.depthwiseKernel.read(),this.pointwiseKernel.read(),this.strides,this.padding,this.dilationRate,"NHWC")),this.useBias&&(t=hg(t,this.bias.read(),this.dataFormat)),null!=this.activation&&(t=this.activation.apply(t)),"channelsFirst"===this.dataFormat&&(t=gi(t,[0,3,1,2])),t}))}getConfig(){const e=super.getConfig();return delete e.rank,delete e.kernelInitializer,delete e.kernelRegularizer,delete e.kernelConstraint,e.depthwiseInitializer=Og(this.depthwiseInitializer),e.pointwiseInitializer=Og(this.pointwiseInitializer),e.depthwiseRegularizer=ov(this.depthwiseRegularizer),e.pointwiseRegularizer=ov(this.pointwiseRegularizer),e.depthwiseConstraint=Cm(this.depthwiseConstraint),e.pointwiseConstraint=Cm(this.pointwiseConstraint),e}}Iv.className="SeparableConv";class Av extends Iv{constructor(e){super(2,e)}}Av.className="SeparableConv2D",Gi(Av);class Dv extends kv{constructor(e){super(1,e),Dv.verifyArgs(e),this.inputSpec=[{ndim:3}]}getConfig(){const e=super.getConfig();return delete e.rank,delete e.dataFormat,e}static verifyArgs(e){if("number"!=typeof e.kernelSize&&!pm(e.kernelSize,"number",1,1))throw new Yp(`Conv1D expects config.kernelSize to be number or number[] with length 1, but received ${JSON.stringify(e.kernelSize)}.`)}}Dv.className="Conv1D",Gi(Dv);class Tv extends py{constructor(e){super(e),"number"==typeof e.cropping?this.cropping=[[e.cropping,e.cropping],[e.cropping,e.cropping]]:"number"==typeof e.cropping[0]?this.cropping=[[e.cropping[0],e.cropping[0]],[e.cropping[1],e.cropping[1]]]:this.cropping=e.cropping,this.dataFormat=void 0===e.dataFormat?"channelsLast":e.dataFormat,this.inputSpec=[{ndim:4}]}computeOutputShape(e){return"channelsFirst"===this.dataFormat?[e[0],e[1],e[2]-this.cropping[0][0]-this.cropping[0][1],e[3]-this.cropping[1][0]-this.cropping[1][1]]:[e[0],e[1]-this.cropping[0][0]-this.cropping[0][1],e[2]-this.cropping[1][0]-this.cropping[1][1],e[3]]}call(e,t){return po((()=>{if(e=ry(e),"channelsLast"===this.dataFormat){const t=ng(e,this.cropping[0][0],e.shape[1]-this.cropping[0][0]-this.cropping[0][1],2);return ng(t,this.cropping[1][0],e.shape[2]-this.cropping[1][1]-this.cropping[1][0],3)}{const t=ng(e,this.cropping[0][0],e.shape[2]-this.cropping[0][0]-this.cropping[0][1],3);return ng(t,this.cropping[1][0],e.shape[3]-this.cropping[1][1]-this.cropping[1][0],4)}}))}getConfig(){const e={cropping:this.cropping,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}}Tv.className="Cropping2D",Gi(Tv);class Fv extends py{constructor(e){var t;super(e),this.DEFAULT_SIZE=[2,2],this.inputSpec=[{ndim:4}],this.size=null==e.size?this.DEFAULT_SIZE:e.size,this.dataFormat=null==e.dataFormat?"channelsLast":e.dataFormat,Pm(this.dataFormat),this.interpolation=null==e.interpolation?"nearest":e.interpolation,t=this.interpolation,dm(Rm,"InterpolationFormat",t)}computeOutputShape(e){if("channelsFirst"===this.dataFormat){const t=null==e[2]?null:this.size[0]*e[2],n=null==e[3]?null:this.size[1]*e[3];return[e[0],e[1],t,n]}{const t=null==e[1]?null:this.size[0]*e[1],n=null==e[2]?null:this.size[1]*e[2];return[e[0],t,n,e[3]]}}call(e,t){return po((()=>{let t=ry(e);const n=t.shape;if("channelsFirst"===this.dataFormat){t=gi(t,[0,2,3,1]);const e=this.size[0]*n[2],r=this.size[1]*n[3],a="nearest"===this.interpolation?t.resizeNearestNeighbor([e,r]):t.resizeBilinear([e,r]);return gi(a,[0,3,1,2])}{const e=this.size[0]*n[1],r=this.size[1]*n[2];return"nearest"===this.interpolation?t.resizeNearestNeighbor([e,r]):t.resizeBilinear([e,r])}}))}getConfig(){const e={size:this.size,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}}Fv.className="UpSampling2D",Gi(Fv);class Rv extends xv{constructor(e){super(2,e),this.depthwiseKernel=null,this.depthMultiplier=null==e.depthMultiplier?1:e.depthMultiplier,this.depthwiseInitializer=Mg(e.depthwiseInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.depthwiseConstraint=Nm(e.depthwiseConstraint),this.depthwiseRegularizer=uv(e.depthwiseRegularizer)}build(e){if((e=ay(e)).length<4)throw new Yp(`Inputs to DepthwiseConv2D should have rank 4. Received input shape: ${JSON.stringify(e)}.`);const t="channelsFirst"===this.dataFormat?1:3;if(null==e[t]||e[t]<0)throw new Yp(`The channel dimension of the inputs to DepthwiseConv2D should be defined, but is not (${e[t]}).`);const n=e[t],r=[this.kernelSize[0],this.kernelSize[1],n,this.depthMultiplier];this.depthwiseKernel=this.addWeight("depthwise_kernel",r,null,this.depthwiseInitializer,this.depthwiseRegularizer,!0,this.depthwiseConstraint),this.useBias?this.bias=this.addWeight("bias",[n*this.depthMultiplier],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint):this.bias=null,this.built=!0}call(e,t){return po((()=>{let t=function(e,t,n=[1,1],r="valid",a,s){return po((()=>{null==a&&(a="channelsLast"),Pm(a);let i=vv(e,a);if(4!==e.rank)throw new Yp(`Input for depthwiseConv2d is required to be 4-D, but is instead ${e.rank}-D`);if(4!==t.rank)throw new Yp(`depthwiseKernel is required to be 4-D, but is instead ${t.rank}-D`);return i=lu(i,t,n,"same"===r?"same":"valid","NHWC",s),"channelsFirst"===a&&(i=gi(i,[0,3,1,2])),i}))}(e=ry(e),this.depthwiseKernel.read(),this.strides,this.padding,this.dataFormat,null);return this.useBias&&(t=hg(t,this.bias.read(),this.dataFormat)),null!=this.activation&&(t=this.activation.apply(t)),t}))}computeOutputShape(e){e=ay(e);const t="channelsFirst"===this.dataFormat?e[2]:e[1],n="channelsFirst"===this.dataFormat?e[3]:e[2],r="channelsFirst"===this.dataFormat?e[1]*this.depthMultiplier:e[3]*this.depthMultiplier,a=yv(t,this.kernelSize[0],this.padding,this.strides[0]),s=yv(n,this.kernelSize[1],this.padding,this.strides[1]);return"channelsFirst"===this.dataFormat?[e[0],r,a,s]:[e[0],a,s,r]}getConfig(){const e=super.getConfig();return e.depthMultiplier=this.depthMultiplier,e.depthwiseInitializer=Og(this.depthwiseInitializer),e.depthwiseRegularizer=ov(this.depthwiseRegularizer),e.depthwiseConstraint=Cm(this.depthwiseRegularizer),e}}function Ov(e,t,n,r){if(Array.isArray(e)){if(null!=t||null!=n)throw new Yp("When inputs is an array, neither initialState or constants should be provided");null!=r&&(n=e.slice(e.length-r,e.length),e=e.slice(0,e.length-r)),e.length>1&&(t=e.slice(1,e.length)),e=e[0]}function a(e){return null==e||Array.isArray(e)?e:[e]}return{inputs:e,initialState:t=a(t),constants:n=a(n)}}function Mv(e,t,n,r=!1,a,s,i=!1,o=!1){return po((()=>{const l=t.shape.length;if(l<3)throw new Yp(`Input should be at least 3D, but is ${l}D.`);const u=[1,0].concat(Jm(2,l));if(t=gi(t,u),null!=s)throw new Jp("The rnn() functoin of the deeplearn.js backend does not support constants yet.");i&&console.warn("Backend rnn(): the unroll = true option is not applicable to the imperative deeplearn.js backend."),null!=a&&((a=a.asType("bool").asType("float32")).rank===l-1&&(a=vu(a,-1)),a=gi(a,u)),r&&(t=Uc(t,0),null!=a&&(a=Uc(a,0)));const c=[];let h,f=n;const d=t.shape[0],p=Sh(t);let m,g;null!=a&&(m=Sh(a));for(let t=0;te(n,f)));if(null==a)h=r[0],f=r[1];else{const e=po((()=>{const e=m[t],n=bc(e).sub(e);return{output:r[0].mul(e).add(f[0].mul(n)),newStates:f.map(((t,a)=>r[1][a].mul(e).add(t.mul(n))))}}));h=e.output,f=e.newStates}o&&c.push(h)}return o&&(g=dh(c,1)),[h,g,f]}))}Rv.className="DepthwiseConv2D",Gi(Rv);class Bv extends py{constructor(e){let t;if(super(e),null==e.cell)throw new Yp("cell property is missing for the constructor of RNN.");if(t=Array.isArray(e.cell)?new jv({cells:e.cell}):e.cell,null==t.stateSize)throw new Yp("The RNN cell should have an attribute `stateSize` (tuple of integers, one integer per RNN state).");this.cell=t,this.returnSequences=null!=e.returnSequences&&e.returnSequences,this.returnState=null!=e.returnState&&e.returnState,this.goBackwards=null!=e.goBackwards&&e.goBackwards,this._stateful=null!=e.stateful&&e.stateful,this.unroll=null!=e.unroll&&e.unroll,this.supportsMasking=!0,this.inputSpec=[new uy({ndim:3})],this.stateSpec=null,this.states_=null,this.numConstants=null,this.keptStates=[]}getStates(){return null==this.states_?Jm(0,Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1).map((e=>null)):this.states_}setStates(e){this.states_=e}computeOutputShape(e){ty(e)&&(e=e[0]),e=e;let t=this.cell.stateSize;Array.isArray(t)||(t=[t]);const n=t[0];let r;if(r=this.returnSequences?[e[0],e[1],n]:[e[0],n],this.returnState){const n=[];for(const r of t)n.push([e[0],r]);return[r].concat(n)}return r}computeMask(e,t){return po((()=>{Array.isArray(t)&&(t=t[0]);const e=this.returnSequences?t:null;if(this.returnState){const t=this.states.map((e=>null));return[e].concat(t)}return e}))}get states(){if(null==this.states_){const e=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1,t=[];for(let n=0;ne.shape[e.shape.length-1])),a))throw new Yp(`An initialState was passed that is not compatible with cell.stateSize. Received stateSpec=${this.stateSpec}; However cell.stateSize is ${this.cell.stateSize}`)}else this.stateSpec=a.map((e=>new uy({shape:[null,e]})));this.stateful&&this.resetStates()}resetStates(e,t=!1){po((()=>{if(!this.stateful)throw new Kp("Cannot call resetStates() on an RNN Layer that is not stateful.");const n=this.inputSpec[0].shape[0];if(null==n)throw new Yp("If an RNN is stateful, it needs to know its batch size. Specify the batch size of your input tensors: \n- If using a Sequential model, specify the batch size by passing a `batchInputShape` option to your first layer.\n- If using the functional API, specify the batch size by passing a `batchShape` option to your Input layer.");if(null==this.states_)Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map((e=>oc([n,e]))):this.states_=[oc([n,this.cell.stateSize])];else if(null==e)mo(this.states_),null!=this.keptStates&&(mo(this.keptStates),this.keptStates=[]),Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map((e=>oc([n,e]))):this.states_[0]=oc([n,this.cell.stateSize]);else{if(Array.isArray(e)||(e=[e]),e.length!==this.states_.length)throw new Yp(`Layer ${this.name} expects ${this.states_.length} state(s), but it received ${e.length} state value(s). Input received: ${e}`);!0===t?this.keptStates.push(this.states_.slice()):mo(this.states_);for(let t=0;tgo(e.clone())))}))}apply(e,t){let n=null==t?null:t.initialState,r=null==t?null:t.constants;null==t&&(t={});const a=Ov(e,n,r,this.numConstants);e=a.inputs,n=a.initialState,r=a.constants;let s=[],i=[];if(null!=n){t.initialState=n,s=s.concat(n),this.stateSpec=[];for(const e of n)this.stateSpec.push(new uy({shape:e.shape}));i=i.concat(this.stateSpec)}if(null!=r&&(t.constants=r,s=s.concat(r),this.numConstants=r.length),s[0]instanceof cy){const n=[e].concat(s),r=this.inputSpec.concat(i),a=this.inputSpec;this.inputSpec=r;const o=super.apply(n,t);return this.inputSpec=a,o}return super.apply(e,t)}call(e,t){return po((()=>{const n=null==t?null:t.mask,r=null==t?null:t.training;let a=null==t?null:t.initialState;e=ry(e),null==a&&(a=this.stateful?this.states_:this.getInitialState(e));const s=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1;if(a.length!==s)throw new Yp(`RNN Layer has ${s} state(s) but was passed ${a.length} initial state(s).`);this.unroll&&console.warn("Ignoring unroll = true for RNN layer, due to imperative backend.");const i={training:r},o=Mv(((e,t)=>{const n=this.cell.call([e].concat(t),i);return[n[0],n.slice(1)]}),e,a,this.goBackwards,n,null,this.unroll,this.returnSequences),l=o[0],u=o[1],c=o[2];this.stateful&&this.resetStates(c,r);const h=this.returnSequences?u:l;return this.returnState?[h].concat(c):h}))}getInitialState(e){return po((()=>{let t=oc(e.shape);return t=Uu(t,[1,2]),t=Qm(t),Array.isArray(this.cell.stateSize)?this.cell.stateSize.map((e=>e>1?sg(t,[1,e]):t)):this.cell.stateSize>1?[sg(t,[1,this.cell.stateSize])]:[t]}))}get trainableWeights(){return this.trainable?this.cell.trainableWeights:[]}get nonTrainableWeights(){return this.trainable?this.cell.nonTrainableWeights:this.cell.weights}setFastWeightInitDuringBuild(e){super.setFastWeightInitDuringBuild(e),null!=this.cell&&this.cell.setFastWeightInitDuringBuild(e)}getConfig(){const e=super.getConfig(),t={returnSequences:this.returnSequences,returnState:this.returnState,goBackwards:this.goBackwards,stateful:this.stateful,unroll:this.unroll};null!=this.numConstants&&(t.numConstants=this.numConstants);const n=this.cell.getConfig();return this.getClassName()===Bv.className&&(t.cell={className:this.cell.getClassName(),config:n}),Object.assign({},n,e,t)}static fromConfig(e,t,n={}){const r=Ay(t.cell,n);return new e(Object.assign(t,{cell:r}))}}Bv.className="RNN",Gi(Bv);class $v extends py{}class Pv extends $v{constructor(e){super(e),this.DEFAULT_ACTIVATION="tanh",this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_RECURRENT_INITIALIZER="orthogonal",this.DEFAULT_BIAS_INITIALIZER="zeros",this.units=e.units,mm(this.units,"units"),this.activation=nv(null==e.activation?this.DEFAULT_ACTIVATION:e.activation),this.useBias=null==e.useBias||e.useBias,this.kernelInitializer=Mg(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=Mg(e.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=Mg(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelRegularizer=uv(e.kernelRegularizer),this.recurrentRegularizer=uv(e.recurrentRegularizer),this.biasRegularizer=uv(e.biasRegularizer),this.kernelConstraint=Nm(e.kernelConstraint),this.recurrentConstraint=Nm(e.recurrentConstraint),this.biasConstraint=Nm(e.biasConstraint),this.dropout=qm([1,Ym([0,null==e.dropout?0:e.dropout])]),this.recurrentDropout=qm([1,Ym([0,null==e.recurrentDropout?0:e.recurrentDropout])]),this.stateSize=this.units,this.dropoutMask=null,this.recurrentDropoutMask=null}build(e){e=ay(e),this.kernel=this.addWeight("kernel",[e[e.length-1],this.units],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.recurrentKernel=this.addWeight("recurrent_kernel",[this.units,this.units],null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint),this.useBias?this.bias=this.addWeight("bias",[this.units],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint):this.bias=null,this.built=!0}call(e,t){return po((()=>{if(2!==(e=e).length)throw new Yp(`SimpleRNNCell expects 2 input Tensors, got ${e.length}.`);let n=e[1];e=e[0];const r=null!=t.training&&t.training;let a;0bc(e),rate:this.dropout,training:r})),0bc(n),rate:this.recurrentDropout,training:r}));const s=this.dropoutMask,i=this.recurrentDropoutMask;a=og(null!=s?Do(e,s):e,this.kernel.read()),null!=this.bias&&(a=hg(a,this.bias.read())),null!=i&&(n=Do(n,i));let o=No(a,og(n,this.recurrentKernel.read()));return null!=this.activation&&(o=this.activation.apply(o)),[o,o]}))}getConfig(){const e=super.getConfig(),t={units:this.units,activation:ev(this.activation),useBias:this.useBias,kernelInitializer:Og(this.kernelInitializer),recurrentInitializer:Og(this.recurrentInitializer),biasInitializer:Og(this.biasInitializer),kernelRegularizer:ov(this.kernelRegularizer),recurrentRegularizer:ov(this.recurrentRegularizer),biasRegularizer:ov(this.biasRegularizer),activityRegularizer:ov(this.activityRegularizer),kernelConstraint:Cm(this.kernelConstraint),recurrentConstraint:Cm(this.recurrentConstraint),biasConstraint:Cm(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout};return Object.assign({},e,t)}}Pv.className="SimpleRNNCell",Gi(Pv);class Lv extends Bv{constructor(e){e.cell=new Pv(e),super(e)}call(e,t){return po((()=>{null!=this.cell.dropoutMask&&(mo(this.cell.dropoutMask),this.cell.dropoutMask=null),null!=this.cell.recurrentDropoutMask&&(mo(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);const n=null==t?null:t.mask,r=null==t?null:t.training,a=null==t?null:t.initialState;return super.call(e,{mask:n,training:r,initialState:a})}))}static fromConfig(e,t){return new e(t)}}Lv.className="SimpleRNN",Gi(Lv);class zv extends $v{constructor(e){if(super(e),this.DEFAULT_ACTIVATION="tanh",this.DEFAULT_RECURRENT_ACTIVATION="hardSigmoid",this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_RECURRENT_INITIALIZER="orthogonal",this.DEFAULT_BIAS_INITIALIZER="zeros",e.resetAfter)throw new Yp("GRUCell does not support reset_after parameter set to true.");this.units=e.units,mm(this.units,"units"),this.activation=nv(void 0===e.activation?this.DEFAULT_ACTIVATION:e.activation),this.recurrentActivation=nv(void 0===e.recurrentActivation?this.DEFAULT_RECURRENT_ACTIVATION:e.recurrentActivation),this.useBias=null==e.useBias||e.useBias,this.kernelInitializer=Mg(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=Mg(e.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=Mg(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelRegularizer=uv(e.kernelRegularizer),this.recurrentRegularizer=uv(e.recurrentRegularizer),this.biasRegularizer=uv(e.biasRegularizer),this.kernelConstraint=Nm(e.kernelConstraint),this.recurrentConstraint=Nm(e.recurrentConstraint),this.biasConstraint=Nm(e.biasConstraint),this.dropout=qm([1,Ym([0,null==e.dropout?0:e.dropout])]),this.recurrentDropout=qm([1,Ym([0,null==e.recurrentDropout?0:e.recurrentDropout])]),this.implementation=e.implementation,this.stateSize=this.units,this.dropoutMask=null,this.recurrentDropoutMask=null}build(e){const t=(e=ay(e))[e.length-1];this.kernel=this.addWeight("kernel",[t,3*this.units],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.recurrentKernel=this.addWeight("recurrent_kernel",[this.units,3*this.units],null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint),this.useBias?this.bias=this.addWeight("bias",[3*this.units],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint):this.bias=null,this.built=!0}call(e,t){return po((()=>{if(2!==(e=e).length)throw new Yp(`GRUCell expects 2 input Tensors (inputs, h, c), got ${e.length}.`);const n=null!=t.training&&t.training;let r=e[1];e=e[0],0bc(e),rate:this.dropout,training:n,count:3})),0bc(r),rate:this.recurrentDropout,training:n,count:3}));const a=this.dropoutMask,s=this.recurrentDropoutMask;let i,o,l;0{null!=this.cell.dropoutMask&&(mo(this.cell.dropoutMask),this.cell.dropoutMask=null),null!=this.cell.recurrentDropoutMask&&(mo(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);const n=null==t?null:t.mask,r=null==t?null:t.training,a=null==t?null:t.initialState;return super.call(e,{mask:n,training:r,initialState:a})}))}static fromConfig(e,t){return 0===t.implmentation&&(t.implementation=1),new e(t)}}Wv.className="GRU",Gi(Wv);class Uv extends $v{constructor(e){super(e),this.DEFAULT_ACTIVATION="tanh",this.DEFAULT_RECURRENT_ACTIVATION="hardSigmoid",this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_RECURRENT_INITIALIZER="orthogonal",this.DEFAULT_BIAS_INITIALIZER="zeros",this.units=e.units,mm(this.units,"units"),this.activation=nv(void 0===e.activation?this.DEFAULT_ACTIVATION:e.activation),this.recurrentActivation=nv(void 0===e.recurrentActivation?this.DEFAULT_RECURRENT_ACTIVATION:e.recurrentActivation),this.useBias=null==e.useBias||e.useBias,this.kernelInitializer=Mg(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=Mg(e.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=Mg(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.unitForgetBias=e.unitForgetBias,this.kernelRegularizer=uv(e.kernelRegularizer),this.recurrentRegularizer=uv(e.recurrentRegularizer),this.biasRegularizer=uv(e.biasRegularizer),this.kernelConstraint=Nm(e.kernelConstraint),this.recurrentConstraint=Nm(e.recurrentConstraint),this.biasConstraint=Nm(e.biasConstraint),this.dropout=qm([1,Ym([0,null==e.dropout?0:e.dropout])]),this.recurrentDropout=qm([1,Ym([0,null==e.recurrentDropout?0:e.recurrentDropout])]),this.implementation=e.implementation,this.stateSize=[this.units,this.units],this.dropoutMask=null,this.recurrentDropoutMask=null}build(e){var t;const n=(e=ay(e))[e.length-1];let r;if(this.kernel=this.addWeight("kernel",[n,4*this.units],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.recurrentKernel=this.addWeight("recurrent_kernel",[this.units,4*this.units],null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint),this.useBias){if(this.unitForgetBias){const e=this.biasInitializer,n=this.units;r=new((t=class extends gg{apply(t,r){const a=e.apply([n]),s=(new bg).apply([n]),i=e.apply([2*n]);return ag(ag(a,s),i)}}).className="CustomInit",t)}else r=this.biasInitializer;this.bias=this.addWeight("bias",[4*this.units],null,r,this.biasRegularizer,!0,this.biasConstraint)}else this.bias=null;this.built=!0}call(e,t){return po((()=>{const n=null!=t.training&&t.training;if(3!==(e=e).length)throw new Yp(`LSTMCell expects 3 input Tensors (inputs, h, c), got ${e.length}.`);let r=e[1];const a=e[2];e=e[0],0bc(e),rate:this.dropout,training:n,count:4})),0bc(r),rate:this.recurrentDropout,training:n,count:4}));const s=this.dropoutMask,i=this.recurrentDropoutMask;let o,l,u,c;0{null!=this.cell.dropoutMask&&(mo(this.cell.dropoutMask),this.cell.dropoutMask=null),null!=this.cell.recurrentDropoutMask&&(mo(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);const n=null==t?null:t.mask,r=null==t?null:t.training,a=null==t?null:t.initialState;return super.call(e,{mask:n,training:r,initialState:a})}))}static fromConfig(e,t){return 0===t.implmentation&&(t.implementation=1),new e(t)}}Vv.className="LSTM",Gi(Vv);class jv extends $v{constructor(e){super(e),this.cells=e.cells}get stateSize(){const e=[];for(const t of this.cells.slice().reverse())Array.isArray(t.stateSize)?e.push(...t.stateSize):e.push(t.stateSize);return e}call(e,t){return po((()=>{let n=(e=e).slice(1);const r=[];for(const e of this.cells.slice().reverse())Array.isArray(e.stateSize)?r.push(n.splice(0,e.stateSize.length)):r.push(n.splice(0,1));r.reverse();const a=[];let s;for(let i=0;i{Um(`RNNCell_${r}`,(()=>{n.build(e),t=Array.isArray(n.stateSize)?n.stateSize[0]:n.stateSize,e=[e[0],t]}))})),this.built=!0}getConfig(){const e=super.getConfig(),t={cells:this.cells.map((e=>({className:e.getClassName(),config:e.getConfig()})))};return Object.assign({},e,t)}static fromConfig(e,t,n={}){const r=[];for(const e of t.cells)r.push(Ay(e,n));return new e({cells:r})}get trainableWeights(){if(!this.trainable)return[];const e=[];for(const t of this.cells)e.push(...t.trainableWeights);return e}get nonTrainableWeights(){const e=[];for(const t of this.cells)e.push(...t.nonTrainableWeights);if(!this.trainable){const t=[];for(const e of this.cells)t.push(...e.trainableWeights);return t.concat(e)}return e}getWeights(){const e=[];for(const t of this.cells)e.push(...t.weights);return oy(e)}setWeights(e){const t=[];for(const n of this.cells){const r=n.weights.length,a=e.splice(r);for(let e=0;efg(t(),n),i=()=>dg(s,t,r);return!a||a<=1?go(i().clone()):Array(a).fill(void 0).map(i).map((e=>go(e.clone())))}jv.className="StackedRNNCells",Gi(jv);class Gv extends Bv{constructor(e){if(e.unroll)throw new Jp("Unrolling is not possible with convolutional RNNs.");if(Array.isArray(e.cell))throw new Jp("It is not possible at the moment to stack convolutional cells.");super(e),this.inputSpec=[new uy({ndim:5})]}call(e,t){return po((()=>{if(null!=this.cell.dropoutMask&&(mo(this.cell.dropoutMask),this.cell.dropoutMask=null),null!=this.cell.recurrentDropoutMask&&(mo(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null),t&&t.constants)throw new Yp("ConvRNN2D cell does not support constants");const n=null==t?null:t.mask,r=null==t?null:t.training,a=null==t?null:t.initialState;return super.call(e,{mask:n,training:r,initialState:a})}))}computeOutputShape(e){let t=this.computeSingleOutputShape(e);return this.returnSequences||(t=[t[0],...t.slice(2)]),this.returnState&&(t=[t,...Array(2).fill([e[0],...t.slice(-3)])]),t}getInitialState(e){return po((()=>{const{stateSize:t}=this.cell,n=e.shape,r=this.computeSingleOutputShape(n),a=oc([r[0],...r.slice(2)]);return Array.isArray(t)?Array(t.length).fill(a):[a]}))}resetStates(e,t=!1){po((()=>{if(!this.stateful)throw new Kp("Cannot call resetStates() on an RNN Layer that is not stateful.");const n=this.inputSpec[0].shape,r=this.computeSingleOutputShape(n),a=[r[0],...r.slice(2)];if(null==n[0])throw new Yp("If an RNN is stateful, it needs to know its batch size. Specify the batch size of your input tensors: \n- If using a Sequential model, specify the batch size by passing a `batchInputShape` option to your first layer.\n- If using the functional API, specify the batch size by passing a `batchShape` option to your Input layer.");if(null==this.getStates())Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map((()=>oc(a))):this.states_=[oc(a)];else if(null==e)mo(this.states_),null!=this.keptStates&&(mo(this.keptStates),this.keptStates=[]),Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map((()=>oc(a))):this.states_[0]=oc(a);else{if(Array.isArray(e)||(e=[e]),e.length!==this.states_.length)throw new Yp(`Layer ${this.name} expects ${this.states_.length} state(s), but it received ${e.length} state value(s). Input received: ${e}`);t?this.keptStates.push(this.states_.slice()):mo(this.states_);for(let t=0;tgo(e.clone())))}))}computeSingleOutputShape(e){const{dataFormat:t,filters:n,kernelSize:r,padding:a,strides:s,dilationRate:i}=this.cell,o="channelsFirst"===t,l=e[o?3:2],u=e[o?4:3],c=yv(l,r[0],a,s[0],i[0]),h=yv(u,r[1],a,s[1],i[1]);return[...e.slice(0,2),...o?[n,c,h]:[c,h,n]]}}Gv.className="ConvRNN2D";class Xv extends Uv{constructor(e){const{filters:t,kernelSize:n,strides:r,padding:a,dataFormat:s,dilationRate:i}=e;super(Object.assign({},e,{units:t})),this.filters=t,mm(this.filters,"filters"),this.kernelSize=gv(n,2,"kernelSize"),this.kernelSize.forEach((e=>mm(e,"kernelSize"))),this.strides=gv(r||1,2,"strides"),this.strides.forEach((e=>mm(e,"strides"))),this.padding=a||"valid",Lm(this.padding),this.dataFormat=s||"channelsLast",Pm(this.dataFormat),this.dilationRate=gv(i||1,2,"dilationRate"),this.dilationRate.forEach((e=>mm(e,"dilationRate")))}build(e){var t;e=ay(e);const n="channelsFirst"===this.dataFormat?1:e.length-1;if(null==e[n])throw new Yp(`The channel dimension of the input should be defined. Found ${e[n]}`);const r=e[n],a=this.kernelSize.concat([r,4*this.filters]);this.kernel=this.addWeight("kernel",a,null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint);const s=this.kernelSize.concat([this.filters,4*this.filters]);if(this.recurrentKernel=this.addWeight("recurrent_kernel",s,null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint),this.useBias){let e;if(this.unitForgetBias){const n=this.biasInitializer,r=this.filters;e=new((t=class extends gg{apply(e,t){return rg([n.apply([r]),lc([r]),n.apply([2*r])])}}).className="CustomInit",t)}else e=this.biasInitializer;this.bias=this.addWeight("bias",[4*this.filters],null,e,this.biasRegularizer,!0,this.biasConstraint)}this.built=!0}call(e,t){return po((()=>{if(3!==e.length)throw new Yp(`ConvLSTM2DCell expects 3 input Tensors (inputs, h, c), got ${e.length}.`);const n=t.training||!1,r=e[0],a=e[1],s=e[2];0bc(r),rate:this.dropout,training:n,count:4}));const i=this.dropoutMask,o=(e,t,n)=>t&&t[n]?Do(t[n],e):e;let l=o(r,i,0),u=o(r,i,1),c=o(r,i,2),h=o(r,i,3);0bc(a),rate:this.recurrentDropout,training:n,count:4}));const f=this.recurrentDropoutMask;let d=o(a,f,0),p=o(a,f,1),m=o(a,f,2),g=o(a,f,3);const[y,b,v,w]=uh(this.kernel.read(),4,3),[_,x,k,E]=this.useBias?uh(this.bias.read(),4):[null,null,null,null];l=this.inputConv(l,y,_,this.padding),u=this.inputConv(u,b,x,this.padding),c=this.inputConv(c,v,k,this.padding),h=this.inputConv(h,w,E,this.padding);const[C,S,N,I]=uh(this.recurrentKernel.read(),4,3);d=this.recurrentConv(d,C),p=this.recurrentConv(p,S),m=this.recurrentConv(m,N),g=this.recurrentConv(g,I);const A=this.recurrentActivation.apply(No(l,d)),D=this.recurrentActivation.apply(No(u,p)),T=No(Do(D,s),Do(A,this.activation.apply(No(c,m)))),F=Do(this.recurrentActivation.apply(No(h,g)),this.activation.apply(T));return[F,F,T]}))}getConfig(){const e=super.getConfig(),{units:t}=e,n=function(e,t){var n={};for(var r in e)Object.prototype.hasOwnProperty.call(e,r)&&t.indexOf(r)<0&&(n[r]=e[r]);if(null!=e&&"function"==typeof Object.getOwnPropertySymbols){var a=0;for(r=Object.getOwnPropertySymbols(e);a{this.invokeCallHook(e,t);const n=ry(e);if(0fg(n,this.rate,r,this.seed)),(()=>n),e)}return e}))}getConfig(){const e={rate:this.rate,noiseShape:this.noiseShape,seed:this.seed},t=super.getConfig();return Object.assign(e,t),e}dispose(){return super.dispose()}}qv.className="Dropout",Gi(qv);class Yv extends qv{constructor(e){super(e),this.inputSpec=[{ndim:3}]}getNoiseShape(e){const t=e.shape;return[t[0],1,t[2]]}}Yv.className="SpatialDropout1D",Gi(Yv);class Jv extends py{constructor(e){if(super(e),this.activation=null,this.useBias=!0,this.kernel=null,this.bias=null,this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_BIAS_INITIALIZER="zeros",null==e.batchInputShape&&null==e.inputShape&&null!=e.inputDim){let t=null;null!=e.batchSize&&(t=e.batchSize),this.batchInputShape=[t,e.inputDim]}this.units=e.units,mm(this.units,"units"),this.activation=nv(e.activation),null!=e.useBias&&(this.useBias=e.useBias),this.kernelInitializer=Mg(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.biasInitializer=Mg(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelConstraint=Nm(e.kernelConstraint),this.biasConstraint=Nm(e.biasConstraint),this.kernelRegularizer=uv(e.kernelRegularizer),this.biasRegularizer=uv(e.biasRegularizer),this.activityRegularizer=uv(e.activityRegularizer),this.supportsMasking=!0,this.inputSpec=[{minNDim:2}]}build(e){const t=(e=ay(e))[e.length-1];null==this.kernel&&(this.kernel=this.addWeight("kernel",[t,this.units],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.units],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint))),this.inputSpec=[{minNDim:2,axes:{[-1]:t}}],this.built=!0}computeOutputShape(e){const t=(e=ay(e)).slice();return t[t.length-1]=this.units,t}call(e,t){return po((()=>{this.invokeCallHook(e,t);const n=ry(e),r=ym(this.activation.getClassName());let a;return null!=r?a=og(n,this.kernel.read(),r,this.bias?this.bias.read():null):(a=og(n,this.kernel.read()),null!=this.bias&&(a=hg(a,this.bias.read())),null!=this.activation&&(a=this.activation.apply(a))),a}))}getConfig(){const e={units:this.units,activation:ev(this.activation),useBias:this.useBias,kernelInitializer:Og(this.kernelInitializer),biasInitializer:Og(this.biasInitializer),kernelRegularizer:ov(this.kernelRegularizer),biasRegularizer:ov(this.biasRegularizer),activityRegularizer:ov(this.activityRegularizer),kernelConstraint:Cm(this.kernelConstraint),biasConstraint:Cm(this.biasConstraint)},t=super.getConfig();return Object.assign(e,t),e}}Jv.className="Dense",Gi(Jv);class Zv extends py{constructor(e){super(e=e||{}),this.inputSpec=[{minNDim:3}],this.dataFormat=e.dataFormat}computeOutputShape(e){e=ay(e);for(const t of e.slice(1))if(null==t)throw new Yp(`The shape of the input to "Flatten" is not fully defined (got ${e.slice(1)}). Make sure to pass a complete "input_shape" or "batch_input_shape" argument to the first layer in your model.`);return[e[0],Xm(e,1)]}call(e,t){return po((()=>{this.invokeCallHook(e,t);let n=ry(e);if("channelsFirst"===this.dataFormat&&n.rank>1){const e=[0];for(let t=2;t{this.invokeCallHook(e,t);const n=ry(e);return this.activation.apply(n)}))}getConfig(){const e={activation:ev(this.activation)},t=super.getConfig();return Object.assign(e,t),e}}Qv.className="Activation",Gi(Qv);class ew extends py{constructor(e){super(e),this.n=e.n,this.inputSpec=[{ndim:2}]}computeOutputShape(e){return[e[0],this.n,e[1]]}call(e,t){return po((()=>{return e=ry(e),t=e,n=this.n,po((()=>{if(2!==t.shape.length)throw new Yp(`repeat() expects a rank-2 tensor, but received a rank-${t.shape.length} tensor.`);return sg(Qm(t,1),[1,n,1])}));var t,n}))}getConfig(){const e={n:this.n},t=super.getConfig();return Object.assign(e,t),e}}ew.className="RepeatVector",Gi(ew);class tw extends py{constructor(e){super(e),this.targetShape=e.targetShape;for(let e=0;e{this.invokeCallHook(e,t);const n=ry(e),r=n.shape,a=r.slice(0,1).concat(this.fixUnknownDimension(r.slice(1),this.targetShape));return n.reshape(a)}))}getConfig(){const e={targetShape:this.targetShape},t=super.getConfig();return Object.assign(e,t),e}}tw.className="Reshape",Gi(tw);class nw extends py{constructor(e){if(super(e),null==e.dims)throw new Error("Required configuration field `dims` is missing during Permute constructor call.");if(!Array.isArray(e.dims))throw new Error(`Permute constructor requires \`dims\` to be an Array, but received ${e.dims} instead.`);const t=Jm(1,e.dims.length+1);if(!se(e.dims.slice().sort(),t))throw new Error("Invalid permutation `dims`: "+JSON.stringify(e.dims)+" `dims` must contain consecutive integers starting from 1.");this.dims=e.dims,this.dimsIncludingBatch=[0].concat(this.dims),this.inputSpec=[new uy({ndim:this.dims.length+1})]}computeOutputShape(e){const t=(e=ay(e)).slice();return this.dims.forEach(((n,r)=>{t[r+1]=e[n]})),t}call(e,t){return gi(ry(e),this.dimsIncludingBatch)}getConfig(){const e={dims:this.dims},t=super.getConfig();return Object.assign(e,t),e}}nw.className="Permute",Gi(nw);class rw extends py{constructor(e){super(null==e?{}:e),this.supportsMasking=!0,this.maskValue=null!=e?null==e.maskValue?0:e.maskValue:0}computeOutputShape(e){return e}getConfig(){const e=super.getConfig(),t={maskValue:this.maskValue};return Object.assign(t,e),t}computeMask(e,t){const n=ry(e);return ll(yc(n,this.maskValue),-1)}call(e,t){return po((()=>{this.invokeCallHook(e,t);const n=ry(e),r=ll(yc(n,this.maskValue),-1,!0);return n.mul(r.asType(n.dtype))}))}}rw.className="Masking",Gi(rw);class aw extends py{constructor(e){if(super(e),this.embeddings=null,this.DEFAULT_EMBEDDINGS_INITIALIZER="randomUniform",null==e.batchInputShape&&null==e.inputShape){let t=null;null!=e.batchSize&&(t=e.batchSize),null==e.inputLength?this.batchInputShape=[t,null]:this.batchInputShape=[t].concat(rm(e.inputLength))}this.inputDim=e.inputDim,mm(this.inputDim,"inputDim"),this.outputDim=e.outputDim,mm(this.outputDim,"outputDim"),this.embeddingsInitializer=Mg(e.embeddingsInitializer||this.DEFAULT_EMBEDDINGS_INITIALIZER),this.embeddingsRegularizer=uv(e.embeddingsRegularizer),this.activityRegularizer=uv(e.activityRegularizer),this.embeddingsConstraint=Nm(e.embeddingsConstraint),this.maskZero=e.maskZero,this.supportsMasking=e.maskZero,this.inputLength=e.inputLength}build(e){this.embeddings=this.addWeight("embeddings",[this.inputDim,this.outputDim],this.dtype,this.embeddingsInitializer,this.embeddingsRegularizer,!0,this.embeddingsConstraint),this.built=!0}warnOnIncompatibleInputShape(e){}computeMask(e,t){return po((()=>this.maskZero?(e=ry(e),yc(e,Ro(e))):null))}computeOutputShape(e){if(e=ay(e),null==this.inputLength)return[...e,this.outputDim];const t=rm(this.inputLength);if(t.length!==e.length-1)throw new Yp(`"inputLength" is ${this.inputLength}, but received input shape has shape ${e}`);{let n=0;for(let r=0;r{this.invokeCallHook(e,t);let n=ry(e);return"int32"!==n.dtype&&(n=Zm(n,"int32")),lg(this.embeddings.read(),n.as1D()).reshape(ay(this.computeOutputShape(n.shape)))}))}getConfig(){const e={inputDim:this.inputDim,outputDim:this.outputDim,embeddingsInitializer:Og(this.embeddingsInitializer),embeddingsRegularizer:ov(this.embeddingsRegularizer),activityRegularizer:ov(this.activityRegularizer),embeddingsConstraint:Cm(this.embeddingsConstraint),maskZero:this.maskZero,inputLength:this.inputLength},t=super.getConfig();return Object.assign(e,t),e}}aw.className="Embedding",Gi(aw);class sw extends py{constructor(e){super(e||{}),this.supportsMasking=!0}mergeFunction(e){throw new Jp}computeElementwiseOpOutputShape(e,t){if(null==e||null==t)return null;if(e.length1)throw new Yp(`Can not merge tensors with different batch sizes. Got tensors with shapes: ${JSON.stringify(e)}.`);let n=null==e[0]?null:e[0].slice(1);for(let t=1;te.length));-1===e.indexOf(null)&&1===hm(r).length?this.reshapeRequired=!1:this.reshapeRequired=!0}call(e,t){return po((()=>{if(e=e,this.reshapeRequired){const t=[],n=e.map((e=>e.rank));if(-1===n.indexOf(null)){const r=Ym(n);for(let n of e){const e=n.rank;for(let t=0;t1){const a=Jm(1,e).concat([0]);t.push(gi(r,a)),n=!0}else t.push(r)}let r=this.mergeFunction(t);const a=r.rank;if(n)if(null==a){const e=r.shape,t=e[e.length-1],n=[t].concat(e.slice(0,e.length-1));r=gi(r.reshape([-1,t]),[1,0]).reshape(n)}else if(a>1){const e=[a-1].concat(Jm(0,a-1));r=gi(r,e)}return r}}return this.mergeFunction(e)}))}computeOutputShape(e){let t;t=null==(e=e)[0]?null:e[0].slice(1);for(let n=1;n{if(null==t)return null;if(!Array.isArray(t))throw new Yp("`mask` should be an Array");if(!Array.isArray(e))throw new Yp("`inputs` should be an Array");if(t.length!==e.length)throw new Yp(`The Array 'inputs' and 'mask' are expected to have the same length, but have different lengths (${e.length} vs ${t.length})`);if(t.every((e=>null==e)))return null;let n=(t=t.map((e=>null==e?e:vu(e,0))))[0];for(let e=1;e{let t=e[0].clone();for(let n=1;n{let t=e[0].clone();for(let n=1;n{let t=e[0].clone();for(let n=1;n{let t=e[0];for(let n=1;n{let t=e[0];for(let n=1;n1)throw new Yp("A `Concatenate` layer requires inputs with matching shapes except for the concat axis. Got input shapes: "+JSON.stringify(e))}mergeFunction(e){return po((()=>rg(e,this.axis)))}computeOutputShape(e){if(!Array.isArray(e)||!Array.isArray(e[0]))throw new Yp("A `Concatenate` layer should be called on a list of inputs.");const t=e,n=t[0].slice(),r=this.axis<0?n.length+this.axis:this.axis;for(const e of t.slice(1)){if(null==n[r]||null==e[r]){n[r]=null;break}n[r]+=e[r]}return n}computeMask(e,t){if(null==t)return null;if(!Array.isArray(t))throw new Yp("`mask` should be an array for Concatenate");if(!Array.isArray(e))throw new Yp("`inputs` should be an array for Concatenate");if(t.length!==e.length)throw new Yp(`Mismatch in the length of mask (${t.length}) and the legnth of inputs (${e.length})`);return po((()=>{let n=!0;if(t.forEach((e=>{null==e||(n=!1)})),n)return null;const r=[];for(let n=0;n"A `Dot` layer should be called on a list of exactly 2 inputs."));const t=e[0],n=e[1];if(t.length>3||n.length>3)throw new Jp("Dot layer does not support tensors of 4D or higher rank yet.");const r=this.interpretAxes(t,n);if(t[r[0]]!==n[r[1]])throw new Yp(`Dimension incompatibility: ${t[r[0]]} !== ${n[r[1]]}`)}mergeFunction(e){if(2!==e.length)throw new Yp(`A \`Dot\` layer must be called on exactly 2 inputs, but received ${e.length} input(s).`);let t,n=e[0],r=e[1];return t=Array.isArray(this.axes)?this.axes.map(((t,n)=>fw(t,e[n].shape.length))):[fw(this.axes,n.shape.length),fw(this.axes,r.shape.length)],this.normalize&&(n=Dy(n,t[0]),r=Dy(r,t[1])),function(e,t,n){if(e.shape.length>3||t.shape.length>3)throw new Jp("batchDot is not implemented for tensors of 4D or higher rank yet");if(Q(e.shape.length>=2,(()=>`batchDot requires the rank of x to be >= 2, but got ${e.shape.length}`)),Q(e.shape.length>=2,(()=>`batchDot requires the rank of y to be >= 2, but got ${t.shape.length}`)),"number"==typeof n&&(n=[n,n]),"complex64"===e.dtype||"complex64"===t.dtype)throw new Jp("batchDot is not implemented for complex64-type Tensors yet.");const r=e.shape.length,a=t.shape.length;null==n&&(n=[r-1,a-2]);const s=n;return po((()=>{let n,i;if(r>a){n=r-a;const e=[];for(let t=0;tr){n=a-r;const t=[];for(let e=0;e0){let e;e=r>a?r+a-3:r-1;const t=[];for(let r=e;r"A `Dot` layer should be called on a list of exactly 2 inputs."));const t=e[0].slice(),n=e[1].slice();if(t.length>3||n.length>3)throw new Jp("Dot layer does not support tensors of 4D or higher rank yet.");const r=this.interpretAxes(t,n);t.splice(r[0],1),n.splice(r[1],1),n.splice(0,1);const a=t.concat(n);return 1===a.length&&a.push(1),a}computeMask(e,t){return null}getConfig(){const e={axes:this.axes,normalize:this.normalize},t=super.getConfig();return Object.assign(e,t),e}}dw.className="Dot",Gi(dw);class pw extends py{constructor(e){super(e),this.supportsMasking=!0,this.stddev=e.stddev}computeOutputShape(e){return e}getConfig(){const e=super.getConfig(),t={stddev:this.stddev};return Object.assign(t,e),t}call(e,t){return po((()=>{this.invokeCallHook(e,t);const n=ry(e);return dg((()=>ig(n.shape,0,this.stddev).add(n)),(()=>n),t.training||!1)}))}}pw.className="GaussianNoise",Gi(pw);class mw extends py{constructor(e){super(e),this.supportsMasking=!0,this.rate=e.rate}computeOutputShape(e){return e}getConfig(){const e=super.getConfig(),t={rate:this.rate};return Object.assign(t,e),t}call(e,t){return po((()=>{this.invokeCallHook(e,t);const n=ry(e);return this.rate>0&&this.rate<1?dg((()=>{const e=Math.sqrt(this.rate/(1-this.rate));return n.mul(ig(n.shape,1,e))}),(()=>n),t.training||!1):n}))}}mw.className="GaussianDropout",Gi(mw);class gw extends py{constructor(e){super(e),this.supportsMasking=!0,this.rate=e.rate,this.noiseShape=e.noiseShape}_getNoiseShape(e){return this.noiseShape||ry(e).shape}computeOutputShape(e){return e}getConfig(){const e=super.getConfig(),t={rate:this.rate};return Object.assign(t,e),t}call(e,t){return po((()=>{if(this.rate<1&&this.rate>0){const n=this._getNoiseShape(e);return dg((()=>{const t=ry(e),r=-1.7580993408473766;let a=Su(Bc(n),this.rate);a=Zm(a,"float32");const s=((1-this.rate)*(1+this.rate*r**2))**-.5,i=-s*r*this.rate;return t.mul(a).add(a.add(-1).mul(r)).mul(s).add(i)}),(()=>ry(e)),t.training||!1)}return e}))}}function yw(e,t,n,r,a,s=.001){let i;if(2===e.rank)i=Ll(e,t,n,r,a,s);else if(3===e.rank)i=zl(e,t,n,r,a,s);else{if(4!==e.rank)throw new Jp(`batchNormalization is not implemented for array of rank ${e.rank} yet`);i=Wl(e,t,n,r,a,s)}return i}gw.className="AlphaDropout",Gi(gw);class bw extends py{constructor(e){null==e&&(e={}),super(e),this.supportsMasking=!0,this.axis=null==e.axis?-1:e.axis,this.momentum=null==e.momentum?.99:e.momentum,this.epsilon=null==e.epsilon?.001:e.epsilon,this.center=null==e.center||e.center,this.scale=null==e.scale||e.scale,this.betaInitializer=Mg(e.betaInitializer||"zeros"),this.gammaInitializer=Mg(e.gammaInitializer||"ones"),this.movingMeanInitializer=Mg(e.movingMeanInitializer||"zeros"),this.movingVarianceInitializer=Mg(e.movingVarianceInitializer||"ones"),this.betaConstraint=Nm(e.betaConstraint),this.gammaConstraint=Nm(e.gammaConstraint),this.betaRegularizer=uv(e.betaRegularizer),this.gammaRegularizer=uv(e.gammaRegularizer)}build(e){e=ay(e);const t=this.axis>=0?this.axis:this.axis+e.length,n=e[t];if(null==n)throw new Yp(`Axis ${t} of input tensor should have a defined dimension but the layer received an input with shape ${JSON.stringify(e)}.`);this.inputSpec=[new uy({ndim:e.length,axes:{[t]:n}})];const r=[n];this.scale&&(this.gamma=this.addWeight("gamma",r,null,this.gammaInitializer,this.gammaRegularizer,!0,this.gammaConstraint)),this.center&&(this.beta=this.addWeight("beta",r,null,this.betaInitializer,this.betaRegularizer,!0,this.betaConstraint)),this.movingMean=this.addWeight("moving_mean",r,null,this.movingMeanInitializer,null,!1),this.movingVariance=this.addWeight("moving_variance",r,null,this.movingVarianceInitializer,null,!1),this.built=!0}call(e,t){return po((()=>{const n=null!=t.training&&t.training,r=ry(e),a=r.shape,s=a.length,i=Jm(0,s),o=this.axis>=0?this.axis:this.axis+s;i.splice(o,1);const l=Qp(1,s);l[o]=a[o];const u=i.slice();u.sort();const c=!se(u,Jm(0,s).slice(0,s-1));if(!n)return(()=>{if(c){const e=this.movingMean.read().reshape(l),t=this.movingVariance.read().reshape(l),n=this.center?this.beta.read().reshape(l):null,a=this.scale?this.gamma.read().reshape(l):null;return yw(r,e,t,n,a,this.epsilon)}return yw(r,this.movingMean.read(),this.movingVariance.read(),null==this.beta?null:this.beta.read(),null==this.gamma?null:this.gamma.read(),this.epsilon)})();const[h,f,d]=function(e,t,n,r,a=.001){return se(r.slice().sort(),Jm(0,e.rank-1))?function(e,t,n,r,a=.001){return po((()=>{const s=pc(e,r),i=s.mean,o=s.variance;return[yw(e,i,o,n,t,a),i,o]}))}(e,t,n,r,a):function(e,t,n,r,a=.001){return po((()=>{const s=pc(e,r),i=s.mean,o=s.variance,l=[];for(const t of Jm(0,e.rank))-1!==r.indexOf(t)?l.push(1):l.push(e.shape[t]);const u=i.reshape(l),c=o.reshape(l),h=null==t?null:t.reshape(l),f=null==n?null:n.reshape(l);return[yw(e,u,c,f,h,a),i,o]}))}(e,t,n,r,a)}(r,this.gamma.read(),this.beta.read(),i,this.epsilon),p=(e,t,n)=>{po((()=>{const r=1-n,a=e.read(),s=a.sub(t).mul(r);e.write(a.sub(s))}))};return(()=>{p(this.movingMean,f,this.momentum),p(this.movingVariance,d,this.momentum)})(),h}))}getConfig(){const e={axis:this.axis,momentum:this.momentum,epsilon:this.epsilon,center:this.center,scale:this.scale,betaInitializer:Og(this.betaInitializer),gammaInitializer:Og(this.gammaInitializer),movingMeanInitializer:Og(this.movingMeanInitializer),movingVarianceInitializer:Og(this.movingVarianceInitializer),betaRegularizer:ov(this.betaRegularizer),gammaRegularizer:ov(this.gammaRegularizer),betaConstraint:Cm(this.betaConstraint),gammaConstraint:Cm(this.gammaConstraint)},t=super.getConfig();return Object.assign(e,t),e}}bw.className="BatchNormalization",Gi(bw);class vw extends py{constructor(e){if(null==e&&(e={}),super(e),this.axis=null==e.axis?-1:e.axis,"number"==typeof this.axis){if(!Number.isInteger(this.axis))throw new Error(`Expected axis to be an integer, but received ${this.axis}`)}else{if(!Array.isArray(this.axis))throw new Error(`Expected axis to be an integer or an array of integers, but received ${JSON.stringify(this.axis)}`);for(const e of this.axis)if(!Number.isInteger(e))throw new Error(`Expected axis to be an array of integers, but received ${JSON.stringify(this.axis)}`)}this.epsilon=null==e.epsilon?.001:e.epsilon,this.center=null==e.center||e.center,this.scale=null==e.scale||e.scale,this.betaInitializer=Mg(e.betaInitializer||"zeros"),this.gammaInitializer=Mg(e.gammaInitializer||"ones"),this.betaRegularizer=uv(e.betaRegularizer),this.gammaRegularizer=uv(e.gammaRegularizer),this.supportsMasking=!0}build(e){const t=(e=ay(e)).length;"number"==typeof this.axis&&(this.axis=[this.axis]);for(let e=0;e=t)throw new Error(`Invalid axis: ${e}`);if(this.axis.length!==hm(this.axis).length)throw new Error(`Found duplicate axes in: ${this.axis}`);const n=this.axis.map((t=>e[t]));this.scale?this.gamma=this.addWeight("gamma",n,"float32",this.gammaInitializer,this.gammaRegularizer,!0):this.gamma=null,this.center?this.beta=this.addWeight("beta",n,"float32",this.betaInitializer,this.betaRegularizer,!0):this.beta=null,this.built=!0}call(e,t){const n=ry(e),r=n.shape,a=r.length;return po((()=>{let{mean:e,variance:t}=pc(n,this.axis,!0);const s=Qp(1,a);for(const e of this.axis)s[e]=r[e];const i=e=>null!=e&&e.shape.length!==a&&this.axis!==[a-1]?e.reshape(s):e;let o=i(this.gamma.read()),l=i(this.beta.read());const u=[],c=[];for(let e=0;e=0?e[2]+this.padding[0][0]+this.padding[0][1]:null,n=null!=e[3]&&e[3]>=0?e[3]+this.padding[1][0]+this.padding[1][1]:null,[e[0],e[1],t,n]):(t=null!=e[1]&&e[1]>=0?e[1]+this.padding[0][0]+this.padding[0][1]:null,n=null!=e[2]&&e[2]>=0?e[2]+this.padding[1][0]+this.padding[1][1]:null,[e[0],t,n,e[3]])}call(e,t){return po((()=>{return t=ry(e),n=this.padding,r=this.dataFormat,po((()=>{if(4!==t.rank)throw new Yp(`temporalPadding expects input tensor to be 4-D, but received a ${t.rank}-D tensor.`);if(null==n&&(n=[[1,1],[1,1]]),2!==n.length||2!==n[0].length||2!==n[1].length)throw new Yp("spatial2dPadding expects `padding` to be an Array of two Arrays, each of which is an Array of two integers.");if(null==r&&(r="channelsLast"),"channelsLast"!==r&&"channelsFirst"!==r)throw new Yp(`Unknown data format: ${r}. Supported data formats are 'channelsLast' and 'channelsFirst.`);let e;return e="channelsFirst"===r?[[0,0],[0,0],n[0],n[1]]:[[0,0],n[0],n[1],[0,0]],wc(t,e)}));var t,n,r}))}getConfig(){const e={padding:this.padding,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}}function _w(e,t,n,r,a,s){return po((()=>{let i;Pm(a),zm(s),Lm(r),null==n&&(n=[1,1]),null==r&&(r="valid"),null==a&&(a="channelsLast"),null==s&&(s="max"),e=vv(e,a);const o="same"===r?"same":"valid";return i="max"===s?rc(e,t,n,o):Dl(e,t,n,o),"channelsFirst"===a&&(i=gi(i,[0,3,1,2])),i}))}function xw(e,t,n,r,a,s){return po((()=>{let i;Pm(a),zm(s),Lm(r),null==n&&(n=[1,1,1]),null==r&&(r="valid"),null==a&&(a="channelsLast"),null==s&&(s="max"),e=wv(e,a);const o="same"===r?"same":"valid";return i="max"===s?ac(e,t,n,o):Tl(e,t,n,o),"channelsFirst"===a&&(i=gi(i,[0,4,1,2,3])),i}))}ww.className="ZeroPadding2D",Gi(ww);class kw extends py{constructor(e){if(null==e.poolSize&&(e.poolSize=2),super(e),"number"==typeof e.poolSize)this.poolSize=[e.poolSize];else{if(!Array.isArray(e.poolSize)||1!==e.poolSize.length||"number"!=typeof e.poolSize[0])throw new Yp(`poolSize for 1D convolutional layer must be a number or an Array of a single number, but received ${JSON.stringify(e.poolSize)}`);this.poolSize=e.poolSize}if(mm(this.poolSize,"poolSize"),null==e.strides)this.strides=this.poolSize;else if("number"==typeof e.strides)this.strides=[e.strides];else{if(!Array.isArray(e.strides)||1!==e.strides.length||"number"!=typeof e.strides[0])throw new Yp(`strides for 1D convolutional layer must be a number or an Array of a single number, but received ${JSON.stringify(e.strides)}`);this.strides=e.strides}mm(this.strides,"strides"),this.padding=null==e.padding?"valid":e.padding,Lm(this.padding),this.inputSpec=[new uy({ndim:3})]}computeOutputShape(e){const t=yv((e=ay(e))[1],this.poolSize[0],this.padding,this.strides[0]);return[e[0],t,e[2]]}call(e,t){return po((()=>{this.invokeCallHook(e,t),e=Qm(ry(e),2);const n=this.poolingFunction(ry(e),[this.poolSize[0],1],[this.strides[0],1],this.padding,"channelsLast");return fh(n,[2])}))}getConfig(){const e={poolSize:this.poolSize,padding:this.padding,strides:this.strides},t=super.getConfig();return Object.assign(e,t),e}}class Ew extends kw{constructor(e){super(e)}poolingFunction(e,t,n,r,a){return Pm(a),Lm(r),_w(e,t,n,r,a,"max")}}Ew.className="MaxPooling1D",Gi(Ew);class Cw extends kw{constructor(e){super(e)}poolingFunction(e,t,n,r,a){return Pm(a),Lm(r),_w(e,t,n,r,a,"avg")}}Cw.className="AveragePooling1D",Gi(Cw);class Sw extends py{constructor(e){if(null==e.poolSize&&(e.poolSize=[2,2]),super(e),this.poolSize=Array.isArray(e.poolSize)?e.poolSize:[e.poolSize,e.poolSize],null==e.strides)this.strides=this.poolSize;else if(Array.isArray(e.strides)){if(2!==e.strides.length)throw new Yp(`If the strides property of a 2D pooling layer is an Array, it is expected to have a length of 2, but received length ${e.strides.length}.`);this.strides=e.strides}else this.strides=[e.strides,e.strides];mm(this.poolSize,"poolSize"),mm(this.strides,"strides"),this.padding=null==e.padding?"valid":e.padding,this.dataFormat=null==e.dataFormat?"channelsLast":e.dataFormat,Pm(this.dataFormat),Lm(this.padding),this.inputSpec=[new uy({ndim:4})]}computeOutputShape(e){e=ay(e);let t="channelsFirst"===this.dataFormat?e[2]:e[1],n="channelsFirst"===this.dataFormat?e[3]:e[2];return t=yv(t,this.poolSize[0],this.padding,this.strides[0]),n=yv(n,this.poolSize[1],this.padding,this.strides[1]),"channelsFirst"===this.dataFormat?[e[0],e[1],t,n]:[e[0],t,n,e[3]]}call(e,t){return po((()=>(this.invokeCallHook(e,t),this.poolingFunction(ry(e),this.poolSize,this.strides,this.padding,this.dataFormat))))}getConfig(){const e={poolSize:this.poolSize,padding:this.padding,strides:this.strides,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}}class Nw extends Sw{constructor(e){super(e)}poolingFunction(e,t,n,r,a){return Pm(a),Lm(r),_w(e,t,n,r,a,"max")}}Nw.className="MaxPooling2D",Gi(Nw);class Iw extends Sw{constructor(e){super(e)}poolingFunction(e,t,n,r,a){return Pm(a),Lm(r),_w(e,t,n,r,a,"avg")}}Iw.className="AveragePooling2D",Gi(Iw);class Aw extends py{constructor(e){if(null==e.poolSize&&(e.poolSize=[2,2,2]),super(e),this.poolSize=Array.isArray(e.poolSize)?e.poolSize:[e.poolSize,e.poolSize,e.poolSize],null==e.strides)this.strides=this.poolSize;else if(Array.isArray(e.strides)){if(3!==e.strides.length)throw new Yp(`If the strides property of a 3D pooling layer is an Array, it is expected to have a length of 3, but received length ${e.strides.length}.`);this.strides=e.strides}else this.strides=[e.strides,e.strides,e.strides];mm(this.poolSize,"poolSize"),mm(this.strides,"strides"),this.padding=null==e.padding?"valid":e.padding,this.dataFormat=null==e.dataFormat?"channelsLast":e.dataFormat,Pm(this.dataFormat),Lm(this.padding),this.inputSpec=[new uy({ndim:5})]}computeOutputShape(e){e=ay(e);let t="channelsFirst"===this.dataFormat?e[2]:e[1],n="channelsFirst"===this.dataFormat?e[3]:e[2],r="channelsFirst"===this.dataFormat?e[4]:e[3];return t=yv(t,this.poolSize[0],this.padding,this.strides[0]),n=yv(n,this.poolSize[1],this.padding,this.strides[1]),r=yv(r,this.poolSize[2],this.padding,this.strides[2]),"channelsFirst"===this.dataFormat?[e[0],e[1],t,n,r]:[e[0],t,n,r,e[4]]}call(e,t){return po((()=>(this.invokeCallHook(e,t),this.poolingFunction(ry(e),this.poolSize,this.strides,this.padding,this.dataFormat))))}getConfig(){const e={poolSize:this.poolSize,padding:this.padding,strides:this.strides,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}}class Dw extends Aw{constructor(e){super(e)}poolingFunction(e,t,n,r,a){return Pm(a),Lm(r),xw(e,t,n,r,a,"max")}}Dw.className="MaxPooling3D",Gi(Dw);class Tw extends Aw{constructor(e){super(e)}poolingFunction(e,t,n,r,a){return Pm(a),Lm(r),xw(e,t,n,r,a,"avg")}}Tw.className="AveragePooling3D",Gi(Tw);class Fw extends py{constructor(e){super(e),this.inputSpec=[new uy({ndim:3})]}computeOutputShape(e){return[e[0],e[2]]}call(e,t){throw new Jp}}class Rw extends Fw{constructor(e){super(e||{})}call(e,t){return po((()=>{const t=ry(e);return ic(t,1)}))}}Rw.className="GlobalAveragePooling1D",Gi(Rw);class Ow extends Fw{constructor(e){super(e||{})}call(e,t){return po((()=>{const t=ry(e);return Wu(t,1)}))}}Ow.className="GlobalMaxPooling1D",Gi(Ow);class Mw extends py{constructor(e){super(e),this.dataFormat=null==e.dataFormat?"channelsLast":e.dataFormat,Pm(this.dataFormat),this.inputSpec=[new uy({ndim:4})]}computeOutputShape(e){return e=e,"channelsLast"===this.dataFormat?[e[0],e[3]]:[e[0],e[1]]}call(e,t){throw new Jp}getConfig(){const e={dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}}class Bw extends Mw{call(e,t){return po((()=>{const t=ry(e);return"channelsLast"===this.dataFormat?ic(t,[1,2]):ic(t,[2,3])}))}}Bw.className="GlobalAveragePooling2D",Gi(Bw);class $w extends Mw{call(e,t){return po((()=>{const t=ry(e);return"channelsLast"===this.dataFormat?Wu(t,[1,2]):Wu(t,[2,3])}))}}$w.className="GlobalMaxPooling2D",Gi($w);class Pw extends py{constructor(e){super(e),this.layer=e.layer}build(e){this.built=!0}get trainable(){return null!=this.layer&&this.layer.trainable}set trainable(e){null!=this.layer&&(this.layer.trainable=e)}get trainableWeights(){return this.layer.trainableWeights}get nonTrainableWeights(){return this.layer.nonTrainableWeights}get updates(){return this.layer._updates}get losses(){return this.layer.losses}getWeights(){return this.layer.getWeights()}setWeights(e){this.layer.setWeights(e)}getConfig(){const e={layer:{className:this.layer.getClassName(),config:this.layer.getConfig()}},t=super.getConfig();return Object.assign(e,t),e}setFastWeightInitDuringBuild(e){super.setFastWeightInitDuringBuild(e),null!=this.layer&&this.layer.setFastWeightInitDuringBuild(e)}static fromConfig(e,t,n={}){const r=Ay(t.layer,n);delete t.layer;const a={layer:r};return Object.assign(a,t),new e(a)}}class Lw extends Pw{constructor(e){super(e),this.supportsMasking=!0}build(e){if((e=ay(e)).length<3)throw new Yp(`TimeDistributed layer expects an input shape >= 3D, but received input shape ${JSON.stringify(e)}`);this.inputSpec=[{shape:e}];const t=[e[0]].concat(e.slice(2));this.layer.built||(this.layer.build(t),this.layer.built=!0),super.build(e)}computeOutputShape(e){const t=[(e=ay(e))[0]].concat(e.slice(2)),n=this.layer.computeOutputShape(t),r=e[1];return[n[0],r].concat(n.slice(1))}call(e,t){return po((()=>Mv(((e,n)=>[ry(this.layer.call(e,t)),[]]),e=ry(e),[],!1,null,null,!1,!0)[1]))}}Lw.className="TimeDistributed",Gi(Lw);class zw extends Pw{constructor(e){super(e);const t=e.layer.getConfig(),n={};n.className=e.layer.getClassName(),n.config=t,this.forwardLayer=Ay(n),t.goBackwards=!0!==t.goBackwards;const r={};var a;if(r.className=e.layer.getClassName(),r.config=t,this.backwardLayer=Ay(r),this.forwardLayer.name="forward_"+this.forwardLayer.name,this.backwardLayer.name="backward_"+this.backwardLayer.name,this.mergeMode=void 0===e.mergeMode?"concat":e.mergeMode,a=this.mergeMode,dm(Bm,"BidirectionalMergeMode",a),e.weights)throw new Jp("weights support is not implemented for Bidirectional layer yet.");this._stateful=e.layer.stateful,this.returnSequences=e.layer.returnSequences,this.returnState=e.layer.returnState,this.supportsMasking=!0,this._trainable=!0,this.inputSpec=e.layer.inputSpec,this.numConstants=null}get trainable(){return this._trainable}set trainable(e){this._trainable=e,null!=this.forwardLayer&&(this.forwardLayer.trainable=e),null!=this.backwardLayer&&(this.backwardLayer.trainable=e)}getWeights(){return this.forwardLayer.getWeights().concat(this.backwardLayer.getWeights())}setWeights(e){const t=e.length,n=Math.floor(t/2);this.forwardLayer.setWeights(e.slice(0,n)),this.backwardLayer.setWeights(e.slice(n))}computeOutputShape(e){let t,n,r,a=this.forwardLayer.computeOutputShape(e);return Array.isArray(a)&&Array.isArray(a[0])||(a=[a]),a=a,this.returnState?(r=a.slice(1),t=a[0]):t=a[0],t=t,"concat"===this.mergeMode?(t[t.length-1]*=2,n=[t]):n=null==this.mergeMode?[t,t.slice()]:[t],this.returnState?null==this.mergeMode?n.concat(r).concat(r.slice()):[t].concat(r).concat(r.slice()):nm(n)}apply(e,t){let n=null==t?null:t.initialState,r=null==t?null:t.constants;null==t&&(t={});const a=Ov(e,n,r,this.numConstants);if(e=a.inputs,n=a.initialState,r=a.constants,Array.isArray(e)&&(n=e.slice(1),e=e[0]),(null==n||0===n.length)&&null==r)return super.apply(e,t);const s=[],i=[];if(null!=n){const e=n.length;if(e%2>0)throw new Yp("When passing `initialState` to a Bidrectional RNN, the state should be an Array containing the states of the underlying RNNs.");t.initialState=n,s.push(...n);const r=n.map((e=>new uy({shape:e.shape})));this.forwardLayer.stateSpec=r.slice(0,e/2),this.backwardLayer.stateSpec=r.slice(e/2),i.push(...r)}if(null!=r)throw new Jp("Support for constants in Bidirectional layers is not implemented yet.");const o=s[0]instanceof cy;for(const e of s)if(e instanceof cy!==o)throw new Yp("The initial state of a Bidirectional layer cannot be specified as a mix of symbolic and non-symbolic tensors");if(o){const n=[e].concat(s),r=this.inputSpec.concat(i),a=this.inputSpec;this.inputSpec=r;const o=super.apply(n,t);return this.inputSpec=a,o}return super.apply(e,t)}call(e,t){return po((()=>{const n=t.initialState;let r,a,s,i;if(null==n)r=this.forwardLayer.call(e,t),a=this.backwardLayer.call(e,t);else{const s=n.slice(0,n.length/2),i=n.slice(n.length/2);r=this.forwardLayer.call(e,Object.assign(t,{initialState:s})),a=this.backwardLayer.call(e,Object.assign(t,{initialState:i}))}return this.returnState&&(Array.isArray(r)&&(s=r.slice(1).concat(a.slice(1))),r=r[0],a=a[0]),this.returnSequences&&(a=Uc(a,1)),"concat"===this.mergeMode?i=rg([r,a]):"sum"===this.mergeMode?i=No(r,a):"ave"===this.mergeMode?i=Do(.5,No(r,a)):"mul"===this.mergeMode?i=Do(r,a):null==this.mergeMode&&(i=[r,a]),this.returnState?null==this.mergeMode?i.concat(s):[i].concat(s):i}))}resetStates(e){this.forwardLayer.resetStates(),this.backwardLayer.resetStates()}build(e){Um(this.forwardLayer.name,(()=>{this.forwardLayer.build(e)})),Um(this.backwardLayer.name,(()=>{this.backwardLayer.build(e)})),this.built=!0}computeMask(e,t){let n;if(Array.isArray(t)&&(t=t[0]),n=this.returnSequences?null==this.mergeMode?[t,t]:t:null==this.mergeMode?[null,null]:null,this.returnState){const e=this.forwardLayer.states.map((e=>null));return Array.isArray(n)?n.concat(e).concat(e):[n].concat(e).concat(e)}return n}get trainableWeights(){return this.forwardLayer.trainableWeights.concat(this.backwardLayer.trainableWeights)}get 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rk{static get Instance(){return this._instance||(this._instance=new this)}constructor(){const e=[].concat(...[E,C,S,N,I,A,D,O,R,T,M,B,$,P,L,z,F].map((e=>e.json)));this.opMappers=e.reduce(((e,t)=>(e[t.tfOpName]=t,e)),{})}transformGraph(e,t={}){const n=e.node,r=[],a=[],s=[],i=n.reduce(((e,t)=>(e[t.name]=this.mapNode(t),t.op.startsWith("Placeholder")?r.push(e[t.name]):"Const"===t.op?a.push(e[t.name]):null!=t.input&&0!==t.input.length||s.push(e[t.name]),e)),{});let o=[];const l=[];let u={},c={};null!=t&&(u=this.mapSignatureEntries(t.inputs),c=this.mapSignatureEntries(t.outputs));const h=Object.keys(i);h.forEach((e=>{const t=i[e];t.inputNames.forEach((e=>{const[n]=Mx(e);t.inputs.push(i[n]),i[n].children.push(t)}))})),0===Object.keys(c).length?h.forEach((e=>{const t=i[e];0===t.children.length&&l.push(t)})):Object.keys(c).forEach((e=>{const[t]=Mx(e),n=i[t];null!=n&&(n.signatureKey=c[e],l.push(n))})),Object.keys(u).length>0?Object.keys(u).forEach((e=>{const[t]=Mx(e),n=i[t];n&&(n.signatureKey=u[e],o.push(n))})):o=r;let f={};null!=e.library&&null!=e.library.function&&(f=e.library.function.reduce(((e,t)=>(e[t.signature.name]=this.mapFunction(t),e)),{}));const d={nodes:i,inputs:o,outputs:l,weights:a,placeholders:r,signature:t,functions:f};return s.length>0&&(d.initNodes=s),d}mapSignatureEntries(e){return Object.keys(e||{}).reduce(((t,n)=>(t[e[n].name]=n,t)),{})}mapNode(e){const t=Tx(e.op)||this.opMappers[e.op]||{};null==e.attr&&(e.attr={});const n={name:e.name,op:e.op,category:t.category,inputNames:(e.input||[]).map((e=>e.startsWith("^")?e.substr(1):e)),inputs:[],children:[],inputParams:{},attrParams:{},rawAttrs:e.attr};return 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r={};null!=t&&(r=t.reduce(((e,t)=>(e[t.name]=this.mapNode(t),"Const"===t.op&&n.push(e[t.name]),e)),{}));const a=[],s=[];e.signature.inputArg.forEach((e=>{const[t]=Mx(e.name),n={name:t,op:"Placeholder",inputs:[],inputNames:[],category:"graph",inputParams:{},attrParams:{dtype:{value:lk(e.type),type:"dtype"}},children:[]};n.signatureKey=e.name,a.push(n),r[t]=n})),Object.keys(r).forEach((e=>{const t=r[e];t.inputNames.forEach((e=>{const[n]=Mx(e);t.inputs.push(r[n]),r[n].children.push(t)}))}));const i=e.ret;e.signature.outputArg.forEach((e=>{const[t,n]=Mx(i[e.name]),a=r[t];null!=a&&(a.defaultOutput=n,s.push(a))}));const o=this.mapArgsToSignature(e);return{nodes:r,inputs:a,outputs:s,weights:n,placeholders:[],signature:o}}mapArgsToSignature(e){return{methodName:e.signature.name,inputs:e.signature.inputArg.reduce(((e,t)=>(e[t.name]=this.mapArgToTensorInfo(t),e)),{}),outputs:e.signature.outputArg.reduce(((t,n)=>(t[n.name]=this.mapArgToTensorInfo(n,e.ret),t)),{})}}mapArgToTensorInfo(e,t){let n=e.name;return null!=t&&(n=t[n]),{name:n,dtype:e.type}}}function ak(e,t){const n=Array.isArray(e)?String.fromCharCode.apply(null,e):function(e){const t=We().global;if(void 0!==t.atob)return t.atob(e);if("undefined"!=typeof Buffer)return new Buffer(e,"base64").toString();throw new Error("Unable to decode base64 in this environment. Missing built-in atob() or Buffer()")}(e);return t?n:n.toLowerCase()}function sk(e,t,n,r=!1){const a=e[t];return null!=a?ak(a.s,r):n}function ik(e,t,n){const r=e[t];return r?r.b:n}function ok(e,t,n){const r=e[t]||{},a=null!=r.i?r.i:null!=r.f?r.f:n;return"number"==typeof a?a:parseInt(a,10)}function lk(e){switch("string"==typeof e&&(e=Nx[e]),e){case Nx.DT_FLOAT:return"float32";case Nx.DT_INT32:case Nx.DT_INT64:case Nx.DT_INT8:case Nx.DT_UINT8:return"int32";case Nx.DT_BOOL:return"bool";case Nx.DT_DOUBLE:return"float32";case Nx.DT_STRING:return"string";default:return null}}function uk(e,t,n){const r=e[t];return r&&r.func?r.func.name:n}function ck(e,t,n){const r=e[t];return r&&r.type?lk(r.type):n}function hk(e,t,n){const r=e[t];return r&&r.list&&r.list.type?r.list.type.map((e=>lk(e))):n}function fk(e){if(!e.unknownRank)return null!=e.dim?e.dim.map((e=>"number"==typeof e.size?e.size:parseInt(e.size,10))):[]}function dk(e,t,n){const r=e[t];return r&&r.shape?fk(r.shape):n}function pk(e,t,n){const r=e[t];return r?((r.list.f&&r.list.f.length?r.list.f:r.list.i)||[]).map((e=>"number"==typeof e?e:parseInt(e,10))):n}function mk(e,t,n,r=!1){const a=e[t];return a&&a.list&&a.list.s?a.list.s.map((e=>ak(e,r))):n}function gk(e,t,n){const r=e[t];return r&&r.list&&r.list.shape?r.list.shape.map((e=>fk(e))):n}function yk(e,t,n){const r=e[t];return r&&r.list&&r.list.b?r.list.b:n}class bk{constructor(e,t,n){this.node=e,this.tensorMap=t,this.context=n,this.inputs=[],this.attrs={},this.inputs=e.inputNames.map((e=>this.getInput(e))),null!=e.rawAttrs&&(this.attrs=Object.keys(e.rawAttrs).reduce(((e,t)=>(e[t]=this.getAttr(t),e)),{}))}getInput(e){return Ox(e,this.tensorMap,this.context)}getAttr(e,t){const n=this.node.rawAttrs[e];if(null!=n.tensor)return Ox(e,this.tensorMap,this.context);if(null!=n.i||null!=n.f)return ok(this.node.rawAttrs,e,t);if(null!=n.s)return sk(this.node.rawAttrs,e,t);if(null!=n.b)return ik(this.node.rawAttrs,e,t);if(null!=n.shape)return dk(this.node.rawAttrs,e,t);if(null!=n.type)return ck(this.node.rawAttrs,e,t);if(null!=n.list){if(null!=n.list.i||null!=n.list.f)return pk(this.node.rawAttrs,e,t);if(null!=n.list.s)return mk(this.node.rawAttrs,e,t);if(null!=n.list.shape)return gk(this.node.rawAttrs,e,t);if(null!=n.list.b)return yk(this.node.rawAttrs,e,t);if(null!=n.list.type)return hk(this.node.rawAttrs,e,t)}return t}}function vk(e,t,n=""){if("number"!=typeof e&&"number"!=typeof t){Q(e.length===t.length,(()=>n+` Shapes ${e} and ${t} must match`));for(let r=0;rn+` Shapes ${e} and ${t} must match`))}}}function wk(e){return"number"!=typeof e&&!e.some((e=>e<0))}function _k(e,t,n){let r=xk(e,n);const a=!wk(r);if(a&&0===t.length)throw new Error(`Tried to calculate elements of an empty list with non-fully-defined elementShape: ${r}`);if(a&&t.forEach((e=>{r=xk(e.shape,r)})),!wk(r))throw new Error(`Non-fully-defined elementShape: ${r}`);return r}function xk(e,t){if("number"==typeof e)return t;if("number"==typeof t)return e;if(e.length!==t.length)throw new Error(`Incompatible ranks during merge: ${e} vs. ${t}`);const n=[];for(let r=0;r=0&&s>=0&&a!==s)throw new Error(`Incompatible shape during merge: ${e} vs. ${t}`);n[r]=a>=0?a:s}return n}class kk{constructor(e,t,n,r,a,s,i){this.name=e,this.dtype=t,this.maxSize=n,this.elementShape=r,this.identicalElementShapes=a,this.dynamicSize=s,this.clearAfterRead=i,this.tensors=[],this.closed_=!1,this.idTensor=Wo(0),go(this.idTensor)}get id(){return this.idTensor.id}get closed(){return this.closed_}clearAndClose(e){this.tensors.forEach((t=>{null!=e&&e.has(t.tensor.id)||t.tensor.dispose()})),this.tensors=[],this.closed_=!0,this.idTensor.dispose()}size(){return this.tensors.length}read(e){if(this.closed_)throw new Error(`TensorArray ${this.name} has already been closed.`);if(e<0||e>=this.size())throw new Error(`Tried to read from index ${e}, but array size is: ${this.size()}`);const t=this.tensors[e];if(t.cleared)throw new Error(`TensorArray ${this.name}: Could not read index ${e} twice because it was cleared after a previous read (perhaps try setting clear_after_read = false?).`);return this.clearAfterRead&&(t.cleared=!0),t.read=!0,t.tensor}readMany(e){return e.map((e=>this.read(e)))}write(e,t){if(this.closed_)throw new Error(`TensorArray ${this.name} has already been closed.`);if(e<0||!this.dynamicSize&&e>=this.maxSize)throw new Error(`Tried to write to index ${e}, but array is not resizeable and size is: ${this.maxSize}`);const n=this.tensors[e]||{};if(t.dtype!==this.dtype)throw new Error(`TensorArray ${this.name}: Could not write to TensorArray index ${e},\n because the value dtype is ${t.dtype}, but TensorArray dtype is ${this.dtype}.`);if(0!==this.size()||null!=this.elementShape&&0!==this.elementShape.length||(this.elementShape=t.shape),vk(this.elementShape,t.shape,`TensorArray ${this.name}: Could not write to TensorArray index ${e}.`),n.read)throw new Error(`TensorArray ${this.name}: Could not write to TensorArray index ${e}, because it has already been read.`);if(n.written)throw new Error(`TensorArray ${this.name}: Could not write to TensorArray index ${e}, because it has already been written.`);n.tensor=t,go(t),n.written=!0,this.tensors[e]=n}writeMany(e,t){if(e.length!==t.length)throw new Error(`TensorArray ${this.name}: could not write multiple tensors,because the index size: ${e.length} is not the same as tensors size: ${t.length}.`);e.forEach(((e,n)=>this.write(e,t[n])))}gather(e,t){if(t&&t!==this.dtype)throw new Error(`TensorArray dtype is ${this.dtype} but gather requested dtype ${t}`);if(e)e=e.slice(0,this.size());else{e=[];for(let t=0;t=this.maxSize)throw new Error(`Max index must be < array size (${n} vs. ${this.maxSize})`);this.writeMany(e,Sh(t,0))}split(e,t){if(t.dtype!==this.dtype)throw new Error(`TensorArray dtype is ${this.dtype} but tensor has dtype ${t.dtype}`);let n=0;const r=e.map((e=>(n+=e,n)));if(n!==t.shape[0])throw new Error(`Expected sum of lengths to be equal to\n tensor.shape[0], but sum of lengths is\n ${n}, and tensor's shape is: ${t.shape}`);if(!this.dynamicSize&&e.length!==this.maxSize)throw new Error(`TensorArray's size is not equal to the size of lengths (${this.maxSize} vs. ${e.length}), and the TensorArray is not marked as dynamically resizeable`);const a=0===n?0:t.size/n,s=[];po((()=>{t=Al(t,[1,n,a]);for(let n=0;n{if(n!==e.dtype)throw new Error(`Invalid data types; op elements ${n}, but list elements ${e.dtype}`);vk(t,e.shape,"TensorList shape mismatch: "),go(e)})),this.idTensor=Wo(0),this.maxNumElements=r,go(this.idTensor)}get id(){return this.idTensor.id}copy(){return new Ek([...this.tensors],this.elementShape,this.elementDtype)}clearAndClose(e){this.tensors.forEach((t=>{null!=e&&e.has(t.id)||t.dispose()})),this.tensors.length=0,this.idTensor.dispose()}size(){return this.tensors.length}stack(e,t,n=-1){if(t!==this.elementDtype)throw new Error(`Invalid data types; op elements ${t}, but list elements ${this.elementDtype}`);if(-1!==n&&this.tensors.length!==n)throw new Error(`Operation expected a list with ${n} elements but got a list with ${this.tensors.length} elements.`);vk(e,this.elementShape,"TensorList shape mismatch: ");const r=_k(this.elementShape,this.tensors,e);return po((()=>{const e=this.tensors.map((e=>Al(e,r)));return dh(e,0)}))}popBack(e,t){if(t!==this.elementDtype)throw new Error(`Invalid data types; op elements ${t}, but list elements ${this.elementDtype}`);if(0===this.size())throw new Error("Trying to pop from an empty list.");const n=_k(this.elementShape,this.tensors,e),r=this.tensors.pop();return vk(r.shape,e,"TensorList shape mismatch: "),Al(r,n)}pushBack(e){if(e.dtype!==this.elementDtype)throw new Error(`Invalid data types; op elements ${e.dtype}, but list elements ${this.elementDtype}`);if(vk(e.shape,this.elementShape,"TensorList shape mismatch: "),this.maxNumElements===this.size())throw new Error("Trying to push element into a full list.");go(e),this.tensors.push(e)}resize(e){if(e<0)throw new Error(`TensorListResize expects size to be non-negative. Got: ${e}`);if(-1!==this.maxNumElements&&e>this.maxNumElements)throw new Error(`TensorListResize input size ${e} is greater maxNumElement ${this.maxNumElements}.`);this.tensors.length=e}getItem(e,t,n){if(n!==this.elementDtype)throw new Error(`Invalid data types; op elements ${n}, but list elements ${this.elementDtype}`);if(e<0||e>this.tensors.length)throw new Error(`Trying to access element ${e} in a list with ${this.tensors.length} elements.`);if(null==this.tensors[e])throw new Error(`element at index ${e} is null.`);vk(this.tensors[e].shape,t,"TensorList shape mismatch: ");const r=_k(this.elementShape,this.tensors,t);return Al(this.tensors[e],r)}setItem(e,t){if(t.dtype!==this.elementDtype)throw new Error(`Invalid data types; op elements ${t.dtype}, but list elements ${this.elementDtype}`);if(e<0||-1!==this.maxNumElements&&e>=this.maxNumElements)throw new Error(`Trying to set element ${e} in a list with max ${this.maxNumElements} elements.`);vk(this.elementShape,t.shape,"TensorList shape mismatch: "),go(t),this.tensors[e]=t}gather(e,t,n){if(t!==this.elementDtype)throw new Error(`Invalid data types; op elements ${t}, but list elements ${this.elementDtype}`);vk(this.elementShape,n,"TensorList shape mismatch: "),e=e.slice(0,this.size());const r=_k(this.elementShape,this.tensors,n);return 0===e.length?ts([],[0].concat(r)):po((()=>{const t=e.map((e=>Al(this.tensors[e],r)));return dh(t,0)}))}concat(e,t){if(e&&e!==this.elementDtype)throw new Error(`TensorList dtype is ${this.elementDtype} but concat requested dtype ${e}`);vk(this.elementShape,t,"TensorList shape mismatch: ");const n=_k(this.elementShape,this.tensors,t);return 0===this.size()?ts([],[0].concat(n)):po((()=>{const e=this.tensors.map((e=>Al(e,n)));return Fl(e,0)}))}}function Ck(e,t,n){const[r,a]=Rx("fusedOps",e,t,n),s="biasadd"===r,i="prelu"===a,o="fusedbatchnorm"===r,l=Rx("numArgs",e,t,n);if(s){if(i&&2!==l)throw new Error("FusedConv2d and DepthwiseConv2d with BiasAdd and Prelu must have two extra arguments: bias and alpha.");if(!i&&1!==l)throw new Error("FusedConv2d and DepthwiseConv2d with BiasAdd must have one extra argument: bias.")}if(o)throw new Error("FusedConv2d and DepthwiseConv2d with FusedBatchNorm is not supported");const u=Rx("strides",e,t,n),c=Px(e,t,n),h=Rx("dataFormat",e,t,n).toUpperCase(),f=Rx("dilations",e,t,n),[d,p]=Rx("args",e,t,n);return{stride:u,pad:c,dataFormat:h,dilations:f,biasArg:d,preluArg:p,activationFunc:a,leakyreluAlpha:Rx("leakyreluAlpha",e,t,n)}}function Sk(e,t,n){return{boxes:Rx("boxes",e,t,n),scores:Rx("scores",e,t,n),maxOutputSize:Rx("maxOutputSize",e,t,n),iouThreshold:Rx("iouThreshold",e,t,n),scoreThreshold:Rx("scoreThreshold",e,t,n),softNmsSigma:Rx("softNmsSigma",e,t,n)}}class Nk{constructor(e,t){this.keyDType=e,this.valueDType=t,this.handle=Wo(0),this.tensorMap=new Map,go(this.handle)}get id(){return this.handle.id}clearAndClose(){this.tensorMap.forEach((e=>e.dispose())),this.tensorMap.clear(),this.handle.dispose()}size(){return this.tensorMap.size}tensorSize(){return Wo(this.size(),"int32")}async import(e,t){this.checkKeyAndValueTensor(e,t);const n=await e.data();return this.tensorMap.forEach((e=>e.dispose())),this.tensorMap.clear(),po((()=>{const e=Sh(t),r=n.length,a=e.length;Q(r===a,(()=>`The number of elements doesn't match, keys has ${r} elements, the values has ${a} elements.`));for(let t=0;t{const e=[];for(let r=0;r{switch(e.category){case"arithmetic":return po((()=>((e,t,n)=>{switch(e.op){case"BiasAdd":case"AddV2":case"Add":return[No(Rx("a",e,t,n),Rx("b",e,t,n))];case"AddN":return[il(Rx("tensors",e,t,n))];case"FloorMod":case"Mod":return[dc(Rx("a",e,t,n),Rx("b",e,t,n))];case"Mul":return[Do(Rx("a",e,t,n),Rx("b",e,t,n))];case"RealDiv":case"Div":return[Ao(Rx("a",e,t,n),Rx("b",e,t,n))];case"DivNoNan":return[du(Rx("a",e,t,n),Rx("b",e,t,n))];case"FloorDiv":return[Io(Rx("a",e,t,n),Rx("b",e,t,n))];case"Sub":return[Xo(Rx("a",e,t,n),Rx("b",e,t,n))];case"Minimum":return[hc(Rx("a",e,t,n),Rx("b",e,t,n))];case"Maximum":return[Qo(Rx("a",e,t,n),Rx("b",e,t,n))];case"Pow":return[Go(Rx("a",e,t,n),Rx("b",e,t,n))];case"SquaredDifference":return[hh(Rx("a",e,t,n),Rx("b",e,t,n))];default:throw TypeError(`Node type ${e.op} is not implemented`)}})(e,t,n)));case"basic_math":return po((()=>((e,t,n)=>{switch(e.op){case"Abs":case"ComplexAbs":return[qo(Rx("x",e,t,n))];case"Acos":return[al(Rx("x",e,t,n))];case"Acosh":return[sl(Rx("x",e,t,n))];case"Asin":return[hl(Rx("x",e,t,n))];case"Asinh":return[fl(Rx("x",e,t,n))];case"Atan":return[dl(Rx("x",e,t,n))];case"Atan2":return[pl(Rx("x",e,t,n),Rx("y",e,t,n))];case"Atanh":return[ml(Rx("x",e,t,n))];case"Ceil":return[jl(Rx("x",e,t,n))];case"Complex":return[Qa(Rx("real",e,t,n),Rx("imag",e,t,n))];case"Cos":return[ru(Rx("x",e,t,n))];case"Cosh":return[au(Rx("x",e,t,n))];case"Elu":return[gu(Rx("x",e,t,n))];case"Erf":return[yu(Rx("x",e,t,n))];case"Exp":return[bu(Rx("x",e,t,n))];case"Expm1":return[wu(Rx("x",e,t,n))];case"Floor":return[ku(Rx("x",e,t,n))];case"Log":return[Bu(Rx("x",e,t,n))];case"Log1p":return[$u(Rx("x",e,t,n))];case"Imag":return[Nu(Rx("x",e,t,n))];case"Neg":return[Pu(Rx("x",e,t,n))];case"Reciprocal":return[Lc(Rx("x",e,t,n))];case"Real":return[Pc(Rx("x",e,t,n))];case"Relu":return[zc(Rx("x",e,t,n))];case"Round":return[Xc(Rx("x",e,t,n))];case"Selu":return[qc(Rx("x",e,t,n))];case"Sigmoid":return[Rl(Rx("x",e,t,n))];case"Sin":return[Qc(Rx("x",e,t,n))];case"Sign":return[Zc(Rx("x",e,t,n))];case"Sinh":return[eh(Rx("x",e,t,n))];case"Softplus":return[Lu(Rx("x",e,t,n))];case"Sqrt":return[To(Rx("x",e,t,n))];case"Square":return[Fo(Rx("x",e,t,n))];case"Tanh":return[Ml(Rx("x",e,t,n))];case"Tan":return[gh(Rx("x",e,t,n))];case"ClipByValue":return[Hl(Rx("x",e,t,n),Rx("clipValueMin",e,t,n),Rx("clipValueMax",e,t,n))];case"Relu6":return[Wc(Rx("x",e,t,n))];case"Rsqrt":return[Kc(Ox(e.inputNames[0],t,n))];case"Prod":return[Ic(Rx("x",e,t,n),Rx("axes",e,t,n))];case"LeakyRelu":return[Tu(Rx("x",e,t,n),Rx("alpha",e,t,n))];case"Prelu":return[Nc(Rx("x",e,t,n),Rx("alpha",e,t,n))];case"IsNan":return[Du(Ox(e.inputNames[0],t,n))];default:throw TypeError(`Node type ${e.op} is not implemented`)}})(e,t,n)));case"control":return(async(e,t,n)=>{switch(e.op){case"If":case"StatelessIf":{const r=Rx("thenBranch",e,t,n),a=Rx("elseBranch",e,t,n),s=Rx("cond",e,t,n),i=Rx("args",e,t,n);return(await s.data())[0]?n.functionMap[r].executeFunctionAsync(i,n.tensorArrayMap,n.tensorListMap):n.functionMap[a].executeFunctionAsync(i,n.tensorArrayMap,n.tensorListMap)}case"While":case"StatelessWhile":{const r=Rx("body",e,t,n),a=Rx("cond",e,t,n),s=Rx("args",e,t,n),i=await n.functionMap[a].executeFunctionAsync(s,n.tensorArrayMap,n.tensorListMap),o=s.map((e=>e.id));let l=await i[0].data();i.forEach((e=>{e.kept||-1!==o.indexOf(e.id)||e.dispose()}));let u=s;for(;l[0];){const e=u;u=await n.functionMap[r].executeFunctionAsync(u,n.tensorArrayMap,n.tensorListMap);const t=u.map((e=>e.id));e.forEach((e=>{e.kept||-1!==o.indexOf(e.id)||-1!==t.indexOf(e.id)||e.dispose()}));const s=await n.functionMap[a].executeFunctionAsync(u,n.tensorArrayMap,n.tensorListMap);l=await s[0].data(),s.forEach((e=>{e.kept||-1!==o.indexOf(e.id)||-1!==t.indexOf(e.id)||e.dispose()}))}return u}case"LoopCond":return[Lx(Rx("pred",e,t,n))];case"Switch":{const r=Rx("pred",e,t,n);let a=Rx("data",e,t,n);return a.kept||(a=Lx(a)),(await r.data())[0]?[void 0,a]:[a,void 0]}case"Merge":{const r=e.inputNames.find((e=>void 0!==Ox(e,t,n)));return r?[Lx(Ox(r,t,n))]:void 0}case"Enter":{const r=Rx("frameName",e,t,n),a=Rx("tensor",e,t,n);return n.enterFrame(r),[Lx(a)]}case"Exit":{const r=Rx("tensor",e,t,n);return n.exitFrame(),[Lx(r)]}case"NextIteration":{const r=Rx("tensor",e,t,n);return n.nextIteration(),[Lx(r)]}case"TensorArrayV3":{const r=Rx("size",e,t,n),a=Rx("dtype",e,t,n),s=Rx("elementShape",e,t,n),i=Rx("dynamicSize",e,t,n),o=Rx("clearAfterRead",e,t,n),l=Rx("identicalElementShapes",e,t,n),u=Rx("name",e,t,n),c=new kk(u,a,r,s,l,i,o);return n.addTensorArray(c),[c.idTensor,Wo(1)]}case"TensorArrayWriteV3":{const r=Rx("tensorArrayId",e,t,n),a=Rx("index",e,t,n),s=Rx("tensor",e,t,n),i=n.getTensorArray(r.id);return i.write(a,s),[i.idTensor]}case"TensorArrayReadV3":{const r=Rx("tensorArrayId",e,t,n),a=Rx("index",e,t,n);return[n.getTensorArray(r.id).read(a)]}case"TensorArrayGatherV3":{const r=Rx("tensorArrayId",e,t,n),a=Rx("indices",e,t,n),s=Rx("dtype",e,t,n);return[n.getTensorArray(r.id).gather(a,s)]}case"TensorArrayScatterV3":{const r=Rx("tensorArrayId",e,t,n),a=Rx("indices",e,t,n),s=Rx("tensor",e,t,n),i=n.getTensorArray(r.id);return i.scatter(a,s),[i.idTensor]}case"TensorArrayConcatV3":{const r=Rx("tensorArrayId",e,t,n),a=n.getTensorArray(r.id),s=Rx("dtype",e,t,n);return[a.concat(s)]}case"TensorArraySplitV3":{const r=Rx("tensorArrayId",e,t,n),a=Rx("tensor",e,t,n),s=Rx("lengths",e,t,n),i=n.getTensorArray(r.id);return i.split(s,a),[i.idTensor]}case"TensorArraySizeV3":{const r=Rx("tensorArrayId",e,t,n);return[Wo(n.getTensorArray(r.id).size(),"int32")]}case"TensorArrayCloseV3":{const r=Rx("tensorArrayId",e,t,n),a=n.getTensorArray(r.id);return a.clearAndClose(),[a.idTensor]}case"TensorListSetItem":{const r=Rx("tensorListId",e,t,n),a=Rx("index",e,t,n),s=Rx("tensor",e,t,n),i=n.getTensorList(r.id);return i.setItem(a,s),[i.idTensor]}case"TensorListGetItem":{const r=Rx("tensorListId",e,t,n),a=Rx("index",e,t,n),s=Rx("elementShape",e,t,n),i=Rx("elementDType",e,t,n);return[n.getTensorList(r.id).getItem(a,s,i)]}case"TensorListScatterV2":case"TensorListScatter":{const r=Rx("indices",e,t,n),a=function(e,t,n,r){if(t.length!==e.shape[0])throw new Error(`Expected len(indices) == tensor.shape[0], but saw: ${t.length} vs. ${e.shape[0]}`);const a=Math.max(...t);if(null!=r&&-1!==r&&a>=r)throw new Error(`Max index must be < array size (${a} vs. ${r})`);const s=new Ek([],n,e.dtype,r),i=Sh(e,0);return t.forEach(((e,t)=>{s.setItem(e,i[t])})),s}(Rx("tensor",e,t,n),r,Rx("elementShape",e,t,n),Rx("numElements",e,t,n));return n.addTensorList(a),[a.idTensor]}case"TensorListReserve":case"EmptyTensorList":{const r=Rx("elementShape",e,t,n),a=Rx("elementDType",e,t,n);let s;s="TensorListReserve"===e.op?"numElements":"maxNumElements";const i=function(e,t,n){return new Ek([],e,t,n)}(r,a,Rx(s,e,t,n));return n.addTensorList(i),[i.idTensor]}case"TensorListGather":{const r=Rx("tensorListId",e,t,n),a=Rx("indices",e,t,n),s=Rx("elementShape",e,t,n),i=Rx("elementDType",e,t,n);return[n.getTensorList(r.id).gather(a,i,s)]}case"TensorListStack":{const r=Rx("tensorListId",e,t,n),a=Rx("elementShape",e,t,n),s=Rx("elementDType",e,t,n),i=Rx("numElements",e,t,n);return[n.getTensorList(r.id).stack(a,s,i)]}case"TensorListFromTensor":{const r=function(e,t,n){const r=e.dtype;if(e.shape.length<1)throw new Error(`Tensor must be at least a vector, but saw shape: ${e.shape}`);if(e.dtype!==n)throw new Error(`Invalid data 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s=xk(e.shape.slice(1),n),i=0===r?0:e.size/r,o=po((()=>{const n=[];e=Al(e,[1,r,i]);for(let r=0;r((e,t,n)=>{switch(e.op){case"Conv1D":{const r=Rx("stride",e,t,n),a=Rx("pad",e,t,n),s=Rx("dataFormat",e,t,n).toUpperCase(),i=Rx("dilation",e,t,n);return[Jl(Rx("x",e,t,n),Rx("filter",e,t,n),r,a,s,i)]}case"Conv2D":{const 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po((()=>((e,t,n)=>{switch(e.op){case"TopKV2":{const r=Rx("x",e,t,n),a=Rx("k",e,t,n),s=Rx("sorted",e,t,n),i=xh(r,a,s);return[i.values,i.indices]}case"Unique":{const r=Rx("x",e,t,n),a=Eh(r);return[a.values,a.indices]}case"UniqueV2":{const r=Rx("x",e,t,n),a=Rx("axis",e,t,n),s=Eh(r,a);return[s.values,s.indices]}default:throw TypeError(`Node type ${e.op} is not implemented`)}})(e,t,n)));case"image":return po((()=>((e,t,n)=>{switch(e.op){case"ResizeBilinear":{const r=Rx("images",e,t,n),a=Rx("size",e,t,n),s=Rx("alignCorners",e,t,n),i=Rx("halfPixelCenters",e,t,n);return[zf.resizeBilinear(r,[a[0],a[1]],s,i)]}case"ResizeNearestNeighbor":{const r=Rx("images",e,t,n),a=Rx("size",e,t,n),s=Rx("alignCorners",e,t,n),i=Rx("halfPixelCenters",e,t,n);return[zf.resizeNearestNeighbor(r,[a[0],a[1]],s,i)]}case"CropAndResize":{const 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po((()=>((e,t,n)=>{switch(e.op){case"BatchMatMul":case"BatchMatMulV2":case"MatMul":return[pi(Rx("a",e,t,n),Rx("b",e,t,n),Rx("transposeA",e,t,n),Rx("transposeB",e,t,n))];case"Einsum":return[mu(Rx("equation",e,t,n),...Rx("tensors",e,t,n))];case"Transpose":return[gi(Rx("x",e,t,n),Rx("perm",e,t,n))];case"_FusedMatMul":const[r,a]=Rx("fusedOps",e,t,n),s="biasadd"===r,i="prelu"===a,o=Rx("numArgs",e,t,n),l=Rx("leakyreluAlpha",e,t,n);if(s){if(i&&2!==o)throw new Error("Fused MatMul with BiasAdd and Prelu must have two extra arguments: bias and alpha.");if(!i&&1!==o)throw new Error("Fused MatMul with BiasAdd must have one extra argument: bias.")}const[u,c]=Rx("args",e,t,n);return[Yh({a:Rx("a",e,t,n),b:Rx("b",e,t,n),transposeA:Rx("transposeA",e,t,n),transposeB:Rx("transposeB",e,t,n),bias:u,activation:a,preluActivationWeights:c,leakyreluAlpha:l})];default:throw TypeError(`Node type ${e.op} is not implemented`)}})(e,t,n)));case"normalization":return po((()=>((e,t,n)=>{switch(e.op){case"FusedBatchNorm":case"FusedBatchNormV2":case"FusedBatchNormV3":return[Pl(Rx("x",e,t,n),Rx("mean",e,t,n),Rx("variance",e,t,n),Rx("offset",e,t,n),Rx("scale",e,t,n),Rx("epsilon",e,t,n))];case"LRN":return[Mu(Rx("x",e,t,n),Rx("radius",e,t,n),Rx("bias",e,t,n),Rx("alpha",e,t,n),Rx("beta",e,t,n))];case"Softmax":return[sh(Rx("x",e,t,n))];case"LogSoftmax":return[Vu(Rx("x",e,t,n))];case"SparseToDense":return[Mh(Rx("sparseIndices",e,t,n),Rx("outputShape",e,t,n),Rx("sparseValues",e,t,n),Rx("defaultValue",e,t,n))];default:throw TypeError(`Node type ${e.op} is not implemented`)}})(e,t,n)));case"reduction":return po((()=>((e,t,n)=>{switch(e.op){case"Max":{const r=Rx("axis",e,t,n),a=Rx("keepDims",e,t,n);return[Wu(Rx("x",e,t,n),r,a)]}case"Mean":{const r=Rx("axis",e,t,n),a=Rx("keepDims",e,t,n);return[ic(Rx("x",e,t,n),r,a)]}case"Min":{const r=Rx("axis",e,t,n),a=Rx("keepDims",e,t,n);return[cc(Rx("x",e,t,n),r,a)]}case"Sum":{const 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r=Rx("n",e,t,n),a=Rx("axis",e,t,n);let s=Rx("tensors",e,t,n);return s=s.slice(0,r),[Fl(s,a)]}case"Gather":{const r=Rx("x",e,t,n),a=Rx("indices",e,t,n);return[Eu(r,Ks(a,"int32"),0)]}case"GatherV2":{const r=Rx("axis",e,t,n),a=Rx("batchDims",e,t,n),s=Rx("x",e,t,n),i=Rx("indices",e,t,n);return[Eu(s,Ks(i,"int32"),r,a)]}case"Reverse":{const r=Rx("dims",e,t,n),a=[];for(let e=0;e{const r=Rx("axis",e,t,n),a=Rx("tensors",e,t,n),s=a[0].shape,i=fh(a[0]).shape,o=a.map((e=>{const t=se(e.shape,s);if(!t&&!se(fh(e).shape,i))throw new Error("the input tensors shape does not match");return t?e:Al(e,s)}));return[dh(o,r)]}));case"Unpack":{const r=Rx("axis",e,t,n),a=Rx("tensor",e,t,n);return Sh(a,r)}case"Tile":{const r=Rx("reps",e,t,n);return[_u(Rx("x",e,t,n),r)]}case"Split":case"SplitV":{const r=Rx("axis",e,t,n),a=Rx("numOrSizeSplits",e,t,n),s=Rx("x",e,t,n);return uh(s,a,r)}case"ScatterNd":{const r=Rx("indices",e,t,n),a=Rx("values",e,t,n),s=Rx("shape",e,t,n);return[Oh(r,a,s)]}case"GatherNd":{const r=Rx("x",e,t,n),a=Rx("indices",e,t,n);return[Bh(r,a)]}case"SparseToDense":{const r=Rx("sparseIndices",e,t,n),a=Rx("outputShape",e,t,n),s=Rx("sparseValues",e,t,n),i=Rx("defaultValue",e,t,n);return[Mh(r,s,a,s.dtype===i.dtype?i:Ks(i,s.dtype))]}default:throw TypeError(`Node type ${e.op} is not implemented`)}})(e,t,n)));case"sparse":return po((()=>((e,t,n)=>{switch(e.op){case"SparseReshape":{const{outputIndices:r,outputShape:a}=Vf.sparseReshape(Rx("inputIndices",e,t,n),Rx("inputShape",e,t,n),Rx("newShape",e,t,n));return[r,a]}default:throw TypeError(`Node type ${e.op} is not implemented`)}})(e,t,n)));case"spectral":return po((()=>((e,t,n)=>{switch(e.op){case"FFT":return[ih(Rx("x",e,t,n))];case"IFFT":return[oh(Rx("x",e,t,n))];case"RFFT":return[ch(Rx("x",e,t,n))];case"IRFFT":return[lh(Rx("x",e,t,n))];default:throw TypeError(`Node type ${e.op} is not implemented`)}})(e,t,n)));case"transformation":return po((()=>((e,t,n)=>{switch(e.op){case"Cast":return[Ks(Rx("x",e,t,n),Rx("dtype",e,t,n))];case"ExpandDims":{const r=Rx("axis",e,t,n);return[vu(Rx("x",e,t,n),r)]}case"Squeeze":{const r=Rx("axis",e,t,n);return[fh(Rx("x",e,t,n),r)]}case"Reshape":return[Al(Rx("x",e,t,n),Rx("shape",e,t,n))];case"MirrorPad":return[fc(Rx("x",e,t,n),Rx("padding",e,t,n),Rx("mode",e,t,n))];case"PadV2":case"Pad":return[wc(Rx("x",e,t,n),Rx("padding",e,t,n),Rx("constantValue",e,t,n))];case"SpaceToBatchND":{const r=Rx("blockShape",e,t,n),a=Rx("paddings",e,t,n);return[Cc(Rx("x",e,t,n),r,a)]}case"BatchToSpaceND":{const r=Rx("blockShape",e,t,n),a=Rx("crops",e,t,n);return[$l(Rx("x",e,t,n),r,a)]}case"DepthToSpace":{const r=Rx("blockSize",e,t,n),a=Rx("dataFormat",e,t,n).toUpperCase();return[ou(Rx("x",e,t,n),r,a)]}case"BroadcastTo":return[Vl(Rx("x",e,t,n),Rx("shape",e,t,n))];default:throw TypeError(`Node type ${e.op} is not implemented`)}})(e,t,n)));case"hash_table":return(async(e,t,n,r)=>{switch(e.op){case"HashTable":case"HashTableV2":{const a=Rx("keyDType",e,t,n),s=Rx("valueDType",e,t,n),i=new Nk(a,s);return r.addHashTable(e.name,i),[i.handle]}case"LookupTableImport":case"LookupTableImportV2":{const a=Rx("tableHandle",e,t,n,r),s=Rx("keys",e,t,n),i=Rx("values",e,t,n),o=r.getHashTableById(a.id);return[await o.import(s,i)]}case"LookupTableFind":case"LookupTableFindV2":{const a=Rx("tableHandle",e,t,n,r),s=Rx("keys",e,t,n),i=Rx("defaultValue",e,t,n),o=r.getHashTableById(a.id);return[await o.find(s,i)]}case"LookupTableSize":case"LookupTableSizeV2":{const a=Rx("tableHandle",e,t,n,r);return[r.getHashTableById(a.id).tensorSize()]}default:throw TypeError(`Node type ${e.op} is not implemented`)}})(e,t,n,r);case"custom":const a=Tx(e.op);if(a&&a.customExecutor)return a.customExecutor(new bk(e,t,n));throw TypeError(`Custom op ${e.op} is not registered.`);default:throw TypeError(`Unknown op '${e.op}'. File an issue at https://github.com/tensorflow/tfjs/issues so we can add it, or register a custom execution with tf.registerOp()`)}})(e,t,n);return Pe(a)?a.then((e=>[].concat(e))):[].concat(a)}class Ak{constructor(e={},t={},n={},r={}){this.weightMap=e,this.tensorArrayMap=t,this.tensorListMap=n,this.functionMap=r,this.rootContext={id:0,frameName:"",iterationId:0},this.contexts=[this.rootContext],this.lastId=0,this.generateCurrentContextIds()}newFrame(e,t){return{id:e,frameName:t,iterationId:0}}set currentContext(e){this.contexts!==e&&(this.contexts=e,this.generateCurrentContextIds())}get currentContext(){return this.contexts}get currentContextId(){return this._currentContextIds[0]}get currentContextIds(){return this._currentContextIds}generateCurrentContextIds(){const e=[];for(let t=0;t0===e.id&&0===e.iterationId?"":`${e.frameName}-${e.iterationId}`)).join("/"):""}enterFrame(e){this.contexts&&(this.lastId++,this.contexts=this.contexts.slice(),this.contexts.push(this.newFrame(this.lastId,e)),this._currentContextIds.unshift(this.contextIdforContexts(this.contexts)))}exitFrame(){if(!(this.contexts&&this.contexts.length>1))throw new Error("Cannot exit frame, the context is empty");this.contexts=this.contexts.slice(),this.contexts.splice(-1),this.currentContextIds.shift()}nextIteration(){if(!(this.contexts&&this.contexts.length>0))throw new Error("Cannot increase frame iteration, the context is empty");{this.contexts=this.contexts.slice(),this.lastId++;const e=Object.assign({},this.contexts[this.contexts.length-1]);e.iterationId+=1,e.id=this.lastId,this.contexts.splice(-1,1,e),this._currentContextIds.splice(0,1,this.contextIdforContexts(this.contexts))}}getWeight(e){return this.weightMap[e]}addTensorArray(e){this.tensorArrayMap[e.id]=e}getTensorArray(e){return this.tensorArrayMap[e]}addTensorList(e){this.tensorListMap[e.id]=e}getTensorList(e){return this.tensorListMap[e]}dispose(e){for(const t in this.tensorArrayMap)this.tensorArrayMap[t].clearAndClose(e);for(const t in this.tensorListMap)this.tensorListMap[t].clearAndClose(e)}}function Dk(e,t,n,r){const a=new Set,s=[];let i=null,o=null;const l=new Set,u=Object.keys(e).map((e=>$x(e)[0]));let c=[];null!=r&&(c=r.map((e=>$x(e.name)[0])));const h=[...t];for(;h.length>0;){const e=h.pop();(Ok(e)||Mk(e)||Bk(e))&&null==i&&(i=e,o=i.children.map((e=>e.name)).filter((e=>a.has(e)))),a.add(e.name),null==n[e.name]&&-1===u.indexOf(e.name)&&-1===c.indexOf(e.name)&&(0!==e.inputs.length?e.inputs.forEach((e=>{l.has(e.name)||(l.add(e.name),h.push(e))})):s.push(e.name))}return{inputs:e,outputs:t,usedNodes:a,missingInputs:s,dynamicNode:i,syncInputs:o}}const Tk=["Switch","Merge","Enter","Exit","NextIteration","StatelessIf","StatelessWhile","if","While"],Fk=["NonMaxSuppressionV2","NonMaxSuppressionV3","NonMaxSuppressionV5","Where"],Rk=["HashTable","HashTableV2","LookupTableImport","LookupTableImportV2","LookupTableFind","LookupTableFindV2","LookupTableSize","LookupTableSizeV2"];function Ok(e){return Tk.indexOf(e.op)>=0}function Mk(e){return Fk.indexOf(e.op)>=0}function Bk(e){return Rk.indexOf(e.op)>=0}class $k{constructor(e,t){this.graph=e,this.parent=t,this.compiledMap=new Map,this._weightMap={},this.SEPERATOR=",",this._functions={},this._functionExecutorMap={},this._outputs=e.outputs,this._inputs=e.inputs,this._initNodes=e.initNodes,this._signature=e.signature,this._functions=e.functions,null!=e.functions&&Object.keys(e.functions).forEach((t=>{this._functionExecutorMap[t]=new $k(e.functions[t],this)}))}get weightIds(){return this.parent?this.parent.weightIds:this._weightIds}get functionExecutorMap(){return this.parent?this.parent.functionExecutorMap:this._functionExecutorMap}get weightMap(){return this.parent?this.parent.weightMap:this._weightMap}set weightMap(e){const t=Object.keys(e).map((t=>e[t].map((e=>e.id))));this._weightIds=[].concat(...t),this._weightMap=e}set resourceManager(e){this._resourceManager=e}get inputs(){return this._inputs.map((e=>({name:e.name,shape:e.attrParams.shape?e.attrParams.shape.value:void 0,dtype:e.attrParams.dtype?e.attrParams.dtype.value:void 0})))}get outputs(){return this._outputs.map((e=>({name:e.name,shape:e.attrParams.shape?e.attrParams.shape.value:void 0,dtype:e.attrParams.dtype?e.attrParams.dtype.value:void 0})))}get inputNodes(){return this._inputs.map((e=>e.signatureKey||e.name))}get outputNodes(){return this._outputs.map((e=>{const t=e.signatureKey||e.name;return e.defaultOutput?`${t}:${e.defaultOutput}`:t}))}get functions(){return Object.keys(this._functions).reduce(((e,t)=>(e[t]=this._functions[t].signature,e)),{})}getCompilationKey(e,t){const n=e.map((e=>e.name)).sort(),r=t.map((e=>e.name)).sort();return n.join(this.SEPERATOR)+"--"+r.join(this.SEPERATOR)}compile(e,t){const n=Dk(e,t,this.weightMap,this._initNodes),{missingInputs:r,dynamicNode:a,syncInputs:s}=n;if(null!=a)throw new Error(`This execution contains the node '${a.name}', which has the dynamic op '${a.op}'. Please use model.executeAsync() instead. Alternatively, to avoid the dynamic ops, specify the inputs [${s}]`);if(r.length>0){const n=t.map((e=>e.name)),a=Object.keys(e);throw new Error(`Cannot compute the outputs [${n}] from the provided inputs [${a}]. Missing the following inputs: [${r}]`)}return function(e,t,n){const{usedNodes:r,inputs:a}=n,s=[],i=Object.keys(a).map((e=>$x(e)[0])).map((t=>e.nodes[t])),o=e.initNodes;i.forEach((e=>{r.has(e.name)&&s.push(e)})),e.weights.forEach((e=>{r.has(e.name)&&s.push(e)})),null!=o&&o.forEach((e=>{r.has(e.name)&&s.push(e)}));const l=new Set,u=[];for(;s.length>0;){const e=s.pop();l.add(e.name),t[e.name]||u.push(e),e.children.forEach((e=>{!l.has(e.name)&&r.has(e.name)&&e.inputs.every((e=>l.has(e.name)))&&s.push(e)}))}return u}(this.graph,this.weightMap,n)}execute(e,t){e=this.mapInputs(e);const n=Object.keys(e).sort();this.checkInputs(e),this.checkInputShapeAndType(e),t=this.mapOutputs(t),this.checkOutputs(t);const r=n.map((e=>this.graph.nodes[$x(e)[0]])),a=t.map((e=>$x(e)[0]));let s=a.map((e=>this.graph.nodes[e]));0===s.length&&(s=this._outputs);const i=this.getCompilationKey(r,s);let o=this.compiledMap.get(i);null==o&&(o=this.compile(e,s),this.compiledMap.set(i,o));const l={},u={};return po((()=>{const n=new Ak(this.weightMap,l,u,this.functionExecutorMap),r=Object.assign({},this.weightMap);Object.keys(e).forEach((t=>{const[n,a]=$x(t),s=[];s[a]=e[t],r[n]=s}));const s=this.getFrozenTensorIds(r),i={};for(let e=0;eOx(e,r,n)))}))}getFrozenTensorIds(e){const t=[].concat.apply([],Object.keys(e).map((t=>e[t])).map((e=>e.map((e=>e.id)))));return new Set(t)}checkTensorForDisposal(e,t,n,r,a,s,i){"control"!==t.category&&-1===s.indexOf(e)&&(n[e].forEach((e=>{null!=e&&(i[e.id]=(i[e.id]||0)+t.children.length)})),t.inputs.forEach((e=>{if("control"!==e.category){const t=function(e,t,n){return t[Bx(e,n.currentContextId)]}(e.name,n,r);null!=t&&t.forEach((e=>{if(e&&!e.kept&&!a.has(e.id)){const t=i[e.id];1===t?(e.dispose(),delete i[e.id]):null!=t&&i[e.id]--}}))}})))}async executeAsync(e,t){return this._executeAsync(e,t)}async _executeAsync(e,t,n=!1,r={},a={}){n||(e=this.mapInputs(e),this.checkInputs(e),this.checkInputShapeAndType(e),t=this.mapOutputs(t),this.checkOutputs(t));const s=new Ak(this.weightMap,r,a,this.functionExecutorMap),i=await this.executeWithControlFlow(e,s,t,n),o=t.map((e=>Ox(e,i,s))),l=o.map((e=>e.id)),u=Object.keys(e).map((t=>e[t].id)),c=new Set([...l,...u,...this.weightIds]);return Object.keys(i).forEach((e=>{i[e].forEach((e=>{!e||e.kept||e.isDisposed||c.has(e.id)||e.dispose()}))})),null==this.parent&&s.dispose(c),o}async executeFunctionAsync(e,t,n){const r=e.reduce(((e,t,n)=>(e[this.inputs[n].name]=t,e)),{});return this._executeAsync(r,this.outputNodes,!0,t,n)}async executeWithControlFlow(e,t,n,r){const a=Object.keys(e),s=a.map((e=>this.graph.nodes[$x(e)[0]])),i=n.map((e=>$x(e)[0]));let o=i.map((e=>this.graph.nodes[e]));0===o.length&&(o=this._outputs);const{usedNodes:l,missingInputs:u,dynamicNode:c,syncInputs:h}=Dk(e,o,this.weightMap,this._initNodes),f=[...s,...this.graph.weights,...this._initNodes||[]].map((e=>({node:e,contexts:t.currentContext}))),d=Object.assign({},this.weightMap);Object.keys(e).forEach((t=>{const[n,r]=$x(t),a=[];a[r]=e[t],d[n]=a}));const p={},m=this.getFrozenTensorIds(d),g={};for(;f.length>0;){const e=this.processStack(s,f,t,d,g,m,i,p,l);await Promise.all(e)}null!=c||r||console.warn("This model execution did not contain any nodes with control flow or dynamic output shapes. You can use model.execute() instead.");const y=o.filter((e=>!Ok(e)&&!Ox(e.name,d,t))).map((e=>e.name));if(y.length>0){let e="";throw null!=c&&(e=`Alternatively, to avoid the dynamic ops, use model.execute() and specify the inputs [${h}]`),new Error(`Cannot compute the outputs [${y}] from the provided inputs [${a}]. Consider providing the following inputs: [${u}]. ${e}`)}return d}processStack(e,t,n,r,a,s,i,o,l){const u=[];for(;t.length>0;){const e=t.pop();n.currentContext=e.contexts;let c="";if("Enter"===e.node.op&&Rx("isConstant",e.node,r,n)&&([c]=Mx(e.node.name,n)),null==r[e.node.name]){const h=Ik(e.node,r,n,this._resourceManager);c||([c]=Mx(e.node.name,n));const f=n.currentContext;Pe(h)?u.push(h.then((u=>(r[c]=u,n.currentContext=f,this.checkTensorForDisposal(c,e.node,r,n,s,i,o),this.processChildNodes(e.node,t,n,r,a,l),u)))):(r[c]=h,this.checkTensorForDisposal(c,e.node,r,n,s,i,o),this.processChildNodes(e.node,t,n,r,a,l))}else this.processChildNodes(e.node,t,n,r,a,l)}return u}processChildNodes(e,t,n,r,a,s){e.children.forEach((e=>{const[i]=Mx(e.name,n);!a[i]&&s.has(e.name)&&("Merge"===e.op?e.inputNames.some((e=>!!Ox(e,r,n)))&&(a[i]=!0,t.push({contexts:n.currentContext,node:e})):e.inputNames.every((e=>!!Ox(e,r,n)))&&(a[i]=!0,t.push({contexts:n.currentContext,node:e})))}))}dispose(){Object.keys(this.weightMap).forEach((e=>this.weightMap[e].forEach((e=>e.dispose()))))}checkInputShapeAndType(e){Object.keys(e).forEach((t=>{const n=e[t],[r]=$x(t),a=this.graph.nodes[r];if(a.attrParams.shape&&a.attrParams.shape.value){const e=a.attrParams.shape.value;Q(e.length===n.shape.length&&n.shape.every(((t,n)=>-1===e[n]||e[n]===t)),(()=>`The shape of dict['${a.name}'] provided in model.execute(dict) must be [${e}], but was [${n.shape}]`))}a.attrParams.dtype&&a.attrParams.dtype.value&&Q(n.dtype===a.attrParams.dtype.value,(()=>`The dtype of dict['${a.name}'] provided in model.execute(dict) must be ${a.attrParams.dtype.value}, but was ${n.dtype}`))}))}mapInputs(e){const t={};for(const n in e)null!=this._signature&&null!=this._signature.inputs&&null!=this._signature.inputs[n]?t[this._signature.inputs[n].name]=e[n]:t[n]=e[n];return t}checkInputs(e){const t=Object.keys(e).filter((e=>{const[t]=$x(e);return null==this.graph.nodes[t]}));if(t.length>0)throw new Error(`The dict provided in model.execute(dict) has keys: [${t}] that are not part of graph`)}mapOutputs(e){return e.map((e=>null!=this._signature&&null!=this._signature.outputs&&null!=this._signature.outputs[e]?this._signature.outputs[e].name:e),{})}checkOutputs(e){e.forEach((e=>{const[t]=$x(e);if(!this.graph.nodes[t])throw new Error(`The output '${e}' is not found in the graph`)}))}}class Pk{constructor(e={},t={}){this.hashTableNameToHandle=e,this.hashTableMap=t}addHashTable(e,t){this.hashTableNameToHandle[e]=t.handle,this.hashTableMap[t.id]=t}getHashTableHandleByName(e){return this.hashTableNameToHandle[e]}getHashTableById(e){return this.hashTableMap[e]}dispose(){for(const e in this.hashTableMap)this.hashTableMap[e].clearAndClose(),delete this.hashTableMap[e];for(const e in this.hashTableNameToHandle)this.hashTableNameToHandle[e].dispose(),delete this.hashTableNameToHandle[e]}}class Lk{constructor(e,t={}){this.modelUrl=e,this.loadOptions=t,this.version="n/a",null==t&&(this.loadOptions={}),this.resourceManager=new Pk}get modelVersion(){return this.version}get inputNodes(){return this.executor.inputNodes}get outputNodes(){return this.executor.outputNodes}get inputs(){return this.executor.inputs}get outputs(){return this.executor.outputs}get weights(){return this.executor.weightMap}get metadata(){return this.artifacts.userDefinedMetadata}get modelSignature(){return this.signature}findIOHandler(){const e=this.modelUrl;if(null!=e.load)this.handler=e;else if(null!=this.loadOptions.requestInit)this.handler=ui(e,this.loadOptions);else{const t=gs(e,this.loadOptions);if(0===t.length)t.push(ui(e,this.loadOptions));else if(t.length>1)throw new Error(`Found more than one (${t.length}) load handlers for URL '${[e]}'`);this.handler=t[0]}}async load(){if(this.findIOHandler(),null==this.handler.load)throw new Error("Cannot proceed with model loading because the IOHandler provided does not have the `load` method implemented.");const e=await this.handler.load();return this.loadSync(e)}loadSync(e){this.artifacts=e;const t=this.artifacts.modelTopology;let n;n=null!=this.artifacts.userDefinedMetadata&&null!=this.artifacts.userDefinedMetadata.signature?this.artifacts.userDefinedMetadata.signature:this.artifacts.signature,this.signature=n,this.version=`${t.versions.producer}.${t.versions.minConsumer}`;const r=as(this.artifacts.weightData,this.artifacts.weightSpecs);if(this.executor=new $k(rk.Instance.transformGraph(t,this.signature)),this.executor.weightMap=this.convertTensorMapToTensorsMap(r),this.executor.resourceManager=this.resourceManager,null!=e.modelInitializer&&null!=e.modelInitializer.node){const t=rk.Instance.transformGraph(e.modelInitializer);this.initializer=new $k(t),this.initializer.weightMap=this.executor.weightMap,this.initializer.resourceManager=this.resourceManager,this.initializer.executeAsync({},[])}return!0}async save(e,t){if("string"==typeof e){const t=ms(e);if(0===t.length)throw new Error(`Cannot find any save handlers for URL '${e}'`);if(t.length>1)throw new Error(`Found more than one (${t.length}) save handlers for URL '${e}'`);e=t[0]}if(null==e.save)throw new Error("GraphModel.save() cannot proceed because the IOHandler provided does not have the `save` attribute defined.");return e.save(this.artifacts)}predict(e,t){return this.execute(e,this.outputNodes)}normalizeInputs(e){if(!(e instanceof _a||Array.isArray(e)))return e;if((e=Array.isArray(e)?e:[e]).length!==this.inputNodes.length)throw new Error(`Input tensor count mismatch,the graph model has ${this.inputNodes.length} placeholders, while there are ${e.length} input tensors.`);return this.inputNodes.reduce(((t,n,r)=>(t[n]=e[r],t)),{})}normalizeOutputs(e){return e=e||this.outputNodes,Array.isArray(e)?e:[e]}execute(e,t){e=this.normalizeInputs(e),t=this.normalizeOutputs(t);const n=this.executor.execute(e,t);return n.length>1?n:n[0]}async executeAsync(e,t){e=this.normalizeInputs(e),t=this.normalizeOutputs(t);const n=await this.executor.executeAsync(e,t);return n.length>1?n:n[0]}convertTensorMapToTensorsMap(e){return Object.keys(e).reduce(((t,n)=>(t[n]=[e[n]],t)),{})}dispose(){this.executor.dispose(),this.initializer&&this.initializer.dispose(),this.resourceManager.dispose()}}async function zk(e,t={}){if(null==e)throw new Error("modelUrl in loadGraphModel() cannot be null. Please provide a url or an IOHandler that loads the model");null==t&&(t={}),t.fromTFHub&&null==e.load&&(e.endsWith("/")||(e+="/"),e=`${e}model.json?tfjs-format=file`);const n=new Lk(e,t);return await n.load(),n}const Wk="3.6.0";function Uk(e,t,n=new Map,r=new Set){if(null==e)return null;if(r.has(e))throw new Error("Circular references are not supported.");if(n.has(e))return n.get(e);const a=t(e);if(a.recurse&&null!==a.value)throw new Error("A deep map function may not return both a value and recurse=true.");if(a.recurse){if(Xk(e)){const a=Array.isArray(e)?[]:{};r.add(e);for(const s in e){const i=Uk(e[s],t,n,r);a[s]=i}return r.delete(e),a}throw new Error(`Can't recurse into non-iterable type: ${e}`)}return n.set(e,a.value),a.value}function Vk(e,t=Hk){return jk(e,t)}function jk(e,t,n=new Set){const r=e[0];if(n.has(r))throw new Error("Circular references are not supported.");const a=t(e);if(a.recurse&&null!==a.value)throw new Error("A deep zip function may not return both a value and recurse=true.");if(a.recurse){if(Xk(r)){const a=Array.isArray(r)?[]:{};n.add(r);for(const s in r){const r=jk(e.map((e=>e[s])),t,n);a[s]=r}return n.delete(r),a}throw new Error(`Can't recurse into non-iterable type: ${r}`)}return a.value}function Hk(e){return null===e?null:Xk(e[0])?{value:null,recurse:!0}:{value:e,recurse:!1}}async function Gk(e,t){const n=new Map;Uk(e,t,n);for(const e of Array.from(n.keys())){const t=n.get(e);if(Pe(t)){const r=await t;n.set(e,r)}}return Uk(e,t,n)}function Xk(e){return null!=e&&!ArrayBuffer.isView(e)&&(Array.isArray(e)||"object"==typeof e&&!(e instanceof _a))}function Kk(e){return e instanceof _a?{value:e.clone(),recurse:!1}:Xk(e)?{value:null,recurse:!0}:{value:e,recurse:!1}}class qk{constructor(e){if(this.capacity=e,this.begin=0,this.end=0,null==e)throw new RangeError("Can't create a ring buffer of unknown capacity.");if(e<1)throw new RangeError("Can't create ring buffer of capacity < 1.");this.data=new Array(e),this.doubledCapacity=2*e}wrap(e){for(;e<0;)e+=this.doubledCapacity;return e%this.doubledCapacity}get(e){if(e<0)throw new RangeError("Can't get item at a negative index.");return this.data[e%this.capacity]}set(e,t){if(e<0)throw new RangeError("Can't set item at a negative index.");this.data[e%this.capacity]=t}length(){let e=this.end-this.begin;return e<0&&(e=this.doubledCapacity+e),e}isFull(){return this.length()===this.capacity}isEmpty(){return 0===this.length()}push(e){if(this.isFull())throw new RangeError("Ring buffer is full.");this.set(this.end,e),this.end=this.wrap(this.end+1)}pushAll(e){for(const t of e)this.push(t)}pop(){if(this.isEmpty())throw new RangeError("Ring buffer is empty.");this.end=this.wrap(this.end-1);const e=this.get(this.end);return this.set(this.end,void 0),e}unshift(e){if(this.isFull())throw new RangeError("Ring buffer is full.");this.begin=this.wrap(this.begin-1),this.set(this.begin,e)}shift(){if(this.isEmpty())throw new RangeError("Ring buffer is empty.");const e=this.get(this.begin);return this.set(this.begin,void 0),this.begin=this.wrap(this.begin+1),e}shuffleExcise(e){if(this.isEmpty())throw new RangeError("Ring buffer is empty.");const t=this.wrap(this.begin+e),n=this.get(t);return this.set(t,this.pop()),n}}class Yk extends qk{constructor(){super(Yk.INITIAL_CAPACITY)}isFull(){return!1}push(e){super.isFull()&&this.expand(),super.push(e)}unshift(e){super.isFull()&&this.expand(),super.unshift(e)}expand(){const e=2*this.capacity,t=new Array(e),n=this.length();for(let e=0;e!0===e))}rowMajorBatch(e,t=!0){return new sE(this,e,t)}columnMajorBatch(e,t=!0,n=Hk){return this.rowMajorBatch(e,t).map((e=>Vk(e,n)))}concatenate(e,t){return new fE(Jk([this,e]),t)}take(e){return e<0||null==e?this:new aE(this,e)}skip(e){return e<0||null==e?this:new rE(this,e)}prefetch(e){return new mE(this,e)}shuffle(e,t){return new gE(this,e,t)}serial(){return new nE(this)}}class eE extends Qk{constructor(e){super(),this.items=e,this.trav=0}summary(){return`Array of ${this.items.length} items`}async next(){if(this.trav>=this.items.length)return{value:null,done:!0};const e=this.items[this.trav];return this.trav++,{value:(t=e,function(e,t){return Uk(e,t)}(t,Kk)),done:!1};var t}}class tE extends Qk{constructor(e){super(),this.nextFn=e}summary(){return"Function call"}async next(){try{return this.nextFn()}catch(e){throw e.message=`Error thrown while iterating through a dataset: ${e.message}`,e}}}class nE extends Qk{constructor(e){super(),this.upstream=e,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> Serial`}async next(){return this.lastRead=this.lastRead.then((()=>this.serialNext())),this.lastRead}async serialNext(){return this.upstream.next()}}class rE extends Qk{constructor(e,t){super(),this.upstream=e,this.maxCount=t,this.count=0,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> Skip`}async next(){return this.lastRead=this.lastRead.then((()=>this.serialNext())),this.lastRead}async serialNext(){for(;this.count++ Take`}async next(){return this.count++>=this.maxCount?{value:null,done:!0}:this.upstream.next()}}class sE extends Qk{constructor(e,t,n=!0){super(),this.upstream=e,this.batchSize=t,this.enableSmallLastBatch=n,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> RowMajorBatch`}async next(){return this.lastRead=this.lastRead.then((()=>this.serialNext())),this.lastRead}async serialNext(){const e=[];for(;e.length0?{value:e,done:!1}:{value:null,done:!0};e.push(t.value)}return{value:e,done:!1}}}class iE extends Qk{constructor(e,t){super(),this.upstream=e,this.predicate=t,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> Filter`}async next(){return this.lastRead=this.lastRead.then((()=>this.serialNext())),this.lastRead}async serialNext(){for(;;){const e=await this.upstream.next();if(e.done||this.predicate(e.value))return e;mo(e.value)}}}class oE extends Qk{constructor(e,t){super(),this.upstream=e,this.transform=t}summary(){return`${this.upstream.summary()} -> Map`}async next(){const e=await this.upstream.next();if(e.done)return{value:null,done:!0};const t=Ma(e.value),n=this.transform(e.value),r=Ma(n);for(const e of t)Oa(e,r)||e.dispose();return{value:n,done:!1}}}class lE extends Qk{constructor(e,t){super(),this.upstream=e,this.handler=t,this.count=0,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> handleErrors`}async next(){return this.lastRead=this.lastRead.then((()=>this.serialNext())),this.lastRead}async serialNext(){for(;;)try{return await this.upstream.next()}catch(e){if(!this.handler(e))return{value:null,done:!0}}}}class uE extends Qk{constructor(e,t){super(),this.upstream=e,this.transform=t}summary(){return`${this.upstream.summary()} -> AsyncMap`}async next(){const e=await this.upstream.next();if(e.done)return{value:null,done:!0};const t=Ma(e.value),n=await this.transform(e.value),r=Ma(n);for(const e of t)Oa(e,r)||e.dispose();return{value:n,done:!1}}}class cE extends Qk{constructor(){super(),this.outputQueue=new Yk,this.lastRead=Promise.resolve({value:null,done:!1})}async next(){return this.lastRead=this.lastRead.then((()=>this.serialNext())),this.lastRead}async serialNext(){for(;0===this.outputQueue.length();)if(!await this.pump())return{value:null,done:!0};return{value:this.outputQueue.shift(),done:!1}}}class hE extends cE{constructor(e,t){super(),this.upstream=e,this.transform=t}summary(){return`${this.upstream.summary()} -> Flatmap`}async pump(){const e=await this.upstream.next();if(e.done)return!1;const t=Ma(e.value),n=this.transform(e.value),r=Ma(n);this.outputQueue.pushAll(n);for(const e of t)Oa(e,r)||e.dispose();return!0}}class fE extends Qk{constructor(e,t){super(),this.baseErrorHandler=t,this.lastRead=null,this.iterator=null,this.moreIterators=e}summary(){return"TODO: fill in upstream of chained summaries -> Chained"}async next(){return this.lastRead=this.readFromChain(this.lastRead),this.lastRead}async readFromChain(e){if(await e,null==this.iterator){const e=await this.moreIterators.next();if(e.done)return{value:null,done:!0};this.iterator=e.value,null!=this.baseErrorHandler&&(this.iterator=this.iterator.handleErrors(this.baseErrorHandler))}const t=await this.iterator.next();return t.done?(this.iterator=null,this.readFromChain(e)):t}}var dE;!function(e){e[e.FAIL=0]="FAIL",e[e.SHORTEST=1]="SHORTEST",e[e.LONGEST=2]="LONGEST"}(dE||(dE={}));class pE extends Qk{constructor(e,t=dE.FAIL){super(),this.iterators=e,this.mismatchMode=t,this.count=0,this.currentPromise=null}summary(){return"{TODO: fill in upstream of zip summaries} -> Zip"}async nextState(e){await e;let t=0,n=0;const r=await Gk(this.iterators,(function(e){return e instanceof Qk?{value:e.next().then((e=>(t++,e.done&&n++,e.value))),recurse:!1}:{value:null,recurse:!0}}));if(t===n)return{value:null,done:!0};if(n>0)switch(this.mismatchMode){case dE.FAIL:throw new Error(`Zipped streams should have the same length. Mismatched at element ${this.count}.`);case dE.SHORTEST:return{value:null,done:!0};case dE.LONGEST:}return this.count++,{value:r,done:!1}}async next(){return this.currentPromise=this.nextState(this.currentPromise),this.currentPromise}}class mE extends Qk{constructor(e,t){super(),this.upstream=e,this.bufferSize=t,this.buffer=new qk(t)}summary(){return`${this.upstream.summary()} -> Prefetch`}refill(){for(;!this.buffer.isFull();){const e=this.upstream.next();this.buffer.push(e)}}next(){return this.refill(),this.buffer.shift()}}class gE extends mE{constructor(e,t,n){super(e,t),this.upstream=e,this.windowSize=t,this.upstreamExhausted=!1,this.random=Dc.alea(n||sa().toString()),this.lastRead=Promise.resolve({value:null,done:!1})}async next(){return this.lastRead=this.lastRead.then((()=>this.serialNext())),this.lastRead}randomInt(e){return Math.floor(this.random()*e)}chooseIndex(){return this.randomInt(this.buffer.length())}async serialNext(){for(this.upstreamExhausted||this.refill();!this.buffer.isEmpty();){const e=this.chooseIndex(),t=await this.buffer.shuffleExcise(e);if(!t.done)return this.refill(),t;this.upstreamExhausted=!0}return{value:null,done:!0}}}class yE{constructor(){this.size=null}batch(e,t=!0){const n=this;let r;return Q(e>0,(()=>`batchSize needs to be positive, but it is\n ${e}`)),r=this.size===1/0||null==this.size?this.size:t?Math.ceil(this.size/e):Math.floor(this.size/e),bE((async()=>(await n.iterator()).columnMajorBatch(e,t,_E)),r)}concatenate(e){const t=this;let n;return n=this.size===1/0||e.size===1/0?1/0:null!=this.size&&null!=e.size?this.size+e.size:null,bE((async()=>(await t.iterator()).concatenate(await e.iterator())),n)}filter(e){const t=this;let n;return n=this.size===1/0?1/0:null,bE((async()=>(await t.iterator()).filter((t=>po((()=>e(t)))))),n)}async forEachAsync(e){return(await this.iterator()).forEachAsync(e)}map(e){const t=this;return bE((async()=>(await t.iterator()).map((t=>po((()=>e(t)))))),this.size)}mapAsync(e){const t=this;return bE((async()=>(await t.iterator()).mapAsync(e)),this.size)}prefetch(e){if(null==e)throw new RangeError("`Dataset.prefetch()` requires bufferSize to be specified.");const t=this;return bE((async()=>(await t.iterator()).prefetch(e)),this.size)}repeat(e){const t=this;let n;return n=null!=this.size&&e>0?this.size*e:0===e?0:null!=this.size&&(void 0===e||e<0)?1/0:null,bE((async()=>{return n=Zk((async()=>({value:await t.iterator(),done:!1}))).take(e),new fE(n,r);var n,r}),n)}skip(e){const t=this;let n;return n=null!=this.size&&e>=0&&this.size>=e?this.size-e:null!=this.size&&(this.size(await t.iterator()).skip(e)),n)}shuffle(e,t,n=!0){if(null==e||e<0)throw null==this.size?new RangeError("`Dataset.shuffle()` requires bufferSize to be specified."):new RangeError(`\`Dataset.shuffle()\` requires bufferSize to be specified. If your data fits in main memory (for regular JS objects), and/or GPU memory (for \`tf.Tensor\`s), consider setting bufferSize to the dataset size (${this.size} elements)`);const r=this,a=Dc.alea(t||sa().toString());return bE((async()=>{let t=a.int32();return n&&(t+=a.int32()),(await r.iterator()).shuffle(e,t.toString())}),this.size)}take(e){const t=this;let n;return n=null!=this.size&&this.size>e?e:null!=this.size&&this.size<=e?this.size:null,bE((async()=>(await t.iterator()).take(e)),n)}async toArray(){if(this.size===1/0)throw new Error("Can not convert infinite data stream to array.");return(await this.iterator()).toArray()}async toArrayForTest(){if(this.size===1/0)throw new Error("Can not convert infinite data stream to array.");return(await this.iterator()).toArrayForTest()}}function bE(e,t=null){return new class extends yE{constructor(){super(...arguments),this.size=t}async iterator(){return e()}}}function vE(e){return bE((async()=>Jk(e)),e.length)}function wE(e){if(!Xk(e))throw new Error("The argument to zip() must be an object or array.");let t;if(Array.isArray(e))for(let n=0;nfunction(e,t=dE.FAIL){return new pE(e,t)}(await Gk(e,(e=>{if(e instanceof yE)return{value:e.iterator(),recurse:!1};if(Xk(e))return{value:null,recurse:!0};throw new Error("Leaves of the structure passed to zip() must be Datasets, not primitives.")})),dE.SHORTEST)),t)}function _E(e){if(null===e)return null;return null==(t=e[0])||null===(n=t)||"object"!=typeof n&&"function"!=typeof n||Array.isArray(t)||"object"==typeof t&&t instanceof _a||we(t)?{value:function(e){if(0===e.length)throw new Error("Can't make a batch of zero elements.");return e[0]instanceof _a?dh(e):ts(e)}(e),recurse:!1}:{value:null,recurse:!0};var t,n}yE.MAX_BUFFER_SIZE=1e4;class xE extends yE{constructor(e){super(),this.input=e}async iterator(){return(await this.input.iterator()).decodeUTF8().split("\n").map((e=>(e.endsWith("\r")&&(e=e.slice(0,-1)),e)))}}const kE='"',EE=Symbol("out"),CE=Symbol("field"),SE=Symbol("quote"),NE=Symbol("quoteafterquote"),IE=Symbol("quoteinquote");class AE extends yE{constructor(e,t){super(),this.input=e,this.hasHeader=!0,this.fullColumnNames=null,this.columnNamesValidated=!1,this.columnConfigs=null,this.configuredColumnsOnly=!1,this.delimiter=",",this.delimWhitespace=!1,this.base=new xE(e),t||(t={}),this.hasHeader=!1!==t.hasHeader,this.fullColumnNames=t.columnNames,this.columnConfigs=t.columnConfigs,this.configuredColumnsOnly=t.configuredColumnsOnly,t.delimWhitespace?(Q(null==t.delimiter,(()=>"Delimiter should not be provided when delimWhitespace is true.")),this.delimWhitespace=!0,this.delimiter=" "):this.delimiter=t.delimiter?t.delimiter:","}async columnNames(){return this.columnNamesValidated||await this.setColumnNames(),this.configuredColumnsOnly?Object.keys(this.columnConfigs):this.fullColumnNames}async setColumnNames(){const e=await this.maybeReadHeaderLine();if(!this.fullColumnNames&&!e)throw new Error("Column names must be provided if there is no header line.");this.fullColumnNames&&e&&Q(e.length===this.fullColumnNames.length,(()=>"The length of provided columnNames ("+this.fullColumnNames.length.toString()+") does not match the length of the header line read from file ("+e.length.toString()+").")),this.fullColumnNames||(this.fullColumnNames=e);const t=this.fullColumnNames.reduce(((e,t)=>(e[t]=e[t]+1||1,e)),{}),n=Object.keys(t).filter((e=>t[e]>1));if(Q(0===n.length,(()=>"Duplicate column names found: "+n.toString())),this.columnConfigs)for(const e of Object.keys(this.columnConfigs))if(-1===this.fullColumnNames.indexOf(e))throw new Error('The key "'+e+'" provided in columnConfigs does not match any of the column names ('+this.fullColumnNames.toString()+").");this.columnNamesValidated=!0}async maybeReadHeaderLine(){if(this.hasHeader){const e=await this.base.iterator(),t=await e.next();if(t.done)throw new Error("No data was found for CSV parsing.");const n=t.value;return this.parseRow(n,!1)}return null}async iterator(){this.columnNamesValidated||await this.setColumnNames();let e=await this.base.iterator();return this.hasHeader&&(e=e.skip(1)),e.map((e=>this.makeDataElement(e)))}makeDataElement(e){const t=this.parseRow(e),n={},r={};for(let a=0;a14||!Number.isInteger(t))throw new Error(`Invalid fftSize: it must be a power of 2 between 2 to 4 and 2 to 14, but got ${this.fftSize}`);if(this.numFrames=e.numFramesPerSpectrogram||43,this.sampleRateHz=e.sampleRateHz,this.columnTruncateLength=e.columnTruncateLength||this.fftSize,this.audioTrackConstraints=e.audioTrackConstraints,this.smoothingTimeConstant=e.smoothingTimeConstant||0,this.includeSpectrogram=!1!==e.includeSpectrogram,this.includeWaveform=!0===e.includeWaveform,!this.includeSpectrogram&&!this.includeWaveform)throw new Error("Both includeSpectrogram and includeWaveform are false. At least one type of data should be returned.")}summary(){return"microphone"}static async create(e={}){if(We().get("IS_NODE"))throw new Error("microphone API is only supported in browser environment.");const t=new DE(e);return await t.start(),t}async start(){try{this.stream=await navigator.mediaDevices.getUserMedia({audio:null==this.audioTrackConstraints||this.audioTrackConstraints,video:!1})}catch(e){throw new Error(`Error thrown while initializing video stream: ${e.message}`)}if(!this.stream)throw new Error("Could not obtain audio from microphone.");const e=window.AudioContext||window.webkitAudioContext;if(this.audioContext=new e,this.sampleRateHz){if(this.audioContext.sampleRate!==this.sampleRateHz)throw new Error(`Mismatch in sampling rate: Expected: ${this.sampleRateHz}; Actual: ${this.audioContext.sampleRate}`)}else this.sampleRateHz=this.audioContext.sampleRate;const t=this.audioContext.createMediaStreamSource(this.stream);this.analyser=this.audioContext.createAnalyser(),this.analyser.fftSize=2*this.fftSize,this.analyser.smoothingTimeConstant=this.smoothingTimeConstant,t.connect(this.analyser),this.freqData=new Float32Array(this.fftSize),this.timeData=new Float32Array(this.fftSize)}async next(){if(this.isClosed)return{value:null,done:!0};let e,t;const n=await this.getAudioData();if(this.includeSpectrogram){const t=this.flattenQueue(n.freqDataQueue);e=this.getTensorFromAudioDataArray(t,[this.numFrames,this.columnTruncateLength,1])}if(this.includeWaveform){const e=this.flattenQueue(n.timeDataQueue);t=this.getTensorFromAudioDataArray(e,[this.numFrames*this.fftSize,1])}return{value:{spectrogram:e,waveform:t},done:!1}}async capture(){return(await this.next()).value}async getAudioData(){const e=[],t=[];let n=0;return new Promise((r=>{const a=setInterval((()=>{this.includeSpectrogram&&(this.analyser.getFloatFrequencyData(this.freqData),this.freqData[0]===-1/0&&r({freqDataQueue:e,timeDataQueue:t}),e.push(this.freqData.slice(0,this.columnTruncateLength))),this.includeWaveform&&(this.analyser.getFloatTimeDomainData(this.timeData),t.push(this.timeData.slice())),++n===this.numFrames&&(clearInterval(a),r({freqDataQueue:e,timeDataQueue:t}))}),this.fftSize/this.sampleRateHz*1e3)}))}stop(){this.isClosed||(this.isClosed=!0,this.analyser.disconnect(),this.audioContext.close(),null!=this.stream&&this.stream.getTracks().length>0&&this.stream.getTracks()[0].stop())}toArray(){throw new Error("Can not convert infinite audio stream to array.")}getSampleRate(){return this.sampleRateHz}flattenQueue(e){const t=e[0].length,n=new Float32Array(e.length*t);return e.forEach(((e,r)=>n.set(e,r*t))),n}getTensorFromAudioDataArray(e,t){const n=new Float32Array(re(t));return n.set(e,n.length-e.length),ts(n,t)}}class TE extends Qk{constructor(e,t){if(super(),this.webcamVideoElement=e,this.webcamConfig=t,this.isClosed=!0,this.resize=!1,this.needToResize())if(this.resize=!0,this.cropSize=[this.webcamConfig.resizeHeight,this.webcamConfig.resizeWidth],this.cropBoxInd=yh([0],"int32"),this.webcamConfig.centerCrop){const e=1*this.webcamConfig.resizeWidth/this.webcamVideoElement.width,t=1*this.webcamConfig.resizeHeight/this.webcamVideoElement.height,n=(1-e)/2,r=(1-t)/2,a=n+e,s=t+r;this.cropBox=bh([r,n,s,a],[1,4])}else this.cropBox=bh([0,0,1,1],[1,4])}summary(){return"webcam"}static async create(e,t={}){if(We().get("IS_NODE"))throw new Error("tf.data.webcam is only supported in browser environment.");if(!e){if(e=document.createElement("video"),!t.resizeWidth||!t.resizeHeight)throw new Error("Please provide webcam video element, or resizeWidth and resizeHeight to create a hidden video element.");e.width=t.resizeWidth,e.height=t.resizeHeight}const n=new TE(e,t);return await n.start(),n}async start(){this.webcamConfig.facingMode&&Q("user"===this.webcamConfig.facingMode||"environment"===this.webcamConfig.facingMode,(()=>`Invalid webcam facing mode: ${this.webcamConfig.facingMode}. Please provide 'user' or 'environment'`));try{this.stream=await navigator.mediaDevices.getUserMedia({video:{deviceId:this.webcamConfig.deviceId,facingMode:this.webcamConfig.facingMode?this.webcamConfig.facingMode:"user",width:this.webcamVideoElement.width,height:this.webcamVideoElement.height}})}catch(e){throw e.message=`Error thrown while initializing video stream: ${e.message}`,e}if(!this.stream)throw new Error("Could not obtain video from webcam.");try{this.webcamVideoElement.srcObject=this.stream}catch(e){console.log(e),this.webcamVideoElement.src=window.URL.createObjectURL(this.stream)}return this.webcamVideoElement.play(),this.isClosed=!1,new Promise((e=>{this.webcamVideoElement.onloadedmetadata=()=>{e()}}))}async next(){if(this.isClosed)return{value:null,done:!0};let e;try{e=ki(this.webcamVideoElement)}catch(e){throw new Error(`Error thrown converting video to pixels: ${JSON.stringify(e)}`)}if(!this.resize)return{value:e,done:!1};try{return{value:this.cropAndResizeFrame(e),done:!1}}catch(e){throw new Error(`Error thrown cropping the video: ${e.message}`)}finally{e.dispose()}}needToResize(){return!(!this.webcamConfig.resizeWidth||!this.webcamConfig.resizeHeight||this.webcamVideoElement.width===this.webcamConfig.resizeWidth&&this.webcamVideoElement.height===this.webcamConfig.resizeHeight)}cropAndResizeFrame(e){return po((()=>{const t=vu(Ks(e,"float32"),0);let n;n=zf.cropAndResize(t,this.cropBox,this.cropBoxInd,this.cropSize,"bilinear");const r=n.shape;return Al(n,r.slice(1))}))}async capture(){return(await this.next()).value}stop(){this.stream.getTracks().forEach((e=>e.stop()));try{this.webcamVideoElement.srcObject=null}catch(e){console.log(e),this.webcamVideoElement.src=null}this.isClosed=!0}toArray(){throw new Error("Can not convert infinite video stream to array.")}}class FE{}class RE extends Qk{split(e){return new OE(this,e)}}class OE extends RE{constructor(e,t){super(),this.upstream=e,this.impl=new ME(e,t)}summary(){return this.impl.summary()}async next(){return this.impl.next()}}class ME extends cE{constructor(e,t){super(),this.upstream=e,this.separator=t,this.carryover=""}summary(){return`${this.upstream.summary()} -> Split('${this.separator}')`}async pump(){const e=await this.upstream.next();if(e.done)return""!==this.carryover&&(this.outputQueue.push(this.carryover),this.carryover="",!0);const t=e.value.split(this.separator);t[0]=this.carryover+t[0];for(const e of t.slice(0,-1))this.outputQueue.push(e);return this.carryover=t[t.length-1],!0}}class BE extends Qk{decodeUTF8(){return new $E(this)}}class $E extends RE{constructor(e){super(),this.upstream=e,this.impl=new PE(e)}summary(){return this.impl.summary()}async next(){return this.impl.next()}}class PE extends cE{constructor(e){if(super(),this.upstream=e,We().get("IS_BROWSER"))this.decoder=new TextDecoder("utf-8");else{const{StringDecoder:e}=n(4589);this.decoder=new e("utf8")}}summary(){return`${this.upstream.summary()} -> Utf8`}async pump(){const e=await this.upstream.next();let t,n;return!e.done&&(t=e.value,n=We().get("IS_BROWSER")?this.decoder.decode(t,{stream:!0}):this.decoder.write(Buffer.from(t.buffer)),this.outputQueue.push(n),!0)}}class LE extends BE{constructor(e,t={}){super(),this.file=e,this.options=t,Q(e instanceof Uint8Array||!!We().get("IS_BROWSER")&&(e instanceof File||e instanceof Blob),(()=>"FileChunkIterator only supports File, Blob and Uint8Array right now.")),this.offset=t.offset||0,this.chunkSize=t.chunkSize||1048576}summary(){return`FileChunks ${this.file}`}async next(){if(this.offset>=(this.file instanceof Uint8Array?this.file.byteLength:this.file.size))return{value:null,done:!0};const e=new Promise(((e,t)=>{const n=this.offset+this.chunkSize;if(this.file instanceof Uint8Array)e(new Uint8Array(this.file.slice(this.offset,n)));else{const r=new FileReader;r.onload=n=>{let a=r.result;if(a instanceof ArrayBuffer&&(a=new Uint8Array(a)),!(a instanceof Uint8Array))return t(new TypeError("FileReader returned unknown type."));e(a)},r.onabort=e=>t(new Error("Aborted")),r.onerror=e=>t(new Error(e.type));const a=this.file.slice(this.offset,n);r.readAsArrayBuffer(a)}this.offset=n}));return{value:await e,done:!1}}}function zE(e){return"string"==typeof e&&"file://"===e.substr(0,7)}class WE extends FE{constructor(e,t={}){super(),this.input=e,this.options=t}async iterator(){if(zE(this.input)&&We().get("IS_NODE")){const e=n(2993);this.input=e.readFileSync(this.input.substr(7))}return new LE(this.input,this.options)}}class UE extends FE{constructor(e,t={}){super(),this.url=e,this.fileOptions=t}async iterator(){return zE(this.url)?new WE(this.url,this.fileOptions).iterator():async function(e,t={}){let n,r;var a;"string"==typeof e?n=e:(n=e.url,r={method:(a=e).method,headers:a.headers,body:a.body,mode:a.mode,credentials:a.credentials,cache:a.cache,redirect:a.redirect,referrer:a.referrer,integrity:a.integrity});const s=await ia(n,r);if(s.ok){const e=new Uint8Array(await s.arrayBuffer());return new LE(e,t)}throw new Error(s.statusText)}(this.url,this.fileOptions)}}function VE(e,t={}){return new AE(new UE(e),t)}function jE(e){const t=Zk(e);return bE((async()=>t))}function HE(e){return bE((async()=>{const t=await e();return Zk((()=>t.next()))}))}async function GE(e,t){return TE.create(e,t)}async function XE(e){return DE.create(e)}const KE="3.6.0";function qE(e,t){Array.isArray(e)||(e=[e]),e.forEach((e=>{null!=e&&Q("complex64"!==e.dtype,(()=>`${t} does not support complex64 tensors in the CPU backend.`))}))}const YE=Ih;class JE extends j{constructor(){super(),this.blockSize=48,this.firstUse=!0,this.data=new V(this,co())}nextDataId(){return JE.nextDataId++}write(e,t,n){this.firstUse&&(this.firstUse=!1,We().get("IS_NODE")&&fd("\n============================\nHi there 👋. Looks like you are running TensorFlow.js in Node.js. To speed things up dramatically, install our node backend, which binds to TensorFlow C++, by running npm i @tensorflow/tfjs-node, or npm i @tensorflow/tfjs-node-gpu if you have CUDA. Then call require('@tensorflow/tfjs-node'); (-gpu suffix for CUDA) at the start of your program. Visit https://github.com/tensorflow/tfjs-node for more details.\n============================"));const r={id:this.nextDataId()};return this.data.set(r,{values:e,dtype:n,refCount:1}),r}makeTensorInfo(e,t,n){let r;if("string"===t&&null!=n&&n.length>0&&ke(n[0])){const a=n.map((e=>oa(e)));r=this.write(a,e,t)}else r=this.write(n,e,t);return{dataId:r,shape:e,dtype:t}}refCount(e){return this.data.has(e)?this.data.get(e).refCount:0}incRef(e){this.data.get(e).refCount++}decRef(e){this.data.has(e)&&this.data.get(e).refCount--}move(e,t,n,r,a){this.data.set(e,{values:t,dtype:r,refCount:a})}numDataIds(){return this.data.numDataIds()}async read(e){return this.readSync(e)}readSync(e){const{dtype:t,complexTensorInfos:n}=this.data.get(e);return"complex64"===t?pd(this.readSync(n.real.dataId),this.readSync(n.imag.dataId)):this.data.get(e).values}bufferSync(e){const t=this.readSync(e.dataId);let n=t;if("string"===e.dtype)try{n=t.map((e=>la(e)))}catch(e){throw new Error("Failed to decode encoded string bytes into utf-8")}return Xs(e.shape,e.dtype,n)}makeOutput(e,t,n){const r=this.write(e,t,n);return co().makeTensorFromDataId(r,t,n,this)}disposeData(e,t=!1){if(this.data.has(e)){if(this.data.get(e).refCount--,!t&&this.data.get(e).refCount>0)return!1;const{complexTensorInfos:n}=this.data.get(e);null!=n&&(this.disposeData(n.real.dataId,!0),this.disposeData(n.imag.dataId,!0)),this.data.delete(e)}return!0}disposeIntermediateTensorInfo(e){this.disposeData(e.dataId)}async time(e){const t=sa();return e(),{kernelMs:sa()-t}}memory(){return{unreliable:!0,reasons:["The reported memory is an upper bound. Due to automatic garbage collection, the true allocated memory may be less."]}}where(e){qE([e],"where");const t=this.readSync(e.dataId);return YE(e.shape,t)}dispose(){}floatPrecision(){return 32}epsilon(){return super.epsilon()}}function ZE(e,t,n){return({inputs:r,attrs:a,backend:s})=>{const{x:i}=r;if(qE(i,e),"string"===i.dtype||"string"===n)throw new Error("unaryKernelFunc does not support string input/output");const o=s,l=o.data.get(i.dataId).values,u=re(i.shape),c=n||i.dtype,h=ge(c,u);for(let e=0;e{const{x:i}=r;if(qE(i,e),"string"===i.dtype||"string"===n)throw new Error("unaryKernelFunc does not support string input/output");const o=s,l=o.data.get(i.dataId).values,u=n||i.dtype,c=t(l,u,a);return o.makeTensorInfo(i.shape,u,c)}}JE.nextDataId=0,Eo("cpu",(()=>new JE),1);const eC=ZE(Wt,(e=>e>=0?e:Math.exp(e)-1)),tC={kernelName:Wt,backendName:"cpu",kernelFunc:eC};function nC(e){const{inputs:t,backend:n}=e,{x:r}=t;return n.incRef(r.dataId),{dataId:r.dataId,shape:r.shape,dtype:r.dtype}}const rC={kernelName:an,backendName:"cpu",kernelFunc:nC};function aC(e){const{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{alpha:s}=r;qE([a],"leakyRelu");const i=re(a.shape),o=n.data.get(a.dataId).values,l=me("float32",i);for(let e=0;e{const i=Zo(t,n),o=i.length,l=Ae(i),u=me(s,re(i)),c=t.length,h=n.length,f=Ae(t),d=Ae(n),p=Yo(t,i),m=Yo(n,i);if(p.length+m.length===0)for(let t=0;ts[e]=0));const i=Be(s,c,f),g=n.slice(-h);m.forEach((e=>g[e]=0));const y=Be(g,h,d);u[t]=e(r[i],a[y])}return[u,i]}}const oC=iC(((e,t)=>e<0?t*e:e));function lC(e){const{inputs:t,backend:n}=e,{x:r,alpha:a}=t;qE([r,a],"prelu");const s=n.data.get(r.dataId).values,i=n.data.get(a.dataId).values,[o,l]=oC(r.shape,a.shape,s,i,r.dtype);return n.makeTensorInfo(l,r.dtype,o)}const uC={kernelName:Kn,backendName:"cpu",kernelFunc:lC},cC=ZE(Qn,(e=>Math.max(0,e))),hC={kernelName:Qn,backendName:"cpu",kernelFunc:cC},fC=ZE(sr,(e=>Math.min(Math.max(0,e),6))),dC={kernelName:sr,backendName:"cpu",kernelFunc:fC},pC=ZE(gr,(e=>1/(1+Math.exp(-e)))),mC={kernelName:gr,backendName:"cpu",kernelFunc:pC};function gC(e,t,n,r,a){if("linear"===n)return nC({inputs:{x:t},backend:e});if("relu"===n)return cC({inputs:{x:t},backend:e});if("elu"===n)return eC({inputs:{x:t},backend:e});if("relu6"===n)return fC({inputs:{x:t},backend:e});if("prelu"===n)return lC({inputs:{x:t,alpha:r},backend:e});if("leakyrelu"===n)return aC({inputs:{x:t},backend:e,attrs:{alpha:a}});if("sigmoid"===n)return pC({inputs:{x:t},backend:e});throw new Error(`Activation ${n} has not been implemented for the CPU backend.`)}function yC(e){const{inputs:t,backend:n}=e,{real:r,imag:a}=t,s=n.data.get(r.dataId).values,i=n.data.get(a.dataId).values,o=n.makeTensorInfo(r.shape,"complex64");return n.data.get(o.dataId).complexTensorInfos={real:n.makeTensorInfo(r.shape,"float32",s),imag:n.makeTensorInfo(a.shape,"float32",i)},o}const bC={kernelName:yt,backendName:"cpu",kernelFunc:yC};function vC(e,t,n="float32"){if("complex64"===n)return yC({inputs:{real:vC(e,t,"float32"),imag:vC(e,t,"float32")},backend:e});const r=Re(re(t),n);return e.makeTensorInfo(t,n,r)}function wC(e){const{inputs:t,backend:n}=e,{input:r}=t,a=n.data.get(r.dataId).complexTensorInfos.real,s=n.data.get(a.dataId).values;return n.makeTensorInfo(a.shape,a.dtype,s)}const _C={kernelName:Jn,backendName:"cpu",kernelFunc:wC};function xC(e){const{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{dtype:s}=r;if("complex64"===s){if("complex64"===a.dtype)return nC({inputs:{x:a},backend:n});const e=vC(n,a.shape,a.dtype),t=xC({inputs:{x:a},backend:n,attrs:{dtype:"float32"}}),r=yC({inputs:{real:t,imag:e},backend:n});return n.disposeIntermediateTensorInfo(e),n.disposeIntermediateTensorInfo(t),r}if("complex64"===a.dtype){const e=wC({inputs:{input:a},backend:n}),t=xC({inputs:{x:e},backend:n,attrs:{dtype:s}});return n.disposeIntermediateTensorInfo(e),t}if(!ve(a.dtype,s)){const e=nC({inputs:{x:a},backend:n});return{dataId:e.dataId,shape:e.shape,dtype:s}}if("int32"===s){const e=n.data.get(a.dataId).values,t=Int32Array.from(e);return n.makeTensorInfo(a.shape,"int32",t)}if("bool"===s){const e=n.data.get(a.dataId).values,t=aa([0],a.dtype),[r,s]=iC(((e,t)=>e!==t?1:0))(a.shape,[],e,t,"bool");return n.makeTensorInfo(s,"bool",r)}throw new Error(`Error in Cast: failed to cast ${a.dtype} to ${s}`)}const kC={kernelName:pt,backendName:"cpu",kernelFunc:xC};function EC(e,t,n,r){return null==n?({inputs:n,backend:a})=>{const{a:s,b:i}=n,o=a;qE([s,i],e);const l=o.data.get(s.dataId).values,u=o.data.get(i.dataId).values,c=r||s.dtype,[h,f]=t(s.shape,i.shape,l,u,c);return o.makeTensorInfo(f,c,h)}:({inputs:e,backend:a})=>{const{a:s,b:i}=e,o=a;if("complex64"===s.dtype||"complex64"===i.dtype){const e=xC({inputs:{x:s},backend:o,attrs:{dtype:"complex64"}}),t=o.data.get(e.dataId),r=t.complexTensorInfos.real,a=t.complexTensorInfos.imag,l=o.data.get(r.dataId).values,u=o.data.get(a.dataId).values,c=xC({inputs:{x:i},backend:o,attrs:{dtype:"complex64"}}),h=o.data.get(c.dataId),f=h.complexTensorInfos.real,d=h.complexTensorInfos.imag,p=o.data.get(f.dataId).values,m=o.data.get(d.dataId).values,[g,y,b]=n(s.shape,i.shape,l,u,p,m),v=o.makeTensorInfo(b,"float32",g),w=o.makeTensorInfo(b,"float32",y),_=yC({inputs:{real:v,imag:w},backend:o});return o.disposeIntermediateTensorInfo(e),o.disposeIntermediateTensorInfo(c),o.disposeIntermediateTensorInfo(v),o.disposeIntermediateTensorInfo(w),_}{const e=o.data.get(s.dataId).values,n=o.data.get(i.dataId).values,a=r||s.dtype,[l,u]=t(s.shape,i.shape,e,n,a);return o.makeTensorInfo(u,a,l)}}}function CC(e){return(t,n,r,a,s,i)=>{const o=Zo(t,n),l=re(o),u=o.length,c=Ae(o),h=me("float32",l),f=me("float32",l),d=Yo(t,o),p=Yo(n,o),m=pd(r,a),g=pd(s,i),y=t.length,b=Ae(t),v=n.length,w=Ae(n);if(d.length+p.length===0)for(let t=0;tr[e]=0));const a=Be(r,y,b),s=n.slice(-v);p.forEach((e=>s[e]=0));const i=Be(s,v,w),o=e(m[2*a],m[2*a+1],g[2*i],g[2*i+1]);h[t]=o.real,f[t]=o.imag}return[h,f,o]}}const SC=iC(((e,t)=>e+t)),NC=CC(((e,t,n,r)=>({real:e+n,imag:t+r}))),IC=EC(qe,SC,NC),AC={kernelName:qe,backendName:"cpu",kernelFunc:IC};function DC(e){const{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{shape:s}=r,i=re(a.shape),o=fe(s,i),l=re(o);Q(i===l,(()=>`The new shape (${o}) has ${l} elements and the old shape (${a.shape}) has ${i} elements. 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e.bindTexture(e.TEXTURE_2D,null),e.bindFramebuffer(e.FRAMEBUFFER,null),e.deleteTexture(r),e.deleteFramebuffer(a),s}(t,n)}return!1}return!!oT(t,"OES_texture_float")&&!!oT(t,"WEBGL_color_buffer_float")&&uT(t)}(hT.getNumber("WEBGL_VERSION")))),hT.registerFlag("WEBGL_FENCE_API_ENABLED",(()=>{return 2===(e=hT.getNumber("WEBGL_VERSION"))&&null!=MD(e).fenceSync;var e})),hT.registerFlag("WEBGL_SIZE_UPLOAD_UNIFORM",(()=>hT.getBool("WEBGL_RENDER_FLOAT32_ENABLED")?4:0)),hT.registerFlag("WEBGL_DELETE_TEXTURE_THRESHOLD",(()=>-1),(e=>{if(e<0&&-1!==e)throw new Error(`WEBGL_DELETE_TEXTURE_THRESHOLD must be -1 (indicating never delete) or at least 0, but got ${e}.`)})),hT.registerFlag("WEBGL_FLUSH_THRESHOLD",(()=>Va()&&hT.getBool("IS_CHROME")?1:-1),(e=>{if(e<0&&-1!==e)throw new Error(`WEBGL_FLUSH_THRESHOLD must be -1 (indicating never manual flush) or at least 0, but got ${e}.`)}));const mT="\n const float FLOAT_MAX = 1.70141184e38;\n const float FLOAT_MIN = 1.17549435e-38;\n\n lowp vec4 encode_float(highp float v) {\n if (isnan(v)) {\n return vec4(255, 255, 255, 255);\n }\n\n highp float av = abs(v);\n\n if(av < FLOAT_MIN) {\n return vec4(0.0, 0.0, 0.0, 0.0);\n } else if(v > FLOAT_MAX) {\n return vec4(0.0, 0.0, 128.0, 127.0) / 255.0;\n } else if(v < -FLOAT_MAX) {\n return vec4(0.0, 0.0, 128.0, 255.0) / 255.0;\n }\n\n highp vec4 c = vec4(0,0,0,0);\n\n highp float e = floor(log2(av));\n highp float m = exp2(fract(log2(av))) - 1.0;\n\n c[2] = floor(128.0 * m);\n m -= c[2] / 128.0;\n c[1] = floor(32768.0 * m);\n m -= c[1] / 32768.0;\n c[0] = floor(8388608.0 * m);\n\n highp float ebias = e + 127.0;\n c[3] = floor(ebias / 2.0);\n ebias -= c[3] * 2.0;\n c[2] += floor(ebias) * 128.0;\n\n c[3] += 128.0 * step(0.0, -v);\n\n return c / 255.0;\n }\n";class gT{constructor(e){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0,this.outPackingScheme=BD.DENSE;const t=zD(e),n=fT();this.outputShape=e,this.userCode=`\n ivec3 outCoordsFromFlatIndex(int index) {\n ${dT(["r","c","d"],e)}\n return ivec3(r, c, d);\n }\n\n void main() {\n ivec2 resTexRC = ivec2(resultUV.yx *\n vec2(${t[0]}, ${t[1]}));\n int index = 4 * (resTexRC.x * ${t[1]} + resTexRC.y);\n\n vec4 result = vec4(0.);\n\n for (int i=0; i<4; i++) {\n int flatIndex = index + i;\n ivec3 rc = outCoordsFromFlatIndex(flatIndex);\n result[i] = getA(rc.x, rc.y, rc.z);\n }\n\n ${n.output} = result;\n }\n `}}class yT{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outPackingScheme=BD.DENSE;const t=zD(e),n=fT();this.outputShape=e,this.userCode=`\n ivec3 outCoordsFromFlatIndex(int index) {\n ${dT(["r","c","d"],e)}\n return ivec3(r, c, d);\n }\n\n void main() {\n ivec2 resTexRC = ivec2(resultUV.yx *\n vec2(${t[0]}, ${t[1]}));\n int index = 4 * (resTexRC.x * ${t[1]} + resTexRC.y);\n\n vec4 result = vec4(0.);\n\n for (int i=0; i<4; i++) {\n int flatIndex = index + i;\n ivec3 rc = outCoordsFromFlatIndex(flatIndex);\n result[i] = getChannel(getA(rc.x, rc.y, rc.z), vec2(rc.y, rc.z));\n }\n\n ${n.output} = result;\n }\n `}}class bT{constructor(e){this.variableNames=["A"],this.outTexUsage=$D.DOWNLOAD;const t=fT();this.outputShape=e,this.userCode=`\n ${mT}\n\n void main() {\n float x = getAAtOutCoords();\n ${t.output} = encode_float(x);\n }\n `}}class vT{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!1,this.outTexUsage=$D.DOWNLOAD;const t=fT();this.outputShape=e,this.userCode=`\n ${mT}\n\n void main() {\n ivec3 coords = getOutputCoords();\n float x = getChannel(getAAtOutCoords(), vec2(coords.y, coords.z));\n ${t.output} = encode_float(x);\n }\n `}}class wT{constructor(e,t,n=!1){this.variableNames=["A"];const r=fT(),[a,s]=t;this.outputShape=e;let i="result";n&&(i="floor(result * 255. + 0.5)"),this.userCode=`\n ${pT(e)}\n\n void main() {\n ivec3 coords = getOutputCoords();\n\n int flatIndex = getFlatIndex(coords);\n int offset = imod(flatIndex, 4);\n\n flatIndex = idiv(flatIndex, 4, 1.);\n\n int r = flatIndex / 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vec2(${s}.0, ${a}.0);\n values = ${r.texture2D}(A, uv);\n\n if(offset == 0) {\n result[${o}] = values[0];\n } else if(offset == 1) {\n result[${o}] = values[1];\n } else if(offset == 2) {\n result[${o}] = values[2];\n } else {\n result[${o}] = values[3];\n }\n }\n }\n `}this.userCode=`\n ${pT(e)}\n\n void main() {\n ivec3 coords = getOutputCoords();\n\n vec4 result = vec4(0.);\n int flatIndex, r, c, offset;\n ivec3 localCoords;\n vec2 uv;\n vec4 values;\n\n ${i}\n\n ${r.output} = ${o};\n }\n `}}function xT(e,t,n,r,a,s){!function(e,t){const n=We().getNumber("WEBGL_MAX_TEXTURE_SIZE");if(e<=0||t<=0)throw new Error(`Requested texture size [${e}x${t}] is invalid.`);if(e>n||t>n)throw new Error(`Requested texture size [${e}x${t}] greater than WebGL maximum on this browser / GPU [${n}x${n}].`)}(t,n);const i=function(e){return QD(e,(()=>e.createTexture()),"Unable to create WebGLTexture.")}(e),o=e.TEXTURE_2D;return VD(e,(()=>e.bindTexture(o,i))),VD(e,(()=>e.texParameteri(o,e.TEXTURE_WRAP_S,e.CLAMP_TO_EDGE))),VD(e,(()=>e.texParameteri(o,e.TEXTURE_WRAP_T,e.CLAMP_TO_EDGE))),VD(e,(()=>e.texParameteri(o,e.TEXTURE_MIN_FILTER,e.NEAREST))),VD(e,(()=>e.texParameteri(o,e.TEXTURE_MAG_FILTER,e.NEAREST))),VD(e,(()=>e.texImage2D(o,0,r,t,n,0,a,s,null))),VD(e,(()=>e.bindTexture(e.TEXTURE_2D,null))),i}function kT(e){return e.internalFormatFloat}function ET(e){return e.internalFormatHalfFloat}function CT(e){return e.downloadTextureFormat}function ST(e){return e.internalFormatPackedFloat}function NT(e){return e.internalFormatPackedHalfFloat}class IT{constructor(e){this.outputTexture=null,this.program=null,this.disposed=!1,this.vertexAttrsAreBound=!1,this.itemsToPoll=[];const t=We().getNumber("WEBGL_VERSION");null!=e?(this.gl=e,function(e,t){RD[e]=t}(t,e)):this.gl=MD(t);let n="WEBGL_color_buffer_float";const r="EXT_color_buffer_half_float";if(1===We().getNumber("WEBGL_VERSION")){const e="OES_texture_float",t="OES_texture_half_float";if(this.textureFloatExtension=HD(this.gl,e),oT(this.gl,t))this.textureHalfFloatExtension=HD(this.gl,t);else if(We().get("WEBGL_FORCE_F16_TEXTURES"))throw new Error("GL context does not support half float textures, yet the environment flag WEBGL_FORCE_F16_TEXTURES is set to true.");if(this.colorBufferFloatExtension=this.gl.getExtension(n),oT(this.gl,r))this.colorBufferHalfFloatExtension=HD(this.gl,r);else if(We().get("WEBGL_FORCE_F16_TEXTURES"))throw new Error("GL context does not support color renderable half floats, yet the environment flag WEBGL_FORCE_F16_TEXTURES is set to true.")}else if(n="EXT_color_buffer_float",oT(this.gl,n))this.colorBufferFloatExtension=this.gl.getExtension(n);else{if(!oT(this.gl,r))throw new Error("GL context does not support color renderable floats");this.colorBufferHalfFloatExtension=this.gl.getExtension(r)}this.vertexBuffer=function(e){return function(e,t){const n=QD(e,(()=>e.createBuffer()),"Unable to 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This is probably a resource leak, delete the program with GPGPUContext.deleteProgram before disposing."),null!=this.outputTexture&&console.warn("Disposing a GPGPUContext that still has a bound output matrix texture. This is probably a resource leak, delete the output matrix texture with GPGPUContext.deleteMatrixTexture before disposing.");const e=this.gl;VD(e,(()=>e.finish())),VD(e,(()=>e.bindFramebuffer(e.FRAMEBUFFER,null))),VD(e,(()=>e.deleteFramebuffer(this.framebuffer))),VD(e,(()=>e.bindBuffer(e.ARRAY_BUFFER,null))),VD(e,(()=>e.bindBuffer(e.ELEMENT_ARRAY_BUFFER,null))),VD(e,(()=>e.deleteBuffer(this.indexBuffer))),this.disposed=!0}createFloat32MatrixTexture(e,t){return this.throwIfDisposed(),function(e,t,n,r){const[a,s]=LD(t,n);return xT(e,a,s,kT(r),r.textureFormatFloat,e.FLOAT)}(this.gl,e,t,this.textureConfig)}createFloat16MatrixTexture(e,t){return this.throwIfDisposed(),function(e,t,n,r){const[a,s]=LD(t,n);return xT(e,a,s,ET(r),r.textureFormatFloat,r.textureTypeHalfFloat)}(this.gl,e,t,this.textureConfig)}createUnsignedBytesMatrixTexture(e,t){return this.throwIfDisposed(),function(e,t,n,r){const[a,s]=LD(t,n);return xT(e,a,s,CT(r),e.RGBA,e.UNSIGNED_BYTE)}(this.gl,e,t,this.textureConfig)}uploadPixelDataToTexture(e,t){this.throwIfDisposed(),function(e,t,n){VD(e,(()=>e.bindTexture(e.TEXTURE_2D,t))),n.data instanceof Uint8Array?VD(e,(()=>e.texImage2D(e.TEXTURE_2D,0,e.RGBA,n.width,n.height,0,e.RGBA,e.UNSIGNED_BYTE,n.data))):VD(e,(()=>e.texImage2D(e.TEXTURE_2D,0,e.RGBA,e.RGBA,e.UNSIGNED_BYTE,n))),VD(e,(()=>e.bindTexture(e.TEXTURE_2D,null)))}(this.gl,e,t)}uploadDenseMatrixToTexture(e,t,n,r){this.throwIfDisposed(),function(e,t,n,r,a,s){let i,o,l;VD(e,(()=>e.bindTexture(e.TEXTURE_2D,t))),a instanceof Uint8Array?(i=new Uint8Array(n*r*4),o=e.UNSIGNED_BYTE,l=e.RGBA):(i=new Float32Array(n*r*4),o=e.FLOAT,l=s.internalFormatPackedFloat),i.set(a),VD(e,(()=>e.texImage2D(e.TEXTURE_2D,0,l,n,r,0,e.RGBA,o,i))),VD(e,(()=>e.bindTexture(e.TEXTURE_2D,null)))}(this.gl,e,t,n,r,this.textureConfig)}createFloat16PackedMatrixTexture(e,t){return this.throwIfDisposed(),function(e,t,n,r){const[a,s]=WD(t,n);return xT(e,a,s,NT(r),e.RGBA,r.textureTypeHalfFloat)}(this.gl,e,t,this.textureConfig)}createPackedMatrixTexture(e,t){return this.throwIfDisposed(),function(e,t,n,r){const[a,s]=WD(t,n);return xT(e,a,s,ST(r),e.RGBA,e.FLOAT)}(this.gl,e,t,this.textureConfig)}deleteMatrixTexture(e){this.throwIfDisposed(),this.outputTexture===e&&(JD(this.gl,this.framebuffer),this.outputTexture=null),VD(this.gl,(()=>this.gl.deleteTexture(e)))}downloadByteEncodedFloatMatrixFromOutputTexture(e,t,n){return this.downloadMatrixDriver(e,(()=>function(e,t,n,r){const[a,s]=LD(t,n),i=new Uint8Array(t*n*4);return VD(e,(()=>e.readPixels(0,0,a,s,r.downloadTextureFormat,e.UNSIGNED_BYTE,i))),new Float32Array(i.buffer)}(this.gl,t,n,this.textureConfig)))}downloadPackedMatrixFromBuffer(e,t,n,r,a,s){return function(e,t,n,r,a,s,i,o){const l=e,u=new Float32Array(function(e,t){const[n,r]=WD(e,t);return n*r*4}(s,i));return l.bindBuffer(l.PIXEL_PACK_BUFFER,t),l.getBufferSubData(l.PIXEL_PACK_BUFFER,0,u),l.bindBuffer(l.PIXEL_PACK_BUFFER,null),u}(this.gl,e,0,0,0,a,s,this.textureConfig)}downloadFloat32MatrixFromBuffer(e,t){return function(e,t,n){const r=e,a=new Float32Array(n);return r.bindBuffer(r.PIXEL_PACK_BUFFER,t),r.getBufferSubData(r.PIXEL_PACK_BUFFER,0,a),r.bindBuffer(r.PIXEL_PACK_BUFFER,null),a}(this.gl,e,t)}createBufferFromTexture(e,t,n){this.bindTextureToFrameBuffer(e);const r=function(e,t,n,r){const a=e.createBuffer();VD(e,(()=>e.bindBuffer(e.PIXEL_PACK_BUFFER,a)));const s=16*t*n;return VD(e,(()=>e.bufferData(e.PIXEL_PACK_BUFFER,s,e.STREAM_READ))),VD(e,(()=>e.readPixels(0,0,n,t,e.RGBA,e.FLOAT,0))),VD(e,(()=>e.bindBuffer(e.PIXEL_PACK_BUFFER,null))),a}(this.gl,t,n,this.textureConfig);return this.unbindTextureToFrameBuffer(),r}createAndWaitForFence(){const e=this.createFence(this.gl);return this.pollFence(e)}createFence(e){let t,n;if(We().getBool("WEBGL_FENCE_API_ENABLED")){const r=e,a=r.fenceSync(r.SYNC_GPU_COMMANDS_COMPLETE,0);e.flush(),n=()=>{const e=r.clientWaitSync(a,0,0);return e===r.ALREADY_SIGNALED||e===r.CONDITION_SATISFIED},t=a}else We().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")>0?(t=this.beginQuery(),this.endQuery(),n=()=>this.isQueryAvailable(t,We().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION"))):n=()=>!0;return{query:t,isFencePassed:n}}downloadMatrixFromPackedTexture(e,t,n){return this.downloadMatrixDriver(e,(()=>function(e,t,n){const r=new Float32Array(t*n*4);return VD(e,(()=>e.readPixels(0,0,n,t,e.RGBA,e.FLOAT,r))),r}(this.gl,t,n)))}createProgram(e){this.throwIfDisposed();const t=this.gl,n=function(e,t){const n=QD(e,(()=>e.createShader(e.FRAGMENT_SHADER)),"Unable to create fragment WebGLShader.");if(VD(e,(()=>e.shaderSource(n,t))),VD(e,(()=>e.compileShader(n))),!1===e.getShaderParameter(n,e.COMPILE_STATUS))throw function(e,t){const n=GD.exec(t);if(null==n)return console.log(`Couldn't parse line number in error: ${t}`),void console.log(e);const r=+n[1],a=e.split("\n"),s=a.length.toString().length+2,i=a.map(((e,t)=>ce((t+1).toString(),s)+e));let o=0;for(let e=0;ee.createShader(e.VERTEX_SHADER)),"Unable to create vertex WebGLShader.");if(VD(e,(()=>e.shaderSource(n,t))),VD(e,(()=>e.compileShader(n))),!1===e.getShaderParameter(n,e.COMPILE_STATUS))throw console.log(e.getShaderInfoLog(n)),new Error("Failed to compile vertex shader.");return n}(e,`${t.version}\n precision highp float;\n ${t.attribute} vec3 clipSpacePos;\n ${t.attribute} vec2 uv;\n ${t.varyingVs} vec2 resultUV;\n\n void main() {\n gl_Position = vec4(clipSpacePos, 1);\n resultUV = uv;\n }`)}(t));const r=function(e){return QD(e,(()=>e.createProgram()),"Unable to create WebGLProgram.")}(t);return VD(t,(()=>t.attachShader(r,this.vertexShader))),VD(t,(()=>t.attachShader(r,n))),function(e,t){if(VD(e,(()=>e.linkProgram(t))),!1===e.getProgramParameter(t,e.LINK_STATUS))throw console.log(e.getProgramInfoLog(t)),new Error("Failed to link vertex and fragment shaders.")}(t,r),this.debug&&XD(t,r),this.vertexAttrsAreBound||(this.setProgram(r),this.vertexAttrsAreBound=function(e,t,n){return VD(e,(()=>e.bindBuffer(e.ARRAY_BUFFER,n))),KD(e,t,"clipSpacePos",n,3,20,0)&&KD(e,t,"uv",n,2,20,12)}(t,this.program,this.vertexBuffer)),r}deleteProgram(e){this.throwIfDisposed(),e===this.program&&(this.program=null),null!=e&&VD(this.gl,(()=>this.gl.deleteProgram(e)))}setProgram(e){this.throwIfDisposed(),this.program=e,null!=this.program&&this.debug&&XD(this.gl,this.program),VD(this.gl,(()=>this.gl.useProgram(e)))}getUniformLocation(e,t,n=!0){return this.throwIfDisposed(),n?function(e,t,n){return QD(e,(()=>e.getUniformLocation(t,n)),'uniform "'+n+'" not present in program.')}(this.gl,e,t):function(e,t,n){return e.getUniformLocation(t,n)}(this.gl,e,t)}getAttributeLocation(e,t){return this.throwIfDisposed(),VD(this.gl,(()=>this.gl.getAttribLocation(e,t)))}getUniformLocationNoThrow(e,t){return this.throwIfDisposed(),this.gl.getUniformLocation(e,t)}setInputMatrixTexture(e,t,n){this.throwIfDisposed(),this.throwIfNoProgram(),qD(this.gl,e,t,n)}setOutputMatrixTexture(e,t,n){this.setOutputMatrixTextureDriver(e,n,t)}setOutputPackedMatrixTexture(e,t,n){this.throwIfDisposed();const[r,a]=WD(t,n);this.setOutputMatrixTextureDriver(e,r,a)}setOutputMatrixWriteRegion(e,t,n,r){this.setOutputMatrixWriteRegionDriver(n,e,r,t)}setOutputPackedMatrixWriteRegion(e,t,n,r){throw new Error("setOutputPackedMatrixWriteRegion not implemented.")}debugValidate(){null!=this.program&&XD(this.gl,this.program),ZD(this.gl)}executeProgram(){this.throwIfDisposed(),this.throwIfNoProgram();const e=this.gl;this.debug&&this.debugValidate(),VD(e,(()=>e.drawElements(e.TRIANGLES,6,e.UNSIGNED_SHORT,0)))}blockUntilAllProgramsCompleted(){this.throwIfDisposed(),VD(this.gl,(()=>this.gl.finish()))}getQueryTimerExtension(){return null==this.disjointQueryTimerExtension&&(this.disjointQueryTimerExtension=HD(this.gl,2===We().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")?"EXT_disjoint_timer_query_webgl2":"EXT_disjoint_timer_query")),this.disjointQueryTimerExtension}getQueryTimerExtensionWebGL2(){return this.getQueryTimerExtension()}getQueryTimerExtensionWebGL1(){return this.getQueryTimerExtension()}beginQuery(){if(2===We().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")){const e=this.gl,t=this.getQueryTimerExtensionWebGL2(),n=e.createQuery();return e.beginQuery(t.TIME_ELAPSED_EXT,n),n}const e=this.getQueryTimerExtensionWebGL1(),t=e.createQueryEXT();return e.beginQueryEXT(e.TIME_ELAPSED_EXT,t),t}endQuery(){if(2===We().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")){const e=this.gl,t=this.getQueryTimerExtensionWebGL2();return void e.endQuery(t.TIME_ELAPSED_EXT)}const e=this.getQueryTimerExtensionWebGL1();e.endQueryEXT(e.TIME_ELAPSED_EXT)}async waitForQueryAndGetTime(e){return await he((()=>this.disposed||this.isQueryAvailable(e,We().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")))),this.getQueryTime(e,We().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION"))}getQueryTime(e,t){if(0===t)return null;if(2===t){const t=this.gl;return t.getQueryParameter(e,t.QUERY_RESULT)/1e6}{const t=this.getQueryTimerExtensionWebGL1();return t.getQueryObjectEXT(e,t.QUERY_RESULT_EXT)/1e6}}isQueryAvailable(e,t){if(0===t)return!0;if(2===t){const t=this.gl,n=this.getQueryTimerExtensionWebGL2(),r=t.getQueryParameter(e,t.QUERY_RESULT_AVAILABLE);return null==this.disjoint&&(this.disjoint=this.gl.getParameter(n.GPU_DISJOINT_EXT)),r&&!this.disjoint}{const t=this.getQueryTimerExtensionWebGL1(),n=t.getQueryObjectEXT(e,t.QUERY_RESULT_AVAILABLE_EXT);return null==this.disjoint&&(this.disjoint=this.gl.getParameter(t.GPU_DISJOINT_EXT)),n&&!this.disjoint}}pollFence(e){return new Promise((t=>{this.addItemToPoll((()=>e.isFencePassed()),(()=>t()))}))}pollItems(){const e=function(e){let t=0;for(;te.isDoneFn)));for(let t=0;t<=e;++t){const{resolveFn:e}=this.itemsToPoll[t];e()}this.itemsToPoll=this.itemsToPoll.slice(e+1)}addItemToPoll(e,t){this.itemsToPoll.push({isDoneFn:e,resolveFn:t}),this.itemsToPoll.length>1||he((()=>(this.pollItems(),0===this.itemsToPoll.length)))}bindTextureToFrameBuffer(e){this.throwIfDisposed(),YD(this.gl,e,this.framebuffer),this.debug&&ZD(this.gl)}unbindTextureToFrameBuffer(){null!=this.outputTexture?(YD(this.gl,this.outputTexture,this.framebuffer),this.debug&&ZD(this.gl)):JD(this.gl,this.framebuffer)}downloadMatrixDriver(e,t){this.bindTextureToFrameBuffer(e);const n=t();return this.unbindTextureToFrameBuffer(),n}setOutputMatrixTextureDriver(e,t,n){this.throwIfDisposed();const r=this.gl;YD(r,e,this.framebuffer),this.debug&&ZD(r),this.outputTexture=e,VD(r,(()=>r.viewport(0,0,t,n))),VD(r,(()=>r.scissor(0,0,t,n)))}setOutputMatrixWriteRegionDriver(e,t,n,r){this.throwIfDisposed(),VD(this.gl,(()=>this.gl.scissor(e,t,n,r)))}throwIfDisposed(){if(this.disposed)throw new Error("Attempted to use disposed GPGPUContext.")}throwIfNoProgram(){if(null==this.program)throw new Error("No GPU program is currently set.")}}const{getBroadcastDims:AT}=g;function DT(e,t,n,r){const a=[];e.forEach((e=>{const t=re(e.shapeInfo.logicalShape);e.shapeInfo.isUniform?a.push(`uniform float ${e.name}${t>1?`[${t}]`:""};`):(a.push(`uniform sampler2D ${e.name};`),a.push(`uniform int offset${e.name};`))}));const s=a.join("\n"),i=e.map((e=>function(e,t,n=!1){let r="";r+=n?FT(e):TT(e);const a=e.shapeInfo.logicalShape,s=t.logicalShape;return a.length<=s.length&&(r+=n?function(e,t){const n=e.name,r=n.charAt(0).toUpperCase()+n.slice(1),a="get"+r+"AtOutCoords",s=e.shapeInfo.logicalShape.length,i=t.logicalShape.length,o=AT(e.shapeInfo.logicalShape,t.logicalShape),l=LT(i),u=i-s;let c;const h=["x","y","z","w","u","v"];c=0===s?"":i<2&&o.length>=1?"coords = 0;":o.map((e=>`coords.${h[e+u]} = 0;`)).join("\n");let f="";f=i<2&&s>0?"coords":e.shapeInfo.logicalShape.map(((e,t)=>`coords.${h[t+u]}`)).join(", ");let d="return outputValue;";const p=1===re(e.shapeInfo.logicalShape),m=1===re(t.logicalShape);if(1!==s||p||m){if(p&&!m)d=1===i?"\n return vec4(outputValue.x, outputValue.x, 0., 0.);\n ":"\n return vec4(outputValue.x);\n ";else if(o.length){const e=s-2,t=s-1;o.indexOf(e)>-1&&o.indexOf(t)>-1?d="return vec4(outputValue.x);":o.indexOf(e)>-1?d="return vec4(outputValue.x, outputValue.y, outputValue.x, outputValue.y);":o.indexOf(t)>-1&&(d="return vec4(outputValue.xx, outputValue.zz);")}}else d="\n return vec4(outputValue.xy, outputValue.xy);\n ";return`\n vec4 ${a}() {\n ${l} coords = getOutputCoords();\n ${c}\n vec4 outputValue = get${r}(${f});\n ${d}\n }\n `}(e,t):function(e,t){const n=e.name,r=n.charAt(0).toUpperCase()+n.slice(1),a="get"+r+"AtOutCoords",s=t.texShape,i=e.shapeInfo.texShape,o=e.shapeInfo.logicalShape.length,l=t.logicalShape.length;if(!e.shapeInfo.isUniform&&o===l&&null==e.shapeInfo.flatOffset&&se(i,s))return`\n float ${a}() {\n return sampleTexture(${n}, resultUV);\n }\n `;const u=LT(l),c=AT(e.shapeInfo.logicalShape,t.logicalShape),h=l-o;let f;const d=["x","y","z","w","u","v"];f=0===o?"":l<2&&c.length>=1?"coords = 0;":c.map((e=>`coords.${d[e+h]} = 0;`)).join("\n");let p="";return p=l<2&&o>0?"coords":e.shapeInfo.logicalShape.map(((e,t)=>`coords.${d[t+h]}`)).join(", "),`\n float ${a}() {\n ${u} coords = getOutputCoords();\n ${f}\n return get${r}(${p});\n }\n `}(e,t)),r}(e,t,r))).join("\n"),o=t.texShape,l=fT(),u=function(e){return`\n float sampleTexture(sampler2D textureSampler, vec2 uv) {\n return ${e.texture2D}(textureSampler, uv).r;\n }\n `}(l);let c,h,f=function(e){return`${e.version}\n precision highp float;\n precision highp int;\n precision highp sampler2D;\n ${e.varyingFs} vec2 resultUV;\n ${e.defineOutput}\n const vec2 halfCR = vec2(0.5, 0.5);\n\n struct ivec5\n {\n int x;\n int y;\n int z;\n int w;\n int u;\n };\n\n struct ivec6\n {\n int x;\n int y;\n int z;\n int w;\n int u;\n int v;\n };\n\n uniform float NAN;\n ${e.defineSpecialNaN}\n ${e.defineSpecialInf}\n ${e.defineRound}\n\n int imod(int x, int y) {\n return x - y * (x / y);\n }\n\n int idiv(int a, int b, float sign) {\n int res = a / b;\n int mod = imod(a, b);\n if (sign < 0. && mod != 0) {\n res -= 1;\n }\n return res;\n }\n\n //Based on the work of Dave Hoskins\n //https://www.shadertoy.com/view/4djSRW\n #define HASHSCALE1 443.8975\n float random(float seed){\n vec2 p = resultUV * seed;\n vec3 p3 = fract(vec3(p.xyx) * HASHSCALE1);\n p3 += dot(p3, p3.yzx + 19.19);\n return fract((p3.x + p3.y) * p3.z);\n }\n\n ${RT}\n ${OT}\n ${MT}\n `}(l);return t.isPacked?(c=function(e,t){switch(e.length){case 0:return"\n int getOutputCoords() {\n return 0;\n }\n ";case 1:return function(e,t){const n=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)];return 1===n[0]?`\n int getOutputCoords() {\n return 2 * int(resultUV.x * ${n[1]}.0);\n }\n `:1===n[1]?`\n int getOutputCoords() {\n return 2 * int(resultUV.y * ${n[0]}.0);\n }\n `:`\n int getOutputCoords() {\n ivec2 resTexRC = ivec2(resultUV.yx *\n vec2(${n[0]}, ${n[1]}));\n return 2 * (resTexRC.x * ${n[1]} + resTexRC.y);\n }\n `}(0,t);case 2:return function(e,t){const n=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)];if(se(e,t))return`\n ivec2 getOutputCoords() {\n return 2 * ivec2(resultUV.yx * vec2(${n[0]}, ${n[1]}));\n }\n `;const r=Math.ceil(e[1]/2);return`\n ivec2 getOutputCoords() {\n ivec2 resTexRC = ivec2(resultUV.yx *\n vec2(${n[0]}, ${n[1]}));\n\n int index = resTexRC.x * ${n[1]} + resTexRC.y;\n int r = 2 * (index / ${r});\n int c = imod(index, ${r}) * 2;\n\n return ivec2(r, c);\n }\n `}(e,t);case 3:return function(e,t){const n=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)],r=Math.ceil(e[2]/2),a=r*Math.ceil(e[1]/2);return`\n ivec3 getOutputCoords() {\n ivec2 resTexRC = ivec2(resultUV.yx *\n vec2(${n[0]}, ${n[1]}));\n int index = resTexRC.x * ${n[1]} + resTexRC.y;\n\n int b = index / ${a};\n index -= b * ${a};\n\n int r = 2 * (index / ${r});\n int c = imod(index, ${r}) * 2;\n\n return ivec3(b, r, c);\n }\n `}(e,t);default:return function(e,t){const n=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)],r=Math.ceil(e[e.length-1]/2),a=r*Math.ceil(e[e.length-2]/2);let s=a,i="",o="b, r, c";for(let t=2;te[t])).join(", ")}function UT(e,t){if(e.length!==t.length)throw Error(`Binary was compiled with ${e.length} inputs, but was executed with ${t.length} inputs`);e.forEach(((e,n)=>{const r=e.logicalShape,a=t[n],s=a.shape;if(!se(r,s))throw Error(`Binary was compiled with different shapes than the current args. Shapes ${r} and ${s} must match`);if(e.isUniform&&a.isUniform)return;const i=e.texShape,o=a.isUniform?null:a.texData.texShape;if(!se(i,o))throw Error(`Binary was compiled with different texture shapes than the current args. Shape ${i} and ${o} must match`)}))}const{addImpl:VT,bincountImpl:jT,bincountReduceImpl:HT,ceilImpl:GT,concatImpl:XT,expImpl:KT,expm1Impl:qT,floorImpl:YT,gatherV2Impl:JT,greaterImpl:ZT,lessImpl:QT,linSpaceImpl:eF,logImpl:tF,maxImpl:nF,maximumImpl:rF,minimumImpl:aF,multiplyImpl:sF,negImpl:iF,prodImpl:oF,rangeImpl:lF,rsqrtImpl:uF,simpleAbsImpl:cF,sliceImpl:hF,sparseFillEmptyRowsImpl:fF,sparseReshapeImpl:dF,stridedSliceImpl:pF,subImpl:mF,tileImpl:gF,topKImpl:yF,transposeImpl:bF,uniqueImpl:vF}=U;function wF(e,t){return["x","y","z","w","u","v"].slice(0,t).map((t=>`${e}.${t}`))}function _F(e,t){return 1===t?[e]:wF(e,t)}class xF{constructor(e){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0,this.outputShape=e;const t=e.length;if(0===t)this.userCode="\n void main() {\n setOutput(vec4(getA(), 0., 0., 0.));\n }\n ";else{const n=_F("rc",t),r=LT(t),a=function(e,t,n){if(1===e)return`rc > ${t[0]}`;let r="";for(let a=e-2;a= ${t[a]}`,a= ${t};\n bool rEdge = rp1 >= ${n};\n `}(t,e[e.length-1],e[e.length-2],n),i=function(e,t){const n=e.length,r=function(e,t){const n=[];for(let r=0;r<=1;r++)for(let a=0;a<=1;a++){let s=`${0===r?"r":"rp1"}, ${0===a?"c":"cp1"}`;for(let n=2;n= ${e[0]} ? 0. : getA(rc + 1),\n 0, 0`:`getA(${r[0]}),\n cEdge ? 0. : getA(${r[1]}),\n rEdge ? 0. : getA(${r[2]}),\n rEdge || cEdge ? 0. : getA(${r[3]})`}(e,n);this.userCode=`\n void main() {\n ${r} rc = getOutputCoords();\n\n if(${a}) {\n setOutput(vec4(0));\n } else {\n ${s}\n\n setOutput(vec4(${i}));\n }\n }\n `}}}class kF{constructor(e,t){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e;let n="";for(let e=0;e<4;e++){let t="thisRC = rc;";e%2==1&&(t+="thisRC.z += 1;"),e>1&&(t+="thisRC.y += 1;"),n+=`\n ${t}\n ${e>0?"if(thisRC.y < rows && thisRC.z < cols){":""}\n int flatIndex = getFlatIndex(thisRC);\n\n ivec3 inputRC = inputCoordsFromReshapedOutCoords(flatIndex);\n vec2 inputRCInnerDims = vec2(float(inputRC.y),float(inputRC.z));\n\n result[${e}] =\n getChannel(getA(inputRC.x, inputRC.y, inputRC.z), inputRCInnerDims);\n ${e>0?"}":""}\n `}var r;this.userCode=`\n ${r=t,`\n ivec3 inputCoordsFromReshapedOutCoords(int index) {\n ${dT(["r","c","d"],r)}\n return ivec3(r, c, d);\n }\n `}\n ${pT(e)}\n\n void main() {\n ivec3 rc = getOutputCoords();\n\n vec4 result = vec4(0.);\n\n ivec3 thisRC;\n int rows = ${e[1]};\n int cols = ${e[2]};\n\n ${n}\n\n setOutput(result);\n }\n `}}class EF{constructor(e){this.gpgpu=e,this.numUsedTextures=0,this.numFreeTextures=0,this._numBytesAllocated=0,this._numBytesFree=0,this.freeTextures={},this.logEnabled=!1,this.usedTextures={}}acquireTexture(e,t,n){const r=SF(t,n),a=NF(e,r,n);a in this.freeTextures||(this.freeTextures[a]=[]),a in this.usedTextures||(this.usedTextures[a]=[]);const s=CF(e,r,this.gpgpu.gl,this.gpgpu.textureConfig,n);if(this.freeTextures[a].length>0){this.numFreeTextures--,this.numUsedTextures++,this._numBytesFree-=s,this.log();const e=this.freeTextures[a].shift();return this.usedTextures[a].push(e),e}let i;return r===PD.PACKED_2X2_FLOAT32?i=this.gpgpu.createPackedMatrixTexture(e[0],e[1]):r===PD.PACKED_2X2_FLOAT16?i=this.gpgpu.createFloat16PackedMatrixTexture(e[0],e[1]):r===PD.UNPACKED_FLOAT32?i=this.gpgpu.createFloat32MatrixTexture(e[0],e[1]):r===PD.UNPACKED_FLOAT16?i=this.gpgpu.createFloat16MatrixTexture(e[0],e[1]):r===PD.PACKED_4X1_UNSIGNED_BYTE&&(i=this.gpgpu.createUnsignedBytesMatrixTexture(e[0],e[1])),this.usedTextures[a].push(i),this.numUsedTextures++,this._numBytesAllocated+=s,this.log(),i}releaseTexture(e,t,n,r){if(null==this.freeTextures)return;const a=SF(n,r),s=NF(t,a,r);s in this.freeTextures||(this.freeTextures[s]=[]);const i=CF(t,a,this.gpgpu.gl,this.gpgpu.textureConfig,r),o=We().get("WEBGL_DELETE_TEXTURE_THRESHOLD");-1!==o&&this._numBytesAllocated>o?(this.gpgpu.deleteMatrixTexture(e),this._numBytesAllocated-=i):(this.freeTextures[s].push(e),this.numFreeTextures++,this._numBytesFree+=i),this.numUsedTextures--;const l=this.usedTextures[s],u=l.indexOf(e);if(u<0)throw new Error("Cannot release a texture that was never provided by this texture manager");l.splice(u,1),this.log()}log(){if(!this.logEnabled)return;const e=this.numFreeTextures+this.numUsedTextures;console.log("Free/Used",`${this.numFreeTextures} / ${this.numUsedTextures}`,`(${e})`);const t=this._numBytesFree/this._numBytesAllocated;console.log(`Bytes allocated: ${this._numBytesAllocated}`),console.log(`Bytes unused: ${this._numBytesFree} (${Math.round(100*t)}%)`)}get numBytesAllocated(){return this._numBytesAllocated}get numBytesFree(){return this._numBytesFree}getNumUsedTextures(){return this.numUsedTextures}getNumFreeTextures(){return this.numFreeTextures}dispose(){if(null!=this.freeTextures){for(const e in this.freeTextures)this.freeTextures[e].forEach((e=>{this.gpgpu.deleteMatrixTexture(e)}));for(const e in this.usedTextures)this.usedTextures[e].forEach((e=>{this.gpgpu.deleteMatrixTexture(e)}));this.freeTextures=null,this.usedTextures=null,this.numUsedTextures=0,this.numFreeTextures=0,this._numBytesAllocated=0,this._numBytesFree=0}}}function CF(e,t,n,r,a){const s=function(e,t){switch(e){case PD.PACKED_2X2_FLOAT32:return ST(t);case PD.PACKED_2X2_FLOAT16:return NT(t);case PD.UNPACKED_FLOAT32:return kT(t);case PD.UNPACKED_FLOAT16:return ET(t);case PD.PACKED_4X1_UNSIGNED_BYTE:return CT(t);default:throw new Error(`Unknown physical texture type ${e}`)}}(t,r);let i;if(a){const[t,n]=WD(e[0],e[1]);i=t*n}else{const[t,n]=LD(e[0],e[1]);i=t*n}return i*function(e,t){const n=e;if(t===n.R32F)return 4;if(t===n.R16F)return 2;if(t===n.RGBA32F)return 16;if(t===e.RGBA)return 16;if(t===n.RGBA16F)return 8;throw new Error(`Unknown internal format ${t}`)}(n,s)}function SF(e,t){if(e===$D.UPLOAD)return PD.PACKED_2X2_FLOAT32;if(e===$D.RENDER||null==e)return function(e){return We().getBool("WEBGL_RENDER_FLOAT32_ENABLED")?e?PD.PACKED_2X2_FLOAT32:PD.UNPACKED_FLOAT32:e?PD.PACKED_2X2_FLOAT16:PD.UNPACKED_FLOAT16}(t);if(e===$D.DOWNLOAD||e===$D.PIXELS)return PD.PACKED_4X1_UNSIGNED_BYTE;throw new Error(`Unknown logical texture type ${e}`)}function NF(e,t,n){return`${e[0]}_${e[1]}_${t}_${n}`}class IF{constructor(e,t){this.variableNames=["A"],this.outputShape=e,this.userCode=`\n float unaryOperation(float x) {\n ${t}\n }\n\n void main() {\n float x = getAAtOutCoords();\n float y = unaryOperation(x);\n\n setOutput(y);\n }\n `}}const AF="return abs(x);",DF="return x;";class TF{constructor(e,t){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.userCode=`\n vec4 unaryOperation(vec4 x) {\n ${t}\n }\n\n void main() {\n vec4 x = getAAtOutCoords();\n vec4 y = unaryOperation(x);\n\n setOutput(y);\n }\n `}}class FF{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!1,this.outputShape=e;const t=e.length,n=_F("rc",t),r=LT(t),a=function(e,t){if(1===e)return"rc";let n="";for(let r=0;rt.push(e)))}const t=this.texData.get(e),{values:n,shape:r,slice:a,dtype:s,complexTensorInfos:i,isPacked:o}=t;if(null!=a){let t;t=o?new TF(r,DF):new IF(r,DF);const n=this.runWebGLProgram(t,[{dataId:e,shape:r,dtype:s}],s),a=this.read(n.dataId);return this.disposeIntermediateTensorInfo(n),a}if(null!=n)return this.convertAndCacheOnCPU(e);if(!We().getBool("WEBGL_DOWNLOAD_FLOAT_ENABLED")&&2===We().getNumber("WEBGL_VERSION"))throw new Error("tensor.data() with WEBGL_DOWNLOAD_FLOAT_ENABLED=false and WEBGL_VERSION=2 not yet supported.");let l,u,c=null;if("complex64"!==s&&We().get("WEBGL_BUFFER_SUPPORTED")){l=this.decode(e);const t=this.texData.get(l.dataId);c=this.gpgpu.createBufferFromTexture(t.texture,...zD(r))}if(this.pendingRead.set(e,[]),"complex64"!==s&&await this.gpgpu.createAndWaitForFence(),"complex64"===s){const e=await Promise.all([this.read(i.real.dataId),this.read(i.imag.dataId)]);u=pd(e[0],e[1])}else if(null==c)u=this.getValuesFromTexture(e);else{const e=re(r);u=this.gpgpu.downloadFloat32MatrixFromBuffer(c,e)}null!=l&&this.disposeIntermediateTensorInfo(l);const h=this.convertAndCacheOnCPU(e,u),f=this.pendingRead.get(e);return this.pendingRead.delete(e),f.forEach((e=>e(h))),this.pendingDisposal.has(e)&&(this.pendingDisposal.delete(e),this.disposeData(e)&&co().removeDataId(e,this),this.pendingDeletes--),h}bufferSync(e){const t=this.readSync(e.dataId);let n=t;if("string"===e.dtype)try{n=t.map((e=>la(e)))}catch(e){throw new Error("Failed to decode encoded string bytes into utf-8")}return Xs(e.shape,e.dtype,n)}checkNumericalProblems(e){if(null!=e)for(let t=0;t0}async time(e){const t=this.activeTimers,n=[];let r=!1;null==this.programTimersStack?(this.programTimersStack=n,r=!0):this.activeTimers.push(n),this.activeTimers=n,e();const a=ne(this.activeTimers.map((e=>e.query))).filter((e=>null!=e)),s=ne(this.activeTimers.map((e=>e.name))).filter((e=>null!=e));this.activeTimers=t,r&&(this.programTimersStack=null);const i={uploadWaitMs:this.uploadWaitMs,downloadWaitMs:this.downloadWaitMs,kernelMs:null,wallMs:null};if(We().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0){const e=await Promise.all(a);i.kernelMs=Y(e),i.getExtraProfileInfo=()=>e.map(((e,t)=>({name:s[t],ms:e}))).map((e=>`${e.name}: ${e.ms}`)).join(", ")}else i.kernelMs={error:"WebGL query timers are not supported in this environment."};return this.uploadWaitMs=0,this.downloadWaitMs=0,i}memory(){return{unreliable:!1,numBytesInGPU:this.numBytesInGPU,numBytesInGPUAllocated:this.textureManager.numBytesAllocated,numBytesInGPUFree:this.textureManager.numBytesFree}}startTimer(){return We().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0?this.gpgpu.beginQuery():{startMs:sa(),endMs:null}}endTimer(e){return We().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0?(this.gpgpu.endQuery(),e):(e.endMs=sa(),e)}async getQueryTime(e){if(We().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0)return this.gpgpu.waitForQueryAndGetTime(e);const t=e;return t.endMs-t.startMs}disposeData(e,t=!1){if(this.pendingDisposal.has(e))return!1;if(!this.texData.has(e))return!0;if(t?this.texData.get(e).refCount=0:this.texData.get(e).refCount--,!t&&this.texData.get(e).refCount>0)return!1;if(this.pendingRead.has(e))return this.pendingDisposal.add(e),this.pendingDeletes++,!1;this.releaseGPUData(e);const{complexTensorInfos:n}=this.texData.get(e);return null!=n&&(this.disposeData(n.real.dataId,t),this.disposeData(n.imag.dataId,t)),this.texData.delete(e),!0}releaseGPUData(e){const{texture:t,dtype:n,texShape:r,usage:a,isPacked:s,slice:i}=this.texData.get(e),o=i&&i.origDataId||e,l=this.dataRefCount.get(o);l>1?this.dataRefCount.set(o,l-1):(this.dataRefCount.delete(o),null!=t&&(this.numBytesInGPU-=this.computeBytes(r,n),this.textureManager.releaseTexture(t,r,a,s)));const u=this.texData.get(e);u.texture=null,u.texShape=null,u.isPacked=!1,u.slice=null}getTexture(e){return this.uploadToGPU(e),this.texData.get(e).texture}getDataInfo(e){return this.texData.get(e)}shouldExecuteOnCPU(e,t=128){return We().getBool("WEBGL_CPU_FORWARD")&&e.every((e=>null==this.texData.get(e.dataId).texture&&re(e.shape)0&&ke(n[0])){const a=n.map((e=>oa(e)));r=this.write(a,e,t)}else r=this.write(n,e,t);return this.texData.get(r).usage=null,{dataId:r,shape:e,dtype:t}}makeOutput(e,t,n){const{dataId:r}=this.makeTensorInfo(e,t,n);return co().makeTensorFromDataId(r,e,t,this)}unpackTensor(e){const t=new FF(e.shape);return this.runWebGLProgram(t,[e],e.dtype)}packTensor(e){const t=new xF(e.shape);return this.runWebGLProgram(t,[e],e.dtype,null,!0)}packedReshape(e,t){const n=[eT(e.shape),...tT(e.shape)],r={dtype:e.dtype,shape:n,dataId:e.dataId},a=[eT(t),...tT(t)],s=new kF(a,n),i=this.runWebGLProgram(s,[r],e.dtype,null,!0);return{dataId:i.dataId,shape:t,dtype:i.dtype}}decode(e){const t=this.texData.get(e),{isPacked:n,shape:r,dtype:a}=t,s=nT(r);let i;return i=n?new yT(s):new gT(s),{dtype:a,shape:r,dataId:this.runWebGLProgram(i,[{shape:s,dtype:a,dataId:e}],a,null,!0).dataId}}runWebGLProgram(e,t,n,r,a=!1){const s=this.makeTensorInfo(e.outputShape,n),i=this.texData.get(s.dataId);if(e.packedOutput&&(i.isPacked=!0),e.outPackingScheme===BD.DENSE){const t=zD(e.outputShape);i.texShape=t.map((e=>2*e))}if(null!=e.outTexUsage&&(i.usage=e.outTexUsage),0===re(s.shape))return i.values=me(s.dtype,0),s;const o=[],l=t.map((t=>{if("complex64"===t.dtype)throw new Error("GPGPUProgram does not support complex64 input. For complex64 dtypes, please separate the program into real and imaginary parts.");let n=this.texData.get(t.dataId);if(null==n.texture){if(!e.packedInputs&&re(t.shape)<=We().getNumber("WEBGL_SIZE_UPLOAD_UNIFORM"))return{shape:t.shape,texData:null,isUniform:!0,uniformValues:n.values};e.packedInputs&&(n.isPacked=!0,n.shape=t.shape)}else if(!!n.isPacked!=!!e.packedInputs)t=n.isPacked?this.unpackTensor(t):this.packTensor(t),o.push(t),n=this.texData.get(t.dataId);else if(n.isPacked&&!aT(n.shape,t.shape)){const e=t,r=t.shape;t.shape=n.shape,t=this.packedReshape(t,r),o.push(t),n=this.texData.get(t.dataId),e.shape=r}return this.uploadToGPU(t.dataId),{shape:t.shape,texData:n,isUniform:!1}}));this.uploadToGPU(s.dataId);const u={shape:s.shape,texData:i,isUniform:!1},c=function(e,t,n){let r="";t.concat(n).forEach((e=>{const t=null!=e.texData&&null!=e.texData.slice&&e.texData.slice.flatOffset>0,n=e.isUniform?"uniform":e.texData.texShape;r+=`${e.shape}_${n}_${t}`}));const a=e.userCode;let s=e.constructor.name;return s+="_"+r+"_"+a,s}(e,l,u),h=this.getAndSaveBinary(c,(()=>function(e,t,n,r){const a=t.userCode,s=n.map(((e,n)=>{const r={logicalShape:e.shape,texShape:e.isUniform?null:e.texData.texShape,isUniform:e.isUniform,isPacked:!e.isUniform&&e.texData.isPacked,flatOffset:null};return null!=e.texData&&null!=e.texData.slice&&e.texData.slice.flatOffset>0&&(r.flatOffset=e.texData.slice.flatOffset),{name:t.variableNames[n],shapeInfo:r}})),i=s.map((e=>e.shapeInfo)),o={logicalShape:r.shape,texShape:r.texData.texShape,isUniform:!1,isPacked:r.texData.isPacked,flatOffset:null},l=DT(s,o,a,t.packedInputs),u=e.createProgram(l);let c=null;const h=e.getUniformLocation(u,"NAN",!1);1===We().getNumber("WEBGL_VERSION")&&(c=e.getUniformLocation(u,"INFINITY",!1));const f={};for(let n=0;n{const a=t.program.variableNames[r],s=t.uniformLocations[a],i=t.uniformLocations[`offset${a}`];if(null!=s)if(n.isUniform)if(re(n.shape)<2)e.gl.uniform1f(s,n.uniformValues[0]);else{let t=n.uniformValues;t instanceof Float32Array||(t=new Float32Array(t)),e.gl.uniform1fv(s,t)}else null!=n.texData.slice&&null!=i&&e.gl.uniform1i(i,n.texData.slice.flatOffset),e.setInputMatrixTexture(n.texData.texture,s,r)})),null!=a&&a(e,t.webGLProgram),e.executeProgram()}(this.gpgpu,h,l,u,r),o.forEach((e=>this.disposeIntermediateTensorInfo(e))),f&&(d=this.endTimer(d),this.activeTimers.push({name:e.constructor.name,query:this.getQueryTime(d)}));const p=We().get("WEBGL_FLUSH_THRESHOLD");if(p>0){const e=sa();e-this.lastGlFlushTime>p&&(this.gpgpu.gl.flush(),this.lastGlFlushTime=e)}if(!We().getBool("WEBGL_LAZILY_UNPACK")&&i.isPacked&&!1===a){const e=this.unpackTensor(s);return this.disposeIntermediateTensorInfo(s),e}return s}compileAndRun(e,t,n,r,a=!1){return n=n||t[0].dtype,this.runWebGLProgram(e,t,n,r,a)}getAndSaveBinary(e,t){return e in this.binaryCache||(this.binaryCache[e]=t()),this.binaryCache[e]}getTextureManager(){return this.textureManager}dispose(){this.disposed||(We().getBool("IS_TEST")||Object.keys(this.binaryCache).forEach((e=>{this.gpgpu.deleteProgram(this.binaryCache[e].webGLProgram),delete this.binaryCache[e]})),this.textureManager.dispose(),null!=this.canvas&&"undefined"!=typeof HTMLCanvasElement&&this.canvas instanceof HTMLCanvasElement?this.canvas.remove():this.canvas=null,this.gpgpuCreatedLocally&&(this.gpgpu.program=null,this.gpgpu.dispose()),this.disposed=!0)}floatPrecision(){return null==this.floatPrecisionValue&&(this.floatPrecisionValue=po((()=>{if(!We().get("WEBGL_RENDER_FLOAT32_ENABLED")){const e=We().getBool("DEBUG");We().set("DEBUG",!1);const t=this.abs(Wo(1e-8)).dataSync()[0];if(We().set("DEBUG",e),t>0)return 32}return 16}))),this.floatPrecisionValue}epsilon(){return 32===this.floatPrecision()?1e-7:1e-4}uploadToGPU(e){const t=this.texData.get(e),{shape:n,dtype:r,values:a,texture:s,usage:i,isPacked:o}=t;if(null!=s)return;const l=null!=this.activeTimers;let u;l&&(u=sa());let c=t.texShape;if(null==c&&(c=function(e,t=!1){let n=We().getNumber("WEBGL_MAX_TEXTURE_SIZE");if(t&&(n*=2,1===(e=e.map(((t,n)=>n>=e.length-2?q(e[n]):e[n]))).length&&(e=[2,e[0]])),2!==e.length){const t=pe(e);e=t.newShape}let r=re(e);if(e.length<=1&&r<=n)return[1,r];if(2===e.length&&e[0]<=n&&e[1]<=n)return e;if(3===e.length&&e[0]*e[1]<=n&&e[2]<=n)return[e[0]*e[1],e[2]];if(3===e.length&&e[0]<=n&&e[1]*e[2]<=n)return[e[0],e[1]*e[2]];if(4===e.length&&e[0]*e[1]*e[2]<=n&&e[3]<=n)return[e[0]*e[1]*e[2],e[3]];if(4===e.length&&e[0]<=n&&e[1]*e[2]*e[3]<=n)return[e[0],e[1]*e[2]*e[3]];if(t){const t=eT(e);let n=2,a=2;return e.length&&([n,a]=tT(e)),r=t*(n/2)*(a/2),le(r).map((e=>2*e))}return le(r)}(n,o),t.texShape=c),null!=a){const e=nT(n);let s,i=c[1],h=c[0];const f=a instanceof Uint8Array;o?([i,h]=WD(c[0],c[1]),s=new _T(e,[h,i],f)):s=new wT(e,[h,i],f);const d=this.makeTensorInfo([h,i],r);this.texData.get(d.dataId).usage=f?$D.PIXELS:$D.UPLOAD,this.gpgpu.uploadDenseMatrixToTexture(this.getTexture(d.dataId),i,h,a);const p=!0,m=this.runWebGLProgram(s,[d],r,null,p),g=this.texData.get(m.dataId);t.texture=g.texture,t.texShape=g.texShape,t.isPacked=g.isPacked,t.usage=g.usage,this.disposeIntermediateTensorInfo(d),this.texData.delete(m.dataId),t.values=null,l&&(this.uploadWaitMs+=sa()-u)}else{const e=this.acquireTexture(c,i,r,o);t.texture=e}}convertAndCacheOnCPU(e,t){const n=this.texData.get(e),{dtype:r}=n;return this.releaseGPUData(e),null!=t&&(n.values=function(e,t){if("float32"===t||"complex64"===t)return e;if("int32"===t||"bool"===t){const n="int32"===t?new Int32Array(e.length):new Uint8Array(e.length);for(let t=0;t1024*this.numMBBeforeWarning*1024){const e=(this.numBytesInGPU/1024/1024).toFixed(2);this.warnedAboutMemory=!0,console.warn(`High memory usage in GPU: ${e} MB, most likely due to a memory leak`)}return this.textureManager.acquireTexture(e,t,r)}computeBytes(e,t){return e[0]*e[1]*_e(t)}}MF.nextDataId=0,ja()&&Eo("webgl",(()=>new MF),2);class BF{constructor(e,t,n){this.variableNames=["A","B"],this.outputShape=Zo(t,n),this.userCode=`\n float binaryOperation(float a, float b) {\n ${e}\n }\n\n void main() {\n float a = getAAtOutCoords();\n float b = getBAtOutCoords();\n setOutput(binaryOperation(a, b));\n }\n `}}class $F{constructor(e,t,n,r=!1){this.variableNames=["A","B"],this.supportsBroadcasting=!0,this.packedInputs=!0,this.packedOutput=!0,this.outputShape=Zo(t,n);const a=this.outputShape.length;let s="";if(r)if(0===a||1===re(this.outputShape))s="\n result.y = 0.;\n result.z = 0.;\n result.w = 0.;\n ";else if(s=`\n ${LT(a)} coords = getOutputCoords();\n `,1===a)s+=`\n result.y = (coords + 1) >= ${this.outputShape[0]} ? 0. : result.y;\n result.z = 0.;\n result.w = 0.;\n `;else{const e=_F("coords",a);s+=`\n bool nextRowOutOfBounds =\n (${e[a-2]} + 1) >= ${this.outputShape[a-2]};\n bool nextColOutOfBounds =\n (${e[a-1]} + 1) >= ${this.outputShape[a-1]};\n result.y = nextColOutOfBounds ? 0. : result.y;\n result.z = nextRowOutOfBounds ? 0. : result.z;\n result.w = nextColOutOfBounds || nextRowOutOfBounds ? 0. : result.w;\n `}this.userCode=`\n vec4 binaryOperation(vec4 a, vec4 b) {\n ${e}\n }\n\n void main() {\n vec4 a = getAAtOutCoords();\n vec4 b = getBAtOutCoords();\n\n vec4 result = binaryOperation(a, b);\n ${s}\n\n setOutput(result);\n }\n `}}function PF(e){const{inputs:t,backend:n}=e,{x:r}=t;return n.incRef(r.dataId),{dataId:r.dataId,shape:r.shape,dtype:r.dtype}}const LF={kernelName:an,backendName:"webgl",kernelFunc:PF};function zF(e){const{inputs:t,backend:n}=e,{real:r,imag:a}=t,s=n.makeTensorInfo(r.shape,"complex64"),i=n.texData.get(s.dataId),o=PF({inputs:{x:r},backend:n}),l=PF({inputs:{x:a},backend:n});return i.complexTensorInfos={real:o,imag:l},s}const WF={kernelName:yt,backendName:"webgl",kernelFunc:zF},UF="return (a < 0.) ? b * a : a;",VF="\n vec4 aLessThanZero = vec4(lessThan(a, vec4(0.)));\n return (aLessThanZero * (b * a)) + ((vec4(1.0) - aLessThanZero) * a);\n",jF={kernelName:hn,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{alpha:s}=r,i=n.makeTensorInfo([],"float32",ra(s,"float32")),o=We().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new $F(VF,a.shape,i.shape):new BF(UF,a.shape,i.shape),l=n.runWebGLProgram(o,[a,i],a.dtype);return n.disposeIntermediateTensorInfo(i),l}},HF="return (a < 0.) ? b * a : a;",GF="\n vec4 aLessThanZero = vec4(lessThan(a, vec4(0.)));\n return (aLessThanZero * (b * a)) + ((vec4(1.0) - aLessThanZero) * a);\n",XF={kernelName:Kn,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n}=e,{x:r,alpha:a}=t,s=We().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new $F(GF,r.shape,a.shape):new BF(HF,r.shape,a.shape);return n.runWebGLProgram(s,[r,a],r.dtype)}};function KF({opSnippet:e,packedOpSnippet:t,cpuKernelImpl:n,dtype:r}){return({inputs:a,backend:s})=>{const{x:i}=a,o=s,l=r||i.dtype;if(o.shouldExecuteOnCPU([i])&&null!=n){const e=o.texData.get(i.dataId),t=n(e.values,l);return o.makeTensorInfo(i.shape,l,t)}let u;return u=We().getBool("WEBGL_PACK_UNARY_OPERATIONS")&&null!=t?new TF(i.shape,t):new IF(i.shape,e),o.runWebGLProgram(u,[i],l)}}function qF({opSnippet:e,packedOpSnippet:t,checkOutOfBounds:n=!1,supportsComplex:r=!1,cpuKernelImpl:a,dtype:s}){return({inputs:i,backend:o})=>{const{a:l,b:u}=i,c=o;if(r&&"complex64"===l.dtype){const t=c.texData.get(l.dataId),n=c.texData.get(u.dataId),[r,a]=[[t.complexTensorInfos.real,n.complexTensorInfos.real],[t.complexTensorInfos.imag,n.complexTensorInfos.imag]].map((t=>{const[n,r]=t,a={dataId:n.dataId,dtype:n.dtype,shape:l.shape},s={dataId:r.dataId,dtype:r.dtype,shape:u.shape},i=new BF(e,l.shape,u.shape);return c.runWebGLProgram(i,[a,s],Da(n.dtype,r.dtype))})),s=zF({inputs:{real:r,imag:a},backend:c});return c.disposeIntermediateTensorInfo(r),c.disposeIntermediateTensorInfo(a),s}const h=s||Da(l.dtype,u.dtype);if(c.shouldExecuteOnCPU([l,u])&&null!=a){const e=c.texData.get(l.dataId),t=c.texData.get(u.dataId),[n,r]=a(l.shape,u.shape,e.values,t.values,h),s=c.makeTensorInfo(r,h);return c.texData.get(s.dataId).values=n,s}let f;return f=We().getBool("WEBGL_PACK_BINARY_OPERATIONS")&&null!=t?new $F(t,l.shape,u.shape,n):new BF(e,l.shape,u.shape),c.runWebGLProgram(f,[l,u],h)}}function YF(e,t=!1){if("linear"===e)return"return x;";if("relu"===e)return t?"\n vec4 result = x * vec4(greaterThanEqual(x, vec4(0.0)));\n bvec4 isNaN = isnan(x);\n\n result.r = isNaN.r ? x.r : result.r;\n result.g = isNaN.g ? x.g : result.g;\n result.b = isNaN.b ? x.b : result.b;\n result.a = isNaN.a ? x.a : result.a;\n\n return result;\n":"if (isnan(x)) return x;\n return (x < 0.0) ? 0.0 : x;\n";if("elu"===e)return t?"\n vec4 result;\n\n result.r = (x.r >= 0.0) ? x.r : (exp(x.r) - 1.0);\n result.g = (x.g >= 0.0) ? x.g : (exp(x.g) - 1.0);\n result.b = (x.b >= 0.0) ? x.b : (exp(x.b) - 1.0);\n result.a = (x.a >= 0.0) ? x.a : (exp(x.a) - 1.0);\n\n return result;\n":"return (x >= 0.0) ? x : (exp(x) - 1.0);";if("relu6"===e)return t?"\n vec4 result = min(x, vec4(6.)) * vec4(greaterThanEqual(x, vec4(0.0)));\n bvec4 isNaN = isnan(x);\n\n result.r = isNaN.r ? x.r : result.r;\n result.g = isNaN.g ? x.g : result.g;\n result.b = isNaN.b ? x.b : result.b;\n result.a = isNaN.a ? x.a : result.a;\n\n return result;\n":"if (isnan(x)) return x;\n return (x < 0.0) ? 0.0 : min(6.0, x);\n";if("prelu"===e)return t?GF:HF;if("leakyrelu"===e)return t?VF:UF;if("sigmoid"===e)return"return 1.0 / (1.0 + exp(-1.0 * x));";throw new Error(`Activation ${e} has not been implemented for the WebGL backend.`)}class JF{constructor(e,t,n,r=!1,a=!1,s=!1,i=null,o=!1,l=!1){this.variableNames=["matrixA","matrixB"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=n;const u=r?e[1]:e[2],c=Math.ceil(u/2),h=r?"i * 2, rc.y":"rc.y, i * 2",f=a?"rc.z, i * 2":"i * 2, rc.z",d=r?["a.xxyy","a.zzww"]:["a.xxzz","a.yyww"],p=a?["b.xzxz","b.ywyw"]:["b.xyxy","b.zwzw"];let m="",g="";i&&(m=o?`vec4 activation(vec4 a) {\n vec4 b = getPreluActivationWeightsAtOutCoords();\n ${i}\n }`:l?`vec4 activation(vec4 a) {\n vec4 b = getLeakyreluAlphaAtOutCoords();\n ${i}\n }`:`vec4 activation(vec4 x) {\n ${i}\n }`,g="result = activation(result);");const y=s?"result += getBiasAtOutCoords();":"";s&&this.variableNames.push("bias"),o&&this.variableNames.push("preluActivationWeights"),l&&this.variableNames.push("leakyreluAlpha");let b="rc.x",v="rc.x";e[0]`The new shape (${l}) has ${u} elements and the old shape (${a.shape}) has ${o} elements. The new shape and old shape must have the same number of elements.`));const c=i.texData.get(a.dataId);return!c.isPacked||aT(a.shape,l)||null!==c.texture&&aT(c.shape,l)?(i.incRef(a.dataId),{dataId:a.dataId,shape:l,dtype:a.dtype}):function(e,t,n){const r=[eT(e.shape),...tT(e.shape)],a={dtype:e.dtype,shape:r,dataId:e.dataId},s=[eT(t),...tT(t)],i=new kF(s,r),o=n.runWebGLProgram(i,[a],e.dtype,null,!0);return{dataId:o.dataId,shape:t,dtype:o.dtype}}(a,l,i)}const rR={kernelName:er,backendName:"webgl",kernelFunc:nR};class aR{constructor(e,t){this.variableNames=["x"];const{windowSize:n,batchSize:r,inSize:a,outSize:s}=e;this.outputShape=[r,s];const i=4*Math.floor(n/4),o=n%4;let l="sumValue += dot(values, ones);";if(null!=t){const e=1/t;l=`sumValue += dot(values * ${ie(e)?e.toPrecision(2):e}, ones);`}let u="";a%n>0&&(u=`\n if (inIdx < 0 || inIdx >= ${a}) {\n return 0.0;\n }\n `),this.userCode=`\n const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0);\n\n float getValue(int batch, int inIdx) {\n ${u}\n return getX(batch, inIdx);\n }\n\n void main() {\n ivec2 coords = getOutputCoords();\n int batch = coords[0];\n int outIdx = coords[1];\n int inOffset = outIdx * ${n};\n\n float sumValue = 0.0;\n\n for (int i = 0; i < ${i}; i += 4) {\n int inIdx = inOffset + i;\n vec4 values = vec4(\n getValue(batch, inIdx),\n getValue(batch, inIdx + 1),\n getValue(batch, inIdx + 2),\n getValue(batch, inIdx + 3)\n );\n\n ${l}\n }\n\n int inIdx = inOffset + ${i};\n if (${1===o}) {\n vec4 values = vec4(getValue(batch, inIdx), 0.0, 0.0, 0.0);\n\n ${l}\n } else if (${2===o}) {\n vec4 values = vec4(\n getValue(batch, inIdx),\n getValue(batch, inIdx + 1), 0.0, 0.0);\n\n ${l}\n } else if (${3===o}) {\n vec4 values = vec4(\n getValue(batch, inIdx),\n getValue(batch, inIdx + 1),\n getValue(batch, inIdx + 2), 0.0);\n\n ${l}\n }\n setOutput(sumValue);\n }\n `}}class sR{constructor(e,t){this.variableNames=["x"];const{windowSize:n,batchSize:r,inSize:a,outSize:s}=e;this.outputShape=[r,s];let i="0.0",o="";"prod"===t?i="1.0":"min"===t?(i="1.0 / 1e-20",o="min"):"max"===t&&(i="-1.0 / 1e-20",o="max");let l=`${t}(${t}(${t}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;"sum"===t?l="sumValue":"prod"===t?l="prodValue":"all"===t?l="allValue":"any"===t&&(l="anyValue");const u=4*Math.floor(n/4),c=n%4;let h=`\n if (${"sum"===t}) {\n sumValue += dot(values, ones);\n } else if (${"prod"===t}) {\n vec2 tmp = vec2(values[0], values[1]) * vec2(values[2], values[3]);\n prodValue *= tmp[0] * tmp[1];\n } else {\n minMaxValue = ${o}(values, minMaxValue);\n }\n `,f="vec4";"all"===t?(i="1.0",h="\n bool reducedAllValue = all(values);\n float floatedReducedAllValue = float(reducedAllValue);\n allValue = float(allValue >= 1.0 && floatedReducedAllValue >= 1.0);\n ",f="bvec4"):"any"===t&&(i="0.0",h="\n bool reducedAnyValue = any(values);\n float floatedReducedAnyValue = float(reducedAnyValue);\n anyValue = float(anyValue >= 1.0 || floatedReducedAnyValue >= 1.0);\n ",f="bvec4");let d="";a%n>0&&(d=`\n if (inIdx < 0 || inIdx >= ${a}) {\n return initializationValue;\n }\n `),this.userCode=`\n const float initializationValue = ${i};\n const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0);\n\n float getValue(int batch, int inIdx) {\n ${d}\n return getX(batch, inIdx);\n }\n\n void main() {\n ivec2 coords = getOutputCoords();\n int batch = coords[0];\n int outIdx = coords[1];\n int inOffset = outIdx * ${n};\n\n vec4 minMaxValue = vec4(${i});\n float prodValue = 1.0;\n float sumValue = 0.0;\n float allValue = 1.0;\n float anyValue = 0.0;\n\n for (int i = 0; i < ${u}; i += 4) {\n int inIdx = inOffset + i;\n ${f} values = ${f}(\n getValue(batch, inIdx),\n getValue(batch, inIdx + 1),\n getValue(batch, inIdx + 2),\n getValue(batch, inIdx + 3)\n );\n\n ${h}\n }\n\n int inIdx = inOffset + ${u};\n if (${1===c}) {\n ${f} values = ${f}(\n getValue(batch, inIdx),\n initializationValue,\n initializationValue,\n initializationValue\n );\n\n ${h}\n } else if (${2===c}) {\n ${f} values = ${f}(\n getValue(batch, inIdx),\n getValue(batch, inIdx + 1),\n initializationValue,\n initializationValue\n );\n\n ${h}\n } else if (${3===c}) {\n ${f} values = ${f}(\n getValue(batch, inIdx),\n getValue(batch, inIdx + 1),\n getValue(batch, inIdx + 2),\n initializationValue\n );\n\n ${h}\n }\n setOutput(${l});\n }\n `}}function iR(e,t,n,r){const a=function(e){const t=[];for(;0===t.length||1!==t[t.length-1].outSize;){const n=t.length?t[t.length-1].outSize:e[1],r=Jf(n);t.push({inSize:n,windowSize:r,outSize:Math.ceil(n/r)})}return t}(e.shape);let s=e;for(let i=0;i6)throw Error(`Transpose for rank ${t} is not yet supported`);const n=["resRC.x","resRC.y","resRC.z","resRC.w","resRC.u","resRC.v"],r=new Array(t);for(let t=0;t6)throw Error(`Packed transpose for rank ${this.rank} is not yet supported.`);const r=LT(this.rank),a=wF("rc",this.rank),s=new Array(this.rank);for(let e=0;e=2&&c>=2&&(y===b||1===y||1===b),(()=>`Error in matMul: the input batch dimensions must either be the same or at least one input batch dimension must be 1. Got input batch dimensions of (${m}) and (${g}).`));const v=(y>b?e.shape.slice(0,-2):t.shape.slice(0,-2)).concat([d,p]);Q(h===f,(()=>`Error in matMul: inner shapes (${h}) and (${f}) of Tensors with shapes ${e.shape} and ${t.shape} and transposeA=${n} and transposeB=${r} must match.`));const w=n?[y,h,d]:[y,d,h],_=r?[b,p,f]:[b,f,p],x=nR({inputs:{x:e},backend:a,attrs:{shape:w}}),k=nR({inputs:{x:t},backend:a,attrs:{shape:_}}),E=[x,k],C=Math.max(y,b),S=n?x.shape[1]:x.shape[2],N=null!=s,I=null!=i,A="leakyrelu"===l,D=null!=l?YF(l,!0):null;let T;if((1===d||1===p)&&S>1e3&&!1===(N||I||A||null!=D)){let e=x,t=k;n&&(e=fR({inputs:{x},backend:a,attrs:{perm:[0,2,1]}}),E.push(e)),r&&(t=fR({inputs:{x:k},backend:a,attrs:{perm:[0,2,1]}}),E.push(t));const s=1===p;let i=e;1!==p&&(i=nR({inputs:{x:e},backend:a,attrs:{shape:[C,S,1]}}),E.push(i));const o=1===p?2:1;let l=t;s&&(l=nR({inputs:{x:t},backend:a,attrs:{shape:[C,1,S]}}),E.push(l));const u=eR({inputs:{a:i,b:l},backend:a});T=cR({inputs:{x:u},backend:a,attrs:{axis:o,keepDims:!0}}),E.push(u)}else{const l=Da(e.dtype,t.dtype),u=new JF(w,_,[C,d,p],n,r,N,D,I,A),c=[x,k];if(null!=s&&c.push(s),I&&c.push(i),A){const e=a.makeTensorInfo([],"float32",ra(o,"float32"));c.push(e),E.push(e)}T=a.runWebGLProgram(u,c,l)}const F=nR({inputs:{x:T},backend:a,attrs:{shape:v}});E.push(T);for(const e of E)a.disposeIntermediateTensorInfo(e);return F}const mR={kernelName:Vr,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n,attrs:r}=e,{a,b:s,bias:i,preluActivationWeights:o}=t,{transposeA:l,transposeB:u,activation:c,leakyreluAlpha:h}=r;return pR({a,b:s,transposeA:l,transposeB:u,backend:n,bias:i,preluActivationWeights:o,leakyreluAlpha:h,activation:c})}},gR="return abs(x);",yR={kernelName:Ge,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n}=e,{x:r}=t;if(n.shouldExecuteOnCPU([r])&&"complex64"!==r.dtype){const e=n.texData.get(r.dataId),t=cF(e.values);return n.makeTensorInfo(r.shape,r.dtype,t)}let a;return a=We().getBool("WEBGL_PACK_UNARY_OPERATIONS")?new TF(r.shape,gR):new IF(r.shape,gR),n.runWebGLProgram(a,[r],r.dtype)}},bR=KF({opSnippet:"if (isnan(x)) return x;\n if (abs(x) > 1.) {\n return NAN;\n }\n return acos(x);\n"}),vR={kernelName:Xe,backendName:"webgl",kernelFunc:bR},wR=KF({opSnippet:"if (isnan(x)) return x;\n if (x < 1.0) return NAN;\nreturn log(x + sqrt(x * x - 1.0));"}),_R={kernelName:Ke,backendName:"webgl",kernelFunc:wR},xR="return a + b;",kR=qF({opSnippet:xR,packedOpSnippet:xR,supportsComplex:!0,cpuKernelImpl:VT}),ER={kernelName:qe,backendName:"webgl",kernelFunc:kR};class CR{constructor(e,t){this.outputShape=[],this.outputShape=e,this.variableNames=t.map(((e,t)=>`T${t}`));const n=[];this.variableNames.forEach((e=>{n.push(`float v${e} = get${e}AtOutCoords();`)}));const r=this.variableNames.map((e=>`v${e}`)).join(" + ");this.userCode=`\n void main() {\n ${n.join("\n ")}\n\n float result = ${r};\n setOutput(result);\n }\n `}}class SR{constructor(e,t){this.outputShape=[],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.variableNames=t.map(((e,t)=>`T${t}`));const n=[];this.variableNames.forEach((e=>{n.push(`vec4 v${e} = get${e}AtOutCoords();`)}));const r=this.variableNames.map((e=>`v${e}`)).join(" + ");this.userCode=`\n void main() {\n ${n.join("\n ")}\n\n vec4 result = ${r};\n setOutput(result);\n }\n `}}const NR={kernelName:Ye,backendName:"webgl",kernelFunc:function e(t){const{inputs:n,backend:r}=t,a=n;if(1===a.length)return PF({inputs:{x:a[0]},backend:r});if(a.length>We().get("WEBGL_MAX_TEXTURES_IN_SHADER")){const t=Math.floor(a.length/2),n=e({inputs:a.slice(0,t),backend:r}),s=e({inputs:a.slice(t),backend:r});return e({inputs:[n,s],backend:r})}const s=a.map((e=>e.dtype)).reduce(((e,t)=>Da(e,t))),i=a.map((e=>e.shape)),o=We().getBool("WEBGL_PACK")?new SR(a[0].shape,i):new CR(a[0].shape,i);return r.runWebGLProgram(o,a,s)}},IR={kernelName:Je,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{axis:s,keepDims:i}=r,o=a.shape.length,l=de(s,a.shape);let u=l;const c=qu(u,o);let h=a;null!=c&&(h=fR({inputs:{x:a},backend:n,attrs:{perm:c}}),u=Ju(u.length,o)),Ku("all",u,o);const[f,d]=Gu(h.shape,u),p=nR({inputs:{x:h},backend:n,attrs:{shape:[-1,re(d)]}}),m=iR(p,p.dtype,"all",n);let g;return g=nR(i?{inputs:{x:m},backend:n,attrs:{shape:Xu(f,l)}}:{inputs:{x:m},backend:n,attrs:{shape:f}}),n.disposeIntermediateTensorInfo(p),n.disposeIntermediateTensorInfo(m),null!=c&&n.disposeIntermediateTensorInfo(h),g}},AR={kernelName:Ze,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{axis:s,keepDims:i}=r,o=a.shape.length,l=de(s,a.shape);let u=l;const c=qu(u,o);let h=a;null!=c&&(h=fR({inputs:{x:a},backend:n,attrs:{perm:c}}),u=Ju(u.length,o)),Ku("any",u,o);const[f,d]=Gu(h.shape,u),p=nR({inputs:{x:h},backend:n,attrs:{shape:[-1,re(d)]}}),m=iR(p,p.dtype,"any",n);let g;return g=nR(i?{inputs:{x:m},backend:n,attrs:{shape:Xu(f,l)}}:{inputs:{x:m},backend:n,attrs:{shape:f}}),n.disposeIntermediateTensorInfo(p),n.disposeIntermediateTensorInfo(m),null!=c&&n.disposeIntermediateTensorInfo(h),g}};class DR{constructor(e,t,n){this.variableNames=["A"];const{windowSize:r,batchSize:a,outSize:s}=e;n||this.variableNames.push("bestIndicesA"),this.outputShape=[a,s];const i="max"===t?">":"<",o=n?"inOffset + i;":"round(getBestIndicesA(batch, inOffset + i));";this.userCode=`\n void main() {\n ivec2 coords = getOutputCoords();\n int batch = coords[0];\n int outIdx = coords[1];\n int inOffset = outIdx * ${r};\n\n int bestIndex = inOffset;\n float bestValue = getA(batch, bestIndex);\n\n for (int i = 0; i < ${r}; i++) {\n int inIdx = ${o};\n float candidate = getA(batch, inIdx);\n if (candidate ${i} bestValue) {\n bestValue = candidate;\n bestIndex = inIdx;\n }\n }\n setOutput(float(bestIndex));\n }\n `}}class TR{constructor(e,t,n,r){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,Q(e.length>2,(()=>`Packed arg${n.charAt(0).toUpperCase()+n.slice(1)} supports only inputs with rank above 2.`));const a=e[e.length-1],s=Math.ceil(a/t);this.outputShape=e.slice(0,-1),s>1&&this.outputShape.push(s),r||this.variableNames.push("bestIndicesA");const i=this.outputShape,o=i.length,l=LT(o),u=_F("coords",o);let c,h;if(1===s){h=o+1;const e=LT(h);c=`\n ${e} sourceLocR = ${e}(${u.join()}, 0);\n ++${u[o-1]};\n ${e} sourceLocG = ${e}(${u.join()}, 0);\n ++${u[o-2]};\n ${e} sourceLocA = ${e}(${u.join()}, 0);\n --${u[o-1]};\n ${e} sourceLocB = ${e}(${u.join()}, 0);\n --${u[o-2]};`}else h=o,c=`\n ${l} sourceLocR = coords;\n ++${u[o-1]};\n ${l} sourceLocG = coords;\n ++${u[o-2]};\n ${l} sourceLocA = coords;\n --${u[o-1]};\n ${l} sourceLocB = coords;\n --${u[o-2]};`;const f=["x","y","z","w","u","v"].slice(0,h),d="."+f[h-1],p=f.map((e=>"int "+e)),m=_F("sourceLocR",h-1).concat("inIdx.r"),g=_F("sourceLocG",h-1).concat("inIdx.g"),y=_F("sourceLocB",h-1).concat("inIdx.b"),b=_F("sourceLocA",h-1).concat("inIdx.a"),v="max"===n?"greaterThan":"lessThan",w=r?"":`\n inIdx = round(vec4(getBestIndicesAChannel(${m.join()}),\n getBestIndicesAChannel(${g.join()}),\n getBestIndicesAChannel(${y.join()}),\n getBestIndicesAChannel(${b.join()})));`,_=`vec4(\n getAChannel(${m.join()}),\n hasNextCol ? getAChannel(${g.join()}) : 0.,\n hasNextRow ? getAChannel(${y.join()}) : 0.,\n hasNextRow && hasNextCol ? getAChannel(${b.join()}) : 0.)`,x=r?"":`\n float getBestIndicesAChannel(${p.join()}) {\n return getChannel(getBestIndicesA(${f.join()}),\n vec2(${f.slice(-2).join()}));\n }`;this.userCode=`\n float getAChannel(${p.join()}) {\n return getChannel(getA(${f.join()}),\n vec2(${f.slice(-2).join()}));\n }\n ${x}\n void main() {\n ${l} coords = getOutputCoords();\n bool hasNextCol = ${u[o-1]} < ${i[o-1]-1};\n bool hasNextRow = ${u[o-2]} < ${i[o-2]-1};\n ${c}\n ivec4 srcIdx = ivec4(sourceLocR${d}, sourceLocG${d},\n sourceLocB${d}, sourceLocA${d}) * ${t};\n ivec4 inIdx = srcIdx;\n vec4 bestIndex = vec4(inIdx);\n vec4 bestValue = ${_};\n\n for (int i = 0; i < ${t}; i++) {\n inIdx = srcIdx;\n ${w}\n vec4 candidate = ${_};\n bvec4 nan = isnan(candidate);\n bvec4 replace = bvec4(\n vec4(${v}(candidate, bestValue)) * (vec4(1.0) - vec4(nan)));\n\n bestValue = vec4(replace.x ? candidate.x : bestValue.x,\n replace.y ? candidate.y : bestValue.y,\n replace.z ? candidate.z : bestValue.z,\n replace.w ? candidate.w : bestValue.w);\n bestIndex = mix(bestIndex, vec4(inIdx), vec4(replace));\n srcIdx++;\n }\n setOutput(bestIndex);\n }\n `}}function FR(e,t,n,r=null){let a=t.shape[0],s=t.shape[1];null!=r&&(a=r.shape[0],s=r.shape[1]);const i=Jf(s),o={windowSize:i,inSize:s,batchSize:a,outSize:Math.ceil(s/i)},l=new DR(o,n,null==r),u=[t];null!=r&&u.push(r);const c=e.runWebGLProgram(l,u,"int32");if(1===c.shape[1])return c;const h=FR(e,t,n,c);return e.disposeIntermediateTensorInfo(c),h}function RR(e,t,n,r=null){const a=null!=r?r.shape:t.shape,s=Jf(a[a.length-1]),i=new TR(a,s,n,null==r),o=null==r?[t]:[t,r],l=e.runWebGLProgram(i,o,"int32");if(l.shape.length===t.shape.length){const r=RR(e,t,n,l);return e.disposeIntermediateTensorInfo(l),r}return l}function OR(e,t,n,r){const a=[n];if(Ku("arg"+r.charAt(0).toUpperCase()+r.slice(1),a,t.shape.length),!We().getBool("WEBGL_PACK_REDUCE")||t.shape.length<=2){const n=[],[s,i]=Gu(t.shape,a),o=re(i),l=nR({inputs:{x:t},backend:e,attrs:{shape:[-1,o]}});n.push(l);const u=FR(e,l,r);n.push(u);const c=nR({inputs:{x:u},backend:e,attrs:{shape:s}});return n.forEach((t=>e.disposeIntermediateTensorInfo(t))),c}return RR(e,t,r)}const MR={kernelName:Qe,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{axis:s}=r;let i=de(s,a.shape);const o=qu(i,a.shape.length);let l=a;const u=[];null!=o&&(l=fR({inputs:{x:a},backend:n,attrs:{perm:o}}),u.push(l),i=Ju(i.length,l.shape.length)),Ku("argMax",[i[0]],l.shape.length);const c=OR(n,l,i[0],"max");return u.forEach((e=>n.disposeIntermediateTensorInfo(e))),c}},BR={kernelName:et,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{axis:s}=r;let i=de(s,a.shape);const o=qu(i,a.shape.length);let l=a;const u=[];null!=o&&(l=fR({inputs:{x:a},backend:n,attrs:{perm:o}}),u.push(l),i=Ju(i.length,l.shape.length)),Ku("argMin",[i[0]],l.shape.length);const c=OR(n,l,i[0],"min");return u.forEach((e=>n.disposeIntermediateTensorInfo(e))),c}},$R=KF({opSnippet:"if (isnan(x)) return x;\n if (abs(x) > 1.) {\n return NAN;\n }\n return asin(x);\n"}),PR={kernelName:tt,backendName:"webgl",kernelFunc:$R},LR=KF({opSnippet:"if (isnan(x)) return x;return log(x + sqrt(x * x + 1.0));"}),zR={kernelName:nt,backendName:"webgl",kernelFunc:LR},WR=KF({opSnippet:"if (isnan(x)) return x;\n return atan(x);\n"}),UR={kernelName:rt,backendName:"webgl",kernelFunc:WR},VR=qF({opSnippet:"\n if (isnan(a)) return a;\n if (isnan(b)) return b;\n\n return atan(a, b);\n",packedOpSnippet:"\n vec4 result = atan(a, b);\n vec4 isNaN = min(vec4(isnan(a)) + vec4(isnan(b)), vec4(1.0));\n \n result.r = isNaN.r > 0. ? NAN : result.r;\n result.g = isNaN.g > 0. ? NAN : result.g;\n result.b = isNaN.b > 0. ? NAN : result.b;\n result.a = isNaN.a > 0. ? NAN : result.a;\n\n return result;\n"}),jR={kernelName:st,backendName:"webgl",kernelFunc:VR},HR=KF({opSnippet:"if (isnan(x)) return x;\n if ((x < -1.0) || (x > 1.0)) return NAN;\nreturn (log(1.0 + x) - log(1.0 - x)) / 2.0;"}),GR={kernelName:at,backendName:"webgl",kernelFunc:HR};class XR{constructor(e,t,n,r=!1,a=!1){if(this.variableNames=["x"],"avg"===t&&n)throw new Error("Cannot compute positions for average pool.");const s=e.filterWidth,i=e.strideHeight,o=e.strideWidth,l=e.dilationHeight,u=e.dilationWidth,c=e.effectiveFilterHeight,h=e.effectiveFilterWidth,f=e.padInfo.top,d=e.padInfo.left;this.outputShape=e.outShape;const p="avg"===t,m=`((batch * ${e.inHeight} + xR) * ${e.inWidth} + xC) * ${e.inChannels} + d`,g=`(xR * ${e.inWidth} + xC) * ${e.inChannels} + d`;let y="0.0";if(p||(y="-1.0 / 1e-20"),n){const t=">=";return void(this.userCode=`\n const ivec2 strides = ivec2(${i}, ${o});\n const ivec2 pads = ivec2(${f}, ${d});\n\n void main() {\n ivec4 coords = getOutputCoords();\n int batch = coords[0];\n int d = coords[3];\n\n ivec2 xRCCorner = coords.yz * strides - pads;\n int xRCorner = xRCCorner.x;\n int xCCorner = xRCCorner.y;\n\n // max/min x(?, ?, d) to get y(yR, yC, d).\n // ? = to be determined\n float minMaxValue = 0.0;\n float minMaxValueFound = 0.0;\n int minMaxPosition = 0;\n float avgValue = 0.0;\n\n for (int wR = 0; wR < ${c};\n wR += ${l}) {\n int xR = xRCorner + wR;\n\n if (xR < 0 || xR >= ${e.inHeight}) {\n continue;\n }\n\n for (int wC = 0; wC < ${h};\n wC += ${u}) {\n int xC = xCCorner + wC;\n\n if (xC < 0 || xC >= ${e.inWidth}) {\n continue;\n }\n\n float value = getX(batch, xR, xC, d);\n\n // If a min / max value has already been found, use it. If not,\n // use the current value.\n float currMinMaxValue = mix(\n value, minMaxValue, minMaxValueFound);\n if (value ${t} currMinMaxValue) {\n minMaxValue = value;\n minMaxValueFound = 1.0;\n minMaxPosition = ${r?a?m:g:`wR * ${h} + wC`};\n }\n }\n }\n setOutput(float(minMaxPosition));\n }\n `)}let b=`${t}(${t}(${t}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;"avg"===t&&(b="avgValue / count");const v=4*Math.floor(s/4),w=s%4,_=`\n if (${p}) {\n avgValue += dot(values, ones);\n } else {\n minMaxValue = max(values, minMaxValue);\n }\n `;this.userCode=`\n const ivec2 strides = ivec2(${i}, ${o});\n const ivec2 pads = ivec2(${f}, ${d});\n const float initializationValue = ${y};\n const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0);\n\n float count = 0.0;\n\n float getValue(int batch, int xR, int xC, int d) {\n if (xC < 0 || xC >= ${e.inWidth}) {\n return initializationValue;\n }\n count += 1.0;\n return getX(batch, xR, xC, d);\n }\n\n void main() {\n ivec4 coords = getOutputCoords();\n int batch = coords[0];\n int d = coords[3];\n\n ivec2 xRCCorner = coords.yz * strides - pads;\n int xRCorner = xRCCorner.x;\n int xCCorner = xRCCorner.y;\n\n // max/min x(?, ?, d) to get y(yR, yC, d).\n // ? = to be determined\n vec4 minMaxValue = vec4(${y});\n float avgValue = 0.0;\n count = 0.0;\n\n for (int wR = 0; wR < ${c};\n wR += ${l}) {\n int xR = xRCorner + wR;\n\n if (xR < 0 || xR >= ${e.inHeight}) {\n continue;\n }\n\n for (int wC = 0; wC < ${v}; wC += 4) {\n int xC = xCCorner + wC * ${u};\n\n vec4 values = vec4(\n getValue(batch, xR, xC, d),\n getValue(batch, xR, xC + ${u}, d),\n getValue(batch, xR, xC + 2 * ${u}, d),\n getValue(batch, xR, xC + 3 * ${u}, d)\n );\n\n ${_}\n }\n\n int xC = xCCorner + ${v};\n if (${1===w}) {\n vec4 values = vec4(\n getValue(batch, xR, xC, d),\n initializationValue,\n initializationValue,\n initializationValue\n );\n\n ${_}\n } else if (${2===w}) {\n vec4 values = vec4(\n getValue(batch, xR, xC, d),\n getValue(batch, xR, xC + ${u}, d),\n initializationValue,\n initializationValue\n );\n\n ${_}\n } else if (${3===w}) {\n vec4 values = vec4(\n getValue(batch, xR, xC, d),\n getValue(batch, xR, xC + ${u}, d),\n getValue(batch, xR, xC + 2 * ${u}, d),\n initializationValue\n );\n\n ${_}\n }\n }\n setOutput(${b});\n }\n `}}class KR{constructor(e,t,n,r=!1,a=!1){if(this.variableNames=["x"],"avg"===t&&n)throw new Error("Cannot compute positions for average pool.");const s=e.filterWidth,i=e.strideDepth,o=e.strideHeight,l=e.strideWidth,u=e.dilationDepth,c=e.dilationHeight,h=e.dilationWidth,f=e.effectiveFilterDepth,d=e.effectiveFilterHeight,p=e.effectiveFilterWidth,m=e.padInfo.front,g=e.padInfo.top,y=e.padInfo.left;this.outputShape=e.outShape;const b="avg"===t;let v="0.0";if(b||(v="-1.0 / 1e-20"),n){const t=">=";return void(this.userCode=`\n const ivec3 strides =\n ivec3(${i}, ${o}, ${l});\n const ivec3 pads = ivec3(${m}, ${g}, ${y});\n\n void main() {\n ivec5 coords = getOutputCoords();\n int batch = coords.x;\n int ch = coords.u;\n\n ivec3 xCorner = ivec3(coords.y, coords.z, coords.w) * strides - pads;\n int xDCorner = xCorner.x;\n int xRCorner = xCorner.y;\n int xCCorner = xCorner.z;\n\n // max/min x(?, ?, ?, ch) to get y(yD, yR, yC, ch).\n // ? = to be determined\n float minMaxValue = 0.0;\n float minMaxValueFound = 0.0;\n int minMaxPosition = 0;\n\n for (int wD = 0; wD < ${f};\n wD += ${u}) {\n int xD = xDCorner + wD;\n\n if (xD < 0 || xD >= ${e.inDepth}) {\n continue;\n }\n\n for (int wR = 0; wR < ${d};\n wR += ${c}) {\n int xR = xRCorner + wR;\n\n if (xR < 0 || xR >= ${e.inHeight}) {\n continue;\n }\n\n for (int wC = 0; wC < ${p};\n wC += ${h}) {\n int xC = xCCorner + wC;\n\n if (xC < 0 || xC >= ${e.inWidth}) {\n continue;\n }\n\n float value = getX(batch, xD, xR, xC, ch);\n\n // If a min / max value has already been found, use it. If not,\n // use the current value.\n float currMinMaxValue = mix(\n value, minMaxValue, minMaxValueFound);\n if (value ${t} currMinMaxValue) {\n minMaxValue = value;\n minMaxValueFound = 1.0;\n minMaxPosition = ${r?a?`(((batch * ${e.inDepth} + xD) * ${e.inHeight} + xR) * ${e.inWidth} + xC) * ${e.inChannels} + ch`:`((xD * ${e.inHeight} + xR) * ${e.inWidth} + xC) * ${e.inChannels} + ch`:`wD * ${d} * ${p} +\n wR * ${p} + wC`};\n }\n }\n }\n }\n setOutput(float(minMaxPosition));\n }\n `)}let w=`${t}(${t}(${t}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;"avg"===t&&(w="avgValue / count");const _=4*Math.floor(s/4),x=s%4,k=`\n if (${b}) {\n avgValue += dot(values, ones);\n } else {\n minMaxValue = max(values, minMaxValue);\n }\n `;this.userCode=`\n const ivec3 strides =\n ivec3(${i}, ${o}, ${l});\n const ivec3 pads = ivec3(${m}, ${g}, ${y});\n const float initializationValue = ${v};\n const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0);\n\n float count = 0.0;\n\n float getValue(int batch, int xD, int xR, int xC, int ch) {\n if (xC < 0 || xC >= ${e.inWidth}) {\n return initializationValue;\n }\n count += 1.0;\n return getX(batch, xD, xR, xC, ch);\n }\n\n void main() {\n ivec5 coords = getOutputCoords();\n int batch = coords.x;\n int ch = coords.u;\n\n ivec3 xCorner = ivec3(coords.y, coords.z, coords.w) * strides - pads;\n int xDCorner = xCorner.x;\n int xRCorner = xCorner.y;\n int xCCorner = xCorner.z;\n\n // max/min x(?, ?, ?, d) to get y(yD, yR, yC, ch).\n // ? = to be determined\n vec4 minMaxValue = vec4(${v});\n float avgValue = 0.0;\n count = 0.0;\n\n for (int wD = 0; wD < ${f};\n wD += ${u}) {\n int xD = xDCorner + wD;\n\n if (xD < 0 || xD >= ${e.inDepth}) {\n continue;\n }\n\n for (int wR = 0; wR < ${d};\n wR += ${c}) {\n int xR = xRCorner + wR;\n\n if (xR < 0 || xR >= ${e.inHeight}) {\n continue;\n }\n\n for (int wC = 0; wC < ${_}; wC += 4) {\n int xC = xCCorner + wC * ${h};\n\n vec4 values = vec4(\n getValue(batch, xD, xR, xC, ch),\n getValue(batch, xD, xR, xC + ${h}, ch),\n getValue(batch, xD, xR, xC + 2 * ${h}, ch),\n getValue(batch, xD, xR, xC + 3 * ${h}, ch)\n );\n\n ${k}\n }\n\n int xC = xCCorner + ${_};\n if (${1===x}) {\n vec4 values = vec4(\n getValue(batch, xD, xR, xC, ch),\n initializationValue,\n initializationValue,\n initializationValue\n );\n\n ${k}\n } else if (${2===x}) {\n vec4 values = vec4(\n getValue(batch, xD, xR, xC, ch),\n getValue(batch, xD, xR, xC + ${h}, ch),\n initializationValue,\n initializationValue\n );\n\n ${k}\n } else if (${3===x}) {\n vec4 values = vec4(\n getValue(batch, xD, xR, xC, ch),\n getValue(batch, xD, xR, xC + ${h}, ch),\n getValue(batch, xD, xR, xC + 2 * ${h}, ch),\n initializationValue\n );\n\n ${k}\n }\n }\n setOutput(${w});\n }\n }\n `}}const qR={kernelName:it,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n,attrs:r}=e,{x:a}=t;cT(a,"avgPool");const{filterSize:s,strides:i,pad:o,dimRoundingMode:l}=r;Q(Nl(i,1),(()=>`Error in avgPool: Either strides or dilations must be 1. Got strides ${i} and dilations '1'`));const u=yl(a.shape,s,i,1,o,l);if(1===u.filterWidth&&1===u.filterHeight&&se(u.inShape,u.outShape))return PF({inputs:{x:a},backend:n});const c=new XR(u,"avg",!1);return n.runWebGLProgram(c,[a],"float32")}},YR={kernelName:lt,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{filterSize:s,strides:i,pad:o,dimRoundingMode:l,dataFormat:u}=r,c=bl(a.shape,s,i,[1,1,1],o,l,u),h=new KR(c,"avg",!1);return n.runWebGLProgram(h,[a],"float32")}};class JR{constructor(e){this.variableNames=["dy"],this.outputShape=e.inShape;const t=e.filterHeight,n=e.filterWidth,r=e.strideHeight,a=e.strideWidth,s=e.dilationHeight,i=e.dilationWidth,o=e.effectiveFilterHeight,l=e.effectiveFilterWidth,u=o-1-e.padInfo.top,c=l-1-e.padInfo.left,h=1/(t*n);this.userCode=`\n const ivec2 pads = ivec2(${u}, ${c});\n const float avgMultiplier = float(${h});\n\n void main() {\n ivec4 coords = getOutputCoords();\n int b = coords[0];\n int d = coords[3];\n\n ivec2 dyRCCorner = coords.yz - pads;\n int dyRCorner = dyRCCorner.x;\n int dyCCorner = dyRCCorner.y;\n\n // Convolve dy(?, ?, d) with pos mask(:, :, d) to get dx(xR, xC, d).\n // ? = to be determined. : = across all values in that axis.\n float dotProd = 0.0;\n for (int wR = 0; wR < ${o};\n wR += ${s}) {\n float dyR = float(dyRCorner + wR) / ${r}.0;\n\n if (dyR < 0.0 || dyR >= ${e.outHeight}.0 || fract(dyR) > 0.0) {\n continue;\n }\n int idyR = int(dyR);\n\n for (int wC = 0; wC < ${l};\n wC+= ${i}) {\n float dyC = float(dyCCorner + wC) / ${a}.0;\n\n if (dyC < 0.0 || dyC >= ${e.outWidth}.0 ||\n fract(dyC) > 0.0) {\n continue;\n }\n int idyC = int(dyC);\n\n float dyValue = getDy(b, idyR, idyC, d);\n\n dotProd += dyValue * avgMultiplier;\n }\n }\n setOutput(dotProd);\n }\n `}}class ZR{constructor(e){this.variableNames=["dy"],this.outputShape=e.inShape;const t=e.filterDepth,n=e.filterHeight,r=e.filterWidth,a=e.strideDepth,s=e.strideHeight,i=e.strideWidth,o=e.dilationDepth,l=e.dilationHeight,u=e.dilationWidth,c=e.effectiveFilterDepth,h=e.effectiveFilterHeight,f=e.effectiveFilterWidth,d=c-1-e.padInfo.front,p=h-1-e.padInfo.top,m=f-1-e.padInfo.left,g=1/(t*n*r);this.userCode=`\n const ivec3 pads = ivec3(${d}, ${p}, ${m});\n const float avgMultiplier = float(${g});\n\n void main() {\n ivec5 coords = getOutputCoords();\n int batch = coords.x;\n int ch = coords.u;\n\n ivec3 dyCorner = ivec3(coords.y, coords.z, coords.w) - pads;\n int dyDCorner = dyCorner.x;\n int dyRCorner = dyCorner.y;\n int dyCCorner = dyCorner.z;\n\n // Convolve dy(?, ?, ?, d) with pos mask(:, :, :, ch) to get\n // dx(xD, xR, xC, ch).\n // ? = to be determined. : = across all values in that axis.\n float dotProd = 0.0;\n\n for (int wD = 0; wD < ${c};\n wD += ${o}) {\n float dyD = float(dyDCorner + wD) / ${a}.0;\n\n if (dyD < 0.0 || dyD >= ${e.outDepth}.0 || fract(dyD) > 0.0) {\n continue;\n }\n int idyD = int(dyD);\n\n for (int wR = 0; wR < ${h};\n wR += ${l}) {\n float dyR = float(dyRCorner + wR) / ${s}.0;\n\n if (dyR < 0.0 || dyR >= ${e.outHeight}.0 ||\n fract(dyR) > 0.0) {\n continue;\n }\n int idyR = int(dyR);\n\n for (int wC = 0; wC < ${f};\n wC += ${u}) {\n float dyC = float(dyCCorner + wC) / ${i}.0;\n\n if (dyC < 0.0 || dyC >= ${e.outWidth}.0 ||\n fract(dyC) > 0.0) {\n continue;\n }\n int idyC = int(dyC);\n\n float dyValue = getDy(batch, idyD, idyR, idyC, ch);\n\n dotProd += dyValue * avgMultiplier;\n }\n }\n }\n setOutput(dotProd);\n }\n `}}const QR={kernelName:ut,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n,attrs:r}=e,{dy:a,input:s}=t,i=s,{filterSize:o,strides:l,pad:u,dimRoundingMode:c}=r,h=bl(i.shape,o,l,[1,1,1],u,c),f=new ZR(h);return n.runWebGLProgram(f,[a],i.dtype)}},eO={kernelName:ot,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n,attrs:r}=e,{dy:a,input:s}=t,i=s;cT([a,s],"avgPoolGrad");const{filterSize:o,strides:l,pad:u}=r,c=yl(i.shape,o,l,1,u),h=new JR(c);return n.runWebGLProgram(h,[a],i.dtype)}},tO={kernelName:ct,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n,attrs:r}=e,{a,b:s}=t,{transposeA:i,transposeB:o}=r;return pR({a,b:s,transposeA:i,transposeB:o,backend:n})}};class nO{constructor(e,t,n,r,a,s){this.outputShape=[],this.variableNames=["x","mean","variance"],Zo(e,t),Zo(e,n);let i="0.0";null!=r&&(Zo(e,r),this.variableNames.push("offset"),i="getOffsetAtOutCoords()");let o="1.0";null!=a&&(Zo(e,a),this.variableNames.push("scale"),o="getScaleAtOutCoords()"),this.outputShape=e,this.userCode=`\n void main() {\n float x = getXAtOutCoords();\n float mean = getMeanAtOutCoords();\n float variance = getVarianceAtOutCoords();\n float offset = ${i};\n float scale = ${o};\n float inv = scale * inversesqrt(variance + float(${s}));\n setOutput(dot(vec3(x, -mean, offset), vec3(inv, inv, 1)));\n }\n `}}class rO{constructor(e,t,n,r,a,s){this.packedInputs=!0,this.packedOutput=!0,this.variableNames=["x","mean","variance"],Zo(e,t),Zo(e,n);let i="vec4(0.0)";null!=r&&(Zo(e,r),this.variableNames.push("offset"),i="getOffsetAtOutCoords()");let o="vec4(1.0)";null!=a&&(Zo(e,a),this.variableNames.push("scale"),o="getScaleAtOutCoords()"),this.outputShape=e,this.userCode=`\n void main() {\n vec4 offset = ${i};\n vec4 scale = ${o};\n\n vec4 x = getXAtOutCoords();\n vec4 mean = getMeanAtOutCoords();\n vec4 variance = getVarianceAtOutCoords();\n\n vec4 inv = scale * inversesqrt(variance + vec4(${s}));\n\n setOutput((x - mean) * inv + offset);\n }\n `}}const aO={kernelName:Qt,backendName:"webgl",kernelFunc:({inputs:e,backend:t,attrs:n})=>{const{x:r,mean:a,variance:s,offset:i,scale:o}=e;Q(a.shape.length===s.shape.length,(()=>"Batch normalization gradient requires mean and variance to have equal ranks.")),Q(null==i||a.shape.length===i.shape.length,(()=>"Batch normalization gradient requires mean and offset to have equal ranks.")),Q(null==o||a.shape.length===o.shape.length,(()=>"Batch normalization gradient requires mean and scale to have equal ranks."));let{varianceEpsilon:l}=n;null==l&&(l=.001);const u=[r,a,s];let c=null;null!=i&&(c=i.shape,u.push(i));let h=null;null!=o&&(h=o.shape,u.push(o));const f=We().getBool("WEBGL_PACK_NORMALIZATION")?new rO(r.shape,a.shape,s.shape,c,h,l):new nO(r.shape,a.shape,s.shape,c,h,l);return t.runWebGLProgram(f,u,u[0].dtype)}};class sO{constructor(e){this.variableNames=["source"],this.outputShape=e,this.rank=e.length;const t=LT(this.rank),n=`uniform int start[${this.rank}];`,r=function(e){if(1===e)return"sourceLoc";if(e<=6)return iO.slice(0,e).map((e=>"sourceLoc."+e)).join(",");throw Error(`Slicing for rank ${e} is not yet supported`)}(this.rank);let a;a=`\n ${t} sourceLoc;\n ${t} coords = getOutputCoords();\n ${e.map(((e,t)=>`sourceLoc.${iO[t]} = start[${t}] + coords.${iO[t]};`)).join("\n")}\n `,this.userCode=`\n ${n}\n void main() {\n ${a}\n setOutput(getSource(${r}));\n }\n `}getCustomSetupFunc(e){if(e.length!==this.rank)throw Error(`The rank (${this.rank}) of the program must match the length of start (${e.length})`);return(t,n)=>{null==this.startLoc&&(this.startLoc=t.getUniformLocationNoThrow(n,"start"),null==this.startLoc)||t.gl.uniform1iv(this.startLoc,e)}}}const iO=["x","y","z","w","u","v"];class oO{constructor(e){this.variableNames=["source"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.rank=e.length;const t=LT(this.rank),n=_F("coords",this.rank),r=_F("sourceLoc",this.rank),a=1===this.rank?"sourceLoc":`vec2(${r.slice(-2).join()})`,s=`getChannel(getSource(${r.join()}), ${a})`,i=`\n result.x = ${s};\n if (++${n[this.rank-1]} < ${e[this.rank-1]}) {\n ++${r[this.rank-1]};\n result.y = ${s};\n --${r[this.rank-1]};\n }\n `,o=1===this.rank?"":`\n --${n[this.rank-1]};\n if (++${n[this.rank-2]} < ${e[this.rank-2]}) {\n ++${r[this.rank-2]};\n result.z = ${s};\n if (++${n[this.rank-1]} < ${e[this.rank-1]}) {\n ++${r[this.rank-1]};\n result.w = ${s};\n }\n }\n `,l=this.rank<=4?`sourceLoc = coords +\n ${t}(${e.map(((e,t)=>`start[${t}]`)).join()});`:e.map(((e,t)=>`${r[t]} = ${n[t]} + start[${t}];`)).join("\n");this.userCode=`\n uniform int start[${this.rank}];\n void main() {\n ${t} coords = getOutputCoords();\n ${t} sourceLoc;\n ${l}\n vec4 result = vec4(0.);\n ${i}\n ${o}\n setOutput(result);\n }\n `}getCustomSetupFunc(e){if(e.length!==this.rank)throw Error(`The rank (${this.rank}) of the program must match the length of start (${e.length})`);return(t,n)=>{null==this.startLoc&&(this.startLoc=t.getUniformLocationNoThrow(n,"start"),null==this.startLoc)||t.gl.uniform1iv(this.startLoc,e)}}}function lO(e){const{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{begin:s,size:i}=r,[o,l]=Ui(a,s,i);if(Ii(a,o,l),0===re(l))return n.makeTensorInfo(l,a.dtype,[]);if(n.shouldExecuteOnCPU([a])||"string"===a.dtype){const e=n.texData.get(a.dataId),t=hF(e.values,o,l,a.shape,a.dtype);return n.makeTensorInfo(l,a.dtype,t)}const{isPacked:u}=n.texData.get(a.dataId),c=zi(a.shape,o,l);if(u||!c){const e=We().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new oO(l):new sO(l),t=e.getCustomSetupFunc(o);return n.runWebGLProgram(e,[a],a.dtype,t)}return n.uploadToGPU(a.dataId),function(e,t,n,r){const a=r.texData.get(e.dataId),s=r.makeTensorInfo(n,e.dtype),i=r.texData.get(s.dataId);Object.assign(i,a),i.refCount=1,i.shape=n,i.dtype=e.dtype;let o=Wi(t,Ae(e.shape));a.slice&&(o+=a.slice.flatOffset),i.slice={flatOffset:o,origDataId:a.slice&&a.slice.origDataId||e.dataId};const l=r.dataRefCount.get(i.slice.origDataId)||1;return r.dataRefCount.set(i.slice.origDataId,l+1),s}(a,o,l,n)}const uO={kernelName:fr,backendName:"webgl",kernelFunc:lO},cO={kernelName:ht,backendName:"webgl",kernelFunc:e=>{const{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{blockShape:s,crops:i}=r;Q(a.shape.length<=4,(()=>"batchToSpaceND for rank > 4 with a WebGL backend not implemented yet"));const o=s.reduce(((e,t)=>e*t)),l=Qf(a.shape,s,o),u=ed(l.length,s.length),c=td(a.shape,s,o),h=nd(i,s.length),f=rd(c,i,s.length),d=[],p=nR({inputs:{x:a},backend:n,attrs:{shape:l}}),m=fR({inputs:{x:p},backend:n,attrs:{perm:u}}),g=nR({inputs:{x:m},backend:n,attrs:{shape:c}}),y=lO({inputs:{x:g},backend:n,attrs:{begin:h,size:f}});return d.push(p),d.push(m),d.push(g),d.forEach((e=>n.disposeIntermediateTensorInfo(e))),y}},hO={kernelName:ft,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n,attrs:r}=e,{x:a,weights:s}=t,{size:i}=r,o=n.readSync(a.dataId),l=n.readSync(s.dataId),u=jT(o,l,s.dtype,s.shape,i);return n.makeTensorInfo([i],s.dtype,u)}},fO=qF({opSnippet:"return float(a != b);",dtype:"bool"}),dO={kernelName:Pn,backendName:"webgl",kernelFunc:fO};function pO(e){const{inputs:t,backend:n}=e,{input:r}=t;return PF({inputs:{x:n.texData.get(r.dataId).complexTensorInfos.real},backend:n})}const mO={kernelName:Jn,backendName:"webgl",kernelFunc:pO},gO={kernelName:pt,backendName:"webgl",kernelFunc:function e(t){const{inputs:n,backend:r,attrs:a}=t,{x:s}=n,{dtype:i}=a;if("complex64"===i){if("complex64"===s.dtype)return PF({inputs:{x:s},backend:r});const t=oc(s.shape),n=e({inputs:{x:s},backend:r,attrs:{dtype:"float32"}}),a=zF({inputs:{real:n,imag:t},backend:r});return t.dispose(),r.disposeIntermediateTensorInfo(n),a}if("complex64"===s.dtype){const t=pO({inputs:{input:s},backend:r}),n=e({inputs:{x:t},backend:r,attrs:{dtype:i}});return r.disposeIntermediateTensorInfo(t),n}if(!ve(s.dtype,i)){const e=PF({inputs:{x:s},backend:r});return{dataId:e.dataId,shape:e.shape,dtype:i}}if("int32"===i)return function(e,t){const n=new IF(e.shape,"return float(int(x));"),r=t.runWebGLProgram(n,[e],"int32");return{dataId:r.dataId,shape:r.shape,dtype:r.dtype}}(s,r);if("bool"===i){const e=r.makeTensorInfo([],"bool",me("bool",1)),t=fO({inputs:{a:s,b:e},backend:r});return r.disposeIntermediateTensorInfo(e),t}throw new Error(`Error in Cast: failed to cast ${s.dtype} to ${i}`)}},yO="return ceil(x);",bO=KF({opSnippet:yO,packedOpSnippet:yO,cpuKernelImpl:GT}),vO={kernelName:mt,backendName:"webgl",kernelFunc:bO};class wO{constructor(e){this.variableNames=["A"],this.outputShape=e,this.userCode="\n uniform float minVal;\n uniform float maxVal;\n\n void main() {\n float value = getAAtOutCoords();\n if (isnan(value)) {\n setOutput(value);\n return;\n }\n\n setOutput(clamp(value, minVal, maxVal));\n }\n "}getCustomSetupFunc(e,t){return(n,r)=>{null==this.minLoc&&(this.minLoc=n.getUniformLocationNoThrow(r,"minVal"),this.maxLoc=n.getUniformLocationNoThrow(r,"maxVal")),n.gl.uniform1f(this.minLoc,e),n.gl.uniform1f(this.maxLoc,t)}}}class _O{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.userCode="\n uniform float minVal;\n uniform float maxVal;\n\n void main() {\n vec4 value = getAAtOutCoords();\n\n if (any(isnan(value))) {\n setOutput(value);\n return;\n }\n\n setOutput(clamp(value, vec4(minVal), vec4(maxVal)));\n }\n "}getCustomSetupFunc(e,t){return(n,r)=>{null==this.minLoc&&(this.minLoc=n.getUniformLocationNoThrow(r,"minVal"),this.maxLoc=n.getUniformLocationNoThrow(r,"maxVal")),n.gl.uniform1f(this.minLoc,e),n.gl.uniform1f(this.maxLoc,t)}}}const xO={kernelName:gt,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{clipValueMin:s,clipValueMax:i}=r;let o;o=We().getBool("WEBGL_PACK_CLIP")?new _O(a.shape):new wO(a.shape);const l=o.getCustomSetupFunc(s,i);return n.runWebGLProgram(o,[a],a.dtype,l)}};class kO{constructor(e){this.variableNames=["real","imag"],this.outputShape=e,this.userCode="\n void main() {\n float re = abs(getRealAtOutCoords());\n float im = abs(getImagAtOutCoords());\n float mx = max(re, im);\n\n // sadly the length function in glsl is not underflow-safe\n // (at least not on Intel GPUs). So the safe solution is\n // to ensure underflow-safety in all cases.\n setOutput(\n mx == 0.0 ? 0.0 : mx * length(vec2(1, min(re, im)/mx))\n );\n }\n "}}function EO(e,t){return{dataId:t.dataId,dtype:t.dtype,shape:e.shape}}const CO={kernelName:bt,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n}=e,{x:r}=t,a=n.texData.get(r.dataId),s=new kO(r.shape),i=[EO(r,a.complexTensorInfos.real),EO(r,a.complexTensorInfos.imag)];return n.runWebGLProgram(s,i,i[0].dtype)}};class SO{constructor(e){this.outputShape=[],this.outputShape=qf(e,1),this.variableNames=e.map(((e,t)=>`T${t}`));const t=new Array(e.length-1);t[0]=e[0][1];for(let n=1;n`T${t}`));const o=new Array(e.length-1);o[0]=e[0][t];for(let n=1;n= ${o[e-1]}) {\n return getChannel(\n getT${e}(${IO(i,l,t)}),\n vec2(${IO(u,l,t)}));\n }`}const f=o.length,d=o[o.length-1];h+=`\n return getChannel(\n getT${f}(${IO(i,l,d)}),\n vec2(${IO(u,l,d)}));`,this.userCode=`\n float getValue(${i.map((e=>"int "+e))}) {\n ${h}\n }\n\n void main() {\n ${a} coords = getOutputCoords();\n vec4 result = vec4(getValue(${s}), 0., 0., 0.);\n\n ${s[r-1]} = ${s[r-1]} + 1;\n if (${s[r-1]} < ${n[r-1]}) {\n result.g = getValue(${s});\n }\n\n ${s[r-2]} = ${s[r-2]} + 1;\n if (${s[r-2]} < ${n[r-2]}) {\n result.a = getValue(${s});\n }\n\n ${s[r-1]} = ${s[r-1]} - 1;\n if (${s[r-2]} < ${n[r-2]} &&\n ${s[r-1]} < ${n[r-1]}) {\n result.b = getValue(${s});\n }\n setOutput(result);\n }\n `}}function IO(e,t,n){const r=e.indexOf(t);return e.map(((e,t)=>t===r?`${e} - ${n}`:e)).join()}function AO(e){const{inputs:t,backend:n}=e,{input:r}=t;return PF({inputs:{x:n.texData.get(r.dataId).complexTensorInfos.imag},backend:n})}const DO={kernelName:on,backendName:"webgl",kernelFunc:AO};function TO(e,t,n){const r=e[0].dtype;if("complex64"===r){const r=e.map((e=>pO({inputs:{input:e},backend:n}))),a=e.map((e=>AO({inputs:{input:e},backend:n}))),s=TO(r,t,n),i=TO(a,t,n),o=zF({inputs:{real:s,imag:i},backend:n});return r.forEach((e=>n.disposeIntermediateTensorInfo(e))),a.forEach((e=>n.disposeIntermediateTensorInfo(e))),n.disposeIntermediateTensorInfo(s),n.disposeIntermediateTensorInfo(i),o}let a=n.shouldExecuteOnCPU(e);if("string"===r&&(a=!0),a){const a=e.map((e=>{const r=re(e.shape.slice(t));return nR({inputs:{x:e},backend:n,attrs:{shape:[-1,r]}})})),s=a.map((e=>({vals:n.readSync(e.dataId),shape:e.shape}))),i=qf(a.map((e=>e.shape)),1),o=1===a[0].shape[0],l=XT(s,i,r,o),u=qf(e.map((e=>e.shape)),t),c=n.makeTensorInfo(u,r,l);return a.forEach((e=>n.disposeIntermediateTensorInfo(e))),c}if(e.length>We().getNumber("WEBGL_MAX_TEXTURES_IN_SHADER")){const r=Math.floor(e.length/2),a=TO(e.slice(0,r),t,n),s=TO(e.slice(r),t,n),i=TO([a,s],t,n);return n.disposeIntermediateTensorInfo(a),n.disposeIntermediateTensorInfo(s),i}if(We().getBool("WEBGL_PACK_ARRAY_OPERATIONS")&&e[0].shape.length>1){const a=new NO(e.map((e=>e.shape)),t);return n.runWebGLProgram(a,e,r)}const{tensors2D:s,outShape:i}=function(e,t,n){const r=qf(e.map((e=>e.shape)),t);return{tensors2D:e.map((e=>nR({inputs:{x:e},attrs:{shape:[-1,re(e.shape.slice(t))]},backend:n}))),outShape:r}}(e,t,n),o=new SO(s.map((e=>e.shape))),l=n.runWebGLProgram(o,s,r);s.forEach((e=>n.disposeIntermediateTensorInfo(e)));const u=nR({inputs:{x:l},attrs:{shape:i},backend:n});return n.disposeIntermediateTensorInfo(l),u}function FO(e){const{inputs:t,backend:n,attrs:r}=e,{axis:a}=r,s=de(a,t[0].shape)[0],i=qf(t.map((e=>e.shape)),s);if(0===re(i))return n.makeTensorInfo(i,t[0].dtype,[]);const o=t.filter((e=>re(e.shape)>0));return 1===o.length?PF({inputs:{x:o[0]},backend:n}):(Kf(o.map((e=>e.shape)),s),TO(o,s,n))}const RO={kernelName:vt,backendName:"webgl",kernelFunc:FO};class OO{constructor(e,t=!1,n=null,r=!1,a=!1){this.variableNames=["x","W"],this.outputShape=e.outShape;const s=e.padInfo.top,i=e.padInfo.left,o=e.strideHeight,l=e.strideWidth,u=e.dilationHeight,c=e.dilationWidth,h=e.filterHeight,f=e.filterWidth,d=4*Math.floor(e.inChannels/4),p=e.inChannels%4,m="channelsLast"===e.dataFormat,g=m?1:2,y=m?2:3,b=m?3:1;let v="",w="";n&&(v=r?`float activation(float a) {\n float b = getPreluActivationWeightsAtOutCoords();\n ${n}\n }`:a?`float activation(float a) {\n float b = getLeakyreluAlphaAtOutCoords();\n ${n}\n }`:`\n float activation(float x) {\n ${n}\n }\n `,w="result = activation(result);");const _=t?"result += getBiasAtOutCoords();":"";t&&this.variableNames.push("bias"),r&&this.variableNames.push("preluActivationWeights"),a&&this.variableNames.push("leakyreluAlpha"),this.userCode=`\n ${v}\n\n const ivec2 strides = ivec2(${o}, ${l});\n const ivec2 pads = ivec2(${s}, ${i});\n\n void main() {\n ivec4 coords = getOutputCoords();\n int batch = coords[0];\n int d2 = coords[${b}];\n\n ivec2 xRCCorner =\n ivec2(coords[${g}], coords[${y}]) * strides - pads;\n int xRCorner = xRCCorner.x;\n int xCCorner = xRCCorner.y;\n\n // Convolve x(?, ?, d1) with w(:, :, d1, d2) to get y(yR, yC, d2).\n // ? = to be determined. : = across all values in that axis.\n float dotProd = 0.0;\n for (int wR = 0; wR < ${h}; wR++) {\n int xR = xRCorner + wR * ${u};\n\n if (xR < 0 || xR >= ${e.inHeight}) {\n continue;\n }\n\n for (int wC = 0; wC < ${f}; wC++) {\n int xC = xCCorner + wC * ${c};\n\n if (xC < 0 || xC >= ${e.inWidth}) {\n continue;\n }\n\n for (int d1 = 0; d1 < ${d}; d1 += 4) {\n vec4 wValues = vec4(\n getW(wR, wC, d1, d2),\n getW(wR, wC, d1 + 1, d2),\n getW(wR, wC, d1 + 2, d2),\n getW(wR, wC, d1 + 3, d2)\n );\n\n if (${m}) {\n vec4 xValues = vec4(\n getX(batch, xR, xC, d1),\n getX(batch, xR, xC, d1 + 1),\n getX(batch, xR, xC, d1 + 2),\n getX(batch, xR, xC, d1 + 3)\n );\n dotProd += dot(xValues, wValues);\n } else {\n vec4 xValues = vec4(\n getX(batch, d1, xR, xC),\n getX(batch, d1 + 1, xR, xC),\n getX(batch, d1 + 2, xR, xC),\n getX(batch, d1 + 3, xR, xC)\n );\n dotProd += dot(xValues, wValues);\n }\n }\n\n if (${1===p}) {\n\n if (${m}) {\n dotProd +=\n getX(batch, xR, xC, ${d}) *\n getW(wR, wC, ${d}, d2);\n } else {\n dotProd +=\n getX(batch, ${d}, xR, xC) *\n getW(wR, wC, ${d}, d2);\n }\n\n } else if (${2===p}) {\n vec2 wValues = vec2(\n getW(wR, wC, ${d}, d2),\n getW(wR, wC, ${d} + 1, d2)\n );\n\n if (${m}) {\n vec2 xValues = vec2(\n getX(batch, xR, xC, ${d}),\n getX(batch, xR, xC, ${d} + 1)\n );\n dotProd += dot(xValues, wValues);\n } else {\n vec2 xValues = vec2(\n getX(batch, ${d}, xR, xC),\n getX(batch, ${d} + 1, xR, xC)\n );\n dotProd += dot(xValues, wValues);\n }\n\n } else if (${3===p}) {\n vec3 wValues = vec3(\n getW(wR, wC, ${d}, d2),\n getW(wR, wC, ${d} + 1, d2),\n getW(wR, wC, ${d} + 2, d2)\n );\n\n if (${m}) {\n vec3 xValues = vec3(\n getX(batch, xR, xC, ${d}),\n getX(batch, xR, xC, ${d} + 1),\n getX(batch, xR, xC, ${d} + 2)\n );\n dotProd += dot(xValues, wValues);\n } else {\n vec3 xValues = vec3(\n getX(batch, ${d}, xR, xC),\n getX(batch, ${d} + 1, xR, xC),\n getX(batch, ${d} + 2, xR, xC)\n );\n dotProd += dot(xValues, wValues);\n }\n\n }\n }\n }\n\n float result = dotProd;\n ${_}\n ${w}\n setOutput(result);\n }\n `}}class MO{constructor(e){this.variableNames=["x","W"],this.outputShape=e.outShape;const t=e.padInfo.front,n=e.padInfo.top,r=e.padInfo.left,a=e.strideDepth,s=e.strideHeight,i=e.strideWidth,o=e.dilationDepth,l=e.dilationHeight,u=e.dilationWidth,c=e.filterDepth,h=e.filterHeight,f=e.filterWidth,d=4*Math.floor(e.inChannels/4),p=e.inChannels%4;this.userCode=`\n const ivec3 strides = ivec3(${a}, ${s}, ${i});\n const ivec3 pads = ivec3(${t}, ${n}, ${r});\n\n void main() {\n ivec5 coords = getOutputCoords();\n int batch = coords.x;\n int d2 = coords.u;\n\n ivec3 xFRCCorner = ivec3(coords.y, coords.z, coords.w) * strides - pads;\n int xFCorner = xFRCCorner.x;\n int xRCorner = xFRCCorner.y;\n int xCCorner = xFRCCorner.z;\n\n // Convolve x(?, ?, ?, d1) with w(:, :, :, d1, d2) to get\n // y(yF, yR, yC, d2). ? = to be determined. : = across all\n // values in that axis.\n float dotProd = 0.0;\n for (int wF = 0; wF < ${c}; wF++) {\n int xF = xFCorner + wF * ${o};\n\n if (xF < 0 || xF >= ${e.inDepth}) {\n continue;\n }\n\n for (int wR = 0; wR < ${h}; wR++) {\n int xR = xRCorner + wR * ${l};\n\n if (xR < 0 || xR >= ${e.inHeight}) {\n continue;\n }\n\n for (int wC = 0; wC < ${f}; wC++) {\n int xC = xCCorner + wC * ${u};\n\n if (xC < 0 || xC >= ${e.inWidth}) {\n continue;\n }\n\n for (int d1 = 0; d1 < ${d}; d1 += 4) {\n vec4 xValues = vec4(\n getX(batch, xF, xR, xC, d1),\n getX(batch, xF, xR, xC, d1 + 1),\n getX(batch, xF, xR, xC, d1 + 2),\n getX(batch, xF, xR, xC, d1 + 3)\n );\n vec4 wValues = vec4(\n getW(wF, wR, wC, d1, d2),\n getW(wF, wR, wC, d1 + 1, d2),\n getW(wF, wR, wC, d1 + 2, d2),\n getW(wF, wR, wC, d1 + 3, d2)\n );\n\n dotProd += dot(xValues, wValues);\n }\n\n if (${1===p}) {\n dotProd +=\n getX(batch, xF, xR, xC, ${d}) *\n getW(wF, wR, wC, ${d}, d2);\n } else if (${2===p}) {\n vec2 xValues = vec2(\n getX(batch, xF, xR, xC, ${d}),\n getX(batch, xF, xR, xC, ${d} + 1)\n );\n vec2 wValues = vec2(\n getW(wF, wR, wC, ${d}, d2),\n getW(wF, wR, wC, ${d} + 1, d2)\n );\n dotProd += dot(xValues, wValues);\n } else if (${3===p}) {\n vec3 xValues = vec3(\n getX(batch, xF, xR, xC, ${d}),\n getX(batch, xF, xR, xC, ${d} + 1),\n getX(batch, xF, xR, xC, ${d} + 2)\n );\n vec3 wValues = vec3(\n getW(wF, wR, wC, ${d}, d2),\n getW(wF, wR, wC, ${d} + 1, d2),\n getW(wF, wR, wC, ${d} + 2, d2)\n );\n dotProd += dot(xValues, wValues);\n }\n }\n }\n }\n setOutput(dotProd);\n }\n `}}class BO{constructor(e,t,n){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e;const{filterWidth:r,inChannels:a,strideWidth:s,strideHeight:i,padInfo:o,outWidth:l,dilationWidth:u,dilationHeight:c,dataFormat:h}=n,{left:f,top:d}=o,p=a*r,m=fT(),g="channelsLast"===h,y=g?0:1,b=g?1:2;let v="";for(let n=0;n<=1;n++)for(let r=0;r<=1;r++)v+=`\n blockIndex = rc.y + ${r};\n pos = rc.x + ${n};\n\n if(blockIndex < ${e[1]} && pos < ${e[0]}) {\n offsetY = int(blockIndex / (${l})) * ${i} - ${d};\n d0 = offsetY + ${c} * (pos / ${p});\n\n if(d0 < ${t[y]} && d0 >= 0) {\n\n offsetX = int(mod(float(blockIndex), ${l}.) * ${s}. - ${f}.);\n d1 = offsetX + ${u} * (int(mod(float(pos), ${p}.) / ${a}.));\n\n if(d1 < ${t[b]} && d1 >= 0) {\n\n ch = int(mod(float(pos), ${a}.));\n\n if (${g}) {\n innerDims = vec2(d1, ch);\n result[${2*n+r}] = getChannel(\n getA(d0, int(innerDims.x),\n int(innerDims.y)), innerDims);\n } else {\n innerDims = vec2(d0, d1);\n result[${2*n+r}] = getChannel(\n getA(ch, int(innerDims.x),\n int(innerDims.y)), innerDims);\n }\n }\n }\n }\n `;this.userCode=`\n void main() {\n ivec2 rc = getOutputCoords();\n\n vec4 result = vec4(0);\n\n int blockIndex, pos, offsetY, d0, offsetX, d1, ch;\n vec2 innerDims;\n\n ${v}\n\n ${m.output} = result;\n }\n `}}function $O({x:e,filter:t,convInfo:n,backend:r,bias:a=null,preluActivationWeights:s=null,leakyreluAlpha:i=0,activation:o=null}){const l=e.shape,u=r.texData.get(e.dataId),c=n.inChannels,h=l[0]*l[1]*l[2],f=n.outChannels,d="channelsLast"===n.dataFormat;let p;const m=[],g=(1===h||1===f)&&c>1e3,y=l[2]%2!=0&&!!u.isPacked;if(!g&&We().getBool("WEBGL_LAZILY_UNPACK")&&We().getBool("WEBGL_PACK_BINARY_OPERATIONS")&&y){const c=d?l[0]*l[1]*(l[2]+1):l[0]*l[2]*(l[3]+1),h={dataId:e.dataId,shape:[1,c,n.inChannels],dtype:e.dtype},f=u.shape;u.shape=u.shape.slice(),u.shape[u.shape.length-2]++,Q(aT(u.shape,h.shape),(()=>`packed reshape ${u.shape} to ${h.shape} isn't free`));const g=nR({inputs:{x:t},backend:r,attrs:{shape:[1,n.inChannels,n.outChannels]}});m.push(g);const y=pR({a:h,b:g,backend:r,transposeA:!1,transposeB:!1,bias:a,activation:o,preluActivationWeights:s,leakyreluAlpha:i}),b=r.texData.get(y.dataId);Q(b.isPacked,(()=>"batchMatMul result is expected to be packed")),u.shape=f,b.shape=n.outShape,p=PF({inputs:{x:y},backend:r}),p.shape=n.outShape,m.push(y)}else{const u=nR({inputs:{x:e},backend:r,attrs:{shape:[1,d?l[0]*l[1]*l[2]:l[0]*l[2]*l[3],n.inChannels]}}),c=nR({inputs:{x:t},backend:r,attrs:{shape:[1,n.inChannels,n.outChannels]}}),h=pR({a:u,b:c,transposeA:!1,transposeB:!1,backend:r,bias:a,activation:o,preluActivationWeights:s,leakyreluAlpha:i});p=nR({inputs:{x:h},backend:r,attrs:{shape:n.outShape}}),m.push(u),m.push(c),m.push(h)}for(const e of m)r.disposeIntermediateTensorInfo(e);return p}function PO({x:e,filter:t,convInfo:n,backend:r,bias:a=null,preluActivationWeights:s=null,leakyreluAlpha:i=0,activation:o=null}){const{filterWidth:l,filterHeight:u,inChannels:c,outWidth:h,outHeight:f,dataFormat:d}=n,p="channelsLast"===d,m=l*u*c,g=f*h,y=[m,g],b=[],v=nR({inputs:{x:e},backend:r,attrs:{shape:e.shape.slice(1)}}),w=nR({inputs:{x:t},backend:r,attrs:{shape:[1,m,re(t.shape)/m]}});b.push(v),b.push(w);const _=new BO(y,v.shape,n),x=r.runWebGLProgram(_,[v],"float32"),k=nR({inputs:{x},backend:r,attrs:{shape:[1,y[0],y[1]]}});b.push(x),b.push(k);const E=null!=a,C=null!=s,S="leakyrelu"===o,N=o?YF(o,!0):null,I=new JF(k.shape,w.shape,[1,g,n.outChannels],!0,!1,E,N,C,S),A=[k,w];if(a&&A.push(a),C&&A.push(s),S){const e=r.makeTensorInfo([],"float32",ra(i,"float32"));A.push(e),b.push(e)}const D=r.runWebGLProgram(I,A,"float32"),T=nR({inputs:{x:D},backend:r,attrs:{shape:p?[1,f,h,n.outChannels]:[1,n.outChannels,f,h]}});b.push(D);for(const e of b)r.disposeIntermediateTensorInfo(e);return T}const LO={kernelName:wt,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n,attrs:r}=e,{x:a,filter:s}=t,{strides:i,pad:o,dataFormat:l,dilations:u,dimRoundingMode:c}=r,h=Il(l),f=vl(a.shape,s.shape,i,u,o,c,!1,h);let d;if(1!==f.filterHeight||1!==f.filterWidth||1!==f.dilationHeight||1!==f.dilationWidth||1!==f.strideHeight||1!==f.strideWidth||"SAME"!==f.padInfo.type&&"VALID"!==f.padInfo.type)if(We().getBool("WEBGL_CONV_IM2COL")&&1===a.shape[0])d=PO({x:a,filter:s,convInfo:f,backend:n});else{const e=new OO(f);d=n.runWebGLProgram(e,[a,s],"float32")}else d=$O({x:a,filter:s,convInfo:f,backend:n});const p=nR({inputs:{x:d},backend:n,attrs:{shape:f.outShape}});return n.disposeIntermediateTensorInfo(d),p}};class zO{constructor(e){this.variableNames=["x","dy"],this.outputShape=e.filterShape;const t=e.strideHeight,n=e.strideWidth,r=e.padInfo.top,a=e.padInfo.left,s="channelsLast"===e.dataFormat;this.userCode=`\n void main() {\n ivec4 coords = getOutputCoords();\n int wR = coords.x;\n int wC = coords.y;\n int d1 = coords.z;\n int d2 = coords.w;\n\n // Convolve x(?, ?, d1) with dy(:, :, d2) to get dw(wR, wC, d1, d2).\n // ? = to be determined. : = across all values in that axis.\n float dotProd = 0.0;\n\n for (int b = 0; b < ${e.batchSize}; b++) {\n for (int yR = 0; yR < ${e.outHeight}; yR++) {\n int xR = wR + yR * ${t} - ${r};\n\n if (xR < 0 || xR >= ${e.inHeight}) {\n continue;\n }\n\n for (int yC = 0; yC < ${e.outWidth}; yC++) {\n int xC = wC + yC * ${n} - ${a};\n\n if (xC < 0 || xC >= ${e.inWidth}) {\n continue;\n }\n\n if (${s}) {\n float dyValue = getDy(b, yR, yC, d2);\n float xValue = getX(b, xR, xC, d1);\n dotProd += (xValue * dyValue);\n } else {\n float dyValue = getDy(b, d2, yR, yC);\n float xValue = getX(b, d1, xR, xC);\n dotProd += (xValue * dyValue);\n }\n\n }\n }\n }\n setOutput(dotProd);\n }\n `}}class WO{constructor(e){this.variableNames=["dy","W"],this.outputShape=e.inShape;const t=e.filterHeight,n=e.filterWidth,r=e.strideHeight,a=e.strideWidth,s="channelsLast"===e.dataFormat,i=t-1-e.padInfo.top,o=n-1-e.padInfo.left,l=s?1:2,u=s?2:3,c=s?3:1;this.userCode=`\n const ivec2 pads = ivec2(${i}, ${o});\n\n void main() {\n ivec4 coords = getOutputCoords();\n int batch = coords[0];\n int d1 = coords[${c}];\n\n ivec2 dyCorner = ivec2(coords[${l}], coords[${u}]) - pads;\n int dyRCorner = dyCorner.x;\n int dyCCorner = dyCorner.y;\n\n // Convolve dy(?, ?, d2) with w(:, :, d1, d2) to compute dx(xR, xC, d1).\n // ? = to be determined. : = across all values in that axis.\n float dotProd = 0.0;\n for (int wR = 0; wR < ${t}; wR++) {\n float dyR = float(dyRCorner + wR) / ${r}.0;\n\n if (dyR < 0.0 || dyR >= ${e.outHeight}.0 || fract(dyR) > 0.0) {\n continue;\n }\n int idyR = int(dyR);\n\n int wRPerm = ${t} - 1 - wR;\n\n for (int wC = 0; wC < ${n}; wC++) {\n float dyC = float(dyCCorner + wC) / ${a}.0;\n\n if (dyC < 0.0 || dyC >= ${e.outWidth}.0 ||\n fract(dyC) > 0.0) {\n continue;\n }\n int idyC = int(dyC);\n\n int wCPerm = ${n} - 1 - wC;\n\n for (int d2 = 0; d2 < ${e.outChannels}; d2++) {\n\n if (${s}) {\n float xValue = getDy(batch, idyR, idyC, d2);\n float wValue = getW(wRPerm, wCPerm, d1, d2);\n dotProd += xValue * wValue;\n } else {\n float xValue = getDy(batch, d2, idyR, idyC);\n float wValue = getW(wRPerm, wCPerm, d1, d2);\n dotProd += xValue * wValue;\n }\n\n }\n }\n }\n setOutput(dotProd);\n }\n `}}class UO{constructor(e){this.variableNames=["x","dy"],this.outputShape=e.filterShape;const t=e.strideDepth,n=e.strideHeight,r=e.strideWidth,a=e.padInfo.front,s=e.padInfo.top,i=e.padInfo.left;this.userCode=`\n void main() {\n ivec5 coords = getOutputCoords();\n int wF = coords.x;\n int wR = coords.y;\n int wC = coords.z;\n int d1 = coords.w;\n int d2 = coords.u;\n\n float dotProd = 0.0;\n\n for (int b = 0; b < ${e.batchSize}; b++) {\n for (int yF = 0; yF < ${e.outDepth}; yF++) {\n int xF = wF + yF * ${t} - ${a};\n\n if (xF < 0 || xF >= ${e.inDepth}) {\n continue;\n }\n\n for (int yR = 0; yR < ${e.outHeight}; yR++) {\n int xR = wR + yR * ${n} - ${s};\n\n if (xR < 0 || xR >= ${e.inHeight}) {\n continue;\n }\n\n for (int yC = 0; yC < ${e.outWidth}; yC++) {\n int xC = wC + yC * ${r} - ${i};\n\n if (xC < 0 || xC >= ${e.inWidth}) {\n continue;\n }\n\n float dyValue = getDy(b, yF, yR, yC, d2);\n float xValue = getX(b, xF, xR, xC, d1);\n dotProd += (xValue * dyValue);\n }\n }\n }\n }\n setOutput(dotProd);\n }\n `}}class VO{constructor(e){this.variableNames=["dy","W"],this.outputShape=e.inShape;const t=e.filterDepth,n=e.filterHeight,r=e.filterWidth,a=e.strideDepth,s=e.strideHeight,i=e.strideWidth,o=t-1-e.padInfo.front,l=n-1-e.padInfo.top,u=r-1-e.padInfo.left;this.userCode=`\n const ivec3 pads = ivec3(${o}, ${l}, ${u});\n\n void main() {\n ivec5 coords = getOutputCoords();\n int batch = coords.x;\n int d1 = coords.u;\n\n\n ivec3 dyCorner = ivec3(coords.y, coords.z, coords.w) - pads;\n int dyFCorner = dyCorner.x;\n int dyRCorner = dyCorner.y;\n int dyCCorner = dyCorner.z;\n\n float dotProd = 0.0;\n for (int wF = 0; wF < ${t}; wF++) {\n float dyF = float(dyFCorner + wF) / ${a}.0;\n\n if (dyF < 0.0 || dyF >= ${e.outDepth}.0 || fract(dyF) > 0.0) {\n continue;\n }\n int idyF = int(dyF);\n\n int wFPerm = ${t} - 1 - wF;\n\n for (int wR = 0; wR < ${n}; wR++) {\n float dyR = float(dyRCorner + wR) / ${s}.0;\n\n if (dyR < 0.0 || dyR >= ${e.outHeight}.0 ||\n fract(dyR) > 0.0) {\n continue;\n }\n int idyR = int(dyR);\n\n int wRPerm = ${n} - 1 - wR;\n\n for (int wC = 0; wC < ${r}; wC++) {\n float dyC = float(dyCCorner + wC) / ${i}.0;\n\n if (dyC < 0.0 || dyC >= ${e.outWidth}.0 ||\n fract(dyC) > 0.0) {\n continue;\n }\n int idyC = int(dyC);\n\n int wCPerm = ${r} - 1 - wC;\n\n for (int d2 = 0; d2 < ${e.outChannels}; d2++) {\n float xValue = getDy(batch, idyF, idyR, idyC, d2);\n float wValue = getW(wFPerm, wRPerm, wCPerm, d1, d2);\n dotProd += xValue * wValue;\n }\n }\n }\n }\n setOutput(dotProd);\n }\n `}}const jO={kernelName:_t,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n,attrs:r}=e,{x:a,dy:s}=t,{strides:i,pad:o,dataFormat:l,dimRoundingMode:u,filterShape:c}=r,h=Il(l),f=vl(a.shape,c,i,1,o,u,!1,h),d=new zO(f);return n.runWebGLProgram(d,[a,s],"float32")}},HO={kernelName:xt,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n,attrs:r}=e,{dy:a,filter:s}=t,{inputShape:i,strides:o,pad:l,dataFormat:u,dimRoundingMode:c}=r,h=Il(u),f=vl(i,s.shape,o,1,l,c,!1,h),d=new WO(f);return n.runWebGLProgram(d,[a,s],"float32")}},GO={kernelName:kt,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n,attrs:r}=e,{x:a,filter:s}=t,{strides:i,pad:o,dilations:l}=r,u=wl(a.shape,s.shape,i,l,o),c=new MO(u);return n.runWebGLProgram(c,[a,s],"float32")}},XO={kernelName:Et,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n,attrs:r}=e,{x:a,dy:s}=t,{strides:i,pad:o,filterShape:l}=r,u=wl(a.shape,l,i,1,o),c=new UO(u);return n.runWebGLProgram(c,[a,s],"float32")}},KO={kernelName:Ct,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n,attrs:r}=e,{dy:a,filter:s}=t,{pad:i,strides:o,inputShape:l}=r,u=wl(l,s.shape,o,1,i),c=new VO(u);return n.runWebGLProgram(c,[a,s],"float32")}},qO=KF({opSnippet:"if (isnan(x)) return x;\n return cos(x);\n"}),YO={kernelName:St,backendName:"webgl",kernelFunc:qO},JO=KF({opSnippet:"\n float e2x = exp(-x);\n return (e2x + 1.0 / e2x) / 2.0;\n"}),ZO={kernelName:Nt,backendName:"webgl",kernelFunc:JO};class QO{constructor(e,t,n,r,a){this.variableNames=["Image","Boxes","BoxInd"],this.outputShape=[];const[s,i,o,l]=e,[u]=t,[c,h]=n;this.outputShape=[u,c,h,l];const f="bilinear"===r?1:0,[d,p]=[i-1+".0",o-1+".0"],[m,g,y]=c>1?[""+(i-1)/(c-1),"(y2-y1) * height_ratio",`y1*${d} + float(y)*(height_scale)`]:["0.0","0.0",`0.5 * (y1+y2) * ${d}`],[b,v,w]=h>1?[""+(o-1)/(h-1),"(x2-x1) * width_ratio",`x1*${p} + float(x)*(width_scale)`]:["0.0","0.0",`0.5 * (x1+x2) * ${p}`];this.userCode=`\n const float height_ratio = float(${m});\n const float width_ratio = float(${b});\n void main() {\n ivec4 coords = getOutputCoords();\n int b = coords[0];\n int y = coords[1];\n int x = coords[2];\n int d = coords[3];\n\n // get box vals\n float y1 = getBoxes(b,0);\n float x1 = getBoxes(b,1);\n float y2 = getBoxes(b,2);\n float x2 = getBoxes(b,3);\n\n // get image in batch index\n int bInd = round(getBoxInd(b));\n if(bInd < 0 || bInd >= ${s}) {\n return;\n }\n\n float height_scale = ${g};\n float width_scale = ${v};\n\n float in_y = ${y};\n if( in_y < 0.0 || in_y > ${d} ) {\n setOutput(float(${a}));\n return;\n }\n float in_x = ${w};\n if( in_x < 0.0 || in_x > ${p} ) {\n setOutput(float(${a}));\n return;\n }\n\n vec2 sourceFracIndexCR = vec2(in_x,in_y);\n if(${f} == 1) {\n // Compute the four integer indices.\n ivec2 sourceFloorCR = ivec2(sourceFracIndexCR);\n ivec2 sourceCeilCR = ivec2(ceil(sourceFracIndexCR));\n\n float topLeft = getImage(b, sourceFloorCR.y, sourceFloorCR.x, d);\n float bottomLeft = getImage(b, sourceCeilCR.y, sourceFloorCR.x, d);\n float topRight = getImage(b, sourceFloorCR.y, sourceCeilCR.x, d);\n float bottomRight = getImage(b, sourceCeilCR.y, sourceCeilCR.x, d);\n\n vec2 fracCR = sourceFracIndexCR - vec2(sourceFloorCR);\n\n float top = topLeft + (topRight - topLeft) * fracCR.x;\n float bottom = bottomLeft + (bottomRight - bottomLeft) * fracCR.x;\n float newValue = top + (bottom - top) * fracCR.y;\n setOutput(newValue);\n } else {\n // Compute the coordinators of nearest neighbor point.\n ivec2 sourceNearestCR = ivec2(floor(\n sourceFracIndexCR + vec2(0.5,0.5)));\n float newValue = getImage(b, sourceNearestCR.y, sourceNearestCR.x, d);\n setOutput(newValue);\n }\n }\n `}}const eM={kernelName:At,backendName:"webgl",kernelFunc:e=>{const{inputs:t,backend:n,attrs:r}=e,{image:a,boxes:s,boxInd:i}=t,{cropSize:o,method:l,extrapolationValue:u}=r,c=new QO(a.shape,s.shape,o,l,u);return n.runWebGLProgram(c,[a,s,i],"float32")}};class tM{constructor(e,t,n){this.variableNames=["x"],this.outputShape=e;const r=e.length,a=t?"0.0":`getX(${nM(r,"coords")})`,s=e[e.length-1];let i="",o="";t?(i=n?"end != "+(s-1):"end != 0",o=n?"end + 1":"end - 1"):(i=n?`end + pow2 < ${s}`:"end >= pow2",o=n?"end + pow2":"end - pow2"),this.userCode=`\n uniform float index;\n void main() {\n ${LT(r)} coords = getOutputCoords();\n int end = ${rM(r,"coords")};\n float val = ${a};\n int pow2 = int(pow(2.0, index));\n if (${i}) {\n int idx = ${o};\n ${rM(r,"coords")} = idx;\n val += getX(${nM(r,"coords")});\n }\n setOutput(val);\n }\n `}getCustomSetupFunc(e){return(t,n)=>{null==this.index&&(this.index=t.getUniformLocation(n,"index")),t.gl.uniform1f(this.index,e)}}}function nM(e,t){if(1===e)return`${t}`;if(2===e)return`${t}.x, ${t}.y`;if(3===e)return`${t}.x, ${t}.y, ${t}.z`;if(4===e)return`${t}.x, ${t}.y, ${t}.z, ${t}.w`;throw Error(`Cumulative sum for rank ${e} is not yet supported`)}function rM(e,t){if(1===e)return`${t}`;if(2===e)return`${t}.y`;if(3===e)return`${t}.z`;if(4===e)return`${t}.w`;throw Error(`Cumulative sum for rank ${e} is not yet supported`)}const aM={kernelName:It,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{axis:s,exclusive:i,reverse:o}=r,l=a.shape.length,u=qu([s],l);let c=a;null!=u&&(c=fR({inputs:{x:a},backend:n,attrs:{perm:u}}));const h=Ju(1,l)[0];if(h!==l-1)throw new Error(`WebGL cumsum shader expects an inner-most axis=${a.shape.length-1} but got axis=${s}`);const f=c.shape[h];let d=PF({inputs:{x:c},backend:n});for(let e=0;e<=Math.ceil(Math.log2(f))-1;e++){const t=new tM(c.shape,!1,o),r=t.getCustomSetupFunc(e),a=d;d=n.runWebGLProgram(t,[d],d.dtype,r),n.disposeIntermediateTensorInfo(a)}if(i){const e=new tM(c.shape,i,o),t=d;d=n.runWebGLProgram(e,[d],d.dtype),n.disposeIntermediateTensorInfo(t)}if(null!=u){const e=fR({inputs:{x:d},backend:n,attrs:{perm:Yu(u)}});return n.disposeIntermediateTensorInfo(d),n.disposeIntermediateTensorInfo(c),e}return d}},sM={kernelName:Dt,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n,attrs:r}=e,{x:a,weights:s}=t,{size:i,binaryOutput:o}=r;if(1===a.shape.length){const e=n.readSync(a.dataId),t=n.readSync(s.dataId),r=jT(e,t,s.dtype,s.shape,i);return n.makeTensorInfo([i],s.dtype,r)}if(2===a.shape.length){const e=n.bufferSync(a),t=n.bufferSync(s),r=HT(e,t,i,o);return n.makeTensorInfo(r.shape,s.dtype,r.values)}throw new Error(`Error in denseBincount: input must be at most rank 2, but got rank${a.shape.length}.`)}};class iM{constructor(e,t,n){this.variableNames=["x"],this.outputShape=[],this.outputShape=e,this.blockSize=t,this.dataFormat=n,this.userCode=`\n void main() {\n ivec4 coords = getOutputCoords();\n int b = coords[0];\n int h = ${this.getHeightCoordString()};\n int w = ${this.getWidthCoordString()};\n int d = ${this.getDepthCoordString()};\n\n int in_h = h / ${t};\n int offset_h = imod(h, ${t});\n int in_w = w / ${t};\n int offset_w = imod(w, ${t});\n int offset_d = (offset_h * ${t} + offset_w) *\n ${this.getOutputDepthSize()};\n int in_d = d + offset_d;\n\n float result = ${this.getInputSamplingString()};\n setOutput(result);\n }\n `}getHeightCoordString(){return"NHWC"===this.dataFormat?"coords[1]":"coords[2]"}getWidthCoordString(){return"NHWC"===this.dataFormat?"coords[2]":"coords[3]"}getDepthCoordString(){return"NHWC"===this.dataFormat?"coords[3]":"coords[1]"}getOutputDepthSize(){return"NHWC"===this.dataFormat?this.outputShape[3]:this.outputShape[1]}getInputSamplingString(){return"NHWC"===this.dataFormat?"getX(b, in_h, in_w, in_d)":"getX(b, in_d, in_h, in_w)"}}const oM={kernelName:Tt,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{blockSize:s,dataFormat:i}=r;Q(s>1,(()=>`blockSize should be > 1 for depthToSpace, but was: ${s}`));const o=a.shape[0],l="NHWC"===i?a.shape[1]:a.shape[2],u="NHWC"===i?a.shape[2]:a.shape[3],c="NHWC"===i?a.shape[3]:a.shape[1],h=l*s,f=u*s,d=c/(s*s),p=new iM("NHWC"===i?[o,h,f,d]:[o,d,h,f],s,i);return n.runWebGLProgram(p,[a],a.dtype)}};class lM{constructor(e,t=!1,n=null,r=!1,a=!1){this.variableNames=["x","W"],this.outputShape=e.outShape;const s=e.inHeight,i=e.inWidth,o=e.padInfo.top,l=e.padInfo.left,u=e.strideHeight,c=e.strideWidth,h=e.dilationHeight,f=e.dilationWidth,d=e.filterHeight,p=e.filterWidth,m=e.outChannels/e.inChannels;let g="",y="";n&&(g=r?`float activation(float a) {\n float b = getPreluActivationWeightsAtOutCoords();\n ${n}\n }`:a?`float activation(float a) {\n float b = getLeakyreluAlphaAtOutCoords();\n ${n}\n }`:`\n float activation(float x) {\n ${n}\n }\n `,y="result = activation(result);");const b=t?"result += getBiasAtOutCoords();":"";t&&this.variableNames.push("bias"),r&&this.variableNames.push("preluActivationWeights"),a&&this.variableNames.push("leakyreluAlpha"),this.userCode=`\n ${g}\n\n const ivec2 strides = ivec2(${u}, ${c});\n const ivec2 pads = ivec2(${o}, ${l});\n\n void main() {\n ivec4 coords = getOutputCoords();\n int batch = coords.x;\n ivec2 xRCCorner = coords.yz * strides - pads;\n int d2 = coords.w;\n int d1 = d2 / ${m};\n int q = d2 - d1 * ${m};\n\n int xRCorner = xRCCorner.x;\n int xCCorner = xRCCorner.y;\n\n // Convolve x(?, ?, d1) with w(:, :, d1, q) to get y(yR, yC, d2).\n // ? = to be determined. : = across all values in that axis.\n float dotProd = 0.0;\n // TO DO(dsmilkov): Flatten the two for loops and vec4 the operations.\n for (int wR = 0; wR < ${d}; wR++) {\n int xR = xRCorner + wR * ${h};\n\n if (xR < 0 || xR >= ${s}) {\n continue;\n }\n\n for (int wC = 0; wC < ${p}; wC++) {\n int xC = xCCorner + wC * ${f};\n\n if (xC < 0 || xC >= ${i}) {\n continue;\n }\n\n float xVal = getX(batch, xR, xC, d1);\n float wVal = getW(wR, wC, d1, q);\n dotProd += xVal * wVal;\n }\n }\n\n float result = dotProd;\n ${b}\n ${y}\n setOutput(result);\n }\n `}}class uM{constructor(e,t=!1,n=null,r=!1,a=!1){this.variableNames=["x","W"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e.outShape;const s=e.outChannels/e.inChannels,i=e.inHeight,o=e.inWidth,l=e.padInfo.top,u=e.padInfo.left,c=e.strideHeight,h=e.strideWidth,f=e.dilationHeight,d=e.dilationWidth,p=e.filterHeight,m=e.filterWidth,g=m;let y="\n int xR; int xC; int xCOffset;\n vec4 wTexel; vec4 previous; vec4 final;";for(let e=0;e=0 && xR < ${i}) {\n `;for(let t=0;t<(g+1)/2;t++){const n=2*t,r=n*d;if(y+=`\n xC = xCCorner + ${r};\n `,1===h){if(n= 0 && xCOffset < ${o} && xTexelC${r}Ready == 0) {\n xTexelC${r} = getX(batch, xR, xCOffset, d1);\n\n // Need to manually clear unused channels in case\n // we're reading from recycled texture.\n if (xCOffset + 1 >= ${o}) {\n xTexelC${r}.zw = vec2(0.0);\n }\n xTexelC${r}Ready = 1;\n }\n `,y+=1===d&&r>0?`\n xC${n} = vec4(xTexelC${r-2}.zw, xTexelC${r}.xy);\n `:`\n xCOffset = xC + 1 - 2;\n\n if (xCOffset >= 0 && xCOffset < ${o}) {\n previous = getX(batch, xR, xCOffset, d1);\n\n // Need to manually clear unused channels in case\n // we're reading from recycled texture.\n if (xCOffset + 1 >= ${o}) {\n previous.zw = vec2(0.0);\n }\n\n xC${n} = vec4(previous.zw, xTexelC${r}.xy);\n } else {\n xC${n} = vec4(0.0, 0.0, xTexelC${r}.xy);\n }\n `):y+=`\n if (xC >= 0 && xC < ${o} && xTexelC${r}Ready == 0) {\n xTexelC${r} = getX(batch, xR, xC, d1);\n if (xC + 1 >= ${o}) {\n xTexelC${r}.zw = vec2(0.0);\n }\n xTexelC${r}Ready = 1;\n }\n\n xC${n} = xTexelC${r};\n `,r+1= 0 && xCOffset < ${o} && xTexelC${r+2}Ready == 0) {\n xTexelC${r+2} = getX(batch, xR, xCOffset, d1);\n\n // Need to manually clear unused channels in case\n // we're reading from recycled texture.\n if (xCOffset + 1 >= ${o}) {\n xTexelC${r+2}.zw = vec2(0.0);\n }\n xTexelC${r+2}Ready = 1;\n }\n `,d>1&&(y+=`\n xCOffset -= 2;\n if (xCOffset >= 0 && xCOffset < ${o} && xTexelC${r}Ready == 0) {\n xTexelC${r} = getX(batch, xR, xCOffset, d1);\n xTexelC${r}Ready = 1;\n }\n `),y+=`\n xC${n+1} = vec4(xTexelC${r}.zw, xTexelC${r+2}.xy);\n `):y+=1===e?`\n xC${n+1} = xTexelC${r};\n `:`\n xCOffset = xC + ${e};\n\n if (xCOffset >= 0 && xCOffset < ${o} && xTexelC${r+2}Ready == 0) {\n xTexelC${r+2} = getX(batch, xR, xCOffset, d1);\n if (xCOffset + 1 >= ${o}) {\n xTexelC${r+2}.zw = vec2(0.0);\n }\n xTexelC${r+2}Ready = 1;\n }\n\n xC${n+1} = xTexelC${r+2};\n `}}else r= 0 && xCOffset < ${o} && xTexelC${r}Ready == 0) {\n xTexelC${r} = getX(batch, xR, xCOffset, d1);\n // Need to manually clear unused channels in case\n // we're reading from recycled texture.\n if (xCOffset + 1 >= ${o}) {\n xTexelC${r}.zw = vec2(0.0);\n }\n xTexelC${r}Ready = 1;\n }\n\n if(xC + 1 >= 0 && xC + 1 < ${o} && xTexelC${r+2}Ready == 0) {\n xTexelC${r+2} = getX(batch, xR, xC + 1, d1);\n // Need to manually clear unused channels in case\n // we're reading from recycled texture.\n if (xC + 2 >= ${o}) {\n xTexelC${r+2}.zw = vec2(0.0);\n }\n xTexelC${r+2}Ready = 1;\n }\n\n xC${n} = vec4(xTexelC${r}.zw, xTexelC${r+2}.zw);\n `,r+1= 0 && xCOffset < ${o}) {\n final = getX(batch, xR, xCOffset, d1);\n }\n xC${n+1} = vec4(xTexelC${r+2}.xy, final.xy);\n `)):(y+=`\n if(xC >= 0 && xC < ${o} && xTexelC${r}Ready == 0) {\n xTexelC${r} = getX(batch, xR, xC, d1);\n if (xC + 1 >= ${o}) {\n xTexelC${r}.zw = vec2(0.0);\n }\n xTexelC${r}Ready = 1;\n }\n\n xCOffset = xC + ${h};\n if(xCOffset >= 0 && xCOffset < ${o} && xTexelC${r+2}Ready == 0) {\n xTexelC${r+2} = getX(batch, xR, xCOffset, d1);\n if (xCOffset + 1 >= ${o}) {\n xTexelC${r+2}.zw = vec2(0.);\n }\n xTexelC${r+2}Ready = 1;\n }\n\n xC${n} = vec4(\n xTexelC${r}.xy, xTexelC${r+2}.xy);\n `,r+1`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${i} and dilations '${c}'`));const h=vl(a.shape,s.shape,i,c,o,u,!0);let f;return f=We().getBool("WEBGL_PACK_DEPTHWISECONV")&&h.strideWidth<=2&&h.outChannels/h.inChannels==1?new uM(h):new lM(h),n.runWebGLProgram(f,[a,s],"float32")}};class hM{constructor(e){this.variableNames=["x","dy"],this.outputShape=e.filterShape;const t=e.strideHeight,n=e.strideWidth,r=e.padInfo.top,a=e.padInfo.left,s=e.outChannels/e.inChannels;this.userCode=`\n void main() {\n ivec4 coords = getOutputCoords();\n int wR = coords.x;\n int wC = coords.y;\n int d1 = coords.z;\n int dm = coords.w;\n int d2 = d1 * ${s} + dm;\n\n float dotProd = 0.0;\n\n // TO DO: Vec4 over the batch size\n for (int b = 0; b < ${e.batchSize}; b++) {\n for (int yR = 0; yR < ${e.outHeight}; yR++) {\n int xR = wR + yR * ${t} - ${r};\n\n if (xR < 0 || xR >= ${e.inHeight}) {\n continue;\n }\n\n for (int yC = 0; yC < ${e.outWidth}; yC++) {\n int xC = wC + yC * ${n} - ${a};\n\n if (xC < 0 || xC >= ${e.inWidth}) {\n continue;\n }\n\n float dyValue = getDy(b, yR, yC, d2);\n float xValue = getX(b, xR, xC, d1);\n dotProd += (xValue * dyValue);\n }\n }\n }\n setOutput(dotProd);\n }\n `}}class fM{constructor(e){this.variableNames=["dy","W"],this.outputShape=e.inShape;const t=e.filterHeight,n=e.filterWidth,r=e.strideHeight,a=e.strideWidth,s=t-1-e.padInfo.top,i=n-1-e.padInfo.left,o=e.outChannels/e.inChannels;this.userCode=`\n const ivec2 pads = ivec2(${s}, ${i});\n\n void main() {\n ivec4 coords = getOutputCoords();\n int batch = coords[0];\n int d1 = coords[3];\n ivec2 dyCorner = coords.yz - pads;\n int dyRCorner = dyCorner.x;\n int dyCCorner = dyCorner.y;\n\n float dotProd = 0.0;\n\n for (int wR = 0; wR < ${t}; wR++) {\n float dyR = float(dyRCorner + wR) / ${r}.0;\n\n if (dyR < 0.0 || dyR >= ${e.outHeight}.0 || fract(dyR) > 0.0) {\n continue;\n }\n int idyR = int(dyR);\n\n int wRPerm = ${t} - 1 - wR;\n\n for (int wC = 0; wC < ${n}; wC++) {\n float dyC = float(dyCCorner + wC) / ${a}.0;\n\n if (dyC < 0.0 || dyC >= ${e.outWidth}.0 ||\n fract(dyC) > 0.0) {\n continue;\n }\n int idyC = int(dyC);\n\n int wCPerm = ${n} - 1 - wC;\n\n // TO DO: Vec4 over the channelMul\n for (int dm = 0; dm < ${o}; dm++) {\n int d2 = d1 * ${o} + dm;\n float xValue = getDy(batch, idyR, idyC, d2);\n float wValue = getW(wRPerm, wCPerm, d1, dm);\n dotProd += xValue * wValue;\n }\n }\n }\n setOutput(dotProd);\n }\n `}}const dM={kernelName:Rt,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n,attrs:r}=e,{x:a,dy:s}=t,{strides:i,dilations:o,pad:l,dimRoundingMode:u,filterShape:c}=r,h=vl(a.shape,c,i,o,l,u,!0),f=new hM(h);return n.runWebGLProgram(f,[a,s],"float32")}},pM={kernelName:Ot,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n,attrs:r}=e,{dy:a,filter:s}=t,{strides:i,dilations:o,pad:l,dimRoundingMode:u,inputShape:c}=r,h=vl(c,s.shape,i,o,l,u,!0),f=new fM(h);return n.runWebGLProgram(f,[a,s],"float32")}};class mM{constructor(e){this.variableNames=["X"],this.outputShape=[e,e],this.userCode="\n void main() {\n ivec2 coords = getOutputCoords();\n float val = coords[0] == coords[1] ? getX(coords[0]) : 0.0;\n setOutput(val);\n }\n "}}const gM={kernelName:Mt,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n}=e,{x:r}=t,a=[...r.shape,...r.shape],s=re(r.shape),i=nR({inputs:{x:r},backend:n,attrs:{shape:[s]}}),o=new mM(s),l=n.runWebGLProgram(o,[i],i.dtype),u=nR({inputs:{x:l},backend:n,attrs:{shape:a}});return n.disposeIntermediateTensorInfo(i),n.disposeIntermediateTensorInfo(l),u}};class yM{constructor(e){this.variableNames=["x","W"],this.outputShape=e.outShape;const{inHeight:t,inWidth:n,padInfo:r,strideHeight:a,strideWidth:s,filterHeight:i,filterWidth:o,dilationHeight:l,dilationWidth:u}=e,{top:c,left:h}=r;this.userCode=`\n const ivec2 strides = ivec2(${a}, ${s});\n const ivec2 pads = ivec2(${c}, ${h});\n const float neg_infinity = -3.4e38;\n\n void main() {\n ivec4 coords = getOutputCoords();\n int batch = coords.x;\n int d1 = coords.w;\n ivec2 outTopLeftCorner =\n coords.yz * strides - pads;\n int hBeg = outTopLeftCorner.x;\n int wBeg = outTopLeftCorner.y;\n\n float curVal = neg_infinity;\n for (int h = 0; h < ${i}; h++) {\n int hIn = hBeg + h * ${l};\n\n if (hIn >= 0 && hIn < ${t}) {\n for (int w = 0; w < ${o}; w++) {\n int wIn = wBeg + w * ${u};\n\n if (wIn >= 0 && wIn < ${n}) {\n float xVal = getX(batch, hIn, wIn, d1);\n float wVal = getW(h, w, d1);\n\n float val = xVal + wVal;\n if (val > curVal) {\n curVal = val;\n }\n }\n }\n }\n }\n\n float result = curVal;\n setOutput(result);\n }\n `}}const bM={kernelName:Bt,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n,attrs:r}=e,{x:a,filter:s}=t,{strides:i,pad:o,dilations:l}=r,u=gl(a.shape,s.shape,i,o,"NHWC",l);let c;const h=new yM(u);c=n.runWebGLProgram(h,[a,s],"float32");const f=nR({inputs:{x:c},backend:n,attrs:{shape:u.outShape}});return n.disposeIntermediateTensorInfo(c),f}},vM={kernelName:zt,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n,attrs:r}=e,{equation:a}=r,s=t,{allDims:i,summedDims:o,idDims:l}=kd(a,s.length);Cd(i.length,l,s);const{path:u,steps:c}=Sd(o,l),h=c.length;let f=null,d=i.length;const p=[];for(let e=0;e=0&&(f=cR({inputs:{x:f},backend:n,attrs:{axis:u[e]-(i.length-d),keepDims:!1}}),p.push(f)),d--)}for(const e of p)e!==f&&n.disposeIntermediateTensorInfo(e);return f}},wM=KF({opSnippet:"return (x >= 0.0) ? x : (exp(x) - 1.0);",packedOpSnippet:"\n vec4 result;\n\n result.r = (x.r >= 0.0) ? x.r : (exp(x.r) - 1.0);\n result.g = (x.g >= 0.0) ? x.g : (exp(x.g) - 1.0);\n result.b = (x.b >= 0.0) ? x.b : (exp(x.b) - 1.0);\n result.a = (x.a >= 0.0) ? x.a : (exp(x.a) - 1.0);\n\n return result;\n"}),_M={kernelName:Wt,backendName:"webgl",kernelFunc:wM},xM={kernelName:Ut,backendName:"webgl",kernelFunc:e=>{const{inputs:t,backend:n}=e,{dy:r,y:a}=t,s=We().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new $F("\n vec4 bGTEZero = vec4(greaterThanEqual(b, vec4(0.)));\n return (bGTEZero * a) + ((vec4(1.0) - bGTEZero) * (a * (b + vec4(1.0))));\n",r.shape,a.shape):new BF("return (b >= 1.0) ? a : a * (b + 1.0);",r.shape,a.shape);return n.runWebGLProgram(s,[r,a],r.dtype)}},kM=qF({opSnippet:"return float(a == b);",packedOpSnippet:"\n return vec4(equal(a, b));\n",dtype:"bool"}),EM={kernelName:jt,backendName:"webgl",kernelFunc:kM},CM=KF({opSnippet:`\n // Error function is calculated approximately with elementary function.\n // See "Handbook of Mathematical Functions with Formulas,\n // Graphs, and Mathematical Tables", Abramowitz and Stegun.\n float p = ${id};\n float a1 = ${od};\n float a2 = ${ld};\n float a3 = ${ud};\n float a4 = ${cd};\n float a5 = ${hd};\n\n float sign = sign(x);\n x = abs(x);\n float t = 1.0 / (1.0 + p * x);\n return sign * (1.0 - (((((a5*t + a4)*t) + a3)*t + a2)*t + a1)*t*exp(-x*x));\n`}),SM={kernelName:Vt,backendName:"webgl",kernelFunc:CM},NM="return exp(x);",IM=KF({opSnippet:NM,packedOpSnippet:NM,cpuKernelImpl:KT}),AM={kernelName:Ht,backendName:"webgl",kernelFunc:IM};function DM(e){const{inputs:t,attrs:n,backend:r}=e,{dim:a}=n,{input:s}=t,i=s.shape.length,o=s.shape.slice();let l=a;return a<0&&(Q(-(i+1)<=a,(()=>`Axis must be in the interval [${-(i+1)}, ${i}]`)),l=i+a+1),o.splice(l,0,1),nR({inputs:{x:s},backend:r,attrs:{shape:o}})}const TM={kernelName:Gt,backendName:"webgl",kernelFunc:DM},FM="return exp(x) - 1.0;",RM=KF({opSnippet:FM,packedOpSnippet:FM,cpuKernelImpl:qT}),OM={kernelName:Xt,backendName:"webgl",kernelFunc:RM};class MM{constructor(e,t,n){this.variableNames=["real","imag"];const r=t[1];this.outputShape=t;const a=n?`2.0 * ${Math.PI}`:`-2.0 * ${Math.PI}`,s=n?`${r}.0`:"1.0";let i;if("real"===e)i="return real * expR - imag * expI;";else{if("imag"!==e)throw new Error(`FFT component must be either "real" or "imag", got ${e}.`);i="return real * expI + imag * expR;"}this.userCode=`\n const float exponentMultiplier = ${a};\n\n float unaryOpComplex(float real, float expR, float imag, float expI) {\n ${i}\n }\n\n float mulMatDFT(int batch, int index) {\n float indexRatio = float(index) / float(${r});\n float exponentMultiplierTimesIndexRatio =\n exponentMultiplier * indexRatio;\n\n float result = 0.0;\n\n for (int i = 0; i < ${r}; i++) {\n // x = (-2|2 * PI / N) * index * i;\n float x = exponentMultiplierTimesIndexRatio * float(i);\n float expR = cos(x);\n float expI = sin(x);\n float real = getReal(batch, i);\n float imag = getImag(batch, i);\n\n result +=\n unaryOpComplex(real, expR, imag, expI) / ${s};\n }\n\n return result;\n }\n\n void main() {\n ivec2 coords = getOutputCoords();\n setOutput(mulMatDFT(coords[0], coords[1]));\n }\n `}}function BM(e,t,n){const r=n.texData.get(e.dataId),a=re(e.shape),s=e.shape[e.shape.length-1],i=nR({inputs:{x:e},backend:n,attrs:{shape:[a/s,s]}}),o=i.shape,l=new MM("real",o,t),u=new MM("imag",o,t),c=[{dataId:r.complexTensorInfos.real.dataId,dtype:r.complexTensorInfos.real.dtype,shape:o},{dataId:r.complexTensorInfos.imag.dataId,dtype:r.complexTensorInfos.imag.dtype,shape:o}],h=n.runWebGLProgram(l,c,"float32"),f=n.runWebGLProgram(u,c,"float32"),d=zF({inputs:{real:h,imag:f},backend:n});n.disposeIntermediateTensorInfo(h),n.disposeIntermediateTensorInfo(f);const p=nR({inputs:{x:d},backend:n,attrs:{shape:e.shape}});return n.disposeIntermediateTensorInfo(i),n.disposeIntermediateTensorInfo(d),p}const $M={kernelName:Kt,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n}=e,{input:r}=t;return BM(r,!1,n)}};class PM{constructor(e,t){this.outputShape=[],this.variableNames=["x"],this.outputShape=e,this.userCode="\n uniform float value;\n void main() {\n // Input can be obtained from uniform value.\n setOutput(value);\n }\n "}getCustomSetupFunc(e){return(t,n)=>{null==this.valueLoc&&(this.valueLoc=t.getUniformLocationNoThrow(n,"value")),t.gl.uniform1f(this.valueLoc,e)}}}function LM(e){const{backend:t,attrs:n}=e,{shape:r,value:a}=n;let{dtype:s}=n;if(s=s||Se(a),"string"===s){const e=ge(s,re(r));return e.fill(a),t.makeTensorInfo(r,s,e)}{const e=new PM(r,a),n=e.getCustomSetupFunc(a);return t.runWebGLProgram(e,[],s,n)}}const zM={kernelName:qt,backendName:"webgl",kernelFunc:LM};class WM{constructor(e){this.variableNames=["Image"],this.outputShape=[];const t=e[2];this.outputShape=e,this.userCode=`\n void main() {\n ivec4 coords = getOutputCoords();\n int x = coords[2];\n\n int coordX = ${t} - x;\n float outputValue;\n if(coordX >= 0 && coordX < ${t}) {\n outputValue = getImage(coords[0], coords[1], coordX, coords[3]);\n } else {\n outputValue = getImage(coords[0], coords[1], coords[2], coords[3]);\n }\n setOutput(outputValue);\n }\n `}}const UM={kernelName:Yt,backendName:"webgl",kernelFunc:({inputs:e,backend:t})=>{const{image:n}=e,r=t,a=new WM(n.shape);return r.runWebGLProgram(a,[n],n.dtype)}},VM="return floor(x);",jM=KF({opSnippet:VM,packedOpSnippet:VM,cpuKernelImpl:YT}),HM={kernelName:Jt,backendName:"webgl",kernelFunc:jM},GM=qF({opSnippet:"\n float s = sign(a) * sign(b);\n int ia = round(a);\n int ib = round(b);\n if (ib != 0) {\n // Windows (D3D) wants guaranteed non-zero int division at compile-time.\n return float(idiv(ia, ib, s));\n } else {\n return NAN;\n }\n",packedOpSnippet:"\n ivec4 ia = round(a);\n ivec4 ib = round(b);\n bvec4 cond = notEqual(ib, ivec4(0));\n ivec4 result = ivec4(0);\n vec4 s = sign(a) * sign(b);\n\n // Windows (D3D) wants guaranteed non-zero int division at compile-time.\n if (cond[0]) {\n result[0] = idiv(ia[0], ib[0], s[0]);\n }\n if (cond[1]) {\n result[1] = idiv(ia[1], ib[1], s[1]);\n }\n if (cond[2]) {\n result[2] = idiv(ia[2], ib[2], s[2]);\n }\n if (cond[3]) {\n result[3] = idiv(ia[3], ib[3], s[3]);\n }\n return vec4(result);\n",dtype:"int32"}),XM={kernelName:Zt,backendName:"webgl",kernelFunc:GM};class KM{constructor(e){this.variableNames=["A"];const t=fT(),[n,r]=e;this.outputShape=e,this.userCode=`\n void main() {\n ivec3 coords = getOutputCoords();\n int texR = coords[0];\n int texC = coords[1];\n int depth = coords[2];\n vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${r}.0, ${n}.0);\n\n vec4 values = ${t.texture2D}(A, uv);\n float value;\n if (depth == 0) {\n value = values.r;\n } else if (depth == 1) {\n value = values.g;\n } else if (depth == 2) {\n value = values.b;\n } else if (depth == 3) {\n value = values.a;\n }\n\n setOutput(floor(value * 255.0 + 0.5));\n }\n `}}class qM{constructor(e){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0;const t=fT(),[n,r]=e;this.outputShape=e,this.userCode=`\n void main() {\n ivec3 coords = getOutputCoords();\n int texR = coords[0];\n int texC = coords[1];\n int depth = coords[2];\n\n vec4 result = vec4(0.);\n\n for(int row=0; row<=1; row++) {\n for(int col=0; col<=1; col++) {\n texC = coords[1] + row;\n depth = coords[2] + col;\n\n vec2 uv = (vec2(texC, texR) + halfCR) /\n vec2(${r}.0, ${n}.0);\n vec4 values = ${t.texture2D}(A, uv);\n float value;\n if (depth == 0) {\n value = values.r;\n } else if (depth == 1) {\n value = values.g;\n } else if (depth == 2) {\n value = values.b;\n } else if (depth == 3) {\n value = values.a;\n }\n\n result[row * 2 + col] = floor(value * 255.0 + 0.5);\n }\n }\n\n ${t.output} = result;\n }\n `}}const YM={kernelName:Wr,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n,attrs:r}=e;let{pixels:a}=t;const{numChannels:s}=r,i="undefined"!=typeof HTMLVideoElement&&a instanceof HTMLVideoElement,o="undefined"!=typeof HTMLImageElement&&a instanceof HTMLImageElement,[l,u]=i?[a.videoWidth,a.videoHeight]:[a.width,a.height],c=[u,l],h=[u,l,s];(o||i)&&(null==JM&&(JM=document.createElement("canvas").getContext("2d")),JM.canvas.width=l,JM.canvas.height=u,JM.drawImage(a,0,0,l,u),a=JM.canvas);const f=n.makeTensorInfo(c,"int32");n.texData.get(f.dataId).usage=$D.PIXELS,n.gpgpu.uploadPixelDataToTexture(n.getTexture(f.dataId),a);const d=We().getBool("WEBGL_PACK")?new qM(h):new KM(h),p=n.runWebGLProgram(d,[f],"int32");return n.disposeData(f.dataId),p}};let JM;const ZM={kernelName:jr,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n,attrs:r}=e,{x:a,filter:s,bias:i,preluActivationWeights:o}=t,{strides:l,pad:u,dataFormat:c,dilations:h,dimRoundingMode:f,activation:d,leakyreluAlpha:p}=r,m=Il(c),g=vl(a.shape,s.shape,l,h,u,f,!1,m);let y;const b=[];if(1!==g.filterHeight||1!==g.filterWidth||1!==g.dilationHeight||1!==g.dilationWidth||1!==g.strideHeight||1!==g.strideWidth||"SAME"!==g.padInfo.type&&"VALID"!==g.padInfo.type)if(We().getBool("WEBGL_CONV_IM2COL")&&1===a.shape[0])y=PO({x:a,filter:s,convInfo:g,backend:n,bias:i,activation:d,preluActivationWeights:o,leakyreluAlpha:p});else{const e=null!=i,t=null!=o,r="leakyrelu"===d,l=d?YF(d,!1):null,u=new OO(g,e,l,t,r),c=[a,s];if(i&&c.push(i),o&&c.push(o),r){const e=n.makeTensorInfo([],"float32",ra(p,"float32"));c.push(e),b.push(e)}y=n.runWebGLProgram(u,c,"float32")}else y=$O({x:a,filter:s,convInfo:g,backend:n,bias:i,activation:d,preluActivationWeights:o,leakyreluAlpha:p});const v=nR({inputs:{x:y},backend:n,attrs:{shape:g.outShape}});return b.push(y),b.forEach((e=>n.disposeIntermediateTensorInfo(e))),v}},QM={kernelName:Hr,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n,attrs:r}=e,{x:a,filter:s,bias:i,preluActivationWeights:o}=t,{strides:l,pad:u,dilations:c,dimRoundingMode:h,activation:f,leakyreluAlpha:d}=r,p=[];let m=c;null==m&&(m=[1,1]),Q(Nl(l,m),(()=>`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${l} and dilations '${m}'`));const g=vl(a.shape,s.shape,l,m,u,h,!0),y=We().getBool("WEBGL_PACK_DEPTHWISECONV")&&g.strideWidth<=2&&g.outChannels/g.inChannels==1,b=f?YF(f,y):null,v=[a,s],w=null!=i,_=null!=o,x="leakyrelu"===f;if(w&&v.push(i),_&&v.push(o),x){const e=n.makeTensorInfo([],"float32",ra(d,"float32"));v.push(e),p.push(e)}let k;k=y?new uM(g,w,b,_,x):new lM(g,w,b,_,x);const E=n.runWebGLProgram(k,v,"float32");return p.forEach((e=>n.disposeIntermediateTensorInfo(e))),E}};class eB{constructor(e,t,n){this.sliceDim=e,this.strides=t,this.variableNames=["x","indices"],this.outputShape=n;const r=LT(t.length),a=LT(n.length),s=this.sliceDim>1?"strides[j]":"strides";this.userCode=`\n ${r} strides = ${r}(${this.strides});\n void main() {\n ${a} coords = getOutputCoords();\n int flattenIndex = 0;\n for (int j = 0; j < ${this.sliceDim}; j++) {\n int index = round(getIndices(coords[0], j));\n flattenIndex += index * ${s};\n }\n setOutput(getX(flattenIndex, coords[1]));\n }\n `}}const tB={kernelName:tn,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n}=e,{params:r,indices:a}=t,s=a.shape,i=s[s.length-1],[o,l,u,c]=Ei(r,a),h=nR({inputs:{x:a},backend:n,attrs:{shape:[l,i]}}),f=nR({inputs:{x:r},backend:n,attrs:{shape:[re(r.shape)/u,u]}}),d=new eB(i,c,[l,u]),p=n.runWebGLProgram(d,[f,h],f.dtype),m=nR({inputs:{x:p},backend:n,attrs:{shape:o}});return n.disposeIntermediateTensorInfo(h),n.disposeIntermediateTensorInfo(f),n.disposeIntermediateTensorInfo(p),m}};class nB{constructor(e,t){this.variableNames=["A","indices"],this.outputShape=t,this.rank=t.length;const n=LT(this.rank),r=function(e,t){const n=["resRC.x","resRC.y","resRC.z","resRC.w"],r=[];for(let t=0;tn.disposeIntermediateTensorInfo(e))),n.makeTensorInfo(l.outputShape,r.dtype,r.values)}const p=new nB(h.shape,d),m=n.runWebGLProgram(p,[h,f],h.dtype);c.push(m);const g=nR({inputs:{x:m},backend:n,attrs:{shape:l.outputShape}});return c.forEach((e=>n.disposeIntermediateTensorInfo(e))),g}},aB=qF({opSnippet:"return float(a > b);",packedOpSnippet:"\n return vec4(greaterThan(a, b));\n",cpuKernelImpl:ZT,dtype:"bool"}),sB={kernelName:nn,backendName:"webgl",kernelFunc:aB},iB=qF({opSnippet:"return float(a >= b);",packedOpSnippet:"\n return vec4(greaterThanEqual(a, b));\n",dtype:"bool"}),oB={kernelName:rn,backendName:"webgl",kernelFunc:iB},lB={kernelName:sn,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n}=e,{input:r}=t;return BM(r,!0,n)}},uB=KF({opSnippet:"return float(!isnan(x) && !isinf(x));",dtype:"bool"}),cB={kernelName:ln,backendName:"webgl",kernelFunc:uB},hB=KF({opSnippet:"return float(isinf(x));",dtype:"bool"}),fB={kernelName:un,backendName:"webgl",kernelFunc:hB},dB=KF({opSnippet:"return float(isnan(x));",dtype:"bool"}),pB={kernelName:cn,backendName:"webgl",kernelFunc:dB},mB=qF({opSnippet:"return float(a < b);",packedOpSnippet:"\n return vec4(lessThan(a, b));\n",cpuKernelImpl:QT,dtype:"bool"}),gB={kernelName:fn,backendName:"webgl",kernelFunc:mB},yB=qF({opSnippet:"return float(a <= b);",packedOpSnippet:"\n return vec4(lessThanEqual(a, b));\n",dtype:"bool"}),bB={kernelName:dn,backendName:"webgl",kernelFunc:yB},vB={kernelName:pn,backendName:"webgl",kernelFunc:function(e){const{backend:t,attrs:n}=e,{start:r,stop:a,num:s}=n,i=eF(r,a,s);return t.makeTensorInfo([i.length],"float32",i)}},wB=KF({opSnippet:"if (x < 0.0) return NAN;\n return log(x);",packedOpSnippet:"\n vec4 result = log(x);\n vec4 isNaN = vec4(lessThan(x, vec4(0.0)));\n result.r = isNaN.r == 1.0 ? NAN : result.r;\n result.g = isNaN.g == 1.0 ? NAN : result.g;\n result.b = isNaN.b == 1.0 ? NAN : result.b;\n result.a = isNaN.a == 1.0 ? NAN : result.a;\n\n return result;\n",cpuKernelImpl:tF}),_B={kernelName:mn,backendName:"webgl",kernelFunc:wB},xB=KF({opSnippet:"return log(1.0 + x);"}),kB={kernelName:gn,backendName:"webgl",kernelFunc:xB},EB=qF({opSnippet:"return float(a >= 1.0 && b >= 1.0);",packedOpSnippet:"\n return vec4(\n vec4(greaterThanEqual(a, vec4(1.0))) *\n vec4(greaterThanEqual(b, vec4(1.0))));\n",dtype:"bool"}),CB={kernelName:yn,backendName:"webgl",kernelFunc:EB},SB=KF({opSnippet:"return float(!(x >= 1.0));"}),NB={kernelName:bn,backendName:"webgl",kernelFunc:SB},IB=qF({opSnippet:"return float(a >= 1.0 || b >= 1.0);",packedOpSnippet:"\n return min(\n vec4(greaterThanEqual(a, vec4(1.0))) +\n vec4(greaterThanEqual(b, vec4(1.0))),\n vec4(1.0));\n",dtype:"bool"}),AB={kernelName:vn,backendName:"webgl",kernelFunc:IB};class DB{constructor(e,t,n,r,a){this.variableNames=["x"],this.outputShape=[];const s=t,i=e[3]-1;let o;this.outputShape=e;const l=`float(${n}) + float(${r}) * sum`;o=.5===a?`inversesqrt(${l})`:1===a?`1.0/(${l})`:`exp(log(${l}) * float(-${a}));`,this.userCode=`\n void main() {\n ivec4 coords = getOutputCoords();\n int b = coords[0];\n int r = coords[1];\n int c = coords[2];\n int d = coords[3];\n float x = getX(b, r, c, d);\n float sum = 0.0;\n for (int j = -${s}; j <= ${s}; j++) {\n int idx = d + j;\n if (idx >= 0 && idx <= ${i}) {\n float z = getX(b, r, c, idx);\n sum += z * z;\n }\n }\n float val = x * ${o};\n setOutput(val);\n }\n `}}class TB{constructor(e,t,n,r,a){this.variableNames=["x"],this.outputShape=[],this.packedInputs=!0,this.packedOutput=!0;const s=t,i=e[3]-1;let o;this.outputShape=e;const l=`float(${n}) + float(${r}) * sum`;o=.5===a?`inversesqrt(${l})`:1===a?`1.0/(${l})`:`exp(log(${l}) * float(-${a}));`,this.userCode=`\n void main() {\n ivec4 coords = getOutputCoords();\n int b = coords.x;\n int r = coords.y;\n int c = coords.z;\n int d = coords.w;\n\n bool hasNextCol = d < ${this.outputShape[3]};\n bool hasNextRow = c < ${this.outputShape[2]};\n\n vec4 sum = vec4(0.);\n vec4 xFragAtOutputCoords = getX(b, r, c, d);\n\n vec4 xAtOutputCoords = vec4(\n getChannel(xFragAtOutputCoords, vec2(c, d)),\n hasNextCol ?\n getChannel(xFragAtOutputCoords, vec2(c, d + 1)) : 0.0,\n hasNextRow ?\n getChannel(xFragAtOutputCoords , vec2(c + 1, d)) : 0.0,\n (hasNextRow && hasNextCol) ?\n getChannel(xFragAtOutputCoords, vec2(c + 1, d + 1)) : 0.0\n );\n\n int firstChannel = d - ${s};\n vec2 cache = vec2(0.);\n if(firstChannel >= 0){\n vec4 firstChannelFrag = getX(b, r, c, firstChannel);\n cache.x = getChannel(firstChannelFrag, vec2(c, firstChannel));\n if(hasNextRow){\n cache.y = getChannel(firstChannelFrag, vec2(c + 1, firstChannel));\n }\n }\n\n ivec2 depth = ivec2(d, d + 1);\n for (int j = - ${s}; j <= ${s}; j++) {\n ivec2 idx = depth + j;\n bvec2 aboveLowerBound = greaterThanEqual(idx, ivec2(0));\n bvec2 belowUpperBound = lessThanEqual(idx, ivec2(${i}));\n\n bool depthInRange = aboveLowerBound.x && belowUpperBound.x;\n bool depthPlusOneInRange = aboveLowerBound.y && belowUpperBound.y;\n\n if(depthInRange || depthPlusOneInRange){\n vec4 z = vec4(0.);\n vec4 xFragAtCurrentDepth;\n z.xz = cache.xy;\n if(depthPlusOneInRange && hasNextCol){\n xFragAtCurrentDepth = idx.y != d ?\n getX(b, r, c, idx.y) : xFragAtOutputCoords;\n z.y = getChannel(xFragAtCurrentDepth, vec2(c, idx.y));\n if(hasNextRow){\n z.w = getChannel(xFragAtCurrentDepth, vec2(c + 1, idx.y));\n }\n }\n cache.xy = z.yw;\n sum += z * z;\n }\n }\n vec4 result = xAtOutputCoords * ${o};\n setOutput(result);\n }\n `}}const FB={kernelName:_n,backendName:"webgl",kernelFunc:e=>{const{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{depthRadius:s,bias:i,alpha:o,beta:l}=r,u=We().getBool("WEBGL_PACK_NORMALIZATION")?new TB(a.shape,s,i,o,l):new DB(a.shape,s,i,o,l);return n.runWebGLProgram(u,[a],a.dtype)}};class RB{constructor(e,t,n,r,a){this.variableNames=["inputImage","outputImage","dy"],this.outputShape=[],this.outputShape=e,this.depth=e[3],this.depthRadius=t,this.bias=n,this.alpha=r,this.beta=a,this.userCode=`\n void main() {\n ivec4 coords = getOutputCoords();\n int b = coords[0];\n int r = coords[1];\n int c = coords[2];\n\n float result = 0.0;\n for (int d = 0; d < ${this.depth}; ++d) {\n int depthBegin = int(max(0.0, float(d - ${t})));\n int depthEnd = int(min(float(${this.depth}),\n float(d + ${t} + 1)));\n\n const int MIN_DEPTH_BEGIN = 0;\n const int MAX_DEPTH_END = ${this.depth};\n\n float norm = 0.0;\n for (int k = MIN_DEPTH_BEGIN; k < MAX_DEPTH_END; ++k) {\n if (k < depthBegin){\n continue;\n }\n else if (k >= depthBegin && k < depthEnd) {\n norm += getInputImage(b, r, c, k) * getInputImage(b, r, c, k);\n }\n else {\n break;\n }\n }\n\n norm = float(${r}) * norm + float(${n});\n\n for(int k = MIN_DEPTH_BEGIN; k < MAX_DEPTH_END; ++k){\n if (k < depthBegin){\n continue;\n }\n else if (k >= depthBegin && k < depthEnd){\n float dyi = -2.0 * float(${r})\n * float(${a})\n * getInputImage(b ,r ,c, k) * getOutputImage(b, r, c, d)\n / norm;\n if (k == d) {\n dyi += pow(norm, -1.0 * ${a});\n }\n if (k == coords[3]) {\n dyi *= getDy(b, r, c, d);\n result += dyi;\n }\n }\n else {\n break;\n }\n }\n }\n setOutput(result);\n }\n `}}const OB={kernelName:xn,backendName:"webgl",kernelFunc:e=>{const{inputs:t,backend:n,attrs:r}=e,{x:a,y:s,dy:i}=t,{depthRadius:o,bias:l,alpha:u,beta:c}=r,h=new RB(a.shape,o,l,u,c);return n.runWebGLProgram(h,[a,s,i],a.dtype)}};function MB(e){const{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{reductionIndices:s,keepDims:i}=r,o=a.shape.length,l=de(s,a.shape);let u=l;const c=qu(u,o),h=null!=c,f=n.shouldExecuteOnCPU([a]);let d=a;if(h){if(f){const e=n.texData.get(d.dataId).values,t=new Array(o);for(let e=0;e 0. ? NAN : result.r;\n result.g = isNaN.g > 0. ? NAN : result.g;\n result.b = isNaN.b > 0. ? NAN : result.b;\n result.a = isNaN.a > 0. ? NAN : result.a;\n\n return result;\n",cpuKernelImpl:rF}),PB={kernelName:En,backendName:"webgl",kernelFunc:$B},LB={kernelName:Cn,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n,attrs:r}=e,{x:a}=t;cT(a,"maxPool");const{filterSize:s,strides:i,pad:o,dimRoundingMode:l}=r;Q(Nl(i,1),(()=>`Error in maxPool: Either strides or dilations must be 1. Got strides ${i} and dilations '1'`));const u=yl(a.shape,s,i,1,o,l);if(1===u.filterWidth&&1===u.filterHeight&&se(u.inShape,u.outShape))return PF({inputs:{x:a},backend:n});const c=new XR(u,"max",!1);return n.runWebGLProgram(c,[a],a.dtype)}},zB={kernelName:Nn,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{filterSize:s,strides:i,pad:o,dataFormat:l,dimRoundingMode:u}=r,c=bl(a.shape,s,i,[1,1,1],o,u,l),h=new KR(c,"max",!1);return n.runWebGLProgram(h,[a],a.dtype)}};class WB{constructor(e){this.variableNames=["dy","maxPos"],this.outputShape=e.inShape;const t=e.strideHeight,n=e.strideWidth,r=e.dilationHeight,a=e.effectiveFilterHeight,s=e.effectiveFilterWidth,i=a-1-e.padInfo.top,o=s-1-e.padInfo.left,l=a*s-1;this.userCode=`\n const ivec2 pads = ivec2(${i}, ${o});\n\n void main() {\n ivec4 coords = getOutputCoords();\n int b = coords[0];\n int d = coords[3];\n\n ivec2 dyRCCorner = coords.yz - pads;\n int dyRCorner = dyRCCorner.x;\n int dyCCorner = dyRCCorner.y;\n\n // Convolve dy(?, ?, d) with pos mask(:, :, d) to get dx(xR, xC, d).\n // ? = to be determined. : = across all values in that axis.\n float dotProd = 0.0;\n for (int wR = 0; wR < ${a};\n wR += ${r}) {\n float dyR = float(dyRCorner + wR) / ${t}.0;\n\n if (dyR < 0.0 || dyR >= ${e.outHeight}.0 || fract(dyR) > 0.0) {\n continue;\n }\n int idyR = int(dyR);\n\n for (int wC = 0; wC < ${s}; wC++) {\n float dyC = float(dyCCorner + wC) / ${n}.0;\n\n if (dyC < 0.0 || dyC >= ${e.outWidth}.0 ||\n fract(dyC) > 0.0) {\n continue;\n }\n int idyC = int(dyC);\n\n float dyValue = getDy(b, idyR, idyC, d);\n int maxPosValue = ${l} - int(getMaxPos(b, idyR, idyC, d));\n\n // Get the current value, check it against the value from the\n // position matrix.\n int curPosValue = wR * ${s} + wC;\n float mask = float(maxPosValue == curPosValue ? 1.0 : 0.0);\n\n dotProd += dyValue * mask;\n }\n }\n setOutput(dotProd);\n }\n `}}class UB{constructor(e){this.variableNames=["dy","maxPos"],this.outputShape=e.inShape;const t=e.strideDepth,n=e.strideHeight,r=e.strideWidth,a=e.dilationDepth,s=e.dilationHeight,i=e.dilationWidth,o=e.effectiveFilterDepth,l=e.effectiveFilterHeight,u=e.effectiveFilterWidth,c=o-1-e.padInfo.front,h=l-1-e.padInfo.top,f=u-1-e.padInfo.left,d=o*l*u-1;this.userCode=`\n const ivec3 pads = ivec3(${c}, ${h}, ${f});\n\n void main() {\n ivec5 coords = getOutputCoords();\n int batch = coords.x;\n int ch = coords.u;\n\n ivec3 dyCorner = ivec3(coords.y, coords.z, coords.w) - pads;\n int dyDCorner = dyCorner.x;\n int dyRCorner = dyCorner.y;\n int dyCCorner = dyCorner.z;\n\n // Convolve dy(?, ?, ?, ch) with pos mask(:, :, :, d) to get\n // dx(xD, xR, xC, ch).\n // ? = to be determined. : = across all values in that axis.\n float dotProd = 0.0;\n\n for (int wD = 0; wD < ${o};\n wD += ${a}) {\n float dyD = float(dyDCorner + wD) / ${t}.0;\n\n if (dyD < 0.0 || dyD >= ${e.outDepth}.0 || fract(dyD) > 0.0) {\n continue;\n }\n int idyD = int(dyD);\n\n for (int wR = 0; wR < ${l};\n wR += ${s}) {\n float dyR = float(dyRCorner + wR) / ${n}.0;\n\n if (dyR < 0.0 || dyR >= ${e.outHeight}.0 ||\n fract(dyR) > 0.0) {\n continue;\n }\n int idyR = int(dyR);\n\n for (int wC = 0; wC < ${u};\n wC += ${i}) {\n float dyC = float(dyCCorner + wC) / ${r}.0;\n\n if (dyC < 0.0 || dyC >= ${e.outWidth}.0 ||\n fract(dyC) > 0.0) {\n continue;\n }\n int idyC = int(dyC);\n\n float dyValue = getDy(batch, idyD, idyR, idyC, ch);\n int maxPosValue = ${d} -\n int(getMaxPos(batch, idyD, idyR, idyC, ch));\n\n // Get the current value, check it against the value from the\n // position matrix.\n int curPosValue =\n wD * ${l} * ${u} +\n wR * ${u} + wC;\n float mask = float(maxPosValue == curPosValue ? 1.0 : 0.0);\n\n dotProd += dyValue * mask;\n }\n }\n }\n setOutput(dotProd);\n }\n `}}const VB={kernelName:In,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n,attrs:r}=e,{dy:a,input:s}=t,i=s,{filterSize:o,strides:l,pad:u,dimRoundingMode:c}=r,h=bl(i.shape,o,l,[1,1,1],u,c),f=new KR(h,"max",!0),d=n.runWebGLProgram(f,[i],i.dtype),p=new UB(h),m=n.runWebGLProgram(p,[a,d],i.dtype);return n.disposeIntermediateTensorInfo(d),m}},jB={kernelName:Sn,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n,attrs:r}=e,{dy:a,input:s,output:i}=t,o=s;cT([s,i],"maxPoolGrad");const{filterSize:l,strides:u,pad:c,dimRoundingMode:h}=r,f=yl(o.shape,l,u,1,c,h),d=new XR(f,"max",!0),p=n.runWebGLProgram(d,[o],o.dtype),m=new WB(f),g=n.runWebGLProgram(m,[a,p],o.dtype);return n.disposeIntermediateTensorInfo(p),g}},HB={kernelName:An,backendName:"webgl",kernelFunc:({inputs:e,attrs:t,backend:n})=>{const{x:r}=e,{filterSize:a,strides:s,pad:i,includeBatchInIndex:o}=t,l=n;Q(4===r.shape.length,(()=>`Error in maxPool: input must be rank 4 but got rank ${r.shape.length}.`));const u=[1,1];Q(Nl(s,u),(()=>`Error in maxPool: Either strides or dilations must be 1. Got strides ${s} and dilations '${u}'`));const c=yl(r.shape,a,s,u,i),[h,f]=function(e,t,n,r){let a=new XR(n,"max",!1);const s=r.runWebGLProgram(a,[e],"float32");return a=new XR(n,"max",!0,!0,t),[s,r.runWebGLProgram(a,[e],"float32")]}(r,o,c,l);return[h,f]}},GB={kernelName:Dn,backendName:"webgl",kernelFunc:({inputs:e,attrs:t,backend:n})=>{const{x:r}=e,{keepDims:a,axis:s}=t,i=n,o=r.shape.length,l=de(s,r.shape);let u=l;const c=qu(u,o),h=null!=c,f=i.shouldExecuteOnCPU([r]),d=[];let p=r;if(h){if(f){const e=i.texData.get(p.dataId).values,t=new Array(o);for(let e=0;e 0. ? NAN : result.r;\n result.g = isNaN.g > 0. ? NAN : result.g;\n result.b = isNaN.b > 0. ? NAN : result.b;\n result.a = isNaN.a > 0. ? NAN : result.a;\n\n return result;\n",cpuKernelImpl:aF}),qB={kernelName:Fn,backendName:"webgl",kernelFunc:KB};class YB{constructor(e,t,n){this.variableNames=["x"],this.outputShape=t.map(((t,n)=>t[0]+e[n]+t[1]));const r=e.length,a=LT(r),s=t.map((e=>e[0])).join(","),i=t.map(((t,n)=>t[0]+e[n])).join(","),o=["coords[0]","coords[1]","coords[2]","coords[3]"].slice(0,r),l="reflect"===n?0:1;this.userCode=1!==r?`\n ${a} start = ${a}(${s});\n ${a} end = ${a}(${i});\n\n void main() {\n ${a} outC = getOutputCoords();\n for (int i = 0; i < ${r}; i++) {\n if (outC[i] < start[i]) {\n outC[i] = start[i] * 2 - outC[i] - ${l};\n } else if(outC[i] >= end[i]) {\n outC[i] = (end[i] - 1) * 2 - outC[i] + ${l};\n }\n }\n ${a} coords = outC - start;\n setOutput(getX(${o}));\n }\n `:`\n int start = ${s};\n int end = ${i};\n\n void main() {\n int outC = getOutputCoords();\n if (outC < start) {\n outC = start * 2 - outC - ${l};\n } else if(outC >= end) {\n outC = (end - 1) * 2 - outC + ${l};\n }\n setOutput(getX(outC - start));\n }\n `}}class JB{constructor(e,t,n){this.variableNames=["x"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=t.map(((t,n)=>t[0]+e[n]+t[1]));const r=e.length,a=LT(r),s=t.map((e=>e[0])).join(","),i=t.map(((t,n)=>t[0]+e[n])).join(","),o=_F("rc",r),l=_F("source",r),u=`${o[r-1]} < ${this.outputShape[r-1]}`,c=1===r?"source":`vec2(${l.slice(-2).join()})`,h="reflect"===n?0:1;let f="";if(1===r){const e=`\n ${a} source = rc;\n if (source < start) {\n source = start * 2 - source - ${h};\n } else if (source >= end) {\n source = (end - 1) * 2 - source + ${h};\n }\n source -= start;\n `;f=`\n ${a} rc = outputLoc;\n ${e}\n result[0] = getChannel(getX(${l.join()}), ${c});\n ${o[r-1]} += 1;\n if(${u}) {\n ${e}\n result[1] = getChannel(getX(${l.join()}), ${c});\n }\n `}else{const e=`\n ${a} source = rc;\n ${a} lt = ${a}(lessThan(source, start));\n ${a} gte = ${a}(greaterThanEqual(source, end));\n ${a} orig = 1 - (lt + gte);\n source = orig * source +\n lt * (start * 2 - source - ${h}) +\n gte * ((end - 1) * 2 - source + ${h});\n source -= start;\n `;f=`\n ${a} rc = outputLoc;\n ${e}\n result[0] = getChannel(getX(${l.join()}), ${c});\n ${o[r-1]} += 1;\n if(${u}) {\n ${e}\n result[1] = getChannel(getX(${l.join()}), ${c});\n }\n rc = outputLoc;\n ${o[r-2]} += 1;\n if(${o[r-2]} < ${this.outputShape[r-2]}) {\n ${e}\n result[2] = getChannel(getX(${l.join()}), ${c});\n ${o[r-1]} += 1;\n if(${u}) {\n ${e}\n result[3] = getChannel(getX(${l.join()}), ${c});\n }\n }\n `}this.userCode=`\n const ${a} start = ${a}(${s});\n const ${a} end = ${a}(${i});\n\n void main() {\n ${a} outputLoc = getOutputCoords();\n vec4 result = vec4(0.);\n ${f}\n setOutput(result);\n }\n `}}const ZB={kernelName:Rn,backendName:"webgl",kernelFunc:({inputs:e,backend:t,attrs:n})=>{const{x:r}=e,{paddings:a,mode:s}=n,i=We().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new JB(r.shape,a,s):new YB(r.shape,a,s);return t.runWebGLProgram(i,[r],r.dtype)}},QB=qF({opSnippet:"if (b == 0.0) return NAN;\n return mod(a, b);",packedOpSnippet:"\n vec4 result = mod(a, b);\n vec4 isNaN = vec4(equal(b, vec4(0.0)));\n \n result.r = isNaN.r > 0. ? NAN : result.r;\n result.g = isNaN.g > 0. ? NAN : result.g;\n result.b = isNaN.b > 0. ? NAN : result.b;\n result.a = isNaN.a > 0. ? NAN : result.a;\n\n return result;\n"}),e$={kernelName:On,backendName:"webgl",kernelFunc:QB};class t${constructor(e,t,n){this.variableNames=["probs"],this.outputShape=[e,n],this.userCode=`\n uniform float seed;\n\n void main() {\n ivec2 coords = getOutputCoords();\n int batch = coords[0];\n\n float r = random(seed);\n float cdf = 0.0;\n\n for (int i = 0; i < ${t-1}; i++) {\n cdf += getProbs(batch, i);\n\n if (r < cdf) {\n setOutput(float(i));\n return;\n }\n }\n\n // If no other event happened, last event happened.\n setOutput(float(${t-1}));\n }\n `}getCustomSetupFunc(e){return(t,n)=>{null==this.seedLoc&&(this.seedLoc=t.getUniformLocation(n,"seed")),t.gl.uniform1f(this.seedLoc,e)}}}const n$=qF({opSnippet:"\nif (a == b) {\n return 1.0;\n};\nreturn a / b;",packedOpSnippet:"\n // vec4 one = vec4(equal(a, b));\n // return one + (vec4(1.0) - one) * a / b;\n vec4 result = a / b;\n if(a.x == b.x) {\n result.x = 1.;\n }\n if(a.y == b.y) {\n result.y = 1.;\n }\n if(a.z == b.z) {\n result.z = 1.;\n }\n if(a.w == b.w) {\n result.w = 1.;\n }\n\n return result;\n",checkOutOfBounds:!0}),r$={kernelName:Lt,backendName:"webgl",kernelFunc:n$},a$="return a - b;",s$=qF({opSnippet:a$,packedOpSnippet:a$,supportsComplex:!0,cpuKernelImpl:mF}),i$={kernelName:Ar,backendName:"webgl",kernelFunc:s$};function o$(e){const{inputs:t,backend:n,attrs:r}=e,{logits:a}=t,{dim:s}=r,i=de([s],a.shape),o=MB({inputs:{x:a},backend:n,attrs:{reductionIndices:i,keepDims:!1}}),l=Xu(o.shape,i),u=nR({inputs:{x:o},backend:n,attrs:{shape:l}}),c=s$({inputs:{a,b:u},backend:n}),h=IM({inputs:{x:c},backend:n}),f=cR({inputs:{x:h},backend:n,attrs:{axis:i,keepDims:!1}}),d=nR({inputs:{x:f},backend:n,attrs:{shape:l}}),p=n$({inputs:{a:h,b:d},backend:n});return n.disposeIntermediateTensorInfo(o),n.disposeIntermediateTensorInfo(u),n.disposeIntermediateTensorInfo(c),n.disposeIntermediateTensorInfo(h),n.disposeIntermediateTensorInfo(f),n.disposeIntermediateTensorInfo(d),p}const l$={kernelName:xr,backendName:"webgl",kernelFunc:o$},u$={kernelName:Mn,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n,attrs:r}=e,{logits:a}=t,{numSamples:s,seed:i,normalized:o}=r,l=o?a:o$({inputs:{logits:a},backend:n,attrs:{dim:a.shape.length-1}}),u=l.shape[0],c=l.shape[1],h=new t$(u,c,s),f=h.getCustomSetupFunc(i),d=n.runWebGLProgram(h,[l],"int32",f);return o||n.disposeIntermediateTensorInfo(l),d}},c$="return -x;",h$={kernelName:$n,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n}=e,{x:r}=t;if(n.shouldExecuteOnCPU([r])){const e=n.texData.get(r.dataId),[t,a]=iF(e.values,r.shape,r.dtype);return n.makeTensorInfo(a,r.dtype,t)}let a;return a=We().getBool("WEBGL_PACK_UNARY_OPERATIONS")?new TF(r.shape,c$):new IF(r.shape,c$),n.runWebGLProgram(a,[r],r.dtype)}},f$=uf,d$={kernelName:Ln,backendName:"webgl",kernelFunc:function(e){fd("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");const{inputs:t,backend:n,attrs:r}=e,{boxes:a,scores:s}=t,{maxOutputSize:i,iouThreshold:o,scoreThreshold:l}=r,u=n.readSync(a.dataId),c=n.readSync(s.dataId),{selectedIndices:h}=f$(u,c,i,o,l);return n.makeTensorInfo([h.length],"int32",new Int32Array(h))}},p$=cf,m$={kernelName:zn,backendName:"webgl",kernelFunc:function(e){fd("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");const{inputs:t,backend:n,attrs:r}=e,{boxes:a,scores:s}=t,{maxOutputSize:i,iouThreshold:o,scoreThreshold:l,padToMaxOutputSize:u}=r,c=n.readSync(a.dataId),h=n.readSync(s.dataId),{selectedIndices:f,validOutputs:d}=p$(c,h,i,o,l,u);return[n.makeTensorInfo([f.length],"int32",new Int32Array(f)),n.makeTensorInfo([],"int32",new Int32Array([d]))]}},g$=hf,y$={kernelName:Wn,backendName:"webgl",kernelFunc:function(e){fd("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");const{inputs:t,backend:n,attrs:r}=e,{boxes:a,scores:s}=t,{maxOutputSize:i,iouThreshold:o,scoreThreshold:l,softNmsSigma:u}=r,c=n.readSync(a.dataId),h=n.readSync(s.dataId),f=i,d=o,p=l,m=u,{selectedIndices:g,selectedScores:y}=g$(c,h,f,d,p,m);return[n.makeTensorInfo([g.length],"int32",new Int32Array(g)),n.makeTensorInfo([y.length],"float32",new Float32Array(y))]}};class b${constructor(e,t,n,r){this.variableNames=["indices"],this.outputShape=[e,t],this.userCode=`\n void main() {\n ivec2 coords = getOutputCoords();\n int index = round(getIndices(coords.x));\n setOutput(mix(float(${r}), float(${n}),\n float(index == coords.y)));\n }\n `}}const v$={kernelName:Vn,backendName:"webgl",kernelFunc:e=>{const{inputs:t,backend:n,attrs:r}=e,{indices:a}=t,{depth:s,onValue:i,offValue:o}=r,l=re(a.shape),u=new b$(l,s,i,o),c=nR({inputs:{x:a},backend:n,attrs:{shape:[l]}}),h=n.runWebGLProgram(u,[c],a.dtype);n.disposeIntermediateTensorInfo(c);const f=nR({inputs:{x:h},backend:n,attrs:{shape:[...a.shape,s]}});return n.disposeIntermediateTensorInfo(h),f}};function w$(e){const{inputs:t,backend:n}=e,{x:r}=t;if("complex64"===r.dtype){const e=pO({inputs:{input:r},backend:n}),t=w$({inputs:{x:e},backend:n}),a=AO({inputs:{input:r},backend:n}),s=w$({inputs:{x:a},backend:n}),i=zF({inputs:{real:t,imag:s},backend:n});return n.disposeIntermediateTensorInfo(e),n.disposeIntermediateTensorInfo(t),n.disposeIntermediateTensorInfo(a),n.disposeIntermediateTensorInfo(s),i}return LM({attrs:{shape:r.shape,dtype:r.dtype,value:"string"===r.dtype?"":0},backend:n})}const _$={kernelName:Lr,backendName:"webgl",kernelFunc:w$},x$={kernelName:Un,backendName:"webgl",kernelFunc:function e(t){const{inputs:n,backend:r}=t,{x:a}=n;if("string"===a.dtype)throw new Error("onesLike is not supported under string dtype");if("complex64"===a.dtype){const t=pO({inputs:{input:a},backend:r}),n=e({inputs:{x:t},backend:r}),s=AO({inputs:{input:a},backend:r}),i=w$({inputs:{x:s},backend:r}),o=zF({inputs:{real:n,imag:i},backend:r});return r.disposeIntermediateTensorInfo(t),r.disposeIntermediateTensorInfo(n),r.disposeIntermediateTensorInfo(s),r.disposeIntermediateTensorInfo(i),o}return LM({attrs:{shape:a.shape,dtype:a.dtype,value:1},backend:r})}},k$={kernelName:jn,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n,attrs:r}=e,{axis:a}=r;if(1===t.length)return DM({inputs:{input:t[0]},backend:n,attrs:{dim:a}});const s=t[0].shape,i=t[0].dtype;t.forEach((e=>{ee(s,e.shape,"All tensors passed to stack must have matching shapes"),Q(i===e.dtype,(()=>"All tensors passed to stack must have matching dtypes"))}));const o=[],l=FO({inputs:t.map((e=>{const t=DM({inputs:{input:e},backend:n,attrs:{dim:a}});return o.push(t),t})),backend:n,attrs:{axis:a}});return o.forEach((e=>n.disposeIntermediateTensorInfo(e))),l}};class E${constructor(e,t,n){this.variableNames=["x"],this.outputShape=t.map(((t,n)=>t[0]+e[n]+t[1]));const r=e.length,a=LT(r),s=t.map((e=>e[0])).join(","),i=t.map(((t,n)=>t[0]+e[n])).join(","),o=["coords[0]","coords[1]","coords[2]","coords[3]"].slice(0,r);this.userCode=1!==r?`\n ${a} start = ${a}(${s});\n ${a} end = ${a}(${i});\n uniform float value;\n\n void main() {\n ${a} outC = getOutputCoords();\n if (any(lessThan(outC, start)) || any(greaterThanEqual(outC, end))) {\n setOutput(value);\n } else {\n ${a} coords = outC - start;\n setOutput(getX(${o}));\n }\n }\n `:`\n int start = ${s};\n int end = ${i};\n uniform float value;\n\n void main() {\n int outC = getOutputCoords();\n if (outC < start || outC >= end) {\n setOutput(value);\n } else {\n setOutput(getX(outC - start));\n }\n }\n `}getCustomSetupFunc(e){return(t,n)=>{null==this.valueLoc&&(this.valueLoc=t.getUniformLocationNoThrow(n,"value")),t.gl.uniform1f(this.valueLoc,e)}}}class C${constructor(e,t,n){this.variableNames=["x"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=t.map(((t,n)=>t[0]+e[n]+t[1]));const r=e.length,a=LT(r),s=t.map((e=>e[0])).join(","),i=t.map(((t,n)=>t[0]+e[n])).join(","),o=_F("rc",r),l=_F("source",r),u=`${o[r-1]} < ${this.outputShape[r-1]}`,c=1===r?"source":`vec2(${l.slice(-2).join()})`,h=[`${a} rc = outputLoc;`,`${o[r-1]} += 1;\n if(${u}) {\n `,1===r?"":`}\n rc = outputLoc;\n ${o[r-2]} += 1;\n if(${o[r-2]} < ${this.outputShape[r-2]}) {`,1===r?"":` ${o[r-1]} += 1;\n if(${u}) {`],f=1===r?"rc < start || rc >= end":"any(lessThan(rc, start)) || any(greaterThanEqual(rc, end))";let d="";for(let e=0,t=1===r?2:4;e{null==this.valueLoc&&(this.valueLoc=t.getUniformLocationNoThrow(n,"value")),t.gl.uniform1f(this.valueLoc,e)}}}const S$=e=>{const{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{paddings:s,constantValue:i}=r,o=We().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new C$(a.shape,s,i):new E$(a.shape,s,i),l=o.getCustomSetupFunc(i);return n.runWebGLProgram(o,[a],a.dtype,l)},N$={kernelName:Hn,backendName:"webgl",kernelFunc:S$},I$=qF({opSnippet:"\n if(a < 0.0 && floor(b) < b){\n return NAN;\n }\n if (b == 0.0) {\n return 1.0;\n }\n return (round(mod(b, 2.0)) != 1) ?\n pow(abs(a), b) : sign(a) * pow(abs(a), b);\n",packedOpSnippet:"\n // isModRound1 has 1 for components with round(mod(b, 2.0)) == 1, 0 otherwise.\n vec4 isModRound1 = vec4(equal(round(mod(b, 2.0)), ivec4(1)));\n vec4 multiplier = sign(a) * isModRound1 + (vec4(1.0) - isModRound1);\n vec4 result = multiplier * pow(abs(a), b);\n\n // Ensure that a^0 = 1, including 0^0 = 1 as this correspond to TF and JS\n bvec4 isExpZero = equal(b, vec4(0.0));\n result.r = isExpZero.r ? 1.0 : result.r;\n result.g = isExpZero.g ? 1.0 : result.g;\n result.b = isExpZero.b ? 1.0 : result.b;\n result.a = isExpZero.a ? 1.0 : result.a;\n\n vec4 isNaN = vec4(lessThan(a, vec4(0.0))) * vec4(lessThan(floor(b), b));\n \n result.r = isNaN.r > 0. ? NAN : result.r;\n result.g = isNaN.g > 0. ? NAN : result.g;\n result.b = isNaN.b > 0. ? NAN : result.b;\n result.a = isNaN.a > 0. ? NAN : result.a;\n\n return result;\n"}),A$={kernelName:Xn,backendName:"webgl",kernelFunc:I$},D$={kernelName:qn,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{axis:s,keepDims:i}=r,o=a.shape.length,l=[],u=de(s,a.shape);let c=u;const h=qu(c,o);let f,d=a;if(null!=h&&(d=fR({inputs:{x:a},backend:n,attrs:{perm:h}}),c=Ju(c.length,o),l.push(d)),Ku("prod",c,o),n.shouldExecuteOnCPU([d])){const e=n.texData.get(d.dataId).values,{outVals:t,outShape:r,outDtype:a}=oF(d.shape,d.dtype,e,c);f=n.makeTensorInfo(r,a,t)}else{const[e,t]=Gu(d.shape,c),r=re(t),s=nR({inputs:{x:d},backend:n,attrs:{shape:[-1,r]}}),i=iR(s,Ta(a.dtype),"prod",n);f=nR({inputs:{x:i},backend:n,attrs:{shape:e}}),l.push(s),l.push(i)}if(i){l.push(f);const e=Xu(f.shape,u);f=nR({inputs:{x:f},backend:n,attrs:{shape:e}})}return l.forEach((e=>n.disposeIntermediateTensorInfo(e))),f}},T$=e=>{const{backend:t,attrs:n}=e,{start:r,stop:a,step:s,dtype:i}=n,o=lF(r,a,s,i);return t.makeTensorInfo([o.length],i,o)},F$={kernelName:Yn,backendName:"webgl",kernelFunc:T$},R$=KF({opSnippet:"return 1.0 / x;"}),O$={kernelName:Zn,backendName:"webgl",kernelFunc:R$},M$=KF({opSnippet:"if (isnan(x)) return x;\n return (x < 0.0) ? 0.0 : x;\n",packedOpSnippet:"\n vec4 result = x * vec4(greaterThanEqual(x, vec4(0.0)));\n bvec4 isNaN = isnan(x);\n\n result.r = isNaN.r ? x.r : result.r;\n result.g = isNaN.g ? x.g : result.g;\n result.b = isNaN.b ? x.b : result.b;\n result.a = isNaN.a ? x.a : result.a;\n\n return result;\n"}),B$={kernelName:Qn,backendName:"webgl",kernelFunc:M$},$$=KF({opSnippet:"if (isnan(x)) return x;\n return (x < 0.0) ? 0.0 : min(6.0, x);\n",packedOpSnippet:"\n vec4 result = min(x, vec4(6.)) * vec4(greaterThanEqual(x, vec4(0.0)));\n bvec4 isNaN = isnan(x);\n\n result.r = isNaN.r ? x.r : result.r;\n result.g = isNaN.g ? x.g : result.g;\n result.b = isNaN.b ? x.b : result.b;\n result.a = isNaN.a ? x.a : result.a;\n\n return result;\n"}),P$={kernelName:sr,backendName:"webgl",kernelFunc:$$};class L${constructor(e,t,n,r,a){this.variableNames=["A"],this.outputShape=[];const[s,i,o,l]=e;this.outputShape=[s,t,n,l];const u=[r&&t>1?i-1:i,r&&n>1?o-1:o],c=[r&&t>1?t-1:t,r&&n>1?n-1:n];let h;h=a?"(vec2(yRC) + vec2(0.5)) * effectiveInputOverOutputRatioRC - vec2(0.5)":"vec2(yRC) * effectiveInputOverOutputRatioRC",this.userCode=`\n const vec2 effectiveInputOverOutputRatioRC = vec2(\n ${u[0]/c[0]},\n ${u[1]/c[1]});\n const vec2 inputShapeRC = vec2(${i}.0, ${o}.0);\n\n void main() {\n ivec4 coords = getOutputCoords();\n int b = coords[0];\n int d = coords[3];\n ivec2 yRC = coords.yz;\n\n // Fractional source index.\n vec2 sourceFracIndexRC = ${h};\n\n // Compute the four integer indices.\n ivec2 sourceFloorRC = ivec2(max(sourceFracIndexRC, vec2(0.0)));\n ivec2 sourceCeilRC = ivec2(\n min(inputShapeRC - 1.0, ceil(sourceFracIndexRC)));\n\n float topLeft = getA(b, sourceFloorRC.x, sourceFloorRC.y, d);\n float bottomLeft = getA(b, sourceCeilRC.x, sourceFloorRC.y, d);\n float topRight = getA(b, sourceFloorRC.x, sourceCeilRC.y, d);\n float bottomRight = getA(b, sourceCeilRC.x, sourceCeilRC.y, d);\n\n vec2 fracRC = sourceFracIndexRC - vec2(sourceFloorRC);\n\n float top = topLeft + (topRight - topLeft) * fracRC.y;\n float bottom = bottomLeft + (bottomRight - bottomLeft) * fracRC.y;\n float newValue = top + (bottom - top) * fracRC.x;\n\n setOutput(newValue);\n }\n `}}class z${constructor(e,t,n,r,a){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=[];const[s,i,o,l]=e;this.outputShape=[s,t,n,l];const u=[r&&t>1?i-1:i,r&&n>1?o-1:o],c=[r&&t>1?t-1:t,r&&n>1?n-1:n];let h;h=a?"(vec3(yRC) + vec3(0.5)) * effectiveInputOverOutputRatioRC - vec3(0.5)":"vec3(yRC) * effectiveInputOverOutputRatioRC",this.userCode=`\n const vec3 effectiveInputOverOutputRatioRC = vec3(\n ${u[0]/c[0]},\n ${u[1]/c[1]},\n ${u[1]/c[1]});\n const vec3 inputShapeRC = vec3(${i}.0, ${o}.0,\n ${o}.0);\n\n float getAValue(int b, int r, int c, int d) {\n return getChannel(getA(b, r, c, d), vec2(c, d));\n }\n\n void main() {\n ivec4 coords = getOutputCoords();\n int b = coords[0];\n int d = coords[3];\n // Calculate values for next column in yRC.z.\n ivec3 yRC = coords.yzz + ivec3(0, 0, 1);\n\n // Fractional source index.\n vec3 sourceFracIndexRC = ${h};\n\n // Compute the four integer indices.\n ivec3 sourceFloorRC = ivec3(max(sourceFracIndexRC, vec3(0.0)));\n ivec3 sourceCeilRC = ivec3(\n min(inputShapeRC - 1.0, ceil(sourceFracIndexRC)));\n\n // Should we calculate next column and row elements in 2x2 packed cell.\n bool hasNextCol = d < ${l-1};\n bool hasNextRow = coords.z < ${n-1};\n\n // In parallel, construct four corners for all four components in\n // packed 2x2 cell.\n vec4 topLeft = vec4(\n getAValue(b, sourceFloorRC.x, sourceFloorRC.y, d),\n hasNextCol ? getAValue(b, sourceFloorRC.x, sourceFloorRC.y, d + 1)\n : 0.0,\n hasNextRow ? getAValue(b, sourceFloorRC.x, sourceFloorRC.z, d)\n : 0.0,\n (hasNextRow && hasNextCol) ?\n getAValue(b, sourceFloorRC.x, sourceFloorRC.z, d + 1) : 0.0);\n\n vec4 bottomLeft = vec4(\n getAValue(b, sourceCeilRC.x, sourceFloorRC.y, d),\n hasNextCol ? getAValue(b, sourceCeilRC.x, sourceFloorRC.y, d + 1)\n : 0.0,\n hasNextRow ? getAValue(b, sourceCeilRC.x, sourceFloorRC.z, d)\n : 0.0,\n (hasNextRow && hasNextCol) ?\n getAValue(b, sourceCeilRC.x, sourceFloorRC.z, d + 1) : 0.0);\n\n vec4 topRight = vec4(\n getAValue(b, sourceFloorRC.x, sourceCeilRC.y, d),\n hasNextCol ? getAValue(b, sourceFloorRC.x, sourceCeilRC.y, d + 1)\n : 0.0,\n hasNextRow ? getAValue(b, sourceFloorRC.x, sourceCeilRC.z, d)\n : 0.0,\n (hasNextRow && hasNextCol) ?\n getAValue(b, sourceFloorRC.x, sourceCeilRC.z, d + 1) : 0.0);\n\n vec4 bottomRight = vec4(\n getAValue(b, sourceCeilRC.x, sourceCeilRC.y, d),\n hasNextCol ? getAValue(b, sourceCeilRC.x, sourceCeilRC.y, d + 1)\n : 0.0,\n hasNextRow ? getAValue(b, sourceCeilRC.x, sourceCeilRC.z, d)\n : 0.0,\n (hasNextRow && hasNextCol) ?\n getAValue(b, sourceCeilRC.x, sourceCeilRC.z, d + 1) : 0.0);\n\n vec3 fracRC = sourceFracIndexRC - vec3(sourceFloorRC);\n\n vec4 top = mix(topLeft, topRight, fracRC.yyzz);\n vec4 bottom = mix(bottomLeft, bottomRight, fracRC.yyzz);\n vec4 newValue = mix(top, bottom, fracRC.x);\n\n setOutput(newValue);\n }\n `}}const W$={kernelName:rr,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n,attrs:r}=e,{images:a}=t,{alignCorners:s,halfPixelCenters:i,size:o}=r,[l,u]=o,c=We().getBool("WEBGL_PACK_IMAGE_OPERATIONS")?new z$(a.shape,l,u,s,i):new L$(a.shape,l,u,s,i);return n.runWebGLProgram(c,[a],"float32")}};class U${constructor(e,t,n){this.variableNames=["dy"],this.outputShape=[],this.outputShape=t;const[,r,a]=t,[,s,i]=e,o=[n&&s>1?r-1:r,n&&i>1?a-1:a],l=[n&&s>1?s-1:s,n&&i>1?i-1:i],u=o[0]/l[0],c=o[1]/l[1],h=1/u,f=1/c,d=2*Math.ceil(h)+2,p=2*Math.ceil(f)+2;this.userCode=`\n void main() {\n ivec4 coords = getOutputCoords();\n int b = coords[0];\n int d = coords[3];\n int r = coords[1];\n int c = coords[2];\n\n float accumulator = 0.0;\n\n const float heightScale = float(${u});\n const float widthScale = float(${c});\n\n const float invHeightScale = float(${h});\n const float invWidthScale = float(${f});\n\n const int winHeight = int(${d});\n const int winWidth = int(${p});\n\n // Compute bounds for where in dy we will look\n float startRLerp = floor(float(r) * invHeightScale);\n int startDyR = int(startRLerp - float(winHeight / 2));\n\n float startCLerp = floor(float(c) * invWidthScale);\n int startDyC = int(startCLerp - float(winWidth / 2));\n\n // Loop over dy\n for (int dyROffset = 0; dyROffset < winHeight; dyROffset++) {\n int dyR = dyROffset + startDyR;\n\n // Guard against the window exceeding the bounds of dy\n if (dyR < 0 || dyR >= ${s}) {\n continue;\n }\n\n for (int dyCOffset = 0; dyCOffset < winWidth; dyCOffset++) {\n int dyC = dyCOffset + startDyC;\n\n // Guard against the window exceeding the bounds of dy\n if (dyC < 0 || dyC >= ${i}) {\n continue;\n }\n\n float dxR = float(dyR) * heightScale;\n int topDxRIndex = int(floor(dxR));\n int bottomDxRIndex = int(min(ceil(dxR), ${r-1}.0));\n float dxRLerp = dxR - float(topDxRIndex);\n float inverseDxRLerp = 1.0 - dxRLerp;\n\n float dxC = float(dyC) * widthScale;\n int leftDxCIndex = int(floor(dxC));\n int rightDxCIndex = int(min(ceil(dxC), ${a-1}.0));\n float dxCLerp = dxC - float(leftDxCIndex);\n float inverseDxCLerp = 1.0 - dxCLerp;\n\n if (r == topDxRIndex && c == leftDxCIndex) {\n // topLeft\n accumulator +=\n getDy(b, dyR, dyC, d) * inverseDxRLerp * inverseDxCLerp;\n }\n\n if (r == topDxRIndex && c == rightDxCIndex) {\n // topRight\n accumulator += getDy(b, dyR, dyC, d) * inverseDxRLerp * dxCLerp;\n }\n\n if (r == bottomDxRIndex && c == leftDxCIndex) {\n // bottomLeft\n accumulator += getDy(b, dyR, dyC, d) * dxRLerp * inverseDxCLerp;\n }\n\n if (r == bottomDxRIndex && c == rightDxCIndex) {\n // bottomRight\n accumulator += getDy(b, dyR, dyC, d) * dxRLerp * dxCLerp;\n }\n }\n }\n // End loop over dy\n\n setOutput(accumulator);\n }\n `}}const V$={kernelName:ar,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n,attrs:r}=e,{images:a,dy:s}=t,{alignCorners:i}=r,o=new U$(s.shape,a.shape,i);return n.runWebGLProgram(o,[s],s.dtype)}};class j${constructor(e,t,n,r,a){this.variableNames=["A"],this.outputShape=[];const[s,i,o,l]=e;this.outputShape=[s,t,n,l];const u=[r&&t>1?i-1:i,r&&n>1?o-1:o],c=[r&&t>1?t-1:t,r&&n>1?n-1:n],h=r?"0.5":"0.0";let f;f=a?"max((vec2(yRC) + vec2(0.5)) * effectiveInputOverOutputRatioRC, vec2(0.0))":"vec2(yRC) * effectiveInputOverOutputRatioRC",this.userCode=`\n const vec2 effectiveInputOverOutputRatioRC = vec2(\n ${u[0]/c[0]},\n ${u[1]/c[1]});\n const vec2 inputShapeRC = vec2(${i}.0, ${o}.0);\n\n void main() {\n ivec4 coords = getOutputCoords();\n int b = coords[0];\n int d = coords[3];\n ivec2 yRC = coords.yz;\n\n // Fractional source index.\n vec2 sourceFracIndexRC = ${f};\n\n // Compute the coordinators of nearest neighbor point.\n ivec2 sourceNearestRC = ivec2(\n min(inputShapeRC - 1.0, floor(sourceFracIndexRC + ${h})));\n float newValue = getA(b, sourceNearestRC.x, sourceNearestRC.y, d);\n\n setOutput(newValue);\n }\n `}}class H${constructor(e,t,n,r,a){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=[];const[s,i,o,l]=e;this.outputShape=[s,t,n,l];const u=[r&&t>1?i-1:i,r&&n>1?o-1:o],c=[r&&t>1?t-1:t,r&&n>1?n-1:n],h=r?"0.5":"0.0";let f;f=a?"max((vec3(yRC) + vec3(0.5)) * effectiveInputOverOutputRatioRC, vec3(0.0))":"vec3(yRC) * effectiveInputOverOutputRatioRC",this.userCode=`\n const vec3 effectiveInputOverOutputRatioRC = vec3(\n ${u[0]/c[0]},\n ${u[1]/c[1]},\n ${u[1]/c[1]});\n const vec3 inputShapeRC = vec3(${i}.0, ${o}.0,\n ${o}.0);\n\n float getAValue(int b, int r, int c, int d) {\n return getChannel(getA(b, r, c, d), vec2(c, d));\n }\n\n void main() {\n ivec4 coords = getOutputCoords();\n int b = coords[0];\n int d = coords[3];\n // Calculate values for next column in yRC.z.\n ivec3 yRC = coords.yzz + ivec3(0, 0, 1);\n\n // Fractional source index.\n vec3 sourceFracIndexRC = ${f};\n\n // Compute the coordinators of nearest neighbor point.\n ivec3 sourceNearestRC = ivec3(\n min(inputShapeRC - 1.0, floor(sourceFracIndexRC + ${h})));\n\n // Should we calculate next column and row elements in 2x2 packed cell.\n bool hasNextCol = d < ${l-1};\n bool hasNextRow = coords.z < ${n-1};\n\n vec4 newValue = vec4(\n getAValue(b, sourceNearestRC.x, sourceNearestRC.y, d),\n hasNextCol ? getAValue(b, sourceNearestRC.x, sourceNearestRC.y, d + 1)\n : 0.0,\n hasNextRow ? getAValue(b, sourceNearestRC.x, sourceNearestRC.z, d)\n : 0.0,\n (hasNextRow && hasNextCol) ?\n getAValue(b, sourceNearestRC.x, sourceNearestRC.z, d + 1) : 0.0);\n\n setOutput(newValue);\n }\n `}}const G$={kernelName:tr,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n,attrs:r}=e,{images:a}=t,{alignCorners:s,halfPixelCenters:i,size:o}=r,[l,u]=o,c=We().getBool("WEBGL_PACK_IMAGE_OPERATIONS")?new H$(a.shape,l,u,s,i):new j$(a.shape,l,u,s,i);return n.runWebGLProgram(c,[a],a.dtype)}};class X${constructor(e,t,n){this.variableNames=["dy"],this.outputShape=[],this.outputShape=t;const[,r,a]=t,[,s,i]=e,o=[n&&s>1?r-1:r,n&&i>1?a-1:a],l=[n&&s>1?s-1:s,n&&i>1?i-1:i],u=o[0]/l[0],c=o[1]/l[1],h=1/u,f=1/c,d=2*Math.ceil(h)+2,p=2*Math.ceil(f)+2;this.userCode=`\n void main() {\n ivec4 coords = getOutputCoords();\n int b = coords[0];\n int d = coords[3];\n int r = coords[1];\n int c = coords[2];\n\n float accumulator = 0.0;\n\n const float heightScale = float(${u});\n const float widthScale = float(${c});\n\n const float invHeightScale = float(${h});\n const float invWidthScale = float(${f});\n\n const int winHeight = int(${d});\n const int winWidth = int(${p});\n\n // Compute bounds for where in dy we will look\n float startRLerp = floor(float(r) * invHeightScale);\n int startDyR = int(floor(startRLerp - float(winHeight / 2)));\n\n float startCLerp = floor(float(c) * invWidthScale);\n int startDyC = int(floor(startCLerp - float(winWidth / 2)));\n\n // Loop over dy\n for (int dyROffset = 0; dyROffset < winHeight; dyROffset++) {\n int dyR = dyROffset + startDyR;\n\n // Guard against the window exceeding the bounds of dy\n if (dyR < 0 || dyR >= ${s}) {\n continue;\n }\n\n for (int dyCOffset = 0; dyCOffset < winWidth; dyCOffset++) {\n int dyC = dyCOffset + startDyC;\n\n // Guard against the window exceeding the bounds of dy\n if (dyC < 0 || dyC >= ${i}) {\n continue;\n }\n\n float sourceFracRow =\n float(${o[0]}) *\n (float(dyR) / float(${l[0]}));\n\n float sourceFracCol =\n float(${o[1]}) *\n (float(dyC) / float(${l[1]}));\n\n int sourceNearestRow = int(min(\n float(int(${r}) - 1),\n ${n} ? float(round(sourceFracRow)) :\n float(floor(sourceFracRow))));\n\n int sourceNearestCol = int(min(\n float(int(${a}) - 1),\n ${n} ? float(round(sourceFracCol)) :\n float(floor(sourceFracCol))));\n\n if (r == sourceNearestRow && c == sourceNearestCol) {\n accumulator += getDy(b, dyR, dyC, d);\n }\n }\n }\n // End loop over dy\n\n setOutput(accumulator);\n }\n `}}const K$={kernelName:nr,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n,attrs:r}=e,{images:a,dy:s}=t,{alignCorners:i}=r,o=new X$(s.shape,a.shape,i);return n.runWebGLProgram(o,[s],s.dtype)}};class q${constructor(e,t){this.variableNames=["x"];const n=e.length;if(n>4)throw new Error(`WebGL backend: Reverse of rank-${n} tensor is not yet supported`);if(this.outputShape=e,1===n)return void(this.userCode=`\n void main() {\n int coord = getOutputCoords();\n setOutput(getX(${e[0]} - coord - 1));\n }\n `);const r=e.map(((n,r)=>(n=>-1!==t.indexOf(n)&&1!==e[n]?`${e[n]} - coords[${n}] - 1`:`coords[${n}]`)(r))).join(","),a=LT(n);this.userCode=`\n void main() {\n ${a} coords = getOutputCoords();\n setOutput(getX(${r}));\n }\n `}}class Y${constructor(e,t){this.variableNames=["x"],this.packedInputs=!0,this.packedOutput=!0;const n=e.length;if(n>4)throw new Error(`WebGL backend: Reverse of rank-${n} tensor is not yet supported`);this.outputShape=e;const r=_F("rc",n),a=`${r[n-1]} + 1 < ${this.outputShape[n-1]}`,s=`${r[n-2]} + 1 < ${this.outputShape[n-2]}`,i=LT(n);function o(n){const r=e.map(((r,a)=>function(n,r){return-1!==t.indexOf(n)&&1!==e[n]?`${e[n]} - ${r[n]} - 1`:`${r[n]}`}(a,n)));return`getChannel(getX(${r.join(",")}), vec2(${r.slice(-2).join(",")}))`}this.userCode=1===n?`\n void main(){\n int rc = getOutputCoords();\n vec4 result = vec4(0.);\n result.r = getChannel(getX(${e[0]} - rc - 1),\n ${e[0]} - rc - 1);\n if(${a}){\n result.g = getChannel(getX(${e[0]} - (rc + 1) - 1),\n ${e[0]} - (rc + 1) - 1);\n }\n setOutput(result);\n }\n `:`\n void main() {\n ${i} rc = getOutputCoords();\n vec4 result = vec4(0.);\n result.r = ${function(e){return o(e)}(r.slice())};\n if(${a}){\n result.g = ${function(e){return e[n-1]="("+e[n-1]+" + 1)",o(e)}(r.slice())};\n }\n if(${s}) {\n result.b = ${function(e){return e[n-2]="("+e[n-2]+" + 1)",o(e)}(r.slice())};\n if(${a}) {\n result.a = ${function(e){return e[n-1]="("+e[n-1]+" + 1)",e[n-2]="("+e[n-2]+" + 1)",o(e)}(r.slice())};\n }\n }\n setOutput(result);\n }\n `}}const J$={kernelName:ir,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{dims:s}=r,i=a.shape.length,o=de(s,a.shape);if(0===i)return PF({inputs:{x:a},backend:n});const l=We().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new Y$(a.shape,o):new q$(a.shape,o);return n.runWebGLProgram(l,[a],a.dtype)}};class Z${constructor(e,t){this.variableNames=["Image"],this.outputShape=[];const n=e[1],r=e[2];this.outputShape=e;let a="";a="number"==typeof t?`float outputValue = ${t.toFixed(2)};`:`\n vec3 fill = vec3(${t.join(",")});\n float outputValue = fill[coords[3]];`,this.userCode=`\n uniform vec4 params;\n void main() {\n ivec4 coords = getOutputCoords();\n int x = coords[2];\n int y = coords[1];\n float coordXFloat = (float(x) - params[0]) * params[3] -\n (float(y) - params[1]) * params[2];\n float coordYFloat = (float(x) - params[0]) * params[2] +\n (float(y) - params[1]) * params[3];\n int coordX = int(round(coordXFloat + params[0]));\n int coordY = int(round(coordYFloat + params[1]));\n ${a}\n if(coordX >= 0 && coordX < ${r} && coordY >= 0 && coordY < ${n}) {\n outputValue = getImage(coords[0], coordY, coordX, coords[3]);\n }\n setOutput(outputValue);\n }\n `}getCustomSetupFunc(e,t,n,r){return(a,s)=>{null==this.paramsLoc&&(this.paramsLoc=a.getUniformLocationNoThrow(s,"params")),a.gl.uniform4f(this.paramsLoc,e,t,n,r)}}}const Q$={kernelName:Ur,backendName:"webgl",kernelFunc:({inputs:e,attrs:t,backend:n})=>{const{image:r}=e,{radians:a,fillValue:s,center:i}=t,o=n,l=new Z$(r.shape,s),[u,c]=Zf(i,r.shape[1],r.shape[2]),h=l.getCustomSetupFunc(u,c,Math.sin(a),Math.cos(a));return o.runWebGLProgram(l,[r],r.dtype,h)}},eP=KF({opSnippet:"\n // OpenGL ES does not support round function.\n // The algorithm is based on banker's rounding.\n float base = floor(x);\n if ((x - base) < 0.5) {\n return floor(x);\n } else if ((x - base) > 0.5) {\n return ceil(x);\n } else {\n if (mod(base, 2.0) == 0.0) {\n return base;\n } else {\n return base + 1.0;\n }\n }\n"}),tP={kernelName:or,backendName:"webgl",kernelFunc:eP},nP=KF({opSnippet:"return inversesqrt(x);",cpuKernelImpl:uF}),rP={kernelName:lr,backendName:"webgl",kernelFunc:nP};class aP{constructor(e,t,n,r,a,s,i=!0){this.variableNames=["updates","indices","defaultValue"],this.outputShape=s;const o=LT(a.length),l=LT(s.length);let u="";1===n?u="i":2===n&&(u="i, j");const c=`getIndices(${u})`;let h="";1===r?h="i":2===r&&(h="i, coords[1]");const f=`getUpdates(${h})`,d=t>1?"strides[j]":"strides";this.userCode=`\n ${o} strides = ${o}(${a});\n\n void main() {\n ${l} coords = getOutputCoords();\n float sum = 0.0;\n bool found = false;\n for (int i = 0; i < ${e}; i++) {\n int flattenedIndex = 0;\n for (int j = 0; j < ${t}; j++) {\n int index = round(${c});\n flattenedIndex += index * ${d};\n }\n if (flattenedIndex == coords[0]) {\n sum += ${f};\n found = true;\n }\n }\n setOutput(mix(getDefaultValue(), sum, float(found)));\n }\n `}}const sP={kernelName:ur,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n,attrs:r}=e,{indices:a,updates:s}=t,{shape:i}=r,{sliceRank:o,numUpdates:l,sliceSize:u,strides:c,outputSize:h}=Ni(0,a,i),f=[h/u,u];if(0===h)return n.makeTensorInfo(i,a.dtype);const d=nR({inputs:{x:a},backend:n,attrs:{shape:[l,o]}}),p=nR({inputs:{x:s},backend:n,attrs:{shape:[l,u]}}),m=n.makeTensorInfo([],"float32",new Float32Array([0])),g=new aP(l,o,d.shape.length,p.shape.length,c,f),y=n.runWebGLProgram(g,[p,d,m],p.dtype),b=nR({inputs:{x:y},backend:n,attrs:{shape:i}});return n.disposeIntermediateTensorInfo(d),n.disposeIntermediateTensorInfo(p),n.disposeIntermediateTensorInfo(y),n.disposeIntermediateTensorInfo(m),b}};class iP{constructor(e,t,n){let r,a;if(this.variableNames=["c","a","b"],this.outputShape=t,n>4)throw Error(`Where for rank ${n} is not yet supported`);if(1===n)a="resRC",r="resRC";else{const n=["resRC.x","resRC.y","resRC.z","resRC.w"],s=[],i=[];for(let r=0;r= 1.0) {\n setOutput(getA(${a}));\n } else {\n setOutput(getB(${a}));\n }\n }\n `}}const oP={kernelName:cr,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n}=e,{condition:r,t:a,e:s}=t,i=new iP(r.shape.length,a.shape,a.shape.length);return n.runWebGLProgram(i,[r,a,s],Da(a.dtype,s.dtype))}},lP=KF({opSnippet:`\n // Stable and Attracting Fixed Point (0, 1) for Normalized Weights.\n // see: https://arxiv.org/abs/1706.02515\n float scaleAlpha = ${ad};\n float scale = ${sd};\n return (x >= 0.0) ? scale * x : scaleAlpha * (exp(x) - 1.0);\n`}),uP={kernelName:hr,backendName:"webgl",kernelFunc:lP},cP=KF({opSnippet:"return 1.0 / (1.0 + exp(-1.0 * x));"}),hP={kernelName:gr,backendName:"webgl",kernelFunc:cP},fP=KF({opSnippet:"\n if (isnan(x)) { return 0.0; }\n return sign(x);\n"}),dP={kernelName:mr,backendName:"webgl",kernelFunc:fP},pP=KF({opSnippet:"if (isnan(x)) return x;\n return sin(x);\n"}),mP={kernelName:dr,backendName:"webgl",kernelFunc:pP},gP=KF({opSnippet:"\n float e2x = exp(x);\n return (e2x - 1.0 / e2x) / 2.0;\n"}),yP={kernelName:pr,backendName:"webgl",kernelFunc:gP},bP=KF({opSnippet:"\n float epsilon = 1.1920928955078125e-7;\n float threshold = log(epsilon) + 2.0;\n\n bool too_large = x > -threshold;\n bool too_small = x < threshold;\n\n float result;\n float exp_x = exp(x);\n\n if (too_large){\n result = x;\n }\n else if (too_small){\n result = exp_x;\n }\n else{\n result = log(exp_x + 1.0);\n }\n return result;\n"}),vP={kernelName:yr,backendName:"webgl",kernelFunc:bP},wP={kernelName:wr,backendName:"webgl",kernelFunc:e=>{const{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{blockShape:s,paddings:i}=r;Q(a.shape.length<=4,(()=>"spaceToBatchND for rank > 4 with a WebGL backend not implemented yet"));const o=s.reduce(((e,t)=>e*t)),l=[[0,0]];l.push(...i);for(let e=1+s.length;en.disposeIntermediateTensorInfo(e))),g}},_P={kernelName:kr,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n}=e,{indices:r,values:a,denseShape:s,defaultValue:i}=t;if(1!==s.shape.length)throw new Error(`Dense shape must be a vector, saw:\n ${s.shape}`);if(2!==r.shape.length)throw new Error(`Indices must be a matrix, saw:\n ${r.shape}`);if(1!==a.shape.length)throw new Error(`Values must be a vector, saw:\n ${a.shape}`);if(0!==i.shape.length)throw new Error(`Default value must be a scalar, saw:\n ${i.shape}`);const o=n.readSync(r.dataId),l=n.readSync(a.dataId),u=n.readSync(s.dataId),c=n.readSync(i.dataId)[0],[h,f,d,p,m]=fF(o,r.shape,r.dtype,l,a.dtype,u,c);return[n.makeTensorInfo(f,r.dtype,h),n.makeTensorInfo([f[0]],a.dtype,d),n.makeTensorInfo([p.length],"bool",new Uint8Array(p.map((e=>Number(e))))),n.makeTensorInfo([m.length],r.dtype,new Int32Array(m))]}},xP={kernelName:Er,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n}=e,{inputIndices:r,inputShape:a,newShape:s}=t;if(2!==r.shape.length)throw new Error(`Input indices should be a matrix but received shape ${r.shape}`);if(1!==a.shape.length)throw new Error(`Input shape should be a vector but received shape ${a.shape}`);if(1!==s.shape.length)throw new Error(`Target shape should be a vector but received shape ${s.shape}`);const i=Array.from(n.readSync(a.dataId)),o=n.readSync(r.dataId),l=Array.from(n.readSync(s.dataId)),[u,c,h]=dF(o,r.shape,r.dtype,i,l);return[n.makeTensorInfo(c,r.dtype,u),n.makeTensorInfo([h.length],s.dtype,new Int32Array(h))]}},kP={kernelName:Cr,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n,attrs:r}=e,{sparseIndices:a,sparseValues:s,defaultValue:i}=t,{outputShape:o}=r,{sliceRank:l,numUpdates:u,strides:c,outputSize:h}=Ni(0,a,o),f=new aP(u,l,a.shape.length,s.shape.length,c,[h,1],!1),d=n.runWebGLProgram(f,[s,a,i],s.dtype),p=nR({inputs:{x:d},backend:n,attrs:{shape:o}});return n.disposeIntermediateTensorInfo(d),p}},EP={kernelName:_r,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{numOrSizeSplits:s,axis:i}=r,o=de(i,a.shape)[0],l=Ad(a,s,o),u=a.shape.length,c=new Array(u).fill(0),h=a.shape.slice();return l.map((e=>{const t=[...h];t[o]=e;const r=lO({inputs:{x:a},backend:n,attrs:{begin:c,size:t}});return c[o]+=e,r}))}},CP=KF({opSnippet:"return sqrt(x);"}),SP={kernelName:br,backendName:"webgl",kernelFunc:CP},NP=KF({opSnippet:"return x * x;"}),IP={kernelName:Nr,backendName:"webgl",kernelFunc:NP},AP="return (a - b) * (a - 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OP={kernelName:Ir,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{begin:s,end:i,strides:o,beginMask:l,endMask:u,ellipsisMask:c,newAxisMask:h,shrinkAxisMask:f}=r,{nonStrided:d,$begin:p,$strides:m,size:g,newShape:y,outShape:b}=Vi(a.shape,s,i,o,l,u,c,h,f),v=nR({inputs:{x:a},backend:n,attrs:{shape:y}});let w;if(d){const e=lO({inputs:{x:v},backend:n,attrs:{begin:p,size:g}});w=nR({inputs:{x:e},backend:n,attrs:{shape:b}}),n.disposeIntermediateTensorInfo(e)}else if(b.some((e=>0===e)))w=n.makeTensorInfo(b,a.dtype,[]);else if(n.shouldExecuteOnCPU([v])){const e=n.texData.get(v.dataId).values,t=Xs(v.shape,v.dtype,e),r=pF(b,t,m,p);w=n.makeTensorInfo(b,v.dtype,r.values)}else{const e=new RP(p,m,b);w=n.runWebGLProgram(e,[v],v.dtype)}const _=nR({inputs:{x:w},backend:n,attrs:{shape:b}});return n.disposeIntermediateTensorInfo(v),n.disposeIntermediateTensorInfo(w),_}},MP=KF({opSnippet:"return 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Axis of 1 must be accompanied by an array of column(s) names");var a=this,s={},i=[],o=r.map((function(e){var t=a.columns.indexOf(e);if(-1==t)throw new Error('column "'.concat(e,'" does not exist'));return t}));if(this.col_data.forEach((function(e,t){o.includes(t)||(s[a.column_names[t]]=e,i.push(a.dtypes[t]))})),!t.inplace){var l=a.columns,u=Object.keys(s),c=new DataFrame(s,{index:a.index,dtypes:i});return c.__set_col_property(c,c.col_data,u,l),c}var h=a.columns,f=Object.keys(s);this.__update_frame_in_place(null,null,s,null,i),this.__set_col_property(a,a.col_data,f,h)}else{if(!utils.__key_in_object(t,"index"))throw Error("No index label found. Axis of 0 must be accompanied by an array of index labels");r.forEach((function(t){if(!e.index.includes(t))throw new Error("".concat(t," does not exist in index"))}));var d,p,m=this.values,g=[];if("string"==typeof r[0]?(this.index.forEach((function(e,t){r.includes(e)&&g.push(t)})),d=utils.__remove_arr(m,g),p=utils.__remove_arr(this.index,g)):(d=utils.__remove_arr(m,r),p=utils.__remove_arr(this.index,r)),!t.inplace)return new DataFrame(d,{columns:this.columns,index:p});this.row_data_tensor=_tensorflow_tfjs__WEBPACK_IMPORTED_MODULE_10__.tensor(d),this.data=d,this.__set_index(p)}}},{key:"loc",value:function(){var e=arguments.length>0&&void 0!==arguments[0]?arguments[0]:{},t=["columns","rows"];utils._throw_wrong_params_error(e,t),e.type="loc";var n=(0,_indexing__WEBPACK_IMPORTED_MODULE_13__.j)(this,e),r=_babel_runtime_helpers_slicedToArray__WEBPACK_IMPORTED_MODULE_1___default()(n,3),a=r[0],s=r[1],i=r[2],o={columns:s},l=new DataFrame(a,o);return l.__set_index(i),l}},{key:"iloc",value:function(){var e=arguments.length>0&&void 0!==arguments[0]?arguments[0]:{},t=["columns","rows"];utils._throw_wrong_params_error(e,t),e.type="iloc";var n=(0,_indexing__WEBPACK_IMPORTED_MODULE_13__.j)(this,e),r=_babel_runtime_helpers_slicedToArray__WEBPACK_IMPORTED_MODULE_1___default()(n,3),a=r[0],s=r[1],i=r[2],o={columns:s},l=new DataFrame(a,o);return l.__set_index(i),l}},{key:"head",value:function(){var e=arguments.length>0&&void 0!==arguments[0]?arguments[0]:5;if(e>this.values.length||e<1)return this;var t=this.values.slice(0,e),n=this.index.slice(0,e),r={columns:this.column_names,index:n},a=new DataFrame(t,r);return a}},{key:"tail",value:function(){var e=arguments.length>0&&void 0!==arguments[0]?arguments[0]:5,t=this.values.length;if(e>t||e<1)return this;var n=this.values.slice(t-e),r=this.index.slice(t-e),a={columns:this.column_names,index:r},s=new DataFrame(n,a);return s}},{key:"sample",value:(_sample=_babel_runtime_helpers_asyncToGenerator__WEBPACK_IMPORTED_MODULE_3___default()(_babel_runtime_regenerator__WEBPACK_IMPORTED_MODULE_2___default().mark((function e(){var t,n,r,a,s=arguments;return _babel_runtime_regenerator__WEBPACK_IMPORTED_MODULE_2___default().wrap((function(e){for(;;)switch(e.prev=e.next){case 0:if(t=s.length>0&&void 0!==s[0]?s[0]:-1,n=s.length>1&&void 0!==s[1]?s[1]:1,!(t>this.shape[0])){e.next=4;break}throw new Error("Sample size n cannot be bigger than size of dataset");case 4:if(!(t<-1||0==t)){e.next=6;break}throw new Error("Sample size cannot be less than -1 or 0");case 6:return t=-1===t?this.shape[0]:t,e.next=9,_tensorflow_tfjs__WEBPACK_IMPORTED_MODULE_10__.data.array(this.index).shuffle(t,n).take(t).toArray();case 9:return r=e.sent,a=this.iloc({rows:r}),e.abrupt("return",a);case 12:case"end":return e.stop()}}),e,this)}))),function(){return 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Error("TypeError: Dtypes of columns must be Float of Int")}},{key:"pow",value:function(e,t){if(this.__frame_is_compactible_for_operation){var n=this.__get_ops_tensors([this,e],t),r=n[0].pow(n[1]),a=this.columns;return this.__get_df_from_tensor(r,a)}throw Error("TypeError: Dtypes of columns must be Float of Int")}},{key:"mod",value:function(e,t){if(this.__frame_is_compactible_for_operation){var n=this.__get_ops_tensors([this,e],t),r=n[0].mod(n[1]),a=this.columns;return this.__get_df_from_tensor(r,a)}throw Error("TypeError: Dtypes of columns must be Float of Int")}},{key:"mean",value:function(){var e=arguments.length>0&&void 0!==arguments[0]?arguments[0]:1;if(this.__frame_is_compactible_for_operation()){var t,n=[];(1==e?this.col_data:this.values).map((function(e){var t=utils._remove_nans(e),r=_tensorflow_tfjs__WEBPACK_IMPORTED_MODULE_10__.tensor(t).mean().arraySync();n.push(Number(r.toFixed(5)))})),t=1==e?this.column_names:this.index;var r=new _series__WEBPACK_IMPORTED_MODULE_12__.F(n,{columns:"sum",index:t});return r}throw Error("Dtype Error: Operation can not be performed on string type")}},{key:"median",value:function(){var e=arguments.length>0&&void 0!==arguments[0]?arguments[0]:1;if(this.__frame_is_compactible_for_operation){var t,n;1==e?(t=this.col_data_tensor.arraySync(),n=this.column_names):(t=this.row_data_tensor.arraySync(),n=this.index);var r=utils.__median(t,!1),a=new _series__WEBPACK_IMPORTED_MODULE_12__.F(r,{index:n});return a}throw Error("TypeError: Dtypes of columns must be Float of Int")}},{key:"min",value:function(){var e=arguments.length>0&&void 0!==arguments[0]?arguments[0]:1;if(this.__frame_is_compactible_for_operation){var t=this.__get_tensor_and_idx(this,e),n=t[0],r=t[1],a=n.min(t[2]),s=new _series__WEBPACK_IMPORTED_MODULE_12__.F(a.arraySync(),{index:r});return s}throw Error("TypeError: Dtypes of columns must be Float of Int")}},{key:"max",value:function(){var e=arguments.length>0&&void 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0!==arguments[0]?arguments[0]:0,t=arguments.length>1?arguments[1]:void 0;if(0!=e&&1!=e)throw new Error("axis must be between 0 or 1");if(this.__frame_is_compactible_for_operation){var n=[],r=null;r=0==e?this.col_data:this.values;for(var a=0;ai?(i=u,o.push(u)):o.push(i);break;case"min":u0&&void 0!==arguments[0]?arguments[0]:{};e=utils.__key_in_object(t,"axis")?t.axis:0;var n=this.__cum_ops(e,"sum");return n}},{key:"cummin",value:function(){var e,t=arguments.length>0&&void 0!==arguments[0]?arguments[0]:{};e=utils.__key_in_object(t,"axis")?t.axis:0;var n=this.__cum_ops(e,"min");return n}},{key:"cummax",value:function(){var e,t=arguments.length>0&&void 0!==arguments[0]?arguments[0]:{};e=utils.__key_in_object(t,"axis")?t.axis:0;var n=this.__cum_ops(e,"max");return n}},{key:"cumprod",value:function(){var e,t=arguments.length>0&&void 0!==arguments[0]?arguments[0]:{};e=utils.__key_in_object(t,"axis")?t.axis:0;var n=this.__cum_ops(e,"prod");return n}},{key:"copy",value:function(){return new DataFrame(_babel_runtime_helpers_toConsumableArray__WEBPACK_IMPORTED_MODULE_0___default()(this.values),{columns:_babel_runtime_helpers_toConsumableArray__WEBPACK_IMPORTED_MODULE_0___default()(this.column_names),index:this.index,dtypes:this.dtypes})}},{key:"reset_index",value:function(){var e=arguments.length>0&&void 0!==arguments[0]&&arguments[0];if(!e){var t=this.copy();return t.__reset_index(),t}this.__reset_index()}},{key:"set_index",value:function(){var e=arguments.length>0&&void 0!==arguments[0]?arguments[0]:{},t=["key","drop","inplace"];if(utils._throw_wrong_params_error(e,t),!utils.__key_in_object(e,"key"))throw Error("Index ValueError: You must specify an array of index");if(utils.__key_in_object(e,"inplace")||(e.inplace=!1),utils.__key_in_object(e,"drop")||(e.drop=!0),Array.isArray(e.key)&&e.key.length!=this.index.length)throw Error("Index LengthError: Lenght of new Index array ".concat(e.key.length," must match lenght of existing index 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1==t?(n=e.row_data_tensor,r=e.column_names,a=0):(n=e.row_data_tensor,r=e.index,a=1),[n,r,a]}},{key:"query",value:function query(kwargs){var operators=[">","<","<=",">=","==","!="],column_index,operator,value;if(utils.__key_in_object(kwargs,"inplace")||(kwargs.inplace=!1),!utils.__key_in_object(kwargs,"column"))throw new Error("specify the column");if(!this.columns.includes(kwargs.column))throw new Error("column ".concat(kwargs.column," does not exist"));if(column_index=this.columns.indexOf(kwargs.column),!utils.__key_in_object(kwargs,"is"))throw new Error("specify an operator in param [is]");if(!operators.includes(kwargs.is))throw new Error(" ".concat(kwargs.is," is not a supported logical operator"));if(operator=kwargs.is,!utils.__key_in_object(kwargs,"to"))throw new Error("specify a value in param [to]");value=kwargs.to;for(var data=this.values,index=this.index,new_data=[],new_index=[],i=0;i0&&void 0!==arguments[0]?arguments[0]:{},n=["columns","values","inplace"];if(utils._throw_wrong_params_error(t,n),utils.__key_in_object(t,"inplace")||(t.inplace=!1),utils.__key_in_object(t,"columns")){if(t.columns.map((function(t){if(!e.column_names.includes(t))throw Error("Value Error: Specified columns must be one of ".concat(e.column_names,", got ").concat(t))})),t.columns.length!=t.values.length)throw Error("Lenght Error: The lenght of the columns names must be equal to the lenght of the values,\n got column of length ".concat(t.columns.length," but values of length ").concat(t.values.length));var r=this.col_data;t.columns.map((function(n,a){var s=e.column_names.indexOf(n),i=e.col_data[s],o=[];i.map((function(e){isNaN(e)&&"string"!=typeof e?o.push(t.values[a]):o.push(e)})),r[s]=o}));var a={};if(r.map((function(t,n){a[e.column_names[n]]=t})),!t.inplace)return new DataFrame(a,{index:this.index});this.__update_frame_in_place(null,null,a,null,null)}else{if(!utils.__key_in_object(t,"values"))throw Error("Value Error: Please specify a fill value");var s;s=Array.isArray(t.values)?t.values[0]:t.values;for(var i=[],o=this.values,l=this.columns,u=0;u0&&void 0!==arguments[0]?arguments[0]:{},t=e.axis||0,n=e.inplace||!1;if(0!=t&&1!=t)throw new Error("axis must either be 1 or 0");var r=null,a=null;0==t?(r=this.values,a=this.columns):(r=this.col_data,a=[]);for(var s=[],i=0;i0&&void 0!==arguments[0]?arguments[0]:{},n=["replace","with","in"];if(utils._throw_wrong_params_error(t,n),utils.__key_in_object(t,"in")){if(t.in.map((function(t){if(!e.column_names.includes(t))throw Error("Value Error: Specified columns must be one of ".concat(e.column_names,", got ").concat(t))})),utils.__key_in_object(t,"replace")&&utils.__key_in_object(t,"with")){var r={};return this.column_names.map((function(n,a){if(t.in.includes(n)){var s=e.col_data[a],i=[];s.map((function(e){e==t.replace?i.push(t.with):i.push(e)})),r[n]=i}else r[n]=e.col_data[a]})),new DataFrame(r,{columns:this.column_names,index:this.index})}throw Error("Params Error: Must specify both 'replace' and 'with' parameters.")}if(utils.__key_in_object(t,"replace")&&utils.__key_in_object(t,"with")){var a=[],s=this.values;s.map((function(e){var n=[];e.map((function(e){e==t.replace?n.push(t.with):n.push(e)})),a.push(n)}));var i=new DataFrame(a,{index:this.index,columns:this.column_names});return i}throw Error("Params Error: Must specify both 'replace' and 'with' parameters.")}},{key:"__logical_ops",value:function(e,t,n){var r,a;if(utils.__is_number(e))a=e;else if(e.series)if(0==n){if(e.values.length!=this.shape[0])throw Error("Shape Error: Operands could not be broadcast together with shapes ".concat(this.shape," and ").concat(e.values.length,"."));a=_tensorflow_tfjs__WEBPACK_IMPORTED_MODULE_10__.tensor(e.values)}else{if(e.values.length!=this.shape[1])throw 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DataFrame(utils.__map_int_to_bool(r,2),{columns:this.column_names,index:this.index})}},{key:"__get_df_from_tensor",value:function(e,t){for(var n=e.shape[0],r=[],a=0;a0&&void 0!==arguments[0]?arguments[0]:{};if(!utils.__key_in_object(e,"column"))throw Error("Value Error: Please specify a column to cast");if(!utils.__key_in_object(e,"dtype"))throw Error("Value Error: Please specify dtype to cast to");if(!this.column_names.includes(e.column))throw Error("'".concat(e.column,"' not found in columns"));var t=this.column_names.indexOf(e.column),n=this.col_types,r=this.col_data;n[t]=e.dtype;var a=[],s=r[t];switch(e.dtype){case"float32":s.map((function(e){a.push(Number(e))})),r[t]=a;break;case"int32":s.map((function(e){a.push(Number(Number(e).toFixed()))})),r[t]=a;break;case"string":s.map((function(e){a.push(String(e))})),r[t]=a}var i={};this.column_names.forEach((function(e,t){i[e]=r[t]}));var o=new DataFrame(i,{dtypes:n,index:this.index});return o}},{key:"unique",value:function(){var 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t=e.typed,n=e.config,r=e.matrix,a=e.DenseMatrix,s=He({typed:t}),i=qe({typed:t,DenseMatrix:a}),o=Ke({typed:t,DenseMatrix:a}),l=Ve({typed:t}),u=je({typed:t});return t(at,{"boolean, boolean":function(e,t){return et},"number, number":function(e,t){return e>t&&!(0,g.RI)(e,t,n.epsilon)},"BigNumber, BigNumber":function(e,t){return e.gt(t)&&!ce(e,t,n.epsilon)},"Fraction, Fraction":function(e,t){return 1===e.compare(t)},"Complex, Complex":function(){throw new TypeError("No ordering relation is defined for complex numbers")},"Unit, Unit":function(e,t){if(!e.equalBase(t))throw new Error("Cannot compare units with different base");return this(e.value,t.value)},"SparseMatrix, SparseMatrix":function(e,t){return i(e,t,this)},"SparseMatrix, DenseMatrix":function(e,t){return s(t,e,this,!0)},"DenseMatrix, SparseMatrix":function(e,t){return s(e,t,this,!1)},"DenseMatrix, DenseMatrix":function(e,t){return l(e,t,this)},"Array, Array":function(e,t){return this(r(e),r(t)).valueOf()},"Array, 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s(e,t,n){t.left.right=t.right,t.right.left=t.left,n.degree--,n.child===t&&(n.child=t.right),0===n.degree&&(n.child=null),t.left=e,t.right=e.right,e.right=t,t.right.left=t,t.parent=null,t.mark=!1}function i(e,t){var n=t.parent;n&&(t.mark?(s(e,t,n),i(n)):t.mark=!0)}a.prototype.type="FibonacciHeap",a.prototype.isFibonacciHeap=!0,a.prototype.insert=function(e,n){var r={key:e,value:n,degree:0};if(this._minimum){var a=this._minimum;r.left=a,r.right=a.right,a.right=r,r.right.left=r,t(e,a.key)&&(this._minimum=r)}else r.left=r,r.right=r,this._minimum=r;return this._size++,r},a.prototype.size=function(){return this._size},a.prototype.clear=function(){this._minimum=null,this._size=0},a.prototype.isEmpty=function(){return 0===this._size},a.prototype.extractMinimum=function(){var e=this._minimum;if(null===e)return e;for(var a=this._minimum,s=e.degree,i=e.child;s>0;){var l=i.right;i.left.right=i.right,i.right.left=i.left,i.left=a,i.right=a.right,a.right=i,i.right.left=i,i.parent=null,i=l,s--}return e.left.right=e.right,e.right.left=e.left,a=e===e.right?null:function(e,a){var s,i=Math.floor(Math.log(a)*r)+1,l=new Array(i),u=0,c=e;if(c)for(u++,c=c.right;c!==e;)u++,c=c.right;for(;u>0;){for(var h=c.degree,f=c.right;s=l[h];){if(n(c.key,s.key)){var d=s;s=c,c=d}o(s,c),l[h]=null,h++}l[h]=c,c=f,u--}e=null;for(var p=0;pm?d++:p===m&&(u=c(u,h(s[f],l[d])),f++,d++)}return u}});function o(e,t){var n,r,a=l(e),s=l(t);if(1===a.length)n=a[0];else{if(2!==a.length||1!==a[1])throw new RangeError("Expected a column vector, instead got a matrix of size ("+a.join(", ")+")");n=a[0]}if(1===s.length)r=s[0];else{if(2!==s.length||1!==s[1])throw new RangeError("Expected a column vector, instead got a matrix of size ("+s.join(", ")+")");r=s[0]}if(n!==r)throw new RangeError("Vectors must have equal length ("+n+" != "+r+")");if(0===n)throw new RangeError("Cannot calculate the dot product of empty vectors");return n}function l(e){return(0,i.SW)(e)?e.size():s(e)}})),ft=f("abs",["typed"],(function(e){return(0,e.typed)("abs",{number:ge,Complex:function(e){return e.abs()},BigNumber:function(e){return e.abs()},Fraction:function(e){return e.abs()},"Array | Matrix":function(e){return ne(e,this,!0)},Unit:function(e){return e.abs()}})})),dt="multiply",pt=f(dt,["typed","matrix","addScalar","multiplyScalar","equalScalar","dot"],(function(e){var t=e.typed,n=e.matrix,r=e.addScalar,a=e.multiplyScalar,s=e.equalScalar,o=e.dot,l=Xe({typed:t,equalScalar:s}),c=je({typed:t});function h(e,t){switch(e.length){case 1:switch(t.length){case 1:if(e[0]!==t[0])throw new RangeError("Dimension mismatch in multiplication. 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n.value=this(n.value,t.value),n.fixPrefix=!1,n},"SparseMatrix, SparseMatrix":function(e,t){return kt(e,t),u(e,t,this)},"SparseMatrix, DenseMatrix":function(e,t){return kt(e,t),l(t,e,this,!0)},"DenseMatrix, SparseMatrix":function(e,t){return kt(e,t),o(e,t,this,!1)},"DenseMatrix, DenseMatrix":function(e,t){return kt(e,t),h(e,t,this)},"Array, Array":function(e,t){return this(n(e),n(t)).valueOf()},"Array, Matrix":function(e,t){return this(n(e),t)},"Matrix, Array":function(e,t){return this(e,n(t))},"SparseMatrix, any":function(e,t){return c(e,s(t),a)},"DenseMatrix, any":function(e,t){return f(e,t,this)},"any, SparseMatrix":function(e,t){return c(t,e,this,!0)},"any, DenseMatrix":function(e,t){return f(t,e,this,!0)},"Array, any":function(e,t){return f(n(e),t,this,!1).valueOf()},"any, Array":function(e,t){return f(n(t),e,this,!0).valueOf()}})}));function kt(e,t){var n=e.size(),r=t.size();if(n.length!==r.length)throw new M(n.length,r.length)}var Et="equal",Ct=(f(Et,["typed","equalScalar"],(function(e){var t=e.typed,n=e.equalScalar;return t(Et,{"any, any":function(e,t){return null===e?null===t:null===t?null===e:void 0===e?void 0===t:void 0===t?void 0===e:n(e,t)}})})),"divideScalar"),St=f(Ct,["typed","numeric"],(function(e){var t=e.typed,n=e.numeric;return t(Ct,{"number, number":function(e,t){return e/t},"Complex, Complex":function(e,t){return e.div(t)},"BigNumber, BigNumber":function(e,t){return e.div(t)},"Fraction, Fraction":function(e,t){return e.div(t)},"Unit, number | Fraction | BigNumber":function(e,t){var r=e.clone(),a=n(1,(0,i.kM)(t));return r.value=this(null===r.value?r._normalize(a):r.value,t),r},"number | Fraction | BigNumber, Unit":function(e,t){var r=t.clone();r=r.pow(-1);var a=n(1,(0,i.kM)(e));return r.value=this(e,null===t.value?t._normalize(a):t.value),r},"Unit, Unit":function(e,t){return e.divide(t)}})})),Nt=f("Spa",["addScalar","equalScalar","FibonacciHeap"],(function(e){var t=e.addScalar,n=e.equalScalar,r=e.FibonacciHeap;function a(){if(!(this instanceof a))throw new SyntaxError("Constructor must be called with the new operator");this._values=[],this._heap=new r}return a.prototype.type="Spa",a.prototype.isSpa=!0,a.prototype.set=function(e,t){if(this._values[e])this._values[e].value=t;else{var n=this._heap.insert(e,t);this._values[e]=n}},a.prototype.get=function(e){var t=this._values[e];return t?t.value:0},a.prototype.accumulate=function(e,n){var r=this._values[e];r?r.value=t(r.value,n):(r=this._heap.insert(e,n),this._values[e]=r)},a.prototype.forEach=function(e,t,r){var a=this._heap,s=this._values,i=[],o=a.extractMinimum();for(o&&i.push(o);o&&o.key<=t;)o.key>=e&&(n(o.value,0)||r(o.key,o.value,this)),(o=a.extractMinimum())&&i.push(o);for(var l=0;l0&&e.forEach(0,n-1,(function(t,n){d._forEachRow(t,b,v,w,(function(r,a){r>t&&e.accumulate(r,h(i(a,n)))}))}));var I,A,D,T,F=n,R=e.get(n),O=r(R);e.forEach(n+1,o-1,(function(e,t){var n=r(t);u(n,O)&&(F=e,O=n,R=t)})),n!==F&&(d._swapRows(n,F,_[1],b,v,w),d._swapRows(n,F,C[1],x,k,E),e.swap(n,F),A=F,D=N[I=n],T=N[A],S[D]=A,S[T]=I,N[I]=T,N[A]=D),e.forEach(0,o-1,(function(e,t){e<=n?(x.push(t),k.push(e)):(t=s(t,R),c(t,0)||(b.push(t),v.push(e)))}))};for(n=0;n0)for(t=0;t=t)break;for(var p=f,m=0;!d[i.p[p]];)d[i.p[p]]=!0,p=i.p[p],m++;m%2==0&&h++}return h%2==0?u:s(u)}(e.clone().valueOf(),u);throw new RangeError("Matrix must be square (size: "+R(t)+")");default:throw new RangeError("Matrix must be two dimensional (size: "+R(t)+")")}}})})),Dt=f("inv",["typed","matrix","divideScalar","addScalar","multiply","unaryMinus","det","identity","abs"],(function(e){var t=e.typed,n=e.matrix,r=e.divideScalar,a=e.addScalar,s=e.multiply,o=e.unaryMinus,l=e.det,u=e.identity,c=e.abs;return t("inv",{"Array | Matrix":function(e){var t=(0,i.SW)(e)?e.size():$(e);switch(t.length){case 1:if(1===t[0])return(0,i.SW)(e)?n([r(1,e.valueOf()[0])]):[r(1,e[0])];throw new RangeError("Matrix must be square (size: 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0+e]=r[245][e]);for(r[246]="����������������������������������������������������������������鯜鯝鯞鯟鯠鯡鯢鯣鯤鯥鯦鯧鯨鯩鯪鯫鯬鯭鯮鯯鯰鯱鯲鯳鯴鯵鯶鯷鯸鯹鯺鯻鯼鯽鯾鯿鰀鰁鰂鰃鰄鰅鰆鰇鰈鰉鰊鰋鰌鰍鰎鰏鰐鰑鰒鰓鰔鰕鰖鰗鰘鰙鰚�鰛鰜鰝鰞鰟鰠鰡鰢鰣鰤鰥鰦鰧鰨鰩鰪鰫鰬鰭鰮鰯鰰鰱鰲鰳鰴鰵鰶鰷鰸鰹鰺鰻觥觫觯訾謦靓雩雳雯霆霁霈霏霎霪霭霰霾龀龃龅龆龇龈龉龊龌黾鼋鼍隹隼隽雎雒瞿雠銎銮鋈錾鍪鏊鎏鐾鑫鱿鲂鲅鲆鲇鲈稣鲋鲎鲐鲑鲒鲔鲕鲚鲛鲞鲟鲠鲡鲢鲣鲥鲦鲧鲨鲩鲫鲭鲮鲰鲱鲲鲳鲴鲵鲶鲷鲺鲻鲼鲽鳄鳅鳆鳇鳊鳋�".split(""),e=0;e!=r[246].length;++e)65533!==r[246][e].charCodeAt(0)&&(n[r[246][e]]=62976+e,t[62976+e]=r[246][e]);for(r[247]="����������������������������������������������������������������鰼鰽鰾鰿鱀鱁鱂鱃鱄鱅鱆鱇鱈鱉鱊鱋鱌鱍鱎鱏鱐鱑鱒鱓鱔鱕鱖鱗鱘鱙鱚鱛鱜鱝鱞鱟鱠鱡鱢鱣鱤鱥鱦鱧鱨鱩鱪鱫鱬鱭鱮鱯鱰鱱鱲鱳鱴鱵鱶鱷鱸鱹鱺�鱻鱽鱾鲀鲃鲄鲉鲊鲌鲏鲓鲖鲗鲘鲙鲝鲪鲬鲯鲹鲾鲿鳀鳁鳂鳈鳉鳑鳒鳚鳛鳠鳡鳌鳍鳎鳏鳐鳓鳔鳕鳗鳘鳙鳜鳝鳟鳢靼鞅鞑鞒鞔鞯鞫鞣鞲鞴骱骰骷鹘骶骺骼髁髀髅髂髋髌髑魅魃魇魉魈魍魑飨餍餮饕饔髟髡髦髯髫髻髭髹鬈鬏鬓鬟鬣麽麾縻麂麇麈麋麒鏖麝麟黛黜黝黠黟黢黩黧黥黪黯鼢鼬鼯鼹鼷鼽鼾齄�".split(""),e=0;e!=r[247].length;++e)65533!==r[247][e].charCodeAt(0)&&(n[r[247][e]]=63232+e,t[63232+e]=r[247][e]);for(r[248]="����������������������������������������������������������������鳣鳤鳥鳦鳧鳨鳩鳪鳫鳬鳭鳮鳯鳰鳱鳲鳳鳴鳵鳶鳷鳸鳹鳺鳻鳼鳽鳾鳿鴀鴁鴂鴃鴄鴅鴆鴇鴈鴉鴊鴋鴌鴍鴎鴏鴐鴑鴒鴓鴔鴕鴖鴗鴘鴙鴚鴛鴜鴝鴞鴟鴠鴡�鴢鴣鴤鴥鴦鴧鴨鴩鴪鴫鴬鴭鴮鴯鴰鴱鴲鴳鴴鴵鴶鴷鴸鴹鴺鴻鴼鴽鴾鴿鵀鵁鵂�����������������������������������������������������������������������������������������������".split(""),e=0;e!=r[248].length;++e)65533!==r[248][e].charCodeAt(0)&&(n[r[248][e]]=63488+e,t[63488+e]=r[248][e]);for(r[249]="����������������������������������������������������������������鵃鵄鵅鵆鵇鵈鵉鵊鵋鵌鵍鵎鵏鵐鵑鵒鵓鵔鵕鵖鵗鵘鵙鵚鵛鵜鵝鵞鵟鵠鵡鵢鵣鵤鵥鵦鵧鵨鵩鵪鵫鵬鵭鵮鵯鵰鵱鵲鵳鵴鵵鵶鵷鵸鵹鵺鵻鵼鵽鵾鵿鶀鶁�鶂鶃鶄鶅鶆鶇鶈鶉鶊鶋鶌鶍鶎鶏鶐鶑鶒鶓鶔鶕鶖鶗鶘鶙鶚鶛鶜鶝鶞鶟鶠鶡鶢�����������������������������������������������������������������������������������������������".split(""),e=0;e!=r[249].length;++e)65533!==r[249][e].charCodeAt(0)&&(n[r[249][e]]=63744+e,t[63744+e]=r[249][e]);for(r[250]="����������������������������������������������������������������鶣鶤鶥鶦鶧鶨鶩鶪鶫鶬鶭鶮鶯鶰鶱鶲鶳鶴鶵鶶鶷鶸鶹鶺鶻鶼鶽鶾鶿鷀鷁鷂鷃鷄鷅鷆鷇鷈鷉鷊鷋鷌鷍鷎鷏鷐鷑鷒鷓鷔鷕鷖鷗鷘鷙鷚鷛鷜鷝鷞鷟鷠鷡�鷢鷣鷤鷥鷦鷧鷨鷩鷪鷫鷬鷭鷮鷯鷰鷱鷲鷳鷴鷵鷶鷷鷸鷹鷺鷻鷼鷽鷾鷿鸀鸁鸂�����������������������������������������������������������������������������������������������".split(""),e=0;e!=r[250].length;++e)65533!==r[250][e].charCodeAt(0)&&(n[r[250][e]]=64e3+e,t[64e3+e]=r[250][e]);for(r[251]="����������������������������������������������������������������鸃鸄鸅鸆鸇鸈鸉鸊鸋鸌鸍鸎鸏鸐鸑鸒鸓鸔鸕鸖鸗鸘鸙鸚鸛鸜鸝鸞鸤鸧鸮鸰鸴鸻鸼鹀鹍鹐鹒鹓鹔鹖鹙鹝鹟鹠鹡鹢鹥鹮鹯鹲鹴鹵鹶鹷鹸鹹鹺鹻鹼鹽麀�麁麃麄麅麆麉麊麌麍麎麏麐麑麔麕麖麗麘麙麚麛麜麞麠麡麢麣麤麥麧麨麩麪�����������������������������������������������������������������������������������������������".split(""),e=0;e!=r[251].length;++e)65533!==r[251][e].charCodeAt(0)&&(n[r[251][e]]=64256+e,t[64256+e]=r[251][e]);for(r[252]="����������������������������������������������������������������麫麬麭麮麯麰麱麲麳麵麶麷麹麺麼麿黀黁黂黃黅黆黇黈黊黋黌黐黒黓黕黖黗黙黚點黡黣黤黦黨黫黬黭黮黰黱黲黳黴黵黶黷黸黺黽黿鼀鼁鼂鼃鼄鼅�鼆鼇鼈鼉鼊鼌鼏鼑鼒鼔鼕鼖鼘鼚鼛鼜鼝鼞鼟鼡鼣鼤鼥鼦鼧鼨鼩鼪鼫鼭鼮鼰鼱�����������������������������������������������������������������������������������������������".split(""),e=0;e!=r[252].length;++e)65533!==r[252][e].charCodeAt(0)&&(n[r[252][e]]=64512+e,t[64512+e]=r[252][e]);for(r[253]="����������������������������������������������������������������鼲鼳鼴鼵鼶鼸鼺鼼鼿齀齁齂齃齅齆齇齈齉齊齋齌齍齎齏齒齓齔齕齖齗齘齙齚齛齜齝齞齟齠齡齢齣齤齥齦齧齨齩齪齫齬齭齮齯齰齱齲齳齴齵齶齷齸�齹齺齻齼齽齾龁龂龍龎龏龐龑龒龓龔龕龖龗龘龜龝龞龡龢龣龤龥郎凉秊裏隣�����������������������������������������������������������������������������������������������".split(""),e=0;e!=r[253].length;++e)65533!==r[253][e].charCodeAt(0)&&(n[r[253][e]]=64768+e,t[64768+e]=r[253][e]);for(r[254]="����������������������������������������������������������������兀嗀﨎﨏﨑﨓﨔礼﨟蘒﨡﨣﨤﨧﨨﨩��������������������������������������������������������������������������������������������������������������������������������������������������������������������������������".split(""),e=0;e!=r[254].length;++e)65533!==r[254][e].charCodeAt(0)&&(n[r[254][e]]=65024+e,t[65024+e]=r[254][e]);return{enc:n,dec:t}}(),t[949]=function(){var e,t=[],n={},r=[];for(r[0]="\0\b\t\n\v\f\r !\"#$%&'()*+,-./0123456789:;<=>?@ABCDEFGHIJKLMNOPQRSTUVWXYZ[\\]^_`abcdefghijklmnopqrstuvwxyz{|}~��������������������������������������������������������������������������������������������������������������������������������".split(""),e=0;e!=r[0].length;++e)65533!==r[0][e].charCodeAt(0)&&(n[r[0][e]]=0+e,t[0+e]=r[0][e]);for(r[129]="�����������������������������������������������������������������갂갃갅갆갋갌갍갎갏갘갞갟갡갢갣갥갦갧갨갩갪갫갮갲갳갴������갵갶갷갺갻갽갾갿걁걂걃걄걅걆걇걈걉걊걌걎걏걐걑걒걓걕������걖걗걙걚걛걝걞걟걠걡걢걣걤걥걦걧걨걩걪걫걬걭걮걯걲걳걵걶걹걻걼걽걾걿겂겇겈겍겎겏겑겒겓겕겖겗겘겙겚겛겞겢겣겤겥겦겧겫겭겮겱겲겳겴겵겶겷겺겾겿곀곂곃곅곆곇곉곊곋곍곎곏곐곑곒곓곔곖곘곙곚곛곜곝곞곟곢곣곥곦곩곫곭곮곲곴곷곸곹곺곻곾곿괁괂괃괅괇괈괉괊괋괎괐괒괓�".split(""),e=0;e!=r[129].length;++e)65533!==r[129][e].charCodeAt(0)&&(n[r[129][e]]=33024+e,t[33024+e]=r[129][e]);for(r[130]="�����������������������������������������������������������������괔괕괖괗괙괚괛괝괞괟괡괢괣괤괥괦괧괨괪괫괮괯괰괱괲괳������괶괷괹괺괻괽괾괿굀굁굂굃굆굈굊굋굌굍굎굏굑굒굓굕굖굗������굙굚굛굜굝굞굟굠굢굤굥굦굧굨굩굪굫굮굯굱굲굷굸굹굺굾궀궃궄궅궆궇궊궋궍궎궏궑궒궓궔궕궖궗궘궙궚궛궞궟궠궡궢궣궥궦궧궨궩궪궫궬궭궮궯궰궱궲궳궴궵궶궸궹궺궻궼궽궾궿귂귃귅귆귇귉귊귋귌귍귎귏귒귔귕귖귗귘귙귚귛귝귞귟귡귢귣귥귦귧귨귩귪귫귬귭귮귯귰귱귲귳귴귵귶귷�".split(""),e=0;e!=r[130].length;++e)65533!==r[130][e].charCodeAt(0)&&(n[r[130][e]]=33280+e,t[33280+e]=r[130][e]);for(r[131]="�����������������������������������������������������������������귺귻귽귾긂긃긄긅긆긇긊긌긎긏긐긑긒긓긕긖긗긘긙긚긛긜������긝긞긟긠긡긢긣긤긥긦긧긨긩긪긫긬긭긮긯긲긳긵긶긹긻긼������긽긾긿깂깄깇깈깉깋깏깑깒깓깕깗깘깙깚깛깞깢깣깤깦깧깪깫깭깮깯깱깲깳깴깵깶깷깺깾깿꺀꺁꺂꺃꺆꺇꺈꺉꺊꺋꺍꺎꺏꺐꺑꺒꺓꺔꺕꺖꺗꺘꺙꺚꺛꺜꺝꺞꺟꺠꺡꺢꺣꺤꺥꺦꺧꺨꺩꺪꺫꺬꺭꺮꺯꺰꺱꺲꺳꺴꺵꺶꺷꺸꺹꺺꺻꺿껁껂껃껅껆껇껈껉껊껋껎껒껓껔껕껖껗껚껛껝껞껟껠껡껢껣껤껥�".split(""),e=0;e!=r[131].length;++e)65533!==r[131][e].charCodeAt(0)&&(n[r[131][e]]=33536+e,t[33536+e]=r[131][e]);for(r[132]="�����������������������������������������������������������������껦껧껩껪껬껮껯껰껱껲껳껵껶껷껹껺껻껽껾껿꼀꼁꼂꼃꼄꼅������꼆꼉꼊꼋꼌꼎꼏꼑꼒꼓꼔꼕꼖꼗꼘꼙꼚꼛꼜꼝꼞꼟꼠꼡꼢꼣������꼤꼥꼦꼧꼨꼩꼪꼫꼮꼯꼱꼳꼵꼶꼷꼸꼹꼺꼻꼾꽀꽄꽅꽆꽇꽊꽋꽌꽍꽎꽏꽑꽒꽓꽔꽕꽖꽗꽘꽙꽚꽛꽞꽟꽠꽡꽢꽣꽦꽧꽨꽩꽪꽫꽬꽭꽮꽯꽰꽱꽲꽳꽴꽵꽶꽷꽸꽺꽻꽼꽽꽾꽿꾁꾂꾃꾅꾆꾇꾉꾊꾋꾌꾍꾎꾏꾒꾓꾔꾖꾗꾘꾙꾚꾛꾝꾞꾟꾠꾡꾢꾣꾤꾥꾦꾧꾨꾩꾪꾫꾬꾭꾮꾯꾰꾱꾲꾳꾴꾵꾶꾷꾺꾻꾽꾾�".split(""),e=0;e!=r[132].length;++e)65533!==r[132][e].charCodeAt(0)&&(n[r[132][e]]=33792+e,t[33792+e]=r[132][e]);for(r[133]="�����������������������������������������������������������������꾿꿁꿂꿃꿄꿅꿆꿊꿌꿏꿐꿑꿒꿓꿕꿖꿗꿘꿙꿚꿛꿝꿞꿟꿠꿡������꿢꿣꿤꿥꿦꿧꿪꿫꿬꿭꿮꿯꿲꿳꿵꿶꿷꿹꿺꿻꿼꿽꿾꿿뀂뀃������뀅뀆뀇뀈뀉뀊뀋뀍뀎뀏뀑뀒뀓뀕뀖뀗뀘뀙뀚뀛뀞뀟뀠뀡뀢뀣뀤뀥뀦뀧뀩뀪뀫뀬뀭뀮뀯뀰뀱뀲뀳뀴뀵뀶뀷뀸뀹뀺뀻뀼뀽뀾뀿끀끁끂끃끆끇끉끋끍끏끐끑끒끖끘끚끛끜끞끟끠끡끢끣끤끥끦끧끨끩끪끫끬끭끮끯끰끱끲끳끴끵끶끷끸끹끺끻끾끿낁낂낃낅낆낇낈낉낊낋낎낐낒낓낔낕낖낗낛낝낞낣낤�".split(""),e=0;e!=r[133].length;++e)65533!==r[133][e].charCodeAt(0)&&(n[r[133][e]]=34048+e,t[34048+e]=r[133][e]);for(r[134]="�����������������������������������������������������������������낥낦낧낪낰낲낶낷낹낺낻낽낾낿냀냁냂냃냆냊냋냌냍냎냏냒������냓냕냖냗냙냚냛냜냝냞냟냡냢냣냤냦냧냨냩냪냫냬냭냮냯냰������냱냲냳냴냵냶냷냸냹냺냻냼냽냾냿넀넁넂넃넄넅넆넇넊넍넎넏넑넔넕넖넗넚넞넟넠넡넢넦넧넩넪넫넭넮넯넰넱넲넳넶넺넻넼넽넾넿녂녃녅녆녇녉녊녋녌녍녎녏녒녓녖녗녙녚녛녝녞녟녡녢녣녤녥녦녧녨녩녪녫녬녭녮녯녰녱녲녳녴녵녶녷녺녻녽녾녿놁놃놄놅놆놇놊놌놎놏놐놑놕놖놗놙놚놛놝�".split(""),e=0;e!=r[134].length;++e)65533!==r[134][e].charCodeAt(0)&&(n[r[134][e]]=34304+e,t[34304+e]=r[134][e]);for(r[135]="�����������������������������������������������������������������놞놟놠놡놢놣놤놥놦놧놩놪놫놬놭놮놯놰놱놲놳놴놵놶놷놸������놹놺놻놼놽놾놿뇀뇁뇂뇃뇄뇅뇆뇇뇈뇉뇊뇋뇍뇎뇏뇑뇒뇓뇕������뇖뇗뇘뇙뇚뇛뇞뇠뇡뇢뇣뇤뇥뇦뇧뇪뇫뇭뇮뇯뇱뇲뇳뇴뇵뇶뇷뇸뇺뇼뇾뇿눀눁눂눃눆눇눉눊눍눎눏눐눑눒눓눖눘눚눛눜눝눞눟눡눢눣눤눥눦눧눨눩눪눫눬눭눮눯눰눱눲눳눵눶눷눸눹눺눻눽눾눿뉀뉁뉂뉃뉄뉅뉆뉇뉈뉉뉊뉋뉌뉍뉎뉏뉐뉑뉒뉓뉔뉕뉖뉗뉙뉚뉛뉝뉞뉟뉡뉢뉣뉤뉥뉦뉧뉪뉫뉬뉭뉮�".split(""),e=0;e!=r[135].length;++e)65533!==r[135][e].charCodeAt(0)&&(n[r[135][e]]=34560+e,t[34560+e]=r[135][e]);for(r[136]="�����������������������������������������������������������������뉯뉰뉱뉲뉳뉶뉷뉸뉹뉺뉻뉽뉾뉿늀늁늂늃늆늇늈늊늋늌늍늎������늏늒늓늕늖늗늛늜늝늞늟늢늤늧늨늩늫늭늮늯늱늲늳늵늶늷������늸늹늺늻늼늽늾늿닀닁닂닃닄닅닆닇닊닋닍닎닏닑닓닔닕닖닗닚닜닞닟닠닡닣닧닩닪닰닱닲닶닼닽닾댂댃댅댆댇댉댊댋댌댍댎댏댒댖댗댘댙댚댛댝댞댟댠댡댢댣댤댥댦댧댨댩댪댫댬댭댮댯댰댱댲댳댴댵댶댷댸댹댺댻댼댽댾댿덀덁덂덃덄덅덆덇덈덉덊덋덌덍덎덏덐덑덒덓덗덙덚덝덠덡덢덣�".split(""),e=0;e!=r[136].length;++e)65533!==r[136][e].charCodeAt(0)&&(n[r[136][e]]=34816+e,t[34816+e]=r[136][e]);for(r[137]="�����������������������������������������������������������������덦덨덪덬덭덯덲덳덵덶덷덹덺덻덼덽덾덿뎂뎆뎇뎈뎉뎊뎋뎍������뎎뎏뎑뎒뎓뎕뎖뎗뎘뎙뎚뎛뎜뎝뎞뎟뎢뎣뎤뎥뎦뎧뎩뎪뎫뎭������뎮뎯뎰뎱뎲뎳뎴뎵뎶뎷뎸뎹뎺뎻뎼뎽뎾뎿돀돁돂돃돆돇돉돊돍돏돑돒돓돖돘돚돜돞돟돡돢돣돥돦돧돩돪돫돬돭돮돯돰돱돲돳돴돵돶돷돸돹돺돻돽돾돿됀됁됂됃됄됅됆됇됈됉됊됋됌됍됎됏됑됒됓됔됕됖됗됙됚됛됝됞됟됡됢됣됤됥됦됧됪됬됭됮됯됰됱됲됳됵됶됷됸됹됺됻됼됽됾됿둀둁둂둃둄�".split(""),e=0;e!=r[137].length;++e)65533!==r[137][e].charCodeAt(0)&&(n[r[137][e]]=35072+e,t[35072+e]=r[137][e]);for(r[138]="�����������������������������������������������������������������둅둆둇둈둉둊둋둌둍둎둏둒둓둕둖둗둙둚둛둜둝둞둟둢둤둦������둧둨둩둪둫둭둮둯둰둱둲둳둴둵둶둷둸둹둺둻둼둽둾둿뒁뒂������뒃뒄뒅뒆뒇뒉뒊뒋뒌뒍뒎뒏뒐뒑뒒뒓뒔뒕뒖뒗뒘뒙뒚뒛뒜뒞뒟뒠뒡뒢뒣뒥뒦뒧뒩뒪뒫뒭뒮뒯뒰뒱뒲뒳뒴뒶뒸뒺뒻뒼뒽뒾뒿듁듂듃듅듆듇듉듊듋듌듍듎듏듑듒듓듔듖듗듘듙듚듛듞듟듡듢듥듧듨듩듪듫듮듰듲듳듴듵듶듷듹듺듻듼듽듾듿딀딁딂딃딄딅딆딇딈딉딊딋딌딍딎딏딐딑딒딓딖딗딙딚딝�".split(""),e=0;e!=r[138].length;++e)65533!==r[138][e].charCodeAt(0)&&(n[r[138][e]]=35328+e,t[35328+e]=r[138][e]);for(r[139]="�����������������������������������������������������������������딞딟딠딡딢딣딦딫딬딭딮딯딲딳딵딶딷딹딺딻딼딽딾딿땂땆������땇땈땉땊땎땏땑땒땓땕땖땗땘땙땚땛땞땢땣땤땥땦땧땨땩땪������땫땬땭땮땯땰땱땲땳땴땵땶땷땸땹땺땻땼땽땾땿떀떁떂떃떄떅떆떇떈떉떊떋떌떍떎떏떐떑떒떓떔떕떖떗떘떙떚떛떜떝떞떟떢떣떥떦떧떩떬떭떮떯떲떶떷떸떹떺떾떿뗁뗂뗃뗅뗆뗇뗈뗉뗊뗋뗎뗒뗓뗔뗕뗖뗗뗙뗚뗛뗜뗝뗞뗟뗠뗡뗢뗣뗤뗥뗦뗧뗨뗩뗪뗫뗭뗮뗯뗰뗱뗲뗳뗴뗵뗶뗷뗸뗹뗺뗻뗼뗽뗾뗿�".split(""),e=0;e!=r[139].length;++e)65533!==r[139][e].charCodeAt(0)&&(n[r[139][e]]=35584+e,t[35584+e]=r[139][e]);for(r[140]="�����������������������������������������������������������������똀똁똂똃똄똅똆똇똈똉똊똋똌똍똎똏똒똓똕똖똗똙똚똛똜똝������똞똟똠똡똢똣똤똦똧똨똩똪똫똭똮똯똰똱똲똳똵똶똷똸똹똺������똻똼똽똾똿뙀뙁뙂뙃뙄뙅뙆뙇뙉뙊뙋뙌뙍뙎뙏뙐뙑뙒뙓뙔뙕뙖뙗뙘뙙뙚뙛뙜뙝뙞뙟뙠뙡뙢뙣뙥뙦뙧뙩뙪뙫뙬뙭뙮뙯뙰뙱뙲뙳뙴뙵뙶뙷뙸뙹뙺뙻뙼뙽뙾뙿뚀뚁뚂뚃뚄뚅뚆뚇뚈뚉뚊뚋뚌뚍뚎뚏뚐뚑뚒뚓뚔뚕뚖뚗뚘뚙뚚뚛뚞뚟뚡뚢뚣뚥뚦뚧뚨뚩뚪뚭뚮뚯뚰뚲뚳뚴뚵뚶뚷뚸뚹뚺뚻뚼뚽뚾뚿뛀뛁뛂�".split(""),e=0;e!=r[140].length;++e)65533!==r[140][e].charCodeAt(0)&&(n[r[140][e]]=35840+e,t[35840+e]=r[140][e]);for(r[141]="�����������������������������������������������������������������뛃뛄뛅뛆뛇뛈뛉뛊뛋뛌뛍뛎뛏뛐뛑뛒뛓뛕뛖뛗뛘뛙뛚뛛뛜뛝������뛞뛟뛠뛡뛢뛣뛤뛥뛦뛧뛨뛩뛪뛫뛬뛭뛮뛯뛱뛲뛳뛵뛶뛷뛹뛺������뛻뛼뛽뛾뛿뜂뜃뜄뜆뜇뜈뜉뜊뜋뜌뜍뜎뜏뜐뜑뜒뜓뜔뜕뜖뜗뜘뜙뜚뜛뜜뜝뜞뜟뜠뜡뜢뜣뜤뜥뜦뜧뜪뜫뜭뜮뜱뜲뜳뜴뜵뜶뜷뜺뜼뜽뜾뜿띀띁띂띃띅띆띇띉띊띋띍띎띏띐띑띒띓띖띗띘띙띚띛띜띝띞띟띡띢띣띥띦띧띩띪띫띬띭띮띯띲띴띶띷띸띹띺띻띾띿랁랂랃랅랆랇랈랉랊랋랎랓랔랕랚랛랝랞�".split(""),e=0;e!=r[141].length;++e)65533!==r[141][e].charCodeAt(0)&&(n[r[141][e]]=36096+e,t[36096+e]=r[141][e]);for(r[142]="�����������������������������������������������������������������랟랡랢랣랤랥랦랧랪랮랯랰랱랲랳랶랷랹랺랻랼랽랾랿럀럁������럂럃럄럅럆럈럊럋럌럍럎럏럐럑럒럓럔럕럖럗럘럙럚럛럜럝������럞럟럠럡럢럣럤럥럦럧럨럩럪럫럮럯럱럲럳럵럶럷럸럹럺럻럾렂렃렄렅렆렊렋렍렎렏렑렒렓렔렕렖렗렚렜렞렟렠렡렢렣렦렧렩렪렫렭렮렯렰렱렲렳렶렺렻렼렽렾렿롁롂롃롅롆롇롈롉롊롋롌롍롎롏롐롒롔롕롖롗롘롙롚롛롞롟롡롢롣롥롦롧롨롩롪롫롮롰롲롳롴롵롶롷롹롺롻롽롾롿뢀뢁뢂뢃뢄�".split(""),e=0;e!=r[142].length;++e)65533!==r[142][e].charCodeAt(0)&&(n[r[142][e]]=36352+e,t[36352+e]=r[142][e]);for(r[143]="�����������������������������������������������������������������뢅뢆뢇뢈뢉뢊뢋뢌뢎뢏뢐뢑뢒뢓뢔뢕뢖뢗뢘뢙뢚뢛뢜뢝뢞뢟������뢠뢡뢢뢣뢤뢥뢦뢧뢩뢪뢫뢬뢭뢮뢯뢱뢲뢳뢵뢶뢷뢹뢺뢻뢼뢽������뢾뢿룂룄룆룇룈룉룊룋룍룎룏룑룒룓룕룖룗룘룙룚룛룜룞룠룢룣룤룥룦룧룪룫룭룮룯룱룲룳룴룵룶룷룺룼룾룿뤀뤁뤂뤃뤅뤆뤇뤈뤉뤊뤋뤌뤍뤎뤏뤐뤑뤒뤓뤔뤕뤖뤗뤙뤚뤛뤜뤝뤞뤟뤡뤢뤣뤤뤥뤦뤧뤨뤩뤪뤫뤬뤭뤮뤯뤰뤱뤲뤳뤴뤵뤶뤷뤸뤹뤺뤻뤾뤿륁륂륃륅륆륇륈륉륊륋륍륎륐륒륓륔륕륖륗�".split(""),e=0;e!=r[143].length;++e)65533!==r[143][e].charCodeAt(0)&&(n[r[143][e]]=36608+e,t[36608+e]=r[143][e]);for(r[144]="�����������������������������������������������������������������륚륛륝륞륟륡륢륣륤륥륦륧륪륬륮륯륰륱륲륳륶륷륹륺륻륽������륾륿릀릁릂릃릆릈릋릌릏릐릑릒릓릔릕릖릗릘릙릚릛릜릝릞������릟릠릡릢릣릤릥릦릧릨릩릪릫릮릯릱릲릳릵릶릷릸릹릺릻릾맀맂맃맄맅맆맇맊맋맍맓맔맕맖맗맚맜맟맠맢맦맧맩맪맫맭맮맯맰맱맲맳맶맻맼맽맾맿먂먃먄먅먆먇먉먊먋먌먍먎먏먐먑먒먓먔먖먗먘먙먚먛먜먝먞먟먠먡먢먣먤먥먦먧먨먩먪먫먬먭먮먯먰먱먲먳먴먵먶먷먺먻먽먾먿멁멃멄멅멆�".split(""),e=0;e!=r[144].length;++e)65533!==r[144][e].charCodeAt(0)&&(n[r[144][e]]=36864+e,t[36864+e]=r[144][e]);for(r[145]="�����������������������������������������������������������������멇멊멌멏멐멑멒멖멗멙멚멛멝멞멟멠멡멢멣멦멪멫멬멭멮멯������멲멳멵멶멷멹멺멻멼멽멾멿몀몁몂몆몈몉몊몋몍몎몏몐몑몒������몓몔몕몖몗몘몙몚몛몜몝몞몟몠몡몢몣몤몥몦몧몪몭몮몯몱몳몴몵몶몷몺몼몾몿뫀뫁뫂뫃뫅뫆뫇뫉뫊뫋뫌뫍뫎뫏뫐뫑뫒뫓뫔뫕뫖뫗뫚뫛뫜뫝뫞뫟뫠뫡뫢뫣뫤뫥뫦뫧뫨뫩뫪뫫뫬뫭뫮뫯뫰뫱뫲뫳뫴뫵뫶뫷뫸뫹뫺뫻뫽뫾뫿묁묂묃묅묆묇묈묉묊묋묌묎묐묒묓묔묕묖묗묙묚묛묝묞묟묡묢묣묤묥묦묧�".split(""),e=0;e!=r[145].length;++e)65533!==r[145][e].charCodeAt(0)&&(n[r[145][e]]=37120+e,t[37120+e]=r[145][e]);for(r[146]="�����������������������������������������������������������������묨묪묬묭묮묯묰묱묲묳묷묹묺묿뭀뭁뭂뭃뭆뭈뭊뭋뭌뭎뭑뭒������뭓뭕뭖뭗뭙뭚뭛뭜뭝뭞뭟뭠뭢뭤뭥뭦뭧뭨뭩뭪뭫뭭뭮뭯뭰뭱������뭲뭳뭴뭵뭶뭷뭸뭹뭺뭻뭼뭽뭾뭿뮀뮁뮂뮃뮄뮅뮆뮇뮉뮊뮋뮍뮎뮏뮑뮒뮓뮔뮕뮖뮗뮘뮙뮚뮛뮜뮝뮞뮟뮠뮡뮢뮣뮥뮦뮧뮩뮪뮫뮭뮮뮯뮰뮱뮲뮳뮵뮶뮸뮹뮺뮻뮼뮽뮾뮿믁믂믃믅믆믇믉믊믋믌믍믎믏믑믒믔믕믖믗믘믙믚믛믜믝믞믟믠믡믢믣믤믥믦믧믨믩믪믫믬믭믮믯믰믱믲믳믴믵믶믷믺믻믽믾밁�".split(""),e=0;e!=r[146].length;++e)65533!==r[146][e].charCodeAt(0)&&(n[r[146][e]]=37376+e,t[37376+e]=r[146][e]);for(r[147]="�����������������������������������������������������������������밃밄밅밆밇밊밎밐밒밓밙밚밠밡밢밣밦밨밪밫밬밮밯밲밳밵������밶밷밹밺밻밼밽밾밿뱂뱆뱇뱈뱊뱋뱎뱏뱑뱒뱓뱔뱕뱖뱗뱘뱙������뱚뱛뱜뱞뱟뱠뱡뱢뱣뱤뱥뱦뱧뱨뱩뱪뱫뱬뱭뱮뱯뱰뱱뱲뱳뱴뱵뱶뱷뱸뱹뱺뱻뱼뱽뱾뱿벀벁벂벃벆벇벉벊벍벏벐벑벒벓벖벘벛벜벝벞벟벢벣벥벦벩벪벫벬벭벮벯벲벶벷벸벹벺벻벾벿볁볂볃볅볆볇볈볉볊볋볌볎볒볓볔볖볗볙볚볛볝볞볟볠볡볢볣볤볥볦볧볨볩볪볫볬볭볮볯볰볱볲볳볷볹볺볻볽�".split(""),e=0;e!=r[147].length;++e)65533!==r[147][e].charCodeAt(0)&&(n[r[147][e]]=37632+e,t[37632+e]=r[147][e]);for(r[148]="�����������������������������������������������������������������볾볿봀봁봂봃봆봈봊봋봌봍봎봏봑봒봓봕봖봗봘봙봚봛봜봝������봞봟봠봡봢봣봥봦봧봨봩봪봫봭봮봯봰봱봲봳봴봵봶봷봸봹������봺봻봼봽봾봿뵁뵂뵃뵄뵅뵆뵇뵊뵋뵍뵎뵏뵑뵒뵓뵔뵕뵖뵗뵚뵛뵜뵝뵞뵟뵠뵡뵢뵣뵥뵦뵧뵩뵪뵫뵬뵭뵮뵯뵰뵱뵲뵳뵴뵵뵶뵷뵸뵹뵺뵻뵼뵽뵾뵿붂붃붅붆붋붌붍붎붏붒붔붖붗붘붛붝붞붟붠붡붢붣붥붦붧붨붩붪붫붬붭붮붯붱붲붳붴붵붶붷붹붺붻붼붽붾붿뷀뷁뷂뷃뷄뷅뷆뷇뷈뷉뷊뷋뷌뷍뷎뷏뷐뷑�".split(""),e=0;e!=r[148].length;++e)65533!==r[148][e].charCodeAt(0)&&(n[r[148][e]]=37888+e,t[37888+e]=r[148][e]);for(r[149]="�����������������������������������������������������������������뷒뷓뷖뷗뷙뷚뷛뷝뷞뷟뷠뷡뷢뷣뷤뷥뷦뷧뷨뷪뷫뷬뷭뷮뷯뷱������뷲뷳뷵뷶뷷뷹뷺뷻뷼뷽뷾뷿븁븂븄븆븇븈븉븊븋븎븏븑븒븓������븕븖븗븘븙븚븛븞븠븡븢븣븤븥븦븧븨븩븪븫븬븭븮븯븰븱븲븳븴븵븶븷븸븹븺븻븼븽븾븿빀빁빂빃빆빇빉빊빋빍빏빐빑빒빓빖빘빜빝빞빟빢빣빥빦빧빩빫빬빭빮빯빲빶빷빸빹빺빾빿뺁뺂뺃뺅뺆뺇뺈뺉뺊뺋뺎뺒뺓뺔뺕뺖뺗뺚뺛뺜뺝뺞뺟뺠뺡뺢뺣뺤뺥뺦뺧뺩뺪뺫뺬뺭뺮뺯뺰뺱뺲뺳뺴뺵뺶뺷�".split(""),e=0;e!=r[149].length;++e)65533!==r[149][e].charCodeAt(0)&&(n[r[149][e]]=38144+e,t[38144+e]=r[149][e]);for(r[150]="�����������������������������������������������������������������뺸뺹뺺뺻뺼뺽뺾뺿뻀뻁뻂뻃뻄뻅뻆뻇뻈뻉뻊뻋뻌뻍뻎뻏뻒뻓������뻕뻖뻙뻚뻛뻜뻝뻞뻟뻡뻢뻦뻧뻨뻩뻪뻫뻭뻮뻯뻰뻱뻲뻳뻴뻵������뻶뻷뻸뻹뻺뻻뻼뻽뻾뻿뼀뼂뼃뼄뼅뼆뼇뼊뼋뼌뼍뼎뼏뼐뼑뼒뼓뼔뼕뼖뼗뼚뼞뼟뼠뼡뼢뼣뼤뼥뼦뼧뼨뼩뼪뼫뼬뼭뼮뼯뼰뼱뼲뼳뼴뼵뼶뼷뼸뼹뼺뼻뼼뼽뼾뼿뽂뽃뽅뽆뽇뽉뽊뽋뽌뽍뽎뽏뽒뽓뽔뽖뽗뽘뽙뽚뽛뽜뽝뽞뽟뽠뽡뽢뽣뽤뽥뽦뽧뽨뽩뽪뽫뽬뽭뽮뽯뽰뽱뽲뽳뽴뽵뽶뽷뽸뽹뽺뽻뽼뽽뽾뽿뾀뾁뾂�".split(""),e=0;e!=r[150].length;++e)65533!==r[150][e].charCodeAt(0)&&(n[r[150][e]]=38400+e,t[38400+e]=r[150][e]);for(r[151]="�����������������������������������������������������������������뾃뾄뾅뾆뾇뾈뾉뾊뾋뾌뾍뾎뾏뾐뾑뾒뾓뾕뾖뾗뾘뾙뾚뾛뾜뾝������뾞뾟뾠뾡뾢뾣뾤뾥뾦뾧뾨뾩뾪뾫뾬뾭뾮뾯뾱뾲뾳뾴뾵뾶뾷뾸������뾹뾺뾻뾼뾽뾾뾿뿀뿁뿂뿃뿄뿆뿇뿈뿉뿊뿋뿎뿏뿑뿒뿓뿕뿖뿗뿘뿙뿚뿛뿝뿞뿠뿢뿣뿤뿥뿦뿧뿨뿩뿪뿫뿬뿭뿮뿯뿰뿱뿲뿳뿴뿵뿶뿷뿸뿹뿺뿻뿼뿽뿾뿿쀀쀁쀂쀃쀄쀅쀆쀇쀈쀉쀊쀋쀌쀍쀎쀏쀐쀑쀒쀓쀔쀕쀖쀗쀘쀙쀚쀛쀜쀝쀞쀟쀠쀡쀢쀣쀤쀥쀦쀧쀨쀩쀪쀫쀬쀭쀮쀯쀰쀱쀲쀳쀴쀵쀶쀷쀸쀹쀺쀻쀽쀾쀿�".split(""),e=0;e!=r[151].length;++e)65533!==r[151][e].charCodeAt(0)&&(n[r[151][e]]=38656+e,t[38656+e]=r[151][e]);for(r[152]="�����������������������������������������������������������������쁀쁁쁂쁃쁄쁅쁆쁇쁈쁉쁊쁋쁌쁍쁎쁏쁐쁒쁓쁔쁕쁖쁗쁙쁚쁛������쁝쁞쁟쁡쁢쁣쁤쁥쁦쁧쁪쁫쁬쁭쁮쁯쁰쁱쁲쁳쁴쁵쁶쁷쁸쁹������쁺쁻쁼쁽쁾쁿삀삁삂삃삄삅삆삇삈삉삊삋삌삍삎삏삒삓삕삖삗삙삚삛삜삝삞삟삢삤삦삧삨삩삪삫삮삱삲삷삸삹삺삻삾샂샃샄샆샇샊샋샍샎샏샑샒샓샔샕샖샗샚샞샟샠샡샢샣샦샧샩샪샫샭샮샯샰샱샲샳샶샸샺샻샼샽샾샿섁섂섃섅섆섇섉섊섋섌섍섎섏섑섒섓섔섖섗섘섙섚섛섡섢섥섨섩섪섫섮�".split(""),e=0;e!=r[152].length;++e)65533!==r[152][e].charCodeAt(0)&&(n[r[152][e]]=38912+e,t[38912+e]=r[152][e]);for(r[153]="�����������������������������������������������������������������섲섳섴섵섷섺섻섽섾섿셁셂셃셄셅셆셇셊셎셏셐셑셒셓셖셗������셙셚셛셝셞셟셠셡셢셣셦셪셫셬셭셮셯셱셲셳셵셶셷셹셺셻������셼셽셾셿솀솁솂솃솄솆솇솈솉솊솋솏솑솒솓솕솗솘솙솚솛솞솠솢솣솤솦솧솪솫솭솮솯솱솲솳솴솵솶솷솸솹솺솻솼솾솿쇀쇁쇂쇃쇅쇆쇇쇉쇊쇋쇍쇎쇏쇐쇑쇒쇓쇕쇖쇙쇚쇛쇜쇝쇞쇟쇡쇢쇣쇥쇦쇧쇩쇪쇫쇬쇭쇮쇯쇲쇴쇵쇶쇷쇸쇹쇺쇻쇾쇿숁숂숃숅숆숇숈숉숊숋숎숐숒숓숔숕숖숗숚숛숝숞숡숢숣�".split(""),e=0;e!=r[153].length;++e)65533!==r[153][e].charCodeAt(0)&&(n[r[153][e]]=39168+e,t[39168+e]=r[153][e]);for(r[154]="�����������������������������������������������������������������숤숥숦숧숪숬숮숰숳숵숶숷숸숹숺숻숼숽숾숿쉀쉁쉂쉃쉄쉅������쉆쉇쉉쉊쉋쉌쉍쉎쉏쉒쉓쉕쉖쉗쉙쉚쉛쉜쉝쉞쉟쉡쉢쉣쉤쉦������쉧쉨쉩쉪쉫쉮쉯쉱쉲쉳쉵쉶쉷쉸쉹쉺쉻쉾슀슂슃슄슅슆슇슊슋슌슍슎슏슑슒슓슔슕슖슗슙슚슜슞슟슠슡슢슣슦슧슩슪슫슮슯슰슱슲슳슶슸슺슻슼슽슾슿싀싁싂싃싄싅싆싇싈싉싊싋싌싍싎싏싐싑싒싓싔싕싖싗싘싙싚싛싞싟싡싢싥싦싧싨싩싪싮싰싲싳싴싵싷싺싽싾싿쌁쌂쌃쌄쌅쌆쌇쌊쌋쌎쌏�".split(""),e=0;e!=r[154].length;++e)65533!==r[154][e].charCodeAt(0)&&(n[r[154][e]]=39424+e,t[39424+e]=r[154][e]);for(r[155]="�����������������������������������������������������������������쌐쌑쌒쌖쌗쌙쌚쌛쌝쌞쌟쌠쌡쌢쌣쌦쌧쌪쌫쌬쌭쌮쌯쌰쌱쌲������쌳쌴쌵쌶쌷쌸쌹쌺쌻쌼쌽쌾쌿썀썁썂썃썄썆썇썈썉썊썋썌썍������썎썏썐썑썒썓썔썕썖썗썘썙썚썛썜썝썞썟썠썡썢썣썤썥썦썧썪썫썭썮썯썱썳썴썵썶썷썺썻썾썿쎀쎁쎂쎃쎅쎆쎇쎉쎊쎋쎍쎎쎏쎐쎑쎒쎓쎔쎕쎖쎗쎘쎙쎚쎛쎜쎝쎞쎟쎠쎡쎢쎣쎤쎥쎦쎧쎨쎩쎪쎫쎬쎭쎮쎯쎰쎱쎲쎳쎴쎵쎶쎷쎸쎹쎺쎻쎼쎽쎾쎿쏁쏂쏃쏄쏅쏆쏇쏈쏉쏊쏋쏌쏍쏎쏏쏐쏑쏒쏓쏔쏕쏖쏗쏚�".split(""),e=0;e!=r[155].length;++e)65533!==r[155][e].charCodeAt(0)&&(n[r[155][e]]=39680+e,t[39680+e]=r[155][e]);for(r[156]="�����������������������������������������������������������������쏛쏝쏞쏡쏣쏤쏥쏦쏧쏪쏫쏬쏮쏯쏰쏱쏲쏳쏶쏷쏹쏺쏻쏼쏽쏾������쏿쐀쐁쐂쐃쐄쐅쐆쐇쐉쐊쐋쐌쐍쐎쐏쐑쐒쐓쐔쐕쐖쐗쐘쐙쐚������쐛쐜쐝쐞쐟쐠쐡쐢쐣쐥쐦쐧쐨쐩쐪쐫쐭쐮쐯쐱쐲쐳쐵쐶쐷쐸쐹쐺쐻쐾쐿쑀쑁쑂쑃쑄쑅쑆쑇쑉쑊쑋쑌쑍쑎쑏쑐쑑쑒쑓쑔쑕쑖쑗쑘쑙쑚쑛쑜쑝쑞쑟쑠쑡쑢쑣쑦쑧쑩쑪쑫쑭쑮쑯쑰쑱쑲쑳쑶쑷쑸쑺쑻쑼쑽쑾쑿쒁쒂쒃쒄쒅쒆쒇쒈쒉쒊쒋쒌쒍쒎쒏쒐쒑쒒쒓쒕쒖쒗쒘쒙쒚쒛쒝쒞쒟쒠쒡쒢쒣쒤쒥쒦쒧쒨쒩�".split(""),e=0;e!=r[156].length;++e)65533!==r[156][e].charCodeAt(0)&&(n[r[156][e]]=39936+e,t[39936+e]=r[156][e]);for(r[157]="�����������������������������������������������������������������쒪쒫쒬쒭쒮쒯쒰쒱쒲쒳쒴쒵쒶쒷쒹쒺쒻쒽쒾쒿쓀쓁쓂쓃쓄쓅������쓆쓇쓈쓉쓊쓋쓌쓍쓎쓏쓐쓑쓒쓓쓔쓕쓖쓗쓘쓙쓚쓛쓜쓝쓞쓟������쓠쓡쓢쓣쓤쓥쓦쓧쓨쓪쓫쓬쓭쓮쓯쓲쓳쓵쓶쓷쓹쓻쓼쓽쓾씂씃씄씅씆씇씈씉씊씋씍씎씏씑씒씓씕씖씗씘씙씚씛씝씞씟씠씡씢씣씤씥씦씧씪씫씭씮씯씱씲씳씴씵씶씷씺씼씾씿앀앁앂앃앆앇앋앏앐앑앒앖앚앛앜앟앢앣앥앦앧앩앪앫앬앭앮앯앲앶앷앸앹앺앻앾앿얁얂얃얅얆얈얉얊얋얎얐얒얓얔�".split(""),e=0;e!=r[157].length;++e)65533!==r[157][e].charCodeAt(0)&&(n[r[157][e]]=40192+e,t[40192+e]=r[157][e]);for(r[158]="�����������������������������������������������������������������얖얙얚얛얝얞얟얡얢얣얤얥얦얧얨얪얫얬얭얮얯얰얱얲얳얶������얷얺얿엀엁엂엃엋엍엏엒엓엕엖엗엙엚엛엜엝엞엟엢엤엦엧������엨엩엪엫엯엱엲엳엵엸엹엺엻옂옃옄옉옊옋옍옎옏옑옒옓옔옕옖옗옚옝옞옟옠옡옢옣옦옧옩옪옫옯옱옲옶옸옺옼옽옾옿왂왃왅왆왇왉왊왋왌왍왎왏왒왖왗왘왙왚왛왞왟왡왢왣왤왥왦왧왨왩왪왫왭왮왰왲왳왴왵왶왷왺왻왽왾왿욁욂욃욄욅욆욇욊욌욎욏욐욑욒욓욖욗욙욚욛욝욞욟욠욡욢욣욦�".split(""),e=0;e!=r[158].length;++e)65533!==r[158][e].charCodeAt(0)&&(n[r[158][e]]=40448+e,t[40448+e]=r[158][e]);for(r[159]="�����������������������������������������������������������������욨욪욫욬욭욮욯욲욳욵욶욷욻욼욽욾욿웂웄웆웇웈웉웊웋웎������웏웑웒웓웕웖웗웘웙웚웛웞웟웢웣웤웥웦웧웪웫웭웮웯웱웲������웳웴웵웶웷웺웻웼웾웿윀윁윂윃윆윇윉윊윋윍윎윏윐윑윒윓윖윘윚윛윜윝윞윟윢윣윥윦윧윩윪윫윬윭윮윯윲윴윶윸윹윺윻윾윿읁읂읃읅읆읇읈읉읋읎읐읙읚읛읝읞읟읡읢읣읤읥읦읧읩읪읬읭읮읯읰읱읲읳읶읷읹읺읻읿잀잁잂잆잋잌잍잏잒잓잕잙잛잜잝잞잟잢잧잨잩잪잫잮잯잱잲잳잵잶잷�".split(""),e=0;e!=r[159].length;++e)65533!==r[159][e].charCodeAt(0)&&(n[r[159][e]]=40704+e,t[40704+e]=r[159][e]);for(r[160]="�����������������������������������������������������������������잸잹잺잻잾쟂쟃쟄쟅쟆쟇쟊쟋쟍쟏쟑쟒쟓쟔쟕쟖쟗쟙쟚쟛쟜������쟞쟟쟠쟡쟢쟣쟥쟦쟧쟩쟪쟫쟭쟮쟯쟰쟱쟲쟳쟴쟵쟶쟷쟸쟹쟺������쟻쟼쟽쟾쟿젂젃젅젆젇젉젋젌젍젎젏젒젔젗젘젙젚젛젞젟젡젢젣젥젦젧젨젩젪젫젮젰젲젳젴젵젶젷젹젺젻젽젾젿졁졂졃졄졅졆졇졊졋졎졏졐졑졒졓졕졖졗졘졙졚졛졜졝졞졟졠졡졢졣졤졥졦졧졨졩졪졫졬졭졮졯졲졳졵졶졷졹졻졼졽졾졿좂좄좈좉좊좎좏좐좑좒좓좕좖좗좘좙좚좛좜좞좠좢좣좤�".split(""),e=0;e!=r[160].length;++e)65533!==r[160][e].charCodeAt(0)&&(n[r[160][e]]=40960+e,t[40960+e]=r[160][e]);for(r[161]="�����������������������������������������������������������������좥좦좧좩좪좫좬좭좮좯좰좱좲좳좴좵좶좷좸좹좺좻좾좿죀죁������죂죃죅죆죇죉죊죋죍죎죏죐죑죒죓죖죘죚죛죜죝죞죟죢죣죥������죦죧죨죩죪죫죬죭죮죯죰죱죲죳죴죶죷죸죹죺죻죾죿줁줂줃줇줈줉줊줋줎　、。·‥…¨〃­―∥＼∼‘’“”〔〕〈〉《》「」『』【】±×÷≠≤≥∞∴°′″℃Å￠￡￥♂♀∠⊥⌒∂∇≡≒§※☆★○●◎◇◆□■△▲▽▼→←↑↓↔〓≪≫√∽∝∵∫∬∈∋⊆⊇⊂⊃∪∩∧∨￢�".split(""),e=0;e!=r[161].length;++e)65533!==r[161][e].charCodeAt(0)&&(n[r[161][e]]=41216+e,t[41216+e]=r[161][e]);for(r[162]="�����������������������������������������������������������������줐줒줓줔줕줖줗줙줚줛줜줝줞줟줠줡줢줣줤줥줦줧줨줩줪줫������줭줮줯줰줱줲줳줵줶줷줸줹줺줻줼줽줾줿쥀쥁쥂쥃쥄쥅쥆쥇������쥈쥉쥊쥋쥌쥍쥎쥏쥒쥓쥕쥖쥗쥙쥚쥛쥜쥝쥞쥟쥢쥤쥥쥦쥧쥨쥩쥪쥫쥭쥮쥯⇒⇔∀∃´～ˇ˘˝˚˙¸˛¡¿ː∮∑∏¤℉‰◁◀▷▶♤♠♡♥♧♣⊙◈▣◐◑▒▤▥▨▧▦▩♨☏☎☜☞¶†‡↕↗↙↖↘♭♩♪♬㉿㈜№㏇™㏂㏘℡€®������������������������".split(""),e=0;e!=r[162].length;++e)65533!==r[162][e].charCodeAt(0)&&(n[r[162][e]]=41472+e,t[41472+e]=r[162][e]);for(r[163]="�����������������������������������������������������������������쥱쥲쥳쥵쥶쥷쥸쥹쥺쥻쥽쥾쥿즀즁즂즃즄즅즆즇즊즋즍즎즏������즑즒즓즔즕즖즗즚즜즞즟즠즡즢즣즤즥즦즧즨즩즪즫즬즭즮������즯즰즱즲즳즴즵즶즷즸즹즺즻즼즽즾즿짂짃짅짆짉짋짌짍짎짏짒짔짗짘짛！＂＃＄％＆＇（）＊＋，－．／０１２３４５６７８９：；＜＝＞？＠ＡＢＣＤＥＦＧＨＩＪＫＬＭＮＯＰＱＲＳＴＵＶＷＸＹＺ［￦］＾＿｀ａｂｃｄｅｆｇｈｉｊｋｌｍｎｏｐｑｒｓｔｕｖｗｘｙｚ｛｜｝￣�".split(""),e=0;e!=r[163].length;++e)65533!==r[163][e].charCodeAt(0)&&(n[r[163][e]]=41728+e,t[41728+e]=r[163][e]);for(r[164]="�����������������������������������������������������������������짞짟짡짣짥짦짨짩짪짫짮짲짳짴짵짶짷짺짻짽짾짿쨁쨂쨃쨄������쨅쨆쨇쨊쨎쨏쨐쨑쨒쨓쨕쨖쨗쨙쨚쨛쨜쨝쨞쨟쨠쨡쨢쨣쨤쨥������쨦쨧쨨쨪쨫쨬쨭쨮쨯쨰쨱쨲쨳쨴쨵쨶쨷쨸쨹쨺쨻쨼쨽쨾쨿쩀쩁쩂쩃쩄쩅쩆ㄱㄲㄳㄴㄵㄶㄷㄸㄹㄺㄻㄼㄽㄾㄿㅀㅁㅂㅃㅄㅅㅆㅇㅈㅉㅊㅋㅌㅍㅎㅏㅐㅑㅒㅓㅔㅕㅖㅗㅘㅙㅚㅛㅜㅝㅞㅟㅠㅡㅢㅣㅤㅥㅦㅧㅨㅩㅪㅫㅬㅭㅮㅯㅰㅱㅲㅳㅴㅵㅶㅷㅸㅹㅺㅻㅼㅽㅾㅿㆀㆁㆂㆃㆄㆅㆆㆇㆈㆉㆊㆋㆌㆍㆎ�".split(""),e=0;e!=r[164].length;++e)65533!==r[164][e].charCodeAt(0)&&(n[r[164][e]]=41984+e,t[41984+e]=r[164][e]);for(r[165]="�����������������������������������������������������������������쩇쩈쩉쩊쩋쩎쩏쩑쩒쩓쩕쩖쩗쩘쩙쩚쩛쩞쩢쩣쩤쩥쩦쩧쩩쩪������쩫쩬쩭쩮쩯쩰쩱쩲쩳쩴쩵쩶쩷쩸쩹쩺쩻쩼쩾쩿쪀쪁쪂쪃쪅쪆������쪇쪈쪉쪊쪋쪌쪍쪎쪏쪐쪑쪒쪓쪔쪕쪖쪗쪙쪚쪛쪜쪝쪞쪟쪠쪡쪢쪣쪤쪥쪦쪧ⅰⅱⅲⅳⅴⅵⅶⅷⅸⅹ�����ⅠⅡⅢⅣⅤⅥⅦⅧⅨⅩ�������ΑΒΓΔΕΖΗΘΙΚΛΜΝΞΟΠΡΣΤΥΦΧΨΩ��������αβγδεζηθικλμνξοπρστυφχψω�������".split(""),e=0;e!=r[165].length;++e)65533!==r[165][e].charCodeAt(0)&&(n[r[165][e]]=42240+e,t[42240+e]=r[165][e]);for(r[166]="�����������������������������������������������������������������쪨쪩쪪쪫쪬쪭쪮쪯쪰쪱쪲쪳쪴쪵쪶쪷쪸쪹쪺쪻쪾쪿쫁쫂쫃쫅������쫆쫇쫈쫉쫊쫋쫎쫐쫒쫔쫕쫖쫗쫚쫛쫜쫝쫞쫟쫡쫢쫣쫤쫥쫦쫧������쫨쫩쫪쫫쫭쫮쫯쫰쫱쫲쫳쫵쫶쫷쫸쫹쫺쫻쫼쫽쫾쫿쬀쬁쬂쬃쬄쬅쬆쬇쬉쬊─│┌┐┘└├┬┤┴┼━┃┏┓┛┗┣┳┫┻╋┠┯┨┷┿┝┰┥┸╂┒┑┚┙┖┕┎┍┞┟┡┢┦┧┩┪┭┮┱┲┵┶┹┺┽┾╀╁╃╄╅╆╇╈╉╊���������������������������".split(""),e=0;e!=r[166].length;++e)65533!==r[166][e].charCodeAt(0)&&(n[r[166][e]]=42496+e,t[42496+e]=r[166][e]);for(r[167]="�����������������������������������������������������������������쬋쬌쬍쬎쬏쬑쬒쬓쬕쬖쬗쬙쬚쬛쬜쬝쬞쬟쬢쬣쬤쬥쬦쬧쬨쬩������쬪쬫쬬쬭쬮쬯쬰쬱쬲쬳쬴쬵쬶쬷쬸쬹쬺쬻쬼쬽쬾쬿쭀쭂쭃쭄������쭅쭆쭇쭊쭋쭍쭎쭏쭑쭒쭓쭔쭕쭖쭗쭚쭛쭜쭞쭟쭠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c=8+(r.w_bits-8<<4)<<8;c|=(r.strategy>=2||r.level<2?0:r.level<6?1:6===r.level?2:3)<<6,0!==r.strstart&&(c|=32),c+=31-c%31,r.status=f,w(r,c),0!==r.strstart&&(w(r,e.adler>>>16),w(r,65535&e.adler)),e.adler=1}if(69===r.status)if(r.gzhead.extra){for(s=r.pending;r.gzindex<(65535&r.gzhead.extra.length)&&(r.pending!==r.pending_buf_size||(r.gzhead.hcrc&&r.pending>s&&(e.adler=i(e.adler,r.pending_buf,r.pending-s,s)),y(e),s=r.pending,r.pending!==r.pending_buf_size));)v(r,255&r.gzhead.extra[r.gzindex]),r.gzindex++;r.gzhead.hcrc&&r.pending>s&&(e.adler=i(e.adler,r.pending_buf,r.pending-s,s)),r.gzindex===r.gzhead.extra.length&&(r.gzindex=0,r.status=73)}else r.status=73;if(73===r.status)if(r.gzhead.name){s=r.pending;do{if(r.pending===r.pending_buf_size&&(r.gzhead.hcrc&&r.pending>s&&(e.adler=i(e.adler,r.pending_buf,r.pending-s,s)),y(e),s=r.pending,r.pending===r.pending_buf_size)){o=1;break}o=r.gzindexs&&(e.adler=i(e.adler,r.pending_buf,r.pending-s,s)),0===o&&(r.gzindex=0,r.status=91)}else r.status=91;if(91===r.status)if(r.gzhead.comment){s=r.pending;do{if(r.pending===r.pending_buf_size&&(r.gzhead.hcrc&&r.pending>s&&(e.adler=i(e.adler,r.pending_buf,r.pending-s,s)),y(e),s=r.pending,r.pending===r.pending_buf_size)){o=1;break}o=r.gzindexs&&(e.adler=i(e.adler,r.pending_buf,r.pending-s,s)),0===o&&(r.status=h)}else r.status=h;if(r.status===h&&(r.gzhead.hcrc?(r.pending+2>r.pending_buf_size&&y(e),r.pending+2<=r.pending_buf_size&&(v(r,255&e.adler),v(r,e.adler>>8&255),e.adler=0,r.status=f)):r.status=f),0!==r.pending){if(y(e),0===e.avail_out)return r.last_flush=-1,0}else if(0===e.avail_in&&m(t)<=m(n)&&4!==t)return p(e,-5);if(r.status===d&&0!==e.avail_in)return p(e,-5);if(0!==e.avail_in||0!==r.lookahead||0!==t&&r.status!==d){var _=2===r.strategy?function(e,t){for(var n;;){if(0===e.lookahead&&(x(e),0===e.lookahead)){if(0===t)return 1;break}if(e.match_length=0,n=a._tr_tally(e,0,e.window[e.strstart]),e.lookahead--,e.strstart++,n&&(b(e,!1),0===e.strm.avail_out))return 1}return e.insert=0,4===t?(b(e,!0),0===e.strm.avail_out?3:4):e.last_lit&&(b(e,!1),0===e.strm.avail_out)?1:2}(r,t):3===r.strategy?function(e,t){for(var n,r,s,i,o=e.window;;){if(e.lookahead<=u){if(x(e),e.lookahead<=u&&0===t)return 1;if(0===e.lookahead)break}if(e.match_length=0,e.lookahead>=3&&e.strstart>0&&(r=o[s=e.strstart-1])===o[++s]&&r===o[++s]&&r===o[++s]){i=e.strstart+u;do{}while(r===o[++s]&&r===o[++s]&&r===o[++s]&&r===o[++s]&&r===o[++s]&&r===o[++s]&&r===o[++s]&&r===o[++s]&&se.lookahead&&(e.match_length=e.lookahead)}if(e.match_length>=3?(n=a._tr_tally(e,1,e.match_length-3),e.lookahead-=e.match_length,e.strstart+=e.match_length,e.match_length=0):(n=a._tr_tally(e,0,e.window[e.strstart]),e.lookahead--,e.strstart++),n&&(b(e,!1),0===e.strm.avail_out))return 1}return e.insert=0,4===t?(b(e,!0),0===e.strm.avail_out?3:4):e.last_lit&&(b(e,!1),0===e.strm.avail_out)?1:2}(r,t):C[r.level].func(r,t);if(3!==_&&4!==_||(r.status=d),1===_||3===_)return 0===e.avail_out&&(r.last_flush=-1),0;if(2===_&&(1===t?a._tr_align(r):5!==t&&(a._tr_stored_block(r,0,0,!1),3===t&&(g(r.head),0===r.lookahead&&(r.strstart=0,r.block_start=0,r.insert=0))),y(e),0===e.avail_out))return r.last_flush=-1,0}return 4!==t?0:r.wrap<=0?1:(2===r.wrap?(v(r,255&e.adler),v(r,e.adler>>8&255),v(r,e.adler>>16&255),v(r,e.adler>>24&255),v(r,255&e.total_in),v(r,e.total_in>>8&255),v(r,e.total_in>>16&255),v(r,e.total_in>>24&255)):(w(r,e.adler>>>16),w(r,65535&e.adler)),y(e),r.wrap>0&&(r.wrap=-r.wrap),0!==r.pending?0:1)},n.deflateEnd=function(e){var t;return e&&e.state?42!==(t=e.state.status)&&69!==t&&73!==t&&91!==t&&t!==h&&t!==f&&t!==d?p(e,l):(e.state=null,t===f?p(e,-3):0):l},n.deflateInfo="pako deflate (from Nodeca project)"},{"../utils/common":27,"./adler32":29,"./crc32":31,"./messages":37,"./trees":38}],33:[function(e,t,n){"use strict";t.exports=function(){this.text=0,this.time=0,this.xflags=0,this.os=0,this.extra=null,this.extra_len=0,this.name="",this.comment="",this.hcrc=0,this.done=!1}},{}],34:[function(e,t,n){"use strict";t.exports=function(e,t){var n,r,a,s,i,o,l,u,c,h,f,d,p,m,g,y,b,v,w,_,x,k,E,C,S;n=e.state,r=e.next_in,C=e.input,a=r+(e.avail_in-5),s=e.next_out,S=e.output,i=s-(t-e.avail_out),o=s+(e.avail_out-257),l=n.dmax,u=n.wsize,c=n.whave,h=n.wnext,f=n.window,d=n.hold,p=n.bits,m=n.lencode,g=n.distcode,y=(1<>>=w=v>>>24,p-=w,0==(w=v>>>16&255))S[s++]=65535&v;else{if(!(16&w)){if(0==(64&w)){v=m[(65535&v)+(d&(1<>>=w,p-=w),p<15&&(d+=C[r++]<>>=w=v>>>24,p-=w,!(16&(w=v>>>16&255))){if(0==(64&w)){v=g[(65535&v)+(d&(1<l){e.msg="invalid distance too far back",n.mode=30;break e}if(d>>>=w,p-=w,x>(w=s-i)){if((w=x-w)>c&&n.sane){e.msg="invalid distance too far back",n.mode=30;break e}if(k=0,E=f,0===h){if(k+=u-w,w<_){_-=w;do{S[s++]=f[k++]}while(--w);k=s-x,E=S}}else if(h2;)S[s++]=E[k++],S[s++]=E[k++],S[s++]=E[k++],_-=3;_&&(S[s++]=E[k++],_>1&&(S[s++]=E[k++]))}else{k=s-x;do{S[s++]=S[k++],S[s++]=S[k++],S[s++]=S[k++],_-=3}while(_>2);_&&(S[s++]=S[k++],_>1&&(S[s++]=S[k++]))}break}}break}}while(r>3,d&=(1<<(p-=_<<3))-1,e.next_in=r,e.next_out=s,e.avail_in=r>>24&255)+(e>>>8&65280)+((65280&e)<<8)+((255&e)<<24)}function f(){this.mode=0,this.last=!1,this.wrap=0,this.havedict=!1,this.flags=0,this.dmax=0,this.check=0,this.total=0,this.head=null,this.wbits=0,this.wsize=0,this.whave=0,this.wnext=0,this.window=null,this.hold=0,this.bits=0,this.length=0,this.offset=0,this.extra=0,this.lencode=null,this.distcode=null,this.lenbits=0,this.distbits=0,this.ncode=0,this.nlen=0,this.ndist=0,this.have=0,this.next=null,this.lens=new r.Buf16(320),this.work=new r.Buf16(288),this.lendyn=null,this.distdyn=null,this.sane=0,this.back=0,this.was=0}function d(e){var t;return e&&e.state?(t=e.state,e.total_in=e.total_out=t.total=0,e.msg="",t.wrap&&(e.adler=1&t.wrap),t.mode=1,t.last=0,t.havedict=0,t.dmax=32768,t.head=null,t.hold=0,t.bits=0,t.lencode=t.lendyn=new r.Buf32(852),t.distcode=t.distdyn=new r.Buf32(592),t.sane=1,t.back=-1,0):l}function p(e){var t;return e&&e.state?((t=e.state).wsize=0,t.whave=0,t.wnext=0,d(e)):l}function m(e,t){var n,r;return e&&e.state?(r=e.state,t<0?(n=0,t=-t):(n=1+(t>>4),t<48&&(t&=15)),t&&(t<8||t>15)?l:(null!==r.window&&r.wbits!==t&&(r.window=null),r.wrap=n,r.wbits=t,p(e))):l}function g(e,t){var n,r;return e?(r=new f,e.state=r,r.window=null,0!==(n=m(e,t))&&(e.state=null),n):l}var y,b,v=!0;function w(e){if(v){var t;for(y=new r.Buf32(512),b=new r.Buf32(32),t=0;t<144;)e.lens[t++]=8;for(;t<256;)e.lens[t++]=9;for(;t<280;)e.lens[t++]=7;for(;t<288;)e.lens[t++]=8;for(o(1,e.lens,0,288,y,0,e.work,{bits:9}),t=0;t<32;)e.lens[t++]=5;o(2,e.lens,0,32,b,0,e.work,{bits:5}),v=!1}e.lencode=y,e.lenbits=9,e.distcode=b,e.distbits=5}n.inflateReset=p,n.inflateReset2=m,n.inflateResetKeep=d,n.inflateInit=function(e){return g(e,15)},n.inflateInit2=g,n.inflate=function(e,t){var n,f,d,p,m,g,y,b,v,_,x,k,E,C,S,N,I,A,D,T,F,R,O,M,B=0,$=new r.Buf8(4),P=[16,17,18,0,8,7,9,6,10,5,11,4,12,3,13,2,14,1,15];if(!e||!e.state||!e.output||!e.input&&0!==e.avail_in)return l;(n=e.state).mode===u&&(n.mode=13),m=e.next_out,d=e.output,y=e.avail_out,p=e.next_in,f=e.input,g=e.avail_in,b=n.hold,v=n.bits,_=g,x=y,R=0;e:for(;;)switch(n.mode){case 1:if(0===n.wrap){n.mode=13;break}for(;v<16;){if(0===g)break e;g--,b+=f[p++]<>>8&255,n.check=s(n.check,$,2,0),b=0,v=0,n.mode=2;break}if(n.flags=0,n.head&&(n.head.done=!1),!(1&n.wrap)||(((255&b)<<8)+(b>>8))%31){e.msg="incorrect header check",n.mode=c;break}if(8!=(15&b)){e.msg="unknown compression method",n.mode=c;break}if(v-=4,F=8+(15&(b>>>=4)),0===n.wbits)n.wbits=F;else if(F>n.wbits){e.msg="invalid window size",n.mode=c;break}n.dmax=1<>8&1),512&n.flags&&($[0]=255&b,$[1]=b>>>8&255,n.check=s(n.check,$,2,0)),b=0,v=0,n.mode=3;case 3:for(;v<32;){if(0===g)break e;g--,b+=f[p++]<>>8&255,$[2]=b>>>16&255,$[3]=b>>>24&255,n.check=s(n.check,$,4,0)),b=0,v=0,n.mode=4;case 4:for(;v<16;){if(0===g)break e;g--,b+=f[p++]<>8),512&n.flags&&($[0]=255&b,$[1]=b>>>8&255,n.check=s(n.check,$,2,0)),b=0,v=0,n.mode=5;case 5:if(1024&n.flags){for(;v<16;){if(0===g)break e;g--,b+=f[p++]<>>8&255,n.check=s(n.check,$,2,0)),b=0,v=0}else n.head&&(n.head.extra=null);n.mode=6;case 6:if(1024&n.flags&&((k=n.length)>g&&(k=g),k&&(n.head&&(F=n.head.extra_len-n.length,n.head.extra||(n.head.extra=new Array(n.head.extra_len)),r.arraySet(n.head.extra,f,p,k,F)),512&n.flags&&(n.check=s(n.check,f,k,p)),g-=k,p+=k,n.length-=k),n.length))break e;n.length=0,n.mode=7;case 7:if(2048&n.flags){if(0===g)break e;k=0;do{F=f[p+k++],n.head&&F&&n.length<65536&&(n.head.name+=String.fromCharCode(F))}while(F&&k>9&1,n.head.done=!0),e.adler=n.check=0,n.mode=u;break;case 10:for(;v<32;){if(0===g)break e;g--,b+=f[p++]<>>=7&v,v-=7&v,n.mode=27;break}for(;v<3;){if(0===g)break e;g--,b+=f[p++]<>>=1)){case 0:n.mode=14;break;case 1:if(w(n),n.mode=20,6===t){b>>>=2,v-=2;break e}break;case 2:n.mode=17;break;case 3:e.msg="invalid block type",n.mode=c}b>>>=2,v-=2;break;case 14:for(b>>>=7&v,v-=7&v;v<32;){if(0===g)break e;g--,b+=f[p++]<>>16^65535)){e.msg="invalid stored block lengths",n.mode=c;break}if(n.length=65535&b,b=0,v=0,n.mode=15,6===t)break e;case 15:n.mode=16;case 16:if(k=n.length){if(k>g&&(k=g),k>y&&(k=y),0===k)break e;r.arraySet(d,f,p,k,m),g-=k,p+=k,y-=k,m+=k,n.length-=k;break}n.mode=u;break;case 17:for(;v<14;){if(0===g)break e;g--,b+=f[p++]<>>=5,v-=5,n.ndist=1+(31&b),b>>>=5,v-=5,n.ncode=4+(15&b),b>>>=4,v-=4,n.nlen>286||n.ndist>30){e.msg="too many length or distance symbols",n.mode=c;break}n.have=0,n.mode=18;case 18:for(;n.have>>=3,v-=3}for(;n.have<19;)n.lens[P[n.have++]]=0;if(n.lencode=n.lendyn,n.lenbits=7,O={bits:n.lenbits},R=o(0,n.lens,0,19,n.lencode,0,n.work,O),n.lenbits=O.bits,R){e.msg="invalid code lengths set",n.mode=c;break}n.have=0,n.mode=19;case 19:for(;n.have>>16&255,I=65535&B,!((S=B>>>24)<=v);){if(0===g)break e;g--,b+=f[p++]<>>=S,v-=S,n.lens[n.have++]=I;else{if(16===I){for(M=S+2;v>>=S,v-=S,0===n.have){e.msg="invalid bit length repeat",n.mode=c;break}F=n.lens[n.have-1],k=3+(3&b),b>>>=2,v-=2}else if(17===I){for(M=S+3;v>>=S)),b>>>=3,v-=3}else{for(M=S+7;v>>=S)),b>>>=7,v-=7}if(n.have+k>n.nlen+n.ndist){e.msg="invalid bit length repeat",n.mode=c;break}for(;k--;)n.lens[n.have++]=F}}if(n.mode===c)break;if(0===n.lens[256]){e.msg="invalid code -- missing end-of-block",n.mode=c;break}if(n.lenbits=9,O={bits:n.lenbits},R=o(1,n.lens,0,n.nlen,n.lencode,0,n.work,O),n.lenbits=O.bits,R){e.msg="invalid literal/lengths set",n.mode=c;break}if(n.distbits=6,n.distcode=n.distdyn,O={bits:n.distbits},R=o(2,n.lens,n.nlen,n.ndist,n.distcode,0,n.work,O),n.distbits=O.bits,R){e.msg="invalid distances set",n.mode=c;break}if(n.mode=20,6===t)break e;case 20:n.mode=21;case 21:if(g>=6&&y>=258){e.next_out=m,e.avail_out=y,e.next_in=p,e.avail_in=g,n.hold=b,n.bits=v,i(e,x),m=e.next_out,d=e.output,y=e.avail_out,p=e.next_in,f=e.input,g=e.avail_in,b=n.hold,v=n.bits,n.mode===u&&(n.back=-1);break}for(n.back=0;N=(B=n.lencode[b&(1<>>16&255,I=65535&B,!((S=B>>>24)<=v);){if(0===g)break e;g--,b+=f[p++]<>A)])>>>16&255,I=65535&B,!(A+(S=B>>>24)<=v);){if(0===g)break e;g--,b+=f[p++]<>>=A,v-=A,n.back+=A}if(b>>>=S,v-=S,n.back+=S,n.length=I,0===N){n.mode=26;break}if(32&N){n.back=-1,n.mode=u;break}if(64&N){e.msg="invalid literal/length code",n.mode=c;break}n.extra=15&N,n.mode=22;case 22:if(n.extra){for(M=n.extra;v>>=n.extra,v-=n.extra,n.back+=n.extra}n.was=n.length,n.mode=23;case 23:for(;N=(B=n.distcode[b&(1<>>16&255,I=65535&B,!((S=B>>>24)<=v);){if(0===g)break e;g--,b+=f[p++]<>A)])>>>16&255,I=65535&B,!(A+(S=B>>>24)<=v);){if(0===g)break e;g--,b+=f[p++]<>>=A,v-=A,n.back+=A}if(b>>>=S,v-=S,n.back+=S,64&N){e.msg="invalid distance code",n.mode=c;break}n.offset=I,n.extra=15&N,n.mode=24;case 24:if(n.extra){for(M=n.extra;v>>=n.extra,v-=n.extra,n.back+=n.extra}if(n.offset>n.dmax){e.msg="invalid distance too far back",n.mode=c;break}n.mode=25;case 25:if(0===y)break e;if(k=x-y,n.offset>k){if((k=n.offset-k)>n.whave&&n.sane){e.msg="invalid distance too far back",n.mode=c;break}k>n.wnext?(k-=n.wnext,E=n.wsize-k):E=n.wnext-k,k>n.length&&(k=n.length),C=n.window}else C=d,E=m-n.offset,k=n.length;k>y&&(k=y),y-=k,n.length-=k;do{d[m++]=C[E++]}while(--k);0===n.length&&(n.mode=21);break;case 26:if(0===y)break e;d[m++]=n.length,y--,n.mode=21;break;case 27:if(n.wrap){for(;v<32;){if(0===g)break 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All Rights Reserved. - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - * ============================================================================= - */ - -/** - * @license - * Copyright 2018 Google LLC - * - * Use of this source code is governed by an MIT-style - * license that can be found in the LICENSE file or at - * https://opensource.org/licenses/MIT. - * ============================================================================= - */ - -/** - * @license - * Copyright 2018 Google LLC. All Rights Reserved. - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - * - * ============================================================================= - */ - -/** - * @license - * Copyright 2018 Google LLC. All Rights Reserved. - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - * ============================================================================= - */ - -/** - * @license - * Copyright 2019 Google LLC. All Rights Reserved. - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - * - * ============================================================================= - */ - -/** - * @license - * Copyright 2019 Google LLC. All Rights Reserved. - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - * ============================================================================= - */ - -/** - * @license - * Copyright 2020 Google Inc. All Rights Reserved. - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - * ============================================================================= - */ - -/** - * @license - * Copyright 2020 Google LLC. All Rights Reserved. - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - * ============================================================================= - */ - -/** - * @license - * Copyright 2020 Google LLC. All Rights Reserved. - * Licensed under the Apache License, Version 2.0 (the License); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an AS IS BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - * ============================================================================= - */ - -/** - * @license - * Copyright 2021 Google LLC. All Rights Reserved. - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - * ============================================================================= - */ - -/** - * @license - * Copyright 2021 Google LLC. All Rights Reserved. - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * https://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - * ============================================================================= - */ - -/** - * @license Complex.js v2.0.11 11/02/2016 - * - * Copyright (c) 2016, Robert Eisele (robert@xarg.org) - * Dual licensed under the MIT or GPL Version 2 licenses. - **/ - -/** - * @license Fraction.js v4.0.12 09/09/2015 - * http://www.xarg.org/2014/03/rational-numbers-in-javascript/ - * - * Copyright (c) 2015, Robert 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true;\n Object.defineProperty(target, descriptor.key, descriptor);\n }\n}\n\nfunction _createClass(Constructor, protoProps, staticProps) {\n if (protoProps) _defineProperties(Constructor.prototype, protoProps);\n if (staticProps) _defineProperties(Constructor, staticProps);\n return Constructor;\n}\n\nmodule.exports = _createClass;","var superPropBase = require(\"./superPropBase\");\n\nfunction _get(target, property, receiver) {\n if (typeof Reflect !== \"undefined\" && Reflect.get) {\n module.exports = _get = Reflect.get;\n } else {\n module.exports = _get = function _get(target, property, receiver) {\n var base = superPropBase(target, property);\n if (!base) return;\n var desc = Object.getOwnPropertyDescriptor(base, property);\n\n if (desc.get) {\n return desc.get.call(receiver);\n }\n\n return desc.value;\n };\n }\n\n return _get(target, property, receiver || target);\n}\n\nmodule.exports = _get;","function _getPrototypeOf(o) {\n module.exports = _getPrototypeOf = Object.setPrototypeOf ? Object.getPrototypeOf : function _getPrototypeOf(o) {\n return o.__proto__ || Object.getPrototypeOf(o);\n };\n return _getPrototypeOf(o);\n}\n\nmodule.exports = _getPrototypeOf;","var setPrototypeOf = require(\"./setPrototypeOf\");\n\nfunction _inherits(subClass, superClass) {\n if (typeof superClass !== \"function\" && superClass !== null) {\n throw new TypeError(\"Super expression must either be null or a function\");\n }\n\n subClass.prototype = Object.create(superClass && superClass.prototype, {\n constructor: {\n value: subClass,\n writable: true,\n configurable: true\n }\n });\n if (superClass) setPrototypeOf(subClass, superClass);\n}\n\nmodule.exports = _inherits;","function _iterableToArray(iter) {\n if (typeof Symbol !== \"undefined\" && Symbol.iterator in Object(iter)) return Array.from(iter);\n}\n\nmodule.exports = _iterableToArray;","function _iterableToArrayLimit(arr, i) {\n if (typeof Symbol === \"undefined\" || !(Symbol.iterator in Object(arr))) return;\n var _arr = [];\n var _n = true;\n var _d = false;\n var _e = undefined;\n\n try {\n for (var _i = arr[Symbol.iterator](), _s; !(_n = (_s = _i.next()).done); _n = true) {\n _arr.push(_s.value);\n\n if (i && _arr.length === i) break;\n }\n } catch (err) {\n _d = true;\n _e = err;\n } finally {\n try {\n if (!_n && _i[\"return\"] != null) _i[\"return\"]();\n } finally {\n if (_d) throw _e;\n }\n }\n\n return _arr;\n}\n\nmodule.exports = _iterableToArrayLimit;","function _nonIterableRest() {\n throw new TypeError(\"Invalid attempt to destructure non-iterable instance.\\nIn order to be iterable, non-array objects must have a [Symbol.iterator]() method.\");\n}\n\nmodule.exports = _nonIterableRest;","function _nonIterableSpread() {\n throw new TypeError(\"Invalid attempt to spread non-iterable instance.\\nIn order to be iterable, non-array objects must have a [Symbol.iterator]() method.\");\n}\n\nmodule.exports = _nonIterableSpread;","var _typeof = require(\"@babel/runtime/helpers/typeof\");\n\nvar assertThisInitialized = require(\"./assertThisInitialized\");\n\nfunction _possibleConstructorReturn(self, call) {\n if (call && (_typeof(call) === \"object\" || typeof call === \"function\")) {\n return call;\n }\n\n return assertThisInitialized(self);\n}\n\nmodule.exports = _possibleConstructorReturn;","function _setPrototypeOf(o, p) {\n module.exports = _setPrototypeOf = Object.setPrototypeOf || function _setPrototypeOf(o, p) {\n o.__proto__ = p;\n return o;\n };\n\n return _setPrototypeOf(o, p);\n}\n\nmodule.exports = _setPrototypeOf;","var arrayWithHoles = require(\"./arrayWithHoles\");\n\nvar iterableToArrayLimit = require(\"./iterableToArrayLimit\");\n\nvar unsupportedIterableToArray = require(\"./unsupportedIterableToArray\");\n\nvar nonIterableRest = require(\"./nonIterableRest\");\n\nfunction _slicedToArray(arr, i) {\n return arrayWithHoles(arr) || iterableToArrayLimit(arr, i) || unsupportedIterableToArray(arr, i) || nonIterableRest();\n}\n\nmodule.exports = _slicedToArray;","var getPrototypeOf = require(\"./getPrototypeOf\");\n\nfunction _superPropBase(object, property) {\n while (!Object.prototype.hasOwnProperty.call(object, property)) {\n object = getPrototypeOf(object);\n if (object === null) break;\n }\n\n return object;\n}\n\nmodule.exports = _superPropBase;","var arrayWithoutHoles = require(\"./arrayWithoutHoles\");\n\nvar iterableToArray = require(\"./iterableToArray\");\n\nvar unsupportedIterableToArray = require(\"./unsupportedIterableToArray\");\n\nvar nonIterableSpread = require(\"./nonIterableSpread\");\n\nfunction _toConsumableArray(arr) {\n return arrayWithoutHoles(arr) || iterableToArray(arr) || unsupportedIterableToArray(arr) || nonIterableSpread();\n}\n\nmodule.exports = _toConsumableArray;","function _typeof(obj) {\n \"@babel/helpers - typeof\";\n\n if (typeof Symbol === \"function\" && typeof Symbol.iterator === \"symbol\") {\n module.exports = _typeof = function _typeof(obj) {\n return typeof obj;\n };\n } else {\n module.exports = _typeof = function _typeof(obj) {\n return obj && typeof Symbol === \"function\" && obj.constructor === Symbol && obj !== Symbol.prototype ? \"symbol\" : typeof obj;\n };\n }\n\n return _typeof(obj);\n}\n\nmodule.exports = _typeof;","var arrayLikeToArray = require(\"./arrayLikeToArray\");\n\nfunction _unsupportedIterableToArray(o, minLen) {\n if (!o) return;\n if (typeof o === \"string\") return arrayLikeToArray(o, minLen);\n var n = Object.prototype.toString.call(o).slice(8, -1);\n if (n === \"Object\" && o.constructor) n = o.constructor.name;\n if (n === \"Map\" || n === \"Set\") return Array.from(o);\n if (n === \"Arguments\" || /^(?:Ui|I)nt(?:8|16|32)(?:Clamped)?Array$/.test(n)) return arrayLikeToArray(o, minLen);\n}\n\nmodule.exports = _unsupportedIterableToArray;","module.exports = require(\"regenerator-runtime\");\n","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport const EPSILON_FLOAT32 = 1e-7;\nexport const EPSILON_FLOAT16 = 1e-4;\n/** Convenient class for storing tensor-related data. */\nexport class DataStorage {\n constructor(backend, dataMover) {\n this.backend = backend;\n this.dataMover = dataMover;\n this.data = new WeakMap();\n this.dataIdsCount = 0;\n }\n get(dataId) {\n if (!this.data.has(dataId)) {\n this.dataMover.moveData(this.backend, dataId);\n }\n return this.data.get(dataId);\n }\n set(dataId, value) {\n this.dataIdsCount++;\n this.data.set(dataId, value);\n }\n has(dataId) {\n return this.data.has(dataId);\n }\n delete(dataId) {\n this.dataIdsCount--;\n return this.data.delete(dataId);\n }\n numDataIds() {\n return this.dataIdsCount;\n }\n}\n/**\n * The interface that defines the kernels that should be implemented when\n * adding a new backend. New backends don't need to implement every one of the\n * methods, this can be done gradually (throw an error for unimplemented\n * methods).\n */\nexport class KernelBackend {\n refCount(dataId) {\n return notYetImplemented('refCount');\n }\n incRef(dataId) {\n return notYetImplemented('incRef');\n }\n timerAvailable() {\n return true;\n }\n time(f) {\n return notYetImplemented('time');\n }\n read(dataId) {\n return notYetImplemented('read');\n }\n readSync(dataId) {\n return notYetImplemented('readSync');\n }\n numDataIds() {\n return notYetImplemented('numDataIds');\n }\n disposeData(dataId, force) {\n return notYetImplemented('disposeData');\n }\n write(values, shape, dtype) {\n return notYetImplemented('write');\n }\n move(dataId, values, shape, dtype, refCount) {\n return notYetImplemented('move');\n }\n memory() {\n return notYetImplemented('memory');\n }\n /** Returns the highest precision for floats in bits (e.g. 16 or 32) */\n floatPrecision() {\n return notYetImplemented('floatPrecision');\n }\n /** Returns the smallest representable number. */\n epsilon() {\n return this.floatPrecision() === 32 ? EPSILON_FLOAT32 : EPSILON_FLOAT16;\n }\n dispose() {\n return notYetImplemented('dispose');\n }\n}\nfunction notYetImplemented(kernelName) {\n throw new Error(`'${kernelName}' not yet implemented or not found in the registry. ` +\n `This kernel may not be supported by the tfjs backend you have chosen`);\n}\n//# sourceMappingURL=backend.js.map","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * Shuffles the array in-place using Fisher-Yates algorithm.\n *\n * ```js\n * const a = [1, 2, 3, 4, 5];\n * tf.util.shuffle(a);\n * console.log(a);\n * ```\n *\n * @param array The array to shuffle in-place.\n *\n * @doc {heading: 'Util', namespace: 'util'}\n */\n// tslint:disable-next-line:no-any\nexport function shuffle(array) {\n let counter = array.length;\n let temp = 0;\n let index = 0;\n // While there are elements in the array\n while (counter > 0) {\n // Pick a random index\n index = (Math.random() * counter) | 0;\n // Decrease counter by 1\n counter--;\n // And swap the last element with it\n temp = array[counter];\n array[counter] = array[index];\n array[index] = temp;\n }\n}\n/**\n * Shuffles two arrays in-place the same way using Fisher-Yates algorithm.\n *\n * ```js\n * const a = [1,2,3,4,5];\n * const b = [11,22,33,44,55];\n * tf.util.shuffleCombo(a, b);\n * console.log(a, b);\n * ```\n *\n * @param array The first array to shuffle in-place.\n * @param array2 The second array to shuffle in-place with the same permutation\n * as the first array.\n *\n * @doc {heading: 'Util', namespace: 'util'}\n */\nexport function shuffleCombo(\n// tslint:disable-next-line:no-any\narray, \n// tslint:disable-next-line:no-any\narray2) {\n if (array.length !== array2.length) {\n throw new Error(`Array sizes must match to be shuffled together ` +\n `First array length was ${array.length}` +\n `Second array length was ${array2.length}`);\n }\n let counter = array.length;\n let temp, temp2;\n let index = 0;\n // While there are elements in the array\n while (counter > 0) {\n // Pick a random index\n index = (Math.random() * counter) | 0;\n // Decrease counter by 1\n counter--;\n // And swap the last element of each array with it\n temp = array[counter];\n temp2 = array2[counter];\n array[counter] = array[index];\n array2[counter] = array2[index];\n array[index] = temp;\n array2[index] = temp2;\n }\n}\n/** Clamps a value to a specified range. */\nexport function clamp(min, x, max) {\n return Math.max(min, Math.min(x, max));\n}\nexport function nearestLargerEven(val) {\n return val % 2 === 0 ? val : val + 1;\n}\nexport function sum(arr) {\n let sum = 0;\n for (let i = 0; i < arr.length; i++) {\n sum += arr[i];\n }\n return sum;\n}\n/**\n * Returns a sample from a uniform [a, b) distribution.\n *\n * @param a The minimum support (inclusive).\n * @param b The maximum support (exclusive).\n * @return A pseudorandom number on the half-open interval [a,b).\n */\nexport function randUniform(a, b) {\n const r = Math.random();\n return (b * r) + (1 - r) * a;\n}\n/** Returns the squared Euclidean distance between two vectors. */\nexport function distSquared(a, b) {\n let result = 0;\n for (let i = 0; i < a.length; i++) {\n const diff = Number(a[i]) - Number(b[i]);\n result += diff * diff;\n }\n return result;\n}\n/**\n * Asserts that the expression is true. Otherwise throws an error with the\n * provided message.\n *\n * ```js\n * const x = 2;\n * tf.util.assert(x === 2, 'x is not 2');\n * ```\n *\n * @param expr The expression to assert (as a boolean).\n * @param msg A function that returns the message to report when throwing an\n * error. We use a function for performance reasons.\n *\n * @doc {heading: 'Util', namespace: 'util'}\n */\nexport function assert(expr, msg) {\n if (!expr) {\n throw new Error(typeof msg === 'string' ? msg : msg());\n }\n}\nexport function assertShapesMatch(shapeA, shapeB, errorMessagePrefix = '') {\n assert(arraysEqual(shapeA, shapeB), () => errorMessagePrefix + ` Shapes ${shapeA} and ${shapeB} must match`);\n}\nexport function assertNonNull(a) {\n assert(a != null, () => `The input to the tensor constructor must be a non-null value.`);\n}\n// NOTE: We explicitly type out what T extends instead of any so that\n// util.flatten on a nested array of number doesn't try to infer T as a\n// number[][], causing us to explicitly type util.flatten().\n/**\n * Flattens an arbitrarily nested array.\n *\n * ```js\n * const a = [[1, 2], [3, 4], [5, [6, [7]]]];\n * const flat = tf.util.flatten(a);\n * console.log(flat);\n * ```\n *\n * @param arr The nested array to flatten.\n * @param result The destination array which holds the elements.\n * @param skipTypedArray If true, avoids flattening the typed arrays. Defaults\n * to false.\n *\n * @doc {heading: 'Util', namespace: 'util'}\n */\nexport function flatten(arr, result = [], skipTypedArray = false) {\n if (result == null) {\n result = [];\n }\n if (Array.isArray(arr) || isTypedArray(arr) && !skipTypedArray) {\n for (let i = 0; i < arr.length; ++i) {\n flatten(arr[i], result, skipTypedArray);\n }\n }\n else {\n result.push(arr);\n }\n return result;\n}\n/**\n * Returns the size (number of elements) of the tensor given its shape.\n *\n * ```js\n * const shape = [3, 4, 2];\n * const size = tf.util.sizeFromShape(shape);\n * console.log(size);\n * ```\n *\n * @doc {heading: 'Util', namespace: 'util'}\n */\nexport function sizeFromShape(shape) {\n if (shape.length === 0) {\n // Scalar.\n return 1;\n }\n let size = shape[0];\n for (let i = 1; i < shape.length; i++) {\n size *= shape[i];\n }\n return size;\n}\nexport function isScalarShape(shape) {\n return shape.length === 0;\n}\nexport function arraysEqual(n1, n2) {\n if (n1 === n2) {\n return true;\n }\n if (n1 == null || n2 == null) {\n return false;\n }\n if (n1.length !== n2.length) {\n return false;\n }\n for (let i = 0; i < n1.length; i++) {\n if (n1[i] !== n2[i]) {\n return false;\n }\n }\n return true;\n}\nexport function isInt(a) {\n return a % 1 === 0;\n}\nexport function tanh(x) {\n // tslint:disable-next-line:no-any\n if (Math.tanh != null) {\n // tslint:disable-next-line:no-any\n return Math.tanh(x);\n }\n if (x === Infinity) {\n return 1;\n }\n else if (x === -Infinity) {\n return -1;\n }\n else {\n const e2x = Math.exp(2 * x);\n return (e2x - 1) / (e2x + 1);\n }\n}\nexport function sizeToSquarishShape(size) {\n const width = Math.ceil(Math.sqrt(size));\n return [width, Math.ceil(size / width)];\n}\n/**\n * Creates a new array with randomized indicies to a given quantity.\n *\n * ```js\n * const randomTen = tf.util.createShuffledIndices(10);\n * console.log(randomTen);\n * ```\n *\n * @param number Quantity of how many shuffled indicies to create.\n *\n * @doc {heading: 'Util', namespace: 'util'}\n */\nexport function createShuffledIndices(n) {\n const shuffledIndices = new Uint32Array(n);\n for (let i = 0; i < n; ++i) {\n shuffledIndices[i] = i;\n }\n shuffle(shuffledIndices);\n return shuffledIndices;\n}\nexport function rightPad(a, size) {\n if (size <= a.length) {\n return a;\n }\n return a + ' '.repeat(size - a.length);\n}\nexport function repeatedTry(checkFn, delayFn = (counter) => 0, maxCounter) {\n return new Promise((resolve, reject) => {\n let tryCount = 0;\n const tryFn = () => {\n if (checkFn()) {\n resolve();\n return;\n }\n tryCount++;\n const nextBackoff = delayFn(tryCount);\n if (maxCounter != null && tryCount >= maxCounter) {\n reject();\n return;\n }\n setTimeout(tryFn, nextBackoff);\n };\n tryFn();\n });\n}\n/**\n * Given the full size of the array and a shape that may contain -1 as the\n * implicit dimension, returns the inferred shape where -1 is replaced.\n * E.g. For shape=[2, -1, 3] and size=24, it will return [2, 4, 3].\n *\n * @param shape The shape, which may contain -1 in some dimension.\n * @param size The full size (number of elements) of the array.\n * @return The inferred shape where -1 is replaced with the inferred size.\n */\nexport function inferFromImplicitShape(shape, size) {\n let shapeProd = 1;\n let implicitIdx = -1;\n for (let i = 0; i < shape.length; ++i) {\n if (shape[i] >= 0) {\n shapeProd *= shape[i];\n }\n else if (shape[i] === -1) {\n if (implicitIdx !== -1) {\n throw Error(`Shapes can only have 1 implicit size. ` +\n `Found -1 at dim ${implicitIdx} and dim ${i}`);\n }\n implicitIdx = i;\n }\n else if (shape[i] < 0) {\n throw Error(`Shapes can not be < 0. Found ${shape[i]} at dim ${i}`);\n }\n }\n if (implicitIdx === -1) {\n if (size > 0 && size !== shapeProd) {\n throw Error(`Size(${size}) must match the product of shape ${shape}`);\n }\n return shape;\n }\n if (shapeProd === 0) {\n throw Error(`Cannot infer the missing size in [${shape}] when ` +\n `there are 0 elements`);\n }\n if (size % shapeProd !== 0) {\n throw Error(`The implicit shape can't be a fractional number. ` +\n `Got ${size} / ${shapeProd}`);\n }\n const newShape = shape.slice();\n newShape[implicitIdx] = size / shapeProd;\n return newShape;\n}\nexport function parseAxisParam(axis, shape) {\n const rank = shape.length;\n // Normalize input\n axis = axis == null ? shape.map((s, i) => i) : [].concat(axis);\n // Check for valid range\n assert(axis.every(ax => ax >= -rank && ax < rank), () => `All values in axis param must be in range [-${rank}, ${rank}) but ` +\n `got axis ${axis}`);\n // Check for only integers\n assert(axis.every(ax => isInt(ax)), () => `All values in axis param must be integers but ` +\n `got axis ${axis}`);\n // Handle negative axis.\n return axis.map(a => a < 0 ? rank + a : a);\n}\n/** Reduces the shape by removing all dimensions of shape 1. */\nexport function squeezeShape(shape, axis) {\n const newShape = [];\n const keptDims = [];\n const isEmptyArray = axis != null && Array.isArray(axis) && axis.length === 0;\n const axes = (axis == null || isEmptyArray) ?\n null :\n parseAxisParam(axis, shape).sort();\n let j = 0;\n for (let i = 0; i < shape.length; ++i) {\n if (axes != null) {\n if (axes[j] === i && shape[i] !== 1) {\n throw new Error(`Can't squeeze axis ${i} since its dim '${shape[i]}' is not 1`);\n }\n if ((axes[j] == null || axes[j] > i) && shape[i] === 1) {\n newShape.push(shape[i]);\n keptDims.push(i);\n }\n if (axes[j] <= i) {\n j++;\n }\n }\n if (shape[i] !== 1) {\n newShape.push(shape[i]);\n keptDims.push(i);\n }\n }\n return { newShape, keptDims };\n}\nexport function getTypedArrayFromDType(dtype, size) {\n let values = null;\n if (dtype == null || dtype === 'float32') {\n values = new Float32Array(size);\n }\n else if (dtype === 'int32') {\n values = new Int32Array(size);\n }\n else if (dtype === 'bool') {\n values = new Uint8Array(size);\n }\n else {\n throw new Error(`Unknown data type ${dtype}`);\n }\n return values;\n}\nexport function getArrayFromDType(dtype, size) {\n let values = null;\n if (dtype == null || dtype === 'float32') {\n values = new Float32Array(size);\n }\n else if (dtype === 'int32') {\n values = new Int32Array(size);\n }\n else if (dtype === 'bool') {\n values = new Uint8Array(size);\n }\n else if (dtype === 'string') {\n values = new Array(size);\n }\n else {\n throw new Error(`Unknown data type ${dtype}`);\n }\n return values;\n}\nexport function checkConversionForErrors(vals, dtype) {\n for (let i = 0; i < vals.length; i++) {\n const num = vals[i];\n if (isNaN(num) || !isFinite(num)) {\n throw Error(`A tensor of type ${dtype} being uploaded contains ${num}.`);\n }\n }\n}\n/** Returns true if the dtype is valid. */\nexport function isValidDtype(dtype) {\n return dtype === 'bool' || dtype === 'complex64' || dtype === 'float32' ||\n dtype === 'int32' || dtype === 'string';\n}\n/**\n * Returns true if the new type can't encode the old type without loss of\n * precision.\n */\nexport function hasEncodingLoss(oldType, newType) {\n if (newType === 'complex64') {\n return false;\n }\n if (newType === 'float32' && oldType !== 'complex64') {\n return false;\n }\n if (newType === 'int32' && oldType !== 'float32' && oldType !== 'complex64') {\n return false;\n }\n if (newType === 'bool' && oldType === 'bool') {\n return false;\n }\n return true;\n}\nexport function isTypedArray(a) {\n return a instanceof Float32Array || a instanceof Int32Array ||\n a instanceof Uint8Array;\n}\nexport function bytesPerElement(dtype) {\n if (dtype === 'float32' || dtype === 'int32') {\n return 4;\n }\n else if (dtype === 'complex64') {\n return 8;\n }\n else if (dtype === 'bool') {\n return 1;\n }\n else {\n throw new Error(`Unknown dtype ${dtype}`);\n }\n}\n/**\n * Returns the approximate number of bytes allocated in the string array - 2\n * bytes per character. Computing the exact bytes for a native string in JS is\n * not possible since it depends on the encoding of the html page that serves\n * the website.\n */\nexport function bytesFromStringArray(arr) {\n if (arr == null) {\n return 0;\n }\n let bytes = 0;\n arr.forEach(x => bytes += x.length);\n return bytes;\n}\n/** Returns true if the value is a string. */\nexport function isString(value) {\n return typeof value === 'string' || value instanceof String;\n}\nexport function isBoolean(value) {\n return typeof value === 'boolean';\n}\nexport function isNumber(value) {\n return typeof value === 'number';\n}\nexport function inferDtype(values) {\n if (Array.isArray(values)) {\n return inferDtype(values[0]);\n }\n if (values instanceof Float32Array) {\n return 'float32';\n }\n else if (values instanceof Int32Array || values instanceof Uint8Array) {\n return 'int32';\n }\n else if (isNumber(values)) {\n return 'float32';\n }\n else if (isString(values)) {\n return 'string';\n }\n else if (isBoolean(values)) {\n return 'bool';\n }\n return 'float32';\n}\nexport function isFunction(f) {\n return !!(f && f.constructor && f.call && f.apply);\n}\nexport function nearestDivisor(size, start) {\n for (let i = start; i < size; ++i) {\n if (size % i === 0) {\n return i;\n }\n }\n return size;\n}\nexport function computeStrides(shape) {\n const rank = shape.length;\n if (rank < 2) {\n return [];\n }\n // Last dimension has implicit stride of 1, thus having D-1 (instead of D)\n // strides.\n const strides = new Array(rank - 1);\n strides[rank - 2] = shape[rank - 1];\n for (let i = rank - 3; i >= 0; --i) {\n strides[i] = strides[i + 1] * shape[i + 1];\n }\n return strides;\n}\nfunction createNestedArray(offset, shape, a, isComplex = false) {\n const ret = new Array();\n if (shape.length === 1) {\n const d = shape[0] * (isComplex ? 2 : 1);\n for (let i = 0; i < d; i++) {\n ret[i] = a[offset + i];\n }\n }\n else {\n const d = shape[0];\n const rest = shape.slice(1);\n const len = rest.reduce((acc, c) => acc * c) * (isComplex ? 2 : 1);\n for (let i = 0; i < d; i++) {\n ret[i] = createNestedArray(offset + i * len, rest, a, isComplex);\n }\n }\n return ret;\n}\n// Provide a nested array of TypedArray in given shape.\nexport function toNestedArray(shape, a, isComplex = false) {\n if (shape.length === 0) {\n // Scalar type should return a single number.\n return a[0];\n }\n const size = shape.reduce((acc, c) => acc * c) * (isComplex ? 2 : 1);\n if (size === 0) {\n // A tensor with shape zero should be turned into empty list.\n return [];\n }\n if (size !== a.length) {\n throw new Error(`[${shape}] does not match the input size ${a.length}${isComplex ? ' for a complex tensor' : ''}.`);\n }\n return createNestedArray(0, shape, a, isComplex);\n}\nexport function makeOnesTypedArray(size, dtype) {\n const array = makeZerosTypedArray(size, dtype);\n for (let i = 0; i < array.length; i++) {\n array[i] = 1;\n }\n return array;\n}\nexport function makeZerosTypedArray(size, dtype) {\n if (dtype == null || dtype === 'float32' || dtype === 'complex64') {\n return new Float32Array(size);\n }\n else if (dtype === 'int32') {\n return new Int32Array(size);\n }\n else if (dtype === 'bool') {\n return new Uint8Array(size);\n }\n else {\n throw new Error(`Unknown data type ${dtype}`);\n }\n}\n/**\n * Make nested `TypedArray` filled with zeros.\n * @param shape The shape information for the nested array.\n * @param dtype dtype of the array element.\n */\nexport function makeZerosNestedTypedArray(shape, dtype) {\n const size = shape.reduce((prev, curr) => prev * curr, 1);\n if (dtype == null || dtype === 'float32') {\n return toNestedArray(shape, new Float32Array(size));\n }\n else if (dtype === 'int32') {\n return toNestedArray(shape, new Int32Array(size));\n }\n else if (dtype === 'bool') {\n return toNestedArray(shape, new Uint8Array(size));\n }\n else {\n throw new Error(`Unknown data type ${dtype}`);\n }\n}\nexport function assertNonNegativeIntegerDimensions(shape) {\n shape.forEach(dimSize => {\n assert(Number.isInteger(dimSize) && dimSize >= 0, () => `Tensor must have a shape comprised of positive integers but got ` +\n `shape [${shape}].`);\n });\n}\n/**\n * Computes flat index for a given location (multidimentionsal index) in a\n * Tensor/multidimensional array.\n *\n * @param locs Location in the tensor.\n * @param rank Rank of the tensor.\n * @param strides Tensor strides.\n */\nexport function locToIndex(locs, rank, strides) {\n if (rank === 0) {\n return 0;\n }\n else if (rank === 1) {\n return locs[0];\n }\n let index = locs[locs.length - 1];\n for (let i = 0; i < locs.length - 1; ++i) {\n index += strides[i] * locs[i];\n }\n return index;\n}\n/**\n * Computes the location (multidimensional index) in a tensor/multidimentional\n * array for a given flat index.\n *\n * @param index Index in flat array.\n * @param rank Rank of tensor.\n * @param strides Strides of tensor.\n */\nexport function indexToLoc(index, rank, strides) {\n if (rank === 0) {\n return [];\n }\n else if (rank === 1) {\n return [index];\n }\n const locs = new Array(rank);\n for (let i = 0; i < locs.length - 1; ++i) {\n locs[i] = Math.floor(index / strides[i]);\n index -= locs[i] * strides[i];\n }\n locs[locs.length - 1] = index;\n return locs;\n}\n/**\n * This method asserts whether an object is a Promise instance.\n * @param object\n */\n// tslint:disable-next-line: no-any\nexport function isPromise(object) {\n // We chose to not use 'obj instanceOf Promise' for two reasons:\n // 1. It only reliably works for es6 Promise, not other Promise\n // implementations.\n // 2. It doesn't work with framework that uses zone.js. zone.js monkey patch\n // the async calls, so it is possible the obj (patched) is comparing to a\n // pre-patched Promise.\n return object && object.then && typeof object.then === 'function';\n}\n//# sourceMappingURL=util_base.js.map","/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { isPromise } from './util_base';\n// Expects flags from URL in the format ?tfjsflags=FLAG1:1,FLAG2:true.\nconst TENSORFLOWJS_FLAGS_PREFIX = 'tfjsflags';\n/**\n * The environment contains evaluated flags as well as the registered platform.\n * This is always used as a global singleton and can be retrieved with\n * `tf.env()`.\n *\n * @doc {heading: 'Environment'}\n */\nexport class Environment {\n // tslint:disable-next-line: no-any\n constructor(global) {\n this.global = global;\n this.flags = {};\n this.flagRegistry = {};\n this.urlFlags = {};\n // Jasmine spies on this in 'environment_test.ts'\n this.getQueryParams = getQueryParams;\n this.populateURLFlags();\n }\n setPlatform(platformName, platform) {\n if (this.platform != null) {\n console.warn(`Platform ${this.platformName} has already been set. ` +\n `Overwriting the platform with ${platform}.`);\n }\n this.platformName = platformName;\n this.platform = platform;\n }\n registerFlag(flagName, evaluationFn, setHook) {\n this.flagRegistry[flagName] = { evaluationFn, setHook };\n // Override the flag value from the URL. This has to happen here because the\n // environment is initialized before flags get registered.\n if (this.urlFlags[flagName] != null) {\n const flagValue = this.urlFlags[flagName];\n console.warn(`Setting feature override from URL ${flagName}: ${flagValue}.`);\n this.set(flagName, flagValue);\n }\n }\n async getAsync(flagName) {\n if (flagName in this.flags) {\n return this.flags[flagName];\n }\n this.flags[flagName] = await this.evaluateFlag(flagName);\n return this.flags[flagName];\n }\n get(flagName) {\n if (flagName in this.flags) {\n return this.flags[flagName];\n }\n const flagValue = this.evaluateFlag(flagName);\n if (isPromise(flagValue)) {\n throw new Error(`Flag ${flagName} cannot be synchronously evaluated. ` +\n `Please use getAsync() instead.`);\n }\n this.flags[flagName] = flagValue;\n return this.flags[flagName];\n }\n getNumber(flagName) {\n return this.get(flagName);\n }\n getBool(flagName) {\n return this.get(flagName);\n }\n getFlags() {\n return this.flags;\n }\n // For backwards compatibility.\n get features() {\n return this.flags;\n }\n set(flagName, value) {\n if (this.flagRegistry[flagName] == null) {\n throw new Error(`Cannot set flag ${flagName} as it has not been registered.`);\n }\n this.flags[flagName] = value;\n if (this.flagRegistry[flagName].setHook != null) {\n this.flagRegistry[flagName].setHook(value);\n }\n }\n evaluateFlag(flagName) {\n if (this.flagRegistry[flagName] == null) {\n throw new Error(`Cannot evaluate flag '${flagName}': no evaluation function found.`);\n }\n return this.flagRegistry[flagName].evaluationFn();\n }\n setFlags(flags) {\n this.flags = Object.assign({}, flags);\n }\n reset() {\n this.flags = {};\n this.urlFlags = {};\n this.populateURLFlags();\n }\n populateURLFlags() {\n if (typeof this.global === 'undefined' ||\n typeof this.global.location === 'undefined' ||\n typeof this.global.location.search === 'undefined') {\n return;\n }\n const urlParams = this.getQueryParams(this.global.location.search);\n if (TENSORFLOWJS_FLAGS_PREFIX in urlParams) {\n const keyValues = urlParams[TENSORFLOWJS_FLAGS_PREFIX].split(',');\n keyValues.forEach(keyValue => {\n const [key, value] = keyValue.split(':');\n this.urlFlags[key] = parseValue(key, value);\n });\n }\n }\n}\nexport function getQueryParams(queryString) {\n const params = {};\n queryString.replace(/[?&]([^=?&]+)(?:=([^&]*))?/g, (s, ...t) => {\n decodeParam(params, t[0], t[1]);\n return t.join('=');\n });\n return params;\n}\nfunction decodeParam(params, name, value) {\n params[decodeURIComponent(name)] = decodeURIComponent(value || '');\n}\nfunction parseValue(flagName, value) {\n value = value.toLowerCase();\n if (value === 'true' || value === 'false') {\n return value === 'true';\n }\n else if (`${+value}` === value) {\n return +value;\n }\n throw new Error(`Could not parse value flag value ${value} for flag ${flagName}.`);\n}\n/**\n * Returns the current environment (a global singleton).\n *\n * The environment object contains the evaluated feature values as well as the\n * active platform.\n *\n * @doc {heading: 'Environment'}\n */\nexport function env() {\n return ENV;\n}\nexport let ENV = null;\nexport function setEnvironmentGlobal(environment) {\n ENV = environment;\n}\n//# sourceMappingURL=environment.js.map","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// Note that the identifier globalNameSpace is scoped to this module, but will\n// always resolve to the same global object regardless of how the module is\n// resolved.\n// tslint:disable-next-line:no-any\nlet globalNameSpace;\n// tslint:disable-next-line:no-any\nexport function getGlobalNamespace() {\n if (globalNameSpace == null) {\n // tslint:disable-next-line:no-any\n let ns;\n if (typeof (window) !== 'undefined') {\n ns = window;\n }\n else if (typeof (global) !== 'undefined') {\n ns = global;\n }\n else if (typeof (process) !== 'undefined') {\n ns = process;\n }\n else if (typeof (self) !== 'undefined') {\n ns = self;\n }\n else {\n throw new Error('Could not find a global object');\n }\n globalNameSpace = ns;\n }\n return globalNameSpace;\n}\n// tslint:disable-next-line:no-any\nfunction getGlobalMap() {\n const ns = getGlobalNamespace();\n if (ns._tfGlobals == null) {\n ns._tfGlobals = new Map();\n }\n return ns._tfGlobals;\n}\n/**\n * Returns a globally accessible 'singleton' object.\n *\n * @param key the name of the object\n * @param init a function to initialize to initialize this object\n * the first time it is fetched.\n */\nexport function getGlobal(key, init) {\n const globalMap = getGlobalMap();\n if (globalMap.has(key)) {\n return globalMap.get(key);\n }\n else {\n const singleton = init();\n globalMap.set(key, singleton);\n return globalMap.get(key);\n }\n}\n//# sourceMappingURL=global_util.js.map","export const Abs = 'Abs';\nexport const Acos = 'Acos';\nexport const Acosh = 'Acosh';\nexport const Add = 'Add';\nexport const AddN = 'AddN';\nexport const All = 'All';\nexport const Any = 'Any';\nexport const ArgMax = 'ArgMax';\nexport const ArgMin = 'ArgMin';\nexport const Asin = 'Asin';\nexport const Asinh = 'Asinh';\nexport const Atan = 'Atan';\nexport const Atanh = 'Atanh';\nexport const Atan2 = 'Atan2';\nexport const AvgPool = 'AvgPool';\nexport const AvgPoolGrad = 'AvgPoolGrad';\nexport const AvgPool3D = 'AvgPool3D';\nexport const AvgPool3DGrad = 'AvgPool3DGrad';\nexport const BatchMatMul = 'BatchMatMul';\nexport const BatchToSpaceND = 'BatchToSpaceND';\nexport const Bincount = 'Bincount';\nexport const BroadcastTo = 'BroadcastTo';\nexport const Cast = 'Cast';\nexport const Ceil = 'Ceil';\nexport const ClipByValue = 'ClipByValue';\nexport const Complex = 'Complex';\nexport const ComplexAbs = 'ComplexAbs';\nexport const Concat = 'Concat';\nexport const Conv2D = 'Conv2D';\nexport const Conv2DBackpropFilter = 'Conv2DBackpropFilter';\nexport const Conv2DBackpropInput = 'Conv2DBackpropInput';\nexport const Conv3D = 'Conv3D';\nexport const Conv3DBackpropFilterV2 = 'Conv3DBackpropFilterV2';\nexport const Conv3DBackpropInputV2 = 'Conv3DBackpropInputV2';\nexport const Cos = 'Cos';\nexport const Cosh = 'Cosh';\nexport const Cumsum = 'Cumsum';\nexport const CropAndResize = 'CropAndResize';\nexport const DenseBincount = 'DenseBincount';\nexport const DepthToSpace = 'DepthToSpace';\nexport const DepthwiseConv2dNative = 'DepthwiseConv2dNative';\nexport const DepthwiseConv2dNativeBackpropFilter = 'DepthwiseConv2dNativeBackpropFilter';\nexport const DepthwiseConv2dNativeBackpropInput = 'DepthwiseConv2dNativeBackpropInput';\nexport const Diag = 'Diag';\nexport const Dilation2D = 'Dilation2D';\nexport const Dilation2DBackpropInput = 'Dilation2DBackpropInput';\nexport const Dilation2DBackpropFilter = 'Dilation2DBackpropFilter';\nexport const RealDiv = 'RealDiv';\nexport const Einsum = 'Einsum';\nexport const Elu = 'Elu';\nexport const EluGrad = 'EluGrad';\nexport const Erf = 'Erf';\nexport const Equal = 'Equal';\nexport const Exp = 'Exp';\nexport const ExpandDims = 'ExpandDims';\nexport const Expm1 = 'Expm1';\nexport const FFT = 'FFT';\nexport const Fill = 'Fill';\nexport const FlipLeftRight = 'FlipLeftRight';\nexport const Floor = 'Floor';\nexport const FloorDiv = 'FloorDiv';\nexport const FusedBatchNorm = 'FusedBatchNorm';\nexport const GatherV2 = 'GatherV2';\nexport const GatherNd = 'GatherNd';\nexport const Greater = 'Greater';\nexport const GreaterEqual = 'GreaterEqual';\nexport const Identity = 'Identity';\nexport const IFFT = 'IFFT';\nexport const Imag = 'Imag';\nexport const IsFinite = 'IsFinite';\nexport const IsInf = 'IsInf';\nexport const IsNan = 'IsNan';\nexport const LeakyRelu = 'LeakyRelu';\nexport const Less = 'Less';\nexport const LessEqual = 'LessEqual';\nexport const LinSpace = 'LinSpace';\nexport const Log = 'Log';\nexport const Log1p = 'Log1p';\nexport const LogicalAnd = 'LogicalAnd';\nexport const LogicalNot = 'LogicalNot';\nexport const LogicalOr = 'LogicalOr';\nexport const LogSoftmax = 'LogSoftmax';\nexport const LRN = 'LRN';\nexport const LRNGrad = 'LRNGrad';\nexport const Max = 'Max';\nexport const Maximum = 'Maximum';\nexport const MaxPool = 'MaxPool';\nexport const MaxPoolGrad = 'MaxPoolGrad';\nexport const MaxPool3D = 'MaxPool3D';\nexport const MaxPool3DGrad = 'MaxPool3DGrad';\nexport const MaxPoolWithArgmax = 'MaxPoolWithArgmax';\nexport const Mean = 'Mean';\nexport const Min = 'Min';\nexport const Minimum = 'Minimum';\nexport const MirrorPad = 'MirrorPad';\nexport const Mod = 'Mod';\nexport const Multinomial = 'Multinomial';\nexport const Multiply = 'Multiply';\nexport const Neg = 'Neg';\nexport const NotEqual = 'NotEqual';\nexport const NonMaxSuppressionV3 = 'NonMaxSuppressionV3';\nexport const NonMaxSuppressionV4 = 'NonMaxSuppressionV4';\nexport const NonMaxSuppressionV5 = 'NonMaxSuppressionV5';\nexport const OnesLike = 'OnesLike';\nexport const OneHot = 'OneHot';\nexport const Pack = 'Pack';\nexport const PadV2 = 'PadV2';\nexport const Pool = 'Pool';\nexport const Pow = 'Pow';\nexport const Prelu = 'Prelu';\nexport const Prod = 'Prod';\nexport const Range = 'Range';\nexport const Real = 'Real';\nexport const Reciprocal = 'Reciprocal';\nexport const Relu = 'Relu';\nexport const Reshape = 'Reshape';\nexport const ResizeNearestNeighbor = 'ResizeNearestNeighbor';\nexport const ResizeNearestNeighborGrad = 'ResizeNearestNeighborGrad';\nexport const ResizeBilinear = 'ResizeBilinear';\nexport const ResizeBilinearGrad = 'ResizeBilinearGrad';\nexport const Relu6 = 'Relu6';\nexport const Reverse = 'Reverse';\nexport const Round = 'Round';\nexport const Rsqrt = 'Rsqrt';\nexport const ScatterNd = 'ScatterNd';\nexport const Select = 'Select';\nexport const Selu = 'Selu';\nexport const Slice = 'Slice';\nexport const Sin = 'Sin';\nexport const Sinh = 'Sinh';\nexport const Sign = 'Sign';\nexport const Sigmoid = 'Sigmoid';\nexport const Softplus = 'Softplus';\nexport const Sqrt = 'Sqrt';\nexport const Sum = 'Sum';\nexport const SpaceToBatchND = 'SpaceToBatchND';\nexport const SplitV = 'SplitV';\nexport const Softmax = 'Softmax';\nexport const SparseFillEmptyRows = 'SparseFillEmptyRows';\nexport const SparseReshape = 'SparseReshape';\nexport const SparseToDense = 'SparseToDense';\nexport const SquaredDifference = 'SquaredDifference';\nexport const Square = 'Square';\nexport const StridedSlice = 'StridedSlice';\nexport const Sub = 'Sub';\nexport const Tan = 'Tan';\nexport const Tanh = 'Tanh';\nexport const Tile = 'Tile';\nexport const TopK = 'TopK';\nexport const Transform = 'Transform';\nexport const Transpose = 'Transpose';\nexport const Unique = 'Unique';\nexport const Unpack = 'Unpack';\nexport const UnsortedSegmentSum = 'UnsortedSegmentSum';\nexport const ZerosLike = 'ZerosLike';\n/**\n * TensorFlow.js-only kernels\n */\nexport const Step = 'Step';\nexport const FromPixels = 'FromPixels';\nexport const RotateWithOffset = 'RotateWithOffset';\nexport const _FusedMatMul = '_FusedMatMul';\nexport const FusedConv2D = 'FusedConv2D';\nexport const FusedDepthwiseConv2D = 'FusedDepthwiseConv2D';\n//# sourceMappingURL=kernel_names.js.map","/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { env } from './environment';\nimport { getGlobal } from './global_util';\nconst kernelRegistry = getGlobal('kernelRegistry', () => new Map());\nconst gradRegistry = getGlobal('gradRegistry', () => new Map());\n/**\n * Returns the kernel function (code) associated with the provided names.\n *\n * @param kernelName The official name of the kernel.\n * @param backendName The official name of the backend.\n */\nexport function getKernel(kernelName, backendName) {\n const key = makeKey(kernelName, backendName);\n return kernelRegistry.get(key);\n}\n/**\n * Returns the registered gradient info associated with the provided kernel.\n * @param kernelName The official TF kernel name.\n */\nexport function getGradient(kernelName) {\n return gradRegistry.get(kernelName);\n}\nexport function getKernelsForBackend(backendName) {\n const it = kernelRegistry.entries();\n const result = [];\n while (true) {\n const { done, value } = it.next();\n if (done) {\n break;\n }\n const [key, config] = value;\n const [backend,] = key.split('_');\n if (backend === backendName) {\n result.push(config);\n }\n }\n return result;\n}\n/**\n * Registers the function (forward pass) for the kernel in a global registry.\n *\n * @param config A config object with the following properties:\n * - `kernelName` The official name of the kernel.\n * - `backendName` The official name of the backend.\n * - `kernelFunc` The function to run during the forward pass of the kernel.\n * - `setupFunc` Optional. Gets called once, after the backend initializes.\n * - `disposeFunc` Optional. Gets called once, right before the backend is\n * disposed.\n */\nexport function registerKernel(config) {\n const { kernelName, backendName } = config;\n const key = makeKey(kernelName, backendName);\n if (kernelRegistry.has(key)) {\n console.warn(`The kernel '${kernelName}' for backend ` +\n `'${backendName}' is already registered`);\n }\n kernelRegistry.set(key, config);\n}\n/**\n * Registers a gradient function for a given kernel in the global registry,\n * to be used during the back-propagation of that kernel.\n *\n * @param config An object with the following properties:\n * - `kernelName` The name of the kernel that the gradient function is for.\n * - `gradFunc` The function to run during back-propagation.\n */\nexport function registerGradient(config) {\n const { kernelName } = config;\n if (gradRegistry.has(kernelName)) {\n // TODO (yassogba) after 3.0 assess whether we need to keep this gated\n // to debug mode.\n if (env().getBool('DEBUG')) {\n console.warn(`Overriding the gradient for '${kernelName}'`);\n }\n }\n gradRegistry.set(kernelName, config);\n}\n/**\n * Removes the kernel function from the registry.\n *\n * @param kernelName The official name of the kernel.\n * @param backendName The official name of the backend.\n *\n */\nexport function unregisterKernel(kernelName, backendName) {\n const key = makeKey(kernelName, backendName);\n if (!kernelRegistry.has(key)) {\n throw new Error(`The kernel '${kernelName}' for backend ` +\n `'${backendName}' is not registered`);\n }\n kernelRegistry.delete(key);\n}\n/** Removes the registered gradient from the global registry. */\nexport function unregisterGradient(kernelName) {\n if (!gradRegistry.has(kernelName)) {\n throw new Error(`The gradient '${kernelName}' for backend is not registered`);\n }\n gradRegistry.delete(kernelName);\n}\n/**\n * Finds kernels that have already been registered to a backend and re-registers\n * them for a new backend. Useful for registering custom backends.\n * @param registeredBackendName Already registered backend.\n * @param newBackendName New backend.\n */\nexport function copyRegisteredKernels(registeredBackendName, newBackendName) {\n const kernels = getKernelsForBackend(registeredBackendName);\n kernels.forEach(kernelConfig => {\n const newKernelConfig = Object.assign({}, kernelConfig, { backendName: newBackendName });\n registerKernel(newKernelConfig);\n });\n}\nfunction makeKey(kernelName, backendName) {\n return `${backendName}_${kernelName}`;\n}\n//# sourceMappingURL=kernel_registry.js.map","/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { env } from './environment';\nimport * as base from './util_base';\nexport * from './util_base';\n/**\n * Create typed array for scalar value. Used for storing in `DataStorage`.\n */\nexport function createScalarValue(value, dtype) {\n if (dtype === 'string') {\n return encodeString(value);\n }\n return toTypedArray([value], dtype);\n}\nfunction noConversionNeeded(a, dtype) {\n return (a instanceof Float32Array && dtype === 'float32') ||\n (a instanceof Int32Array && dtype === 'int32') ||\n (a instanceof Uint8Array && dtype === 'bool');\n}\nexport function toTypedArray(a, dtype) {\n if (dtype === 'string') {\n throw new Error('Cannot convert a string[] to a TypedArray');\n }\n if (Array.isArray(a)) {\n a = base.flatten(a);\n }\n if (env().getBool('DEBUG')) {\n base.checkConversionForErrors(a, dtype);\n }\n if (noConversionNeeded(a, dtype)) {\n return a;\n }\n if (dtype == null || dtype === 'float32' || dtype === 'complex64') {\n return new Float32Array(a);\n }\n else if (dtype === 'int32') {\n return new Int32Array(a);\n }\n else if (dtype === 'bool') {\n const bool = new Uint8Array(a.length);\n for (let i = 0; i < bool.length; ++i) {\n if (Math.round(a[i]) !== 0) {\n bool[i] = 1;\n }\n }\n return bool;\n }\n else {\n throw new Error(`Unknown data type ${dtype}`);\n }\n}\n/**\n * Returns the current high-resolution time in milliseconds relative to an\n * arbitrary time in the past. It works across different platforms (node.js,\n * browsers).\n *\n * ```js\n * console.log(tf.util.now());\n * ```\n *\n * @doc {heading: 'Util', namespace: 'util'}\n */\nexport function now() {\n return env().platform.now();\n}\n/**\n * Returns a platform-specific implementation of\n * [`fetch`](https://developer.mozilla.org/en-US/docs/Web/API/Fetch_API).\n *\n * If `fetch` is defined on the global object (`window`, `process`, etc.),\n * `tf.util.fetch` returns that function.\n *\n * If not, `tf.util.fetch` returns a platform-specific solution.\n *\n * ```js\n * const resource = await tf.util.fetch('https://unpkg.com/@tensorflow/tfjs');\n * // handle response\n * ```\n *\n * @doc {heading: 'Util'}\n */\nexport function fetch(path, requestInits) {\n return env().platform.fetch(path, requestInits);\n}\n/**\n * Encodes the provided string into bytes using the provided encoding scheme.\n *\n * @param s The string to encode.\n * @param encoding The encoding scheme. Defaults to utf-8.\n *\n * @doc {heading: 'Util'}\n */\nexport function encodeString(s, encoding = 'utf-8') {\n encoding = encoding || 'utf-8';\n return env().platform.encode(s, encoding);\n}\n/**\n * Decodes the provided bytes into a string using the provided encoding scheme.\n * @param bytes The bytes to decode.\n *\n * @param encoding The encoding scheme. Defaults to utf-8.\n *\n * @doc {heading: 'Util'}\n */\nexport function decodeString(bytes, encoding = 'utf-8') {\n encoding = encoding || 'utf-8';\n return env().platform.decode(bytes, encoding);\n}\n//# sourceMappingURL=util.js.map","/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { env } from './environment';\nimport * as util from './util';\nexport class Profiler {\n constructor(backendTimer, logger) {\n this.backendTimer = backendTimer;\n this.logger = logger;\n if (logger == null) {\n this.logger = new Logger();\n }\n }\n profileKernel(kernelName, inputs, f) {\n let outputs;\n const holdResultWrapperFn = () => {\n outputs = f();\n };\n let timer;\n const start = util.now();\n if (this.backendTimer.timerAvailable()) {\n timer = this.backendTimer.time(holdResultWrapperFn);\n }\n else {\n holdResultWrapperFn();\n for (const output of outputs) {\n output.dataSync();\n }\n timer = Promise.resolve({ kernelMs: util.now() - start });\n }\n if (env().getBool('CHECK_COMPUTATION_FOR_ERRORS')) {\n for (let i = 0; i < outputs.length; i++) {\n const output = outputs[i];\n // Dangling promise here because we don't want to propagate up\n // asynchronicity.\n output.data().then(tensorVals => {\n checkComputationForErrors(tensorVals, output.dtype, kernelName);\n });\n }\n }\n const kernelProfile = {\n kernelName,\n outputs,\n inputs,\n timeMs: timer.then(timing => timing.kernelMs),\n extraInfo: timer.then(timing => timing.getExtraProfileInfo != null ?\n timing.getExtraProfileInfo() :\n '')\n };\n return kernelProfile;\n }\n logKernelProfile(kernelProfile) {\n const { kernelName, outputs, timeMs, inputs, extraInfo } = kernelProfile;\n outputs.forEach(result => {\n Promise.all([result.data(), timeMs, extraInfo]).then(valueContainer => {\n this.logger.logKernelProfile(kernelName, result, valueContainer[0], valueContainer[1], inputs, valueContainer[2]);\n });\n });\n }\n}\nexport function checkComputationForErrors(vals, dtype, kernelName) {\n if (dtype !== 'float32') {\n // Only floating point computations will generate NaN values\n return false;\n }\n for (let i = 0; i < vals.length; i++) {\n const num = vals[i];\n if (isNaN(num) || !isFinite(num)) {\n // Throwing custom exception so behavior is testable.\n console.warn(`Found ${num} in the result of '${kernelName}'`);\n return true;\n }\n }\n return false;\n}\nexport class Logger {\n logKernelProfile(name, result, vals, timeMs, inputs, extraInfo) {\n const time = typeof timeMs === 'number' ? util.rightPad(`${timeMs}ms`, 9) :\n timeMs['error'];\n const paddedName = util.rightPad(name, 25);\n const rank = result.rank;\n const size = result.size;\n const shape = util.rightPad(result.shape.toString(), 14);\n let inputShapesDescription = '';\n for (const name in inputs) {\n const input = inputs[name];\n if (input != null) {\n // The input might be a non-tensor (e.g HTMLImageElement), in which case\n // we claim the output shape as input shape.\n const inputShape = input.shape || result.shape;\n const inputRank = inputShape.length;\n inputShapesDescription +=\n `${name}: ${inputRank}D ${inputRank > 0 ? inputShape : ''} `;\n }\n }\n console.log(`%c${paddedName}\\t%c${time}\\t%c${rank}D ${shape}\\t%c${size}\\t%c${inputShapesDescription}\\t%c${extraInfo}`, 'font-weight:bold', 'color:red', 'color:blue', 'color: orange', 'color: green', 'color: steelblue');\n }\n}\n//# sourceMappingURL=profiler.js.map","/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { computeStrides, isString, rightPad, sizeFromShape } from './util';\n// Maximum number of values before we decide to show ellipsis.\nconst FORMAT_LIMIT_NUM_VALS = 20;\n// Number of first and last values to show when displaying a, b,...,y, z.\nconst FORMAT_NUM_FIRST_LAST_VALS = 3;\n// Number of significant digits to show.\nconst FORMAT_NUM_SIG_DIGITS = 7;\nexport function tensorToString(vals, shape, dtype, verbose) {\n const strides = computeStrides(shape);\n const padPerCol = computeMaxSizePerColumn(vals, shape, dtype, strides);\n const rank = shape.length;\n const valsLines = subTensorToString(vals, shape, dtype, strides, padPerCol);\n const lines = ['Tensor'];\n if (verbose) {\n lines.push(` dtype: ${dtype}`);\n lines.push(` rank: ${rank}`);\n lines.push(` shape: [${shape}]`);\n lines.push(` values:`);\n }\n lines.push(valsLines.map(l => ' ' + l).join('\\n'));\n return lines.join('\\n');\n}\nfunction computeMaxSizePerColumn(vals, shape, dtype, strides) {\n const n = sizeFromShape(shape);\n const numCols = strides[strides.length - 1];\n const padPerCol = new Array(numCols).fill(0);\n const rank = shape.length;\n const valuesOrTuples = dtype === 'complex64' ? createComplexTuples(vals) : vals;\n if (rank > 1) {\n for (let row = 0; row < n / numCols; row++) {\n const offset = row * numCols;\n for (let j = 0; j < numCols; j++) {\n padPerCol[j] = Math.max(padPerCol[j], valToString(valuesOrTuples[offset + j], 0, dtype).length);\n }\n }\n }\n return padPerCol;\n}\nfunction valToString(val, pad, dtype) {\n let valStr;\n if (Array.isArray(val)) {\n valStr = `${parseFloat(val[0].toFixed(FORMAT_NUM_SIG_DIGITS))} + ` +\n `${parseFloat(val[1].toFixed(FORMAT_NUM_SIG_DIGITS))}j`;\n }\n else if (isString(val)) {\n valStr = `'${val}'`;\n }\n else if (dtype === 'bool') {\n valStr = boolNumToString(val);\n }\n else {\n valStr = parseFloat(val.toFixed(FORMAT_NUM_SIG_DIGITS)).toString();\n }\n return rightPad(valStr, pad);\n}\nfunction boolNumToString(v) {\n return v === 0 ? 'false' : 'true';\n}\nfunction subTensorToString(vals, shape, dtype, strides, padPerCol, isLast = true) {\n const storagePerElement = dtype === 'complex64' ? 2 : 1;\n const size = shape[0];\n const rank = shape.length;\n if (rank === 0) {\n if (dtype === 'complex64') {\n const complexTuple = createComplexTuples(vals);\n return [valToString(complexTuple[0], 0, dtype)];\n }\n if (dtype === 'bool') {\n return [boolNumToString(vals[0])];\n }\n return [vals[0].toString()];\n }\n if (rank === 1) {\n if (size > FORMAT_LIMIT_NUM_VALS) {\n const firstValsSize = FORMAT_NUM_FIRST_LAST_VALS * storagePerElement;\n let firstVals = Array.from(vals.slice(0, firstValsSize));\n let lastVals = Array.from(vals.slice((size - FORMAT_NUM_FIRST_LAST_VALS) * storagePerElement, size * storagePerElement));\n if (dtype === 'complex64') {\n firstVals = createComplexTuples(firstVals);\n lastVals = createComplexTuples(lastVals);\n }\n return [\n '[' +\n firstVals.map((x, i) => valToString(x, padPerCol[i], dtype))\n .join(', ') +\n ', ..., ' +\n lastVals\n .map((x, i) => valToString(x, padPerCol[size - FORMAT_NUM_FIRST_LAST_VALS + i], dtype))\n .join(', ') +\n ']'\n ];\n }\n const displayVals = dtype === 'complex64' ? createComplexTuples(vals) :\n Array.from(vals);\n return [\n '[' +\n displayVals.map((x, i) => valToString(x, padPerCol[i], dtype))\n .join(', ') +\n ']'\n ];\n }\n // The array is rank 2 or more.\n const subshape = shape.slice(1);\n const substrides = strides.slice(1);\n const stride = strides[0] * storagePerElement;\n const lines = [];\n if (size > FORMAT_LIMIT_NUM_VALS) {\n for (let i = 0; i < FORMAT_NUM_FIRST_LAST_VALS; i++) {\n const start = i * stride;\n const end = start + stride;\n lines.push(...subTensorToString(vals.slice(start, end), subshape, dtype, substrides, padPerCol, false /* isLast */));\n }\n lines.push('...');\n for (let i = size - FORMAT_NUM_FIRST_LAST_VALS; i < size; i++) {\n const start = i * stride;\n const end = start + stride;\n lines.push(...subTensorToString(vals.slice(start, end), subshape, dtype, substrides, padPerCol, i === size - 1 /* isLast */));\n }\n }\n else {\n for (let i = 0; i < size; i++) {\n const start = i * stride;\n const end = start + stride;\n lines.push(...subTensorToString(vals.slice(start, end), subshape, dtype, substrides, padPerCol, i === size - 1 /* isLast */));\n }\n }\n const sep = rank === 2 ? ',' : '';\n lines[0] = '[' + lines[0] + sep;\n for (let i = 1; i < lines.length - 1; i++) {\n lines[i] = ' ' + lines[i] + sep;\n }\n let newLineSep = ',\\n';\n for (let i = 2; i < rank; i++) {\n newLineSep += '\\n';\n }\n lines[lines.length - 1] =\n ' ' + lines[lines.length - 1] + ']' + (isLast ? '' : newLineSep);\n return lines;\n}\nfunction createComplexTuples(vals) {\n const complexTuples = [];\n for (let i = 0; i < vals.length; i += 2) {\n complexTuples.push([vals[i], vals[i + 1]]);\n }\n return complexTuples;\n}\n//# sourceMappingURL=tensor_format.js.map","/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { getGlobal } from './global_util';\nimport { tensorToString } from './tensor_format';\nimport * as util from './util';\nimport { computeStrides, toNestedArray } from './util';\n/**\n * A mutable object, similar to `tf.Tensor`, that allows users to set values\n * at locations before converting to an immutable `tf.Tensor`.\n *\n * See `tf.buffer` for creating a tensor buffer.\n *\n * @doc {heading: 'Tensors', subheading: 'Classes'}\n */\nexport class TensorBuffer {\n constructor(shape, dtype, values) {\n this.dtype = dtype;\n this.shape = shape.slice();\n this.size = util.sizeFromShape(shape);\n if (values != null) {\n const n = values.length;\n util.assert(n === this.size, () => `Length of values '${n}' does not match the size ` +\n `inferred by the shape '${this.size}'.`);\n }\n if (dtype === 'complex64') {\n throw new Error(`complex64 dtype TensorBuffers are not supported. Please create ` +\n `a TensorBuffer for the real and imaginary parts separately and ` +\n `call tf.complex(real, imag).`);\n }\n this.values = values || util.getArrayFromDType(dtype, this.size);\n this.strides = computeStrides(shape);\n }\n /**\n * Sets a value in the buffer at a given location.\n *\n * @param value The value to set.\n * @param locs The location indices.\n *\n * @doc {heading: 'Tensors', subheading: 'Creation'}\n */\n set(value, ...locs) {\n if (locs.length === 0) {\n locs = [0];\n }\n util.assert(locs.length === this.rank, () => `The number of provided coordinates (${locs.length}) must ` +\n `match the rank (${this.rank})`);\n const index = this.locToIndex(locs);\n this.values[index] = value;\n }\n /**\n * Returns the value in the buffer at the provided location.\n *\n * @param locs The location indices.\n *\n * @doc {heading: 'Tensors', subheading: 'Creation'}\n */\n get(...locs) {\n if (locs.length === 0) {\n locs = [0];\n }\n let i = 0;\n for (const loc of locs) {\n if (loc < 0 || loc >= this.shape[i]) {\n const msg = `Requested out of range element at ${locs}. ` +\n ` Buffer shape=${this.shape}`;\n throw new Error(msg);\n }\n i++;\n }\n let index = locs[locs.length - 1];\n for (let i = 0; i < locs.length - 1; ++i) {\n index += this.strides[i] * locs[i];\n }\n return this.values[index];\n }\n locToIndex(locs) {\n if (this.rank === 0) {\n return 0;\n }\n else if (this.rank === 1) {\n return locs[0];\n }\n let index = locs[locs.length - 1];\n for (let i = 0; i < locs.length - 1; ++i) {\n index += this.strides[i] * locs[i];\n }\n return index;\n }\n indexToLoc(index) {\n if (this.rank === 0) {\n return [];\n }\n else if (this.rank === 1) {\n return [index];\n }\n const locs = new Array(this.shape.length);\n for (let i = 0; i < locs.length - 1; ++i) {\n locs[i] = Math.floor(index / this.strides[i]);\n index -= locs[i] * this.strides[i];\n }\n locs[locs.length - 1] = index;\n return locs;\n }\n get rank() {\n return this.shape.length;\n }\n /**\n * Creates an immutable `tf.Tensor` object from the buffer.\n *\n * @doc {heading: 'Tensors', subheading: 'Creation'}\n */\n toTensor() {\n return trackerFn().makeTensor(this.values, this.shape, this.dtype);\n }\n}\n// For tracking tensor creation and disposal.\nlet trackerFn = null;\n// Used by chaining methods to call into ops.\nlet opHandler = null;\n// Used to warn about deprecated methods.\nlet deprecationWarningFn = null;\n// This here so that we can use this method on dev branches and keep the\n// functionality at master.\n// tslint:disable-next-line:no-unused-expression\n[deprecationWarningFn];\n/**\n * An external consumer can register itself as the tensor tracker. This way\n * the Tensor class can notify the tracker for every tensor created and\n * disposed.\n */\nexport function setTensorTracker(fn) {\n trackerFn = fn;\n}\n/**\n * An external consumer can register itself as the op handler. This way the\n * Tensor class can have chaining methods that call into ops via the op\n * handler.\n */\nexport function setOpHandler(handler) {\n opHandler = handler;\n}\n/**\n * Sets the deprecation warning function to be used by this file. This way the\n * Tensor class can be a leaf but still use the environment.\n */\nexport function setDeprecationWarningFn(fn) {\n deprecationWarningFn = fn;\n}\n/**\n * A `tf.Tensor` object represents an immutable, multidimensional array of\n * numbers that has a shape and a data type.\n *\n * For performance reasons, functions that create tensors do not necessarily\n * perform a copy of the data passed to them (e.g. if the data is passed as a\n * `Float32Array`), and changes to the data will change the tensor. This is not\n * a feature and is not supported. To avoid this behavior, use the tensor before\n * changing the input data or create a copy with `copy = tf.add(yourTensor, 0)`.\n *\n * See `tf.tensor` for details on how to create a `tf.Tensor`.\n *\n * @doc {heading: 'Tensors', subheading: 'Classes'}\n */\nexport class Tensor {\n constructor(shape, dtype, dataId, id) {\n /** Whether this tensor has been globally kept. */\n this.kept = false;\n this.isDisposedInternal = false;\n this.shape = shape.slice();\n this.dtype = dtype || 'float32';\n this.size = util.sizeFromShape(shape);\n this.strides = computeStrides(shape);\n this.dataId = dataId;\n this.id = id;\n this.rankType = (this.rank < 5 ? this.rank.toString() : 'higher');\n }\n get rank() {\n return this.shape.length;\n }\n /**\n * Returns a promise of `tf.TensorBuffer` that holds the underlying data.\n *\n * @doc {heading: 'Tensors', subheading: 'Classes'}\n */\n async buffer() {\n const vals = await this.data();\n return opHandler.buffer(this.shape, this.dtype, vals);\n }\n /**\n * Returns a `tf.TensorBuffer` that holds the underlying data.\n * @doc {heading: 'Tensors', subheading: 'Classes'}\n */\n bufferSync() {\n return opHandler.buffer(this.shape, this.dtype, this.dataSync());\n }\n /**\n * Returns the tensor data as a nested array. The transfer of data is done\n * asynchronously.\n *\n * @doc {heading: 'Tensors', subheading: 'Classes'}\n */\n async array() {\n const vals = await this.data();\n return toNestedArray(this.shape, vals, this.dtype === 'complex64');\n }\n /**\n * Returns the tensor data as a nested array. The transfer of data is done\n * synchronously.\n *\n * @doc {heading: 'Tensors', subheading: 'Classes'}\n */\n arraySync() {\n return toNestedArray(this.shape, this.dataSync(), this.dtype === 'complex64');\n }\n /**\n * Asynchronously downloads the values from the `tf.Tensor`. Returns a\n * promise of `TypedArray` that resolves when the computation has finished.\n *\n * @doc {heading: 'Tensors', subheading: 'Classes'}\n */\n async data() {\n this.throwIfDisposed();\n const data = trackerFn().read(this.dataId);\n if (this.dtype === 'string') {\n const bytes = await data;\n try {\n return bytes.map(b => util.decodeString(b));\n }\n catch (_a) {\n throw new Error('Failed to decode the string bytes into utf-8. ' +\n 'To get the original bytes, call tensor.bytes().');\n }\n }\n return data;\n }\n /**\n * Synchronously downloads the values from the `tf.Tensor`. This blocks the\n * UI thread until the values are ready, which can cause performance issues.\n *\n * @doc {heading: 'Tensors', subheading: 'Classes'}\n */\n dataSync() {\n this.throwIfDisposed();\n const data = trackerFn().readSync(this.dataId);\n if (this.dtype === 'string') {\n try {\n return data.map(b => util.decodeString(b));\n }\n catch (_a) {\n throw new Error('Failed to decode the string bytes into utf-8. ' +\n 'To get the original bytes, call tensor.bytes().');\n }\n }\n return data;\n }\n /** Returns the underlying bytes of the tensor's data. */\n async bytes() {\n this.throwIfDisposed();\n const data = await trackerFn().read(this.dataId);\n if (this.dtype === 'string') {\n return data;\n }\n else {\n return new Uint8Array(data.buffer);\n }\n }\n /**\n * Disposes `tf.Tensor` from memory.\n *\n * @doc {heading: 'Tensors', subheading: 'Classes'}\n */\n dispose() {\n if (this.isDisposed) {\n return;\n }\n trackerFn().disposeTensor(this);\n this.isDisposedInternal = true;\n }\n get isDisposed() {\n return this.isDisposedInternal;\n }\n throwIfDisposed() {\n if (this.isDisposed) {\n throw new Error(`Tensor is disposed.`);\n }\n }\n /**\n * Prints the `tf.Tensor`. See `tf.print` for details.\n *\n * @param verbose Whether to print verbose information about the tensor,\n * including dtype and size.\n *\n * @doc {heading: 'Tensors', subheading: 'Classes'}\n */\n print(verbose = false) {\n return opHandler.print(this, verbose);\n }\n /**\n * Returns a copy of the tensor. See `tf.clone` for details.\n * @doc {heading: 'Tensors', subheading: 'Classes'}\n */\n clone() {\n this.throwIfDisposed();\n return opHandler.clone(this);\n }\n /**\n * Returns a human-readable description of the tensor. Useful for logging.\n *\n * @doc {heading: 'Tensors', subheading: 'Classes'}\n */\n toString(verbose = false) {\n const vals = this.dataSync();\n return tensorToString(vals, this.shape, this.dtype, verbose);\n }\n cast(dtype) {\n this.throwIfDisposed();\n return opHandler.cast(this, dtype);\n }\n variable(trainable = true, name, dtype) {\n this.throwIfDisposed();\n return trackerFn().makeVariable(this, trainable, name, dtype);\n }\n}\nObject.defineProperty(Tensor, Symbol.hasInstance, {\n value: (instance) => {\n // Implementation note: we should use properties of the object that will be\n // defined before the constructor body has finished executing (methods).\n // This is because when this code is transpiled by babel, babel will call\n // classCallCheck before the constructor body is run.\n // See https://github.com/tensorflow/tfjs/issues/3384 for backstory.\n return !!instance && instance.data != null && instance.dataSync != null &&\n instance.throwIfDisposed != null;\n }\n});\nexport function getGlobalTensorClass() {\n // Use getGlobal so that we can augment the Tensor class across package\n // boundaries becase the node resolution alg may result in different modules\n // being returned for this file depending on the path they are loaded from.\n return getGlobal('Tensor', () => {\n return Tensor;\n });\n}\n// Global side effect. Cache global reference to Tensor class\ngetGlobalTensorClass();\n/**\n * A mutable `tf.Tensor`, useful for persisting state, e.g. for training.\n *\n * @doc {heading: 'Tensors', subheading: 'Classes'}\n */\nexport class Variable extends Tensor {\n constructor(initialValue, trainable, name, tensorId) {\n super(initialValue.shape, initialValue.dtype, initialValue.dataId, tensorId);\n this.trainable = trainable;\n this.name = name;\n }\n /**\n * Assign a new `tf.Tensor` to this variable. The new `tf.Tensor` must have\n * the same shape and dtype as the old `tf.Tensor`.\n *\n * @param newValue New tensor to be assigned to this variable.\n *\n * @doc {heading: 'Tensors', subheading: 'Classes'}\n */\n assign(newValue) {\n if (newValue.dtype !== this.dtype) {\n throw new Error(`dtype of the new value (${newValue.dtype}) and ` +\n `previous value (${this.dtype}) must match`);\n }\n if (!util.arraysEqual(newValue.shape, this.shape)) {\n throw new Error(`shape of the new value (${newValue.shape}) and ` +\n `previous value (${this.shape}) must match`);\n }\n trackerFn().disposeTensor(this);\n this.dataId = newValue.dataId;\n trackerFn().incRef(this, null /* backend */);\n }\n dispose() {\n trackerFn().disposeVariable(this);\n this.isDisposedInternal = true;\n }\n}\nObject.defineProperty(Variable, Symbol.hasInstance, {\n value: (instance) => {\n return instance instanceof Tensor && instance.assign != null &&\n instance.assign instanceof Function;\n }\n});\n//# sourceMappingURL=tensor.js.map","/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport var Rank;\n(function (Rank) {\n Rank[\"R0\"] = \"R0\";\n Rank[\"R1\"] = \"R1\";\n Rank[\"R2\"] = \"R2\";\n Rank[\"R3\"] = \"R3\";\n Rank[\"R4\"] = \"R4\";\n Rank[\"R5\"] = \"R5\";\n Rank[\"R6\"] = \"R6\";\n})(Rank || (Rank = {}));\n// Looks for upcasting types. Used, for example, in operations with mixed dtype\n// inputs.\nvar UpcastInt32AndMap;\n(function (UpcastInt32AndMap) {\n UpcastInt32AndMap[\"float32\"] = \"float32\";\n UpcastInt32AndMap[\"int32\"] = \"int32\";\n UpcastInt32AndMap[\"bool\"] = \"int32\";\n UpcastInt32AndMap[\"complex64\"] = \"complex64\";\n})(UpcastInt32AndMap || (UpcastInt32AndMap = {}));\nvar UpcastBoolAndMap;\n(function (UpcastBoolAndMap) {\n UpcastBoolAndMap[\"float32\"] = \"float32\";\n UpcastBoolAndMap[\"int32\"] = \"int32\";\n UpcastBoolAndMap[\"bool\"] = \"bool\";\n UpcastBoolAndMap[\"complex64\"] = \"complex64\";\n})(UpcastBoolAndMap || (UpcastBoolAndMap = {}));\nvar UpcastFloat32AndMap;\n(function (UpcastFloat32AndMap) {\n UpcastFloat32AndMap[\"float32\"] = \"float32\";\n UpcastFloat32AndMap[\"int32\"] = \"float32\";\n UpcastFloat32AndMap[\"bool\"] = \"float32\";\n UpcastFloat32AndMap[\"complex64\"] = \"complex64\";\n})(UpcastFloat32AndMap || (UpcastFloat32AndMap = {}));\nvar UpcastComplex64AndMap;\n(function (UpcastComplex64AndMap) {\n UpcastComplex64AndMap[\"float32\"] = \"complex64\";\n UpcastComplex64AndMap[\"int32\"] = \"complex64\";\n UpcastComplex64AndMap[\"bool\"] = \"complex64\";\n UpcastComplex64AndMap[\"complex64\"] = \"complex64\";\n})(UpcastComplex64AndMap || (UpcastComplex64AndMap = {}));\nconst upcastTypeMap = {\n 'float32': UpcastFloat32AndMap,\n 'int32': UpcastInt32AndMap,\n 'bool': UpcastBoolAndMap,\n 'complex64': UpcastComplex64AndMap\n};\nexport function upcastType(typeA, typeB) {\n if (typeA === 'string' || typeB === 'string') {\n if (typeA === 'string' && typeB === 'string') {\n return 'string';\n }\n throw new Error(`Can not upcast ${typeA} with ${typeB}`);\n }\n return upcastTypeMap[typeA][typeB];\n}\n/** Returns the output type after summation. */\nexport function sumOutType(type) {\n return upcastType(type, 'int32');\n}\n//# sourceMappingURL=types.js.map","/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { Tensor } from './tensor';\nimport { upcastType } from './types';\nimport { assert } from './util';\nexport function makeTypesMatch(a, b) {\n if (a.dtype === b.dtype) {\n return [a, b];\n }\n const dtype = upcastType(a.dtype, b.dtype);\n return [a.cast(dtype), b.cast(dtype)];\n}\nexport function assertTypesMatch(a, b) {\n assert(a.dtype === b.dtype, () => `The dtypes of the first(${a.dtype}) and` +\n ` second(${b.dtype}) input must match`);\n}\nexport function isTensorInList(tensor, tensorList) {\n return tensorList.some(x => x.id === tensor.id);\n}\n/**\n * Extracts any `Tensor`s found within the provided object.\n *\n * @param container an object that may be a `Tensor` or may directly contain\n * `Tensor`s, such as a `Tensor[]` or `{key: Tensor, ...}`. In general it\n * is safe to pass any object here, except that `Promise`s are not\n * supported.\n * @returns An array of `Tensors` found within the passed object. If the\n * argument is simply a `Tensor', a list containing that `Tensor` is\n * returned. If the object is not a `Tensor` or does not\n * contain `Tensors`, an empty list is returned.\n */\nexport function getTensorsInContainer(result) {\n const list = [];\n const seen = new Set();\n walkTensorContainer(result, list, seen);\n return list;\n}\nfunction walkTensorContainer(container, list, seen) {\n if (container == null) {\n return;\n }\n if (container instanceof Tensor) {\n list.push(container);\n return;\n }\n if (!isIterable(container)) {\n return;\n }\n // Iteration over keys works also for arrays.\n const iterable = container;\n for (const k in iterable) {\n const val = iterable[k];\n if (!seen.has(val)) {\n seen.add(val);\n walkTensorContainer(val, list, seen);\n }\n }\n}\n// tslint:disable-next-line:no-any\nfunction isIterable(obj) {\n return Array.isArray(obj) || typeof obj === 'object';\n}\n//# sourceMappingURL=tensor_util.js.map","/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { KernelBackend } from './backends/backend';\nimport { Environment, setEnvironmentGlobal } from './environment';\nimport { getGlobalNamespace } from './global_util';\nimport { Add, Cast, Identity } from './kernel_names';\nimport { getGradient, getKernel, getKernelsForBackend } from './kernel_registry';\nimport { Profiler } from './profiler';\nimport { backpropagateGradients, getFilteredNodesXToY } from './tape';\nimport { setTensorTracker, Tensor, Variable } from './tensor';\nimport { getTensorsInContainer } from './tensor_util';\nimport * as util from './util';\nimport { bytesFromStringArray, makeOnesTypedArray, now, sizeFromShape } from './util';\nfunction isRegisteredKernelInvocation(kernelInvocation) {\n return kernelInvocation.kernelName != null;\n}\nclass EngineState {\n constructor() {\n // Public since optimizers will use it.\n this.registeredVariables = {};\n this.nextTapeNodeId = 0;\n this.numBytes = 0;\n this.numTensors = 0;\n this.numStringTensors = 0;\n this.numDataBuffers = 0;\n // Number of nested tf.grad() statements when computing higher-order\n // gradients. E.g. `1` for first-order gradients and `2` for second-order\n // gradients. Used to track if the tape should be removed after a backprop.\n this.gradientDepth = 0;\n // Number of nested kernel calls. When kernel depth is greater than 1, we turn\n // off the tape.\n this.kernelDepth = 0;\n this.scopeStack = [];\n /**\n * Keeps track of the number of data moves during a kernel execution. We\n * maintain a stack since kernels can call other kernels, recursively.\n */\n this.numDataMovesStack = [];\n this.nextScopeId = 0;\n this.tensorInfo = new WeakMap();\n this.profiling = false;\n this.activeProfile = {\n newBytes: 0,\n newTensors: 0,\n peakBytes: 0,\n kernels: [],\n result: null,\n get kernelNames() {\n return Array.from(new Set(this.kernels.map(k => k.name)));\n }\n };\n }\n dispose() {\n for (const variableName in this.registeredVariables) {\n this.registeredVariables[variableName].dispose();\n }\n }\n}\nexport class Engine {\n constructor(ENV) {\n this.ENV = ENV;\n this.registry = {};\n this.registryFactory = {};\n this.pendingBackendInitId = 0;\n this.state = new EngineState();\n }\n async ready() {\n if (this.pendingBackendInit != null) {\n return this.pendingBackendInit.then(() => { });\n }\n if (this.backendInstance != null) {\n return;\n }\n const sortedBackends = this.getSortedBackends();\n for (let i = 0; i < sortedBackends.length; i++) {\n const backendName = sortedBackends[i];\n const success = await this.initializeBackend(backendName).success;\n if (success) {\n await this.setBackend(backendName);\n return;\n }\n }\n throw new Error(`Could not initialize any backends, all backend initializations ` +\n `failed.`);\n }\n get backend() {\n if (this.pendingBackendInit != null) {\n throw new Error(`Backend '${this.backendName}' has not yet been initialized. Make ` +\n `sure to await tf.ready() or await tf.setBackend() before calling ` +\n `other methods`);\n }\n if (this.backendInstance == null) {\n const { name, asyncInit } = this.initializeBackendsAndReturnBest();\n if (asyncInit) {\n throw new Error(`The highest priority backend '${name}' has not yet been ` +\n `initialized. Make sure to await tf.ready() or ` +\n `await tf.setBackend() before calling other methods`);\n }\n this.setBackend(name);\n }\n return this.backendInstance;\n }\n backendNames() {\n return Object.keys(this.registryFactory);\n }\n findBackend(backendName) {\n if (!(backendName in this.registry)) {\n // If the backend hasn't been initialized but we have a registry entry for\n // it, initialize it and return it.\n if (backendName in this.registryFactory) {\n const { asyncInit } = this.initializeBackend(backendName);\n if (asyncInit) {\n // Backend is not ready yet.\n return null;\n }\n }\n else {\n return null;\n }\n }\n return this.registry[backendName];\n }\n findBackendFactory(backendName) {\n if (!(backendName in this.registryFactory)) {\n return null;\n }\n return this.registryFactory[backendName].factory;\n }\n registerBackend(backendName, factory, priority = 1) {\n if (backendName in this.registryFactory) {\n console.warn(`${backendName} backend was already registered. ` +\n `Reusing existing backend factory.`);\n return false;\n }\n this.registryFactory[backendName] = { factory, priority };\n return true;\n }\n async setBackend(backendName) {\n if (this.registryFactory[backendName] == null) {\n throw new Error(`Backend name '${backendName}' not found in registry`);\n }\n this.backendName = backendName;\n if (this.registry[backendName] == null) {\n this.backendInstance = null;\n const { success, asyncInit } = this.initializeBackend(backendName);\n const result = asyncInit ? await success : success;\n if (!result) {\n return false;\n }\n }\n this.backendInstance = this.registry[backendName];\n this.setupRegisteredKernels();\n // Reset the profiler.\n this.profiler = new Profiler(this.backendInstance);\n return true;\n }\n setupRegisteredKernels() {\n const kernels = getKernelsForBackend(this.backendName);\n kernels.forEach(kernel => {\n if (kernel.setupFunc != null) {\n kernel.setupFunc(this.backendInstance);\n }\n });\n }\n disposeRegisteredKernels(backendName) {\n const kernels = getKernelsForBackend(backendName);\n kernels.forEach(kernel => {\n if (kernel.disposeFunc != null) {\n kernel.disposeFunc(this.registry[backendName]);\n }\n });\n }\n /**\n * Initializes a backend by looking up the backend name in the factory\n * registry and calling the factory method. Returns a boolean representing\n * whether the initialization of the backend suceeded. Throws an error if\n * there is no backend in the factory registry.\n */\n initializeBackend(backendName) {\n const registryFactoryEntry = this.registryFactory[backendName];\n if (registryFactoryEntry == null) {\n throw new Error(`Cannot initialize backend ${backendName}, no registration found.`);\n }\n try {\n const backend = registryFactoryEntry.factory();\n /* Test if the factory returns a promise.\n Done in a more liberal way than\n previous 'Promise.resolve(backend)===backend'\n as we needed to account for custom Promise\n implementations (e.g. Angular) */\n if (backend && !(backend instanceof KernelBackend) &&\n typeof backend.then === 'function') {\n const promiseId = ++this.pendingBackendInitId;\n const success = backend\n .then(backendInstance => {\n // Outdated promise. Another backend was set in the meantime.\n if (promiseId < this.pendingBackendInitId) {\n return false;\n }\n this.registry[backendName] = backendInstance;\n this.pendingBackendInit = null;\n return true;\n })\n .catch(err => {\n // Outdated promise. Another backend was set in the meantime.\n if (promiseId < this.pendingBackendInitId) {\n return false;\n }\n this.pendingBackendInit = null;\n console.warn(`Initialization of backend ${backendName} failed`);\n console.warn(err.stack || err.message);\n return false;\n });\n this.pendingBackendInit = success;\n return { success, asyncInit: true };\n }\n else {\n this.registry[backendName] = backend;\n return { success: true, asyncInit: false };\n }\n }\n catch (err) {\n console.warn(`Initialization of backend ${backendName} failed`);\n console.warn(err.stack || err.message);\n return { success: false, asyncInit: false };\n }\n }\n removeBackend(backendName) {\n if (!(backendName in this.registryFactory)) {\n throw new Error(`${backendName} backend not found in registry`);\n }\n if (this.backendName === backendName && this.pendingBackendInit != null) {\n // There is a pending promise of the backend we want to remove. Make it\n // obsolete.\n this.pendingBackendInitId++;\n }\n if (backendName in this.registry) {\n this.disposeRegisteredKernels(backendName);\n this.registry[backendName].dispose();\n delete this.registry[backendName];\n }\n delete this.registryFactory[backendName];\n // Unset the backend if it is active.\n if (this.backendName === backendName) {\n this.pendingBackendInit = null;\n this.backendName = null;\n this.backendInstance = null;\n }\n }\n getSortedBackends() {\n if (Object.keys(this.registryFactory).length === 0) {\n throw new Error('No backend found in registry.');\n }\n return Object.keys(this.registryFactory).sort((a, b) => {\n // Highest priority comes first.\n return this.registryFactory[b].priority -\n this.registryFactory[a].priority;\n });\n }\n initializeBackendsAndReturnBest() {\n const sortedBackends = this.getSortedBackends();\n for (let i = 0; i < sortedBackends.length; i++) {\n const backendName = sortedBackends[i];\n const { success, asyncInit } = this.initializeBackend(backendName);\n if (asyncInit || success) {\n return { name: backendName, asyncInit };\n }\n }\n throw new Error(`Could not initialize any backends, all backend initializations ` +\n `failed.`);\n }\n moveData(backend, dataId) {\n const info = this.state.tensorInfo.get(dataId);\n const srcBackend = info.backend;\n const values = this.readSync(dataId);\n const refCount = srcBackend.refCount(dataId);\n // Delete the tensor from the old backend and move it to the new\n // backend.\n srcBackend.disposeData(dataId, true);\n info.backend = backend;\n backend.move(dataId, values, info.shape, info.dtype, refCount);\n if (this.shouldCheckForMemLeaks()) {\n // Track the number of moves during a kernel execution to correctly\n // detect memory leaks.\n this.state.numDataMovesStack[this.state.numDataMovesStack.length - 1]++;\n }\n }\n tidy(nameOrFn, fn) {\n let name = null;\n if (fn == null) {\n // Called with only 1 argument.\n if (typeof nameOrFn !== 'function') {\n throw new Error('Please provide a function to tidy()');\n }\n fn = nameOrFn;\n }\n else {\n // Called with 2 arguments.\n if (typeof nameOrFn !== 'string' && !(nameOrFn instanceof String)) {\n throw new Error('When calling with two arguments, the first argument ' +\n 'to tidy() must be a string');\n }\n if (typeof fn !== 'function') {\n throw new Error('When calling with two arguments, the 2nd argument ' +\n 'to tidy() must be a function');\n }\n name = nameOrFn;\n // TODO(nsthorat,smilkov): Do operation logging and performance\n // profiling.\n }\n let result;\n return this.scopedRun(() => this.startScope(name), () => this.endScope(result), () => {\n result = fn();\n if (result instanceof Promise) {\n console.error('Cannot return a Promise inside of tidy.');\n }\n return result;\n });\n }\n scopedRun(start, end, f) {\n start();\n try {\n const res = f();\n end();\n return res;\n }\n catch (ex) {\n end();\n throw ex;\n }\n }\n nextTensorId() {\n return Engine.nextTensorId++;\n }\n nextVariableId() {\n return Engine.nextVariableId++;\n }\n /**\n * This method is called instead of the public-facing tensor.clone() when\n * saving a tensor for backwards pass. It makes sure to add the clone\n * operation to the tape regardless of being called inside a kernel\n * execution.\n */\n clone(x) {\n const y = ENGINE.runKernel(Identity, { x });\n const inputs = { x };\n const grad = (dy) => ({\n x: () => {\n const dtype = 'float32';\n const gradInputs = { x: dy };\n const attrs = { dtype };\n return ENGINE.runKernel(Cast, gradInputs, \n // tslint:disable-next-line: no-unnecessary-type-assertion\n attrs);\n }\n });\n const saved = [];\n this.addTapeNode(this.state.activeScope.name, inputs, [y], grad, saved, {});\n return y;\n }\n /**\n * Execute a kernel with the given name and return the output tensor.\n *\n * @param kernelName The name of the kernel to execute.\n * @param inputs A map of input names to tensors.\n * @param attrs A map of attribute names to their values. An attribute is a\n * primitive (non-tensor) input to the kernel.\n * @param inputsToSave A list of tensors, inputs to save for the backprop\n * computation.\n * @param outputsToSave A list of booleans, specifying which output to save\n * for the backprop computation. These are booleans since the output\n * tensors are not visible to the user.\n */\n runKernel(kernelName, inputs, attrs) {\n const hasKernel = getKernel(kernelName, this.backendName) != null;\n if (!hasKernel) {\n throw new Error(`Kernel '${kernelName}' not registered for backend '${this.backendName}'`);\n }\n return this.runKernelFunc({ kernelName, inputs, attrs });\n }\n shouldCheckForMemLeaks() {\n return this.ENV.getBool('IS_TEST');\n }\n checkKernelForMemLeak(kernelName, numDataIdsBefore, outInfos) {\n const numDataIdsAfter = this.backend.numDataIds();\n // Count the number of data ids associated with the result of the kernel.\n let numOutputDataIds = 0;\n outInfos.forEach(info => {\n // Complex numbers allocate 3 data ids, one for 'real', one for\n // 'imaginary', and one for the container that holds the former two.\n numOutputDataIds += (info.dtype === 'complex64' ? 3 : 1);\n });\n // Account for the number of moves during kernel execution. A \"data move\"\n // can happen in the middle of a kernel execution, placing a new (key,value)\n // pair in the data storage. Since data moves have net zero effect (we\n // always remove the data from the old backend), we have to cancel them out\n // when detecting memory leaks.\n const numMoves = this.state.numDataMovesStack[this.state.numDataMovesStack.length - 1];\n const dataIdsLeaked = numDataIdsAfter - numDataIdsBefore - numOutputDataIds - numMoves;\n if (dataIdsLeaked > 0) {\n throw new Error(`Backend '${this.backendName}' has an internal memory leak ` +\n `(${dataIdsLeaked} data ids) after running '${kernelName}'`);\n }\n }\n /**\n * Internal helper method to execute a kernel Func\n *\n * Use `runKernel` to execute kernels from outside of engine.\n */\n runKernelFunc(kernelParams) {\n let outputs;\n let saved = [];\n const isTapeOn = this.isTapeOn();\n const startingBytecount = this.state.numBytes;\n const startingNumTensors = this.state.numTensors;\n if (this.shouldCheckForMemLeaks()) {\n this.state.numDataMovesStack.push(0);\n }\n let kernelFunc;\n if (this.backendName == null) {\n // backend has not been initialized yet (backend initialization is lazy\n // can be deferred until an op/ kernel is run).\n // The below getter has side effects that will try to initialize the\n // backend and set properties like this.backendName\n // tslint:disable-next-line: no-unused-expression\n this.backend;\n }\n let out;\n const kernelOrScopeName = isRegisteredKernelInvocation(kernelParams) ?\n kernelParams.kernelName :\n this.state.activeScope != null ? this.state.activeScope.name : '';\n // Create the kernelFunc from either a registered kernel OR passed in\n // forward/backward functions (used by custom grad). In this context a\n // kernelFunc wraps a kernel implementation with some bookkeeping.\n if (isRegisteredKernelInvocation(kernelParams)) {\n const { kernelName, inputs, attrs } = kernelParams;\n if (this.backendName == null) {\n // backend has not been initialized yet (backend initialization is lazy\n // can be deferred until an op/ kernel is run).\n // The below getter has side effects that will try to initialize the\n // backend and set properties like this.backendName\n // tslint:disable-next-line: no-unused-expression\n this.backend;\n }\n const kernel = getKernel(kernelName, this.backendName);\n util.assert(kernel != null, () => `Cannot find registered kernel '${kernelName}' for backend '${this.backendName}'`);\n kernelFunc = () => {\n const numDataIdsBefore = this.backend.numDataIds();\n out = kernel.kernelFunc({ inputs, attrs, backend: this.backend });\n const outInfos = Array.isArray(out) ? out : [out];\n if (this.shouldCheckForMemLeaks()) {\n this.checkKernelForMemLeak(kernelName, numDataIdsBefore, outInfos);\n }\n const outTensors = outInfos.map((outInfo) => {\n // todo (yassogba) remove this option (Tensor) when node backend\n // methods have been modularized and they all return tensorInfo.\n // TensorInfos do not have a rank attribute.\n if (outInfo.rank != null) {\n return outInfo;\n }\n const { dataId, shape, dtype } = outInfo;\n return this.makeTensorFromDataId(dataId, shape, dtype);\n });\n // Save any required inputs and outputs.\n // Do not save unless we are recording to the tape. Otherwise it would\n // cause a mem leak since there would be no backprop for these tensors\n // (which would otherwise dispose them).\n if (isTapeOn) {\n const tensorsToSave = this.getTensorsForGradient(kernelName, inputs, outTensors);\n saved = this.saveTensorsForBackwardMode(tensorsToSave);\n }\n return outTensors;\n };\n }\n else {\n const { forwardFunc } = kernelParams;\n // Running a customGrad op.\n const saveFunc = (tensors) => {\n // Do not save unless we are recording to the tape. Otherwise it would\n // cause a mem leak since we would never run backprop, which disposes\n // the kept tensors.\n if (!isTapeOn) {\n return;\n }\n saved = tensors.map(tensor => this.keep(this.clone(tensor)));\n };\n kernelFunc = () => {\n const numDataIdsBefore = this.backend.numDataIds();\n out = this.tidy(() => forwardFunc(this.backend, saveFunc));\n const outs = (Array.isArray(out) ? out : [out]);\n if (this.shouldCheckForMemLeaks()) {\n // Scope name is used to print a more helpful error message if needed.\n this.checkKernelForMemLeak(kernelOrScopeName, numDataIdsBefore, outs);\n }\n return outs;\n };\n }\n //\n // Run the kernelFunc. Optionally profiling it.\n //\n const { inputs, attrs } = kernelParams;\n const backwardsFunc = isRegisteredKernelInvocation(kernelParams) ?\n null :\n kernelParams.backwardsFunc;\n let kernelProfile;\n this.scopedRun(\n // Stop recording to a tape when running a kernel.\n () => this.state.kernelDepth++, () => this.state.kernelDepth--, () => {\n if (!this.ENV.getBool('DEBUG') && !this.state.profiling) {\n outputs = kernelFunc();\n }\n else {\n kernelProfile = this.profiler.profileKernel(kernelOrScopeName, inputs, () => kernelFunc());\n if (this.ENV.getBool('DEBUG')) {\n this.profiler.logKernelProfile(kernelProfile);\n }\n outputs = kernelProfile.outputs;\n }\n });\n if (isTapeOn) {\n this.addTapeNode(kernelOrScopeName, inputs, outputs, backwardsFunc, saved, attrs);\n }\n if (this.state.profiling) {\n this.state.activeProfile.kernels.push({\n name: kernelOrScopeName,\n bytesAdded: this.state.numBytes - startingBytecount,\n totalBytesSnapshot: this.state.numBytes,\n tensorsAdded: this.state.numTensors - startingNumTensors,\n totalTensorsSnapshot: this.state.numTensors,\n inputShapes: Object.keys(inputs).map(key => inputs[key] != null ? inputs[key].shape : null),\n outputShapes: outputs.map(item => item.shape),\n kernelTimeMs: kernelProfile.timeMs,\n extraInfo: kernelProfile.extraInfo\n });\n }\n return (Array.isArray(out) ? outputs : outputs[0]);\n }\n /**\n * Saves tensors used in forward mode for use in backward mode.\n *\n * @param tensors the list of tensors to save.\n */\n saveTensorsForBackwardMode(tensors) {\n const saved = tensors.map(tensor => this.keep(this.clone(tensor)));\n return saved;\n }\n /**\n * Returns a list of tensors to save for a given gradient calculation.\n *\n * @param kernelName name of kernel to look up gradient for.\n * @param inputs a map of input tensors.\n * @param outputs an array of output tensors from forward mode of kernel.\n */\n getTensorsForGradient(kernelName, inputs, outputs) {\n const gradConfig = getGradient(kernelName);\n if (gradConfig != null) {\n const inputsToSave = gradConfig.inputsToSave || [];\n const outputsToSave = gradConfig.outputsToSave || [];\n // If saveAllInputs is true, all inputs will be saved. Otherwise, inputs\n // specified in inputsToSave will be saved.\n let inputTensorsToSave;\n if (gradConfig.saveAllInputs) {\n util.assert(Array.isArray(inputs), () => 'saveAllInputs is true, expected inputs to be an array.');\n inputTensorsToSave = Object.keys(inputs).map((key) => inputs[key]);\n }\n else {\n inputTensorsToSave = inputsToSave.map((inputName) => inputs[inputName]);\n }\n const outputTensorsToSave = outputs.filter((_, i) => outputsToSave[i]);\n return inputTensorsToSave.concat(outputTensorsToSave);\n }\n // We return an empty list rather than throw an error because the kernel we\n // are looking up may not actually be relevant to backproping through the\n // overall function\n //\n // See 'does not error if irrelevant (pruned) ops are missing grads' test\n // in gradients_test.ts for an example.\n return [];\n }\n /**\n * Internal method used by public APIs for tensor creation. Makes a new\n * tensor with the provided shape, dtype and values. It always\n * creates a new data id and writes the values to the underlying backend.\n */\n makeTensor(values, shape, dtype, backend) {\n if (values == null) {\n throw new Error('Values passed to engine.makeTensor() are null');\n }\n dtype = dtype || 'float32';\n backend = backend || this.backend;\n let backendVals = values;\n if (dtype === 'string' && util.isString(values[0])) {\n backendVals = values.map(d => util.encodeString(d));\n }\n const dataId = backend.write(backendVals, shape, dtype);\n const t = new Tensor(shape, dtype, dataId, this.nextTensorId());\n this.trackTensor(t, backend);\n // Count bytes for string tensors.\n if (dtype === 'string') {\n const info = this.state.tensorInfo.get(dataId);\n const newBytes = bytesFromStringArray(backendVals);\n this.state.numBytes += newBytes - info.bytes;\n info.bytes = newBytes;\n }\n return t;\n }\n /**\n * Internal method used by backends. Makes a new tensor\n * that is a wrapper around an existing data id. It doesn't create\n * a new data id, only increments the ref count used in memory tracking.\n */\n makeTensorFromDataId(dataId, shape, dtype, backend) {\n dtype = dtype || 'float32';\n const t = new Tensor(shape, dtype, dataId, this.nextTensorId());\n this.trackTensor(t, backend);\n return t;\n }\n makeVariable(initialValue, trainable = true, name, dtype) {\n name = name || this.nextVariableId().toString();\n if (dtype != null && dtype !== initialValue.dtype) {\n initialValue = initialValue.cast(dtype);\n }\n const v = new Variable(initialValue, trainable, name, this.nextTensorId());\n if (this.state.registeredVariables[v.name] != null) {\n throw new Error(`Variable with name ${v.name} was already registered`);\n }\n this.state.registeredVariables[v.name] = v;\n this.incRef(v, this.backend);\n return v;\n }\n trackTensor(a, backend) {\n this.state.numTensors++;\n if (a.dtype === 'string') {\n this.state.numStringTensors++;\n }\n // Bytes for complex numbers are counted by their components. Bytes for\n // string tensors are counted when writing values.\n let bytes = 0;\n if (a.dtype !== 'complex64' && a.dtype !== 'string') {\n bytes = a.size * util.bytesPerElement(a.dtype);\n }\n this.state.numBytes += bytes;\n if (!this.state.tensorInfo.has(a.dataId)) {\n this.state.numDataBuffers++;\n this.state.tensorInfo.set(a.dataId, {\n backend: backend || this.backend,\n dtype: a.dtype,\n shape: a.shape,\n bytes\n });\n }\n if (!(a instanceof Variable)) {\n this.track(a);\n }\n }\n // Track the tensor by dataId and increase the refCount for the dataId in the\n // backend.\n // TODO(pyu10055): This is currently used by makeVariable method, to increase\n // refCount on the backend for the dataId. It can potentially be replaced with\n // Identity op indead of calling backend directly.\n incRef(a, backend) {\n this.trackTensor(a, backend);\n this.backend.incRef(a.dataId);\n }\n removeDataId(dataId, backend) {\n if (this.state.tensorInfo.has(dataId) &&\n this.state.tensorInfo.get(dataId).backend === backend) {\n this.state.tensorInfo.delete(dataId);\n this.state.numDataBuffers--;\n }\n }\n disposeTensor(a) {\n if (!this.state.tensorInfo.has(a.dataId)) {\n return;\n }\n const info = this.state.tensorInfo.get(a.dataId);\n this.state.numTensors--;\n if (a.dtype === 'string') {\n this.state.numStringTensors--;\n this.state.numBytes -= info.bytes;\n }\n // Don't count bytes for complex numbers as they are counted by their\n // components.\n if (a.dtype !== 'complex64' && a.dtype !== 'string') {\n const bytes = a.size * util.bytesPerElement(a.dtype);\n this.state.numBytes -= bytes;\n }\n // Remove the reference to dataId if backend dispose the data successfully\n if (info.backend.disposeData(a.dataId)) {\n this.removeDataId(a.dataId, info.backend);\n }\n // TODO(nsthorat): Construct an error and save the stack trace for\n // debugging when in debug mode. Creating a stack trace is too expensive\n // to do unconditionally.\n }\n disposeVariables() {\n for (const varName in this.state.registeredVariables) {\n const v = this.state.registeredVariables[varName];\n this.disposeVariable(v);\n }\n }\n disposeVariable(v) {\n this.disposeTensor(v);\n if (this.state.registeredVariables[v.name] != null) {\n delete this.state.registeredVariables[v.name];\n }\n }\n memory() {\n const info = this.backend.memory();\n info.numTensors = this.state.numTensors;\n info.numDataBuffers = this.state.numDataBuffers;\n info.numBytes = this.state.numBytes;\n if (this.state.numStringTensors > 0) {\n info.unreliable = true;\n if (info.reasons == null) {\n info.reasons = [];\n }\n info.reasons.push('Memory usage by string tensors is approximate ' +\n '(2 bytes per character)');\n }\n return info;\n }\n async profile(query) {\n this.state.profiling = true;\n const startBytes = this.state.numBytes;\n const startNumTensors = this.state.numTensors;\n this.state.activeProfile.kernels = [];\n this.state.activeProfile.result = await query();\n this.state.profiling = false;\n this.state.activeProfile.peakBytes = Math.max(...this.state.activeProfile.kernels.map(d => d.totalBytesSnapshot));\n this.state.activeProfile.newBytes = this.state.numBytes - startBytes;\n this.state.activeProfile.newTensors =\n this.state.numTensors - startNumTensors;\n for (const kernel of this.state.activeProfile.kernels) {\n kernel.kernelTimeMs = await kernel.kernelTimeMs;\n kernel.extraInfo = await kernel.extraInfo;\n }\n return this.state.activeProfile;\n }\n isTapeOn() {\n return this.state.gradientDepth > 0 && this.state.kernelDepth === 0;\n }\n addTapeNode(kernelName, inputs, outputs, gradientsFunc, saved, attrs) {\n const tapeNode = { id: this.state.nextTapeNodeId++, kernelName, inputs, outputs, saved };\n const gradConfig = getGradient(kernelName);\n if (gradConfig != null) {\n gradientsFunc = gradConfig.gradFunc;\n }\n if (gradientsFunc != null) {\n tapeNode.gradient = (dys) => {\n // TODO(smilkov): To optimize back-prop, pass dys that are not used in\n // the backprop graph to the user as null instead of zeros\n dys = dys.map((dy, i) => {\n if (dy == null) {\n const output = outputs[i];\n const vals = util.makeZerosTypedArray(output.size, output.dtype);\n return this.makeTensor(vals, output.shape, output.dtype);\n }\n return dy;\n });\n // Grad functions of ops with single outputs expect a dy, while ops\n // with multiple outputs expect dys (array of dy).\n return gradientsFunc(dys.length > 1 ? dys : dys[0], saved, attrs);\n };\n }\n this.state.activeTape.push(tapeNode);\n }\n keep(result) {\n result.kept = true;\n return result;\n }\n startTape() {\n if (this.state.gradientDepth === 0) {\n this.state.activeTape = [];\n }\n this.state.gradientDepth++;\n }\n endTape() {\n this.state.gradientDepth--;\n }\n /**\n * Start a scope. Use this with endScope() to achieve the same functionality\n * as scope() without the need for a function closure.\n */\n startScope(name) {\n const scopeInfo = {\n track: [],\n name: 'unnamed scope',\n id: this.state.nextScopeId++\n };\n if (name) {\n scopeInfo.name = name;\n }\n this.state.scopeStack.push(scopeInfo);\n this.state.activeScope = scopeInfo;\n }\n /**\n * End a scope. Use this with startScope() to achieve the same functionality\n * as scope() without the need for a function closure.\n */\n endScope(result) {\n const tensorsToTrackInParent = getTensorsInContainer(result);\n const tensorsToTrackInParentSet = new Set(tensorsToTrackInParent.map(t => t.id));\n // Dispose the arrays tracked in this scope.\n for (let i = 0; i < this.state.activeScope.track.length; i++) {\n const tensor = this.state.activeScope.track[i];\n if (!tensor.kept && !tensorsToTrackInParentSet.has(tensor.id)) {\n tensor.dispose();\n }\n }\n const oldScope = this.state.scopeStack.pop();\n this.state.activeScope = this.state.scopeStack.length === 0 ?\n null :\n this.state.scopeStack[this.state.scopeStack.length - 1];\n // Track the current result in the parent scope.\n tensorsToTrackInParent.forEach(tensor => {\n // Only track the tensor if was allocated in the inner scope and is not\n // globally kept.\n if (!tensor.kept && tensor.scopeId === oldScope.id) {\n this.track(tensor);\n }\n });\n }\n /**\n * Returns gradients of `f` with respect to each of the `xs`. The gradients\n * returned are of the same length as `xs`, but some might be null if `f`\n * was not a function of that `x`. It also takes optional dy to multiply the\n * gradient, which defaults to `1`.\n */\n gradients(f, xs, dy, allowNoGradients = false) {\n util.assert(xs.length > 0, () => 'gradients() received an empty list of xs.');\n if (dy != null && dy.dtype !== 'float32') {\n throw new Error(`dy must have 'float32' dtype, but has '${dy.dtype}'`);\n }\n const y = this.scopedRun(() => this.startTape(), () => this.endTape(), () => this.tidy('forward', f));\n util.assert(y instanceof Tensor, () => 'The result y returned by f() must be a tensor.');\n // Filter out the nodes that don't connect x => y.\n const filteredTape = getFilteredNodesXToY(this.state.activeTape, xs, y);\n if (!allowNoGradients && filteredTape.length === 0 && xs.length > 0) {\n throw new Error('Cannot compute gradient of y=f(x) with respect to x. Make sure ' +\n 'that the f you passed encloses all operations that lead from x ' +\n 'to y.');\n }\n return this.tidy('backward', () => {\n const accumulatedGradientMap = {};\n accumulatedGradientMap[y.id] = (dy == null) ? ones(y.shape) : dy;\n // Backprop gradients through the filtered nodes.\n backpropagateGradients(accumulatedGradientMap, filteredTape, \n // Pass the tidy function to avoid circular dep with `tape.ts`.\n f => this.tidy(f), \n // Pass an add function to avoide a circular dep with `tape.ts`.\n add);\n const grads = xs.map(x => accumulatedGradientMap[x.id]);\n if (this.state.gradientDepth === 0) {\n // This means that we are not computing higher-order gradients\n // and can clean up the tape.\n this.state.activeTape.forEach(node => {\n for (const tensor of node.saved) {\n tensor.dispose();\n }\n });\n this.state.activeTape = null;\n }\n return { value: y, grads };\n });\n }\n customGrad(f) {\n util.assert(util.isFunction(f), () => 'The f passed in customGrad(f) must be a function.');\n return (...inputs) => {\n util.assert(inputs.every(t => t instanceof Tensor), () => 'The args passed in customGrad(f)(x1, x2,...) must all be ' +\n 'tensors');\n let res;\n const inputMap = {};\n inputs.forEach((input, i) => {\n inputMap[i] = input;\n });\n const forwardFunc = (_, save) => {\n res = f(...[...inputs, save]);\n util.assert(res.value instanceof Tensor, () => 'The function f passed in customGrad(f) must return an ' +\n 'object where `obj.value` is a tensor');\n util.assert(util.isFunction(res.gradFunc), () => 'The function f passed in customGrad(f) must return an ' +\n 'object where `obj.gradFunc` is a function.');\n return res.value;\n };\n const backwardsFunc = (dy, saved) => {\n const gradRes = res.gradFunc(dy, saved);\n const grads = Array.isArray(gradRes) ? gradRes : [gradRes];\n util.assert(grads.length === inputs.length, () => 'The function f passed in customGrad(f) must return an ' +\n 'object where `obj.gradFunc` is a function that returns ' +\n 'the same number of tensors as inputs passed to f(...).');\n util.assert(grads.every(t => t instanceof Tensor), () => 'The function f passed in customGrad(f) must return an ' +\n 'object where `obj.gradFunc` is a function that returns ' +\n 'a list of only tensors.');\n const gradMap = {};\n grads.forEach((grad, i) => {\n gradMap[i] = () => grad;\n });\n return gradMap;\n };\n return this.runKernelFunc({\n forwardFunc,\n backwardsFunc,\n inputs: inputMap,\n });\n };\n }\n readSync(dataId) {\n // Route the read to the correct backend.\n const info = this.state.tensorInfo.get(dataId);\n return info.backend.readSync(dataId);\n }\n read(dataId) {\n // Route the read to the correct backend.\n const info = this.state.tensorInfo.get(dataId);\n return info.backend.read(dataId);\n }\n async time(query) {\n const start = now();\n const timingInfo = await this.backend.time(query);\n timingInfo.wallMs = now() - start;\n return timingInfo;\n }\n /**\n * Tracks a Tensor in the current scope to be automatically cleaned up\n * when the current scope ends, and returns the value.\n *\n * @param result The Tensor to track in the current scope.\n */\n track(result) {\n if (this.state.activeScope != null) {\n result.scopeId = this.state.activeScope.id;\n this.state.activeScope.track.push(result);\n }\n return result;\n }\n get registeredVariables() {\n return this.state.registeredVariables;\n }\n /**\n * Resets the engine state. Removes all backends but does not remove\n * registered backend factories.\n */\n reset() {\n // Make any pending promise obsolete.\n this.pendingBackendInitId++;\n this.state.dispose();\n this.ENV.reset();\n this.state = new EngineState();\n for (const backendName in this.registry) {\n this.disposeRegisteredKernels(backendName);\n this.registry[backendName].dispose();\n delete this.registry[backendName];\n }\n this.backendName = null;\n this.backendInstance = null;\n this.pendingBackendInit = null;\n }\n}\nEngine.nextTensorId = 0;\nEngine.nextVariableId = 0;\nfunction ones(shape) {\n const values = makeOnesTypedArray(sizeFromShape(shape), 'float32');\n return ENGINE.makeTensor(values, shape, 'float32');\n}\nexport function getOrMakeEngine() {\n const ns = getGlobalNamespace();\n if (ns._tfengine == null) {\n const environment = new Environment(ns);\n ns._tfengine = new Engine(environment);\n }\n setEnvironmentGlobal(ns._tfengine.ENV);\n // Tell the current tensor interface that the global engine is responsible\n // for tracking.\n setTensorTracker(() => ns._tfengine);\n return ns._tfengine;\n}\nexport const ENGINE = getOrMakeEngine();\n/**\n * A implementation of the add op for use within engine and tape.\n *\n * This allows us to avoid a circular dependency between add.ts and engine.\n * It is exported to be available in tape tests.\n */\nexport function add(a, b) {\n // We duplicate Add here to avoid a circular dependency with add.ts.\n const inputs = { a, b };\n return ENGINE.runKernel(Add, inputs);\n}\n//# sourceMappingURL=engine.js.map","/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport * as util from './util';\n/**\n * Computes a list of TapeNodes that connect x to y, filtering everything else\n * out and preserving the order of the original tape elements.\n *\n * @param tape The tape elements to filter.\n * @param xs The input Tensors.\n * @param y The output Tensor.\n */\nexport function getFilteredNodesXToY(tape, xs, y) {\n // Forward pass to compute all the nodes and Tensors that are transitively a\n // function of x.\n const tensorsFromX = {};\n const nodesFromX = {};\n for (let i = 0; i < xs.length; i++) {\n tensorsFromX[xs[i].id] = true;\n }\n for (let i = 0; i < tape.length; i++) {\n const node = tape[i];\n const nodeInputs = node.inputs;\n for (const inputName in nodeInputs) {\n const input = nodeInputs[inputName];\n let anyInputFromX = false;\n for (let j = 0; j < xs.length; j++) {\n if (tensorsFromX[input.id]) {\n node.outputs.forEach(output => tensorsFromX[output.id] = true);\n anyInputFromX = true;\n nodesFromX[node.id] = true;\n break;\n }\n }\n if (anyInputFromX) {\n break;\n }\n }\n }\n // Backward pass to find all of the nodes and Tensors that lead to y.\n const tensorsLeadToY = {};\n tensorsLeadToY[y.id] = true;\n const nodesToY = {};\n for (let i = tape.length - 1; i >= 0; i--) {\n const node = tape[i];\n const nodeInputs = node.inputs;\n // If any of the outputs lead to y, mark all of the inputs as leading to y.\n for (let j = 0; j < node.outputs.length; j++) {\n if (tensorsLeadToY[node.outputs[j].id]) {\n for (const inputName in nodeInputs) {\n tensorsLeadToY[nodeInputs[inputName].id] = true;\n nodesToY[node.id] = true;\n }\n break;\n }\n }\n }\n // Return the paths that come from x and lead to y.\n const filteredTape = [];\n for (let i = 0; i < tape.length; i++) {\n const node = tape[i];\n if (nodesFromX[node.id] && nodesToY[node.id]) {\n // Prune the inputs from the node that aren't a function of x.\n const prunedInputs = {};\n for (const inputName in node.inputs) {\n const nodeInput = node.inputs[inputName];\n if (tensorsFromX[nodeInput.id]) {\n prunedInputs[inputName] = nodeInput;\n }\n }\n // Copy the node and overwrite inputsAndArgs to the pruned version.\n const prunedNode = Object.assign({}, node);\n prunedNode.inputs = prunedInputs;\n prunedNode.outputs = node.outputs;\n filteredTape.push(prunedNode);\n }\n }\n return filteredTape;\n}\n/**\n * Backpropagate gradients through the filtered TapeNodes.\n *\n * @param tensorAccumulatedGradientMap A map of Tensor to its gradient. This map\n * is mutated by this method.\n * @param filteredTape The filtered TapeNodes to backprop through.\n */\nexport function backpropagateGradients(tensorAccumulatedGradientMap, filteredTape, tidy, add) {\n // Walk the tape backward and keep a map of Tensor to its gradient.\n for (let i = filteredTape.length - 1; i >= 0; i--) {\n const node = filteredTape[i];\n const dys = [];\n node.outputs.forEach(o => {\n const gradTensor = tensorAccumulatedGradientMap[o.id];\n if (gradTensor != null) {\n dys.push(gradTensor);\n }\n else {\n // This particular output is not in the back-propagation subgraph, so it\n // does not affect the final output, thus we put null for its dy.\n dys.push(null);\n }\n });\n if (node.gradient == null) {\n throw new Error(`Cannot compute gradient: gradient function not found ` +\n `for ${node.kernelName}.`);\n }\n // Backprop dy through this node and accumulate gradients over the inputs.\n const inputGradients = node.gradient(dys);\n for (const inputName in node.inputs) {\n if (!(inputName in inputGradients)) {\n throw new Error(`Cannot backprop through input ${inputName}. ` +\n `Available gradients found: ${Object.keys(inputGradients)}.`);\n }\n // Call the gradient function.\n const dx = tidy(() => inputGradients[inputName]());\n if (dx.dtype !== 'float32') {\n throw new Error(`Error in gradient for op ${node.kernelName}. The gradient of input ` +\n `${inputName} must have 'float32' dtype, but has '${dx.dtype}'`);\n }\n const x = node.inputs[inputName];\n if (!util.arraysEqual(dx.shape, x.shape)) {\n throw new Error(`Error in gradient for op ${node.kernelName}. The gradient of input ` +\n `'${inputName}' has shape '${dx.shape}', which does not match ` +\n `the shape of the input '${x.shape}'`);\n }\n if (tensorAccumulatedGradientMap[x.id] == null) {\n tensorAccumulatedGradientMap[x.id] = dx;\n }\n else {\n const curGradient = tensorAccumulatedGradientMap[x.id];\n tensorAccumulatedGradientMap[x.id] = add(curGradient, dx);\n curGradient.dispose();\n }\n }\n }\n}\n//# sourceMappingURL=tape.js.map","/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// tslint:disable-next-line:no-any\nfunction _isNavigatorDefined() {\n return typeof navigator !== 'undefined' && navigator != null;\n}\nexport function isMobile(nav) {\n if (nav || _isNavigatorDefined()) {\n if (!nav) {\n nav = navigator;\n }\n if (nav.product === 'ReactNative') {\n return true;\n }\n // tslint:disable-next-line:no-any\n const a = nav.userAgent || nav.vendor || window.opera;\n // tslint:disable-next-line:max-line-length\n return /(android|bb\\d+|meego).+mobile|avantgo|bada\\/|blackberry|blazer|compal|elaine|fennec|hiptop|iemobile|ip(hone|od)|iris|kindle|lge |maemo|midp|mmp|mobile.+firefox|netfront|opera m(ob|in)i|palm( os)?|phone|p(ixi|re)\\/|plucker|pocket|psp|series(4|6)0|symbian|treo|up\\.(browser|link)|vodafone|wap|windows ce|xda|xiino/i\n .test(a) ||\n // tslint:disable-next-line:max-line-length\n /1207|6310|6590|3gso|4thp|50[1-6]i|770s|802s|a wa|abac|ac(er|oo|s\\-)|ai(ko|rn)|al(av|ca|co)|amoi|an(ex|ny|yw)|aptu|ar(ch|go)|as(te|us)|attw|au(di|\\-m|r |s )|avan|be(ck|ll|nq)|bi(lb|rd)|bl(ac|az)|br(e|v)w|bumb|bw\\-(n|u)|c55\\/|capi|ccwa|cdm\\-|cell|chtm|cldc|cmd\\-|co(mp|nd)|craw|da(it|ll|ng)|dbte|dc\\-s|devi|dica|dmob|do(c|p)o|ds(12|\\-d)|el(49|ai)|em(l2|ul)|er(ic|k0)|esl8|ez([4-7]0|os|wa|ze)|fetc|fly(\\-|_)|g1 u|g560|gene|gf\\-5|g\\-mo|go(\\.w|od)|gr(ad|un)|haie|hcit|hd\\-(m|p|t)|hei\\-|hi(pt|ta)|hp( i|ip)|hs\\-c|ht(c(\\-| |_|a|g|p|s|t)|tp)|hu(aw|tc)|i\\-(20|go|ma)|i230|iac( |\\-|\\/)|ibro|idea|ig01|ikom|im1k|inno|ipaq|iris|ja(t|v)a|jbro|jemu|jigs|kddi|keji|kgt( |\\/)|klon|kpt |kwc\\-|kyo(c|k)|le(no|xi)|lg( g|\\/(k|l|u)|50|54|\\-[a-w])|libw|lynx|m1\\-w|m3ga|m50\\/|ma(te|ui|xo)|mc(01|21|ca)|m\\-cr|me(rc|ri)|mi(o8|oa|ts)|mmef|mo(01|02|bi|de|do|t(\\-| |o|v)|zz)|mt(50|p1|v )|mwbp|mywa|n10[0-2]|n20[2-3]|n30(0|2)|n50(0|2|5)|n7(0(0|1)|10)|ne((c|m)\\-|on|tf|wf|wg|wt)|nok(6|i)|nzph|o2im|op(ti|wv)|oran|owg1|p800|pan(a|d|t)|pdxg|pg(13|\\-([1-8]|c))|phil|pire|pl(ay|uc)|pn\\-2|po(ck|rt|se)|prox|psio|pt\\-g|qa\\-a|qc(07|12|21|32|60|\\-[2-7]|i\\-)|qtek|r380|r600|raks|rim9|ro(ve|zo)|s55\\/|sa(ge|ma|mm|ms|ny|va)|sc(01|h\\-|oo|p\\-)|sdk\\/|se(c(\\-|0|1)|47|mc|nd|ri)|sgh\\-|shar|sie(\\-|m)|sk\\-0|sl(45|id)|sm(al|ar|b3|it|t5)|so(ft|ny)|sp(01|h\\-|v\\-|v )|sy(01|mb)|t2(18|50)|t6(00|10|18)|ta(gt|lk)|tcl\\-|tdg\\-|tel(i|m)|tim\\-|t\\-mo|to(pl|sh)|ts(70|m\\-|m3|m5)|tx\\-9|up(\\.b|g1|si)|utst|v400|v750|veri|vi(rg|te)|vk(40|5[0-3]|\\-v)|vm40|voda|vulc|vx(52|53|60|61|70|80|81|83|85|98)|w3c(\\-| )|webc|whit|wi(g |nc|nw)|wmlb|wonu|x700|yas\\-|your|zeto|zte\\-/i\n .test(a.substr(0, 4));\n }\n return false;\n}\nexport function isBrowser() {\n return (typeof window !== 'undefined' && window.document != null) ||\n //@ts-ignore\n (typeof WorkerGlobalScope !== 'undefined');\n}\n//# sourceMappingURL=device_util.js.map","/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport './engine';\nimport * as device_util from './device_util';\nimport { env } from './environment';\nconst ENV = env();\n/**\n * This file contains environment-related flag registrations.\n */\n/** Whether to enable debug mode. */\nENV.registerFlag('DEBUG', () => false, debugValue => {\n if (debugValue) {\n console.warn('Debugging mode is ON. The output of every math call will ' +\n 'be downloaded to CPU and checked for NaNs. ' +\n 'This significantly impacts performance.');\n }\n});\n/** Whether we are in a browser (as versus, say, node.js) environment. */\nENV.registerFlag('IS_BROWSER', () => device_util.isBrowser());\n/** Whether we are in a browser (as versus, say, node.js) environment. */\nENV.registerFlag('IS_NODE', () => (typeof process !== 'undefined') &&\n (typeof process.versions !== 'undefined') &&\n (typeof process.versions.node !== 'undefined'));\n/** Whether this browser is Chrome. */\nENV.registerFlag('IS_CHROME', () => typeof navigator !== 'undefined' && navigator != null &&\n navigator.userAgent != null && /Chrome/.test(navigator.userAgent) &&\n /Google Inc/.test(navigator.vendor));\n/**\n * True when the environment is \"production\" where we disable safety checks\n * to gain performance.\n */\nENV.registerFlag('PROD', () => false);\n/**\n * Whether to do sanity checks when inferring a shape from user-provided\n * values, used when creating a new tensor.\n */\nENV.registerFlag('TENSORLIKE_CHECK_SHAPE_CONSISTENCY', () => ENV.getBool('DEBUG'));\n/** Whether deprecation warnings are enabled. */\nENV.registerFlag('DEPRECATION_WARNINGS_ENABLED', () => true);\n/** True if running unit tests. */\nENV.registerFlag('IS_TEST', () => false);\n/** Whether to check computation result for errors. */\nENV.registerFlag('CHECK_COMPUTATION_FOR_ERRORS', () => true);\n/** Whether the backend needs to wrap input to imageBitmap. */\nENV.registerFlag('WRAP_TO_IMAGEBITMAP', () => false);\n//# sourceMappingURL=flags.js.map","/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from './engine';\nimport { env } from './environment';\nimport { Tensor } from './tensor';\nimport { assert, flatten, inferDtype, isTypedArray, toTypedArray } from './util';\nexport function inferShape(val, dtype) {\n let firstElem = val;\n if (isTypedArray(val)) {\n return dtype === 'string' ? [] : [val.length];\n }\n if (!Array.isArray(val)) {\n return []; // Scalar.\n }\n const shape = [];\n while (Array.isArray(firstElem) ||\n isTypedArray(firstElem) && dtype !== 'string') {\n shape.push(firstElem.length);\n firstElem = firstElem[0];\n }\n if (Array.isArray(val) &&\n env().getBool('TENSORLIKE_CHECK_SHAPE_CONSISTENCY')) {\n deepAssertShapeConsistency(val, shape, []);\n }\n return shape;\n}\nfunction deepAssertShapeConsistency(val, shape, indices) {\n indices = indices || [];\n if (!(Array.isArray(val)) && !isTypedArray(val)) {\n assert(shape.length === 0, () => `Element arr[${indices.join('][')}] is a primitive, ` +\n `but should be an array/TypedArray of ${shape[0]} elements`);\n return;\n }\n assert(shape.length > 0, () => `Element arr[${indices.join('][')}] should be a primitive, ` +\n `but is an array of ${val.length} elements`);\n assert(val.length === shape[0], () => `Element arr[${indices.join('][')}] should have ${shape[0]} ` +\n `elements, but has ${val.length} elements`);\n const subShape = shape.slice(1);\n for (let i = 0; i < val.length; ++i) {\n deepAssertShapeConsistency(val[i], subShape, indices.concat(i));\n }\n}\nfunction assertDtype(expectedDtype, actualDType, argName, functionName) {\n if (expectedDtype === 'string_or_numeric') {\n return;\n }\n if (expectedDtype == null) {\n throw new Error(`Expected dtype cannot be null.`);\n }\n if (expectedDtype !== 'numeric' && expectedDtype !== actualDType ||\n expectedDtype === 'numeric' && actualDType === 'string') {\n throw new Error(`Argument '${argName}' passed to '${functionName}' must ` +\n `be ${expectedDtype} tensor, but got ${actualDType} tensor`);\n }\n}\nexport function convertToTensor(x, argName, functionName, parseAsDtype = 'numeric') {\n if (x instanceof Tensor) {\n assertDtype(parseAsDtype, x.dtype, argName, functionName);\n return x;\n }\n let inferredDtype = inferDtype(x);\n // If the user expects a bool/int/float, use that info to update the\n // inferredDtype when it is not a string.\n if (inferredDtype !== 'string' &&\n ['bool', 'int32', 'float32'].indexOf(parseAsDtype) >= 0) {\n inferredDtype = parseAsDtype;\n }\n assertDtype(parseAsDtype, inferredDtype, argName, functionName);\n if ((x == null) ||\n (!isTypedArray(x) && !Array.isArray(x) && typeof x !== 'number' &&\n typeof x !== 'boolean' && typeof x !== 'string')) {\n const type = x == null ? 'null' : x.constructor.name;\n throw new Error(`Argument '${argName}' passed to '${functionName}' must be a ` +\n `Tensor or TensorLike, but got '${type}'`);\n }\n const inferredShape = inferShape(x, inferredDtype);\n if (!isTypedArray(x) && !Array.isArray(x)) {\n x = [x];\n }\n const skipTypedArray = true;\n const values = inferredDtype !== 'string' ?\n toTypedArray(x, inferredDtype) :\n flatten(x, [], skipTypedArray);\n return ENGINE.makeTensor(values, inferredShape, inferredDtype);\n}\nexport function convertToTensorArray(arg, argName, functionName, parseAsDtype = 'numeric') {\n if (!Array.isArray(arg)) {\n throw new Error(`Argument ${argName} passed to ${functionName} must be a ` +\n '`Tensor[]` or `TensorLike[]`');\n }\n const tensors = arg;\n return tensors.map((t, i) => convertToTensor(t, `${argName}[${i}]`, functionName, parseAsDtype));\n}\n//# sourceMappingURL=tensor_util_env.js.map","/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { isPromise } from '../util';\nexport const OP_SCOPE_SUFFIX = '__op';\n/**\n * Used for wrapping functions that perform math operations on\n * Tensors. The function will be wrapped in a named scope that cleans all\n * memory usage after the function is done.\n */\nexport function op(f) {\n const keys = Object.keys(f);\n if (keys.length !== 1) {\n throw new Error(`Please provide an object with a single key ` +\n `(operation name) mapping to a function. Got an object with ` +\n `${keys.length} keys.`);\n }\n let opName = keys[0];\n const fn = f[opName];\n // Strip the underscore from the end of the function name.\n if (opName.endsWith('_')) {\n opName = opName.substring(0, opName.length - 1);\n }\n // add an __op suffix to distinguish ops from kernels in tf.profile\n opName = opName + OP_SCOPE_SUFFIX;\n // tslint:disable-next-line:no-any\n const f2 = (...args) => {\n ENGINE.startScope(opName);\n try {\n const result = fn(...args);\n if (isPromise(result)) {\n console.error('Cannot return a Promise inside of tidy.');\n }\n ENGINE.endScope(result);\n return result;\n }\n catch (ex) {\n ENGINE.endScope(null);\n throw ex;\n }\n };\n Object.defineProperty(f2, 'name', { value: opName, configurable: true });\n // tslint:disable-next-line:no-any\n return f2;\n}\n//# sourceMappingURL=operation.js.map","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Complex } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport { op } from './operation';\n/**\n * Converts two real numbers to a complex number.\n *\n * Given a tensor `real` representing the real part of a complex number, and a\n * tensor `imag` representing the imaginary part of a complex number, this\n * operation returns complex numbers elementwise of the form [r0, i0, r1, i1],\n * where r represents the real part and i represents the imag part.\n *\n * The input tensors real and imag must have the same shape.\n *\n * ```js\n * const real = tf.tensor1d([2.25, 3.25]);\n * const imag = tf.tensor1d([4.75, 5.75]);\n * const complex = tf.complex(real, imag);\n *\n * complex.print();\n * ```\n *\n * @doc {heading: 'Tensors', subheading: 'Creation'}\n */\nfunction complex_(real, imag) {\n const $real = convertToTensor(real, 'real', 'complex');\n const $imag = convertToTensor(imag, 'imag', 'complex');\n util.assertShapesMatch($real.shape, $imag.shape, `real and imag shapes, ${$real.shape} and ${$imag.shape}, ` +\n `must match in call to tf.complex().`);\n const inputs = { real: $real, imag: $imag };\n return ENGINE.runKernel(Complex, inputs);\n}\nexport const complex = op({ complex_ });\n//# sourceMappingURL=complex.js.map","/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { assert, assertNonNegativeIntegerDimensions, flatten, inferDtype, isTypedArray, sizeFromShape, toTypedArray } from '../util';\n/** This is shared code across all tensor creation methods. */\nexport function makeTensor(values, shape, inferredShape, dtype) {\n if (dtype == null) {\n dtype = inferDtype(values);\n }\n if (dtype === 'complex64') {\n throw new Error(`Cannot construct a complex64 tensor directly. ` +\n `Please use tf.complex(real, imag).`);\n }\n if (!isTypedArray(values) && !Array.isArray(values) &&\n typeof values !== 'number' && typeof values !== 'boolean' &&\n typeof values !== 'string') {\n throw new Error('values passed to tensor(values) must be a number/boolean/string or ' +\n 'an array of numbers/booleans/strings, or a TypedArray');\n }\n if (shape != null) {\n assertNonNegativeIntegerDimensions(shape);\n const providedSize = sizeFromShape(shape);\n const inferredSize = sizeFromShape(inferredShape);\n assert(providedSize === inferredSize, () => `Based on the provided shape, [${shape}], the tensor should have ` +\n `${providedSize} values but has ${inferredSize}`);\n for (let i = 0; i < inferredShape.length; ++i) {\n const inferred = inferredShape[i];\n const flatDimsDontMatch = i === inferredShape.length - 1 ?\n inferred !== sizeFromShape(shape.slice(i)) :\n true;\n assert(inferredShape[i] === shape[i] || !flatDimsDontMatch, () => `Error creating a new Tensor. Inferred shape ` +\n `(${inferredShape}) does not match the provided ` +\n `shape (${shape}). `);\n }\n }\n if (!isTypedArray(values) && !Array.isArray(values)) {\n values = [values];\n }\n shape = shape || inferredShape;\n values = dtype !== 'string' ?\n toTypedArray(values, dtype) :\n flatten(values, [], true);\n return ENGINE.makeTensor(values, shape, dtype);\n}\n//# sourceMappingURL=tensor_ops_util.js.map","/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { inferShape } from '../tensor_util_env';\nimport { makeTensor } from './tensor_ops_util';\n/**\n * Creates a `tf.Tensor` with the provided values, shape and dtype.\n *\n * ```js\n * // Pass an array of values to create a vector.\n * tf.tensor([1, 2, 3, 4]).print();\n * ```\n *\n * ```js\n * // Pass a nested array of values to make a matrix or a higher\n * // dimensional tensor.\n * tf.tensor([[1, 2], [3, 4]]).print();\n * ```\n *\n * ```js\n * // Pass a flat array and specify a shape yourself.\n * tf.tensor([1, 2, 3, 4], [2, 2]).print();\n * ```\n *\n * @param values The values of the tensor. Can be nested array of numbers,\n * or a flat array, or a `TypedArray`. If the values are strings,\n * they will be encoded as utf-8 and kept as `Uint8Array[]`.\n * @param shape The shape of the tensor. Optional. If not provided,\n * it is inferred from `values`.\n * @param dtype The data type.\n *\n * @doc {heading: 'Tensors', subheading: 'Creation'}\n */\nexport function tensor(values, shape, dtype) {\n const inferredShape = inferShape(values, dtype);\n return makeTensor(values, shape, inferredShape, dtype);\n}\n//# sourceMappingURL=tensor.js.map","/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/* Type definitions for exporting and importing of models. */\n/**\n * A map from Tensor dtype to number of bytes per element of the Tensor.\n */\nexport const DTYPE_VALUE_SIZE_MAP = {\n 'float32': 4,\n 'float16': 2,\n 'int32': 4,\n 'uint16': 2,\n 'uint8': 1,\n 'bool': 1,\n 'complex64': 8\n};\n//# sourceMappingURL=types.js.map","/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { complex } from '../ops/complex';\nimport { tensor } from '../ops/tensor';\nimport { sizeFromShape } from '../util';\nimport { DTYPE_VALUE_SIZE_MAP } from './types';\n/** Number of bytes reserved for the length of the string. (32bit integer). */\nconst NUM_BYTES_STRING_LENGTH = 4;\n/**\n * Encode a map from names to weight values as an ArrayBuffer, along with an\n * `Array` of `WeightsManifestEntry` as specification of the encoded weights.\n *\n * This function does not perform sharding.\n *\n * This function is the reverse of `decodeWeights`.\n *\n * @param tensors A map (\"dict\") from names to tensors.\n * @param group Group to which the weights belong (optional).\n * @returns A `Promise` of\n * - A flat `ArrayBuffer` with all the binary values of the `Tensor`s\n * concatenated.\n * - An `Array` of `WeightManifestEntry`s, carrying information including\n * tensor names, `dtype`s and shapes.\n * @throws Error: on unsupported tensor `dtype`.\n */\nexport async function encodeWeights(tensors, group) {\n // TODO(adarob, cais): Support quantization.\n const specs = [];\n const dataPromises = [];\n const names = Array.isArray(tensors) ?\n tensors.map(tensor => tensor.name) :\n Object.keys(tensors);\n for (let i = 0; i < names.length; ++i) {\n const name = names[i];\n const t = Array.isArray(tensors) ? tensors[i].tensor : tensors[name];\n if (t.dtype !== 'float32' && t.dtype !== 'int32' && t.dtype !== 'bool' &&\n t.dtype !== 'string' && t.dtype !== 'complex64') {\n throw new Error(`Unsupported dtype in weight '${name}': ${t.dtype}`);\n }\n const spec = { name, shape: t.shape, dtype: t.dtype };\n if (t.dtype === 'string') {\n const utf8bytes = new Promise(async (resolve) => {\n const vals = await t.bytes();\n const totalNumBytes = vals.reduce((p, c) => p + c.length, 0) +\n NUM_BYTES_STRING_LENGTH * vals.length;\n const bytes = new Uint8Array(totalNumBytes);\n let offset = 0;\n for (let i = 0; i < vals.length; i++) {\n const val = vals[i];\n const bytesOfLength = new Uint8Array(new Uint32Array([val.length]).buffer);\n bytes.set(bytesOfLength, offset);\n offset += NUM_BYTES_STRING_LENGTH;\n bytes.set(val, offset);\n offset += val.length;\n }\n resolve(bytes);\n });\n dataPromises.push(utf8bytes);\n }\n else {\n dataPromises.push(t.data());\n }\n if (group != null) {\n spec.group = group;\n }\n specs.push(spec);\n }\n const tensorValues = await Promise.all(dataPromises);\n return { data: concatenateTypedArrays(tensorValues), specs };\n}\n/**\n * Decode flat ArrayBuffer as weights.\n *\n * This function does not handle sharding.\n *\n * This function is the reverse of `encodeWeights`.\n *\n * @param buffer A flat ArrayBuffer carrying the binary values of the tensors\n * concatenated in the order specified in `specs`.\n * @param specs Specifications of the names, dtypes and shapes of the tensors\n * whose value are encoded by `buffer`.\n * @return A map from tensor name to tensor value, with the names corresponding\n * to names in `specs`.\n * @throws Error, if any of the tensors has unsupported dtype.\n */\nexport function decodeWeights(buffer, specs) {\n // TODO(adarob, cais): Support quantization.\n const out = {};\n let float16Decode;\n let offset = 0;\n for (const spec of specs) {\n const name = spec.name;\n const dtype = spec.dtype;\n const shape = spec.shape;\n const size = sizeFromShape(shape);\n let values;\n if ('quantization' in spec) {\n const quantization = spec.quantization;\n if (quantization.dtype === 'uint8' || quantization.dtype === 'uint16') {\n if (!('min' in quantization && 'scale' in quantization)) {\n throw new Error(`Weight ${spec.name} with quantization ${quantization.dtype} ` +\n `doesn't have corresponding metadata min and scale.`);\n }\n }\n else if (quantization.dtype === 'float16') {\n if (dtype !== 'float32') {\n throw new Error(`Weight ${spec.name} is quantized with ${quantization.dtype} ` +\n `which only supports weights of type float32 not ${dtype}.`);\n }\n }\n else {\n throw new Error(`Weight ${spec.name} has unknown ` +\n `quantization dtype ${quantization.dtype}. ` +\n `Supported quantization dtypes are: ` +\n `'uint8', 'uint16', and 'float16'.`);\n }\n const quantizationSizeFactor = DTYPE_VALUE_SIZE_MAP[quantization.dtype];\n const byteBuffer = buffer.slice(offset, offset + size * quantizationSizeFactor);\n const quantizedArray = (quantization.dtype === 'uint8') ?\n new Uint8Array(byteBuffer) :\n new Uint16Array(byteBuffer);\n if (dtype === 'float32') {\n if (quantization.dtype === 'uint8' || quantization.dtype === 'uint16') {\n values = new Float32Array(quantizedArray.length);\n for (let i = 0; i < quantizedArray.length; i++) {\n const v = quantizedArray[i];\n values[i] = v * quantization.scale + quantization.min;\n }\n }\n else if (quantization.dtype === 'float16') {\n if (float16Decode === undefined) {\n float16Decode = getFloat16Decoder();\n }\n values = float16Decode(quantizedArray);\n }\n else {\n throw new Error(`Unsupported quantization type ${quantization.dtype} ` +\n `for weight type float32.`);\n }\n }\n else if (dtype === 'int32') {\n if (quantization.dtype !== 'uint8' && quantization.dtype !== 'uint16') {\n throw new Error(`Unsupported quantization type ${quantization.dtype} ` +\n `for weight type int32.`);\n }\n values = new Int32Array(quantizedArray.length);\n for (let i = 0; i < quantizedArray.length; i++) {\n const v = quantizedArray[i];\n values[i] = Math.round(v * quantization.scale + quantization.min);\n }\n }\n else {\n throw new Error(`Unsupported dtype in weight '${name}': ${dtype}`);\n }\n offset += size * quantizationSizeFactor;\n }\n else if (dtype === 'string') {\n const size = sizeFromShape(spec.shape);\n values = [];\n for (let i = 0; i < size; i++) {\n const byteLength = new Uint32Array(buffer.slice(offset, offset + NUM_BYTES_STRING_LENGTH))[0];\n offset += NUM_BYTES_STRING_LENGTH;\n const bytes = new Uint8Array(buffer.slice(offset, offset + byteLength));\n values.push(bytes);\n offset += byteLength;\n }\n }\n else {\n const dtypeFactor = DTYPE_VALUE_SIZE_MAP[dtype];\n const byteBuffer = buffer.slice(offset, offset + size * dtypeFactor);\n if (dtype === 'float32') {\n values = new Float32Array(byteBuffer);\n }\n else if (dtype === 'int32') {\n values = new Int32Array(byteBuffer);\n }\n else if (dtype === 'bool') {\n values = new Uint8Array(byteBuffer);\n }\n else if (dtype === 'complex64') {\n values = new Float32Array(byteBuffer);\n const real = new Float32Array(values.length / 2);\n const image = new Float32Array(values.length / 2);\n for (let i = 0; i < real.length; i++) {\n real[i] = values[i * 2];\n image[i] = values[i * 2 + 1];\n }\n const realTensor = tensor(real, shape, 'float32');\n const imageTensor = tensor(image, shape, 'float32');\n out[name] = complex(realTensor, imageTensor);\n realTensor.dispose();\n imageTensor.dispose();\n }\n else {\n throw new Error(`Unsupported dtype in weight '${name}': ${dtype}`);\n }\n offset += size * dtypeFactor;\n }\n if (dtype !== 'complex64') {\n out[name] = tensor(values, shape, dtype);\n }\n }\n return out;\n}\n/**\n * Concatenate TypedArrays into an ArrayBuffer.\n */\nexport function concatenateTypedArrays(xs) {\n // TODO(adarob, cais): Support quantization.\n if (xs === null) {\n throw new Error(`Invalid input value: ${JSON.stringify(xs)}`);\n }\n let totalByteLength = 0;\n // `normalizedXs` is here for this reason: a `TypedArray`'s `buffer'\n // can have a different byte length from that of the `TypedArray` itself,\n // for example, when the `TypedArray` is created from an offset in an\n // `ArrayBuffer`. `normliazedXs` holds `TypedArray`s whose `buffer`s match\n // the `TypedArray` in byte length. If an element of `xs` does not show\n // this property, a new `TypedArray` that satisfy this property will be\n // constructed and pushed into `normalizedXs`.\n const normalizedXs = [];\n xs.forEach((x) => {\n totalByteLength += x.byteLength;\n // tslint:disable:no-any\n normalizedXs.push(x.byteLength === x.buffer.byteLength ? x :\n new x.constructor(x));\n if (!(x instanceof Float32Array || x instanceof Int32Array ||\n x instanceof Uint8Array)) {\n throw new Error(`Unsupported TypedArray subtype: ${x.constructor.name}`);\n }\n // tslint:enable:no-any\n });\n const y = new Uint8Array(totalByteLength);\n let offset = 0;\n normalizedXs.forEach((x) => {\n y.set(new Uint8Array(x.buffer), offset);\n offset += x.byteLength;\n });\n return y.buffer;\n}\n// Use Buffer on Node.js instead of Blob/atob/btoa\nconst useNodeBuffer = typeof Buffer !== 'undefined' &&\n (typeof Blob === 'undefined' || typeof atob === 'undefined' ||\n typeof btoa === 'undefined');\n/**\n * Calculate the byte length of a JavaScript string.\n *\n * Note that a JavaScript string can contain wide characters, therefore the\n * length of the string is not necessarily equal to the byte length.\n *\n * @param str Input string.\n * @returns Byte length.\n */\nexport function stringByteLength(str) {\n if (useNodeBuffer) {\n return Buffer.byteLength(str);\n }\n return new Blob([str]).size;\n}\n/**\n * Encode an ArrayBuffer as a base64 encoded string.\n *\n * @param buffer `ArrayBuffer` to be converted.\n * @returns A string that base64-encodes `buffer`.\n */\nexport function arrayBufferToBase64String(buffer) {\n if (useNodeBuffer) {\n return Buffer.from(buffer).toString('base64');\n }\n const buf = new Uint8Array(buffer);\n let s = '';\n for (let i = 0, l = buf.length; i < l; i++) {\n s += String.fromCharCode(buf[i]);\n }\n return btoa(s);\n}\n/**\n * Decode a base64 string as an ArrayBuffer.\n *\n * @param str Base64 string.\n * @returns Decoded `ArrayBuffer`.\n */\nexport function base64StringToArrayBuffer(str) {\n if (useNodeBuffer) {\n const buf = Buffer.from(str, 'base64');\n return buf.buffer.slice(buf.byteOffset, buf.byteOffset + buf.byteLength);\n }\n const s = atob(str);\n const buffer = new Uint8Array(s.length);\n for (let i = 0; i < s.length; ++i) {\n buffer.set([s.charCodeAt(i)], i);\n }\n return buffer.buffer;\n}\n/**\n * Concatenate a number of ArrayBuffers into one.\n *\n * @param buffers A number of array buffers to concatenate.\n * @returns Result of concatenating `buffers` in order.\n */\nexport function concatenateArrayBuffers(buffers) {\n if (buffers.length === 1) {\n return buffers[0];\n }\n let totalByteLength = 0;\n buffers.forEach((buffer) => {\n totalByteLength += buffer.byteLength;\n });\n const temp = new Uint8Array(totalByteLength);\n let offset = 0;\n buffers.forEach((buffer) => {\n temp.set(new Uint8Array(buffer), offset);\n offset += buffer.byteLength;\n });\n return temp.buffer;\n}\n/**\n * Get the basename of a path.\n *\n * Behaves in a way analogous to Linux's basename command.\n *\n * @param path\n */\nexport function basename(path) {\n const SEPARATOR = '/';\n path = path.trim();\n while (path.endsWith(SEPARATOR)) {\n path = path.slice(0, path.length - 1);\n }\n const items = path.split(SEPARATOR);\n return items[items.length - 1];\n}\n/**\n * Populate ModelArtifactsInfo fields for a model with JSON topology.\n * @param modelArtifacts\n * @returns A ModelArtifactsInfo object.\n */\nexport function getModelArtifactsInfoForJSON(modelArtifacts) {\n if (modelArtifacts.modelTopology instanceof ArrayBuffer) {\n throw new Error('Expected JSON model topology, received ArrayBuffer.');\n }\n return {\n dateSaved: new Date(),\n modelTopologyType: 'JSON',\n modelTopologyBytes: modelArtifacts.modelTopology == null ?\n 0 :\n stringByteLength(JSON.stringify(modelArtifacts.modelTopology)),\n weightSpecsBytes: modelArtifacts.weightSpecs == null ?\n 0 :\n stringByteLength(JSON.stringify(modelArtifacts.weightSpecs)),\n weightDataBytes: modelArtifacts.weightData == null ?\n 0 :\n modelArtifacts.weightData.byteLength,\n };\n}\n/**\n * Computes mantisa table for casting Float16 to Float32\n * See http://www.fox-toolkit.org/ftp/fasthalffloatconversion.pdf\n *\n * @returns Uint32Array, 2048 mantissa lookup values.\n */\nfunction computeFloat16MantisaTable() {\n const convertMantissa = (i) => {\n let m = i << 13;\n let e = 0;\n while ((m & 0x00800000) === 0) {\n e -= 0x00800000;\n m <<= 1;\n }\n m &= ~0x00800000;\n e += 0x38800000;\n return m | e;\n };\n const mantisaTable = new Uint32Array(2048);\n mantisaTable[0] = 0;\n for (let i = 1; i < 1024; i++) {\n mantisaTable[i] = convertMantissa(i);\n }\n for (let i = 1024; i < 2048; i++) {\n mantisaTable[i] = 0x38000000 + ((i - 1024) << 13);\n }\n return mantisaTable;\n}\n/**\n * Computes exponent table for casting Float16 to Float32\n * See http://www.fox-toolkit.org/ftp/fasthalffloatconversion.pdf\n *\n * @returns Uint32Array, 64 exponent lookup values.\n */\nfunction computeFloat16ExponentTable() {\n const exponentTable = new Uint32Array(64);\n exponentTable[0] = 0;\n exponentTable[31] = 0x47800000;\n exponentTable[32] = 0x80000000;\n exponentTable[63] = 0xc7800000;\n for (let i = 1; i < 31; i++) {\n exponentTable[i] = i << 23;\n }\n for (let i = 33; i < 63; i++) {\n exponentTable[i] = 0x80000000 + ((i - 32) << 23);\n }\n return exponentTable;\n}\n/**\n * Computes offset table for casting Float16 to Float32\n * See http://www.fox-toolkit.org/ftp/fasthalffloatconversion.pdf\n *\n * @returns Uint32Array, 6d offset values.\n */\nfunction computeFloat16OffsetTable() {\n const offsetTable = new Uint32Array(64);\n for (let i = 0; i < 64; i++) {\n offsetTable[i] = 1024;\n }\n offsetTable[0] = offsetTable[32] = 0;\n return offsetTable;\n}\n/**\n * Retrieve a Float16 decoder which will decode a ByteArray of Float16 values\n * to a Float32Array.\n *\n * @returns Function (buffer: Uint16Array) => Float32Array which decodes\n * the Uint16Array of Float16 bytes to a Float32Array.\n */\nexport function getFloat16Decoder() {\n // Algorithm is based off of\n // http://www.fox-toolkit.org/ftp/fasthalffloatconversion.pdf\n // Cache lookup tables\n const mantisaTable = computeFloat16MantisaTable();\n const exponentTable = computeFloat16ExponentTable();\n const offsetTable = computeFloat16OffsetTable();\n return (quantizedArray) => {\n const buffer = new ArrayBuffer(4 * quantizedArray.length);\n const bufferUint32View = new Uint32Array(buffer);\n for (let index = 0; index < quantizedArray.length; index++) {\n const float16Bits = quantizedArray[index];\n const float32Bits = mantisaTable[offsetTable[float16Bits >> 10] + (float16Bits & 0x3ff)] +\n exponentTable[float16Bits >> 10];\n bufferUint32View[index] = float32Bits;\n }\n return new Float32Array(buffer);\n };\n}\n//# sourceMappingURL=io_utils.js.map","/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nexport class IORouterRegistry {\n constructor() {\n this.saveRouters = [];\n this.loadRouters = [];\n }\n static getInstance() {\n if (IORouterRegistry.instance == null) {\n IORouterRegistry.instance = new IORouterRegistry();\n }\n return IORouterRegistry.instance;\n }\n /**\n * Register a save-handler router.\n *\n * @param saveRouter A function that maps a URL-like string onto an instance\n * of `IOHandler` with the `save` method defined or `null`.\n */\n static registerSaveRouter(saveRouter) {\n IORouterRegistry.getInstance().saveRouters.push(saveRouter);\n }\n /**\n * Register a load-handler router.\n *\n * @param loadRouter A function that maps a URL-like string onto an instance\n * of `IOHandler` with the `load` method defined or `null`.\n */\n static registerLoadRouter(loadRouter) {\n IORouterRegistry.getInstance().loadRouters.push(loadRouter);\n }\n /**\n * Look up IOHandler for saving, given a URL-like string.\n *\n * @param url\n * @returns If only one match is found, an instance of IOHandler with the\n * `save` method defined. If no match is found, `null`.\n * @throws Error, if more than one match is found.\n */\n static getSaveHandlers(url) {\n return IORouterRegistry.getHandlers(url, 'save');\n }\n /**\n * Look up IOHandler for loading, given a URL-like string.\n *\n * @param url\n * @param loadOptions Optional, custom load options.\n * @returns All valid handlers for `url`, given the currently registered\n * handler routers.\n */\n static getLoadHandlers(url, loadOptions) {\n return IORouterRegistry.getHandlers(url, 'load', loadOptions);\n }\n static getHandlers(url, handlerType, loadOptions) {\n const validHandlers = [];\n const routers = handlerType === 'load' ?\n IORouterRegistry.getInstance().loadRouters :\n IORouterRegistry.getInstance().saveRouters;\n routers.forEach(router => {\n const handler = router(url, loadOptions);\n if (handler !== null) {\n validHandlers.push(handler);\n }\n });\n return validHandlers;\n }\n}\nexport const registerSaveRouter = (loudRouter) => IORouterRegistry.registerSaveRouter(loudRouter);\nexport const registerLoadRouter = (loudRouter) => IORouterRegistry.registerLoadRouter(loudRouter);\nexport const getSaveHandlers = (url) => IORouterRegistry.getSaveHandlers(url);\nexport const getLoadHandlers = (url, loadOptions) => IORouterRegistry.getLoadHandlers(url, loadOptions);\n//# sourceMappingURL=router_registry.js.map","/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport '../flags';\nimport { env } from '../environment';\nimport { getModelArtifactsInfoForJSON } from './io_utils';\nimport { IORouterRegistry } from './router_registry';\nconst DATABASE_NAME = 'tensorflowjs';\nconst DATABASE_VERSION = 1;\n// Model data and ModelArtifactsInfo (metadata) are stored in two separate\n// stores for efficient access of the list of stored models and their metadata.\n// 1. The object store for model data: topology, weights and weight manifests.\nconst MODEL_STORE_NAME = 'models_store';\n// 2. The object store for ModelArtifactsInfo, including meta-information such\n// as the type of topology (JSON vs binary), byte size of the topology, byte\n// size of the weights, etc.\nconst INFO_STORE_NAME = 'model_info_store';\n/**\n * Delete the entire database for tensorflow.js, including the models store.\n */\nexport async function deleteDatabase() {\n const idbFactory = getIndexedDBFactory();\n return new Promise((resolve, reject) => {\n const deleteRequest = idbFactory.deleteDatabase(DATABASE_NAME);\n deleteRequest.onsuccess = () => resolve();\n deleteRequest.onerror = error => reject(error);\n });\n}\nfunction getIndexedDBFactory() {\n if (!env().getBool('IS_BROWSER')) {\n // TODO(cais): Add more info about what IOHandler subtypes are available.\n // Maybe point to a doc page on the web and/or automatically determine\n // the available IOHandlers and print them in the error message.\n throw new Error('Failed to obtain IndexedDB factory because the current environment' +\n 'is not a web browser.');\n }\n // tslint:disable-next-line:no-any\n const theWindow = typeof window === 'undefined' ? self : window;\n const factory = theWindow.indexedDB || theWindow.mozIndexedDB ||\n theWindow.webkitIndexedDB || theWindow.msIndexedDB ||\n theWindow.shimIndexedDB;\n if (factory == null) {\n throw new Error('The current browser does not appear to support IndexedDB.');\n }\n return factory;\n}\nfunction setUpDatabase(openRequest) {\n const db = openRequest.result;\n db.createObjectStore(MODEL_STORE_NAME, { keyPath: 'modelPath' });\n db.createObjectStore(INFO_STORE_NAME, { keyPath: 'modelPath' });\n}\n/**\n * IOHandler subclass: Browser IndexedDB.\n *\n * See the doc string of `browserIndexedDB` for more details.\n */\nexport class BrowserIndexedDB {\n constructor(modelPath) {\n this.indexedDB = getIndexedDBFactory();\n if (modelPath == null || !modelPath) {\n throw new Error('For IndexedDB, modelPath must not be null, undefined or empty.');\n }\n this.modelPath = modelPath;\n }\n async save(modelArtifacts) {\n // TODO(cais): Support saving GraphDef models.\n if (modelArtifacts.modelTopology instanceof ArrayBuffer) {\n throw new Error('BrowserLocalStorage.save() does not support saving model topology ' +\n 'in binary formats yet.');\n }\n return this.databaseAction(this.modelPath, modelArtifacts);\n }\n async load() {\n return this.databaseAction(this.modelPath);\n }\n /**\n * Perform database action to put model artifacts into or read model artifacts\n * from IndexedDB object store.\n *\n * Whether the action is put or get depends on whether `modelArtifacts` is\n * specified. If it is specified, the action will be put; otherwise the action\n * will be get.\n *\n * @param modelPath A unique string path for the model.\n * @param modelArtifacts If specified, it will be the model artifacts to be\n * stored in IndexedDB.\n * @returns A `Promise` of `SaveResult`, if the action is put, or a `Promise`\n * of `ModelArtifacts`, if the action is get.\n */\n databaseAction(modelPath, modelArtifacts) {\n return new Promise((resolve, reject) => {\n const openRequest = this.indexedDB.open(DATABASE_NAME, DATABASE_VERSION);\n openRequest.onupgradeneeded = () => setUpDatabase(openRequest);\n openRequest.onsuccess = () => {\n const db = openRequest.result;\n if (modelArtifacts == null) {\n // Read model out from object store.\n const modelTx = db.transaction(MODEL_STORE_NAME, 'readonly');\n const modelStore = modelTx.objectStore(MODEL_STORE_NAME);\n const getRequest = modelStore.get(this.modelPath);\n getRequest.onsuccess = () => {\n if (getRequest.result == null) {\n db.close();\n return reject(new Error(`Cannot find model with path '${this.modelPath}' ` +\n `in IndexedDB.`));\n }\n else {\n resolve(getRequest.result.modelArtifacts);\n }\n };\n getRequest.onerror = error => {\n db.close();\n return reject(getRequest.error);\n };\n modelTx.oncomplete = () => db.close();\n }\n else {\n // Put model into object store.\n const modelArtifactsInfo = getModelArtifactsInfoForJSON(modelArtifacts);\n // First, put ModelArtifactsInfo into info store.\n const infoTx = db.transaction(INFO_STORE_NAME, 'readwrite');\n let infoStore = infoTx.objectStore(INFO_STORE_NAME);\n const putInfoRequest = infoStore.put({ modelPath: this.modelPath, modelArtifactsInfo });\n let modelTx;\n putInfoRequest.onsuccess = () => {\n // Second, put model data into model store.\n modelTx = db.transaction(MODEL_STORE_NAME, 'readwrite');\n const modelStore = modelTx.objectStore(MODEL_STORE_NAME);\n const putModelRequest = modelStore.put({\n modelPath: this.modelPath,\n modelArtifacts,\n modelArtifactsInfo\n });\n putModelRequest.onsuccess = () => resolve({ modelArtifactsInfo });\n putModelRequest.onerror = error => {\n // If the put-model request fails, roll back the info entry as\n // well.\n infoStore = infoTx.objectStore(INFO_STORE_NAME);\n const deleteInfoRequest = infoStore.delete(this.modelPath);\n deleteInfoRequest.onsuccess = () => {\n db.close();\n return reject(putModelRequest.error);\n };\n deleteInfoRequest.onerror = error => {\n db.close();\n return reject(putModelRequest.error);\n };\n };\n };\n putInfoRequest.onerror = error => {\n db.close();\n return reject(putInfoRequest.error);\n };\n infoTx.oncomplete = () => {\n if (modelTx == null) {\n db.close();\n }\n else {\n modelTx.oncomplete = () => db.close();\n }\n };\n }\n };\n openRequest.onerror = error => reject(openRequest.error);\n });\n }\n}\nBrowserIndexedDB.URL_SCHEME = 'indexeddb://';\nexport const indexedDBRouter = (url) => {\n if (!env().getBool('IS_BROWSER')) {\n return null;\n }\n else {\n if (!Array.isArray(url) && url.startsWith(BrowserIndexedDB.URL_SCHEME)) {\n return browserIndexedDB(url.slice(BrowserIndexedDB.URL_SCHEME.length));\n }\n else {\n return null;\n }\n }\n};\nIORouterRegistry.registerSaveRouter(indexedDBRouter);\nIORouterRegistry.registerLoadRouter(indexedDBRouter);\n/**\n * Creates a browser IndexedDB IOHandler for saving and loading models.\n *\n * ```js\n * const model = tf.sequential();\n * model.add(\n * tf.layers.dense({units: 1, inputShape: [100], activation: 'sigmoid'}));\n *\n * const saveResult = await model.save('indexeddb://MyModel'));\n * console.log(saveResult);\n * ```\n *\n * @param modelPath A unique identifier for the model to be saved. Must be a\n * non-empty string.\n * @returns An instance of `BrowserIndexedDB` (sublcass of `IOHandler`),\n * which can be used with, e.g., `tf.Model.save`.\n */\nexport function browserIndexedDB(modelPath) {\n return new BrowserIndexedDB(modelPath);\n}\nfunction maybeStripScheme(key) {\n return key.startsWith(BrowserIndexedDB.URL_SCHEME) ?\n key.slice(BrowserIndexedDB.URL_SCHEME.length) :\n key;\n}\nexport class BrowserIndexedDBManager {\n constructor() {\n this.indexedDB = getIndexedDBFactory();\n }\n async listModels() {\n return new Promise((resolve, reject) => {\n const openRequest = this.indexedDB.open(DATABASE_NAME, DATABASE_VERSION);\n openRequest.onupgradeneeded = () => setUpDatabase(openRequest);\n openRequest.onsuccess = () => {\n const db = openRequest.result;\n const tx = db.transaction(INFO_STORE_NAME, 'readonly');\n const store = tx.objectStore(INFO_STORE_NAME);\n // tslint:disable:max-line-length\n // Need to cast `store` as `any` here because TypeScript's DOM\n // library does not have the `getAll()` method even though the\n // method is supported in the latest version of most mainstream\n // browsers:\n // https://developer.mozilla.org/en-US/docs/Web/API/IDBObjectStore/getAll\n // tslint:enable:max-line-length\n // tslint:disable-next-line:no-any\n const getAllInfoRequest = store.getAll();\n getAllInfoRequest.onsuccess = () => {\n const out = {};\n for (const item of getAllInfoRequest.result) {\n out[item.modelPath] = item.modelArtifactsInfo;\n }\n resolve(out);\n };\n getAllInfoRequest.onerror = error => {\n db.close();\n return reject(getAllInfoRequest.error);\n };\n tx.oncomplete = () => db.close();\n };\n openRequest.onerror = error => reject(openRequest.error);\n });\n }\n async removeModel(path) {\n path = maybeStripScheme(path);\n return new Promise((resolve, reject) => {\n const openRequest = this.indexedDB.open(DATABASE_NAME, DATABASE_VERSION);\n openRequest.onupgradeneeded = () => setUpDatabase(openRequest);\n openRequest.onsuccess = () => {\n const db = openRequest.result;\n const infoTx = db.transaction(INFO_STORE_NAME, 'readwrite');\n const infoStore = infoTx.objectStore(INFO_STORE_NAME);\n const getInfoRequest = infoStore.get(path);\n let modelTx;\n getInfoRequest.onsuccess = () => {\n if (getInfoRequest.result == null) {\n db.close();\n return reject(new Error(`Cannot find model with path '${path}' ` +\n `in IndexedDB.`));\n }\n else {\n // First, delete the entry in the info store.\n const deleteInfoRequest = infoStore.delete(path);\n const deleteModelData = () => {\n // Second, delete the entry in the model store.\n modelTx = db.transaction(MODEL_STORE_NAME, 'readwrite');\n const modelStore = modelTx.objectStore(MODEL_STORE_NAME);\n const deleteModelRequest = modelStore.delete(path);\n deleteModelRequest.onsuccess = () => resolve(getInfoRequest.result.modelArtifactsInfo);\n deleteModelRequest.onerror = error => reject(getInfoRequest.error);\n };\n // Proceed with deleting model data regardless of whether deletion\n // of info data succeeds or not.\n deleteInfoRequest.onsuccess = deleteModelData;\n deleteInfoRequest.onerror = error => {\n deleteModelData();\n db.close();\n return reject(getInfoRequest.error);\n };\n }\n };\n getInfoRequest.onerror = error => {\n db.close();\n return reject(getInfoRequest.error);\n };\n infoTx.oncomplete = () => {\n if (modelTx == null) {\n db.close();\n }\n else {\n modelTx.oncomplete = () => db.close();\n }\n };\n };\n openRequest.onerror = error => reject(openRequest.error);\n });\n }\n}\n//# sourceMappingURL=indexed_db.js.map","/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport '../flags';\nimport { env } from '../environment';\nimport { assert } from '../util';\nimport { arrayBufferToBase64String, base64StringToArrayBuffer, getModelArtifactsInfoForJSON } from './io_utils';\nimport { IORouterRegistry } from './router_registry';\nconst PATH_SEPARATOR = '/';\nconst PATH_PREFIX = 'tensorflowjs_models';\nconst INFO_SUFFIX = 'info';\nconst MODEL_TOPOLOGY_SUFFIX = 'model_topology';\nconst WEIGHT_SPECS_SUFFIX = 'weight_specs';\nconst WEIGHT_DATA_SUFFIX = 'weight_data';\nconst MODEL_METADATA_SUFFIX = 'model_metadata';\n/**\n * Purge all tensorflow.js-saved model artifacts from local storage.\n *\n * @returns Paths of the models purged.\n */\nexport function purgeLocalStorageArtifacts() {\n if (!env().getBool('IS_BROWSER') || typeof window === 'undefined' ||\n typeof window.localStorage === 'undefined') {\n throw new Error('purgeLocalStorageModels() cannot proceed because local storage is ' +\n 'unavailable in the current environment.');\n }\n const LS = window.localStorage;\n const purgedModelPaths = [];\n for (let i = 0; i < LS.length; ++i) {\n const key = LS.key(i);\n const prefix = PATH_PREFIX + PATH_SEPARATOR;\n if (key.startsWith(prefix) && key.length > prefix.length) {\n LS.removeItem(key);\n const modelName = getModelPathFromKey(key);\n if (purgedModelPaths.indexOf(modelName) === -1) {\n purgedModelPaths.push(modelName);\n }\n }\n }\n return purgedModelPaths;\n}\nfunction getModelKeys(path) {\n return {\n info: [PATH_PREFIX, path, INFO_SUFFIX].join(PATH_SEPARATOR),\n topology: [PATH_PREFIX, path, MODEL_TOPOLOGY_SUFFIX].join(PATH_SEPARATOR),\n weightSpecs: [PATH_PREFIX, path, WEIGHT_SPECS_SUFFIX].join(PATH_SEPARATOR),\n weightData: [PATH_PREFIX, path, WEIGHT_DATA_SUFFIX].join(PATH_SEPARATOR),\n modelMetadata: [PATH_PREFIX, path, MODEL_METADATA_SUFFIX].join(PATH_SEPARATOR)\n };\n}\n/**\n * Get model path from a local-storage key.\n *\n * E.g., 'tensorflowjs_models/my/model/1/info' --> 'my/model/1'\n *\n * @param key\n */\nfunction getModelPathFromKey(key) {\n const items = key.split(PATH_SEPARATOR);\n if (items.length < 3) {\n throw new Error(`Invalid key format: ${key}`);\n }\n return items.slice(1, items.length - 1).join(PATH_SEPARATOR);\n}\nfunction maybeStripScheme(key) {\n return key.startsWith(BrowserLocalStorage.URL_SCHEME) ?\n key.slice(BrowserLocalStorage.URL_SCHEME.length) :\n key;\n}\n/**\n * IOHandler subclass: Browser Local Storage.\n *\n * See the doc string to `browserLocalStorage` for more details.\n */\nexport class BrowserLocalStorage {\n constructor(modelPath) {\n if (!env().getBool('IS_BROWSER') || typeof window === 'undefined' ||\n typeof window.localStorage === 'undefined') {\n // TODO(cais): Add more info about what IOHandler subtypes are\n // available.\n // Maybe point to a doc page on the web and/or automatically determine\n // the available IOHandlers and print them in the error message.\n throw new Error('The current environment does not support local storage.');\n }\n this.LS = window.localStorage;\n if (modelPath == null || !modelPath) {\n throw new Error('For local storage, modelPath must not be null, undefined or empty.');\n }\n this.modelPath = modelPath;\n this.keys = getModelKeys(this.modelPath);\n }\n /**\n * Save model artifacts to browser local storage.\n *\n * See the documentation to `browserLocalStorage` for details on the saved\n * artifacts.\n *\n * @param modelArtifacts The model artifacts to be stored.\n * @returns An instance of SaveResult.\n */\n async save(modelArtifacts) {\n if (modelArtifacts.modelTopology instanceof ArrayBuffer) {\n throw new Error('BrowserLocalStorage.save() does not support saving model topology ' +\n 'in binary formats yet.');\n }\n else {\n const topology = JSON.stringify(modelArtifacts.modelTopology);\n const weightSpecs = JSON.stringify(modelArtifacts.weightSpecs);\n const modelArtifactsInfo = getModelArtifactsInfoForJSON(modelArtifacts);\n try {\n this.LS.setItem(this.keys.info, JSON.stringify(modelArtifactsInfo));\n this.LS.setItem(this.keys.topology, topology);\n this.LS.setItem(this.keys.weightSpecs, weightSpecs);\n this.LS.setItem(this.keys.weightData, arrayBufferToBase64String(modelArtifacts.weightData));\n const result = {\n format: modelArtifacts.format,\n generatedBy: modelArtifacts.generatedBy,\n convertedBy: modelArtifacts.convertedBy\n };\n if (modelArtifacts.signature != null) {\n result.signature = modelArtifacts.signature;\n }\n if (modelArtifacts.userDefinedMetadata != null) {\n result.userDefinedMetadata = modelArtifacts.userDefinedMetadata;\n }\n if (modelArtifacts.modelInitializer != null) {\n result.modelInitializer = modelArtifacts.modelInitializer;\n }\n this.LS.setItem(this.keys.modelMetadata, JSON.stringify(result));\n return { modelArtifactsInfo };\n }\n catch (err) {\n // If saving failed, clean up all items saved so far.\n this.LS.removeItem(this.keys.info);\n this.LS.removeItem(this.keys.topology);\n this.LS.removeItem(this.keys.weightSpecs);\n this.LS.removeItem(this.keys.weightData);\n this.LS.removeItem(this.keys.modelMetadata);\n throw new Error(`Failed to save model '${this.modelPath}' to local storage: ` +\n `size quota being exceeded is a possible cause of this failure: ` +\n `modelTopologyBytes=${modelArtifactsInfo.modelTopologyBytes}, ` +\n `weightSpecsBytes=${modelArtifactsInfo.weightSpecsBytes}, ` +\n `weightDataBytes=${modelArtifactsInfo.weightDataBytes}.`);\n }\n }\n }\n /**\n * Load a model from local storage.\n *\n * See the documentation to `browserLocalStorage` for details on the saved\n * artifacts.\n *\n * @returns The loaded model (if loading succeeds).\n */\n async load() {\n const info = JSON.parse(this.LS.getItem(this.keys.info));\n if (info == null) {\n throw new Error(`In local storage, there is no model with name '${this.modelPath}'`);\n }\n if (info.modelTopologyType !== 'JSON') {\n throw new Error('BrowserLocalStorage does not support loading non-JSON model ' +\n 'topology yet.');\n }\n const out = {};\n // Load topology.\n const topology = JSON.parse(this.LS.getItem(this.keys.topology));\n if (topology == null) {\n throw new Error(`In local storage, the topology of model '${this.modelPath}' ` +\n `is missing.`);\n }\n out.modelTopology = topology;\n // Load weight specs.\n const weightSpecs = JSON.parse(this.LS.getItem(this.keys.weightSpecs));\n if (weightSpecs == null) {\n throw new Error(`In local storage, the weight specs of model '${this.modelPath}' ` +\n `are missing.`);\n }\n out.weightSpecs = weightSpecs;\n // Load meta-data fields.\n const metadataString = this.LS.getItem(this.keys.modelMetadata);\n if (metadataString != null) {\n const metadata = JSON.parse(metadataString);\n out.format = metadata['format'];\n out.generatedBy = metadata['generatedBy'];\n out.convertedBy = metadata['convertedBy'];\n if (metadata['signature'] != null) {\n out.signature = metadata['signature'];\n }\n if (metadata['userDefinedMetadata'] != null) {\n out.userDefinedMetadata = metadata['userDefinedMetadata'];\n }\n if (metadata['modelInitializer'] != null) {\n out.modelInitializer = metadata['modelInitializer'];\n }\n }\n // Load weight data.\n const weightDataBase64 = this.LS.getItem(this.keys.weightData);\n if (weightDataBase64 == null) {\n throw new Error(`In local storage, the binary weight values of model ` +\n `'${this.modelPath}' are missing.`);\n }\n out.weightData = base64StringToArrayBuffer(weightDataBase64);\n return out;\n }\n}\nBrowserLocalStorage.URL_SCHEME = 'localstorage://';\nexport const localStorageRouter = (url) => {\n if (!env().getBool('IS_BROWSER')) {\n return null;\n }\n else {\n if (!Array.isArray(url) && url.startsWith(BrowserLocalStorage.URL_SCHEME)) {\n return browserLocalStorage(url.slice(BrowserLocalStorage.URL_SCHEME.length));\n }\n else {\n return null;\n }\n }\n};\nIORouterRegistry.registerSaveRouter(localStorageRouter);\nIORouterRegistry.registerLoadRouter(localStorageRouter);\n/**\n * Factory function for local storage IOHandler.\n *\n * This `IOHandler` supports both `save` and `load`.\n *\n * For each model's saved artifacts, four items are saved to local storage.\n * - `${PATH_SEPARATOR}/${modelPath}/info`: Contains meta-info about the\n * model, such as date saved, type of the topology, size in bytes, etc.\n * - `${PATH_SEPARATOR}/${modelPath}/topology`: Model topology. For Keras-\n * style models, this is a stringized JSON.\n * - `${PATH_SEPARATOR}/${modelPath}/weight_specs`: Weight specs of the\n * model, can be used to decode the saved binary weight values (see\n * item below).\n * - `${PATH_SEPARATOR}/${modelPath}/weight_data`: Concatenated binary\n * weight values, stored as a base64-encoded string.\n *\n * Saving may throw an `Error` if the total size of the artifacts exceed the\n * browser-specific quota.\n *\n * @param modelPath A unique identifier for the model to be saved. Must be a\n * non-empty string.\n * @returns An instance of `IOHandler`, which can be used with, e.g.,\n * `tf.Model.save`.\n */\nexport function browserLocalStorage(modelPath) {\n return new BrowserLocalStorage(modelPath);\n}\nexport class BrowserLocalStorageManager {\n constructor() {\n assert(env().getBool('IS_BROWSER'), () => 'Current environment is not a web browser');\n assert(typeof window === 'undefined' ||\n typeof window.localStorage !== 'undefined', () => 'Current browser does not appear to support localStorage');\n this.LS = window.localStorage;\n }\n async listModels() {\n const out = {};\n const prefix = PATH_PREFIX + PATH_SEPARATOR;\n const suffix = PATH_SEPARATOR + INFO_SUFFIX;\n for (let i = 0; i < this.LS.length; ++i) {\n const key = this.LS.key(i);\n if (key.startsWith(prefix) && key.endsWith(suffix)) {\n const modelPath = getModelPathFromKey(key);\n out[modelPath] = JSON.parse(this.LS.getItem(key));\n }\n }\n return out;\n }\n async removeModel(path) {\n path = maybeStripScheme(path);\n const keys = getModelKeys(path);\n if (this.LS.getItem(keys.info) == null) {\n throw new Error(`Cannot find model at path '${path}'`);\n }\n const info = JSON.parse(this.LS.getItem(keys.info));\n this.LS.removeItem(keys.info);\n this.LS.removeItem(keys.topology);\n this.LS.removeItem(keys.weightSpecs);\n this.LS.removeItem(keys.weightData);\n return info;\n }\n}\n//# sourceMappingURL=local_storage.js.map","/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * Classes and functions for model management across multiple storage mediums.\n *\n * Supported client actions:\n * - Listing models on all registered storage mediums.\n * - Remove model by URL from any registered storage mediums, by using URL\n * string.\n * - Moving or copying model from one path to another in the same medium or from\n * one medium to another, by using URL strings.\n */\nimport { assert } from '../util';\nimport { IORouterRegistry } from './router_registry';\nconst URL_SCHEME_SUFFIX = '://';\nexport class ModelStoreManagerRegistry {\n constructor() {\n this.managers = {};\n }\n static getInstance() {\n if (ModelStoreManagerRegistry.instance == null) {\n ModelStoreManagerRegistry.instance = new ModelStoreManagerRegistry();\n }\n return ModelStoreManagerRegistry.instance;\n }\n /**\n * Register a save-handler router.\n *\n * @param saveRouter A function that maps a URL-like string onto an instance\n * of `IOHandler` with the `save` method defined or `null`.\n */\n static registerManager(scheme, manager) {\n assert(scheme != null, () => 'scheme must not be undefined or null.');\n if (scheme.endsWith(URL_SCHEME_SUFFIX)) {\n scheme = scheme.slice(0, scheme.indexOf(URL_SCHEME_SUFFIX));\n }\n assert(scheme.length > 0, () => 'scheme must not be an empty string.');\n const registry = ModelStoreManagerRegistry.getInstance();\n assert(registry.managers[scheme] == null, () => `A model store manager is already registered for scheme '${scheme}'.`);\n registry.managers[scheme] = manager;\n }\n static getManager(scheme) {\n const manager = this.getInstance().managers[scheme];\n if (manager == null) {\n throw new Error(`Cannot find model manager for scheme '${scheme}'`);\n }\n return manager;\n }\n static getSchemes() {\n return Object.keys(this.getInstance().managers);\n }\n}\n/**\n * Helper method for parsing a URL string into a scheme and a path.\n *\n * @param url E.g., 'localstorage://my-model'\n * @returns A dictionary with two fields: scheme and path.\n * Scheme: e.g., 'localstorage' in the example above.\n * Path: e.g., 'my-model' in the example above.\n */\nfunction parseURL(url) {\n if (url.indexOf(URL_SCHEME_SUFFIX) === -1) {\n throw new Error(`The url string provided does not contain a scheme. ` +\n `Supported schemes are: ` +\n `${ModelStoreManagerRegistry.getSchemes().join(',')}`);\n }\n return {\n scheme: url.split(URL_SCHEME_SUFFIX)[0],\n path: url.split(URL_SCHEME_SUFFIX)[1],\n };\n}\nasync function cloneModelInternal(sourceURL, destURL, deleteSource = false) {\n assert(sourceURL !== destURL, () => `Old path and new path are the same: '${sourceURL}'`);\n const loadHandlers = IORouterRegistry.getLoadHandlers(sourceURL);\n assert(loadHandlers.length > 0, () => `Copying failed because no load handler is found for source URL ${sourceURL}.`);\n assert(loadHandlers.length < 2, () => `Copying failed because more than one (${loadHandlers.length}) ` +\n `load handlers for source URL ${sourceURL}.`);\n const loadHandler = loadHandlers[0];\n const saveHandlers = IORouterRegistry.getSaveHandlers(destURL);\n assert(saveHandlers.length > 0, () => `Copying failed because no save handler is found for destination ` +\n `URL ${destURL}.`);\n assert(saveHandlers.length < 2, () => `Copying failed because more than one (${loadHandlers.length}) ` +\n `save handlers for destination URL ${destURL}.`);\n const saveHandler = saveHandlers[0];\n const sourceScheme = parseURL(sourceURL).scheme;\n const sourcePath = parseURL(sourceURL).path;\n const sameMedium = sourceScheme === parseURL(sourceURL).scheme;\n const modelArtifacts = await loadHandler.load();\n // If moving within the same storage medium, remove the old model as soon as\n // the loading is done. Without doing this, it is possible that the combined\n // size of the two models will cause the cloning to fail.\n if (deleteSource && sameMedium) {\n await ModelStoreManagerRegistry.getManager(sourceScheme)\n .removeModel(sourcePath);\n }\n const saveResult = await saveHandler.save(modelArtifacts);\n // If moving between mediums, the deletion is done after the save succeeds.\n // This guards against the case in which saving to the destination medium\n // fails.\n if (deleteSource && !sameMedium) {\n await ModelStoreManagerRegistry.getManager(sourceScheme)\n .removeModel(sourcePath);\n }\n return saveResult.modelArtifactsInfo;\n}\n/**\n * List all models stored in registered storage mediums.\n *\n * For a web browser environment, the registered mediums are Local Storage and\n * IndexedDB.\n *\n * ```js\n * // First create and save a model.\n * const model = tf.sequential();\n * model.add(tf.layers.dense(\n * {units: 1, inputShape: [10], activation: 'sigmoid'}));\n * await model.save('localstorage://demo/management/model1');\n *\n * // Then list existing models.\n * console.log(JSON.stringify(await tf.io.listModels()));\n *\n * // Delete the model.\n * await tf.io.removeModel('localstorage://demo/management/model1');\n *\n * // List models again.\n * console.log(JSON.stringify(await tf.io.listModels()));\n * ```\n *\n * @returns A `Promise` of a dictionary mapping URLs of existing models to\n * their model artifacts info. URLs include medium-specific schemes, e.g.,\n * 'indexeddb://my/model/1'. Model artifacts info include type of the\n * model's topology, byte sizes of the topology, weights, etc.\n *\n * @doc {\n * heading: 'Models',\n * subheading: 'Management',\n * namespace: 'io',\n * ignoreCI: true\n * }\n */\nasync function listModels() {\n const schemes = ModelStoreManagerRegistry.getSchemes();\n const out = {};\n for (const scheme of schemes) {\n const schemeOut = await ModelStoreManagerRegistry.getManager(scheme).listModels();\n for (const path in schemeOut) {\n const url = scheme + URL_SCHEME_SUFFIX + path;\n out[url] = schemeOut[path];\n }\n }\n return out;\n}\n/**\n * Remove a model specified by URL from a reigstered storage medium.\n *\n * ```js\n * // First create and save a model.\n * const model = tf.sequential();\n * model.add(tf.layers.dense(\n * {units: 1, inputShape: [10], activation: 'sigmoid'}));\n * await model.save('localstorage://demo/management/model1');\n *\n * // Then list existing models.\n * console.log(JSON.stringify(await tf.io.listModels()));\n *\n * // Delete the model.\n * await tf.io.removeModel('localstorage://demo/management/model1');\n *\n * // List models again.\n * console.log(JSON.stringify(await tf.io.listModels()));\n * ```\n *\n * @param url A URL to a stored model, with a scheme prefix, e.g.,\n * 'localstorage://my-model-1', 'indexeddb://my/model/2'.\n * @returns ModelArtifactsInfo of the deleted model (if and only if deletion\n * is successful).\n * @throws Error if deletion fails, e.g., if no model exists at `path`.\n *\n * @doc {\n * heading: 'Models',\n * subheading: 'Management',\n * namespace: 'io',\n * ignoreCI: true\n * }\n */\nasync function removeModel(url) {\n const schemeAndPath = parseURL(url);\n const manager = ModelStoreManagerRegistry.getManager(schemeAndPath.scheme);\n return manager.removeModel(schemeAndPath.path);\n}\n/**\n * Copy a model from one URL to another.\n *\n * This function supports:\n *\n * 1. Copying within a storage medium, e.g.,\n * `tf.io.copyModel('localstorage://model-1', 'localstorage://model-2')`\n * 2. Copying between two storage mediums, e.g.,\n * `tf.io.copyModel('localstorage://model-1', 'indexeddb://model-1')`\n *\n * ```js\n * // First create and save a model.\n * const model = tf.sequential();\n * model.add(tf.layers.dense(\n * {units: 1, inputShape: [10], activation: 'sigmoid'}));\n * await model.save('localstorage://demo/management/model1');\n *\n * // Then list existing models.\n * console.log(JSON.stringify(await tf.io.listModels()));\n *\n * // Copy the model, from Local Storage to IndexedDB.\n * await tf.io.copyModel(\n * 'localstorage://demo/management/model1',\n * 'indexeddb://demo/management/model1');\n *\n * // List models again.\n * console.log(JSON.stringify(await tf.io.listModels()));\n *\n * // Remove both models.\n * await tf.io.removeModel('localstorage://demo/management/model1');\n * await tf.io.removeModel('indexeddb://demo/management/model1');\n * ```\n *\n * @param sourceURL Source URL of copying.\n * @param destURL Destination URL of copying.\n * @returns ModelArtifactsInfo of the copied model (if and only if copying\n * is successful).\n * @throws Error if copying fails, e.g., if no model exists at `sourceURL`, or\n * if `oldPath` and `newPath` are identical.\n *\n * @doc {\n * heading: 'Models',\n * subheading: 'Management',\n * namespace: 'io',\n * ignoreCI: true\n * }\n */\nasync function copyModel(sourceURL, destURL) {\n const deleteSource = false;\n return cloneModelInternal(sourceURL, destURL, deleteSource);\n}\n/**\n * Move a model from one URL to another.\n *\n * This function supports:\n *\n * 1. Moving within a storage medium, e.g.,\n * `tf.io.moveModel('localstorage://model-1', 'localstorage://model-2')`\n * 2. Moving between two storage mediums, e.g.,\n * `tf.io.moveModel('localstorage://model-1', 'indexeddb://model-1')`\n *\n * ```js\n * // First create and save a model.\n * const model = tf.sequential();\n * model.add(tf.layers.dense(\n * {units: 1, inputShape: [10], activation: 'sigmoid'}));\n * await model.save('localstorage://demo/management/model1');\n *\n * // Then list existing models.\n * console.log(JSON.stringify(await tf.io.listModels()));\n *\n * // Move the model, from Local Storage to IndexedDB.\n * await tf.io.moveModel(\n * 'localstorage://demo/management/model1',\n * 'indexeddb://demo/management/model1');\n *\n * // List models again.\n * console.log(JSON.stringify(await tf.io.listModels()));\n *\n * // Remove the moved model.\n * await tf.io.removeModel('indexeddb://demo/management/model1');\n * ```\n *\n * @param sourceURL Source URL of moving.\n * @param destURL Destination URL of moving.\n * @returns ModelArtifactsInfo of the copied model (if and only if copying\n * is successful).\n * @throws Error if moving fails, e.g., if no model exists at `sourceURL`, or\n * if `oldPath` and `newPath` are identical.\n *\n * @doc {\n * heading: 'Models',\n * subheading: 'Management',\n * namespace: 'io',\n * ignoreCI: true\n * }\n */\nasync function moveModel(sourceURL, destURL) {\n const deleteSource = true;\n return cloneModelInternal(sourceURL, destURL, deleteSource);\n}\nexport { moveModel, copyModel, removeModel, listModels };\n//# sourceMappingURL=model_management.js.map","/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport '../flags';\nimport { env } from '../environment';\nimport { BrowserIndexedDB, BrowserIndexedDBManager } from '../io/indexed_db';\nimport { BrowserLocalStorage, BrowserLocalStorageManager } from '../io/local_storage';\nimport { ModelStoreManagerRegistry } from '../io/model_management';\nexport class PlatformBrowser {\n fetch(path, init) {\n return fetch(path, init);\n }\n now() {\n return performance.now();\n }\n encode(text, encoding) {\n if (encoding !== 'utf-8' && encoding !== 'utf8') {\n throw new Error(`Browser's encoder only supports utf-8, but got ${encoding}`);\n }\n if (this.textEncoder == null) {\n this.textEncoder = new TextEncoder();\n }\n return this.textEncoder.encode(text);\n }\n decode(bytes, encoding) {\n return new TextDecoder(encoding).decode(bytes);\n }\n}\nif (env().get('IS_BROWSER')) {\n env().setPlatform('browser', new PlatformBrowser());\n // Register LocalStorage IOHandler\n try {\n ModelStoreManagerRegistry.registerManager(BrowserLocalStorage.URL_SCHEME, new BrowserLocalStorageManager());\n }\n catch (err) {\n }\n // Register IndexedDB IOHandler\n try {\n ModelStoreManagerRegistry.registerManager(BrowserIndexedDB.URL_SCHEME, new BrowserIndexedDBManager());\n }\n catch (err) {\n }\n}\n//# sourceMappingURL=platform_browser.js.map","/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { env } from '../environment';\n// We are wrapping this within an object so it can be stubbed by Jasmine.\nexport const getNodeFetch = {\n // tslint:disable-next-line:no-require-imports\n importFetch: () => require('node-fetch')\n};\nlet systemFetch;\n// These getters and setters are for testing so we don't export a mutable\n// variable.\nexport function resetSystemFetch() {\n systemFetch = null;\n}\nexport function setSystemFetch(fetchFn) {\n systemFetch = fetchFn;\n}\nexport function getSystemFetch() {\n return systemFetch;\n}\nexport class PlatformNode {\n constructor() {\n // tslint:disable-next-line:no-require-imports\n this.util = require('util');\n // According to the spec, the built-in encoder can do only UTF-8 encoding.\n // https://developer.mozilla.org/en-US/docs/Web/API/TextEncoder/TextEncoder\n this.textEncoder = new this.util.TextEncoder();\n }\n fetch(path, requestInits) {\n if (env().global.fetch != null) {\n return env().global.fetch(path, requestInits);\n }\n if (systemFetch == null) {\n systemFetch = getNodeFetch.importFetch();\n }\n return systemFetch(path, requestInits);\n }\n now() {\n const time = process.hrtime();\n return time[0] * 1000 + time[1] / 1000000;\n }\n encode(text, encoding) {\n if (encoding !== 'utf-8' && encoding !== 'utf8') {\n throw new Error(`Node built-in encoder only supports utf-8, but got ${encoding}`);\n }\n return this.textEncoder.encode(text);\n }\n decode(bytes, encoding) {\n if (bytes.length === 0) {\n return '';\n }\n return new this.util.TextDecoder(encoding).decode(bytes);\n }\n}\nif (env().get('IS_NODE')) {\n env().setPlatform('node', new PlatformNode());\n}\n//# sourceMappingURL=platform_node.js.map","/**\n * @license\n * Copyright 2020 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { TensorBuffer } from '../tensor';\nimport * as util from '../util';\n/**\n * Creates an empty `tf.TensorBuffer` with the specified `shape` and `dtype`.\n *\n * The values are stored in CPU as `TypedArray`. Fill the buffer using\n * `buffer.set()`, or by modifying directly `buffer.values`.\n *\n * When done, call `buffer.toTensor()` to get an immutable `tf.Tensor` with\n * those values.\n *\n * ```js\n * // Create a buffer and set values at particular indices.\n * const buffer = tf.buffer([2, 2]);\n * buffer.set(3, 0, 0);\n * buffer.set(5, 1, 0);\n *\n * // Convert the buffer back to a tensor.\n * buffer.toTensor().print();\n * ```\n *\n * @param shape An array of integers defining the output tensor shape.\n * @param dtype The dtype of the buffer. Defaults to 'float32'.\n * @param values The values of the buffer as `TypedArray`. Defaults to\n * zeros.\n *\n * @doc {heading: 'Tensors', subheading: 'Creation'}\n */\nexport function buffer(shape, dtype = 'float32', values) {\n dtype = dtype || 'float32';\n util.assertNonNegativeIntegerDimensions(shape);\n return new TensorBuffer(shape, dtype, values);\n}\n//# sourceMappingURL=buffer.js.map","/**\n * @license\n * Copyright 2020 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Cast } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport { op } from './operation';\n/**\n * Casts a `tf.Tensor` to a new dtype.\n *\n * ```js\n * const x = tf.tensor1d([1.5, 2.5, 3]);\n * tf.cast(x, 'int32').print();\n * ```\n * @param x The input tensor to be casted.\n * @param dtype The dtype to cast the input tensor to.\n *\n * @doc {heading: 'Tensors', subheading: 'Transformations'}\n */\nfunction cast_(x, dtype) {\n const $x = convertToTensor(x, 'x', 'cast');\n // Sanity checks.\n if (!util.isValidDtype(dtype)) {\n throw new Error(`Failed to cast to unknown dtype ${dtype}`);\n }\n if (dtype === 'string' && $x.dtype !== 'string' ||\n dtype !== 'string' && $x.dtype === 'string') {\n throw new Error('Only strings can be casted to strings');\n }\n const inputs = { x: $x };\n const attrs = { dtype };\n return ENGINE.runKernel(Cast, inputs, attrs);\n}\nexport const cast = op({ cast_ });\n//# sourceMappingURL=cast.js.map","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Identity } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Creates a new tensor with the same values and shape as the specified\n * tensor.\n *\n * ```js\n * const x = tf.tensor([1, 2]);\n *\n * x.clone().print();\n * ```\n *\n * @param x The tensor to clone.\n *\n * @doc {heading: 'Tensors', subheading: 'Creation'}\n */\nfunction clone_(x) {\n const $x = convertToTensor(x, 'x', 'clone', 'string_or_numeric');\n const inputs = { x: $x };\n // Note this op is called tf.identity in python. Hence the kernel name used\n // here.\n return ENGINE.runKernel(Identity, inputs);\n}\nexport const clone = op({ clone_ });\n//# sourceMappingURL=clone.js.map","/**\n * @license\n * Copyright 2020 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * Prints information about the `tf.Tensor` including its data.\n *\n * ```js\n * const verbose = true;\n * tf.tensor2d([1, 2, 3, 4], [2, 2]).print(verbose);\n * ```\n * @param x The tensor to be printed.\n * @param verbose Whether to print verbose information about the ` Tensor`,\n * including dtype and size.\n *\n * @doc {heading: 'Tensors', subheading: 'Creation'}\n */\nexport function print(x, verbose = false) {\n console.log(x.toString(verbose));\n}\n//# sourceMappingURL=print.js.map","/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * IOHandlers related to files, such as browser-triggered file downloads,\n * user-selected files in browser.\n */\nimport '../flags';\nimport { env } from '../environment';\nimport { basename, concatenateArrayBuffers, getModelArtifactsInfoForJSON } from './io_utils';\nimport { IORouterRegistry } from './router_registry';\nconst DEFAULT_FILE_NAME_PREFIX = 'model';\nconst DEFAULT_JSON_EXTENSION_NAME = '.json';\nconst DEFAULT_WEIGHT_DATA_EXTENSION_NAME = '.weights.bin';\nfunction defer(f) {\n return new Promise(resolve => setTimeout(resolve)).then(f);\n}\nexport class BrowserDownloads {\n constructor(fileNamePrefix) {\n if (!env().getBool('IS_BROWSER')) {\n // TODO(cais): Provide info on what IOHandlers are available under the\n // current environment.\n throw new Error('browserDownloads() cannot proceed because the current environment ' +\n 'is not a browser.');\n }\n if (fileNamePrefix.startsWith(BrowserDownloads.URL_SCHEME)) {\n fileNamePrefix = fileNamePrefix.slice(BrowserDownloads.URL_SCHEME.length);\n }\n if (fileNamePrefix == null || fileNamePrefix.length === 0) {\n fileNamePrefix = DEFAULT_FILE_NAME_PREFIX;\n }\n this.modelTopologyFileName = fileNamePrefix + DEFAULT_JSON_EXTENSION_NAME;\n this.weightDataFileName =\n fileNamePrefix + DEFAULT_WEIGHT_DATA_EXTENSION_NAME;\n }\n async save(modelArtifacts) {\n if (typeof (document) === 'undefined') {\n throw new Error('Browser downloads are not supported in ' +\n 'this environment since `document` is not present');\n }\n const weightsURL = window.URL.createObjectURL(new Blob([modelArtifacts.weightData], { type: 'application/octet-stream' }));\n if (modelArtifacts.modelTopology instanceof ArrayBuffer) {\n throw new Error('BrowserDownloads.save() does not support saving model topology ' +\n 'in binary formats yet.');\n }\n else {\n const weightsManifest = [{\n paths: ['./' + this.weightDataFileName],\n weights: modelArtifacts.weightSpecs\n }];\n const modelTopologyAndWeightManifest = {\n modelTopology: modelArtifacts.modelTopology,\n format: modelArtifacts.format,\n generatedBy: modelArtifacts.generatedBy,\n convertedBy: modelArtifacts.convertedBy,\n weightsManifest\n };\n if (modelArtifacts.signature != null) {\n modelTopologyAndWeightManifest.signature = modelArtifacts.signature;\n }\n if (modelArtifacts.userDefinedMetadata != null) {\n modelTopologyAndWeightManifest.userDefinedMetadata =\n modelArtifacts.userDefinedMetadata;\n }\n if (modelArtifacts.modelInitializer != null) {\n modelTopologyAndWeightManifest.modelInitializer =\n modelArtifacts.modelInitializer;\n }\n const modelTopologyAndWeightManifestURL = window.URL.createObjectURL(new Blob([JSON.stringify(modelTopologyAndWeightManifest)], { type: 'application/json' }));\n // If anchor elements are not provided, create them without attaching them\n // to parents, so that the downloaded file names can be controlled.\n const jsonAnchor = this.jsonAnchor == null ? document.createElement('a') :\n this.jsonAnchor;\n jsonAnchor.download = this.modelTopologyFileName;\n jsonAnchor.href = modelTopologyAndWeightManifestURL;\n // Trigger downloads by evoking a click event on the download anchors.\n // When multiple downloads are started synchronously, Firefox will only\n // save the last one.\n await defer(() => jsonAnchor.dispatchEvent(new MouseEvent('click')));\n if (modelArtifacts.weightData != null) {\n const weightDataAnchor = this.weightDataAnchor == null ?\n document.createElement('a') :\n this.weightDataAnchor;\n weightDataAnchor.download = this.weightDataFileName;\n weightDataAnchor.href = weightsURL;\n await defer(() => weightDataAnchor.dispatchEvent(new MouseEvent('click')));\n }\n return { modelArtifactsInfo: getModelArtifactsInfoForJSON(modelArtifacts) };\n }\n }\n}\nBrowserDownloads.URL_SCHEME = 'downloads://';\nclass BrowserFiles {\n constructor(files) {\n if (files == null || files.length < 1) {\n throw new Error(`When calling browserFiles, at least 1 file is required, ` +\n `but received ${files}`);\n }\n this.files = files;\n }\n async load() {\n const jsonFile = this.files[0];\n const weightFiles = this.files.slice(1);\n return new Promise((resolve, reject) => {\n const jsonReader = new FileReader();\n jsonReader.onload = (event) => {\n // tslint:disable-next-line:no-any\n const modelJSON = JSON.parse(event.target.result);\n const modelTopology = modelJSON.modelTopology;\n if (modelTopology == null) {\n reject(new Error(`modelTopology field is missing from file ${jsonFile.name}`));\n return;\n }\n if (weightFiles.length === 0) {\n resolve({ modelTopology });\n }\n const weightsManifest = modelJSON.weightsManifest;\n if (weightsManifest == null) {\n reject(new Error(`weightManifest field is missing from file ${jsonFile.name}`));\n return;\n }\n let pathToFile;\n try {\n pathToFile =\n this.checkManifestAndWeightFiles(weightsManifest, weightFiles);\n }\n catch (err) {\n reject(err);\n return;\n }\n const weightSpecs = [];\n const paths = [];\n const perFileBuffers = [];\n weightsManifest.forEach(weightsGroup => {\n weightsGroup.paths.forEach(path => {\n paths.push(path);\n perFileBuffers.push(null);\n });\n weightSpecs.push(...weightsGroup.weights);\n });\n weightsManifest.forEach(weightsGroup => {\n weightsGroup.paths.forEach(path => {\n const weightFileReader = new FileReader();\n weightFileReader.onload = (event) => {\n // tslint:disable-next-line:no-any\n const weightData = event.target.result;\n const index = paths.indexOf(path);\n perFileBuffers[index] = weightData;\n if (perFileBuffers.indexOf(null) === -1) {\n const result = {\n modelTopology,\n weightSpecs,\n weightData: concatenateArrayBuffers(perFileBuffers),\n format: modelJSON.format,\n generatedBy: modelJSON.generatedBy,\n convertedBy: modelJSON.convertedBy\n };\n if (modelJSON.signature != null) {\n result.signature = modelJSON.signature;\n }\n if (modelJSON.userDefinedMetadata != null) {\n result.userDefinedMetadata = modelJSON.userDefinedMetadata;\n }\n if (modelJSON.modelInitializer != null) {\n result.modelInitializer = modelJSON.modelInitializer;\n }\n resolve(result);\n }\n };\n weightFileReader.onerror = error => reject(`Failed to weights data from file of path '${path}'.`);\n weightFileReader.readAsArrayBuffer(pathToFile[path]);\n });\n });\n };\n jsonReader.onerror = error => reject(`Failed to read model topology and weights manifest JSON ` +\n `from file '${jsonFile.name}'. BrowserFiles supports loading ` +\n `Keras-style tf.Model artifacts only.`);\n jsonReader.readAsText(jsonFile);\n });\n }\n /**\n * Check the compatibility between weights manifest and weight files.\n */\n checkManifestAndWeightFiles(manifest, files) {\n const basenames = [];\n const fileNames = files.map(file => basename(file.name));\n const pathToFile = {};\n for (const group of manifest) {\n group.paths.forEach(path => {\n const pathBasename = basename(path);\n if (basenames.indexOf(pathBasename) !== -1) {\n throw new Error(`Duplicate file basename found in weights manifest: ` +\n `'${pathBasename}'`);\n }\n basenames.push(pathBasename);\n if (fileNames.indexOf(pathBasename) === -1) {\n throw new Error(`Weight file with basename '${pathBasename}' is not provided.`);\n }\n else {\n pathToFile[path] = files[fileNames.indexOf(pathBasename)];\n }\n });\n }\n if (basenames.length !== files.length) {\n throw new Error(`Mismatch in the number of files in weights manifest ` +\n `(${basenames.length}) and the number of weight files provided ` +\n `(${files.length}).`);\n }\n return pathToFile;\n }\n}\nexport const browserDownloadsRouter = (url) => {\n if (!env().getBool('IS_BROWSER')) {\n return null;\n }\n else {\n if (!Array.isArray(url) && url.startsWith(BrowserDownloads.URL_SCHEME)) {\n return browserDownloads(url.slice(BrowserDownloads.URL_SCHEME.length));\n }\n else {\n return null;\n }\n }\n};\nIORouterRegistry.registerSaveRouter(browserDownloadsRouter);\n/**\n * Creates an IOHandler that triggers file downloads from the browser.\n *\n * The returned `IOHandler` instance can be used as model exporting methods such\n * as `tf.Model.save` and supports only saving.\n *\n * ```js\n * const model = tf.sequential();\n * model.add(tf.layers.dense(\n * {units: 1, inputShape: [10], activation: 'sigmoid'}));\n * const saveResult = await model.save('downloads://mymodel');\n * // This will trigger downloading of two files:\n * // 'mymodel.json' and 'mymodel.weights.bin'.\n * console.log(saveResult);\n * ```\n *\n * @param fileNamePrefix Prefix name of the files to be downloaded. For use with\n * `tf.Model`, `fileNamePrefix` should follow either of the following two\n * formats:\n * 1. `null` or `undefined`, in which case the default file\n * names will be used:\n * - 'model.json' for the JSON file containing the model topology and\n * weights manifest.\n * - 'model.weights.bin' for the binary file containing the binary weight\n * values.\n * 2. A single string or an Array of a single string, as the file name prefix.\n * For example, if `'foo'` is provided, the downloaded JSON\n * file and binary weights file will be named 'foo.json' and\n * 'foo.weights.bin', respectively.\n * @param config Additional configuration for triggering downloads.\n * @returns An instance of `BrowserDownloads` `IOHandler`.\n *\n * @doc {\n * heading: 'Models',\n * subheading: 'Loading',\n * namespace: 'io',\n * ignoreCI: true\n * }\n */\nexport function browserDownloads(fileNamePrefix = 'model') {\n return new BrowserDownloads(fileNamePrefix);\n}\n/**\n * Creates an IOHandler that loads model artifacts from user-selected files.\n *\n * This method can be used for loading from files such as user-selected files\n * in the browser.\n * When used in conjunction with `tf.loadLayersModel`, an instance of\n * `tf.LayersModel` (Keras-style) can be constructed from the loaded artifacts.\n *\n * ```js\n * // Note: This code snippet won't run properly without the actual file input\n * // elements in the HTML DOM.\n *\n * // Suppose there are two HTML file input (``)\n * // elements.\n * const uploadJSONInput = document.getElementById('upload-json');\n * const uploadWeightsInput = document.getElementById('upload-weights');\n * const model = await tf.loadLayersModel(tf.io.browserFiles(\n * [uploadJSONInput.files[0], uploadWeightsInput.files[0]]));\n * ```\n *\n * @param files `File`s to load from. Currently, this function supports only\n * loading from files that contain Keras-style models (i.e., `tf.Model`s), for\n * which an `Array` of `File`s is expected (in that order):\n * - A JSON file containing the model topology and weight manifest.\n * - Optionally, One or more binary files containing the binary weights.\n * These files must have names that match the paths in the `weightsManifest`\n * contained by the aforementioned JSON file, or errors will be thrown\n * during loading. These weights files have the same format as the ones\n * generated by `tensorflowjs_converter` that comes with the `tensorflowjs`\n * Python PIP package. If no weights files are provided, only the model\n * topology will be loaded from the JSON file above.\n * @returns An instance of `Files` `IOHandler`.\n *\n * @doc {\n * heading: 'Models',\n * subheading: 'Loading',\n * namespace: 'io',\n * ignoreCI: true\n * }\n */\nexport function browserFiles(files) {\n return new BrowserFiles(files);\n}\n//# sourceMappingURL=browser_files.js.map","/**\n * @license\n * Copyright 2020 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n// Required side effectful code for tfjs-core\n// Set up Engine and ENV\nimport { getOrMakeEngine } from './engine';\ngetOrMakeEngine();\n// Register backend-agnostic flags.\nimport './flags';\n// Register platforms\nimport './platforms/platform_browser';\nimport './platforms/platform_node';\n// Set up OpHandler\nimport { buffer } from './ops/buffer';\nimport { cast } from './ops/cast';\nimport { clone } from './ops/clone';\nimport { print } from './ops/print';\nimport { setOpHandler } from './tensor';\nconst opHandler = {\n buffer,\n cast,\n clone,\n print\n};\nsetOpHandler(opHandler);\n//# sourceMappingURL=base_side_effects.js.map","/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { assert } from '../util';\n/**\n * Monitor Promise.all progress, fire onProgress callback function.\n *\n * @param promises Promise list going to be monitored\n * @param onProgress Callback function. Fired when a promise resolved.\n * @param startFraction Optional fraction start. Default to 0.\n * @param endFraction Optional fraction end. Default to 1.\n */\nexport function monitorPromisesProgress(promises, onProgress, startFraction, endFraction) {\n checkPromises(promises);\n startFraction = startFraction == null ? 0 : startFraction;\n endFraction = endFraction == null ? 1 : endFraction;\n checkFraction(startFraction, endFraction);\n let resolvedPromise = 0;\n const registerMonitor = (promise) => {\n promise.then(value => {\n const fraction = startFraction +\n ++resolvedPromise / promises.length * (endFraction - startFraction);\n // pass fraction as parameter to callback function.\n onProgress(fraction);\n return value;\n });\n return promise;\n };\n function checkPromises(promises) {\n assert(promises != null && Array.isArray(promises) && promises.length > 0, () => 'promises must be a none empty array');\n }\n function checkFraction(startFraction, endFraction) {\n assert(startFraction >= 0 && startFraction <= 1, () => `Progress fraction must be in range [0, 1], but ` +\n `got startFraction ${startFraction}`);\n assert(endFraction >= 0 && endFraction <= 1, () => `Progress fraction must be in range [0, 1], but ` +\n `got endFraction ${endFraction}`);\n assert(endFraction >= startFraction, () => `startFraction must be no more than endFraction, but ` +\n `got startFraction ${startFraction} and endFraction ` +\n `${endFraction}`);\n }\n return Promise.all(promises.map(registerMonitor));\n}\n//# sourceMappingURL=progress.js.map","/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { env } from '../environment';\nimport * as util from '../util';\nimport { decodeWeights } from './io_utils';\nimport { monitorPromisesProgress } from './progress';\nimport { DTYPE_VALUE_SIZE_MAP } from './types';\n/**\n * Reads binary weights data from a number of URLs.\n *\n * @param fetchURLs URLs to send the HTTP requests at, using `fetch` calls.\n * @param requestOptions RequestInit (options) for the HTTP requests.\n * @param fetchFunc Optional overriding value for the `window.fetch` function.\n * @param onProgress Optional, progress callback function, fired periodically\n * before the load is completed.\n * @returns A `Promise` of an Array of `ArrayBuffer`. The Array has the same\n * length as `fetchURLs`.\n */\nexport async function loadWeightsAsArrayBuffer(fetchURLs, loadOptions) {\n if (loadOptions == null) {\n loadOptions = {};\n }\n const fetchFunc = loadOptions.fetchFunc == null ? env().platform.fetch :\n loadOptions.fetchFunc;\n // Create the requests for all of the weights in parallel.\n const requests = fetchURLs.map(fetchURL => fetchFunc(fetchURL, loadOptions.requestInit, { isBinary: true }));\n const fetchStartFraction = 0;\n const fetchEndFraction = 0.5;\n const responses = loadOptions.onProgress == null ?\n await Promise.all(requests) :\n await monitorPromisesProgress(requests, loadOptions.onProgress, fetchStartFraction, fetchEndFraction);\n const bufferPromises = responses.map(response => response.arrayBuffer());\n const bufferStartFraction = 0.5;\n const bufferEndFraction = 1;\n const buffers = loadOptions.onProgress == null ?\n await Promise.all(bufferPromises) :\n await monitorPromisesProgress(bufferPromises, loadOptions.onProgress, bufferStartFraction, bufferEndFraction);\n return buffers;\n}\n/**\n * Reads a weights manifest JSON configuration, fetches the weights and\n * returns them as `Tensor`s.\n *\n * @param manifest The weights manifest JSON.\n * @param filePathPrefix The path prefix for filenames given in the manifest.\n * Defaults to the empty string.\n * @param weightNames The names of the weights to be fetched.\n */\nexport async function loadWeights(manifest, filePathPrefix = '', weightNames, requestInit) {\n // TODO(nsthorat): Groups are currently fetched atomically. If you need a\n // single weight from a group, the whole group will be fetched. At a future\n // date, we should support fetching only the individual shards within a\n // group that are needed to reconstruct the requested weight.\n // TODO(cais): Use `decodeWeights` for implementation.\n const fetchWeights = (fetchUrls) => loadWeightsAsArrayBuffer(fetchUrls, { requestInit });\n const loadWeights = weightsLoaderFactory(fetchWeights);\n return loadWeights(manifest, filePathPrefix, weightNames);\n}\n/**\n * Creates a function, which reads a weights manifest JSON configuration,\n * fetches the weight files using the specified function and returns them as\n * `Tensor`s.\n *\n * ```js\n * // example for creating a nodejs weight loader, which reads the weight files\n * // from disk using fs.readFileSync\n *\n * import * as fs from 'fs'\n *\n * const fetchWeightsFromDisk = (filePaths: string[]) =>\n * filePaths.map(filePath => fs.readFileSync(filePath).buffer)\n *\n * const loadWeights = tf.io.weightsLoaderFactory(fetchWeightsFromDisk)\n *\n * const manifest = JSON.parse(\n * fs.readFileSync('./my_model-weights_manifest').toString()\n * )\n * const weightMap = await loadWeights(manifest, './')\n * ```\n * @param fetchWeightsFunction The function used for fetching the weight files.\n * @returns Weight loading function.\n */\nexport function weightsLoaderFactory(fetchWeightsFunction) {\n return async (manifest, filePathPrefix = '', weightNames) => {\n // Collect all the groups, weights, and their relative offsets to be\n // fetched.\n const groupIndicesToFetchMap = manifest.map(() => false);\n const groupWeightsToFetch = {};\n const weightsFound = weightNames != null ? weightNames.map(() => false) : [];\n const allManifestWeightNames = [];\n manifest.forEach((manifestGroupConfig, groupIndex) => {\n let groupOffset = 0;\n manifestGroupConfig.weights.forEach(weightsEntry => {\n const rawDtype = ('quantization' in weightsEntry) ?\n weightsEntry.quantization.dtype :\n weightsEntry.dtype;\n const weightsBytes = DTYPE_VALUE_SIZE_MAP[rawDtype] *\n util.sizeFromShape(weightsEntry.shape);\n const enqueueWeightsForFetchingFn = () => {\n groupIndicesToFetchMap[groupIndex] = true;\n if (groupWeightsToFetch[groupIndex] == null) {\n groupWeightsToFetch[groupIndex] = [];\n }\n groupWeightsToFetch[groupIndex].push({\n manifestEntry: weightsEntry,\n groupOffset,\n sizeBytes: weightsBytes\n });\n };\n if (weightNames != null) {\n weightNames.forEach((weightName, weightIndex) => {\n if (weightName === weightsEntry.name) {\n enqueueWeightsForFetchingFn();\n weightsFound[weightIndex] = true;\n }\n });\n }\n else {\n enqueueWeightsForFetchingFn();\n }\n allManifestWeightNames.push(weightsEntry.name);\n groupOffset += weightsBytes;\n });\n });\n if (!weightsFound.every(found => found)) {\n const weightsNotFound = weightNames.filter((_, i) => !weightsFound[i]);\n throw new Error(`Could not find weights in manifest with names: ` +\n `${weightsNotFound.join(', ')}. \\n` +\n `Manifest JSON has weights with names: ` +\n `${allManifestWeightNames.join(', ')}.`);\n }\n // Convert the one-hot boolean groupId => shouldFetch map to a list of group\n // IDs.\n const groupIndicesToFetch = groupIndicesToFetchMap.reduce((accumulator, shouldFetch, i) => {\n if (shouldFetch) {\n accumulator.push(i);\n }\n return accumulator;\n }, []);\n const fetchUrls = [];\n groupIndicesToFetch.forEach(i => {\n manifest[i].paths.forEach(filepath => {\n const fetchUrl = filePathPrefix +\n (!filePathPrefix.endsWith('/') ? '/' : '') + filepath;\n fetchUrls.push(fetchUrl);\n });\n });\n const buffers = await fetchWeightsFunction(fetchUrls);\n const weightsTensorMap = {};\n let bufferIndexOffset = 0;\n groupIndicesToFetch.forEach(i => {\n const numBuffers = manifest[i].paths.length;\n let groupBytes = 0;\n for (let i = 0; i < numBuffers; i++) {\n groupBytes += buffers[bufferIndexOffset + i].byteLength;\n }\n // Create a buffer for the whole group.\n const groupBuffer = new ArrayBuffer(groupBytes);\n const groupByteBuffer = new Uint8Array(groupBuffer);\n let groupBufferOffset = 0;\n for (let i = 0; i < numBuffers; i++) {\n const buffer = new Uint8Array(buffers[bufferIndexOffset + i]);\n groupByteBuffer.set(buffer, groupBufferOffset);\n groupBufferOffset += buffer.byteLength;\n }\n const weightsEntries = groupWeightsToFetch[i];\n weightsEntries.forEach(weightsEntry => {\n const byteBuffer = groupBuffer.slice(weightsEntry.groupOffset, weightsEntry.groupOffset + weightsEntry.sizeBytes);\n const nameToTensorMap = decodeWeights(byteBuffer, [weightsEntry.manifestEntry]);\n for (const name in nameToTensorMap) {\n weightsTensorMap[name] = nameToTensorMap[name];\n }\n });\n bufferIndexOffset += numBuffers;\n });\n return weightsTensorMap;\n };\n}\n//# sourceMappingURL=weights_loader.js.map","/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * IOHandler implementations based on HTTP requests in the web browser.\n *\n * Uses [`fetch`](https://developer.mozilla.org/en-US/docs/Web/API/Fetch_API).\n */\nimport { env } from '../environment';\nimport { assert } from '../util';\nimport { concatenateArrayBuffers, getModelArtifactsInfoForJSON } from './io_utils';\nimport { IORouterRegistry } from './router_registry';\nimport { loadWeightsAsArrayBuffer } from './weights_loader';\nconst OCTET_STREAM_MIME_TYPE = 'application/octet-stream';\nconst JSON_TYPE = 'application/json';\nexport class HTTPRequest {\n constructor(path, loadOptions) {\n this.DEFAULT_METHOD = 'POST';\n if (loadOptions == null) {\n loadOptions = {};\n }\n this.weightPathPrefix = loadOptions.weightPathPrefix;\n this.onProgress = loadOptions.onProgress;\n this.weightUrlConverter = loadOptions.weightUrlConverter;\n if (loadOptions.fetchFunc != null) {\n assert(typeof loadOptions.fetchFunc === 'function', () => 'Must pass a function that matches the signature of ' +\n '`fetch` (see ' +\n 'https://developer.mozilla.org/en-US/docs/Web/API/Fetch_API)');\n this.fetch = loadOptions.fetchFunc;\n }\n else {\n this.fetch = env().platform.fetch;\n }\n assert(path != null && path.length > 0, () => 'URL path for http must not be null, undefined or ' +\n 'empty.');\n if (Array.isArray(path)) {\n assert(path.length === 2, () => 'URL paths for http must have a length of 2, ' +\n `(actual length is ${path.length}).`);\n }\n this.path = path;\n if (loadOptions.requestInit != null &&\n loadOptions.requestInit.body != null) {\n throw new Error('requestInit is expected to have no pre-existing body, but has one.');\n }\n this.requestInit = loadOptions.requestInit || {};\n }\n async save(modelArtifacts) {\n if (modelArtifacts.modelTopology instanceof ArrayBuffer) {\n throw new Error('BrowserHTTPRequest.save() does not support saving model topology ' +\n 'in binary formats yet.');\n }\n const init = Object.assign({ method: this.DEFAULT_METHOD }, this.requestInit);\n init.body = new FormData();\n const weightsManifest = [{\n paths: ['./model.weights.bin'],\n weights: modelArtifacts.weightSpecs,\n }];\n const modelTopologyAndWeightManifest = {\n modelTopology: modelArtifacts.modelTopology,\n format: modelArtifacts.format,\n generatedBy: modelArtifacts.generatedBy,\n convertedBy: modelArtifacts.convertedBy,\n weightsManifest\n };\n if (modelArtifacts.signature != null) {\n modelTopologyAndWeightManifest.signature = modelArtifacts.signature;\n }\n if (modelArtifacts.userDefinedMetadata != null) {\n modelTopologyAndWeightManifest.userDefinedMetadata =\n modelArtifacts.userDefinedMetadata;\n }\n if (modelArtifacts.modelInitializer != null) {\n modelTopologyAndWeightManifest.modelInitializer =\n modelArtifacts.modelInitializer;\n }\n init.body.append('model.json', new Blob([JSON.stringify(modelTopologyAndWeightManifest)], { type: JSON_TYPE }), 'model.json');\n if (modelArtifacts.weightData != null) {\n init.body.append('model.weights.bin', new Blob([modelArtifacts.weightData], { type: OCTET_STREAM_MIME_TYPE }), 'model.weights.bin');\n }\n const response = await this.fetch(this.path, init);\n if (response.ok) {\n return {\n modelArtifactsInfo: getModelArtifactsInfoForJSON(modelArtifacts),\n responses: [response],\n };\n }\n else {\n throw new Error(`BrowserHTTPRequest.save() failed due to HTTP response status ` +\n `${response.status}.`);\n }\n }\n /**\n * Load model artifacts via HTTP request(s).\n *\n * See the documentation to `tf.io.http` for details on the saved\n * artifacts.\n *\n * @returns The loaded model artifacts (if loading succeeds).\n */\n async load() {\n const modelConfigRequest = await this.fetch(this.path, this.requestInit);\n if (!modelConfigRequest.ok) {\n throw new Error(`Request to ${this.path} failed with status code ` +\n `${modelConfigRequest.status}. Please verify this URL points to ` +\n `the model JSON of the model to load.`);\n }\n let modelConfig;\n try {\n modelConfig = await modelConfigRequest.json();\n }\n catch (e) {\n let message = `Failed to parse model JSON of response from ${this.path}.`;\n // TODO(nsthorat): Remove this after some time when we're comfortable that\n // .pb files are mostly gone.\n if (this.path.endsWith('.pb')) {\n message += ' Your path contains a .pb file extension. ' +\n 'Support for .pb models have been removed in TensorFlow.js 1.0 ' +\n 'in favor of .json models. You can re-convert your Python ' +\n 'TensorFlow model using the TensorFlow.js 1.0 conversion scripts ' +\n 'or you can convert your.pb models with the \\'pb2json\\'' +\n 'NPM script in the tensorflow/tfjs-converter repository.';\n }\n else {\n message += ' Please make sure the server is serving valid ' +\n 'JSON for this request.';\n }\n throw new Error(message);\n }\n const modelTopology = modelConfig.modelTopology;\n const weightsManifest = modelConfig.weightsManifest;\n const generatedBy = modelConfig.generatedBy;\n const convertedBy = modelConfig.convertedBy;\n const format = modelConfig.format;\n const signature = modelConfig.signature;\n const userDefinedMetadata = modelConfig.userDefinedMetadata;\n // We do not allow both modelTopology and weightsManifest to be missing.\n if (modelTopology == null && weightsManifest == null) {\n throw new Error(`The JSON from HTTP path ${this.path} contains neither model ` +\n `topology or manifest for weights.`);\n }\n let weightSpecs;\n let weightData;\n if (weightsManifest != null) {\n const results = await this.loadWeights(weightsManifest);\n [weightSpecs, weightData] = results;\n }\n const artifacts = {\n modelTopology,\n weightSpecs,\n weightData,\n generatedBy,\n convertedBy,\n format\n };\n if (signature != null) {\n artifacts.signature = signature;\n }\n if (userDefinedMetadata != null) {\n artifacts.userDefinedMetadata = userDefinedMetadata;\n }\n const initializer = modelConfig.modelInitializer;\n if (initializer) {\n artifacts.modelInitializer = initializer;\n }\n return artifacts;\n }\n async loadWeights(weightsManifest) {\n const weightPath = Array.isArray(this.path) ? this.path[1] : this.path;\n const [prefix, suffix] = parseUrl(weightPath);\n const pathPrefix = this.weightPathPrefix || prefix;\n const weightSpecs = [];\n for (const entry of weightsManifest) {\n weightSpecs.push(...entry.weights);\n }\n const fetchURLs = [];\n const urlPromises = [];\n for (const weightsGroup of weightsManifest) {\n for (const path of weightsGroup.paths) {\n if (this.weightUrlConverter != null) {\n urlPromises.push(this.weightUrlConverter(path));\n }\n else {\n fetchURLs.push(pathPrefix + path + suffix);\n }\n }\n }\n if (this.weightUrlConverter) {\n fetchURLs.push(...await Promise.all(urlPromises));\n }\n const buffers = await loadWeightsAsArrayBuffer(fetchURLs, {\n requestInit: this.requestInit,\n fetchFunc: this.fetch,\n onProgress: this.onProgress\n });\n return [weightSpecs, concatenateArrayBuffers(buffers)];\n }\n}\nHTTPRequest.URL_SCHEME_REGEX = /^https?:\\/\\//;\n/**\n * Extract the prefix and suffix of the url, where the prefix is the path before\n * the last file, and suffix is the search params after the last file.\n * ```\n * const url = 'http://tfhub.dev/model/1/tensorflowjs_model.pb?tfjs-format=file'\n * [prefix, suffix] = parseUrl(url)\n * // prefix = 'http://tfhub.dev/model/1/'\n * // suffix = '?tfjs-format=file'\n * ```\n * @param url the model url to be parsed.\n */\nexport function parseUrl(url) {\n const lastSlash = url.lastIndexOf('/');\n const lastSearchParam = url.lastIndexOf('?');\n const prefix = url.substring(0, lastSlash);\n const suffix = lastSearchParam > lastSlash ? url.substring(lastSearchParam) : '';\n return [prefix + '/', suffix];\n}\nexport function isHTTPScheme(url) {\n return url.match(HTTPRequest.URL_SCHEME_REGEX) != null;\n}\nexport const httpRouter = (url, loadOptions) => {\n if (typeof fetch === 'undefined' &&\n (loadOptions == null || loadOptions.fetchFunc == null)) {\n // `http` uses `fetch` or `node-fetch`, if one wants to use it in\n // an environment that is not the browser or node they have to setup a\n // global fetch polyfill.\n return null;\n }\n else {\n let isHTTP = true;\n if (Array.isArray(url)) {\n isHTTP = url.every(urlItem => isHTTPScheme(urlItem));\n }\n else {\n isHTTP = isHTTPScheme(url);\n }\n if (isHTTP) {\n return http(url, loadOptions);\n }\n }\n return null;\n};\nIORouterRegistry.registerSaveRouter(httpRouter);\nIORouterRegistry.registerLoadRouter(httpRouter);\n/**\n * Creates an IOHandler subtype that sends model artifacts to HTTP server.\n *\n * An HTTP request of the `multipart/form-data` mime type will be sent to the\n * `path` URL. The form data includes artifacts that represent the topology\n * and/or weights of the model. In the case of Keras-style `tf.Model`, two\n * blobs (files) exist in form-data:\n * - A JSON file consisting of `modelTopology` and `weightsManifest`.\n * - A binary weights file consisting of the concatenated weight values.\n * These files are in the same format as the one generated by\n * [tfjs_converter](https://js.tensorflow.org/tutorials/import-keras.html).\n *\n * The following code snippet exemplifies the client-side code that uses this\n * function:\n *\n * ```js\n * const model = tf.sequential();\n * model.add(\n * tf.layers.dense({units: 1, inputShape: [100], activation: 'sigmoid'}));\n *\n * const saveResult = await model.save(tf.io.http(\n * 'http://model-server:5000/upload', {requestInit: {method: 'PUT'}}));\n * console.log(saveResult);\n * ```\n *\n * If the default `POST` method is to be used, without any custom parameters\n * such as headers, you can simply pass an HTTP or HTTPS URL to `model.save`:\n *\n * ```js\n * const saveResult = await model.save('http://model-server:5000/upload');\n * ```\n *\n * The following GitHub Gist\n * https://gist.github.com/dsmilkov/1b6046fd6132d7408d5257b0976f7864\n * implements a server based on [flask](https://github.com/pallets/flask) that\n * can receive the request. Upon receiving the model artifacts via the requst,\n * this particular server reconsistutes instances of [Keras\n * Models](https://keras.io/models/model/) in memory.\n *\n *\n * @param path A URL path to the model.\n * Can be an absolute HTTP path (e.g.,\n * 'http://localhost:8000/model-upload)') or a relative path (e.g.,\n * './model-upload').\n * @param requestInit Request configurations to be used when sending\n * HTTP request to server using `fetch`. It can contain fields such as\n * `method`, `credentials`, `headers`, `mode`, etc. See\n * https://developer.mozilla.org/en-US/docs/Web/API/Request/Request\n * for more information. `requestInit` must not have a body, because the\n * body will be set by TensorFlow.js. File blobs representing the model\n * topology (filename: 'model.json') and the weights of the model (filename:\n * 'model.weights.bin') will be appended to the body. If `requestInit` has a\n * `body`, an Error will be thrown.\n * @param loadOptions Optional configuration for the loading. It includes the\n * following fields:\n * - weightPathPrefix Optional, this specifies the path prefix for weight\n * files, by default this is calculated from the path param.\n * - fetchFunc Optional, custom `fetch` function. E.g., in Node.js,\n * the `fetch` from node-fetch can be used here.\n * - onProgress Optional, progress callback function, fired periodically\n * before the load is completed.\n * @returns An instance of `IOHandler`.\n *\n * @doc {\n * heading: 'Models',\n * subheading: 'Loading',\n * namespace: 'io',\n * ignoreCI: true\n * }\n */\nexport function http(path, loadOptions) {\n return new HTTPRequest(path, loadOptions);\n}\n/**\n * Deprecated. Use `tf.io.http`.\n * @param path\n * @param loadOptions\n */\nexport function browserHTTPRequest(path, loadOptions) {\n return http(path, loadOptions);\n}\n//# sourceMappingURL=http.js.map","/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nclass PassthroughLoader {\n constructor(modelArtifacts) {\n this.modelArtifacts = modelArtifacts;\n }\n async load() {\n return this.modelArtifacts;\n }\n}\nclass PassthroughSaver {\n constructor(saveHandler) {\n this.saveHandler = saveHandler;\n }\n async save(modelArtifacts) {\n return this.saveHandler(modelArtifacts);\n }\n}\n/**\n * Creates an IOHandler that loads model artifacts from memory.\n *\n * When used in conjunction with `tf.loadLayersModel`, an instance of\n * `tf.LayersModel` (Keras-style) can be constructed from the loaded artifacts.\n *\n * ```js\n * const model = await tf.loadLayersModel(tf.io.fromMemory(\n * modelTopology, weightSpecs, weightData));\n * ```\n *\n * @param modelArtifacts a object containing model topology (i.e., parsed from\n * the JSON format).\n * @param weightSpecs An array of `WeightsManifestEntry` objects describing the\n * names, shapes, types, and quantization of the weight data.\n * @param weightData A single `ArrayBuffer` containing the weight data,\n * concatenated in the order described by the weightSpecs.\n * @param trainingConfig Model training configuration. Optional.\n *\n * @returns A passthrough `IOHandler` that simply loads the provided data.\n */\nexport function fromMemory(modelArtifacts, weightSpecs, weightData, trainingConfig) {\n if (arguments.length === 1) {\n const isModelArtifacts = modelArtifacts.modelTopology != null ||\n modelArtifacts.weightSpecs != null;\n if (isModelArtifacts) {\n return new PassthroughLoader(modelArtifacts);\n }\n else {\n // Legacy support: with only modelTopology.\n // TODO(cais): Remove this deprecated API.\n console.warn('Please call tf.io.fromMemory() with only one argument. ' +\n 'The argument should be of type ModelArtifacts. ' +\n 'The multi-argument signature of tf.io.fromMemory() has been ' +\n 'deprecated and will be removed in a future release.');\n return new PassthroughLoader({ modelTopology: modelArtifacts });\n }\n }\n else {\n // Legacy support.\n // TODO(cais): Remove this deprecated API.\n console.warn('Please call tf.io.fromMemory() with only one argument. ' +\n 'The argument should be of type ModelArtifacts. ' +\n 'The multi-argument signature of tf.io.fromMemory() has been ' +\n 'deprecated and will be removed in a future release.');\n return new PassthroughLoader({\n modelTopology: modelArtifacts,\n weightSpecs,\n weightData,\n trainingConfig\n });\n }\n}\n/**\n * Creates an IOHandler that passes saved model artifacts to a callback.\n *\n * ```js\n * function handleSave(artifacts) {\n * // ... do something with the artifacts ...\n * return {modelArtifactsInfo: {...}, ...};\n * }\n *\n * const saveResult = model.save(tf.io.withSaveHandler(handleSave));\n * ```\n *\n * @param saveHandler A function that accepts a `ModelArtifacts` and returns a\n * `SaveResult`.\n */\nexport function withSaveHandler(saveHandler) {\n return new PassthroughSaver(saveHandler);\n}\n//# sourceMappingURL=passthrough.js.map","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { BatchMatMul } from '../kernel_names';\nimport { makeTypesMatch } from '../tensor_util';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Computes the dot product of two matrices, A * B. These must be matrices.\n *\n * ```js\n * const a = tf.tensor2d([1, 2], [1, 2]);\n * const b = tf.tensor2d([1, 2, 3, 4], [2, 2]);\n *\n * a.matMul(b).print(); // or tf.matMul(a, b)\n * ```\n * @param a First matrix in dot product operation.\n * @param b Second matrix in dot product operation.\n * @param transposeA If true, `a` is transposed before multiplication.\n * @param transposeB If true, `b` is transposed before multiplication.\n *\n * @doc {heading: 'Operations', subheading: 'Matrices'}\n */\nfunction matMul_(a, b, transposeA = false, transposeB = false) {\n let $a = convertToTensor(a, 'a', 'matMul');\n let $b = convertToTensor(b, 'b', 'matMul');\n [$a, $b] = makeTypesMatch($a, $b);\n const inputs = { a: $a, b: $b };\n const attrs = { transposeA, transposeB };\n return ENGINE.runKernel(BatchMatMul, inputs, attrs);\n}\nexport const matMul = op({ matMul_ });\n//# sourceMappingURL=mat_mul.js.map","/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { OneHot } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport { op } from './operation';\n/**\n * Creates a one-hot `tf.Tensor`. The locations represented by `indices` take\n * value `onValue` (defaults to 1), while all other locations take value\n * `offValue` (defaults to 0). If `indices` is rank `R`, the output has rank\n * `R+1` with the last axis of size `depth`.\n *\n * ```js\n * tf.oneHot(tf.tensor1d([0, 1], 'int32'), 3).print();\n * ```\n *\n * @param indices `tf.Tensor` of indices with dtype `int32`.\n * @param depth The depth of the one hot dimension.\n * @param onValue A number used to fill in the output when the index matches\n * the location.\n * @param offValue A number used to fill in the output when the index does\n * not match the location.\n *\n * @doc {heading: 'Tensors', subheading: 'Creation'}\n */\nfunction oneHot_(indices, depth, onValue = 1, offValue = 0) {\n if (depth < 2) {\n throw new Error(`Error in oneHot: depth must be >=2, but it is ${depth}`);\n }\n const $indices = convertToTensor(indices, 'indices', 'oneHot', 'int32');\n const inputs = { indices: $indices };\n const attrs = { depth, onValue, offValue };\n return ENGINE.runKernel(OneHot, inputs, attrs);\n}\nexport const oneHot = op({ oneHot_ });\n//# sourceMappingURL=one_hot.js.map","/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { Transpose } from '../kernel_names';\nimport { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport { op } from './operation';\n/**\n * Transposes the `tf.Tensor`. Permutes the dimensions according to `perm`.\n *\n * The returned `tf.Tensor`'s dimension `i` will correspond to the input\n * dimension `perm[i]`. If `perm` is not given, it is set to `[n-1...0]`,\n * where `n` is the rank of the input `tf.Tensor`. Hence by default, this\n * operation performs a regular matrix transpose on 2-D input `tf.Tensor`s.\n *\n * ```js\n * const a = tf.tensor2d([1, 2, 3, 4, 5, 6], [2, 3]);\n *\n * a.transpose().print(); // or tf.transpose(a)\n * ```\n *\n * @param x The tensor to transpose.\n * @param perm The permutation of the dimensions of a.\n *\n * @doc {heading: 'Operations', subheading: 'Matrices'}\n */\nfunction transpose_(x, perm) {\n const $x = convertToTensor(x, 'x', 'transpose');\n if (perm == null) {\n perm = $x.shape.map((s, i) => i).reverse();\n }\n util.assert($x.rank === perm.length, () => `Error in transpose: rank of input ${$x.rank} ` +\n `must match length of perm ${perm}.`);\n perm.forEach(axis => {\n util.assert(axis >= 0 && axis < $x.rank, () => `All entries in 'perm' must be between 0 and ${$x.rank - 1}` +\n ` but got ${perm}`);\n });\n if ($x.rank <= 1) {\n return $x.clone();\n }\n const inputs = { x: $x };\n const attrs = { perm };\n return ENGINE.runKernel(Transpose, inputs, attrs);\n}\nexport const transpose = op({ transpose_ });\n//# sourceMappingURL=transpose.js.map","/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { convertToTensor } from '../tensor_util_env';\nimport * as util from '../util';\nimport { cast } from './cast';\nimport { matMul } from './mat_mul';\nimport { oneHot } from './one_hot';\nimport { op } from './operation';\nimport { transpose } from './transpose';\n/**\n * Computes the confusion matrix from true labels and predicted labels.\n *\n * ```js\n * const labels = tf.tensor1d([0, 1, 2, 1, 0], 'int32');\n * const predictions = tf.tensor1d([0, 2, 2, 1, 0], 'int32');\n * const numClasses = 3;\n * const out = tf.math.confusionMatrix(labels, predictions, numClasses);\n * out.print();\n * // Expected output matrix:\n * // [[2, 0, 0],\n * // [0, 1, 1],\n * // [0, 0, 1]]\n * ```\n *\n * @param labels The target labels, assumed to be 0-based integers\n * for the classes. The shape is `[numExamples]`, where\n * `numExamples` is the number of examples included.\n * @param predictions The predicted classes, assumed to be\n * 0-based integers for the classes. Must have the same shape as `labels`.\n * @param numClasses Number of all classes, as an integer.\n * Its value must be larger than the largest element in `labels` and\n * `predictions`.\n * @returns The confusion matrix as a int32-type 2D tensor. The value at\n * row `r` and column `c` is the number of times examples of actual class\n * `r` were predicted as class `c`.\n *\n * @doc {heading: 'Operations', subheading: 'Evaluation'}\n */\nexport function confusionMatrix_(labels, predictions, numClasses) {\n const $labels = convertToTensor(labels, 'labels', 'confusionMatrix');\n const $predictions = convertToTensor(predictions, 'predictions', 'confusionMatrix');\n util.assert(numClasses == null || numClasses > 0 && Number.isInteger(numClasses), () => `If provided, numClasses must be a positive integer, ` +\n `but got ${numClasses}`);\n util.assert($labels.rank === 1, () => `Expected the rank of labels to be 1, but got ${$labels.rank}`);\n util.assert($predictions.rank === 1, () => `Expected the rank of predictions to be 1, ` +\n `but got ${$predictions.rank}`);\n util.assert($labels.shape[0] === $predictions.shape[0], () => `Mismatch in the number of examples: ` +\n `${$labels.shape[0]} vs. ${$predictions.shape[0]}. ` +\n `Labels and predictions should have the same number of elements.`);\n util.assert(numClasses > 0 && Number.isInteger(numClasses), () => `numClasses is required to be a positive integer, but got ` +\n `${numClasses}`);\n // TODO(cais): In the future, if oneHot supports tensors inputs for\n // `numClasses`, `confusionMatrix` can make `numClasses` optional.\n const oneHotLabels = oneHot(cast($labels, 'int32'), numClasses);\n const oneHotPredictions = oneHot(cast($predictions, 'int32'), numClasses);\n const oneHotLabelsT = transpose(oneHotLabels);\n const product = matMul(oneHotLabelsT, oneHotPredictions);\n return cast(product, 'int32');\n}\nexport const confusionMatrix = op({ confusionMatrix_ });\n//# sourceMappingURL=confusion_matrix.js.map","/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { inferShape } from '../tensor_util_env';\nimport { assertNonNull } from '../util';\nimport { makeTensor } from './tensor_ops_util';\n/**\n * Creates rank-3 `tf.Tensor` with the provided values, shape and dtype.\n *\n * The same functionality can be achieved with `tf.tensor`, but in general\n * we recommend using `tf.tensor3d` as it makes the code more readable.\n *\n * ```js\n * // Pass a nested array.\n * tf.tensor3d([[[1], [2]], [[3], [4]]]).print();\n * ```\n * ```js\n * // Pass a flat array and specify a shape.\n * tf.tensor3d([1, 2, 3, 4], [2, 2, 1]).print();\n * ```\n *\n * @param values The values of the tensor. Can be nested array of numbers,\n * or a flat array, or a `TypedArray`.\n * @param shape The shape of the tensor. If not provided, it is inferred from\n * `values`.\n * @param dtype The data type.\n *\n * @doc {heading: 'Tensors', subheading: 'Creation'}\n */\nexport function tensor3d(values, shape, dtype) {\n assertNonNull(values);\n if (shape != null && shape.length !== 3) {\n throw new Error('tensor3d() requires shape to have three numbers');\n }\n const inferredShape = inferShape(values, dtype);\n if (inferredShape.length !== 3 && inferredShape.length !== 1) {\n throw new Error('tensor3d() requires values to be number[][][] or flat/TypedArray');\n }\n if (inferredShape.length === 1 && shape == null) {\n throw new Error('tensor3d() requires shape to be provided when `values` ' +\n 'are a flat array');\n }\n return makeTensor(values, shape, inferredShape, dtype);\n}\n//# sourceMappingURL=tensor3d.js.map","/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport { ENGINE } from '../engine';\nimport { env } from '../environment';\nimport { FromPixels } from '../kernel_names';\nimport { getKernel } from '../kernel_registry';\nimport { Tensor } from '../tensor';\nimport { convertToTensor } from '../tensor_util_env';\nimport { cast } from './cast';\nimport { op } from './operation';\nimport { tensor3d } from './tensor3d';\nlet fromPixels2DContext;\n/**\n * Creates a `tf.Tensor` from an image.\n *\n * ```js\n * const image = new ImageData(1, 1);\n * image.data[0] = 100;\n * image.data[1] = 150;\n * image.data[2] = 200;\n * image.data[3] = 255;\n *\n * tf.browser.fromPixels(image).print();\n * ```\n *\n * @param pixels The input image to construct the tensor from. The\n * supported image types are all 4-channel. You can also pass in an image\n * object with following attributes:\n * `{data: Uint8Array; width: number; height: number}`\n * @param numChannels The number of channels of the output tensor. A\n * numChannels value less than 4 allows you to ignore channels. Defaults to\n * 3 (ignores alpha channel of input image).\n *\n * @returns A Tensor3D with the shape `[height, width, numChannels]`.\n *\n * @doc {heading: 'Browser', namespace: 'browser', ignoreCI: true}\n */\nfunction fromPixels_(pixels, numChannels = 3) {\n // Sanity checks.\n if (numChannels > 4) {\n throw new Error('Cannot construct Tensor with more than 4 channels from pixels.');\n }\n if (pixels == null) {\n throw new Error('pixels passed to tf.browser.fromPixels() can not be null');\n }\n let isPixelData = false;\n let isImageData = false;\n let isVideo = false;\n let isImage = false;\n let isCanvasLike = false;\n let isImageBitmap = false;\n if (pixels.data instanceof Uint8Array) {\n isPixelData = true;\n }\n else if (typeof (ImageData) !== 'undefined' && pixels instanceof ImageData) {\n isImageData = true;\n }\n else if (typeof (HTMLVideoElement) !== 'undefined' &&\n pixels instanceof HTMLVideoElement) {\n isVideo = true;\n }\n else if (typeof (HTMLImageElement) !== 'undefined' &&\n pixels instanceof HTMLImageElement) {\n isImage = true;\n // tslint:disable-next-line: no-any\n }\n else if (pixels.getContext != null) {\n isCanvasLike = true;\n }\n else if (typeof (ImageBitmap) !== 'undefined' && pixels instanceof ImageBitmap) {\n isImageBitmap = true;\n }\n else {\n throw new Error('pixels passed to tf.browser.fromPixels() must be either an ' +\n `HTMLVideoElement, HTMLImageElement, HTMLCanvasElement, ImageData ` +\n `in browser, or OffscreenCanvas, ImageData in webworker` +\n ` or {data: Uint32Array, width: number, height: number}, ` +\n `but was ${pixels.constructor.name}`);\n }\n if (isVideo) {\n const HAVE_CURRENT_DATA_READY_STATE = 2;\n if (isVideo &&\n pixels.readyState <\n HAVE_CURRENT_DATA_READY_STATE) {\n throw new Error('The video element has not loaded data yet. Please wait for ' +\n '`loadeddata` event on the ': break;\n\t\t\tcase '': pass = false; break;\n\t\t\tdefault:\n\t\t\t\tif(y[0].charCodeAt(1) !== 47 && !pass) throw new Error('Unrecognized rich format ' + y[0]);\n\t\t}\n\t}\n\treturn font;\n}\n\nvar parse_rs = (function() {\n\tvar tregex = matchtag(\"t\"), rpregex = matchtag(\"rPr\");\n\t/* 18.4.4 r CT_RElt */\n\tfunction parse_r(r) {\n\t\t/* 18.4.12 t ST_Xstring */\n\t\tvar t = r.match(tregex)/*, cp = 65001*/;\n\t\tif(!t) return {t:\"s\", v:\"\"};\n\n\t\tvar o = ({t:'s', v:unescapexml(t[1])});\n\t\tvar rpr = r.match(rpregex);\n\t\tif(rpr) o.s = parse_rpr(rpr[1]);\n\t\treturn o;\n\t}\n\tvar rregex = /<(?:\\w+:)?r>/g, rend = /<\\/(?:\\w+:)?r>/;\n\treturn function parse_rs(rs) {\n\t\treturn rs.replace(rregex,\"\").split(rend).map(parse_r).filter(function(r) { return r.v; });\n\t};\n})();\n\n\n/* Parse a list of tags */\nvar rs_to_html = (function parse_rs_factory() {\n\tvar nlregex = /(\\r\\n|\\n)/g;\n\tfunction parse_rpr2(font, intro, outro) {\n\t\tvar style = [];\n\n\t\tif(font.u) style.push(\"text-decoration: underline;\");\n\t\tif(font.uval) style.push(\"text-underline-style:\" + font.uval + \";\");\n\t\tif(font.sz) style.push(\"font-size:\" + font.sz + \"pt;\");\n\t\tif(font.outline) style.push(\"text-effect: outline;\");\n\t\tif(font.shadow) style.push(\"text-shadow: auto;\");\n\t\tintro.push('');\n\n\t\tif(font.b) { intro.push(\"\"); outro.push(\"\"); }\n\t\tif(font.i) { intro.push(\"\"); outro.push(\"\"); }\n\t\tif(font.strike) { intro.push(\"\"); outro.push(\"\"); }\n\n\t\tvar align = font.valign || \"\";\n\t\tif(align == \"superscript\" || align == \"super\") align = \"sup\";\n\t\telse if(align == \"subscript\") align = \"sub\";\n\t\tif(align != \"\") { intro.push(\"<\" + align + \">\"); outro.push(\"\"); }\n\n\t\toutro.push(\"\");\n\t\treturn font;\n\t}\n\n\t/* 18.4.4 r CT_RElt */\n\tfunction r_to_html(r) {\n\t\tvar terms = [[],r.v,[]];\n\t\tif(!r.v) return \"\";\n\n\t\tif(r.s) parse_rpr2(r.s, terms[0], terms[2]);\n\n\t\treturn terms[0].join(\"\") + terms[1].replace(nlregex,'
') + terms[2].join(\"\");\n\t}\n\n\treturn function parse_rs(rs) {\n\t\treturn rs.map(r_to_html).join(\"\");\n\t};\n})();\n\n/* 18.4.8 si CT_Rst */\nvar sitregex = /<(?:\\w+:)?t[^>]*>([^<]*)<\\/(?:\\w+:)?t>/g, sirregex = /<(?:\\w+:)?r>/;\nvar sirphregex = /<(?:\\w+:)?rPh.*?>([\\s\\S]*?)<\\/(?:\\w+:)?rPh>/g;\nfunction parse_si(x, opts) {\n\tvar html = opts ? opts.cellHTML : true;\n\tvar z = {};\n\tif(!x) return { t: \"\" };\n\t//var y;\n\t/* 18.4.12 t ST_Xstring (Plaintext String) */\n\t// TODO: is whitespace actually valid here?\n\tif(x.match(/^\\s*<(?:\\w+:)?t[^>]*>/)) {\n\t\tz.t = unescapexml(utf8read(x.slice(x.indexOf(\">\")+1).split(/<\\/(?:\\w+:)?t>/)[0]||\"\"));\n\t\tz.r = utf8read(x);\n\t\tif(html) z.h = escapehtml(z.t);\n\t}\n\t/* 18.4.4 r CT_RElt (Rich Text Run) */\n\telse if((/*y = */x.match(sirregex))) {\n\t\tz.r = utf8read(x);\n\t\tz.t = unescapexml(utf8read((x.replace(sirphregex, '').match(sitregex)||[]).join(\"\").replace(tagregex,\"\")));\n\t\tif(html) z.h = rs_to_html(parse_rs(z.r));\n\t}\n\t/* 18.4.3 phoneticPr CT_PhoneticPr (TODO: needed for Asian support) */\n\t/* 18.4.6 rPh CT_PhoneticRun (TODO: needed for Asian support) */\n\treturn z;\n}\n\n/* 18.4 Shared String Table */\nvar sstr0 = /<(?:\\w+:)?sst([^>]*)>([\\s\\S]*)<\\/(?:\\w+:)?sst>/;\nvar sstr1 = /<(?:\\w+:)?(?:si|sstItem)>/g;\nvar sstr2 = /<\\/(?:\\w+:)?(?:si|sstItem)>/;\nfunction parse_sst_xml(data, opts) {\n\tvar s = ([]), ss = \"\";\n\tif(!data) return s;\n\t/* 18.4.9 sst CT_Sst */\n\tvar sst = data.match(sstr0);\n\tif(sst) {\n\t\tss = sst[2].replace(sstr1,\"\").split(sstr2);\n\t\tfor(var i = 0; i != ss.length; ++i) {\n\t\t\tvar o = parse_si(ss[i].trim(), opts);\n\t\t\tif(o != null) s[s.length] = o;\n\t\t}\n\t\tsst = parsexmltag(sst[1]); s.Count = sst.count; s.Unique = sst.uniqueCount;\n\t}\n\treturn s;\n}\n\nRELS.SST = \"http://schemas.openxmlformats.org/officeDocument/2006/relationships/sharedStrings\";\nvar straywsregex = /^\\s|\\s$|[\\t\\n\\r]/;\nfunction write_sst_xml(sst, opts) {\n\tif(!opts.bookSST) return \"\";\n\tvar o = [XML_HEADER];\n\to[o.length] = (writextag('sst', null, {\n\t\txmlns: XMLNS.main[0],\n\t\tcount: sst.Count,\n\t\tuniqueCount: sst.Unique\n\t}));\n\tfor(var i = 0; i != sst.length; ++i) { if(sst[i] == null) continue;\n\t\tvar s = sst[i];\n\t\tvar sitag = \"\";\n\t\tif(s.r) sitag += s.r;\n\t\telse {\n\t\t\tsitag += \"\" + escapexml(s.t) + \"\";\n\t\t}\n\t\tsitag += \"\";\n\t\to[o.length] = (sitag);\n\t}\n\tif(o.length>2){ o[o.length] = (''); o[1]=o[1].replace(\"/>\",\">\"); }\n\treturn o.join(\"\");\n}\n/* [MS-XLSB] 2.4.221 BrtBeginSst */\nfunction parse_BrtBeginSst(data) {\n\treturn [data.read_shift(4), data.read_shift(4)];\n}\n\n/* [MS-XLSB] 2.1.7.45 Shared Strings */\nfunction parse_sst_bin(data, opts) {\n\tvar s = ([]);\n\tvar pass = false;\n\trecordhopper(data, function hopper_sst(val, R_n, RT) {\n\t\tswitch(RT) {\n\t\t\tcase 0x009F: /* 'BrtBeginSst' */\n\t\t\t\ts.Count = val[0]; s.Unique = val[1]; break;\n\t\t\tcase 0x0013: /* 'BrtSSTItem' */\n\t\t\t\ts.push(val); break;\n\t\t\tcase 0x00A0: /* 'BrtEndSst' */\n\t\t\t\treturn true;\n\n\t\t\tcase 0x0023: /* 'BrtFRTBegin' */\n\t\t\t\tpass = true; break;\n\t\t\tcase 0x0024: /* 'BrtFRTEnd' */\n\t\t\t\tpass = false; break;\n\n\t\t\tdefault:\n\t\t\t\tif(R_n.indexOf(\"Begin\") > 0){/* empty */}\n\t\t\t\telse if(R_n.indexOf(\"End\") > 0){/* empty */}\n\t\t\t\tif(!pass || opts.WTF) throw new Error(\"Unexpected record \" + RT + \" \" + R_n);\n\t\t}\n\t});\n\treturn s;\n}\n\nfunction write_BrtBeginSst(sst, o) {\n\tif(!o) o = new_buf(8);\n\to.write_shift(4, sst.Count);\n\to.write_shift(4, sst.Unique);\n\treturn o;\n}\n\nvar write_BrtSSTItem = write_RichStr;\n\nfunction write_sst_bin(sst) {\n\tvar ba = buf_array();\n\twrite_record(ba, \"BrtBeginSst\", write_BrtBeginSst(sst));\n\tfor(var i = 0; i < sst.length; ++i) write_record(ba, \"BrtSSTItem\", write_BrtSSTItem(sst[i]));\n\t/* FRTSST */\n\twrite_record(ba, \"BrtEndSst\");\n\treturn ba.end();\n}\nfunction _JS2ANSI(str) {\n\tif(typeof cptable !== 'undefined') return cptable.utils.encode(current_ansi, str);\n\tvar o = [], oo = str.split(\"\");\n\tfor(var i = 0; i < oo.length; ++i) o[i] = oo[i].charCodeAt(0);\n\treturn o;\n}\n\n/* [MS-OFFCRYPTO] 2.1.4 Version */\nfunction parse_CRYPTOVersion(blob, length) {\n\tvar o = {};\n\to.Major = blob.read_shift(2);\n\to.Minor = blob.read_shift(2);\nif(length >= 4) blob.l += length - 4;\n\treturn o;\n}\n\n/* [MS-OFFCRYPTO] 2.1.5 DataSpaceVersionInfo */\nfunction parse_DataSpaceVersionInfo(blob) {\n\tvar o = {};\n\to.id = blob.read_shift(0, 'lpp4');\n\to.R = parse_CRYPTOVersion(blob, 4);\n\to.U = parse_CRYPTOVersion(blob, 4);\n\to.W = parse_CRYPTOVersion(blob, 4);\n\treturn o;\n}\n\n/* [MS-OFFCRYPTO] 2.1.6.1 DataSpaceMapEntry Structure */\nfunction parse_DataSpaceMapEntry(blob) {\n\tvar len = blob.read_shift(4);\n\tvar end = blob.l + len - 4;\n\tvar o = {};\n\tvar cnt = blob.read_shift(4);\n\tvar comps = [];\n\t/* [MS-OFFCRYPTO] 2.1.6.2 DataSpaceReferenceComponent Structure */\n\twhile(cnt-- > 0) comps.push({ t: blob.read_shift(4), v: blob.read_shift(0, 'lpp4') });\n\to.name = blob.read_shift(0, 'lpp4');\n\to.comps = comps;\n\tif(blob.l != end) throw new Error(\"Bad DataSpaceMapEntry: \" + blob.l + \" != \" + end);\n\treturn o;\n}\n\n/* [MS-OFFCRYPTO] 2.1.6 DataSpaceMap */\nfunction parse_DataSpaceMap(blob) {\n\tvar o = [];\n\tblob.l += 4; // must be 0x8\n\tvar cnt = blob.read_shift(4);\n\twhile(cnt-- > 0) o.push(parse_DataSpaceMapEntry(blob));\n\treturn o;\n}\n\n/* [MS-OFFCRYPTO] 2.1.7 DataSpaceDefinition */\nfunction parse_DataSpaceDefinition(blob) {\n\tvar o = [];\n\tblob.l += 4; // must be 0x8\n\tvar cnt = blob.read_shift(4);\n\twhile(cnt-- > 0) o.push(blob.read_shift(0, 'lpp4'));\n\treturn o;\n}\n\n/* [MS-OFFCRYPTO] 2.1.8 DataSpaceDefinition */\nfunction parse_TransformInfoHeader(blob) {\n\tvar o = {};\n\t/*var len = */blob.read_shift(4);\n\tblob.l += 4; // must be 0x1\n\to.id = blob.read_shift(0, 'lpp4');\n\to.name = blob.read_shift(0, 'lpp4');\n\to.R = parse_CRYPTOVersion(blob, 4);\n\to.U = parse_CRYPTOVersion(blob, 4);\n\to.W = parse_CRYPTOVersion(blob, 4);\n\treturn o;\n}\n\nfunction parse_Primary(blob) {\n\t/* [MS-OFFCRYPTO] 2.2.6 IRMDSTransformInfo */\n\tvar hdr = parse_TransformInfoHeader(blob);\n\t/* [MS-OFFCRYPTO] 2.1.9 EncryptionTransformInfo */\n\thdr.ename = blob.read_shift(0, '8lpp4');\n\thdr.blksz = blob.read_shift(4);\n\thdr.cmode = blob.read_shift(4);\n\tif(blob.read_shift(4) != 0x04) throw new Error(\"Bad !Primary record\");\n\treturn hdr;\n}\n\n/* [MS-OFFCRYPTO] 2.3.2 Encryption Header */\nfunction parse_EncryptionHeader(blob, length) {\n\tvar tgt = blob.l + length;\n\tvar o = {};\n\to.Flags = (blob.read_shift(4) & 0x3F);\n\tblob.l += 4;\n\to.AlgID = blob.read_shift(4);\n\tvar valid = false;\n\tswitch(o.AlgID) {\n\t\tcase 0x660E: case 0x660F: case 0x6610: valid = (o.Flags == 0x24); break;\n\t\tcase 0x6801: valid = (o.Flags == 0x04); break;\n\t\tcase 0: valid = (o.Flags == 0x10 || o.Flags == 0x04 || o.Flags == 0x24); break;\n\t\tdefault: throw 'Unrecognized encryption algorithm: ' + o.AlgID;\n\t}\n\tif(!valid) throw new Error(\"Encryption Flags/AlgID mismatch\");\n\to.AlgIDHash = blob.read_shift(4);\n\to.KeySize = blob.read_shift(4);\n\to.ProviderType = blob.read_shift(4);\n\tblob.l += 8;\n\to.CSPName = blob.read_shift((tgt-blob.l)>>1, 'utf16le');\n\tblob.l = tgt;\n\treturn o;\n}\n\n/* [MS-OFFCRYPTO] 2.3.3 Encryption Verifier */\nfunction parse_EncryptionVerifier(blob, length) {\n\tvar o = {}, tgt = blob.l + length;\n\tblob.l += 4; // SaltSize must be 0x10\n\to.Salt = blob.slice(blob.l, blob.l+16); blob.l += 16;\n\to.Verifier = blob.slice(blob.l, blob.l+16); blob.l += 16;\n\t/*var sz = */blob.read_shift(4);\n\to.VerifierHash = blob.slice(blob.l, tgt); blob.l = tgt;\n\treturn o;\n}\n\n/* [MS-OFFCRYPTO] 2.3.4.* EncryptionInfo Stream */\nfunction parse_EncryptionInfo(blob) {\n\tvar vers = parse_CRYPTOVersion(blob);\n\tswitch(vers.Minor) {\n\t\tcase 0x02: return [vers.Minor, parse_EncInfoStd(blob, vers)];\n\t\tcase 0x03: return [vers.Minor, parse_EncInfoExt(blob, vers)];\n\t\tcase 0x04: return [vers.Minor, parse_EncInfoAgl(blob, vers)];\n\t}\n\tthrow new Error(\"ECMA-376 Encrypted file unrecognized Version: \" + vers.Minor);\n}\n\n/* [MS-OFFCRYPTO] 2.3.4.5 EncryptionInfo Stream (Standard Encryption) */\nfunction parse_EncInfoStd(blob) {\n\tvar flags = blob.read_shift(4);\n\tif((flags & 0x3F) != 0x24) throw new Error(\"EncryptionInfo mismatch\");\n\tvar sz = blob.read_shift(4);\n\t//var tgt = blob.l + sz;\n\tvar hdr = parse_EncryptionHeader(blob, sz);\n\tvar verifier = parse_EncryptionVerifier(blob, blob.length - blob.l);\n\treturn { t:\"Std\", h:hdr, v:verifier };\n}\n/* [MS-OFFCRYPTO] 2.3.4.6 EncryptionInfo Stream (Extensible Encryption) */\nfunction parse_EncInfoExt() { throw new Error(\"File is password-protected: ECMA-376 Extensible\"); }\n/* [MS-OFFCRYPTO] 2.3.4.10 EncryptionInfo Stream (Agile Encryption) */\nfunction parse_EncInfoAgl(blob) {\n\tvar KeyData = [\"saltSize\",\"blockSize\",\"keyBits\",\"hashSize\",\"cipherAlgorithm\",\"cipherChaining\",\"hashAlgorithm\",\"saltValue\"];\n\tblob.l+=4;\n\tvar xml = blob.read_shift(blob.length - blob.l, 'utf8');\n\tvar o = {};\n\txml.replace(tagregex, function xml_agile(x) {\n\t\tvar y = parsexmltag(x);\n\t\tswitch(strip_ns(y[0])) {\n\t\t\tcase '': break;\n\t\t\tcase '': case '': break;\n\n\t\t\tcase '': break;\n\t\t\tcase ' 4 || vers.Major < 2) throw new Error('unrecognized major version code: ' + vers.Major);\n\to.Flags = blob.read_shift(4); length -= 4;\n\tvar sz = blob.read_shift(4); length -= 4;\n\to.EncryptionHeader = parse_EncryptionHeader(blob, sz); length -= sz;\n\to.EncryptionVerifier = parse_EncryptionVerifier(blob, length);\n\treturn o;\n}\n/* [MS-OFFCRYPTO] 2.3.6.1 RC4 Encryption Header */\nfunction parse_RC4Header(blob) {\n\tvar o = {};\n\tvar vers = o.EncryptionVersionInfo = parse_CRYPTOVersion(blob, 4);\n\tif(vers.Major != 1 || vers.Minor != 1) throw 'unrecognized version code ' + vers.Major + ' : ' + vers.Minor;\n\to.Salt = blob.read_shift(16);\n\to.EncryptedVerifier = blob.read_shift(16);\n\to.EncryptedVerifierHash = blob.read_shift(16);\n\treturn o;\n}\n\n/* [MS-OFFCRYPTO] 2.3.7.1 Binary Document Password Verifier Derivation */\nfunction crypto_CreatePasswordVerifier_Method1(Password) {\n\tvar Verifier = 0x0000, PasswordArray;\n\tvar PasswordDecoded = _JS2ANSI(Password);\n\tvar len = PasswordDecoded.length + 1, i, PasswordByte;\n\tvar Intermediate1, Intermediate2, Intermediate3;\n\tPasswordArray = new_raw_buf(len);\n\tPasswordArray[0] = PasswordDecoded.length;\n\tfor(i = 1; i != len; ++i) PasswordArray[i] = PasswordDecoded[i-1];\n\tfor(i = len-1; i >= 0; --i) {\n\t\tPasswordByte = PasswordArray[i];\n\t\tIntermediate1 = ((Verifier & 0x4000) === 0x0000) ? 0 : 1;\n\t\tIntermediate2 = (Verifier << 1) & 0x7FFF;\n\t\tIntermediate3 = Intermediate1 | Intermediate2;\n\t\tVerifier = Intermediate3 ^ PasswordByte;\n\t}\n\treturn Verifier ^ 0xCE4B;\n}\n\n/* [MS-OFFCRYPTO] 2.3.7.2 Binary Document XOR Array Initialization */\nvar crypto_CreateXorArray_Method1 = (function() {\n\tvar PadArray = [0xBB, 0xFF, 0xFF, 0xBA, 0xFF, 0xFF, 0xB9, 0x80, 0x00, 0xBE, 0x0F, 0x00, 0xBF, 0x0F, 0x00];\n\tvar InitialCode = [0xE1F0, 0x1D0F, 0xCC9C, 0x84C0, 0x110C, 0x0E10, 0xF1CE, 0x313E, 0x1872, 0xE139, 0xD40F, 0x84F9, 0x280C, 0xA96A, 0x4EC3];\n\tvar XorMatrix = [0xAEFC, 0x4DD9, 0x9BB2, 0x2745, 0x4E8A, 0x9D14, 0x2A09, 0x7B61, 0xF6C2, 0xFDA5, 0xEB6B, 0xC6F7, 0x9DCF, 0x2BBF, 0x4563, 0x8AC6, 0x05AD, 0x0B5A, 0x16B4, 0x2D68, 0x5AD0, 0x0375, 0x06EA, 0x0DD4, 0x1BA8, 0x3750, 0x6EA0, 0xDD40, 0xD849, 0xA0B3, 0x5147, 0xA28E, 0x553D, 0xAA7A, 0x44D5, 0x6F45, 0xDE8A, 0xAD35, 0x4A4B, 0x9496, 0x390D, 0x721A, 0xEB23, 0xC667, 0x9CEF, 0x29FF, 0x53FE, 0xA7FC, 0x5FD9, 0x47D3, 0x8FA6, 0x0F6D, 0x1EDA, 0x3DB4, 0x7B68, 0xF6D0, 0xB861, 0x60E3, 0xC1C6, 0x93AD, 0x377B, 0x6EF6, 0xDDEC, 0x45A0, 0x8B40, 0x06A1, 0x0D42, 0x1A84, 0x3508, 0x6A10, 0xAA51, 0x4483, 0x8906, 0x022D, 0x045A, 0x08B4, 0x1168, 0x76B4, 0xED68, 0xCAF1, 0x85C3, 0x1BA7, 0x374E, 0x6E9C, 0x3730, 0x6E60, 0xDCC0, 0xA9A1, 0x4363, 0x86C6, 0x1DAD, 0x3331, 0x6662, 0xCCC4, 0x89A9, 0x0373, 0x06E6, 0x0DCC, 0x1021, 0x2042, 0x4084, 0x8108, 0x1231, 0x2462, 0x48C4];\n\tvar Ror = function(Byte) { return ((Byte/2) | (Byte*128)) & 0xFF; };\n\tvar XorRor = function(byte1, byte2) { return Ror(byte1 ^ byte2); };\n\tvar CreateXorKey_Method1 = function(Password) {\n\t\tvar XorKey = InitialCode[Password.length - 1];\n\t\tvar CurrentElement = 0x68;\n\t\tfor(var i = Password.length-1; i >= 0; --i) {\n\t\t\tvar Char = Password[i];\n\t\t\tfor(var j = 0; j != 7; ++j) {\n\t\t\t\tif(Char & 0x40) XorKey ^= XorMatrix[CurrentElement];\n\t\t\t\tChar *= 2; --CurrentElement;\n\t\t\t}\n\t\t}\n\t\treturn XorKey;\n\t};\n\treturn function(password) {\n\t\tvar Password = _JS2ANSI(password);\n\t\tvar XorKey = CreateXorKey_Method1(Password);\n\t\tvar Index = Password.length;\n\t\tvar ObfuscationArray = new_raw_buf(16);\n\t\tfor(var i = 0; i != 16; ++i) ObfuscationArray[i] = 0x00;\n\t\tvar Temp, PasswordLastChar, PadIndex;\n\t\tif((Index & 1) === 1) {\n\t\t\tTemp = XorKey >> 8;\n\t\t\tObfuscationArray[Index] = XorRor(PadArray[0], Temp);\n\t\t\t--Index;\n\t\t\tTemp = XorKey & 0xFF;\n\t\t\tPasswordLastChar = Password[Password.length - 1];\n\t\t\tObfuscationArray[Index] = XorRor(PasswordLastChar, Temp);\n\t\t}\n\t\twhile(Index > 0) {\n\t\t\t--Index;\n\t\t\tTemp = XorKey >> 8;\n\t\t\tObfuscationArray[Index] = XorRor(Password[Index], Temp);\n\t\t\t--Index;\n\t\t\tTemp = XorKey & 0xFF;\n\t\t\tObfuscationArray[Index] = XorRor(Password[Index], Temp);\n\t\t}\n\t\tIndex = 15;\n\t\tPadIndex = 15 - Password.length;\n\t\twhile(PadIndex > 0) {\n\t\t\tTemp = XorKey >> 8;\n\t\t\tObfuscationArray[Index] = XorRor(PadArray[PadIndex], Temp);\n\t\t\t--Index;\n\t\t\t--PadIndex;\n\t\t\tTemp = XorKey & 0xFF;\n\t\t\tObfuscationArray[Index] = XorRor(Password[Index], Temp);\n\t\t\t--Index;\n\t\t\t--PadIndex;\n\t\t}\n\t\treturn ObfuscationArray;\n\t};\n})();\n\n/* [MS-OFFCRYPTO] 2.3.7.3 Binary Document XOR Data Transformation Method 1 */\nvar crypto_DecryptData_Method1 = function(password, Data, XorArrayIndex, XorArray, O) {\n\t/* If XorArray is set, use it; if O is not set, make changes in-place */\n\tif(!O) O = Data;\n\tif(!XorArray) XorArray = crypto_CreateXorArray_Method1(password);\n\tvar Index, Value;\n\tfor(Index = 0; Index != Data.length; ++Index) {\n\t\tValue = Data[Index];\n\t\tValue ^= XorArray[XorArrayIndex];\n\t\tValue = ((Value>>5) | (Value<<3)) & 0xFF;\n\t\tO[Index] = Value;\n\t\t++XorArrayIndex;\n\t}\n\treturn [O, XorArrayIndex, XorArray];\n};\n\nvar crypto_MakeXorDecryptor = function(password) {\n\tvar XorArrayIndex = 0, XorArray = crypto_CreateXorArray_Method1(password);\n\treturn function(Data) {\n\t\tvar O = crypto_DecryptData_Method1(\"\", Data, XorArrayIndex, XorArray);\n\t\tXorArrayIndex = O[1];\n\t\treturn O[0];\n\t};\n};\n\n/* 2.5.343 */\nfunction parse_XORObfuscation(blob, length, opts, out) {\n\tvar o = ({ key: parseuint16(blob), verificationBytes: parseuint16(blob) });\n\tif(opts.password) o.verifier = crypto_CreatePasswordVerifier_Method1(opts.password);\n\tout.valid = o.verificationBytes === o.verifier;\n\tif(out.valid) out.insitu = crypto_MakeXorDecryptor(opts.password);\n\treturn o;\n}\n\n/* 2.4.117 */\nfunction parse_FilePassHeader(blob, length, oo) {\n\tvar o = oo || {}; o.Info = blob.read_shift(2); blob.l -= 2;\n\tif(o.Info === 1) o.Data = parse_RC4Header(blob, length);\n\telse o.Data = parse_RC4CryptoHeader(blob, length);\n\treturn o;\n}\nfunction parse_FilePass(blob, length, opts) {\n\tvar o = ({ Type: opts.biff >= 8 ? blob.read_shift(2) : 0 }); /* wEncryptionType */\n\tif(o.Type) parse_FilePassHeader(blob, length-2, o);\n\telse parse_XORObfuscation(blob, opts.biff >= 8 ? length : length - 2, opts, o);\n\treturn o;\n}\n\n\nvar RTF = (function() {\n\tfunction rtf_to_sheet(d, opts) {\n\t\tswitch(opts.type) {\n\t\t\tcase 'base64': return rtf_to_sheet_str(Base64.decode(d), opts);\n\t\t\tcase 'binary': return rtf_to_sheet_str(d, opts);\n\t\t\tcase 'buffer': return rtf_to_sheet_str(d.toString('binary'), opts);\n\t\t\tcase 'array': return rtf_to_sheet_str(cc2str(d), opts);\n\t\t}\n\t\tthrow new Error(\"Unrecognized type \" + opts.type);\n\t}\n\n\tfunction rtf_to_sheet_str(str, opts) {\n\t\tvar o = opts || {};\n\t\tvar ws = o.dense ? ([]) : ({});\n\t\tvar range = ({s: {c:0, r:0}, e: {c:0, r:0}});\n\n\t\t// TODO: parse\n\t\tif(!str.match(/\\\\trowd/)) throw new Error(\"RTF missing table\");\n\n\t\tws['!ref'] = encode_range(range);\n\t\treturn ws;\n\t}\n\n\tfunction rtf_to_workbook(d, opts) { return sheet_to_workbook(rtf_to_sheet(d, opts), opts); }\n\n\t/* TODO: this is a stub */\n\tfunction sheet_to_rtf(ws) {\n\t\tvar o = [\"{\\\\rtf1\\\\ansi\"];\n\t\tvar r = safe_decode_range(ws['!ref']), cell;\n\t\tvar dense = Array.isArray(ws);\n\t\tfor(var R = r.s.r; R <= r.e.r; ++R) {\n\t\t\to.push(\"\\\\trowd\\\\trautofit1\");\n\t\t\tfor(var C = r.s.c; C <= r.e.c; ++C) o.push(\"\\\\cellx\" + (C+1));\n\t\t\to.push(\"\\\\pard\\\\intbl\");\n\t\t\tfor(C = r.s.c; C <= r.e.c; ++C) {\n\t\t\t\tvar coord = encode_cell({r:R,c:C});\n\t\t\t\tcell = dense ? (ws[R]||[])[C]: ws[coord];\n\t\t\t\tif(!cell || cell.v == null && (!cell.f || cell.F)) continue;\n\t\t\t\to.push(\" \" + (cell.w || (format_cell(cell), cell.w)));\n\t\t\t\to.push(\"\\\\cell\");\n\t\t\t}\n\t\t\to.push(\"\\\\pard\\\\intbl\\\\row\");\n\t\t}\n\t\treturn o.join(\"\") + \"}\";\n\t}\n\n\treturn {\n\t\tto_workbook: rtf_to_workbook,\n\t\tto_sheet: rtf_to_sheet,\n\t\tfrom_sheet: sheet_to_rtf\n\t};\n})();\nfunction hex2RGB(h) {\n\tvar o = h.slice(h[0]===\"#\"?1:0).slice(0,6);\n\treturn [parseInt(o.slice(0,2),16),parseInt(o.slice(2,4),16),parseInt(o.slice(4,6),16)];\n}\nfunction rgb2Hex(rgb) {\n\tfor(var i=0,o=1; i!=3; ++i) o = o*256 + (rgb[i]>255?255:rgb[i]<0?0:rgb[i]);\n\treturn o.toString(16).toUpperCase().slice(1);\n}\n\nfunction rgb2HSL(rgb) {\n\tvar R = rgb[0]/255, G = rgb[1]/255, B=rgb[2]/255;\n\tvar M = Math.max(R, G, B), m = Math.min(R, G, B), C = M - m;\n\tif(C === 0) return [0, 0, R];\n\n\tvar H6 = 0, S = 0, L2 = (M + m);\n\tS = C / (L2 > 1 ? 2 - L2 : L2);\n\tswitch(M){\n\t\tcase R: H6 = ((G - B) / C + 6)%6; break;\n\t\tcase G: H6 = ((B - R) / C + 2); break;\n\t\tcase B: H6 = ((R - G) / C + 4); break;\n\t}\n\treturn [H6 / 6, S, L2 / 2];\n}\n\nfunction hsl2RGB(hsl){\n\tvar H = hsl[0], S = hsl[1], L = hsl[2];\n\tvar C = S * 2 * (L < 0.5 ? L : 1 - L), m = L - C/2;\n\tvar rgb = [m,m,m], h6 = 6*H;\n\n\tvar X;\n\tif(S !== 0) switch(h6|0) {\n\t\tcase 0: case 6: X = C * h6; rgb[0] += C; rgb[1] += X; break;\n\t\tcase 1: X = C * (2 - h6); rgb[0] += X; rgb[1] += C; break;\n\t\tcase 2: X = C * (h6 - 2); rgb[1] += C; rgb[2] += X; break;\n\t\tcase 3: X = C * (4 - h6); rgb[1] += X; rgb[2] += C; break;\n\t\tcase 4: X = C * (h6 - 4); rgb[2] += C; rgb[0] += X; break;\n\t\tcase 5: X = C * (6 - h6); rgb[2] += X; rgb[0] += C; break;\n\t}\n\tfor(var i = 0; i != 3; ++i) rgb[i] = Math.round(rgb[i]*255);\n\treturn rgb;\n}\n\n/* 18.8.3 bgColor tint algorithm */\nfunction rgb_tint(hex, tint) {\n\tif(tint === 0) return hex;\n\tvar hsl = rgb2HSL(hex2RGB(hex));\n\tif (tint < 0) hsl[2] = hsl[2] * (1 + tint);\n\telse hsl[2] = 1 - (1 - hsl[2]) * (1 - tint);\n\treturn rgb2Hex(hsl2RGB(hsl));\n}\n\n/* 18.3.1.13 width calculations */\n/* [MS-OI29500] 2.1.595 Column Width & Formatting */\nvar DEF_MDW = 6, MAX_MDW = 15, MIN_MDW = 1, MDW = DEF_MDW;\nfunction width2px(width) { return Math.floor(( width + (Math.round(128/MDW))/256 )* MDW ); }\nfunction px2char(px) { return (Math.floor((px - 5)/MDW * 100 + 0.5))/100; }\nfunction char2width(chr) { return (Math.round((chr * MDW + 5)/MDW*256))/256; }\n//function px2char_(px) { return (((px - 5)/MDW * 100 + 0.5))/100; }\n//function char2width_(chr) { return (((chr * MDW + 5)/MDW*256))/256; }\nfunction cycle_width(collw) { return char2width(px2char(width2px(collw))); }\n/* XLSX/XLSB/XLS specify width in units of MDW */\nfunction find_mdw_colw(collw) {\n\tvar delta = Math.abs(collw - cycle_width(collw)), _MDW = MDW;\n\tif(delta > 0.005) for(MDW=MIN_MDW; MDW 0.5) guess--;\n\t\tif(Math.abs(guess) < delta) { delta = Math.abs(guess); _MDW = MDW; }\n\t}\n\tMDW = _MDW;\n}*/\n\nfunction process_col(coll) {\n\tif(coll.width) {\n\t\tcoll.wpx = width2px(coll.width);\n\t\tcoll.wch = px2char(coll.wpx);\n\t\tcoll.MDW = MDW;\n\t} else if(coll.wpx) {\n\t\tcoll.wch = px2char(coll.wpx);\n\t\tcoll.width = char2width(coll.wch);\n\t\tcoll.MDW = MDW;\n\t} else if(typeof coll.wch == 'number') {\n\t\tcoll.width = char2width(coll.wch);\n\t\tcoll.wpx = width2px(coll.width);\n\t\tcoll.MDW = MDW;\n\t}\n\tif(coll.customWidth) delete coll.customWidth;\n}\n\nvar DEF_PPI = 96, PPI = DEF_PPI;\nfunction px2pt(px) { return px * 96 / PPI; }\nfunction pt2px(pt) { return pt * PPI / 96; }\n\n/* [MS-EXSPXML3] 2.4.54 ST_enmPattern */\nvar XLMLPatternTypeMap = {\n\t\"None\": \"none\",\n\t\"Solid\": \"solid\",\n\t\"Gray50\": \"mediumGray\",\n\t\"Gray75\": \"darkGray\",\n\t\"Gray25\": \"lightGray\",\n\t\"HorzStripe\": \"darkHorizontal\",\n\t\"VertStripe\": \"darkVertical\",\n\t\"ReverseDiagStripe\": \"darkDown\",\n\t\"DiagStripe\": \"darkUp\",\n\t\"DiagCross\": \"darkGrid\",\n\t\"ThickDiagCross\": \"darkTrellis\",\n\t\"ThinHorzStripe\": \"lightHorizontal\",\n\t\"ThinVertStripe\": \"lightVertical\",\n\t\"ThinReverseDiagStripe\": \"lightDown\",\n\t\"ThinHorzCross\": \"lightGrid\"\n};\n\n/* 18.8.5 borders CT_Borders */\nfunction parse_borders(t, styles, themes, opts) {\n\tstyles.Borders = [];\n\tvar border = {};\n\tvar pass = false;\n\t(t[0].match(tagregex)||[]).forEach(function(x) {\n\t\tvar y = parsexmltag(x);\n\t\tswitch(strip_ns(y[0])) {\n\t\t\tcase '': case '': break;\n\n\t\t\t/* 18.8.4 border CT_Border */\n\t\t\tcase '': case '':\n\t\t\t\tborder = {};\n\t\t\t\tif(y.diagonalUp) border.diagonalUp = parsexmlbool(y.diagonalUp);\n\t\t\t\tif(y.diagonalDown) border.diagonalDown = parsexmlbool(y.diagonalDown);\n\t\t\t\tstyles.Borders.push(border);\n\t\t\t\tbreak;\n\t\t\tcase '': break;\n\n\t\t\t/* note: not in spec, appears to be CT_BorderPr */\n\t\t\tcase '': break;\n\t\t\tcase '': break;\n\t\t\tcase '': break;\n\n\t\t\t/* note: not in spec, appears to be CT_BorderPr */\n\t\t\tcase '': break;\n\t\t\tcase '': break;\n\t\t\tcase '': break;\n\n\t\t\t/* 18.8.43 top CT_BorderPr */\n\t\t\tcase '': break;\n\t\t\tcase '': break;\n\t\t\tcase '': break;\n\n\t\t\t/* 18.8.6 bottom CT_BorderPr */\n\t\t\tcase '': break;\n\t\t\tcase '': break;\n\t\t\tcase '': break;\n\n\t\t\t/* 18.8.13 diagonal CT_BorderPr */\n\t\t\tcase '': case '': break;\n\t\t\tcase '': break;\n\n\t\t\t/* 18.8.25 horizontal CT_BorderPr */\n\t\t\tcase '': case '': break;\n\t\t\tcase '': break;\n\n\t\t\t/* 18.8.44 vertical CT_BorderPr */\n\t\t\tcase '': case '': break;\n\t\t\tcase '': break;\n\n\t\t\t/* 18.8.37 start CT_BorderPr */\n\t\t\tcase '': case '': break;\n\t\t\tcase '': break;\n\n\t\t\t/* 18.8.16 end CT_BorderPr */\n\t\t\tcase '': case '': break;\n\t\t\tcase '': break;\n\n\t\t\t/* 18.8.? color CT_Color */\n\t\t\tcase '':\n\t\t\t\tbreak;\n\t\t\tcase '': case '': break;\n\n\t\t\t/* 18.2.10 extLst CT_ExtensionList ? */\n\t\t\tcase '': case '': break;\n\t\t\tcase '': pass = false; break;\n\t\t\tdefault: if(opts && opts.WTF) {\n\t\t\t\tif(!pass) throw new Error('unrecognized ' + y[0] + ' in borders');\n\t\t\t}\n\t\t}\n\t});\n}\n\n/* 18.8.21 fills CT_Fills */\nfunction parse_fills(t, styles, themes, opts) {\n\tstyles.Fills = [];\n\tvar fill = {};\n\tvar pass = false;\n\t(t[0].match(tagregex)||[]).forEach(function(x) {\n\t\tvar y = parsexmltag(x);\n\t\tswitch(strip_ns(y[0])) {\n\t\t\tcase '': case '': break;\n\n\t\t\t/* 18.8.20 fill CT_Fill */\n\t\t\tcase '': case '':\n\t\t\t\tfill = {}; styles.Fills.push(fill); break;\n\t\t\tcase '': break;\n\n\t\t\t/* 18.8.24 gradientFill CT_GradientFill */\n\t\t\tcase '': break;\n\t\t\tcase '': styles.Fills.push(fill); fill = {}; break;\n\n\t\t\t/* 18.8.32 patternFill CT_PatternFill */\n\t\t\tcase '':\n\t\t\t\tif(y.patternType) fill.patternType = y.patternType;\n\t\t\t\tbreak;\n\t\t\tcase '': case '': break;\n\n\t\t\t/* 18.8.3 bgColor CT_Color */\n\t\t\tcase '': case '': break;\n\n\t\t\t/* 18.8.19 fgColor CT_Color */\n\t\t\tcase '': case '': break;\n\n\t\t\t/* 18.8.38 stop CT_GradientStop */\n\t\t\tcase '': break;\n\t\t\tcase '': break;\n\n\t\t\t/* 18.8.? color CT_Color */\n\t\t\tcase '': break;\n\t\t\tcase '': break;\n\n\t\t\t/* 18.2.10 extLst CT_ExtensionList ? */\n\t\t\tcase '': case '': break;\n\t\t\tcase '': pass = false; break;\n\t\t\tdefault: if(opts && opts.WTF) {\n\t\t\t\tif(!pass) throw new Error('unrecognized ' + y[0] + ' in fills');\n\t\t\t}\n\t\t}\n\t});\n}\n\n/* 18.8.23 fonts CT_Fonts */\nfunction parse_fonts(t, styles, themes, opts) {\n\tstyles.Fonts = [];\n\tvar font = {};\n\tvar pass = false;\n\t(t[0].match(tagregex)||[]).forEach(function(x) {\n\t\tvar y = parsexmltag(x);\n\t\tswitch(strip_ns(y[0])) {\n\t\t\tcase '': case '': break;\n\n\t\t\t/* 18.8.22 font CT_Font */\n\t\t\tcase '': break;\n\t\t\tcase '': case '':\n\t\t\t\tstyles.Fonts.push(font);\n\t\t\t\tfont = {};\n\t\t\t\tbreak;\n\n\t\t\t/* 18.8.29 name CT_FontName */\n\t\t\tcase '': case '': break;\n\n\t\t\t/* 18.8.2 b CT_BooleanProperty */\n\t\t\tcase '': font.bold = 1; break;\n\n\t\t\t/* 18.8.26 i CT_BooleanProperty */\n\t\t\tcase '': font.italic = 1; break;\n\n\t\t\t/* 18.4.13 u CT_UnderlineProperty */\n\t\t\tcase '': font.underline = 1; break;\n\n\t\t\t/* 18.4.10 strike CT_BooleanProperty */\n\t\t\tcase '': font.strike = 1; break;\n\n\t\t\t/* 18.4.2 outline CT_BooleanProperty */\n\t\t\tcase '': font.outline = 1; break;\n\n\t\t\t/* 18.8.36 shadow CT_BooleanProperty */\n\t\t\tcase '': font.shadow = 1; break;\n\n\t\t\t/* 18.8.12 condense CT_BooleanProperty */\n\t\t\tcase '': font.condense = 1; break;\n\n\t\t\t/* 18.8.17 extend CT_BooleanProperty */\n\t\t\tcase '': font.extend = 1; break;\n\n\t\t\t/* 18.4.11 sz CT_FontSize */\n\t\t\tcase '': case '': break;\n\n\t\t\t/* 18.4.14 vertAlign CT_VerticalAlignFontProperty */\n\t\t\tcase '': case '': break;\n\n\t\t\t/* 18.8.18 family CT_FontFamily */\n\t\t\tcase '': case '': break;\n\n\t\t\t/* 18.8.35 scheme CT_FontScheme */\n\t\t\tcase '': case '': break;\n\n\t\t\t/* 18.4.1 charset CT_IntProperty */\n\t\t\tcase '': case '': break;\n\n\t\t\t/* note: sometimes mc:AlternateContent appears bare */\n\t\t\tcase '': pass = false; break;\n\n\t\t\t/* 18.2.10 extLst CT_ExtensionList ? */\n\t\t\tcase '': case '': break;\n\t\t\tcase '': pass = false; break;\n\t\t\tdefault: if(opts && opts.WTF) {\n\t\t\t\tif(!pass) throw new Error('unrecognized ' + y[0] + ' in fonts');\n\t\t\t}\n\t\t}\n\t});\n}\n\n/* 18.8.31 numFmts CT_NumFmts */\nfunction parse_numFmts(t, styles, opts) {\n\tstyles.NumberFmt = [];\n\tvar k/*Array*/ = (keys(SSF._table));\n\tfor(var i=0; i < k.length; ++i) styles.NumberFmt[k[i]] = SSF._table[k[i]];\n\tvar m = t[0].match(tagregex);\n\tif(!m) return;\n\tfor(i=0; i < m.length; ++i) {\n\t\tvar y = parsexmltag(m[i]);\n\t\tswitch(strip_ns(y[0])) {\n\t\t\tcase '': case '': case '': break;\n\t\t\tcase '0) {\n\t\t\t\t\tif(j > 0x188) {\n\t\t\t\t\t\tfor(j = 0x188; j > 0x3c; --j) if(styles.NumberFmt[j] == null) break;\n\t\t\t\t\t\tstyles.NumberFmt[j] = f;\n\t\t\t\t\t}\n\t\t\t\t\tSSF.load(f,j);\n\t\t\t\t}\n\t\t\t} break;\n\t\t\tcase '': break;\n\t\t\tdefault: if(opts.WTF) throw new Error('unrecognized ' + y[0] + ' in numFmts');\n\t\t}\n\t}\n}\n\nfunction write_numFmts(NF) {\n\tvar o = [\"\"];\n\t[[5,8],[23,26],[41,44],[/*63*/50,/*66],[164,*/392]].forEach(function(r) {\n\t\tfor(var i = r[0]; i <= r[1]; ++i) if(NF[i] != null) o[o.length] = (writextag('numFmt',null,{numFmtId:i,formatCode:escapexml(NF[i])}));\n\t});\n\tif(o.length === 1) return \"\";\n\to[o.length] = (\"\");\n\to[0] = writextag('numFmts', null, { count:o.length-2 }).replace(\"/>\", \">\");\n\treturn o.join(\"\");\n}\n\n/* 18.8.10 cellXfs CT_CellXfs */\nvar cellXF_uint = [ \"numFmtId\", \"fillId\", \"fontId\", \"borderId\", \"xfId\" ];\nvar cellXF_bool = [ \"applyAlignment\", \"applyBorder\", \"applyFill\", \"applyFont\", \"applyNumberFormat\", \"applyProtection\", \"pivotButton\", \"quotePrefix\" ];\nfunction parse_cellXfs(t, styles, opts) {\n\tstyles.CellXf = [];\n\tvar xf;\n\tvar pass = false;\n\t(t[0].match(tagregex)||[]).forEach(function(x) {\n\t\tvar y = parsexmltag(x), i = 0;\n\t\tswitch(strip_ns(y[0])) {\n\t\t\tcase '': case '': case '': break;\n\n\t\t\t/* 18.8.45 xf CT_Xf */\n\t\t\tcase '':\n\t\t\t\txf = y;\n\t\t\t\tdelete xf[0];\n\t\t\t\tfor(i = 0; i < cellXF_uint.length; ++i) if(xf[cellXF_uint[i]])\n\t\t\t\t\txf[cellXF_uint[i]] = parseInt(xf[cellXF_uint[i]], 10);\n\t\t\t\tfor(i = 0; i < cellXF_bool.length; ++i) if(xf[cellXF_bool[i]])\n\t\t\t\t\txf[cellXF_bool[i]] = parsexmlbool(xf[cellXF_bool[i]]);\n\t\t\t\tif(xf.numFmtId > 0x188) {\n\t\t\t\t\tfor(i = 0x188; i > 0x3c; --i) if(styles.NumberFmt[xf.numFmtId] == styles.NumberFmt[i]) { xf.numFmtId = i; break; }\n\t\t\t\t}\n\t\t\t\tstyles.CellXf.push(xf); break;\n\t\t\tcase '': break;\n\n\t\t\t/* 18.8.1 alignment CT_CellAlignment */\n\t\t\tcase '':\n\t\t\t\tvar alignment = {};\n\t\t\t\tif(y.vertical) alignment.vertical = y.vertical;\n\t\t\t\tif(y.horizontal) alignment.horizontal = y.horizontal;\n\t\t\t\tif(y.textRotation != null) alignment.textRotation = y.textRotation;\n\t\t\t\tif(y.indent) alignment.indent = y.indent;\n\t\t\t\tif(y.wrapText) alignment.wrapText = parsexmlbool(y.wrapText);\n\t\t\t\txf.alignment = alignment;\n\t\t\t\tbreak;\n\t\t\tcase '': break;\n\n\t\t\t/* 18.8.33 protection CT_CellProtection */\n\t\t\tcase '': case '': break;\n\n\t\t\t/* note: sometimes mc:AlternateContent appears bare */\n\t\t\tcase '': pass = false; break;\n\n\t\t\t/* 18.2.10 extLst CT_ExtensionList ? */\n\t\t\tcase '': case '': break;\n\t\t\tcase '': pass = false; break;\n\t\t\tdefault: if(opts && opts.WTF) {\n\t\t\t\tif(!pass) throw new Error('unrecognized ' + y[0] + ' in cellXfs');\n\t\t\t}\n\t\t}\n\t});\n}\n\nfunction write_cellXfs(cellXfs) {\n\tvar o = [];\n\to[o.length] = (writextag('cellXfs',null));\n\tcellXfs.forEach(function(c) {\n\t\to[o.length] = (writextag('xf', null, c));\n\t});\n\to[o.length] = (\"\");\n\tif(o.length === 2) return \"\";\n\to[0] = writextag('cellXfs',null, {count:o.length-2}).replace(\"/>\",\">\");\n\treturn o.join(\"\");\n}\n\n/* 18.8 Styles CT_Stylesheet*/\nvar parse_sty_xml= (function make_pstyx() {\nvar numFmtRegex = /<(?:\\w+:)?numFmts([^>]*)>[\\S\\s]*?<\\/(?:\\w+:)?numFmts>/;\nvar cellXfRegex = /<(?:\\w+:)?cellXfs([^>]*)>[\\S\\s]*?<\\/(?:\\w+:)?cellXfs>/;\nvar fillsRegex = /<(?:\\w+:)?fills([^>]*)>[\\S\\s]*?<\\/(?:\\w+:)?fills>/;\nvar fontsRegex = /<(?:\\w+:)?fonts([^>]*)>[\\S\\s]*?<\\/(?:\\w+:)?fonts>/;\nvar bordersRegex = /<(?:\\w+:)?borders([^>]*)>[\\S\\s]*?<\\/(?:\\w+:)?borders>/;\n\nreturn function parse_sty_xml(data, themes, opts) {\n\tvar styles = {};\n\tif(!data) return styles;\n\tdata = data.replace(//mg,\"\").replace(//gm,\"\");\n\t/* 18.8.39 styleSheet CT_Stylesheet */\n\tvar t;\n\n\t/* 18.8.31 numFmts CT_NumFmts ? */\n\tif((t=data.match(numFmtRegex))) parse_numFmts(t, styles, opts);\n\n\t/* 18.8.23 fonts CT_Fonts ? */\n\tif((t=data.match(fontsRegex))) parse_fonts(t, styles, themes, opts);\n\n\t/* 18.8.21 fills CT_Fills ? */\n\tif((t=data.match(fillsRegex))) parse_fills(t, styles, themes, opts);\n\n\t/* 18.8.5 borders CT_Borders ? */\n\tif((t=data.match(bordersRegex))) parse_borders(t, styles, themes, opts);\n\n\t/* 18.8.9 cellStyleXfs CT_CellStyleXfs ? */\n\t/* 18.8.8 cellStyles CT_CellStyles ? */\n\n\t/* 18.8.10 cellXfs CT_CellXfs ? */\n\tif((t=data.match(cellXfRegex))) parse_cellXfs(t, styles, opts);\n\n\t/* 18.8.15 dxfs CT_Dxfs ? */\n\t/* 18.8.42 tableStyles CT_TableStyles ? */\n\t/* 18.8.11 colors CT_Colors ? */\n\t/* 18.2.10 extLst CT_ExtensionList ? */\n\n\treturn styles;\n};\n})();\n\nvar STYLES_XML_ROOT = writextag('styleSheet', null, {\n\t'xmlns': XMLNS.main[0],\n\t'xmlns:vt': XMLNS.vt\n});\n\nRELS.STY = \"http://schemas.openxmlformats.org/officeDocument/2006/relationships/styles\";\n\nfunction write_sty_xml(wb, opts) {\n\tvar o = [XML_HEADER, STYLES_XML_ROOT], w;\n\tif(wb.SSF && (w = write_numFmts(wb.SSF)) != null) o[o.length] = w;\n\to[o.length] = ('');\n\to[o.length] = ('');\n\to[o.length] = ('');\n\to[o.length] = ('');\n\tif((w = write_cellXfs(opts.cellXfs))) o[o.length] = (w);\n\to[o.length] = ('');\n\to[o.length] = ('');\n\to[o.length] = ('');\n\n\tif(o.length>2){ o[o.length] = (''); o[1]=o[1].replace(\"/>\",\">\"); }\n\treturn o.join(\"\");\n}\n/* [MS-XLSB] 2.4.657 BrtFmt */\nfunction parse_BrtFmt(data, length) {\n\tvar numFmtId = data.read_shift(2);\n\tvar stFmtCode = parse_XLWideString(data,length-2);\n\treturn [numFmtId, stFmtCode];\n}\nfunction write_BrtFmt(i, f, o) {\n\tif(!o) o = new_buf(6 + 4 * f.length);\n\to.write_shift(2, i);\n\twrite_XLWideString(f, o);\n\tvar out = (o.length > o.l) ? o.slice(0, o.l) : o;\n\tif(o.l == null) o.l = o.length;\n\treturn out;\n}\n\n/* [MS-XLSB] 2.4.659 BrtFont TODO */\nfunction parse_BrtFont(data, length, opts) {\n\tvar out = ({});\n\n\tout.sz = data.read_shift(2) / 20;\n\n\tvar grbit = parse_FontFlags(data, 2, opts);\n\tif(grbit.fItalic) out.italic = 1;\n\tif(grbit.fCondense) out.condense = 1;\n\tif(grbit.fExtend) out.extend = 1;\n\tif(grbit.fShadow) out.shadow = 1;\n\tif(grbit.fOutline) out.outline = 1;\n\tif(grbit.fStrikeout) out.strike = 1;\n\n\tvar bls = data.read_shift(2);\n\tif(bls === 0x02BC) out.bold = 1;\n\n\tswitch(data.read_shift(2)) {\n\t\t/* case 0: out.vertAlign = \"baseline\"; break; */\n\t\tcase 1: out.vertAlign = \"superscript\"; break;\n\t\tcase 2: out.vertAlign = \"subscript\"; break;\n\t}\n\n\tvar underline = data.read_shift(1);\n\tif(underline != 0) out.underline = underline;\n\n\tvar family = data.read_shift(1);\n\tif(family > 0) out.family = family;\n\n\tvar bCharSet = data.read_shift(1);\n\tif(bCharSet > 0) out.charset = bCharSet;\n\n\tdata.l++;\n\tout.color = parse_BrtColor(data, 8);\n\n\tswitch(data.read_shift(1)) {\n\t\t/* case 0: out.scheme = \"none\": break; */\n\t\tcase 1: out.scheme = \"major\"; break;\n\t\tcase 2: out.scheme = \"minor\"; break;\n\t}\n\n\tout.name = parse_XLWideString(data, length - 21);\n\n\treturn out;\n}\nfunction write_BrtFont(font, o) {\n\tif(!o) o = new_buf(25+4*32);\n\to.write_shift(2, font.sz * 20);\n\twrite_FontFlags(font, o);\n\to.write_shift(2, font.bold ? 0x02BC : 0x0190);\n\tvar sss = 0;\n\tif(font.vertAlign == \"superscript\") sss = 1;\n\telse if(font.vertAlign == \"subscript\") sss = 2;\n\to.write_shift(2, sss);\n\to.write_shift(1, font.underline || 0);\n\to.write_shift(1, font.family || 0);\n\to.write_shift(1, font.charset || 0);\n\to.write_shift(1, 0);\n\twrite_BrtColor(font.color, o);\n\tvar scheme = 0;\n\tif(font.scheme == \"major\") scheme = 1;\n\tif(font.scheme == \"minor\") scheme = 2;\n\to.write_shift(1, scheme);\n\twrite_XLWideString(font.name, o);\n\treturn o.length > o.l ? o.slice(0, o.l) : o;\n}\n\n/* [MS-XLSB] 2.4.650 BrtFill */\nvar XLSBFillPTNames = [\n\t\"none\",\n\t\"solid\",\n\t\"mediumGray\",\n\t\"darkGray\",\n\t\"lightGray\",\n\t\"darkHorizontal\",\n\t\"darkVertical\",\n\t\"darkDown\",\n\t\"darkUp\",\n\t\"darkGrid\",\n\t\"darkTrellis\",\n\t\"lightHorizontal\",\n\t\"lightVertical\",\n\t\"lightDown\",\n\t\"lightUp\",\n\t\"lightGrid\",\n\t\"lightTrellis\",\n\t\"gray125\",\n\t\"gray0625\"\n];\nvar rev_XLSBFillPTNames = (evert(XLSBFillPTNames));\n/* TODO: gradient fill representation */\nvar parse_BrtFill = parsenoop;\nfunction write_BrtFill(fill, o) {\n\tif(!o) o = new_buf(4*3 + 8*7 + 16*1);\n\tvar fls = rev_XLSBFillPTNames[fill.patternType];\n\tif(fls == null) fls = 0x28;\n\to.write_shift(4, fls);\n\tvar j = 0;\n\tif(fls != 0x28) {\n\t\t/* TODO: custom FG Color */\n\t\twrite_BrtColor({auto:1}, o);\n\t\t/* TODO: custom BG Color */\n\t\twrite_BrtColor({auto:1}, o);\n\n\t\tfor(; j < 12; ++j) o.write_shift(4, 0);\n\t} else {\n\t\tfor(; j < 4; ++j) o.write_shift(4, 0);\n\n\t\tfor(; j < 12; ++j) o.write_shift(4, 0); /* TODO */\n\t\t/* iGradientType */\n\t\t/* xnumDegree */\n\t\t/* xnumFillToLeft */\n\t\t/* xnumFillToRight */\n\t\t/* xnumFillToTop */\n\t\t/* xnumFillToBottom */\n\t\t/* cNumStop */\n\t\t/* xfillGradientStop */\n\t}\n\treturn o.length > o.l ? o.slice(0, o.l) : o;\n}\n\n/* [MS-XLSB] 2.4.824 BrtXF */\nfunction parse_BrtXF(data, length) {\n\tvar tgt = data.l + length;\n\tvar ixfeParent = data.read_shift(2);\n\tvar ifmt = data.read_shift(2);\n\tdata.l = tgt;\n\treturn {ixfe:ixfeParent, numFmtId:ifmt };\n}\nfunction write_BrtXF(data, ixfeP, o) {\n\tif(!o) o = new_buf(16);\n\to.write_shift(2, ixfeP||0);\n\to.write_shift(2, data.numFmtId||0);\n\to.write_shift(2, 0); /* iFont */\n\to.write_shift(2, 0); /* iFill */\n\to.write_shift(2, 0); /* ixBorder */\n\to.write_shift(1, 0); /* trot */\n\to.write_shift(1, 0); /* indent */\n\tvar flow = 0;\n\to.write_shift(1, flow); /* flags */\n\to.write_shift(1, 0); /* flags */\n\to.write_shift(1, 0); /* xfGrbitAtr */\n\to.write_shift(1, 0);\n\treturn o;\n}\n\n/* [MS-XLSB] 2.5.4 Blxf TODO */\nfunction write_Blxf(data, o) {\n\tif(!o) o = new_buf(10);\n\to.write_shift(1, 0); /* dg */\n\to.write_shift(1, 0);\n\to.write_shift(4, 0); /* color */\n\to.write_shift(4, 0); /* color */\n\treturn o;\n}\n/* [MS-XLSB] 2.4.302 BrtBorder TODO */\nvar parse_BrtBorder = parsenoop;\nfunction write_BrtBorder(border, o) {\n\tif(!o) o = new_buf(51);\n\to.write_shift(1, 0); /* diagonal */\n\twrite_Blxf(null, o); /* top */\n\twrite_Blxf(null, o); /* bottom */\n\twrite_Blxf(null, o); /* left */\n\twrite_Blxf(null, o); /* right */\n\twrite_Blxf(null, o); /* diag */\n\treturn o.length > o.l ? o.slice(0, o.l) : o;\n}\n\n/* [MS-XLSB] 2.4.763 BrtStyle TODO */\nfunction write_BrtStyle(style, o) {\n\tif(!o) o = new_buf(12+4*10);\n\to.write_shift(4, style.xfId);\n\to.write_shift(2, 1);\n\to.write_shift(1, +style.builtinId);\n\to.write_shift(1, 0); /* iLevel */\n\twrite_XLNullableWideString(style.name || \"\", o);\n\treturn o.length > o.l ? o.slice(0, o.l) : o;\n}\n\n/* [MS-XLSB] 2.4.272 BrtBeginTableStyles */\nfunction write_BrtBeginTableStyles(cnt, defTableStyle, defPivotStyle) {\n\tvar o = new_buf(4+256*2*4);\n\to.write_shift(4, cnt);\n\twrite_XLNullableWideString(defTableStyle, o);\n\twrite_XLNullableWideString(defPivotStyle, o);\n\treturn o.length > o.l ? o.slice(0, o.l) : o;\n}\n\n/* [MS-XLSB] 2.1.7.50 Styles */\nfunction parse_sty_bin(data, themes, opts) {\n\tvar styles = {};\n\tstyles.NumberFmt = ([]);\n\tfor(var y in SSF._table) styles.NumberFmt[y] = SSF._table[y];\n\n\tstyles.CellXf = [];\n\tstyles.Fonts = [];\n\tvar state = [];\n\tvar pass = false;\n\trecordhopper(data, function hopper_sty(val, R_n, RT) {\n\t\tswitch(RT) {\n\t\t\tcase 0x002C: /* 'BrtFmt' */\n\t\t\t\tstyles.NumberFmt[val[0]] = val[1]; SSF.load(val[1], val[0]);\n\t\t\t\tbreak;\n\t\t\tcase 0x002B: /* 'BrtFont' */\n\t\t\t\tstyles.Fonts.push(val);\n\t\t\t\tif(val.color.theme != null && themes && themes.themeElements && themes.themeElements.clrScheme) {\n\t\t\t\t\tval.color.rgb = rgb_tint(themes.themeElements.clrScheme[val.color.theme].rgb, val.color.tint || 0);\n\t\t\t\t}\n\t\t\t\tbreak;\n\t\t\tcase 0x0401: /* 'BrtKnownFonts' */ break;\n\t\t\tcase 0x002D: /* 'BrtFill' */\n\t\t\t\tbreak;\n\t\t\tcase 0x002E: /* 'BrtBorder' */\n\t\t\t\tbreak;\n\t\t\tcase 0x002F: /* 'BrtXF' */\n\t\t\t\tif(state[state.length - 1] == \"BrtBeginCellXFs\") {\n\t\t\t\t\tstyles.CellXf.push(val);\n\t\t\t\t}\n\t\t\t\tbreak;\n\t\t\tcase 0x0030: /* 'BrtStyle' */\n\t\t\tcase 0x01FB: /* 'BrtDXF' */\n\t\t\tcase 0x023C: /* 'BrtMRUColor' */\n\t\t\tcase 0x01DB: /* 'BrtIndexedColor': */\n\t\t\t\tbreak;\n\n\t\t\tcase 0x0493: /* 'BrtDXF14' */\n\t\t\tcase 0x0836: /* 'BrtDXF15' */\n\t\t\tcase 0x046A: /* 'BrtSlicerStyleElement' */\n\t\t\tcase 0x0200: /* 'BrtTableStyleElement' */\n\t\t\tcase 0x082F: /* 'BrtTimelineStyleElement' */\n\t\t\tcase 0x0C00: /* 'BrtUid' */\n\t\t\t\tbreak;\n\n\t\t\tcase 0x0023: /* 'BrtFRTBegin' */\n\t\t\t\tpass = true; break;\n\t\t\tcase 0x0024: /* 'BrtFRTEnd' */\n\t\t\t\tpass = false; break;\n\t\t\tcase 0x0025: /* 'BrtACBegin' */\n\t\t\t\tstate.push(R_n); pass = true; break;\n\t\t\tcase 0x0026: /* 'BrtACEnd' */\n\t\t\t\tstate.pop(); pass = false; break;\n\n\t\t\tdefault:\n\t\t\t\tif((R_n||\"\").indexOf(\"Begin\") > 0) state.push(R_n);\n\t\t\t\telse if((R_n||\"\").indexOf(\"End\") > 0) state.pop();\n\t\t\t\telse if(!pass || (opts.WTF && state[state.length-1] != \"BrtACBegin\")) throw new Error(\"Unexpected record \" + RT + \" \" + R_n);\n\t\t}\n\t});\n\treturn styles;\n}\n\nfunction write_FMTS_bin(ba, NF) {\n\tif(!NF) return;\n\tvar cnt = 0;\n\t[[5,8],[23,26],[41,44],[/*63*/50,/*66],[164,*/392]].forEach(function(r) {\nfor(var i = r[0]; i <= r[1]; ++i) if(NF[i] != null) ++cnt;\n\t});\n\n\tif(cnt == 0) return;\n\twrite_record(ba, \"BrtBeginFmts\", write_UInt32LE(cnt));\n\t[[5,8],[23,26],[41,44],[/*63*/50,/*66],[164,*/392]].forEach(function(r) {\nfor(var i = r[0]; i <= r[1]; ++i) if(NF[i] != null) write_record(ba, \"BrtFmt\", write_BrtFmt(i, NF[i]));\n\t});\n\twrite_record(ba, \"BrtEndFmts\");\n}\n\nfunction write_FONTS_bin(ba) {\n\tvar cnt = 1;\n\n\tif(cnt == 0) return;\n\twrite_record(ba, \"BrtBeginFonts\", write_UInt32LE(cnt));\n\twrite_record(ba, \"BrtFont\", write_BrtFont({\n\t\tsz:12,\n\t\tcolor: {theme:1},\n\t\tname: \"Calibri\",\n\t\tfamily: 2,\n\t\tscheme: \"minor\"\n\t}));\n\t/* 1*65491BrtFont [ACFONTS] */\n\twrite_record(ba, \"BrtEndFonts\");\n}\n\nfunction write_FILLS_bin(ba) {\n\tvar cnt = 2;\n\n\tif(cnt == 0) return;\n\twrite_record(ba, \"BrtBeginFills\", write_UInt32LE(cnt));\n\twrite_record(ba, \"BrtFill\", write_BrtFill({patternType:\"none\"}));\n\twrite_record(ba, \"BrtFill\", write_BrtFill({patternType:\"gray125\"}));\n\t/* 1*65431BrtFill */\n\twrite_record(ba, \"BrtEndFills\");\n}\n\nfunction write_BORDERS_bin(ba) {\n\tvar cnt = 1;\n\n\tif(cnt == 0) return;\n\twrite_record(ba, \"BrtBeginBorders\", write_UInt32LE(cnt));\n\twrite_record(ba, \"BrtBorder\", write_BrtBorder({}));\n\t/* 1*65430BrtBorder */\n\twrite_record(ba, \"BrtEndBorders\");\n}\n\nfunction write_CELLSTYLEXFS_bin(ba) {\n\tvar cnt = 1;\n\twrite_record(ba, \"BrtBeginCellStyleXFs\", write_UInt32LE(cnt));\n\twrite_record(ba, \"BrtXF\", write_BrtXF({\n\t\tnumFmtId: 0,\n\t\tfontId: 0,\n\t\tfillId: 0,\n\t\tborderId: 0\n\t}, 0xFFFF));\n\t/* 1*65430(BrtXF *FRT) */\n\twrite_record(ba, \"BrtEndCellStyleXFs\");\n}\n\nfunction write_CELLXFS_bin(ba, data) {\n\twrite_record(ba, \"BrtBeginCellXFs\", write_UInt32LE(data.length));\n\tdata.forEach(function(c) { write_record(ba, \"BrtXF\", write_BrtXF(c,0)); });\n\t/* 1*65430(BrtXF *FRT) */\n\twrite_record(ba, \"BrtEndCellXFs\");\n}\n\nfunction write_STYLES_bin(ba) {\n\tvar cnt = 1;\n\n\twrite_record(ba, \"BrtBeginStyles\", write_UInt32LE(cnt));\n\twrite_record(ba, \"BrtStyle\", write_BrtStyle({\n\t\txfId:0,\n\t\tbuiltinId:0,\n\t\tname:\"Normal\"\n\t}));\n\t/* 1*65430(BrtStyle *FRT) */\n\twrite_record(ba, \"BrtEndStyles\");\n}\n\nfunction write_DXFS_bin(ba) {\n\tvar cnt = 0;\n\n\twrite_record(ba, \"BrtBeginDXFs\", write_UInt32LE(cnt));\n\t/* *2147483647(BrtDXF *FRT) */\n\twrite_record(ba, \"BrtEndDXFs\");\n}\n\nfunction write_TABLESTYLES_bin(ba) {\n\tvar cnt = 0;\n\n\twrite_record(ba, \"BrtBeginTableStyles\", write_BrtBeginTableStyles(cnt, \"TableStyleMedium9\", \"PivotStyleMedium4\"));\n\t/* *TABLESTYLE */\n\twrite_record(ba, \"BrtEndTableStyles\");\n}\n\nfunction write_COLORPALETTE_bin() {\n\treturn;\n\t/* BrtBeginColorPalette [INDEXEDCOLORS] [MRUCOLORS] BrtEndColorPalette */\n}\n\n/* [MS-XLSB] 2.1.7.50 Styles */\nfunction write_sty_bin(wb, opts) {\n\tvar ba = buf_array();\n\twrite_record(ba, \"BrtBeginStyleSheet\");\n\twrite_FMTS_bin(ba, wb.SSF);\n\twrite_FONTS_bin(ba, wb);\n\twrite_FILLS_bin(ba, wb);\n\twrite_BORDERS_bin(ba, wb);\n\twrite_CELLSTYLEXFS_bin(ba, wb);\n\twrite_CELLXFS_bin(ba, opts.cellXfs);\n\twrite_STYLES_bin(ba, wb);\n\twrite_DXFS_bin(ba, wb);\n\twrite_TABLESTYLES_bin(ba, wb);\n\twrite_COLORPALETTE_bin(ba, wb);\n\t/* FRTSTYLESHEET*/\n\twrite_record(ba, \"BrtEndStyleSheet\");\n\treturn ba.end();\n}\nRELS.THEME = \"http://schemas.openxmlformats.org/officeDocument/2006/relationships/theme\";\n\n/* Even though theme layout is dk1 lt1 dk2 lt2, true order is lt1 dk1 lt2 dk2 */\nvar XLSXThemeClrScheme = [\n\t'', '', '', '',\n\t'', '', '',\n\t'', '', '',\n\t'', ''\n];\n/* 20.1.6.2 clrScheme CT_ColorScheme */\nfunction parse_clrScheme(t, themes, opts) {\n\tthemes.themeElements.clrScheme = [];\n\tvar color = {};\n\t(t[0].match(tagregex)||[]).forEach(function(x) {\n\t\tvar y = parsexmltag(x);\n\t\tswitch(y[0]) {\n\t\t\t/* 20.1.6.2 clrScheme (Color Scheme) CT_ColorScheme */\n\t\t\tcase '': break;\n\n\t\t\t/* 20.1.2.3.32 srgbClr CT_SRgbColor */\n\t\t\tcase '': case '':\n\t\t\tcase '': case '':\n\t\t\tcase '': case '':\n\t\t\tcase '': case '':\n\t\t\tcase '': case '':\n\t\t\tcase '': case '':\n\t\t\tcase '': case '':\n\t\t\tcase '': case '':\n\t\t\tcase '': case '':\n\t\t\tcase '': case '':\n\t\t\tcase '': case '':\n\t\t\tcase '': case '':\n\t\t\t\tif (y[0].charAt(1) === '/') {\n\t\t\t\t\tthemes.themeElements.clrScheme[XLSXThemeClrScheme.indexOf(y[0])] = color;\n\t\t\t\t\tcolor = {};\n\t\t\t\t} else {\n\t\t\t\t\tcolor.name = y[0].slice(3, y[0].length - 1);\n\t\t\t\t}\n\t\t\t\tbreak;\n\n\t\t\tdefault: if(opts && opts.WTF) throw new Error('Unrecognized ' + y[0] + ' in clrScheme');\n\t\t}\n\t});\n}\n\n/* 20.1.4.1.18 fontScheme CT_FontScheme */\nfunction parse_fontScheme() { }\n\n/* 20.1.4.1.15 fmtScheme CT_StyleMatrix */\nfunction parse_fmtScheme() { }\n\nvar clrsregex = /]*)>[\\s\\S]*<\\/a:clrScheme>/;\nvar fntsregex = /]*)>[\\s\\S]*<\\/a:fontScheme>/;\nvar fmtsregex = /]*)>[\\s\\S]*<\\/a:fmtScheme>/;\n\n/* 20.1.6.10 themeElements CT_BaseStyles */\nfunction parse_themeElements(data, themes, opts) {\n\tthemes.themeElements = {};\n\n\tvar t;\n\n\t[\n\t\t/* clrScheme CT_ColorScheme */\n\t\t['clrScheme', clrsregex, parse_clrScheme],\n\t\t/* fontScheme CT_FontScheme */\n\t\t['fontScheme', fntsregex, parse_fontScheme],\n\t\t/* fmtScheme CT_StyleMatrix */\n\t\t['fmtScheme', fmtsregex, parse_fmtScheme]\n\t].forEach(function(m) {\n\t\tif(!(t=data.match(m[1]))) throw new Error(m[0] + ' not found in themeElements');\n\t\tm[2](t, themes, opts);\n\t});\n}\n\nvar themeltregex = /]*)>[\\s\\S]*<\\/a:themeElements>/;\n\n/* 14.2.7 Theme Part */\nfunction parse_theme_xml(data, opts) {\n\t/* 20.1.6.9 theme CT_OfficeStyleSheet */\n\tif(!data || data.length === 0) return parse_theme_xml(write_theme());\n\n\tvar t;\n\tvar themes = {};\n\n\t/* themeElements CT_BaseStyles */\n\tif(!(t=data.match(themeltregex))) throw new Error('themeElements not found in theme');\n\tparse_themeElements(t[0], themes, opts);\n\tthemes.raw = data;\n\treturn themes;\n}\n\nfunction write_theme(Themes, opts) {\n\tif(opts && opts.themeXLSX) return opts.themeXLSX;\n\tif(Themes && typeof Themes.raw == \"string\") return Themes.raw;\n\tvar o = [XML_HEADER];\n\to[o.length] = '';\n\to[o.length] = '';\n\n\to[o.length] = '';\n\to[o.length] = '';\n\to[o.length] = '';\n\to[o.length] = '';\n\to[o.length] = '';\n\to[o.length] = '';\n\to[o.length] = '';\n\to[o.length] = '';\n\to[o.length] = '';\n\to[o.length] = '';\n\to[o.length] = '';\n\to[o.length] = '';\n\to[o.length] = '';\n\to[o.length] = '';\n\n\to[o.length] = '';\n\to[o.length] = '';\n\to[o.length] = '';\n\to[o.length] = '';\n\to[o.length] = '';\n\to[o.length] = '';\n\to[o.length] = '';\n\to[o.length] = '';\n\to[o.length] = '';\n\to[o.length] = '';\n\to[o.length] = '';\n\to[o.length] = '';\n\to[o.length] = '';\n\to[o.length] = '';\n\to[o.length] = '';\n\to[o.length] = '';\n\to[o.length] = '';\n\to[o.length] = '';\n\to[o.length] = '';\n\to[o.length] = '';\n\to[o.length] = '';\n\to[o.length] = '';\n\to[o.length] = '';\n\to[o.length] = '';\n\to[o.length] = '';\n\to[o.length] = '';\n\to[o.length] = '';\n\to[o.length] = '';\n\to[o.length] = '';\n\to[o.length] = '';\n\to[o.length] = '';\n\to[o.length] = '';\n\to[o.length] = '';\n\to[o.length] = '';\n\to[o.length] = '';\n\to[o.length] = '';\n\to[o.length] = '';\n\to[o.length] = '';\n\to[o.length] = '';\n\to[o.length] = '';\n\to[o.length] = '';\n\to[o.length] = '';\n\to[o.length] = '';\n\to[o.length] = '';\n\to[o.length] = '';\n\to[o.length] = '';\n\to[o.length] = '';\n\to[o.length] = '';\n\to[o.length] = '';\n\to[o.length] = '';\n\to[o.length] = '';\n\to[o.length] = '';\n\to[o.length] = '';\n\to[o.length] = '';\n\to[o.length] = '';\n\to[o.length] = '';\n\to[o.length] = '';\n\to[o.length] = '';\n\to[o.length] = '';\n\to[o.length] = '';\n\to[o.length] = '';\n\to[o.length] = '';\n\to[o.length] = '';\n\to[o.length] = '';\n\to[o.length] = '';\n\to[o.length] = '';\n\to[o.length] = '';\n\to[o.length] = '';\n\to[o.length] = '';\n\to[o.length] = '';\n\to[o.length] = '';\n\to[o.length] = '';\n\n\to[o.length] = '';\n\to[o.length] = '';\n\to[o.length] = '';\n\to[o.length] = '';\n\to[o.length] = '';\n\to[o.length] = '';\n\to[o.length] = '';\n\to[o.length] = '';\n\to[o.length] = '';\n\to[o.length] = '';\n\to[o.length] = '';\n\to[o.length] = '';\n\to[o.length] = '';\n\to[o.length] = '';\n\to[o.length] = '';\n\to[o.length] = '';\n\to[o.length] = '';\n\to[o.length] = '';\n\to[o.length] = '';\n\to[o.length] = '';\n\to[o.length] = '';\n\to[o.length] = '';\n\to[o.length] = '';\n\to[o.length] = '';\n\to[o.length] = '';\n\to[o.length] = '';\n\to[o.length] = '';\n\to[o.length] = '';\n\to[o.length] = '';\n\to[o.length] = '';\n\to[o.length] = '';\n\to[o.length] = '';\n\to[o.length] = '';\n\to[o.length] = '';\n\to[o.length] = '';\n\to[o.length] = '';\n\to[o.length] = '';\n\to[o.length] = '';\n\to[o.length] = '';\n\to[o.length] = '';\n\to[o.length] = '';\n\to[o.length] = '';\n\to[o.length] = '';\n\to[o.length] = '';\n\to[o.length] = '';\n\to[o.length] = '';\n\to[o.length] = '';\n\to[o.length] = '';\n\to[o.length] = '';\n\to[o.length] = '';\n\to[o.length] = '';\n\to[o.length] = '';\n\to[o.length] = '';\n\to[o.length] = '';\n\to[o.length] = '';\n\to[o.length] = '';\n\to[o.length] = '';\n\to[o.length] = '';\n\to[o.length] = '';\n\to[o.length] = '';\n\to[o.length] = '';\n\to[o.length] = '';\n\to[o.length] = '';\n\n\to[o.length] = '';\n\to[o.length] = '';\n\to[o.length] = '';\n\to[o.length] = '';\n\to[o.length] = '';\n\to[o.length] = '';\n\to[o.length] = '';\n\to[o.length] = '';\n\to[o.length] = '';\n\to[o.length] = '';\n\treturn o.join(\"\");\n}\n/* [MS-XLS] 2.4.326 TODO: payload is a zip file */\nfunction parse_Theme(blob, length, opts) {\n\tvar end = blob.l + length;\n\tvar dwThemeVersion = blob.read_shift(4);\n\tif(dwThemeVersion === 124226) return;\n\tif(!opts.cellStyles || !jszip) { blob.l = end; return; }\n\tvar data = blob.slice(blob.l);\n\tblob.l = end;\n\tvar zip; try { zip = new jszip(data); } catch(e) { return; }\n\tvar themeXML = getzipstr(zip, \"theme/theme/theme1.xml\", true);\n\tif(!themeXML) return;\n\treturn parse_theme_xml(themeXML, opts);\n}\n\n/* 2.5.49 */\nfunction parse_ColorTheme(blob) { return blob.read_shift(4); }\n\n/* 2.5.155 */\nfunction parse_FullColorExt(blob) {\n\tvar o = {};\n\to.xclrType = blob.read_shift(2);\n\to.nTintShade = blob.read_shift(2);\n\tswitch(o.xclrType) {\n\t\tcase 0: blob.l += 4; break;\n\t\tcase 1: o.xclrValue = parse_IcvXF(blob, 4); break;\n\t\tcase 2: o.xclrValue = parse_LongRGBA(blob, 4); break;\n\t\tcase 3: o.xclrValue = parse_ColorTheme(blob, 4); break;\n\t\tcase 4: blob.l += 4; break;\n\t}\n\tblob.l += 8;\n\treturn o;\n}\n\n/* 2.5.164 TODO: read 7 bits*/\nfunction parse_IcvXF(blob, length) {\n\treturn parsenoop(blob, length);\n}\n\n/* 2.5.280 */\nfunction parse_XFExtGradient(blob, length) {\n\treturn parsenoop(blob, length);\n}\n\n/* [MS-XLS] 2.5.108 */\nfunction parse_ExtProp(blob) {\n\tvar extType = blob.read_shift(2);\n\tvar cb = blob.read_shift(2) - 4;\n\tvar o = [extType];\n\tswitch(extType) {\n\t\tcase 0x04: case 0x05: case 0x07: case 0x08:\n\t\tcase 0x09: case 0x0A: case 0x0B: case 0x0D:\n\t\t\to[1] = parse_FullColorExt(blob, cb); break;\n\t\tcase 0x06: o[1] = parse_XFExtGradient(blob, cb); break;\n\t\tcase 0x0E: case 0x0F: o[1] = blob.read_shift(cb === 1 ? 1 : 2); break;\n\t\tdefault: throw new Error(\"Unrecognized ExtProp type: \" + extType + \" \" + cb);\n\t}\n\treturn o;\n}\n\n/* 2.4.355 */\nfunction parse_XFExt(blob, length) {\n\tvar end = blob.l + length;\n\tblob.l += 2;\n\tvar ixfe = blob.read_shift(2);\n\tblob.l += 2;\n\tvar cexts = blob.read_shift(2);\n\tvar ext = [];\n\twhile(cexts-- > 0) ext.push(parse_ExtProp(blob, end-blob.l));\n\treturn {ixfe:ixfe, ext:ext};\n}\n\n/* xf is an XF, see parse_XFExt for xfext */\nfunction update_xfext(xf, xfext) {\n\txfext.forEach(function(xfe) {\n\t\tswitch(xfe[0]) { /* 2.5.108 extPropData */\n\t\t\tcase 0x04: break; /* foreground color */\n\t\t\tcase 0x05: break; /* background color */\n\t\t\tcase 0x06: break; /* gradient fill */\n\t\t\tcase 0x07: break; /* top cell border color */\n\t\t\tcase 0x08: break; /* bottom cell border color */\n\t\t\tcase 0x09: break; /* left cell border color */\n\t\t\tcase 0x0a: break; /* right cell border color */\n\t\t\tcase 0x0b: break; /* diagonal cell border color */\n\t\t\tcase 0x0d: /* text color */\n\t\t\t\tbreak;\n\t\t\tcase 0x0e: break; /* font scheme */\n\t\t\tcase 0x0f: break; /* indentation level */\n\t\t}\n\t});\n}\n\n/* 18.6 Calculation Chain */\nfunction parse_cc_xml(data) {\n\tvar d = [];\n\tif(!data) return d;\n\tvar i = 1;\n\t(data.match(tagregex)||[]).forEach(function(x) {\n\t\tvar y = parsexmltag(x);\n\t\tswitch(y[0]) {\n\t\t\tcase '': case '': break;\n\t\t\t/* 18.6.1 c CT_CalcCell 1 */\n\t\t\tcase ' 0){/* empty */}\n\t\t\t\telse if((R_n||\"\").indexOf(\"End\") > 0){/* empty */}\n\t\t\t\telse if(!pass || opts.WTF) throw new Error(\"Unexpected record \" + RT + \" \" + R_n);\n\t\t}\n\t});\n\treturn out;\n}\n\n//function write_cc_bin(data, opts) { }\n/* 18.14 Supplementary Workbook Data */\nfunction parse_xlink_xml() {\n\t//var opts = _opts || {};\n\t//if(opts.WTF) throw \"XLSX External Link\";\n}\n\n/* [MS-XLSB] 2.1.7.25 External Link */\nfunction parse_xlink_bin(data, rel, name, _opts) {\n\tif(!data) return data;\n\tvar opts = _opts || {};\n\n\tvar pass = false, end = false;\n\n\trecordhopper(data, function xlink_parse(val, R_n, RT) {\n\t\tif(end) return;\n\t\tswitch(RT) {\n\t\t\tcase 0x0167: /* 'BrtSupTabs' */\n\t\t\tcase 0x016B: /* 'BrtExternTableStart' */\n\t\t\tcase 0x016C: /* 'BrtExternTableEnd' */\n\t\t\tcase 0x016E: /* 'BrtExternRowHdr' */\n\t\t\tcase 0x016F: /* 'BrtExternCellBlank' */\n\t\t\tcase 0x0170: /* 'BrtExternCellReal' */\n\t\t\tcase 0x0171: /* 'BrtExternCellBool' */\n\t\t\tcase 0x0172: /* 'BrtExternCellError' */\n\t\t\tcase 0x0173: /* 'BrtExternCellString' */\n\t\t\tcase 0x01D8: /* 'BrtExternValueMeta' */\n\t\t\tcase 0x0241: /* 'BrtSupNameStart' */\n\t\t\tcase 0x0242: /* 'BrtSupNameValueStart' */\n\t\t\tcase 0x0243: /* 'BrtSupNameValueEnd' */\n\t\t\tcase 0x0244: /* 'BrtSupNameNum' */\n\t\t\tcase 0x0245: /* 'BrtSupNameErr' */\n\t\t\tcase 0x0246: /* 'BrtSupNameSt' */\n\t\t\tcase 0x0247: /* 'BrtSupNameNil' */\n\t\t\tcase 0x0248: /* 'BrtSupNameBool' */\n\t\t\tcase 0x0249: /* 'BrtSupNameFmla' */\n\t\t\tcase 0x024A: /* 'BrtSupNameBits' */\n\t\t\tcase 0x024B: /* 'BrtSupNameEnd' */\n\t\t\t\tbreak;\n\n\t\t\tcase 0x0023: /* 'BrtFRTBegin' */\n\t\t\t\tpass = true; break;\n\t\t\tcase 0x0024: /* 'BrtFRTEnd' */\n\t\t\t\tpass = false; break;\n\n\t\t\tdefault:\n\t\t\t\tif((R_n||\"\").indexOf(\"Begin\") > 0){/* empty */}\n\t\t\t\telse if((R_n||\"\").indexOf(\"End\") > 0){/* empty */}\n\t\t\t\telse if(!pass || opts.WTF) throw new Error(\"Unexpected record \" + RT.toString(16) + \" \" + R_n);\n\t\t}\n\t}, opts);\n}\n/* 20.5 DrawingML - SpreadsheetML Drawing */\nRELS.IMG = \"http://schemas.openxmlformats.org/officeDocument/2006/relationships/image\";\nRELS.DRAW = \"http://schemas.openxmlformats.org/officeDocument/2006/relationships/drawing\";\n\n/* 20.5.2.35 wsDr CT_Drawing */\nfunction parse_drawing(data, rels) {\n\tif(!data) return \"??\";\n\t/*\n\t Chartsheet Drawing:\n\t - 20.5.2.35 wsDr CT_Drawing\n\t - 20.5.2.1 absoluteAnchor CT_AbsoluteAnchor\n\t - 20.5.2.16 graphicFrame CT_GraphicalObjectFrame\n\t - 20.1.2.2.16 graphic CT_GraphicalObject\n\t - 20.1.2.2.17 graphicData CT_GraphicalObjectData\n - chart reference\n\t the actual type is based on the URI of the graphicData\n\t\tTODO: handle embedded charts and other types of graphics\n\t*/\n\tvar id = (data.match(/]*r:id=\"([^\"]*)\"/)||[\"\",\"\"])[1];\n\n\treturn rels['!id'][id].Target;\n}\n\n/* L.5.5.2 SpreadsheetML Comments + VML Schema */\nvar _shapeid = 1024;\nfunction write_comments_vml(rId, comments) {\n\tvar csize = [21600, 21600];\n\t/* L.5.2.1.2 Path Attribute */\n\tvar bbox = [\"m0,0l0\",csize[1],csize[0],csize[1],csize[0],\"0xe\"].join(\",\");\n\tvar o = [\n\t\twritextag(\"xml\", null, { 'xmlns:v': XLMLNS.v, 'xmlns:o': XLMLNS.o, 'xmlns:x': XLMLNS.x, 'xmlns:mv': XLMLNS.mv }).replace(/\\/>/,\">\"),\n\t\twritextag(\"o:shapelayout\", writextag(\"o:idmap\", null, {'v:ext':\"edit\", 'data':rId}), {'v:ext':\"edit\"}),\n\t\twritextag(\"v:shapetype\", [\n\t\t\twritextag(\"v:stroke\", null, {joinstyle:\"miter\"}),\n\t\t\twritextag(\"v:path\", null, {gradientshapeok:\"t\", 'o:connecttype':\"rect\"})\n\t\t].join(\"\"), {id:\"_x0000_t202\", 'o:spt':202, coordsize:csize.join(\",\"),path:bbox})\n\t];\n\twhile(_shapeid < rId * 1000) _shapeid += 1000;\n\n\tcomments.forEach(function(x) {\n\tvar c = decode_cell(x[0]);\n\tvar fillopts = {'color2':\"#BEFF82\", 'type':\"gradient\"};\n\tif(fillopts.type == \"gradient\") fillopts.angle = \"-180\";\n\tvar fillparm = fillopts.type == \"gradient\" ? writextag(\"o:fill\", null, {type:\"gradientUnscaled\", 'v:ext':\"view\"}) : null;\n\tvar fillxml = writextag('v:fill', fillparm, fillopts);\n\n\tvar shadata = ({on:\"t\", 'obscured':\"t\"});\n\t++_shapeid;\n\n\to = o.concat([\n\t'',\n\t\tfillxml,\n\t\twritextag(\"v:shadow\", null, shadata),\n\t\twritextag(\"v:path\", null, {'o:connecttype':\"none\"}),\n\t\t'
',\n\t\t'',\n\t\t\t'',\n\t\t\t'',\n\t\t\t/* Part 4 19.4.2.3 Anchor (Anchor) */\n\t\t\twritetag('x:Anchor', [c.c+1, 0, c.r+1, 0, c.c+3, 20, c.r+5, 20].join(\",\")),\n\t\t\twritetag('x:AutoFill', \"False\"),\n\t\t\twritetag('x:Row', String(c.r)),\n\t\t\twritetag('x:Column', String(c.c)),\n\t\t\tx[1].hidden ? '' : '',\n\t\t'',\n\t''\n\t]); });\n\to.push('');\n\treturn o.join(\"\");\n}\nRELS.CMNT = \"http://schemas.openxmlformats.org/officeDocument/2006/relationships/comments\";\n\nfunction sheet_insert_comments(sheet, comments) {\n\tvar dense = Array.isArray(sheet);\n\tvar cell;\n\tcomments.forEach(function(comment) {\n\t\tvar r = decode_cell(comment.ref);\n\t\tif(dense) {\n\t\t\tif(!sheet[r.r]) sheet[r.r] = [];\n\t\t\tcell = sheet[r.r][r.c];\n\t\t} else cell = sheet[comment.ref];\n\t\tif (!cell) {\n\t\t\tcell = ({t:\"z\"});\n\t\t\tif(dense) sheet[r.r][r.c] = cell;\n\t\t\telse sheet[comment.ref] = cell;\n\t\t\tvar range = safe_decode_range(sheet[\"!ref\"]||\"BDWGO1000001:A1\");\n\t\t\tif(range.s.r > r.r) range.s.r = r.r;\n\t\t\tif(range.e.r < r.r) range.e.r = r.r;\n\t\t\tif(range.s.c > r.c) range.s.c = r.c;\n\t\t\tif(range.e.c < r.c) range.e.c = r.c;\n\t\t\tvar encoded = encode_range(range);\n\t\t\tif (encoded !== sheet[\"!ref\"]) sheet[\"!ref\"] = encoded;\n\t\t}\n\n\t\tif (!cell.c) cell.c = [];\n\t\tvar o = ({a: comment.author, t: comment.t, r: comment.r});\n\t\tif(comment.h) o.h = comment.h;\n\t\tcell.c.push(o);\n\t});\n}\n\n/* 18.7 Comments */\nfunction parse_comments_xml(data, opts) {\n\t/* 18.7.6 CT_Comments */\n\tif(data.match(/<(?:\\w+:)?comments *\\/>/)) return [];\n\tvar authors = [];\n\tvar commentList = [];\n\tvar authtag = data.match(/<(?:\\w+:)?authors>([\\s\\S]*)<\\/(?:\\w+:)?authors>/);\n\tif(authtag && authtag[1]) authtag[1].split(/<\\/\\w*:?author>/).forEach(function(x) {\n\t\tif(x === \"\" || x.trim() === \"\") return;\n\t\tvar a = x.match(/<(?:\\w+:)?author[^>]*>(.*)/);\n\t\tif(a) authors.push(a[1]);\n\t});\n\tvar cmnttag = data.match(/<(?:\\w+:)?commentList>([\\s\\S]*)<\\/(?:\\w+:)?commentList>/);\n\tif(cmnttag && cmnttag[1]) cmnttag[1].split(/<\\/\\w*:?comment>/).forEach(function(x) {\n\t\tif(x === \"\" || x.trim() === \"\") return;\n\t\tvar cm = x.match(/<(?:\\w+:)?comment[^>]*>/);\n\t\tif(!cm) return;\n\t\tvar y = parsexmltag(cm[0]);\n\t\tvar comment = ({ author: y.authorId && authors[y.authorId] || \"sheetjsghost\", ref: y.ref, guid: y.guid });\n\t\tvar cell = decode_cell(y.ref);\n\t\tif(opts.sheetRows && opts.sheetRows <= cell.r) return;\n\t\tvar textMatch = x.match(/<(?:\\w+:)?text>([\\s\\S]*)<\\/(?:\\w+:)?text>/);\n\t\tvar rt = !!textMatch && !!textMatch[1] && parse_si(textMatch[1]) || {r:\"\",t:\"\",h:\"\"};\n\t\tcomment.r = rt.r;\n\t\tif(rt.r == \"\") rt.t = rt.h = \"\";\n\t\tcomment.t = rt.t.replace(/\\r\\n/g,\"\\n\").replace(/\\r/g,\"\\n\");\n\t\tif(opts.cellHTML) comment.h = rt.h;\n\t\tcommentList.push(comment);\n\t});\n\treturn commentList;\n}\n\nvar CMNT_XML_ROOT = writextag('comments', null, { 'xmlns': XMLNS.main[0] });\nfunction write_comments_xml(data) {\n\tvar o = [XML_HEADER, CMNT_XML_ROOT];\n\n\tvar iauthor = [];\n\to.push(\"\");\n\tdata.forEach(function(x) { x[1].forEach(function(w) { var a = escapexml(w.a);\n\t\tif(iauthor.indexOf(a) > -1) return;\n\t\tiauthor.push(a);\n\t\to.push(\"\" + a + \"\");\n\t}); });\n\to.push(\"\");\n\to.push(\"\");\n\tdata.forEach(function(d) {\n\t\td[1].forEach(function(c) {\n\t\t\t/* 18.7.3 CT_Comment */\n\t\t\to.push('');\n\t\t\to.push(writetag(\"t\", c.t == null ? \"\" : escapexml(c.t)));\n\t\t\to.push('');\n\t\t});\n\t});\n\to.push(\"\");\n\tif(o.length>2) { o[o.length] = (''); o[1]=o[1].replace(\"/>\",\">\"); }\n\treturn o.join(\"\");\n}\n/* [MS-XLSB] 2.4.28 BrtBeginComment */\nfunction parse_BrtBeginComment(data) {\n\tvar out = {};\n\tout.iauthor = data.read_shift(4);\n\tvar rfx = parse_UncheckedRfX(data, 16);\n\tout.rfx = rfx.s;\n\tout.ref = encode_cell(rfx.s);\n\tdata.l += 16; /*var guid = parse_GUID(data); */\n\treturn out;\n}\nfunction write_BrtBeginComment(data, o) {\n\tif(o == null) o = new_buf(36);\n\to.write_shift(4, data[1].iauthor);\n\twrite_UncheckedRfX((data[0]), o);\n\to.write_shift(4, 0);\n\to.write_shift(4, 0);\n\to.write_shift(4, 0);\n\to.write_shift(4, 0);\n\treturn o;\n}\n\n/* [MS-XLSB] 2.4.327 BrtCommentAuthor */\nvar parse_BrtCommentAuthor = parse_XLWideString;\nfunction write_BrtCommentAuthor(data) { return write_XLWideString(data.slice(0, 54)); }\n\n/* [MS-XLSB] 2.1.7.8 Comments */\nfunction parse_comments_bin(data, opts) {\n\tvar out = [];\n\tvar authors = [];\n\tvar c = {};\n\tvar pass = false;\n\trecordhopper(data, function hopper_cmnt(val, R_n, RT) {\n\t\tswitch(RT) {\n\t\t\tcase 0x0278: /* 'BrtCommentAuthor' */\n\t\t\t\tauthors.push(val); break;\n\t\t\tcase 0x027B: /* 'BrtBeginComment' */\n\t\t\t\tc = val; break;\n\t\t\tcase 0x027D: /* 'BrtCommentText' */\n\t\t\t\tc.t = val.t; c.h = val.h; c.r = val.r; break;\n\t\t\tcase 0x027C: /* 'BrtEndComment' */\n\t\t\t\tc.author = authors[c.iauthor];\n\t\t\t\tdelete (c).iauthor;\n\t\t\t\tif(opts.sheetRows && c.rfx && opts.sheetRows <= c.rfx.r) break;\n\t\t\t\tif(!c.t) c.t = \"\";\n\t\t\t\tdelete c.rfx; out.push(c); break;\n\n\t\t\tcase 0x0C00: /* 'BrtUid' */\n\t\t\t\tbreak;\n\n\t\t\tcase 0x0023: /* 'BrtFRTBegin' */\n\t\t\t\tpass = true; break;\n\t\t\tcase 0x0024: /* 'BrtFRTEnd' */\n\t\t\t\tpass = false; break;\n\t\t\tcase 0x0025: /* 'BrtACBegin' */ break;\n\t\t\tcase 0x0026: /* 'BrtACEnd' */ break;\n\n\n\t\t\tdefault:\n\t\t\t\tif((R_n||\"\").indexOf(\"Begin\") > 0){/* empty */}\n\t\t\t\telse if((R_n||\"\").indexOf(\"End\") > 0){/* empty */}\n\t\t\t\telse if(!pass || opts.WTF) throw new Error(\"Unexpected record \" + RT + \" \" + R_n);\n\t\t}\n\t});\n\treturn out;\n}\n\nfunction write_comments_bin(data) {\n\tvar ba = buf_array();\n\tvar iauthor = [];\n\twrite_record(ba, \"BrtBeginComments\");\n\n\twrite_record(ba, \"BrtBeginCommentAuthors\");\n\tdata.forEach(function(comment) {\n\t\tcomment[1].forEach(function(c) {\n\t\t\tif(iauthor.indexOf(c.a) > -1) return;\n\t\t\tiauthor.push(c.a.slice(0,54));\n\t\t\twrite_record(ba, \"BrtCommentAuthor\", write_BrtCommentAuthor(c.a));\n\t\t});\n\t});\n\twrite_record(ba, \"BrtEndCommentAuthors\");\n\n\twrite_record(ba, \"BrtBeginCommentList\");\n\tdata.forEach(function(comment) {\n\t\tcomment[1].forEach(function(c) {\n\t\t\tc.iauthor = iauthor.indexOf(c.a);\n\t\t\tvar range = {s:decode_cell(comment[0]),e:decode_cell(comment[0])};\n\t\t\twrite_record(ba, \"BrtBeginComment\", write_BrtBeginComment([range, c]));\n\t\t\tif(c.t && c.t.length > 0) write_record(ba, \"BrtCommentText\", write_BrtCommentText(c));\n\t\t\twrite_record(ba, \"BrtEndComment\");\n\t\t\tdelete c.iauthor;\n\t\t});\n\t});\n\twrite_record(ba, \"BrtEndCommentList\");\n\n\twrite_record(ba, \"BrtEndComments\");\n\treturn ba.end();\n}\nvar CT_VBA = \"application/vnd.ms-office.vbaProject\";\nfunction make_vba_xls(cfb) {\n\tvar newcfb = CFB.utils.cfb_new({root:\"R\"});\n\tcfb.FullPaths.forEach(function(p, i) {\n\t\tif(p.slice(-1) === \"/\" || !p.match(/_VBA_PROJECT_CUR/)) return;\n\t\tvar newpath = p.replace(/^[^\\/]*/,\"R\").replace(/\\/_VBA_PROJECT_CUR\\u0000*/, \"\");\n\t\tCFB.utils.cfb_add(newcfb, newpath, cfb.FileIndex[i].content);\n\t});\n\treturn CFB.write(newcfb);\n}\n\nfunction fill_vba_xls(cfb, vba) {\n\tvba.FullPaths.forEach(function(p, i) {\n\t\tif(i == 0) return;\n\t\tvar newpath = p.replace(/[^\\/]*[\\/]/, \"/_VBA_PROJECT_CUR/\");\n\t\tif(newpath.slice(-1) !== \"/\") CFB.utils.cfb_add(cfb, newpath, vba.FileIndex[i].content);\n\t});\n}\n\nvar VBAFMTS = [ \"xlsb\", \"xlsm\", \"xlam\", \"biff8\", \"xla\" ];\n\nRELS.DS = \"http://schemas.openxmlformats.org/officeDocument/2006/relationships/dialogsheet\";\nRELS.MS = \"http://schemas.microsoft.com/office/2006/relationships/xlMacrosheet\";\n\n/* macro and dialog sheet stubs */\nfunction parse_ds_bin() { return {'!type':'dialog'}; }\nfunction parse_ds_xml() { return {'!type':'dialog'}; }\nfunction parse_ms_bin() { return {'!type':'macro'}; }\nfunction parse_ms_xml() { return {'!type':'macro'}; }\n/* TODO: it will be useful to parse the function str */\nvar rc_to_a1 = (function(){\n\tvar rcregex = /(^|[^A-Za-z_])R(\\[?-?\\d+\\]|[1-9]\\d*|)C(\\[?-?\\d+\\]|[1-9]\\d*|)(?![A-Za-z0-9_])/g;\n\tvar rcbase = ({r:0,c:0});\n\tfunction rcfunc($$,$1,$2,$3) {\n\t\tvar cRel = false, rRel = false;\n\n\t\tif($2.length == 0) rRel = true;\n\t\telse if($2.charAt(0) == \"[\") { rRel = true; $2 = $2.slice(1, -1); }\n\n\t\tif($3.length == 0) cRel = true;\n\t\telse if($3.charAt(0) == \"[\") { cRel = true; $3 = $3.slice(1, -1); }\n\n\t\tvar R = $2.length>0?parseInt($2,10)|0:0, C = $3.length>0?parseInt($3,10)|0:0;\n\n\t\tif(cRel) C += rcbase.c; else --C;\n\t\tif(rRel) R += rcbase.r; else --R;\n\t\treturn $1 + (cRel ? \"\" : \"$\") + encode_col(C) + (rRel ? \"\" : \"$\") + encode_row(R);\n\t}\n\treturn function rc_to_a1(fstr, base) {\n\t\trcbase = base;\n\t\treturn fstr.replace(rcregex, rcfunc);\n\t};\n})();\n\nvar crefregex = /(^|[^._A-Z0-9])([$]?)([A-Z]{1,2}|[A-W][A-Z]{2}|X[A-E][A-Z]|XF[A-D])([$]?)(10[0-3]\\d{4}|104[0-7]\\d{3}|1048[0-4]\\d{2}|10485[0-6]\\d|104857[0-6]|[1-9]\\d{0,5})(?![_.\\(A-Za-z0-9])/g;\nvar a1_to_rc =(function(){\n\treturn function a1_to_rc(fstr, base) {\n\t\treturn fstr.replace(crefregex, function($0, $1, $2, $3, $4, $5) {\n\t\t\tvar c = decode_col($3) - ($2 ? 0 : base.c);\n\t\t\tvar r = decode_row($5) - ($4 ? 0 : base.r);\n\t\t\tvar R = (r == 0 ? \"\" : !$4 ? \"[\" + r + \"]\" : (r+1));\n\t\t\tvar C = (c == 0 ? \"\" : !$2 ? \"[\" + c + \"]\" : (c+1));\n\t\t\treturn $1 + \"R\" + R + \"C\" + C;\n\t\t});\n\t};\n})();\n\n/* no defined name can collide with a valid cell address A1:XFD1048576 ... except LOG10! */\nfunction shift_formula_str(f, delta) {\n\treturn f.replace(crefregex, function($0, $1, $2, $3, $4, $5) {\n\t\treturn $1+($2==\"$\" ? $2+$3 : encode_col(decode_col($3)+delta.c))+($4==\"$\" ? $4+$5 : encode_row(decode_row($5) + delta.r));\n\t});\n}\n\nfunction shift_formula_xlsx(f, range, cell) {\n\tvar r = decode_range(range), s = r.s, c = decode_cell(cell);\n\tvar delta = {r:c.r - s.r, c:c.c - s.c};\n\treturn shift_formula_str(f, delta);\n}\n\n/* TODO: parse formula */\nfunction fuzzyfmla(f) {\n\tif(f.length == 1) return false;\n\treturn true;\n}\n\nfunction _xlfn(f) {\n\treturn f.replace(/_xlfn\\./g,\"\");\n}\nfunction parseread1(blob) { blob.l+=1; return; }\n\n/* [MS-XLS] 2.5.51 */\nfunction parse_ColRelU(blob, length) {\n\tvar c = blob.read_shift(length == 1 ? 1 : 2);\n\treturn [c & 0x3FFF, (c >> 14) & 1, (c >> 15) & 1];\n}\n\n/* [MS-XLS] 2.5.198.105 ; [MS-XLSB] 2.5.97.89 */\nfunction parse_RgceArea(blob, length, opts) {\n\tvar w = 2;\n\tif(opts) {\n\t\tif(opts.biff >= 2 && opts.biff <= 5) return parse_RgceArea_BIFF2(blob, length, opts);\n\t\telse if(opts.biff == 12) w = 4;\n\t}\n\tvar r=blob.read_shift(w), R=blob.read_shift(w);\n\tvar c=parse_ColRelU(blob, 2);\n\tvar C=parse_ColRelU(blob, 2);\n\treturn { s:{r:r, c:c[0], cRel:c[1], rRel:c[2]}, e:{r:R, c:C[0], cRel:C[1], rRel:C[2]} };\n}\n/* BIFF 2-5 encodes flags in the row field */\nfunction parse_RgceArea_BIFF2(blob) {\n\tvar r=parse_ColRelU(blob, 2), R=parse_ColRelU(blob, 2);\n\tvar c=blob.read_shift(1);\n\tvar C=blob.read_shift(1);\n\treturn { s:{r:r[0], c:c, cRel:r[1], rRel:r[2]}, e:{r:R[0], c:C, cRel:R[1], rRel:R[2]} };\n}\n\n/* [MS-XLS] 2.5.198.105 ; [MS-XLSB] 2.5.97.90 */\nfunction parse_RgceAreaRel(blob, length, opts) {\n\tif(opts.biff < 8) return parse_RgceArea_BIFF2(blob, length, opts);\n\tvar r=blob.read_shift(opts.biff == 12 ? 4 : 2), R=blob.read_shift(opts.biff == 12 ? 4 : 2);\n\tvar c=parse_ColRelU(blob, 2);\n\tvar C=parse_ColRelU(blob, 2);\n\treturn { s:{r:r, c:c[0], cRel:c[1], rRel:c[2]}, e:{r:R, c:C[0], cRel:C[1], rRel:C[2]} };\n}\n\n/* [MS-XLS] 2.5.198.109 ; [MS-XLSB] 2.5.97.91 */\nfunction parse_RgceLoc(blob, length, opts) {\n\tif(opts && opts.biff >= 2 && opts.biff <= 5) return parse_RgceLoc_BIFF2(blob, length, opts);\n\tvar r = blob.read_shift(opts && opts.biff == 12 ? 4 : 2);\n\tvar c = parse_ColRelU(blob, 2);\n\treturn {r:r, c:c[0], cRel:c[1], rRel:c[2]};\n}\nfunction parse_RgceLoc_BIFF2(blob) {\n\tvar r = parse_ColRelU(blob, 2);\n\tvar c = blob.read_shift(1);\n\treturn {r:r[0], c:c, cRel:r[1], rRel:r[2]};\n}\n\n/* [MS-XLS] 2.5.198.107, 2.5.47 */\nfunction parse_RgceElfLoc(blob) {\n\tvar r = blob.read_shift(2);\n\tvar c = blob.read_shift(2);\n\treturn {r:r, c:c & 0xFF, fQuoted:!!(c & 0x4000), cRel:c>>15, rRel:c>>15 };\n}\n\n/* [MS-XLS] 2.5.198.111 ; [MS-XLSB] 2.5.97.92 TODO */\nfunction parse_RgceLocRel(blob, length, opts) {\n\tvar biff = opts && opts.biff ? opts.biff : 8;\n\tif(biff >= 2 && biff <= 5) return parse_RgceLocRel_BIFF2(blob, length, opts);\n\tvar r = blob.read_shift(biff >= 12 ? 4 : 2);\n\tvar cl = blob.read_shift(2);\n\tvar cRel = (cl & 0x4000) >> 14, rRel = (cl & 0x8000) >> 15;\n\tcl &= 0x3FFF;\n\tif(rRel == 1) while(r > 0x7FFFF) r -= 0x100000;\n\tif(cRel == 1) while(cl > 0x1FFF) cl = cl - 0x4000;\n\treturn {r:r,c:cl,cRel:cRel,rRel:rRel};\n}\nfunction parse_RgceLocRel_BIFF2(blob) {\n\tvar rl = blob.read_shift(2);\n\tvar c = blob.read_shift(1);\n\tvar rRel = (rl & 0x8000) >> 15, cRel = (rl & 0x4000) >> 14;\n\trl &= 0x3FFF;\n\tif(rRel == 1 && rl >= 0x2000) rl = rl - 0x4000;\n\tif(cRel == 1 && c >= 0x80) c = c - 0x100;\n\treturn {r:rl,c:c,cRel:cRel,rRel:rRel};\n}\n\n/* [MS-XLS] 2.5.198.27 ; [MS-XLSB] 2.5.97.18 */\nfunction parse_PtgArea(blob, length, opts) {\n\tvar type = (blob[blob.l++] & 0x60) >> 5;\n\tvar area = parse_RgceArea(blob, opts.biff >= 2 && opts.biff <= 5 ? 6 : 8, opts);\n\treturn [type, area];\n}\n\n/* [MS-XLS] 2.5.198.28 ; [MS-XLSB] 2.5.97.19 */\nfunction parse_PtgArea3d(blob, length, opts) {\n\tvar type = (blob[blob.l++] & 0x60) >> 5;\n\tvar ixti = blob.read_shift(2, 'i');\n\tvar w = 8;\n\tif(opts) switch(opts.biff) {\n\t\tcase 5: blob.l += 12; w = 6; break;\n\t\tcase 12: w = 12; break;\n\t}\n\tvar area = parse_RgceArea(blob, w, opts);\n\treturn [type, ixti, area];\n}\n\n/* [MS-XLS] 2.5.198.29 ; [MS-XLSB] 2.5.97.20 */\nfunction parse_PtgAreaErr(blob, length, opts) {\n\tvar type = (blob[blob.l++] & 0x60) >> 5;\n\tblob.l += opts && (opts.biff > 8) ? 12 : (opts.biff < 8 ? 6 : 8);\n\treturn [type];\n}\n/* [MS-XLS] 2.5.198.30 ; [MS-XLSB] 2.5.97.21 */\nfunction parse_PtgAreaErr3d(blob, length, opts) {\n\tvar type = (blob[blob.l++] & 0x60) >> 5;\n\tvar ixti = blob.read_shift(2);\n\tvar w = 8;\n\tif(opts) switch(opts.biff) {\n\t\tcase 5: blob.l += 12; w = 6; break;\n\t\tcase 12: w = 12; break;\n\t}\n\tblob.l += w;\n\treturn [type, ixti];\n}\n\n/* [MS-XLS] 2.5.198.31 ; [MS-XLSB] 2.5.97.22 */\nfunction parse_PtgAreaN(blob, length, opts) {\n\tvar type = (blob[blob.l++] & 0x60) >> 5;\n\tvar area = parse_RgceAreaRel(blob, length - 1, opts);\n\treturn [type, area];\n}\n\n/* [MS-XLS] 2.5.198.32 ; [MS-XLSB] 2.5.97.23 */\nfunction parse_PtgArray(blob, length, opts) {\n\tvar type = (blob[blob.l++] & 0x60) >> 5;\n\tblob.l += opts.biff == 2 ? 6 : opts.biff == 12 ? 14 : 7;\n\treturn [type];\n}\n\n/* [MS-XLS] 2.5.198.33 ; [MS-XLSB] 2.5.97.24 */\nfunction parse_PtgAttrBaxcel(blob) {\n\tvar bitSemi = blob[blob.l+1] & 0x01; /* 1 = volatile */\n\tvar bitBaxcel = 1;\n\tblob.l += 4;\n\treturn [bitSemi, bitBaxcel];\n}\n\n/* [MS-XLS] 2.5.198.34 ; [MS-XLSB] 2.5.97.25 */\nfunction parse_PtgAttrChoose(blob, length, opts) {\n\tblob.l +=2;\n\tvar offset = blob.read_shift(opts && opts.biff == 2 ? 1 : 2);\n\tvar o = [];\n\t/* offset is 1 less than the number of elements */\n\tfor(var i = 0; i <= offset; ++i) o.push(blob.read_shift(opts && opts.biff == 2 ? 1 : 2));\n\treturn o;\n}\n\n/* [MS-XLS] 2.5.198.35 ; [MS-XLSB] 2.5.97.26 */\nfunction parse_PtgAttrGoto(blob, length, opts) {\n\tvar bitGoto = (blob[blob.l+1] & 0xFF) ? 1 : 0;\n\tblob.l += 2;\n\treturn [bitGoto, blob.read_shift(opts && opts.biff == 2 ? 1 : 2)];\n}\n\n/* [MS-XLS] 2.5.198.36 ; [MS-XLSB] 2.5.97.27 */\nfunction parse_PtgAttrIf(blob, length, opts) {\n\tvar bitIf = (blob[blob.l+1] & 0xFF) ? 1 : 0;\n\tblob.l += 2;\n\treturn [bitIf, blob.read_shift(opts && opts.biff == 2 ? 1 : 2)];\n}\n\n/* [MS-XLSB] 2.5.97.28 */\nfunction parse_PtgAttrIfError(blob) {\n\tvar bitIf = (blob[blob.l+1] & 0xFF) ? 1 : 0;\n\tblob.l += 2;\n\treturn [bitIf, blob.read_shift(2)];\n}\n\n/* [MS-XLS] 2.5.198.37 ; [MS-XLSB] 2.5.97.29 */\nfunction parse_PtgAttrSemi(blob, length, opts) {\n\tvar bitSemi = (blob[blob.l+1] & 0xFF) ? 1 : 0;\n\tblob.l += opts && opts.biff == 2 ? 3 : 4;\n\treturn [bitSemi];\n}\n\n/* [MS-XLS] 2.5.198.40 ; [MS-XLSB] 2.5.97.32 */\nfunction parse_PtgAttrSpaceType(blob) {\n\tvar type = blob.read_shift(1), cch = blob.read_shift(1);\n\treturn [type, cch];\n}\n\n/* [MS-XLS] 2.5.198.38 ; [MS-XLSB] 2.5.97.30 */\nfunction parse_PtgAttrSpace(blob) {\n\tblob.read_shift(2);\n\treturn parse_PtgAttrSpaceType(blob, 2);\n}\n\n/* [MS-XLS] 2.5.198.39 ; [MS-XLSB] 2.5.97.31 */\nfunction parse_PtgAttrSpaceSemi(blob) {\n\tblob.read_shift(2);\n\treturn parse_PtgAttrSpaceType(blob, 2);\n}\n\n/* [MS-XLS] 2.5.198.84 ; [MS-XLSB] 2.5.97.68 TODO */\nfunction parse_PtgRef(blob, length, opts) {\n\t//var ptg = blob[blob.l] & 0x1F;\n\tvar type = (blob[blob.l] & 0x60)>>5;\n\tblob.l += 1;\n\tvar loc = parse_RgceLoc(blob, 0, opts);\n\treturn [type, loc];\n}\n\n/* [MS-XLS] 2.5.198.88 ; [MS-XLSB] 2.5.97.72 TODO */\nfunction parse_PtgRefN(blob, length, opts) {\n\tvar type = (blob[blob.l] & 0x60)>>5;\n\tblob.l += 1;\n\tvar loc = parse_RgceLocRel(blob, 0, opts);\n\treturn [type, loc];\n}\n\n/* [MS-XLS] 2.5.198.85 ; [MS-XLSB] 2.5.97.69 TODO */\nfunction parse_PtgRef3d(blob, length, opts) {\n\tvar type = (blob[blob.l] & 0x60)>>5;\n\tblob.l += 1;\n\tvar ixti = blob.read_shift(2); // XtiIndex\n\tif(opts && opts.biff == 5) blob.l += 12;\n\tvar loc = parse_RgceLoc(blob, 0, opts); // TODO: or RgceLocRel\n\treturn [type, ixti, loc];\n}\n\n\n/* [MS-XLS] 2.5.198.62 ; [MS-XLSB] 2.5.97.45 TODO */\nfunction parse_PtgFunc(blob, length, opts) {\n\t//var ptg = blob[blob.l] & 0x1F;\n\tvar type = (blob[blob.l] & 0x60)>>5;\n\tblob.l += 1;\n\tvar iftab = blob.read_shift(opts && opts.biff <= 3 ? 1 : 2);\n\treturn [FtabArgc[iftab], Ftab[iftab], type];\n}\n/* [MS-XLS] 2.5.198.63 ; [MS-XLSB] 2.5.97.46 TODO */\nfunction parse_PtgFuncVar(blob, length, opts) {\n\tvar type = blob[blob.l++];\n\tvar cparams = blob.read_shift(1), tab = opts && opts.biff <= 3 ? [(type == 0x58 ? -1 : 0), blob.read_shift(1)]: parsetab(blob);\n\treturn [cparams, (tab[0] === 0 ? Ftab : Cetab)[tab[1]]];\n}\n\nfunction parsetab(blob) {\n\treturn [blob[blob.l+1]>>7, blob.read_shift(2) & 0x7FFF];\n}\n\n/* [MS-XLS] 2.5.198.41 ; [MS-XLSB] 2.5.97.33 */\nfunction parse_PtgAttrSum(blob, length, opts) {\n\tblob.l += opts && opts.biff == 2 ? 3 : 4; return;\n}\n\n/* [MS-XLS] 2.5.198.58 ; [MS-XLSB] 2.5.97.40 */\nfunction parse_PtgExp(blob, length, opts) {\n\tblob.l++;\n\tif(opts && opts.biff == 12) return [blob.read_shift(4, 'i'), 0];\n\tvar row = blob.read_shift(2);\n\tvar col = blob.read_shift(opts && opts.biff == 2 ? 1 : 2);\n\treturn [row, col];\n}\n\n/* [MS-XLS] 2.5.198.57 ; [MS-XLSB] 2.5.97.39 */\nfunction parse_PtgErr(blob) { blob.l++; return BErr[blob.read_shift(1)]; }\n\n/* [MS-XLS] 2.5.198.66 ; [MS-XLSB] 2.5.97.49 */\nfunction parse_PtgInt(blob) { blob.l++; return blob.read_shift(2); }\n\n/* [MS-XLS] 2.5.198.42 ; [MS-XLSB] 2.5.97.34 */\nfunction parse_PtgBool(blob) { blob.l++; return blob.read_shift(1)!==0;}\n\n/* [MS-XLS] 2.5.198.79 ; [MS-XLSB] 2.5.97.63 */\nfunction parse_PtgNum(blob) { blob.l++; return parse_Xnum(blob, 8); }\n\n/* [MS-XLS] 2.5.198.89 ; [MS-XLSB] 2.5.97.74 */\nfunction parse_PtgStr(blob, length, opts) { blob.l++; return parse_ShortXLUnicodeString(blob, length-1, opts); }\n\n/* [MS-XLS] 2.5.192.112 + 2.5.192.11{3,4,5,6,7} */\n/* [MS-XLSB] 2.5.97.93 + 2.5.97.9{4,5,6,7} */\nfunction parse_SerAr(blob, biff) {\n\tvar val = [blob.read_shift(1)];\n\tif(biff == 12) switch(val[0]) {\n\t\tcase 0x02: val[0] = 0x04; break; /* SerBool */\n\t\tcase 0x04: val[0] = 0x10; break; /* SerErr */\n\t\tcase 0x00: val[0] = 0x01; break; /* SerNum */\n\t\tcase 0x01: val[0] = 0x02; break; /* SerStr */\n\t}\n\tswitch(val[0]) {\n\t\tcase 0x04: /* SerBool -- boolean */\n\t\t\tval[1] = parsebool(blob, 1) ? 'TRUE' : 'FALSE';\n\t\t\tif(biff != 12) blob.l += 7; break;\n\t\tcase 0x25: /* appears to be an alias */\n\t\tcase 0x10: /* SerErr -- error */\n\t\t\tval[1] = BErr[blob[blob.l]];\n\t\t\tblob.l += ((biff == 12) ? 4 : 8); break;\n\t\tcase 0x00: /* SerNil -- honestly, I'm not sure how to reproduce this */\n\t\t\tblob.l += 8; break;\n\t\tcase 0x01: /* SerNum -- Xnum */\n\t\t\tval[1] = parse_Xnum(blob, 8); break;\n\t\tcase 0x02: /* SerStr -- XLUnicodeString (<256 chars) */\n\t\t\tval[1] = parse_XLUnicodeString2(blob, 0, {biff:biff > 0 && biff < 8 ? 2 : biff}); break;\n\t\tdefault: throw new Error(\"Bad SerAr: \" + val[0]); /* Unreachable */\n\t}\n\treturn val;\n}\n\n/* [MS-XLS] 2.5.198.61 ; [MS-XLSB] 2.5.97.44 */\nfunction parse_PtgExtraMem(blob, cce, opts) {\n\tvar count = blob.read_shift((opts.biff == 12) ? 4 : 2);\n\tvar out = [];\n\tfor(var i = 0; i != count; ++i) out.push(((opts.biff == 12) ? parse_UncheckedRfX : parse_Ref8U)(blob, 8));\n\treturn out;\n}\n\n/* [MS-XLS] 2.5.198.59 ; [MS-XLSB] 2.5.97.41 */\nfunction parse_PtgExtraArray(blob, length, opts) {\n\tvar rows = 0, cols = 0;\n\tif(opts.biff == 12) {\n\t\trows = blob.read_shift(4); // DRw\n\t\tcols = blob.read_shift(4); // DCol\n\t} else {\n\t\tcols = 1 + blob.read_shift(1); //DColByteU\n\t\trows = 1 + blob.read_shift(2); //DRw\n\t}\n\tif(opts.biff >= 2 && opts.biff < 8) { --rows; if(--cols == 0) cols = 0x100; }\n\t// $FlowIgnore\n\tfor(var i = 0, o = []; i != rows && (o[i] = []); ++i)\n\t\tfor(var j = 0; j != cols; ++j) o[i][j] = parse_SerAr(blob, opts.biff);\n\treturn o;\n}\n\n/* [MS-XLS] 2.5.198.76 ; [MS-XLSB] 2.5.97.60 */\nfunction parse_PtgName(blob, length, opts) {\n\tvar type = (blob.read_shift(1) >>> 5) & 0x03;\n\tvar w = (!opts || (opts.biff >= 8)) ? 4 : 2;\n\tvar nameindex = blob.read_shift(w);\n\tswitch(opts.biff) {\n\t\tcase 2: blob.l += 5; break;\n\t\tcase 3: case 4: blob.l += 8; break;\n\t\tcase 5: blob.l += 12; break;\n\t}\n\treturn [type, 0, nameindex];\n}\n\n/* [MS-XLS] 2.5.198.77 ; [MS-XLSB] 2.5.97.61 */\nfunction parse_PtgNameX(blob, length, opts) {\n\tif(opts.biff == 5) return parse_PtgNameX_BIFF5(blob, length, opts);\n\tvar type = (blob.read_shift(1) >>> 5) & 0x03;\n\tvar ixti = blob.read_shift(2); // XtiIndex\n\tvar nameindex = blob.read_shift(4);\n\treturn [type, ixti, nameindex];\n}\nfunction parse_PtgNameX_BIFF5(blob) {\n\tvar type = (blob.read_shift(1) >>> 5) & 0x03;\n\tvar ixti = blob.read_shift(2, 'i'); // XtiIndex\n\tblob.l += 8;\n\tvar nameindex = blob.read_shift(2);\n\tblob.l += 12;\n\treturn [type, ixti, nameindex];\n}\n\n/* [MS-XLS] 2.5.198.70 ; [MS-XLSB] 2.5.97.54 */\nfunction parse_PtgMemArea(blob, length, opts) {\n\tvar type = (blob.read_shift(1) >>> 5) & 0x03;\n\tblob.l += (opts && opts.biff == 2 ? 3 : 4);\n\tvar cce = blob.read_shift(opts && opts.biff == 2 ? 1 : 2);\n\treturn [type, cce];\n}\n\n/* [MS-XLS] 2.5.198.72 ; [MS-XLSB] 2.5.97.56 */\nfunction parse_PtgMemFunc(blob, length, opts) {\n\tvar type = (blob.read_shift(1) >>> 5) & 0x03;\n\tvar cce = blob.read_shift(opts && opts.biff == 2 ? 1 : 2);\n\treturn [type, cce];\n}\n\n\n/* [MS-XLS] 2.5.198.86 ; [MS-XLSB] 2.5.97.69 */\nfunction parse_PtgRefErr(blob, length, opts) {\n\tvar type = (blob.read_shift(1) >>> 5) & 0x03;\n\tblob.l += 4;\n\tif(opts.biff < 8) blob.l--;\n\tif(opts.biff == 12) blob.l += 2;\n\treturn [type];\n}\n\n/* [MS-XLS] 2.5.198.87 ; [MS-XLSB] 2.5.97.71 */\nfunction parse_PtgRefErr3d(blob, length, opts) {\n\tvar type = (blob[blob.l++] & 0x60) >> 5;\n\tvar ixti = blob.read_shift(2);\n\tvar w = 4;\n\tif(opts) switch(opts.biff) {\n\t\tcase 5: w = 15; break;\n\t\tcase 12: w = 6; break;\n\t}\n\tblob.l += w;\n\treturn [type, ixti];\n}\n\n/* [MS-XLS] 2.5.198.71 ; [MS-XLSB] 2.5.97.55 */\nvar parse_PtgMemErr = parsenoop;\n/* [MS-XLS] 2.5.198.73 ; [MS-XLSB] 2.5.97.57 */\nvar parse_PtgMemNoMem = parsenoop;\n/* [MS-XLS] 2.5.198.92 */\nvar parse_PtgTbl = parsenoop;\n\nfunction parse_PtgElfLoc(blob, length, opts) {\n\tblob.l += 2;\n\treturn [parse_RgceElfLoc(blob, 4, opts)];\n}\nfunction parse_PtgElfNoop(blob) {\n\tblob.l += 6;\n\treturn [];\n}\n/* [MS-XLS] 2.5.198.46 */\nvar parse_PtgElfCol = parse_PtgElfLoc;\n/* [MS-XLS] 2.5.198.47 */\nvar parse_PtgElfColS = parse_PtgElfNoop;\n/* [MS-XLS] 2.5.198.48 */\nvar parse_PtgElfColSV = parse_PtgElfNoop;\n/* [MS-XLS] 2.5.198.49 */\nvar parse_PtgElfColV = parse_PtgElfLoc;\n/* [MS-XLS] 2.5.198.50 */\nfunction parse_PtgElfLel(blob) {\n\tblob.l += 2;\n\treturn [parseuint16(blob), blob.read_shift(2) & 0x01];\n}\n/* [MS-XLS] 2.5.198.51 */\nvar parse_PtgElfRadical = parse_PtgElfLoc;\n/* [MS-XLS] 2.5.198.52 */\nvar parse_PtgElfRadicalLel = parse_PtgElfLel;\n/* [MS-XLS] 2.5.198.53 */\nvar parse_PtgElfRadicalS = parse_PtgElfNoop;\n/* [MS-XLS] 2.5.198.54 */\nvar parse_PtgElfRw = parse_PtgElfLoc;\n/* [MS-XLS] 2.5.198.55 */\nvar parse_PtgElfRwV = parse_PtgElfLoc;\n\n/* [MS-XLSB] 2.5.97.52 TODO */\nvar PtgListRT = [\n\t\"Data\",\n\t\"All\",\n\t\"Headers\",\n\t\"??\",\n\t\"?Data2\",\n\t\"??\",\n\t\"?DataHeaders\",\n\t\"??\",\n\t\"Totals\",\n\t\"??\",\n\t\"??\",\n\t\"??\",\n\t\"?DataTotals\",\n\t\"??\",\n\t\"??\",\n\t\"??\",\n\t\"?Current\"\n];\nfunction parse_PtgList(blob) {\n\tblob.l += 2;\n\tvar ixti = blob.read_shift(2);\n\tvar flags = blob.read_shift(2);\n\tvar idx = blob.read_shift(4);\n\tvar c = blob.read_shift(2);\n\tvar C = blob.read_shift(2);\n\tvar rt = PtgListRT[(flags >> 2) & 0x1F];\n\treturn {ixti: ixti, coltype:(flags&0x3), rt:rt, idx:idx, c:c, C:C};\n}\n/* [MS-XLS] 2.5.198.91 ; [MS-XLSB] 2.5.97.76 */\nfunction parse_PtgSxName(blob) {\n\tblob.l += 2;\n\treturn [blob.read_shift(4)];\n}\n\n/* [XLS] old spec */\nfunction parse_PtgSheet(blob, length, opts) {\n\tblob.l += 5;\n\tblob.l += 2;\n\tblob.l += (opts.biff == 2 ? 1 : 4);\n\treturn [\"PTGSHEET\"];\n}\nfunction parse_PtgEndSheet(blob, length, opts) {\n\tblob.l += (opts.biff == 2 ? 4 : 5);\n\treturn [\"PTGENDSHEET\"];\n}\nfunction parse_PtgMemAreaN(blob) {\n\tvar type = (blob.read_shift(1) >>> 5) & 0x03;\n\tvar cce = blob.read_shift(2);\n\treturn [type, cce];\n}\nfunction parse_PtgMemNoMemN(blob) {\n\tvar type = (blob.read_shift(1) >>> 5) & 0x03;\n\tvar cce = blob.read_shift(2);\n\treturn [type, cce];\n}\nfunction parse_PtgAttrNoop(blob) {\n\tblob.l += 4;\n\treturn [0, 0];\n}\n\n/* [MS-XLS] 2.5.198.25 ; [MS-XLSB] 2.5.97.16 */\nvar PtgTypes = {\n0x01: { n:'PtgExp', f:parse_PtgExp },\n0x02: { n:'PtgTbl', f:parse_PtgTbl },\n0x03: { n:'PtgAdd', f:parseread1 },\n0x04: { n:'PtgSub', f:parseread1 },\n0x05: { n:'PtgMul', f:parseread1 },\n0x06: { n:'PtgDiv', f:parseread1 },\n0x07: { n:'PtgPower', f:parseread1 },\n0x08: { n:'PtgConcat', f:parseread1 },\n0x09: { n:'PtgLt', f:parseread1 },\n0x0A: { n:'PtgLe', f:parseread1 },\n0x0B: { n:'PtgEq', f:parseread1 },\n0x0C: { n:'PtgGe', f:parseread1 },\n0x0D: { n:'PtgGt', f:parseread1 },\n0x0E: { n:'PtgNe', f:parseread1 },\n0x0F: { n:'PtgIsect', f:parseread1 },\n0x10: { n:'PtgUnion', f:parseread1 },\n0x11: { n:'PtgRange', f:parseread1 },\n0x12: { n:'PtgUplus', f:parseread1 },\n0x13: { n:'PtgUminus', f:parseread1 },\n0x14: { n:'PtgPercent', f:parseread1 },\n0x15: { n:'PtgParen', f:parseread1 },\n0x16: { n:'PtgMissArg', f:parseread1 },\n0x17: { n:'PtgStr', f:parse_PtgStr },\n0x1A: { n:'PtgSheet', f:parse_PtgSheet },\n0x1B: { n:'PtgEndSheet', f:parse_PtgEndSheet },\n0x1C: { n:'PtgErr', f:parse_PtgErr },\n0x1D: { n:'PtgBool', f:parse_PtgBool },\n0x1E: { n:'PtgInt', f:parse_PtgInt },\n0x1F: { n:'PtgNum', f:parse_PtgNum },\n0x20: { n:'PtgArray', f:parse_PtgArray },\n0x21: { n:'PtgFunc', f:parse_PtgFunc },\n0x22: { n:'PtgFuncVar', f:parse_PtgFuncVar },\n0x23: { n:'PtgName', f:parse_PtgName },\n0x24: { n:'PtgRef', f:parse_PtgRef },\n0x25: { n:'PtgArea', f:parse_PtgArea },\n0x26: { n:'PtgMemArea', f:parse_PtgMemArea },\n0x27: { n:'PtgMemErr', f:parse_PtgMemErr },\n0x28: { n:'PtgMemNoMem', f:parse_PtgMemNoMem },\n0x29: { n:'PtgMemFunc', f:parse_PtgMemFunc },\n0x2A: { n:'PtgRefErr', f:parse_PtgRefErr },\n0x2B: { n:'PtgAreaErr', f:parse_PtgAreaErr },\n0x2C: { n:'PtgRefN', f:parse_PtgRefN },\n0x2D: { n:'PtgAreaN', f:parse_PtgAreaN },\n0x2E: { n:'PtgMemAreaN', f:parse_PtgMemAreaN },\n0x2F: { n:'PtgMemNoMemN', f:parse_PtgMemNoMemN },\n0x39: { n:'PtgNameX', f:parse_PtgNameX },\n0x3A: { n:'PtgRef3d', f:parse_PtgRef3d },\n0x3B: { n:'PtgArea3d', f:parse_PtgArea3d },\n0x3C: { n:'PtgRefErr3d', f:parse_PtgRefErr3d },\n0x3D: { n:'PtgAreaErr3d', f:parse_PtgAreaErr3d },\n0xFF: {}\n};\n/* These are duplicated in the PtgTypes table */\nvar PtgDupes = {\n0x40: 0x20, 0x60: 0x20,\n0x41: 0x21, 0x61: 0x21,\n0x42: 0x22, 0x62: 0x22,\n0x43: 0x23, 0x63: 0x23,\n0x44: 0x24, 0x64: 0x24,\n0x45: 0x25, 0x65: 0x25,\n0x46: 0x26, 0x66: 0x26,\n0x47: 0x27, 0x67: 0x27,\n0x48: 0x28, 0x68: 0x28,\n0x49: 0x29, 0x69: 0x29,\n0x4A: 0x2A, 0x6A: 0x2A,\n0x4B: 0x2B, 0x6B: 0x2B,\n0x4C: 0x2C, 0x6C: 0x2C,\n0x4D: 0x2D, 0x6D: 0x2D,\n0x4E: 0x2E, 0x6E: 0x2E,\n0x4F: 0x2F, 0x6F: 0x2F,\n0x58: 0x22, 0x78: 0x22,\n0x59: 0x39, 0x79: 0x39,\n0x5A: 0x3A, 0x7A: 0x3A,\n0x5B: 0x3B, 0x7B: 0x3B,\n0x5C: 0x3C, 0x7C: 0x3C,\n0x5D: 0x3D, 0x7D: 0x3D\n};\n(function(){for(var y in PtgDupes) PtgTypes[y] = PtgTypes[PtgDupes[y]];})();\n\nvar Ptg18 = {\n0x01: { n:'PtgElfLel', f:parse_PtgElfLel },\n0x02: { n:'PtgElfRw', f:parse_PtgElfRw },\n0x03: { n:'PtgElfCol', f:parse_PtgElfCol },\n0x06: { n:'PtgElfRwV', f:parse_PtgElfRwV },\n0x07: { n:'PtgElfColV', f:parse_PtgElfColV },\n0x0A: { n:'PtgElfRadical', f:parse_PtgElfRadical },\n0x0B: { n:'PtgElfRadicalS', f:parse_PtgElfRadicalS },\n0x0D: { n:'PtgElfColS', f:parse_PtgElfColS },\n0x0F: { n:'PtgElfColSV', f:parse_PtgElfColSV },\n0x10: { n:'PtgElfRadicalLel', f:parse_PtgElfRadicalLel },\n0x19: { n:'PtgList', f:parse_PtgList },\n0x1D: { n:'PtgSxName', f:parse_PtgSxName },\n0xFF: {}\n};\nvar Ptg19 = {\n0x00: { n:'PtgAttrNoop', f:parse_PtgAttrNoop },\n0x01: { n:'PtgAttrSemi', f:parse_PtgAttrSemi },\n0x02: { n:'PtgAttrIf', f:parse_PtgAttrIf },\n0x04: { n:'PtgAttrChoose', f:parse_PtgAttrChoose },\n0x08: { n:'PtgAttrGoto', f:parse_PtgAttrGoto },\n0x10: { n:'PtgAttrSum', f:parse_PtgAttrSum },\n0x20: { n:'PtgAttrBaxcel', f:parse_PtgAttrBaxcel },\n0x40: { n:'PtgAttrSpace', f:parse_PtgAttrSpace },\n0x41: { n:'PtgAttrSpaceSemi', f:parse_PtgAttrSpaceSemi },\n0x80: { n:'PtgAttrIfError', f:parse_PtgAttrIfError },\n0xFF: {}\n};\nPtg19[0x21] = Ptg19[0x20];\n\n/* [MS-XLS] 2.5.198.103 ; [MS-XLSB] 2.5.97.87 */\nfunction parse_RgbExtra(blob, length, rgce, opts) {\n\tif(opts.biff < 8) return parsenoop(blob, length);\n\tvar target = blob.l + length;\n\tvar o = [];\n\tfor(var i = 0; i !== rgce.length; ++i) {\n\t\tswitch(rgce[i][0]) {\n\t\t\tcase 'PtgArray': /* PtgArray -> PtgExtraArray */\n\t\t\t\trgce[i][1] = parse_PtgExtraArray(blob, 0, opts);\n\t\t\t\to.push(rgce[i][1]);\n\t\t\t\tbreak;\n\t\t\tcase 'PtgMemArea': /* PtgMemArea -> PtgExtraMem */\n\t\t\t\trgce[i][2] = parse_PtgExtraMem(blob, rgce[i][1], opts);\n\t\t\t\to.push(rgce[i][2]);\n\t\t\t\tbreak;\n\t\t\tcase 'PtgExp': /* PtgExp -> PtgExtraCol */\n\t\t\t\tif(opts && opts.biff == 12) {\n\t\t\t\t\trgce[i][1][1] = blob.read_shift(4);\n\t\t\t\t\to.push(rgce[i][1]);\n\t\t\t\t} break;\n\t\t\tcase 'PtgList': /* TODO: PtgList -> PtgExtraList */\n\t\t\tcase 'PtgElfRadicalS': /* TODO: PtgElfRadicalS -> PtgExtraElf */\n\t\t\tcase 'PtgElfColS': /* TODO: PtgElfColS -> PtgExtraElf */\n\t\t\tcase 'PtgElfColSV': /* TODO: PtgElfColSV -> PtgExtraElf */\n\t\t\t\tthrow \"Unsupported \" + rgce[i][0];\n\t\t\tdefault: break;\n\t\t}\n\t}\n\tlength = target - blob.l;\n\t/* note: this is technically an error but Excel disregards */\n\t//if(target !== blob.l && blob.l !== target - length) throw new Error(target + \" != \" + blob.l);\n\tif(length !== 0) o.push(parsenoop(blob, length));\n\treturn o;\n}\n\n/* [MS-XLS] 2.5.198.104 ; [MS-XLSB] 2.5.97.88 */\nfunction parse_Rgce(blob, length, opts) {\n\tvar target = blob.l + length;\n\tvar R, id, ptgs = [];\n\twhile(target != blob.l) {\n\t\tlength = target - blob.l;\n\t\tid = blob[blob.l];\n\t\tR = PtgTypes[id];\n\t\tif(id === 0x18 || id === 0x19) R = (id === 0x18 ? Ptg18 : Ptg19)[blob[blob.l + 1]];\n\t\tif(!R || !R.f) { /*ptgs.push*/(parsenoop(blob, length)); }\n\t\telse { ptgs.push([R.n, R.f(blob, length, opts)]); }\n\t}\n\treturn ptgs;\n}\n\nfunction stringify_array(f) {\n\tvar o = [];\n\tfor(var i = 0; i < f.length; ++i) {\n\t\tvar x = f[i], r = [];\n\t\tfor(var j = 0; j < x.length; ++j) {\n\t\t\tvar y = x[j];\n\t\t\tif(y) switch(y[0]) {\n\t\t\t\t// TODO: handle embedded quotes\n\t\t\t\tcase 0x02:\nr.push('\"' + y[1].replace(/\"/g,'\"\"') + '\"'); break;\n\t\t\t\tdefault: r.push(y[1]);\n\t\t\t} else r.push(\"\");\n\t\t}\n\t\to.push(r.join(\",\"));\n\t}\n\treturn o.join(\";\");\n}\n\n/* [MS-XLS] 2.2.2 ; [MS-XLSB] 2.2.2 TODO */\nvar PtgBinOp = {\n\tPtgAdd: \"+\",\n\tPtgConcat: \"&\",\n\tPtgDiv: \"/\",\n\tPtgEq: \"=\",\n\tPtgGe: \">=\",\n\tPtgGt: \">\",\n\tPtgLe: \"<=\",\n\tPtgLt: \"<\",\n\tPtgMul: \"*\",\n\tPtgNe: \"<>\",\n\tPtgPower: \"^\",\n\tPtgSub: \"-\"\n};\n\n// List of invalid characters needs to be tested further\nvar quoteCharacters = new RegExp(/[^\\w\\u4E00-\\u9FFF\\u3040-\\u30FF]/);\nfunction formula_quote_sheet_name(sname, opts) {\n\tif(!sname && !(opts && opts.biff <= 5 && opts.biff >= 2)) throw new Error(\"empty sheet name\");\n\tif (quoteCharacters.test(sname)) return \"'\" + sname + \"'\";\n\treturn sname;\n}\nfunction get_ixti_raw(supbooks, ixti, opts) {\n\tif(!supbooks) return \"SH33TJSERR0\";\n\tif(opts.biff > 8 && (!supbooks.XTI || !supbooks.XTI[ixti])) return supbooks.SheetNames[ixti];\n\tif(!supbooks.XTI) return \"SH33TJSERR6\";\n\tvar XTI = supbooks.XTI[ixti];\n\tif(opts.biff < 8) {\n\t\tif(ixti > 10000) ixti-= 65536;\n\t\tif(ixti < 0) ixti = -ixti;\n\t\treturn ixti == 0 ? \"\" : supbooks.XTI[ixti - 1];\n\t}\n\tif(!XTI) return \"SH33TJSERR1\";\n\tvar o = \"\";\n\tif(opts.biff > 8) switch(supbooks[XTI[0]][0]) {\n\t\tcase 0x0165: /* 'BrtSupSelf' */\n\t\t\to = XTI[1] == -1 ? \"#REF\" : supbooks.SheetNames[XTI[1]];\n\t\t\treturn XTI[1] == XTI[2] ? o : o + \":\" + supbooks.SheetNames[XTI[2]];\n\t\tcase 0x0166: /* 'BrtSupSame' */\n\t\t\tif(opts.SID != null) return supbooks.SheetNames[opts.SID];\n\t\t\treturn \"SH33TJSSAME\" + supbooks[XTI[0]][0];\n\t\tcase 0x0163: /* 'BrtSupBookSrc' */\n\t\t\t/* falls through */\n\t\tdefault: return \"SH33TJSSRC\" + supbooks[XTI[0]][0];\n\t}\n\tswitch(supbooks[XTI[0]][0][0]) {\n\t\tcase 0x0401:\n\t\t\to = XTI[1] == -1 ? \"#REF\" : (supbooks.SheetNames[XTI[1]] || \"SH33TJSERR3\");\n\t\t\treturn XTI[1] == XTI[2] ? o : o + \":\" + supbooks.SheetNames[XTI[2]];\n\t\tcase 0x3A01: return supbooks[XTI[0]].slice(1).map(function(name) { return name.Name; }).join(\";;\"); //return \"SH33TJSERR8\";\n\t\tdefault:\n\t\t\tif(!supbooks[XTI[0]][0][3]) return \"SH33TJSERR2\";\n\t\t\to = XTI[1] == -1 ? \"#REF\" : (supbooks[XTI[0]][0][3][XTI[1]] || \"SH33TJSERR4\");\n\t\t\treturn XTI[1] == XTI[2] ? o : o + \":\" + supbooks[XTI[0]][0][3][XTI[2]];\n\t}\n}\nfunction get_ixti(supbooks, ixti, opts) {\n\treturn formula_quote_sheet_name(get_ixti_raw(supbooks, ixti, opts), opts);\n}\nfunction stringify_formula(formula/*Array*/, range, cell, supbooks, opts) {\n\tvar biff = (opts && opts.biff) || 8;\n\tvar _range = /*range != null ? range :*/ {s:{c:0, r:0},e:{c:0, r:0}};\n\tvar stack = [], e1, e2, c, ixti=0, nameidx=0, r, sname=\"\";\n\tif(!formula[0] || !formula[0][0]) return \"\";\n\tvar last_sp = -1, sp = \"\";\n\tfor(var ff = 0, fflen = formula[0].length; ff < fflen; ++ff) {\n\t\tvar f = formula[0][ff];\n\t\tswitch(f[0]) {\n\t\t\tcase 'PtgUminus': /* [MS-XLS] 2.5.198.93 */\n\t\t\t\tstack.push(\"-\" + stack.pop()); break;\n\t\t\tcase 'PtgUplus': /* [MS-XLS] 2.5.198.95 */\n\t\t\t\tstack.push(\"+\" + stack.pop()); break;\n\t\t\tcase 'PtgPercent': /* [MS-XLS] 2.5.198.81 */\n\t\t\t\tstack.push(stack.pop() + \"%\"); break;\n\n\t\t\tcase 'PtgAdd': /* [MS-XLS] 2.5.198.26 */\n\t\t\tcase 'PtgConcat': /* [MS-XLS] 2.5.198.43 */\n\t\t\tcase 'PtgDiv': /* [MS-XLS] 2.5.198.45 */\n\t\t\tcase 'PtgEq': /* [MS-XLS] 2.5.198.56 */\n\t\t\tcase 'PtgGe': /* [MS-XLS] 2.5.198.64 */\n\t\t\tcase 'PtgGt': /* [MS-XLS] 2.5.198.65 */\n\t\t\tcase 'PtgLe': /* [MS-XLS] 2.5.198.68 */\n\t\t\tcase 'PtgLt': /* [MS-XLS] 2.5.198.69 */\n\t\t\tcase 'PtgMul': /* [MS-XLS] 2.5.198.75 */\n\t\t\tcase 'PtgNe': /* [MS-XLS] 2.5.198.78 */\n\t\t\tcase 'PtgPower': /* [MS-XLS] 2.5.198.82 */\n\t\t\tcase 'PtgSub': /* [MS-XLS] 2.5.198.90 */\n\t\t\t\te1 = stack.pop(); e2 = stack.pop();\n\t\t\t\tif(last_sp >= 0) {\n\t\t\t\t\tswitch(formula[0][last_sp][1][0]) {\n\t\t\t\t\t\tcase 0:\n\t\t\t\t\t\t\t// $FlowIgnore\n\t\t\t\t\t\t\tsp = fill(\" \", formula[0][last_sp][1][1]); break;\n\t\t\t\t\t\tcase 1:\n\t\t\t\t\t\t\t// $FlowIgnore\n\t\t\t\t\t\t\tsp = fill(\"\\r\", formula[0][last_sp][1][1]); break;\n\t\t\t\t\t\tdefault:\n\t\t\t\t\t\t\tsp = \"\";\n\t\t\t\t\t\t\t// $FlowIgnore\n\t\t\t\t\t\t\tif(opts.WTF) throw new Error(\"Unexpected PtgAttrSpaceType \" + formula[0][last_sp][1][0]);\n\t\t\t\t\t}\n\t\t\t\t\te2 = e2 + sp;\n\t\t\t\t\tlast_sp = -1;\n\t\t\t\t}\n\t\t\t\tstack.push(e2+PtgBinOp[f[0]]+e1);\n\t\t\t\tbreak;\n\n\t\t\tcase 'PtgIsect': /* [MS-XLS] 2.5.198.67 */\n\t\t\t\te1 = stack.pop(); e2 = stack.pop();\n\t\t\t\tstack.push(e2+\" \"+e1);\n\t\t\t\tbreak;\n\t\t\tcase 'PtgUnion': /* [MS-XLS] 2.5.198.94 */\n\t\t\t\te1 = stack.pop(); e2 = stack.pop();\n\t\t\t\tstack.push(e2+\",\"+e1);\n\t\t\t\tbreak;\n\t\t\tcase 'PtgRange': /* [MS-XLS] 2.5.198.83 */\n\t\t\t\te1 = stack.pop(); e2 = stack.pop();\n\t\t\t\tstack.push(e2+\":\"+e1);\n\t\t\t\tbreak;\n\n\t\t\tcase 'PtgAttrChoose': /* [MS-XLS] 2.5.198.34 */\n\t\t\t\tbreak;\n\t\t\tcase 'PtgAttrGoto': /* [MS-XLS] 2.5.198.35 */\n\t\t\t\tbreak;\n\t\t\tcase 'PtgAttrIf': /* [MS-XLS] 2.5.198.36 */\n\t\t\t\tbreak;\n\t\t\tcase 'PtgAttrIfError': /* [MS-XLSB] 2.5.97.28 */\n\t\t\t\tbreak;\n\n\n\t\t\tcase 'PtgRef': /* [MS-XLS] 2.5.198.84 */\nc = shift_cell_xls((f[1][1]), _range, opts);\n\t\t\t\tstack.push(encode_cell_xls(c, biff));\n\t\t\t\tbreak;\n\t\t\tcase 'PtgRefN': /* [MS-XLS] 2.5.198.88 */\nc = cell ? shift_cell_xls((f[1][1]), cell, opts) : (f[1][1]);\n\t\t\t\tstack.push(encode_cell_xls(c, biff));\n\t\t\t\tbreak;\n\t\t\tcase 'PtgRef3d': /* [MS-XLS] 2.5.198.85 */\nixti = f[1][1]; c = shift_cell_xls((f[1][2]), _range, opts);\n\t\t\t\tsname = get_ixti(supbooks, ixti, opts);\n\t\t\t\tvar w = sname; /* IE9 fails on defined names */ // eslint-disable-line no-unused-vars\n\t\t\t\tstack.push(sname + \"!\" + encode_cell_xls(c, biff));\n\t\t\t\tbreak;\n\n\t\t\tcase 'PtgFunc': /* [MS-XLS] 2.5.198.62 */\n\t\t\tcase 'PtgFuncVar': /* [MS-XLS] 2.5.198.63 */\n\t\t\t\t/* f[1] = [argc, func, type] */\n\t\t\t\tvar argc = (f[1][0]), func = (f[1][1]);\n\t\t\t\tif(!argc) argc = 0;\n\t\t\t\targc &= 0x7F;\n\t\t\t\tvar args = argc == 0 ? [] : stack.slice(-argc);\n\t\t\t\tstack.length -= argc;\n\t\t\t\tif(func === 'User') func = args.shift();\n\t\t\t\tstack.push(func + \"(\" + args.join(\",\") + \")\");\n\t\t\t\tbreak;\n\n\t\t\tcase 'PtgBool': /* [MS-XLS] 2.5.198.42 */\n\t\t\t\tstack.push(f[1] ? \"TRUE\" : \"FALSE\"); break;\n\t\t\tcase 'PtgInt': /* [MS-XLS] 2.5.198.66 */\n\t\t\t\tstack.push(f[1]); break;\n\t\t\tcase 'PtgNum': /* [MS-XLS] 2.5.198.79 TODO: precision? */\n\t\t\t\tstack.push(String(f[1])); break;\n\t\t\tcase 'PtgStr': /* [MS-XLS] 2.5.198.89 */\n\t\t\t\t// $FlowIgnore\n\t\t\t\tstack.push('\"' + f[1].replace(/\"/g, '\"\"') + '\"'); break;\n\t\t\tcase 'PtgErr': /* [MS-XLS] 2.5.198.57 */\n\t\t\t\tstack.push(f[1]); break;\n\t\t\tcase 'PtgAreaN': /* [MS-XLS] 2.5.198.31 TODO */\nr = shift_range_xls(f[1][1], cell ? {s:cell} : _range, opts);\n\t\t\t\tstack.push(encode_range_xls((r), opts));\n\t\t\t\tbreak;\n\t\t\tcase 'PtgArea': /* [MS-XLS] 2.5.198.27 TODO: fixed points */\nr = shift_range_xls(f[1][1], _range, opts);\n\t\t\t\tstack.push(encode_range_xls((r), opts));\n\t\t\t\tbreak;\n\t\t\tcase 'PtgArea3d': /* [MS-XLS] 2.5.198.28 TODO */\nixti = f[1][1]; r = f[1][2];\n\t\t\t\tsname = get_ixti(supbooks, ixti, opts);\n\t\t\t\tstack.push(sname + \"!\" + encode_range_xls((r), opts));\n\t\t\t\tbreak;\n\t\t\tcase 'PtgAttrSum': /* [MS-XLS] 2.5.198.41 */\n\t\t\t\tstack.push(\"SUM(\" + stack.pop() + \")\");\n\t\t\t\tbreak;\n\n\t\t\tcase 'PtgAttrBaxcel': /* [MS-XLS] 2.5.198.33 */\n\t\t\tcase 'PtgAttrSemi': /* [MS-XLS] 2.5.198.37 */\n\t\t\t\tbreak;\n\n\t\t\tcase 'PtgName': /* [MS-XLS] 2.5.198.76 ; [MS-XLSB] 2.5.97.60 TODO: revisions */\n\t\t\t\t/* f[1] = type, 0, nameindex */\n\t\t\t\tnameidx = (f[1][2]);\n\t\t\t\tvar lbl = (supbooks.names||[])[nameidx-1] || (supbooks[0]||[])[nameidx];\n\t\t\t\tvar name = lbl ? lbl.Name : \"SH33TJSNAME\" + String(nameidx);\n\t\t\t\tif(name in XLSXFutureFunctions) name = XLSXFutureFunctions[name];\n\t\t\t\tstack.push(name);\n\t\t\t\tbreak;\n\n\t\t\tcase 'PtgNameX': /* [MS-XLS] 2.5.198.77 ; [MS-XLSB] 2.5.97.61 TODO: revisions */\n\t\t\t\t/* f[1] = type, ixti, nameindex */\n\t\t\t\tvar bookidx = (f[1][1]); nameidx = (f[1][2]); var externbook;\n\t\t\t\t/* TODO: Properly handle missing values -- this should be using get_ixti_raw primarily */\n\t\t\t\tif(opts.biff <= 5) {\n\t\t\t\t\tif(bookidx < 0) bookidx = -bookidx;\n\t\t\t\t\tif(supbooks[bookidx]) externbook = supbooks[bookidx][nameidx];\n\t\t\t\t} else {\n\t\t\t\t\tvar o = \"\";\n\t\t\t\t\tif(((supbooks[bookidx]||[])[0]||[])[0] == 0x3A01){/* empty */}\n\t\t\t\t\telse if(((supbooks[bookidx]||[])[0]||[])[0] == 0x0401){\n\t\t\t\t\t\tif(supbooks[bookidx][nameidx] && supbooks[bookidx][nameidx].itab > 0) {\n\t\t\t\t\t\t\to = supbooks.SheetNames[supbooks[bookidx][nameidx].itab-1] + \"!\";\n\t\t\t\t\t\t}\n\t\t\t\t\t}\n\t\t\t\t\telse o = supbooks.SheetNames[nameidx-1]+ \"!\";\n\t\t\t\t\tif(supbooks[bookidx] && supbooks[bookidx][nameidx]) o += supbooks[bookidx][nameidx].Name;\n\t\t\t\t\telse if(supbooks[0] && supbooks[0][nameidx]) o += supbooks[0][nameidx].Name;\n\t\t\t\t\telse {\n\t\t\t\t\t\tvar ixtidata = get_ixti_raw(supbooks, bookidx, opts).split(\";;\");\n\t\t\t\t\t\tif(ixtidata[nameidx - 1]) o = ixtidata[nameidx - 1]; // TODO: confirm this is correct\n\t\t\t\t\t\telse o += \"SH33TJSERRX\";\n\t\t\t\t\t}\n\t\t\t\t\tstack.push(o);\n\t\t\t\t\tbreak;\n\t\t\t\t}\n\t\t\t\tif(!externbook) externbook = {Name: \"SH33TJSERRY\"};\n\t\t\t\tstack.push(externbook.Name);\n\t\t\t\tbreak;\n\n\t\t\tcase 'PtgParen': /* [MS-XLS] 2.5.198.80 */\n\t\t\t\tvar lp = '(', rp = ')';\n\t\t\t\tif(last_sp >= 0) {\n\t\t\t\t\tsp = \"\";\n\t\t\t\t\tswitch(formula[0][last_sp][1][0]) {\n\t\t\t\t\t\t// $FlowIgnore\n\t\t\t\t\t\tcase 2: lp = fill(\" \", formula[0][last_sp][1][1]) + lp; break;\n\t\t\t\t\t\t// $FlowIgnore\n\t\t\t\t\t\tcase 3: lp = fill(\"\\r\", formula[0][last_sp][1][1]) + lp; break;\n\t\t\t\t\t\t// $FlowIgnore\n\t\t\t\t\t\tcase 4: rp = fill(\" \", formula[0][last_sp][1][1]) + rp; break;\n\t\t\t\t\t\t// $FlowIgnore\n\t\t\t\t\t\tcase 5: rp = fill(\"\\r\", formula[0][last_sp][1][1]) + rp; break;\n\t\t\t\t\t\tdefault:\n\t\t\t\t\t\t\t// $FlowIgnore\n\t\t\t\t\t\t\tif(opts.WTF) throw new Error(\"Unexpected PtgAttrSpaceType \" + formula[0][last_sp][1][0]);\n\t\t\t\t\t}\n\t\t\t\t\tlast_sp = -1;\n\t\t\t\t}\n\t\t\t\tstack.push(lp + stack.pop() + rp); break;\n\n\t\t\tcase 'PtgRefErr': /* [MS-XLS] 2.5.198.86 */\n\t\t\t\tstack.push('#REF!'); break;\n\n\t\t\tcase 'PtgRefErr3d': /* [MS-XLS] 2.5.198.87 */\n\t\t\t\tstack.push('#REF!'); break;\n\n\t\t\tcase 'PtgExp': /* [MS-XLS] 2.5.198.58 TODO */\n\t\t\t\tc = {c:(f[1][1]),r:(f[1][0])};\n\t\t\t\tvar q = ({c: cell.c, r:cell.r});\n\t\t\t\tif(supbooks.sharedf[encode_cell(c)]) {\n\t\t\t\t\tvar parsedf = (supbooks.sharedf[encode_cell(c)]);\n\t\t\t\t\tstack.push(stringify_formula(parsedf, _range, q, supbooks, opts));\n\t\t\t\t}\n\t\t\t\telse {\n\t\t\t\t\tvar fnd = false;\n\t\t\t\t\tfor(e1=0;e1!=supbooks.arrayf.length; ++e1) {\n\t\t\t\t\t\t/* TODO: should be something like range_has */\n\t\t\t\t\t\te2 = supbooks.arrayf[e1];\n\t\t\t\t\t\tif(c.c < e2[0].s.c || c.c > e2[0].e.c) continue;\n\t\t\t\t\t\tif(c.r < e2[0].s.r || c.r > e2[0].e.r) continue;\n\t\t\t\t\t\tstack.push(stringify_formula(e2[1], _range, q, supbooks, opts));\n\t\t\t\t\t\tfnd = true;\n\t\t\t\t\t\tbreak;\n\t\t\t\t\t}\n\t\t\t\t\tif(!fnd) stack.push(f[1]);\n\t\t\t\t}\n\t\t\t\tbreak;\n\n\t\t\tcase 'PtgArray': /* [MS-XLS] 2.5.198.32 TODO */\n\t\t\t\tstack.push(\"{\" + stringify_array(f[1]) + \"}\");\n\t\t\t\tbreak;\n\n\t\t\tcase 'PtgMemArea': /* [MS-XLS] 2.5.198.70 TODO: confirm this is a non-display */\n\t\t\t\t//stack.push(\"(\" + f[2].map(encode_range).join(\",\") + \")\");\n\t\t\t\tbreak;\n\n\t\t\tcase 'PtgAttrSpace': /* [MS-XLS] 2.5.198.38 */\n\t\t\tcase 'PtgAttrSpaceSemi': /* [MS-XLS] 2.5.198.39 */\n\t\t\t\tlast_sp = ff;\n\t\t\t\tbreak;\n\n\t\t\tcase 'PtgTbl': /* [MS-XLS] 2.5.198.92 TODO */\n\t\t\t\tbreak;\n\n\t\t\tcase 'PtgMemErr': /* [MS-XLS] 2.5.198.71 */\n\t\t\t\tbreak;\n\n\t\t\tcase 'PtgMissArg': /* [MS-XLS] 2.5.198.74 */\n\t\t\t\tstack.push(\"\");\n\t\t\t\tbreak;\n\n\t\t\tcase 'PtgAreaErr': /* [MS-XLS] 2.5.198.29 */\n\t\t\t\tstack.push(\"#REF!\"); break;\n\n\t\t\tcase 'PtgAreaErr3d': /* [MS-XLS] 2.5.198.30 */\n\t\t\t\tstack.push(\"#REF!\"); break;\n\n\t\t\tcase 'PtgList': /* [MS-XLSB] 2.5.97.52 */\n\t\t\t\t// $FlowIgnore\n\t\t\t\tstack.push(\"Table\" + f[1].idx + \"[#\" + f[1].rt + \"]\");\n\t\t\t\tbreak;\n\n\t\t\tcase 'PtgMemAreaN':\n\t\t\tcase 'PtgMemNoMemN':\n\t\t\tcase 'PtgAttrNoop':\n\t\t\tcase 'PtgSheet':\n\t\t\tcase 'PtgEndSheet':\n\t\t\t\tbreak;\n\n\t\t\tcase 'PtgMemFunc': /* [MS-XLS] 2.5.198.72 TODO */\n\t\t\t\tbreak;\n\t\t\tcase 'PtgMemNoMem': /* [MS-XLS] 2.5.198.73 TODO */\n\t\t\t\tbreak;\n\n\t\t\tcase 'PtgElfCol': /* [MS-XLS] 2.5.198.46 */\n\t\t\tcase 'PtgElfColS': /* [MS-XLS] 2.5.198.47 */\n\t\t\tcase 'PtgElfColSV': /* [MS-XLS] 2.5.198.48 */\n\t\t\tcase 'PtgElfColV': /* [MS-XLS] 2.5.198.49 */\n\t\t\tcase 'PtgElfLel': /* [MS-XLS] 2.5.198.50 */\n\t\t\tcase 'PtgElfRadical': /* [MS-XLS] 2.5.198.51 */\n\t\t\tcase 'PtgElfRadicalLel': /* [MS-XLS] 2.5.198.52 */\n\t\t\tcase 'PtgElfRadicalS': /* [MS-XLS] 2.5.198.53 */\n\t\t\tcase 'PtgElfRw': /* [MS-XLS] 2.5.198.54 */\n\t\t\tcase 'PtgElfRwV': /* [MS-XLS] 2.5.198.55 */\n\t\t\t\tthrow new Error(\"Unsupported ELFs\");\n\n\t\t\tcase 'PtgSxName': /* [MS-XLS] 2.5.198.91 TODO -- find a test case */\n\t\t\t\tthrow new Error('Unrecognized Formula Token: ' + String(f));\n\t\t\tdefault: throw new Error('Unrecognized Formula Token: ' + String(f));\n\t\t}\n\t\tvar PtgNonDisp = ['PtgAttrSpace', 'PtgAttrSpaceSemi', 'PtgAttrGoto'];\n\t\tif(opts.biff != 3) if(last_sp >= 0 && PtgNonDisp.indexOf(formula[0][ff][0]) == -1) {\n\t\t\tf = formula[0][last_sp];\n\t\t\tvar _left = true;\n\t\t\tswitch(f[1][0]) {\n\t\t\t\t/* note: some bad XLSB files omit the PtgParen */\n\t\t\t\tcase 4: _left = false;\n\t\t\t\t/* falls through */\n\t\t\t\tcase 0:\n\t\t\t\t\t// $FlowIgnore\n\t\t\t\t\tsp = fill(\" \", f[1][1]); break;\n\t\t\t\tcase 5: _left = false;\n\t\t\t\t/* falls through */\n\t\t\t\tcase 1:\n\t\t\t\t\t// $FlowIgnore\n\t\t\t\t\tsp = fill(\"\\r\", f[1][1]); break;\n\t\t\t\tdefault:\n\t\t\t\t\tsp = \"\";\n\t\t\t\t\t// $FlowIgnore\n\t\t\t\t\tif(opts.WTF) throw new Error(\"Unexpected PtgAttrSpaceType \" + f[1][0]);\n\t\t\t}\n\t\t\tstack.push((_left ? sp : \"\") + stack.pop() + (_left ? \"\" : sp));\n\t\t\tlast_sp = -1;\n\t\t}\n\t}\n\tif(stack.length > 1 && opts.WTF) throw new Error(\"bad formula stack\");\n\treturn stack[0];\n}\n\n/* [MS-XLS] 2.5.198.1 TODO */\nfunction parse_ArrayParsedFormula(blob, length, opts) {\n\tvar target = blob.l + length, len = opts.biff == 2 ? 1 : 2;\n\tvar rgcb, cce = blob.read_shift(len); // length of rgce\n\tif(cce == 0xFFFF) return [[],parsenoop(blob, length-2)];\n\tvar rgce = parse_Rgce(blob, cce, opts);\n\tif(length !== cce + len) rgcb = parse_RgbExtra(blob, length - cce - len, rgce, opts);\n\tblob.l = target;\n\treturn [rgce, rgcb];\n}\n\n/* [MS-XLS] 2.5.198.3 TODO */\nfunction parse_XLSCellParsedFormula(blob, length, opts) {\n\tvar target = blob.l + length, len = opts.biff == 2 ? 1 : 2;\n\tvar rgcb, cce = blob.read_shift(len); // length of rgce\n\tif(cce == 0xFFFF) return [[],parsenoop(blob, length-2)];\n\tvar rgce = parse_Rgce(blob, cce, opts);\n\tif(length !== cce + len) rgcb = parse_RgbExtra(blob, length - cce - len, rgce, opts);\n\tblob.l = target;\n\treturn [rgce, rgcb];\n}\n\n/* [MS-XLS] 2.5.198.21 */\nfunction parse_NameParsedFormula(blob, length, opts, cce) {\n\tvar target = blob.l + length;\n\tvar rgce = parse_Rgce(blob, cce, opts);\n\tvar rgcb;\n\tif(target !== blob.l) rgcb = parse_RgbExtra(blob, target - blob.l, rgce, opts);\n\treturn [rgce, rgcb];\n}\n\n/* [MS-XLS] 2.5.198.118 TODO */\nfunction parse_SharedParsedFormula(blob, length, opts) {\n\tvar target = blob.l + length;\n\tvar rgcb, cce = blob.read_shift(2); // length of rgce\n\tvar rgce = parse_Rgce(blob, cce, opts);\n\tif(cce == 0xFFFF) return [[],parsenoop(blob, length-2)];\n\tif(length !== cce + 2) rgcb = parse_RgbExtra(blob, target - cce - 2, rgce, opts);\n\treturn [rgce, rgcb];\n}\n\n/* [MS-XLS] 2.5.133 TODO: how to emit empty strings? */\nfunction parse_FormulaValue(blob) {\n\tvar b;\n\tif(__readUInt16LE(blob,blob.l + 6) !== 0xFFFF) return [parse_Xnum(blob),'n'];\n\tswitch(blob[blob.l]) {\n\t\tcase 0x00: blob.l += 8; return [\"String\", 's'];\n\t\tcase 0x01: b = blob[blob.l+2] === 0x1; blob.l += 8; return [b,'b'];\n\t\tcase 0x02: b = blob[blob.l+2]; blob.l += 8; return [b,'e'];\n\t\tcase 0x03: blob.l += 8; return [\"\",'s'];\n\t}\n\treturn [];\n}\nfunction write_FormulaValue(value) {\n\tif(value == null) {\n\t\t// Blank String Value\n\t\tvar o = new_buf(8);\n\t\to.write_shift(1, 0x03);\n\t\to.write_shift(1, 0);\n\t\to.write_shift(2, 0);\n\t\to.write_shift(2, 0);\n\t\to.write_shift(2, 0xFFFF);\n\t\treturn o;\n\t} else if(typeof value == \"number\") return write_Xnum(value);\n\treturn write_Xnum(0);\n}\n\n/* [MS-XLS] 2.4.127 TODO */\nfunction parse_Formula(blob, length, opts) {\n\tvar end = blob.l + length;\n\tvar cell = parse_XLSCell(blob, 6);\n\tif(opts.biff == 2) ++blob.l;\n\tvar val = parse_FormulaValue(blob,8);\n\tvar flags = blob.read_shift(1);\n\tif(opts.biff != 2) {\n\t\tblob.read_shift(1);\n\t\tif(opts.biff >= 5) {\n\t\t\t/*var chn = */blob.read_shift(4);\n\t\t}\n\t}\n\tvar cbf = parse_XLSCellParsedFormula(blob, end - blob.l, opts);\n\treturn {cell:cell, val:val[0], formula:cbf, shared: (flags >> 3) & 1, tt:val[1]};\n}\nfunction write_Formula(cell, R, C, opts, os) {\n\t// Cell\n\tvar o1 = write_XLSCell(R, C, os);\n\n\t// FormulaValue\n\tvar o2 = write_FormulaValue(cell.v);\n\n\t// flags + cache\n\tvar o3 = new_buf(6);\n\tvar flags = 0x01 | 0x20;\n\to3.write_shift(2, flags);\n\to3.write_shift(4, 0);\n\n\t// CellParsedFormula\n\tvar bf = new_buf(cell.bf.length);\n\tfor(var i = 0; i < cell.bf.length; ++i) bf[i] = cell.bf[i];\n\n\tvar out = bconcat([o1, o2, o3, bf]);\n\treturn out;\n}\n\n\n/* XLSB Parsed Formula records have the same shape */\nfunction parse_XLSBParsedFormula(data, length, opts) {\n\tvar cce = data.read_shift(4);\n\tvar rgce = parse_Rgce(data, cce, opts);\n\tvar cb = data.read_shift(4);\n\tvar rgcb = cb > 0 ? parse_RgbExtra(data, cb, rgce, opts) : null;\n\treturn [rgce, rgcb];\n}\n\n/* [MS-XLSB] 2.5.97.1 ArrayParsedFormula */\nvar parse_XLSBArrayParsedFormula = parse_XLSBParsedFormula;\n/* [MS-XLSB] 2.5.97.4 CellParsedFormula */\nvar parse_XLSBCellParsedFormula = parse_XLSBParsedFormula;\n/* [MS-XLSB] 2.5.97.8 DVParsedFormula */\n//var parse_XLSBDVParsedFormula = parse_XLSBParsedFormula;\n/* [MS-XLSB] 2.5.97.9 FRTParsedFormula */\n//var parse_XLSBFRTParsedFormula = parse_XLSBParsedFormula2;\n/* [MS-XLSB] 2.5.97.12 NameParsedFormula */\nvar parse_XLSBNameParsedFormula = parse_XLSBParsedFormula;\n/* [MS-XLSB] 2.5.97.98 SharedParsedFormula */\nvar parse_XLSBSharedParsedFormula = parse_XLSBParsedFormula;\n/* [MS-XLS] 2.5.198.4 */\nvar Cetab = {\n0x0000: 'BEEP',\n0x0001: 'OPEN',\n0x0002: 'OPEN.LINKS',\n0x0003: 'CLOSE.ALL',\n0x0004: 'SAVE',\n0x0005: 'SAVE.AS',\n0x0006: 'FILE.DELETE',\n0x0007: 'PAGE.SETUP',\n0x0008: 'PRINT',\n0x0009: 'PRINTER.SETUP',\n0x000A: 'QUIT',\n0x000B: 'NEW.WINDOW',\n0x000C: 'ARRANGE.ALL',\n0x000D: 'WINDOW.SIZE',\n0x000E: 'WINDOW.MOVE',\n0x000F: 'FULL',\n0x0010: 'CLOSE',\n0x0011: 'RUN',\n0x0016: 'SET.PRINT.AREA',\n0x0017: 'SET.PRINT.TITLES',\n0x0018: 'SET.PAGE.BREAK',\n0x0019: 'REMOVE.PAGE.BREAK',\n0x001A: 'FONT',\n0x001B: 'DISPLAY',\n0x001C: 'PROTECT.DOCUMENT',\n0x001D: 'PRECISION',\n0x001E: 'A1.R1C1',\n0x001F: 'CALCULATE.NOW',\n0x0020: 'CALCULATION',\n0x0022: 'DATA.FIND',\n0x0023: 'EXTRACT',\n0x0024: 'DATA.DELETE',\n0x0025: 'SET.DATABASE',\n0x0026: 'SET.CRITERIA',\n0x0027: 'SORT',\n0x0028: 'DATA.SERIES',\n0x0029: 'TABLE',\n0x002A: 'FORMAT.NUMBER',\n0x002B: 'ALIGNMENT',\n0x002C: 'STYLE',\n0x002D: 'BORDER',\n0x002E: 'CELL.PROTECTION',\n0x002F: 'COLUMN.WIDTH',\n0x0030: 'UNDO',\n0x0031: 'CUT',\n0x0032: 'COPY',\n0x0033: 'PASTE',\n0x0034: 'CLEAR',\n0x0035: 'PASTE.SPECIAL',\n0x0036: 'EDIT.DELETE',\n0x0037: 'INSERT',\n0x0038: 'FILL.RIGHT',\n0x0039: 'FILL.DOWN',\n0x003D: 'DEFINE.NAME',\n0x003E: 'CREATE.NAMES',\n0x003F: 'FORMULA.GOTO',\n0x0040: 'FORMULA.FIND',\n0x0041: 'SELECT.LAST.CELL',\n0x0042: 'SHOW.ACTIVE.CELL',\n0x0043: 'GALLERY.AREA',\n0x0044: 'GALLERY.BAR',\n0x0045: 'GALLERY.COLUMN',\n0x0046: 'GALLERY.LINE',\n0x0047: 'GALLERY.PIE',\n0x0048: 'GALLERY.SCATTER',\n0x0049: 'COMBINATION',\n0x004A: 'PREFERRED',\n0x004B: 'ADD.OVERLAY',\n0x004C: 'GRIDLINES',\n0x004D: 'SET.PREFERRED',\n0x004E: 'AXES',\n0x004F: 'LEGEND',\n0x0050: 'ATTACH.TEXT',\n0x0051: 'ADD.ARROW',\n0x0052: 'SELECT.CHART',\n0x0053: 'SELECT.PLOT.AREA',\n0x0054: 'PATTERNS',\n0x0055: 'MAIN.CHART',\n0x0056: 'OVERLAY',\n0x0057: 'SCALE',\n0x0058: 'FORMAT.LEGEND',\n0x0059: 'FORMAT.TEXT',\n0x005A: 'EDIT.REPEAT',\n0x005B: 'PARSE',\n0x005C: 'JUSTIFY',\n0x005D: 'HIDE',\n0x005E: 'UNHIDE',\n0x005F: 'WORKSPACE',\n0x0060: 'FORMULA',\n0x0061: 'FORMULA.FILL',\n0x0062: 'FORMULA.ARRAY',\n0x0063: 'DATA.FIND.NEXT',\n0x0064: 'DATA.FIND.PREV',\n0x0065: 'FORMULA.FIND.NEXT',\n0x0066: 'FORMULA.FIND.PREV',\n0x0067: 'ACTIVATE',\n0x0068: 'ACTIVATE.NEXT',\n0x0069: 'ACTIVATE.PREV',\n0x006A: 'UNLOCKED.NEXT',\n0x006B: 'UNLOCKED.PREV',\n0x006C: 'COPY.PICTURE',\n0x006D: 'SELECT',\n0x006E: 'DELETE.NAME',\n0x006F: 'DELETE.FORMAT',\n0x0070: 'VLINE',\n0x0071: 'HLINE',\n0x0072: 'VPAGE',\n0x0073: 'HPAGE',\n0x0074: 'VSCROLL',\n0x0075: 'HSCROLL',\n0x0076: 'ALERT',\n0x0077: 'NEW',\n0x0078: 'CANCEL.COPY',\n0x0079: 'SHOW.CLIPBOARD',\n0x007A: 'MESSAGE',\n0x007C: 'PASTE.LINK',\n0x007D: 'APP.ACTIVATE',\n0x007E: 'DELETE.ARROW',\n0x007F: 'ROW.HEIGHT',\n0x0080: 'FORMAT.MOVE',\n0x0081: 'FORMAT.SIZE',\n0x0082: 'FORMULA.REPLACE',\n0x0083: 'SEND.KEYS',\n0x0084: 'SELECT.SPECIAL',\n0x0085: 'APPLY.NAMES',\n0x0086: 'REPLACE.FONT',\n0x0087: 'FREEZE.PANES',\n0x0088: 'SHOW.INFO',\n0x0089: 'SPLIT',\n0x008A: 'ON.WINDOW',\n0x008B: 'ON.DATA',\n0x008C: 'DISABLE.INPUT',\n0x008E: 'OUTLINE',\n0x008F: 'LIST.NAMES',\n0x0090: 'FILE.CLOSE',\n0x0091: 'SAVE.WORKBOOK',\n0x0092: 'DATA.FORM',\n0x0093: 'COPY.CHART',\n0x0094: 'ON.TIME',\n0x0095: 'WAIT',\n0x0096: 'FORMAT.FONT',\n0x0097: 'FILL.UP',\n0x0098: 'FILL.LEFT',\n0x0099: 'DELETE.OVERLAY',\n0x009B: 'SHORT.MENUS',\n0x009F: 'SET.UPDATE.STATUS',\n0x00A1: 'COLOR.PALETTE',\n0x00A2: 'DELETE.STYLE',\n0x00A3: 'WINDOW.RESTORE',\n0x00A4: 'WINDOW.MAXIMIZE',\n0x00A6: 'CHANGE.LINK',\n0x00A7: 'CALCULATE.DOCUMENT',\n0x00A8: 'ON.KEY',\n0x00A9: 'APP.RESTORE',\n0x00AA: 'APP.MOVE',\n0x00AB: 'APP.SIZE',\n0x00AC: 'APP.MINIMIZE',\n0x00AD: 'APP.MAXIMIZE',\n0x00AE: 'BRING.TO.FRONT',\n0x00AF: 'SEND.TO.BACK',\n0x00B9: 'MAIN.CHART.TYPE',\n0x00BA: 'OVERLAY.CHART.TYPE',\n0x00BB: 'SELECT.END',\n0x00BC: 'OPEN.MAIL',\n0x00BD: 'SEND.MAIL',\n0x00BE: 'STANDARD.FONT',\n0x00BF: 'CONSOLIDATE',\n0x00C0: 'SORT.SPECIAL',\n0x00C1: 'GALLERY.3D.AREA',\n0x00C2: 'GALLERY.3D.COLUMN',\n0x00C3: 'GALLERY.3D.LINE',\n0x00C4: 'GALLERY.3D.PIE',\n0x00C5: 'VIEW.3D',\n0x00C6: 'GOAL.SEEK',\n0x00C7: 'WORKGROUP',\n0x00C8: 'FILL.GROUP',\n0x00C9: 'UPDATE.LINK',\n0x00CA: 'PROMOTE',\n0x00CB: 'DEMOTE',\n0x00CC: 'SHOW.DETAIL',\n0x00CE: 'UNGROUP',\n0x00CF: 'OBJECT.PROPERTIES',\n0x00D0: 'SAVE.NEW.OBJECT',\n0x00D1: 'SHARE',\n0x00D2: 'SHARE.NAME',\n0x00D3: 'DUPLICATE',\n0x00D4: 'APPLY.STYLE',\n0x00D5: 'ASSIGN.TO.OBJECT',\n0x00D6: 'OBJECT.PROTECTION',\n0x00D7: 'HIDE.OBJECT',\n0x00D8: 'SET.EXTRACT',\n0x00D9: 'CREATE.PUBLISHER',\n0x00DA: 'SUBSCRIBE.TO',\n0x00DB: 'ATTRIBUTES',\n0x00DC: 'SHOW.TOOLBAR',\n0x00DE: 'PRINT.PREVIEW',\n0x00DF: 'EDIT.COLOR',\n0x00E0: 'SHOW.LEVELS',\n0x00E1: 'FORMAT.MAIN',\n0x00E2: 'FORMAT.OVERLAY',\n0x00E3: 'ON.RECALC',\n0x00E4: 'EDIT.SERIES',\n0x00E5: 'DEFINE.STYLE',\n0x00F0: 'LINE.PRINT',\n0x00F3: 'ENTER.DATA',\n0x00F9: 'GALLERY.RADAR',\n0x00FA: 'MERGE.STYLES',\n0x00FB: 'EDITION.OPTIONS',\n0x00FC: 'PASTE.PICTURE',\n0x00FD: 'PASTE.PICTURE.LINK',\n0x00FE: 'SPELLING',\n0x0100: 'ZOOM',\n0x0103: 'INSERT.OBJECT',\n0x0104: 'WINDOW.MINIMIZE',\n0x0109: 'SOUND.NOTE',\n0x010A: 'SOUND.PLAY',\n0x010B: 'FORMAT.SHAPE',\n0x010C: 'EXTEND.POLYGON',\n0x010D: 'FORMAT.AUTO',\n0x0110: 'GALLERY.3D.BAR',\n0x0111: 'GALLERY.3D.SURFACE',\n0x0112: 'FILL.AUTO',\n0x0114: 'CUSTOMIZE.TOOLBAR',\n0x0115: 'ADD.TOOL',\n0x0116: 'EDIT.OBJECT',\n0x0117: 'ON.DOUBLECLICK',\n0x0118: 'ON.ENTRY',\n0x0119: 'WORKBOOK.ADD',\n0x011A: 'WORKBOOK.MOVE',\n0x011B: 'WORKBOOK.COPY',\n0x011C: 'WORKBOOK.OPTIONS',\n0x011D: 'SAVE.WORKSPACE',\n0x0120: 'CHART.WIZARD',\n0x0121: 'DELETE.TOOL',\n0x0122: 'MOVE.TOOL',\n0x0123: 'WORKBOOK.SELECT',\n0x0124: 'WORKBOOK.ACTIVATE',\n0x0125: 'ASSIGN.TO.TOOL',\n0x0127: 'COPY.TOOL',\n0x0128: 'RESET.TOOL',\n0x0129: 'CONSTRAIN.NUMERIC',\n0x012A: 'PASTE.TOOL',\n0x012E: 'WORKBOOK.NEW',\n0x0131: 'SCENARIO.CELLS',\n0x0132: 'SCENARIO.DELETE',\n0x0133: 'SCENARIO.ADD',\n0x0134: 'SCENARIO.EDIT',\n0x0135: 'SCENARIO.SHOW',\n0x0136: 'SCENARIO.SHOW.NEXT',\n0x0137: 'SCENARIO.SUMMARY',\n0x0138: 'PIVOT.TABLE.WIZARD',\n0x0139: 'PIVOT.FIELD.PROPERTIES',\n0x013A: 'PIVOT.FIELD',\n0x013B: 'PIVOT.ITEM',\n0x013C: 'PIVOT.ADD.FIELDS',\n0x013E: 'OPTIONS.CALCULATION',\n0x013F: 'OPTIONS.EDIT',\n0x0140: 'OPTIONS.VIEW',\n0x0141: 'ADDIN.MANAGER',\n0x0142: 'MENU.EDITOR',\n0x0143: 'ATTACH.TOOLBARS',\n0x0144: 'VBAActivate',\n0x0145: 'OPTIONS.CHART',\n0x0148: 'VBA.INSERT.FILE',\n0x014A: 'VBA.PROCEDURE.DEFINITION',\n0x0150: 'ROUTING.SLIP',\n0x0152: 'ROUTE.DOCUMENT',\n0x0153: 'MAIL.LOGON',\n0x0156: 'INSERT.PICTURE',\n0x0157: 'EDIT.TOOL',\n0x0158: 'GALLERY.DOUGHNUT',\n0x015E: 'CHART.TREND',\n0x0160: 'PIVOT.ITEM.PROPERTIES',\n0x0162: 'WORKBOOK.INSERT',\n0x0163: 'OPTIONS.TRANSITION',\n0x0164: 'OPTIONS.GENERAL',\n0x0172: 'FILTER.ADVANCED',\n0x0175: 'MAIL.ADD.MAILER',\n0x0176: 'MAIL.DELETE.MAILER',\n0x0177: 'MAIL.REPLY',\n0x0178: 'MAIL.REPLY.ALL',\n0x0179: 'MAIL.FORWARD',\n0x017A: 'MAIL.NEXT.LETTER',\n0x017B: 'DATA.LABEL',\n0x017C: 'INSERT.TITLE',\n0x017D: 'FONT.PROPERTIES',\n0x017E: 'MACRO.OPTIONS',\n0x017F: 'WORKBOOK.HIDE',\n0x0180: 'WORKBOOK.UNHIDE',\n0x0181: 'WORKBOOK.DELETE',\n0x0182: 'WORKBOOK.NAME',\n0x0184: 'GALLERY.CUSTOM',\n0x0186: 'ADD.CHART.AUTOFORMAT',\n0x0187: 'DELETE.CHART.AUTOFORMAT',\n0x0188: 'CHART.ADD.DATA',\n0x0189: 'AUTO.OUTLINE',\n0x018A: 'TAB.ORDER',\n0x018B: 'SHOW.DIALOG',\n0x018C: 'SELECT.ALL',\n0x018D: 'UNGROUP.SHEETS',\n0x018E: 'SUBTOTAL.CREATE',\n0x018F: 'SUBTOTAL.REMOVE',\n0x0190: 'RENAME.OBJECT',\n0x019C: 'WORKBOOK.SCROLL',\n0x019D: 'WORKBOOK.NEXT',\n0x019E: 'WORKBOOK.PREV',\n0x019F: 'WORKBOOK.TAB.SPLIT',\n0x01A0: 'FULL.SCREEN',\n0x01A1: 'WORKBOOK.PROTECT',\n0x01A4: 'SCROLLBAR.PROPERTIES',\n0x01A5: 'PIVOT.SHOW.PAGES',\n0x01A6: 'TEXT.TO.COLUMNS',\n0x01A7: 'FORMAT.CHARTTYPE',\n0x01A8: 'LINK.FORMAT',\n0x01A9: 'TRACER.DISPLAY',\n0x01AE: 'TRACER.NAVIGATE',\n0x01AF: 'TRACER.CLEAR',\n0x01B0: 'TRACER.ERROR',\n0x01B1: 'PIVOT.FIELD.GROUP',\n0x01B2: 'PIVOT.FIELD.UNGROUP',\n0x01B3: 'CHECKBOX.PROPERTIES',\n0x01B4: 'LABEL.PROPERTIES',\n0x01B5: 'LISTBOX.PROPERTIES',\n0x01B6: 'EDITBOX.PROPERTIES',\n0x01B7: 'PIVOT.REFRESH',\n0x01B8: 'LINK.COMBO',\n0x01B9: 'OPEN.TEXT',\n0x01BA: 'HIDE.DIALOG',\n0x01BB: 'SET.DIALOG.FOCUS',\n0x01BC: 'ENABLE.OBJECT',\n0x01BD: 'PUSHBUTTON.PROPERTIES',\n0x01BE: 'SET.DIALOG.DEFAULT',\n0x01BF: 'FILTER',\n0x01C0: 'FILTER.SHOW.ALL',\n0x01C1: 'CLEAR.OUTLINE',\n0x01C2: 'FUNCTION.WIZARD',\n0x01C3: 'ADD.LIST.ITEM',\n0x01C4: 'SET.LIST.ITEM',\n0x01C5: 'REMOVE.LIST.ITEM',\n0x01C6: 'SELECT.LIST.ITEM',\n0x01C7: 'SET.CONTROL.VALUE',\n0x01C8: 'SAVE.COPY.AS',\n0x01CA: 'OPTIONS.LISTS.ADD',\n0x01CB: 'OPTIONS.LISTS.DELETE',\n0x01CC: 'SERIES.AXES',\n0x01CD: 'SERIES.X',\n0x01CE: 'SERIES.Y',\n0x01CF: 'ERRORBAR.X',\n0x01D0: 'ERRORBAR.Y',\n0x01D1: 'FORMAT.CHART',\n0x01D2: 'SERIES.ORDER',\n0x01D3: 'MAIL.LOGOFF',\n0x01D4: 'CLEAR.ROUTING.SLIP',\n0x01D5: 'APP.ACTIVATE.MICROSOFT',\n0x01D6: 'MAIL.EDIT.MAILER',\n0x01D7: 'ON.SHEET',\n0x01D8: 'STANDARD.WIDTH',\n0x01D9: 'SCENARIO.MERGE',\n0x01DA: 'SUMMARY.INFO',\n0x01DB: 'FIND.FILE',\n0x01DC: 'ACTIVE.CELL.FONT',\n0x01DD: 'ENABLE.TIPWIZARD',\n0x01DE: 'VBA.MAKE.ADDIN',\n0x01E0: 'INSERTDATATABLE',\n0x01E1: 'WORKGROUP.OPTIONS',\n0x01E2: 'MAIL.SEND.MAILER',\n0x01E5: 'AUTOCORRECT',\n0x01E9: 'POST.DOCUMENT',\n0x01EB: 'PICKLIST',\n0x01ED: 'VIEW.SHOW',\n0x01EE: 'VIEW.DEFINE',\n0x01EF: 'VIEW.DELETE',\n0x01FD: 'SHEET.BACKGROUND',\n0x01FE: 'INSERT.MAP.OBJECT',\n0x01FF: 'OPTIONS.MENONO',\n0x0205: 'MSOCHECKS',\n0x0206: 'NORMAL',\n0x0207: 'LAYOUT',\n0x0208: 'RM.PRINT.AREA',\n0x0209: 'CLEAR.PRINT.AREA',\n0x020A: 'ADD.PRINT.AREA',\n0x020B: 'MOVE.BRK',\n0x0221: 'HIDECURR.NOTE',\n0x0222: 'HIDEALL.NOTES',\n0x0223: 'DELETE.NOTE',\n0x0224: 'TRAVERSE.NOTES',\n0x0225: 'ACTIVATE.NOTES',\n0x026C: 'PROTECT.REVISIONS',\n0x026D: 'UNPROTECT.REVISIONS',\n0x0287: 'OPTIONS.ME',\n0x028D: 'WEB.PUBLISH',\n0x029B: 'NEWWEBQUERY',\n0x02A1: 'PIVOT.TABLE.CHART',\n0x02F1: 'OPTIONS.SAVE',\n0x02F3: 'OPTIONS.SPELL',\n0x0328: 'HIDEALL.INKANNOTS'\n};\n\n/* [MS-XLS] 2.5.198.17 */\n/* [MS-XLSB] 2.5.97.10 */\nvar Ftab = {\n0x0000: 'COUNT',\n0x0001: 'IF',\n0x0002: 'ISNA',\n0x0003: 'ISERROR',\n0x0004: 'SUM',\n0x0005: 'AVERAGE',\n0x0006: 'MIN',\n0x0007: 'MAX',\n0x0008: 'ROW',\n0x0009: 'COLUMN',\n0x000A: 'NA',\n0x000B: 'NPV',\n0x000C: 'STDEV',\n0x000D: 'DOLLAR',\n0x000E: 'FIXED',\n0x000F: 'SIN',\n0x0010: 'COS',\n0x0011: 'TAN',\n0x0012: 'ATAN',\n0x0013: 'PI',\n0x0014: 'SQRT',\n0x0015: 'EXP',\n0x0016: 'LN',\n0x0017: 'LOG10',\n0x0018: 'ABS',\n0x0019: 'INT',\n0x001A: 'SIGN',\n0x001B: 'ROUND',\n0x001C: 'LOOKUP',\n0x001D: 'INDEX',\n0x001E: 'REPT',\n0x001F: 'MID',\n0x0020: 'LEN',\n0x0021: 'VALUE',\n0x0022: 'TRUE',\n0x0023: 'FALSE',\n0x0024: 'AND',\n0x0025: 'OR',\n0x0026: 'NOT',\n0x0027: 'MOD',\n0x0028: 'DCOUNT',\n0x0029: 'DSUM',\n0x002A: 'DAVERAGE',\n0x002B: 'DMIN',\n0x002C: 'DMAX',\n0x002D: 'DSTDEV',\n0x002E: 'VAR',\n0x002F: 'DVAR',\n0x0030: 'TEXT',\n0x0031: 'LINEST',\n0x0032: 'TREND',\n0x0033: 'LOGEST',\n0x0034: 'GROWTH',\n0x0035: 'GOTO',\n0x0036: 'HALT',\n0x0037: 'RETURN',\n0x0038: 'PV',\n0x0039: 'FV',\n0x003A: 'NPER',\n0x003B: 'PMT',\n0x003C: 'RATE',\n0x003D: 'MIRR',\n0x003E: 'IRR',\n0x003F: 'RAND',\n0x0040: 'MATCH',\n0x0041: 'DATE',\n0x0042: 'TIME',\n0x0043: 'DAY',\n0x0044: 'MONTH',\n0x0045: 'YEAR',\n0x0046: 'WEEKDAY',\n0x0047: 'HOUR',\n0x0048: 'MINUTE',\n0x0049: 'SECOND',\n0x004A: 'NOW',\n0x004B: 'AREAS',\n0x004C: 'ROWS',\n0x004D: 'COLUMNS',\n0x004E: 'OFFSET',\n0x004F: 'ABSREF',\n0x0050: 'RELREF',\n0x0051: 'ARGUMENT',\n0x0052: 'SEARCH',\n0x0053: 'TRANSPOSE',\n0x0054: 'ERROR',\n0x0055: 'STEP',\n0x0056: 'TYPE',\n0x0057: 'ECHO',\n0x0058: 'SET.NAME',\n0x0059: 'CALLER',\n0x005A: 'DEREF',\n0x005B: 'WINDOWS',\n0x005C: 'SERIES',\n0x005D: 'DOCUMENTS',\n0x005E: 'ACTIVE.CELL',\n0x005F: 'SELECTION',\n0x0060: 'RESULT',\n0x0061: 'ATAN2',\n0x0062: 'ASIN',\n0x0063: 'ACOS',\n0x0064: 'CHOOSE',\n0x0065: 'HLOOKUP',\n0x0066: 'VLOOKUP',\n0x0067: 'LINKS',\n0x0068: 'INPUT',\n0x0069: 'ISREF',\n0x006A: 'GET.FORMULA',\n0x006B: 'GET.NAME',\n0x006C: 'SET.VALUE',\n0x006D: 'LOG',\n0x006E: 'EXEC',\n0x006F: 'CHAR',\n0x0070: 'LOWER',\n0x0071: 'UPPER',\n0x0072: 'PROPER',\n0x0073: 'LEFT',\n0x0074: 'RIGHT',\n0x0075: 'EXACT',\n0x0076: 'TRIM',\n0x0077: 'REPLACE',\n0x0078: 'SUBSTITUTE',\n0x0079: 'CODE',\n0x007A: 'NAMES',\n0x007B: 'DIRECTORY',\n0x007C: 'FIND',\n0x007D: 'CELL',\n0x007E: 'ISERR',\n0x007F: 'ISTEXT',\n0x0080: 'ISNUMBER',\n0x0081: 'ISBLANK',\n0x0082: 'T',\n0x0083: 'N',\n0x0084: 'FOPEN',\n0x0085: 'FCLOSE',\n0x0086: 'FSIZE',\n0x0087: 'FREADLN',\n0x0088: 'FREAD',\n0x0089: 'FWRITELN',\n0x008A: 'FWRITE',\n0x008B: 'FPOS',\n0x008C: 'DATEVALUE',\n0x008D: 'TIMEVALUE',\n0x008E: 'SLN',\n0x008F: 'SYD',\n0x0090: 'DDB',\n0x0091: 'GET.DEF',\n0x0092: 'REFTEXT',\n0x0093: 'TEXTREF',\n0x0094: 'INDIRECT',\n0x0095: 'REGISTER',\n0x0096: 'CALL',\n0x0097: 'ADD.BAR',\n0x0098: 'ADD.MENU',\n0x0099: 'ADD.COMMAND',\n0x009A: 'ENABLE.COMMAND',\n0x009B: 'CHECK.COMMAND',\n0x009C: 'RENAME.COMMAND',\n0x009D: 'SHOW.BAR',\n0x009E: 'DELETE.MENU',\n0x009F: 'DELETE.COMMAND',\n0x00A0: 'GET.CHART.ITEM',\n0x00A1: 'DIALOG.BOX',\n0x00A2: 'CLEAN',\n0x00A3: 'MDETERM',\n0x00A4: 'MINVERSE',\n0x00A5: 'MMULT',\n0x00A6: 'FILES',\n0x00A7: 'IPMT',\n0x00A8: 'PPMT',\n0x00A9: 'COUNTA',\n0x00AA: 'CANCEL.KEY',\n0x00AB: 'FOR',\n0x00AC: 'WHILE',\n0x00AD: 'BREAK',\n0x00AE: 'NEXT',\n0x00AF: 'INITIATE',\n0x00B0: 'REQUEST',\n0x00B1: 'POKE',\n0x00B2: 'EXECUTE',\n0x00B3: 'TERMINATE',\n0x00B4: 'RESTART',\n0x00B5: 'HELP',\n0x00B6: 'GET.BAR',\n0x00B7: 'PRODUCT',\n0x00B8: 'FACT',\n0x00B9: 'GET.CELL',\n0x00BA: 'GET.WORKSPACE',\n0x00BB: 'GET.WINDOW',\n0x00BC: 'GET.DOCUMENT',\n0x00BD: 'DPRODUCT',\n0x00BE: 'ISNONTEXT',\n0x00BF: 'GET.NOTE',\n0x00C0: 'NOTE',\n0x00C1: 'STDEVP',\n0x00C2: 'VARP',\n0x00C3: 'DSTDEVP',\n0x00C4: 'DVARP',\n0x00C5: 'TRUNC',\n0x00C6: 'ISLOGICAL',\n0x00C7: 'DCOUNTA',\n0x00C8: 'DELETE.BAR',\n0x00C9: 'UNREGISTER',\n0x00CC: 'USDOLLAR',\n0x00CD: 'FINDB',\n0x00CE: 'SEARCHB',\n0x00CF: 'REPLACEB',\n0x00D0: 'LEFTB',\n0x00D1: 'RIGHTB',\n0x00D2: 'MIDB',\n0x00D3: 'LENB',\n0x00D4: 'ROUNDUP',\n0x00D5: 'ROUNDDOWN',\n0x00D6: 'ASC',\n0x00D7: 'DBCS',\n0x00D8: 'RANK',\n0x00DB: 'ADDRESS',\n0x00DC: 'DAYS360',\n0x00DD: 'TODAY',\n0x00DE: 'VDB',\n0x00DF: 'ELSE',\n0x00E0: 'ELSE.IF',\n0x00E1: 'END.IF',\n0x00E2: 'FOR.CELL',\n0x00E3: 'MEDIAN',\n0x00E4: 'SUMPRODUCT',\n0x00E5: 'SINH',\n0x00E6: 'COSH',\n0x00E7: 'TANH',\n0x00E8: 'ASINH',\n0x00E9: 'ACOSH',\n0x00EA: 'ATANH',\n0x00EB: 'DGET',\n0x00EC: 'CREATE.OBJECT',\n0x00ED: 'VOLATILE',\n0x00EE: 'LAST.ERROR',\n0x00EF: 'CUSTOM.UNDO',\n0x00F0: 'CUSTOM.REPEAT',\n0x00F1: 'FORMULA.CONVERT',\n0x00F2: 'GET.LINK.INFO',\n0x00F3: 'TEXT.BOX',\n0x00F4: 'INFO',\n0x00F5: 'GROUP',\n0x00F6: 'GET.OBJECT',\n0x00F7: 'DB',\n0x00F8: 'PAUSE',\n0x00FB: 'RESUME',\n0x00FC: 'FREQUENCY',\n0x00FD: 'ADD.TOOLBAR',\n0x00FE: 'DELETE.TOOLBAR',\n0x00FF: 'User',\n0x0100: 'RESET.TOOLBAR',\n0x0101: 'EVALUATE',\n0x0102: 'GET.TOOLBAR',\n0x0103: 'GET.TOOL',\n0x0104: 'SPELLING.CHECK',\n0x0105: 'ERROR.TYPE',\n0x0106: 'APP.TITLE',\n0x0107: 'WINDOW.TITLE',\n0x0108: 'SAVE.TOOLBAR',\n0x0109: 'ENABLE.TOOL',\n0x010A: 'PRESS.TOOL',\n0x010B: 'REGISTER.ID',\n0x010C: 'GET.WORKBOOK',\n0x010D: 'AVEDEV',\n0x010E: 'BETADIST',\n0x010F: 'GAMMALN',\n0x0110: 'BETAINV',\n0x0111: 'BINOMDIST',\n0x0112: 'CHIDIST',\n0x0113: 'CHIINV',\n0x0114: 'COMBIN',\n0x0115: 'CONFIDENCE',\n0x0116: 'CRITBINOM',\n0x0117: 'EVEN',\n0x0118: 'EXPONDIST',\n0x0119: 'FDIST',\n0x011A: 'FINV',\n0x011B: 'FISHER',\n0x011C: 'FISHERINV',\n0x011D: 'FLOOR',\n0x011E: 'GAMMADIST',\n0x011F: 'GAMMAINV',\n0x0120: 'CEILING',\n0x0121: 'HYPGEOMDIST',\n0x0122: 'LOGNORMDIST',\n0x0123: 'LOGINV',\n0x0124: 'NEGBINOMDIST',\n0x0125: 'NORMDIST',\n0x0126: 'NORMSDIST',\n0x0127: 'NORMINV',\n0x0128: 'NORMSINV',\n0x0129: 'STANDARDIZE',\n0x012A: 'ODD',\n0x012B: 'PERMUT',\n0x012C: 'POISSON',\n0x012D: 'TDIST',\n0x012E: 'WEIBULL',\n0x012F: 'SUMXMY2',\n0x0130: 'SUMX2MY2',\n0x0131: 'SUMX2PY2',\n0x0132: 'CHITEST',\n0x0133: 'CORREL',\n0x0134: 'COVAR',\n0x0135: 'FORECAST',\n0x0136: 'FTEST',\n0x0137: 'INTERCEPT',\n0x0138: 'PEARSON',\n0x0139: 'RSQ',\n0x013A: 'STEYX',\n0x013B: 'SLOPE',\n0x013C: 'TTEST',\n0x013D: 'PROB',\n0x013E: 'DEVSQ',\n0x013F: 'GEOMEAN',\n0x0140: 'HARMEAN',\n0x0141: 'SUMSQ',\n0x0142: 'KURT',\n0x0143: 'SKEW',\n0x0144: 'ZTEST',\n0x0145: 'LARGE',\n0x0146: 'SMALL',\n0x0147: 'QUARTILE',\n0x0148: 'PERCENTILE',\n0x0149: 'PERCENTRANK',\n0x014A: 'MODE',\n0x014B: 'TRIMMEAN',\n0x014C: 'TINV',\n0x014E: 'MOVIE.COMMAND',\n0x014F: 'GET.MOVIE',\n0x0150: 'CONCATENATE',\n0x0151: 'POWER',\n0x0152: 'PIVOT.ADD.DATA',\n0x0153: 'GET.PIVOT.TABLE',\n0x0154: 'GET.PIVOT.FIELD',\n0x0155: 'GET.PIVOT.ITEM',\n0x0156: 'RADIANS',\n0x0157: 'DEGREES',\n0x0158: 'SUBTOTAL',\n0x0159: 'SUMIF',\n0x015A: 'COUNTIF',\n0x015B: 'COUNTBLANK',\n0x015C: 'SCENARIO.GET',\n0x015D: 'OPTIONS.LISTS.GET',\n0x015E: 'ISPMT',\n0x015F: 'DATEDIF',\n0x0160: 'DATESTRING',\n0x0161: 'NUMBERSTRING',\n0x0162: 'ROMAN',\n0x0163: 'OPEN.DIALOG',\n0x0164: 'SAVE.DIALOG',\n0x0165: 'VIEW.GET',\n0x0166: 'GETPIVOTDATA',\n0x0167: 'HYPERLINK',\n0x0168: 'PHONETIC',\n0x0169: 'AVERAGEA',\n0x016A: 'MAXA',\n0x016B: 'MINA',\n0x016C: 'STDEVPA',\n0x016D: 'VARPA',\n0x016E: 'STDEVA',\n0x016F: 'VARA',\n0x0170: 'BAHTTEXT',\n0x0171: 'THAIDAYOFWEEK',\n0x0172: 'THAIDIGIT',\n0x0173: 'THAIMONTHOFYEAR',\n0x0174: 'THAINUMSOUND',\n0x0175: 'THAINUMSTRING',\n0x0176: 'THAISTRINGLENGTH',\n0x0177: 'ISTHAIDIGIT',\n0x0178: 'ROUNDBAHTDOWN',\n0x0179: 'ROUNDBAHTUP',\n0x017A: 'THAIYEAR',\n0x017B: 'RTD',\n\n0x017C: 'CUBEVALUE',\n0x017D: 'CUBEMEMBER',\n0x017E: 'CUBEMEMBERPROPERTY',\n0x017F: 'CUBERANKEDMEMBER',\n0x0180: 'HEX2BIN',\n0x0181: 'HEX2DEC',\n0x0182: 'HEX2OCT',\n0x0183: 'DEC2BIN',\n0x0184: 'DEC2HEX',\n0x0185: 'DEC2OCT',\n0x0186: 'OCT2BIN',\n0x0187: 'OCT2HEX',\n0x0188: 'OCT2DEC',\n0x0189: 'BIN2DEC',\n0x018A: 'BIN2OCT',\n0x018B: 'BIN2HEX',\n0x018C: 'IMSUB',\n0x018D: 'IMDIV',\n0x018E: 'IMPOWER',\n0x018F: 'IMABS',\n0x0190: 'IMSQRT',\n0x0191: 'IMLN',\n0x0192: 'IMLOG2',\n0x0193: 'IMLOG10',\n0x0194: 'IMSIN',\n0x0195: 'IMCOS',\n0x0196: 'IMEXP',\n0x0197: 'IMARGUMENT',\n0x0198: 'IMCONJUGATE',\n0x0199: 'IMAGINARY',\n0x019A: 'IMREAL',\n0x019B: 'COMPLEX',\n0x019C: 'IMSUM',\n0x019D: 'IMPRODUCT',\n0x019E: 'SERIESSUM',\n0x019F: 'FACTDOUBLE',\n0x01A0: 'SQRTPI',\n0x01A1: 'QUOTIENT',\n0x01A2: 'DELTA',\n0x01A3: 'GESTEP',\n0x01A4: 'ISEVEN',\n0x01A5: 'ISODD',\n0x01A6: 'MROUND',\n0x01A7: 'ERF',\n0x01A8: 'ERFC',\n0x01A9: 'BESSELJ',\n0x01AA: 'BESSELK',\n0x01AB: 'BESSELY',\n0x01AC: 'BESSELI',\n0x01AD: 'XIRR',\n0x01AE: 'XNPV',\n0x01AF: 'PRICEMAT',\n0x01B0: 'YIELDMAT',\n0x01B1: 'INTRATE',\n0x01B2: 'RECEIVED',\n0x01B3: 'DISC',\n0x01B4: 'PRICEDISC',\n0x01B5: 'YIELDDISC',\n0x01B6: 'TBILLEQ',\n0x01B7: 'TBILLPRICE',\n0x01B8: 'TBILLYIELD',\n0x01B9: 'PRICE',\n0x01BA: 'YIELD',\n0x01BB: 'DOLLARDE',\n0x01BC: 'DOLLARFR',\n0x01BD: 'NOMINAL',\n0x01BE: 'EFFECT',\n0x01BF: 'CUMPRINC',\n0x01C0: 'CUMIPMT',\n0x01C1: 'EDATE',\n0x01C2: 'EOMONTH',\n0x01C3: 'YEARFRAC',\n0x01C4: 'COUPDAYBS',\n0x01C5: 'COUPDAYS',\n0x01C6: 'COUPDAYSNC',\n0x01C7: 'COUPNCD',\n0x01C8: 'COUPNUM',\n0x01C9: 'COUPPCD',\n0x01CA: 'DURATION',\n0x01CB: 'MDURATION',\n0x01CC: 'ODDLPRICE',\n0x01CD: 'ODDLYIELD',\n0x01CE: 'ODDFPRICE',\n0x01CF: 'ODDFYIELD',\n0x01D0: 'RANDBETWEEN',\n0x01D1: 'WEEKNUM',\n0x01D2: 'AMORDEGRC',\n0x01D3: 'AMORLINC',\n0x01D4: 'CONVERT',\n0x02D4: 'SHEETJS',\n0x01D5: 'ACCRINT',\n0x01D6: 'ACCRINTM',\n0x01D7: 'WORKDAY',\n0x01D8: 'NETWORKDAYS',\n0x01D9: 'GCD',\n0x01DA: 'MULTINOMIAL',\n0x01DB: 'LCM',\n0x01DC: 'FVSCHEDULE',\n0x01DD: 'CUBEKPIMEMBER',\n0x01DE: 'CUBESET',\n0x01DF: 'CUBESETCOUNT',\n0x01E0: 'IFERROR',\n0x01E1: 'COUNTIFS',\n0x01E2: 'SUMIFS',\n0x01E3: 'AVERAGEIF',\n0x01E4: 'AVERAGEIFS'\n};\nvar FtabArgc = {\n0x0002: 1, /* ISNA */\n0x0003: 1, /* ISERROR */\n0x000A: 0, /* NA */\n0x000F: 1, /* SIN */\n0x0010: 1, /* COS */\n0x0011: 1, /* TAN */\n0x0012: 1, /* ATAN */\n0x0013: 0, /* PI */\n0x0014: 1, /* SQRT */\n0x0015: 1, /* EXP */\n0x0016: 1, /* LN */\n0x0017: 1, /* LOG10 */\n0x0018: 1, /* ABS */\n0x0019: 1, /* INT */\n0x001A: 1, /* SIGN */\n0x001B: 2, /* ROUND */\n0x001E: 2, /* REPT */\n0x001F: 3, /* MID */\n0x0020: 1, /* LEN */\n0x0021: 1, /* VALUE */\n0x0022: 0, /* TRUE */\n0x0023: 0, /* FALSE */\n0x0026: 1, /* NOT */\n0x0027: 2, /* MOD */\n0x0028: 3, /* DCOUNT */\n0x0029: 3, /* DSUM */\n0x002A: 3, /* DAVERAGE */\n0x002B: 3, /* DMIN */\n0x002C: 3, /* DMAX */\n0x002D: 3, /* DSTDEV */\n0x002F: 3, /* DVAR */\n0x0030: 2, /* TEXT */\n0x0035: 1, /* GOTO */\n0x003D: 3, /* MIRR */\n0x003F: 0, /* RAND */\n0x0041: 3, /* DATE */\n0x0042: 3, /* TIME */\n0x0043: 1, /* DAY */\n0x0044: 1, /* MONTH */\n0x0045: 1, /* YEAR */\n0x0046: 1, /* WEEKDAY */\n0x0047: 1, /* HOUR */\n0x0048: 1, /* MINUTE */\n0x0049: 1, /* SECOND */\n0x004A: 0, /* NOW */\n0x004B: 1, /* AREAS */\n0x004C: 1, /* ROWS */\n0x004D: 1, /* COLUMNS */\n0x004F: 2, /* ABSREF */\n0x0050: 2, /* RELREF */\n0x0053: 1, /* TRANSPOSE */\n0x0055: 0, /* STEP */\n0x0056: 1, /* TYPE */\n0x0059: 0, /* CALLER */\n0x005A: 1, /* DEREF */\n0x005E: 0, /* ACTIVE.CELL */\n0x005F: 0, /* SELECTION */\n0x0061: 2, /* ATAN2 */\n0x0062: 1, /* ASIN */\n0x0063: 1, /* ACOS */\n0x0065: 3, /* HLOOKUP */\n0x0066: 3, /* VLOOKUP */\n0x0069: 1, /* ISREF */\n0x006A: 1, /* GET.FORMULA */\n0x006C: 2, /* SET.VALUE */\n0x006F: 1, /* CHAR */\n0x0070: 1, /* LOWER */\n0x0071: 1, /* UPPER */\n0x0072: 1, /* PROPER */\n0x0075: 2, /* EXACT */\n0x0076: 1, /* TRIM */\n0x0077: 4, /* REPLACE */\n0x0079: 1, /* CODE */\n0x007E: 1, /* ISERR */\n0x007F: 1, /* ISTEXT */\n0x0080: 1, /* ISNUMBER */\n0x0081: 1, /* ISBLANK */\n0x0082: 1, /* T */\n0x0083: 1, /* N */\n0x0085: 1, /* FCLOSE */\n0x0086: 1, /* FSIZE */\n0x0087: 1, /* FREADLN */\n0x0088: 2, /* FREAD */\n0x0089: 2, /* FWRITELN */\n0x008A: 2, /* FWRITE */\n0x008C: 1, /* DATEVALUE */\n0x008D: 1, /* TIMEVALUE */\n0x008E: 3, /* SLN */\n0x008F: 4, /* SYD */\n0x0090: 4, /* DDB */\n0x00A1: 1, /* DIALOG.BOX */\n0x00A2: 1, /* CLEAN */\n0x00A3: 1, /* MDETERM */\n0x00A4: 1, /* MINVERSE */\n0x00A5: 2, /* MMULT */\n0x00AC: 1, /* WHILE */\n0x00AF: 2, /* INITIATE */\n0x00B0: 2, /* REQUEST */\n0x00B1: 3, /* POKE */\n0x00B2: 2, /* EXECUTE */\n0x00B3: 1, /* TERMINATE */\n0x00B8: 1, /* FACT */\n0x00BA: 1, /* GET.WORKSPACE */\n0x00BD: 3, /* DPRODUCT */\n0x00BE: 1, /* ISNONTEXT */\n0x00C3: 3, /* DSTDEVP */\n0x00C4: 3, /* DVARP */\n0x00C5: 1, /* TRUNC */\n0x00C6: 1, /* ISLOGICAL */\n0x00C7: 3, /* DCOUNTA */\n0x00C9: 1, /* UNREGISTER */\n0x00CF: 4, /* REPLACEB */\n0x00D2: 3, /* MIDB */\n0x00D3: 1, /* LENB */\n0x00D4: 2, /* ROUNDUP */\n0x00D5: 2, /* ROUNDDOWN */\n0x00D6: 1, /* ASC */\n0x00D7: 1, /* DBCS */\n0x00E1: 0, /* END.IF */\n0x00E5: 1, /* SINH */\n0x00E6: 1, /* COSH */\n0x00E7: 1, /* TANH */\n0x00E8: 1, /* ASINH */\n0x00E9: 1, /* ACOSH */\n0x00EA: 1, /* ATANH */\n0x00EB: 3, /* DGET */\n0x00F4: 1, /* INFO */\n0x00F7: 4, /* DB */\n0x00FC: 2, /* FREQUENCY */\n0x0101: 1, /* EVALUATE */\n0x0105: 1, /* ERROR.TYPE */\n0x010F: 1, /* GAMMALN */\n0x0111: 4, /* BINOMDIST */\n0x0112: 2, /* CHIDIST */\n0x0113: 2, /* CHIINV */\n0x0114: 2, /* COMBIN */\n0x0115: 3, /* CONFIDENCE */\n0x0116: 3, /* CRITBINOM */\n0x0117: 1, /* EVEN */\n0x0118: 3, /* EXPONDIST */\n0x0119: 3, /* FDIST */\n0x011A: 3, /* FINV */\n0x011B: 1, /* FISHER */\n0x011C: 1, /* FISHERINV */\n0x011D: 2, /* FLOOR */\n0x011E: 4, /* GAMMADIST */\n0x011F: 3, /* GAMMAINV */\n0x0120: 2, /* CEILING */\n0x0121: 4, /* HYPGEOMDIST */\n0x0122: 3, /* LOGNORMDIST */\n0x0123: 3, /* LOGINV */\n0x0124: 3, /* NEGBINOMDIST */\n0x0125: 4, /* NORMDIST */\n0x0126: 1, /* NORMSDIST */\n0x0127: 3, /* NORMINV */\n0x0128: 1, /* NORMSINV */\n0x0129: 3, /* STANDARDIZE */\n0x012A: 1, /* ODD */\n0x012B: 2, /* PERMUT */\n0x012C: 3, /* POISSON */\n0x012D: 3, /* TDIST */\n0x012E: 4, /* WEIBULL */\n0x012F: 2, /* SUMXMY2 */\n0x0130: 2, /* SUMX2MY2 */\n0x0131: 2, /* SUMX2PY2 */\n0x0132: 2, /* CHITEST */\n0x0133: 2, /* CORREL */\n0x0134: 2, /* COVAR */\n0x0135: 3, /* FORECAST */\n0x0136: 2, /* FTEST */\n0x0137: 2, /* INTERCEPT */\n0x0138: 2, /* PEARSON */\n0x0139: 2, /* RSQ */\n0x013A: 2, /* STEYX */\n0x013B: 2, /* SLOPE */\n0x013C: 4, /* TTEST */\n0x0145: 2, /* LARGE */\n0x0146: 2, /* SMALL */\n0x0147: 2, /* QUARTILE */\n0x0148: 2, /* PERCENTILE */\n0x014B: 2, /* TRIMMEAN */\n0x014C: 2, /* TINV */\n0x0151: 2, /* POWER */\n0x0156: 1, /* RADIANS */\n0x0157: 1, /* DEGREES */\n0x015A: 2, /* COUNTIF */\n0x015B: 1, /* COUNTBLANK */\n0x015E: 4, /* ISPMT */\n0x015F: 3, /* DATEDIF */\n0x0160: 1, /* DATESTRING */\n0x0161: 2, /* NUMBERSTRING */\n0x0168: 1, /* PHONETIC */\n0x0170: 1, /* BAHTTEXT */\n0x0171: 1, /* THAIDAYOFWEEK */\n0x0172: 1, /* THAIDIGIT */\n0x0173: 1, /* THAIMONTHOFYEAR */\n0x0174: 1, /* THAINUMSOUND */\n0x0175: 1, /* THAINUMSTRING */\n0x0176: 1, /* THAISTRINGLENGTH */\n0x0177: 1, /* ISTHAIDIGIT */\n0x0178: 1, /* ROUNDBAHTDOWN */\n0x0179: 1, /* ROUNDBAHTUP */\n0x017A: 1, /* THAIYEAR */\n0x017E: 3, /* CUBEMEMBERPROPERTY */\n0x0181: 1, /* HEX2DEC */\n0x0188: 1, /* OCT2DEC */\n0x0189: 1, /* BIN2DEC */\n0x018C: 2, /* IMSUB */\n0x018D: 2, /* IMDIV */\n0x018E: 2, /* IMPOWER */\n0x018F: 1, /* IMABS */\n0x0190: 1, /* IMSQRT */\n0x0191: 1, /* IMLN */\n0x0192: 1, /* IMLOG2 */\n0x0193: 1, /* IMLOG10 */\n0x0194: 1, /* IMSIN */\n0x0195: 1, /* IMCOS */\n0x0196: 1, /* IMEXP */\n0x0197: 1, /* IMARGUMENT */\n0x0198: 1, /* IMCONJUGATE */\n0x0199: 1, /* IMAGINARY */\n0x019A: 1, /* IMREAL */\n0x019E: 4, /* SERIESSUM */\n0x019F: 1, /* FACTDOUBLE */\n0x01A0: 1, /* SQRTPI */\n0x01A1: 2, /* QUOTIENT */\n0x01A4: 1, /* ISEVEN */\n0x01A5: 1, /* ISODD */\n0x01A6: 2, /* MROUND */\n0x01A8: 1, /* ERFC */\n0x01A9: 2, /* BESSELJ */\n0x01AA: 2, /* BESSELK */\n0x01AB: 2, /* BESSELY */\n0x01AC: 2, /* BESSELI */\n0x01AE: 3, /* XNPV */\n0x01B6: 3, /* TBILLEQ */\n0x01B7: 3, /* TBILLPRICE */\n0x01B8: 3, /* TBILLYIELD */\n0x01BB: 2, /* DOLLARDE */\n0x01BC: 2, /* DOLLARFR */\n0x01BD: 2, /* NOMINAL */\n0x01BE: 2, /* EFFECT */\n0x01BF: 6, /* CUMPRINC */\n0x01C0: 6, /* CUMIPMT */\n0x01C1: 2, /* EDATE */\n0x01C2: 2, /* EOMONTH */\n0x01D0: 2, /* RANDBETWEEN */\n0x01D4: 3, /* CONVERT */\n0x01DC: 2, /* FVSCHEDULE */\n0x01DF: 1, /* CUBESETCOUNT */\n0x01E0: 2, /* IFERROR */\n0xFFFF: 0\n};\n/* [MS-XLSX] 2.2.3 Functions */\n/* [MS-XLSB] 2.5.97.10 Ftab */\nvar XLSXFutureFunctions = {\n\t\"_xlfn.ACOT\": \"ACOT\",\n\t\"_xlfn.ACOTH\": \"ACOTH\",\n\t\"_xlfn.AGGREGATE\": \"AGGREGATE\",\n\t\"_xlfn.ARABIC\": \"ARABIC\",\n\t\"_xlfn.AVERAGEIF\": \"AVERAGEIF\",\n\t\"_xlfn.AVERAGEIFS\": \"AVERAGEIFS\",\n\t\"_xlfn.BASE\": \"BASE\",\n\t\"_xlfn.BETA.DIST\": \"BETA.DIST\",\n\t\"_xlfn.BETA.INV\": \"BETA.INV\",\n\t\"_xlfn.BINOM.DIST\": \"BINOM.DIST\",\n\t\"_xlfn.BINOM.DIST.RANGE\": \"BINOM.DIST.RANGE\",\n\t\"_xlfn.BINOM.INV\": \"BINOM.INV\",\n\t\"_xlfn.BITAND\": \"BITAND\",\n\t\"_xlfn.BITLSHIFT\": \"BITLSHIFT\",\n\t\"_xlfn.BITOR\": \"BITOR\",\n\t\"_xlfn.BITRSHIFT\": \"BITRSHIFT\",\n\t\"_xlfn.BITXOR\": \"BITXOR\",\n\t\"_xlfn.CEILING.MATH\": \"CEILING.MATH\",\n\t\"_xlfn.CEILING.PRECISE\": \"CEILING.PRECISE\",\n\t\"_xlfn.CHISQ.DIST\": \"CHISQ.DIST\",\n\t\"_xlfn.CHISQ.DIST.RT\": \"CHISQ.DIST.RT\",\n\t\"_xlfn.CHISQ.INV\": \"CHISQ.INV\",\n\t\"_xlfn.CHISQ.INV.RT\": \"CHISQ.INV.RT\",\n\t\"_xlfn.CHISQ.TEST\": \"CHISQ.TEST\",\n\t\"_xlfn.COMBINA\": \"COMBINA\",\n\t\"_xlfn.CONCAT\": \"CONCAT\",\n\t\"_xlfn.CONFIDENCE.NORM\": \"CONFIDENCE.NORM\",\n\t\"_xlfn.CONFIDENCE.T\": \"CONFIDENCE.T\",\n\t\"_xlfn.COT\": \"COT\",\n\t\"_xlfn.COTH\": \"COTH\",\n\t\"_xlfn.COUNTIFS\": \"COUNTIFS\",\n\t\"_xlfn.COVARIANCE.P\": \"COVARIANCE.P\",\n\t\"_xlfn.COVARIANCE.S\": \"COVARIANCE.S\",\n\t\"_xlfn.CSC\": \"CSC\",\n\t\"_xlfn.CSCH\": \"CSCH\",\n\t\"_xlfn.DAYS\": \"DAYS\",\n\t\"_xlfn.DECIMAL\": \"DECIMAL\",\n\t\"_xlfn.ECMA.CEILING\": \"ECMA.CEILING\",\n\t\"_xlfn.ERF.PRECISE\": \"ERF.PRECISE\",\n\t\"_xlfn.ERFC.PRECISE\": \"ERFC.PRECISE\",\n\t\"_xlfn.EXPON.DIST\": \"EXPON.DIST\",\n\t\"_xlfn.F.DIST\": \"F.DIST\",\n\t\"_xlfn.F.DIST.RT\": \"F.DIST.RT\",\n\t\"_xlfn.F.INV\": \"F.INV\",\n\t\"_xlfn.F.INV.RT\": \"F.INV.RT\",\n\t\"_xlfn.F.TEST\": \"F.TEST\",\n\t\"_xlfn.FILTERXML\": \"FILTERXML\",\n\t\"_xlfn.FLOOR.MATH\": \"FLOOR.MATH\",\n\t\"_xlfn.FLOOR.PRECISE\": \"FLOOR.PRECISE\",\n\t\"_xlfn.FORECAST.ETS\": \"FORECAST.ETS\",\n\t\"_xlfn.FORECAST.ETS.CONFINT\": \"FORECAST.ETS.CONFINT\",\n\t\"_xlfn.FORECAST.ETS.SEASONALITY\": \"FORECAST.ETS.SEASONALITY\",\n\t\"_xlfn.FORECAST.ETS.STAT\": \"FORECAST.ETS.STAT\",\n\t\"_xlfn.FORECAST.LINEAR\": \"FORECAST.LINEAR\",\n\t\"_xlfn.FORMULATEXT\": \"FORMULATEXT\",\n\t\"_xlfn.GAMMA\": \"GAMMA\",\n\t\"_xlfn.GAMMA.DIST\": \"GAMMA.DIST\",\n\t\"_xlfn.GAMMA.INV\": \"GAMMA.INV\",\n\t\"_xlfn.GAMMALN.PRECISE\": \"GAMMALN.PRECISE\",\n\t\"_xlfn.GAUSS\": \"GAUSS\",\n\t\"_xlfn.HYPGEOM.DIST\": \"HYPGEOM.DIST\",\n\t\"_xlfn.IFERROR\": \"IFERROR\",\n\t\"_xlfn.IFNA\": \"IFNA\",\n\t\"_xlfn.IFS\": \"IFS\",\n\t\"_xlfn.IMCOSH\": \"IMCOSH\",\n\t\"_xlfn.IMCOT\": \"IMCOT\",\n\t\"_xlfn.IMCSC\": \"IMCSC\",\n\t\"_xlfn.IMCSCH\": \"IMCSCH\",\n\t\"_xlfn.IMSEC\": \"IMSEC\",\n\t\"_xlfn.IMSECH\": \"IMSECH\",\n\t\"_xlfn.IMSINH\": \"IMSINH\",\n\t\"_xlfn.IMTAN\": \"IMTAN\",\n\t\"_xlfn.ISFORMULA\": \"ISFORMULA\",\n\t\"_xlfn.ISO.CEILING\": \"ISO.CEILING\",\n\t\"_xlfn.ISOWEEKNUM\": \"ISOWEEKNUM\",\n\t\"_xlfn.LOGNORM.DIST\": \"LOGNORM.DIST\",\n\t\"_xlfn.LOGNORM.INV\": \"LOGNORM.INV\",\n\t\"_xlfn.MAXIFS\": \"MAXIFS\",\n\t\"_xlfn.MINIFS\": \"MINIFS\",\n\t\"_xlfn.MODE.MULT\": \"MODE.MULT\",\n\t\"_xlfn.MODE.SNGL\": \"MODE.SNGL\",\n\t\"_xlfn.MUNIT\": \"MUNIT\",\n\t\"_xlfn.NEGBINOM.DIST\": \"NEGBINOM.DIST\",\n\t\"_xlfn.NETWORKDAYS.INTL\": \"NETWORKDAYS.INTL\",\n\t\"_xlfn.NIGBINOM\": \"NIGBINOM\",\n\t\"_xlfn.NORM.DIST\": \"NORM.DIST\",\n\t\"_xlfn.NORM.INV\": \"NORM.INV\",\n\t\"_xlfn.NORM.S.DIST\": \"NORM.S.DIST\",\n\t\"_xlfn.NORM.S.INV\": \"NORM.S.INV\",\n\t\"_xlfn.NUMBERVALUE\": \"NUMBERVALUE\",\n\t\"_xlfn.PDURATION\": \"PDURATION\",\n\t\"_xlfn.PERCENTILE.EXC\": \"PERCENTILE.EXC\",\n\t\"_xlfn.PERCENTILE.INC\": \"PERCENTILE.INC\",\n\t\"_xlfn.PERCENTRANK.EXC\": \"PERCENTRANK.EXC\",\n\t\"_xlfn.PERCENTRANK.INC\": \"PERCENTRANK.INC\",\n\t\"_xlfn.PERMUTATIONA\": \"PERMUTATIONA\",\n\t\"_xlfn.PHI\": \"PHI\",\n\t\"_xlfn.POISSON.DIST\": \"POISSON.DIST\",\n\t\"_xlfn.QUARTILE.EXC\": \"QUARTILE.EXC\",\n\t\"_xlfn.QUARTILE.INC\": \"QUARTILE.INC\",\n\t\"_xlfn.QUERYSTRING\": \"QUERYSTRING\",\n\t\"_xlfn.RANK.AVG\": \"RANK.AVG\",\n\t\"_xlfn.RANK.EQ\": \"RANK.EQ\",\n\t\"_xlfn.RRI\": \"RRI\",\n\t\"_xlfn.SEC\": \"SEC\",\n\t\"_xlfn.SECH\": \"SECH\",\n\t\"_xlfn.SHEET\": \"SHEET\",\n\t\"_xlfn.SHEETS\": \"SHEETS\",\n\t\"_xlfn.SKEW.P\": \"SKEW.P\",\n\t\"_xlfn.STDEV.P\": \"STDEV.P\",\n\t\"_xlfn.STDEV.S\": \"STDEV.S\",\n\t\"_xlfn.SUMIFS\": \"SUMIFS\",\n\t\"_xlfn.SWITCH\": \"SWITCH\",\n\t\"_xlfn.T.DIST\": \"T.DIST\",\n\t\"_xlfn.T.DIST.2T\": \"T.DIST.2T\",\n\t\"_xlfn.T.DIST.RT\": \"T.DIST.RT\",\n\t\"_xlfn.T.INV\": \"T.INV\",\n\t\"_xlfn.T.INV.2T\": \"T.INV.2T\",\n\t\"_xlfn.T.TEST\": \"T.TEST\",\n\t\"_xlfn.TEXTJOIN\": \"TEXTJOIN\",\n\t\"_xlfn.UNICHAR\": \"UNICHAR\",\n\t\"_xlfn.UNICODE\": \"UNICODE\",\n\t\"_xlfn.VAR.P\": \"VAR.P\",\n\t\"_xlfn.VAR.S\": \"VAR.S\",\n\t\"_xlfn.WEBSERVICE\": \"WEBSERVICE\",\n\t\"_xlfn.WEIBULL.DIST\": \"WEIBULL.DIST\",\n\t\"_xlfn.WORKDAY.INTL\": \"WORKDAY.INTL\",\n\t\"_xlfn.XOR\": \"XOR\",\n\t\"_xlfn.Z.TEST\": \"Z.TEST\"\n};\n\n/* Part 3 TODO: actually parse formulae */\nfunction ods_to_csf_formula(f) {\n\tif(f.slice(0,3) == \"of:\") f = f.slice(3);\n\t/* 5.2 Basic Expressions */\n\tif(f.charCodeAt(0) == 61) {\n\t\tf = f.slice(1);\n\t\tif(f.charCodeAt(0) == 61) f = f.slice(1);\n\t}\n\tf = f.replace(/COM\\.MICROSOFT\\./g, \"\");\n\t/* Part 3 Section 5.8 References */\n\tf = f.replace(/\\[((?:\\.[A-Z]+[0-9]+)(?::\\.[A-Z]+[0-9]+)?)\\]/g, function($$, $1) { return $1.replace(/\\./g,\"\"); });\n\t/* TODO: something other than this */\n\tf = f.replace(/\\[.(#[A-Z]*[?!])\\]/g, \"$1\");\n\treturn f.replace(/[;~]/g,\",\").replace(/\\|/g,\";\");\n}\n\nfunction csf_to_ods_formula(f) {\n\tvar o = \"of:=\" + f.replace(crefregex, \"$1[.$2$3$4$5]\").replace(/\\]:\\[/g,\":\");\n\t/* TODO: something other than this */\n\treturn o.replace(/;/g, \"|\").replace(/,/g,\";\");\n}\n\nfunction ods_to_csf_3D(r) {\n\tvar a = r.split(\":\");\n\tvar s = a[0].split(\".\")[0];\n\treturn [s, a[0].split(\".\")[1] + (a.length > 1 ? (\":\" + (a[1].split(\".\")[1] || a[1].split(\".\")[0])) : \"\")];\n}\n\nfunction csf_to_ods_3D(r) {\n\treturn r.replace(/\\./,\"!\");\n}\n\nvar strs = {}; // shared strings\nvar _ssfopts = {}; // spreadsheet formatting options\n\nRELS.WS = [\n\t\"http://schemas.openxmlformats.org/officeDocument/2006/relationships/worksheet\",\n\t\"http://purl.oclc.org/ooxml/officeDocument/relationships/worksheet\"\n];\n\n/*global Map */\nvar browser_has_Map = typeof Map !== 'undefined';\n\nfunction get_sst_id(sst, str, rev) {\n\tvar i = 0, len = sst.length;\n\tif(rev) {\n\t\tif(browser_has_Map ? rev.has(str) : Object.prototype.hasOwnProperty.call(rev, str)) {\n\t\t\tvar revarr = browser_has_Map ? rev.get(str) : rev[str];\n\t\t\tfor(; i < revarr.length; ++i) {\n\t\t\t\tif(sst[revarr[i]].t === str) { sst.Count ++; return revarr[i]; }\n\t\t\t}\n\t\t}\n\t} else for(; i < len; ++i) {\n\t\tif(sst[i].t === str) { sst.Count ++; return i; }\n\t}\n\tsst[len] = ({t:str}); sst.Count ++; sst.Unique ++;\n\tif(rev) {\n\t\tif(browser_has_Map) {\n\t\t\tif(!rev.has(str)) rev.set(str, []);\n\t\t\trev.get(str).push(len);\n\t\t} else {\n\t\t\tif(!Object.prototype.hasOwnProperty.call(rev, str)) rev[str] = [];\n\t\t\trev[str].push(len);\n\t\t}\n\t}\n\treturn len;\n}\n\nfunction col_obj_w(C, col) {\n\tvar p = ({min:C+1,max:C+1});\n\t/* wch (chars), wpx (pixels) */\n\tvar wch = -1;\n\tif(col.MDW) MDW = col.MDW;\n\tif(col.width != null) p.customWidth = 1;\n\telse if(col.wpx != null) wch = px2char(col.wpx);\n\telse if(col.wch != null) wch = col.wch;\n\tif(wch > -1) { p.width = char2width(wch); p.customWidth = 1; }\n\telse if(col.width != null) p.width = col.width;\n\tif(col.hidden) p.hidden = true;\n\treturn p;\n}\n\nfunction default_margins(margins, mode) {\n\tif(!margins) return;\n\tvar defs = [0.7, 0.7, 0.75, 0.75, 0.3, 0.3];\n\tif(mode == 'xlml') defs = [1, 1, 1, 1, 0.5, 0.5];\n\tif(margins.left == null) margins.left = defs[0];\n\tif(margins.right == null) margins.right = defs[1];\n\tif(margins.top == null) margins.top = defs[2];\n\tif(margins.bottom == null) margins.bottom = defs[3];\n\tif(margins.header == null) margins.header = defs[4];\n\tif(margins.footer == null) margins.footer = defs[5];\n}\n\nfunction get_cell_style(styles, cell, opts) {\n\tvar z = opts.revssf[cell.z != null ? cell.z : \"General\"];\n\tvar i = 0x3c, len = styles.length;\n\tif(z == null && opts.ssf) {\n\t\tfor(; i < 0x188; ++i) if(opts.ssf[i] == null) {\n\t\t\tSSF.load(cell.z, i);\n\t\t\t// $FlowIgnore\n\t\t\topts.ssf[i] = cell.z;\n\t\t\topts.revssf[cell.z] = z = i;\n\t\t\tbreak;\n\t\t}\n\t}\n\tfor(i = 0; i != len; ++i) if(styles[i].numFmtId === z) return i;\n\tstyles[len] = {\n\t\tnumFmtId:z,\n\t\tfontId:0,\n\t\tfillId:0,\n\t\tborderId:0,\n\t\txfId:0,\n\t\tapplyNumberFormat:1\n\t};\n\treturn len;\n}\n\nfunction safe_format(p, fmtid, fillid, opts, themes, styles) {\n\ttry {\n\t\tif(opts.cellNF) p.z = SSF._table[fmtid];\n\t} catch(e) { if(opts.WTF) throw e; }\n\tif(p.t === 'z') return;\n\tif(p.t === 'd' && typeof p.v === 'string') p.v = parseDate(p.v);\n\tif(!opts || opts.cellText !== false) try {\n\t\tif(SSF._table[fmtid] == null) SSF.load(SSFImplicit[fmtid] || \"General\", fmtid);\n\t\tif(p.t === 'e') p.w = p.w || BErr[p.v];\n\t\telse if(fmtid === 0) {\n\t\t\tif(p.t === 'n') {\n\t\t\t\tif((p.v|0) === p.v) p.w = SSF._general_int(p.v);\n\t\t\t\telse p.w = SSF._general_num(p.v);\n\t\t\t}\n\t\t\telse if(p.t === 'd') {\n\t\t\t\tvar dd = datenum(p.v);\n\t\t\t\tif((dd|0) === dd) p.w = SSF._general_int(dd);\n\t\t\t\telse p.w = SSF._general_num(dd);\n\t\t\t}\n\t\t\telse if(p.v === undefined) return \"\";\n\t\t\telse p.w = SSF._general(p.v,_ssfopts);\n\t\t}\n\t\telse if(p.t === 'd') p.w = SSF.format(fmtid,datenum(p.v),_ssfopts);\n\t\telse p.w = SSF.format(fmtid,p.v,_ssfopts);\n\t} catch(e) { if(opts.WTF) throw e; }\n\tif(!opts.cellStyles) return;\n\tif(fillid != null) try {\n\t\tp.s = styles.Fills[fillid];\n\t\tif (p.s.fgColor && p.s.fgColor.theme && !p.s.fgColor.rgb) {\n\t\t\tp.s.fgColor.rgb = rgb_tint(themes.themeElements.clrScheme[p.s.fgColor.theme].rgb, p.s.fgColor.tint || 0);\n\t\t\tif(opts.WTF) p.s.fgColor.raw_rgb = themes.themeElements.clrScheme[p.s.fgColor.theme].rgb;\n\t\t}\n\t\tif (p.s.bgColor && p.s.bgColor.theme) {\n\t\t\tp.s.bgColor.rgb = rgb_tint(themes.themeElements.clrScheme[p.s.bgColor.theme].rgb, p.s.bgColor.tint || 0);\n\t\t\tif(opts.WTF) p.s.bgColor.raw_rgb = themes.themeElements.clrScheme[p.s.bgColor.theme].rgb;\n\t\t}\n\t} catch(e) { if(opts.WTF && styles.Fills) throw e; }\n}\n\nfunction check_ws(ws, sname, i) {\n\tif(ws && ws['!ref']) {\n\t\tvar range = safe_decode_range(ws['!ref']);\n\t\tif(range.e.c < range.s.c || range.e.r < range.s.r) throw new Error(\"Bad range (\" + i + \"): \" + ws['!ref']);\n\t}\n}\nfunction parse_ws_xml_dim(ws, s) {\n\tvar d = safe_decode_range(s);\n\tif(d.s.r<=d.e.r && d.s.c<=d.e.c && d.s.r>=0 && d.s.c>=0) ws[\"!ref\"] = encode_range(d);\n}\nvar mergecregex = /<(?:\\w:)?mergeCell ref=\"[A-Z0-9:]+\"\\s*[\\/]?>/g;\nvar sheetdataregex = /<(?:\\w+:)?sheetData[^>]*>([\\s\\S]*)<\\/(?:\\w+:)?sheetData>/;\nvar hlinkregex = /<(?:\\w:)?hyperlink [^>]*>/mg;\nvar dimregex = /\"(\\w*:\\w*)\"/;\nvar colregex = /<(?:\\w:)?col\\b[^>]*[\\/]?>/g;\nvar afregex = /<(?:\\w:)?autoFilter[^>]*([\\/]|>([\\s\\S]*)<\\/(?:\\w:)?autoFilter)>/g;\nvar marginregex= /<(?:\\w:)?pageMargins[^>]*\\/>/g;\nvar sheetprregex = /<(?:\\w:)?sheetPr\\b(?:[^>a-z][^>]*)?\\/>/;\nvar svsregex = /<(?:\\w:)?sheetViews[^>]*(?:[\\/]|>([\\s\\S]*)<\\/(?:\\w:)?sheetViews)>/;\n\n/* 18.3 Worksheets */\nfunction parse_ws_xml(data, opts, idx, rels, wb, themes, styles) {\n\tif(!data) return data;\n\tif(!rels) rels = {'!id':{}};\n\tif(DENSE != null && opts.dense == null) opts.dense = DENSE;\n\n\t/* 18.3.1.99 worksheet CT_Worksheet */\n\tvar s = opts.dense ? ([]) : ({});\n\tvar refguess = ({s: {r:2000000, c:2000000}, e: {r:0, c:0} });\n\n\tvar data1 = \"\", data2 = \"\";\n\tvar mtch = data.match(sheetdataregex);\n\tif(mtch) {\n\t\tdata1 = data.slice(0, mtch.index);\n\t\tdata2 = data.slice(mtch.index + mtch[0].length);\n\t} else data1 = data2 = data;\n\n\t/* 18.3.1.82 sheetPr CT_SheetPr */\n\tvar sheetPr = data1.match(sheetprregex);\n\tif(sheetPr) parse_ws_xml_sheetpr(sheetPr[0], s, wb, idx);\n\n\t/* 18.3.1.35 dimension CT_SheetDimension */\n\tvar ridx = (data1.match(/<(?:\\w*:)?dimension/)||{index:-1}).index;\n\tif(ridx > 0) {\n\t\tvar ref = data1.slice(ridx,ridx+50).match(dimregex);\n\t\tif(ref) parse_ws_xml_dim(s, ref[1]);\n\t}\n\n\t/* 18.3.1.88 sheetViews CT_SheetViews */\n\tvar svs = data1.match(svsregex);\n\tif(svs && svs[1]) parse_ws_xml_sheetviews(svs[1], wb);\n\n\t/* 18.3.1.17 cols CT_Cols */\n\tvar columns = [];\n\tif(opts.cellStyles) {\n\t\t/* 18.3.1.13 col CT_Col */\n\t\tvar cols = data1.match(colregex);\n\t\tif(cols) parse_ws_xml_cols(columns, cols);\n\t}\n\n\t/* 18.3.1.80 sheetData CT_SheetData ? */\n\tif(mtch) parse_ws_xml_data(mtch[1], s, opts, refguess, themes, styles);\n\n\t/* 18.3.1.2 autoFilter CT_AutoFilter */\n\tvar afilter = data2.match(afregex);\n\tif(afilter) s['!autofilter'] = parse_ws_xml_autofilter(afilter[0]);\n\n\t/* 18.3.1.55 mergeCells CT_MergeCells */\n\tvar merges = [];\n\tvar _merge = data2.match(mergecregex);\n\tif(_merge) for(ridx = 0; ridx != _merge.length; ++ridx)\n\t\tmerges[ridx] = safe_decode_range(_merge[ridx].slice(_merge[ridx].indexOf(\"\\\"\")+1));\n\n\t/* 18.3.1.48 hyperlinks CT_Hyperlinks */\n\tvar hlink = data2.match(hlinkregex);\n\tif(hlink) parse_ws_xml_hlinks(s, hlink, rels);\n\n\t/* 18.3.1.62 pageMargins CT_PageMargins */\n\tvar margins = data2.match(marginregex);\n\tif(margins) s['!margins'] = parse_ws_xml_margins(parsexmltag(margins[0]));\n\n\tif(!s[\"!ref\"] && refguess.e.c >= refguess.s.c && refguess.e.r >= refguess.s.r) s[\"!ref\"] = encode_range(refguess);\n\tif(opts.sheetRows > 0 && s[\"!ref\"]) {\n\t\tvar tmpref = safe_decode_range(s[\"!ref\"]);\n\t\tif(opts.sheetRows <= +tmpref.e.r) {\n\t\t\ttmpref.e.r = opts.sheetRows - 1;\n\t\t\tif(tmpref.e.r > refguess.e.r) tmpref.e.r = refguess.e.r;\n\t\t\tif(tmpref.e.r < tmpref.s.r) tmpref.s.r = tmpref.e.r;\n\t\t\tif(tmpref.e.c > refguess.e.c) tmpref.e.c = refguess.e.c;\n\t\t\tif(tmpref.e.c < tmpref.s.c) tmpref.s.c = tmpref.e.c;\n\t\t\ts[\"!fullref\"] = s[\"!ref\"];\n\t\t\ts[\"!ref\"] = encode_range(tmpref);\n\t\t}\n\t}\n\tif(columns.length > 0) s[\"!cols\"] = columns;\n\tif(merges.length > 0) s[\"!merges\"] = merges;\n\treturn s;\n}\n\nfunction write_ws_xml_merges(merges) {\n\tif(merges.length === 0) return \"\";\n\tvar o = '';\n\tfor(var i = 0; i != merges.length; ++i) o += '';\n\treturn o + '';\n}\n\n/* 18.3.1.82-3 sheetPr CT_ChartsheetPr / CT_SheetPr */\nfunction parse_ws_xml_sheetpr(sheetPr, s, wb, idx) {\n\tvar data = parsexmltag(sheetPr);\n\tif(!wb.Sheets[idx]) wb.Sheets[idx] = {};\n\tif(data.codeName) wb.Sheets[idx].CodeName = unescapexml(utf8read(data.codeName));\n}\nfunction write_ws_xml_sheetpr(ws, wb, idx, opts, o) {\n\tvar needed = false;\n\tvar props = {}, payload = null;\n\tif(opts.bookType !== 'xlsx' && wb.vbaraw) {\n\t\tvar cname = wb.SheetNames[idx];\n\t\ttry { if(wb.Workbook) cname = wb.Workbook.Sheets[idx].CodeName || cname; } catch(e) {}\n\t\tneeded = true;\n\t\tprops.codeName = utf8write(escapexml(cname));\n\t}\n\n\tif(ws && ws[\"!outline\"]) {\n\t\tvar outlineprops = {summaryBelow:1, summaryRight:1};\n\t\tif(ws[\"!outline\"].above) outlineprops.summaryBelow = 0;\n\t\tif(ws[\"!outline\"].left) outlineprops.summaryRight = 0;\n\t\tpayload = (payload||\"\") + writextag('outlinePr', null, outlineprops);\n\t}\n\n\tif(!needed && !payload) return;\n\to[o.length] = (writextag('sheetPr', payload, props));\n}\n\n/* 18.3.1.85 sheetProtection CT_SheetProtection */\nvar sheetprot_deffalse = [\"objects\", \"scenarios\", \"selectLockedCells\", \"selectUnlockedCells\"];\nvar sheetprot_deftrue = [\n\t\"formatColumns\", \"formatRows\", \"formatCells\",\n\t\"insertColumns\", \"insertRows\", \"insertHyperlinks\",\n\t\"deleteColumns\", \"deleteRows\",\n\t\"sort\", \"autoFilter\", \"pivotTables\"\n];\nfunction write_ws_xml_protection(sp) {\n\t// algorithmName, hashValue, saltValue, spinCount\n\tvar o = ({sheet:1});\n\tsheetprot_deffalse.forEach(function(n) { if(sp[n] != null && sp[n]) o[n] = \"1\"; });\n\tsheetprot_deftrue.forEach(function(n) { if(sp[n] != null && !sp[n]) o[n] = \"0\"; });\n\t/* TODO: algorithm */\n\tif(sp.password) o.password = crypto_CreatePasswordVerifier_Method1(sp.password).toString(16).toUpperCase();\n\treturn writextag('sheetProtection', null, o);\n}\n\nfunction parse_ws_xml_hlinks(s, data, rels) {\n\tvar dense = Array.isArray(s);\n\tfor(var i = 0; i != data.length; ++i) {\n\t\tvar val = parsexmltag(utf8read(data[i]), true);\n\t\tif(!val.ref) return;\n\t\tvar rel = ((rels || {})['!id']||[])[val.id];\n\t\tif(rel) {\n\t\t\tval.Target = rel.Target;\n\t\t\tif(val.location) val.Target += \"#\"+val.location;\n\t\t} else {\n\t\t\tval.Target = \"#\" + val.location;\n\t\t\trel = {Target: val.Target, TargetMode: 'Internal'};\n\t\t}\n\t\tval.Rel = rel;\n\t\tif(val.tooltip) { val.Tooltip = val.tooltip; delete val.tooltip; }\n\t\tvar rng = safe_decode_range(val.ref);\n\t\tfor(var R=rng.s.r;R<=rng.e.r;++R) for(var C=rng.s.c;C<=rng.e.c;++C) {\n\t\t\tvar addr = encode_cell({c:C,r:R});\n\t\t\tif(dense) {\n\t\t\t\tif(!s[R]) s[R] = [];\n\t\t\t\tif(!s[R][C]) s[R][C] = {t:\"z\",v:undefined};\n\t\t\t\ts[R][C].l = val;\n\t\t\t} else {\n\t\t\t\tif(!s[addr]) s[addr] = {t:\"z\",v:undefined};\n\t\t\t\ts[addr].l = val;\n\t\t\t}\n\t\t}\n\t}\n}\n\nfunction parse_ws_xml_margins(margin) {\n\tvar o = {};\n\t[\"left\", \"right\", \"top\", \"bottom\", \"header\", \"footer\"].forEach(function(k) {\n\t\tif(margin[k]) o[k] = parseFloat(margin[k]);\n\t});\n\treturn o;\n}\nfunction write_ws_xml_margins(margin) {\n\tdefault_margins(margin);\n\treturn writextag('pageMargins', null, margin);\n}\n\nfunction parse_ws_xml_cols(columns, cols) {\n\tvar seencol = false;\n\tfor(var coli = 0; coli != cols.length; ++coli) {\n\t\tvar coll = parsexmltag(cols[coli], true);\n\t\tif(coll.hidden) coll.hidden = parsexmlbool(coll.hidden);\n\t\tvar colm=parseInt(coll.min, 10)-1, colM=parseInt(coll.max,10)-1;\n\t\tdelete coll.min; delete coll.max; coll.width = +coll.width;\n\t\tif(!seencol && coll.width) { seencol = true; find_mdw_colw(coll.width); }\n\t\tprocess_col(coll);\n\t\twhile(colm <= colM) columns[colm++] = dup(coll);\n\t}\n}\nfunction write_ws_xml_cols(ws, cols) {\n\tvar o = [\"\"], col;\n\tfor(var i = 0; i != cols.length; ++i) {\n\t\tif(!(col = cols[i])) continue;\n\t\to[o.length] = (writextag('col', null, col_obj_w(i, col)));\n\t}\n\to[o.length] = \"\";\n\treturn o.join(\"\");\n}\n\nfunction parse_ws_xml_autofilter(data) {\n\tvar o = { ref: (data.match(/ref=\"([^\"]*)\"/)||[])[1]};\n\treturn o;\n}\nfunction write_ws_xml_autofilter(data, ws, wb, idx) {\n\tvar ref = typeof data.ref == \"string\" ? data.ref : encode_range(data.ref);\n\tif(!wb.Workbook) wb.Workbook = ({Sheets:[]});\n\tif(!wb.Workbook.Names) wb.Workbook.Names = [];\n\tvar names = wb.Workbook.Names;\n\tvar range = decode_range(ref);\n\tif(range.s.r == range.e.r) { range.e.r = decode_range(ws[\"!ref\"]).e.r; ref = encode_range(range); }\n\tfor(var i = 0; i < names.length; ++i) {\n\t\tvar name = names[i];\n\t\tif(name.Name != '_xlnm._FilterDatabase') continue;\n\t\tif(name.Sheet != idx) continue;\n\t\tname.Ref = \"'\" + wb.SheetNames[idx] + \"'!\" + ref; break;\n\t}\n\tif(i == names.length) names.push({ Name: '_xlnm._FilterDatabase', Sheet: idx, Ref: \"'\" + wb.SheetNames[idx] + \"'!\" + ref });\n\treturn writextag(\"autoFilter\", null, {ref:ref});\n}\n\n/* 18.3.1.88 sheetViews CT_SheetViews */\n/* 18.3.1.87 sheetView CT_SheetView */\nvar sviewregex = /<(?:\\w:)?sheetView(?:[^>a-z][^>]*)?\\/?>/;\nfunction parse_ws_xml_sheetviews(data, wb) {\n\tif(!wb.Views) wb.Views = [{}];\n\t(data.match(sviewregex)||[]).forEach(function(r, i) {\n\t\tvar tag = parsexmltag(r);\n\t\t// $FlowIgnore\n\t\tif(!wb.Views[i]) wb.Views[i] = {};\n\t\t// $FlowIgnore\n\t\tif(parsexmlbool(tag.rightToLeft)) wb.Views[i].RTL = true;\n\t});\n}\nfunction write_ws_xml_sheetviews(ws, opts, idx, wb) {\n\tvar sview = ({workbookViewId:\"0\"});\n\t// $FlowIgnore\n\tif((((wb||{}).Workbook||{}).Views||[])[0]) sview.rightToLeft = wb.Workbook.Views[0].RTL ? \"1\" : \"0\";\n\treturn writextag(\"sheetViews\", writextag(\"sheetView\", null, sview), {});\n}\n\nfunction write_ws_xml_cell(cell, ref, ws, opts) {\n\tif(cell.v === undefined && typeof cell.f !== \"string\" || cell.t === 'z') return \"\";\n\tvar vv = \"\";\n\tvar oldt = cell.t, oldv = cell.v;\n\tif(cell.t !== \"z\") switch(cell.t) {\n\t\tcase 'b': vv = cell.v ? \"1\" : \"0\"; break;\n\t\tcase 'n': vv = ''+cell.v; break;\n\t\tcase 'e': vv = BErr[cell.v]; break;\n\t\tcase 'd':\n\t\t\tif(opts && opts.cellDates) vv = parseDate(cell.v, -1).toISOString();\n\t\t\telse {\n\t\t\t\tcell = dup(cell);\n\t\t\t\tcell.t = 'n';\n\t\t\t\tvv = ''+(cell.v = datenum(parseDate(cell.v)));\n\t\t\t}\n\t\t\tif(typeof cell.z === 'undefined') cell.z = SSF._table[14];\n\t\t\tbreak;\n\t\tdefault: vv = cell.v; break;\n\t}\n\tvar v = writetag('v', escapexml(vv)), o = ({r:ref});\n\t/* TODO: cell style */\n\tvar os = get_cell_style(opts.cellXfs, cell, opts);\n\tif(os !== 0) o.s = os;\n\tswitch(cell.t) {\n\t\tcase 'n': break;\n\t\tcase 'd': o.t = \"d\"; break;\n\t\tcase 'b': o.t = \"b\"; break;\n\t\tcase 'e': o.t = \"e\"; break;\n\t\tcase 'z': break;\n\t\tdefault: if(cell.v == null) { delete cell.t; break; }\n\t\t\tif(opts && opts.bookSST) {\n\t\t\t\tv = writetag('v', ''+get_sst_id(opts.Strings, cell.v, opts.revStrings));\n\t\t\t\to.t = \"s\"; break;\n\t\t\t}\n\t\t\to.t = \"str\"; break;\n\t}\n\tif(cell.t != oldt) { cell.t = oldt; cell.v = oldv; }\n\tif(typeof cell.f == \"string\" && cell.f) {\n\t\tvar ff = cell.F && cell.F.slice(0, ref.length) == ref ? {t:\"array\", ref:cell.F} : null;\n\t\tv = writextag('f', escapexml(cell.f), ff) + (cell.v != null ? v : \"\");\n\t}\n\tif(cell.l) ws['!links'].push([ref, cell.l]);\n\tif(cell.c) ws['!comments'].push([ref, cell.c]);\n\treturn writextag('c', v, o);\n}\n\nvar parse_ws_xml_data = (function() {\n\tvar cellregex = /<(?:\\w+:)?c[ \\/>]/, rowregex = /<\\/(?:\\w+:)?row>/;\n\tvar rregex = /r=[\"']([^\"']*)[\"']/, isregex = /<(?:\\w+:)?is>([\\S\\s]*?)<\\/(?:\\w+:)?is>/;\n\tvar refregex = /ref=[\"']([^\"']*)[\"']/;\n\tvar match_v = matchtag(\"v\"), match_f = matchtag(\"f\");\n\nreturn function parse_ws_xml_data(sdata, s, opts, guess, themes, styles) {\n\tvar ri = 0, x = \"\", cells = [], cref = [], idx=0, i=0, cc=0, d=\"\", p;\n\tvar tag, tagr = 0, tagc = 0;\n\tvar sstr, ftag;\n\tvar fmtid = 0, fillid = 0;\n\tvar do_format = Array.isArray(styles.CellXf), cf;\n\tvar arrayf = [];\n\tvar sharedf = [];\n\tvar dense = Array.isArray(s);\n\tvar rows = [], rowobj = {}, rowrite = false;\n\tvar sheetStubs = !!opts.sheetStubs;\n\tfor(var marr = sdata.split(rowregex), mt = 0, marrlen = marr.length; mt != marrlen; ++mt) {\n\t\tx = marr[mt].trim();\n\t\tvar xlen = x.length;\n\t\tif(xlen === 0) continue;\n\n\t\t/* 18.3.1.73 row CT_Row */\n\t\tfor(ri = 0; ri < xlen; ++ri) if(x.charCodeAt(ri) === 62) break; ++ri;\n\t\ttag = parsexmltag(x.slice(0,ri), true);\n\t\ttagr = tag.r != null ? parseInt(tag.r, 10) : tagr+1; tagc = -1;\n\t\tif(opts.sheetRows && opts.sheetRows < tagr) continue;\n\t\tif(guess.s.r > tagr - 1) guess.s.r = tagr - 1;\n\t\tif(guess.e.r < tagr - 1) guess.e.r = tagr - 1;\n\n\t\tif(opts && opts.cellStyles) {\n\t\t\trowobj = {}; rowrite = false;\n\t\t\tif(tag.ht) { rowrite = true; rowobj.hpt = parseFloat(tag.ht); rowobj.hpx = pt2px(rowobj.hpt); }\n\t\t\tif(tag.hidden == \"1\") { rowrite = true; rowobj.hidden = true; }\n\t\t\tif(tag.outlineLevel != null) { rowrite = true; rowobj.level = +tag.outlineLevel; }\n\t\t\tif(rowrite) rows[tagr-1] = rowobj;\n\t\t}\n\n\t\t/* 18.3.1.4 c CT_Cell */\n\t\tcells = x.slice(ri).split(cellregex);\n\t\tfor(var rslice = 0; rslice != cells.length; ++rslice) if(cells[rslice].trim().charAt(0) != \"<\") break;\n\t\tcells = cells.slice(rslice);\n\t\tfor(ri = 0; ri != cells.length; ++ri) {\n\t\t\tx = cells[ri].trim();\n\t\t\tif(x.length === 0) continue;\n\t\t\tcref = x.match(rregex); idx = ri; i=0; cc=0;\n\t\t\tx = \"\":\"\") + x;\n\t\t\tif(cref != null && cref.length === 2) {\n\t\t\t\tidx = 0; d=cref[1];\n\t\t\t\tfor(i=0; i != d.length; ++i) {\n\t\t\t\t\tif((cc=d.charCodeAt(i)-64) < 1 || cc > 26) break;\n\t\t\t\t\tidx = 26*idx + cc;\n\t\t\t\t}\n\t\t\t\t--idx;\n\t\t\t\ttagc = idx;\n\t\t\t} else ++tagc;\n\t\t\tfor(i = 0; i != x.length; ++i) if(x.charCodeAt(i) === 62) break; ++i;\n\t\t\ttag = parsexmltag(x.slice(0,i), true);\n\t\t\tif(!tag.r) tag.r = encode_cell({r:tagr-1, c:tagc});\n\t\t\td = x.slice(i);\n\t\t\tp = ({t:\"\"});\n\n\t\t\tif((cref=d.match(match_v))!= null && cref[1] !== '') p.v=unescapexml(cref[1]);\n\t\t\tif(opts.cellFormula) {\n\t\t\t\tif((cref=d.match(match_f))!= null && cref[1] !== '') {\n\t\t\t\t\t/* TODO: match against XLSXFutureFunctions */\n\t\t\t\t\tp.f=unescapexml(utf8read(cref[1])).replace(/\\r\\n/g, \"\\n\");\n\t\t\t\t\tif(!opts.xlfn) p.f = _xlfn(p.f);\n\t\t\t\t\tif(cref[0].indexOf('t=\"array\"') > -1) {\n\t\t\t\t\t\tp.F = (d.match(refregex)||[])[1];\n\t\t\t\t\t\tif(p.F.indexOf(\":\") > -1) arrayf.push([safe_decode_range(p.F), p.F]);\n\t\t\t\t\t} else if(cref[0].indexOf('t=\"shared\"') > -1) {\n\t\t\t\t\t\t// TODO: parse formula\n\t\t\t\t\t\tftag = parsexmltag(cref[0]);\n\t\t\t\t\t\tvar ___f = unescapexml(utf8read(cref[1]));\n\t\t\t\t\t\tif(!opts.xlfn) ___f = _xlfn(___f);\n\t\t\t\t\t\tsharedf[parseInt(ftag.si, 10)] = [ftag, ___f, tag.r];\n\t\t\t\t\t}\n\t\t\t\t} else if((cref=d.match(/]*\\/>/))) {\n\t\t\t\t\tftag = parsexmltag(cref[0]);\n\t\t\t\t\tif(sharedf[ftag.si]) p.f = shift_formula_xlsx(sharedf[ftag.si][1], sharedf[ftag.si][2]/*[0].ref*/, tag.r);\n\t\t\t\t}\n\t\t\t\t/* TODO: factor out contains logic */\n\t\t\t\tvar _tag = decode_cell(tag.r);\n\t\t\t\tfor(i = 0; i < arrayf.length; ++i)\n\t\t\t\t\tif(_tag.r >= arrayf[i][0].s.r && _tag.r <= arrayf[i][0].e.r)\n\t\t\t\t\t\tif(_tag.c >= arrayf[i][0].s.c && _tag.c <= arrayf[i][0].e.c)\n\t\t\t\t\t\t\tp.F = arrayf[i][1];\n\t\t\t}\n\n\t\t\tif(tag.t == null && p.v === undefined) {\n\t\t\t\tif(p.f || p.F) {\n\t\t\t\t\tp.v = 0; p.t = \"n\";\n\t\t\t\t} else if(!sheetStubs) continue;\n\t\t\t\telse p.t = \"z\";\n\t\t\t}\n\t\t\telse p.t = tag.t || \"n\";\n\t\t\tif(guess.s.c > tagc) guess.s.c = tagc;\n\t\t\tif(guess.e.c < tagc) guess.e.c = tagc;\n\t\t\t/* 18.18.11 t ST_CellType */\n\t\t\tswitch(p.t) {\n\t\t\t\tcase 'n':\n\t\t\t\t\tif(p.v == \"\" || p.v == null) {\n\t\t\t\t\t\tif(!sheetStubs) continue;\n\t\t\t\t\t\tp.t = 'z';\n\t\t\t\t\t} else p.v = parseFloat(p.v);\n\t\t\t\t\tbreak;\n\t\t\t\tcase 's':\n\t\t\t\t\tif(typeof p.v == 'undefined') {\n\t\t\t\t\t\tif(!sheetStubs) continue;\n\t\t\t\t\t\tp.t = 'z';\n\t\t\t\t\t} else {\n\t\t\t\t\t\tsstr = strs[parseInt(p.v, 10)];\n\t\t\t\t\t\tp.v = sstr.t;\n\t\t\t\t\t\tp.r = sstr.r;\n\t\t\t\t\t\tif(opts.cellHTML) p.h = sstr.h;\n\t\t\t\t\t}\n\t\t\t\t\tbreak;\n\t\t\t\tcase 'str':\n\t\t\t\t\tp.t = \"s\";\n\t\t\t\t\tp.v = (p.v!=null) ? utf8read(p.v) : '';\n\t\t\t\t\tif(opts.cellHTML) p.h = escapehtml(p.v);\n\t\t\t\t\tbreak;\n\t\t\t\tcase 'inlineStr':\n\t\t\t\t\tcref = d.match(isregex);\n\t\t\t\t\tp.t = 's';\n\t\t\t\t\tif(cref != null && (sstr = parse_si(cref[1]))) {\n\t\t\t\t\t\tp.v = sstr.t;\n\t\t\t\t\t\tif(opts.cellHTML) p.h = sstr.h;\n\t\t\t\t\t} else p.v = \"\";\n\t\t\t\t\tbreak;\n\t\t\t\tcase 'b': p.v = parsexmlbool(p.v); break;\n\t\t\t\tcase 'd':\n\t\t\t\t\tif(opts.cellDates) p.v = parseDate(p.v, 1);\n\t\t\t\t\telse { p.v = datenum(parseDate(p.v, 1)); p.t = 'n'; }\n\t\t\t\t\tbreak;\n\t\t\t\t/* error string in .w, number in .v */\n\t\t\t\tcase 'e':\n\t\t\t\t\tif(!opts || opts.cellText !== false) p.w = p.v;\n\t\t\t\t\tp.v = RBErr[p.v]; break;\n\t\t\t}\n\t\t\t/* formatting */\n\t\t\tfmtid = fillid = 0;\n\t\t\tcf = null;\n\t\t\tif(do_format && tag.s !== undefined) {\n\t\t\t\tcf = styles.CellXf[tag.s];\n\t\t\t\tif(cf != null) {\n\t\t\t\t\tif(cf.numFmtId != null) fmtid = cf.numFmtId;\n\t\t\t\t\tif(opts.cellStyles) {\n\t\t\t\t\t\tif(cf.fillId != null) fillid = cf.fillId;\n\t\t\t\t\t}\n\t\t\t\t}\n\t\t\t}\n\t\t\tsafe_format(p, fmtid, fillid, opts, themes, styles);\n\t\t\tif(opts.cellDates && do_format && p.t == 'n' && SSF.is_date(SSF._table[fmtid])) { p.t = 'd'; p.v = numdate(p.v); }\n\t\t\tif(dense) {\n\t\t\t\tvar _r = decode_cell(tag.r);\n\t\t\t\tif(!s[_r.r]) s[_r.r] = [];\n\t\t\t\ts[_r.r][_r.c] = p;\n\t\t\t} else s[tag.r] = p;\n\t\t}\n\t}\n\tif(rows.length > 0) s['!rows'] = rows;\n}; })();\n\nfunction write_ws_xml_data(ws, opts, idx, wb) {\n\tvar o = [], r = [], range = safe_decode_range(ws['!ref']), cell=\"\", ref, rr = \"\", cols = [], R=0, C=0, rows = ws['!rows'];\n\tvar dense = Array.isArray(ws);\n\tvar params = ({r:rr}), row, height = -1;\n\tfor(C = range.s.c; C <= range.e.c; ++C) cols[C] = encode_col(C);\n\tfor(R = range.s.r; R <= range.e.r; ++R) {\n\t\tr = [];\n\t\trr = encode_row(R);\n\t\tfor(C = range.s.c; C <= range.e.c; ++C) {\n\t\t\tref = cols[C] + rr;\n\t\t\tvar _cell = dense ? (ws[R]||[])[C]: ws[ref];\n\t\t\tif(_cell === undefined) continue;\n\t\t\tif((cell = write_ws_xml_cell(_cell, ref, ws, opts, idx, wb)) != null) r.push(cell);\n\t\t}\n\t\tif(r.length > 0 || (rows && rows[R])) {\n\t\t\tparams = ({r:rr});\n\t\t\tif(rows && rows[R]) {\n\t\t\t\trow = rows[R];\n\t\t\t\tif(row.hidden) params.hidden = 1;\n\t\t\t\theight = -1;\n\t\t\t\tif(row.hpx) height = px2pt(row.hpx);\n\t\t\t\telse if(row.hpt) height = row.hpt;\n\t\t\t\tif(height > -1) { params.ht = height; params.customHeight = 1; }\n\t\t\t\tif(row.level) { params.outlineLevel = row.level; }\n\t\t\t}\n\t\t\to[o.length] = (writextag('row', r.join(\"\"), params));\n\t\t}\n\t}\n\tif(rows) for(; R < rows.length; ++R) {\n\t\tif(rows && rows[R]) {\n\t\t\tparams = ({r:R+1});\n\t\t\trow = rows[R];\n\t\t\tif(row.hidden) params.hidden = 1;\n\t\t\theight = -1;\n\t\t\tif (row.hpx) height = px2pt(row.hpx);\n\t\t\telse if (row.hpt) height = row.hpt;\n\t\t\tif (height > -1) { params.ht = height; params.customHeight = 1; }\n\t\t\tif (row.level) { params.outlineLevel = row.level; }\n\t\t\to[o.length] = (writextag('row', \"\", params));\n\t\t}\n\t}\n\treturn o.join(\"\");\n}\n\nvar WS_XML_ROOT = writextag('worksheet', null, {\n\t'xmlns': XMLNS.main[0],\n\t'xmlns:r': XMLNS.r\n});\n\nfunction write_ws_xml(idx, opts, wb, rels) {\n\tvar o = [XML_HEADER, WS_XML_ROOT];\n\tvar s = wb.SheetNames[idx], sidx = 0, rdata = \"\";\n\tvar ws = wb.Sheets[s];\n\tif(ws == null) ws = {};\n\tvar ref = ws['!ref'] || 'A1';\n\tvar range = safe_decode_range(ref);\n\tif(range.e.c > 0x3FFF || range.e.r > 0xFFFFF) {\n\t\tif(opts.WTF) throw new Error(\"Range \" + ref + \" exceeds format limit A1:XFD1048576\");\n\t\trange.e.c = Math.min(range.e.c, 0x3FFF);\n\t\trange.e.r = Math.min(range.e.c, 0xFFFFF);\n\t\tref = encode_range(range);\n\t}\n\tif(!rels) rels = {};\n\tws['!comments'] = [];\n\tvar _drawing = [];\n\n\twrite_ws_xml_sheetpr(ws, wb, idx, opts, o);\n\n\to[o.length] = (writextag('dimension', null, {'ref': ref}));\n\n\to[o.length] = write_ws_xml_sheetviews(ws, opts, idx, wb);\n\n\t/* TODO: store in WB, process styles */\n\tif(opts.sheetFormat) o[o.length] = (writextag('sheetFormatPr', null, {\n\t\tdefaultRowHeight:opts.sheetFormat.defaultRowHeight||'16',\n\t\tbaseColWidth:opts.sheetFormat.baseColWidth||'10',\n\t\toutlineLevelRow:opts.sheetFormat.outlineLevelRow||'7'\n\t}));\n\n\tif(ws['!cols'] != null && ws['!cols'].length > 0) o[o.length] = (write_ws_xml_cols(ws, ws['!cols']));\n\n\to[sidx = o.length] = '';\n\tws['!links'] = [];\n\tif(ws['!ref'] != null) {\n\t\trdata = write_ws_xml_data(ws, opts, idx, wb, rels);\n\t\tif(rdata.length > 0) o[o.length] = (rdata);\n\t}\n\tif(o.length>sidx+1) { o[o.length] = (''); o[sidx]=o[sidx].replace(\"/>\",\">\"); }\n\n\t/* sheetCalcPr */\n\n\tif(ws['!protect'] != null) o[o.length] = write_ws_xml_protection(ws['!protect']);\n\n\t/* protectedRanges */\n\t/* scenarios */\n\n\tif(ws['!autofilter'] != null) o[o.length] = write_ws_xml_autofilter(ws['!autofilter'], ws, wb, idx);\n\n\t/* sortState */\n\t/* dataConsolidate */\n\t/* customSheetViews */\n\n\tif(ws['!merges'] != null && ws['!merges'].length > 0) o[o.length] = (write_ws_xml_merges(ws['!merges']));\n\n\t/* phoneticPr */\n\t/* conditionalFormatting */\n\t/* dataValidations */\n\n\tvar relc = -1, rel, rId = -1;\n\tif(ws['!links'].length > 0) {\n\t\to[o.length] = \"\";\nws['!links'].forEach(function(l) {\n\t\t\tif(!l[1].Target) return;\n\t\t\trel = ({\"ref\":l[0]});\n\t\t\tif(l[1].Target.charAt(0) != \"#\") {\n\t\t\t\trId = add_rels(rels, -1, escapexml(l[1].Target).replace(/#.*$/, \"\"), RELS.HLINK);\n\t\t\t\trel[\"r:id\"] = \"rId\"+rId;\n\t\t\t}\n\t\t\tif((relc = l[1].Target.indexOf(\"#\")) > -1) rel.location = escapexml(l[1].Target.slice(relc+1));\n\t\t\tif(l[1].Tooltip) rel.tooltip = escapexml(l[1].Tooltip);\n\t\t\to[o.length] = writextag(\"hyperlink\",null,rel);\n\t\t});\n\t\to[o.length] = \"\";\n\t}\n\tdelete ws['!links'];\n\n\t/* printOptions */\n\n\tif(ws['!margins'] != null) o[o.length] = write_ws_xml_margins(ws['!margins']);\n\n\t/* pageSetup */\n\t/* headerFooter */\n\t/* rowBreaks */\n\t/* colBreaks */\n\t/* customProperties */\n\t/* cellWatches */\n\n\tif(!opts || opts.ignoreEC || (opts.ignoreEC == (void 0))) o[o.length] = writetag(\"ignoredErrors\", writextag(\"ignoredError\", null, {numberStoredAsText:1, sqref:ref}));\n\n\t/* smartTags */\n\n\tif(_drawing.length > 0) {\n\t\trId = add_rels(rels, -1, \"../drawings/drawing\" + (idx+1) + \".xml\", RELS.DRAW);\n\t\to[o.length] = writextag(\"drawing\", null, {\"r:id\":\"rId\" + rId});\n\t\tws['!drawing'] = _drawing;\n\t}\n\n\tif(ws['!comments'].length > 0) {\n\t\trId = add_rels(rels, -1, \"../drawings/vmlDrawing\" + (idx+1) + \".vml\", RELS.VML);\n\t\to[o.length] = writextag(\"legacyDrawing\", null, {\"r:id\":\"rId\" + rId});\n\t\tws['!legacy'] = rId;\n\t}\n\n\t/* legacyDrawingHF */\n\t/* picture */\n\t/* oleObjects */\n\t/* controls */\n\t/* webPublishItems */\n\t/* tableParts */\n\t/* extLst */\n\n\tif(o.length>1) { o[o.length] = (''); o[1]=o[1].replace(\"/>\",\">\"); }\n\treturn o.join(\"\");\n}\n\n/* [MS-XLSB] 2.4.726 BrtRowHdr */\nfunction parse_BrtRowHdr(data, length) {\n\tvar z = ({});\n\tvar tgt = data.l + length;\n\tz.r = data.read_shift(4);\n\tdata.l += 4; // TODO: ixfe\n\tvar miyRw = data.read_shift(2);\n\tdata.l += 1; // TODO: top/bot padding\n\tvar flags = data.read_shift(1);\n\tdata.l = tgt;\n\tif(flags & 0x07) z.level = flags & 0x07;\n\tif(flags & 0x10) z.hidden = true;\n\tif(flags & 0x20) z.hpt = miyRw / 20;\n\treturn z;\n}\nfunction write_BrtRowHdr(R, range, ws) {\n\tvar o = new_buf(17+8*16);\n\tvar row = (ws['!rows']||[])[R]||{};\n\to.write_shift(4, R);\n\n\to.write_shift(4, 0); /* TODO: ixfe */\n\n\tvar miyRw = 0x0140;\n\tif(row.hpx) miyRw = px2pt(row.hpx) * 20;\n\telse if(row.hpt) miyRw = row.hpt * 20;\n\to.write_shift(2, miyRw);\n\n\to.write_shift(1, 0); /* top/bot padding */\n\n\tvar flags = 0x0;\n\tif(row.level) flags |= row.level;\n\tif(row.hidden) flags |= 0x10;\n\tif(row.hpx || row.hpt) flags |= 0x20;\n\to.write_shift(1, flags);\n\n\to.write_shift(1, 0); /* phonetic guide */\n\n\t/* [MS-XLSB] 2.5.8 BrtColSpan explains the mechanism */\n\tvar ncolspan = 0, lcs = o.l;\n\to.l += 4;\n\n\tvar caddr = {r:R, c:0};\n\tfor(var i = 0; i < 16; ++i) {\n\t\tif((range.s.c > ((i+1) << 10)) || (range.e.c < (i << 10))) continue;\n\t\tvar first = -1, last = -1;\n\t\tfor(var j = (i<<10); j < ((i+1)<<10); ++j) {\n\t\t\tcaddr.c = j;\n\t\t\tvar cell = Array.isArray(ws) ? (ws[caddr.r]||[])[caddr.c] : ws[encode_cell(caddr)];\n\t\t\tif(cell) { if(first < 0) first = j; last = j; }\n\t\t}\n\t\tif(first < 0) continue;\n\t\t++ncolspan;\n\t\to.write_shift(4, first);\n\t\to.write_shift(4, last);\n\t}\n\n\tvar l = o.l;\n\to.l = lcs;\n\to.write_shift(4, ncolspan);\n\to.l = l;\n\n\treturn o.length > o.l ? o.slice(0, o.l) : o;\n}\nfunction write_row_header(ba, ws, range, R) {\n\tvar o = write_BrtRowHdr(R, range, ws);\n\tif((o.length > 17) || (ws['!rows']||[])[R]) write_record(ba, 'BrtRowHdr', o);\n}\n\n/* [MS-XLSB] 2.4.820 BrtWsDim */\nvar parse_BrtWsDim = parse_UncheckedRfX;\nvar write_BrtWsDim = write_UncheckedRfX;\n\n/* [MS-XLSB] 2.4.821 BrtWsFmtInfo */\nfunction parse_BrtWsFmtInfo() {\n}\n//function write_BrtWsFmtInfo(ws, o) { }\n\n/* [MS-XLSB] 2.4.823 BrtWsProp */\nfunction parse_BrtWsProp(data, length) {\n\tvar z = {};\n\t/* TODO: pull flags */\n\tdata.l += 19;\n\tz.name = parse_XLSBCodeName(data, length - 19);\n\treturn z;\n}\nfunction write_BrtWsProp(str, o) {\n\tif(o == null) o = new_buf(84+4*str.length);\n\tfor(var i = 0; i < 3; ++i) o.write_shift(1,0);\n\twrite_BrtColor({auto:1}, o);\n\to.write_shift(-4,-1);\n\to.write_shift(-4,-1);\n\twrite_XLSBCodeName(str, o);\n\treturn o.slice(0, o.l);\n}\n\n/* [MS-XLSB] 2.4.306 BrtCellBlank */\nfunction parse_BrtCellBlank(data) {\n\tvar cell = parse_XLSBCell(data);\n\treturn [cell];\n}\nfunction write_BrtCellBlank(cell, ncell, o) {\n\tif(o == null) o = new_buf(8);\n\treturn write_XLSBCell(ncell, o);\n}\n\n\n/* [MS-XLSB] 2.4.307 BrtCellBool */\nfunction parse_BrtCellBool(data) {\n\tvar cell = parse_XLSBCell(data);\n\tvar fBool = data.read_shift(1);\n\treturn [cell, fBool, 'b'];\n}\nfunction write_BrtCellBool(cell, ncell, o) {\n\tif(o == null) o = new_buf(9);\n\twrite_XLSBCell(ncell, o);\n\to.write_shift(1, cell.v ? 1 : 0);\n\treturn o;\n}\n\n/* [MS-XLSB] 2.4.308 BrtCellError */\nfunction parse_BrtCellError(data) {\n\tvar cell = parse_XLSBCell(data);\n\tvar bError = data.read_shift(1);\n\treturn [cell, bError, 'e'];\n}\n\n/* [MS-XLSB] 2.4.311 BrtCellIsst */\nfunction parse_BrtCellIsst(data) {\n\tvar cell = parse_XLSBCell(data);\n\tvar isst = data.read_shift(4);\n\treturn [cell, isst, 's'];\n}\nfunction write_BrtCellIsst(cell, ncell, o) {\n\tif(o == null) o = new_buf(12);\n\twrite_XLSBCell(ncell, o);\n\to.write_shift(4, ncell.v);\n\treturn o;\n}\n\n/* [MS-XLSB] 2.4.313 BrtCellReal */\nfunction parse_BrtCellReal(data) {\n\tvar cell = parse_XLSBCell(data);\n\tvar value = parse_Xnum(data);\n\treturn [cell, value, 'n'];\n}\nfunction write_BrtCellReal(cell, ncell, o) {\n\tif(o == null) o = new_buf(16);\n\twrite_XLSBCell(ncell, o);\n\twrite_Xnum(cell.v, o);\n\treturn o;\n}\n\n/* [MS-XLSB] 2.4.314 BrtCellRk */\nfunction parse_BrtCellRk(data) {\n\tvar cell = parse_XLSBCell(data);\n\tvar value = parse_RkNumber(data);\n\treturn [cell, value, 'n'];\n}\nfunction write_BrtCellRk(cell, ncell, o) {\n\tif(o == null) o = new_buf(12);\n\twrite_XLSBCell(ncell, o);\n\twrite_RkNumber(cell.v, o);\n\treturn o;\n}\n\n\n/* [MS-XLSB] 2.4.317 BrtCellSt */\nfunction parse_BrtCellSt(data) {\n\tvar cell = parse_XLSBCell(data);\n\tvar value = parse_XLWideString(data);\n\treturn [cell, value, 'str'];\n}\nfunction write_BrtCellSt(cell, ncell, o) {\n\tif(o == null) o = new_buf(12 + 4 * cell.v.length);\n\twrite_XLSBCell(ncell, o);\n\twrite_XLWideString(cell.v, o);\n\treturn o.length > o.l ? o.slice(0, o.l) : o;\n}\n\n/* [MS-XLSB] 2.4.653 BrtFmlaBool */\nfunction parse_BrtFmlaBool(data, length, opts) {\n\tvar end = data.l + length;\n\tvar cell = parse_XLSBCell(data);\n\tcell.r = opts['!row'];\n\tvar value = data.read_shift(1);\n\tvar o = [cell, value, 'b'];\n\tif(opts.cellFormula) {\n\t\tdata.l += 2;\n\t\tvar formula = parse_XLSBCellParsedFormula(data, end - data.l, opts);\n\t\to[3] = stringify_formula(formula, null/*range*/, cell, opts.supbooks, opts);/* TODO */\n\t}\n\telse data.l = end;\n\treturn o;\n}\n\n/* [MS-XLSB] 2.4.654 BrtFmlaError */\nfunction parse_BrtFmlaError(data, length, opts) {\n\tvar end = data.l + length;\n\tvar cell = parse_XLSBCell(data);\n\tcell.r = opts['!row'];\n\tvar value = data.read_shift(1);\n\tvar o = [cell, value, 'e'];\n\tif(opts.cellFormula) {\n\t\tdata.l += 2;\n\t\tvar formula = parse_XLSBCellParsedFormula(data, end - data.l, opts);\n\t\to[3] = stringify_formula(formula, null/*range*/, cell, opts.supbooks, opts);/* TODO */\n\t}\n\telse data.l = end;\n\treturn o;\n}\n\n/* [MS-XLSB] 2.4.655 BrtFmlaNum */\nfunction parse_BrtFmlaNum(data, length, opts) {\n\tvar end = data.l + length;\n\tvar cell = parse_XLSBCell(data);\n\tcell.r = opts['!row'];\n\tvar value = parse_Xnum(data);\n\tvar o = [cell, value, 'n'];\n\tif(opts.cellFormula) {\n\t\tdata.l += 2;\n\t\tvar formula = parse_XLSBCellParsedFormula(data, end - data.l, opts);\n\t\to[3] = stringify_formula(formula, null/*range*/, cell, opts.supbooks, opts);/* TODO */\n\t}\n\telse data.l = end;\n\treturn o;\n}\n\n/* [MS-XLSB] 2.4.656 BrtFmlaString */\nfunction parse_BrtFmlaString(data, length, opts) {\n\tvar end = data.l + length;\n\tvar cell = parse_XLSBCell(data);\n\tcell.r = opts['!row'];\n\tvar value = parse_XLWideString(data);\n\tvar o = [cell, value, 'str'];\n\tif(opts.cellFormula) {\n\t\tdata.l += 2;\n\t\tvar formula = parse_XLSBCellParsedFormula(data, end - data.l, opts);\n\t\to[3] = stringify_formula(formula, null/*range*/, cell, opts.supbooks, opts);/* TODO */\n\t}\n\telse data.l = end;\n\treturn o;\n}\n\n/* [MS-XLSB] 2.4.682 BrtMergeCell */\nvar parse_BrtMergeCell = parse_UncheckedRfX;\nvar write_BrtMergeCell = write_UncheckedRfX;\n/* [MS-XLSB] 2.4.107 BrtBeginMergeCells */\nfunction write_BrtBeginMergeCells(cnt, o) {\n\tif(o == null) o = new_buf(4);\n\to.write_shift(4, cnt);\n\treturn o;\n}\n\n/* [MS-XLSB] 2.4.662 BrtHLink */\nfunction parse_BrtHLink(data, length) {\n\tvar end = data.l + length;\n\tvar rfx = parse_UncheckedRfX(data, 16);\n\tvar relId = parse_XLNullableWideString(data);\n\tvar loc = parse_XLWideString(data);\n\tvar tooltip = parse_XLWideString(data);\n\tvar display = parse_XLWideString(data);\n\tdata.l = end;\n\tvar o = ({rfx:rfx, relId:relId, loc:loc, display:display});\n\tif(tooltip) o.Tooltip = tooltip;\n\treturn o;\n}\nfunction write_BrtHLink(l, rId) {\n\tvar o = new_buf(50+4*(l[1].Target.length + (l[1].Tooltip || \"\").length));\n\twrite_UncheckedRfX({s:decode_cell(l[0]), e:decode_cell(l[0])}, o);\n\twrite_RelID(\"rId\" + rId, o);\n\tvar locidx = l[1].Target.indexOf(\"#\");\n\tvar loc = locidx == -1 ? \"\" : l[1].Target.slice(locidx+1);\n\twrite_XLWideString(loc || \"\", o);\n\twrite_XLWideString(l[1].Tooltip || \"\", o);\n\twrite_XLWideString(\"\", o);\n\treturn o.slice(0, o.l);\n}\n\n/* [MS-XLSB] 2.4.692 BrtPane */\nfunction parse_BrtPane(/*data, length, opts*/) {\n}\n\n/* [MS-XLSB] 2.4.6 BrtArrFmla */\nfunction parse_BrtArrFmla(data, length, opts) {\n\tvar end = data.l + length;\n\tvar rfx = parse_RfX(data, 16);\n\tvar fAlwaysCalc = data.read_shift(1);\n\tvar o = [rfx]; o[2] = fAlwaysCalc;\n\tif(opts.cellFormula) {\n\t\tvar formula = parse_XLSBArrayParsedFormula(data, end - data.l, opts);\n\t\to[1] = formula;\n\t} else data.l = end;\n\treturn o;\n}\n\n/* [MS-XLSB] 2.4.750 BrtShrFmla */\nfunction parse_BrtShrFmla(data, length, opts) {\n\tvar end = data.l + length;\n\tvar rfx = parse_UncheckedRfX(data, 16);\n\tvar o = [rfx];\n\tif(opts.cellFormula) {\n\t\tvar formula = parse_XLSBSharedParsedFormula(data, end - data.l, opts);\n\t\to[1] = formula;\n\t\tdata.l = end;\n\t} else data.l = end;\n\treturn o;\n}\n\n/* [MS-XLSB] 2.4.323 BrtColInfo */\n/* TODO: once XLS ColInfo is set, combine the functions */\nfunction write_BrtColInfo(C, col, o) {\n\tif(o == null) o = new_buf(18);\n\tvar p = col_obj_w(C, col);\n\to.write_shift(-4, C);\n\to.write_shift(-4, C);\n\to.write_shift(4, (p.width || 10) * 256);\n\to.write_shift(4, 0/*ixfe*/); // style\n\tvar flags = 0;\n\tif(col.hidden) flags |= 0x01;\n\tif(typeof p.width == 'number') flags |= 0x02;\n\tif(col.level) flags |= (col.level << 8);\n\to.write_shift(2, flags); // bit flag\n\treturn o;\n}\n\n/* [MS-XLSB] 2.4.678 BrtMargins */\nvar BrtMarginKeys = [\"left\",\"right\",\"top\",\"bottom\",\"header\",\"footer\"];\nfunction parse_BrtMargins(data) {\n\tvar margins = ({});\n\tBrtMarginKeys.forEach(function(k) { margins[k] = parse_Xnum(data, 8); });\n\treturn margins;\n}\nfunction write_BrtMargins(margins, o) {\n\tif(o == null) o = new_buf(6*8);\n\tdefault_margins(margins);\n\tBrtMarginKeys.forEach(function(k) { write_Xnum((margins)[k], o); });\n\treturn o;\n}\n\n/* [MS-XLSB] 2.4.299 BrtBeginWsView */\nfunction parse_BrtBeginWsView(data) {\n\tvar f = data.read_shift(2);\n\tdata.l += 28;\n\treturn { RTL: f & 0x20 };\n}\nfunction write_BrtBeginWsView(ws, Workbook, o) {\n\tif(o == null) o = new_buf(30);\n\tvar f = 0x39c;\n\tif((((Workbook||{}).Views||[])[0]||{}).RTL) f |= 0x20;\n\to.write_shift(2, f); // bit flag\n\to.write_shift(4, 0);\n\to.write_shift(4, 0); // view first row\n\to.write_shift(4, 0); // view first col\n\to.write_shift(1, 0); // gridline color ICV\n\to.write_shift(1, 0);\n\to.write_shift(2, 0);\n\to.write_shift(2, 100); // zoom scale\n\to.write_shift(2, 0);\n\to.write_shift(2, 0);\n\to.write_shift(2, 0);\n\to.write_shift(4, 0); // workbook view id\n\treturn o;\n}\n\n/* [MS-XLSB] 2.4.309 BrtCellIgnoreEC */\nfunction write_BrtCellIgnoreEC(ref) {\n\tvar o = new_buf(24);\n\to.write_shift(4, 4);\n\to.write_shift(4, 1);\n\twrite_UncheckedRfX(ref, o);\n\treturn o;\n}\n\n/* [MS-XLSB] 2.4.748 BrtSheetProtection */\nfunction write_BrtSheetProtection(sp, o) {\n\tif(o == null) o = new_buf(16*4+2);\n\to.write_shift(2, sp.password ? crypto_CreatePasswordVerifier_Method1(sp.password) : 0);\n\to.write_shift(4, 1); // this record should not be written if no protection\n\t[\n\t\t[\"objects\", false], // fObjects\n\t\t[\"scenarios\", false], // fScenarios\n\t\t[\"formatCells\", true], // fFormatCells\n\t\t[\"formatColumns\", true], // fFormatColumns\n\t\t[\"formatRows\", true], // fFormatRows\n\t\t[\"insertColumns\", true], // fInsertColumns\n\t\t[\"insertRows\", true], // fInsertRows\n\t\t[\"insertHyperlinks\", true], // fInsertHyperlinks\n\t\t[\"deleteColumns\", true], // fDeleteColumns\n\t\t[\"deleteRows\", true], // fDeleteRows\n\t\t[\"selectLockedCells\", false], // fSelLockedCells\n\t\t[\"sort\", true], // fSort\n\t\t[\"autoFilter\", true], // fAutoFilter\n\t\t[\"pivotTables\", true], // fPivotTables\n\t\t[\"selectUnlockedCells\", false] // fSelUnlockedCells\n\t].forEach(function(n) {\nif(n[1]) o.write_shift(4, sp[n[0]] != null && !sp[n[0]] ? 1 : 0);\n\t\telse o.write_shift(4, sp[n[0]] != null && sp[n[0]] ? 0 : 1);\n\t});\n\treturn o;\n}\n\nfunction parse_BrtDVal(/*data, length, opts*/) {\n}\nfunction parse_BrtDVal14(/*data, length, opts*/) {\n}\n/* [MS-XLSB] 2.1.7.61 Worksheet */\nfunction parse_ws_bin(data, _opts, idx, rels, wb, themes, styles) {\n\tif(!data) return data;\n\tvar opts = _opts || {};\n\tif(!rels) rels = {'!id':{}};\n\tif(DENSE != null && opts.dense == null) opts.dense = DENSE;\n\tvar s = (opts.dense ? [] : {});\n\n\tvar ref;\n\tvar refguess = {s: {r:2000000, c:2000000}, e: {r:0, c:0} };\n\n\tvar state = [];\n\tvar pass = false, end = false;\n\tvar row, p, cf, R, C, addr, sstr, rr, cell;\n\tvar merges = [];\n\topts.biff = 12;\n\topts['!row'] = 0;\n\n\tvar ai = 0, af = false;\n\n\tvar arrayf = [];\n\tvar sharedf = {};\n\tvar supbooks = opts.supbooks || wb.supbooks || ([[]]);\n\tsupbooks.sharedf = sharedf;\n\tsupbooks.arrayf = arrayf;\n\tsupbooks.SheetNames = wb.SheetNames || wb.Sheets.map(function(x) { return x.name; });\n\tif(!opts.supbooks) {\n\t\topts.supbooks = supbooks;\n\t\tif(wb.Names) for(var i = 0; i < wb.Names.length; ++i) supbooks[0][i+1] = wb.Names[i];\n\t}\n\n\tvar colinfo = [], rowinfo = [];\n\tvar seencol = false;\n\n\trecordhopper(data, function ws_parse(val, R_n, RT) {\n\t\tif(end) return;\n\t\tswitch(RT) {\n\t\t\tcase 0x0094: /* 'BrtWsDim' */\n\t\t\t\tref = val; break;\n\t\t\tcase 0x0000: /* 'BrtRowHdr' */\n\t\t\t\trow = val;\n\t\t\t\tif(opts.sheetRows && opts.sheetRows <= row.r) end=true;\n\t\t\t\trr = encode_row(R = row.r);\n\t\t\t\topts['!row'] = row.r;\n\t\t\t\tif(val.hidden || val.hpt || val.level != null) {\n\t\t\t\t\tif(val.hpt) val.hpx = pt2px(val.hpt);\n\t\t\t\t\trowinfo[val.r] = val;\n\t\t\t\t}\n\t\t\t\tbreak;\n\n\t\t\tcase 0x0002: /* 'BrtCellRk' */\n\t\t\tcase 0x0003: /* 'BrtCellError' */\n\t\t\tcase 0x0004: /* 'BrtCellBool' */\n\t\t\tcase 0x0005: /* 'BrtCellReal' */\n\t\t\tcase 0x0006: /* 'BrtCellSt' */\n\t\t\tcase 0x0007: /* 'BrtCellIsst' */\n\t\t\tcase 0x0008: /* 'BrtFmlaString' */\n\t\t\tcase 0x0009: /* 'BrtFmlaNum' */\n\t\t\tcase 0x000A: /* 'BrtFmlaBool' */\n\t\t\tcase 0x000B: /* 'BrtFmlaError' */\n\t\t\t\tp = ({t:val[2]});\n\t\t\t\tswitch(val[2]) {\n\t\t\t\t\tcase 'n': p.v = val[1]; break;\n\t\t\t\t\tcase 's': sstr = strs[val[1]]; p.v = sstr.t; p.r = sstr.r; break;\n\t\t\t\t\tcase 'b': p.v = val[1] ? true : false; break;\n\t\t\t\t\tcase 'e': p.v = val[1]; if(opts.cellText !== false) p.w = BErr[p.v]; break;\n\t\t\t\t\tcase 'str': p.t = 's'; p.v = val[1]; break;\n\t\t\t\t}\n\t\t\t\tif((cf = styles.CellXf[val[0].iStyleRef])) safe_format(p,cf.numFmtId,null,opts, themes, styles);\n\t\t\t\tC = val[0].c;\n\t\t\t\tif(opts.dense) { if(!s[R]) s[R] = []; s[R][C] = p; }\n\t\t\t\telse s[encode_col(C) + rr] = p;\n\t\t\t\tif(opts.cellFormula) {\n\t\t\t\t\taf = false;\n\t\t\t\t\tfor(ai = 0; ai < arrayf.length; ++ai) {\n\t\t\t\t\t\tvar aii = arrayf[ai];\n\t\t\t\t\t\tif(row.r >= aii[0].s.r && row.r <= aii[0].e.r)\n\t\t\t\t\t\t\tif(C >= aii[0].s.c && C <= aii[0].e.c) {\n\t\t\t\t\t\t\t\tp.F = encode_range(aii[0]); af = true;\n\t\t\t\t\t\t\t}\n\t\t\t\t\t}\n\t\t\t\t\tif(!af && val.length > 3) p.f = val[3];\n\t\t\t\t}\n\t\t\t\tif(refguess.s.r > row.r) refguess.s.r = row.r;\n\t\t\t\tif(refguess.s.c > C) refguess.s.c = C;\n\t\t\t\tif(refguess.e.r < row.r) refguess.e.r = row.r;\n\t\t\t\tif(refguess.e.c < C) refguess.e.c = C;\n\t\t\t\tif(opts.cellDates && cf && p.t == 'n' && SSF.is_date(SSF._table[cf.numFmtId])) {\n\t\t\t\t\tvar _d = SSF.parse_date_code(p.v); if(_d) { p.t = 'd'; p.v = new Date(_d.y, _d.m-1,_d.d,_d.H,_d.M,_d.S,_d.u); }\n\t\t\t\t}\n\t\t\t\tbreak;\n\n\t\t\tcase 0x0001: /* 'BrtCellBlank' */\n\t\t\t\tif(!opts.sheetStubs || pass) break;\n\t\t\t\tp = ({t:'z',v:undefined});\n\t\t\t\tC = val[0].c;\n\t\t\t\tif(opts.dense) { if(!s[R]) s[R] = []; s[R][C] = p; }\n\t\t\t\telse s[encode_col(C) + rr] = p;\n\t\t\t\tif(refguess.s.r > row.r) refguess.s.r = row.r;\n\t\t\t\tif(refguess.s.c > C) refguess.s.c = C;\n\t\t\t\tif(refguess.e.r < row.r) refguess.e.r = row.r;\n\t\t\t\tif(refguess.e.c < C) refguess.e.c = C;\n\t\t\t\tbreak;\n\n\t\t\tcase 0x00B0: /* 'BrtMergeCell' */\n\t\t\t\tmerges.push(val); break;\n\n\t\t\tcase 0x01EE: /* 'BrtHLink' */\n\t\t\t\tvar rel = rels['!id'][val.relId];\n\t\t\t\tif(rel) {\n\t\t\t\t\tval.Target = rel.Target;\n\t\t\t\t\tif(val.loc) val.Target += \"#\"+val.loc;\n\t\t\t\t\tval.Rel = rel;\n\t\t\t\t} else if(val.relId == '') {\n\t\t\t\t\tval.Target = \"#\" + val.loc;\n\t\t\t\t}\n\t\t\t\tfor(R=val.rfx.s.r;R<=val.rfx.e.r;++R) for(C=val.rfx.s.c;C<=val.rfx.e.c;++C) {\n\t\t\t\t\tif(opts.dense) {\n\t\t\t\t\t\tif(!s[R]) s[R] = [];\n\t\t\t\t\t\tif(!s[R][C]) s[R][C] = {t:'z',v:undefined};\n\t\t\t\t\t\ts[R][C].l = val;\n\t\t\t\t\t} else {\n\t\t\t\t\t\taddr = encode_cell({c:C,r:R});\n\t\t\t\t\t\tif(!s[addr]) s[addr] = {t:'z',v:undefined};\n\t\t\t\t\t\ts[addr].l = val;\n\t\t\t\t\t}\n\t\t\t\t}\n\t\t\t\tbreak;\n\n\t\t\tcase 0x01AA: /* 'BrtArrFmla' */\n\t\t\t\tif(!opts.cellFormula) break;\n\t\t\t\tarrayf.push(val);\n\t\t\t\tcell = ((opts.dense ? s[R][C] : s[encode_col(C) + rr]));\n\t\t\t\tcell.f = stringify_formula(val[1], refguess, {r:row.r, c:C}, supbooks, opts);\n\t\t\t\tcell.F = encode_range(val[0]);\n\t\t\t\tbreak;\n\t\t\tcase 0x01AB: /* 'BrtShrFmla' */\n\t\t\t\tif(!opts.cellFormula) break;\n\t\t\t\tsharedf[encode_cell(val[0].s)] = val[1];\n\t\t\t\tcell = (opts.dense ? s[R][C] : s[encode_col(C) + rr]);\n\t\t\t\tcell.f = stringify_formula(val[1], refguess, {r:row.r, c:C}, supbooks, opts);\n\t\t\t\tbreak;\n\n\t\t\t/* identical to 'ColInfo' in XLS */\n\t\t\tcase 0x003C: /* 'BrtColInfo' */\n\t\t\t\tif(!opts.cellStyles) break;\n\t\t\t\twhile(val.e >= val.s) {\n\t\t\t\t\tcolinfo[val.e--] = { width: val.w/256, hidden: !!(val.flags & 0x01), level: val.level };\n\t\t\t\t\tif(!seencol) { seencol = true; find_mdw_colw(val.w/256); }\n\t\t\t\t\tprocess_col(colinfo[val.e+1]);\n\t\t\t\t}\n\t\t\t\tbreak;\n\n\t\t\tcase 0x00A1: /* 'BrtBeginAFilter' */\n\t\t\t\ts['!autofilter'] = { ref:encode_range(val) };\n\t\t\t\tbreak;\n\n\t\t\tcase 0x01DC: /* 'BrtMargins' */\n\t\t\t\ts['!margins'] = val;\n\t\t\t\tbreak;\n\n\t\t\tcase 0x0093: /* 'BrtWsProp' */\n\t\t\t\tif(!wb.Sheets[idx]) wb.Sheets[idx] = {};\n\t\t\t\tif(val.name) wb.Sheets[idx].CodeName = val.name;\n\t\t\t\tbreak;\n\n\t\t\tcase 0x0089: /* 'BrtBeginWsView' */\n\t\t\t\tif(!wb.Views) wb.Views = [{}];\n\t\t\t\tif(!wb.Views[0]) wb.Views[0] = {};\n\t\t\t\tif(val.RTL) wb.Views[0].RTL = true;\n\t\t\t\tbreak;\n\n\t\t\tcase 0x01E5: /* 'BrtWsFmtInfo' */\n\t\t\t\tbreak;\n\n\t\t\tcase 0x0040: /* 'BrtDVal' */\n\t\t\tcase 0x041D: /* 'BrtDVal14' */\n\t\t\t\tbreak;\n\n\t\t\tcase 0x0097: /* 'BrtPane' */\n\t\t\t\tbreak;\n\t\t\tcase 0x00AF: /* 'BrtAFilterDateGroupItem' */\n\t\t\tcase 0x0284: /* 'BrtActiveX' */\n\t\t\tcase 0x0271: /* 'BrtBigName' */\n\t\t\tcase 0x0232: /* 'BrtBkHim' */\n\t\t\tcase 0x018C: /* 'BrtBrk' */\n\t\t\tcase 0x0458: /* 'BrtCFIcon' */\n\t\t\tcase 0x047A: /* 'BrtCFRuleExt' */\n\t\t\tcase 0x01D7: /* 'BrtCFVO' */\n\t\t\tcase 0x041A: /* 'BrtCFVO14' */\n\t\t\tcase 0x0289: /* 'BrtCellIgnoreEC' */\n\t\t\tcase 0x0451: /* 'BrtCellIgnoreEC14' */\n\t\t\tcase 0x0031: /* 'BrtCellMeta' */\n\t\t\tcase 0x024D: /* 'BrtCellSmartTagProperty' */\n\t\t\tcase 0x025F: /* 'BrtCellWatch' */\n\t\t\tcase 0x0234: /* 'BrtColor' */\n\t\t\tcase 0x041F: /* 'BrtColor14' */\n\t\t\tcase 0x00A8: /* 'BrtColorFilter' */\n\t\t\tcase 0x00AE: /* 'BrtCustomFilter' */\n\t\t\tcase 0x049C: /* 'BrtCustomFilter14' */\n\t\t\tcase 0x01F3: /* 'BrtDRef' */\n\t\t\tcase 0x01FB: /* 'BrtDXF' */\n\t\t\tcase 0x0226: /* 'BrtDrawing' */\n\t\t\tcase 0x00AB: /* 'BrtDynamicFilter' */\n\t\t\tcase 0x00A7: /* 'BrtFilter' */\n\t\t\tcase 0x0499: /* 'BrtFilter14' */\n\t\t\tcase 0x00A9: /* 'BrtIconFilter' */\n\t\t\tcase 0x049D: /* 'BrtIconFilter14' */\n\t\t\tcase 0x0227: /* 'BrtLegacyDrawing' */\n\t\t\tcase 0x0228: /* 'BrtLegacyDrawingHF' */\n\t\t\tcase 0x0295: /* 'BrtListPart' */\n\t\t\tcase 0x027F: /* 'BrtOleObject' */\n\t\t\tcase 0x01DE: /* 'BrtPageSetup' */\n\t\t\tcase 0x0219: /* 'BrtPhoneticInfo' */\n\t\t\tcase 0x01DD: /* 'BrtPrintOptions' */\n\t\t\tcase 0x0218: /* 'BrtRangeProtection' */\n\t\t\tcase 0x044F: /* 'BrtRangeProtection14' */\n\t\t\tcase 0x02A8: /* 'BrtRangeProtectionIso' */\n\t\t\tcase 0x0450: /* 'BrtRangeProtectionIso14' */\n\t\t\tcase 0x0400: /* 'BrtRwDescent' */\n\t\t\tcase 0x0098: /* 'BrtSel' */\n\t\t\tcase 0x0297: /* 'BrtSheetCalcProp' */\n\t\t\tcase 0x0217: /* 'BrtSheetProtection' */\n\t\t\tcase 0x02A6: /* 'BrtSheetProtectionIso' */\n\t\t\tcase 0x01F8: /* 'BrtSlc' */\n\t\t\tcase 0x0413: /* 'BrtSparkline' */\n\t\t\tcase 0x01AC: /* 'BrtTable' */\n\t\t\tcase 0x00AA: /* 'BrtTop10Filter' */\n\t\t\tcase 0x0C00: /* 'BrtUid' */\n\t\t\tcase 0x0032: /* 'BrtValueMeta' */\n\t\t\tcase 0x0816: /* 'BrtWebExtension' */\n\t\t\tcase 0x0415: /* 'BrtWsFmtInfoEx14' */\n\t\t\t\tbreak;\n\n\t\t\tcase 0x0023: /* 'BrtFRTBegin' */\n\t\t\t\tpass = true; break;\n\t\t\tcase 0x0024: /* 'BrtFRTEnd' */\n\t\t\t\tpass = false; break;\n\t\t\tcase 0x0025: /* 'BrtACBegin' */\n\t\t\t\tstate.push(R_n); pass = true; break;\n\t\t\tcase 0x0026: /* 'BrtACEnd' */\n\t\t\t\tstate.pop(); pass = false; break;\n\n\t\t\tdefault:\n\t\t\t\tif((R_n||\"\").indexOf(\"Begin\") > 0){/* empty */}\n\t\t\t\telse if((R_n||\"\").indexOf(\"End\") > 0){/* empty */}\n\t\t\t\telse if(!pass || opts.WTF) throw new Error(\"Unexpected record \" + RT + \" \" + R_n);\n\t\t}\n\t}, opts);\n\n\tdelete opts.supbooks;\n\tdelete opts['!row'];\n\n\tif(!s[\"!ref\"] && (refguess.s.r < 2000000 || ref && (ref.e.r > 0 || ref.e.c > 0 || ref.s.r > 0 || ref.s.c > 0))) s[\"!ref\"] = encode_range(ref || refguess);\n\tif(opts.sheetRows && s[\"!ref\"]) {\n\t\tvar tmpref = safe_decode_range(s[\"!ref\"]);\n\t\tif(opts.sheetRows <= +tmpref.e.r) {\n\t\t\ttmpref.e.r = opts.sheetRows - 1;\n\t\t\tif(tmpref.e.r > refguess.e.r) tmpref.e.r = refguess.e.r;\n\t\t\tif(tmpref.e.r < tmpref.s.r) tmpref.s.r = tmpref.e.r;\n\t\t\tif(tmpref.e.c > refguess.e.c) tmpref.e.c = refguess.e.c;\n\t\t\tif(tmpref.e.c < tmpref.s.c) tmpref.s.c = tmpref.e.c;\n\t\t\ts[\"!fullref\"] = s[\"!ref\"];\n\t\t\ts[\"!ref\"] = encode_range(tmpref);\n\t\t}\n\t}\n\tif(merges.length > 0) s[\"!merges\"] = merges;\n\tif(colinfo.length > 0) s[\"!cols\"] = colinfo;\n\tif(rowinfo.length > 0) s[\"!rows\"] = rowinfo;\n\treturn s;\n}\n\n/* TODO: something useful -- this is a stub */\nfunction write_ws_bin_cell(ba, cell, R, C, opts, ws) {\n\tif(cell.v === undefined) return;\n\tvar vv = \"\";\n\tswitch(cell.t) {\n\t\tcase 'b': vv = cell.v ? \"1\" : \"0\"; break;\n\t\tcase 'd': // no BrtCellDate :(\n\t\t\tcell = dup(cell);\n\t\t\tcell.z = cell.z || SSF._table[14];\n\t\t\tcell.v = datenum(parseDate(cell.v)); cell.t = 'n';\n\t\t\tbreak;\n\t\t/* falls through */\n\t\tcase 'n': case 'e': vv = ''+cell.v; break;\n\t\tdefault: vv = cell.v; break;\n\t}\n\tvar o = ({r:R, c:C});\n\t/* TODO: cell style */\n\to.s = get_cell_style(opts.cellXfs, cell, opts);\n\tif(cell.l) ws['!links'].push([encode_cell(o), cell.l]);\n\tif(cell.c) ws['!comments'].push([encode_cell(o), cell.c]);\n\tswitch(cell.t) {\n\t\tcase 's': case 'str':\n\t\t\tif(opts.bookSST) {\n\t\t\t\tvv = get_sst_id(opts.Strings, (cell.v), opts.revStrings);\n\t\t\t\to.t = \"s\"; o.v = vv;\n\t\t\t\twrite_record(ba, \"BrtCellIsst\", write_BrtCellIsst(cell, o));\n\t\t\t} else {\n\t\t\t\to.t = \"str\";\n\t\t\t\twrite_record(ba, \"BrtCellSt\", write_BrtCellSt(cell, o));\n\t\t\t}\n\t\t\treturn;\n\t\tcase 'n':\n\t\t\t/* TODO: determine threshold for Real vs RK */\n\t\t\tif(cell.v == (cell.v | 0) && cell.v > -1000 && cell.v < 1000) write_record(ba, \"BrtCellRk\", write_BrtCellRk(cell, o));\n\t\t\telse write_record(ba, \"BrtCellReal\", write_BrtCellReal(cell, o));\n\t\t\treturn;\n\t\tcase 'b':\n\t\t\to.t = \"b\";\n\t\t\twrite_record(ba, \"BrtCellBool\", write_BrtCellBool(cell, o));\n\t\t\treturn;\n\t\tcase 'e': /* TODO: error */ o.t = \"e\"; break;\n\t}\n\twrite_record(ba, \"BrtCellBlank\", write_BrtCellBlank(cell, o));\n}\n\nfunction write_CELLTABLE(ba, ws, idx, opts) {\n\tvar range = safe_decode_range(ws['!ref'] || \"A1\"), ref, rr = \"\", cols = [];\n\twrite_record(ba, 'BrtBeginSheetData');\n\tvar dense = Array.isArray(ws);\n\tvar cap = range.e.r;\n\tif(ws['!rows']) cap = Math.max(range.e.r, ws['!rows'].length - 1);\n\tfor(var R = range.s.r; R <= cap; ++R) {\n\t\trr = encode_row(R);\n\t\t/* [ACCELLTABLE] */\n\t\t/* BrtRowHdr */\n\t\twrite_row_header(ba, ws, range, R);\n\t\tif(R <= range.e.r) for(var C = range.s.c; C <= range.e.c; ++C) {\n\t\t\t/* *16384CELL */\n\t\t\tif(R === range.s.r) cols[C] = encode_col(C);\n\t\t\tref = cols[C] + rr;\n\t\t\tvar cell = dense ? (ws[R]||[])[C] : ws[ref];\n\t\t\tif(!cell) continue;\n\t\t\t/* write cell */\n\t\t\twrite_ws_bin_cell(ba, cell, R, C, opts, ws);\n\t\t}\n\t}\n\twrite_record(ba, 'BrtEndSheetData');\n}\n\nfunction write_MERGECELLS(ba, ws) {\n\tif(!ws || !ws['!merges']) return;\n\twrite_record(ba, 'BrtBeginMergeCells', write_BrtBeginMergeCells(ws['!merges'].length));\n\tws['!merges'].forEach(function(m) { write_record(ba, 'BrtMergeCell', write_BrtMergeCell(m)); });\n\twrite_record(ba, 'BrtEndMergeCells');\n}\n\nfunction write_COLINFOS(ba, ws) {\n\tif(!ws || !ws['!cols']) return;\n\twrite_record(ba, 'BrtBeginColInfos');\n\tws['!cols'].forEach(function(m, i) { if(m) write_record(ba, 'BrtColInfo', write_BrtColInfo(i, m)); });\n\twrite_record(ba, 'BrtEndColInfos');\n}\n\nfunction write_IGNOREECS(ba, ws) {\n\tif(!ws || !ws['!ref']) return;\n\twrite_record(ba, 'BrtBeginCellIgnoreECs');\n\twrite_record(ba, 'BrtCellIgnoreEC', write_BrtCellIgnoreEC(safe_decode_range(ws['!ref'])));\n\twrite_record(ba, 'BrtEndCellIgnoreECs');\n}\n\nfunction write_HLINKS(ba, ws, rels) {\n\t/* *BrtHLink */\n\tws['!links'].forEach(function(l) {\n\t\tif(!l[1].Target) return;\n\t\tvar rId = add_rels(rels, -1, l[1].Target.replace(/#.*$/, \"\"), RELS.HLINK);\n\t\twrite_record(ba, \"BrtHLink\", write_BrtHLink(l, rId));\n\t});\n\tdelete ws['!links'];\n}\nfunction write_LEGACYDRAWING(ba, ws, idx, rels) {\n\t/* [BrtLegacyDrawing] */\n\tif(ws['!comments'].length > 0) {\n\t\tvar rId = add_rels(rels, -1, \"../drawings/vmlDrawing\" + (idx+1) + \".vml\", RELS.VML);\n\t\twrite_record(ba, \"BrtLegacyDrawing\", write_RelID(\"rId\" + rId));\n\t\tws['!legacy'] = rId;\n\t}\n}\n\nfunction write_AUTOFILTER(ba, ws, wb, idx) {\n\tif(!ws['!autofilter']) return;\n\tvar data = ws['!autofilter'];\n\tvar ref = typeof data.ref === \"string\" ? data.ref : encode_range(data.ref);\n\n\t/* Update FilterDatabase defined name for the worksheet */\n\tif(!wb.Workbook) wb.Workbook = ({Sheets:[]});\n\tif(!wb.Workbook.Names) wb.Workbook.Names = [];\n\tvar names = wb.Workbook.Names;\n\tvar range = decode_range(ref);\n\tif(range.s.r == range.e.r) { range.e.r = decode_range(ws[\"!ref\"]).e.r; ref = encode_range(range); }\n\tfor(var i = 0; i < names.length; ++i) {\n\t\tvar name = names[i];\n\t\tif(name.Name != '_xlnm._FilterDatabase') continue;\n\t\tif(name.Sheet != idx) continue;\n\t\tname.Ref = \"'\" + wb.SheetNames[idx] + \"'!\" + ref; break;\n\t}\n\tif(i == names.length) names.push({ Name: '_xlnm._FilterDatabase', Sheet: idx, Ref: \"'\" + wb.SheetNames[idx] + \"'!\" + ref });\n\n\twrite_record(ba, \"BrtBeginAFilter\", write_UncheckedRfX(safe_decode_range(ref)));\n\t/* *FILTERCOLUMN */\n\t/* [SORTSTATE] */\n\t/* BrtEndAFilter */\n\twrite_record(ba, \"BrtEndAFilter\");\n}\n\nfunction write_WSVIEWS2(ba, ws, Workbook) {\n\twrite_record(ba, \"BrtBeginWsViews\");\n\t{ /* 1*WSVIEW2 */\n\t\t/* [ACUID] */\n\t\twrite_record(ba, \"BrtBeginWsView\", write_BrtBeginWsView(ws, Workbook));\n\t\t/* [BrtPane] */\n\t\t/* *4BrtSel */\n\t\t/* *4SXSELECT */\n\t\t/* *FRT */\n\t\twrite_record(ba, \"BrtEndWsView\");\n\t}\n\t/* *FRT */\n\twrite_record(ba, \"BrtEndWsViews\");\n}\n\nfunction write_WSFMTINFO() {\n\t/* [ACWSFMTINFO] */\n\t//write_record(ba, \"BrtWsFmtInfo\", write_BrtWsFmtInfo(ws));\n}\n\nfunction write_SHEETPROTECT(ba, ws) {\n\tif(!ws['!protect']) return;\n\t/* [BrtSheetProtectionIso] */\n\twrite_record(ba, \"BrtSheetProtection\", write_BrtSheetProtection(ws['!protect']));\n}\n\nfunction write_ws_bin(idx, opts, wb, rels) {\n\tvar ba = buf_array();\n\tvar s = wb.SheetNames[idx], ws = wb.Sheets[s] || {};\n\tvar c = s; try { if(wb && wb.Workbook) c = wb.Workbook.Sheets[idx].CodeName || c; } catch(e) {}\n\tvar r = safe_decode_range(ws['!ref'] || \"A1\");\n\tif(r.e.c > 0x3FFF || r.e.r > 0xFFFFF) {\n\t\tif(opts.WTF) throw new Error(\"Range \" + (ws['!ref'] || \"A1\") + \" exceeds format limit A1:XFD1048576\");\n\t\tr.e.c = Math.min(r.e.c, 0x3FFF);\n\t\tr.e.r = Math.min(r.e.c, 0xFFFFF);\n\t}\n\tws['!links'] = [];\n\t/* passed back to write_zip and removed there */\n\tws['!comments'] = [];\n\twrite_record(ba, \"BrtBeginSheet\");\n\tif(wb.vbaraw) write_record(ba, \"BrtWsProp\", write_BrtWsProp(c));\n\twrite_record(ba, \"BrtWsDim\", write_BrtWsDim(r));\n\twrite_WSVIEWS2(ba, ws, wb.Workbook);\n\twrite_WSFMTINFO(ba, ws);\n\twrite_COLINFOS(ba, ws, idx, opts, wb);\n\twrite_CELLTABLE(ba, ws, idx, opts, wb);\n\t/* [BrtSheetCalcProp] */\n\twrite_SHEETPROTECT(ba, ws);\n\t/* *([BrtRangeProtectionIso] BrtRangeProtection) */\n\t/* [SCENMAN] */\n\twrite_AUTOFILTER(ba, ws, wb, idx);\n\t/* [SORTSTATE] */\n\t/* [DCON] */\n\t/* [USERSHVIEWS] */\n\twrite_MERGECELLS(ba, ws);\n\t/* [BrtPhoneticInfo] */\n\t/* *CONDITIONALFORMATTING */\n\t/* [DVALS] */\n\twrite_HLINKS(ba, ws, rels);\n\t/* [BrtPrintOptions] */\n\tif(ws['!margins']) write_record(ba, \"BrtMargins\", write_BrtMargins(ws['!margins']));\n\t/* [BrtPageSetup] */\n\t/* [HEADERFOOTER] */\n\t/* [RWBRK] */\n\t/* [COLBRK] */\n\t/* *BrtBigName */\n\t/* [CELLWATCHES] */\n\tif(!opts || opts.ignoreEC || (opts.ignoreEC == (void 0))) write_IGNOREECS(ba, ws);\n\t/* [SMARTTAGS] */\n\t/* [BrtDrawing] */\n\twrite_LEGACYDRAWING(ba, ws, idx, rels);\n\t/* [BrtLegacyDrawingHF] */\n\t/* [BrtBkHim] */\n\t/* [OLEOBJECTS] */\n\t/* [ACTIVEXCONTROLS] */\n\t/* [WEBPUBITEMS] */\n\t/* [LISTPARTS] */\n\t/* FRTWORKSHEET */\n\twrite_record(ba, \"BrtEndSheet\");\n\treturn ba.end();\n}\nRELS.CHART = \"http://schemas.openxmlformats.org/officeDocument/2006/relationships/chart\";\nRELS.CHARTEX = \"http://schemas.microsoft.com/office/2014/relationships/chartEx\";\n\nfunction parse_Cache(data) {\n\tvar col = [];\n\tvar num = data.match(/^/);\n\tvar f;\n\n\t/* 21.2.2.150 pt CT_NumVal */\n\t(data.match(/(.*?)<\\/c:pt>/mg)||[]).forEach(function(pt) {\n\t\tvar q = pt.match(/(.*)<\\/c:v><\\/c:pt>/);\n\t\tif(!q) return;\n\t\tcol[+q[1]] = num ? +q[2] : q[2];\n\t});\n\n\t/* 21.2.2.71 formatCode CT_Xstring */\n\tvar nf = unescapexml((data.match(/([\\s\\S]*?)<\\/c:formatCode>/) || [\"\",\"General\"])[1]);\n\n\t(data.match(/(.*?)<\\/c:f>/mg)||[]).forEach(function(F) { f = F.replace(/<.*?>/g,\"\"); });\n\n\treturn [col, nf, f];\n}\n\n/* 21.2 DrawingML - Charts */\nfunction parse_chart(data, name, opts, rels, wb, csheet) {\n\tvar cs = ((csheet || {\"!type\":\"chart\"}));\n\tif(!data) return csheet;\n\t/* 21.2.2.27 chart CT_Chart */\n\n\tvar C = 0, R = 0, col = \"A\";\n\tvar refguess = {s: {r:2000000, c:2000000}, e: {r:0, c:0} };\n\n\t/* 21.2.2.120 numCache CT_NumData */\n\t(data.match(/[\\s\\S]*?<\\/c:numCache>/gm)||[]).forEach(function(nc) {\n\t\tvar cache = parse_Cache(nc);\n\t\trefguess.s.r = refguess.s.c = 0;\n\t\trefguess.e.c = C;\n\t\tcol = encode_col(C);\n\t\tcache[0].forEach(function(n,i) {\n\t\t\tcs[col + encode_row(i)] = {t:'n', v:n, z:cache[1] };\n\t\t\tR = i;\n\t\t});\n\t\tif(refguess.e.r < R) refguess.e.r = R;\n\t\t++C;\n\t});\n\tif(C > 0) cs[\"!ref\"] = encode_range(refguess);\n\treturn cs;\n}\nRELS.CS = \"http://schemas.openxmlformats.org/officeDocument/2006/relationships/chartsheet\";\n\nvar CS_XML_ROOT = writextag('chartsheet', null, {\n\t'xmlns': XMLNS.main[0],\n\t'xmlns:r': XMLNS.r\n});\n\n/* 18.3 Worksheets also covers Chartsheets */\nfunction parse_cs_xml(data, opts, idx, rels, wb) {\n\tif(!data) return data;\n\t/* 18.3.1.12 chartsheet CT_ChartSheet */\n\tif(!rels) rels = {'!id':{}};\n\tvar s = ({'!type':\"chart\", '!drawel':null, '!rel':\"\"});\n\tvar m;\n\n\t/* 18.3.1.83 sheetPr CT_ChartsheetPr */\n\tvar sheetPr = data.match(sheetprregex);\n\tif(sheetPr) parse_ws_xml_sheetpr(sheetPr[0], s, wb, idx);\n\n\t/* 18.3.1.36 drawing CT_Drawing */\n\tif((m = data.match(/drawing r:id=\"(.*?)\"/))) s['!rel'] = m[1];\n\n\tif(rels['!id'][s['!rel']]) s['!drawel'] = rels['!id'][s['!rel']];\n\treturn s;\n}\nfunction write_cs_xml(idx, opts, wb, rels) {\n\tvar o = [XML_HEADER, CS_XML_ROOT];\n\to[o.length] = writextag(\"drawing\", null, {\"r:id\": \"rId1\"});\n\tadd_rels(rels, -1, \"../drawings/drawing\" + (idx+1) + \".xml\", RELS.DRAW);\n\tif(o.length>2) { o[o.length] = (''); o[1]=o[1].replace(\"/>\",\">\"); }\n\treturn o.join(\"\");\n}\n\n/* [MS-XLSB] 2.4.331 BrtCsProp */\nfunction parse_BrtCsProp(data, length) {\n\tdata.l += 10;\n\tvar name = parse_XLWideString(data, length - 10);\n\treturn { name: name };\n}\n\n/* [MS-XLSB] 2.1.7.7 Chart Sheet */\nfunction parse_cs_bin(data, opts, idx, rels, wb) {\n\tif(!data) return data;\n\tif(!rels) rels = {'!id':{}};\n\tvar s = {'!type':\"chart\", '!drawel':null, '!rel':\"\"};\n\tvar state = [];\n\tvar pass = false;\n\trecordhopper(data, function cs_parse(val, R_n, RT) {\n\t\tswitch(RT) {\n\n\t\t\tcase 0x0226: /* 'BrtDrawing' */\n\t\t\t\ts['!rel'] = val; break;\n\n\t\t\tcase 0x028B: /* 'BrtCsProp' */\n\t\t\t\tif(!wb.Sheets[idx]) wb.Sheets[idx] = {};\n\t\t\t\tif(val.name) wb.Sheets[idx].CodeName = val.name;\n\t\t\t\tbreak;\n\n\t\t\tcase 0x0232: /* 'BrtBkHim' */\n\t\t\tcase 0x028C: /* 'BrtCsPageSetup' */\n\t\t\tcase 0x029D: /* 'BrtCsProtection' */\n\t\t\tcase 0x02A7: /* 'BrtCsProtectionIso' */\n\t\t\tcase 0x0227: /* 'BrtLegacyDrawing' */\n\t\t\tcase 0x0228: /* 'BrtLegacyDrawingHF' */\n\t\t\tcase 0x01DC: /* 'BrtMargins' */\n\t\t\tcase 0x0C00: /* 'BrtUid' */\n\t\t\t\tbreak;\n\n\t\t\tcase 0x0023: /* 'BrtFRTBegin' */\n\t\t\t\tpass = true; break;\n\t\t\tcase 0x0024: /* 'BrtFRTEnd' */\n\t\t\t\tpass = false; break;\n\t\t\tcase 0x0025: /* 'BrtACBegin' */\n\t\t\t\tstate.push(R_n); break;\n\t\t\tcase 0x0026: /* 'BrtACEnd' */\n\t\t\t\tstate.pop(); break;\n\n\t\t\tdefault:\n\t\t\t\tif((R_n||\"\").indexOf(\"Begin\") > 0) state.push(R_n);\n\t\t\t\telse if((R_n||\"\").indexOf(\"End\") > 0) state.pop();\n\t\t\t\telse if(!pass || opts.WTF) throw new Error(\"Unexpected record \" + RT + \" \" + R_n);\n\t\t}\n\t}, opts);\n\n\tif(rels['!id'][s['!rel']]) s['!drawel'] = rels['!id'][s['!rel']];\n\treturn s;\n}\nfunction write_cs_bin() {\n\tvar ba = buf_array();\n\twrite_record(ba, \"BrtBeginSheet\");\n\t/* [BrtCsProp] */\n\t/* CSVIEWS */\n\t/* [[BrtCsProtectionIso] BrtCsProtection] */\n\t/* [USERCSVIEWS] */\n\t/* [BrtMargins] */\n\t/* [BrtCsPageSetup] */\n\t/* [HEADERFOOTER] */\n\t/* BrtDrawing */\n\t/* [BrtLegacyDrawing] */\n\t/* [BrtLegacyDrawingHF] */\n\t/* [BrtBkHim] */\n\t/* [WEBPUBITEMS] */\n\t/* FRTCHARTSHEET */\n\twrite_record(ba, \"BrtEndSheet\");\n\treturn ba.end();\n}\n/* 18.2.28 (CT_WorkbookProtection) Defaults */\nvar WBPropsDef = [\n\t['allowRefreshQuery', false, \"bool\"],\n\t['autoCompressPictures', true, \"bool\"],\n\t['backupFile', false, \"bool\"],\n\t['checkCompatibility', false, \"bool\"],\n\t['CodeName', ''],\n\t['date1904', false, \"bool\"],\n\t['defaultThemeVersion', 0, \"int\"],\n\t['filterPrivacy', false, \"bool\"],\n\t['hidePivotFieldList', false, \"bool\"],\n\t['promptedSolutions', false, \"bool\"],\n\t['publishItems', false, \"bool\"],\n\t['refreshAllConnections', false, \"bool\"],\n\t['saveExternalLinkValues', true, \"bool\"],\n\t['showBorderUnselectedTables', true, \"bool\"],\n\t['showInkAnnotation', true, \"bool\"],\n\t['showObjects', 'all'],\n\t['showPivotChartFilter', false, \"bool\"],\n\t['updateLinks', 'userSet']\n];\n\n/* 18.2.30 (CT_BookView) Defaults */\nvar WBViewDef = [\n\t['activeTab', 0, \"int\"],\n\t['autoFilterDateGrouping', true, \"bool\"],\n\t['firstSheet', 0, \"int\"],\n\t['minimized', false, \"bool\"],\n\t['showHorizontalScroll', true, \"bool\"],\n\t['showSheetTabs', true, \"bool\"],\n\t['showVerticalScroll', true, \"bool\"],\n\t['tabRatio', 600, \"int\"],\n\t['visibility', 'visible']\n\t//window{Height,Width}, {x,y}Window\n];\n\n/* 18.2.19 (CT_Sheet) Defaults */\nvar SheetDef = [\n\t//['state', 'visible']\n];\n\n/* 18.2.2 (CT_CalcPr) Defaults */\nvar CalcPrDef = [\n\t['calcCompleted', 'true'],\n\t['calcMode', 'auto'],\n\t['calcOnSave', 'true'],\n\t['concurrentCalc', 'true'],\n\t['fullCalcOnLoad', 'false'],\n\t['fullPrecision', 'true'],\n\t['iterate', 'false'],\n\t['iterateCount', '100'],\n\t['iterateDelta', '0.001'],\n\t['refMode', 'A1']\n];\n\n/* 18.2.3 (CT_CustomWorkbookView) Defaults */\n/*var CustomWBViewDef = [\n\t['autoUpdate', 'false'],\n\t['changesSavedWin', 'false'],\n\t['includeHiddenRowCol', 'true'],\n\t['includePrintSettings', 'true'],\n\t['maximized', 'false'],\n\t['minimized', 'false'],\n\t['onlySync', 'false'],\n\t['personalView', 'false'],\n\t['showComments', 'commIndicator'],\n\t['showFormulaBar', 'true'],\n\t['showHorizontalScroll', 'true'],\n\t['showObjects', 'all'],\n\t['showSheetTabs', 'true'],\n\t['showStatusbar', 'true'],\n\t['showVerticalScroll', 'true'],\n\t['tabRatio', '600'],\n\t['xWindow', '0'],\n\t['yWindow', '0']\n];*/\n\nfunction push_defaults_array(target, defaults) {\n\tfor(var j = 0; j != target.length; ++j) { var w = target[j];\n\t\tfor(var i=0; i != defaults.length; ++i) { var z = defaults[i];\n\t\t\tif(w[z[0]] == null) w[z[0]] = z[1];\n\t\t\telse switch(z[2]) {\n\t\t\tcase \"bool\": if(typeof w[z[0]] == \"string\") w[z[0]] = parsexmlbool(w[z[0]]); break;\n\t\t\tcase \"int\": if(typeof w[z[0]] == \"string\") w[z[0]] = parseInt(w[z[0]], 10); break;\n\t\t\t}\n\t\t}\n\t}\n}\nfunction push_defaults(target, defaults) {\n\tfor(var i = 0; i != defaults.length; ++i) { var z = defaults[i];\n\t\tif(target[z[0]] == null) target[z[0]] = z[1];\n\t\telse switch(z[2]) {\n\t\t\tcase \"bool\": if(typeof target[z[0]] == \"string\") target[z[0]] = parsexmlbool(target[z[0]]); break;\n\t\t\tcase \"int\": if(typeof target[z[0]] == \"string\") target[z[0]] = parseInt(target[z[0]], 10); break;\n\t\t}\n\t}\n}\n\nfunction parse_wb_defaults(wb) {\n\tpush_defaults(wb.WBProps, WBPropsDef);\n\tpush_defaults(wb.CalcPr, CalcPrDef);\n\n\tpush_defaults_array(wb.WBView, WBViewDef);\n\tpush_defaults_array(wb.Sheets, SheetDef);\n\n\t_ssfopts.date1904 = parsexmlbool(wb.WBProps.date1904);\n}\n\nfunction safe1904(wb) {\n\t/* TODO: store date1904 somewhere else */\n\tif(!wb.Workbook) return \"false\";\n\tif(!wb.Workbook.WBProps) return \"false\";\n\treturn parsexmlbool(wb.Workbook.WBProps.date1904) ? \"true\" : \"false\";\n}\n\nvar badchars = \"][*?\\/\\\\\".split(\"\");\nfunction check_ws_name(n, safe) {\n\tif(n.length > 31) { if(safe) return false; throw new Error(\"Sheet names cannot exceed 31 chars\"); }\n\tvar _good = true;\n\tbadchars.forEach(function(c) {\n\t\tif(n.indexOf(c) == -1) return;\n\t\tif(!safe) throw new Error(\"Sheet name cannot contain : \\\\ / ? * [ ]\");\n\t\t_good = false;\n\t});\n\treturn _good;\n}\nfunction check_wb_names(N, S, codes) {\n\tN.forEach(function(n,i) {\n\t\tcheck_ws_name(n);\n\t\tfor(var j = 0; j < i; ++j) if(n == N[j]) throw new Error(\"Duplicate Sheet Name: \" + n);\n\t\tif(codes) {\n\t\t\tvar cn = (S && S[i] && S[i].CodeName) || n;\n\t\t\tif(cn.charCodeAt(0) == 95 && cn.length > 22) throw new Error(\"Bad Code Name: Worksheet\" + cn);\n\t\t}\n\t});\n}\nfunction check_wb(wb) {\n\tif(!wb || !wb.SheetNames || !wb.Sheets) throw new Error(\"Invalid Workbook\");\n\tif(!wb.SheetNames.length) throw new Error(\"Workbook is empty\");\n\tvar Sheets = (wb.Workbook && wb.Workbook.Sheets) || [];\n\tcheck_wb_names(wb.SheetNames, Sheets, !!wb.vbaraw);\n\tfor(var i = 0; i < wb.SheetNames.length; ++i) check_ws(wb.Sheets[wb.SheetNames[i]], wb.SheetNames[i], i);\n\t/* TODO: validate workbook */\n}\n/* 18.2 Workbook */\nvar wbnsregex = /<\\w+:workbook/;\nfunction parse_wb_xml(data, opts) {\n\tif(!data) throw new Error(\"Could not find file\");\n\tvar wb = { AppVersion:{}, WBProps:{}, WBView:[], Sheets:[], CalcPr:{}, Names:[], xmlns: \"\" };\n\tvar pass = false, xmlns = \"xmlns\";\n\tvar dname = {}, dnstart = 0;\n\tdata.replace(tagregex, function xml_wb(x, idx) {\n\t\tvar y = parsexmltag(x);\n\t\tswitch(strip_ns(y[0])) {\n\t\t\tcase '': break;\n\n\t\t\t/* 18.2.13 fileVersion CT_FileVersion ? */\n\t\t\tcase '': case '': break;\n\n\t\t\t/* 18.2.12 fileSharing CT_FileSharing ? */\n\t\t\tcase '': break;\n\n\t\t\t/* 18.2.28 workbookPr CT_WorkbookPr ? */\n\t\t\tcase '':\n\t\t\t\tWBPropsDef.forEach(function(w) {\n\t\t\t\t\tif(y[w[0]] == null) return;\n\t\t\t\t\tswitch(w[2]) {\n\t\t\t\t\t\tcase \"bool\": wb.WBProps[w[0]] = parsexmlbool(y[w[0]]); break;\n\t\t\t\t\t\tcase \"int\": wb.WBProps[w[0]] = parseInt(y[w[0]], 10); break;\n\t\t\t\t\t\tdefault: wb.WBProps[w[0]] = y[w[0]];\n\t\t\t\t\t}\n\t\t\t\t});\n\t\t\t\tif(y.codeName) wb.WBProps.CodeName = utf8read(y.codeName);\n\t\t\t\tbreak;\n\t\t\tcase '': break;\n\n\t\t\t/* 18.2.29 workbookProtection CT_WorkbookProtection ? */\n\t\t\tcase '': break;\n\n\t\t\t/* 18.2.1 bookViews CT_BookViews ? */\n\t\t\tcase '': case '': break;\n\t\t\t/* 18.2.30 workbookView CT_BookView + */\n\t\t\tcase '': delete y[0]; wb.WBView.push(y); break;\n\t\t\tcase '': break;\n\n\t\t\t/* 18.2.20 sheets CT_Sheets 1 */\n\t\t\tcase '': case '': break; // aggregate sheet\n\t\t\t/* 18.2.19 sheet CT_Sheet + */\n\t\t\tcase '': break;\n\n\t\t\t/* 18.2.15 functionGroups CT_FunctionGroups ? */\n\t\t\tcase '': break;\n\t\t\t/* 18.2.14 functionGroup CT_FunctionGroup + */\n\t\t\tcase '': case '': break;\n\t\t\t/* 18.2.8 externalReference CT_ExternalReference + */\n\t\t\tcase '': break;\n\t\t\tcase '': case '': pass=false; break;\n\t\t\t/* 18.2.5 definedName CT_DefinedName + */\n\t\t\tcase '': {\n\t\t\t\tdname.Ref = unescapexml(utf8read(data.slice(dnstart, idx)));\n\t\t\t\twb.Names.push(dname);\n\t\t\t} break;\n\t\t\tcase '': break;\n\n\t\t\t/* 18.2.2 calcPr CT_CalcPr ? */\n\t\t\tcase '': delete y[0]; wb.CalcPr = y; break;\n\t\t\tcase '': break;\n\n\t\t\t/* 18.2.16 oleSize CT_OleSize ? (ref required) */\n\t\t\tcase '': case '': case '': break;\n\n\t\t\t/* 18.2.18 pivotCaches CT_PivotCaches ? */\n\t\t\tcase '': case '': case '': break;\n\n\t\t\t/* 18.2.23 smartTagTypes CT_SmartTagTypes ? */\n\t\t\tcase '': case '': break;\n\t\t\t/* 18.2.22 smartTagType CT_SmartTagType ? */\n\t\t\tcase '': break;\n\n\t\t\t/* 18.2.11 fileRecoveryPr CT_FileRecoveryPr ? */\n\t\t\tcase '': break;\n\n\t\t\t/* 18.2.26 webPublishObjects CT_WebPublishObjects ? */\n\t\t\tcase '': case '': break;\n\t\t\t/* 18.2.25 webPublishObject CT_WebPublishObject ? */\n\t\t\tcase '': case '': case '': break;\n\t\t\t/* 18.2.7 ext CT_Extension + */\n\t\t\tcase '': pass=false; break;\n\n\t\t\t/* Others */\n\t\t\tcase '': pass=true; break;\n\t\t\tcase '': pass=false; break;\n\n\t\t\t/* TODO */\n\t\t\tcase ' 0);\n\n\t/* fileVersion */\n\t/* fileSharing */\n\n\tvar workbookPr = ({codeName:\"ThisWorkbook\"});\n\tif(wb.Workbook && wb.Workbook.WBProps) {\n\t\tWBPropsDef.forEach(function(x) {\nif((wb.Workbook.WBProps[x[0]]) == null) return;\n\t\t\tif((wb.Workbook.WBProps[x[0]]) == x[1]) return;\n\t\t\tworkbookPr[x[0]] = (wb.Workbook.WBProps[x[0]]);\n\t\t});\nif(wb.Workbook.WBProps.CodeName) { workbookPr.codeName = wb.Workbook.WBProps.CodeName; delete workbookPr.CodeName; }\n\t}\n\to[o.length] = (writextag('workbookPr', null, workbookPr));\n\n\t/* workbookProtection */\n\n\tvar sheets = wb.Workbook && wb.Workbook.Sheets || [];\n\tvar i = 0;\n\n\t/* bookViews only written if first worksheet is hidden */\n\tif(sheets && sheets[0] && !!sheets[0].Hidden) {\n\t\to[o.length] = \"\";\n\t\tfor(i = 0; i != wb.SheetNames.length; ++i) {\n\t\t\tif(!sheets[i]) break;\n\t\t\tif(!sheets[i].Hidden) break;\n\t\t}\n\t\tif(i == wb.SheetNames.length) i = 0;\n\t\to[o.length] = '';\n\t\to[o.length] = \"\";\n\t}\n\n\to[o.length] = \"\";\n\tfor(i = 0; i != wb.SheetNames.length; ++i) {\n\t\tvar sht = ({name:escapexml(wb.SheetNames[i].slice(0,31))});\n\t\tsht.sheetId = \"\"+(i+1);\n\t\tsht[\"r:id\"] = \"rId\"+(i+1);\n\t\tif(sheets[i]) switch(sheets[i].Hidden) {\n\t\t\tcase 1: sht.state = \"hidden\"; break;\n\t\t\tcase 2: sht.state = \"veryHidden\"; break;\n\t\t}\n\t\to[o.length] = (writextag('sheet',null,sht));\n\t}\n\to[o.length] = \"\";\n\n\t/* functionGroups */\n\t/* externalReferences */\n\n\tif(write_names) {\n\t\to[o.length] = \"\";\n\t\tif(wb.Workbook && wb.Workbook.Names) wb.Workbook.Names.forEach(function(n) {\n\t\t\tvar d = {name:n.Name};\n\t\t\tif(n.Comment) d.comment = n.Comment;\n\t\t\tif(n.Sheet != null) d.localSheetId = \"\"+n.Sheet;\n\t\t\tif(n.Hidden) d.hidden = \"1\";\n\t\t\tif(!n.Ref) return;\n\t\t\to[o.length] = writextag('definedName', escapexml(n.Ref), d);\n\t\t});\n\t\to[o.length] = \"\";\n\t}\n\n\t/* calcPr */\n\t/* oleSize */\n\t/* customWorkbookViews */\n\t/* pivotCaches */\n\t/* smartTagPr */\n\t/* smartTagTypes */\n\t/* webPublishing */\n\t/* fileRecoveryPr */\n\t/* webPublishObjects */\n\t/* extLst */\n\n\tif(o.length>2){ o[o.length] = ''; o[1]=o[1].replace(\"/>\",\">\"); }\n\treturn o.join(\"\");\n}\n/* [MS-XLSB] 2.4.304 BrtBundleSh */\nfunction parse_BrtBundleSh(data, length) {\n\tvar z = {};\n\tz.Hidden = data.read_shift(4); //hsState ST_SheetState\n\tz.iTabID = data.read_shift(4);\n\tz.strRelID = parse_RelID(data,length-8);\n\tz.name = parse_XLWideString(data);\n\treturn z;\n}\nfunction write_BrtBundleSh(data, o) {\n\tif(!o) o = new_buf(127);\n\to.write_shift(4, data.Hidden);\n\to.write_shift(4, data.iTabID);\n\twrite_RelID(data.strRelID, o);\n\twrite_XLWideString(data.name.slice(0,31), o);\n\treturn o.length > o.l ? o.slice(0, o.l) : o;\n}\n\n/* [MS-XLSB] 2.4.815 BrtWbProp */\nfunction parse_BrtWbProp(data, length) {\n\tvar o = ({});\n\tvar flags = data.read_shift(4);\n\to.defaultThemeVersion = data.read_shift(4);\n\tvar strName = (length > 8) ? parse_XLWideString(data) : \"\";\n\tif(strName.length > 0) o.CodeName = strName;\n\to.autoCompressPictures = !!(flags & 0x10000);\n\to.backupFile = !!(flags & 0x40);\n\to.checkCompatibility = !!(flags & 0x1000);\n\to.date1904 = !!(flags & 0x01);\n\to.filterPrivacy = !!(flags & 0x08);\n\to.hidePivotFieldList = !!(flags & 0x400);\n\to.promptedSolutions = !!(flags & 0x10);\n\to.publishItems = !!(flags & 0x800);\n\to.refreshAllConnections = !!(flags & 0x40000);\n\to.saveExternalLinkValues = !!(flags & 0x80);\n\to.showBorderUnselectedTables = !!(flags & 0x04);\n\to.showInkAnnotation = !!(flags & 0x20);\n\to.showObjects = [\"all\", \"placeholders\", \"none\"][(flags >> 13) & 0x03];\n\to.showPivotChartFilter = !!(flags & 0x8000);\n\to.updateLinks = [\"userSet\", \"never\", \"always\"][(flags >> 8) & 0x03];\n\treturn o;\n}\nfunction write_BrtWbProp(data, o) {\n\tif(!o) o = new_buf(72);\n\tvar flags = 0;\n\tif(data) {\n\t\t/* TODO: mirror parse_BrtWbProp fields */\n\t\tif(data.filterPrivacy) flags |= 0x08;\n\t}\n\to.write_shift(4, flags);\n\to.write_shift(4, 0);\n\twrite_XLSBCodeName(data && data.CodeName || \"ThisWorkbook\", o);\n\treturn o.slice(0, o.l);\n}\n\nfunction parse_BrtFRTArchID$(data, length) {\n\tvar o = {};\n\tdata.read_shift(4);\n\to.ArchID = data.read_shift(4);\n\tdata.l += length - 8;\n\treturn o;\n}\n\n/* [MS-XLSB] 2.4.687 BrtName */\nfunction parse_BrtName(data, length, opts) {\n\tvar end = data.l + length;\n\tdata.l += 4; //var flags = data.read_shift(4);\n\tdata.l += 1; //var chKey = data.read_shift(1);\n\tvar itab = data.read_shift(4);\n\tvar name = parse_XLNameWideString(data);\n\tvar formula = parse_XLSBNameParsedFormula(data, 0, opts);\n\tvar comment = parse_XLNullableWideString(data);\n\t//if(0 /* fProc */) {\n\t\t// unusedstring1: XLNullableWideString\n\t\t// description: XLNullableWideString\n\t\t// helpTopic: XLNullableWideString\n\t\t// unusedstring2: XLNullableWideString\n\t//}\n\tdata.l = end;\n\tvar out = ({Name:name, Ptg:formula});\n\tif(itab < 0xFFFFFFF) out.Sheet = itab;\n\tif(comment) out.Comment = comment;\n\treturn out;\n}\n\n/* [MS-XLSB] 2.1.7.61 Workbook */\nfunction parse_wb_bin(data, opts) {\n\tvar wb = { AppVersion:{}, WBProps:{}, WBView:[], Sheets:[], CalcPr:{}, xmlns: \"\" };\n\tvar state = [];\n\tvar pass = false;\n\n\tif(!opts) opts = {};\n\topts.biff = 12;\n\n\tvar Names = [];\n\tvar supbooks = ([[]]);\n\tsupbooks.SheetNames = [];\n\tsupbooks.XTI = [];\n\n\trecordhopper(data, function hopper_wb(val, R_n, RT) {\n\t\tswitch(RT) {\n\t\t\tcase 0x009C: /* 'BrtBundleSh' */\n\t\t\t\tsupbooks.SheetNames.push(val.name);\n\t\t\t\twb.Sheets.push(val); break;\n\n\t\t\tcase 0x0099: /* 'BrtWbProp' */\n\t\t\t\twb.WBProps = val; break;\n\n\t\t\tcase 0x0027: /* 'BrtName' */\n\t\t\t\tif(val.Sheet != null) opts.SID = val.Sheet;\n\t\t\t\tval.Ref = stringify_formula(val.Ptg, null, null, supbooks, opts);\n\t\t\t\tdelete opts.SID;\n\t\t\t\tdelete val.Ptg;\n\t\t\t\tNames.push(val);\n\t\t\t\tbreak;\n\t\t\tcase 0x040C: /* 'BrtNameExt' */ break;\n\n\t\t\tcase 0x0165: /* 'BrtSupSelf' */\n\t\t\tcase 0x0166: /* 'BrtSupSame' */\n\t\t\tcase 0x0163: /* 'BrtSupBookSrc' */\n\t\t\tcase 0x029B: /* 'BrtSupAddin' */\n\t\t\t\tif(!supbooks[0].length) supbooks[0] = [RT, val];\n\t\t\t\telse supbooks.push([RT, val]);\n\t\t\t\tsupbooks[supbooks.length - 1].XTI = [];\n\t\t\t\tbreak;\n\t\t\tcase 0x016A: /* 'BrtExternSheet' */\n\t\t\t\tif(supbooks.length === 0) { supbooks[0] = []; supbooks[0].XTI = []; }\n\t\t\t\tsupbooks[supbooks.length - 1].XTI = supbooks[supbooks.length - 1].XTI.concat(val);\n\t\t\t\tsupbooks.XTI = supbooks.XTI.concat(val);\n\t\t\t\tbreak;\n\t\t\tcase 0x0169: /* 'BrtPlaceholderName' */\n\t\t\t\tbreak;\n\n\t\t\t/* case 'BrtModelTimeGroupingCalcCol' */\n\t\t\tcase 0x0C00: /* 'BrtUid' */\n\t\t\tcase 0x0C01: /* 'BrtRevisionPtr' */\n\t\t\tcase 0x0817: /* 'BrtAbsPath15' */\n\t\t\tcase 0x0216: /* 'BrtBookProtection' */\n\t\t\tcase 0x02A5: /* 'BrtBookProtectionIso' */\n\t\t\tcase 0x009E: /* 'BrtBookView' */\n\t\t\tcase 0x009D: /* 'BrtCalcProp' */\n\t\t\tcase 0x0262: /* 'BrtCrashRecErr' */\n\t\t\tcase 0x0802: /* 'BrtDecoupledPivotCacheID' */\n\t\t\tcase 0x009B: /* 'BrtFileRecover' */\n\t\t\tcase 0x0224: /* 'BrtFileSharing' */\n\t\t\tcase 0x02A4: /* 'BrtFileSharingIso' */\n\t\t\tcase 0x0080: /* 'BrtFileVersion' */\n\t\t\tcase 0x0299: /* 'BrtFnGroup' */\n\t\t\tcase 0x0850: /* 'BrtModelRelationship' */\n\t\t\tcase 0x084D: /* 'BrtModelTable' */\n\t\t\tcase 0x0225: /* 'BrtOleSize' */\n\t\t\tcase 0x0805: /* 'BrtPivotTableRef' */\n\t\t\tcase 0x0254: /* 'BrtSmartTagType' */\n\t\t\tcase 0x081C: /* 'BrtTableSlicerCacheID' */\n\t\t\tcase 0x081B: /* 'BrtTableSlicerCacheIDs' */\n\t\t\tcase 0x0822: /* 'BrtTimelineCachePivotCacheID' */\n\t\t\tcase 0x018D: /* 'BrtUserBookView' */\n\t\t\tcase 0x009A: /* 'BrtWbFactoid' */\n\t\t\tcase 0x045D: /* 'BrtWbProp14' */\n\t\t\tcase 0x0229: /* 'BrtWebOpt' */\n\t\t\tcase 0x082B: /* 'BrtWorkBookPr15' */\n\t\t\t\tbreak;\n\n\t\t\tcase 0x0023: /* 'BrtFRTBegin' */\n\t\t\t\tstate.push(R_n); pass = true; break;\n\t\t\tcase 0x0024: /* 'BrtFRTEnd' */\n\t\t\t\tstate.pop(); pass = false; break;\n\t\t\tcase 0x0025: /* 'BrtACBegin' */\n\t\t\t\tstate.push(R_n); pass = true; break;\n\t\t\tcase 0x0026: /* 'BrtACEnd' */\n\t\t\t\tstate.pop(); pass = false; break;\n\n\t\t\tcase 0x0010: /* 'BrtFRTArchID$' */ break;\n\n\t\t\tdefault:\n\t\t\t\tif((R_n||\"\").indexOf(\"Begin\") > 0){/* empty */}\n\t\t\t\telse if((R_n||\"\").indexOf(\"End\") > 0){/* empty */}\n\t\t\t\telse if(!pass || (opts.WTF && state[state.length-1] != \"BrtACBegin\" && state[state.length-1] != \"BrtFRTBegin\")) throw new Error(\"Unexpected record \" + RT + \" \" + R_n);\n\t\t}\n\t}, opts);\n\n\tparse_wb_defaults(wb);\n\n\t// $FlowIgnore\n\twb.Names = Names;\n\n\t(wb).supbooks = supbooks;\n\treturn wb;\n}\n\nfunction write_BUNDLESHS(ba, wb) {\n\twrite_record(ba, \"BrtBeginBundleShs\");\n\tfor(var idx = 0; idx != wb.SheetNames.length; ++idx) {\n\t\tvar viz = wb.Workbook && wb.Workbook.Sheets && wb.Workbook.Sheets[idx] && wb.Workbook.Sheets[idx].Hidden || 0;\n\t\tvar d = { Hidden: viz, iTabID: idx+1, strRelID: 'rId' + (idx+1), name: wb.SheetNames[idx] };\n\t\twrite_record(ba, \"BrtBundleSh\", write_BrtBundleSh(d));\n\t}\n\twrite_record(ba, \"BrtEndBundleShs\");\n}\n\n/* [MS-XLSB] 2.4.649 BrtFileVersion */\nfunction write_BrtFileVersion(data, o) {\n\tif(!o) o = new_buf(127);\n\tfor(var i = 0; i != 4; ++i) o.write_shift(4, 0);\n\twrite_XLWideString(\"SheetJS\", o);\n\twrite_XLWideString(XLSX.version, o);\n\twrite_XLWideString(XLSX.version, o);\n\twrite_XLWideString(\"7262\", o);\n\to.length = o.l;\n\treturn o.length > o.l ? o.slice(0, o.l) : o;\n}\n\n/* [MS-XLSB] 2.4.301 BrtBookView */\nfunction write_BrtBookView(idx, o) {\n\tif(!o) o = new_buf(29);\n\to.write_shift(-4, 0);\n\to.write_shift(-4, 460);\n\to.write_shift(4, 28800);\n\to.write_shift(4, 17600);\n\to.write_shift(4, 500);\n\to.write_shift(4, idx);\n\to.write_shift(4, idx);\n\tvar flags = 0x78;\n\to.write_shift(1, flags);\n\treturn o.length > o.l ? o.slice(0, o.l) : o;\n}\n\nfunction write_BOOKVIEWS(ba, wb) {\n\t/* required if hidden tab appears before visible tab */\n\tif(!wb.Workbook || !wb.Workbook.Sheets) return;\n\tvar sheets = wb.Workbook.Sheets;\n\tvar i = 0, vistab = -1, hidden = -1;\n\tfor(; i < sheets.length; ++i) {\n\t\tif(!sheets[i] || !sheets[i].Hidden && vistab == -1) vistab = i;\n\t\telse if(sheets[i].Hidden == 1 && hidden == -1) hidden = i;\n\t}\n\tif(hidden > vistab) return;\n\twrite_record(ba, \"BrtBeginBookViews\");\n\twrite_record(ba, \"BrtBookView\", write_BrtBookView(vistab));\n\t/* 1*(BrtBookView *FRT) */\n\twrite_record(ba, \"BrtEndBookViews\");\n}\n\n/* [MS-XLSB] 2.4.305 BrtCalcProp */\n/*function write_BrtCalcProp(data, o) {\n\tif(!o) o = new_buf(26);\n\to.write_shift(4,0); // force recalc\n\to.write_shift(4,1);\n\to.write_shift(4,0);\n\twrite_Xnum(0, o);\n\to.write_shift(-4, 1023);\n\to.write_shift(1, 0x33);\n\to.write_shift(1, 0x00);\n\treturn o;\n}*/\n\n/* [MS-XLSB] 2.4.646 BrtFileRecover */\n/*function write_BrtFileRecover(data, o) {\n\tif(!o) o = new_buf(1);\n\to.write_shift(1,0);\n\treturn o;\n}*/\n\n/* [MS-XLSB] 2.1.7.61 Workbook */\nfunction write_wb_bin(wb, opts) {\n\tvar ba = buf_array();\n\twrite_record(ba, \"BrtBeginBook\");\n\twrite_record(ba, \"BrtFileVersion\", write_BrtFileVersion());\n\t/* [[BrtFileSharingIso] BrtFileSharing] */\n\twrite_record(ba, \"BrtWbProp\", write_BrtWbProp(wb.Workbook && wb.Workbook.WBProps || null));\n\t/* [ACABSPATH] */\n\t/* [[BrtBookProtectionIso] BrtBookProtection] */\n\twrite_BOOKVIEWS(ba, wb, opts);\n\twrite_BUNDLESHS(ba, wb, opts);\n\t/* [FNGROUP] */\n\t/* [EXTERNALS] */\n\t/* *BrtName */\n\t/* write_record(ba, \"BrtCalcProp\", write_BrtCalcProp()); */\n\t/* [BrtOleSize] */\n\t/* *(BrtUserBookView *FRT) */\n\t/* [PIVOTCACHEIDS] */\n\t/* [BrtWbFactoid] */\n\t/* [SMARTTAGTYPES] */\n\t/* [BrtWebOpt] */\n\t/* write_record(ba, \"BrtFileRecover\", write_BrtFileRecover()); */\n\t/* [WEBPUBITEMS] */\n\t/* [CRERRS] */\n\t/* FRTWORKBOOK */\n\twrite_record(ba, \"BrtEndBook\");\n\n\treturn ba.end();\n}\nfunction parse_wb(data, name, opts) {\n\tif(name.slice(-4)===\".bin\") return parse_wb_bin((data), opts);\n\treturn parse_wb_xml((data), opts);\n}\n\nfunction parse_ws(data, name, idx, opts, rels, wb, themes, styles) {\n\tif(name.slice(-4)===\".bin\") return parse_ws_bin((data), opts, idx, rels, wb, themes, styles);\n\treturn parse_ws_xml((data), opts, idx, rels, wb, themes, styles);\n}\n\nfunction parse_cs(data, name, idx, opts, rels, wb, themes, styles) {\n\tif(name.slice(-4)===\".bin\") return parse_cs_bin((data), opts, idx, rels, wb, themes, styles);\n\treturn parse_cs_xml((data), opts, idx, rels, wb, themes, styles);\n}\n\nfunction parse_ms(data, name, idx, opts, rels, wb, themes, styles) {\n\tif(name.slice(-4)===\".bin\") return parse_ms_bin((data), opts, idx, rels, wb, themes, styles);\n\treturn parse_ms_xml((data), opts, idx, rels, wb, themes, styles);\n}\n\nfunction parse_ds(data, name, idx, opts, rels, wb, themes, styles) {\n\tif(name.slice(-4)===\".bin\") return parse_ds_bin((data), opts, idx, rels, wb, themes, styles);\n\treturn parse_ds_xml((data), opts, idx, rels, wb, themes, styles);\n}\n\nfunction parse_sty(data, name, themes, opts) {\n\tif(name.slice(-4)===\".bin\") return parse_sty_bin((data), themes, opts);\n\treturn parse_sty_xml((data), themes, opts);\n}\n\nfunction parse_theme(data, name, opts) {\n\treturn parse_theme_xml(data, opts);\n}\n\nfunction parse_sst(data, name, opts) {\n\tif(name.slice(-4)===\".bin\") return parse_sst_bin((data), opts);\n\treturn parse_sst_xml((data), opts);\n}\n\nfunction parse_cmnt(data, name, opts) {\n\tif(name.slice(-4)===\".bin\") return parse_comments_bin((data), opts);\n\treturn parse_comments_xml((data), opts);\n}\n\nfunction parse_cc(data, name, opts) {\n\tif(name.slice(-4)===\".bin\") return parse_cc_bin((data), name, opts);\n\treturn parse_cc_xml((data), name, opts);\n}\n\nfunction parse_xlink(data, rel, name, opts) {\n\tif(name.slice(-4)===\".bin\") return parse_xlink_bin((data), rel, name, opts);\n\treturn parse_xlink_xml((data), rel, name, opts);\n}\n\nfunction write_wb(wb, name, opts) {\n\treturn (name.slice(-4)===\".bin\" ? write_wb_bin : write_wb_xml)(wb, opts);\n}\n\nfunction write_ws(data, name, opts, wb, rels) {\n\treturn (name.slice(-4)===\".bin\" ? write_ws_bin : write_ws_xml)(data, opts, wb, rels);\n}\n\n// eslint-disable-next-line no-unused-vars\nfunction write_cs(data, name, opts, wb, rels) {\n\treturn (name.slice(-4)===\".bin\" ? write_cs_bin : write_cs_xml)(data, opts, wb, rels);\n}\n\nfunction write_sty(data, name, opts) {\n\treturn (name.slice(-4)===\".bin\" ? write_sty_bin : write_sty_xml)(data, opts);\n}\n\nfunction write_sst(data, name, opts) {\n\treturn (name.slice(-4)===\".bin\" ? write_sst_bin : write_sst_xml)(data, opts);\n}\n\nfunction write_cmnt(data, name, opts) {\n\treturn (name.slice(-4)===\".bin\" ? write_comments_bin : write_comments_xml)(data, opts);\n}\n/*\nfunction write_cc(data, name:string, opts) {\n\treturn (name.slice(-4)===\".bin\" ? write_cc_bin : write_cc_xml)(data, opts);\n}\n*/\nvar attregexg2=/([\\w:]+)=((?:\")([^\"]*)(?:\")|(?:')([^']*)(?:'))/g;\nvar attregex2=/([\\w:]+)=((?:\")(?:[^\"]*)(?:\")|(?:')(?:[^']*)(?:'))/;\nfunction xlml_parsexmltag(tag, skip_root) {\n\tvar words = tag.split(/\\s+/);\n\tvar z = ([]); if(!skip_root) z[0] = words[0];\n\tif(words.length === 1) return z;\n\tvar m = tag.match(attregexg2), y, j, w, i;\n\tif(m) for(i = 0; i != m.length; ++i) {\n\t\ty = m[i].match(attregex2);\nif((j=y[1].indexOf(\":\")) === -1) z[y[1]] = y[2].slice(1,y[2].length-1);\n\t\telse {\n\t\t\tif(y[1].slice(0,6) === \"xmlns:\") w = \"xmlns\"+y[1].slice(6);\n\t\t\telse w = y[1].slice(j+1);\n\t\t\tz[w] = y[2].slice(1,y[2].length-1);\n\t\t}\n\t}\n\treturn z;\n}\nfunction xlml_parsexmltagobj(tag) {\n\tvar words = tag.split(/\\s+/);\n\tvar z = {};\n\tif(words.length === 1) return z;\n\tvar m = tag.match(attregexg2), y, j, w, i;\n\tif(m) for(i = 0; i != m.length; ++i) {\n\t\ty = m[i].match(attregex2);\nif((j=y[1].indexOf(\":\")) === -1) z[y[1]] = y[2].slice(1,y[2].length-1);\n\t\telse {\n\t\t\tif(y[1].slice(0,6) === \"xmlns:\") w = \"xmlns\"+y[1].slice(6);\n\t\t\telse w = y[1].slice(j+1);\n\t\t\tz[w] = y[2].slice(1,y[2].length-1);\n\t\t}\n\t}\n\treturn z;\n}\n\n// ----\n\nfunction xlml_format(format, value) {\n\tvar fmt = XLMLFormatMap[format] || unescapexml(format);\n\tif(fmt === \"General\") return SSF._general(value);\n\treturn SSF.format(fmt, value);\n}\n\nfunction xlml_set_custprop(Custprops, key, cp, val) {\n\tvar oval = val;\n\tswitch((cp[0].match(/dt:dt=\"([\\w.]+)\"/)||[\"\",\"\"])[1]) {\n\t\tcase \"boolean\": oval = parsexmlbool(val); break;\n\t\tcase \"i2\": case \"int\": oval = parseInt(val, 10); break;\n\t\tcase \"r4\": case \"float\": oval = parseFloat(val); break;\n\t\tcase \"date\": case \"dateTime.tz\": oval = parseDate(val); break;\n\t\tcase \"i8\": case \"string\": case \"fixed\": case \"uuid\": case \"bin.base64\": break;\n\t\tdefault: throw new Error(\"bad custprop:\" + cp[0]);\n\t}\n\tCustprops[unescapexml(key)] = oval;\n}\n\nfunction safe_format_xlml(cell, nf, o) {\n\tif(cell.t === 'z') return;\n\tif(!o || o.cellText !== false) try {\n\t\tif(cell.t === 'e') { cell.w = cell.w || BErr[cell.v]; }\n\t\telse if(nf === \"General\") {\n\t\t\tif(cell.t === 'n') {\n\t\t\t\tif((cell.v|0) === cell.v) cell.w = SSF._general_int(cell.v);\n\t\t\t\telse cell.w = SSF._general_num(cell.v);\n\t\t\t}\n\t\t\telse cell.w = SSF._general(cell.v);\n\t\t}\n\t\telse cell.w = xlml_format(nf||\"General\", cell.v);\n\t} catch(e) { if(o.WTF) throw e; }\n\ttry {\n\t\tvar z = XLMLFormatMap[nf]||nf||\"General\";\n\t\tif(o.cellNF) cell.z = z;\n\t\tif(o.cellDates && cell.t == 'n' && SSF.is_date(z)) {\n\t\t\tvar _d = SSF.parse_date_code(cell.v); if(_d) { cell.t = 'd'; cell.v = new Date(_d.y, _d.m-1,_d.d,_d.H,_d.M,_d.S,_d.u); }\n\t\t}\n\t} catch(e) { if(o.WTF) throw e; }\n}\n\nfunction process_style_xlml(styles, stag, opts) {\n\tif(opts.cellStyles) {\n\t\tif(stag.Interior) {\n\t\t\tvar I = stag.Interior;\n\t\t\tif(I.Pattern) I.patternType = XLMLPatternTypeMap[I.Pattern] || I.Pattern;\n\t\t}\n\t}\n\tstyles[stag.ID] = stag;\n}\n\n/* TODO: there must exist some form of OSP-blessed spec */\nfunction parse_xlml_data(xml, ss, data, cell, base, styles, csty, row, arrayf, o) {\n\tvar nf = \"General\", sid = cell.StyleID, S = {}; o = o || {};\n\tvar interiors = [];\n\tvar i = 0;\n\tif(sid === undefined && row) sid = row.StyleID;\n\tif(sid === undefined && csty) sid = csty.StyleID;\n\twhile(styles[sid] !== undefined) {\n\t\tif(styles[sid].nf) nf = styles[sid].nf;\n\t\tif(styles[sid].Interior) interiors.push(styles[sid].Interior);\n\t\tif(!styles[sid].Parent) break;\n\t\tsid = styles[sid].Parent;\n\t}\n\tswitch(data.Type) {\n\t\tcase 'Boolean':\n\t\t\tcell.t = 'b';\n\t\t\tcell.v = parsexmlbool(xml);\n\t\t\tbreak;\n\t\tcase 'String':\n\t\t\tcell.t = 's'; cell.r = xlml_fixstr(unescapexml(xml));\n\t\t\tcell.v = (xml.indexOf(\"<\") > -1 ? unescapexml(ss||xml).replace(/<.*?>/g, \"\") : cell.r); // todo: BR etc\n\t\t\tbreak;\n\t\tcase 'DateTime':\n\t\t\tif(xml.slice(-1) != \"Z\") xml += \"Z\";\n\t\t\tcell.v = (parseDate(xml) - new Date(Date.UTC(1899, 11, 30))) / (24 * 60 * 60 * 1000);\n\t\t\tif(cell.v !== cell.v) cell.v = unescapexml(xml);\n\t\t\telse if(cell.v<60) cell.v = cell.v -1;\n\t\t\tif(!nf || nf == \"General\") nf = \"yyyy-mm-dd\";\n\t\t\t/* falls through */\n\t\tcase 'Number':\n\t\t\tif(cell.v === undefined) cell.v=+xml;\n\t\t\tif(!cell.t) cell.t = 'n';\n\t\t\tbreak;\n\t\tcase 'Error': cell.t = 'e'; cell.v = RBErr[xml]; if(o.cellText !== false) cell.w = xml; break;\n\t\tdefault:\n\t\t\tif(xml == \"\" && ss == \"\") { cell.t = 'z'; }\n\t\t\telse { cell.t = 's'; cell.v = xlml_fixstr(ss||xml); }\n\t\t\tbreak;\n\t}\n\tsafe_format_xlml(cell, nf, o);\n\tif(o.cellFormula !== false) {\n\t\tif(cell.Formula) {\n\t\t\tvar fstr = unescapexml(cell.Formula);\n\t\t\t/* strictly speaking, the leading = is required but some writers omit */\n\t\t\tif(fstr.charCodeAt(0) == 61 /* = */) fstr = fstr.slice(1);\n\t\t\tcell.f = rc_to_a1(fstr, base);\n\t\t\tdelete cell.Formula;\n\t\t\tif(cell.ArrayRange == \"RC\") cell.F = rc_to_a1(\"RC:RC\", base);\n\t\t\telse if(cell.ArrayRange) {\n\t\t\t\tcell.F = rc_to_a1(cell.ArrayRange, base);\n\t\t\t\tarrayf.push([safe_decode_range(cell.F), cell.F]);\n\t\t\t}\n\t\t} else {\n\t\t\tfor(i = 0; i < arrayf.length; ++i)\n\t\t\t\tif(base.r >= arrayf[i][0].s.r && base.r <= arrayf[i][0].e.r)\n\t\t\t\t\tif(base.c >= arrayf[i][0].s.c && base.c <= arrayf[i][0].e.c)\n\t\t\t\t\t\tcell.F = arrayf[i][1];\n\t\t}\n\t}\n\tif(o.cellStyles) {\n\t\tinteriors.forEach(function(x) {\n\t\t\tif(!S.patternType && x.patternType) S.patternType = x.patternType;\n\t\t});\n\t\tcell.s = S;\n\t}\n\tif(cell.StyleID !== undefined) cell.ixfe = cell.StyleID;\n}\n\nfunction xlml_clean_comment(comment) {\n\tcomment.t = comment.v || \"\";\n\tcomment.t = comment.t.replace(/\\r\\n/g,\"\\n\").replace(/\\r/g,\"\\n\");\n\tcomment.v = comment.w = comment.ixfe = undefined;\n}\n\nfunction xlml_normalize(d) {\n\tif(has_buf && Buffer.isBuffer(d)) return d.toString('utf8');\n\tif(typeof d === 'string') return d;\n\t/* duktape */\n\tif(typeof Uint8Array !== 'undefined' && d instanceof Uint8Array) return utf8read(a2s(ab2a(d)));\n\tthrow new Error(\"Bad input format: expected Buffer or string\");\n}\n\n/* TODO: Everything */\n/* UOS uses CJK in tags */\nvar xlmlregex = /<(\\/?)([^\\s?>:\\/]+)(?:[\\s?:\\/][^>]*)?>/mg;\n//var xlmlregex = /<(\\/?)([a-z0-9]*:|)(\\w+)[^>]*>/mg;\nfunction parse_xlml_xml(d, _opts) {\n\tvar opts = _opts || {};\n\tmake_ssf(SSF);\n\tvar str = debom(xlml_normalize(d));\n\tif(opts.type == 'binary' || opts.type == 'array' || opts.type == 'base64') {\n\t\tif(typeof cptable !== 'undefined') str = cptable.utils.decode(65001, char_codes(str));\n\t\telse str = utf8read(str);\n\t}\n\tvar opening = str.slice(0, 1024).toLowerCase(), ishtml = false;\n\tif(opening.indexOf(\"= 0) ishtml = true; });\n\tif(ishtml) return HTML_.to_workbook(str, opts);\n\tvar Rn;\n\tvar state = [], tmp;\n\tif(DENSE != null && opts.dense == null) opts.dense = DENSE;\n\tvar sheets = {}, sheetnames = [], cursheet = (opts.dense ? [] : {}), sheetname = \"\";\n\tvar table = {}, cell = ({}), row = {};// eslint-disable-line no-unused-vars\n\tvar dtag = xlml_parsexmltag(''), didx = 0;\n\tvar c = 0, r = 0;\n\tvar refguess = {s: {r:2000000, c:2000000}, e: {r:0, c:0} };\n\tvar styles = {}, stag = {};\n\tvar ss = \"\", fidx = 0;\n\tvar merges = [];\n\tvar Props = {}, Custprops = {}, pidx = 0, cp = [];\n\tvar comments = [], comment = ({});\n\tvar cstys = [], csty, seencol = false;\n\tvar arrayf = [];\n\tvar rowinfo = [], rowobj = {}, cc = 0, rr = 0;\n\tvar Workbook = ({ Sheets:[], WBProps:{date1904:false} }), wsprops = {};\n\txlmlregex.lastIndex = 0;\n\tstr = str.replace(//mg,\"\");\n\tvar raw_Rn3 = \"\";\n\twhile((Rn = xlmlregex.exec(str))) switch((Rn[3] = (raw_Rn3 = Rn[3]).toLowerCase())) {\n\t\tcase 'data' /*case 'Data'*/:\n\t\t\tif(raw_Rn3 == \"data\") {\n\t\t\t\tif(Rn[1]==='/'){if((tmp=state.pop())[0]!==Rn[3]) throw new Error(\"Bad state: \"+tmp.join(\"|\"));}\n\t\t\t\telse if(Rn[0].charAt(Rn[0].length-2) !== '/') state.push([Rn[3], true]);\n\t\t\t\tbreak;\n\t\t\t}\n\t\t\tif(state[state.length-1][1]) break;\n\t\t\tif(Rn[1]==='/') parse_xlml_data(str.slice(didx, Rn.index), ss, dtag, state[state.length-1][0]==/*\"Comment\"*/\"comment\"?comment:cell, {c:c,r:r}, styles, cstys[c], row, arrayf, opts);\n\t\t\telse { ss = \"\"; dtag = xlml_parsexmltag(Rn[0]); didx = Rn.index + Rn[0].length; }\n\t\t\tbreak;\n\t\tcase 'cell' /*case 'Cell'*/:\n\t\t\tif(Rn[1]==='/'){\n\t\t\t\tif(comments.length > 0) cell.c = comments;\n\t\t\t\tif((!opts.sheetRows || opts.sheetRows > r) && cell.v !== undefined) {\n\t\t\t\t\tif(opts.dense) {\n\t\t\t\t\t\tif(!cursheet[r]) cursheet[r] = [];\n\t\t\t\t\t\tcursheet[r][c] = cell;\n\t\t\t\t\t} else cursheet[encode_col(c) + encode_row(r)] = cell;\n\t\t\t\t}\n\t\t\t\tif(cell.HRef) {\n\t\t\t\t\tcell.l = ({Target:cell.HRef});\n\t\t\t\t\tif(cell.HRefScreenTip) cell.l.Tooltip = cell.HRefScreenTip;\n\t\t\t\t\tdelete cell.HRef; delete cell.HRefScreenTip;\n\t\t\t\t}\n\t\t\t\tif(cell.MergeAcross || cell.MergeDown) {\n\t\t\t\t\tcc = c + (parseInt(cell.MergeAcross,10)|0);\n\t\t\t\t\trr = r + (parseInt(cell.MergeDown,10)|0);\n\t\t\t\t\tmerges.push({s:{c:c,r:r},e:{c:cc,r:rr}});\n\t\t\t\t}\n\t\t\t\tif(!opts.sheetStubs) { if(cell.MergeAcross) c = cc + 1; else ++c; }\n\t\t\t\telse if(cell.MergeAcross || cell.MergeDown) {\nfor(var cma = c; cma <= cc; ++cma) {\n\t\t\t\t\t\tfor(var cmd = r; cmd <= rr; ++cmd) {\n\t\t\t\t\t\t\tif(cma > c || cmd > r) {\n\t\t\t\t\t\t\t\tif(opts.dense) {\n\t\t\t\t\t\t\t\t\tif(!cursheet[cmd]) cursheet[cmd] = [];\n\t\t\t\t\t\t\t\t\tcursheet[cmd][cma] = {t:'z'};\n\t\t\t\t\t\t\t\t} else cursheet[encode_col(cma) + encode_row(cmd)] = {t:'z'};\n\t\t\t\t\t\t\t}\n\t\t\t\t\t\t}\n\t\t\t\t\t}\n\t\t\t\t\tc = cc + 1;\n\t\t\t\t}\n\t\t\t\telse ++c;\n\t\t\t} else {\n\t\t\t\tcell = xlml_parsexmltagobj(Rn[0]);\n\t\t\t\tif(cell.Index) c = +cell.Index - 1;\n\t\t\t\tif(c < refguess.s.c) refguess.s.c = c;\n\t\t\t\tif(c > refguess.e.c) refguess.e.c = c;\n\t\t\t\tif(Rn[0].slice(-2) === \"/>\") ++c;\n\t\t\t\tcomments = [];\n\t\t\t}\n\t\t\tbreak;\n\t\tcase 'row' /*case 'Row'*/:\n\t\t\tif(Rn[1]==='/' || Rn[0].slice(-2) === \"/>\") {\n\t\t\t\tif(r < refguess.s.r) refguess.s.r = r;\n\t\t\t\tif(r > refguess.e.r) refguess.e.r = r;\n\t\t\t\tif(Rn[0].slice(-2) === \"/>\") {\n\t\t\t\t\trow = xlml_parsexmltag(Rn[0]);\n\t\t\t\t\tif(row.Index) r = +row.Index - 1;\n\t\t\t\t}\n\t\t\t\tc = 0; ++r;\n\t\t\t} else {\n\t\t\t\trow = xlml_parsexmltag(Rn[0]);\n\t\t\t\tif(row.Index) r = +row.Index - 1;\n\t\t\t\trowobj = {};\n\t\t\t\tif(row.AutoFitHeight == \"0\" || row.Height) {\n\t\t\t\t\trowobj.hpx = parseInt(row.Height, 10); rowobj.hpt = px2pt(rowobj.hpx);\n\t\t\t\t\trowinfo[r] = rowobj;\n\t\t\t\t}\n\t\t\t\tif(row.Hidden == \"1\") { rowobj.hidden = true; rowinfo[r] = rowobj; }\n\t\t\t}\n\t\t\tbreak;\n\t\tcase 'worksheet' /*case 'Worksheet'*/: /* TODO: read range from FullRows/FullColumns */\n\t\t\tif(Rn[1]==='/'){\n\t\t\t\tif((tmp=state.pop())[0]!==Rn[3]) throw new Error(\"Bad state: \"+tmp.join(\"|\"));\n\t\t\t\tsheetnames.push(sheetname);\n\t\t\t\tif(refguess.s.r <= refguess.e.r && refguess.s.c <= refguess.e.c) {\n\t\t\t\t\tcursheet[\"!ref\"] = encode_range(refguess);\n\t\t\t\t\tif(opts.sheetRows && opts.sheetRows <= refguess.e.r) {\n\t\t\t\t\t\tcursheet[\"!fullref\"] = cursheet[\"!ref\"];\n\t\t\t\t\t\trefguess.e.r = opts.sheetRows - 1;\n\t\t\t\t\t\tcursheet[\"!ref\"] = encode_range(refguess);\n\t\t\t\t\t}\n\t\t\t\t}\n\t\t\t\tif(merges.length) cursheet[\"!merges\"] = merges;\n\t\t\t\tif(cstys.length > 0) cursheet[\"!cols\"] = cstys;\n\t\t\t\tif(rowinfo.length > 0) cursheet[\"!rows\"] = rowinfo;\n\t\t\t\tsheets[sheetname] = cursheet;\n\t\t\t} else {\n\t\t\t\trefguess = {s: {r:2000000, c:2000000}, e: {r:0, c:0} };\n\t\t\t\tr = c = 0;\n\t\t\t\tstate.push([Rn[3], false]);\n\t\t\t\ttmp = xlml_parsexmltag(Rn[0]);\n\t\t\t\tsheetname = unescapexml(tmp.Name);\n\t\t\t\tcursheet = (opts.dense ? [] : {});\n\t\t\t\tmerges = [];\n\t\t\t\tarrayf = [];\n\t\t\t\trowinfo = [];\n\t\t\t\twsprops = {name:sheetname, Hidden:0};\n\t\t\t\tWorkbook.Sheets.push(wsprops);\n\t\t\t}\n\t\t\tbreak;\n\t\tcase 'table' /*case 'Table'*/:\n\t\t\tif(Rn[1]==='/'){if((tmp=state.pop())[0]!==Rn[3]) throw new Error(\"Bad state: \"+tmp.join(\"|\"));}\n\t\t\telse if(Rn[0].slice(-2) == \"/>\") break;\n\t\t\telse {\n\t\t\t\ttable = xlml_parsexmltag(Rn[0]);\n\t\t\t\tstate.push([Rn[3], false]);\n\t\t\t\tcstys = []; seencol = false;\n\t\t\t}\n\t\t\tbreak;\n\n\t\tcase 'style' /*case 'Style'*/:\n\t\t\tif(Rn[1]==='/') process_style_xlml(styles, stag, opts);\n\t\t\telse stag = xlml_parsexmltag(Rn[0]);\n\t\t\tbreak;\n\n\t\tcase 'numberformat' /*case 'NumberFormat'*/:\n\t\t\tstag.nf = unescapexml(xlml_parsexmltag(Rn[0]).Format || \"General\");\n\t\t\tif(XLMLFormatMap[stag.nf]) stag.nf = XLMLFormatMap[stag.nf];\n\t\t\tfor(var ssfidx = 0; ssfidx != 0x188; ++ssfidx) if(SSF._table[ssfidx] == stag.nf) break;\n\t\t\tif(ssfidx == 0x188) for(ssfidx = 0x39; ssfidx != 0x188; ++ssfidx) if(SSF._table[ssfidx] == null) { SSF.load(stag.nf, ssfidx); break; }\n\t\t\tbreak;\n\n\t\tcase 'column' /*case 'Column'*/:\n\t\t\tif(state[state.length-1][0] !== /*'Table'*/'table') break;\n\t\t\tcsty = xlml_parsexmltag(Rn[0]);\n\t\t\tif(csty.Hidden) { csty.hidden = true; delete csty.Hidden; }\n\t\t\tif(csty.Width) csty.wpx = parseInt(csty.Width, 10);\n\t\t\tif(!seencol && csty.wpx > 10) {\n\t\t\t\tseencol = true; MDW = DEF_MDW; //find_mdw_wpx(csty.wpx);\n\t\t\t\tfor(var _col = 0; _col < cstys.length; ++_col) if(cstys[_col]) process_col(cstys[_col]);\n\t\t\t}\n\t\t\tif(seencol) process_col(csty);\n\t\t\tcstys[(csty.Index-1||cstys.length)] = csty;\n\t\t\tfor(var i = 0; i < +csty.Span; ++i) cstys[cstys.length] = dup(csty);\n\t\t\tbreak;\n\n\t\tcase 'namedrange' /*case 'NamedRange'*/:\n\t\t\tif(Rn[1]==='/') break;\n\t\t\tif(!Workbook.Names) Workbook.Names = [];\n\t\t\tvar _NamedRange = parsexmltag(Rn[0]);\n\t\t\tvar _DefinedName = ({\n\t\t\t\tName: _NamedRange.Name,\n\t\t\t\tRef: rc_to_a1(_NamedRange.RefersTo.slice(1), {r:0, c:0})\n\t\t\t});\n\t\t\tif(Workbook.Sheets.length>0) _DefinedName.Sheet=Workbook.Sheets.length-1;\nWorkbook.Names.push(_DefinedName);\n\t\t\tbreak;\n\n\t\tcase 'namedcell' /*case 'NamedCell'*/: break;\n\t\tcase 'b' /*case 'B'*/: break;\n\t\tcase 'i' /*case 'I'*/: break;\n\t\tcase 'u' /*case 'U'*/: break;\n\t\tcase 's' /*case 'S'*/: break;\n\t\tcase 'em' /*case 'EM'*/: break;\n\t\tcase 'h2' /*case 'H2'*/: break;\n\t\tcase 'h3' /*case 'H3'*/: break;\n\t\tcase 'sub' /*case 'Sub'*/: break;\n\t\tcase 'sup' /*case 'Sup'*/: break;\n\t\tcase 'span' /*case 'Span'*/: break;\n\t\tcase 'alignment' /*case 'Alignment'*/:\n\t\t\tbreak;\n\t\tcase 'borders' /*case 'Borders'*/: break;\n\t\tcase 'border' /*case 'Border'*/: break;\n\t\tcase 'font' /*case 'Font'*/:\n\t\t\tif(Rn[0].slice(-2) === \"/>\") break;\n\t\t\telse if(Rn[1]===\"/\") ss += str.slice(fidx, Rn.index);\n\t\t\telse fidx = Rn.index + Rn[0].length;\n\t\t\tbreak;\n\t\tcase 'interior' /*case 'Interior'*/:\n\t\t\tif(!opts.cellStyles) break;\n\t\t\tstag.Interior = xlml_parsexmltag(Rn[0]);\n\t\t\tbreak;\n\t\tcase 'protection' /*case 'Protection'*/: break;\n\n\t\tcase 'author' /*case 'Author'*/:\n\t\tcase 'title' /*case 'Title'*/:\n\t\tcase 'description' /*case 'Description'*/:\n\t\tcase 'created' /*case 'Created'*/:\n\t\tcase 'keywords' /*case 'Keywords'*/:\n\t\tcase 'subject' /*case 'Subject'*/:\n\t\tcase 'category' /*case 'Category'*/:\n\t\tcase 'company' /*case 'Company'*/:\n\t\tcase 'lastauthor' /*case 'LastAuthor'*/:\n\t\tcase 'lastsaved' /*case 'LastSaved'*/:\n\t\tcase 'lastprinted' /*case 'LastPrinted'*/:\n\t\tcase 'version' /*case 'Version'*/:\n\t\tcase 'revision' /*case 'Revision'*/:\n\t\tcase 'totaltime' /*case 'TotalTime'*/:\n\t\tcase 'hyperlinkbase' /*case 'HyperlinkBase'*/:\n\t\tcase 'manager' /*case 'Manager'*/:\n\t\tcase 'contentstatus' /*case 'ContentStatus'*/:\n\t\tcase 'identifier' /*case 'Identifier'*/:\n\t\tcase 'language' /*case 'Language'*/:\n\t\tcase 'appname' /*case 'AppName'*/:\n\t\t\tif(Rn[0].slice(-2) === \"/>\") break;\n\t\t\telse if(Rn[1]===\"/\") xlml_set_prop(Props, raw_Rn3, str.slice(pidx, Rn.index));\n\t\t\telse pidx = Rn.index + Rn[0].length;\n\t\t\tbreak;\n\t\tcase 'paragraphs' /*case 'Paragraphs'*/: break;\n\n\t\tcase 'styles' /*case 'Styles'*/:\n\t\tcase 'workbook' /*case 'Workbook'*/:\n\t\t\tif(Rn[1]==='/'){if((tmp=state.pop())[0]!==Rn[3]) throw new Error(\"Bad state: \"+tmp.join(\"|\"));}\n\t\t\telse state.push([Rn[3], false]);\n\t\t\tbreak;\n\n\t\tcase 'comment' /*case 'Comment'*/:\n\t\t\tif(Rn[1]==='/'){\n\t\t\t\tif((tmp=state.pop())[0]!==Rn[3]) throw new Error(\"Bad state: \"+tmp.join(\"|\"));\n\t\t\t\txlml_clean_comment(comment);\n\t\t\t\tcomments.push(comment);\n\t\t\t} else {\n\t\t\t\tstate.push([Rn[3], false]);\n\t\t\t\ttmp = xlml_parsexmltag(Rn[0]);\n\t\t\t\tcomment = ({a:tmp.Author});\n\t\t\t}\n\t\t\tbreak;\n\n\t\tcase 'autofilter' /*case 'AutoFilter'*/:\n\t\t\tif(Rn[1]==='/'){if((tmp=state.pop())[0]!==Rn[3]) throw new Error(\"Bad state: \"+tmp.join(\"|\"));}\n\t\t\telse if(Rn[0].charAt(Rn[0].length-2) !== '/') {\n\t\t\t\tvar AutoFilter = xlml_parsexmltag(Rn[0]);\n\t\t\t\tcursheet['!autofilter'] = { ref:rc_to_a1(AutoFilter.Range).replace(/\\$/g,\"\") };\n\t\t\t\tstate.push([Rn[3], true]);\n\t\t\t}\n\t\t\tbreak;\n\n\t\tcase 'name' /*case 'Name'*/: break;\n\n\t\tcase 'datavalidation' /*case 'DataValidation'*/:\n\t\t\tif(Rn[1]==='/'){\n\t\t\t\tif((tmp=state.pop())[0]!==Rn[3]) throw new Error(\"Bad state: \"+tmp.join(\"|\"));\n\t\t\t} else {\n\t\t\t\tif(Rn[0].charAt(Rn[0].length-2) !== '/') state.push([Rn[3], true]);\n\t\t\t}\n\t\t\tbreak;\n\n\t\tcase 'pixelsperinch' /*case 'PixelsPerInch'*/:\n\t\t\tbreak;\n\t\tcase 'componentoptions' /*case 'ComponentOptions'*/:\n\t\tcase 'documentproperties' /*case 'DocumentProperties'*/:\n\t\tcase 'customdocumentproperties' /*case 'CustomDocumentProperties'*/:\n\t\tcase 'officedocumentsettings' /*case 'OfficeDocumentSettings'*/:\n\t\tcase 'pivottable' /*case 'PivotTable'*/:\n\t\tcase 'pivotcache' /*case 'PivotCache'*/:\n\t\tcase 'names' /*case 'Names'*/:\n\t\tcase 'mapinfo' /*case 'MapInfo'*/:\n\t\tcase 'pagebreaks' /*case 'PageBreaks'*/:\n\t\tcase 'querytable' /*case 'QueryTable'*/:\n\t\tcase 'sorting' /*case 'Sorting'*/:\n\t\tcase 'schema' /*case 'Schema'*/: //case 'data' /*case 'data'*/:\n\t\tcase 'conditionalformatting' /*case 'ConditionalFormatting'*/:\n\t\tcase 'smarttagtype' /*case 'SmartTagType'*/:\n\t\tcase 'smarttags' /*case 'SmartTags'*/:\n\t\tcase 'excelworkbook' /*case 'ExcelWorkbook'*/:\n\t\tcase 'workbookoptions' /*case 'WorkbookOptions'*/:\n\t\tcase 'worksheetoptions' /*case 'WorksheetOptions'*/:\n\t\t\tif(Rn[1]==='/'){if((tmp=state.pop())[0]!==Rn[3]) throw new Error(\"Bad state: \"+tmp.join(\"|\"));}\n\t\t\telse if(Rn[0].charAt(Rn[0].length-2) !== '/') state.push([Rn[3], true]);\n\t\t\tbreak;\n\n\t\tdefault:\n\t\t\t/* FODS file root is */\n\t\t\tif(state.length == 0 && Rn[3] == \"document\") return parse_fods(str, opts);\n\t\t\t/* UOS file root is */\n\t\t\tif(state.length == 0 && Rn[3] == \"uof\"/*\"UOF\"*/) return parse_fods(str, opts);\n\n\t\t\tvar seen = true;\n\t\t\tswitch(state[state.length-1][0]) {\n\t\t\t\t/* OfficeDocumentSettings */\n\t\t\t\tcase 'officedocumentsettings' /*case 'OfficeDocumentSettings'*/: switch(Rn[3]) {\n\t\t\t\t\tcase 'allowpng' /*case 'AllowPNG'*/: break;\n\t\t\t\t\tcase 'removepersonalinformation' /*case 'RemovePersonalInformation'*/: break;\n\t\t\t\t\tcase 'downloadcomponents' /*case 'DownloadComponents'*/: break;\n\t\t\t\t\tcase 'locationofcomponents' /*case 'LocationOfComponents'*/: break;\n\t\t\t\t\tcase 'colors' /*case 'Colors'*/: break;\n\t\t\t\t\tcase 'color' /*case 'Color'*/: break;\n\t\t\t\t\tcase 'index' /*case 'Index'*/: break;\n\t\t\t\t\tcase 'rgb' /*case 'RGB'*/: break;\n\t\t\t\t\tcase 'targetscreensize' /*case 'TargetScreenSize'*/: break;\n\t\t\t\t\tcase 'readonlyrecommended' /*case 'ReadOnlyRecommended'*/: break;\n\t\t\t\t\tdefault: seen = false;\n\t\t\t\t} break;\n\n\t\t\t\t/* ComponentOptions */\n\t\t\t\tcase 'componentoptions' /*case 'ComponentOptions'*/: switch(Rn[3]) {\n\t\t\t\t\tcase 'toolbar' /*case 'Toolbar'*/: break;\n\t\t\t\t\tcase 'hideofficelogo' /*case 'HideOfficeLogo'*/: break;\n\t\t\t\t\tcase 'spreadsheetautofit' /*case 'SpreadsheetAutoFit'*/: break;\n\t\t\t\t\tcase 'label' /*case 'Label'*/: break;\n\t\t\t\t\tcase 'caption' /*case 'Caption'*/: break;\n\t\t\t\t\tcase 'maxheight' /*case 'MaxHeight'*/: break;\n\t\t\t\t\tcase 'maxwidth' /*case 'MaxWidth'*/: break;\n\t\t\t\t\tcase 'nextsheetnumber' /*case 'NextSheetNumber'*/: break;\n\t\t\t\t\tdefault: seen = false;\n\t\t\t\t} break;\n\n\t\t\t\t/* ExcelWorkbook */\n\t\t\t\tcase 'excelworkbook' /*case 'ExcelWorkbook'*/: switch(Rn[3]) {\n\t\t\t\t\tcase 'date1904' /*case 'Date1904'*/:\nWorkbook.WBProps.date1904 = true;\n\t\t\t\t\t\tbreak;\n\t\t\t\t\tcase 'windowheight' /*case 'WindowHeight'*/: break;\n\t\t\t\t\tcase 'windowwidth' /*case 'WindowWidth'*/: break;\n\t\t\t\t\tcase 'windowtopx' /*case 'WindowTopX'*/: break;\n\t\t\t\t\tcase 'windowtopy' /*case 'WindowTopY'*/: break;\n\t\t\t\t\tcase 'tabratio' /*case 'TabRatio'*/: break;\n\t\t\t\t\tcase 'protectstructure' /*case 'ProtectStructure'*/: break;\n\t\t\t\t\tcase 'protectwindow' /*case 'ProtectWindow'*/: break;\n\t\t\t\t\tcase 'protectwindows' /*case 'ProtectWindows'*/: break;\n\t\t\t\t\tcase 'activesheet' /*case 'ActiveSheet'*/: break;\n\t\t\t\t\tcase 'displayinknotes' /*case 'DisplayInkNotes'*/: break;\n\t\t\t\t\tcase 'firstvisiblesheet' /*case 'FirstVisibleSheet'*/: break;\n\t\t\t\t\tcase 'supbook' /*case 'SupBook'*/: break;\n\t\t\t\t\tcase 'sheetname' /*case 'SheetName'*/: break;\n\t\t\t\t\tcase 'sheetindex' /*case 'SheetIndex'*/: break;\n\t\t\t\t\tcase 'sheetindexfirst' /*case 'SheetIndexFirst'*/: break;\n\t\t\t\t\tcase 'sheetindexlast' /*case 'SheetIndexLast'*/: break;\n\t\t\t\t\tcase 'dll' /*case 'Dll'*/: break;\n\t\t\t\t\tcase 'acceptlabelsinformulas' /*case 'AcceptLabelsInFormulas'*/: break;\n\t\t\t\t\tcase 'donotsavelinkvalues' /*case 'DoNotSaveLinkValues'*/: break;\n\t\t\t\t\tcase 'iteration' /*case 'Iteration'*/: break;\n\t\t\t\t\tcase 'maxiterations' /*case 'MaxIterations'*/: break;\n\t\t\t\t\tcase 'maxchange' /*case 'MaxChange'*/: break;\n\t\t\t\t\tcase 'path' /*case 'Path'*/: break;\n\t\t\t\t\tcase 'xct' /*case 'Xct'*/: break;\n\t\t\t\t\tcase 'count' /*case 'Count'*/: break;\n\t\t\t\t\tcase 'selectedsheets' /*case 'SelectedSheets'*/: break;\n\t\t\t\t\tcase 'calculation' /*case 'Calculation'*/: break;\n\t\t\t\t\tcase 'uncalced' /*case 'Uncalced'*/: break;\n\t\t\t\t\tcase 'startupprompt' /*case 'StartupPrompt'*/: break;\n\t\t\t\t\tcase 'crn' /*case 'Crn'*/: break;\n\t\t\t\t\tcase 'externname' /*case 'ExternName'*/: break;\n\t\t\t\t\tcase 'formula' /*case 'Formula'*/: break;\n\t\t\t\t\tcase 'colfirst' /*case 'ColFirst'*/: break;\n\t\t\t\t\tcase 'collast' /*case 'ColLast'*/: break;\n\t\t\t\t\tcase 'wantadvise' /*case 'WantAdvise'*/: break;\n\t\t\t\t\tcase 'boolean' /*case 'Boolean'*/: break;\n\t\t\t\t\tcase 'error' /*case 'Error'*/: break;\n\t\t\t\t\tcase 'text' /*case 'Text'*/: break;\n\t\t\t\t\tcase 'ole' /*case 'OLE'*/: break;\n\t\t\t\t\tcase 'noautorecover' /*case 'NoAutoRecover'*/: break;\n\t\t\t\t\tcase 'publishobjects' /*case 'PublishObjects'*/: break;\n\t\t\t\t\tcase 'donotcalculatebeforesave' /*case 'DoNotCalculateBeforeSave'*/: break;\n\t\t\t\t\tcase 'number' /*case 'Number'*/: break;\n\t\t\t\t\tcase 'refmoder1c1' /*case 'RefModeR1C1'*/: break;\n\t\t\t\t\tcase 'embedsavesmarttags' /*case 'EmbedSaveSmartTags'*/: break;\n\t\t\t\t\tdefault: seen = false;\n\t\t\t\t} break;\n\n\t\t\t\t/* WorkbookOptions */\n\t\t\t\tcase 'workbookoptions' /*case 'WorkbookOptions'*/: switch(Rn[3]) {\n\t\t\t\t\tcase 'owcversion' /*case 'OWCVersion'*/: break;\n\t\t\t\t\tcase 'height' /*case 'Height'*/: break;\n\t\t\t\t\tcase 'width' /*case 'Width'*/: break;\n\t\t\t\t\tdefault: seen = false;\n\t\t\t\t} break;\n\n\t\t\t\t/* WorksheetOptions */\n\t\t\t\tcase 'worksheetoptions' /*case 'WorksheetOptions'*/: switch(Rn[3]) {\n\t\t\t\t\tcase 'visible' /*case 'Visible'*/:\n\t\t\t\t\t\tif(Rn[0].slice(-2) === \"/>\"){/* empty */}\n\t\t\t\t\t\telse if(Rn[1]===\"/\") switch(str.slice(pidx, Rn.index)) {\n\t\t\t\t\t\t\tcase \"SheetHidden\": wsprops.Hidden = 1; break;\n\t\t\t\t\t\t\tcase \"SheetVeryHidden\": wsprops.Hidden = 2; break;\n\t\t\t\t\t\t}\n\t\t\t\t\t\telse pidx = Rn.index + Rn[0].length;\n\t\t\t\t\t\tbreak;\n\t\t\t\t\tcase 'header' /*case 'Header'*/:\n\t\t\t\t\t\tif(!cursheet['!margins']) default_margins(cursheet['!margins']={}, 'xlml');\n\t\t\t\t\t\tcursheet['!margins'].header = parsexmltag(Rn[0]).Margin;\n\t\t\t\t\t\tbreak;\n\t\t\t\t\tcase 'footer' /*case 'Footer'*/:\n\t\t\t\t\t\tif(!cursheet['!margins']) default_margins(cursheet['!margins']={}, 'xlml');\n\t\t\t\t\t\tcursheet['!margins'].footer = parsexmltag(Rn[0]).Margin;\n\t\t\t\t\t\tbreak;\n\t\t\t\t\tcase 'pagemargins' /*case 'PageMargins'*/:\n\t\t\t\t\t\tvar pagemargins = parsexmltag(Rn[0]);\n\t\t\t\t\t\tif(!cursheet['!margins']) default_margins(cursheet['!margins']={},'xlml');\n\t\t\t\t\t\tif(pagemargins.Top) cursheet['!margins'].top = pagemargins.Top;\n\t\t\t\t\t\tif(pagemargins.Left) cursheet['!margins'].left = pagemargins.Left;\n\t\t\t\t\t\tif(pagemargins.Right) cursheet['!margins'].right = pagemargins.Right;\n\t\t\t\t\t\tif(pagemargins.Bottom) cursheet['!margins'].bottom = pagemargins.Bottom;\n\t\t\t\t\t\tbreak;\n\t\t\t\t\tcase 'displayrighttoleft' /*case 'DisplayRightToLeft'*/:\n\t\t\t\t\t\tif(!Workbook.Views) Workbook.Views = [];\n\t\t\t\t\t\tif(!Workbook.Views[0]) Workbook.Views[0] = {};\n\t\t\t\t\t\tWorkbook.Views[0].RTL = true;\n\t\t\t\t\t\tbreak;\n\n\t\t\t\t\tcase 'freezepanes' /*case 'FreezePanes'*/: break;\n\t\t\t\t\tcase 'frozennosplit' /*case 'FrozenNoSplit'*/: break;\n\n\t\t\t\t\tcase 'splithorizontal' /*case 'SplitHorizontal'*/:\n\t\t\t\t\tcase 'splitvertical' /*case 'SplitVertical'*/:\n\t\t\t\t\t\tbreak;\n\n\t\t\t\t\tcase 'donotdisplaygridlines' /*case 'DoNotDisplayGridlines'*/:\n\t\t\t\t\t\tbreak;\n\n\t\t\t\t\tcase 'activerow' /*case 'ActiveRow'*/: break;\n\t\t\t\t\tcase 'activecol' /*case 'ActiveCol'*/: break;\n\t\t\t\t\tcase 'toprowbottompane' /*case 'TopRowBottomPane'*/: break;\n\t\t\t\t\tcase 'leftcolumnrightpane' /*case 'LeftColumnRightPane'*/: break;\n\n\t\t\t\t\tcase 'unsynced' /*case 'Unsynced'*/: break;\n\t\t\t\t\tcase 'print' /*case 'Print'*/: break;\n\t\t\t\t\tcase 'panes' /*case 'Panes'*/: break;\n\t\t\t\t\tcase 'scale' /*case 'Scale'*/: break;\n\t\t\t\t\tcase 'pane' /*case 'Pane'*/: break;\n\t\t\t\t\tcase 'number' /*case 'Number'*/: break;\n\t\t\t\t\tcase 'layout' /*case 'Layout'*/: break;\n\t\t\t\t\tcase 'pagesetup' /*case 'PageSetup'*/: break;\n\t\t\t\t\tcase 'selected' /*case 'Selected'*/: break;\n\t\t\t\t\tcase 'protectobjects' /*case 'ProtectObjects'*/: break;\n\t\t\t\t\tcase 'enableselection' /*case 'EnableSelection'*/: break;\n\t\t\t\t\tcase 'protectscenarios' /*case 'ProtectScenarios'*/: break;\n\t\t\t\t\tcase 'validprinterinfo' /*case 'ValidPrinterInfo'*/: break;\n\t\t\t\t\tcase 'horizontalresolution' /*case 'HorizontalResolution'*/: break;\n\t\t\t\t\tcase 'verticalresolution' /*case 'VerticalResolution'*/: break;\n\t\t\t\t\tcase 'numberofcopies' /*case 'NumberofCopies'*/: break;\n\t\t\t\t\tcase 'activepane' /*case 'ActivePane'*/: break;\n\t\t\t\t\tcase 'toprowvisible' /*case 'TopRowVisible'*/: break;\n\t\t\t\t\tcase 'leftcolumnvisible' /*case 'LeftColumnVisible'*/: break;\n\t\t\t\t\tcase 'fittopage' /*case 'FitToPage'*/: break;\n\t\t\t\t\tcase 'rangeselection' /*case 'RangeSelection'*/: break;\n\t\t\t\t\tcase 'papersizeindex' /*case 'PaperSizeIndex'*/: break;\n\t\t\t\t\tcase 'pagelayoutzoom' /*case 'PageLayoutZoom'*/: break;\n\t\t\t\t\tcase 'pagebreakzoom' /*case 'PageBreakZoom'*/: break;\n\t\t\t\t\tcase 'filteron' /*case 'FilterOn'*/: break;\n\t\t\t\t\tcase 'fitwidth' /*case 'FitWidth'*/: break;\n\t\t\t\t\tcase 'fitheight' /*case 'FitHeight'*/: break;\n\t\t\t\t\tcase 'commentslayout' /*case 'CommentsLayout'*/: break;\n\t\t\t\t\tcase 'zoom' /*case 'Zoom'*/: break;\n\t\t\t\t\tcase 'lefttoright' /*case 'LeftToRight'*/: break;\n\t\t\t\t\tcase 'gridlines' /*case 'Gridlines'*/: break;\n\t\t\t\t\tcase 'allowsort' /*case 'AllowSort'*/: break;\n\t\t\t\t\tcase 'allowfilter' /*case 'AllowFilter'*/: break;\n\t\t\t\t\tcase 'allowinsertrows' /*case 'AllowInsertRows'*/: break;\n\t\t\t\t\tcase 'allowdeleterows' /*case 'AllowDeleteRows'*/: break;\n\t\t\t\t\tcase 'allowinsertcols' /*case 'AllowInsertCols'*/: break;\n\t\t\t\t\tcase 'allowdeletecols' /*case 'AllowDeleteCols'*/: break;\n\t\t\t\t\tcase 'allowinserthyperlinks' /*case 'AllowInsertHyperlinks'*/: break;\n\t\t\t\t\tcase 'allowformatcells' /*case 'AllowFormatCells'*/: break;\n\t\t\t\t\tcase 'allowsizecols' /*case 'AllowSizeCols'*/: break;\n\t\t\t\t\tcase 'allowsizerows' /*case 'AllowSizeRows'*/: break;\n\t\t\t\t\tcase 'nosummaryrowsbelowdetail' /*case 'NoSummaryRowsBelowDetail'*/: break;\n\t\t\t\t\tcase 'tabcolorindex' /*case 'TabColorIndex'*/: break;\n\t\t\t\t\tcase 'donotdisplayheadings' /*case 'DoNotDisplayHeadings'*/: break;\n\t\t\t\t\tcase 'showpagelayoutzoom' /*case 'ShowPageLayoutZoom'*/: break;\n\t\t\t\t\tcase 'nosummarycolumnsrightdetail' /*case 'NoSummaryColumnsRightDetail'*/: break;\n\t\t\t\t\tcase 'blackandwhite' /*case 'BlackAndWhite'*/: break;\n\t\t\t\t\tcase 'donotdisplayzeros' /*case 'DoNotDisplayZeros'*/: break;\n\t\t\t\t\tcase 'displaypagebreak' /*case 'DisplayPageBreak'*/: break;\n\t\t\t\t\tcase 'rowcolheadings' /*case 'RowColHeadings'*/: break;\n\t\t\t\t\tcase 'donotdisplayoutline' /*case 'DoNotDisplayOutline'*/: break;\n\t\t\t\t\tcase 'noorientation' /*case 'NoOrientation'*/: break;\n\t\t\t\t\tcase 'allowusepivottables' /*case 'AllowUsePivotTables'*/: break;\n\t\t\t\t\tcase 'zeroheight' /*case 'ZeroHeight'*/: break;\n\t\t\t\t\tcase 'viewablerange' /*case 'ViewableRange'*/: break;\n\t\t\t\t\tcase 'selection' /*case 'Selection'*/: break;\n\t\t\t\t\tcase 'protectcontents' /*case 'ProtectContents'*/: break;\n\t\t\t\t\tdefault: seen = false;\n\t\t\t\t} break;\n\n\t\t\t\t/* PivotTable */\n\t\t\t\tcase 'pivottable' /*case 'PivotTable'*/: case 'pivotcache' /*case 'PivotCache'*/: switch(Rn[3]) {\n\t\t\t\t\tcase 'immediateitemsondrop' /*case 'ImmediateItemsOnDrop'*/: break;\n\t\t\t\t\tcase 'showpagemultipleitemlabel' /*case 'ShowPageMultipleItemLabel'*/: break;\n\t\t\t\t\tcase 'compactrowindent' /*case 'CompactRowIndent'*/: break;\n\t\t\t\t\tcase 'location' /*case 'Location'*/: break;\n\t\t\t\t\tcase 'pivotfield' /*case 'PivotField'*/: break;\n\t\t\t\t\tcase 'orientation' /*case 'Orientation'*/: break;\n\t\t\t\t\tcase 'layoutform' /*case 'LayoutForm'*/: break;\n\t\t\t\t\tcase 'layoutsubtotallocation' /*case 'LayoutSubtotalLocation'*/: break;\n\t\t\t\t\tcase 'layoutcompactrow' /*case 'LayoutCompactRow'*/: break;\n\t\t\t\t\tcase 'position' /*case 'Position'*/: break;\n\t\t\t\t\tcase 'pivotitem' /*case 'PivotItem'*/: break;\n\t\t\t\t\tcase 'datatype' /*case 'DataType'*/: break;\n\t\t\t\t\tcase 'datafield' /*case 'DataField'*/: break;\n\t\t\t\t\tcase 'sourcename' /*case 'SourceName'*/: break;\n\t\t\t\t\tcase 'parentfield' /*case 'ParentField'*/: break;\n\t\t\t\t\tcase 'ptlineitems' /*case 'PTLineItems'*/: break;\n\t\t\t\t\tcase 'ptlineitem' /*case 'PTLineItem'*/: break;\n\t\t\t\t\tcase 'countofsameitems' /*case 'CountOfSameItems'*/: break;\n\t\t\t\t\tcase 'item' /*case 'Item'*/: break;\n\t\t\t\t\tcase 'itemtype' /*case 'ItemType'*/: break;\n\t\t\t\t\tcase 'ptsource' /*case 'PTSource'*/: break;\n\t\t\t\t\tcase 'cacheindex' /*case 'CacheIndex'*/: break;\n\t\t\t\t\tcase 'consolidationreference' /*case 'ConsolidationReference'*/: break;\n\t\t\t\t\tcase 'filename' /*case 'FileName'*/: break;\n\t\t\t\t\tcase 'reference' /*case 'Reference'*/: break;\n\t\t\t\t\tcase 'nocolumngrand' /*case 'NoColumnGrand'*/: break;\n\t\t\t\t\tcase 'norowgrand' /*case 'NoRowGrand'*/: break;\n\t\t\t\t\tcase 'blanklineafteritems' /*case 'BlankLineAfterItems'*/: break;\n\t\t\t\t\tcase 'hidden' /*case 'Hidden'*/: break;\n\t\t\t\t\tcase 'subtotal' /*case 'Subtotal'*/: break;\n\t\t\t\t\tcase 'basefield' /*case 'BaseField'*/: break;\n\t\t\t\t\tcase 'mapchilditems' /*case 'MapChildItems'*/: break;\n\t\t\t\t\tcase 'function' /*case 'Function'*/: break;\n\t\t\t\t\tcase 'refreshonfileopen' /*case 'RefreshOnFileOpen'*/: break;\n\t\t\t\t\tcase 'printsettitles' /*case 'PrintSetTitles'*/: break;\n\t\t\t\t\tcase 'mergelabels' /*case 'MergeLabels'*/: break;\n\t\t\t\t\tcase 'defaultversion' /*case 'DefaultVersion'*/: break;\n\t\t\t\t\tcase 'refreshname' /*case 'RefreshName'*/: break;\n\t\t\t\t\tcase 'refreshdate' /*case 'RefreshDate'*/: break;\n\t\t\t\t\tcase 'refreshdatecopy' /*case 'RefreshDateCopy'*/: break;\n\t\t\t\t\tcase 'versionlastrefresh' /*case 'VersionLastRefresh'*/: break;\n\t\t\t\t\tcase 'versionlastupdate' /*case 'VersionLastUpdate'*/: break;\n\t\t\t\t\tcase 'versionupdateablemin' /*case 'VersionUpdateableMin'*/: break;\n\t\t\t\t\tcase 'versionrefreshablemin' /*case 'VersionRefreshableMin'*/: break;\n\t\t\t\t\tcase 'calculation' /*case 'Calculation'*/: break;\n\t\t\t\t\tdefault: seen = false;\n\t\t\t\t} break;\n\n\t\t\t\t/* PageBreaks */\n\t\t\t\tcase 'pagebreaks' /*case 'PageBreaks'*/: switch(Rn[3]) {\n\t\t\t\t\tcase 'colbreaks' /*case 'ColBreaks'*/: break;\n\t\t\t\t\tcase 'colbreak' /*case 'ColBreak'*/: break;\n\t\t\t\t\tcase 'rowbreaks' /*case 'RowBreaks'*/: break;\n\t\t\t\t\tcase 'rowbreak' /*case 'RowBreak'*/: break;\n\t\t\t\t\tcase 'colstart' /*case 'ColStart'*/: break;\n\t\t\t\t\tcase 'colend' /*case 'ColEnd'*/: break;\n\t\t\t\t\tcase 'rowend' /*case 'RowEnd'*/: break;\n\t\t\t\t\tdefault: seen = false;\n\t\t\t\t} break;\n\n\t\t\t\t/* AutoFilter */\n\t\t\t\tcase 'autofilter' /*case 'AutoFilter'*/: switch(Rn[3]) {\n\t\t\t\t\tcase 'autofiltercolumn' /*case 'AutoFilterColumn'*/: break;\n\t\t\t\t\tcase 'autofiltercondition' /*case 'AutoFilterCondition'*/: break;\n\t\t\t\t\tcase 'autofilterand' /*case 'AutoFilterAnd'*/: break;\n\t\t\t\t\tcase 'autofilteror' /*case 'AutoFilterOr'*/: break;\n\t\t\t\t\tdefault: seen = false;\n\t\t\t\t} break;\n\n\t\t\t\t/* QueryTable */\n\t\t\t\tcase 'querytable' /*case 'QueryTable'*/: switch(Rn[3]) {\n\t\t\t\t\tcase 'id' /*case 'Id'*/: break;\n\t\t\t\t\tcase 'autoformatfont' /*case 'AutoFormatFont'*/: break;\n\t\t\t\t\tcase 'autoformatpattern' /*case 'AutoFormatPattern'*/: break;\n\t\t\t\t\tcase 'querysource' /*case 'QuerySource'*/: break;\n\t\t\t\t\tcase 'querytype' /*case 'QueryType'*/: break;\n\t\t\t\t\tcase 'enableredirections' /*case 'EnableRedirections'*/: break;\n\t\t\t\t\tcase 'refreshedinxl9' /*case 'RefreshedInXl9'*/: break;\n\t\t\t\t\tcase 'urlstring' /*case 'URLString'*/: break;\n\t\t\t\t\tcase 'htmltables' /*case 'HTMLTables'*/: break;\n\t\t\t\t\tcase 'connection' /*case 'Connection'*/: break;\n\t\t\t\t\tcase 'commandtext' /*case 'CommandText'*/: break;\n\t\t\t\t\tcase 'refreshinfo' /*case 'RefreshInfo'*/: break;\n\t\t\t\t\tcase 'notitles' /*case 'NoTitles'*/: break;\n\t\t\t\t\tcase 'nextid' /*case 'NextId'*/: break;\n\t\t\t\t\tcase 'columninfo' /*case 'ColumnInfo'*/: break;\n\t\t\t\t\tcase 'overwritecells' /*case 'OverwriteCells'*/: break;\n\t\t\t\t\tcase 'donotpromptforfile' /*case 'DoNotPromptForFile'*/: break;\n\t\t\t\t\tcase 'textwizardsettings' /*case 'TextWizardSettings'*/: break;\n\t\t\t\t\tcase 'source' /*case 'Source'*/: break;\n\t\t\t\t\tcase 'number' /*case 'Number'*/: break;\n\t\t\t\t\tcase 'decimal' /*case 'Decimal'*/: break;\n\t\t\t\t\tcase 'thousandseparator' /*case 'ThousandSeparator'*/: break;\n\t\t\t\t\tcase 'trailingminusnumbers' /*case 'TrailingMinusNumbers'*/: break;\n\t\t\t\t\tcase 'formatsettings' /*case 'FormatSettings'*/: break;\n\t\t\t\t\tcase 'fieldtype' /*case 'FieldType'*/: break;\n\t\t\t\t\tcase 'delimiters' /*case 'Delimiters'*/: break;\n\t\t\t\t\tcase 'tab' /*case 'Tab'*/: break;\n\t\t\t\t\tcase 'comma' /*case 'Comma'*/: break;\n\t\t\t\t\tcase 'autoformatname' /*case 'AutoFormatName'*/: break;\n\t\t\t\t\tcase 'versionlastedit' /*case 'VersionLastEdit'*/: break;\n\t\t\t\t\tcase 'versionlastrefresh' /*case 'VersionLastRefresh'*/: break;\n\t\t\t\t\tdefault: seen = false;\n\t\t\t\t} break;\n\n\t\t\t\tcase 'datavalidation' /*case 'DataValidation'*/:\n\t\t\t\tswitch(Rn[3]) {\n\t\t\t\t\tcase 'range' /*case 'Range'*/: break;\n\n\t\t\t\t\tcase 'type' /*case 'Type'*/: break;\n\t\t\t\t\tcase 'min' /*case 'Min'*/: break;\n\t\t\t\t\tcase 'max' /*case 'Max'*/: break;\n\t\t\t\t\tcase 'sort' /*case 'Sort'*/: break;\n\t\t\t\t\tcase 'descending' /*case 'Descending'*/: break;\n\t\t\t\t\tcase 'order' /*case 'Order'*/: break;\n\t\t\t\t\tcase 'casesensitive' /*case 'CaseSensitive'*/: break;\n\t\t\t\t\tcase 'value' /*case 'Value'*/: break;\n\t\t\t\t\tcase 'errorstyle' /*case 'ErrorStyle'*/: break;\n\t\t\t\t\tcase 'errormessage' /*case 'ErrorMessage'*/: break;\n\t\t\t\t\tcase 'errortitle' /*case 'ErrorTitle'*/: break;\n\t\t\t\t\tcase 'inputmessage' /*case 'InputMessage'*/: break;\n\t\t\t\t\tcase 'inputtitle' /*case 'InputTitle'*/: break;\n\t\t\t\t\tcase 'combohide' /*case 'ComboHide'*/: break;\n\t\t\t\t\tcase 'inputhide' /*case 'InputHide'*/: break;\n\t\t\t\t\tcase 'condition' /*case 'Condition'*/: break;\n\t\t\t\t\tcase 'qualifier' /*case 'Qualifier'*/: break;\n\t\t\t\t\tcase 'useblank' /*case 'UseBlank'*/: break;\n\t\t\t\t\tcase 'value1' /*case 'Value1'*/: break;\n\t\t\t\t\tcase 'value2' /*case 'Value2'*/: break;\n\t\t\t\t\tcase 'format' /*case 'Format'*/: break;\n\n\t\t\t\t\tcase 'cellrangelist' /*case 'CellRangeList'*/: break;\n\t\t\t\t\tdefault: seen = false;\n\t\t\t\t} break;\n\n\t\t\t\tcase 'sorting' /*case 'Sorting'*/:\n\t\t\t\tcase 'conditionalformatting' /*case 'ConditionalFormatting'*/:\n\t\t\t\tswitch(Rn[3]) {\n\t\t\t\t\tcase 'range' /*case 'Range'*/: break;\n\t\t\t\t\tcase 'type' /*case 'Type'*/: break;\n\t\t\t\t\tcase 'min' /*case 'Min'*/: break;\n\t\t\t\t\tcase 'max' /*case 'Max'*/: break;\n\t\t\t\t\tcase 'sort' /*case 'Sort'*/: break;\n\t\t\t\t\tcase 'descending' /*case 'Descending'*/: break;\n\t\t\t\t\tcase 'order' /*case 'Order'*/: break;\n\t\t\t\t\tcase 'casesensitive' /*case 'CaseSensitive'*/: break;\n\t\t\t\t\tcase 'value' /*case 'Value'*/: break;\n\t\t\t\t\tcase 'errorstyle' /*case 'ErrorStyle'*/: break;\n\t\t\t\t\tcase 'errormessage' /*case 'ErrorMessage'*/: break;\n\t\t\t\t\tcase 'errortitle' /*case 'ErrorTitle'*/: break;\n\t\t\t\t\tcase 'cellrangelist' /*case 'CellRangeList'*/: break;\n\t\t\t\t\tcase 'inputmessage' /*case 'InputMessage'*/: break;\n\t\t\t\t\tcase 'inputtitle' /*case 'InputTitle'*/: break;\n\t\t\t\t\tcase 'combohide' /*case 'ComboHide'*/: break;\n\t\t\t\t\tcase 'inputhide' /*case 'InputHide'*/: break;\n\t\t\t\t\tcase 'condition' /*case 'Condition'*/: break;\n\t\t\t\t\tcase 'qualifier' /*case 'Qualifier'*/: break;\n\t\t\t\t\tcase 'useblank' /*case 'UseBlank'*/: break;\n\t\t\t\t\tcase 'value1' /*case 'Value1'*/: break;\n\t\t\t\t\tcase 'value2' /*case 'Value2'*/: break;\n\t\t\t\t\tcase 'format' /*case 'Format'*/: break;\n\t\t\t\t\tdefault: seen = false;\n\t\t\t\t} break;\n\n\t\t\t\t/* MapInfo (schema) */\n\t\t\t\tcase 'mapinfo' /*case 'MapInfo'*/: case 'schema' /*case 'Schema'*/: case 'data' /*case 'data'*/: switch(Rn[3]) {\n\t\t\t\t\tcase 'map' /*case 'Map'*/: break;\n\t\t\t\t\tcase 'entry' /*case 'Entry'*/: break;\n\t\t\t\t\tcase 'range' /*case 'Range'*/: break;\n\t\t\t\t\tcase 'xpath' /*case 'XPath'*/: break;\n\t\t\t\t\tcase 'field' /*case 'Field'*/: break;\n\t\t\t\t\tcase 'xsdtype' /*case 'XSDType'*/: break;\n\t\t\t\t\tcase 'filteron' /*case 'FilterOn'*/: break;\n\t\t\t\t\tcase 'aggregate' /*case 'Aggregate'*/: break;\n\t\t\t\t\tcase 'elementtype' /*case 'ElementType'*/: break;\n\t\t\t\t\tcase 'attributetype' /*case 'AttributeType'*/: break;\n\t\t\t\t/* These are from xsd (XML Schema Definition) */\n\t\t\t\t\tcase 'schema' /*case 'schema'*/:\n\t\t\t\t\tcase 'element' /*case 'element'*/:\n\t\t\t\t\tcase 'complextype' /*case 'complexType'*/:\n\t\t\t\t\tcase 'datatype' /*case 'datatype'*/:\n\t\t\t\t\tcase 'all' /*case 'all'*/:\n\t\t\t\t\tcase 'attribute' /*case 'attribute'*/:\n\t\t\t\t\tcase 'extends' /*case 'extends'*/: break;\n\n\t\t\t\t\tcase 'row' /*case 'row'*/: break;\n\t\t\t\t\tdefault: seen = false;\n\t\t\t\t} break;\n\n\t\t\t\t/* SmartTags (can be anything) */\n\t\t\t\tcase 'smarttags' /*case 'SmartTags'*/: break;\n\n\t\t\t\tdefault: seen = false; break;\n\t\t\t}\n\t\t\tif(seen) break;\n\t\t\t/* CustomDocumentProperties */\n\t\t\tif(Rn[3].match(/!\\[CDATA/)) break;\n\t\t\tif(!state[state.length-1][1]) throw 'Unrecognized tag: ' + Rn[3] + \"|\" + state.join(\"|\");\n\t\t\tif(state[state.length-1][0]===/*'CustomDocumentProperties'*/'customdocumentproperties') {\n\t\t\t\tif(Rn[0].slice(-2) === \"/>\") break;\n\t\t\t\telse if(Rn[1]===\"/\") xlml_set_custprop(Custprops, raw_Rn3, cp, str.slice(pidx, Rn.index));\n\t\t\t\telse { cp = Rn; pidx = Rn.index + Rn[0].length; }\n\t\t\t\tbreak;\n\t\t\t}\n\t\t\tif(opts.WTF) throw 'Unrecognized tag: ' + Rn[3] + \"|\" + state.join(\"|\");\n\t}\n\tvar out = ({});\n\tif(!opts.bookSheets && !opts.bookProps) out.Sheets = sheets;\n\tout.SheetNames = sheetnames;\n\tout.Workbook = Workbook;\n\tout.SSF = SSF.get_table();\n\tout.Props = Props;\n\tout.Custprops = Custprops;\n\treturn out;\n}\n\nfunction parse_xlml(data, opts) {\n\tfix_read_opts(opts=opts||{});\n\tswitch(opts.type||\"base64\") {\n\t\tcase \"base64\": return parse_xlml_xml(Base64.decode(data), opts);\n\t\tcase \"binary\": case \"buffer\": case \"file\": return parse_xlml_xml(data, opts);\n\t\tcase \"array\": return parse_xlml_xml(a2s(data), opts);\n\t}\n}\n\n/* TODO */\nfunction write_props_xlml(wb, opts) {\n\tvar o = [];\n\t/* DocumentProperties */\n\tif(wb.Props) o.push(xlml_write_docprops(wb.Props, opts));\n\t/* CustomDocumentProperties */\n\tif(wb.Custprops) o.push(xlml_write_custprops(wb.Props, wb.Custprops, opts));\n\treturn o.join(\"\");\n}\n/* TODO */\nfunction write_wb_xlml() {\n\t/* OfficeDocumentSettings */\n\t/* ExcelWorkbook */\n\treturn \"\";\n}\n/* TODO */\nfunction write_sty_xlml(wb, opts) {\n\t/* Styles */\n\tvar styles = [''];\n\topts.cellXfs.forEach(function(xf, id) {\n\t\tvar payload = [];\n\t\tpayload.push(writextag('NumberFormat', null, {\"ss:Format\": escapexml(SSF._table[xf.numFmtId])}));\n\n\t\tvar o = {\"ss:ID\": \"s\" + (21+id)};\n\t\tstyles.push(writextag('Style', payload.join(\"\"), o));\n\t});\n\treturn writextag(\"Styles\", styles.join(\"\"));\n}\nfunction write_name_xlml(n) { return writextag(\"NamedRange\", null, {\"ss:Name\": n.Name, \"ss:RefersTo\":\"=\" + a1_to_rc(n.Ref, {r:0,c:0})}); }\nfunction write_names_xlml(wb) {\n\tif(!((wb||{}).Workbook||{}).Names) return \"\";\nvar names = wb.Workbook.Names;\n\tvar out = [];\n\tfor(var i = 0; i < names.length; ++i) {\n\t\tvar n = names[i];\n\t\tif(n.Sheet != null) continue;\n\t\tif(n.Name.match(/^_xlfn\\./)) continue;\n\t\tout.push(write_name_xlml(n));\n\t}\n\treturn writextag(\"Names\", out.join(\"\"));\n}\nfunction write_ws_xlml_names(ws, opts, idx, wb) {\n\tif(!ws) return \"\";\n\tif(!((wb||{}).Workbook||{}).Names) return \"\";\nvar names = wb.Workbook.Names;\n\tvar out = [];\n\tfor(var i = 0; i < names.length; ++i) {\n\t\tvar n = names[i];\n\t\tif(n.Sheet != idx) continue;\n\t\t/*switch(n.Name) {\n\t\t\tcase \"_\": continue;\n\t\t}*/\n\t\tif(n.Name.match(/^_xlfn\\./)) continue;\n\t\tout.push(write_name_xlml(n));\n\t}\n\treturn out.join(\"\");\n}\n/* WorksheetOptions */\nfunction write_ws_xlml_wsopts(ws, opts, idx, wb) {\n\tif(!ws) return \"\";\n\tvar o = [];\n\t/* NOTE: spec technically allows any order, but stick with implied order */\n\n\t/* FitToPage */\n\t/* DoNotDisplayColHeaders */\n\t/* DoNotDisplayRowHeaders */\n\t/* ViewableRange */\n\t/* Selection */\n\t/* GridlineColor */\n\t/* Name */\n\t/* ExcelWorksheetType */\n\t/* IntlMacro */\n\t/* Unsynced */\n\t/* Selected */\n\t/* CodeName */\n\n\tif(ws['!margins']) {\n\t\to.push(\"\");\n\t\tif(ws['!margins'].header) o.push(writextag(\"Header\", null, {'x:Margin':ws['!margins'].header}));\n\t\tif(ws['!margins'].footer) o.push(writextag(\"Footer\", null, {'x:Margin':ws['!margins'].footer}));\n\t\to.push(writextag(\"PageMargins\", null, {\n\t\t\t'x:Bottom': ws['!margins'].bottom || \"0.75\",\n\t\t\t'x:Left': ws['!margins'].left || \"0.7\",\n\t\t\t'x:Right': ws['!margins'].right || \"0.7\",\n\t\t\t'x:Top': ws['!margins'].top || \"0.75\"\n\t\t}));\n\t\to.push(\"\");\n\t}\n\n\t/* PageSetup */\n\t/* DisplayPageBreak */\n\t/* TransitionExpressionEvaluation */\n\t/* TransitionFormulaEntry */\n\t/* Print */\n\t/* Zoom */\n\t/* PageLayoutZoom */\n\t/* PageBreakZoom */\n\t/* ShowPageBreakZoom */\n\t/* DefaultRowHeight */\n\t/* DefaultColumnWidth */\n\t/* StandardWidth */\n\n\tif(wb && wb.Workbook && wb.Workbook.Sheets && wb.Workbook.Sheets[idx]) {\n\t\t/* Visible */\n\t\tif(wb.Workbook.Sheets[idx].Hidden) o.push(writextag(\"Visible\", (wb.Workbook.Sheets[idx].Hidden == 1 ? \"SheetHidden\" : \"SheetVeryHidden\"), {}));\n\t\telse {\n\t\t\t/* Selected */\n\t\t\tfor(var i = 0; i < idx; ++i) if(wb.Workbook.Sheets[i] && !wb.Workbook.Sheets[i].Hidden) break;\n\t\t\tif(i == idx) o.push(\"\");\n\t\t}\n\t}\n\n\t/* LeftColumnVisible */\n\n\tif(((((wb||{}).Workbook||{}).Views||[])[0]||{}).RTL) o.push(\"\");\n\n\t/* GridlineColorIndex */\n\t/* DisplayFormulas */\n\t/* DoNotDisplayGridlines */\n\t/* DoNotDisplayHeadings */\n\t/* DoNotDisplayOutline */\n\t/* ApplyAutomaticOutlineStyles */\n\t/* NoSummaryRowsBelowDetail */\n\t/* NoSummaryColumnsRightDetail */\n\t/* DoNotDisplayZeros */\n\t/* ActiveRow */\n\t/* ActiveColumn */\n\t/* FilterOn */\n\t/* RangeSelection */\n\t/* TopRowVisible */\n\t/* TopRowBottomPane */\n\t/* LeftColumnRightPane */\n\t/* ActivePane */\n\t/* SplitHorizontal */\n\t/* SplitVertical */\n\t/* FreezePanes */\n\t/* FrozenNoSplit */\n\t/* TabColorIndex */\n\t/* Panes */\n\n\t/* NOTE: Password not supported in XLML Format */\n\tif(ws['!protect']) {\n\t\to.push(writetag(\"ProtectContents\", \"True\"));\n\t\tif(ws['!protect'].objects) o.push(writetag(\"ProtectObjects\", \"True\"));\n\t\tif(ws['!protect'].scenarios) o.push(writetag(\"ProtectScenarios\", \"True\"));\n\t\tif(ws['!protect'].selectLockedCells != null && !ws['!protect'].selectLockedCells) o.push(writetag(\"EnableSelection\", \"NoSelection\"));\n\t\telse if(ws['!protect'].selectUnlockedCells != null && !ws['!protect'].selectUnlockedCells) o.push(writetag(\"EnableSelection\", \"UnlockedCells\"));\n\t[\n\t\t[ \"formatCells\", \"AllowFormatCells\" ],\n\t\t[ \"formatColumns\", \"AllowSizeCols\" ],\n\t\t[ \"formatRows\", \"AllowSizeRows\" ],\n\t\t[ \"insertColumns\", \"AllowInsertCols\" ],\n\t\t[ \"insertRows\", \"AllowInsertRows\" ],\n\t\t[ \"insertHyperlinks\", \"AllowInsertHyperlinks\" ],\n\t\t[ \"deleteColumns\", \"AllowDeleteCols\" ],\n\t\t[ \"deleteRows\", \"AllowDeleteRows\" ],\n\t\t[ \"sort\", \"AllowSort\" ],\n\t\t[ \"autoFilter\", \"AllowFilter\" ],\n\t\t[ \"pivotTables\", \"AllowUsePivotTables\" ]\n\t].forEach(function(x) { if(ws['!protect'][x[0]]) o.push(\"<\"+x[1]+\"/>\"); });\n\t}\n\n\tif(o.length == 0) return \"\";\n\treturn writextag(\"WorksheetOptions\", o.join(\"\"), {xmlns:XLMLNS.x});\n}\nfunction write_ws_xlml_comment(comments) {\n\treturn comments.map(function(c) {\n\t\t// TODO: formatted text\n\t\tvar t = xlml_unfixstr(c.t||\"\");\n\t\tvar d =writextag(\"ss:Data\", t, {\"xmlns\":\"http://www.w3.org/TR/REC-html40\"});\n\t\treturn writextag(\"Comment\", d, {\"ss:Author\":c.a});\n\t}).join(\"\");\n}\nfunction write_ws_xlml_cell(cell, ref, ws, opts, idx, wb, addr){\n\tif(!cell || (cell.v == undefined && cell.f == undefined)) return \"\";\n\n\tvar attr = {};\n\tif(cell.f) attr[\"ss:Formula\"] = \"=\" + escapexml(a1_to_rc(cell.f, addr));\n\tif(cell.F && cell.F.slice(0, ref.length) == ref) {\n\t\tvar end = decode_cell(cell.F.slice(ref.length + 1));\n\t\tattr[\"ss:ArrayRange\"] = \"RC:R\" + (end.r == addr.r ? \"\" : \"[\" + (end.r - addr.r) + \"]\") + \"C\" + (end.c == addr.c ? \"\" : \"[\" + (end.c - addr.c) + \"]\");\n\t}\n\n\tif(cell.l && cell.l.Target) {\n\t\tattr[\"ss:HRef\"] = escapexml(cell.l.Target);\n\t\tif(cell.l.Tooltip) attr[\"x:HRefScreenTip\"] = escapexml(cell.l.Tooltip);\n\t}\n\n\tif(ws['!merges']) {\n\t\tvar marr = ws['!merges'];\n\t\tfor(var mi = 0; mi != marr.length; ++mi) {\n\t\t\tif(marr[mi].s.c != addr.c || marr[mi].s.r != addr.r) continue;\n\t\t\tif(marr[mi].e.c > marr[mi].s.c) attr['ss:MergeAcross'] = marr[mi].e.c - marr[mi].s.c;\n\t\t\tif(marr[mi].e.r > marr[mi].s.r) attr['ss:MergeDown'] = marr[mi].e.r - marr[mi].s.r;\n\t\t}\n\t}\n\n\tvar t = \"\", p = \"\";\n\tswitch(cell.t) {\n\t\tcase 'z': if(!opts.sheetStubs) return \"\"; break;\n\t\tcase 'n': t = 'Number'; p = String(cell.v); break;\n\t\tcase 'b': t = 'Boolean'; p = (cell.v ? \"1\" : \"0\"); break;\n\t\tcase 'e': t = 'Error'; p = BErr[cell.v]; break;\n\t\tcase 'd': t = 'DateTime'; p = new Date(cell.v).toISOString(); if(cell.z == null) cell.z = cell.z || SSF._table[14]; break;\n\t\tcase 's': t = 'String'; p = escapexlml(cell.v||\"\"); break;\n\t}\n\t/* TODO: cell style */\n\tvar os = get_cell_style(opts.cellXfs, cell, opts);\n\tattr[\"ss:StyleID\"] = \"s\" + (21+os);\n\tattr[\"ss:Index\"] = addr.c + 1;\n\tvar _v = (cell.v != null ? p : \"\");\n\tvar m = cell.t == 'z' ? \"\" : ('' + _v + '');\n\n\tif((cell.c||[]).length > 0) m += write_ws_xlml_comment(cell.c);\n\n\treturn writextag(\"Cell\", m, attr);\n}\nfunction write_ws_xlml_row(R, row) {\n\tvar o = '';\n}\n/* TODO */\nfunction write_ws_xlml_table(ws, opts, idx, wb) {\n\tif(!ws['!ref']) return \"\";\n\tvar range = safe_decode_range(ws['!ref']);\n\tvar marr = ws['!merges'] || [], mi = 0;\n\tvar o = [];\n\tif(ws['!cols']) ws['!cols'].forEach(function(n, i) {\n\t\tprocess_col(n);\n\t\tvar w = !!n.width;\n\t\tvar p = col_obj_w(i, n);\n\t\tvar k = {\"ss:Index\":i+1};\n\t\tif(w) k['ss:Width'] = width2px(p.width);\n\t\tif(n.hidden) k['ss:Hidden']=\"1\";\n\t\to.push(writextag(\"Column\",null,k));\n\t});\n\tvar dense = Array.isArray(ws);\n\tfor(var R = range.s.r; R <= range.e.r; ++R) {\n\t\tvar row = [write_ws_xlml_row(R, (ws['!rows']||[])[R])];\n\t\tfor(var C = range.s.c; C <= range.e.c; ++C) {\n\t\t\tvar skip = false;\n\t\t\tfor(mi = 0; mi != marr.length; ++mi) {\n\t\t\t\tif(marr[mi].s.c > C) continue;\n\t\t\t\tif(marr[mi].s.r > R) continue;\n\t\t\t\tif(marr[mi].e.c < C) continue;\n\t\t\t\tif(marr[mi].e.r < R) continue;\n\t\t\t\tif(marr[mi].s.c != C || marr[mi].s.r != R) skip = true;\n\t\t\t\tbreak;\n\t\t\t}\n\t\t\tif(skip) continue;\n\t\t\tvar addr = {r:R,c:C};\n\t\t\tvar ref = encode_cell(addr), cell = dense ? (ws[R]||[])[C] : ws[ref];\n\t\t\trow.push(write_ws_xlml_cell(cell, ref, ws, opts, idx, wb, addr));\n\t\t}\n\t\trow.push(\"\");\n\t\tif(row.length > 2) o.push(row.join(\"\"));\n\t}\n\treturn o.join(\"\");\n}\nfunction write_ws_xlml(idx, opts, wb) {\n\tvar o = [];\n\tvar s = wb.SheetNames[idx];\n\tvar ws = wb.Sheets[s];\n\n\tvar t = ws ? write_ws_xlml_names(ws, opts, idx, wb) : \"\";\n\tif(t.length > 0) o.push(\"\" + t + \"\");\n\n\t/* Table */\n\tt = ws ? write_ws_xlml_table(ws, opts, idx, wb) : \"\";\n\tif(t.length > 0) o.push(\"\" + t + \"
\");\n\n\t/* WorksheetOptions */\n\to.push(write_ws_xlml_wsopts(ws, opts, idx, wb));\n\n\treturn o.join(\"\");\n}\nfunction write_xlml(wb, opts) {\n\tif(!opts) opts = {};\n\tif(!wb.SSF) wb.SSF = SSF.get_table();\n\tif(wb.SSF) {\n\t\tmake_ssf(SSF); SSF.load_table(wb.SSF);\n\t\t// $FlowIgnore\n\t\topts.revssf = evert_num(wb.SSF); opts.revssf[wb.SSF[65535]] = 0;\n\t\topts.ssf = wb.SSF;\n\t\topts.cellXfs = [];\n\t\tget_cell_style(opts.cellXfs, {}, {revssf:{\"General\":0}});\n\t}\n\tvar d = [];\n\td.push(write_props_xlml(wb, opts));\n\td.push(write_wb_xlml(wb, opts));\n\td.push(\"\");\n\td.push(\"\");\n\tfor(var i = 0; i < wb.SheetNames.length; ++i)\n\t\td.push(writextag(\"Worksheet\", write_ws_xlml(i, opts, wb), {\"ss:Name\":escapexml(wb.SheetNames[i])}));\n\td[2] = write_sty_xlml(wb, opts);\n\td[3] = write_names_xlml(wb, opts);\n\treturn XML_HEADER + writextag(\"Workbook\", d.join(\"\"), {\n\t\t'xmlns': XLMLNS.ss,\n\t\t'xmlns:o': XLMLNS.o,\n\t\t'xmlns:x': XLMLNS.x,\n\t\t'xmlns:ss': XLMLNS.ss,\n\t\t'xmlns:dt': XLMLNS.dt,\n\t\t'xmlns:html': XLMLNS.html\n\t});\n}\n/* [MS-OLEDS] 2.3.8 CompObjStream */\nfunction parse_compobj(obj) {\n\tvar v = {};\n\tvar o = obj.content;\n/* [MS-OLEDS] 2.3.7 CompObjHeader -- All fields MUST be ignored */\n\to.l = 28;\n\n\tv.AnsiUserType = o.read_shift(0, \"lpstr-ansi\");\n\tv.AnsiClipboardFormat = parse_ClipboardFormatOrAnsiString(o);\n\n\tif(o.length - o.l <= 4) return v;\n\n\tvar m = o.read_shift(4);\n\tif(m == 0 || m > 40) return v;\n\to.l-=4; v.Reserved1 = o.read_shift(0, \"lpstr-ansi\");\n\n\tif(o.length - o.l <= 4) return v;\n\tm = o.read_shift(4);\n\tif(m !== 0x71b239f4) return v;\n\tv.UnicodeClipboardFormat = parse_ClipboardFormatOrUnicodeString(o);\n\n\tm = o.read_shift(4);\n\tif(m == 0 || m > 40) return v;\n\to.l-=4; v.Reserved2 = o.read_shift(0, \"lpwstr\");\n}\n\n/*\n\tContinue logic for:\n\t- 2.4.58 Continue\n\t- 2.4.59 ContinueBigName\n\t- 2.4.60 ContinueFrt\n\t- 2.4.61 ContinueFrt11\n\t- 2.4.62 ContinueFrt12\n*/\nfunction slurp(R, blob, length, opts) {\n\tvar l = length;\n\tvar bufs = [];\n\tvar d = blob.slice(blob.l,blob.l+l);\n\tif(opts && opts.enc && opts.enc.insitu) switch(R.n) {\n\tcase 'BOF': case 'FilePass': case 'FileLock': case 'InterfaceHdr': case 'RRDInfo': case 'RRDHead': case 'UsrExcl': break;\n\tdefault:\n\t\tif(d.length === 0) break;\n\t\topts.enc.insitu(d);\n\t}\n\tbufs.push(d);\n\tblob.l += l;\n\tvar next = (XLSRecordEnum[__readUInt16LE(blob,blob.l)]);\n\tvar start = 0;\n\twhile(next != null && next.n.slice(0,8) === 'Continue') {\n\t\tl = __readUInt16LE(blob,blob.l+2);\n\t\tstart = blob.l + 4;\n\t\tif(next.n == 'ContinueFrt') start += 4;\n\t\telse if(next.n.slice(0,11) == 'ContinueFrt') start += 12;\n\t\tbufs.push(blob.slice(start,blob.l+4+l));\n\t\tblob.l += 4+l;\n\t\tnext = (XLSRecordEnum[__readUInt16LE(blob, blob.l)]);\n\t}\n\tvar b = (bconcat(bufs));\n\tprep_blob(b, 0);\n\tvar ll = 0; b.lens = [];\n\tfor(var j = 0; j < bufs.length; ++j) { b.lens.push(ll); ll += bufs[j].length; }\n\treturn R.f(b, b.length, opts);\n}\n\nfunction safe_format_xf(p, opts, date1904) {\n\tif(p.t === 'z') return;\n\tif(!p.XF) return;\n\tvar fmtid = 0;\n\ttry {\n\t\tfmtid = p.z || p.XF.numFmtId || 0;\n\t\tif(opts.cellNF) p.z = SSF._table[fmtid];\n\t} catch(e) { if(opts.WTF) throw e; }\n\tif(!opts || opts.cellText !== false) try {\n\t\tif(p.t === 'e') { p.w = p.w || BErr[p.v]; }\n\t\telse if(fmtid === 0 || fmtid == \"General\") {\n\t\t\tif(p.t === 'n') {\n\t\t\t\tif((p.v|0) === p.v) p.w = SSF._general_int(p.v);\n\t\t\t\telse p.w = SSF._general_num(p.v);\n\t\t\t}\n\t\t\telse p.w = SSF._general(p.v);\n\t\t}\n\t\telse p.w = SSF.format(fmtid,p.v, {date1904:!!date1904});\n\t} catch(e) { if(opts.WTF) throw e; }\n\tif(opts.cellDates && fmtid && p.t == 'n' && SSF.is_date(SSF._table[fmtid] || String(fmtid))) {\n\t\tvar _d = SSF.parse_date_code(p.v); if(_d) { p.t = 'd'; p.v = new Date(_d.y, _d.m-1,_d.d,_d.H,_d.M,_d.S,_d.u); }\n\t}\n}\n\nfunction make_cell(val, ixfe, t) {\n\treturn ({v:val, ixfe:ixfe, t:t});\n}\n\n// 2.3.2\nfunction parse_workbook(blob, options) {\n\tvar wb = ({opts:{}});\n\tvar Sheets = {};\n\tif(DENSE != null && options.dense == null) options.dense = DENSE;\n\tvar out = ((options.dense ? [] : {}));\n\tvar Directory = {};\n\tvar range = ({});\n\tvar last_formula = null;\n\tvar sst = ([]);\n\tvar cur_sheet = \"\";\n\tvar Preamble = {};\n\tvar lastcell, last_cell = \"\", cc, cmnt, rngC, rngR;\n\tvar sharedf = {};\n\tvar arrayf = [];\n\tvar temp_val;\n\tvar country;\n\tvar cell_valid = true;\n\tvar XFs = []; /* XF records */\n\tvar palette = [];\n\tvar Workbook = ({ Sheets:[], WBProps:{date1904:false}, Views:[{}] }), wsprops = {};\n\tvar get_rgb = function getrgb(icv) {\n\t\tif(icv < 8) return XLSIcv[icv];\n\t\tif(icv < 64) return palette[icv-8] || XLSIcv[icv];\n\t\treturn XLSIcv[icv];\n\t};\n\tvar process_cell_style = function pcs(cell, line, options) {\n\t\tvar xfd = line.XF.data;\n\t\tif(!xfd || !xfd.patternType || !options || !options.cellStyles) return;\n\t\tline.s = ({});\n\t\tline.s.patternType = xfd.patternType;\n\t\tvar t;\n\t\tif((t = rgb2Hex(get_rgb(xfd.icvFore)))) { line.s.fgColor = {rgb:t}; }\n\t\tif((t = rgb2Hex(get_rgb(xfd.icvBack)))) { line.s.bgColor = {rgb:t}; }\n\t};\n\tvar addcell = function addcell(cell, line, options) {\n\t\tif(file_depth > 1) return;\n\t\tif(options.sheetRows && cell.r >= options.sheetRows) cell_valid = false;\n\t\tif(!cell_valid) return;\n\t\tif(options.cellStyles && line.XF && line.XF.data) process_cell_style(cell, line, options);\n\t\tdelete line.ixfe; delete line.XF;\n\t\tlastcell = cell;\n\t\tlast_cell = encode_cell(cell);\n\t\tif(!range || !range.s || !range.e) range = {s:{r:0,c:0},e:{r:0,c:0}};\n\t\tif(cell.r < range.s.r) range.s.r = cell.r;\n\t\tif(cell.c < range.s.c) range.s.c = cell.c;\n\t\tif(cell.r + 1 > range.e.r) range.e.r = cell.r + 1;\n\t\tif(cell.c + 1 > range.e.c) range.e.c = cell.c + 1;\n\t\tif(options.cellFormula && line.f) {\n\t\t\tfor(var afi = 0; afi < arrayf.length; ++afi) {\n\t\t\t\tif(arrayf[afi][0].s.c > cell.c || arrayf[afi][0].s.r > cell.r) continue;\n\t\t\t\tif(arrayf[afi][0].e.c < cell.c || arrayf[afi][0].e.r < cell.r) continue;\n\t\t\t\tline.F = encode_range(arrayf[afi][0]);\n\t\t\t\tif(arrayf[afi][0].s.c != cell.c || arrayf[afi][0].s.r != cell.r) delete line.f;\n\t\t\t\tif(line.f) line.f = \"\" + stringify_formula(arrayf[afi][1], range, cell, supbooks, opts);\n\t\t\t\tbreak;\n\t\t\t}\n\t\t}\n\t\t{\n\t\t\tif(options.dense) {\n\t\t\t\tif(!out[cell.r]) out[cell.r] = [];\n\t\t\t\tout[cell.r][cell.c] = line;\n\t\t\t} else out[last_cell] = line;\n\t\t}\n\t};\n\tvar opts = ({\n\t\tenc: false, // encrypted\n\t\tsbcch: 0, // cch in the preceding SupBook\n\t\tsnames: [], // sheetnames\n\t\tsharedf: sharedf, // shared formulae by address\n\t\tarrayf: arrayf, // array formulae array\n\t\trrtabid: [], // RRTabId\n\t\tlastuser: \"\", // Last User from WriteAccess\n\t\tbiff: 8, // BIFF version\n\t\tcodepage: 0, // CP from CodePage record\n\t\twinlocked: 0, // fLockWn from WinProtect\n\t\tcellStyles: !!options && !!options.cellStyles,\n\t\tWTF: !!options && !!options.wtf\n\t});\n\tif(options.password) opts.password = options.password;\n\tvar themes;\n\tvar merges = [];\n\tvar objects = [];\n\tvar colinfo = [], rowinfo = [];\n\t// eslint-disable-next-line no-unused-vars\n\tvar defwidth = 0, defheight = 0; // twips / MDW respectively\n\tvar seencol = false;\n\tvar supbooks = ([]); // 1-indexed, will hold extern names\n\tsupbooks.SheetNames = opts.snames;\n\tsupbooks.sharedf = opts.sharedf;\n\tsupbooks.arrayf = opts.arrayf;\n\tsupbooks.names = [];\n\tsupbooks.XTI = [];\n\tvar last_Rn = '';\n\tvar file_depth = 0; /* TODO: make a real stack */\n\tvar BIFF2Fmt = 0, BIFF2FmtTable = [];\n\tvar FilterDatabases = []; /* TODO: sort out supbooks and process elsewhere */\n\tvar last_lbl;\n\n\t/* explicit override for some broken writers */\n\topts.codepage = 1200;\n\tset_cp(1200);\n\tvar seen_codepage = false;\n\twhile(blob.l < blob.length - 1) {\n\t\tvar s = blob.l;\n\t\tvar RecordType = blob.read_shift(2);\n\t\tif(RecordType === 0 && last_Rn === 'EOF') break;\n\t\tvar length = (blob.l === blob.length ? 0 : blob.read_shift(2));\n\t\tvar R = XLSRecordEnum[RecordType];\n\t\t//console.log(RecordType.toString(16), RecordType, R, blob.l, length, blob.length);\n\t\t//if(!R) console.log(blob.slice(blob.l, blob.l + length));\n\t\tif(R && R.f) {\n\t\t\tif(options.bookSheets) {\n\t\t\t\tif(last_Rn === 'BoundSheet8' && R.n !== 'BoundSheet8') break;\n\t\t\t}\n\t\t\tlast_Rn = R.n;\n\t\t\tif(R.r === 2 || R.r == 12) {\n\t\t\t\tvar rt = blob.read_shift(2); length -= 2;\n\t\t\t\tif(!opts.enc && rt !== RecordType && (((rt&0xFF)<<8)|(rt>>8)) !== RecordType) throw new Error(\"rt mismatch: \" + rt + \"!=\" + RecordType);\n\t\t\t\tif(R.r == 12){ blob.l += 10; length -= 10; } // skip FRT\n\t\t\t}\n\t\t\t//console.error(R,blob.l,length,blob.length);\n\t\t\tvar val = ({});\n\t\t\tif(R.n === 'EOF') val = R.f(blob, length, opts);\n\t\t\telse val = slurp(R, blob, length, opts);\n\t\t\tvar Rn = R.n;\nif(file_depth == 0 && Rn != 'BOF') continue;\n\t\t\t/* nested switch statements to workaround V8 128 limit */\n\t\t\tswitch(Rn) {\n\t\t\t\t/* Workbook Options */\n\t\t\t\tcase 'Date1904':\nwb.opts.Date1904 = Workbook.WBProps.date1904 = val; break;\n\t\t\t\tcase 'WriteProtect': wb.opts.WriteProtect = true; break;\n\t\t\t\tcase 'FilePass':\n\t\t\t\t\tif(!opts.enc) blob.l = 0;\n\t\t\t\t\topts.enc = val;\n\t\t\t\t\tif(!options.password) throw new Error(\"File is password-protected\");\n\t\t\t\t\tif(val.valid == null) throw new Error(\"Encryption scheme unsupported\");\n\t\t\t\t\tif(!val.valid) throw new Error(\"Password is incorrect\");\n\t\t\t\t\tbreak;\n\t\t\t\tcase 'WriteAccess': opts.lastuser = val; break;\n\t\t\t\tcase 'FileSharing': break; //TODO\n\t\t\t\tcase 'CodePage':\n\t\t\t\t\tvar cpval = Number(val);\n\t\t\t\t\t/* overrides based on test cases */\n\t\t\t\t\tswitch(cpval) {\n\t\t\t\t\t\tcase 0x5212: cpval = 1200; break;\n\t\t\t\t\t\tcase 0x8000: cpval = 10000; break;\n\t\t\t\t\t\tcase 0x8001: cpval = 1252; break;\n\t\t\t\t\t}\n\t\t\t\t\tset_cp(opts.codepage = cpval);\n\t\t\t\t\tseen_codepage = true;\n\t\t\t\t\tbreak;\n\t\t\t\tcase 'RRTabId': opts.rrtabid = val; break;\n\t\t\t\tcase 'WinProtect': opts.winlocked = val; break;\n\t\t\t\tcase 'Template': break; // TODO\n\t\t\t\tcase 'BookBool': break; // TODO\n\t\t\t\tcase 'UsesELFs': break;\n\t\t\t\tcase 'MTRSettings': break;\n\t\t\t\tcase 'RefreshAll':\n\t\t\t\tcase 'CalcCount':\n\t\t\t\tcase 'CalcDelta':\n\t\t\t\tcase 'CalcIter':\n\t\t\t\tcase 'CalcMode':\n\t\t\t\tcase 'CalcPrecision':\n\t\t\t\tcase 'CalcSaveRecalc':\n\t\t\t\t\twb.opts[Rn] = val; break;\n\t\t\t\tcase 'CalcRefMode': opts.CalcRefMode = val; break; // TODO: implement R1C1\n\t\t\t\tcase 'Uncalced': break;\n\t\t\t\tcase 'ForceFullCalculation': wb.opts.FullCalc = val; break;\n\t\t\t\tcase 'WsBool':\n\t\t\t\t\tif(val.fDialog) out[\"!type\"] = \"dialog\";\n\t\t\t\t\tbreak; // TODO\n\t\t\t\tcase 'XF':\n\t\t\t\t\tXFs.push(val); break;\n\t\t\t\tcase 'ExtSST': break; // TODO\n\t\t\t\tcase 'BookExt': break; // TODO\n\t\t\t\tcase 'RichTextStream': break;\n\t\t\t\tcase 'BkHim': break;\n\n\t\t\t\tcase 'SupBook':\n\t\t\t\t\tsupbooks.push([val]);\n\t\t\t\t\tsupbooks[supbooks.length-1].XTI = [];\n\t\t\t\t\tbreak;\n\t\t\t\tcase 'ExternName':\n\t\t\t\t\tsupbooks[supbooks.length-1].push(val);\n\t\t\t\t\tbreak;\n\t\t\t\tcase 'Index': break; // TODO\n\t\t\t\tcase 'Lbl':\n\t\t\t\t\tlast_lbl = ({\n\t\t\t\t\t\tName: val.Name,\n\t\t\t\t\t\tRef: stringify_formula(val.rgce,range,null,supbooks,opts)\n\t\t\t\t\t});\n\t\t\t\t\tif(val.itab > 0) last_lbl.Sheet = val.itab - 1;\n\t\t\t\t\tsupbooks.names.push(last_lbl);\n\t\t\t\t\tif(!supbooks[0]) { supbooks[0] = []; supbooks[0].XTI = []; }\n\t\t\t\t\tsupbooks[supbooks.length-1].push(val);\n\t\t\t\t\tif(val.Name == \"_xlnm._FilterDatabase\" && val.itab > 0)\n\t\t\t\t\t\tif(val.rgce && val.rgce[0] && val.rgce[0][0] && val.rgce[0][0][0] == 'PtgArea3d')\n\t\t\t\t\t\t\tFilterDatabases[val.itab - 1] = { ref: encode_range(val.rgce[0][0][1][2]) };\n\t\t\t\t\tbreak;\n\t\t\t\tcase 'ExternCount': opts.ExternCount = val; break;\n\t\t\t\tcase 'ExternSheet':\n\t\t\t\t\tif(supbooks.length == 0) { supbooks[0] = []; supbooks[0].XTI = []; }\n\t\t\t\t\tsupbooks[supbooks.length - 1].XTI = supbooks[supbooks.length - 1].XTI.concat(val); supbooks.XTI = supbooks.XTI.concat(val); break;\n\t\t\t\tcase 'NameCmt':\n\t\t\t\t\t/* TODO: search for correct name */\n\t\t\t\t\tif(opts.biff < 8) break;\n\t\t\t\t\tif(last_lbl != null) last_lbl.Comment = val[1];\n\t\t\t\t\tbreak;\n\n\t\t\t\tcase 'Protect': out[\"!protect\"] = val; break; /* for sheet or book */\n\t\t\t\tcase 'Password': if(val !== 0 && opts.WTF) console.error(\"Password verifier: \" + val); break;\n\t\t\t\tcase 'Prot4Rev': case 'Prot4RevPass': break; /*TODO: Revision Control*/\n\n\t\t\t\tcase 'BoundSheet8': {\n\t\t\t\t\tDirectory[val.pos] = val;\n\t\t\t\t\topts.snames.push(val.name);\n\t\t\t\t} break;\n\t\t\t\tcase 'EOF': {\n\t\t\t\t\tif(--file_depth) break;\n\t\t\t\t\tif(range.e) {\n\t\t\t\t\t\tif(range.e.r > 0 && range.e.c > 0) {\n\t\t\t\t\t\t\trange.e.r--; range.e.c--;\n\t\t\t\t\t\t\tout[\"!ref\"] = encode_range(range);\n\t\t\t\t\t\t\tif(options.sheetRows && options.sheetRows <= range.e.r) {\n\t\t\t\t\t\t\t\tvar tmpri = range.e.r;\n\t\t\t\t\t\t\t\trange.e.r = options.sheetRows - 1;\n\t\t\t\t\t\t\t\tout[\"!fullref\"] = out[\"!ref\"];\n\t\t\t\t\t\t\t\tout[\"!ref\"] = encode_range(range);\n\t\t\t\t\t\t\t\trange.e.r = tmpri;\n\t\t\t\t\t\t\t}\n\t\t\t\t\t\t\trange.e.r++; range.e.c++;\n\t\t\t\t\t\t}\n\t\t\t\t\t\tif(merges.length > 0) out[\"!merges\"] = merges;\n\t\t\t\t\t\tif(objects.length > 0) out[\"!objects\"] = objects;\n\t\t\t\t\t\tif(colinfo.length > 0) out[\"!cols\"] = colinfo;\n\t\t\t\t\t\tif(rowinfo.length > 0) out[\"!rows\"] = rowinfo;\n\t\t\t\t\t\tWorkbook.Sheets.push(wsprops);\n\t\t\t\t\t}\n\t\t\t\t\tif(cur_sheet === \"\") Preamble = out; else Sheets[cur_sheet] = out;\n\t\t\t\t\tout = ((options.dense ? [] : {}));\n\t\t\t\t} break;\n\t\t\t\tcase 'BOF': {\n\t\t\t\t\tif(opts.biff === 8) opts.biff = {\n0x0009:2,\n0x0209:3,\n0x0409:4\n\t\t\t\t\t}[RecordType] || {\n0x0200:2,\n0x0300:3,\n0x0400:4,\n0x0500:5,\n0x0600:8,\n0x0002:2,\n0x0007:2\n\t\t\t\t\t}[val.BIFFVer] || 8;\n\t\t\t\t\tif(opts.biff == 8 && val.BIFFVer == 0 && val.dt == 16) opts.biff = 2;\n\t\t\t\t\tif(file_depth++) break;\n\t\t\t\t\tcell_valid = true;\n\t\t\t\t\tout = ((options.dense ? [] : {}));\n\n\t\t\t\t\tif(opts.biff < 8 && !seen_codepage) { seen_codepage = true; set_cp(opts.codepage = options.codepage || 1252); }\n\t\t\t\t\tif(opts.biff < 5) {\n\t\t\t\t\t\tif(cur_sheet === \"\") cur_sheet = \"Sheet1\";\n\t\t\t\t\t\trange = {s:{r:0,c:0},e:{r:0,c:0}};\n\t\t\t\t\t\t/* fake BoundSheet8 */\n\t\t\t\t\t\tvar fakebs8 = {pos: blob.l - length, name:cur_sheet};\n\t\t\t\t\t\tDirectory[fakebs8.pos] = fakebs8;\n\t\t\t\t\t\topts.snames.push(cur_sheet);\n\t\t\t\t\t}\n\t\t\t\t\telse cur_sheet = (Directory[s] || {name:\"\"}).name;\n\t\t\t\t\tif(val.dt == 0x20) out[\"!type\"] = \"chart\";\n\t\t\t\t\tif(val.dt == 0x40) out[\"!type\"] = \"macro\";\n\t\t\t\t\tmerges = [];\n\t\t\t\t\tobjects = [];\n\t\t\t\t\topts.arrayf = arrayf = [];\n\t\t\t\t\tcolinfo = []; rowinfo = [];\n\t\t\t\t\tdefwidth = defheight = 0;\n\t\t\t\t\tseencol = false;\n\t\t\t\t\twsprops = {Hidden:(Directory[s]||{hs:0}).hs, name:cur_sheet };\n\t\t\t\t} break;\n\n\t\t\t\tcase 'Number': case 'BIFF2NUM': case 'BIFF2INT': {\n\t\t\t\t\tif(out[\"!type\"] == \"chart\") if(options.dense ? (out[val.r]||[])[val.c]: out[encode_cell({c:val.c, r:val.r})]) ++val.c;\n\t\t\t\t\ttemp_val = ({ixfe: val.ixfe, XF: XFs[val.ixfe]||{}, v:val.val, t:'n'});\n\t\t\t\t\tif(BIFF2Fmt > 0) temp_val.z = BIFF2FmtTable[(temp_val.ixfe>>8) & 0x1F];\n\t\t\t\t\tsafe_format_xf(temp_val, options, wb.opts.Date1904);\n\t\t\t\t\taddcell({c:val.c, r:val.r}, temp_val, options);\n\t\t\t\t} break;\n\t\t\t\tcase 'BoolErr': {\n\t\t\t\t\ttemp_val = ({ixfe: val.ixfe, XF: XFs[val.ixfe], v:val.val, t:val.t});\n\t\t\t\t\tif(BIFF2Fmt > 0) temp_val.z = BIFF2FmtTable[(temp_val.ixfe>>8) & 0x1F];\n\t\t\t\t\tsafe_format_xf(temp_val, options, wb.opts.Date1904);\n\t\t\t\t\taddcell({c:val.c, r:val.r}, temp_val, options);\n\t\t\t\t} break;\n\t\t\t\tcase 'RK': {\n\t\t\t\t\ttemp_val = ({ixfe: val.ixfe, XF: XFs[val.ixfe], v:val.rknum, t:'n'});\n\t\t\t\t\tif(BIFF2Fmt > 0) temp_val.z = BIFF2FmtTable[(temp_val.ixfe>>8) & 0x1F];\n\t\t\t\t\tsafe_format_xf(temp_val, options, wb.opts.Date1904);\n\t\t\t\t\taddcell({c:val.c, r:val.r}, temp_val, options);\n\t\t\t\t} break;\n\t\t\t\tcase 'MulRk': {\n\t\t\t\t\tfor(var j = val.c; j <= val.C; ++j) {\n\t\t\t\t\t\tvar ixfe = val.rkrec[j-val.c][0];\n\t\t\t\t\t\ttemp_val= ({ixfe:ixfe, XF:XFs[ixfe], v:val.rkrec[j-val.c][1], t:'n'});\n\t\t\t\t\t\tif(BIFF2Fmt > 0) temp_val.z = BIFF2FmtTable[(temp_val.ixfe>>8) & 0x1F];\n\t\t\t\t\t\tsafe_format_xf(temp_val, options, wb.opts.Date1904);\n\t\t\t\t\t\taddcell({c:j, r:val.r}, temp_val, options);\n\t\t\t\t\t}\n\t\t\t\t} break;\n\t\t\t\tcase 'Formula': {\n\t\t\t\t\tif(val.val == 'String') { last_formula = val; break; }\n\t\t\t\t\ttemp_val = make_cell(val.val, val.cell.ixfe, val.tt);\n\t\t\t\t\ttemp_val.XF = XFs[temp_val.ixfe];\n\t\t\t\t\tif(options.cellFormula) {\n\t\t\t\t\t\tvar _f = val.formula;\n\t\t\t\t\t\tif(_f && _f[0] && _f[0][0] && _f[0][0][0] == 'PtgExp') {\n\t\t\t\t\t\t\tvar _fr = _f[0][0][1][0], _fc = _f[0][0][1][1];\n\t\t\t\t\t\t\tvar _fe = encode_cell({r:_fr, c:_fc});\n\t\t\t\t\t\t\tif(sharedf[_fe]) temp_val.f = \"\"+stringify_formula(val.formula,range,val.cell,supbooks, opts);\n\t\t\t\t\t\t\telse temp_val.F = ((options.dense ? (out[_fr]||[])[_fc]: out[_fe]) || {}).F;\n\t\t\t\t\t\t} else temp_val.f = \"\"+stringify_formula(val.formula,range,val.cell,supbooks, opts);\n\t\t\t\t\t}\n\t\t\t\t\tif(BIFF2Fmt > 0) temp_val.z = BIFF2FmtTable[(temp_val.ixfe>>8) & 0x1F];\n\t\t\t\t\tsafe_format_xf(temp_val, options, wb.opts.Date1904);\n\t\t\t\t\taddcell(val.cell, temp_val, options);\n\t\t\t\t\tlast_formula = val;\n\t\t\t\t} break;\n\t\t\t\tcase 'String': {\n\t\t\t\t\tif(last_formula) { /* technically always true */\n\t\t\t\t\t\tlast_formula.val = val;\n\t\t\t\t\t\ttemp_val = make_cell(val, last_formula.cell.ixfe, 's');\n\t\t\t\t\t\ttemp_val.XF = XFs[temp_val.ixfe];\n\t\t\t\t\t\tif(options.cellFormula) {\n\t\t\t\t\t\t\ttemp_val.f = \"\"+stringify_formula(last_formula.formula, range, last_formula.cell, supbooks, opts);\n\t\t\t\t\t\t}\n\t\t\t\t\t\tif(BIFF2Fmt > 0) temp_val.z = BIFF2FmtTable[(temp_val.ixfe>>8) & 0x1F];\n\t\t\t\t\t\tsafe_format_xf(temp_val, options, wb.opts.Date1904);\n\t\t\t\t\t\taddcell(last_formula.cell, temp_val, options);\n\t\t\t\t\t\tlast_formula = null;\n\t\t\t\t\t} else throw new Error(\"String record expects Formula\");\n\t\t\t\t} break;\n\t\t\t\tcase 'Array': {\n\t\t\t\t\tarrayf.push(val);\n\t\t\t\t\tvar _arraystart = encode_cell(val[0].s);\n\t\t\t\t\tcc = options.dense ? (out[val[0].s.r]||[])[val[0].s.c] : out[_arraystart];\n\t\t\t\t\tif(options.cellFormula && cc) {\n\t\t\t\t\t\tif(!last_formula) break; /* technically unreachable */\n\t\t\t\t\t\tif(!_arraystart || !cc) break;\n\t\t\t\t\t\tcc.f = \"\"+stringify_formula(val[1], range, val[0], supbooks, opts);\n\t\t\t\t\t\tcc.F = encode_range(val[0]);\n\t\t\t\t\t}\n\t\t\t\t} break;\n\t\t\t\tcase 'ShrFmla': {\n\t\t\t\t\tif(!cell_valid) break;\n\t\t\t\t\tif(!options.cellFormula) break;\n\t\t\t\t\tif(last_cell) {\n\t\t\t\t\t\t/* TODO: capture range */\n\t\t\t\t\t\tif(!last_formula) break; /* technically unreachable */\n\t\t\t\t\t\tsharedf[encode_cell(last_formula.cell)]= val[0];\n\t\t\t\t\t\tcc = options.dense ? (out[last_formula.cell.r]||[])[last_formula.cell.c] : out[encode_cell(last_formula.cell)];\n\t\t\t\t\t\t(cc||{}).f = \"\"+stringify_formula(val[0], range, lastcell, supbooks, opts);\n\t\t\t\t\t}\n\t\t\t\t} break;\n\t\t\t\tcase 'LabelSst':\n\t\t\t\t\ttemp_val=make_cell(sst[val.isst].t, val.ixfe, 's');\n\t\t\t\t\tif(sst[val.isst].h) temp_val.h = sst[val.isst].h;\n\t\t\t\t\ttemp_val.XF = XFs[temp_val.ixfe];\n\t\t\t\t\tif(BIFF2Fmt > 0) temp_val.z = BIFF2FmtTable[(temp_val.ixfe>>8) & 0x1F];\n\t\t\t\t\tsafe_format_xf(temp_val, options, wb.opts.Date1904);\n\t\t\t\t\taddcell({c:val.c, r:val.r}, temp_val, options);\n\t\t\t\t\tbreak;\n\t\t\t\tcase 'Blank': if(options.sheetStubs) {\n\t\t\t\t\ttemp_val = ({ixfe: val.ixfe, XF: XFs[val.ixfe], t:'z'});\n\t\t\t\t\tif(BIFF2Fmt > 0) temp_val.z = BIFF2FmtTable[(temp_val.ixfe>>8) & 0x1F];\n\t\t\t\t\tsafe_format_xf(temp_val, options, wb.opts.Date1904);\n\t\t\t\t\taddcell({c:val.c, r:val.r}, temp_val, options);\n\t\t\t\t} break;\n\t\t\t\tcase 'MulBlank': if(options.sheetStubs) {\n\t\t\t\t\tfor(var _j = val.c; _j <= val.C; ++_j) {\n\t\t\t\t\t\tvar _ixfe = val.ixfe[_j-val.c];\n\t\t\t\t\t\ttemp_val= ({ixfe:_ixfe, XF:XFs[_ixfe], t:'z'});\n\t\t\t\t\t\tif(BIFF2Fmt > 0) temp_val.z = BIFF2FmtTable[(temp_val.ixfe>>8) & 0x1F];\n\t\t\t\t\t\tsafe_format_xf(temp_val, options, wb.opts.Date1904);\n\t\t\t\t\t\taddcell({c:_j, r:val.r}, temp_val, options);\n\t\t\t\t\t}\n\t\t\t\t} break;\n\t\t\t\tcase 'RString':\n\t\t\t\tcase 'Label': case 'BIFF2STR':\n\t\t\t\t\ttemp_val=make_cell(val.val, val.ixfe, 's');\n\t\t\t\t\ttemp_val.XF = XFs[temp_val.ixfe];\n\t\t\t\t\tif(BIFF2Fmt > 0) temp_val.z = BIFF2FmtTable[(temp_val.ixfe>>8) & 0x1F];\n\t\t\t\t\tsafe_format_xf(temp_val, options, wb.opts.Date1904);\n\t\t\t\t\taddcell({c:val.c, r:val.r}, temp_val, options);\n\t\t\t\t\tbreak;\n\n\t\t\t\tcase 'Dimensions': {\n\t\t\t\t\tif(file_depth === 1) range = val; /* TODO: stack */\n\t\t\t\t} break;\n\t\t\t\tcase 'SST': {\n\t\t\t\t\tsst = val;\n\t\t\t\t} break;\n\t\t\t\tcase 'Format': { /* val = [id, fmt] */\n\t\t\t\t\tif(opts.biff == 4) {\n\t\t\t\t\t\tBIFF2FmtTable[BIFF2Fmt++] = val[1];\n\t\t\t\t\t\tfor(var b4idx = 0; b4idx < BIFF2Fmt + 163; ++b4idx) if(SSF._table[b4idx] == val[1]) break;\n\t\t\t\t\t\tif(b4idx >= 163) SSF.load(val[1], BIFF2Fmt + 163);\n\t\t\t\t\t}\n\t\t\t\t\telse SSF.load(val[1], val[0]);\n\t\t\t\t} break;\n\t\t\t\tcase 'BIFF2FORMAT': {\n\t\t\t\t\tBIFF2FmtTable[BIFF2Fmt++] = val;\n\t\t\t\t\tfor(var b2idx = 0; b2idx < BIFF2Fmt + 163; ++b2idx) if(SSF._table[b2idx] == val) break;\n\t\t\t\t\tif(b2idx >= 163) SSF.load(val, BIFF2Fmt + 163);\n\t\t\t\t} break;\n\n\t\t\t\tcase 'MergeCells': merges = merges.concat(val); break;\n\n\t\t\t\tcase 'Obj': objects[val.cmo[0]] = opts.lastobj = val; break;\n\t\t\t\tcase 'TxO': opts.lastobj.TxO = val; break;\n\t\t\t\tcase 'ImData': opts.lastobj.ImData = val; break;\n\n\t\t\t\tcase 'HLink': {\n\t\t\t\t\tfor(rngR = val[0].s.r; rngR <= val[0].e.r; ++rngR)\n\t\t\t\t\t\tfor(rngC = val[0].s.c; rngC <= val[0].e.c; ++rngC) {\n\t\t\t\t\t\t\tcc = options.dense ? (out[rngR]||[])[rngC] : out[encode_cell({c:rngC,r:rngR})];\n\t\t\t\t\t\t\tif(cc) cc.l = val[1];\n\t\t\t\t\t\t}\n\t\t\t\t} break;\n\t\t\t\tcase 'HLinkTooltip': {\n\t\t\t\t\tfor(rngR = val[0].s.r; rngR <= val[0].e.r; ++rngR)\n\t\t\t\t\t\tfor(rngC = val[0].s.c; rngC <= val[0].e.c; ++rngC) {\n\t\t\t\t\t\t\tcc = options.dense ? (out[rngR]||[])[rngC] : out[encode_cell({c:rngC,r:rngR})];\n\t\t\t\t\t\t\tif(cc && cc.l) cc.l.Tooltip = val[1];\n\t\t\t\t\t\t\t}\n\t\t\t\t} break;\n\n\t\t\t\t/* Comments */\n\t\t\t\tcase 'Note': {\n\t\t\t\t\tif(opts.biff <= 5 && opts.biff >= 2) break; /* TODO: BIFF5 */\n\t\t\t\t\tcc = options.dense ? (out[val[0].r]||[])[val[0].c] : out[encode_cell(val[0])];\n\t\t\t\t\tvar noteobj = objects[val[2]];\n\t\t\t\t\tif(!cc) {\n\t\t\t\t\t\tif(options.dense) {\n\t\t\t\t\t\t\tif(!out[val[0].r]) out[val[0].r] = [];\n\t\t\t\t\t\t\tcc = out[val[0].r][val[0].c] = ({t:\"z\"});\n\t\t\t\t\t\t} else {\n\t\t\t\t\t\t\tcc = out[encode_cell(val[0])] = ({t:\"z\"});\n\t\t\t\t\t\t}\n\t\t\t\t\t\trange.e.r = Math.max(range.e.r, val[0].r);\n\t\t\t\t\t\trange.s.r = Math.min(range.s.r, val[0].r);\n\t\t\t\t\t\trange.e.c = Math.max(range.e.c, val[0].c);\n\t\t\t\t\t\trange.s.c = Math.min(range.s.c, val[0].c);\n\t\t\t\t\t}\n\t\t\t\t\tif(!cc.c) cc.c = [];\n\t\t\t\t\tcmnt = {a:val[1],t:noteobj.TxO.t};\n\t\t\t\t\tcc.c.push(cmnt);\n\t\t\t\t} break;\n\n\t\t\t\tdefault: switch(R.n) { /* nested */\n\t\t\t\tcase 'ClrtClient': break;\n\t\t\t\tcase 'XFExt': update_xfext(XFs[val.ixfe], val.ext); break;\n\n\t\t\t\tcase 'DefColWidth': defwidth = val; break;\n\t\t\t\tcase 'DefaultRowHeight': defheight = val[1]; break; // TODO: flags\n\n\t\t\t\tcase 'ColInfo': {\n\t\t\t\t\tif(!opts.cellStyles) break;\n\t\t\t\t\twhile(val.e >= val.s) {\n\t\t\t\t\t\tcolinfo[val.e--] = { width: val.w/256 };\n\t\t\t\t\t\tif(!seencol) { seencol = true; find_mdw_colw(val.w/256); }\n\t\t\t\t\t\tprocess_col(colinfo[val.e+1]);\n\t\t\t\t\t}\n\t\t\t\t} break;\n\t\t\t\tcase 'Row': {\n\t\t\t\t\tvar rowobj = {};\n\t\t\t\t\tif(val.level != null) { rowinfo[val.r] = rowobj; rowobj.level = val.level; }\n\t\t\t\t\tif(val.hidden) { rowinfo[val.r] = rowobj; rowobj.hidden = true; }\n\t\t\t\t\tif(val.hpt) {\n\t\t\t\t\t\trowinfo[val.r] = rowobj;\n\t\t\t\t\t\trowobj.hpt = val.hpt; rowobj.hpx = pt2px(val.hpt);\n\t\t\t\t\t}\n\t\t\t\t} break;\n\n\t\t\t\tcase 'LeftMargin':\n\t\t\t\tcase 'RightMargin':\n\t\t\t\tcase 'TopMargin':\n\t\t\t\tcase 'BottomMargin':\n\t\t\t\t\tif(!out['!margins']) default_margins(out['!margins'] = {});\n\t\t\t\t\tout['!margins'][Rn.slice(0,-6).toLowerCase()] = val;\n\t\t\t\t\tbreak;\n\n\t\t\t\tcase 'Setup': // TODO\n\t\t\t\t\tif(!out['!margins']) default_margins(out['!margins'] = {});\n\t\t\t\t\tout['!margins'].header = val.header;\n\t\t\t\t\tout['!margins'].footer = val.footer;\n\t\t\t\t\tbreak;\n\n\t\t\t\tcase 'Window2': // TODO\n\t\t\t\t\t// $FlowIgnore\n\t\t\t\t\tif(val.RTL) Workbook.Views[0].RTL = true;\n\t\t\t\t\tbreak;\n\n\t\t\t\tcase 'Header': break; // TODO\n\t\t\t\tcase 'Footer': break; // TODO\n\t\t\t\tcase 'HCenter': break; // TODO\n\t\t\t\tcase 'VCenter': break; // TODO\n\t\t\t\tcase 'Pls': break; // TODO\n\t\t\t\tcase 'GCW': break;\n\t\t\t\tcase 'LHRecord': break;\n\t\t\t\tcase 'DBCell': break; // TODO\n\t\t\t\tcase 'EntExU2': break; // TODO\n\t\t\t\tcase 'SxView': break; // TODO\n\t\t\t\tcase 'Sxvd': break; // TODO\n\t\t\t\tcase 'SXVI': break; // TODO\n\t\t\t\tcase 'SXVDEx': break; // TODO\n\t\t\t\tcase 'SxIvd': break; // TODO\n\t\t\t\tcase 'SXString': break; // TODO\n\t\t\t\tcase 'Sync': break;\n\t\t\t\tcase 'Addin': break;\n\t\t\t\tcase 'SXDI': break; // TODO\n\t\t\t\tcase 'SXLI': break; // TODO\n\t\t\t\tcase 'SXEx': break; // TODO\n\t\t\t\tcase 'QsiSXTag': break; // TODO\n\t\t\t\tcase 'Selection': break;\n\t\t\t\tcase 'Feat': break;\n\t\t\t\tcase 'FeatHdr': case 'FeatHdr11': break;\n\t\t\t\tcase 'Feature11': case 'Feature12': case 'List12': break;\n\t\t\t\tcase 'Country': country = val; break;\n\t\t\t\tcase 'RecalcId': break;\n\t\t\t\tcase 'DxGCol': break; // TODO: htmlify\n\t\t\t\tcase 'Fbi': case 'Fbi2': case 'GelFrame': break;\n\t\t\t\tcase 'Font': break; // TODO\n\t\t\t\tcase 'XFCRC': break; // TODO\n\t\t\t\tcase 'Style': break; // TODO\n\t\t\t\tcase 'StyleExt': break; // TODO\n\t\t\t\tcase 'Palette': palette = val; break;\n\t\t\t\tcase 'Theme': themes = val; break;\n\t\t\t\t/* Protection */\n\t\t\t\tcase 'ScenarioProtect': break;\n\t\t\t\tcase 'ObjProtect': break;\n\n\t\t\t\t/* Conditional Formatting */\n\t\t\t\tcase 'CondFmt12': break;\n\n\t\t\t\t/* Table */\n\t\t\t\tcase 'Table': break; // TODO\n\t\t\t\tcase 'TableStyles': break; // TODO\n\t\t\t\tcase 'TableStyle': break; // TODO\n\t\t\t\tcase 'TableStyleElement': break; // TODO\n\n\t\t\t\t/* PivotTable */\n\t\t\t\tcase 'SXStreamID': break; // TODO\n\t\t\t\tcase 'SXVS': break; // TODO\n\t\t\t\tcase 'DConRef': break; // TODO\n\t\t\t\tcase 'SXAddl': break; // TODO\n\t\t\t\tcase 'DConBin': break; // TODO\n\t\t\t\tcase 'DConName': break; // TODO\n\t\t\t\tcase 'SXPI': break; // TODO\n\t\t\t\tcase 'SxFormat': break; // TODO\n\t\t\t\tcase 'SxSelect': break; // TODO\n\t\t\t\tcase 'SxRule': break; // TODO\n\t\t\t\tcase 'SxFilt': break; // TODO\n\t\t\t\tcase 'SxItm': break; // TODO\n\t\t\t\tcase 'SxDXF': break; // TODO\n\n\t\t\t\t/* Scenario Manager */\n\t\t\t\tcase 'ScenMan': break;\n\n\t\t\t\t/* Data Consolidation */\n\t\t\t\tcase 'DCon': break;\n\n\t\t\t\t/* Watched Cell */\n\t\t\t\tcase 'CellWatch': break;\n\n\t\t\t\t/* Print Settings */\n\t\t\t\tcase 'PrintRowCol': break;\n\t\t\t\tcase 'PrintGrid': break;\n\t\t\t\tcase 'PrintSize': break;\n\n\t\t\t\tcase 'XCT': break;\n\t\t\t\tcase 'CRN': break;\n\n\t\t\t\tcase 'Scl': {\n\t\t\t\t\t//console.log(\"Zoom Level:\", val[0]/val[1],val);\n\t\t\t\t} break;\n\t\t\t\tcase 'SheetExt': {\n\t\t\t\t\t/* empty */\n\t\t\t\t} break;\n\t\t\t\tcase 'SheetExtOptional': {\n\t\t\t\t\t/* empty */\n\t\t\t\t} break;\n\n\t\t\t\t/* VBA */\n\t\t\t\tcase 'ObNoMacros': {\n\t\t\t\t\t/* empty */\n\t\t\t\t} break;\n\t\t\t\tcase 'ObProj': {\n\t\t\t\t\t/* empty */\n\t\t\t\t} break;\n\t\t\t\tcase 'CodeName': {\nif(!cur_sheet) Workbook.WBProps.CodeName = val || \"ThisWorkbook\";\n\t\t\t\t\telse wsprops.CodeName = val || wsprops.name;\n\t\t\t\t} break;\n\t\t\t\tcase 'GUIDTypeLib': {\n\t\t\t\t\t/* empty */\n\t\t\t\t} break;\n\n\t\t\t\tcase 'WOpt': break; // TODO: WTF?\n\t\t\t\tcase 'PhoneticInfo': break;\n\n\t\t\t\tcase 'OleObjectSize': break;\n\n\t\t\t\t/* Differential Formatting */\n\t\t\t\tcase 'DXF': case 'DXFN': case 'DXFN12': case 'DXFN12List': case 'DXFN12NoCB': break;\n\n\t\t\t\t/* Data Validation */\n\t\t\t\tcase 'Dv': case 'DVal': break;\n\n\t\t\t\t/* Data Series */\n\t\t\t\tcase 'BRAI': case 'Series': case 'SeriesText': break;\n\n\t\t\t\t/* Data Connection */\n\t\t\t\tcase 'DConn': break;\n\t\t\t\tcase 'DbOrParamQry': break;\n\t\t\t\tcase 'DBQueryExt': break;\n\n\t\t\t\tcase 'OleDbConn': break;\n\t\t\t\tcase 'ExtString': break;\n\n\t\t\t\t/* Formatting */\n\t\t\t\tcase 'IFmtRecord': break;\n\t\t\t\tcase 'CondFmt': case 'CF': case 'CF12': case 'CFEx': break;\n\n\t\t\t\t/* Explicitly Ignored */\n\t\t\t\tcase 'Excel9File': break;\n\t\t\t\tcase 'Units': break;\n\t\t\t\tcase 'InterfaceHdr': case 'Mms': case 'InterfaceEnd': case 'DSF': break;\n\t\t\t\tcase 'BuiltInFnGroupCount': /* 2.4.30 0x0E or 0x10 but excel 2011 generates 0x11? */ break;\n\t\t\t\t/* View Stuff */\n\t\t\t\tcase 'Window1': case 'HideObj': case 'GridSet': case 'Guts':\n\t\t\t\tcase 'UserBView': case 'UserSViewBegin': case 'UserSViewEnd': break;\n\t\t\t\tcase 'Pane': break;\n\t\t\t\tdefault: switch(R.n) { /* nested */\n\t\t\t\t/* Chart */\n\t\t\t\tcase 'Dat':\n\t\t\t\tcase 'Begin': case 'End':\n\t\t\t\tcase 'StartBlock': case 'EndBlock':\n\t\t\t\tcase 'Frame': case 'Area':\n\t\t\t\tcase 'Axis': case 'AxisLine': case 'Tick': break;\n\t\t\t\tcase 'AxesUsed':\n\t\t\t\tcase 'CrtLayout12': case 'CrtLayout12A': case 'CrtLink': case 'CrtLine': case 'CrtMlFrt': case 'CrtMlFrtContinue': break;\n\t\t\t\tcase 'LineFormat': case 'AreaFormat':\n\t\t\t\tcase 'Chart': case 'Chart3d': case 'Chart3DBarShape': case 'ChartFormat': case 'ChartFrtInfo': break;\n\t\t\t\tcase 'PlotArea': case 'PlotGrowth': break;\n\t\t\t\tcase 'SeriesList': case 'SerParent': case 'SerAuxTrend': break;\n\t\t\t\tcase 'DataFormat': case 'SerToCrt': case 'FontX': break;\n\t\t\t\tcase 'CatSerRange': case 'AxcExt': case 'SerFmt': break;\n\t\t\t\tcase 'ShtProps': break;\n\t\t\t\tcase 'DefaultText': case 'Text': case 'CatLab': break;\n\t\t\t\tcase 'DataLabExtContents': break;\n\t\t\t\tcase 'Legend': case 'LegendException': break;\n\t\t\t\tcase 'Pie': case 'Scatter': break;\n\t\t\t\tcase 'PieFormat': case 'MarkerFormat': break;\n\t\t\t\tcase 'StartObject': case 'EndObject': break;\n\t\t\t\tcase 'AlRuns': case 'ObjectLink': break;\n\t\t\t\tcase 'SIIndex': break;\n\t\t\t\tcase 'AttachedLabel': case 'YMult': break;\n\n\t\t\t\t/* Chart Group */\n\t\t\t\tcase 'Line': case 'Bar': break;\n\t\t\t\tcase 'Surf': break;\n\n\t\t\t\t/* Axis Group */\n\t\t\t\tcase 'AxisParent': break;\n\t\t\t\tcase 'Pos': break;\n\t\t\t\tcase 'ValueRange': break;\n\n\t\t\t\t/* Pivot Chart */\n\t\t\t\tcase 'SXViewEx9': break; // TODO\n\t\t\t\tcase 'SXViewLink': break;\n\t\t\t\tcase 'PivotChartBits': break;\n\t\t\t\tcase 'SBaseRef': break;\n\t\t\t\tcase 'TextPropsStream': break;\n\n\t\t\t\t/* Chart Misc */\n\t\t\t\tcase 'LnExt': break;\n\t\t\t\tcase 'MkrExt': break;\n\t\t\t\tcase 'CrtCoopt': break;\n\n\t\t\t\t/* Query Table */\n\t\t\t\tcase 'Qsi': case 'Qsif': case 'Qsir': case 'QsiSXTag': break;\n\t\t\t\tcase 'TxtQry': break;\n\n\t\t\t\t/* Filter */\n\t\t\t\tcase 'FilterMode': break;\n\t\t\t\tcase 'AutoFilter': case 'AutoFilterInfo': break;\n\t\t\t\tcase 'AutoFilter12': break;\n\t\t\t\tcase 'DropDownObjIds': break;\n\t\t\t\tcase 'Sort': break;\n\t\t\t\tcase 'SortData': break;\n\n\t\t\t\t/* Drawing */\n\t\t\t\tcase 'ShapePropsStream': break;\n\t\t\t\tcase 'MsoDrawing': case 'MsoDrawingGroup': case 'MsoDrawingSelection': break;\n\t\t\t\t/* Pub Stuff */\n\t\t\t\tcase 'WebPub': case 'AutoWebPub': break;\n\n\t\t\t\t/* Print Stuff */\n\t\t\t\tcase 'HeaderFooter': case 'HFPicture': case 'PLV':\n\t\t\t\tcase 'HorizontalPageBreaks': case 'VerticalPageBreaks': break;\n\t\t\t\t/* Behavioral */\n\t\t\t\tcase 'Backup': case 'CompressPictures': case 'Compat12': break;\n\n\t\t\t\t/* Should not Happen */\n\t\t\t\tcase 'Continue': case 'ContinueFrt12': break;\n\n\t\t\t\t/* Future Records */\n\t\t\t\tcase 'FrtFontList': case 'FrtWrapper': break;\n\n\t\t\t\tdefault: switch(R.n) { /* nested */\n\t\t\t\t/* BIFF5 records */\n\t\t\t\tcase 'TabIdConf': case 'Radar': case 'RadarArea': case 'DropBar': case 'Intl': case 'CoordList': case 'SerAuxErrBar': break;\n\n\t\t\t\t/* BIFF2-4 records */\n\t\t\t\tcase 'BIFF2FONTCLR': case 'BIFF2FMTCNT': case 'BIFF2FONTXTRA': break;\n\t\t\t\tcase 'BIFF2XF': case 'BIFF3XF': case 'BIFF4XF': break;\n\t\t\t\tcase 'BIFF4FMTCNT': case 'BIFF2ROW': case 'BIFF2WINDOW2': break;\n\n\t\t\t\t/* Miscellaneous */\n\t\t\t\tcase 'SCENARIO': case 'DConBin': case 'PicF': case 'DataLabExt':\n\t\t\t\tcase 'Lel': case 'BopPop': case 'BopPopCustom': case 'RealTimeData':\n\t\t\t\tcase 'Name': break;\n\t\t\t\tcase 'LHNGraph': case 'FnGroupName': case 'AddMenu': case 'LPr': break;\n\t\t\t\tcase 'ListObj': case 'ListField': break;\n\t\t\t\tcase 'RRSort': break;\n\t\t\t\tcase 'BigName': break;\n\t\t\t\tcase 'ToolbarHdr': case 'ToolbarEnd': break;\n\t\t\t\tcase 'DDEObjName': break;\n\t\t\t\tcase 'FRTArchId$': break;\n\t\t\t\tdefault: if(options.WTF) throw 'Unrecognized Record ' + R.n;\n\t\t\t}}}}\n\t\t} else blob.l += length;\n\t}\n\twb.SheetNames=keys(Directory).sort(function(a,b) { return Number(a) - Number(b); }).map(function(x){return Directory[x].name;});\n\tif(!options.bookSheets) wb.Sheets=Sheets;\n\tif(wb.Sheets) FilterDatabases.forEach(function(r,i) { wb.Sheets[wb.SheetNames[i]]['!autofilter'] = r; });\n\twb.Preamble=Preamble;\n\twb.Strings = sst;\n\twb.SSF = SSF.get_table();\n\tif(opts.enc) wb.Encryption = opts.enc;\n\tif(themes) wb.Themes = themes;\n\twb.Metadata = {};\n\tif(country !== undefined) wb.Metadata.Country = country;\n\tif(supbooks.names.length > 0) Workbook.Names = supbooks.names;\n\twb.Workbook = Workbook;\n\treturn wb;\n}\n\n/* TODO: split props*/\nvar PSCLSID = {\n\tSI: \"e0859ff2f94f6810ab9108002b27b3d9\",\n\tDSI: \"02d5cdd59c2e1b10939708002b2cf9ae\",\n\tUDI: \"05d5cdd59c2e1b10939708002b2cf9ae\"\n};\nfunction parse_xls_props(cfb, props, o) {\n\t/* [MS-OSHARED] 2.3.3.2.2 Document Summary Information Property Set */\n\tvar DSI = CFB.find(cfb, '!DocumentSummaryInformation');\n\tif(DSI && DSI.size > 0) try {\n\t\tvar DocSummary = parse_PropertySetStream(DSI, DocSummaryPIDDSI, PSCLSID.DSI);\n\t\tfor(var d in DocSummary) props[d] = DocSummary[d];\n\t} catch(e) {if(o.WTF) throw e;/* empty */}\n\n\t/* [MS-OSHARED] 2.3.3.2.1 Summary Information Property Set*/\n\tvar SI = CFB.find(cfb, '!SummaryInformation');\n\tif(SI && SI.size > 0) try {\n\t\tvar Summary = parse_PropertySetStream(SI, SummaryPIDSI, PSCLSID.SI);\n\t\tfor(var s in Summary) if(props[s] == null) props[s] = Summary[s];\n\t} catch(e) {if(o.WTF) throw e;/* empty */}\n\n\tif(props.HeadingPairs && props.TitlesOfParts) {\n\t\tload_props_pairs(props.HeadingPairs, props.TitlesOfParts, props, o);\n\t\tdelete props.HeadingPairs; delete props.TitlesOfParts;\n\t}\n}\nfunction write_xls_props(wb, cfb) {\n\tvar DSEntries = [], SEntries = [], CEntries = [];\n\tvar i = 0, Keys;\n\tif(wb.Props) {\n\t\tKeys = keys(wb.Props);\n\t\t// $FlowIgnore\n\t\tfor(i = 0; i < Keys.length; ++i) (Object.prototype.hasOwnProperty.call(DocSummaryRE, Keys[i]) ? DSEntries : Object.prototype.hasOwnProperty.call(SummaryRE, Keys[i]) ? SEntries : CEntries).push([Keys[i], wb.Props[Keys[i]]]);\n\t}\n\tif(wb.Custprops) {\n\t\tKeys = keys(wb.Custprops);\n\t\t// $FlowIgnore\n\t\tfor(i = 0; i < Keys.length; ++i) if(!Object.prototype.hasOwnProperty.call((wb.Props||{}), Keys[i])) (Object.prototype.hasOwnProperty.call(DocSummaryRE, Keys[i]) ? DSEntries : Object.prototype.hasOwnProperty.call(SummaryRE, Keys[i]) ? SEntries : CEntries).push([Keys[i], wb.Custprops[Keys[i]]]);\n\t}\n\tvar CEntries2 = [];\n\tfor(i = 0; i < CEntries.length; ++i) {\n\t\tif(XLSPSSkip.indexOf(CEntries[i][0]) > -1) continue;\n\t\tif(CEntries[i][1] == null) continue;\n\t\tCEntries2.push(CEntries[i]);\n\t}\n\tif(SEntries.length) CFB.utils.cfb_add(cfb, \"/\\u0005SummaryInformation\", write_PropertySetStream(SEntries, PSCLSID.SI, SummaryRE, SummaryPIDSI));\n\tif(DSEntries.length || CEntries2.length) CFB.utils.cfb_add(cfb, \"/\\u0005DocumentSummaryInformation\", write_PropertySetStream(DSEntries, PSCLSID.DSI, DocSummaryRE, DocSummaryPIDDSI, CEntries2.length ? CEntries2 : null, PSCLSID.UDI));\n}\n\nfunction parse_xlscfb(cfb, options) {\nif(!options) options = {};\nfix_read_opts(options);\nreset_cp();\nif(options.codepage) set_ansi(options.codepage);\nvar CompObj, WB;\nif(cfb.FullPaths) {\n\tif(CFB.find(cfb, '/encryption')) throw new Error(\"File is password-protected\");\n\tCompObj = CFB.find(cfb, '!CompObj');\n\tWB = CFB.find(cfb, '/Workbook') || CFB.find(cfb, '/Book');\n} else {\n\tswitch(options.type) {\n\t\tcase 'base64': cfb = s2a(Base64.decode(cfb)); break;\n\t\tcase 'binary': cfb = s2a(cfb); break;\n\t\tcase 'buffer': break;\n\t\tcase 'array': if(!Array.isArray(cfb)) cfb = Array.prototype.slice.call(cfb); break;\n\t}\n\tprep_blob(cfb, 0);\n\tWB = ({content: cfb});\n}\nvar WorkbookP;\n\nvar _data;\nif(CompObj) parse_compobj(CompObj);\nif(options.bookProps && !options.bookSheets) WorkbookP = ({});\nelse {\n\tvar T = has_buf ? 'buffer' : 'array';\n\tif(WB && WB.content) WorkbookP = parse_workbook(WB.content, options);\n\t/* Quattro Pro 7-8 */\n\telse if((_data=CFB.find(cfb, 'PerfectOffice_MAIN')) && _data.content) WorkbookP = WK_.to_workbook(_data.content, (options.type = T, options));\n\t/* Quattro Pro 9 */\n\telse if((_data=CFB.find(cfb, 'NativeContent_MAIN')) && _data.content) WorkbookP = WK_.to_workbook(_data.content, (options.type = T, options));\n\telse throw new Error(\"Cannot find Workbook stream\");\n\tif(options.bookVBA && cfb.FullPaths && CFB.find(cfb, '/_VBA_PROJECT_CUR/VBA/dir')) WorkbookP.vbaraw = make_vba_xls(cfb);\n}\n\nvar props = {};\nif(cfb.FullPaths) parse_xls_props(cfb, props, options);\n\nWorkbookP.Props = WorkbookP.Custprops = props; /* TODO: split up properties */\nif(options.bookFiles) WorkbookP.cfb = cfb;\n/*WorkbookP.CompObjP = CompObjP; // TODO: storage? */\nreturn WorkbookP;\n}\n\n\nfunction write_xlscfb(wb, opts) {\n\tvar o = opts || {};\n\tvar cfb = CFB.utils.cfb_new({root:\"R\"});\n\tvar wbpath = \"/Workbook\";\n\tswitch(o.bookType || \"xls\") {\n\t\tcase \"xls\": o.bookType = \"biff8\";\n\t\t/* falls through */\n\t\tcase \"xla\": if(!o.bookType) o.bookType = \"xla\";\n\t\t/* falls through */\n\t\tcase \"biff8\": wbpath = \"/Workbook\"; o.biff = 8; break;\n\t\tcase \"biff5\": wbpath = \"/Book\"; o.biff = 5; break;\n\t\tdefault: throw new Error(\"invalid type \" + o.bookType + \" for XLS CFB\");\n\t}\n\tCFB.utils.cfb_add(cfb, wbpath, write_biff_buf(wb, o));\n\tif(o.biff == 8 && (wb.Props || wb.Custprops)) write_xls_props(wb, cfb);\n\t// TODO: SI, DSI, CO\n\tif(o.biff == 8 && wb.vbaraw) fill_vba_xls(cfb, CFB.read(wb.vbaraw, {type: typeof wb.vbaraw == \"string\" ? \"binary\" : \"buffer\"}));\n\treturn cfb;\n}\n/* [MS-XLSB] 2.3 Record Enumeration */\nvar XLSBRecordEnum = {\n0x0000: { n:\"BrtRowHdr\", f:parse_BrtRowHdr },\n0x0001: { n:\"BrtCellBlank\", f:parse_BrtCellBlank },\n0x0002: { n:\"BrtCellRk\", f:parse_BrtCellRk },\n0x0003: { n:\"BrtCellError\", f:parse_BrtCellError },\n0x0004: { n:\"BrtCellBool\", f:parse_BrtCellBool },\n0x0005: { n:\"BrtCellReal\", f:parse_BrtCellReal },\n0x0006: { n:\"BrtCellSt\", f:parse_BrtCellSt },\n0x0007: { n:\"BrtCellIsst\", f:parse_BrtCellIsst },\n0x0008: { n:\"BrtFmlaString\", f:parse_BrtFmlaString },\n0x0009: { n:\"BrtFmlaNum\", f:parse_BrtFmlaNum },\n0x000A: { n:\"BrtFmlaBool\", f:parse_BrtFmlaBool },\n0x000B: { n:\"BrtFmlaError\", f:parse_BrtFmlaError },\n0x0010: { n:\"BrtFRTArchID$\", f:parse_BrtFRTArchID$ },\n0x0013: { n:\"BrtSSTItem\", f:parse_RichStr },\n0x0014: { n:\"BrtPCDIMissing\" },\n0x0015: { n:\"BrtPCDINumber\" },\n0x0016: { n:\"BrtPCDIBoolean\" },\n0x0017: { n:\"BrtPCDIError\" },\n0x0018: { n:\"BrtPCDIString\" },\n0x0019: { n:\"BrtPCDIDatetime\" },\n0x001A: { n:\"BrtPCDIIndex\" },\n0x001B: { n:\"BrtPCDIAMissing\" },\n0x001C: { n:\"BrtPCDIANumber\" },\n0x001D: { n:\"BrtPCDIABoolean\" },\n0x001E: { n:\"BrtPCDIAError\" },\n0x001F: { n:\"BrtPCDIAString\" },\n0x0020: { n:\"BrtPCDIADatetime\" },\n0x0021: { n:\"BrtPCRRecord\" },\n0x0022: { n:\"BrtPCRRecordDt\" },\n0x0023: { n:\"BrtFRTBegin\" },\n0x0024: { n:\"BrtFRTEnd\" },\n0x0025: { n:\"BrtACBegin\" },\n0x0026: { n:\"BrtACEnd\" },\n0x0027: { n:\"BrtName\", f:parse_BrtName },\n0x0028: { n:\"BrtIndexRowBlock\" },\n0x002A: { n:\"BrtIndexBlock\" },\n0x002B: { n:\"BrtFont\", f:parse_BrtFont },\n0x002C: { n:\"BrtFmt\", f:parse_BrtFmt },\n0x002D: { n:\"BrtFill\", f:parse_BrtFill },\n0x002E: { n:\"BrtBorder\", f:parse_BrtBorder },\n0x002F: { n:\"BrtXF\", f:parse_BrtXF },\n0x0030: { n:\"BrtStyle\" },\n0x0031: { n:\"BrtCellMeta\" },\n0x0032: { n:\"BrtValueMeta\" },\n0x0033: { n:\"BrtMdb\" },\n0x0034: { n:\"BrtBeginFmd\" },\n0x0035: { n:\"BrtEndFmd\" },\n0x0036: { n:\"BrtBeginMdx\" },\n0x0037: { n:\"BrtEndMdx\" },\n0x0038: { n:\"BrtBeginMdxTuple\" },\n0x0039: { n:\"BrtEndMdxTuple\" },\n0x003A: { n:\"BrtMdxMbrIstr\" },\n0x003B: { n:\"BrtStr\" },\n0x003C: { n:\"BrtColInfo\", f:parse_ColInfo },\n0x003E: { n:\"BrtCellRString\" },\n0x003F: { n:\"BrtCalcChainItem$\", f:parse_BrtCalcChainItem$ },\n0x0040: { n:\"BrtDVal\", f:parse_BrtDVal },\n0x0041: { n:\"BrtSxvcellNum\" },\n0x0042: { n:\"BrtSxvcellStr\" },\n0x0043: { n:\"BrtSxvcellBool\" },\n0x0044: { n:\"BrtSxvcellErr\" },\n0x0045: { n:\"BrtSxvcellDate\" },\n0x0046: { n:\"BrtSxvcellNil\" },\n0x0080: { n:\"BrtFileVersion\" },\n0x0081: { n:\"BrtBeginSheet\" },\n0x0082: { n:\"BrtEndSheet\" },\n0x0083: { n:\"BrtBeginBook\", f:parsenoop, p:0 },\n0x0084: { n:\"BrtEndBook\" },\n0x0085: { n:\"BrtBeginWsViews\" },\n0x0086: { n:\"BrtEndWsViews\" },\n0x0087: { n:\"BrtBeginBookViews\" },\n0x0088: { n:\"BrtEndBookViews\" },\n0x0089: { n:\"BrtBeginWsView\", f:parse_BrtBeginWsView },\n0x008A: { n:\"BrtEndWsView\" },\n0x008B: { n:\"BrtBeginCsViews\" },\n0x008C: { n:\"BrtEndCsViews\" },\n0x008D: { n:\"BrtBeginCsView\" },\n0x008E: { n:\"BrtEndCsView\" },\n0x008F: { n:\"BrtBeginBundleShs\" },\n0x0090: { n:\"BrtEndBundleShs\" },\n0x0091: { n:\"BrtBeginSheetData\" },\n0x0092: { n:\"BrtEndSheetData\" },\n0x0093: { n:\"BrtWsProp\", f:parse_BrtWsProp },\n0x0094: { n:\"BrtWsDim\", f:parse_BrtWsDim, p:16 },\n0x0097: { n:\"BrtPane\", f:parse_BrtPane },\n0x0098: { n:\"BrtSel\" },\n0x0099: { n:\"BrtWbProp\", f:parse_BrtWbProp },\n0x009A: { n:\"BrtWbFactoid\" },\n0x009B: { n:\"BrtFileRecover\" },\n0x009C: { n:\"BrtBundleSh\", f:parse_BrtBundleSh },\n0x009D: { n:\"BrtCalcProp\" },\n0x009E: { n:\"BrtBookView\" },\n0x009F: { n:\"BrtBeginSst\", f:parse_BrtBeginSst },\n0x00A0: { n:\"BrtEndSst\" },\n0x00A1: { n:\"BrtBeginAFilter\", f:parse_UncheckedRfX },\n0x00A2: { n:\"BrtEndAFilter\" },\n0x00A3: { n:\"BrtBeginFilterColumn\" },\n0x00A4: { n:\"BrtEndFilterColumn\" },\n0x00A5: { n:\"BrtBeginFilters\" },\n0x00A6: { n:\"BrtEndFilters\" },\n0x00A7: { n:\"BrtFilter\" },\n0x00A8: { n:\"BrtColorFilter\" },\n0x00A9: { n:\"BrtIconFilter\" },\n0x00AA: { n:\"BrtTop10Filter\" },\n0x00AB: { n:\"BrtDynamicFilter\" },\n0x00AC: { n:\"BrtBeginCustomFilters\" },\n0x00AD: { n:\"BrtEndCustomFilters\" },\n0x00AE: { n:\"BrtCustomFilter\" },\n0x00AF: { n:\"BrtAFilterDateGroupItem\" },\n0x00B0: { n:\"BrtMergeCell\", f:parse_BrtMergeCell },\n0x00B1: { n:\"BrtBeginMergeCells\" },\n0x00B2: { n:\"BrtEndMergeCells\" },\n0x00B3: { n:\"BrtBeginPivotCacheDef\" },\n0x00B4: { n:\"BrtEndPivotCacheDef\" },\n0x00B5: { n:\"BrtBeginPCDFields\" },\n0x00B6: { n:\"BrtEndPCDFields\" },\n0x00B7: { n:\"BrtBeginPCDField\" },\n0x00B8: { n:\"BrtEndPCDField\" },\n0x00B9: { n:\"BrtBeginPCDSource\" },\n0x00BA: { n:\"BrtEndPCDSource\" },\n0x00BB: { n:\"BrtBeginPCDSRange\" },\n0x00BC: { n:\"BrtEndPCDSRange\" },\n0x00BD: { n:\"BrtBeginPCDFAtbl\" },\n0x00BE: { n:\"BrtEndPCDFAtbl\" },\n0x00BF: { n:\"BrtBeginPCDIRun\" },\n0x00C0: { n:\"BrtEndPCDIRun\" },\n0x00C1: { n:\"BrtBeginPivotCacheRecords\" },\n0x00C2: { n:\"BrtEndPivotCacheRecords\" },\n0x00C3: { n:\"BrtBeginPCDHierarchies\" },\n0x00C4: { n:\"BrtEndPCDHierarchies\" },\n0x00C5: { n:\"BrtBeginPCDHierarchy\" },\n0x00C6: { n:\"BrtEndPCDHierarchy\" },\n0x00C7: { n:\"BrtBeginPCDHFieldsUsage\" },\n0x00C8: { n:\"BrtEndPCDHFieldsUsage\" },\n0x00C9: { n:\"BrtBeginExtConnection\" },\n0x00CA: { n:\"BrtEndExtConnection\" },\n0x00CB: { n:\"BrtBeginECDbProps\" },\n0x00CC: { n:\"BrtEndECDbProps\" },\n0x00CD: { n:\"BrtBeginECOlapProps\" },\n0x00CE: { n:\"BrtEndECOlapProps\" },\n0x00CF: { n:\"BrtBeginPCDSConsol\" },\n0x00D0: { n:\"BrtEndPCDSConsol\" },\n0x00D1: { n:\"BrtBeginPCDSCPages\" },\n0x00D2: { n:\"BrtEndPCDSCPages\" },\n0x00D3: { n:\"BrtBeginPCDSCPage\" },\n0x00D4: { n:\"BrtEndPCDSCPage\" },\n0x00D5: { n:\"BrtBeginPCDSCPItem\" },\n0x00D6: { n:\"BrtEndPCDSCPItem\" },\n0x00D7: { n:\"BrtBeginPCDSCSets\" },\n0x00D8: { n:\"BrtEndPCDSCSets\" },\n0x00D9: { n:\"BrtBeginPCDSCSet\" },\n0x00DA: { n:\"BrtEndPCDSCSet\" },\n0x00DB: { n:\"BrtBeginPCDFGroup\" },\n0x00DC: { n:\"BrtEndPCDFGroup\" },\n0x00DD: { n:\"BrtBeginPCDFGItems\" },\n0x00DE: { n:\"BrtEndPCDFGItems\" },\n0x00DF: { n:\"BrtBeginPCDFGRange\" },\n0x00E0: { n:\"BrtEndPCDFGRange\" },\n0x00E1: { n:\"BrtBeginPCDFGDiscrete\" },\n0x00E2: { n:\"BrtEndPCDFGDiscrete\" },\n0x00E3: { n:\"BrtBeginPCDSDTupleCache\" },\n0x00E4: { n:\"BrtEndPCDSDTupleCache\" },\n0x00E5: { n:\"BrtBeginPCDSDTCEntries\" },\n0x00E6: { n:\"BrtEndPCDSDTCEntries\" },\n0x00E7: { n:\"BrtBeginPCDSDTCEMembers\" },\n0x00E8: { n:\"BrtEndPCDSDTCEMembers\" },\n0x00E9: { n:\"BrtBeginPCDSDTCEMember\" },\n0x00EA: { n:\"BrtEndPCDSDTCEMember\" },\n0x00EB: { n:\"BrtBeginPCDSDTCQueries\" },\n0x00EC: { n:\"BrtEndPCDSDTCQueries\" },\n0x00ED: { n:\"BrtBeginPCDSDTCQuery\" },\n0x00EE: { n:\"BrtEndPCDSDTCQuery\" },\n0x00EF: { n:\"BrtBeginPCDSDTCSets\" },\n0x00F0: { n:\"BrtEndPCDSDTCSets\" },\n0x00F1: { n:\"BrtBeginPCDSDTCSet\" },\n0x00F2: { n:\"BrtEndPCDSDTCSet\" },\n0x00F3: { n:\"BrtBeginPCDCalcItems\" },\n0x00F4: { n:\"BrtEndPCDCalcItems\" },\n0x00F5: { n:\"BrtBeginPCDCalcItem\" },\n0x00F6: { n:\"BrtEndPCDCalcItem\" },\n0x00F7: { n:\"BrtBeginPRule\" },\n0x00F8: { n:\"BrtEndPRule\" },\n0x00F9: { n:\"BrtBeginPRFilters\" },\n0x00FA: { n:\"BrtEndPRFilters\" },\n0x00FB: { n:\"BrtBeginPRFilter\" },\n0x00FC: { n:\"BrtEndPRFilter\" },\n0x00FD: { n:\"BrtBeginPNames\" },\n0x00FE: { n:\"BrtEndPNames\" },\n0x00FF: { n:\"BrtBeginPName\" },\n0x0100: { n:\"BrtEndPName\" },\n0x0101: { n:\"BrtBeginPNPairs\" },\n0x0102: { n:\"BrtEndPNPairs\" },\n0x0103: { n:\"BrtBeginPNPair\" },\n0x0104: { n:\"BrtEndPNPair\" },\n0x0105: { n:\"BrtBeginECWebProps\" },\n0x0106: { n:\"BrtEndECWebProps\" },\n0x0107: { n:\"BrtBeginEcWpTables\" },\n0x0108: { n:\"BrtEndECWPTables\" },\n0x0109: { n:\"BrtBeginECParams\" },\n0x010A: { n:\"BrtEndECParams\" },\n0x010B: { n:\"BrtBeginECParam\" },\n0x010C: { n:\"BrtEndECParam\" },\n0x010D: { n:\"BrtBeginPCDKPIs\" },\n0x010E: { n:\"BrtEndPCDKPIs\" },\n0x010F: { n:\"BrtBeginPCDKPI\" },\n0x0110: { n:\"BrtEndPCDKPI\" },\n0x0111: { n:\"BrtBeginDims\" },\n0x0112: { n:\"BrtEndDims\" },\n0x0113: { n:\"BrtBeginDim\" },\n0x0114: { n:\"BrtEndDim\" },\n0x0115: { n:\"BrtIndexPartEnd\" },\n0x0116: { n:\"BrtBeginStyleSheet\" },\n0x0117: { n:\"BrtEndStyleSheet\" },\n0x0118: { n:\"BrtBeginSXView\" },\n0x0119: { n:\"BrtEndSXVI\" },\n0x011A: { n:\"BrtBeginSXVI\" },\n0x011B: { n:\"BrtBeginSXVIs\" },\n0x011C: { n:\"BrtEndSXVIs\" },\n0x011D: { n:\"BrtBeginSXVD\" },\n0x011E: { n:\"BrtEndSXVD\" },\n0x011F: { n:\"BrtBeginSXVDs\" },\n0x0120: { n:\"BrtEndSXVDs\" },\n0x0121: { n:\"BrtBeginSXPI\" },\n0x0122: { n:\"BrtEndSXPI\" },\n0x0123: { n:\"BrtBeginSXPIs\" },\n0x0124: { n:\"BrtEndSXPIs\" },\n0x0125: { n:\"BrtBeginSXDI\" },\n0x0126: { n:\"BrtEndSXDI\" },\n0x0127: { n:\"BrtBeginSXDIs\" },\n0x0128: { n:\"BrtEndSXDIs\" },\n0x0129: { n:\"BrtBeginSXLI\" },\n0x012A: { n:\"BrtEndSXLI\" },\n0x012B: { n:\"BrtBeginSXLIRws\" },\n0x012C: { n:\"BrtEndSXLIRws\" },\n0x012D: { n:\"BrtBeginSXLICols\" },\n0x012E: { n:\"BrtEndSXLICols\" },\n0x012F: { n:\"BrtBeginSXFormat\" },\n0x0130: { n:\"BrtEndSXFormat\" },\n0x0131: { n:\"BrtBeginSXFormats\" },\n0x0132: { n:\"BrtEndSxFormats\" },\n0x0133: { n:\"BrtBeginSxSelect\" },\n0x0134: { n:\"BrtEndSxSelect\" },\n0x0135: { n:\"BrtBeginISXVDRws\" },\n0x0136: { n:\"BrtEndISXVDRws\" },\n0x0137: { n:\"BrtBeginISXVDCols\" },\n0x0138: { n:\"BrtEndISXVDCols\" },\n0x0139: { n:\"BrtEndSXLocation\" },\n0x013A: { n:\"BrtBeginSXLocation\" },\n0x013B: { n:\"BrtEndSXView\" },\n0x013C: { n:\"BrtBeginSXTHs\" },\n0x013D: { n:\"BrtEndSXTHs\" },\n0x013E: { n:\"BrtBeginSXTH\" },\n0x013F: { n:\"BrtEndSXTH\" },\n0x0140: { n:\"BrtBeginISXTHRws\" },\n0x0141: { n:\"BrtEndISXTHRws\" },\n0x0142: { n:\"BrtBeginISXTHCols\" },\n0x0143: { n:\"BrtEndISXTHCols\" },\n0x0144: { n:\"BrtBeginSXTDMPS\" },\n0x0145: { n:\"BrtEndSXTDMPs\" },\n0x0146: { n:\"BrtBeginSXTDMP\" },\n0x0147: { n:\"BrtEndSXTDMP\" },\n0x0148: { n:\"BrtBeginSXTHItems\" },\n0x0149: { n:\"BrtEndSXTHItems\" },\n0x014A: { n:\"BrtBeginSXTHItem\" },\n0x014B: { n:\"BrtEndSXTHItem\" },\n0x014C: { n:\"BrtBeginMetadata\" },\n0x014D: { n:\"BrtEndMetadata\" },\n0x014E: { n:\"BrtBeginEsmdtinfo\" },\n0x014F: { n:\"BrtMdtinfo\" },\n0x0150: { n:\"BrtEndEsmdtinfo\" },\n0x0151: { n:\"BrtBeginEsmdb\" },\n0x0152: { n:\"BrtEndEsmdb\" },\n0x0153: { n:\"BrtBeginEsfmd\" },\n0x0154: { n:\"BrtEndEsfmd\" },\n0x0155: { n:\"BrtBeginSingleCells\" },\n0x0156: { n:\"BrtEndSingleCells\" },\n0x0157: { n:\"BrtBeginList\" },\n0x0158: { n:\"BrtEndList\" },\n0x0159: { n:\"BrtBeginListCols\" },\n0x015A: { n:\"BrtEndListCols\" },\n0x015B: { n:\"BrtBeginListCol\" },\n0x015C: { n:\"BrtEndListCol\" },\n0x015D: { n:\"BrtBeginListXmlCPr\" },\n0x015E: { n:\"BrtEndListXmlCPr\" },\n0x015F: { n:\"BrtListCCFmla\" },\n0x0160: { n:\"BrtListTrFmla\" },\n0x0161: { n:\"BrtBeginExternals\" },\n0x0162: { n:\"BrtEndExternals\" },\n0x0163: { n:\"BrtSupBookSrc\", f:parse_RelID},\n0x0165: { n:\"BrtSupSelf\" },\n0x0166: { n:\"BrtSupSame\" },\n0x0167: { n:\"BrtSupTabs\" },\n0x0168: { n:\"BrtBeginSupBook\" },\n0x0169: { n:\"BrtPlaceholderName\" },\n0x016A: { n:\"BrtExternSheet\", f:parse_ExternSheet },\n0x016B: { n:\"BrtExternTableStart\" },\n0x016C: { n:\"BrtExternTableEnd\" },\n0x016E: { n:\"BrtExternRowHdr\" },\n0x016F: { n:\"BrtExternCellBlank\" },\n0x0170: { n:\"BrtExternCellReal\" },\n0x0171: { n:\"BrtExternCellBool\" },\n0x0172: { n:\"BrtExternCellError\" },\n0x0173: { n:\"BrtExternCellString\" },\n0x0174: { n:\"BrtBeginEsmdx\" },\n0x0175: { n:\"BrtEndEsmdx\" },\n0x0176: { n:\"BrtBeginMdxSet\" },\n0x0177: { n:\"BrtEndMdxSet\" },\n0x0178: { n:\"BrtBeginMdxMbrProp\" },\n0x0179: { n:\"BrtEndMdxMbrProp\" },\n0x017A: { n:\"BrtBeginMdxKPI\" },\n0x017B: { n:\"BrtEndMdxKPI\" },\n0x017C: { n:\"BrtBeginEsstr\" },\n0x017D: { n:\"BrtEndEsstr\" },\n0x017E: { n:\"BrtBeginPRFItem\" },\n0x017F: { n:\"BrtEndPRFItem\" },\n0x0180: { n:\"BrtBeginPivotCacheIDs\" },\n0x0181: { n:\"BrtEndPivotCacheIDs\" },\n0x0182: { n:\"BrtBeginPivotCacheID\" },\n0x0183: { n:\"BrtEndPivotCacheID\" },\n0x0184: { n:\"BrtBeginISXVIs\" },\n0x0185: { n:\"BrtEndISXVIs\" },\n0x0186: { n:\"BrtBeginColInfos\" },\n0x0187: { n:\"BrtEndColInfos\" },\n0x0188: { n:\"BrtBeginRwBrk\" },\n0x0189: { n:\"BrtEndRwBrk\" },\n0x018A: { n:\"BrtBeginColBrk\" },\n0x018B: { n:\"BrtEndColBrk\" },\n0x018C: { n:\"BrtBrk\" },\n0x018D: { n:\"BrtUserBookView\" },\n0x018E: { n:\"BrtInfo\" },\n0x018F: { n:\"BrtCUsr\" },\n0x0190: { n:\"BrtUsr\" },\n0x0191: { n:\"BrtBeginUsers\" },\n0x0193: { n:\"BrtEOF\" },\n0x0194: { n:\"BrtUCR\" },\n0x0195: { n:\"BrtRRInsDel\" },\n0x0196: { n:\"BrtRREndInsDel\" },\n0x0197: { n:\"BrtRRMove\" },\n0x0198: { n:\"BrtRREndMove\" },\n0x0199: { n:\"BrtRRChgCell\" },\n0x019A: { n:\"BrtRREndChgCell\" },\n0x019B: { n:\"BrtRRHeader\" },\n0x019C: { n:\"BrtRRUserView\" },\n0x019D: { n:\"BrtRRRenSheet\" },\n0x019E: { n:\"BrtRRInsertSh\" },\n0x019F: { n:\"BrtRRDefName\" },\n0x01A0: { n:\"BrtRRNote\" },\n0x01A1: { n:\"BrtRRConflict\" },\n0x01A2: { n:\"BrtRRTQSIF\" },\n0x01A3: { n:\"BrtRRFormat\" },\n0x01A4: { n:\"BrtRREndFormat\" },\n0x01A5: { n:\"BrtRRAutoFmt\" },\n0x01A6: { n:\"BrtBeginUserShViews\" },\n0x01A7: { n:\"BrtBeginUserShView\" },\n0x01A8: { n:\"BrtEndUserShView\" },\n0x01A9: { n:\"BrtEndUserShViews\" },\n0x01AA: { n:\"BrtArrFmla\", f:parse_BrtArrFmla },\n0x01AB: { n:\"BrtShrFmla\", f:parse_BrtShrFmla },\n0x01AC: { n:\"BrtTable\" },\n0x01AD: { n:\"BrtBeginExtConnections\" },\n0x01AE: { n:\"BrtEndExtConnections\" },\n0x01AF: { n:\"BrtBeginPCDCalcMems\" },\n0x01B0: { n:\"BrtEndPCDCalcMems\" },\n0x01B1: { n:\"BrtBeginPCDCalcMem\" },\n0x01B2: { n:\"BrtEndPCDCalcMem\" },\n0x01B3: { n:\"BrtBeginPCDHGLevels\" },\n0x01B4: { n:\"BrtEndPCDHGLevels\" },\n0x01B5: { n:\"BrtBeginPCDHGLevel\" },\n0x01B6: { n:\"BrtEndPCDHGLevel\" },\n0x01B7: { n:\"BrtBeginPCDHGLGroups\" },\n0x01B8: { n:\"BrtEndPCDHGLGroups\" },\n0x01B9: { n:\"BrtBeginPCDHGLGroup\" },\n0x01BA: { n:\"BrtEndPCDHGLGroup\" },\n0x01BB: { n:\"BrtBeginPCDHGLGMembers\" },\n0x01BC: { n:\"BrtEndPCDHGLGMembers\" },\n0x01BD: { n:\"BrtBeginPCDHGLGMember\" },\n0x01BE: { n:\"BrtEndPCDHGLGMember\" },\n0x01BF: { n:\"BrtBeginQSI\" },\n0x01C0: { n:\"BrtEndQSI\" },\n0x01C1: { n:\"BrtBeginQSIR\" },\n0x01C2: { n:\"BrtEndQSIR\" },\n0x01C3: { n:\"BrtBeginDeletedNames\" },\n0x01C4: { n:\"BrtEndDeletedNames\" },\n0x01C5: { n:\"BrtBeginDeletedName\" },\n0x01C6: { n:\"BrtEndDeletedName\" },\n0x01C7: { n:\"BrtBeginQSIFs\" },\n0x01C8: { n:\"BrtEndQSIFs\" },\n0x01C9: { n:\"BrtBeginQSIF\" },\n0x01CA: { n:\"BrtEndQSIF\" },\n0x01CB: { n:\"BrtBeginAutoSortScope\" },\n0x01CC: { n:\"BrtEndAutoSortScope\" },\n0x01CD: { n:\"BrtBeginConditionalFormatting\" },\n0x01CE: { n:\"BrtEndConditionalFormatting\" },\n0x01CF: { n:\"BrtBeginCFRule\" },\n0x01D0: { n:\"BrtEndCFRule\" },\n0x01D1: { n:\"BrtBeginIconSet\" },\n0x01D2: { n:\"BrtEndIconSet\" },\n0x01D3: { n:\"BrtBeginDatabar\" },\n0x01D4: { n:\"BrtEndDatabar\" },\n0x01D5: { n:\"BrtBeginColorScale\" },\n0x01D6: { n:\"BrtEndColorScale\" },\n0x01D7: { n:\"BrtCFVO\" },\n0x01D8: { n:\"BrtExternValueMeta\" },\n0x01D9: { n:\"BrtBeginColorPalette\" },\n0x01DA: { n:\"BrtEndColorPalette\" },\n0x01DB: { n:\"BrtIndexedColor\" },\n0x01DC: { n:\"BrtMargins\", f:parse_BrtMargins },\n0x01DD: { n:\"BrtPrintOptions\" },\n0x01DE: { n:\"BrtPageSetup\" },\n0x01DF: { n:\"BrtBeginHeaderFooter\" },\n0x01E0: { n:\"BrtEndHeaderFooter\" },\n0x01E1: { n:\"BrtBeginSXCrtFormat\" },\n0x01E2: { n:\"BrtEndSXCrtFormat\" },\n0x01E3: { n:\"BrtBeginSXCrtFormats\" },\n0x01E4: { n:\"BrtEndSXCrtFormats\" },\n0x01E5: { n:\"BrtWsFmtInfo\", f:parse_BrtWsFmtInfo },\n0x01E6: { n:\"BrtBeginMgs\" },\n0x01E7: { n:\"BrtEndMGs\" },\n0x01E8: { n:\"BrtBeginMGMaps\" },\n0x01E9: { n:\"BrtEndMGMaps\" },\n0x01EA: { n:\"BrtBeginMG\" },\n0x01EB: { n:\"BrtEndMG\" },\n0x01EC: { n:\"BrtBeginMap\" },\n0x01ED: { n:\"BrtEndMap\" },\n0x01EE: { n:\"BrtHLink\", f:parse_BrtHLink },\n0x01EF: { n:\"BrtBeginDCon\" },\n0x01F0: { n:\"BrtEndDCon\" },\n0x01F1: { n:\"BrtBeginDRefs\" },\n0x01F2: { n:\"BrtEndDRefs\" },\n0x01F3: { n:\"BrtDRef\" },\n0x01F4: { n:\"BrtBeginScenMan\" },\n0x01F5: { n:\"BrtEndScenMan\" },\n0x01F6: { n:\"BrtBeginSct\" },\n0x01F7: { n:\"BrtEndSct\" },\n0x01F8: { n:\"BrtSlc\" },\n0x01F9: { n:\"BrtBeginDXFs\" },\n0x01FA: { n:\"BrtEndDXFs\" },\n0x01FB: { n:\"BrtDXF\" },\n0x01FC: { n:\"BrtBeginTableStyles\" },\n0x01FD: { n:\"BrtEndTableStyles\" },\n0x01FE: { n:\"BrtBeginTableStyle\" },\n0x01FF: { n:\"BrtEndTableStyle\" },\n0x0200: { n:\"BrtTableStyleElement\" },\n0x0201: { n:\"BrtTableStyleClient\" },\n0x0202: { n:\"BrtBeginVolDeps\" },\n0x0203: { n:\"BrtEndVolDeps\" },\n0x0204: { n:\"BrtBeginVolType\" },\n0x0205: { n:\"BrtEndVolType\" },\n0x0206: { n:\"BrtBeginVolMain\" },\n0x0207: { n:\"BrtEndVolMain\" },\n0x0208: { n:\"BrtBeginVolTopic\" },\n0x0209: { n:\"BrtEndVolTopic\" },\n0x020A: { n:\"BrtVolSubtopic\" },\n0x020B: { n:\"BrtVolRef\" },\n0x020C: { n:\"BrtVolNum\" },\n0x020D: { n:\"BrtVolErr\" },\n0x020E: { n:\"BrtVolStr\" },\n0x020F: { n:\"BrtVolBool\" },\n0x0210: { n:\"BrtBeginCalcChain$\" },\n0x0211: { n:\"BrtEndCalcChain$\" },\n0x0212: { n:\"BrtBeginSortState\" },\n0x0213: { n:\"BrtEndSortState\" },\n0x0214: { n:\"BrtBeginSortCond\" },\n0x0215: { n:\"BrtEndSortCond\" },\n0x0216: { n:\"BrtBookProtection\" },\n0x0217: { n:\"BrtSheetProtection\" },\n0x0218: { n:\"BrtRangeProtection\" },\n0x0219: { n:\"BrtPhoneticInfo\" },\n0x021A: { n:\"BrtBeginECTxtWiz\" },\n0x021B: { n:\"BrtEndECTxtWiz\" },\n0x021C: { n:\"BrtBeginECTWFldInfoLst\" },\n0x021D: { n:\"BrtEndECTWFldInfoLst\" },\n0x021E: { n:\"BrtBeginECTwFldInfo\" },\n0x0224: { n:\"BrtFileSharing\" },\n0x0225: { n:\"BrtOleSize\" },\n0x0226: { n:\"BrtDrawing\", f:parse_RelID },\n0x0227: { n:\"BrtLegacyDrawing\" },\n0x0228: { n:\"BrtLegacyDrawingHF\" },\n0x0229: { n:\"BrtWebOpt\" },\n0x022A: { n:\"BrtBeginWebPubItems\" },\n0x022B: { n:\"BrtEndWebPubItems\" },\n0x022C: { n:\"BrtBeginWebPubItem\" },\n0x022D: { n:\"BrtEndWebPubItem\" },\n0x022E: { n:\"BrtBeginSXCondFmt\" },\n0x022F: { n:\"BrtEndSXCondFmt\" },\n0x0230: { n:\"BrtBeginSXCondFmts\" },\n0x0231: { n:\"BrtEndSXCondFmts\" },\n0x0232: { n:\"BrtBkHim\" },\n0x0234: { n:\"BrtColor\" },\n0x0235: { n:\"BrtBeginIndexedColors\" },\n0x0236: { n:\"BrtEndIndexedColors\" },\n0x0239: { n:\"BrtBeginMRUColors\" },\n0x023A: { n:\"BrtEndMRUColors\" },\n0x023C: { n:\"BrtMRUColor\" },\n0x023D: { n:\"BrtBeginDVals\" },\n0x023E: { n:\"BrtEndDVals\" },\n0x0241: { n:\"BrtSupNameStart\" },\n0x0242: { n:\"BrtSupNameValueStart\" },\n0x0243: { n:\"BrtSupNameValueEnd\" },\n0x0244: { n:\"BrtSupNameNum\" },\n0x0245: { n:\"BrtSupNameErr\" },\n0x0246: { n:\"BrtSupNameSt\" },\n0x0247: { n:\"BrtSupNameNil\" },\n0x0248: { n:\"BrtSupNameBool\" },\n0x0249: { n:\"BrtSupNameFmla\" },\n0x024A: { n:\"BrtSupNameBits\" },\n0x024B: { n:\"BrtSupNameEnd\" },\n0x024C: { n:\"BrtEndSupBook\" },\n0x024D: { n:\"BrtCellSmartTagProperty\" },\n0x024E: { n:\"BrtBeginCellSmartTag\" },\n0x024F: { n:\"BrtEndCellSmartTag\" },\n0x0250: { n:\"BrtBeginCellSmartTags\" },\n0x0251: { n:\"BrtEndCellSmartTags\" },\n0x0252: { n:\"BrtBeginSmartTags\" },\n0x0253: { n:\"BrtEndSmartTags\" },\n0x0254: { n:\"BrtSmartTagType\" },\n0x0255: { n:\"BrtBeginSmartTagTypes\" },\n0x0256: { n:\"BrtEndSmartTagTypes\" },\n0x0257: { n:\"BrtBeginSXFilters\" },\n0x0258: { n:\"BrtEndSXFilters\" },\n0x0259: { n:\"BrtBeginSXFILTER\" },\n0x025A: { n:\"BrtEndSXFilter\" },\n0x025B: { n:\"BrtBeginFills\" },\n0x025C: { n:\"BrtEndFills\" },\n0x025D: { n:\"BrtBeginCellWatches\" },\n0x025E: { n:\"BrtEndCellWatches\" },\n0x025F: { n:\"BrtCellWatch\" },\n0x0260: { n:\"BrtBeginCRErrs\" },\n0x0261: { n:\"BrtEndCRErrs\" },\n0x0262: { n:\"BrtCrashRecErr\" },\n0x0263: { n:\"BrtBeginFonts\" },\n0x0264: { n:\"BrtEndFonts\" },\n0x0265: { n:\"BrtBeginBorders\" },\n0x0266: { n:\"BrtEndBorders\" },\n0x0267: { n:\"BrtBeginFmts\" },\n0x0268: { n:\"BrtEndFmts\" },\n0x0269: { n:\"BrtBeginCellXFs\" },\n0x026A: { n:\"BrtEndCellXFs\" },\n0x026B: { n:\"BrtBeginStyles\" },\n0x026C: { n:\"BrtEndStyles\" },\n0x0271: { n:\"BrtBigName\" },\n0x0272: { n:\"BrtBeginCellStyleXFs\" },\n0x0273: { n:\"BrtEndCellStyleXFs\" },\n0x0274: { n:\"BrtBeginComments\" },\n0x0275: { n:\"BrtEndComments\" },\n0x0276: { n:\"BrtBeginCommentAuthors\" },\n0x0277: { n:\"BrtEndCommentAuthors\" },\n0x0278: { n:\"BrtCommentAuthor\", f:parse_BrtCommentAuthor },\n0x0279: { n:\"BrtBeginCommentList\" },\n0x027A: { n:\"BrtEndCommentList\" },\n0x027B: { n:\"BrtBeginComment\", f:parse_BrtBeginComment},\n0x027C: { n:\"BrtEndComment\" },\n0x027D: { n:\"BrtCommentText\", f:parse_BrtCommentText },\n0x027E: { n:\"BrtBeginOleObjects\" },\n0x027F: { n:\"BrtOleObject\" },\n0x0280: { n:\"BrtEndOleObjects\" },\n0x0281: { n:\"BrtBeginSxrules\" },\n0x0282: { n:\"BrtEndSxRules\" },\n0x0283: { n:\"BrtBeginActiveXControls\" },\n0x0284: { n:\"BrtActiveX\" },\n0x0285: { n:\"BrtEndActiveXControls\" },\n0x0286: { n:\"BrtBeginPCDSDTCEMembersSortBy\" },\n0x0288: { n:\"BrtBeginCellIgnoreECs\" },\n0x0289: { n:\"BrtCellIgnoreEC\" },\n0x028A: { n:\"BrtEndCellIgnoreECs\" },\n0x028B: { n:\"BrtCsProp\", f:parse_BrtCsProp },\n0x028C: { n:\"BrtCsPageSetup\" },\n0x028D: { n:\"BrtBeginUserCsViews\" },\n0x028E: { n:\"BrtEndUserCsViews\" },\n0x028F: { n:\"BrtBeginUserCsView\" },\n0x0290: { n:\"BrtEndUserCsView\" },\n0x0291: { n:\"BrtBeginPcdSFCIEntries\" },\n0x0292: { n:\"BrtEndPCDSFCIEntries\" },\n0x0293: { n:\"BrtPCDSFCIEntry\" },\n0x0294: { n:\"BrtBeginListParts\" },\n0x0295: { n:\"BrtListPart\" },\n0x0296: { n:\"BrtEndListParts\" },\n0x0297: { n:\"BrtSheetCalcProp\" },\n0x0298: { n:\"BrtBeginFnGroup\" },\n0x0299: { n:\"BrtFnGroup\" },\n0x029A: { n:\"BrtEndFnGroup\" },\n0x029B: { n:\"BrtSupAddin\" },\n0x029C: { n:\"BrtSXTDMPOrder\" },\n0x029D: { n:\"BrtCsProtection\" },\n0x029F: { n:\"BrtBeginWsSortMap\" },\n0x02A0: { n:\"BrtEndWsSortMap\" },\n0x02A1: { n:\"BrtBeginRRSort\" },\n0x02A2: { n:\"BrtEndRRSort\" },\n0x02A3: { n:\"BrtRRSortItem\" },\n0x02A4: { n:\"BrtFileSharingIso\" },\n0x02A5: { n:\"BrtBookProtectionIso\" },\n0x02A6: { n:\"BrtSheetProtectionIso\" },\n0x02A7: { n:\"BrtCsProtectionIso\" },\n0x02A8: { n:\"BrtRangeProtectionIso\" },\n0x02A9: { n:\"BrtDValList\" },\n0x0400: { n:\"BrtRwDescent\" },\n0x0401: { n:\"BrtKnownFonts\" },\n0x0402: { n:\"BrtBeginSXTupleSet\" },\n0x0403: { n:\"BrtEndSXTupleSet\" },\n0x0404: { n:\"BrtBeginSXTupleSetHeader\" },\n0x0405: { n:\"BrtEndSXTupleSetHeader\" },\n0x0406: { n:\"BrtSXTupleSetHeaderItem\" },\n0x0407: { n:\"BrtBeginSXTupleSetData\" },\n0x0408: { n:\"BrtEndSXTupleSetData\" },\n0x0409: { n:\"BrtBeginSXTupleSetRow\" },\n0x040A: { n:\"BrtEndSXTupleSetRow\" },\n0x040B: { n:\"BrtSXTupleSetRowItem\" },\n0x040C: { n:\"BrtNameExt\" },\n0x040D: { n:\"BrtPCDH14\" },\n0x040E: { n:\"BrtBeginPCDCalcMem14\" },\n0x040F: { n:\"BrtEndPCDCalcMem14\" },\n0x0410: { n:\"BrtSXTH14\" },\n0x0411: { n:\"BrtBeginSparklineGroup\" },\n0x0412: { n:\"BrtEndSparklineGroup\" },\n0x0413: { n:\"BrtSparkline\" },\n0x0414: { n:\"BrtSXDI14\" },\n0x0415: { n:\"BrtWsFmtInfoEx14\" },\n0x0416: { n:\"BrtBeginConditionalFormatting14\" },\n0x0417: { n:\"BrtEndConditionalFormatting14\" },\n0x0418: { n:\"BrtBeginCFRule14\" },\n0x0419: { n:\"BrtEndCFRule14\" },\n0x041A: { n:\"BrtCFVO14\" },\n0x041B: { n:\"BrtBeginDatabar14\" },\n0x041C: { n:\"BrtBeginIconSet14\" },\n0x041D: { n:\"BrtDVal14\", f: parse_BrtDVal14 },\n0x041E: { n:\"BrtBeginDVals14\" },\n0x041F: { n:\"BrtColor14\" },\n0x0420: { n:\"BrtBeginSparklines\" },\n0x0421: { n:\"BrtEndSparklines\" },\n0x0422: { n:\"BrtBeginSparklineGroups\" },\n0x0423: { n:\"BrtEndSparklineGroups\" },\n0x0425: { n:\"BrtSXVD14\" },\n0x0426: { n:\"BrtBeginSXView14\" },\n0x0427: { n:\"BrtEndSXView14\" },\n0x0428: { n:\"BrtBeginSXView16\" },\n0x0429: { n:\"BrtEndSXView16\" },\n0x042A: { n:\"BrtBeginPCD14\" },\n0x042B: { n:\"BrtEndPCD14\" },\n0x042C: { n:\"BrtBeginExtConn14\" },\n0x042D: { n:\"BrtEndExtConn14\" },\n0x042E: { n:\"BrtBeginSlicerCacheIDs\" },\n0x042F: { n:\"BrtEndSlicerCacheIDs\" },\n0x0430: { n:\"BrtBeginSlicerCacheID\" },\n0x0431: { n:\"BrtEndSlicerCacheID\" },\n0x0433: { n:\"BrtBeginSlicerCache\" },\n0x0434: { n:\"BrtEndSlicerCache\" },\n0x0435: { n:\"BrtBeginSlicerCacheDef\" },\n0x0436: { n:\"BrtEndSlicerCacheDef\" },\n0x0437: { n:\"BrtBeginSlicersEx\" },\n0x0438: { n:\"BrtEndSlicersEx\" },\n0x0439: { n:\"BrtBeginSlicerEx\" },\n0x043A: { n:\"BrtEndSlicerEx\" },\n0x043B: { n:\"BrtBeginSlicer\" },\n0x043C: { n:\"BrtEndSlicer\" },\n0x043D: { n:\"BrtSlicerCachePivotTables\" },\n0x043E: { n:\"BrtBeginSlicerCacheOlapImpl\" },\n0x043F: { n:\"BrtEndSlicerCacheOlapImpl\" },\n0x0440: { n:\"BrtBeginSlicerCacheLevelsData\" },\n0x0441: { n:\"BrtEndSlicerCacheLevelsData\" },\n0x0442: { n:\"BrtBeginSlicerCacheLevelData\" },\n0x0443: { n:\"BrtEndSlicerCacheLevelData\" },\n0x0444: { n:\"BrtBeginSlicerCacheSiRanges\" },\n0x0445: { n:\"BrtEndSlicerCacheSiRanges\" },\n0x0446: { n:\"BrtBeginSlicerCacheSiRange\" },\n0x0447: { n:\"BrtEndSlicerCacheSiRange\" },\n0x0448: { n:\"BrtSlicerCacheOlapItem\" },\n0x0449: { n:\"BrtBeginSlicerCacheSelections\" },\n0x044A: { n:\"BrtSlicerCacheSelection\" },\n0x044B: { n:\"BrtEndSlicerCacheSelections\" },\n0x044C: { n:\"BrtBeginSlicerCacheNative\" },\n0x044D: { n:\"BrtEndSlicerCacheNative\" },\n0x044E: { n:\"BrtSlicerCacheNativeItem\" },\n0x044F: { n:\"BrtRangeProtection14\" },\n0x0450: { n:\"BrtRangeProtectionIso14\" },\n0x0451: { n:\"BrtCellIgnoreEC14\" },\n0x0457: { n:\"BrtList14\" },\n0x0458: { n:\"BrtCFIcon\" },\n0x0459: { n:\"BrtBeginSlicerCachesPivotCacheIDs\" },\n0x045A: { n:\"BrtEndSlicerCachesPivotCacheIDs\" },\n0x045B: { n:\"BrtBeginSlicers\" },\n0x045C: { n:\"BrtEndSlicers\" },\n0x045D: { n:\"BrtWbProp14\" },\n0x045E: { n:\"BrtBeginSXEdit\" },\n0x045F: { n:\"BrtEndSXEdit\" },\n0x0460: { n:\"BrtBeginSXEdits\" },\n0x0461: { n:\"BrtEndSXEdits\" },\n0x0462: { n:\"BrtBeginSXChange\" },\n0x0463: { n:\"BrtEndSXChange\" },\n0x0464: { n:\"BrtBeginSXChanges\" },\n0x0465: { n:\"BrtEndSXChanges\" },\n0x0466: { n:\"BrtSXTupleItems\" },\n0x0468: { n:\"BrtBeginSlicerStyle\" },\n0x0469: { n:\"BrtEndSlicerStyle\" },\n0x046A: { n:\"BrtSlicerStyleElement\" },\n0x046B: { n:\"BrtBeginStyleSheetExt14\" },\n0x046C: { n:\"BrtEndStyleSheetExt14\" },\n0x046D: { n:\"BrtBeginSlicerCachesPivotCacheID\" },\n0x046E: { n:\"BrtEndSlicerCachesPivotCacheID\" },\n0x046F: { n:\"BrtBeginConditionalFormattings\" },\n0x0470: { n:\"BrtEndConditionalFormattings\" },\n0x0471: { n:\"BrtBeginPCDCalcMemExt\" },\n0x0472: { n:\"BrtEndPCDCalcMemExt\" },\n0x0473: { n:\"BrtBeginPCDCalcMemsExt\" },\n0x0474: { n:\"BrtEndPCDCalcMemsExt\" },\n0x0475: { n:\"BrtPCDField14\" },\n0x0476: { n:\"BrtBeginSlicerStyles\" },\n0x0477: { n:\"BrtEndSlicerStyles\" },\n0x0478: { n:\"BrtBeginSlicerStyleElements\" },\n0x0479: { n:\"BrtEndSlicerStyleElements\" },\n0x047A: { n:\"BrtCFRuleExt\" },\n0x047B: { n:\"BrtBeginSXCondFmt14\" },\n0x047C: { n:\"BrtEndSXCondFmt14\" },\n0x047D: { n:\"BrtBeginSXCondFmts14\" },\n0x047E: { n:\"BrtEndSXCondFmts14\" },\n0x0480: { n:\"BrtBeginSortCond14\" },\n0x0481: { n:\"BrtEndSortCond14\" },\n0x0482: { n:\"BrtEndDVals14\" },\n0x0483: { n:\"BrtEndIconSet14\" },\n0x0484: { n:\"BrtEndDatabar14\" },\n0x0485: { n:\"BrtBeginColorScale14\" },\n0x0486: { n:\"BrtEndColorScale14\" },\n0x0487: { n:\"BrtBeginSxrules14\" },\n0x0488: { n:\"BrtEndSxrules14\" },\n0x0489: { n:\"BrtBeginPRule14\" },\n0x048A: { n:\"BrtEndPRule14\" },\n0x048B: { n:\"BrtBeginPRFilters14\" },\n0x048C: { n:\"BrtEndPRFilters14\" },\n0x048D: { n:\"BrtBeginPRFilter14\" },\n0x048E: { n:\"BrtEndPRFilter14\" },\n0x048F: { n:\"BrtBeginPRFItem14\" },\n0x0490: { n:\"BrtEndPRFItem14\" },\n0x0491: { n:\"BrtBeginCellIgnoreECs14\" },\n0x0492: { n:\"BrtEndCellIgnoreECs14\" },\n0x0493: { n:\"BrtDxf14\" },\n0x0494: { n:\"BrtBeginDxF14s\" },\n0x0495: { n:\"BrtEndDxf14s\" },\n0x0499: { n:\"BrtFilter14\" },\n0x049A: { n:\"BrtBeginCustomFilters14\" },\n0x049C: { n:\"BrtCustomFilter14\" },\n0x049D: { n:\"BrtIconFilter14\" },\n0x049E: { n:\"BrtPivotCacheConnectionName\" },\n0x0800: { n:\"BrtBeginDecoupledPivotCacheIDs\" },\n0x0801: { n:\"BrtEndDecoupledPivotCacheIDs\" },\n0x0802: { n:\"BrtDecoupledPivotCacheID\" },\n0x0803: { n:\"BrtBeginPivotTableRefs\" },\n0x0804: { n:\"BrtEndPivotTableRefs\" },\n0x0805: { n:\"BrtPivotTableRef\" },\n0x0806: { n:\"BrtSlicerCacheBookPivotTables\" },\n0x0807: { n:\"BrtBeginSxvcells\" },\n0x0808: { n:\"BrtEndSxvcells\" },\n0x0809: { n:\"BrtBeginSxRow\" },\n0x080A: { n:\"BrtEndSxRow\" },\n0x080C: { n:\"BrtPcdCalcMem15\" },\n0x0813: { n:\"BrtQsi15\" },\n0x0814: { n:\"BrtBeginWebExtensions\" },\n0x0815: { n:\"BrtEndWebExtensions\" },\n0x0816: { n:\"BrtWebExtension\" },\n0x0817: { n:\"BrtAbsPath15\" },\n0x0818: { n:\"BrtBeginPivotTableUISettings\" },\n0x0819: { n:\"BrtEndPivotTableUISettings\" },\n0x081B: { n:\"BrtTableSlicerCacheIDs\" },\n0x081C: { n:\"BrtTableSlicerCacheID\" },\n0x081D: { n:\"BrtBeginTableSlicerCache\" },\n0x081E: { n:\"BrtEndTableSlicerCache\" },\n0x081F: { n:\"BrtSxFilter15\" },\n0x0820: { n:\"BrtBeginTimelineCachePivotCacheIDs\" },\n0x0821: { n:\"BrtEndTimelineCachePivotCacheIDs\" },\n0x0822: { n:\"BrtTimelineCachePivotCacheID\" },\n0x0823: { n:\"BrtBeginTimelineCacheIDs\" },\n0x0824: { n:\"BrtEndTimelineCacheIDs\" },\n0x0825: { n:\"BrtBeginTimelineCacheID\" },\n0x0826: { n:\"BrtEndTimelineCacheID\" },\n0x0827: { n:\"BrtBeginTimelinesEx\" },\n0x0828: { n:\"BrtEndTimelinesEx\" },\n0x0829: { n:\"BrtBeginTimelineEx\" },\n0x082A: { n:\"BrtEndTimelineEx\" },\n0x082B: { n:\"BrtWorkBookPr15\" },\n0x082C: { n:\"BrtPCDH15\" },\n0x082D: { n:\"BrtBeginTimelineStyle\" },\n0x082E: { n:\"BrtEndTimelineStyle\" },\n0x082F: { n:\"BrtTimelineStyleElement\" },\n0x0830: { n:\"BrtBeginTimelineStylesheetExt15\" },\n0x0831: { n:\"BrtEndTimelineStylesheetExt15\" },\n0x0832: { n:\"BrtBeginTimelineStyles\" },\n0x0833: { n:\"BrtEndTimelineStyles\" },\n0x0834: { n:\"BrtBeginTimelineStyleElements\" },\n0x0835: { n:\"BrtEndTimelineStyleElements\" },\n0x0836: { n:\"BrtDxf15\" },\n0x0837: { n:\"BrtBeginDxfs15\" },\n0x0838: { n:\"brtEndDxfs15\" },\n0x0839: { n:\"BrtSlicerCacheHideItemsWithNoData\" },\n0x083A: { n:\"BrtBeginItemUniqueNames\" },\n0x083B: { n:\"BrtEndItemUniqueNames\" },\n0x083C: { n:\"BrtItemUniqueName\" },\n0x083D: { n:\"BrtBeginExtConn15\" },\n0x083E: { n:\"BrtEndExtConn15\" },\n0x083F: { n:\"BrtBeginOledbPr15\" },\n0x0840: { n:\"BrtEndOledbPr15\" },\n0x0841: { n:\"BrtBeginDataFeedPr15\" },\n0x0842: { n:\"BrtEndDataFeedPr15\" },\n0x0843: { n:\"BrtTextPr15\" },\n0x0844: { n:\"BrtRangePr15\" },\n0x0845: { n:\"BrtDbCommand15\" },\n0x0846: { n:\"BrtBeginDbTables15\" },\n0x0847: { n:\"BrtEndDbTables15\" },\n0x0848: { n:\"BrtDbTable15\" },\n0x0849: { n:\"BrtBeginDataModel\" },\n0x084A: { n:\"BrtEndDataModel\" },\n0x084B: { n:\"BrtBeginModelTables\" },\n0x084C: { n:\"BrtEndModelTables\" },\n0x084D: { n:\"BrtModelTable\" },\n0x084E: { n:\"BrtBeginModelRelationships\" },\n0x084F: { n:\"BrtEndModelRelationships\" },\n0x0850: { n:\"BrtModelRelationship\" },\n0x0851: { n:\"BrtBeginECTxtWiz15\" },\n0x0852: { n:\"BrtEndECTxtWiz15\" },\n0x0853: { n:\"BrtBeginECTWFldInfoLst15\" },\n0x0854: { n:\"BrtEndECTWFldInfoLst15\" },\n0x0855: { n:\"BrtBeginECTWFldInfo15\" },\n0x0856: { n:\"BrtFieldListActiveItem\" },\n0x0857: { n:\"BrtPivotCacheIdVersion\" },\n0x0858: { n:\"BrtSXDI15\" },\n0x0859: { n:\"BrtBeginModelTimeGroupings\" },\n0x085A: { n:\"BrtEndModelTimeGroupings\" },\n0x085B: { n:\"BrtBeginModelTimeGrouping\" },\n0x085C: { n:\"BrtEndModelTimeGrouping\" },\n0x085D: { n:\"BrtModelTimeGroupingCalcCol\" },\n0x0C00: { n:\"BrtUid\" },\n0x0C01: { n:\"BrtRevisionPtr\" },\n0x13e7: { n:\"BrtBeginCalcFeatures\" },\n0x13e8: { n:\"BrtEndCalcFeatures\" },\n0x13e9: { n:\"BrtCalcFeature\" },\n0xFFFF: { n:\"\" }\n};\n\nvar XLSBRE = evert_key(XLSBRecordEnum, 'n');\n\n/* [MS-XLS] 2.3 Record Enumeration */\nvar XLSRecordEnum = {\n0x0003: { n:\"BIFF2NUM\", f:parse_BIFF2NUM },\n0x0004: { n:\"BIFF2STR\", f:parse_BIFF2STR },\n0x0006: { n:\"Formula\", f:parse_Formula },\n0x0009: { n:'BOF', f:parse_BOF },\n0x000a: { n:'EOF', f:parsenoop2 },\n0x000c: { n:\"CalcCount\", f:parseuint16 },\n0x000d: { n:\"CalcMode\", f:parseuint16 },\n0x000e: { n:\"CalcPrecision\", f:parsebool },\n0x000f: { n:\"CalcRefMode\", f:parsebool },\n0x0010: { n:\"CalcDelta\", f:parse_Xnum },\n0x0011: { n:\"CalcIter\", f:parsebool },\n0x0012: { n:\"Protect\", f:parsebool },\n0x0013: { n:\"Password\", f:parseuint16 },\n0x0014: { n:\"Header\", f:parse_XLHeaderFooter },\n0x0015: { n:\"Footer\", f:parse_XLHeaderFooter },\n0x0017: { n:\"ExternSheet\", f:parse_ExternSheet },\n0x0018: { n:\"Lbl\", f:parse_Lbl },\n0x0019: { n:\"WinProtect\", f:parsebool },\n0x001a: { n:\"VerticalPageBreaks\" },\n0x001b: { n:\"HorizontalPageBreaks\" },\n0x001c: { n:\"Note\", f:parse_Note },\n0x001d: { n:\"Selection\" },\n0x0022: { n:\"Date1904\", f:parsebool },\n0x0023: { n:\"ExternName\", f:parse_ExternName },\n0x0024: { n:\"COLWIDTH\" },\n0x0026: { n:\"LeftMargin\", f:parse_Xnum },\n0x0027: { n:\"RightMargin\", f:parse_Xnum },\n0x0028: { n:\"TopMargin\", f:parse_Xnum },\n0x0029: { n:\"BottomMargin\", f:parse_Xnum },\n0x002a: { n:\"PrintRowCol\", f:parsebool },\n0x002b: { n:\"PrintGrid\", f:parsebool },\n0x002f: { n:\"FilePass\", f:parse_FilePass },\n0x0031: { n:\"Font\", f:parse_Font },\n0x0033: { n:\"PrintSize\", f:parseuint16 },\n0x003c: { n:\"Continue\" },\n0x003d: { n:\"Window1\", f:parse_Window1 },\n0x0040: { n:\"Backup\", f:parsebool },\n0x0041: { n:\"Pane\", f:parse_Pane },\n0x0042: { n:'CodePage', f:parseuint16 },\n0x004d: { n:\"Pls\" },\n0x0050: { n:\"DCon\" },\n0x0051: { n:\"DConRef\" },\n0x0052: { n:\"DConName\" },\n0x0055: { n:\"DefColWidth\", f:parseuint16 },\n0x0059: { n:\"XCT\" },\n0x005a: { n:\"CRN\" },\n0x005b: { n:\"FileSharing\" },\n0x005c: { n:'WriteAccess', f:parse_WriteAccess },\n0x005d: { n:\"Obj\", f:parse_Obj },\n0x005e: { n:\"Uncalced\" },\n0x005f: { n:\"CalcSaveRecalc\", f:parsebool },\n0x0060: { n:\"Template\" },\n0x0061: { n:\"Intl\" },\n0x0063: { n:\"ObjProtect\", f:parsebool },\n0x007d: { n:\"ColInfo\", f:parse_ColInfo },\n0x0080: { n:\"Guts\", f:parse_Guts },\n0x0081: { n:\"WsBool\", f:parse_WsBool },\n0x0082: { n:\"GridSet\", f:parseuint16 },\n0x0083: { n:\"HCenter\", f:parsebool },\n0x0084: { n:\"VCenter\", f:parsebool },\n0x0085: { n:'BoundSheet8', f:parse_BoundSheet8 },\n0x0086: { n:\"WriteProtect\" },\n0x008c: { n:\"Country\", f:parse_Country },\n0x008d: { n:\"HideObj\", f:parseuint16 },\n0x0090: { n:\"Sort\" },\n0x0092: { n:\"Palette\", f:parse_Palette },\n0x0097: { n:\"Sync\" },\n0x0098: { n:\"LPr\" },\n0x0099: { n:\"DxGCol\" },\n0x009a: { n:\"FnGroupName\" },\n0x009b: { n:\"FilterMode\" },\n0x009c: { n:\"BuiltInFnGroupCount\", f:parseuint16 },\n0x009d: { n:\"AutoFilterInfo\" },\n0x009e: { n:\"AutoFilter\" },\n0x00a0: { n:\"Scl\", f:parse_Scl },\n0x00a1: { n:\"Setup\", f:parse_Setup },\n0x00ae: { n:\"ScenMan\" },\n0x00af: { n:\"SCENARIO\" },\n0x00b0: { n:\"SxView\" },\n0x00b1: { n:\"Sxvd\" },\n0x00b2: { n:\"SXVI\" },\n0x00b4: { n:\"SxIvd\" },\n0x00b5: { n:\"SXLI\" },\n0x00b6: { n:\"SXPI\" },\n0x00b8: { n:\"DocRoute\" },\n0x00b9: { n:\"RecipName\" },\n0x00bd: { n:\"MulRk\", f:parse_MulRk },\n0x00be: { n:\"MulBlank\", f:parse_MulBlank },\n0x00c1: { n:'Mms', f:parsenoop2 },\n0x00c5: { n:\"SXDI\" },\n0x00c6: { n:\"SXDB\" },\n0x00c7: { n:\"SXFDB\" },\n0x00c8: { n:\"SXDBB\" },\n0x00c9: { n:\"SXNum\" },\n0x00ca: { n:\"SxBool\", f:parsebool },\n0x00cb: { n:\"SxErr\" },\n0x00cc: { n:\"SXInt\" },\n0x00cd: { n:\"SXString\" },\n0x00ce: { n:\"SXDtr\" },\n0x00cf: { n:\"SxNil\" },\n0x00d0: { n:\"SXTbl\" },\n0x00d1: { n:\"SXTBRGIITM\" },\n0x00d2: { n:\"SxTbpg\" },\n0x00d3: { n:\"ObProj\" },\n0x00d5: { n:\"SXStreamID\" },\n0x00d7: { n:\"DBCell\" },\n0x00d8: { n:\"SXRng\" },\n0x00d9: { n:\"SxIsxoper\" },\n0x00da: { n:\"BookBool\", f:parseuint16 },\n0x00dc: { n:\"DbOrParamQry\" },\n0x00dd: { n:\"ScenarioProtect\", f:parsebool },\n0x00de: { n:\"OleObjectSize\" },\n0x00e0: { n:\"XF\", f:parse_XF },\n0x00e1: { n:'InterfaceHdr', f:parse_InterfaceHdr },\n0x00e2: { n:'InterfaceEnd', f:parsenoop2 },\n0x00e3: { n:\"SXVS\" },\n0x00e5: { n:\"MergeCells\", f:parse_MergeCells },\n0x00e9: { n:\"BkHim\" },\n0x00eb: { n:\"MsoDrawingGroup\" },\n0x00ec: { n:\"MsoDrawing\" },\n0x00ed: { n:\"MsoDrawingSelection\" },\n0x00ef: { n:\"PhoneticInfo\" },\n0x00f0: { n:\"SxRule\" },\n0x00f1: { n:\"SXEx\" },\n0x00f2: { n:\"SxFilt\" },\n0x00f4: { n:\"SxDXF\" },\n0x00f5: { n:\"SxItm\" },\n0x00f6: { n:\"SxName\" },\n0x00f7: { n:\"SxSelect\" },\n0x00f8: { n:\"SXPair\" },\n0x00f9: { n:\"SxFmla\" },\n0x00fb: { n:\"SxFormat\" },\n0x00fc: { n:\"SST\", f:parse_SST },\n0x00fd: { n:\"LabelSst\", f:parse_LabelSst },\n0x00ff: { n:\"ExtSST\", f:parse_ExtSST },\n0x0100: { n:\"SXVDEx\" },\n0x0103: { n:\"SXFormula\" },\n0x0122: { n:\"SXDBEx\" },\n0x0137: { n:\"RRDInsDel\" },\n0x0138: { n:\"RRDHead\" },\n0x013b: { n:\"RRDChgCell\" },\n0x013d: { n:\"RRTabId\", f:parseuint16a },\n0x013e: { n:\"RRDRenSheet\" },\n0x013f: { n:\"RRSort\" },\n0x0140: { n:\"RRDMove\" },\n0x014a: { n:\"RRFormat\" },\n0x014b: { n:\"RRAutoFmt\" },\n0x014d: { n:\"RRInsertSh\" },\n0x014e: { n:\"RRDMoveBegin\" },\n0x014f: { n:\"RRDMoveEnd\" },\n0x0150: { n:\"RRDInsDelBegin\" },\n0x0151: { n:\"RRDInsDelEnd\" },\n0x0152: { n:\"RRDConflict\" },\n0x0153: { n:\"RRDDefName\" },\n0x0154: { n:\"RRDRstEtxp\" },\n0x015f: { n:\"LRng\" },\n0x0160: { n:\"UsesELFs\", f:parsebool },\n0x0161: { n:\"DSF\", f:parsenoop2 },\n0x0191: { n:\"CUsr\" },\n0x0192: { n:\"CbUsr\" },\n0x0193: { n:\"UsrInfo\" },\n0x0194: { n:\"UsrExcl\" },\n0x0195: { n:\"FileLock\" },\n0x0196: { n:\"RRDInfo\" },\n0x0197: { n:\"BCUsrs\" },\n0x0198: { n:\"UsrChk\" },\n0x01a9: { n:\"UserBView\" },\n0x01aa: { n:\"UserSViewBegin\" },\n0x01ab: { n:\"UserSViewEnd\" },\n0x01ac: { n:\"RRDUserView\" },\n0x01ad: { n:\"Qsi\" },\n0x01ae: { n:\"SupBook\", f:parse_SupBook },\n0x01af: { n:\"Prot4Rev\", f:parsebool },\n0x01b0: { n:\"CondFmt\" },\n0x01b1: { n:\"CF\" },\n0x01b2: { n:\"DVal\" },\n0x01b5: { n:\"DConBin\" },\n0x01b6: { n:\"TxO\", f:parse_TxO },\n0x01b7: { n:\"RefreshAll\", f:parsebool },\n0x01b8: { n:\"HLink\", f:parse_HLink },\n0x01b9: { n:\"Lel\" },\n0x01ba: { n:\"CodeName\", f:parse_XLUnicodeString },\n0x01bb: { n:\"SXFDBType\" },\n0x01bc: { n:\"Prot4RevPass\", f:parseuint16 },\n0x01bd: { n:\"ObNoMacros\" },\n0x01be: { n:\"Dv\" },\n0x01c0: { n:\"Excel9File\", f:parsenoop2 },\n0x01c1: { n:\"RecalcId\", f:parse_RecalcId, r:2},\n0x01c2: { n:\"EntExU2\", f:parsenoop2 },\n0x0200: { n:\"Dimensions\", f:parse_Dimensions },\n0x0201: { n:\"Blank\", f:parse_Blank },\n0x0203: { n:\"Number\", f:parse_Number },\n0x0204: { n:\"Label\", f:parse_Label },\n0x0205: { n:\"BoolErr\", f:parse_BoolErr },\n0x0206: { n:\"Formula\", f:parse_Formula },\n0x0207: { n:\"String\", f:parse_String },\n0x0208: { n:'Row', f:parse_Row },\n0x020b: { n:\"Index\" },\n0x0221: { n:\"Array\", f:parse_Array },\n0x0225: { n:\"DefaultRowHeight\", f:parse_DefaultRowHeight },\n0x0236: { n:\"Table\" },\n0x023e: { n:\"Window2\", f:parse_Window2 },\n0x027e: { n:\"RK\", f:parse_RK },\n0x0293: { n:\"Style\" },\n0x0406: { n:\"Formula\", f:parse_Formula },\n0x0418: { n:\"BigName\" },\n0x041e: { n:\"Format\", f:parse_Format },\n0x043c: { n:\"ContinueBigName\" },\n0x04bc: { n:\"ShrFmla\", f:parse_ShrFmla },\n0x0800: { n:\"HLinkTooltip\", f:parse_HLinkTooltip },\n0x0801: { n:\"WebPub\" },\n0x0802: { n:\"QsiSXTag\" },\n0x0803: { n:\"DBQueryExt\" },\n0x0804: { n:\"ExtString\" },\n0x0805: { n:\"TxtQry\" },\n0x0806: { n:\"Qsir\" },\n0x0807: { n:\"Qsif\" },\n0x0808: { n:\"RRDTQSIF\" },\n0x0809: { n:'BOF', f:parse_BOF },\n0x080a: { n:\"OleDbConn\" },\n0x080b: { n:\"WOpt\" },\n0x080c: { n:\"SXViewEx\" },\n0x080d: { n:\"SXTH\" },\n0x080e: { n:\"SXPIEx\" },\n0x080f: { n:\"SXVDTEx\" },\n0x0810: { n:\"SXViewEx9\" },\n0x0812: { n:\"ContinueFrt\" },\n0x0813: { n:\"RealTimeData\" },\n0x0850: { n:\"ChartFrtInfo\" },\n0x0851: { n:\"FrtWrapper\" },\n0x0852: { n:\"StartBlock\" },\n0x0853: { n:\"EndBlock\" },\n0x0854: { n:\"StartObject\" },\n0x0855: { n:\"EndObject\" },\n0x0856: { n:\"CatLab\" },\n0x0857: { n:\"YMult\" },\n0x0858: { n:\"SXViewLink\" },\n0x0859: { n:\"PivotChartBits\" },\n0x085a: { n:\"FrtFontList\" },\n0x0862: { n:\"SheetExt\" },\n0x0863: { n:\"BookExt\", r:12},\n0x0864: { n:\"SXAddl\" },\n0x0865: { n:\"CrErr\" },\n0x0866: { n:\"HFPicture\" },\n0x0867: { n:'FeatHdr', f:parsenoop2 },\n0x0868: { n:\"Feat\" },\n0x086a: { n:\"DataLabExt\" },\n0x086b: { n:\"DataLabExtContents\" },\n0x086c: { n:\"CellWatch\" },\n0x0871: { n:\"FeatHdr11\" },\n0x0872: { n:\"Feature11\" },\n0x0874: { n:\"DropDownObjIds\" },\n0x0875: { n:\"ContinueFrt11\" },\n0x0876: { n:\"DConn\" },\n0x0877: { n:\"List12\" },\n0x0878: { n:\"Feature12\" },\n0x0879: { n:\"CondFmt12\" },\n0x087a: { n:\"CF12\" },\n0x087b: { n:\"CFEx\" },\n0x087c: { n:\"XFCRC\", f:parse_XFCRC, r:12 },\n0x087d: { n:\"XFExt\", f:parse_XFExt, r:12 },\n0x087e: { n:\"AutoFilter12\" },\n0x087f: { n:\"ContinueFrt12\" },\n0x0884: { n:\"MDTInfo\" },\n0x0885: { n:\"MDXStr\" },\n0x0886: { n:\"MDXTuple\" },\n0x0887: { n:\"MDXSet\" },\n0x0888: { n:\"MDXProp\" },\n0x0889: { n:\"MDXKPI\" },\n0x088a: { n:\"MDB\" },\n0x088b: { n:\"PLV\" },\n0x088c: { n:\"Compat12\", f:parsebool, r:12 },\n0x088d: { n:\"DXF\" },\n0x088e: { n:\"TableStyles\", r:12 },\n0x088f: { n:\"TableStyle\" },\n0x0890: { n:\"TableStyleElement\" },\n0x0892: { n:\"StyleExt\" },\n0x0893: { n:\"NamePublish\" },\n0x0894: { n:\"NameCmt\", f:parse_NameCmt, r:12 },\n0x0895: { n:\"SortData\" },\n0x0896: { n:\"Theme\", f:parse_Theme, r:12 },\n0x0897: { n:\"GUIDTypeLib\" },\n0x0898: { n:\"FnGrp12\" },\n0x0899: { n:\"NameFnGrp12\" },\n0x089a: { n:\"MTRSettings\", f:parse_MTRSettings, r:12 },\n0x089b: { n:\"CompressPictures\", f:parsenoop2 },\n0x089c: { n:\"HeaderFooter\" },\n0x089d: { n:\"CrtLayout12\" },\n0x089e: { n:\"CrtMlFrt\" },\n0x089f: { n:\"CrtMlFrtContinue\" },\n0x08a3: { n:\"ForceFullCalculation\", f:parse_ForceFullCalculation },\n0x08a4: { n:\"ShapePropsStream\" },\n0x08a5: { n:\"TextPropsStream\" },\n0x08a6: { n:\"RichTextStream\" },\n0x08a7: { n:\"CrtLayout12A\" },\n0x1001: { n:\"Units\" },\n0x1002: { n:\"Chart\" },\n0x1003: { n:\"Series\" },\n0x1006: { n:\"DataFormat\" },\n0x1007: { n:\"LineFormat\" },\n0x1009: { n:\"MarkerFormat\" },\n0x100a: { n:\"AreaFormat\" },\n0x100b: { n:\"PieFormat\" },\n0x100c: { n:\"AttachedLabel\" },\n0x100d: { n:\"SeriesText\" },\n0x1014: { n:\"ChartFormat\" },\n0x1015: { n:\"Legend\" },\n0x1016: { n:\"SeriesList\" },\n0x1017: { n:\"Bar\" },\n0x1018: { n:\"Line\" },\n0x1019: { n:\"Pie\" },\n0x101a: { n:\"Area\" },\n0x101b: { n:\"Scatter\" },\n0x101c: { n:\"CrtLine\" },\n0x101d: { n:\"Axis\" },\n0x101e: { n:\"Tick\" },\n0x101f: { n:\"ValueRange\" },\n0x1020: { n:\"CatSerRange\" },\n0x1021: { n:\"AxisLine\" },\n0x1022: { n:\"CrtLink\" },\n0x1024: { n:\"DefaultText\" },\n0x1025: { n:\"Text\" },\n0x1026: { n:\"FontX\", f:parseuint16 },\n0x1027: { n:\"ObjectLink\" },\n0x1032: { n:\"Frame\" },\n0x1033: { n:\"Begin\" },\n0x1034: { n:\"End\" },\n0x1035: { n:\"PlotArea\" },\n0x103a: { n:\"Chart3d\" },\n0x103c: { n:\"PicF\" },\n0x103d: { n:\"DropBar\" },\n0x103e: { n:\"Radar\" },\n0x103f: { n:\"Surf\" },\n0x1040: { n:\"RadarArea\" },\n0x1041: { n:\"AxisParent\" },\n0x1043: { n:\"LegendException\" },\n0x1044: { n:\"ShtProps\", f:parse_ShtProps },\n0x1045: { n:\"SerToCrt\" },\n0x1046: { n:\"AxesUsed\" },\n0x1048: { n:\"SBaseRef\" },\n0x104a: { n:\"SerParent\" },\n0x104b: { n:\"SerAuxTrend\" },\n0x104e: { n:\"IFmtRecord\" },\n0x104f: { n:\"Pos\" },\n0x1050: { n:\"AlRuns\" },\n0x1051: { n:\"BRAI\" },\n0x105b: { n:\"SerAuxErrBar\" },\n0x105c: { n:\"ClrtClient\", f:parse_ClrtClient },\n0x105d: { n:\"SerFmt\" },\n0x105f: { n:\"Chart3DBarShape\" },\n0x1060: { n:\"Fbi\" },\n0x1061: { n:\"BopPop\" },\n0x1062: { n:\"AxcExt\" },\n0x1063: { n:\"Dat\" },\n0x1064: { n:\"PlotGrowth\" },\n0x1065: { n:\"SIIndex\" },\n0x1066: { n:\"GelFrame\" },\n0x1067: { n:\"BopPopCustom\" },\n0x1068: { n:\"Fbi2\" },\n\n0x0000: { n:\"Dimensions\", f:parse_Dimensions },\n0x0002: { n:\"BIFF2INT\", f:parse_BIFF2INT },\n0x0005: { n:\"BoolErr\", f:parse_BoolErr },\n0x0007: { n:\"String\", f:parse_BIFF2STRING },\n0x0008: { n:\"BIFF2ROW\" },\n0x000b: { n:\"Index\" },\n0x0016: { n:\"ExternCount\", f:parseuint16 },\n0x001e: { n:\"BIFF2FORMAT\", f:parse_BIFF2Format },\n0x001f: { n:\"BIFF2FMTCNT\" }, /* 16-bit cnt of BIFF2FORMAT records */\n0x0020: { n:\"BIFF2COLINFO\" },\n0x0021: { n:\"Array\", f:parse_Array },\n0x0025: { n:\"DefaultRowHeight\", f:parse_DefaultRowHeight },\n0x0032: { n:\"BIFF2FONTXTRA\", f:parse_BIFF2FONTXTRA },\n0x0034: { n:\"DDEObjName\" },\n0x003e: { n:\"BIFF2WINDOW2\" },\n0x0043: { n:\"BIFF2XF\" },\n0x0045: { n:\"BIFF2FONTCLR\" },\n0x0056: { n:\"BIFF4FMTCNT\" }, /* 16-bit cnt, similar to BIFF2 */\n0x007e: { n:\"RK\" }, /* Not necessarily same as 0x027e */\n0x007f: { n:\"ImData\", f:parse_ImData },\n0x0087: { n:\"Addin\" },\n0x0088: { n:\"Edg\" },\n0x0089: { n:\"Pub\" },\n0x0091: { n:\"Sub\" },\n0x0094: { n:\"LHRecord\" },\n0x0095: { n:\"LHNGraph\" },\n0x0096: { n:\"Sound\" },\n0x00a9: { n:\"CoordList\" },\n0x00ab: { n:\"GCW\" },\n0x00bc: { n:\"ShrFmla\" }, /* Not necessarily same as 0x04bc */\n0x00bf: { n:\"ToolbarHdr\" },\n0x00c0: { n:\"ToolbarEnd\" },\n0x00c2: { n:\"AddMenu\" },\n0x00c3: { n:\"DelMenu\" },\n0x00d6: { n:\"RString\", f:parse_RString },\n0x00df: { n:\"UDDesc\" },\n0x00ea: { n:\"TabIdConf\" },\n0x0162: { n:\"XL5Modify\" },\n0x01a5: { n:\"FileSharing2\" },\n0x0209: { n:'BOF', f:parse_BOF },\n0x0218: { n:\"Lbl\", f:parse_Lbl },\n0x0223: { n:\"ExternName\", f:parse_ExternName },\n0x0231: { n:\"Font\" },\n0x0243: { n:\"BIFF3XF\" },\n0x0409: { n:'BOF', f:parse_BOF },\n0x0443: { n:\"BIFF4XF\" },\n0x086d: { n:\"FeatInfo\" },\n0x0873: { n:\"FeatInfo11\" },\n0x0881: { n:\"SXAddl12\" },\n0x08c0: { n:\"AutoWebPub\" },\n0x08c1: { n:\"ListObj\" },\n0x08c2: { n:\"ListField\" },\n0x08c3: { n:\"ListDV\" },\n0x08c4: { n:\"ListCondFmt\" },\n0x08c5: { n:\"ListCF\" },\n0x08c6: { n:\"FMQry\" },\n0x08c7: { n:\"FMSQry\" },\n0x08c8: { n:\"PLV\" },\n0x08c9: { n:\"LnExt\" },\n0x08ca: { n:\"MkrExt\" },\n0x08cb: { n:\"CrtCoopt\" },\n0x08d6: { n:\"FRTArchId$\", r:12 },\n\n0x7262: {}\n};\n\nvar XLSRE = evert_key(XLSRecordEnum, 'n');\nfunction write_biff_rec(ba, type, payload, length) {\n\tvar t = +type || +XLSRE[type];\n\tif(isNaN(t)) return;\n\tvar len = length || (payload||[]).length || 0;\n\tvar o = ba.next(4);\n\to.write_shift(2, t);\n\to.write_shift(2, len);\n\tif(len > 0 && is_buf(payload)) ba.push(payload);\n}\n\nfunction write_biff_continue(ba, type, payload, length) {\n\tvar len = length || (payload||[]).length || 0;\n\tif(len <= 8224) return write_biff_rec(ba, type, payload, len);\n\tvar t = +type || +XLSRE[type];\n\tif(isNaN(t)) return;\n\tvar parts = payload.parts || [], sidx = 0;\n\tvar i = 0, w = 0;\n\twhile(w + (parts[sidx] || 8224) <= 8224) { w+= (parts[sidx] || 8224); sidx++; }\n\tvar o = ba.next(4);\n\to.write_shift(2, t);\n\to.write_shift(2, w);\n\tba.push(payload.slice(i, i + w));\n\ti += w;\n\twhile(i < len) {\n\t\to = ba.next(4);\n\t\to.write_shift(2, 0x3c); // TODO: figure out correct continue type\n\t\tw = 0;\n\t\twhile(w + (parts[sidx] || 8224) <= 8224) { w+= (parts[sidx] || 8224); sidx++; }\n\t\to.write_shift(2, w);\n\t\tba.push(payload.slice(i, i+w)); i+= w;\n\t}\n}\n\nfunction write_BIFF2Cell(out, r, c) {\n\tif(!out) out = new_buf(7);\n\tout.write_shift(2, r);\n\tout.write_shift(2, c);\n\tout.write_shift(2, 0);\n\tout.write_shift(1, 0);\n\treturn out;\n}\n\nfunction write_BIFF2BERR(r, c, val, t) {\n\tvar out = new_buf(9);\n\twrite_BIFF2Cell(out, r, c);\n\tif(t == 'e') { out.write_shift(1, val); out.write_shift(1, 1); }\n\telse { out.write_shift(1, val?1:0); out.write_shift(1, 0); }\n\treturn out;\n}\n\n/* TODO: codepage, large strings */\nfunction write_BIFF2LABEL(r, c, val) {\n\tvar out = new_buf(8 + 2*val.length);\n\twrite_BIFF2Cell(out, r, c);\n\tout.write_shift(1, val.length);\n\tout.write_shift(val.length, val, 'sbcs');\n\treturn out.l < out.length ? out.slice(0, out.l) : out;\n}\n\nfunction write_ws_biff2_cell(ba, cell, R, C) {\n\tif(cell.v != null) switch(cell.t) {\n\t\tcase 'd': case 'n':\n\t\t\tvar v = cell.t == 'd' ? datenum(parseDate(cell.v)) : cell.v;\n\t\t\tif((v == (v|0)) && (v >= 0) && (v < 65536))\n\t\t\t\twrite_biff_rec(ba, 0x0002, write_BIFF2INT(R, C, v));\n\t\t\telse\n\t\t\t\twrite_biff_rec(ba, 0x0003, write_BIFF2NUM(R,C, v));\n\t\t\treturn;\n\t\tcase 'b': case 'e': write_biff_rec(ba, 0x0005, write_BIFF2BERR(R, C, cell.v, cell.t)); return;\n\t\t/* TODO: codepage, sst */\n\t\tcase 's': case 'str':\n\t\t\twrite_biff_rec(ba, 0x0004, write_BIFF2LABEL(R, C, cell.v));\n\t\t\treturn;\n\t}\n\twrite_biff_rec(ba, 0x0001, write_BIFF2Cell(null, R, C));\n}\n\nfunction write_ws_biff2(ba, ws, idx, opts) {\n\tvar dense = Array.isArray(ws);\n\tvar range = safe_decode_range(ws['!ref'] || \"A1\"), ref, rr = \"\", cols = [];\n\tif(range.e.c > 0xFF || range.e.r > 0x3FFF) {\n\t\tif(opts.WTF) throw new Error(\"Range \" + (ws['!ref'] || \"A1\") + \" exceeds format limit A1:IV16384\");\n\t\trange.e.c = Math.min(range.e.c, 0xFF);\n\t\trange.e.r = Math.min(range.e.c, 0x3FFF);\n\t\tref = encode_range(range);\n\t}\n\tfor(var R = range.s.r; R <= range.e.r; ++R) {\n\t\trr = encode_row(R);\n\t\tfor(var C = range.s.c; C <= range.e.c; ++C) {\n\t\t\tif(R === range.s.r) cols[C] = encode_col(C);\n\t\t\tref = cols[C] + rr;\n\t\t\tvar cell = dense ? (ws[R]||[])[C] : ws[ref];\n\t\t\tif(!cell) continue;\n\t\t\t/* write cell */\n\t\t\twrite_ws_biff2_cell(ba, cell, R, C, opts);\n\t\t}\n\t}\n}\n\n/* Based on test files */\nfunction write_biff2_buf(wb, opts) {\n\tvar o = opts || {};\n\tif(DENSE != null && o.dense == null) o.dense = DENSE;\n\tvar ba = buf_array();\n\tvar idx = 0;\n\tfor(var i=0;i 0xFF || range.e.r >= MAX_ROWS) {\n\t\tif(opts.WTF) throw new Error(\"Range \" + (ws['!ref'] || \"A1\") + \" exceeds format limit A1:IV16384\");\n\t\trange.e.c = Math.min(range.e.c, 0xFF);\n\t\trange.e.r = Math.min(range.e.c, MAX_ROWS-1);\n\t}\n\n\twrite_biff_rec(ba, 0x0809, write_BOF(wb, 0x10, opts));\n\t/* [Uncalced] Index */\n\twrite_biff_rec(ba, \"CalcMode\", writeuint16(1));\n\twrite_biff_rec(ba, \"CalcCount\", writeuint16(100));\n\twrite_biff_rec(ba, \"CalcRefMode\", writebool(true));\n\twrite_biff_rec(ba, \"CalcIter\", writebool(false));\n\twrite_biff_rec(ba, \"CalcDelta\", write_Xnum(0.001));\n\twrite_biff_rec(ba, \"CalcSaveRecalc\", writebool(true));\n\twrite_biff_rec(ba, \"PrintRowCol\", writebool(false));\n\twrite_biff_rec(ba, \"PrintGrid\", writebool(false));\n\twrite_biff_rec(ba, \"GridSet\", writeuint16(1));\n\twrite_biff_rec(ba, \"Guts\", write_Guts([0,0]));\n\t/* DefaultRowHeight WsBool [Sync] [LPr] [HorizontalPageBreaks] [VerticalPageBreaks] */\n\t/* Header (string) */\n\t/* Footer (string) */\n\twrite_biff_rec(ba, \"HCenter\", writebool(false));\n\twrite_biff_rec(ba, \"VCenter\", writebool(false));\n\t/* ... */\n\twrite_biff_rec(ba, 0x200, write_Dimensions(range, opts));\n\t/* ... */\n\n\tif(b8) ws['!links'] = [];\n\tfor(var R = range.s.r; R <= range.e.r; ++R) {\n\t\trr = encode_row(R);\n\t\tfor(var C = range.s.c; C <= range.e.c; ++C) {\n\t\t\tif(R === range.s.r) cols[C] = encode_col(C);\n\t\t\tref = cols[C] + rr;\n\t\t\tvar cell = dense ? (ws[R]||[])[C] : ws[ref];\n\t\t\tif(!cell) continue;\n\t\t\t/* write cell */\n\t\t\twrite_ws_biff8_cell(ba, cell, R, C, opts);\n\t\t\tif(b8 && cell.l) ws['!links'].push([ref, cell.l]);\n\t\t}\n\t}\n\tvar cname = _sheet.CodeName || _sheet.name || s;\n\t/* ... */\n\tif(b8) write_biff_rec(ba, \"Window2\", write_Window2((_WB.Views||[])[0]));\n\t/* ... */\n\tif(b8 && (ws['!merges']||[]).length) write_biff_rec(ba, \"MergeCells\", write_MergeCells(ws['!merges']));\n\t/* [LRng] *QUERYTABLE [PHONETICINFO] CONDFMTS */\n\tif(b8) write_ws_biff8_hlinks(ba, ws);\n\t/* [DVAL] */\n\twrite_biff_rec(ba, \"CodeName\", write_XLUnicodeString(cname, opts));\n\t/* *WebPub *CellWatch [SheetExt] */\n\tif(b8) write_FEAT(ba, ws);\n\t/* *FEAT11 *RECORD12 */\n\twrite_biff_rec(ba, \"EOF\");\n\treturn ba.end();\n}\n\n/* [MS-XLS] 2.1.7.20.3 */\nfunction write_biff8_global(wb, bufs, opts) {\n\tvar A = buf_array();\n\tvar _WB = ((wb||{}).Workbook||{});\n\tvar _sheets = (_WB.Sheets||[]);\n\tvar _wb = _WB.WBProps||{};\n\tvar b8 = opts.biff == 8, b5 = opts.biff == 5;\n\twrite_biff_rec(A, 0x0809, write_BOF(wb, 0x05, opts));\n\tif(opts.bookType == \"xla\") write_biff_rec(A, \"Addin\");\n\twrite_biff_rec(A, \"InterfaceHdr\", b8 ? writeuint16(0x04b0) : null);\n\twrite_biff_rec(A, \"Mms\", writezeroes(2));\n\tif(b5) write_biff_rec(A, \"ToolbarHdr\");\n\tif(b5) write_biff_rec(A, \"ToolbarEnd\");\n\twrite_biff_rec(A, \"InterfaceEnd\");\n\twrite_biff_rec(A, \"WriteAccess\", write_WriteAccess(\"SheetJS\", opts));\n\t/* [FileSharing] */\n\twrite_biff_rec(A, \"CodePage\", writeuint16(b8 ? 0x04b0 : 0x04E4));\n\t/* *2047 Lel */\n\tif(b8) write_biff_rec(A, \"DSF\", writeuint16(0));\n\tif(b8) write_biff_rec(A, \"Excel9File\");\n\twrite_biff_rec(A, \"RRTabId\", write_RRTabId(wb.SheetNames.length));\n\tif(b8 && wb.vbaraw) write_biff_rec(A, \"ObProj\");\n\t/* [ObNoMacros] */\n\tif(b8 && wb.vbaraw) {\n\t\tvar cname = _wb.CodeName || \"ThisWorkbook\";\n\t\twrite_biff_rec(A, \"CodeName\", write_XLUnicodeString(cname, opts));\n\t}\n\twrite_biff_rec(A, \"BuiltInFnGroupCount\", writeuint16(0x11));\n\t/* *FnGroupName *FnGrp12 */\n\t/* *Lbl */\n\t/* [OleObjectSize] */\n\twrite_biff_rec(A, \"WinProtect\", writebool(false));\n\twrite_biff_rec(A, \"Protect\", writebool(false));\n\twrite_biff_rec(A, \"Password\", writeuint16(0));\n\tif(b8) write_biff_rec(A, \"Prot4Rev\", writebool(false));\n\tif(b8) write_biff_rec(A, \"Prot4RevPass\", writeuint16(0));\n\twrite_biff_rec(A, \"Window1\", write_Window1(opts));\n\twrite_biff_rec(A, \"Backup\", writebool(false));\n\twrite_biff_rec(A, \"HideObj\", writeuint16(0));\n\twrite_biff_rec(A, \"Date1904\", writebool(safe1904(wb)==\"true\"));\n\twrite_biff_rec(A, \"CalcPrecision\", writebool(true));\n\tif(b8) write_biff_rec(A, \"RefreshAll\", writebool(false));\n\twrite_biff_rec(A, \"BookBool\", writeuint16(0));\n\t/* ... */\n\twrite_FONTS_biff8(A, wb, opts);\n\twrite_FMTS_biff8(A, wb.SSF, opts);\n\twrite_CELLXFS_biff8(A, opts);\n\t/* ... */\n\tif(b8) write_biff_rec(A, \"UsesELFs\", writebool(false));\n\tvar a = A.end();\n\n\tvar C = buf_array();\n\t/* METADATA [MTRSettings] [ForceFullCalculation] */\n\tif(b8) write_biff_rec(C, \"Country\", write_Country());\n\t/* *SUPBOOK *LBL *RTD [RecalcId] *HFPicture *MSODRAWINGGROUP */\n\n\t/* BIFF8: [SST *Continue] ExtSST */\n\tif(b8 && opts.Strings) write_biff_continue(C, \"SST\", write_SST(opts.Strings, opts));\n\n\t/* *WebPub [WOpt] [CrErr] [BookExt] *FeatHdr *DConn [THEME] [CompressPictures] [Compat12] [GUIDTypeLib] */\n\twrite_biff_rec(C, \"EOF\");\n\tvar c = C.end();\n\n\tvar B = buf_array();\n\tvar blen = 0, j = 0;\n\tfor(j = 0; j < wb.SheetNames.length; ++j) blen += (b8 ? 12 : 11) + (b8 ? 2 : 1) * wb.SheetNames[j].length;\n\tvar start = a.length + blen + c.length;\n\tfor(j = 0; j < wb.SheetNames.length; ++j) {\n\t\tvar _sheet = _sheets[j] || ({});\n\t\twrite_biff_rec(B, \"BoundSheet8\", write_BoundSheet8({pos:start, hs:_sheet.Hidden||0, dt:0, name:wb.SheetNames[j]}, opts));\n\t\tstart += bufs[j].length;\n\t}\n\t/* 1*BoundSheet8 */\n\tvar b = B.end();\n\tif(blen != b.length) throw new Error(\"BS8 \" + blen + \" != \" + b.length);\n\n\tvar out = [];\n\tif(a.length) out.push(a);\n\tif(b.length) out.push(b);\n\tif(c.length) out.push(c);\n\treturn __toBuffer([out]);\n}\n\n/* [MS-XLS] 2.1.7.20 Workbook Stream */\nfunction write_biff8_buf(wb, opts) {\n\tvar o = opts || {};\n\tvar bufs = [];\n\n\tif(wb && !wb.SSF) {\n\t\twb.SSF = SSF.get_table();\n\t}\n\tif(wb && wb.SSF) {\n\t\tmake_ssf(SSF); SSF.load_table(wb.SSF);\n\t\t// $FlowIgnore\n\t\to.revssf = evert_num(wb.SSF); o.revssf[wb.SSF[65535]] = 0;\n\t\to.ssf = wb.SSF;\n\t}\n\n\to.Strings = []; o.Strings.Count = 0; o.Strings.Unique = 0;\n\tfix_write_opts(o);\n\n\to.cellXfs = [];\n\tget_cell_style(o.cellXfs, {}, {revssf:{\"General\":0}});\n\n\tif(!wb.Props) wb.Props = {};\n\n\tfor(var i = 0; i < wb.SheetNames.length; ++i) bufs[bufs.length] = write_ws_biff8(i, o, wb);\n\tbufs.unshift(write_biff8_global(wb, bufs, o));\n\treturn __toBuffer([bufs]);\n}\n\nfunction write_biff_buf(wb, opts) {\n\tvar o = opts || {};\n\tswitch(o.biff || 2) {\n\t\tcase 8: case 5: return write_biff8_buf(wb, opts);\n\t\tcase 4: case 3: case 2: return write_biff2_buf(wb, opts);\n\t}\n\tthrow new Error(\"invalid type \" + o.bookType + \" for BIFF\");\n}\n/* note: browser DOM element cannot see mso- style attrs, must parse */\nvar HTML_ = (function() {\n\tfunction html_to_sheet(str, _opts) {\n\t\tvar opts = _opts || {};\n\t\tif(DENSE != null && opts.dense == null) opts.dense = DENSE;\n\t\tvar ws = opts.dense ? ([]) : ({});\n\t\tstr = str.replace(//g, \"\");\n\t\tvar mtch = str.match(/\");\n\t\tvar mtch2 = str.match(/<\\/table/i);\n\t\tvar i = mtch.index, j = mtch2 && mtch2.index || str.length;\n\t\tvar rows = split_regex(str.slice(i, j), /(:?]*>)/i, \"\");\n\t\tvar R = -1, C = 0, RS = 0, CS = 0;\n\t\tvar range = {s:{r:10000000, c:10000000},e:{r:0,c:0}};\n\t\tvar merges = [];\n\t\tfor(i = 0; i < rows.length; ++i) {\n\t\t\tvar row = rows[i].trim();\n\t\t\tvar hd = row.slice(0,3).toLowerCase();\n\t\t\tif(hd == \"/i);\n\t\t\tfor(j = 0; j < cells.length; ++j) {\n\t\t\t\tvar cell = cells[j].trim();\n\t\t\t\tif(!cell.match(/\")) > -1) m = m.slice(cc+1);\n\t\t\t\tfor(var midx = 0; midx < merges.length; ++midx) {\n\t\t\t\t\tvar _merge = merges[midx];\n\t\t\t\t\tif(_merge.s.c == C && _merge.s.r < R && R <= _merge.e.r) { C = _merge.e.c + 1; midx = -1; }\n\t\t\t\t}\n\t\t\t\tvar tag = parsexmltag(cell.slice(0, cell.indexOf(\">\")));\n\t\t\t\tCS = tag.colspan ? +tag.colspan : 1;\n\t\t\t\tif((RS = +tag.rowspan)>1 || CS>1) merges.push({s:{r:R,c:C},e:{r:R + (RS||1) - 1, c:C + CS - 1}});\n\t\t\t\tvar _t = tag.t || \"\";\n\t\t\t\t/* TODO: generate stub cells */\n\t\t\t\tif(!m.length) { C += CS; continue; }\n\t\t\t\tm = htmldecode(m);\n\t\t\t\tif(range.s.r > R) range.s.r = R; if(range.e.r < R) range.e.r = R;\n\t\t\t\tif(range.s.c > C) range.s.c = C; if(range.e.c < C) range.e.c = C;\n\t\t\t\tif(!m.length) continue;\n\t\t\t\tvar o = {t:'s', v:m};\n\t\t\t\tif(opts.raw || !m.trim().length || _t == 's'){}\n\t\t\t\telse if(m === 'TRUE') o = {t:'b', v:true};\n\t\t\t\telse if(m === 'FALSE') o = {t:'b', v:false};\n\t\t\t\telse if(!isNaN(fuzzynum(m))) o = {t:'n', v:fuzzynum(m)};\n\t\t\t\telse if(!isNaN(fuzzydate(m).getDate())) {\n\t\t\t\t\to = ({t:'d', v:parseDate(m)});\n\t\t\t\t\tif(!opts.cellDates) o = ({t:'n', v:datenum(o.v)});\n\t\t\t\t\to.z = opts.dateNF || SSF._table[14];\n\t\t\t\t}\n\t\t\t\tif(opts.dense) { if(!ws[R]) ws[R] = []; ws[R][C] = o; }\n\t\t\t\telse ws[encode_cell({r:R, c:C})] = o;\n\t\t\t\tC += CS;\n\t\t\t}\n\t\t}\n\t\tws['!ref'] = encode_range(range);\n\t\tif(merges.length) ws[\"!merges\"] = merges;\n\t\treturn ws;\n\t}\n\tfunction html_to_book(str, opts) {\n\t\treturn sheet_to_workbook(html_to_sheet(str, opts), opts);\n\t}\n\tfunction make_html_row(ws, r, R, o) {\n\t\tvar M = (ws['!merges'] ||[]);\n\t\tvar oo = [];\n\t\tfor(var C = r.s.c; C <= r.e.c; ++C) {\n\t\t\tvar RS = 0, CS = 0;\n\t\t\tfor(var j = 0; j < M.length; ++j) {\n\t\t\t\tif(M[j].s.r > R || M[j].s.c > C) continue;\n\t\t\t\tif(M[j].e.r < R || M[j].e.c < C) continue;\n\t\t\t\tif(M[j].s.r < R || M[j].s.c < C) { RS = -1; break; }\n\t\t\t\tRS = M[j].e.r - M[j].s.r + 1; CS = M[j].e.c - M[j].s.c + 1; break;\n\t\t\t}\n\t\t\tif(RS < 0) continue;\n\t\t\tvar coord = encode_cell({r:R,c:C});\n\t\t\tvar cell = o.dense ? (ws[R]||[])[C] : ws[coord];\n\t\t\t/* TODO: html entities */\n\t\t\tvar w = (cell && cell.v != null) && (cell.h || escapehtml(cell.w || (format_cell(cell), cell.w) || \"\")) || \"\";\n\t\t\tvar sp = ({});\n\t\t\tif(RS > 1) sp.rowspan = RS;\n\t\t\tif(CS > 1) sp.colspan = CS;\n\t\t\tsp.t = cell && cell.t || 'z';\n\t\t\tif(o.editable) w = '' + w + '';\n\t\t\tsp.id = (o.id || \"sjs\") + \"-\" + coord;\n\t\t\tif(sp.t != \"z\") { sp.v = cell.v; if(cell.z != null) sp.z = cell.z; }\n\t\t\too.push(writextag('td', w, sp));\n\t\t}\n\t\tvar preamble = \"\";\n\t\treturn preamble + oo.join(\"\") + \"\";\n\t}\n\tfunction make_html_preamble(ws, R, o) {\n\t\tvar out = [];\n\t\treturn out.join(\"\") + '';\n\t}\n\tvar _BEGIN = 'SheetJS Table Export';\n\tvar _END = '';\n\tfunction sheet_to_html(ws, opts/*, wb:?Workbook*/) {\n\t\tvar o = opts || {};\n\t\tvar header = o.header != null ? o.header : _BEGIN;\n\t\tvar footer = o.footer != null ? o.footer : _END;\n\t\tvar out = [header];\n\t\tvar r = decode_range(ws['!ref']);\n\t\to.dense = Array.isArray(ws);\n\t\tout.push(make_html_preamble(ws, r, o));\n\t\tfor(var R = r.s.r; R <= r.e.r; ++R) out.push(make_html_row(ws, r, R, o));\n\t\tout.push(\"
\" + footer);\n\t\treturn out.join(\"\");\n\n\t}\n\treturn {\n\t\tto_workbook: html_to_book,\n\t\tto_sheet: html_to_sheet,\n\t\t_row: make_html_row,\n\t\tBEGIN: _BEGIN,\n\t\tEND: _END,\n\t\t_preamble: make_html_preamble,\n\t\tfrom_sheet: sheet_to_html\n\t};\n})();\n\nfunction sheet_add_dom(ws, table, _opts) {\n\tvar opts = _opts || {};\n\tif(DENSE != null) opts.dense = DENSE;\n\tvar or_R = 0, or_C = 0;\n\tif(opts.origin != null) {\n\t\tif(typeof opts.origin == 'number') or_R = opts.origin;\n\t\telse {\n\t\t\tvar _origin = typeof opts.origin == \"string\" ? decode_cell(opts.origin) : opts.origin;\n\t\t\tor_R = _origin.r; or_C = _origin.c;\n\t\t}\n\t}\n\tvar rows = table.getElementsByTagName('tr');\n\tvar sheetRows = Math.min(opts.sheetRows||10000000, rows.length);\n\tvar range = {s:{r:0,c:0},e:{r:or_R,c:or_C}};\n\tif(ws[\"!ref\"]) {\n\t\tvar _range = decode_range(ws[\"!ref\"]);\n\t\trange.s.r = Math.min(range.s.r, _range.s.r);\n\t\trange.s.c = Math.min(range.s.c, _range.s.c);\n\t\trange.e.r = Math.max(range.e.r, _range.e.r);\n\t\trange.e.c = Math.max(range.e.c, _range.e.c);\n\t\tif(or_R == -1) range.e.r = or_R = _range.e.r + 1;\n\t}\n\tvar merges = [], midx = 0;\n\tvar rowinfo = ws[\"!rows\"] || (ws[\"!rows\"] = []);\n\tvar _R = 0, R = 0, _C = 0, C = 0, RS = 0, CS = 0;\n\tif(!ws[\"!cols\"]) ws['!cols'] = [];\n\tfor(; _R < rows.length && R < sheetRows; ++_R) {\n\t\tvar row = rows[_R];\n\t\tif (is_dom_element_hidden(row)) {\n\t\t\tif (opts.display) continue;\n\t\t\trowinfo[R] = {hidden: true};\n\t\t}\n\t\tvar elts = (row.children);\n\t\tfor(_C = C = 0; _C < elts.length; ++_C) {\n\t\t\tvar elt = elts[_C];\n\t\t\tif (opts.display && is_dom_element_hidden(elt)) continue;\n\t\t\tvar v = elt.hasAttribute('v') ? elt.getAttribute('v') : htmldecode(elt.innerHTML);\n\t\t\tvar z = elt.getAttribute('z');\n\t\t\tfor(midx = 0; midx < merges.length; ++midx) {\n\t\t\t\tvar m = merges[midx];\n\t\t\t\tif(m.s.c == C + or_C && m.s.r < R + or_R && R + or_R <= m.e.r) { C = m.e.c+1 - or_C; midx = -1; }\n\t\t\t}\n\t\t\t/* TODO: figure out how to extract nonstandard mso- style */\n\t\t\tCS = +elt.getAttribute(\"colspan\") || 1;\n\t\t\tif( ((RS = (+elt.getAttribute(\"rowspan\") || 1)))>1 || CS>1) merges.push({s:{r:R + or_R,c:C + or_C},e:{r:R + or_R + (RS||1) - 1, c:C + or_C + (CS||1) - 1}});\n\t\t\tvar o = {t:'s', v:v};\n\t\t\tvar _t = elt.getAttribute(\"t\") || \"\";\n\t\t\tif(v != null) {\n\t\t\t\tif(v.length == 0) o.t = _t || 'z';\n\t\t\t\telse if(opts.raw || v.trim().length == 0 || _t == \"s\"){}\n\t\t\t\telse if(v === 'TRUE') o = {t:'b', v:true};\n\t\t\t\telse if(v === 'FALSE') o = {t:'b', v:false};\n\t\t\t\telse if(!isNaN(fuzzynum(v))) o = {t:'n', v:fuzzynum(v)};\n\t\t\t\telse if(!isNaN(fuzzydate(v).getDate())) {\n\t\t\t\t\to = ({t:'d', v:parseDate(v)});\n\t\t\t\t\tif(!opts.cellDates) o = ({t:'n', v:datenum(o.v)});\n\t\t\t\t\to.z = opts.dateNF || SSF._table[14];\n\t\t\t\t}\n\t\t\t}\n\t\t\tif(o.z === undefined && z != null) o.z = z;\n\t\t\tif(opts.dense) { if(!ws[R + or_R]) ws[R + or_R] = []; ws[R + or_R][C + or_C] = o; }\n\t\t\telse ws[encode_cell({c:C + or_C, r:R + or_R})] = o;\n\t\t\tif(range.e.c < C + or_C) range.e.c = C + or_C;\n\t\t\tC += CS;\n\t\t}\n\t\t++R;\n\t}\n\tif(merges.length) ws['!merges'] = (ws[\"!merges\"] || []).concat(merges);\n\trange.e.r = Math.max(range.e.r, R - 1 + or_R);\n\tws['!ref'] = encode_range(range);\n\tif(R >= sheetRows) ws['!fullref'] = encode_range((range.e.r = rows.length-_R+R-1 + or_R,range)); // We can count the real number of rows to parse but we don't to improve the performance\n\treturn ws;\n}\n\nfunction parse_dom_table(table, _opts) {\n\tvar opts = _opts || {};\n\tvar ws = opts.dense ? ([]) : ({});\n\treturn sheet_add_dom(ws, table, _opts);\n}\n\nfunction table_to_book(table, opts) {\n\treturn sheet_to_workbook(parse_dom_table(table, opts), opts);\n}\n\nfunction is_dom_element_hidden(element) {\n\tvar display = '';\n\tvar get_computed_style = get_get_computed_style_function(element);\n\tif(get_computed_style) display = get_computed_style(element).getPropertyValue('display');\n\tif(!display) display = element.style.display; // Fallback for cases when getComputedStyle is not available (e.g. an old browser or some Node.js environments) or doesn't work (e.g. if the element is not inserted to a document)\n\treturn display === 'none';\n}\n\n/* global getComputedStyle */\nfunction get_get_computed_style_function(element) {\n\t// The proper getComputedStyle implementation is the one defined in the element window\n\tif(element.ownerDocument.defaultView && typeof element.ownerDocument.defaultView.getComputedStyle === 'function') return element.ownerDocument.defaultView.getComputedStyle;\n\t// If it is not available, try to get one from the global namespace\n\tif(typeof getComputedStyle === 'function') return getComputedStyle;\n\treturn null;\n}\n/* OpenDocument */\nvar parse_content_xml = (function() {\n\n\tvar parse_text_p = function(text) {\n\t\t/* 6.1.2 White Space Characters */\n\t\tvar fixed = text\n\t\t\t.replace(/[\\t\\r\\n]/g, \" \").trim().replace(/ +/g, \" \")\n\t\t\t.replace(//g,\" \")\n\t\t\t.replace(//g, function($$,$1) { return Array(parseInt($1,10)+1).join(\" \"); })\n\t\t\t.replace(/]*\\/>/g,\"\\t\")\n\t\t\t.replace(//g,\"\\n\");\n\t\tvar v = unescapexml(fixed.replace(/<[^>]*>/g,\"\"));\n\n\t\treturn [v];\n\t};\n\n\tvar number_formats = {\n\t\t/* ods name: [short ssf fmt, long ssf fmt] */\n\t\tday: [\"d\", \"dd\"],\n\t\tmonth: [\"m\", \"mm\"],\n\t\tyear: [\"y\", \"yy\"],\n\t\thours: [\"h\", \"hh\"],\n\t\tminutes: [\"m\", \"mm\"],\n\t\tseconds: [\"s\", \"ss\"],\n\t\t\"am-pm\": [\"A/P\", \"AM/PM\"],\n\t\t\"day-of-week\": [\"ddd\", \"dddd\"],\n\t\tera: [\"e\", \"ee\"],\n\t\t/* there is no native representation of LO \"Q\" format */\n\t\tquarter: [\"\\\\Qm\", \"m\\\\\\\"th quarter\\\"\"]\n\t};\n\n\treturn function pcx(d, _opts) {\n\t\tvar opts = _opts || {};\n\t\tif(DENSE != null && opts.dense == null) opts.dense = DENSE;\n\t\tvar str = xlml_normalize(d);\n\t\tvar state = [], tmp;\n\t\tvar tag;\n\t\tvar NFtag = {name:\"\"}, NF = \"\", pidx = 0;\n\t\tvar sheetag;\n\t\tvar rowtag;\n\t\tvar Sheets = {}, SheetNames = [];\n\t\tvar ws = opts.dense ? ([]) : ({});\n\t\tvar Rn, q;\n\t\tvar ctag = ({value:\"\"});\n\t\tvar textp = \"\", textpidx = 0, textptag;\n\t\tvar textR = [];\n\t\tvar R = -1, C = -1, range = {s: {r:1000000,c:10000000}, e: {r:0, c:0}};\n\t\tvar row_ol = 0;\n\t\tvar number_format_map = {};\n\t\tvar merges = [], mrange = {}, mR = 0, mC = 0;\n\t\tvar rowinfo = [], rowpeat = 1, colpeat = 1;\n\t\tvar arrayf = [];\n\t\tvar WB = {Names:[]};\n\t\tvar atag = ({});\n\t\tvar _Ref = [\"\", \"\"];\n\t\tvar comments = [], comment = ({});\n\t\tvar creator = \"\", creatoridx = 0;\n\t\tvar isstub = false, intable = false;\n\t\tvar i = 0;\n\t\txlmlregex.lastIndex = 0;\n\t\tstr = str.replace(//mg,\"\").replace(//gm,\"\");\n\t\twhile((Rn = xlmlregex.exec(str))) switch((Rn[3]=Rn[3].replace(/_.*$/,\"\"))) {\n\n\t\t\tcase 'table': case '工作表': // 9.1.2 \n\t\t\t\tif(Rn[1]==='/') {\n\t\t\t\t\tif(range.e.c >= range.s.c && range.e.r >= range.s.r) ws['!ref'] = encode_range(range);\n\t\t\t\t\telse ws['!ref'] = \"A1:A1\";\n\t\t\t\t\tif(opts.sheetRows > 0 && opts.sheetRows <= range.e.r) {\n\t\t\t\t\t\tws['!fullref'] = ws['!ref'];\n\t\t\t\t\t\trange.e.r = opts.sheetRows - 1;\n\t\t\t\t\t\tws['!ref'] = encode_range(range);\n\t\t\t\t\t}\n\t\t\t\t\tif(merges.length) ws['!merges'] = merges;\n\t\t\t\t\tif(rowinfo.length) ws[\"!rows\"] = rowinfo;\n\t\t\t\t\tsheetag.name = sheetag['名称'] || sheetag.name;\n\t\t\t\t\tif(typeof JSON !== 'undefined') JSON.stringify(sheetag);\n\t\t\t\t\tSheetNames.push(sheetag.name);\n\t\t\t\t\tSheets[sheetag.name] = ws;\n\t\t\t\t\tintable = false;\n\t\t\t\t}\n\t\t\t\telse if(Rn[0].charAt(Rn[0].length-2) !== '/') {\n\t\t\t\t\tsheetag = parsexmltag(Rn[0], false);\n\t\t\t\t\tR = C = -1;\n\t\t\t\t\trange.s.r = range.s.c = 10000000; range.e.r = range.e.c = 0;\n\t\t\t\t\tws = opts.dense ? ([]) : ({}); merges = [];\n\t\t\t\t\trowinfo = [];\n\t\t\t\t\tintable = true;\n\t\t\t\t}\n\t\t\t\tbreak;\n\n\t\t\tcase 'table-row-group': // 9.1.9 \n\t\t\t\tif(Rn[1] === \"/\") --row_ol; else ++row_ol;\n\t\t\t\tbreak;\n\t\t\tcase 'table-row': case '行': // 9.1.3 \n\t\t\t\tif(Rn[1] === '/') { R+=rowpeat; rowpeat = 1; break; }\n\t\t\t\trowtag = parsexmltag(Rn[0], false);\n\t\t\t\tif(rowtag['行号']) R = rowtag['行号'] - 1; else if(R == -1) R = 0;\n\t\t\t\trowpeat = +rowtag['number-rows-repeated'] || 1;\n\t\t\t\t/* TODO: remove magic */\n\t\t\t\tif(rowpeat < 10) for(i = 0; i < rowpeat; ++i) if(row_ol > 0) rowinfo[R + i] = {level: row_ol};\n\t\t\t\tC = -1; break;\n\t\t\tcase 'covered-table-cell': // 9.1.5 \n\t\t\t\tif(Rn[1] !== '/') ++C;\n\t\t\t\tif(opts.sheetStubs) {\n\t\t\t\t\tif(opts.dense) { if(!ws[R]) ws[R] = []; ws[R][C] = {t:'z'}; }\n\t\t\t\t\telse ws[encode_cell({r:R,c:C})] = {t:'z'};\n\t\t\t\t}\n\t\t\t\ttextp = \"\"; textR = [];\n\t\t\t\tbreak; /* stub */\n\t\t\tcase 'table-cell': case '数据':\n\t\t\t\tif(Rn[0].charAt(Rn[0].length-2) === '/') {\n\t\t\t\t\t++C;\n\t\t\t\t\tctag = parsexmltag(Rn[0], false);\n\t\t\t\t\tcolpeat = parseInt(ctag['number-columns-repeated']||\"1\", 10);\n\t\t\t\t\tq = ({t:'z', v:null});\n\t\t\t\t\tif(ctag.formula && opts.cellFormula != false) q.f = ods_to_csf_formula(unescapexml(ctag.formula));\n\t\t\t\t\tif((ctag['数据类型'] || ctag['value-type']) == \"string\") {\n\t\t\t\t\t\tq.t = \"s\"; q.v = unescapexml(ctag['string-value'] || \"\");\n\t\t\t\t\t\tif(opts.dense) {\n\t\t\t\t\t\t\tif(!ws[R]) ws[R] = [];\n\t\t\t\t\t\t\tws[R][C] = q;\n\t\t\t\t\t\t} else {\n\t\t\t\t\t\t\tws[encode_cell({r:R,c:C})] = q;\n\t\t\t\t\t\t}\n\t\t\t\t\t}\n\t\t\t\t\tC+= colpeat-1;\n\t\t\t\t} else if(Rn[1]!=='/') {\n\t\t\t\t\t++C;\n\t\t\t\t\tcolpeat = 1;\n\t\t\t\t\tvar rptR = rowpeat ? R + rowpeat - 1 : R;\n\t\t\t\t\tif(C > range.e.c) range.e.c = C;\n\t\t\t\t\tif(C < range.s.c) range.s.c = C;\n\t\t\t\t\tif(R < range.s.r) range.s.r = R;\n\t\t\t\t\tif(rptR > range.e.r) range.e.r = rptR;\n\t\t\t\t\tctag = parsexmltag(Rn[0], false);\n\t\t\t\t\tcomments = []; comment = ({});\n\t\t\t\t\tq = ({t:ctag['数据类型'] || ctag['value-type'], v:null});\n\t\t\t\t\tif(opts.cellFormula) {\n\t\t\t\t\t\tif(ctag.formula) ctag.formula = unescapexml(ctag.formula);\n\t\t\t\t\t\tif(ctag['number-matrix-columns-spanned'] && ctag['number-matrix-rows-spanned']) {\n\t\t\t\t\t\t\tmR = parseInt(ctag['number-matrix-rows-spanned'],10) || 0;\n\t\t\t\t\t\t\tmC = parseInt(ctag['number-matrix-columns-spanned'],10) || 0;\n\t\t\t\t\t\t\tmrange = {s: {r:R,c:C}, e:{r:R + mR-1,c:C + mC-1}};\n\t\t\t\t\t\t\tq.F = encode_range(mrange);\n\t\t\t\t\t\t\tarrayf.push([mrange, q.F]);\n\t\t\t\t\t\t}\n\t\t\t\t\t\tif(ctag.formula) q.f = ods_to_csf_formula(ctag.formula);\n\t\t\t\t\t\telse for(i = 0; i < arrayf.length; ++i)\n\t\t\t\t\t\t\tif(R >= arrayf[i][0].s.r && R <= arrayf[i][0].e.r)\n\t\t\t\t\t\t\t\tif(C >= arrayf[i][0].s.c && C <= arrayf[i][0].e.c)\n\t\t\t\t\t\t\t\t\tq.F = arrayf[i][1];\n\t\t\t\t\t}\n\t\t\t\t\tif(ctag['number-columns-spanned'] || ctag['number-rows-spanned']) {\n\t\t\t\t\t\tmR = parseInt(ctag['number-rows-spanned'],10) || 0;\n\t\t\t\t\t\tmC = parseInt(ctag['number-columns-spanned'],10) || 0;\n\t\t\t\t\t\tmrange = {s: {r:R,c:C}, e:{r:R + mR-1,c:C + mC-1}};\n\t\t\t\t\t\tmerges.push(mrange);\n\t\t\t\t\t}\n\n\t\t\t\t\t/* 19.675.2 table:number-columns-repeated */\n\t\t\t\t\tif(ctag['number-columns-repeated']) colpeat = parseInt(ctag['number-columns-repeated'], 10);\n\n\t\t\t\t\t/* 19.385 office:value-type */\n\t\t\t\t\tswitch(q.t) {\n\t\t\t\t\t\tcase 'boolean': q.t = 'b'; q.v = parsexmlbool(ctag['boolean-value']); break;\n\t\t\t\t\t\tcase 'float': q.t = 'n'; q.v = parseFloat(ctag.value); break;\n\t\t\t\t\t\tcase 'percentage': q.t = 'n'; q.v = parseFloat(ctag.value); break;\n\t\t\t\t\t\tcase 'currency': q.t = 'n'; q.v = parseFloat(ctag.value); break;\n\t\t\t\t\t\tcase 'date': q.t = 'd'; q.v = parseDate(ctag['date-value']);\n\t\t\t\t\t\t\tif(!opts.cellDates) { q.t = 'n'; q.v = datenum(q.v); }\n\t\t\t\t\t\t\tq.z = 'm/d/yy'; break;\n\t\t\t\t\t\tcase 'time': q.t = 'n'; q.v = parse_isodur(ctag['time-value'])/86400; break;\n\t\t\t\t\t\tcase 'number': q.t = 'n'; q.v = parseFloat(ctag['数据数值']); break;\n\t\t\t\t\t\tdefault:\n\t\t\t\t\t\t\tif(q.t === 'string' || q.t === 'text' || !q.t) {\n\t\t\t\t\t\t\t\tq.t = 's';\n\t\t\t\t\t\t\t\tif(ctag['string-value'] != null) { textp = unescapexml(ctag['string-value']); textR = []; }\n\t\t\t\t\t\t\t} else throw new Error('Unsupported value type ' + q.t);\n\t\t\t\t\t}\n\t\t\t\t} else {\n\t\t\t\t\tisstub = false;\n\t\t\t\t\tif(q.t === 's') {\n\t\t\t\t\t\tq.v = textp || '';\n\t\t\t\t\t\tif(textR.length) q.R = textR;\n\t\t\t\t\t\tisstub = textpidx == 0;\n\t\t\t\t\t}\n\t\t\t\t\tif(atag.Target) q.l = atag;\n\t\t\t\t\tif(comments.length > 0) { q.c = comments; comments = []; }\n\t\t\t\t\tif(textp && opts.cellText !== false) q.w = textp;\n\t\t\t\t\tif(isstub) { q.t = \"z\"; delete q.v; }\n\t\t\t\t\tif(!isstub || opts.sheetStubs) {\n\t\t\t\t\t\tif(!(opts.sheetRows && opts.sheetRows <= R)) {\n\t\t\t\t\t\t\tfor(var rpt = 0; rpt < rowpeat; ++rpt) {\n\t\t\t\t\t\t\t\tcolpeat = parseInt(ctag['number-columns-repeated']||\"1\", 10);\n\t\t\t\t\t\t\t\tif(opts.dense) {\n\t\t\t\t\t\t\t\t\tif(!ws[R + rpt]) ws[R + rpt] = [];\n\t\t\t\t\t\t\t\t\tws[R + rpt][C] = rpt == 0 ? q : dup(q);\n\t\t\t\t\t\t\t\t\twhile(--colpeat > 0) ws[R + rpt][C + colpeat] = dup(q);\n\t\t\t\t\t\t\t\t} else {\n\t\t\t\t\t\t\t\t\tws[encode_cell({r:R + rpt,c:C})] = q;\n\t\t\t\t\t\t\t\t\twhile(--colpeat > 0) ws[encode_cell({r:R + rpt,c:C + colpeat})] = dup(q);\n\t\t\t\t\t\t\t\t}\n\t\t\t\t\t\t\t\tif(range.e.c <= C) range.e.c = C;\n\t\t\t\t\t\t\t}\n\t\t\t\t\t\t}\n\t\t\t\t\t}\n\t\t\t\t\tcolpeat = parseInt(ctag['number-columns-repeated']||\"1\", 10);\n\t\t\t\t\tC += colpeat-1; colpeat = 0;\n\t\t\t\t\tq = {};\n\t\t\t\t\ttextp = \"\"; textR = [];\n\t\t\t\t}\n\t\t\t\tatag = ({});\n\t\t\t\tbreak; // 9.1.4 \n\n\t\t\t/* pure state */\n\t\t\tcase 'document': // TODO: is the root for FODS\n\t\t\tcase 'document-content': case '电子表格文档': // 3.1.3.2 \n\t\t\tcase 'spreadsheet': case '主体': // 3.7 \n\t\t\tcase 'scripts': // 3.12 \n\t\t\tcase 'styles': // TODO \n\t\t\tcase 'font-face-decls': // 3.14 \n\t\t\tcase 'master-styles': //3.15.4 -- relevant for FODS\n\t\t\t\tif(Rn[1]==='/'){if((tmp=state.pop())[0]!==Rn[3]) throw \"Bad state: \"+tmp;}\n\t\t\t\telse if(Rn[0].charAt(Rn[0].length-2) !== '/') state.push([Rn[3], true]);\n\t\t\t\tbreak;\n\n\t\t\tcase 'annotation': // 14.1 \n\t\t\t\tif(Rn[1]==='/'){\n\t\t\t\t\tif((tmp=state.pop())[0]!==Rn[3]) throw \"Bad state: \"+tmp;\n\t\t\t\t\tcomment.t = textp;\n\t\t\t\t\tif(textR.length) comment.R = textR;\n\t\t\t\t\tcomment.a = creator;\n\t\t\t\t\tcomments.push(comment);\n\t\t\t\t}\n\t\t\t\telse if(Rn[0].charAt(Rn[0].length-2) !== '/') {state.push([Rn[3], false]);}\n\t\t\t\tcreator = \"\"; creatoridx = 0;\n\t\t\t\ttextp = \"\"; textpidx = 0; textR = [];\n\t\t\t\tbreak;\n\n\t\t\tcase 'creator': // 4.3.2.7 \n\t\t\t\tif(Rn[1]==='/') { creator = str.slice(creatoridx,Rn.index); }\n\t\t\t\telse creatoridx = Rn.index + Rn[0].length;\n\t\t\t\tbreak;\n\n\t\t\t/* ignore state */\n\t\t\tcase 'meta': case '元数据': // TODO: FODS/UOF\n\t\t\tcase 'settings': // TODO: \n\t\t\tcase 'config-item-set': // TODO: \n\t\t\tcase 'config-item-map-indexed': // TODO: \n\t\t\tcase 'config-item-map-entry': // TODO: \n\t\t\tcase 'config-item-map-named': // TODO: \n\t\t\tcase 'shapes': // 9.2.8 \n\t\t\tcase 'frame': // 10.4.2 \n\t\t\tcase 'text-box': // 10.4.3 \n\t\t\tcase 'image': // 10.4.4 \n\t\t\tcase 'data-pilot-tables': // 9.6.2 \n\t\t\tcase 'list-style': // 16.30 \n\t\t\tcase 'form': // 13.13 \n\t\t\tcase 'dde-links': // 9.8 \n\t\t\tcase 'event-listeners': // TODO\n\t\t\tcase 'chart': // TODO\n\t\t\t\tif(Rn[1]==='/'){if((tmp=state.pop())[0]!==Rn[3]) throw \"Bad state: \"+tmp;}\n\t\t\t\telse if(Rn[0].charAt(Rn[0].length-2) !== '/') state.push([Rn[3], false]);\n\t\t\t\ttextp = \"\"; textpidx = 0; textR = [];\n\t\t\t\tbreak;\n\n\t\t\tcase 'scientific-number': // TODO: \n\t\t\t\tbreak;\n\t\t\tcase 'currency-symbol': // TODO: \n\t\t\t\tbreak;\n\t\t\tcase 'currency-style': // TODO: \n\t\t\t\tbreak;\n\t\t\tcase 'number-style': // 16.27.2 \n\t\t\tcase 'percentage-style': // 16.27.9 \n\t\t\tcase 'date-style': // 16.27.10 \n\t\t\tcase 'time-style': // 16.27.18 \n\t\t\t\tif(Rn[1]==='/'){\n\t\t\t\t\tnumber_format_map[NFtag.name] = NF;\n\t\t\t\t\tif((tmp=state.pop())[0]!==Rn[3]) throw \"Bad state: \"+tmp;\n\t\t\t\t} else if(Rn[0].charAt(Rn[0].length-2) !== '/') {\n\t\t\t\t\tNF = \"\";\n\t\t\t\t\tNFtag = parsexmltag(Rn[0], false);\n\t\t\t\t\tstate.push([Rn[3], true]);\n\t\t\t\t} break;\n\n\t\t\tcase 'script': break; // 3.13 \n\t\t\tcase 'libraries': break; // TODO: \n\t\t\tcase 'automatic-styles': break; // 3.15.3 \n\n\t\t\tcase 'default-style': // TODO: \n\t\t\tcase 'page-layout': break; // TODO: \n\t\t\tcase 'style': // 16.2 \n\t\t\t\tbreak;\n\t\t\tcase 'map': break; // 16.3 \n\t\t\tcase 'font-face': break; // 16.21 \n\n\t\t\tcase 'paragraph-properties': break; // 17.6 \n\t\t\tcase 'table-properties': break; // 17.15 \n\t\t\tcase 'table-column-properties': break; // 17.16 \n\t\t\tcase 'table-row-properties': break; // 17.17 \n\t\t\tcase 'table-cell-properties': break; // 17.18 \n\n\t\t\tcase 'number': // 16.27.3 \n\t\t\t\tswitch(state[state.length-1][0]) {\n\t\t\t\t\tcase 'time-style':\n\t\t\t\t\tcase 'date-style':\n\t\t\t\t\t\ttag = parsexmltag(Rn[0], false);\n\t\t\t\t\t\tNF += number_formats[Rn[3]][tag.style==='long'?1:0]; break;\n\t\t\t\t} break;\n\n\t\t\tcase 'fraction': break; // TODO 16.27.6 \n\n\t\t\tcase 'day': // 16.27.11 \n\t\t\tcase 'month': // 16.27.12 \n\t\t\tcase 'year': // 16.27.13 \n\t\t\tcase 'era': // 16.27.14 \n\t\t\tcase 'day-of-week': // 16.27.15 \n\t\t\tcase 'week-of-year': // 16.27.16 \n\t\t\tcase 'quarter': // 16.27.17 \n\t\t\tcase 'hours': // 16.27.19 \n\t\t\tcase 'minutes': // 16.27.20 \n\t\t\tcase 'seconds': // 16.27.21 \n\t\t\tcase 'am-pm': // 16.27.22 \n\t\t\t\tswitch(state[state.length-1][0]) {\n\t\t\t\t\tcase 'time-style':\n\t\t\t\t\tcase 'date-style':\n\t\t\t\t\t\ttag = parsexmltag(Rn[0], false);\n\t\t\t\t\t\tNF += number_formats[Rn[3]][tag.style==='long'?1:0]; break;\n\t\t\t\t} break;\n\n\t\t\tcase 'boolean-style': break; // 16.27.23 \n\t\t\tcase 'boolean': break; // 16.27.24 \n\t\t\tcase 'text-style': break; // 16.27.25 \n\t\t\tcase 'text': // 16.27.26 \n\t\t\t\tif(Rn[0].slice(-2) === \"/>\") break;\n\t\t\t\telse if(Rn[1]===\"/\") switch(state[state.length-1][0]) {\n\t\t\t\t\tcase 'number-style':\n\t\t\t\t\tcase 'date-style':\n\t\t\t\t\tcase 'time-style':\n\t\t\t\t\t\tNF += str.slice(pidx, Rn.index);\n\t\t\t\t\t\tbreak;\n\t\t\t\t}\n\t\t\t\telse pidx = Rn.index + Rn[0].length;\n\t\t\t\tbreak;\n\n\t\t\tcase 'named-range': // 9.4.12 \n\t\t\t\ttag = parsexmltag(Rn[0], false);\n\t\t\t\t_Ref = ods_to_csf_3D(tag['cell-range-address']);\n\t\t\t\tvar nrange = ({Name:tag.name, Ref:_Ref[0] + '!' + _Ref[1]});\n\t\t\t\tif(intable) nrange.Sheet = SheetNames.length;\n\t\t\t\tWB.Names.push(nrange);\n\t\t\t\tbreak;\n\n\t\t\tcase 'text-content': break; // 16.27.27 \n\t\t\tcase 'text-properties': break; // 16.27.27 \n\t\t\tcase 'embedded-text': break; // 16.27.4 \n\n\t\t\tcase 'body': case '电子表格': break; // 3.3 16.9.6 19.726.3\n\n\t\t\tcase 'forms': break; // 12.25.2 13.2\n\t\t\tcase 'table-column': break; // 9.1.6 \n\t\t\tcase 'table-header-rows': break; // 9.1.7 \n\t\t\tcase 'table-rows': break; // 9.1.12 \n\t\t\t/* TODO: outline levels */\n\t\t\tcase 'table-column-group': break; // 9.1.10 \n\t\t\tcase 'table-header-columns': break; // 9.1.11 \n\t\t\tcase 'table-columns': break; // 9.1.12 \n\n\t\t\tcase 'null-date': break; // 9.4.2 TODO: date1904\n\n\t\t\tcase 'graphic-properties': break; // 17.21 \n\t\t\tcase 'calculation-settings': break; // 9.4.1 \n\t\t\tcase 'named-expressions': break; // 9.4.11 \n\t\t\tcase 'label-range': break; // 9.4.9 \n\t\t\tcase 'label-ranges': break; // 9.4.10 \n\t\t\tcase 'named-expression': break; // 9.4.13 \n\t\t\tcase 'sort': break; // 9.4.19 \n\t\t\tcase 'sort-by': break; // 9.4.20 \n\t\t\tcase 'sort-groups': break; // 9.4.22 \n\n\t\t\tcase 'tab': break; // 6.1.4 \n\t\t\tcase 'line-break': break; // 6.1.5 \n\t\t\tcase 'span': break; // 6.1.7 \n\t\t\tcase 'p': case '文本串': // 5.1.3 \n\t\t\t\tif(['master-styles'].indexOf(state[state.length-1][0]) > -1) break;\n\t\t\t\tif(Rn[1]==='/' && (!ctag || !ctag['string-value'])) {\n\t\t\t\t\tvar ptp = parse_text_p(str.slice(textpidx,Rn.index), textptag);\n\t\t\t\t\ttextp = (textp.length > 0 ? textp + \"\\n\" : \"\") + ptp[0];\n\t\t\t\t} else { textptag = parsexmltag(Rn[0], false); textpidx = Rn.index + Rn[0].length; }\n\t\t\t\tbreak; // \n\t\t\tcase 's': break; // \n\n\t\t\tcase 'database-range': // 9.4.15 \n\t\t\t\tif(Rn[1]==='/') break;\n\t\t\t\ttry {\n\t\t\t\t\t_Ref = ods_to_csf_3D(parsexmltag(Rn[0])['target-range-address']);\n\t\t\t\t\tSheets[_Ref[0]]['!autofilter'] = { ref:_Ref[1] };\n\t\t\t\t} catch(e) {/* empty */}\n\t\t\t\tbreak;\n\n\t\t\tcase 'date': break; // <*:date>\n\n\t\t\tcase 'object': break; // 10.4.6.2 \n\t\t\tcase 'title': case '标题': break; // <*:title> OR \n\t\t\tcase 'desc': break; // <*:desc>\n\t\t\tcase 'binary-data': break; // 10.4.5 TODO: b64 blob\n\n\t\t\t/* 9.2 Advanced Tables */\n\t\t\tcase 'table-source': break; // 9.2.6\n\t\t\tcase 'scenario': break; // 9.2.6\n\n\t\t\tcase 'iteration': break; // 9.4.3 \n\t\t\tcase 'content-validations': break; // 9.4.4 \n\t\t\tcase 'filter': break; // 9.5.2 \n\t\t\tcase 'filter-and': break; // 9.5.3 \n\t\t\tcase 'filter-or': break; // 9.5.4 \n\t\t\tcase 'filter-condition': break; // 9.5.5 \n\n\t\t\tcase 'list-level-style-bullet': break; // 16.31 \n\t\t\tcase 'page-count': break; // TODO \n\t\t\tcase 'time': break; // TODO \n\n\t\t\t/* 9.3 Advanced Table Cells */\n\t\t\tcase 'cell-range-source': break; // 9.3.1 \n\t\t\tcase 'property': break; // 13.8 \n\n\t\t\tcase 'a': // 6.1.8 hyperlink\n\t\t\t\tif(Rn[1]!== '/') {\n\t\t\t\t\tatag = parsexmltag(Rn[0], false);\n\t\t\t\t\tif(!atag.href) break;\n\t\t\t\t\tatag.Target = atag.href; delete atag.href;\n\t\t\t\t\tif(atag.Target.charAt(0) == \"#\" && atag.Target.indexOf(\".\") > -1) {\n\t\t\t\t\t\t_Ref = ods_to_csf_3D(atag.Target.slice(1));\n\t\t\t\t\t\tatag.Target = \"#\" + _Ref[0] + \"!\" + _Ref[1];\n\t\t\t\t\t}\n\t\t\t\t}\n\t\t\t\tbreak;\n\n\t\t\t/* non-standard */\n\t\t\tcase 'table-protection': break;\n\t\t\tcase 'data-pilot-grand-total': break; // ';\n\treturn function wso() {\n\t\treturn XML_HEADER + payload;\n\t};\n})();\nvar write_content_ods = (function() {\n\t/* 6.1.2 White Space Characters */\n\tvar write_text_p = function(text) {\n\t\treturn escapexml(text)\n\t\t\t.replace(/ +/g, function($$){return '';})\n\t\t\t.replace(/\\t/g, \"\")\n\t\t\t.replace(/\\n/g, \"\")\n\t\t\t.replace(/^ /, \"\").replace(/ $/, \"\");\n\t};\n\n\tvar null_cell_xml = ' \\n';\n\tvar covered_cell_xml = ' \\n';\n\tvar write_ws = function(ws, wb, i) {\n\t\t/* Section 9 Tables */\n\t\tvar o = [];\n\t\to.push(' \\n');\n\t\tvar R=0,C=0, range = decode_range(ws['!ref']);\n\t\tvar marr = ws['!merges'] || [], mi = 0;\n\t\tvar dense = Array.isArray(ws);\n\t\tfor(R = 0; R < range.s.r; ++R) o.push(' \\n');\n\t\tfor(; R <= range.e.r; ++R) {\n\t\t\to.push(' \\n');\n\t\t\tfor(C=0; C < range.s.c; ++C) o.push(null_cell_xml);\n\t\t\tfor(; C <= range.e.c; ++C) {\n\t\t\t\tvar skip = false, ct = {}, textp = \"\";\n\t\t\t\tfor(mi = 0; mi != marr.length; ++mi) {\n\t\t\t\t\tif(marr[mi].s.c > C) continue;\n\t\t\t\t\tif(marr[mi].s.r > R) continue;\n\t\t\t\t\tif(marr[mi].e.c < C) continue;\n\t\t\t\t\tif(marr[mi].e.r < R) continue;\n\t\t\t\t\tif(marr[mi].s.c != C || marr[mi].s.r != R) skip = true;\n\t\t\t\t\tct['table:number-columns-spanned'] = (marr[mi].e.c - marr[mi].s.c + 1);\n\t\t\t\t\tct['table:number-rows-spanned'] = (marr[mi].e.r - marr[mi].s.r + 1);\n\t\t\t\t\tbreak;\n\t\t\t\t}\n\t\t\t\tif(skip) { o.push(covered_cell_xml); continue; }\n\t\t\t\tvar ref = encode_cell({r:R, c:C}), cell = dense ? (ws[R]||[])[C]: ws[ref];\n\t\t\t\tif(cell && cell.f) {\n\t\t\t\t\tct['table:formula'] = escapexml(csf_to_ods_formula(cell.f));\n\t\t\t\t\tif(cell.F) {\n\t\t\t\t\t\tif(cell.F.slice(0, ref.length) == ref) {\n\t\t\t\t\t\t\tvar _Fref = decode_range(cell.F);\n\t\t\t\t\t\t\tct['table:number-matrix-columns-spanned'] = (_Fref.e.c - _Fref.s.c + 1);\n\t\t\t\t\t\t\tct['table:number-matrix-rows-spanned'] = (_Fref.e.r - _Fref.s.r + 1);\n\t\t\t\t\t\t}\n\t\t\t\t\t}\n\t\t\t\t}\n\t\t\t\tif(!cell) { o.push(null_cell_xml); continue; }\n\t\t\t\tswitch(cell.t) {\n\t\t\t\t\tcase 'b':\n\t\t\t\t\t\ttextp = (cell.v ? 'TRUE' : 'FALSE');\n\t\t\t\t\t\tct['office:value-type'] = \"boolean\";\n\t\t\t\t\t\tct['office:boolean-value'] = (cell.v ? 'true' : 'false');\n\t\t\t\t\t\tbreak;\n\t\t\t\t\tcase 'n':\n\t\t\t\t\t\ttextp = (cell.w||String(cell.v||0));\n\t\t\t\t\t\tct['office:value-type'] = \"float\";\n\t\t\t\t\t\tct['office:value'] = (cell.v||0);\n\t\t\t\t\t\tbreak;\n\t\t\t\t\tcase 's': case 'str':\n\t\t\t\t\t\ttextp = cell.v == null ? \"\" : cell.v;\n\t\t\t\t\t\tct['office:value-type'] = \"string\";\n\t\t\t\t\t\tbreak;\n\t\t\t\t\tcase 'd':\n\t\t\t\t\t\ttextp = (cell.w||(parseDate(cell.v).toISOString()));\n\t\t\t\t\t\tct['office:value-type'] = \"date\";\n\t\t\t\t\t\tct['office:date-value'] = (parseDate(cell.v).toISOString());\n\t\t\t\t\t\tct['table:style-name'] = \"ce1\";\n\t\t\t\t\t\tbreak;\n\t\t\t\t\t//case 'e':\n\t\t\t\t\tdefault: o.push(null_cell_xml); continue;\n\t\t\t\t}\n\t\t\t\tvar text_p = write_text_p(textp);\n\t\t\t\tif(cell.l && cell.l.Target) {\n\t\t\t\t\tvar _tgt = cell.l.Target; _tgt = _tgt.charAt(0) == \"#\" ? \"#\" + csf_to_ods_3D(_tgt.slice(1)) : _tgt;\n\t\t\t\t\ttext_p = writextag('text:a', text_p, {'xlink:href': _tgt});\n\t\t\t\t}\n\t\t\t\to.push(' ' + writextag('table:table-cell', writextag('text:p', text_p, {}), ct) + '\\n');\n\t\t\t}\n\t\t\to.push(' \\n');\n\t\t}\n\t\to.push(' \\n');\n\t\treturn o.join(\"\");\n\t};\n\n\tvar write_automatic_styles_ods = function(o) {\n\t\to.push(' \\n');\n\n\t\to.push(' \\n');\n\t\to.push(' \\n');\n\t\to.push(' /\\n');\n\t\to.push(' \\n');\n\t\to.push(' /\\n');\n\t\to.push(' \\n');\n\t\to.push(' \\n');\n\n\t\t/* table */\n\t\to.push(' \\n'); // style:master-page-name=\"mp1\">\\n');\n\t\to.push(' \\n');\n\t\to.push(' \\n');\n\n\t\t/* table cells, text */\n\t\to.push(' \\n');\n\n\t\t/* page-layout */\n\n\t\to.push(' \\n');\n\t};\n\n\treturn function wcx(wb, opts) {\n\t\tvar o = [XML_HEADER];\n\t\t/* 3.1.3.2 */\n\t\tvar attr = wxt_helper({\n\t\t\t'xmlns:office': \"urn:oasis:names:tc:opendocument:xmlns:office:1.0\",\n\t\t\t'xmlns:table': \"urn:oasis:names:tc:opendocument:xmlns:table:1.0\",\n\t\t\t'xmlns:style': \"urn:oasis:names:tc:opendocument:xmlns:style:1.0\",\n\t\t\t'xmlns:text': \"urn:oasis:names:tc:opendocument:xmlns:text:1.0\",\n\t\t\t'xmlns:draw': \"urn:oasis:names:tc:opendocument:xmlns:drawing:1.0\",\n\t\t\t'xmlns:fo': \"urn:oasis:names:tc:opendocument:xmlns:xsl-fo-compatible:1.0\",\n\t\t\t'xmlns:xlink': \"http://www.w3.org/1999/xlink\",\n\t\t\t'xmlns:dc': \"http://purl.org/dc/elements/1.1/\",\n\t\t\t'xmlns:meta': \"urn:oasis:names:tc:opendocument:xmlns:meta:1.0\",\n\t\t\t'xmlns:number': \"urn:oasis:names:tc:opendocument:xmlns:datastyle:1.0\",\n\t\t\t'xmlns:presentation': \"urn:oasis:names:tc:opendocument:xmlns:presentation:1.0\",\n\t\t\t'xmlns:svg': \"urn:oasis:names:tc:opendocument:xmlns:svg-compatible:1.0\",\n\t\t\t'xmlns:chart': \"urn:oasis:names:tc:opendocument:xmlns:chart:1.0\",\n\t\t\t'xmlns:dr3d': \"urn:oasis:names:tc:opendocument:xmlns:dr3d:1.0\",\n\t\t\t'xmlns:math': \"http://www.w3.org/1998/Math/MathML\",\n\t\t\t'xmlns:form': \"urn:oasis:names:tc:opendocument:xmlns:form:1.0\",\n\t\t\t'xmlns:script': \"urn:oasis:names:tc:opendocument:xmlns:script:1.0\",\n\t\t\t'xmlns:ooo': \"http://openoffice.org/2004/office\",\n\t\t\t'xmlns:ooow': \"http://openoffice.org/2004/writer\",\n\t\t\t'xmlns:oooc': \"http://openoffice.org/2004/calc\",\n\t\t\t'xmlns:dom': \"http://www.w3.org/2001/xml-events\",\n\t\t\t'xmlns:xforms': \"http://www.w3.org/2002/xforms\",\n\t\t\t'xmlns:xsd': \"http://www.w3.org/2001/XMLSchema\",\n\t\t\t'xmlns:xsi': \"http://www.w3.org/2001/XMLSchema-instance\",\n\t\t\t'xmlns:sheet': \"urn:oasis:names:tc:opendocument:sh33tjs:1.0\",\n\t\t\t'xmlns:rpt': \"http://openoffice.org/2005/report\",\n\t\t\t'xmlns:of': \"urn:oasis:names:tc:opendocument:xmlns:of:1.2\",\n\t\t\t'xmlns:xhtml': \"http://www.w3.org/1999/xhtml\",\n\t\t\t'xmlns:grddl': \"http://www.w3.org/2003/g/data-view#\",\n\t\t\t'xmlns:tableooo': \"http://openoffice.org/2009/table\",\n\t\t\t'xmlns:drawooo': \"http://openoffice.org/2010/draw\",\n\t\t\t'xmlns:calcext': \"urn:org:documentfoundation:names:experimental:calc:xmlns:calcext:1.0\",\n\t\t\t'xmlns:loext': \"urn:org:documentfoundation:names:experimental:office:xmlns:loext:1.0\",\n\t\t\t'xmlns:field': \"urn:openoffice:names:experimental:ooo-ms-interop:xmlns:field:1.0\",\n\t\t\t'xmlns:formx': \"urn:openoffice:names:experimental:ooxml-odf-interop:xmlns:form:1.0\",\n\t\t\t'xmlns:css3t': \"http://www.w3.org/TR/css3-text/\",\n\t\t\t'office:version': \"1.2\"\n\t\t});\n\n\t\tvar fods = wxt_helper({\n\t\t\t'xmlns:config': \"urn:oasis:names:tc:opendocument:xmlns:config:1.0\",\n\t\t\t'office:mimetype': \"application/vnd.oasis.opendocument.spreadsheet\"\n\t\t});\n\n\t\tif(opts.bookType == \"fods\") o.push('\\n');\n\t\telse o.push('\\n');\n\t\twrite_automatic_styles_ods(o);\n\t\to.push(' \\n');\n\t\to.push(' \\n');\n\t\tfor(var i = 0; i != wb.SheetNames.length; ++i) o.push(write_ws(wb.Sheets[wb.SheetNames[i]], wb, i, opts));\n\t\to.push(' \\n');\n\t\to.push(' \\n');\n\t\tif(opts.bookType == \"fods\") o.push('');\n\t\telse o.push('');\n\t\treturn o.join(\"\");\n\t};\n})();\n\nfunction write_ods(wb, opts) {\n\tif(opts.bookType == \"fods\") return write_content_ods(wb, opts);\n\nvar zip = zip_new();\n\tvar f = \"\";\n\n\tvar manifest = [];\n\tvar rdf = [];\n\n\t/* Part 3 Section 3.3 MIME Media Type */\n\tf = \"mimetype\";\n\tzip_add_file(zip, f, \"application/vnd.oasis.opendocument.spreadsheet\");\n\n\t/* Part 1 Section 2.2 Documents */\n\tf = \"content.xml\";\n\tzip_add_file(zip, f, write_content_ods(wb, opts));\n\tmanifest.push([f, \"text/xml\"]);\n\trdf.push([f, \"ContentFile\"]);\n\n\t/* TODO: these are hard-coded styles to satiate excel */\n\tf = \"styles.xml\";\n\tzip_add_file(zip, f, write_styles_ods(wb, opts));\n\tmanifest.push([f, \"text/xml\"]);\n\trdf.push([f, \"StylesFile\"]);\n\n\t/* TODO: this is hard-coded to satiate excel */\n\tf = \"meta.xml\";\n\tzip_add_file(zip, f, write_meta_ods());\n\tmanifest.push([f, \"text/xml\"]);\n\trdf.push([f, \"MetadataFile\"]);\n\n\t/* Part 3 Section 6 Metadata Manifest File */\n\tf = \"manifest.rdf\";\n\tzip_add_file(zip, f, write_rdf(rdf/*, opts*/));\n\tmanifest.push([f, \"application/rdf+xml\"]);\n\n\t/* Part 3 Section 4 Manifest File */\n\tf = \"META-INF/manifest.xml\";\n\tzip_add_file(zip, f, write_manifest(manifest/*, opts*/));\n\n\treturn zip;\n}\n\nfunction write_sheet_index(wb, sheet) {\n\tif(!sheet) return 0;\n\tvar idx = wb.SheetNames.indexOf(sheet);\n\tif(idx == -1) throw new Error(\"Sheet not found: \" + sheet);\n\treturn idx;\n}\n\nfunction write_obj_str(factory) {\n\treturn function write_str(wb, o) {\n\t\tvar idx = write_sheet_index(wb, o.sheet);\n\t\treturn factory.from_sheet(wb.Sheets[wb.SheetNames[idx]], o, wb);\n\t};\n}\n\nvar write_htm_str = write_obj_str(HTML_);\nvar write_csv_str = write_obj_str({from_sheet:sheet_to_csv});\nvar write_slk_str = write_obj_str(typeof SYLK !== \"undefined\" ? SYLK : {});\nvar write_dif_str = write_obj_str(typeof DIF !== \"undefined\" ? DIF : {});\nvar write_prn_str = write_obj_str(typeof PRN !== \"undefined\" ? PRN : {});\nvar write_rtf_str = write_obj_str(typeof RTF !== \"undefined\" ? RTF : {});\nvar write_txt_str = write_obj_str({from_sheet:sheet_to_txt});\nvar write_dbf_buf = write_obj_str(typeof DBF !== \"undefined\" ? DBF : {});\nvar write_eth_str = write_obj_str(typeof ETH !== \"undefined\" ? ETH : {});\n\nfunction fix_opts_func(defaults) {\n\treturn function fix_opts(opts) {\n\t\tfor(var i = 0; i != defaults.length; ++i) {\n\t\t\tvar d = defaults[i];\n\t\t\tif(opts[d[0]] === undefined) opts[d[0]] = d[1];\n\t\t\tif(d[2] === 'n') opts[d[0]] = Number(opts[d[0]]);\n\t\t}\n\t};\n}\n\nvar fix_read_opts = function(opts) {\nfix_opts_func([\n\t['cellNF', false], /* emit cell number format string as .z */\n\t['cellHTML', true], /* emit html string as .h */\n\t['cellFormula', true], /* emit formulae as .f */\n\t['cellStyles', false], /* emits style/theme as .s */\n\t['cellText', true], /* emit formatted text as .w */\n\t['cellDates', false], /* emit date cells with type `d` */\n\n\t['sheetStubs', false], /* emit empty cells */\n\t['sheetRows', 0, 'n'], /* read n rows (0 = read all rows) */\n\n\t['bookDeps', false], /* parse calculation chains */\n\t['bookSheets', false], /* only try to get sheet names (no Sheets) */\n\t['bookProps', false], /* only try to get properties (no Sheets) */\n\t['bookFiles', false], /* include raw file structure (keys, files, cfb) */\n\t['bookVBA', false], /* include vba raw data (vbaraw) */\n\n\t['password',''], /* password */\n\t['WTF', false] /* WTF mode (throws errors) */\n])(opts);\n};\n\nvar fix_write_opts = fix_opts_func([\n\t['cellDates', false], /* write date cells with type `d` */\n\n\t['bookSST', false], /* Generate Shared String Table */\n\n\t['bookType', 'xlsx'], /* Type of workbook (xlsx/m/b) */\n\n\t['compression', false], /* Use file compression */\n\n\t['WTF', false] /* WTF mode (throws errors) */\n]);\nfunction get_sheet_type(n) {\n\tif(RELS.WS.indexOf(n) > -1) return \"sheet\";\n\tif(RELS.CS && n == RELS.CS) return \"chart\";\n\tif(RELS.DS && n == RELS.DS) return \"dialog\";\n\tif(RELS.MS && n == RELS.MS) return \"macro\";\n\treturn (n && n.length) ? n : \"sheet\";\n}\nfunction safe_parse_wbrels(wbrels, sheets) {\n\tif(!wbrels) return 0;\n\ttry {\n\t\twbrels = sheets.map(function pwbr(w) { if(!w.id) w.id = w.strRelID; return [w.name, wbrels['!id'][w.id].Target, get_sheet_type(wbrels['!id'][w.id].Type)]; });\n\t} catch(e) { return null; }\n\treturn !wbrels || wbrels.length === 0 ? null : wbrels;\n}\n\nfunction safe_parse_sheet(zip, path, relsPath, sheet, idx, sheetRels, sheets, stype, opts, wb, themes, styles) {\n\ttry {\n\t\tsheetRels[sheet]=parse_rels(getzipstr(zip, relsPath, true), path);\n\t\tvar data = getzipdata(zip, path);\n\t\tvar _ws;\n\t\tswitch(stype) {\n\t\t\tcase 'sheet': _ws = parse_ws(data, path, idx, opts, sheetRels[sheet], wb, themes, styles); break;\n\t\t\tcase 'chart': _ws = parse_cs(data, path, idx, opts, sheetRels[sheet], wb, themes, styles);\n\t\t\t\tif(!_ws || !_ws['!drawel']) break;\n\t\t\t\tvar dfile = resolve_path(_ws['!drawel'].Target, path);\n\t\t\t\tvar drelsp = get_rels_path(dfile);\n\t\t\t\tvar draw = parse_drawing(getzipstr(zip, dfile, true), parse_rels(getzipstr(zip, drelsp, true), dfile));\n\t\t\t\tvar chartp = resolve_path(draw, dfile);\n\t\t\t\tvar crelsp = get_rels_path(chartp);\n\t\t\t\t_ws = parse_chart(getzipstr(zip, chartp, true), chartp, opts, parse_rels(getzipstr(zip, crelsp, true), chartp), wb, _ws);\n\t\t\t\tbreak;\n\t\t\tcase 'macro': _ws = parse_ms(data, path, idx, opts, sheetRels[sheet], wb, themes, styles); break;\n\t\t\tcase 'dialog': _ws = parse_ds(data, path, idx, opts, sheetRels[sheet], wb, themes, styles); break;\n\t\t\tdefault: throw new Error(\"Unrecognized sheet type \" + stype);\n\t\t}\n\t\tsheets[sheet] = _ws;\n\n\t\t/* scan rels for comments */\n\t\tvar comments = [];\n\t\tif(sheetRels && sheetRels[sheet]) keys(sheetRels[sheet]).forEach(function(n) {\n\t\t\tif(sheetRels[sheet][n].Type == RELS.CMNT) {\n\t\t\t\tvar dfile = resolve_path(sheetRels[sheet][n].Target, path);\n\t\t\t\tcomments = parse_cmnt(getzipdata(zip, dfile, true), dfile, opts);\n\t\t\t\tif(!comments || !comments.length) return;\n\t\t\t\tsheet_insert_comments(_ws, comments);\n\t\t\t}\n\t\t});\n\t} catch(e) { if(opts.WTF) throw e; }\n}\n\nfunction strip_front_slash(x) { return x.charAt(0) == '/' ? x.slice(1) : x; }\n\nfunction parse_zip(zip, opts) {\n\tmake_ssf(SSF);\n\topts = opts || {};\n\tfix_read_opts(opts);\n\n\t/* OpenDocument Part 3 Section 2.2.1 OpenDocument Package */\n\tif(safegetzipfile(zip, 'META-INF/manifest.xml')) return parse_ods(zip, opts);\n\t/* UOC */\n\tif(safegetzipfile(zip, 'objectdata.xml')) return parse_ods(zip, opts);\n\t/* Numbers */\n\tif(safegetzipfile(zip, 'Index/Document.iwa')) throw new Error('Unsupported NUMBERS file');\n\n\tvar entries = zipentries(zip);\n\tvar dir = parse_ct((getzipstr(zip, '[Content_Types].xml')));\n\tvar xlsb = false;\n\tvar sheets, binname;\n\tif(dir.workbooks.length === 0) {\n\t\tbinname = \"xl/workbook.xml\";\n\t\tif(getzipdata(zip,binname, true)) dir.workbooks.push(binname);\n\t}\n\tif(dir.workbooks.length === 0) {\n\t\tbinname = \"xl/workbook.bin\";\n\t\tif(!getzipdata(zip,binname,true)) throw new Error(\"Could not find workbook\");\n\t\tdir.workbooks.push(binname);\n\t\txlsb = true;\n\t}\n\tif(dir.workbooks[0].slice(-3) == \"bin\") xlsb = true;\n\n\tvar themes = ({});\n\tvar styles = ({});\n\tif(!opts.bookSheets && !opts.bookProps) {\n\t\tstrs = [];\n\t\tif(dir.sst) try { strs=parse_sst(getzipdata(zip, strip_front_slash(dir.sst)), dir.sst, opts); } catch(e) { if(opts.WTF) throw e; }\n\n\t\tif(opts.cellStyles && dir.themes.length) themes = parse_theme(getzipstr(zip, dir.themes[0].replace(/^\\//,''), true)||\"\",dir.themes[0], opts);\n\n\t\tif(dir.style) styles = parse_sty(getzipdata(zip, strip_front_slash(dir.style)), dir.style, themes, opts);\n\t}\n\n\t/*var externbooks = */dir.links.map(function(link) {\n\t\ttry {\n\t\t\tvar rels = parse_rels(getzipstr(zip, get_rels_path(strip_front_slash(link))), link);\n\t\t\treturn parse_xlink(getzipdata(zip, strip_front_slash(link)), rels, link, opts);\n\t\t} catch(e) {}\n\t});\n\n\tvar wb = parse_wb(getzipdata(zip, strip_front_slash(dir.workbooks[0])), dir.workbooks[0], opts);\n\n\tvar props = {}, propdata = \"\";\n\n\tif(dir.coreprops.length) {\n\t\tpropdata = getzipdata(zip, strip_front_slash(dir.coreprops[0]), true);\n\t\tif(propdata) props = parse_core_props(propdata);\n\t\tif(dir.extprops.length !== 0) {\n\t\t\tpropdata = getzipdata(zip, strip_front_slash(dir.extprops[0]), true);\n\t\t\tif(propdata) parse_ext_props(propdata, props, opts);\n\t\t}\n\t}\n\n\tvar custprops = {};\n\tif(!opts.bookSheets || opts.bookProps) {\n\t\tif (dir.custprops.length !== 0) {\n\t\t\tpropdata = getzipstr(zip, strip_front_slash(dir.custprops[0]), true);\n\t\t\tif(propdata) custprops = parse_cust_props(propdata, opts);\n\t\t}\n\t}\n\n\tvar out = ({});\n\tif(opts.bookSheets || opts.bookProps) {\n\t\tif(wb.Sheets) sheets = wb.Sheets.map(function pluck(x){ return x.name; });\n\t\telse if(props.Worksheets && props.SheetNames.length > 0) sheets=props.SheetNames;\n\t\tif(opts.bookProps) { out.Props = props; out.Custprops = custprops; }\n\t\tif(opts.bookSheets && typeof sheets !== 'undefined') out.SheetNames = sheets;\n\t\tif(opts.bookSheets ? out.SheetNames : opts.bookProps) return out;\n\t}\n\tsheets = {};\n\n\tvar deps = {};\n\tif(opts.bookDeps && dir.calcchain) deps=parse_cc(getzipdata(zip, strip_front_slash(dir.calcchain)),dir.calcchain,opts);\n\n\tvar i=0;\n\tvar sheetRels = ({});\n\tvar path, relsPath;\n\n\t{\n\t\tvar wbsheets = wb.Sheets;\n\t\tprops.Worksheets = wbsheets.length;\n\t\tprops.SheetNames = [];\n\t\tfor(var j = 0; j != wbsheets.length; ++j) {\n\t\t\tprops.SheetNames[j] = wbsheets[j].name;\n\t\t}\n\t}\n\n\tvar wbext = xlsb ? \"bin\" : \"xml\";\n\tvar wbrelsi = dir.workbooks[0].lastIndexOf(\"/\");\n\tvar wbrelsfile = (dir.workbooks[0].slice(0, wbrelsi+1) + \"_rels/\" + dir.workbooks[0].slice(wbrelsi+1) + \".rels\").replace(/^\\//,\"\");\n\tif(!safegetzipfile(zip, wbrelsfile)) wbrelsfile = 'xl/_rels/workbook.' + wbext + '.rels';\n\tvar wbrels = parse_rels(getzipstr(zip, wbrelsfile, true), wbrelsfile);\n\tif(wbrels) wbrels = safe_parse_wbrels(wbrels, wb.Sheets);\n\n\t/* Numbers iOS hack */\n\tvar nmode = (getzipdata(zip,\"xl/worksheets/sheet.xml\",true))?1:0;\n\twsloop: for(i = 0; i != props.Worksheets; ++i) {\n\t\tvar stype = \"sheet\";\n\t\tif(wbrels && wbrels[i]) {\n\t\t\tpath = 'xl/' + (wbrels[i][1]).replace(/[\\/]?xl\\//, \"\");\n\t\t\tif(!safegetzipfile(zip, path)) path = wbrels[i][1];\n\t\t\tif(!safegetzipfile(zip, path)) path = wbrelsfile.replace(/_rels\\/.*$/,\"\") + wbrels[i][1];\n\t\t\tstype = wbrels[i][2];\n\t\t} else {\n\t\t\tpath = 'xl/worksheets/sheet'+(i+1-nmode)+\".\" + wbext;\n\t\t\tpath = path.replace(/sheet0\\./,\"sheet.\");\n\t\t}\n\t\trelsPath = path.replace(/^(.*)(\\/)([^\\/]*)$/, \"$1/_rels/$3.rels\");\n\t\tif(opts && opts.sheets != null) switch(typeof opts.sheets) {\n\t\t\tcase \"number\": if(i != opts.sheets) continue wsloop; break;\n\t\t\tcase \"string\": if(props.SheetNames[i].toLowerCase() != opts.sheets.toLowerCase()) continue wsloop; break;\n\t\t\tdefault: if(Array.isArray && Array.isArray(opts.sheets)) {\n\t\t\t\tvar snjseen = false;\n\t\t\t\tfor(var snj = 0; snj != opts.sheets.length; ++snj) {\n\t\t\t\t\tif(typeof opts.sheets[snj] == \"number\" && opts.sheets[snj] == i) snjseen=1;\n\t\t\t\t\tif(typeof opts.sheets[snj] == \"string\" && opts.sheets[snj].toLowerCase() == props.SheetNames[i].toLowerCase()) snjseen = 1;\n\t\t\t\t}\n\t\t\t\tif(!snjseen) continue wsloop;\n\t\t\t}\n\t\t}\n\t\tsafe_parse_sheet(zip, path, relsPath, props.SheetNames[i], i, sheetRels, sheets, stype, opts, wb, themes, styles);\n\t}\n\n\tout = ({\n\t\tDirectory: dir,\n\t\tWorkbook: wb,\n\t\tProps: props,\n\t\tCustprops: custprops,\n\t\tDeps: deps,\n\t\tSheets: sheets,\n\t\tSheetNames: props.SheetNames,\n\t\tStrings: strs,\n\t\tStyles: styles,\n\t\tThemes: themes,\n\t\tSSF: SSF.get_table()\n\t});\n\tif(opts && opts.bookFiles) {\n\t\tout.keys = entries;\n\t\tout.files = zip.files;\n\t}\n\tif(opts && opts.bookVBA) {\n\t\tif(dir.vba.length > 0) out.vbaraw = getzipdata(zip,strip_front_slash(dir.vba[0]),true);\n\t\telse if(dir.defaults && dir.defaults.bin === CT_VBA) out.vbaraw = getzipdata(zip, 'xl/vbaProject.bin',true);\n\t}\n\treturn out;\n}\n\n/* [MS-OFFCRYPTO] 2.1.1 */\nfunction parse_xlsxcfb(cfb, _opts) {\n\tvar opts = _opts || {};\n\tvar f = 'Workbook', data = CFB.find(cfb, f);\n\ttry {\n\tf = '/!DataSpaces/Version';\n\tdata = CFB.find(cfb, f); if(!data || !data.content) throw new Error(\"ECMA-376 Encrypted file missing \" + f);\n\t/*var version = */parse_DataSpaceVersionInfo(data.content);\n\n\t/* 2.3.4.1 */\n\tf = '/!DataSpaces/DataSpaceMap';\n\tdata = CFB.find(cfb, f); if(!data || !data.content) throw new Error(\"ECMA-376 Encrypted file missing \" + f);\n\tvar dsm = parse_DataSpaceMap(data.content);\n\tif(dsm.length !== 1 || dsm[0].comps.length !== 1 || dsm[0].comps[0].t !== 0 || dsm[0].name !== \"StrongEncryptionDataSpace\" || dsm[0].comps[0].v !== \"EncryptedPackage\")\n\t\tthrow new Error(\"ECMA-376 Encrypted file bad \" + f);\n\n\t/* 2.3.4.2 */\n\tf = '/!DataSpaces/DataSpaceInfo/StrongEncryptionDataSpace';\n\tdata = CFB.find(cfb, f); if(!data || !data.content) throw new Error(\"ECMA-376 Encrypted file missing \" + f);\n\tvar seds = parse_DataSpaceDefinition(data.content);\n\tif(seds.length != 1 || seds[0] != \"StrongEncryptionTransform\")\n\t\tthrow new Error(\"ECMA-376 Encrypted file bad \" + f);\n\n\t/* 2.3.4.3 */\n\tf = '/!DataSpaces/TransformInfo/StrongEncryptionTransform/!Primary';\n\tdata = CFB.find(cfb, f); if(!data || !data.content) throw new Error(\"ECMA-376 Encrypted file missing \" + f);\n\t/*var hdr = */parse_Primary(data.content);\n\t} catch(e) {}\n\n\tf = '/EncryptionInfo';\n\tdata = CFB.find(cfb, f); if(!data || !data.content) throw new Error(\"ECMA-376 Encrypted file missing \" + f);\n\tvar einfo = parse_EncryptionInfo(data.content);\n\n\t/* 2.3.4.4 */\n\tf = '/EncryptedPackage';\n\tdata = CFB.find(cfb, f); if(!data || !data.content) throw new Error(\"ECMA-376 Encrypted file missing \" + f);\n\n/*global decrypt_agile */\nif(einfo[0] == 0x04 && typeof decrypt_agile !== 'undefined') return decrypt_agile(einfo[1], data.content, opts.password || \"\", opts);\n/*global decrypt_std76 */\nif(einfo[0] == 0x02 && typeof decrypt_std76 !== 'undefined') return decrypt_std76(einfo[1], data.content, opts.password || \"\", opts);\n\tthrow new Error(\"File is password-protected\");\n}\n\nfunction write_zip(wb, opts) {\n\t_shapeid = 1024;\n\tif(opts.bookType == \"ods\") return write_ods(wb, opts);\n\tif(wb && !wb.SSF) {\n\t\twb.SSF = SSF.get_table();\n\t}\n\tif(wb && wb.SSF) {\n\t\tmake_ssf(SSF); SSF.load_table(wb.SSF);\n\t\t// $FlowIgnore\n\t\topts.revssf = evert_num(wb.SSF); opts.revssf[wb.SSF[65535]] = 0;\n\t\topts.ssf = wb.SSF;\n\t}\n\topts.rels = {}; opts.wbrels = {};\n\topts.Strings = []; opts.Strings.Count = 0; opts.Strings.Unique = 0;\n\tif(browser_has_Map) opts.revStrings = new Map();\n\telse { opts.revStrings = {}; opts.revStrings.foo = []; delete opts.revStrings.foo; }\n\tvar wbext = opts.bookType == \"xlsb\" ? \"bin\" : \"xml\";\n\tvar vbafmt = VBAFMTS.indexOf(opts.bookType) > -1;\n\tvar ct = new_ct();\n\tfix_write_opts(opts = opts || {});\nvar zip = zip_new();\n\tvar f = \"\", rId = 0;\n\n\topts.cellXfs = [];\n\tget_cell_style(opts.cellXfs, {}, {revssf:{\"General\":0}});\n\n\tif(!wb.Props) wb.Props = {};\n\n\tf = \"docProps/core.xml\";\n\tzip_add_file(zip, f, write_core_props(wb.Props, opts));\n\tct.coreprops.push(f);\n\tadd_rels(opts.rels, 2, f, RELS.CORE_PROPS);\n\nf = \"docProps/app.xml\";\n\tif(wb.Props && wb.Props.SheetNames){/* empty */}\n\telse if(!wb.Workbook || !wb.Workbook.Sheets) wb.Props.SheetNames = wb.SheetNames;\n\telse {\n\t\tvar _sn = [];\n\t\tfor(var _i = 0; _i < wb.SheetNames.length; ++_i)\n\t\t\tif((wb.Workbook.Sheets[_i]||{}).Hidden != 2) _sn.push(wb.SheetNames[_i]);\n\t\twb.Props.SheetNames = _sn;\n\t}\n\twb.Props.Worksheets = wb.Props.SheetNames.length;\n\tzip_add_file(zip, f, write_ext_props(wb.Props, opts));\n\tct.extprops.push(f);\n\tadd_rels(opts.rels, 3, f, RELS.EXT_PROPS);\n\n\tif(wb.Custprops !== wb.Props && keys(wb.Custprops||{}).length > 0) {\n\t\tf = \"docProps/custom.xml\";\n\t\tzip_add_file(zip, f, write_cust_props(wb.Custprops, opts));\n\t\tct.custprops.push(f);\n\t\tadd_rels(opts.rels, 4, f, RELS.CUST_PROPS);\n\t}\n\n\tfor(rId=1;rId <= wb.SheetNames.length; ++rId) {\n\t\tvar wsrels = {'!id':{}};\n\t\tvar ws = wb.Sheets[wb.SheetNames[rId-1]];\n\t\tvar _type = (ws || {})[\"!type\"] || \"sheet\";\n\t\tswitch(_type) {\n\t\tcase \"chart\":\n\t\t\t/* falls through */\n\t\tdefault:\n\t\t\tf = \"xl/worksheets/sheet\" + rId + \".\" + wbext;\n\t\t\tzip_add_file(zip, f, write_ws(rId-1, f, opts, wb, wsrels));\n\t\t\tct.sheets.push(f);\n\t\t\tadd_rels(opts.wbrels, -1, \"worksheets/sheet\" + rId + \".\" + wbext, RELS.WS[0]);\n\t\t}\n\n\t\tif(ws) {\n\t\t\tvar comments = ws['!comments'];\n\t\t\tvar need_vml = false;\n\t\t\tif(comments && comments.length > 0) {\n\t\t\t\tvar cf = \"xl/comments\" + rId + \".\" + wbext;\n\t\t\t\tzip_add_file(zip, cf, write_cmnt(comments, cf, opts));\n\t\t\t\tct.comments.push(cf);\n\t\t\t\tadd_rels(wsrels, -1, \"../comments\" + rId + \".\" + wbext, RELS.CMNT);\n\t\t\t\tneed_vml = true;\n\t\t\t}\n\t\t\tif(ws['!legacy']) {\n\t\t\t\tif(need_vml) zip_add_file(zip, \"xl/drawings/vmlDrawing\" + (rId) + \".vml\", write_comments_vml(rId, ws['!comments']));\n\t\t\t}\n\t\t\tdelete ws['!comments'];\n\t\t\tdelete ws['!legacy'];\n\t\t}\n\n\t\tif(wsrels['!id'].rId1) zip_add_file(zip, get_rels_path(f), write_rels(wsrels));\n\t}\n\n\tif(opts.Strings != null && opts.Strings.length > 0) {\n\t\tf = \"xl/sharedStrings.\" + wbext;\n\t\tzip_add_file(zip, f, write_sst(opts.Strings, f, opts));\n\t\tct.strs.push(f);\n\t\tadd_rels(opts.wbrels, -1, \"sharedStrings.\" + wbext, RELS.SST);\n\t}\n\n\tf = \"xl/workbook.\" + wbext;\n\tzip_add_file(zip, f, write_wb(wb, f, opts));\n\tct.workbooks.push(f);\n\tadd_rels(opts.rels, 1, f, RELS.WB);\n\n\t/* TODO: something more intelligent with themes */\n\n\tf = \"xl/theme/theme1.xml\";\n\tzip_add_file(zip, f, write_theme(wb.Themes, opts));\n\tct.themes.push(f);\n\tadd_rels(opts.wbrels, -1, \"theme/theme1.xml\", RELS.THEME);\n\n\t/* TODO: something more intelligent with styles */\n\n\tf = \"xl/styles.\" + wbext;\n\tzip_add_file(zip, f, write_sty(wb, f, opts));\n\tct.styles.push(f);\n\tadd_rels(opts.wbrels, -1, \"styles.\" + wbext, RELS.STY);\n\n\tif(wb.vbaraw && vbafmt) {\n\t\tf = \"xl/vbaProject.bin\";\n\t\tzip_add_file(zip, f, wb.vbaraw);\n\t\tct.vba.push(f);\n\t\tadd_rels(opts.wbrels, -1, \"vbaProject.bin\", RELS.VBA);\n\t}\n\n\tzip_add_file(zip, \"[Content_Types].xml\", write_ct(ct, opts));\n\tzip_add_file(zip, '_rels/.rels', write_rels(opts.rels));\n\tzip_add_file(zip, 'xl/_rels/workbook.' + wbext + '.rels', write_rels(opts.wbrels));\n\n\tdelete opts.revssf; delete opts.ssf;\n\treturn zip;\n}\nfunction firstbyte(f,o) {\n\tvar x = \"\";\n\tswitch((o||{}).type || \"base64\") {\n\t\tcase 'buffer': return [f[0], f[1], f[2], f[3], f[4], f[5], f[6], f[7]];\n\t\tcase 'base64': x = Base64.decode(f.slice(0,12)); break;\n\t\tcase 'binary': x = f; break;\n\t\tcase 'array': return [f[0], f[1], f[2], f[3], f[4], f[5], f[6], f[7]];\n\t\tdefault: throw new Error(\"Unrecognized type \" + (o && o.type || \"undefined\"));\n\t}\n\treturn [x.charCodeAt(0), x.charCodeAt(1), x.charCodeAt(2), x.charCodeAt(3), x.charCodeAt(4), x.charCodeAt(5), x.charCodeAt(6), x.charCodeAt(7)];\n}\n\nfunction read_cfb(cfb, opts) {\n\tif(CFB.find(cfb, \"EncryptedPackage\")) return parse_xlsxcfb(cfb, opts);\n\treturn parse_xlscfb(cfb, opts);\n}\n\nfunction read_zip(data, opts) {\nvar zip, d = data;\n\tvar o = opts||{};\n\tif(!o.type) o.type = (has_buf && Buffer.isBuffer(data)) ? \"buffer\" : \"base64\";\n\tzip = zip_read(d, o);\n\treturn parse_zip(zip, o);\n}\n\nfunction read_plaintext(data, o) {\n\tvar i = 0;\n\tmain: while(i < data.length) switch(data.charCodeAt(i)) {\n\t\tcase 0x0A: case 0x0D: case 0x20: ++i; break;\n\t\tcase 0x3C: return parse_xlml(data.slice(i),o);\n\t\tdefault: break main;\n\t}\n\treturn PRN.to_workbook(data, o);\n}\n\nfunction read_plaintext_raw(data, o) {\n\tvar str = \"\", bytes = firstbyte(data, o);\n\tswitch(o.type) {\n\t\tcase 'base64': str = Base64.decode(data); break;\n\t\tcase 'binary': str = data; break;\n\t\tcase 'buffer': str = data.toString('binary'); break;\n\t\tcase 'array': str = cc2str(data); break;\n\t\tdefault: throw new Error(\"Unrecognized type \" + o.type);\n\t}\n\tif(bytes[0] == 0xEF && bytes[1] == 0xBB && bytes[2] == 0xBF) str = utf8read(str);\n\treturn read_plaintext(str, o);\n}\n\nfunction read_utf16(data, o) {\n\tvar d = data;\n\tif(o.type == 'base64') d = Base64.decode(d);\n\td = cptable.utils.decode(1200, d.slice(2), 'str');\n\to.type = \"binary\";\n\treturn read_plaintext(d, o);\n}\n\nfunction bstrify(data) {\n\treturn !data.match(/[^\\x00-\\x7F]/) ? data : utf8write(data);\n}\n\nfunction read_prn(data, d, o, str) {\n\tif(str) { o.type = \"string\"; return PRN.to_workbook(data, o); }\n\treturn PRN.to_workbook(d, o);\n}\n\nfunction readSync(data, opts) {\n\treset_cp();\n\tif(typeof ArrayBuffer !== 'undefined' && data instanceof ArrayBuffer) return readSync(new Uint8Array(data), opts);\n\tvar d = data, n = [0,0,0,0], str = false;\n\tvar o = opts||{};\n\tif(o.cellStyles) { o.cellNF = true; o.sheetStubs = true; }\n\t_ssfopts = {};\n\tif(o.dateNF) _ssfopts.dateNF = o.dateNF;\n\tif(!o.type) o.type = (has_buf && Buffer.isBuffer(data)) ? \"buffer\" : \"base64\";\n\tif(o.type == \"file\") { o.type = has_buf ? \"buffer\" : \"binary\"; d = read_binary(data); }\n\tif(o.type == \"string\") { str = true; o.type = \"binary\"; o.codepage = 65001; d = bstrify(data); }\n\tif(o.type == 'array' && typeof Uint8Array !== 'undefined' && data instanceof Uint8Array && typeof ArrayBuffer !== 'undefined') {\n\t\t// $FlowIgnore\n\t\tvar ab=new ArrayBuffer(3), vu=new Uint8Array(ab); vu.foo=\"bar\";\n\t\t// $FlowIgnore\n\t\tif(!vu.foo) {o=dup(o); o.type='array'; return readSync(ab2a(d), o);}\n\t}\n\tswitch((n = firstbyte(d, o))[0]) {\n\t\tcase 0xD0: if(n[1] === 0xCF && n[2] === 0x11 && n[3] === 0xE0 && n[4] === 0xA1 && n[5] === 0xB1 && n[6] === 0x1A && n[7] === 0xE1) return read_cfb(CFB.read(d, o), o); break;\n\t\tcase 0x09: if(n[1] <= 0x04) return parse_xlscfb(d, o); break;\n\t\tcase 0x3C: return parse_xlml(d, o);\n\t\tcase 0x49: if(n[1] === 0x44) return read_wb_ID(d, o); break;\n\t\tcase 0x54: if(n[1] === 0x41 && n[2] === 0x42 && n[3] === 0x4C) return DIF.to_workbook(d, o); break;\n\t\tcase 0x50: return (n[1] === 0x4B && n[2] < 0x09 && n[3] < 0x09) ? read_zip(d, o) : read_prn(data, d, o, str);\n\t\tcase 0xEF: return n[3] === 0x3C ? parse_xlml(d, o) : read_prn(data, d, o, str);\n\t\tcase 0xFF: if(n[1] === 0xFE) { return read_utf16(d, o); } break;\n\t\tcase 0x00: if(n[1] === 0x00 && n[2] >= 0x02 && n[3] === 0x00) return WK_.to_workbook(d, o); break;\n\t\tcase 0x03: case 0x83: case 0x8B: case 0x8C: return DBF.to_workbook(d, o);\n\t\tcase 0x7B: if(n[1] === 0x5C && n[2] === 0x72 && n[3] === 0x74) return RTF.to_workbook(d, o); break;\n\t\tcase 0x0A: case 0x0D: case 0x20: return read_plaintext_raw(d, o);\n\t}\n\tif(DBF.versions.indexOf(n[0]) > -1 && n[2] <= 12 && n[3] <= 31) return DBF.to_workbook(d, o);\n\treturn read_prn(data, d, o, str);\n}\n\nfunction readFileSync(filename, opts) {\n\tvar o = opts||{}; o.type = 'file';\n\treturn readSync(filename, o);\n}\nfunction write_cfb_ctr(cfb, o) {\n\tswitch(o.type) {\n\t\tcase \"base64\": case \"binary\": break;\n\t\tcase \"buffer\": case \"array\": o.type = \"\"; break;\n\t\tcase \"file\": return write_dl(o.file, CFB.write(cfb, {type:has_buf ? 'buffer' : \"\"}));\n\t\tcase \"string\": throw new Error(\"'string' output type invalid for '\" + o.bookType + \"' files\");\n\t\tdefault: throw new Error(\"Unrecognized type \" + o.type);\n\t}\n\treturn CFB.write(cfb, o);\n}\n\nfunction write_zip_type(wb, opts) {\n\tvar o = opts||{};\n\tvar z = write_zip(wb, o);\n\tvar oopts = {};\n\tif(o.compression) oopts.compression = 'DEFLATE';\n\tif(o.password) oopts.type = has_buf ? \"nodebuffer\" : \"string\";\n\telse switch(o.type) {\n\t\tcase \"base64\": oopts.type = \"base64\"; break;\n\t\tcase \"binary\": oopts.type = \"string\"; break;\n\t\tcase \"string\": throw new Error(\"'string' output type invalid for '\" + o.bookType + \"' files\");\n\t\tcase \"buffer\":\n\t\tcase \"file\": oopts.type = has_buf ? \"nodebuffer\" : \"string\"; break;\n\t\tdefault: throw new Error(\"Unrecognized type \" + o.type);\n\t}\n\tvar out = z.FullPaths ? CFB.write(z, {fileType:\"zip\", type: {\"nodebuffer\": \"buffer\", \"string\": \"binary\"}[oopts.type] || oopts.type}) : z.generate(oopts);\n/*jshint -W083 */\n\tif(o.password && typeof encrypt_agile !== 'undefined') return write_cfb_ctr(encrypt_agile(out, o.password), o); // eslint-disable-line no-undef\n/*jshint +W083 */\n\tif(o.type === \"file\") return write_dl(o.file, out);\n\treturn o.type == \"string\" ? utf8read(out) : out;\n}\n\nfunction write_cfb_type(wb, opts) {\n\tvar o = opts||{};\n\tvar cfb = write_xlscfb(wb, o);\n\treturn write_cfb_ctr(cfb, o);\n}\n\nfunction write_string_type(out, opts, bom) {\n\tif(!bom) bom = \"\";\n\tvar o = bom + out;\n\tswitch(opts.type) {\n\t\tcase \"base64\": return Base64.encode(utf8write(o));\n\t\tcase \"binary\": return utf8write(o);\n\t\tcase \"string\": return out;\n\t\tcase \"file\": return write_dl(opts.file, o, 'utf8');\n\t\tcase \"buffer\": {\n\t\t\tif(has_buf) return Buffer_from(o, 'utf8');\n\t\t\telse return write_string_type(o, {type:'binary'}).split(\"\").map(function(c) { return c.charCodeAt(0); });\n\t\t}\n\t}\n\tthrow new Error(\"Unrecognized type \" + opts.type);\n}\n\nfunction write_stxt_type(out, opts) {\n\tswitch(opts.type) {\n\t\tcase \"base64\": return Base64.encode(out);\n\t\tcase \"binary\": return out;\n\t\tcase \"string\": return out; /* override in sheet_to_txt */\n\t\tcase \"file\": return write_dl(opts.file, out, 'binary');\n\t\tcase \"buffer\": {\n\t\t\tif(has_buf) return Buffer_from(out, 'binary');\n\t\t\telse return out.split(\"\").map(function(c) { return c.charCodeAt(0); });\n\t\t}\n\t}\n\tthrow new Error(\"Unrecognized type \" + opts.type);\n}\n\n/* TODO: test consistency */\nfunction write_binary_type(out, opts) {\n\tswitch(opts.type) {\n\t\tcase \"string\":\n\t\tcase \"base64\":\n\t\tcase \"binary\":\n\t\t\tvar bstr = \"\";\n\t\t\t// $FlowIgnore\n\t\t\tfor(var i = 0; i < out.length; ++i) bstr += String.fromCharCode(out[i]);\n\t\t\treturn opts.type == 'base64' ? Base64.encode(bstr) : opts.type == 'string' ? utf8read(bstr) : bstr;\n\t\tcase \"file\": return write_dl(opts.file, out);\n\t\tcase \"buffer\": return out;\n\t\tdefault: throw new Error(\"Unrecognized type \" + opts.type);\n\t}\n}\n\nfunction writeSync(wb, opts) {\n\treset_cp();\n\tcheck_wb(wb);\n\tvar o = opts||{};\n\tif(o.cellStyles) { o.cellNF = true; o.sheetStubs = true; }\n\tif(o.type == \"array\") { o.type = \"binary\"; var out = (writeSync(wb, o)); o.type = \"array\"; return s2ab(out); }\n\tswitch(o.bookType || 'xlsb') {\n\t\tcase 'xml':\n\t\tcase 'xlml': return write_string_type(write_xlml(wb, o), o);\n\t\tcase 'slk':\n\t\tcase 'sylk': return write_string_type(write_slk_str(wb, o), o);\n\t\tcase 'htm':\n\t\tcase 'html': return write_string_type(write_htm_str(wb, o), o);\n\t\tcase 'txt': return write_stxt_type(write_txt_str(wb, o), o);\n\t\tcase 'csv': return write_string_type(write_csv_str(wb, o), o, \"\\ufeff\");\n\t\tcase 'dif': return write_string_type(write_dif_str(wb, o), o);\n\t\tcase 'dbf': return write_binary_type(write_dbf_buf(wb, o), o);\n\t\tcase 'prn': return write_string_type(write_prn_str(wb, o), o);\n\t\tcase 'rtf': return write_string_type(write_rtf_str(wb, o), o);\n\t\tcase 'eth': return write_string_type(write_eth_str(wb, o), o);\n\t\tcase 'fods': return write_string_type(write_ods(wb, o), o);\n\t\tcase 'biff2': if(!o.biff) o.biff = 2; /* falls through */\n\t\tcase 'biff3': if(!o.biff) o.biff = 3; /* falls through */\n\t\tcase 'biff4': if(!o.biff) o.biff = 4; return write_binary_type(write_biff_buf(wb, o), o);\n\t\tcase 'biff5': if(!o.biff) o.biff = 5; /* falls through */\n\t\tcase 'biff8':\n\t\tcase 'xla':\n\t\tcase 'xls': if(!o.biff) o.biff = 8; return write_cfb_type(wb, o);\n\t\tcase 'xlsx':\n\t\tcase 'xlsm':\n\t\tcase 'xlam':\n\t\tcase 'xlsb':\n\t\tcase 'ods': return write_zip_type(wb, o);\n\t\tdefault: throw new Error (\"Unrecognized bookType |\" + o.bookType + \"|\");\n\t}\n}\n\nfunction resolve_book_type(o) {\n\tif(o.bookType) return;\n\tvar _BT = {\n\t\t\"xls\": \"biff8\",\n\t\t\"htm\": \"html\",\n\t\t\"slk\": \"sylk\",\n\t\t\"socialcalc\": \"eth\",\n\t\t\"Sh33tJS\": \"WTF\"\n\t};\n\tvar ext = o.file.slice(o.file.lastIndexOf(\".\")).toLowerCase();\n\tif(ext.match(/^\\.[a-z]+$/)) o.bookType = ext.slice(1);\n\to.bookType = _BT[o.bookType] || o.bookType;\n}\n\nfunction writeFileSync(wb, filename, opts) {\n\tvar o = opts||{}; o.type = 'file';\n\to.file = filename;\n\tresolve_book_type(o);\n\treturn writeSync(wb, o);\n}\n\nfunction writeFileAsync(filename, wb, opts, cb) {\n\tvar o = opts||{}; o.type = 'file';\n\to.file = filename;\n\tresolve_book_type(o);\n\to.type = 'buffer';\n\tvar _cb = cb; if(!(_cb instanceof Function)) _cb = (opts);\n\treturn _fs.writeFile(filename, writeSync(wb, o), _cb);\n}\nfunction make_json_row(sheet, r, R, cols, header, hdr, dense, o) {\n\tvar rr = encode_row(R);\n\tvar defval = o.defval, raw = o.raw || !Object.prototype.hasOwnProperty.call(o, \"raw\");\n\tvar isempty = true;\n\tvar row = (header === 1) ? [] : {};\n\tif(header !== 1) {\n\t\tif(Object.defineProperty) try { Object.defineProperty(row, '__rowNum__', {value:R, enumerable:false}); } catch(e) { row.__rowNum__ = R; }\n\t\telse row.__rowNum__ = R;\n\t}\n\tif(!dense || sheet[R]) for (var C = r.s.c; C <= r.e.c; ++C) {\n\t\tvar val = dense ? sheet[R][C] : sheet[cols[C] + rr];\n\t\tif(val === undefined || val.t === undefined) {\n\t\t\tif(defval === undefined) continue;\n\t\t\tif(hdr[C] != null) { row[hdr[C]] = defval; }\n\t\t\tcontinue;\n\t\t}\n\t\tvar v = val.v;\n\t\tswitch(val.t){\n\t\t\tcase 'z': if(v == null) break; continue;\n\t\t\tcase 'e': v = void 0; break;\n\t\t\tcase 's': case 'd': case 'b': case 'n': break;\n\t\t\tdefault: throw new Error('unrecognized type ' + val.t);\n\t\t}\n\t\tif(hdr[C] != null) {\n\t\t\tif(v == null) {\n\t\t\t\tif(defval !== undefined) row[hdr[C]] = defval;\n\t\t\t\telse if(raw && v === null) row[hdr[C]] = null;\n\t\t\t\telse continue;\n\t\t\t} else {\n\t\t\t\trow[hdr[C]] = raw || (o.rawNumbers && val.t == \"n\") ? v : format_cell(val,v,o);\n\t\t\t}\n\t\t\tif(v != null) isempty = false;\n\t\t}\n\t}\n\treturn { row: row, isempty: isempty };\n}\n\n\nfunction sheet_to_json(sheet, opts) {\n\tif(sheet == null || sheet[\"!ref\"] == null) return [];\n\tvar val = {t:'n',v:0}, header = 0, offset = 1, hdr = [], v=0, vv=\"\";\n\tvar r = {s:{r:0,c:0},e:{r:0,c:0}};\n\tvar o = opts || {};\n\tvar range = o.range != null ? o.range : sheet[\"!ref\"];\n\tif(o.header === 1) header = 1;\n\telse if(o.header === \"A\") header = 2;\n\telse if(Array.isArray(o.header)) header = 3;\n\telse if(o.header == null) header = 0;\n\tswitch(typeof range) {\n\t\tcase 'string': r = safe_decode_range(range); break;\n\t\tcase 'number': r = safe_decode_range(sheet[\"!ref\"]); r.s.r = range; break;\n\t\tdefault: r = range;\n\t}\n\tif(header > 0) offset = 0;\n\tvar rr = encode_row(r.s.r);\n\tvar cols = [];\n\tvar out = [];\n\tvar outi = 0, counter = 0;\n\tvar dense = Array.isArray(sheet);\n\tvar R = r.s.r, C = 0, CC = 0;\n\tif(dense && !sheet[R]) sheet[R] = [];\n\tfor(C = r.s.c; C <= r.e.c; ++C) {\n\t\tcols[C] = encode_col(C);\n\t\tval = dense ? sheet[R][C] : sheet[cols[C] + rr];\n\t\tswitch(header) {\n\t\t\tcase 1: hdr[C] = C - r.s.c; break;\n\t\t\tcase 2: hdr[C] = cols[C]; break;\n\t\t\tcase 3: hdr[C] = o.header[C - r.s.c]; break;\n\t\t\tdefault:\n\t\t\t\tif(val == null) val = {w: \"__EMPTY\", t: \"s\"};\n\t\t\t\tvv = v = format_cell(val, null, o);\n\t\t\t\tcounter = 0;\n\t\t\t\tfor(CC = 0; CC < hdr.length; ++CC) if(hdr[CC] == vv) vv = v + \"_\" + (++counter);\n\t\t\t\thdr[C] = vv;\n\t\t}\n\t}\n\tfor (R = r.s.r + offset; R <= r.e.r; ++R) {\n\t\tvar row = make_json_row(sheet, r, R, cols, header, hdr, dense, o);\n\t\tif((row.isempty === false) || (header === 1 ? o.blankrows !== false : !!o.blankrows)) out[outi++] = row.row;\n\t}\n\tout.length = outi;\n\treturn out;\n}\n\nvar qreg = /\"/g;\nfunction make_csv_row(sheet, r, R, cols, fs, rs, FS, o) {\n\tvar isempty = true;\n\tvar row = [], txt = \"\", rr = encode_row(R);\n\tfor(var C = r.s.c; C <= r.e.c; ++C) {\n\t\tif (!cols[C]) continue;\n\t\tvar val = o.dense ? (sheet[R]||[])[C]: sheet[cols[C] + rr];\n\t\tif(val == null) txt = \"\";\n\t\telse if(val.v != null) {\n\t\t\tisempty = false;\n\t\t\ttxt = ''+(o.rawNumbers && val.t == \"n\" ? val.v : format_cell(val, null, o));\n\t\t\tfor(var i = 0, cc = 0; i !== txt.length; ++i) if((cc = txt.charCodeAt(i)) === fs || cc === rs || cc === 34 || o.forceQuotes) {txt = \"\\\"\" + txt.replace(qreg, '\"\"') + \"\\\"\"; break; }\n\t\t\tif(txt == \"ID\") txt = '\"ID\"';\n\t\t} else if(val.f != null && !val.F) {\n\t\t\tisempty = false;\n\t\t\ttxt = '=' + val.f; if(txt.indexOf(\",\") >= 0) txt = '\"' + txt.replace(qreg, '\"\"') + '\"';\n\t\t} else txt = \"\";\n\t\t/* NOTE: Excel CSV does not support array formulae */\n\t\trow.push(txt);\n\t}\n\tif(o.blankrows === false && isempty) return null;\n\treturn row.join(FS);\n}\n\nfunction sheet_to_csv(sheet, opts) {\n\tvar out = [];\n\tvar o = opts == null ? {} : opts;\n\tif(sheet == null || sheet[\"!ref\"] == null) return \"\";\n\tvar r = safe_decode_range(sheet[\"!ref\"]);\n\tvar FS = o.FS !== undefined ? o.FS : \",\", fs = FS.charCodeAt(0);\n\tvar RS = o.RS !== undefined ? o.RS : \"\\n\", rs = RS.charCodeAt(0);\n\tvar endregex = new RegExp((FS==\"|\" ? \"\\\\|\" : FS)+\"+$\");\n\tvar row = \"\", cols = [];\n\to.dense = Array.isArray(sheet);\n\tvar colinfo = o.skipHidden && sheet[\"!cols\"] || [];\n\tvar rowinfo = o.skipHidden && sheet[\"!rows\"] || [];\n\tfor(var C = r.s.c; C <= r.e.c; ++C) if (!((colinfo[C]||{}).hidden)) cols[C] = encode_col(C);\n\tfor(var R = r.s.r; R <= r.e.r; ++R) {\n\t\tif ((rowinfo[R]||{}).hidden) continue;\n\t\trow = make_csv_row(sheet, r, R, cols, fs, rs, FS, o);\n\t\tif(row == null) { continue; }\n\t\tif(o.strip) row = row.replace(endregex,\"\");\n\t\tout.push(row + RS);\n\t}\n\tdelete o.dense;\n\treturn out.join(\"\");\n}\n\nfunction sheet_to_txt(sheet, opts) {\n\tif(!opts) opts = {}; opts.FS = \"\\t\"; opts.RS = \"\\n\";\n\tvar s = sheet_to_csv(sheet, opts);\n\tif(typeof cptable == 'undefined' || opts.type == 'string') return s;\n\tvar o = cptable.utils.encode(1200, s, 'str');\n\treturn String.fromCharCode(255) + String.fromCharCode(254) + o;\n}\n\nfunction sheet_to_formulae(sheet) {\n\tvar y = \"\", x, val=\"\";\n\tif(sheet == null || sheet[\"!ref\"] == null) return [];\n\tvar r = safe_decode_range(sheet['!ref']), rr = \"\", cols = [], C;\n\tvar cmds = [];\n\tvar dense = Array.isArray(sheet);\n\tfor(C = r.s.c; C <= r.e.c; ++C) cols[C] = encode_col(C);\n\tfor(var R = r.s.r; R <= r.e.r; ++R) {\n\t\trr = encode_row(R);\n\t\tfor(C = r.s.c; C <= r.e.c; ++C) {\n\t\t\ty = cols[C] + rr;\n\t\t\tx = dense ? (sheet[R]||[])[C] : sheet[y];\n\t\t\tval = \"\";\n\t\t\tif(x === undefined) continue;\n\t\t\telse if(x.F != null) {\n\t\t\t\ty = x.F;\n\t\t\t\tif(!x.f) continue;\n\t\t\t\tval = x.f;\n\t\t\t\tif(y.indexOf(\":\") == -1) y = y + \":\" + y;\n\t\t\t}\n\t\t\tif(x.f != null) val = x.f;\n\t\t\telse if(x.t == 'z') continue;\n\t\t\telse if(x.t == 'n' && x.v != null) val = \"\" + x.v;\n\t\t\telse if(x.t == 'b') val = x.v ? \"TRUE\" : \"FALSE\";\n\t\t\telse if(x.w !== undefined) val = \"'\" + x.w;\n\t\t\telse if(x.v === undefined) continue;\n\t\t\telse if(x.t == 's') val = \"'\" + x.v;\n\t\t\telse val = \"\"+x.v;\n\t\t\tcmds[cmds.length] = y + \"=\" + val;\n\t\t}\n\t}\n\treturn cmds;\n}\n\nfunction sheet_add_json(_ws, js, opts) {\n\tvar o = opts || {};\n\tvar offset = +!o.skipHeader;\n\tvar ws = _ws || ({});\n\tvar _R = 0, _C = 0;\n\tif(ws && o.origin != null) {\n\t\tif(typeof o.origin == 'number') _R = o.origin;\n\t\telse {\n\t\t\tvar _origin = typeof o.origin == \"string\" ? decode_cell(o.origin) : o.origin;\n\t\t\t_R = _origin.r; _C = _origin.c;\n\t\t}\n\t}\n\tvar cell;\n\tvar range = ({s: {c:0, r:0}, e: {c:_C, r:_R + js.length - 1 + offset}});\n\tif(ws['!ref']) {\n\t\tvar _range = safe_decode_range(ws['!ref']);\n\t\trange.e.c = Math.max(range.e.c, _range.e.c);\n\t\trange.e.r = Math.max(range.e.r, _range.e.r);\n\t\tif(_R == -1) { _R = _range.e.r + 1; range.e.r = _R + js.length - 1 + offset; }\n\t} else {\n\t\tif(_R == -1) { _R = 0; range.e.r = js.length - 1 + offset; }\n\t}\n\tvar hdr = o.header || [], C = 0;\n\n\tjs.forEach(function (JS, R) {\n\t\tkeys(JS).forEach(function(k) {\n\t\t\tif((C=hdr.indexOf(k)) == -1) hdr[C=hdr.length] = k;\n\t\t\tvar v = JS[k];\n\t\t\tvar t = 'z';\n\t\t\tvar z = \"\";\n\t\t\tvar ref = encode_cell({c:_C + C,r:_R + R + offset});\n\t\t\tcell = utils.sheet_get_cell(ws, ref);\n\t\t\tif(v && typeof v === 'object' && !(v instanceof Date)){\n\t\t\t\tws[ref] = v;\n\t\t\t} else {\n\t\t\t\tif(typeof v == 'number') t = 'n';\n\t\t\t\telse if(typeof v == 'boolean') t = 'b';\n\t\t\t\telse if(typeof v == 'string') t = 's';\n\t\t\t\telse if(v instanceof Date) {\n\t\t\t\t\tt = 'd';\n\t\t\t\t\tif(!o.cellDates) { t = 'n'; v = datenum(v); }\n\t\t\t\t\tz = (o.dateNF || SSF._table[14]);\n\t\t\t\t}\n\t\t\t\tif(!cell) ws[ref] = cell = ({t:t, v:v});\n\t\t\t\telse {\n\t\t\t\t\tcell.t = t; cell.v = v;\n\t\t\t\t\tdelete cell.w; delete cell.R;\n\t\t\t\t\tif(z) cell.z = z;\n\t\t\t\t}\n\t\t\t\tif(z) cell.z = z;\n\t\t\t}\n\t\t});\n\t});\n\trange.e.c = Math.max(range.e.c, _C + hdr.length - 1);\n\tvar __R = encode_row(_R);\n\tif(offset) for(C = 0; C < hdr.length; ++C) ws[encode_col(C + _C) + __R] = {t:'s', v:hdr[C]};\n\tws['!ref'] = encode_range(range);\n\treturn ws;\n}\nfunction json_to_sheet(js, opts) { return sheet_add_json(null, js, opts); }\n\nvar utils = {\n\tencode_col: encode_col,\n\tencode_row: encode_row,\n\tencode_cell: encode_cell,\n\tencode_range: encode_range,\n\tdecode_col: decode_col,\n\tdecode_row: decode_row,\n\tsplit_cell: split_cell,\n\tdecode_cell: decode_cell,\n\tdecode_range: decode_range,\n\tformat_cell: format_cell,\n\tget_formulae: sheet_to_formulae,\n\tmake_csv: sheet_to_csv,\n\tmake_json: sheet_to_json,\n\tmake_formulae: sheet_to_formulae,\n\tsheet_add_aoa: sheet_add_aoa,\n\tsheet_add_json: sheet_add_json,\n\tsheet_add_dom: sheet_add_dom,\n\taoa_to_sheet: aoa_to_sheet,\n\tjson_to_sheet: json_to_sheet,\n\ttable_to_sheet: parse_dom_table,\n\ttable_to_book: table_to_book,\n\tsheet_to_csv: sheet_to_csv,\n\tsheet_to_txt: sheet_to_txt,\n\tsheet_to_json: sheet_to_json,\n\tsheet_to_html: HTML_.from_sheet,\n\tsheet_to_formulae: sheet_to_formulae,\n\tsheet_to_row_object_array: sheet_to_json\n};\n\n(function(utils) {\nutils.consts = utils.consts || {};\nfunction add_consts(R/*Array*/) { R.forEach(function(a){ utils.consts[a[0]] = a[1]; }); }\n\nfunction get_default(x, y, z) { return x[y] != null ? x[y] : (x[y] = z); }\n\n/* get cell, creating a stub if necessary */\nfunction ws_get_cell_stub(ws, R, C) {\n\t/* A1 cell address */\n\tif(typeof R == \"string\") {\n\t\t/* dense */\n\t\tif(Array.isArray(ws)) {\n\t\t\tvar RC = decode_cell(R);\n\t\t\tif(!ws[RC.r]) ws[RC.r] = [];\n\t\t\treturn ws[RC.r][RC.c] || (ws[RC.r][RC.c] = {t:'z'});\n\t\t}\n\t\treturn ws[R] || (ws[R] = {t:'z'});\n\t}\n\t/* cell address object */\n\tif(typeof R != \"number\") return ws_get_cell_stub(ws, encode_cell(R));\n\t/* R and C are 0-based indices */\n\treturn ws_get_cell_stub(ws, encode_cell({r:R,c:C||0}));\n}\nutils.sheet_get_cell = ws_get_cell_stub;\n\n/* find sheet index for given name / validate index */\nfunction wb_sheet_idx(wb, sh) {\n\tif(typeof sh == \"number\") {\n\t\tif(sh >= 0 && wb.SheetNames.length > sh) return sh;\n\t\tthrow new Error(\"Cannot find sheet # \" + sh);\n\t} else if(typeof sh == \"string\") {\n\t\tvar idx = wb.SheetNames.indexOf(sh);\n\t\tif(idx > -1) return idx;\n\t\tthrow new Error(\"Cannot find sheet name |\" + sh + \"|\");\n\t} else throw new Error(\"Cannot find sheet |\" + sh + \"|\");\n}\n\n/* simple blank workbook object */\nutils.book_new = function() {\n\treturn { SheetNames: [], Sheets: {} };\n};\n\n/* add a worksheet to the end of a given workbook */\nutils.book_append_sheet = function(wb, ws, name) {\n\tif(!name) for(var i = 1; i <= 0xFFFF; ++i, name = undefined) if(wb.SheetNames.indexOf(name = \"Sheet\" + i) == -1) break;\n\tif(!name || wb.SheetNames.length >= 0xFFFF) throw new Error(\"Too many worksheets\");\n\tcheck_ws_name(name);\n\tif(wb.SheetNames.indexOf(name) >= 0) throw new Error(\"Worksheet with name |\" + name + \"| already exists!\");\n\n\twb.SheetNames.push(name);\n\twb.Sheets[name] = ws;\n};\n\n/* set sheet visibility (visible/hidden/very hidden) */\nutils.book_set_sheet_visibility = function(wb, sh, vis) {\n\tget_default(wb,\"Workbook\",{});\n\tget_default(wb.Workbook,\"Sheets\",[]);\n\n\tvar idx = wb_sheet_idx(wb, sh);\n\t// $FlowIgnore\n\tget_default(wb.Workbook.Sheets,idx, {});\n\n\tswitch(vis) {\n\t\tcase 0: case 1: case 2: break;\n\t\tdefault: throw new Error(\"Bad sheet visibility setting \" + vis);\n\t}\n\t// $FlowIgnore\n\twb.Workbook.Sheets[idx].Hidden = vis;\n};\nadd_consts([\n\t[\"SHEET_VISIBLE\", 0],\n\t[\"SHEET_HIDDEN\", 1],\n\t[\"SHEET_VERY_HIDDEN\", 2]\n]);\n\n/* set number format */\nutils.cell_set_number_format = function(cell, fmt) {\n\tcell.z = fmt;\n\treturn cell;\n};\n\n/* set cell hyperlink */\nutils.cell_set_hyperlink = function(cell, target, tooltip) {\n\tif(!target) {\n\t\tdelete cell.l;\n\t} else {\n\t\tcell.l = ({ Target: target });\n\t\tif(tooltip) cell.l.Tooltip = tooltip;\n\t}\n\treturn cell;\n};\nutils.cell_set_internal_link = function(cell, range, tooltip) { return utils.cell_set_hyperlink(cell, \"#\" + range, tooltip); };\n\n/* add to cell comments */\nutils.cell_add_comment = function(cell, text, author) {\n\tif(!cell.c) cell.c = [];\n\tcell.c.push({t:text, a:author||\"SheetJS\"});\n};\n\n/* set array formula and flush related cells */\nutils.sheet_set_array_formula = function(ws, range, formula) {\n\tvar rng = typeof range != \"string\" ? range : safe_decode_range(range);\n\tvar rngstr = typeof range == \"string\" ? range : encode_range(range);\n\tfor(var R = rng.s.r; R <= rng.e.r; ++R) for(var C = rng.s.c; C <= rng.e.c; ++C) {\n\t\tvar cell = ws_get_cell_stub(ws, R, C);\n\t\tcell.t = 'n';\n\t\tcell.F = rngstr;\n\t\tdelete cell.v;\n\t\tif(R == rng.s.r && C == rng.s.c) cell.f = formula;\n\t}\n\treturn ws;\n};\n\nreturn utils;\n})(utils);\n\nif(has_buf && typeof require != 'undefined') (function() {\n\tvar Readable = require('stream').Readable;\n\n\tvar write_csv_stream = function(sheet, opts) {\n\t\tvar stream = Readable();\n\t\tvar o = opts == null ? {} : opts;\n\t\tif(sheet == null || sheet[\"!ref\"] == null) { stream.push(null); return stream; }\n\t\tvar r = safe_decode_range(sheet[\"!ref\"]);\n\t\tvar FS = o.FS !== undefined ? o.FS : \",\", fs = FS.charCodeAt(0);\n\t\tvar RS = o.RS !== undefined ? o.RS : \"\\n\", rs = RS.charCodeAt(0);\n\t\tvar endregex = new RegExp((FS==\"|\" ? \"\\\\|\" : FS)+\"+$\");\n\t\tvar row = \"\", cols = [];\n\t\to.dense = Array.isArray(sheet);\n\t\tvar colinfo = o.skipHidden && sheet[\"!cols\"] || [];\n\t\tvar rowinfo = o.skipHidden && sheet[\"!rows\"] || [];\n\t\tfor(var C = r.s.c; C <= r.e.c; ++C) if (!((colinfo[C]||{}).hidden)) cols[C] = encode_col(C);\n\t\tvar R = r.s.r;\n\t\tvar BOM = false;\n\t\tstream._read = function() {\n\t\t\tif(!BOM) { BOM = true; return stream.push(\"\\uFEFF\"); }\n\t\t\twhile(R <= r.e.r) {\n\t\t\t\t++R;\n\t\t\t\tif ((rowinfo[R-1]||{}).hidden) continue;\n\t\t\t\trow = make_csv_row(sheet, r, R-1, cols, fs, rs, FS, o);\n\t\t\t\tif(row != null) {\n\t\t\t\t\tif(o.strip) row = row.replace(endregex,\"\");\n\t\t\t\t\tstream.push(row + RS);\n\t\t\t\t\tbreak;\n\t\t\t\t}\n\t\t\t}\n\t\t\tif(R > r.e.r) return stream.push(null);\n\t\t};\n\t\treturn stream;\n\t};\n\n\tvar write_html_stream = function(ws, opts) {\n\t\tvar stream = Readable();\n\n\t\tvar o = opts || {};\n\t\tvar header = o.header != null ? o.header : HTML_.BEGIN;\n\t\tvar footer = o.footer != null ? o.footer : HTML_.END;\n\t\tstream.push(header);\n\t\tvar r = decode_range(ws['!ref']);\n\t\to.dense = Array.isArray(ws);\n\t\tstream.push(HTML_._preamble(ws, r, o));\n\t\tvar R = r.s.r;\n\t\tvar end = false;\n\t\tstream._read = function() {\n\t\t\tif(R > r.e.r) {\n\t\t\t\tif(!end) { end = true; stream.push(\"\" + footer); }\n\t\t\t\treturn stream.push(null);\n\t\t\t}\n\t\t\twhile(R <= r.e.r) {\n\t\t\t\tstream.push(HTML_._row(ws, r, R, o));\n\t\t\t\t++R;\n\t\t\t\tbreak;\n\t\t\t}\n\t\t};\n\t\treturn stream;\n\t};\n\n\tvar write_json_stream = function(sheet, opts) {\n\t\tvar stream = Readable({objectMode:true});\n\n\t\tif(sheet == null || sheet[\"!ref\"] == null) { stream.push(null); return stream; }\n\t\tvar val = {t:'n',v:0}, header = 0, offset = 1, hdr = [], v=0, vv=\"\";\n\t\tvar r = {s:{r:0,c:0},e:{r:0,c:0}};\n\t\tvar o = opts || {};\n\t\tvar range = o.range != null ? o.range : sheet[\"!ref\"];\n\t\tif(o.header === 1) header = 1;\n\t\telse if(o.header === \"A\") header = 2;\n\t\telse if(Array.isArray(o.header)) header = 3;\n\t\tswitch(typeof range) {\n\t\t\tcase 'string': r = safe_decode_range(range); break;\n\t\t\tcase 'number': r = safe_decode_range(sheet[\"!ref\"]); r.s.r = range; break;\n\t\t\tdefault: r = range;\n\t\t}\n\t\tif(header > 0) offset = 0;\n\t\tvar rr = encode_row(r.s.r);\n\t\tvar cols = [];\n\t\tvar counter = 0;\n\t\tvar dense = Array.isArray(sheet);\n\t\tvar R = r.s.r, C = 0, CC = 0;\n\t\tif(dense && !sheet[R]) sheet[R] = [];\n\t\tfor(C = r.s.c; C <= r.e.c; ++C) {\n\t\t\tcols[C] = encode_col(C);\n\t\t\tval = dense ? sheet[R][C] : sheet[cols[C] + rr];\n\t\t\tswitch(header) {\n\t\t\t\tcase 1: hdr[C] = C - r.s.c; break;\n\t\t\t\tcase 2: hdr[C] = cols[C]; break;\n\t\t\t\tcase 3: hdr[C] = o.header[C - r.s.c]; break;\n\t\t\t\tdefault:\n\t\t\t\t\tif(val == null) val = {w: \"__EMPTY\", t: \"s\"};\n\t\t\t\t\tvv = v = format_cell(val, null, o);\n\t\t\t\t\tcounter = 0;\n\t\t\t\t\tfor(CC = 0; CC < hdr.length; ++CC) if(hdr[CC] == vv) vv = v + \"_\" + (++counter);\n\t\t\t\t\thdr[C] = vv;\n\t\t\t}\n\t\t}\n\t\tR = r.s.r + offset;\n\t\tstream._read = function() {\n\t\t\tif(R > r.e.r) return stream.push(null);\n\t\t\twhile(R <= r.e.r) {\n\t\t\t\t//if ((rowinfo[R-1]||{}).hidden) continue;\n\t\t\t\tvar row = make_json_row(sheet, r, R, cols, header, hdr, dense, o);\n\t\t\t\t++R;\n\t\t\t\tif((row.isempty === false) || (header === 1 ? o.blankrows !== false : !!o.blankrows)) {\n\t\t\t\t\tstream.push(row.row);\n\t\t\t\t\tbreak;\n\t\t\t\t}\n\t\t\t}\n\t\t};\n\t\treturn stream;\n\t};\n\n\tXLSX.stream = {\n\t\tto_json: write_json_stream,\n\t\tto_html: write_html_stream,\n\t\tto_csv: write_csv_stream\n\t};\n})();\n\nif(typeof parse_xlscfb !== \"undefined\") XLSX.parse_xlscfb = parse_xlscfb;\nXLSX.parse_zip = parse_zip;\nXLSX.read = readSync; //xlsread\nXLSX.readFile = readFileSync; //readFile\nXLSX.readFileSync = readFileSync;\nXLSX.write = writeSync;\nXLSX.writeFile = writeFileSync;\nXLSX.writeFileSync = writeFileSync;\nXLSX.writeFileAsync = writeFileAsync;\nXLSX.utils = utils;\nXLSX.SSF = SSF;\nif(typeof CFB !== \"undefined\") XLSX.CFB = CFB;\n}\n/*global define */\nif(typeof exports !== 'undefined') make_xlsx_lib(exports);\nelse if(typeof module !== 'undefined' && module.exports) make_xlsx_lib(module.exports);\nelse if(typeof define === 'function' && define.amd) define(function() { if(!XLSX.version) make_xlsx_lib(XLSX); return XLSX; });\nelse make_xlsx_lib(XLSX);\n/*exported XLS, ODS */\nvar XLS = XLSX, ODS = XLSX;\n","// The module cache\nvar __webpack_module_cache__ = {};\n\n// The require function\nfunction __webpack_require__(moduleId) {\n\t// Check if module is in cache\n\tif(__webpack_module_cache__[moduleId]) {\n\t\treturn __webpack_module_cache__[moduleId].exports;\n\t}\n\t// Create a new module (and put it into the cache)\n\tvar module = __webpack_module_cache__[moduleId] = {\n\t\tid: moduleId,\n\t\tloaded: false,\n\t\texports: {}\n\t};\n\n\t// Execute the module function\n\t__webpack_modules__[moduleId].call(module.exports, module, module.exports, __webpack_require__);\n\n\t// Flag the module as loaded\n\tmodule.loaded = true;\n\n\t// Return the exports of the module\n\treturn module.exports;\n}\n\n","// module exports must be returned from runtime so entry inlining is disabled\n// startup\n// Load entry module and return exports\nreturn __webpack_require__(2476);\n","__webpack_require__.amdD = function () {\n\tthrow new Error('define cannot be used indirect');\n};","__webpack_require__.amdO = {};","// getDefaultExport function for compatibility with non-harmony modules\n__webpack_require__.n = (module) => {\n\tvar getter = module && module.__esModule ?\n\t\t() => (module['default']) :\n\t\t() => (module);\n\t__webpack_require__.d(getter, { a: getter });\n\treturn getter;\n};","// define getter functions for harmony exports\n__webpack_require__.d = (exports, definition) => {\n\tfor(var key in definition) {\n\t\tif(__webpack_require__.o(definition, key) && !__webpack_require__.o(exports, key)) {\n\t\t\tObject.defineProperty(exports, key, { enumerable: true, get: definition[key] });\n\t\t}\n\t}\n};","__webpack_require__.g = (function() {\n\tif (typeof globalThis === 'object') return globalThis;\n\ttry {\n\t\treturn this || new Function('return this')();\n\t} catch (e) {\n\t\tif (typeof window === 'object') return window;\n\t}\n})();","__webpack_require__.o = (obj, prop) => (Object.prototype.hasOwnProperty.call(obj, prop))","// define __esModule on exports\n__webpack_require__.r = (exports) => {\n\tif(typeof Symbol !== 'undefined' && Symbol.toStringTag) {\n\t\tObject.defineProperty(exports, Symbol.toStringTag, { value: 'Module' });\n\t}\n\tObject.defineProperty(exports, '__esModule', { value: true });\n};","__webpack_require__.nmd = (module) => {\n\tmodule.paths = [];\n\tif (!module.children) module.children = [];\n\treturn module;\n};"],"sourceRoot":""} \ No newline at end of file diff --git a/danfojs-browser/src/config/config.js b/danfojs-browser/src/config/config.js deleted file mode 100644 index 268663d2..00000000 --- a/danfojs-browser/src/config/config.js +++ /dev/null @@ -1,54 +0,0 @@ - -/** - * Package wide configuration class - */ -export class Configs { - constructor() { - this.table_width = 17; //set the width of each column printed in console - this.table_truncate = 16; //set the max number of string before text is truncated in printing - this.dtype_test_lim = 10; - this.table_max_row = 21; - this.table_max_col_in_console = 7; - } - - set_width(val) { - this.table_width = val; - } - - get get_width() { - return this.table_width; - } - - set_max_col_in_console(val) { - this.table_max_col_in_console = val; - } - - get get_max_col_in_console() { - return this.table_max_col_in_console; - } - - set_row_num(val) { - this.table_max_row = val; - } - - get get_max_row() { - return this.table_max_row; - } - - - get get_truncate() { - return this.table_truncate; - } - - set_truncate(val) { - this.table_truncate = val; - } - - get get_dtype_test_lim() { - return this.dtype_test_lim; - } - - set_dtype_test_lim(val) { - this.dtype_test_lim = val; - } -} diff --git a/danfojs-browser/src/core/concat.js b/danfojs-browser/src/core/concat.js deleted file mode 100644 index aa9bf9a2..00000000 --- a/danfojs-browser/src/core/concat.js +++ /dev/null @@ -1,221 +0,0 @@ -import { DataFrame } from './frame'; -import { Utils } from "./utils"; -import { Series } from './series'; - -const utils = new Utils(); - - -export class Concat { - constructor(kwargs) { - - - // check if keys exist in kwargs - utils.__in_object(kwargs, "df_list", "df_list not found: specify the list of dataframe"); - utils.__in_object(kwargs, "axis", "axis not found: specify the axis"); - - let df_list = null; //set the df_list to null - let axis = null; // set axis to null - let indexes = null; - - //check if df_list is an array - if (Array.isArray(kwargs["df_list"])) { - - df_list = kwargs["df_list"]; - } else { - throw new Error("df_list must be an Array of dataFrames/Series"); - } - - //check if axis is int and is either 0 or 1 - if (typeof kwargs["axis"] === "number") { - - if (kwargs["axis"] == 0 || kwargs["axis"] == 1) { - - axis = kwargs["axis"]; - } else { - axis = 1; - // throw new Error("Invalid axis: axis must be 0 or 1") - } - - } else { - throw new Error("axis must be a number"); - } - - - let df_object = Object.assign({}, df_list); // convert the array to object - - if (axis == 1) { - - let columns = []; - let duplicate_col_count = {}; - let max_length = 0; - let a_key = Object.keys(df_object)[0]; - indexes = df_object[a_key].index; - for (let key in df_object) { - - let column = df_object[key].columns; - let length = df_object[key].values.length; - - if (length > max_length) { - max_length = length; - } - - for (let index in column) { - - let col_name = column[index]; - if (col_name in duplicate_col_count) { - - let count = duplicate_col_count[col_name]; - let name = `${col_name}_${count + 1}`; - - columns.push(name); - - duplicate_col_count[col_name] = count + 1; - } else { - - columns.push(col_name); - duplicate_col_count[col_name] = 1; - } - } - - - } - - let data = new Array(max_length); - - for (let key in df_list) { - - let values = df_list[key].values; - - for (let index = 0; index < values.length; index++) { - - let val = values[index]; - if (typeof data[index] === "undefined") { - - if (Array.isArray(val)){ - data[index] = val; - } else { - data[index] = [ val ]; - } - - } else { - if (Array.isArray(val)){ - data[index].push(...val); - } else { - data[index].push(val); - } - - } - } - - if (values.length < max_length) { - let column_length = df_list[key].columns.length; - let null_array = Array(column_length); - - for (let col = 0; col < column_length; col++) { - null_array[col] = NaN; - } - - if (typeof data[max_length - 1] === "undefined") { - data[max_length - 1] = null_array; - } else { - data[max_length - 1].push(...null_array); - } - } - } - - let df = new DataFrame(data, { columns: columns, index: indexes }); //convert to dataframe - return df; - } else { - //concatenate base on axis 0 - let columns = []; - let row_indexes = []; - let col_i = 0; - for (let key in df_list) { - let column = df_list[key].columns; - columns.push(...column); - indexes = df_list[key].index; - let r_index = indexes.map((val) => { - return `${val}_row${col_i}`; - }); - row_indexes.push(...r_index); - col_i += 1; - } - - let column_set = new Set(columns); - - columns = Array.from(column_set); - - let data = []; - - for (let key in df_list) { - - let value = df_list[key].values; - - // let col_length = value[0].length - - let df_columns = df_list[key].columns; - - let not_exist = []; - for (let col_index in columns) { - let col_name = columns[col_index]; - - let is_index = df_columns.indexOf(col_name); - - if (is_index == -1) { - not_exist.push(col_name); - } - } - if (not_exist.length > 0) { - for (let i = 0; i < value.length; i++) { - let row_value = value[i]; - - let new_arr = Array(columns.length); - for (let j = 0; j < columns.length; j++) { - - let col_name = columns[j]; - if (not_exist.includes(col_name)) { - - new_arr[j] = NaN; - } else { - let index = df_columns.indexOf(col_name); - if (Array.isArray(row_value)){ - new_arr[j] = row_value[index]; - } else { - new_arr[j] = row_value; - } - - } - - } - data.push(new_arr); - } - } else { - data.push(...value); - } - - } - - if (Array.isArray(data[0])){ - let df = new DataFrame(data, { columns: columns, index: row_indexes }); - return df; - } else { - let sf = new Series(data, { index: row_indexes }); - return sf; - } - - - } - - } -} - - -/** -* Concatenate pandas objects along a particular axis with optional set logic along the other axes. -* @param {kwargs} {df_list: List of DataFrame to concatenate together axis: 0 for row axis and 1 for index axis -* @returns {DataFrame} -*/ -export const concat = (kwargs) => { - let concat_sf = new Concat(kwargs); - return concat_sf; -}; diff --git a/danfojs-browser/src/core/date_range.js b/danfojs-browser/src/core/date_range.js deleted file mode 100644 index 8e1ed433..00000000 --- a/danfojs-browser/src/core/date_range.js +++ /dev/null @@ -1,289 +0,0 @@ -import { Utils } from "./utils"; - -const utils = new Utils; - -/** - * Generate date range between a specified set of date - * @param {kwargs} kwargs { - * start : string - * end : string - * period: int - * freq : string - * } - * @returns Array - */ -export class date_range { - constructor(kwargs){ - - this.offset = null; - - if (utils.__key_in_object(kwargs, "start")){ - this.start = kwargs["start"]; - } else { - this.start = null; - } - - if (utils.__key_in_object(kwargs, "end")){ - this.end = kwargs["end"]; - } else { - this.end = null; - } - - if (utils.__key_in_object(kwargs, "period")){ - this.period = kwargs["period"]; - } else { - this.period = null; - } - - if (utils.__key_in_object(kwargs, "freq")){ - this.freq = kwargs["freq"]; - } else { - this.freq = "D"; - } - - this.freq_list = [ "M", "D", "s", "H", "m", "Y" ]; - - if (this.freq.length == 1){ - if (!this.freq_list.includes(this.freq)){ - throw new Error(`invalid freq ${this.freq}`); - } - } else { - let freq_split = this.freq.split(""); - this.offset = parseInt(freq_split[0]); - this.freq = freq_split[1]; - if (!this.freq_list.includes(this.freq)){ - throw new Error(`invalid freq ${this.freq}`); - } - } - - let rslt = this.range(this.start, this.end, this.period, this.offset); - return rslt; - - } - - range(start, end, period, offset = null){ - - let start_date = null; - let end_date = null; - let start_range = null; - let end_range = null; - if (start && end){ - start_date = new Date(start); - start_range = this.freq_type(start_date, this.freq); - end_date = new Date(end); - end_range = this.freq_type(end_date, this.freq); - - //check if the end year is greater than start year - let start_year = start_date.getFullYear(); - let end_year = end_date.getFullYear(); - if ((start_year < end_year)){ - // end_range = start_range + end_range - if (this.freq == "M"){ - end_range = this.month_end(start_date, end_date); - } else if (this.freq == "D"){ - end_range = this.day_end(start_date, end_date) - start_range; - - } - } - - let range_array = utils.__range(start_range, end_range); - - if (offset){ - range_array = this.offset_count(range_array, offset); - } - - if (this.freq == "M"){ - range_array = this.month_range(range_array); - } - - let date_range = range_array.map((x) => { - return this.set_dateProps(start_date, this.freq, x); - }); - date_range[date_range.length - 1] = end_date; - - let date_string = this.toLocalString(date_range); - - return date_string; - } else if (start && !(end)){ - start_date = new Date(start); - start_range = this.freq_type(start_date, this.freq); - end_range = offset ? ((period * offset) - 1) : period - 1; - - if (start_range > end_range){ - end_range = end_range + start_range; - } - let range_array = utils.__range(start_range, end_range); - - - if (offset){ - range_array = this.offset_count(range_array, offset); - } - - let date_range = range_array.map((x) => { - return this.set_dateProps(start_date, this.freq, x); - }); - - let date_string = this.toLocalString(date_range); - return date_string; - - } else if (end && !(start)){ - end_date = new Date(end); - end_range = this.freq_type(end_date, this.freq); - start_range = (end_range - period) + 1; - - let range_array = utils.__range(start_range, end_range); - - if (offset){ - range_array = this.offset_count(range_array, offset); - } - - let date_range = range_array.map((x) => { - return this.set_dateProps(end_date, this.freq, x); - }); - - let date_string = this.toLocalString(date_range); - return date_string; - } - } - - freq_type(date, ftype){ - - let rslt = null; - switch (ftype){ - - case "M": - rslt = date.getMonth(); - break; - case "Y": - rslt = date.getFullYear(); - break; - case "s": - rslt = date.getSeconds(); - break; - case "D": - rslt = date.getDate(); - break; - case "H": - rslt = date.getHours(); - break; - case "m": - rslt = date.getMinutes(); - break; - } - return rslt; - } - - offset_count(d_array, offset){ - - let r_array = []; - - for (let i = 0; i < d_array.length; i += offset){ - r_array.push(d_array[i]); - } - return r_array; - } - - set_dateProps(date, ftype, val){ - - let new_date = new Date(date.valueOf()); - switch (ftype){ - - case "M": - if (val.length == 2){ - - new_date.setYear(new_date.getFullYear() + val[0]); - new_date.setMonth(parseInt(val[1])); - } else { - new_date.setMonth(val); - } - - break; - case "Y": - new_date.setYear(val); - break; - case "s": - new_date.setSeconds(val); - break; - case "D": - new_date.setDate(val); - break; - case "H": - new_date.setHours(val); - break; - case "m": - new_date.setMinutes(val); - break; - } - return new_date; - } - - toLocalString(d_array){ - - let r_array = d_array.map((x) => { - - return x.toLocaleString(); - }); - - return r_array; - } - - month_end(start_date, end_date){ - - let end_month = end_date.getMonth(); - - let diff_year = end_date.getFullYear() - start_date.getFullYear(); - - let end_range = (12 * diff_year) + end_month; - - return end_range; - } - - month_range(range){ - - let minus = null; - let y_val = 0; - let d_range = range.map((x) => { - - if (x > 11){ - if (x % 12 == 0){ - minus = x; - y_val = x / 12; - return [ y_val, (x - minus) ]; - } else { - return [ y_val, (x - minus) ]; - } - - } - return [ y_val, x ]; - }); - - return d_range; - } - - day_end(start_date, end_date){ - - let month_end = this.month_end(start_date, end_date); - let range = utils.__range(start_date.getMonth(), month_end); - let m_range = this.month_range(range); - - // let s_date = new Date(start_date.getFullYear(),start_date.getMonth(),0) - let sum = 0; - for (let i = 0; i < m_range.length; i++){ - - let val = m_range[i]; - - let d_date = null; - if (i === m_range.length - 1) { - d_date = new Date(start_date.getFullYear() + val[0], val[1], end_date.getDate()).getDate(); - } else { - d_date = new Date(start_date.getFullYear() + val[0], val[1], 0).getDate(); - - } - - sum += d_date; - - } - return sum; - - } -} diff --git a/danfojs-browser/src/core/frame.js b/danfojs-browser/src/core/frame.js deleted file mode 100644 index 10636107..00000000 --- a/danfojs-browser/src/core/frame.js +++ /dev/null @@ -1,2172 +0,0 @@ -/** -* Copyright 2021, JsData. -* All rights reserved. -* -* This source code is licensed under the MIT license found in the -* LICENSE file in the root directory of this source tree. - -* Unless required by applicable law or agreed to in writing, software -* distributed under the License is distributed on an "AS IS" BASIS, -* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -* See the License for the specific language governing permissions and -* limitations under the License. -*/ - -import * as tf from "@tensorflow/tfjs"; -import Ndframe from "./generic"; -import { Series } from "./series"; -import { Utils } from "./utils"; -import { GroupBy } from "./groupby"; -import { Plot } from "../plotting/plot"; -import { indexLoc } from "./indexing"; -import { concat } from "./concat.js"; - -const utils = new Utils(); -import { std, variance } from "mathjs"; - -/** - * A 2D frame object that stores data in structured tabular format - * @param {data} data, JSON, Array, 2D Tensor - * @param {kwargs} Object {columns: Array of column names, defaults to ordered numbers when not specified - * dtypes: strings of data types, automatically inferred when not specified - * index: row index for subseting array, defaults to ordered numbers when not specified} - * - * @returns DataFrame - */ -export class DataFrame extends Ndframe { - constructor(data, kwargs) { - super(data, kwargs); - this._set_column_property(); //set column property on DataFrame Class for easy accessing using the format df['colname'] - } - - _set_column_property() { - let col_vals = this.col_data; - let col_names = this.column_names; - - col_vals.forEach((col, i) => { - this[col_names[i]] = null; - Object.defineProperty(this, col_names[i], { - get() { - return new Series(this.col_data[i], { - columns: col_names[i], - index: this.index - }); - }, - set(value) { - this.addColumn({ column: col_names[i], value: value }); - } - }); - }); - } - /** - * Write a CSV file of the DataFrame contents - * @param {string} csvFilePath Path to save CSV when in Node.js form - * @returns {Promise} CSV representation of Object data - * - */ - async to_csv(csvFilePath = "") { - const csvContent = await super.to_csv(); - // behave differently for Node vs Web - if (typeof window === "undefined") { - // Write CSV on Node.js - // eslint-disable-next-line no-undef - const fs = require("fs"); - fs.writeFileSync( - csvFilePath, - csvContent, - (err) => err && console.error(err) - ); - } else { - // Download CSV on Web - const webCSV = "data:text/csv;charset=utf-8," + csvContent; - window.open(encodeURI(webCSV)); - } - return csvContent; - } - - /** - * Drop a list of rows or columns base on the specified axis - * @param {Object} kwargs Configuration object - * {columns: [Array(Columns| Index)] array of column names to drop - * axis: row=0, columns=1 - * inplace: specify whether to drop the row/column with/without creating a new DataFrame} - * @returns null | DataFrame - * - */ - drop(kwargs = {}) { - let params_needed = [ "columns", "index", "inplace", "axis" ]; - utils._throw_wrong_params_error(kwargs, params_needed); - - kwargs["inplace"] = kwargs["inplace"] || false; - - if (!("axis" in kwargs)) { - kwargs["axis"] = 1; - } - - let to_drop = null; - if ("index" in kwargs && kwargs["axis"] == 0) { - to_drop = kwargs["index"]; - } else { - to_drop = kwargs["columns"]; - } - - if (kwargs["axis"] == 1) { - if (!("columns" in kwargs)) { - throw Error( - "No column found. Axis of 1 must be accompanied by an array of column(s) names" - ); - } - let self = this; - let new_col_data = {}; - let new_dtype = []; - - const index = to_drop.map((x) => { - let col_idx = self.columns.indexOf(x); - if (col_idx == -1) { - throw new Error(`column "${x}" does not exist`); - } - return col_idx; - }); - - this.col_data.forEach((col, idx) => { - if (!index.includes(idx)) { - new_col_data[self.column_names[idx]] = col; - new_dtype.push(self.dtypes[idx]); - } - }); - - if (!kwargs["inplace"]) { - let old_cols = self.columns; - let new_columns = Object.keys(new_col_data); - let df = new DataFrame(new_col_data, { - index: self.index, - dtypes: new_dtype - }); - df.__set_col_property(df, df.col_data, new_columns, old_cols); - return df; - } else { - let old_cols = self.columns; - let new_columns = Object.keys(new_col_data); - this.__update_frame_in_place(null, null, new_col_data, null, new_dtype); - this.__set_col_property(self, self.col_data, new_columns, old_cols); - } - } else { - if (!utils.__key_in_object(kwargs, "index")) { - throw Error( - "No index label found. Axis of 0 must be accompanied by an array of index labels" - ); - } - to_drop.forEach((x) => { - if (!this.index.includes(x)) - throw new Error(`${x} does not exist in index`); - }); - const values = this.values; - let data_idx = []; - let new_data, new_index; - if (typeof to_drop[0] == "string") { - //get index of strings labels in rows - this.index.forEach((idx, i) => { - if (to_drop.includes(idx)) { - data_idx.push(i); - } - }); - new_data = utils.__remove_arr(values, data_idx); - new_index = utils.__remove_arr(this.index, data_idx); - } else { - new_data = utils.__remove_arr(values, to_drop); - new_index = utils.__remove_arr(this.index, to_drop); - } - - if (!kwargs["inplace"]) { - return new DataFrame(new_data, { - columns: this.columns, - index: new_index - }); - } else { - this.row_data_tensor = tf.tensor(new_data); - this.data = new_data; - this.__set_index(new_index); - } - } - } - - /** - * Purely label based indexing. Can accept string label names for both rows and columns - * @param {kwargs} kwargs object {rows: Array of index, columns: Array of column name(s)} - * @return DataFrame data stucture - */ - loc(kwargs = {}) { - let params_needed = [ "columns", "rows" ]; - utils._throw_wrong_params_error(kwargs, params_needed); - - kwargs["type"] = "loc"; - let [ new_data, columns, rows ] = indexLoc(this, kwargs); - let df_columns = { columns: columns }; - let df = new DataFrame(new_data, df_columns); - df.__set_index(rows); - return df; - } - - /** - * Access a dataframe element using row and column index - * @param {*} kwargs object {rows: Array of index, columns: Array of column index} - * @return DataFrame data stucture - */ - iloc(kwargs = {}) { - let params_needed = [ "columns", "rows" ]; - utils._throw_wrong_params_error(kwargs, params_needed); - - kwargs["type"] = "iloc"; - - let [ new_data, columns, rows ] = indexLoc(this, kwargs); - let df_columns = { columns: columns }; - let df = new DataFrame(new_data, df_columns); - df.__set_index(rows); - return df; - } - - /** - * Prints the first n values in a dataframe - * @param {rows} rows --> int - * @returns DataFrame - */ - head(rows = 5) { - if (rows > this.values.length || rows < 1) { - //return all values - return this; - } else { - //Creates a new dataframe with first [rows] - let data = this.values.slice(0, rows); - let idx = this.index.slice(0, rows); - let config = { columns: this.column_names, index: idx }; - let df = new DataFrame(data, config); - return df; - } - } - - /** - * Prints the last n values in a dataframe - * @param {rows} rows --> int - * @returns DataFrame - */ - tail(rows = 5) { - let row_len = this.values.length; - if (rows > row_len || rows < 1) { - //return all values - return this; - } else { - //Creates a new dataframe with last [rows] - let data = this.values.slice(row_len - rows); - let indx = this.index.slice(row_len - rows); - let config = { columns: this.column_names, index: indx }; - let df = new DataFrame(data, config); - return df; - } - } - - /** - * Gets [num] number of random rows in a dataframe - * @param {num} rows --> The number of rows to return - * @param {seed} seed --> (Optional) An integer specifying the random seed that will be used to create the distribution. - * @returns {Promise} resolves to a DataFrame object - */ - async sample(num = -1, seed = 1) { - if (num > this.shape[0]) { - throw new Error("Sample size n cannot be bigger than size of dataset"); - } - if (num < -1 || num == 0) { - throw new Error("Sample size cannot be less than -1 or 0"); - } - num = num === -1 ? this.shape[0] : num; - const shuffled_index = await tf.data.array(this.index).shuffle(num, seed).take(num).toArray(); - const df = this.iloc({ rows: shuffled_index }); - return df; - } - - - /** - * Return Addition of DataFrame and other, element-wise (binary operator add). - * @param {other} DataFrame, Series, Array or Number to add - * @returns {DataFrame} - */ - add(other, axis) { - if (this.__frame_is_compactible_for_operation) { - //check if all types a numeric - let tensors = this.__get_ops_tensors([ this, other ], axis); - let sum_vals = tensors[0].add(tensors[1]); - let col_names = this.columns; - return this.__get_df_from_tensor(sum_vals, col_names); - } else { - throw Error("TypeError: Dtypes of columns must be Float of Int"); - } - } - - /** - * Return subtraction of DataFrame and other, element-wise (binary operator add). - * @param {other} DataFrame, Series, Array or Number to add - * @returns {DataFrame} - */ - sub(other, axis) { - if (this.__frame_is_compactible_for_operation) { - //check if all types are numeric - let tensors = this.__get_ops_tensors([ this, other ], axis); - let result = tensors[0].sub(tensors[1]); - let col_names = this.columns; - return this.__get_df_from_tensor(result, col_names); - } else { - throw Error("TypeError: Dtypes of columns must be Float of Int"); - } - } - - /** - * Return subtraction of DataFrame and other, element-wise (binary operator add). - * @param {other} DataFrame, Series, Array or Number to add - * @returns {DataFrame} - */ - mul(other, axis) { - if (this.__frame_is_compactible_for_operation) { - //check if all types are numeric - let tensors = this.__get_ops_tensors([ this, other ], axis); - let result = tensors[0].mul(tensors[1]); - let col_names = this.columns; - return this.__get_df_from_tensor(result, col_names); - } else { - throw Error("TypeError: Dtypes of columns must be Float of Int"); - } - } - - /** - * Return division of DataFrame and other, element-wise (binary operator add). - * @param {other} DataFrame, Series, Array or Number to add - * @returns {DataFrame} - */ - div(other, axis) { - if (this.__frame_is_compactible_for_operation) { - //check if all types are numeric - let tensors = this.__get_ops_tensors([ this, other ], axis); - let result = tensors[0].div(tensors[1]); - let col_names = this.columns; - return this.__get_df_from_tensor(result, col_names); - } else { - throw Error("TypeError: Dtypes of columns must be Float of Int"); - } - } - - /** - * Return division of DataFrame and other, element-wise (binary operator add). - * @param {other} DataFrame, Series, Array or Number to add - * @returns {DataFrame} - */ - pow(other, axis) { - if (this.__frame_is_compactible_for_operation) { - //check if all types are numeric - let tensors = this.__get_ops_tensors([ this, other ], axis); - let result = tensors[0].pow(tensors[1]); - let col_names = this.columns; - return this.__get_df_from_tensor(result, col_names); - } else { - throw Error("TypeError: Dtypes of columns must be Float of Int"); - } - } - - /** - * Return division of DataFrame and other, element-wise (binary operator add). - * @param {other} DataFrame, Series, Array or Number to add - * @returns {DataFrame} - */ - mod(other, axis) { - if (this.__frame_is_compactible_for_operation) { - //check if all types are numeric - let tensors = this.__get_ops_tensors([ this, other ], axis); - let result = tensors[0].mod(tensors[1]); - let col_names = this.columns; - return this.__get_df_from_tensor(result, col_names); - } else { - throw Error("TypeError: Dtypes of columns must be Float of Int"); - } - } - - /** - * Return mean of DataFrame across specified axis. - * @param {axis} Number {0: row, 1 : column} Axis for the function to be applied on - * @returns {Series} - */ - mean(axis = 1) { - if (this.__frame_is_compactible_for_operation()) { - let values; - let val_mean = []; - if (axis == 1) { - values = this.col_data; - } else { - values = this.values; - } - - values.map((arr) => { - let temp = utils._remove_nans(arr); - let temp_mean = tf.tensor(temp).mean().arraySync(); - val_mean.push(Number(temp_mean.toFixed(5))); - }); - - let new_index; - if (axis == 1) { - new_index = this.column_names; - } else { - new_index = this.index; - } - let sf = new Series(val_mean, { columns: "sum", index: new_index }); - return sf; - } else { - throw Error("Dtype Error: Operation can not be performed on string type"); - } - } - - /** - * Return median of DataFrame across specified axis. - * @param {axis} Number {0: row, 1 : column} Axis for the function to be applied on - * @returns {Series} - */ - median(axis = 1) { - if (this.__frame_is_compactible_for_operation) { - //check if all types are numeric - let tensor_vals, idx; - if (axis == 1) { - tensor_vals = this.col_data_tensor.arraySync(); - idx = this.column_names; - } else { - tensor_vals = this.row_data_tensor.arraySync(); - idx = this.index; - } - let median = utils.__median(tensor_vals, false); - let sf = new Series(median, { index: idx }); - return sf; - } else { - throw Error("TypeError: Dtypes of columns must be Float of Int"); - } - } - - /** - * Return minimum element in a DataFrame across specified axis. - * @param {axis} Number {0: row, 1 : column} Axis for the function to be applied on - * @returns {Series} - */ - min(axis = 1) { - if (this.__frame_is_compactible_for_operation) { - //check if all types are numeric - let operands = this.__get_tensor_and_idx(this, axis); - let tensor_vals = operands[0]; - let idx = operands[1]; - let result = tensor_vals.min(operands[2]); - let sf = new Series(result.arraySync(), { index: idx }); - return sf; - } else { - throw Error("TypeError: Dtypes of columns must be Float of Int"); - } - } - - /** - * Return maximum element of DataFrame across specified axis. - * @param {axis} Number {0: row, 1 : column} Axis for the function to be applied on - * @returns {Series} - */ - max(axis = 1) { - if (this.__frame_is_compactible_for_operation) { - //check if all types are numeric - let operands = this.__get_tensor_and_idx(this, axis); - let tensor_vals = operands[0]; - let idx = operands[1]; - let result = tensor_vals.max(operands[2]); - let sf = new Series(result.arraySync(), { index: idx }); - return sf; - } else { - throw Error("TypeError: Dtypes of columns must be Float of Int"); - } - } - - /** - * Return standard deviation of DataFrame across specified axis. - * @param {axis} Number {0: row, 1 : column} Axis for the function to be applied on - * @returns {Series} - */ - std(axis = 1) { - if (this.__frame_is_compactible_for_operation) { - //check if all types are numeric - let tensor_vals = this.col_data_tensor.arraySync(); - let idx; - if (axis == 1) { - idx = this.column_names; - } else { - idx = this.index; - } - let median = std(tensor_vals, axis); - let sf = new Series(median, { index: idx }); - return sf; - } else { - throw Error("TypeError: Dtypes of columns must be Float of Int"); - } - } - - /** - * Return variance of DataFrame across specified axis. - * @param {axis} Number {0: row, 1 : column} Axis for the function to be applied on - * @returns {Series} - */ - var(axis = 1) { - if (this.__frame_is_compactible_for_operation) { - //check if all types are numeric - let tensor_vals = this.col_data_tensor.arraySync(); - let idx; - if (axis == 1) { - idx = this.column_names; - } else { - idx = this.index; - } - let median = variance(tensor_vals, axis); - let sf = new Series(median, { index: idx }); - return sf; - } else { - throw Error("TypeError: Dtypes of columns must be Float of Int"); - } - } - - /** - * Return number of non-null elements in a Series - * @returns {Series}, Count of non-null values - */ - count(axis = 1) { - if (this.__frame_is_compactible_for_operation) { - //check if all types are numeric - let tensor_vals, idx; - if (axis == 1) { - tensor_vals = this.col_data_tensor.arraySync(); - idx = this.column_names; - } else { - tensor_vals = this.row_data_tensor.arraySync(); - idx = this.index; - } - let counts = utils.__count_nan(tensor_vals, true, false); - let sf = new Series(counts, { index: idx }); - return sf; - } else { - throw Error("TypeError: Dtypes of columns must be Float of Int"); - } - } - - /** - * Rounds values in DataFrame to specified number of dp - * @returns {DataFrame}, New DataFrame with rounded values - */ - round(dp = 1) { - if (this.__frame_is_compactible_for_operation) { - //check if all types are numeric - let values = this.values; - let idx = this.index; - - let new_vals = utils.__round(values, dp, false); - let options = { columns: this.column_names, index: idx }; - let df = new DataFrame(new_vals, options); - return df; - } else { - throw Error("TypeError: Dtypes of columns must be Float of Int"); - } - } - - /** - * Perform Cummulative operations - * @param {axis} axis [int] {0 or 1} - * @param {ops} ops {String} name of operation - * @return {DataFrame} - */ - __cum_ops(axis = 0, ops) { - if (!(axis == 0) && !(axis == 1)) { - throw new Error("axis must be between 0 or 1"); - } - - if (this.__frame_is_compactible_for_operation) { - let data = []; - let df_data = null; - - if (axis == 0) { - df_data = this.col_data; - } else { - df_data = this.values; - } - - for (let i = 0; i < df_data.length; i++) { - let value = df_data[i]; - let temp_val = value[0]; - let temp_data = [ temp_val ]; - for (let j = 1; j < value.length; j++) { - let curr_val = value[j]; - switch (ops) { - case "max": - if (curr_val > temp_val) { - temp_val = curr_val; - temp_data.push(curr_val); - } else { - temp_data.push(temp_val); - } - break; - case "min": - if (curr_val < temp_val) { - temp_val = curr_val; - temp_data.push(curr_val); - } else { - temp_data.push(temp_val); - } - break; - case "sum": - temp_val = temp_val + curr_val; - temp_data.push(temp_val); - - break; - case "prod": - temp_val = temp_val * curr_val; - temp_data.push(temp_val); - - break; - } - } - data.push(temp_data); - } - - if (axis == 0) { - data = utils.__get_col_values(data); - } - - return new DataFrame(data, { columns: this.columns }); - } else { - throw Error("TypeError: Dtypes of columns must be Float of Int"); - } - } - /** - * calculate the cummulative sum along axis - * @param {kwargs} {axis: [int]} - * @returns {DataFrame} - */ - cumsum(kwargs = {}) { - let axis; - if (!utils.__key_in_object(kwargs, "axis")) { - axis = 0; - } else { - axis = kwargs["axis"]; - } - // let axis = kwargs["axis"] || 0 - let data = this.__cum_ops(axis, "sum"); - return data; - } - - /** - * calculate the cummulative min - * @param {kwargs} {axis: [int]} - * @returns {DataFrame} - */ - cummin(kwargs = {}) { - let axis; - if (!utils.__key_in_object(kwargs, "axis")) { - axis = 0; - } else { - axis = kwargs["axis"]; - } - let data = this.__cum_ops(axis, "min"); - return data; - } - - /** - * calculate the cummulative max - * @param {kwargs} {axis: [int]} - * @returns {DataFrame} - */ - cummax(kwargs = {}) { - let axis; - if (!utils.__key_in_object(kwargs, "axis")) { - axis = 0; - } else { - axis = kwargs["axis"]; - } - let data = this.__cum_ops(axis, "max"); - return data; - } - - /** - * calculate the cummulative prod - * @param {kwargs} {axis: [int]} - * @returns {DataFrame} - */ - cumprod(kwargs = {}) { - let axis; - if (!utils.__key_in_object(kwargs, "axis")) { - axis = 0; - } else { - axis = kwargs["axis"]; - } - let data = this.__cum_ops(axis, "prod"); - return data; - } - - /** - * Makes a new copy of a DataFrame - * @returns {DataFrame} - */ - copy() { - let df = new DataFrame([ ...this.values ], { - columns: [ ...this.column_names ], - index: this.index, - dtypes: this.dtypes - }); - return df; - } - - /** - * Generate a new index for the DataFrame. - * This is useful when the index is meaningless and needs to be reset to the default before another operation. - * @param {inplace} boolean: Modify the original object or return a new one. Default to false - */ - reset_index(inplace = false) { - if (inplace) { - this.__reset_index(); - } else { - let df = this.copy(); - df.__reset_index(); - return df; - } - } - - /** - * Set the DataFrame index (row labels) using an array of the same length. - * @param {kwargs} {index: Array of new index values} - */ - set_index(kwargs = {}) { - let params_needed = [ "key", "drop", "inplace" ]; - utils._throw_wrong_params_error(kwargs, params_needed); - - if (!utils.__key_in_object(kwargs, "key")) { - throw Error("Index ValueError: You must specify an array of index"); - } - - if (!utils.__key_in_object(kwargs, "inplace")) { - kwargs["inplace"] = false; - } - - if (!utils.__key_in_object(kwargs, "drop")) { - kwargs["drop"] = true; - } - - if ( - Array.isArray(kwargs["key"]) && - kwargs["key"].length != this.index.length - ) { - throw Error( - `Index LengthError: Lenght of new Index array ${kwargs["key"].length} must match lenght of existing index ${this.index.length}` - ); - } - - if ( - typeof kwargs["key"] == "string" && - this.column_names.includes(kwargs["key"]) - ) { - kwargs["key_name"] = kwargs["key"]; - kwargs["key"] = this[kwargs["key"]].values; - } - if (kwargs["inplace"]) { - // this.index_arr = kwargs['key'] - this.__set_index(kwargs["key"]); - if (kwargs["drop"] && typeof kwargs["key_name"] == "string") { - this.drop({ columns: [ kwargs["key_name"] ], inplace: true, axis: 1 }); - } - } else { - let df = this.copy(); - df.__set_index(kwargs["key"]); - if (kwargs["drop"] && typeof kwargs["key_name"] == "string") { - df.drop({ columns: [ kwargs["key_name"] ], axis: 1, inplace: true }); - } - return df; - } - } - - /** - * Generate descriptive statistics for all numeric columns - * Descriptive statistics include those that summarize the central tendency, - * dispersion and shape of a dataset’s distribution, excluding NaN values. - * @returns {Series} - */ - describe() { - let numeric_df = this.select_dtypes([ "float32", "int32" ]); - let col_names = numeric_df.column_names; - let index = [ "count", "mean", "std", "min", "median", "max", "variance" ]; - - let stats_arr = {}; - col_names.forEach((name) => { - let col_series = numeric_df[name]; - let count = col_series.count(); - let mean = col_series.mean(); - let std = col_series.std(); - let min = col_series.min(); - let median = col_series.median(); - let max = col_series.max(); - let variance = col_series.var(); - - let _stats = [ count, mean, std, min, median, max, variance ]; - stats_arr[name] = _stats; - }); - let df = new DataFrame(stats_arr, { index: index }); - return df.round(6); - } - - /** - * Return a subset of the DataFrame’s columns based on the column dtypes. - * @param {include} scalar or array-like. A selection of dtypes or strings to be included. At least one of these parameters must be supplied. - * @returns {DataFrame, Series} The subset of the frame including the dtypes. - */ - select_dtypes(include) { - let dtypes = this.dtypes; - let col_names = this.column_names; - let col_vals = {}; - let original_col_vals = this.col_data; - const __supported_dtypes = [ "float32", "int32", "string", "boolean" ]; - - if (include == undefined) { - //return all - let df = this.copy(); - return df; - } else { - //check if the right types are included - include.forEach((type) => { - if (!__supported_dtypes.includes(type)) { - throw Error(`Dtype Error: dtype ${type} not supported.`); - } - }); - - dtypes.forEach((dtype, i) => { - if (include.includes(dtype)) { - col_vals[col_names[i]] = original_col_vals[i]; - } - }); - let df = new DataFrame(col_vals); - return df; - } - } - - /** - * Sort a Dataframe in ascending or descending order by a specified column name. - * @param {kwargs} Object, {by: Column name to sort by - * ascending (Bool): Whether to return sorted values in ascending order or not, - * inplace (Bool): Whether to perform sorting on the original Series or not} - * @returns {Series} - */ - // sort_values(kwargs = {}) { - // if (utils.__key_in_object(kwargs, "by")) { - // let sort_col = this.column(kwargs["by"]) - // let sorted_col, sorted_index; - // let new_row_data = [] - - // if (utils.__key_in_object(kwargs, "inplace") && kwargs['inplace'] == true) { - // sort_col.sort_values(kwargs) - // sorted_index = sort_col.index - - // } else { - // sorted_col = sort_col.sort_values(kwargs) - // sorted_index = sorted_col.index - // } - - // sorted_index.map(idx => { - // new_row_data.push(this.values[idx]) - // }) - - // if (utils.__key_in_object(kwargs, "inplace") && kwargs['inplace'] == true) { - // this.data = new_row_data - // this.index_arr = sorted_index - // return null - // } else { - // let df = new DataFrame(new_row_data, { columns: this.column_names, index: sorted_index, dtype: this.dtypes }) - // return df - // } - - // } else { - // throw Error("Value Error: must specify the column to sort by") - // } - - // sorted_index.map(idx => { - // new_row_data.push(this.values[idx]) - // }) - - // if (utils.__key_in_object(kwargs, "inplace") && kwargs['inplace'] == true) { - // this.__update_frame_in_place(new_row_data, null, null, sorted_index, null) - - // } else { - // let df = new DataFrame(new_row_data, { columns: this.column_names, index: sorted_index, dtype: this.dtypes }) - // return df - // } - - // } else { - // throw Error("Value Error: must specify the column to sort by") - // } - - // } - - /** - * Return the sum of the values in a DataFrame across a specified axis. - * @params {kwargs} {axis: 0 for row and 1 for column} - * @returns {Series}, Sum of values accross axis - */ - sum(kwargs = { axis: 1 }) { - if (this.__frame_is_compactible_for_operation()) { - let values; - let val_sums = []; - if (kwargs["axis"] == 1) { - values = this.col_data; - } else { - values = this.values; - } - - values.map((arr) => { - let temp = utils._remove_nans(arr); - let temp_sum = tf.tensor(temp).sum().arraySync(); - val_sums.push(Number(temp_sum.toFixed(5))); - }); - - let new_index; - if (kwargs["axis"] == 1) { - new_index = this.column_names; - } else { - new_index = this.index; - } - let sf = new Series(val_sums, { columns: "sum", index: new_index }); - return sf; - } else { - throw Error("Dtype Error: Operation can not be performed on string type"); - } - } - - /** - * Returns the absolute values in DataFrame - * @return {DataFrame} - */ - abs() { - let data = this.values; - - let tensor_data = tf.tensor(data); - let abs_data = tensor_data.abs().arraySync(); - let df = new DataFrame(utils.__round(abs_data, 2, false), { - columns: this.column_names, - index: this.index - }); - return df; - } - - __get_tensor_and_idx(df, axis) { - let tensor_vals, idx, t_axis; - if (axis == 1) { - //Tensorflow uses 0 for column and 1 for rows, - // we use the opposite for consistency with Pandas (0 : row, 1: columns) - tensor_vals = df.row_data_tensor; - idx = df.column_names; - t_axis = 0; //switch the axis - } else { - tensor_vals = df.row_data_tensor; - idx = df.index; - t_axis = 1; - } - - return [ tensor_vals, idx, t_axis ]; - } - - /** - * Filter DataFrame element base on the element in a column - * @param {kwargs} kwargs {column : coumn name[string], is: String, to: string| int} - * @returns {DataFrame} - */ - query(kwargs) { - //define the set of operators to be used - let operators = [ ">", "<", "<=", ">=", "==", "!=" ]; - - if (!utils.__key_in_object(kwargs, "inplace")) { - kwargs["inplace"] = false; - } - - let column_index, operator, value; - - if (utils.__key_in_object(kwargs, "column")) { - if (this.columns.includes(kwargs["column"])) { - column_index = this.columns.indexOf(kwargs["column"]); - } else { - throw new Error(`column ${kwargs["column"]} does not exist`); - } - } else { - throw new Error("specify the column"); - } - - if (utils.__key_in_object(kwargs, "is")) { - if (operators.includes(kwargs["is"])) { - operator = kwargs["is"]; - } else { - throw new Error(` ${kwargs["is"]} is not a supported logical operator`); - } - } else { - throw new Error("specify an operator in param [is]"); - } - - if (utils.__key_in_object(kwargs, "to")) { - value = kwargs["to"]; - } else { - throw new Error("specify a value in param [to]"); - } - - let data = this.values; - let index = this.index; - let new_data = []; - let new_index = []; - - for (var i = 0; i < data.length; i++) { - let data_value = data[i]; - let elem = data_value[column_index]; - //use eval function for easy operation - //eval() takes in a string expression e.g eval('2>5') - if (eval(`elem${operator}value`)) { - new_data.push(data_value); - new_index.push(index[i]); - } - } - - if (new_data.length == 0) { - throw new Error( - `query returned empty data; is either ${value} does not exist in column ${kwargs["column"]}` - ); - } - if (kwargs["inplace"]) { - this.__update_frame_in_place( - new_data, - this.columns, - null, - new_index, - null - ); - } else { - let new_df = new DataFrame(new_data, { - columns: this.columns, - index: new_index - }); - return new_df; - } - } - - /** - * Add a column with values to the dataframe - * @param {kwargs} Object {column :[string] , value:[Array]} - * - */ - addColumn(kwargs) { - utils.__in_object(kwargs, "column", "column name not specified"); - utils.__in_object(kwargs, "value", "column value not specified"); - - let column_name = kwargs["column"]; - let data_length = this.shape[0]; - let value; - - if (kwargs["value"] instanceof Series) { - value = kwargs["value"].values; - } else { - value = kwargs["value"]; - } - - if (value.length != data_length) { - throw new Error( - `Array length ${value.length} not equal to ${data_length}` - ); - } - - if (this.columns.includes(column_name)) { - let col_idx = this.columns.indexOf(column_name); - let new_data = []; - - this.values.map((val, index) => { - let new_val = val.slice(); - new_val[col_idx] = value[index]; - new_data.push(new_val); - }); - this.__update_frame_in_place(new_data, null, null, null, null); - } else { - let data = this.values; - let new_data = []; - - data.map(function (val, index) { - let new_val = val.slice(); - new_val.push(value[index]); - new_data.push(new_val); - }); - - //add new dtype - let new_dtypes = [ ...this.dtypes ]; - new_dtypes.push(utils.__get_t(value)[0]); - - let new_col_names = [ ...this.columns ]; - new_col_names.push(column_name); - - this.__update_frame_in_place( - new_data, - new_col_names, - null, - null, - new_dtypes - ); - Object.defineProperty(this, column_name, { - get() { - return new Series(value, { columns: column_name, index: this.index }); - }, - set(value) { - this.addColumn({ column: column_name, value: value }); - } - }); - } - } - - /** - * - * @param {col} col is a list of columns - */ - groupby(col) { - const len = this.shape[0]; - const column_names = this.column_names; - const col_index = col.map((val) => column_names.indexOf(val)); - const col_dtype = this.dtypes.filter((val, index) => { - return col_index.includes(index); - }); - - const self = this; - const data = col.map( - (column_name) => { - if (!(column_names.includes(column_name))) - throw new Error(`column ${column_name} does not exist`); - const [ column_data, _ ] = indexLoc(self, { - rows: [ `0:${len}` ], - columns: [ `${column_name}` ], - type: "loc" - }); - return column_data; - } - ); - - const unique_columns = data.map((column_data) => utils.__unique(column_data)); - - function getRecursiveDict(uniq_columns) { - const first_uniq_columns = uniq_columns[0]; - const remaining_columns = uniq_columns.slice(1); - const c_dict = {}; - if (!remaining_columns.length) - first_uniq_columns.forEach((col_value) => c_dict[col_value] = []); - else - first_uniq_columns.forEach((col_value) => c_dict[col_value] = getRecursiveDict(remaining_columns)); - return c_dict; - } - const col_dict = getRecursiveDict(unique_columns); - - return new GroupBy( - col_dict, - col, - this.values, - column_names, - col_dtype - ).group(); - } - - /** - * Return a sequence of axis dimension along row and columns - * @params col_name: the name of a column in the database. - * @returns tensor of shape 1 - */ - column(col_name) { - if (!this.columns.includes(col_name)) { - throw new Error(`column ${col_name} does not exist`); - } - let col_indx_objs = utils.__arr_to_obj(this.columns); - let indx = col_indx_objs[col_name]; - let data = this.col_data[indx]; - return new Series(data, { columns: [ col_name ] }); - } - - /** - * Replace NaN or undefined with a specified value" - * @param {kwargs}, {column(s): Array of column name(s) to fill. If undefined fill all columns; - * value(s): Array | Scalar of value(s) to fill with. If single value is specified, we use it to fill all - * @return {DataFrame} - */ - fillna(kwargs = {}) { - let params_needed = [ "columns", "values", "inplace" ]; - utils._throw_wrong_params_error(kwargs, params_needed); - - if (!utils.__key_in_object(kwargs, "inplace")) { - kwargs["inplace"] = false; - } - - if (utils.__key_in_object(kwargs, "columns")) { - //check if the column(s) exists - kwargs["columns"].map((col) => { - if (!this.column_names.includes(col)) { - throw Error( - `Value Error: Specified columns must be one of ${this.column_names}, got ${col}` - ); - } - }); - - if (kwargs["columns"].length != kwargs["values"].length) { - throw Error(`Lenght Error: The lenght of the columns names must be equal to the lenght of the values, - got column of length ${kwargs["columns"].length} but values of length ${kwargs["values"].length}`); - } - let new_col_data = this.col_data; - kwargs["columns"].map((col, i) => { - let col_idx = this.column_names.indexOf(col); - let col_data = this.col_data[col_idx]; - - let __temp = []; - col_data.map((val) => { - //fill the column - if (isNaN(val) && typeof val != "string") { - __temp.push(kwargs["values"][i]); - } else { - __temp.push(val); - } - }); - new_col_data[col_idx] = __temp; - }); - - let final_data = {}; - new_col_data.map((col, i) => { - final_data[this.column_names[i]] = col; - }); - - if (kwargs["inplace"]) { - this.__update_frame_in_place(null, null, final_data, null, null); - } else { - return new DataFrame(final_data, { index: this.index }); - } - } else { - //fill all columns using same value - if (!utils.__key_in_object(kwargs, "values")) { - throw Error("Value Error: Please specify a fill value"); - } - - let nan_val; - if (Array.isArray(kwargs["values"])) { - nan_val = kwargs["values"][0]; - } else { - nan_val = kwargs["values"]; - } - let data = []; - let values = this.values; - let columns = this.columns; - - for (let i = 0; i < values.length; i++) { - let temp_data = []; - let row_value = values[i]; - for (let j = 0; j < row_value.length; j++) { - if (isNaN(row_value[j]) && typeof row_value[j] != "string") { - temp_data.push(nan_val); - } else { - temp_data.push(row_value[j]); - } - } - data.push(temp_data); - } - if (kwargs["inplace"]) { - this.__update_frame_in_place(data, null, null, null, null); - } else { - return new DataFrame(data, { columns: columns, index: this.index }); - } - } - } - - /** - * Return a boolean same-sized object indicating if the values are NaN. NaN and undefined values, - * gets mapped to True values. Everything else gets mapped to False values. - * @return {DataFrame} - */ - isna() { - let new_row_data = this.__isna(); - let columns = this.column_names; - return new DataFrame(new_row_data, { columns: columns, index: this.index }); - } - - /** - * Obtain index containing nan values - * @return Array list (int) - */ - nanIndex() { - let df_values = this.values; - let index_data = []; - - for (let i = 0; i < df_values.length; i++) { - let row_values = df_values[i]; - - if (row_values.includes(NaN)) { - index_data.push(i); - } - } - return index_data; - } - - /** - * Drop all rows containing NaN - * @param {kwargs} kwargs [Object] {axis: [int]{o or 1}, inplace:[boolean]} - */ - dropna(kwargs = {}) { - let axis = kwargs["axis"] || 0; - let inplace = kwargs["inplace"] || false; - - if (axis != 0 && axis != 1) { - throw new Error("axis must either be 1 or 0"); - } - - let df_values = null; - let columns = null; - if (axis == 0) { - df_values = this.values; - columns = this.columns; - } else { - df_values = this.col_data; - columns = []; - } - let data = []; - - for (let i = 0; i < df_values.length; i++) { - let values = df_values[i]; - - if (!values.includes(NaN)) { - if (axis == 0) { - data.push(values); - } else { - columns.push(this.columns[i]); - if (data.length == 0) { - for (let j = 0; j < values.length; j++) { - data.push([ values[j] ]); - } - } else { - for (let j = 0; j < data.length; j++) { - data[j].push(values[j]); - } - } - } - } - } - - if (inplace == true) { - this.data = data; - this.__reset_index(); - this.columns = columns; - } else { - return new DataFrame(data, { columns: columns }); - } - } - - /** - * Apply a function to each element or along a specified axis of the DataFrame. Supports JavaScipt functions - * when axis is not specified, and accepts Tensorflow functions when axis is specified. - * @param {kwargs} kargs is defined as {axis: undefined, 0 or 1, callable: [FUNCTION]} - * @return Array - */ - apply(kwargs) { - let is_callable = utils.__is_function(kwargs["callable"]); - if (!is_callable) { - throw new Error("the argument must be a function"); - } - - let callable = kwargs["callable"]; - let data = []; - - if (utils.__key_in_object(kwargs, "axis")) { - //This accepts all tensorflow operations - let axis = kwargs["axis"]; - let df_data; - if (axis == 0) { - df_data = this.values; - } else { - df_data = this.col_data; - } - - for (let i = 0; i < df_data.length; i++) { - let value = tf.tensor(df_data[i]); - let callable_data; - try { - callable_data = callable(value).arraySync(); - } catch (error) { - throw Error( - `Callable Error: You can only apply JavaScript functions on DataFrames when axis is not specified. This operation is applied on all element, and returns a DataFrame of the same shape.` - ); - } - - data.push(callable_data); - } - } else { - //perform element wise operation. This accepts any JavaScript function - let df_data = this.values; - let new_data = []; - df_data.forEach((row) => { - let new_row = []; - row.forEach((val) => { - new_row.push(callable(val)); - }); - new_data.push(new_row); - }); - data = new_data; - } - - if (utils.__is_1D_array(data)) { - if (kwargs["axis"] == 0) { - let sf = new Series(data, { index: this.index }); - return sf; - } else { - let sf = new Series(data, { index: this.column_names }); - return sf; - } - } else { - let df = new DataFrame(data, { - columns: this.column_names, - index: this.index - }); - return df; - } - } - - /** - * Returns Less than of DataFrame and other. Supports element wise operations - * @param {other} DataFrame, Series, Scalar - * @param {axis} Number {0 for row, 1 for index} Whether to compare by the index or columns - * @return {DataFrame} - */ - lt(other, axis) { - if (this.__frame_is_compactible_for_operation()) { - if (axis == undefined) { - axis = 0; - } - let df = this.__logical_ops(other, "lt", axis); - return df; - } else { - throw Error("Dtype Error: Operation can not be performed on string type"); - } - } - - /** - * Returns Greater than of DataFrame and other. Supports element wise operations - * @param {other} DataFrame, Series, Scalar - * @param {axis} Number {0 for row, 1 for index} Whether to compare by the index or columns - * @return {DataFrame} - */ - gt(other, axis) { - if (this.__frame_is_compactible_for_operation()) { - if (axis == undefined) { - axis = 0; - } - - let df = this.__logical_ops(other, "gt", axis); - return df; - } else { - throw Error("Dtype Error: Operation can not be performed on string type"); - } - } - - /** - * Returns Less than or Equal to of DataFrame and other. Supports element wise operations - * @param {other} DataFrame, Series, Scalar - * @param {axis} Number {0 for row, 1 for index} Whether to compare by the index or columns - * @return {DataFrame} - */ - le(other, axis) { - if (this.__frame_is_compactible_for_operation()) { - if (axis == undefined) { - axis = 0; - } - let df = this.__logical_ops(other, "le", axis); - return df; - } else { - throw Error("Dtype Error: Operation can not be performed on string type"); - } - } - - /** - * Returns Greater than or Equal to of DataFrame and other. Supports element wise operations - * @param {other} DataFrame, Series, Scalar - * @param {axis} Number {0 for row, 1 for index} Whether to compare by the index or columns - * @return {DataFrame} - */ - ge(other, axis) { - if (this.__frame_is_compactible_for_operation()) { - if (axis == undefined) { - axis = 0; - } - let df = this.__logical_ops(other, "ge", axis); - return df; - } else { - throw Error("Dtype Error: Operation can not be performed on string type"); - } - } - - /** - * Returns Not Equal to of DataFrame and other. Supports element wise operations - * @param {other} DataFrame, Series, Scalar - * @param {axis} Number {0 for row, 1 for index} Whether to compare by the index or columns - * @return {DataFrame} - */ - ne(other, axis) { - if (this.__frame_is_compactible_for_operation()) { - if (axis == undefined) { - axis = 0; - } - let df = this.__logical_ops(other, "ne", axis); - return df; - } else { - throw Error("Dtype Error: Operation can not be performed on string type"); - } - } - - /** - * Returns Greater than or Equal to of DataFrame and other. Supports element wise operations - * @param {other} DataFrame, Series, Scalar - * @param {axis} Number {0 for row, 1 for index} Whether to compare by the index or columns - * @return {DataFrame} - */ - eq(other, axis) { - if (this.__frame_is_compactible_for_operation()) { - if (axis == undefined) { - axis = 0; - } - let df = this.__logical_ops(other, "eq", axis); - return df; - } else { - throw Error("Dtype Error: Operation can not be performed on string type"); - } - } - - /** - * Replace all occurence of a value with a new specified value" - * @param {kwargs}, {"replace": the value you want to replace, - * "with": the new value you want to replace the olde value with - * "in": Array of column names to replace value in, If not specified, replace all columns} - * @return {Series} - */ - replace(kwargs = {}) { - let params_needed = [ "replace", "with", "in" ]; - utils._throw_wrong_params_error(kwargs, params_needed); - - if (utils.__key_in_object(kwargs, "in")) { - //fill specified columns only - //check if the column(s) exists - kwargs["in"].map((col) => { - if (!this.column_names.includes(col)) { - throw Error( - `Value Error: Specified columns must be one of ${this.column_names}, got ${col}` - ); - } - }); - - if ( - utils.__key_in_object(kwargs, "replace") && - utils.__key_in_object(kwargs, "with") - ) { - let new_col_data_obj = {}; - this.column_names.map((col, idx) => { - if (kwargs["in"].includes(col)) { - let temp_col_data = this.col_data[idx]; //retreive the column data - let __temp = []; - temp_col_data.map((val) => { - //replace the values - if (val == kwargs["replace"]) { - __temp.push(kwargs["with"]); - } else { - __temp.push(val); - } - }); - new_col_data_obj[col] = __temp; - } else { - new_col_data_obj[col] = this.col_data[idx]; - } - }); - return new DataFrame(new_col_data_obj, { - columns: this.column_names, - index: this.index - }); - } else { - throw Error( - "Params Error: Must specify both 'replace' and 'with' parameters." - ); - } - } else { - //fill every occurence in all columns and rows - if ( - utils.__key_in_object(kwargs, "replace") && - utils.__key_in_object(kwargs, "with") - ) { - let replaced_arr = []; - let old_arr = this.values; - - old_arr.map((inner_arr) => { - let temp = []; - inner_arr.map((val) => { - if (val == kwargs["replace"]) { - temp.push(kwargs["with"]); - } else { - temp.push(val); - } - }); - replaced_arr.push(temp); - }); - - let df = new DataFrame(replaced_arr, { - index: this.index, - columns: this.column_names - }); - return df; - } else { - throw Error( - "Params Error: Must specify both 'replace' and 'with' parameters." - ); - } - } - } - - //performs logical comparisons on DataFrame using Tensorflow.js - __logical_ops(val, logical_type, axis) { - let int_vals, other; - if (utils.__is_number(val)) { - other = val; - } else { - if (val.series) { - //series - if (axis == 0) { - if (val.values.length != this.shape[0]) { - throw Error( - `Shape Error: Operands could not be broadcast together with shapes ${this.shape} and ${val.values.length}.` - ); - } - other = tf.tensor(val.values); - } else { - if (val.values.length != this.shape[1]) { - throw Error( - `Shape Error: Operands could not be broadcast together with shapes ${this.shape} and ${val.values.length}.` - ); - } - other = tf.tensor(val.values); - } - } else if (Array.isArray(val)) { - //Array of Array - other = tf.tensor(val); - } else { - //DataFrame - other = val.row_data_tensor; - } - } - - switch (logical_type) { - case "lt": - int_vals = tf.tensor(this.values).less(other).arraySync(); - break; - case "gt": - int_vals = tf.tensor(this.values).greater(other).arraySync(); - break; - case "le": - int_vals = tf.tensor(this.values).lessEqual(other).arraySync(); - break; - case "ge": - int_vals = tf.tensor(this.values).greaterEqual(other).arraySync(); - break; - case "ne": - int_vals = tf.tensor(this.values).notEqual(other).arraySync(); - break; - case "eq": - int_vals = tf.tensor(this.values).equal(other).arraySync(); - break; - } - let bool_vals = utils.__map_int_to_bool(int_vals, 2); - let df = new DataFrame(bool_vals, { - columns: this.column_names, - index: this.index - }); - return df; - } - - //slice the corresponding arrays from tensor objects - __get_df_from_tensor(val, col_names) { - let len = val.shape[0]; - let new_array = []; - for (let i = 0; i < len; i++) { - let arr = val.slice([ i ], [ 1 ]).arraySync()[0]; - new_array.push(arr); - } - return new DataFrame(new_array, { columns: col_names }); - } - - //checks if DataFrame is compaticble for arithmetic operation - //compatible Dataframe must have only numerical dtypes - __frame_is_compactible_for_operation() { - let dtypes = this.dtypes; - const str = (element) => element == "string"; - - if (dtypes.some(str)) { - return false; - } else { - return true; - } - } - - //retreives the corresponding tensors based on specified axis - __get_ops_tensors(tensors, axis) { - if (utils.__is_undefined(tensors[1].series)) { - //check if add operation is on a series or DataFrame - let tensors_arr = []; - if (utils.__is_undefined(axis) || axis == 1) { - //axis = 1 (column) - tensors_arr.push(tensors[0].row_data_tensor); - tensors_arr.push(tensors[1]); - return tensors_arr; - } else { - //axis = 0 (rows) - tensors_arr.push(tensors[0].col_data_tensor); - tensors_arr.push(tensors[1]); - return tensors_arr; - } - } else { - //operation is being performed on a Dataframe or Series - let tensors_arr = []; - if (utils.__is_undefined(axis) || axis == 1) { - //axis = 1 (column) - let this_tensor, other_tensor; - - this_tensor = tensors[0].row_data_tensor; //tensorflow uses 1 for rows axis and 0 for column axis - if (tensors[1].series) { - other_tensor = tf.tensor(tensors[1].values, [ - 1, - tensors[1].values.length - ]); - } else { - other_tensor = tensors[1].row_data_tensor; - } - - tensors_arr.push(this_tensor); - tensors_arr.push(other_tensor); - return tensors_arr; - } else { - //axis = 0 (rows) - let this_tensor, other_tensor; - - this_tensor = tensors[0].row_data_tensor; - if (tensors[1].series) { - other_tensor = tf.tensor(tensors[1].values, [ - tensors[1].values.length, - 1 - ]); - } else { - other_tensor = tensors[1].row_data_tensor; - } - - tensors_arr.push(this_tensor); - tensors_arr.push(other_tensor); - return tensors_arr; - } - } - } - - /** - * Transpose index and columns. - * Reflect the DataFrame over its main diagonal by writing rows as columns and vice-versa. - * The property T is an accessor to the method transpose(). - */ - transpose() { - let new_values = this.col_data; - let new_index = this.column_names; - let new_col_names = this.index; - - let df = new DataFrame(new_values, { - columns: new_col_names, - index: new_index - }); - return df; - } - - /** - * The property T is an accessor to the method transpose(). - */ - get T() { - return this.transpose(); - } - - /** - * Returns the data types in the DataFrame - * @return {Array} list of data types for each column - */ - get ctypes() { - let cols = this.column_names; - let d_types = this.col_types; - let sf = new Series(d_types, { index: cols }); - return sf; - } - - /** - * Make plots of Series or DataFrame. - * Uses the Plotly as backend, so supports Plotly's configuration parameters - * @param {string} div Name of the div to show the plot - * @returns {Class} Plot class that expoese different plot type - */ - plot(div) { - const plt = new Plot(this, div); - return plt; - } - - /** - * Returns the Tensorflow tensor backing the DataFrame Object - * @returns {2D tensor} - */ - get tensor() { - return this.row_data_tensor; - } - - /** - * Sets the data types of an DataFrame - * @param {Object} kwargs {column: Name of the column to cast, dtype: [float32, int32, string] data type to cast to} - * @returns {DataFrame} - */ - astype(kwargs = {}) { - if (!utils.__key_in_object(kwargs, "column")) { - throw Error("Value Error: Please specify a column to cast"); - } - - if (!utils.__key_in_object(kwargs, "dtype")) { - throw Error("Value Error: Please specify dtype to cast to"); - } - - if (!this.column_names.includes(kwargs["column"])) { - throw Error(`'${kwargs["column"]}' not found in columns`); - } - - let col_idx = this.column_names.indexOf(kwargs["column"]); - let new_types = this.col_types; - let col_values = this.col_data; - - new_types[col_idx] = kwargs["dtype"]; - let new_col_values = []; - let temp_col = col_values[col_idx]; - - switch (kwargs["dtype"]) { - case "float32": - temp_col.map((val) => { - new_col_values.push(Number(val)); - }); - col_values[col_idx] = new_col_values; - break; - case "int32": - temp_col.map((val) => { - new_col_values.push(Number(Number(val).toFixed())); - }); - col_values[col_idx] = new_col_values; - - break; - case "string": - temp_col.map((val) => { - new_col_values.push(String(val)); - }); - col_values[col_idx] = new_col_values; - break; - default: - break; - } - - let new_col_obj = {}; - this.column_names.forEach((cname, i) => { - new_col_obj[cname] = col_values[i]; - }); - - let df = new DataFrame(new_col_obj, { - dtypes: new_types, - index: this.index - }); - return df; - } - - /** - * Return the unique values along an axis - * @param {axis} Int, 0 for row, and 1 for column. Default to 1 - * @return {Object} - */ - unique(axis = 1) { - if (axis == undefined || axis > 1 || axis < 0) { - throw Error( - `Axis Error: Please specify a correct axis. Axis must either be '0' or '1', got ${axis}` - ); - } - let _unique = {}; - if (axis == 1) { - //column - let col_names = this.column_names; - col_names.forEach((cname) => { - _unique[cname] = this[cname].unique().values; - }); - } else { - let rows = this.values; - let _index = this.index; - rows.forEach((row, i) => { - let data_set = new Set(row); - _unique[_index[i]] = Array.from(data_set); - }); - } - - return _unique; - } - - /** - * Return the number of unique value along an axis - * @param {axis} Int, 0 for row, and 1 for column. Default to 1 - * @return {Series} - */ - nunique(axis = 1) { - if (axis == undefined || axis > 1 || axis < 0) { - throw Error( - `Axis Error: Please specify a correct axis. Axis must either be '0' or '1', got ${axis}` - ); - } - - let _nunique = []; - if (axis == 1) { - //column - let col_names = this.column_names; - col_names.forEach((cname) => { - _nunique.push(this[cname].unique().values.length); - }); - let sf = new Series(_nunique, { index: this.column_names }); - return sf; - } else { - let rows = this.values; - rows.forEach((row) => { - let data_set = new Set(row); - _nunique.push(Array.from(data_set).length); - }); - } - let sf = new Series(_nunique, { index: this.index }); - return sf; - } - - /** - * Change axes labels. Object values must be unique (1-to-1). - * Labels not contained in a dict / Series will be left as-is. Extra labels listed don’t throw an error. - * @param {Object} kwargs {mapper: Dict-like or functions transformations to apply to that axis’ values, - * axis: Int, 0 for row, and 1 for column. Default to 1, - * inplace: Whether to return a new DataFrame. If True then value of copy is ignored. - * @returns {DataFrame} - */ - rename(kwargs = {}) { - let params_needed = [ "mapper", "inplace", "axis" ]; - utils._throw_wrong_params_error(kwargs, params_needed); - - // utils.__in_object(kwargs, "columns", "value not defined") - if (!utils.__key_in_object(kwargs, "inplace")) { - kwargs["inplace"] = false; - } - if (!utils.__key_in_object(kwargs, "axis")) { - kwargs["axis"] = 1; - } - if (!utils.__key_in_object(kwargs, "mapper")) { - throw Error("Please specify a mapper object"); - } - if (kwargs["axis"] == 1) { - //columns - let old_col_names = Object.keys(kwargs["mapper"]); - let new_col_names = Object.values(kwargs["mapper"]); - let col_names = [ ...this.column_names ]; - - old_col_names.forEach((cname, i) => { - if (!col_names.includes(cname)) { - throw Error( - `Label Error: Specified column '${cname}' not found in column axis` - ); - } - let idx = col_names.indexOf(cname); - col_names[idx] = new_col_names[i]; - }); - if (kwargs["inplace"]) { - this.columns = col_names; - this.__set_col_property(this, this.col_data, col_names, old_col_names); - } else { - let df = this.copy(); - df.columns = col_names; - df.__set_col_property(df, df.col_data, col_names, old_col_names); - return df; - } - } else { - //row - let old_index = Object.keys(kwargs["mapper"]); - let row_index = this.index; - let new_index = []; - - row_index.forEach((idx) => { - if (old_index.includes(idx)) { - new_index.push(kwargs["mapper"][idx]); - } else { - new_index.push(idx); - } - }); - - if (kwargs["inplace"]) { - this.__set_index(new_index); - } else { - let df = this.copy(); - df.__set_index(new_index); - return df; - } - } - } - /** - * Sort DataFrame by index - * @param {*} kwargs {inplace: Boolean, ascending: Bool} - * @returns DataFrame - */ - sort_index(kwargs = {}) { - let inplace = - typeof kwargs["inplace"] == "undefined" ? false : kwargs["inplace"]; - let asc = - typeof kwargs["ascending"] == "undefined" ? true : kwargs["ascending"]; - - let index_val = this.index; - let [ data, index ] = this.__sort_by(index_val, index_val, asc); - - if (inplace) { - this.__update_frame_in_place(data, null, null, index, null); - } else { - let df = this.copy(); - df.__update_frame_in_place(data, null, null, index, null); - return df; - } - } - - /** - * Sort a Dataframe in ascending or descending order by a specified column name. - * @param {kwargs} Object, {by: Column name to sort by - * ascending (Bool): Whether to return sorted values in ascending order or not, - * inplace (Bool): Whether to perform sorting on the original Series or not} - * @returns {Series} - */ - sort_values(kwargs = {}) { - if (!utils.__key_in_object(kwargs, "by")) { - throw Error(`use col_name to specify column name`); - } - - let inplace = - typeof kwargs["inplace"] == "undefined" ? false : kwargs["inplace"]; - let asc = - typeof kwargs["ascending"] == "undefined" ? true : kwargs["ascending"]; - let index_val = this.index; - let column_val = this.column(kwargs["by"]).values; - let [ data, index ] = this.__sort_by(column_val, index_val, asc); - - if (inplace) { - this.__update_frame_in_place(data, null, null, index, null); - } else { - let df = this.copy(); - df.__update_frame_in_place(data, null, null, index, null); - return df; - } - } - - //set all columns to DataFrame Property. This ensures easy access to columns as Series - __set_col_property(self, col_vals, col_names, old_col_names) { - //delete old name - old_col_names.forEach((name) => { - delete self[name]; - }); - - col_vals.forEach((col, i) => { - // self[col_names[i]] = new Series(col, { columns: col_names[i], index: self.index }) - Object.defineProperty(self, col_names[i], { - get() { - return new Series(col, { columns: col_names[i], index: self.index }); - }, - set(value) { - this.addColumn({ column: col_names[i], value: value }); - } - }); - }); - } - - //update a DataFrame in place - __update_frame_in_place(row_data, column_names, col_obj, index, dtypes) { - if (row_data != undefined) { - this.data = row_data; - } else { - //check column is available and create row from column - if (col_obj != undefined) { - let _res = utils._get_row_and_col_values(col_obj); - this.data = _res[0]; - this.columns = _res[1]; - column_names = _res[1]; - } - } - - if (col_obj != undefined) { - this.col_data = Object.values(col_obj); - this.columns = Object.keys(col_obj); - column_names = Object.keys(col_obj); - } else { - //check if row data is available and create column data from rows - if (row_data != undefined) { - this.col_data = utils.__get_col_values(row_data); //get column data from row - } - } - - if (column_names != undefined) { - this.columns = column_names; - } - if (index != undefined) { - this.index_arr = index; - } - if (dtypes != undefined) { - this.col_types = dtypes; - } - } - - __sort_by(col_value, df_index, asc) { - let values = this.values; - - let sorted_val = utils.__sort(col_value, asc); - let duplicate_obj = utils.__get_duplicate(col_value); - - let data = []; - let indexs = []; - for (let row_i = 0; row_i < sorted_val.length; row_i++) { - let val = sorted_val[row_i]; - let index = null; - - if (val in duplicate_obj) { - index = duplicate_obj[val]["index"][0]; - duplicate_obj[val]["index"].splice(0, 1); - } else { - index = col_value.indexOf(val); - } - - data.push(values[index]); - indexs.push(df_index[index]); - } - - return [ data, indexs ]; - } - - /** - * Append rows to a DataFrame - * @param {val} val Array | Series to append to the object - * @return DataFrame - */ - append(val) { - let df2 = null; - if (Array.isArray(val)) { - if (Array.isArray(val[0])) { - if (val[0].length != this.shape[1]) { - throw Error( - `length Mixmatch: The lenght of provided value (${val.length}) does not match the original DataFrame (${this.shape[1]})` - ); - } - df2 = new DataFrame(val); - } - } else if (utils.__is_object(val)) { - df2 = new DataFrame(val); - } else if (val instanceof DataFrame) { - df2 = val.copy(); - } - - let concat_df = concat({ df_list: [ this, df2 ], axis: 0 }); - - return concat_df; - } -} diff --git a/danfojs-browser/src/core/generic.js b/danfojs-browser/src/core/generic.js deleted file mode 100644 index 90bb2ec0..00000000 --- a/danfojs-browser/src/core/generic.js +++ /dev/null @@ -1,500 +0,0 @@ -/** -* Copyright 2020, JsData. -* All rights reserved. -* -* This source code is licensed under the MIT license found in the -* LICENSE file in the root directory of this source tree. - -* Unless required by applicable law or agreed to in writing, software -* distributed under the License is distributed on an "AS IS" BASIS, -* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -* See the License for the specific language governing permissions and -* limitations under the License. - * -*/ - -import { tensor, Tensor } from "@tensorflow/tfjs"; -import { table } from "table"; -import { Utils } from "./utils"; -import { Configs } from "../config/config"; - - -const utils = new Utils(); -const config = new Configs(); //package wide configuration object -export default class NDframe { - /** - * N-Dimensiona data structure. Stores multi-dimensional - * data in a size-mutable, labeled data structure. Analogous to the Python Pandas DataFrame. - * - * @param {data} Array JSON, Tensor. Block of data. - * @param {kwargs} Object Optional Configuration Object - * {columns: Array of column names. If not specified and data is an array of array, use range index. - * dtypes: Data types of the columns, - * index: row index for subseting array } - * - * @returns NDframe - */ - - constructor(data, kwargs = {}) { - this.kwargs = kwargs; - - if (data instanceof Tensor) { - data = data.arraySync(); - } - - if (utils.__is_1D_array(data)) { - this.series = true; - this._read_array(data); - } else { - this.series = false; - if (utils.__is_object(data[0])) { - //check the type of the first object in the data - this._read_object(data, 1); //type 1 object are of JSON form [{a: 1, b: 2}, {a: 30, b: 20}] - } else if (utils.__is_object(data)) { - this._read_object(data, 2); //type 2 object are of the form {a: [1,2,3,4], b: [30,20, 30, 20}] - } else if ( - Array.isArray(data[0]) || - utils.__is_number(data[0]) || - utils.__is_string(data[0]) - ) { - this._read_array(data); - } else { - throw new Error("File format not supported"); - } - } - } - - /** - * - * @param {Array} data - * Read array of data into NDFrame - */ - _read_array(data) { - this.data = utils.__replace_undefined_with_NaN(data, this.series); - this.row_data_tensor = tensor(this.data); - - if (this.series) { - this.col_data = [ this.values ]; - } else { - this.col_data = utils.__get_col_values(this.data); - } - - this.col_data_tensor = tensor(this.col_data); //data saved as 2D column tensors - - if ("index" in this.kwargs) { - this.__set_index(this.kwargs["index"]); - } else { - this.index_arr = [ ...Array(this.row_data_tensor.shape[0]).keys() ]; - } - - if (this.ndim == 1) { - //series array - if ("columns" in this.kwargs) { - this.columns = this.kwargs["columns"]; - } else { - this.columns = [ "0" ]; - } - } else { - //2D Array - if ("columns" in this.kwargs) { - if ( - this.kwargs["columns"].length == Number(this.row_data_tensor.shape[1]) - ) { - this.columns = this.kwargs["columns"]; - } else { - throw `Column length mismatch. You provided a column of length ${this.kwargs["columns"].length} but data has length of ${this.row_data_tensor.shape[1]}`; - } - } else { - this.columns = [ ...Array(this.row_data_tensor.shape[1]).keys() ]; - } - } - - if ("dtypes" in this.kwargs) { - this._set_col_types(this.kwargs["dtypes"], false); - } else { - this._set_col_types(null, true); //infer dtypes - } - } - - /** - * Convert Javascript Object of arrays into NDFrame - * @param {*} data Object of Arrays - * @param {*} type type 1 object are of JSON form [{a: 1, b: 2}, {a: 30, b: 20}], - * type 2 object are of the form {a: [1,2,3,4], b: [30,20, 30, 20}] - */ - _read_object(data, type) { - if (type == 2) { - let [ row_arr, col_names ] = utils._get_row_and_col_values(data); - this.kwargs["columns"] = col_names; - this._read_array(row_arr); - } else { - let data_arr = data.map((item) => { - return Object.values(item); - }); - - this.data = utils.__replace_undefined_with_NaN(data_arr, this.series); //Defualt array data in row format - this.row_data_tensor = tensor(this.data); //data saved as row tensors - this.kwargs["columns"] = Object.keys(Object.values(data)[0]); //get names of the column from the first entry - - if (this.series) { - this.col_data = [ this.values ]; //data saved as 1D column tensors - } else { - this.col_data = utils.__get_col_values(this.data); - } - - this.col_data_tensor = tensor(this.col_data); //data saved as 2D column tensors - - if ("index" in this.kwargs) { - this.__set_index(this.kwargs["index"]); - } else { - this.index_arr = [ ...Array(this.row_data_tensor.shape[0]).keys() ]; - } - - if (this.ndim == 1) { - //series array - if (!this.kwargs["columns"]) { - this.columns = [ "0" ]; - } else { - this.columns = this.kwargs["columns"]; - } - } else { - //2D Array - if ("columns" in this.kwargs) { - if ( - this.kwargs["columns"].length == - Number(this.row_data_tensor.shape[1]) - ) { - this.columns = this.kwargs["columns"]; - } else { - throw `Column length mismatch. You provided a column of length ${this.kwargs["columns"].length} but data has column length of ${this.row_data_tensor.shape[1]}`; - } - } else { - this.columns = [ ...Array(this.row_data_tensor.shape[1]).keys() ]; - } - } - - if ("dtypes" in this.kwargs) { - this._set_col_types(this.kwargs["dtypes"], false); - } else { - this._set_col_types(null, true); //infer dtypes - } - } - } - - /** - * Sets the data type of the NDFrame. Supported types are ['float32', "int32", 'string', 'boolean'] - * @param {Array} dtypes Array of data types. - * @param {Boolean} infer Whether to automatically infer the dtypes from the Object - */ - _set_col_types(dtypes, infer) { - const __supported_dtypes = [ "float32", "int32", "string", "boolean" ]; - - if (infer) { - if (this.series) { - this.col_types = utils.__get_t(this.values); - } else { - this.col_types = utils.__get_t(this.col_data); - } - } else { - if (this.series) { - this.col_types = dtypes; - } else { - if (dtypes.length != this.columns.length) { - throw new Error( - `length Mixmatch: Length of specified dtypes is ${dtypes.length}, but length of columns is ${this.columns.length}` - ); - } - if (Array.isArray(dtypes)) { - dtypes.forEach((type, indx) => { - if (!__supported_dtypes.includes(type)) { - throw new Error( - `dtype error: dtype specified at index ${indx} is not supported` - ); - } - }); - this.col_types = dtypes; - } else { - throw new Error(`dtypes must be an Array of types`); - } - } - } - } - - /** - * Returns the data types in the DataFrame - * @return {Array} list of data types for each column - */ - get dtypes() { - // let col_data = utils.get_col_values(this.data) - // this.col_types = utils.__get_t(col_data) - // let sf = new Series({dtypes: this.col_types, index: this.column_names}) - return this.col_types; - } - - /** - * Gets dimension of the NDFrame - * @returns {Integer} dimension of NDFrame - */ - get ndim() { - if (this.series) { - return 1; - } else { - return this.row_data_tensor.shape.length; - } - } - - /** - * Gets values for index and columns - * @return {Object} axes configuration for index and columns of NDFrame - */ - get axes() { - let axes = { - index: this.index, - columns: this.columns - }; - return axes; - } - - /** - * Gets index of the NDframe - * @return {Array} array of index from series - */ - get index() { - return this.index_arr; - } - - /** - * Sets index of the NDFrame - */ - __set_index(labels) { - if (!Array.isArray(labels)) { - throw Error("Value Error: index must be an array"); - } - if (labels.length > this.shape[0] || labels.length < this.shape[0]) { - throw Error("Value Error: length of labels must match row shape of data"); - } - this.index_arr = labels; - } - - /** - * Generate a new index for NDFrame. - */ - __reset_index() { - let new_idx = [ ...Array(this.values.length).keys() ]; - this.index_arr = new_idx; - } - - /** - * Gets a sequence of axis dimension along row and columns - * @returns {Array} the shape of the NDFrame - */ - get shape() { - if (this.series) { - return [ this.values.length, 1 ]; - } else { - return this.row_data_tensor.shape; - } - } - - /** - * Gets the values in the NDFrame in JS array - * @returns {Array} Arrays of arrays of data instances - */ - get values() { - return this.data; - } - - /** - * Gets the column names of the data - * @returns {Array} strings of column names - */ - get column_names() { - return this.columns; - } - - /** - * Return a boolean same-sized object indicating if the values are NaN. NaN and undefined values - * gets mapped to True values. Everything else gets mapped to False values. - * @return {Array} - */ - __isna() { - let new_arr = []; - if (this.series) { - this.values.map((val) => { - // eslint-disable-next-line use-isnan - if (val == NaN) { - new_arr.push(true); - } else if (isNaN(val) && typeof val != "string") { - new_arr.push(true); - } else { - new_arr.push(false); - } - }); - } else { - let row_data = this.values; - row_data.map((arr) => { - let temp_arr = []; - arr.map((val) => { - // eslint-disable-next-line use-isnan - if (val == NaN) { - temp_arr.push(true); - } else if (isNaN(val) && typeof val != "string") { - temp_arr.push(true); - } else { - temp_arr.push(false); - } - }); - new_arr.push(temp_arr); - }); - } - return new_arr; - } - - /* - * Gets binary size of the NDFrame - * @returns {String} size of the NDFrame - */ - get size() { - return this.row_data_tensor.size; - } - - /** - * Return object data as comma-separated values (csv). - * @returns {Promise} CSV representation of Object data - */ - async to_csv() { - if (this.series) { - let csv = this.values.join(","); - return csv; - } else { - let records = this.values; - let header = this.column_names.join(","); - - let csv_str = `${header}\n`; - records.forEach((val) => { - let row = `${val.join(",")}\n`; - csv_str += row; - }); - return csv_str; - } - } - - /** - * Return object as JSON string. - * @returns {Promise } JSON representation of Object data - */ - async to_json() { - if (this.series) { - let obj = {}; - obj[this.column_names[0]] = this.values; - let json = JSON.stringify(obj); - return json; - } else { - let values = this.values; - let header = this.column_names; - let json_arr = []; - values.forEach((val) => { - let obj = {}; - header.forEach((h, i) => { - obj[h] = val[i]; - }); - json_arr.push(obj); - }); - return JSON.stringify(json_arr); - } - } - - /** - * Prints the data in a Series as a grid of row and columns - */ - toString() { - let table_width = config.get_width; - let table_truncate = config.get_truncate; - let max_row = config.get_max_row; - let max_col_in_console = config.get_max_col_in_console; - - // let data; - let data_arr = []; - let table_config = {}; - // let idx = this.index - let col_len = this.columns.length; - // let row_len = this.values.length - 1 - let header = []; - - if (col_len > max_col_in_console) { - //truncate displayed columns to fit in the console - let first_4_cols = this.columns.slice(0, 4); - let last_3_cols = this.columns.slice(col_len - 4); - //join columns with truncate ellipse in the middle - header = [ "" ].concat(first_4_cols).concat([ "..." ]).concat(last_3_cols); - - let sub_idx, values_1, value_2; - - if (this.values.length > max_row) { - //slice Object to show [max_rows] - let df_subset_1 = this.iloc({ - rows: [ `0:${max_row}` ], - columns: [ "0:4" ] - }); - let df_subset_2 = this.iloc({ - rows: [ `0:${max_row}` ], - columns: [ `${col_len - 4}:` ] - }); - sub_idx = this.index.slice(0, max_row); - values_1 = df_subset_1.values; - value_2 = df_subset_2.values; - } else { - let df_subset_1 = this.iloc({ rows: [ "0:" ], columns: [ "0:4" ] }); - let df_subset_2 = this.iloc({ - rows: [ "0:" ], - columns: [ `${col_len - 4}:` ] - }); - sub_idx = this.index.slice(0, max_row); - values_1 = df_subset_1.values; - value_2 = df_subset_2.values; - } - - // merge dfs - sub_idx.map((val, i) => { - let row = [ val ].concat(values_1[i]).concat([ "..." ]).concat(value_2[i]); - data_arr.push(row); - }); - } else { - //display all columns - header = [ "" ].concat(this.columns); - let idx, values; - if (this.values.length > max_row) { - //slice Object to show a max of [max_rows] - let data = this.loc({ rows: [ `0:${max_row}` ], columns: this.columns }); - idx = data.index; - values = data.values; - } else { - values = this.values; - idx = this.index; - } - - // merge cols - idx.forEach((val, i) => { - let row = [ val ].concat(values[i]); - data_arr.push(row); - }); - } - - //set column width of all columns - table_config[0] = 10; - for (let index = 1; index < header.length; index++) { - table_config[index] = { width: table_width, truncate: table_truncate }; - } - - let table_data = [ header ].concat(data_arr); //Adds the column names to values before printing - return table(table_data, { columns: table_config }); - } - - /** - * Pretty prints n number of rows in a DataFrame or Series in the console - * @param {rows} Number of rows to print - */ - print() { - console.log(this + ""); - } -} diff --git a/danfojs-browser/src/core/get_dummies.js b/danfojs-browser/src/core/get_dummies.js deleted file mode 100644 index 32a5391d..00000000 --- a/danfojs-browser/src/core/get_dummies.js +++ /dev/null @@ -1,139 +0,0 @@ -import { DataFrame } from "./frame"; -import { Utils } from "./utils"; - -const utils = new Utils; - -function oneHot(in_data, prefix, prefix_sep) { - let data_set = new Set(in_data); - let labels = Array.from(data_set); - let prefix_labels = null; - if (prefix) { - prefix_labels = labels.map((x) => { - return prefix + prefix_sep + x; - }); - } else { - prefix_labels = labels.map((x) => { - return x; - }); - } - - let onehot_data = utils.__zeros(in_data.length, labels.length); - - for (let i = 0; i < in_data.length; i++) { - - let elem = in_data[i]; - let elem_index = labels.indexOf(elem); - onehot_data[i][elem_index] = 1; - } - - return [ onehot_data, prefix_labels ]; - -} - -/** - * Generate one hot encoding for categorical variable in arrays |Serie | and Dataframe - * @param {kwargs} kwargs { data : Array | Series | DataFrame, - * prefix_sep: String e.g "_", - * prefix: String | Array of String, - * columns: [Array] columns to be encoded in DataFrame. - * } - */ -function get_dummy(kwargs = {}) { - utils.__in_object(kwargs, "data", "data not provided"); - - let prefix = kwargs["prefix"] || null; - let prefix_sep = kwargs["prefix_sep"] || [ "_" ]; - let columns = kwargs["columns"] || null; - - let is_dataframe = false; - let in_data = null; - - if (Array.isArray(kwargs["data"])) { - in_data = kwargs["data"]; - } else if (kwargs["data"] instanceof DataFrame) { - in_data = kwargs["data"]; - is_dataframe = true; - } else { - in_data = kwargs["data"].values; - } - - if (!is_dataframe) { - let [ onehot_data, prefix_labels ] = oneHot(in_data, prefix, prefix_sep); - - return new DataFrame(onehot_data, { columns: prefix_labels }); - } else { - - let column_index = []; - if (!columns) { - columns = []; - in_data.col_types.map((x, i) => { - - if (x == "string") { - let name_column = in_data.columns[i]; - columns.push(name_column); - column_index.push(i); - } - }); - } else { - - columns.forEach((x) => { - let col_idx = columns.indexOf(x); - column_index.push(col_idx); - }); - } - - if (prefix) { - if (Array.isArray(prefix)) { - if (prefix.length != columns.length) { - throw new Error("prefix must be the same length with the number of onehot encoding column"); - } - } else { - throw new Error("prefix for dataframe must be an array"); - } - } else { - prefix = columns; - } - - let df_data = in_data.values; - let df_columns = in_data.columns; - let col_data = in_data.col_data; - - let column_data = []; - column_index.forEach((x) => { - column_data.push(col_data[x]); - }); - - let one_hotColumns = []; - let one_hotData = []; - column_data.forEach((data, i) => { - - let [ onehot_data, prefix_labels ] = oneHot(data, prefix[i], prefix_sep); - one_hotColumns.push(...prefix_labels); - - if (one_hotData.length == 0) { - one_hotData.push(...onehot_data); - } else { - onehot_data.forEach((x, i) => { - one_hotData[i].push(...x); - }); - } - - }); - - let final_data = df_data.map((elem, i) => { - - let ele = elem.slice(); - let dt = utils.__remove_arr(ele, column_index); - dt.push(...one_hotData[i]); - return dt; - }); - - let final_columns = utils.__remove_arr(df_columns, column_index); - final_columns.push(...one_hotColumns); - - return new DataFrame(final_data, { columns: final_columns }); - } - -} - -export const get_dummies = get_dummy; diff --git a/danfojs-browser/src/core/groupby.js b/danfojs-browser/src/core/groupby.js deleted file mode 100644 index 02fcf198..00000000 --- a/danfojs-browser/src/core/groupby.js +++ /dev/null @@ -1,336 +0,0 @@ -import { DataFrame } from "./frame"; -import { Utils } from "./utils"; -import { Series } from "./series"; -const utils = new Utils; - -/** - * The class performs all groupby operation on a dataframe - * involveing all aggregate funciton - * @param {col_dict} col_dict Object of unique keys in the group by column - * @param {key_col} key_col Array contains the column names - * @param {data} Array the dataframe data - * @param {column_name} Array of all column name in the dataframe. - */ -export class GroupBy { - constructor(col_dict, key_col, data, column_name, col_dtype) { - - this.key_col = key_col; - this.col_dict = col_dict; - this.data = data; - this.column_name = column_name; - this.data_tensors = {}; //store the tensor version of the groupby data - this.col_dtype = col_dtype; - - } - - /** - * Group the dataframe by the column by - * creating an object to store the grouping - * @returns Groupby data structure - */ - group(){ - for (const value of this.data){ - const col_indexes = this.key_col.map((key) => this.column_name.indexOf(key)); - const col_values = col_indexes.map((idx) => value[idx]); - - let sub_col_dict = this.col_dict; - for (const col_value of col_values){ - if (!(col_value in sub_col_dict)) - break; - if (col_value === col_values[col_values.length - 1]) - sub_col_dict[col_value].push(value); - else - sub_col_dict = sub_col_dict[col_value]; - } - } - - const self = this; - function dfs(sub_col_dict, sub_data_tensors){ - for (const [ key, value ] of Object.entries(sub_col_dict)){ - if (Array.isArray(value)) { - if (value.length === 0) - delete sub_col_dict[key]; - else - sub_data_tensors[key] = new DataFrame(value, { columns:self.column_name }); - } else { - if (!(key in sub_data_tensors)) - sub_data_tensors[key] = {}; - dfs(value, sub_data_tensors[key]); - } - } - } - dfs(this.col_dict, this.data_tensors); - - return this; - } - - /** - * obtain the column for each group - * @param {col_name} col_name [Array]--> array of column names - * @return Groupby data structure - */ - col(col_names){ - this.selected_column = col_names; // store col_names for use later in .apply - if (Array.isArray(col_names)){ - - for (let i = 0; i < col_names.length; i++){ - - let col_name = col_names[i]; - if (!this.column_name.includes(col_name)){ - throw new Error(`Column ${col_name} does not exist in groups`); - } - } - } else { - throw new Error(`Col_name must be an array of column`); - } - - const group_col = {}; - function dfs(sub_data_tensors, sub_group_col) { - for (const [ key, value ] of Object.entries(sub_data_tensors)){ - if (value instanceof DataFrame) { - sub_group_col[key] = col_names.map((col_name) => value.column(col_name)); - } else { - sub_group_col[key] = {}; - dfs(value, sub_group_col[key]); - } - } - } - dfs(this.data_tensors, group_col); - - const gp = new GroupBy( - null, - this.key_col, - null, - col_names, - this.col_dtype - ); - gp.group_col = group_col; - gp.group_col_name = col_names; - return gp; - } - - - /** - * Basic root of all column arithemetic in groups - * @param {operation} operation String - */ - arithemetic(operation){ - const ops_name = [ "mean", "sum", "count", "mode", "std", "var", "cumsum", "cumprod", - "cummax", "cummin" ]; - const ops_map = { - "mean": "mean()", - "sum": "sum()", - "mode": "mode()", - "count": "count()", - "std" : "std()", - "var" : "var()", - "cumsum" : "cumsum().values", - "cumprod": "cumprod().values", - "cummax" : "cummax().values", - "cummin" : "cummin().values" - }; - const is_array_operation = Array.isArray(operation); - const count_group = {}; - - //the local variable to store variables to be used in eval - // this seems not to be needed in Node version, since local - //variable are easily accessed in the eval function - let local = null; - function dfs(sub_count_group, sub_group_col) { - for (const [ key, value ] of Object.entries(sub_group_col)){ - if (Array.isArray(value)) { - sub_count_group[key] = []; - let data; - if (is_array_operation) { - for (let i = 0; i < value.length; i++){ - const op = operation[i]; - if (!ops_name.includes(op)){ - throw new Error("operation does not exist"); - } - local = value[i]; - data = eval(`local.${ops_map[op]}`); - sub_count_group[key].push(data); - } - } else { - value.forEach((v) => { - local = v; - data = eval(`local.${operation}`); - sub_count_group[key].push(data); - }); - } - } else { - sub_count_group[key] = {}; - dfs(sub_count_group[key], value); - } - } - } - - dfs(count_group, this.group_col); - return count_group; - } - - operations(ops, name) { - if (!this.group_col) { - let column = this.column_name.filter((val) => !this.key_col.includes(val)); - let col_gp = this.col(column); - let value = col_gp.arithemetic(ops); - let df = col_gp.to_DataFrame(col_gp.key_col, col_gp.group_col_name, value, name); - return df; - } else { - let value = this.arithemetic(ops); - let df = this.to_DataFrame(this.key_col, this.group_col_name, value, name); - return df; - } - } - count(){ - return this.operations("count()", "count"); - } - - sum(){ - return this.operations("sum()", "sum"); - } - - std(){ - return this.operations("std()", "std"); - } - - var(){ - return this.operations("var()", "var"); - } - - mean(){ - return this.operations("mean()", "mean"); - } - - cumsum(){ - return this.operations("cumsum().values", "cumsum"); - } - cummax(){ - return this.operations("cummax().values", "cummax"); - } - - cumprod(){ - return this.operations("cumprod().values", "cumprod"); - } - - cummin(){ - return this.operations("cummin().values", "cummin"); - } - - max(){ - return this.operations("max()", "max"); - } - - min(){ - return this.operations("min()", "min"); - } - - /** - * returns dataframe of a group - * @param {*} key [Array] - */ - get_groups(key){ - if (this.key_col.length < 2) - return this.data_tensors[key]; - - if (key.length !== this.key_col.length) - throw new Error("specify the group by column"); - - utils.__is_object(this.data_tensors, key[0], `Key Error: ${key[0]} not in object`); - const last_key = key[key.length - 1]; - let sub_data_tensors = this.data_tensors; - for (const k of key) { - if (k === last_key) - return sub_data_tensors[k]; - else - sub_data_tensors = sub_data_tensors[k]; - } - } - - /** - * Map every column to an operation - * @param {kwargs} kwargs {column name: math operation} - * @example .agg({"A": "mean","B": "sum","C":"count"}) - */ - agg(kwargs = {}){ - - let columns = Object.keys(kwargs); - let operations = columns.map((x) => { return kwargs[x].toLocaleLowerCase(); }); - - let col_gp = this.col(columns); - - let data = col_gp.arithemetic(operations); - let df = this.to_DataFrame(col_gp.key_col, col_gp.group_col_name, data, operations); - - return df; - } - - to_DataFrame(key_col, col, data, ops){ - const df_data = []; - - function concatPathAndNode(path, node, col_dtype) { - if (Array.isArray(node)) { - if (Array.isArray(node[0])) { - const transposed_node = node[0].map((_, colIndex) => node.map((row) => row[colIndex])); - for (const n_array of transposed_node) - df_data.push(path.concat(n_array)); - } else - df_data.push(path.concat(node)); - } else { - for (const [ k, child ] of Object.entries(node)) { - const sanitized_k = col_dtype[0] === "string" ? k : parseInt(k); - concatPathAndNode(path.concat([ sanitized_k ]), child, col_dtype.slice(1)); - } - } - } - - concatPathAndNode([], data, this.col_dtype); - - const column = [ ...key_col ]; - const group_col = col.slice().map((x, i) => { - if (Array.isArray(ops)){ - return `${x}_${ops[i]}`; - } - return `${x}_${ops}`; - }); - column.push(...group_col); - - return new DataFrame(df_data, { columns: column }); - } - - apply(callable){ - let df_data; - let column; - if (!this.group_col) { - column = this.column_name.filter((val) => !this.key_col.includes(val)); - const col_gp = this.col(column); - df_data = col_gp.group_col; - } else { - column = this.group_col_name; - df_data = this.group_col; - } - const count_group = {}; - - function recursiveCount(sub_df_data, sub_count_group) { - for (const [ key, value ] of Object.entries(sub_df_data)) { - if (Array.isArray(value)) { - sub_count_group[key] = value.map(( callable_value ) => { - const callable_rslt = callable(callable_value); - if ((callable_rslt instanceof DataFrame) || (callable_rslt instanceof Series)) - return callable_rslt.values; - else - return callable_rslt; - }); - } else { - sub_count_group[key] = {}; - recursiveCount(value, sub_count_group[key]); - } - } - } - - recursiveCount(df_data, count_group); - - return this.to_DataFrame(this.key_col, column, count_group, "apply"); - } - -} diff --git a/danfojs-browser/src/core/indexing.js b/danfojs-browser/src/core/indexing.js deleted file mode 100644 index a9c399d2..00000000 --- a/danfojs-browser/src/core/indexing.js +++ /dev/null @@ -1,198 +0,0 @@ -import { Utils } from "./utils"; -const utils = new Utils(); - - -/** - * Obtain the defined the set of row and column index - * @param {*} kwargs object {rows:Array, columns:Array of column name, type: ["iloc","loc"]} - * @return Array - */ -export const indexLoc = (ndframe, kwargs) => { - - let rows = null; - let columns = null; - let isColumnSplit = false; - - if (Object.prototype.hasOwnProperty.call(kwargs, "rows")) { //check if the object has the key - if (Array.isArray(kwargs["rows"])) { - - if (kwargs["rows"].length == 1 && typeof kwargs["rows"][0] == "string") { - if (kwargs["rows"][0].includes(":")) { - - let column_split = kwargs["rows"][0].split(":"); - - if (kwargs['type'] == 'loc') { - //get index of first and last occurence of label - let start, end; - if (isNaN(Number(column_split[0]))) { - start = ndframe.index.indexOf(column_split[0]); - } else { - start = Number(column_split[0]); - } - - if (isNaN(Number(column_split[1]))) { - end = ndframe.index.lastIndexOf(column_split[1]) - 1 || (ndframe.values.length - 1); - } else { - end = Number(column_split[1]) - 1 || (ndframe.values.length - 1); - } - rows = utils.__range(start, end); - } else { - let start = parseInt(column_split[0]) || 0; - let end = parseInt(column_split[1]) - 1 || (ndframe.values.length - 1); - - if (typeof start == "number" && typeof end == "number") { - rows = utils.__range(start, end); - } - } - - } else { - if (kwargs["type"] == "loc") { - let row_idx = []; - ndframe.index.map((idx, i) => { - if (kwargs['rows'][0] == idx) { - row_idx.push(i); - } - }); - rows = row_idx; - } else { - throw new Error("Slice index must be separated by ':'"); - } - } - } else { - if (kwargs["type"] == "loc") { - //get all the index of specified labels - let row_idx = []; - ndframe.index.map((idx, i) => { - if (kwargs['rows'].includes(idx)) { - row_idx.push(i); - } - }); - rows = row_idx; - } else { - //return int index - rows = kwargs["rows"]; - } - } - } else { - throw new Error("rows parameter must be a Array"); - } - } else { - if (kwargs["type"] == "loc") { - rows = utils.__range(0, Number(ndframe.shape[0]) - 1); - // throw new Error("Invalid syntax, please specify a slice label") - } else { - rows = utils.__range(0, Number(ndframe.shape[0]) - 1); - } - } - - if (Object.prototype.hasOwnProperty.call(kwargs, "columns")) { - if (Array.isArray(kwargs["columns"])) { - if (kwargs["columns"].length == 1 && kwargs["columns"][0].includes(":")) { - - let column_split = kwargs["columns"][0].split(":"); - let start, end; - - if (kwargs["type"] == "iloc") { - if (column_split[0] == "") { - start = 0; - } else { - start = parseInt(column_split[0]) || 0; - } - - if (column_split[1] == "") { - end = parseInt(ndframe.columns.length - 1); - } else { - end = parseInt(column_split[1]) - 1 === 0 ? 0 : parseInt(column_split[1]) - 1; - } - - } else { - start = parseInt(ndframe.columns.indexOf(column_split[0])); - end = parseInt(ndframe.columns.indexOf(column_split[1])) - 1; - } - - if (typeof start == "number" && typeof end == "number") { - - columns = utils.__range(start, end); - isColumnSplit = true; - } - - } else { - columns = kwargs["columns"]; - } - - } else { - throw new Error("columns must be a list"); - } - } else { - //Return all column - if (kwargs["type"] == "loc") { - columns = ndframe.column_names; - } else { - columns = utils.__range(0, Number(ndframe.shape[1]) - 1); - } - } - - let data_values = ndframe.values; - let new_data = []; // store the data from the for loop - - for (var index = 0; index < rows.length; index++) { - let row_val = rows[index]; - let max_rowIndex = data_values.length - 1; //obtain the maximum row index - - if (row_val > max_rowIndex) { //check if the input row index is greater than the maximum row index - throw new Error(`Specified row index ${row_val} is bigger than maximum row index of ${max_rowIndex}`); - } - - if (Array.isArray(data_values[0])) { - - let value = data_values[row_val]; - let row_data = []; - - - for (var i in columns) { - var col_index; - if (kwargs["type"] == "loc" && !isColumnSplit) { - col_index = ndframe.columns.indexOf(columns[i]); //obtain the column index - - if (col_index == -1) { - throw new Error(`Column ${columns[i]} does not exist`); - } - } else { - col_index = columns[i]; - let max_colIndex = ndframe.columns.length - 1; //assign the maximum column index to a value - - if (col_index > max_colIndex) { - throw new Error(`column index ${col_index} is bigger than ${max_colIndex}`); - } - } - - let elem = value[col_index]; //obtain the element at the column index - row_data.push(elem); - } - - new_data.push(row_data); //store the data for each row in the new_data - - } else { - - new_data.push(data_values[row_val]); - } - - } - - let column_names = []; - if (kwargs["type"] == "iloc" || isColumnSplit) { - // let axes = ndframe.axes - columns.map((col) => { - column_names.push(ndframe.columns[col]); - }); - } else { - column_names = columns; - } - - //get index of columns - let final_row = []; - rows.forEach((i) => { - final_row.push(ndframe.index[i]); - }); - return [ new_data, column_names, final_row ]; -}; diff --git a/danfojs-browser/src/core/merge.js b/danfojs-browser/src/core/merge.js deleted file mode 100644 index 5d4a813f..00000000 --- a/danfojs-browser/src/core/merge.js +++ /dev/null @@ -1,329 +0,0 @@ -import { DataFrame } from './frame'; -import { Utils } from "./utils"; - -const utils = new Utils; -export class Merge { - - constructor(kwargs) { - - //check if keys exist in kwargs - utils.__in_object(kwargs, "left"); - utils.__in_object(kwargs, "right"); - utils.__in_object(kwargs, "on"); - // utils.__in_object(kwargs, "how") - if (!utils.__key_in_object(kwargs, "how")){ - this.how = 'inner'; - kwargs['how'] = 'inner'; - } else { - this.how = kwargs['how']; - } - - this.left = null; - this.right = null; - this.on = null; - // this.how = null; - - let how_keys = [ "outer", "inner", "left", "right" ]; - - if ((kwargs["left"] instanceof DataFrame) && (kwargs["right"] instanceof DataFrame)) { - this.left = kwargs["left"]; - this.right = kwargs["right"]; - } else { - throw new Error("The left and right key value must be a dataFrame"); - } - - if (Array.isArray(kwargs["on"])) { - this.on = kwargs["on"]; - } else { - throw new Error("key 'on' must be a list"); - } - - if (how_keys.includes(kwargs["how"])) { - - this.how = kwargs["how"]; - } else { - throw new Error(`${kwargs["how"]} specify in keyword how is not recognise`); - } - - this.left_col_index = []; - this.right_col_index = []; - - //find the index of the columns - for (let i = 0; i < this.on.length; i++) { - - if (this.left.columns.includes(this.on[i]) && this.right.columns.includes(this.on[i])) { - - let left_index = this.left.columns.indexOf(this.on[i]); - let right_index = this.right.columns.indexOf(this.on[i]); - - this.left_col_index.push(left_index); - this.right_col_index.push(right_index); - } - } - - this.left_key_dict = {}; - this.right_key_dict = {}; - - let left_values = this.left.values; - let right_values = this.right.values; - - - /** - * Create a dictionary for both left and right dataframe - containing the key combination of columns used as keys, and - the value of such keys combination are the rows having this - keys combination. - */ - for (let i = 0; i < left_values.length; i++) { - let left_value = left_values[i]; - let left_key_comb_values = []; - - //get the value in the column index - for (let j = 0; j < this.left_col_index.length; j++) { - let index = this.left_col_index[j]; - - left_key_comb_values.push(left_value[index]); - } - //combine into single hashable string - let left_key_comb = left_key_comb_values.join('_'); - - let self = this; // assign the this scope to self - //filter out the value that are not the column key - let left_value_filter = left_value.filter(function (val, index) { - return !self.left_col_index.includes(index); - }); - - //check if the key combination already exist or not - //before storing the key combination and the value - //associated with it - if (utils.__key_in_object(this.left_key_dict, left_key_comb)) { - this.left_key_dict[left_key_comb].filters.push(left_value_filter); - } else { - this.left_key_dict[left_key_comb] = { - filters: [left_value_filter], - comb_values: left_key_comb_values - }; - } - - } - for (let i = 0; i < right_values.length; i++) { - let right_value = right_values[i]; - let right_key_comb_values = []; - - for (let j = 0; j < this.right_col_index.length; j++) { - let index = this.right_col_index[j]; - - right_key_comb_values.push(right_value[index]); - } - let right_key_comb = right_key_comb_values.join('_'); - - let self = this; - let right_value_filter = right_value.filter(function (val, index) { - return !self.right_col_index.includes(index); - }); - - if (utils.__key_in_object(this.right_key_dict, right_key_comb)) { - this.right_key_dict[right_key_comb].filters.push(right_value_filter); - } else { - this.right_key_dict[right_key_comb] = { - filters: [right_value_filter], - comb_values: right_key_comb_values - }; - } - } - - - //create column - this.__create_columns(); - - let data = null; - switch (this.how) { - - case "outer": - data = this.outer(); - break; - case "inner": - data = this.inner(); - break; - case "left": - data = this.left_merge(); - break; - case "right": - data = this.right_merge(); - break; - } - - // eslint-disable-next-line no-self-assign - let df = new DataFrame(data = data, { columns: this.columns }); - - return df; - } - - __create_columns() { - - let self = this; - self.left_col = self.left.columns.filter((val, index) => { - return !self.left_col_index.includes(index); - }); - - self.right_col = self.right.columns.filter((val, index) => { - return !self.right_col_index.includes(index); - }); - - self.columns = [ ...self.on ]; - let column_duplicate = {}; - - let temp_column = [ ...self.left_col ]; - temp_column.push(...self.right_col); - - for (let i = 0; i < temp_column.length; i++) { - - let col = temp_column[i]; - if (utils.__key_in_object(column_duplicate, col)) { - - let col_name = `${col}_${column_duplicate[col]}`; - self.columns.push(col_name); - - column_duplicate[col] += 1; - } else { - self.columns.push(col); - column_duplicate[col] = 1; - } - } - } - - outer() { - - let keys = Object.keys(this.left_key_dict); // obtain the keys of the left dataframe - - keys.push(...Object.keys(this.right_key_dict)); - - keys = Array.from(new Set(keys)); // obtain the unique keys - - let data = this.basic(keys); - - return data; - - } - - inner() { - - let left_keys = Object.keys(this.left_key_dict); - let right_keys = Object.keys(this.right_key_dict); - - let keys = left_keys.filter((val) => { - return right_keys.includes(val); - }); - - let data = this.basic(keys); - - return data; - } - - left_merge() { - - let keys = Object.keys(this.left_key_dict); - let data = this.basic(keys); - - return data; - - } - - right_merge() { - let keys = Object.keys(this.right_key_dict); - let data = this.basic(keys); - - return data; - } - - basic(keys) { - - let data = []; - - for (let i = 0; i < keys.length; i++) { - let key = keys[i]; - - let key_array = key.split("_").filter((val) => { - return val != ""; - }); - - if (utils.__key_in_object(this.left_key_dict, key)) { - let left_row = this.left_key_dict[key].filters; - let key_array = this.left_key_dict[key].comb_values; - - - for (let left_i = 0; left_i < left_row.length; left_i++) { - - let left_row_row = left_row[left_i]; - - if (utils.__key_in_object(this.right_key_dict, key)) { - - let right_row = this.right_key_dict[key].filters; - - for (let r_i = 0; r_i < right_row.length; r_i++) { - - let right_row_row = right_row[r_i]; - - - let inner_data = key_array.slice(0); - inner_data.push(...left_row_row); - inner_data.push(...right_row_row); - - - data.push(inner_data); - } - } else { - let nan_array = Array(this.right_col.length); - - for (let i = 0; i < this.right_col.length; i++) { - nan_array[i] = NaN; - } - - let inner_data = key_array.slice(0); - inner_data.push(...left_row_row); - inner_data.push(...nan_array); - data.push(inner_data); - } - } - } else { - - let right_row = this.right_key_dict[key].filters; - let key_array = this.right_key_dict[key].comb_values; - - for (let i = 0; i < right_row.length; i++) { - - let right_row_row = right_row[i]; - - let nan_array = Array(this.left_col.length); - - for (let j = 0; j < nan_array.length; j++) { - nan_array[j] = NaN; - } - - let inner_data = key_array.slice(0); - inner_data.push(...nan_array); - inner_data.push(...right_row_row); - data.push(inner_data); - } - } - } - return data; - - } -} - - -/** -* Merge DataFrame or named Series objects with a database-style join. -* The join is done on columns or indexes. If joining columns on columns, the DataFrame indexes will be ignored. Otherwise -* if joining indexes on indexes or indexes on a column or columns, the index will be passed on. -* @param {kwargs}{left: DataFrame; -* right: DataFrame | Named Series, Object to merge with; -* on: label, Column or index level names to join on. These must be found in both DataFrames; -* how: {‘left’, ‘right’, ‘outer’, ‘inner’}. Type of merge to be performed. -* @returns {DataFrame} -*/ -export const merge = (kwargs) => { - let merge = new Merge(kwargs); - return merge; -}; diff --git a/danfojs-browser/src/core/series.js b/danfojs-browser/src/core/series.js deleted file mode 100644 index 25d2adc0..00000000 --- a/danfojs-browser/src/core/series.js +++ /dev/null @@ -1,1302 +0,0 @@ -/** -* Copyright 2020, JsData -* All rights reserved. -* -* This source code is licensed under the MIT license found in the -* LICENSE file in the root directory of this source tree. - -* Unless required by applicable law or agreed to in writing, software -* distributed under the License is distributed on an "AS IS" BASIS, -* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -* See the License for the specific language governing permissions and -* limitations under the License. - * -*/ - - -import * as tf from "@tensorflow/tfjs"; -import { variance, std, median, mode } from 'mathjs'; -import { Utils } from "./utils"; -import { Str } from "./strings"; -import NDframe from "./generic"; -import { table } from 'table'; -import { Configs } from '../config/config'; -import { TimeSeries } from './timeseries'; -import { Plot } from '../plotting/plot'; -import { indexLoc } from './indexing'; - - -const utils = new Utils(); -const config = new Configs(); //package wide configuration object - - -/** - * One-dimensional ndarray with axis labels (including time series). - * The object supports both integer- and label-based indexing and provides a host of methods for performing operations involving the index. - * Operations between Series (+, -, /, , *) align values based on their associated index values– they need not be the same length. - * @param {data} data Array, JSON of 1D values - * @param {kwargs} Object {columns: column names, dtypes : data type of values} - * - * @returns Series - */ -export class Series extends NDframe { - constructor(data, kwargs) { - if (Array.isArray(data[0]) || utils.__is_object(data[0])) { - data = utils.__convert_2D_to_1D(data); - super(data, kwargs); - } else { - super(data, kwargs); - } - } - - - /** - * Returns a Series in Tensorflow's tensor format - * @returns {1D Tensor} - */ - get tensor() { - return tf.tensor(this.values).asType(this.dtypes[0]); - } - - - /** - * Returns the first n values in a Series - * @param {rows} Number of rows to return - * @returns {Series} - */ - head(rows = 5) { - if (rows > this.shape[0] || rows < 1) { - //return all values - return new Series(this.values, { columns: this.column_names }); - } else { - let data = this.values.slice(0, rows); - return new Series(data, { columns: this.column_names }); - } - - } - - - /** - * Returns the last n values in a Series - * @param {rows} number of rows to return - * @returns {Series} - */ - tail(rows = 5) { - if (rows > this.values.length || rows < 1) { - //return all values - return new Series(this.values, { columns: this.column_names }); - } else { - let data = this.values.slice(this.shape[0] - rows); - let idx = this.index.slice(this.shape[0] - rows); - let sf = new Series(data, { columns: this.column_names, index: idx }); - return sf; - } - - } - - /** - * Gets [num] number of random rows in a dataframe - * @param {num} rows --> The number of rows to return - * @param {seed} seed --> (Optional) An integer specifying the random seed that will be used to create the distribution. - * @returns {Promise} resolves to a Series object - */ - async sample(num = 5, seed = 1) { - if (num > this.shape[0]) { - throw new Error("Sample size n cannot be bigger than size of dataset"); - } - if (num < -1 || num == 0) { - throw new Error("Sample size cannot be less than -1 or 0"); - } - num = num === -1 ? this.shape[0] : num; - const shuffled_index = await tf.data.array(this.index).shuffle(num, seed).take(num).toArray(); - const sf = this.iloc(shuffled_index); - return sf; - } - - /** - * Return Addition of series and other, element-wise (binary operator add). - * Equivalent to series + other - * @param {other} Series or Number to add - * @returns {Series} - */ - add(other) { - if (utils.__is_number(other)) { - //broadcast addition - let sum = this.row_data_tensor.add(other).arraySync(); - return new Series(sum, { columns: this.column_names }); - } else { - if (this.__check_series_op_compactibility) { - let sum = this.tensor.add(other.tensor).arraySync(); - return new Series(sum, { columns: this.column_names }); - } - } - } - - - /** - * Returns the subtraction between a series and other, element-wise (binary operator subtraction). - * Equivalent to series - other - * @param {other} Series, Number to subtract - * @returns {Series} - */ - sub(other) { - if (utils.__is_number(other)) { - let sub = this.tensor.sub(other).arraySync(); - return new Series(sub, { columns: this.column_names }); - } else { - if (this.__check_series_op_compactibility) { - let sub = this.tensor.sub(other.tensor).arraySync(); - return new Series(sub, { columns: this.column_names }); - } - } - } - - - /** - * Return Multiplication of series and other, element-wise (binary operator mul). - * Equivalent to series * other - * @param {other} Series, Number to multiply with. - * @returns {Series} - */ - mul(other) { - if (utils.__is_number(other)) { - let mul = this.tensor.mul(other).arraySync(); - return new Series(mul, { columns: this.column_names }); - } else { - if (this.__check_series_op_compactibility) { - let mul = this.tensor.mul(other.tensor).arraySync(); - return new Series(mul, { columns: this.column_names }); - } - } - } - - - /** - * Return division of series and other, element-wise (binary operator div). - * Equivalent to series / other - * @param {other} Series, Number to divide with. - * @returns {Series} - */ - div(other, round = true) { - if (utils.__is_number(other)) { - let div_result = this.tensor.div(other); - return new Series(div_result.arraySync(), { columns: this.column_names, dtypes: [ div_result.dtype ] }); - } else { - if (this.__check_series_op_compactibility) { - let dtype; - //Check if caller needs a float division - if (round) { - dtype = "float32"; - } else { - dtype = "int32"; - } - //dtype may change after division because of how TFJS works internally, so save dtypes first - let tensor1 = this.tensor.asType(dtype); - let tensor2 = other.tensor.asType(dtype); - let result = tensor1.div(tensor2); - return new Series(result.arraySync(), { columns: this.column_names, dtypes: [ result.dtype ] }); - } - } - } - - /** - * Return Exponential power of series and other, element-wise (binary operator pow). - * Equivalent to series ** other - * @param {other} Series, Number to multiply with. - */ - pow(other) { - if (utils.__is_number(other)) { - let pow_result = this.tensor.pow(other).arraySync(); - return new Series(pow_result, { columns: this.column_names }); - } else { - if (this.__check_series_op_compactibility) { - let pow_result = this.tensor.pow(other.tensor).arraySync(); - return new Series(pow_result, { columns: this.column_names }); - } - } - } - - /** - * Return Modulo of series and other, element-wise (binary operator mod). - * Equivalent to series % other - * @param {other} Series, Number - * @returns {Series} - */ - mod(other) { - if (utils.__is_number(other)) { - let mod_result = this.tensor.mod(other).arraySync(); - return new Series(mod_result, { columns: this.column_names }); - } else { - if (this.__check_series_op_compactibility) { - let mod_result = this.tensor.mod(other.tensor).arraySync(); - return new Series(mod_result, { columns: this.column_names }); - } - } - } - - - /** - * Returns the mean of elements in Series - * @returns {Series} - */ - mean() { - utils._throw_str_dtype_error(this, 'mean'); - let values = utils._remove_nans(this.values); - let mean = tf.tensor(values).mean().arraySync(); - return mean; - } - - - /** - * Returns the median of elements in Series - * @returns {Series} - */ - median() { - utils._throw_str_dtype_error(this, 'median'); - let values = utils._remove_nans(this.values); - let median_val = median(values); - return median_val; - } - - - /** - * Returns the modal value of elements in Series - * @returns {Number} - */ - mode() { - utils._throw_str_dtype_error(this, 'median'); - let values = utils._remove_nans(this.values); - let modal_val = mode(values); - return modal_val; - } - - - /** - * Returns the minimum value in a Series - * @returns {Number} - */ - min() { - utils._throw_str_dtype_error(this, 'min'); - let min = this.row_data_tensor.min().arraySync(); - return min; - - } - - /** - * Returns the maximum value in a Series - * @returns {Number} - */ - max() { - utils._throw_str_dtype_error(this, 'max'); - let max = this.row_data_tensor.max().arraySync(); - return max; - - } - - - /** - * Return the sum of the values in a series. - * This is equivalent to the method tf.sum - * @returns {Number}, sum of values in Series - */ - sum() { - utils._throw_str_dtype_error(this, 'sum'); - if (this.dtypes[0] == "boolean") { - let temp = utils._remove_nans(this.values); - let temp_sum = tf.tensor(temp).sum().arraySync(); - return Number(temp_sum); - } - let temp = utils._remove_nans(this.values); - let temp_sum = tf.tensor(temp).sum().arraySync(); - return Number(temp_sum.toFixed(5)); - } - - - /** - * Return number of non-null elements in a Series - * @returns {Number}, Count of non-null values - */ - count() { - return utils.__count_nan(this.values, true, true); - } - - - /** - * Return maximum of series and other, element-wise (binary operator div). - * @param {other} Series, Numbers to check maximum against - * @returns {Series} - */ - maximum(other) { - if (utils.__is_number(other)) { - let max_result = this.row_data_tensor.maximum(other); - return new Series(max_result.arraySync(), { - columns: this.column_names, - dtypes: max_result.dtype, - index: this.index - }); - } else { - if (this.__check_series_op_compactibility) { - let tensor1 = this.row_data_tensor; - let tensor2 = other.tensor; - let result = tensor1.maximum(tensor2).arraySync(); - return new Series(result, { columns: this.column_names, index: this.index }); - } - } - } - - /** - * Return maximum of series and other, element-wise (binary operator div). - * @param {other} Series, Numbers to check maximum against - * @returns {Series} - */ - minimum(other) { - if (utils.__is_number(other)) { - let max_result = this.row_data_tensor.minimum(other); - return new Series(max_result.arraySync(), { - columns: this.column_names, - dtypes: max_result.dtype, - index: this.index - }); - } else { - if (this.__check_series_op_compactibility) { - let tensor1 = this.tensor; - let tensor2 = other.tensor; - let result = tensor1.minimum(tensor2).arraySync(); - return new Series(result, { columns: this.column_names, index: this.index }); - } - } - } - - - /** - * Round each value in a Series to the given number of decimals. - * @param {dp} Number, Numbers of Decimal places to round to - * @returns {Series} - */ - round(dp) { - if (utils.__is_undefined(dp)) { - //use tensorflow round function to roound to the nearest whole number - let result = tf.round(this.row_data_tensor).arraySync(); - return new Series(result, { columns: this.column_names, index: this.index }); - - } else { - let result = utils.__round(this.values, dp, true); - return new Series(result, { columns: this.column_names, index: this.index }); - - } - - } - - /** - * Return sample standard deviation over requested axis. - * @returns {Number} - */ - std() { - utils._throw_str_dtype_error(this, 'std'); - let values = utils._remove_nans(this.values); - // TODO: Use Tensorflow ops for faster computation - let std_val = std(values); //using math.js - return std_val; - - } - - /** - * Return unbiased variance of Series. - * @returns {Number} - */ - var() { - utils._throw_str_dtype_error(this, 'std'); - let values = utils._remove_nans(this.values); - // TODO: Use Tensorflow ops for faster computation - let var_val = variance(values); //using math.js - return var_val; - - } - - /** - * Return a boolean same-sized object indicating if the values are NaN. NaN and undefined values, - * gets mapped to True values. Everything else gets mapped to False values. - * @return {Series} - */ - isna() { - let new_arr = this.__isna(); - let sf = new Series(new_arr, { - index: this.index, - columns: this.column_names, - dtypes: [ "boolean" ] - }); - return sf; - } - - /** - * Replace NaN or undefined with a specified value" - * @param {kwargs}, {"value": the new value to replace the old value with, inplace: Perform operation inplace or not} - * @return {Series} - */ - fillna(kwargs = {}) { - let params_needed = [ "value", "inplace" ]; - utils._throw_wrong_params_error(kwargs, params_needed); - - kwargs['inplace'] = kwargs['inplace'] || false; - - if (!("value" in kwargs)) { - throw Error('Value Error: Must specify value to replace with'); - } - - let new_values = []; - this.values.forEach((val) => { - if (isNaN(val) && typeof val != "string") { - new_values.push(kwargs['value']); - } else { - new_values.push(val); - } - }); - - if (kwargs['inplace']) { - this.data = new_values; - } else { - let sf = new Series(new_values, { - columns: this.column_names, - index: this.index, - dtypes: this.dtypes - }); - return sf; - } - } - - - /** - * Sort a Series in ascending or descending order by some criterion. - * @param {kwargs} Object, {ascending (Bool): Whether to return sorted values in ascending order or not, - * inplace (Bool): Whether to perform sorting on the original Series or not} - * @returns {Series} - */ - sort_values(kwargs = {}) { - let params_needed = [ "inplace", "ascending" ]; - utils._throw_wrong_params_error(kwargs, params_needed); - - if (!('ascending' in kwargs)) { - kwargs['ascending'] = true; - } - - if (!('inplace' in kwargs)) { - kwargs['inplace'] = false; - } - - let sorted_values = []; - let arr_obj = [ ...this.values ]; - let range_idx = utils.__range(0, this.index.length - 1); - let sorted_idx = utils._sort_arr_with_index(range_idx, arr_obj, this.dtypes[0]); - - sorted_idx.forEach((idx) => { - sorted_values.push(this.values[idx]); - }); - - if (kwargs['ascending']) { - sorted_values = sorted_values.reverse(); - sorted_idx = sorted_idx.reverse(); - } - - if (kwargs['inplace']) { - this.data = sorted_values; - this.__set_index(sorted_idx); - } else { - let sf = new Series(sorted_values, { columns: this.column_names, index: sorted_idx }); - return sf; - } - } - - - /** - * Makes a deep copy of a Series - * @returns {Series} - */ - copy() { - - let sf = new Series([ ...this.values ], { - columns: [ ...this.column_names ], - index: [ ...this.index ], - dtypes: [ ...this.dtypes[0] ] - }); - return sf; - } - - - /** - * Generate descriptive statistics. - * Descriptive statistics include those that summarize the central tendency, - * dispersion and shape of a dataset’s distribution, excluding NaN values. - * @returns {Series} - */ - describe() { - if (this.dtypes[0] == "string") { - return null; - } else { - - let index = [ 'count', 'mean', 'std', 'min', 'median', 'max', 'variance' ]; - let count = this.count(); - let mean = this.mean(); - let std = this.std(); - let min = this.min(); - let median = this.median(); - let max = this.max(); - let variance = this.var(); - - let vals = [ count, mean, std, min, median, max, variance ]; - let sf = new Series(vals, { columns: this.columns, index: index }); - return sf; - - } - - - } - - - /** - * Returns Series with the index reset. - * This is useful when index is meaningless and needs to be reset to the default before another operation. - * @param {kwargs} {inplace: Modify the Series in place (do not create a new object} - */ - reset_index(kwargs = {}) { - let params_needed = [ "inplace" ]; - utils._throw_wrong_params_error(kwargs, params_needed); - - kwargs['inplace'] = kwargs['inplace'] || false; - - if (kwargs['inplace']) { - this.__reset_index(); - } else { - let sf = this.copy(); - sf.__reset_index(); - return sf; - } - } - - /** - * Returns Series with the specified index. - * Set the Series index (row labels) using an array of the same length. - * @param {kwargs} {index: Array of new index values, - * inplace: If operation should happen inplace - * } - */ - set_index(kwargs = {}) { - - let params_needed = [ "index", "inplace" ]; - utils._throw_wrong_params_error(kwargs, params_needed); - - - kwargs['inplace'] = kwargs['inplace'] || false; - - if (!('index' in kwargs)) { - throw Error("Index ValueError: You must specify an array of index"); - } - - if (kwargs['index'].length != this.index.length) { - throw Error(`Index LengthError: Lenght of new Index array ${kwargs['index'].length} must match lenght of existing index ${this.index.length}`); - } - - if (kwargs['inplace']) { - this.index_arr = kwargs['index']; - } else { - let sf = this.copy(); - sf.__set_index(kwargs['index']); - return sf; - } - } - - - /** - * Checks if two series are compatible for a mathematical operation - * @param {Series} other Series to compare against - * @returns{boolean} - */ - __check_series_op_compactibility(other) { - if (utils.__is_undefined(other.series)) { - throw Error("param [other] must be a Series or a single value that can be broadcasted"); - } - if (other.values.length != this.values.length) { - throw Error("Shape Error: Series shape do not match"); - } - if (this.dtypes[0] != 'float' || this.dtypes[0] != 'int') { - throw Error(`dtype Error: Cannot perform operation on type ${this.dtypes[0]} with type ${other.dtypes[0]}`); - } - if (other.dtypes[0] != 'float' || other.dtypes[0] != 'int') { - throw Error(`dtype Error: Cannot perform operation on type ${other.dtypes[0]} with type ${this.dtypes[0]}`); - } - - return true; - } - - /** - * map all the element in a column to a variable or function - * @param{callable} callable can either be a funtion or an object - * @return {Array} - */ - map(callable) { - let is_callable = utils.__is_function(callable); - - let data = this.data.map((val) => { - if (is_callable) { - return callable(val); - } else { - if (utils.__is_object(callable)) { - - if (val in callable) { - return callable[val]; - } else { - return NaN; - } - } else { - throw new Error("callable must either be a function or an object"); - } - } - }); - let sf = new Series(data, { - columns: this.column_names, - index: this.index - }); - return sf; - } - - /** - * Applies a function to each element of a Series - * @param {Function} Function to apply to each element of the series - * @return {Array} - */ - apply(callable) { - let is_callable = utils.__is_function(callable); - - if (!is_callable) { - throw new Error("the arguement most be a function"); - } - - let data = this.data.map((val) => { - return callable(val); - }); - return new Series(data, { columns: this.column_names, index: this.index }); - } - - /** - * Returns the unique value(s) in a Series - * @return {Series} - */ - unique() { - - let data_set = new Set(this.values); - let series = new Series(Array.from(data_set)); - - return series; - - } - - /** - * Return the number of unique value in a series - * @return {int} - */ - nunique() { - return this.unique().values.length; - } - - /** - * Returns unique values and their counts in a Series - * @return {Series} - */ - value_counts() { - - let s_data = this.values; - let data_dict = {}; - - for (let i = 0; i < s_data.length; i++) { - let val = s_data[i]; - - if (val in data_dict) { - data_dict[val] += 1; - } else { - data_dict[val] = 1; - } - } - - let index = Object.keys(data_dict).map((x) => { - return parseInt(x) ? parseInt(x) : x; - }); - let data = Object.values(data_dict); - - let series = new Series(data, { index: index }); - return series; - - } - - /** - * Returns the absolute values in Series - * @return {series} - */ - abs() { - let abs_data = this.row_data_tensor.abs().arraySync(); - return new Series(utils.__round(abs_data, 2, true)); - } - - - /** - * Returns the cumulative sum over a Series - * @return {Series} - */ - cumsum() { - let data = this.__cum_ops("sum"); - return data; - } - - /** - * Returns cumulative minimum over a Series - * @returns series - */ - cummin() { - let data = this.__cum_ops("min"); - return data; - } - - /** - * Returns cumulative maximum over a Series - * @returns series - */ - cummax() { - let data = this.__cum_ops("max"); - return data; - } - - /** - * Returns cumulative product over a Series - * @returns series - */ - cumprod() { - let data = this.__cum_ops("prod"); - return data; - } - - - /** - * Returns Less than of series and other. Supports element wise operations - * @param {other} Series, Scalar - * @return {Series} - */ - lt(other) { - return this.__bool_ops(other, "lt"); - } - - /** - * Returns Greater than of series and other. Supports element wise operations - * @param {other} Series, Scalar - * @return {Series} - */ - gt(other) { - return this.__bool_ops(other, "gt"); - } - - /** - * Returns Less than or Equal to of series and other. Supports element wise operations - * @param {other} Series, Scalar - * @return {Series} - */ - le(other) { - return this.__bool_ops(other, "le"); - } - - /** - * Returns Greater than or Equal to of series and other. Supports element wise operations - * @param {other} Series, Scalar - * @return {Series} - */ - ge(other) { - return this.__bool_ops(other, "ge"); - } - - /** - * Returns Not Equal to of series and other. Supports element wise operations - * @param {other} Series, Scalar - * @return {Series} - */ - ne(other) { - return this.__bool_ops(other, "ne"); - } - - - /** - * Returns Equal to of series and other. Supports element wise operations - * @param {other} Series, Scalar - * @return {Series} - */ - eq(other) { - return this.__bool_ops(other, "eq"); - } - - /** - * Replace all occurence of a value with a new value" - * @param {kwargs}, {"replace": the value you want to replace, - * "with": the new value you want to replace the olde value with, - * inplace: Perform operation inplace or not} - * @return {Series} - */ - replace(kwargs = {}) { - let params_needed = [ "replace", "with", "inplace" ]; - utils._throw_wrong_params_error(kwargs, params_needed); - - kwargs['inplace'] = kwargs['inplace'] || false; - - if (!("replace" in kwargs)) { - throw Error("Params Error: Must specify param 'replace'"); - } - - if (!("with" in kwargs)) { - throw Error("Params Error: Must specify param 'with'"); - } - - let replaced_arr = []; - let old_arr = this.values; - - old_arr.forEach((val) => { - if (val == kwargs['replace']) { - replaced_arr.push(kwargs['with']); - } else { - replaced_arr.push(val); - } - }); - - if (kwargs['inplace']) { - this.data = replaced_arr; - } else { - let sf = new Series(replaced_arr, { - index: this.index, - columns: this.columns, - dtypes: this.dtypes - }); - return sf; - } - - } - - - /** - * Return a new Series with missing values (NaN) removed. - * @param {kwargs} {inplace: Perform operation inplace or not} - * @return {Series} - */ - dropna(kwargs = {}) { - let params_needed = [ "inplace" ]; - utils._throw_wrong_params_error(kwargs, params_needed); - - kwargs['inplace'] = kwargs['inplace'] || false; - - let old_values = this.values; - let old_index = this.index; - let new_values = []; - let new_index = []; - let isna_vals = this.isna().values; - - isna_vals.forEach((val, i) => { - if (!val) { - new_values.push(old_values[i]); - new_index.push(old_index[i]); - } - }); - if (kwargs['inplace']) { - this.index_arr = new_index; - this.data = new_values; - } else { - let sf = new Series(new_values, { - columns: this.column_names, - index: new_index, - dtypes: this.dtypes - }); - return sf; - } - - } - - /** - * Return the integer indices that would sort the Series. - * @param {ascending} boolean true: will sort the Series in ascending order, false: will sort in descending order - * @return {Series} - */ - argsort(ascending = true) { - let sorted_index = this.sort_values({ ascending: ascending }).index; - let sf = new Series(sorted_index); - return sf; - } - - /** - * Return int position of the largest value in the Series. - * @return {Number} - */ - argmax() { - return this.row_data_tensor.argMax().arraySync(); - } - - - /** - * Return int position of the smallest value in the Series. - * @param {ascending} boolean true: will sort the Series in ascending order, false: will sort in descending order - * @return {Series} - */ - argmin() { - return this.row_data_tensor.argMin().arraySync(); - - } - - - /** - * Returns dtype of Series - * @return {string} - */ - get dtype() { - return this.dtypes[0]; - } - - /** - * Return Series with duplicate values removed - * @param {kwargs} {inplace: Perform operation inplace or not, - * keep: first | last which dupliate value to keep} - * @return {Series} - */ - drop_duplicates(kwargs = {}) { - let params_needed = [ "inplace", "keep" ]; - utils._throw_wrong_params_error(kwargs, params_needed); - - kwargs['inplace'] = kwargs['inplace'] || false; - kwargs['keep'] = kwargs['keep'] || "first"; - - - let data_arr, old_index; - if (kwargs['keep'] == "last") { - data_arr = this.values.reverse(); - old_index = this.index.reverse(); - } else { - data_arr = this.values; - old_index = this.index; - } - - let new_index = []; - let new_arr = []; - - data_arr.forEach((val, i) => { - if (!new_arr.includes(val)) { - new_index.push(old_index[i]); - new_arr.push(val); - } - }); - - if (kwargs['keep'] == "last") { - //re-reversed the array and index to its true ordering - new_arr = new_arr.reverse(); - new_index = new_index.reverse(); - } - - if (kwargs['inplace']) { - this.data = new_arr; - this.index_arr = new_index; - } else { - let sf = new Series(new_arr, { - index: new_index, - columns: this.column_names, - dtypes: this.dtypes - }); - return sf; - } - - } - - - /** - * Prints the data in a Series as a grid of row and columns - */ - toString() { - let table_width = 20; - let table_truncate = 20; - let max_row = config.get_max_row; - let data_arr = []; - let table_config = {}; - let header = [ "" ].concat(this.columns); - let idx, data; - - if (this.values.length > max_row) { - //slice Object to show a max of [max_rows] - data = this.values.slice(0, max_row); - idx = this.index.slice(0, max_row); - } else { - data = this.values; - idx = this.index; - } - - idx.forEach((val, i) => { - let row = [ val ].concat(data[i]); - data_arr.push(row); - }); - - //set column width of all columns - table_config[0] = 10; - table_config[1] = { width: table_width, truncate: table_truncate }; - - let table_data = [ header ].concat(data_arr); //Add the column names to values before printing - return table(table_data, { columns: table_config }); - } - - /** - * Perform boolean operations on bool values - * @param {*} other Other series to compare with - * @param {string} b_ops name of operation to perform [ne, ge, le, gt, lt, eq] - */ - __bool_ops(other, b_ops) { - let r_series; - let l_series = this.values; - - if (typeof other == "number") { - r_series = [ ...l_series ].fill(other); //create array of repeated value for broadcasting - } else { - if (!(other instanceof Series)) { - throw new Error("Value Error: 'other' must be an instance of Series"); - } - r_series = other.values; - } - - if (!(l_series.length === r_series.length)) { - throw new Error("Length Error: Both series must be of the same length"); - } - - let data = []; - - for (let i = 0; i < l_series.length; i++) { - - let l_val = l_series[i]; - let r_val = r_series[i]; - let bool = null; - switch (b_ops) { - - case "lt": - bool = l_val < r_val ? true : false; - data.push(bool); - break; - case "gt": - bool = l_val > r_val ? true : false; - data.push(bool); - break; - case "le": - bool = l_val <= r_val ? true : false; - data.push(bool); - break; - case "ge": - bool = l_val >= r_val ? true : false; - data.push(bool); - break; - case "ne": - bool = l_val != r_val ? true : false; - data.push(bool); - break; - case "eq": - bool = l_val === r_val ? true : false; - data.push(bool); - break; - } - } - return new Series(data); - - } - - /** - * perform cumulative operation on series data - * @returns array - */ - __cum_ops(ops) { - - let s_data = this.values; - let temp_val = s_data[0]; - let data = [ temp_val ]; - - for (let i = 1; i < s_data.length; i++) { - - let curr_val = s_data[i]; - switch (ops) { - case "max": - if (curr_val > temp_val) { - data.push(curr_val); - temp_val = curr_val; - } else { - data.push(temp_val); - } - break; - case "min": - if (curr_val < temp_val) { - data.push(curr_val); - temp_val = curr_val; - } else { - data.push(temp_val); - } - break; - case "sum": - temp_val = temp_val + curr_val; - data.push(temp_val); - break; - case "prod": - temp_val = temp_val * curr_val; - data.push(temp_val); - break; - - } - } - return new Series(data); - } - - - /** - * Cast Series to specified data type - * @param {string} dtype data type to cast to [float32, int32, string, boolean] - *@returns {Series} - */ - astype(dtype) { - const __supported_dtypes = [ 'float32', "int32", 'string', 'boolean' ]; - - if (!dtype) { - throw Error("Value Error: Please specify dtype to cast to"); - } - - if (!__supported_dtypes.includes(dtype)) { - throw Error(`dtype ${dtype} not supported. dtype must be one of ${__supported_dtypes}`); - } - - let col_values = this.values; - let new_values = []; - - switch (dtype) { - case "float32": - col_values.forEach((val) => { - new_values.push(Number(val)); - }); - break; - case "int32": - col_values.forEach((val) => { - new_values.push(Number(Number(val).toFixed())); - }); - break; - case "string": - col_values.forEach((val) => { - new_values.push(String(val)); - }); - break; - case "boolean": - col_values.forEach((val) => { - new_values.push(Boolean(val)); - }); - break; - default: - break; - } - - let sf = new Series(new_values, { dtypes: dtype, index: this.index }); - return sf; - - } - - - /** - * Exposes numerous string methods to manipulate Series of type string - */ - get str() { - if (this.dtypes[0] == "string") { - return new Str(this); - } else { - throw new Error("Cannot call accessor str on non-string type"); - } - - } - - /** - * Returns Danfo Time Object that exposes different time properties - */ - get dt() { - let timeseries = new TimeSeries({ data: this }); // parsed to date-time - timeseries.preprocessed(); - return timeseries; - - } - - /** - * Displays the data in a console friendly manner - */ - print() { - console.log(this + ""); - } - - - /** - * Make plots of Series or DataFrame. - * Uses the Plotly as backend, so therefore supports Plotly's configuration parameters - * @param {string} div Name of the div to show the plot - * @returns {Class} Plot class that expoese different plot type - */ - plot(div) { - const plt = new Plot(this, div); - return plt; - } - - /** - * Slice series by index - * @param {Array} row list of index to slice by - * @returns Series - */ - iloc(row) { - let kwargs = {}; - kwargs["rows"] = row; - kwargs["type"] = "iloc"; - - let [ new_data, columns, rows ] = indexLoc(this, kwargs); - let sf = new Series(new_data, { columns: columns, index: rows }); - - return sf; - } - - /** - * Adds new values to the end of a Series - * @param {Object} val Single value | Array | Series to append to the object - * @param {Boolean} inplace Whether to perform operation inplace or not - */ - append(val, inplace = false) { - if (inplace) { - let self = this; - if (Array.isArray(val)) { - val.forEach((el, i) => { - self.data.push(el); - self.index_arr.push(i); - }); - } else if (val instanceof Series) { - let value = val.values; - let old_index = val.index; - value.forEach((el, i) => { - self.data.push(el); - self.index_arr.push(old_index[i]); - }); - } else { - self.data.push(val); - self.index_arr.push(0); - } - } else { - let sf = this.copy(); - if (Array.isArray(val)) { - val.forEach((el, i) => { - sf.data.push(el); - sf.index_arr.push(i); - }); - } else if (val instanceof Series) { - let value = val.values; - let old_index = val.index; - value.forEach((el, i) => { - sf.data.push(el); - sf.index_arr.push(old_index[i]); - }); - } else { - sf.data.push(val); - sf.index_arr.push(0); - } - return sf; - } - - } - -} - diff --git a/danfojs-browser/src/core/strings.js b/danfojs-browser/src/core/strings.js deleted file mode 100644 index e767e0ee..00000000 --- a/danfojs-browser/src/core/strings.js +++ /dev/null @@ -1,423 +0,0 @@ -import { Series } from "./series"; - -/** - * String methods applied on Series and DataFrames - */ -export class Str { - constructor(series) { - this.series = series; - this.array = series.values; - } - - /** - * Converts all characters to lowercase. - * @return {array} - */ - toLowerCase() { - let new_arr = []; - this.array.map((val) => { - if (isNaN(val) && typeof val != "string") { - new_arr.push(val); - } else { - new_arr.push(val.toLowerCase()); - } - - }); - let sf = this.__create_new_sf_from(new_arr, this.series); - return sf; - } - - /** - * Converts all characters to uppercase. - * @return {array} - */ - toUpperCase() { - let new_arr = []; - this.array.map((val) => { - if (isNaN(val) && typeof val != "string") { - new_arr.push(val); - } else { - new_arr.push(val.toUpperCase()); - } - - }); - let sf = this.__create_new_sf_from(new_arr, this.series); - return sf; - } - - /** - * Capitalize first string - * @return {array} - */ - capitalize() { - let new_arr = []; - this.array.map((val) => { - if (isNaN(val) && typeof val != "string") { - new_arr.push(val); - } else { - let f_char = val.slice(0, 1); - let l_char = val.slice(1); - let new_str = `${f_char.toUpperCase()}${l_char.toLowerCase()}`; - new_arr.push(new_str); - } - - }); - let sf = this.__create_new_sf_from(new_arr, this.series); - return sf; - - } - - /** - * Returns the character at the specified index (position) - * @params {index} index position of character - * @return {array} - */ - charAt(index = 0) { - let new_arr = []; - this.array.map((val) => { - if (isNaN(val) && typeof val != "string") { - new_arr.push(val); - } else { - new_arr.push(val.charAt(index)); - } - }); - let sf = this.__create_new_sf_from(new_arr, this.series); - return sf; - } - - /** - * Joins two or more strings/arrays. 0 joins from the start - * @params {other} string|array to concatenate with. - * @params {position} where to concat the string from. O concats from the start, 1 concats from the end - * @params {isArray} whether operation is performed on Array or not - * @return {array} - */ - concat(other = "", position = 1) { - if (Array.isArray(other)) { - let final_arr = []; - for (let i = 0; i < other.length; i++) { - let l_str = this.array[i]; - let r_str = other[i]; - if (position == 1) { - final_arr.push(l_str.concat(r_str)); - } else { - final_arr.push(r_str.concat(l_str)); - } - - } - let sf = this.__create_new_sf_from(final_arr, this.series); - return sf; - } else { - - let new_arr = []; - - this.array.map((val) => { - if (position == 1) { - if (isNaN(val) && typeof val != "string") { - new_arr.push(String(val).concat(other)); - } else { - new_arr.push(val.concat(other)); - } - - } else { - if (isNaN(val) && typeof val != "string") { - new_arr.push(other.concat(String(val))); - } else { - new_arr.push(other.concat(val)); - } - } - }); - let sf = this.__create_new_sf_from(new_arr, this.series); - return sf; - } - - } - - - /** - * Checks whether a string begins with specified characters - * @params {String | Character} String or Character to check against - * @return {array} - */ - startsWith(str = "") { - let new_arr = []; - this.array.forEach((val) => { - if (isNaN(val) && typeof val != "string") { - new_arr.push(false); - } else { - new_arr.push(val.startsWith(str)); - } - }); - let sf = this.__create_new_sf_from(new_arr, this.series); - return sf; - } - - /** - * Checks whether a string ends with specified characters - * @params {String | Character} String or Character to check against - * @return {array} - */ - endsWith(str = "") { - let new_arr = []; - this.array.map((val) => { - if (isNaN(val) && typeof val != "string") { - new_arr.push(false); - } else { - new_arr.push(val.endsWith(str)); - } - }); - let sf = this.__create_new_sf_from(new_arr, this.series); - return sf; - } - - /** - * Checks whether a string contains the specified string/characters - * @params {String | Character} String or Character to check against - * @return {array} - */ - includes(str = "") { - let new_arr = []; - this.array.map((val) => { - if (isNaN(val) && typeof val != "string") { - new_arr.push(false); - } else { - new_arr.push(val.includes(str)); - } - }); - let sf = this.__create_new_sf_from(new_arr, this.series); - return sf; - } - - /** - * Returns the position of the first found occurrence of a specified value in a string - * @params {String | Character} String or Character to check against - * @return {array} - */ - indexOf(str = "") { - let new_arr = []; - this.array.map((val) => { - if (isNaN(val) && typeof val != "string") { - new_arr.push(-1); - } else { - new_arr.push(val.indexOf(str)); - } - }); - let sf = this.__create_new_sf_from(new_arr, this.series); - return sf; - } - - /** - * Returns the position of the last found occurrence of a specified value in a string - * @params {str: String | Character} String or Character to check against - * @return {array} - */ - lastIndexOf(str = "") { - let new_arr = []; - this.array.map((val) => { - if (isNaN(val) && typeof val != "string") { - new_arr.push(-1); - } else { - new_arr.push(val.lastIndexOf(str)); - } - }); - let sf = this.__create_new_sf_from(new_arr, this.series); - return sf; - } - - - /** - * Searches a string for a specified value, or a regular expression, and returns a new string where the specified values are replaced - * @params {searchValue: String | Character} string value to replace - * @params {replaceValue: String | Character} string to replace with - * @return {array} - */ - replace(searchValue = "", replaceValue = "") { - let new_arr = []; - this.array.map((val) => { - if (isNaN(val) && typeof val != "string") { - new_arr.push(val); - } else { - new_arr.push(val.replace(searchValue, replaceValue)); - } - }); - let sf = this.__create_new_sf_from(new_arr, this.series); - return sf; - } - - /** - * Returns a new string with a specified number of copies of an existing string - * @params {num: Integer} Number of times to repeat - * @return {array} - */ - repeat(num = 1) { - let new_arr = []; - this.array.map((val) => { - if (isNaN(val) && typeof val != "string") { - new_arr.push(val); - } else { - new_arr.push(val.repeat(num)); - } - }); - let sf = this.__create_new_sf_from(new_arr, this.series); - return sf; - } - - - /** - * Searches a string for a specified value, or regular expression, and returns the position of the match - * @params {str: String | Character} String or Character to check against - * @return {array} - */ - search(str = "") { - let new_arr = []; - this.array.map((val) => { - if (isNaN(val) && typeof val != "string") { - new_arr.push(-1); - } else { - new_arr.push(val.search(str)); - } - }); - let sf = this.__create_new_sf_from(new_arr, this.series); - return sf; - } - - /** - * Extracts a part of a string and returns a new string - * @params {startIndex: Int} index position of start character - * @params {endIndex: Int} index position of last character - * @return {array} - */ - slice(startIndex = 0, endIndex = 1) { - let new_arr = []; - this.array.map((val) => { - if (isNaN(val) && typeof val != "string") { - new_arr.push(val); - } else { - new_arr.push(val.slice(startIndex, endIndex)); - } - }); - let sf = this.__create_new_sf_from(new_arr, this.series); - return sf; - } - - /** - * Splits a string into an array of substrings - * @params {val: string} string or character to split at - * @params {endIndex: Int} index position of last character - * @return {array} - */ - split(splitVal = " ") { - let new_arr = []; - this.array.map((val) => { - if (isNaN(val) && typeof val != "string") { - new_arr.push(val); - } else { - new_arr.push(val.split(splitVal)); - } - }); - let sf = this.__create_new_sf_from(new_arr, this.series); - return sf; - } - - /** - * Extracts the characters from a string, beginning at a specified start position, and through the specified number of character - * @params {startIndex: Int} index position of start character - * @params {num: Int} number of characters to return - * @return {array} - */ - substr(startIndex = 0, num = 1) { - let new_arr = []; - this.array.map((val) => { - if (isNaN(val) && typeof val != "string") { - new_arr.push(val); - } else { - new_arr.push(val.substr(startIndex, num)); - } - }); - let sf = this.__create_new_sf_from(new_arr, this.series); - return sf; - } - - /** - * Extracts the characters from a string, between two specified indices - * @params {startIndex: Int} index position of start character - * @params {endIndex: Int} index position of last character - * @return {array} - */ - substring(startIndex = 0, endIndex = 1) { - let new_arr = []; - this.array.map((val) => { - if (isNaN(val) && typeof val != "string") { - new_arr.push(val); - } else { - new_arr.push(val.substring(startIndex, endIndex)); - } - }); - let sf = this.__create_new_sf_from(new_arr, this.series); - return sf; - } - - /** - * Removes whitespace from both ends of a string - * @return {array} - */ - trim() { - let new_arr = []; - this.array.map((val) => { - if (isNaN(val) && typeof val != "string") { - new_arr.push(val); - } else { - new_arr.push(val.trim()); - } - }); - let sf = this.__create_new_sf_from(new_arr, this.series); - return sf; - } - - /** - * Joins strings to specified value - * @params {valToJoin} string value to join to the array - * @params {joinChar} Character to Join with - * @return {array} - */ - join(valToJoin = "", joinChar = " ") { - let new_arr = []; - this.array.map((val) => { - if (isNaN(val) && typeof val != "string") { - new_arr.push(val); - } else { - let l_char = val; - let r_char = valToJoin; - let new_char = `${l_char}${joinChar}${r_char}`; - new_arr.push(new_char); - } - }); - let sf = this.__create_new_sf_from(new_arr, this.series); - return sf; - } - - /** - * Counts the number of characters in string - * @return {array} - */ - len() { - let new_arr = []; - this.array.map((val) => { - if (isNaN(val) && typeof val != "string") { - new_arr.push(val); - } else { - new_arr.push(val.length); - } - }); - let sf = this.__create_new_sf_from(new_arr, this.series); - return sf; - } - - //create a new series - __create_new_sf_from(new_val, series) { - let sf = new Series(new_val, { columns: series.column_names, index: series.index }); - return sf; - } - - -} diff --git a/danfojs-browser/src/core/timeseries.js b/danfojs-browser/src/core/timeseries.js deleted file mode 100644 index 1d8af672..00000000 --- a/danfojs-browser/src/core/timeseries.js +++ /dev/null @@ -1,305 +0,0 @@ -import { Utils } from "./utils"; -import { Series } from "./series"; -const utils = new Utils; - - -/** - * @class - * @description Handle all datetime operations - * @param {kwargs} Object {"data":[array of string], "format": string} - */ -export class TimeSeries { - constructor(kwargs) { - - utils.__in_object(kwargs, "data", "specify the data"); - - if (kwargs["data"] instanceof Series) { - this.data = kwargs["data"].values; - } else { - this.data = kwargs["data"]; - } - - this.format = kwargs["format"] || null; - - this.keys = { //key: len - "Y": 4, - "m": 2, - "H": 2, - "M": 2, - "S": 2, - "b": 3, - "d": 2, - "-": 1 - }; - - this.__in_format = [ "%Y-m-d%", "%m-d-Y%", "%m-d-Y H%M%S%" ]; - - this.__monthName = [ "Jan", "Feb", "Mar", "Apr", "May", "Jun", "Jul", "Aug", "Sep", "Oct", "Nov", "Dec" ]; - this.__weekName = [ "Sun", "Mon", "Tue", "Wed", "Thu", "Fri", "Sat" ]; - - } - - /** - * @description preprocessed the data into desirable structure - */ - preprocessed() { - - let format_values = null; - if (this.format) { - format_values = this.generate_format(); - } - - this.date_list = []; - - for (let i = 0; i < this.data.length; i++) { - - let date_string = this.data[i]; - - if (this.format && !this.__in_format.includes(this.format)) { - - let format_dateString = this.__apply_format(date_string, format_values); - let valueDate = new Date(format_dateString); - - this.__is_validDate(valueDate); - - this.date_list.push(valueDate); - - } else if (this.is_timestamp(date_string)) { - - let string2int = parseInt(date_string); - let valueDate = new Date(string2int); - - this.__is_validDate(valueDate); - this.date_list.push(valueDate); - } else { - let valueDate = new Date(date_string); - - this.__is_validDate(valueDate); - this.date_list.push(valueDate); - } - } - - // return this.date_list; - - } - - /** - * @description if format is given, apply the format on each element of the data - * @return string - */ - __apply_format(elem, format) { - - let date_string = ""; - - let temp_val = 0; - - - for (let index in format) { - - let value = format[index]; - - if (index == 0) { - date_string += elem.slice(0, value); - - } else if (index > 4) { - - date_string += ":" + elem.slice(temp_val, temp_val + value); - } else if (index == 4) { - date_string += " " + elem.slice(temp_val, temp_val + value); - } else if (index > 0 && index <= 2) { - date_string += "-" + elem.slice(temp_val, temp_val + value); - } - - temp_val += value; - } - - return date_string; - - } - /** - * @description convert format string to their respective value. - */ - generate_format() { - - let format_list = this.format.split(""); - - let self = this; - let format_keys = format_list.filter(function (key) { - return utils.__key_in_object(self.keys, key); - }); - - let format_value = format_keys.map(function (val) { - return self.keys[val]; - }); - - - return format_value; - } - - /** - * @description check if a string is a timestamp - * @param {date_string} date_string [string] - */ - is_timestamp(date_string) { - - let string2int = parseInt(date_string); - let int2string = String(string2int); - - if (isNaN(string2int) || (int2string.length < date_string.length)) { - return false; - } else { - return true; - } - } - - /** - * @description check if a date instance returns Invalid date - * @param {date_instance} instance of new Date() - */ - __is_validDate(date_instance) { - - if (date_instance.toDateString() == "Invalid Date") { - throw new Error("Invalid date, the date format not recognise"); - } - } - - /** - * @description abstract all date operations - * @param {*} callback [FUNCTION] - * @return series - */ - __date_ops(callback) { - - let data = this.date_list.map(function (date_instance) { - - return callback(date_instance); - }); - - // eslint-disable-next-line no-self-assign - let series = new Series(data = data); - - return series; - } - - /** - * @description obtain the month in a date. - * @return Series - */ - month() { - - let series = this.__date_ops(function (date_instance) { - return date_instance.getMonth(); - }); - - return series; - } - - /** - * @return Series - */ - hour() { - let series = this.__date_ops(function (date_instance) { - return date_instance.getHours(); - }); - - return series; - } - - /** - * @return Series - */ - day() { - let series = this.__date_ops(function (date_instance) { - return date_instance.getDay(); - }); - - return series; - } - - /** - * @description generate year frome date instance - * @return Series - */ - year() { - - let series = this.__date_ops(function (date_instance) { - return date_instance.getFullYear(); - }); - - return series; - } - - /** - * @description generate month name - * @return Series - */ - month_name() { - - let self = this; - let series = this.__date_ops(function (date_instance) { - return self.__monthName[date_instance.getMonth()]; - }); - - return series; - } - - /** - * @description generate days of the week - * @return Series - */ - weekdays() { - let self = this; - let series = this.__date_ops(function (date_instance) { - return self.__weekName[date_instance.getDay()]; - }); - - return series; - } - - /** - * @description day of the month - * @return Series - */ - monthday() { - let series = this.__date_ops(function (date_instance) { - return date_instance.getDate(); - }); - - return series; - } - - /** - * @description obtain the seconds in a date - * @return Series - */ - seconds() { - let series = this.__date_ops(function (date_instance) { - return date_instance.getSeconds(); - }); - - return series; - } - - /** - * @description obtain the minutes in a date - * @return Series - */ - minutes() { - let series = this.__date_ops(function (date_instance) { - return date_instance.getMinutes(); - }); - - return series; - } - -} - - -export const to_datetime = (kwargs) => { - - let timeseries = new TimeSeries(kwargs); // parsed to date-time - timeseries.preprocessed(); // generate date-time list - - return timeseries; - -}; diff --git a/danfojs-browser/src/core/utils.js b/danfojs-browser/src/core/utils.js deleted file mode 100644 index 7b6c9475..00000000 --- a/danfojs-browser/src/core/utils.js +++ /dev/null @@ -1,688 +0,0 @@ -import { tensor, linspace } from "@tensorflow/tfjs"; -import { Configs } from "../config/config"; - -const config = new Configs(); - -export class Utils { - //remove an element from an array - remove(arr, index) { - let new_arr = arr.filter(function (val, i) { - return i != index; - }); - - return new_arr; - } - - //filter element present in an aray - __remove_arr(arr, index) { - let new_arr = arr.filter(function (val, i) { - return !index.includes(i); - }); - - return new_arr; - } - - // Returns if a value is a string - __is_string(value) { - return typeof value === "string" || value instanceof String; - } - - // Returns if a value is really a number - __is_number(value) { - return typeof value === "number" && isFinite(value); - } - - // Returns if a value is an object - __is_object(value) { - return value && typeof value === "object" && value.constructor && value.constructor.name === "Object"; - } - - // Returns if a value is null - __is_null(value) { - return value === null; - } - - // Returns if a value is undefined - __is_undefined(value) { - return typeof value === "undefined"; - } - - /** - * Optimized version of random sampling from an array, as implemented in Python - * - * - Chooses k unique random elements from a population sequence or set. - - Returns a new list containing elements from the population while - leaving the original population unchanged. The resulting list is - in selection order so that all sub-slices will also be valid random - samples. This allows raffle winners (the sample) to be partitioned - into grand prize and second place winners (the subslices). - - Members of the population need not be hashable or unique. If the - population contains repeats, then each occurrence is a possible - selection in the sample. - - To choose a sample in a range of integers, use range as an argument. - This is especially fast and space efficient for sampling from a - large population: sample(range(10000000), 60) - - Sampling without replacement entails tracking either potential - selections (the array) in a list or previous selections in a set. - - When the number of selections is small compared to the - population, then tracking selections is efficient, requiring - only a small set and an occasional reselection. For - a larger number of selections, the array tracking method is - preferred since the list takes less space than the - set and it doesn't suffer from frequent reselections. - * - * @param {*} array The array to sample values from randomly - * @param {*} num The number of elements to sample randomly - */ - // Chooses k unique random elements from array. - __sample_from_iter(array, k, destructive) { - var n = array.length; - - if (k < 0 || k > n) - throw new RangeError("Sample larger than population or is negative"); - - if ( - destructive || - n <= (k <= 5 ? 21 : 21 + Math.pow(4, Math.ceil(Math.log(k * 3, 4)))) - ) { - if (!destructive) array = Array.prototype.slice.call(array); - for (var i = 0; i < k; i++) { - // invariant: non-selected at [i,n) - var j = (i + Math.random() * (n - i)) | 0; - var x = array[i]; - array[i] = array[j]; - array[j] = x; - } - array.length = k; // truncate - return array; - } else { - var selected = new Set(); - // eslint-disable-next-line no-empty - while (selected.add((Math.random() * n) | 0).size < k) { } - // eslint-disable-next-line no-undef - return Array.prototype.map.call(selected, (i) => population[i]); - } - } - - //generate integers between two set of numbers - __range(start, end) { - let value = linspace(start, end, end - start + 1).arraySync(); - return value; - } - - //check if key is in object - __key_in_object(object, key) { - if (Object.prototype.hasOwnProperty.call(object, key)) { - return true; - } else { - return false; - } - } - - //transpose row array into column wise array - __get_col_values(data) { - let row_len = data.length; - let cols_len = data[0].length; - var cols_arr = []; - for (var i = 0; i <= cols_len - 1; i++) { - let temp_col = []; - for (let j = 0; j < row_len; j++) { - temp_col.push(data[j][i]); - } - cols_arr.push(temp_col); - } - return cols_arr; - } - - /** - * retrieve row array and column names from object of the form {a: [1,2,3,4], b: [30,20, 30, 20}] - * @param {*} data - */ - _get_row_and_col_values(data) { - let col_names = Object.keys(data); - let col_data = Object.values(data); - let first_col_len = col_data[0].length; - - col_data.forEach((data) => { - if (data.length != first_col_len) { - throw Error("Length Error: Length of columns must be the same!"); - } - }); - let rows_len = col_data[0].length; - let cols_len = col_names.length; - var rows_arr = []; - - for (var i = 0; i <= rows_len - 1; i++) { - var temp_row = []; - for (let j = 0; j < cols_len; j++) { - let _arr = col_data[j]; - temp_row.push(_arr[i]); - } - rows_arr.push(temp_row); - } - return [ rows_arr, col_names ]; - } - - //converts a 2D array of array to 1D for Series Class - __convert_2D_to_1D(data) { - let new_data = []; - data.map((val) => { - if (this.__is_object(val)) { - new_data.push(JSON.stringify(val)); - } else { - new_data.push(`${val}`); - } - }); - return new_data; - } - - __replace_undefined_with_NaN(data, isSeries) { - if (isSeries) { - let temp_arr = []; - data.forEach((val) => { - if (typeof val === "undefined" || val == Infinity || val == null) { - temp_arr.push(NaN); - } else { - temp_arr.push(val); - } - }); - return temp_arr; - } else { - let full_arr = []; - data.forEach((val) => { - var temp_arr = []; - val.forEach((ele) => { - if (typeof ele === "undefined" || ele == Infinity || ele == null) { - temp_arr.push(NaN); - } else { - temp_arr.push(ele); - } - }); - full_arr.push(temp_arr); - }); - return full_arr; - } - } - - __checker(arr_val) { - let dtypes = []; - let lim; - let int_tracker = []; - let float_tracker = []; - let string_tracker = []; - let bool_tracker = []; - - if (arr_val.length == 0) { - dtypes.push("string"); - } - - if (arr_val.length < config.get_dtype_test_lim) { - lim = arr_val.length - 1; - } else { - lim = config.get_dtype_test_lim - 1; - } - arr_val.forEach((ele, indx) => { - let count = indx; - if (typeof ele == "boolean") { - float_tracker.push(false); - int_tracker.push(false); - string_tracker.push(false); - bool_tracker.push(true); - } else if (isNaN(ele) && typeof ele != "string") { - float_tracker.push(true); - int_tracker.push(false); - string_tracker.push(false); - bool_tracker.push(false); - } else if (!isNaN(Number(ele))) { - if (ele.toString().includes(".")) { - float_tracker.push(true); - int_tracker.push(false); - string_tracker.push(false); - bool_tracker.push(false); - } else { - float_tracker.push(false); - int_tracker.push(true); - string_tracker.push(false); - bool_tracker.push(false); - } - } else { - float_tracker.push(false); - int_tracker.push(false); - string_tracker.push(true); - bool_tracker.push(false); - } - - if (count == lim) { - //if atleast one string appears return string dtype - const even = (element) => element == true; - if (string_tracker.some(even)) { - dtypes = "string"; - } else if (float_tracker.some(even)) { - dtypes = "float32"; - } else if (int_tracker.some(even)) { - dtypes = "int32"; - } else if (bool_tracker.some(even)) { - dtypes = "boolean"; - } else { - dtypes = "undefined"; - } - } - }); - - return dtypes; - } - //infer types from an array of array - __get_t(arr_val) { - const self = this; - if (this.__is_1D_array(arr_val)) { - return [ this.__checker(arr_val) ]; - } else { - const dtypes = arr_val.map((arr) => { - return self.__checker(arr); - }); - return dtypes; - } - } - - - __unique(data) { - let unique = new Set(); - - data.map(function (val) { - unique.add(val[0]); - }); - - let unique_array = Array.from(unique); - - return unique_array; - } - - //second version of In object - __in_object(object, key, message) { - if (!Object.prototype.hasOwnProperty.call(object, key)) { - throw new Error(message); - } - } - - //check if a array is 1D - __is_1D_array(arr) { - if ( - typeof arr[0] == "number" || - typeof arr[0] == "string" || - typeof arr[0] == "boolean" - ) { - return true; - } else { - return false; - } - } - - //converts an array to object with index as value - __arr_to_obj(arr) { - let arr_map = {}; - arr.forEach((ele, i) => { - arr_map[ele] = i; - }); - return arr_map; - } - - /** - * count the NaN and non-NaN values present in an array - * @param {Array} arr Array object - * @param {Boolean} val whether to return the value count instead of the null count - * @param {Boolean} isSeries Whether the Obj is of type series or not - */ - __count_nan(arr, return_val = true, isSeries) { - if (isSeries) { - let null_count = 0; - let val_count = 0; - arr.forEach((ele) => { - if (Number.isNaN(ele)) { - null_count = null_count + 1; - } else { - val_count = val_count + 1; - } - }); - if (return_val) { - return val_count; - } else { - return null_count; - } - } else { - let result_arr = []; - arr.forEach((ele_arr) => { - let null_count = 0; - let val_count = 0; - ele_arr.forEach((ele) => { - if (Number.isNaN(ele)) { - null_count = null_count + 1; - } else { - val_count = val_count + 1; - } - }); - if (return_val) { - result_arr.push(val_count); - } else { - result_arr.push(null_count); - } - }); - return result_arr; - } - } - - //computes the median of an array - __median(arr, isSeries) { - if (isSeries) { - const sorted = arr.slice().sort((a, b) => a - b); - const middle = Math.floor(sorted.length / 2); - - if (sorted.length % 2 === 0) { - return (sorted[middle - 1] + sorted[middle]) / 2; - } - - return sorted[middle]; - } else { - let result_arr = []; - arr.map((ele) => { - const sorted = ele.slice().sort((a, b) => a - b); - const middle = Math.floor(sorted.length / 2); - - if (sorted.length % 2 === 0) { - result_arr.push((sorted[middle - 1] + sorted[middle]) / 2); - } else { - result_arr.push(sorted[middle]); - } - }); - return result_arr; - } - } - - //computes the mode(s) of an array - __mode(arr) { - var modes = [], - count = [], - i, - maxIndex = 0; - - arr.forEach((val) => { - count[val] = (count[val] || 0) + 1; - if (count[val] > maxIndex) { - maxIndex = count[val]; - } - }); - - for (i in count) - if (this.__key_in_object(count, i)) { - if (count[i] === maxIndex) { - modes.push(Number(i)); - } - } - - return modes; - } - - //round elements of an array to ndp - __round(arr, dp = 2, isSeries) { - if (dp < 0) { - dp = 1; - } - if (isSeries) { - let new_arr = []; - arr.map((val) => { - new_arr.push(Number(val.toFixed(dp))); - }); - - return new_arr; - } else { - let result_arr = []; - arr.map((arr_ele) => { - let new_arr = []; - arr_ele.map((val) => { - new_arr.push(Number(val.toFixed(dp))); - }); - result_arr.push(new_arr); - }); - return result_arr; - } - } - - //check a variable is a function - __is_function(variable) { - return typeof variable == "function"; - } - - //generate a random list - __randgen(num, start, end) { - let gen_num = []; - - //random int - function randi(a, b) { - return Math.floor(Math.random() * (b - a) + a); - } - - function recursive(val, arr) { - if (!arr.includes(val)) { - return val; - } - - val = randi(start, end); - - recursive(val, arr); - } - - for (let i = 0; i < num; i++) { - let gen_val = randi(start, end); - let recur_val = recursive(gen_val, gen_num); - gen_num.push(recur_val); - } - - return gen_num; - } - - _throw_wrong_params_error(kwargs, params_needed) { - let keys = Object.keys(kwargs); - let bool = []; - for (let i = 0; i < keys.length; i++) { - if (params_needed.includes(keys[i])) { - bool.push(true); - } else { - bool.push(false); - } - } - const truthy = (element) => element == false; - if (bool.some(truthy)) { - throw Error( - `Params Error: A specified parameter is not supported. Your params must be any of the following [${params_needed}]` - ); - } - } - - //maps int values (0, 1) to bools (false, true) - __map_int_to_bool(arr, dim) { - let new_arr = []; - if (dim == 2) { - arr.map((outer_val) => { - let temp_arr = []; - outer_val.map((val) => { - if (val == 1) { - temp_arr.push(true); - } else { - temp_arr.push(false); - } - }); - new_arr.push(temp_arr); - }); - return new_arr; - } else { - arr.map((val) => { - if (val == 1) { - new_arr.push(true); - } else { - new_arr.push(false); - } - }); - return new_arr; - } - } - - __std(data) { - let tensor_data = data; - - let mean = tensor_data.mean(); - let sub_mean_pow = tensor_data.sub(mean).pow(2); - let mean_data = sub_mean_pow.mean(); - let std = mean_data.sqrt(); - - return std; - } - - __zeros(row, column) { - let zero_data = []; - - for (let i = 0; i < row; i++) { - let col_data = Array(column); - for (let j = 0; j < column; j++) { - col_data[j] = 0; - } - zero_data.push(col_data); - } - return zero_data; - } - - __shuffle(num, array) { - //https://stackoverflow.com/questions/18806210/generating-non-repeating-random-numbers-in-js/18806417 - var j, - temp; - - for (let i = 0; i < num; i++) { - j = Math.floor(Math.random() * (array.length - i)) + i; - - // swap randomly chosen element with current element - temp = array[i]; - array[i] = array[j]; - array[j] = temp; - } - - return array.slice(0, num); - } - - __sort(arr, ascending = true) { - let sorted = arr.slice(); - return sorted.sort((a, b) => { - if (ascending) { - if (typeof a === "string" && typeof b === "string") { - return a.charCodeAt() - b.charCodeAt(); - } else { - return a - b; - } - } else { - if (typeof a === "string" && typeof b === "string") { - return b.charCodeAt() - a.charCodeAt(); - } else { - return b - a; - } - } - }); - } - - __is_browser_env() { - var isBrowser = new Function( - "try {return this===window;}catch(e){ return false;}" - ); - // tests if global scope is binded to window - return isBrowser(); - } - - __is_node_env() { - var isNode = new Function( - "try {return this===global;}catch(e){return false;}" - ); - // tests if global scope is binded to window - return isNode(); - } - - _throw_str_dtype_error(obj, ops) { - if (obj.dtypes[0] == "string") { - throw Error( - `dtype error: String data type does not support ${ops} operation` - ); - } - } - - /** - * Remove NaN values from Array - * @param {*} arr - */ - _remove_nans(arr) { - let values = arr.filter((val) => !isNaN(val) && typeof val != "string"); - return values; - } - - /** -* Replace NaN with null before tensor operations -* @param {*} arr -*/ - _replace_nan_with_null(arr) { - let values = arr.map((val) => { - if (isNaN(val)) { - return null; - } else { - return val; - } - }); - return values; - } - - __get_duplicate(arr) { - let temp_obj = {}; - let rslt_obj = {}; - - arr.forEach((val, index) => { - if (val in temp_obj) { - temp_obj[val]["count"] += 1; - temp_obj[val]["index"].push(index); - } else { - temp_obj[val] = {}; - temp_obj[val]["count"] = 1; - temp_obj[val]["index"] = [ index ]; - } - }); - - for (let key in temp_obj) { - if (temp_obj[key]["count"] >= 2) { - rslt_obj[key] = {}; - rslt_obj[key]["count"] = temp_obj[key]["count"]; - rslt_obj[key]["index"] = temp_obj[key]["index"]; - } - } - - return rslt_obj; - } - - /** - * Sorts an array by index - * @param {Array} arr1 - * @param {Array} arr2 - * @param {string} dtype - * - * @returns sorted index - */ - _sort_arr_with_index(arr1, arr2, dtype) { - let sorted_idx = arr1.map((item, index) => { - return [ arr2[index], item ]; - }); - if (dtype == "string") { - sorted_idx.sort(); - } else { - sorted_idx.sort(([ arg1 ], [ arg2 ]) => arg2 - arg1); - } - - return sorted_idx.map(([ , item ]) => item); - } -} diff --git a/danfojs-browser/src/index.js b/danfojs-browser/src/index.js deleted file mode 100644 index b119f684..00000000 --- a/danfojs-browser/src/index.js +++ /dev/null @@ -1,20 +0,0 @@ -import NDframe from "./core/generic"; -import * as tf from "@tensorflow/tfjs"; - -export { Series } from "./core/series"; -export { DataFrame } from "./core/frame"; -export { to_datetime } from "./core/timeseries"; -export { read_csv, read_json, read_excel } from "./io/reader"; -export { merge } from "./core/merge"; -export { concat } from "./core/concat"; -export { LabelEncoder, OneHotEncoder } from "./preprocessing/encodings"; -export { MinMaxScaler, StandardScaler } from "./preprocessing/scalers"; -export { date_range } from "./core/date_range"; -export { get_dummies } from "./core/get_dummies"; -export { Configs } from "./config/config"; -export { NDframe }; -export { Str } from "./core/strings"; -export { Utils } from "./core/utils"; -export { tf }; - -export const _version = "0.2.7"; diff --git a/danfojs-browser/src/io/reader.js b/danfojs-browser/src/io/reader.js deleted file mode 100644 index d736dfa9..00000000 --- a/danfojs-browser/src/io/reader.js +++ /dev/null @@ -1,116 +0,0 @@ -import { data } from "@tensorflow/tfjs"; -import XLSX from "xlsx"; -import { DataFrame } from "../core/frame"; - - -/** - * Reads a CSV file from local or remote storage - * @param {string} source URL to CSV file - * @param {object} config (Optional). A CSV Config object that contains configurations - * for reading and decoding from CSV file(s). - * { start: The index position to start from when reading the CSV file. - * - * end: The end position to stop at when reading the CSV file. - * - * ...csvConfigs: other supported Tensorflow csvConfig parameters. See https://js.tensorflow.org/api/latest/#data.csv - * } - * - * @returns {Promise} DataFrame structure of parsed CSV data - */ -export const read_csv = async (source, configs = {}) => { - let { start, end } = configs; - if (!(source.startsWith("https") || source.startsWith("http") || source.startsWith("blob"))) { - throw new Error("Cannot read local file in browser environment"); - } - let tfdata = []; - await data.csv(source, configs) - .skip(start) - .take(end) - .forEachAsync((row) => { - return tfdata.push(row); - }); - const df = new DataFrame(tfdata); - return df; -}; - -/** - * Reads a JSON file from local or remote address - * @param {string} source URL or local file path to retreive JSON file. - * @returns {Promise} DataFrame structure of parsed CSV data - */ -export const read_json = async (source) => { - let res = await fetch(source, { method: "Get" }); - let json = await res.json(); - let df = new DataFrame(json); - return df; - -}; - -/** - * Reads an Excel file from local or remote address - * @param {string} source URL to Excel file - * @param {object} configs { - * - * sheet : string, (Optional) Name of the sheet which u want to parse. Default will be the first sheet. - * - * header_index : int, (Optional) Index of the row which represents the header(columns) of the data. Default will be the first non empty row. - * - * data_index : int, (Optional) Index of the row from which actual data(content) starts. Default will be the next row of `header_index`. - * - * } - * @returns {Promise} DataFrame structure of parsed Excel data - */ -export const read_excel = async (source, configs = {}) => { - let { sheet, header_index, data_index } = configs; - let workbook; - if (!header_index) { - //default header_index - header_index = 1; - } - if (!data_index) { - //default data_index - data_index = header_index + 1; - } - try { - let res = await fetch(source, { method: "Get" }); - res = await res.arrayBuffer(); - res = new Uint8Array(res); - workbook = XLSX.read(res, { type: "array" }); - - - // Parse worksheet from workbook - const worksheet = workbook.Sheets[sheet || workbook.SheetNames[0]]; - let range = XLSX.utils.decode_range(worksheet["!ref"]); - let column_names = [], - data = []; - for (let R = header_index - 1; R <= range.e.r; ++R) { - let row_data = []; - for (let C = range.s.c; C <= range.e.c; ++C) { - let cell_ref; - - //Populate column_names - if (R == header_index - 1) { - cell_ref = XLSX.utils.encode_cell({ c: C, r: header_index - 1 }); - if (worksheet[cell_ref]) { - column_names.push(worksheet[cell_ref].v); - } - } - - //Populate corresponding data row - if (R >= data_index - 1) { - cell_ref = XLSX.utils.encode_cell({ c: C, r: R }); - if (worksheet[cell_ref]) { - row_data.push(worksheet[cell_ref].v); - } - } - } - if (R >= data_index - 1) { - data.push(row_data); - } - } - let df = new DataFrame(data, { columns: column_names }); - return df; - } catch (err) { - throw new Error(err); - } -}; diff --git a/danfojs-browser/src/plotting/plot.js b/danfojs-browser/src/plotting/plot.js deleted file mode 100644 index 4653d9b5..00000000 --- a/danfojs-browser/src/plotting/plot.js +++ /dev/null @@ -1,867 +0,0 @@ -import { Utils } from "../core/utils"; -import { Series } from "../core/series"; -const utils = new Utils(); - -/** - * Change: Rising Odegua 6th Feb 2021 - * We no longer bundle Plotjs with DanfoJs. I noticed that removing this single - * inport reduces the bundle size from 5.7mb to 2.1mb. I know it's still a huge - * bundle, but it's progress! - * If you need to use Plotly, then you can add the Plotly CDN - * to your script before DanfoJs CDN - */ -try { - const version = Plotly.version; - console.info(`Using Plotly version ${version}`); -} catch (error) { - console.info(`Plotly CDN not found. If you need to make Plots, then add the Plotly CDN to your script`); -} - -/** - * Plotting methods and Functions performed on Series and DataFrames - */ -export class Plot { - constructor(ndframe, div) { - this.div = div; - this.ndframe = ndframe; - } - - - /** - * Plot Series or DataFrame as lines. This function is useful to plot lines using DataFrame’s values as coordinates. - * Make plots of Series or DataFrame. - * Uses the Plotly as backend, so supoorts Plotly's configuration parameters - * @param {string} div Name of the div to show the plot - * @param {Object} config configuration options for making Plots, supports Plotly parameters - */ - line(config = {}) { - - let ret_params = this.__get_plot_params(config); - let this_config = ret_params[0]; - let params = ret_params[1]; - - if (this.ndframe instanceof Series) { - let trace = {}; - let y = this.ndframe.values; - - params.forEach((param) => { - if(!(param == "layout")) { - trace[param] = config[param]; - } - }); - - trace["y"] = y; - trace['type'] = "line"; - - Plotly.newPlot(this.div, [ trace ], this_config['layout'], this_config); - - } else { - //check if plotting two columns against each other - if (utils.__key_in_object(this_config, 'x') && utils.__key_in_object(this_config, 'y')) { - if (!this.ndframe.column_names.includes(this_config['x'])) { - throw Error(`Column Error: ${this_config['x']} not found in columns`); - } - if (!this.ndframe.column_names.includes(this_config['y'])) { - throw Error(`Column Error: ${this_config['y']} not found in columns`); - } - - - let x = this.ndframe[this_config['x']].values; - let y = this.ndframe[this_config['y']].values; - - let trace = {}; - trace["x"] = x; - trace['y'] = y; - - - let xaxis = {}; let yaxis = {}; - xaxis['title'] = this_config['x']; - yaxis['title'] = this_config['y']; - - this_config['layout']['xaxis'] = xaxis; - this_config['layout']['yaxis'] = yaxis; - - Plotly.newPlot(this.div, [ trace ], this_config['layout'], this_config); - - } else if (utils.__key_in_object(this_config, 'x') || utils.__key_in_object(this_config, 'y')) { - //plot single column specified in either of param [x | y] against index - //plot columns against index - let data = []; - let cols_to_plot; - - if (utils.__key_in_object(this_config, "columns")) { - cols_to_plot = this.____check_if_cols_exist(this_config['columns']); - } else { - cols_to_plot = this.ndframe.column_names; - } - - cols_to_plot.forEach((c_name) => { - let trace = {}; - - params.forEach((param) => { //TODO accept individual configuration for traces - trace[param] = config[param]; - }); - if (utils.__key_in_object(this_config, 'x')) { - trace["x"] = this.ndframe[this_config['x']].values; - trace["y"] = this.ndframe[c_name].values; - trace['name'] = c_name; - } else { - trace["y"] = this.ndframe[this_config['y']].values; - trace["x"] = this.ndframe[c_name].values; - trace['name'] = c_name; - } - - data.push(trace); - - }); - Plotly.newPlot(this.div, data, this_config['layout'], this_config); - - } else { - //plot columns against index - let data = []; - let cols_to_plot; - - if (utils.__key_in_object(this_config, "columns")) { - cols_to_plot = this.____check_if_cols_exist(this_config['columns']); - } else { - cols_to_plot = this.ndframe.column_names; - } - - cols_to_plot.forEach((c_name) => { - let trace = {}; - - params.forEach((param) => { //TODO accept individual configuration for traces - trace[param] = config[param]; - }); - trace["x"] = this.ndframe.index; - trace["y"] = this.ndframe[c_name].values; - trace['name'] = c_name; - - data.push(trace); - - }); - Plotly.newPlot(this.div, data, this_config['layout'], this_config); - - } - - } - - - } - - - /** - * Plot Series or DataFrame as Bars. - * Uses the Plotly as backend, so supoorts Plotly's configuration parameters - * @param {string} div Name of the div to show the plot - * @param {Object} config configuration options for making Plots, supports Plotly parameters - */ - bar(config = {}) { - - let ret_params = this.__get_plot_params(config); - let this_config = ret_params[0]; - let params = ret_params[1]; - - if (this.ndframe instanceof Series) { - let trace = {}; - let y = this.ndframe.values; - - params.forEach((param) => { - if (!param == "layout") { - trace[param] = config[param]; - } - }); - - trace["y"] = y; - trace['type'] = "bar"; - - Plotly.newPlot(this.div, [ trace ], this_config['layout'], this_config); - - } else { - //check if plotting two columns against each other - if (utils.__key_in_object(this_config, 'x') && utils.__key_in_object(this_config, 'y')) { - if (!this.ndframe.column_names.includes(this_config['x'])) { - throw Error(`Column Error: ${this_config['x']} not found in columns`); - } - if (!this.ndframe.column_names.includes(this_config['y'])) { - throw Error(`Column Error: ${this_config['y']} not found in columns`); - } - - - let x = this.ndframe[this_config['x']].values; - let y = this.ndframe[this_config['y']].values; - - let trace = {}; - trace["x"] = x; - trace['y'] = y; - trace['type'] = "bar"; - - - let xaxis = {}; let yaxis = {}; - xaxis['title'] = this_config['x']; - yaxis['title'] = this_config['y']; - - this_config['layout']['xaxis'] = xaxis; - this_config['layout']['yaxis'] = yaxis; - - Plotly.newPlot(this.div, [ trace ], this_config['layout'], this_config); - - } else if (utils.__key_in_object(this_config, 'x') || utils.__key_in_object(this_config, 'y')) { - //plot single column specified in either of param [x | y] against index - let trace = {}; - - params.forEach((param) => { - if (!param == "layout") { - trace[param] = config[param]; - } - }); - - if (utils.__key_in_object(this_config, 'x')) { - trace['y'] = this.ndframe[this_config['x']].values; - } else { - trace['y'] = this.ndframe[this_config['y']].values; - } - trace['type'] = "bar"; - - Plotly.newPlot(this.div, [ trace ], this_config['layout'], this_config); - - } else { - //plot columns against index - let data = []; - let cols_to_plot; - - if (utils.__key_in_object(this_config, "columns")) { - cols_to_plot = this.____check_if_cols_exist(this_config['columns']); - } else { - cols_to_plot = this.ndframe.column_names; - } - - cols_to_plot.forEach((c_name) => { - let trace = {}; - - // params.forEach(param => { //TODO accept individual configuration for traces - // trace[param] = config[param] - // }) - trace['x'] = this.ndframe.index; - trace["y"] = this.ndframe[c_name].values; - trace['name'] = c_name; - trace['type'] = "bar"; - - data.push(trace); - - }); - Plotly.newPlot(this.div, data, this_config['layout'], this_config); - - } - - } - - - } - - - /** - * Plot two or more columns in a DataFrame as scatter points. If Series, plot against its index - * Uses the Plotly as backend, so supoorts Plotly's configuration parameters - * @param {string} div Name of the div to show the plot - * @param {Object} config configuration options for making Plots, supports Plotly parameters - */ - scatter(config = {}) { - - let ret_params = this.__get_plot_params(config); - let this_config = ret_params[0]; - let params = ret_params[1]; - - if (this.ndframe instanceof Series) { - let trace = {}; - - params.forEach((param) => { - if (!param == "layout") { - trace[param] = config[param]; - } - }); - - trace["x"] = this.ndframe.values; - trace['y'] = this.ndframe.index; - trace['type'] = "scatter"; - trace['mode'] = "markers"; - - Plotly.newPlot(this.div, [ trace ], this_config['layout'], this_config); - - } else { - //check if plotting two columns against each other - if (utils.__key_in_object(this_config, 'x') && utils.__key_in_object(this_config, 'y')) { - if (!this.ndframe.column_names.includes(this_config['x'])) { - throw Error(`Column Error: ${this_config['x']} not found in columns`); - } - if (!this.ndframe.column_names.includes(this_config['y'])) { - throw Error(`Column Error: ${this_config['y']} not found in columns`); - } - - - let x = this.ndframe[this_config['x']].values; - let y = this.ndframe[this_config['y']].values; - - let trace = {}; - trace["x"] = x; - trace['y'] = y; - trace['type'] = "scatter"; - trace['mode'] = "markers"; - - let xaxis = {}; let yaxis = {}; - xaxis['title'] = this_config['x']; - yaxis['title'] = this_config['y']; - - this_config['layout']['xaxis'] = xaxis; - this_config['layout']['yaxis'] = yaxis; - - Plotly.newPlot(this.div, [ trace ], this_config['layout'], this_config); - - } else if (utils.__key_in_object(this_config, 'x') || utils.__key_in_object(this_config, 'y')) { - //plot single column specified in either of param [x | y] against index - let trace = {}; - - params.forEach((param) => { - if (!param == "layout") { - trace[param] = config[param]; - } - }); - - if (utils.__key_in_object(this_config, 'x')) { - trace['y'] = this.ndframe.index; - trace['x'] = this.ndframe[this_config['x']].values; - - } else { - trace['x'] = this.ndframe.index; - trace['y'] = this.ndframe[this_config['y']].values; - - } - trace['type'] = "scatter"; - trace['mode'] = "markers"; - - Plotly.newPlot(this.div, [ trace ], this_config['layout'], this_config); - - } else { - //plot columns against index - let data = []; - let cols_to_plot; - - if (utils.__key_in_object(this_config, "columns")) { - cols_to_plot = this.____check_if_cols_exist(this_config['columns']); - } else { - cols_to_plot = this.ndframe.column_names; - } - - cols_to_plot.forEach((c_name) => { - let trace = {}; - - // params.forEach(param => { //TODO accept individual configuration for traces - // trace[param] = config[param] - // }) - trace['y'] = this.ndframe.index; - trace["x"] = this.ndframe[c_name].values; - trace['name'] = c_name; - trace['type'] = "scatter"; - trace['mode'] = "markers"; - data.push(trace); - - }); - Plotly.newPlot(this.div, data, this_config['layout'], this_config); - - } - - } - - - } - - - /** - * Plot columns in a Series/DataFrame as Histograms. - * Uses the Plotly as backend, so supoorts Plotly's configuration parameters - * @param {string} div Name of the div to show the plot - * @param {Object} config configuration options for making Plots, supports Plotly parameters - */ - hist(config = {}) { - - let ret_params = this.__get_plot_params(config); - let this_config = ret_params[0]; - let params = ret_params[1]; - - if (this.ndframe instanceof Series) { - let trace = {}; - - params.forEach((param) => { - if (!param == "layout") { - trace[param] = config[param]; - } - }); - - trace["x"] = this.ndframe.values; - trace['type'] = "histogram"; - - Plotly.newPlot(this.div, [ trace ], this_config['layout'], this_config); - - } else if (utils.__key_in_object(this_config, 'x')) { - //plot as vertical histogram - let trace = {}; - params.forEach((param) => { - if (!param == "layout") { - trace[param] = config[param]; - } - }); - - trace['x'] = this.ndframe[this_config['x']].values; - trace['type'] = "histogram"; - - Plotly.newPlot(this.div, [ trace ], this_config['layout'], this_config); - - } else if (utils.__key_in_object(this_config, 'y')) { - //plot as vertical histogram - let trace = {}; - params.forEach((param) => { - if (!param == "layout") { - trace[param] = config[param]; - } - }); - - trace['y'] = this.ndframe[this_config['y']].values; - trace['type'] = "histogram"; - - Plotly.newPlot(this.div, [ trace ], this_config['layout'], this_config); - - } else { - let data = []; - let cols_to_plot; - - if (utils.__key_in_object(this_config, "columns")) { - cols_to_plot = this.____check_if_cols_exist(this_config['columns']); - } else { - cols_to_plot = this.ndframe.column_names; - } - - cols_to_plot.forEach((c_name) => { - let trace = {}; - trace["x"] = this.ndframe[c_name].values; - trace['name'] = c_name; - trace['type'] = "histogram"; - data.push(trace); - - }); - Plotly.newPlot(this.div, data, this_config['layout'], this_config); - - } - - } - - - /** - * Makes Pie Plots from two Columns in a DataFrame. - * Uses the Plotly as backend, so supoorts Plotly's configuration parameters - * @param {string} div Name of the div to show the plot - * @param {Object} config configuration options for making Plots, supports Plotly parameters - */ - pie(config = {}) { - - let ret_params = this.__get_plot_params(config); - let this_config = ret_params[0]; - - - if (this.ndframe instanceof Series) { - let data = [ { - values: this.ndframe.values, - labels: this.ndframe.index, - type: 'pie', - name: this_config['labels'], - hoverinfo: 'label+percent+name', - automargin: true - } ]; - - Plotly.newPlot(this.div, data, this_config['layout'], this_config); - - } else if (utils.__key_in_object(this_config, 'values') && utils.__key_in_object(this_config, 'labels')) { - if (!this.ndframe.column_names.includes(this_config['labels'])) { - throw Error(`Column Error: ${this_config['labels']} not found in columns. labels name must be one of [ ${this.ndframe.column_names}]`); - } - if (!this.ndframe.column_names.includes(this_config['values'])) { - throw Error(`Column Error: ${this_config['values']} not found in columns. value name must be one of [ ${this.ndframe.column_names}]`); - } - let data = [ { - values: this.ndframe[this_config['values']].values, - labels: this.ndframe[this_config['labels']].values, - type: 'pie', - name: this_config['labels'], - hoverinfo: 'label+percent+name', - automargin: true - } ]; - - Plotly.newPlot(this.div, data, this_config['layout'], this_config); - - } else { - let cols_to_plot; - - if (utils.__key_in_object(this_config, "columns")) { - cols_to_plot = this.____check_if_cols_exist(this_config['columns']); - } else { - cols_to_plot = this.ndframe.column_names; - } - - if (utils.__key_in_object(this_config, 'row_pos')) { - if (this_config['row_pos'].length != cols_to_plot.length - 1) { - throw Error(`Lenght of row_pos array must be equal to number of columns. Got ${this_config['row_pos'].length}, expected ${cols_to_plot.length - 1}`); - } - } else { - let temp_arr = []; - for (let i = 0; i < cols_to_plot.length - 1; i++) { - temp_arr.push(0); - } - this_config['row_pos'] = temp_arr; - - } - - if (utils.__key_in_object(this_config, 'col_pos')) { - if (this_config['col_pos'].length != cols_to_plot.length - 1) { - throw Error(`Lenght of col_pos array must be equal to number of columns. Got ${this_config['col_pos'].length}, expected ${cols_to_plot.length - 1}`); - } - } else { - let temp_arr = []; - for (let i = 0; i < cols_to_plot.length - 1; i++) { - temp_arr.push(i); - } - this_config['col_pos'] = temp_arr; - - } - let data = []; - - cols_to_plot.forEach((c_name, i) => { - let trace = {}; - trace["values"] = this.ndframe[c_name].values; - trace['labels'] = this.ndframe[this_config['labels']].values; - trace['name'] = c_name; - trace['type'] = "pie"; - trace['domain'] = { row: this_config['row_pos'][i], column: this_config['col_pos'][i] }; - trace["hoverinfo"] = 'label+percent+name'; - trace['textposition'] = "outside"; - trace['automargin'] = true; - data.push(trace); - - }); - - if (!utils.__key_in_object(this_config, "grid")) { - //set default grid - let size = Number((this.ndframe.shape[1] / 2).toFixed()) + 1; - this_config['grid'] = { rows: size, columns: size }; - } - this_config['layout']['grid'] = this_config['grid']; - Plotly.newPlot(this.div, data, this_config['layout'], this_config); - - - } - - } - - - /** - * Plot Box plots from Series or DataFrame as lines. - * Uses the Plotly as backend, so supoorts Plotly's configuration parameters - * @param {Object} config configuration options for making Plots, supports Plotly parameters - */ - box(config = {}) { - - let ret_params = this.__get_plot_params(config); - let this_config = ret_params[0]; - let params = ret_params[1]; - - if (this.ndframe instanceof Series) { - let trace = {}; - let y = this.ndframe.values; - - params.forEach((param) => { - if (!param == "layout") { - trace[param] = config[param]; - } - }); - - trace["y"] = y; - trace['type'] = "box"; - - Plotly.newPlot(this.div, [ trace ], this_config['layout'], this_config); - - } else { - //check if plotting two columns against each other - if (utils.__key_in_object(this_config, 'x') && utils.__key_in_object(this_config, 'y')) { - if (!this.ndframe.column_names.includes(this_config['x'])) { - throw Error(`Column Error: ${this_config['x']} not found in columns`); - } - if (!this.ndframe.column_names.includes(this_config['y'])) { - throw Error(`Column Error: ${this_config['y']} not found in columns`); - } - - - let x = this.ndframe[this_config['x']].values; - let y = this.ndframe[this_config['y']].values; - - let trace = {}; - trace["x"] = x; - trace['y'] = y; - trace['type'] = 'box'; - - - let xaxis = {}; let yaxis = {}; - xaxis['title'] = this_config['x']; - yaxis['title'] = this_config['y']; - - this_config['layout']['xaxis'] = xaxis; - this_config['layout']['yaxis'] = yaxis; - - Plotly.newPlot(this.div, [ trace ], this_config['layout'], this_config); - - } else if (utils.__key_in_object(this_config, 'x') || utils.__key_in_object(this_config, 'y')) { - //plot single column specified in either of param [x | y] against index - let trace = {}; - - params.forEach((param) => { - if (!param == "layout") { - trace[param] = config[param]; - } - }); - - if (utils.__key_in_object(this_config, 'x')) { - trace['x'] = this.ndframe[this_config['x']].values; - trace['y'] = this.ndframe.index; - trace['type'] = 'box'; - } else { - trace['x'] = this.ndframe.index; - trace['y'] = this_config['y']; - trace['type'] = 'box'; - } - - Plotly.newPlot(this.div, [ trace ], this_config['layout'], this_config); - - } else { - //plot columns against index - let data = []; - let cols_to_plot; - - if (utils.__key_in_object(this_config, "columns")) { - cols_to_plot = this.____check_if_cols_exist(this_config['columns']); - } else { - cols_to_plot = this.ndframe.column_names; - } - - cols_to_plot.forEach((c_name) => { - let trace = {}; - - params.forEach((param) => { //TODO accept individual configuration for traces - trace[param] = config[param]; - }); - trace["x"] = this.ndframe[c_name].values; - trace["y"] = this.ndframe.index; - trace['name'] = c_name; - trace['type'] = 'box'; - data.push(trace); - - }); - Plotly.newPlot(this.div, data, this_config['layout'], this_config); - - } - - } - - - } - - - /** - * Plot Violin plots from Series or DataFrame as lines. - * Uses the Plotly as backend, so supoorts Plotly's configuration parameters - * @param {Object} config configuration options for making Plots, supports Plotly parameters - */ - violin(config = {}) { - - let ret_params = this.__get_plot_params(config); - let this_config = ret_params[0]; - let params = ret_params[1]; - - if (this.ndframe instanceof Series) { - let trace = {}; - let y = this.ndframe.values; - - params.forEach((param) => { - if (!param == "layout") { - trace[param] = config[param]; - } - }); - - trace["y"] = y; - trace['type'] = "violin"; - - Plotly.newPlot(this.div, [ trace ], this_config['layout'], this_config); - - } else { - //check if plotting two columns against each other - if (utils.__key_in_object(this_config, 'x') && utils.__key_in_object(this_config, 'y')) { - if (!this.ndframe.column_names.includes(this_config['x'])) { - throw Error(`Column Error: ${this_config['x']} not found in columns`); - } - if (!this.ndframe.column_names.includes(this_config['y'])) { - throw Error(`Column Error: ${this_config['y']} not found in columns`); - } - - - let x = this.ndframe[this_config['x']].values; - let y = this.ndframe[this_config['y']].values; - - let trace = {}; - trace["x"] = x; - trace['y'] = y; - trace['type'] = 'violin'; - - - let xaxis = {}; let yaxis = {}; - xaxis['title'] = this_config['x']; - yaxis['title'] = this_config['y']; - - this_config['layout']['xaxis'] = xaxis; - this_config['layout']['yaxis'] = yaxis; - - Plotly.newPlot(this.div, [ trace ], this_config['layout'], this_config); - - } else if (utils.__key_in_object(this_config, 'x') || utils.__key_in_object(this_config, 'y')) { - //plot single column specified in either of param [x | y] against index - let trace = {}; - - params.forEach((param) => { - if (!param == "layout") { - trace[param] = config[param]; - } - }); - - if (utils.__key_in_object(this_config, 'x')) { - trace['x'] = this.ndframe[this_config['x']].values; - trace['y'] = this.ndframe.index; - trace['type'] = 'violin'; - } else { - trace['x'] = this.ndframe.index; - trace['y'] = this_config['y']; - trace['type'] = 'violin'; - } - - Plotly.newPlot(this.div, [ trace ], this_config['layout'], this_config); - - } else { - //plot columns against index - let data = []; - let cols_to_plot; - - if (utils.__key_in_object(this_config, "columns")) { - cols_to_plot = this.____check_if_cols_exist(this_config['columns']); - } else { - cols_to_plot = this.ndframe.column_names; - } - - cols_to_plot.forEach((c_name) => { - let trace = {}; - - params.forEach((param) => { //TODO accept individual configuration for traces - trace[param] = config[param]; - }); - trace["y"] = this.ndframe[c_name].values; - trace['name'] = c_name; - trace['type'] = 'violin'; - data.push(trace); - - }); - Plotly.newPlot(this.div, data, this_config['layout'], this_config); - - } - - } - - - } - - /** - * Display DataFrame in a div using D3.js format - * Uses the Plotly as backend, so supoorts Plotly's configuration parameters - * @param {Object} config configuration options for making Plots, supports Plotly parameters - */ - table(config = {}) { - let ret_params = this.__get_plot_params(config); - let this_config = ret_params[0]; - let header = {}; - let cells = {}; - let cols_data = []; - let cols_2_show; - - - if (utils.__key_in_object(this_config, 'columns')) { - - this_config['columns'].forEach((cname) => { - if (!this.ndframe.column_names.includes(cname)) { - throw Error(`Column Error: ${cname} not found in columns. Columns should be one of [ ${this.ndframe.column_names} ]`); - } - - let idx = this.ndframe.column_names.indexOf(cname); - cols_data.push(this.ndframe.col_data[idx]); - }); - cols_2_show = this_config['columns']; - } else { - - cols_2_show = this.ndframe.column_names; - cols_data = this.ndframe.col_data; - - } - - header['values'] = cols_2_show; - cells['values'] = cols_data; - - if (this_config['header_style']) { - Object.keys(this_config['header_style']).forEach((param) => { - header[param] = this_config['header_style'][param]; - }); - } - - if (this_config['cell_style']) { - Object.keys(this_config['cell_style']).forEach((param) => { - cells[param] = this_config['cell_style'][param]; - }); - } - var data = [ { - type: 'table', - header: header, - cells: cells - } ]; - Plotly.newPlot(this.div, data, this_config['layout'], this_config); - - } - - - __get_plot_params(config) { - let params = Object.keys(config); - let this_config = {}; - - params.forEach((param) => { - this_config[param] = config[param]; - }); - - if (!utils.__key_in_object(config, "layout")) { - this_config['layout'] = {}; - } - - - return [ this_config, params ]; - - } - - ____check_if_cols_exist(cols) { - cols.forEach((col) => { - if (!this.ndframe.column_names.includes(col)) { - throw Error(`Column Error: ${col} not found in columns. Columns should be one of [ ${this.ndframe.column_names} ]`); - } - }); - return cols; - } - - -} diff --git a/danfojs-browser/src/preprocessing/encodings.js b/danfojs-browser/src/preprocessing/encodings.js deleted file mode 100644 index ace33885..00000000 --- a/danfojs-browser/src/preprocessing/encodings.js +++ /dev/null @@ -1,108 +0,0 @@ -import { Series } from "../core/series"; -import { Utils } from "../core/utils"; -import { DataFrame } from "../core/frame"; - -const utils = new Utils; - -export class LabelEncoder { - - /** - * - * @param {data} data [Array|Series] - * @returns Array. - */ - fit(data) { - let in_data = null; - if (Array.isArray(data)) { - in_data = data; - } else if (data instanceof Series) { - in_data = data.values; - } else { - throw new Error("data must be an array or a Series"); - } - - let data_set = new Set(in_data); - this.label = Array.from(data_set); - - let self = this; - let output_data = in_data.map((x) => { - return self.label.indexOf(x); - }); - - return new Series(output_data); - } - - /** - * Transform data using the label generated from fitting - * @param {data} data [Array|Series] - * @returns Array - */ - transform(data) { - let in_data = null; - if (Array.isArray(data)) { - in_data = data; - } else if (data instanceof Series) { - in_data = data.values; - } else { - throw new Error("data must be an array or a Series"); - } - - let self = this; - let output_data = in_data.map((x) => { - return self.label.indexOf(x); - }); - return new Series(output_data); - } -} - -export class OneHotEncoder { - - fit(data) { - let in_data = null; - if (Array.isArray(data)) { - in_data = data; - } else if (data instanceof Series) { - in_data = data.values; - } else { - throw new Error("data must be an array"); - } - - let data_set = new Set(in_data); - this.label = Array.from(data_set); - - let onehot_data = utils.__zeros(in_data.length, this.label.length); - - for (let i = 0; i < in_data.length; i++) { - - let elem = in_data[i]; - let elem_index = this.label.indexOf(elem); - onehot_data[i][elem_index] = 1; - } - - return new DataFrame(onehot_data, { columns: this.label }); - - } - - transform(data) { - let in_data = null; - - if (Array.isArray(data)) { - in_data = data; - } else if (data instanceof Series) { - in_data = data.values; - } else { - throw new Error("data must be an array"); - } - - let onehot_data = utils.__zeros(in_data.length, this.label.length); - - for (let i = 0; i < in_data.length; i++) { - let elem = in_data[i]; - let elem_index = this.label.indexOf(elem); - onehot_data[i][elem_index] = 1; - } - - return new DataFrame(onehot_data, { columns: this.label }); - - } -} diff --git a/danfojs-browser/src/preprocessing/scalers.js b/danfojs-browser/src/preprocessing/scalers.js deleted file mode 100644 index 2542512c..00000000 --- a/danfojs-browser/src/preprocessing/scalers.js +++ /dev/null @@ -1,267 +0,0 @@ -import { tensor, moments } from "@tensorflow/tfjs"; -import { Series } from "../core/series"; -import { DataFrame } from "../core/frame"; -import { Utils } from "../core/utils"; - -const utils = new Utils(); - -export class MinMaxScaler { - /** - * Fit minmax scaler on data, to obtain their min and max value - * @param {data} data [DataRame | Series | Array] - * @returns Array - */ - fit(data) { - let tensor_data = null; - if (Array.isArray(data)) { - tensor_data = tensor(data); - } else if (data instanceof DataFrame || data instanceof Series) { - if (data.dtypes.includes("string")) { - throw Error("Dtype Error: Cannot perform operation on string dtypes"); - } - tensor_data = tensor(data.values); - } else { - throw new Error("data must either be an Array, DataFrame or Series"); - } - - this.max = tensor_data.max(0); - this.min = tensor_data.min(0); - - let output_data = tensor_data - .sub(this.min) - .div(this.max.sub(this.min)) - .arraySync(); - - if (data instanceof Series || Array.isArray(data)) { - return new Series(output_data); - } else { - return new DataFrame(output_data); - } - } - - /** - * Transform an array using the min and max generated from the fitting on data - * @param {data} data [Array] - * @returns array - */ - transform(data) { - if (data instanceof Series) { - if (data.dtypes.includes("string")) { - throw Error("Dtype Error: Cannot perform operation on string dtypes"); - } - let tensor_data = tensor(data.values); - let output_data = tensor_data - .sub(this.min) - .div(this.max.sub(this.min)) - .arraySync(); - return new Series(output_data); - } else if (Array.isArray(data)) { - let tensor_data = tensor(data); - let output_data = tensor_data - .sub(this.min) - .div(this.max.sub(this.min)) - .arraySync(); - if (utils.__is_1D_array(data)) { - return new Series(output_data); - } else { - return new DataFrame(output_data); - } - } else if (data instanceof DataFrame) { - if (data.dtypes.includes("string")) { - throw Error("Dtype Error: Cannot perform operation on string dtypes"); - } - let tensor_data = tensor(data.values); - let output_data = tensor_data - .sub(this.min) - .div(this.max.sub(this.min)) - .arraySync(); - return new DataFrame(output_data); - } else { - throw Error("Value Error: Data type not supoorted"); - } - } -} - -export class StandardScaler { - /** - * - * @param {data} data [DataRame | Series | Array] - * @returns Array - */ - fit(data) { - let tensor_data = null; - if (Array.isArray(data)) { - tensor_data = tensor(data); - } else if (data instanceof DataFrame || data instanceof Series) { - if (data.dtypes.includes("string")) { - throw Error("Dtype Error: Cannot perform operation on string dtypes"); - } - tensor_data = tensor(data.values); - } else { - throw new Error("data must either be an Array, DataFrame or Series"); - } - - this.std = moments(tensor_data, 0).variance.sqrt(); - this.mean = tensor_data.mean(0); - let output_data = tensor_data.sub(this.mean).div(this.std).arraySync(); - - if (data instanceof Series || Array.isArray(data)) { - return new Series((data = output_data)); - } else { - return new DataFrame((data = output_data)); - } - } - - transform(data) { - // if(!Array.isArray(data)){ - // throw new Error(data) - // } - - if (data instanceof Series) { - if (data.dtypes.includes("string")) { - throw Error("Dtype Error: Cannot perform operation on string dtypes"); - } - let tensor_data = tensor(data.values); - let output_data = tensor_data.sub(this.mean).div(this.std).arraySync(); - return new Series(output_data); - } else if (Array.isArray(data)) { - let tensor_data = tensor(data); - let output_data = tensor_data.sub(this.mean).div(this.std).arraySync(); - if (utils.__is_1D_array(data)) { - return new Series(output_data); - } else { - return new DataFrame(output_data); - } - } else if (data instanceof DataFrame) { - if (data.dtypes.includes("string")) { - throw Error("Dtype Error: Cannot perform operation on string dtypes"); - } - let tensor_data = tensor(data.values); - let output_data = tensor_data.sub(this.mean).div(this.std).arraySync(); - return new DataFrame(output_data); - } else { - throw Error("Value Error: Data type not supoorted"); - } - } -} - -// export class RobustScaler{ - -// __median(arr, isTensor,return_index) { -// if (!isTensor) { -// const sorted = arr.slice().sort((a, b) => a - b); -// const middle = Math.floor(sorted.length / 2); - -// if (sorted.length % 2 === 0) { - -// return return_index ? [(middle - 1) , middle] : (sorted[middle - 1] + sorted[middle]) / 2; -// } - -// return return_index ? middle : sorted[middle] ; -// } else { -// let result_arr = [] -// arr.map(ele => { -// const sorted = ele.slice().sort((a, b) => a - b); -// const middle = Math.floor(sorted.length / 2); - -// if (sorted.length % 2 === 0) { -// result_arr.push(return_index ? [(middle - 1) , middle] :(sorted[middle - 1] + sorted[middle]) / 2 ) -// } else { -// result_arr.push(return_index ? middle : sorted[middle]) -// } - -// }) -// return result_arr -// } - -// } - -// quantile(data,isTensor){ - -// if(isTensor){ -// data = utils.__get_col_values(data); -// } - -// let median = this.__median(data,isTensor,true) -// let med = this.__median(data,isTensor,false) - -// let q1 = [] -// let q2 = [] - -// if(!isTensor){ -// let sorted = data.slice().sort((a, b) => a - b); - -// if(Array.isArray(median)){ -// let lower = median[0] -// let lower_data = sorted.slice(0,lower+1) -// let upper_data = sorted.slice(lower+1,) - -// q1.push(this.__median(lower_data,isTensor,false)); -// q2.push(this.__median(upper_data,isTensor,false)); - -// }else{ -// let lower_data = sorted.slice(0,median) -// let upper_data = sorted.slice(median+1,) - -// q1.push(this.__median(lower_data,isTensor,false)); -// q2.push(this.__median(upper_data,isTensor,false)); -// } - -// }else{ - -// data.map((x,i)=>{ -// let sorted = x.slice().sort((a, b) => a - b); - -// if(Array.isArray(median[i])){ -// let lower = median[i][0] -// let lower_data = sorted.slice(0,lower+1) -// let upper_data = sorted.slice(lower+1,) - -// q1.push(this.__median(lower_data,!isTensor,false)); -// q2.push(this.__median(upper_data,!isTensor,false)); - -// }else{ -// let lower_data = sorted.slice(0,median[i]) -// let upper_data = sorted.slice(median[i]+1,) - -// q1.push(this.__median(lower_data,!isTensor,false)); -// q2.push(this.__median(upper_data,!isTensor,false)); -// } - -// }) -// } - -// return [q1,q2, med] - -// } - -// /** -// * Fit robust scalar on data to obtain the first quantile and third quantile -// * @param {data} data [DataRame | Series | Array] -// * @returns Array -// */ -// fit(data){ - -// let tensor_data = null -// let isTensor = false; -// if(Array.isArray(data)){ -// tensor_data = tensor(data) -// } -// else if((data instanceof DataFrame)){ -// tensor_data = tensor(data.values) -// isTensor = true; -// } -// else if((data instanceof Series)){ -// tensor_data = tensor(data.values) -// } -// else{ -// throw new Error("data must either be an Array, DataFrame or Series") -// } - -// let [q1, q3, median] = this.quantile(data,isTensor) -// let q3_tensor = tensor(q3) -// let output_data = tensor_data.sub(median).div(q3_tensor.sub(q1)).arraySync() - -// return output_data; -// } -// } diff --git a/danfojs-browser/tests/config/config.js b/danfojs-browser/tests/config/config.js deleted file mode 100644 index 75d2b433..00000000 --- a/danfojs-browser/tests/config/config.js +++ /dev/null @@ -1,24 +0,0 @@ -/* eslint-disable no-undef */ - -describe("Config", function () { - const config = new dfd.Configs(); - - it("gets the default config val for table width", function () { - let table_width = config.get_width; - assert.equal(table_width, 17); - }); - it("gets the default config val for table truncation", function () { - let table_trunc = config.get_truncate; - assert.equal(table_trunc, 16); - }); - it("Sets the config val for table width", function () { - config.set_width(7); - let table_width = config.get_width; - assert.equal(table_width, 7); - }); - it("Sets the config val for table truncation", function () { - config.set_truncate(5); - let table_trunc = config.get_truncate; - assert.equal(table_trunc, 5); - }); -}); diff --git a/danfojs-browser/tests/core/date_range.js b/danfojs-browser/tests/core/date_range.js deleted file mode 100644 index 20bfcb74..00000000 --- a/danfojs-browser/tests/core/date_range.js +++ /dev/null @@ -1,59 +0,0 @@ - -describe("date_range", function(){ - - it("Obtain date between start with end not specified, but period and freq specified", function(){ - - - let d = new dfd.date_range({ "start":'1/1/2018', period:5, freq:'M' }); - let rslt = [ - '1/1/2018, 12:00:00 AM', - '2/1/2018, 12:00:00 AM', - '3/1/2018, 12:00:00 AM', - '4/1/2018, 12:00:00 AM', - '5/1/2018, 12:00:00 AM' - ]; - - assert.deepEqual(d, rslt); - - - }); - it("Obtain date between start with end not specified, but period and freq specified, plus offset", function(){ - - let d = new dfd.date_range({ start:'1/1/2018', period:5, freq:'3M' }); - let rslt = [ - '1/1/2018, 12:00:00 AM', - '4/1/2018, 12:00:00 AM', - '7/1/2018, 12:00:00 AM', - '10/1/2018, 12:00:00 AM', - '1/1/2019, 12:00:00 AM' - ]; - - assert.deepEqual(d, rslt); - - - }); - it("Obtain date range with start not specified but end and period is given", function(){ - - let d = new dfd.date_range({ end:'1/1/2018', period:8 }); - let rslt = [ - '12/25/2017, 12:00:00 AM', - '12/26/2017, 12:00:00 AM', - '12/27/2017, 12:00:00 AM', - '12/28/2017, 12:00:00 AM', - '12/29/2017, 12:00:00 AM', - '12/30/2017, 12:00:00 AM', - '12/31/2017, 12:00:00 AM', - '1/1/2018, 12:00:00 AM' - ]; - - assert.deepEqual(d, rslt); - - - }); - it("inputing wrong freq", function(){ - assert.throws(function () { new dfd.date_range({ end:'1/1/2018', period:8, freq:"d" }); }, Error, 'invalid freq d'); - }); - it("inputing wrong freq with offset", function(){ - assert.throws(function () { new dfd.date_range({ end:'1/1/2018', period:8, freq:"4d" }); }, Error, 'invalid freq d'); - }); -}); diff --git a/danfojs-browser/tests/core/frame.js b/danfojs-browser/tests/core/frame.js deleted file mode 100644 index 10eebc2d..00000000 --- a/danfojs-browser/tests/core/frame.js +++ /dev/null @@ -1,2379 +0,0 @@ -/* eslint-disable no-undef */ - -describe("DataFrame", function () { - - //TODO: Write browser side test for fs - // describe("to_csv", function () { - // afterEach(function(){ - // // Clean up generated file - // fs.unlinkSync(testCSVPath); - // }); - - // it("save dataframe to CSV file", async function () { - // let data = [ [ 1, 2, 3 ], [ 4, 5, 6 ] ]; - // let cols = [ "A", "B", "C" ]; - // let df = new dfd.DataFrame(data, { columns: cols }); - // await df.to_csv(testCSVPath); - // assert.isTrue(fs.existsSync(testCSVPath)); - // }); - - // it("return dataframe csv string", async function () { - // let data = [ [ 1, 2, 3 ], [ 4, 5, 6 ] ]; - // let cols = [ "A", "B", "C" ]; - // let df = new dfd.DataFrame(data, { columns: cols }); - // const csvContent = await df.to_csv(testCSVPath); - // assert.deepEqual(csvContent, "A,B,C\n1,2,3\n4,5,6\n"); - // }); - - // }); - - describe("drop", function () { - it("throw error for wrong row index", function () { - let data = [ [ 1, 2, 3 ], [ 4, 5, 6 ] ]; - let cols = [ "A", "B", "C" ]; - let df = new dfd.DataFrame(data, { columns: cols }); - assert.throws(function () { df.drop({ columns: [ 3 ], axis: 0, inplace: false }); }, Error, 'No index label found. Axis of 0 must be accompanied by an array of index labels'); - }); - it("throw error for wrong row index", function () { - let data = [ [ 1, 2, 3 ], [ 4, 5, 6 ] ]; - let cols = [ "A", "B", "C" ]; - let df = new dfd.DataFrame(data, { columns: cols }); - assert.throws(function () { df.drop({ columns: [ "D" ], axis: 1, inplace: false }); }, Error, 'column "D" does not exist'); - }); - - it("drop a column inplace", function () { - let data = [ [ 1, 2, 3 ], [ 4, 5, 6 ] ]; - let cols = [ "A", "B", "C" ]; - let df = new dfd.DataFrame(data, { columns: cols }); - df.drop({ columns: [ "C", "B" ], axis: 1, inplace: true }); - let column = [ "A" ]; - assert.deepEqual(df.columns, column); - }); - it("check if data is updated after column is dropped", function () { - let data = [ [ 1, 2, 3 ], [ 4, 5, 6 ] ]; - let cols = [ "A", "B", "C" ]; - let df = new dfd.DataFrame(data, { columns: cols }); - df.drop({ columns: [ "C" ], axis: 1, inplace: true }); - let new_data = [ [ 1, 2 ], [ 4, 5 ] ]; - assert.deepEqual(df.values, new_data); - assert.deepEqual(df.dtypes.length, 2); - - }); - - it("check if data is updated after row is dropped", function () { - let data = [ [ 1, 2, 3 ], [ 4, 5, 6 ] ]; - let cols = [ "A", "B", "C" ]; - let df = new dfd.DataFrame(data, { columns: cols }); - df.drop({ index: [ 0 ], axis: 0, inplace: true }); - let new_data = [ [ 4, 5, 6 ] ]; - assert.deepEqual(df.values, new_data); - }); - it("check if new dataframe is properly created after column is dropped (not-in-inplace)", function () { - let data = [ [ 1, 2, 3 ], [ 4, 5, 6 ] ]; - let cols = [ "A", "B", "C" ]; - let df = new dfd.DataFrame(data, { columns: cols }); - let df_drop = df.drop({ columns: [ "C" ], axis: 1, inplace: false }); - - let expected_data = [ [ 1, 2 ], [ 4, 5 ] ]; - let expected_cols = [ "A", "B" ]; - let expected_df = new dfd.DataFrame(expected_data, { columns: expected_cols }); - assert.deepEqual(df_drop.values, expected_df.values); - }); - it("check that the dtype is updated after column drop", function () { - let data = [ [ 1, 2, 3 ], [ 4, 5, 6 ] ]; - let cols = [ "A", "B", "C" ]; - let df = new dfd.DataFrame(data, { columns: cols }); - df.drop({ columns: [ "A" ], axis: 1, inplace: true }); - let dtype = [ 'int32', 'int32' ]; - assert.deepEqual(df.ctypes.values, dtype); - }); - it("drop row by single string labels", function () { - let data = [ [ 1, 2, 3 ], [ 4, 5, 6 ], [ 20, 34, 5 ] ]; - let cols = [ "A", "B", "C" ]; - let df = new dfd.DataFrame(data, { columns: cols, index: [ "a", "b", "c" ] }); - df.drop({ index: [ "a" ], axis: 0, inplace: true }); - let new_data = [ [ 4, 5, 6 ], [ 20, 34, 5 ] ]; - assert.deepEqual(df.values, new_data); - }); - it("drop row by two or more string labels", function () { - let data = [ [ 1, 2, 3 ], [ 4, 5, 6 ], [ 20, 34, 5 ], [ 2, 3.4, 5 ], [ 2.0, 340, 5 ] ]; - let cols = [ "A", "B", "C" ]; - let df = new dfd.DataFrame(data, { columns: cols, index: [ "a", "b", "c", "a", "b" ] }); - df.drop({ index: [ "a", "b" ], axis: 0, inplace: true }); - let new_data = [ [ 20, 34, 5 ] ]; - assert.deepEqual(df.values, new_data); - - }); - }); - - describe("head", function () { - it("Gets the first n rows in a DataFrame", function () { - let data = [ [ 1, 2, 3 ], [ 4, 5, 6 ], [ 20, 30, 40 ], [ 39, 89, 78 ] ]; - let cols = [ "A", "B", "C" ]; - let df = new dfd.DataFrame(data, { columns: cols }); - assert.deepEqual(df.head(2).values, [ [ 1, 2, 3 ], [ 4, 5, 6 ] ]); - }); - it("Return all rows in a DataFrame if row specified is greater than values", function () { - let data = [ [ 1, 2, 3 ], [ 4, 5, 6 ], [ 20, 30, 40 ], [ 39, 89, 78 ] ]; - let cols = [ "A", "B", "C" ]; - let df = new dfd.DataFrame(data, { columns: cols }); - assert.deepEqual(df.head(10).values, data); - }); - it("Return all rows in a DataFrame if row specified is less than 0", function () { - let data = [ [ 1, 2, 3 ], [ 4, 5, 6 ], [ 20, 30, 40 ], [ 39, 89, 78 ] ]; - let cols = [ "A", "B", "C" ]; - let df = new dfd.DataFrame(data, { columns: cols }); - assert.deepEqual(df.head(-1).values, data); - }); - - }); - - describe("tail", function () { - it("Prints the last n rows of a DataFrame", function () { - let data = [ [ 1, 2, 3 ], [ 4, 5, 6 ], [ 20, 30, 40 ], [ 39, 89, 78 ] ]; - let cols = [ "A", "B", "C" ]; - let df = new dfd.DataFrame(data, { columns: cols }); - assert.deepEqual(df.tail(2).values, [ [ 20, 30, 40 ], [ 39, 89, 78 ] ]); - }); - it("Return all rows in a DataFrame if row specified is greater than values", function () { - let data = [ [ 1, 2, 3 ], [ 4, 5, 6 ], [ 20, 30, 40 ], [ 39, 89, 78 ] ]; - let cols = [ "A", "B", "C" ]; - let df = new dfd.DataFrame(data, { columns: cols }); - assert.deepEqual(df.tail(10).values, data); - }); - it("Return all rows in a DataFrame if row specified is less than 0", function () { - let data = [ [ 1, 2, 3 ], [ 4, 5, 6 ], [ 20, 30, 40 ], [ 39, 89, 78 ] ]; - let cols = [ "A", "B", "C" ]; - let df = new dfd.DataFrame(data, { columns: cols }); - assert.deepEqual(df.tail(-1).values, data); - }); - it("Return last 3 row index in a DataFrame", function () { - let data = [ [ 1, 2, 3 ], [ 4, 5, 6 ], [ 20, 30, 40 ], [ 39, 89, 78 ] ]; - let cols = [ "A", "B", "C" ]; - let df = new dfd.DataFrame(data, { columns: cols }); - assert.deepEqual(df.tail(2).index, [ 2, 3 ]); - }); - it("Check print format on head call", function () { - let data = [ [ 1, 2, 34, 5, 0, 6, 4, 5, 6, 7 ], [ 20, 30, 40, 39, 89, 78, 45, 56, 56, 45 ] ]; - let df = new dfd.DataFrame(data); - assert.deepEqual(df.tail(2).values, [ [ 1, 2, 34, 5, 0, 6, 4, 5, 6, 7 ], [ 20, 30, 40, 39, 89, 78, 45, 56, 56, 45 ] ]); - }); - }); - - describe("sample", function () { - it("Samples n number of random elements from a DataFrame", async function () { - let data = [ [ 1, 2, 3 ], [ 4, 5, 6 ], [ 20, 30, 40 ], [ 39, 89, 78 ], [ 100, 200, 300 ] ]; - let cols = [ "A", "B", "C" ]; - let df = new dfd.DataFrame(data, { columns: cols }); - let expected = [ [ 1, 2, 3 ], [ 20, 30, 40 ] ]; - let values = (await df.sample(2)).values; - assert.deepEqual(values, expected); - }); - it("Throw error if n is greater than lenght of Dataframe", async function () { - let data = [ [ 1, 2, 3 ], [ 4, 5, 6 ], [ 20, 30, 40 ], [ 39, 89, 78 ], [ 100, 200, 300 ] ]; - let cols = [ "A", "B", "C" ]; - let df = new dfd.DataFrame(data, { columns: cols }); - try { - await df.sample(100); - } catch (e) { - expect(e).to.be.instanceOf(Error); - expect(e.message).to.eql('Sample size n cannot be bigger than size of dataset'); - } - }); - it("Throw error if n is less than -1", async function () { - let data = [ [ 1, 2, 3 ], [ 4, 5, 6 ], [ 20, 30, 40 ], [ 39, 89, 78 ], [ 100, 200, 300 ] ]; - let cols = [ "A", "B", "C" ]; - let df = new dfd.DataFrame(data, { columns: cols }); - try { - await df.sample(-2); - } catch (e) { - expect(e).to.be.instanceOf(Error); - expect(e.message).to.eql('Sample size cannot be less than -1 or 0'); - } - }); - it("Throw error if n is 0", async function () { - let data = [ [ 1, 2, 3 ], [ 4, 5, 6 ], [ 20, 30, 40 ], [ 39, 89, 78 ], [ 100, 200, 300 ] ]; - let cols = [ "A", "B", "C" ]; - let df = new dfd.DataFrame(data, { columns: cols }); - try { - await df.sample(0); - } catch (e) { - expect(e).to.be.instanceOf(Error); - expect(e.message).to.eql('Sample size cannot be less than -1 or 0'); - } - }); - it("Return all values if n is -1", async function () { - let data = [ [ 1, 2, 3 ], [ 4, 5, 6 ], [ 20, 30, 40 ], [ 39, 89, 78 ], [ 100, 200, 300 ] ]; - let cols = [ "A", "B", "C" ]; - let df = new dfd.DataFrame(data, { columns: cols }); - assert.deepEqual((await df.sample(-1)).shape, [ 5, 3 ]); - }); - }); - - describe("loc", function () { - - it("throw error for wrong column name", function () { - let data = [ [ 1, 2, 3 ], [ 4, 5, 6 ] ]; - let cols = [ "A", "B", "C" ]; - let df = new dfd.DataFrame(data, { columns: cols }); - assert.throws(function () { df.loc({ "rows": [ 0, 1 ], "columns": [ "A", "D" ] }); }, Error, "Column D does not exist"); - }); - it("check data after selecting column", function () { - let data = [ [ 1, 2, 3 ], [ 4, 5, 6 ] ]; - let cols = [ "A", "B", "C" ]; - let df = new dfd.DataFrame(data, { columns: cols }); - - let col_df = df.loc({ "rows": [ 0, 1 ], "columns": [ "B", "C" ] }); - let col_data = [ [ 2, 3 ], [ 5, 6 ] ]; - - assert.deepEqual(col_df.values, col_data); - - }); - it("check data after selecting row index", function () { - let data = [ [ 1, 2, 3 ], [ 4, 5, 6 ] ]; - let cols = [ "A", "B", "C" ]; - let df = new dfd.DataFrame(data, { columns: cols }); - - let col_df = df.loc({ "rows": [ 1 ], "columns": [ "B", "C" ] }); - let col_data = [ [ 5, 6 ] ]; - - assert.deepEqual(col_df.values, col_data); - - }); - it("check data after row and column slice", function () { - let data = [ [ 1, 2, 3 ], [ 4, 5, 6 ], [ 20, 30, 40 ], [ 39, 89, 78 ] ]; - let cols = [ "A", "B", "C" ]; - let df = new dfd.DataFrame(data, { columns: cols }); - - let col_df = df.loc({ "rows": [ "0:2" ], "columns": [ "B:C" ] }); - let col_data = [ [ 2 ], [ 5 ] ]; - - assert.deepEqual(col_df.values, col_data); - - }); - it("check data after row slice", function () { - let data = [ [ 1, 2, 3 ], [ 4, 5, 6 ], [ 20, 30, 40 ], [ 39, 89, 78 ] ]; - let cols = [ "A", "B", "C" ]; - let df = new dfd.DataFrame(data, { columns: cols }); - - let col_df = df.loc({ "rows": [ "0:2" ], "columns": [ "B", "C" ] }); - let col_data = [ [ 2, 3 ], [ 5, 6 ] ]; - - assert.deepEqual(col_df.values, col_data); - - }); - it("check data after column slice", function () { - let data = [ [ 1, 2, 3 ], [ 4, 5, 6 ], [ 20, 30, 40 ], [ 39, 89, 78 ] ]; - let cols = [ "A", "B", "C" ]; - let df = new dfd.DataFrame(data, { columns: cols }); - - let col_df = df.loc({ "rows": [ 0, 1 ], "columns": [ "A:C" ] }); - let col_data = [ [ 1, 2 ], [ 4, 5 ] ]; - assert.deepEqual(col_df.values, col_data); - - }); - it("loc by single string index", function () { - let data = { - "Name": [ "Apples", "Mango", "Banana", "Pear" ], - "Count": [ 21, 5, 30, 10 ], - "Price": [ 200, 300, 40, 250 ] - }; - - let df = new dfd.DataFrame(data); - df.set_index({ key: [ "a", "b", "c", "a" ], inplace: true }); - let sub_df = df.loc({ rows: [ "a" ], columns: [ "Name", "Count" ] }); - let expected = [ [ "Apples", 21 ], [ "Pear", 10 ] ]; - assert.deepEqual(sub_df.values, expected); - - }); - it("loc by multiple string index", function () { - let data = { - "Name": [ "Apples", "Mango", "Banana", "Pear" ], - "Count": [ 21, 5, 30, 10 ], - "Price": [ 200, 300, 40, 250 ] - }; - - let df = new dfd.DataFrame(data); - df.set_index({ key: [ "a", "b", "c", "a" ], inplace: true }); - let sub_df = df.loc({ rows: [ "a", "b" ], columns: [ "Name", "Count" ] }); - let expected = [ [ "Apples", 21 ], [ "Mango", 5 ], [ "Pear", 10 ] ]; - assert.deepEqual(sub_df.values, expected); - - }); - it("loc by slice string index", function () { - let data = { - "Name": [ "Apples", "Mango", "Banana", "Pear" ], - "Count": [ 21, 5, 30, 10 ], - "Price": [ 200, 300, 40, 250 ] - }; - - let df = new dfd.DataFrame(data); - df.set_index({ key: [ "a", "b", "c", "d" ], inplace: true }); - let sub_df = df.loc({ rows: [ "a:c" ], columns: [ "Name", "Count" ] }); - let expected = [ [ "Apples", 21 ], [ "Mango", 5 ] ]; - assert.deepEqual(sub_df.values, expected); - - }); - - - }); - - describe("iloc", function () { - - it("throw error for wrong column index", function () { - let data = [ [ 1, 2, 3 ], [ 4, 5, 6 ] ]; - let cols = [ "A", "B", "C" ]; - let df = new dfd.DataFrame(data, { columns: cols }); - assert.throws(function () { df.iloc({ "rows": [ 0, 1 ], "columns": [ 0, 3 ] }); }, Error, "column index 3 is bigger than 2"); - }); - - it("throw error for wrong row index", function () { - let data = [ [ 1, 2, 3 ], [ 4, 5, 6 ] ]; - let cols = [ "A", "B", "C" ]; - let df = new dfd.DataFrame(data, { columns: cols }); - assert.throws(function () { df.iloc({ "rows": 0, "columns": [ 0, 3 ] }); }, Error, "rows parameter must be a Array"); - }); - - it("throw error for wrong column index", function () { - let data = [ [ 1, 2, 3 ], [ 4, 5, 6 ] ]; - let cols = [ "A", "B", "C" ]; - let df = new dfd.DataFrame(data, { columns: cols }); - assert.throws(function () { df.iloc({ "rows": [ 0, 1 ], "columns": 3 }); }, Error, "columns must be a list"); - }); - - it("throw error for wrong row index", function () { - let data = [ [ 1, 2, 3 ], [ 4, 5, 6 ] ]; - let cols = [ "A", "B", "C" ]; - let df = new dfd.DataFrame(data, { columns: cols }); - assert.throws(function () { df.iloc({ "rows": [ 0, 8 ], "columns": [ 1, 2 ] }); }, Error, "Specified row index 8 is bigger than maximum row index of 1"); - }); - - it("check data after selecting column", function () { - let data = [ [ 1, 2, 3 ], [ 4, 5, 6 ] ]; - let cols = [ "A", "B", "C" ]; - let df = new dfd.DataFrame(data, { columns: cols }); - - let col_df = df.iloc({ "rows": [ 0, 1 ], "columns": [ 1, 2 ] }); - let col_data = [ [ 2, 3 ], [ 5, 6 ] ]; - - assert.deepEqual(col_df.values, col_data); - - }); - it("check data after selecting row index", function () { - let data = [ [ 1, 2, 3 ], [ 4, 5, 6 ] ]; - let cols = [ "A", "B", "C" ]; - let df = new dfd.DataFrame(data, { columns: cols }); - - let col_df = df.iloc({ "rows": [ 1 ], "columns": [ 1, 2 ] }); - let col_data = [ [ 5, 6 ] ]; - - assert.deepEqual(col_df.values, col_data); - - }); - it("check data after row and column slice", function () { - let data = [ [ 1, 2, 3 ], [ 4, 5, 6 ], [ 20, 30, 40 ], [ 39, 89, 78 ] ]; - let cols = [ "A", "B", "C" ]; - let df = new dfd.DataFrame(data, { columns: cols }); - - let col_df = df.iloc({ "rows": [ "0:2" ], "columns": [ "1:2" ] }); - let col_data = [ [ 2 ], [ 5 ] ]; - - assert.deepEqual(col_df.values, col_data); - - }); - it("check data after row slice", function () { - let data = [ [ 1, 2, 3 ], [ 4, 5, 6 ], [ 20, 30, 40 ], [ 39, 89, 78 ] ]; - let cols = [ "A", "B", "C" ]; - let df = new dfd.DataFrame(data, { columns: cols }); - - let col_df = df.iloc({ "rows": [ "0:2" ], "columns": [ 1, 2 ] }); - let col_data = [ [ 2, 3 ], [ 5, 6 ] ]; - - assert.deepEqual(col_df.values, col_data); - - }); - it("check data after column slice", function () { - let data = [ [ 1, 2, 3 ], [ 4, 5, 6 ], [ 20, 30, 40 ], [ 39, 89, 78 ] ]; - let cols = [ "A", "B", "C" ]; - let df = new dfd.DataFrame(data, { columns: cols }); - - let col_df = df.iloc({ "rows": [ 0, 1, 2 ], "columns": [ "1:2" ] }); - let col_data = [ [ 2 ], [ 5 ], [ 30 ] ]; - assert.deepEqual(col_df.values, col_data); - - }); - it("Return all columns if columns parameter is not specified", function () { - let data = [ [ 1, 2, 3 ], [ 4, 5, 6 ], [ 20, 30, 40 ], [ 39, 89, 78 ] ]; - let cols = [ "A", "B", "C" ]; - let df = new dfd.DataFrame(data, { columns: cols }); - - let col_df = df.iloc({ "rows": [ 0, 1, 2 ] }); - let col_data = [ [ 1, 2, 3 ], [ 4, 5, 6 ], [ 20, 30, 40 ] ]; - assert.deepEqual(col_df.values, col_data); - - }); - it("Return all rows if rows parameter is not specified", function () { - let data = [ [ 1, 2, 3 ], [ 4, 5, 6 ], [ 20, 30, 40 ], [ 39, 89, 78 ] ]; - let cols = [ "A", "B", "C" ]; - let df = new dfd.DataFrame(data, { columns: cols }); - - let col_df = df.iloc({ "columns": [ "1:2" ] }); - let col_data = [ [ 2 ], [ 5 ], [ 30 ], [ 89 ] ]; - assert.deepEqual(col_df.values, col_data); - - }); - - it("column slice starting with 0 and returning a single result works", function () { - let data = { - "Name": [ "Apples", "Mango", "Banana", "Pear" ], - "Count": [ 21, 5, 30, 10 ], - "Price": [ 200, 300, 40, 250 ] - }; - let df = new dfd.DataFrame(data); - let sub_df = df.iloc({ rows: [ "2:3" ], columns: [ "0:1" ] }); - const result = [ [ "Banana" ] ]; - assert.deepEqual(sub_df.values, result); - - }); - it("column slice with format '0:' works", function () { - let data = { - "Name": [ "Apples", "Mango", "Banana", "Pear" ], - "Count": [ 21, 5, 30, 10 ], - "Price": [ 200, 300, 40, 250 ] - }; - let df = new dfd.DataFrame(data); - let sub_df = df.iloc({ rows: [ "2:3" ], columns: [ "0:" ] }); - const result = [ [ "Banana", 30, 40 ] ]; - assert.deepEqual(sub_df.values, result); - - }); - it("column slice with format ':2' works", function () { - let data = { - "Name": [ "Apples", "Mango", "Banana", "Pear" ], - "Count": [ 21, 5, 30, 10 ], - "Price": [ 200, 300, 40, 250 ] - }; - let df = new dfd.DataFrame(data); - let sub_df = df.iloc({ rows: [ "2:3" ], columns: [ ":2" ] }); - const result = [ [ "Banana", 30 ] ]; - assert.deepEqual(sub_df.values, result); - - }); - it("row slice with format ':2' works", function () { - let data = { - "Name": [ "Apples", "Mango", "Banana", "Pear" ], - "Count": [ 21, 5, 30, 10 ], - "Price": [ 200, 300, 40, 250 ] - }; - let df = new dfd.DataFrame(data); - let sub_df = df.iloc({ rows: [ ":2" ], columns: [ ":1" ] }); - const result = [ [ 'Apples' ], [ 'Mango' ] ]; - assert.deepEqual(sub_df.values, result); - - }); - it("row slice with format '1:' works", function () { - let data = { - "Name": [ "Apples", "Mango", "Banana", "Pear" ], - "Count": [ 21, 5, 30, 10 ], - "Price": [ 200, 300, 40, 250 ] - }; - let df = new dfd.DataFrame(data); - let sub_df = df.iloc({ rows: [ ":2" ], columns: [ ":2" ] }); - const result = [ [ 'Apples', 21 ], [ 'Mango', 5 ] ]; - assert.deepEqual(sub_df.values, result); - - }); - - - }); - - - describe("add", function () { - it("Return Addition of DataFrame with a single Number", function () { - let data = [ [ 0, 2, 4 ], [ 360, 180, 360 ] ]; - let df = new dfd.DataFrame(data); - assert.deepEqual(df.add(2).values, [ [ 2, 4, 6 ], [ 362, 182, 362 ] ]); - }); - it("Return addition of a DataFrame with a Series along default axis 1", function () { - let data = [ [ 0, 2, 4 ], [ 360, 180, 360 ] ]; - let sf = new dfd.Series([ 1, 2, 1 ]); - let df = new dfd.DataFrame(data); - assert.deepEqual(df.add(sf).values, [ [ 1, 4, 5 ], [ 361, 182, 361 ] ]); - }); - it("Return addition of a DataFrame with a Series along axis 0", function () { - let data = [ [ 0, 2, 4 ], [ 360, 180, 360 ] ]; - let sf = new dfd.Series([ 1, 2 ]); - let df = new dfd.DataFrame(data); - assert.deepEqual(df.add(sf, 0).values, [ [ 1, 3, 5 ], [ 362, 182, 362 ] ]); - }); - it("Return addition of a DataFrame with a DataFrame along default axis 1", function () { - let df1 = new dfd.DataFrame([ [ 0, 2, 4 ], [ 360, 180, 360 ] ]); - let df2 = new dfd.DataFrame([ [ 1, 2, 4 ], [ 10, 5, 0 ] ]); - assert.deepEqual(df1.add(df2).values, [ [ 1, 4, 8 ], [ 370, 185, 360 ] ]); - }); - it("Return addition of a DataFrame with a DataFrame along axis 0", function () { - let df1 = new dfd.DataFrame([ [ 0, 2, 4 ], [ 360, 180, 360 ] ]); - let df2 = new dfd.DataFrame([ [ 1, 2, 4 ], [ 10, 5, 0 ] ]); - assert.deepEqual(df1.add(df2).values, [ [ 1, 4, 8 ], [ 370, 185, 360 ] ]); - }); - - }); - - describe("sub", function () { - it("Return subtraction of DataFrame with a single Number", function () { - let data = [ [ 0, 2, 4 ], [ 360, 180, 360 ] ]; - let df = new dfd.DataFrame(data); - assert.deepEqual(df.sub(2).values, [ [ -2, 0, 2 ], [ 358, 178, 358 ] ]); - }); - it("Return subtraction of a DataFrame with a Series along default axis 1", function () { - let data = [ [ 0, 2, 4 ], [ 360, 180, 360 ] ]; - let sf = new dfd.Series([ 1, 2, 1 ]); - let df = new dfd.DataFrame(data); - assert.deepEqual(df.sub(sf).values, [ [ -1, 0, 3 ], [ 359, 178, 359 ] ]); - }); - it("Return subtraction of a DataFrame with a Series along axis 0", function () { - let data = [ [ 0, 2, 4 ], [ 360, 180, 360 ] ]; - let sf = new dfd.Series([ 1, 2 ]); - let df = new dfd.DataFrame(data); - assert.deepEqual(df.sub(sf, 0).values, [ [ -1, 1, 3 ], [ 358, 178, 358 ] ]); - }); - it("Return subtraction of a DataFrame with a DataFrame along default axis 1", function () { - let df1 = new dfd.DataFrame([ [ 0, 2, 4 ], [ 360, 180, 360 ] ]); - let df2 = new dfd.DataFrame([ [ 1, 2, 4 ], [ 10, 5, 0 ] ]); - assert.deepEqual(df1.sub(df2).values, [ [ -1, 0, 0 ], [ 350, 175, 360 ] ]); - }); - it("Return subtraction of a DataFrame with a DataFrame along axis 0", function () { - let df1 = new dfd.DataFrame([ [ 0, 2, 4 ], [ 360, 180, 360 ] ]); - let df2 = new dfd.DataFrame([ [ 1, 2, 4 ], [ 10, 5, 0 ] ]); - assert.deepEqual(df1.sub(df2).values, [ [ -1, 0, 0 ], [ 350, 175, 360 ] ]); - }); - - }); - - describe("mul", function () { - it("Return multiplication of DataFrame with a single Number", function () { - let data = [ [ 0, 2, 4 ], [ 360, 180, 360 ] ]; - let df = new dfd.DataFrame(data); - assert.deepEqual(df.mul(2).values, [ [ 0, 4, 8 ], [ 720, 360, 720 ] ]); - }); - it("Return multiplication of a DataFrame with a Series along default axis 1", function () { - let data = [ [ 0, 2, 4 ], [ 360, 180, 360 ] ]; - let sf = new dfd.Series([ 1, 2, 1 ]); - let df = new dfd.DataFrame(data); - assert.deepEqual(df.mul(sf).values, [ [ 0, 4, 4 ], [ 360, 360, 360 ] ]); - }); - it("Return multiplication of a DataFrame with a Series along axis 0", function () { - let data = [ [ 0, 2, 4 ], [ 360, 180, 360 ] ]; - let sf = new dfd.Series([ 1, 2 ]); - let df = new dfd.DataFrame(data); - assert.deepEqual(df.mul(sf, 0).values, [ [ 0, 2, 4 ], [ 720, 360, 720 ] ]); - }); - it("Return multiplication of a DataFrame with a DataFrame along default axis 1", function () { - let df1 = new dfd.DataFrame([ [ 0, 2, 4 ], [ 360, 180, 360 ] ]); - let df2 = new dfd.DataFrame([ [ 1, 2, 4 ], [ 10, 5, 0 ] ]); - assert.deepEqual(df1.mul(df2).values, [ [ 0, 4, 16 ], [ 3600, 900, 0 ] ]); - }); - it("Return multiplication of a DataFrame with a DataFrame along axis 0", function () { - let df1 = new dfd.DataFrame([ [ 0, 2, 4 ], [ 360, 180, 360 ] ]); - let df2 = new dfd.DataFrame([ [ 1, 2, 4 ], [ 10, 5, 0 ] ]); - assert.deepEqual(df1.mul(df2, 0).values, [ [ 0, 4, 16 ], [ 3600, 900, 0 ] ]); - }); - - }); - - describe("div", function () { - it("Return division of DataFrame with a single Number", function () { - let data = [ [ 0, 2, 4 ], [ 360, 180, 360 ] ]; - let df = new dfd.DataFrame(data); - assert.deepEqual(df.div(2).values, [ [ 0, 1, 2 ], [ 180, 90, 180 ] ]); - }); - it("Return division of a DataFrame with a Series along default axis 1", function () { - let data = [ [ 0, 2, 4 ], [ 360, 180, 360 ] ]; - let sf = new dfd.Series([ 1, 2, 1 ]); - let df = new dfd.DataFrame(data); - assert.deepEqual(df.div(sf).values, [ [ 0, 1, 4 ], [ 360, 90, 360 ] ]); - }); - it("Return division of a DataFrame with a Series along axis 0", function () { - let data = [ [ 0, 2, 4 ], [ 360, 180, 360 ] ]; - let sf = new dfd.Series([ 1, 2 ]); - let df = new dfd.DataFrame(data); - assert.deepEqual(df.div(sf, 0).values, [ [ 0, 2, 4 ], [ 180, 90, 180 ] ]); - }); - it("Return division of a DataFrame with a DataFrame along default axis 1", function () { - let df1 = new dfd.DataFrame([ [ 0, 2, 4 ], [ 360, 180, 360 ] ]); - let df2 = new dfd.DataFrame([ [ 1, 2, 4 ], [ 10, 5, 0 ] ]); - assert.deepEqual(df1.div(df2).values, [ [ 0, 1, 1 ], [ 36, 36, NaN ] ]); - }); - it("Return division of a DataFrame with a DataFrame along axis 0", function () { - let df1 = new dfd.DataFrame([ [ 0, 2, 4 ], [ 360, 180, 360 ] ]); - assert.deepEqual(df1.div(df1).values, [ [ NaN, 1, 1 ], [ 1, 1, 1 ] ]); - }); - it("Return division of a DataFrame with a DataFrame along axis 0", function () { - let df1 = new dfd.DataFrame([ [ 0, 2, 4 ], [ 360, 180, 360 ] ]); - let df2 = new dfd.DataFrame([ [ 1, 2, 4 ], [ 10, 5, 0 ] ]); - assert.deepEqual(df1.div(df2).values, [ [ 0, 1, 1 ], [ 36, 36, NaN ] ]); - }); - - }); - - describe("pow", function () { - it("Return exponential of DataFrame with a single Number", function () { - let data = [ [ 0, 2, 4 ], [ 360, 180, 360 ] ]; - let df = new dfd.DataFrame(data); - assert.deepEqual(df.pow(2).values, [ [ 0, 4, 16 ], [ 129600, 32400, 129600 ] ]); - }); - it("Return exponential of a DataFrame with a Series along default axis 1", function () { - let data = [ [ 0, 2, 4 ], [ 360, 180, 360 ] ]; - let sf = new dfd.Series([ 1, 2, 1 ]); - let df = new dfd.DataFrame(data); - assert.deepEqual(df.pow(sf).values, [ [ 0, 4, 4 ], [ 360, 32400, 360 ] ]); - }); - it("Return exponential of a DataFrame with a Series along axis 0", function () { - let data = [ [ 0, 2, 4 ], [ 360, 180, 360 ] ]; - let sf = new dfd.Series([ 1, 2 ]); - let df = new dfd.DataFrame(data); - assert.deepEqual(df.pow(sf, 0).values, [ [ 0, 2, 4 ], [ 129600, 32400, 129600 ] ]); - }); - it("Return exponential of a DataFrame with another DataFrame along default axis 1", function () { - let df1 = new dfd.DataFrame([ [ 0, 2, 4 ], [ 3, 10, 4 ] ]); - let df2 = new dfd.DataFrame([ [ 1, 2, 4 ], [ 10, 5, 0 ] ]); - assert.deepEqual(df1.pow(df2).values, [ [ 0, 4, 256 ], [ 59049, 100000, 1 ] ]); - }); - it("Return exponential of a DataFrame with another DataFrame along axis 0", function () { - let df1 = new dfd.DataFrame([ [ 0, 2, 4 ], [ 3, 10, 4 ] ]); - let df2 = new dfd.DataFrame([ [ 1, 2, 4 ], [ 10, 5, 0 ] ]); - assert.deepEqual(df1.pow(df2, 0).values, [ [ 0, 4, 256 ], [ 59049, 100000, 1 ] ]); - }); - - }); - - describe("mod", function () { - it("Return modulus of DataFrame with a single Number", function () { - let data = [ [ 0, 2, 4 ], [ 360, 180, 360 ] ]; - let df = new dfd.DataFrame(data); - assert.deepEqual(df.mod(2).values, [ [ 0, 0, 0 ], [ 0, 0, 0 ] ]); - }); - it("Return modulus of a DataFrame with a Series along default axis 1", function () { - let data = [ [ 0, 2, 4 ], [ 31, 15, 360 ] ]; - let sf = new dfd.Series([ 1, 2, 1 ]); - let df = new dfd.DataFrame(data); - assert.deepEqual(df.mod(sf).values, [ [ 0, 0, 0 ], [ 0, 1, 0 ] ]); - }); - it("Return modulus of a DataFrame with a Series along axis 0", function () { - let data = [ [ 0, 2, 4 ], [ 31, 15, 360 ] ]; - let sf = new dfd.Series([ 1, 2 ]); - let df = new dfd.DataFrame(data); - assert.deepEqual(df.mod(sf, 0).values, [ [ 0, 0, 0 ], [ 1, 1, 0 ] ]); - }); - it("Return modulus of a DataFrame with a DataFrame along default axis 1", function () { - let df1 = new dfd.DataFrame([ [ 0, 2, 4 ], [ 31, 15, 360 ] ]); - let df2 = new dfd.DataFrame([ [ 1, 2, 4 ], [ 10, 5, 0 ] ]); - assert.deepEqual(df1.mod(df2).values, [ [ 0, 0, 0 ], [ 1, 0, NaN ] ]); - }); - it("Return modulus of a DataFrame with a DataFrame along axis 0", function () { - let df1 = new dfd.DataFrame([ [ 0, 2, 4 ], [ 360, 180, 360 ] ]); - let df2 = new dfd.DataFrame([ [ 1, 2, 4 ], [ 10, 5, 0 ] ]); - assert.deepEqual(df1.mod(df2).values, [ [ 0, 0, 0 ], [ 0, 0, NaN ] ]); - }); - - }); - - describe("mean", function () { - it("Returns the mean of a DataFrame (Default axis is [1:column])", function () { - let data = [ [ 0, 2, 4 ], [ 360, 180, 360 ] ]; - let df = new dfd.DataFrame(data, { columns: [ "col1", "col2", "col3" ] }); - assert.deepEqual(df.mean().values, [ 180, 91, 182 ]); - }); - it("Return mean of a DataFrame along axis 0 (row)", function () { - let data = [ [ 0, 2, 4 ], [ 360, 180, 360 ] ]; - let df = new dfd.DataFrame(data); - assert.deepEqual(df.mean(0).values, [ 2, 300 ]); - }); - it("Removes NaN before calculating mean of a DataFrame", function () { - let data = [ [ 11, 20, 3 ], [ NaN, 15, 6 ], [ 2, 30, 40 ], [ 2, 89, 78 ] ]; - let df = new dfd.DataFrame(data); - assert.deepEqual(df.mean().values, [ 5, 38.5, 31.75 ]); - }); - }); - - describe("median", function () { - it("Returns the median of a DataFrame (Default axis is [1:column])", function () { - let data = [ [ 0, 2, 4 ], [ 360, 180, 360 ] ]; - let df = new dfd.DataFrame(data); - assert.deepEqual(df.median().values, [ 180, 91, 182 ]); - }); - it("Return median of a DataFrame along axis 0 (row)", function () { - let data = [ [ 0, 2, 4 ], [ 360, 180, 360 ] ]; - let df = new dfd.DataFrame(data); - assert.deepEqual(df.median({ "axis": 0 }).values, [ 2, 360 ]); - }); - - }); - - // describe("mode", function () { - // it("Returns the mode of a DataFrame (Default axis is [1:column])", function () { - // let data = [[0, 2, 4], [360, 180, 360]] - // let df = new dfd.DataFrame(data) - // assert.deepEqual(df.mode().values, [362, 182, 362]) - // }) - // it("Returns mode of a DataFrame along axis 0 (row)", function () { - // let data = [[0, 2, 4], [360, 180, 360]] - // let df = new dfd.DataFrame(data) - // assert.deepEqual(df.mode({ "axis": 0 }).values, [1, 182]) - // }) - // it("Returns mode of a DataFrame along axis 1", function () { - // let data = [{ "col1": [0, 2, 4] }, { "col2": [360, 180, 360] }] - // let df = new dfd.DataFrame(data) - // assert.deepEqual(df.mode().values, [1, 362, 40]) - // }) - - // }) - - describe("min", function () { - it("Returns the minimum values in a DataFrame (Default axis is [1:column])", function () { - let data = [ [ 0, 2, 4 ], [ 360, 180, 360 ] ]; - let df = new dfd.DataFrame(data); - assert.deepEqual(df.min().values, [ 0, 2, 4 ]); - }); - it("Returns the minimum values of a DataFrame along axis 0 (row)", function () { - let data = [ [ 0, 2, 4 ], [ 360, 180, 360 ] ]; - let df = new dfd.DataFrame(data); - assert.deepEqual(df.min({ "axis": 0 }).values, [ 0, 180 ]); - }); - - }); - - describe("max", function () { - it("Returns the maximum values in a DataFrame (Default axis is [1:column])", function () { - let data = [ [ 0, 2, 4 ], [ 360, 180, 360 ] ]; - let df = new dfd.DataFrame(data); - assert.deepEqual(df.max().values, [ 360, 180, 360 ]); - }); - it("Returns the maximum values of a DataFrame along axis 0 (row)", function () { - let data = [ [ 0, 2, 4 ], [ 360, 180, 360 ] ]; - let df = new dfd.DataFrame(data); - assert.deepEqual(df.max({ "axis": 0 }).values, [ 4, 360 ]); - }); - - }); - - describe("std", function () { - it("Returns the standard deviations of values in a DataFrame (Default axis is [1:column])", function () { - let data = [ [ 0, 2, 4 ], [ 360, 180, 360 ] ]; - let df = new dfd.DataFrame(data); - assert.deepEqual(df.std().values, [ 254.55844122715712, 125.86500705120545, 251.7300141024109 ]); - }); - it("Return the standard deviations of values of a DataFrame along axis 0 (row)", function () { - let data = [ [ 0, 2, 4 ], [ 360, 180, 360 ] ]; - let df = new dfd.DataFrame(data); - assert.deepEqual(df.std(0).values, [ 2, 103.92304845413264 ]); - }); - - - }); - - describe("var", function () { - it("Returns the variance of values in a DataFrame (Default axis is [1:column])", function () { - let data = [ [ 0, 2, 4 ], [ 360, 180, 360 ] ]; - let df = new dfd.DataFrame(data); - assert.deepEqual(df.var().values, [ 64800, 15842, 63368 ]); - }); - it("Return the variance of values of a DataFrame along axis 0 (row)", function () { - let data = [ [ 0, 2, 4 ], [ 360, 180, 360 ] ]; - let df = new dfd.DataFrame(data); - assert.deepEqual(df.var(0).values, [ 4, 10800 ]); - }); - - - }); - - describe("describe", function () { - it("Returns descriptive statistics of columns in a DataFrame created from an array", function () { - let data = [ [ 0, 2, 4, "a" ], - [ 360, 180, 360, "b" ], - [ 2, 4, 6, "c" ] ]; - - let df = new dfd.DataFrame(data); - let res = [ [ 3, 3, 3 ], [ 120.666664, 62, 123.333336 ], - [ 207.271159, 102.19589, 204.961785 ], - [ 0, 2, 4 ], [ 2, 4, 6 ], - [ 360, 180, 360 ], - [ 42961.333333, 10444, 42009.333333 ] ]; - - assert.deepEqual(df.describe().values, res); - }); - it("Returns descriptive statistics of columns in a DataFrame created from an Object", function () { - let data = { - "col1": [ 0, 2, 4 ], - "col2": [ 360, 180, 360 ], - "col3": [ 2, 4, 6 ], - "col4": [ "boy", "girl", "man" ], - "col5": [ "apple", "car", "bee" ] - }; - let df = new dfd.DataFrame(data); - - let res = [ [ 3, 3, 3 ], [ 2, 300, 4 ], - [ 2, 103.923048, 2 ], - [ 0, 180, 2 ], [ 2, 360, 4 ], - [ 4, 360, 6 ], - [ 4, 10800, 4 ] ]; - - assert.deepEqual(df.describe().values, res); - }); - - }); - - describe("count", function () { - it("Returns the count of non-nan values in a DataFrame (Default axis is [1:column])", function () { - let data = [ [ 0, 2, 4 ], [ 360, 180.1, 360.11 ], [ NaN, 2, 4 ], [ 360, undefined, 360 ] ]; - let df = new dfd.DataFrame(data); - assert.deepEqual(df.count().values, [ 3, 3, 4 ]); - }); - it("Return the count of non NaN values of a DataFrame along axis 0", function () { - let data = [ [ 0, 2, 4, NaN ], [ 360, undefined, 360, 70 ] ]; - let df = new dfd.DataFrame(data); - assert.deepEqual(df.count(0).values, [ 3, 3 ]); - }); - - }); - - describe("round", function () { - it("Rounds values in a DataFrame to 3dp", function () { - let data = [ [ 10.1, 2.092, 4.23 ], [ 360.232244, 180.0190290, 36.902612 ] ]; - let df = new dfd.DataFrame(data); - let expected = [ [ 10.1, 2.092, 4.23 ], [ 360.232, 180.0190, 36.903 ] ]; - assert.deepEqual(df.round(3).values, expected); - }); - it("Rounds values in a DataFrame to 1dp", function () { - let data = [ [ 10.1, 2.092, 4.23 ], [ 360.232244, 180.0190290, 36.902612 ] ]; - let df = new dfd.DataFrame(data); - let expected = [ [ 10.1, 2.1, 4.2 ], [ 360.2, 180.0, 36.9 ] ]; - assert.deepEqual(df.round(1).values, expected); - }); - - }); - - describe("sort_values", function () { - it("Sort values in DataFrame by specified column in ascending order (Default)", function () { - let data = [ [ 0, 2, 4, "a" ], - [ 360, 180, 360, "b" ], - [ 2, 4, 6, "c" ] ]; - - let df = new dfd.DataFrame(data, { "columns": [ "col1", "col2", "col3", "col4" ] }); - df.sort_values({ "by": "col1", inplace: true, ascending: true }); - let expected = [ [ 0, 2, 4, "a" ], [ 2, 4, 6, "c" ], [ 360, 180, 360, "b" ] ]; - assert.deepEqual(df.values, expected); - assert.deepEqual(df.index, [ 0, 2, 1 ]); - - }); - - it("Sort values in DataFrame by specified column in ascending order (Default)", function () { - let data = [ [ 0, 2, 4, "a" ], - [ 360, 180, 1, "b" ], - [ 2, 4, 6, "c" ] ]; - - let df = new dfd.DataFrame(data, { "columns": [ "col1", "col2", "col3", "col4" ] }); - let df_sort = df.sort_values({ "by": "col3" }); - let expected = [ [ 360, 180, 1, "b" ], [ 0, 2, 4, "a" ], [ 2, 4, 6, "c" ] ]; - assert.deepEqual(df_sort.values, expected); - assert.deepEqual(df_sort.index, [ 1, 0, 2 ]); - - }); - it("Sort values in DataFrame by specified column in descending order", function () { - let data = [ [ 0, 2, 4, "a" ], - [ 360, 180, 360, "b" ], - [ 2, 4, 6, "c" ] ]; - - let df = new dfd.DataFrame(data, { "columns": [ "col1", "col2", "col3", "col4" ] }); - let expected = [ [ 360, 180, 360, "b" ], [ 2, 4, 6, "c" ], [ 0, 2, 4, "a" ] ]; - assert.deepEqual(df.sort_values({ "by": "col1", "ascending": false }).values, expected); - }); - - it("Sort values in DataFrame by specified column in descending order (second col)", function () { - let data = [ [ 0, 2, 4, "a" ], - [ 360, 180, 1, "b" ], - [ 2, 4, 6, "c" ] ]; - - let df = new dfd.DataFrame(data, { "columns": [ "col1", "col2", "col3", "col4" ] }); - let expected = [ [ 2, 4, 6, "c" ], [ 0, 2, 4, "a" ], [ 360, 180, 1, "b" ] ]; - assert.deepEqual(df.sort_values({ "by": "col3", "ascending": false }).values, expected); - }); - it("Sort values in DataFrame by specified column containing alpha(numeric) values", function () { - let data = [ [ 0, 2, 4, "a" ], - [ 360, 180, 1, "b" ], - [ 2, 4, 6, "c" ] ]; - - let df = new dfd.DataFrame(data, { "columns": [ "col1", "col2", "col3", "col4" ] }); - let expected = [ [ 2, 4, 6, 'c' ], [ 360, 180, 1, 'b' ], [ 0, 2, 4, 'a' ] ]; - assert.deepEqual(df.sort_values({ "by": "col4", "ascending": false }).values, expected); - }); - it("Sort duplicate DataGrame with duplicate columns", function () { - - let data = { - "A": [ 1, 2, 3, 4, 5, 3, 5, 6, 4, 5, 3, 4 ], - "B": [ 2, 3, 4, 5, 6, 7, 8, 9, 1, 2, 3, 4 ] - }; - - let df = new dfd.DataFrame(data); - let expected = [ [ 1, 2 ], - [ 2, 3 ], - [ 3, 4 ], - [ 3, 7 ], - [ 3, 3 ], - [ 4, 5 ], - [ 4, 1 ], - [ 4, 4 ], - [ 5, 6 ], - [ 5, 8 ], - [ 5, 2 ], - [ 6, 9 ] ]; - assert.deepEqual(df.sort_values({ "by": "A", "ascending": true }).values, expected); - }); - - - }); - - describe("copy", function () { - it("Makes a deep copy of DataFrame", function () { - let data = [ [ 0, 2, 4 ], [ 360, 180, 360 ] ]; - let df = new dfd.DataFrame(data); - let df_copy = df.copy(); - assert.deepEqual(df_copy.values, [ [ 0, 2, 4 ], [ 360, 180, 360 ] ]); - }); - it("Confirms child copy modification does not affect parent DataFrame", function () { - let data = [ [ 0, 2, 4 ], [ 360, 180, 360 ] ]; - let df = new dfd.DataFrame(data); - let df_copy = df.copy(); - df_copy.addColumn({ column: "col_new", value: [ "boy", "girl" ] }); - assert.notDeepEqual(df_copy.values, df.values); - }); - - }); - - - describe("set_index", function () { - it("Sets the index of a DataFrame created from an Object", function () { - let data = [ { alpha: "A", count: 1 }, { alpha: "B", count: 2 }, { alpha: "C", count: 3 } ]; - let df = new dfd.DataFrame(data); - let df_new = df.set_index({ "key": [ "one", "two", "three" ] }); - assert.deepEqual(df_new.index, [ "one", "two", "three" ]); - }); - it("Sets the index of a DataFrame from column name", function () { - let data = [ { alpha: "A", count: 1 }, { alpha: "B", count: 2 }, { alpha: "C", count: 3 } ]; - let df = new dfd.DataFrame(data); - let df_new = df.set_index({ "key": "alpha" }); - assert.deepEqual(df_new.index, [ "A", "B", "C" ]); - }); - it("Sets the index of a DataFrame from column name", function () { - let data = [ { alpha: "A", count: 1 }, { alpha: "B", count: 2 }, { alpha: "C", count: 3 } ]; - let df = new dfd.DataFrame(data); - let df_new = df.set_index({ key: "alpha", drop: true }); - assert.deepEqual(df_new.index, [ "A", "B", "C" ]); - }); - it("Sets the index of a DataFrame created from an Array", function () { - let data = [ [ 0, 2, 4 ], [ 360, 180, 360 ], [ 0, 2, 4 ], [ 360, 180, 360 ], [ 0, 2, 4 ] ]; - let df = new dfd.DataFrame(data); - df.set_index({ "key": [ "one", "two", "three", "four", "five" ], "inplace": true }); - assert.deepEqual(df.index, [ "one", "two", "three", "four", "five" ]); - }); - - }); - - describe("reset_index", function () { - it("Resets the index of a DataFrame created from an Object", function () { - let data = [ { alpha: "A", count: 1 }, { alpha: "B", count: 2 }, { alpha: "C", count: 3 } ]; - let df = new dfd.DataFrame(data); - let df_new = df.set_index({ "key": [ "one", "two", "three" ] }); - let df_reset = df_new.reset_index(); - assert.deepEqual(df_reset.index, [ 0, 1, 2 ]); - }); - it("Resets the index of a DataFrame created from an Array", function () { - let data = [ [ 0, 2, 4 ], [ 360, 180, 360 ], [ 0, 2, 4 ], [ 360, 180, 360 ], [ 0, 2, 4 ] ]; - let df = new dfd.DataFrame(data); - df.set_index({ "key": [ "one", "two", "three", "four", "five" ], "inplace": true }); - df.reset_index(true); - assert.deepEqual(df.index, [ 0, 1, 2, 3, 4 ]); - }); - - }); - - - // describe("apply", function () { - // it("Apply a function to all values of a DataFrame", function () { - // let data = [[0, 2, 4], - // [360, 180, 360], - // [0, 2, 4]] - // let df = new dfd.DataFrame(data) - - // let apply_func = (x) => { - // return x + 1000 - // } - // let expected = [[1000, 1002, 1004], [1360, 1180, 1360], [1000, 1002, 1004]] - // assert.deepEqual(df.apply(apply_func), expected) - // }); - - // it("Throws error on applying function to string columns", function () { - // let data = [[0, 2, "ab"], - // [360, 180, "mk"], - // [0, 2, "po"]] - // let df = new dfd.DataFrame(data) - - // let apply_func = (x) => { - // return x + 1000 - // } - // let expected = "Dtypes Error: columns dtypes must be numeric, got strings" - // assert.deepEqual(df.apply(apply_func), expected) - // }); - // }); - - - describe("query", function () { - - it("Get the DataFrame containing rows with the filtered column", function () { - - let data = [ [ 1, 2, 3 ], [ 4, 5, 6 ], [ 20, 30, 40 ], [ 39, 89, 78 ] ]; - let cols = [ "A", "B", "C" ]; - let df = new dfd.DataFrame(data, { columns: cols }); - let query_df = df.query({ "column": "B", "is": ">=", "to": 5 }); - let query_data = [ [ 4, 5, 6 ], [ 20, 30, 40 ], [ 39, 89, 78 ] ]; - assert.deepEqual(query_df.values, query_data); - }); - it("Get the Dataframe containing rows with the filtered column in String values", function () { - let data = { "Abs": [ 20, 30, 47 ], "Count": [ 34, 4, 5 ], "country code": [ "NG", "FR", "GH" ] }; - let cols = [ "Abs", "Count", "country code" ]; - let df = new dfd.DataFrame(data, { columns: cols }); - let query_df = df.query({ column: "country code", is: "==", to: "NG" }); - let query_data = [ [ 20, 34, "NG" ] ]; - assert.deepEqual(query_df.values, query_data); - }); - it("Print Error for value key not specified", function () { - - let data = [ [ 1, 2, 3 ], [ 4, 5, 6 ], [ 20, 30, 40 ], [ 39, 89, 78 ] ]; - let cols = [ "A", "B", "C" ]; - let df = new dfd.DataFrame(data, { columns: cols }); - - assert.throws(function () { df.query({ "column": "B", "is": ">=" }); }, Error, "specify a value in param [to]"); - }); - it("Print Error for operator key not specified", function () { - - let data = [ [ 1, 2, 3 ], [ 4, 5, 6 ], [ 20, 30, 40 ], [ 39, 89, 78 ] ]; - let cols = [ "A", "B", "C" ]; - let df = new dfd.DataFrame(data, { columns: cols }); - - assert.throws(function () { df.query({ "column": "B", "to": 5 }); }, Error, "specify an operator in param [is]"); - }); - - it("Print Error for column key not specified", function () { - - let data = [ [ 1, 2, 3 ], [ 4, 5, 6 ], [ 20, 30, 40 ], [ 39, 89, 78 ] ]; - let cols = [ "A", "B", "C" ]; - let df = new dfd.DataFrame(data, { columns: cols }); - - assert.throws(function () { df.query({ "is": ">=", "to": 5 }); }, Error, "specify the column"); - }); - it("Print Error for column name not in dataframe", function () { - - let data = [ [ 1, 2, 3 ], [ 4, 5, 6 ], [ 20, 30, 40 ], [ 39, 89, 78 ] ]; - let cols = [ "A", "B", "C" ]; - let df = new dfd.DataFrame(data, { columns: cols }); - - assert.throws(function () { df.query({ "column": "D", "is": ">=", "to": 5 }); }, Error, "column D does not exist"); - }); - it("Confirms that query index are updated", function () { - - let data = [ [ 1, 2, 3 ], [ 4, 5, 6 ], [ 20, 30, 40 ], [ 39, 89, 78 ] ]; - let cols = [ "A", "B", "C" ]; - let df = new dfd.DataFrame(data, { columns: cols }); - let df_query = df.query({ "column": "B", "is": ">=", "to": 5 }); - assert.deepEqual(df_query.index, [ 1, 2, 3 ]); - }); - it("Confirms that columns data are updated inplace", function () { - - let data = [ [ 1, 2, 3 ], [ 4, 5, 6 ], [ 20, 30, 40 ], [ 39, 89, 78 ] ]; - let cols = [ "A", "B", "C" ]; - let df = new dfd.DataFrame(data, { columns: cols }); - df.query({ "column": "B", "is": ">=", "to": 5, inplace: true }); - assert.deepEqual(df.col_data, [ [ 4, 20, 39 ], [ 5, 30, 89 ], [ 6, 40, 78 ] ]); - }); - it("Confirms that query happens inplace", function () { - - let data = [ [ 1, 2, 3 ], [ 4, 5, 6 ], [ 20, 30, 40 ], [ 39, 89, 78 ] ]; - let cols = [ "A", "B", "C" ]; - let df = new dfd.DataFrame(data, { columns: cols }); - df.query({ "column": "B", "is": ">=", "to": 5, inplace: true }); - let query_data = [ [ 4, 5, 6 ], [ 20, 30, 40 ], [ 39, 89, 78 ] ]; - assert.deepEqual(df.values, query_data); - }); - it("Confirms that query happens inplace and index are updated", function () { - - let data = [ [ 1, 2, 3 ], [ 4, 5, 6 ], [ 20, 30, 40 ], [ 39, 89, 78 ] ]; - let cols = [ "A", "B", "C" ]; - let df = new dfd.DataFrame(data, { columns: cols }); - df.query({ "column": "B", "is": ">=", "to": 5, inplace: true }); - assert.deepEqual(df.index, [ 1, 2, 3 ]); - }); - it("Wrong query value", function () { - let data = { - "A": [ 30, 1, 2, 3 ], - "B": [ 34, 4, 5, 6 ], - "C": [ 20, 20, 30, 40 ] - }; - - let cols = [ "A", "B", "C" ]; - let df = new dfd.DataFrame(data, { columns: cols }); - - assert.throws(function () { df.query({ "column": "B", "is": ">", "to": 40 }); }, Error, "query returned empty data; is either 40 does not exist in column B"); - }); - - }); - - describe("addColumn", function () { - it("Print the data, after changing a column data", function () { - let data = [ [ 1, 2, 3 ], [ 4, 5, 6 ], [ 20, 30, 40 ], [ 39, 89, 78 ] ]; - let cols = [ "A", "B", "C" ]; - let df = new dfd.DataFrame(data, { columns: cols }); - - let new_col = [ 1, 2, 3, 4 ]; - - df.addColumn({ "column": "C", "value": new_col }); - - let new_data = [ [ 1, 2, 1 ], [ 4, 5, 2 ], [ 20, 30, 3 ], [ 39, 89, 4 ] ]; - - assert.deepEqual(df.values, new_data); - }); - it("Print the Dataframe column names, after a new column is added ", function () { - let data = [ [ 1, 2, 3 ], [ 4, 5, 6 ], [ 20, 30, 40 ], [ 39, 89, 78 ] ]; - let cols = [ "A", "B", "C" ]; - let df = new dfd.DataFrame(data, { columns: cols }); - - let new_col = [ 1, 2, 3, 4 ]; - - df.addColumn({ "column": "D", "value": new_col }); - - let new_column = [ "A", "B", "C", "D" ]; - - assert.deepEqual(df.column_names, new_column); - }); - it("Print Error for column name not in keyword passed", function () { - - let data = [ [ 1, 2, 3 ], [ 4, 5, 6 ], [ 20, 30, 40 ], [ 39, 89, 78 ] ]; - let cols = [ "A", "B", "C" ]; - let df = new dfd.DataFrame(data, { columns: cols }); - let new_col = [ 1, 2, 3, 4 ]; - - assert.throws(function () { df.addColumn({ "value": new_col }); }, Error, "column name not specified"); - }); - it("Check if new column value length is the same with Dataframe length", function () { - let data = [ [ 1, 2, 3 ], [ 4, 5, 6 ], [ 20, 30, 40 ], [ 39, 89, 78 ] ]; - let cols = [ "A", "B", "C" ]; - let df = new dfd.DataFrame(data, { columns: cols }); - let new_col = [ 1, 2, 3 ]; - assert.throws(function () { df.addColumn({ "column": "D", "value": new_col }); }, Error, "Array length 3 not equal to 4"); - }); - - it("Check that dtype is updated after a new column is added ", function () { - let data = [ [ 1, 2, 3 ], [ 4, 5, 6 ], [ 20, 30, 40 ], [ 39, 89, 78 ] ]; - let cols = [ "A", "B", "C" ]; - let df = new dfd.DataFrame(data, { columns: cols }); - let new_col = [ "n", "b", "c", "f" ]; - - df.addColumn({ "column": "D", "value": new_col }); - let dtype = [ "int32", "int32", "int32", "string" ]; - - assert.deepEqual(df.dtypes, dtype); - }); - - it("add series as value to a new column ", function () { - let data = [ [ 1, 2, 3 ], [ 4, 5, 6 ], [ 20, 30, 40 ], [ 39, 89, 78 ] ]; - let cols = [ "A", "B", "C" ]; - let df = new dfd.DataFrame(data, { columns: cols }); - let sf = new dfd.Series([ "n", "b", "c", "f" ]); - - df.addColumn({ "column": "D", "value": sf }); - let dtype = [ "int32", "int32", "int32", "string" ]; - - assert.deepEqual(df.dtypes, dtype); - }); - - }); - - // describe("groupby", function () { - // it("Check group by One column data", function () { - - // let data = [[1, 2, 3], [4, 5, 6], [20, 30, 40], [39, 89, 78]] - // let cols = ["A", "B", "C"] - // let df = new dfd.DataFrame(data, { columns: cols }) - // let group_df = df.groupby(["A"]); - - // let group_dict = { - // '1': [[1, 2, 3]], - // '4': [[4, 5, 6]], - // '20': [[20, 30, 40]], - // '39': [[39, 89, 78]] - // } - - // assert.deepEqual(group_df.col_dict, group_dict); - // }); - // it("Obtain the DataFrame of one of the group", function () { - - // let data = [[1, 2, 3], [4, 5, 6], [20, 30, 40], [39, 89, 78]] - // let cols = ["A", "B", "C"] - // let df = new dfd.DataFrame(data, { columns: cols }) - // let group_df = df.groupby(["A"]); - // let new_data = [[1, 2, 3]] - - // assert.deepEqual(group_df.get_groups([1]).values, new_data); - // }); - // it("Check group by Two column data", function () { - - // let data = [[1, 2, 3], [4, 5, 6], [20, 30, 40], [39, 89, 78]] - // let cols = ["A", "B", "C"] - // let df = new dfd.DataFrame(data, { columns: cols }) - // let group_df = df.groupby(["A", "B"]); - // let new_data = { - // '1': { '2': [[1, 2, 3]] }, - // '4': { '5': [[4, 5, 6]] }, - // '20': { '30': [[20, 30, 40]] }, - // '39': { '89': [[39, 89, 78]] } - // } - - // assert.deepEqual(group_df.col_dict, new_data); - // }); - - // it("Obtain the DataFrame of one of the group, grouped by two column", function () { - - // let data = [[1, 2, 3], [4, 5, 6], [20, 30, 40], [39, 89, 78]] - // let cols = ["A", "B", "C"] - // let df = new dfd.DataFrame(data, { columns: cols }) - // let group_df = df.groupby(["A", "B"]); - // let new_data = [[1, 2, 3]] - - // assert.deepEqual(group_df.get_groups([1, 2]).values, new_data); - // }); - - // it("Count column in group", function () { - - // let data = [[1, 2, 3], [4, 5, 6], [20, 30, 40], [39, 89, 78]] - // let cols = ["A", "B", "C"] - // let df = new dfd.DataFrame(data, { columns: cols }) - // let group_df = df.groupby(["A", "B"]); - // let new_data = { - // '1': { '2': [1] }, - // '4': { '5': [1] }, - // '20': { '30': [1] }, - // '39': { '89': [1] } - // } - - // assert.deepEqual(group_df.col(["C"]).count(), new_data); - // }); - // it("sum column element in group", function () { - - // let data = [[1, 2, 3], [4, 5, 6], [20, 30, 40], [39, 89, 78]] - // let cols = ["A", "B", "C"] - // let df = new dfd.DataFrame(data, { columns: cols }) - // let group_df = df.groupby(["A", "B"]); - // let new_data = { - // '1': { '2': [3] }, - // '4': { '5': [6] }, - // '20': { '30': [40] }, - // '39': { '89': [78] } - // } - - // assert.deepEqual(group_df.col(["C"]).sum(), new_data); - // }); - - // it("sum column element group by one column", function () { - - // let data = [[1, 2, 3], [4, 5, 6], [20, 30, 40], [39, 89, 78]] - // let cols = ["A", "B", "C"] - // let df = new dfd.DataFrame(data, { columns: cols }) - // let group_df = df.groupby(["A"]); - - // let new_data = { '1': [2, 3], '4': [5, 6], '20': [30, 40], '39': [89, 78] } - - // assert.deepEqual(group_df.col(["B", "C"]).sum(), new_data); - // }); - - // it("Perform aggregate on column for groupby", function () { - - // let data = [[1, 2, 3], [4, 5, 6], [20, 30, 40], [39, 89, 78]] - // let cols = ["A", "B", "C"] - // let df = new dfd.DataFrame(data, { columns: cols }) - // let group_df = df.groupby(["A", "B"]); - // let new_data = { - // '1': { '2': [2, 1] }, - // '4': { '5': [5, 1] }, - // '20': { '30': [30, 1] }, - // '39': { '89': [89, 1] } - // } - - // assert.deepEqual(group_df.agg({ "B": "mean", "C": "count" }), new_data); - // }); - - - // }); - - describe("column", function () { - it("Obtain a column from a dataframe created from object", function () { - let data = [ { alpha: "A", count: 1 }, { alpha: "B", count: 2 }, { alpha: "C", count: 3 } ]; - let options = { columns: [ "Gender", "count" ] }; - let df = new dfd.DataFrame(data, options); - let col_data = df.column("count"); - let rslt_data = [ 1, 2, 3 ]; - assert.deepEqual(col_data.values, rslt_data); - }); - it("Obtain a column from a dataframe", function () { - let data = [ [ 1, 2, 3 ], [ 4, 5, 6 ], [ 20, 30, 40 ], [ 39, 89, 78 ] ]; - let cols = [ "A", "B", "C" ]; - let df = new dfd.DataFrame(data, { columns: cols }); - let col_data = df.column("C"); - let rslt_data = [ 3, 6, 40, 78 ]; - assert.deepEqual(col_data.values, rslt_data); - }); - it("Throw Error for wrong column", function () { - let data = [ [ 1, 2, 3 ], [ 4, 5, 6 ], [ 20, 30, 40 ], [ 39, 89, 78 ] ]; - let cols = [ "A", "B", "C" ]; - let df = new dfd.DataFrame(data, { columns: cols }); - assert.throws(() => { df.column("D"); }, Error, "column D does not exist"); - - }); - }); - - // describe("Concatenate", function () { - - // it("Check the axis 0 concatenation", function () { - // let data = [[1, 2, 3], [4, 5, 6], [20, 30, 40], [39, 89, 78]] - // let cols = ["A", "B", "C"] - // let df = new dfd.DataFrame(data, { columns: cols }) - - // let data1 = [[1, 2, 3], [4, 5, 6], [20, 30, 40], [39, 89, 78]] - // let cols1 = ["A", "B", "C"] - // let df1 = new dfd.DataFrame(data1, { columns: cols1 }) - - // let data2 = [[1, 2, 3, 5], [4, 5, 6, 8], [20, 30, 40, 10]] - // let cols2 = ["A", "B", "C", "D"] - // let df2 = new dfd.DataFrame(data2, { columns: cols2 }) - - // let new_df = DataFrame.concat({ "df_list": [df, df1, df2], "axis": 0 }) - - // let data_values = [[1, 2, 3, NaN], [4, 5, 6, NaN], [20, 30, 40, NaN], [39, 89, 78, NaN], - // [1, 2, 3, NaN], [4, 5, 6, NaN], [20, 30, 40, NaN], [39, 89, 78, NaN], - // [1, 2, 3, 5], [4, 5, 6, 8], [20, 30, 40, 10]] - - // assert.deepEqual(new_df.values, data_values); - // }); - - // it("Check the axis 1 concatenation", function () { - // let data = [[1, 2, 3], [4, 5, 6], [20, 30, 40], [39, 89, 78]] - // let cols = ["A", "B", "C"] - // let df = new dfd.DataFrame(data, { columns: cols }) - - // let data1 = [[1, 2, 3], [4, 5, 6], [20, 30, 40], [39, 89, 78]] - // let cols1 = ["A", "B", "C"] - // let df1 = new dfd.DataFrame(data1, { columns: cols1 }) - - // let data2 = [[1, 2, 3, 5], [4, 5, 6, 8], [20, 30, 40, 10]] - // let cols2 = ["A", "B", "C", "D"] - // let df2 = new dfd.DataFrame(data2, { columns: cols2 }) - - // let new_df = DataFrame.concat({ "df_list": [df, df1, df2], "axis": 1 }) - - // let data_values = [[1, 2, 3, 1, 2, 3, 1, 2, 3, 5], [4, 5, 6, 4, 5, 6, 4, 5, 6, 8], - // [20, 30, 40, 20, 30, 40, 20, 30, 40, 10], [39, 89, 78, 39, 89, 78, NaN, - // NaN, NaN, NaN]] - // assert.deepEqual(new_df.values, data_values); - // }); - // }); - - - describe("Apply", function () { - it("Apply math operation on dataframe element wise", function () { - let data = [ [ 1, 2, 3 ], [ 4, 5, 6 ], [ 20, 30, 40 ], [ 39, 89, 78 ] ]; - let cols = [ "A", "B", "C" ]; - let df = new dfd.DataFrame(data, { columns: cols }); - let rslt = [ [ 2, 3, 4 ], [ 5, 6, 7 ], [ 21, 31, 41 ], [ 40, 90, 79 ] ]; - - let apply_rslt = df.apply({ - callable: (x) => { - return x + 1; - } - }); - - assert.deepEqual(apply_rslt.values, rslt); - }); - - it("Apply string function on all elements of a dataframe", function () { - let data = [ [ "BOY", "GIRL", "ALL" ], [ "Man", "Woman", "Girl" ] ]; - let df = new dfd.DataFrame(data); - let rslt = [ [ "boy", "girl", "all" ], [ "man", "woman", "girl" ] ]; - - let apply_rslt = df.apply({ - callable: (x) => { - return x.toLowerCase(); - - } - }); - assert.deepEqual(apply_rslt.values, rslt); - }); - - it("Throws error if you try to run a function that does not operate on axis and axis is specified", function () { - let data = [ [ "BOY", "GIRL", "ALL" ], [ "Man", "Woman", "Girl" ] ]; - let df = new dfd.DataFrame(data); - - let err = `Callable Error: You can only apply JavaScript functions on DataFrames when axis is not specified. This operation is applied on all element, and returns a DataFrame of the same shape.`; - - assert.throws(() => { - df.apply({ - axis: 0, callable: (x) => { - return x.toLowerCase(); - } - }); - }, Error, err); - - }); - - it("Apply math operation on dataframe at axis 1", function () { - let data = [ [ 1, 2, 3 ], - [ 4, 5, 6 ], - [ 20, 30, 40 ], - [ 39, 89, 78 ] ]; - let cols = [ "A", "B", "C" ]; - let df = new dfd.DataFrame(data, { columns: cols }); - let rslt = [ 64, 126, 127 ]; - - let apply_rslt = df.apply({ - axis: 1, callable: (x) => { - return x.sum(); - } - }); - - assert.deepEqual(apply_rslt.values, rslt); - }); - - it("Apply tensor operation on dataframe at axis 0", function () { - let data = [ [ 1, 2, 3 ], [ 4, 5, 6 ], [ 20, 30, 40 ], [ 39, 89, 78 ] ]; - let cols = [ "A", "B", "C" ]; - let df = new dfd.DataFrame(data, { columns: cols }); - - let rslt = [ 6, 15, 90, 206 ]; - - let apply_rslt = df.apply({ - axis: 0, callable: (x) => { - return x.sum(); - } - }); - - assert.deepEqual(apply_rslt.values, rslt); - - }); - - it("Apply add operation element wise dataframe on axis 1", function () { - let data = [ [ 1, 2, 3 ], [ 4, 5, 6 ], [ 20, 30, 40 ], [ 39, 89, 78 ] ]; - let cols = [ "A", "B", "C" ]; - let df = new dfd.DataFrame(data, { columns: cols }); - - let result = [ [ 2, 3, 4 ], - [ 5, 6, 7 ], - [ 21, 31, 41 ], - [ 40, 90, 79 ] ]; - - let apply_rslt = df.apply({ - axis: 0, callable: (x) => { - return x.add(1); - } - }); - - assert.deepEqual(apply_rslt.values, result); - }); - }); - - describe("dropna", function () { - it("drop inplace at axis 0 at inplace false", function () { - let data = [ [ NaN, 1, 2, 3 ], [ 3, 4, NaN, 9 ], [ 5, 6, 7, 8 ] ]; - let column = [ "A", "B", "C", "D" ]; - let df = new dfd.DataFrame(data, { columns: column }); - - let df_val = [ [ 5, 6, 7, 8 ] ]; - - assert.deepEqual(df.dropna().values, df_val); - - }); - it("drop inplace at axis 1, inplace false ", function () { - let data = [ [ NaN, 1, 2, 3 ], [ 3, 4, NaN, 9 ], [ 5, 6, 7, 8 ] ]; - let column = [ "A", "B", "C", "D" ]; - let df = new dfd.DataFrame(data, { columns: column }); - - let df_val = [ [ 1, 3 ], [ 4, 9 ], [ 6, 8 ] ]; - - assert.deepEqual(df.dropna({ axis: 1 }).values, df_val); - - }); - it("drop inplace at axis 1, inplace true ", function () { - let data = [ [ NaN, 1, 2, 3 ], [ 3, 4, NaN, 9 ], [ 5, 6, 7, 8 ] ]; - let column = [ "A", "B", "C", "D" ]; - let df = new dfd.DataFrame(data, { columns: column }); - - let df_val = [ [ 1, 3 ], [ 4, 9 ], [ 6, 8 ] ]; - df.dropna({ axis: 1, inplace: true }); - - - assert.deepEqual(df.values, df_val); - - }); - it("drop inplace at axis 0 at inplace true", function () { - let data = [ [ NaN, 1, 2, 3 ], [ 3, 4, NaN, 9 ], [ 5, 6, 7, 8 ] ]; - let column = [ "A", "B", "C", "D" ]; - let df = new dfd.DataFrame(data, { columns: column }); - - let df_val = [ [ 5, 6, 7, 8 ] ]; - - df.dropna({ inplace: true }); - assert.deepEqual(df.values, df_val); - - }); - }); - - describe("isna", function () { - - it("check if each value are nan", function () { - let data = [ [ NaN, 1, 2, 3 ], [ 3, 4, undefined, 9 ], [ 5, 6, 7, 8 ] ]; - let column = [ "A", "B", "C", "D" ]; - let df = new dfd.DataFrame(data, { columns: column }); - - let df_val = [ - [ true, false, false, false ], - [ false, false, true, false ], - [ false, false, false, false ] - ]; - - assert.deepEqual(df.isna().values, df_val); - }); - }); - - describe("fillna", function () { - - it("replace all NaN value", function () { - let data = [ [ NaN, 1, 2, 3 ], [ 3, 4, NaN, 9 ], [ 5, 6, 7, 8 ] ]; - let column = [ "A", "B", "C", "D" ]; - let df = new dfd.DataFrame(data, { columns: column }); - - let df_val = [ [ -999, 1, 2, 3 ], [ 3, 4, -999, 9 ], [ 5, 6, 7, 8 ] ]; - df.fillna({ values: -999, inplace: true }); - assert.deepEqual(df.values, df_val); - }); - it("replace all NaN value", function () { - let data = [ [ NaN, 1, 2, 3 ], [ 3, 4, NaN, 9 ], [ 5, 6, 7, 8 ] ]; - let column = [ "A", "B", "C", "D" ]; - let df = new dfd.DataFrame(data, { columns: column }); - - let df_val = [ [ -999, 1, 2, 3 ], [ 3, 4, -999, 9 ], [ 5, 6, 7, 8 ] ]; - - let df_filled = df.fillna({ values: [ -999 ] }); - assert.deepEqual(df_filled.values, df_val); - }); - - it("Fills only a specified column", function () { - let data = [ [ 1, 2, 3 ], - [ 4, 5, 6 ], - [ 20, NaN, 40 ], - [ 39, NaN, 78 ] ]; - let cols = [ "A", "B", "C" ]; - let df = new dfd.DataFrame(data, { columns: cols }); - let new_vals = [ [ 1, 2, 3 ], [ 4, 5, 6 ], [ 20, 2, 40 ], [ 39, 2, 78 ] ]; - let df_filled = df.fillna({ columns: [ "B" ], values: [ 2 ] }); - - assert.deepEqual(df_filled.values, new_vals); - }); - it("Fills column with specified values not in place", function () { - let data = [ [ 1, 2, 3 ], [ 4, 5, 6 ], [ NaN, 20, 40 ], [ NaN, -1, 78 ] ]; - let cols = [ "A", "B", "C" ]; - let df = new dfd.DataFrame(data, { columns: cols }); - let new_vals = [ [ 1, 2, 3 ], [ 4, 5, 6 ], [ -2, 20, 40 ], [ -2, -1, 78 ] ]; - let df_filled = df.fillna({ columns: [ "A" ], values: [ -2 ] }); - - assert.deepEqual(df_filled.values, new_vals); - }); - - it("Fills a list of columns with specified values", function () { - let data = [ [ 1, undefined, 3 ], [ 4, undefined, 6 ], [ NaN, "boy", 40 ], [ NaN, "girl", 78 ] ]; - let cols = [ "A", "B", "C" ]; - let df = new dfd.DataFrame(data, { columns: cols }); - let new_vals = [ [ 1, "girl", 3 ], [ 4, "girl", 6 ], [ 200, "boy", 40 ], [ 200, "girl", 78 ] ]; - let df_filled = df.fillna({ columns: [ "A", "B" ], values: [ 200, "girl" ] }); - assert.deepEqual(df_filled.values, new_vals); - }); - it("Fills a list of columns with specified values inplace", function () { - let data = [ [ 1, undefined, 3 ], [ 4, undefined, 6 ], [ NaN, "boy", 40 ], [ NaN, "girl", 78 ] ]; - let cols = [ "A", "B", "C" ]; - let df = new dfd.DataFrame(data, { columns: cols }); - let new_vals = [ [ 1, "girl", 3 ], [ 4, "girl", 6 ], [ 200, "boy", 40 ], [ 200, "girl", 78 ] ]; - df.fillna({ columns: [ "A", "B" ], values: [ 200, "girl" ], inplace: true }); - assert.deepEqual(df.values, new_vals); - }); - }); - - - describe("nanindex", function () { - - it("print out the nanIndex", function () { - let data = [ [ NaN, 1, 2, 3 ], [ 3, 4, NaN, 9 ], [ 5, 6, 7, 8 ] ]; - let column = [ "A", "B", "C", "D" ]; - let df = new dfd.DataFrame(data, { columns: column }); - - let df_val = [ 0, 1 ]; - assert.deepEqual(df.nanIndex(), df_val); - }); - }); - - describe("select_dtypes", function () { - - it("Returns float columns in a DataFrame", function () { - let data = [ [ 30, 1, 2, "boy" ], [ 3.2, 4, 30, "girl" ], [ 5.09, 6, 7, "cat" ] ]; - let column = [ "A", "B", "C", "D" ]; - let df = new dfd.DataFrame(data, { columns: column }); - let df_sub = df.select_dtypes([ 'float32' ]); - assert.deepEqual(df_sub.col_data, [ [ 30, 3.2, 5.09 ] ]); - }); - - it("Returns int columns in a DataFrame", function () { - let data = [ [ 30, 1, 2, "boy" ], - [ 3.2, 4, 30, "girl" ], - [ 5.09, 6, 7, "cat" ] ]; - let column = [ "A", "B", "C", "D" ]; - let df = new dfd.DataFrame(data, { columns: column }); - let df_sub = df.select_dtypes([ 'int32' ]); - assert.deepEqual(df_sub.values, [ [ 1, 2 ], [ 4, 30 ], [ 6, 7 ] ]); - }); - - it("Returns string columns in a DataFrame", function () { - let data = [ [ 30, 1, 2, "boy" ], - [ 3.2, 4, 30, "girl" ], - [ 5.09, 6, 7, "cat" ] ]; - let column = [ "A", "B", "C", "D" ]; - let df = new dfd.DataFrame(data, { columns: column }); - let df_sub = df.select_dtypes([ 'string' ]); - assert.deepEqual(df_sub.col_data, [ [ "boy", "girl", "cat" ] ]); - }); - - it("Returns string and float columns in a DataFrame", function () { - let data = [ [ 30, 1, 2, "boy" ], - [ 3.2, 4, 30, "girl" ], - [ 5.09, 6, 7, "cat" ] ]; - let column = [ "A", "B", "C", "D" ]; - let df = new dfd.DataFrame(data, { columns: column }); - let df_sub = df.select_dtypes([ 'string', 'float32' ]); - assert.deepEqual(df_sub.col_data, [ [ 30, 3.2, 5.09 ], [ "boy", "girl", "cat" ] ]); - }); - - it("Returns int and float columns in a DataFrame", function () { - let data = [ [ 30, 1, 2, "boy" ], - [ 3.2, 4, 30, "girl" ], - [ 5.09, 6, 7, "cat" ] ]; - let column = [ "A", "B", "C", "D" ]; - let df = new dfd.DataFrame(data, { columns: column }); - let df_sub = df.select_dtypes([ 'int32', 'float32' ]); - assert.deepEqual(df_sub.values, [ [ 30, 1, 2 ], [ 3.2, 4, 30 ], [ 5.09, 6, 7 ] ]); - }); - }); - - describe("cum_ops", function () { - - it("check cumsum data", function () { - let data = [ [ 2, 1, 2, 3 ], [ 3, 4, 11, 9 ], [ 5, 6, 7, 8 ] ]; - let column = [ "A", "B", "C", "D" ]; - let df = new dfd.DataFrame(data, { columns: column }); - let rslt = [ [ 2, 1, 2, 3 ], [ 5, 5, 13, 12 ], [ 10, 11, 20, 20 ] ]; - - assert.deepEqual(df.cumsum().values, rslt); - }); - it("check cumsum data along axis 1", function () { - let data = [ [ 2, 1, 2, 3 ], [ 3, 4, 11, 9 ], [ 5, 6, 7, 8 ] ]; - let column = [ "A", "B", "C", "D" ]; - let df = new dfd.DataFrame(data, { columns: column }); - let rslt = [ [ 2, 3, 5, 8 ], [ 3, 7, 18, 27 ], [ 5, 11, 18, 26 ] ]; - - assert.deepEqual(df.cumsum({ axis: 1 }).values, rslt); - }); - }); - - describe("__set_column_property", async function () { - it("Access column object using list subset and name of column", async function () { - let data = [ { alpha: "A", count: 1 }, { alpha: "B", count: 2 }, { alpha: "C", count: 3 } ]; - let df = new dfd.DataFrame(data); - let col1 = [ "A", "B", "C" ]; - let col2 = [ 1, 2, 3 ]; - assert.deepEqual(df['alpha'].values, col1); - assert.deepEqual(df['count'].values, col2); - }); - it("Access column object using list subset and name of column after assigning", function () { - let data = [ [ 1, 2, 3 ], [ 4, 5, 6 ] ]; - let cols = [ "A", "B", "C" ]; - let df = new dfd.DataFrame(data, { columns: cols }); - df["A"] = [ 30, 40 ]; - let col1 = [ 30, 40 ]; - assert.deepEqual(df["A"].values, col1); - }); - }); - - describe("lt", function () { - it("Returns Less than of DataFrame and other DataFrame (element-wise)", function () { - let data1 = [ [ 10, 45, 56, 10 ], [ 25, 23, 20, 10 ] ]; - let data2 = [ [ 100, 450, 590, 5 ], [ 25, 2, 0, 10 ] ]; - - let df = new dfd.DataFrame(data1); - let df2 = new dfd.DataFrame(data2); - let expected = [ [ true, true, true, false ], - [ false, false, false, false ] ]; - assert.deepEqual(df.lt(df2).values, expected); - }); - - it("Return Less than of series scalar (element-wise)", function () { - let data1 = [ [ 10, 45, 56, 10 ], [ 25, 23, 20, 10 ] ]; - let sf = new dfd.DataFrame(data1); - let expected = [ [ true, false, false, true ], - [ true, true, true, true ] ]; - assert.deepEqual(sf.lt(30).values, expected); - }); - it("Return Less than of series and DataFrame scalar along axis 1 (column)", function () { - let data1 = [ [ 10, 45, 56, 10 ], - [ 23, 20, 10, 10 ] ]; - let sf = new dfd.Series([ 10, 23, 56, 100 ]); - let df = new dfd.DataFrame(data1); - let expected = [ [ false, false, false, true ], [ false, true, true, true ] ]; - assert.deepEqual(df.lt(sf, 1).values, expected); - }); - - it("Return Less than of Array and DataFrame scalar along axis 1 (column)", function () { - let data1 = [ [ 10, 45, 56, 10 ], [ 23, 20, 10, 10 ] ]; - let sf = [ 10, 23, 56, 100 ]; - let df = new dfd.DataFrame(data1); - let expected = [ [ false, false, false, true ], [ false, true, true, true ] ]; - assert.deepEqual(df.lt(sf, 1).values, expected); - }); - - }); - - describe("gt", function () { - it("Return Greater than of series and other series (element-wise)", function () { - let data1 = [ [ 10, 45, 56, 10 ], [ 25, 23, 20, 10 ] ]; - let data2 = [ [ 100, 450, 590, 5 ], [ 25, 2, 0, 10 ] ]; - - let df = new dfd.DataFrame(data1); - let df2 = new dfd.DataFrame(data2); - let expected = [ [ false, false, false, true ], [ false, true, true, false ] ]; - assert.deepEqual(df.gt(df2).values, expected); - }); - - it("Return Greater than of series scalar (element-wise)", function () { - let data1 = [ [ 10, 45, 56, 10 ], [ 25, 23, 20, 10 ] ]; - let sf = new dfd.DataFrame(data1); - let expected = [ [ false, true, true, false ], [ false, false, false, false ] ]; - assert.deepEqual(sf.gt(30).values, expected); - }); - - it("Return Less than of Array and DataFrame scalar along axis 1 (column)", function () { - let data1 = [ [ 10, 45, 56, 10 ], [ 23, 20, 10, 10 ] ]; - let sf = [ 10, 23, 56, 100 ]; - let df = new dfd.DataFrame(data1); - let expected = [ [ false, true, false, false ], [ true, false, false, false ] ]; - assert.deepEqual(df.gt(sf, 1).values, expected); - }); - - }); - - describe("le", function () { - it("Return Less than or Equal to of series and other series (element-wise)", function () { - let data1 = [ [ 10, 45, 56, 10 ], [ 25, 23, 20, 10 ] ]; - let data2 = [ [ 100, 450, 590, 5 ], [ 25, 2, 0, 10 ] ]; - - let df = new dfd.DataFrame(data1); - let df2 = new dfd.DataFrame(data2); - let expected = [ [ true, true, true, false ], [ true, false, false, true ] ]; - assert.deepEqual(df.le(df2).values, expected); - }); - - it("Return Less than or Equal to of series scalar (element-wise)", function () { - let data1 = [ [ 10, 45, 56, 10 ], [ 25, 23, 30, 10 ] ]; - let sf = new dfd.DataFrame(data1); - let expected = [ [ true, false, false, true ], [ true, true, true, true ] ]; - assert.deepEqual(sf.le(30).values, expected); - }); - - }); - - describe("ge", function () { - it("Return Greater than or Equal to of series and other series (element-wise)", function () { - let data1 = [ [ 10, 45, 56, 10 ], [ 25, 23, 20, 10 ] ]; - let data2 = [ [ 100, 450, 590, 5 ], [ 25, 2, 0, 10 ] ]; - - let df = new dfd.DataFrame(data1); - let df2 = new dfd.DataFrame(data2); - let expected = [ [ false, false, false, true ], [ true, true, true, true ] ]; - assert.deepEqual(df.ge(df2).values, expected); - }); - - it("Return Greater than or Equal to of series scalar (element-wise)", function () { - let data1 = [ [ 10, 45, 56, 10 ], [ 25, 23, 30, 10 ] ]; - let sf = new dfd.DataFrame(data1); - let expected = [ [ false, true, true, false ], [ false, false, true, false ] ]; - assert.deepEqual(sf.ge(30).values, expected); - }); - - }); - - describe("ne", function () { - it("Return Not Equal to of series and other series (element-wise)", function () { - let data1 = [ [ 10, 45, 56, 10 ], [ 25, 23, 20, 10 ] ]; - let data2 = [ [ 100, 450, 590, 5 ], [ 25, 2, 0, 10 ] ]; - - let df = new dfd.DataFrame(data1); - let df2 = new dfd.DataFrame(data2); - let expected = [ [ true, true, true, true ], [ false, true, true, false ] ]; - assert.deepEqual(df.ne(df2).values, expected); - }); - - it("Return Not Equal to of series scalar (element-wise)", function () { - let data1 = [ [ 10, 45, 56, 10 ], [ 25, 23, 30, 10 ] ]; - let sf = new dfd.DataFrame(data1); - let expected = [ [ true, true, true, true ], [ true, true, false, true ] ]; - assert.deepEqual(sf.ne(30).values, expected); - }); - - }); - - describe("eq", function () { - it("Return Equal to of DataFrame and other DataFrame (element-wise)", function () { - let data1 = [ [ 10, 45, 56, 10 ], [ 25, 23, 20, 10 ] ]; - let data2 = [ [ 100, 450, 590, 5 ], [ 25, 2, 0, 10 ] ]; - - let df = new dfd.DataFrame(data1); - let df2 = new dfd.DataFrame(data2); - let expected = [ [ false, false, false, false ], [ true, false, false, true ] ]; - assert.deepEqual(df.eq(df2).values, expected); - }); - - it("Return Equal to of DataFrame with scalar (element-wise)", function () { - let data1 = [ [ 10, 45, 56, 10 ], [ 25, 23, 30, 10 ] ]; - let sf = new dfd.DataFrame(data1); - let expected = [ [ false, false, false, false ], [ false, false, true, false ] ]; - assert.deepEqual(sf.eq(30).values, expected); - }); - it("Return Equal to of series and DataFrame scalar along axis 1 (column)", function () { - let data1 = { "Col1": [ 10, 45, 56, 10 ], "Col2": [ 23, 20, 10, 10 ] }; - let sf = new dfd.Series([ 10, 23 ]); - let df = new dfd.DataFrame(data1); - let expected = [ [ true, false, false, true ], [ true, false, false, false ] ]; - assert.deepEqual(df.eq(sf, 1).col_data, expected); - }); - - }); - - describe("replace", function () { - it("Replace values given in replace param", function () { - let data1 = [ [ 10, 45, 56, 25 ], [ 23, 20, 10, 24 ] ]; - let sf = new dfd.DataFrame(data1); - let expected = [ [ -999, 45, 56, 25 ], [ 23, 20, -999, 24 ] ]; - let df_rep = sf.replace({ replace: 10, with: -999 }); - assert.deepEqual(df_rep.values, expected); - }); - - it("Replace values given in replace param with value (String type)", function () { - let data1 = [ [ "A", "A", "A", "B" ], [ "B", "C", "C", "D" ] ]; - let df = new dfd.DataFrame(data1); - let expected = [ [ "boy", "boy", "boy", "B" ], [ "B", "C", "C", "D" ] ]; - let df_rep = df.replace({ replace: "A", with: "boy" }); - assert.deepEqual(df_rep.values, expected); - }); - it("Throw error on wrong param passed", function () { - let data1 = [ [ "A", "A", "A", "B" ], [ "B", "C", "C", "D" ] ]; - let sf = new dfd.DataFrame(data1); - let expected = `Params Error: A specified parameter is not supported. Your params must be any of the following [replace,with,in]`; - assert.throws(() => { sf.replace({ replce: "A", with: "boy" }); }, Error, expected); - }); - it("Replace values in specified two column(s)", function () { - let data1 = [ [ "A", "A", 1, "girl" ], - [ "B", "A", 2, "woman" ], - [ "A", "B", 3, "man" ] ]; - let df = new dfd.DataFrame(data1, { columns: [ "col1", "col2", "col3", "col4" ] }); - let expected = [ [ "boy", "boy", 1, "girl" ], - [ "B", "boy", 2, "woman" ], - [ "boy", "B", 3, "man" ] ]; - let df_rep = df.replace({ replace: "A", with: "boy", in: [ "col1", "col2" ] }); - assert.deepEqual(df_rep.values, expected); - }); - - it("Replace values in specified single column(s)", function () { - let data1 = [ [ 2, "A", 1, "girl" ], - [ 3, "A", 2, "woman" ], - [ 4, "B", 3, "man" ] ]; - let df = new dfd.DataFrame(data1, { columns: [ "col1", "col2", "col3", "col4" ] }); - let expected = [ [ 2, "A", 1, "girl" ], - [ 100, "A", 2, "woman" ], - [ 4, "B", 3, "man" ] ]; - let df_rep = df.replace({ replace: 3, with: 100, in: [ "col1" ] }); - assert.deepEqual(df_rep.values, expected); - }); - - - }); - - describe("drop_duplicates", function () { - it("Return Series with duplicate values removed (Default, first values kept)", function () { - let data1 = [ 10, 45, 56, 10, 23, 20, 10, 10 ]; - let sf = new dfd.Series(data1); - let expected = [ 10, 45, 56, 23, 20 ]; - let expected_index = [ 0, 1, 2, 4, 5 ]; - let df_drop = sf.drop_duplicates(); - assert.deepEqual(df_drop.values, expected); - assert.deepEqual(df_drop.index, expected_index); - - }); - - it("Return Series with duplicate values removed (last values kept)", function () { - let data1 = [ 10, 45, 56, 10, 23, 20, 10, 10 ]; - let sf = new dfd.Series(data1); - let expected = [ 45, 56, 23, 20, 10 ]; - let expected_index = [ 1, 2, 4, 5, 7 ]; - let df_drop = sf.drop_duplicates({ keep: "last" }); - assert.deepEqual(df_drop.values, expected); - assert.deepEqual(df_drop.index, expected_index); - - }); - - it("Return Series with duplicate values removed (String)", function () { - let data1 = [ "A", "A", "A", "B", "B", "C", "C", "D" ]; - let sf = new dfd.Series(data1); - let expected = [ "A", "B", "C", "D" ]; - let expected_index = [ 0, 3, 5, 7 ]; - sf.drop_duplicates({ inplace: true }); - assert.deepEqual(sf.values, expected); - assert.deepEqual(sf.index, expected_index); - - }); - - }); - - describe("sum", function () { - it("Sum values of a DataFrame by Default axis column (axis=1)", function () { - let data1 = [ [ 30, 40, 3.1 ], - [ 5, 5, 5.1 ], - [ 5, 5, 3.2 ] ]; - let sf = new dfd.DataFrame(data1); - let res = [ 40, 50, 11.4 ]; - assert.deepEqual(sf.sum().values, res); - }); - it("Sum values of a DataFrame along row axis (axis=0)", function () { - let data1 = [ [ 30, 40, 3.1 ], - [ 5, 5, 5.1 ], - [ 5, 5, 3.2 ] ]; - let df = new dfd.DataFrame(data1); - let res = [ 73.1, 15.1, 13.2 ]; - assert.deepEqual(df.sum({ axis: 0 }).values, res); - }); - it("Sum values of a mixed DataFrame along row axis (axis=0)", function () { - let data1 = [ [ 30, 40, 3.1, true ], - [ 5, 5, 5.1, true ], - [ 5, 5, 3.2, true ] ]; - let df = new dfd.DataFrame(data1); - let res = [ 74.1, 16.1, 14.2 ]; - assert.deepEqual(df.sum({ axis: 0 }).values, res); - }); - it("Sum values of a boolean DataFrame along row axis (axis=0)", function () { - let data1 = [ [ true, true, false, true ], - [ false, false, false, false ], - [ false, true, true, false ] ]; - let df = new dfd.DataFrame(data1); - let res = [ 3, 0, 2 ]; - assert.deepEqual(df.sum({ axis: 0 }).values, res); - }); - it("Sum values of a boolean DataFrame along default column axis (axis=1)", function () { - let data1 = [ [ true, true, false, true ], - [ false, false, false, false ], - [ false, true, true, false ] ]; - let df = new dfd.DataFrame(data1); - let res = [ 1, 2, 1, 1 ]; - assert.deepEqual(df.sum().values, res); - }); - it("Sum values of a df with missing values", function () { - let data1 = [ [ 11, 20, 3 ], [ null, 15, 6 ], [ 2, 30, 40 ], [ 2, 89, 78 ] ]; - let df = new dfd.DataFrame(data1); - let res = [ 15, 154, 127 ]; - assert.deepEqual(df.sum().values, res); - }); - }); - - describe("abs", function () { - it("Returns the absolute values in DataFrame of ints", function () { - let data1 = [ [ -10, 45, 56, 10 ], [ -25, 23, 20, -10 ] ]; - let df = new dfd.DataFrame(data1); - let expected = [ [ 10, 45, 56, 10 ], [ 25, 23, 20, 10 ] ]; - assert.deepEqual(df.abs().values, expected); - }); - - it("Returns the absolute values in mixed DataFrame", function () { - let data1 = [ [ -10, -45.1, 56, 10 ], [ -25, -23.2, 20, -10 ] ]; - let df = new dfd.DataFrame(data1); - let expected = [ [ 10, 45.1, 56, 10 ], [ 25, 23.2, 20, 10 ] ]; - assert.deepEqual(df.abs().values, expected); - }); - }); - - describe("T", function () { - it("Returns the Tranpose of a DataFrame", function () { - let data1 = [ [ 10, 45, 56, 10 ], - [ 25, 23, 20, 10 ] ]; - - let cols = [ "a", "b", "c", "d" ]; - let df = new dfd.DataFrame(data1, { columns: cols }); - let df_trans = df.T; - let expected_vals = [ [ 10, 25 ], [ 45, 23 ], [ 56, 20 ], [ 10, 10 ] ]; - let expected_index = cols; - let expected_col_names = [ 0, 1 ]; - assert.deepEqual(df_trans.index, expected_index); - assert.deepEqual(df_trans.values, expected_vals); - assert.deepEqual(df_trans.column_names, expected_col_names); - - }); - - }); - - - describe("astype", function () { - it("set type of float column to int", function () { - let data = { - "A": [ -20.1, 30, 47.3, -20 ], - "B": [ 34, -4, 5, 6 ], - "C": [ 20.1, -20.23, 30.3, 40.11 ], - "D": [ "a", "b", "c", "c" ] - }; - let ndframe = new dfd.DataFrame(data); - let df = ndframe.astype({ column: "A", dtype: "int32" }); - - assert.deepEqual(df.dtypes, [ 'int32', 'int32', 'float32', 'string' ]); - assert.deepEqual(df['A'].values, [ -20, 30, 47, -20 ]); - - }); - it("set type of int column to float", function () { - let data = { - "A": [ -20.1, 30, 47.3, -20 ], - "B": [ 34, -4, 5, 6 ], - "C": [ 20.1, -20.23, 30.3, 40.11 ], - "D": [ "a", "b", "c", "c" ] - }; - let ndframe = new dfd.DataFrame(data); - let df = ndframe.astype({ column: "B", dtype: "float32" }); - - assert.deepEqual(df.dtypes, [ 'float32', 'float32', 'float32', 'string' ]); - assert.deepEqual(df['B'].values, [ 34, -4, 5, 6 ]); - - }); - it("set type of string column to int", function () { - let data = { - "A": [ -20.1, 30, 47.3, -20 ], - "B": [ 34, -4, 5, 6 ], - "C": [ 20.1, -20.23, 30.3, 40.11 ], - "D": [ "20.1", "21", "23.4", "50.78" ] - }; - let ndframe = new dfd.DataFrame(data); - let df = ndframe.astype({ column: "D", dtype: "int32" }); - - assert.deepEqual(df.dtypes, [ 'float32', 'int32', 'float32', 'int32' ]); - assert.deepEqual(df['D'].values, [ 20, 21, 23, 51 ]); - - }); - it("set type of string column to float", function () { - let data = { - "A": [ -20.1, 30, 47.3, -20 ], - "B": [ 34, -4, 5, 6 ], - "C": [ 20.1, -20.23, 30.3, 40.11 ], - "D": [ "20.1", "21", "23.4", "50.78" ] - }; - let ndframe = new dfd.DataFrame(data); - let df = ndframe.astype({ column: "D", dtype: "float32" }); - - assert.deepEqual(df.dtypes, [ 'float32', 'int32', 'float32', 'float32' ]); - assert.deepEqual(df['D'].values, [ 20.1, 21, 23.4, 50.78 ]); - - }); - }); - - - describe("nunique", function () { - it("Returns the number of unique elements along axis 1", function () { - let data = { - "A": [ -20, 30, 47.3, -20 ], - "B": [ 34, -4, 5, 6 ], - "C": [ 20, 20, 30, 30 ], - "D": [ "a", "b", "c", "c" ] - }; - - let ndframe = new dfd.DataFrame(data); - let df = ndframe.nunique(1); - let res = [ 3, 4, 2, 3 ]; - assert.deepEqual(df.values, res); - - }); - it("Returns the number of unique elements along axis 0", function () { - let data = { - "A": [ 20, 30, 47.3, 30 ], - "B": [ 34, -4, 5, 30 ], - "C": [ 20, 20, 30, 30 ], - "D": [ "a", "b", "c", "c" ] - }; - - let ndframe = new dfd.DataFrame(data); - let df = ndframe.nunique(0); - let res = [ 3, 4, 4, 2 ]; - assert.deepEqual(df.values, res); - - }); - - }); - - - describe("unique", function () { - it("Returns the unique elements along axis 1", function () { - let data = { - "A": [ -20, 30, 47.3, -20 ], - "B": [ 34, -4, 5, 6 ], - "C": [ 20, 20, 30, 30 ], - "D": [ "a", "b", "c", "c" ] - }; - - let ndframe = new dfd.DataFrame(data); - let df = ndframe.unique(1); - let res = { - "A": [ -20, 30, 47.3 ], - "B": [ 34, -4, 5, 6 ], - "C": [ 20, 30 ], - "D": [ "a", "b", "c" ] - }; - - assert.deepEqual(df, res); - - }); - it("Returns the unique elements along axis 0", function () { - let data = { - "A": [ -20, 30, 47.3, -20 ], - "B": [ 34, -4, 5, 6 ], - "C": [ 20, 20, 30, 30 ], - "D": [ "a", "b", "c", "c" ] - }; - - let ndframe = new dfd.DataFrame(data); - let df = ndframe.unique(0); - let res = { - 0: [ -20, 34, 20, "a" ], - 1: [ 30, -4, 20, "b" ], - 2: [ 47.3, 5, 30, "c" ], - 3: [ -20, 6, 30, "c" ] - }; - assert.deepEqual(df, res); - - }); - }); - - - describe("rename", function () { - it("Rename columns along axis 1", function () { - let data = { - "A": [ -20, 30, 47.3, -20 ], - "B": [ 34, -4, 5, 6 ], - "C": [ 20, 20, 30, 30 ], - "D": [ "a", "b", "c", "c" ] - }; - - let ndframe = new dfd.DataFrame(data); - let df = ndframe.rename({ mapper: { "A": "a1", "B": "b1" } }); - let res = [ "a1", "b1", "C", "D" ]; - assert.deepEqual(df.columns, res); - - }); - it("confirms original column name is not modified along axis 1", function () { - let data = { - "A": [ -20, 30, 47.3, -20 ], - "B": [ 34, -4, 5, 6 ], - "C": [ 20, 20, 30, 30 ], - "D": [ "a", "b", "c", "c" ] - }; - - let ndframe = new dfd.DataFrame(data); - // let df = ndframe.rename({ mapper: { "A": "a1", "B": "b1" } }) - let res = [ "A", "B", "C", "D" ]; - assert.deepEqual(ndframe.columns, res); - - }); - it("Rename columns along axis 1 inplace", function () { - let data = { - "A": [ -20, 30, 47.3, -20 ], - "B": [ 34, -4, 5, 6 ], - "C": [ 20, 20, 30, 30 ], - "D": [ "a", "b", "c", "c" ] - }; - - let df = new dfd.DataFrame(data); - df.rename({ mapper: { "A": "a1", "B": "b1" }, inplace: true }); - let res = [ "a1", "b1", "C", "D" ]; - assert.deepEqual(df.columns, res); - - }); - it("Rename string index along axis 0", function () { - let data = { - "A": [ -20, 30, 47.3, -20 ], - "B": [ 34, -4, 5, 6 ], - "C": [ 20, 20, 30, 30 ], - "D": [ "a", "b", "c", "c" ] - }; - - let ndframe = new dfd.DataFrame(data, { index: [ "a", "b", "c", "d" ] }); - let df = ndframe.rename({ mapper: { "a": 0, "b": 1 }, axis: 0 }); - let res = [ 0, 1, "c", "d" ]; - assert.deepEqual(df.index, res); - - }); - it("Rename string index along axis 0 inplace", function () { - let data = { - "A": [ -20, 30, 47.3, -20 ], - "B": [ 34, -4, 5, 6 ], - "C": [ 20, 20, 30, 30 ], - "D": [ "a", "b", "c", "c" ] - }; - - let df = new dfd.DataFrame(data, { index: [ "a", "b", "c", "d" ] }); - df.rename({ mapper: { "a": 0, "b": 1 }, axis: 0, inplace: true }); - let res = [ 0, 1, "c", "d" ]; - assert.deepEqual(df.index, res); - - }); - }); - - describe("sort_index", function () { - - it("sort index in ascending order", function () { - let data = [ [ 0, 2, 4, "b" ], - [ 360, 180, 360, "a" ], - [ 2, 4, 6, "c" ] ]; - - let df = new dfd.DataFrame(data, { "columns": [ "col1", "col2", "col3", "col4" ] }); - df.set_index({ key: [ "b", "a", "c" ], inplace: true }); - - let df2 = df.sort_index(); - let rslt = [ [ 360, 180, 360, 'a' ], [ 0, 2, 4, 'b' ], [ 2, 4, 6, 'c' ] ]; - - assert.deepEqual(df2.values, rslt); - }); - it("sort index in descending order", function () { - let data = [ [ 0, 2, 4, "b" ], - [ 360, 180, 360, "a" ], - [ 2, 4, 6, "c" ] ]; - - let df = new dfd.DataFrame(data, { "columns": [ "col1", "col2", "col3", "col4" ] }); - df.set_index({ key: [ "b", "a", "c" ], inplace: true }); - - let df2 = df.sort_index({ ascending: false }); - let rslt = [ [ 2, 4, 6, 'c' ], [ 0, 2, 4, 'b' ], [ 360, 180, 360, 'a' ] ]; - - assert.deepEqual(df2.values, rslt); - }); - it("sort index in descending order with inplace set to true", function () { - let data = [ [ 0, 2, 4, "b" ], - [ 360, 180, 360, "a" ], - [ 2, 4, 6, "c" ] ]; - - let df = new dfd.DataFrame(data, { "columns": [ "col1", "col2", "col3", "col4" ] }); - df.set_index({ key: [ "b", "a", "c" ], inplace: true }); - - df.sort_index({ ascending: false, inplace: true }); - let rslt = [ [ 2, 4, 6, 'c' ], [ 0, 2, 4, 'b' ], [ 360, 180, 360, 'a' ] ]; - assert.deepEqual(df.values, rslt); - }); - }); - - describe("append", function () { - - it("Add a new single row (array) to the end of a DataFrame", function () { - let data = [ [ 0, 2, 4, "b" ], - [ 360, 180, 360, "a" ], - [ 2, 4, 6, "c" ] ]; - - let df = new dfd.DataFrame(data); - let expected_val = [ [ 0, 2, 4, "b" ], - [ 360, 180, 360, "a" ], - [ 2, 4, 6, "c" ], - [ 20, 40, 60, "d" ] ]; - - let rslt_df = df.append([ [ 20, 40, 60, "d" ] ]); - assert.deepEqual(rslt_df.values, expected_val); - - }); - - it("Add a new single row (object) to the end of a DataFrame", function () { - let data = [ [ 0, 2, 4, "b" ], - [ 360, 180, 360, "a" ], - [ 2, 4, 6, "c" ] ]; - - let df = new dfd.DataFrame(data, { "columns": [ "col1", "col2", "col3", "col4" ] }); - let expected_val = [ [ 0, 2, 4, "b" ], - [ 360, 180, 360, "a" ], - [ 2, 4, 6, "c" ], - [ 20, 40, 60, "d" ] ]; - let rslt_df = df.append({ col1: [ 20 ], col2: [ 40 ], col3: [ 60 ], col4: [ "d" ] }); - assert.deepEqual(rslt_df.values, expected_val); - - }); - - it("Add a new single row (object) to the end of a DataFrame", function () { - let data = [ [ 0, 2, 4, "b" ], - [ 360, 180, 360, "a" ], - [ 2, 4, 6, "c" ] ]; - - let df = new dfd.DataFrame(data, { "columns": [ "col1", "col2", "col3", "col4" ] }); - let df2 = new dfd.DataFrame([ [ 20, 40, 60, "d" ] ], { "columns": [ "col1", "col2", "col3", "col4" ] }); - - let expected_val = [ [ 0, 2, 4, "b" ], - [ 360, 180, 360, "a" ], - [ 2, 4, 6, "c" ], - [ 20, 40, 60, "d" ] ]; - - let rslt_df = df.append(df2); - assert.deepEqual(rslt_df.values, expected_val); - - }); - it("Confirm index Change after append", function () { - let data = [ [ 0, 2, 4, "b" ], - [ 360, 180, 360, "a" ], - [ 2, 4, 6, "c" ] ]; - - let df = new dfd.DataFrame(data, { "columns": [ "col1", "col2", "col3", "col4" ] }); - let df2 = new dfd.DataFrame([ [ 20, 40, 60, "d" ] ], { "columns": [ "col1", "col2", "col3", "col4" ] }); - - let rslt_df = df.append(df2); - assert.deepEqual(rslt_df.index, [ "0_row0", "1_row0", "2_row0", "0_row1" ]); - - }); - }); - describe("Str", function () { - it("Str (startsWith) works for columns selected from a DF", function () { - let data = { - "Name": [ "Apples", "Bake", "Application", undefined ], - "Count": [ 2, 5, 4, 10 ], - "Price": [ 200, 300, 40, 250 ] - }; - - let df = new dfd.DataFrame(data); - let name_sf = df['Name']; - assert.deepEqual(name_sf.str.startsWith("App").values, [ true, false, true, false ]); - }); - it("Str (toLowerCase) works for columns selected from a DF", function () { - let data = { - "Name": [ "Apples", "Bake", "Application", undefined ], - "Count": [ 2, 5, 4, 10 ], - "Price": [ 200, 300, 40, 250 ] - }; - - let df = new dfd.DataFrame(data); - let name_sf = df['Name']; - assert.deepEqual(name_sf.str.toLowerCase().values, [ "apples", "bake", "application", NaN ]); - }); - }); -}); diff --git a/danfojs-browser/tests/core/generic.js b/danfojs-browser/tests/core/generic.js deleted file mode 100644 index c270882a..00000000 --- a/danfojs-browser/tests/core/generic.js +++ /dev/null @@ -1,271 +0,0 @@ -/* eslint-disable no-undef */ -const tf = require("@tensorflow/tfjs"); - -describe("Generic (NDFrame)", function () { - describe("NDframe Created from Array", function () { - it("prints the shape of a 1D array", function () { - let data = [ 1, 2, 3, "Boy", "Girl" ]; - let ndframe = new dfd.NDframe(data); - assert.deepEqual(ndframe.shape, [ 5, 1 ]); - }); - it("prints the default assigned column name in a series", function () { - let data = [ "Boy", 20, 25 ]; - let ndframe = new dfd.NDframe(data); - assert.deepEqual(ndframe.column_names, [ "0" ]); - }); - it("prints the assigned column name in a series", function () { - let data = [ "Boy", 20, 25 ]; - let options = { "columns": 'Records' }; - let ndframe = new dfd.NDframe(data, options); - assert.deepEqual(ndframe.column_names, "Records"); - }); - it("prints the shape of a 2D array", function () { - let data = [ [ "Boy", 20 ], [ "Girl", 25 ] ]; - let ndframe = new dfd.NDframe(data); - assert.deepEqual(ndframe.shape, [ 2, 2 ]); - }); - it("prints the default assigned column names in 2D frame", function () { - let data = [ [ "Boy", 20 ], [ "Girl", 25 ] ]; - let ndframe = new dfd.NDframe(data); - assert.deepEqual(ndframe.column_names, [ 0, 1 ]); - }); - it("prints the assigned column names", function () { - let data = [ [ "Boy", 20 ], [ "Girl", 25 ] ]; - let options = { "columns": [ "Gender", "Age" ] }; - let ndframe = new dfd.NDframe(data, options); - assert.deepEqual(ndframe.column_names, [ "Gender", "Age" ]); - }); - it("prints the size of a frame", function () { - let data = [ [ "Boy", 20, 1 ], [ "Girl", 25, 3 ] ]; - let options = { "columns": [ "Gender", "Age", "count" ] }; - let ndframe = new dfd.NDframe(data, options); - assert.deepEqual(ndframe.size, 6); - }); - it("prints the dimension of a frame", function () { - let data = [ [ "Boy", 20, 1 ], [ "Girl", 25, 3 ] ]; - let options = { "columns": [ "Gender", "Age", "count" ] }; - let ndframe = new dfd.NDframe(data, options); - assert.deepEqual(ndframe.ndim, 2); - }); - it("prints the values of a frame", function () { - let data = [ [ 21, 20, 1 ], [ 20, 25, 3 ] ]; - let ndframe = new dfd.NDframe(data); - assert.deepEqual(ndframe.values, data); - }); - it("prints the values of a frame", function () { - let data = [ [ 21, 20, 1 ], [ 20, 25, 3 ] ]; - let ndframe = new dfd.NDframe(data); - assert.deepEqual(ndframe.values, data); - }); - }); - - describe("NDframe Created from JavaScript Object", function () { - - it("prints the shape of a 2D frame created from an Object", function () { - let data = [ { alpha: "A", count: 1 }, { alpha: "B", count: 2 }, { alpha: "C", count: 3 } ]; - let ndframe = new dfd.NDframe(data); - assert.deepEqual(ndframe.shape, [ 3, 2 ]); - }); - it("prints the column names of frame created from an Object", function () { - let data = [ { alpha: "A", count: 1 }, { alpha: "B", count: 2 }, { alpha: "C", count: 3 } ]; - let ndframe = new dfd.NDframe(data); - assert.deepEqual(ndframe.column_names, [ "alpha", "count" ]); - }); - it("prints the shape of a 1D frame created from an Object", function () { - let data = [ { alpha: "A", count: 1 } ]; - let ndframe = new dfd.NDframe(data); - assert.deepEqual(ndframe.shape, [ 1, 2 ]); - }); - - it("prints the size of a frame created from an Object", function () { - let data = [ { alpha: "A", count: 1 }, { alpha: "B", count: 2 }, { alpha: "C", count: 3 } ]; - let options = { columns: [ "Gender", "Age", "count" ] }; - let ndframe = new dfd.NDframe(data, options); - assert.deepEqual(ndframe.size, 6); - }); - it("prints the dimension of a frame created from an Object", function () { - let data = [ { alpha: "A", count: 1 }, { alpha: "B", count: 2 } ]; - let options = { columns: [ "Gender", "Age", "count" ] }; - let ndframe = new dfd.NDframe(data, options); - assert.deepEqual(ndframe.ndim, 2); - }); - it("prints the values of a frame created from an Object", function () { - let data = [ { alpha: "A", count: 1 }, { alpha: "B", count: 2 } ]; - let ndframe = new dfd.NDframe(data); - assert.deepEqual(ndframe.values, [ [ "A", 1 ], [ "B", 2 ] ]); - }); - it("prints the values of a frame created from an Object with null values", function () { - let data = [ { alpha: "A", count: null }, { alpha: null, count: 2 } ]; - let ndframe = new dfd.NDframe(data); - assert.deepEqual(ndframe.values, [ [ "A", NaN ], [ NaN, 2 ] ]); - }); - }); - - describe("NDframe Created from a Tensor", function () { - - it("prints the shape of a 2D frame created from a 2D tensor", function () { - let data = tf.tensor([ 1, 2, 3, 4 ]); - let ndframe = new dfd.NDframe(data); - assert.deepEqual(ndframe.ndim, 1); - assert.deepEqual(ndframe.values, [ 1, 2, 3, 4 ]); - - }); - it("prints the shape of a 2D frame created from a 1D tensor", function () { - let data = tf.tensor([ [ 2, 3, 4 ], [ 4, 5, 6 ] ]); - let ndframe = new dfd.NDframe(data, { columns: [ "alpha", "count", "sum" ] }); - assert.deepEqual(ndframe.column_names, [ "alpha", "count", "sum" ]); - }); - - }); - - describe("index", function () { - it("Returns the index of an NDframe", function () { - let data = [ { alpha: "A", count: 1 }, { alpha: "B", count: 2 }, { alpha: "C", count: 3 } ]; - let df = new dfd.NDframe(data); - assert.deepEqual(df.index, [ 0, 1, 2 ]); - }); - it("Returns the index of an NDframe created from an Array", function () { - let data = [ [ 12, 2, 20 ], [ 90, 5, 23 ], [ 45, 56, 70 ], [ 9, 10, 19 ] ]; - let df = new dfd.NDframe(data); - assert.deepEqual(df.index, [ 0, 1, 2, 3 ]); - }); - }); - - describe("NDframe Created from JavaScript Object of Arrays", function () { - - it("retrieves the col data created from OA ", function () { - let data = { alpha: [ "A", "B", "C", "D" ], count: [ 1, 2, 3, 4 ] }; - let ndframe = new dfd.NDframe(data); - // assert.deepEqual(ndframe.shape, [4, 2]) - assert.deepEqual(ndframe.col_data, [ [ "A", "B", "C", "D" ], [ 1, 2, 3, 4 ] ]); - }); - it("retrieves the col data ", function () { - let data = { alpha: [ "A", "B", "C", "D" ], count: [ 1, 2, 3, 4 ], sum: [ 20.3, 30.456, 40.90, 90.1 ] }; - let ndframe = new dfd.NDframe(data); - let res = [ [ "A", "B", "C", "D" ], [ 1, 2, 3, 4 ], [ 20.3, 30.456, 40.90, 90.1 ] ]; - assert.deepEqual(ndframe.col_data, res); - }); - it("retrieves the row data created from OA ", function () { - let data = { alpha: [ "A", "B" ], count: [ 1, 2 ] }; - let ndframe = new dfd.NDframe(data); - // assert.deepEqual(ndframe.shape, [4, 2]) - assert.deepEqual(ndframe.values, [ [ "A", 1 ], [ "B", 2 ] ]); - }); - }); - - describe("__set_index", function () { - it("sets the index of an NDframe", function () { - let data = [ { alpha: "A", count: 1 }, { alpha: "B", count: 2 }, { alpha: "C", count: 3 } ]; - let df = new dfd.NDframe(data); - df.__set_index([ "A", "B", "C" ]); - assert.deepEqual(df.index, [ "A", "B", "C" ]); - }); - it("Returns the index of an NDframe created from an Array", function () { - let data = [ [ 12, 2, 20 ], [ 90, 5, 23 ], [ 45, 56, 70 ], [ 9, 10, 19 ] ]; - let df = new dfd.NDframe(data); - df.__set_index([ 10, 20, 30, 40 ]); - assert.deepEqual(df.index, [ 10, 20, 30, 40 ]); - }); - }); - - - describe("dtype", function () { - it("Returns int dtype set during creation of 1DFrame (Series) from an Object", function () { - let data = [ 1, 2, 3, 4, 5, 6, 7, 8, 9 ]; - let options = { dtypes: [ 'int32' ] }; - let ndframe = new dfd.NDframe(data, options); - assert.deepEqual(ndframe.dtypes, [ 'int32' ]); - }); - - it("Returns string dtype set during creation of 1DFrame (Series) from an Array", function () { - let data = [ "Alice", "Yemi", "Rising", "Mark" ]; - let options = { dtypes: [ 'string' ] }; - let ndframe = new dfd.NDframe(data, options); - assert.deepEqual(ndframe.dtypes, [ "string" ]); - }); - - it("Returns string dtype automatically inferred from 1DFrame (Series)", function () { - let data = [ "Alice", "Yemi", "Rising", "Mark" ]; - let options = { columns: 'Names' }; - let ndframe = new dfd.NDframe(data, options); - assert.deepEqual(ndframe.dtypes, [ "string" ]); - }); - - it("Returns int dtype automatically inferred from 1DFrame (Series)", function () { - let data = [ 20, 30, 20, 20 ]; - let options = { columns: 'Size' }; - let ndframe = new dfd.NDframe(data, options); - assert.deepEqual(ndframe.dtypes, [ "int32" ]); - }); - it("Returns float dtype automatically inferred from 1DFrame (Series)", function () { - let data = [ 20.1, 30.4, 20.2, 4.23, 20.1 ]; - let options = { columns: 'Size' }; - let ndframe = new dfd.NDframe(data, options); - assert.deepEqual(ndframe.dtypes, [ "float32" ]); - }); - - - it("Returns dtype set during creation of 2DFrame from an Object", function () { - let data = [ { alpha: "A", count: 1 }, { alpha: "B", count: 2 } ]; - let options = { dtypes: [ 'string', 'int32' ] }; - let ndframe = new dfd.NDframe(data, options); - assert.deepEqual(ndframe.dtypes, [ 'string', 'int32' ]); - }); - it("Returns dtype set during creation of 2DFrame from an Array", function () { - let data = [ [ "Alice", 2, 3.0 ], [ "Boy", 5, 6.1 ], [ "Girl", 30, 40 ], [ 39, 89, 78.2 ] ]; - let cols = [ "Name", "Count", "Score" ]; - let options = { columns: cols, dtypes: [ 'string', 'int32', 'float32' ] }; - let ndframe = new dfd.NDframe(data, options); - assert.deepEqual(ndframe.dtypes, [ "string", "int32", "float32" ]); - }); - - it("Returns dtype automatically inferred from 2DFrame", function () { - let data = [ [ "Alice", 2, 3.1 ], [ "Boy", 5, 6.1 ], [ "Girl", 30, 40.2 ], [ 39, 89, 78.2 ] ]; - let cols = [ "Name", "Count", "Score" ]; - let options = { columns: cols }; - let ndframe = new dfd.NDframe(data, options); - assert.deepEqual(ndframe.dtypes, [ "string", "int32", "float32" ]); - }); - - }); - - - describe("to_csv", async function () { - it("Converts DataFrame to csv format and return string", async function () { - let data = [ { alpha: "A", count: 1 }, { alpha: "B", count: 2 }, { alpha: "C", count: 3 } ]; - let df = new dfd.NDframe(data); - let result = `alpha,count\nA,1\nB,2\nC,3\n`; - df.to_csv().then((csv) => { - assert.deepEqual(csv, result); - }); - }); - it("Converts DataFrame of Series to csv format and return string when path is not specified", async function () { - let data = [ [ 12, 2, 20 ], [ 90, 5, 23 ], [ 45, 56, 70 ], [ 9, 10, 19 ] ]; - let df = new dfd.NDframe(data, { columns: [ "A", "B", "C" ] }); - let result = `A,B,C\n12,2,20\n90,5,23\n45,56,70\n9,10,19\n`; - assert.deepEqual(await df.to_csv(), result); - }); - }); - - describe("to_json", async function () { - it("Converts DataFrame to json format and return string", async function () { - let data = [ { alpha: "A", count: 1 }, { alpha: "B", count: 2 }, { alpha: "C", count: 3 } ]; - let result = JSON.stringify([ { alpha: "A", count: 1 }, { alpha: "B", count: 2 }, { alpha: "C", count: 3 } ]); - - let df = new dfd.NDframe(data); - df.to_json().then((json) => { - assert.deepEqual(json, result); - }); - }); - it("Converts DataFrame to json format", async function () { - let data = [ [ 12, 2, 20 ], [ 90, 5, 23 ], [ 45, 56, 70 ] ]; - let df = new dfd.NDframe(data, { columns: [ "A", "B", "C" ] }); - let result = JSON.stringify([ { A: 12, B: 2, C: 20 }, { A: 90, B: 5, C: 23 }, { A: 45, B: 56, C: 70 } ]); - df.to_json().then((json) => { - assert.deepEqual(json, result); - }); - }); - }); - - -}); diff --git a/danfojs-browser/tests/core/get_dummies.js b/danfojs-browser/tests/core/get_dummies.js deleted file mode 100644 index 2be074b2..00000000 --- a/danfojs-browser/tests/core/get_dummies.js +++ /dev/null @@ -1,149 +0,0 @@ -describe("get_dummies", function(){ - - it("test on array", function(){ - - let data = [ "dog", "male", "female", "male", "female", "male", "dog" ]; - - let df = dfd.get_dummies({ data:data }); - - let df_values = [ - [ 1, 0, 0 ], - [ 0, 1, 0 ], - [ 0, 0, 1 ], - [ 0, 1, 0 ], - [ 0, 0, 1 ], - [ 0, 1, 0 ], - [ 1, 0, 0 ] - ]; - let df_columns = [ 'dog', 'male', 'female' ]; - - assert.deepEqual(df.values, df_values); - assert.deepEqual(df.columns, df_columns); - - }); - it("test on Series", function(){ - - let data = [ "dog", "male", "female", "male", "female", "male", "dog" ]; - let series = new dfd.Series(data); - - let df = dfd.get_dummies({ data:series, prefix:"test", prefix_sep:"/" }); - - let df_values = [ - [ 1, 0, 0 ], - [ 0, 1, 0 ], - [ 0, 0, 1 ], - [ 0, 1, 0 ], - [ 0, 0, 1 ], - [ 0, 1, 0 ], - [ 1, 0, 0 ] - ]; - let df_columns = [ 'test/dog', 'test/male', 'test/female' ]; - - assert.deepEqual(df.values, df_values); - assert.deepEqual(df.columns, df_columns); - }); - - it("get dummies on DataFrame", function(){ - - let data = [ [ 1, "dog", 1.0, "fat" ], [ 3, "fog", 2.0, "good" ], [ 4, "gof", 3.0, "best" ] ]; - let columns = [ "A", "B", "C", "d" ]; - let df = new dfd.DataFrame(data, { columns:columns }); - - let df1 = dfd.get_dummies({ data:df, prefix_sep:"_", columns:[ "A", "d" ] }); - let df1_columns = [ - 'C', 'd', - 'A_1', 'A_3', - 'A_4', 'd_dog', - 'd_fog', 'd_gof' - ]; - - let df1_values = [ - [ 1, 'fat', 1, 0, 0, 1, 0, 0 ], - [ 2, 'good', 0, 1, 0, 0, 1, 0 ], - [ 3, 'best', 0, 0, 1, 0, 0, 1 ] - ]; - - assert.deepEqual(df1.values, df1_values); - assert.deepEqual(df1.columns, df1_columns); - - }); - it("Throw error if the prefix specified is not equal to the column specified", function(){ - - let data = [ [ 1, "dog", 1.0, "fat" ], [ 3, "fog", 2.0, "good" ], [ 4, "gof", 3.0, "best" ] ]; - let columns = [ "A", "B", "C", "d" ]; - let df = new dfd.DataFrame(data, { columns:columns }); - - assert.throws(function () { dfd.get_dummies({ data:df, prefix:[ "fg" ], prefix_sep:"_", columns:[ "A", "d" ] }); }, Error, - 'prefix must be the same length with the number of onehot encoding column'); - - }); - it("replace column sepecified with prefix", function(){ - - let data = [ [ 1, "dog", 1.0, "fat" ], [ 3, "fog", 2.0, "good" ], [ 4, "gof", 3.0, "best" ] ]; - let columns = [ "A", "B", "C", "d" ]; - let df = new dfd.DataFrame(data, { columns:columns }); - - let df1 = dfd.get_dummies({ data:df, prefix:[ "F", "G" ], prefix_sep:"_", columns:[ "A", "d" ] }); - let df1_columns = [ - 'C', 'd', - 'F_1', 'F_3', - 'F_4', 'G_dog', - 'G_fog', 'G_gof' - ]; - - let df1_values = [ - [ 1, 'fat', 1, 0, 0, 1, 0, 0 ], - [ 2, 'good', 0, 1, 0, 0, 1, 0 ], - [ 3, 'best', 0, 0, 1, 0, 0, 1 ] - ]; - - assert.deepEqual(df1.values, df1_values); - assert.deepEqual(df1.columns, df1_columns); - - }); - - it("infer the onehotencoding column base on string dtypes", function(){ - - let data = [ [ 1, "dog", 1.0, "fat" ], [ 3, "fog", 2.0, "good" ], [ 4, "gof", 3.0, "best" ] ]; - let columns = [ "A", "B", "C", "d" ]; - let df = new dfd.DataFrame(data, { columns:columns }); - - let df1 = dfd.get_dummies({ data:df, prefix_sep:"_" }); - let df1_columns = [ - 'A', 'C', - 'B_dog', 'B_fog', - 'B_gof', 'd_fat', - 'd_good', 'd_best' - ]; - let df1_values = [ - [ - 1, 1, 1, 0, - 0, 1, 0, 0 - ], - [ - 3, 2, 0, 1, - 0, 0, 1, 0 - ], - [ - 4, 3, 0, 0, - 1, 0, 0, 1 - ] - ]; - - assert.deepEqual(df1.values, df1_values); - assert.deepEqual(df1.columns, df1_columns); - - }); - it("replace column sepecified with prefix", function(){ - - let data = [ [ 1, "dog", 1.0, "fat" ], [ 3, "fog", 2.0, "good" ], [ 4, "gof", 3.0, "best" ] ]; - let columns = [ "A", "B", "C", "d" ]; - let df = new dfd.DataFrame(data, { columns:columns }); - - assert.throws(function () { dfd.get_dummies({ data:df, prefix:"F", prefix_sep:"_", columns:[ "A", "d" ] }); }, Error, - "prefix for dataframe must be an array"); - - }); - - -}); diff --git a/danfojs-browser/tests/core/series.js b/danfojs-browser/tests/core/series.js deleted file mode 100644 index 3d079cf9..00000000 --- a/danfojs-browser/tests/core/series.js +++ /dev/null @@ -1,1304 +0,0 @@ -/* eslint-disable no-undef */ -const tf = require("@tensorflow/tfjs"); - -describe("Series", function () { - describe("tensor", function () { - it("Returns the tensor object of a Series", function () { - let data = [ 1, 2, 3, 4, 5, 620, 30, 40, 39, 89, 78 ]; - let sf = new dfd.Series(data); - assert.deepEqual(sf.tensor.dtype, "int32"); - }); - it("Returns the float dtype of a tensor object", function () { - let data = [ 1.1, 2.2, 3, 4.1, 5, 620, 30.1, 40, 39, 89, 78 ]; - let sf = new dfd.Series(data); - assert.deepEqual(sf.tensor.dtype, "float32"); - }); - it("Compares a tensor returned from a Series to Tensorflow's tensor", function () { - let data = [ 1.1, 2.2, 3, 4.1, 5, 620, 30.1, 40, 39, 89, 78 ]; - let sf = new dfd.Series(data); - let tf_data = tf.tensor(data); - assert.deepEqual(sf.tensor.arraySync(), tf_data.arraySync()); - }); - }); - describe("tensor", function () { - it("Returns the dtype string Series", function () { - let data = [ "b", "c", "d" ]; - let sf = new dfd.Series(data); - assert.deepEqual(sf.dtype, "string"); - }); - it("Returns the dtype int Series", function () { - let data = [ 1, 2, 3, 4, 5 ]; - let sf = new dfd.Series(data); - assert.deepEqual(sf.dtype, "int32"); - }); - it("Returns the dtype int Series", function () { - let data = [ 1.1, 2.2, 3.3, 4.5, 5 ]; - let sf = new dfd.Series(data); - assert.deepEqual(sf.dtype, "float32"); - }); - }); - - describe("head", function () { - it("Gets the first n rows in a Series", function () { - let data = [ 1, 2, 3, 4, 5, 620, 30, 40, 39, 89, 78 ]; - let cols = [ "A" ]; - let sf = new dfd.Series(data, { columns: cols }); - assert.deepEqual(sf.head(2).values, [ 1, 2 ]); - }); - it("Return all rows in a Series if row specified is greater than values", function () { - let data = [ "Boy", "Girl", "Man", "Woman", "Tall" ]; - let cols = [ "Items" ]; - let sf = new dfd.Series(data, { columns: cols }); - assert.deepEqual(sf.head(10).values, data); - }); - it("Return all rows in a Series if row specified is less than 0", function () { - let data = [ 1, 2, 3, 4, 5, 620, 30, 40, 39, 89, 78 ]; - let sf = new dfd.Series(data); - assert.deepEqual(sf.head(-1).values, data); - }); - }); - - describe("tail", function () { - it("Prints the last n rows of a Series", function () { - let data = [ 1, 2, 3, 4, 5, 620, 30, 40, 39, 89, 78 ]; - let sf = new dfd.Series(data); - assert.deepEqual(sf.tail(2).values, [ 89, 78 ]); - }); - it("Return all rows in a Series if row specified is greater than values", function () { - let data = [ 1, 2, 3, 4, 5, 620, 30, 40, 39, 89, 78 ]; - let sf = new dfd.Series(data); - assert.deepEqual(sf.tail(15).values, data); - }); - it("Return all rows in a Series if row specified is less than 0", function () { - let data = [ "Boy", "Girl", "Man", "Woman", "Tall" ]; - let cols = [ "Items" ]; - let sf = new dfd.Series(data, { columns: cols }); - assert.deepEqual(sf.tail(-1).values, data); - }); - }); - - describe("sample", function () { - it("Samples n number of random elements from a DataFrame", async function () { - let data = [ 1, 2, 3, 4, 5, 620, 30, 40, 39, 89, 78 ]; - let sf = new dfd.Series(data); - assert.deepEqual((await sf.sample(7)).values.length, 7); - }); - it("Return all values if n of sample -1", async function () { - let data = [ 1, 2, 3, 4, 5, 620, 30, 40, 39, 89, 78 ]; - let sf = new dfd.Series(data); - assert.deepEqual((await sf.sample(-1)).values.length, data.length); - }); - it("Throw error if n is greater than lenght of Series", async function () { - let data = [ 1, 2, 3, 4, 5, 620, 30, 40, 39, 89, 78 ]; - let sf = new dfd.Series(data); - try { - await sf.sample(100); - } catch (e) { - expect(e).to.be.instanceOf(Error); - expect(e.message).to.eql('Sample size n cannot be bigger than size of dataset'); - } - }); - }); - - describe("add", function () { - it("Return Addition of series with another series", function () { - let data = [ 1, 2, 3, 4, 5, 6 ]; - let data2 = [ 30, 40, 39, 1, 2, 1 ]; - let sf = new dfd.Series(data); - let sf2 = new dfd.Series(data2); - assert.deepEqual(sf.add(sf2).values, [ 31, 42, 42, 5, 7, 7 ]); - }); - it("Return Addition of series with a single value (Broadcasting)", function () { - let data = [ 1, 2, 3, 4, 5 ]; - let sf = new dfd.Series(data); - assert.deepEqual(sf.add(1).values, [ 2, 3, 4, 5, 6 ]); - }); - it("Throws type error on addition of string type", function () { - let data = [ 1, 2, 3, 4 ]; - let data2 = [ "A", "B", "C", "d" ]; - let sf = new dfd.Series(data); - let sf2 = new dfd.Series(data2); - assert.throws( - () => { - sf.add(sf2); - }, - Error, - "Argument 'x' passed to 'cast' must be numeric tensor, but got string tensor" - ); - }); - // it("Throws length error if series lenght mixmatch", function () { - // let data = [1, 2, 3, 4] - // let data2 = [1, 2, 3, 4, 5, 6] - // let sf = new dfd.Series(data) - // let sf2 = new dfd.Series(data2) - // assert.throws(() => { sf.add(sf2) }, Error, "Incompatible shapes: [4] vs. [6]") - // }) - }); - - describe("sub", function () { - it("Return Subtraction of series with another series", function () { - let data1 = [ 30, 40, 39, 1, 2, 1 ]; - let data2 = [ 1, 2, 3, 4, 5, 6 ]; - let sf1 = new dfd.Series(data1); - let sf2 = new dfd.Series(data2); - assert.deepEqual(sf1.sub(sf2).values, [ 29, 38, 36, -3, -3, -5 ]); - }); - it("Return Subtraction of series with a single value (Broadcasting)", function () { - let data = [ 1, 2, 3, 4, 5 ]; - let sf = new dfd.Series(data); - assert.deepEqual(sf.sub(1).values, [ 0, 1, 2, 3, 4 ]); - }); - it("Throws type error on Subtraction of string type", function () { - let data = [ 1, 2, 3, 4 ]; - let data2 = [ "A", "B", "C", "d" ]; - let sf = new dfd.Series(data); - let sf2 = new dfd.Series(data2); - assert.throws( - () => { - sf.sub(sf2); - }, - Error, - "Argument 'x' passed to 'cast' must be numeric tensor, but got string tensor" - ); - }); - // it("Throws length error if series lenght mixmatch", function () { - // let data = [1, 2, 3, 4] - // let data2 = [1, 2, 3, 4, 5, 6] - // let sf = new dfd.Series(data) - // let sf2 = new dfd.Series(data2) - // assert.throws(() => { sf.sub(sf2) }, Error, "Incompatible shapes: [4] vs. [6]") - // }) - }); - - describe("mul", function () { - it("Return multiplication of series with another series", function () { - let data1 = [ 30, 40, 3, 5 ]; - let data2 = [ 1, 2, 3, 4 ]; - let sf1 = new dfd.Series(data1); - let sf2 = new dfd.Series(data2); - assert.deepEqual(sf1.mul(sf2).values, [ 30, 80, 9, 20 ]); - }); - it("Return multiplication of series with a single value (Broadcasting)", function () { - let data = [ 1, 2, 3, 4, 5 ]; - let sf = new dfd.Series(data); - assert.deepEqual(sf.mul(1).values, [ 1, 2, 3, 4, 5 ]); - }); - // it("Throws type error on multiplication of string type", function () { - // let data = [1, 2, 3, 4] - // let data2 = ["A", "B", "C", "d"] - // let sf = new dfd.Series(data) - // let sf2 = new dfd.Series(data2) - // assert.throws(() => { sf.mul(sf2) }, Error, "Argument 'x' passed to 'cast' must be numeric tensor, but got string tensor") - // }) - // it("Throws length error if series lenght mixmatch", function () { - // let data = [1, 2, 3, 4] - // let data2 = [1, 2, 3, 4, 5, 6] - // let sf = new dfd.Series(data) - // let sf2 = new dfd.Series(data2) - // assert.throws(() => { sf.mul(sf2) }, Error, "Operands could not be broadcast together with shapes 4 and 6") - // }) - }); - - describe("div", function () { - it("Return float division of series with another series", function () { - let data1 = [ 30, 40, 3, 5 ]; - let data2 = [ 1, 2, 3, 4 ]; - let sf1 = new dfd.Series(data1); - let sf2 = new dfd.Series(data2); - assert.deepEqual(sf1.div(sf2).values, [ 30, 20, 1, 1.25 ]); - }); - it("Return integer division of series with another series", function () { - let data1 = [ 30, 40, 3, 5 ]; - let data2 = [ 1, 2, 3, 4 ]; - let sf1 = new dfd.Series(data1); - let sf2 = new dfd.Series(data2); - assert.deepEqual(sf1.div(sf2, false).values, [ 30, 20, 1, 1 ]); - }); - it("Return division of series with a single value (Broadcasting)", function () { - let data = [ 10, 2, 3, 90 ]; - let sf = new dfd.Series(data); - assert.deepEqual(sf.div(2).values, [ 5, 1, 1.5, 45 ]); - }); - // it("Throws type error on division of string type", function () { - // let data = [1, 2, 3, 4] - // let data2 = ["A", "B", "C", "d"] - // let sf = new dfd.Series(data) - // let sf2 = new dfd.Series(data2) - // assert.throws(() => { sf.mul(sf2) }, Error, "Argument 'x' passed to 'cast' must be numeric tensor, but got string tensor") - // }) - // it("Throws length error if series lenght mixmatch", function () { - // let data = [1, 2, 3, 4] - // let data2 = [1, 2, 3, 4, 5, 6] - // let sf = new dfd.Series(data) - // let sf2 = new dfd.Series(data2) - // assert.throws(() => { sf.mul(sf2) }, Error, "Operands could not be broadcast together with shapes 4 and 6") - // }) - }); - - describe("pow", function () { - it("Return Exponetial power of series with another series", function () { - let data1 = [ 2, 3, 4, 5 ]; - let data2 = [ 1, 2, 3, 0 ]; - let sf1 = new dfd.Series(data1); - let sf2 = new dfd.Series(data2); - assert.deepEqual(sf1.pow(sf2).values, [ 2, 9, 64, 1 ]); - }); - it("Return Exponetial power of series with a single value (Broadcasting)", function () { - let data = [ 1, 2, 3, 4, 5 ]; - let sf = new dfd.Series(data); - assert.deepEqual(sf.pow(2).values, [ 1, 4, 9, 16, 25 ]); - }); - }); - - describe("mod", function () { - it("Return modulo of series with another float series", function () { - let data1 = [ 2, 30, 4, 5 ]; - let data2 = [ 1.1, 2.2, 3.3, 2.4 ]; - let sf1 = new dfd.Series(data1); - let sf2 = new dfd.Series(data2); - let expected = [ - 0.8999999761581421, - 1.3999993801116943, - 0.7000000476837158, - 0.19999980926513672 - ]; - assert.deepEqual(sf1.mod(sf2).values, expected); - }); - it("Return modulo of series with another int series", function () { - let data1 = [ 2, 30, 4, 5 ]; - let data2 = [ 1, 2, 3, 1 ]; - let sf1 = new dfd.Series(data1); - let sf2 = new dfd.Series(data2); - assert.deepEqual(sf1.mod(sf2).values, [ 0, 0, 1, 0 ]); - }); - it("Return modulo power of series with a single value (Broadcasting)", function () { - let data = [ 1, 2, 3, 4, 5 ]; - let sf = new dfd.Series(data); - assert.deepEqual(sf.mod(2).values, [ 1, 0, 1, 0, 1 ]); - }); - }); - - describe("mean", function () { - it("Computes the mean of elements in a int series", function () { - let data1 = [ 30, 40, 3, 5 ]; - let sf = new dfd.Series(data1); - assert.deepEqual(sf.mean(), 19.5); - }); - it("Computes the mean of elements in a float series", function () { - let data1 = [ 30.1, 40.2, 3.1, 5.1 ]; - let sf = new dfd.Series(data1); - assert.deepEqual(sf.mean(), 19.625); - }); - it("Computes the mean of elements in a float series with NaN", function () { - let data1 = [ 30.1, 40.2, 3.1, 5.1, NaN ]; - let sf = new dfd.Series(data1); - assert.deepEqual(sf.mean(), 19.625); - }); - it("Throws error if dtype is string", function () { - let data1 = [ "boy", "girl", "Man" ]; - let sf = new dfd.Series(data1); - assert.throws( - () => { - sf.mean(); - }, - Error, - "dtype error: String data type does not support mean operation" - ); - }); - }); - - describe("median", function () { - it("Computes the median value of elements across int Series", function () { - let data1 = [ 30, 40, 3, 5 ]; - let sf = new dfd.Series(data1); - assert.deepEqual(sf.median(), 17.5); - }); - it("Computes the median value of elements across float Series", function () { - let data1 = [ 30.1, 40.2, 3.1, 5.1 ]; - let sf = new dfd.Series(data1); - assert.deepEqual(sf.median(), 17.6); - }); - }); - - describe("sum", function () { - it("Sum values of a Int Series", function () { - let data1 = [ 30, 40, 3, 5, 5, 5, 5, 5, 3, 3, 3, 21, 3 ]; - let sf = new dfd.Series(data1); - assert.deepEqual(sf.sum(), 131); - }); - it("Sum values of a Float Series", function () { - let data1 = [ 30.1, 3.1, 40.2, 3.1, 5.1 ]; - let sf = new dfd.Series(data1); - assert.deepEqual(sf.sum(), 81.6); - }); - it("Sum values of a bool Series", function () { - let data1 = [ true, true, false, false, false ]; - let sf = new dfd.Series(data1); - assert.deepEqual(sf.sum(), 2); - }); - it("Sum values a Series with missing values", function () { - let data1 = [ 11, NaN, 2, 2 ]; - let sf = new dfd.Series(data1); - assert.deepEqual(sf.sum(), 15); - }); - }); - - describe("mode", function () { - it("Computes the multi-modal values of a Series", function () { - let data1 = [ 30, 40, 3, 5, 5, 5, 5, 5, 3, 3, 3, 21, 3 ]; - let sf = new dfd.Series(data1); - assert.deepEqual(sf.mode(), [ 5, 3 ]); - }); - it("Computes the modal value of a Series", function () { - let data1 = [ 30.1, 3.1, 40.2, 3.1, 5.1, NaN ]; - let sf = new dfd.Series(data1); - assert.deepEqual(sf.mode(), [ 3.1 ]); - }); - }); - - describe("min", function () { - it("Returns the single smallest elementin a Series", function () { - let data = [ 30, 40, 3, 5 ]; - let sf = new dfd.Series(data); - assert.deepEqual(sf.min(), 3); - }); - it("Computes the minimum of elements across an float Series", function () { - let data1 = [ 30.1, 40.2, 3.12, 5.1 ]; - let sf = new dfd.Series(data1, { dtypes: [ "float32" ] }); - assert.deepEqual(Number(sf.min().toFixed(2)), 3.12); - }); - }); - - describe("max", function () { - it("Computes the maximum of elements across dimensions of a Series", function () { - let data1 = [ 30, 40, 3, 5 ]; - let sf = new dfd.Series(data1); - assert.deepEqual(sf.max(), 40); - }); - it("Return sum of float values in a series", function () { - let data1 = [ 30.1, 40.21, 3.1, 5.1 ]; - let sf = new dfd.Series(data1); - assert.deepEqual(Number(sf.max().toFixed(2)), 40.21); - }); - it("Throws error on addition of string Series", function () { - let data1 = [ "boy", "gitl", "woman", "man" ]; - let sf = new dfd.Series(data1); - assert.throws( - () => { - sf.max(); - }, - Error, - "dtype error: String data type does not support max operation" - ); - }); - }); - - describe("std", function () { - it("Computes the standard of elements in a int series", function () { - let data1 = [ 30, 40, 3, 5 ]; - let sf = new dfd.Series(data1); - assert.deepEqual(sf.std(), 18.375708603116962); - }); - it("Computes the standard deviation of elements in a float series", function () { - let data1 = [ 30.1, 40.2, 3.1, 5.1 ]; - let sf = new dfd.Series(data1); - assert.deepEqual(sf.std(), 18.412925713566906); - }); - it("Computes the standard deviation of elements in a float series with missing values", function () { - let data1 = [ 30, 40, 3, 5, undefined ]; - let sf = new dfd.Series(data1); - assert.deepEqual(sf.std(), 18.375708603116962); - }); - }); - - describe("var", function () { - it("Computes the variance of elements in a int series", function () { - let data1 = [ 30, 40, 3, 5 ]; - let sf = new dfd.Series(data1); - assert.deepEqual(sf.var(), 337.6666666666667); - }); - it("Computes the variance of elements in a float series", function () { - let data1 = [ 30.1, 40.2, 3.1, 5.1 ]; - let sf = new dfd.Series(data1); - assert.deepEqual(sf.var(), 339.03583333333336); - }); - it("Computes the variance of elements in a int series with missing values", function () { - let data1 = [ 30, undefined, 40, 3, 5 ]; - let sf = new dfd.Series(data1); - assert.deepEqual(sf.var(), 337.6666666666667); - }); - }); - - describe("describe", function () { - it("Computes the descriptive statistics on an int Series", function () { - let data1 = [ 10, 45, 56, 25, 23, 20, 10 ]; - let sf = new dfd.Series(data1); - assert.deepEqual(sf.describe().round().values, [ - 7, - 27, - 17, - 10, - 23, - 56, - 302 - ]); - }); - it("Computes the descriptive statistics on a float Series", function () { - let data1 = [ 30.1, 40.2, 3.1, 5.1 ]; - let sf = new dfd.Series(data1); - assert.deepEqual(sf.describe().round().values, [ - 4, - 20, - 18, - 3, - 18, - 40, - 339 - ]); - }); - it("Computes the descriptive statistics on a float Series", function () { - let data1 = [ 30.1, 40.2, 3.1, 5.1 ]; - let sf = new dfd.Series(data1); - assert.deepEqual(sf.describe().index, [ - "count", - "mean", - "std", - "min", - "median", - "max", - "variance" - ]); - }); - }); - - describe("maximum", function () { - it("Returns the max of a and b (a > b ? a : b) element-wise. Supports broadcasting.", function () { - let data1 = [ 30, 40, 3, 5 ]; - let data2 = [ 10, 41, 2, 0 ]; - let sf1 = new dfd.Series(data1); - let sf2 = new dfd.Series(data2); - assert.deepEqual(sf1.maximum(sf2).values, [ 30, 41, 3, 5 ]); - }); - it("Throws error on checking maximum of incompatible Series", function () { - let data1 = [ 30, 40, 3, 5 ]; - let data2 = [ 10, 41, 2 ]; - let sf1 = new dfd.Series(data1); - let sf2 = new dfd.Series(data2); - assert.throws( - () => { - sf1.maximum(sf2); - }, - Error, - "Operands could not be broadcast together with shapes 4 and 3" - ); - }); - }); - - describe("minimum", function () { - it("Returns the min of a and b (a < b ? a : b) element-wise. Supports broadcasting.", function () { - let data1 = [ 30, 40, 3, 5 ]; - let data2 = [ 10, 41, 2, 0 ]; - let sf1 = new dfd.Series(data1); - let sf2 = new dfd.Series(data2); - assert.deepEqual(sf1.minimum(sf2).values, [ 10, 40, 2, 0 ]); - }); - // it("Return sum of float values in a series", function () { - // let data1 = [30.1, 40.9, 3, 5] - // let data2 = [10.2, 41, 2, 0] - // let sf1 = new dfd.Series(data1) - // let sf2 = new dfd.Series(data2) - // assert.deepEqual(sf1.minimum(sf2).values, [30, 41, 3, 5]) - - // }) - }); - - describe("count", function () { - it("Returns the count of non NaN values in a string Series", function () { - let data = [ "boy", "gitl", "woman", NaN ]; - let sf = new dfd.Series(data); - assert.deepEqual(sf.count(), 3); - }); - it("Returns the count of non NaN values in a string Series", function () { - let data = [ "boy", "gitl", "woman", "Man" ]; - let sf = new dfd.Series(data); - assert.deepEqual(sf.count(), 4); - }); - it("Returns the count of non NaN values in a int Series", function () { - let data = [ 20, 30, NaN, 2, NaN, 30, 21 ]; - let sf = new dfd.Series(data); - assert.deepEqual(sf.count(), 5); - }); - it("Returns the count of non NaN values in a float Series", function () { - let data = [ 20.1, 30.4, NaN, 2.1, NaN, 30.0, 21.3 ]; - let sf = new dfd.Series(data); - assert.deepEqual(sf.count(), 5); - }); - }); - - describe("round", function () { - it("Rounds elements in a Series to nearest whole number", function () { - let data1 = [ 30.21091, 40.190901, 3.564, 5.0212 ]; - let sf = new dfd.Series(data1); - assert.deepEqual(sf.round().values, [ 30, 40, 4, 5 ]); - }); - it("Rounds elements in a Series to 1dp", function () { - let data1 = [ 30.21091, 40.190901, 3.564, 5.0212 ]; - let sf = new dfd.Series(data1); - assert.deepEqual(sf.round(1).values, [ 30.2, 40.2, 3.6, 5.0 ]); - }); - it("Rounds elements in a Series to 2dp", function () { - let data1 = [ 30.2191, 40.190901, 3.564, 5.0212 ]; - let sf = new dfd.Series(data1); - assert.deepEqual(sf.round(2).values, [ 30.22, 40.19, 3.56, 5.02 ]); - }); - }); - - describe("isna", function () { - it("Return a boolean same-sized object indicating if string Series contain NaN", function () { - let data1 = [ NaN, undefined, "girl", "Man" ]; - let sf = new dfd.Series(data1); - assert.deepEqual(sf.isna().values, [ true, true, false, false ]); - }); - it("Return a boolean same-sized object indicating if float Series values are NaN", function () { - let data1 = [ 30.21091, NaN, 3.564, undefined ]; - let sf = new dfd.Series(data1); - assert.deepEqual(sf.isna().values, [ false, true, false, true ]); - }); - it("Return a boolean same-sized object indicating if int Series values are NaN", function () { - let data1 = [ 30, 40, 3, 5, undefined, undefined ]; - let sf = new dfd.Series(data1); - assert.deepEqual(sf.isna().values, [ - false, - false, - false, - false, - true, - true - ]); - }); - }); - - describe("sort_values", function () { - it("Sort values in a Series in ascending order (not inplace)", function () { - let sf = new dfd.Series([ 20, 30, 1, 2, 4, 57, 89, 0, 4 ]); - let result = [ 0, 1, 2, 4, 4, 20, 30, 57, 89 ]; - let sorted_sf = sf.sort_values(); - assert.deepEqual(sorted_sf.values, result); - }); - it("confirms that sort_values in ascending order does not happen inplace", function () { - let sf = new dfd.Series([ 20, 30, 1, 2, 4, 57, 89, 0, 4 ]); - let result = [ 0, 1, 2, 4, 4, 20, 30, 57, 89 ]; - let expected_index = [ 7, 2, 3, 8, 4, 0, 1, 5, 6 ]; - sf.sort_values({ inplace: true }); - assert.deepEqual(sf.values, result); - assert.deepEqual(sf.index, expected_index); - }); - it("Sort values in a Series in Descending order", function () { - let sf = new dfd.Series([ 20, 30, 1, 2, 4, 57, 89, 0, 4 ]); - let result = [ 89, 57, 30, 20, 4, 4, 2, 1, 0 ]; - let sorted_sf = sf.sort_values({ ascending: false }); - assert.deepEqual(sorted_sf.values, result); - }); - it("confirms that sort_values in descending order happens inplace", function () { - let sf = new dfd.Series([ 20, 30, 1, 2, 4, 57, 89, 0, 4 ]); - let result = [ 89, 57, 30, 20, 4, 4, 2, 1, 0 ]; - sf.sort_values({ ascending: false, inplace: true }); - assert.deepEqual(sf.values, result); - }); - it("Confirms that series index is sorted in ascending order (not in inplace)", function () { - let sf = new dfd.Series([ 20, 30, 1, 2, 4, 57, 89, 0, 4 ]); - let result = [ 7, 2, 3, 8, 4, 0, 1, 5, 6 ]; - let sorted_sf = sf.sort_values(); - assert.deepEqual(sorted_sf.index, result); - }); - it("Confirms that series index is sorted in descending order (not in inplace)", function () { - let sf = new dfd.Series([ 20, 30, 1, 2, 4, 57, 89, 0, 4 ]); - let result = [ 6, 5, 1, 0, 4, 8, 3, 2, 7 ]; - let sorted_sf = sf.sort_values({ ascending: false }); - assert.deepEqual(sorted_sf.index, result); - }); - it("Sort string values in a Series", function () { - let sf = new dfd.Series([ "boy", "zebra", "girl", "man" ]); - let result = [ "boy", "girl", "man", "zebra" ]; - let sorted_sf = sf.sort_values({ ascending: false }); - assert.deepEqual(sorted_sf.values, result); - }); - // it("Throws error on sorting of string", function () { - // let sf = new dfd.Series(["boy", "man", "girl"]) - // assert.throws(() => { sf.sort_values() }, Error, "Dtype Error: cannot sort Series of type string") - // }) - }); - - describe("copy", function () { - it("Checks if copied values are the same as the first one", function () { - let sf = new dfd.Series([ 30.21091, 40.190901, 3.564, 5.0212 ]); - let sf_copy = sf.copy(); - assert.deepEqual(sf.values, sf_copy.values); - }); - it("Checks if copied index are the same", function () { - let sf = new dfd.Series([ 30.21091, 40.190901, 3.564, 5.0212 ]); - sf = sf.set_index({ index: [ "a", "b", "c", "d" ] }); - let sf_copy = sf.copy(); - assert.deepEqual(sf.index, sf_copy.index); - }); - // it("Checks if copied dtype is the same", function () { - // let sf = new dfd.Series([30.21091, 40.190901, 3.564, 5.0212]) - // sf.round() - // sf.astype(['int32']) - // let sf_copy = sf.copy() - // assert.deepEqual(sf.dtypes[0], sf_copy.dtypes[0]) - // assert.deepEqual(sf.values, sf_copy.values) - - // }) - }); - - describe("reset_index", function () { - it("resets the index of a Series", function () { - let data = [ - { alpha: "A", count: 1 }, - { alpha: "B", count: 2 }, - { alpha: "C", count: 3 } - ]; - let df = new dfd.Series(data); - let df_new = df.set_index({ index: [ "one", "two", "three" ] }); - let df_reset = df_new.reset_index(); - assert.deepEqual(df_reset.index, [ 0, 1, 2 ]); - }); - it("Reset the index of a Series created from an Array", function () { - let data = [ 1, 2, 3, 4, 5, 6 ]; - let df = new dfd.Series(data); - df.set_index({ - index: [ "one", "two", "three", "four", "five", "six" ], - inplace: true - }); - let df_new = df.reset_index(); - assert.deepEqual(df_new.index, [ 0, 1, 2, 3, 4, 5 ]); - }); - it("checks that the original series changed after reseting new index inplace", function () { - let data = [ - { alpha: "A", count: 1 }, - { alpha: "B", count: 2 }, - { alpha: "C", count: 3 } - ]; - let df = new dfd.Series(data); - df.reset_index({ inplace: true }); - assert.deepEqual(df.index, [ 0, 1, 2 ]); - }); - }); - - describe("set_index", function () { - it("sets the index of an Series", function () { - let data = [ - { alpha: "A", count: 1 }, - { alpha: "B", count: 2 }, - { alpha: "C", count: 3 } - ]; - let df = new dfd.Series(data); - let df_new = df.set_index({ index: [ "one", "two", "three" ] }); - assert.deepEqual(df_new.index, [ "one", "two", "three" ]); - assert.notDeepEqual(df.index, df_new.index); - }); - it("checks that the original series is not modified after setting new index not-inplace", function () { - let data = [ - { alpha: "A", count: 1 }, - { alpha: "B", count: 2 }, - { alpha: "C", count: 3 } - ]; - let df = new dfd.Series(data); - let df_new = df.set_index({ index: [ "one", "two", "three" ] }); - assert.notDeepEqual(df.index, df_new.index); - }); - it("sets the index of an Series inplace", function () { - let data = [ 12, 2, 20, 50 ]; - let df = new dfd.Series(data); - df.set_index({ index: [ "one", "two", "three", "four" ], inplace: true }); - assert.deepEqual(df.index, [ "one", "two", "three", "four" ]); - }); - }); - - describe("Map", function () { - it("map series element to object keys", function () { - let sf = new dfd.Series([ 1, 2, 3, 4 ]); - let map = { 1: "ok", 2: "okie", 3: "frit", 4: "gop" }; - - let rslt = [ "ok", "okie", "frit", "gop" ]; - - assert.deepEqual(sf.map(map).values, rslt); - }); - - it("map series element to a function statement", function () { - let sf = new dfd.Series([ 1, 2, 3, 4 ]); - let func_map = (x) => { - return x + 1; - }; - - let rslt = [ 2, 3, 4, 5 ]; - - assert.deepEqual(sf.map(func_map).values, rslt); - }); - }); - - describe("Apply", function () { - it("apply a function to a series element", function () { - let sf = new dfd.Series([ 1, 2, 3, 4, 5, 6, 7, 8 ]); - - let apply_func = (x) => { - return x + x; - }; - - let rslt = [ 2, 4, 6, 8, 10, 12, 14, 16 ]; - assert.deepEqual(sf.apply(apply_func).values, rslt); - }); - }); - - describe("unique", function () { - it("returns the unique values in a Series of type int", function () { - let sf = new dfd.Series([ 1, 2, 3, 4, 5, 6, 7, 8, 1, 1, 22, 8, 5, 5, 5 ]); - let expected = [ 1, 2, 3, 4, 5, 6, 7, 8, 22 ]; - assert.deepEqual(sf.unique().values, expected); - }); - it("returns the unique values in a Series of type string", function () { - let sf = new dfd.Series([ "a", "a", "b", "c", "c", "d", "e", "d", "d", "e" ]); - let expected = [ "a", "b", "c", "d", "e" ]; - assert.deepEqual(sf.unique().values, expected); - }); - it("returns the unique values in a Series of type string", function () { - let sf = new dfd.Series([ "a", "a", "b", "c", "c", "d", "e", "d", "d", "e" ]); - let expected = [ "a", "b", "c", "d", "e" ]; - assert.deepEqual(sf.unique().values, expected); - }); - it("returns the nunique values in a Series of type string", function () { - let sf = new dfd.Series([ "a", "a", "b", "c", "c", "d", "e", "d", "d", "e" ]); - let expected = 5; - assert.deepEqual(sf.nunique(), expected); - }); - }); - - describe("value_counts", function () { - it("returns the unique values and their counts in a Series of type int", function () { - let sf = new dfd.Series([ 1, 2, 3, 4, 5, 6, 7, 8, 1, 1, 22, 8, 5, 5, 5 ]); - let expected_index = [ 1, 2, 3, 4, 5, 6, 7, 8, 22 ]; - let expected_vals = [ 3, 1, 1, 1, 4, 1, 1, 2, 1 ]; - assert.deepEqual(sf.value_counts().values, expected_vals); - assert.deepEqual(sf.value_counts().index, expected_index); - }); - it("returns the unique values and their counts in a Series of type string", function () { - let sf = new dfd.Series([ "a", "a", "b", "c", "c", "d", "e", "d", "d", "e" ]); - let expected_vals = [ 2, 1, 2, 3, 2 ]; - let expected_index = [ "a", "b", "c", "d", "e" ]; - assert.deepEqual(sf.value_counts().values, expected_vals); - assert.deepEqual(sf.value_counts().index, expected_index); - }); - }); - - describe("abs", function () { - it("Returns the absolute values in Series", function () { - let data1 = [ -10, 45, 56, -25, 23, -20, 10 ]; - let sf = new dfd.Series(data1); - assert.deepEqual(sf.abs().values, [ 10, 45, 56, 25, 23, 20, 10 ]); - }); - it("Computes the descriptive statistics on a float Series", function () { - let data1 = [ -30.1, -40.2, -3.1, -5.1 ]; - let sf = new dfd.Series(data1); - assert.deepEqual(sf.abs().values, [ 30.1, 40.2, 3.1, 5.1 ]); - }); - }); - - describe("fillna", function () { - it("replace all NaN value with specified value", function () { - let data = [ NaN, 1, 2, 33, 4, NaN, 5, 6, 7, 8 ]; - let sf = new dfd.Series(data); - let sf_val = [ -999, 1, 2, 33, 4, -999, 5, 6, 7, 8 ]; - sf.fillna({ value: -999, inplace: true }); - assert.deepEqual(sf.values, sf_val); - }); - it("replace all NaN value in string Series with specified value", function () { - let data = [ NaN, "boy", NaN, "hey", "Man", undefined ]; - let sf = new dfd.Series(data); - let sf_val = [ "filled", "boy", "filled", "hey", "Man", "filled" ]; - let sf_fill = sf.fillna({ value: "filled" }); - assert.deepEqual(sf_fill.values, sf_val); - }); - }); - - describe("cumsum", function () { - it("Return cumulative sum over a Series", function () { - let data1 = [ 10, 45, 56, 25, 23, 20, 10 ]; - let sf = new dfd.Series(data1); - assert.deepEqual(sf.cumsum().values, [ 10, 55, 111, 136, 159, 179, 189 ]); - }); - }); - - describe("cummax", function () { - it("Return cumulative maximum over a Series", function () { - let data1 = [ 10, 45, 56, 25, 23, 20, 10 ]; - let sf = new dfd.Series(data1); - assert.deepEqual(sf.cummax().values, [ 10, 45, 56, 56, 56, 56, 56 ]); - }); - }); - - describe("cummin", function () { - it("Return cumulative minimum over a Series", function () { - let data1 = [ 10, 45, 56, 25, 23, 20, 10 ]; - let sf = new dfd.Series(data1); - assert.deepEqual(sf.cummin().values, [ 10, 10, 10, 10, 10, 10, 10 ]); - }); - }); - - describe("cumprod", function () { - it("Return cumulative product over a Series", function () { - let data1 = [ 1, 2, 10, 3, 12, 14, 1 ]; - let sf = new dfd.Series(data1); - let rslt = [ 1, 2, 20, 60, 720, 10080, 10080 ]; - assert.deepEqual(sf.cumprod().values, rslt); - }); - }); - - describe("lt", function () { - it("Return Less than of series and other series (element-wise)", function () { - let data1 = [ 10, 45, 56, 25, 23, 20, 10 ]; - let data2 = [ 100, 450, 590, 5, 25, 2, 0 ]; - - let sf = new dfd.Series(data1); - let sf2 = new dfd.Series(data2); - let expected = [ true, true, true, false, true, false, false ]; - assert.deepEqual(sf.lt(sf2).values, expected); - }); - - it("Return Less than of series scalar (element-wise)", function () { - let data1 = [ 10, 45, 56, 25, 23, 20, 10 ]; - let sf = new dfd.Series(data1); - let expected = [ true, false, false, true, true, true, true ]; - assert.deepEqual(sf.lt(30).values, expected); - }); - }); - - describe("gt", function () { - it("Return Greater than of series and other series (element-wise)", function () { - let data1 = [ 10, 45, 56, 25, 23, 20, 10 ]; - let data2 = [ 100, 450, 590, 5, 25, 2, 0 ]; - - let sf = new dfd.Series(data1); - let sf2 = new dfd.Series(data2); - let expected = [ false, false, false, true, false, true, true ]; - assert.deepEqual(sf.gt(sf2).values, expected); - }); - - it("Return Greater than of series scalar (element-wise)", function () { - let data1 = [ 10, 45, 56, 25, 23, 20, 10 ]; - let sf = new dfd.Series(data1); - let expected = [ false, true, true, false, false, false, false ]; - assert.deepEqual(sf.gt(30).values, expected); - }); - }); - - describe("le", function () { - it("Return Less than or Equal to of series and other series (element-wise)", function () { - let data1 = [ 10, 45, 56, 25, 23, 20, 10 ]; - let data2 = [ 100, 450, 590, 5, 25, 2, 0 ]; - - let sf = new dfd.Series(data1); - let sf2 = new dfd.Series(data2); - let expected = [ true, true, true, false, true, false, false ]; - assert.deepEqual(sf.le(sf2).values, expected); - }); - - it("Return Less than or Equal to of series scalar (element-wise)", function () { - let data1 = [ 10, 45, 56, 25, 23, 20, 10 ]; - let sf = new dfd.Series(data1); - let expected = [ true, false, false, true, true, true, true ]; - assert.deepEqual(sf.le(30).values, expected); - }); - }); - - describe("ge", function () { - it("Return Greater than or Equal to of series and other series (element-wise)", function () { - let data1 = [ 10, 45, 56, 25, 23, 20, 10 ]; - let data2 = [ 100, 450, 56, 5, 25, 20, 0 ]; - - let sf = new dfd.Series(data1); - let sf2 = new dfd.Series(data2); - let expected = [ false, false, true, true, false, true, true ]; - assert.deepEqual(sf.ge(sf2).values, expected); - }); - - it("Return Greater than or Equal to of series scalar (element-wise)", function () { - let data1 = [ 30, 45, 56, 25, 23, 20, 10 ]; - let sf = new dfd.Series(data1); - let expected = [ true, true, true, false, false, false, false ]; - assert.deepEqual(sf.ge(30).values, expected); - }); - }); - - describe("ne", function () { - it("Return Not Equal to of series and other series (element-wise)", function () { - let data1 = [ 10, 45, 56, 25, 23, 20, 10 ]; - let data2 = [ 10, 450, 56, 5, 25, 2, 0 ]; - - let sf = new dfd.Series(data1); - let sf2 = new dfd.Series(data2); - let expected = [ false, true, false, true, true, true, true ]; - assert.deepEqual(sf.ne(sf2).values, expected); - }); - - it("Return Not Equal to of series scalar (element-wise)", function () { - let data1 = [ 10, 30, 56, 30, 23, 20, 10 ]; - let sf = new dfd.Series(data1); - let expected = [ true, false, true, false, true, true, true ]; - assert.deepEqual(sf.ne(30).values, expected); - }); - }); - - describe("eq", function () { - it("Return Equal to of series and other series (element-wise)", function () { - let data1 = [ 10, 45, 56, 25, 23, 20, 10 ]; - let data2 = [ 100, 450, 590, 25, 25, 2, 0 ]; - - let sf = new dfd.Series(data1); - let sf2 = new dfd.Series(data2); - let expected = [ false, false, false, true, false, false, false ]; - assert.deepEqual(sf.eq(sf2).values, expected); - }); - - it("Return Equal to of series scalar (element-wise)", function () { - let data1 = [ 10, 45, 56, 25, 23, 20, 30 ]; - let sf = new dfd.Series(data1); - let expected = [ false, false, false, false, false, false, true ]; - assert.deepEqual(sf.eq(30).values, expected); - }); - }); - - describe("replace", function () { - it("Replace values given in replace param with value", function () { - let data1 = [ 10, 45, 56, 25, 23, 20, 10 ]; - let sf = new dfd.Series(data1); - let expected = [ -50, 45, 56, 25, 23, 20, -50 ]; - let df_rep = sf.replace({ replace: 10, with: -50 }); - assert.deepEqual(df_rep.values, expected); - }); - - it("Replace values given in replace param with value (String type)", function () { - let data1 = [ "A", "A", "A", "B", "B", "C", "C", "D" ]; - let sf = new dfd.Series(data1); - let expected = [ "boy", "boy", "boy", "B", "B", "C", "C", "D" ]; - sf.replace({ replace: "A", with: "boy", inplace: true }); - assert.deepEqual(sf.values, expected); - }); - it("Throw error on wrong param passed", function () { - let data1 = [ "A", "A", "A", "B", "B", "C", "C", "D" ]; - let sf = new dfd.Series(data1); - let expected = `Params Error: A specified parameter is not supported. Your params must be any of the following [replace,with,inplace]`; - assert.throws( - () => { - sf.replace({ replce: "A", with: "boy", inplace: true }); - }, - Error, - expected - ); - }); - }); - - describe("drop_duplicates", function () { - it("Return Series with duplicate values removed (Default, first values kept)", function () { - let data1 = [ 10, 45, 56, 10, 23, 20, 10, 10 ]; - let sf = new dfd.Series(data1); - let expected = [ 10, 45, 56, 23, 20 ]; - let expected_index = [ 0, 1, 2, 4, 5 ]; - let df_drop = sf.drop_duplicates(); - assert.deepEqual(df_drop.values, expected); - assert.deepEqual(df_drop.index, expected_index); - }); - - it("Return Series with duplicate values removed (last values kept)", function () { - let data1 = [ 10, 45, 56, 10, 23, 20, 10, 10 ]; - let sf = new dfd.Series(data1); - let expected = [ 45, 56, 23, 20, 10 ]; - let expected_index = [ 1, 2, 4, 5, 7 ]; - let df_drop = sf.drop_duplicates({ keep: "last" }); - assert.deepEqual(df_drop.values, expected); - assert.deepEqual(df_drop.index, expected_index); - }); - - it("Return Series with duplicate values removed (String)", function () { - let data1 = [ "A", "A", "A", "B", "B", "C", "C", "D" ]; - let sf = new dfd.Series(data1); - let expected = [ "A", "B", "C", "D" ]; - let expected_index = [ 0, 3, 5, 7 ]; - sf.drop_duplicates({ inplace: true }); - assert.deepEqual(sf.values, expected); - assert.deepEqual(sf.index, expected_index); - }); - }); - - describe("dropna", function () { - it("Return a new Series with missing values removed (Int)", function () { - let data1 = [ 10, 45, undefined, 10, 23, 20, undefined, 10 ]; - let sf = new dfd.Series(data1); - let expected = [ 10, 45, 10, 23, 20, 10 ]; - let expected_index = [ 0, 1, 3, 4, 5, 7 ]; - let sf_drop = sf.dropna(); - assert.deepEqual(sf_drop.values, expected); - assert.deepEqual(sf_drop.index, expected_index); - }); - - it("Return a new Series with missing values removed (String)", function () { - let data1 = [ "A", NaN, "A", "B", "B", NaN, "C", undefined ]; - let sf = new dfd.Series(data1); - let expected = [ "A", "A", "B", "B", "C" ]; - let expected_index = [ 0, 2, 3, 4, 6 ]; - - sf.dropna({ inplace: true }); - assert.deepEqual(sf.values, expected); - assert.deepEqual(sf.index, expected_index); - }); - }); - - describe("argsort", function () { - it("Return the integer indices that would sort the Series values", function () { - let data1 = [ 10, 45, 20, 10, 23, 20, 30, 11 ]; - let sf = new dfd.Series(data1); - let expected = [ 3, 0, 7, 5, 2, 4, 6, 1 ]; - let sf_sort = sf.argsort(); - assert.deepEqual(sf_sort.values, expected); - }); - - it("Return the integer indices that would sort the Series values", function () { - let data1 = [ 10.22, 4.5, 2.0, 10, 23.23, 20.1, 30, 11 ]; - let sf = new dfd.Series(data1); - let expected = [ 6, 4, 5, 7, 0, 3, 1, 2 ]; - let sf_sort = sf.argsort(false); - assert.deepEqual(sf_sort.values, expected); - }); - }); - - describe("argmax", function () { - it("Return int position of the largest value in the Series.", function () { - let data1 = [ 10, 45, 20, 10, 23, 20, 30, 11 ]; - let sf = new dfd.Series(data1); - let expected = 1; - let argmax = sf.argmax(); - assert.deepEqual(argmax, expected); - }); - - it("Return int position of the largest value in the Float Series.", function () { - let data1 = [ 10.22, 4.5, 2.0, 10, 23.23, 20.1, 30, 11 ]; - let sf = new dfd.Series(data1); - let expected = 6; - let argmax = sf.argmax(); - assert.deepEqual(argmax, expected); - }); - }); - - describe("argmin", function () { - it("Return int position of the smallest value in the Series", function () { - let data1 = [ 10, 45, 20, 122, 23, 20, 30, 11 ]; - let sf = new dfd.Series(data1); - let expected = 0; - let argmin = sf.argmin(); - assert.deepEqual(argmin, expected); - }); - - it("Return int position of the smallest value in a Float Series", function () { - let data1 = [ 10.22, 4.5, 2.0, 10, 23.23, 20.1, 30, 11 ]; - let sf = new dfd.Series(data1); - let expected = 2; - let argmin = sf.argmin(); - assert.deepEqual(argmin, expected); - }); - }); - - describe("Str", function () { - it("Converts all characters to lowercase.", function () { - let data = [ "lower", "CAPITALS", "this is a sentence", "SwApCaSe" ]; - let res = [ "lower", "capitals", "this is a sentence", "swapcase" ]; - let sf = new dfd.Series(data); - assert.deepEqual(sf.str.toLowerCase().values, res); - }); - it("Converts all characters to uppercase.", function () { - let data = [ "lower", "CAPITALS", "this is a sentence", "SwApCaSe" ]; - let res = [ "LOWER", "CAPITALS", "THIS IS A SENTENCE", "SWAPCASE" ]; - let sf = new dfd.Series(data); - assert.deepEqual(sf.str.toUpperCase().values, res); - }); - it("Converts all characters to capital case.", function () { - let data = [ "lower", "CAPITALS", "this is a sentence", "SwApCaSe" ]; - let res = [ "Lower", "Capitals", "This is a sentence", "Swapcase" ]; - let sf = new dfd.Series(data); - assert.deepEqual(sf.str.capitalize().values, res); - }); - - it("Returns the character at the specified index (position)", function () { - let data = [ "lower", "CAPITALS", "this is a sentence", "SwApCaSe" ]; - let res = [ "w", "P", "i", "A" ]; - let sf = new dfd.Series(data); - assert.deepEqual(sf.str.charAt(2).values, res); - }); - - it("Throws error on concat of numeric series", function () { - let data = [ 1, 2, 3, 4, 5, 6 ]; - let sf = new dfd.Series(data); - assert.throws( - () => { - sf.str.concat("20", 1); - }, - Error, - "Cannot call accessor str on non-string type" - ); - - }); - }); - - describe("dt", function () { - it("check month generated", function () { - let data = [ "02Sep2019", "03Dec2019", "04Jan2019" ]; - let sf = new dfd.Series(data); - let new_data = [ 8, 11, 0 ]; - assert.deepEqual(sf.dt.month().values, new_data); - }); - - it("check month Name generated", function () { - let data = [ "06-30-02019", "07-29-2019", "08-28-2019" ]; - let sf = new dfd.Series(data); - let new_data = [ "Jun", "Jul", "Aug" ]; - assert.deepEqual(sf.dt.month_name().values, new_data); - }); - - it("check days of the weeks generated", function () { - let data = [ "06-30-02019", "07-29-2019", "08-28-2019" ]; - let sf = new dfd.Series(data); - let new_data = [ "Sun", "Mon", "Wed" ]; - assert.deepEqual(sf.dt.weekdays().values, new_data); - }); - - it("check day of the month generated", function () { - let data = [ "06-30-02019", "07-29-2019", "08-28-2019" ]; - let sf = new dfd.Series(data); - let new_data = [ 30, 29, 28 ]; - assert.deepEqual(sf.dt.monthday().values, new_data); - }); - }); - - describe("astype", function () { - it("set type of float column to int", function () { - let data = [ -20.1, 30, 47.3, -20 ]; - let ndframe = new dfd.Series(data); - let df = ndframe.astype("int32"); - - assert.deepEqual(df.dtypes, "int32"); - assert.deepEqual(df.values, [ -20, 30, 47, -20 ]); - }); - it("set type of int column to float", function () { - let data = [ 34, -4, 5, 6 ]; - let ndframe = new dfd.Series(data); - let df = ndframe.astype("float32"); - assert.deepEqual(df.dtypes, "float32"); - assert.deepEqual(df.values, [ 34, -4, 5, 6 ]); - }); - it("set type of string column to int", function () { - let data = [ "20.1", "21", "23.4", "50.78" ]; - let ndframe = new dfd.Series(data); - let df = ndframe.astype("int32"); - - assert.deepEqual(df.dtypes, "int32"); - assert.deepEqual(df.values, [ 20, 21, 23, 51 ]); - }); - it("set type of string column to float", function () { - let data = [ "20.1", "21", "23.4", "50.78" ]; - let ndframe = new dfd.Series(data); - let df = ndframe.astype("float32"); - - assert.deepEqual(df.dtypes, "float32"); - assert.deepEqual(df.values, [ 20.1, 21, 23.4, 50.78 ]); - }); - }); - - describe("iloc", function () { - it("indexing by list of index", function () { - let data = [ 1, 2, 3, 4, "a", "b", "c" ]; - let sf = new dfd.Series(data); - - let expected_val = [ 2, "a", 3, 4, "b" ]; - - assert.deepEqual(sf.iloc([ 1, 4, 2, 3, 5 ]).values, expected_val); - }); - it("indexing by slicing", function () { - let data = [ 1, 2, 3, 4, "a", "b", "c" ]; - let sf = new dfd.Series(data); - - let expected_val = [ 2, 3, 4 ]; - - assert.deepEqual(sf.iloc([ "1:4" ]).values, expected_val); - }); - it("indexing by slicing format ':5' works", function () { - let data = [ 1, 2, 3, 4, "a", "b", "c" ]; - let sf = new dfd.Series(data); - let expected_val = [ 1, 2, 3, 4, 'a' ]; - assert.deepEqual(sf.iloc([ ":5" ]).values, expected_val); - }); - it("indexing by slicing format '2:` works", function () { - let data = [ 1, 2, 3, 4, "a", "b", "c" ]; - let sf = new dfd.Series(data); - let expected_val = [ 3, 4, "a", "b", "c" ]; - assert.deepEqual(sf.iloc([ "2:" ]).values, expected_val); - }); - }); - - describe("append", function () { - it("Add a new single value to the end of a Series", function () { - let data = [ 1, 2, 3, 4, "a", "b", "c" ]; - let sf = new dfd.Series(data); - let expected_val = [ 1, 2, 3, 4, "a", "b", "c", "d" ]; - sf.append("d", true); - assert.deepEqual(sf.values, expected_val); - }); - it("Add a new array of values to the end of a Series", function () { - let data = [ 1, 2, 3, 4 ]; - let to_add = [ "a", "b", "c" ]; - let sf = new dfd.Series(data); - let expected_val = [ 1, 2, 3, 4, "a", "b", "c" ]; - sf.append(to_add, true); - assert.deepEqual(sf.values, expected_val); - }); - it("Add a Series to the end of another Series", function () { - let sf1 = new dfd.Series([ 1, 2, 3, 4 ]); - let sf2 = new dfd.Series([ "a", "b", "c" ]); - let expected_val = [ 1, 2, 3, 4, "a", "b", "c" ]; - sf1.append(sf2, true); - assert.deepEqual(sf1.values, expected_val); - }); - it("Add a new single value to the end of a Series", function () { - let data = [ 1, 2, 3, 4, "a", "b", "c" ]; - let sf = new dfd.Series(data); - let expected_val = [ 1, 2, 3, 4, "a", "b", "c", "d" ]; - sf = sf.append("d"); - assert.deepEqual(sf.values, expected_val); - }); - it("Add a new array of values to the end of a Series", function () { - let data = [ 1, 2, 3, 4 ]; - let to_add = [ "a", "b", "c" ]; - let sf = new dfd.Series(data); - let expected_val = [ 1, 2, 3, 4, "a", "b", "c" ]; - sf = sf.append(to_add); - assert.deepEqual(sf.values, expected_val); - }); - it("Add a Series to the end of another Series", function () { - let sf1 = new dfd.Series([ 1, 2, 3, 4 ]); - let sf2 = new dfd.Series([ "a", "b", "c" ]); - let expected_val = [ 1, 2, 3, 4, "a", "b", "c" ]; - sf1 = sf1.append(sf2); - assert.deepEqual(sf1.values, expected_val); - }); - it("Confirm index Change after append", function () { - let sf1 = new dfd.Series([ 1, 2, 3, 4 ]); - let sf2 = new dfd.Series([ "a", "b", "c" ]); - sf1 = sf1.append(sf2); - assert.deepEqual(sf1.index, [ 0, 1, 2, 3, 0, 1, 2 ]); - }); - it("Confirm index Change after append inplace", function () { - let sf1 = new dfd.Series([ 1, 2, 3, 4 ]); - let sf2 = new dfd.Series([ "a", "b", "c" ]); - sf1.append(sf2, true); - assert.deepEqual(sf1.index, [ 0, 1, 2, 3, 0, 1, 2 ]); - }); - }); -}); diff --git a/danfojs-browser/tests/core/timeseries.js b/danfojs-browser/tests/core/timeseries.js deleted file mode 100644 index f74ba207..00000000 --- a/danfojs-browser/tests/core/timeseries.js +++ /dev/null @@ -1,88 +0,0 @@ - -describe("TimeSeries", function () { - - it("Check date formatting", function () { - - let data = [ "02Sep2019", "03Sep2019", "04Sep2019" ]; - - let times = dfd.to_datetime({ "data": data, "format": "%d%b%Y%" }); - - let new_data = [ new Date("02-Sep-2019"), new Date("03-Sep-2019"), new Date("04-Sep-2019") ]; - - assert.deepEqual(times.date_list, new_data); - - }); - - it("Check Invalid date Formatting", function () { - - let data = [ "30-06-02019", "29-06-2019", "28-06-2019" ]; - - assert.throws(function () { dfd.to_datetime({ "data": data }); }, Error, "Invalid date, the date format not recognise"); - - }); - - it("check month generated", function () { - - let data = [ "02Sep2019", "03Dec2019", "04Jan2019" ]; - - let times = dfd.to_datetime({ "data": data, "format": "%d%b%Y%" }); - - let new_data = [ 8, 11, 0 ]; - - assert.deepEqual(times.month().values, new_data); - }); - - it("check month Name generated", function () { - - let data = [ "06-30-02019", "07-29-2019", "08-28-2019" ]; - - let times = dfd.to_datetime({ "data": data }); - - let new_data = [ "Jun", "Jul", "Aug" ]; - - assert.deepEqual(times.month_name().values, new_data); - }); - - it("check days of the weeks generated", function () { - - let data = [ "06-30-02019", "07-29-2019", "08-28-2019" ]; - - let times = dfd.to_datetime({ "data": data }); - - let new_data = [ "Sun", "Mon", "Wed" ]; - - assert.deepEqual(times.weekdays().values, new_data); - }); - - it("check day of the month generated", function () { - - let data = [ "06-30-02019", "07-29-2019", "08-28-2019" ]; - - let times = dfd.to_datetime({ "data": data }); - - let new_data = [ 30, 29, 28 ]; - - assert.deepEqual(times.monthday().values, new_data); - }); - it("check the seconds generated", function () { - - let data = [ "06-30-02019 00:00:12", "07-29-2019 00:30:40", "08-28-2019 00:12:04" ]; - - let times = dfd.to_datetime({ "data": data }); - - let new_data = [ 12, 40, 4 ]; - - assert.deepEqual(times.seconds().values, new_data); - }); - it("check the minutes generated", function () { - - let data = [ "06-30-02019 00:00:12", "07-29-2019 00:30:40", "08-28-2019 00:12:04" ]; - - let times = dfd.to_datetime({ "data": data }); - - let new_data = [ 0, 30, 12 ]; - - assert.deepEqual(times.minutes().values, new_data); - }); - -}); diff --git a/danfojs-browser/tests/core/utils.js b/danfojs-browser/tests/core/utils.js deleted file mode 100644 index c0aa1d5c..00000000 --- a/danfojs-browser/tests/core/utils.js +++ /dev/null @@ -1,247 +0,0 @@ -/* eslint-disable no-undef */ - - -describe("Utils Functions", function () { - const utils = new dfd.Utils; - - it("removes an element from an array", function () { - let arr = [ 1, 2, 3, 4 ]; - assert.deepEqual(utils.remove(arr, 2), [ 1, 2, 4 ]); - }); - it("Checks if variable is a string", function () { - let arr = [ "1", "2" ]; - assert.isTrue(utils.__is_string(arr[0])); - }); - it("Checks if variable is a number", function () { - let arr = [ 1, 2, 3, 4 ]; - assert.isTrue(utils.__is_number(arr[0])); - }); - it("Checks if value is null", function () { - let val = null; - let val2 = 1; - assert.isTrue(utils.__is_null(val)); - assert.isFalse(utils.__is_null(val2)); - }); - - it("Checks if value is undefined", function () { - let arr; - assert.isTrue(utils.__is_undefined(arr)); - }); - it("random sample n elements from array", function () { - let data = [ [ 1, 2, 3 ], [ 4, 5, 6 ], [ 20, 30, 40 ], [ 39, 89, 78 ], [ 100, 200, 300 ] ]; - assert.isFalse(utils.__sample_from_iter(data, 2) == utils.__sample_from_iter(data, 2)); - assert.isFalse(utils.__sample_from_iter(data, 3) === utils.__sample_from_iter(data, 3)); - - }); - it("Generate number betwee two set of values", function () { - - let start = 0; - let end = 5; - let data = [ 0, 1, 2, 3, 4, 5 ]; - assert.deepEqual(utils.__range(start, end), data); - }); - - describe("__get_col_values", function () { - it("converts an array of rows to array of columns", function () { - let data = [ [ 1, 2, 3 ], [ 4, 5, 6 ], [ 20, 30, 40 ] ]; - let result = [ [ 1, 4, 20 ], [ 2, 5, 30 ], [ 3, 6, 40 ] ]; - assert.deepEqual(utils.__get_col_values(data), result); - }); - }); - - describe("__get_t", function () { - it("Returns string type present in an 1D array", function () { - let data = [ 'Alice', 'Boy', 'Girl', "39" ]; - let result = [ 'string' ]; - assert.deepEqual(utils.__get_t(data), result); - }); - it("Returns float type present in an 1D array", function () { - let data = [ 1.1, 2.1, 3.2, 4.4 ]; - let result = [ 'float32' ]; - assert.deepEqual(utils.__get_t(data), result); - }); - it("Returns int type present in an 1D array", function () { - let data = [ 1, 2, 3, 45 ]; - let result = [ 'int32' ]; - assert.deepEqual(utils.__get_t(data), result); - }); - it("Returns float when there's a mixture of int and float in a 1D array", function () { - let data = [ 1, 2.1, 3, 45 ]; - let result = [ 'float32' ]; - assert.deepEqual(utils.__get_t(data), result); - }); - it("Returns float type when NaN is present in an 1D array", function () { - let data = [ 1, 2, 3, 45, NaN ]; - let result = [ 'float32' ]; - assert.deepEqual(utils.__get_t(data), result); - }); - it("Returns correct dtype if NaN present in data", function () { - let data = [ - [ 18.7, 17.4, 18, NaN, 19.3 ], - [ 20, NaN, 19, 18, 20 ] ]; - let result = [ 'float32', 'float32' ]; - assert.deepEqual(utils.__get_t(data), result); - }); - it("Returns the data type present in an 2D array", function () { - let data = [ [ 'Alice', 'Boy', 'Girl', "39" ], [ 2, 5, 30, 89 ], [ 3.1, 6.1, 40.1, 78.2 ] ]; - let result = [ 'string', 'int32', 'float32' ]; - assert.deepEqual(utils.__get_t(data), result); - }); - it("Returns the string dtype when there's a mixture of dtyoes in a 2D array", function () { - let data = [ [ 'Alice', 'Boy', 'Girl', 21 ], [ 2, 5, 30, "hey" ], [ 3, 6, 40.1, 78.2 ] ]; - let result = [ 'string', 'string', 'float32' ]; - assert.deepEqual(utils.__get_t(data), result); - }); - it("Returns bool type in a 1D array", function () { - let data = [ true, true, false, false, false, true ]; - let result = [ 'boolean' ]; - assert.deepEqual(utils.__get_t(data), result); - }); - it("Returns bool type in a 2D array", function () { - let data = [ [ true, false, true ], [ "boy", "girl", "man" ], [ 20, 30, 24 ] ]; - let result = [ 'boolean', 'string', 'int32' ]; - assert.deepEqual(utils.__get_t(data), result); - }); - it("Returns string type if values are all NaN", function () { - let data = [ [ true, false, true ], [ "boy", "girl", "boy" ], [ NaN, undefined, NaN ] ]; - let result = [ 'boolean', 'string', 'float32' ]; - assert.deepEqual(utils.__get_t(data), result); - }); - }); - - describe("__map_int_to_bool", function () { - it("map ints to bools in array of arrays", function () { - let data = [ [ 1, 0, 1 ], [ 1, 1, 0 ] ]; - assert.deepEqual(utils.__map_int_to_bool(data, 2), [ [ true, false, true ], [ true, true, false ] ]); - }); - it("map ints to bools in array", function () { - let data = [ 1, 0, 0, 1, 1 ]; - assert.deepEqual(utils.__map_int_to_bool(data, 1), [ true, false, false, true, true ]); - }); - }); - - - describe("__median", function () { - it("Gets the median value of an even array", function () { - let data = [ 100, 2, 3, 20, 30, 40 ]; - assert.deepEqual(utils.__median(data, true), 25); - }); - it("Gets the median value of an odd array", function () { - let data = [ 1, 30, 20, 50, 40 ]; - assert.deepEqual(utils.__median(data, true), 30); - }); - }); - - describe("__mode", function () { - it("Gets the modal value(s) of an array", function () { - let data = [ 100, 2, 3, 20, 20, 40 ]; - assert.deepEqual(utils.__mode(data), [ 20 ]); - }); - it("Returns the multi-modal values of an array", function () { - let data = [ 100, 2, 3, 20, 20, 40, 4, 4, 4, 20 ]; - assert.deepEqual(utils.__mode(data), [ 4, 20 ]); - }); - }); - - describe("__round", function () { - it("round elements in array to 1 dp", function () { - let data = [ 10.01, 2.2, 3.11, 20.505, 20.22, 40.0909 ]; - assert.deepEqual(utils.__round(data, 1, true), [ 10.0, 2.2, 3.1, 20.5, 20.2, 40.1 ]); - }); - it("round elements in array to 2 dp", function () { - let data = [ 10.019, 2.2099, 3.1145, 20.506, 20.22, 40.0909 ]; - assert.deepEqual(utils.__round(data, 2, true), [ 10.02, 2.21, 3.11, 20.51, 20.22, 40.09 ]); - }); - }); - - describe("__replace_undefined_with_NaN", function () { - it("replace undefined in Series with NaN", function () { - let data = [ 10.01, 2.2, undefined, 20.505, 20.22, undefined ]; - assert.deepEqual(utils.__replace_undefined_with_NaN(data, true), [ 10.01, 2.2, NaN, 20.505, 20.22, NaN ]); - }); - it("replace undefined in DataFrame with NaN", function () { - let data = [ [ 10.01, 2.2, undefined, 20.505, 20.22, undefined ], - [ 10.01, undefined, undefined, 20.505, 20, undefined ] ]; - - let result = [ [ 10.01, 2.2, NaN, 20.505, 20.22, NaN ], - [ 10.01, NaN, NaN, 20.505, 20, NaN ] ]; - assert.deepEqual(utils.__replace_undefined_with_NaN(data, false), result); - }); - it("replace null in Series with NaN", function () { - let data = [ 10.01, 2.2, null, 20.505, 20.22, null ]; - assert.deepEqual(utils.__replace_undefined_with_NaN(data, true), [ 10.01, 2.2, NaN, 20.505, 20.22, NaN ]); - }); - it("replace null in DataFrame with NaN", function () { - let data = [ [ 10.01, 2.2, null, 20.505, 20.22, null ], - [ 10.01, null, null, 20.505, 20, null ] ]; - - let result = [ [ 10.01, 2.2, NaN, 20.505, 20.22, NaN ], - [ 10.01, NaN, NaN, 20.505, 20, NaN ] ]; - assert.deepEqual(utils.__replace_undefined_with_NaN(data, false), result); - }); - }); - - describe("__convert_2D_to_1D", function () { - it("convert 2D array of array to 1D of string values", function () { - let data = [ [ 10.01, 2.2, "a" ], [ 20.505, 20.22, "boy" ] ]; - assert.deepEqual(utils.__convert_2D_to_1D(data), [ "10.01,2.2,a", "20.505,20.22,boy" ]); - }); - - }); - - // describe("_throw_wrong_params_error", function () { - // it("check if the right params are passed to a function", function () { - // let params_needed = ["replace", "with", "inplace"] - // let kwargs = { "replae": 2, "with": 12, "inplace": true } - // assert.equal(utils._throw_wrong_params_error(kwargs, params_needed), false) - // }) - // it("check if the right params are passed to a function 2", function () { - // let params_needed = ["replace", "with", "inplace"] - // let kwargs = { "replace": 2, "with": 12, "inplace": true } - // assert.equal(utils._throw_wrong_params_error(kwargs, params_needed), true) - // }) - - // }) - - describe("_get_row_and_col_values", function () { - it("retreive rows and labels from column object", function () { - let data = { "Alpha": [ "A", "B", "C", "D" ], count: [ 1, 2, 3, 4 ], sum: [ 20.3, 30.456, 40.90, 90.1 ] }; - let res = [ [ "A", 1, 20.3 ], [ "B", 2, 30.456 ], [ "C", 3, 40.90 ], [ "D", 4, 90.1 ] ]; - assert.deepEqual(utils._get_row_and_col_values(data)[0], res); - assert.deepEqual(utils._get_row_and_col_values(data)[1], [ "Alpha", "count", "sum" ]); - - }); - - - }); - - - describe("_get_duplicate", function(){ - it("obtain duplicate and their index", function(){ - - let data = [ 1, 2, 3, 4, 5, 3, 4, 6, 4, 5 ]; - let res = { '3': { count: 2, index: [ 2, 5 ] }, - '4': { count: 3, index: [ 3, 6, 8 ] }, - '5': { count: 2, index: [ 4, 9 ] } }; - - assert.deepEqual(utils.__get_duplicate(data), res); - }); - }); - - describe("__shuffle", function() { - it("returns array with correct shape", function() { - let data = [ 1, 2, 3, 4, 5 ]; - assert.deepEqual(utils.__shuffle(3, data).length, 3); - }); - - it("returns original values when num equals total length", function() { - let data = [ 1, 2, 3, 4, 5 ]; - assert.deepEqual(utils.__shuffle(5, data).sort(), [ 1, 2, 3, 4, 5 ]); - }); - - it("returns empty array when num equals zero", function() { - let data = [ 1, 2, 3, 4, 5 ]; - assert.deepEqual(utils.__shuffle(0, data), []); - }); - }); -}); diff --git a/danfojs-browser/tests/io/reader.js b/danfojs-browser/tests/io/reader.js deleted file mode 100644 index 7177cc78..00000000 --- a/danfojs-browser/tests/io/reader.js +++ /dev/null @@ -1,36 +0,0 @@ -/* eslint-disable no-undef */ -describe("read_csv", async function () { - this.timeout(10000); // all tests in this suite get 10 seconds before timeout - it("reads a csv file from source over the internet", async function () { - const csvUrl = - "https://storage.googleapis.com/tfjs-examples/multivariate-linear-regression/data/boston-housing-train.csv"; - - const df = await dfd.read_csv(csvUrl); - const num_of_columns = df.column_names.length; - assert.equal(num_of_columns, 13); - - }); -}); - -describe("read_json", async function () { - this.timeout(10000); // all tests in this suite get 10 seconds before timeout - it("reads a json file from source over the internet", async function () { - const jUrl = - "https://raw.githubusercontent.com/risenW/Tensorflowjs_Projects/master/recommender-sys/Python-Model/web_book_data.json"; - - const df = await dfd.read_json(jUrl); - const num_of_columns = df.column_names.length; - assert.equal(num_of_columns, 4); - - }); -}); - -describe("read_excel", async function () { - this.timeout(10000); // all tests in this suite get 10 seconds before timeout - it("reads an excel file from source over the internet", async function () { - const remote_url = - "https://file-examples-com.github.io/uploads/2017/02/file_example_XLS_100.xls"; - const df = await dfd.read_excel(remote_url); - assert(df.columns.length, 8); - }); -}); diff --git a/danfojs-browser/tests/preprocessing/encodings.js b/danfojs-browser/tests/preprocessing/encodings.js deleted file mode 100644 index 9f05f356..00000000 --- a/danfojs-browser/tests/preprocessing/encodings.js +++ /dev/null @@ -1,94 +0,0 @@ - -describe("Encodings", function () { - - describe("LabelEncoder", function () { - - it("test the label encoding on array", function () { - let data = [ "dog", "cat", "man", "dog", "cat", "man", "man", "cat" ]; - let encode = new dfd.LabelEncoder(); - let fit_data = [ - 0, 1, 2, 0, - 1, 2, 2, 1 - ]; - assert.deepEqual(encode.fit(data).values, fit_data); - assert.deepEqual(encode.transform([ "dog", "man" ]).values, [ 0, 2 ]); - }); - it("test the label encoding on Series", function () { - let data = [ "dog", "cat", "man", "dog", "cat", "man", "man", "cat" ]; - let series = new dfd.Series(data); - let encode = new dfd.LabelEncoder(); - let fit_data = [ - 0, 1, 2, 0, - 1, 2, 2, 1 - ]; - assert.deepEqual(encode.fit(series).values, fit_data); - assert.deepEqual(encode.transform([ "dog", "man" ]).values, [ 0, 2 ]); - }); - it("label encoding directly from a Series", function () { - let data = new dfd.Series([ "dog", "cat", "man", "dog", "cat", "man", "man", "cat" ]); - let to_label_encode = new dfd.Series([ "dog", "man" ]); - let encode = new dfd.LabelEncoder(); - let fit_data = [ - 0, 1, 2, 0, - 1, 2, 2, 1 - ]; - assert.deepEqual(encode.fit(data).values, fit_data); - assert.deepEqual(encode.transform(to_label_encode).values, [ 0, 2 ]); - }); - // it("Label encoding on Series", function () { - // let data = ["dog", "cat", "man", "dog", "cat", "man", "man", "cat"] - // let series = new dfd.Series(data) - // let encode = new dfd.LabelEncoder() - // let fit_data = [ - // 0, 1, 2, 0, - // 1, 2, 2, 1 - // ] - // assert.deepEqual(encode.fit(series).values, fit_data) - // assert.deepEqual(encode.transform(["dog", "man"]).values, [0, 2]) - // }); - - }); - - describe("OneHotEncoder", function () { - - it("test onehotencoding on array", function () { - let data = [ "dog", "cat", "man", "dog", "cat", "man", "man", "cat" ]; - let encode = new dfd.OneHotEncoder(); - let fit_data = [ - [ 1, 0, 0 ], - [ 0, 1, 0 ], - [ 0, 0, 1 ], - [ 1, 0, 0 ], - [ 0, 1, 0 ], - [ 0, 0, 1 ], - [ 0, 0, 1 ], - [ 0, 1, 0 ] - ]; - let transform_data = [ [ 0, 0, 1 ], [ 0, 1, 0 ] ]; - - assert.deepEqual(encode.fit(data).values, fit_data); - assert.deepEqual(encode.transform([ "man", "cat" ]).values, transform_data); - }); - it("test onehotencoding on Series", function () { - let data = [ "dog", "cat", "man", "dog", "cat", "man", "man", "cat" ]; - let series = new dfd.Series(data); - let encode = new dfd.OneHotEncoder(); - let fit_data = [ - [ 1, 0, 0 ], - [ 0, 1, 0 ], - [ 0, 0, 1 ], - [ 1, 0, 0 ], - [ 0, 1, 0 ], - [ 0, 0, 1 ], - [ 0, 0, 1 ], - [ 0, 1, 0 ] - ]; - let transform_data = [ [ 0, 0, 1 ], [ 0, 1, 0 ] ]; - - assert.deepEqual(encode.fit(series).values, fit_data); - assert.deepEqual(encode.transform(new dfd.Series([ "man", "cat" ])).values, transform_data); - }); - }); - - -}); diff --git a/danfojs-browser/tests/preprocessing/scaler.js b/danfojs-browser/tests/preprocessing/scaler.js deleted file mode 100644 index 43ba76da..00000000 --- a/danfojs-browser/tests/preprocessing/scaler.js +++ /dev/null @@ -1,54 +0,0 @@ -describe("Preprocessing", function(){ - - describe("MinMaxscaler", function(){ - - it("Standardize values in a DataFrame", function(){ - let data = [ [ -1, 2 ], [ -0.5, 6 ], [ 0, 10 ], [ 1, 18 ] ]; - let scaler = new dfd.MinMaxScaler(); - let fit_data = [ [ 0, 0 ], [ 0.25, 0.25 ], [ 0.5, 0.5 ], [ 1, 1 ] ]; - let transform_data = [ [ 1.5, 0. ] ]; - scaler.fit(new dfd.DataFrame(data)); - assert.deepEqual(scaler.transform(new dfd.DataFrame(data)).values, fit_data); - assert.deepEqual(scaler.transform([ [ 2, 2 ] ]).values, transform_data); - }); - it("Standardize values in a Series", function(){ - let data = [ -1, 2, -0.5, 60, 101, 18 ]; - let scaler = new dfd.MinMaxScaler(); - let result = [ 0, 0.029411764815449715, 0.0049019609577953815, 0.5980392098426819, 1, 0.18627451360225677 ]; - let transform_data = [ 0.029411764815449715, 0.029411764815449715 ]; - scaler.fit(new dfd.Series(data)); - assert.deepEqual(scaler.transform(new dfd.Series(data)).values, result); - assert.deepEqual(scaler.transform([ 2, 2 ]).values, transform_data); - }); - }); - - describe("StandardScaler", function(){ - - it("basic test", function(){ - let data = [ [ 0, 0 ], [ 0, 0 ], [ 1, 1 ], [ 1, 1 ] ]; - - let scaler = new dfd.StandardScaler(); - let fit_data = [ [ -1, -1 ], [ -1, -1 ], [ 1, 1 ], [ 1, 1 ] ]; - let transform_data = [ [ 3, 3 ] ]; - - assert.deepEqual(scaler.fit(new dfd.DataFrame(data)).round().values, fit_data); - assert.deepEqual(scaler.transform([ [ 2, 2 ] ]).round().values, transform_data); - }); - }); - - - // describe("RobustScaler", function(){ - // it("basic test", function(){ - - // let data = [[3,1], [7,3], [8,4], [5,6], [12,5], [14,12], [21,23], [15,15], [18,2], [14,15]] - // // let data2 = [[4,100,900],[5,110,800],[21,220,890],[20,300,500]] - // let data2 = [0, 4, 4, 4, 7, 10, 11, 12, 14, 16, 17, 25] - - - // let scaler = new RobustScaler() - - // // console.log(scaler.quantile(data2,true)) - - // }); - // }); -}); diff --git a/danfojs-browser/types/config/config.d.ts b/danfojs-browser/types/config/config.d.ts deleted file mode 100644 index cd41525e..00000000 --- a/danfojs-browser/types/config/config.d.ts +++ /dev/null @@ -1,20 +0,0 @@ -/** - * Package wide configuration class - */ -export class Configs { - table_width: number; - table_truncate: number; - dtype_test_lim: number; - table_max_row: number; - table_max_col_in_console: number; - set_width(val?: any): void; - get get_width(): number; - set_max_col_in_console(val?: any): void; - get get_max_col_in_console(): number; - set_row_num(val?: any): void; - get get_max_row(): number; - get get_truncate(): number; - set_truncate(val?: any): void; - get get_dtype_test_lim(): number; - set_dtype_test_lim(val?: any): void; -} diff --git a/danfojs-browser/types/core/concat.d.ts b/danfojs-browser/types/core/concat.d.ts deleted file mode 100644 index 269f31e2..00000000 --- a/danfojs-browser/types/core/concat.d.ts +++ /dev/null @@ -1,5 +0,0 @@ -export class Concat { - constructor(kwargs?: any); -} -export function concat(kwargs?: any): DataFrame; -import { DataFrame } from "./frame"; diff --git a/danfojs-browser/types/core/date_range.d.ts b/danfojs-browser/types/core/date_range.d.ts deleted file mode 100644 index 58f7060a..00000000 --- a/danfojs-browser/types/core/date_range.d.ts +++ /dev/null @@ -1,27 +0,0 @@ -/** - * Generate date range between a specified set of date - * @param {kwargs} kwargs { - * start : string - * end : string - * period: int - * freq : string - * } - * @returns Array - */ -export class date_range { - constructor(kwargs?: any); - offset: number; - start?: any; - end?: any; - period?: any; - freq?: any; - freq_list: string[]; - range(start?: any, end?: any, period?: any, offset?: any): any; - freq_type(date?: any, ftype?: any): any; - offset_count(d_array?: any, offset?: any): any[]; - set_dateProps(date?: any, ftype?: any, val?: any): Date; - toLocalString(d_array?: any): any; - month_end(start_date?: any, end_date?: any): any; - month_range(range?: any): any; - day_end(start_date?: any, end_date?: any): number; -} diff --git a/danfojs-browser/types/core/frame.d.ts b/danfojs-browser/types/core/frame.d.ts deleted file mode 100644 index 466b1ea0..00000000 --- a/danfojs-browser/types/core/frame.d.ts +++ /dev/null @@ -1,404 +0,0 @@ -/** - * A 2D frame object that stores data in structured tabular format - * @param {data} data, JSON, Array, 2D Tensor - * @param {kwargs} Object {columns: Array of column names, defaults to ordered numbers when not specified - * dtypes: strings of data types, automatically inferred when not specified - * index: row index for subseting array, defaults to ordered numbers when not specified} - * - * @returns DataFrame - */ -export class DataFrame extends Ndframe { - constructor(data?: any, kwargs?: any); - _set_column_property(): void; - /** - * Drop a list of rows or columns base on the specified axis - * @param {Object} kwargs Configuration object - * {columns: [Array(Columns| Index)] array of column names to drop - * axis: row=0, columns=1 - * inplace: specify whether to drop the row/column with/without creating a new DataFrame} - * @returns null | DataFrame - * - */ - drop(kwargs?: any): DataFrame; - /** - * Purely label based indexing. Can accept string label names for both rows and columns - * @param {kwargs} kwargs object {rows: Array of index, columns: Array of column name(s)} - * @return DataFrame data stucture - */ - loc(kwargs?: any): DataFrame; - /** - * Access a dataframe element using row and column index - * @param {*} kwargs object {rows: Array of index, columns: Array of column index} - * @return DataFrame data stucture - */ - iloc(kwargs?: any): DataFrame; - /** - * Prints the first n values in a dataframe - * @param {rows} rows --> int - * @returns DataFrame - */ - head(rows?: any): DataFrame; - /** - * Prints the last n values in a dataframe - * @param {rows} rows --> int - * @returns DataFrame - */ - tail(rows?: any): DataFrame; - /** - * Gets [num] number of random rows in a dataframe - * @param {rows} rows --> int - * @returns DataFrame - */ - sample(num?: number): DataFrame; - /** - * Return Addition of DataFrame and other, element-wise (binary operator add). - * @param {other} DataFrame, Series, Array or Number to add - * @returns {DataFrame} - */ - add(other?: any, axis?: any): DataFrame; - /** - * Return subtraction of DataFrame and other, element-wise (binary operator add). - * @param {other} DataFrame, Series, Array or Number to add - * @returns {DataFrame} - */ - sub(other?: any, axis?: any): DataFrame; - /** - * Return subtraction of DataFrame and other, element-wise (binary operator add). - * @param {other} DataFrame, Series, Array or Number to add - * @returns {DataFrame} - */ - mul(other?: any, axis?: any): DataFrame; - /** - * Return division of DataFrame and other, element-wise (binary operator add). - * @param {other} DataFrame, Series, Array or Number to add - * @returns {DataFrame} - */ - div(other?: any, axis?: any): DataFrame; - /** - * Return division of DataFrame and other, element-wise (binary operator add). - * @param {other} DataFrame, Series, Array or Number to add - * @returns {DataFrame} - */ - pow(other?: any, axis?: any): DataFrame; - /** - * Return division of DataFrame and other, element-wise (binary operator add). - * @param {other} DataFrame, Series, Array or Number to add - * @returns {DataFrame} - */ - mod(other?: any, axis?: any): DataFrame; - /** - * Return mean of DataFrame across specified axis. - * @param {axis} Number {0: row, 1 : column} Axis for the function to be applied on - * @returns {Series} - */ - mean(axis?: number): Series; - /** - * Return median of DataFrame across specified axis. - * @param {axis} Number {0: row, 1 : column} Axis for the function to be applied on - * @returns {Series} - */ - median(axis?: number): Series; - /** - * Return minimum element in a DataFrame across specified axis. - * @param {axis} Number {0: row, 1 : column} Axis for the function to be applied on - * @returns {Series} - */ - min(axis?: number): Series; - /** - * Return maximum element of DataFrame across specified axis. - * @param {axis} Number {0: row, 1 : column} Axis for the function to be applied on - * @returns {Series} - */ - max(axis?: number): Series; - /** - * Return standard deviation of DataFrame across specified axis. - * @param {axis} Number {0: row, 1 : column} Axis for the function to be applied on - * @returns {Series} - */ - std(axis?: number): Series; - /** - * Return variance of DataFrame across specified axis. - * @param {axis} Number {0: row, 1 : column} Axis for the function to be applied on - * @returns {Series} - */ - var(axis?: number): Series; - /** - * Return number of non-null elements in a Series - * @returns {Series}, Count of non-null values - */ - count(axis?: number): Series; - /** - * Rounds values in DataFrame to specified number of dp - * @returns {DataFrame}, New DataFrame with rounded values - */ - round(dp?: number): DataFrame; - /** - * Perform Cummulative operations - * @param {axis} axis [int] {0 or 1} - * @param {ops} ops {String} name of operation - * @return {DataFrame} - */ - __cum_ops(axis?: any, ops?: any): DataFrame; - /** - * calculate the cummulative sum along axis - * @param {kwargs} {axis: [int]} - * @returns {DataFrame} - */ - cumsum(kwargs?: {}): DataFrame; - /** - * calculate the cummulative min - * @param {kwargs} {axis: [int]} - * @returns {DataFrame} - */ - cummin(kwargs?: {}): DataFrame; - /** - * calculate the cummulative max - * @param {kwargs} {axis: [int]} - * @returns {DataFrame} - */ - cummax(kwargs?: {}): DataFrame; - /** - * calculate the cummulative prod - * @param {kwargs} {axis: [int]} - * @returns {DataFrame} - */ - cumprod(kwargs?: {}): DataFrame; - /** - * Makes a new copy of a DataFrame - * @returns {DataFrame} - */ - copy(): DataFrame; - /** - * Generate a new index for the DataFrame. - * This is useful when the index is meaningless and needs to be reset to the default before another operation. - * @param {inplace} boolean: Modify the original object or return a new one. Default to false - */ - reset_index(inplace?: boolean): DataFrame; - /** - * Set the DataFrame index (row labels) using an array of the same length. - * @param {kwargs} {index: Array of new index values} - */ - set_index(kwargs?: {}): DataFrame; - /** - * Generate descriptive statistics for all numeric columns - * Descriptive statistics include those that summarize the central tendency, - * dispersion and shape of a dataset’s distribution, excluding NaN values. - * @returns {Series} - */ - describe(): Series; - /** - * Return a subset of the DataFrame’s columns based on the column dtypes. - * @param {include} scalar or array-like. A selection of dtypes or strings to be included. At least one of these parameters must be supplied. - * @returns {DataFrame, Series} The subset of the frame including the dtypes. - */ - select_dtypes(include?: any): DataFrame; - /** - * Sort a Dataframe in ascending or descending order by a specified column name. - * @param {kwargs} Object, {by: Column name to sort by - * ascending (Bool): Whether to return sorted values in ascending order or not, - * inplace (Bool): Whether to perform sorting on the original Series or not} - * @returns {Series} - */ - /** - * Return the sum of the values in a DataFrame across a specified axis. - * @params {kwargs} {axis: 0 for row and 1 for column} - * @returns {Series}, Sum of values accross axis - */ - sum(kwargs?: { - axis: number; - }): Series; - /** - * Returns the absolute values in DataFrame - * @return {DataFrame} - */ - abs(): DataFrame; - __get_tensor_and_idx(df?: any, axis?: any): any[]; - /** - * Filter DataFrame element base on the element in a column - * @param {kwargs} kwargs {column : coumn name[string], is: String, to: string| int} - * @returns {DataFrame} - */ - query(kwargs?: any): DataFrame; - /** - * Add a column with values to the dataframe - * @param {kwargs} Object {column :[string] , value:[Array]} - * - */ - addColumn(kwargs?: any): void; - /** - * - * @param {col} col is a list of column with maximum length of two - */ - groupby(col?: any): GroupBy; - /** - * Return a sequence of axis dimension along row and columns - * @params col_name: the name of a column in the database. - * @returns tensor of shape 1 - */ - column(col_name?: any): Series; - /** - * Replace NaN or undefined with a specified value" - * @param {kwargs}, {column(s): Array of column name(s) to fill. If undefined fill all columns; - * value(s): Array | Scalar of value(s) to fill with. If single value is specified, we use it to fill all - * @return {DataFrame} - */ - fillna(kwargs?: {}): DataFrame; - /** - * Return a boolean same-sized object indicating if the values are NaN. NaN and undefined values, - * gets mapped to True values. Everything else gets mapped to False values. - * @return {DataFrame} - */ - isna(): DataFrame; - /** - * Obtain index containing nan values - * @return Array list (int) - */ - nanIndex(): number[]; - /** - * Drop all rows containing NaN - * @param {kwargs} kwargs [Object] {axis: [int]{o or 1}, inplace:[boolean]} - */ - dropna(kwargs?: any): DataFrame; - /** - * Apply a function to each element or along a specified axis of the DataFrame. Supports JavaScipt functions - * when axis is not specified, and accepts Tensorflow functions when axis is specified. - * @param {kwargs} kargs is defined as {axis: undefined, 0 or 1, callable: [FUNCTION]} - * @return Array - */ - apply(kwargs?: any): Series | DataFrame; - /** - * Returns Less than of DataFrame and other. Supports element wise operations - * @param {other} DataFrame, Series, Scalar - * @param {axis} Number {0 for row, 1 for index} Whether to compare by the index or columns - * @return {DataFrame} - */ - lt(other?: any, axis?: any): DataFrame; - /** - * Returns Greater than of DataFrame and other. Supports element wise operations - * @param {other} DataFrame, Series, Scalar - * @param {axis} Number {0 for row, 1 for index} Whether to compare by the index or columns - * @return {DataFrame} - */ - gt(other?: any, axis?: any): DataFrame; - /** - * Returns Less than or Equal to of DataFrame and other. Supports element wise operations - * @param {other} DataFrame, Series, Scalar - * @param {axis} Number {0 for row, 1 for index} Whether to compare by the index or columns - * @return {DataFrame} - */ - le(other?: any, axis?: any): DataFrame; - /** - * Returns Greater than or Equal to of DataFrame and other. Supports element wise operations - * @param {other} DataFrame, Series, Scalar - * @param {axis} Number {0 for row, 1 for index} Whether to compare by the index or columns - * @return {DataFrame} - */ - ge(other?: any, axis?: any): DataFrame; - /** - * Returns Not Equal to of DataFrame and other. Supports element wise operations - * @param {other} DataFrame, Series, Scalar - * @param {axis} Number {0 for row, 1 for index} Whether to compare by the index or columns - * @return {DataFrame} - */ - ne(other?: any, axis?: any): DataFrame; - /** - * Returns Greater than or Equal to of DataFrame and other. Supports element wise operations - * @param {other} DataFrame, Series, Scalar - * @param {axis} Number {0 for row, 1 for index} Whether to compare by the index or columns - * @return {DataFrame} - */ - eq(other?: any, axis?: any): DataFrame; - /** - * Replace all occurence of a value with a new specified value" - * @param {kwargs}, {"replace": the value you want to replace, - * "with": the new value you want to replace the olde value with - * "in": Array of column names to replace value in, If not specified, replace all columns} - * @return {Series} - */ - replace(kwargs?: {}): Series; - __logical_ops(val?: any, logical_type?: any, axis?: any): DataFrame; - __get_df_from_tensor(val?: any, col_names?: any): DataFrame; - __frame_is_compactible_for_operation(): boolean; - __get_ops_tensors(tensors?: any, axis?: any): any[]; - /** - * Transpose index and columns. - * Reflect the DataFrame over its main diagonal by writing rows as columns and vice-versa. - * The property T is an accessor to the method transpose(). - */ - transpose(): DataFrame; - /** - * The property T is an accessor to the method transpose(). - */ - get T(): DataFrame; - /** - * Returns the data types in the DataFrame - * @return {Array} list of data types for each column - */ - get ctypes(): Series; - /** - * Make plots of Series or DataFrame. - * Uses the Plotly as backend, so supports Plotly's configuration parameters - * @param {string} div Name of the div to show the plot - * @returns {Class} Plot class that expoese different plot type - */ - plot(div: string): any; - /** - * Returns the Tensorflow tensor backing the DataFrame Object - * @returns {2D tensor} - */ - get tensor(): Tensor2D; - /** - * Sets the data types of an DataFrame - * @param {Object} kwargs {column: Name of the column to cast, dtype: [float32, int32, string] data type to cast to} - * @returns {DataFrame} - */ - astype(kwargs?: any): DataFrame; - /** - * Return the unique values along an axis - * @param {axis} Int, 0 for row, and 1 for column. Default to 1 - * @return {Object} - */ - unique(axis?: number): any; - /** - * Return the number of unique value along an axis - * @param {axis} Int, 0 for row, and 1 for column. Default to 1 - * @return {Series} - */ - nunique(axis?: number): Series; - /** - * Change axes labels. Object values must be unique (1-to-1). - * Labels not contained in a dict / Series will be left as-is. Extra labels listed don’t throw an error. - * @param {Object} kwargs {mapper: Dict-like or functions transformations to apply to that axis’ values, - * axis: Int, 0 for row, and 1 for column. Default to 1, - * inplace: Whether to return a new DataFrame. If True then value of copy is ignored. - * @returns {DataFrame} - */ - rename(kwargs?: any): DataFrame; - /** - * Sort DataFrame by index - * @param {*} kwargs {inplace: Boolean, ascending: Bool} - * @returns DataFrame - */ - sort_index(kwargs?: any): DataFrame; - /** - * Sort a Dataframe in ascending or descending order by a specified column name. - * @param {kwargs} Object, {by: Column name to sort by - * ascending (Bool): Whether to return sorted values in ascending order or not, - * inplace (Bool): Whether to perform sorting on the original Series or not} - * @returns {Series} - */ - sort_values(kwargs?: {}): Series; - __set_col_property(self?: any, col_vals?: any, col_names?: any, old_col_names?: any): void; - __update_frame_in_place(row_data?: any, column_names?: any, col_obj?: any, index?: any, dtypes?: any): void; - __sort_by(col_value?: any, df_index?: any, asc?: any): any[][]; - /** - * Append rows to a DataFrame - * @param {val} val Array | Series to append to the object - * @return DataFrame - */ - append(val?: any): DataFrame; -} -import { Tensor2D } from "@tensorflow/tfjs-core"; -import Ndframe from "./generic"; -import { Series } from "./series"; -import { GroupBy } from "./groupby"; diff --git a/danfojs-browser/types/core/generic.d.ts b/danfojs-browser/types/core/generic.d.ts deleted file mode 100644 index edb2146e..00000000 --- a/danfojs-browser/types/core/generic.d.ts +++ /dev/null @@ -1,112 +0,0 @@ -export default class NDframe { - /** - * N-Dimensiona data structure. Stores multi-dimensional - * data in a size-mutable, labeled data structure. Analogous to the Python Pandas DataFrame. - * - * @param {data} Array JSON, Tensor. Block of data. - * @param {kwargs} Object Optional Configuration Object - * {columns: Array of column names. If not specified and data is an array of array, use range index. - * dtypes: Data types of the columns, - * index: row index for subseting array } - * - * @returns NDframe - */ - constructor(data?: any, kwargs?: {}); - kwargs: {}; - series: boolean; - /** - * - * @param {Array} data - * Read array of data into NDFrame - */ - _read_array(data?: any[]): void; - data?: any[]; - row_data_tensor?: any; - col_data?: any[][]; - col_data_tensor?: any; - index_arr?: any[]; - columns?: any; - /** - * Convert Javascript Object of arrays into NDFrame - * @param {*} data Object of Arrays - * @param {*} type type 1 object are of JSON form [{a: 1, b: 2}, {a: 30, b: 20}], - * type 2 object are of the form {a: [1,2,3,4], b: [30,20, 30, 20}] - */ - _read_object(data?: any, type?: any): void; - /** - * Sets the data type of the NDFrame. Supported types are ['float32', "int32", 'string', 'boolean'] - * @param {Array} dtypes Array of data types. - * @param {Boolean} infer Whether to automatically infer the dtypes from the Object - */ - _set_col_types(dtypes: Array, infer: boolean): void; - col_types?: any[]; - /** - * Returns the data types in the DataFrame - * @return {Array} list of data types for each column - */ - get dtypes(): any[]; - /** - * Gets dimension of the NDFrame - * @returns {Integer} dimension of NDFrame - */ - get ndim(): any; - /** - * Gets values for index and columns - * @return {Object} axes configuration for index and columns of NDFrame - */ - get axes(): any; - /** - * Gets index of the NDframe - * @return {Array} array of index from series - */ - get index(): any[]; - /** - * Sets index of the NDFrame - */ - __set_index(labels?: any): void; - /** - * Generate a new index for NDFrame. - */ - __reset_index(): void; - /** - * Gets a sequence of axis dimension along row and columns - * @returns {Array} the shape of the NDFrame - */ - get shape(): any[]; - /** - * Gets the values in the NDFrame in JS array - * @returns {Array} Arrays of arrays of data instances - */ - get values(): any[]; - /** - * Gets the column names of the data - * @returns {Array} strings of column names - */ - get column_names(): any[]; - /** - * Return a boolean same-sized object indicating if the values are NaN. NaN and undefined values - * gets mapped to True values. Everything else gets mapped to False values. - * @return {Array} - */ - __isna(): any[]; - get size(): any; - /** - * Return object data as comma-separated values (csv). - * @returns {Promise} CSV representation of Object data - */ - to_csv(): Promise; - /** - * Return object as JSON string. - * @returns {Promise } JSON representation of Object data - */ - to_json(): Promise; - /** - * Prints the data in a Series as a grid of row and columns - */ - toString(): any; - /** - * Pretty prints n number of rows in a DataFrame or Series in the console - * @param {rows} Number of rows to print - */ - print(): void; -} diff --git a/danfojs-browser/types/core/get_dummies.d.ts b/danfojs-browser/types/core/get_dummies.d.ts deleted file mode 100644 index a0edcaa0..00000000 --- a/danfojs-browser/types/core/get_dummies.d.ts +++ /dev/null @@ -1,10 +0,0 @@ -/** - * Generate one hot encoding for categorical variable in arrays |Serie | and Dataframe - * @param {kwargs} kwargs { data : Array | Series | DataFrame, - * prefix_sep: String e.g "_", - * prefix: String | Array of String, - * columns: [Array] columns to be encoded in DataFrame. - * } - */ -export function get_dummies(kwargs?: any): DataFrame; -import { DataFrame } from "./frame"; diff --git a/danfojs-browser/types/core/groupby.d.ts b/danfojs-browser/types/core/groupby.d.ts deleted file mode 100644 index c8231934..00000000 --- a/danfojs-browser/types/core/groupby.d.ts +++ /dev/null @@ -1,59 +0,0 @@ -/** - * The class performs all groupby operation on a dataframe - * involveing all aggregate funciton - * @param {col_dict} col_dict Object of unique keys in the group by column - * @param {key_col} key_col Array contains the column names - * @param {data} Array the dataframe data - * @param {column_name} Array of all column name in the dataframe. - */ -export class GroupBy { - constructor(col_dict?: any, key_col?: any, data?: any, column_name?: any); - key_col?: any; - col_dict?: any; - data?: any; - column_name?: any; - data_tensors: {}; - /** - * Group the dataframe by the column by - * creating an object to store the grouping - * @returns Groupby data structure - */ - group(): GroupBy; - /** - * obtain the column for each group - * @param {col_name} col_name [Array]--> array of column names - * @return Groupby data structure - */ - col(col_names?: any): GroupBy; - group_col_name?: any[]; - group_col: {}; - /** - * Basic root of all column arithemetic in groups - * @param {operation} operatioin String - */ - arithemetic(operation?: any): {}; - count(): DataFrame; - sum(): DataFrame; - std(): DataFrame; - var(): DataFrame; - mean(): DataFrame; - cumsum(): DataFrame; - cummax(): DataFrame; - cumprod(): DataFrame; - cummin(): DataFrame; - max(): DataFrame; - min(): DataFrame; - /** - * returns dataframe of a group - * @param {*} key [Array] - */ - get_groups(key?: any): any; - /** - * Map every column to an operaton - * @param {kwargs} kwargs {column name: math operation} - * @example .agg({"A": "mean","B": "sum","C":"count"}) - */ - agg(kwargs?: any): DataFrame; - to_DataFrame(key_col?: any, col?: any, data?: any, ops?: any): DataFrame; -} -import { DataFrame } from "./frame"; diff --git a/danfojs-browser/types/core/indexing.d.ts b/danfojs-browser/types/core/indexing.d.ts deleted file mode 100644 index 9615effa..00000000 --- a/danfojs-browser/types/core/indexing.d.ts +++ /dev/null @@ -1 +0,0 @@ -export function indexLoc(ndframe?: any, kwargs?: any): any[]; diff --git a/danfojs-browser/types/core/merge.d.ts b/danfojs-browser/types/core/merge.d.ts deleted file mode 100644 index 8162a6c9..00000000 --- a/danfojs-browser/types/core/merge.d.ts +++ /dev/null @@ -1,19 +0,0 @@ -export class Merge { - constructor(kwargs?: any); - how?: any; - left: DataFrame; - right: DataFrame; - on?: any[]; - left_col_index?: any[]; - right_col_index?: any[]; - left_key_dict: {}; - right_key_dict: {}; - __create_columns(): void; - outer(): any[]; - inner(): any[]; - left_merge(): any[]; - right_merge(): any[]; - basic(keys?: any): any[]; -} -export function merge(kwargs?: any): DataFrame; -import { DataFrame } from "./frame"; diff --git a/danfojs-browser/types/core/series.d.ts b/danfojs-browser/types/core/series.d.ts deleted file mode 100644 index 2461da65..00000000 --- a/danfojs-browser/types/core/series.d.ts +++ /dev/null @@ -1,370 +0,0 @@ -/** - * One-dimensional ndarray with axis labels (including time series). - * The object supports both integer- and label-based indexing and provides a host of methods for performing operations involving the index. - * Operations between Series (+, -, /, , *) align values based on their associated index values– they need not be the same length. - * @param {data} data Array, JSON of 1D values - * @param {kwargs} Object {columns: column names, dtypes : data type of values} - * - * @returns Series - */ -export class Series extends NDframe { - constructor(data?: any, kwargs?: any); - /** - * Returns a Series in Tensorflow's tensor format - * @returns {1D Tensor} - */ - get tensor(): Tensor1D; - /** - * Returns the first n values in a Series - * @param {rows} Number of rows to return - * @returns {Series} - */ - head(rows?: number): Series; - /** - * Returns the last n values in a Series - * @param {rows} number of rows to return - * @returns {Series} - */ - tail(rows?: number): Series; - /** - * Returns n number of random rows in a Series - * @param {rows} number of rows to return - * @returns {Series} - */ - sample(num?: number): Series; - /** - * Return Addition of series and other, element-wise (binary operator add). - * Equivalent to series + other - * @param {other} Series or Number to add - * @returns {Series} - */ - add(other?: any): Series; - /** - * Returns the subtraction between a series and other, element-wise (binary operator subtraction). - * Equivalent to series - other - * @param {other} Series, Number to subtract - * @returns {Series} - */ - sub(other?: any): Series; - /** - * Return Multiplication of series and other, element-wise (binary operator mul). - * Equivalent to series * other - * @param {other} Series, Number to multiply with. - * @returns {Series} - */ - mul(other?: any): Series; - /** - * Return division of series and other, element-wise (binary operator div). - * Equivalent to series / other - * @param {other} Series, Number to divide with. - * @returns {Series} - */ - div(other?: any, round?: boolean): Series; - /** - * Return Exponential power of series and other, element-wise (binary operator pow). - * Equivalent to series ** other - * @param {other} Series, Number to multiply with. - */ - pow(other?: any): Series; - /** - * Return Modulo of series and other, element-wise (binary operator mod). - * Equivalent to series % other - * @param {other} Series, Number - * @returns {Series} - */ - mod(other?: any): Series; - /** - * Returns the mean of elements in Series - * @returns {Series} - */ - mean(): Series; - /** - * Returns the median of elements in Series - * @returns {Series} - */ - median(): Series; - /** - * Returns the modal value of elements in Series - * @returns {Number} - */ - mode(): number; - /** - * Returns the minimum value in a Series - * @returns {Number} - */ - min(): number; - /** - * Returns the maximum value in a Series - * @returns {Number} - */ - max(): number; - /** - * Return the sum of the values in a series. - * This is equivalent to the method tf.sum - * @returns {Number}, sum of values in Series - */ - sum(): number; - /** - * Return number of non-null elements in a Series - * @returns {Number}, Count of non-null values - */ - count(): number; - /** - * Return maximum of series and other, element-wise (binary operator div). - * @param {other} Series, Numbers to check maximum against - * @returns {Series} - */ - maximum(other?: any): Series; - /** - * Return maximum of series and other, element-wise (binary operator div). - * @param {other} Series, Numbers to check maximum against - * @returns {Series} - */ - minimum(other?: any): Series; - /** - * Round each value in a Series to the given number of decimals. - * @param {dp} Number, Numbers of Decimal places to round to - * @returns {Series} - */ - round(dp?: any): Series; - /** - * Return sample standard deviation over requested axis. - * @returns {Number} - */ - std(): number; - /** - * Return unbiased variance of Series. - * @returns {Number} - */ - var(): number; - /** - * Return a boolean same-sized object indicating if the values are NaN. NaN and undefined values, - * gets mapped to True values. Everything else gets mapped to False values. - * @return {Series} - */ - isna(): Series; - /** - * Replace NaN or undefined with a specified value" - * @param {kwargs}, {"value": the new value to replace the old value with, inplace: Perform operation inplace or not} - * @return {Series} - */ - fillna(kwargs?: {}): Series; - /** - * Sort a Series in ascending or descending order by some criterion. - * @param {kwargs} Object, {ascending (Bool): Whether to return sorted values in ascending order or not, - * inplace (Bool): Whether to perform sorting on the original Series or not} - * @returns {Series} - */ - sort_values(kwargs?: {}): Series; - /** - * Makes a deep copy of a Series - * @returns {Series} - */ - copy(): Series; - /** - * Generate descriptive statistics. - * Descriptive statistics include those that summarize the central tendency, - * dispersion and shape of a dataset’s distribution, excluding NaN values. - * @returns {Series} - */ - describe(): Series; - /** - * Returns Series with the index reset. - * This is useful when index is meaningless and needs to be reset to the default before another operation. - * @param {kwargs} {inplace: Modify the Series in place (do not create a new object} - */ - reset_index(kwargs?: {}): Series; - /** - * Returns Series with the specified index. - * Set the Series index (row labels) using an array of the same length. - * @param {kwargs} {index: Array of new index values, - * inplace: If operation should happen inplace - * } - */ - set_index(kwargs?: {}): Series; - /** - * Checks if two series are compatible for a mathematical operation - * @param {Series} other Series to compare against - * @returns{boolean} - */ - __check_series_op_compactibility(other: Series): boolean; - /** - * map all the element in a column to a variable or function - * @param{callable} callable can either be a funtion or an object - * @return {Array} - */ - map(callable?: any): any[]; - /** - * Applies a function to each element of a Series - * @param {Function} Function to apply to each element of the series - * @return {Array} - */ - apply(callable?: any): any[]; - /** - * Returns the unique value(s) in a Series - * @return {Series} - */ - unique(): Series; - /** - * Return the number of unique value in a series - * @return {int} - */ - nunique(): any; - /** - * Returns unique values and their counts in a Series - * @return {Series} - */ - value_counts(): Series; - /** - * Returns the absolute values in Series - * @return {series} - */ - abs(): any; - /** - * Returns the cumulative sum over a Series - * @return {Series} - */ - cumsum(): Series; - /** - * Returns cumulative minimum over a Series - * @returns series - */ - cummin(): Series; - /** - * Returns cumulative maximum over a Series - * @returns series - */ - cummax(): Series; - /** - * Returns cumulative product over a Series - * @returns series - */ - cumprod(): Series; - /** - * Returns Less than of series and other. Supports element wise operations - * @param {other} Series, Scalar - * @return {Series} - */ - lt(other?: any): Series; - /** - * Returns Greater than of series and other. Supports element wise operations - * @param {other} Series, Scalar - * @return {Series} - */ - gt(other?: any): Series; - /** - * Returns Less than or Equal to of series and other. Supports element wise operations - * @param {other} Series, Scalar - * @return {Series} - */ - le(other?: any): Series; - /** - * Returns Greater than or Equal to of series and other. Supports element wise operations - * @param {other} Series, Scalar - * @return {Series} - */ - ge(other?: any): Series; - /** - * Returns Not Equal to of series and other. Supports element wise operations - * @param {other} Series, Scalar - * @return {Series} - */ - ne(other?: any): Series; - /** - * Returns Equal to of series and other. Supports element wise operations - * @param {other} Series, Scalar - * @return {Series} - */ - eq(other?: any): Series; - /** - * Replace all occurence of a value with a new value" - * @param {kwargs}, {"replace": the value you want to replace, - * "with": the new value you want to replace the olde value with, - * inplace: Perform operation inplace or not} - * @return {Series} - */ - replace(kwargs?: {}): Series; - /** - * Return a new Series with missing values (NaN) removed. - * @param {kwargs} {inplace: Perform operation inplace or not} - * @return {Series} - */ - dropna(kwargs?: {}): Series; - /** - * Return the integer indices that would sort the Series. - * @param {ascending} boolean true: will sort the Series in ascending order, false: will sort in descending order - * @return {Series} - */ - argsort(ascending?: boolean): Series; - /** - * Return int position of the largest value in the Series. - * @return {Number} - */ - argmax(): number; - /** - * Return int position of the smallest value in the Series. - * @param {ascending} boolean true: will sort the Series in ascending order, false: will sort in descending order - * @return {Series} - */ - argmin(): Series; - /** - * Returns dtype of Series - * @return {string} - */ - get dtype(): string; - /** - * Return Series with duplicate values removed - * @param {kwargs} {inplace: Perform operation inplace or not, - * keep: first | last which dupliate value to keep} - * @return {Series} - */ - drop_duplicates(kwargs?: {}): Series; - /** - * Perform boolean operations on bool values - * @param {*} other Other series to compare with - * @param {string} b_ops name of operation to perform [ne, ge, le, gt, lt, eq] - */ - __bool_ops(other?: any, b_ops: string): Series; - /** - * perform cumulative operation on series data - * @returns array - */ - __cum_ops(ops?: any): Series; - /** - * Cast Series to specified data type - * @param {string} dtype data type to cast to [float32, int32, string, boolean] - *@returns {Series} - */ - astype(dtype: string): Series; - /** - * Exposes numerous string methods to manipulate Series - */ - get str(): Str; - /** - * Returns Danfo Time Object that exposes different time properties - */ - get dt(): TimeSeries; - /** - * Make plots of Series or DataFrame. - * Uses the Plotly as backend, so therefore supports Plotly's configuration parameters - * @param {string} div Name of the div to show the plot - * @returns {Class} Plot class that expoese different plot type - */ - plot(div: string): any; - /** - * Slice series by index - * @param {Array} row list of index to slice by - * @returns Series - */ - iloc(row?: any[]): Series; - /** - * Adds new values to the end of a Series - * @param {Object} val Single value | Array | Series to append to the object - * @param {Boolean} inplace Whether to perform operation inplace or not - */ - append(val?: any, inplace?: boolean): Series; -} -import { Tensor1D } from "@tensorflow/tfjs"; -import NDframe from "./generic"; -import { Str } from "./strings"; -import { TimeSeries } from "./timeseries"; diff --git a/danfojs-browser/types/core/strings.d.ts b/danfojs-browser/types/core/strings.d.ts deleted file mode 100644 index 22bfb110..00000000 --- a/danfojs-browser/types/core/strings.d.ts +++ /dev/null @@ -1,133 +0,0 @@ -/** - * String methods applied on Series and DataFrames - */ -export class Str { - constructor(series?: any); - series?: any; - array?: any; - /** - * Converts all characters to lowercase. - * @return {array} - */ - toLowerCase(): any[]; - /** - * Converts all characters to uppercase. - * @return {array} - */ - toUpperCase(): any[]; - /** - * Capitalize first string - * @return {array} - */ - capitalize(): any[]; - /** - * Returns the character at the specified index (position) - * @params {index} index position of character - * @return {array} - */ - charAt(index?: number): any[]; - /** - * Joins two or more strings/arrays. 0 joins from the start - * @params {other} string|array to concatenate with. - * @params {position} where to concat the string from. O concats from the start, 1 concats from the end - * @params {isArray} whether operation is performed on Array or not - * @return {array} - */ - concat(other?: string, position?: number): any[]; - /** - * Checks whether a string begins with specified characters - * @params {String | Character} String or Character to check against - * @return {array} - */ - startsWith(str?: string): any[]; - /** - * Checks whether a string ends with specified characters - * @params {String | Character} String or Character to check against - * @return {array} - */ - endsWith(str?: string): any[]; - /** - * Checks whether a string contains the specified string/characters - * @params {String | Character} String or Character to check against - * @return {array} - */ - includes(str?: string): any[]; - /** - * Returns the position of the first found occurrence of a specified value in a string - * @params {String | Character} String or Character to check against - * @return {array} - */ - indexOf(str?: string): any[]; - /** - * Returns the position of the last found occurrence of a specified value in a string - * @params {str: String | Character} String or Character to check against - * @return {array} - */ - lastIndexOf(str?: string): any[]; - /** - * Searches a string for a specified value, or a regular expression, and returns a new string where the specified values are replaced - * @params {searchValue: String | Character} string value to replace - * @params {replaceValue: String | Character} string to replace with - * @return {array} - */ - replace(searchValue?: string, replaceValue?: string): any[]; - /** - * Returns a new string with a specified number of copies of an existing string - * @params {num: Integer} Number of times to repeat - * @return {array} - */ - repeat(num?: number): any[]; - /** - * Searches a string for a specified value, or regular expression, and returns the position of the match - * @params {str: String | Character} String or Character to check against - * @return {array} - */ - search(str?: string): any[]; - /** - * Extracts a part of a string and returns a new string - * @params {startIndex: Int} index position of start character - * @params {endIndex: Int} index position of last character - * @return {array} - */ - slice(startIndex?: number, endIndex?: number): any[]; - /** - * Splits a string into an array of substrings - * @params {val: string} string or character to split at - * @params {endIndex: Int} index position of last character - * @return {array} - */ - split(splitVal?: string): any[]; - /** - * Extracts the characters from a string, beginning at a specified start position, and through the specified number of character - * @params {startIndex: Int} index position of start character - * @params {num: Int} number of characters to return - * @return {array} - */ - substr(startIndex?: number, num?: number): any[]; - /** - * Extracts the characters from a string, between two specified indices - * @params {startIndex: Int} index position of start character - * @params {endIndex: Int} index position of last character - * @return {array} - */ - substring(startIndex?: number, endIndex?: number): any[]; - /** - * Removes whitespace from both ends of a string - * @return {array} - */ - trim(): any[]; - /** - * Joins strings to specified value - * @params {valToJoin} string value to join to the array - * @params {joinChar} Character to Join with - * @return {array} - */ - join(valToJoin?: string, joinChar?: string): any[]; - /** - * Counts the number of characters in string - * @return {array} - */ - len(): any[]; - __create_new_sf_from(new_val?: any, series?: any): Series; -} -import { Series } from "./series"; diff --git a/danfojs-browser/types/core/timeseries.d.ts b/danfojs-browser/types/core/timeseries.d.ts deleted file mode 100644 index d140a554..00000000 --- a/danfojs-browser/types/core/timeseries.d.ts +++ /dev/null @@ -1,98 +0,0 @@ -/** - * @class - * @description Handle all datetime operations - * @param {kwargs} Object {"data":[array of string], "format": string} - */ -export class TimeSeries { - constructor(kwargs?: any); - data?: any; - format?: any; - keys: { - Y: number; - m: number; - H: number; - M: number; - S: number; - b: number; - d: number; - "-": number; - }; - __in_format: string[]; - __monthName: string[]; - __weekName: string[]; - /** - * @description preprocessed the data into desirable structure - */ - preprocessed(): void; - date_list?: any[]; - /** - * @description if format is given, apply the format on each element of the data - * @return string - */ - __apply_format(elem?: any, format?: any): string; - /** - * @description convert format string to their respective value. - */ - generate_format(): any; - /** - * @description check if a string is a timestamp - * @param {date_string} date_string [string] - */ - is_timestamp(date_string?: any): boolean; - /** - * @description check if a date instance returns Invalid date - * @param {date_instance} instance of new Date() - */ - __is_validDate(date_instance?: any): void; - /** - * @description abstract all date operations - * @param {*} callback [FUNCTION] - * @return series - */ - __date_ops(callback?: any): Series; - /** - * @description obtain the month in a date. - * @return Series - */ - month(): Series; - /** - * @return Series - */ - hour(): Series; - /** - * @return Series - */ - day(): Series; - /** - * @description generate year frome date instance - * @return Series - */ - year(): Series; - /** - * @description generate month name - * @return Series - */ - month_name(): Series; - /** - * @description generate days of the week - * @return Series - */ - weekdays(): Series; - /** - * @description day of the month - * @return Series - */ - monthday(): Series; - /** - * @description obtain the seconds in a date - * @return Series - */ - seconds(): Series; - /** - * @description obtain the minutes in a date - * @return Series - */ - minutes(): Series; -} -export function to_datetime(kwargs?: any): TimeSeries; -import { Series } from "./series"; diff --git a/danfojs-browser/types/core/utils.d.ts b/danfojs-browser/types/core/utils.d.ts deleted file mode 100644 index cf122a5c..00000000 --- a/danfojs-browser/types/core/utils.d.ts +++ /dev/null @@ -1,94 +0,0 @@ -export class Utils { - remove(arr?: any, index?: any): any; - __remove_arr(arr?: any, index?: any): any; - __is_string(value?: any): boolean; - __is_number(value?: any): boolean; - __is_object(value?: any): boolean; - __is_null(value?: any): boolean; - __is_undefined(value?: any): boolean; - /** - * Optimized version of random sampling from an array, as implemented in Python - * - * - Chooses k unique random elements from a population sequence or set. - - Returns a new list containing elements from the population while - leaving the original population unchanged. The resulting list is - in selection order so that all sub-slices will also be valid random - samples. This allows raffle winners (the sample) to be partitioned - into grand prize and second place winners (the subslices). - - Members of the population need not be hashable or unique. If the - population contains repeats, then each occurrence is a possible - selection in the sample. - - To choose a sample in a range of integers, use range as an argument. - This is especially fast and space efficient for sampling from a - large population: sample(range(10000000), 60) - - Sampling without replacement entails tracking either potential - selections (the array) in a list or previous selections in a set. - - When the number of selections is small compared to the - population, then tracking selections is efficient, requiring - only a small set and an occasional reselection. For - a larger number of selections, the array tracking method is - preferred since the list takes less space than the - set and it doesn't suffer from frequent reselections. - * - * @param {*} array The array to sample values from randomly - * @param {*} num The number of elements to sample randomly - */ - __sample_from_iter(array?: any, k?: any, destructive?: any): any; - __range(start?: any, end?: any): any; - __key_in_object(object?: any, key?: any): boolean; - __get_col_values(data?: any): any[][]; - /** - * retrieve row array and column names from object of the form {a: [1,2,3,4], b: [30,20, 30, 20}] - * @param {*} data - */ - _get_row_and_col_values(data?: any): (string[] | any[][])[]; - __convert_2D_to_1D(data?: any): any[]; - __replace_undefined_with_NaN(data?: any, isSeries?: any): any[]; - __get_t(arr_val?: any): any[]; - __unique(data?: any): any; - __in_object(object?: any, key?: any, message?: any): void; - __is_1D_array(arr?: any): boolean; - __arr_to_obj(arr?: any): {}; - /** - * count the NaN and non-NaN values present in an array - * @param {Array} arr Array object - * @param {Boolean} val whether to return the value count instead of the null count - * @param {Boolean} isSeries Whether the Obj is of type series or not - */ - __count_nan(arr?: any[], return_val: boolean, isSeries: boolean): number | any[]; - __median(arr?: any, isSeries?: any): any; - __mode(arr?: any): number[]; - __round(arr?: any, dp: number, isSeries?: any): any[]; - __is_function(variable?: any): boolean; - __randgen(num?: any, start?: any, end?: any): any[]; - _throw_wrong_params_error(kwargs?: any, params_needed?: any): void; - __map_int_to_bool(arr?: any, dim?: any): any[]; - __std(data?: any): any; - __zeros(row?: any, column?: any): any[][]; - __shuffle(num?: any, array?: any): any; - __sort(arr?: any, ascending?: boolean): any; - __is_browser_env(): any; - __is_node_env(): any; - _throw_str_dtype_error(obj?: any, ops?: any): void; - /** - * Remove NaN values from Array - * @param {*} arr - */ - _remove_nans(arr?: any): any; - __get_duplicate(arr?: any): {}; - /** - * Sorts an array by index - * @param {Array} arr1 - * @param {Array} arr2 - * @param {string} dtype - * - * @returns sorted index - */ - _sort_arr_with_index(arr1?: any[], arr2?: any[], dtype: string): any[]; -} diff --git a/danfojs-browser/types/index.d.ts b/danfojs-browser/types/index.d.ts deleted file mode 100644 index ab3b0102..00000000 --- a/danfojs-browser/types/index.d.ts +++ /dev/null @@ -1,14 +0,0 @@ -import { Series } from "./core/series"; -import { DataFrame } from "./core/frame"; -import { to_datetime } from "./core/timeseries"; -import { merge } from "./core/merge"; -import { concat } from "./core/concat"; -import { date_range } from "./core/date_range"; -import { get_dummies } from "./core/get_dummies"; -import { read_csv, read_json, read_excel, read } from "./io/reader"; -import { LabelEncoder, OneHotEncoder } from "./preprocessing/encodings"; -import { MinMaxScaler, StandardScaler } from "./preprocessing/scalers"; - -declare module 'danfojs-node' { - export { Series, DataFrame, to_datetime, merge, concat, date_range, get_dummies, read_csv, read_json, read_excel, read, LabelEncoder, OneHotEncoder, MinMaxScaler, StandardScaler } -} \ No newline at end of file diff --git a/danfojs-browser/types/io/reader.d.ts b/danfojs-browser/types/io/reader.d.ts deleted file mode 100644 index 686b9a42..00000000 --- a/danfojs-browser/types/io/reader.d.ts +++ /dev/null @@ -1,4 +0,0 @@ -export function read_csv(source?: any, chunk?: any): Promise; -export function read_json(source?: any): Promise; -export function read_excel(kwargs?: any): Promise; -import { DataFrame } from "../core/frame"; diff --git a/danfojs-browser/types/plotting/plot.d.ts b/danfojs-browser/types/plotting/plot.d.ts deleted file mode 100644 index de3963e9..00000000 --- a/danfojs-browser/types/plotting/plot.d.ts +++ /dev/null @@ -1,66 +0,0 @@ -/** - * Plotting methods and Functions performed on Series and DataFrames - */ -export class Plot { - constructor(ndframe?: any, div?: any); - div?: any; - ndframe?: any; - /** - * Plot Series or DataFrame as lines. This function is useful to plot lines using DataFrame’s values as coordinates. - * Make plots of Series or DataFrame. - * Uses the Plotly as backend, so supoorts Plotly's configuration parameters - * @param {string} div Name of the div to show the plot - * @param {Object} config configuration options for making Plots, supports Plotly parameters - */ - line(config?: any): void; - /** - * Plot Series or DataFrame as Bars. - * Uses the Plotly as backend, so supoorts Plotly's configuration parameters - * @param {string} div Name of the div to show the plot - * @param {Object} config configuration options for making Plots, supports Plotly parameters - */ - bar(config?: any): void; - /** - * Plot two or more columns in a DataFrame as scatter points. If Series, plot against its index - * Uses the Plotly as backend, so supoorts Plotly's configuration parameters - * @param {string} div Name of the div to show the plot - * @param {Object} config configuration options for making Plots, supports Plotly parameters - */ - scatter(config?: any): void; - /** - * Plot columns in a Series/DataFrame as Histograms. - * Uses the Plotly as backend, so supoorts Plotly's configuration parameters - * @param {string} div Name of the div to show the plot - * @param {Object} config configuration options for making Plots, supports Plotly parameters - */ - hist(config?: any): void; - /** - * Makes Pie Plots from two Columns in a DataFrame. - * Uses the Plotly as backend, so supoorts Plotly's configuration parameters - * @param {string} div Name of the div to show the plot - * @param {Object} config configuration options for making Plots, supports Plotly parameters - */ - pie(config?: any): void; - /** - * Plot Box plots from Series or DataFrame as lines. - * Uses the Plotly as backend, so supoorts Plotly's configuration parameters - * @param {Object} config configuration options for making Plots, supports Plotly parameters - */ - box(config?: any): void; - /** - * Plot Violin plots from Series or DataFrame as lines. - * Uses the Plotly as backend, so supoorts Plotly's configuration parameters - * @param {Object} config configuration options for making Plots, supports Plotly parameters - */ - violin(config?: any): void; - /** - * Display DataFrame in a div using D3.js format - * Uses the Plotly as backend, so supoorts Plotly's configuration parameters - * @param {Object} config configuration options for making Plots, supports Plotly parameters - */ - table(config?: any): void; - __get_plot_params(config?: any): (string[] | { - layout: {}; - })[]; - ____check_if_cols_exist(cols?: any): any; -} diff --git a/danfojs-browser/types/preprocessing/encodings.d.ts b/danfojs-browser/types/preprocessing/encodings.d.ts deleted file mode 100644 index 4b866774..00000000 --- a/danfojs-browser/types/preprocessing/encodings.d.ts +++ /dev/null @@ -1,22 +0,0 @@ -export class LabelEncoder { - /** - * - * @param {data} data [Array|Series] - * @returns Array. - */ - fit(data?: any): Series; - label?: any; - /** - * Transform data using the label generated from fitting - * @param {data} data [Array|Series] - * @returns Array - */ - transform(data?: any): Series; -} -export class OneHotEncoder { - fit(data?: any): DataFrame; - label?: any; - transform(data?: any): DataFrame; -} -import { Series } from "../core/series"; -import { DataFrame } from "../core/frame"; diff --git a/danfojs-browser/types/preprocessing/scalers.d.ts b/danfojs-browser/types/preprocessing/scalers.d.ts deleted file mode 100644 index aade7b34..00000000 --- a/danfojs-browser/types/preprocessing/scalers.d.ts +++ /dev/null @@ -1,29 +0,0 @@ -export class MinMaxScaler { - /** - * Fit minmax scaler on data, to obtain their min and max value - * @param {data} data [DataRame | Series | Array] - * @returns Array - */ - fit(data?: any): Series | DataFrame; - max?: any; - min?: any; - /** - * Transform an array using the min and max generated from the fitting on data - * @param {data} data [Array] - * @returns array - */ - transform(data?: any): Series | DataFrame; -} -export class StandardScaler { - /** - * - * @param {data} data [DataRame | Series | Array] - * @returns Array - */ - fit(data?: any): Series | DataFrame; - std?: any; - mean?: any; - transform(data?: any): Series | DataFrame; -} -import { Series } from "../core/series"; -import { DataFrame } from "../core/frame"; diff --git a/danfojs-browser/webpack.config.js b/danfojs-browser/webpack.config.js deleted file mode 100644 index b825af40..00000000 --- a/danfojs-browser/webpack.config.js +++ /dev/null @@ -1,38 +0,0 @@ -/* eslint-disable no-undef */ -const path = require("path"); - -const createConfig = (target) => { - return { - mode: "production", - devtool: "source-map", - context: path.resolve(__dirname), - entry: { - index: `./src/index.js` - }, - target: target, - output: { - path: path.resolve(__dirname, "lib"), - filename: `bundle.js`, - library: "dfd" - }, - module: { - rules: [ - { - use: { - loader: "babel-loader", - options: { presets: [ "@babel/preset-env" ] } - }, - test: /\.(js|jsx)$/, - exclude: /node_modules/ - } - ] - }, - resolve: { - fallback: { - fs: false - } - } - }; -}; - -module.exports = [ createConfig("web") ]; diff --git a/danfojs-node/.editorconfig b/danfojs-node/.editorconfig deleted file mode 100644 index cb8b48c7..00000000 --- a/danfojs-node/.editorconfig +++ /dev/null @@ -1,9 +0,0 @@ -# Editor configuration, see http://editorconfig.org -root = true - -[*] -charset = utf-8 -indent_style = space -indent_size = 2 -insert_final_newline = true -trim_trailing_whitespace = true \ No newline at end of file diff --git a/danfojs-node/dist/config/config.js b/danfojs-node/dist/config/config.js deleted file mode 100644 index d9a6c793..00000000 --- a/danfojs-node/dist/config/config.js +++ /dev/null @@ -1,59 +0,0 @@ -"use strict"; - -Object.defineProperty(exports, "__esModule", { - value: true -}); -exports.Configs = void 0; - -class Configs { - constructor() { - this.table_width = 17; - this.table_truncate = 16; - this.dtype_test_lim = 10; - this.table_max_row = 21; - this.table_max_col_in_console = 7; - } - - set_width(val) { - this.table_width = val; - } - - get get_width() { - return this.table_width; - } - - set_max_col_in_console(val) { - this.table_max_col_in_console = val; - } - - get get_max_col_in_console() { - return this.table_max_col_in_console; - } - - set_row_num(val) { - this.table_max_row = val; - } - - get get_max_row() { - return this.table_max_row; - } - - get get_truncate() { - return this.table_truncate; - } - - set_truncate(val) { - this.table_truncate = val; - } - - get get_dtype_test_lim() { - return this.dtype_test_lim; - } - - set_dtype_test_lim(val) { - this.dtype_test_lim = val; - } - -} - -exports.Configs = Configs; \ No newline at end of file diff --git a/danfojs-node/dist/core/concat.js b/danfojs-node/dist/core/concat.js deleted file mode 100644 index 665e154a..00000000 --- a/danfojs-node/dist/core/concat.js +++ /dev/null @@ -1,204 +0,0 @@ -"use strict"; - -Object.defineProperty(exports, "__esModule", { - value: true -}); -exports.concat = exports.Concat = void 0; - -var _frame = require("./frame"); - -var _utils = require("./utils"); - -var _series = require("./series"); - -const utils = new _utils.Utils(); - -class Concat { - constructor(kwargs) { - utils.__in_object(kwargs, "df_list", "df_list not found: specify the list of dataframe"); - - utils.__in_object(kwargs, "axis", "axis not found: specify the axis"); - - let df_list = null; - let axis = null; - let indexes = null; - - if (Array.isArray(kwargs["df_list"])) { - df_list = kwargs["df_list"]; - } else { - throw new Error("df_list must be an Array of dataFrames/Series"); - } - - if (typeof kwargs["axis"] === "number") { - if (kwargs["axis"] == 0 || kwargs["axis"] == 1) { - axis = kwargs["axis"]; - } else { - axis = 1; - } - } else { - throw new Error("axis must be a number"); - } - - let df_object = Object.assign({}, df_list); - - if (axis == 1) { - let columns = []; - let duplicate_col_count = {}; - let max_length = 0; - let a_key = Object.keys(df_object)[0]; - indexes = df_object[a_key].index; - - for (let key in df_object) { - let column = df_object[key].columns; - let length = df_object[key].values.length; - - if (length > max_length) { - max_length = length; - } - - for (let index in column) { - let col_name = column[index]; - - if (col_name in duplicate_col_count) { - let count = duplicate_col_count[col_name]; - let name = `${col_name}_${count + 1}`; - columns.push(name); - duplicate_col_count[col_name] = count + 1; - } else { - columns.push(col_name); - duplicate_col_count[col_name] = 1; - } - } - } - - let data = new Array(max_length); - - for (let key in df_list) { - let values = df_list[key].values; - - for (let index = 0; index < values.length; index++) { - let val = values[index]; - - if (typeof data[index] === "undefined") { - if (Array.isArray(val)) { - data[index] = val; - } else { - data[index] = [val]; - } - } else { - if (Array.isArray(val)) { - data[index].push(...val); - } else { - data[index].push(val); - } - } - } - - if (values.length < max_length) { - let column_length = df_list[key].columns.length; - let null_array = Array(column_length); - - for (let col = 0; col < column_length; col++) { - null_array[col] = NaN; - } - - if (typeof data[max_length - 1] === "undefined") { - data[max_length - 1] = null_array; - } else { - data[max_length - 1].push(...null_array); - } - } - } - - let df = new _frame.DataFrame(data, { - columns: columns, - index: indexes - }); - return df; - } else { - let columns = []; - let row_indexes = []; - let col_i = 0; - - for (let key in df_list) { - let column = df_list[key].columns; - columns.push(...column); - indexes = df_list[key].index; - let r_index = indexes.map(val => { - return `${val}_row${col_i}`; - }); - row_indexes.push(...r_index); - col_i += 1; - } - - let column_set = new Set(columns); - columns = Array.from(column_set); - let data = []; - - for (let key in df_list) { - let value = df_list[key].values; - let df_columns = df_list[key].columns; - let not_exist = []; - - for (let col_index in columns) { - let col_name = columns[col_index]; - let is_index = df_columns.indexOf(col_name); - - if (is_index == -1) { - not_exist.push(col_name); - } - } - - if (not_exist.length > 0) { - for (let i = 0; i < value.length; i++) { - let row_value = value[i]; - let new_arr = Array(columns.length); - - for (let j = 0; j < columns.length; j++) { - let col_name = columns[j]; - - if (not_exist.includes(col_name)) { - new_arr[j] = NaN; - } else { - let index = df_columns.indexOf(col_name); - - if (Array.isArray(row_value)) { - new_arr[j] = row_value[index]; - } else { - new_arr[j] = row_value; - } - } - } - - data.push(new_arr); - } - } else { - data.push(...value); - } - } - - if (Array.isArray(data[0])) { - let df = new _frame.DataFrame(data, { - columns: columns, - index: row_indexes - }); - return df; - } else { - let sf = new _series.Series(data, { - index: row_indexes - }); - return sf; - } - } - } - -} - -exports.Concat = Concat; - -const concat = kwargs => { - let concat_sf = new Concat(kwargs); - return concat_sf; -}; - -exports.concat = concat; \ No newline at end of file diff --git a/danfojs-node/dist/core/date_range.js b/danfojs-node/dist/core/date_range.js deleted file mode 100644 index 7843571a..00000000 --- a/danfojs-node/dist/core/date_range.js +++ /dev/null @@ -1,276 +0,0 @@ -"use strict"; - -Object.defineProperty(exports, "__esModule", { - value: true -}); -exports.date_range = void 0; - -var _utils = require("./utils"); - -const utils = new _utils.Utils(); - -class date_range { - constructor(kwargs) { - this.offset = null; - - if (utils.__key_in_object(kwargs, "start")) { - this.start = kwargs["start"]; - } else { - this.start = null; - } - - if (utils.__key_in_object(kwargs, "end")) { - this.end = kwargs["end"]; - } else { - this.end = null; - } - - if (utils.__key_in_object(kwargs, "period")) { - this.period = kwargs["period"]; - } else { - this.period = null; - } - - if (utils.__key_in_object(kwargs, "freq")) { - this.freq = kwargs["freq"]; - } else { - this.freq = "D"; - } - - this.freq_list = ["M", "D", "s", "H", "m", "Y"]; - - if (this.freq.length == 1) { - if (!this.freq_list.includes(this.freq)) { - throw new Error(`invalid freq ${this.freq}`); - } - } else { - let freq_split = this.freq.split(""); - this.offset = parseInt(freq_split[0]); - this.freq = freq_split[1]; - - if (!this.freq_list.includes(this.freq)) { - throw new Error(`invalid freq ${this.freq}`); - } - } - - let rslt = this.range(this.start, this.end, this.period, this.offset); - return rslt; - } - - range(start, end, period, offset = null) { - let start_date = null; - let end_date = null; - let start_range = null; - let end_range = null; - - if (start && end) { - start_date = new Date(start); - start_range = this.freq_type(start_date, this.freq); - end_date = new Date(end); - end_range = this.freq_type(end_date, this.freq); - let start_year = start_date.getFullYear(); - let end_year = end_date.getFullYear(); - - if (start_year < end_year) { - if (this.freq == "M") { - end_range = this.month_end(start_date, end_date); - } else if (this.freq == "D") { - end_range = this.day_end(start_date, end_date) - start_range; - } - } - - let range_array = utils.__range(start_range, end_range); - - if (offset) { - range_array = this.offset_count(range_array, offset); - } - - if (this.freq == "M") { - range_array = this.month_range(range_array); - } - - let date_range = range_array.map(x => { - return this.set_dateProps(start_date, this.freq, x); - }); - date_range[date_range.length - 1] = end_date; - let date_string = this.toLocalString(date_range); - return date_string; - } else if (start && !end) { - start_date = new Date(start); - start_range = this.freq_type(start_date, this.freq); - end_range = offset ? period * offset - 1 : period - 1; - - if (start_range > end_range) { - end_range = end_range + start_range; - } - - let range_array = utils.__range(start_range, end_range); - - if (offset) { - range_array = this.offset_count(range_array, offset); - } - - let date_range = range_array.map(x => { - return this.set_dateProps(start_date, this.freq, x); - }); - let date_string = this.toLocalString(date_range); - return date_string; - } else if (end && !start) { - end_date = new Date(end); - end_range = this.freq_type(end_date, this.freq); - start_range = end_range - period + 1; - - let range_array = utils.__range(start_range, end_range); - - if (offset) { - range_array = this.offset_count(range_array, offset); - } - - let date_range = range_array.map(x => { - return this.set_dateProps(end_date, this.freq, x); - }); - let date_string = this.toLocalString(date_range); - return date_string; - } - } - - freq_type(date, ftype) { - let rslt = null; - - switch (ftype) { - case "M": - rslt = date.getMonth(); - break; - - case "Y": - rslt = date.getFullYear(); - break; - - case "s": - rslt = date.getSeconds(); - break; - - case "D": - rslt = date.getDate(); - break; - - case "H": - rslt = date.getHours(); - break; - - case "m": - rslt = date.getMinutes(); - break; - } - - return rslt; - } - - offset_count(d_array, offset) { - let r_array = []; - - for (let i = 0; i < d_array.length; i += offset) { - r_array.push(d_array[i]); - } - - return r_array; - } - - set_dateProps(date, ftype, val) { - let new_date = new Date(date.valueOf()); - - switch (ftype) { - case "M": - if (val.length == 2) { - new_date.setYear(new_date.getFullYear() + val[0]); - new_date.setMonth(parseInt(val[1])); - } else { - new_date.setMonth(val); - } - - break; - - case "Y": - new_date.setYear(val); - break; - - case "s": - new_date.setSeconds(val); - break; - - case "D": - new_date.setDate(val); - break; - - case "H": - new_date.setHours(val); - break; - - case "m": - new_date.setMinutes(val); - break; - } - - return new_date; - } - - toLocalString(d_array) { - let r_array = d_array.map(x => { - return x.toLocaleString(); - }); - return r_array; - } - - month_end(start_date, end_date) { - let end_month = end_date.getMonth(); - let diff_year = end_date.getFullYear() - start_date.getFullYear(); - let end_range = 12 * diff_year + end_month; - return end_range; - } - - month_range(range) { - let minus = null; - let y_val = 0; - let d_range = range.map(x => { - if (x > 11) { - if (x % 12 == 0) { - minus = x; - y_val = x / 12; - return [y_val, x - minus]; - } else { - return [y_val, x - minus]; - } - } - - return [y_val, x]; - }); - return d_range; - } - - day_end(start_date, end_date) { - let month_end = this.month_end(start_date, end_date); - - let range = utils.__range(start_date.getMonth(), month_end); - - let m_range = this.month_range(range); - let sum = 0; - - for (let i = 0; i < m_range.length; i++) { - let val = m_range[i]; - let d_date = null; - - if (i === m_range.length - 1) { - d_date = new Date(start_date.getFullYear() + val[0], val[1], end_date.getDate()).getDate(); - } else { - d_date = new Date(start_date.getFullYear() + val[0], val[1], 0).getDate(); - } - - sum += d_date; - } - - return sum; - } - -} - -exports.date_range = date_range; \ No newline at end of file diff --git a/danfojs-node/dist/core/frame.js b/danfojs-node/dist/core/frame.js deleted file mode 100644 index f3e77241..00000000 --- a/danfojs-node/dist/core/frame.js +++ /dev/null @@ -1,1881 +0,0 @@ -"use strict"; - -var _interopRequireDefault = require("@babel/runtime/helpers/interopRequireDefault"); - -var _interopRequireWildcard = require("@babel/runtime/helpers/interopRequireWildcard"); - -Object.defineProperty(exports, "__esModule", { - value: true -}); -exports.DataFrame = void 0; - -var tf = _interopRequireWildcard(require("@tensorflow/tfjs-node")); - -var _generic = _interopRequireDefault(require("./generic")); - -var _series = require("./series"); - -var _utils = require("./utils"); - -var _groupby = require("./groupby"); - -var _indexing = require("./indexing"); - -var _concat = require("./concat.js"); - -var _mathjs = require("mathjs"); - -const utils = new _utils.Utils(); - -class DataFrame extends _generic.default { - constructor(data, kwargs) { - super(data, kwargs); - - this._set_column_property(); - } - - _set_column_property() { - let col_vals = this.col_data; - let col_names = this.column_names; - col_vals.forEach((col, i) => { - this[col_names[i]] = null; - Object.defineProperty(this, col_names[i], { - get() { - return new _series.Series(this.col_data[i], { - columns: col_names[i], - index: this.index - }); - }, - - set(value) { - this.addColumn({ - column: col_names[i], - value: value - }); - } - - }); - }); - } - - async to_csv(csvFilePath = "") { - const csvContent = await super.to_csv(); - - if (typeof window === "undefined") { - const fs = require("fs"); - - fs.writeFileSync(csvFilePath, csvContent, err => err && console.error(err)); - } else { - const webCSV = "data:text/csv;charset=utf-8," + csvContent; - window.open(encodeURI(webCSV)); - } - - return csvContent; - } - - drop(kwargs = {}) { - let params_needed = ["columns", "index", "inplace", "axis"]; - - utils._throw_wrong_params_error(kwargs, params_needed); - - kwargs["inplace"] = kwargs["inplace"] || false; - - if (!("axis" in kwargs)) { - kwargs["axis"] = 1; - } - - let to_drop = null; - - if ("index" in kwargs && kwargs["axis"] == 0) { - to_drop = kwargs["index"]; - } else { - to_drop = kwargs["columns"]; - } - - if (kwargs["axis"] == 1) { - if (!("columns" in kwargs)) { - throw Error("No column found. Axis of 1 must be accompanied by an array of column(s) names"); - } - - let self = this; - let new_col_data = {}; - let new_dtype = []; - const index = to_drop.map(x => { - let col_idx = self.columns.indexOf(x); - - if (col_idx == -1) { - throw new Error(`column "${x}" does not exist`); - } - - return col_idx; - }); - this.col_data.forEach((col, idx) => { - if (!index.includes(idx)) { - new_col_data[self.column_names[idx]] = col; - new_dtype.push(self.dtypes[idx]); - } - }); - - if (!kwargs["inplace"]) { - let old_cols = self.columns; - let new_columns = Object.keys(new_col_data); - let df = new DataFrame(new_col_data, { - index: self.index, - dtypes: new_dtype - }); - - df.__set_col_property(df, df.col_data, new_columns, old_cols); - - return df; - } else { - let old_cols = self.columns; - let new_columns = Object.keys(new_col_data); - - this.__update_frame_in_place(null, null, new_col_data, null, new_dtype); - - this.__set_col_property(self, self.col_data, new_columns, old_cols); - } - } else { - if (!utils.__key_in_object(kwargs, "index")) { - throw Error("No index label found. Axis of 0 must be accompanied by an array of index labels"); - } - - to_drop.forEach(x => { - if (!this.index.includes(x)) throw new Error(`${x} does not exist in index`); - }); - const values = this.values; - let data_idx = []; - let new_data, new_index; - - if (typeof to_drop[0] == "string") { - this.index.forEach((idx, i) => { - if (to_drop.includes(idx)) { - data_idx.push(i); - } - }); - new_data = utils.__remove_arr(values, data_idx); - new_index = utils.__remove_arr(this.index, data_idx); - } else { - new_data = utils.__remove_arr(values, to_drop); - new_index = utils.__remove_arr(this.index, to_drop); - } - - if (!kwargs["inplace"]) { - return new DataFrame(new_data, { - columns: this.columns, - index: new_index - }); - } else { - this.row_data_tensor = tf.tensor(new_data); - this.data = new_data; - - this.__set_index(new_index); - } - } - } - - loc(kwargs = {}) { - let params_needed = ["columns", "rows"]; - - utils._throw_wrong_params_error(kwargs, params_needed); - - kwargs["type"] = "loc"; - let [new_data, columns, rows] = (0, _indexing.indexLoc)(this, kwargs); - let df_columns = { - columns: columns - }; - let df = new DataFrame(new_data, df_columns); - - df.__set_index(rows); - - return df; - } - - iloc(kwargs = {}) { - let params_needed = ["columns", "rows"]; - - utils._throw_wrong_params_error(kwargs, params_needed); - - kwargs["type"] = "iloc"; - let [new_data, columns, rows] = (0, _indexing.indexLoc)(this, kwargs); - let df_columns = { - columns: columns - }; - let df = new DataFrame(new_data, df_columns); - - df.__set_index(rows); - - return df; - } - - head(rows = 5) { - if (rows > this.values.length || rows < 1) { - return this; - } else { - let data = this.values.slice(0, rows); - let idx = this.index.slice(0, rows); - let config = { - columns: this.column_names, - index: idx - }; - let df = new DataFrame(data, config); - return df; - } - } - - tail(rows = 5) { - let row_len = this.values.length; - - if (rows > row_len || rows < 1) { - return this; - } else { - let data = this.values.slice(row_len - rows); - let indx = this.index.slice(row_len - rows); - let config = { - columns: this.column_names, - index: indx - }; - let df = new DataFrame(data, config); - return df; - } - } - - async sample(num = -1, seed = 1) { - if (num > this.shape[0]) { - throw new Error("Sample size n cannot be bigger than size of dataset"); - } - - if (num < -1 || num == 0) { - throw new Error("Sample size cannot be less than -1 or 0"); - } - - num = num === -1 ? this.shape[0] : num; - const shuffled_index = await tf.data.array(this.index).shuffle(num, seed).take(num).toArray(); - const df = this.iloc({ - rows: shuffled_index - }); - return df; - } - - add(other, axis) { - if (this.__frame_is_compactible_for_operation) { - let tensors = this.__get_ops_tensors([this, other], axis); - - let sum_vals = tensors[0].add(tensors[1]); - let col_names = this.columns; - return this.__get_df_from_tensor(sum_vals, col_names); - } else { - throw Error("TypeError: Dtypes of columns must be Float of Int"); - } - } - - sub(other, axis) { - if (this.__frame_is_compactible_for_operation) { - let tensors = this.__get_ops_tensors([this, other], axis); - - let result = tensors[0].sub(tensors[1]); - let col_names = this.columns; - return this.__get_df_from_tensor(result, col_names); - } else { - throw Error("TypeError: Dtypes of columns must be Float of Int"); - } - } - - mul(other, axis) { - if (this.__frame_is_compactible_for_operation) { - let tensors = this.__get_ops_tensors([this, other], axis); - - let result = tensors[0].mul(tensors[1]); - let col_names = this.columns; - return this.__get_df_from_tensor(result, col_names); - } else { - throw Error("TypeError: Dtypes of columns must be Float of Int"); - } - } - - div(other, axis) { - if (this.__frame_is_compactible_for_operation) { - let tensors = this.__get_ops_tensors([this, other], axis); - - let result = tensors[0].div(tensors[1]); - let col_names = this.columns; - return this.__get_df_from_tensor(result, col_names); - } else { - throw Error("TypeError: Dtypes of columns must be Float of Int"); - } - } - - pow(other, axis) { - if (this.__frame_is_compactible_for_operation) { - let tensors = this.__get_ops_tensors([this, other], axis); - - let result = tensors[0].pow(tensors[1]); - let col_names = this.columns; - return this.__get_df_from_tensor(result, col_names); - } else { - throw Error("TypeError: Dtypes of columns must be Float of Int"); - } - } - - mod(other, axis) { - if (this.__frame_is_compactible_for_operation) { - let tensors = this.__get_ops_tensors([this, other], axis); - - let result = tensors[0].mod(tensors[1]); - let col_names = this.columns; - return this.__get_df_from_tensor(result, col_names); - } else { - throw Error("TypeError: Dtypes of columns must be Float of Int"); - } - } - - mean(axis = 1) { - if (this.__frame_is_compactible_for_operation()) { - let values; - let val_mean = []; - - if (axis == 1) { - values = this.col_data; - } else { - values = this.values; - } - - values.map(arr => { - let temp = utils._remove_nans(arr); - - let temp_mean = tf.tensor(temp).mean().arraySync(); - val_mean.push(Number(temp_mean.toFixed(5))); - }); - let new_index; - - if (axis == 1) { - new_index = this.column_names; - } else { - new_index = this.index; - } - - let sf = new _series.Series(val_mean, { - columns: "sum", - index: new_index - }); - return sf; - } else { - throw Error("Dtype Error: Operation can not be performed on string type"); - } - } - - median(axis = 1) { - if (this.__frame_is_compactible_for_operation) { - let tensor_vals, idx; - - if (axis == 1) { - tensor_vals = this.col_data_tensor.arraySync(); - idx = this.column_names; - } else { - tensor_vals = this.row_data_tensor.arraySync(); - idx = this.index; - } - - let median = utils.__median(tensor_vals, false); - - let sf = new _series.Series(median, { - index: idx - }); - return sf; - } else { - throw Error("TypeError: Dtypes of columns must be Float of Int"); - } - } - - min(axis = 1) { - if (this.__frame_is_compactible_for_operation) { - let operands = this.__get_tensor_and_idx(this, axis); - - let tensor_vals = operands[0]; - let idx = operands[1]; - let result = tensor_vals.min(operands[2]); - let sf = new _series.Series(result.arraySync(), { - index: idx - }); - return sf; - } else { - throw Error("TypeError: Dtypes of columns must be Float of Int"); - } - } - - max(axis = 1) { - if (this.__frame_is_compactible_for_operation) { - let operands = this.__get_tensor_and_idx(this, axis); - - let tensor_vals = operands[0]; - let idx = operands[1]; - let result = tensor_vals.max(operands[2]); - let sf = new _series.Series(result.arraySync(), { - index: idx - }); - return sf; - } else { - throw Error("TypeError: Dtypes of columns must be Float of Int"); - } - } - - std(axis = 1) { - if (this.__frame_is_compactible_for_operation) { - let tensor_vals = this.col_data_tensor.arraySync(); - let idx; - - if (axis == 1) { - idx = this.column_names; - } else { - idx = this.index; - } - - let median = (0, _mathjs.std)(tensor_vals, axis); - let sf = new _series.Series(median, { - index: idx - }); - return sf; - } else { - throw Error("TypeError: Dtypes of columns must be Float of Int"); - } - } - - var(axis = 1) { - if (this.__frame_is_compactible_for_operation) { - let tensor_vals = this.col_data_tensor.arraySync(); - let idx; - - if (axis == 1) { - idx = this.column_names; - } else { - idx = this.index; - } - - let median = (0, _mathjs.variance)(tensor_vals, axis); - let sf = new _series.Series(median, { - index: idx - }); - return sf; - } else { - throw Error("TypeError: Dtypes of columns must be Float of Int"); - } - } - - count(axis = 1) { - if (this.__frame_is_compactible_for_operation) { - let tensor_vals, idx; - - if (axis == 1) { - tensor_vals = this.col_data_tensor.arraySync(); - idx = this.column_names; - } else { - tensor_vals = this.row_data_tensor.arraySync(); - idx = this.index; - } - - let counts = utils.__count_nan(tensor_vals, true, false); - - let sf = new _series.Series(counts, { - index: idx - }); - return sf; - } else { - throw Error("TypeError: Dtypes of columns must be Float of Int"); - } - } - - round(dp = 1) { - if (this.__frame_is_compactible_for_operation) { - let values = this.values; - let idx = this.index; - - let new_vals = utils.__round(values, dp, false); - - let options = { - columns: this.column_names, - index: idx - }; - let df = new DataFrame(new_vals, options); - return df; - } else { - throw Error("TypeError: Dtypes of columns must be Float of Int"); - } - } - - __cum_ops(axis = 0, ops) { - if (!(axis == 0) && !(axis == 1)) { - throw new Error("axis must be between 0 or 1"); - } - - if (this.__frame_is_compactible_for_operation) { - let data = []; - let df_data = null; - - if (axis == 0) { - df_data = this.col_data; - } else { - df_data = this.values; - } - - for (let i = 0; i < df_data.length; i++) { - let value = df_data[i]; - let temp_val = value[0]; - let temp_data = [temp_val]; - - for (let j = 1; j < value.length; j++) { - let curr_val = value[j]; - - switch (ops) { - case "max": - if (curr_val > temp_val) { - temp_val = curr_val; - temp_data.push(curr_val); - } else { - temp_data.push(temp_val); - } - - break; - - case "min": - if (curr_val < temp_val) { - temp_val = curr_val; - temp_data.push(curr_val); - } else { - temp_data.push(temp_val); - } - - break; - - case "sum": - temp_val = temp_val + curr_val; - temp_data.push(temp_val); - break; - - case "prod": - temp_val = temp_val * curr_val; - temp_data.push(temp_val); - break; - } - } - - data.push(temp_data); - } - - if (axis == 0) { - data = utils.__get_col_values(data); - } - - return new DataFrame(data, { - columns: this.columns - }); - } else { - throw Error("TypeError: Dtypes of columns must be Float of Int"); - } - } - - cumsum(kwargs = {}) { - let axis; - - if (!utils.__key_in_object(kwargs, "axis")) { - axis = 0; - } else { - axis = kwargs["axis"]; - } - - let data = this.__cum_ops(axis, "sum"); - - return data; - } - - cummin(kwargs = {}) { - let axis; - - if (!utils.__key_in_object(kwargs, "axis")) { - axis = 0; - } else { - axis = kwargs["axis"]; - } - - let data = this.__cum_ops(axis, "min"); - - return data; - } - - cummax(kwargs = {}) { - let axis; - - if (!utils.__key_in_object(kwargs, "axis")) { - axis = 0; - } else { - axis = kwargs["axis"]; - } - - let data = this.__cum_ops(axis, "max"); - - return data; - } - - cumprod(kwargs = {}) { - let axis; - - if (!utils.__key_in_object(kwargs, "axis")) { - axis = 0; - } else { - axis = kwargs["axis"]; - } - - let data = this.__cum_ops(axis, "prod"); - - return data; - } - - copy() { - let df = new DataFrame([...this.values], { - columns: [...this.column_names], - index: this.index, - dtypes: this.dtypes - }); - return df; - } - - reset_index(inplace = false) { - if (inplace) { - this.__reset_index(); - } else { - let df = this.copy(); - - df.__reset_index(); - - return df; - } - } - - set_index(kwargs = {}) { - let params_needed = ["key", "drop", "inplace"]; - - utils._throw_wrong_params_error(kwargs, params_needed); - - if (!utils.__key_in_object(kwargs, "key")) { - throw Error("Index ValueError: You must specify an array of index"); - } - - if (!utils.__key_in_object(kwargs, "inplace")) { - kwargs["inplace"] = false; - } - - if (!utils.__key_in_object(kwargs, "drop")) { - kwargs["drop"] = true; - } - - if (Array.isArray(kwargs["key"]) && kwargs["key"].length != this.index.length) { - throw Error(`Index LengthError: Lenght of new Index array ${kwargs["key"].length} must match lenght of existing index ${this.index.length}`); - } - - if (typeof kwargs["key"] == "string" && this.column_names.includes(kwargs["key"])) { - kwargs["key_name"] = kwargs["key"]; - kwargs["key"] = this[kwargs["key"]].values; - } - - if (kwargs["inplace"]) { - this.__set_index(kwargs["key"]); - - if (kwargs["drop"] && typeof kwargs["key_name"] == "string") { - this.drop({ - columns: [kwargs["key_name"]], - inplace: true, - axis: 1 - }); - } - } else { - let df = this.copy(); - - df.__set_index(kwargs["key"]); - - if (kwargs["drop"] && typeof kwargs["key_name"] == "string") { - df.drop({ - columns: [kwargs["key_name"]], - axis: 1, - inplace: true - }); - } - - return df; - } - } - - describe() { - let numeric_df = this.select_dtypes(["float32", "int32"]); - let col_names = numeric_df.column_names; - let index = ["count", "mean", "std", "min", "median", "max", "variance"]; - let stats_arr = {}; - col_names.forEach(name => { - let col_series = numeric_df[name]; - let count = col_series.count(); - let mean = col_series.mean(); - let std = col_series.std(); - let min = col_series.min(); - let median = col_series.median(); - let max = col_series.max(); - let variance = col_series.var(); - let _stats = [count, mean, std, min, median, max, variance]; - stats_arr[name] = _stats; - }); - let df = new DataFrame(stats_arr, { - index: index - }); - return df.round(6); - } - - select_dtypes(include) { - let dtypes = this.dtypes; - let col_names = this.column_names; - let col_vals = {}; - let original_col_vals = this.col_data; - const __supported_dtypes = ["float32", "int32", "string", "boolean"]; - - if (include == undefined) { - let df = this.copy(); - return df; - } else { - include.forEach(type => { - if (!__supported_dtypes.includes(type)) { - throw Error(`Dtype Error: dtype ${type} not supported.`); - } - }); - dtypes.forEach((dtype, i) => { - if (include.includes(dtype)) { - col_vals[col_names[i]] = original_col_vals[i]; - } - }); - let df = new DataFrame(col_vals); - return df; - } - } - - sum(kwargs = { - axis: 1 - }) { - if (this.__frame_is_compactible_for_operation()) { - let values; - let val_sums = []; - - if (kwargs["axis"] == 1) { - values = this.col_data; - } else { - values = this.values; - } - - values.map(arr => { - let temp = utils._remove_nans(arr); - - let temp_sum = tf.tensor(temp).sum().arraySync(); - val_sums.push(Number(temp_sum.toFixed(5))); - }); - let new_index; - - if (kwargs["axis"] == 1) { - new_index = this.column_names; - } else { - new_index = this.index; - } - - let sf = new _series.Series(val_sums, { - columns: "sum", - index: new_index - }); - return sf; - } else { - throw Error("Dtype Error: Operation can not be performed on string type"); - } - } - - abs() { - let data = this.values; - let tensor_data = tf.tensor(data); - let abs_data = tensor_data.abs().arraySync(); - let df = new DataFrame(utils.__round(abs_data, 2, false), { - columns: this.column_names, - index: this.index - }); - return df; - } - - __get_tensor_and_idx(df, axis) { - let tensor_vals, idx, t_axis; - - if (axis == 1) { - let temp_tensor_vals = df.row_data_tensor; - let flat_tensor_array = tf.util.flatten(temp_tensor_vals.arraySync()); - - const flat_tensor_array_without_nans = utils._replace_nan_with_null(flat_tensor_array); - - tensor_vals = tf.tensor(flat_tensor_array_without_nans, temp_tensor_vals.shape); - idx = df.column_names; - t_axis = 0; - } else { - tensor_vals = df.row_data_tensor; - idx = df.index; - t_axis = 1; - } - - return [tensor_vals, idx, t_axis]; - } - - query(kwargs) { - let operators = [">", "<", "<=", ">=", "==", "!="]; - - if (!utils.__key_in_object(kwargs, "inplace")) { - kwargs["inplace"] = false; - } - - let column_index, operator, value; - - if (utils.__key_in_object(kwargs, "column")) { - if (this.columns.includes(kwargs["column"])) { - column_index = this.columns.indexOf(kwargs["column"]); - } else { - throw new Error(`column ${kwargs["column"]} does not exist`); - } - } else { - throw new Error("specify the column"); - } - - if (utils.__key_in_object(kwargs, "is")) { - if (operators.includes(kwargs["is"])) { - operator = kwargs["is"]; - } else { - throw new Error(` ${kwargs["is"]} is not a supported logical operator`); - } - } else { - throw new Error("specify an operator in param [is]"); - } - - if (utils.__key_in_object(kwargs, "to")) { - value = kwargs["to"]; - } else { - throw new Error("specify a value in param [to]"); - } - - let data = this.values; - let index = this.index; - let new_data = []; - let new_index = []; - - for (var i = 0; i < data.length; i++) { - let data_value = data[i]; - let elem = data_value[column_index]; - - if (eval(`elem${operator}value`)) { - new_data.push(data_value); - new_index.push(index[i]); - } - } - - if (new_data.length == 0) { - throw new Error(`query returned empty data; is either ${value} does not exist in column ${kwargs["column"]}`); - } - - if (kwargs["inplace"]) { - this.__update_frame_in_place(new_data, this.columns, null, new_index, null); - } else { - let new_df = new DataFrame(new_data, { - columns: this.columns, - index: new_index - }); - return new_df; - } - } - - addColumn(kwargs) { - utils.__in_object(kwargs, "column", "column name not specified"); - - utils.__in_object(kwargs, "value", "column value not specified"); - - let column_name = kwargs["column"]; - let data_length = this.shape[0]; - let value; - - if (kwargs["value"] instanceof _series.Series) { - value = kwargs["value"].values; - } else { - value = kwargs["value"]; - } - - if (value.length != data_length) { - throw new Error(`Array length ${value.length} not equal to ${data_length}`); - } - - if (this.columns.includes(column_name)) { - let col_idx = this.columns.indexOf(column_name); - let new_data = []; - this.values.map((val, index) => { - let new_val = val.slice(); - new_val[col_idx] = value[index]; - new_data.push(new_val); - }); - - this.__update_frame_in_place(new_data, null, null, null, null); - } else { - let data = this.values; - let new_data = []; - data.map(function (val, index) { - let new_val = val.slice(); - new_val.push(value[index]); - new_data.push(new_val); - }); - let new_dtypes = [...this.dtypes]; - new_dtypes.push(utils.__get_t(value)[0]); - let new_col_names = [...this.columns]; - new_col_names.push(column_name); - - this.__update_frame_in_place(new_data, new_col_names, null, null, new_dtypes); - - Object.defineProperty(this, column_name, { - get() { - return new _series.Series(value, { - columns: column_name, - index: this.index - }); - }, - - set(value) { - this.addColumn({ - column: column_name, - value: value - }); - } - - }); - } - } - - groupby(col) { - const len = this.shape[0]; - const column_names = this.column_names; - const col_index = col.map(val => column_names.indexOf(val)); - const col_dtype = this.dtypes.filter((val, index) => { - return col_index.includes(index); - }); - const self = this; - const data = col.map(column_name => { - if (!column_names.includes(column_name)) throw new Error(`column ${column_name} does not exist`); - const [column_data, _] = (0, _indexing.indexLoc)(self, { - rows: [`0:${len}`], - columns: [`${column_name}`], - type: "loc" - }); - return column_data; - }); - const unique_columns = data.map(column_data => utils.__unique(column_data)); - - function getRecursiveDict(uniq_columns) { - const first_uniq_columns = uniq_columns[0]; - const remaining_columns = uniq_columns.slice(1); - const c_dict = {}; - if (!remaining_columns.length) first_uniq_columns.forEach(col_value => c_dict[col_value] = []);else first_uniq_columns.forEach(col_value => c_dict[col_value] = getRecursiveDict(remaining_columns)); - return c_dict; - } - - const col_dict = getRecursiveDict(unique_columns); - return new _groupby.GroupBy(col_dict, col, this.values, column_names, col_dtype).group(); - } - - column(col_name) { - if (!this.columns.includes(col_name)) { - throw new Error(`column ${col_name} does not exist`); - } - - let col_indx_objs = utils.__arr_to_obj(this.columns); - - let indx = col_indx_objs[col_name]; - let data = this.col_data[indx]; - return new _series.Series(data, { - columns: [col_name] - }); - } - - fillna(kwargs = {}) { - let params_needed = ["columns", "values", "inplace"]; - - utils._throw_wrong_params_error(kwargs, params_needed); - - if (!utils.__key_in_object(kwargs, "inplace")) { - kwargs["inplace"] = false; - } - - if (utils.__key_in_object(kwargs, "columns")) { - kwargs["columns"].map(col => { - if (!this.column_names.includes(col)) { - throw Error(`Value Error: Specified columns must be one of ${this.column_names}, got ${col}`); - } - }); - - if (kwargs["columns"].length != kwargs["values"].length) { - throw Error(`Lenght Error: The lenght of the columns names must be equal to the lenght of the values, - got column of length ${kwargs["columns"].length} but values of length ${kwargs["values"].length}`); - } - - let new_col_data = this.col_data; - kwargs["columns"].map((col, i) => { - let col_idx = this.column_names.indexOf(col); - let col_data = this.col_data[col_idx]; - let __temp = []; - col_data.map(val => { - if (isNaN(val) && typeof val != "string") { - __temp.push(kwargs["values"][i]); - } else { - __temp.push(val); - } - }); - new_col_data[col_idx] = __temp; - }); - let final_data = {}; - new_col_data.map((col, i) => { - final_data[this.column_names[i]] = col; - }); - - if (kwargs["inplace"]) { - this.__update_frame_in_place(null, null, final_data, null, null); - } else { - return new DataFrame(final_data, { - index: this.index - }); - } - } else { - if (!utils.__key_in_object(kwargs, "values")) { - throw Error("Value Error: Please specify a fill value"); - } - - let nan_val; - - if (Array.isArray(kwargs["values"])) { - nan_val = kwargs["values"][0]; - } else { - nan_val = kwargs["values"]; - } - - let data = []; - let values = this.values; - let columns = this.columns; - - for (let i = 0; i < values.length; i++) { - let temp_data = []; - let row_value = values[i]; - - for (let j = 0; j < row_value.length; j++) { - if (isNaN(row_value[j]) && typeof row_value[j] != "string") { - temp_data.push(nan_val); - } else { - temp_data.push(row_value[j]); - } - } - - data.push(temp_data); - } - - if (kwargs["inplace"]) { - this.__update_frame_in_place(data, null, null, null, null); - } else { - return new DataFrame(data, { - columns: columns, - index: this.index - }); - } - } - } - - isna() { - let new_row_data = this.__isna(); - - let columns = this.column_names; - return new DataFrame(new_row_data, { - columns: columns, - index: this.index - }); - } - - nanIndex() { - let df_values = this.values; - let index_data = []; - - for (let i = 0; i < df_values.length; i++) { - let row_values = df_values[i]; - - if (row_values.includes(NaN)) { - index_data.push(i); - } - } - - return index_data; - } - - dropna(kwargs = {}) { - let axis = kwargs["axis"] || 0; - let inplace = kwargs["inplace"] || false; - - if (axis != 0 && axis != 1) { - throw new Error("axis must either be 1 or 0"); - } - - let df_values = null; - let columns = null; - - if (axis == 0) { - df_values = this.values; - columns = this.columns; - } else { - df_values = this.col_data; - columns = []; - } - - let data = []; - - for (let i = 0; i < df_values.length; i++) { - let values = df_values[i]; - - if (!values.includes(NaN)) { - if (axis == 0) { - data.push(values); - } else { - columns.push(this.columns[i]); - - if (data.length == 0) { - for (let j = 0; j < values.length; j++) { - data.push([values[j]]); - } - } else { - for (let j = 0; j < data.length; j++) { - data[j].push(values[j]); - } - } - } - } - } - - if (inplace == true) { - this.data = data; - - this.__reset_index(); - - this.columns = columns; - } else { - return new DataFrame(data, { - columns: columns - }); - } - } - - apply(kwargs) { - let is_callable = utils.__is_function(kwargs["callable"]); - - if (!is_callable) { - throw new Error("the argument must be a function"); - } - - let callable = kwargs["callable"]; - let data = []; - - if (utils.__key_in_object(kwargs, "axis")) { - let axis = kwargs["axis"]; - let df_data; - - if (axis == 0) { - df_data = this.values; - } else { - df_data = this.col_data; - } - - for (let i = 0; i < df_data.length; i++) { - let value = tf.tensor(df_data[i]); - let callable_data; - - try { - callable_data = callable(value).arraySync(); - } catch (error) { - throw Error(`Callable Error: You can only apply JavaScript functions on DataFrames when axis is not specified. This operation is applied on all element, and returns a DataFrame of the same shape.`); - } - - data.push(callable_data); - } - } else { - let df_data = this.values; - let new_data = []; - df_data.forEach(row => { - let new_row = []; - row.forEach(val => { - new_row.push(callable(val)); - }); - new_data.push(new_row); - }); - data = new_data; - } - - if (utils.__is_1D_array(data)) { - if (kwargs["axis"] == 0) { - let sf = new _series.Series(data, { - index: this.index - }); - return sf; - } else { - let sf = new _series.Series(data, { - index: this.column_names - }); - return sf; - } - } else { - let df = new DataFrame(data, { - columns: this.column_names, - index: this.index - }); - return df; - } - } - - lt(other, axis) { - if (this.__frame_is_compactible_for_operation()) { - if (axis == undefined) { - axis = 0; - } - - let df = this.__logical_ops(other, "lt", axis); - - return df; - } else { - throw Error("Dtype Error: Operation can not be performed on string type"); - } - } - - gt(other, axis) { - if (this.__frame_is_compactible_for_operation()) { - if (axis == undefined) { - axis = 0; - } - - let df = this.__logical_ops(other, "gt", axis); - - return df; - } else { - throw Error("Dtype Error: Operation can not be performed on string type"); - } - } - - le(other, axis) { - if (this.__frame_is_compactible_for_operation()) { - if (axis == undefined) { - axis = 0; - } - - let df = this.__logical_ops(other, "le", axis); - - return df; - } else { - throw Error("Dtype Error: Operation can not be performed on string type"); - } - } - - ge(other, axis) { - if (this.__frame_is_compactible_for_operation()) { - if (axis == undefined) { - axis = 0; - } - - let df = this.__logical_ops(other, "ge", axis); - - return df; - } else { - throw Error("Dtype Error: Operation can not be performed on string type"); - } - } - - ne(other, axis) { - if (this.__frame_is_compactible_for_operation()) { - if (axis == undefined) { - axis = 0; - } - - let df = this.__logical_ops(other, "ne", axis); - - return df; - } else { - throw Error("Dtype Error: Operation can not be performed on string type"); - } - } - - eq(other, axis) { - if (this.__frame_is_compactible_for_operation()) { - if (axis == undefined) { - axis = 0; - } - - let df = this.__logical_ops(other, "eq", axis); - - return df; - } else { - throw Error("Dtype Error: Operation can not be performed on string type"); - } - } - - replace(kwargs = {}) { - let params_needed = ["replace", "with", "in"]; - - utils._throw_wrong_params_error(kwargs, params_needed); - - if (utils.__key_in_object(kwargs, "in")) { - kwargs["in"].map(col => { - if (!this.column_names.includes(col)) { - throw Error(`Value Error: Specified columns must be one of ${this.column_names}, got ${col}`); - } - }); - - if (utils.__key_in_object(kwargs, "replace") && utils.__key_in_object(kwargs, "with")) { - let new_col_data_obj = {}; - this.column_names.map((col, idx) => { - if (kwargs["in"].includes(col)) { - let temp_col_data = this.col_data[idx]; - let __temp = []; - temp_col_data.map(val => { - if (val == kwargs["replace"]) { - __temp.push(kwargs["with"]); - } else { - __temp.push(val); - } - }); - new_col_data_obj[col] = __temp; - } else { - new_col_data_obj[col] = this.col_data[idx]; - } - }); - return new DataFrame(new_col_data_obj, { - columns: this.column_names, - index: this.index - }); - } else { - throw Error("Params Error: Must specify both 'replace' and 'with' parameters."); - } - } else { - if (utils.__key_in_object(kwargs, "replace") && utils.__key_in_object(kwargs, "with")) { - let replaced_arr = []; - let old_arr = this.values; - old_arr.map(inner_arr => { - let temp = []; - inner_arr.map(val => { - if (val == kwargs["replace"]) { - temp.push(kwargs["with"]); - } else { - temp.push(val); - } - }); - replaced_arr.push(temp); - }); - let df = new DataFrame(replaced_arr, { - index: this.index, - columns: this.column_names - }); - return df; - } else { - throw Error("Params Error: Must specify both 'replace' and 'with' parameters."); - } - } - } - - __logical_ops(val, logical_type, axis) { - let int_vals, other; - - if (utils.__is_number(val)) { - other = val; - } else { - if (val.series) { - if (axis == 0) { - if (val.values.length != this.shape[0]) { - throw Error(`Shape Error: Operands could not be broadcast together with shapes ${this.shape} and ${val.values.length}.`); - } - - other = tf.tensor(val.values); - } else { - if (val.values.length != this.shape[1]) { - throw Error(`Shape Error: Operands could not be broadcast together with shapes ${this.shape} and ${val.values.length}.`); - } - - other = tf.tensor(val.values); - } - } else if (Array.isArray(val)) { - other = tf.tensor(val); - } else { - other = val.row_data_tensor; - } - } - - switch (logical_type) { - case "lt": - int_vals = tf.tensor(this.values).less(other).arraySync(); - break; - - case "gt": - int_vals = tf.tensor(this.values).greater(other).arraySync(); - break; - - case "le": - int_vals = tf.tensor(this.values).lessEqual(other).arraySync(); - break; - - case "ge": - int_vals = tf.tensor(this.values).greaterEqual(other).arraySync(); - break; - - case "ne": - int_vals = tf.tensor(this.values).notEqual(other).arraySync(); - break; - - case "eq": - int_vals = tf.tensor(this.values).equal(other).arraySync(); - break; - } - - let bool_vals = utils.__map_int_to_bool(int_vals, 2); - - let df = new DataFrame(bool_vals, { - columns: this.column_names, - index: this.index - }); - return df; - } - - __get_df_from_tensor(val, col_names) { - let len = val.shape[0]; - let new_array = []; - - for (let i = 0; i < len; i++) { - let arr = val.slice([i], [1]).arraySync()[0]; - new_array.push(arr); - } - - return new DataFrame(new_array, { - columns: col_names - }); - } - - __frame_is_compactible_for_operation() { - let dtypes = this.dtypes; - - const str = element => element == "string"; - - if (dtypes.some(str)) { - return false; - } else { - return true; - } - } - - __get_ops_tensors(tensors, axis) { - if (utils.__is_undefined(tensors[1].series)) { - let tensors_arr = []; - - if (utils.__is_undefined(axis) || axis == 1) { - tensors_arr.push(tensors[0].row_data_tensor); - tensors_arr.push(tensors[1]); - return tensors_arr; - } else { - tensors_arr.push(tensors[0].col_data_tensor); - tensors_arr.push(tensors[1]); - return tensors_arr; - } - } else { - let tensors_arr = []; - - if (utils.__is_undefined(axis) || axis == 1) { - let this_tensor, other_tensor; - this_tensor = tensors[0].row_data_tensor; - - if (tensors[1].series) { - other_tensor = tf.tensor(tensors[1].values, [1, tensors[1].values.length]); - } else { - other_tensor = tensors[1].row_data_tensor; - } - - tensors_arr.push(this_tensor); - tensors_arr.push(other_tensor); - return tensors_arr; - } else { - let this_tensor, other_tensor; - this_tensor = tensors[0].row_data_tensor; - - if (tensors[1].series) { - other_tensor = tf.tensor(tensors[1].values, [tensors[1].values.length, 1]); - } else { - other_tensor = tensors[1].row_data_tensor; - } - - tensors_arr.push(this_tensor); - tensors_arr.push(other_tensor); - return tensors_arr; - } - } - } - - transpose() { - let new_values = this.col_data; - let new_index = this.column_names; - let new_col_names = this.index; - let df = new DataFrame(new_values, { - columns: new_col_names, - index: new_index - }); - return df; - } - - get T() { - return this.transpose(); - } - - get ctypes() { - let cols = this.column_names; - let d_types = this.col_types; - let sf = new _series.Series(d_types, { - index: cols - }); - return sf; - } - - plot(div) { - const plt = new Plot(this, div); - return plt; - } - - get tensor() { - return this.row_data_tensor; - } - - astype(kwargs = {}) { - if (!utils.__key_in_object(kwargs, "column")) { - throw Error("Value Error: Please specify a column to cast"); - } - - if (!utils.__key_in_object(kwargs, "dtype")) { - throw Error("Value Error: Please specify dtype to cast to"); - } - - if (!this.column_names.includes(kwargs["column"])) { - throw Error(`'${kwargs["column"]}' not found in columns`); - } - - let col_idx = this.column_names.indexOf(kwargs["column"]); - let new_types = this.col_types; - let col_values = this.col_data; - new_types[col_idx] = kwargs["dtype"]; - let new_col_values = []; - let temp_col = col_values[col_idx]; - - switch (kwargs["dtype"]) { - case "float32": - temp_col.map(val => { - new_col_values.push(Number(val)); - }); - col_values[col_idx] = new_col_values; - break; - - case "int32": - temp_col.map(val => { - new_col_values.push(Number(Number(val).toFixed())); - }); - col_values[col_idx] = new_col_values; - break; - - case "string": - temp_col.map(val => { - new_col_values.push(String(val)); - }); - col_values[col_idx] = new_col_values; - break; - - default: - break; - } - - let new_col_obj = {}; - this.column_names.forEach((cname, i) => { - new_col_obj[cname] = col_values[i]; - }); - let df = new DataFrame(new_col_obj, { - dtypes: new_types, - index: this.index - }); - return df; - } - - unique(axis = 1) { - if (axis == undefined || axis > 1 || axis < 0) { - throw Error(`Axis Error: Please specify a correct axis. Axis must either be '0' or '1', got ${axis}`); - } - - let _unique = {}; - - if (axis == 1) { - let col_names = this.column_names; - col_names.forEach(cname => { - _unique[cname] = this[cname].unique().values; - }); - } else { - let rows = this.values; - let _index = this.index; - rows.forEach((row, i) => { - let data_set = new Set(row); - _unique[_index[i]] = Array.from(data_set); - }); - } - - return _unique; - } - - nunique(axis = 1) { - if (axis == undefined || axis > 1 || axis < 0) { - throw Error(`Axis Error: Please specify a correct axis. Axis must either be '0' or '1', got ${axis}`); - } - - let _nunique = []; - - if (axis == 1) { - let col_names = this.column_names; - col_names.forEach(cname => { - _nunique.push(this[cname].unique().values.length); - }); - let sf = new _series.Series(_nunique, { - index: this.column_names - }); - return sf; - } else { - let rows = this.values; - rows.forEach(row => { - let data_set = new Set(row); - - _nunique.push(Array.from(data_set).length); - }); - } - - let sf = new _series.Series(_nunique, { - index: this.index - }); - return sf; - } - - rename(kwargs = {}) { - let params_needed = ["mapper", "inplace", "axis"]; - - utils._throw_wrong_params_error(kwargs, params_needed); - - if (!utils.__key_in_object(kwargs, "inplace")) { - kwargs["inplace"] = false; - } - - if (!utils.__key_in_object(kwargs, "axis")) { - kwargs["axis"] = 1; - } - - if (!utils.__key_in_object(kwargs, "mapper")) { - throw Error("Please specify a mapper object"); - } - - if (kwargs["axis"] == 1) { - let old_col_names = Object.keys(kwargs["mapper"]); - let new_col_names = Object.values(kwargs["mapper"]); - let col_names = [...this.column_names]; - old_col_names.forEach((cname, i) => { - if (!col_names.includes(cname)) { - throw Error(`Label Error: Specified column '${cname}' not found in column axis`); - } - - let idx = col_names.indexOf(cname); - col_names[idx] = new_col_names[i]; - }); - - if (kwargs["inplace"]) { - this.columns = col_names; - - this.__set_col_property(this, this.col_data, col_names, old_col_names); - } else { - let df = this.copy(); - df.columns = col_names; - - df.__set_col_property(df, df.col_data, col_names, old_col_names); - - return df; - } - } else { - let old_index = Object.keys(kwargs["mapper"]); - let row_index = this.index; - let new_index = []; - row_index.forEach(idx => { - if (old_index.includes(idx)) { - new_index.push(kwargs["mapper"][idx]); - } else { - new_index.push(idx); - } - }); - - if (kwargs["inplace"]) { - this.__set_index(new_index); - } else { - let df = this.copy(); - - df.__set_index(new_index); - - return df; - } - } - } - - sort_index(kwargs = {}) { - let inplace = typeof kwargs["inplace"] == "undefined" ? false : kwargs["inplace"]; - let asc = typeof kwargs["ascending"] == "undefined" ? true : kwargs["ascending"]; - let index_val = this.index; - - let [data, index] = this.__sort_by(index_val, index_val, asc); - - if (inplace) { - this.__update_frame_in_place(data, null, null, index, null); - } else { - let df = this.copy(); - - df.__update_frame_in_place(data, null, null, index, null); - - return df; - } - } - - sort_values(kwargs = {}) { - if (!utils.__key_in_object(kwargs, "by")) { - throw Error(`use col_name to specify column name`); - } - - let inplace = typeof kwargs["inplace"] == "undefined" ? false : kwargs["inplace"]; - let asc = typeof kwargs["ascending"] == "undefined" ? true : kwargs["ascending"]; - let index_val = this.index; - let column_val = this.column(kwargs["by"]).values; - - let [data, index] = this.__sort_by(column_val, index_val, asc); - - if (inplace) { - this.__update_frame_in_place(data, null, null, index, null); - } else { - let df = this.copy(); - - df.__update_frame_in_place(data, null, null, index, null); - - return df; - } - } - - __set_col_property(self, col_vals, col_names, old_col_names) { - old_col_names.forEach(name => { - delete self[name]; - }); - col_vals.forEach((col, i) => { - Object.defineProperty(self, col_names[i], { - get() { - return new _series.Series(col, { - columns: col_names[i], - index: self.index - }); - }, - - set(value) { - this.addColumn({ - column: col_names[i], - value: value - }); - } - - }); - }); - } - - __update_frame_in_place(row_data, column_names, col_obj, index, dtypes) { - if (row_data != undefined) { - this.data = row_data; - } else { - if (col_obj != undefined) { - let _res = utils._get_row_and_col_values(col_obj); - - this.data = _res[0]; - this.columns = _res[1]; - column_names = _res[1]; - } - } - - if (col_obj != undefined) { - this.col_data = Object.values(col_obj); - this.columns = Object.keys(col_obj); - column_names = Object.keys(col_obj); - } else { - if (row_data != undefined) { - this.col_data = utils.__get_col_values(row_data); - } - } - - if (column_names != undefined) { - this.columns = column_names; - } - - if (index != undefined) { - this.index_arr = index; - } - - if (dtypes != undefined) { - this.col_types = dtypes; - } - } - - __sort_by(col_value, df_index, asc) { - let values = this.values; - - let sorted_val = utils.__sort(col_value, asc); - - let duplicate_obj = utils.__get_duplicate(col_value); - - let data = []; - let indexs = []; - - for (let row_i = 0; row_i < sorted_val.length; row_i++) { - let val = sorted_val[row_i]; - let index = null; - - if (duplicate_obj.hasOwnProperty(val)) { - index = duplicate_obj[val]["index"][0]; - duplicate_obj[val]["index"].splice(0, 1); - } else { - index = col_value.indexOf(val); - } - - data.push(values[index]); - indexs.push(df_index[index]); - } - - return [data, indexs]; - } - - append(val) { - let df2 = null; - - if (Array.isArray(val)) { - if (Array.isArray(val[0])) { - if (val[0].length != this.shape[1]) { - throw Error(`length Mixmatch: The lenght of provided value (${val.length}) does not match the original DataFrame (${this.shape[1]})`); - } - - df2 = new DataFrame(val); - } - } else if (utils.__is_object(val)) { - df2 = new DataFrame(val); - } else if (val instanceof DataFrame) { - df2 = val.copy(); - } - - let concat_df = (0, _concat.concat)({ - df_list: [this, df2], - axis: 0 - }); - return concat_df; - } - -} - -exports.DataFrame = DataFrame; \ No newline at end of file diff --git a/danfojs-node/dist/core/generic.js b/danfojs-node/dist/core/generic.js deleted file mode 100644 index 31c48532..00000000 --- a/danfojs-node/dist/core/generic.js +++ /dev/null @@ -1,397 +0,0 @@ -"use strict"; - -var _interopRequireWildcard = require("@babel/runtime/helpers/interopRequireWildcard"); - -Object.defineProperty(exports, "__esModule", { - value: true -}); -exports.default = void 0; - -var tf = _interopRequireWildcard(require("@tensorflow/tfjs-node")); - -var _table = require("table"); - -var _utils = require("./utils"); - -var _config = require("../config/config"); - -const utils = new _utils.Utils(); -const config = new _config.Configs(); - -class NDframe { - constructor(data, kwargs = {}) { - this.kwargs = kwargs; - - if (data instanceof tf.Tensor) { - data = data.arraySync(); - } - - if (utils.__is_1D_array(data)) { - this.series = true; - - this._read_array(data); - } else { - this.series = false; - - if (utils.__is_object(data[0])) { - this._read_object(data, 1); - } else if (utils.__is_object(data)) { - this._read_object(data, 2); - } else if (Array.isArray(data[0]) || utils.__is_number(data[0]) || utils.__is_string(data[0])) { - this._read_array(data); - } else { - throw new Error("File format not supported"); - } - } - } - - _read_array(data) { - this.data = utils.__replace_undefined_with_NaN(data, this.series); - this.row_data_tensor = tf.tensor(this.data); - - if (this.series) { - this.col_data = [this.values]; - } else { - this.col_data = utils.__get_col_values(this.data); - } - - this.col_data_tensor = tf.tensor(this.col_data); - - if ("index" in this.kwargs) { - this.__set_index(this.kwargs["index"]); - } else { - this.index_arr = [...Array(this.row_data_tensor.shape[0]).keys()]; - } - - if (this.ndim == 1) { - if ("columns" in this.kwargs) { - this.columns = this.kwargs["columns"]; - } else { - this.columns = ["0"]; - } - } else { - if ("columns" in this.kwargs) { - if (this.kwargs["columns"].length == Number(this.row_data_tensor.shape[1])) { - this.columns = this.kwargs["columns"]; - } else { - throw `Column length mismatch. You provided a column of length ${this.kwargs["columns"].length} but data has lenght of ${this.row_data_tensor.shape[1]}`; - } - } else { - this.columns = [...Array(this.row_data_tensor.shape[1]).keys()]; - } - } - - if ("dtypes" in this.kwargs) { - this._set_col_types(this.kwargs["dtypes"], false); - } else { - this._set_col_types(null, true); - } - } - - _read_object(data, type) { - if (type == 2) { - let [row_arr, col_names] = utils._get_row_and_col_values(data); - - this.kwargs["columns"] = col_names; - - this._read_array(row_arr); - } else { - let data_arr = data.map(item => { - return Object.values(item); - }); - this.data = utils.__replace_undefined_with_NaN(data_arr, this.series); - this.row_data_tensor = tf.tensor(this.data); - this.kwargs["columns"] = Object.keys(Object.values(data)[0]); - - if (this.series) { - this.col_data = [this.values]; - } else { - this.col_data = utils.__get_col_values(this.data); - } - - this.col_data_tensor = tf.tensor(this.col_data); - - if ("index" in this.kwargs) { - this.__set_index(this.kwargs["index"]); - } else { - this.index_arr = [...Array(this.row_data_tensor.shape[0]).keys()]; - } - - if (this.ndim == 1) { - if (!this.kwargs["columns"]) { - this.columns = ["0"]; - } else { - this.columns = this.kwargs["columns"]; - } - } else { - if ("columns" in this.kwargs) { - if (this.kwargs["columns"].length == Number(this.row_data_tensor.shape[1])) { - this.columns = this.kwargs["columns"]; - } else { - throw `Column length mismatch. You provided a column of length ${this.kwargs["columns"].length} but data has column length of ${this.row_data_tensor.shape[1]}`; - } - } else { - this.columns = [...Array(this.row_data_tensor.shape[1]).keys()]; - } - } - - if ("dtypes" in this.kwargs) { - this._set_col_types(this.kwargs["dtypes"], false); - } else { - this._set_col_types(null, true); - } - } - } - - _set_col_types(dtypes, infer) { - const __supported_dtypes = ["float32", "int32", "string", "boolean"]; - - if (infer) { - if (this.series) { - this.col_types = utils.__get_t(this.values); - } else { - this.col_types = utils.__get_t(this.col_data); - } - } else { - if (this.series) { - this.col_types = dtypes; - } else { - if (dtypes.length != this.columns.length) { - throw new Error(`length Mixmatch: Length of specified dtypes is ${dtypes.length}, but length of columns is ${this.columns.length}`); - } - - if (Array.isArray(dtypes)) { - dtypes.forEach((type, indx) => { - if (!__supported_dtypes.includes(type)) { - throw new Error(`dtype error: dtype specified at index ${indx} is not supported`); - } - }); - this.col_types = dtypes; - } else { - throw new Error(`dtypes must be an Array of types`); - } - } - } - } - - get dtypes() { - return this.col_types; - } - - get ndim() { - if (this.series) { - return 1; - } else { - return this.row_data_tensor.shape.length; - } - } - - get axes() { - let axes = { - index: this.index, - columns: this.columns - }; - return axes; - } - - get index() { - return this.index_arr; - } - - __set_index(labels) { - if (!Array.isArray(labels)) { - throw Error("Value Error: index must be an array"); - } - - if (labels.length > this.shape[0] || labels.length < this.shape[0]) { - throw Error("Value Error: length of labels must match row shape of data"); - } - - this.index_arr = labels; - } - - __reset_index() { - let new_idx = [...Array(this.values.length).keys()]; - this.index_arr = new_idx; - } - - get shape() { - if (this.series) { - return [this.values.length, 1]; - } else { - return this.row_data_tensor.shape; - } - } - - get values() { - return this.data; - } - - get column_names() { - return this.columns; - } - - __isna() { - let new_arr = []; - - if (this.series) { - this.values.map(val => { - if (val == NaN) { - new_arr.push(true); - } else if (isNaN(val) && typeof val != "string") { - new_arr.push(true); - } else { - new_arr.push(false); - } - }); - } else { - let row_data = this.values; - row_data.map(arr => { - let temp_arr = []; - arr.map(val => { - if (val == NaN) { - temp_arr.push(true); - } else if (isNaN(val) && typeof val != "string") { - temp_arr.push(true); - } else { - temp_arr.push(false); - } - }); - new_arr.push(temp_arr); - }); - } - - return new_arr; - } - - get size() { - return this.row_data_tensor.size; - } - - async to_csv() { - if (this.series) { - let csv = this.values.join(","); - return csv; - } else { - let records = this.values; - let header = this.column_names.join(","); - let csv_str = `${header}\n`; - records.forEach(val => { - let row = `${val.join(",")}\n`; - csv_str += row; - }); - return csv_str; - } - } - - async to_json() { - if (this.series) { - let obj = {}; - obj[this.column_names[0]] = this.values; - let json = JSON.stringify(obj); - return json; - } else { - let values = this.values; - let header = this.column_names; - let json_arr = []; - values.forEach(val => { - let obj = {}; - header.forEach((h, i) => { - obj[h] = val[i]; - }); - json_arr.push(obj); - }); - return JSON.stringify(json_arr); - } - } - - toString() { - let table_width = config.get_width; - let table_truncate = config.get_truncate; - let max_row = config.get_max_row; - let max_col_in_console = config.get_max_col_in_console; - let data_arr = []; - let table_config = {}; - let col_len = this.columns.length; - let header = []; - - if (col_len > max_col_in_console) { - let first_4_cols = this.columns.slice(0, 4); - let last_3_cols = this.columns.slice(col_len - 4); - header = [""].concat(first_4_cols).concat(["..."]).concat(last_3_cols); - let sub_idx, values_1, value_2; - - if (this.values.length > max_row) { - let df_subset_1 = this.iloc({ - rows: [`0:${max_row}`], - columns: ["0:4"] - }); - let df_subset_2 = this.iloc({ - rows: [`0:${max_row}`], - columns: [`${col_len - 4}:`] - }); - sub_idx = this.index.slice(0, max_row); - values_1 = df_subset_1.values; - value_2 = df_subset_2.values; - } else { - let df_subset_1 = this.iloc({ - rows: ["0:"], - columns: ["0:4"] - }); - let df_subset_2 = this.iloc({ - rows: ["0:"], - columns: [`${col_len - 4}:`] - }); - sub_idx = this.index.slice(0, max_row); - values_1 = df_subset_1.values; - value_2 = df_subset_2.values; - } - - sub_idx.map((val, i) => { - let row = [val].concat(values_1[i]).concat(["..."]).concat(value_2[i]); - data_arr.push(row); - }); - } else { - header = [""].concat(this.columns); - let idx, values; - - if (this.values.length > max_row) { - let data = this.loc({ - rows: [`0:${max_row}`], - columns: this.columns - }); - idx = data.index; - values = data.values; - } else { - values = this.values; - idx = this.index; - } - - idx.forEach((val, i) => { - let row = [val].concat(values[i]); - data_arr.push(row); - }); - } - - table_config[0] = 10; - - for (let index = 1; index < header.length; index++) { - table_config[index] = { - width: table_width, - truncate: table_truncate - }; - } - - let table_data = [header].concat(data_arr); - return (0, _table.table)(table_data, { - columns: table_config - }); - } - - print() { - console.log(this + ""); - } - -} - -exports.default = NDframe; \ No newline at end of file diff --git a/danfojs-node/dist/core/get_dummies.js b/danfojs-node/dist/core/get_dummies.js deleted file mode 100644 index cfd7748c..00000000 --- a/danfojs-node/dist/core/get_dummies.js +++ /dev/null @@ -1,134 +0,0 @@ -"use strict"; - -Object.defineProperty(exports, "__esModule", { - value: true -}); -exports.get_dummies = void 0; - -var _frame = require("./frame"); - -var _utils = require("./utils"); - -const utils = new _utils.Utils(); - -function oneHot(in_data, prefix, prefix_sep) { - let data_set = new Set(in_data); - let labels = Array.from(data_set); - let prefix_labels = null; - - if (prefix) { - prefix_labels = labels.map(x => { - return prefix + prefix_sep + x; - }); - } else { - prefix_labels = labels.map(x => { - return x; - }); - } - - let onehot_data = utils.__zeros(in_data.length, labels.length); - - for (let i = 0; i < in_data.length; i++) { - let elem = in_data[i]; - let elem_index = labels.indexOf(elem); - onehot_data[i][elem_index] = 1; - } - - return [onehot_data, prefix_labels]; -} - -function get_dummy(kwargs = {}) { - utils.__in_object(kwargs, "data", "data not provided"); - - let prefix = kwargs["prefix"] || null; - let prefix_sep = kwargs["prefix_sep"] || ["_"]; - let columns = kwargs["columns"] || null; - let is_dataframe = false; - let in_data = null; - - if (Array.isArray(kwargs["data"])) { - in_data = kwargs["data"]; - } else if (kwargs["data"] instanceof _frame.DataFrame) { - in_data = kwargs["data"]; - is_dataframe = true; - } else { - in_data = kwargs["data"].values; - } - - if (!is_dataframe) { - let [onehot_data, prefix_labels] = oneHot(in_data, prefix, prefix_sep); - return new _frame.DataFrame(onehot_data, { - columns: prefix_labels - }); - } else { - let column_index = []; - - if (!columns) { - columns = []; - in_data.col_types.map((x, i) => { - if (x == "string") { - let name_column = in_data.columns[i]; - columns.push(name_column); - column_index.push(i); - } - }); - } else { - columns.forEach(x => { - let col_idx = columns.indexOf(x); - column_index.push(col_idx); - }); - } - - if (prefix) { - if (Array.isArray(prefix)) { - if (prefix.length != columns.length) { - throw new Error("prefix must be the same length with the number of onehot encoding column"); - } - } else { - throw new Error("prefix for dataframe must be an array"); - } - } else { - prefix = columns; - } - - let df_data = in_data.values; - let df_columns = in_data.columns; - let col_data = in_data.col_data; - let column_data = []; - column_index.forEach(x => { - column_data.push(col_data[x]); - }); - let one_hotColumns = []; - let one_hotData = []; - column_data.forEach((data, i) => { - let [onehot_data, prefix_labels] = oneHot(data, prefix[i], prefix_sep); - one_hotColumns.push(...prefix_labels); - - if (one_hotData.length == 0) { - one_hotData.push(...onehot_data); - } else { - onehot_data.forEach((x, i) => { - one_hotData[i].push(...x); - }); - } - }); - let final_data = df_data.map((elem, i) => { - let ele = elem.slice(); - - let dt = utils.__remove_arr(ele, column_index); - - dt.push(...one_hotData[i]); - return dt; - }); - - let final_columns = utils.__remove_arr(df_columns, column_index); - - final_columns.push(...one_hotColumns); - return new _frame.DataFrame(final_data, { - columns: final_columns - }); - } -} - -const get_dummies = get_dummy; -exports.get_dummies = get_dummies; \ No newline at end of file diff --git a/danfojs-node/dist/core/groupby.js b/danfojs-node/dist/core/groupby.js deleted file mode 100644 index 75642fef..00000000 --- a/danfojs-node/dist/core/groupby.js +++ /dev/null @@ -1,294 +0,0 @@ -"use strict"; - -Object.defineProperty(exports, "__esModule", { - value: true -}); -exports.GroupBy = void 0; - -var _frame = require("./frame"); - -var _utils = require("./utils"); - -var _series = require("./series"); - -const utils = new _utils.Utils(); - -class GroupBy { - constructor(col_dict, key_col, data, column_name, col_dtype) { - this.key_col = key_col; - this.col_dict = col_dict; - this.data = data; - this.column_name = column_name; - this.data_tensors = {}; - this.col_dtype = col_dtype; - } - - group() { - for (const value of this.data) { - const col_indexes = this.key_col.map(key => this.column_name.indexOf(key)); - const col_values = col_indexes.map(idx => value[idx]); - let sub_col_dict = this.col_dict; - - for (const col_value of col_values) { - if (!(col_value in sub_col_dict)) break; - if (col_value === col_values[col_values.length - 1]) sub_col_dict[col_value].push(value);else sub_col_dict = sub_col_dict[col_value]; - } - } - - const self = this; - - function dfs(sub_col_dict, sub_data_tensors) { - for (const [key, value] of Object.entries(sub_col_dict)) { - if (Array.isArray(value)) { - if (value.length === 0) delete sub_col_dict[key];else sub_data_tensors[key] = new _frame.DataFrame(value, { - columns: self.column_name - }); - } else { - if (!(key in sub_data_tensors)) sub_data_tensors[key] = {}; - dfs(value, sub_data_tensors[key]); - } - } - } - - dfs(this.col_dict, this.data_tensors); - return this; - } - - col(col_names) { - this.selected_column = col_names; - - if (Array.isArray(col_names)) { - for (let i = 0; i < col_names.length; i++) { - let col_name = col_names[i]; - - if (!this.column_name.includes(col_name)) { - throw new Error(`Column ${col_name} does not exist in groups`); - } - } - } else { - throw new Error(`Col_name must be an array of column`); - } - - const group_col = {}; - - function dfs(sub_data_tensors, sub_group_col) { - for (const [key, value] of Object.entries(sub_data_tensors)) { - if (value instanceof _frame.DataFrame) { - sub_group_col[key] = col_names.map(col_name => value.column(col_name)); - } else { - sub_group_col[key] = {}; - dfs(value, sub_group_col[key]); - } - } - } - - dfs(this.data_tensors, group_col); - const gp = new GroupBy(null, this.key_col, null, col_names, this.col_dtype); - gp.group_col = group_col; - gp.group_col_name = col_names; - return gp; - } - - arithemetic(operation) { - let ops_name = ["mean", "sum", "count", "mode", "std", "var", "cumsum", "cumprod", "cummax", "cummin"]; - let ops_map = { - "mean": "mean()", - "sum": "sum()", - "mode": "mode()", - "count": "count()", - "std": "std()", - "var": "var()", - "cumsum": "cumsum().values", - "cumprod": "cumprod().values", - "cummax": "cummax().values", - "cummin": "cummin().values" - }; - const is_array_operation = Array.isArray(operation); - const count_group = {}; - - function dfs(sub_count_group, sub_group_col) { - for (const [key, value] of Object.entries(sub_group_col)) { - if (Array.isArray(value)) { - sub_count_group[key] = []; - let data; - - if (is_array_operation) { - for (let i = 0; i < value.length; i++) { - const op = operation[i]; - - if (!ops_name.includes(op)) { - throw new Error("operation does not exist"); - } - - data = eval(`value[i].${ops_map[op]}`); - sub_count_group[key].push(data); - } - } else { - value.forEach(v => { - data = eval(`v.${operation}`); - sub_count_group[key].push(data); - }); - } - } else { - sub_count_group[key] = {}; - dfs(sub_count_group[key], value); - } - } - } - - dfs(count_group, this.group_col); - return count_group; - } - - operations(ops, name) { - if (!this.group_col) { - let column = this.column_name.filter(val => !this.key_col.includes(val)); - let col_gp = this.col(column); - let value = col_gp.arithemetic(ops); - let df = col_gp.to_DataFrame(col_gp.key_col, col_gp.group_col_name, value, name); - return df; - } else { - let value = this.arithemetic(ops); - let df = this.to_DataFrame(this.key_col, this.group_col_name, value, name); - return df; - } - } - - count() { - return this.operations("count()", "count"); - } - - sum() { - return this.operations("sum()", "sum"); - } - - std() { - return this.operations("std()", "std"); - } - - var() { - return this.operations("var()", "var"); - } - - mean() { - return this.operations("mean()", "mean"); - } - - cumsum() { - return this.operations("cumsum().values", "cumsum"); - } - - cummax() { - return this.operations("cummax().values", "cummax"); - } - - cumprod() { - return this.operations("cumprod().values", "cumprod"); - } - - cummin() { - return this.operations("cummin().values", "cummin"); - } - - max() { - return this.operations("max()", "max"); - } - - min() { - return this.operations("min()", "min"); - } - - get_groups(key) { - if (this.key_col.length < 2) return this.data_tensors[key]; - if (key.length !== this.key_col.length) throw new Error("specify the group by column"); - - utils.__is_object(this.data_tensors, key[0], `Key Error: ${key[0]} not in object`); - - const last_key = key[key.length - 1]; - let sub_data_tensors = this.data_tensors; - - for (const k of key) { - if (k === last_key) return sub_data_tensors[k];else sub_data_tensors = sub_data_tensors[k]; - } - } - - agg(kwargs = {}) { - let columns = Object.keys(kwargs); - let operations = columns.map(x => { - return kwargs[x].toLocaleLowerCase(); - }); - let col_gp = this.col(columns); - let data = col_gp.arithemetic(operations); - let df = this.to_DataFrame(col_gp.key_col, col_gp.group_col_name, data, operations); - return df; - } - - to_DataFrame(key_col, col, data, ops) { - const df_data = []; - - function concatPathAndNode(path, node, col_dtype) { - if (Array.isArray(node)) { - if (Array.isArray(node[0])) { - const transposed_node = node[0].map((_, colIndex) => node.map(row => row[colIndex])); - - for (const n_array of transposed_node) df_data.push(path.concat(n_array)); - } else df_data.push(path.concat(node)); - } else { - for (const [k, child] of Object.entries(node)) { - const sanitized_k = col_dtype[0] === "string" ? k : parseInt(k); - concatPathAndNode(path.concat([sanitized_k]), child, col_dtype.slice(1)); - } - } - } - - concatPathAndNode([], data, this.col_dtype); - const column = [...key_col]; - const group_col = col.slice().map((x, i) => { - if (Array.isArray(ops)) { - return `${x}_${ops[i]}`; - } - - return `${x}_${ops}`; - }); - column.push(...group_col); - return new _frame.DataFrame(df_data, { - columns: column - }); - } - - apply(callable) { - let df_data; - let column; - - if (!this.group_col) { - column = this.column_name.filter(val => !this.key_col.includes(val)); - const col_gp = this.col(column); - df_data = col_gp.group_col; - } else { - column = this.group_col_name; - df_data = this.group_col; - } - - const count_group = {}; - - function recursiveCount(sub_df_data, sub_count_group) { - for (const [key, value] of Object.entries(sub_df_data)) { - if (Array.isArray(value)) { - sub_count_group[key] = value.map(callable_value => { - const callable_rslt = callable(callable_value); - if (callable_rslt instanceof _frame.DataFrame || callable_rslt instanceof _series.Series) return callable_rslt.values;else return callable_rslt; - }); - } else { - sub_count_group[key] = {}; - recursiveCount(value, sub_count_group[key]); - } - } - } - - recursiveCount(df_data, count_group); - return this.to_DataFrame(this.key_col, column, count_group, "apply"); - } - -} - -exports.GroupBy = GroupBy; \ No newline at end of file diff --git a/danfojs-node/dist/core/indexing.js b/danfojs-node/dist/core/indexing.js deleted file mode 100644 index 11d26588..00000000 --- a/danfojs-node/dist/core/indexing.js +++ /dev/null @@ -1,185 +0,0 @@ -"use strict"; - -Object.defineProperty(exports, "__esModule", { - value: true -}); -exports.indexLoc = void 0; - -var _utils = require("./utils"); - -const utils = new _utils.Utils(); - -const indexLoc = (ndframe, kwargs) => { - let rows = null; - let columns = null; - let isColumnSplit = false; - - if (Object.prototype.hasOwnProperty.call(kwargs, "rows")) { - if (Array.isArray(kwargs["rows"])) { - if (kwargs["rows"].length == 1 && typeof kwargs["rows"][0] == "string") { - if (kwargs["rows"][0].includes(":")) { - let column_split = kwargs["rows"][0].split(":"); - - if (kwargs['type'] == 'loc') { - let start, end; - - if (isNaN(Number(column_split[0]))) { - start = ndframe.index.indexOf(column_split[0]); - } else { - start = Number(column_split[0]); - } - - if (isNaN(Number(column_split[1]))) { - end = ndframe.index.lastIndexOf(column_split[1]) - 1 || ndframe.values.length - 1; - } else { - end = Number(column_split[1]) - 1 || ndframe.values.length - 1; - } - - rows = utils.__range(start, end); - } else { - let start = parseInt(column_split[0]) || 0; - let end = parseInt(column_split[1]) - 1 || ndframe.values.length - 1; - - if (typeof start == "number" && typeof end == "number") { - rows = utils.__range(start, end); - } - } - } else { - if (kwargs["type"] == "loc") { - let row_idx = []; - ndframe.index.map((idx, i) => { - if (kwargs['rows'][0] == idx) { - row_idx.push(i); - } - }); - rows = row_idx; - } else { - throw new Error("Slice index must be separated by ':'"); - } - } - } else { - if (kwargs["type"] == "loc") { - let row_idx = []; - ndframe.index.map((idx, i) => { - if (kwargs['rows'].includes(idx)) { - row_idx.push(i); - } - }); - rows = row_idx; - } else { - rows = kwargs["rows"]; - } - } - } else { - throw new Error("rows parameter must be a Array"); - } - } else { - if (kwargs["type"] == "loc") { - rows = utils.__range(0, Number(ndframe.shape[0]) - 1); - } else { - rows = utils.__range(0, Number(ndframe.shape[0]) - 1); - } - } - - if (Object.prototype.hasOwnProperty.call(kwargs, "columns")) { - if (Array.isArray(kwargs["columns"])) { - if (kwargs["columns"].length == 1 && kwargs["columns"][0].includes(":")) { - let column_split = kwargs["columns"][0].split(":"); - let start, end; - - if (kwargs["type"] == "iloc") { - if (column_split[0] == "") { - start = 0; - } else { - start = parseInt(column_split[0]) || 0; - } - - if (column_split[1] == "") { - end = parseInt(ndframe.columns.length - 1); - } else { - end = parseInt(column_split[1]) - 1 === 0 ? 0 : parseInt(column_split[1]) - 1; - } - } else { - start = parseInt(ndframe.columns.indexOf(column_split[0])); - end = parseInt(ndframe.columns.indexOf(column_split[1])) - 1; - } - - if (typeof start == "number" && typeof end == "number") { - columns = utils.__range(start, end); - isColumnSplit = true; - } - } else { - columns = kwargs["columns"]; - } - } else { - throw new Error("columns must be a list"); - } - } else { - if (kwargs["type"] == "loc") { - columns = ndframe.column_names; - } else { - columns = utils.__range(0, Number(ndframe.shape[1]) - 1); - } - } - - let data_values = ndframe.values; - let new_data = []; - - for (var index = 0; index < rows.length; index++) { - let row_val = rows[index]; - let max_rowIndex = data_values.length - 1; - - if (row_val > max_rowIndex) { - throw new Error(`Specified row index ${row_val} is bigger than maximum row index of ${max_rowIndex}`); - } - - if (Array.isArray(data_values[0])) { - let value = data_values[row_val]; - let row_data = []; - - for (var i in columns) { - var col_index; - - if (kwargs["type"] == "loc" && !isColumnSplit) { - col_index = ndframe.columns.indexOf(columns[i]); - - if (col_index == -1) { - throw new Error(`Column ${columns[i]} does not exist`); - } - } else { - col_index = columns[i]; - let max_colIndex = ndframe.columns.length - 1; - - if (col_index > max_colIndex) { - throw new Error(`column index ${col_index} is bigger than ${max_colIndex}`); - } - } - - let elem = value[col_index]; - row_data.push(elem); - } - - new_data.push(row_data); - } else { - new_data.push(data_values[row_val]); - } - } - - let column_names = []; - - if (kwargs["type"] == "iloc" || isColumnSplit) { - columns.map(col => { - column_names.push(ndframe.columns[col]); - }); - } else { - column_names = columns; - } - - let final_row = []; - rows.forEach(i => { - final_row.push(ndframe.index[i]); - }); - return [new_data, column_names, final_row]; -}; - -exports.indexLoc = indexLoc; \ No newline at end of file diff --git a/danfojs-node/dist/core/merge.js b/danfojs-node/dist/core/merge.js deleted file mode 100644 index 13b79f60..00000000 --- a/danfojs-node/dist/core/merge.js +++ /dev/null @@ -1,276 +0,0 @@ -"use strict"; - -Object.defineProperty(exports, "__esModule", { - value: true -}); -exports.merge = exports.Merge = void 0; - -var _frame = require("./frame"); - -var _utils = require("./utils"); - -const utils = new _utils.Utils(); - -class Merge { - constructor(kwargs) { - utils.__in_object(kwargs, "left"); - - utils.__in_object(kwargs, "right"); - - utils.__in_object(kwargs, "on"); - - if (!utils.__key_in_object(kwargs, "how")) { - this.how = 'inner'; - kwargs['how'] = 'inner'; - } else { - this.how = kwargs['how']; - } - - this.left = null; - this.right = null; - this.on = null; - let how_keys = ["outer", "inner", "left", "right"]; - - if (kwargs["left"] instanceof _frame.DataFrame && kwargs["right"] instanceof _frame.DataFrame) { - this.left = kwargs["left"]; - this.right = kwargs["right"]; - } else { - throw new Error("The left and right key value must be a dataFrame"); - } - - if (Array.isArray(kwargs["on"])) { - this.on = kwargs["on"]; - } else { - throw new Error("key 'on' must be a list"); - } - - if (how_keys.includes(kwargs["how"])) { - this.how = kwargs["how"]; - } else { - throw new Error(`${kwargs["how"]} specify in keyword how is not recognise`); - } - - this.left_col_index = []; - this.right_col_index = []; - - for (let i = 0; i < this.on.length; i++) { - if (this.left.columns.includes(this.on[i]) && this.right.columns.includes(this.on[i])) { - let left_index = this.left.columns.indexOf(this.on[i]); - let right_index = this.right.columns.indexOf(this.on[i]); - this.left_col_index.push(left_index); - this.right_col_index.push(right_index); - } - } - - this.left_key_dict = {}; - this.right_key_dict = {}; - let left_values = this.left.values; - let right_values = this.right.values; - - for (let i = 0; i < left_values.length; i++) { - let left_value = left_values[i]; - let left_key_comb_values = []; - - for (let j = 0; j < this.left_col_index.length; j++) { - let index = this.left_col_index[j]; - left_key_comb_values.push(left_value[index]); - } - - let left_key_comb = left_key_comb_values.join('_'); - let self = this; - let left_value_filter = left_value.filter(function (val, index) { - return !self.left_col_index.includes(index); - }); - - if (utils.__key_in_object(this.left_key_dict, left_key_comb)) { - this.left_key_dict[left_key_comb].filters.push(left_value_filter); - } else { - this.left_key_dict[left_key_comb] = { - filters: [left_value_filter], - comb_values: left_key_comb_values - }; - } - } - - for (let i = 0; i < right_values.length; i++) { - let right_value = right_values[i]; - let right_key_comb_values = []; - - for (let j = 0; j < this.right_col_index.length; j++) { - let index = this.right_col_index[j]; - right_key_comb_values.push(right_value[index]); - } - - let right_key_comb = right_key_comb_values.join('_'); - let self = this; - let right_value_filter = right_value.filter(function (val, index) { - return !self.right_col_index.includes(index); - }); - - if (utils.__key_in_object(this.right_key_dict, right_key_comb)) { - this.right_key_dict[right_key_comb].filters.push(right_value_filter); - } else { - this.right_key_dict[right_key_comb] = { - filters: [right_value_filter], - comb_values: right_key_comb_values - }; - } - } - - this.__create_columns(); - - let data = null; - - switch (this.how) { - case "outer": - data = this.outer(); - break; - - case "inner": - data = this.inner(); - break; - - case "left": - data = this.left_merge(); - break; - - case "right": - data = this.right_merge(); - break; - } - - let df = new _frame.DataFrame(data = data, { - columns: this.columns - }); - return df; - } - - __create_columns() { - let self = this; - self.left_col = self.left.columns.filter((val, index) => { - return !self.left_col_index.includes(index); - }); - self.right_col = self.right.columns.filter((val, index) => { - return !self.right_col_index.includes(index); - }); - self.columns = [...self.on]; - let column_duplicate = {}; - let temp_column = [...self.left_col]; - temp_column.push(...self.right_col); - - for (let i = 0; i < temp_column.length; i++) { - let col = temp_column[i]; - - if (utils.__key_in_object(column_duplicate, col)) { - let col_name = `${col}_${column_duplicate[col]}`; - self.columns.push(col_name); - column_duplicate[col] += 1; - } else { - self.columns.push(col); - column_duplicate[col] = 1; - } - } - } - - outer() { - let keys = Object.keys(this.left_key_dict); - keys.push(...Object.keys(this.right_key_dict)); - keys = Array.from(new Set(keys)); - let data = this.basic(keys); - return data; - } - - inner() { - let left_keys = Object.keys(this.left_key_dict); - let right_keys = Object.keys(this.right_key_dict); - let keys = left_keys.filter(val => { - return right_keys.includes(val); - }); - let data = this.basic(keys); - return data; - } - - left_merge() { - let keys = Object.keys(this.left_key_dict); - let data = this.basic(keys); - return data; - } - - right_merge() { - let keys = Object.keys(this.right_key_dict); - let data = this.basic(keys); - return data; - } - - basic(keys) { - let data = []; - - for (let i = 0; i < keys.length; i++) { - let key = keys[i]; - let key_array = key.split("_").filter(val => { - return val != ""; - }); - - if (utils.__key_in_object(this.left_key_dict, key)) { - let left_row = this.left_key_dict[key].filters; - let key_array = this.left_key_dict[key].comb_values; - - for (let left_i = 0; left_i < left_row.length; left_i++) { - let left_row_row = left_row[left_i]; - - if (utils.__key_in_object(this.right_key_dict, key)) { - let right_row = this.right_key_dict[key].filters; - - for (let r_i = 0; r_i < right_row.length; r_i++) { - let right_row_row = right_row[r_i]; - let inner_data = key_array.slice(0); - inner_data.push(...left_row_row); - inner_data.push(...right_row_row); - data.push(inner_data); - } - } else { - let nan_array = Array(this.right_col.length); - - for (let i = 0; i < this.right_col.length; i++) { - nan_array[i] = NaN; - } - - let inner_data = key_array.slice(0); - inner_data.push(...left_row_row); - inner_data.push(...nan_array); - data.push(inner_data); - } - } - } else { - let right_row = this.right_key_dict[key].filters; - let key_array = this.right_key_dict[key].comb_values; - - for (let i = 0; i < right_row.length; i++) { - let right_row_row = right_row[i]; - let nan_array = Array(this.left_col.length); - - for (let j = 0; j < nan_array.length; j++) { - nan_array[j] = NaN; - } - - let inner_data = key_array.slice(0); - inner_data.push(...nan_array); - inner_data.push(...right_row_row); - data.push(inner_data); - } - } - } - - return data; - } - -} - -exports.Merge = Merge; - -const merge = kwargs => { - let merge = new Merge(kwargs); - return merge; -}; - -exports.merge = merge; \ No newline at end of file diff --git a/danfojs-node/dist/core/series.js b/danfojs-node/dist/core/series.js deleted file mode 100644 index b22d8a10..00000000 --- a/danfojs-node/dist/core/series.js +++ /dev/null @@ -1,1046 +0,0 @@ -"use strict"; - -var _interopRequireDefault = require("@babel/runtime/helpers/interopRequireDefault"); - -var _interopRequireWildcard = require("@babel/runtime/helpers/interopRequireWildcard"); - -Object.defineProperty(exports, "__esModule", { - value: true -}); -exports.Series = void 0; - -var tf = _interopRequireWildcard(require("@tensorflow/tfjs-node")); - -var _mathjs = require("mathjs"); - -var _utils = require("./utils"); - -var _strings = require("./strings"); - -var _generic = _interopRequireDefault(require("./generic")); - -var _table = require("table"); - -var _config = require("../config/config"); - -var _timeseries = require("./timeseries"); - -var _indexing = require("./indexing"); - -const utils = new _utils.Utils(); -const config = new _config.Configs(); - -class Series extends _generic.default { - constructor(data, kwargs) { - if (Array.isArray(data[0]) || utils.__is_object(data[0])) { - data = utils.__convert_2D_to_1D(data); - super(data, kwargs); - } else { - super(data, kwargs); - } - } - - get tensor() { - return tf.tensor(this.values).asType(this.dtypes[0]); - } - - head(rows = 5) { - if (rows > this.shape[0] || rows < 1) { - return new Series(this.values, { - columns: this.column_names - }); - } else { - let data = this.values.slice(0, rows); - return new Series(data, { - columns: this.column_names - }); - } - } - - tail(rows = 5) { - if (rows > this.values.length || rows < 1) { - return new Series(this.values, { - columns: this.column_names - }); - } else { - let data = this.values.slice(this.shape[0] - rows); - let idx = this.index.slice(this.shape[0] - rows); - let sf = new Series(data, { - columns: this.column_names, - index: idx - }); - return sf; - } - } - - async sample(num = 5, seed = 1) { - if (num > this.shape[0]) { - throw new Error("Sample size n cannot be bigger than size of dataset"); - } - - if (num < -1 || num == 0) { - throw new Error("Sample size cannot be less than -1 or 0"); - } - - num = num === -1 ? this.shape[0] : num; - const shuffled_index = await tf.data.array(this.index).shuffle(num, seed).take(num).toArray(); - const sf = this.iloc(shuffled_index); - return sf; - } - - add(other) { - if (utils.__is_number(other)) { - let sum = this.row_data_tensor.add(other).arraySync(); - return new Series(sum, { - columns: this.column_names - }); - } else { - if (this.__check_series_op_compactibility) { - let sum = this.tensor.add(other.tensor).arraySync(); - return new Series(sum, { - columns: this.column_names - }); - } - } - } - - sub(other) { - if (utils.__is_number(other)) { - let sub = this.tensor.sub(other).arraySync(); - return new Series(sub, { - columns: this.column_names - }); - } else { - if (this.__check_series_op_compactibility) { - let sub = this.tensor.sub(other.tensor).arraySync(); - return new Series(sub, { - columns: this.column_names - }); - } - } - } - - mul(other) { - if (utils.__is_number(other)) { - let mul = this.tensor.mul(other).arraySync(); - return new Series(mul, { - columns: this.column_names - }); - } else { - if (this.__check_series_op_compactibility) { - let mul = this.tensor.mul(other.tensor).arraySync(); - return new Series(mul, { - columns: this.column_names - }); - } - } - } - - div(other, round = true) { - if (utils.__is_number(other)) { - let div_result = this.tensor.div(other); - return new Series(div_result.arraySync(), { - columns: this.column_names, - dtypes: [div_result.dtype] - }); - } else { - if (this.__check_series_op_compactibility) { - let dtype; - - if (round) { - dtype = "float32"; - } else { - dtype = "int32"; - } - - let tensor1 = this.tensor.asType(dtype); - let tensor2 = other.tensor.asType(dtype); - let result = tensor1.div(tensor2); - return new Series(result.arraySync(), { - columns: this.column_names, - dtypes: [result.dtype] - }); - } - } - } - - pow(other) { - if (utils.__is_number(other)) { - let pow_result = this.tensor.pow(other).arraySync(); - return new Series(pow_result, { - columns: this.column_names - }); - } else { - if (this.__check_series_op_compactibility) { - let pow_result = this.tensor.pow(other.tensor).arraySync(); - return new Series(pow_result, { - columns: this.column_names - }); - } - } - } - - mod(other) { - if (utils.__is_number(other)) { - let mod_result = this.tensor.mod(other).arraySync(); - return new Series(mod_result, { - columns: this.column_names - }); - } else { - if (this.__check_series_op_compactibility) { - let mod_result = this.tensor.mod(other.tensor).arraySync(); - return new Series(mod_result, { - columns: this.column_names - }); - } - } - } - - mean() { - utils._throw_str_dtype_error(this, 'mean'); - - let values = utils._remove_nans(this.values); - - let mean = tf.tensor(values).mean().arraySync(); - return mean; - } - - median() { - utils._throw_str_dtype_error(this, 'median'); - - let values = utils._remove_nans(this.values); - - let median_val = (0, _mathjs.median)(values); - return median_val; - } - - mode() { - utils._throw_str_dtype_error(this, 'median'); - - let values = utils._remove_nans(this.values); - - let modal_val = (0, _mathjs.mode)(values); - return modal_val; - } - - min() { - utils._throw_str_dtype_error(this, 'min'); - - let min = this.row_data_tensor.min().arraySync(); - return min; - } - - max() { - utils._throw_str_dtype_error(this, 'max'); - - let max = this.row_data_tensor.max().arraySync(); - return max; - } - - sum() { - utils._throw_str_dtype_error(this, 'sum'); - - if (this.dtypes[0] == "boolean") { - let temp = utils._remove_nans(this.values); - - let temp_sum = tf.tensor(temp).sum().arraySync(); - return Number(temp_sum); - } - - let temp = utils._remove_nans(this.values); - - let temp_sum = tf.tensor(temp).sum().arraySync(); - return Number(temp_sum.toFixed(5)); - } - - count() { - return utils.__count_nan(this.values, true, true); - } - - maximum(other) { - if (utils.__is_number(other)) { - let max_result = this.row_data_tensor.maximum(other); - return new Series(max_result.arraySync(), { - columns: this.column_names, - dtypes: max_result.dtype, - index: this.index - }); - } else { - if (this.__check_series_op_compactibility) { - let tensor1 = this.row_data_tensor; - let tensor2 = other.tensor; - let result = tensor1.maximum(tensor2).arraySync(); - return new Series(result, { - columns: this.column_names, - index: this.index - }); - } - } - } - - minimum(other) { - if (utils.__is_number(other)) { - let max_result = this.row_data_tensor.minimum(other); - return new Series(max_result.arraySync(), { - columns: this.column_names, - dtypes: max_result.dtype, - index: this.index - }); - } else { - if (this.__check_series_op_compactibility) { - let tensor1 = this.tensor; - let tensor2 = other.tensor; - let result = tensor1.minimum(tensor2).arraySync(); - return new Series(result, { - columns: this.column_names, - index: this.index - }); - } - } - } - - round(dp) { - if (utils.__is_undefined(dp)) { - let result = tf.round(this.row_data_tensor).arraySync(); - return new Series(result, { - columns: this.column_names, - index: this.index - }); - } else { - let result = utils.__round(this.values, dp, true); - - return new Series(result, { - columns: this.column_names, - index: this.index - }); - } - } - - std() { - utils._throw_str_dtype_error(this, 'std'); - - let values = utils._remove_nans(this.values); - - let std_val = (0, _mathjs.std)(values); - return std_val; - } - - var() { - utils._throw_str_dtype_error(this, 'std'); - - let values = utils._remove_nans(this.values); - - let var_val = (0, _mathjs.variance)(values); - return var_val; - } - - isna() { - let new_arr = this.__isna(); - - let sf = new Series(new_arr, { - index: this.index, - columns: this.column_names, - dtypes: ["boolean"] - }); - return sf; - } - - fillna(kwargs = {}) { - let params_needed = ["value", "inplace"]; - - utils._throw_wrong_params_error(kwargs, params_needed); - - kwargs['inplace'] = kwargs['inplace'] || false; - - if (!("value" in kwargs)) { - throw Error('Value Error: Must specify value to replace with'); - } - - let new_values = []; - this.values.forEach(val => { - if (isNaN(val) && typeof val != "string") { - new_values.push(kwargs['value']); - } else { - new_values.push(val); - } - }); - - if (kwargs['inplace']) { - this.data = new_values; - } else { - let sf = new Series(new_values, { - columns: this.column_names, - index: this.index, - dtypes: this.dtypes - }); - return sf; - } - } - - sort_values(kwargs = {}) { - let params_needed = ["inplace", "ascending"]; - - utils._throw_wrong_params_error(kwargs, params_needed); - - if (!('ascending' in kwargs)) { - kwargs['ascending'] = true; - } - - if (!('inplace' in kwargs)) { - kwargs['inplace'] = false; - } - - let sorted_values = []; - let arr_obj = [...this.values]; - - let range_idx = utils.__range(0, this.index.length - 1); - - let sorted_idx = utils._sort_arr_with_index(range_idx, arr_obj, this.dtypes[0]); - - sorted_idx.forEach(idx => { - sorted_values.push(this.values[idx]); - }); - - if (kwargs['ascending']) { - sorted_values = sorted_values.reverse(); - sorted_idx = sorted_idx.reverse(); - } - - if (kwargs['inplace']) { - this.data = sorted_values; - - this.__set_index(sorted_idx); - } else { - let sf = new Series(sorted_values, { - columns: this.column_names, - index: sorted_idx - }); - return sf; - } - } - - copy() { - let sf = new Series([...this.values], { - columns: [...this.column_names], - index: [...this.index], - dtypes: [...this.dtypes[0]] - }); - return sf; - } - - describe() { - if (this.dtypes[0] == "string") { - return null; - } else { - let index = ['count', 'mean', 'std', 'min', 'median', 'max', 'variance']; - let count = this.count(); - let mean = this.mean(); - let std = this.std(); - let min = this.min(); - let median = this.median(); - let max = this.max(); - let variance = this.var(); - let vals = [count, mean, std, min, median, max, variance]; - let sf = new Series(vals, { - columns: this.columns, - index: index - }); - return sf; - } - } - - reset_index(kwargs = {}) { - let params_needed = ["inplace"]; - - utils._throw_wrong_params_error(kwargs, params_needed); - - kwargs['inplace'] = kwargs['inplace'] || false; - - if (kwargs['inplace']) { - this.__reset_index(); - } else { - let sf = this.copy(); - - sf.__reset_index(); - - return sf; - } - } - - set_index(kwargs = {}) { - let params_needed = ["index", "inplace"]; - - utils._throw_wrong_params_error(kwargs, params_needed); - - kwargs['inplace'] = kwargs['inplace'] || false; - - if (!('index' in kwargs)) { - throw Error("Index ValueError: You must specify an array of index"); - } - - if (kwargs['index'].length != this.index.length) { - throw Error(`Index LengthError: Lenght of new Index array ${kwargs['index'].length} must match lenght of existing index ${this.index.length}`); - } - - if (kwargs['inplace']) { - this.index_arr = kwargs['index']; - } else { - let sf = this.copy(); - - sf.__set_index(kwargs['index']); - - return sf; - } - } - - __check_series_op_compactibility(other) { - if (utils.__is_undefined(other.series)) { - throw Error("param [other] must be a Series or a single value that can be broadcasted"); - } - - if (other.values.length != this.values.length) { - throw Error("Shape Error: Series shape do not match"); - } - - if (this.dtypes[0] != 'float' || this.dtypes[0] != 'int') { - throw Error(`dtype Error: Cannot perform operation on type ${this.dtypes[0]} with type ${other.dtypes[0]}`); - } - - if (other.dtypes[0] != 'float' || other.dtypes[0] != 'int') { - throw Error(`dtype Error: Cannot perform operation on type ${other.dtypes[0]} with type ${this.dtypes[0]}`); - } - - return true; - } - - map(callable) { - let is_callable = utils.__is_function(callable); - - let data = this.data.map(val => { - if (is_callable) { - return callable(val); - } else { - if (utils.__is_object(callable)) { - if (val in callable) { - return callable[val]; - } else { - return NaN; - } - } else { - throw new Error("callable must either be a function or an object"); - } - } - }); - let sf = new Series(data, { - columns: this.column_names, - index: this.index - }); - return sf; - } - - apply(callable) { - let is_callable = utils.__is_function(callable); - - if (!is_callable) { - throw new Error("the arguement most be a function"); - } - - let data = this.data.map(val => { - return callable(val); - }); - return new Series(data, { - columns: this.column_names, - index: this.index - }); - } - - unique() { - let data_set = new Set(this.values); - let series = new Series(Array.from(data_set)); - return series; - } - - nunique() { - return this.unique().values.length; - } - - value_counts() { - let s_data = this.values; - let data_dict = {}; - - for (let i = 0; i < s_data.length; i++) { - let val = s_data[i]; - - if (val in data_dict) { - data_dict[val] += 1; - } else { - data_dict[val] = 1; - } - } - - let index = Object.keys(data_dict).map(x => { - return parseInt(x) ? parseInt(x) : x; - }); - let data = Object.values(data_dict); - let series = new Series(data, { - index: index - }); - return series; - } - - abs() { - let abs_data = this.row_data_tensor.abs().arraySync(); - return new Series(utils.__round(abs_data, 2, true)); - } - - cumsum() { - let data = this.__cum_ops("sum"); - - return data; - } - - cummin() { - let data = this.__cum_ops("min"); - - return data; - } - - cummax() { - let data = this.__cum_ops("max"); - - return data; - } - - cumprod() { - let data = this.__cum_ops("prod"); - - return data; - } - - lt(other) { - return this.__bool_ops(other, "lt"); - } - - gt(other) { - return this.__bool_ops(other, "gt"); - } - - le(other) { - return this.__bool_ops(other, "le"); - } - - ge(other) { - return this.__bool_ops(other, "ge"); - } - - ne(other) { - return this.__bool_ops(other, "ne"); - } - - eq(other) { - return this.__bool_ops(other, "eq"); - } - - replace(kwargs = {}) { - let params_needed = ["replace", "with", "inplace"]; - - utils._throw_wrong_params_error(kwargs, params_needed); - - kwargs['inplace'] = kwargs['inplace'] || false; - - if (!("replace" in kwargs)) { - throw Error("Params Error: Must specify param 'replace'"); - } - - if (!("with" in kwargs)) { - throw Error("Params Error: Must specify param 'with'"); - } - - let replaced_arr = []; - let old_arr = this.values; - old_arr.forEach(val => { - if (val == kwargs['replace']) { - replaced_arr.push(kwargs['with']); - } else { - replaced_arr.push(val); - } - }); - - if (kwargs['inplace']) { - this.data = replaced_arr; - } else { - let sf = new Series(replaced_arr, { - index: this.index, - columns: this.columns, - dtypes: this.dtypes - }); - return sf; - } - } - - dropna(kwargs = {}) { - let params_needed = ["inplace"]; - - utils._throw_wrong_params_error(kwargs, params_needed); - - kwargs['inplace'] = kwargs['inplace'] || false; - let old_values = this.values; - let old_index = this.index; - let new_values = []; - let new_index = []; - let isna_vals = this.isna().values; - isna_vals.forEach((val, i) => { - if (!val) { - new_values.push(old_values[i]); - new_index.push(old_index[i]); - } - }); - - if (kwargs['inplace']) { - this.index_arr = new_index; - this.data = new_values; - } else { - let sf = new Series(new_values, { - columns: this.column_names, - index: new_index, - dtypes: this.dtypes - }); - return sf; - } - } - - argsort(ascending = true) { - let sorted_index = this.sort_values({ - ascending: ascending - }).index; - let sf = new Series(sorted_index); - return sf; - } - - argmax() { - return this.row_data_tensor.argMax().arraySync(); - } - - argmin() { - return this.row_data_tensor.argMin().arraySync(); - } - - get dtype() { - return this.dtypes[0]; - } - - drop_duplicates(kwargs = {}) { - let params_needed = ["inplace", "keep"]; - - utils._throw_wrong_params_error(kwargs, params_needed); - - kwargs['inplace'] = kwargs['inplace'] || false; - kwargs['keep'] = kwargs['keep'] || "first"; - let data_arr, old_index; - - if (kwargs['keep'] == "last") { - data_arr = this.values.reverse(); - old_index = this.index.reverse(); - } else { - data_arr = this.values; - old_index = this.index; - } - - let new_index = []; - let new_arr = []; - data_arr.forEach((val, i) => { - if (!new_arr.includes(val)) { - new_index.push(old_index[i]); - new_arr.push(val); - } - }); - - if (kwargs['keep'] == "last") { - new_arr = new_arr.reverse(); - new_index = new_index.reverse(); - } - - if (kwargs['inplace']) { - this.data = new_arr; - this.index_arr = new_index; - } else { - let sf = new Series(new_arr, { - index: new_index, - columns: this.column_names, - dtypes: this.dtypes - }); - return sf; - } - } - - toString() { - let table_width = 20; - let table_truncate = 20; - let max_row = config.get_max_row; - let data_arr = []; - let table_config = {}; - let header = [""].concat(this.columns); - let idx, data; - - if (this.values.length > max_row) { - data = this.values.slice(0, max_row); - idx = this.index.slice(0, max_row); - } else { - data = this.values; - idx = this.index; - } - - idx.forEach((val, i) => { - let row = [val].concat(data[i]); - data_arr.push(row); - }); - table_config[0] = 10; - table_config[1] = { - width: table_width, - truncate: table_truncate - }; - let table_data = [header].concat(data_arr); - return (0, _table.table)(table_data, { - columns: table_config - }); - } - - __bool_ops(other, b_ops) { - let r_series; - let l_series = this.values; - - if (typeof other == "number") { - r_series = [...l_series].fill(other); - } else { - if (!(other instanceof Series)) { - throw new Error("Value Error: 'other' must be an instance of Series"); - } - - r_series = other.values; - } - - if (!(l_series.length === r_series.length)) { - throw new Error("Length Error: Both series must be of the same length"); - } - - let data = []; - - for (let i = 0; i < l_series.length; i++) { - let l_val = l_series[i]; - let r_val = r_series[i]; - let bool = null; - - switch (b_ops) { - case "lt": - bool = l_val < r_val ? true : false; - data.push(bool); - break; - - case "gt": - bool = l_val > r_val ? true : false; - data.push(bool); - break; - - case "le": - bool = l_val <= r_val ? true : false; - data.push(bool); - break; - - case "ge": - bool = l_val >= r_val ? true : false; - data.push(bool); - break; - - case "ne": - bool = l_val != r_val ? true : false; - data.push(bool); - break; - - case "eq": - bool = l_val === r_val ? true : false; - data.push(bool); - break; - } - } - - return new Series(data); - } - - __cum_ops(ops) { - let s_data = this.values; - let temp_val = s_data[0]; - let data = [temp_val]; - - for (let i = 1; i < s_data.length; i++) { - let curr_val = s_data[i]; - - switch (ops) { - case "max": - if (curr_val > temp_val) { - data.push(curr_val); - temp_val = curr_val; - } else { - data.push(temp_val); - } - - break; - - case "min": - if (curr_val < temp_val) { - data.push(curr_val); - temp_val = curr_val; - } else { - data.push(temp_val); - } - - break; - - case "sum": - temp_val = temp_val + curr_val; - data.push(temp_val); - break; - - case "prod": - temp_val = temp_val * curr_val; - data.push(temp_val); - break; - } - } - - return new Series(data); - } - - astype(dtype) { - const __supported_dtypes = ['float32', "int32", 'string', 'boolean']; - - if (!dtype) { - throw Error("Value Error: Please specify dtype to cast to"); - } - - if (!__supported_dtypes.includes(dtype)) { - throw Error(`dtype ${dtype} not supported. dtype must be one of ${__supported_dtypes}`); - } - - let col_values = this.values; - let new_values = []; - - switch (dtype) { - case "float32": - col_values.forEach(val => { - new_values.push(Number(val)); - }); - break; - - case "int32": - col_values.forEach(val => { - new_values.push(Number(Number(val).toFixed())); - }); - break; - - case "string": - col_values.forEach(val => { - new_values.push(String(val)); - }); - break; - - case "boolean": - col_values.forEach(val => { - new_values.push(Boolean(val)); - }); - break; - - default: - break; - } - - let sf = new Series(new_values, { - dtypes: dtype, - index: this.index - }); - return sf; - } - - get str() { - if (this.dtypes[0] == "string") { - return new _strings.Str(this); - } else { - throw new Error("Cannot call accessor str on non-string type"); - } - } - - get dt() { - let timeseries = new _timeseries.TimeSeries({ - data: this - }); - timeseries.preprocessed(); - return timeseries; - } - - print() { - console.log(this + ""); - } - - plot(div) { - const plt = new Plot(this, div); - return plt; - } - - iloc(row) { - let kwargs = {}; - kwargs["rows"] = row; - kwargs["type"] = "iloc"; - let [new_data, columns, rows] = (0, _indexing.indexLoc)(this, kwargs); - let sf = new Series(new_data, { - columns: columns, - index: rows - }); - return sf; - } - - append(val, inplace = false) { - if (inplace) { - let self = this; - - if (Array.isArray(val)) { - val.forEach((el, i) => { - self.data.push(el); - self.index_arr.push(i); - }); - } else if (val instanceof Series) { - let value = val.values; - let old_index = val.index; - value.forEach((el, i) => { - self.data.push(el); - self.index_arr.push(old_index[i]); - }); - } else { - self.data.push(val); - self.index_arr.push(0); - } - } else { - let sf = this.copy(); - - if (Array.isArray(val)) { - val.forEach((el, i) => { - sf.data.push(el); - sf.index_arr.push(i); - }); - } else if (val instanceof Series) { - let value = val.values; - let old_index = val.index; - value.forEach((el, i) => { - sf.data.push(el); - sf.index_arr.push(old_index[i]); - }); - } else { - sf.data.push(val); - sf.index_arr.push(0); - } - - return sf; - } - } - -} - -exports.Series = Series; \ No newline at end of file diff --git a/danfojs-node/dist/core/strings.js b/danfojs-node/dist/core/strings.js deleted file mode 100644 index 55680aae..00000000 --- a/danfojs-node/dist/core/strings.js +++ /dev/null @@ -1,359 +0,0 @@ -"use strict"; - -Object.defineProperty(exports, "__esModule", { - value: true -}); -exports.Str = void 0; - -var _series = require("./series"); - -class Str { - constructor(series) { - this.series = series; - this.array = series.values; - } - - toLowerCase() { - let new_arr = []; - this.array.map(val => { - if (isNaN(val) && typeof val != "string") { - new_arr.push(val); - } else { - new_arr.push(val.toLowerCase()); - } - }); - - let sf = this.__create_new_sf_from(new_arr, this.series); - - return sf; - } - - toUpperCase() { - let new_arr = []; - this.array.map(val => { - if (isNaN(val) && typeof val != "string") { - new_arr.push(val); - } else { - new_arr.push(val.toUpperCase()); - } - }); - - let sf = this.__create_new_sf_from(new_arr, this.series); - - return sf; - } - - capitalize() { - let new_arr = []; - this.array.map(val => { - if (isNaN(val) && typeof val != "string") { - new_arr.push(val); - } else { - let f_char = val.slice(0, 1); - let l_char = val.slice(1); - let new_str = `${f_char.toUpperCase()}${l_char.toLowerCase()}`; - new_arr.push(new_str); - } - }); - - let sf = this.__create_new_sf_from(new_arr, this.series); - - return sf; - } - - charAt(index = 0) { - let new_arr = []; - this.array.map(val => { - if (isNaN(val) && typeof val != "string") { - new_arr.push(val); - } else { - new_arr.push(val.charAt(index)); - } - }); - - let sf = this.__create_new_sf_from(new_arr, this.series); - - return sf; - } - - concat(other = "", position = 1) { - if (Array.isArray(other)) { - let final_arr = []; - - for (let i = 0; i < other.length; i++) { - let l_str = this.array[i]; - let r_str = other[i]; - - if (position == 1) { - final_arr.push(l_str.concat(r_str)); - } else { - final_arr.push(r_str.concat(l_str)); - } - } - - let sf = this.__create_new_sf_from(final_arr, this.series); - - return sf; - } else { - let new_arr = []; - this.array.map(val => { - if (position == 1) { - if (isNaN(val) && typeof val != "string") { - new_arr.push(String(val).concat(other)); - } else { - new_arr.push(val.concat(other)); - } - } else { - if (isNaN(val) && typeof val != "string") { - new_arr.push(other.concat(String(val))); - } else { - new_arr.push(other.concat(val)); - } - } - }); - - let sf = this.__create_new_sf_from(new_arr, this.series); - - return sf; - } - } - - startsWith(str = "") { - let new_arr = []; - this.array.forEach(val => { - if (isNaN(val) && typeof val != "string") { - new_arr.push(false); - } else { - new_arr.push(val.startsWith(str)); - } - }); - - let sf = this.__create_new_sf_from(new_arr, this.series); - - return sf; - } - - endsWith(str = "") { - let new_arr = []; - this.array.map(val => { - if (isNaN(val) && typeof val != "string") { - new_arr.push(false); - } else { - new_arr.push(val.endsWith(str)); - } - }); - - let sf = this.__create_new_sf_from(new_arr, this.series); - - return sf; - } - - includes(str = "") { - let new_arr = []; - this.array.map(val => { - if (isNaN(val) && typeof val != "string") { - new_arr.push(false); - } else { - new_arr.push(val.includes(str)); - } - }); - - let sf = this.__create_new_sf_from(new_arr, this.series); - - return sf; - } - - indexOf(str = "") { - let new_arr = []; - this.array.map(val => { - if (isNaN(val) && typeof val != "string") { - new_arr.push(-1); - } else { - new_arr.push(val.indexOf(str)); - } - }); - - let sf = this.__create_new_sf_from(new_arr, this.series); - - return sf; - } - - lastIndexOf(str = "") { - let new_arr = []; - this.array.map(val => { - if (isNaN(val) && typeof val != "string") { - new_arr.push(-1); - } else { - new_arr.push(val.lastIndexOf(str)); - } - }); - - let sf = this.__create_new_sf_from(new_arr, this.series); - - return sf; - } - - replace(searchValue = "", replaceValue = "") { - let new_arr = []; - this.array.map(val => { - if (isNaN(val) && typeof val != "string") { - new_arr.push(val); - } else { - new_arr.push(val.replace(searchValue, replaceValue)); - } - }); - - let sf = this.__create_new_sf_from(new_arr, this.series); - - return sf; - } - - repeat(num = 1) { - let new_arr = []; - this.array.map(val => { - if (isNaN(val) && typeof val != "string") { - new_arr.push(val); - } else { - new_arr.push(val.repeat(num)); - } - }); - - let sf = this.__create_new_sf_from(new_arr, this.series); - - return sf; - } - - search(str = "") { - let new_arr = []; - this.array.map(val => { - if (isNaN(val) && typeof val != "string") { - new_arr.push(-1); - } else { - new_arr.push(val.search(str)); - } - }); - - let sf = this.__create_new_sf_from(new_arr, this.series); - - return sf; - } - - slice(startIndex = 0, endIndex = 1) { - let new_arr = []; - this.array.map(val => { - if (isNaN(val) && typeof val != "string") { - new_arr.push(val); - } else { - new_arr.push(val.slice(startIndex, endIndex)); - } - }); - - let sf = this.__create_new_sf_from(new_arr, this.series); - - return sf; - } - - split(splitVal = " ") { - let new_arr = []; - this.array.map(val => { - if (isNaN(val) && typeof val != "string") { - new_arr.push(val); - } else { - new_arr.push(val.split(splitVal)); - } - }); - - let sf = this.__create_new_sf_from(new_arr, this.series); - - return sf; - } - - substr(startIndex = 0, num = 1) { - let new_arr = []; - this.array.map(val => { - if (isNaN(val) && typeof val != "string") { - new_arr.push(val); - } else { - new_arr.push(val.substr(startIndex, num)); - } - }); - - let sf = this.__create_new_sf_from(new_arr, this.series); - - return sf; - } - - substring(startIndex = 0, endIndex = 1) { - let new_arr = []; - this.array.map(val => { - if (isNaN(val) && typeof val != "string") { - new_arr.push(val); - } else { - new_arr.push(val.substring(startIndex, endIndex)); - } - }); - - let sf = this.__create_new_sf_from(new_arr, this.series); - - return sf; - } - - trim() { - let new_arr = []; - this.array.map(val => { - if (isNaN(val) && typeof val != "string") { - new_arr.push(val); - } else { - new_arr.push(val.trim()); - } - }); - - let sf = this.__create_new_sf_from(new_arr, this.series); - - return sf; - } - - join(valToJoin = "", joinChar = " ") { - let new_arr = []; - this.array.map(val => { - if (isNaN(val) && typeof val != "string") { - new_arr.push(val); - } else { - let l_char = val; - let r_char = valToJoin; - let new_char = `${l_char}${joinChar}${r_char}`; - new_arr.push(new_char); - } - }); - - let sf = this.__create_new_sf_from(new_arr, this.series); - - return sf; - } - - len() { - let new_arr = []; - this.array.map(val => { - if (isNaN(val) && typeof val != "string") { - new_arr.push(val); - } else { - new_arr.push(val.length); - } - }); - - let sf = this.__create_new_sf_from(new_arr, this.series); - - return sf; - } - - __create_new_sf_from(new_val, series) { - let sf = new _series.Series(new_val, { - columns: series.column_names, - index: series.index - }); - return sf; - } - -} - -exports.Str = Str; \ No newline at end of file diff --git a/danfojs-node/dist/core/timeseries.js b/danfojs-node/dist/core/timeseries.js deleted file mode 100644 index e9f545e7..00000000 --- a/danfojs-node/dist/core/timeseries.js +++ /dev/null @@ -1,223 +0,0 @@ -"use strict"; - -Object.defineProperty(exports, "__esModule", { - value: true -}); -exports.to_datetime = exports.TimeSeries = void 0; - -var _utils = require("./utils"); - -var _series = require("./series"); - -const utils = new _utils.Utils(); - -class TimeSeries { - constructor(kwargs) { - utils.__in_object(kwargs, "data", "specify the data"); - - if (kwargs["data"] instanceof _series.Series) { - this.data = kwargs["data"].values; - } else { - this.data = kwargs["data"]; - } - - this.format = kwargs["format"] || null; - this.keys = { - "Y": 4, - "m": 2, - "H": 2, - "M": 2, - "S": 2, - "b": 3, - "d": 2, - "-": 1 - }; - this.__in_format = ["%Y-m-d%", "%m-d-Y%", "%m-d-Y H%M%S%"]; - this.__monthName = ["Jan", "Feb", "Mar", "Apr", "May", "Jun", "Jul", "Aug", "Sep", "Oct", "Nov", "Dec"]; - this.__weekName = ["Sun", "Mon", "Tue", "Wed", "Thu", "Fri", "Sat"]; - } - - preprocessed() { - let format_values = null; - - if (this.format) { - format_values = this.generate_format(); - } - - this.date_list = []; - - for (let i = 0; i < this.data.length; i++) { - let date_string = this.data[i]; - - if (this.format && !this.__in_format.includes(this.format)) { - let format_dateString = this.__apply_format(date_string, format_values); - - let valueDate = new Date(format_dateString); - - this.__is_validDate(valueDate); - - this.date_list.push(valueDate); - } else if (this.is_timestamp(date_string)) { - let string2int = parseInt(date_string); - let valueDate = new Date(string2int); - - this.__is_validDate(valueDate); - - this.date_list.push(valueDate); - } else { - let valueDate = new Date(date_string); - - this.__is_validDate(valueDate); - - this.date_list.push(valueDate); - } - } - } - - __apply_format(elem, format) { - let date_string = ""; - let temp_val = 0; - - for (let index in format) { - let value = format[index]; - - if (index == 0) { - date_string += elem.slice(0, value); - } else if (index > 4) { - date_string += ":" + elem.slice(temp_val, temp_val + value); - } else if (index == 4) { - date_string += " " + elem.slice(temp_val, temp_val + value); - } else if (index > 0 && index <= 2) { - date_string += "-" + elem.slice(temp_val, temp_val + value); - } - - temp_val += value; - } - - return date_string; - } - - generate_format() { - let format_list = this.format.split(""); - let self = this; - let format_keys = format_list.filter(function (key) { - return utils.__key_in_object(self.keys, key); - }); - let format_value = format_keys.map(function (val) { - return self.keys[val]; - }); - return format_value; - } - - is_timestamp(date_string) { - let string2int = parseInt(date_string); - let int2string = String(string2int); - - if (isNaN(string2int) || int2string.length < date_string.length) { - return false; - } else { - return true; - } - } - - __is_validDate(date_instance) { - if (date_instance.toDateString() == "Invalid Date") { - throw new Error("Invalid date, the date format not recognise"); - } - } - - __date_ops(callback) { - let data = this.date_list.map(function (date_instance) { - return callback(date_instance); - }); - let series = new _series.Series(data = data); - return series; - } - - month() { - let series = this.__date_ops(function (date_instance) { - return date_instance.getMonth(); - }); - - return series; - } - - hour() { - let series = this.__date_ops(function (date_instance) { - return date_instance.getHours(); - }); - - return series; - } - - day() { - let series = this.__date_ops(function (date_instance) { - return date_instance.getDay(); - }); - - return series; - } - - year() { - let series = this.__date_ops(function (date_instance) { - return date_instance.getFullYear(); - }); - - return series; - } - - month_name() { - let self = this; - - let series = this.__date_ops(function (date_instance) { - return self.__monthName[date_instance.getMonth()]; - }); - - return series; - } - - weekdays() { - let self = this; - - let series = this.__date_ops(function (date_instance) { - return self.__weekName[date_instance.getDay()]; - }); - - return series; - } - - monthday() { - let series = this.__date_ops(function (date_instance) { - return date_instance.getDate(); - }); - - return series; - } - - seconds() { - let series = this.__date_ops(function (date_instance) { - return date_instance.getSeconds(); - }); - - return series; - } - - minutes() { - let series = this.__date_ops(function (date_instance) { - return date_instance.getMinutes(); - }); - - return series; - } - -} - -exports.TimeSeries = TimeSeries; - -const to_datetime = kwargs => { - let timeseries = new TimeSeries(kwargs); - timeseries.preprocessed(); - return timeseries; -}; - -exports.to_datetime = to_datetime; \ No newline at end of file diff --git a/danfojs-node/dist/core/utils.js b/danfojs-node/dist/core/utils.js deleted file mode 100644 index e7f3132c..00000000 --- a/danfojs-node/dist/core/utils.js +++ /dev/null @@ -1,607 +0,0 @@ -"use strict"; - -var _interopRequireWildcard = require("@babel/runtime/helpers/interopRequireWildcard"); - -Object.defineProperty(exports, "__esModule", { - value: true -}); -exports.Utils = void 0; - -var tf = _interopRequireWildcard(require("@tensorflow/tfjs-node")); - -var _config = require("../config/config"); - -const config = new _config.Configs(); - -class Utils { - remove(arr, index) { - let new_arr = arr.filter(function (val, i) { - return i != index; - }); - return new_arr; - } - - __remove_arr(arr, index) { - let new_arr = arr.filter(function (val, i) { - return !index.includes(i); - }); - return new_arr; - } - - __is_string(value) { - return typeof value === "string" || value instanceof String; - } - - __is_number(value) { - return typeof value === "number" && isFinite(value); - } - - __is_object(value) { - return value && typeof value === "object" && value.constructor && value.constructor.name === "Object"; - } - - __is_null(value) { - return value === null; - } - - __is_undefined(value) { - return typeof value === "undefined"; - } - - __sample_from_iter(array, k, destructive) { - var n = array.length; - if (k < 0 || k > n) throw new RangeError("Sample larger than population or is negative"); - - if (destructive || n <= (k <= 5 ? 21 : 21 + Math.pow(4, Math.ceil(Math.log(k * 3, 4))))) { - if (!destructive) array = Array.prototype.slice.call(array); - - for (var i = 0; i < k; i++) { - var j = i + Math.random() * (n - i) | 0; - var x = array[i]; - array[i] = array[j]; - array[j] = x; - } - - array.length = k; - return array; - } else { - var selected = new Set(); - - while (selected.add(Math.random() * n | 0).size < k) {} - - return Array.prototype.map.call(selected, i => population[i]); - } - } - - __range(start, end) { - let value = tf.linspace(start, end, end - start + 1).arraySync(); - return value; - } - - __key_in_object(object, key) { - if (Object.prototype.hasOwnProperty.call(object, key)) { - return true; - } else { - return false; - } - } - - __get_col_values(data) { - let row_len = data.length; - let cols_len = data[0].length; - var cols_arr = []; - - for (var i = 0; i <= cols_len - 1; i++) { - let temp_col = []; - - for (let j = 0; j < row_len; j++) { - temp_col.push(data[j][i]); - } - - cols_arr.push(temp_col); - } - - return cols_arr; - } - - _get_row_and_col_values(data) { - let col_names = Object.keys(data); - let col_data = Object.values(data); - let first_col_len = col_data[0].length; - col_data.forEach(data => { - if (data.length != first_col_len) { - throw Error("Length Error: Length of columns must be the same!"); - } - }); - let rows_len = col_data[0].length; - let cols_len = col_names.length; - var rows_arr = []; - - for (var i = 0; i <= rows_len - 1; i++) { - var temp_row = []; - - for (let j = 0; j < cols_len; j++) { - let _arr = col_data[j]; - temp_row.push(_arr[i]); - } - - rows_arr.push(temp_row); - } - - return [rows_arr, col_names]; - } - - __convert_2D_to_1D(data) { - let new_data = []; - data.map(val => { - if (this.__is_object(val)) { - new_data.push(JSON.stringify(val)); - } else { - new_data.push(`${val}`); - } - }); - return new_data; - } - - __replace_undefined_with_NaN(data, isSeries) { - if (isSeries) { - let temp_arr = []; - data.forEach(val => { - if (typeof val === "undefined" || val == Infinity || val == null) { - temp_arr.push(NaN); - } else { - temp_arr.push(val); - } - }); - return temp_arr; - } else { - let full_arr = []; - data.forEach(val => { - var temp_arr = []; - val.forEach(ele => { - if (typeof ele === "undefined" || ele == Infinity || ele == null) { - temp_arr.push(NaN); - } else { - temp_arr.push(ele); - } - }); - full_arr.push(temp_arr); - }); - return full_arr; - } - } - - __checker(arr_val) { - let dtypes = []; - let lim; - let int_tracker = []; - let float_tracker = []; - let string_tracker = []; - let bool_tracker = []; - - if (arr_val.length == 0) { - dtypes.push("string"); - } - - if (arr_val.length < config.get_dtype_test_lim) { - lim = arr_val.length - 1; - } else { - lim = config.get_dtype_test_lim - 1; - } - - arr_val.forEach((ele, indx) => { - let count = indx; - - if (typeof ele == "boolean") { - float_tracker.push(false); - int_tracker.push(false); - string_tracker.push(false); - bool_tracker.push(true); - } else if (isNaN(ele) && typeof ele != "string") { - float_tracker.push(true); - int_tracker.push(false); - string_tracker.push(false); - bool_tracker.push(false); - } else if (!isNaN(Number(ele))) { - if (ele.toString().includes(".")) { - float_tracker.push(true); - int_tracker.push(false); - string_tracker.push(false); - bool_tracker.push(false); - } else { - float_tracker.push(false); - int_tracker.push(true); - string_tracker.push(false); - bool_tracker.push(false); - } - } else { - float_tracker.push(false); - int_tracker.push(false); - string_tracker.push(true); - bool_tracker.push(false); - } - - if (count == lim) { - const even = element => element == true; - - if (string_tracker.some(even)) { - dtypes = "string"; - } else if (float_tracker.some(even)) { - dtypes = "float32"; - } else if (int_tracker.some(even)) { - dtypes = "int32"; - } else if (bool_tracker.some(even)) { - dtypes = "boolean"; - } else { - dtypes = "undefined"; - } - } - }); - return dtypes; - } - - __get_t(arr_val) { - const self = this; - - if (this.__is_1D_array(arr_val)) { - return [this.__checker(arr_val)]; - } else { - const dtypes = arr_val.map(arr => { - return self.__checker(arr); - }); - return dtypes; - } - } - - __unique(data) { - let unique = new Set(); - data.map(function (val) { - unique.add(val[0]); - }); - let unique_array = Array.from(unique); - return unique_array; - } - - __in_object(object, key, message) { - if (!Object.prototype.hasOwnProperty.call(object, key)) { - throw new Error(message); - } - } - - __is_1D_array(arr) { - if (typeof arr[0] == "number" || typeof arr[0] == "string" || typeof arr[0] == "boolean") { - return true; - } else { - return false; - } - } - - __arr_to_obj(arr) { - let arr_map = {}; - arr.forEach((ele, i) => { - arr_map[ele] = i; - }); - return arr_map; - } - - __count_nan(arr, return_val = true, isSeries) { - if (isSeries) { - let null_count = 0; - let val_count = 0; - arr.forEach(ele => { - if (Number.isNaN(ele)) { - null_count = null_count + 1; - } else { - val_count = val_count + 1; - } - }); - - if (return_val) { - return val_count; - } else { - return null_count; - } - } else { - let result_arr = []; - arr.forEach(ele_arr => { - let null_count = 0; - let val_count = 0; - ele_arr.forEach(ele => { - if (Number.isNaN(ele)) { - null_count = null_count + 1; - } else { - val_count = val_count + 1; - } - }); - - if (return_val) { - result_arr.push(val_count); - } else { - result_arr.push(null_count); - } - }); - return result_arr; - } - } - - __median(arr, isSeries) { - if (isSeries) { - const sorted = arr.slice().sort((a, b) => a - b); - const middle = Math.floor(sorted.length / 2); - - if (sorted.length % 2 === 0) { - return (sorted[middle - 1] + sorted[middle]) / 2; - } - - return sorted[middle]; - } else { - let result_arr = []; - arr.map(ele => { - const sorted = ele.slice().sort((a, b) => a - b); - const middle = Math.floor(sorted.length / 2); - - if (sorted.length % 2 === 0) { - result_arr.push((sorted[middle - 1] + sorted[middle]) / 2); - } else { - result_arr.push(sorted[middle]); - } - }); - return result_arr; - } - } - - __mode(arr) { - var modes = [], - count = [], - i, - maxIndex = 0; - arr.forEach(val => { - count[val] = (count[val] || 0) + 1; - - if (count[val] > maxIndex) { - maxIndex = count[val]; - } - }); - - for (i in count) if (this.__key_in_object(count, i)) { - if (count[i] === maxIndex) { - modes.push(Number(i)); - } - } - - return modes; - } - - __round(arr, dp = 2, isSeries) { - if (dp < 0) { - dp = 1; - } - - if (isSeries) { - let new_arr = []; - arr.map(val => { - new_arr.push(Number(val.toFixed(dp))); - }); - return new_arr; - } else { - let result_arr = []; - arr.map(arr_ele => { - let new_arr = []; - arr_ele.map(val => { - new_arr.push(Number(val.toFixed(dp))); - }); - result_arr.push(new_arr); - }); - return result_arr; - } - } - - __is_function(variable) { - return typeof variable == "function"; - } - - __randgen(num, start, end) { - let gen_num = []; - - function randi(a, b) { - return Math.floor(Math.random() * (b - a) + a); - } - - function recursive(val, arr) { - if (!arr.includes(val)) { - return val; - } - - val = randi(start, end); - recursive(val, arr); - } - - for (let i = 0; i < num; i++) { - let gen_val = randi(start, end); - let recur_val = recursive(gen_val, gen_num); - gen_num.push(recur_val); - } - - return gen_num; - } - - _throw_wrong_params_error(kwargs, params_needed) { - let keys = Object.keys(kwargs); - let bool = []; - - for (let i = 0; i < keys.length; i++) { - if (params_needed.includes(keys[i])) { - bool.push(true); - } else { - bool.push(false); - } - } - - const truthy = element => element == false; - - if (bool.some(truthy)) { - throw Error(`Params Error: A specified parameter is not supported. Your params must be any of the following [${params_needed}]`); - } - } - - __map_int_to_bool(arr, dim) { - let new_arr = []; - - if (dim == 2) { - arr.map(outer_val => { - let temp_arr = []; - outer_val.map(val => { - if (val == 1) { - temp_arr.push(true); - } else { - temp_arr.push(false); - } - }); - new_arr.push(temp_arr); - }); - return new_arr; - } else { - arr.map(val => { - if (val == 1) { - new_arr.push(true); - } else { - new_arr.push(false); - } - }); - return new_arr; - } - } - - __std(data) { - let tensor_data = data; - let mean = tensor_data.mean(); - let sub_mean_pow = tensor_data.sub(mean).pow(2); - let mean_data = sub_mean_pow.mean(); - let std = mean_data.sqrt(); - return std; - } - - __zeros(row, column) { - let zero_data = []; - - for (let i = 0; i < row; i++) { - let col_data = Array(column); - - for (let j = 0; j < column; j++) { - col_data[j] = 0; - } - - zero_data.push(col_data); - } - - return zero_data; - } - - __shuffle(num, array) { - var i = array.length, - j = 0, - temp; - - while (i--) { - j = Math.floor(Math.random() * (i + 1)); - temp = array[i]; - array[i] = array[j]; - array[j] = temp; - } - - return array.slice(0, num); - } - - __sort(arr, ascending = true) { - let sorted = arr.slice(); - return sorted.sort((a, b) => { - if (ascending) { - if (typeof a === "string" && typeof b === "string") { - return a.charCodeAt() - b.charCodeAt(); - } else { - return a - b; - } - } else { - if (typeof a === "string" && typeof b === "string") { - return b.charCodeAt() - a.charCodeAt(); - } else { - return b - a; - } - } - }); - } - - __is_browser_env() { - var isBrowser = new Function("try {return this===window;}catch(e){ return false;}"); - return isBrowser(); - } - - __is_node_env() { - var isNode = new Function("try {return this===global;}catch(e){return false;}"); - return isNode(); - } - - _throw_str_dtype_error(obj, ops) { - if (obj.dtypes[0] == "string") { - throw Error(`dtype error: String data type does not support ${ops} operation`); - } - } - - _remove_nans(arr) { - let values = arr.filter(val => !isNaN(val) && typeof val != "string"); - return values; - } - - _replace_nan_with_null(arr) { - let values = arr.map(val => { - if (isNaN(val)) { - return null; - } else { - return val; - } - }); - return values; - } - - __get_duplicate(arr) { - let temp_obj = {}; - let rslt_obj = {}; - arr.forEach((val, index) => { - if (temp_obj.hasOwnProperty(val)) { - temp_obj[val]["count"] += 1; - temp_obj[val]["index"].push(index); - } else { - temp_obj[val] = {}; - temp_obj[val]["count"] = 1; - temp_obj[val]["index"] = [index]; - } - }); - - for (let key in temp_obj) { - if (temp_obj[key]["count"] >= 2) { - rslt_obj[key] = {}; - rslt_obj[key]["count"] = temp_obj[key]["count"]; - rslt_obj[key]["index"] = temp_obj[key]["index"]; - } - } - - return rslt_obj; - } - - _sort_arr_with_index(arr1, arr2, dtype) { - let sorted_idx = arr1.map((item, index) => { - return [arr2[index], item]; - }); - - if (dtype == "string") { - sorted_idx.sort(); - } else { - sorted_idx.sort(([arg1], [arg2]) => arg2 - arg1); - } - - return sorted_idx.map(([, item]) => item); - } - -} - -exports.Utils = Utils; \ No newline at end of file diff --git a/danfojs-node/dist/index.js b/danfojs-node/dist/index.js deleted file mode 100644 index 5c2b93e8..00000000 --- a/danfojs-node/dist/index.js +++ /dev/null @@ -1,159 +0,0 @@ -"use strict"; - -var _interopRequireWildcard = require("@babel/runtime/helpers/interopRequireWildcard"); - -var _interopRequireDefault = require("@babel/runtime/helpers/interopRequireDefault"); - -Object.defineProperty(exports, "__esModule", { - value: true -}); -Object.defineProperty(exports, "NDframe", { - enumerable: true, - get: function () { - return _generic.default; - } -}); -Object.defineProperty(exports, "Series", { - enumerable: true, - get: function () { - return _series.Series; - } -}); -Object.defineProperty(exports, "DataFrame", { - enumerable: true, - get: function () { - return _frame.DataFrame; - } -}); -Object.defineProperty(exports, "to_datetime", { - enumerable: true, - get: function () { - return _timeseries.to_datetime; - } -}); -Object.defineProperty(exports, "read_csv", { - enumerable: true, - get: function () { - return _reader.read_csv; - } -}); -Object.defineProperty(exports, "read_json", { - enumerable: true, - get: function () { - return _reader.read_json; - } -}); -Object.defineProperty(exports, "read_excel", { - enumerable: true, - get: function () { - return _reader.read_excel; - } -}); -Object.defineProperty(exports, "read", { - enumerable: true, - get: function () { - return _reader.read; - } -}); -Object.defineProperty(exports, "merge", { - enumerable: true, - get: function () { - return _merge.merge; - } -}); -Object.defineProperty(exports, "concat", { - enumerable: true, - get: function () { - return _concat.concat; - } -}); -Object.defineProperty(exports, "LabelEncoder", { - enumerable: true, - get: function () { - return _encodings.LabelEncoder; - } -}); -Object.defineProperty(exports, "OneHotEncoder", { - enumerable: true, - get: function () { - return _encodings.OneHotEncoder; - } -}); -Object.defineProperty(exports, "MinMaxScaler", { - enumerable: true, - get: function () { - return _scalers.MinMaxScaler; - } -}); -Object.defineProperty(exports, "StandardScaler", { - enumerable: true, - get: function () { - return _scalers.StandardScaler; - } -}); -Object.defineProperty(exports, "date_range", { - enumerable: true, - get: function () { - return _date_range.date_range; - } -}); -Object.defineProperty(exports, "get_dummies", { - enumerable: true, - get: function () { - return _get_dummies.get_dummies; - } -}); -Object.defineProperty(exports, "Configs", { - enumerable: true, - get: function () { - return _config.Configs; - } -}); -Object.defineProperty(exports, "Str", { - enumerable: true, - get: function () { - return _strings.Str; - } -}); -Object.defineProperty(exports, "Utils", { - enumerable: true, - get: function () { - return _utils.Utils; - } -}); -exports.tf = exports._version = void 0; - -var _generic = _interopRequireDefault(require("./core/generic")); - -var tf = _interopRequireWildcard(require("@tensorflow/tfjs-node")); - -exports.tf = tf; - -var _series = require("./core/series"); - -var _frame = require("./core/frame"); - -var _timeseries = require("./core/timeseries"); - -var _reader = require("./io/reader"); - -var _merge = require("./core/merge"); - -var _concat = require("./core/concat"); - -var _encodings = require("./preprocessing/encodings"); - -var _scalers = require("./preprocessing/scalers"); - -var _date_range = require("./core/date_range"); - -var _get_dummies = require("./core/get_dummies"); - -var _config = require("./config/config"); - -var _strings = require("./core/strings"); - -var _utils = require("./core/utils"); - -const _version = "0.2.7"; -exports._version = _version; \ No newline at end of file diff --git a/danfojs-node/dist/io/reader.js b/danfojs-node/dist/io/reader.js deleted file mode 100644 index 27aabe15..00000000 --- a/danfojs-node/dist/io/reader.js +++ /dev/null @@ -1,118 +0,0 @@ -"use strict"; - -var _interopRequireDefault = require("@babel/runtime/helpers/interopRequireDefault"); - -Object.defineProperty(exports, "__esModule", { - value: true -}); -exports.read = exports.read_excel = exports.read_json = exports.read_csv = void 0; - -var _interopRequireWildcard2 = _interopRequireDefault(require("@babel/runtime/helpers/interopRequireWildcard")); - -var _tfjsNode = require("@tensorflow/tfjs-node"); - -var _nodeFetch = _interopRequireDefault(require("node-fetch")); - -var _frictionless = require("frictionless.js"); - -var _streamToArray = _interopRequireDefault(require("stream-to-array")); - -var _frame = require("../core/frame"); - -const read_csv = async (source, configs = {}) => { - let { - start, - end - } = configs; - - if (!(source.startsWith("file://") || source.startsWith("http") || source.startsWith("blob"))) { - source = source.startsWith("/") ? `file://${source}` : `file://${process.cwd()}/${source}`; - } - - let tfdata = []; - await _tfjsNode.data.csv(source, configs).skip(start).take(end).forEachAsync(row => { - return tfdata.push(row); - }); - const df = new _frame.DataFrame(tfdata); - return df; -}; - -exports.read_csv = read_csv; - -const read_json = async source => { - if (source.startsWith("https://") || source.startsWith("http://") || source.startsWith("file://")) { - let res = await (0, _nodeFetch.default)(source, { - method: "Get" - }); - let json = await res.json(); - let df = new _frame.DataFrame(json); - return df; - } else { - let fs = await Promise.resolve().then(() => (0, _interopRequireWildcard2.default)(require("fs"))); - return new Promise((resolve, reject) => { - fs.readFile(source, (err, data) => { - if (err) reject(err); - let df = new _frame.DataFrame(JSON.parse(data)); - resolve(df); - }); - }); - } -}; - -exports.read_json = read_json; - -const read_excel = async (source, configs) => { - const df = await read(source, configs); - return df; -}; - -exports.read_excel = read_excel; - -const read = async (source, configs = {}) => { - let { - data_num, - header, - sheet - } = configs; - data_num = data_num === undefined ? 0 : data_num; - header = header === undefined ? true : header; - let rows, file; - - if ((0, _frictionless.isDataset)(source)) { - console.info("datapackage.json found. Loading Dataset package from Datahub.io"); - const dataset = await _frictionless.Dataset.load(source); - file = dataset.resources[data_num]; - rows = await (0, _streamToArray.default)(await file.rows()); - } else { - try { - file = (0, _frictionless.open)(source); - - if (sheet) { - rows = await (0, _streamToArray.default)(await file.rows({ - sheet - })); - } else { - rows = await (0, _streamToArray.default)(await file.rows()); - } - } catch (error) { - console.log(error); - } - } - - if (["csv", "xls", "xlsx"].includes(await file.descriptor.format)) { - if (header) { - let df = new _frame.DataFrame(rows.slice(1), { - columns: rows[0] - }); - return df; - } else { - let df = new _frame.DataFrame(rows); - return df; - } - } else { - let df = new _frame.DataFrame(rows); - return df; - } -}; - -exports.read = read; \ No newline at end of file diff --git a/danfojs-node/dist/plotting/plot.js b/danfojs-node/dist/plotting/plot.js deleted file mode 100644 index ff98bd6e..00000000 --- a/danfojs-node/dist/plotting/plot.js +++ /dev/null @@ -1,681 +0,0 @@ -"use strict"; - -Object.defineProperty(exports, "__esModule", { - value: true -}); -exports.Plot = void 0; - -var _utils = require("../core/utils"); - -var _series = require("../core/series"); - -const utils = new _utils.Utils(); - -try { - const version = Plotly.version; - console.info(`Using Plotly version ${version}`); -} catch (error) { - console.info(`Plotly CDN not found. If you need to make Plots, then add the Plotly CDN to your script`); -} - -class Plot { - constructor(ndframe, div) { - this.div = div; - this.ndframe = ndframe; - } - - line(config = {}) { - let ret_params = this.__get_plot_params(config); - - let this_config = ret_params[0]; - let params = ret_params[1]; - - if (this.ndframe instanceof _series.Series) { - let trace = {}; - let y = this.ndframe.values; - params.forEach(param => { - if (!param == "layout") { - trace[param] = config[param]; - } - }); - trace["y"] = y; - trace['type'] = "line"; - Plotly.newPlot(this.div, [trace], this_config['layout'], this_config); - } else { - if (utils.__key_in_object(this_config, 'x') && utils.__key_in_object(this_config, 'y')) { - if (!this.ndframe.column_names.includes(this_config['x'])) { - throw Error(`Column Error: ${this_config['x']} not found in columns`); - } - - if (!this.ndframe.column_names.includes(this_config['y'])) { - throw Error(`Column Error: ${this_config['y']} not found in columns`); - } - - let x = this.ndframe[this_config['x']].values; - let y = this.ndframe[this_config['y']].values; - let trace = {}; - trace["x"] = x; - trace['y'] = y; - let xaxis = {}; - let yaxis = {}; - xaxis['title'] = this_config['x']; - yaxis['title'] = this_config['y']; - this_config['layout']['xaxis'] = xaxis; - this_config['layout']['yaxis'] = yaxis; - Plotly.newPlot(this.div, [trace], this_config['layout'], this_config); - } else if (utils.__key_in_object(this_config, 'x') || utils.__key_in_object(this_config, 'y')) { - let data = []; - let cols_to_plot; - - if (utils.__key_in_object(this_config, "columns")) { - cols_to_plot = this.____check_if_cols_exist(this_config['columns']); - } else { - cols_to_plot = this.ndframe.column_names; - } - - cols_to_plot.forEach(c_name => { - let trace = {}; - params.forEach(param => { - trace[param] = config[param]; - }); - - if (utils.__key_in_object(this_config, 'x')) { - trace["x"] = this.ndframe[this_config['x']].values; - trace["y"] = this.ndframe[c_name].values; - trace['name'] = c_name; - } else { - trace["y"] = this.ndframe[this_config['y']].values; - trace["x"] = this.ndframe[c_name].values; - trace['name'] = c_name; - } - - data.push(trace); - }); - Plotly.newPlot(this.div, data, this_config['layout'], this_config); - } else { - let data = []; - let cols_to_plot; - - if (utils.__key_in_object(this_config, "columns")) { - cols_to_plot = this.____check_if_cols_exist(this_config['columns']); - } else { - cols_to_plot = this.ndframe.column_names; - } - - cols_to_plot.forEach(c_name => { - let trace = {}; - params.forEach(param => { - trace[param] = config[param]; - }); - trace["x"] = this.ndframe.index; - trace["y"] = this.ndframe[c_name].values; - trace['name'] = c_name; - data.push(trace); - }); - Plotly.newPlot(this.div, data, this_config['layout'], this_config); - } - } - } - - bar(config = {}) { - let ret_params = this.__get_plot_params(config); - - let this_config = ret_params[0]; - let params = ret_params[1]; - - if (this.ndframe instanceof _series.Series) { - let trace = {}; - let y = this.ndframe.values; - params.forEach(param => { - if (!param == "layout") { - trace[param] = config[param]; - } - }); - trace["y"] = y; - trace['type'] = "bar"; - Plotly.newPlot(this.div, [trace], this_config['layout'], this_config); - } else { - if (utils.__key_in_object(this_config, 'x') && utils.__key_in_object(this_config, 'y')) { - if (!this.ndframe.column_names.includes(this_config['x'])) { - throw Error(`Column Error: ${this_config['x']} not found in columns`); - } - - if (!this.ndframe.column_names.includes(this_config['y'])) { - throw Error(`Column Error: ${this_config['y']} not found in columns`); - } - - let x = this.ndframe[this_config['x']].values; - let y = this.ndframe[this_config['y']].values; - let trace = {}; - trace["x"] = x; - trace['y'] = y; - trace['type'] = "bar"; - let xaxis = {}; - let yaxis = {}; - xaxis['title'] = this_config['x']; - yaxis['title'] = this_config['y']; - this_config['layout']['xaxis'] = xaxis; - this_config['layout']['yaxis'] = yaxis; - Plotly.newPlot(this.div, [trace], this_config['layout'], this_config); - } else if (utils.__key_in_object(this_config, 'x') || utils.__key_in_object(this_config, 'y')) { - let trace = {}; - params.forEach(param => { - if (!param == "layout") { - trace[param] = config[param]; - } - }); - - if (utils.__key_in_object(this_config, 'x')) { - trace['y'] = this.ndframe[this_config['x']].values; - } else { - trace['y'] = this.ndframe[this_config['y']].values; - } - - trace['type'] = "bar"; - Plotly.newPlot(this.div, [trace], this_config['layout'], this_config); - } else { - let data = []; - let cols_to_plot; - - if (utils.__key_in_object(this_config, "columns")) { - cols_to_plot = this.____check_if_cols_exist(this_config['columns']); - } else { - cols_to_plot = this.ndframe.column_names; - } - - cols_to_plot.forEach(c_name => { - let trace = {}; - trace['x'] = this.ndframe.index; - trace["y"] = this.ndframe[c_name].values; - trace['name'] = c_name; - trace['type'] = "bar"; - data.push(trace); - }); - Plotly.newPlot(this.div, data, this_config['layout'], this_config); - } - } - } - - scatter(config = {}) { - let ret_params = this.__get_plot_params(config); - - let this_config = ret_params[0]; - let params = ret_params[1]; - - if (this.ndframe instanceof _series.Series) { - let trace = {}; - params.forEach(param => { - if (!param == "layout") { - trace[param] = config[param]; - } - }); - trace["x"] = this.ndframe.values; - trace['y'] = this.ndframe.index; - trace['type'] = "scatter"; - trace['mode'] = "markers"; - Plotly.newPlot(this.div, [trace], this_config['layout'], this_config); - } else { - if (utils.__key_in_object(this_config, 'x') && utils.__key_in_object(this_config, 'y')) { - if (!this.ndframe.column_names.includes(this_config['x'])) { - throw Error(`Column Error: ${this_config['x']} not found in columns`); - } - - if (!this.ndframe.column_names.includes(this_config['y'])) { - throw Error(`Column Error: ${this_config['y']} not found in columns`); - } - - let x = this.ndframe[this_config['x']].values; - let y = this.ndframe[this_config['y']].values; - let trace = {}; - trace["x"] = x; - trace['y'] = y; - trace['type'] = "scatter"; - trace['mode'] = "markers"; - let xaxis = {}; - let yaxis = {}; - xaxis['title'] = this_config['x']; - yaxis['title'] = this_config['y']; - this_config['layout']['xaxis'] = xaxis; - this_config['layout']['yaxis'] = yaxis; - Plotly.newPlot(this.div, [trace], this_config['layout'], this_config); - } else if (utils.__key_in_object(this_config, 'x') || utils.__key_in_object(this_config, 'y')) { - let trace = {}; - params.forEach(param => { - if (!param == "layout") { - trace[param] = config[param]; - } - }); - - if (utils.__key_in_object(this_config, 'x')) { - trace['y'] = this.ndframe.index; - trace['x'] = this.ndframe[this_config['x']].values; - } else { - trace['x'] = this.ndframe.index; - trace['y'] = this.ndframe[this_config['y']].values; - } - - trace['type'] = "scatter"; - trace['mode'] = "markers"; - Plotly.newPlot(this.div, [trace], this_config['layout'], this_config); - } else { - let data = []; - let cols_to_plot; - - if (utils.__key_in_object(this_config, "columns")) { - cols_to_plot = this.____check_if_cols_exist(this_config['columns']); - } else { - cols_to_plot = this.ndframe.column_names; - } - - cols_to_plot.forEach(c_name => { - let trace = {}; - trace['y'] = this.ndframe.index; - trace["x"] = this.ndframe[c_name].values; - trace['name'] = c_name; - trace['type'] = "scatter"; - trace['mode'] = "markers"; - data.push(trace); - }); - Plotly.newPlot(this.div, data, this_config['layout'], this_config); - } - } - } - - hist(config = {}) { - let ret_params = this.__get_plot_params(config); - - let this_config = ret_params[0]; - let params = ret_params[1]; - - if (this.ndframe instanceof _series.Series) { - let trace = {}; - params.forEach(param => { - if (!param == "layout") { - trace[param] = config[param]; - } - }); - trace["x"] = this.ndframe.values; - trace['type'] = "histogram"; - Plotly.newPlot(this.div, [trace], this_config['layout'], this_config); - } else if (utils.__key_in_object(this_config, 'x')) { - let trace = {}; - params.forEach(param => { - if (!param == "layout") { - trace[param] = config[param]; - } - }); - trace['x'] = this.ndframe[this_config['y']].values; - trace['type'] = "histogram"; - Plotly.newPlot(this.div, [trace], this_config['layout'], this_config); - } else if (utils.__key_in_object(this_config, 'y')) { - let trace = {}; - params.forEach(param => { - if (!param == "layout") { - trace[param] = config[param]; - } - }); - trace['y'] = this.ndframe[this_config['y']].values; - trace['type'] = "histogram"; - Plotly.newPlot(this.div, [trace], this_config['layout'], this_config); - } else { - let data = []; - let cols_to_plot; - - if (utils.__key_in_object(this_config, "columns")) { - cols_to_plot = this.____check_if_cols_exist(this_config['columns']); - } else { - cols_to_plot = this.ndframe.column_names; - } - - cols_to_plot.forEach(c_name => { - let trace = {}; - trace["x"] = this.ndframe[c_name].values; - trace['name'] = c_name; - trace['type'] = "histogram"; - data.push(trace); - }); - Plotly.newPlot(this.div, data, this_config['layout'], this_config); - } - } - - pie(config = {}) { - let ret_params = this.__get_plot_params(config); - - let this_config = ret_params[0]; - - if (this.ndframe instanceof _series.Series) { - let data = [{ - values: this.ndframe.values, - labels: this.ndframe.index, - type: 'pie', - name: this_config['labels'], - hoverinfo: 'label+percent+name', - automargin: true - }]; - Plotly.newPlot(this.div, data, this_config['layout'], this_config); - } else if (utils.__key_in_object(this_config, 'values') && utils.__key_in_object(this_config, 'labels')) { - if (!this.ndframe.column_names.includes(this_config['labels'])) { - throw Error(`Column Error: ${this_config['labels']} not found in columns. labels name must be one of [ ${this.ndframe.column_names}]`); - } - - if (!this.ndframe.column_names.includes(this_config['values'])) { - throw Error(`Column Error: ${this_config['values']} not found in columns. value name must be one of [ ${this.ndframe.column_names}]`); - } - - let data = [{ - values: this.ndframe[this_config['values']].values, - labels: this.ndframe[this_config['labels']].values, - type: 'pie', - name: this_config['labels'], - hoverinfo: 'label+percent+name', - automargin: true - }]; - Plotly.newPlot(this.div, data, this_config['layout'], this_config); - } else { - let cols_to_plot; - - if (utils.__key_in_object(this_config, "columns")) { - cols_to_plot = this.____check_if_cols_exist(this_config['columns']); - } else { - cols_to_plot = this.ndframe.column_names; - } - - if (utils.__key_in_object(this_config, 'row_pos')) { - if (this_config['row_pos'].length != cols_to_plot.length - 1) { - throw Error(`Lenght of row_pos array must be equal to number of columns. Got ${this_config['row_pos'].length}, expected ${cols_to_plot.length - 1}`); - } - } else { - let temp_arr = []; - - for (let i = 0; i < cols_to_plot.length - 1; i++) { - temp_arr.push(0); - } - - this_config['row_pos'] = temp_arr; - } - - if (utils.__key_in_object(this_config, 'col_pos')) { - if (this_config['col_pos'].length != cols_to_plot.length - 1) { - throw Error(`Lenght of col_pos array must be equal to number of columns. Got ${this_config['col_pos'].length}, expected ${cols_to_plot.length - 1}`); - } - } else { - let temp_arr = []; - - for (let i = 0; i < cols_to_plot.length - 1; i++) { - temp_arr.push(i); - } - - this_config['col_pos'] = temp_arr; - } - - let data = []; - cols_to_plot.forEach((c_name, i) => { - let trace = {}; - trace["values"] = this.ndframe[c_name].values; - trace['labels'] = this.ndframe[this_config['labels']].values; - trace['name'] = c_name; - trace['type'] = "pie"; - trace['domain'] = { - row: this_config['row_pos'][i], - column: this_config['col_pos'][i] - }; - trace["hoverinfo"] = 'label+percent+name'; - trace['textposition'] = "outside"; - trace['automargin'] = true; - data.push(trace); - }); - - if (!utils.__key_in_object(this_config, "grid")) { - let size = Number((this.ndframe.shape[1] / 2).toFixed()) + 1; - this_config['grid'] = { - rows: size, - columns: size - }; - } - - this_config['layout']['grid'] = this_config['grid']; - Plotly.newPlot(this.div, data, this_config['layout'], this_config); - } - } - - box(config = {}) { - let ret_params = this.__get_plot_params(config); - - let this_config = ret_params[0]; - let params = ret_params[1]; - - if (this.ndframe instanceof _series.Series) { - let trace = {}; - let y = this.ndframe.values; - params.forEach(param => { - if (!param == "layout") { - trace[param] = config[param]; - } - }); - trace["y"] = y; - trace['type'] = "box"; - Plotly.newPlot(this.div, [trace], this_config['layout'], this_config); - } else { - if (utils.__key_in_object(this_config, 'x') && utils.__key_in_object(this_config, 'y')) { - if (!this.ndframe.column_names.includes(this_config['x'])) { - throw Error(`Column Error: ${this_config['x']} not found in columns`); - } - - if (!this.ndframe.column_names.includes(this_config['y'])) { - throw Error(`Column Error: ${this_config['y']} not found in columns`); - } - - let x = this.ndframe[this_config['x']].values; - let y = this.ndframe[this_config['y']].values; - let trace = {}; - trace["x"] = x; - trace['y'] = y; - trace['type'] = 'box'; - let xaxis = {}; - let yaxis = {}; - xaxis['title'] = this_config['x']; - yaxis['title'] = this_config['y']; - this_config['layout']['xaxis'] = xaxis; - this_config['layout']['yaxis'] = yaxis; - Plotly.newPlot(this.div, [trace], this_config['layout'], this_config); - } else if (utils.__key_in_object(this_config, 'x') || utils.__key_in_object(this_config, 'y')) { - let trace = {}; - params.forEach(param => { - if (!param == "layout") { - trace[param] = config[param]; - } - }); - - if (utils.__key_in_object(this_config, 'x')) { - trace['x'] = this.ndframe[this_config['x']].values; - trace['y'] = this.ndframe.index; - trace['type'] = 'box'; - } else { - trace['x'] = this.ndframe.index; - trace['y'] = this_config['y']; - trace['type'] = 'box'; - } - - Plotly.newPlot(this.div, [trace], this_config['layout'], this_config); - } else { - let data = []; - let cols_to_plot; - - if (utils.__key_in_object(this_config, "columns")) { - cols_to_plot = this.____check_if_cols_exist(this_config['columns']); - } else { - cols_to_plot = this.ndframe.column_names; - } - - cols_to_plot.forEach(c_name => { - let trace = {}; - params.forEach(param => { - trace[param] = config[param]; - }); - trace["y"] = this.ndframe[c_name].values; - trace['name'] = c_name; - trace['type'] = 'box'; - data.push(trace); - }); - Plotly.newPlot(this.div, data, this_config['layout'], this_config); - } - } - } - - violin(config = {}) { - let ret_params = this.__get_plot_params(config); - - let this_config = ret_params[0]; - let params = ret_params[1]; - - if (this.ndframe instanceof _series.Series) { - let trace = {}; - let y = this.ndframe.values; - params.forEach(param => { - if (!param == "layout") { - trace[param] = config[param]; - } - }); - trace["y"] = y; - trace['type'] = "violin"; - Plotly.newPlot(this.div, [trace], this_config['layout'], this_config); - } else { - if (utils.__key_in_object(this_config, 'x') && utils.__key_in_object(this_config, 'y')) { - if (!this.ndframe.column_names.includes(this_config['x'])) { - throw Error(`Column Error: ${this_config['x']} not found in columns`); - } - - if (!this.ndframe.column_names.includes(this_config['y'])) { - throw Error(`Column Error: ${this_config['y']} not found in columns`); - } - - let x = this.ndframe[this_config['x']].values; - let y = this.ndframe[this_config['y']].values; - let trace = {}; - trace["x"] = x; - trace['y'] = y; - trace['type'] = 'violin'; - let xaxis = {}; - let yaxis = {}; - xaxis['title'] = this_config['x']; - yaxis['title'] = this_config['y']; - this_config['layout']['xaxis'] = xaxis; - this_config['layout']['yaxis'] = yaxis; - Plotly.newPlot(this.div, [trace], this_config['layout'], this_config); - } else if (utils.__key_in_object(this_config, 'x') || utils.__key_in_object(this_config, 'y')) { - let trace = {}; - params.forEach(param => { - if (!param == "layout") { - trace[param] = config[param]; - } - }); - - if (utils.__key_in_object(this_config, 'x')) { - trace['x'] = this.ndframe[this_config['x']].values; - trace['y'] = this.ndframe.index; - trace['type'] = 'violin'; - } else { - trace['x'] = this.ndframe.index; - trace['y'] = this_config['y']; - trace['type'] = 'violin'; - } - - Plotly.newPlot(this.div, [trace], this_config['layout'], this_config); - } else { - let data = []; - let cols_to_plot; - - if (utils.__key_in_object(this_config, "columns")) { - cols_to_plot = this.____check_if_cols_exist(this_config['columns']); - } else { - cols_to_plot = this.ndframe.column_names; - } - - cols_to_plot.forEach(c_name => { - let trace = {}; - params.forEach(param => { - trace[param] = config[param]; - }); - trace["y"] = this.ndframe[c_name].values; - trace['name'] = c_name; - trace['type'] = 'violin'; - data.push(trace); - }); - Plotly.newPlot(this.div, data, this_config['layout'], this_config); - } - } - } - - table(config = {}) { - let ret_params = this.__get_plot_params(config); - - let this_config = ret_params[0]; - let header = {}; - let cells = {}; - let cols_data = []; - let cols_2_show; - - if (utils.__key_in_object(this_config, 'columns')) { - this_config['columns'].forEach(cname => { - if (!this.ndframe.column_names.includes(cname)) { - throw Error(`Column Error: ${cname} not found in columns. Columns should be one of [ ${this.ndframe.column_names} ]`); - } - - let idx = this.ndframe.column_names.indexOf(cname); - cols_data.push(this.ndframe.col_data[idx]); - }); - cols_2_show = this_config['columns']; - } else { - cols_2_show = this.ndframe.column_names; - cols_data = this.ndframe.col_data; - } - - header['values'] = cols_2_show; - cells['values'] = cols_data; - - if (this_config['header_style']) { - Object.keys(this_config['header_style']).forEach(param => { - header[param] = this_config['header_style'][param]; - }); - } - - if (this_config['cell_style']) { - Object.keys(this_config['cell_style']).forEach(param => { - cells[param] = this_config['cell_style'][param]; - }); - } - - var data = [{ - type: 'table', - header: header, - cells: cells - }]; - Plotly.newPlot(this.div, data, this_config['layout'], this_config); - } - - __get_plot_params(config) { - let params = Object.keys(config); - let this_config = {}; - params.forEach(param => { - this_config[param] = config[param]; - }); - - if (!utils.__key_in_object(config, "layout")) { - this_config['layout'] = {}; - } - - return [this_config, params]; - } - - ____check_if_cols_exist(cols) { - cols.forEach(col => { - if (!this.ndframe.column_names.includes(col)) { - throw Error(`Column Error: ${col} not found in columns. Columns should be one of [ ${this.ndframe.column_names} ]`); - } - }); - return cols; - } - -} - -exports.Plot = Plot; \ No newline at end of file diff --git a/danfojs-node/dist/preprocessing/encodings.js b/danfojs-node/dist/preprocessing/encodings.js deleted file mode 100644 index ec79dd12..00000000 --- a/danfojs-node/dist/preprocessing/encodings.js +++ /dev/null @@ -1,113 +0,0 @@ -"use strict"; - -Object.defineProperty(exports, "__esModule", { - value: true -}); -exports.OneHotEncoder = exports.LabelEncoder = void 0; - -var _series = require("../core/series"); - -var _utils = require("../core/utils"); - -var _frame = require("../core/frame"); - -const utils = new _utils.Utils(); - -class LabelEncoder { - fit(data) { - let in_data = null; - - if (Array.isArray(data)) { - in_data = data; - } else if (data instanceof _series.Series) { - in_data = data.values; - } else { - throw new Error("data must be an array or a Series"); - } - - let data_set = new Set(in_data); - this.label = Array.from(data_set); - let self = this; - let output_data = in_data.map(x => { - return self.label.indexOf(x); - }); - return new _series.Series(output_data); - } - - transform(data) { - let in_data = null; - - if (Array.isArray(data)) { - in_data = data; - } else if (data instanceof _series.Series) { - in_data = data.values; - } else { - throw new Error("data must be an array or a Series"); - } - - let self = this; - let output_data = in_data.map(x => { - return self.label.indexOf(x); - }); - return new _series.Series(output_data); - } - -} - -exports.LabelEncoder = LabelEncoder; - -class OneHotEncoder { - fit(data) { - let in_data = null; - - if (Array.isArray(data)) { - in_data = data; - } else if (data instanceof _series.Series) { - in_data = data.values; - } else { - throw new Error("data must be an array"); - } - - let data_set = new Set(in_data); - this.label = Array.from(data_set); - - let onehot_data = utils.__zeros(in_data.length, this.label.length); - - for (let i = 0; i < in_data.length; i++) { - let elem = in_data[i]; - let elem_index = this.label.indexOf(elem); - onehot_data[i][elem_index] = 1; - } - - return new _frame.DataFrame(onehot_data, { - columns: this.label - }); - } - - transform(data) { - let in_data = null; - - if (Array.isArray(data)) { - in_data = data; - } else if (data instanceof _series.Series) { - in_data = data.values; - } else { - throw new Error("data must be an array"); - } - - let onehot_data = utils.__zeros(in_data.length, this.label.length); - - for (let i = 0; i < in_data.length; i++) { - let elem = in_data[i]; - let elem_index = this.label.indexOf(elem); - onehot_data[i][elem_index] = 1; - } - - return new _frame.DataFrame(onehot_data, { - columns: this.label - }); - } - -} - -exports.OneHotEncoder = OneHotEncoder; \ No newline at end of file diff --git a/danfojs-node/dist/preprocessing/scalers.js b/danfojs-node/dist/preprocessing/scalers.js deleted file mode 100644 index 157b0163..00000000 --- a/danfojs-node/dist/preprocessing/scalers.js +++ /dev/null @@ -1,142 +0,0 @@ -"use strict"; - -var _interopRequireWildcard = require("@babel/runtime/helpers/interopRequireWildcard"); - -Object.defineProperty(exports, "__esModule", { - value: true -}); -exports.StandardScaler = exports.MinMaxScaler = void 0; - -var tf = _interopRequireWildcard(require("@tensorflow/tfjs-node")); - -var _series = require("../core/series"); - -var _frame = require("../core/frame"); - -var _utils = require("../core/utils"); - -const utils = new _utils.Utils(); - -class MinMaxScaler { - fit(data) { - let tensor_data = null; - - if (Array.isArray(data)) { - tensor_data = tf.tensor(data); - } else if (data instanceof _frame.DataFrame || data instanceof _series.Series) { - if (data.dtypes.includes("string")) { - throw Error("Dtype Error: Cannot perform operation on string dtypes"); - } - - tensor_data = tf.tensor(data.values); - } else { - throw new Error("data must either be an Array, DataFrame or Series"); - } - - this.max = tensor_data.max(0); - this.min = tensor_data.min(0); - let output_data = tensor_data.sub(this.min).div(this.max.sub(this.min)).arraySync(); - - if (data instanceof _series.Series || Array.isArray(data)) { - return new _series.Series(output_data); - } else { - return new _frame.DataFrame(output_data); - } - } - - transform(data) { - if (data instanceof _series.Series) { - if (data.dtypes.includes("string")) { - throw Error("Dtype Error: Cannot perform operation on string dtypes"); - } - - let tensor_data = tf.tensor(data.values); - let output_data = tensor_data.sub(this.min).div(this.max.sub(this.min)).arraySync(); - return new _series.Series(output_data); - } else if (Array.isArray(data)) { - let tensor_data = tf.tensor(data); - let output_data = tensor_data.sub(this.min).div(this.max.sub(this.min)).arraySync(); - - if (utils.__is_1D_array(data)) { - return new _series.Series(output_data); - } else { - return new _frame.DataFrame(output_data); - } - } else if (data instanceof _frame.DataFrame) { - if (data.dtypes.includes("string")) { - throw Error("Dtype Error: Cannot perform operation on string dtypes"); - } - - let tensor_data = tf.tensor(data.values); - let output_data = tensor_data.sub(this.min).div(this.max.sub(this.min)).arraySync(); - return new _frame.DataFrame(output_data); - } else { - throw Error("Value Error: Data type not supoorted"); - } - } - -} - -exports.MinMaxScaler = MinMaxScaler; - -class StandardScaler { - fit(data) { - let tensor_data = null; - - if (Array.isArray(data)) { - tensor_data = tf.tensor(data); - } else if (data instanceof _frame.DataFrame || data instanceof _series.Series) { - if (data.dtypes.includes("string")) { - throw Error("Dtype Error: Cannot perform operation on string dtypes"); - } - - tensor_data = tf.tensor(data.values); - } else { - throw new Error("data must either be an Array, DataFrame or Series"); - } - - this.std = tf.moments(tensor_data, 0).variance.sqrt(); - this.mean = tensor_data.mean(0); - let output_data = tensor_data.sub(this.mean).div(this.std).arraySync(); - - if (data instanceof _series.Series || Array.isArray(data)) { - return new _series.Series(data = output_data); - } else { - return new _frame.DataFrame(data = output_data); - } - } - - transform(data) { - if (data instanceof _series.Series) { - if (data.dtypes.includes("string")) { - throw Error("Dtype Error: Cannot perform operation on string dtypes"); - } - - let tensor_data = tf.tensor(data.values); - let output_data = tensor_data.sub(this.mean).div(this.std).arraySync(); - return new _series.Series(output_data); - } else if (Array.isArray(data)) { - let tensor_data = tf.tensor(data); - let output_data = tensor_data.sub(this.mean).div(this.std).arraySync(); - - if (utils.__is_1D_array(data)) { - return new _series.Series(output_data); - } else { - return new _frame.DataFrame(output_data); - } - } else if (data instanceof _frame.DataFrame) { - if (data.dtypes.includes("string")) { - throw Error("Dtype Error: Cannot perform operation on string dtypes"); - } - - let tensor_data = tf.tensor(data.values); - let output_data = tensor_data.sub(this.mean).div(this.std).arraySync(); - return new _frame.DataFrame(output_data); - } else { - throw Error("Value Error: Data type not supoorted"); - } - } - -} - -exports.StandardScaler = StandardScaler; \ No newline at end of file diff --git a/danfojs-node/src/config/config.js b/danfojs-node/src/config/config.js deleted file mode 100644 index 268663d2..00000000 --- a/danfojs-node/src/config/config.js +++ /dev/null @@ -1,54 +0,0 @@ - -/** - * Package wide configuration class - */ -export class Configs { - constructor() { - this.table_width = 17; //set the width of each column printed in console - this.table_truncate = 16; //set the max number of string before text is truncated in printing - this.dtype_test_lim = 10; - this.table_max_row = 21; - this.table_max_col_in_console = 7; - } - - set_width(val) { - this.table_width = val; - } - - get get_width() { - return this.table_width; - } - - set_max_col_in_console(val) { - this.table_max_col_in_console = val; - } - - get get_max_col_in_console() { - return this.table_max_col_in_console; - } - - set_row_num(val) { - this.table_max_row = val; - } - - get get_max_row() { - return this.table_max_row; - } - - - get get_truncate() { - return this.table_truncate; - } - - set_truncate(val) { - this.table_truncate = val; - } - - get get_dtype_test_lim() { - return this.dtype_test_lim; - } - - set_dtype_test_lim(val) { - this.dtype_test_lim = val; - } -} diff --git a/danfojs-node/src/core/concat.js b/danfojs-node/src/core/concat.js deleted file mode 100644 index aa9bf9a2..00000000 --- a/danfojs-node/src/core/concat.js +++ /dev/null @@ -1,221 +0,0 @@ -import { DataFrame } from './frame'; -import { Utils } from "./utils"; -import { Series } from './series'; - -const utils = new Utils(); - - -export class Concat { - constructor(kwargs) { - - - // check if keys exist in kwargs - utils.__in_object(kwargs, "df_list", "df_list not found: specify the list of dataframe"); - utils.__in_object(kwargs, "axis", "axis not found: specify the axis"); - - let df_list = null; //set the df_list to null - let axis = null; // set axis to null - let indexes = null; - - //check if df_list is an array - if (Array.isArray(kwargs["df_list"])) { - - df_list = kwargs["df_list"]; - } else { - throw new Error("df_list must be an Array of dataFrames/Series"); - } - - //check if axis is int and is either 0 or 1 - if (typeof kwargs["axis"] === "number") { - - if (kwargs["axis"] == 0 || kwargs["axis"] == 1) { - - axis = kwargs["axis"]; - } else { - axis = 1; - // throw new Error("Invalid axis: axis must be 0 or 1") - } - - } else { - throw new Error("axis must be a number"); - } - - - let df_object = Object.assign({}, df_list); // convert the array to object - - if (axis == 1) { - - let columns = []; - let duplicate_col_count = {}; - let max_length = 0; - let a_key = Object.keys(df_object)[0]; - indexes = df_object[a_key].index; - for (let key in df_object) { - - let column = df_object[key].columns; - let length = df_object[key].values.length; - - if (length > max_length) { - max_length = length; - } - - for (let index in column) { - - let col_name = column[index]; - if (col_name in duplicate_col_count) { - - let count = duplicate_col_count[col_name]; - let name = `${col_name}_${count + 1}`; - - columns.push(name); - - duplicate_col_count[col_name] = count + 1; - } else { - - columns.push(col_name); - duplicate_col_count[col_name] = 1; - } - } - - - } - - let data = new Array(max_length); - - for (let key in df_list) { - - let values = df_list[key].values; - - for (let index = 0; index < values.length; index++) { - - let val = values[index]; - if (typeof data[index] === "undefined") { - - if (Array.isArray(val)){ - data[index] = val; - } else { - data[index] = [ val ]; - } - - } else { - if (Array.isArray(val)){ - data[index].push(...val); - } else { - data[index].push(val); - } - - } - } - - if (values.length < max_length) { - let column_length = df_list[key].columns.length; - let null_array = Array(column_length); - - for (let col = 0; col < column_length; col++) { - null_array[col] = NaN; - } - - if (typeof data[max_length - 1] === "undefined") { - data[max_length - 1] = null_array; - } else { - data[max_length - 1].push(...null_array); - } - } - } - - let df = new DataFrame(data, { columns: columns, index: indexes }); //convert to dataframe - return df; - } else { - //concatenate base on axis 0 - let columns = []; - let row_indexes = []; - let col_i = 0; - for (let key in df_list) { - let column = df_list[key].columns; - columns.push(...column); - indexes = df_list[key].index; - let r_index = indexes.map((val) => { - return `${val}_row${col_i}`; - }); - row_indexes.push(...r_index); - col_i += 1; - } - - let column_set = new Set(columns); - - columns = Array.from(column_set); - - let data = []; - - for (let key in df_list) { - - let value = df_list[key].values; - - // let col_length = value[0].length - - let df_columns = df_list[key].columns; - - let not_exist = []; - for (let col_index in columns) { - let col_name = columns[col_index]; - - let is_index = df_columns.indexOf(col_name); - - if (is_index == -1) { - not_exist.push(col_name); - } - } - if (not_exist.length > 0) { - for (let i = 0; i < value.length; i++) { - let row_value = value[i]; - - let new_arr = Array(columns.length); - for (let j = 0; j < columns.length; j++) { - - let col_name = columns[j]; - if (not_exist.includes(col_name)) { - - new_arr[j] = NaN; - } else { - let index = df_columns.indexOf(col_name); - if (Array.isArray(row_value)){ - new_arr[j] = row_value[index]; - } else { - new_arr[j] = row_value; - } - - } - - } - data.push(new_arr); - } - } else { - data.push(...value); - } - - } - - if (Array.isArray(data[0])){ - let df = new DataFrame(data, { columns: columns, index: row_indexes }); - return df; - } else { - let sf = new Series(data, { index: row_indexes }); - return sf; - } - - - } - - } -} - - -/** -* Concatenate pandas objects along a particular axis with optional set logic along the other axes. -* @param {kwargs} {df_list: List of DataFrame to concatenate together axis: 0 for row axis and 1 for index axis -* @returns {DataFrame} -*/ -export const concat = (kwargs) => { - let concat_sf = new Concat(kwargs); - return concat_sf; -}; diff --git a/danfojs-node/src/core/date_range.js b/danfojs-node/src/core/date_range.js deleted file mode 100644 index 4676c97d..00000000 --- a/danfojs-node/src/core/date_range.js +++ /dev/null @@ -1,288 +0,0 @@ -import { Utils } from "./utils"; - -const utils = new Utils; - -/** - * Generate date range between a specified set of date - * @param {kwargs} kwargs { - * start : string - * end : string - * period: int - * freq : string - * } - * @returns Array - */ -export class date_range { - constructor(kwargs){ - - this.offset = null; - - if (utils.__key_in_object(kwargs, "start")){ - this.start = kwargs["start"]; - } else { - this.start = null; - } - - if (utils.__key_in_object(kwargs, "end")){ - this.end = kwargs["end"]; - } else { - this.end = null; - } - - if (utils.__key_in_object(kwargs, "period")){ - this.period = kwargs["period"]; - } else { - this.period = null; - } - - if (utils.__key_in_object(kwargs, "freq")){ - this.freq = kwargs["freq"]; - } else { - this.freq = "D"; - } - - this.freq_list = [ "M", "D", "s", "H", "m", "Y" ]; - - if (this.freq.length == 1){ - if (!this.freq_list.includes(this.freq)){ - throw new Error(`invalid freq ${this.freq}`); - } - } else { - let freq_split = this.freq.split(""); - this.offset = parseInt(freq_split[0]); - this.freq = freq_split[1]; - if (!this.freq_list.includes(this.freq)){ - throw new Error(`invalid freq ${this.freq}`); - } - } - - let rslt = this.range(this.start, this.end, this.period, this.offset); - return rslt; - - } - - range(start, end, period, offset = null){ - - let start_date = null; - let end_date = null; - let start_range = null; - let end_range = null; - if (start && end){ - start_date = new Date(start); - start_range = this.freq_type(start_date, this.freq); - end_date = new Date(end); - end_range = this.freq_type(end_date, this.freq); - - //check if the end year is greater than start year - let start_year = start_date.getFullYear(); - let end_year = end_date.getFullYear(); - if ((start_year < end_year)){ - // end_range = start_range + end_range - if (this.freq == "M"){ - end_range = this.month_end(start_date, end_date); - } else if (this.freq == "D"){ - end_range = this.day_end(start_date, end_date) - start_range; - - } - } - - let range_array = utils.__range(start_range, end_range); - - if (offset){ - range_array = this.offset_count(range_array, offset); - } - - if (this.freq == "M"){ - range_array = this.month_range(range_array); - } - - let date_range = range_array.map((x) => { - return this.set_dateProps(start_date, this.freq, x); - }); - date_range[date_range.length - 1] = end_date; - - let date_string = this.toLocalString(date_range); - - return date_string; - } else if (start && !(end)){ - start_date = new Date(start); - start_range = this.freq_type(start_date, this.freq); - end_range = offset ? ((period * offset) - 1) : period - 1; - - if (start_range > end_range){ - end_range = end_range + start_range; - } - let range_array = utils.__range(start_range, end_range); - - - if (offset){ - range_array = this.offset_count(range_array, offset); - } - - let date_range = range_array.map((x) => { - return this.set_dateProps(start_date, this.freq, x); - }); - - let date_string = this.toLocalString(date_range); - return date_string; - - } else if (end && !(start)){ - end_date = new Date(end); - end_range = this.freq_type(end_date, this.freq); - start_range = (end_range - period) + 1; - - let range_array = utils.__range(start_range, end_range); - - if (offset){ - range_array = this.offset_count(range_array, offset); - } - - let date_range = range_array.map((x) => { - return this.set_dateProps(end_date, this.freq, x); - }); - - let date_string = this.toLocalString(date_range); - return date_string; - } - } - - freq_type(date, ftype){ - - let rslt = null; - switch (ftype){ - - case "M": - rslt = date.getMonth(); - break; - case "Y": - rslt = date.getFullYear(); - break; - case "s": - rslt = date.getSeconds(); - break; - case "D": - rslt = date.getDate(); - break; - case "H": - rslt = date.getHours(); - break; - case "m": - rslt = date.getMinutes(); - break; - } - return rslt; - } - - offset_count(d_array, offset){ - - let r_array = []; - - for (let i = 0; i < d_array.length; i += offset){ - r_array.push(d_array[i]); - } - return r_array; - } - - set_dateProps(date, ftype, val){ - - let new_date = new Date(date.valueOf()); - switch (ftype){ - - case "M": - if (val.length == 2){ - - new_date.setYear(new_date.getFullYear() + val[0]); - new_date.setMonth(parseInt(val[1])); - } else { - new_date.setMonth(val); - } - - break; - case "Y": - new_date.setYear(val); - break; - case "s": - new_date.setSeconds(val); - break; - case "D": - new_date.setDate(val); - break; - case "H": - new_date.setHours(val); - break; - case "m": - new_date.setMinutes(val); - break; - } - return new_date; - } - - toLocalString(d_array){ - - let r_array = d_array.map((x) => { - - return x.toLocaleString(); - }); - - return r_array; - } - - month_end(start_date, end_date){ - - let end_month = end_date.getMonth(); - - let diff_year = end_date.getFullYear() - start_date.getFullYear(); - - let end_range = (12 * diff_year) + end_month; - - return end_range; - } - - month_range(range){ - - let minus = null; - let y_val = 0; - let d_range = range.map((x) => { - - if (x > 11){ - if (x % 12 == 0){ - minus = x; - y_val = x / 12; - return [ y_val, (x - minus) ]; - } else { - return [ y_val, (x - minus) ]; - } - - } - return [ y_val, x ]; - }); - - return d_range; - } - - day_end(start_date, end_date){ - - let month_end = this.month_end(start_date, end_date); - let range = utils.__range(start_date.getMonth(), month_end); - let m_range = this.month_range(range); - - // let s_date = new Date(start_date.getFullYear(),start_date.getMonth(),0) - let sum = 0; - for (let i = 0; i < m_range.length; i++){ - - let val = m_range[i]; - let d_date = null; - if (i === m_range.length - 1) { - d_date = new Date(start_date.getFullYear() + val[0], val[1], end_date.getDate()).getDate(); - } else { - d_date = new Date(start_date.getFullYear() + val[0], val[1], 0).getDate(); - - } - - sum += d_date; - - } - return sum; - - } -} diff --git a/danfojs-node/src/core/frame.js b/danfojs-node/src/core/frame.js deleted file mode 100644 index a5ff8820..00000000 --- a/danfojs-node/src/core/frame.js +++ /dev/null @@ -1,2175 +0,0 @@ -/** -* Copyright 2020, JsData. -* All rights reserved. -* -* This source code is licensed under the MIT license found in the -* LICENSE file in the root directory of this source tree. - -* Unless required by applicable law or agreed to in writing, software -* distributed under the License is distributed on an "AS IS" BASIS, -* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -* See the License for the specific language governing permissions and -* limitations under the License. -*/ - -import * as tf from '@tensorflow/tfjs-node'; -import Ndframe from "./generic"; -import { Series } from "./series"; -import { Utils } from "./utils"; -import { GroupBy } from "./groupby"; -import { indexLoc } from "./indexing"; -import { concat } from "./concat.js"; - -const utils = new Utils(); -import { std, variance } from "mathjs"; - -/** - * A 2D frame object that stores data in structured tabular format - * @param {data} data, JSON, Array, 2D Tensor - * @param {kwargs} Object {columns: Array of column names, defaults to ordered numbers when not specified - * dtypes: strings of data types, automatically inferred when not specified - * index: row index for subseting array, defaults to ordered numbers when not specified} - * - * @returns DataFrame - */ -export class DataFrame extends Ndframe { - constructor(data, kwargs) { - super(data, kwargs); - this._set_column_property(); //set column property on DataFrame Class for easy accessing using the format df['colname'] - } - - _set_column_property() { - let col_vals = this.col_data; - let col_names = this.column_names; - - col_vals.forEach((col, i) => { - this[col_names[i]] = null; - Object.defineProperty(this, col_names[i], { - get() { - return new Series(this.col_data[i], { - columns: col_names[i], - index: this.index - }); - }, - set(value) { - this.addColumn({ column: col_names[i], value: value }); - } - }); - }); - } - /** - * Write a CSV file of the DataFrame contents - * @param {string} csvFilePath Path to save CSV when in Node.js form - * @returns {Promise} CSV representation of Object data - * - */ - async to_csv(csvFilePath = "") { - const csvContent = await super.to_csv(); - // behave differently for Node vs Web - if (typeof window === "undefined") { - // Write CSV on Node.js - // eslint-disable-next-line no-undef - const fs = require("fs"); - fs.writeFileSync( - csvFilePath, - csvContent, - (err) => err && console.error(err) - ); - } else { - // Download CSV on Web - const webCSV = "data:text/csv;charset=utf-8," + csvContent; - window.open(encodeURI(webCSV)); - } - return csvContent; - } - - /** - * Drop a list of rows or columns base on the specified axis - * @param {Object} kwargs Configuration object - * {columns: [Array(Columns| Index)] array of column names to drop - * axis: row=0, columns=1 - * inplace: specify whether to drop the row/column with/without creating a new DataFrame} - * @returns null | DataFrame - * - */ - drop(kwargs = {}) { - let params_needed = ["columns", "index", "inplace", "axis"]; - utils._throw_wrong_params_error(kwargs, params_needed); - - kwargs["inplace"] = kwargs["inplace"] || false; - - if (!("axis" in kwargs)) { - kwargs["axis"] = 1; - } - - let to_drop = null; - if ("index" in kwargs && kwargs["axis"] == 0) { - to_drop = kwargs["index"]; - } else { - to_drop = kwargs["columns"]; - } - - if (kwargs["axis"] == 1) { - if (!("columns" in kwargs)) { - throw Error( - "No column found. Axis of 1 must be accompanied by an array of column(s) names" - ); - } - let self = this; - let new_col_data = {}; - let new_dtype = []; - - const index = to_drop.map((x) => { - let col_idx = self.columns.indexOf(x); - if (col_idx == -1) { - throw new Error(`column "${x}" does not exist`); - } - return col_idx; - }); - - this.col_data.forEach((col, idx) => { - if (!index.includes(idx)) { - new_col_data[self.column_names[idx]] = col; - new_dtype.push(self.dtypes[idx]); - } - }); - - if (!kwargs["inplace"]) { - let old_cols = self.columns; - let new_columns = Object.keys(new_col_data); - let df = new DataFrame(new_col_data, { - index: self.index, - dtypes: new_dtype - }); - df.__set_col_property(df, df.col_data, new_columns, old_cols); - return df; - } else { - let old_cols = self.columns; - let new_columns = Object.keys(new_col_data); - this.__update_frame_in_place(null, null, new_col_data, null, new_dtype); - this.__set_col_property(self, self.col_data, new_columns, old_cols); - } - } else { - if (!utils.__key_in_object(kwargs, "index")) { - throw Error( - "No index label found. Axis of 0 must be accompanied by an array of index labels" - ); - } - to_drop.forEach((x) => { - if (!this.index.includes(x)) - throw new Error(`${x} does not exist in index`); - }); - const values = this.values; - let data_idx = []; - let new_data, new_index; - if (typeof to_drop[0] == "string") { - //get index of strings labels in rows - this.index.forEach((idx, i) => { - if (to_drop.includes(idx)) { - data_idx.push(i); - } - }); - new_data = utils.__remove_arr(values, data_idx); - new_index = utils.__remove_arr(this.index, data_idx); - } else { - new_data = utils.__remove_arr(values, to_drop); - new_index = utils.__remove_arr(this.index, to_drop); - } - - if (!kwargs["inplace"]) { - return new DataFrame(new_data, { - columns: this.columns, - index: new_index - }); - } else { - this.row_data_tensor = tf.tensor(new_data); - this.data = new_data; - this.__set_index(new_index); - } - } - } - - /** - * Purely label based indexing. Can accept string label names for both rows and columns - * @param {kwargs} kwargs object {rows: Array of index, columns: Array of column name(s)} - * @return DataFrame data stucture - */ - loc(kwargs = {}) { - let params_needed = ["columns", "rows"]; - utils._throw_wrong_params_error(kwargs, params_needed); - - kwargs["type"] = "loc"; - let [new_data, columns, rows] = indexLoc(this, kwargs); - let df_columns = { columns: columns }; - let df = new DataFrame(new_data, df_columns); - df.__set_index(rows); - return df; - } - - /** - * Access a dataframe element using row and column index - * @param {*} kwargs object {rows: Array of index, columns: Array of column index} - * @return DataFrame data stucture - */ - iloc(kwargs = {}) { - let params_needed = ["columns", "rows"]; - utils._throw_wrong_params_error(kwargs, params_needed); - - kwargs["type"] = "iloc"; - - let [new_data, columns, rows] = indexLoc(this, kwargs); - let df_columns = { columns: columns }; - let df = new DataFrame(new_data, df_columns); - df.__set_index(rows); - return df; - } - - /** - * Prints the first n values in a dataframe - * @param {rows} rows --> int - * @returns DataFrame - */ - head(rows = 5) { - if (rows > this.values.length || rows < 1) { - //return all values - return this; - } else { - //Creates a new dataframe with first [rows] - let data = this.values.slice(0, rows); - let idx = this.index.slice(0, rows); - let config = { columns: this.column_names, index: idx }; - let df = new DataFrame(data, config); - return df; - } - } - - /** - * Prints the last n values in a dataframe - * @param {rows} rows --> int - * @returns DataFrame - */ - tail(rows = 5) { - let row_len = this.values.length; - if (rows > row_len || rows < 1) { - //return all values - return this; - } else { - //Creates a new dataframe with last [rows] - let data = this.values.slice(row_len - rows); - let indx = this.index.slice(row_len - rows); - let config = { columns: this.column_names, index: indx }; - let df = new DataFrame(data, config); - return df; - } - } - - /** - * Gets [num] number of random rows in a dataframe - * @param {num} rows --> The number of rows to return - * @param {seed} seed --> (Optional) An integer specifying the random seed that will be used to create the distribution. - * @returns {Promise} resolves to a DataFrame object - */ - async sample(num = -1, seed = 1) { - if (num > this.shape[0]) { - throw new Error("Sample size n cannot be bigger than size of dataset"); - } - if (num < -1 || num == 0) { - throw new Error("Sample size cannot be less than -1 or 0"); - } - num = num === -1 ? this.shape[0] : num; - const shuffled_index = await tf.data.array(this.index).shuffle(num, seed).take(num).toArray(); - const df = this.iloc({ rows: shuffled_index }); - return df; - } - - /** - * Return Addition of DataFrame and other, element-wise (binary operator add). - * @param {other} DataFrame, Series, Array or Number to add - * @returns {DataFrame} - */ - add(other, axis) { - if (this.__frame_is_compactible_for_operation) { - //check if all types a numeric - let tensors = this.__get_ops_tensors([this, other], axis); - let sum_vals = tensors[0].add(tensors[1]); - let col_names = this.columns; - return this.__get_df_from_tensor(sum_vals, col_names); - } else { - throw Error("TypeError: Dtypes of columns must be Float of Int"); - } - } - - /** - * Return subtraction of DataFrame and other, element-wise (binary operator add). - * @param {other} DataFrame, Series, Array or Number to add - * @returns {DataFrame} - */ - sub(other, axis) { - if (this.__frame_is_compactible_for_operation) { - //check if all types are numeric - let tensors = this.__get_ops_tensors([this, other], axis); - let result = tensors[0].sub(tensors[1]); - let col_names = this.columns; - return this.__get_df_from_tensor(result, col_names); - } else { - throw Error("TypeError: Dtypes of columns must be Float of Int"); - } - } - - /** - * Return subtraction of DataFrame and other, element-wise (binary operator add). - * @param {other} DataFrame, Series, Array or Number to add - * @returns {DataFrame} - */ - mul(other, axis) { - if (this.__frame_is_compactible_for_operation) { - //check if all types are numeric - let tensors = this.__get_ops_tensors([this, other], axis); - let result = tensors[0].mul(tensors[1]); - let col_names = this.columns; - return this.__get_df_from_tensor(result, col_names); - } else { - throw Error("TypeError: Dtypes of columns must be Float of Int"); - } - } - - /** - * Return division of DataFrame and other, element-wise (binary operator add). - * @param {other} DataFrame, Series, Array or Number to add - * @returns {DataFrame} - */ - div(other, axis) { - if (this.__frame_is_compactible_for_operation) { - //check if all types are numeric - let tensors = this.__get_ops_tensors([this, other], axis); - let result = tensors[0].div(tensors[1]); - let col_names = this.columns; - return this.__get_df_from_tensor(result, col_names); - } else { - throw Error("TypeError: Dtypes of columns must be Float of Int"); - } - } - - /** - * Return division of DataFrame and other, element-wise (binary operator add). - * @param {other} DataFrame, Series, Array or Number to add - * @returns {DataFrame} - */ - pow(other, axis) { - if (this.__frame_is_compactible_for_operation) { - //check if all types are numeric - let tensors = this.__get_ops_tensors([this, other], axis); - let result = tensors[0].pow(tensors[1]); - let col_names = this.columns; - return this.__get_df_from_tensor(result, col_names); - } else { - throw Error("TypeError: Dtypes of columns must be Float of Int"); - } - } - - /** - * Return division of DataFrame and other, element-wise (binary operator add). - * @param {other} DataFrame, Series, Array or Number to add - * @returns {DataFrame} - */ - mod(other, axis) { - if (this.__frame_is_compactible_for_operation) { - //check if all types are numeric - let tensors = this.__get_ops_tensors([this, other], axis); - let result = tensors[0].mod(tensors[1]); - let col_names = this.columns; - return this.__get_df_from_tensor(result, col_names); - } else { - throw Error("TypeError: Dtypes of columns must be Float of Int"); - } - } - - /** - * Return mean of DataFrame across specified axis. - * @param {axis} Number {0: row, 1 : column} Axis for the function to be applied on - * @returns {Series} - */ - mean(axis = 1) { - if (this.__frame_is_compactible_for_operation()) { - let values; - let val_mean = []; - if (axis == 1) { - values = this.col_data; - } else { - values = this.values; - } - - values.map((arr) => { - let temp = utils._remove_nans(arr); - let temp_mean = tf.tensor(temp).mean().arraySync(); - val_mean.push(Number(temp_mean.toFixed(5))); - }); - - let new_index; - if (axis == 1) { - new_index = this.column_names; - } else { - new_index = this.index; - } - let sf = new Series(val_mean, { columns: "sum", index: new_index }); - return sf; - } else { - throw Error("Dtype Error: Operation can not be performed on string type"); - } - - } - - /** - * Return median of DataFrame across specified axis. - * @param {axis} Number {0: row, 1 : column} Axis for the function to be applied on - * @returns {Series} - */ - median(axis = 1) { - if (this.__frame_is_compactible_for_operation) { - //check if all types are numeric - let tensor_vals, idx; - if (axis == 1) { - tensor_vals = this.col_data_tensor.arraySync(); - idx = this.column_names; - } else { - tensor_vals = this.row_data_tensor.arraySync(); - idx = this.index; - } - let median = utils.__median(tensor_vals, false); - let sf = new Series(median, { index: idx }); - return sf; - } else { - throw Error("TypeError: Dtypes of columns must be Float of Int"); - } - } - - /** - * Return minimum element in a DataFrame across specified axis. - * @param {axis} Number {0: row, 1 : column} Axis for the function to be applied on - * @returns {Series} - */ - min(axis = 1) { - if (this.__frame_is_compactible_for_operation) { - //check if all types are numeric - let operands = this.__get_tensor_and_idx(this, axis); - let tensor_vals = operands[0]; - let idx = operands[1]; - let result = tensor_vals.min(operands[2]); - let sf = new Series(result.arraySync(), { index: idx }); - return sf; - } else { - throw Error("TypeError: Dtypes of columns must be Float of Int"); - } - } - - /** - * Return maximum element of DataFrame across specified axis. - * @param {axis} Number {0: row, 1 : column} Axis for the function to be applied on - * @returns {Series} - */ - max(axis = 1) { - if (this.__frame_is_compactible_for_operation) { - //check if all types are numeric - let operands = this.__get_tensor_and_idx(this, axis); - let tensor_vals = operands[0]; - let idx = operands[1]; - let result = tensor_vals.max(operands[2]); - let sf = new Series(result.arraySync(), { index: idx }); - return sf; - } else { - throw Error("TypeError: Dtypes of columns must be Float of Int"); - } - } - - /** - * Return standard deviation of DataFrame across specified axis. - * @param {axis} Number {0: row, 1 : column} Axis for the function to be applied on - * @returns {Series} - */ - std(axis = 1) { - if (this.__frame_is_compactible_for_operation) { - //check if all types are numeric - let tensor_vals = this.col_data_tensor.arraySync(); - let idx; - if (axis == 1) { - idx = this.column_names; - } else { - idx = this.index; - } - let median = std(tensor_vals, axis); - let sf = new Series(median, { index: idx }); - return sf; - } else { - throw Error("TypeError: Dtypes of columns must be Float of Int"); - } - } - - /** - * Return variance of DataFrame across specified axis. - * @param {axis} Number {0: row, 1 : column} Axis for the function to be applied on - * @returns {Series} - */ - var(axis = 1) { - if (this.__frame_is_compactible_for_operation) { - //check if all types are numeric - let tensor_vals = this.col_data_tensor.arraySync(); - let idx; - if (axis == 1) { - idx = this.column_names; - } else { - idx = this.index; - } - let median = variance(tensor_vals, axis); - let sf = new Series(median, { index: idx }); - return sf; - } else { - throw Error("TypeError: Dtypes of columns must be Float of Int"); - } - } - - /** - * Return number of non-null elements in a Series - * @returns {Series}, Count of non-null values - */ - count(axis = 1) { - if (this.__frame_is_compactible_for_operation) { - //check if all types are numeric - let tensor_vals, idx; - if (axis == 1) { - tensor_vals = this.col_data_tensor.arraySync(); - idx = this.column_names; - } else { - tensor_vals = this.row_data_tensor.arraySync(); - idx = this.index; - } - let counts = utils.__count_nan(tensor_vals, true, false); - let sf = new Series(counts, { index: idx }); - return sf; - } else { - throw Error("TypeError: Dtypes of columns must be Float of Int"); - } - } - - /** - * Rounds values in DataFrame to specified number of dp - * @returns {DataFrame}, New DataFrame with rounded values - */ - round(dp = 1) { - if (this.__frame_is_compactible_for_operation) { - //check if all types are numeric - let values = this.values; - let idx = this.index; - - let new_vals = utils.__round(values, dp, false); - let options = { columns: this.column_names, index: idx }; - let df = new DataFrame(new_vals, options); - return df; - } else { - throw Error("TypeError: Dtypes of columns must be Float of Int"); - } - } - - /** - * Perform Cummulative operations - * @param {axis} axis [int] {0 or 1} - * @param {ops} ops {String} name of operation - * @return {DataFrame} - */ - __cum_ops(axis = 0, ops) { - if (!(axis == 0) && !(axis == 1)) { - throw new Error("axis must be between 0 or 1"); - } - - if (this.__frame_is_compactible_for_operation) { - let data = []; - let df_data = null; - - if (axis == 0) { - df_data = this.col_data; - } else { - df_data = this.values; - } - - for (let i = 0; i < df_data.length; i++) { - let value = df_data[i]; - let temp_val = value[0]; - let temp_data = [temp_val]; - for (let j = 1; j < value.length; j++) { - let curr_val = value[j]; - switch (ops) { - case "max": - if (curr_val > temp_val) { - temp_val = curr_val; - temp_data.push(curr_val); - } else { - temp_data.push(temp_val); - } - break; - case "min": - if (curr_val < temp_val) { - temp_val = curr_val; - temp_data.push(curr_val); - } else { - temp_data.push(temp_val); - } - break; - case "sum": - temp_val = temp_val + curr_val; - temp_data.push(temp_val); - - break; - case "prod": - temp_val = temp_val * curr_val; - temp_data.push(temp_val); - - break; - } - } - data.push(temp_data); - } - - if (axis == 0) { - data = utils.__get_col_values(data); - } - - return new DataFrame(data, { columns: this.columns }); - } else { - throw Error("TypeError: Dtypes of columns must be Float of Int"); - } - } - /** - * calculate the cummulative sum along axis - * @param {kwargs} {axis: [int]} - * @returns {DataFrame} - */ - cumsum(kwargs = {}) { - let axis; - if (!utils.__key_in_object(kwargs, "axis")) { - axis = 0; - } else { - axis = kwargs["axis"]; - } - // let axis = kwargs["axis"] || 0 - let data = this.__cum_ops(axis, "sum"); - return data; - } - - /** - * calculate the cummulative min - * @param {kwargs} {axis: [int]} - * @returns {DataFrame} - */ - cummin(kwargs = {}) { - let axis; - if (!utils.__key_in_object(kwargs, "axis")) { - axis = 0; - } else { - axis = kwargs["axis"]; - } - let data = this.__cum_ops(axis, "min"); - return data; - } - - /** - * calculate the cummulative max - * @param {kwargs} {axis: [int]} - * @returns {DataFrame} - */ - cummax(kwargs = {}) { - let axis; - if (!utils.__key_in_object(kwargs, "axis")) { - axis = 0; - } else { - axis = kwargs["axis"]; - } - let data = this.__cum_ops(axis, "max"); - return data; - } - - /** - * calculate the cummulative prod - * @param {kwargs} {axis: [int]} - * @returns {DataFrame} - */ - cumprod(kwargs = {}) { - let axis; - if (!utils.__key_in_object(kwargs, "axis")) { - axis = 0; - } else { - axis = kwargs["axis"]; - } - let data = this.__cum_ops(axis, "prod"); - return data; - } - - /** - * Makes a new copy of a DataFrame - * @returns {DataFrame} - */ - copy() { - let df = new DataFrame([...this.values], { - columns: [...this.column_names], - index: this.index, - dtypes: this.dtypes - }); - return df; - } - - /** - * Generate a new index for the DataFrame. - * This is useful when the index is meaningless and needs to be reset to the default before another operation. - * @param {inplace} boolean: Modify the original object or return a new one. Default to false - */ - reset_index(inplace = false) { - if (inplace) { - this.__reset_index(); - } else { - let df = this.copy(); - df.__reset_index(); - return df; - } - } - - /** - * Set the DataFrame index (row labels) using an array of the same length. - * @param {kwargs} {index: Array of new index values} - */ - set_index(kwargs = {}) { - let params_needed = ["key", "drop", "inplace"]; - utils._throw_wrong_params_error(kwargs, params_needed); - - if (!utils.__key_in_object(kwargs, "key")) { - throw Error("Index ValueError: You must specify an array of index"); - } - - if (!utils.__key_in_object(kwargs, "inplace")) { - kwargs["inplace"] = false; - } - - if (!utils.__key_in_object(kwargs, "drop")) { - kwargs["drop"] = true; - } - - if ( - Array.isArray(kwargs["key"]) && - kwargs["key"].length != this.index.length - ) { - throw Error( - `Index LengthError: Lenght of new Index array ${kwargs["key"].length} must match lenght of existing index ${this.index.length}` - ); - } - - if ( - typeof kwargs["key"] == "string" && - this.column_names.includes(kwargs["key"]) - ) { - kwargs["key_name"] = kwargs["key"]; - kwargs["key"] = this[kwargs["key"]].values; - } - if (kwargs["inplace"]) { - // this.index_arr = kwargs['key'] - this.__set_index(kwargs["key"]); - if (kwargs["drop"] && typeof kwargs["key_name"] == "string") { - this.drop({ columns: [kwargs["key_name"]], inplace: true, axis: 1 }); - } - } else { - let df = this.copy(); - df.__set_index(kwargs["key"]); - if (kwargs["drop"] && typeof kwargs["key_name"] == "string") { - df.drop({ columns: [kwargs["key_name"]], axis: 1, inplace: true }); - } - return df; - } - } - - /** - * Generate descriptive statistics for all numeric columns - * Descriptive statistics include those that summarize the central tendency, - * dispersion and shape of a dataset’s distribution, excluding NaN values. - * @returns {Series} - */ - describe() { - let numeric_df = this.select_dtypes(["float32", "int32"]); - let col_names = numeric_df.column_names; - let index = ["count", "mean", "std", "min", "median", "max", "variance"]; - - let stats_arr = {}; - col_names.forEach((name) => { - let col_series = numeric_df[name]; - let count = col_series.count(); - let mean = col_series.mean(); - let std = col_series.std(); - let min = col_series.min(); - let median = col_series.median(); - let max = col_series.max(); - let variance = col_series.var(); - - let _stats = [count, mean, std, min, median, max, variance]; - stats_arr[name] = _stats; - }); - let df = new DataFrame(stats_arr, { index: index }); - return df.round(6); - } - - /** - * Return a subset of the DataFrame’s columns based on the column dtypes. - * @param {include} scalar or array-like. A selection of dtypes or strings to be included. At least one of these parameters must be supplied. - * @returns {DataFrame, Series} The subset of the frame including the dtypes. - */ - select_dtypes(include) { - let dtypes = this.dtypes; - let col_names = this.column_names; - let col_vals = {}; - let original_col_vals = this.col_data; - const __supported_dtypes = ["float32", "int32", "string", "boolean"]; - - if (include == undefined) { - //return all - let df = this.copy(); - return df; - } else { - //check if the right types are included - include.forEach((type) => { - if (!__supported_dtypes.includes(type)) { - throw Error(`Dtype Error: dtype ${type} not supported.`); - } - }); - - dtypes.forEach((dtype, i) => { - if (include.includes(dtype)) { - col_vals[col_names[i]] = original_col_vals[i]; - } - }); - let df = new DataFrame(col_vals); - return df; - } - } - - /** - * Sort a Dataframe in ascending or descending order by a specified column name. - * @param {kwargs} Object, {by: Column name to sort by - * ascending (Bool): Whether to return sorted values in ascending order or not, - * inplace (Bool): Whether to perform sorting on the original Series or not} - * @returns {Series} - */ - // sort_values(kwargs = {}) { - // if (utils.__key_in_object(kwargs, "by")) { - // let sort_col = this.column(kwargs["by"]) - // let sorted_col, sorted_index; - // let new_row_data = [] - - // if (utils.__key_in_object(kwargs, "inplace") && kwargs['inplace'] == true) { - // sort_col.sort_values(kwargs) - // sorted_index = sort_col.index - - // } else { - // sorted_col = sort_col.sort_values(kwargs) - // sorted_index = sorted_col.index - // } - - // sorted_index.map(idx => { - // new_row_data.push(this.values[idx]) - // }) - - // if (utils.__key_in_object(kwargs, "inplace") && kwargs['inplace'] == true) { - // this.data = new_row_data - // this.index_arr = sorted_index - // return null - // } else { - // let df = new DataFrame(new_row_data, { columns: this.column_names, index: sorted_index, dtype: this.dtypes }) - // return df - // } - - // } else { - // throw Error("Value Error: must specify the column to sort by") - // } - - // sorted_index.map(idx => { - // new_row_data.push(this.values[idx]) - // }) - - // if (utils.__key_in_object(kwargs, "inplace") && kwargs['inplace'] == true) { - // this.__update_frame_in_place(new_row_data, null, null, sorted_index, null) - - // } else { - // let df = new DataFrame(new_row_data, { columns: this.column_names, index: sorted_index, dtype: this.dtypes }) - // return df - // } - - // } else { - // throw Error("Value Error: must specify the column to sort by") - // } - - // } - - /** - * Return the sum of the values in a DataFrame across a specified axis. - * @params {kwargs} {axis: 0 for row and 1 for column} - * @returns {Series}, Sum of values accross axis - */ - sum(kwargs = { axis: 1 }) { - if (this.__frame_is_compactible_for_operation()) { - let values; - let val_sums = []; - if (kwargs["axis"] == 1) { - values = this.col_data; - } else { - values = this.values; - } - - values.map((arr) => { - let temp = utils._remove_nans(arr); - let temp_sum = tf.tensor(temp).sum().arraySync(); - val_sums.push(Number(temp_sum.toFixed(5))); - }); - - let new_index; - if (kwargs["axis"] == 1) { - new_index = this.column_names; - } else { - new_index = this.index; - } - let sf = new Series(val_sums, { columns: "sum", index: new_index }); - return sf; - } else { - throw Error("Dtype Error: Operation can not be performed on string type"); - } - } - - /** - * Returns the absolute values in DataFrame - * @return {DataFrame} - */ - abs() { - let data = this.values; - - let tensor_data = tf.tensor(data); - let abs_data = tensor_data.abs().arraySync(); - let df = new DataFrame(utils.__round(abs_data, 2, false), { - columns: this.column_names, - index: this.index - }); - return df; - } - - __get_tensor_and_idx(df, axis) { - let tensor_vals, idx, t_axis; - if (axis == 1) { - let temp_tensor_vals = df.row_data_tensor; - // Why do we flatten and replace NaNs with Null? See https://github.com/opensource9ja/danfojs/issues/200 - let flat_tensor_array = tf.util.flatten(temp_tensor_vals.arraySync()); - const flat_tensor_array_without_nans = utils._replace_nan_with_null(flat_tensor_array); - tensor_vals = tf.tensor(flat_tensor_array_without_nans, temp_tensor_vals.shape); - idx = df.column_names; - //Tensorflow uses 0 for column and 1 for rows, - // we use the opposite for consistency with the Pandas API (0 : row, 1: columns) - t_axis = 0; //switch the axis - } else { - tensor_vals = df.row_data_tensor; - idx = df.index; - t_axis = 1; - } - - return [tensor_vals, idx, t_axis]; - } - - /** - * Filter DataFrame element base on the element in a column - * @param {kwargs} kwargs {column : coumn name[string], is: String, to: string| int} - * @returns {DataFrame} - */ - query(kwargs) { - //define the set of operators to be used - let operators = [">", "<", "<=", ">=", "==", "!="]; - - if (!utils.__key_in_object(kwargs, "inplace")) { - kwargs["inplace"] = false; - } - - let column_index, operator, value; - - if (utils.__key_in_object(kwargs, "column")) { - if (this.columns.includes(kwargs["column"])) { - column_index = this.columns.indexOf(kwargs["column"]); - } else { - throw new Error(`column ${kwargs["column"]} does not exist`); - } - } else { - throw new Error("specify the column"); - } - - if (utils.__key_in_object(kwargs, "is")) { - if (operators.includes(kwargs["is"])) { - operator = kwargs["is"]; - } else { - throw new Error(` ${kwargs["is"]} is not a supported logical operator`); - } - } else { - throw new Error("specify an operator in param [is]"); - } - - if (utils.__key_in_object(kwargs, "to")) { - value = kwargs["to"]; - } else { - throw new Error("specify a value in param [to]"); - } - - let data = this.values; - let index = this.index; - let new_data = []; - let new_index = []; - - for (var i = 0; i < data.length; i++) { - let data_value = data[i]; - let elem = data_value[column_index]; - //use eval function for easy operation - //eval() takes in a string expression e.g eval('2>5') - if (eval(`elem${operator}value`)) { - new_data.push(data_value); - new_index.push(index[i]); - } - } - - if (new_data.length == 0) { - throw new Error( - `query returned empty data; is either ${value} does not exist in column ${kwargs["column"]}` - ); - } - if (kwargs["inplace"]) { - this.__update_frame_in_place( - new_data, - this.columns, - null, - new_index, - null - ); - } else { - let new_df = new DataFrame(new_data, { - columns: this.columns, - index: new_index - }); - return new_df; - } - } - - /** - * Add a column with values to the dataframe - * @param {kwargs} Object {column :[string] , value:[Array]} - * - */ - addColumn(kwargs) { - utils.__in_object(kwargs, "column", "column name not specified"); - utils.__in_object(kwargs, "value", "column value not specified"); - - let column_name = kwargs["column"]; - let data_length = this.shape[0]; - let value; - - if (kwargs["value"] instanceof Series) { - value = kwargs["value"].values; - } else { - value = kwargs["value"]; - } - - if (value.length != data_length) { - throw new Error( - `Array length ${value.length} not equal to ${data_length}` - ); - } - - if (this.columns.includes(column_name)) { - let col_idx = this.columns.indexOf(column_name); - let new_data = []; - - this.values.map((val, index) => { - let new_val = val.slice(); - new_val[col_idx] = value[index]; - new_data.push(new_val); - }); - this.__update_frame_in_place(new_data, null, null, null, null); - } else { - let data = this.values; - let new_data = []; - - data.map(function (val, index) { - let new_val = val.slice(); - new_val.push(value[index]); - new_data.push(new_val); - }); - - //add new dtype - let new_dtypes = [...this.dtypes]; - new_dtypes.push(utils.__get_t(value)[0]); - - let new_col_names = [...this.columns]; - new_col_names.push(column_name); - - this.__update_frame_in_place( - new_data, - new_col_names, - null, - null, - new_dtypes - ); - Object.defineProperty(this, column_name, { - get() { - return new Series(value, { columns: column_name, index: this.index }); - }, - set(value) { - this.addColumn({ column: column_name, value: value }); - } - }); - } - } - - /** - * - * @param {col} col is a list of column with maximum length of two - */ - groupby(col) { - const len = this.shape[0]; - const column_names = this.column_names; - const col_index = col.map((val) => column_names.indexOf(val)); - const col_dtype = this.dtypes.filter((val, index) => { - return col_index.includes(index); - }); - - const self = this; - const data = col.map( - (column_name) => { - if (!(column_names.includes(column_name))) - throw new Error(`column ${column_name} does not exist`); - const [column_data, _] = indexLoc(self, { - rows: [`0:${len}`], - columns: [`${column_name}`], - type: "loc" - }); - return column_data; - } - ); - - const unique_columns = data.map((column_data) => utils.__unique(column_data)); - - function getRecursiveDict(uniq_columns) { - const first_uniq_columns = uniq_columns[0]; - const remaining_columns = uniq_columns.slice(1); - const c_dict = {}; - if (!remaining_columns.length) - first_uniq_columns.forEach((col_value) => c_dict[col_value] = []); - else - first_uniq_columns.forEach((col_value) => c_dict[col_value] = getRecursiveDict(remaining_columns)); - return c_dict; - } - const col_dict = getRecursiveDict(unique_columns); - - return new GroupBy( - col_dict, - col, - this.values, - column_names, - col_dtype - ).group(); - } - - /** - * Return a sequence of axis dimension along row and columns - * @params col_name: the name of a column in the database. - * @returns tensor of shape 1 - */ - column(col_name) { - if (!this.columns.includes(col_name)) { - throw new Error(`column ${col_name} does not exist`); - } - let col_indx_objs = utils.__arr_to_obj(this.columns); - let indx = col_indx_objs[col_name]; - let data = this.col_data[indx]; - return new Series(data, { columns: [col_name] }); - } - - /** - * Replace NaN or undefined with a specified value" - * @param {kwargs}, {column(s): Array of column name(s) to fill. If undefined fill all columns; - * value(s): Array | Scalar of value(s) to fill with. If single value is specified, we use it to fill all - * @return {DataFrame} - */ - fillna(kwargs = {}) { - let params_needed = ["columns", "values", "inplace"]; - utils._throw_wrong_params_error(kwargs, params_needed); - - if (!utils.__key_in_object(kwargs, "inplace")) { - kwargs["inplace"] = false; - } - - if (utils.__key_in_object(kwargs, "columns")) { - //check if the column(s) exists - kwargs["columns"].map((col) => { - if (!this.column_names.includes(col)) { - throw Error( - `Value Error: Specified columns must be one of ${this.column_names}, got ${col}` - ); - } - }); - - if (kwargs["columns"].length != kwargs["values"].length) { - throw Error(`Lenght Error: The lenght of the columns names must be equal to the lenght of the values, - got column of length ${kwargs["columns"].length} but values of length ${kwargs["values"].length}`); - } - let new_col_data = this.col_data; - kwargs["columns"].map((col, i) => { - let col_idx = this.column_names.indexOf(col); - let col_data = this.col_data[col_idx]; - - let __temp = []; - col_data.map((val) => { - //fill the column - if (isNaN(val) && typeof val != "string") { - __temp.push(kwargs["values"][i]); - } else { - __temp.push(val); - } - }); - new_col_data[col_idx] = __temp; - }); - - let final_data = {}; - new_col_data.map((col, i) => { - final_data[this.column_names[i]] = col; - }); - - if (kwargs["inplace"]) { - this.__update_frame_in_place(null, null, final_data, null, null); - } else { - return new DataFrame(final_data, { index: this.index }); - } - } else { - //fill all columns using same value - if (!utils.__key_in_object(kwargs, "values")) { - throw Error("Value Error: Please specify a fill value"); - } - - let nan_val; - if (Array.isArray(kwargs["values"])) { - nan_val = kwargs["values"][0]; - } else { - nan_val = kwargs["values"]; - } - let data = []; - let values = this.values; - let columns = this.columns; - - for (let i = 0; i < values.length; i++) { - let temp_data = []; - let row_value = values[i]; - for (let j = 0; j < row_value.length; j++) { - if (isNaN(row_value[j]) && typeof row_value[j] != "string") { - temp_data.push(nan_val); - } else { - temp_data.push(row_value[j]); - } - } - data.push(temp_data); - } - if (kwargs["inplace"]) { - this.__update_frame_in_place(data, null, null, null, null); - } else { - return new DataFrame(data, { columns: columns, index: this.index }); - } - } - } - - /** - * Return a boolean same-sized object indicating if the values are NaN. NaN and undefined values, - * gets mapped to True values. Everything else gets mapped to False values. - * @return {DataFrame} - */ - isna() { - let new_row_data = this.__isna(); - let columns = this.column_names; - return new DataFrame(new_row_data, { columns: columns, index: this.index }); - } - - /** - * Obtain index containing nan values - * @return Array list (int) - */ - nanIndex() { - let df_values = this.values; - let index_data = []; - - for (let i = 0; i < df_values.length; i++) { - let row_values = df_values[i]; - - if (row_values.includes(NaN)) { - index_data.push(i); - } - } - return index_data; - } - - /** - * Drop all rows containing NaN - * @param {kwargs} kwargs [Object] {axis: [int]{o or 1}, inplace:[boolean]} - */ - dropna(kwargs = {}) { - let axis = kwargs["axis"] || 0; - let inplace = kwargs["inplace"] || false; - - if (axis != 0 && axis != 1) { - throw new Error("axis must either be 1 or 0"); - } - - let df_values = null; - let columns = null; - if (axis == 0) { - df_values = this.values; - columns = this.columns; - } else { - df_values = this.col_data; - columns = []; - } - let data = []; - - for (let i = 0; i < df_values.length; i++) { - let values = df_values[i]; - - if (!values.includes(NaN)) { - if (axis == 0) { - data.push(values); - } else { - columns.push(this.columns[i]); - if (data.length == 0) { - for (let j = 0; j < values.length; j++) { - data.push([values[j]]); - } - } else { - for (let j = 0; j < data.length; j++) { - data[j].push(values[j]); - } - } - } - } - } - - if (inplace == true) { - this.data = data; - this.__reset_index(); - this.columns = columns; - } else { - return new DataFrame(data, { columns: columns }); - } - } - - /** - * Apply a function to each element or along a specified axis of the DataFrame. Supports JavaScipt functions - * when axis is not specified, and accepts Tensorflow functions when axis is specified. - * @param {kwargs} kargs is defined as {axis: undefined, 0 or 1, callable: [FUNCTION]} - * @return Array - */ - apply(kwargs) { - let is_callable = utils.__is_function(kwargs["callable"]); - if (!is_callable) { - throw new Error("the argument must be a function"); - } - - let callable = kwargs["callable"]; - let data = []; - - if (utils.__key_in_object(kwargs, "axis")) { - //This accepts all tensorflow operations - let axis = kwargs["axis"]; - let df_data; - if (axis == 0) { - df_data = this.values; - } else { - df_data = this.col_data; - } - - for (let i = 0; i < df_data.length; i++) { - let value = tf.tensor(df_data[i]); - let callable_data; - try { - callable_data = callable(value).arraySync(); - } catch (error) { - throw Error( - `Callable Error: You can only apply JavaScript functions on DataFrames when axis is not specified. This operation is applied on all element, and returns a DataFrame of the same shape.` - ); - } - - data.push(callable_data); - } - } else { - //perform element wise operation. This accepts any JavaScript function - let df_data = this.values; - let new_data = []; - df_data.forEach((row) => { - let new_row = []; - row.forEach((val) => { - new_row.push(callable(val)); - }); - new_data.push(new_row); - }); - data = new_data; - } - - if (utils.__is_1D_array(data)) { - if (kwargs["axis"] == 0) { - let sf = new Series(data, { index: this.index }); - return sf; - } else { - let sf = new Series(data, { index: this.column_names }); - return sf; - } - } else { - let df = new DataFrame(data, { - columns: this.column_names, - index: this.index - }); - return df; - } - } - - /** - * Returns Less than of DataFrame and other. Supports element wise operations - * @param {other} DataFrame, Series, Scalar - * @param {axis} Number {0 for row, 1 for index} Whether to compare by the index or columns - * @return {DataFrame} - */ - lt(other, axis) { - if (this.__frame_is_compactible_for_operation()) { - if (axis == undefined) { - axis = 0; - } - let df = this.__logical_ops(other, "lt", axis); - return df; - } else { - throw Error("Dtype Error: Operation can not be performed on string type"); - } - } - - /** - * Returns Greater than of DataFrame and other. Supports element wise operations - * @param {other} DataFrame, Series, Scalar - * @param {axis} Number {0 for row, 1 for index} Whether to compare by the index or columns - * @return {DataFrame} - */ - gt(other, axis) { - if (this.__frame_is_compactible_for_operation()) { - if (axis == undefined) { - axis = 0; - } - - let df = this.__logical_ops(other, "gt", axis); - return df; - } else { - throw Error("Dtype Error: Operation can not be performed on string type"); - } - } - - /** - * Returns Less than or Equal to of DataFrame and other. Supports element wise operations - * @param {other} DataFrame, Series, Scalar - * @param {axis} Number {0 for row, 1 for index} Whether to compare by the index or columns - * @return {DataFrame} - */ - le(other, axis) { - if (this.__frame_is_compactible_for_operation()) { - if (axis == undefined) { - axis = 0; - } - let df = this.__logical_ops(other, "le", axis); - return df; - } else { - throw Error("Dtype Error: Operation can not be performed on string type"); - } - } - - /** - * Returns Greater than or Equal to of DataFrame and other. Supports element wise operations - * @param {other} DataFrame, Series, Scalar - * @param {axis} Number {0 for row, 1 for index} Whether to compare by the index or columns - * @return {DataFrame} - */ - ge(other, axis) { - if (this.__frame_is_compactible_for_operation()) { - if (axis == undefined) { - axis = 0; - } - let df = this.__logical_ops(other, "ge", axis); - return df; - } else { - throw Error("Dtype Error: Operation can not be performed on string type"); - } - } - - /** - * Returns Not Equal to of DataFrame and other. Supports element wise operations - * @param {other} DataFrame, Series, Scalar - * @param {axis} Number {0 for row, 1 for index} Whether to compare by the index or columns - * @return {DataFrame} - */ - ne(other, axis) { - if (this.__frame_is_compactible_for_operation()) { - if (axis == undefined) { - axis = 0; - } - let df = this.__logical_ops(other, "ne", axis); - return df; - } else { - throw Error("Dtype Error: Operation can not be performed on string type"); - } - } - - /** - * Returns Greater than or Equal to of DataFrame and other. Supports element wise operations - * @param {other} DataFrame, Series, Scalar - * @param {axis} Number {0 for row, 1 for index} Whether to compare by the index or columns - * @return {DataFrame} - */ - eq(other, axis) { - if (this.__frame_is_compactible_for_operation()) { - if (axis == undefined) { - axis = 0; - } - let df = this.__logical_ops(other, "eq", axis); - return df; - } else { - throw Error("Dtype Error: Operation can not be performed on string type"); - } - } - - /** - * Replace all occurence of a value with a new specified value" - * @param {kwargs}, {"replace": the value you want to replace, - * "with": the new value you want to replace the olde value with - * "in": Array of column names to replace value in, If not specified, replace all columns} - * @return {Series} - */ - replace(kwargs = {}) { - let params_needed = ["replace", "with", "in"]; - utils._throw_wrong_params_error(kwargs, params_needed); - - if (utils.__key_in_object(kwargs, "in")) { - //fill specified columns only - //check if the column(s) exists - kwargs["in"].map((col) => { - if (!this.column_names.includes(col)) { - throw Error( - `Value Error: Specified columns must be one of ${this.column_names}, got ${col}` - ); - } - }); - - if ( - utils.__key_in_object(kwargs, "replace") && - utils.__key_in_object(kwargs, "with") - ) { - let new_col_data_obj = {}; - this.column_names.map((col, idx) => { - if (kwargs["in"].includes(col)) { - let temp_col_data = this.col_data[idx]; //retreive the column data - let __temp = []; - temp_col_data.map((val) => { - //replace the values - if (val == kwargs["replace"]) { - __temp.push(kwargs["with"]); - } else { - __temp.push(val); - } - }); - new_col_data_obj[col] = __temp; - } else { - new_col_data_obj[col] = this.col_data[idx]; - } - }); - return new DataFrame(new_col_data_obj, { - columns: this.column_names, - index: this.index - }); - } else { - throw Error( - "Params Error: Must specify both 'replace' and 'with' parameters." - ); - } - } else { - //fill every occurence in all columns and rows - if ( - utils.__key_in_object(kwargs, "replace") && - utils.__key_in_object(kwargs, "with") - ) { - let replaced_arr = []; - let old_arr = this.values; - - old_arr.map((inner_arr) => { - let temp = []; - inner_arr.map((val) => { - if (val == kwargs["replace"]) { - temp.push(kwargs["with"]); - } else { - temp.push(val); - } - }); - replaced_arr.push(temp); - }); - - let df = new DataFrame(replaced_arr, { - index: this.index, - columns: this.column_names - }); - return df; - } else { - throw Error( - "Params Error: Must specify both 'replace' and 'with' parameters." - ); - } - } - } - - //performs logical comparisons on DataFrame using Tensorflow.js - __logical_ops(val, logical_type, axis) { - let int_vals, other; - if (utils.__is_number(val)) { - other = val; - } else { - if (val.series) { - //series - if (axis == 0) { - if (val.values.length != this.shape[0]) { - throw Error( - `Shape Error: Operands could not be broadcast together with shapes ${this.shape} and ${val.values.length}.` - ); - } - other = tf.tensor(val.values); - } else { - if (val.values.length != this.shape[1]) { - throw Error( - `Shape Error: Operands could not be broadcast together with shapes ${this.shape} and ${val.values.length}.` - ); - } - other = tf.tensor(val.values); - } - } else if (Array.isArray(val)) { - //Array of Array - other = tf.tensor(val); - } else { - //DataFrame - other = val.row_data_tensor; - } - } - - switch (logical_type) { - case "lt": - int_vals = tf.tensor(this.values).less(other).arraySync(); - break; - case "gt": - int_vals = tf.tensor(this.values).greater(other).arraySync(); - break; - case "le": - int_vals = tf.tensor(this.values).lessEqual(other).arraySync(); - break; - case "ge": - int_vals = tf.tensor(this.values).greaterEqual(other).arraySync(); - break; - case "ne": - int_vals = tf.tensor(this.values).notEqual(other).arraySync(); - break; - case "eq": - int_vals = tf.tensor(this.values).equal(other).arraySync(); - break; - } - let bool_vals = utils.__map_int_to_bool(int_vals, 2); - let df = new DataFrame(bool_vals, { - columns: this.column_names, - index: this.index - }); - return df; - } - - //slice the corresponding arrays from tensor objects - __get_df_from_tensor(val, col_names) { - let len = val.shape[0]; - let new_array = []; - for (let i = 0; i < len; i++) { - let arr = val.slice([i], [1]).arraySync()[0]; - new_array.push(arr); - } - return new DataFrame(new_array, { columns: col_names }); - } - - //checks if DataFrame is compaticble for arithmetic operation - //compatible Dataframe must have only numerical dtypes - __frame_is_compactible_for_operation() { - let dtypes = this.dtypes; - const str = (element) => element == "string"; - - if (dtypes.some(str)) { - return false; - } else { - return true; - } - } - - //retreives the corresponding tensors based on specified axis - __get_ops_tensors(tensors, axis) { - if (utils.__is_undefined(tensors[1].series)) { - //check if add operation is on a series or DataFrame - let tensors_arr = []; - if (utils.__is_undefined(axis) || axis == 1) { - //axis = 1 (column) - tensors_arr.push(tensors[0].row_data_tensor); - tensors_arr.push(tensors[1]); - return tensors_arr; - } else { - //axis = 0 (rows) - tensors_arr.push(tensors[0].col_data_tensor); - tensors_arr.push(tensors[1]); - return tensors_arr; - } - } else { - //operation is being performed on a Dataframe or Series - let tensors_arr = []; - if (utils.__is_undefined(axis) || axis == 1) { - //axis = 1 (column) - let this_tensor, other_tensor; - - this_tensor = tensors[0].row_data_tensor; //tensorflow uses 1 for rows axis and 0 for column axis - if (tensors[1].series) { - other_tensor = tf.tensor(tensors[1].values, [ - 1, - tensors[1].values.length - ]); - } else { - other_tensor = tensors[1].row_data_tensor; - } - - tensors_arr.push(this_tensor); - tensors_arr.push(other_tensor); - return tensors_arr; - } else { - //axis = 0 (rows) - let this_tensor, other_tensor; - - this_tensor = tensors[0].row_data_tensor; - if (tensors[1].series) { - other_tensor = tf.tensor(tensors[1].values, [ - tensors[1].values.length, - 1 - ]); - } else { - other_tensor = tensors[1].row_data_tensor; - } - - tensors_arr.push(this_tensor); - tensors_arr.push(other_tensor); - return tensors_arr; - } - } - } - - /** - * Transpose index and columns. - * Reflect the DataFrame over its main diagonal by writing rows as columns and vice-versa. - * The property T is an accessor to the method transpose(). - */ - transpose() { - let new_values = this.col_data; - let new_index = this.column_names; - let new_col_names = this.index; - - let df = new DataFrame(new_values, { - columns: new_col_names, - index: new_index - }); - return df; - } - - /** - * The property T is an accessor to the method transpose(). - */ - get T() { - return this.transpose(); - } - - /** - * Returns the data types in the DataFrame - * @return {Array} list of data types for each column - */ - get ctypes() { - let cols = this.column_names; - let d_types = this.col_types; - let sf = new Series(d_types, { index: cols }); - return sf; - } - - /** - * Make plots of Series or DataFrame. - * Uses the Plotly as backend, so supports Plotly's configuration parameters - * @param {string} div Name of the div to show the plot - * @returns {Class} Plot class that expoese different plot type - */ - plot(div) { - const plt = new Plot(this, div); - return plt; - } - - /** - * Returns the Tensorflow tensor backing the DataFrame Object - * @returns {2D tensor} - */ - get tensor() { - return this.row_data_tensor; - } - - /** - * Sets the data types of an DataFrame - * @param {Object} kwargs {column: Name of the column to cast, dtype: [float32, int32, string] data type to cast to} - * @returns {DataFrame} - */ - astype(kwargs = {}) { - if (!utils.__key_in_object(kwargs, "column")) { - throw Error("Value Error: Please specify a column to cast"); - } - - if (!utils.__key_in_object(kwargs, "dtype")) { - throw Error("Value Error: Please specify dtype to cast to"); - } - - if (!this.column_names.includes(kwargs["column"])) { - throw Error(`'${kwargs["column"]}' not found in columns`); - } - - let col_idx = this.column_names.indexOf(kwargs["column"]); - let new_types = this.col_types; - let col_values = this.col_data; - - new_types[col_idx] = kwargs["dtype"]; - let new_col_values = []; - let temp_col = col_values[col_idx]; - - switch (kwargs["dtype"]) { - case "float32": - temp_col.map((val) => { - new_col_values.push(Number(val)); - }); - col_values[col_idx] = new_col_values; - break; - case "int32": - temp_col.map((val) => { - new_col_values.push(Number(Number(val).toFixed())); - }); - col_values[col_idx] = new_col_values; - - break; - case "string": - temp_col.map((val) => { - new_col_values.push(String(val)); - }); - col_values[col_idx] = new_col_values; - break; - default: - break; - } - - let new_col_obj = {}; - this.column_names.forEach((cname, i) => { - new_col_obj[cname] = col_values[i]; - }); - - let df = new DataFrame(new_col_obj, { - dtypes: new_types, - index: this.index - }); - return df; - } - - /** - * Return the unique values along an axis - * @param {axis} Int, 0 for row, and 1 for column. Default to 1 - * @return {Object} - */ - unique(axis = 1) { - if (axis == undefined || axis > 1 || axis < 0) { - throw Error( - `Axis Error: Please specify a correct axis. Axis must either be '0' or '1', got ${axis}` - ); - } - let _unique = {}; - if (axis == 1) { - //column - let col_names = this.column_names; - col_names.forEach((cname) => { - _unique[cname] = this[cname].unique().values; - }); - } else { - let rows = this.values; - let _index = this.index; - rows.forEach((row, i) => { - let data_set = new Set(row); - _unique[_index[i]] = Array.from(data_set); - }); - } - - return _unique; - } - - /** - * Return the number of unique value along an axis - * @param {axis} Int, 0 for row, and 1 for column. Default to 1 - * @return {Series} - */ - nunique(axis = 1) { - if (axis == undefined || axis > 1 || axis < 0) { - throw Error( - `Axis Error: Please specify a correct axis. Axis must either be '0' or '1', got ${axis}` - ); - } - - let _nunique = []; - if (axis == 1) { - //column - let col_names = this.column_names; - col_names.forEach((cname) => { - _nunique.push(this[cname].unique().values.length); - }); - let sf = new Series(_nunique, { index: this.column_names }); - return sf; - } else { - let rows = this.values; - rows.forEach((row) => { - let data_set = new Set(row); - _nunique.push(Array.from(data_set).length); - }); - } - let sf = new Series(_nunique, { index: this.index }); - return sf; - } - - /** - * Change axes labels. Object values must be unique (1-to-1). - * Labels not contained in a dict / Series will be left as-is. Extra labels listed don’t throw an error. - * @param {Object} kwargs {mapper: Dict-like or functions transformations to apply to that axis’ values, - * axis: Int, 0 for row, and 1 for column. Default to 1, - * inplace: Whether to return a new DataFrame. If True then value of copy is ignored. - * @returns {DataFrame} - */ - rename(kwargs = {}) { - let params_needed = ["mapper", "inplace", "axis"]; - utils._throw_wrong_params_error(kwargs, params_needed); - - // utils.__in_object(kwargs, "columns", "value not defined") - if (!utils.__key_in_object(kwargs, "inplace")) { - kwargs["inplace"] = false; - } - if (!utils.__key_in_object(kwargs, "axis")) { - kwargs["axis"] = 1; - } - if (!utils.__key_in_object(kwargs, "mapper")) { - throw Error("Please specify a mapper object"); - } - if (kwargs["axis"] == 1) { - //columns - let old_col_names = Object.keys(kwargs["mapper"]); - let new_col_names = Object.values(kwargs["mapper"]); - let col_names = [...this.column_names]; - - old_col_names.forEach((cname, i) => { - if (!col_names.includes(cname)) { - throw Error( - `Label Error: Specified column '${cname}' not found in column axis` - ); - } - let idx = col_names.indexOf(cname); - col_names[idx] = new_col_names[i]; - }); - if (kwargs["inplace"]) { - this.columns = col_names; - this.__set_col_property(this, this.col_data, col_names, old_col_names); - } else { - let df = this.copy(); - df.columns = col_names; - df.__set_col_property(df, df.col_data, col_names, old_col_names); - return df; - } - } else { - //row - let old_index = Object.keys(kwargs["mapper"]); - let row_index = this.index; - let new_index = []; - - row_index.forEach((idx) => { - if (old_index.includes(idx)) { - new_index.push(kwargs["mapper"][idx]); - } else { - new_index.push(idx); - } - }); - - if (kwargs["inplace"]) { - this.__set_index(new_index); - } else { - let df = this.copy(); - df.__set_index(new_index); - return df; - } - } - } - /** - * Sort DataFrame by index - * @param {*} kwargs {inplace: Boolean, ascending: Bool} - * @returns DataFrame - */ - sort_index(kwargs = {}) { - let inplace = - typeof kwargs["inplace"] == "undefined" ? false : kwargs["inplace"]; - let asc = - typeof kwargs["ascending"] == "undefined" ? true : kwargs["ascending"]; - - let index_val = this.index; - let [data, index] = this.__sort_by(index_val, index_val, asc); - - if (inplace) { - this.__update_frame_in_place(data, null, null, index, null); - } else { - let df = this.copy(); - df.__update_frame_in_place(data, null, null, index, null); - return df; - } - } - - /** - * Sort a Dataframe in ascending or descending order by a specified column name. - * @param {kwargs} Object, {by: Column name to sort by - * ascending (Bool): Whether to return sorted values in ascending order or not, - * inplace (Bool): Whether to perform sorting on the original Series or not} - * @returns {Series} - */ - sort_values(kwargs = {}) { - if (!utils.__key_in_object(kwargs, "by")) { - throw Error(`use col_name to specify column name`); - } - - let inplace = - typeof kwargs["inplace"] == "undefined" ? false : kwargs["inplace"]; - let asc = - typeof kwargs["ascending"] == "undefined" ? true : kwargs["ascending"]; - let index_val = this.index; - let column_val = this.column(kwargs["by"]).values; - let [data, index] = this.__sort_by(column_val, index_val, asc); - - if (inplace) { - this.__update_frame_in_place(data, null, null, index, null); - } else { - let df = this.copy(); - df.__update_frame_in_place(data, null, null, index, null); - return df; - } - } - - //set all columns to DataFrame Property. This ensures easy access to columns as Series - __set_col_property(self, col_vals, col_names, old_col_names) { - //delete old name - old_col_names.forEach((name) => { - delete self[name]; - }); - - col_vals.forEach((col, i) => { - // self[col_names[i]] = new Series(col, { columns: col_names[i], index: self.index }) - Object.defineProperty(self, col_names[i], { - get() { - return new Series(col, { columns: col_names[i], index: self.index }); - }, - set(value) { - this.addColumn({ column: col_names[i], value: value }); - } - }); - }); - } - - //update a DataFrame in place - __update_frame_in_place(row_data, column_names, col_obj, index, dtypes) { - if (row_data != undefined) { - this.data = row_data; - } else { - //check column is available and create row from column - if (col_obj != undefined) { - let _res = utils._get_row_and_col_values(col_obj); - this.data = _res[0]; - this.columns = _res[1]; - column_names = _res[1]; - } - } - - if (col_obj != undefined) { - this.col_data = Object.values(col_obj); - this.columns = Object.keys(col_obj); - column_names = Object.keys(col_obj); - } else { - //check if row data is available and create column data from rows - if (row_data != undefined) { - this.col_data = utils.__get_col_values(row_data); //get column data from row - } - } - - if (column_names != undefined) { - this.columns = column_names; - } - if (index != undefined) { - this.index_arr = index; - } - if (dtypes != undefined) { - this.col_types = dtypes; - } - } - - __sort_by(col_value, df_index, asc) { - let values = this.values; - - let sorted_val = utils.__sort(col_value, asc); - let duplicate_obj = utils.__get_duplicate(col_value); - - let data = []; - let indexs = []; - for (let row_i = 0; row_i < sorted_val.length; row_i++) { - let val = sorted_val[row_i]; - let index = null; - - if (duplicate_obj.hasOwnProperty(val)) { - index = duplicate_obj[val]["index"][0]; - duplicate_obj[val]["index"].splice(0, 1); - } else { - index = col_value.indexOf(val); - } - - data.push(values[index]); - indexs.push(df_index[index]); - } - - return [data, indexs]; - } - - /** - * Append rows to a DataFrame - * @param {val} val Array | Series to append to the object - * @return DataFrame - */ - append(val) { - let df2 = null; - if (Array.isArray(val)) { - if (Array.isArray(val[0])) { - if (val[0].length != this.shape[1]) { - throw Error( - `length Mixmatch: The lenght of provided value (${val.length}) does not match the original DataFrame (${this.shape[1]})` - ); - } - df2 = new DataFrame(val); - } - } else if (utils.__is_object(val)) { - df2 = new DataFrame(val); - } else if (val instanceof DataFrame) { - df2 = val.copy(); - } - - let concat_df = concat({ df_list: [this, df2], axis: 0 }); - - return concat_df; - } -} diff --git a/danfojs-node/src/core/generic.js b/danfojs-node/src/core/generic.js deleted file mode 100644 index 8b41f412..00000000 --- a/danfojs-node/src/core/generic.js +++ /dev/null @@ -1,500 +0,0 @@ -/** -* Copyright 2020, JsData. -* All rights reserved. -* -* This source code is licensed under the MIT license found in the -* LICENSE file in the root directory of this source tree. - -* Unless required by applicable law or agreed to in writing, software -* distributed under the License is distributed on an "AS IS" BASIS, -* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -* See the License for the specific language governing permissions and -* limitations under the License. - * -*/ - -import * as tf from '@tensorflow/tfjs-node'; -import { table } from "table"; -import { Utils } from "./utils"; -import { Configs } from "../config/config"; - - -const utils = new Utils(); -const config = new Configs(); //package wide configuration object -export default class NDframe { - /** - * N-Dimensiona data structure. Stores multi-dimensional - * data in a size-mutable, labeled data structure. Analogous to the Python Pandas DataFrame. - * - * @param {data} Array JSON, Tensor. Block of data. - * @param {kwargs} Object Optional Configuration Object - * {columns: Array of column names. If not specified and data is an array of array, use range index. - * dtypes: Data types of the columns, - * index: row index for subseting array } - * - * @returns NDframe - */ - - constructor(data, kwargs = {}) { - this.kwargs = kwargs; - - if (data instanceof tf.Tensor) { - data = data.arraySync(); - } - - if (utils.__is_1D_array(data)) { - this.series = true; - this._read_array(data); - } else { - this.series = false; - if (utils.__is_object(data[0])) { - //check the type of the first object in the data - this._read_object(data, 1); //type 1 object are of JSON form [{a: 1, b: 2}, {a: 30, b: 20}] - } else if (utils.__is_object(data)) { - this._read_object(data, 2); //type 2 object are of the form {a: [1,2,3,4], b: [30,20, 30, 20}] - } else if ( - Array.isArray(data[0]) || - utils.__is_number(data[0]) || - utils.__is_string(data[0]) - ) { - this._read_array(data); - } else { - throw new Error("File format not supported"); - } - } - } - - /** - * - * @param {Array} data - * Read array of data into NDFrame - */ - _read_array(data) { - this.data = utils.__replace_undefined_with_NaN(data, this.series); - this.row_data_tensor = tf.tensor(this.data); - - if (this.series) { - this.col_data = [ this.values ]; - } else { - this.col_data = utils.__get_col_values(this.data); - } - - this.col_data_tensor = tf.tensor(this.col_data); //data saved as 2D column tensors - - if ("index" in this.kwargs) { - this.__set_index(this.kwargs["index"]); - } else { - this.index_arr = [ ...Array(this.row_data_tensor.shape[0]).keys() ]; - } - - if (this.ndim == 1) { - //series array - if ("columns" in this.kwargs) { - this.columns = this.kwargs["columns"]; - } else { - this.columns = [ "0" ]; - } - } else { - //2D Array - if ("columns" in this.kwargs) { - if ( - this.kwargs["columns"].length == Number(this.row_data_tensor.shape[1]) - ) { - this.columns = this.kwargs["columns"]; - } else { - throw `Column length mismatch. You provided a column of length ${this.kwargs["columns"].length} but data has lenght of ${this.row_data_tensor.shape[1]}`; - } - } else { - this.columns = [ ...Array(this.row_data_tensor.shape[1]).keys() ]; - } - } - - if ("dtypes" in this.kwargs) { - this._set_col_types(this.kwargs["dtypes"], false); - } else { - this._set_col_types(null, true); //infer dtypes - } - } - - /** - * Convert Javascript Object of arrays into NDFrame - * @param {*} data Object of Arrays - * @param {*} type type 1 object are of JSON form [{a: 1, b: 2}, {a: 30, b: 20}], - * type 2 object are of the form {a: [1,2,3,4], b: [30,20, 30, 20}] - */ - _read_object(data, type) { - if (type == 2) { - let [ row_arr, col_names ] = utils._get_row_and_col_values(data); - this.kwargs["columns"] = col_names; - this._read_array(row_arr); - } else { - let data_arr = data.map((item) => { - return Object.values(item); - }); - - this.data = utils.__replace_undefined_with_NaN(data_arr, this.series); //Defualt array data in row format - this.row_data_tensor = tf.tensor(this.data); //data saved as row tensors - this.kwargs["columns"] = Object.keys(Object.values(data)[0]); //get names of the column from the first entry - - if (this.series) { - this.col_data = [ this.values ]; //data saved as 1D column tensors - } else { - this.col_data = utils.__get_col_values(this.data); - } - - this.col_data_tensor = tf.tensor(this.col_data); //data saved as 2D column tensors - - if ("index" in this.kwargs) { - this.__set_index(this.kwargs["index"]); - } else { - this.index_arr = [ ...Array(this.row_data_tensor.shape[0]).keys() ]; - } - - if (this.ndim == 1) { - //series array - if (!this.kwargs["columns"]) { - this.columns = [ "0" ]; - } else { - this.columns = this.kwargs["columns"]; - } - } else { - //2D Array - if ("columns" in this.kwargs) { - if ( - this.kwargs["columns"].length == - Number(this.row_data_tensor.shape[1]) - ) { - this.columns = this.kwargs["columns"]; - } else { - throw `Column length mismatch. You provided a column of length ${this.kwargs["columns"].length} but data has column length of ${this.row_data_tensor.shape[1]}`; - } - } else { - this.columns = [ ...Array(this.row_data_tensor.shape[1]).keys() ]; - } - } - - if ("dtypes" in this.kwargs) { - this._set_col_types(this.kwargs["dtypes"], false); - } else { - this._set_col_types(null, true); //infer dtypes - } - } - } - - /** - * Sets the data type of the NDFrame. Supported types are ['float32', "int32", 'string', 'boolean'] - * @param {Array} dtypes Array of data types. - * @param {Boolean} infer Whether to automatically infer the dtypes from the Object - */ - _set_col_types(dtypes, infer) { - const __supported_dtypes = [ "float32", "int32", "string", "boolean" ]; - - if (infer) { - if (this.series) { - this.col_types = utils.__get_t(this.values); - } else { - this.col_types = utils.__get_t(this.col_data); - } - } else { - if (this.series) { - this.col_types = dtypes; - } else { - if (dtypes.length != this.columns.length) { - throw new Error( - `length Mixmatch: Length of specified dtypes is ${dtypes.length}, but length of columns is ${this.columns.length}` - ); - } - if (Array.isArray(dtypes)) { - dtypes.forEach((type, indx) => { - if (!__supported_dtypes.includes(type)) { - throw new Error( - `dtype error: dtype specified at index ${indx} is not supported` - ); - } - }); - this.col_types = dtypes; - } else { - throw new Error(`dtypes must be an Array of types`); - } - } - } - } - - /** - * Returns the data types in the DataFrame - * @return {Array} list of data types for each column - */ - get dtypes() { - // let col_data = utils.get_col_values(this.data) - // this.col_types = utils.__get_t(col_data) - // let sf = new Series({dtypes: this.col_types, index: this.column_names}) - return this.col_types; - } - - /** - * Gets dimension of the NDFrame - * @returns {Integer} dimension of NDFrame - */ - get ndim() { - if (this.series) { - return 1; - } else { - return this.row_data_tensor.shape.length; - } - } - - /** - * Gets values for index and columns - * @return {Object} axes configuration for index and columns of NDFrame - */ - get axes() { - let axes = { - index: this.index, - columns: this.columns - }; - return axes; - } - - /** - * Gets index of the NDframe - * @return {Array} array of index from series - */ - get index() { - return this.index_arr; - } - - /** - * Sets index of the NDFrame - */ - __set_index(labels) { - if (!Array.isArray(labels)) { - throw Error("Value Error: index must be an array"); - } - if (labels.length > this.shape[0] || labels.length < this.shape[0]) { - throw Error("Value Error: length of labels must match row shape of data"); - } - this.index_arr = labels; - } - - /** - * Generate a new index for NDFrame. - */ - __reset_index() { - let new_idx = [ ...Array(this.values.length).keys() ]; - this.index_arr = new_idx; - } - - /** - * Gets a sequence of axis dimension along row and columns - * @returns {Array} the shape of the NDFrame - */ - get shape() { - if (this.series) { - return [ this.values.length, 1 ]; - } else { - return this.row_data_tensor.shape; - } - } - - /** - * Gets the values in the NDFrame in JS array - * @returns {Array} Arrays of arrays of data instances - */ - get values() { - return this.data; - } - - /** - * Gets the column names of the data - * @returns {Array} strings of column names - */ - get column_names() { - return this.columns; - } - - /** - * Return a boolean same-sized object indicating if the values are NaN. NaN and undefined values - * gets mapped to True values. Everything else gets mapped to False values. - * @return {Array} - */ - __isna() { - let new_arr = []; - if (this.series) { - this.values.map((val) => { - // eslint-disable-next-line use-isnan - if (val == NaN) { - new_arr.push(true); - } else if (isNaN(val) && typeof val != "string") { - new_arr.push(true); - } else { - new_arr.push(false); - } - }); - } else { - let row_data = this.values; - row_data.map((arr) => { - let temp_arr = []; - arr.map((val) => { - // eslint-disable-next-line use-isnan - if (val == NaN) { - temp_arr.push(true); - } else if (isNaN(val) && typeof val != "string") { - temp_arr.push(true); - } else { - temp_arr.push(false); - } - }); - new_arr.push(temp_arr); - }); - } - return new_arr; - } - - /* - * Gets binary size of the NDFrame - * @returns {String} size of the NDFrame - */ - get size() { - return this.row_data_tensor.size; - } - - /** - * Return object data as comma-separated values (csv). - * @returns {Promise} CSV representation of Object data - */ - async to_csv() { - if (this.series) { - let csv = this.values.join(","); - return csv; - } else { - let records = this.values; - let header = this.column_names.join(","); - - let csv_str = `${header}\n`; - records.forEach((val) => { - let row = `${val.join(",")}\n`; - csv_str += row; - }); - return csv_str; - } - } - - /** - * Return object as JSON string. - * @returns {Promise } JSON representation of Object data - */ - async to_json() { - if (this.series) { - let obj = {}; - obj[this.column_names[0]] = this.values; - let json = JSON.stringify(obj); - return json; - } else { - let values = this.values; - let header = this.column_names; - let json_arr = []; - values.forEach((val) => { - let obj = {}; - header.forEach((h, i) => { - obj[h] = val[i]; - }); - json_arr.push(obj); - }); - return JSON.stringify(json_arr); - } - } - - /** - * Prints the data in a Series as a grid of row and columns - */ - toString() { - let table_width = config.get_width; - let table_truncate = config.get_truncate; - let max_row = config.get_max_row; - let max_col_in_console = config.get_max_col_in_console; - - // let data; - let data_arr = []; - let table_config = {}; - // let idx = this.index - let col_len = this.columns.length; - // let row_len = this.values.length - 1 - let header = []; - - if (col_len > max_col_in_console) { - //truncate displayed columns to fit in the console - let first_4_cols = this.columns.slice(0, 4); - let last_3_cols = this.columns.slice(col_len - 4); - //join columns with truncate ellipse in the middle - header = [ "" ].concat(first_4_cols).concat([ "..." ]).concat(last_3_cols); - - let sub_idx, values_1, value_2; - - if (this.values.length > max_row) { - //slice Object to show [max_rows] - let df_subset_1 = this.iloc({ - rows: [ `0:${max_row}` ], - columns: [ "0:4" ] - }); - let df_subset_2 = this.iloc({ - rows: [ `0:${max_row}` ], - columns: [ `${col_len - 4}:` ] - }); - sub_idx = this.index.slice(0, max_row); - values_1 = df_subset_1.values; - value_2 = df_subset_2.values; - } else { - let df_subset_1 = this.iloc({ rows: [ "0:" ], columns: [ "0:4" ] }); - let df_subset_2 = this.iloc({ - rows: [ "0:" ], - columns: [ `${col_len - 4}:` ] - }); - sub_idx = this.index.slice(0, max_row); - values_1 = df_subset_1.values; - value_2 = df_subset_2.values; - } - - // merge dfs - sub_idx.map((val, i) => { - let row = [ val ].concat(values_1[i]).concat([ "..." ]).concat(value_2[i]); - data_arr.push(row); - }); - } else { - //display all columns - header = [ "" ].concat(this.columns); - let idx, values; - if (this.values.length > max_row) { - //slice Object to show a max of [max_rows] - let data = this.loc({ rows: [ `0:${max_row}` ], columns: this.columns }); - idx = data.index; - values = data.values; - } else { - values = this.values; - idx = this.index; - } - - // merge cols - idx.forEach((val, i) => { - let row = [ val ].concat(values[i]); - data_arr.push(row); - }); - } - - //set column width of all columns - table_config[0] = 10; - for (let index = 1; index < header.length; index++) { - table_config[index] = { width: table_width, truncate: table_truncate }; - } - - let table_data = [ header ].concat(data_arr); //Adds the column names to values before printing - return table(table_data, { columns: table_config }); - } - - /** - * Pretty prints n number of rows in a DataFrame or Series in the console - * @param {rows} Number of rows to print - */ - print() { - console.log(this + ""); - } -} diff --git a/danfojs-node/src/core/get_dummies.js b/danfojs-node/src/core/get_dummies.js deleted file mode 100644 index 32a5391d..00000000 --- a/danfojs-node/src/core/get_dummies.js +++ /dev/null @@ -1,139 +0,0 @@ -import { DataFrame } from "./frame"; -import { Utils } from "./utils"; - -const utils = new Utils; - -function oneHot(in_data, prefix, prefix_sep) { - let data_set = new Set(in_data); - let labels = Array.from(data_set); - let prefix_labels = null; - if (prefix) { - prefix_labels = labels.map((x) => { - return prefix + prefix_sep + x; - }); - } else { - prefix_labels = labels.map((x) => { - return x; - }); - } - - let onehot_data = utils.__zeros(in_data.length, labels.length); - - for (let i = 0; i < in_data.length; i++) { - - let elem = in_data[i]; - let elem_index = labels.indexOf(elem); - onehot_data[i][elem_index] = 1; - } - - return [ onehot_data, prefix_labels ]; - -} - -/** - * Generate one hot encoding for categorical variable in arrays |Serie | and Dataframe - * @param {kwargs} kwargs { data : Array | Series | DataFrame, - * prefix_sep: String e.g "_", - * prefix: String | Array of String, - * columns: [Array] columns to be encoded in DataFrame. - * } - */ -function get_dummy(kwargs = {}) { - utils.__in_object(kwargs, "data", "data not provided"); - - let prefix = kwargs["prefix"] || null; - let prefix_sep = kwargs["prefix_sep"] || [ "_" ]; - let columns = kwargs["columns"] || null; - - let is_dataframe = false; - let in_data = null; - - if (Array.isArray(kwargs["data"])) { - in_data = kwargs["data"]; - } else if (kwargs["data"] instanceof DataFrame) { - in_data = kwargs["data"]; - is_dataframe = true; - } else { - in_data = kwargs["data"].values; - } - - if (!is_dataframe) { - let [ onehot_data, prefix_labels ] = oneHot(in_data, prefix, prefix_sep); - - return new DataFrame(onehot_data, { columns: prefix_labels }); - } else { - - let column_index = []; - if (!columns) { - columns = []; - in_data.col_types.map((x, i) => { - - if (x == "string") { - let name_column = in_data.columns[i]; - columns.push(name_column); - column_index.push(i); - } - }); - } else { - - columns.forEach((x) => { - let col_idx = columns.indexOf(x); - column_index.push(col_idx); - }); - } - - if (prefix) { - if (Array.isArray(prefix)) { - if (prefix.length != columns.length) { - throw new Error("prefix must be the same length with the number of onehot encoding column"); - } - } else { - throw new Error("prefix for dataframe must be an array"); - } - } else { - prefix = columns; - } - - let df_data = in_data.values; - let df_columns = in_data.columns; - let col_data = in_data.col_data; - - let column_data = []; - column_index.forEach((x) => { - column_data.push(col_data[x]); - }); - - let one_hotColumns = []; - let one_hotData = []; - column_data.forEach((data, i) => { - - let [ onehot_data, prefix_labels ] = oneHot(data, prefix[i], prefix_sep); - one_hotColumns.push(...prefix_labels); - - if (one_hotData.length == 0) { - one_hotData.push(...onehot_data); - } else { - onehot_data.forEach((x, i) => { - one_hotData[i].push(...x); - }); - } - - }); - - let final_data = df_data.map((elem, i) => { - - let ele = elem.slice(); - let dt = utils.__remove_arr(ele, column_index); - dt.push(...one_hotData[i]); - return dt; - }); - - let final_columns = utils.__remove_arr(df_columns, column_index); - final_columns.push(...one_hotColumns); - - return new DataFrame(final_data, { columns: final_columns }); - } - -} - -export const get_dummies = get_dummy; diff --git a/danfojs-node/src/core/groupby.js b/danfojs-node/src/core/groupby.js deleted file mode 100644 index 7d11d916..00000000 --- a/danfojs-node/src/core/groupby.js +++ /dev/null @@ -1,332 +0,0 @@ -import { DataFrame } from "./frame"; -import { Utils } from "./utils"; -import { Series } from "./series"; -const utils = new Utils; - -/** - * The class performs all groupby operation on a dataframe - * involveing all aggregate funciton - * @param {col_dict} col_dict Object of unique keys in the group by column - * @param {key_col} key_col Array contains the column names - * @param {data} Array the dataframe data - * @param {column_name} Array of all column name in the dataframe. - */ -export class GroupBy { - constructor(col_dict, key_col, data, column_name, col_dtype) { - - this.key_col = key_col; - this.col_dict = col_dict; - this.data = data; - this.column_name = column_name; - this.data_tensors = {}; //store the tensor version of the groupby data - this.col_dtype = col_dtype; - - } - - /** - * Group the dataframe by the column by - * creating an object to store the grouping - * @returns Groupby data structure - */ - group(){ - for (const value of this.data){ - const col_indexes = this.key_col.map((key) => this.column_name.indexOf(key)); - const col_values = col_indexes.map((idx) => value[idx]); - - let sub_col_dict = this.col_dict; - for (const col_value of col_values){ - if (!(col_value in sub_col_dict)) - break; - if (col_value === col_values[col_values.length - 1]) - sub_col_dict[col_value].push(value); - else - sub_col_dict = sub_col_dict[col_value]; - } - } - - const self = this; - function dfs(sub_col_dict, sub_data_tensors){ - for (const [ key, value ] of Object.entries(sub_col_dict)){ - if (Array.isArray(value)) { - if (value.length === 0) - delete sub_col_dict[key]; - else - sub_data_tensors[key] = new DataFrame(value, { columns:self.column_name }); - } else { - if (!(key in sub_data_tensors)) - sub_data_tensors[key] = {}; - dfs(value, sub_data_tensors[key]); - } - } - } - dfs(this.col_dict, this.data_tensors); - - - return this; - - } - - /** - * obtain the column for each group - * @param {col_name} col_name [Array]--> array of column names - * @return Groupby data structure - */ - col(col_names){ - this.selected_column = col_names; // store col_names for use later in .apply - if (Array.isArray(col_names)){ - - for (let i = 0; i < col_names.length; i++){ - - let col_name = col_names[i]; - if (!this.column_name.includes(col_name)){ - throw new Error(`Column ${col_name} does not exist in groups`); - } - } - } else { - throw new Error(`Col_name must be an array of column`); - } - - const group_col = {}; - function dfs(sub_data_tensors, sub_group_col) { - for (const [ key, value ] of Object.entries(sub_data_tensors)){ - if (value instanceof DataFrame) { - sub_group_col[key] = col_names.map((col_name) => value.column(col_name)); - } else { - sub_group_col[key] = {}; - dfs(value, sub_group_col[key]); - } - } - } - dfs(this.data_tensors, group_col); - - const gp = new GroupBy( - null, - this.key_col, - null, - col_names, - this.col_dtype - ); - gp.group_col = group_col; - gp.group_col_name = col_names; - return gp; - } - - /** - * Basic root of all column arithemetic in groups - * @param {operation} operation String - */ - arithemetic(operation){ - - let ops_name = [ "mean", "sum", "count", "mode", "std", "var", "cumsum", "cumprod", - "cummax", "cummin" ]; - let ops_map = { - "mean": "mean()", - "sum": "sum()", - "mode": "mode()", - "count": "count()", - "std" : "std()", - "var" : "var()", - "cumsum" : "cumsum().values", - "cumprod": "cumprod().values", - "cummax" : "cummax().values", - "cummin" : "cummin().values" - }; - const is_array_operation = Array.isArray(operation); - const count_group = {}; - - function dfs(sub_count_group, sub_group_col) { - for (const [ key, value ] of Object.entries(sub_group_col)){ - if (Array.isArray(value)) { - sub_count_group[key] = []; - let data; - if (is_array_operation) { - for (let i = 0; i < value.length; i++){ - const op = operation[i]; - if (!ops_name.includes(op)){ - throw new Error("operation does not exist"); - } - data = eval(`value[i].${ops_map[op]}`); - sub_count_group[key].push(data); - } - } else { - value.forEach((v) => { - data = eval(`v.${operation}`); - sub_count_group[key].push(data); - }); - } - } else { - sub_count_group[key] = {}; - dfs(sub_count_group[key], value); - } - } - } - - dfs(count_group, this.group_col); - return count_group; - } - - operations(ops, name) { - if (!this.group_col) { - let column = this.column_name.filter((val) => !this.key_col.includes(val)); - let col_gp = this.col(column); - let value = col_gp.arithemetic(ops); - let df = col_gp.to_DataFrame(col_gp.key_col, col_gp.group_col_name, value, name); - return df; - } else { - let value = this.arithemetic(ops); - let df = this.to_DataFrame(this.key_col, this.group_col_name, value, name); - return df; - } - } - count(){ - return this.operations("count()", "count"); - } - - sum(){ - return this.operations("sum()", "sum"); - } - - std(){ - return this.operations("std()", "std"); - } - - var(){ - return this.operations("var()", "var"); - } - - mean(){ - return this.operations("mean()", "mean"); - } - - cumsum(){ - return this.operations("cumsum().values", "cumsum"); - } - cummax(){ - return this.operations("cummax().values", "cummax"); - } - - cumprod(){ - return this.operations("cumprod().values", "cumprod"); - } - - cummin(){ - return this.operations("cummin().values", "cummin"); - } - - max(){ - return this.operations("max()", "max"); - } - - min(){ - return this.operations("min()", "min"); - } - - /** - * returns dataframe of a group - * @param {*} key [Array] - */ - get_groups(key){ - if (this.key_col.length < 2) - return this.data_tensors[key]; - - if (key.length !== this.key_col.length) - throw new Error("specify the group by column"); - - utils.__is_object(this.data_tensors, key[0], `Key Error: ${key[0]} not in object`); - const last_key = key[key.length - 1]; - let sub_data_tensors = this.data_tensors; - for (const k of key) { - if (k === last_key) - return sub_data_tensors[k]; - else - sub_data_tensors = sub_data_tensors[k]; - } - } - - /** - * Map every column to an operaton - * @param {kwargs} kwargs {column name: math operation} - * @example .agg({"A": "mean","B": "sum","C":"count"}) - */ - agg(kwargs = {}){ - - let columns = Object.keys(kwargs); - let operations = columns.map((x) => { return kwargs[x].toLocaleLowerCase(); }); - - let col_gp = this.col(columns); - - let data = col_gp.arithemetic(operations); - let df = this.to_DataFrame(col_gp.key_col, col_gp.group_col_name, data, operations); - - return df; - } - - to_DataFrame(key_col, col, data, ops){ - const df_data = []; - - function concatPathAndNode(path, node, col_dtype) { - if (Array.isArray(node)) { - if (Array.isArray(node[0])) { - const transposed_node = node[0].map((_, colIndex) => node.map((row) => row[colIndex])); - for (const n_array of transposed_node) - df_data.push(path.concat(n_array)); - } else - df_data.push(path.concat(node)); - } else { - for (const [ k, child ] of Object.entries(node)) { - const sanitized_k = col_dtype[0] === "string" ? k : parseInt(k); - concatPathAndNode(path.concat([ sanitized_k ]), child, col_dtype.slice(1)); - } - } - } - - concatPathAndNode([], data, this.col_dtype); - - const column = [ ...key_col ]; - const group_col = col.slice().map((x, i) => { - if (Array.isArray(ops)){ - return `${x}_${ops[i]}`; - } - return `${x}_${ops}`; - }); - column.push(...group_col); - - return new DataFrame(df_data, { columns: column }); - } - - apply(callable){ - let df_data; - let column; - if (!this.group_col) { - column = this.column_name.filter((val) => !this.key_col.includes(val)); - const col_gp = this.col(column); - df_data = col_gp.group_col; - } else { - column = this.group_col_name; - df_data = this.group_col; - } - const count_group = {}; - - function recursiveCount(sub_df_data, sub_count_group) { - for (const [ key, value ] of Object.entries(sub_df_data)) { - if (Array.isArray(value)) { - sub_count_group[key] = value.map(( callable_value ) => { - const callable_rslt = callable(callable_value); - if ((callable_rslt instanceof DataFrame) || (callable_rslt instanceof Series)) - return callable_rslt.values; - else - return callable_rslt; - }); - } else { - sub_count_group[key] = {}; - recursiveCount(value, sub_count_group[key]); - } - } - } - - recursiveCount(df_data, count_group); - - return this.to_DataFrame(this.key_col, column, count_group, "apply"); - } - -} diff --git a/danfojs-node/src/core/indexing.js b/danfojs-node/src/core/indexing.js deleted file mode 100644 index a69da513..00000000 --- a/danfojs-node/src/core/indexing.js +++ /dev/null @@ -1,198 +0,0 @@ -import { Utils } from "./utils"; -const utils = new Utils(); - - -/** - * Obtain the defined the set of row and column index - * @param {*} kwargs object {rows:Array, columns:Array of column name, type: ["iloc","loc"]} - * @return Array - */ -export const indexLoc = (ndframe, kwargs) => { - - let rows = null; - let columns = null; - let isColumnSplit = false; - - if (Object.prototype.hasOwnProperty.call(kwargs, "rows")) { //check if the object has the key - if (Array.isArray(kwargs["rows"])) { - - if (kwargs["rows"].length == 1 && typeof kwargs["rows"][0] == "string") { - if (kwargs["rows"][0].includes(":")) { - - let column_split = kwargs["rows"][0].split(":"); - - if (kwargs['type'] == 'loc') { - //get index of first and last occurence of label - let start, end; - if (isNaN(Number(column_split[0]))) { - start = ndframe.index.indexOf(column_split[0]); - } else { - start = Number(column_split[0]); - } - - if (isNaN(Number(column_split[1]))) { - end = ndframe.index.lastIndexOf(column_split[1]) - 1 || (ndframe.values.length - 1); - } else { - end = Number(column_split[1]) - 1 || (ndframe.values.length - 1); - } - rows = utils.__range(start, end); - } else { - let start = parseInt(column_split[0]) || 0; - let end = parseInt(column_split[1]) - 1 || (ndframe.values.length - 1); - - if (typeof start == "number" && typeof end == "number") { - rows = utils.__range(start, end); - } - } - - } else { - if (kwargs["type"] == "loc") { - let row_idx = []; - ndframe.index.map((idx, i) => { - if (kwargs['rows'][0] == idx) { - row_idx.push(i); - } - }); - rows = row_idx; - } else { - throw new Error("Slice index must be separated by ':'"); - } - } - } else { - if (kwargs["type"] == "loc") { - //get all the index of specified labels - let row_idx = []; - ndframe.index.map((idx, i) => { - if (kwargs['rows'].includes(idx)) { - row_idx.push(i); - } - }); - rows = row_idx; - } else { - //return int index - rows = kwargs["rows"]; - } - } - } else { - throw new Error("rows parameter must be a Array"); - } - } else { - if (kwargs["type"] == "loc") { - rows = utils.__range(0, Number(ndframe.shape[0]) - 1); - // throw new Error("Invalid syntax, please specify a slice label") - } else { - rows = utils.__range(0, Number(ndframe.shape[0]) - 1); - } - } - - if (Object.prototype.hasOwnProperty.call(kwargs, "columns")) { - if (Array.isArray(kwargs["columns"])) { - if (kwargs["columns"].length == 1 && kwargs["columns"][0].includes(":")) { - - let column_split = kwargs["columns"][0].split(":"); - let start, end; - - if (kwargs["type"] == "iloc") { - if (column_split[0] == "") { - start = 0; - } else { - start = parseInt(column_split[0]) || 0; - } - - if (column_split[1] == "") { - end = parseInt(ndframe.columns.length - 1); - } else { - end = parseInt(column_split[1]) - 1 === 0 ? 0 : parseInt(column_split[1]) - 1; - } - - } else { - start = parseInt(ndframe.columns.indexOf(column_split[0])); - end = parseInt(ndframe.columns.indexOf(column_split[1])) - 1; - } - - if (typeof start == "number" && typeof end == "number") { - - columns = utils.__range(start, end); - isColumnSplit = true; - } - - } else { - columns = kwargs["columns"]; - } - - } else { - throw new Error("columns must be a list"); - } - } else { - //Return all column - if (kwargs["type"] == "loc") { - columns = ndframe.column_names; - } else { - columns = utils.__range(0, Number(ndframe.shape[1]) - 1); - } - } - - let data_values = ndframe.values; - let new_data = []; // store the data from the for loop - - for (var index = 0; index < rows.length; index++) { - let row_val = rows[index]; - let max_rowIndex = data_values.length - 1; //obtain the maximum row index - - if (row_val > max_rowIndex) { //check if the input row index is greater than the maximum row index - throw new Error(`Specified row index ${row_val} is bigger than maximum row index of ${max_rowIndex}`); - } - - if (Array.isArray(data_values[0])) { - - let value = data_values[row_val]; - let row_data = []; - - - for (var i in columns) { - var col_index; - if (kwargs["type"] == "loc" && !isColumnSplit) { - col_index = ndframe.columns.indexOf(columns[i]); //obtain the column index - - if (col_index == -1) { - throw new Error(`Column ${columns[i]} does not exist`); - } - } else { - col_index = columns[i]; - let max_colIndex = ndframe.columns.length - 1; //assign the maximum column index to a value - - if (col_index > max_colIndex) { - throw new Error(`column index ${col_index} is bigger than ${max_colIndex}`); - } - } - - let elem = value[col_index]; //obtain the element at the column index - row_data.push(elem); - } - - new_data.push(row_data); //store the data for each row in the new_data - - } else { - - new_data.push(data_values[row_val]); - } - - } - - let column_names = []; - if (kwargs["type"] == "iloc" || isColumnSplit) { - // let axes = ndframe.axes - columns.map((col) => { - column_names.push(ndframe.columns[col]); - }); - } else { - column_names = columns; - } - - //get index of columns - let final_row = []; - rows.forEach((i) => { - final_row.push(ndframe.index[i]); - }); - return [new_data, column_names, final_row]; -}; diff --git a/danfojs-node/src/core/merge.js b/danfojs-node/src/core/merge.js deleted file mode 100644 index 5d4a813f..00000000 --- a/danfojs-node/src/core/merge.js +++ /dev/null @@ -1,329 +0,0 @@ -import { DataFrame } from './frame'; -import { Utils } from "./utils"; - -const utils = new Utils; -export class Merge { - - constructor(kwargs) { - - //check if keys exist in kwargs - utils.__in_object(kwargs, "left"); - utils.__in_object(kwargs, "right"); - utils.__in_object(kwargs, "on"); - // utils.__in_object(kwargs, "how") - if (!utils.__key_in_object(kwargs, "how")){ - this.how = 'inner'; - kwargs['how'] = 'inner'; - } else { - this.how = kwargs['how']; - } - - this.left = null; - this.right = null; - this.on = null; - // this.how = null; - - let how_keys = [ "outer", "inner", "left", "right" ]; - - if ((kwargs["left"] instanceof DataFrame) && (kwargs["right"] instanceof DataFrame)) { - this.left = kwargs["left"]; - this.right = kwargs["right"]; - } else { - throw new Error("The left and right key value must be a dataFrame"); - } - - if (Array.isArray(kwargs["on"])) { - this.on = kwargs["on"]; - } else { - throw new Error("key 'on' must be a list"); - } - - if (how_keys.includes(kwargs["how"])) { - - this.how = kwargs["how"]; - } else { - throw new Error(`${kwargs["how"]} specify in keyword how is not recognise`); - } - - this.left_col_index = []; - this.right_col_index = []; - - //find the index of the columns - for (let i = 0; i < this.on.length; i++) { - - if (this.left.columns.includes(this.on[i]) && this.right.columns.includes(this.on[i])) { - - let left_index = this.left.columns.indexOf(this.on[i]); - let right_index = this.right.columns.indexOf(this.on[i]); - - this.left_col_index.push(left_index); - this.right_col_index.push(right_index); - } - } - - this.left_key_dict = {}; - this.right_key_dict = {}; - - let left_values = this.left.values; - let right_values = this.right.values; - - - /** - * Create a dictionary for both left and right dataframe - containing the key combination of columns used as keys, and - the value of such keys combination are the rows having this - keys combination. - */ - for (let i = 0; i < left_values.length; i++) { - let left_value = left_values[i]; - let left_key_comb_values = []; - - //get the value in the column index - for (let j = 0; j < this.left_col_index.length; j++) { - let index = this.left_col_index[j]; - - left_key_comb_values.push(left_value[index]); - } - //combine into single hashable string - let left_key_comb = left_key_comb_values.join('_'); - - let self = this; // assign the this scope to self - //filter out the value that are not the column key - let left_value_filter = left_value.filter(function (val, index) { - return !self.left_col_index.includes(index); - }); - - //check if the key combination already exist or not - //before storing the key combination and the value - //associated with it - if (utils.__key_in_object(this.left_key_dict, left_key_comb)) { - this.left_key_dict[left_key_comb].filters.push(left_value_filter); - } else { - this.left_key_dict[left_key_comb] = { - filters: [left_value_filter], - comb_values: left_key_comb_values - }; - } - - } - for (let i = 0; i < right_values.length; i++) { - let right_value = right_values[i]; - let right_key_comb_values = []; - - for (let j = 0; j < this.right_col_index.length; j++) { - let index = this.right_col_index[j]; - - right_key_comb_values.push(right_value[index]); - } - let right_key_comb = right_key_comb_values.join('_'); - - let self = this; - let right_value_filter = right_value.filter(function (val, index) { - return !self.right_col_index.includes(index); - }); - - if (utils.__key_in_object(this.right_key_dict, right_key_comb)) { - this.right_key_dict[right_key_comb].filters.push(right_value_filter); - } else { - this.right_key_dict[right_key_comb] = { - filters: [right_value_filter], - comb_values: right_key_comb_values - }; - } - } - - - //create column - this.__create_columns(); - - let data = null; - switch (this.how) { - - case "outer": - data = this.outer(); - break; - case "inner": - data = this.inner(); - break; - case "left": - data = this.left_merge(); - break; - case "right": - data = this.right_merge(); - break; - } - - // eslint-disable-next-line no-self-assign - let df = new DataFrame(data = data, { columns: this.columns }); - - return df; - } - - __create_columns() { - - let self = this; - self.left_col = self.left.columns.filter((val, index) => { - return !self.left_col_index.includes(index); - }); - - self.right_col = self.right.columns.filter((val, index) => { - return !self.right_col_index.includes(index); - }); - - self.columns = [ ...self.on ]; - let column_duplicate = {}; - - let temp_column = [ ...self.left_col ]; - temp_column.push(...self.right_col); - - for (let i = 0; i < temp_column.length; i++) { - - let col = temp_column[i]; - if (utils.__key_in_object(column_duplicate, col)) { - - let col_name = `${col}_${column_duplicate[col]}`; - self.columns.push(col_name); - - column_duplicate[col] += 1; - } else { - self.columns.push(col); - column_duplicate[col] = 1; - } - } - } - - outer() { - - let keys = Object.keys(this.left_key_dict); // obtain the keys of the left dataframe - - keys.push(...Object.keys(this.right_key_dict)); - - keys = Array.from(new Set(keys)); // obtain the unique keys - - let data = this.basic(keys); - - return data; - - } - - inner() { - - let left_keys = Object.keys(this.left_key_dict); - let right_keys = Object.keys(this.right_key_dict); - - let keys = left_keys.filter((val) => { - return right_keys.includes(val); - }); - - let data = this.basic(keys); - - return data; - } - - left_merge() { - - let keys = Object.keys(this.left_key_dict); - let data = this.basic(keys); - - return data; - - } - - right_merge() { - let keys = Object.keys(this.right_key_dict); - let data = this.basic(keys); - - return data; - } - - basic(keys) { - - let data = []; - - for (let i = 0; i < keys.length; i++) { - let key = keys[i]; - - let key_array = key.split("_").filter((val) => { - return val != ""; - }); - - if (utils.__key_in_object(this.left_key_dict, key)) { - let left_row = this.left_key_dict[key].filters; - let key_array = this.left_key_dict[key].comb_values; - - - for (let left_i = 0; left_i < left_row.length; left_i++) { - - let left_row_row = left_row[left_i]; - - if (utils.__key_in_object(this.right_key_dict, key)) { - - let right_row = this.right_key_dict[key].filters; - - for (let r_i = 0; r_i < right_row.length; r_i++) { - - let right_row_row = right_row[r_i]; - - - let inner_data = key_array.slice(0); - inner_data.push(...left_row_row); - inner_data.push(...right_row_row); - - - data.push(inner_data); - } - } else { - let nan_array = Array(this.right_col.length); - - for (let i = 0; i < this.right_col.length; i++) { - nan_array[i] = NaN; - } - - let inner_data = key_array.slice(0); - inner_data.push(...left_row_row); - inner_data.push(...nan_array); - data.push(inner_data); - } - } - } else { - - let right_row = this.right_key_dict[key].filters; - let key_array = this.right_key_dict[key].comb_values; - - for (let i = 0; i < right_row.length; i++) { - - let right_row_row = right_row[i]; - - let nan_array = Array(this.left_col.length); - - for (let j = 0; j < nan_array.length; j++) { - nan_array[j] = NaN; - } - - let inner_data = key_array.slice(0); - inner_data.push(...nan_array); - inner_data.push(...right_row_row); - data.push(inner_data); - } - } - } - return data; - - } -} - - -/** -* Merge DataFrame or named Series objects with a database-style join. -* The join is done on columns or indexes. If joining columns on columns, the DataFrame indexes will be ignored. Otherwise -* if joining indexes on indexes or indexes on a column or columns, the index will be passed on. -* @param {kwargs}{left: DataFrame; -* right: DataFrame | Named Series, Object to merge with; -* on: label, Column or index level names to join on. These must be found in both DataFrames; -* how: {‘left’, ‘right’, ‘outer’, ‘inner’}. Type of merge to be performed. -* @returns {DataFrame} -*/ -export const merge = (kwargs) => { - let merge = new Merge(kwargs); - return merge; -}; diff --git a/danfojs-node/src/core/series.js b/danfojs-node/src/core/series.js deleted file mode 100644 index 0c184a61..00000000 --- a/danfojs-node/src/core/series.js +++ /dev/null @@ -1,1302 +0,0 @@ -/** -* Copyright 2020, JsData -* All rights reserved. -* -* This source code is licensed under the MIT license found in the -* LICENSE file in the root directory of this source tree. - -* Unless required by applicable law or agreed to in writing, software -* distributed under the License is distributed on an "AS IS" BASIS, -* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -* See the License for the specific language governing permissions and -* limitations under the License. - * -*/ - - -import * as tf from '@tensorflow/tfjs-node'; -import { variance, std, median, mode } from 'mathjs'; -import { Utils } from "./utils"; -import { Str } from "./strings"; -import NDframe from "./generic"; -import { table } from 'table'; -import { Configs } from '../config/config'; -import { TimeSeries } from './timeseries'; -import { indexLoc } from './indexing'; - - -const utils = new Utils(); -const config = new Configs(); //package wide configuration object - - -/** - * One-dimensional ndarray with axis labels (including time series). - * The object supports both integer- and label-based indexing and provides a host of methods for performing operations involving the index. - * Operations between Series (+, -, /, , *) align values based on their associated index values– they need not be the same length. - * @param {data} data Array, JSON of 1D values - * @param {kwargs} Object {columns: column names, dtypes : data type of values} - * - * @returns Series - */ -export class Series extends NDframe { - constructor(data, kwargs) { - if (Array.isArray(data[0]) || utils.__is_object(data[0])) { - data = utils.__convert_2D_to_1D(data); - super(data, kwargs); - } else { - super(data, kwargs); - } - } - - - /** - * Returns a Series in Tensorflow's tensor format - * @returns {1D Tensor} - */ - get tensor() { - return tf.tensor(this.values).asType(this.dtypes[0]); - } - - - /** - * Returns the first n values in a Series - * @param {rows} Number of rows to return - * @returns {Series} - */ - head(rows = 5) { - if (rows > this.shape[0] || rows < 1) { - //return all values - return new Series(this.values, { columns: this.column_names }); - } else { - let data = this.values.slice(0, rows); - return new Series(data, { columns: this.column_names }); - } - - } - - - /** - * Returns the last n values in a Series - * @param {rows} number of rows to return - * @returns {Series} - */ - tail(rows = 5) { - if (rows > this.values.length || rows < 1) { - //return all values - return new Series(this.values, { columns: this.column_names }); - } else { - let data = this.values.slice(this.shape[0] - rows); - let idx = this.index.slice(this.shape[0] - rows); - let sf = new Series(data, { columns: this.column_names, index: idx }); - return sf; - } - - } - - /** - * Gets [num] number of random rows in a dataframe - * @param {num} rows --> The number of rows to return - * @param {seed} seed --> (Optional) An integer specifying the random seed that will be used to create the distribution. - * @returns {Promise} resolves to a Series object - */ - async sample(num = 5, seed = 1) { - if (num > this.shape[0]) { - throw new Error("Sample size n cannot be bigger than size of dataset"); - } - if (num < -1 || num == 0) { - throw new Error("Sample size cannot be less than -1 or 0"); - } - num = num === -1 ? this.shape[0] : num; - const shuffled_index = await tf.data.array(this.index).shuffle(num, seed).take(num).toArray(); - const sf = this.iloc(shuffled_index); - return sf; - } - - /** - * Return Addition of series and other, element-wise (binary operator add). - * Equivalent to series + other - * @param {other} Series or Number to add - * @returns {Series} - */ - add(other) { - if (utils.__is_number(other)) { - //broadcast addition - let sum = this.row_data_tensor.add(other).arraySync(); - return new Series(sum, { columns: this.column_names }); - } else { - if (this.__check_series_op_compactibility) { - let sum = this.tensor.add(other.tensor).arraySync(); - return new Series(sum, { columns: this.column_names }); - } - } - } - - - /** - * Returns the subtraction between a series and other, element-wise (binary operator subtraction). - * Equivalent to series - other - * @param {other} Series, Number to subtract - * @returns {Series} - */ - sub(other) { - if (utils.__is_number(other)) { - let sub = this.tensor.sub(other).arraySync(); - return new Series(sub, { columns: this.column_names }); - } else { - if (this.__check_series_op_compactibility) { - let sub = this.tensor.sub(other.tensor).arraySync(); - return new Series(sub, { columns: this.column_names }); - } - } - } - - - /** - * Return Multiplication of series and other, element-wise (binary operator mul). - * Equivalent to series * other - * @param {other} Series, Number to multiply with. - * @returns {Series} - */ - mul(other) { - if (utils.__is_number(other)) { - let mul = this.tensor.mul(other).arraySync(); - return new Series(mul, { columns: this.column_names }); - } else { - if (this.__check_series_op_compactibility) { - let mul = this.tensor.mul(other.tensor).arraySync(); - return new Series(mul, { columns: this.column_names }); - } - } - } - - - /** - * Return division of series and other, element-wise (binary operator div). - * Equivalent to series / other - * @param {other} Series, Number to divide with. - * @returns {Series} - */ - div(other, round = true) { - if (utils.__is_number(other)) { - let div_result = this.tensor.div(other); - return new Series(div_result.arraySync(), { columns: this.column_names, dtypes: [div_result.dtype] }); - } else { - if (this.__check_series_op_compactibility) { - let dtype; - //Check if caller needs a float division - if (round) { - dtype = "float32"; - } else { - dtype = "int32"; - } - //dtype may change after division because of how TFJS works internally, so save dtypes first - let tensor1 = this.tensor.asType(dtype); - let tensor2 = other.tensor.asType(dtype); - let result = tensor1.div(tensor2); - return new Series(result.arraySync(), { columns: this.column_names, dtypes: [result.dtype] }); - } - } - } - - /** - * Return Exponential power of series and other, element-wise (binary operator pow). - * Equivalent to series ** other - * @param {other} Series, Number to multiply with. - */ - pow(other) { - if (utils.__is_number(other)) { - let pow_result = this.tensor.pow(other).arraySync(); - return new Series(pow_result, { columns: this.column_names }); - } else { - if (this.__check_series_op_compactibility) { - let pow_result = this.tensor.pow(other.tensor).arraySync(); - return new Series(pow_result, { columns: this.column_names }); - } - } - } - - /** - * Return Modulo of series and other, element-wise (binary operator mod). - * Equivalent to series % other - * @param {other} Series, Number - * @returns {Series} - */ - mod(other) { - if (utils.__is_number(other)) { - let mod_result = this.tensor.mod(other).arraySync(); - return new Series(mod_result, { columns: this.column_names }); - } else { - if (this.__check_series_op_compactibility) { - let mod_result = this.tensor.mod(other.tensor).arraySync(); - return new Series(mod_result, { columns: this.column_names }); - } - } - } - - - /** - * Returns the mean of elements in Series - * @returns {Series} - */ - mean() { - utils._throw_str_dtype_error(this, 'mean'); - let values = utils._remove_nans(this.values); - let mean = tf.tensor(values).mean().arraySync(); - return mean; - } - - - /** - * Returns the median of elements in Series - * @returns {Series} - */ - median() { - utils._throw_str_dtype_error(this, 'median'); - let values = utils._remove_nans(this.values); - let median_val = median(values); - return median_val; - } - - - /** - * Returns the modal value of elements in Series - * @returns {Number} - */ - mode() { - utils._throw_str_dtype_error(this, 'median'); - let values = utils._remove_nans(this.values); - let modal_val = mode(values); - return modal_val; - } - - - /** - * Returns the minimum value in a Series - * @returns {Number} - */ - min() { - utils._throw_str_dtype_error(this, 'min'); - let min = this.row_data_tensor.min().arraySync(); - return min; - - } - - /** - * Returns the maximum value in a Series - * @returns {Number} - */ - max() { - utils._throw_str_dtype_error(this, 'max'); - let max = this.row_data_tensor.max().arraySync(); - return max; - - } - - - /** - * Return the sum of the values in a series. - * This is equivalent to the method tf.sum - * @returns {Number}, sum of values in Series - */ - sum() { - utils._throw_str_dtype_error(this, 'sum'); - - if (this.dtypes[0] == "boolean") { - let temp = utils._remove_nans(this.values); - let temp_sum = tf.tensor(temp).sum().arraySync(); - return Number(temp_sum); - } - let temp = utils._remove_nans(this.values); - let temp_sum = tf.tensor(temp).sum().arraySync(); - return Number(temp_sum.toFixed(5)); - } - - - /** - * Return number of non-null elements in a Series - * @returns {Number}, Count of non-null values - */ - count() { - return utils.__count_nan(this.values, true, true); - } - - - /** - * Return maximum of series and other, element-wise (binary operator div). - * @param {other} Series, Numbers to check maximum against - * @returns {Series} - */ - maximum(other) { - if (utils.__is_number(other)) { - let max_result = this.row_data_tensor.maximum(other); - return new Series(max_result.arraySync(), { - columns: this.column_names, - dtypes: max_result.dtype, - index: this.index - }); - } else { - if (this.__check_series_op_compactibility) { - let tensor1 = this.row_data_tensor; - let tensor2 = other.tensor; - let result = tensor1.maximum(tensor2).arraySync(); - return new Series(result, { columns: this.column_names, index: this.index }); - } - } - } - - /** - * Return maximum of series and other, element-wise (binary operator div). - * @param {other} Series, Numbers to check maximum against - * @returns {Series} - */ - minimum(other) { - if (utils.__is_number(other)) { - let max_result = this.row_data_tensor.minimum(other); - return new Series(max_result.arraySync(), { - columns: this.column_names, - dtypes: max_result.dtype, - index: this.index - }); - } else { - if (this.__check_series_op_compactibility) { - let tensor1 = this.tensor; - let tensor2 = other.tensor; - let result = tensor1.minimum(tensor2).arraySync(); - return new Series(result, { columns: this.column_names, index: this.index }); - } - } - } - - - /** - * Round each value in a Series to the given number of decimals. - * @param {dp} Number, Numbers of Decimal places to round to - * @returns {Series} - */ - round(dp) { - if (utils.__is_undefined(dp)) { - //use tensorflow round function to roound to the nearest whole number - let result = tf.round(this.row_data_tensor).arraySync(); - return new Series(result, { columns: this.column_names, index: this.index }); - - } else { - let result = utils.__round(this.values, dp, true); - return new Series(result, { columns: this.column_names, index: this.index }); - - } - - } - - /** - * Return sample standard deviation over requested axis. - * @returns {Number} - */ - std() { - utils._throw_str_dtype_error(this, 'std'); - let values = utils._remove_nans(this.values); - // TODO: Use Tensorflow ops for faster computation - let std_val = std(values); //using math.js - return std_val; - - } - - /** - * Return unbiased variance of Series. - * @returns {Number} - */ - var() { - utils._throw_str_dtype_error(this, 'std'); - let values = utils._remove_nans(this.values); - // TODO: Use Tensorflow ops for faster computation - let var_val = variance(values); //using math.js - return var_val; - - } - - /** - * Return a boolean same-sized object indicating if the values are NaN. NaN and undefined values, - * gets mapped to True values. Everything else gets mapped to False values. - * @return {Series} - */ - isna() { - let new_arr = this.__isna(); - let sf = new Series(new_arr, { - index: this.index, - columns: this.column_names, - dtypes: ["boolean"] - }); - return sf; - } - - /** - * Replace NaN or undefined with a specified value" - * @param {kwargs}, {"value": the new value to replace the old value with, inplace: Perform operation inplace or not} - * @return {Series} - */ - fillna(kwargs = {}) { - let params_needed = ["value", "inplace"]; - utils._throw_wrong_params_error(kwargs, params_needed); - - kwargs['inplace'] = kwargs['inplace'] || false; - - if (!("value" in kwargs)) { - throw Error('Value Error: Must specify value to replace with'); - } - - let new_values = []; - this.values.forEach((val) => { - if (isNaN(val) && typeof val != "string") { - new_values.push(kwargs['value']); - } else { - new_values.push(val); - } - }); - - if (kwargs['inplace']) { - this.data = new_values; - } else { - let sf = new Series(new_values, { - columns: this.column_names, - index: this.index, - dtypes: this.dtypes - }); - return sf; - } - } - - - /** - * Sort a Series in ascending or descending order by some criterion. - * @param {kwargs} Object, {ascending (Bool): Whether to return sorted values in ascending order or not, - * inplace (Bool): Whether to perform sorting on the original Series or not} - * @returns {Series} - */ - sort_values(kwargs = {}) { - let params_needed = ["inplace", "ascending"]; - utils._throw_wrong_params_error(kwargs, params_needed); - - if (!('ascending' in kwargs)) { - kwargs['ascending'] = true; - } - - if (!('inplace' in kwargs)) { - kwargs['inplace'] = false; - } - - let sorted_values = []; - let arr_obj = [...this.values]; - let range_idx = utils.__range(0, this.index.length - 1); - let sorted_idx = utils._sort_arr_with_index(range_idx, arr_obj, this.dtypes[0]); - - sorted_idx.forEach((idx) => { - sorted_values.push(this.values[idx]); - }); - - if (kwargs['ascending']) { - sorted_values = sorted_values.reverse(); - sorted_idx = sorted_idx.reverse(); - } - - if (kwargs['inplace']) { - this.data = sorted_values; - this.__set_index(sorted_idx); - } else { - let sf = new Series(sorted_values, { columns: this.column_names, index: sorted_idx }); - return sf; - } - } - - - /** - * Makes a deep copy of a Series - * @returns {Series} - */ - copy() { - - let sf = new Series([...this.values], { - columns: [...this.column_names], - index: [...this.index], - dtypes: [...this.dtypes[0]] - }); - return sf; - } - - - /** - * Generate descriptive statistics. - * Descriptive statistics include those that summarize the central tendency, - * dispersion and shape of a dataset’s distribution, excluding NaN values. - * @returns {Series} - */ - describe() { - if (this.dtypes[0] == "string") { - return null; - } else { - - let index = ['count', 'mean', 'std', 'min', 'median', 'max', 'variance']; - let count = this.count(); - let mean = this.mean(); - let std = this.std(); - let min = this.min(); - let median = this.median(); - let max = this.max(); - let variance = this.var(); - - let vals = [count, mean, std, min, median, max, variance]; - let sf = new Series(vals, { columns: this.columns, index: index }); - return sf; - - } - - - } - - - /** - * Returns Series with the index reset. - * This is useful when index is meaningless and needs to be reset to the default before another operation. - * @param {kwargs} {inplace: Modify the Series in place (do not create a new object} - */ - reset_index(kwargs = {}) { - let params_needed = ["inplace"]; - utils._throw_wrong_params_error(kwargs, params_needed); - - kwargs['inplace'] = kwargs['inplace'] || false; - - if (kwargs['inplace']) { - this.__reset_index(); - } else { - let sf = this.copy(); - sf.__reset_index(); - return sf; - } - } - - /** - * Returns Series with the specified index. - * Set the Series index (row labels) using an array of the same length. - * @param {kwargs} {index: Array of new index values, - * inplace: If operation should happen inplace - * } - */ - set_index(kwargs = {}) { - - let params_needed = ["index", "inplace"]; - utils._throw_wrong_params_error(kwargs, params_needed); - - - kwargs['inplace'] = kwargs['inplace'] || false; - - if (!('index' in kwargs)) { - throw Error("Index ValueError: You must specify an array of index"); - } - - if (kwargs['index'].length != this.index.length) { - throw Error(`Index LengthError: Lenght of new Index array ${kwargs['index'].length} must match lenght of existing index ${this.index.length}`); - } - - if (kwargs['inplace']) { - this.index_arr = kwargs['index']; - } else { - let sf = this.copy(); - sf.__set_index(kwargs['index']); - return sf; - } - } - - - /** - * Checks if two series are compatible for a mathematical operation - * @param {Series} other Series to compare against - * @returns{boolean} - */ - __check_series_op_compactibility(other) { - if (utils.__is_undefined(other.series)) { - throw Error("param [other] must be a Series or a single value that can be broadcasted"); - } - if (other.values.length != this.values.length) { - throw Error("Shape Error: Series shape do not match"); - } - if (this.dtypes[0] != 'float' || this.dtypes[0] != 'int') { - throw Error(`dtype Error: Cannot perform operation on type ${this.dtypes[0]} with type ${other.dtypes[0]}`); - } - if (other.dtypes[0] != 'float' || other.dtypes[0] != 'int') { - throw Error(`dtype Error: Cannot perform operation on type ${other.dtypes[0]} with type ${this.dtypes[0]}`); - } - - return true; - } - - /** - * map all the element in a column to a variable or function - * @param{callable} callable can either be a funtion or an object - * @return {Array} - */ - map(callable) { - let is_callable = utils.__is_function(callable); - - let data = this.data.map((val) => { - if (is_callable) { - return callable(val); - } else { - if (utils.__is_object(callable)) { - - if (val in callable) { - return callable[val]; - } else { - return NaN; - } - } else { - throw new Error("callable must either be a function or an object"); - } - } - }); - let sf = new Series(data, { - columns: this.column_names, - index: this.index - }); - return sf; - } - - /** - * Applies a function to each element of a Series - * @param {Function} Function to apply to each element of the series - * @return {Array} - */ - apply(callable) { - let is_callable = utils.__is_function(callable); - - if (!is_callable) { - throw new Error("the arguement most be a function"); - } - - let data = this.data.map((val) => { - return callable(val); - }); - return new Series(data, { columns: this.column_names, index: this.index }); - } - - /** - * Returns the unique value(s) in a Series - * @return {Series} - */ - unique() { - - let data_set = new Set(this.values); - let series = new Series(Array.from(data_set)); - - return series; - - } - - /** - * Return the number of unique value in a series - * @return {int} - */ - nunique() { - return this.unique().values.length; - } - - /** - * Returns unique values and their counts in a Series - * @return {Series} - */ - value_counts() { - - let s_data = this.values; - let data_dict = {}; - - for (let i = 0; i < s_data.length; i++) { - let val = s_data[i]; - - if (val in data_dict) { - data_dict[val] += 1; - } else { - data_dict[val] = 1; - } - } - - let index = Object.keys(data_dict).map((x) => { - return parseInt(x) ? parseInt(x) : x; - }); - let data = Object.values(data_dict); - - let series = new Series(data, { index: index }); - return series; - - } - - /** - * Returns the absolute values in Series - * @return {series} - */ - abs() { - let abs_data = this.row_data_tensor.abs().arraySync(); - return new Series(utils.__round(abs_data, 2, true)); - } - - - /** - * Returns the cumulative sum over a Series - * @return {Series} - */ - cumsum() { - let data = this.__cum_ops("sum"); - return data; - } - - /** - * Returns cumulative minimum over a Series - * @returns series - */ - cummin() { - let data = this.__cum_ops("min"); - return data; - } - - /** - * Returns cumulative maximum over a Series - * @returns series - */ - cummax() { - let data = this.__cum_ops("max"); - return data; - } - - /** - * Returns cumulative product over a Series - * @returns series - */ - cumprod() { - let data = this.__cum_ops("prod"); - return data; - } - - - /** - * Returns Less than of series and other. Supports element wise operations - * @param {other} Series, Scalar - * @return {Series} - */ - lt(other) { - return this.__bool_ops(other, "lt"); - } - - /** - * Returns Greater than of series and other. Supports element wise operations - * @param {other} Series, Scalar - * @return {Series} - */ - gt(other) { - return this.__bool_ops(other, "gt"); - } - - /** - * Returns Less than or Equal to of series and other. Supports element wise operations - * @param {other} Series, Scalar - * @return {Series} - */ - le(other) { - return this.__bool_ops(other, "le"); - } - - /** - * Returns Greater than or Equal to of series and other. Supports element wise operations - * @param {other} Series, Scalar - * @return {Series} - */ - ge(other) { - return this.__bool_ops(other, "ge"); - } - - /** - * Returns Not Equal to of series and other. Supports element wise operations - * @param {other} Series, Scalar - * @return {Series} - */ - ne(other) { - return this.__bool_ops(other, "ne"); - } - - - /** - * Returns Equal to of series and other. Supports element wise operations - * @param {other} Series, Scalar - * @return {Series} - */ - eq(other) { - return this.__bool_ops(other, "eq"); - } - - /** - * Replace all occurence of a value with a new value" - * @param {kwargs}, {"replace": the value you want to replace, - * "with": the new value you want to replace the olde value with, - * inplace: Perform operation inplace or not} - * @return {Series} - */ - replace(kwargs = {}) { - let params_needed = ["replace", "with", "inplace"]; - utils._throw_wrong_params_error(kwargs, params_needed); - - kwargs['inplace'] = kwargs['inplace'] || false; - - if (!("replace" in kwargs)) { - throw Error("Params Error: Must specify param 'replace'"); - } - - if (!("with" in kwargs)) { - throw Error("Params Error: Must specify param 'with'"); - } - - let replaced_arr = []; - let old_arr = this.values; - - old_arr.forEach((val) => { - if (val == kwargs['replace']) { - replaced_arr.push(kwargs['with']); - } else { - replaced_arr.push(val); - } - }); - - if (kwargs['inplace']) { - this.data = replaced_arr; - } else { - let sf = new Series(replaced_arr, { - index: this.index, - columns: this.columns, - dtypes: this.dtypes - }); - return sf; - } - - } - - - /** - * Return a new Series with missing values (NaN) removed. - * @param {kwargs} {inplace: Perform operation inplace or not} - * @return {Series} - */ - dropna(kwargs = {}) { - let params_needed = ["inplace"]; - utils._throw_wrong_params_error(kwargs, params_needed); - - kwargs['inplace'] = kwargs['inplace'] || false; - - let old_values = this.values; - let old_index = this.index; - let new_values = []; - let new_index = []; - let isna_vals = this.isna().values; - - isna_vals.forEach((val, i) => { - if (!val) { - new_values.push(old_values[i]); - new_index.push(old_index[i]); - } - }); - if (kwargs['inplace']) { - this.index_arr = new_index; - this.data = new_values; - } else { - let sf = new Series(new_values, { - columns: this.column_names, - index: new_index, - dtypes: this.dtypes - }); - return sf; - } - - } - - /** - * Return the integer indices that would sort the Series. - * @param {ascending} boolean true: will sort the Series in ascending order, false: will sort in descending order - * @return {Series} - */ - argsort(ascending = true) { - let sorted_index = this.sort_values({ ascending: ascending }).index; - let sf = new Series(sorted_index); - return sf; - } - - /** - * Return int position of the largest value in the Series. - * @return {Number} - */ - argmax() { - return this.row_data_tensor.argMax().arraySync(); - } - - - /** - * Return int position of the smallest value in the Series. - * @param {ascending} boolean true: will sort the Series in ascending order, false: will sort in descending order - * @return {Series} - */ - argmin() { - return this.row_data_tensor.argMin().arraySync(); - - } - - - /** - * Returns dtype of Series - * @return {string} - */ - get dtype() { - return this.dtypes[0]; - } - - /** - * Return Series with duplicate values removed - * @param {kwargs} {inplace: Perform operation inplace or not, - * keep: first | last which dupliate value to keep} - * @return {Series} - */ - drop_duplicates(kwargs = {}) { - let params_needed = ["inplace", "keep"]; - utils._throw_wrong_params_error(kwargs, params_needed); - - kwargs['inplace'] = kwargs['inplace'] || false; - kwargs['keep'] = kwargs['keep'] || "first"; - - - let data_arr, old_index; - if (kwargs['keep'] == "last") { - data_arr = this.values.reverse(); - old_index = this.index.reverse(); - } else { - data_arr = this.values; - old_index = this.index; - } - - let new_index = []; - let new_arr = []; - - data_arr.forEach((val, i) => { - if (!new_arr.includes(val)) { - new_index.push(old_index[i]); - new_arr.push(val); - } - }); - - if (kwargs['keep'] == "last") { - //re-reversed the array and index to its true ordering - new_arr = new_arr.reverse(); - new_index = new_index.reverse(); - } - - if (kwargs['inplace']) { - this.data = new_arr; - this.index_arr = new_index; - } else { - let sf = new Series(new_arr, { - index: new_index, - columns: this.column_names, - dtypes: this.dtypes - }); - return sf; - } - - } - - - /** - * Prints the data in a Series as a grid of row and columns - */ - toString() { - let table_width = 20; - let table_truncate = 20; - let max_row = config.get_max_row; - let data_arr = []; - let table_config = {}; - let header = [""].concat(this.columns); - let idx, data; - - if (this.values.length > max_row) { - //slice Object to show a max of [max_rows] - data = this.values.slice(0, max_row); - idx = this.index.slice(0, max_row); - } else { - data = this.values; - idx = this.index; - } - - idx.forEach((val, i) => { - let row = [val].concat(data[i]); - data_arr.push(row); - }); - - //set column width of all columns - table_config[0] = 10; - table_config[1] = { width: table_width, truncate: table_truncate }; - - let table_data = [header].concat(data_arr); //Add the column names to values before printing - return table(table_data, { columns: table_config }); - } - - /** - * Perform boolean operations on bool values - * @param {*} other Other series to compare with - * @param {string} b_ops name of operation to perform [ne, ge, le, gt, lt, eq] - */ - __bool_ops(other, b_ops) { - let r_series; - let l_series = this.values; - - if (typeof other == "number") { - r_series = [...l_series].fill(other); //create array of repeated value for broadcasting - } else { - if (!(other instanceof Series)) { - throw new Error("Value Error: 'other' must be an instance of Series"); - } - r_series = other.values; - } - - if (!(l_series.length === r_series.length)) { - throw new Error("Length Error: Both series must be of the same length"); - } - - let data = []; - - for (let i = 0; i < l_series.length; i++) { - - let l_val = l_series[i]; - let r_val = r_series[i]; - let bool = null; - switch (b_ops) { - - case "lt": - bool = l_val < r_val ? true : false; - data.push(bool); - break; - case "gt": - bool = l_val > r_val ? true : false; - data.push(bool); - break; - case "le": - bool = l_val <= r_val ? true : false; - data.push(bool); - break; - case "ge": - bool = l_val >= r_val ? true : false; - data.push(bool); - break; - case "ne": - bool = l_val != r_val ? true : false; - data.push(bool); - break; - case "eq": - bool = l_val === r_val ? true : false; - data.push(bool); - break; - } - } - return new Series(data); - - } - - /** - * perform cumulative operation on series data - * @returns array - */ - __cum_ops(ops) { - - let s_data = this.values; - let temp_val = s_data[0]; - let data = [temp_val]; - - for (let i = 1; i < s_data.length; i++) { - - let curr_val = s_data[i]; - switch (ops) { - case "max": - if (curr_val > temp_val) { - data.push(curr_val); - temp_val = curr_val; - } else { - data.push(temp_val); - } - break; - case "min": - if (curr_val < temp_val) { - data.push(curr_val); - temp_val = curr_val; - } else { - data.push(temp_val); - } - break; - case "sum": - temp_val = temp_val + curr_val; - data.push(temp_val); - break; - case "prod": - temp_val = temp_val * curr_val; - data.push(temp_val); - break; - - } - } - return new Series(data); - } - - - /** - * Cast Series to specified data type - * @param {string} dtype data type to cast to [float32, int32, string, boolean] - *@returns {Series} - */ - astype(dtype) { - const __supported_dtypes = ['float32', "int32", 'string', 'boolean']; - - if (!dtype) { - throw Error("Value Error: Please specify dtype to cast to"); - } - - if (!__supported_dtypes.includes(dtype)) { - throw Error(`dtype ${dtype} not supported. dtype must be one of ${__supported_dtypes}`); - } - - let col_values = this.values; - let new_values = []; - - switch (dtype) { - case "float32": - col_values.forEach((val) => { - new_values.push(Number(val)); - }); - break; - case "int32": - col_values.forEach((val) => { - new_values.push(Number(Number(val).toFixed())); - }); - break; - case "string": - col_values.forEach((val) => { - new_values.push(String(val)); - }); - break; - case "boolean": - col_values.forEach((val) => { - new_values.push(Boolean(val)); - }); - break; - default: - break; - } - - let sf = new Series(new_values, { dtypes: dtype, index: this.index }); - return sf; - - } - - - /** - * Exposes numerous string methods to manipulate Series of type string - */ - get str() { - if (this.dtypes[0] == "string") { - return new Str(this); - } else { - throw new Error("Cannot call accessor str on non-string type"); - } - - } - - /** - * Returns Danfo Time Object that exposes different time properties - */ - get dt() { - let timeseries = new TimeSeries({ data: this }); // parsed to date-time - timeseries.preprocessed(); - return timeseries; - - } - - /** - * Displays the data in a console friendly manner - */ - print() { - console.log(this + ""); - } - - - /** - * Make plots of Series or DataFrame. - * Uses the Plotly as backend, so therefore supports Plotly's configuration parameters - * @param {string} div Name of the div to show the plot - * @returns {Class} Plot class that expoese different plot type - */ - plot(div) { - const plt = new Plot(this, div); - return plt; - } - - /** - * Slice series by index - * @param {Array} row list of index to slice by - * @returns Series - */ - iloc(row) { - let kwargs = {}; - kwargs["rows"] = row; - kwargs["type"] = "iloc"; - - let [new_data, columns, rows] = indexLoc(this, kwargs); - let sf = new Series(new_data, { columns: columns, index: rows }); - - return sf; - } - - /** - * Adds new values to the end of a Series - * @param {Object} val Single value | Array | Series to append to the object - * @param {Boolean} inplace Whether to perform operation inplace or not - */ - append(val, inplace = false) { - if (inplace) { - let self = this; - if (Array.isArray(val)) { - val.forEach((el, i) => { - self.data.push(el); - self.index_arr.push(i); - }); - } else if (val instanceof Series) { - let value = val.values; - let old_index = val.index; - value.forEach((el, i) => { - self.data.push(el); - self.index_arr.push(old_index[i]); - }); - } else { - self.data.push(val); - self.index_arr.push(0); - } - } else { - let sf = this.copy(); - if (Array.isArray(val)) { - val.forEach((el, i) => { - sf.data.push(el); - sf.index_arr.push(i); - }); - } else if (val instanceof Series) { - let value = val.values; - let old_index = val.index; - value.forEach((el, i) => { - sf.data.push(el); - sf.index_arr.push(old_index[i]); - }); - } else { - sf.data.push(val); - sf.index_arr.push(0); - } - return sf; - } - - } - -} - diff --git a/danfojs-node/src/core/strings.js b/danfojs-node/src/core/strings.js deleted file mode 100644 index e767e0ee..00000000 --- a/danfojs-node/src/core/strings.js +++ /dev/null @@ -1,423 +0,0 @@ -import { Series } from "./series"; - -/** - * String methods applied on Series and DataFrames - */ -export class Str { - constructor(series) { - this.series = series; - this.array = series.values; - } - - /** - * Converts all characters to lowercase. - * @return {array} - */ - toLowerCase() { - let new_arr = []; - this.array.map((val) => { - if (isNaN(val) && typeof val != "string") { - new_arr.push(val); - } else { - new_arr.push(val.toLowerCase()); - } - - }); - let sf = this.__create_new_sf_from(new_arr, this.series); - return sf; - } - - /** - * Converts all characters to uppercase. - * @return {array} - */ - toUpperCase() { - let new_arr = []; - this.array.map((val) => { - if (isNaN(val) && typeof val != "string") { - new_arr.push(val); - } else { - new_arr.push(val.toUpperCase()); - } - - }); - let sf = this.__create_new_sf_from(new_arr, this.series); - return sf; - } - - /** - * Capitalize first string - * @return {array} - */ - capitalize() { - let new_arr = []; - this.array.map((val) => { - if (isNaN(val) && typeof val != "string") { - new_arr.push(val); - } else { - let f_char = val.slice(0, 1); - let l_char = val.slice(1); - let new_str = `${f_char.toUpperCase()}${l_char.toLowerCase()}`; - new_arr.push(new_str); - } - - }); - let sf = this.__create_new_sf_from(new_arr, this.series); - return sf; - - } - - /** - * Returns the character at the specified index (position) - * @params {index} index position of character - * @return {array} - */ - charAt(index = 0) { - let new_arr = []; - this.array.map((val) => { - if (isNaN(val) && typeof val != "string") { - new_arr.push(val); - } else { - new_arr.push(val.charAt(index)); - } - }); - let sf = this.__create_new_sf_from(new_arr, this.series); - return sf; - } - - /** - * Joins two or more strings/arrays. 0 joins from the start - * @params {other} string|array to concatenate with. - * @params {position} where to concat the string from. O concats from the start, 1 concats from the end - * @params {isArray} whether operation is performed on Array or not - * @return {array} - */ - concat(other = "", position = 1) { - if (Array.isArray(other)) { - let final_arr = []; - for (let i = 0; i < other.length; i++) { - let l_str = this.array[i]; - let r_str = other[i]; - if (position == 1) { - final_arr.push(l_str.concat(r_str)); - } else { - final_arr.push(r_str.concat(l_str)); - } - - } - let sf = this.__create_new_sf_from(final_arr, this.series); - return sf; - } else { - - let new_arr = []; - - this.array.map((val) => { - if (position == 1) { - if (isNaN(val) && typeof val != "string") { - new_arr.push(String(val).concat(other)); - } else { - new_arr.push(val.concat(other)); - } - - } else { - if (isNaN(val) && typeof val != "string") { - new_arr.push(other.concat(String(val))); - } else { - new_arr.push(other.concat(val)); - } - } - }); - let sf = this.__create_new_sf_from(new_arr, this.series); - return sf; - } - - } - - - /** - * Checks whether a string begins with specified characters - * @params {String | Character} String or Character to check against - * @return {array} - */ - startsWith(str = "") { - let new_arr = []; - this.array.forEach((val) => { - if (isNaN(val) && typeof val != "string") { - new_arr.push(false); - } else { - new_arr.push(val.startsWith(str)); - } - }); - let sf = this.__create_new_sf_from(new_arr, this.series); - return sf; - } - - /** - * Checks whether a string ends with specified characters - * @params {String | Character} String or Character to check against - * @return {array} - */ - endsWith(str = "") { - let new_arr = []; - this.array.map((val) => { - if (isNaN(val) && typeof val != "string") { - new_arr.push(false); - } else { - new_arr.push(val.endsWith(str)); - } - }); - let sf = this.__create_new_sf_from(new_arr, this.series); - return sf; - } - - /** - * Checks whether a string contains the specified string/characters - * @params {String | Character} String or Character to check against - * @return {array} - */ - includes(str = "") { - let new_arr = []; - this.array.map((val) => { - if (isNaN(val) && typeof val != "string") { - new_arr.push(false); - } else { - new_arr.push(val.includes(str)); - } - }); - let sf = this.__create_new_sf_from(new_arr, this.series); - return sf; - } - - /** - * Returns the position of the first found occurrence of a specified value in a string - * @params {String | Character} String or Character to check against - * @return {array} - */ - indexOf(str = "") { - let new_arr = []; - this.array.map((val) => { - if (isNaN(val) && typeof val != "string") { - new_arr.push(-1); - } else { - new_arr.push(val.indexOf(str)); - } - }); - let sf = this.__create_new_sf_from(new_arr, this.series); - return sf; - } - - /** - * Returns the position of the last found occurrence of a specified value in a string - * @params {str: String | Character} String or Character to check against - * @return {array} - */ - lastIndexOf(str = "") { - let new_arr = []; - this.array.map((val) => { - if (isNaN(val) && typeof val != "string") { - new_arr.push(-1); - } else { - new_arr.push(val.lastIndexOf(str)); - } - }); - let sf = this.__create_new_sf_from(new_arr, this.series); - return sf; - } - - - /** - * Searches a string for a specified value, or a regular expression, and returns a new string where the specified values are replaced - * @params {searchValue: String | Character} string value to replace - * @params {replaceValue: String | Character} string to replace with - * @return {array} - */ - replace(searchValue = "", replaceValue = "") { - let new_arr = []; - this.array.map((val) => { - if (isNaN(val) && typeof val != "string") { - new_arr.push(val); - } else { - new_arr.push(val.replace(searchValue, replaceValue)); - } - }); - let sf = this.__create_new_sf_from(new_arr, this.series); - return sf; - } - - /** - * Returns a new string with a specified number of copies of an existing string - * @params {num: Integer} Number of times to repeat - * @return {array} - */ - repeat(num = 1) { - let new_arr = []; - this.array.map((val) => { - if (isNaN(val) && typeof val != "string") { - new_arr.push(val); - } else { - new_arr.push(val.repeat(num)); - } - }); - let sf = this.__create_new_sf_from(new_arr, this.series); - return sf; - } - - - /** - * Searches a string for a specified value, or regular expression, and returns the position of the match - * @params {str: String | Character} String or Character to check against - * @return {array} - */ - search(str = "") { - let new_arr = []; - this.array.map((val) => { - if (isNaN(val) && typeof val != "string") { - new_arr.push(-1); - } else { - new_arr.push(val.search(str)); - } - }); - let sf = this.__create_new_sf_from(new_arr, this.series); - return sf; - } - - /** - * Extracts a part of a string and returns a new string - * @params {startIndex: Int} index position of start character - * @params {endIndex: Int} index position of last character - * @return {array} - */ - slice(startIndex = 0, endIndex = 1) { - let new_arr = []; - this.array.map((val) => { - if (isNaN(val) && typeof val != "string") { - new_arr.push(val); - } else { - new_arr.push(val.slice(startIndex, endIndex)); - } - }); - let sf = this.__create_new_sf_from(new_arr, this.series); - return sf; - } - - /** - * Splits a string into an array of substrings - * @params {val: string} string or character to split at - * @params {endIndex: Int} index position of last character - * @return {array} - */ - split(splitVal = " ") { - let new_arr = []; - this.array.map((val) => { - if (isNaN(val) && typeof val != "string") { - new_arr.push(val); - } else { - new_arr.push(val.split(splitVal)); - } - }); - let sf = this.__create_new_sf_from(new_arr, this.series); - return sf; - } - - /** - * Extracts the characters from a string, beginning at a specified start position, and through the specified number of character - * @params {startIndex: Int} index position of start character - * @params {num: Int} number of characters to return - * @return {array} - */ - substr(startIndex = 0, num = 1) { - let new_arr = []; - this.array.map((val) => { - if (isNaN(val) && typeof val != "string") { - new_arr.push(val); - } else { - new_arr.push(val.substr(startIndex, num)); - } - }); - let sf = this.__create_new_sf_from(new_arr, this.series); - return sf; - } - - /** - * Extracts the characters from a string, between two specified indices - * @params {startIndex: Int} index position of start character - * @params {endIndex: Int} index position of last character - * @return {array} - */ - substring(startIndex = 0, endIndex = 1) { - let new_arr = []; - this.array.map((val) => { - if (isNaN(val) && typeof val != "string") { - new_arr.push(val); - } else { - new_arr.push(val.substring(startIndex, endIndex)); - } - }); - let sf = this.__create_new_sf_from(new_arr, this.series); - return sf; - } - - /** - * Removes whitespace from both ends of a string - * @return {array} - */ - trim() { - let new_arr = []; - this.array.map((val) => { - if (isNaN(val) && typeof val != "string") { - new_arr.push(val); - } else { - new_arr.push(val.trim()); - } - }); - let sf = this.__create_new_sf_from(new_arr, this.series); - return sf; - } - - /** - * Joins strings to specified value - * @params {valToJoin} string value to join to the array - * @params {joinChar} Character to Join with - * @return {array} - */ - join(valToJoin = "", joinChar = " ") { - let new_arr = []; - this.array.map((val) => { - if (isNaN(val) && typeof val != "string") { - new_arr.push(val); - } else { - let l_char = val; - let r_char = valToJoin; - let new_char = `${l_char}${joinChar}${r_char}`; - new_arr.push(new_char); - } - }); - let sf = this.__create_new_sf_from(new_arr, this.series); - return sf; - } - - /** - * Counts the number of characters in string - * @return {array} - */ - len() { - let new_arr = []; - this.array.map((val) => { - if (isNaN(val) && typeof val != "string") { - new_arr.push(val); - } else { - new_arr.push(val.length); - } - }); - let sf = this.__create_new_sf_from(new_arr, this.series); - return sf; - } - - //create a new series - __create_new_sf_from(new_val, series) { - let sf = new Series(new_val, { columns: series.column_names, index: series.index }); - return sf; - } - - -} diff --git a/danfojs-node/src/core/timeseries.js b/danfojs-node/src/core/timeseries.js deleted file mode 100644 index 1d8af672..00000000 --- a/danfojs-node/src/core/timeseries.js +++ /dev/null @@ -1,305 +0,0 @@ -import { Utils } from "./utils"; -import { Series } from "./series"; -const utils = new Utils; - - -/** - * @class - * @description Handle all datetime operations - * @param {kwargs} Object {"data":[array of string], "format": string} - */ -export class TimeSeries { - constructor(kwargs) { - - utils.__in_object(kwargs, "data", "specify the data"); - - if (kwargs["data"] instanceof Series) { - this.data = kwargs["data"].values; - } else { - this.data = kwargs["data"]; - } - - this.format = kwargs["format"] || null; - - this.keys = { //key: len - "Y": 4, - "m": 2, - "H": 2, - "M": 2, - "S": 2, - "b": 3, - "d": 2, - "-": 1 - }; - - this.__in_format = [ "%Y-m-d%", "%m-d-Y%", "%m-d-Y H%M%S%" ]; - - this.__monthName = [ "Jan", "Feb", "Mar", "Apr", "May", "Jun", "Jul", "Aug", "Sep", "Oct", "Nov", "Dec" ]; - this.__weekName = [ "Sun", "Mon", "Tue", "Wed", "Thu", "Fri", "Sat" ]; - - } - - /** - * @description preprocessed the data into desirable structure - */ - preprocessed() { - - let format_values = null; - if (this.format) { - format_values = this.generate_format(); - } - - this.date_list = []; - - for (let i = 0; i < this.data.length; i++) { - - let date_string = this.data[i]; - - if (this.format && !this.__in_format.includes(this.format)) { - - let format_dateString = this.__apply_format(date_string, format_values); - let valueDate = new Date(format_dateString); - - this.__is_validDate(valueDate); - - this.date_list.push(valueDate); - - } else if (this.is_timestamp(date_string)) { - - let string2int = parseInt(date_string); - let valueDate = new Date(string2int); - - this.__is_validDate(valueDate); - this.date_list.push(valueDate); - } else { - let valueDate = new Date(date_string); - - this.__is_validDate(valueDate); - this.date_list.push(valueDate); - } - } - - // return this.date_list; - - } - - /** - * @description if format is given, apply the format on each element of the data - * @return string - */ - __apply_format(elem, format) { - - let date_string = ""; - - let temp_val = 0; - - - for (let index in format) { - - let value = format[index]; - - if (index == 0) { - date_string += elem.slice(0, value); - - } else if (index > 4) { - - date_string += ":" + elem.slice(temp_val, temp_val + value); - } else if (index == 4) { - date_string += " " + elem.slice(temp_val, temp_val + value); - } else if (index > 0 && index <= 2) { - date_string += "-" + elem.slice(temp_val, temp_val + value); - } - - temp_val += value; - } - - return date_string; - - } - /** - * @description convert format string to their respective value. - */ - generate_format() { - - let format_list = this.format.split(""); - - let self = this; - let format_keys = format_list.filter(function (key) { - return utils.__key_in_object(self.keys, key); - }); - - let format_value = format_keys.map(function (val) { - return self.keys[val]; - }); - - - return format_value; - } - - /** - * @description check if a string is a timestamp - * @param {date_string} date_string [string] - */ - is_timestamp(date_string) { - - let string2int = parseInt(date_string); - let int2string = String(string2int); - - if (isNaN(string2int) || (int2string.length < date_string.length)) { - return false; - } else { - return true; - } - } - - /** - * @description check if a date instance returns Invalid date - * @param {date_instance} instance of new Date() - */ - __is_validDate(date_instance) { - - if (date_instance.toDateString() == "Invalid Date") { - throw new Error("Invalid date, the date format not recognise"); - } - } - - /** - * @description abstract all date operations - * @param {*} callback [FUNCTION] - * @return series - */ - __date_ops(callback) { - - let data = this.date_list.map(function (date_instance) { - - return callback(date_instance); - }); - - // eslint-disable-next-line no-self-assign - let series = new Series(data = data); - - return series; - } - - /** - * @description obtain the month in a date. - * @return Series - */ - month() { - - let series = this.__date_ops(function (date_instance) { - return date_instance.getMonth(); - }); - - return series; - } - - /** - * @return Series - */ - hour() { - let series = this.__date_ops(function (date_instance) { - return date_instance.getHours(); - }); - - return series; - } - - /** - * @return Series - */ - day() { - let series = this.__date_ops(function (date_instance) { - return date_instance.getDay(); - }); - - return series; - } - - /** - * @description generate year frome date instance - * @return Series - */ - year() { - - let series = this.__date_ops(function (date_instance) { - return date_instance.getFullYear(); - }); - - return series; - } - - /** - * @description generate month name - * @return Series - */ - month_name() { - - let self = this; - let series = this.__date_ops(function (date_instance) { - return self.__monthName[date_instance.getMonth()]; - }); - - return series; - } - - /** - * @description generate days of the week - * @return Series - */ - weekdays() { - let self = this; - let series = this.__date_ops(function (date_instance) { - return self.__weekName[date_instance.getDay()]; - }); - - return series; - } - - /** - * @description day of the month - * @return Series - */ - monthday() { - let series = this.__date_ops(function (date_instance) { - return date_instance.getDate(); - }); - - return series; - } - - /** - * @description obtain the seconds in a date - * @return Series - */ - seconds() { - let series = this.__date_ops(function (date_instance) { - return date_instance.getSeconds(); - }); - - return series; - } - - /** - * @description obtain the minutes in a date - * @return Series - */ - minutes() { - let series = this.__date_ops(function (date_instance) { - return date_instance.getMinutes(); - }); - - return series; - } - -} - - -export const to_datetime = (kwargs) => { - - let timeseries = new TimeSeries(kwargs); // parsed to date-time - timeseries.preprocessed(); // generate date-time list - - return timeseries; - -}; diff --git a/danfojs-node/src/core/utils.js b/danfojs-node/src/core/utils.js deleted file mode 100644 index b4931c1a..00000000 --- a/danfojs-node/src/core/utils.js +++ /dev/null @@ -1,688 +0,0 @@ -import * as tf from '@tensorflow/tfjs-node'; -import { Configs } from "../config/config"; - -const config = new Configs(); - -export class Utils { - //remove an element from an array - remove(arr, index) { - let new_arr = arr.filter(function (val, i) { - return i != index; - }); - - return new_arr; - } - - //filter element present in an aray - __remove_arr(arr, index) { - let new_arr = arr.filter(function (val, i) { - return !index.includes(i); - }); - - return new_arr; - } - - // Returns if a value is a string - __is_string(value) { - return typeof value === "string" || value instanceof String; - } - - // Returns if a value is really a number - __is_number(value) { - return typeof value === "number" && isFinite(value); - } - - // Returns if a value is an object - __is_object(value) { - return value && typeof value === "object" && value.constructor && value.constructor.name === "Object"; - } - - // Returns if a value is null - __is_null(value) { - return value === null; - } - - // Returns if a value is undefined - __is_undefined(value) { - return typeof value === "undefined"; - } - - /** - * Optimized version of random sampling from an array, as implemented in Python - * - * - Chooses k unique random elements from a population sequence or set. - - Returns a new list containing elements from the population while - leaving the original population unchanged. The resulting list is - in selection order so that all sub-slices will also be valid random - samples. This allows raffle winners (the sample) to be partitioned - into grand prize and second place winners (the subslices). - - Members of the population need not be hashable or unique. If the - population contains repeats, then each occurrence is a possible - selection in the sample. - - To choose a sample in a range of integers, use range as an argument. - This is especially fast and space efficient for sampling from a - large population: sample(range(10000000), 60) - - Sampling without replacement entails tracking either potential - selections (the array) in a list or previous selections in a set. - - When the number of selections is small compared to the - population, then tracking selections is efficient, requiring - only a small set and an occasional reselection. For - a larger number of selections, the array tracking method is - preferred since the list takes less space than the - set and it doesn't suffer from frequent reselections. - * - * @param {*} array The array to sample values from randomly - * @param {*} num The number of elements to sample randomly - */ - // Chooses k unique random elements from array. - __sample_from_iter(array, k, destructive) { - var n = array.length; - - if (k < 0 || k > n) - throw new RangeError("Sample larger than population or is negative"); - - if ( - destructive || - n <= (k <= 5 ? 21 : 21 + Math.pow(4, Math.ceil(Math.log(k * 3, 4)))) - ) { - if (!destructive) array = Array.prototype.slice.call(array); - for (var i = 0; i < k; i++) { - // invariant: non-selected at [i,n) - var j = (i + Math.random() * (n - i)) | 0; - var x = array[i]; - array[i] = array[j]; - array[j] = x; - } - array.length = k; // truncate - return array; - } else { - var selected = new Set(); - // eslint-disable-next-line no-empty - while (selected.add((Math.random() * n) | 0).size < k) { } - // eslint-disable-next-line no-undef - return Array.prototype.map.call(selected, (i) => population[i]); - } - } - - //generate integers between two set of numbers - __range(start, end) { - let value = tf.linspace(start, end, end - start + 1).arraySync(); - return value; - } - - //check if key is in object - __key_in_object(object, key) { - if (Object.prototype.hasOwnProperty.call(object, key)) { - return true; - } else { - return false; - } - } - - //transpose row array into column wise array - __get_col_values(data) { - let row_len = data.length; - let cols_len = data[0].length; - var cols_arr = []; - for (var i = 0; i <= cols_len - 1; i++) { - let temp_col = []; - for (let j = 0; j < row_len; j++) { - temp_col.push(data[j][i]); - } - cols_arr.push(temp_col); - } - return cols_arr; - } - - /** - * retrieve row array and column names from object of the form {a: [1,2,3,4], b: [30,20, 30, 20}] - * @param {*} data - */ - _get_row_and_col_values(data) { - let col_names = Object.keys(data); - let col_data = Object.values(data); - let first_col_len = col_data[0].length; - - col_data.forEach((data) => { - if (data.length != first_col_len) { - throw Error("Length Error: Length of columns must be the same!"); - } - }); - let rows_len = col_data[0].length; - let cols_len = col_names.length; - var rows_arr = []; - - for (var i = 0; i <= rows_len - 1; i++) { - var temp_row = []; - for (let j = 0; j < cols_len; j++) { - let _arr = col_data[j]; - temp_row.push(_arr[i]); - } - rows_arr.push(temp_row); - } - return [rows_arr, col_names]; - } - - //converts a 2D array of array to 1D for Series Class - __convert_2D_to_1D(data) { - let new_data = []; - data.map((val) => { - if (this.__is_object(val)) { - new_data.push(JSON.stringify(val)); - } else { - new_data.push(`${val}`); - } - }); - return new_data; - } - - __replace_undefined_with_NaN(data, isSeries) { - if (isSeries) { - let temp_arr = []; - data.forEach((val) => { - if (typeof val === "undefined" || val == Infinity || val == null) { - temp_arr.push(NaN); - } else { - temp_arr.push(val); - } - }); - return temp_arr; - } else { - let full_arr = []; - data.forEach((val) => { - var temp_arr = []; - val.forEach((ele) => { - if (typeof ele === "undefined" || ele == Infinity || ele == null) { - temp_arr.push(NaN); - } else { - temp_arr.push(ele); - } - }); - full_arr.push(temp_arr); - }); - return full_arr; - } - } - - __checker(arr_val) { - let dtypes = []; - let lim; - let int_tracker = []; - let float_tracker = []; - let string_tracker = []; - let bool_tracker = []; - - if (arr_val.length == 0) { - dtypes.push("string"); - } - - if (arr_val.length < config.get_dtype_test_lim) { - lim = arr_val.length - 1; - } else { - lim = config.get_dtype_test_lim - 1; - } - arr_val.forEach((ele, indx) => { - let count = indx; - if (typeof ele == "boolean") { - float_tracker.push(false); - int_tracker.push(false); - string_tracker.push(false); - bool_tracker.push(true); - } else if (isNaN(ele) && typeof ele != "string") { - float_tracker.push(true); - int_tracker.push(false); - string_tracker.push(false); - bool_tracker.push(false); - } else if (!isNaN(Number(ele))) { - if (ele.toString().includes(".")) { - float_tracker.push(true); - int_tracker.push(false); - string_tracker.push(false); - bool_tracker.push(false); - } else { - float_tracker.push(false); - int_tracker.push(true); - string_tracker.push(false); - bool_tracker.push(false); - } - } else { - float_tracker.push(false); - int_tracker.push(false); - string_tracker.push(true); - bool_tracker.push(false); - } - - if (count == lim) { - //if atleast one string appears return string dtype - const even = (element) => element == true; - if (string_tracker.some(even)) { - dtypes = "string"; - } else if (float_tracker.some(even)) { - dtypes = "float32"; - } else if (int_tracker.some(even)) { - dtypes = "int32"; - } else if (bool_tracker.some(even)) { - dtypes = "boolean"; - } else { - dtypes = "undefined"; - } - } - }); - - return dtypes; - } - //infer types from an array of array - __get_t(arr_val) { - const self = this; - if (this.__is_1D_array(arr_val)) { - return [this.__checker(arr_val)]; - } else { - const dtypes = arr_val.map((arr) => { - return self.__checker(arr); - }); - return dtypes; - } - } - - __unique(data) { - let unique = new Set(); - - data.map(function (val) { - unique.add(val[0]); - }); - - let unique_array = Array.from(unique); - - return unique_array; - } - - //second version of In object - __in_object(object, key, message) { - if (!Object.prototype.hasOwnProperty.call(object, key)) { - throw new Error(message); - } - } - - //check if a array is 1D - __is_1D_array(arr) { - if ( - typeof arr[0] == "number" || - typeof arr[0] == "string" || - typeof arr[0] == "boolean" - ) { - return true; - } else { - return false; - } - } - - //converts an array to object with index as value - __arr_to_obj(arr) { - let arr_map = {}; - arr.forEach((ele, i) => { - arr_map[ele] = i; - }); - return arr_map; - } - - /** - * count the NaN and non-NaN values present in an array - * @param {Array} arr Array object - * @param {Boolean} val whether to return the value count instead of the null count - * @param {Boolean} isSeries Whether the Obj is of type series or not - */ - __count_nan(arr, return_val = true, isSeries) { - if (isSeries) { - let null_count = 0; - let val_count = 0; - arr.forEach((ele) => { - if (Number.isNaN(ele)) { - null_count = null_count + 1; - } else { - val_count = val_count + 1; - } - }); - if (return_val) { - return val_count; - } else { - return null_count; - } - } else { - let result_arr = []; - arr.forEach((ele_arr) => { - let null_count = 0; - let val_count = 0; - ele_arr.forEach((ele) => { - if (Number.isNaN(ele)) { - null_count = null_count + 1; - } else { - val_count = val_count + 1; - } - }); - if (return_val) { - result_arr.push(val_count); - } else { - result_arr.push(null_count); - } - }); - return result_arr; - } - } - - //computes the median of an array - __median(arr, isSeries) { - if (isSeries) { - const sorted = arr.slice().sort((a, b) => a - b); - const middle = Math.floor(sorted.length / 2); - - if (sorted.length % 2 === 0) { - return (sorted[middle - 1] + sorted[middle]) / 2; - } - - return sorted[middle]; - } else { - let result_arr = []; - arr.map((ele) => { - const sorted = ele.slice().sort((a, b) => a - b); - const middle = Math.floor(sorted.length / 2); - - if (sorted.length % 2 === 0) { - result_arr.push((sorted[middle - 1] + sorted[middle]) / 2); - } else { - result_arr.push(sorted[middle]); - } - }); - return result_arr; - } - } - - //computes the mode(s) of an array - __mode(arr) { - var modes = [], - count = [], - i, - maxIndex = 0; - - arr.forEach((val) => { - count[val] = (count[val] || 0) + 1; - if (count[val] > maxIndex) { - maxIndex = count[val]; - } - }); - - for (i in count) - if (this.__key_in_object(count, i)) { - if (count[i] === maxIndex) { - modes.push(Number(i)); - } - } - - return modes; - } - - //round elements of an array to ndp - __round(arr, dp = 2, isSeries) { - if (dp < 0) { - dp = 1; - } - if (isSeries) { - let new_arr = []; - arr.map((val) => { - new_arr.push(Number(val.toFixed(dp))); - }); - - return new_arr; - } else { - let result_arr = []; - arr.map((arr_ele) => { - let new_arr = []; - arr_ele.map((val) => { - new_arr.push(Number(val.toFixed(dp))); - }); - result_arr.push(new_arr); - }); - return result_arr; - } - } - - //check a variable is a function - __is_function(variable) { - return typeof variable == "function"; - } - - //generate a random list - __randgen(num, start, end) { - let gen_num = []; - - //random int - function randi(a, b) { - return Math.floor(Math.random() * (b - a) + a); - } - - function recursive(val, arr) { - if (!arr.includes(val)) { - return val; - } - - val = randi(start, end); - - recursive(val, arr); - } - - for (let i = 0; i < num; i++) { - let gen_val = randi(start, end); - let recur_val = recursive(gen_val, gen_num); - gen_num.push(recur_val); - } - - return gen_num; - } - - _throw_wrong_params_error(kwargs, params_needed) { - let keys = Object.keys(kwargs); - let bool = []; - for (let i = 0; i < keys.length; i++) { - if (params_needed.includes(keys[i])) { - bool.push(true); - } else { - bool.push(false); - } - } - const truthy = (element) => element == false; - if (bool.some(truthy)) { - throw Error( - `Params Error: A specified parameter is not supported. Your params must be any of the following [${params_needed}]` - ); - } - } - - //maps int values (0, 1) to bools (false, true) - __map_int_to_bool(arr, dim) { - let new_arr = []; - if (dim == 2) { - arr.map((outer_val) => { - let temp_arr = []; - outer_val.map((val) => { - if (val == 1) { - temp_arr.push(true); - } else { - temp_arr.push(false); - } - }); - new_arr.push(temp_arr); - }); - return new_arr; - } else { - arr.map((val) => { - if (val == 1) { - new_arr.push(true); - } else { - new_arr.push(false); - } - }); - return new_arr; - } - } - - __std(data) { - let tensor_data = data; - - let mean = tensor_data.mean(); - let sub_mean_pow = tensor_data.sub(mean).pow(2); - let mean_data = sub_mean_pow.mean(); - let std = mean_data.sqrt(); - - return std; - } - - __zeros(row, column) { - let zero_data = []; - - for (let i = 0; i < row; i++) { - let col_data = Array(column); - for (let j = 0; j < column; j++) { - col_data[j] = 0; - } - zero_data.push(col_data); - } - return zero_data; - } - - __shuffle(num, array) { - //https://stackoverflow.com/questions/18806210/generating-non-repeating-random-numbers-in-js/18806417 - var i = array.length, - j = 0, - temp; - - while (i--) { - j = Math.floor(Math.random() * (i + 1)); - - // swap randomly chosen element with current element - temp = array[i]; - array[i] = array[j]; - array[j] = temp; - } - - return array.slice(0, num); - } - - __sort(arr, ascending = true) { - let sorted = arr.slice(); - return sorted.sort((a, b) => { - if (ascending) { - if (typeof a === "string" && typeof b === "string") { - return a.charCodeAt() - b.charCodeAt(); - } else { - return a - b; - } - } else { - if (typeof a === "string" && typeof b === "string") { - return b.charCodeAt() - a.charCodeAt(); - } else { - return b - a; - } - } - }); - } - - __is_browser_env() { - var isBrowser = new Function( - "try {return this===window;}catch(e){ return false;}" - ); - // tests if global scope is binded to window - return isBrowser(); - } - - __is_node_env() { - var isNode = new Function( - "try {return this===global;}catch(e){return false;}" - ); - // tests if global scope is binded to window - return isNode(); - } - - _throw_str_dtype_error(obj, ops) { - if (obj.dtypes[0] == "string") { - throw Error( - `dtype error: String data type does not support ${ops} operation` - ); - } - } - - /** - * Remove NaN values from Array - * @param {*} arr - */ - _remove_nans(arr) { - let values = arr.filter((val) => !isNaN(val) && typeof val != "string"); - return values; - } - - /** - * Replace NaN with null before tensor operations - * @param {*} arr - */ - _replace_nan_with_null(arr) { - let values = arr.map((val) => { - if (isNaN(val)) { - return null; - } else { - return val; - } - }); - return values; - } - - __get_duplicate(arr) { - let temp_obj = {}; - let rslt_obj = {}; - - arr.forEach((val, index) => { - if (temp_obj.hasOwnProperty(val)) { - temp_obj[val]["count"] += 1; - temp_obj[val]["index"].push(index); - } else { - temp_obj[val] = {}; - temp_obj[val]["count"] = 1; - temp_obj[val]["index"] = [index]; - } - }); - - for (let key in temp_obj) { - if (temp_obj[key]["count"] >= 2) { - rslt_obj[key] = {}; - rslt_obj[key]["count"] = temp_obj[key]["count"]; - rslt_obj[key]["index"] = temp_obj[key]["index"]; - } - } - - return rslt_obj; - } - - /** - * Sorts an array by index - * @param {Array} arr1 - * @param {Array} arr2 - * @param {string} dtype - * - * @returns sorted index - */ - _sort_arr_with_index(arr1, arr2, dtype) { - let sorted_idx = arr1.map((item, index) => { - return [arr2[index], item]; - }); - if (dtype == "string") { - sorted_idx.sort(); - } else { - sorted_idx.sort(([arg1], [arg2]) => arg2 - arg1); - } - - return sorted_idx.map(([, item]) => item); - } -} diff --git a/danfojs-node/src/index.js b/danfojs-node/src/index.js deleted file mode 100644 index 6b9415a0..00000000 --- a/danfojs-node/src/index.js +++ /dev/null @@ -1,20 +0,0 @@ -import NDframe from "./core/generic"; -import * as tf from "@tensorflow/tfjs-node"; - -export { Series } from "./core/series"; -export { DataFrame } from "./core/frame"; -export { to_datetime } from "./core/timeseries"; -export { read_csv, read_json, read_excel, read } from "./io/reader"; -export { merge } from "./core/merge"; -export { concat } from "./core/concat"; -export { LabelEncoder, OneHotEncoder } from "./preprocessing/encodings"; -export { MinMaxScaler, StandardScaler } from "./preprocessing/scalers"; -export { date_range } from "./core/date_range"; -export { get_dummies } from "./core/get_dummies"; -export { Configs } from "./config/config"; -export { NDframe }; -export { Str } from "./core/strings"; -export { Utils } from "./core/utils"; -export { tf }; - -export const _version = "0.2.7"; diff --git a/danfojs-node/src/io/reader.js b/danfojs-node/src/io/reader.js deleted file mode 100644 index f4738e21..00000000 --- a/danfojs-node/src/io/reader.js +++ /dev/null @@ -1,138 +0,0 @@ -import { data } from "@tensorflow/tfjs-node"; -import fetch from "node-fetch"; -import { open, Dataset, isDataset } from "frictionless.js"; -import toArray from "stream-to-array"; -import { DataFrame } from "../core/frame"; - - -/** - * Reads a CSV file from local or remote storage - * - * @param {source} URL or local file path to retreive CSV file. If it's a local path, it - * must have prefix `file://` and it only works in node environment. - * @param {config} (Optional). A CSV Config object that contains configurations - * for reading and decoding from CSV file(s). - * - * @returns {Promise} DataFrame structure of parsed CSV data - */ -export const read_csv = async (source, configs = {}) => { - let { start, end } = configs; - if (!(source.startsWith("file://") || source.startsWith("http") || source.startsWith("blob"))) { - //probabily a relative path, append file:// to it - source = source.startsWith("/") ? `file://${source}` : `file://${process.cwd()}/${source}`; - } - - let tfdata = []; - await data.csv(source, configs) - .skip(start) - .take(end) - .forEachAsync((row) => { - return tfdata.push(row); - }); - const df = new DataFrame(tfdata); - return df; -}; - -/** - * Reads a JSON file from local or remote address - * - * @param {source} URL or local file path to retreive JSON file. - * @returns {Promise} DataFrame structure of parsed CSV data - */ -export const read_json = async (source) => { - if ( - source.startsWith("https://") || - source.startsWith("http://") || - source.startsWith("file://") - ) { - //read from URL - let res = await fetch(source, { method: "Get" }); - let json = await res.json(); - let df = new DataFrame(json); - return df; - } else { - //Try reading file from local env - let fs = await import("fs"); - return new Promise((resolve, reject) => { - fs.readFile(source, (err, data) => { - if (err) reject(err); - let df = new DataFrame(JSON.parse(data)); - resolve(df); - }); - }); - } -}; - -/** - * Reads an Excel file from local or remote address - * @param {string} source URL or local file path to retreive Excel file. - * @param {object} configs (Optional) Configuration options when reading excel files - * - * { - * sheet : string, (Optional) number of the sheet to parse. Default will be the first sheet. - * } - * @returns {Promise} DataFrame structure of parsed Excel data - */ -export const read_excel = async (source, configs) => { - const df = await read(source, configs); - return df; -}; - -/** - * Opens a file using frictionless.js specification. - * @param {string} source A path to the file/resources. It can be a local file, - * a URL to a tabular data (CSV, EXCEL) or Datahub.io Data Resource. - * Data comes with extra properties and specification conforming to the Frictionless Data standards. - * @param {object} configs { - * - * data_num (Defaults => 0): The specific dataset to load, when reading data from a datapackage.json - * - * header (Defaults => true): Whether the dataset contains header or not. - * - * sheet (Defaults => 0): Number of the excel sheet which u want to load. - * } - * @returns {DataFrame} Danfo DataFrame/Series - */ -export const read = async ( - source, - configs = {} -) => { - let { data_num, header, sheet } = configs; - data_num = data_num === undefined ? 0 : data_num; - header = header === undefined ? true : header; - let rows, file; - - if (isDataset(source)) { - console.info( - "datapackage.json found. Loading Dataset package from Datahub.io" - ); - const dataset = await Dataset.load(source); - file = dataset.resources[data_num]; - rows = await toArray(await file.rows()); - } else { - try { - file = open(source); - if (sheet) { - rows = await toArray(await file.rows({ sheet })); - } else { - rows = await toArray(await file.rows()); - } - } catch (error) { - console.log(error); - } - } - - if ([ "csv", "xls", "xlsx" ].includes(await file.descriptor.format)) { - if (header) { - let df = new DataFrame(rows.slice(1), { columns: rows[0] }); - return df; - } else { - let df = new DataFrame(rows); - return df; - } - } else { - let df = new DataFrame(rows); - return df; - } -}; - diff --git a/danfojs-node/src/preprocessing/encodings.js b/danfojs-node/src/preprocessing/encodings.js deleted file mode 100644 index ace33885..00000000 --- a/danfojs-node/src/preprocessing/encodings.js +++ /dev/null @@ -1,108 +0,0 @@ -import { Series } from "../core/series"; -import { Utils } from "../core/utils"; -import { DataFrame } from "../core/frame"; - -const utils = new Utils; - -export class LabelEncoder { - - /** - * - * @param {data} data [Array|Series] - * @returns Array. - */ - fit(data) { - let in_data = null; - if (Array.isArray(data)) { - in_data = data; - } else if (data instanceof Series) { - in_data = data.values; - } else { - throw new Error("data must be an array or a Series"); - } - - let data_set = new Set(in_data); - this.label = Array.from(data_set); - - let self = this; - let output_data = in_data.map((x) => { - return self.label.indexOf(x); - }); - - return new Series(output_data); - } - - /** - * Transform data using the label generated from fitting - * @param {data} data [Array|Series] - * @returns Array - */ - transform(data) { - let in_data = null; - if (Array.isArray(data)) { - in_data = data; - } else if (data instanceof Series) { - in_data = data.values; - } else { - throw new Error("data must be an array or a Series"); - } - - let self = this; - let output_data = in_data.map((x) => { - return self.label.indexOf(x); - }); - return new Series(output_data); - } -} - -export class OneHotEncoder { - - fit(data) { - let in_data = null; - if (Array.isArray(data)) { - in_data = data; - } else if (data instanceof Series) { - in_data = data.values; - } else { - throw new Error("data must be an array"); - } - - let data_set = new Set(in_data); - this.label = Array.from(data_set); - - let onehot_data = utils.__zeros(in_data.length, this.label.length); - - for (let i = 0; i < in_data.length; i++) { - - let elem = in_data[i]; - let elem_index = this.label.indexOf(elem); - onehot_data[i][elem_index] = 1; - } - - return new DataFrame(onehot_data, { columns: this.label }); - - } - - transform(data) { - let in_data = null; - - if (Array.isArray(data)) { - in_data = data; - } else if (data instanceof Series) { - in_data = data.values; - } else { - throw new Error("data must be an array"); - } - - let onehot_data = utils.__zeros(in_data.length, this.label.length); - - for (let i = 0; i < in_data.length; i++) { - let elem = in_data[i]; - let elem_index = this.label.indexOf(elem); - onehot_data[i][elem_index] = 1; - } - - return new DataFrame(onehot_data, { columns: this.label }); - - } -} diff --git a/danfojs-node/src/preprocessing/scalers.js b/danfojs-node/src/preprocessing/scalers.js deleted file mode 100644 index f966cf13..00000000 --- a/danfojs-node/src/preprocessing/scalers.js +++ /dev/null @@ -1,267 +0,0 @@ -import * as tf from '@tensorflow/tfjs-node'; -import { Series } from "../core/series"; -import { DataFrame } from "../core/frame"; -import { Utils } from "../core/utils"; - -const utils = new Utils(); - -export class MinMaxScaler { - /** - * Fit minmax scaler on data, to obtain their min and max value - * @param {data} data [DataRame | Series | Array] - * @returns Array - */ - fit(data) { - let tensor_data = null; - if (Array.isArray(data)) { - tensor_data = tf.tensor(data); - } else if (data instanceof DataFrame || data instanceof Series) { - if (data.dtypes.includes("string")) { - throw Error("Dtype Error: Cannot perform operation on string dtypes"); - } - tensor_data = tf.tensor(data.values); - } else { - throw new Error("data must either be an Array, DataFrame or Series"); - } - - this.max = tensor_data.max(0); - this.min = tensor_data.min(0); - - let output_data = tensor_data - .sub(this.min) - .div(this.max.sub(this.min)) - .arraySync(); - - if (data instanceof Series || Array.isArray(data)) { - return new Series(output_data); - } else { - return new DataFrame(output_data); - } - } - - /** - * Transform an array using the min and max generated from the fitting on data - * @param {data} data [Array] - * @returns array - */ - transform(data) { - if (data instanceof Series) { - if (data.dtypes.includes("string")) { - throw Error("Dtype Error: Cannot perform operation on string dtypes"); - } - let tensor_data = tf.tensor(data.values); - let output_data = tensor_data - .sub(this.min) - .div(this.max.sub(this.min)) - .arraySync(); - return new Series(output_data); - } else if (Array.isArray(data)) { - let tensor_data = tf.tensor(data); - let output_data = tensor_data - .sub(this.min) - .div(this.max.sub(this.min)) - .arraySync(); - if (utils.__is_1D_array(data)) { - return new Series(output_data); - } else { - return new DataFrame(output_data); - } - } else if (data instanceof DataFrame) { - if (data.dtypes.includes("string")) { - throw Error("Dtype Error: Cannot perform operation on string dtypes"); - } - let tensor_data = tf.tensor(data.values); - let output_data = tensor_data - .sub(this.min) - .div(this.max.sub(this.min)) - .arraySync(); - return new DataFrame(output_data); - } else { - throw Error("Value Error: Data type not supoorted"); - } - } -} - -export class StandardScaler { - /** - * - * @param {data} data [DataRame | Series | Array] - * @returns Array - */ - fit(data) { - let tensor_data = null; - if (Array.isArray(data)) { - tensor_data = tf.tensor(data); - } else if (data instanceof DataFrame || data instanceof Series) { - if (data.dtypes.includes("string")) { - throw Error("Dtype Error: Cannot perform operation on string dtypes"); - } - tensor_data = tf.tensor(data.values); - } else { - throw new Error("data must either be an Array, DataFrame or Series"); - } - - this.std = tf.moments(tensor_data, 0).variance.sqrt(); - this.mean = tensor_data.mean(0); - let output_data = tensor_data.sub(this.mean).div(this.std).arraySync(); - - if (data instanceof Series || Array.isArray(data)) { - return new Series((data = output_data)); - } else { - return new DataFrame((data = output_data)); - } - } - - transform(data) { - // if(!Array.isArray(data)){ - // throw new Error(data) - // } - - if (data instanceof Series) { - if (data.dtypes.includes("string")) { - throw Error("Dtype Error: Cannot perform operation on string dtypes"); - } - let tensor_data = tf.tensor(data.values); - let output_data = tensor_data.sub(this.mean).div(this.std).arraySync(); - return new Series(output_data); - } else if (Array.isArray(data)) { - let tensor_data = tf.tensor(data); - let output_data = tensor_data.sub(this.mean).div(this.std).arraySync(); - if (utils.__is_1D_array(data)) { - return new Series(output_data); - } else { - return new DataFrame(output_data); - } - } else if (data instanceof DataFrame) { - if (data.dtypes.includes("string")) { - throw Error("Dtype Error: Cannot perform operation on string dtypes"); - } - let tensor_data = tf.tensor(data.values); - let output_data = tensor_data.sub(this.mean).div(this.std).arraySync(); - return new DataFrame(output_data); - } else { - throw Error("Value Error: Data type not supoorted"); - } - } -} - -// export class RobustScaler{ - -// __median(arr, isTensor,return_index) { -// if (!isTensor) { -// const sorted = arr.slice().sort((a, b) => a - b); -// const middle = Math.floor(sorted.length / 2); - -// if (sorted.length % 2 === 0) { - -// return return_index ? [(middle - 1) , middle] : (sorted[middle - 1] + sorted[middle]) / 2; -// } - -// return return_index ? middle : sorted[middle] ; -// } else { -// let result_arr = [] -// arr.map(ele => { -// const sorted = ele.slice().sort((a, b) => a - b); -// const middle = Math.floor(sorted.length / 2); - -// if (sorted.length % 2 === 0) { -// result_arr.push(return_index ? [(middle - 1) , middle] :(sorted[middle - 1] + sorted[middle]) / 2 ) -// } else { -// result_arr.push(return_index ? middle : sorted[middle]) -// } - -// }) -// return result_arr -// } - -// } - -// quantile(data,isTensor){ - -// if(isTensor){ -// data = utils.__get_col_values(data); -// } - -// let median = this.__median(data,isTensor,true) -// let med = this.__median(data,isTensor,false) - -// let q1 = [] -// let q2 = [] - -// if(!isTensor){ -// let sorted = data.slice().sort((a, b) => a - b); - -// if(Array.isArray(median)){ -// let lower = median[0] -// let lower_data = sorted.slice(0,lower+1) -// let upper_data = sorted.slice(lower+1,) - -// q1.push(this.__median(lower_data,isTensor,false)); -// q2.push(this.__median(upper_data,isTensor,false)); - -// }else{ -// let lower_data = sorted.slice(0,median) -// let upper_data = sorted.slice(median+1,) - -// q1.push(this.__median(lower_data,isTensor,false)); -// q2.push(this.__median(upper_data,isTensor,false)); -// } - -// }else{ - -// data.map((x,i)=>{ -// let sorted = x.slice().sort((a, b) => a - b); - -// if(Array.isArray(median[i])){ -// let lower = median[i][0] -// let lower_data = sorted.slice(0,lower+1) -// let upper_data = sorted.slice(lower+1,) - -// q1.push(this.__median(lower_data,!isTensor,false)); -// q2.push(this.__median(upper_data,!isTensor,false)); - -// }else{ -// let lower_data = sorted.slice(0,median[i]) -// let upper_data = sorted.slice(median[i]+1,) - -// q1.push(this.__median(lower_data,!isTensor,false)); -// q2.push(this.__median(upper_data,!isTensor,false)); -// } - -// }) -// } - -// return [q1,q2, med] - -// } - -// /** -// * Fit robust scalar on data to obtain the first quantile and third quantile -// * @param {data} data [DataRame | Series | Array] -// * @returns Array -// */ -// fit(data){ - -// let tensor_data = null -// let isTensor = false; -// if(Array.isArray(data)){ -// tensor_data = tf.tensor(data) -// } -// else if((data instanceof DataFrame)){ -// tensor_data = tf.tensor(data.values) -// isTensor = true; -// } -// else if((data instanceof Series)){ -// tensor_data = tf.tensor(data.values) -// } -// else{ -// throw new Error("data must either be an Array, DataFrame or Series") -// } - -// let [q1, q3, median] = this.quantile(data,isTensor) -// let q3_tensor = tf.tensor(q3) -// let output_data = tensor_data.sub(median).div(q3_tensor.sub(q1)).arraySync() - -// return output_data; -// } -// } diff --git a/danfojs-node/tests/config/config.js b/danfojs-node/tests/config/config.js deleted file mode 100644 index f6f39af5..00000000 --- a/danfojs-node/tests/config/config.js +++ /dev/null @@ -1,25 +0,0 @@ -import { Configs } from '../../src/config/config'; -import { assert } from 'chai'; - -const config = new Configs(); - -describe("Config", function () { - it("gets the default config val for table width", function () { - let table_width = config.get_width; - assert.equal(table_width, 17); - }); - it("gets the default config val for table truncation", function () { - let table_trunc = config.get_truncate; - assert.equal(table_trunc, 16); - }); - it("Sets the config val for table width", function () { - config.set_width(7); - let table_width = config.get_width; - assert.equal(table_width, 7); - }); - it("Sets the config val for table truncation", function () { - config.set_truncate(5); - let table_trunc = config.get_truncate; - assert.equal(table_trunc, 5); - }); -}); diff --git a/danfojs-node/tests/core/date_range.js b/danfojs-node/tests/core/date_range.js deleted file mode 100644 index 1652ff64..00000000 --- a/danfojs-node/tests/core/date_range.js +++ /dev/null @@ -1,75 +0,0 @@ -import { assert } from "chai"; -import { date_range } from '../../src/core/date_range'; - -describe("date_range", function(){ - - // it("Obtain date between start and end specified", function(){ - - // let d = new date_range({"start":'2018-04-24',"end":'2018-04-27'}) - // let rslt = [ - // '4/24/2018, 12:00:00 AM', - // '4/25/2018, 12:00:00 AM', - // '4/26/2018, 12:00:00 AM', - // '4/27/2018, 12:00:00 AM' - // ] - - // assert.deepEqual(d,rslt) - - - // }); - it("Obtain date between start with end not specified, but period and freq specified", function(){ - - - let d = new date_range({ "start":'1/1/2018', period:5, freq:'M' }); - let rslt = [ - '1/1/2018, 12:00:00 AM', - '2/1/2018, 12:00:00 AM', - '3/1/2018, 12:00:00 AM', - '4/1/2018, 12:00:00 AM', - '5/1/2018, 12:00:00 AM' - ]; - - assert.deepEqual(d, rslt); - - - }); - it("Obtain date between start with end not specified, but period and freq specified, plus offset", function(){ - - let d = new date_range({ start:'1/1/2018', period:5, freq:'3M' }); - let rslt = [ - '1/1/2018, 12:00:00 AM', - '4/1/2018, 12:00:00 AM', - '7/1/2018, 12:00:00 AM', - '10/1/2018, 12:00:00 AM', - '1/1/2019, 12:00:00 AM' - ]; - - assert.deepEqual(d, rslt); - - - }); - it("Obtain date range with start not specified but end and period is given", function(){ - - let d = new date_range({ end:'1/1/2018', period:8 }); - let rslt = [ - '12/25/2017, 12:00:00 AM', - '12/26/2017, 12:00:00 AM', - '12/27/2017, 12:00:00 AM', - '12/28/2017, 12:00:00 AM', - '12/29/2017, 12:00:00 AM', - '12/30/2017, 12:00:00 AM', - '12/31/2017, 12:00:00 AM', - '1/1/2018, 12:00:00 AM' - ]; - - assert.deepEqual(d, rslt); - - - }); - it("inputing wrong freq", function(){ - assert.throws(function () { new date_range({ end:'1/1/2018', period:8, freq:"d" }); }, Error, 'invalid freq d'); - }); - it("inputing wrong freq with offset", function(){ - assert.throws(function () { new date_range({ end:'1/1/2018', period:8, freq:"4d" }); }, Error, 'invalid freq d'); - }); -}); diff --git a/danfojs-node/tests/core/frame.js b/danfojs-node/tests/core/frame.js deleted file mode 100644 index 01e1250e..00000000 --- a/danfojs-node/tests/core/frame.js +++ /dev/null @@ -1,2384 +0,0 @@ -import { assert, expect } from "chai"; -import { DataFrame } from '../../src/core/frame'; -import { Series } from "../../src/core/series"; -import fs from "fs"; - -const testCSVPath = "./tester.csv"; - -describe("DataFrame", function () { - - describe("to_csv", function () { - afterEach(function () { - // Clean up generated file - fs.unlinkSync(testCSVPath); - }); - - it("save dataframe to CSV file", async function () { - let data = [[1, 2, 3], [4, 5, 6]]; - let cols = ["A", "B", "C"]; - let df = new DataFrame(data, { columns: cols }); - await df.to_csv(testCSVPath); - assert.isTrue(fs.existsSync(testCSVPath)); - }); - - it("return dataframe csv string", async function () { - let data = [[1, 2, 3], [4, 5, 6]]; - let cols = ["A", "B", "C"]; - let df = new DataFrame(data, { columns: cols }); - const csvContent = await df.to_csv(testCSVPath); - assert.deepEqual(csvContent, "A,B,C\n1,2,3\n4,5,6\n"); - }); - - }); - - describe("drop", function () { - it("throw error for wrong row index", function () { - let data = [[1, 2, 3], [4, 5, 6]]; - let cols = ["A", "B", "C"]; - let df = new DataFrame(data, { columns: cols }); - assert.throws(function () { df.drop({ columns: [3], axis: 0, inplace: false }); }, Error, 'No index label found. Axis of 0 must be accompanied by an array of index labels'); - }); - it("throw error for wrong row index", function () { - let data = [[1, 2, 3], [4, 5, 6]]; - let cols = ["A", "B", "C"]; - let df = new DataFrame(data, { columns: cols }); - assert.throws(function () { df.drop({ columns: ["D"], axis: 1, inplace: false }); }, Error, 'column "D" does not exist'); - }); - - it("drop a column inplace", function () { - let data = [[1, 2, 3], [4, 5, 6]]; - let cols = ["A", "B", "C"]; - let df = new DataFrame(data, { columns: cols }); - df.drop({ columns: ["C", "B"], axis: 1, inplace: true }); - let column = ["A"]; - assert.deepEqual(df.columns, column); - }); - it("check if data is updated after column is dropped", function () { - let data = [[1, 2, 3], [4, 5, 6]]; - let cols = ["A", "B", "C"]; - let df = new DataFrame(data, { columns: cols }); - df.drop({ columns: ["C"], axis: 1, inplace: true }); - let new_data = [[1, 2], [4, 5]]; - assert.deepEqual(df.values, new_data); - assert.deepEqual(df.dtypes.length, 2); - - }); - - it("check if data is updated after row is dropped", function () { - let data = [[1, 2, 3], [4, 5, 6]]; - let cols = ["A", "B", "C"]; - let df = new DataFrame(data, { columns: cols }); - df.drop({ index: [0], axis: 0, inplace: true }); - let new_data = [[4, 5, 6]]; - assert.deepEqual(df.values, new_data); - }); - it("check if new dataframe is properly created after column is dropped (not-in-inplace)", function () { - let data = [[1, 2, 3], [4, 5, 6]]; - let cols = ["A", "B", "C"]; - let df = new DataFrame(data, { columns: cols }); - let df_drop = df.drop({ columns: ["C"], axis: 1, inplace: false }); - - let expected_data = [[1, 2], [4, 5]]; - let expected_cols = ["A", "B"]; - let expected_df = new DataFrame(expected_data, { columns: expected_cols }); - assert.deepEqual(df_drop.values, expected_df.values); - }); - it("check that the dtype is updated after column drop", function () { - let data = [[1, 2, 3], [4, 5, 6]]; - let cols = ["A", "B", "C"]; - let df = new DataFrame(data, { columns: cols }); - df.drop({ columns: ["A"], axis: 1, inplace: true }); - let dtype = ['int32', 'int32']; - assert.deepEqual(df.ctypes.values, dtype); - }); - it("drop row by single string labels", function () { - let data = [[1, 2, 3], [4, 5, 6], [20, 34, 5]]; - let cols = ["A", "B", "C"]; - let df = new DataFrame(data, { columns: cols, index: ["a", "b", "c"] }); - df.drop({ index: ["a"], axis: 0, inplace: true }); - let new_data = [[4, 5, 6], [20, 34, 5]]; - assert.deepEqual(df.values, new_data); - }); - it("drop row by two or more string labels", function () { - let data = [[1, 2, 3], [4, 5, 6], [20, 34, 5], [2, 3.4, 5], [2.0, 340, 5]]; - let cols = ["A", "B", "C"]; - let df = new DataFrame(data, { columns: cols, index: ["a", "b", "c", "a", "b"] }); - df.drop({ index: ["a", "b"], axis: 0, inplace: true }); - let new_data = [[20, 34, 5]]; - assert.deepEqual(df.values, new_data); - - }); - }); - - describe("head", function () { - it("Gets the first n rows in a DataFrame", function () { - let data = [[1, 2, 3], [4, 5, 6], [20, 30, 40], [39, 89, 78]]; - let cols = ["A", "B", "C"]; - let df = new DataFrame(data, { columns: cols }); - assert.deepEqual(df.head(2).values, [[1, 2, 3], [4, 5, 6]]); - }); - it("Return all rows in a DataFrame if row specified is greater than values", function () { - let data = [[1, 2, 3], [4, 5, 6], [20, 30, 40], [39, 89, 78]]; - let cols = ["A", "B", "C"]; - let df = new DataFrame(data, { columns: cols }); - assert.deepEqual(df.head(10).values, data); - }); - it("Return all rows in a DataFrame if row specified is less than 0", function () { - let data = [[1, 2, 3], [4, 5, 6], [20, 30, 40], [39, 89, 78]]; - let cols = ["A", "B", "C"]; - let df = new DataFrame(data, { columns: cols }); - assert.deepEqual(df.head(-1).values, data); - }); - - }); - - describe("tail", function () { - it("Prints the last n rows of a DataFrame", function () { - let data = [[1, 2, 3], [4, 5, 6], [20, 30, 40], [39, 89, 78]]; - let cols = ["A", "B", "C"]; - let df = new DataFrame(data, { columns: cols }); - assert.deepEqual(df.tail(2).values, [[20, 30, 40], [39, 89, 78]]); - }); - it("Return all rows in a DataFrame if row specified is greater than values", function () { - let data = [[1, 2, 3], [4, 5, 6], [20, 30, 40], [39, 89, 78]]; - let cols = ["A", "B", "C"]; - let df = new DataFrame(data, { columns: cols }); - assert.deepEqual(df.tail(10).values, data); - }); - it("Return all rows in a DataFrame if row specified is less than 0", function () { - let data = [[1, 2, 3], [4, 5, 6], [20, 30, 40], [39, 89, 78]]; - let cols = ["A", "B", "C"]; - let df = new DataFrame(data, { columns: cols }); - assert.deepEqual(df.tail(-1).values, data); - }); - it("Return last 3 row index in a DataFrame", function () { - let data = [[1, 2, 3], [4, 5, 6], [20, 30, 40], [39, 89, 78]]; - let cols = ["A", "B", "C"]; - let df = new DataFrame(data, { columns: cols }); - assert.deepEqual(df.tail(2).index, [2, 3]); - }); - it("Check print format on head call", function () { - let data = [[1, 2, 34, 5, 0, 6, 4, 5, 6, 7], [20, 30, 40, 39, 89, 78, 45, 56, 56, 45]]; - let df = new DataFrame(data); - assert.deepEqual(df.tail(2).values, [[1, 2, 34, 5, 0, 6, 4, 5, 6, 7], [20, 30, 40, 39, 89, 78, 45, 56, 56, 45]]); - }); - }); - - describe("sample", function () { - it("Samples n number of random elements from a DataFrame", async function () { - let data = [[1, 2, 3], [4, 5, 6], [20, 30, 40], [39, 89, 78], [100, 200, 300]]; - let cols = ["A", "B", "C"]; - let df = new DataFrame(data, { columns: cols }); - let expected = [[1, 2, 3], [20, 30, 40]]; - let values = (await df.sample(2)).values; - assert.deepEqual(values, expected); - }); - it("Throw error if n is greater than lenght of Dataframe", async function () { - let data = [[1, 2, 3], [4, 5, 6], [20, 30, 40], [39, 89, 78], [100, 200, 300]]; - let cols = ["A", "B", "C"]; - let df = new DataFrame(data, { columns: cols }); - try { - await df.sample(100); - } catch (e) { - expect(e).to.be.instanceOf(Error); - expect(e.message).to.eql('Sample size n cannot be bigger than size of dataset'); - } - }); - it("Throw error if n is less than -1", async function () { - let data = [[1, 2, 3], [4, 5, 6], [20, 30, 40], [39, 89, 78], [100, 200, 300]]; - let cols = ["A", "B", "C"]; - let df = new DataFrame(data, { columns: cols }); - try { - await df.sample(-2); - } catch (e) { - expect(e).to.be.instanceOf(Error); - expect(e.message).to.eql('Sample size cannot be less than -1 or 0'); - } - }); - it("Throw error if n is 0", async function () { - let data = [[1, 2, 3], [4, 5, 6], [20, 30, 40], [39, 89, 78], [100, 200, 300]]; - let cols = ["A", "B", "C"]; - let df = new DataFrame(data, { columns: cols }); - try { - await df.sample(0); - } catch (e) { - expect(e).to.be.instanceOf(Error); - expect(e.message).to.eql('Sample size cannot be less than -1 or 0'); - } - }); - it("Return all values if n is -1", async function () { - let data = [[1, 2, 3], [4, 5, 6], [20, 30, 40], [39, 89, 78], [100, 200, 300]]; - let cols = ["A", "B", "C"]; - let df = new DataFrame(data, { columns: cols }); - assert.deepEqual((await df.sample(-1)).shape, [5, 3]); - }); - }); - - describe("loc", function () { - - it("throw error for wrong column name", function () { - let data = [[1, 2, 3], [4, 5, 6]]; - let cols = ["A", "B", "C"]; - let df = new DataFrame(data, { columns: cols }); - assert.throws(function () { df.loc({ "rows": [0, 1], "columns": ["A", "D"] }); }, Error, "Column D does not exist"); - }); - it("check data after selecting column", function () { - let data = [[1, 2, 3], [4, 5, 6]]; - let cols = ["A", "B", "C"]; - let df = new DataFrame(data, { columns: cols }); - - let col_df = df.loc({ "rows": [0, 1], "columns": ["B", "C"] }); - let col_data = [[2, 3], [5, 6]]; - - assert.deepEqual(col_df.values, col_data); - - }); - it("check data after selecting row index", function () { - let data = [[1, 2, 3], [4, 5, 6]]; - let cols = ["A", "B", "C"]; - let df = new DataFrame(data, { columns: cols }); - - let col_df = df.loc({ "rows": [1], "columns": ["B", "C"] }); - let col_data = [[5, 6]]; - - assert.deepEqual(col_df.values, col_data); - - }); - it("check data after row and column slice", function () { - let data = [[1, 2, 3], [4, 5, 6], [20, 30, 40], [39, 89, 78]]; - let cols = ["A", "B", "C"]; - let df = new DataFrame(data, { columns: cols }); - - let col_df = df.loc({ "rows": ["0:2"], "columns": ["B:C"] }); - let col_data = [[2], [5]]; - - assert.deepEqual(col_df.values, col_data); - - }); - it("check data after row slice", function () { - let data = [[1, 2, 3], [4, 5, 6], [20, 30, 40], [39, 89, 78]]; - let cols = ["A", "B", "C"]; - let df = new DataFrame(data, { columns: cols }); - - let col_df = df.loc({ "rows": ["0:2"], "columns": ["B", "C"] }); - let col_data = [[2, 3], [5, 6]]; - - assert.deepEqual(col_df.values, col_data); - - }); - it("check data after column slice", function () { - let data = [[1, 2, 3], [4, 5, 6], [20, 30, 40], [39, 89, 78]]; - let cols = ["A", "B", "C"]; - let df = new DataFrame(data, { columns: cols }); - - let col_df = df.loc({ "rows": [0, 1], "columns": ["A:C"] }); - let col_data = [[1, 2], [4, 5]]; - assert.deepEqual(col_df.values, col_data); - - }); - it("loc by single string index", function () { - let data = { - "Name": ["Apples", "Mango", "Banana", "Pear"], - "Count": [21, 5, 30, 10], - "Price": [200, 300, 40, 250] - }; - - let df = new DataFrame(data); - df.set_index({ key: ["a", "b", "c", "a"], inplace: true }); - let sub_df = df.loc({ rows: ["a"], columns: ["Name", "Count"] }); - let expected = [["Apples", 21], ["Pear", 10]]; - assert.deepEqual(sub_df.values, expected); - - }); - it("loc by multiple string index", function () { - let data = { - "Name": ["Apples", "Mango", "Banana", "Pear"], - "Count": [21, 5, 30, 10], - "Price": [200, 300, 40, 250] - }; - - let df = new DataFrame(data); - df.set_index({ key: ["a", "b", "c", "a"], inplace: true }); - let sub_df = df.loc({ rows: ["a", "b"], columns: ["Name", "Count"] }); - let expected = [["Apples", 21], ["Mango", 5], ["Pear", 10]]; - assert.deepEqual(sub_df.values, expected); - - }); - it("loc by slice string index", function () { - let data = { - "Name": ["Apples", "Mango", "Banana", "Pear"], - "Count": [21, 5, 30, 10], - "Price": [200, 300, 40, 250] - }; - - let df = new DataFrame(data); - df.set_index({ key: ["a", "b", "c", "d"], inplace: true }); - let sub_df = df.loc({ rows: ["a:c"], columns: ["Name", "Count"] }); - let expected = [["Apples", 21], ["Mango", 5]]; - assert.deepEqual(sub_df.values, expected); - - }); - - - }); - - describe("iloc", function () { - - it("throw error for wrong column index", function () { - let data = [[1, 2, 3], [4, 5, 6]]; - let cols = ["A", "B", "C"]; - let df = new DataFrame(data, { columns: cols }); - assert.throws(function () { df.iloc({ "rows": [0, 1], "columns": [0, 3] }); }, Error, "column index 3 is bigger than 2"); - }); - - it("throw error for wrong row index", function () { - let data = [[1, 2, 3], [4, 5, 6]]; - let cols = ["A", "B", "C"]; - let df = new DataFrame(data, { columns: cols }); - assert.throws(function () { df.iloc({ "rows": 0, "columns": [0, 3] }); }, Error, "rows parameter must be a Array"); - }); - - it("throw error for wrong column index", function () { - let data = [[1, 2, 3], [4, 5, 6]]; - let cols = ["A", "B", "C"]; - let df = new DataFrame(data, { columns: cols }); - assert.throws(function () { df.iloc({ "rows": [0, 1], "columns": 3 }); }, Error, "columns must be a list"); - }); - - it("throw error for wrong row index", function () { - let data = [[1, 2, 3], [4, 5, 6]]; - let cols = ["A", "B", "C"]; - let df = new DataFrame(data, { columns: cols }); - assert.throws(function () { df.iloc({ "rows": [0, 8], "columns": [1, 2] }); }, Error, "Specified row index 8 is bigger than maximum row index of 1"); - }); - - it("check data after selecting column", function () { - let data = [[1, 2, 3], [4, 5, 6]]; - let cols = ["A", "B", "C"]; - let df = new DataFrame(data, { columns: cols }); - - let col_df = df.iloc({ "rows": [0, 1], "columns": [1, 2] }); - let col_data = [[2, 3], [5, 6]]; - - assert.deepEqual(col_df.values, col_data); - - }); - it("check data after selecting row index", function () { - let data = [[1, 2, 3], [4, 5, 6]]; - let cols = ["A", "B", "C"]; - let df = new DataFrame(data, { columns: cols }); - - let col_df = df.iloc({ "rows": [1], "columns": [1, 2] }); - let col_data = [[5, 6]]; - - assert.deepEqual(col_df.values, col_data); - - }); - it("check data after row and column slice", function () { - let data = [[1, 2, 3], [4, 5, 6], [20, 30, 40], [39, 89, 78]]; - let cols = ["A", "B", "C"]; - let df = new DataFrame(data, { columns: cols }); - - let col_df = df.iloc({ "rows": ["0:2"], "columns": ["1:2"] }); - let col_data = [[2], [5]]; - - assert.deepEqual(col_df.values, col_data); - - }); - it("check data after row slice", function () { - let data = [[1, 2, 3], [4, 5, 6], [20, 30, 40], [39, 89, 78]]; - let cols = ["A", "B", "C"]; - let df = new DataFrame(data, { columns: cols }); - - let col_df = df.iloc({ "rows": ["0:2"], "columns": [1, 2] }); - let col_data = [[2, 3], [5, 6]]; - - assert.deepEqual(col_df.values, col_data); - - }); - it("check data after column slice", function () { - let data = [[1, 2, 3], [4, 5, 6], [20, 30, 40], [39, 89, 78]]; - let cols = ["A", "B", "C"]; - let df = new DataFrame(data, { columns: cols }); - - let col_df = df.iloc({ "rows": [0, 1, 2], "columns": ["1:2"] }); - let col_data = [[2], [5], [30]]; - assert.deepEqual(col_df.values, col_data); - - }); - it("Return all columns if columns parameter is not specified", function () { - let data = [[1, 2, 3], [4, 5, 6], [20, 30, 40], [39, 89, 78]]; - let cols = ["A", "B", "C"]; - let df = new DataFrame(data, { columns: cols }); - - let col_df = df.iloc({ "rows": [0, 1, 2] }); - let col_data = [[1, 2, 3], [4, 5, 6], [20, 30, 40]]; - assert.deepEqual(col_df.values, col_data); - - }); - it("Return all rows if rows parameter is not specified", function () { - let data = [[1, 2, 3], [4, 5, 6], [20, 30, 40], [39, 89, 78]]; - let cols = ["A", "B", "C"]; - let df = new DataFrame(data, { columns: cols }); - - let col_df = df.iloc({ "columns": ["1:2"] }); - let col_data = [[2], [5], [30], [89]]; - assert.deepEqual(col_df.values, col_data); - - }); - it("column slice starting with 0 and returning a single result works", function () { - let data = { - "Name": ["Apples", "Mango", "Banana", "Pear"], - "Count": [21, 5, 30, 10], - "Price": [200, 300, 40, 250] - }; - let df = new DataFrame(data); - let sub_df = df.iloc({ rows: ["2:3"], columns: ["0:1"] }); - const result = [["Banana"]]; - assert.deepEqual(sub_df.values, result); - - }); - it("column slice with format '0:' works", function () { - let data = { - "Name": ["Apples", "Mango", "Banana", "Pear"], - "Count": [21, 5, 30, 10], - "Price": [200, 300, 40, 250] - }; - let df = new DataFrame(data); - let sub_df = df.iloc({ rows: ["2:3"], columns: ["0:"] }); - const result = [["Banana", 30, 40]]; - assert.deepEqual(sub_df.values, result); - - }); - it("column slice with format ':2' works", function () { - let data = { - "Name": ["Apples", "Mango", "Banana", "Pear"], - "Count": [21, 5, 30, 10], - "Price": [200, 300, 40, 250] - }; - let df = new DataFrame(data); - let sub_df = df.iloc({ rows: ["2:3"], columns: [":2"] }); - const result = [["Banana", 30]]; - assert.deepEqual(sub_df.values, result); - - }); - it("row slice with format ':2' works", function () { - let data = { - "Name": ["Apples", "Mango", "Banana", "Pear"], - "Count": [21, 5, 30, 10], - "Price": [200, 300, 40, 250] - }; - let df = new DataFrame(data); - let sub_df = df.iloc({ rows: [":2"], columns: [":1"] }); - const result = [['Apples'], ['Mango']]; - assert.deepEqual(sub_df.values, result); - - }); - it("row slice with format '1:' works", function () { - let data = { - "Name": ["Apples", "Mango", "Banana", "Pear"], - "Count": [21, 5, 30, 10], - "Price": [200, 300, 40, 250] - }; - let df = new DataFrame(data); - let sub_df = df.iloc({ rows: [":2"], columns: [":2"] }); - const result = [['Apples', 21], ['Mango', 5]]; - assert.deepEqual(sub_df.values, result); - - }); - - }); - - - describe("add", function () { - it("Return Addition of DataFrame with a single Number", function () { - let data = [[0, 2, 4], [360, 180, 360]]; - let df = new DataFrame(data); - assert.deepEqual(df.add(2).values, [[2, 4, 6], [362, 182, 362]]); - }); - it("Return addition of a DataFrame with a Series along default axis 1", function () { - let data = [[0, 2, 4], [360, 180, 360]]; - let sf = new Series([1, 2, 1]); - let df = new DataFrame(data); - assert.deepEqual(df.add(sf).values, [[1, 4, 5], [361, 182, 361]]); - }); - it("Return addition of a DataFrame with a Series along axis 0", function () { - let data = [[0, 2, 4], [360, 180, 360]]; - let sf = new Series([1, 2]); - let df = new DataFrame(data); - assert.deepEqual(df.add(sf, 0).values, [[1, 3, 5], [362, 182, 362]]); - }); - it("Return addition of a DataFrame with a DataFrame along default axis 1", function () { - let df1 = new DataFrame([[0, 2, 4], [360, 180, 360]]); - let df2 = new DataFrame([[1, 2, 4], [10, 5, 0]]); - assert.deepEqual(df1.add(df2).values, [[1, 4, 8], [370, 185, 360]]); - }); - it("Return addition of a DataFrame with a DataFrame along axis 0", function () { - let df1 = new DataFrame([[0, 2, 4], [360, 180, 360]]); - let df2 = new DataFrame([[1, 2, 4], [10, 5, 0]]); - assert.deepEqual(df1.add(df2).values, [[1, 4, 8], [370, 185, 360]]); - }); - - }); - - describe("sub", function () { - it("Return subtraction of DataFrame with a single Number", function () { - let data = [[0, 2, 4], [360, 180, 360]]; - let df = new DataFrame(data); - assert.deepEqual(df.sub(2).values, [[-2, 0, 2], [358, 178, 358]]); - }); - it("Return subtraction of a DataFrame with a Series along default axis 1", function () { - let data = [[0, 2, 4], [360, 180, 360]]; - let sf = new Series([1, 2, 1]); - let df = new DataFrame(data); - assert.deepEqual(df.sub(sf).values, [[-1, 0, 3], [359, 178, 359]]); - }); - it("Return subtraction of a DataFrame with a Series along axis 0", function () { - let data = [[0, 2, 4], [360, 180, 360]]; - let sf = new Series([1, 2]); - let df = new DataFrame(data); - assert.deepEqual(df.sub(sf, 0).values, [[-1, 1, 3], [358, 178, 358]]); - }); - it("Return subtraction of a DataFrame with a DataFrame along default axis 1", function () { - let df1 = new DataFrame([[0, 2, 4], [360, 180, 360]]); - let df2 = new DataFrame([[1, 2, 4], [10, 5, 0]]); - assert.deepEqual(df1.sub(df2).values, [[-1, 0, 0], [350, 175, 360]]); - }); - it("Return subtraction of a DataFrame with a DataFrame along axis 0", function () { - let df1 = new DataFrame([[0, 2, 4], [360, 180, 360]]); - let df2 = new DataFrame([[1, 2, 4], [10, 5, 0]]); - assert.deepEqual(df1.sub(df2).values, [[-1, 0, 0], [350, 175, 360]]); - }); - - }); - - describe("mul", function () { - it("Return multiplication of DataFrame with a single Number", function () { - let data = [[0, 2, 4], [360, 180, 360]]; - let df = new DataFrame(data); - assert.deepEqual(df.mul(2).values, [[0, 4, 8], [720, 360, 720]]); - }); - it("Return multiplication of a DataFrame with a Series along default axis 1", function () { - let data = [[0, 2, 4], [360, 180, 360]]; - let sf = new Series([1, 2, 1]); - let df = new DataFrame(data); - assert.deepEqual(df.mul(sf).values, [[0, 4, 4], [360, 360, 360]]); - }); - it("Return multiplication of a DataFrame with a Series along axis 0", function () { - let data = [[0, 2, 4], [360, 180, 360]]; - let sf = new Series([1, 2]); - let df = new DataFrame(data); - assert.deepEqual(df.mul(sf, 0).values, [[0, 2, 4], [720, 360, 720]]); - }); - it("Return multiplication of a DataFrame with a DataFrame along default axis 1", function () { - let df1 = new DataFrame([[0, 2, 4], [360, 180, 360]]); - let df2 = new DataFrame([[1, 2, 4], [10, 5, 0]]); - assert.deepEqual(df1.mul(df2).values, [[0, 4, 16], [3600, 900, 0]]); - }); - it("Return multiplication of a DataFrame with a DataFrame along axis 0", function () { - let df1 = new DataFrame([[0, 2, 4], [360, 180, 360]]); - let df2 = new DataFrame([[1, 2, 4], [10, 5, 0]]); - assert.deepEqual(df1.mul(df2, 0).values, [[0, 4, 16], [3600, 900, 0]]); - }); - - }); - - describe("div", function () { - it("Return division of DataFrame with a single Number", function () { - let data = [[0, 2, 4], [360, 180, 360]]; - let df = new DataFrame(data); - assert.deepEqual(df.div(2).values, [[0, 1, 2], [180, 90, 180]]); - }); - it("Return division of a DataFrame with a Series along default axis 1", function () { - let data = [[0, 2, 4], [360, 180, 360]]; - let sf = new Series([1, 2, 1]); - let df = new DataFrame(data); - assert.deepEqual(df.div(sf).values, [[0, 1, 4], [360, 90, 360]]); - }); - it("Return division of a DataFrame with a Series along axis 0", function () { - let data = [[0, 2, 4], [360, 180, 360]]; - let sf = new Series([1, 2]); - let df = new DataFrame(data); - assert.deepEqual(df.div(sf, 0).values, [[0, 2, 4], [180, 90, 180]]); - }); - it("Return division of a DataFrame with a DataFrame along default axis 1", function () { - let df1 = new DataFrame([[0, 2, 4], [360, 180, 360]]); - let df2 = new DataFrame([[1, 2, 4], [10, 5, 0]]); - assert.deepEqual(df1.div(df2).values, [[0, 1, 1], [36, 36, NaN]]); - }); - it("Return division of a DataFrame with a DataFrame along axis 0", function () { - let df1 = new DataFrame([[0, 2, 4], [360, 180, 360]]); - assert.deepEqual(df1.div(df1).values, [[NaN, 1, 1], [1, 1, 1]]); - }); - it("Return division of a DataFrame with a DataFrame along axis 0", function () { - let df1 = new DataFrame([[0, 2, 4], [360, 180, 360]]); - let df2 = new DataFrame([[1, 2, 4], [10, 5, 0]]); - assert.deepEqual(df1.div(df2).values, [[0, 1, 1], [36, 36, NaN]]); - }); - - }); - - describe("pow", function () { - it("Return exponential of DataFrame with a single Number", function () { - let data = [[0, 2, 4], [360, 180, 360]]; - let df = new DataFrame(data); - assert.deepEqual(df.pow(2).values, [[0, 4, 16], [129600, 32400, 129600]]); - }); - it("Return exponential of a DataFrame with a Series along default axis 1", function () { - let data = [[0, 2, 4], [360, 180, 360]]; - let sf = new Series([1, 2, 1]); - let df = new DataFrame(data); - assert.deepEqual(df.pow(sf).values, [[0, 4, 4], [360, 32400, 360]]); - }); - it("Return exponential of a DataFrame with a Series along axis 0", function () { - let data = [[0, 2, 4], [360, 180, 360]]; - let sf = new Series([1, 2]); - let df = new DataFrame(data); - assert.deepEqual(df.pow(sf, 0).values, [[0, 2, 4], [129600, 32400, 129600]]); - }); - it("Return exponential of a DataFrame with another DataFrame along default axis 1", function () { - let df1 = new DataFrame([[0, 2, 4], [3, 10, 4]]); - let df2 = new DataFrame([[1, 2, 4], [10, 5, 0]]); - assert.deepEqual(df1.pow(df2).values, [[0, 4, 256], [59049, 100000, 1]]); - }); - it("Return exponential of a DataFrame with another DataFrame along axis 0", function () { - let df1 = new DataFrame([[0, 2, 4], [3, 10, 4]]); - let df2 = new DataFrame([[1, 2, 4], [10, 5, 0]]); - assert.deepEqual(df1.pow(df2, 0).values, [[0, 4, 256], [59049, 100000, 1]]); - }); - - }); - - describe("mod", function () { - it("Return modulus of DataFrame with a single Number", function () { - let data = [[0, 2, 4], [360, 180, 360]]; - let df = new DataFrame(data); - assert.deepEqual(df.mod(2).values, [[0, 0, 0], [0, 0, 0]]); - }); - it("Return modulus of a DataFrame with a Series along default axis 1", function () { - let data = [[0, 2, 4], [31, 15, 360]]; - let sf = new Series([1, 2, 1]); - let df = new DataFrame(data); - assert.deepEqual(df.mod(sf).values, [[0, 0, 0], [0, 1, 0]]); - }); - it("Return modulus of a DataFrame with a Series along axis 0", function () { - let data = [[0, 2, 4], [31, 15, 360]]; - let sf = new Series([1, 2]); - let df = new DataFrame(data); - assert.deepEqual(df.mod(sf, 0).values, [[0, 0, 0], [1, 1, 0]]); - }); - it("Return modulus of a DataFrame with a DataFrame along default axis 1", function () { - let df1 = new DataFrame([[0, 2, 4], [31, 15, 360]]); - let df2 = new DataFrame([[1, 2, 4], [10, 5, 0]]); - assert.deepEqual(df1.mod(df2).values, [[0, 0, 0], [1, 0, NaN]]); - }); - it("Return modulus of a DataFrame with a DataFrame along axis 0", function () { - let df1 = new DataFrame([[0, 2, 4], [360, 180, 360]]); - let df2 = new DataFrame([[1, 2, 4], [10, 5, 0]]); - assert.deepEqual(df1.mod(df2).values, [[0, 0, 0], [0, 0, NaN]]); - }); - - }); - - describe("mean", function () { - it("Returns the mean of a DataFrame (Default axis is [1:column])", function () { - let data = [[0, 2, 4], [360, 180, 360]]; - let df = new DataFrame(data, { columns: ["col1", "col2", "col3"] }); - assert.deepEqual(df.mean().values, [180, 91, 182]); - }); - it("Return mean of a DataFrame along axis 0 (row)", function () { - let data = [[0, 2, 4], [360, 180, 360]]; - let df = new DataFrame(data); - assert.deepEqual(df.mean(0).values, [2, 300]); - }); - it("Removes NaN before calculating mean of a DataFrame", function () { - let data = [[11, 20, 3], [NaN, 15, 6], [2, 30, 40], [2, 89, 78]]; - let df = new DataFrame(data); - assert.deepEqual(df.mean().values, [5, 38.5, 31.75]); - }); - }); - - describe("median", function () { - it("Returns the median of a DataFrame (Default axis is [1:column])", function () { - let data = [[0, 2, 4], [360, 180, 360]]; - let df = new DataFrame(data); - assert.deepEqual(df.median().values, [180, 91, 182]); - }); - it("Return median of a DataFrame along axis 0 (row)", function () { - let data = [[0, 2, 4], [360, 180, 360]]; - let df = new DataFrame(data); - assert.deepEqual(df.median({ "axis": 0 }).values, [2, 360]); - }); - - }); - - // describe("mode", function () { - // it("Returns the mode of a DataFrame (Default axis is [1:column])", function () { - // let data = [[0, 2, 4], [360, 180, 360]] - // let df = new DataFrame(data) - // assert.deepEqual(df.mode().values, [362, 182, 362]) - // }) - // it("Returns mode of a DataFrame along axis 0 (row)", function () { - // let data = [[0, 2, 4], [360, 180, 360]] - // let df = new DataFrame(data) - // assert.deepEqual(df.mode({ "axis": 0 }).values, [1, 182]) - // }) - // it("Returns mode of a DataFrame along axis 1", function () { - // let data = [{ "col1": [0, 2, 4] }, { "col2": [360, 180, 360] }] - // let df = new DataFrame(data) - // assert.deepEqual(df.mode().values, [1, 362, 40]) - // }) - - // }) - - describe("min", function () { - it("Returns the minimum values in a DataFrame (Default axis is [1:column])", function () { - let data = [[0, 2, 4], [360, 180, 360]]; - let df = new DataFrame(data); - assert.deepEqual(df.min().values, [0, 2, 4]); - }); - it("Returns the minimum values of a DataFrame along axis 0 (row)", function () { - let data = [[0, 2, 4], [360, 180, 360]]; - let df = new DataFrame(data); - assert.deepEqual(df.min({ "axis": 0 }).values, [0, 180]); - }); - - }); - - describe("max", function () { - it("Returns the maximum values in a DataFrame (Default axis is [1:column])", function () { - let data = [[0, 2, 4], [360, 180, 360]]; - let df = new DataFrame(data); - assert.deepEqual(df.max().values, [360, 180, 360]); - }); - it("Returns the maximum values of a DataFrame along axis 0 (row)", function () { - let data = [[0, 2, 4], [360, 180, 360]]; - let df = new DataFrame(data); - assert.deepEqual(df.max({ "axis": 0 }).values, [4, 360]); - }); - - }); - - describe("std", function () { - it("Returns the standard deviations of values in a DataFrame (Default axis is [1:column])", function () { - let data = [[0, 2, 4], [360, 180, 360]]; - let df = new DataFrame(data); - assert.deepEqual(df.std().values, [254.55844122715712, 125.86500705120545, 251.7300141024109]); - }); - it("Return the standard deviations of values of a DataFrame along axis 0 (row)", function () { - let data = [[0, 2, 4], [360, 180, 360]]; - let df = new DataFrame(data); - assert.deepEqual(df.std(0).values, [2, 103.92304845413264]); - }); - - - }); - - describe("var", function () { - it("Returns the variance of values in a DataFrame (Default axis is [1:column])", function () { - let data = [[0, 2, 4], [360, 180, 360]]; - let df = new DataFrame(data); - assert.deepEqual(df.var().values, [64800, 15842, 63368]); - }); - it("Return the variance of values of a DataFrame along axis 0 (row)", function () { - let data = [[0, 2, 4], [360, 180, 360]]; - let df = new DataFrame(data); - assert.deepEqual(df.var(0).values, [4, 10800]); - }); - - - }); - - describe("describe", function () { - it("Returns descriptive statistics of columns in a DataFrame created from an array", function () { - let data = [[0, 2, 4, "a"], - [360, 180, 360, "b"], - [2, 4, 6, "c"]]; - - let df = new DataFrame(data); - let res = [[3, 3, 3], [120.666664, 62, 123.333336], - [207.271159, 102.19589, 204.961785], - [0, 2, 4], [2, 4, 6], - [360, 180, 360], - [42961.333333, 10444, 42009.333333]]; - - assert.deepEqual(df.describe().values, res); - }); - it("Returns descriptive statistics of columns in a DataFrame created from an Object", function () { - let data = { - "col1": [0, 2, 4], - "col2": [360, 180, 360], - "col3": [2, 4, 6], - "col4": ["boy", "girl", "man"], - "col5": ["apple", "car", "bee"] - }; - let df = new DataFrame(data); - - let res = [[3, 3, 3], [2, 300, 4], - [2, 103.923048, 2], - [0, 180, 2], [2, 360, 4], - [4, 360, 6], - [4, 10800, 4]]; - - assert.deepEqual(df.describe().values, res); - }); - - }); - - describe("count", function () { - it("Returns the count of non-nan values in a DataFrame (Default axis is [1:column])", function () { - let data = [[0, 2, 4], [360, 180.1, 360.11], [NaN, 2, 4], [360, undefined, 360]]; - let df = new DataFrame(data); - assert.deepEqual(df.count().values, [3, 3, 4]); - }); - it("Return the count of non NaN values of a DataFrame along axis 0", function () { - let data = [[0, 2, 4, NaN], [360, undefined, 360, 70]]; - let df = new DataFrame(data); - assert.deepEqual(df.count(0).values, [3, 3]); - }); - - }); - - describe("round", function () { - it("Rounds values in a DataFrame to 3dp", function () { - let data = [[10.1, 2.092, 4.23], [360.232244, 180.0190290, 36.902612]]; - let df = new DataFrame(data); - let expected = [[10.1, 2.092, 4.23], [360.232, 180.0190, 36.903]]; - assert.deepEqual(df.round(3).values, expected); - }); - it("Rounds values in a DataFrame to 1dp", function () { - let data = [[10.1, 2.092, 4.23], [360.232244, 180.0190290, 36.902612]]; - let df = new DataFrame(data); - let expected = [[10.1, 2.1, 4.2], [360.2, 180.0, 36.9]]; - assert.deepEqual(df.round(1).values, expected); - }); - - }); - - describe("sort_values", function () { - it("Sort values in DataFrame by specified column in ascending order (Default)", function () { - let data = [[0, 2, 4, "a"], - [360, 180, 360, "b"], - [2, 4, 6, "c"]]; - - let df = new DataFrame(data, { "columns": ["col1", "col2", "col3", "col4"] }); - df.sort_values({ "by": "col1", inplace: true, ascending: true }); - let expected = [[0, 2, 4, "a"], [2, 4, 6, "c"], [360, 180, 360, "b"]]; - assert.deepEqual(df.values, expected); - assert.deepEqual(df.index, [0, 2, 1]); - - }); - - it("Sort values in DataFrame by specified column in ascending order (Default)", function () { - let data = [[0, 2, 4, "a"], - [360, 180, 1, "b"], - [2, 4, 6, "c"]]; - - let df = new DataFrame(data, { "columns": ["col1", "col2", "col3", "col4"] }); - let df_sort = df.sort_values({ "by": "col3" }); - let expected = [[360, 180, 1, "b"], [0, 2, 4, "a"], [2, 4, 6, "c"]]; - assert.deepEqual(df_sort.values, expected); - assert.deepEqual(df_sort.index, [1, 0, 2]); - - }); - it("Sort values in DataFrame by specified column in descending order", function () { - let data = [[0, 2, 4, "a"], - [360, 180, 360, "b"], - [2, 4, 6, "c"]]; - - let df = new DataFrame(data, { "columns": ["col1", "col2", "col3", "col4"] }); - let expected = [[360, 180, 360, "b"], [2, 4, 6, "c"], [0, 2, 4, "a"]]; - assert.deepEqual(df.sort_values({ "by": "col1", "ascending": false }).values, expected); - }); - - it("Sort values in DataFrame by specified column in descending order (second col)", function () { - let data = [[0, 2, 4, "a"], - [360, 180, 1, "b"], - [2, 4, 6, "c"]]; - - let df = new DataFrame(data, { "columns": ["col1", "col2", "col3", "col4"] }); - let expected = [[2, 4, 6, "c"], [0, 2, 4, "a"], [360, 180, 1, "b"]]; - assert.deepEqual(df.sort_values({ "by": "col3", "ascending": false }).values, expected); - }); - it("Sort values in DataFrame by specified column containing alpha(numeric) values", function () { - let data = [[0, 2, 4, "a"], - [360, 180, 1, "b"], - [2, 4, 6, "c"]]; - - let df = new DataFrame(data, { "columns": ["col1", "col2", "col3", "col4"] }); - let expected = [[2, 4, 6, 'c'], [360, 180, 1, 'b'], [0, 2, 4, 'a']]; - assert.deepEqual(df.sort_values({ "by": "col4", "ascending": false }).values, expected); - }); - it("Sort duplicate DataGrame with duplicate columns", function () { - - let data = { - "A": [1, 2, 3, 4, 5, 3, 5, 6, 4, 5, 3, 4], - "B": [2, 3, 4, 5, 6, 7, 8, 9, 1, 2, 3, 4] - }; - - let df = new DataFrame(data); - let expected = [[1, 2], - [2, 3], - [3, 4], - [3, 7], - [3, 3], - [4, 5], - [4, 1], - [4, 4], - [5, 6], - [5, 8], - [5, 2], - [6, 9]]; - assert.deepEqual(df.sort_values({ "by": "A", "ascending": true }).values, expected); - }); - - - }); - - describe("copy", function () { - it("Makes a deep copy of DataFrame", function () { - let data = [[0, 2, 4], [360, 180, 360]]; - let df = new DataFrame(data); - let df_copy = df.copy(); - assert.deepEqual(df_copy.values, [[0, 2, 4], [360, 180, 360]]); - }); - it("Confirms child copy modification does not affect parent DataFrame", function () { - let data = [[0, 2, 4], [360, 180, 360]]; - let df = new DataFrame(data); - let df_copy = df.copy(); - df_copy.addColumn({ column: "col_new", value: ["boy", "girl"] }); - assert.notDeepEqual(df_copy.values, df.values); - }); - - }); - - - describe("set_index", function () { - it("Sets the index of a DataFrame created from an Object", function () { - let data = [{ alpha: "A", count: 1 }, { alpha: "B", count: 2 }, { alpha: "C", count: 3 }]; - let df = new DataFrame(data); - let df_new = df.set_index({ "key": ["one", "two", "three"] }); - assert.deepEqual(df_new.index, ["one", "two", "three"]); - }); - it("Sets the index of a DataFrame from column name", function () { - let data = [{ alpha: "A", count: 1 }, { alpha: "B", count: 2 }, { alpha: "C", count: 3 }]; - let df = new DataFrame(data); - let df_new = df.set_index({ "key": "alpha" }); - assert.deepEqual(df_new.index, ["A", "B", "C"]); - }); - it("Sets the index of a DataFrame from column name", function () { - let data = [{ alpha: "A", count: 1 }, { alpha: "B", count: 2 }, { alpha: "C", count: 3 }]; - let df = new DataFrame(data); - let df_new = df.set_index({ key: "alpha", drop: true }); - assert.deepEqual(df_new.index, ["A", "B", "C"]); - }); - it("Sets the index of a DataFrame created from an Array", function () { - let data = [[0, 2, 4], [360, 180, 360], [0, 2, 4], [360, 180, 360], [0, 2, 4]]; - let df = new DataFrame(data); - df.set_index({ "key": ["one", "two", "three", "four", "five"], "inplace": true }); - assert.deepEqual(df.index, ["one", "two", "three", "four", "five"]); - }); - - }); - - describe("reset_index", function () { - it("Resets the index of a DataFrame created from an Object", function () { - let data = [{ alpha: "A", count: 1 }, { alpha: "B", count: 2 }, { alpha: "C", count: 3 }]; - let df = new DataFrame(data); - let df_new = df.set_index({ "key": ["one", "two", "three"] }); - let df_reset = df_new.reset_index(); - assert.deepEqual(df_reset.index, [0, 1, 2]); - }); - it("Resets the index of a DataFrame created from an Array", function () { - let data = [[0, 2, 4], [360, 180, 360], [0, 2, 4], [360, 180, 360], [0, 2, 4]]; - let df = new DataFrame(data); - df.set_index({ "key": ["one", "two", "three", "four", "five"], "inplace": true }); - df.reset_index(true); - assert.deepEqual(df.index, [0, 1, 2, 3, 4]); - }); - - }); - - - // describe("apply", function () { - // it("Apply a function to all values of a DataFrame", function () { - // let data = [[0, 2, 4], - // [360, 180, 360], - // [0, 2, 4]] - // let df = new DataFrame(data) - - // let apply_func = (x) => { - // return x + 1000 - // } - // let expected = [[1000, 1002, 1004], [1360, 1180, 1360], [1000, 1002, 1004]] - // assert.deepEqual(df.apply(apply_func), expected) - // }); - - // it("Throws error on applying function to string columns", function () { - // let data = [[0, 2, "ab"], - // [360, 180, "mk"], - // [0, 2, "po"]] - // let df = new DataFrame(data) - - // let apply_func = (x) => { - // return x + 1000 - // } - // let expected = "Dtypes Error: columns dtypes must be numeric, got strings" - // assert.deepEqual(df.apply(apply_func), expected) - // }); - // }); - - - describe("query", function () { - - it("Get the DataFrame containing rows with the filtered column", function () { - - let data = [[1, 2, 3], [4, 5, 6], [20, 30, 40], [39, 89, 78]]; - let cols = ["A", "B", "C"]; - let df = new DataFrame(data, { columns: cols }); - let query_df = df.query({ "column": "B", "is": ">=", "to": 5 }); - let query_data = [[4, 5, 6], [20, 30, 40], [39, 89, 78]]; - assert.deepEqual(query_df.values, query_data); - }); - it("Get the Dataframe containing rows with the filtered column in String values", function () { - let data = { "Abs": [20, 30, 47], "Count": [34, 4, 5], "country code": ["NG", "FR", "GH"] }; - let cols = ["Abs", "Count", "country code"]; - let df = new DataFrame(data, { columns: cols }); - let query_df = df.query({ column: "country code", is: "==", to: "NG" }); - let query_data = [[20, 34, "NG"]]; - assert.deepEqual(query_df.values, query_data); - }); - it("Print Error for value key not specified", function () { - - let data = [[1, 2, 3], [4, 5, 6], [20, 30, 40], [39, 89, 78]]; - let cols = ["A", "B", "C"]; - let df = new DataFrame(data, { columns: cols }); - - assert.throws(function () { df.query({ "column": "B", "is": ">=" }); }, Error, "specify a value in param [to]"); - }); - it("Print Error for operator key not specified", function () { - - let data = [[1, 2, 3], [4, 5, 6], [20, 30, 40], [39, 89, 78]]; - let cols = ["A", "B", "C"]; - let df = new DataFrame(data, { columns: cols }); - - assert.throws(function () { df.query({ "column": "B", "to": 5 }); }, Error, "specify an operator in param [is]"); - }); - - it("Print Error for column key not specified", function () { - - let data = [[1, 2, 3], [4, 5, 6], [20, 30, 40], [39, 89, 78]]; - let cols = ["A", "B", "C"]; - let df = new DataFrame(data, { columns: cols }); - - assert.throws(function () { df.query({ "is": ">=", "to": 5 }); }, Error, "specify the column"); - }); - it("Print Error for column name not in dataframe", function () { - - let data = [[1, 2, 3], [4, 5, 6], [20, 30, 40], [39, 89, 78]]; - let cols = ["A", "B", "C"]; - let df = new DataFrame(data, { columns: cols }); - - assert.throws(function () { df.query({ "column": "D", "is": ">=", "to": 5 }); }, Error, "column D does not exist"); - }); - it("Confirms that query index are updated", function () { - - let data = [[1, 2, 3], [4, 5, 6], [20, 30, 40], [39, 89, 78]]; - let cols = ["A", "B", "C"]; - let df = new DataFrame(data, { columns: cols }); - let df_query = df.query({ "column": "B", "is": ">=", "to": 5 }); - assert.deepEqual(df_query.index, [1, 2, 3]); - }); - it("Confirms that columns data are updated inplace", function () { - - let data = [[1, 2, 3], [4, 5, 6], [20, 30, 40], [39, 89, 78]]; - let cols = ["A", "B", "C"]; - let df = new DataFrame(data, { columns: cols }); - df.query({ "column": "B", "is": ">=", "to": 5, inplace: true }); - assert.deepEqual(df.col_data, [[4, 20, 39], [5, 30, 89], [6, 40, 78]]); - }); - it("Confirms that query happens inplace", function () { - - let data = [[1, 2, 3], [4, 5, 6], [20, 30, 40], [39, 89, 78]]; - let cols = ["A", "B", "C"]; - let df = new DataFrame(data, { columns: cols }); - df.query({ "column": "B", "is": ">=", "to": 5, inplace: true }); - let query_data = [[4, 5, 6], [20, 30, 40], [39, 89, 78]]; - assert.deepEqual(df.values, query_data); - }); - it("Confirms that query happens inplace and index are updated", function () { - - let data = [[1, 2, 3], [4, 5, 6], [20, 30, 40], [39, 89, 78]]; - let cols = ["A", "B", "C"]; - let df = new DataFrame(data, { columns: cols }); - df.query({ "column": "B", "is": ">=", "to": 5, inplace: true }); - assert.deepEqual(df.index, [1, 2, 3]); - }); - it("Wrong query value", function () { - let data = { - "A": [30, 1, 2, 3], - "B": [34, 4, 5, 6], - "C": [20, 20, 30, 40] - }; - - let cols = ["A", "B", "C"]; - let df = new DataFrame(data, { columns: cols }); - - assert.throws(function () { df.query({ "column": "B", "is": ">", "to": 40 }); }, Error, "query returned empty data; is either 40 does not exist in column B"); - }); - - }); - - describe("addColumn", function () { - it("Print the data, after changing a column data", function () { - let data = [[1, 2, 3], [4, 5, 6], [20, 30, 40], [39, 89, 78]]; - let cols = ["A", "B", "C"]; - let df = new DataFrame(data, { columns: cols }); - - let new_col = [1, 2, 3, 4]; - - df.addColumn({ "column": "C", "value": new_col }); - - let new_data = [[1, 2, 1], [4, 5, 2], [20, 30, 3], [39, 89, 4]]; - - assert.deepEqual(df.values, new_data); - }); - it("Print the Dataframe column names, after a new column is added ", function () { - let data = [[1, 2, 3], [4, 5, 6], [20, 30, 40], [39, 89, 78]]; - let cols = ["A", "B", "C"]; - let df = new DataFrame(data, { columns: cols }); - - let new_col = [1, 2, 3, 4]; - - df.addColumn({ "column": "D", "value": new_col }); - - let new_column = ["A", "B", "C", "D"]; - - assert.deepEqual(df.column_names, new_column); - }); - it("Print Error for column name not in keyword passed", function () { - - let data = [[1, 2, 3], [4, 5, 6], [20, 30, 40], [39, 89, 78]]; - let cols = ["A", "B", "C"]; - let df = new DataFrame(data, { columns: cols }); - let new_col = [1, 2, 3, 4]; - - assert.throws(function () { df.addColumn({ "value": new_col }); }, Error, "column name not specified"); - }); - it("Check if new column value length is the same with Dataframe length", function () { - let data = [[1, 2, 3], [4, 5, 6], [20, 30, 40], [39, 89, 78]]; - let cols = ["A", "B", "C"]; - let df = new DataFrame(data, { columns: cols }); - let new_col = [1, 2, 3]; - assert.throws(function () { df.addColumn({ "column": "D", "value": new_col }); }, Error, "Array length 3 not equal to 4"); - }); - - it("Check that dtype is updated after a new column is added ", function () { - let data = [[1, 2, 3], [4, 5, 6], [20, 30, 40], [39, 89, 78]]; - let cols = ["A", "B", "C"]; - let df = new DataFrame(data, { columns: cols }); - let new_col = ["n", "b", "c", "f"]; - - df.addColumn({ "column": "D", "value": new_col }); - let dtype = ["int32", "int32", "int32", "string"]; - - assert.deepEqual(df.dtypes, dtype); - }); - - it("add series as value to a new column ", function () { - let data = [[1, 2, 3], [4, 5, 6], [20, 30, 40], [39, 89, 78]]; - let cols = ["A", "B", "C"]; - let df = new DataFrame(data, { columns: cols }); - let sf = new Series(["n", "b", "c", "f"]); - - df.addColumn({ "column": "D", "value": sf }); - let dtype = ["int32", "int32", "int32", "string"]; - - assert.deepEqual(df.dtypes, dtype); - }); - - }); - - // describe("groupby", function () { - // it("Check group by One column data", function () { - - // let data = [[1, 2, 3], [4, 5, 6], [20, 30, 40], [39, 89, 78]] - // let cols = ["A", "B", "C"] - // let df = new DataFrame(data, { columns: cols }) - // let group_df = df.groupby(["A"]); - - // let group_dict = { - // '1': [[1, 2, 3]], - // '4': [[4, 5, 6]], - // '20': [[20, 30, 40]], - // '39': [[39, 89, 78]] - // } - - // assert.deepEqual(group_df.col_dict, group_dict); - // }); - // it("Obtain the DataFrame of one of the group", function () { - - // let data = [[1, 2, 3], [4, 5, 6], [20, 30, 40], [39, 89, 78]] - // let cols = ["A", "B", "C"] - // let df = new DataFrame(data, { columns: cols }) - // let group_df = df.groupby(["A"]); - // let new_data = [[1, 2, 3]] - - // assert.deepEqual(group_df.get_groups([1]).values, new_data); - // }); - // it("Check group by Two column data", function () { - - // let data = [[1, 2, 3], [4, 5, 6], [20, 30, 40], [39, 89, 78]] - // let cols = ["A", "B", "C"] - // let df = new DataFrame(data, { columns: cols }) - // let group_df = df.groupby(["A", "B"]); - // let new_data = { - // '1': { '2': [[1, 2, 3]] }, - // '4': { '5': [[4, 5, 6]] }, - // '20': { '30': [[20, 30, 40]] }, - // '39': { '89': [[39, 89, 78]] } - // } - - // assert.deepEqual(group_df.col_dict, new_data); - // }); - - // it("Obtain the DataFrame of one of the group, grouped by two column", function () { - - // let data = [[1, 2, 3], [4, 5, 6], [20, 30, 40], [39, 89, 78]] - // let cols = ["A", "B", "C"] - // let df = new DataFrame(data, { columns: cols }) - // let group_df = df.groupby(["A", "B"]); - // let new_data = [[1, 2, 3]] - - // assert.deepEqual(group_df.get_groups([1, 2]).values, new_data); - // }); - - // it("Count column in group", function () { - - // let data = [[1, 2, 3], [4, 5, 6], [20, 30, 40], [39, 89, 78]] - // let cols = ["A", "B", "C"] - // let df = new DataFrame(data, { columns: cols }) - // let group_df = df.groupby(["A", "B"]); - // let new_data = { - // '1': { '2': [1] }, - // '4': { '5': [1] }, - // '20': { '30': [1] }, - // '39': { '89': [1] } - // } - - // assert.deepEqual(group_df.col(["C"]).count(), new_data); - // }); - // it("sum column element in group", function () { - - // let data = [[1, 2, 3], [4, 5, 6], [20, 30, 40], [39, 89, 78]] - // let cols = ["A", "B", "C"] - // let df = new DataFrame(data, { columns: cols }) - // let group_df = df.groupby(["A", "B"]); - // let new_data = { - // '1': { '2': [3] }, - // '4': { '5': [6] }, - // '20': { '30': [40] }, - // '39': { '89': [78] } - // } - - // assert.deepEqual(group_df.col(["C"]).sum(), new_data); - // }); - - // it("sum column element group by one column", function () { - - // let data = [[1, 2, 3], [4, 5, 6], [20, 30, 40], [39, 89, 78]] - // let cols = ["A", "B", "C"] - // let df = new DataFrame(data, { columns: cols }) - // let group_df = df.groupby(["A"]); - - // let new_data = { '1': [2, 3], '4': [5, 6], '20': [30, 40], '39': [89, 78] } - - // assert.deepEqual(group_df.col(["B", "C"]).sum(), new_data); - // }); - - // it("Perform aggregate on column for groupby", function () { - - // let data = [[1, 2, 3], [4, 5, 6], [20, 30, 40], [39, 89, 78]] - // let cols = ["A", "B", "C"] - // let df = new DataFrame(data, { columns: cols }) - // let group_df = df.groupby(["A", "B"]); - // let new_data = { - // '1': { '2': [2, 1] }, - // '4': { '5': [5, 1] }, - // '20': { '30': [30, 1] }, - // '39': { '89': [89, 1] } - // } - - // assert.deepEqual(group_df.agg({ "B": "mean", "C": "count" }), new_data); - // }); - - - // }); - - describe("column", function () { - it("Obtain a column from a dataframe created from object", function () { - let data = [{ alpha: "A", count: 1 }, { alpha: "B", count: 2 }, { alpha: "C", count: 3 }]; - let options = { columns: ["Gender", "count"] }; - let df = new DataFrame(data, options); - let col_data = df.column("count"); - let rslt_data = [1, 2, 3]; - assert.deepEqual(col_data.values, rslt_data); - }); - it("Obtain a column from a dataframe", function () { - let data = [[1, 2, 3], [4, 5, 6], [20, 30, 40], [39, 89, 78]]; - let cols = ["A", "B", "C"]; - let df = new DataFrame(data, { columns: cols }); - let col_data = df.column("C"); - let rslt_data = [3, 6, 40, 78]; - assert.deepEqual(col_data.values, rslt_data); - }); - it("Throw Error for wrong column", function () { - let data = [[1, 2, 3], [4, 5, 6], [20, 30, 40], [39, 89, 78]]; - let cols = ["A", "B", "C"]; - let df = new DataFrame(data, { columns: cols }); - assert.throws(() => { df.column("D"); }, Error, "column D does not exist"); - - }); - }); - - // describe("Concatenate", function () { - - // it("Check the axis 0 concatenation", function () { - // let data = [[1, 2, 3], [4, 5, 6], [20, 30, 40], [39, 89, 78]] - // let cols = ["A", "B", "C"] - // let df = new DataFrame(data, { columns: cols }) - - // let data1 = [[1, 2, 3], [4, 5, 6], [20, 30, 40], [39, 89, 78]] - // let cols1 = ["A", "B", "C"] - // let df1 = new DataFrame(data1, { columns: cols1 }) - - // let data2 = [[1, 2, 3, 5], [4, 5, 6, 8], [20, 30, 40, 10]] - // let cols2 = ["A", "B", "C", "D"] - // let df2 = new DataFrame(data2, { columns: cols2 }) - - // let new_df = DataFrame.concat({ "df_list": [df, df1, df2], "axis": 0 }) - - // let data_values = [[1, 2, 3, NaN], [4, 5, 6, NaN], [20, 30, 40, NaN], [39, 89, 78, NaN], - // [1, 2, 3, NaN], [4, 5, 6, NaN], [20, 30, 40, NaN], [39, 89, 78, NaN], - // [1, 2, 3, 5], [4, 5, 6, 8], [20, 30, 40, 10]] - - // assert.deepEqual(new_df.values, data_values); - // }); - - // it("Check the axis 1 concatenation", function () { - // let data = [[1, 2, 3], [4, 5, 6], [20, 30, 40], [39, 89, 78]] - // let cols = ["A", "B", "C"] - // let df = new DataFrame(data, { columns: cols }) - - // let data1 = [[1, 2, 3], [4, 5, 6], [20, 30, 40], [39, 89, 78]] - // let cols1 = ["A", "B", "C"] - // let df1 = new DataFrame(data1, { columns: cols1 }) - - // let data2 = [[1, 2, 3, 5], [4, 5, 6, 8], [20, 30, 40, 10]] - // let cols2 = ["A", "B", "C", "D"] - // let df2 = new DataFrame(data2, { columns: cols2 }) - - // let new_df = DataFrame.concat({ "df_list": [df, df1, df2], "axis": 1 }) - - // let data_values = [[1, 2, 3, 1, 2, 3, 1, 2, 3, 5], [4, 5, 6, 4, 5, 6, 4, 5, 6, 8], - // [20, 30, 40, 20, 30, 40, 20, 30, 40, 10], [39, 89, 78, 39, 89, 78, NaN, - // NaN, NaN, NaN]] - // assert.deepEqual(new_df.values, data_values); - // }); - // }); - - - describe("Apply", function () { - it("Apply math operation on dataframe element wise", function () { - let data = [[1, 2, 3], [4, 5, 6], [20, 30, 40], [39, 89, 78]]; - let cols = ["A", "B", "C"]; - let df = new DataFrame(data, { columns: cols }); - let rslt = [[2, 3, 4], [5, 6, 7], [21, 31, 41], [40, 90, 79]]; - - let apply_rslt = df.apply({ - callable: (x) => { - return x + 1; - } - }); - - assert.deepEqual(apply_rslt.values, rslt); - }); - - it("Apply string function on all elements of a dataframe", function () { - let data = [["BOY", "GIRL", "ALL"], ["Man", "Woman", "Girl"]]; - let df = new DataFrame(data); - let rslt = [["boy", "girl", "all"], ["man", "woman", "girl"]]; - - let apply_rslt = df.apply({ - callable: (x) => { - return x.toLowerCase(); - - } - }); - assert.deepEqual(apply_rslt.values, rslt); - }); - - it("Throws error if you try to run a function that does not operate on axis and axis is specified", function () { - let data = [["BOY", "GIRL", "ALL"], ["Man", "Woman", "Girl"]]; - let df = new DataFrame(data); - - let err = `Callable Error: You can only apply JavaScript functions on DataFrames when axis is not specified. This operation is applied on all element, and returns a DataFrame of the same shape.`; - - assert.throws(() => { - df.apply({ - axis: 0, callable: (x) => { - return x.toLowerCase(); - } - }); - }, Error, err); - - }); - - it("Apply math operation on dataframe at axis 1", function () { - let data = [[1, 2, 3], - [4, 5, 6], - [20, 30, 40], - [39, 89, 78]]; - let cols = ["A", "B", "C"]; - let df = new DataFrame(data, { columns: cols }); - let rslt = [64, 126, 127]; - - let apply_rslt = df.apply({ - axis: 1, callable: (x) => { - return x.sum(); - } - }); - - assert.deepEqual(apply_rslt.values, rslt); - }); - - it("Apply tensor operation on dataframe at axis 0", function () { - let data = [[1, 2, 3], [4, 5, 6], [20, 30, 40], [39, 89, 78]]; - let cols = ["A", "B", "C"]; - let df = new DataFrame(data, { columns: cols }); - - let rslt = [6, 15, 90, 206]; - - let apply_rslt = df.apply({ - axis: 0, callable: (x) => { - return x.sum(); - } - }); - - assert.deepEqual(apply_rslt.values, rslt); - - }); - - it("Apply add operation element wise dataframe on axis 1", function () { - let data = [[1, 2, 3], [4, 5, 6], [20, 30, 40], [39, 89, 78]]; - let cols = ["A", "B", "C"]; - let df = new DataFrame(data, { columns: cols }); - - let result = [[2, 3, 4], - [5, 6, 7], - [21, 31, 41], - [40, 90, 79]]; - - let apply_rslt = df.apply({ - axis: 0, callable: (x) => { - return x.add(1); - } - }); - - assert.deepEqual(apply_rslt.values, result); - }); - }); - - describe("dropna", function () { - it("drop inplace at axis 0 at inplace false", function () { - let data = [[NaN, 1, 2, 3], [3, 4, NaN, 9], [5, 6, 7, 8]]; - let column = ["A", "B", "C", "D"]; - let df = new DataFrame(data, { columns: column }); - - let df_val = [[5, 6, 7, 8]]; - - assert.deepEqual(df.dropna().values, df_val); - - }); - it("drop inplace at axis 1, inplace false ", function () { - let data = [[NaN, 1, 2, 3], [3, 4, NaN, 9], [5, 6, 7, 8]]; - let column = ["A", "B", "C", "D"]; - let df = new DataFrame(data, { columns: column }); - - let df_val = [[1, 3], [4, 9], [6, 8]]; - - assert.deepEqual(df.dropna({ axis: 1 }).values, df_val); - - }); - it("drop inplace at axis 1, inplace true ", function () { - let data = [[NaN, 1, 2, 3], [3, 4, NaN, 9], [5, 6, 7, 8]]; - let column = ["A", "B", "C", "D"]; - let df = new DataFrame(data, { columns: column }); - - let df_val = [[1, 3], [4, 9], [6, 8]]; - df.dropna({ axis: 1, inplace: true }); - - - assert.deepEqual(df.values, df_val); - - }); - it("drop inplace at axis 0 at inplace true", function () { - let data = [[NaN, 1, 2, 3], [3, 4, NaN, 9], [5, 6, 7, 8]]; - let column = ["A", "B", "C", "D"]; - let df = new DataFrame(data, { columns: column }); - - let df_val = [[5, 6, 7, 8]]; - - df.dropna({ inplace: true }); - assert.deepEqual(df.values, df_val); - - }); - }); - - describe("isna", function () { - - it("check if each value are nan", function () { - let data = [[NaN, 1, 2, 3], [3, 4, undefined, 9], [5, 6, 7, 8]]; - let column = ["A", "B", "C", "D"]; - let df = new DataFrame(data, { columns: column }); - - let df_val = [ - [true, false, false, false], - [false, false, true, false], - [false, false, false, false] - ]; - - assert.deepEqual(df.isna().values, df_val); - }); - }); - - describe("fillna", function () { - - it("replace all NaN value", function () { - let data = [[NaN, 1, 2, 3], [3, 4, NaN, 9], [5, 6, 7, 8]]; - let column = ["A", "B", "C", "D"]; - let df = new DataFrame(data, { columns: column }); - - let df_val = [[-999, 1, 2, 3], [3, 4, -999, 9], [5, 6, 7, 8]]; - df.fillna({ values: -999, inplace: true }); - assert.deepEqual(df.values, df_val); - }); - it("replace all NaN value", function () { - let data = [[NaN, 1, 2, 3], [3, 4, NaN, 9], [5, 6, 7, 8]]; - let column = ["A", "B", "C", "D"]; - let df = new DataFrame(data, { columns: column }); - - let df_val = [[-999, 1, 2, 3], [3, 4, -999, 9], [5, 6, 7, 8]]; - - let df_filled = df.fillna({ values: [-999] }); - assert.deepEqual(df_filled.values, df_val); - }); - - it("Fills only a specified column", function () { - let data = [[1, 2, 3], - [4, 5, 6], - [20, NaN, 40], - [39, NaN, 78]]; - let cols = ["A", "B", "C"]; - let df = new DataFrame(data, { columns: cols }); - let new_vals = [[1, 2, 3], [4, 5, 6], [20, 2, 40], [39, 2, 78]]; - let df_filled = df.fillna({ columns: ["B"], values: [2] }); - - assert.deepEqual(df_filled.values, new_vals); - }); - it("Fills column with specified values not in place", function () { - let data = [[1, 2, 3], [4, 5, 6], [NaN, 20, 40], [NaN, -1, 78]]; - let cols = ["A", "B", "C"]; - let df = new DataFrame(data, { columns: cols }); - let new_vals = [[1, 2, 3], [4, 5, 6], [-2, 20, 40], [-2, -1, 78]]; - let df_filled = df.fillna({ columns: ["A"], values: [-2] }); - - assert.deepEqual(df_filled.values, new_vals); - }); - - it("Fills a list of columns with specified values", function () { - let data = [[1, undefined, 3], [4, undefined, 6], [NaN, "boy", 40], [NaN, "girl", 78]]; - let cols = ["A", "B", "C"]; - let df = new DataFrame(data, { columns: cols }); - let new_vals = [[1, "girl", 3], [4, "girl", 6], [200, "boy", 40], [200, "girl", 78]]; - let df_filled = df.fillna({ columns: ["A", "B"], values: [200, "girl"] }); - assert.deepEqual(df_filled.values, new_vals); - }); - it("Fills a list of columns with specified values inplace", function () { - let data = [[1, undefined, 3], [4, undefined, 6], [NaN, "boy", 40], [NaN, "girl", 78]]; - let cols = ["A", "B", "C"]; - let df = new DataFrame(data, { columns: cols }); - let new_vals = [[1, "girl", 3], [4, "girl", 6], [200, "boy", 40], [200, "girl", 78]]; - df.fillna({ columns: ["A", "B"], values: [200, "girl"], inplace: true }); - assert.deepEqual(df.values, new_vals); - }); - }); - - - describe("nanindex", function () { - - it("print out the nanIndex", function () { - let data = [[NaN, 1, 2, 3], [3, 4, NaN, 9], [5, 6, 7, 8]]; - let column = ["A", "B", "C", "D"]; - let df = new DataFrame(data, { columns: column }); - - let df_val = [0, 1]; - assert.deepEqual(df.nanIndex(), df_val); - }); - }); - - describe("select_dtypes", function () { - - it("Returns float columns in a DataFrame", function () { - let data = [[30, 1, 2, "boy"], [3.2, 4, 30, "girl"], [5.09, 6, 7, "cat"]]; - let column = ["A", "B", "C", "D"]; - let df = new DataFrame(data, { columns: column }); - let df_sub = df.select_dtypes(['float32']); - assert.deepEqual(df_sub.col_data, [[30, 3.2, 5.09]]); - }); - - it("Returns int columns in a DataFrame", function () { - let data = [[30, 1, 2, "boy"], - [3.2, 4, 30, "girl"], - [5.09, 6, 7, "cat"]]; - let column = ["A", "B", "C", "D"]; - let df = new DataFrame(data, { columns: column }); - let df_sub = df.select_dtypes(['int32']); - assert.deepEqual(df_sub.values, [[1, 2], [4, 30], [6, 7]]); - }); - - it("Returns string columns in a DataFrame", function () { - let data = [[30, 1, 2, "boy"], - [3.2, 4, 30, "girl"], - [5.09, 6, 7, "cat"]]; - let column = ["A", "B", "C", "D"]; - let df = new DataFrame(data, { columns: column }); - let df_sub = df.select_dtypes(['string']); - assert.deepEqual(df_sub.col_data, [["boy", "girl", "cat"]]); - }); - - it("Returns string and float columns in a DataFrame", function () { - let data = [[30, 1, 2, "boy"], - [3.2, 4, 30, "girl"], - [5.09, 6, 7, "cat"]]; - let column = ["A", "B", "C", "D"]; - let df = new DataFrame(data, { columns: column }); - let df_sub = df.select_dtypes(['string', 'float32']); - assert.deepEqual(df_sub.col_data, [[30, 3.2, 5.09], ["boy", "girl", "cat"]]); - }); - - it("Returns int and float columns in a DataFrame", function () { - let data = [[30, 1, 2, "boy"], - [3.2, 4, 30, "girl"], - [5.09, 6, 7, "cat"]]; - let column = ["A", "B", "C", "D"]; - let df = new DataFrame(data, { columns: column }); - let df_sub = df.select_dtypes(['int32', 'float32']); - assert.deepEqual(df_sub.values, [[30, 1, 2], [3.2, 4, 30], [5.09, 6, 7]]); - }); - }); - - describe("cum_ops", function () { - - it("check cumsum data", function () { - let data = [[2, 1, 2, 3], [3, 4, 11, 9], [5, 6, 7, 8]]; - let column = ["A", "B", "C", "D"]; - let df = new DataFrame(data, { columns: column }); - let rslt = [[2, 1, 2, 3], [5, 5, 13, 12], [10, 11, 20, 20]]; - - assert.deepEqual(df.cumsum().values, rslt); - }); - it("check cumsum data along axis 1", function () { - let data = [[2, 1, 2, 3], [3, 4, 11, 9], [5, 6, 7, 8]]; - let column = ["A", "B", "C", "D"]; - let df = new DataFrame(data, { columns: column }); - let rslt = [[2, 3, 5, 8], [3, 7, 18, 27], [5, 11, 18, 26]]; - - assert.deepEqual(df.cumsum({ axis: 1 }).values, rslt); - }); - }); - - describe("__set_column_property", async function () { - it("Access column object using list subset and name of column", async function () { - let data = [{ alpha: "A", count: 1 }, { alpha: "B", count: 2 }, { alpha: "C", count: 3 }]; - let df = new DataFrame(data); - let col1 = ["A", "B", "C"]; - let col2 = [1, 2, 3]; - assert.deepEqual(df['alpha'].values, col1); - assert.deepEqual(df['count'].values, col2); - }); - it("Access column object using list subset and name of column after assigning", function () { - let data = [[1, 2, 3], [4, 5, 6]]; - let cols = ["A", "B", "C"]; - let df = new DataFrame(data, { columns: cols }); - df["A"] = [30, 40]; - let col1 = [30, 40]; - assert.deepEqual(df["A"].values, col1); - }); - }); - - describe("lt", function () { - it("Returns Less than of DataFrame and other DataFrame (element-wise)", function () { - let data1 = [[10, 45, 56, 10], [25, 23, 20, 10]]; - let data2 = [[100, 450, 590, 5], [25, 2, 0, 10]]; - - let df = new DataFrame(data1); - let df2 = new DataFrame(data2); - let expected = [[true, true, true, false], - [false, false, false, false]]; - assert.deepEqual(df.lt(df2).values, expected); - }); - - it("Return Less than of series scalar (element-wise)", function () { - let data1 = [[10, 45, 56, 10], [25, 23, 20, 10]]; - let sf = new DataFrame(data1); - let expected = [[true, false, false, true], - [true, true, true, true]]; - assert.deepEqual(sf.lt(30).values, expected); - }); - it("Return Less than of series and DataFrame scalar along axis 1 (column)", function () { - let data1 = [[10, 45, 56, 10], - [23, 20, 10, 10]]; - let sf = new Series([10, 23, 56, 100]); - let df = new DataFrame(data1); - let expected = [[false, false, false, true], [false, true, true, true]]; - assert.deepEqual(df.lt(sf, 1).values, expected); - }); - - it("Return Less than of Array and DataFrame scalar along axis 1 (column)", function () { - let data1 = [[10, 45, 56, 10], [23, 20, 10, 10]]; - let sf = [10, 23, 56, 100]; - let df = new DataFrame(data1); - let expected = [[false, false, false, true], [false, true, true, true]]; - assert.deepEqual(df.lt(sf, 1).values, expected); - }); - - }); - - describe("gt", function () { - it("Return Greater than of series and other series (element-wise)", function () { - let data1 = [[10, 45, 56, 10], [25, 23, 20, 10]]; - let data2 = [[100, 450, 590, 5], [25, 2, 0, 10]]; - - let df = new DataFrame(data1); - let df2 = new DataFrame(data2); - let expected = [[false, false, false, true], [false, true, true, false]]; - assert.deepEqual(df.gt(df2).values, expected); - }); - - it("Return Greater than of series scalar (element-wise)", function () { - let data1 = [[10, 45, 56, 10], [25, 23, 20, 10]]; - let sf = new DataFrame(data1); - let expected = [[false, true, true, false], [false, false, false, false]]; - assert.deepEqual(sf.gt(30).values, expected); - }); - - it("Return Less than of Array and DataFrame scalar along axis 1 (column)", function () { - let data1 = [[10, 45, 56, 10], [23, 20, 10, 10]]; - let sf = [10, 23, 56, 100]; - let df = new DataFrame(data1); - let expected = [[false, true, false, false], [true, false, false, false]]; - assert.deepEqual(df.gt(sf, 1).values, expected); - }); - - }); - - describe("le", function () { - it("Return Less than or Equal to of series and other series (element-wise)", function () { - let data1 = [[10, 45, 56, 10], [25, 23, 20, 10]]; - let data2 = [[100, 450, 590, 5], [25, 2, 0, 10]]; - - let df = new DataFrame(data1); - let df2 = new DataFrame(data2); - let expected = [[true, true, true, false], [true, false, false, true]]; - assert.deepEqual(df.le(df2).values, expected); - }); - - it("Return Less than or Equal to of series scalar (element-wise)", function () { - let data1 = [[10, 45, 56, 10], [25, 23, 30, 10]]; - let sf = new DataFrame(data1); - let expected = [[true, false, false, true], [true, true, true, true]]; - assert.deepEqual(sf.le(30).values, expected); - }); - - }); - - describe("ge", function () { - it("Return Greater than or Equal to of series and other series (element-wise)", function () { - let data1 = [[10, 45, 56, 10], [25, 23, 20, 10]]; - let data2 = [[100, 450, 590, 5], [25, 2, 0, 10]]; - - let df = new DataFrame(data1); - let df2 = new DataFrame(data2); - let expected = [[false, false, false, true], [true, true, true, true]]; - assert.deepEqual(df.ge(df2).values, expected); - }); - - it("Return Greater than or Equal to of series scalar (element-wise)", function () { - let data1 = [[10, 45, 56, 10], [25, 23, 30, 10]]; - let sf = new DataFrame(data1); - let expected = [[false, true, true, false], [false, false, true, false]]; - assert.deepEqual(sf.ge(30).values, expected); - }); - - }); - - describe("ne", function () { - it("Return Not Equal to of series and other series (element-wise)", function () { - let data1 = [[10, 45, 56, 10], [25, 23, 20, 10]]; - let data2 = [[100, 450, 590, 5], [25, 2, 0, 10]]; - - let df = new DataFrame(data1); - let df2 = new DataFrame(data2); - let expected = [[true, true, true, true], [false, true, true, false]]; - assert.deepEqual(df.ne(df2).values, expected); - }); - - it("Return Not Equal to of series scalar (element-wise)", function () { - let data1 = [[10, 45, 56, 10], [25, 23, 30, 10]]; - let sf = new DataFrame(data1); - let expected = [[true, true, true, true], [true, true, false, true]]; - assert.deepEqual(sf.ne(30).values, expected); - }); - - }); - - describe("eq", function () { - it("Return Equal to of DataFrame and other DataFrame (element-wise)", function () { - let data1 = [[10, 45, 56, 10], [25, 23, 20, 10]]; - let data2 = [[100, 450, 590, 5], [25, 2, 0, 10]]; - - let df = new DataFrame(data1); - let df2 = new DataFrame(data2); - let expected = [[false, false, false, false], [true, false, false, true]]; - assert.deepEqual(df.eq(df2).values, expected); - }); - - it("Return Equal to of DataFrame with scalar (element-wise)", function () { - let data1 = [[10, 45, 56, 10], [25, 23, 30, 10]]; - let sf = new DataFrame(data1); - let expected = [[false, false, false, false], [false, false, true, false]]; - assert.deepEqual(sf.eq(30).values, expected); - }); - it("Return Equal to of series and DataFrame scalar along axis 1 (column)", function () { - let data1 = { "Col1": [10, 45, 56, 10], "Col2": [23, 20, 10, 10] }; - let sf = new Series([10, 23]); - let df = new DataFrame(data1); - let expected = [[true, false, false, true], [true, false, false, false]]; - assert.deepEqual(df.eq(sf, 1).col_data, expected); - }); - - }); - - describe("replace", function () { - it("Replace values given in replace param", function () { - let data1 = [[10, 45, 56, 25], [23, 20, 10, 24]]; - let sf = new DataFrame(data1); - let expected = [[-999, 45, 56, 25], [23, 20, -999, 24]]; - let df_rep = sf.replace({ replace: 10, with: -999 }); - assert.deepEqual(df_rep.values, expected); - }); - - it("Replace values given in replace param with value (String type)", function () { - let data1 = [["A", "A", "A", "B"], ["B", "C", "C", "D"]]; - let df = new DataFrame(data1); - let expected = [["boy", "boy", "boy", "B"], ["B", "C", "C", "D"]]; - let df_rep = df.replace({ replace: "A", with: "boy" }); - assert.deepEqual(df_rep.values, expected); - }); - it("Throw error on wrong param passed", function () { - let data1 = [["A", "A", "A", "B"], ["B", "C", "C", "D"]]; - let sf = new DataFrame(data1); - let expected = `Params Error: A specified parameter is not supported. Your params must be any of the following [replace,with,in]`; - assert.throws(() => { sf.replace({ replce: "A", with: "boy" }); }, Error, expected); - }); - it("Replace values in specified two column(s)", function () { - let data1 = [["A", "A", 1, "girl"], - ["B", "A", 2, "woman"], - ["A", "B", 3, "man"]]; - let df = new DataFrame(data1, { columns: ["col1", "col2", "col3", "col4"] }); - let expected = [["boy", "boy", 1, "girl"], - ["B", "boy", 2, "woman"], - ["boy", "B", 3, "man"]]; - let df_rep = df.replace({ replace: "A", with: "boy", in: ["col1", "col2"] }); - assert.deepEqual(df_rep.values, expected); - }); - - it("Replace values in specified single column(s)", function () { - let data1 = [[2, "A", 1, "girl"], - [3, "A", 2, "woman"], - [4, "B", 3, "man"]]; - let df = new DataFrame(data1, { columns: ["col1", "col2", "col3", "col4"] }); - let expected = [[2, "A", 1, "girl"], - [100, "A", 2, "woman"], - [4, "B", 3, "man"]]; - let df_rep = df.replace({ replace: 3, with: 100, in: ["col1"] }); - assert.deepEqual(df_rep.values, expected); - }); - - - }); - - describe("drop_duplicates", function () { - it("Return Series with duplicate values removed (Default, first values kept)", function () { - let data1 = [10, 45, 56, 10, 23, 20, 10, 10]; - let sf = new Series(data1); - let expected = [10, 45, 56, 23, 20]; - let expected_index = [0, 1, 2, 4, 5]; - let df_drop = sf.drop_duplicates(); - assert.deepEqual(df_drop.values, expected); - assert.deepEqual(df_drop.index, expected_index); - - }); - - it("Return Series with duplicate values removed (last values kept)", function () { - let data1 = [10, 45, 56, 10, 23, 20, 10, 10]; - let sf = new Series(data1); - let expected = [45, 56, 23, 20, 10]; - let expected_index = [1, 2, 4, 5, 7]; - let df_drop = sf.drop_duplicates({ keep: "last" }); - assert.deepEqual(df_drop.values, expected); - assert.deepEqual(df_drop.index, expected_index); - - }); - - it("Return Series with duplicate values removed (String)", function () { - let data1 = ["A", "A", "A", "B", "B", "C", "C", "D"]; - let sf = new Series(data1); - let expected = ["A", "B", "C", "D"]; - let expected_index = [0, 3, 5, 7]; - sf.drop_duplicates({ inplace: true }); - assert.deepEqual(sf.values, expected); - assert.deepEqual(sf.index, expected_index); - - }); - - }); - - describe("sum", function () { - it("Sum values of a DataFrame by Default axis column (axis=1)", function () { - let data1 = [[30, 40, 3.1], - [5, 5, 5.1], - [5, 5, 3.2]]; - let sf = new DataFrame(data1); - let res = [40, 50, 11.4]; - assert.deepEqual(sf.sum().values, res); - }); - it("Sum values of a DataFrame along row axis (axis=0)", function () { - let data1 = [[30, 40, 3.1], - [5, 5, 5.1], - [5, 5, 3.2]]; - let df = new DataFrame(data1); - let res = [73.1, 15.1, 13.2]; - assert.deepEqual(df.sum({ axis: 0 }).values, res); - }); - it("Sum values of a mixed DataFrame along row axis (axis=0)", function () { - let data1 = [[30, 40, 3.1, true], - [5, 5, 5.1, true], - [5, 5, 3.2, true]]; - let df = new DataFrame(data1); - let res = [74.1, 16.1, 14.2]; - assert.deepEqual(df.sum({ axis: 0 }).values, res); - }); - it("Sum values of a boolean DataFrame along row axis (axis=0)", function () { - let data1 = [[true, true, false, true], - [false, false, false, false], - [false, true, true, false]]; - let df = new DataFrame(data1); - let res = [3, 0, 2]; - assert.deepEqual(df.sum({ axis: 0 }).values, res); - }); - it("Sum values of a boolean DataFrame along default column axis (axis=1)", function () { - let data1 = [[true, true, false, true], - [false, false, false, false], - [false, true, true, false]]; - let df = new DataFrame(data1); - let res = [1, 2, 1, 1]; - assert.deepEqual(df.sum().values, res); - }); - it("Sum values of a df with missing values", function () { - let data1 = [[11, 20, 3], [null, 15, 6], [2, 30, 40], [2, 89, 78]]; - let df = new DataFrame(data1); - let res = [15, 154, 127]; - assert.deepEqual(df.sum().values, res); - }); - - }); - - describe("abs", function () { - it("Returns the absolute values in DataFrame of ints", function () { - let data1 = [[-10, 45, 56, 10], [-25, 23, 20, -10]]; - let df = new DataFrame(data1); - let expected = [[10, 45, 56, 10], [25, 23, 20, 10]]; - assert.deepEqual(df.abs().values, expected); - }); - - it("Returns the absolute values in mixed DataFrame", function () { - let data1 = [[-10, -45.1, 56, 10], [-25, -23.2, 20, -10]]; - let df = new DataFrame(data1); - let expected = [[10, 45.1, 56, 10], [25, 23.2, 20, 10]]; - assert.deepEqual(df.abs().values, expected); - }); - }); - - describe("T", function () { - it("Returns the Tranpose of a DataFrame", function () { - let data1 = [[10, 45, 56, 10], - [25, 23, 20, 10]]; - - let cols = ["a", "b", "c", "d"]; - let df = new DataFrame(data1, { columns: cols }); - let df_trans = df.T; - let expected_vals = [[10, 25], [45, 23], [56, 20], [10, 10]]; - let expected_index = cols; - let expected_col_names = [0, 1]; - assert.deepEqual(df_trans.index, expected_index); - assert.deepEqual(df_trans.values, expected_vals); - assert.deepEqual(df_trans.column_names, expected_col_names); - - }); - - }); - - - describe("astype", function () { - it("set type of float column to int", function () { - let data = { - "A": [-20.1, 30, 47.3, -20], - "B": [34, -4, 5, 6], - "C": [20.1, -20.23, 30.3, 40.11], - "D": ["a", "b", "c", "c"] - }; - let ndframe = new DataFrame(data); - let df = ndframe.astype({ column: "A", dtype: "int32" }); - - assert.deepEqual(df.dtypes, ['int32', 'int32', 'float32', 'string']); - assert.deepEqual(df['A'].values, [-20, 30, 47, -20]); - - }); - it("set type of int column to float", function () { - let data = { - "A": [-20.1, 30, 47.3, -20], - "B": [34, -4, 5, 6], - "C": [20.1, -20.23, 30.3, 40.11], - "D": ["a", "b", "c", "c"] - }; - let ndframe = new DataFrame(data); - let df = ndframe.astype({ column: "B", dtype: "float32" }); - - assert.deepEqual(df.dtypes, ['float32', 'float32', 'float32', 'string']); - assert.deepEqual(df['B'].values, [34, -4, 5, 6]); - - }); - it("set type of string column to int", function () { - let data = { - "A": [-20.1, 30, 47.3, -20], - "B": [34, -4, 5, 6], - "C": [20.1, -20.23, 30.3, 40.11], - "D": ["20.1", "21", "23.4", "50.78"] - }; - let ndframe = new DataFrame(data); - let df = ndframe.astype({ column: "D", dtype: "int32" }); - - assert.deepEqual(df.dtypes, ['float32', 'int32', 'float32', 'int32']); - assert.deepEqual(df['D'].values, [20, 21, 23, 51]); - - }); - it("set type of string column to float", function () { - let data = { - "A": [-20.1, 30, 47.3, -20], - "B": [34, -4, 5, 6], - "C": [20.1, -20.23, 30.3, 40.11], - "D": ["20.1", "21", "23.4", "50.78"] - }; - let ndframe = new DataFrame(data); - let df = ndframe.astype({ column: "D", dtype: "float32" }); - - assert.deepEqual(df.dtypes, ['float32', 'int32', 'float32', 'float32']); - assert.deepEqual(df['D'].values, [20.1, 21, 23.4, 50.78]); - - }); - }); - - - describe("nunique", function () { - it("Returns the number of unique elements along axis 1", function () { - let data = { - "A": [-20, 30, 47.3, -20], - "B": [34, -4, 5, 6], - "C": [20, 20, 30, 30], - "D": ["a", "b", "c", "c"] - }; - - let ndframe = new DataFrame(data); - let df = ndframe.nunique(1); - let res = [3, 4, 2, 3]; - assert.deepEqual(df.values, res); - - }); - it("Returns the number of unique elements along axis 0", function () { - let data = { - "A": [20, 30, 47.3, 30], - "B": [34, -4, 5, 30], - "C": [20, 20, 30, 30], - "D": ["a", "b", "c", "c"] - }; - - let ndframe = new DataFrame(data); - let df = ndframe.nunique(0); - let res = [3, 4, 4, 2]; - assert.deepEqual(df.values, res); - - }); - - }); - - - describe("unique", function () { - it("Returns the unique elements along axis 1", function () { - let data = { - "A": [-20, 30, 47.3, -20], - "B": [34, -4, 5, 6], - "C": [20, 20, 30, 30], - "D": ["a", "b", "c", "c"] - }; - - let ndframe = new DataFrame(data); - let df = ndframe.unique(1); - let res = { - "A": [-20, 30, 47.3], - "B": [34, -4, 5, 6], - "C": [20, 30], - "D": ["a", "b", "c"] - }; - - assert.deepEqual(df, res); - - }); - it("Returns the unique elements along axis 0", function () { - let data = { - "A": [-20, 30, 47.3, -20], - "B": [34, -4, 5, 6], - "C": [20, 20, 30, 30], - "D": ["a", "b", "c", "c"] - }; - - let ndframe = new DataFrame(data); - let df = ndframe.unique(0); - let res = { - 0: [-20, 34, 20, "a"], - 1: [30, -4, 20, "b"], - 2: [47.3, 5, 30, "c"], - 3: [-20, 6, 30, "c"] - }; - assert.deepEqual(df, res); - - }); - }); - - - describe("rename", function () { - it("Rename columns along axis 1", function () { - let data = { - "A": [-20, 30, 47.3, -20], - "B": [34, -4, 5, 6], - "C": [20, 20, 30, 30], - "D": ["a", "b", "c", "c"] - }; - - let ndframe = new DataFrame(data); - let df = ndframe.rename({ mapper: { "A": "a1", "B": "b1" } }); - let res = ["a1", "b1", "C", "D"]; - assert.deepEqual(df.columns, res); - - }); - it("confirms original column name is not modified along axis 1", function () { - let data = { - "A": [-20, 30, 47.3, -20], - "B": [34, -4, 5, 6], - "C": [20, 20, 30, 30], - "D": ["a", "b", "c", "c"] - }; - - let ndframe = new DataFrame(data); - // let df = ndframe.rename({ mapper: { "A": "a1", "B": "b1" } }) - let res = ["A", "B", "C", "D"]; - assert.deepEqual(ndframe.columns, res); - - }); - it("Rename columns along axis 1 inplace", function () { - let data = { - "A": [-20, 30, 47.3, -20], - "B": [34, -4, 5, 6], - "C": [20, 20, 30, 30], - "D": ["a", "b", "c", "c"] - }; - - let df = new DataFrame(data); - df.rename({ mapper: { "A": "a1", "B": "b1" }, inplace: true }); - let res = ["a1", "b1", "C", "D"]; - assert.deepEqual(df.columns, res); - - }); - it("Rename string index along axis 0", function () { - let data = { - "A": [-20, 30, 47.3, -20], - "B": [34, -4, 5, 6], - "C": [20, 20, 30, 30], - "D": ["a", "b", "c", "c"] - }; - - let ndframe = new DataFrame(data, { index: ["a", "b", "c", "d"] }); - let df = ndframe.rename({ mapper: { "a": 0, "b": 1 }, axis: 0 }); - let res = [0, 1, "c", "d"]; - assert.deepEqual(df.index, res); - - }); - it("Rename string index along axis 0 inplace", function () { - let data = { - "A": [-20, 30, 47.3, -20], - "B": [34, -4, 5, 6], - "C": [20, 20, 30, 30], - "D": ["a", "b", "c", "c"] - }; - - let df = new DataFrame(data, { index: ["a", "b", "c", "d"] }); - df.rename({ mapper: { "a": 0, "b": 1 }, axis: 0, inplace: true }); - let res = [0, 1, "c", "d"]; - assert.deepEqual(df.index, res); - - }); - }); - - describe("sort_index", function () { - - it("sort index in ascending order", function () { - let data = [[0, 2, 4, "b"], - [360, 180, 360, "a"], - [2, 4, 6, "c"]]; - - let df = new DataFrame(data, { "columns": ["col1", "col2", "col3", "col4"] }); - df.set_index({ key: ["b", "a", "c"], inplace: true }); - - let df2 = df.sort_index(); - let rslt = [[360, 180, 360, 'a'], [0, 2, 4, 'b'], [2, 4, 6, 'c']]; - - assert.deepEqual(df2.values, rslt); - }); - it("sort index in descending order", function () { - let data = [[0, 2, 4, "b"], - [360, 180, 360, "a"], - [2, 4, 6, "c"]]; - - let df = new DataFrame(data, { "columns": ["col1", "col2", "col3", "col4"] }); - df.set_index({ key: ["b", "a", "c"], inplace: true }); - - let df2 = df.sort_index({ ascending: false }); - let rslt = [[2, 4, 6, 'c'], [0, 2, 4, 'b'], [360, 180, 360, 'a']]; - - assert.deepEqual(df2.values, rslt); - }); - it("sort index in descending order with inplace set to true", function () { - let data = [[0, 2, 4, "b"], - [360, 180, 360, "a"], - [2, 4, 6, "c"]]; - - let df = new DataFrame(data, { "columns": ["col1", "col2", "col3", "col4"] }); - df.set_index({ key: ["b", "a", "c"], inplace: true }); - - df.sort_index({ ascending: false, inplace: true }); - let rslt = [[2, 4, 6, 'c'], [0, 2, 4, 'b'], [360, 180, 360, 'a']]; - assert.deepEqual(df.values, rslt); - }); - }); - - describe("append", function () { - - it("Add a new single row (array) to the end of a DataFrame", function () { - let data = [[0, 2, 4, "b"], - [360, 180, 360, "a"], - [2, 4, 6, "c"]]; - - let df = new DataFrame(data); - let expected_val = [[0, 2, 4, "b"], - [360, 180, 360, "a"], - [2, 4, 6, "c"], - [20, 40, 60, "d"]]; - - let rslt_df = df.append([[20, 40, 60, "d"]]); - assert.deepEqual(rslt_df.values, expected_val); - - }); - - it("Add a new single row (object) to the end of a DataFrame", function () { - let data = [[0, 2, 4, "b"], - [360, 180, 360, "a"], - [2, 4, 6, "c"]]; - - let df = new DataFrame(data, { "columns": ["col1", "col2", "col3", "col4"] }); - let expected_val = [[0, 2, 4, "b"], - [360, 180, 360, "a"], - [2, 4, 6, "c"], - [20, 40, 60, "d"]]; - let rslt_df = df.append({ col1: [20], col2: [40], col3: [60], col4: ["d"] }); - assert.deepEqual(rslt_df.values, expected_val); - - }); - - it("Add a new single row (object) to the end of a DataFrame", function () { - let data = [[0, 2, 4, "b"], - [360, 180, 360, "a"], - [2, 4, 6, "c"]]; - - let df = new DataFrame(data, { "columns": ["col1", "col2", "col3", "col4"] }); - let df2 = new DataFrame([[20, 40, 60, "d"]], { "columns": ["col1", "col2", "col3", "col4"] }); - - let expected_val = [[0, 2, 4, "b"], - [360, 180, 360, "a"], - [2, 4, 6, "c"], - [20, 40, 60, "d"]]; - - let rslt_df = df.append(df2); - assert.deepEqual(rslt_df.values, expected_val); - - }); - it("Confirm index Change after append", function () { - let data = [[0, 2, 4, "b"], - [360, 180, 360, "a"], - [2, 4, 6, "c"]]; - - let df = new DataFrame(data, { "columns": ["col1", "col2", "col3", "col4"] }); - let df2 = new DataFrame([[20, 40, 60, "d"]], { "columns": ["col1", "col2", "col3", "col4"] }); - - let rslt_df = df.append(df2); - assert.deepEqual(rslt_df.index, ["0_row0", "1_row0", "2_row0", "0_row1"]); - - }); - }); - - describe("Str", function () { - it("Str (startsWith) works for columns selected from a DF", function () { - let data = { - "Name": ["Apples", "Bake", "Application", undefined], - "Count": [2, 5, 4, 10], - "Price": [200, 300, 40, 250] - }; - - let df = new DataFrame(data); - let name_sf = df['Name']; - assert.deepEqual(name_sf.str.startsWith("App").values, [true, false, true, false]); - }); - it("Str (toLowerCase) works for columns selected from a DF", function () { - let data = { - "Name": ["Apples", "Bake", "Application", undefined], - "Count": [2, 5, 4, 10], - "Price": [200, 300, 40, 250] - }; - - let df = new DataFrame(data); - let name_sf = df['Name']; - assert.deepEqual(name_sf.str.toLowerCase().values, ["apples", "bake", "application", NaN]); - }); - }); - -}); diff --git a/danfojs-node/tests/core/generic.js b/danfojs-node/tests/core/generic.js deleted file mode 100644 index 84632a6e..00000000 --- a/danfojs-node/tests/core/generic.js +++ /dev/null @@ -1,273 +0,0 @@ -import { assert } from "chai"; -import NDframe from '../../src/core/generic'; -import * as tf from '@tensorflow/tfjs-node'; - - -describe("Generic (NDFrame)", function () { - describe("NDframe Created from Array", function () { - it("prints the shape of a 1D array", function () { - let data = [ 1, 2, 3, "Boy", "Girl" ]; - let ndframe = new NDframe(data); - assert.deepEqual(ndframe.shape, [ 5, 1 ]); - }); - it("prints the default assigned column name in a series", function () { - let data = [ "Boy", 20, 25 ]; - let ndframe = new NDframe(data); - assert.deepEqual(ndframe.column_names, [ "0" ]); - }); - it("prints the assigned column name in a series", function () { - let data = [ "Boy", 20, 25 ]; - let options = { "columns": 'Records' }; - let ndframe = new NDframe(data, options); - assert.deepEqual(ndframe.column_names, "Records"); - }); - it("prints the shape of a 2D array", function () { - let data = [ [ "Boy", 20 ], [ "Girl", 25 ] ]; - let ndframe = new NDframe(data); - assert.deepEqual(ndframe.shape, [ 2, 2 ]); - }); - it("prints the default assigned column names in 2D frame", function () { - let data = [ [ "Boy", 20 ], [ "Girl", 25 ] ]; - let ndframe = new NDframe(data); - assert.deepEqual(ndframe.column_names, [ 0, 1 ]); - }); - it("prints the assigned column names", function () { - let data = [ [ "Boy", 20 ], [ "Girl", 25 ] ]; - let options = { "columns": [ "Gender", "Age" ] }; - let ndframe = new NDframe(data, options); - assert.deepEqual(ndframe.column_names, [ "Gender", "Age" ]); - }); - it("prints the size of a frame", function () { - let data = [ [ "Boy", 20, 1 ], [ "Girl", 25, 3 ] ]; - let options = { "columns": [ "Gender", "Age", "count" ] }; - let ndframe = new NDframe(data, options); - assert.deepEqual(ndframe.size, 6); - }); - it("prints the dimension of a frame", function () { - let data = [ [ "Boy", 20, 1 ], [ "Girl", 25, 3 ] ]; - let options = { "columns": [ "Gender", "Age", "count" ] }; - let ndframe = new NDframe(data, options); - assert.deepEqual(ndframe.ndim, 2); - }); - it("prints the values of a frame", function () { - let data = [ [ 21, 20, 1 ], [ 20, 25, 3 ] ]; - let ndframe = new NDframe(data); - assert.deepEqual(ndframe.values, data); - }); - it("prints the values of a frame", function () { - let data = [ [ 21, 20, 1 ], [ 20, 25, 3 ] ]; - let ndframe = new NDframe(data); - assert.deepEqual(ndframe.values, data); - }); - }); - - describe("NDframe Created from JavaScript Object", function () { - - it("prints the shape of a 2D frame created from an Object", function () { - let data = [ { alpha: "A", count: 1 }, { alpha: "B", count: 2 }, { alpha: "C", count: 3 } ]; - let ndframe = new NDframe(data); - assert.deepEqual(ndframe.shape, [ 3, 2 ]); - }); - it("prints the column names of frame created from an Object", function () { - let data = [ { alpha: "A", count: 1 }, { alpha: "B", count: 2 }, { alpha: "C", count: 3 } ]; - let ndframe = new NDframe(data); - assert.deepEqual(ndframe.column_names, [ "alpha", "count" ]); - }); - it("prints the shape of a 1D frame created from an Object", function () { - let data = [ { alpha: "A", count: 1 } ]; - let ndframe = new NDframe(data); - assert.deepEqual(ndframe.shape, [ 1, 2 ]); - }); - - it("prints the size of a frame created from an Object", function () { - let data = [ { alpha: "A", count: 1 }, { alpha: "B", count: 2 }, { alpha: "C", count: 3 } ]; - let options = { columns: [ "Gender", "Age", "count" ] }; - let ndframe = new NDframe(data, options); - assert.deepEqual(ndframe.size, 6); - }); - it("prints the dimension of a frame created from an Object", function () { - let data = [ { alpha: "A", count: 1 }, { alpha: "B", count: 2 } ]; - let options = { columns: [ "Gender", "Age", "count" ] }; - let ndframe = new NDframe(data, options); - assert.deepEqual(ndframe.ndim, 2); - }); - it("prints the values of a frame created from an Object", function () { - let data = [ { alpha: "A", count: 1 }, { alpha: "B", count: 2 } ]; - let ndframe = new NDframe(data); - assert.deepEqual(ndframe.values, [ [ "A", 1 ], [ "B", 2 ] ]); - }); - it("prints the values of a frame created from an Object with null values", function () { - let data = [ { alpha: "A", count: null }, { alpha: null, count: 2 } ]; - let ndframe = new NDframe(data); - assert.deepEqual(ndframe.values, [ [ "A", NaN ], [ NaN, 2 ] ]); - }); - }); - - describe("NDframe Created from a Tensor", function () { - - it("prints the shape of a 2D frame created from a 2D tensor", function () { - let data = tf.tensor([ 1, 2, 3, 4 ]); - let ndframe = new NDframe(data); - assert.deepEqual(ndframe.ndim, 1); - assert.deepEqual(ndframe.values, [ 1, 2, 3, 4 ]); - - }); - it("prints the shape of a 2D frame created from a 1D tensor", function () { - let data = tf.tensor([ [ 2, 3, 4 ], [ 4, 5, 6 ] ]); - let ndframe = new NDframe(data, { columns: [ "alpha", "count", "sum" ] }); - assert.deepEqual(ndframe.column_names, [ "alpha", "count", "sum" ]); - }); - - }); - - describe("index", function () { - it("Returns the index of an NDframe", function () { - let data = [ { alpha: "A", count: 1 }, { alpha: "B", count: 2 }, { alpha: "C", count: 3 } ]; - let df = new NDframe(data); - assert.deepEqual(df.index, [ 0, 1, 2 ]); - }); - it("Returns the index of an NDframe created from an Array", function () { - let data = [ [ 12, 2, 20 ], [ 90, 5, 23 ], [ 45, 56, 70 ], [ 9, 10, 19 ] ]; - let df = new NDframe(data); - assert.deepEqual(df.index, [ 0, 1, 2, 3 ]); - }); - }); - - describe("NDframe Created from JavaScript Object of Arrays", function () { - - it("retrieves the col data created from OA ", function () { - let data = { alpha: [ "A", "B", "C", "D" ], count: [ 1, 2, 3, 4 ] }; - let ndframe = new NDframe(data); - // assert.deepEqual(ndframe.shape, [4, 2]) - assert.deepEqual(ndframe.col_data, [ [ "A", "B", "C", "D" ], [ 1, 2, 3, 4 ] ]); - }); - it("retrieves the col data ", function () { - let data = { alpha: [ "A", "B", "C", "D" ], count: [ 1, 2, 3, 4 ], sum: [ 20.3, 30.456, 40.90, 90.1 ] }; - let ndframe = new NDframe(data); - let res = [ [ "A", "B", "C", "D" ], [ 1, 2, 3, 4 ], [ 20.3, 30.456, 40.90, 90.1 ] ]; - assert.deepEqual(ndframe.col_data, res); - }); - it("retrieves the row data created from OA ", function () { - let data = { alpha: [ "A", "B" ], count: [ 1, 2 ] }; - let ndframe = new NDframe(data); - // assert.deepEqual(ndframe.shape, [4, 2]) - assert.deepEqual(ndframe.values, [ [ "A", 1 ], [ "B", 2 ] ]); - }); - }); - - describe("__set_index", function () { - it("sets the index of an NDframe", function () { - let data = [ { alpha: "A", count: 1 }, { alpha: "B", count: 2 }, { alpha: "C", count: 3 } ]; - let df = new NDframe(data); - df.__set_index([ "A", "B", "C" ]); - assert.deepEqual(df.index, [ "A", "B", "C" ]); - }); - it("Returns the index of an NDframe created from an Array", function () { - let data = [ [ 12, 2, 20 ], [ 90, 5, 23 ], [ 45, 56, 70 ], [ 9, 10, 19 ] ]; - let df = new NDframe(data); - df.__set_index([ 10, 20, 30, 40 ]); - assert.deepEqual(df.index, [ 10, 20, 30, 40 ]); - }); - }); - - - describe("dtype", function () { - it("Returns int dtype set during creation of 1DFrame (Series) from an Object", function () { - let data = [ 1, 2, 3, 4, 5, 6, 7, 8, 9 ]; - let options = { dtypes: [ 'int32' ] }; - let ndframe = new NDframe(data, options); - assert.deepEqual(ndframe.dtypes, [ 'int32' ]); - }); - - it("Returns string dtype set during creation of 1DFrame (Series) from an Array", function () { - let data = [ "Alice", "Yemi", "Rising", "Mark" ]; - let options = { dtypes: [ 'string' ] }; - let ndframe = new NDframe(data, options); - assert.deepEqual(ndframe.dtypes, [ "string" ]); - }); - - it("Returns string dtype automatically inferred from 1DFrame (Series)", function () { - let data = [ "Alice", "Yemi", "Rising", "Mark" ]; - let options = { columns: 'Names' }; - let ndframe = new NDframe(data, options); - assert.deepEqual(ndframe.dtypes, [ "string" ]); - }); - - it("Returns int dtype automatically inferred from 1DFrame (Series)", function () { - let data = [ 20, 30, 20, 20 ]; - let options = { columns: 'Size' }; - let ndframe = new NDframe(data, options); - assert.deepEqual(ndframe.dtypes, [ "int32" ]); - }); - it("Returns float dtype automatically inferred from 1DFrame (Series)", function () { - let data = [ 20.1, 30.4, 20.2, 4.23, 20.1 ]; - let options = { columns: 'Size' }; - let ndframe = new NDframe(data, options); - assert.deepEqual(ndframe.dtypes, [ "float32" ]); - }); - - - it("Returns dtype set during creation of 2DFrame from an Object", function () { - let data = [ { alpha: "A", count: 1 }, { alpha: "B", count: 2 } ]; - let options = { dtypes: [ 'string', 'int32' ] }; - let ndframe = new NDframe(data, options); - assert.deepEqual(ndframe.dtypes, [ 'string', 'int32' ]); - }); - it("Returns dtype set during creation of 2DFrame from an Array", function () { - let data = [ [ "Alice", 2, 3.0 ], [ "Boy", 5, 6.1 ], [ "Girl", 30, 40 ], [ 39, 89, 78.2 ] ]; - let cols = [ "Name", "Count", "Score" ]; - let options = { columns: cols, dtypes: [ 'string', 'int32', 'float32' ] }; - let ndframe = new NDframe(data, options); - assert.deepEqual(ndframe.dtypes, [ "string", "int32", "float32" ]); - }); - - it("Returns dtype automatically inferred from 2DFrame", function () { - let data = [ [ "Alice", 2, 3.1 ], [ "Boy", 5, 6.1 ], [ "Girl", 30, 40.2 ], [ 39, 89, 78.2 ] ]; - let cols = [ "Name", "Count", "Score" ]; - let options = { columns: cols }; - let ndframe = new NDframe(data, options); - assert.deepEqual(ndframe.dtypes, [ "string", "int32", "float32" ]); - }); - - }); - - - describe("to_csv", async function () { - it("Converts DataFrame to csv format and return string", async function () { - let data = [ { alpha: "A", count: 1 }, { alpha: "B", count: 2 }, { alpha: "C", count: 3 } ]; - let df = new NDframe(data); - let result = `alpha,count\nA,1\nB,2\nC,3\n`; - df.to_csv().then((csv) => { - assert.deepEqual(csv, result); - }); - }); - it("Converts DataFrame of Series to csv format and return string when path is not specified", async function () { - let data = [ [ 12, 2, 20 ], [ 90, 5, 23 ], [ 45, 56, 70 ], [ 9, 10, 19 ] ]; - let df = new NDframe(data, { columns: [ "A", "B", "C" ] }); - let result = `A,B,C\n12,2,20\n90,5,23\n45,56,70\n9,10,19\n`; - assert.deepEqual(await df.to_csv(), result); - }); - }); - - describe("to_json", async function () { - it("Converts DataFrame to json format and return string", async function () { - let data = [ { alpha: "A", count: 1 }, { alpha: "B", count: 2 }, { alpha: "C", count: 3 } ]; - let result = JSON.stringify([ { alpha: "A", count: 1 }, { alpha: "B", count: 2 }, { alpha: "C", count: 3 } ]); - - let df = new NDframe(data); - df.to_json().then((json) => { - assert.deepEqual(json, result); - }); - }); - it("Converts DataFrame to json format", async function () { - let data = [ [ 12, 2, 20 ], [ 90, 5, 23 ], [ 45, 56, 70 ] ]; - let df = new NDframe(data, { columns: [ "A", "B", "C" ] }); - let result = JSON.stringify([ { A: 12, B: 2, C: 20 }, { A: 90, B: 5, C: 23 }, { A: 45, B: 56, C: 70 } ]); - df.to_json().then((json) => { - assert.deepEqual(json, result); - }); - }); - }); - - -}); diff --git a/danfojs-node/tests/core/get_dummies.js b/danfojs-node/tests/core/get_dummies.js deleted file mode 100644 index 28178a86..00000000 --- a/danfojs-node/tests/core/get_dummies.js +++ /dev/null @@ -1,154 +0,0 @@ -import { assert } from "chai"; -import { get_dummies } from '../../src/core/get_dummies'; -import { DataFrame } from "../../src/core/frame"; -import { Series } from "../../src/core/series"; - -describe("get_dummies", function(){ - - it("test on array", function(){ - - let data = [ "dog", "male", "female", "male", "female", "male", "dog" ]; - - let df = get_dummies({ data:data }); - - let df_values = [ - [ 1, 0, 0 ], - [ 0, 1, 0 ], - [ 0, 0, 1 ], - [ 0, 1, 0 ], - [ 0, 0, 1 ], - [ 0, 1, 0 ], - [ 1, 0, 0 ] - ]; - let df_columns = [ 'dog', 'male', 'female' ]; - - assert.deepEqual(df.values, df_values); - assert.deepEqual(df.columns, df_columns); - - }); - it("test on Series", function(){ - - let data = [ "dog", "male", "female", "male", "female", "male", "dog" ]; - let series = new Series(data); - - let df = get_dummies({ data:series, prefix:"test", prefix_sep:"/" }); - - let df_values = [ - [ 1, 0, 0 ], - [ 0, 1, 0 ], - [ 0, 0, 1 ], - [ 0, 1, 0 ], - [ 0, 0, 1 ], - [ 0, 1, 0 ], - [ 1, 0, 0 ] - ]; - let df_columns = [ 'test/dog', 'test/male', 'test/female' ]; - - assert.deepEqual(df.values, df_values); - assert.deepEqual(df.columns, df_columns); - }); - - it("get dummies on DataFrame", function(){ - - let data = [ [ 1, "dog", 1.0, "fat" ], [ 3, "fog", 2.0, "good" ], [ 4, "gof", 3.0, "best" ] ]; - let columns = [ "A", "B", "C", "d" ]; - let df = new DataFrame(data, { columns:columns }); - - let df1 = get_dummies({ data:df, prefix_sep:"_", columns:[ "A", "d" ] }); - let df1_columns = [ - 'C', 'd', - 'A_1', 'A_3', - 'A_4', 'd_dog', - 'd_fog', 'd_gof' - ]; - - let df1_values = [ - [ 1, 'fat', 1, 0, 0, 1, 0, 0 ], - [ 2, 'good', 0, 1, 0, 0, 1, 0 ], - [ 3, 'best', 0, 0, 1, 0, 0, 1 ] - ]; - - assert.deepEqual(df1.values, df1_values); - assert.deepEqual(df1.columns, df1_columns); - - }); - it("Throw error if the prefix specified is not equal to the column specified", function(){ - - let data = [ [ 1, "dog", 1.0, "fat" ], [ 3, "fog", 2.0, "good" ], [ 4, "gof", 3.0, "best" ] ]; - let columns = [ "A", "B", "C", "d" ]; - let df = new DataFrame(data, { columns:columns }); - - assert.throws(function () { get_dummies({ data:df, prefix:[ "fg" ], prefix_sep:"_", columns:[ "A", "d" ] }); }, Error, - 'prefix must be the same length with the number of onehot encoding column'); - - }); - it("replace column sepecified with prefix", function(){ - - let data = [ [ 1, "dog", 1.0, "fat" ], [ 3, "fog", 2.0, "good" ], [ 4, "gof", 3.0, "best" ] ]; - let columns = [ "A", "B", "C", "d" ]; - let df = new DataFrame(data, { columns:columns }); - - let df1 = get_dummies({ data:df, prefix:[ "F", "G" ], prefix_sep:"_", columns:[ "A", "d" ] }); - let df1_columns = [ - 'C', 'd', - 'F_1', 'F_3', - 'F_4', 'G_dog', - 'G_fog', 'G_gof' - ]; - - let df1_values = [ - [ 1, 'fat', 1, 0, 0, 1, 0, 0 ], - [ 2, 'good', 0, 1, 0, 0, 1, 0 ], - [ 3, 'best', 0, 0, 1, 0, 0, 1 ] - ]; - - assert.deepEqual(df1.values, df1_values); - assert.deepEqual(df1.columns, df1_columns); - - }); - - it("infer the onehotencoding column base on string dtypes", function(){ - - let data = [ [ 1, "dog", 1.0, "fat" ], [ 3, "fog", 2.0, "good" ], [ 4, "gof", 3.0, "best" ] ]; - let columns = [ "A", "B", "C", "d" ]; - let df = new DataFrame(data, { columns:columns }); - - let df1 = get_dummies({ data:df, prefix_sep:"_" }); - let df1_columns = [ - 'A', 'C', - 'B_dog', 'B_fog', - 'B_gof', 'd_fat', - 'd_good', 'd_best' - ]; - let df1_values = [ - [ - 1, 1, 1, 0, - 0, 1, 0, 0 - ], - [ - 3, 2, 0, 1, - 0, 0, 1, 0 - ], - [ - 4, 3, 0, 0, - 1, 0, 0, 1 - ] - ]; - - assert.deepEqual(df1.values, df1_values); - assert.deepEqual(df1.columns, df1_columns); - - }); - it("replace column sepecified with prefix", function(){ - - let data = [ [ 1, "dog", 1.0, "fat" ], [ 3, "fog", 2.0, "good" ], [ 4, "gof", 3.0, "best" ] ]; - let columns = [ "A", "B", "C", "d" ]; - let df = new DataFrame(data, { columns:columns }); - - assert.throws(function () { get_dummies({ data:df, prefix:"F", prefix_sep:"_", columns:[ "A", "d" ] }); }, Error, - "prefix for dataframe must be an array"); - - }); - - -}); diff --git a/danfojs-node/tests/core/groupby.js b/danfojs-node/tests/core/groupby.js deleted file mode 100644 index 58d3c799..00000000 --- a/danfojs-node/tests/core/groupby.js +++ /dev/null @@ -1,296 +0,0 @@ -import { assert } from "chai"; -import { DataFrame } from '../../src/core/frame'; - -describe("groupby", function () { - it("Check group by One column data", function () { - - let data = [ [ 1, 2, 3 ], [ 4, 5, 6 ], [ 20, 30, 40 ], [ 39, 89, 78 ] ]; - let cols = [ "A", "B", "C" ]; - let df = new DataFrame(data, { columns: cols }); - let group_df = df.groupby([ "A" ]); - - let group_dict = { - '1': [ [ 1, 2, 3 ] ], - '4': [ [ 4, 5, 6 ] ], - '20': [ [ 20, 30, 40 ] ], - '39': [ [ 39, 89, 78 ] ] - }; - - assert.deepEqual(group_df.col_dict, group_dict); - }); - it("Obtain the DataFrame of one of the group", function () { - - let data = [ [ 1, 2, 3 ], [ 4, 5, 6 ], [ 20, 30, 40 ], [ 39, 89, 78 ] ]; - let cols = [ "A", "B", "C" ]; - let df = new DataFrame(data, { columns: cols }); - let group_df = df.groupby([ "A" ]); - let new_data = [ [ 1, 2, 3 ] ]; - - assert.deepEqual(group_df.get_groups([ 1 ]).values, new_data); - }); - it("Check group by Two column data", function () { - - let data = [ [ 1, 2, 3 ], [ 4, 5, 6 ], [ 20, 30, 40 ], [ 39, 89, 78 ] ]; - let cols = [ "A", "B", "C" ]; - let df = new DataFrame(data, { columns: cols }); - let group_df = df.groupby([ "A", "B" ]); - let new_data = { - '1': { '2': [ [ 1, 2, 3 ] ] }, - '4': { '5': [ [ 4, 5, 6 ] ] }, - '20': { '30': [ [ 20, 30, 40 ] ] }, - '39': { '89': [ [ 39, 89, 78 ] ] } - }; - - assert.deepEqual(group_df.col_dict, new_data); - }); - - it("Obtain the DataFrame of one of the group, grouped by two column", function () { - - let data = [ [ 1, 2, 3 ], [ 4, 5, 6 ], [ 20, 30, 40 ], [ 39, 89, 78 ] ]; - let cols = [ "A", "B", "C" ]; - let df = new DataFrame(data, { columns: cols }); - let group_df = df.groupby([ "A", "B" ]); - let new_data = [ [ 1, 2, 3 ] ]; - - assert.deepEqual(group_df.get_groups([ 1, 2 ]).values, new_data); - }); - - it("Count column in group", function () { - - let data = [ [ 1, 2, 3 ], [ 4, 5, 6 ], [ 20, 30, 40 ], [ 39, 89, 78 ] ]; - let cols = [ "A", "B", "C" ]; - let df = new DataFrame(data, { columns: cols }); - let group_df = df.groupby([ "A", "B" ]); - let new_data = [ - [ 1, 2, 1 ], - [ 4, 5, 1 ], - [ 20, 30, 1 ], - [ 39, 89, 1 ] - ]; - - assert.deepEqual(group_df.col([ "C" ]).count().values, new_data); - }); - it("sum column element in group", function () { - - let data = [ [ 1, 2, 3 ], [ 4, 5, 6 ], [ 20, 30, 40 ], [ 39, 89, 78 ] ]; - let cols = [ "A", "B", "C" ]; - let df = new DataFrame(data, { columns: cols }); - let group_df = df.groupby([ "A", "B" ]); - let new_data = [ - [ 1, 2, 3 ], - [ 4, 5, 6 ], - [ 20, 30, 40 ], - [ 39, 89, 78 ] - ]; - assert.deepEqual(group_df.col([ "C" ]).sum().values, new_data); - }); - - it("sum column element group by one column", function () { - - let data = [ [ 1, 2, 3 ], [ 1, 5, 6 ], [ 20, 30, 40 ], [ 39, 89, 78 ] ]; - let cols = [ "A", "B", "C" ]; - let df = new DataFrame(data, { columns: cols }); - let group_df = df.groupby([ "A", "B" ]); - - let new_data = [ - [ 1, 2, 2, 3 ], - [ 1, 5, 5, 6 ], - [ 20, 30, 30, 40 ], - [ 39, 89, 89, 78 ] - ]; - - assert.deepEqual(group_df.col([ "B", "C" ]).sum().values, new_data); - }); - - it("Perform aggregate on column for groupby", function () { - - let data = [ [ 1, 2, 3 ], [ 4, 5, 6 ], [ 20, 30, 40 ], [ 39, 89, 78 ] ]; - let cols = [ "A", "B", "C" ]; - let df = new DataFrame(data, { columns: cols }); - let group_df = df.groupby([ "A", "B" ]); - let new_data = [ - [ 1, 2, 2, 1 ], - [ 4, 5, 5, 1 ], - [ 20, 30, 30, 1 ], - [ 39, 89, 89, 1 ] - ]; - - assert.deepEqual(group_df.agg({ "B": "mean", "C": "count" }).values, new_data); - }); - it("cummulative sum for groupby", function () { - - let data = [ [ 1, 2, 3 ], [ 4, 5, 6 ], [ 20, 30, 40 ], [ 39, 89, 78 ] ]; - let cols = [ "A", "B", "C" ]; - let df = new DataFrame(data, { columns: cols }); - let group_df = df.groupby([ "A", "B" ]); - let new_data = [ - [ 1, 2, 2, 3 ], - [ 4, 5, 5, 6 ], - [ 20, 30, 30, 40 ], - [ 39, 89, 89, 78 ] - ]; - assert.deepEqual(group_df.col([ "B", "C" ]).cumsum().values, new_data); - }); - it("cummulative max for groupby", function () { - - let data = [ [ 1, 2, 3 ], [ 4, 5, 6 ], [ 20, 30, 40 ], [ 39, 89, 78 ] ]; - let cols = [ "A", "B", "C" ]; - let df = new DataFrame(data, { columns: cols }); - let group_df = df.groupby([ "A" ]); - let new_data = [ [ 1, 3 ], [ 4, 6 ], [ 20, 40 ], [ 39, 78 ] ]; - - - assert.deepEqual(group_df.col([ "C" ]).cummax().values, new_data); - }); - it("cummulative min for groupby", function () { - - let data = [ [ 1, 2, 3 ], [ 4, 5, 6 ], [ 20, 30, 40 ], [ 39, 89, 78 ] ]; - let cols = [ "A", "B", "C" ]; - let df = new DataFrame(data, { columns: cols }); - let group_df = df.groupby([ "A" ]); - let new_data = [ [ 1, 3 ], [ 4, 6 ], [ 20, 40 ], [ 39, 78 ] ]; - - assert.deepEqual(group_df.col([ "C" ]).cummin().values, new_data); - }); - - it("cummulative prod for groupby", function () { - - let data = [ [ 1, 2, 3 ], [ 4, 5, 6 ], [ 20, 30, 40 ], [ 39, 89, 78 ] ]; - let cols = [ "A", "B", "C" ]; - let df = new DataFrame(data, { columns: cols }); - let group_df = df.groupby([ "A" ]); - let new_data = [ [ 1, 3 ], [ 4, 6 ], [ 20, 40 ], [ 39, 78 ] ]; - - assert.deepEqual(group_df.col([ "C" ]).cumprod().values, new_data); - }); - it("mean for groupby", function () { - - let data = [ [ 1, 2, 3 ], [ 4, 5, 6 ], [ 20, 30, 40 ], [ 39, 89, 78 ] ]; - let cols = [ "A", "B", "C" ]; - let df = new DataFrame(data, { columns: cols }); - let group_df = df.groupby([ "A", "B" ]); - let new_data = [ - [ 1, 2, 2, 3 ], - [ 4, 5, 5, 6 ], - [ 20, 30, 30, 40 ], - [ 39, 89, 89, 78 ] - ]; - - assert.deepEqual(group_df.col([ "B", "C" ]).mean().values, new_data); - }); - - it("printing multiindex table, example with cumsum operation for dataframe group by one column", function(){ - let data = { 'A': [ 'foo', 'bar', 'foo', 'bar', - 'foo', 'bar', 'foo', 'foo' ], - 'B': [ 'one', 'one', 'two', 'three', - 'two', 'two', 'one', 'three' ], - 'C': [ 1, 3, 2, 4, 5, 2, 6, 7 ], - 'D': [ 3, 2, 4, 1, 5, 6, 7, 8 ] }; - - - let df = new DataFrame(data); - - let grp = df.groupby([ "A" ]); - let rslt = [ - [ 'foo', 1 ], - [ 'foo', 3 ], - [ 'foo', 8 ], - [ 'foo', 14 ], - [ 'foo', 21 ], - [ 'bar', 3 ], - [ 'bar', 7 ], - [ 'bar', 9 ] - ]; - assert.deepEqual(grp.col([ "C" ]).cumsum().values, rslt); - - }); - it("printing multiindex table, example with cumsum operation for dataframe group by one column", function(){ - let data = { 'A': [ 'foo', 'bar', 'foo', 'bar', - 'foo', 'bar', 'foo', 'foo' ], - 'B': [ 'one', 'one', 'two', 'three', - 'two', 'two', 'one', 'three' ], - 'C': [ 1, 3, 2, 4, 5, 2, 6, 7 ], - 'D': [ 3, 2, 4, 1, 5, 6, 7, 8 ] }; - - let df = new DataFrame(data); - - - let grp = df.groupby([ "A", "B" ]); - let rslt = [ - [ 'foo', 'one', 1, 3 ], - [ 'foo', 'one', 7, 10 ], - [ 'foo', 'two', 2, 4 ], - [ 'foo', 'two', 7, 9 ], - [ 'foo', 'three', 7, 8 ], - [ 'bar', 'one', 3, 2 ], - [ 'bar', 'two', 2, 6 ], - [ 'bar', 'three', 4, 1 ] - ]; - assert.deepEqual(grp.col([ "C", "D" ]).cumsum().values, rslt); - - }); - it("should apply grouby operation to all column", function(){ - let data = { 'A': [ 'foo', 'bar', 'foo', 'bar', - 'foo', 'bar', 'foo', 'foo' ], - 'B': [ 'one', 'one', 'two', 'three', - 'two', 'two', 'one', 'three' ], - 'C': [ 1, 3, 2, 4, 5, 2, 6, 7 ], - 'D': [ 3, 2, 4, 1, 5, 6, 7, 8 ] }; - - let df = new DataFrame(data); - - let grp = df.groupby([ "A", "B" ]); - let rslt = [ - [ 'foo', 'one', 2, 2 ], - [ 'foo', 'two', 2, 2 ], - [ 'foo', 'three', 1, 1 ], - [ 'bar', 'one', 1, 1 ], - [ 'bar', 'two', 1, 1 ], - [ 'bar', 'three', 1, 1 ] - ]; - - assert.deepEqual(grp.count().values, rslt); - }); - it("should apply function to specific column", function () { - - let data = { 'A': [ 'foo', 'bar', 'foo', 'bar', - 'foo', 'bar', 'foo', 'foo' ], - 'B': [ 'one', 'one', 'two', 'three', - 'two', 'two', 'one', 'three' ], - 'C': [ 1, 3, 2, 4, 5, 2, 6, 7 ], - 'D': [ 3, 2, 4, 1, 5, 6, 7, 8 ] }; - let df = new DataFrame(data); - let group_df = df.groupby([ "A"]); - let rslt = [ - [ 'foo', 5, 3 ], - [ 'foo', 6, 4 ], - [ 'foo', 7, 7 ], - [ 'foo', 9, 8 ], - [ 'foo', 10, 9 ], - [ 'bar', 4, 5 ], - [ 'bar', 3, 6 ], - [ 'bar', 8, 4 ] - ]; - assert.deepEqual(group_df.col(['D', 'C']).apply((x) => x.add(2)).values, rslt); - }); - it("should apply function to group column", function () { - - let data = { 'A': [ 'foo', 'bar', 'foo', 'bar', - 'foo', 'bar', 'foo', 'foo' ], - 'B': [ 'one', 'one', 'two', 'three', - 'two', 'two', 'one', 'three' ], - 'C': [ 1, 3, 2, 4, 5, 2, 6, 7 ], - 'D': [ 3, 2, 4, 1, 5, 6, 7, 8 ] }; - let df = new DataFrame(data); - let group_df = df.groupby([ "A", "B"]); - let rslt = [ - [ 'foo', 'one', 2, 2 ], - [ 'foo', 'two', 2, 2 ], - [ 'foo', 'three', 1, 1 ], - [ 'bar', 'one', 1, 1 ], - [ 'bar', 'two', 1, 1 ], - [ 'bar', 'three', 1, 1 ] - ]; - assert.deepEqual(group_df.apply((x) => x.count()).values, rslt); - }); -}); diff --git a/danfojs-node/tests/core/series.js b/danfojs-node/tests/core/series.js deleted file mode 100644 index 2c8cfd0c..00000000 --- a/danfojs-node/tests/core/series.js +++ /dev/null @@ -1,1305 +0,0 @@ -import { assert, expect } from "chai"; -import { Series } from "../../src/core/series"; -import * as tf from '@tensorflow/tfjs-node'; - -describe("Series", function () { - describe("tensor", function () { - it("Returns the tensor object of a Series", function () { - let data = [1, 2, 3, 4, 5, 620, 30, 40, 39, 89, 78]; - let sf = new Series(data); - assert.deepEqual(sf.tensor.dtype, "int32"); - }); - it("Returns the float dtype of a tensor object", function () { - let data = [1.1, 2.2, 3, 4.1, 5, 620, 30.1, 40, 39, 89, 78]; - let sf = new Series(data); - assert.deepEqual(sf.tensor.dtype, "float32"); - }); - it("Compares a tensor returned from a Series to Tensorflow's tensor", function () { - let data = [1.1, 2.2, 3, 4.1, 5, 620, 30.1, 40, 39, 89, 78]; - let sf = new Series(data); - let tf_data = tf.tensor(data); - assert.deepEqual(sf.tensor.arraySync(), tf_data.arraySync()); - }); - }); - describe("tensor", function () { - it("Returns the dtype string Series", function () { - let data = ["b", "c", "d"]; - let sf = new Series(data); - assert.deepEqual(sf.dtype, "string"); - }); - it("Returns the dtype int Series", function () { - let data = [1, 2, 3, 4, 5]; - let sf = new Series(data); - assert.deepEqual(sf.dtype, "int32"); - }); - it("Returns the dtype int Series", function () { - let data = [1.1, 2.2, 3.3, 4.5, 5]; - let sf = new Series(data); - assert.deepEqual(sf.dtype, "float32"); - }); - }); - - describe("head", function () { - it("Gets the first n rows in a Series", function () { - let data = [1, 2, 3, 4, 5, 620, 30, 40, 39, 89, 78]; - let cols = ["A"]; - let sf = new Series(data, { columns: cols }); - assert.deepEqual(sf.head(2).values, [1, 2]); - }); - it("Return all rows in a Series if row specified is greater than values", function () { - let data = ["Boy", "Girl", "Man", "Woman", "Tall"]; - let cols = ["Items"]; - let sf = new Series(data, { columns: cols }); - assert.deepEqual(sf.head(10).values, data); - }); - it("Return all rows in a Series if row specified is less than 0", function () { - let data = [1, 2, 3, 4, 5, 620, 30, 40, 39, 89, 78]; - let sf = new Series(data); - assert.deepEqual(sf.head(-1).values, data); - }); - }); - - describe("tail", function () { - it("Prints the last n rows of a Series", function () { - let data = [1, 2, 3, 4, 5, 620, 30, 40, 39, 89, 78]; - let sf = new Series(data); - assert.deepEqual(sf.tail(2).values, [89, 78]); - }); - it("Return all rows in a Series if row specified is greater than values", function () { - let data = [1, 2, 3, 4, 5, 620, 30, 40, 39, 89, 78]; - let sf = new Series(data); - assert.deepEqual(sf.tail(15).values, data); - }); - it("Return all rows in a Series if row specified is less than 0", function () { - let data = ["Boy", "Girl", "Man", "Woman", "Tall"]; - let cols = ["Items"]; - let sf = new Series(data, { columns: cols }); - assert.deepEqual(sf.tail(-1).values, data); - }); - }); - - describe("sample", function () { - it("Samples n number of random elements from a DataFrame", async function () { - let data = [1, 2, 3, 4, 5, 620, 30, 40, 39, 89, 78]; - let sf = new Series(data); - assert.deepEqual((await sf.sample(7)).values.length, 7); - }); - it("Return all values if n of sample -1", async function () { - let data = [1, 2, 3, 4, 5, 620, 30, 40, 39, 89, 78]; - let sf = new Series(data); - assert.deepEqual((await sf.sample(-1)).values.length, data.length); - }); - it("Throw error if n is greater than lenght of Series", async function () { - let data = [1, 2, 3, 4, 5, 620, 30, 40, 39, 89, 78]; - let sf = new Series(data); - try { - await sf.sample(100); - } catch (e) { - expect(e).to.be.instanceOf(Error); - expect(e.message).to.eql('Sample size n cannot be bigger than size of dataset'); - } - }); - }); - - describe("add", function () { - it("Return Addition of series with another series", function () { - let data = [1, 2, 3, 4, 5, 6]; - let data2 = [30, 40, 39, 1, 2, 1]; - let sf = new Series(data); - let sf2 = new Series(data2); - assert.deepEqual(sf.add(sf2).values, [31, 42, 42, 5, 7, 7]); - }); - it("Return Addition of series with a single value (Broadcasting)", function () { - let data = [1, 2, 3, 4, 5]; - let sf = new Series(data); - assert.deepEqual(sf.add(1).values, [2, 3, 4, 5, 6]); - }); - it("Throws type error on addition of string type", function () { - let data = [1, 2, 3, 4]; - let data2 = ["A", "B", "C", "d"]; - let sf = new Series(data); - let sf2 = new Series(data2); - assert.throws( - () => { - sf.add(sf2); - }, - Error, - "Argument 'x' passed to 'cast' must be numeric tensor, but got string tensor" - ); - }); - // it("Throws length error if series lenght mixmatch", function () { - // let data = [1, 2, 3, 4] - // let data2 = [1, 2, 3, 4, 5, 6] - // let sf = new Series(data) - // let sf2 = new Series(data2) - // assert.throws(() => { sf.add(sf2) }, Error, "Incompatible shapes: [4] vs. [6]") - // }) - }); - - describe("sub", function () { - it("Return Subtraction of series with another series", function () { - let data1 = [30, 40, 39, 1, 2, 1]; - let data2 = [1, 2, 3, 4, 5, 6]; - let sf1 = new Series(data1); - let sf2 = new Series(data2); - assert.deepEqual(sf1.sub(sf2).values, [29, 38, 36, -3, -3, -5]); - }); - it("Return Subtraction of series with a single value (Broadcasting)", function () { - let data = [1, 2, 3, 4, 5]; - let sf = new Series(data); - assert.deepEqual(sf.sub(1).values, [0, 1, 2, 3, 4]); - }); - it("Throws type error on Subtraction of string type", function () { - let data = [1, 2, 3, 4]; - let data2 = ["A", "B", "C", "d"]; - let sf = new Series(data); - let sf2 = new Series(data2); - assert.throws( - () => { - sf.sub(sf2); - }, - Error, - "Argument 'x' passed to 'cast' must be numeric tensor, but got string tensor" - ); - }); - // it("Throws length error if series lenght mixmatch", function () { - // let data = [1, 2, 3, 4] - // let data2 = [1, 2, 3, 4, 5, 6] - // let sf = new Series(data) - // let sf2 = new Series(data2) - // assert.throws(() => { sf.sub(sf2) }, Error, "Incompatible shapes: [4] vs. [6]") - // }) - }); - - describe("mul", function () { - it("Return multiplication of series with another series", function () { - let data1 = [30, 40, 3, 5]; - let data2 = [1, 2, 3, 4]; - let sf1 = new Series(data1); - let sf2 = new Series(data2); - assert.deepEqual(sf1.mul(sf2).values, [30, 80, 9, 20]); - }); - it("Return multiplication of series with a single value (Broadcasting)", function () { - let data = [1, 2, 3, 4, 5]; - let sf = new Series(data); - assert.deepEqual(sf.mul(1).values, [1, 2, 3, 4, 5]); - }); - // it("Throws type error on multiplication of string type", function () { - // let data = [1, 2, 3, 4] - // let data2 = ["A", "B", "C", "d"] - // let sf = new Series(data) - // let sf2 = new Series(data2) - // assert.throws(() => { sf.mul(sf2) }, Error, "Argument 'x' passed to 'cast' must be numeric tensor, but got string tensor") - // }) - // it("Throws length error if series lenght mixmatch", function () { - // let data = [1, 2, 3, 4] - // let data2 = [1, 2, 3, 4, 5, 6] - // let sf = new Series(data) - // let sf2 = new Series(data2) - // assert.throws(() => { sf.mul(sf2) }, Error, "Operands could not be broadcast together with shapes 4 and 6") - // }) - }); - - describe("div", function () { - it("Return float division of series with another series", function () { - let data1 = [30, 40, 3, 5]; - let data2 = [1, 2, 3, 4]; - let sf1 = new Series(data1); - let sf2 = new Series(data2); - assert.deepEqual(sf1.div(sf2).values, [30, 20, 1, 1.25]); - }); - it("Return integer division of series with another series", function () { - let data1 = [30, 40, 3, 5]; - let data2 = [1, 2, 3, 4]; - let sf1 = new Series(data1); - let sf2 = new Series(data2); - assert.deepEqual(sf1.div(sf2, false).values, [30, 20, 1, 1]); - }); - it("Return division of series with a single value (Broadcasting)", function () { - let data = [10, 2, 3, 90]; - let sf = new Series(data); - assert.deepEqual(sf.div(2).values, [5, 1, 1.5, 45]); - }); - // it("Throws type error on division of string type", function () { - // let data = [1, 2, 3, 4] - // let data2 = ["A", "B", "C", "d"] - // let sf = new Series(data) - // let sf2 = new Series(data2) - // assert.throws(() => { sf.mul(sf2) }, Error, "Argument 'x' passed to 'cast' must be numeric tensor, but got string tensor") - // }) - // it("Throws length error if series lenght mixmatch", function () { - // let data = [1, 2, 3, 4] - // let data2 = [1, 2, 3, 4, 5, 6] - // let sf = new Series(data) - // let sf2 = new Series(data2) - // assert.throws(() => { sf.mul(sf2) }, Error, "Operands could not be broadcast together with shapes 4 and 6") - // }) - }); - - describe("pow", function () { - it("Return Exponetial power of series with another series", function () { - let data1 = [2, 3, 4, 5]; - let data2 = [1, 2, 3, 0]; - let sf1 = new Series(data1); - let sf2 = new Series(data2); - assert.deepEqual(sf1.pow(sf2).values, [2, 9, 64, 1]); - }); - it("Return Exponetial power of series with a single value (Broadcasting)", function () { - let data = [1, 2, 3, 4, 5]; - let sf = new Series(data); - assert.deepEqual(sf.pow(2).values, [1, 4, 9, 16, 25]); - }); - }); - - describe("mod", function () { - it("Return modulo of series with another float series", function () { - let data1 = [2, 30, 4, 5]; - let data2 = [1.1, 2.2, 3.3, 2.4]; - let sf1 = new Series(data1); - let sf2 = new Series(data2); - let expected = [ - 0.8999999761581421, - 1.3999993801116943, - 0.7000000476837158, - 0.19999980926513672 - ]; - assert.deepEqual(sf1.mod(sf2).values, expected); - }); - it("Return modulo of series with another int series", function () { - let data1 = [2, 30, 4, 5]; - let data2 = [1, 2, 3, 1]; - let sf1 = new Series(data1); - let sf2 = new Series(data2); - assert.deepEqual(sf1.mod(sf2).values, [0, 0, 1, 0]); - }); - it("Return modulo power of series with a single value (Broadcasting)", function () { - let data = [1, 2, 3, 4, 5]; - let sf = new Series(data); - assert.deepEqual(sf.mod(2).values, [1, 0, 1, 0, 1]); - }); - }); - - describe("mean", function () { - it("Computes the mean of elements in a int series", function () { - let data1 = [30, 40, 3, 5]; - let sf = new Series(data1); - assert.deepEqual(sf.mean(), 19.5); - }); - it("Computes the mean of elements in a float series", function () { - let data1 = [30.1, 40.2, 3.1, 5.1]; - let sf = new Series(data1); - assert.deepEqual(sf.mean(), 19.625); - }); - it("Computes the mean of elements in a float series with NaN", function () { - let data1 = [30.1, 40.2, 3.1, 5.1, NaN]; - let sf = new Series(data1); - assert.deepEqual(sf.mean(), 19.625); - }); - it("Throws error if dtype is string", function () { - let data1 = ["boy", "girl", "Man"]; - let sf = new Series(data1); - assert.throws( - () => { - sf.mean(); - }, - Error, - "dtype error: String data type does not support mean operation" - ); - }); - }); - - describe("median", function () { - it("Computes the median value of elements across int Series", function () { - let data1 = [30, 40, 3, 5]; - let sf = new Series(data1); - assert.deepEqual(sf.median(), 17.5); - }); - it("Computes the median value of elements across float Series", function () { - let data1 = [30.1, 40.2, 3.1, 5.1, NaN]; - let sf = new Series(data1); - assert.deepEqual(sf.median(), 17.6); - }); - }); - - describe("sum", function () { - it("Sum values of a Int Series", function () { - let data1 = [30, 40, 3, 5, 5, 5, 5, 5, 3, 3, 3, 21, 3]; - let sf = new Series(data1); - assert.deepEqual(sf.sum(), 131); - }); - it("Sum values of a Float Series", function () { - let data1 = [30.1, 3.1, 40.2, 3.1, 5.1]; - let sf = new Series(data1); - assert.deepEqual(sf.sum(), 81.6); - }); - it("Sum values of a bool Series", function () { - let data1 = [true, true, false, false, false]; - let sf = new Series(data1); - assert.deepEqual(sf.sum(), 2); - }); - it("Sum values a Series with missing values", function () { - let data1 = [11, NaN, 2, 2]; - let sf = new Series(data1); - assert.deepEqual(sf.sum(), 15); - }); - }); - - describe("mode", function () { - it("Computes the multi-modal values of a Series", function () { - let data1 = [30, 40, 3, 5, 5, 5, 5, 5, 3, 3, 3, 21, 3]; - let sf = new Series(data1); - assert.deepEqual(sf.mode(), [5, 3]); - }); - it("Computes the modal value of a Series", function () { - let data1 = [30.1, 3.1, 40.2, 3.1, 5.1]; - let sf = new Series(data1); - assert.deepEqual(sf.mode(), [3.1]); - }); - }); - - describe("min", function () { - it("Returns the single smallest elementin a Series", function () { - let data = [30, 40, 3, 5]; - let sf = new Series(data); - assert.deepEqual(sf.min(), 3); - }); - it("Computes the minimum of elements across an float Series", function () { - let data1 = [30.1, 40.2, 3.12, 5.1]; - let sf = new Series(data1, { dtypes: ["float32"] }); - assert.deepEqual(Number(sf.min().toFixed(2)), 3.12); - }); - }); - - describe("max", function () { - it("Computes the maximum of elements across dimensions of a Series", function () { - let data1 = [30, 40, 3, 5]; - let sf = new Series(data1); - assert.deepEqual(sf.max(), 40); - }); - it("Return sum of float values in a series", function () { - let data1 = [30.1, 40.21, 3.1, 5.1]; - let sf = new Series(data1); - assert.deepEqual(Number(sf.max().toFixed(2)), 40.21); - }); - it("Throws error on addition of string Series", function () { - let data1 = ["boy", "gitl", "woman", "man"]; - let sf = new Series(data1); - assert.throws( - () => { - sf.max(); - }, - Error, - "dtype error: String data type does not support max operation" - ); - }); - }); - - describe("std", function () { - it("Computes the standard of elements in a int series", function () { - let data1 = [30, 40, 3, 5]; - let sf = new Series(data1); - assert.deepEqual(sf.std(), 18.375708603116962); - }); - it("Computes the standard deviation of elements in a float series", function () { - let data1 = [30.1, 40.2, 3.1, 5.1]; - let sf = new Series(data1); - assert.deepEqual(sf.std(), 18.412925713566906); - }); - it("Computes the standard deviation of elements in a float series with missing values", function () { - let data1 = [30, 40, 3, 5, undefined]; - let sf = new Series(data1); - assert.deepEqual(sf.std(), 18.375708603116962); - }); - }); - - describe("var", function () { - it("Computes the variance of elements in a int series", function () { - let data1 = [30, 40, 3, 5]; - let sf = new Series(data1); - assert.deepEqual(sf.var(), 337.6666666666667); - }); - it("Computes the variance of elements in a float series", function () { - let data1 = [30.1, 40.2, 3.1, 5.1]; - let sf = new Series(data1); - assert.deepEqual(sf.var(), 339.03583333333336); - }); - it("Computes the variance of elements in a int series with missing values", function () { - let data1 = [30, undefined, 40, 3, 5]; - let sf = new Series(data1); - assert.deepEqual(sf.var(), 337.6666666666667); - }); - }); - - describe("describe", function () { - it("Computes the descriptive statistics on an int Series", function () { - let data1 = [10, 45, 56, 25, 23, 20, 10]; - let sf = new Series(data1); - assert.deepEqual(sf.describe().round().values, [ - 7, - 27, - 17, - 10, - 23, - 56, - 302 - ]); - }); - it("Computes the descriptive statistics on a float Series", function () { - let data1 = [30.1, 40.2, 3.1, 5.1]; - let sf = new Series(data1); - assert.deepEqual(sf.describe().round().values, [ - 4, - 20, - 18, - 3, - 18, - 40, - 339 - ]); - }); - it("Computes the descriptive statistics on a float Series", function () { - let data1 = [30.1, 40.2, 3.1, 5.1]; - let sf = new Series(data1); - assert.deepEqual(sf.describe().index, [ - "count", - "mean", - "std", - "min", - "median", - "max", - "variance" - ]); - }); - }); - - describe("maximum", function () { - it("Returns the max of a and b (a > b ? a : b) element-wise. Supports broadcasting.", function () { - let data1 = [30, 40, 3, 5]; - let data2 = [10, 41, 2, 0]; - let sf1 = new Series(data1); - let sf2 = new Series(data2); - assert.deepEqual(sf1.maximum(sf2).values, [30, 41, 3, 5]); - }); - it("Throws error on checking maximum of incompatible Series", function () { - let data1 = [30, 40, 3, 5]; - let data2 = [10, 41, 2]; - let sf1 = new Series(data1); - let sf2 = new Series(data2); - assert.throws( - () => { - sf1.maximum(sf2); - }, - Error, - "Operands could not be broadcast together with shapes 4 and 3" - ); - }); - }); - - describe("minimum", function () { - it("Returns the min of a and b (a < b ? a : b) element-wise. Supports broadcasting.", function () { - let data1 = [30, 40, 3, 5]; - let data2 = [10, 41, 2, 0]; - let sf1 = new Series(data1); - let sf2 = new Series(data2); - assert.deepEqual(sf1.minimum(sf2).values, [10, 40, 2, 0]); - }); - // it("Return sum of float values in a series", function () { - // let data1 = [30.1, 40.9, 3, 5] - // let data2 = [10.2, 41, 2, 0] - // let sf1 = new Series(data1) - // let sf2 = new Series(data2) - // assert.deepEqual(sf1.minimum(sf2).values, [30, 41, 3, 5]) - - // }) - }); - - describe("count", function () { - it("Returns the count of non NaN values in a string Series", function () { - let data = ["boy", "gitl", "woman", NaN]; - let sf = new Series(data); - assert.deepEqual(sf.count(), 3); - }); - it("Returns the count of non NaN values in a string Series", function () { - let data = ["boy", "gitl", "woman", "Man"]; - let sf = new Series(data); - assert.deepEqual(sf.count(), 4); - }); - it("Returns the count of non NaN values in a int Series", function () { - let data = [20, 30, NaN, 2, NaN, 30, 21]; - let sf = new Series(data); - assert.deepEqual(sf.count(), 5); - }); - it("Returns the count of non NaN values in a float Series", function () { - let data = [20.1, 30.4, NaN, 2.1, NaN, 30.0, 21.3]; - let sf = new Series(data); - assert.deepEqual(sf.count(), 5); - }); - }); - - describe("round", function () { - it("Rounds elements in a Series to nearest whole number", function () { - let data1 = [30.21091, 40.190901, 3.564, 5.0212]; - let sf = new Series(data1); - assert.deepEqual(sf.round().values, [30, 40, 4, 5]); - }); - it("Rounds elements in a Series to 1dp", function () { - let data1 = [30.21091, 40.190901, 3.564, 5.0212]; - let sf = new Series(data1); - assert.deepEqual(sf.round(1).values, [30.2, 40.2, 3.6, 5.0]); - }); - it("Rounds elements in a Series to 2dp", function () { - let data1 = [30.2191, 40.190901, 3.564, 5.0212]; - let sf = new Series(data1); - assert.deepEqual(sf.round(2).values, [30.22, 40.19, 3.56, 5.02]); - }); - }); - - describe("isna", function () { - it("Return a boolean same-sized object indicating if string Series contain NaN", function () { - let data1 = [NaN, undefined, "girl", "Man"]; - let sf = new Series(data1); - assert.deepEqual(sf.isna().values, [true, true, false, false]); - }); - it("Return a boolean same-sized object indicating if float Series values are NaN", function () { - let data1 = [30.21091, NaN, 3.564, undefined]; - let sf = new Series(data1); - assert.deepEqual(sf.isna().values, [false, true, false, true]); - }); - it("Return a boolean same-sized object indicating if int Series values are NaN", function () { - let data1 = [30, 40, 3, 5, undefined, undefined]; - let sf = new Series(data1); - assert.deepEqual(sf.isna().values, [ - false, - false, - false, - false, - true, - true - ]); - }); - }); - - describe("sort_values", function () { - it("Sort values in a Series in ascending order (not inplace)", function () { - let sf = new Series([20, 30, 1, 2, 4, 57, 89, 0, 4]); - let result = [0, 1, 2, 4, 4, 20, 30, 57, 89]; - let sorted_sf = sf.sort_values(); - assert.deepEqual(sorted_sf.values, result); - }); - it("confirms that sort_values in ascending order does not happen inplace", function () { - let sf = new Series([20, 30, 1, 2, 4, 57, 89, 0, 4]); - let result = [0, 1, 2, 4, 4, 20, 30, 57, 89]; - let expected_index = [7, 2, 3, 8, 4, 0, 1, 5, 6]; - sf.sort_values({ inplace: true }); - assert.deepEqual(sf.values, result); - assert.deepEqual(sf.index, expected_index); - }); - it("Sort values in a Series in Descending order", function () { - let sf = new Series([20, 30, 1, 2, 4, 57, 89, 0, 4]); - let result = [89, 57, 30, 20, 4, 4, 2, 1, 0]; - let sorted_sf = sf.sort_values({ ascending: false }); - assert.deepEqual(sorted_sf.values, result); - }); - it("confirms that sort_values in descending order happens inplace", function () { - let sf = new Series([20, 30, 1, 2, 4, 57, 89, 0, 4]); - let result = [89, 57, 30, 20, 4, 4, 2, 1, 0]; - sf.sort_values({ ascending: false, inplace: true }); - assert.deepEqual(sf.values, result); - }); - it("Confirms that series index is sorted in ascending order (not in inplace)", function () { - let sf = new Series([20, 30, 1, 2, 4, 57, 89, 0, 4]); - let result = [7, 2, 3, 8, 4, 0, 1, 5, 6]; - let sorted_sf = sf.sort_values(); - assert.deepEqual(sorted_sf.index, result); - }); - it("Confirms that series index is sorted in descending order (not in inplace)", function () { - let sf = new Series([20, 30, 1, 2, 4, 57, 89, 0, 4]); - let result = [6, 5, 1, 0, 4, 8, 3, 2, 7]; - let sorted_sf = sf.sort_values({ ascending: false }); - assert.deepEqual(sorted_sf.index, result); - }); - it("Sort string values in a Series", function () { - let sf = new Series(["boy", "zebra", "girl", "man"]); - let result = ["boy", "girl", "man", "zebra"]; - let sorted_sf = sf.sort_values({ ascending: false }); - assert.deepEqual(sorted_sf.values, result); - }); - // it("Throws error on sorting of string", function () { - // let sf = new Series(["boy", "man", "girl"]) - // assert.throws(() => { sf.sort_values() }, Error, "Dtype Error: cannot sort Series of type string") - // }) - }); - - describe("copy", function () { - it("Checks if copied values are the same as the first one", function () { - let sf = new Series([30.21091, 40.190901, 3.564, 5.0212]); - let sf_copy = sf.copy(); - assert.deepEqual(sf.values, sf_copy.values); - }); - it("Checks if copied index are the same", function () { - let sf = new Series([30.21091, 40.190901, 3.564, 5.0212]); - sf = sf.set_index({ index: ["a", "b", "c", "d"] }); - let sf_copy = sf.copy(); - assert.deepEqual(sf.index, sf_copy.index); - }); - // it("Checks if copied dtype is the same", function () { - // let sf = new Series([30.21091, 40.190901, 3.564, 5.0212]) - // sf.round() - // sf.astype(['int32']) - // let sf_copy = sf.copy() - // assert.deepEqual(sf.dtypes[0], sf_copy.dtypes[0]) - // assert.deepEqual(sf.values, sf_copy.values) - - // }) - }); - - describe("reset_index", function () { - it("resets the index of a Series", function () { - let data = [ - { alpha: "A", count: 1 }, - { alpha: "B", count: 2 }, - { alpha: "C", count: 3 } - ]; - let df = new Series(data); - let df_new = df.set_index({ index: ["one", "two", "three"] }); - let df_reset = df_new.reset_index(); - assert.deepEqual(df_reset.index, [0, 1, 2]); - }); - it("Reset the index of a Series created from an Array", function () { - let data = [1, 2, 3, 4, 5, 6]; - let df = new Series(data); - df.set_index({ - index: ["one", "two", "three", "four", "five", "six"], - inplace: true - }); - let df_new = df.reset_index(); - assert.deepEqual(df_new.index, [0, 1, 2, 3, 4, 5]); - }); - it("checks that the original series changed after reseting new index inplace", function () { - let data = [ - { alpha: "A", count: 1 }, - { alpha: "B", count: 2 }, - { alpha: "C", count: 3 } - ]; - let df = new Series(data); - df.reset_index({ inplace: true }); - assert.deepEqual(df.index, [0, 1, 2]); - }); - }); - - describe("set_index", function () { - it("sets the index of an Series", function () { - let data = [ - { alpha: "A", count: 1 }, - { alpha: "B", count: 2 }, - { alpha: "C", count: 3 } - ]; - let df = new Series(data); - let df_new = df.set_index({ index: ["one", "two", "three"] }); - assert.deepEqual(df_new.index, ["one", "two", "three"]); - assert.notDeepEqual(df.index, df_new.index); - }); - it("checks that the original series is not modified after setting new index not-inplace", function () { - let data = [ - { alpha: "A", count: 1 }, - { alpha: "B", count: 2 }, - { alpha: "C", count: 3 } - ]; - let df = new Series(data); - let df_new = df.set_index({ index: ["one", "two", "three"] }); - assert.notDeepEqual(df.index, df_new.index); - }); - it("sets the index of an Series inplace", function () { - let data = [12, 2, 20, 50]; - let df = new Series(data); - df.set_index({ index: ["one", "two", "three", "four"], inplace: true }); - assert.deepEqual(df.index, ["one", "two", "three", "four"]); - }); - }); - - describe("Map", function () { - it("map series element to object keys", function () { - let sf = new Series([1, 2, 3, 4]); - let map = { 1: "ok", 2: "okie", 3: "frit", 4: "gop" }; - - let rslt = ["ok", "okie", "frit", "gop"]; - - assert.deepEqual(sf.map(map).values, rslt); - }); - - it("map series element to a function statement", function () { - let sf = new Series([1, 2, 3, 4]); - let func_map = (x) => { - return x + 1; - }; - - let rslt = [2, 3, 4, 5]; - - assert.deepEqual(sf.map(func_map).values, rslt); - }); - }); - - describe("Apply", function () { - it("apply a function to a series element", function () { - let sf = new Series([1, 2, 3, 4, 5, 6, 7, 8]); - - let apply_func = (x) => { - return x + x; - }; - - let rslt = [2, 4, 6, 8, 10, 12, 14, 16]; - assert.deepEqual(sf.apply(apply_func).values, rslt); - }); - }); - - describe("unique", function () { - it("returns the unique values in a Series of type int", function () { - let sf = new Series([1, 2, 3, 4, 5, 6, 7, 8, 1, 1, 22, 8, 5, 5, 5]); - let expected = [1, 2, 3, 4, 5, 6, 7, 8, 22]; - assert.deepEqual(sf.unique().values, expected); - }); - it("returns the unique values in a Series of type string", function () { - let sf = new Series(["a", "a", "b", "c", "c", "d", "e", "d", "d", "e"]); - let expected = ["a", "b", "c", "d", "e"]; - assert.deepEqual(sf.unique().values, expected); - }); - it("returns the unique values in a Series of type string", function () { - let sf = new Series(["a", "a", "b", "c", "c", "d", "e", "d", "d", "e"]); - let expected = ["a", "b", "c", "d", "e"]; - assert.deepEqual(sf.unique().values, expected); - }); - it("returns the nunique values in a Series of type string", function () { - let sf = new Series(["a", "a", "b", "c", "c", "d", "e", "d", "d", "e"]); - let expected = 5; - assert.deepEqual(sf.nunique(), expected); - }); - }); - - describe("value_counts", function () { - it("returns the unique values and their counts in a Series of type int", function () { - let sf = new Series([1, 2, 3, 4, 5, 6, 7, 8, 1, 1, 22, 8, 5, 5, 5]); - let expected_index = [1, 2, 3, 4, 5, 6, 7, 8, 22]; - let expected_vals = [3, 1, 1, 1, 4, 1, 1, 2, 1]; - assert.deepEqual(sf.value_counts().values, expected_vals); - assert.deepEqual(sf.value_counts().index, expected_index); - }); - it("returns the unique values and their counts in a Series of type string", function () { - let sf = new Series(["a", "a", "b", "c", "c", "d", "e", "d", "d", "e"]); - let expected_vals = [2, 1, 2, 3, 2]; - let expected_index = ["a", "b", "c", "d", "e"]; - assert.deepEqual(sf.value_counts().values, expected_vals); - assert.deepEqual(sf.value_counts().index, expected_index); - }); - }); - - describe("abs", function () { - it("Returns the absolute values in Series", function () { - let data1 = [-10, 45, 56, -25, 23, -20, 10]; - let sf = new Series(data1); - assert.deepEqual(sf.abs().values, [10, 45, 56, 25, 23, 20, 10]); - }); - it("Computes the descriptive statistics on a float Series", function () { - let data1 = [-30.1, -40.2, -3.1, -5.1]; - let sf = new Series(data1); - assert.deepEqual(sf.abs().values, [30.1, 40.2, 3.1, 5.1]); - }); - }); - - describe("fillna", function () { - it("replace all NaN value with specified value", function () { - let data = [NaN, 1, 2, 33, 4, NaN, 5, 6, 7, 8]; - let sf = new Series(data); - let sf_val = [-999, 1, 2, 33, 4, -999, 5, 6, 7, 8]; - sf.fillna({ value: -999, inplace: true }); - assert.deepEqual(sf.values, sf_val); - }); - it("replace all NaN value in string Series with specified value", function () { - let data = [NaN, "boy", NaN, "hey", "Man", undefined]; - let sf = new Series(data); - let sf_val = ["filled", "boy", "filled", "hey", "Man", "filled"]; - let sf_fill = sf.fillna({ value: "filled" }); - assert.deepEqual(sf_fill.values, sf_val); - }); - }); - - describe("cumsum", function () { - it("Return cumulative sum over a Series", function () { - let data1 = [10, 45, 56, 25, 23, 20, 10]; - let sf = new Series(data1); - assert.deepEqual(sf.cumsum().values, [10, 55, 111, 136, 159, 179, 189]); - }); - }); - - describe("cummax", function () { - it("Return cumulative maximum over a Series", function () { - let data1 = [10, 45, 56, 25, 23, 20, 10]; - let sf = new Series(data1); - assert.deepEqual(sf.cummax().values, [10, 45, 56, 56, 56, 56, 56]); - }); - }); - - describe("cummin", function () { - it("Return cumulative minimum over a Series", function () { - let data1 = [10, 45, 56, 25, 23, 20, 10]; - let sf = new Series(data1); - assert.deepEqual(sf.cummin().values, [10, 10, 10, 10, 10, 10, 10]); - }); - }); - - describe("cumprod", function () { - it("Return cumulative product over a Series", function () { - let data1 = [1, 2, 10, 3, 12, 14, 1]; - let sf = new Series(data1); - let rslt = [1, 2, 20, 60, 720, 10080, 10080]; - assert.deepEqual(sf.cumprod().values, rslt); - }); - }); - - describe("lt", function () { - it("Return Less than of series and other series (element-wise)", function () { - let data1 = [10, 45, 56, 25, 23, 20, 10]; - let data2 = [100, 450, 590, 5, 25, 2, 0]; - - let sf = new Series(data1); - let sf2 = new Series(data2); - let expected = [true, true, true, false, true, false, false]; - assert.deepEqual(sf.lt(sf2).values, expected); - }); - - it("Return Less than of series scalar (element-wise)", function () { - let data1 = [10, 45, 56, 25, 23, 20, 10]; - let sf = new Series(data1); - let expected = [true, false, false, true, true, true, true]; - assert.deepEqual(sf.lt(30).values, expected); - }); - }); - - describe("gt", function () { - it("Return Greater than of series and other series (element-wise)", function () { - let data1 = [10, 45, 56, 25, 23, 20, 10]; - let data2 = [100, 450, 590, 5, 25, 2, 0]; - - let sf = new Series(data1); - let sf2 = new Series(data2); - let expected = [false, false, false, true, false, true, true]; - assert.deepEqual(sf.gt(sf2).values, expected); - }); - - it("Return Greater than of series scalar (element-wise)", function () { - let data1 = [10, 45, 56, 25, 23, 20, 10]; - let sf = new Series(data1); - let expected = [false, true, true, false, false, false, false]; - assert.deepEqual(sf.gt(30).values, expected); - }); - }); - - describe("le", function () { - it("Return Less than or Equal to of series and other series (element-wise)", function () { - let data1 = [10, 45, 56, 25, 23, 20, 10]; - let data2 = [100, 450, 590, 5, 25, 2, 0]; - - let sf = new Series(data1); - let sf2 = new Series(data2); - let expected = [true, true, true, false, true, false, false]; - assert.deepEqual(sf.le(sf2).values, expected); - }); - - it("Return Less than or Equal to of series scalar (element-wise)", function () { - let data1 = [10, 45, 56, 25, 23, 20, 10]; - let sf = new Series(data1); - let expected = [true, false, false, true, true, true, true]; - assert.deepEqual(sf.le(30).values, expected); - }); - }); - - describe("ge", function () { - it("Return Greater than or Equal to of series and other series (element-wise)", function () { - let data1 = [10, 45, 56, 25, 23, 20, 10]; - let data2 = [100, 450, 56, 5, 25, 20, 0]; - - let sf = new Series(data1); - let sf2 = new Series(data2); - let expected = [false, false, true, true, false, true, true]; - assert.deepEqual(sf.ge(sf2).values, expected); - }); - - it("Return Greater than or Equal to of series scalar (element-wise)", function () { - let data1 = [30, 45, 56, 25, 23, 20, 10]; - let sf = new Series(data1); - let expected = [true, true, true, false, false, false, false]; - assert.deepEqual(sf.ge(30).values, expected); - }); - }); - - describe("ne", function () { - it("Return Not Equal to of series and other series (element-wise)", function () { - let data1 = [10, 45, 56, 25, 23, 20, 10]; - let data2 = [10, 450, 56, 5, 25, 2, 0]; - - let sf = new Series(data1); - let sf2 = new Series(data2); - let expected = [false, true, false, true, true, true, true]; - assert.deepEqual(sf.ne(sf2).values, expected); - }); - - it("Return Not Equal to of series scalar (element-wise)", function () { - let data1 = [10, 30, 56, 30, 23, 20, 10]; - let sf = new Series(data1); - let expected = [true, false, true, false, true, true, true]; - assert.deepEqual(sf.ne(30).values, expected); - }); - }); - - describe("eq", function () { - it("Return Equal to of series and other series (element-wise)", function () { - let data1 = [10, 45, 56, 25, 23, 20, 10]; - let data2 = [100, 450, 590, 25, 25, 2, 0]; - - let sf = new Series(data1); - let sf2 = new Series(data2); - let expected = [false, false, false, true, false, false, false]; - assert.deepEqual(sf.eq(sf2).values, expected); - }); - - it("Return Equal to of series scalar (element-wise)", function () { - let data1 = [10, 45, 56, 25, 23, 20, 30]; - let sf = new Series(data1); - let expected = [false, false, false, false, false, false, true]; - assert.deepEqual(sf.eq(30).values, expected); - }); - }); - - describe("replace", function () { - it("Replace values given in replace param with value", function () { - let data1 = [10, 45, 56, 25, 23, 20, 10]; - let sf = new Series(data1); - let expected = [-50, 45, 56, 25, 23, 20, -50]; - let df_rep = sf.replace({ replace: 10, with: -50 }); - assert.deepEqual(df_rep.values, expected); - }); - - it("Replace values given in replace param with value (String type)", function () { - let data1 = ["A", "A", "A", "B", "B", "C", "C", "D"]; - let sf = new Series(data1); - let expected = ["boy", "boy", "boy", "B", "B", "C", "C", "D"]; - sf.replace({ replace: "A", with: "boy", inplace: true }); - assert.deepEqual(sf.values, expected); - }); - it("Throw error on wrong param passed", function () { - let data1 = ["A", "A", "A", "B", "B", "C", "C", "D"]; - let sf = new Series(data1); - let expected = `Params Error: A specified parameter is not supported. Your params must be any of the following [replace,with,inplace]`; - assert.throws( - () => { - sf.replace({ replce: "A", with: "boy", inplace: true }); - }, - Error, - expected - ); - }); - }); - - describe("drop_duplicates", function () { - it("Return Series with duplicate values removed (Default, first values kept)", function () { - let data1 = [10, 45, 56, 10, 23, 20, 10, 10]; - let sf = new Series(data1); - let expected = [10, 45, 56, 23, 20]; - let expected_index = [0, 1, 2, 4, 5]; - let df_drop = sf.drop_duplicates(); - assert.deepEqual(df_drop.values, expected); - assert.deepEqual(df_drop.index, expected_index); - }); - - it("Return Series with duplicate values removed (last values kept)", function () { - let data1 = [10, 45, 56, 10, 23, 20, 10, 10]; - let sf = new Series(data1); - let expected = [45, 56, 23, 20, 10]; - let expected_index = [1, 2, 4, 5, 7]; - let df_drop = sf.drop_duplicates({ keep: "last" }); - assert.deepEqual(df_drop.values, expected); - assert.deepEqual(df_drop.index, expected_index); - }); - - it("Return Series with duplicate values removed (String)", function () { - let data1 = ["A", "A", "A", "B", "B", "C", "C", "D"]; - let sf = new Series(data1); - let expected = ["A", "B", "C", "D"]; - let expected_index = [0, 3, 5, 7]; - sf.drop_duplicates({ inplace: true }); - assert.deepEqual(sf.values, expected); - assert.deepEqual(sf.index, expected_index); - }); - }); - - describe("dropna", function () { - it("Return a new Series with missing values removed (Int)", function () { - let data1 = [10, 45, undefined, 10, 23, 20, undefined, 10]; - let sf = new Series(data1); - let expected = [10, 45, 10, 23, 20, 10]; - let expected_index = [0, 1, 3, 4, 5, 7]; - let sf_drop = sf.dropna(); - assert.deepEqual(sf_drop.values, expected); - assert.deepEqual(sf_drop.index, expected_index); - }); - - it("Return a new Series with missing values removed (String)", function () { - let data1 = ["A", NaN, "A", "B", "B", NaN, "C", undefined]; - let sf = new Series(data1); - let expected = ["A", "A", "B", "B", "C"]; - let expected_index = [0, 2, 3, 4, 6]; - - sf.dropna({ inplace: true }); - assert.deepEqual(sf.values, expected); - assert.deepEqual(sf.index, expected_index); - }); - }); - - describe("argsort", function () { - it("Return the integer indices that would sort the Series values", function () { - let data1 = [10, 45, 20, 10, 23, 20, 30, 11]; - let sf = new Series(data1); - let expected = [3, 0, 7, 5, 2, 4, 6, 1]; - let sf_sort = sf.argsort(); - assert.deepEqual(sf_sort.values, expected); - }); - - it("Return the integer indices that would sort the Series values", function () { - let data1 = [10.22, 4.5, 2.0, 10, 23.23, 20.1, 30, 11]; - let sf = new Series(data1); - let expected = [6, 4, 5, 7, 0, 3, 1, 2]; - let sf_sort = sf.argsort(false); - assert.deepEqual(sf_sort.values, expected); - }); - }); - - describe("argmax", function () { - it("Return int position of the largest value in the Series.", function () { - let data1 = [10, 45, 20, 10, 23, 20, 30, 11]; - let sf = new Series(data1); - let expected = 1; - let argmax = sf.argmax(); - assert.deepEqual(argmax, expected); - }); - - it("Return int position of the largest value in the Float Series.", function () { - let data1 = [10.22, 4.5, 2.0, 10, 23.23, 20.1, 30, 11]; - let sf = new Series(data1); - let expected = 6; - let argmax = sf.argmax(); - assert.deepEqual(argmax, expected); - }); - }); - - describe("argmin", function () { - it("Return int position of the smallest value in the Series", function () { - let data1 = [10, 45, 20, 122, 23, 20, 30, 11]; - let sf = new Series(data1); - let expected = 0; - let argmin = sf.argmin(); - assert.deepEqual(argmin, expected); - }); - - it("Return int position of the smallest value in a Float Series", function () { - let data1 = [10.22, 4.5, 2.0, 10, 23.23, 20.1, 30, 11]; - let sf = new Series(data1); - let expected = 2; - let argmin = sf.argmin(); - assert.deepEqual(argmin, expected); - }); - }); - - describe("Str", function () { - it("Converts all characters to lowercase.", function () { - let data = ["lower", "CAPITALS", "this is a sentence", "SwApCaSe"]; - let res = ["lower", "capitals", "this is a sentence", "swapcase"]; - let sf = new Series(data); - assert.deepEqual(sf.str.toLowerCase().values, res); - }); - it("Converts all characters to uppercase.", function () { - let data = ["lower", "CAPITALS", "this is a sentence", "SwApCaSe"]; - let res = ["LOWER", "CAPITALS", "THIS IS A SENTENCE", "SWAPCASE"]; - let sf = new Series(data); - assert.deepEqual(sf.str.toUpperCase().values, res); - }); - it("Converts all characters to capital case.", function () { - let data = ["lower", "CAPITALS", "this is a sentence", "SwApCaSe"]; - let res = ["Lower", "Capitals", "This is a sentence", "Swapcase"]; - let sf = new Series(data); - assert.deepEqual(sf.str.capitalize().values, res); - }); - - it("Returns the character at the specified index (position)", function () { - let data = ["lower", "CAPITALS", "this is a sentence", "SwApCaSe"]; - let res = ["w", "P", "i", "A"]; - let sf = new Series(data); - assert.deepEqual(sf.str.charAt(2).values, res); - }); - - it("Throws error on concat of numeric series", function () { - let data = [1, 2, 3, 4, 5, 6]; - let sf = new Series(data); - assert.throws( - () => { - sf.str.concat("20", 1); - }, - Error, - "Cannot call accessor str on non-string type" - ); - - }); - }); - - describe("dt", function () { - it("check month generated", function () { - let data = ["02Sep2019", "03Dec2019", "04Jan2019"]; - let sf = new Series(data); - let new_data = [8, 11, 0]; - assert.deepEqual(sf.dt.month().values, new_data); - }); - - it("check month Name generated", function () { - let data = ["06-30-02019", "07-29-2019", "08-28-2019"]; - let sf = new Series(data); - let new_data = ["Jun", "Jul", "Aug"]; - assert.deepEqual(sf.dt.month_name().values, new_data); - }); - - it("check days of the weeks generated", function () { - let data = ["06-30-02019", "07-29-2019", "08-28-2019"]; - let sf = new Series(data); - let new_data = ["Sun", "Mon", "Wed"]; - assert.deepEqual(sf.dt.weekdays().values, new_data); - }); - - it("check day of the month generated", function () { - let data = ["06-30-02019", "07-29-2019", "08-28-2019"]; - let sf = new Series(data); - let new_data = [30, 29, 28]; - assert.deepEqual(sf.dt.monthday().values, new_data); - }); - }); - - describe("astype", function () { - it("set type of float column to int", function () { - let data = [-20.1, 30, 47.3, -20]; - let ndframe = new Series(data); - let df = ndframe.astype("int32"); - - assert.deepEqual(df.dtypes, "int32"); - assert.deepEqual(df.values, [-20, 30, 47, -20]); - }); - it("set type of int column to float", function () { - let data = [34, -4, 5, 6]; - let ndframe = new Series(data); - let df = ndframe.astype("float32"); - assert.deepEqual(df.dtypes, "float32"); - assert.deepEqual(df.values, [34, -4, 5, 6]); - }); - it("set type of string column to int", function () { - let data = ["20.1", "21", "23.4", "50.78"]; - let ndframe = new Series(data); - let df = ndframe.astype("int32"); - - assert.deepEqual(df.dtypes, "int32"); - assert.deepEqual(df.values, [20, 21, 23, 51]); - }); - it("set type of string column to float", function () { - let data = ["20.1", "21", "23.4", "50.78"]; - let ndframe = new Series(data); - let df = ndframe.astype("float32"); - - assert.deepEqual(df.dtypes, "float32"); - assert.deepEqual(df.values, [20.1, 21, 23.4, 50.78]); - }); - }); - - describe("iloc", function () { - it("indexing by list of index", function () { - let data = [1, 2, 3, 4, "a", "b", "c"]; - let sf = new Series(data); - - let expected_val = [2, "a", 3, 4, "b"]; - - assert.deepEqual(sf.iloc([1, 4, 2, 3, 5]).values, expected_val); - }); - it("indexing by slicing", function () { - let data = [1, 2, 3, 4, "a", "b", "c"]; - let sf = new Series(data); - - let expected_val = [2, 3, 4]; - - assert.deepEqual(sf.iloc(["1:4"]).values, expected_val); - }); - it("indexing by slicing format ':5' works", function () { - let data = [1, 2, 3, 4, "a", "b", "c"]; - let sf = new Series(data); - let expected_val = [1, 2, 3, 4, 'a']; - assert.deepEqual(sf.iloc([":5"]).values, expected_val); - }); - it("indexing by slicing format '2:` works", function () { - let data = [1, 2, 3, 4, "a", "b", "c"]; - let sf = new Series(data); - let expected_val = [3, 4, "a", "b", "c"]; - assert.deepEqual(sf.iloc(["2:"]).values, expected_val); - }); - }); - - describe("append", function () { - it("Add a new single value to the end of a Series", function () { - let data = [1, 2, 3, 4, "a", "b", "c"]; - let sf = new Series(data); - let expected_val = [1, 2, 3, 4, "a", "b", "c", "d"]; - sf.append("d", true); - assert.deepEqual(sf.values, expected_val); - }); - it("Add a new array of values to the end of a Series", function () { - let data = [1, 2, 3, 4]; - let to_add = ["a", "b", "c"]; - let sf = new Series(data); - let expected_val = [1, 2, 3, 4, "a", "b", "c"]; - sf.append(to_add, true); - assert.deepEqual(sf.values, expected_val); - }); - it("Add a Series to the end of another Series", function () { - let sf1 = new Series([1, 2, 3, 4]); - let sf2 = new Series(["a", "b", "c"]); - let expected_val = [1, 2, 3, 4, "a", "b", "c"]; - sf1.append(sf2, true); - assert.deepEqual(sf1.values, expected_val); - }); - it("Add a new single value to the end of a Series", function () { - let data = [1, 2, 3, 4, "a", "b", "c"]; - let sf = new Series(data); - let expected_val = [1, 2, 3, 4, "a", "b", "c", "d"]; - sf = sf.append("d"); - assert.deepEqual(sf.values, expected_val); - }); - it("Add a new array of values to the end of a Series", function () { - let data = [1, 2, 3, 4]; - let to_add = ["a", "b", "c"]; - let sf = new Series(data); - let expected_val = [1, 2, 3, 4, "a", "b", "c"]; - sf = sf.append(to_add); - assert.deepEqual(sf.values, expected_val); - }); - it("Add a Series to the end of another Series", function () { - let sf1 = new Series([1, 2, 3, 4]); - let sf2 = new Series(["a", "b", "c"]); - let expected_val = [1, 2, 3, 4, "a", "b", "c"]; - sf1 = sf1.append(sf2); - assert.deepEqual(sf1.values, expected_val); - }); - it("Confirm index Change after append", function () { - let sf1 = new Series([1, 2, 3, 4]); - let sf2 = new Series(["a", "b", "c"]); - sf1 = sf1.append(sf2); - assert.deepEqual(sf1.index, [0, 1, 2, 3, 0, 1, 2]); - }); - it("Confirm index Change after append inplace", function () { - let sf1 = new Series([1, 2, 3, 4]); - let sf2 = new Series(["a", "b", "c"]); - sf1.append(sf2, true); - assert.deepEqual(sf1.index, [0, 1, 2, 3, 0, 1, 2]); - }); - }); -}); diff --git a/danfojs-node/tests/core/strings.js b/danfojs-node/tests/core/strings.js deleted file mode 100644 index 294b7d5b..00000000 --- a/danfojs-node/tests/core/strings.js +++ /dev/null @@ -1,168 +0,0 @@ -import { assert } from "chai"; -import { Str } from '../../src/core/strings'; -import { Series } from "../../src/core/series"; - - -describe("Str", function () { - it("Converts all characters to lowercase.", function () { - let data = ['lower', 'CAPITALS', 'this is a sentence', 'SwApCaSe']; - let res = ['lower', 'capitals', 'this is a sentence', 'swapcase']; - let str = new Str(new Series(data)); - assert.deepEqual(str.toLowerCase().values, res); - }); - it("Converts all characters to uppercase.", function () { - let data = ['lower', 'CAPITALS', 'this is a sentence', 'SwApCaSe']; - let res = ['LOWER', 'CAPITALS', 'THIS IS A SENTENCE', 'SWAPCASE']; - let str = new Str(new Series(data)); - assert.deepEqual(str.toUpperCase().values, res); - }); - it("Converts all characters to capital case.", function () { - let data = ['lower', 'CAPITALS', 'this is a sentence', 'SwApCaSe']; - let res = ['Lower', 'Capitals', 'This is a sentence', 'Swapcase']; - let str = new Str(new Series(data)); - assert.deepEqual(str.capitalize().values, res); - }); - - it("Returns the character at the specified index (position)", function () { - let data = ['lower', 'CAPITALS', 'this is a sentence', 'SwApCaSe']; - let res = ["w", "P", "i", "A"]; - let str = new Str(new Series(data)); - assert.deepEqual(str.charAt(2).values, res); - }); - it("Joins two or more strings. 0 joins from the start", function () { - let data = ['lower', 'CAPITALS', 'sentence', 'SwApCaSe']; - let data2 = ['XX', 'YY', 'BB', '01']; - - let res = ['XXlower', 'YYCAPITALS', 'BBsentence', '01SwApCaSe']; - let str = new Str(new Series(data)); - assert.deepEqual(str.concat(data2, 0).values, res); - }); - - it("Joins two or more strings. 1 joins from the end", function () { - let data = ['lower', 'CAPITALS', 'sentence', 'SwApCaSe']; - let data2 = ['XX', 'YY', 'BB', '01']; - - let res = ['lowerXX', 'CAPITALSYY', 'sentenceBB', 'SwApCaSe01']; - let str = new Str(new Series(data)); - assert.deepEqual(str.concat(data2, 1).values, res); - }); - - it("Joins two arrays of strings. 0 joins from the start", function () { - let data = ['lower', 'CAPITALS', 'sentence', 'SwApCaSe']; - let res = ['prelower', 'preCAPITALS', 'presentence', 'preSwApCaSe']; - let str = new Str(new Series(data)); - assert.deepEqual(str.concat("pre", 0).values, res); - }); - - it("Joins two or more strings. 1 joins from the end", function () { - let data = ['lower', 'CAPITALS', 'this is a sentence', 'SwApCaSe']; - let res = ['lowerpost', 'CAPITALSpost', 'this is a sentencepost', 'SwApCaSepost']; - let str = new Str(new Series(data)); - assert.deepEqual(str.concat("post", 1).values, res); - }); - - it("Checks whether a string begins with specified characters", function () { - let data = ['lower', 'CAPITALS', 'this is a sentence', 'SwApCaSe']; - let res = [true, false, false, false]; - let str = new Str(new Series(data)); - assert.deepEqual(str.startsWith("l").values, res); - }); - it("Checks whether a string ends with specified characters", function () { - let data = ['lower', 'CAPITALS', 'this is a sentence', 'SwApCaSe']; - let res = [false, false, true, true]; - let str = new Str(new Series(data)); - assert.deepEqual(str.endsWith("e").values, res); - }); - - it("Checks whether a string contains the specified string/characters", function () { - let data = ['lower', 'CAPITALS', 'this is a sentence', 'SwApCaSe']; - let res = [false, false, true, false]; - let str = new Str(new Series(data)); - assert.deepEqual(str.includes("sentence").values, res); - }); - - it("Returns the position of the first found occurrence of a specified value in a string", function () { - let data = ['lower', 'CAPITALS', 'this is a sentence', 'SwApCaSe']; - let res = [-1, 0, -1, 4]; - let str = new Str(new Series(data)); - assert.deepEqual(str.indexOf("C").values, res); - }); - - it("Returns the position of the last found occurrence of a specified value in a string", function () { - let data = ['lower', 'CAPITALS', 'this is a sentence', 'SwApCaSe']; - let res = [4, -1, -1, -1]; - let str = new Str(new Series(data)); - assert.deepEqual(str.lastIndexOf("r").values, res); - }); - - it("Returns a new string with a specified number of copies of an existing string", function () { - let data = ['a', 'b', 'c', 'd']; - let res = ['aaa', 'bbb', 'ccc', 'ddd']; - let str = new Str(new Series(data)); - assert.deepEqual(str.repeat(3).values, res); - }); - - it("Searches a string for a specified value, or a regular expression, and returns a new string where the specified values are replaced", function () { - let data = ['lower', 'CAPITALS', 'this is a sentence', 'SwApCaSe']; - let res = ['lower', 'CXXXPITALS', 'this is a sentence', 'SwXXXpCaSe']; - let str = new Str(new Series(data)); - assert.deepEqual(str.replace("A", "XXX").values, res); - }); - - it("Searches a string for a specified value, or regular expression, and returns the position of the match", function () { - let data = ['lower', 'CAPITALS', 'this is a sentence', 'SwApCaSe']; - let res = [-1, 1, -1, 2]; - let str = new Str(new Series(data)); - assert.deepEqual(str.search("A").values, res); - }); - - it("Extracts a part of a string and returns a new string", function () { - let data = ['lower', 'CAPITALS', 'this is a sentence', 'SwApCaSe']; - let res = ['ow', 'AP', 'hi', 'wA']; - let str = new Str(new Series(data)); - assert.deepEqual(str.slice(1, 3).values, res); - }); - - it("Splits a string into an array of substrings", function () { - let data = ['lower part', 'CAPITALS city', 'is a sentence', 'SwAp CaSe']; - let res = ["lower,part", "CAPITALS,city", "is,a,sentence", "SwAp,CaSe"]; - let str = new Str(new Series(data)); - assert.deepEqual(str.split(" ").values, res); - }); - - it("Extracts the characters from a string, beginning at a specified start position, and through the specified number of character", function () { - let data = ['lower part', 'CAPITALS city', 'this is a sentence', 'SwAp CaSe']; - let res = [" p", "AL", "is", "Ca"]; - let str = new Str(new Series(data)); - assert.deepEqual(str.substr(5, 2).values, res); - }); - - it("Extracts the characters from a string, between two specified indices", function () { - let data = ['lower part', 'CAPITALS city', 'this is a sentence', 'SwAp CaSe']; - let res = ["w", "P", "i", "A"]; - let str = new Str(new Series(data)); - assert.deepEqual(str.substring(2, 3).values, res); - }); - - it("Removes whitespace from both ends of a string", function () { - let data = ['lower part ', ' CAPITALS city', ' this is a sentence', ' SwAp CaSe']; - let res = ['lower part', 'CAPITALS city', 'this is a sentence', 'SwAp CaSe']; - let str = new Str(new Series(data)); - assert.deepEqual(str.trim().values, res); - }); - - it("Joins strings to specified value", function () { - let data = ['lower part', 'CAPITALS city', 'this is a sentence', 'SwAp CaSe']; - let res = ['lower part,new', 'CAPITALS city,new', 'this is a sentence,new', 'SwAp CaSe,new']; - let str = new Str(new Series(data)); - assert.deepEqual(str.join("new", ",").values, res); - }); - - it("Counts the number of characters in string", function () { - let data = ['lower part', 'CAPITALS', 'this is a sentence', 'SwApCaSe']; - let res = [10, 8, 18, 8]; - let str = new Str(new Series(data)); - assert.deepEqual(str.len().values, res); - }); - -}); diff --git a/danfojs-node/tests/core/timeseries.js b/danfojs-node/tests/core/timeseries.js deleted file mode 100644 index 917d7186..00000000 --- a/danfojs-node/tests/core/timeseries.js +++ /dev/null @@ -1,90 +0,0 @@ -import { assert } from "chai"; -import { to_datetime } from '../../src/core/timeseries'; - -describe("TimeSeries", function () { - - it("Check date formatting", function () { - - let data = [ "02Sep2019", "03Sep2019", "04Sep2019" ]; - - let times = to_datetime({ "data": data, "format": "%d%b%Y%" }); - - let new_data = [ new Date("02-Sep-2019"), new Date("03-Sep-2019"), new Date("04-Sep-2019") ]; - - assert.deepEqual(times.date_list, new_data); - - }); - - it("Check Invalid date Formatting", function () { - - let data = [ "30-06-02019", "29-06-2019", "28-06-2019" ]; - - assert.throws(function () { to_datetime({ "data": data }); }, Error, "Invalid date, the date format not recognise"); - - }); - - it("check month generated", function () { - - let data = [ "02Sep2019", "03Dec2019", "04Jan2019" ]; - - let times = to_datetime({ "data": data, "format": "%d%b%Y%" }); - - let new_data = [ 8, 11, 0 ]; - - assert.deepEqual(times.month().values, new_data); - }); - - it("check month Name generated", function () { - - let data = [ "06-30-02019", "07-29-2019", "08-28-2019" ]; - - let times = to_datetime({ "data": data }); - - let new_data = [ "Jun", "Jul", "Aug" ]; - - assert.deepEqual(times.month_name().values, new_data); - }); - - it("check days of the weeks generated", function () { - - let data = [ "06-30-02019", "07-29-2019", "08-28-2019" ]; - - let times = to_datetime({ "data": data }); - - let new_data = [ "Sun", "Mon", "Wed" ]; - - assert.deepEqual(times.weekdays().values, new_data); - }); - - it("check day of the month generated", function () { - - let data = [ "06-30-02019", "07-29-2019", "08-28-2019" ]; - - let times = to_datetime({ "data": data }); - - let new_data = [ 30, 29, 28 ]; - - assert.deepEqual(times.monthday().values, new_data); - }); - it("check the seconds generated", function () { - - let data = [ "06-30-02019 00:00:12", "07-29-2019 00:30:40", "08-28-2019 00:12:04" ]; - - let times = to_datetime({ "data": data }); - - let new_data = [ 12, 40, 4 ]; - - assert.deepEqual(times.seconds().values, new_data); - }); - it("check the minutes generated", function () { - - let data = [ "06-30-02019 00:00:12", "07-29-2019 00:30:40", "08-28-2019 00:12:04" ]; - - let times = to_datetime({ "data": data }); - - let new_data = [ 0, 30, 12 ]; - - assert.deepEqual(times.minutes().values, new_data); - }); - -}); diff --git a/danfojs-node/tests/core/utils.js b/danfojs-node/tests/core/utils.js deleted file mode 100644 index 1d9d3222..00000000 --- a/danfojs-node/tests/core/utils.js +++ /dev/null @@ -1,231 +0,0 @@ -import { assert } from "chai"; -import { Utils } from '../../src/core/utils'; -const utils = new Utils; - - -describe("Utils Functions", function () { - it("removes an element from an array", function () { - let arr = [ 1, 2, 3, 4 ]; - assert.deepEqual(utils.remove(arr, 2), [ 1, 2, 4 ]); - }); - it("Checks if variable is a string", function () { - let arr = [ "1", "2" ]; - assert.isTrue(utils.__is_string(arr[0])); - }); - it("Checks if variable is a number", function () { - let arr = [ 1, 2, 3, 4 ]; - assert.isTrue(utils.__is_number(arr[0])); - }); - it("Checks if value is null", function () { - let val = null; - let val2 = 1; - assert.isTrue(utils.__is_null(val)); - assert.isFalse(utils.__is_null(val2)); - }); - - it("Checks if value is undefined", function () { - let arr; - assert.isTrue(utils.__is_undefined(arr)); - }); - it("random sample n elements from array", function () { - let data = [ [ 1, 2, 3 ], [ 4, 5, 6 ], [ 20, 30, 40 ], [ 39, 89, 78 ], [ 100, 200, 300 ] ]; - assert.isFalse(utils.__sample_from_iter(data, 2) == utils.__sample_from_iter(data, 2)); - assert.isFalse(utils.__sample_from_iter(data, 3) === utils.__sample_from_iter(data, 3)); - - }); - it("Generate number betwee two set of values", function () { - - let start = 0; - let end = 5; - let data = [ 0, 1, 2, 3, 4, 5 ]; - assert.deepEqual(utils.__range(start, end), data); - }); - - describe("__get_col_values", function () { - it("converts an array of rows to array of columns", function () { - let data = [ [ 1, 2, 3 ], [ 4, 5, 6 ], [ 20, 30, 40 ] ]; - let result = [ [ 1, 4, 20 ], [ 2, 5, 30 ], [ 3, 6, 40 ] ]; - assert.deepEqual(utils.__get_col_values(data), result); - }); - }); - - describe("__get_t", function () { - it("Returns string type present in an 1D array", function () { - let data = [ 'Alice', 'Boy', 'Girl', "39" ]; - let result = [ 'string' ]; - assert.deepEqual(utils.__get_t(data), result); - }); - it("Returns float type present in an 1D array", function () { - let data = [ 1.1, 2.1, 3.2, 4.4 ]; - let result = [ 'float32' ]; - assert.deepEqual(utils.__get_t(data), result); - }); - it("Returns int type present in an 1D array", function () { - let data = [ 1, 2, 3, 45 ]; - let result = [ 'int32' ]; - assert.deepEqual(utils.__get_t(data), result); - }); - it("Returns float when there's a mixture of int and float in a 1D array", function () { - let data = [ 1, 2.1, 3, 45 ]; - let result = [ 'float32' ]; - assert.deepEqual(utils.__get_t(data), result); - }); - it("Returns float type when NaN is present in an 1D array", function () { - let data = [ 1, 2, 3, 45, NaN ]; - let result = [ 'float32' ]; - assert.deepEqual(utils.__get_t(data), result); - }); - it("Returns correct dtype if NaN present in data", function () { - let data = [ - [18.7, 17.4, 18, NaN, 19.3], - [ 20, NaN, 19, 18, 20 ]]; - let result = [ 'float32', 'float32' ]; - assert.deepEqual(utils.__get_t(data), result); - }); - it("Returns the data type present in an 2D array", function () { - let data = [ [ 'Alice', 'Boy', 'Girl', "39" ], [ 2, 5, 30, 89 ], [ 3.1, 6.1, 40.1, 78.2 ] ]; - let result = [ 'string', 'int32', 'float32' ]; - assert.deepEqual(utils.__get_t(data), result); - }); - it("Returns the string dtype when there's a mixture of dtyoes in a 2D array", function () { - let data = [ [ 'Alice', 'Boy', 'Girl', 21 ], [ 2, 5, 30, "hey" ], [ 3, 6, 40.1, 78.2 ] ]; - let result = [ 'string', 'string', 'float32' ]; - assert.deepEqual(utils.__get_t(data), result); - }); - it("Returns bool type in a 1D array", function () { - let data = [ true, true, false, false, false, true ]; - let result = [ 'boolean' ]; - assert.deepEqual(utils.__get_t(data), result); - }); - it("Returns bool type in a 2D array", function () { - let data = [ [ true, false, true ], [ "boy", "girl", "man" ], [ 20, 30, 24 ] ]; - let result = [ 'boolean', 'string', 'int32' ]; - assert.deepEqual(utils.__get_t(data), result); - }); - it("Returns string type if values are all NaN", function () { - let data = [ [ true, false, true ], [ "boy", "girl", "boy" ], [ NaN, undefined, NaN ] ]; - let result = [ 'boolean', 'string', 'float32' ]; - assert.deepEqual(utils.__get_t(data), result); - }); - }); - - describe("__map_int_to_bool", function () { - it("map ints to bools in array of arrays", function () { - let data = [ [ 1, 0, 1 ], [ 1, 1, 0 ] ]; - assert.deepEqual(utils.__map_int_to_bool(data, 2), [ [ true, false, true ], [ true, true, false ] ]); - }); - it("map ints to bools in array", function () { - let data = [ 1, 0, 0, 1, 1 ]; - assert.deepEqual(utils.__map_int_to_bool(data, 1), [ true, false, false, true, true ]); - }); - }); - - - describe("__median", function () { - it("Gets the median value of an even array", function () { - let data = [ 100, 2, 3, 20, 30, 40 ]; - assert.deepEqual(utils.__median(data, true), 25); - }); - it("Gets the median value of an odd array", function () { - let data = [ 1, 30, 20, 50, 40 ]; - assert.deepEqual(utils.__median(data, true), 30); - }); - }); - - describe("__mode", function () { - it("Gets the modal value(s) of an array", function () { - let data = [ 100, 2, 3, 20, 20, 40 ]; - assert.deepEqual(utils.__mode(data), [ 20 ]); - }); - it("Returns the multi-modal values of an array", function () { - let data = [ 100, 2, 3, 20, 20, 40, 4, 4, 4, 20 ]; - assert.deepEqual(utils.__mode(data), [ 4, 20 ]); - }); - }); - - describe("__round", function () { - it("round elements in array to 1 dp", function () { - let data = [ 10.01, 2.2, 3.11, 20.505, 20.22, 40.0909 ]; - assert.deepEqual(utils.__round(data, 1, true), [ 10.0, 2.2, 3.1, 20.5, 20.2, 40.1 ]); - }); - it("round elements in array to 2 dp", function () { - let data = [ 10.019, 2.2099, 3.1145, 20.506, 20.22, 40.0909 ]; - assert.deepEqual(utils.__round(data, 2, true), [ 10.02, 2.21, 3.11, 20.51, 20.22, 40.09 ]); - }); - }); - - describe("__replace_undefined_with_NaN", function () { - it("replace undefined in Series with NaN", function () { - let data = [ 10.01, 2.2, undefined, 20.505, 20.22, undefined ]; - assert.deepEqual(utils.__replace_undefined_with_NaN(data, true), [ 10.01, 2.2, NaN, 20.505, 20.22, NaN ]); - }); - it("replace undefined in DataFrame with NaN", function () { - let data = [ [ 10.01, 2.2, undefined, 20.505, 20.22, undefined ], - [ 10.01, undefined, undefined, 20.505, 20, undefined ] ]; - - let result = [ [ 10.01, 2.2, NaN, 20.505, 20.22, NaN ], - [ 10.01, NaN, NaN, 20.505, 20, NaN ] ]; - assert.deepEqual(utils.__replace_undefined_with_NaN(data, false), result); - }); - it("replace null in Series with NaN", function () { - let data = [ 10.01, 2.2, null, 20.505, 20.22, null ]; - assert.deepEqual(utils.__replace_undefined_with_NaN(data, true), [ 10.01, 2.2, NaN, 20.505, 20.22, NaN ]); - }); - it("replace null in DataFrame with NaN", function () { - let data = [ [ 10.01, 2.2, null, 20.505, 20.22, null ], - [ 10.01, null, null, 20.505, 20, null ] ]; - - let result = [ [ 10.01, 2.2, NaN, 20.505, 20.22, NaN ], - [ 10.01, NaN, NaN, 20.505, 20, NaN ] ]; - assert.deepEqual(utils.__replace_undefined_with_NaN(data, false), result); - }); - }); - - describe("__convert_2D_to_1D", function () { - it("convert 2D array of array to 1D of string values", function () { - let data = [ [ 10.01, 2.2, "a" ], [ 20.505, 20.22, "boy" ] ]; - assert.deepEqual(utils.__convert_2D_to_1D(data), [ "10.01,2.2,a", "20.505,20.22,boy" ]); - }); - - }); - - // describe("_throw_wrong_params_error", function () { - // it("check if the right params are passed to a function", function () { - // let params_needed = ["replace", "with", "inplace"] - // let kwargs = { "replae": 2, "with": 12, "inplace": true } - // assert.equal(utils._throw_wrong_params_error(kwargs, params_needed), false) - // }) - // it("check if the right params are passed to a function 2", function () { - // let params_needed = ["replace", "with", "inplace"] - // let kwargs = { "replace": 2, "with": 12, "inplace": true } - // assert.equal(utils._throw_wrong_params_error(kwargs, params_needed), true) - // }) - - // }) - - describe("_get_row_and_col_values", function () { - it("retreive rows and labels from column object", function () { - let data = { "Alpha": [ "A", "B", "C", "D" ], count: [ 1, 2, 3, 4 ], sum: [ 20.3, 30.456, 40.90, 90.1 ] }; - let res = [ [ "A", 1, 20.3 ], [ "B", 2, 30.456 ], [ "C", 3, 40.90 ], [ "D", 4, 90.1 ] ]; - assert.deepEqual(utils._get_row_and_col_values(data)[0], res); - assert.deepEqual(utils._get_row_and_col_values(data)[1], [ "Alpha", "count", "sum" ]); - - }); - - - }); - - - describe("_get_duplicate", function(){ - it("obtain duplicate and their index", function(){ - - let data = [ 1, 2, 3, 4, 5, 3, 4, 6, 4, 5 ]; - let res = { '3': { count: 2, index: [ 2, 5 ] }, - '4': { count: 3, index: [ 3, 6, 8 ] }, - '5': { count: 2, index: [ 4, 9 ] } }; - - assert.deepEqual(utils.__get_duplicate(data), res); - }); - }); - -}); diff --git a/danfojs-node/tests/io/reader.js b/danfojs-node/tests/io/reader.js deleted file mode 100644 index 854b0266..00000000 --- a/danfojs-node/tests/io/reader.js +++ /dev/null @@ -1,118 +0,0 @@ -import { assert } from "chai"; -import { read_csv, read_json, read_excel, read } from "../../src/io/reader"; - -describe("read_csv", async function () { - this.timeout(10000); // all tests in this suite get 10 seconds before timeout - it("reads a csv file from source over the internet", async function () { - const csvUrl = - "https://storage.googleapis.com/tfjs-examples/multivariate-linear-regression/data/boston-housing-train.csv"; - - const df = await read_csv(csvUrl); - const num_of_columns = df.column_names.length; - assert.equal(num_of_columns, 13); - - }); - - it("reads a csv file from source from local disk", async function () { - const csvUrl = "tests/samples/titanic.csv"; - const df = await read_csv(csvUrl); - const num_of_columns = df.column_names.length; - assert.equal(num_of_columns, 8); - }); -}); - -describe("read_json", async function () { - this.timeout(10000); // all tests in this suite get 10 seconds before timeout - it("reads a json file from source over the internet", async function () { - const jUrl = - "https://raw.githubusercontent.com/risenW/Tensorflowjs_Projects/master/recommender-sys/Python-Model/web_book_data.json"; - - const df = await read_json(jUrl); - const num_of_columns = df.column_names.length; - assert.equal(num_of_columns, 4); - - }); - - it("reads a json file from source from local disk", async function () { - const jUrl = "tests/samples/book.json"; - const df = await read_json(jUrl); - const num_of_columns = df.column_names.length; - assert.equal(num_of_columns, 4); - }); -}); - -describe("read_excel", async function () { - this.timeout(10000); // all tests in this suite get 10 seconds before timeout - it("reads an excel file from source over the internet", async function () { - const remote_url = - "https://file-examples-com.github.io/uploads/2017/02/file_example_XLS_100.xls"; - const df = await read_excel(remote_url, { sheet: 0 }); - assert(df.columns.length, 8); - }); - - it("reads an excel file from source from local disk", async function () { - const file_url = "tests/samples/SampleData.xlsx"; - const df = await read_excel(file_url); - assert(df.columns.length, 4); - }); -}); - -describe("read: Generic read function from frictionless.js", async function () { - this.timeout(20000); // all tests in this suite get 10 seconds before timeout - - it("read an excel file from source over the internet", async function () { - const remote_url = - "https://file-examples-com.github.io/uploads/2017/02/file_example_XLS_100.xls"; - const df = await read(remote_url); - assert(df.columns.length, 8); - - }); - - it("read an excel file from source from local disk", async function () { - const file_url = "tests/samples/SampleData.xlsx"; - const df = await read(file_url); - assert(df.columns.length, 4); - }); - - it("read a csv file from source over the internet", async function () { - const csvUrl = - "https://storage.googleapis.com/tfjs-examples/multivariate-linear-regression/data/boston-housing-train.csv"; - - const df = await read(csvUrl); - const num_of_columns = df.column_names.length; - assert.equal(num_of_columns, 13); - }); - - it("read a csv file from source from local disk", async function () { - const csvUrl = "tests/samples/titanic.csv"; - - const df = await read(csvUrl); - const num_of_columns = df.column_names.length; - assert.equal(num_of_columns, 8); - - }); - it("read a csv file from source from local disk with header set to false", async function () { - const csvUrl = "tests/samples/titanic.csv"; - const df = await read(csvUrl, { header: false }); - const num_of_columns = df.column_names.length; - assert.equal(num_of_columns, 8); - }); - - it("read a Dataset package", async function () { - const jUrl = - "https://raw.githubusercontent.com/frictionlessdata/frictionless-js/master/test/fixtures/co2-ppm/datapackage.json"; - - const df = await read(jUrl); - const num_of_columns = df.column_names.length; - assert.equal(num_of_columns, 6); - }); - - it("read a specific data from Dataset package", async function () { - const jUrl = - "https://raw.githubusercontent.com/frictionlessdata/frictionless-js/master/test/fixtures/co2-ppm/datapackage.json"; - - const df = await read(jUrl, { data_num: 2 }); - const num_of_columns = df.column_names.length; - assert.equal(num_of_columns, 3); - }); -}); diff --git a/danfojs-node/tests/preprocessing/encodings.js b/danfojs-node/tests/preprocessing/encodings.js deleted file mode 100644 index 2f8df208..00000000 --- a/danfojs-node/tests/preprocessing/encodings.js +++ /dev/null @@ -1,97 +0,0 @@ -import { assert } from "chai"; -import { LabelEncoder, OneHotEncoder } from "../../src/preprocessing/encodings"; -import { Series } from "../../src/core/series"; - -describe("Encodings", function () { - - describe("LabelEncoder", function () { - - it("test the label encoding on array", function () { - let data = [ "dog", "cat", "man", "dog", "cat", "man", "man", "cat" ]; - let encode = new LabelEncoder(); - let fit_data = [ - 0, 1, 2, 0, - 1, 2, 2, 1 - ]; - assert.deepEqual(encode.fit(data).values, fit_data); - assert.deepEqual(encode.transform([ "dog", "man" ]).values, [ 0, 2 ]); - }); - it("test the label encoding on Series", function () { - let data = [ "dog", "cat", "man", "dog", "cat", "man", "man", "cat" ]; - let series = new Series(data); - let encode = new LabelEncoder(); - let fit_data = [ - 0, 1, 2, 0, - 1, 2, 2, 1 - ]; - assert.deepEqual(encode.fit(series).values, fit_data); - assert.deepEqual(encode.transform([ "dog", "man" ]).values, [ 0, 2 ]); - }); - it("label encoding directly from a Series", function () { - let data = new Series([ "dog", "cat", "man", "dog", "cat", "man", "man", "cat" ]); - let to_label_encode = new Series([ "dog", "man" ]); - let encode = new LabelEncoder(); - let fit_data = [ - 0, 1, 2, 0, - 1, 2, 2, 1 - ]; - assert.deepEqual(encode.fit(data).values, fit_data); - assert.deepEqual(encode.transform(to_label_encode).values, [ 0, 2 ]); - }); - // it("Label encoding on Series", function () { - // let data = ["dog", "cat", "man", "dog", "cat", "man", "man", "cat"] - // let series = new Series(data) - // let encode = new LabelEncoder() - // let fit_data = [ - // 0, 1, 2, 0, - // 1, 2, 2, 1 - // ] - // assert.deepEqual(encode.fit(series).values, fit_data) - // assert.deepEqual(encode.transform(["dog", "man"]).values, [0, 2]) - // }); - - }); - - describe("OneHotEncoder", function () { - - it("test onehotencoding on array", function () { - let data = [ "dog", "cat", "man", "dog", "cat", "man", "man", "cat" ]; - let encode = new OneHotEncoder(); - let fit_data = [ - [ 1, 0, 0 ], - [ 0, 1, 0 ], - [ 0, 0, 1 ], - [ 1, 0, 0 ], - [ 0, 1, 0 ], - [ 0, 0, 1 ], - [ 0, 0, 1 ], - [ 0, 1, 0 ] - ]; - let transform_data = [ [ 0, 0, 1 ], [ 0, 1, 0 ] ]; - - assert.deepEqual(encode.fit(data).values, fit_data); - assert.deepEqual(encode.transform([ "man", "cat" ]).values, transform_data); - }); - it("test onehotencoding on Series", function () { - let data = [ "dog", "cat", "man", "dog", "cat", "man", "man", "cat" ]; - let series = new Series(data); - let encode = new OneHotEncoder(); - let fit_data = [ - [ 1, 0, 0 ], - [ 0, 1, 0 ], - [ 0, 0, 1 ], - [ 1, 0, 0 ], - [ 0, 1, 0 ], - [ 0, 0, 1 ], - [ 0, 0, 1 ], - [ 0, 1, 0 ] - ]; - let transform_data = [ [ 0, 0, 1 ], [ 0, 1, 0 ] ]; - - assert.deepEqual(encode.fit(series).values, fit_data); - assert.deepEqual(encode.transform(new Series([ "man", "cat" ])).values, transform_data); - }); - }); - - -}); diff --git a/danfojs-node/tests/preprocessing/scaler.js b/danfojs-node/tests/preprocessing/scaler.js deleted file mode 100644 index 0d5b7167..00000000 --- a/danfojs-node/tests/preprocessing/scaler.js +++ /dev/null @@ -1,60 +0,0 @@ -import { assert } from "chai"; -import { MinMaxScaler, StandardScaler } from "../../src/preprocessing/scalers"; -import { DataFrame } from '../../src/core/frame'; -import { Series } from '../../src/core/series'; - - -describe("Preprocessing", function(){ - - describe("MinMaxscaler", function(){ - - it("Standardize values in a DataFrame", function(){ - let data = [ [ -1, 2 ], [ -0.5, 6 ], [ 0, 10 ], [ 1, 18 ] ]; - let scaler = new MinMaxScaler(); - let fit_data = [ [ 0, 0 ], [ 0.25, 0.25 ], [ 0.5, 0.5 ], [ 1, 1 ] ]; - let transform_data = [ [ 1.5, 0. ] ]; - scaler.fit(new DataFrame(data)); - assert.deepEqual(scaler.transform(new DataFrame(data)).values, fit_data); - assert.deepEqual(scaler.transform([ [ 2, 2 ] ]).values, transform_data); - }); - it("Standardize values in a Series", function(){ - let data = [ -1, 2, -0.5, 60, 101, 18 ]; - let scaler = new MinMaxScaler(); - let result = [ 0, 0.029411764815449715, 0.0049019609577953815, 0.5980392098426819, 1, 0.18627451360225677 ]; - let transform_data = [ 0.029411764815449715, 0.029411764815449715 ]; - scaler.fit(new Series(data)); - assert.deepEqual(scaler.transform(new Series(data)).values, result); - assert.deepEqual(scaler.transform([ 2, 2 ]).values, transform_data); - }); - }); - - describe("StandardScaler", function(){ - - it("basic test", function(){ - let data = [ [ 0, 0 ], [ 0, 0 ], [ 1, 1 ], [ 1, 1 ] ]; - - let scaler = new StandardScaler(); - let fit_data = [ [ -1, -1 ], [ -1, -1 ], [ 1, 1 ], [ 1, 1 ] ]; - let transform_data = [ [ 3, 3 ] ]; - - assert.deepEqual(scaler.fit(new DataFrame(data)).round().values, fit_data); - assert.deepEqual(scaler.transform([ [ 2, 2 ] ]).round().values, transform_data); - }); - }); - - - // describe("RobustScaler", function(){ - // it("basic test", function(){ - - // let data = [[3,1], [7,3], [8,4], [5,6], [12,5], [14,12], [21,23], [15,15], [18,2], [14,15]] - // // let data2 = [[4,100,900],[5,110,800],[21,220,890],[20,300,500]] - // let data2 = [0, 4, 4, 4, 7, 10, 11, 12, 14, 16, 17, 25] - - - // let scaler = new RobustScaler() - - // // console.log(scaler.quantile(data2,true)) - - // }); - // }); -}); diff --git a/danfojs-node/tests/samples/SampleData.xlsx b/danfojs-node/tests/samples/SampleData.xlsx deleted file mode 100644 index 67a11847..00000000 Binary files a/danfojs-node/tests/samples/SampleData.xlsx and /dev/null differ diff --git a/danfojs-node/tests/samples/book.json b/danfojs-node/tests/samples/book.json deleted file mode 100644 index 39974173..00000000 --- a/danfojs-node/tests/samples/book.json +++ /dev/null @@ -1 +0,0 @@ -[{"book_id":1,"title":"Harry Potter and the Half-Blood Prince (Harry Potter, #6)","image_url":"https:\/\/images.gr-assets.com\/books\/1361039191m\/1.jpg","authors":"J.K. Rowling, Mary GrandPr\u00e9"},{"book_id":2,"title":"Harry Potter and the Order of the Phoenix (Harry Potter, #5)","image_url":"https:\/\/images.gr-assets.com\/books\/1387141547m\/2.jpg","authors":"J.K. Rowling, Mary GrandPr\u00e9"},{"book_id":3,"title":"Harry Potter and the Sorcerer's Stone (Harry Potter, #1)","image_url":"https:\/\/images.gr-assets.com\/books\/1474154022m\/3.jpg","authors":"J.K. Rowling, Mary GrandPr\u00e9"},{"book_id":5,"title":"Harry Potter and the Prisoner of Azkaban (Harry Potter, #3)","image_url":"https:\/\/images.gr-assets.com\/books\/1499277281m\/5.jpg","authors":"J.K. Rowling, Mary GrandPr\u00e9, Rufus Beck"},{"book_id":6,"title":"Harry Potter and the Goblet of Fire (Harry Potter, #4)","image_url":"https:\/\/images.gr-assets.com\/books\/1361482611m\/6.jpg","authors":"J.K. Rowling, Mary GrandPr\u00e9"},{"book_id":8,"title":"Harry Potter Boxed Set, Books 1-5 (Harry Potter, #1-5)","image_url":"https:\/\/s.gr-assets.com\/assets\/nophoto\/book\/111x148-bcc042a9c91a29c1d680899eff700a03.png","authors":"J.K. Rowling, Mary GrandPr\u00e9"},{"book_id":10,"title":"Harry Potter Collection (Harry Potter, #1-6)","image_url":"https:\/\/images.gr-assets.com\/books\/1328867351m\/10.jpg","authors":"J.K. 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Elise Lurette,female,58,0,0,146.5208 -0,3,Mr. Robert Mernagh,male,28,0,0,7.75 -0,3,Mr. Karl Siegwart Andreas Olsen,male,42,0,1,8.4042 -1,3,Miss. Margaret Madigan,female,21,0,0,7.75 -0,2,Miss. Henriette Yrois,female,24,0,0,13 -0,3,Mr. Nestor Cyriel Vande Walle,male,28,0,0,9.5 -0,3,Mr. Frederick Sage,male,17,8,2,69.55 -0,3,Mr. Jakob Alfred Johanson,male,34,0,0,6.4958 -0,3,Mr. Gerious Youseff,male,45.5,0,0,7.225 -1,3,Mr. Gurshon Cohen,male,18,0,0,8.05 -0,3,Miss. Telma Matilda Strom,female,2,0,1,10.4625 -0,3,Mr. Karl Alfred Backstrom,male,32,1,0,15.85 -1,3,Mr. Nassef Cassem Albimona,male,26,0,0,18.7875 -1,3,Miss. Helen Carr,female,16,0,0,7.75 -1,1,Mr. Henry Blank,male,40,0,0,31 -0,3,Mr. Ahmed Ali,male,24,0,0,7.05 -1,2,Miss. Clear Annie Cameron,female,35,0,0,21 -0,3,Mr. John Henry Perkin,male,22,0,0,7.25 -0,2,Mr. Hans Kristensen Givard,male,30,0,0,13 -0,3,Mr. Philip Kiernan,male,22,1,0,7.75 -1,1,Miss. Madeleine Newell,female,31,1,0,113.275 -1,3,Miss. Eliina Honkanen,female,27,0,0,7.925 -0,2,Mr. Sidney Samuel Jacobsohn,male,42,1,0,27 -1,1,Miss. Albina Bazzani,female,32,0,0,76.2917 -0,2,Mr. Walter Harris,male,30,0,0,10.5 -1,3,Mr. Victor Francis Sunderland,male,16,0,0,8.05 -0,2,Mr. James H Bracken,male,27,0,0,13 -0,3,Mr. George Henry Green,male,51,0,0,8.05 -0,3,Mr. Christo Nenkoff,male,22,0,0,7.8958 -1,1,Mr. Frederick Maxfield Hoyt,male,38,1,0,90 -0,3,Mr. Karl Ivar Sven Berglund,male,22,0,0,9.35 -1,2,Mr. William John Mellors,male,19,0,0,10.5 -0,3,Mr. John Hall Lovell,male,20.5,0,0,7.25 -0,2,Mr. Arne Jonas Fahlstrom,male,18,0,0,13 -0,3,Miss. Mathilde Lefebre,female,12,3,1,25.4667 -1,1,Mrs. Henry Birkhardt (Irene Wallach) Harris,female,35,1,0,83.475 -0,3,Mr. Bengt Edvin Larsson,male,29,0,0,7.775 -0,2,Mr. Ernst Adolf Sjostedt,male,59,0,0,13.5 -1,3,Miss. Lillian Gertrud Asplund,female,5,4,2,31.3875 -0,2,Mr. Robert William Norman Leyson,male,24,0,0,10.5 -0,3,Miss. Alice Phoebe Harknett,female,21,0,0,7.55 -0,2,Mr. Stephen Hold,male,44,1,0,26 -1,2,Miss. Marjorie Collyer,female,8,0,2,26.25 -0,2,Mr. Frederick William Pengelly,male,19,0,0,10.5 -0,2,Mr. George Henry Hunt,male,33,0,0,12.275 -0,3,Miss. Thamine Zabour,female,19,1,0,14.4542 -1,3,Miss. Katherine Murphy,female,18,1,0,15.5 -0,2,Mr. Reginald Charles Coleridge,male,29,0,0,10.5 -0,3,Mr. Matti Alexanteri Maenpaa,male,22,0,0,7.125 -0,3,Mr. Sleiman Attalah,male,30,0,0,7.225 -0,1,Dr. William Edward Minahan,male,44,2,0,90 -0,3,Miss. Agda Thorilda Viktoria Lindahl,female,25,0,0,7.775 -1,2,Mrs. William (Anna) Hamalainen,female,24,0,2,14.5 -1,1,Mr. Richard Leonard Beckwith,male,37,1,1,52.5542 -0,2,Rev. Ernest Courtenay Carter,male,54,1,0,26 -0,3,Mr. James George Reed,male,18,0,0,7.25 -0,3,Mrs. Wilhelm (Elna Matilda Persson) Strom,female,29,1,1,10.4625 -0,1,Mr. William Thomas Stead,male,62,0,0,26.55 -0,3,Mr. William Arthur Lobb,male,30,1,0,16.1 -0,3,Mrs. Viktor (Helena Wilhelmina) Rosblom,female,41,0,2,20.2125 -1,3,Mrs. Darwis (Hanne Youssef Razi) Touma,female,29,0,2,15.2458 -1,1,Mrs. Gertrude Maybelle Thorne,female,38,0,0,79.2 -1,1,Miss. Gladys Cherry,female,30,0,0,86.5 -1,1,Miss. Anna Ward,female,35,0,0,512.3292 -1,2,Mrs. (Lutie Davis) Parrish,female,50,0,1,26 -1,3,Master. Edvin Rojj Felix Asplund,male,3,4,2,31.3875 -0,1,Mr. Emil Taussig,male,52,1,1,79.65 -0,1,Mr. William Harrison,male,40,0,0,0 -0,3,Miss. Delia Henry,female,21,0,0,7.75 -0,2,Mr. David Reeves,male,36,0,0,10.5 -0,3,Mr. Ernesti Arvid Panula,male,16,4,1,39.6875 -1,3,Mr. Ernst Ulrik Persson,male,25,1,0,7.775 -1,1,Mrs. William Thompson (Edith Junkins) Graham,female,58,0,1,153.4625 -1,1,Miss. Amelia Bissette,female,35,0,0,135.6333 -0,1,Mr. Alexander Cairns,male,28,0,0,31 -1,3,Mr. William Henry Tornquist,male,25,0,0,0 -1,2,Mrs. (Elizabeth Anne Maidment) Mellinger,female,41,0,1,19.5 -0,1,Mr. Charles H Natsch,male,37,0,1,29.7 -1,3,Miss. Hanora Healy,female,33,0,0,7.75 -1,1,Miss. Kornelia Theodosia Andrews,female,63,1,0,77.9583 -0,3,Miss. Augusta Charlotta Lindblom,female,45,0,0,7.75 -0,2,Mr. Francis Parkes,male,21,0,0,0 -0,3,Master. Eric Rice,male,7,4,1,29.125 -1,3,Mrs. Stanton (Rosa Hunt) Abbott,female,35,1,1,20.25 -0,3,Mr. Frank Duane,male,65,0,0,7.75 -0,3,Mr. Nils Johan Goransson Olsson,male,28,0,0,7.8542 -0,3,Mr. Alfons de Pelsmaeker,male,16,0,0,9.5 -1,3,Mr. Edward Arthur Dorking,male,19,0,0,8.05 -0,1,Mr. Richard William Smith,male,57,0,0,26 -0,3,Mr. Ivan Stankovic,male,33,0,0,8.6625 -1,3,Mr. Theodore de Mulder,male,30,0,0,9.5 -0,3,Mr. Penko Naidenoff,male,22,0,0,7.8958 -1,2,Mr. Masabumi Hosono,male,42,0,0,13 -1,3,Miss. Kate Connolly,female,22,0,0,7.75 -1,1,Miss. Ellen Barber,female,26,0,0,78.85 -1,1,Mrs. Dickinson H (Helen Walton) Bishop,female,19,1,0,91.0792 -0,2,Mr. Rene Jacques Levy,male,36,0,0,12.875 -0,3,Miss. Aloisia Haas,female,24,0,0,8.85 -0,3,Mr. Ivan Mineff,male,24,0,0,7.8958 -0,1,Mr. Ervin G Lewy,male,30,0,0,27.7208 -0,3,Mr. Mansour Hanna,male,23.5,0,0,7.2292 -0,1,Miss. Helen Loraine Allison,female,2,1,2,151.55 -1,1,Mr. Adolphe Saalfeld,male,47,0,0,30.5 -1,1,Mrs. James (Helene DeLaudeniere Chaput) Baxter,female,50,0,1,247.5208 -1,3,Miss. Anna Katherine Kelly,female,20,0,0,7.75 -1,3,Mr. Bernard McCoy,male,24,2,0,23.25 -0,3,Mr. William Cahoone Jr Johnson,male,19,0,0,0 -1,2,Miss. Nora A Keane,female,46,0,0,12.35 -0,3,Mr. Howard Hugh Williams,male,28,0,0,8.05 -1,1,Master. Hudson Trevor Allison,male,0.92,1,2,151.55 -1,1,Miss. Margaret Fleming,female,42,0,0,110.8833 -1,1,Mrs. Victor de Satode (Maria Josefa Perez de Soto y Vallejo) Penasco y Castellana,female,17,1,0,108.9 -0,2,Mr. Samuel Abelson,male,30,1,0,24 -1,1,Miss. Laura Mabel Francatelli,female,30,0,0,56.9292 -1,1,Miss. Margaret Bechstein Hays,female,24,0,0,83.1583 -1,1,Miss. Emily Borie Ryerson,female,18,2,2,262.375 -0,2,Mrs. William (Anna Sylfven) Lahtinen,female,26,1,1,26 -0,3,Mr. Ignjac Hendekovic,male,28,0,0,7.8958 -0,2,Mr. Benjamin Hart,male,43,1,1,26.25 -1,3,Miss. Helmina Josefina Nilsson,female,26,0,0,7.8542 -1,2,Mrs. Sinai (Miriam Sternin) Kantor,female,24,1,0,26 -0,2,Dr. Ernest Moraweck,male,54,0,0,14 -1,1,Miss. Mary Natalie Wick,female,31,0,2,164.8667 -1,1,Mrs. Frederic Oakley (Margaretta Corning Stone) Spedden,female,40,1,1,134.5 -0,3,Mr. Samuel Dennis,male,22,0,0,7.25 -0,3,Mr. Yoto Danoff,male,27,0,0,7.8958 -1,2,Miss. Hilda Mary Slayter,female,30,0,0,12.35 -1,2,Mrs. Albert Francis (Sylvia Mae Harbaugh) Caldwell,female,22,1,1,29 -0,3,Mr. George John Jr Sage,male,20,8,2,69.55 -1,1,Miss. Marie Grice Young,female,36,0,0,135.6333 -0,3,Mr. Johan Hansen Nysveen,male,61,0,0,6.2375 -1,2,Mrs. (Ada E Hall) Ball,female,36,0,0,13 -1,3,Mrs. Frank John (Emily Alice Brown) Goldsmith,female,31,1,1,20.525 -1,1,Miss. Jean Gertrude Hippach,female,16,0,1,57.9792 -1,3,Miss. Agnes McCoy,female,28,2,0,23.25 -0,1,Mr. Austen Partner,male,45.5,0,0,28.5 -0,1,Mr. George Edward Graham,male,38,0,1,153.4625 -0,3,Mr. Leo Edmondus Vander Planke,male,16,2,0,18 -1,1,Mrs. Henry William (Clara Heinsheimer) Frauenthal,female,42,1,0,133.65 -0,3,Mr. Mitto Denkoff,male,30,0,0,7.8958 -0,1,Mr. Thomas Clinton Pears,male,29,1,0,66.6 -1,1,Miss. Elizabeth Margaret Burns,female,41,0,0,134.5 -1,3,Mr. Karl Edwart Dahl,male,45,0,0,8.05 -0,1,Mr. Stephen Weart Blackwell,male,45,0,0,35.5 -1,2,Master. Edmond Roger Navratil,male,2,1,1,26 -1,1,Miss. Alice Elizabeth Fortune,female,24,3,2,263 -0,2,Mr. Erik Gustaf Collander,male,28,0,0,13 -0,2,Mr. Charles Frederick Waddington Sedgwick,male,25,0,0,13 -0,2,Mr. Stanley Hubert Fox,male,36,0,0,13 -1,2,Miss. Amelia Brown,female,24,0,0,13 -1,2,Miss. Marion Elsie Smith,female,40,0,0,13 -1,3,Mrs. Thomas Henry (Mary E Finck) Davison,female,34,1,0,16.1 -1,3,Master. William Loch Coutts,male,3,1,1,15.9 -0,3,Mr. Jovan Dimic,male,42,0,0,8.6625 -0,3,Mr. Nils Martin Odahl,male,23,0,0,9.225 -0,1,Mr. Fletcher Fellows Williams-Lambert,male,43,0,0,35 -0,3,Mr. Tannous Elias,male,15,1,1,7.2292 -0,3,Mr. Josef Arnold-Franchi,male,25,1,0,17.8 -0,3,Mr. Wazli Yousif,male,23,0,0,7.225 -0,3,Mr. Leo Peter Vanden Steen,male,28,0,0,9.5 -1,1,Miss. Elsie Edith Bowerman,female,22,0,1,55 -0,2,Miss. Annie Clemmer Funk,female,38,0,0,13 -1,3,Miss. Mary McGovern,female,22,0,0,7.8792 -1,3,Miss. Helen Mary Mockler,female,23,0,0,7.8792 -0,3,Mr. Wilhelm Skoog,male,40,1,4,27.9 -0,2,Mr. Sebastiano del Carlo,male,29,1,0,27.7208 -0,3,Mrs. (Catherine David) Barbara,female,45,0,1,14.4542 -0,3,Mr. Adola Asim,male,35,0,0,7.05 -0,3,Mr. Thomas O'Brien,male,27,1,0,15.5 -0,3,Mr. Mauritz Nils Martin Adahl,male,30,0,0,7.25 -1,1,Mrs. Frank Manley (Anna Sophia Atkinson) Warren,female,60,1,0,75.25 -1,3,Mrs. (Mantoura Boulos) Moussa,female,35,0,0,7.2292 -1,3,Miss. Annie Jermyn,female,22,0,0,7.75 -1,1,Mme. Leontine Pauline Aubart,female,24,0,0,69.3 -1,1,Mr. George Achilles Harder,male,25,1,0,55.4417 -0,3,Mr. Jakob Alfred Wiklund,male,18,1,0,6.4958 -0,3,Mr. William Thomas Beavan,male,19,0,0,8.05 -0,1,Mr. Sante Ringhini,male,22,0,0,135.6333 -0,3,Miss. Stina Viola Palsson,female,3,3,1,21.075 -1,1,Mrs. Edgar Joseph (Leila Saks) Meyer,female,25,1,0,82.1708 -1,3,Miss. Aurora Adelia Landergren,female,22,0,0,7.25 -0,1,Mr. Harry Elkins Widener,male,27,0,2,211.5 -0,3,Mr. Tannous Betros,male,20,0,0,4.0125 -0,3,Mr. Karl Gideon Gustafsson,male,19,0,0,7.775 -1,1,Miss. Rosalie Bidois,female,42,0,0,227.525 -1,3,Miss. Maria Nakid,female,1,0,2,15.7417 -0,3,Mr. Juho Tikkanen,male,32,0,0,7.925 -1,1,Mrs. Alexander Oskar (Mary Aline Towner) Holverson,female,35,1,0,52 -0,3,Mr. Vasil Plotcharsky,male,27,0,0,7.8958 -0,2,Mr. Charles Henry Davies,male,18,0,0,73.5 -0,3,Master. Sidney Leonard Goodwin,male,1,5,2,46.9 -1,2,Miss. Kate Buss,female,36,0,0,13 -0,3,Mr. Matthew Sadlier,male,19,0,0,7.7292 -1,2,Miss. Bertha Lehmann,female,17,0,0,12 -1,1,Mr. William Ernest Carter,male,36,1,2,120 -1,3,Mr. Carl Olof Jansson,male,21,0,0,7.7958 -0,3,Mr. Johan Birger Gustafsson,male,28,2,0,7.925 -1,1,Miss. Marjorie Newell,female,23,1,0,113.275 -1,3,Mrs. Hjalmar (Agnes Charlotta Bengtsson) Sandstrom,female,24,0,2,16.7 -0,3,Mr. Erik Johansson,male,22,0,0,7.7958 -0,3,Miss. Elina Olsson,female,31,0,0,7.8542 -0,2,Mr. Peter David McKane,male,46,0,0,26 -0,2,Dr. Alfred Pain,male,23,0,0,10.5 -1,2,Mrs. William H (Jessie L) Trout,female,28,0,0,12.65 -1,3,Mr. Juha Niskanen,male,39,0,0,7.925 -0,3,Mr. John Adams,male,26,0,0,8.05 -0,3,Miss. Mari Aina Jussila,female,21,1,0,9.825 -0,3,Mr. Pekka Pietari Hakkarainen,male,28,1,0,15.85 -0,3,Miss. Marija Oreskovic,female,20,0,0,8.6625 -0,2,Mr. Shadrach Gale,male,34,1,0,21 -0,3,Mr. Carl/Charles Peter Widegren,male,51,0,0,7.75 -1,2,Master. William Rowe Richards,male,3,1,1,18.75 -0,3,Mr. Hans Martin Monsen Birkeland,male,21,0,0,7.775 -0,3,Miss. Ida Lefebre,female,3,3,1,25.4667 -0,3,Mr. Todor Sdycoff,male,42,0,0,7.8958 -0,3,Mr. Henry Hart,male,27,0,0,6.8583 -1,1,Miss. Daisy E Minahan,female,33,1,0,90 -0,2,Mr. Alfred Fleming Cunningham,male,22,0,0,0 -1,3,Mr. Johan Julian Sundman,male,44,0,0,7.925 -0,3,Mrs. Thomas (Annie Louise Rowley) Meek,female,32,0,0,8.05 -1,2,Mrs. James Vivian (Lulu Thorne Christian) Drew,female,34,1,1,32.5 -1,2,Miss. Lyyli Karoliina Silven,female,18,0,2,13 -0,2,Mr. William John Matthews,male,30,0,0,13 -0,3,Miss. Catharina Van Impe,female,10,0,2,24.15 -0,3,Mr. David Charters,male,21,0,0,7.7333 -0,3,Mr. Leo Zimmerman,male,29,0,0,7.875 -0,3,Mrs. Ernst Gilbert (Anna Sigrid Maria Brogren) Danbom,female,28,1,1,14.4 -0,3,Mr. Viktor Richard Rosblom,male,18,1,1,20.2125 -0,3,Mr. Phillippe Wiseman,male,54,0,0,7.25 -1,2,Mrs. Charles V (Ada Maria Winfield) Clarke,female,28,1,0,26 -1,2,Miss. Kate Florence Phillips,female,19,0,0,26 -0,3,Mr. James Flynn,male,28,0,0,7.75 -1,3,Mr. Berk (Berk Trembisky) Pickard,male,32,0,0,8.05 -1,1,Mr. Mauritz Hakan Bjornstrom-Steffansson,male,28,0,0,26.55 -1,3,Mrs. Percival (Florence Kate White) Thorneycroft,female,33,1,0,16.1 -1,2,Mrs. Charles Alexander (Alice Adelaide Slow) Louch,female,42,1,0,26 -0,3,Mr. Nikolai Erland Kallio,male,17,0,0,7.125 -0,1,Mr. William Baird Silvey,male,50,1,0,55.9 -1,1,Miss. Lucile Polk Carter,female,14,1,2,120 -0,3,Miss. Doolina Margaret Ford,female,21,2,2,34.375 -1,2,Mrs. Sidney (Emily Hocking) Richards,female,24,2,3,18.75 -0,1,Mr. Mark Fortune,male,64,1,4,263 -0,2,Mr. Johan Henrik Johannesson Kvillner,male,31,0,0,10.5 -1,2,Mrs. Benjamin (Esther Ada Bloomfield) Hart,female,45,1,1,26.25 -0,3,Mr. Leon Hampe,male,20,0,0,9.5 -0,3,Mr. Johan Emil Petterson,male,25,1,0,7.775 -1,2,Ms. Encarnacion Reynaldo,female,28,0,0,13 -1,3,Mr. Bernt Johannesen-Bratthammer,male,29,0,0,8.1125 -1,1,Master. Washington Dodge,male,4,0,2,81.8583 -1,2,Miss. Madeleine Violet Mellinger,female,13,0,1,19.5 -1,1,Mr. Frederic Kimber Seward,male,34,0,0,26.55 -1,3,Miss. Marie Catherine Baclini,female,5,2,1,19.2583 -1,1,Major. Arthur Godfrey Peuchen,male,52,0,0,30.5 -0,2,Mr. Edwy Arthur West,male,36,1,2,27.75 -0,3,Mr. Ingvald Olai Olsen Hagland,male,28,1,0,19.9667 -0,1,Mr. Benjamin Laventall Foreman,male,30,0,0,27.75 -1,1,Mr. Samuel L Goldenberg,male,49,1,0,89.1042 -0,3,Mr. Joseph Peduzzi,male,24,0,0,8.05 -1,3,Mr. Ivan Jalsevac,male,29,0,0,7.8958 -0,1,Mr. Francis Davis Millet,male,65,0,0,26.55 -1,1,Mrs. Frederick R (Marion) Kenyon,female,41,1,0,51.8625 -1,2,Miss. Ellen Toomey,female,50,0,0,10.5 -0,3,Mr. Maurice O'Connor,male,17,0,0,7.75 -1,1,Mr. Harry Anderson,male,48,0,0,26.55 -0,3,Mr. William Morley,male,34,0,0,8.05 -0,1,Mr. Arthur H Gee,male,47,0,0,38.5 -0,2,Mr. Jacob Christian Milling,male,48,0,0,13 -0,3,Mr. Simon Maisner,male,34,0,0,8.05 -0,3,Mr. Manuel Estanslas Goncalves,male,38,0,0,7.05 -0,2,Mr. William Campbell,male,21,0,0,0 -0,1,Mr. John Montgomery Smart,male,56,0,0,26.55 -0,3,Mr. James Scanlan,male,22,0,0,7.725 -1,3,Miss. Helene Barbara Baclini,female,0.75,2,1,19.2583 -0,3,Mr. Arthur Keefe,male,39,0,0,7.25 -0,3,Mr. Luka Cacic,male,38,0,0,8.6625 -1,2,Mrs. Edwy Arthur (Ada Mary Worth) West,female,33,1,2,27.75 -1,2,Mrs. Amin S (Marie Marthe Thuillard) Jerwan,female,23,0,0,13.7917 -0,3,Miss. Ida Sofia Strandberg,female,22,0,0,9.8375 -0,1,Mr. George Quincy Clifford,male,40,0,0,52 -0,2,Mr. Peter Henry Renouf,male,34,1,0,21 -0,3,Mr. Lewis Richard Braund,male,29,1,0,7.0458 -0,3,Mr. Nils August Karlsson,male,22,0,0,7.5208 -1,3,Miss. Hildur E Hirvonen,female,2,0,1,12.2875 -0,3,Master. Harold Victor Goodwin,male,9,5,2,46.9 -0,2,Mr. Anthony Wood Frost,male,37,0,0,0 -0,3,Mr. Richard Henry Rouse,male,50,0,0,8.05 -1,3,Mrs. (Hedwig) Turkula,female,63,0,0,9.5875 -1,1,Mr. Dickinson H Bishop,male,25,1,0,91.0792 -0,3,Miss. Jeannie Lefebre,female,8,3,1,25.4667 -1,1,Mrs. Frederick Maxfield (Jane Anne Forby) Hoyt,female,35,1,0,90 -0,1,Mr. Edward Austin Kent,male,58,0,0,29.7 -0,3,Mr. Francis William Somerton,male,30,0,0,8.05 -1,3,Master. Eden Leslie Coutts,male,9,1,1,15.9 -0,3,Mr. Konrad Mathias Reiersen Hagland,male,19,1,0,19.9667 -0,3,Mr. Einar Windelov,male,21,0,0,7.25 -0,1,Mr. Harry Markland Molson,male,55,0,0,30.5 -0,1,Mr. Ramon Artagaveytia,male,71,0,0,49.5042 -0,3,Mr. Edward Roland Stanley,male,21,0,0,8.05 -0,3,Mr. Gerious Yousseff,male,26,0,0,14.4583 -1,1,Miss. Elizabeth Mussey Eustis,female,54,1,0,78.2667 -0,3,Mr. Frederick William Shellard,male,55,0,0,15.1 -0,1,Mrs. Hudson J C (Bessie Waldo Daniels) Allison,female,25,1,2,151.55 -0,3,Mr. Olof Svensson,male,24,0,0,7.7958 -0,3,Mr. Petar Calic,male,17,0,0,8.6625 -0,3,Miss. Mary Canavan,female,21,0,0,7.75 -0,3,Miss. Bridget Mary O'Sullivan,female,21,0,0,7.6292 -0,3,Miss. Kristina Sofia Laitinen,female,37,0,0,9.5875 -1,1,Miss. Roberta Maioni,female,16,0,0,86.5 -0,1,Mr. Victor de Satode Penasco y Castellana,male,18,1,0,108.9 -1,2,Mrs. Frederick Charles (Jane Richards) Quick,female,33,0,2,26 -1,1,Mr. George Bradley,male,37,0,0,26.55 -0,3,Mr. Henry Margido Olsen,male,28,0,0,22.525 -1,3,Mr. Fang Lang,male,26,0,0,56.4958 -1,3,Mr. Eugene Patrick Daly,male,29,0,0,7.75 -0,3,Mr. James Webber,male,66,0,0,8.05 -1,1,Mr. James Robert McGough,male,36,0,0,26.2875 -1,1,Mrs. Martin (Elizabeth L. Barrett) Rothschild,female,54,1,0,59.4 -0,3,Mr. Satio Coleff,male,24,0,0,7.4958 -0,1,Mr. William Anderson Walker,male,47,0,0,34.0208 -1,2,Mrs. (Amelia Milley) Lemore,female,34,0,0,10.5 -0,3,Mr. Patrick Ryan,male,30,0,0,24.15 -1,2,Mrs. William A (Florence Agnes Hughes) Angle,female,36,1,0,26 -0,3,Mr. Stefo Pavlovic,male,32,0,0,7.8958 -1,1,Miss. Anne Perreault,female,30,0,0,93.5 -0,3,Mr. Janko Vovk,male,22,0,0,7.8958 -0,3,Mr. Sarkis Lahoud,male,35,0,0,7.225 -1,1,Mrs. Louis Albert (Ida Sophia Fischer) Hippach,female,44,0,1,57.9792 -0,3,Mr. Fared Kassem,male,18,0,0,7.2292 -0,3,Mr. James Farrell,male,40.5,0,0,7.75 -1,2,Miss. Lucy Ridsdale,female,50,0,0,10.5 -0,1,Mr. John Farthing,male,49,0,0,221.7792 -0,3,Mr. Johan Werner Salonen,male,39,0,0,7.925 -0,2,Mr. Richard George Hocking,male,23,2,1,11.5 -1,2,Miss. Phyllis May Quick,female,2,1,1,26 -0,3,Mr. Nakli Toufik,male,17,0,0,7.2292 -0,3,Mr. Joseph Jr Elias,male,17,1,1,7.2292 -1,3,Mrs. Catherine (Catherine Rizk) Peter,female,24,0,2,22.3583 -0,3,Miss. Marija Cacic,female,30,0,0,8.6625 -1,2,Miss. Eva Miriam Hart,female,7,0,2,26.25 -0,1,Major. Archibald Willingham Butt,male,45,0,0,26.55 -1,1,Miss. Bertha LeRoy,female,30,0,0,106.425 -0,3,Mr. Samuel Beard Risien,male,69,0,0,14.5 -1,1,Miss. Hedwig Margaritha Frolicher,female,22,0,2,49.5 -1,1,Miss. Harriet R Crosby,female,36,0,2,71 -0,3,Miss. Ingeborg Constanzia Andersson,female,9,4,2,31.275 -0,3,Miss. Sigrid Elisabeth Andersson,female,11,4,2,31.275 -1,2,Mr. Edward Beane,male,32,1,0,26 -0,1,Mr. Walter Donald Douglas,male,50,1,0,106.425 -0,1,Mr. Arthur Ernest Nicholson,male,64,0,0,26 -1,2,Mrs. Edward (Ethel Clarke) Beane,female,19,1,0,26 -1,2,Mr. Julian Padro y Manent,male,27,0,0,13.8625 -0,3,Mr. Frank John Goldsmith,male,33,1,1,20.525 -1,2,Master. John Morgan Jr Davies,male,8,1,1,36.75 -1,1,Mr. John Borland Jr Thayer,male,17,0,2,110.8833 -0,2,Mr. Percival James R Sharp,male,27,0,0,26 -0,3,Mr. Timothy O'Brien,male,21,0,0,7.8292 -1,3,Mr. Fahim Leeni,male,22,0,0,7.225 -1,3,Miss. Velin Ohman,female,22,0,0,7.775 -0,1,Mr. George Wright,male,62,0,0,26.55 -1,1,Lady. (Lucille Christiana Sutherland)Duff Gordon,female,48,1,0,39.6 -0,1,Mr. Victor Robbins,male,45,0,0,227.525 -1,1,Mrs. Emil (Tillie Mandelbaum) Taussig,female,39,1,1,79.65 -1,3,Mrs. Guillaume Joseph (Emma) de Messemaeker,female,36,1,0,17.4 -0,3,Mr. Thomas Rowan Morrow,male,30,0,0,7.75 -0,3,Mr. Husein Sivic,male,40,0,0,7.8958 -0,2,Mr. Robert Douglas Norman,male,28,0,0,13.5 -0,3,Mr. John Simmons,male,40,0,0,8.05 -0,3,Miss. (Marion Ogden) Meanwell,female,62,0,0,8.05 -0,3,Mr. Alfred J Davies,male,24,2,0,24.15 -0,3,Mr. Ilia Stoytcheff,male,19,0,0,7.8958 -0,3,Mrs. Nils (Alma Cornelia Berglund) Palsson,female,29,0,4,21.075 -0,3,Mr. Tannous Doharr,male,28,0,0,7.2292 -1,3,Mr. Carl Jonsson,male,32,0,0,7.8542 -1,2,Mr. George Harris,male,62,0,0,10.5 -1,1,Mrs. Edward Dale (Charlotte Lamson) Appleton,female,53,2,0,51.4792 -1,1,Mr. John Irwin Flynn,male,36,0,0,26.3875 -1,3,Miss. Mary Kelly,female,22,0,0,7.75 -0,3,Mr. Alfred George John Rush,male,16,0,0,8.05 -0,3,Mr. George Patchett,male,19,0,0,14.5 -1,2,Miss. Ethel Garside,female,34,0,0,13 -1,1,Mrs. William Baird (Alice Munger) Silvey,female,39,1,0,55.9 -0,3,Mrs. Joseph (Maria Elias) Caram,female,18,1,0,14.4583 -1,3,Mr. Eiriik Jussila,male,32,0,0,7.925 -1,2,Miss. Julie Rachel Christy,female,25,1,1,30 -1,1,Mrs. John Borland (Marian Longstreth Morris) Thayer,female,39,1,1,110.8833 -0,2,Mr. William James Downton,male,54,0,0,26 -0,1,Mr. John Hugo Ross,male,36,0,0,40.125 -0,3,Mr. Uscher Paulner,male,16,0,0,8.7125 -1,1,Miss. Ruth Taussig,female,18,0,2,79.65 -0,2,Mr. John Denzil Jarvis,male,47,0,0,15 -1,1,Mr. Maxmillian Frolicher-Stehli,male,60,1,1,79.2 -0,3,Mr. Eliezer Gilinski,male,22,0,0,8.05 -0,3,Mr. Joseph Murdlin,male,22,0,0,8.05 -0,3,Mr. Matti Rintamaki,male,35,0,0,7.125 -1,1,Mrs. Walter Bertram (Martha Eustis) Stephenson,female,52,1,0,78.2667 -0,3,Mr. William James Elsbury,male,47,0,0,7.25 -0,3,Miss. Mary Bourke,female,40,0,2,7.75 -0,2,Mr. John Henry Chapman,male,37,1,0,26 -0,3,Mr. Jean Baptiste Van Impe,male,36,1,1,24.15 -1,2,Miss. Jessie Wills Leitch,female,31,0,0,33 -0,3,Mr. Alfred Johnson,male,49,0,0,0 -0,3,Mr. Hanna Boulos,male,18,0,0,7.225 -1,1,Sir. Cosmo Edmund Duff Gordon,male,49,1,0,56.9292 -1,2,Mrs. Sidney Samuel (Amy Frances Christy) Jacobsohn,female,24,2,1,27 -0,3,Mr. Petco Slabenoff,male,42,0,0,7.8958 -0,1,Mr. Charles H Harrington,male,37,0,0,42.4 -0,3,Mr. Ernst William Torber,male,44,0,0,8.05 -1,1,Mr. Harry Homer,male,35,0,0,26.55 -0,3,Mr. Edvard Bengtsson Lindell,male,36,1,0,15.55 -0,3,Mr. Milan Karaic,male,30,0,0,7.8958 -1,1,Mr. Robert Williams Daniel,male,27,0,0,30.5 -1,2,Mrs. Joseph (Juliette Marie Louise Lafargue) Laroche,female,22,1,2,41.5792 -1,1,Miss. Elizabeth W Shutes,female,40,0,0,153.4625 -0,3,Mrs. Anders Johan (Alfrida Konstantia Brogren) Andersson,female,39,1,5,31.275 -0,3,Mr. Jose Neto Jardin,male,21,0,0,7.05 -1,3,Miss. Margaret Jane Murphy,female,18,1,0,15.5 -0,3,Mr. John Horgan,male,22,0,0,7.75 -0,3,Mr. William Alfred Brocklebank,male,35,0,0,8.05 -1,2,Miss. Alice Herman,female,24,1,2,65 -0,3,Mr. Ernst Gilbert Danbom,male,34,1,1,14.4 -0,3,Mrs. William Arthur (Cordelia K Stanlick) Lobb,female,26,1,0,16.1 -1,2,Miss. Marion Louise Becker,female,4,2,1,39 -0,2,Mr. Lawrence Gavey,male,26,0,0,10.5 -0,3,Mr. Antoni Yasbeck,male,27,1,0,14.4542 -1,1,Mr. Edwin Nelson Jr Kimball,male,42,1,0,52.5542 -1,3,Mr. Sahid Nakid,male,20,1,1,15.7417 -0,3,Mr. Henry Damsgaard Hansen,male,21,0,0,7.8542 -0,3,Mr. David John Bowen,male,21,0,0,16.1 -0,1,Mr. Frederick Sutton,male,61,0,0,32.3208 -0,2,Rev. Charles Leonard Kirkland,male,57,0,0,12.35 -1,1,Miss. Gretchen Fiske Longley,female,21,0,0,77.9583 -0,3,Mr. Guentcho Bostandyeff,male,26,0,0,7.8958 -0,3,Mr. Patrick D O'Connell,male,18,0,0,7.7333 -1,1,Mr. Algernon Henry Wilson Barkworth,male,80,0,0,30 -0,3,Mr. Johan Svensson Lundahl,male,51,0,0,7.0542 -1,1,Dr. Max Stahelin-Maeglin,male,32,0,0,30.5 -0,1,Mr. William Henry Marsh Parr,male,30,0,0,0 -0,3,Miss. Mabel Skoog,female,9,3,2,27.9 -1,2,Miss. Mary Davis,female,28,0,0,13 -0,3,Mr. Antti Gustaf Leinonen,male,32,0,0,7.925 -0,2,Mr. Harvey Collyer,male,31,1,1,26.25 -0,3,Mrs. Juha (Maria Emilia Ojala) Panula,female,41,0,5,39.6875 -0,3,Mr. Percival Thorneycroft,male,37,1,0,16.1 -0,3,Mr. Hans Peder Jensen,male,20,0,0,7.8542 -1,1,Mlle. Emma Sagesser,female,24,0,0,69.3 -0,3,Miss. Margit Elizabeth Skoog,female,2,3,2,27.9 -1,3,Mr. Choong Foo,male,32,0,0,56.4958 -1,3,Miss. Eugenie Baclini,female,0.75,2,1,19.2583 -1,1,Mr. Henry Sleeper Harper,male,48,1,0,76.7292 -0,3,Mr. Liudevit Cor,male,19,0,0,7.8958 -1,1,Col. Oberst Alfons Simonius-Blumer,male,56,0,0,35.5 -0,3,Mr. Edward Willey,male,21,0,0,7.55 -1,3,Miss. Amy Zillah Elsie Stanley,female,23,0,0,7.55 -0,3,Mr. Mito Mitkoff,male,23,0,0,7.8958 -1,2,Miss. Elsie Doling,female,18,0,1,23 -0,3,Mr. Johannes Halvorsen Kalvik,male,21,0,0,8.4333 -1,3,Miss. Hanora O'Leary,female,16,0,0,7.8292 -0,3,Miss. Hanora Hegarty,female,18,0,0,6.75 -0,2,Mr. Leonard Mark Hickman,male,24,2,0,73.5 -0,3,Mr. Alexander Radeff,male,27,0,0,7.8958 -0,3,Mrs. John (Catherine) Bourke,female,32,1,1,15.5 -0,2,Mr. George Floyd Eitemiller,male,23,0,0,13 -0,1,Mr. Arthur Webster Newell,male,58,0,2,113.275 -1,1,Dr. Henry William Frauenthal,male,50,2,0,133.65 -0,3,Mr. Mohamed Badt,male,40,0,0,7.225 -0,1,Mr. Edward Pomeroy Colley,male,47,0,0,25.5875 -0,3,Mr. Peju Coleff,male,36,0,0,7.4958 -1,3,Mr. Eino William Lindqvist,male,20,1,0,7.925 -0,2,Mr. Lewis Hickman,male,32,2,0,73.5 -0,2,Mr. Reginald Fenton Butler,male,25,0,0,13 -0,3,Mr. Knud Paust Rommetvedt,male,49,0,0,7.775 -0,3,Mr. Jacob Cook,male,43,0,0,8.05 -1,1,Mrs. Elmer Zebley (Juliet Cummins Wright) Taylor,female,48,1,0,52 -1,2,Mrs. Thomas William Solomon (Elizabeth Catherine Ford) Brown,female,40,1,1,39 -0,1,Mr. Thornton Davidson,male,31,1,0,52 -0,2,Mr. Henry Michael Mitchell,male,70,0,0,10.5 -1,2,Mr. Charles Wilhelms,male,31,0,0,13 -0,2,Mr. Ennis Hastings Watson,male,19,0,0,0 -0,3,Mr. Gustaf Hjalmar Edvardsson,male,18,0,0,7.775 -0,3,Mr. Frederick Charles Sawyer,male,24.5,0,0,8.05 -1,3,Miss. Anna Sofia Turja,female,18,0,0,9.8417 -0,3,Mrs. Frederick (Augusta Tyler) Goodwin,female,43,1,6,46.9 -1,1,Mr. Thomas Drake Martinez Cardeza,male,36,0,1,512.3292 -0,3,Miss. Katie Peters,female,28,0,0,8.1375 -1,1,Mr. Hammad Hassab,male,27,0,0,76.7292 -0,3,Mr. Thor Anderson Olsvigen,male,20,0,0,9.225 -0,3,Mr. Charles Edward Goodwin,male,14,5,2,46.9 -0,2,Mr. Thomas William Solomon Brown,male,60,1,1,39 -0,2,Mr. Joseph Philippe Lemercier Laroche,male,25,1,2,41.5792 -0,3,Mr. Jaako Arnold Panula,male,14,4,1,39.6875 -0,3,Mr. Branko Dakic,male,19,0,0,10.1708 -0,3,Mr. Eberhard Thelander Fischer,male,18,0,0,7.7958 -1,1,Miss. Georgette Alexandra Madill,female,15,0,1,211.3375 -1,1,Mr. Albert Adrian Dick,male,31,1,0,57 -1,3,Miss. Manca Karun,female,4,0,1,13.4167 -1,3,Mr. Ali Lam,male,37,0,0,56.4958 -0,3,Mr. Khalil Saad,male,25,0,0,7.225 -0,1,Col. John Weir,male,60,0,0,26.55 -0,2,Mr. Charles Henry Chapman,male,52,0,0,13.5 -0,3,Mr. James Kelly,male,44,0,0,8.05 -1,3,Miss. Katherine Mullens,female,19,0,0,7.7333 -0,1,Mr. John Borland Thayer,male,49,1,1,110.8833 -0,3,Mr. Adolf Mathias Nicolai Olsen Humblen,male,42,0,0,7.65 -1,1,Mrs. John Jacob (Madeleine Talmadge Force) Astor,female,18,1,0,227.525 -1,1,Mr. Spencer Victor Silverthorne,male,35,0,0,26.2875 -0,3,Miss. Saiide Barbara,female,18,0,1,14.4542 -0,3,Mr. Martin Gallagher,male,25,0,0,7.7417 -0,3,Mr. Henrik Juul Hansen,male,26,1,0,7.8542 -0,2,Mr. Henry Samuel Morley,male,39,0,0,26 -1,2,Mrs. Florence Kelly,female,45,0,0,13.5 -1,1,Mr. Edward Pennington Calderhead,male,42,0,0,26.2875 -1,1,Miss. Alice Cleaver,female,22,0,0,151.55 -1,3,Master. Halim Gonios Moubarek,male,4,1,1,15.2458 -1,1,Mlle. Berthe Antonine Mayne,female,24,0,0,49.5042 -0,1,Mr. Herman Klaber,male,41,0,0,26.55 -1,1,Mr. Elmer Zebley Taylor,male,48,1,0,52 -0,3,Mr. August Viktor Larsson,male,29,0,0,9.4833 -0,2,Mr. Samuel Greenberg,male,52,0,0,13 -0,3,Mr. Peter Andreas Lauritz Andersen Soholt,male,19,0,0,7.65 -1,1,Miss. Caroline Louise Endres,female,38,0,0,227.525 -1,2,Miss. Edwina Celia Troutt,female,27,0,0,10.5 -0,3,Mr. Malkolm Joackim Johnson,male,33,0,0,7.775 -1,2,Miss. Annie Jessie Harper,female,6,0,1,33 -0,3,Mr. Svend Lauritz Jensen,male,17,1,0,7.0542 -0,2,Mr. William Henry Gillespie,male,34,0,0,13 -0,2,Mr. Henry Price Hodges,male,50,0,0,13 -1,1,Mr. Norman Campbell Chambers,male,27,1,0,53.1 -0,3,Mr. Luka Oreskovic,male,20,0,0,8.6625 -1,2,Mrs. Peter Henry (Lillian Jefferys) Renouf,female,30,3,0,21 -1,3,Miss. Margareth Mannion,female,28,0,0,7.7375 -0,2,Mr. Kurt Arnold Gottfrid Bryhl,male,25,1,0,26 -0,3,Miss. Pieta Sofia Ilmakangas,female,25,1,0,7.925 -1,1,Miss. Elisabeth Walton Allen,female,29,0,0,211.3375 -0,3,Mr. Houssein G N Hassan,male,11,0,0,18.7875 -0,2,Mr. Robert J Knight,male,41,0,0,0 -0,2,Mr. William John Berriman,male,23,0,0,13 -0,2,Mr. Moses Aaron Troupiansky,male,23,0,0,13 -0,3,Mr. Leslie Williams,male,28.5,0,0,16.1 -0,3,Mrs. Edward (Margaret Ann Watson) Ford,female,48,1,3,34.375 -1,1,Mr. Gustave J Lesurer,male,35,0,0,512.3292 -0,3,Mr. Kanio Ivanoff,male,20,0,0,7.8958 -0,3,Mr. Minko Nankoff,male,32,0,0,7.8958 -1,1,Mr. Walter James Hawksford,male,45,0,0,30 -0,1,Mr. Tyrell William Cavendish,male,36,1,0,78.85 -1,1,Miss. Susan Parker Ryerson,female,21,2,2,262.375 -0,3,Mr. Neal McNamee,male,24,1,0,16.1 -1,3,Mr. Juho Stranden,male,31,0,0,7.925 -0,1,Capt. Edward Gifford Crosby,male,70,1,1,71 -0,3,Mr. Rossmore Edward Abbott,male,16,1,1,20.25 -1,2,Miss. Anna Sinkkonen,female,30,0,0,13 -0,1,Mr. Daniel Warner Marvin,male,19,1,0,53.1 -0,3,Mr. Michael Connaghton,male,31,0,0,7.75 -1,2,Miss. Joan Wells,female,4,1,1,23 -1,3,Master. Meier Moor,male,6,0,1,12.475 -0,3,Mr. Johannes Joseph Vande Velde,male,33,0,0,9.5 -0,3,Mr. Lalio Jonkoff,male,23,0,0,7.8958 -1,2,Mrs. Samuel (Jane Laver) Herman,female,48,1,2,65 -1,2,Master. Viljo Hamalainen,male,0.67,1,1,14.5 -0,3,Mr. August Sigfrid Carlsson,male,28,0,0,7.7958 -0,2,Mr. Percy Andrew Bailey,male,18,0,0,11.5 -0,3,Mr. Thomas Leonard Theobald,male,34,0,0,8.05 -1,1,the Countess. of (Lucy Noel Martha Dyer-Edwards) Rothes,female,33,0,0,86.5 -0,3,Mr. John Garfirth,male,23,0,0,14.5 -0,3,Mr. Iisakki Antino Aijo Nirva,male,41,0,0,7.125 -1,3,Mr. Hanna Assi Barah,male,20,0,0,7.2292 -1,1,Mrs. William Ernest (Lucile Polk) Carter,female,36,1,2,120 -0,3,Mr. Hans Linus Eklund,male,16,0,0,7.775 -1,1,Mrs. John C (Anna Andrews) Hogeboom,female,51,1,0,77.9583 -0,1,Dr. Arthur Jackson Brewe,male,46,0,0,39.6 -0,3,Miss. Mary Mangan,female,30.5,0,0,7.75 -0,3,Mr. Daniel J Moran,male,28,1,0,24.15 -0,3,Mr. Daniel Danielsen Gronnestad,male,32,0,0,8.3625 -0,3,Mr. Rene Aime Lievens,male,24,0,0,9.5 -0,3,Mr. Niels Peder Jensen,male,48,0,0,7.8542 -0,2,Mrs. (Mary) Mack,female,57,0,0,10.5 -0,3,Mr. Dibo Elias,male,29,0,0,7.225 -1,2,Mrs. Elizabeth (Eliza Needs) Hocking,female,54,1,3,23 -0,3,Mr. Pehr Fabian Oliver Malkolm Myhrman,male,18,0,0,7.75 -0,3,Mr. Roger Tobin,male,20,0,0,7.75 -1,3,Miss. Virginia Ethel Emanuel,female,5,0,0,12.475 -0,3,Mr. Thomas J Kilgannon,male,22,0,0,7.7375 -1,1,Mrs. Edward Scott (Elisabeth Walton McMillan) Robert,female,43,0,1,211.3375 -1,3,Miss. Banoura Ayoub,female,13,0,0,7.2292 -1,1,Mrs. Albert Adrian (Vera Gillespie) Dick,female,17,1,0,57 -0,1,Mr. Milton Clyde Long,male,29,0,0,30 -0,3,Mr. Andrew G Johnston,male,35,1,2,23.45 -0,3,Mr. William Ali,male,25,0,0,7.05 -0,3,Mr. Abraham (David Lishin) Harmer,male,25,0,0,7.25 -1,3,Miss. Anna Sofia Sjoblom,female,18,0,0,7.4958 -0,3,Master. George Hugh Rice,male,8,4,1,29.125 -1,3,Master. Bertram Vere Dean,male,1,1,2,20.575 -0,1,Mr. Benjamin Guggenheim,male,46,0,0,79.2 -0,3,Mr. Andrew Keane,male,20,0,0,7.75 -0,2,Mr. Alfred Gaskell,male,16,0,0,26 -0,3,Miss. Stella Anna Sage,female,21,8,2,69.55 -0,1,Mr. William Fisher Hoyt,male,43,0,0,30.6958 -0,3,Mr. Ristiu Dantcheff,male,25,0,0,7.8958 -0,2,Mr. Richard Otter,male,39,0,0,13 -1,1,Dr. Alice (Farnham) Leader,female,49,0,0,25.9292 -1,3,Mrs. Mara Osman,female,31,0,0,8.6833 -0,3,Mr. Yousseff Ibrahim Shawah,male,30,0,0,7.2292 -0,3,Mrs. Jean Baptiste (Rosalie Paula Govaert) Van Impe,female,30,1,1,24.15 -0,2,Mr. Martin Ponesell,male,34,0,0,13 -1,2,Mrs. Harvey (Charlotte Annie Tate) Collyer,female,31,1,1,26.25 -1,1,Master. William Thornton II Carter,male,11,1,2,120 -1,3,Master. Assad Alexander Thomas,male,0.42,0,1,8.5167 -1,3,Mr. Oskar Arvid Hedman,male,27,0,0,6.975 -0,3,Mr. Karl Johan Johansson,male,31,0,0,7.775 -0,1,Mr. Thomas Jr Andrews,male,39,0,0,0 -0,3,Miss. Ellen Natalia Pettersson,female,18,0,0,7.775 -0,2,Mr. August Meyer,male,39,0,0,13 -1,1,Mrs. Norman Campbell (Bertha Griggs) Chambers,female,33,1,0,53.1 -0,3,Mr. William Alexander,male,26,0,0,7.8875 -0,3,Mr. James Lester,male,39,0,0,24.15 -0,2,Mr. Richard James Slemen,male,35,0,0,10.5 -0,3,Miss. Ebba Iris Alfrida Andersson,female,6,4,2,31.275 -0,3,Mr. Ernest Portage Tomlin,male,30.5,0,0,8.05 -0,1,Mr. Richard Fry,male,39,0,0,0 -0,3,Miss. Wendla Maria Heininen,female,23,0,0,7.925 -0,2,Mr. Albert Mallet,male,31,1,1,37.0042 -0,3,Mr. John Fredrik Alexander Holm,male,43,0,0,6.45 -0,3,Master. Karl Thorsten Skoog,male,10,3,2,27.9 -1,1,Mrs. Charles Melville (Clara Jennings Gregg) Hays,female,52,1,1,93.5 -1,3,Mr. Nikola Lulic,male,27,0,0,8.6625 -0,1,Jonkheer. John George Reuchlin,male,38,0,0,0 -1,3,Mrs. (Beila) Moor,female,27,0,1,12.475 -0,3,Master. Urho Abraham Panula,male,2,4,1,39.6875 -0,3,Mr. John Flynn,male,36,0,0,6.95 -0,3,Mr. Len Lam,male,23,0,0,56.4958 -1,2,Master. Andre Mallet,male,1,0,2,37.0042 -1,3,Mr. Thomas Joseph McCormack,male,19,0,0,7.75 -1,1,Mrs. George Nelson (Martha Evelyn) Stone,female,62,0,0,80 -1,3,Mrs. Antoni (Selini Alexander) Yasbeck,female,15,1,0,14.4542 -1,2,Master. George Sibley Richards,male,0.83,1,1,18.75 -0,3,Mr. Amin Saad,male,30,0,0,7.2292 -0,3,Mr. Albert Augustsson,male,23,0,0,7.8542 -0,3,Mr. Owen George Allum,male,18,0,0,8.3 -1,1,Miss. Sara Rebecca Compton,female,39,1,1,83.1583 -0,3,Mr. Jakob Pasic,male,21,0,0,8.6625 -0,3,Mr. Maurice Sirota,male,20,0,0,8.05 -1,3,Mr. Chang Chip,male,32,0,0,56.4958 -1,1,Mr. Pierre Marechal,male,29,0,0,29.7 -0,3,Mr. Ilmari Rudolf Alhomaki,male,20,0,0,7.925 -0,2,Mr. Thomas Charles Mudd,male,16,0,0,10.5 -1,1,Miss. Augusta Serepeca,female,30,0,0,31 -0,3,Mr. Peter L Lemberopolous,male,34.5,0,0,6.4375 -0,3,Mr. Jeso Culumovic,male,17,0,0,8.6625 -0,3,Mr. Anthony Abbing,male,42,0,0,7.55 -0,3,Mr. Douglas Bullen Sage,male,18,8,2,69.55 -0,3,Mr. Marin Markoff,male,35,0,0,7.8958 -0,2,Rev. John Harper,male,28,0,1,33 -1,1,Mrs. Samuel L (Edwiga Grabowska) Goldenberg,female,40,1,0,89.1042 -0,3,Master. Sigvard Harald Elias Andersson,male,4,4,2,31.275 -0,3,Mr. Johan Svensson,male,74,0,0,7.775 -0,3,Miss. Nourelain Boulos,female,9,1,1,15.2458 -1,1,Miss. Mary Conover Lines,female,16,0,1,39.4 -0,2,Mrs. Ernest Courtenay (Lilian Hughes) Carter,female,44,1,0,26 -1,3,Mrs. Sam (Leah Rosen) Aks,female,18,0,1,9.35 -1,1,Mrs. George Dennick (Mary Hitchcock) Wick,female,45,1,1,164.8667 -1,1,Mr. Peter Denis Daly,male,51,0,0,26.55 -1,3,Mrs. Solomon (Latifa Qurban) Baclini,female,24,0,3,19.2583 -0,3,Mr. Raihed Razi,male,30,0,0,7.2292 -0,3,Mr. Claus Peter Hansen,male,41,2,0,14.1083 -0,2,Mr. Frederick Edward Giles,male,21,1,0,11.5 -1,1,Mrs. Frederick Joel (Margaret Welles Barron) Swift,female,48,0,0,25.9292 -0,3,Miss. Dorothy Edith Sage,female,14,8,2,69.55 -0,2,Mr. John William Gill,male,24,0,0,13 -1,2,Mrs. (Karolina) Bystrom,female,42,0,0,13 -1,2,Miss. Asuncion Duran y More,female,27,1,0,13.8583 -0,1,Mr. Washington Augustus II Roebling,male,31,0,0,50.4958 -0,3,Mr. Philemon van Melkebeke,male,23,0,0,9.5 -1,3,Master. Harold Theodor Johnson,male,4,1,1,11.1333 -0,3,Mr. Cerin Balkic,male,26,0,0,7.8958 -1,1,Mrs. Richard Leonard (Sallie Monypeny) Beckwith,female,47,1,1,52.5542 -0,1,Mr. Frans Olof Carlsson,male,33,0,0,5 -0,3,Mr. Victor Vander Cruyssen,male,47,0,0,9 -1,2,Mrs. Samuel (Hannah Wizosky) Abelson,female,28,1,0,24 -1,3,Miss. Adele Kiamie Najib,female,15,0,0,7.225 -0,3,Mr. Alfred Ossian Gustafsson,male,20,0,0,9.8458 -0,3,Mr. Nedelio Petroff,male,19,0,0,7.8958 -0,3,Mr. Kristo Laleff,male,23,0,0,7.8958 -1,1,Mrs. Thomas Jr (Lily Alexenia Wilson) Potter,female,56,0,1,83.1583 -1,2,Mrs. William (Imanita Parrish Hall) Shelley,female,25,0,1,26 -0,3,Mr. Johann Markun,male,33,0,0,7.8958 -0,3,Miss. Gerda Ulrika Dahlberg,female,22,0,0,10.5167 -0,2,Mr. Frederick James Banfield,male,28,0,0,10.5 -0,3,Mr. Henry Jr Sutehall,male,25,0,0,7.05 -0,3,Mrs. William (Margaret Norton) Rice,female,39,0,5,29.125 -0,2,Rev. Juozas Montvila,male,27,0,0,13 -1,1,Miss. Margaret Edith Graham,female,19,0,0,30 -0,3,Miss. Catherine Helen Johnston,female,7,1,2,23.45 -1,1,Mr. Karl Howell Behr,male,26,0,0,30 -0,3,Mr. Patrick Dooley,male,32,0,0,7.75 \ No newline at end of file diff --git a/danfojs-node/types/config/config.d.ts b/danfojs-node/types/config/config.d.ts deleted file mode 100644 index cd41525e..00000000 --- a/danfojs-node/types/config/config.d.ts +++ /dev/null @@ -1,20 +0,0 @@ -/** - * Package wide configuration class - */ -export class Configs { - table_width: number; - table_truncate: number; - dtype_test_lim: number; - table_max_row: number; - table_max_col_in_console: number; - set_width(val?: any): void; - get get_width(): number; - set_max_col_in_console(val?: any): void; - get get_max_col_in_console(): number; - set_row_num(val?: any): void; - get get_max_row(): number; - get get_truncate(): number; - set_truncate(val?: any): void; - get get_dtype_test_lim(): number; - set_dtype_test_lim(val?: any): void; -} diff --git a/danfojs-node/types/core/concat.d.ts b/danfojs-node/types/core/concat.d.ts deleted file mode 100644 index 269f31e2..00000000 --- a/danfojs-node/types/core/concat.d.ts +++ /dev/null @@ -1,5 +0,0 @@ -export class Concat { - constructor(kwargs?: any); -} -export function concat(kwargs?: any): DataFrame; -import { DataFrame } from "./frame"; diff --git a/danfojs-node/types/core/date_range.d.ts b/danfojs-node/types/core/date_range.d.ts deleted file mode 100644 index 58f7060a..00000000 --- a/danfojs-node/types/core/date_range.d.ts +++ /dev/null @@ -1,27 +0,0 @@ -/** - * Generate date range between a specified set of date - * @param {kwargs} kwargs { - * start : string - * end : string - * period: int - * freq : string - * } - * @returns Array - */ -export class date_range { - constructor(kwargs?: any); - offset: number; - start?: any; - end?: any; - period?: any; - freq?: any; - freq_list: string[]; - range(start?: any, end?: any, period?: any, offset?: any): any; - freq_type(date?: any, ftype?: any): any; - offset_count(d_array?: any, offset?: any): any[]; - set_dateProps(date?: any, ftype?: any, val?: any): Date; - toLocalString(d_array?: any): any; - month_end(start_date?: any, end_date?: any): any; - month_range(range?: any): any; - day_end(start_date?: any, end_date?: any): number; -} diff --git a/danfojs-node/types/core/frame.d.ts b/danfojs-node/types/core/frame.d.ts deleted file mode 100644 index 4913c6f1..00000000 --- a/danfojs-node/types/core/frame.d.ts +++ /dev/null @@ -1,403 +0,0 @@ -/** - * A 2D frame object that stores data in structured tabular format - * @param {data} data, JSON, Array, 2D Tensor - * @param {kwargs} Object {columns: Array of column names, defaults to ordered numbers when not specified - * dtypes: strings of data types, automatically inferred when not specified - * index: row index for subseting array, defaults to ordered numbers when not specified} - * - * @returns DataFrame - */ -export class DataFrame extends Ndframe { - constructor(data?: any, kwargs?: any); - _set_column_property(): void; - /** - * Drop a list of rows or columns base on the specified axis - * @param {Object} kwargs Configuration object - * {columns: [Array(Columns| Index)] array of column names to drop - * axis: row=0, columns=1 - * inplace: specify whether to drop the row/column with/without creating a new DataFrame} - * @returns null | DataFrame - * - */ - drop(kwargs?: any): DataFrame; - /** - * Purely label based indexing. Can accept string label names for both rows and columns - * @param {kwargs} kwargs object {rows: Array of index, columns: Array of column name(s)} - * @return DataFrame data stucture - */ - loc(kwargs?: any): DataFrame; - /** - * Access a dataframe element using row and column index - * @param {*} kwargs object {rows: Array of index, columns: Array of column index} - * @return DataFrame data stucture - */ - iloc(kwargs?: any): DataFrame; - /** - * Prints the first n values in a dataframe - * @param {rows} rows --> int - * @returns DataFrame - */ - head(rows?: any): DataFrame; - /** - * Prints the last n values in a dataframe - * @param {rows} rows --> int - * @returns DataFrame - */ - tail(rows?: any): DataFrame; - /** - * Gets [num] number of random rows in a dataframe - * @param {rows} rows --> int - * @returns DataFrame - */ - sample(num?: number): DataFrame; - /** - * Return Addition of DataFrame and other, element-wise (binary operator add). - * @param {other} DataFrame, Series, Array or Number to add - * @returns {DataFrame} - */ - add(other?: any, axis?: any): DataFrame; - /** - * Return subtraction of DataFrame and other, element-wise (binary operator add). - * @param {other} DataFrame, Series, Array or Number to add - * @returns {DataFrame} - */ - sub(other?: any, axis?: any): DataFrame; - /** - * Return subtraction of DataFrame and other, element-wise (binary operator add). - * @param {other} DataFrame, Series, Array or Number to add - * @returns {DataFrame} - */ - mul(other?: any, axis?: any): DataFrame; - /** - * Return division of DataFrame and other, element-wise (binary operator add). - * @param {other} DataFrame, Series, Array or Number to add - * @returns {DataFrame} - */ - div(other?: any, axis?: any): DataFrame; - /** - * Return division of DataFrame and other, element-wise (binary operator add). - * @param {other} DataFrame, Series, Array or Number to add - * @returns {DataFrame} - */ - pow(other?: any, axis?: any): DataFrame; - /** - * Return division of DataFrame and other, element-wise (binary operator add). - * @param {other} DataFrame, Series, Array or Number to add - * @returns {DataFrame} - */ - mod(other?: any, axis?: any): DataFrame; - /** - * Return mean of DataFrame across specified axis. - * @param {axis} Number {0: row, 1 : column} Axis for the function to be applied on - * @returns {Series} - */ - mean(axis?: number): Series; - /** - * Return median of DataFrame across specified axis. - * @param {axis} Number {0: row, 1 : column} Axis for the function to be applied on - * @returns {Series} - */ - median(axis?: number): Series; - /** - * Return minimum element in a DataFrame across specified axis. - * @param {axis} Number {0: row, 1 : column} Axis for the function to be applied on - * @returns {Series} - */ - min(axis?: number): Series; - /** - * Return maximum element of DataFrame across specified axis. - * @param {axis} Number {0: row, 1 : column} Axis for the function to be applied on - * @returns {Series} - */ - max(axis?: number): Series; - /** - * Return standard deviation of DataFrame across specified axis. - * @param {axis} Number {0: row, 1 : column} Axis for the function to be applied on - * @returns {Series} - */ - std(axis?: number): Series; - /** - * Return variance of DataFrame across specified axis. - * @param {axis} Number {0: row, 1 : column} Axis for the function to be applied on - * @returns {Series} - */ - var(axis?: number): Series; - /** - * Return number of non-null elements in a Series - * @returns {Series}, Count of non-null values - */ - count(axis?: number): Series; - /** - * Rounds values in DataFrame to specified number of dp - * @returns {DataFrame}, New DataFrame with rounded values - */ - round(dp?: number): DataFrame; - /** - * Perform Cummulative operations - * @param {axis} axis [int] {0 or 1} - * @param {ops} ops {String} name of operation - * @return {DataFrame} - */ - __cum_ops(axis?: any, ops?: any): DataFrame; - /** - * calculate the cummulative sum along axis - * @param {kwargs} {axis: [int]} - * @returns {DataFrame} - */ - cumsum(kwargs?: {}): DataFrame; - /** - * calculate the cummulative min - * @param {kwargs} {axis: [int]} - * @returns {DataFrame} - */ - cummin(kwargs?: {}): DataFrame; - /** - * calculate the cummulative max - * @param {kwargs} {axis: [int]} - * @returns {DataFrame} - */ - cummax(kwargs?: {}): DataFrame; - /** - * calculate the cummulative prod - * @param {kwargs} {axis: [int]} - * @returns {DataFrame} - */ - cumprod(kwargs?: {}): DataFrame; - /** - * Makes a new copy of a DataFrame - * @returns {DataFrame} - */ - copy(): DataFrame; - /** - * Generate a new index for the DataFrame. - * This is useful when the index is meaningless and needs to be reset to the default before another operation. - * @param {inplace} boolean: Modify the original object or return a new one. Default to false - */ - reset_index(inplace?: boolean): DataFrame; - /** - * Set the DataFrame index (row labels) using an array of the same length. - * @param {kwargs} {index: Array of new index values} - */ - set_index(kwargs?: {}): DataFrame; - /** - * Generate descriptive statistics for all numeric columns - * Descriptive statistics include those that summarize the central tendency, - * dispersion and shape of a dataset’s distribution, excluding NaN values. - * @returns {Series} - */ - describe(): Series; - /** - * Return a subset of the DataFrame’s columns based on the column dtypes. - * @param {include} scalar or array-like. A selection of dtypes or strings to be included. At least one of these parameters must be supplied. - * @returns {DataFrame, Series} The subset of the frame including the dtypes. - */ - select_dtypes(include?: any): DataFrame; - /** - * Sort a Dataframe in ascending or descending order by a specified column name. - * @param {kwargs} Object, {by: Column name to sort by - * ascending (Bool): Whether to return sorted values in ascending order or not, - * inplace (Bool): Whether to perform sorting on the original Series or not} - * @returns {Series} - */ - /** - * Return the sum of the values in a DataFrame across a specified axis. - * @params {kwargs} {axis: 0 for row and 1 for column} - * @returns {Series}, Sum of values accross axis - */ - sum(kwargs?: { - axis: number; - }): Series; - /** - * Returns the absolute values in DataFrame - * @return {DataFrame} - */ - abs(): DataFrame; - __get_tensor_and_idx(df?: any, axis?: any): any[]; - /** - * Filter DataFrame element base on the element in a column - * @param {kwargs} kwargs {column : coumn name[string], is: String, to: string| int} - * @returns {DataFrame} - */ - query(kwargs?: any): DataFrame; - /** - * Add a column with values to the dataframe - * @param {kwargs} Object {column :[string] , value:[Array]} - * - */ - addColumn(kwargs?: any): void; - /** - * - * @param {col} col is a list of column with maximum length of two - */ - groupby(col?: any): GroupBy; - /** - * Return a sequence of axis dimension along row and columns - * @params col_name: the name of a column in the database. - * @returns tensor of shape 1 - */ - column(col_name?: any): Series; - /** - * Replace NaN or undefined with a specified value" - * @param {kwargs}, {column(s): Array of column name(s) to fill. If undefined fill all columns; - * value(s): Array | Scalar of value(s) to fill with. If single value is specified, we use it to fill all - * @return {DataFrame} - */ - fillna(kwargs?: {}): DataFrame; - /** - * Return a boolean same-sized object indicating if the values are NaN. NaN and undefined values, - * gets mapped to True values. Everything else gets mapped to False values. - * @return {DataFrame} - */ - isna(): DataFrame; - /** - * Obtain index containing nan values - * @return Array list (int) - */ - nanIndex(): number[]; - /** - * Drop all rows containing NaN - * @param {kwargs} kwargs [Object] {axis: [int]{o or 1}, inplace:[boolean]} - */ - dropna(kwargs?: any): DataFrame; - /** - * Apply a function to each element or along a specified axis of the DataFrame. Supports JavaScipt functions - * when axis is not specified, and accepts Tensorflow functions when axis is specified. - * @param {kwargs} kargs is defined as {axis: undefined, 0 or 1, callable: [FUNCTION]} - * @return Array - */ - apply(kwargs?: any): Series | DataFrame; - /** - * Returns Less than of DataFrame and other. Supports element wise operations - * @param {other} DataFrame, Series, Scalar - * @param {axis} Number {0 for row, 1 for index} Whether to compare by the index or columns - * @return {DataFrame} - */ - lt(other?: any, axis?: any): DataFrame; - /** - * Returns Greater than of DataFrame and other. Supports element wise operations - * @param {other} DataFrame, Series, Scalar - * @param {axis} Number {0 for row, 1 for index} Whether to compare by the index or columns - * @return {DataFrame} - */ - gt(other?: any, axis?: any): DataFrame; - /** - * Returns Less than or Equal to of DataFrame and other. Supports element wise operations - * @param {other} DataFrame, Series, Scalar - * @param {axis} Number {0 for row, 1 for index} Whether to compare by the index or columns - * @return {DataFrame} - */ - le(other?: any, axis?: any): DataFrame; - /** - * Returns Greater than or Equal to of DataFrame and other. Supports element wise operations - * @param {other} DataFrame, Series, Scalar - * @param {axis} Number {0 for row, 1 for index} Whether to compare by the index or columns - * @return {DataFrame} - */ - ge(other?: any, axis?: any): DataFrame; - /** - * Returns Not Equal to of DataFrame and other. Supports element wise operations - * @param {other} DataFrame, Series, Scalar - * @param {axis} Number {0 for row, 1 for index} Whether to compare by the index or columns - * @return {DataFrame} - */ - ne(other?: any, axis?: any): DataFrame; - /** - * Returns Greater than or Equal to of DataFrame and other. Supports element wise operations - * @param {other} DataFrame, Series, Scalar - * @param {axis} Number {0 for row, 1 for index} Whether to compare by the index or columns - * @return {DataFrame} - */ - eq(other?: any, axis?: any): DataFrame; - /** - * Replace all occurence of a value with a new specified value" - * @param {kwargs}, {"replace": the value you want to replace, - * "with": the new value you want to replace the olde value with - * "in": Array of column names to replace value in, If not specified, replace all columns} - * @return {Series} - */ - replace(kwargs?: {}): Series; - __logical_ops(val?: any, logical_type?: any, axis?: any): DataFrame; - __get_df_from_tensor(val?: any, col_names?: any): DataFrame; - __frame_is_compactible_for_operation(): boolean; - __get_ops_tensors(tensors?: any, axis?: any): any[]; - /** - * Transpose index and columns. - * Reflect the DataFrame over its main diagonal by writing rows as columns and vice-versa. - * The property T is an accessor to the method transpose(). - */ - transpose(): DataFrame; - /** - * The property T is an accessor to the method transpose(). - */ - get T(): DataFrame; - /** - * Returns the data types in the DataFrame - * @return {Array} list of data types for each column - */ - get ctypes(): Series; - /** - * Make plots of Series or DataFrame. - * Uses the Plotly as backend, so supports Plotly's configuration parameters - * @param {string} div Name of the div to show the plot - * @returns {Class} Plot class that expoese different plot type - */ - plot(div: string): any; - /** - * Returns the Tensorflow tensor backing the DataFrame Object - * @returns {2D tensor} - */ - get tensor(): 2; - /** - * Sets the data types of an DataFrame - * @param {Object} kwargs {column: Name of the column to cast, dtype: [float32, int32, string] data type to cast to} - * @returns {DataFrame} - */ - astype(kwargs?: any): DataFrame; - /** - * Return the unique values along an axis - * @param {axis} Int, 0 for row, and 1 for column. Default to 1 - * @return {Object} - */ - unique(axis?: number): any; - /** - * Return the number of unique value along an axis - * @param {axis} Int, 0 for row, and 1 for column. Default to 1 - * @return {Series} - */ - nunique(axis?: number): Series; - /** - * Change axes labels. Object values must be unique (1-to-1). - * Labels not contained in a dict / Series will be left as-is. Extra labels listed don’t throw an error. - * @param {Object} kwargs {mapper: Dict-like or functions transformations to apply to that axis’ values, - * axis: Int, 0 for row, and 1 for column. Default to 1, - * inplace: Whether to return a new DataFrame. If True then value of copy is ignored. - * @returns {DataFrame} - */ - rename(kwargs?: any): DataFrame; - /** - * Sort DataFrame by index - * @param {*} kwargs {inplace: Boolean, ascending: Bool} - * @returns DataFrame - */ - sort_index(kwargs?: any): DataFrame; - /** - * Sort a Dataframe in ascending or descending order by a specified column name. - * @param {kwargs} Object, {by: Column name to sort by - * ascending (Bool): Whether to return sorted values in ascending order or not, - * inplace (Bool): Whether to perform sorting on the original Series or not} - * @returns {Series} - */ - sort_values(kwargs?: {}): Series; - __set_col_property(self?: any, col_vals?: any, col_names?: any, old_col_names?: any): void; - __update_frame_in_place(row_data?: any, column_names?: any, col_obj?: any, index?: any, dtypes?: any): void; - __sort_by(col_value?: any, df_index?: any, asc?: any): any[][]; - /** - * Append rows to a DataFrame - * @param {val} val Array | Series to append to the object - * @return DataFrame - */ - append(val?: any): DataFrame; -} -import Ndframe from "./generic"; -import { Series } from "./series"; -import { GroupBy } from "./groupby"; diff --git a/danfojs-node/types/core/generic.d.ts b/danfojs-node/types/core/generic.d.ts deleted file mode 100644 index edb2146e..00000000 --- a/danfojs-node/types/core/generic.d.ts +++ /dev/null @@ -1,112 +0,0 @@ -export default class NDframe { - /** - * N-Dimensiona data structure. Stores multi-dimensional - * data in a size-mutable, labeled data structure. Analogous to the Python Pandas DataFrame. - * - * @param {data} Array JSON, Tensor. Block of data. - * @param {kwargs} Object Optional Configuration Object - * {columns: Array of column names. If not specified and data is an array of array, use range index. - * dtypes: Data types of the columns, - * index: row index for subseting array } - * - * @returns NDframe - */ - constructor(data?: any, kwargs?: {}); - kwargs: {}; - series: boolean; - /** - * - * @param {Array} data - * Read array of data into NDFrame - */ - _read_array(data?: any[]): void; - data?: any[]; - row_data_tensor?: any; - col_data?: any[][]; - col_data_tensor?: any; - index_arr?: any[]; - columns?: any; - /** - * Convert Javascript Object of arrays into NDFrame - * @param {*} data Object of Arrays - * @param {*} type type 1 object are of JSON form [{a: 1, b: 2}, {a: 30, b: 20}], - * type 2 object are of the form {a: [1,2,3,4], b: [30,20, 30, 20}] - */ - _read_object(data?: any, type?: any): void; - /** - * Sets the data type of the NDFrame. Supported types are ['float32', "int32", 'string', 'boolean'] - * @param {Array} dtypes Array of data types. - * @param {Boolean} infer Whether to automatically infer the dtypes from the Object - */ - _set_col_types(dtypes: Array, infer: boolean): void; - col_types?: any[]; - /** - * Returns the data types in the DataFrame - * @return {Array} list of data types for each column - */ - get dtypes(): any[]; - /** - * Gets dimension of the NDFrame - * @returns {Integer} dimension of NDFrame - */ - get ndim(): any; - /** - * Gets values for index and columns - * @return {Object} axes configuration for index and columns of NDFrame - */ - get axes(): any; - /** - * Gets index of the NDframe - * @return {Array} array of index from series - */ - get index(): any[]; - /** - * Sets index of the NDFrame - */ - __set_index(labels?: any): void; - /** - * Generate a new index for NDFrame. - */ - __reset_index(): void; - /** - * Gets a sequence of axis dimension along row and columns - * @returns {Array} the shape of the NDFrame - */ - get shape(): any[]; - /** - * Gets the values in the NDFrame in JS array - * @returns {Array} Arrays of arrays of data instances - */ - get values(): any[]; - /** - * Gets the column names of the data - * @returns {Array} strings of column names - */ - get column_names(): any[]; - /** - * Return a boolean same-sized object indicating if the values are NaN. NaN and undefined values - * gets mapped to True values. Everything else gets mapped to False values. - * @return {Array} - */ - __isna(): any[]; - get size(): any; - /** - * Return object data as comma-separated values (csv). - * @returns {Promise} CSV representation of Object data - */ - to_csv(): Promise; - /** - * Return object as JSON string. - * @returns {Promise } JSON representation of Object data - */ - to_json(): Promise; - /** - * Prints the data in a Series as a grid of row and columns - */ - toString(): any; - /** - * Pretty prints n number of rows in a DataFrame or Series in the console - * @param {rows} Number of rows to print - */ - print(): void; -} diff --git a/danfojs-node/types/core/get_dummies.d.ts b/danfojs-node/types/core/get_dummies.d.ts deleted file mode 100644 index a0edcaa0..00000000 --- a/danfojs-node/types/core/get_dummies.d.ts +++ /dev/null @@ -1,10 +0,0 @@ -/** - * Generate one hot encoding for categorical variable in arrays |Serie | and Dataframe - * @param {kwargs} kwargs { data : Array | Series | DataFrame, - * prefix_sep: String e.g "_", - * prefix: String | Array of String, - * columns: [Array] columns to be encoded in DataFrame. - * } - */ -export function get_dummies(kwargs?: any): DataFrame; -import { DataFrame } from "./frame"; diff --git a/danfojs-node/types/core/groupby.d.ts b/danfojs-node/types/core/groupby.d.ts deleted file mode 100644 index c8231934..00000000 --- a/danfojs-node/types/core/groupby.d.ts +++ /dev/null @@ -1,59 +0,0 @@ -/** - * The class performs all groupby operation on a dataframe - * involveing all aggregate funciton - * @param {col_dict} col_dict Object of unique keys in the group by column - * @param {key_col} key_col Array contains the column names - * @param {data} Array the dataframe data - * @param {column_name} Array of all column name in the dataframe. - */ -export class GroupBy { - constructor(col_dict?: any, key_col?: any, data?: any, column_name?: any); - key_col?: any; - col_dict?: any; - data?: any; - column_name?: any; - data_tensors: {}; - /** - * Group the dataframe by the column by - * creating an object to store the grouping - * @returns Groupby data structure - */ - group(): GroupBy; - /** - * obtain the column for each group - * @param {col_name} col_name [Array]--> array of column names - * @return Groupby data structure - */ - col(col_names?: any): GroupBy; - group_col_name?: any[]; - group_col: {}; - /** - * Basic root of all column arithemetic in groups - * @param {operation} operatioin String - */ - arithemetic(operation?: any): {}; - count(): DataFrame; - sum(): DataFrame; - std(): DataFrame; - var(): DataFrame; - mean(): DataFrame; - cumsum(): DataFrame; - cummax(): DataFrame; - cumprod(): DataFrame; - cummin(): DataFrame; - max(): DataFrame; - min(): DataFrame; - /** - * returns dataframe of a group - * @param {*} key [Array] - */ - get_groups(key?: any): any; - /** - * Map every column to an operaton - * @param {kwargs} kwargs {column name: math operation} - * @example .agg({"A": "mean","B": "sum","C":"count"}) - */ - agg(kwargs?: any): DataFrame; - to_DataFrame(key_col?: any, col?: any, data?: any, ops?: any): DataFrame; -} -import { DataFrame } from "./frame"; diff --git a/danfojs-node/types/core/indexing.d.ts b/danfojs-node/types/core/indexing.d.ts deleted file mode 100644 index 9615effa..00000000 --- a/danfojs-node/types/core/indexing.d.ts +++ /dev/null @@ -1 +0,0 @@ -export function indexLoc(ndframe?: any, kwargs?: any): any[]; diff --git a/danfojs-node/types/core/merge.d.ts b/danfojs-node/types/core/merge.d.ts deleted file mode 100644 index 8162a6c9..00000000 --- a/danfojs-node/types/core/merge.d.ts +++ /dev/null @@ -1,19 +0,0 @@ -export class Merge { - constructor(kwargs?: any); - how?: any; - left: DataFrame; - right: DataFrame; - on?: any[]; - left_col_index?: any[]; - right_col_index?: any[]; - left_key_dict: {}; - right_key_dict: {}; - __create_columns(): void; - outer(): any[]; - inner(): any[]; - left_merge(): any[]; - right_merge(): any[]; - basic(keys?: any): any[]; -} -export function merge(kwargs?: any): DataFrame; -import { DataFrame } from "./frame"; diff --git a/danfojs-node/types/core/series.d.ts b/danfojs-node/types/core/series.d.ts deleted file mode 100644 index 58e8efb4..00000000 --- a/danfojs-node/types/core/series.d.ts +++ /dev/null @@ -1,369 +0,0 @@ -/** - * One-dimensional ndarray with axis labels (including time series). - * The object supports both integer- and label-based indexing and provides a host of methods for performing operations involving the index. - * Operations between Series (+, -, /, , *) align values based on their associated index values– they need not be the same length. - * @param {data} data Array, JSON of 1D values - * @param {kwargs} Object {columns: column names, dtypes : data type of values} - * - * @returns Series - */ -export class Series extends NDframe { - constructor(data?: any, kwargs?: any); - /** - * Returns a Series in Tensorflow's tensor format - * @returns {1D Tensor} - */ - get tensor(): 1; - /** - * Returns the first n values in a Series - * @param {rows} Number of rows to return - * @returns {Series} - */ - head(rows?: number): Series; - /** - * Returns the last n values in a Series - * @param {rows} number of rows to return - * @returns {Series} - */ - tail(rows?: number): Series; - /** - * Returns n number of random rows in a Series - * @param {rows} number of rows to return - * @returns {Series} - */ - sample(num?: number): Series; - /** - * Return Addition of series and other, element-wise (binary operator add). - * Equivalent to series + other - * @param {other} Series or Number to add - * @returns {Series} - */ - add(other?: any): Series; - /** - * Returns the subtraction between a series and other, element-wise (binary operator subtraction). - * Equivalent to series - other - * @param {other} Series, Number to subtract - * @returns {Series} - */ - sub(other?: any): Series; - /** - * Return Multiplication of series and other, element-wise (binary operator mul). - * Equivalent to series * other - * @param {other} Series, Number to multiply with. - * @returns {Series} - */ - mul(other?: any): Series; - /** - * Return division of series and other, element-wise (binary operator div). - * Equivalent to series / other - * @param {other} Series, Number to divide with. - * @returns {Series} - */ - div(other?: any, round?: boolean): Series; - /** - * Return Exponential power of series and other, element-wise (binary operator pow). - * Equivalent to series ** other - * @param {other} Series, Number to multiply with. - */ - pow(other?: any): Series; - /** - * Return Modulo of series and other, element-wise (binary operator mod). - * Equivalent to series % other - * @param {other} Series, Number - * @returns {Series} - */ - mod(other?: any): Series; - /** - * Returns the mean of elements in Series - * @returns {Series} - */ - mean(): Series; - /** - * Returns the median of elements in Series - * @returns {Series} - */ - median(): Series; - /** - * Returns the modal value of elements in Series - * @returns {Number} - */ - mode(): number; - /** - * Returns the minimum value in a Series - * @returns {Number} - */ - min(): number; - /** - * Returns the maximum value in a Series - * @returns {Number} - */ - max(): number; - /** - * Return the sum of the values in a series. - * This is equivalent to the method tf.sum - * @returns {Number}, sum of values in Series - */ - sum(): number; - /** - * Return number of non-null elements in a Series - * @returns {Number}, Count of non-null values - */ - count(): number; - /** - * Return maximum of series and other, element-wise (binary operator div). - * @param {other} Series, Numbers to check maximum against - * @returns {Series} - */ - maximum(other?: any): Series; - /** - * Return maximum of series and other, element-wise (binary operator div). - * @param {other} Series, Numbers to check maximum against - * @returns {Series} - */ - minimum(other?: any): Series; - /** - * Round each value in a Series to the given number of decimals. - * @param {dp} Number, Numbers of Decimal places to round to - * @returns {Series} - */ - round(dp?: any): Series; - /** - * Return sample standard deviation over requested axis. - * @returns {Number} - */ - std(): number; - /** - * Return unbiased variance of Series. - * @returns {Number} - */ - var(): number; - /** - * Return a boolean same-sized object indicating if the values are NaN. NaN and undefined values, - * gets mapped to True values. Everything else gets mapped to False values. - * @return {Series} - */ - isna(): Series; - /** - * Replace NaN or undefined with a specified value" - * @param {kwargs}, {"value": the new value to replace the old value with, inplace: Perform operation inplace or not} - * @return {Series} - */ - fillna(kwargs?: {}): Series; - /** - * Sort a Series in ascending or descending order by some criterion. - * @param {kwargs} Object, {ascending (Bool): Whether to return sorted values in ascending order or not, - * inplace (Bool): Whether to perform sorting on the original Series or not} - * @returns {Series} - */ - sort_values(kwargs?: {}): Series; - /** - * Makes a deep copy of a Series - * @returns {Series} - */ - copy(): Series; - /** - * Generate descriptive statistics. - * Descriptive statistics include those that summarize the central tendency, - * dispersion and shape of a dataset’s distribution, excluding NaN values. - * @returns {Series} - */ - describe(): Series; - /** - * Returns Series with the index reset. - * This is useful when index is meaningless and needs to be reset to the default before another operation. - * @param {kwargs} {inplace: Modify the Series in place (do not create a new object} - */ - reset_index(kwargs?: {}): Series; - /** - * Returns Series with the specified index. - * Set the Series index (row labels) using an array of the same length. - * @param {kwargs} {index: Array of new index values, - * inplace: If operation should happen inplace - * } - */ - set_index(kwargs?: {}): Series; - /** - * Checks if two series are compatible for a mathematical operation - * @param {Series} other Series to compare against - * @returns{boolean} - */ - __check_series_op_compactibility(other: Series): boolean; - /** - * map all the element in a column to a variable or function - * @param{callable} callable can either be a funtion or an object - * @return {Array} - */ - map(callable?: any): any[]; - /** - * Applies a function to each element of a Series - * @param {Function} Function to apply to each element of the series - * @return {Array} - */ - apply(callable?: any): any[]; - /** - * Returns the unique value(s) in a Series - * @return {Series} - */ - unique(): Series; - /** - * Return the number of unique value in a series - * @return {int} - */ - nunique(): any; - /** - * Returns unique values and their counts in a Series - * @return {Series} - */ - value_counts(): Series; - /** - * Returns the absolute values in Series - * @return {series} - */ - abs(): any; - /** - * Returns the cumulative sum over a Series - * @return {Series} - */ - cumsum(): Series; - /** - * Returns cumulative minimum over a Series - * @returns series - */ - cummin(): Series; - /** - * Returns cumulative maximum over a Series - * @returns series - */ - cummax(): Series; - /** - * Returns cumulative product over a Series - * @returns series - */ - cumprod(): Series; - /** - * Returns Less than of series and other. Supports element wise operations - * @param {other} Series, Scalar - * @return {Series} - */ - lt(other?: any): Series; - /** - * Returns Greater than of series and other. Supports element wise operations - * @param {other} Series, Scalar - * @return {Series} - */ - gt(other?: any): Series; - /** - * Returns Less than or Equal to of series and other. Supports element wise operations - * @param {other} Series, Scalar - * @return {Series} - */ - le(other?: any): Series; - /** - * Returns Greater than or Equal to of series and other. Supports element wise operations - * @param {other} Series, Scalar - * @return {Series} - */ - ge(other?: any): Series; - /** - * Returns Not Equal to of series and other. Supports element wise operations - * @param {other} Series, Scalar - * @return {Series} - */ - ne(other?: any): Series; - /** - * Returns Equal to of series and other. Supports element wise operations - * @param {other} Series, Scalar - * @return {Series} - */ - eq(other?: any): Series; - /** - * Replace all occurence of a value with a new value" - * @param {kwargs}, {"replace": the value you want to replace, - * "with": the new value you want to replace the olde value with, - * inplace: Perform operation inplace or not} - * @return {Series} - */ - replace(kwargs?: {}): Series; - /** - * Return a new Series with missing values (NaN) removed. - * @param {kwargs} {inplace: Perform operation inplace or not} - * @return {Series} - */ - dropna(kwargs?: {}): Series; - /** - * Return the integer indices that would sort the Series. - * @param {ascending} boolean true: will sort the Series in ascending order, false: will sort in descending order - * @return {Series} - */ - argsort(ascending?: boolean): Series; - /** - * Return int position of the largest value in the Series. - * @return {Number} - */ - argmax(): number; - /** - * Return int position of the smallest value in the Series. - * @param {ascending} boolean true: will sort the Series in ascending order, false: will sort in descending order - * @return {Series} - */ - argmin(): Series; - /** - * Returns dtype of Series - * @return {string} - */ - get dtype(): string; - /** - * Return Series with duplicate values removed - * @param {kwargs} {inplace: Perform operation inplace or not, - * keep: first | last which dupliate value to keep} - * @return {Series} - */ - drop_duplicates(kwargs?: {}): Series; - /** - * Perform boolean operations on bool values - * @param {*} other Other series to compare with - * @param {string} b_ops name of operation to perform [ne, ge, le, gt, lt, eq] - */ - __bool_ops(other?: any, b_ops: string): Series; - /** - * perform cumulative operation on series data - * @returns array - */ - __cum_ops(ops?: any): Series; - /** - * Cast Series to specified data type - * @param {string} dtype data type to cast to [float32, int32, string, boolean] - *@returns {Series} - */ - astype(dtype: string): Series; - /** - * Exposes numerous string methods to manipulate Series - */ - get str(): Str; - /** - * Returns Danfo Time Object that exposes different time properties - */ - get dt(): TimeSeries; - /** - * Make plots of Series or DataFrame. - * Uses the Plotly as backend, so therefore supports Plotly's configuration parameters - * @param {string} div Name of the div to show the plot - * @returns {Class} Plot class that expoese different plot type - */ - plot(div: string): any; - /** - * Slice series by index - * @param {Array} row list of index to slice by - * @returns Series - */ - iloc(row?: any[]): Series; - /** - * Adds new values to the end of a Series - * @param {Object} val Single value | Array | Series to append to the object - * @param {Boolean} inplace Whether to perform operation inplace or not - */ - append(val?: any, inplace?: boolean): Series; -} -import NDframe from "./generic"; -import { Str } from "./strings"; -import { TimeSeries } from "./timeseries"; diff --git a/danfojs-node/types/core/strings.d.ts b/danfojs-node/types/core/strings.d.ts deleted file mode 100644 index 06eb778f..00000000 --- a/danfojs-node/types/core/strings.d.ts +++ /dev/null @@ -1,133 +0,0 @@ -/** - * String methods applied on Series and DataFrames - */ -export class Str { - constructor(series?: any); - series?: any; - array?: any; - /** - * Converts all characters to lowercase. - * @return {array} - */ - toLowerCase(): any[]; - /** - * Converts all characters to uppercase. - * @return {array} - */ - toUpperCase(): any[]; - /** - * Capitalize first string - * @return {array} - */ - capitalize(): any[]; - /** - * Returns the character at the specified index (position) - * @params {index} index position of character - * @return {array} - */ - charAt(index?: number): any[]; - /** - * Joins two or more strings/arrays. 0 joins from the start - * @params {other} string|array to concatenate with. - * @params {position} where to concat the string from. O concats from the start, 1 concats from the end - * @params {isArray} whether operation is performed on Array or not - * @return {array} - */ - concat(other?: string, position?: number): any[]; - /** - * Checks whether a string begins with specified characters - * @params {String | Character} String or Character to check against - * @return {array} - */ - startsWith(str?: string): any[]; - /** - * Checks whether a string ends with specified characters - * @params {String | Character} String or Character to check against - * @return {array} - */ - endsWith(str?: string): any[]; - /** - * Checks whether a string contains the specified string/characters - * @params {String | Character} String or Character to check against - * @return {array} - */ - includes(str?: string): any[]; - /** - * Returns the position of the first found occurrence of a specified value in a string - * @params {String | Character} String or Character to check against - * @return {array} - */ - indexOf(str?: string): any[]; - /** - * Returns the position of the last found occurrence of a specified value in a string - * @params {str: String | Character} String or Character to check against - * @return {array} - */ - lastIndexOf(str?: string): any[]; - /** - * Searches a string for a specified value, or a regular expression, and returns a new string where the specified values are replaced - * @params {searchValue: String | Character} string value to replace - * @params {replaceValue: String | Character} string to replace with - * @return {array} - */ - replace(searchValue?: string | RegExp, replaceValue?: string): any[]; - /** - * Returns a new string with a specified number of copies of an existing string - * @params {num: Integer} Number of times to repeat - * @return {array} - */ - repeat(num?: number): any[]; - /** - * Searches a string for a specified value, or regular expression, and returns the position of the match - * @params {str: String | Character} String or Character to check against - * @return {array} - */ - search(str?: string): any[]; - /** - * Extracts a part of a string and returns a new string - * @params {startIndex: Int} index position of start character - * @params {endIndex: Int} index position of last character - * @return {array} - */ - slice(startIndex?: number, endIndex?: number): any[]; - /** - * Splits a string into an array of substrings - * @params {val: string} string or character to split at - * @params {endIndex: Int} index position of last character - * @return {array} - */ - split(splitVal?: string): any[]; - /** - * Extracts the characters from a string, beginning at a specified start position, and through the specified number of character - * @params {startIndex: Int} index position of start character - * @params {num: Int} number of characters to return - * @return {array} - */ - substr(startIndex?: number, num?: number): any[]; - /** - * Extracts the characters from a string, between two specified indices - * @params {startIndex: Int} index position of start character - * @params {endIndex: Int} index position of last character - * @return {array} - */ - substring(startIndex?: number, endIndex?: number): any[]; - /** - * Removes whitespace from both ends of a string - * @return {array} - */ - trim(): any[]; - /** - * Joins strings to specified value - * @params {valToJoin} string value to join to the array - * @params {joinChar} Character to Join with - * @return {array} - */ - join(valToJoin?: string, joinChar?: string): any[]; - /** - * Counts the number of characters in string - * @return {array} - */ - len(): any[]; - __create_new_sf_from(new_val?: any, series?: any): Series; -} -import { Series } from "./series"; diff --git a/danfojs-node/types/core/timeseries.d.ts b/danfojs-node/types/core/timeseries.d.ts deleted file mode 100644 index d140a554..00000000 --- a/danfojs-node/types/core/timeseries.d.ts +++ /dev/null @@ -1,98 +0,0 @@ -/** - * @class - * @description Handle all datetime operations - * @param {kwargs} Object {"data":[array of string], "format": string} - */ -export class TimeSeries { - constructor(kwargs?: any); - data?: any; - format?: any; - keys: { - Y: number; - m: number; - H: number; - M: number; - S: number; - b: number; - d: number; - "-": number; - }; - __in_format: string[]; - __monthName: string[]; - __weekName: string[]; - /** - * @description preprocessed the data into desirable structure - */ - preprocessed(): void; - date_list?: any[]; - /** - * @description if format is given, apply the format on each element of the data - * @return string - */ - __apply_format(elem?: any, format?: any): string; - /** - * @description convert format string to their respective value. - */ - generate_format(): any; - /** - * @description check if a string is a timestamp - * @param {date_string} date_string [string] - */ - is_timestamp(date_string?: any): boolean; - /** - * @description check if a date instance returns Invalid date - * @param {date_instance} instance of new Date() - */ - __is_validDate(date_instance?: any): void; - /** - * @description abstract all date operations - * @param {*} callback [FUNCTION] - * @return series - */ - __date_ops(callback?: any): Series; - /** - * @description obtain the month in a date. - * @return Series - */ - month(): Series; - /** - * @return Series - */ - hour(): Series; - /** - * @return Series - */ - day(): Series; - /** - * @description generate year frome date instance - * @return Series - */ - year(): Series; - /** - * @description generate month name - * @return Series - */ - month_name(): Series; - /** - * @description generate days of the week - * @return Series - */ - weekdays(): Series; - /** - * @description day of the month - * @return Series - */ - monthday(): Series; - /** - * @description obtain the seconds in a date - * @return Series - */ - seconds(): Series; - /** - * @description obtain the minutes in a date - * @return Series - */ - minutes(): Series; -} -export function to_datetime(kwargs?: any): TimeSeries; -import { Series } from "./series"; diff --git a/danfojs-node/types/core/utils.d.ts b/danfojs-node/types/core/utils.d.ts deleted file mode 100644 index cf122a5c..00000000 --- a/danfojs-node/types/core/utils.d.ts +++ /dev/null @@ -1,94 +0,0 @@ -export class Utils { - remove(arr?: any, index?: any): any; - __remove_arr(arr?: any, index?: any): any; - __is_string(value?: any): boolean; - __is_number(value?: any): boolean; - __is_object(value?: any): boolean; - __is_null(value?: any): boolean; - __is_undefined(value?: any): boolean; - /** - * Optimized version of random sampling from an array, as implemented in Python - * - * - Chooses k unique random elements from a population sequence or set. - - Returns a new list containing elements from the population while - leaving the original population unchanged. The resulting list is - in selection order so that all sub-slices will also be valid random - samples. This allows raffle winners (the sample) to be partitioned - into grand prize and second place winners (the subslices). - - Members of the population need not be hashable or unique. If the - population contains repeats, then each occurrence is a possible - selection in the sample. - - To choose a sample in a range of integers, use range as an argument. - This is especially fast and space efficient for sampling from a - large population: sample(range(10000000), 60) - - Sampling without replacement entails tracking either potential - selections (the array) in a list or previous selections in a set. - - When the number of selections is small compared to the - population, then tracking selections is efficient, requiring - only a small set and an occasional reselection. For - a larger number of selections, the array tracking method is - preferred since the list takes less space than the - set and it doesn't suffer from frequent reselections. - * - * @param {*} array The array to sample values from randomly - * @param {*} num The number of elements to sample randomly - */ - __sample_from_iter(array?: any, k?: any, destructive?: any): any; - __range(start?: any, end?: any): any; - __key_in_object(object?: any, key?: any): boolean; - __get_col_values(data?: any): any[][]; - /** - * retrieve row array and column names from object of the form {a: [1,2,3,4], b: [30,20, 30, 20}] - * @param {*} data - */ - _get_row_and_col_values(data?: any): (string[] | any[][])[]; - __convert_2D_to_1D(data?: any): any[]; - __replace_undefined_with_NaN(data?: any, isSeries?: any): any[]; - __get_t(arr_val?: any): any[]; - __unique(data?: any): any; - __in_object(object?: any, key?: any, message?: any): void; - __is_1D_array(arr?: any): boolean; - __arr_to_obj(arr?: any): {}; - /** - * count the NaN and non-NaN values present in an array - * @param {Array} arr Array object - * @param {Boolean} val whether to return the value count instead of the null count - * @param {Boolean} isSeries Whether the Obj is of type series or not - */ - __count_nan(arr?: any[], return_val: boolean, isSeries: boolean): number | any[]; - __median(arr?: any, isSeries?: any): any; - __mode(arr?: any): number[]; - __round(arr?: any, dp: number, isSeries?: any): any[]; - __is_function(variable?: any): boolean; - __randgen(num?: any, start?: any, end?: any): any[]; - _throw_wrong_params_error(kwargs?: any, params_needed?: any): void; - __map_int_to_bool(arr?: any, dim?: any): any[]; - __std(data?: any): any; - __zeros(row?: any, column?: any): any[][]; - __shuffle(num?: any, array?: any): any; - __sort(arr?: any, ascending?: boolean): any; - __is_browser_env(): any; - __is_node_env(): any; - _throw_str_dtype_error(obj?: any, ops?: any): void; - /** - * Remove NaN values from Array - * @param {*} arr - */ - _remove_nans(arr?: any): any; - __get_duplicate(arr?: any): {}; - /** - * Sorts an array by index - * @param {Array} arr1 - * @param {Array} arr2 - * @param {string} dtype - * - * @returns sorted index - */ - _sort_arr_with_index(arr1?: any[], arr2?: any[], dtype: string): any[]; -} diff --git a/danfojs-node/types/index.d.ts b/danfojs-node/types/index.d.ts deleted file mode 100644 index ab3b0102..00000000 --- a/danfojs-node/types/index.d.ts +++ /dev/null @@ -1,14 +0,0 @@ -import { Series } from "./core/series"; -import { DataFrame } from "./core/frame"; -import { to_datetime } from "./core/timeseries"; -import { merge } from "./core/merge"; -import { concat } from "./core/concat"; -import { date_range } from "./core/date_range"; -import { get_dummies } from "./core/get_dummies"; -import { read_csv, read_json, read_excel, read } from "./io/reader"; -import { LabelEncoder, OneHotEncoder } from "./preprocessing/encodings"; -import { MinMaxScaler, StandardScaler } from "./preprocessing/scalers"; - -declare module 'danfojs-node' { - export { Series, DataFrame, to_datetime, merge, concat, date_range, get_dummies, read_csv, read_json, read_excel, read, LabelEncoder, OneHotEncoder, MinMaxScaler, StandardScaler } -} \ No newline at end of file diff --git a/danfojs-node/types/io/reader.d.ts b/danfojs-node/types/io/reader.d.ts deleted file mode 100644 index 24fe997c..00000000 --- a/danfojs-node/types/io/reader.d.ts +++ /dev/null @@ -1,5 +0,0 @@ -export function read_csv(source?: any, chunk?: any): Promise; -export function read_json(source?: any): Promise; -export function read_excel(kwargs?: any): Promise; -export function read(path_or_descriptor?: any, configs?: object): DataFrame; -import { DataFrame } from "../core/frame"; diff --git a/danfojs-node/types/plotting/plot.d.ts b/danfojs-node/types/plotting/plot.d.ts deleted file mode 100644 index de3963e9..00000000 --- a/danfojs-node/types/plotting/plot.d.ts +++ /dev/null @@ -1,66 +0,0 @@ -/** - * Plotting methods and Functions performed on Series and DataFrames - */ -export class Plot { - constructor(ndframe?: any, div?: any); - div?: any; - ndframe?: any; - /** - * Plot Series or DataFrame as lines. This function is useful to plot lines using DataFrame’s values as coordinates. - * Make plots of Series or DataFrame. - * Uses the Plotly as backend, so supoorts Plotly's configuration parameters - * @param {string} div Name of the div to show the plot - * @param {Object} config configuration options for making Plots, supports Plotly parameters - */ - line(config?: any): void; - /** - * Plot Series or DataFrame as Bars. - * Uses the Plotly as backend, so supoorts Plotly's configuration parameters - * @param {string} div Name of the div to show the plot - * @param {Object} config configuration options for making Plots, supports Plotly parameters - */ - bar(config?: any): void; - /** - * Plot two or more columns in a DataFrame as scatter points. If Series, plot against its index - * Uses the Plotly as backend, so supoorts Plotly's configuration parameters - * @param {string} div Name of the div to show the plot - * @param {Object} config configuration options for making Plots, supports Plotly parameters - */ - scatter(config?: any): void; - /** - * Plot columns in a Series/DataFrame as Histograms. - * Uses the Plotly as backend, so supoorts Plotly's configuration parameters - * @param {string} div Name of the div to show the plot - * @param {Object} config configuration options for making Plots, supports Plotly parameters - */ - hist(config?: any): void; - /** - * Makes Pie Plots from two Columns in a DataFrame. - * Uses the Plotly as backend, so supoorts Plotly's configuration parameters - * @param {string} div Name of the div to show the plot - * @param {Object} config configuration options for making Plots, supports Plotly parameters - */ - pie(config?: any): void; - /** - * Plot Box plots from Series or DataFrame as lines. - * Uses the Plotly as backend, so supoorts Plotly's configuration parameters - * @param {Object} config configuration options for making Plots, supports Plotly parameters - */ - box(config?: any): void; - /** - * Plot Violin plots from Series or DataFrame as lines. - * Uses the Plotly as backend, so supoorts Plotly's configuration parameters - * @param {Object} config configuration options for making Plots, supports Plotly parameters - */ - violin(config?: any): void; - /** - * Display DataFrame in a div using D3.js format - * Uses the Plotly as backend, so supoorts Plotly's configuration parameters - * @param {Object} config configuration options for making Plots, supports Plotly parameters - */ - table(config?: any): void; - __get_plot_params(config?: any): (string[] | { - layout: {}; - })[]; - ____check_if_cols_exist(cols?: any): any; -} diff --git a/danfojs-node/types/preprocessing/encodings.d.ts b/danfojs-node/types/preprocessing/encodings.d.ts deleted file mode 100644 index 4b866774..00000000 --- a/danfojs-node/types/preprocessing/encodings.d.ts +++ /dev/null @@ -1,22 +0,0 @@ -export class LabelEncoder { - /** - * - * @param {data} data [Array|Series] - * @returns Array. - */ - fit(data?: any): Series; - label?: any; - /** - * Transform data using the label generated from fitting - * @param {data} data [Array|Series] - * @returns Array - */ - transform(data?: any): Series; -} -export class OneHotEncoder { - fit(data?: any): DataFrame; - label?: any; - transform(data?: any): DataFrame; -} -import { Series } from "../core/series"; -import { DataFrame } from "../core/frame"; diff --git a/danfojs-node/types/preprocessing/scalers.d.ts b/danfojs-node/types/preprocessing/scalers.d.ts deleted file mode 100644 index aade7b34..00000000 --- a/danfojs-node/types/preprocessing/scalers.d.ts +++ /dev/null @@ -1,29 +0,0 @@ -export class MinMaxScaler { - /** - * Fit minmax scaler on data, to obtain their min and max value - * @param {data} data [DataRame | Series | Array] - * @returns Array - */ - fit(data?: any): Series | DataFrame; - max?: any; - min?: any; - /** - * Transform an array using the min and max generated from the fitting on data - * @param {data} data [Array] - * @returns array - */ - transform(data?: any): Series | DataFrame; -} -export class StandardScaler { - /** - * - * @param {data} data [DataRame | Series | Array] - * @returns Array - */ - fit(data?: any): Series | DataFrame; - std?: any; - mean?: any; - transform(data?: any): Series | DataFrame; -} -import { Series } from "../core/series"; -import { DataFrame } from "../core/frame"; diff --git a/package.json b/package.json index 7520cbe9..3ac3875d 100644 --- a/package.json +++ b/package.json @@ -6,7 +6,8 @@ "danfojs-browser/**" ], "scripts": { - "build": "cd danfojs-node && yarn build && cd ../danfojs-browser && yarn bundle", - "test": "cd danfojs-node && yarn test && cd ../danfojs-browser && yarn test" + "install": "cd src/danfojs-base && yarn && cd ../danfojs-browser && yarn && cd ../danfojs-node && yarn", + "build": "cd src/danfojs-node && yarn build:clean && cd ../danfojs-browser && yarn build:clean", + "test": "cd src/danfojs-base && yarn && cd ../danfojs-node && yarn && yarn test:clean && cd ../danfojs-browser && yarn && yarn test:clean" } } \ No newline at end of file diff --git a/src/danfojs-base/README.md b/src/danfojs-base/README.md new file mode 100644 index 00000000..69ec125c --- /dev/null +++ b/src/danfojs-base/README.md @@ -0,0 +1,46 @@ +## danfojs-base + +**danfojs-base** is the core module of Danfo.js. Danfojs-node and Danfojs-browser folders simply extends/export this functions and classes from this module. + +## Folders and Files + + - __aggregators__: All files that contain functions that aggregate data. + - __core__: Holds the core classes of Danfo.js. + - `daterange`: Class that represents a date range. + - `datetime`: Class that represents a date and time. + - `frame`: Class that represents a frame. + - `series`: Class that represents a series. + - `generic`: Class that represents a generic object. + - `indexing`: Class that represents an indexing. + - `math.ops`: Class that represents a math operation. + - `strings`: Class that represents a string. + - __io__: Holds the IO classes of Danfo.js. + - __browser__: Holds the browser IO classes. + - `io.csv`: Holds the CSV IO classes. + - `io.json`: Holds the JSON IO classes. + - `io.excel`: Holds the excel IO classes. + - __node__: Holds the node IO classes for Node.js + - `io.csv`: Holds the CSV IO classes for Node.js + - `io.json`: Holds the JSON IO classes for Node.js + - `io.excel`: Holds the excel IO classes for Node.js + - __plotting__: Holds the plotting classes + - __plotly__: Holds the plotting class for Plotly charts. + - __vega__: (Stub) Holds the plotting class for Vega charts. + - __shared__ + - `config`: Holds the configuration class. + - `defaults`: Holds the default values for the configuration class. + - `errors`: Holds the error classes. + - `tensorflowlib`: Autogenerated tensorflow library export. + - `utils`: Holds the utility classes. + - `types`: Holds the type classes. + - __transformers__: Holds the transformers files + - __encoders__: Holds the encoder classes. + - `dummy.encoder`: Holds the dummy encoder class + - `one.hot.encoder`: Holds the onehot encoder class + - `label.encoder`: Holds the label encoder class + - __scalers__ + - `min.max.scalers`: Holds the min max scaler class + - `standard.scalers`: Holds the standard scaler class + - `concat`: Holds the concatenation class + - `merge`: Holds the merge class + - `index.ts`: Entry point for the module. \ No newline at end of file diff --git a/src/danfojs-base/aggregators/groupby.ts b/src/danfojs-base/aggregators/groupby.ts new file mode 100644 index 00000000..d12a8d43 --- /dev/null +++ b/src/danfojs-base/aggregators/groupby.ts @@ -0,0 +1,652 @@ +/** +* @license +* Copyright 2022 JsData. All rights reserved. +* +* This source code is licensed under the MIT license found in the +* LICENSE file in the root directory of this source tree. + +* Unless required by applicable law or agreed to in writing, software +* distributed under the License is distributed on an "AS IS" BASIS, +* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +* See the License for the specific language governing permissions and +* limitations under the License. +* ========================================================================== +*/ +import DataFrame from "../core/frame" +import { ArrayType1D, ArrayType2D } from "../shared/types" +import { variance, std, median, mode } from 'mathjs'; +import concat from "../transformers/concat" +import Series from "../core/series"; + + + +/** + * The class performs all groupby operation on a dataframe + * involving all aggregate funciton + * @param {colDict} colDict Object of unique keys in the group by column + * @param {keyCol} keyCol Array contains the column names + * @param {data} Array the dataframe data + * @param {columnName} Array of all column name in the dataframe. + * @param {colDtype} Array columns dtype + */ +export default class Groupby { + colDict: { [key: string ]: {} } = {} + keyCol: ArrayType1D + data?: ArrayType2D | null + columnName: ArrayType1D + colDtype: ArrayType1D + colIndex: ArrayType1D + groupDict?: any + groupColNames?: Array + keyToValue: { + [key: string] : ArrayType1D + } = {} + + constructor(keyCol: ArrayType1D, data: ArrayType2D | null, columnName: ArrayType1D, colDtype:ArrayType1D, colIndex: ArrayType1D) { + + this.keyCol = keyCol; + this.data = data; + this.columnName = columnName; + //this.dataTensors = {}; //store the tensor version of the groupby data + this.colDtype = colDtype; + this.colIndex = colIndex + + } + /** + * Generate group object data needed for group operations + * let data = [ [ 1, 2, 3 ], [ 4, 5, 6 ], [ 20, 30, 40 ], [ 39, 89, 78 ] ]; + * let cols = [ "A", "B", "C" ]; + * let df = new dfd.DataFrame(data, { columns: cols }); + * let groupDf = df.groupby([ "A" ]); + * The following internal object is generated and save to this.colDict + * { + * '1': { A: [ 1 ], B: [ 2 ], C: [ 3 ] }, + * '4': { A: [ 4 ], B: [ 5 ], C: [ 6 ] }, + * '20': { A: [ 20 ], B: [ 30 ], C: [ 40 ] }, + * '39': { A: [ 39 ], B: [ 89 ], C: [ 78 ] } + * } + * Since for groupby using more than one columns is index via '-' + * e.g for df.groupby(['A','B']) + * the result will look like this + * { + * '1-2': {A: [ 1 ], B: [ 2 ], C: [ 3 ]}, + * '4-5': {A: [ 4 ], B: [ 5 ], C: [ 6 ]} + * } + * but in doing analysis on a specific column like this + * df.groupby(['A','B']).col(['C']) + * will have the following set of internal result + * { + * '1-2': { C: [ 3 ]}, + * '4-5': {C: [ 6 ]} + * } + * In building our multindex type of DataFrame for this data, + * we've somehow loose track of value for column A and B. + * This could actually be generated by using split('-') on the object keys + * e.g '1-2'.split('-') will give us the value for A and B. + * But we might have weird case scenerio where A and B value has '-` + * e.g + * { + * '1--2-': { C: [ 3 ]}, + * '4--5-': {C: [ 6 ]} + * } + * using `.split('-') might not work well + * Hence we create a key-value `keyToValue` object to store index and their + * associated value + * NOTE: In the previous implementation we made use of Graph representation + * for the group by data and Depth First search (DFS). But we decided to use key-value + * object in javascript as an hashmap to reduce search time compared to using Grpah and DFS + */ + group(): Groupby{ + const self = this + let keyToValue:{ + [key: string] : ArrayType1D + } = {} + const group = this.data?.reduce((prev: any, current)=>{ + let indexes= [] + for(let i in self.colIndex) { + let index = self.colIndex[i] as number + indexes.push(current[index]) + } + let index = indexes.join('-') + + if(!keyToValue[index]) { + keyToValue[index] = indexes + } + + if(prev[index]) { + let data = prev[index] + for (let i in self.columnName) { + let colName = self.columnName[i] as string + data[colName].push(current[i]) + } + } else { + prev[index] = {} + for (let i in self.columnName) { + let colName = self.columnName[i] as string + prev[index][colName] = [current[i]] + } + } + return prev + + }, {}) + this.colDict = group + this.keyToValue = keyToValue + return this + } + + /** + * Generate new internal groupby data + * group = df.groupby(['A', 'B']).col('C') + * This filter the colDict property as generated by `.group()` + * it filter each group to contain only column `C` in their internal object + * e.g + * { + * '1-2': {A: [ 1 ], B: [ 2 ], C: [ 3 ]}, + * '4-5': {A: [ 4 ], B: [ 5 ], C: [ 6 ]} + * } + * to + * { + * '1-2': { C: [ 3 ]}, + * '4-5': {C: [ 6 ]} + * } + * @param colNames column names + * @return Groupby + */ + col(colNames: ArrayType1D | undefined): Groupby { + + if (typeof colNames === "undefined") { + colNames = this.columnName.filter((_, index)=>{ + return !this.colIndex.includes(index) + }) + } + let self = this + colNames.forEach((val) => { + if (!self.columnName.includes(val)) + throw new Error(`Column ${val} does not exist in groups`) + }) + let colDict: { [key: string ]: {} } = {...this.colDict} + for(let [key, values] of Object.entries(colDict)) { + let c: { [key: string ]: [] } = {} + let keyVal: any = {...values} + for(let colKey in colNames) { + let colName = colNames[colKey] as string + c[colName] = keyVal[colName] + } + colDict[key] = c + } + const gp = new Groupby( + this.keyCol, + null, + this.columnName, + this.colDtype, + this.colIndex + ) + gp.colDict = colDict + gp.groupColNames = colNames as Array + gp.keyToValue = this.keyToValue + + return gp + } + + /** + * Perform all groupby arithmetic operations + * In the previous implementation all groups data are + * stord as DataFrame, which involve lot of memory usage + * Hence each groups are just pure javascrit object + * and all arithmetic operation is done directly on javascript + * arrays. + * e.g + * using this internal data + * { + * '1-2': {A: [ 1,3 ], B: [ 2,5 ], C: [ 3, 5 ]}, + * '4-5': {A: [ 4,1 ], B: [ 5,0 ], C: [ 6, 12 ]} + * } + * 1) using groupby(['A', 'B']).arithmetic("mean") + * result: * { + * '1-2': {A_mean: [ 2 ], B_mean: [ 3.5 ], C_mean: [ 4 ]}, + * '4-5': {A_mean: [ 2.5 ], B: [ 2.5 ], C_mean: [ 9 ]} + * } + * 2) .arithmetic({ + * A: 'mean', + * B: 'sum', + * C: 'min' + * }) + * result: + * { + * '1-2': {A_mean: [ 2 ], B_sum: [ 7 ], C_min: [ 3 ]}, + * '4-5': {A_mean: [ 2.5 ], B_sum: [ 5 ], C_min: [ 6 ]} + * } + * 3) .arithmetic({ + * A: 'mean', + * B: 'sum', + * C: ['min', 'max'] + * }) + * result: + * { + * '1-2': {A_mean: [ 2 ], B_sum: [ 7 ], C_min: [ 3 ], C_max: [5]}, + * '4-5': {A_mean: [ 2.5 ], B_sum: [ 5 ], C_min: [ 6 ], C_max: [12]} + * } + * @param operation + */ + private arithemetic(operation: {[key: string] : Array | string} | string): { [key: string ]: {} } { + + const opsName = [ "mean", "sum", "count", "mode", "std", "var", "cumsum", "cumprod", + "cummax", "cummin", "median" , "min"]; + if (typeof operation === "string" ) { + if (!opsName.includes(operation)) { + throw new Error(`group operation: ${operation} is not valid`) + } + } else { + Object.keys(operation).forEach((key)=>{ + let ops = operation[key] + if(Array.isArray(ops)) { + for(let op of ops) { + if (!opsName.includes(op)) { + throw new Error(`group operation: ${op} for column ${key} is not valid`) + } + } + } else { + if (!opsName.includes(ops)) { + throw new Error(`group operation: ${ops} for column ${key} is not valid`) + } + } + + }) + } + let colDict: { [key: string ]: {} } = {...this.colDict} + for(const [key, values] of Object.entries(colDict)) { + let colVal: { [key: string ]: Array } = {} + let keyVal: any = {...values} + let groupColNames: Array = this.groupColNames as Array + for(let colKey=0; colKey < groupColNames.length; colKey++) { + let colName = groupColNames[colKey] + let colIndex = this.columnName.indexOf(colName) + let colDtype = this.colDtype[colIndex] + if (colDtype === "string") throw new Error(`Can't perform math operation on column ${colName}`) + + if (typeof operation === "string") { + let colName2 = `${colName}_${operation}` + colVal[colName2] = this.groupMathLog(keyVal[colName], operation) + } + else { + if(Array.isArray(operation[colName])) { + for(let ops of operation[colName]) { + let colName2 = `${colName}_${ops}` + colVal[colName2] = this.groupMathLog(keyVal[colName],ops) + } + } else { + let ops: string = operation[colName] as string + let colName2 = `${colName}_${ops}` + colVal[colName2] = this.groupMathLog(keyVal[colName], ops) + } + + } + } + colDict[key] = colVal + } + return colDict + } + + /** + * Peform all arithmetic logic + * @param colVal + * @param ops + */ + private groupMathLog(colVal: Array, ops: string): Array{ + let data = [] + switch(ops) { + case "max": + let max = colVal.reduce((prev, curr)=> { + if (prev > curr) { + return prev + } + return curr + }) + data.push(max) + break; + case "min": + let min = colVal.reduce((prev, curr)=> { + if (prev < curr) { + return prev + } + return curr + }) + data.push(min) + break; + case "sum": + let sum = colVal.reduce((prev, curr)=> { + return prev + curr + }) + data.push(sum) + break; + case "count": + data.push(colVal.length) + break; + case "mean": + let sumMean = colVal.reduce((prev, curr)=> { + return prev + curr + }) + data.push(sumMean / colVal.length) + break; + case "std": + data.push(std(colVal)) + break; + case "var": + data.push(variance(colVal)) + break; + case "median": + data.push(median(colVal)) + break; + case "mode": + data.push(mode(colVal)) + break; + case "cumsum": + colVal.reduce((prev, curr) => { + let sum = prev + curr + data.push(sum) + return sum + }, 0) + break; + case "cummin": + data = [colVal[0]] + colVal.slice(1,).reduce((prev, curr)=>{ + if (prev < curr) { + data.push(prev) + return prev + } + data.push(curr) + return curr + }, data[0]) + break; + case "cummax": + data = [colVal[0]] + colVal.slice(1,).reduce((prev, curr)=> { + if (prev > curr) { + data.push(prev) + return prev + } + data.push(curr) + return curr + }, data[0]) + break; + case "cumprod": + colVal.reduce((prev, curr) => { + let sum = prev * curr + data.push(sum) + return sum + }, 1) + break; + } + return data + } + + /** + * Takes in internal groupby internal data and convert + * them to a single data frame. + * @param colDict + */ + private toDataFrame(colDict: { [key: string ]: {} }): DataFrame { + let data: { [key: string ]: ArrayType1D } = {} + + for(let key of this.colKeyDict(colDict)) { + let value = colDict[key] + let keyDict: { [key: string ]: ArrayType1D } = {} + let oneValue = Object.values(value)[0] as ArrayType1D + let valueLen = oneValue.length + for(let key1 in this.keyCol) { + let keyName = this.keyCol[key1] as string + let keyValue = this.keyToValue[key][key1] + keyDict[keyName] = Array(valueLen).fill(keyValue) + } + let combine: { [key: string ]: ArrayType1D } = {...keyDict, ...value} + if(Object.keys(data).length < 1) { + data = combine + } else { + for(let dataKey of Object.keys(data)) { + let dataValue = combine[dataKey] as ArrayType1D + data[dataKey] = [...data[dataKey], ...dataValue] + } + } + } + return new DataFrame(data) + } + + private operations(ops: string): DataFrame { + if (!this.groupColNames) { + let colGroup = this.col(undefined) + let colDict = colGroup.arithemetic(ops) + let df = colGroup.toDataFrame(colDict) + return df + } + let colDict = this.arithemetic(ops) + let df = this.toDataFrame(colDict) + return df + } + + /** + * Obtain the count for each group + * @returns DataFrame + * + */ + count(): DataFrame { + return this.operations("count") + } + + /** + * Obtain the sum of columns for each group + * @returns DataFrame + * + */ + sum(): DataFrame{ + return this.operations("sum") + } + + /** + * Obtain the variance of columns for each group + * @returns DataFrame + */ + var(): DataFrame{ + return this.operations("var") + } + + /** + * Obtain the mean of columns for each group + * @returns DataFrame + */ + mean(): DataFrame{ + return this.operations("mean") + } + + /** + * Obtain the cumsum of columns for each group + * @returns DataFrame + * + */ + cumSum(): DataFrame{ + return this.operations("cumsum") + } + + /** + * Obtain the cummax of columns for each group + * @returns DataFrame + */ + cumMax(): DataFrame{ + return this.operations("cummax") + } + + /** + * Obtain the cumprod of columns for each group + * @returns DataFrame + */ + cumProd(): DataFrame{ + return this.operations("cumprod") + } + + /** + * Obtain the cummin of columns for each group + * @returns DataFrame + */ + cumMin(): DataFrame{ + return this.operations("cummin") + } + + /** + * Obtain the max value of columns for each group + * @returns DataFrame + * + */ + max(): DataFrame{ + return this.operations("max") + } + + /** + * Obtain the min of columns for each group + * @returns DataFrame + */ + min(): DataFrame{ + return this.operations("min") + } + + /** + * Obtain a specific group + * @param keys Array + * @returns DataFrame + */ + getGroup(keys: Array): DataFrame { + let dictKey = keys.join("-") + let colDict: { [key: string ]: {} } = {} + colDict[dictKey] = {...this.colDict[dictKey]} + return this.toDataFrame(colDict) + } + + /** + * Perform aggregation on all groups + * @param ops + * @returns DataFrame + */ + agg(ops: { [key: string ]: Array | string }): DataFrame { + let columns = Object.keys(ops); + let col_gp = this.col(columns); + let data = col_gp.arithemetic(ops); + let df = col_gp.toDataFrame(data); + return df; + } + + /** + * Apply custom aggregator function + * to each group + * @param callable + * @returns DataFrame + * @example + * let grp = df.groupby(['A']) + * grp.apply((x) => x.count()) + */ + apply(callable: (x: DataFrame)=> DataFrame | Series ): DataFrame { + let colDict: { [key: string ]: DataFrame | Series } = {} + for(const key of this.colKeyDict(this.colDict)) { + let valDataframe = new DataFrame(this.colDict[key]) + colDict[key] = callable(valDataframe) + } + return this.concatGroups(colDict) + } + + private concatGroups(colDict: {[key: string]: DataFrame | Series}): DataFrame { + let data: Array = [] + for(const [key, values] of Object.entries(colDict)) { + let copyDf: DataFrame; + if (values instanceof DataFrame) { + copyDf = values.copy() + } + else { + let columns = values.index as string[] + columns = columns.length > 1 ? columns : ['applyOps'] + copyDf = new DataFrame([values.values], {columns: columns }) + } + let len = copyDf.shape[0] + let key1: any; + for(key1 in this.keyCol){ + + let keyName = this.keyCol[key1] as string + let keyValue = this.keyToValue[key][key1] + let dfValue = Array(len).fill(keyValue) + let atIndex: number = parseInt(key1) + if (this.groupColNames) { + copyDf.addColumn(keyName, dfValue, {inplace: true, atIndex: atIndex }) + } + else { + copyDf.addColumn(`${keyName}_Group`, dfValue, {inplace: true, atIndex: atIndex }) + } + + } + data.push(copyDf) + } + return concat({dfList: data, axis:0}) as DataFrame + } + + /** + * obtain the total number of groups + * @returns number + */ + get ngroups(): number{ + let keys = Object.keys(this.colDict) + return keys.length + } + + /** + * obtaind the internal group data + * @returns {[keys: string]: {}} + */ + get groups(): {[keys: string]: {}}{ + return this.colDict + } + + /** + * Obtain the first row of each group + * @returns DataFrame + */ + first(): DataFrame{ + return this.apply((x)=>{ + return x.head(1) + }) + } + + /** + * Obtain the last row of each group + * @returns DataFrame + */ + last(): DataFrame { + return this.apply((x)=>{ + return x.tail(1) + }) + } + + /** + * Obtains the dataframe se of each groups + * @returns DataFrame + */ + size(): DataFrame { + return this.apply((x)=>{ + return new Series([x.shape[0]]) + }) + } + + private colKeyDict(colDict: { [key: string ]: {} }): string[]{ + let keyDict :{ [key: string ]: string[] } = {} + + for(let key of Object.keys(colDict)) { + let firstKey = key.split("-")[0] + if (firstKey in keyDict) { + keyDict[firstKey].push(key) + } + else { + keyDict[firstKey] = [key] + } + } + let keys = [] + for(let key of Object.keys(keyDict)) { + keys.push(...keyDict[key]) + } + return keys + } + +} \ No newline at end of file diff --git a/src/danfojs-base/core/daterange.ts b/src/danfojs-base/core/daterange.ts new file mode 100644 index 00000000..7d342645 --- /dev/null +++ b/src/danfojs-base/core/daterange.ts @@ -0,0 +1,271 @@ +/** +* @license +* Copyright 2022 JsData. All rights reserved. +* +* This source code is licensed under the MIT license found in the +* LICENSE file in the root directory of this source tree. + +* Unless required by applicable law or agreed to in writing, software +* distributed under the License is distributed on an "AS IS" BASIS, +* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +* See the License for the specific language governing permissions and +* limitations under the License. +* ========================================================================== +*/ +import Utils from "../shared/utils"; + +const utils = new Utils(); + +interface Params { + start?: string; + offset?: number; + end?: string; + freq?: string; + period?: number +} +class DateRange { + private offset?: number + private start?: string + private end?: string + private freq: string + private period?: number + private freqList: string[] + + constructor({start, end, offset, freq, period}: Params){ + this.start = start + this.end = end + this.offset = offset + this.freq = freq ? freq : "D" + this.period = period + this.freqList = [ "M", "D", "s", "H", "m", "Y" ] + + if (this.freq.length == 1){ + if (!this.freqList.includes(this.freq)){ + throw new Error(`invalid freq ${this.freq}`); + } + } else { + this.offset = parseInt(this.freq.slice(0, -1)); + if (!Number.isFinite(this.offset)){ + throw new Error(`invalid freq offset ${this.freq.slice(0, -1)}`); + } + this.freq = this.freq.slice(-1); + if (!this.freqList.includes(this.freq)){ + throw new Error(`invalid freq ${this.freq}`); + } + } + + } + + range(): string[] { + let start = this.start + let period = this.period + let end = this.end + let offset = this.offset + let startDate: Date + let endDate: Date + let startRange: number + let endRange: number + if (start && end) { + startDate = new Date(start) + startRange = this.freqType(startDate, this.freq) + endDate = new Date(end) + endRange = this.freqType(endDate, this.freq) + + let startYear = startDate.getFullYear() + let endYear = endDate.getFullYear() + if ((startYear <= endYear) && (startDate.getMonth() !== endDate.getMonth())){ + if (this.freq == "M") { + endRange = this.monthEnd(startDate, endDate) + } + else if (this.freq === "D") { + endRange = this.dayEnd(startDate, endDate) - startRange + } + } + let rangeArray = utils.range(startRange, endRange) + if ( offset ) { + rangeArray = this.offsetCount(rangeArray, offset) + } + let dateRange = rangeArray.map((x) => { + return this.setDateProps(startDate, this.freq, x) + }) + dateRange[dateRange.length -1] = endDate + let dateString = this.toLocalString(dateRange) + return dateString + } + else if ( start && !(end) ) { + startDate = new Date(start) + startRange = this.freqType(startDate, this.freq) + period = period as number + endRange = offset ? ((period * offset) - 1) : period -1; + + if ( startRange > endRange ) { + endRange = endRange + startRange + } + let rangeArray = utils.range(startRange, endRange) + + if ( offset ) { + rangeArray = this.offsetCount(rangeArray, offset) + } + let dateRange = rangeArray.map((x) => { + return this.setDateProps(startDate, this.freq, x) + }) + + let dateString = this.toLocalString(dateRange) + return dateString + } + // if end and not start given + endDate = new Date(end as string) + endRange = this.freqType(endDate, this.freq) + period = period as number + startRange = (endRange - period) + 1 + let rangeArray = utils.range(startRange, endRange) + + if ( offset ) { + rangeArray = this.offsetCount(rangeArray, offset) + } + let dateRange = rangeArray.map((x) => { + return this.setDateProps(endDate, this.freq, x) + }) + let dateString = this.toLocalString(dateRange) + return dateString + } + + /** + * @param date Date + * @param ftype string: frequency type, month, Year, day etc + * @param number + */ + private freqType(date: Date, ftype: string): number{ + let rslt: number = 0; + switch (ftype){ + + case "M": + rslt = date.getMonth(); + break; + case "Y": + rslt = date.getFullYear(); + break; + case "s": + rslt = date.getSeconds(); + break; + case "D": + rslt = date.getDate(); + break; + case "H": + rslt = date.getHours(); + break; + case "m": + rslt = date.getMinutes(); + break; + } + return rslt; + } + + + private offsetCount(dArray: number[], offset: number) :number[] { + let rArray: number[] = [] + for (let i = 0; i < dArray.length; i += offset){ + rArray.push(dArray[i]); + } + return rArray; + } + + private setDateProps(date: Date, ftype: string, val: number[] | number): Date { + let newDate = new Date(date.valueOf()) + switch (ftype){ + case "M": + if (Array.isArray(val)){ + + newDate.setFullYear(newDate.getFullYear() + val[0]); + newDate.setMonth(val[1]); + } else { + newDate.setMonth(val); + } + break; + case "Y": + newDate.setFullYear(val as number); + break; + case "s": + newDate.setSeconds(val as number); + break; + case "D": + newDate.setDate(val as number); + break; + case "H": + newDate.setHours(val as number); + break; + case "m": + newDate.setMinutes(val as number); + break; + } + return newDate; + } + + + private toLocalString(dArray: Date[]) { + let r_array = dArray.map((x) => { + return x.toLocaleString(); + }); + return r_array; + } + + private monthEnd(startDate: Date, endDate: Date) { + let endMonth = endDate.getMonth() + let diffYear = endDate.getFullYear() - startDate.getFullYear() + let endRange = (12 * diffYear) + endMonth + return endRange + } + + private monthRange(range: number[]): number[][] { + let minus: number; + let yVal = 0 + let dateRange: number[][] = range.map((x) => { + if (x > 11) { + if ( x % 12 == 0) { + minus = x + yVal = x / 12 + return [yVal, (x - minus)] + } + else { + return [yVal, (x -minus)] + } + } + return [yVal, x] + }) + return dateRange + } + + private dayEnd(startDate: Date, endDate: Date): number{ + let monthEnd = this.monthEnd(startDate, endDate) + let range = utils.range(startDate.getMonth(), monthEnd) + let mRange = this.monthRange(range) + + let sum = 0 + for (let i=0; i < mRange.length; i++) { + let val = mRange[i] + let dDate: number + if (i === mRange.length - 1) { + dDate = new Date(startDate.getUTCFullYear() + val[0], val[1], endDate.getDate()).getDate() + } + else { + dDate = new Date(startDate.getUTCFullYear() + val[0], val[1], 0).getDate() + } + sum += dDate + } + return sum + } +} + +/** + * Generate sequence of Dates + * @param start : signify the date to start with + * @param end : signify the date to end with + * @param period : the total number of date to generate + * @param offset : set the date range offset + * @param freq: set the date range frequency and offset + * @return string[] + */ +export default function dateRange(param: Params): string[] { + const dateRange = new DateRange(param) + return dateRange.range() +} \ No newline at end of file diff --git a/src/danfojs-base/core/datetime.ts b/src/danfojs-base/core/datetime.ts new file mode 100644 index 00000000..264f0e10 --- /dev/null +++ b/src/danfojs-base/core/datetime.ts @@ -0,0 +1,243 @@ +/** +* @license +* Copyright 2022 JsData. All rights reserved. +* +* This source code is licensed under the MIT license found in the +* LICENSE file in the root directory of this source tree. + +* Unless required by applicable law or agreed to in writing, software +* distributed under the License is distributed on an "AS IS" BASIS, +* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +* See the License for the specific language governing permissions and +* limitations under the License. +* ========================================================================== +*/ +import { ArrayType1D, DateTime } from "../shared/types"; +import Series from "./series"; + +const WEEK_NAME = ["Sunday", "Monday", "Tuesday", "Wednesday", "Thursday", "Friday", "Saturday"]; +const MONTH_NAME = ["January", "February", "March", "April", "May", "June", "July", "August", "September", "October", "November", "December"]; + +/** + * Format and handle all datetime operations on Series or Array of date strings + * @param data Series or Array of date strings + */ +export default class TimeSeries implements DateTime { + private $dateObjectArray: Array + + constructor(data: Series | ArrayType1D) { + if (data instanceof Series) { + this.$dateObjectArray = this.processData(data.values as ArrayType1D) + } else { + this.$dateObjectArray = this.processData(data) + } + } + + /** + * Processed the data values into internal structure for easy access + * @param dateArray An array of date strings + */ + private processData(dateArray: ArrayType1D): Array { + const values = dateArray.map(dateString => new Date(`${dateString}`)) + return values + } + + /** + * Returns the month, in local time. + * @example + * ``` + * import { Dataframe } from "danfojs-node" + * const data = [ + * "2019-01-01", + * "2019-02-01", + * "2019-03-01", + * "2019-04-01", + * ] + * const df = new Dataframe(data) + * const dfNew = df.dt.month() + * console.log(dfNew.values) + * // [1, 2, 3, 4] + * ``` + */ + month(): Series { + const newValues = this.$dateObjectArray.map(date => date.getMonth()) + return new Series(newValues); + } + + /** + * Returns the day of the week, in local time + * @example + * ``` + * import { Dataframe } from "danfojs-node" + * const data = [ + * "2019-01-01", + * "2019-02-01", + * "2019-03-01", + * "2019-04-01", + * ] + * const df = new Dataframe(data) + * const dayOfWeek = df.dt.dayOfWeek() + * console.log(dayOfWeek.values) + * ``` + */ + dayOfWeek() { + const newValues = this.$dateObjectArray.map(date => date.getDay()) + return new Series(newValues); + } + + /** + * Returns the year, in local time + * @example + * ``` + * import { Dataframe } from "danfojs-node" + * const data = [ + * "2019-01-01", + * "2019-02-01", + * "2021-03-01", + * "2020-04-01", + * ] + * const df = new Dataframe(data) + * const year = df.dt.year() + * console.log(year.values) + * // [2019, 2019, 2021, 2020] + * ``` + */ + year() { + const newValues = this.$dateObjectArray.map(date => date.getFullYear()) + return new Series(newValues); + } + + /** + * Returns the name of the month, in local time + * @example + * ``` + * import { Dataframe } from "danfojs-node" + * const data = [ + * "2019-01-01", + * "2019-02-01", + * "2021-03-01", + * "2020-04-01", + * ] + * const df = new Dataframe(data) + * const monthName = df.dt.monthName().values + * console.log(monthName) + * // ["January", "February", "March", "April"] + * ``` + */ + monthName() { + const newValues = this.$dateObjectArray.map(date => MONTH_NAME[date.getMonth()]) + return new Series(newValues); + } + + /** + * Returns the name of the day, of the week, in local time + * @example + * ``` + * import { Dataframe } from "danfojs-node" + * const data = [ + * "2019-01-01", + * "2019-02-01", + * "2021-03-01", + * "2020-04-01", + * ] + * const df = new Dataframe(data) + * const dayOfWeekName = df.dt.dayOfWeekName().values + * console.log(dayOfWeekName) + * ``` + */ + dayOfWeekName() { + const newValues = this.$dateObjectArray.map(date => WEEK_NAME[date.getDay()]) + return new Series(newValues); + } + + /** + * Returns the day of the month, in local time + * @example + * ``` + * import { Dataframe } from "danfojs-node" + * const data = [ + * "2019-01-01", + * "2019-02-05", + * "2021-03-02", + * "2020-04-01", + * ] + * const df = new Dataframe(data) + * const dayOfMonth = df.dt.dayOfMonth().values + * console.log(dayOfMonth) + * // [1, 5, 2, 1] + * ``` + */ + dayOfMonth() { + const newValues = this.$dateObjectArray.map(date => date.getDate()) + return new Series(newValues); + } + + /** + * Returns the hour of the day, in local time + * @example + * ``` + * import { Dataframe } from "danfojs-node" + * const data = [ + * "2019-01-01", + * "2019-02-05", + * "2021-03-02", + * "2020-04-01", + * ] + * const df = new Dataframe(data) + * const hour = df.dt.hour().values + * console.log(hour) + * // [0, 0, 0, 0] + * ``` + */ + hours() { + const newValues = this.$dateObjectArray.map(date => date.getHours()) + return new Series(newValues); + } + + /** + * Returns the second of the day, in local time + * @example + * ``` + * import { Dataframe } from "danfojs-node" + * const data = [ + * "2019-01-01", + * "2019-02-05", + * "2021-03-02", + * "2020-04-01", + * ] + * const df = new Dataframe(data) + * const second = df.dt.second().values + * console.log(second) + * ``` + */ + seconds() { + const newValues = this.$dateObjectArray.map(date => date.getSeconds()) + return new Series(newValues); + } + + /** + * Returns the minute of the day, in local time + * @example + * ``` + * import { Dataframe } from "danfojs-node" + * const data = [ + * "2019-01-01", + * "2019-02-05", + * "2021-03-02", + * "2020-04-01", + * ] + * const df = new Dataframe(data) + * const minute = df.dt.minute().values + * console.log(minute) + * ``` + */ + minutes() { + const newValues = this.$dateObjectArray.map(date => date.getMinutes()) + return new Series(newValues); + } + +} + +export const toDateTime = (data: Series | ArrayType1D) => { + return new TimeSeries(data); +}; diff --git a/src/danfojs-base/core/frame.ts b/src/danfojs-base/core/frame.ts new file mode 100644 index 00000000..7cdd004f --- /dev/null +++ b/src/danfojs-base/core/frame.ts @@ -0,0 +1,3330 @@ +/** +* @license +* Copyright 2022 JsData. All rights reserved. +* +* This source code is licensed under the MIT license found in the +* LICENSE file in the root directory of this source tree. + +* Unless required by applicable law or agreed to in writing, software +* distributed under the License is distributed on an "AS IS" BASIS, +* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +* See the License for the specific language governing permissions and +* limitations under the License. +* ========================================================================== +*/ +import { toCSVBrowser, toExcelBrowser, toJSONBrowser } from "../io/browser"; +import { toCSVNode, toExcelNode, toJSONNode } from "../io/node"; +import dummyEncode from "../transformers/encoders/dummy.encoder"; +import { variance, std, median, mode, mean } from 'mathjs'; +import tensorflow from '../shared/tensorflowlib' +import { DATA_TYPES } from '../shared/defaults'; +import { _genericMathOp } from "./math.ops"; +import Groupby from '../aggregators/groupby'; +import ErrorThrower from "../shared/errors" +import { _iloc, _loc } from "./indexing"; +import { PlotlyLib } from "../plotting"; +import Utils from "../shared/utils" +import NDframe from "./generic"; +import { table } from "table"; +import Series from './series'; +import { + ArrayType1D, + ArrayType2D, + DataFrameInterface, + BaseDataOptionType, + CsvOutputOptionsBrowser, + ExcelOutputOptionsBrowser, + JsonOutputOptionsBrowser, + CsvOutputOptionsNode, + ExcelOutputOptionsNode, + JsonOutputOptionsNode +} from "../shared/types"; + +const utils = new Utils(); + +/** + * Two-dimensional ndarray with axis labels. + * The object supports both integer- and label-based indexing and provides a host of methods for performing operations involving the index. + * Operations between DataFrame (+, -, /, , *) align values based on their associated index values– they need not be the same length. + * @param data 2D Array, JSON, Tensor, Block of data. + * @param options.index Array of numeric or string names for subseting array. If not specified, indexes are auto generated. + * @param options.columns Array of column names. If not specified, column names are auto generated. + * @param options.dtypes Array of data types for each the column. If not specified, dtypes are/is inferred. + * @param options.config General configuration object for extending or setting NDframe behavior. + */ +export default class DataFrame extends NDframe implements DataFrameInterface { + [key: string]: any + constructor(data: any, options: BaseDataOptionType = {}) { + const { index, columns, dtypes, config } = options; + super({ data, index, columns, dtypes, config, isSeries: false }); + this.$setInternalColumnDataProperty(); + } + + /** + * Maps all column names to their corresponding data, and return them as Series objects. + * This makes column subsetting works. E.g this can work ==> `df["col1"]` + * @param column Optional, a single column name to map + */ + private $setInternalColumnDataProperty(column?: string) { + const self = this; + if (column && typeof column === "string") { + Object.defineProperty(self, column, { + get() { + return self.$getColumnData(column) + }, + set(arr: ArrayType1D | Series) { + self.$setColumnData(column, arr); + } + }) + } else { + const columns = this.columns; + for (let i = 0; i < columns.length; i++) { + const column = columns[i]; + Object.defineProperty(this, column, { + get() { + return self.$getColumnData(column) + }, + set(arr: ArrayType1D | Series) { + self.$setColumnData(column, arr); + } + }) + } + } + + } + + /** + * Returns the column data from the DataFrame by column name. + * @param column column name to get the column data + * @param returnSeries Whether to return the data in series format or not. Defaults to true + */ + private $getColumnData(column: string, returnSeries: boolean = true) { + const columnIndex = this.columns.indexOf(column) + + if (columnIndex == -1) { + ErrorThrower.throwColumnNotFoundError(this) + } + + const dtypes = [this.$dtypes[columnIndex]] + const index = [...this.$index] + const columns = [column] + const config = { ...this.$config } + + if (this.$config.isLowMemoryMode) { + const data: ArrayType1D = [] + for (let i = 0; i < this.values.length; i++) { + const row: any = this.values[i]; + data.push(row[columnIndex]) + } + if (returnSeries) { + return new Series(data, { + dtypes, + index, + columns, + config + }) + } else { + return data + } + + } else { + const data = this.$dataIncolumnFormat[columnIndex] + if (returnSeries) { + return new Series(data, { + dtypes, + index, + columns, + config + }) + } else { + return data + } + } + + } + + + /** + * Updates the internal column data via column name. + * @param column The name of the column to update. + * @param arr The new column data + */ + private $setColumnData(column: string, arr: ArrayType1D | Series): void { + + const columnIndex = this.$columns.indexOf(column) + + if (columnIndex == -1) { + throw new Error(`ParamError: column ${column} not found in ${this.$columns}. If you need to add a new column, use the df.addColumn method. `) + } + + let colunmValuesToAdd: ArrayType1D + + if (arr instanceof Series) { + colunmValuesToAdd = arr.values as ArrayType1D + } else if (Array.isArray(arr)) { + colunmValuesToAdd = arr; + } else { + throw new Error("ParamError: specified value not supported. It must either be an Array or a Series of the same length") + } + + if (colunmValuesToAdd.length !== this.shape[0]) { + ErrorThrower.throwColumnLengthError(this, colunmValuesToAdd.length) + } + + if (this.$config.isLowMemoryMode) { + //Update row ($data) array + for (let i = 0; i < this.$data.length; i++) { + (this.$data as any)[i][columnIndex] = colunmValuesToAdd[i] + } + //Update the dtypes + this.$dtypes[columnIndex] = utils.inferDtype(colunmValuesToAdd)[0] + } else { + //Update row ($data) array + for (let i = 0; i < this.values.length; i++) { + (this.$data as any)[i][columnIndex] = colunmValuesToAdd[i] + } + //Update column ($dataIncolumnFormat) array since it's available in object + (this.$dataIncolumnFormat as any)[columnIndex] = arr + + //Update the dtypes + this.$dtypes[columnIndex] = utils.inferDtype(colunmValuesToAdd)[0] + } + + } + + /** + * Return data with missing values removed from a specified axis + * @param axis 0 or 1. If 0, column-wise, if 1, row-wise + */ + private $getDataByAxisWithMissingValuesRemoved(axis: number): Array { + const oldValues = this.$getDataArraysByAxis(axis); + const cleanValues = []; + for (let i = 0; i < oldValues.length; i++) { + const values = oldValues[i] as number[] + cleanValues.push(utils.removeMissingValuesFromArray(values) as number[]); + } + return cleanValues; + } + + /** + * Return data aligned to the specified axis. Transposes the array if needed. + * @param axis 0 or 1. If 0, column-wise, if 1, row-wise + */ + private $getDataArraysByAxis(axis: number): ArrayType2D { + if (axis === 1) { + return this.values as ArrayType2D + } else { + let dfValues; + if (this.config.isLowMemoryMode) { + dfValues = utils.transposeArray(this.values) as ArrayType2D + } else { + dfValues = this.$dataIncolumnFormat as ArrayType2D + } + return dfValues + } + } + + /* + * checks if DataFrame is compactible for arithmetic operation + * compatible Dataframe must have only numerical dtypes + **/ + private $frameIsNotCompactibleForArithmeticOperation() { + const dtypes = this.dtypes; + const str = (element: any) => element == "string"; + return dtypes.some(str) + } + + /** + * Return Tensors in the right axis for math operations. + * @param other DataFrame or Series or number or array + * @param axis 0 or 1. If 0, column-wise, if 1, row-wise + * */ + private $getTensorsForArithmeticOperationByAxis( + other: DataFrame | Series | number | Array, + axis: number + ) { + if (typeof other === "number") { + return [this.tensor, tensorflow.scalar(other)]; + } else if (other instanceof DataFrame) { + return [this.tensor, other.tensor]; + } else if (other instanceof Series) { + if (axis === 0) { + return [this.tensor, tensorflow.tensor2d(other.values as Array, [other.shape[0], 1])]; + } else { + return [this.tensor, tensorflow.tensor2d(other.values as Array, [other.shape[0], 1]).transpose()]; + } + } else if (Array.isArray(other)) { + if (axis === 0) { + return [this.tensor, tensorflow.tensor2d(other, [other.length, 1])]; + } else { + return [this.tensor, tensorflow.tensor2d(other, [other.length, 1]).transpose()]; + } + } else { + throw new Error("ParamError: Invalid type for other parameter. other must be one of Series, DataFrame or number.") + } + + } + + /** + * Returns the dtype for a given column name + * @param column + */ + private $getColumnDtype(column: string): string { + const columnIndex = this.columns.indexOf(column) + if (columnIndex === -1) { + throw Error(`ColumnNameError: Column "${column}" does not exist`) + } + return this.dtypes[columnIndex] + } + + private $logicalOps(tensors: typeof tensorflow.Tensor[], operation: string) { + let newValues: number[] = [] + + switch (operation) { + case 'gt': + newValues = tensors[0].greater(tensors[1]).arraySync() as number[] + break; + case 'lt': + newValues = tensors[0].less(tensors[1]).arraySync() as number[] + break; + case 'ge': + newValues = tensors[0].greaterEqual(tensors[1]).arraySync() as number[] + break; + case 'le': + newValues = tensors[0].lessEqual(tensors[1]).arraySync() as number[] + break; + case 'eq': + newValues = tensors[0].equal(tensors[1]).arraySync() as number[] + break; + case 'ne': + newValues = tensors[0].notEqual(tensors[1]).arraySync() as number[] + break; + + } + + const newData = utils.mapIntegersToBooleans(newValues, 2) + return new DataFrame( + newData, + { + index: [...this.index], + columns: [...this.columns], + dtypes: [...this.dtypes], + config: { ...this.config } + }) + } + + private $MathOps(tensors: typeof tensorflow.Tensor[], operation: string, inplace: boolean) { + let tensorResult + + switch (operation) { + case 'add': + tensorResult = tensors[0].add(tensors[1]) + break; + case 'sub': + tensorResult = tensors[0].sub(tensors[1]) + break; + case 'pow': + tensorResult = tensors[0].pow(tensors[1]) + break; + case 'div': + tensorResult = tensors[0].div(tensors[1]) + break; + case 'mul': + tensorResult = tensors[0].mul(tensors[1]) + break; + case 'mod': + tensorResult = tensors[0].mod(tensors[1]) + break; + } + + if (inplace) { + const newData = tensorResult?.arraySync() as ArrayType2D + this.$setValues(newData) + } else { + return new DataFrame( + tensorResult, + { + index: [...this.index], + columns: [...this.columns], + dtypes: [...this.dtypes], + config: { ...this.config } + }) + + } + } + + /** + * Purely integer-location based indexing for selection by position. + * ``.iloc`` is primarily integer position based (from ``0`` to + * ``length-1`` of the axis), but may also be used with a boolean array. + * + * @param rows Array of row indexes + * @param columns Array of column indexes + * + * Allowed inputs are in rows and columns params are: + * + * - An array of single integer, e.g. ``[5]``. + * - A list or array of integers, e.g. ``[4, 3, 0]``. + * - A slice array string with ints, e.g. ``["1:7"]``. + * - A boolean array. + * - A ``callable`` function with one argument (the calling Series or + * DataFrame) and that returns valid output for indexing (one of the above). + * This is useful in method chains, when you don't have a reference to the + * calling object, but would like to base your selection on some value. + * + * ``.iloc`` will raise ``IndexError`` if a requested indexer is + * out-of-bounds. + * + * @example + * ``` + * const df = new DataFrame([[1, 2], [3, 4]], { columns: ['A', 'B'] }) + * const df2 = df.iloc({ rows: [1], columns: ["A"] }) + * ``` + */ + iloc({ rows, columns }: { + rows?: Array | Series, + columns?: Array + }): DataFrame { + return _iloc({ ndFrame: this, rows, columns }) as DataFrame; + } + + + /** + * Access a group of rows and columns by label(s) or a boolean array. + * ``loc`` is primarily label based, but may also be used with a boolean array. + * + * @param rows Array of row indexes + * @param columns Array of column indexes + * + * Allowed inputs are: + * + * - A single label, e.g. ``["5"]`` or ``['a']``, (note that ``5`` is interpreted as a + * *label* of the index, and **never** as an integer position along the index). + * + * - A list or array of labels, e.g. ``['a', 'b', 'c']``. + * + * - A slice object with labels, e.g. ``["a:f"]``. Note that start and the stop are included + * + * - A boolean array of the same length as the axis being sliced, + * e.g. ``[True, False, True]``. + * + * - A ``callable`` function with one argument (the calling Series or + * DataFrame) and that returns valid output for indexing (one of the above) + * @example + * ``` + * const df = new DataFrame([[1, 2], [3, 4]], { columns: ['A', 'B'] }) + * const df2 = df.loc({ rows: [1], columns: ["A"] }) + * ``` + */ + loc({ rows, columns }: { + rows?: Array | Series, + columns?: Array + }): DataFrame { + return _loc({ ndFrame: this, rows, columns }) as DataFrame + } + + /** + * Prints DataFrame to console as a formatted grid of row and columns. + */ + toString(): string { + const maxRow = this.config.getMaxRow; + const maxColToDisplayInConsole = this.config.getTableMaxColInConsole; + + // let data; + const dataArr: ArrayType2D = []; + const colLen = this.columns.length; + + let header = []; + + if (colLen > maxColToDisplayInConsole) { + //truncate displayed columns to fit in the console + let firstFourcolNames = this.columns.slice(0, 4); + let lastThreecolNames = this.columns.slice(colLen - 3); + //join columns with truncate ellipse in the middle + header = ["", ...firstFourcolNames, "...", ...lastThreecolNames]; + + let subIdx: Array + let firstHalfValues: ArrayType2D + let lastHalfValueS: ArrayType2D + + if (this.values.length > maxRow) { + //slice Object to show [max_rows] + let dfSubset1 = this.iloc({ + rows: [`0:${maxRow}`], + columns: ["0:4"] + }); + + let dfSubset2 = this.iloc({ + rows: [`0:${maxRow}`], + columns: [`${colLen - 3}:`] + }); + + subIdx = this.index.slice(0, maxRow); + firstHalfValues = dfSubset1.values as ArrayType2D + lastHalfValueS = dfSubset2.values as ArrayType2D + + } else { + let dfSubset1 = this.iloc({ columns: ["0:4"] }); + let dfSubset2 = this.iloc({ columns: [`${colLen - 3}:`] }); + + subIdx = this.index.slice(0, maxRow); + firstHalfValues = dfSubset1.values as ArrayType2D + lastHalfValueS = dfSubset2.values as ArrayType2D + } + + // merge subset + for (let i = 0; i < subIdx.length; i++) { + const idx = subIdx[i]; + const row = [idx, ...firstHalfValues[i], "...", ...lastHalfValueS[i]] + dataArr.push(row); + } + + } else { + //display all columns + header = ["", ...this.columns] + let subIdx + let values: ArrayType2D + if (this.values.length > maxRow) { + //slice Object to show a max of [max_rows] + let data = this.iloc({ rows: [`0:${maxRow}`] }); + subIdx = data.index; + values = data.values as ArrayType2D + } else { + values = this.values as ArrayType2D + subIdx = this.index; + } + + // merge subset + for (let i = 0; i < subIdx.length; i++) { + const idx = subIdx[i]; + const row = [idx, ...values[i]]; + dataArr.push(row); + } + } + + + const columnsConfig: any = {}; + columnsConfig[0] = { width: 10 }; //set column width for index column + + for (let index = 1; index < header.length; index++) { + columnsConfig[index] = { width: 17, truncate: 16 }; + } + + const tableData: any = [header, ...dataArr]; //Adds the column names to values before printing + + return table(tableData, { columns: columnsConfig, ...this.config.getTableDisplayConfig }); + } + + /** + * Returns the first n values in a DataFrame + * @param rows The number of rows to return + * @example + * ``` + * const df = new DataFrame([[1, 2], [3, 4]], { columns: ['A', 'B']}) + * const df2 = df.head(1) + * ``` + */ + head(rows: number = 5): DataFrame { + if (rows > this.shape[0]) { + throw new Error("ParamError: Number of rows cannot be greater than available rows in data") + } + if (rows <= 0) { + throw new Error("ParamError: Number of rows cannot be less than 1") + } + + return this.iloc({ rows: [`0:${rows}`] }) + } + + /** + * Returns the last n values in a DataFrame + * @param rows The number of rows to return + * @example + * ``` + * const df = new DataFrame([[1, 2], [3, 4]], { columns: ['A', 'B']}) + * const df2 = df.tail(1) + * ``` + */ + tail(rows: number = 5): any { + if (rows > this.shape[0]) { + throw new Error("ParamError: Number of rows cannot be greater than available rows in data") + } + if (rows <= 0) { + throw new Error("ParamError: Number of rows cannot be less than 1") + } + rows = this.shape[0] - rows + return this.iloc({ rows: [`${rows}:`] }) + } + + /** + * Gets n number of random rows in a dataframe. Sample is reproducible if seed is provided. + * @param num The number of rows to return. Default to 5. + * @param options.seed An integer specifying the random seed that will be used to create the distribution. + * @example + * ``` + * const df = new DataFrame([[1, 2], [3, 4]], { columns: ['A', 'B'] }) + * const df2 = await df.sample(1) + * ``` + * @example + * ``` + * const df = new DataFrame([[1, 2], [3, 4]], { columns: ['A', 'B'] }) + * const df2 = await df.sample(1, { seed: 1 }) + * ``` + */ + async sample(num = 5, options?: { seed?: number }): Promise { + const { seed } = { seed: 1, ...options } + + if (num > this.shape[0]) { + throw new Error("ParamError: Sample size cannot be bigger than number of rows"); + } + if (num <= 0) { + throw new Error("ParamError: Sample size cannot be less than 1"); + } + + const shuffledIndex = await tensorflow.data.array(this.index).shuffle(num, `${seed}`).take(num).toArray(); + const df = this.iloc({ rows: shuffledIndex }); + return df; + } + + /** + * Return Addition of DataFrame and other, element-wise (binary operator add). + * @param other DataFrame, Series, Array or Scalar number to add + * @param options.axis 0 or 1. If 0, add column-wise, if 1, add row-wise + * @param options.inplace Boolean indicating whether to perform the operation inplace or not. Defaults to false + * @example + * ``` + * const df = new DataFrame([[1, 2], [3, 4]], { columns: ['A', 'B'] }) + * const df2 = df.add(1) + * df2.print() + * ``` + * @example + * ``` + * const df = new DataFrame([[1, 2], [3, 4]], { columns: ['A', 'B'] }) + * df.add([1, 2], { axis: 0, inplace: true}) + * df.print() + * ``` + */ + add(other: DataFrame | Series | number[] | number, options?: { axis?: 0 | 1, inplace?: boolean }): DataFrame + add(other: DataFrame | Series | number[] | number, options?: { axis?: 0 | 1, inplace?: boolean }): DataFrame | void { + const { inplace, axis } = { inplace: false, axis: 1, ...options } + + if (this.$frameIsNotCompactibleForArithmeticOperation()) { + throw Error("TypeError: add operation is not supported for string dtypes"); + } + + if ([0, 1].indexOf(axis) === -1) { + throw Error("ParamError: Axis must be 0 or 1"); + } + + const tensors = this.$getTensorsForArithmeticOperationByAxis(other, axis); + return this.$MathOps(tensors, "add", inplace) + } + + /** + * Return substraction between DataFrame and other. + * @param other DataFrame, Series, Array or Scalar number to substract from DataFrame + * @param options.axis 0 or 1. If 0, compute the subtraction column-wise, if 1, row-wise + * @param options.inplace Boolean indicating whether to perform the operation inplace or not. Defaults to false + * @example + * ``` + * const df = new DataFrame([[1, 2], [3, 4]], { columns: ['A', 'B'] }) + * const df2 = df.sub(1) + * df2.print() + * ``` + * @example + * ``` + * const df = new DataFrame([[1, 2], [3, 4]], { columns: ['A', 'B'] }) + * df.sub([1, 2], { axis: 0, inplace: true}) + * df.print() + * ``` + */ + sub(other: DataFrame | Series | number[] | number, options?: { axis?: 0 | 1, inplace?: boolean }): DataFrame + sub(other: DataFrame | Series | number[] | number, options?: { axis?: 0 | 1, inplace?: boolean }): DataFrame | void { + const { inplace, axis } = { inplace: false, axis: 1, ...options } + + if (this.$frameIsNotCompactibleForArithmeticOperation()) { + throw Error("TypeError: sub operation is not supported for string dtypes"); + } + + if ([0, 1].indexOf(axis) === -1) { + throw Error("ParamError: Axis must be 0 or 1"); + } + + const tensors = this.$getTensorsForArithmeticOperationByAxis(other, axis); + return this.$MathOps(tensors, "sub", inplace) + + + } + /** + * Return multiplciation between DataFrame and other. + * @param other DataFrame, Series, Array or Scalar number to multiply with. + * @param options.axis 0 or 1. If 0, compute the multiplication column-wise, if 1, row-wise + * @param options.inplace Boolean indicating whether to perform the operation inplace or not. Defaults to false + * @example + * ``` + * const df = new DataFrame([[1, 2], [3, 4]], { columns: ['A', 'B'] }) + * const df2 = df.mul(2) + * df2.print() + * ``` + * @example + * ``` + * const df = new DataFrame([[1, 2], [3, 4]], { columns: ['A', 'B'] }) + * df.mul([1, 2], { axis: 0, inplace: true}) + * df.print() + * ``` + */ + mul(other: DataFrame | Series | number[] | number, options?: { axis?: 0 | 1, inplace?: boolean }): DataFrame + mul(other: DataFrame | Series | number[] | number, options?: { axis?: 0 | 1, inplace?: boolean }): DataFrame | void { + const { inplace, axis } = { inplace: false, axis: 1, ...options } + + if (this.$frameIsNotCompactibleForArithmeticOperation()) { + throw Error("TypeError: mul operation is not supported for string dtypes"); + } + + if ([0, 1].indexOf(axis) === -1) { + throw Error("ParamError: Axis must be 0 or 1"); + } + const tensors = this.$getTensorsForArithmeticOperationByAxis(other, axis); + return this.$MathOps(tensors, "mul", inplace) + + + } + + /** + * Return division of DataFrame with other. + * @param other DataFrame, Series, Array or Scalar number to divide with. + * @param options.axis 0 or 1. If 0, compute the division column-wise, if 1, row-wise + * @param options.inplace Boolean indicating whether to perform the operation inplace or not. Defaults to false + * @example + * ``` + * const df = new DataFrame([[1, 2], [3, 4]], { columns: ['A', 'B'] }) + * const df2 = df.div(2) + * df2.print() + * ``` + */ + div(other: DataFrame | Series | number[] | number, options?: { axis?: 0 | 1, inplace?: boolean }): DataFrame + div(other: DataFrame | Series | number[] | number, options?: { axis?: 0 | 1, inplace?: boolean }): DataFrame | void { + const { inplace, axis } = { inplace: false, axis: 1, ...options } + + if (this.$frameIsNotCompactibleForArithmeticOperation()) { + throw Error("TypeError: div operation is not supported for string dtypes"); + } + + if ([0, 1].indexOf(axis) === -1) { + throw Error("ParamError: Axis must be 0 or 1"); + } + + const tensors = this.$getTensorsForArithmeticOperationByAxis(other, axis); + return this.$MathOps(tensors, "div", inplace) + + + } + + /** + * Return DataFrame raised to the power of other. + * @param other DataFrame, Series, Array or Scalar number to to raise to. + * @param options.axis 0 or 1. If 0, compute the power column-wise, if 1, row-wise + * @param options.inplace Boolean indicating whether to perform the operation inplace or not. Defaults to false + * @example + * ``` + * const df = new DataFrame([[1, 2], [3, 4]], { columns: ['A', 'B'] }) + * const df2 = df.pow(2) + * df2.print() + * ``` + */ + pow(other: DataFrame | Series | number[] | number, options?: { axis?: 0 | 1, inplace?: boolean }): DataFrame + pow(other: DataFrame | Series | number[] | number, options?: { axis?: 0 | 1, inplace?: boolean }): DataFrame | void { + const { inplace, axis } = { inplace: false, axis: 1, ...options } + + if (this.$frameIsNotCompactibleForArithmeticOperation()) { + throw Error("TypeError: pow operation is not supported for string dtypes"); + } + + if ([0, 1].indexOf(axis) === -1) { + throw Error("ParamError: Axis must be 0 or 1"); + } + + const tensors = this.$getTensorsForArithmeticOperationByAxis(other, axis); + return this.$MathOps(tensors, "pow", inplace) + + + } + + /** + * Return modulus between DataFrame and other. + * @param other DataFrame, Series, Array or Scalar number to modulus with. + * @param options.axis 0 or 1. If 0, compute the mod column-wise, if 1, row-wise + * @param options.inplace Boolean indicating whether to perform the operation inplace or not. Defaults to false + * @example + * ``` + * const df = new DataFrame([[1, 2], [3, 4]], { columns: ['A', 'B'] }) + * const df2 = df.mod(2) + * df2.print() + * ``` + */ + mod(other: DataFrame | Series | number[] | number, options?: { axis?: 0 | 1, inplace?: boolean }): DataFrame + mod(other: DataFrame | Series | number[] | number, options?: { axis?: 0 | 1, inplace?: boolean }): DataFrame | void { + const { inplace, axis } = { inplace: false, axis: 1, ...options } + + if (this.$frameIsNotCompactibleForArithmeticOperation()) { + throw Error("TypeError: mod operation is not supported for string dtypes"); + } + + if ([0, 1].indexOf(axis) === -1) { + throw Error("ParamError: Axis must be 0 or 1"); + } + + const tensors = this.$getTensorsForArithmeticOperationByAxis(other, axis); + return this.$MathOps(tensors, "mod", inplace) + + } + + /** + * Return mean of DataFrame across specified axis. + * @param options.axis 0 or 1. If 0, compute the mean column-wise, if 1, row-wise. Defaults to 1 + * @example + * ``` + * const df = new DataFrame([[1, 2], [3, 4]], { columns: ['A', 'B'] }) + * df.mean().print() + * ``` + * @example + * ``` + * const df = new DataFrame([[1, 2], [3, 4]], { columns: ['A', 'B'] }) + * df.mean({ axis: 0 }).print() + * ``` + */ + mean(options?: { axis?: 0 | 1 }): Series { + const { axis } = { axis: 1, ...options } + + if (this.$frameIsNotCompactibleForArithmeticOperation()) { + throw Error("TypeError: mean operation is not supported for string dtypes"); + } + + if ([0, 1].indexOf(axis) === -1) { + throw Error("ParamError: Axis must be 0 or 1"); + } + + const newData = this.$getDataByAxisWithMissingValuesRemoved(axis) + const resultArr = newData.map(arr => arr.reduce((a, b) => a + b, 0) / arr.length) + if (axis === 0) { + return new Series(resultArr, { index: this.columns }); + } else { + return new Series(resultArr, { index: this.index }); + } + } + + /** + * Return median of DataFrame across specified axis. + * @param options.axis 0 or 1. If 0, compute the median column-wise, if 1, row-wise. Defaults to 1 + * @example + * ``` + * const df = new DataFrame([[1, 2, 4], [3, 4, 5], [6, 7, 8]], { columns: ['A', 'B', 'C'] }); + * df.median().print() + * ``` + */ + median(options?: { axis?: 0 | 1 }): Series { + const { axis } = { axis: 1, ...options } + + if (this.$frameIsNotCompactibleForArithmeticOperation()) { + throw Error("TypeError: median operation is not supported for string dtypes"); + } + + if ([0, 1].indexOf(axis) === -1) { + throw Error("ParamError: Axis must be 0 or 1"); + } + + const newData = this.$getDataByAxisWithMissingValuesRemoved(axis) + const resultArr = newData.map(arr => median(arr)) + if (axis === 0) { + return new Series(resultArr, { index: this.columns }); + } else { + return new Series(resultArr, { index: this.index }); + } + + } + + /** + * Return mode of DataFrame across specified axis. + * @param options.axis 0 or 1. If 0, compute the mode column-wise, if 1, row-wise. Defaults to 1 + * @param options.keep If there are more than one modes, returns the mode at position [keep]. Defaults to 0 + * @example + * ``` + * const df = new DataFrame([[1, 2, 4], [3, 4, 5], [6, 7, 8]], { columns: ['A', 'B', 'C'] }); + * df.mode().print() + * ``` + * @example + * ``` + * const df = new DataFrame([[1, 2, 4], [3, 4, 5], [6, 7, 8]], { columns: ['A', 'B', 'C'] }); + * df.mode({ keep: 1 }).print() + * ``` + */ + mode(options?: { axis?: 0 | 1, keep?: number }): Series { + const { axis, keep } = { axis: 1, keep: 0, ...options } + + if (this.$frameIsNotCompactibleForArithmeticOperation()) { + throw Error("TypeError: mode operation is not supported for string dtypes"); + } + + if ([0, 1].indexOf(axis) === -1) { + throw Error("ParamError: Axis must be 0 or 1"); + } + + const newData = this.$getDataByAxisWithMissingValuesRemoved(axis) + const resultArr = newData.map(arr => { + const tempMode = mode(arr) + if (tempMode.length === 1) { + return tempMode[0] + } else { + return tempMode[keep] + } + }) + if (axis === 0) { + return new Series(resultArr, { index: this.columns }); + } else { + return new Series(resultArr, { index: this.index }); + } + + } + + /** + * Return minimum of values in a DataFrame across specified axis. + * @param options.axis 0 or 1. If 0, compute the minimum value column-wise, if 1, row-wise. Defaults to 1 + * @example + * ``` + * const df = new DataFrame([[1, 2], [3, 4]], { columns: ['A', 'B']}) + * df.min().print() + * ``` + * @example + * ``` + * const df = new DataFrame([[1, 2], [3, 4]], { columns: ['A', 'B']}) + * df.min({ axis: 0 }).print() + * ``` + */ + min(options?: { axis?: 0 | 1 }): Series { + const { axis } = { axis: 1, ...options } + + if (this.$frameIsNotCompactibleForArithmeticOperation()) { + throw Error("TypeError: min operation is not supported for string dtypes"); + } + + if ([0, 1].indexOf(axis) === -1) { + throw Error("ParamError: Axis must be 0 or 1"); + } + + const newData = this.$getDataByAxisWithMissingValuesRemoved(axis) + + const resultArr = newData.map(arr => { + let smallestValue = arr[0] + for (let i = 0; i < arr.length; i++) { + smallestValue = smallestValue < arr[i] ? smallestValue : arr[i] + } + return smallestValue + }) + if (axis === 0) { + return new Series(resultArr, { index: this.columns }); + } else { + return new Series(resultArr, { index: this.index }); + } + } + + /** + * Return maximum of values in a DataFrame across specified axis. + * @param options.axis 0 or 1. If 0, compute the maximum column-wise, if 1, row-wise. Defaults to 1 + * @example + * ``` + * const df = new DataFrame([[1, 2], [3, 4]], { columns: ['A', 'B']}) + * df.max().print() + * ``` + * @example + * ``` + * const df = new DataFrame([[1, 2], [3, 4]], { columns: ['A', 'B']}) + * df.max({ axis: 0 }).print() + * ``` + */ + max(options?: { axis?: 0 | 1 }): Series { + const { axis } = { axis: 1, ...options } + + if (this.$frameIsNotCompactibleForArithmeticOperation()) { + throw Error("TypeError: max operation is not supported for string dtypes"); + } + + if ([0, 1].indexOf(axis) === -1) { + throw Error("ParamError: Axis must be 0 or 1"); + } + + const newData = this.$getDataByAxisWithMissingValuesRemoved(axis) + + const resultArr = newData.map(arr => { + let biggestValue = arr[0] + for (let i = 0; i < arr.length; i++) { + biggestValue = biggestValue > arr[i] ? biggestValue : arr[i] + } + return biggestValue + }) + if (axis === 0) { + return new Series(resultArr, { index: this.columns }); + } else { + return new Series(resultArr, { index: this.index }); + } + + } + + /** + * Return standard deviation of values in a DataFrame across specified axis. + * @param options.axis 0 or 1. If 0, compute the standard deviation column-wise, if 1, row-wise. Defaults to 1 + * @example + * ``` + * const df = new DataFrame([[1, 2], [3, 4]], { columns: ['A', 'B']}) + * df.std().print() + * ``` + * @example + * ``` + * const df = new DataFrame([[1, 2], [3, 4]], { columns: ['A', 'B']}) + * df.std({ axis: 0 }).print() + * ``` + */ + std(options?: { axis?: 0 | 1 }): Series { + const { axis } = { axis: 1, ...options } + + if (this.$frameIsNotCompactibleForArithmeticOperation()) { + throw Error("TypeError: std operation is not supported for string dtypes"); + } + + if ([0, 1].indexOf(axis) === -1) { + throw Error("ParamError: Axis must be 0 or 1"); + } + + const newData = this.$getDataByAxisWithMissingValuesRemoved(axis) + const resultArr = newData.map(arr => std(arr)) + if (axis === 0) { + return new Series(resultArr, { index: this.columns }); + } else { + return new Series(resultArr, { index: this.index }); + } + + } + + /** + * Return variance of values in a DataFrame across specified axis. + * @param options.axis 0 or 1. If 0, compute the variance column-wise, if 1, add row-wise. Defaults to 1 + * @example + * ``` + * const df = new DataFrame([[1, 2], [3, 4]], { columns: ['A', 'B']}) + * df.var().print() + * ``` + * @example + * ``` + * const df = new DataFrame([[1, 2], [3, 4]], { columns: ['A', 'B']}) + * df.var({ axis: 0 }).print() + * ``` + */ + var(options?: { axis?: 0 | 1 }): Series { + const { axis } = { axis: 1, ...options } + + if (this.$frameIsNotCompactibleForArithmeticOperation()) { + throw Error("TypeError: var operation is not supported for string dtypes"); + } + + if ([0, 1].indexOf(axis) === -1) { + throw Error("ParamError: Axis must be 0 or 1"); + } + + const newData = this.$getDataByAxisWithMissingValuesRemoved(axis) + const resultArr = newData.map(arr => variance(arr)) + if (axis === 0) { + return new Series(resultArr, { index: this.columns }); + } else { + return new Series(resultArr, { index: this.index }); + } + + } + + /** + * Get Less than of dataframe and other, element-wise (binary operator lt). + * @param other DataFrame, Series, Array or Scalar number to compare with + * @param options.axis 0 or 1. If 0, add column-wise, if 1, add row-wise + * @example + * ``` + * const df = new DataFrame([[1, 2], [3, 4]], { columns: ['A', 'B']}) + * df.lt(2).print() + * ``` + * @example + * ``` + * const df = new DataFrame([[1, 2], [3, 4]], { columns: ['A', 'B']}) + * df.lt([2, 3], { axis: 0 }).print() + * ``` + * @example + * ``` + * const df = new DataFrame([[1, 2], [3, 4]], { columns: ['A', 'B']}) + * const sf = new Series([2, 3]) + * df.lt(sf, { axis: 1 }).print() + * ``` + */ + lt(other: DataFrame | Series | number | Array, options?: { axis?: 0 | 1 }): DataFrame { + const { axis } = { axis: 1, ...options } + + if (this.$frameIsNotCompactibleForArithmeticOperation()) { + throw Error("TypeError: lt operation is not supported for string dtypes"); + } + + if ([0, 1].indexOf(axis) === -1) { + throw Error("ParamError: Axis must be 0 or 1"); + } + + const tensors = this.$getTensorsForArithmeticOperationByAxis(other, axis); + return this.$logicalOps(tensors, "lt") + } + + /** + * Returns "greater than" of dataframe and other. + * @param other DataFrame, Series, Array or Scalar number to compare with + * @param options.axis 0 or 1. If 0, add column-wise, if 1, add row-wise + * @example + * ``` + * const df = new DataFrame([[1, 2], [3, 4]], { columns: ['A', 'B']}) + * df.gt(2).print() + * ``` + * @example + * ``` + * const df = new DataFrame([[1, 2], [3, 4]], { columns: ['A', 'B']}) + * df.gt([2, 3], { axis: 0 }).print() + * ``` + * @example + * ``` + * const df = new DataFrame([[1, 2], [3, 4]], { columns: ['A', 'B']}) + * const sf = new Series([2, 3]) + * df.gt(sf, { axis: 1 }).print() + * ``` + */ + gt(other: DataFrame | Series | number | Array, options?: { axis?: 0 | 1 }): DataFrame { + const { axis } = { axis: 1, ...options } + + if (this.$frameIsNotCompactibleForArithmeticOperation()) { + throw Error("TypeError: gt operation is not supported for string dtypes"); + } + + if ([0, 1].indexOf(axis) === -1) { + throw Error("ParamError: Axis must be 0 or 1"); + } + + const tensors = this.$getTensorsForArithmeticOperationByAxis(other, axis); + return this.$logicalOps(tensors, "gt") + } + + /** + * Returns "equals to" of dataframe and other. + * @param other DataFrame, Series, Array or Scalar number to compare with + * @param options.axis 0 or 1. If 0, add column-wise, if 1, add row-wise + * @example + * ``` + * const df = new DataFrame([[1, 2], [3, 4]], { columns: ['A', 'B']}) + * df.eq(2).print() + * ``` + * @example + * ``` + * const df = new DataFrame([[1, 2], [3, 4]], { columns: ['A', 'B']}) + * df.eq([2, 3], { axis: 0 }).print() + * ``` + * @example + * ``` + * const df = new DataFrame([[1, 2], [3, 4]], { columns: ['A', 'B']}) + * const sf = new Series([2, 3]) + * df.eq(sf, { axis: 1 }).print() + * ``` + */ + eq(other: DataFrame | Series | number | Array, options?: { axis?: 0 | 1 }): DataFrame { + const { axis } = { axis: 1, ...options } + + if (this.$frameIsNotCompactibleForArithmeticOperation()) { + throw Error("TypeError: eq operation is not supported for string dtypes"); + } + + if ([0, 1].indexOf(axis) === -1) { + throw Error("ParamError: Axis must be 0 or 1"); + } + + const tensors = this.$getTensorsForArithmeticOperationByAxis(other, axis); + return this.$logicalOps(tensors, "eq") + } + + /** + * Returns "not equal to" of dataframe and other. + * @param other DataFrame, Series, Array or Scalar number to compare with + * @param options.axis 0 or 1. If 0, add column-wise, if 1, add row-wise + * @example + * ``` + * const df = new DataFrame([[1, 2], [3, 4]], { columns: ['A', 'B']}) + * df.ne(2).print() + * ``` + * @example + * ``` + * const df = new DataFrame([[1, 2], [3, 4]], { columns: ['A', 'B']}) + * df.ne([2, 3], { axis: 0 }).print() + * ``` + * @example + * ``` + * const df = new DataFrame([[1, 2], [3, 4]], { columns: ['A', 'B']}) + * const sf = new Series([2, 3]) + * df.ne(sf, { axis: 1 }).print() + * ``` + */ + ne(other: DataFrame | Series | number | Array, options?: { axis?: 0 | 1 }): DataFrame { + const { axis } = { axis: 1, ...options } + + if (this.$frameIsNotCompactibleForArithmeticOperation()) { + throw Error("TypeError: ne operation is not supported for string dtypes"); + } + + if ([0, 1].indexOf(axis) === -1) { + throw Error("ParamError: Axis must be 0 or 1"); + } + + const tensors = this.$getTensorsForArithmeticOperationByAxis(other, axis); + return this.$logicalOps(tensors, "ne") + } + + /** + * Returns "less than or equal to" of dataframe and other. + * @param other DataFrame, Series, Array or Scalar number to compare with + * @param options.axis 0 or 1. If 0, add column-wise, if 1, add row-wise + * @example + * ``` + * const df = new DataFrame([[1, 2], [3, 4]], { columns: ['A', 'B']}) + * df.le(2).print() + * ``` + * @example + * ``` + * const df = new DataFrame([[1, 2], [3, 4]], { columns: ['A', 'B']}) + * df.le([2, 3], { axis: 0 }).print() + * ``` + * @example + * ``` + * const df = new DataFrame([[1, 2], [3, 4]], { columns: ['A', 'B']}) + * const sf = new Series([2, 3]) + * df.le(sf, { axis: 1 }).print() + * ``` + */ + le(other: DataFrame | Series | number | Array, options?: { axis?: 0 | 1 }): DataFrame { + const { axis } = { axis: 1, ...options } + + if (this.$frameIsNotCompactibleForArithmeticOperation()) { + throw Error("TypeError: le operation is not supported for string dtypes"); + } + + if ([0, 1].indexOf(axis) === -1) { + throw Error("ParamError: Axis must be 0 or 1"); + } + + const tensors = this.$getTensorsForArithmeticOperationByAxis(other, axis); + return this.$logicalOps(tensors, "le") + } + + /** + * Returns "greater than or equal to" between dataframe and other. + * @param other DataFrame, Series, Array or Scalar number to compare with + * @param options.axis 0 or 1. If 0, add column-wise, if 1, add row-wise + * @example + * ``` + * const df = new DataFrame([[1, 2], [3, 4]], { columns: ['A', 'B']}) + * df.ge(2).print() + * ``` + * @example + * ``` + * const df = new DataFrame([[1, 2], [3, 4]], { columns: ['A', 'B']}) + * df.ge([2, 3], { axis: 0 }).print() + * ``` + * @example + * ``` + * const df = new DataFrame([[1, 2], [3, 4]], { columns: ['A', 'B']}) + * const sf = new Series([2, 3]) + * df.ge(sf, { axis: 1 }).print() + * ``` + */ + ge(other: DataFrame | Series | number | Array, options?: { axis?: 0 | 1 }): DataFrame { + const { axis } = { axis: 1, ...options } + + if (this.$frameIsNotCompactibleForArithmeticOperation()) { + throw Error("TypeError: ge operation is not supported for string dtypes"); + } + + if ([0, 1].indexOf(axis) === -1) { + throw Error("ParamError: Axis must be 0 or 1"); + } + + const tensors = this.$getTensorsForArithmeticOperationByAxis(other, axis); + return this.$logicalOps(tensors, "ge") + } + + /** + * Return number of non-null elements in a Series + * @param options.axis 0 or 1. If 0, count column-wise, if 1, add row-wise. Defaults to 1 + * @example + * ``` + * const df = new DataFrame([[1, 2], [3, 4]], { columns: ['A', 'B']}) + * df.count().print() + * ``` + * + * @example + * ``` + * const df = new DataFrame([[1, 2], [3, 4]], { columns: ['A', 'B']}) + * df.count({ axis: 0 }).print() + * ``` + */ + count(options?: { axis?: 0 | 1 }): Series { + const { axis } = { axis: 1, ...options } + + if ([0, 1].indexOf(axis) === -1) { + throw Error("ParamError: Axis must be 0 or 1"); + } + + const newData = this.$getDataByAxisWithMissingValuesRemoved(axis) + const resultArr = newData.map(arr => arr.length) + if (axis === 0) { + return new Series(resultArr, { index: this.columns }); + } else { + return new Series(resultArr, { index: this.index }); + } + + } + + /** + * Return the sum of values across an axis. + * @param options.axis 0 or 1. If 0, count column-wise, if 1, add row-wise. Defaults to 1 + * @example + * ``` + * const df = new DataFrame([[1, 2], [3, 4]], { columns: ['A', 'B']}) + * df.sum().print() + * ``` + * + * @example + * ``` + * const df = new DataFrame([[1, 2], [3, 4]], { columns: ['A', 'B']}) + * df.sum({ axis: 0 }).print() + * ``` + */ + sum(options?: { axis?: 0 | 1 }): Series { + const { axis } = { axis: 1, ...options } + + if ([0, 1].indexOf(axis) === -1) { + throw Error("ParamError: Axis must be 0 or 1"); + } + + const result = this.$getDataByAxisWithMissingValuesRemoved(axis) + const sumArr = result.map((innerArr) => { + return innerArr.reduce((a, b) => Number(a) + Number(b), 0) + }) + if (axis === 0) { + return new Series(sumArr, { + index: [...this.columns] + }) + } else { + return new Series(sumArr, { + index: [...this.index] + }) + } + + } + + /** + * Return the absolute value of elements in a DataFrame. + * @param options.inplace Boolean indicating whether to perform the operation inplace or not. Defaults to false + * @example + * ``` + * const df = new DataFrame([[1.0, 2.1], [3.1, 4]], { columns: ['A', 'B']}) + * df.abs().print() + * ``` + * + * @example + * ``` + * const df = new DataFrame([[1.0, 2], [3.3, 4]], { columns: ['A', 'B']}) + * df.abs({ inplace: true }).print() + * ``` + */ + abs(options?: { inplace?: boolean }): DataFrame + abs(options?: { inplace?: boolean }): DataFrame | void { + const { inplace } = { inplace: false, ...options } + + const newData = (this.values as number[][]).map(arr => arr.map(val => Math.abs(val))) + if (inplace) { + this.$setValues(newData) + } else { + return new DataFrame(newData, { + index: [...this.index], + columns: [...this.columns], + dtypes: [...this.dtypes], + config: { ...this.config } + }) + } + } + + /** + * Rounds all element in the DataFrame to specified number of decimal places. + * @param dp Number of decimal places to round to. Defaults to 1 + * @param options.inplace Boolean indicating whether to perform the operation inplace or not. Defaults to false + * @example + * ``` + * const df = new DataFrame([[1.12, 2.34], [3.43, 4.0]], { columns: ['A', 'B']}) + * const df2 = df.round(2) + * df2.print() + * ``` + * + * @example + * ``` + * const df = new DataFrame([[1.12, 2.34], [3.43, 4.0]], { columns: ['A', 'B']}) + * df.round(2, { inplace: true }).print() + * ``` + */ + round(dp: number, options?: { inplace: boolean }): DataFrame + round(dp: number = 1, options?: { inplace: boolean }): DataFrame | void { + const { inplace } = { inplace: false, ...options } + + if (this.$frameIsNotCompactibleForArithmeticOperation()) { + throw Error("TypeError: round operation is not supported for string dtypes"); + } + + if (typeof dp !== "number") { + throw Error("ParamError: dp must be a number"); + } + + const newData = utils.round(this.values as number[], dp, false); + + if (inplace) { + this.$setValues(newData) + } else { + return new DataFrame( + newData, + { + index: [...this.index], + columns: [...this.columns], + config: { ...this.config } + }) + } + } + + /** + * Returns cumulative product accross specified axis. + * @param options.axis 0 or 1. If 0, count column-wise, if 1, add row-wise. Defaults to 1 + * @param options.inplace Boolean indicating whether to perform the operation inplace or not. Defaults to false + * + * @example + * ``` + * const df = new DataFrame([[1, 2], [3, 4]], { columns: ['A', 'B']}) + * const df2 = df.cumprod() + * df2.print() + * ``` + * + * @example + * ``` + * const df = new DataFrame([[1, 2], [3, 4]], { columns: ['A', 'B']}) + * const df2 = df.cumprod({ axis: 0 }) + * df2.print() + * ``` + * + * @example + * ``` + * const df = new DataFrame([[1, 2], [3, 4]], { columns: ['A', 'B']}) + * df.cumprod({ axis: 0, inplace: true }).print() + * ``` + */ + cumProd(options?: { axis?: 0 | 1, inplace?: boolean }): DataFrame + cumProd(options?: { axis?: 0 | 1, inplace?: boolean }): DataFrame | void { + const { axis, inplace } = { axis: 1, inplace: false, ...options } + return this.cumOps("prod", axis, inplace); + } + + /** + * Returns cumulative sum accross specified axis. + * @param options.axis 0 or 1. If 0, count column-wise, if 1, add row-wise. Defaults to 1 + * @param options.inplace Boolean indicating whether to perform the operation inplace or not. Defaults to false + * @example + * ``` + * const df = new DataFrame([[1, 2], [3, 4]], { columns: ['A', 'B']}) + * const df2 = df.cumSum() + * df2.print() + * ``` + * + * @example + * ``` + * const df = new DataFrame([[1, 2], [3, 4]], { columns: ['A', 'B']}) + * const df2 = df.cumSum({ axis: 0 }) + * df2.print() + * ``` + * + * @example + * ``` + * const df = new DataFrame([[1, 2], [3, 4]], { columns: ['A', 'B']}) + * df.cumSum({ axis: 0, inplace: true }).print() + * ``` + */ + cumSum(options?: { axis?: 0 | 1, inplace?: boolean }): DataFrame + cumSum(options?: { axis?: 0 | 1, inplace?: boolean }): DataFrame | void { + const { axis, inplace } = { axis: 1, inplace: false, ...options } + return this.cumOps("sum", axis, inplace); + } + + /** + * Returns cumulative minimum accross specified axis. + * @param options.axis 0 or 1. If 0, count column-wise, if 1, add row-wise. Defaults to 1 + * @param options.inplace Boolean indicating whether to perform the operation inplace or not. Defaults to false + * @example + * ``` + * const df = new DataFrame([[1, 2], [3, 4]], { columns: ['A', 'B']}) + * const df2 = df.cumMin() + * df2.print() + * ``` + * + * @example + * ``` + * const df = new DataFrame([[1, 2], [3, 4]], { columns: ['A', 'B']}) + * const df2 = df.cumMin({ axis: 0 }) + * df2.print() + * ``` + * + * @example + * ``` + * const df = new DataFrame([[1, 2], [3, 4]], { columns: ['A', 'B']}) + * df.cumMin({ axis: 0, inplace: true }).print() + * ``` + */ + cumMin(options?: { axis?: 0 | 1, inplace?: boolean }): DataFrame + cumMin(options?: { axis?: 0 | 1, inplace?: boolean }): DataFrame | void { + const { axis, inplace } = { axis: 1, inplace: false, ...options } + return this.cumOps("min", axis, inplace); + } + + /** + * Returns cumulative maximum accross specified axis. + * @param options.axis 0 or 1. If 0, count column-wise, if 1, add row-wise. Defaults to 1 + * @param options.inplace Boolean indicating whether to perform the operation inplace or not. Defaults to false + * @example + * ``` + * const df = new DataFrame([[1, 2], [3, 4]], { columns: ['A', 'B']}) + * const df2 = df.cumMax() + * df2.print() + * ``` + * + * @example + * ``` + * const df = new DataFrame([[1, 2], [3, 4]], { columns: ['A', 'B']}) + * const df2 = df.cumMax({ axis: 0 }) + * df2.print() + * ``` + * + * @example + * ``` + * const df = new DataFrame([[1, 2], [3, 4]], { columns: ['A', 'B']}) + * df.cumMax({ axis: 0, inplace: true }).print() + * ``` + */ + cumMax(options?: { axis?: 0 | 1, inplace?: boolean }): DataFrame + cumMax(options?: { axis?: 0 | 1, inplace?: boolean }): DataFrame | void { + const { axis, inplace } = { axis: 1, inplace: false, ...options } + return this.cumOps("max", axis, inplace); + } + + /** + * Internal helper function for cumulative operation on DataFrame + */ + private cumOps(ops: string, axis: number, inplace: boolean): DataFrame + private cumOps(ops: string, axis: number, inplace: boolean): DataFrame | void { + if (this.dtypes.includes("string")) ErrorThrower.throwStringDtypeOperationError(ops) + + const result = this.$getDataByAxisWithMissingValuesRemoved(axis) + + let newData: ArrayType2D = result.map((sData) => { + let tempval = sData[0]; + const data = [tempval]; + + for (let i = 1; i < sData.length; i++) { + let currVal = sData[i]; + switch (ops) { + case "max": + if (currVal > tempval) { + data.push(currVal); + tempval = currVal; + } else { + data.push(tempval); + } + break; + case "min": + if (currVal < tempval) { + data.push(currVal); + tempval = currVal; + } else { + data.push(tempval); + } + break; + case "sum": + tempval = (tempval as number) + (currVal as number) + data.push(tempval); + break; + case "prod": + tempval = (tempval as number) * (currVal as number) + data.push(tempval); + break; + + } + } + return data; + }) + + if (axis === 0) { + newData = utils.transposeArray(newData) as ArrayType2D + } + + if (inplace) { + this.$setValues(newData) + } else { + return new DataFrame(newData, { + index: [...this.index], + columns: [...this.columns], + dtypes: [...this.dtypes], + config: { ...this.config } + }) + } + } + + /** + * Generate descriptive statistics for all numeric columns. + * Descriptive statistics include those that summarize the central tendency, + * dispersion and shape of a dataset’s distribution, excluding NaN values. + * @example + * ``` + * const df = new DataFrame([[1, 2], [3, 4]], { columns: ['A', 'B']}) + * df.describe().print() + * ``` + */ + describe(): DataFrame { + const numericColumnNames = this.columns.filter(name => this.$getColumnDtype(name) !== "string") + const index = ["count", "mean", "std", "min", "median", "max", "variance"]; + const statsObject: any = {}; + for (let i = 0; i < numericColumnNames.length; i++) { + const colName = numericColumnNames[i]; + const $count = (this.$getColumnData(colName) as Series).count(); + const $mean = mean(this.$getColumnData(colName, false) as number[]); + const $std = std(this.$getColumnData(colName, false) as number[]); + const $min = (this.$getColumnData(colName) as Series).min(); + const $median = median(this.$getColumnData(colName, false) as number[]); + const $max = (this.$getColumnData(colName) as Series).max(); + const $variance = variance(this.$getColumnData(colName, false) as number[]); + + const stats = [$count, $mean, $std, $min, $median, $max, $variance]; + statsObject[colName] = stats; + + } + + const df = new DataFrame(statsObject, { index }); + return df + } + + /** + * Drops all rows or columns with missing values (NaN) + * @param axis 0 or 1. If 0, drop columns with NaNs, if 1, drop rows with NaNs + * @param options.inplace Boolean indicating whether to perform the operation inplace or not. Defaults to false + * @example + * ``` + * const df = new DataFrame([[1, 2], [3, 4], [NaN, NaN]], { columns: ['A', 'B']}) + * const df2 = df.dropna() + * df2.print() + * ``` + * + * @example + * ``` + * const df = new DataFrame([[1, 2], [3, 4], [NaN, NaN]], { columns: ['A', 'B']}) + * df.dropna({ axis: 0, inplace: true }).print() + * ``` + */ + dropNa(options?: { axis: 0 | 1, inplace?: boolean }): DataFrame + dropNa(options?: { axis: 0 | 1, inplace?: boolean }): DataFrame | void { + const { axis, inplace } = { axis: 1, inplace: false, ...options } + + if ([0, 1].indexOf(axis) === -1) { + throw Error("ParamError: Axis must be 0 or 1"); + } + + const newIndex: Array = []; + + if (axis == 1) { + const newData = []; + + const dfValues = this.values as ArrayType2D; + for (let i = 0; i < dfValues.length; i++) { + const values: ArrayType1D = dfValues[i]; + //@ts-ignore + if (!values.includes(NaN) && !values.includes(undefined) && !values.includes(null)) { + newData.push(values); + newIndex.push(this.index[i]) + } + } + + if (inplace) { + this.$setValues(newData, false) + this.$setIndex(newIndex) + } else { + return new DataFrame( + newData, + { + index: newIndex, + columns: [...this.columns], + dtypes: [...this.dtypes], + config: { ...this.config } + }) + } + + } else { + const newColumnNames = [] + const newDtypes = [] + let dfValues: ArrayType2D = [] + + if (this.config.isLowMemoryMode) { + dfValues = utils.transposeArray(this.values) as ArrayType2D + } else { + dfValues = this.$dataIncolumnFormat as ArrayType2D + } + const tempColArr = [] + + for (let i = 0; i < dfValues.length; i++) { + const values: ArrayType1D = dfValues[i]; + if (!values.includes(NaN)) { + tempColArr.push(values); + newColumnNames.push(this.columns[i]) + newDtypes.push(this.dtypes[i]) + } + } + + const newData = utils.transposeArray(tempColArr) as ArrayType2D + + if (inplace) { + this.$setValues(newData, false, false) + this.$setColumnNames(newColumnNames) + this.$setDtypes(newDtypes) + } else { + return new DataFrame( + newData, + { + index: [...this.index], + columns: newColumnNames, + dtypes: newDtypes, + config: { ...this.config } + }) + } + } + + } + + /** + * Adds a new column to the DataFrame. If column exists, then the column values is replaced. + * @param column The name of the column to add or replace. + * @param values An array of values to be inserted into the DataFrame. Must be the same length as the columns + * @param options.inplace Boolean indicating whether to perform the operation inplace or not. Defaults to false + * @param options.atIndex Column index to insert after. Defaults to the end of the columns. + * @example + * ``` + * const df = new DataFrame([[1, 2], [3, 4]], { columns: ['A', 'B']}) + * const df2 = df.addColumn('C', [5, 6]) + * df2.print() + * ``` + * + * @example + * ``` + * const df = new DataFrame([[1, 2], [3, 4]], { columns: ['A', 'B']}) + * df.addColumn('C', [5, 6], { inplace: true }).print() + * ``` + * + * @example + * ``` + * const df = new DataFrame([[1, 2], [3, 4]], { columns: ['A', 'B']}) + * df.addColumn('C', [5, 6], { inplace: true, atIndex: 0 }).print() + * ``` + */ + addColumn( + column: string, + values: Series | ArrayType1D, + options?: { inplace?: boolean, atIndex?: number | string } + ): DataFrame + addColumn( + column: string, + values: Series | ArrayType1D, + options?: { inplace?: boolean, atIndex?: number | string } + ): DataFrame | void { + let { inplace, atIndex } = { inplace: false, atIndex: this.columns.length, ...options }; + if (typeof atIndex === "string" ) { + if (!(this.columns.includes(atIndex))){ + throw new Error(`${atIndex} not a column`) + } + atIndex = this.columns.indexOf(atIndex) + } + + if (!column) { + throw new Error("ParamError: column must be specified") + } + + if (!values) { + throw new Error("ParamError: values must be specified") + } + + const columnIndex = this.$columns.indexOf(column) + + if (columnIndex === -1) { + let colunmValuesToAdd: ArrayType1D + + if (values instanceof Series) { + colunmValuesToAdd = values.values as ArrayType1D + } else if (Array.isArray(values)) { + colunmValuesToAdd = values; + } else { + throw new Error("ParamError: specified value not supported. It must either be an Array or a Series of the same length") + } + + if (colunmValuesToAdd.length !== this.shape[0]) { + ErrorThrower.throwColumnLengthError(this, colunmValuesToAdd.length) + } + + const newData = [] + const oldValues = this.$data + for (let i = 0; i < oldValues.length; i++) { + const innerArr = [...oldValues[i]] as ArrayType1D + innerArr.splice(atIndex, 0, colunmValuesToAdd[i]) + newData.push(innerArr) + } + + if (inplace) { + this.$setValues(newData, true, false) + let columns = [...this.columns] + columns.splice(atIndex, 0, column) + this.$setColumnNames(columns) + this.$setInternalColumnDataProperty(column); + + } else { + let columns = [...this.columns] + columns.splice(atIndex, 0, column) + + const df = new DataFrame(newData, { + index: [...this.index], + columns: columns, + dtypes: [...this.dtypes, utils.inferDtype(colunmValuesToAdd)[0]], + config: { ...this.$config } + }) + return df + } + } else { + this.$setColumnData(column, values); + } + + } + + /** + * Makes a deep copy of a DataFrame. + * @example + * ``` + * const df = new DataFrame([[1, 2], [3, 4]], { columns: ['A', 'B']}) + * const df2 = df.copy() + * df2.print() + * ``` + */ + copy(): DataFrame { + let df = new DataFrame([...this.$data], { + columns: [...this.columns], + index: [...this.index], + dtypes: [...this.dtypes], + config: { ...this.$config } + }); + return df; + } + + /** + * Return a boolean, same-sized object indicating where elements are empty (NaN, undefined, null). + * NaN, undefined and null values gets mapped to true, and everything else gets mapped to false. + * @example + * ``` + * const df = new DataFrame([[1, 2], [3, 4]], { columns: ['A', 'B']}) + * df.isNa().print() + * ``` + */ + isNa(): DataFrame { + const newData = [] + for (let i = 0; i < this.values.length; i++) { + const valueArr = this.values[i] as ArrayType1D + const tempData = valueArr.map((value) => { + if (utils.isEmpty(value)) { + return true; + } else { + return false; + } + }) + newData.push(tempData) + } + + const df = new DataFrame(newData, + { + index: [...this.index], + columns: [...this.columns], + config: { ...this.config } + }); + return df; + } + + /** + * Replace all empty elements with a specified value. Replace params expect columns array to map to values array. + * @param values The list of values to use for replacement. + * @param options.columns The list of column names to be replaced. + * @param options.inplace Boolean indicating whether to perform the operation inplace or not. Defaults to false. + * @example + * ``` + * const df = new DataFrame([[1, 2], [3, 4], [NaN, NaN]], { columns: ['A', 'B']}) + * const df2 = df.fillNa(-99) + * df2.print() + * ``` + * + * @example + * ``` + * const df = new DataFrame([[1, 2], [3, 4], [NaN, NaN]], { columns: ['A', 'B']}) + * df.fillNa(-99, { inplace: true }).print() + * ``` + * + * @example + * ``` + * const df = new DataFrame([[1, 2], [3, 4], [NaN, NaN]], { columns: ['A', 'B']}) + * df.fillNa(-99, { columns: ["A"], inplace: true }).print() + * ``` + * + */ + fillNa( + values: number | string | boolean | ArrayType1D, + options?: + { + columns?: Array, + inplace?: boolean + } + ): DataFrame + fillNa( + values: number | string | boolean | ArrayType1D, + options?: + { + columns?: Array, + inplace?: boolean + } + ): DataFrame | void { + let { columns, inplace } = { inplace: false, ...options } + + if (!values && typeof values !== "boolean" && typeof values !== "number") { + throw Error('ParamError: value must be specified'); + } + + if (Array.isArray(values)) { + if (!Array.isArray(columns)) { + throw Error('ParamError: value is an array, hence columns must also be an array of same length'); + } + + if (values.length !== columns.length) { + throw Error('ParamError: specified column and values must have the same length'); + } + + columns.forEach((col) => { + if (!this.columns.includes(col)) { + throw Error( + `ValueError: Specified column "${col}" must be one of ${this.columns}` + ); + } + }); + } + + const newData = [] + const oldValues = [...this.values] + + if (!columns) { + //Fill all columns + for (let i = 0; i < oldValues.length; i++) { + const valueArr = [...oldValues[i] as ArrayType1D] + + const tempArr = valueArr.map((innerVal) => { + if (utils.isEmpty(innerVal)) { + const replaceWith = Array.isArray(values) ? values[i] : values + return replaceWith + } else { + return innerVal + } + }) + newData.push(tempArr) + } + + } else { + //Fill specific columns + const tempData = [...this.values] + + for (let i = 0; i < tempData.length; i++) { + const valueArr = tempData[i] as ArrayType1D + + for (let i = 0; i < columns.length; i++) { //B + const columnIndex = this.columns.indexOf(columns[i]) + const replaceWith = Array.isArray(values) ? values[i] : values + valueArr[columnIndex] = utils.isEmpty(valueArr[columnIndex]) ? replaceWith : valueArr[columnIndex] + } + newData.push(valueArr) + } + } + + if (inplace) { + this.$setValues(newData as ArrayType2D) + } else { + const df = new DataFrame(newData, + { + index: [...this.index], + columns: [...this.columns], + dtypes: [...this.dtypes], + config: { ...this.config } + }); + return df; + } + } + + /** + * Drop specified columns or rows. + * @param options.columns Array of column names to drop. + * @param options.index Array of index to drop. + * @param options.inplace Boolean indicating whether to perform the operation inplace or not. Defaults to false. + * @example + * ``` + * const df = new DataFrame([[1, 2], [3, 4]], { columns: ['A', 'B']}) + * const df2 = df.drop({ columns: ['A'] }) + * df2.print() + * ``` + * + * @example + * ``` + * const df = new DataFrame([[1, 2], [3, 4]], { columns: ['A', 'B']}) + * df.drop({ index: [0], inplace: true }).print() + * ``` + */ + drop(options?: + { + columns?: string | Array, + index?: Array, + inplace?: boolean + } + ): DataFrame + drop(options?: + { + columns?: string | Array, + index?: Array, + inplace?: boolean + } + ): DataFrame | void { + let { columns, index, inplace } = { inplace: false, ...options } + + if (!columns && !index) { + throw Error('ParamError: Must specify one of columns or index'); + } + + if (columns && index) { + throw Error('ParamError: Can only specify one of columns or index'); + } + + if (columns) { + const columnIndices: Array = [] + + if (typeof columns === "string") { + columnIndices.push(this.columns.indexOf(columns)) + } else if (Array.isArray(columns)) { + for (let column of columns) { + if (this.columns.indexOf(column) === -1) { + throw Error(`ParamError: specified column "${column}" not found in columns`); + } + columnIndices.push(this.columns.indexOf(column)) + } + + } else { + throw Error('ParamError: columns must be an array of column names or a string of column name'); + } + + let newRowData: ArrayType2D = [] + let newColumnNames = [] + let newDtypes = [] + + for (let i = 0; i < this.values.length; i++) { + const tempInnerArr = [] + const innerArr = this.values[i] as ArrayType1D + for (let j = 0; j < innerArr.length; j++) { + if (!(columnIndices.includes(j))) { + tempInnerArr.push(innerArr[j]) + } + } + newRowData.push(tempInnerArr) + } + + for (let i = 0; i < this.columns.length; i++) { + const element = this.columns[i] + if (!(columns.includes(element))) { + newColumnNames.push(element) + newDtypes.push(this.dtypes[i]) + } + } + + if (inplace) { + this.$setValues(newRowData, true, false) + this.$setColumnNames(newColumnNames) + } else { + const df = new DataFrame(newRowData, + { + index: [...this.index], + columns: newColumnNames, + dtypes: newDtypes, + config: { ...this.config } + }); + return df; + } + + } + + if (index) { + const rowIndices: Array = [] + + if (typeof index === "string" || typeof index === "number" || typeof index === "boolean") { + rowIndices.push(this.index.indexOf(index)) + } else if (Array.isArray(index)) { + for (let indx of index) { + if (this.index.indexOf(indx) === -1) { + throw Error(`ParamError: specified index "${indx}" not found in indices`); + } + rowIndices.push(this.index.indexOf(indx)); + } + } else { + throw Error('ParamError: index must be an array of indices or a scalar index'); + } + + let newRowData: ArrayType2D = [] + let newIndex = [] + + for (let i = 0; i < this.values.length; i++) { + const innerArr = this.values[i] as ArrayType1D + if (!(rowIndices.includes(i))) { + newRowData.push(innerArr) + } + } + + for (let i = 0; i < this.index.length; i++) { + const indx = this.index[i] + if (!(index.includes(indx))) { + newIndex.push(indx) + } + } + + if (inplace) { + this.$setValues(newRowData, false) + this.$setIndex(newIndex) + } else { + const df = new DataFrame(newRowData, + { + index: newIndex, + columns: [...this.columns], + dtypes: [...this.dtypes], + config: { ...this.config } + }); + return df; + } + } + + } + + /** + * Sorts a Dataframe by a specified column values + * @param column Column name to sort by. + * @param options.ascending Whether to sort values in ascending order or not. Defaults to true. + * @param options.inplace Boolean indicating whether to perform the operation inplace or not. Defaults to false. + * @example + * ``` + * const df = new DataFrame([[1, 2], [3, 4]], { columns: ['A', 'B']}) + * const df2 = df.sortBy('A') + * df2.print() + * ``` + * + * @example + * ``` + * const df = new DataFrame([[1, 2], [3, 4]], { columns: ['A', 'B']}) + * df.sortBy('A', { ascending: false, inplace: true }).print() + * ``` + */ + sortValues( + column: string, + options?: + { + ascending?: boolean + inplace?: boolean + } + ): DataFrame + sortValues( + column: string, + options?: + { + ascending?: boolean + inplace?: boolean + } + ): DataFrame | void { + const { ascending, inplace } = { ascending: true, inplace: false, ...options } + + if (!column) { + throw Error(`ParamError: must specify a column to sort by`); + } + + if (this.columns.indexOf(column) === -1) { + throw Error(`ParamError: specified column "${column}" not found in columns`); + } + + const columnValues = this.$getColumnData(column, false) as ArrayType1D + const index = [...this.index] + + const objToSort = columnValues.map((value, i) => { + return { index: index[i], value } + }) + + const sortedObjectArr = utils.sortObj(objToSort, ascending) + const sortedIndex = sortedObjectArr.map(obj => obj.index) + + const newDf = _loc({ ndFrame: this, rows: sortedIndex }) as DataFrame + + if (inplace) { + this.$setValues(newDf.values) + this.$setIndex(newDf.index) + } else { + return newDf + } + + } + + /** + * Sets the index of the DataFrame to the specified value. + * @param options.index An array of index values to set + * @param options.column A column name whose values set in place of the index + * @param options.drop Whether to drop the column whose index was set. Defaults to false + * @param options.inplace Boolean indicating whether to perform the operation inplace or not. Defaults to false + * @example + * ``` + * const df = new DataFrame([[1, 2], [3, 4]], { columns: ['A', 'B']}) + * const df2 = df.setIndex({ index: ['a', 'b'] }) + * df2.print() + * ``` + * + * @example + * ``` + * const df = new DataFrame([[1, 2], [3, 4]], { columns: ['A', 'B']}) + * df.setIndex({ column: "A", inplace: true }) + * df.print() + * ``` + */ + setIndex( + options: + { + index?: Array, + column?: string, + drop?: boolean, + inplace?: boolean + } + ): DataFrame + setIndex( + options: + { + index?: Array, + column?: string, + drop?: boolean, + inplace?: boolean + } + ): DataFrame | void { + const { index, column, drop, inplace } = { drop: false, inplace: false, ...options } + + if (!index && !column) { + throw new Error("ParamError: must specify either index or column") + } + + let newIndex: Array = []; + + if (index) { + if (!Array.isArray(index)) { + throw Error(`ParamError: index must be an array`); + } + + if (index.length !== this.values.length) { + throw Error(`ParamError: index must be the same length as the number of rows`); + } + newIndex = index; + } + + if (column) { + if (this.columns.indexOf(column) === -1) { + throw Error(`ParamError: column not found in column names`); + } + + newIndex = this.$getColumnData(column, false) as Array + } + + if (drop) { + const dfDropped = this.drop({ columns: [column as string] }) + + const newData = dfDropped?.values as ArrayType2D + const newColumns = dfDropped?.columns + const newDtypes = dfDropped?.dtypes + + if (inplace) { + this.$setValues(newData, true, false) + this.$setIndex(newIndex) + this.$setColumnNames(newColumns) + } else { + const df = new DataFrame(newData, + { + index: newIndex, + columns: newColumns, + dtypes: newDtypes, + config: { ...this.config } + }); + return df; + } + } else { + if (inplace) { + this.$setIndex(newIndex) + } else { + const df = new DataFrame(this.values, + { + index: newIndex, + columns: [...this.columns], + dtypes: [...this.dtypes], + config: { ...this.config } + }); + return df; + } + } + + } + + /** + * Resets the index of the DataFrame to default. + * @param options.inplace Boolean indicating whether to perform the operation inplace or not. Defaults to false + * @example + * ``` + * const df = new DataFrame([[1, 2], [3, 4]], { columns: ['A', 'B']}) + * df.resetIndex({ inplace: true }) + * df.print() + * ``` + */ + resetIndex(options?: { inplace?: boolean }): DataFrame + resetIndex(options?: { inplace?: boolean }): DataFrame | void { + const { inplace } = { inplace: false, ...options } + + if (inplace) { + this.$resetIndex() + } else { + const df = new DataFrame(this.values, + { + index: this.index.map((_, i) => i), + columns: [...this.columns], + dtypes: [...this.dtypes], + config: { ...this.config } + }); + return df; + } + + } + + /** + * Apply a function along an axis of the DataFrame. To apply a function element-wise, use `applyMap`. + * Objects passed to the function are Series values whose + * index is either the DataFrame’s index (axis=0) or the DataFrame’s columns (axis=1) + * @param callable Function to apply to each column or row. + * @param options.axis 0 or 1. If 0, compute the power column-wise, if 1, row-wise + * + * @example + * ``` + * const df = new DataFrame([[1, 2], [3, 4]], { columns: ['A', 'B']}) + * const df2 = df.apply(Math.sqrt, { axis: 0 }) + * df2.print() + * ``` + */ + apply(callable: any, options?: { axis?: 0 | 1 }): DataFrame | Series { + const { axis } = { axis: 1, ...options } + + if ([0, 1].indexOf(axis) === -1) { + throw Error(`ParamError: axis must be 0 or 1`); + } + + const valuesForFunc = this.$getDataByAxisWithMissingValuesRemoved(axis) + + const result = valuesForFunc.map(row => { + return callable(row) + }) + + if (axis === 0) { + if (utils.is1DArray(result)) { + return new Series(result, { + index: [...this.columns] + }) + } else { + return new DataFrame(result, { + index: [...this.columns], + columns: [...this.columns], + dtypes: [...this.dtypes], + config: { ...this.config } + }) + } + } else { + if (utils.is1DArray(result)) { + return new Series(result, { + index: [...this.index] + }) + } else { + return new DataFrame(result, { + index: [...this.index], + columns: [...this.columns], + dtypes: [...this.dtypes], + config: { ...this.config } + }) + } + } + + } + + /** + * Apply a function to a Dataframe values element-wise. + * @param callable Function to apply to each column or row + * @param options.inplace Boolean indicating whether to perform the operation inplace or not. Defaults to false + * @example + * ``` + * const df = new DataFrame([[1, 2], [3, 4]], { columns: ['A', 'B']}) + * function square(x) { return x * x } + * const df2 = df.applyMap(square) + * df2.print() + * ``` + */ + applyMap(callable: any, options?: { inplace?: boolean }): DataFrame + applyMap(callable: any, options?: { inplace?: boolean }): DataFrame | void { + const { inplace } = { inplace: false, ...options } + + const newData = (this.values as ArrayType2D).map((row) => { + const tempData = row.map((val) => { + return callable(val); + }); + return tempData; + }); + + if (inplace) { + this.$setValues(newData); + } else { + return new DataFrame(newData, + { + index: [...this.index], + columns: [...this.columns], + dtypes: [...this.dtypes], + config: { ...this.config } + }); + } + } + + /** + * Returns the specified column data as a Series object. + * @param column The name of the column to return + * @example + * ``` + * const df = new DataFrame([[1, 2], [3, 4]], { columns: ['A', 'B']}) + * const sf = df.column('A') + * sf.print() + * ``` + * + */ + column(column: string): Series { + return this.$getColumnData(column) as Series + } + + /** + * Return a subset of the DataFrame based on the column dtypes. + * @param include An array of dtypes or strings to be included. + * @example + * ``` + * const df = new DataFrame([[1, 2.1, "Dog"], [3, 4.3, "Cat"]], { columns: ['A', 'B', 'C']}) + * const df2 = df.selectDtypes(['float32']) + * df2.print() + * ``` + * + * @example + * ``` + * const df = new DataFrame([[1, 2.1, "Dog"], [3, 4.3, "Cat"]], { columns: ['A', 'B', 'C']}) + * const df2 = df.selectDtypes(['float32', 'int32']) + * df2.print() + * ``` + * + */ + selectDtypes(include: Array): DataFrame { + const supportedDtypes = ["float32", "int32", "string", "boolean", 'undefined'] + + if (Array.isArray(include) === false) { + throw Error(`ParamError: include must be an array`); + } + + include.forEach(dtype => { + if (supportedDtypes.indexOf(dtype) === -1) { + throw Error(`ParamError: include must be an array of valid dtypes`); + } + }) + const newColumnNames = [] + + for (let i = 0; i < this.dtypes.length; i++) { + if (include.includes(this.dtypes[i])) { + newColumnNames.push(this.columns[i]) + } + } + return this.loc({ columns: newColumnNames }) + + } + + /** + * Returns the transpose of the DataFrame. + * @example + * ``` + * const df = new DataFrame([[1, 2], [3, 4]], { columns: ['A', 'B']}) + * const df2 = df.transpose() + * df2.print() + * ``` + * + * @example + * ``` + * const df = new DataFrame([[1, 2], [3, 4]], { columns: ['A', 'B']}) + * df.transpose({ inplace: true }) + * df.print() + * ``` + **/ + transpose(options?: { inplace?: boolean }): DataFrame + transpose(options?: { inplace?: boolean }): DataFrame | void { + const { inplace } = { inplace: false, ...options } + const newData = utils.transposeArray(this.values) + const newColNames = [...this.index.map(i => i.toString())] + + if (inplace) { + this.$setValues(newData, false, false) + this.$setIndex([...this.columns]) + this.$setColumnNames(newColNames) + } else { + return new DataFrame(newData, { + index: [...this.columns], + columns: newColNames, + config: { ...this.config } + }) + } + } + + /** + * Returns the Transpose of the DataFrame. Similar to `transpose`. + * @example + * ``` + * const df = new DataFrame([[1, 2], [3, 4]], { columns: ['A', 'B']}) + * const df2 = df.T() + * df2.print() + * ``` + **/ + get T(): DataFrame { + const newData = utils.transposeArray(this.values) + return new DataFrame(newData, { + index: [...this.columns], + columns: [...this.index.map(i => i.toString())], + config: { ...this.config } + }) + } + + /** + * Replace all occurence of a value with a new value. + * @param oldValue The value you want to replace + * @param newValue The new value you want to replace the old value with + * @param options.columns An array of column names you want to replace. If not provided replace accross all columns. + * @param options.inplace Boolean indicating whether to perform the operation inplace or not. Defaults to false + * + * @example + * ``` + * const df = new DataFrame([[1, 2], [3, 4]], { columns: ['A', 'B']}) + * const df2 = df.replace(2, 5) + * df.print() + * ``` + * + * @example + * ``` + * const df = new DataFrame([[1, 2], [2, 20]], { columns: ['A', 'B']}) + * const df2 = df.replace(2, 5, { columns: ['A'] }) + * df2.print() + * ``` + */ + replace( + oldValue: number | string | boolean, + newValue: number | string | boolean, + options?: { + columns?: Array + inplace?: boolean + } + ): DataFrame + replace( + oldValue: number | string | boolean, + newValue: number | string | boolean, + options?: { + columns?: Array + inplace?: boolean + } + ): DataFrame | void { + const { columns, inplace } = { inplace: false, ...options } + + if (!oldValue && typeof oldValue !== 'boolean') { + throw Error(`Params Error: Must specify param 'oldValue' to replace`); + } + + if (!newValue && typeof newValue !== 'boolean') { + throw Error(`Params Error: Must specify param 'newValue' to replace with`); + } + + let newData: ArrayType2D = [] + + if (columns) { + if (!Array.isArray(columns)) { + throw Error(`Params Error: column must be an array of column(s)`); + } + const columnIndex: Array = [] + + columns.forEach(column => { + const _indx = this.columns.indexOf(column) + if (_indx === -1) { + throw Error(`Params Error: column not found in columns`); + } + columnIndex.push(_indx) + }) + + newData = (this.values as ArrayType2D).map(([...row]) => { + for (const colIndx of columnIndex) { + if (row[colIndx] === oldValue) { + row[colIndx] = newValue; + } + } + return row; + }) + } else { + newData = (this.values as ArrayType2D).map(([...row]) => { + return row.map((cell => { + if (cell === oldValue) { + return newValue + } else { + return cell + } + })) + }) + } + + if (inplace) { + this.$setValues(newData) + } else { + return new DataFrame(newData, { + index: [...this.index], + columns: [...this.columns], + dtypes: [...this.dtypes], + config: { ...this.config } + }) + } + } + + + + /** + * Cast the values of a column to specified data type + * @param column The name of the column to cast + * @param dtype Data type to cast to. One of [float32, int32, string, boolean] + * @param options.inplace Boolean indicating whether to perform the operation inplace or not. Defaults to false + * + * @example + * ``` + * const df = new DataFrame([[1, 2.2], [3, 4.3]], { columns: ['A', 'B']}) + * const df2 = df.asType('B', 'int32') + * df2.print() + * ``` + * + * @example + * ``` + * const df = new DataFrame([[1, 2.2], [3, 4.3]], { columns: ['A', 'B']}) + * df.asType('B', 'int32', { inplace: true }) + * df.print() + * ``` + */ + asType( + column: string, + dtype: "float32" | "int32" | "string" | "boolean", + options?: { inplace?: boolean } + ): DataFrame + asType( + column: string, + dtype: "float32" | "int32" | "string" | "boolean", + options?: { inplace?: boolean } + ): DataFrame | void { + const { inplace } = { inplace: false, ...options } + const columnIndex = this.columns.indexOf(column) + + if (columnIndex === -1) { + throw Error(`Params Error: column not found in columns`); + } + + if (!(DATA_TYPES.includes(dtype))) { + throw Error(`dtype ${dtype} not supported. dtype must be one of ${DATA_TYPES}`); + } + + const data = this.values as ArrayType2D + + const newData = data.map((row) => { + if (dtype === "float32") { + row[columnIndex] = Number(row[columnIndex]) + return row + } else if (dtype === "int32") { + row[columnIndex] = parseInt(row[columnIndex] as any) + return row + } else if (dtype === "string") { + row[columnIndex] = row[columnIndex].toString() + return row + } else if (dtype === "boolean") { + row[columnIndex] = Boolean(row[columnIndex]) + return row + } + }) + + if (inplace) { + this.$setValues(newData as any) + } else { + const newDtypes = [...this.dtypes] + newDtypes[columnIndex] = dtype + + return new DataFrame(newData, { + index: [...this.index], + columns: [...this.columns], + dtypes: newDtypes, + config: { ...this.config } + }) + } + } + + /** + * Return the number of unique elements in a column, across the specified axis. + * To get the values use `.unique()` instead. + * @param axis The axis to count unique elements across. Defaults to 1 + * @example + * ``` + * const df = new DataFrame([[1, 2], [3, 4], [1, 2], [5, 6]], { columns: ['A', 'B'] }) + * df.nunique().print() + * ``` + * + */ + nUnique(axis: 0 | 1 = 1): Series { + if ([0, 1].indexOf(axis) === -1) { + throw Error(`ParamError: axis must be 0 or 1`); + } + const data = this.$getDataArraysByAxis(axis) + const newData = data.map(row => new Set(row).size) + + if (axis === 0) { + return new Series(newData, { + index: [...this.columns], + dtypes: ["int32"] + }) + } else { + return new Series(newData, { + index: [...this.index], + dtypes: ["int32"] + }) + } + } + + /** + * Renames a column or index to specified value. + * @param mapper An object that maps each column or index in the DataFrame to a new value + * @param options.inplace Boolean indicating whether to perform the operation inplace or not. Defaults to false + * @param options.axis The axis to perform the operation on. Defaults to 1 + * @example + * ``` + * const df = new DataFrame([[1, 2], [3, 4], [1, 2], [5, 6]], { columns: ['A', 'B'] }) + * const df2 = df.rename({ A: 'a', B: 'b' }) + * df2.print() + * ``` + * + * @example + * ``` + * const df = new DataFrame([[1, 2], [3, 4], [1, 2], [5, 6]], { columns: ['A', 'B'] }) + * df.rename({ A: 'a', B: 'b' }, { inplace: true }) + * df.print() + * ``` + * + * @example + * ``` + * const df = new DataFrame([[1, 2], [3, 4], [1, 2], [5, 6]], { columns: ['A', 'B'] }) + * df.rename({ 0: 'a', 1: 'b' }, { axis: 0, inplace: true}) + * df.print() + * ``` + * + */ + rename( + mapper: any, + options?: { + axis?: 0 | 1 + inplace?: boolean + } + ): DataFrame + rename( + mapper: any, + options?: { + axis?: 0 | 1 + inplace?: boolean + } + ): DataFrame | void { + const { axis, inplace } = { axis: 1, inplace: false, ...options } + + if ([0, 1].indexOf(axis) === -1) { + throw Error(`ParamError: axis must be 0 or 1`); + } + + if (axis === 1) { + const colsAdded: string[] = []; + const newColumns = this.columns.map(col => { + if (mapper[col] !== undefined) { + colsAdded.push(mapper[col]); + return mapper[col] + } else { + return col + } + }) + + if (inplace) { + this.$setColumnNames(newColumns) + for (const col of colsAdded) { + this.$setInternalColumnDataProperty(col); + } + } else { + return new DataFrame([...this.values], { + index: [...this.index], + columns: newColumns, + dtypes: [...this.dtypes], + config: { ...this.config } + }) + } + } else { + const newIndex = this.index.map(col => { + if (mapper[col] !== undefined) { + return mapper[col] + } else { + return col + } + }) + + if (inplace) { + this.$setIndex(newIndex) + } else { + return new DataFrame([...this.values], { + index: newIndex, + columns: [...this.columns], + dtypes: [...this.dtypes], + config: { ...this.config } + }) + } + } + + } + + /** + * Sorts the Dataframe by the index. + * @param options.inplace Boolean indicating whether to perform the operation inplace or not. Defaults to false + * @param options.ascending Whether to sort values in ascending order or not. Defaults to true + * @example + * ``` + * const df = new DataFrame([[1, 2], [3, 4], [1, 2], [5, 6]], { columns: ['A', 'B'] }) + * const df2 = df.sortIndex() + * df2.print() + * ``` + * + * @example + * ``` + * const df = new DataFrame([[1, 2], [3, 4], [1, 2], [5, 6]], { columns: ['A', 'B'] }) + * df.sortIndex({ inplace: true }) + * df.print() + * ``` + * + * @example + * ``` + * const df = new DataFrame([[1, 2], [3, 4], [1, 2], [5, 6]], { columns: ['A', 'B'] }) + * df.sortIndex({ ascending: false, inplace: true }) + * df.print() + * ``` + */ + sortIndex(options?: + { + inplace?: boolean + ascending?: boolean + } + ): DataFrame + sortIndex(options?: + { + inplace?: boolean + ascending?: boolean + } + ): DataFrame | void { + const { ascending, inplace } = { ascending: true, inplace: false, ...options } + + const indexPosition = utils.range(0, this.index.length - 1) + const index = [...this.index] + + const objToSort = index.map((idx, i) => { + return { index: indexPosition[i], value: idx } + }) + + const sortedObjectArr = utils.sortObj(objToSort, ascending) + let sortedIndex = sortedObjectArr.map((obj) => obj.index); + const newData = sortedIndex.map(i => (this.values as ArrayType2D)[i as number]) + sortedIndex = sortedIndex.map((i) => index[i as number]); + + if (inplace) { + this.$setValues(newData) + this.$setIndex(sortedIndex) + } else { + return new DataFrame(newData, { + index: sortedIndex, + columns: [...this.columns], + dtypes: [...this.dtypes], + config: { ...this.config } + }) + } + + } + + /** + * Add new rows at the end of the DataFrame. + * @param newValues Array, Series or DataFrame to append to the DataFrame + * @param index The new index value(s) to append to the Series. Must contain the same number of values as `newValues` + * as they map `1 - 1`. + * @param options.inplace Boolean indicating whether to perform the operation inplace or not. Defaults to false + * + * @example + * ``` + * const df = new DataFrame([[1, 2], [3, 4], [1, 2], [5, 6]], { columns: ['A', 'B'] }) + * const values = [7, 8] + * const index = ['a', 'b'] + * const df2 = df.append(values, index) + * df2.print() + * ``` + * + * @example + * ``` + * const df = new DataFrame([[1, 2], [3, 4], [1, 2], [5, 6]], { columns: ['A', 'B'] }) + * const values = new Series([7, 8, 9, 10]) + * const index = ['a', 'b', 'c', 'd'] + * const df2 = df.append(values, index) + * df2.print() + * ``` + * + * @example + * ``` + * const df = new DataFrame([[1, 2], [3, 4], [1, 2], [5, 6]], { columns: ['A', 'B'] }) + * const values = new DataFrame([[7, 8], [9, 10]], { columns: ['C', 'D'] }) + * const index = ['a', 'b'] + * const df2 = df.append(values, index) + * df2.print() + * ``` + */ + append( + newValues: ArrayType1D | ArrayType2D | Series | DataFrame, + index: Array | number | string, + options?: { + inplace?: boolean, + } + ): DataFrame + append( + newValues: ArrayType1D | ArrayType2D | Series | DataFrame, + index: Array | number | string, + options?: { + inplace?: boolean, + } + ): DataFrame | void { + const { inplace } = { inplace: false, ...options } + + if (!newValues) { + throw Error(`ParamError: newValues must be a Series, DataFrame or Array`); + } + + if (!index) { + throw Error(`ParamError: index must be specified`); + } + + let rowsToAdd = [] + if (newValues instanceof Series) { + + if (newValues.values.length !== this.shape[1]) { + throw Error(`ValueError: length of newValues must be the same as the number of columns.`); + } + rowsToAdd = [newValues.values] + + } else if (newValues instanceof DataFrame) { + + if (newValues.shape[1] !== this.shape[1]) { + throw Error(`ValueError: length of newValues must be the same as the number of columns.`); + } + rowsToAdd = newValues.values + + } else if (Array.isArray(newValues)) { + + if (utils.is1DArray(newValues)) { + rowsToAdd = [newValues] + } else { + rowsToAdd = newValues + } + + if ((rowsToAdd[0] as any).length !== this.shape[1]) { + throw Error(`ValueError: length of newValues must be the same as the number of columns.`); + } + + } else { + throw Error(`ValueError: newValues must be a Series, DataFrame or Array`); + } + + + let indexInArrFormat: Array = [] + if (!Array.isArray(index)) { + indexInArrFormat = [index] + } else { + indexInArrFormat = index + } + + if (rowsToAdd.length !== indexInArrFormat.length) { + throw Error(`ParamError: index must contain the same number of values as newValues`); + } + + const newData = [...this.values] as ArrayType2D + const newIndex = [...this.index] + + rowsToAdd.forEach((row, i) => { + newData.push(row as ArrayType1D) + newIndex.push(indexInArrFormat[i]) + }) + + if (inplace) { + this.$setValues(newData) + this.$setIndex(newIndex) + } else { + return new DataFrame(newData, { + index: newIndex, + columns: [...this.columns], + dtypes: [...this.dtypes], + config: { ...this.config } + }) + } + + } + + /** + * Queries the DataFrame for rows that meet the boolean criteria. This is just a wrapper for the `iloc` method. + * @param condition An array of boolean mask, one for each row in the DataFrame. Rows where the value are true will be returned. + * @param options.inplace Boolean indicating whether to perform the operation inplace or not. Defaults to false + * + * @example + * ``` + * const df = new DataFrame([[1, 2], [3, 4], [1, 2], [5, 6]], { columns: ['A', 'B'] }) + * const df2 = df.query([true, false, true, true]) + * df2.print() + * ``` + * + * @example + * ``` + * const df = new DataFrame([[1, 2], [3, 4], [1, 2], [5, 6]], { columns: ['A', 'B'] }) + * const df2 = df.query(df["A"].gt(2)) + * df2.print() + * ``` + * + * @example + * ``` + * const df = new DataFrame([[1, 2], [3, 4], [1, 2], [5, 6]], { columns: ['A', 'B'] }) + * const df2 = df.query(df["A"].gt(2).and(df["B"].lt(5))) + * df2.print() + * ``` + * + * @example + * ``` + * const df = new DataFrame([[1, 2], [3, 4], [1, 2], [5, 6]], { columns: ['A', 'B'] }) + * df.query(df["A"].gt(2), { inplace: true }) + * df.print() + * ``` + **/ + query(condition: Series | Array, options?: { inplace?: boolean }): DataFrame + query(condition: Series | Array, options?: { inplace?: boolean }): DataFrame | void { + const { inplace } = { inplace: false, ...options } + + if (!condition) { + throw new Error("ParamError: condition must be specified"); + } + + const result = _iloc({ + ndFrame: this, + rows: condition, + }) as DataFrame + + if (inplace) { + this.$setValues(result.values, false, false) + this.$setIndex(result.index) + } else { + return result + } + + } + + /** + * Returns the data types for each column as a Series. + * @example + * ``` + * const df = new DataFrame([[1, 2.1, "Dog"], [3, 4.3, "Cat"]], { columns: ['A', 'B', 'C'] }) + * df.ctypes().print() + * ``` + */ + get ctypes(): Series { + return new Series(this.dtypes, { index: this.columns }) + } + + /** + * One-hot encode specified columns in the DataFrame. If columns are not specified, all columns of string dtype will be encoded. + * @param options Options for the operation. The following options are available: + * - `columns`: A single column name or an array of column names to encode. Defaults to all columns of dtype string. + * - `prefix`: Prefix to add to the column names. Defaults to unique labels. + * - `prefixSeparator`: Separator to use for the prefix. Defaults to '_'. + * - `inplace`: Boolean indicating whether to perform the operation inplace or not. Defaults to false + * @example + * ``` + * const df = new DataFrame([[1, 2], [3, 4]], { columns: ['A', 'B']}) + * const df2 = df.getDummies() + * ``` + * @example + * ``` + * const df = new DataFrame([[1, 2], [3, 4]], { columns: ['A', 'B']}) + * const df2 = df.getDummies({ columns: ['A'] }) + * ``` + * + * @example + * ``` + * const df = new DataFrame([[1, 2], [3, 4]], { columns: ['A', 'B']}) + * const df2 = df.getDummies({ prefix: 'cat' }) + * ``` + * + * @example + * ``` + * const df = new DataFrame([[1, 2], [3, 4]], { columns: ['A', 'B']}) + * const df2 = df.getDummies({ prefix: 'cat', prefixSeparator: '_' }) + * ``` + * + * @example + * ``` + * const df = new DataFrame([[1, 2], [3, 4]], { columns: ['A', 'B']}) + * const df2 = df.getDummies({ inplace: true }) + * ``` + */ + getDummies(options?: { + columns?: string | Array, + prefix?: string | Array, + prefixSeparator?: string | Array, + inplace?: boolean + }): DataFrame + getDummies(options?: { + columns?: string | Array, + prefix?: string | Array, + prefixSeparator?: string | Array, + inplace?: boolean + }): DataFrame | void { + const { inplace } = { inplace: false, ...options } + + const encodedDF = dummyEncode(this, options) + if (inplace) { + this.$setValues(encodedDF.values, false, false) + this.$setColumnNames(encodedDF.columns) + } else { + return encodedDF + } + + } + + /** + * Groupby + * @params col a list of column + * @returns Groupby + * @example + * let data = [ [ 1, 2, 3 ], [ 4, 5, 6 ], [ 20, 30, 40 ], [ 39, 89, 78 ] ]; + * let cols = [ "A", "B", "C" ]; + * let df = new dfd.DataFrame(data, { columns: cols }); + * let groupDf = df.groupby([ "A" ]); + */ + groupby(col: Array): Groupby { + const columns = this.columns + const colIndex = col.map((val) => columns.indexOf(val)) + const colDtype = this.dtypes.filter((val, index) => { + return colIndex.includes(index) + }) + + return new Groupby( + col, + this.values as ArrayType2D, + columns, + colDtype, + colIndex + ).group() + + } + + /** + * Exposes functions for creating charts from a DataFrame. + * Charts are created using the Plotly.js library, so all Plotly's configuration parameters are available. + * @param divId name of the HTML Div to render the chart in. + */ + plot(divId: string) { + //TODO: Add support for check plot library to use. So we can support other plot library like d3, vega, etc + if (utils.isBrowserEnv()) { + const plt = new PlotlyLib(this, divId); + return plt; + } else { + throw new Error("Not supported in NodeJS"); + } + } + + /** + * Converts a DataFrame to CSV. + * @param options Configuration object. Supports the following options: + * - `filePath`: Local file path to write the CSV file. If not specified, the CSV will be returned as a string. Option is only available in NodeJS. + * - `fileName`: Name of the CSV file. Defaults to `data.csv`. Option is only available in Browser. + * - `download`: If true, the CSV will be downloaded. Defaults to false. Option is only available in Browser. + * - `header`: Boolean indicating whether to include a header row in the CSV file. + * - `sep`: Character to be used as a separator in the CSV file. + * + * @example + * ``` + * const df = new DataFrame([[1, 2], [3, 4]], { columns: ['A', 'B']}) + * const csv = df.toCSV() + * console.log(csv) + * //output + * "A","B" + * 1,2 + * 3,4 + * ``` + * + * @example + * ``` + * const df = new DataFrame([[1, 2], [3, 4]], { columns: ['A', 'B']}) + * const csv = df.toCSV({ header: false }) + * console.log(csv) + * //output + * 1,2 + * 3,4 + * ``` + * + * @example + * ``` + * const df = new DataFrame([[1, 2], [3, 4]], { columns: ['A', 'B']}) + * const csv = df.toCSV({ sep: ';' }) + * console.log(csv) + * //output + * "A";"B" + * 1;2 + * 3;4 + * ``` + * + * @example + * ``` + * const df = new DataFrame([[1, 2], [3, 4]], { columns: ['A', 'B']}) + * df.toCSV({ filePath: './data.csv' }) //write to local file in NodeJS + * ``` + * + * @example + * ``` + * const df = new DataFrame([[1, 2], [3, 4]], { columns: ['A', 'B']}) + * df.toCSV({ fileName: 'data.csv', download: true }) //Downloads file in Browser + * ``` + * + */ + toCSV(options?: CsvOutputOptionsBrowser | CsvOutputOptionsNode): string + toCSV(options?: CsvOutputOptionsBrowser | CsvOutputOptionsNode): string | void { + if (utils.isBrowserEnv()) { + return toCSVBrowser(this, options as CsvOutputOptionsBrowser) + } else { + return toCSVNode(this, options as CsvOutputOptionsNode) + } + } + + /** + * Converts a DataFrame to JSON. + * @param options Configuration object. Supported options: + * - `filePath`: The file path to write the JSON to. If not specified, the JSON object is returned. Option is only available in NodeJS. + * - `fileName`: The name of the JSON file. Defaults to `data.json`. Option is only available in Browser. + * - `download`: If true, the JSON will be downloaded. Defaults to false. Option is only available in Browser. + * - `format`: The format of the JSON. Supported values are `'column'` and `'row'`. Defaults to `'column'`. + * + * @example + * ``` + * const df = new DataFrame([[1, 2], [3, 4]], { columns: ['A', 'B']}) + * const json = df.toJSON() + * ``` + * + * @example + * ``` + * const df = new DataFrame([[1, 2], [3, 4]], { columns: ['A', 'B']}) + * const json = df.toJSON({ format: 'row' }) + * console.log(json) + * //output + * [{"A":1,"B":2},{"A":3,"B":4}] + * ``` + * + * @example + * ``` + * const df = new DataFrame([[1, 2], [3, 4]], { columns: ['A', 'B']}) + * const json = df.toJSON({ format: "column" }) + * console.log(json) + * //output + * {"A":[1,3],"B":[2,4]} + * ``` + * + * @example + * ``` + * const df = new DataFrame([[1, 2], [3, 4]], { columns: ['A', 'B']}) + * df.toJSON({ filePath: './data.json' }) // downloads to local file system as data.json in NodeJS + * ``` + * + * @example + * ``` + * const df = new DataFrame([[1, 2], [3, 4]], { columns: ['A', 'B']}) + * df.toJSON({ fileName: 'data.json', download: true }) // downloads file browser + * ``` + */ + toJSON(options?: JsonOutputOptionsBrowser | JsonOutputOptionsNode): object + toJSON(options?: JsonOutputOptionsBrowser | JsonOutputOptionsNode): object | void { + if (utils.isBrowserEnv()) { + return toJSONBrowser(this, options as JsonOutputOptionsBrowser) + } else { + return toJSONNode(this, options as JsonOutputOptionsNode) + } + } + + + /** + * Converts a DataFrame to Excel file format. + * @param options Configuration object. Supported options: + * - `sheetName`: The sheet name to be written to. Defaults to `'Sheet1'`. + * - `filePath`: The filePath to be written to. Defaults to `'./output.xlsx'`. Option is only available in NodeJs + * - `fileName`: The fileName to be written to. Defaults to `'output.xlsx'`. Option is only available in Browser + * + * @example + * ``` + * const df = new DataFrame([[1, 2], [3, 4]], { columns: ['A', 'B']}) + * df.toExcel({ filePath: './output.xlsx' }) // writes to local file system as output.xlsx in NodeJS + * ``` + * + * @example + * ``` + * const df = new DataFrame([[1, 2], [3, 4]], { columns: ['A', 'B']}) + * df.toExcel({ fileName: 'output.xlsx', download: true }) // downloads file browser + * ``` + * + * @example + * ``` + * const df = new DataFrame([[1, 2], [3, 4]], { columns: ['A', 'B']}) + * df.toExcel({ sheetName: 'Sheet2' }) // writes to Sheet2 in Excel + * ``` + * + */ + toExcel(options?: ExcelOutputOptionsBrowser | ExcelOutputOptionsNode): void { + if (utils.isBrowserEnv()) { + toExcelBrowser(this, options as ExcelOutputOptionsBrowser) + } else { + return toExcelNode(this, options as ExcelOutputOptionsNode) + } + } +} diff --git a/src/danfojs-base/core/generic.ts b/src/danfojs-base/core/generic.ts new file mode 100644 index 00000000..49942d12 --- /dev/null +++ b/src/danfojs-base/core/generic.ts @@ -0,0 +1,471 @@ +/** +* @license +* Copyright 2022 JsData. All rights reserved. +* +* This source code is licensed under the MIT license found in the +* LICENSE file in the root directory of this source tree. + +* Unless required by applicable law or agreed to in writing, software +* distributed under the License is distributed on an "AS IS" BASIS, +* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +* See the License for the specific language governing permissions and +* limitations under the License. +* ========================================================================== +*/ + +import Utils from "../shared/utils"; +import Configs from "../shared/config"; +import { _iloc } from "./indexing" +import { + NDframeInterface, + NdframeInputDataType, + LoadArrayDataType, + LoadObjectDataType, + AxisType, + ArrayType1D, + ArrayType2D +} from '../shared/types' +import ErrorThrower from '../shared/errors'; +import { BASE_CONFIG, DATA_TYPES } from '../shared/defaults'; +import tensorflow from '../shared/tensorflowlib' + + +const utils = new Utils(); + +/** + * N-Dimension data structure. Stores multi-dimensional + * data in a size-mutable, labeled data structure. Analogous to the Python Pandas DataFrame. + * + * @param Object + * + * data: 1D or 2D Array, JSON, Tensor, Block of data. + * + * index: Array of numeric or string names for subseting array. If not specified, indexes are auto generated. + * + * columns: Array of column names. If not specified, column names are auto generated. + * + * dtypes: Array of data types for each the column. If not specified, dtypes inferred. + * + * config: General configuration object for NDframe + * + * @returns NDframe + */ +export default class NDframe implements NDframeInterface { + $isSeries: boolean; + protected $data: any + protected $dataIncolumnFormat: ArrayType1D | ArrayType2D = [] + protected $index: Array = [] + protected $columns: string[] = [] + protected $dtypes: Array = [] + protected $config: Configs + + constructor({ data, index, columns, dtypes, config, isSeries }: NdframeInputDataType) { + this.$isSeries = isSeries + if (config) { + this.$config = new Configs({ ...BASE_CONFIG, ...config }); + } else { + this.$config = new Configs(BASE_CONFIG); + } + + if (data instanceof tensorflow.Tensor) { + data = data.arraySync(); + } + + if (data === undefined || (Array.isArray(data) && data.length === 0)) { + this.loadArrayIntoNdframe({ data: [], index: [], columns: [], dtypes: [] }); + } else if (utils.is1DArray(data)) { + this.loadArrayIntoNdframe({ data, index, columns, dtypes }); + } else { + + if (Array.isArray(data) && utils.isObject(data[0])) { + this.loadObjectIntoNdframe({ data, type: 1, index, columns, dtypes }); + + } else if (utils.isObject(data)) { + this.loadObjectIntoNdframe({ data, type: 2, index, columns, dtypes }); + + } else if ( + Array.isArray((data)[0]) || + utils.isNumber((data)[0]) || + utils.isString((data)[0]) + ) { + this.loadArrayIntoNdframe({ data, index, columns, dtypes }); + } else { + throw new Error("File format not supported!"); + } + } + } + + /** + * Internal function to load array of data into NDFrame + * @param data The array of data to load into NDFrame + * @param index Array of numeric or string names for subsetting array. + * @param columns Array of column names. + * @param dtypes Array of data types for each the column. + */ + private loadArrayIntoNdframe({ data, index, columns, dtypes }: LoadArrayDataType): void { + // this.$data = utils.replaceUndefinedWithNaN(data, this.$isSeries); + this.$data = data + if (!this.$config.isLowMemoryMode) { + //In NOT low memory mode, we transpose the array and save in column format. + //This makes column data retrieval run in constant time + this.$dataIncolumnFormat = utils.transposeArray(data) + } + this.$setIndex(index); + this.$setDtypes(dtypes); + this.$setColumnNames(columns); + } + + /** + * Internal function to format and load a Javascript object or object of arrays into NDFrame. + * @param data Object or object of arrays. + * @param type The type of the object. There are two recognized types: + * + * - type 1 object are in JSON format `[{a: 1, b: 2}, {a: 30, b: 20}]`. + * + * - type 2 object are of the form `{a: [1,2,3,4], b: [30,20, 30, 20}]}` + * @param index Array of numeric or string names for subsetting array. + * @param columns Array of column names. + * @param dtypes Array of data types for each the column. + */ + private loadObjectIntoNdframe({ data, type, index, columns, dtypes }: LoadObjectDataType): void { + if (type === 1 && Array.isArray(data)) { + const _data = (data).map((item) => { + return Object.values(item); + }); + + let _columnNames; + + if (columns) { + _columnNames = columns + } else { + _columnNames = Object.keys((data)[0]); + } + + this.loadArrayIntoNdframe({ data: _data, index, columns: _columnNames, dtypes }); + + } else { + const [_data, _colNames] = utils.getRowAndColValues(data); + let _columnNames; + + if (columns) { + _columnNames = columns + } else { + _columnNames = _colNames + } + this.loadArrayIntoNdframe({ data: _data, index, columns: _columnNames, dtypes }); + } + } + + /** + * Converts and returns the data in the NDframe as a Tensorflow.js Tensor. + */ + get tensor() { + if (this.$isSeries) { + return tensorflow.tensor1d(this.$data, this.$dtypes[0]); + } else { + return tensorflow.tensor2d(this.$data, this.shape, "float32") + } + } + + /** + * Returns the dtypes of the columns + */ + get dtypes(): Array { + return this.$dtypes + } + + /** + * Internal function to set the Dtypes of the NDFrame from an array. This function + * performs the necessary checks. + */ + $setDtypes(dtypes: Array | undefined): void { + if (this.$isSeries) { + if (dtypes) { + if (this.$data.length != 0 && dtypes.length != 1) { + ErrorThrower.throwDtypesLengthError(this, dtypes) + } + + if (!(DATA_TYPES.includes(`${dtypes[0]}`))) { + ErrorThrower.throwDtypeNotSupportedError(dtypes[0]) + } + + this.$dtypes = dtypes + } else { + this.$dtypes = utils.inferDtype(this.$data) + } + + } else { + if (dtypes) { + if (this.$data.length != 0 && dtypes.length != this.shape[1]) { + ErrorThrower.throwDtypesLengthError(this, dtypes) + } + + if (this.$data.length == 0 && dtypes.length == 0) { + this.$dtypes = dtypes + } else { + dtypes.forEach((dtype) => { + if (!(DATA_TYPES.includes(dtype))) { + ErrorThrower.throwDtypeNotSupportedError(dtype) + } + }) + + this.$dtypes = dtypes + + } + + } else { + this.$dtypes = utils.inferDtype(this.$data) + } + } + } + + /** + * Returns the dimension of the data. Series have a dimension of 1, + * while DataFrames have a dimension of 2. + */ + get ndim(): number { + if (this.$isSeries) { + return 1; + } else { + return 2 + } + } + + /** + * Returns the axis labels of the NDFrame. + */ + get axis(): AxisType { + return { + index: this.$index, + columns: this.$columns + }; + } + + /** + * Returns the configuration object of the NDFrame. + */ + get config(): Configs { + return this.$config + + } + + /** + * Internal function to set the configuration of the ndframe + */ + $setConfig(config: Configs): void { + this.$config = config + } + + /** + * Returns the indices of the NDFrame + */ + get index(): Array { + return this.$index + + } + + /** + * Internal function to set the index of the NDFrame with the specified + * array of indices. Performs all necessary checks to ensure that the + * index is valid. + */ + $setIndex(index: Array | undefined): void { + if (index) { + + if (this.$data.length != 0 && index.length != this.shape[0]) { + ErrorThrower.throwIndexLengthError(this, index) + } + if (Array.from(new Set(index)).length !== this.shape[0]) { + ErrorThrower.throwIndexDuplicateError() + } + + this.$index = index + } else { + this.$index = utils.range(0, this.shape[0] - 1) //generate index + } + } + + /** + * Internal function to reset the index of the NDFrame using a range of indices. + */ + $resetIndex(): void { + this.$index = utils.range(0, this.shape[0] - 1) + } + + /** + * Returns the column names of the NDFrame + */ + get columns(): string[] { + return this.$columns + } + + /** + * Internal function to set the column names for the NDFrame. This function + * performs a check to ensure that the column names are unique, and same length as the + * number of columns in the data. + */ + $setColumnNames(columns?: string[]) { + + if (this.$isSeries) { + if (columns) { + if (this.$data.length != 0 && columns.length != 1 && typeof columns != 'string') { + ErrorThrower.throwColumnNamesLengthError(this, columns) + } + this.$columns = columns + } else { + this.$columns = ["0"] + } + } else { + if (columns) { + + if (this.$data.length != 0 && columns.length != this.shape[1]) { + + ErrorThrower.throwColumnNamesLengthError(this, columns) + } + if (Array.from(new Set(columns)).length !== this.shape[1]) { + ErrorThrower.throwColumnDuplicateError() + } + + this.$columns = columns + } else { + this.$columns = (utils.range(0, this.shape[1] - 1)).map((val) => `${val}`) //generate columns + } + } + } + + /** + * Returns the shape of the NDFrame. Shape is determined by [row lenght, column length] + */ + get shape(): Array { + if (this.$data.length === 0) return [0, 0] + if (this.$isSeries) { + return [this.$data.length, 1]; + } else { + const rowLen = (this.$data).length + const colLen = (this.$data[0] as []).length + return [rowLen, colLen] + } + + } + + /** + * Returns the underlying data in Array format. + */ + get values(): ArrayType1D | ArrayType2D { + return this.$data; + } + + /** + * Updates the internal $data property to the specified value + * @param values An array of values to set + * @param checkLength Whether to check the length of the new values and the existing row length + * @param checkColumnLength Whether to check the length of the new values and the existing column length + * */ + $setValues(values: ArrayType1D | ArrayType2D, checkLength: boolean = true, checkColumnLength: boolean = true): void { + if (this.$isSeries) { + if (checkLength && values.length != this.shape[0]) { + ErrorThrower.throwRowLengthError(this, values.length) + } + + this.$data = values + this.$dtypes = utils.inferDtype(values) //Dtype may change depeneding on the value set + + if (!this.$config.isLowMemoryMode) { + this.$dataIncolumnFormat = values + } + + } else { + if (checkLength && values.length != this.shape[0]) { + ErrorThrower.throwRowLengthError(this, values.length) + } + + if (checkColumnLength) { + values.forEach(value => { + if ((value as ArrayType1D).length != this.shape[1]) { + ErrorThrower.throwColumnLengthError(this, values.length) + } + }) + } + + this.$data = values + this.$dtypes = utils.inferDtype(values) + + if (!this.$config.isLowMemoryMode) { + this.$dataIncolumnFormat = utils.transposeArray(values) + } + + } + + } + + /** + * Returns the underlying data in Array column format. + * Similar to this.values, but in column format. + */ + get getColumnData() { + if (this.config.isLowMemoryMode) { + return utils.transposeArray(this.values); + } else { + return this.$dataIncolumnFormat; + } + } + + /** + * Returns the size of the NDFrame object + * + */ + get size(): number { + return this.shape[0] * this.shape[1] + } + + /** + * Converts a DataFrame or Series to CSV. + * @param options Configuration object. Supports the following options: + * - `filePath`: Local file path to write the CSV file. If not specified, the CSV will be returned as a string. + * - `header`: Boolean indicating whether to include a header row in the CSV file. + * - `sep`: Character to be used as a separator in the CSV file. + */ + // toCSV(options?: CsvOutputOptionsNode): string + // toCSV(options?: CsvOutputOptionsNode): string | void { + // return toCSV(this, options); + // } + + /** + * Converts a DataFrame or Series to JSON. + * @param options Configuration object. Supported options: + * - `filePath`: The file path to write the JSON to. If not specified, the JSON object is returned. + * - `format`: The format of the JSON. Defaults to `'column'`. E.g for using `column` format: + * ``` + * [{ "a": 1, "b": 2, "c": 3, "d": 4 }, + * { "a": 5, "b": 6, "c": 7, "d": 8 }] + * ``` + * and `row` format: + * ``` + * { "a": [1, 5, 9], + * "b": [2, 6, 10] + * } + * ``` + */ + // toJSON(options?: { format?: "row" | "column", filePath?: string }): object + // toJSON(options?: { format?: "row" | "column", filePath?: string }): object | void { + // return toJSON(this, options); + // } + + + /** + * Converts a DataFrame or Series to Excel Sheet. + * @param options Configuration object. Supported options: + * - `sheetName`: The sheet name to be written to. Defaults to `'Sheet1'`. + * - `filePath`: The filePath to be written to. Defaults to `'./output.xlsx'`. + */ + // toExcel(options?: { filePath?: string, sheetName?: string }): void { + // return toExcel(this, options); + // } + + /** + * Pretty prints a DataFrame or Series to the console + */ + print() { + console.log(this + ""); + } +} \ No newline at end of file diff --git a/src/danfojs-base/core/indexing.ts b/src/danfojs-base/core/indexing.ts new file mode 100644 index 00000000..5c2b668b --- /dev/null +++ b/src/danfojs-base/core/indexing.ts @@ -0,0 +1,420 @@ +/** +* @license +* Copyright 2022 JsData. All rights reserved. +* +* This source code is licensed under the MIT license found in the +* LICENSE file in the root directory of this source tree. + +* Unless required by applicable law or agreed to in writing, software +* distributed under the License is distributed on an "AS IS" BASIS, +* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +* See the License for the specific language governing permissions and +* limitations under the License. +* ========================================================================== +*/ + +import Series from "./series"; +import Utils from "../shared/utils" +import DataFrame from "./frame"; +import { NDframeInterface } from "../shared/types"; + +const utils = new Utils(); + +/** +* Internal function to slice a Series/DataFrame by index based labels +* @param Object +*/ +export function _iloc({ ndFrame, rows, columns }: { + ndFrame: NDframeInterface + rows?: Array | Series + columns?: Array +}): Series | DataFrame { + + let _rowIndexes: Array + let _columnIndexes: Array + + const _data = ndFrame.values; + const _index = ndFrame.index; + + if (rows instanceof Series) { + rows = rows.values as Array + } + + if (rows !== undefined && !Array.isArray(rows)) { + throw new Error(`rows parameter must be an Array. For example: rows: [1,2] or rows: ["0:10"]`) + } + + if (columns !== undefined && !Array.isArray(columns)) { + throw new Error(`columns parameter must be an Array. For example: columns: [1,2] or columns: ["0:10"]`) + } + + if (!rows) { + _rowIndexes = utils.range(0, ndFrame.shape[0] - 1) + + } else if (rows.length == 1 && typeof rows[0] == "string") { + const rowSplit = rows[0].split(":") + + if (rowSplit.length != 2) { + throw new Error(`Invalid row split parameter: If using row split string, it must be of the form; rows: ["start:end"]`); + } + if (isNaN(parseInt(rowSplit[0])) && rowSplit[0] != "") { + throw new Error(`Invalid row split parameter. Split parameter must be a number`); + } + + if (isNaN(parseInt(rowSplit[1])) && rowSplit[1] != "") { + throw new Error(`Invalid row split parameter. Split parameter must be a number`); + } + + const start = rowSplit[0] == "" ? 0 : parseInt(rowSplit[0]) + const end = rowSplit[1] == "" ? ndFrame.shape[0] : parseInt(rowSplit[1]) + + if (start < 0) { + throw new Error(`row slice [start] index cannot be less than 0`); + } + + if (end > ndFrame.shape[0]) { + throw new Error(`row slice [end] index cannot be bigger than ${ndFrame.shape[0]}`); + } + _rowIndexes = utils.range(start, end - 1) + } else { + const _formatedRows = [] + for (let i = 0; i < rows.length; i++) { + let _indexToUse = rows[i]; + if (_indexToUse > ndFrame.shape[0]) { + throw new Error(`Invalid row parameter: Specified index ${_indexToUse} cannot be bigger than index length ${ndFrame.shape[0]}`); + } + + if (typeof _indexToUse !== "number" && typeof _indexToUse !== "boolean") { + throw new Error(`Invalid row parameter: row index ${_indexToUse} must be a number or boolean`); + } + + if (typeof _indexToUse === "boolean" && _indexToUse === true) { + _formatedRows.push(_index[i]) + } + + if (typeof _indexToUse === "number") { + _formatedRows.push(_indexToUse) + } + } + + _rowIndexes = _formatedRows as number[] + } + + if (!columns) { + _columnIndexes = utils.range(0, ndFrame.shape[1] - 1) + + } else if (columns.length == 1 && typeof columns[0] == "string") { + const columnSplit = columns[0].split(":") + + if (columnSplit.length != 2) { + throw new Error(`Invalid column split parameter: If using column split string, it must be of the form; columns: ["start:end"]`); + } + if (isNaN(parseInt(columnSplit[0])) && columnSplit[0] != "") { + throw new Error(`Invalid column split parameter. Split parameter must be a number`); + } + + if (isNaN(parseInt(columnSplit[1])) && columnSplit[1] != "") { + throw new Error(`Invalid column split parameter. Split parameter must be a number`); + } + + const start = columnSplit[0] == "" ? 0 : parseInt(columnSplit[0]) + const end = columnSplit[1] == "" ? ndFrame.shape[1] : parseInt(columnSplit[1]) + + if (start < 0) { + throw new Error(`column slice [start] index cannot be less than 0`); + } + + if (end > ndFrame.shape[1]) { + throw new Error(`column slice [end] index cannot be bigger than ${ndFrame.shape[1]}`); + } + _columnIndexes = utils.range(start, end - 1) + } else { + + for (let i = 0; i < columns.length; i++) { + const _indexToUse = columns[i]; + if (_indexToUse > ndFrame.shape[1]) { + throw new Error(`Invalid column parameter: Specified index ${_indexToUse} cannot be bigger than index length ${ndFrame.shape[1]}`); + } + + if (typeof _indexToUse != "number") { + throw new Error(`Invalid column parameter: column index ${_indexToUse} must be a number`); + } + + } + + _columnIndexes = columns as number[] + } + + if (ndFrame instanceof Series) { + const newData = [] + const newIndex = [] + + for (let i = 0; i < _rowIndexes.length; i++) { + const rowIndx = _rowIndexes[i] + newData.push(_data[rowIndx]) + newIndex.push(_index[rowIndx]) + } + const sf = new Series( + newData, + { + index: newIndex, + columns: ndFrame.columns, + dtypes: ndFrame.dtypes, + config: ndFrame.config + }) + + return sf + } else { + const newData = [] + const newIndex = [] + const newColumnNames: string[] = [] + const newDtypes = [] + + for (let i = 0; i < _rowIndexes.length; i++) { + const rowIndx = _rowIndexes[i] + const rowData: any = _data[rowIndx] + const newRowDataWithRequiredCols = [] + + for (let j = 0; j < _columnIndexes.length; j++) { + const colIndx = _columnIndexes[j] + newRowDataWithRequiredCols.push(rowData[colIndx]) + } + newData.push(newRowDataWithRequiredCols) + newIndex.push(_index[rowIndx]) + } + + for (let i = 0; i < _columnIndexes.length; i++) { + const colIndx = _columnIndexes[i] + newColumnNames.push(ndFrame.columns[colIndx]) + newDtypes.push(ndFrame.dtypes[colIndx]) + + } + + const df = new DataFrame( + newData, + { + index: newIndex, + columns: newColumnNames, + dtypes: newDtypes, + config: ndFrame.config + }) + + return df + + } + +} + +/** +* Internal function to slice a Series/DataFrame by specified string location based labels +* @param Object +*/ +export function _loc({ ndFrame, rows, columns }: { + ndFrame: NDframeInterface + rows?: Array | Series + columns?: Array +}): Series | DataFrame { + + let _rowIndexes: Array + let _columnIndexes: Array + + const _data = ndFrame.values; + const _index = ndFrame.index; + + if (rows instanceof Series) { + rows = rows.values as Array + } + + if (rows !== undefined && !Array.isArray(rows)) { + throw new Error(`rows parameter must be an Array. For example: rows: [1,2] or rows: ["0:10"]`) + } + + if (columns !== undefined && !Array.isArray(columns)) { + throw new Error(`columns parameter must be an Array. For example: columns: ["a","b"] or columns: ["a:c"]`) + } + + if (!rows) { + _rowIndexes = _index.map(indexValue => _index.indexOf(indexValue)) // Return all row index + + } else if (rows.length == 1 && typeof rows[0] == "string") { + + if (rows[0].indexOf(":") === -1) { // Input type ==> ["1"] or [`"1"`] + let temp; + if (rows[0].startsWith(`"`) || rows[0].startsWith(`'`) || rows[0].startsWith("`")) { + temp = _index.indexOf(rows[0].replace(/['"`]/g, '')) + } else { + temp = _index.indexOf(Number(rows[0])) + } + + if (temp === -1) { + throw new Error(`IndexError: Specified index (${rows[0]}) not found`); + } + + _rowIndexes = [temp] + + } else { + // Input type ==> ["1:2"] or [`"1":"4"`] + const rowSplit = rows[0].split(":") + + if (rowSplit.length != 2) { + throw new Error(`Invalid row split parameter: If using row split string, it must be of the form; rows: ["start:end"]`); + } + + let start: number + let end: number + + if (rowSplit[0] === "") { + start = _index.indexOf(_index[0]) + } else { + if (rowSplit[0].startsWith(`"`) || rowSplit[0].startsWith(`'`) || rowSplit[0].startsWith("`")) { + start = _index.indexOf(rowSplit[0].replace(/['"`]/g, '')) + } else { + start = _index.indexOf(Number(rowSplit[0])) + } + + } + + if (rowSplit[1] === "") { + end = _index.indexOf(_index[_index.length - 1]) + 1 + } else { + if (rowSplit[0].startsWith(`"`) || rowSplit[0].startsWith(`'`) || rowSplit[0].startsWith("`")) { + end = _index.indexOf(rowSplit[1].replace(/['"`]/g, '')) + } else { + end = _index.indexOf(Number(rowSplit[1])) + } + + } + + if (start === -1) { + throw new Error(`IndexError: Specified start index not found`); + } + + if (end === -1) { + throw new Error(`IndexError: Specified end index not found`); + } + + _rowIndexes = _index.slice(start, end).map(indexValue => _index.indexOf(indexValue)) + } + + } else { + // Input type ==> ["1", "2"] or [1, 5] or [true, false] + const rowsIndexToUse = [] + for (let i = 0; i < rows.length; i++) { + const isBoolean = typeof rows[i] === "boolean" + if (isBoolean && rows[i]) { + rowsIndexToUse.push(_index.indexOf(_index[i])) + } + + if (!isBoolean) { + const rowIndex = _index.indexOf(rows[i] as number | string) + if (rowIndex === -1) { + throw new Error(`IndexError: Specified index (${rows[i]}) not found`); + } + rowsIndexToUse.push(rowIndex) + } + } + + _rowIndexes = rowsIndexToUse + } + + const _columnNames = ndFrame.columns + + if (!columns) { + _columnIndexes = _columnNames.map(columnName => _columnNames.indexOf(columnName))// Return all column index + + } else if (columns.length == 1) { + if (typeof columns[0] !== "string") { + throw new Error(`ColumnIndexError: columns parameter must be an array of a string name. For example: columns: ["b"]`) + } + + if (columns[0].indexOf(":") == -1) { // Input type ==> ["A"] + _columnIndexes = [_columnNames.indexOf(columns[0])] + + } else { // Input type ==> ["a:b"] or [`"col1":"col5"`] + const columnSplit = columns[0].split(":") + + if (columnSplit.length != 2) { + throw new Error(`ColumnIndexError: Invalid row split parameter. If using row split string, it must be of the form; rows: ["start:end"]`); + } + + const start = columnSplit[0] == "" ? _columnNames.indexOf(_columnNames[0]) : _columnNames.indexOf(columnSplit[0]) + const end = columnSplit[1] == "" ? _columnNames.indexOf(_columnNames[_columnNames.length - 1]) : _columnNames.indexOf(columnSplit[1]) + + if (start === -1) { + throw new Error(`ColumnIndexError: Specified start index not found`); + } + + if (end === -1) { + throw new Error(`ColumnIndexError: Specified end index not found`); + } + _columnIndexes = _columnNames.slice(start, end + 1).map(columnName => _columnNames.indexOf(columnName)) + _columnIndexes.pop() //Remove the last element + + } + } else {// Input type ==> ["A", "B"] or ["col1", "col2"] + for (let i = 0; i < columns.length; i++) { + if (_columnNames.indexOf(columns[i]) === -1) { + throw new Error(`ColumnIndexError: Specified column (${columns[i]}) not found`); + } + } + _columnIndexes = columns.map(columnName => _columnNames.indexOf(columnName)) + } + + if (ndFrame instanceof Series) { + const newData = [] + const newIndex = [] + + for (let i = 0; i < _rowIndexes.length; i++) { + const rowIndx = _rowIndexes[i] + newData.push(_data[rowIndx]) + newIndex.push(_index[rowIndx]) + } + const sf = new Series( + newData, + { + index: newIndex, + columns: ndFrame.columns, + dtypes: ndFrame.dtypes, + config: ndFrame.config + }) + + return sf + } else { + const newData = [] + const newIndex = [] + const newColumnNames: string[] = [] + const newDtypes = [] + + for (let i = 0; i < _rowIndexes.length; i++) { + const rowIndx: number = _rowIndexes[i] + const rowData: any = _data[rowIndx] + const newRowDataWithRequiredCols = [] + + for (let j = 0; j < _columnIndexes.length; j++) { + const colIndx = _columnIndexes[j] + newRowDataWithRequiredCols.push(rowData[colIndx]) + } + newData.push(newRowDataWithRequiredCols) + newIndex.push(_index[rowIndx]) + } + + for (let i = 0; i < _columnIndexes.length; i++) { + const colIndx = _columnIndexes[i] + newColumnNames.push(ndFrame.columns[colIndx]) + newDtypes.push(ndFrame.dtypes[colIndx]) + + } + + const df = new DataFrame( + newData, + { + index: newIndex, + columns: newColumnNames, + dtypes: newDtypes, + config: ndFrame.config + }) + return df + + } + +} \ No newline at end of file diff --git a/src/danfojs-base/core/math.ops.ts b/src/danfojs-base/core/math.ops.ts new file mode 100644 index 00000000..6b141a2e --- /dev/null +++ b/src/danfojs-base/core/math.ops.ts @@ -0,0 +1,167 @@ +/** +* @license +* Copyright 2022 JsData. All rights reserved. +* +* This source code is licensed under the MIT license found in the +* LICENSE file in the root directory of this source tree. + +* Unless required by applicable law or agreed to in writing, software +* distributed under the License is distributed on an "AS IS" BASIS, +* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +* See the License for the specific language governing permissions and +* limitations under the License. +* ========================================================================== +*/ +import Series from "./series"; +import Utils from "../shared/utils" + +const utils = new Utils(); + +/** + * Generic function for performing math operations on a series + * @param object + * + * ndframe ==> The current Series + * + * other ==> The Series or number to perform math operation with + * + * operation ==> The type of operation to perform +*/ +export function _genericMathOp({ ndFrame, other, operation }: { + ndFrame: Series + other: Series | number | Array + operation: string +}) { + if (typeof other === 'number') { + //broadcast operation + let newData; + switch (operation) { + case 'add': + newData = (ndFrame.values as number[]).map((ele => ele + other)) + return newData + + case 'sub': + newData = (ndFrame.values as number[]).map((ele => ele - other)) + return newData + + case 'mul': + newData = (ndFrame.values as number[]).map((ele => ele * other)) + return newData + + case 'div': + newData = (ndFrame.values as number[]).map((ele => ele / other)) + return newData + + case 'mod': + newData = (ndFrame.values as number[]).map((ele => ele % other)) + return newData + + case 'pow': + newData = (ndFrame.values as number[]).map((ele => ele ** other)) + return newData + + case 'minimum': + newData = (ndFrame.values as number[]).map((ele => Math.min(ele, other))) + return newData + + case 'maximum': + newData = (ndFrame.values as number[]).map((ele => Math.max(ele, other))) + return newData + + default: + throw new Error(`${operation} is not implemented`); + + } + } else if (other instanceof Series) { + utils.checkSeriesOpCompactibility({ firstSeries: ndFrame, secondSeries: other, operation }) + + let newData; + switch (operation) { + case 'add': + + newData = (ndFrame.values as number[]).map((ele, index) => { return ele + (other.values as number[])[index] }) + return newData + + case 'sub': + + newData = (ndFrame.values as number[]).map((ele, index) => { return ele - (other.values as number[])[index] }) + return newData + + case 'mul': + + newData = (ndFrame.values as number[]).map((ele, index) => { return ele * (other.values as number[])[index] }) + return newData + + case 'div': + + newData = (ndFrame.values as number[]).map((ele, index) => { return ele / (other.values as number[])[index] }) + return newData + + case 'mod': + + newData = (ndFrame.values as number[]).map((ele, index) => { return ele % (other.values as number[])[index] }) + return newData + + case 'pow': + newData = (ndFrame.values as number[]).map((ele, index) => { return ele ** (other.values as number[])[index] }) + return newData + + case 'minimum': + newData = (ndFrame.values as number[]).map((ele, index) => { return Math.min(ele, (other.values as number[])[index]) }) + return newData + + case 'maximum': + newData = (ndFrame.values as number[]).map((ele, index) => { return Math.max(ele, (other.values as number[])[index]) }) + return newData + + default: + throw new Error(`${operation} is not implemented`); + } + } else if (Array.isArray(other)) { + if(other.length !== ndFrame.values.length){ + throw new Error(`ParamError: Length of array must be equal to length of Series`) + } + let newData; + switch (operation) { + case 'add': + + newData = (ndFrame.values as number[]).map((ele, index) => { return ele + (other as number[])[index] }) + return newData + + case 'sub': + + newData = (ndFrame.values as number[]).map((ele, index) => { return ele - (other as number[])[index] }) + return newData + + case 'mul': + + newData = (ndFrame.values as number[]).map((ele, index) => { return ele * (other as number[])[index] }) + return newData + + case 'div': + + newData = (ndFrame.values as number[]).map((ele, index) => { return ele / (other as number[])[index] }) + return newData + + case 'mod': + + newData = (ndFrame.values as number[]).map((ele, index) => { return ele % (other as number[])[index] }) + return newData + + case 'pow': + newData = (ndFrame.values as number[]).map((ele, index) => { return ele ** (other as number[])[index] }) + return newData + + case 'minimum': + newData = (ndFrame.values as number[]).map((ele, index) => { return Math.min(ele, (other as number[])[index]) }) + return newData + + case 'maximum': + newData = (ndFrame.values as number[]).map((ele, index) => { return Math.max(ele, (other as number[])[index]) }) + return newData + + } + } else { + throw new Error("ParamError: value for other not supported. It must be either a scalar, Array or Series"); + } +} \ No newline at end of file diff --git a/src/danfojs-base/core/series.ts b/src/danfojs-base/core/series.ts new file mode 100644 index 00000000..3b9659b0 --- /dev/null +++ b/src/danfojs-base/core/series.ts @@ -0,0 +1,2242 @@ +/** +* @license +* Copyright 2022 JsData. All rights reserved. +* +* This source code is licensed under the MIT license found in the +* LICENSE file in the root directory of this source tree. + +* Unless required by applicable law or agreed to in writing, software +* distributed under the License is distributed on an "AS IS" BASIS, +* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +* See the License for the specific language governing permissions and +* limitations under the License. +* ========================================================================== +*/ +import { toCSVBrowser, toExcelBrowser, toJSONBrowser } from "../io/browser"; +import { toCSVNode, toExcelNode, toJSONNode } from "../io/node"; +import dummyEncode from "../transformers/encoders/dummy.encoder"; +import { variance, std, median, mode } from 'mathjs'; +import tensorflow from '../shared/tensorflowlib' +import { DATA_TYPES } from '../shared/defaults' +import { _genericMathOp } from "./math.ops"; +import ErrorThrower from "../shared/errors" +import { _iloc, _loc } from "./indexing"; +import { PlotlyLib } from "../plotting"; +import Utils from "../shared/utils" +import NDframe from "./generic"; +import { table } from "table"; +import Str from './strings'; +import Dt from './datetime'; +import DataFrame from "./frame"; +import { + ArrayType1D, + BaseDataOptionType, + SeriesInterface, + CsvOutputOptionsBrowser, + ExcelOutputOptionsBrowser, + JsonOutputOptionsBrowser, + CsvOutputOptionsNode, + ExcelOutputOptionsNode, + JsonOutputOptionsNode, + mapParam +} from "../shared/types"; + +const utils = new Utils(); + + +/** + * One-dimensional ndarray with axis labels. + * The object supports both integer- and label-based indexing and provides a host of methods for performing operations involving the index. + * Operations between Series (+, -, /, , *) align values based on their associated index values – they need not be the same length. + * @param data 1D Array, JSON, Tensor, Block of data. + * @param options.index Array of numeric or string index for subseting array. If not specified, indices are auto generated. + * @param options.columns Column name. This is like the name of the Series. If not specified, column name is set to 0. + * @param options.dtypes Data types of the Series data. If not specified, dtypes is inferred. + * @param options.config General configuration object for extending or setting Series behavior. + */ +export default class Series extends NDframe implements SeriesInterface { + + constructor(data: any = [], options: BaseDataOptionType = {}) { + const { index, columns, dtypes, config } = options; + if (Array.isArray(data[0]) || utils.isObject(data[0])) { + data = utils.convert2DArrayToSeriesArray(data); + super({ + data, + index, + columns, + dtypes, + config, + isSeries: true + }); + } else { + super({ + data, + index, + columns, + dtypes, + config, + isSeries: true + }); + } + } + + /** + * Purely integer-location based indexing for selection by position. + * ``.iloc`` is primarily integer position based (from ``0`` to + * ``length-1`` of the axis), but may also be used with a boolean array. + * + * @param rows Array of row indexes + * + * Allowed inputs are in rows and columns params are: + * + * - An array of single integer, e.g. ``[5]``. + * - A list or array of integers, e.g. ``[4, 3, 0]``. + * - A slice array string with ints, e.g. ``["1:7"]``. + * - A boolean array. + * - A ``callable`` function with one argument (the calling Series or + * DataFrame) and that returns valid output for indexing (one of the above). + * This is useful in method chains, when you don't have a reference to the + * calling object, but would like to base your selection on some value. + * + * ``.iloc`` will raise ``IndexError`` if a requested indexer is + * out-of-bounds. + * + * @example + * ``` + * const sf = new Series([1, 2, 3, 4, 5, 6], { index: ['a', 'b', 'c', 'd', 'e', 'f'] }); + * const sf2 = sf.iloc([0, 2, 4]); + * sf2.print(); + * ``` + */ + iloc(rows: Array): Series { + return _iloc({ ndFrame: this, rows }) as Series + } + + /** + * Access a group of rows by label(s) or a boolean array. + * ``loc`` is primarily label based, but may also be used with a boolean array. + * + * @param rows Array of row indexes + * + * Allowed inputs are: + * + * - A single label, e.g. ``["5"]`` or ``['a']``, (note that ``5`` is interpreted as a + * *label* of the index, and **never** as an integer position along the index). + * + * - A list or array of labels, e.g. ``['a', 'b', 'c']``. + * + * - A slice object with labels, e.g. ``["a:f"]``. Note that start and the stop are included + * + * - A boolean array of the same length as the axis being sliced, + * e.g. ``[True, False, True]``. + * + * - A ``callable`` function with one argument (the calling Series or + * DataFrame) and that returns valid output for indexing (one of the above) + * + * @example + * ``` + * const sf = new Series([1, 2, 3, 4, 5, 6], { index: ['a', 'b', 'c', 'd', 'e', 'f'] }); + * const sf2 = sf.loc(['a', 'c', 'e']); + * sf2.print(); + * ``` + * + * @example + * ``` + * const sf = new Series([1, 2, 3, 4, 5, 6], { index: ['a', 'b', 'c', 'd', 'e', 'f'] }); + * const sf2 = sf.loc(sf.gt(2)); + * sf2.print(); + * ``` + */ + loc(rows: Array): Series { + return _loc({ ndFrame: this, rows }) as Series + } + + /** + * Returns the first n values in a Series + * @param rows The number of rows to return + * @example + * ``` + * const sf = new Series([1, 2, 3, 4, 5, 6], { index: ['a', 'b', 'c', 'd', 'e', 'f'] }); + * const sf2 = sf.head(3); + * sf2.print(); + * ``` + */ + head(rows: number = 5): Series { + return this.iloc([`0:${rows}`]) + } + + /** + * Returns the last n values in a Series + * @param rows The number of rows to return + * @example + * ``` + * const sf = new Series([1, 2, 3, 4, 5, 6], { index: ['a', 'b', 'c', 'd', 'e', 'f'] }); + * const sf2 = sf.tail(3); + * sf2.print(); + * ``` + */ + tail(rows: number = 5): Series { + const startIdx = this.shape[0] - rows + return this.iloc([`${startIdx}:`]) + } + + /** + * Returns specified number of random rows in a Series + * @param num The number of rows to return + * @param options.seed An integer specifying the random seed that will be used to create the distribution. + * @example + * ``` + * const df = new Series([1, 2, 3, 4]) + * const df2 = await df.sample(2) + * df2.print() + * ``` + * @example + * ``` + * const df = new Series([1, 2, 3, 4]) + * const df2 = await df.sample(1, { seed: 1 }) + * df2.print() + * ``` + */ + async sample(num = 5, options?: { seed?: number }): Promise { + const { seed } = { seed: 1, ...options } + + if (num > this.shape[0]) { + throw new Error("Sample size n cannot be bigger than size of dataset"); + } + if (num < -1 || num == 0) { + throw new Error("Sample size cannot be less than -1 or be equal to 0"); + } + num = num === -1 ? this.shape[0] : num; + + const shuffledIndex = await tensorflow.data.array(this.index).shuffle(num, `${seed}`).take(num).toArray(); + const sf = this.iloc(shuffledIndex); + return sf; + } + + /** + * Return Addition of series and other, element-wise (binary operator add). + * Equivalent to series + other + * @param other Series, Array of same length or scalar number to add + * @param options.inplace Boolean indicating whether to perform the operation inplace or not. Defaults to false + * + * @example + * ``` + * const sf = new Series([1, 2, 3, 4, 5, 6], { index: ['a', 'b', 'c', 'd', 'e', 'f'] }); + * const sf2 = sf.add(2); + * console.log(sf2.values); + * //output [3, 4, 5, 6, 7, 8] + * ``` + * + * @example + * ``` + * const sf = new Series([1, 2, 3, 4, 5, 6], { index: ['a', 'b', 'c', 'd', 'e', 'f'] }); + * const sf2 = sf.add([2, 3, 4, 5, 6, 7]); + * console.log(sf2.values); + * //output [3, 5, 7, 9, 11, 13] + * ``` + * + * @example + * ``` + * const sf = new Series([1, 2, 3, 4, 5, 6], { index: ['a', 'b', 'c', 'd', 'e', 'f'] }); + * sf.add(2, { inplace: true }); + * console.log(sf.values); + * //output [3, 4, 5, 6, 7, 8] + * ``` + */ + add(other: Series | Array | number, options?: { inplace?: boolean }): Series + add(other: Series | Array | number, options?: { inplace?: boolean }): Series | void { + const { inplace } = { inplace: false, ...options } + + if (this.dtypes[0] == "string") ErrorThrower.throwStringDtypeOperationError("add") + + const newData = _genericMathOp({ ndFrame: this, other, operation: "add" }) + + if (inplace) { + this.$setValues(newData as ArrayType1D) + } else { + return utils.createNdframeFromNewDataWithOldProps({ ndFrame: this, newData, isSeries: true }) as Series + } + } + + /** + * Returns the subtraction between a series and other, element-wise (binary operator subtraction). + * Equivalent to series - other + * @param other Number to subtract + * @param options.inplace Boolean indicating whether to perform the operation inplace or not. Defaults to false + * @example + * ``` + * const sf = new Series([1, 2, 3, 4, 5, 6], { index: ['a', 'b', 'c', 'd', 'e', 'f'] }); + * const sf2 = sf.sub(2); + * console.log(sf2.values); + * //output [-1, 0, 1, 2, 3, 4] + * ``` + * + * @example + * ``` + * const sf = new Series([1, 2, 3, 4, 5, 6], { index: ['a', 'b', 'c', 'd', 'e', 'f'] }); + * const sf2 = sf.sub([2, 3, 4, 5, 6, 7]); + * console.log(sf2.values); + * //output [-1, -1, -1, -1, -1, -1] + * ``` + * + * @example + * ``` + * const sf = new Series([1, 2, 3, 4, 5, 6], { index: ['a', 'b', 'c', 'd', 'e', 'f'] }); + * sf.sub(2, { inplace: true }); + * console.log(sf.values); + * //output [-1, 0, 1, 2, 3, 4] + * ``` + */ + sub(other: Series | number | Array, options?: { inplace?: boolean }): Series + sub(other: Series | number | Array, options?: { inplace?: boolean }): Series | void { + const { inplace } = { inplace: false, ...options } + + if (this.dtypes[0] == "string") ErrorThrower.throwStringDtypeOperationError("sub") + + const newData = _genericMathOp({ ndFrame: this, other, operation: "sub" }) + + if (inplace) { + this.$setValues(newData as ArrayType1D) + } else { + return utils.createNdframeFromNewDataWithOldProps({ ndFrame: this, newData, isSeries: true }) as Series + } + + } + + /** + * Return Multiplication of series and other, element-wise (binary operator mul). + * Equivalent to series * other + * @param other Number to multiply with. + * @param options.inplace Boolean indicating whether to perform the operation inplace or not. Defaults to false + * @example + * ``` + * const sf = new Series([1, 2, 3, 4, 5, 6], { index: ['a', 'b', 'c', 'd', 'e', 'f'] }); + * const sf2 = sf.mul(2); + * console.log(sf2.values); + * //output [2, 4, 6, 8, 10, 12] + * ``` + * + * @example + * ``` + * const sf = new Series([1, 2, 3, 4, 5, 6], { index: ['a', 'b', 'c', 'd', 'e', 'f'] }); + * const sf2 = sf.mul([2, 3, 4, 5, 6, 7]); + * console.log(sf2.values); + * //output [2, 6, 12, 20, 30, 42] + * ``` + * + * @example + * ``` + * const sf = new Series([1, 2, 3, 4, 5, 6], { index: ['a', 'b', 'c', 'd', 'e', 'f'] }); + * sf.mul(2, { inplace: true }); + * console.log(sf.values); + * //output [2, 4, 6, 8, 10, 12] + * ``` + */ + mul(other: Series | number | Array, options?: { inplace?: boolean }): Series + mul(other: Series | number | Array, options?: { inplace?: boolean }): Series | void { + const { inplace } = { inplace: false, ...options } + + if (this.dtypes[0] == "string") ErrorThrower.throwStringDtypeOperationError("mul") + + const newData = _genericMathOp({ ndFrame: this, other, operation: "mul" }) + + if (inplace) { + this.$setValues(newData as ArrayType1D) + } else { + return utils.createNdframeFromNewDataWithOldProps({ ndFrame: this, newData, isSeries: true }) as Series + } + } + + /** + * Return division of series and other, element-wise (binary operator div). + * Equivalent to series / other + * @param other Series or number to divide with. + * @param options.inplace Boolean indicating whether to perform the operation inplace or not. Defaults to false + * @example + * ``` + * const sf = new Series([1, 2, 3, 4, 5, 6], { index: ['a', 'b', 'c', 'd', 'e', 'f'] }); + * const sf2 = sf.div(2); + * console.log(sf2.values); + * //output [0.5, 1, 1.5, 2, 2.5, 3] + * ``` + * + * @example + * ``` + * const sf = new Series([1, 2, 3, 4, 5, 6], { index: ['a', 'b', 'c', 'd', 'e', 'f'] }); + * const sf2 = sf.div([2, 3, 4, 5, 6, 7]); + * console.log(sf2.values); + * ``` + * + * @example + * ``` + * const sf = new Series([1, 2, 3, 4, 5, 6], { index: ['a', 'b', 'c', 'd', 'e', 'f'] }); + * sf.div(2, { inplace: true }); + * console.log(sf.values); + * //output [0.5, 1, 1.5, 2, 2.5, 3] + * ``` + */ + div(other: Series | number | Array, options?: { inplace?: boolean }): Series + div(other: Series | number | Array, options?: { inplace?: boolean }): Series | void { + const { inplace } = { inplace: false, ...options } + + if (this.dtypes[0] == "string") ErrorThrower.throwStringDtypeOperationError("div") + + const newData = _genericMathOp({ ndFrame: this, other, operation: "div" }) + + if (inplace) { + this.$setValues(newData as ArrayType1D) + } else { + return utils.createNdframeFromNewDataWithOldProps({ ndFrame: this, newData, isSeries: true }) as Series + } + } + + /** + * Return Exponential power of series and other, element-wise (binary operator pow). + * Equivalent to series ** other + * @param other Number to raise to power. + * @param options.inplace Boolean indicating whether to perform the operation inplace or not. Defaults to false + * @example + * ``` + * const sf = new Series([1, 2, 3, 4, 5, 6], { index: ['a', 'b', 'c', 'd', 'e', 'f'] }); + * const sf2 = sf.pow(2); + * console.log(sf2.values); + * //output [1, 4, 9, 16, 25, 36] + * ``` + * + * @example + * ``` + * const sf = new Series([1, 2, 3, 4, 5, 6], { index: ['a', 'b', 'c', 'd', 'e', 'f'] }); + * const sf2 = sf.pow(new Series([2, 3, 4, 5, 6, 7])); + * console.log(sf2.values); + * //output [ 1, 8, 81, 1024, 15625, 279936 ] + * ``` + * + */ + pow(other: Series | number | Array, options?: { inplace?: boolean }): Series + pow(other: Series | number | Array, options?: { inplace?: boolean }): Series | void { + const { inplace } = { inplace: false, ...options } + + if (this.dtypes[0] == "string") ErrorThrower.throwStringDtypeOperationError("pow") + + const newData = _genericMathOp({ ndFrame: this, other, operation: "pow" }) + + if (inplace) { + this.$setValues(newData as ArrayType1D) + } else { + return utils.createNdframeFromNewDataWithOldProps({ ndFrame: this, newData, isSeries: true }) as Series + } + } + + /** + * Return Modulo of series and other, element-wise (binary operator mod). + * Equivalent to series % other + * @param other Number to modulo with + * @param options.inplace Boolean indicating whether to perform the operation inplace or not. Defaults to false + * @example + * ``` + * const sf = new Series([1, 2, 3, 4, 5, 6], { index: ['a', 'b', 'c', 'd', 'e', 'f'] }); + * const sf2 = sf.mod(2); + * console.log(sf2.values); + * //output [1, 0, 1, 0, 1, 0] + * ``` + */ + mod(other: Series | number | Array, options?: { inplace?: boolean }): Series + mod(other: Series | number | Array, options?: { inplace?: boolean }): Series | void { + const { inplace } = { inplace: false, ...options } + + if (this.dtypes[0] == "string") ErrorThrower.throwStringDtypeOperationError("mod") + + const newData = _genericMathOp({ ndFrame: this, other, operation: "mod" }) + + if (inplace) { + this.$setValues(newData as ArrayType1D) + } else { + return utils.createNdframeFromNewDataWithOldProps({ ndFrame: this, newData, isSeries: true }) as Series + } + } + + /** + * Checks if the array value passed has a compatible dtype, removes NaN values, and if + * boolean values are present, converts them to integer values. + */ + private $checkAndCleanValues(values: ArrayType1D, operation: string): number[] { + if (this.dtypes[0] == "string") ErrorThrower.throwStringDtypeOperationError(operation) + values = utils.removeMissingValuesFromArray(values); + + if (this.dtypes[0] == "boolean") { + values = (utils.mapBooleansToIntegers(values as boolean[], 1) as ArrayType1D); + } + return values as number[] + } + + /** + * Returns the mean of elements in Series. + * @example + * ``` + * const sf = new Series([1, 2, 3, 4, 5, 6]); + * console.log(sf.mean()); + * //output 3.5 + * ``` + */ + mean(): number { + const values = this.$checkAndCleanValues(this.values as ArrayType1D, "mean") + return (values.reduce((a, b) => a + b) / values.length) as number + } + + + /** + * Returns the median of elements in Series + * @example + * ``` + * const sf = new Series([1, 2, 3, 4, 5, 6]); + * console.log(sf.median()); + * //output 3.5 + * ``` + */ + median(): number { + const values = this.$checkAndCleanValues(this.values as ArrayType1D, "median") + return median(values); + } + + /** + * Returns the modal value of elements in Series + * @example + * ``` + * const sf = new Series([1, 2, 3, 4, 4, 5, 6]); + * console.log(sf.mode()); + * //output [ 4 ] + * ``` + * + * @example + * ``` + * const sf = new Series([1, 2, 3, 4, 4, 5, 5, 6]); + * console.log(sf.mode()); + * //output [ 4, 5 ] + * ``` + * + */ + mode() { + const values = this.$checkAndCleanValues(this.values as ArrayType1D, "mode") + return mode(values); + } + + /** + * Returns the minimum value in a Series + * @example + * ``` + * const sf = new Series([1, 2, 3, 4, 5, 6]); + * console.log(sf.min()); + * //output 1 + * ``` + * + */ + min(): number { + const values = this.$checkAndCleanValues(this.values as ArrayType1D, "min") + let smallestValue = values[0] + for (let i = 0; i < values.length; i++) { + smallestValue = smallestValue < values[i] ? smallestValue : values[i] + } + return smallestValue + } + + /** + * Returns the maximum value in a Series + * @example + * ``` + * const sf = new Series([1, 2, 3, 4, 5, 6]); + * console.log(sf.max()); + * //output 6 + * ``` + */ + max(): number { + const values = this.$checkAndCleanValues(this.values as ArrayType1D, "max") + let biggestValue = values[0] + for (let i = 0; i < values.length; i++) { + biggestValue = biggestValue > values[i] ? biggestValue : values[i] + } + return biggestValue + } + + /** + * Return the sum of the values in a series. + * @example + * ``` + * const sf = new Series([1, 2, 3, 4, 5, 6]); + * console.log(sf.sum()); + * //output 21 + * ``` + */ + sum(): number { + const values = this.$checkAndCleanValues(this.values as ArrayType1D, "sum") + return values.reduce((sum, value) => sum + value, 0) + } + + /** + * Return number of non-null elements in a Series + * @example + * ``` + * const sf = new Series([1, 2, 3, 4, 5, 6]); + * console.log(sf.count()); + * //output 6 + * ``` + * + * @example + * ``` + * const sf = new Series([1, 2, 3, 4, 5, 6, NaN]); + * console.log(sf.count()); + * //output 6 + * ``` + */ + count(): number { + const values = utils.removeMissingValuesFromArray(this.values as ArrayType1D) + return values.length + } + + /** + * Return maximum of series and other. + * @param other Series, number or Array of numbers to check against + * @example + * ``` + * const sf = new Series([1, 2, 3, 4, 5, 6]); + * const sf2 = sf.maximum(3); + * console.log(sf2.values); + * //output [ 3, 3, 3, 4, 5, 6 ] + * ``` + * + * @example + * ``` + * const sf = new Series([1, 2, 3, 4, 5, 6]); + * const sf2 = new Series([4, 1, 3, 40, 5, 3]); + * const sf3 = sf.maximum(sf2); + * console.log(sf3.values); + * //output [ 4, 2, 3, 40, 5, 6 ] + * ``` + */ + maximum(other: Series | number | Array): Series { + if (this.dtypes[0] == "string") ErrorThrower.throwStringDtypeOperationError("maximum") + + const newData = _genericMathOp({ ndFrame: this, other, operation: "maximum" }) + return new Series(newData, { + columns: this.columns, + index: this.index + }); + } + + /** + * Return minimum of series and other. + * @param other Series, number of Array of numbers to check against + * @example + * ``` + * const sf = new Series([1, 2, 3, 4, 5, 6]); + * const sf2 = sf.minimum(3); + * console.log(sf2.values); + * //output [ 1, 2, 3, 3, 3, 3 ] + * ``` + * + * @example + * ``` + * const sf = new Series([1, 2, 3, 4, 5, 6]); + * const sf2 = new Series([4, 1, 3, 40, 5, 3]); + * const sf3 = sf.minimum(sf2); + * console.log(sf3.values); + * //output [ 1, 1, 3, 4, 5, 3 ] + * ``` + * + */ + minimum(other: Series | number | Array): Series { + if (this.dtypes[0] == "string") ErrorThrower.throwStringDtypeOperationError("maximum") + + const newData = _genericMathOp({ ndFrame: this, other, operation: "minimum" }) + return new Series(newData, { + columns: this.columns, + index: this.index + }); + } + + /** + * Round each value in a Series to the specified number of decimals. + * @param dp Number of Decimal places to round to + * @param options.inplace Boolean indicating whether to perform the operation inplace or not. Defaults to false + * + * @example + * ``` + * const sf = new Series([1.23, 2.4, 3.123, 4.1234, 5.12345]); + * const sf2 = sf.round(2); + * console.log(sf2.values); + * //output [ 1.23, 2.4, 3.12, 4.12, 5.12 ] + * ``` + * + * @example + * ``` + * const sf = new Series([1.23, 2.4, 3.123, 4.1234, 5.12345]); + * sf.round(2, { inplace: true }); + * console.log(sf.values); + * //output [ 1.23, 2.4, 3.12, 4.12, 5.12 ] + * ``` + */ + round(dp = 1, options?: { inplace?: boolean }): Series | void { + const { inplace } = { inplace: false, ...options } + + const newValues = utils.round(this.values as number[], dp, true); + + if (inplace) { + this.$setValues(newValues) + } else { + return utils.createNdframeFromNewDataWithOldProps({ + ndFrame: this, + newData: newValues, + isSeries: true + }) as Series + } + + } + + /** + * Return sample standard deviation of elements in Series + * @example + * ``` + * const sf = new Series([1, 2, 3, 4, 5, 6]); + * console.log(sf.std()); + * //output 1.8708286933869707 + * ``` + */ + std(): number { + const values = this.$checkAndCleanValues(this.values as ArrayType1D, "max") + return std(values); + } + + /** + * Return unbiased variance of elements in a Series. + * @example + * ``` + * const sf = new Series([1, 2, 3, 4, 5, 6]); + * console.log(sf.var()); + * //output 3.5 + * ``` + */ + var(): number { + const values = this.$checkAndCleanValues(this.values as ArrayType1D, "max") + return variance(values); + } + + /** + * Return a boolean same-sized object indicating where elements are NaN. + * NaN and undefined values gets mapped to true, and everything else gets mapped to false. + * @example + * ``` + * const sf = new Series([1, 2, 3, 4, NaN, 6]); + * console.log(sf.isNaN()); + * //output [ false, false, false, false, true, false ] + * ``` + * + */ + isNa(): Series { + const newData = this.values.map((value) => { + + if (utils.isEmpty(value)) { + return true; + } else { + return false; + } + }) + const sf = new Series(newData, + { + index: this.index, + dtypes: ["boolean"], + config: this.config + }); + return sf; + } + + /** + * Replace all missing values with a specified value + * @param value The value to replace NaN with + * @param options.inplace Boolean indicating whether to perform the operation inplace or not. Defaults to false + * + * @example + * ``` + * const sf = new Series([1, 2, 3, 4, NaN, 6]); + * const sf2 = sf.fillNa(-99); + * console.log(sf2.values); + * //output [ 1, 2, 3, 4, -99, 6 ] + * ``` + * + * @example + * ``` + * const sf = new Series([1, 2, 3, 4, NaN, 6]); + * sf.fillNa(-99, { inplace: true }); + * console.log(sf.values); + * //output [ 1, 2, 3, 4, -99, 6 ] + * ``` + */ + fillNa(value: number | string | boolean, options?: { inplace?: boolean }): Series + fillNa(value: number | string | boolean, options?: { inplace?: boolean }): Series | void { + const { inplace } = { inplace: false, ...options } + + if (!value && typeof value !== "boolean" && typeof value !== "number") { + throw Error('ParamError: value must be specified'); + } + + const newValues: ArrayType1D = []; + (this.values as ArrayType1D).forEach((val) => { + if (utils.isEmpty(val)) { + newValues.push(value); + } else { + newValues.push(val); + } + }); + + if (inplace) { + this.$setValues(newValues) + } else { + return utils.createNdframeFromNewDataWithOldProps({ + ndFrame: this, + newData: newValues, + isSeries: true + }) as Series + } + } + + + /** + * Sort a Series in ascending or descending order by some criterion. + * @param options Method options + * @param ascending Whether to return sorted values in ascending order or not. Defaults to true + * @param options.inplace Boolean indicating whether to perform the operation inplace or not. Defaults to false + * @example + * ``` + * const sf = new Series([2, 1, 3, 4, 6, 5]); + * const sf2 = sf.sortValues(); + * console.log(sf2.values); + * //output [ 1, 2, 3, 4, 5, 6 ] + * ``` + */ + sortValues(options?: { ascending?: boolean, inplace?: boolean }): Series + sortValues(options?: { ascending?: boolean, inplace?: boolean }): Series | void { + const { ascending, inplace, } = { ascending: true, inplace: false, ...options } + + let sortedValues = []; + const rangeIdx = utils.range(0, this.index.length - 1); + let sortedIdx = utils.sortArrayByIndex(rangeIdx, this.values, this.dtypes[0]); + + for (let indx of sortedIdx) { + sortedValues.push(this.values[indx]) + } + + if (ascending) { + sortedValues = sortedValues.reverse(); + sortedIdx = sortedIdx.reverse(); + } + + if (inplace) { + this.$setValues(sortedValues as ArrayType1D) + this.$setIndex(sortedIdx); + } else { + const sf = new Series(sortedValues, { + index: sortedIdx, + dtypes: this.dtypes, + config: this.config + }); + return sf; + + } + } + + + /** + * Makes a deep copy of a Series + * @example + * ``` + * const sf = new Series([1, 2, 3, 4, 5, 6]); + * const sf2 = sf.copy(); + * ``` + * + */ + copy(): Series { + const sf = new Series([...this.values], { + columns: [...this.columns], + index: [...this.index], + dtypes: [...this.dtypes], + config: { ...this.config } + }); + return sf; + } + + + /** + * Generate descriptive statistics. + * Descriptive statistics include those that summarize the central tendency, + * dispersion and shape of a dataset’s distribution, excluding NaN values. + * @example + * ``` + * const sf = new Series([1, 2, 3, 4, 5, 6]); + * const sf2 = sf.describe(); + * sf2.print(); + * ``` + */ + describe(): Series { + if (this.dtypes[0] == "string") { + throw new Error("DType Error: Cannot generate descriptive statistics for Series with string dtype") + } else { + + const index = ['count', 'mean', 'std', 'min', 'median', 'max', 'variance']; + const count = this.count(); + const mean = this.mean(); + const std = this.std(); + const min = this.min(); + const median = this.median(); + const max = this.max(); + const variance = this.var(); + + const data = [count, mean, std, min, median, max, variance]; + const sf = new Series(data, { index: index }); + return sf; + + } + } + + + /** + * Resets the index of the Series to default values. + * @example + * ``` + * const sf = new Series([1, 2, 3, 4, 5, 6], { index: ['a', 'b', 'c', 'd', 'e', 'f'] }); + * const sf2 = sf.resetIndex(); + * console.log(sf2.index); + * //output [ 0, 1, 2, 3, 4, 5 ] + * ``` + * + * @example + * ``` + * const sf = new Series([1, 2, 3, 4, 5, 6], { index: ['a', 'b', 'c', 'd', 'e', 'f'] }); + * sf.resetIndex({ inplace: true }); + * console.log(sf.index); + * //output [ 0, 1, 2, 3, 4, 5 ] + * ``` + */ + resetIndex(options?: { inplace?: boolean }): Series + resetIndex(options?: { inplace?: boolean }): Series | void { + const { inplace } = { inplace: false, ...options } + + if (inplace) { + this.$resetIndex(); + } else { + const sf = this.copy(); + sf.$resetIndex(); + return sf; + } + } + + /** + * Set the Series index (row labels) using an array of the same length. + * @param index Array of new index values, + * @param options.inplace Boolean indicating whether to perform the operation inplace or not. Defaults to false + * @example + * ``` + * const sf = new Series([1, 2, 3, 4, 5, 6], { index: ['a', 'b', 'c', 'd', 'e', 'f'] }); + * const sf2 = sf.setIndex(['g', 'h', 'i', 'j', 'k', 'l']); + * console.log(sf2.index); + * //output [ 'g', 'h', 'i', 'j', 'k', 'l' ] + * ``` + */ + setIndex(index: Array, options?: { inplace?: boolean }): Series + setIndex(index: Array, options?: { inplace?: boolean }): Series | void { + const { inplace } = { inplace: false, ...options } + + if (!index) { + throw Error('Param Error: Must specify index array'); + } + + if (inplace) { + this.$setIndex(index) + } else { + const sf = this.copy(); + sf.$setIndex(index) + return sf; + } + } + + + /** + * map all the element in a Series to a function or object. + * @param callable callable can either be a funtion or an object. If function, then each value and the corresponding index is passed. + * @param options.inplace Boolean indicating whether to perform the operation inplace or not. Defaults to false + * @example + * ``` + * const sf = new Series([1, 2, 3, 4, 5, 6]); + * const sf2 = sf.map((x) => x * 2); + * console.log(sf2.values); + * //output [ 2, 4, 6, 8, 10, 12 ] + * ``` + * + * @example + * ``` + * const sf = new Series([1, 2, 3, 4, 5, 6]); + * const sf2 = sf.map({ + * 1: -99, + * 3: -99 + * }); + * console.log(sf2.values); + * //output [ -99, 2, -99, 4, -99, 6 ] + * ``` + */ + map(callable: mapParam, options?: { inplace?: boolean }): Series + map(callable: mapParam, options?: { inplace?: boolean }): Series | void { + const { inplace } = { inplace: false, ...options } + + const isCallable = utils.isFunction(callable); + + const data = (this.values as ArrayType1D).map((val: any, i: number) => { + if (isCallable) { + return (callable as Function)(val, i); + } else if (utils.isObject(callable)) { + if (val in callable) { + //@ts-ignore + return callable[val]; + } else { + return val + } + } else { + throw new Error("Param Error: callable must either be a function or an object"); + } + }); + + if (inplace) { + this.$setValues(data) + } else { + const sf = this.copy(); + sf.$setValues(data) + return sf; + } + } + + /** + * Applies a function to each element of a Series + * @param callable Function to apply to each element of the series + * @param options.inplace Boolean indicating whether to perform the operation inplace or not. Defaults to false + * @example + * ``` + * const sf = new Series([1, 2, 3, 4, 5, 6]); + * const sf2 = sf.apply((x) => x * 2); + * console.log(sf2.values); + * //output [ 2, 4, 6, 8, 10, 12 ] + * ``` + * + * @example + * ``` + * const sf = new Series([1, 2, 3, 4, 5, 6]); + * sf.apply((x) => x * 2, { inplace: true }); + * console.log(sf.values); + * //output [ 2, 4, 6, 8, 10, 12 ] + * ``` + * + */ + apply(callable: (value: any) => any, options?: { inplace?: boolean }): Series + apply(callable: (value: any) => any, options?: { inplace?: boolean }): Series | void { + const { inplace } = { inplace: false, ...options } + + const isCallable = utils.isFunction(callable); + if (!isCallable) { + throw new Error("Param Error: callable must be a function"); + } + + const data = this.values.map((val) => { + return callable(val); + }); + + if (inplace) { + this.$setValues(data) + } else { + const sf = this.copy(); + sf.$setValues(data) + return sf; + } + } + + /** + * Returns a Series with only the unique value(s) in the original Series + * @example + * ``` + * const sf = new Series([1, 2, 3, 4, 5, 6, 1, 2, 3, 4, 5, 6]); + * const sf2 = sf.unique(); + * console.log(sf2.values); + * //output [ 1, 2, 3, 4, 5, 6 ] + * ``` + */ + unique(): Series { + const newValues = new Set(this.values as ArrayType1D); + let series = new Series(Array.from(newValues)); + return series; + } + + /** + * Return the number of unique elements in a Series + * @example + * ``` + * const sf = new Series([1, 2, 3, 4, 5, 6, 1, 2, 3, 4, 5, 6]); + * console.log(sf.nUnique()); + * //output 6 + * ``` + * + */ + nUnique(): number { + return (new Set(this.values as ArrayType1D)).size; + } + + /** + * Returns unique values and their counts in a Series + * @example + * ``` + * const sf = new Series([1, 2, 3, 4, 5, 6, 1, 2, 3, 4, 5, 6]); + * const sf2 = sf.valueCounts(); + * sf2.print(); + * ``` + */ + valueCounts(): Series { + const sData = this.values; + const dataDict: any = {}; + for (let i = 0; i < sData.length; i++) { + const val = sData[i]; + if (`${val}` in dataDict) { + dataDict[`${val}`] = dataDict[`${val}`] + 1; + } else { + dataDict[`${val}`] = 1; + } + } + + const index = Object.keys(dataDict).map((x) => { + return parseInt(x) ? parseInt(x) : x; + }); + const data = Object.values(dataDict); + + const series = new Series(data, { index: index }); + return series; + + } + + /** + * Returns the absolute of values in Series + * @param options.inplace Boolean indicating whether to perform the operation inplace or not. Defaults to false + * @example + * ``` + * const sf = new Series([1, -2, 3, -4, 5, -6]); + * const sf2 = sf.abs(); + * console.log(sf2.values); + * //output [ 1, 2, 3, 4, 5, 6 ] + * ``` + * + * @example + * ``` + * const sf = new Series([1, -2, 3, -4, 5, -6]); + * sf.abs({ inplace: true }); + * console.log(sf.values); + * //output [ 1, 2, 3, 4, 5, 6 ] + * ``` + */ + abs(options?: { inplace?: boolean }): Series + abs(options?: { inplace?: boolean }): Series | void { + const { inplace } = { inplace: false, ...options } + + if (this.dtypes[0] == "string") ErrorThrower.throwStringDtypeOperationError("abs") + let newValues; + + + newValues = this.values.map(val => Math.abs(val as number)); + + if (inplace) { + this.$setValues(newValues as ArrayType1D) + } else { + const sf = this.copy(); + sf.$setValues(newValues as ArrayType1D) + return sf; + } + } + + /** + * Returns the cumulative sum over a Series + * @param options.inplace Boolean indicating whether to perform the operation inplace or not. Defaults to false + * @example + * ``` + * const sf = new Series([1, 2, 3, 4, 5, 6]); + * const sf2 = sf.cumsum(); + * console.log(sf2.values); + * //output [ 1, 3, 6, 10, 15, 21 ] + * ``` + * + * @example + * ``` + * const sf = new Series([1, 2, 3, 4, 5, 6]); + * sf.cumSum({ inplace: true }); + * console.log(sf.values); + * //output [ 1, 3, 6, 10, 15, 21 ] + * ``` + */ + cumSum(options?: { inplace?: boolean }): Series + cumSum(options?: { inplace?: boolean }): Series | void { + const ops = { inplace: false, ...options } + return this.cumOps("sum", ops); + } + + /** + * Returns cumulative minimum over a Series + * @param options.inplace Boolean indicating whether to perform the operation inplace or not. Defaults to false + * @example + * ``` + * const sf = new Series([1, 2, 3, 4, 5, 6]); + * const sf2 = sf.cumMin(); + * console.log(sf2.values); + * //output [ 1, 1, 1, 1, 1, 1 ] + * ``` + * + */ + cumMin(options?: { inplace?: boolean }): Series + cumMin(options?: { inplace?: boolean }): Series | void { + const ops = { inplace: false, ...options } + return this.cumOps("min", ops); + } + + + /** + * Returns cumulative maximum over a Series + * @param options.inplace Boolean indicating whether to perform the operation inplace or not. Defaults to false + * @example + * ``` + * const sf = new Series([1, 2, 3, 4, 5, 6]); + * const sf2 = sf.cumMax(); + * console.log(sf2.values); + * //output [ 1, 2, 3, 4, 5, 6 ] + * ``` + */ + cumMax(options?: { inplace?: boolean }): Series + cumMax(options?: { inplace?: boolean }): Series | void { + const ops = { inplace: false, ...options } + return this.cumOps("max", ops); + } + + /** + * Returns cumulative product over a Series + * @param options.inplace Boolean indicating whether to perform the operation inplace or not. Defaults to false + * @example + * ``` + * const sf = new Series([1, 2, 3, 4, 5, 6]); + * const sf2 = sf.cumProd(); + * console.log(sf2.values); + * //output [ 1, 2, 6, 24, 120, 720 ] + * ``` + */ + cumProd(options?: { inplace?: boolean }): Series + cumProd(options?: { inplace?: boolean }): Series | void { + const ops = { inplace: false, ...options } + return this.cumOps("prod", ops); + } + + /** + * Internal helper function to calculate cumulative operations on series data + */ + private cumOps(ops: string, options: { inplace: boolean }): Series | void { + if (this.dtypes[0] == "string") ErrorThrower.throwStringDtypeOperationError(ops) + const { inplace } = options; + + const sData = this.values; + let tempval = sData[0]; + const data = [tempval]; + + for (let i = 1; i < sData.length; i++) { + let currVal = sData[i]; + switch (ops) { + case "max": + if (currVal > tempval) { + data.push(currVal); + tempval = currVal; + } else { + data.push(tempval); + } + break; + case "min": + if (currVal < tempval) { + data.push(currVal); + tempval = currVal; + } else { + data.push(tempval); + } + break; + case "sum": + tempval = (tempval as number) + (currVal as number) + data.push(tempval); + break; + case "prod": + tempval = (tempval as number) * (currVal as number) + data.push(tempval); + break; + + } + } + + if (inplace) { + this.$setValues(data as ArrayType1D) + } else { + return new Series(data, { + index: this.index, + config: { ...this.config } + }); + } + } + + + /** + * Returns less than of series and other. Supports element wise operations + * @param other Series, number, or Array of numbers to compare against + * @example + * ``` + * const sf = new Series([1, 2, 3, 4, 5, 6]); + * const sf2 = sf.lt(3); + * console.log(sf2.values); + * //output [ true, true, false, false, false, false ] + * ``` + * + * @example + * ``` + * const sf = new Series([1, 2, 3, 4, 5, 6]); + * const sf2 = sf.lt([3, 4, 5, 6, 7, 8]); + * console.log(sf2.values); + * //output [ true, true, false, false, false, false ] + * ``` + */ + lt(other: Series | number | Array | boolean[]): Series { + return this.boolOps(other, "lt"); + } + + /** + * Returns Greater than of series and other. Supports element wise operations + * @param other Series, number or Array of numbers to compare against + * @example + * ``` + * const sf = new Series([1, 2, 3, 4, 5, 6]); + * const sf2 = sf.gt(3); + * console.log(sf2.values); + * //output [ false, false, true, true, true, true ] + * ``` + * + * @example + * ``` + * const sf = new Series([1, 2, 3, 4, 5, 6]); + * const sf2 = sf.gt([3, 4, 5, 6, 7, 8]); + * console.log(sf2.values); + * //output [ false, false, true, true, true, true ] + * ``` + */ + gt(other: Series | number | Array | boolean[]): Series { + return this.boolOps(other, "gt"); + } + + /** + * Returns Less than or Equal to of series and other. Supports element wise operations + * @param other Series, number or Array of numbers to compare against + * @example + * ``` + * const sf = new Series([1, 2, 3, 4, 5, 6]); + * const sf2 = sf.le(3); + * console.log(sf2.values); + * //output [ true, true, true, true, false, false ] + * ``` + * + * @example + * ``` + * const sf = new Series([1, 2, 3, 4, 5, 6]); + * const sf2 = sf.le([3, 4, 5, 6, 7, 8]); + * console.log(sf2.values); + * //output [ true, true, true, true, false, false ] + * ``` + * + */ + le(other: Series | number | Array | boolean[]): Series { + return this.boolOps(other, "le"); + } + + /** + * Returns Greater than or Equal to of series and other. Supports element wise operations + * @param other Series, number or Array of numbers to compare against + * @example + * ``` + * const sf = new Series([1, 2, 3, 4, 5, 6]); + * const sf2 = sf.ge(3); + * console.log(sf2.values); + * //output [ false, false, true, true, true, true ] + * ``` + * + * @example + * ``` + * const sf = new Series([1, 2, 3, 4, 5, 6]); + * const sf2 = sf.ge([3, 4, 5, 6, 7, 8]); + * console.log(sf2.values); + * //output [ false, false, true, true, true, true ] + * ``` + */ + ge(other: Series | number | Array | boolean[]): Series { + return this.boolOps(other, "ge"); + } + + /** + * Returns Not Equal to of series and other. Supports element wise operations + * @param other Series, number or Array of numbers to compare against + * @example + * ``` + * const sf = new Series([1, 2, 3, 4, 5, 6]); + * const sf2 = sf.ne(3); + * console.log(sf2.values); + * //output [ true, true, false, true, true, true ] + * ``` + * + * @example + * ``` + * const sf = new Series([1, 2, 3, 4, 5, 6]); + * const sf2 = sf.ne([3, 2, 5, 6, 7, 8]); + * console.log(sf2.values); + * //output [ true, false, true, true, true, true ] + * ``` + * + */ + ne(other: Series | number | Array | boolean[]): Series { + return this.boolOps(other, "ne"); + } + + /** + * Returns Equal to of series and other. Supports element wise operations + * @param other Series, number or Array of numbers to compare against + * @example + * ``` + * const sf = new Series([1, 2, 3, 4, 5, 6]); + * const sf2 = sf.eq(3); + * console.log(sf2.values); + * //output [ false, false, true, false, false, false ] + * ``` + * + * @example + * ``` + * const sf = new Series([1, 2, 3, 4, 5, 6]); + * const sf2 = sf.eq(new Series([3, 2, 5, 6, 7, 8])); + * console.log(sf2.values); + * //output [ false, true, false, false, false, false ] + * ``` + */ + eq(other: Series | number | Array | boolean[]): Series { + return this.boolOps(other, "eq"); + } + + /** + * Internal function to perform boolean operations + * @param other Other Series or number to compare with + * @param bOps Name of operation to perform [ne, ge, le, gt, lt, eq] + */ + private boolOps(other: Series | number | Array | boolean[], bOps: string) { + const data = []; + const lSeries = this.values; + let rSeries; + + if (typeof other == "number") { + rSeries = Array(this.values.length).fill(other); //create array of repeated value for broadcasting + } else if (typeof other == "string" && ["eq", "ne"].includes(bOps)) { + rSeries = Array(this.values.length).fill(other); + } else if (other instanceof Series) { + rSeries = other.values; + } else if (Array.isArray(other)) { + rSeries = other; + } else { + throw new Error("ParamError: value for other not supported. It must be either a scalar, Array or Series"); + } + + if (!(lSeries.length === rSeries.length)) { + throw new Error("LengthError: Lenght of other must be equal to length of Series"); + } + + + for (let i = 0; i < lSeries.length; i++) { + let lVal = lSeries[i]; + let rVal = rSeries[i]; + let bool = null; + switch (bOps) { + case "lt": + bool = lVal < rVal ? true : false; + data.push(bool); + break; + case "gt": + bool = lVal > rVal ? true : false; + data.push(bool); + break; + case "le": + bool = lVal <= rVal ? true : false; + data.push(bool); + break; + case "ge": + bool = lVal >= rVal ? true : false; + data.push(bool); + break; + case "ne": + bool = lVal !== rVal ? true : false; + data.push(bool); + break; + case "eq": + bool = lVal === rVal ? true : false; + data.push(bool); + break; + } + } + return new Series(data, { + index: this.index, + config: { ...this.config } + }); + } + + /** + * Replace all occurence of a value with a new value + * @param oldValue The value you want to replace + * @param newValue The new value you want to replace the old value with + * @param options.inplace Boolean indicating whether to perform the operation inplace or not. Defaults to false + * + * @example + * ``` + * const sf = new Series([1, 2, 3, 4, 5, 6]); + * const sf2 = sf.replace(3, 10); + * console.log(sf2.values); + * //output [ 1, 2, 10, 4, 5, 6 ] + * ``` + * + * @example + * ``` + * const sf = new Series([1, 2, 3, 4, 5, 6]); + * sf.replace(3, 10, { inplace: true }); + * console.log(sf.values); + * //output [ 1, 2, 10, 4, 5, 6 ] + * ``` + */ + replace( + oldValue: string | number | boolean, + newValue: string | number | boolean, + options?: { inplace?: boolean } + ): Series + replace( + oldValue: string | number | boolean, + newValue: string | number | boolean, + options?: { inplace?: boolean } + ): Series | void { + const { inplace } = { inplace: false, ...options } + + if (!oldValue && typeof oldValue !== 'boolean') { + throw Error(`Params Error: Must specify param 'oldValue' to replace`); + } + + if (!newValue && typeof newValue !== 'boolean') { + throw Error(`Params Error: Must specify param 'newValue' to replace with`); + } + + const newArr = [...this.values].map((val) => { + if (val === oldValue) { + return newValue + } else { + return val + } + }); + + if (inplace) { + this.$setValues(newArr as ArrayType1D) + } else { + const sf = this.copy(); + sf.$setValues(newArr as ArrayType1D) + return sf; + } + + } + + /** + * Drops all missing values (NaN, null, undefined) from a Series. + * @param options.inplace Boolean indicating whether to perform the operation inplace or not. Defaults to false + * + * @example + * ``` + * const sf = new Series([1, 2, NaN, 4, 5, NaN]); + * const sf2 = sf.dropNa(); + * console.log(sf2.values); + * //output [ 1, 2, 4, 5 ] + * ``` + * + * @example + * ``` + * const sf = new Series([1, 2, NaN, 4, 5, null]); + * sf.dropNa({ inplace: true }); + * console.log(sf.values); + * //output [ 1, 2, 4, 5 ] + * ``` + */ + dropNa(options?: { inplace?: boolean }): Series + dropNa(options?: { inplace?: boolean }): Series | void { + const { inplace } = { inplace: false, ...options } + + const oldValues = this.values; + const oldIndex = this.index; + const newValues: ArrayType1D = []; + const newIndex: Array = []; + const isNaVals = this.isNa().values; + + isNaVals.forEach((val, i) => { + if (!val) { + newValues.push((oldValues as ArrayType1D)[i]); + newIndex.push(oldIndex[i]) + } + }); + + if (inplace) { + this.$setValues(newValues, false) + this.$setIndex(newIndex) + } else { + const sf = this.copy(); + sf.$setValues(newValues, false) + sf.$setIndex(newIndex) + return sf; + } + + } + + /** + * Returns the integer indices that would sort the Series. + * @param ascending Boolean indicating whether to sort in ascending order or not. Defaults to true + * @example + * ``` + * const sf = new Series([3, 1, 2]); + * const sf2 = sf.argSort(); + * console.log(sf2.values); + * //output [ 1, 2, 0 ] + * ``` + * + * @example + * ``` + * const sf = new Series([3, 1, 2]); + * const sf2 = sf.argSort({ascending: false}); + * console.log(sf2.values); + * //output [ 0, 2, 1 ] + * + */ + argSort(options?: { ascending: boolean }): Series { + const { ascending } = { ascending: true, ...options } + const sortedIndex = this.sortValues({ ascending }); + const sf = new Series(sortedIndex.index); + return sf; + } + + /** + * Returns integer position of the largest value in the Series. + * @example + * ``` + * const sf = new Series([3, 1, 2]); + * const sf2 = sf.argMax(); + * console.log(sf2); + * //output 0 + * ``` + * + */ + argMax(): number { + return this.tensor.argMax().arraySync() as number + } + + + /** + * Returns integer position of the smallest value in the Series. + * @example + * ``` + * const sf = new Series([3, 1, 2]); + * const sf2 = sf.argMin(); + * console.log(sf2); + * //output 1 + * ``` + * + */ + argMin(): number { + return this.tensor.argMin().arraySync() as number + } + + /** + * Remove duplicate values from a Series + * @param keep "first" | "last", which dupliate value to keep. Defaults to "first". + * @param options.inplace Boolean indicating whether to perform the operation inplace or not. Defaults to false + * @example + * ``` + * const sf = new Series([1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10]); + * const sf2 = sf.dropDuplicates(); + * console.log(sf2.values); + * //output [ 1, 2, 3, 4, 5, 6, 7, 8, 9, 10 ] + * ``` + * + * @example + * ``` + * const sf = new Series([1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10]); + * sf.dropDuplicates({ keep: "last", inplace: true }); + * console.log(sf.values); + * //output [ 1, 2, 3, 4, 5, 6, 7, 8, 9, 10 ] + * ``` + * + */ + dropDuplicates(options?: { keep?: "first" | "last", inplace?: boolean }): Series + dropDuplicates(options?: { keep?: "first" | "last", inplace?: boolean }): Series | void { + const { keep, inplace } = { keep: "first", inplace: false, ...options } + + if (!(["first", "last"].includes(keep))) { + throw Error(`Params Error: Keep must be one of 'first' or 'last'`); + } + + let dataArr: ArrayType1D + let newArr: ArrayType1D = []; + let oldIndex: Array + let newIndex: Array = []; + + if (keep === "last") { + dataArr = (this.values as ArrayType1D).reverse(); + oldIndex = this.index.reverse(); + } else { + dataArr = (this.values as ArrayType1D) + oldIndex = this.index; + } + + dataArr.forEach((val, i) => { + if (!newArr.includes(val)) { + newIndex.push(oldIndex[i]); + newArr.push(val); + } + }); + + if (keep === "last") { + //re-reversed the array and index to its true order + newArr = newArr.reverse(); + newIndex = newIndex.reverse(); + } + + if (inplace) { + this.$setValues(newArr, false) + this.$setIndex(newIndex) + } else { + const sf = this.copy(); + sf.$setValues(newArr, false) + sf.$setIndex(newIndex) + return sf; + } + + } + + /** + * Cast Series to specified data type + * @param dtype Data type to cast to. One of [float32, int32, string, boolean, undefined] + * @param options.inplace Boolean indicating whether to perform the operation inplace or not. Defaults to false + * + * @example + * ``` + * const sf = new Series([1, 2, 3, 4, 5, 6, 7, 8, 9, 10]); + * const sf2 = sf.asType("float32"); + * console.log(sf2.dtype); + * //output "float32" + * ``` + * + * @example + * ``` + * const sf = new Series([1, 2, 3, 4, 5, 6, 7, 8, 9, 10]); + * sf.asType("float32", {inplace: true}); + * console.log(sf.dtype); + * //output "float32" + * ``` + */ + asType(dtype: "float32" | "int32" | "string" | "boolean" | "undefined", options?: { inplace?: boolean }): Series + asType(dtype: "float32" | "int32" | "string" | "boolean" | "undefined", options?: { inplace?: boolean }): Series | void { + const { inplace } = { inplace: false, ...options } + + if (!dtype) { + throw Error("Param Error: Please specify dtype to cast to"); + } + + if (!(DATA_TYPES.includes(dtype))) { + throw Error(`dtype ${dtype} not supported. dtype must be one of ${DATA_TYPES}`); + } + + const oldValues = [...this.values]; + const newValues: ArrayType1D = []; + + switch (dtype) { + case "float32": + oldValues.forEach((val) => { + newValues.push(Number(val)); + }); + break; + case "int32": + oldValues.forEach((val) => { + newValues.push(parseInt(val as any)); + }); + break; + case "string": + oldValues.forEach((val) => { + newValues.push(String(val)); + }); + break; + case "boolean": + oldValues.forEach((val) => { + newValues.push(Boolean(val)); + }); + break; + case "undefined": + oldValues.forEach((_) => { + newValues.push(NaN); + }); + break; + default: + break; + } + + if (inplace) { + this.$setValues(newValues, false) + this.$setDtypes([dtype]) + } else { + const sf = this.copy(); + sf.$setValues(newValues, false) + sf.$setDtypes([dtype]) + return sf; + } + + } + + /** + * Appends a new value or values to the end of the Series + * @param newValue Single value | Array | Series to append to the Series + * @param index The new index value(s) to append to the Series. Must contain the same number of values as `newValues` + * as they map `1 - 1`. + * @param options.inplace Boolean indicating whether to perform the operation inplace or not. Defaults to false + * @example + * ``` + * const sf = new Series([1, 2, 3, 4, 5, 6, 7, 8, 9, 10]); + * sf.append(11); + * console.log(sf.values); + * //output [ 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11 ] + * ``` + * + * @example + * ``` + * const sf = new Series([1, 2, 3, 4, 5, 6, 7, 8, 9, 10]); + * sf.append([11, 12, 13]); + * console.log(sf.values); + * //output [ 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13 ] + * ``` + * + * @example + * ``` + * const sf = new Series([1, 2, 3, 4, 5, 6, 7, 8, 9, 10]); + * sf.append(new Series([11, 12, 13]), { inplace: true}); + * console.log(sf.values); + * //output [ 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13 ] + * ``` + */ + append( + newValue: string | number | boolean | Series | ArrayType1D, + index: Array | number | string, + options?: { inplace?: boolean } + ): Series | void { + const { inplace } = { inplace: false, ...options } + + if (!newValue && typeof newValue !== "boolean") { + throw Error("Param Error: newValues cannot be null or undefined"); + } + + if (!index) { + throw Error("Param Error: index cannot be null or undefined"); + } + + const newData = [...this.values] + const newIndx = [...this.index] + + if (Array.isArray(newValue) && Array.isArray(index)) { + + if (newValue.length !== index.length) { + throw Error("Param Error: Length of new values and index must be the same"); + } + + newValue.forEach((el, i) => { + newData.push(el); + newIndx.push(index[i]); + }); + + } else if (newValue instanceof Series) { + const _value = newValue.values; + + if (!Array.isArray(index)) { + throw Error("Param Error: index must be an array"); + } + + if (index.length !== _value.length) { + throw Error("Param Error: Length of new values and index must be the same"); + } + + _value.forEach((el, i) => { + newData.push(el); + newIndx.push(index[i]); + }); + } else { + newData.push(newValue); + newIndx.push(index as string | number); + } + + if (inplace) { + this.$setValues(newData as ArrayType1D, false) + this.$setIndex(newIndx) + } else { + const sf = new Series( + newData, + { + index: newIndx, + columns: this.columns, + dtypes: this.dtypes, + config: this.config + }) + + return sf + } + } + + /** + * Returns dtype of Series + * @example + * ``` + * const sf = new Series([1, 2, 3, 4, 5, 6, 7, 8, 9, 10]); + * console.log(sf.dtype); + * //output "int32" + * ``` + */ + get dtype(): string { + return this.dtypes[0]; + } + + /** + * Exposes numerous string methods to manipulate Series of string dtype + * @example + * ``` + * const sf = new Series(["a", "b", "c", "d", "e", "f", "g", "h", "i", "j"]); + * const sfs = sf.str.join("HelloWorld", ""); + * console.log(sfs.values); + * //output ["aHelloWorld", "bHelloWorld", "cHelloWorld", "dHelloWorld", "eHelloWorld", "fHelloWorld", "gHelloWorld", "hHelloWorld", "iHelloWorld", "jHelloWorld"] + * ``` + */ + get str() { + if (this.dtypes[0] == "string") { + return new Str(this); + } else { + throw new Error("Cannot call accessor str on non-string type"); + } + } + + /** + * Returns time class that exposes different date time method + * @example + * ``` + * const sf = new Series([ + * "2020-01-01", + * "2020-01-02", + * "2020-01-03", + * "2020-01-04", + * "2020-01-05", + * ]); + * const sfd = sf.dt.dayOfWeekName(); + * console.log(sfd.values); + * //output [ 'Wednesday', 'Thursday', 'Friday', 'Saturday', 'Sunday' ] + * ``` + */ + get dt() { + if (this.dtypes[0] == "string") { + return new Dt(this) + } else { + throw new Error("Cannot call accessor dt on non-string type"); + } + } + + /** + * Overrides default toString implementation. This essentially makes `print()` works. + */ + toString(): string { + const maxRow = this.$config.getMaxRow; + let indx: (string | number)[] + let values = [] + + if (this.shape[0] > maxRow) { + //slice rows to show [max_rows] rows + const sfSlice = this.iloc([`0:${maxRow}`]); + + indx = sfSlice.index + values = sfSlice.values; + + } else { + indx = this.index + values = this.values; + } + + const tabledata = values.map((x, i) => [indx[i], x]) + return table(tabledata as any); + } + + /** + * Returns the logical AND between Series and other. Supports element wise operations and broadcasting. + * @param other Series, Scalar, Array of Scalars + * @example + * ``` + * const sf = new Series([true, true, false, false, true]); + * const sf2 = new Series([true, false, true, false, true]); + * const sf3 = sf.and(sf2); + * console.log(sf3.values); + * //output [ true, false, false, false, false ] + * ``` + */ + and(other: any): Series { + + if (other === undefined) { + throw new Error("Param Error: other cannot be undefined"); + } + const newValues: ArrayType1D = []; + + if (other instanceof Series) { + if (this.dtypes[0] !== other.dtypes[0]) { + throw new Error("Param Error: Series must be of same dtype"); + } + + if (this.shape[0] !== other.shape[0]) { + throw new Error("Param Error: Series must be of same shape"); + } + + this.values.forEach((val, i) => { + newValues.push(Boolean(val) && Boolean(other.values[i])); + }); + + } else if (typeof other === "boolean") { + + this.values.forEach((val) => { + newValues.push(Boolean(val) && Boolean(other)); + }); + + } else if (Array.isArray(other)) { + + this.values.forEach((val, i) => { + newValues.push(Boolean(val) && Boolean(other[i])); + }); + + } else { + throw new Error("Param Error: other must be a Series, Scalar, or Array of Scalars"); + } + return new Series(newValues, { + index: this.index, + config: { ...this.config } + }); + } + + /** + * Returns the logical OR between Series and other. Supports element wise operations and broadcasting. + * @param other Series, Scalar, Array of Scalars + * @example + * ``` + * const sf = new Series([true, true, false, false, true]); + * const sf2 = new Series([true, false, true, false, true]); + * const sf3 = sf.or(sf2); + * console.log(sf3.values); + * //output [ true, true, true, false, true ] + * ``` + * + */ + or(other: any): Series { + + if (other === undefined) { + throw new Error("Param Error: other cannot be undefined"); + } + const newValues: ArrayType1D = []; + + if (other instanceof Series) { + if (this.dtypes[0] !== other.dtypes[0]) { + throw new Error("Param Error: Series must be of same dtype"); + } + + if (this.shape[0] !== other.shape[0]) { + throw new Error("Param Error: Series must be of same shape"); + } + + this.values.forEach((val, i) => { + newValues.push(Boolean(val) || Boolean(other.values[i])); + }); + + } else if (typeof other === "boolean") { + + this.values.forEach((val) => { + newValues.push(Boolean(val) || Boolean(other)); + }); + + } else if (Array.isArray(other)) { + + this.values.forEach((val, i) => { + newValues.push(Boolean(val) || Boolean(other[i])); + }); + + } else { + throw new Error("Param Error: other must be a Series, Scalar, or Array of Scalars"); + } + + return new Series(newValues, { + index: this.index, + config: { ...this.config } + }); + } + + /** + * One-hot encode values in the Series. + * @param options Options for the operation. The following options are available: + * - `prefix`: Prefix to add to the new column. Defaults to unique labels. + * - `prefixSeparator`: Separator to use for the prefix. Defaults to '_'. + * @example + * ``` + * const sf = new Series(["a", "b", "c", "a"]); + * const sf2 = sf.getDummies({ prefix: "category" }); + * console.log(sf2.values); + * //output [ [ 1, 0, 0 ], [ 0, 1, 0 ], [ 0, 0, 1 ], [ 1, 0, 0 ] ] + * ``` + * + * @example + * ``` + * const sf = new Series(["a", "b", "c", "a"]); + * const sf2 = sf.getDummies({ prefix: "category", prefixSeparator: "-" }); + * console.log(sf2.values); + * //output [ [ 1, 0, 0 ], [ 0, 1, 0 ], [ 0, 0, 1 ], [ 1, 0, 0 ] ] + * ``` + */ + getDummies(options?: { + columns?: string | Array, + prefix?: string | Array, + prefixSeparator?: string, + }): DataFrame { + return dummyEncode(this, options) + } + + + /** + * Exposes functions for creating charts from a Series. + * Charts are created using the Plotly.js library, so all Plotly's configuration parameters are available. + * @param divId name of the HTML Div to render the chart in. + * @example + * ``` + * const sf = new Series([1, 2, 3, 4, 5]); + * sf.plot("myDiv").line() //renders the chart in the div with id "myDiv" + * ``` + */ + plot(divId: string) { + //TODO: Add support for check plot library to use + // So we can support other plot library like d3, vega, etc + if (utils.isBrowserEnv()) { + const plt = new PlotlyLib(this, divId); + return plt; + } else { + throw new Error("Not supported in NodeJS"); + } + } + + /** + * Converts a Series to CSV. + * @param options Configuration object. Supports the following options: + * - `filePath`: Local file path to write the CSV file. If not specified, the CSV will be returned as a string. Option is only available in NodeJS. + * - `fileName`: Name of the CSV file. Defaults to `data.csv`. Option is only available in Browser. + * - `download`: If true, the CSV will be downloaded. Defaults to false. Option is only available in Browser. + * + * @example + * ``` + * const df = new Series([1, 2, 3, 4]) + * const csv = df.toCSV() + * console.log(csv) + * //output "1,2,3,4" + * ``` + * + * @example + * ``` + * const df = new Series([1, 2, 3, 4]) + * df.toCSV({ filePath: './data.csv' }) //write to local file in NodeJS + * ``` + * + * @example + * ``` + * const df = new Series([1, 2, 3, 4]) + * df.toCSV({ fileName: 'data.csv', download: true }) //Downloads file in Browser + * ``` + * + */ + toCSV(options?: CsvOutputOptionsBrowser | CsvOutputOptionsNode): string + toCSV(options?: CsvOutputOptionsBrowser | CsvOutputOptionsNode): string | void { + if (utils.isBrowserEnv()) { + return toCSVBrowser(this, options as CsvOutputOptionsBrowser) + } else { + return toCSVNode(this, options as CsvOutputOptionsNode) + } + } + + /** + * Converts a Series to JSON. + * @param options Configuration object. Supported options: + * - `filePath`: The file path to write the JSON to. If not specified, the JSON object is returned. Option is only available in NodeJS. + * - `fileName`: The name of the JSON file. Defaults to `data.json`. Option is only available in Browser. + * - `download`: If true, the JSON will be downloaded. Defaults to false. Option is only available in Browser. + * + * @example + * ``` + * const df = new Series([[1, 2, 3, 4]], { columns: ['A']}) + * const json = df.toJSON() + * console.log(json) + * //output { A: [ '1,2,3,4' ] } + * ``` + * + * @example + * ``` + * const df = new Series([1, 2, 3, 4]) + * df.toJSON({ filePath: './data.json' }) // downloads to local file system as data.json in NodeJS + * ``` + * + * @example + * ``` + * const df = new Series([1, 2, 3, 4]) + * df.toJSON({ fileName: 'data.json', download: true }) // downloads file browser + * ``` + */ + toJSON(options?: JsonOutputOptionsBrowser | JsonOutputOptionsNode): object + toJSON(options?: JsonOutputOptionsBrowser | JsonOutputOptionsNode): object | void { + if (utils.isBrowserEnv()) { + return toJSONBrowser(this, options as JsonOutputOptionsBrowser) + } else { + return toJSONNode(this, options as JsonOutputOptionsNode) + } + } + + + /** + * Converts a Series to Excel file format. + * @param options Configuration object. Supported options: + * - `sheetName`: The sheet name to be written to. Defaults to `'Sheet1'`. + * - `filePath`: The filePath to be written to. Defaults to `'./output.xlsx'`. Option is only available in NodeJs + * - `fileName`: The fileName to be written to. Defaults to `'output.xlsx'`. Option is only available in Browser + * + * @example + * ``` + * const df = new Series([1, 2, 3, 4]) + * df.toExcel({ filePath: './output.xlsx' }) // writes to local file system as output.xlsx in NodeJS + * ``` + * + * @example + * ``` + * const df = new Series([1, 2, 3, 4]) + * df.toExcel({ fileName: 'output.xlsx', download: true }) // downloads file browser + * ``` + * + * @example + * ``` + * const df = new Series([1, 2, 3, 4]) + * df.toExcel({ sheetName: 'Sheet2' }) // writes to Sheet2 in Excel + * ``` + * + */ + toExcel(options?: ExcelOutputOptionsBrowser | ExcelOutputOptionsNode): void { + if (utils.isBrowserEnv()) { + toExcelBrowser(this, options as ExcelOutputOptionsBrowser) + } else { + return toExcelNode(this, options as ExcelOutputOptionsNode) + } + } +} \ No newline at end of file diff --git a/src/danfojs-base/core/strings.ts b/src/danfojs-base/core/strings.ts new file mode 100644 index 00000000..0f5e2b68 --- /dev/null +++ b/src/danfojs-base/core/strings.ts @@ -0,0 +1,766 @@ +/** +* @license +* Copyright 2022 JsData. All rights reserved. +* +* This source code is licensed under the MIT license found in the +* LICENSE file in the root directory of this source tree. + +* Unless required by applicable law or agreed to in writing, software +* distributed under the License is distributed on an "AS IS" BASIS, +* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +* See the License for the specific language governing permissions and +* limitations under the License. +* ========================================================================== +*/ +import { ArrayType1D } from "../shared/types"; +import Utils from "../shared/utils" +import Series from "./series"; + +const utils = new Utils(); +/** + * Exposes numerous String methods. All methods are applied Element-wise + */ +export default class Str { + private series: Series + private values: ArrayType1D + + constructor(series: Series) { + this.series = series; + this.values = (series.values as ArrayType1D); + } + + /** + * Converts all characters to lowercase. + * @param options The following optional parameters are supported: + * - `inplace` Boolean, indicating whether to perform the operation inplace or not. Defaults to `false` + * @example + * ``` + * import { Series } from "danfojs-node" + * const sf = new Series(["GooD", "Bad", "CrAzy"]) + * const newSf = sf.str.toLowerCase() + * console.log(newSf.values) + * // ["good", "bad", "crazy"] + * ``` + */ + toLowerCase(options?: { inplace?: boolean }): Series + toLowerCase(options?: { inplace?: boolean }): Series | void { + const { inplace } = { inplace: false, ...options } + const newArr: Array = []; + this.values.map((val) => { + if (utils.isEmpty(val)) { + newArr.push(NaN); + } else { + newArr.push(`${val}`.toLowerCase()); + } + + }); + + if (inplace) { + this.series.$setValues(newArr as ArrayType1D) + this.series.print() + } else { + const sf = this.series.copy() + sf.$setValues(newArr as ArrayType1D) + return sf; + } + + } + + /** + * Converts all characters to uppercase. + * @param options The following optional parameters are supported: + * - `inplace` Boolean, indicating whether to perform the operation inplace or not. Defaults to `false` + * @example + * ``` + * import { Series } from "danfojs-node" + * const sf = new Series(["GooD", "Bad", "CrAzy"]) + * const newSf = sf.str.toUpperCase() + * console.log(newSf.values) + * // ["GOOD", "BAD", "CRAZY"] + * ``` + */ + toUpperCase(options?: { inplace?: boolean }): Series + toUpperCase(options?: { inplace?: boolean }): Series | void { + const { inplace } = { inplace: false, ...options } + const newArr: Array = []; + this.values.map((val) => { + if (utils.isEmpty(val)) { + newArr.push(NaN); + } else { + newArr.push(`${val}`.toUpperCase()); + } + + }); + if (inplace) { + this.series.$setValues(newArr as ArrayType1D) + this.series.print() + } else { + const sf = this.series.copy() + sf.$setValues(newArr as ArrayType1D) + return sf; + + } + } + + /** + * Capitalize first string + * @param options The following optional parameters are supported: + * - `inplace` Boolean, indicating whether to perform the operation inplace or not. Defaults to `false` + * @example + * ``` + * import { Series } from "danfojs-node" + * const sf = new Series(["Good", "bad", "crazy"]) + * const newSf = sf.str.capitalize() + * console.log(newSf.values) + * // ["Good", "Bad", "Crazy"] + * ``` + */ + capitalize(options?: { inplace?: boolean }): Series + capitalize(options?: { inplace?: boolean }): Series | void { + const { inplace } = { inplace: false, ...options } + const newArr: Array = []; + this.values.map((val) => { + if (utils.isEmpty(val)) { + newArr.push(NaN); + } else { + let firstChar = `${val}`.slice(0, 1); + let leftChar = `${val}`.slice(1); + let newStr = `${firstChar.toUpperCase()}${leftChar.toLowerCase()}`; + newArr.push(newStr); + } + + }); + + if (inplace) { + this.series.$setValues(newArr as ArrayType1D) + this.series.print() + } else { + const sf = this.series.copy() + sf.$setValues(newArr as ArrayType1D) + return sf; + } + + } + + /** + * Returns the character at the specified index (position) + * @param index position of character + * @param options The following optional parameters are supported: + * - `inplace` Boolean, indicating whether to perform the operation inplace or not. Defaults to `false` + * @example + * ``` + * import { Series } from "danfojs-node" + * const sf = new Series(["Good", "bad", "crazy"]) + * const newSf = sf.str.charAt(1) + * console.log(newSf.values) + * // ["o", "a", "r"] + * ``` + */ + charAt(index: number, options?: { inplace?: boolean }): Series + charAt(index = 0, options?: { inplace?: boolean }): Series | void { + const { inplace } = { inplace: false, ...options } + const newArr: Array = []; + this.values.map((val) => { + if (utils.isEmpty(val)) { + newArr.push(NaN); + } else { + newArr.push(`${val}`.charAt(index)); + } + }); + if (inplace) { + this.series.$setValues(newArr as ArrayType1D) + this.series.print() + } else { + const sf = this.series.copy() + sf.$setValues(newArr as ArrayType1D) + return sf; + } + } + + /** + * Joins specified `other` with values in the Series. + * @param other string|values to concatenate with. + * @param position where to concat the string from. O concats from the start, 1 concats from the end. Defaults to 1. + * @param options The following optional parameters are supported: + * - `inplace` Boolean, indicating whether to perform the operation inplace or not. Defaults to `false` + * @example + * ``` + * import { Series } from "danfojs-node" + * const sf = new Series(["Good", "bad", "crazy"]) + * const newSf = sf.str.concat("_new") + * console.log(newSf.values) + * // ["Good_new", "bad_new", "crazy_new" + * ``` + */ + concat(other: Array | string, position: number, options?: { inplace?: boolean }): Series + concat(other: Array | string, position = 1, options?: { inplace?: boolean }): Series | void { + const { inplace } = { inplace: false, ...options } + const newArr: Array = []; + + if (Array.isArray(other)) { + for (let i = 0; i < other.length; i++) { + let leftStr = `${this.values[i]}`; + let rightStr = `${other[i]}`; + if (position == 1) { + newArr.push(leftStr.concat(rightStr)); + } else { + newArr.push(rightStr.concat(leftStr)); + } + + } + } else { + this.values.map((val) => { + if (position == 1) { + if (utils.isEmpty(val)) { + newArr.push(NaN); + } else { + newArr.push(`${val}`.concat(`${other}`)); + } + + } else { + if (utils.isEmpty(val)) { + newArr.push(NaN); + } else { + newArr.push(other.concat(`${val}`)); + } + } + }); + } + + if (inplace) { + this.series.$setValues(newArr as ArrayType1D) + this.series.print() + } else { + const sf = this.series.copy() + sf.$setValues(newArr as ArrayType1D) + return sf; + } + + } + + + /** + * Checks whether a string begins with specified characters + * @param str String or Character to check against + * @param options The following optional parameters are supported: + * - `inplace` Boolean, indicating whether to perform the operation inplace or not. Defaults to `false` + * @example + * ``` + * import { Series } from "danfojs-node" + * const sf = new Series(["Good", "bad", "crazy"]) + * const newSf = sf.str.startsWith("G") + * console.log(newSf.values) + * // [true, false, false] + * ``` + */ + startsWith(str: string, options?: { inplace?: boolean }): Series + startsWith(str = "", options?: { inplace?: boolean }): Series | void { + const { inplace } = { inplace: false, ...options } + const newArr: Array = []; + this.values.forEach((val) => { + if (utils.isEmpty(val)) { + newArr.push(NaN); + } else { + newArr.push(`${val}`.startsWith(str)); + } + }); + if (inplace) { + this.series.$setValues(newArr as ArrayType1D) + this.series.print() + } else { + const sf = this.series.copy() + sf.$setValues(newArr as ArrayType1D) + return sf; + } + } + + /** + * Checks whether a string ends with specified characters + * @param str String or Character to check against + * @param options The following optional parameters are supported: + * - `inplace` Boolean, indicating whether to perform the operation inplace or not. Defaults to `false` + * @example + * ``` + * import { Series } from "danfojs-node" + * const sf = new Series(["Good", "bad", "crazy"]) + * const newSf = sf.str.endsWith("d") + * console.log(newSf.values) + * // [true, true, false] + * ``` + */ + endsWith(str: string, options?: { inplace?: boolean }): Series + endsWith(str = "", options?: { inplace?: boolean }): Series | void { + const { inplace } = { inplace: false, ...options } + const newArr: Array = []; + this.values.map((val) => { + if (utils.isEmpty(val)) { + newArr.push(NaN); + } else { + newArr.push(`${val}`.endsWith(str)); + } + }); + if (inplace) { + this.series.$setValues(newArr as ArrayType1D) + this.series.print() + } else { + const sf = this.series.copy() + sf.$setValues(newArr as ArrayType1D) + return sf; + } + } + + /** + * Checks whether a string contains the specified string/characters + * @param str String or Character to check against + * @param options The following optional parameters are supported: + * - `inplace` Boolean, indicating whether to perform the operation inplace or not. Defaults to `false` + * @example + * ``` + * import { Series } from "danfojs-node" + * const sf = new Series(["Good", "bad", "crazy"]) + * const newSf = sf.str.includes("d") + * console.log(newSf.values) + * // [true, true, false] + * ``` + */ + includes(str: string, options?: { inplace?: boolean }): Series + includes(str = "", options?: { inplace?: boolean }): Series | void { + const { inplace } = { inplace: false, ...options } + const newArr: Array = []; + this.values.map((val) => { + if (utils.isEmpty(val)) { + newArr.push(NaN); + } else { + newArr.push(`${val}`.includes(str)); + } + }); + if (inplace) { + this.series.$setValues(newArr as ArrayType1D) + this.series.print() + } else { + const sf = this.series.copy() + sf.$setValues(newArr as ArrayType1D) + return sf; + } + } + + /** + * Returns the position of the first occurrence of a specified value in a string. + * @param str String or Character to check against + * @param options The following optional parameters are supported: + * - `inplace` Boolean, indicating whether to perform the operation inplace or not. Defaults to `false` + * @example + * ``` + * import { Series } from "danfojs-node" + * const sf = new Series(["Good", "bad", "crazy"]) + * const newSf = sf.str.indexOf("d") + * console.log(newSf.values) + * // [3, 2, -1] + * ``` + */ + indexOf(str: string, options?: { inplace?: boolean }): Series + indexOf(str = "", options?: { inplace?: boolean }): Series | void { + const { inplace } = { inplace: false, ...options } + const newArr: Array = []; + this.values.map((val) => { + if (utils.isEmpty(val)) { + newArr.push(NaN); + } else { + newArr.push(`${val}`.indexOf(str)); + } + }); + if (inplace) { + this.series.$setValues(newArr as ArrayType1D) + this.series.print() + } else { + const sf = this.series.copy() + sf.$setValues(newArr as ArrayType1D) + return sf; + } + } + + /** + * Returns the position of the last found occurrence of a specified value in a string + * @param str String or Character to check against + * @param options The following optional parameters are supported: + * - `inplace` Boolean, indicating whether to perform the operation inplace or not. Defaults to `false` + * @example + * ``` + * import { Series } from "danfojs-node" + * const sf = new Series(["Good", "odd", "crazy"]) + * const newSf = sf.str.lastIndexOf("d") + * console.log(newSf.values) + * // [3, 2, -1] + * ``` + */ + lastIndexOf(str: string, options?: { inplace?: boolean }): Series + lastIndexOf(str = "", options?: { inplace?: boolean }): Series | void { + const { inplace } = { inplace: false, ...options } + const newArr: Array = []; + this.values.map((val) => { + if (utils.isEmpty(val)) { + newArr.push(NaN); + } else { + newArr.push(`${val}`.lastIndexOf(str)); + } + }); + if (inplace) { + this.series.$setValues(newArr as ArrayType1D) + this.series.print() + } else { + const sf = this.series.copy() + sf.$setValues(newArr as ArrayType1D) + return sf; + } + } + + + /** + * Searches a string for a specified value, or a regular expression, and returns a new string where the specified values are replaced + * @param searchValue String | Character value to replace + * @param replaceValue String | Character string to replace with + * @param options The following optional parameters are supported: + * - `inplace` Boolean, indicating whether to perform the operation inplace or not. Defaults to `false` + * @example + * ``` + * import { Series } from "danfojs-node" + * const sf = new Series(["Good", "odd", "crazy"]) + * const newSf = sf.str.replace("d", 7) + * console.log(newSf.values) + * // ["Goo7", "o77", "crazy"] + * ``` + */ + replace(searchValue: string, replaceValue: string, options?: { inplace?: boolean }): Series + replace(searchValue = "", replaceValue = "", options?: { inplace?: boolean }): Series | void { + const { inplace } = { inplace: false, ...options } + const newArr: Array = []; + this.values.map((val) => { + if (utils.isEmpty(val)) { + newArr.push(NaN); + } else { + newArr.push(`${val}`.replace(searchValue, replaceValue)); + } + }); + if (inplace) { + this.series.$setValues(newArr as ArrayType1D) + this.series.print() + } else { + const sf = this.series.copy() + sf.$setValues(newArr as ArrayType1D) + return sf; + } + } + + /** + * Returns a new string with a specified number of copies of an existing string + * @param num Number of times to repeat + * @param options The following optional parameters are supported: + * - `inplace` Boolean, indicating whether to perform the operation inplace or not. Defaults to `false` + * @example + * ``` + * import { Series } from "danfojs-node" + * const sf = new Series(["Good", "odd", "crazy"]) + * const newSf = sf.str.replace(2) + * console.log(newSf.values) + * // ["GoodGood", "oddodd", "crazycrazy"] + * ``` + */ + repeat(num: number, options?: { inplace?: boolean }): Series + repeat(num = 1, options?: { inplace?: boolean }): Series | void { + const { inplace } = { inplace: false, ...options } + const newArr: Array = []; + this.values.map((val) => { + if (utils.isEmpty(val)) { + newArr.push(NaN); + } else { + newArr.push(`${val}`.repeat(num)); + } + }); + if (inplace) { + this.series.$setValues(newArr as ArrayType1D) + this.series.print() + } else { + const sf = this.series.copy() + sf.$setValues(newArr as ArrayType1D) + return sf; + } + } + + + /** + * Searches a string for a specified value, or regular expression, and returns the position of the match + * @param str String or Character to check against + * @param options The following optional parameters are supported: + * - `inplace` Boolean, indicating whether to perform the operation inplace or not. Defaults to `false` + * @example + * ``` + * import { Series } from "danfojs-node" + * const sf = new Series(["Good", "odd", "crazy"]) + * const newSf = sf.str.search("d") + * console.log(newSf.values) + * ``` + */ + search(str: string, options?: { inplace?: boolean }): Series + search(str = "", options?: { inplace?: boolean }): Series | void { + const { inplace } = { inplace: false, ...options } + const newArr: Array = []; + this.values.map((val) => { + if (utils.isEmpty(val)) { + newArr.push(NaN); + } else { + newArr.push(`${val}`.search(str)); + } + }); + if (inplace) { + this.series.$setValues(newArr as ArrayType1D) + this.series.print() + } else { + const sf = this.series.copy() + sf.$setValues(newArr as ArrayType1D) + return sf; + } + } + + /** + * Extracts a part of a string and returns a new string + * @param startIndex index position of start character + * @param endIndex index position of last character + * @param options The following optional parameters are supported: + * - `inplace` Boolean, indicating whether to perform the operation inplace or not. Defaults to `false` + * @example + * ``` + * import { Series } from "danfojs-node" + * const sf = new Series(["Good", "odd", "crazy"]) + * const newSf = sf.str.slice(0,1) + * console.log(newSf.values) + * // ["G", "o", "c"] + * ``` + */ + slice(startIndex: number, endIndex: number, options?: { inplace?: boolean }): Series + slice(startIndex = 0, endIndex = 1, options?: { inplace?: boolean }): Series | void { + const { inplace } = { inplace: false, ...options } + const newArr: Array = []; + this.values.map((val) => { + if (utils.isEmpty(val)) { + newArr.push(NaN); + } else { + newArr.push(`${val}`.slice(startIndex, endIndex)); + } + }); + if (inplace) { + this.series.$setValues(newArr as ArrayType1D) + this.series.print() + } else { + const sf = this.series.copy() + sf.$setValues(newArr as ArrayType1D) + return sf; + } + } + + /** + * Splits a string into an values of substrings + * @param splitVal string or character to split at + * @param options The following optional parameters are supported: + * - `inplace` Boolean, indicating whether to perform the operation inplace or not. Defaults to `false` + * @example + * ``` + * import { Series } from "danfojs-node" + * const sf = new Series(["Good", "odd", "grade"]) + * const newSf = sf.str.split(d) + * console.log(newSf.values) + * ``` + */ + split(splitVal: string, options?: { inplace?: boolean }): Series + split(splitVal = " ", options?: { inplace?: boolean }): Series | void { + const { inplace } = { inplace: false, ...options } + const newArr: Array = []; + this.values.map((val) => { + if (utils.isEmpty(val)) { + newArr.push(NaN); + } else { + newArr.push(`${String(val).split(splitVal)}`); + } + }); + if (inplace) { + this.series.$setValues(newArr as ArrayType1D) + this.series.print() + } else { + const sf = this.series.copy() + sf.$setValues(newArr as ArrayType1D) + return sf; + } + } + + /** + * Extracts the characters from a string, beginning at a specified start position, and through the specified number of character + * @param startIndex index position of start character + * @param num number of characters to return + * @param options The following optional parameters are supported: + * - `inplace` Boolean, indicating whether to perform the operation inplace or not. Defaults to `false` + * @example + * ``` + * import { Series } from "danfojs-node" + * const sf = new Series(["Good", "odd", "grade"]) + * const newSf = sf.str.substr(d) + * ``` + */ + substr(startIndex: number, num: number, options?: { inplace?: boolean }): Series + substr(startIndex = 0, num = 1, options?: { inplace?: boolean }): Series | void { + const { inplace } = { inplace: false, ...options } + const newArr: Array = []; + this.values.map((val) => { + if (utils.isEmpty(val)) { + newArr.push(NaN); + } else { + newArr.push(`${String(val).substr(startIndex, num)}`); + } + }); + if (inplace) { + this.series.$setValues(newArr as ArrayType1D) + this.series.print() + } else { + const sf = this.series.copy() + sf.$setValues(newArr as ArrayType1D) + return sf; + } + } + + /** + * Extracts the characters from a string, between two specified indices + * @param startIndex index position of start character + * @param endIndex index position of last character + * @param options The following optional parameters are supported: + * - `inplace` Boolean, indicating whether to perform the operation inplace or not. Defaults to `false` + * @example + * ``` + * import { Series } from "danfojs-node" + * const sf = new Series(["Good", "odd", "grade"]) + * const newSf = sf.str.substring(d) + * ``` + */ + substring(startIndex: number, endIndex: number, options?: { inplace?: boolean }): Series + substring(startIndex = 0, endIndex = 1, options?: { inplace?: boolean }): Series | void { + const { inplace } = { inplace: false, ...options } + const newArr: Array = []; + this.values.map((val) => { + if (utils.isEmpty(val)) { + newArr.push(NaN); + } else { + newArr.push(`${String(val).substring(startIndex, endIndex)}`); + } + }); + if (inplace) { + this.series.$setValues(newArr as ArrayType1D) + this.series.print() + } else { + const sf = this.series.copy() + sf.$setValues(newArr as ArrayType1D) + return sf; + } + } + + /** + * Removes whitespace from both ends of a string + * @param options The following optional parameters are supported: + * - `inplace` Boolean, indicating whether to perform the operation inplace or not. Defaults to `false` + * @example + * ``` + * import { Series } from "danfojs-node" + * const sf = new Series([" Good", "odd ", " grade "]) + * const newSf = sf.str.trim(d) + * ["Good", "odd", "grade"] + * ``` + */ + trim(options?: { inplace?: boolean }): Series + trim(options?: { inplace?: boolean }): Series | void { + const { inplace } = { inplace: false, ...options } + const newArr: Array = []; + this.values.map((val) => { + if (utils.isEmpty(val)) { + newArr.push(NaN); + } else { + newArr.push(`${val}`.trim()); + } + }); + if (inplace) { + this.series.$setValues(newArr as ArrayType1D) + this.series.print() + } else { + const sf = this.series.copy() + sf.$setValues(newArr as ArrayType1D) + return sf; + } + } + + /** + * Joins strings to specified value + * @param valToJoin string value to join to the values + * @param joinChar Character to Join with + * @param options The following optional parameters are supported: + * - `inplace` Boolean, indicating whether to perform the operation inplace or not. Defaults to `false` + * @example + * ``` + * import { Series } from "danfojs-node" + * const sf = new Series(["Good", "odd", "grade"]) + * const newSf = sf.str.join("new", "_") + * // ["Good_new", "odd_new", "grade_new"] + * ``` + */ + join(valToJoin: string, joinChar: string, options?: { inplace?: boolean }): Series + join(valToJoin = "", joinChar = " ", options?: { inplace?: boolean }): Series | void { + const { inplace } = { inplace: false, ...options } + const newArr: Array = []; + this.values.map((val) => { + if (utils.isEmpty(val)) { + newArr.push(NaN); + } else { + let leftChar = val; + let rightChar = valToJoin; + let new_char = `${leftChar}${joinChar}${rightChar}`; + newArr.push(new_char); + } + }); + if (inplace) { + this.series.$setValues(newArr as ArrayType1D) + this.series.print() + } else { + const sf = this.series.copy() + sf.$setValues(newArr as ArrayType1D) + return sf; + } + } + + /** + * Counts the number of characters in string + * @param options The following optional parameters are supported: + * - `inplace` Boolean, indicating whether to perform the operation inplace or not. Defaults to `false` + * @example + * ``` + * import { Series } from "danfojs-node" + * const sf = new Series(["Good", "odd", "grade"]) + * const newSf = sf.str.len(d) + * // [4,3,5] + * ``` + */ + len(options?: { inplace?: boolean }): Series + len(options?: { inplace?: boolean }): Series | void { + const { inplace } = { inplace: false, ...options } + const newArr: Array = []; + this.values.map((val) => { + if (utils.isEmpty(val)) { + newArr.push(NaN); + } else { + newArr.push(`${val}`.length); + } + }); + if (inplace) { + this.series.$setValues(newArr as ArrayType1D) + this.series.print() + } else { + const sf = this.series.copy() + sf.$setValues(newArr as ArrayType1D) + return sf; + } + } + +} diff --git a/src/danfojs-base/index.ts b/src/danfojs-base/index.ts new file mode 100644 index 00000000..8e3d62e5 --- /dev/null +++ b/src/danfojs-base/index.ts @@ -0,0 +1,53 @@ +/** +* @license +* Copyright 2022 JsData. All rights reserved. +* +* This source code is licensed under the MIT license found in the +* LICENSE file in the root directory of this source tree. + +* Unless required by applicable law or agreed to in writing, software +* distributed under the License is distributed on an "AS IS" BASIS, +* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +* See the License for the specific language governing permissions and +* limitations under the License. +* ========================================================================== +*/ +import NDframe from "./core/generic"; +import Config from './shared/config'; +import Utils from './shared/utils'; +import Series from "./core/series"; +import DataFrame from "./core/frame"; +import Str from "./core/strings" +import Dt, { toDateTime } from "./core/datetime" +import MinMaxScaler from "./transformers/scalers/min.max.scaler"; +import StandardScaler from "./transformers/scalers/standard.scaler"; +import LabelEncoder from "./transformers/encoders/label.encoder"; +import OneHotEncoder from "./transformers/encoders/one.hot.encoder"; +import getDummies from "./transformers/encoders/dummy.encoder" +import concat from "./transformers/concat" +import merge from "./transformers/merge" +import dateRange from "./core/daterange" +import tensorflow from "./shared/tensorflowlib" + +const __version = "1.0.0" + +export { + NDframe, + Config, + Utils, + Str, + Dt, + toDateTime, + Series, + DataFrame, + MinMaxScaler, + StandardScaler, + LabelEncoder, + OneHotEncoder, + getDummies, + concat, + merge, + dateRange, + tensorflow, + __version, +} diff --git a/src/danfojs-base/io/browser/index.ts b/src/danfojs-base/io/browser/index.ts new file mode 100644 index 00000000..b3df0460 --- /dev/null +++ b/src/danfojs-base/io/browser/index.ts @@ -0,0 +1,34 @@ +/** +* @license +* Copyright 2022 JsData. All rights reserved. +* +* This source code is licensed under the MIT license found in the +* LICENSE file in the root directory of this source tree. + +* Unless required by applicable law or agreed to in writing, software +* distributed under the License is distributed on an "AS IS" BASIS, +* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +* See the License for the specific language governing permissions and +* limitations under the License. +* ========================================================================== +*/ +import { + $readCSV, + $streamCSV, + $toCSV, +} from "./io.csv" +import { + $readJSON, + $toJSON, +} from "./io.json" +import { $readExcel, $toExcel } from "./io.excel" + +export { + $readCSV as readCSVBrowser, + $streamCSV as streamCSVBrowser, + $toCSV as toCSVBrowser, + $readJSON as readJSONBrowser, + $toJSON as toJSONBrowser, + $readExcel as readExcelBrowser, + $toExcel as toExcelBrowser, +} \ No newline at end of file diff --git a/src/danfojs-base/io/browser/io.csv.ts b/src/danfojs-base/io/browser/io.csv.ts new file mode 100644 index 00000000..e05d42da --- /dev/null +++ b/src/danfojs-base/io/browser/io.csv.ts @@ -0,0 +1,174 @@ +/** +* @license +* Copyright 2022 JsData. All rights reserved. +* +* This source code is licensed under the MIT license found in the +* LICENSE file in the root directory of this source tree. + +* Unless required by applicable law or agreed to in writing, software +* distributed under the License is distributed on an "AS IS" BASIS, +* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +* See the License for the specific language governing permissions and +* limitations under the License. +* ========================================================================== +*/ +import { CsvInputOptionsBrowser, CsvOutputOptionsBrowser, ArrayType2D } from "../../shared/types" +import { DataFrame, NDframe, Series } from '../../' +import Papa from 'papaparse' + + +/** + * Reads a CSV file from local or remote location into a DataFrame. + * @param filePath URL or local file path to CSV file. `readCSV` uses PapaParse to parse the CSV file, + * hence all PapaParse options are supported. + * @param options Configuration object. Supports all Papaparse parse config options. + * @returns DataFrame containing the parsed CSV file. + * @example + * ``` + * import { readCSV } from "danfojs-node" + * const df = await readCSV("https://raw.githubusercontent.com/test.csv") + * ``` + * @example + * ``` + * import { readCSV } from "danfojs-node" + * const df = await readCSV("https://raw.githubusercontent.com/test.csv", { + * delimiter: ",", + * headers: { + * Accept: "text/csv", + * Authorization: "Bearer YWRtaW46YWRtaW4=" + * } + * }) + * ``` + * @example + * ``` + * import { readCSV } from "danfojs-node" + * const df = await readCSV("./data/sample.csv") + * ``` + */ +const $readCSV = async (file: any, options?: CsvInputOptionsBrowser): Promise => { + const frameConfig = options?.frameConfig || {} + + return new Promise(resolve => { + Papa.parse(file, { + header: true, + ...options, + download: true, + complete: results => { + const df = new DataFrame(results.data, frameConfig); + resolve(df); + } + }); + }); +}; + +/** + * Streams a CSV file from local or remote location in chunks. Intermediate chunks is passed as a DataFrame to the callback function. + * @param filePath URL or local file path to CSV file. `readCSV` uses PapaParse to parse the CSV file, + * hence all PapaParse options are supported. + * @param options Configuration object. Supports all Papaparse parse config options. + * @param callback Callback function to be called once the specifed rows are parsed into DataFrame. + * @example + * ``` + * import { streamCSV } from "danfojs-node" + * streamCSV("https://raw.githubusercontent.com/test.csv", (dfRow) => { + * const dfModified = dfRow["Names"].map((name) => name.split(",")[0]) + * return dfModified + * }) + * ``` + */ +const $streamCSV = async (file: string, callback: (df: DataFrame) => void, options: CsvInputOptionsBrowser,): Promise => { + const frameConfig = options?.frameConfig || {} + + return new Promise(resolve => { + let count = -1 + Papa.parse(file, { + ...options, + header: true, + download: true, + step: results => { + const df = new DataFrame([results.data], { ...frameConfig, index: [count++] }); + callback(df); + }, + complete: () => resolve(null) + }); + }); +}; + + +/** + * Converts a DataFrame or Series to CSV. + * @param df DataFrame or Series to be converted to CSV. + * @param options Configuration object. Supports the following options: + * - `filePath`: Local file path to write the CSV file. If not specified, the CSV will be returned as a string. + * - `header`: Boolean indicating whether to include a header row in the CSV file. + * - `sep`: Character to be used as a separator in the CSV file. + * @example + * ``` + * import { toCSV } from "danfojs-node" + * const df = new DataFrame([[1, 2, 3], [4, 5, 6]]) + * const csv = toCSV(df) + * ``` + * @example + * ``` + * import { toCSV } from "danfojs-node" + * const df = new DataFrame([[1, 2, 3], [4, 5, 6]]) + * toCSV(df, { + * filePath: "./data/sample.csv", + * header: true, + * sep: "+" + * }) + * ``` + */ +const $toCSV = (df: NDframe | DataFrame | Series, options?: CsvOutputOptionsBrowser): string | void => { + let { fileName, download, sep, header } = { fileName: "output.csv", sep: ",", header: true, download: false, ...options } + + if (df.$isSeries) { + const csv = df.values.join(sep); + + if (download) { + if (!(fileName.endsWith(".csv"))) { + fileName = fileName + ".csv" + } + $downloadFileInBrowser(csv, fileName); + } else { + return csv; + } + } else { + const rows = df.values as ArrayType2D + let csvStr = header === true ? `${df.columns.join(sep)}\n` : "" + + for (let i = 0; i < rows.length; i++) { + const row = `${rows[i].join(sep)}\n`; + csvStr += row; + } + + if (download) { + if (!(fileName.endsWith(".csv"))) { + fileName = fileName + ".csv" + } + $downloadFileInBrowser(csvStr, fileName); + } else { + return csvStr; + } + } +}; + +/** + * Internal function to download a CSV file in the browser. + * @param content A string of CSV file contents + * @param fileName The name of the file to be downloaded + */ +const $downloadFileInBrowser = (content: any, fileName: string) => { + var hiddenElement = document.createElement('a'); + hiddenElement.href = 'data:text/csv;charset=utf-8,' + encodeURI(content); + hiddenElement.target = '_blank'; + hiddenElement.download = fileName; + hiddenElement.click(); +} + + +export { + $readCSV, + $streamCSV, + $toCSV, +} \ No newline at end of file diff --git a/src/danfojs-base/io/browser/io.excel.ts b/src/danfojs-base/io/browser/io.excel.ts new file mode 100644 index 00000000..574171c4 --- /dev/null +++ b/src/danfojs-base/io/browser/io.excel.ts @@ -0,0 +1,126 @@ +/** +* @license +* Copyright 2022 JsData. All rights reserved. +* +* This source code is licensed under the MIT license found in the +* LICENSE file in the root directory of this source tree. + +* Unless required by applicable law or agreed to in writing, software +* distributed under the License is distributed on an "AS IS" BASIS, +* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +* See the License for the specific language governing permissions and +* limitations under the License. +* ========================================================================== +*/ +import { + ArrayType1D, + ArrayType2D, + ExcelOutputOptionsBrowser, + ExcelInputOptionsBrowser +} from "../../shared/types" +import { DataFrame, NDframe, Series } from '../../' +import XLSX from 'xlsx'; + +/** + * Reads a JSON file from local or remote location into a DataFrame. + * @param file URL or local file path to JSON file. + * @param options Configuration object. Supported options: + * - `method`: The HTTP method to use. Defaults to `'GET'`. + * - `headers`: Additional headers to send with the request. Supports the `node-fetch` [HeadersInit] + * @example + * ``` + * import { readExcel } from "danfojs-node" + * const df = await readExcel("https://raw.githubusercontent.com/test.xlsx") + * ``` + * @example + * ``` + * import { readExcel } from "danfojs-node" + * const df = await readExcel("https://raw.githubusercontent.com/test.xlsx", { + * method: "GET", + * headers: { + * Accept: "text/csv", + * Authorization: "Bearer YWRtaW46YWRtaW4=" + * } + * }) + * ``` + */ +const $readExcel = async (file: any, options?: ExcelInputOptionsBrowser) => { + const { sheet, method, headers, frameConfig } = { sheet: 0, method: "GET", headers: {}, frameConfig: {}, ...options } + + if (typeof file === "string" && file.startsWith("http")) { + + return new Promise(resolve => { + fetch(file, { method, headers }).then(response => { + if (response.status !== 200) { + throw new Error(`Failed to load ${file}`) + } + response.arrayBuffer().then(arrBuf => { + const arrBufInt8 = new Uint8Array(arrBuf); + const workbook = XLSX.read(arrBufInt8, { type: "array" }) + const worksheet = workbook.Sheets[workbook.SheetNames[sheet]]; + const data = XLSX.utils.sheet_to_json(worksheet); + const df = new DataFrame(data, frameConfig); + resolve(df); + }); + }).catch((err) => { + throw new Error(err) + }) + }) + + } else if (file instanceof File) { + const arrBuf = await file.arrayBuffer() + const arrBufInt8 = new Uint8Array(arrBuf); + const workbook = XLSX.read(arrBufInt8, { type: "array" }) + const worksheet = workbook.Sheets[workbook.SheetNames[sheet]]; + const data = XLSX.utils.sheet_to_json(worksheet); + const df = new DataFrame(data, frameConfig); + return df; + } else { + throw new Error("ParamError: File not supported. file must be a url or an input File object") + } +}; + +/** + * Converts a DataFrame or Series to Excel Sheet. + * @param df DataFrame or Series to be converted to JSON. + * @param options Configuration object. Supported options: + * - `sheetName`: The sheet name to be written to. Defaults to `'Sheet1'`. + * - `file`: The file to be written to. Defaults to `'./output.xlsx'`. + * @example + * ``` + * import { toExcel } from "danfojs-node" + * const df = new DataFrame([[1, 2, 3], [4, 5, 6]]) + * toExcel(df, { + * file: "./data/sample.xlsx", + * sheetName: "MySheet", + * }) + * ``` + */ +const $toExcel = (df: NDframe | DataFrame | Series, options?: ExcelOutputOptionsBrowser) => { + let { fileName, sheetName } = { fileName: "./output.xlsx", sheetName: "Sheet1", ...options } + + if (!(fileName.endsWith(".xlsx"))) { + fileName = fileName + ".xlsx" + } + let data; + + if (df.$isSeries) { + const row = df.values as ArrayType1D + const col = df.columns + data = [col, ...(row.map(x => [x]))] + } else { + const row = df.values as ArrayType2D + const cols = df.columns + data = [cols, ...row] + } + + const worksheet = XLSX.utils.aoa_to_sheet(data); + const wb = XLSX.utils.book_new(); + XLSX.utils.book_append_sheet(wb, worksheet, sheetName); + XLSX.writeFile(wb, `${fileName}`) +}; + +export { + $readExcel, + $toExcel +} \ No newline at end of file diff --git a/src/danfojs-base/io/browser/io.json.ts b/src/danfojs-base/io/browser/io.json.ts new file mode 100644 index 00000000..131140c0 --- /dev/null +++ b/src/danfojs-base/io/browser/io.json.ts @@ -0,0 +1,187 @@ +/** +* @license +* Copyright 2022 JsData. All rights reserved. +* +* This source code is licensed under the MIT license found in the +* LICENSE file in the root directory of this source tree. + +* Unless required by applicable law or agreed to in writing, software +* distributed under the License is distributed on an "AS IS" BASIS, +* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +* See the License for the specific language governing permissions and +* limitations under the License. +* ========================================================================== +*/ +import { + ArrayType1D, + ArrayType2D, + JsonInputOptionsBrowser, + JsonOutputOptionsBrowser +} from '../../shared/types' +import { DataFrame, NDframe, Series } from '../../' + +/** + * Reads a JSON file from local or remote location into a DataFrame. + * @param fileName URL or local file path to JSON file. + * @param options Configuration object. Supported options: + * - `method`: The HTTP method to use. Defaults to `'GET'`. + * - `headers`: Additional headers to send with the request. Supports the `node-fetch` [HeadersInit] + * @example + * ``` + * import { readJSON } from "danfojs-node" + * const df = await readJSON("https://raw.githubusercontent.com/test.json") + * ``` + * @example + * ``` + * import { readJSON } from "danfojs-node" + * const df = await readJSON("https://raw.githubusercontent.com/test.json", { + * headers: { + * Accept: "text/json", + * Authorization: "Bearer YWRtaW46YWRtaW4=" + * } + * }) + * ``` + * @example + * ``` + * import { readJSON } from "danfojs-node" + * const df = await readJSON("./data/sample.json") + * ``` + */ +const $readJSON = async (file: any, options?: JsonInputOptionsBrowser) => { + const { method, headers, frameConfig } = { method: "GET", headers: {}, frameConfig: {}, ...options } + + if (typeof file === "string" && file.startsWith("http")) { + + return new Promise(resolve => { + fetch(file, { method, headers }).then(response => { + if (response.status !== 200) { + throw new Error(`Failed to load ${file}`) + } + response.json().then(json => { + resolve(new DataFrame(json, frameConfig)); + }); + }).catch((err) => { + throw new Error(err) + }) + }) + + } else if (file instanceof File) { + return new Promise(resolve => { + const reader = new FileReader(); + reader.readAsText(file); + reader.onload = (event) => { + const jsonObj = JSON.parse(event?.target?.result as string); + resolve(new DataFrame(jsonObj, frameConfig)); + } + }) + } else { + throw new Error("ParamError: File not supported. file must be a url or an input File object") + } +}; + + +/** + * Converts a DataFrame or Series to JSON. + * @param df DataFrame or Series to be converted to JSON. + * @param options Configuration object. Supported options: + * - `fileName`: The file path to write the JSON to. If not specified, the JSON object is returned. + * - `format`: The format of the JSON. Defaults to `'column'`. E.g for using `column` format: + * ``` + * [{ "a": 1, "b": 2, "c": 3, "d": 4 }, + * { "a": 5, "b": 6, "c": 7, "d": 8 }] + * ``` + * and `row` format: + * ``` + * { "a": [1, 5, 9], + * "b": [2, 6, 10] + * } + * ``` + * @example + * ``` + * import { toJSON } from "danfojs-node" + * const df = new DataFrame([[1, 2, 3], [4, 5, 6]]) + * const json = toJSON(df) + * ``` + * @example + * ``` + * import { toJSON } from "danfojs-node" + * const df = new DataFrame([[1, 2, 3], [4, 5, 6]]) + * toJSON(df, { + * fileName: "./data/sample.json", + * format: "row" + * }) + * ``` + */ +const $toJSON = (df: NDframe | DataFrame | Series, options?: JsonOutputOptionsBrowser): object | void => { + let { fileName, format, download } = { fileName: "output.json", download: false, format: "column", ...options } + + if (df.$isSeries) { + const obj: { [key: string]: ArrayType1D } = {}; + obj[df.columns[0]] = df.values as ArrayType1D; + + if (download) { + if (!fileName.endsWith(".json")) { + fileName = fileName + ".json" + } + $downloadFileInBrowser(obj, fileName) + } else { + return obj + } + + } else { + if (format === "row") { + const obj: { [key: string]: ArrayType1D } = {}; + for (let i = 0; i < df.columns.length; i++) { + obj[df.columns[i]] = (df as DataFrame).column(df.columns[i]).values as ArrayType1D; + } + if (download) { + if (!(fileName.endsWith(".json"))) { + fileName = fileName + ".json" + } + + $downloadFileInBrowser(obj, fileName) + } else { + return obj + } + } else { + const values = df.values as ArrayType2D + const header = df.columns + const jsonArr: any = []; + + values.forEach((val) => { + const obj: any = {}; + header.forEach((h, i) => { + obj[h] = val[i] + }); + jsonArr.push(obj); + }); + if (download) { + if (!fileName.endsWith(".json")) { + fileName = fileName + ".json" + } + $downloadFileInBrowser(jsonArr, fileName) + } else { + return jsonArr + } + } + } +}; + +/** + * Internal function to download a JSON file in the browser. + * @param content A string of JSON file contents + * @param fileName The name of the file to be downloaded + */ +const $downloadFileInBrowser = (content: any, fileName: string) => { + var hiddenElement = document.createElement('a'); + hiddenElement.href = 'data:text/json;charset=utf-8,' + encodeURI(JSON.stringify(content)); + hiddenElement.target = '_blank'; + hiddenElement.download = fileName; + hiddenElement.click(); +} + + +export { + $readJSON, + $toJSON +} \ No newline at end of file diff --git a/src/danfojs-base/io/node/index.ts b/src/danfojs-base/io/node/index.ts new file mode 100644 index 00000000..3f5eb6ce --- /dev/null +++ b/src/danfojs-base/io/node/index.ts @@ -0,0 +1,40 @@ +/** +* @license +* Copyright 2022 JsData. All rights reserved. +* +* This source code is licensed under the MIT license found in the +* LICENSE file in the root directory of this source tree. + +* Unless required by applicable law or agreed to in writing, software +* distributed under the License is distributed on an "AS IS" BASIS, +* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +* See the License for the specific language governing permissions and +* limitations under the License. +* ========================================================================== +*/ +import { + $readCSV, + $streamCSV, + $toCSV, + $openCsvInputStream, + $writeCsvOutputStream, +} from "./io.csv" +import { + $readJSON, + $toJSON, + $streamJSON +} from "./io.json" +import { $readExcel, $toExcel } from "./io.excel" + +export { + $readCSV as readCSVNode, + $streamCSV as streamCSVNode, + $toCSV as toCSVNode, + $openCsvInputStream as openCsvInputStreamNode, + $writeCsvOutputStream as writeCsvOutputStreamNode, + $readJSON as readJSONNode, + $toJSON as toJSONNode, + $streamJSON as streamJSONNode, + $readExcel as readExcelNode, + $toExcel as toExcelNode, +} \ No newline at end of file diff --git a/src/danfojs-base/io/node/io.csv.ts b/src/danfojs-base/io/node/io.csv.ts new file mode 100644 index 00000000..1de24619 --- /dev/null +++ b/src/danfojs-base/io/node/io.csv.ts @@ -0,0 +1,356 @@ +/** +* @license +* Copyright 2022 JsData. All rights reserved. +* +* This source code is licensed under the MIT license found in the +* LICENSE file in the root directory of this source tree. + +* Unless required by applicable law or agreed to in writing, software +* distributed under the License is distributed on an "AS IS" BASIS, +* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +* See the License for the specific language governing permissions and +* limitations under the License. +* ========================================================================== +*/ +import { CsvInputOptionsNode, CsvOutputOptionsNode, ArrayType2D } from "../../shared/types" +import { DataFrame, NDframe, Series } from '../../' +import request from "request" +import Papa from 'papaparse' +import stream from "stream" +import fs from 'fs' + +/** + * Reads a CSV file from local or remote location into a DataFrame. + * @param filePath URL or local file path to CSV file. `readCSV` uses PapaParse to parse the CSV file, + * hence all PapaParse options are supported. + * @param options Configuration object. Supports all Papaparse parse config options. + * @returns DataFrame containing the parsed CSV file. + * @example + * ``` + * import { readCSV } from "danfojs-node" + * const df = await readCSV("https://raw.githubusercontent.com/test.csv") + * ``` + * @example + * ``` + * import { readCSV } from "danfojs-node" + * const df = await readCSV("https://raw.githubusercontent.com/test.csv", { + * delimiter: ",", + * headers: { + * Accept: "text/csv", + * Authorization: "Bearer YWRtaW46YWRtaW4=" + * } + * }) + * ``` + * @example + * ``` + * import { readCSV } from "danfojs-node" + * const df = await readCSV("./data/sample.csv") + * ``` + */ +const $readCSV = async (filePath: string, options?: CsvInputOptionsNode): Promise => { + const frameConfig = options?.frameConfig || {} + + if (filePath.startsWith("http") || filePath.startsWith("https")) { + return new Promise(resolve => { + const optionsWithDefaults = { + header: true, + ...options, + } + + const dataStream = request.get(filePath); + const parseStream: any = Papa.parse(Papa.NODE_STREAM_INPUT, optionsWithDefaults); + dataStream.pipe(parseStream); + + const data: any = []; + parseStream.on("data", (chunk: any) => { + data.push(chunk); + }); + + parseStream.on("finish", () => { + resolve(new DataFrame(data, frameConfig)); + }); + }); + + } else { + return new Promise(resolve => { + const fileStream = fs.createReadStream(filePath) + Papa.parse(fileStream, { + header: true, + ...options, + complete: results => { + const df = new DataFrame(results.data, frameConfig); + resolve(df); + } + }); + }); + } +}; + +/** + * Streams a CSV file from local or remote location in chunks. Intermediate chunks is passed as a DataFrame to the callback function. + * @param filePath URL or local file path to CSV file. `readCSV` uses PapaParse to parse the CSV file, + * hence all PapaParse options are supported. + * @param callback Callback function to be called once the specifed rows are parsed into DataFrame. + * @param options Configuration object. Supports all Papaparse parse config options. + * @example + * ``` + * import { streamCSV } from "danfojs-node" + * streamCSV("https://raw.githubusercontent.com/test.csv", (dfRow) => { + * const dfModified = dfRow["Names"].map((name) => name.split(",")[0]) + * return dfModified + * }) + * ``` + */ +const $streamCSV = async (filePath: string, callback: (df: DataFrame) => void, options?: CsvInputOptionsNode): Promise => { + const frameConfig = options?.frameConfig || {} + + if (filePath.startsWith("http") || filePath.startsWith("https")) { + const optionsWithDefaults = { + header: true, + ...options, + } + return new Promise(resolve => { + let count = -1 + const dataStream = request.get(filePath); + const parseStream: any = Papa.parse(Papa.NODE_STREAM_INPUT, optionsWithDefaults); + dataStream.pipe(parseStream); + + parseStream.on("data", (chunk: any) => { + const df = new DataFrame([chunk], { ...frameConfig, index: [count++], }); + callback(df); + }); + + parseStream.on("finish", () => { + resolve(null); + }); + + }); + } else { + const fileStream = fs.createReadStream(filePath) + + return new Promise(resolve => { + let count = -1 + Papa.parse(fileStream, { + header: true, + ...options, + step: results => { + const df = new DataFrame([results.data], { ...frameConfig, index: [count++] }); + callback(df); + }, + complete: () => resolve(null) + }); + }); + } +}; + + +/** + * Converts a DataFrame or Series to CSV. + * @param df DataFrame or Series to be converted to CSV. + * @param options Configuration object. Supports the following options: + * - `filePath`: Local file path to write the CSV file. If not specified, the CSV will be returned as a string. + * - `header`: Boolean indicating whether to include a header row in the CSV file. + * - `sep`: Character to be used as a separator in the CSV file. + * @example + * ``` + * import { toCSV } from "danfojs-node" + * const df = new DataFrame([[1, 2, 3], [4, 5, 6]]) + * const csv = toCSV(df) + * ``` + * @example + * ``` + * import { toCSV } from "danfojs-node" + * const df = new DataFrame([[1, 2, 3], [4, 5, 6]]) + * toCSV(df, { + * filePath: "./data/sample.csv", + * header: true, + * sep: "+" + * }) + * ``` + */ +const $toCSV = (df: NDframe | DataFrame | Series, options?: CsvOutputOptionsNode): string | void => { + let { filePath, sep, header } = { sep: ",", header: true, filePath: undefined, ...options } + + if (df.$isSeries) { + const csv = df.values.join(sep); + + if (filePath !== undefined) { + if (!(filePath.endsWith(".csv"))) { + filePath = filePath + ".csv" + } + fs.writeFileSync(filePath, csv, "utf8") + } else { + return csv; + } + } else { + const rows = df.values as ArrayType2D + let csvStr = header === true ? `${df.columns.join(sep)}\n` : "" + + for (let i = 0; i < rows.length; i++) { + const row = `${rows[i].join(sep)}\n`; + csvStr += row; + } + + if (filePath !== undefined) { + if (!(filePath.endsWith(".csv"))) { + filePath = filePath + ".csv" + } + fs.writeFileSync(filePath, csvStr, "utf8") + } else { + return csvStr; + } + } +}; + + +/** + * Opens a CSV file from local or remote location as a Stream. Intermediate row is returned as a DataFrame object. + * @param filePath URL or local file path to CSV file. + * @param options Configuration object. Supports all Papaparse config options. + * @example + * ``` + * import { openCsvInputStream } from "danfojs-node" + * const csvStream = openCsvInputStream("./data/sample.csv") + * ``` + */ +const $openCsvInputStream = (filePath: string, options: CsvInputOptionsNode) => { + const { header } = { header: true, ...options } + let isFirstChunk = true + let ndFrameColumnNames: any = [] + + const csvInputStream = new stream.Readable({ objectMode: true }); + csvInputStream._read = () => { }; + + if (filePath.startsWith("http") || filePath.startsWith("https")) { + const dataStream = request.get(filePath); + const parseStream: any = Papa.parse(Papa.NODE_STREAM_INPUT, { header, ...options }); + dataStream.pipe(parseStream); + let count = -1 + + parseStream.on("data", (chunk: any) => { + if (isFirstChunk) { + if (header === true) { + ndFrameColumnNames = Object.keys(chunk) + } else { + ndFrameColumnNames = chunk + } + isFirstChunk = false + return + } + + const df = new DataFrame([Object.values(chunk)], { + columns: ndFrameColumnNames, + index: [count++] + }) + csvInputStream.push(df); + }); + + parseStream.on("finish", () => { + csvInputStream.push(null); + return (null); + }); + + return csvInputStream; + } else { + const fileStream = fs.createReadStream(filePath) + let count = -1 + Papa.parse(fileStream, { + ...{ header, ...options }, + step: results => { + if (isFirstChunk) { + if (header === true) { + ndFrameColumnNames = results.meta.fields || [] + } else { + ndFrameColumnNames = results.data + } + isFirstChunk = false + return + } + + const df = new DataFrame([results.data], { + columns: ndFrameColumnNames, + index: [count++] + }) + + csvInputStream.push(df); + }, + complete: (result: any) => { + csvInputStream.push(null); + return null + }, + error: (err) => { + csvInputStream.emit("error", err); + } + }); + + return csvInputStream; + } +}; + + +/** + * Writes a file stream to local storage. Stream objects must be a Series or DataFrame. + * @param filePath URL or local file path to write to. + * @param options Configuration object. Supports all `toCSV` options. + * @example + * ``` + * import { openCsvInputStream, + * writeCsvOutputStream, + * convertFunctionTotransformer } from "danfojs-node" + * + * const csvStream = openCsvInputStream("./data/sample.csv") + * const outStream = writeCsvOutputStream("./data/sampleOut.csv") + * + * const transformer = (dfRow) => { + * const dfModified = dfRow["Names"].map((name) => name.split(",")[0]) + * return dfModified + * } + * csvStream.pipe(convertFunctionTotransformer(transformer)).pipe(outStream) + * ``` + */ +const $writeCsvOutputStream = (filePath: string, options: CsvInputOptionsNode) => { + let isFirstRow = true + const fileOutputStream = fs.createWriteStream(filePath) + const csvOutputStream = new stream.Writable({ objectMode: true }) + + csvOutputStream._write = (chunk: DataFrame | Series, encoding, callback) => { + + if (chunk instanceof DataFrame) { + + if (isFirstRow) { + isFirstRow = false + fileOutputStream.write($toCSV(chunk, { header: true, ...options })); + callback(); + } else { + fileOutputStream.write($toCSV(chunk, { header: false, ...options })); + callback(); + } + + } else if (chunk instanceof Series) { + + fileOutputStream.write($toCSV(chunk)); + callback(); + + } else { + csvOutputStream.emit("error", new Error("ValueError: Intermediate chunk must be either a Series or DataFrame")) + } + + } + + csvOutputStream.on("finish", () => { + fileOutputStream.end() + }) + + return csvOutputStream +} + + + + +export { + $readCSV, + $streamCSV, + $toCSV, + $writeCsvOutputStream, + $openCsvInputStream, +} \ No newline at end of file diff --git a/src/danfojs-base/io/node/io.excel.ts b/src/danfojs-base/io/node/io.excel.ts new file mode 100644 index 00000000..d4811949 --- /dev/null +++ b/src/danfojs-base/io/node/io.excel.ts @@ -0,0 +1,125 @@ +/** +* @license +* Copyright 2022 JsData. All rights reserved. +* +* This source code is licensed under the MIT license found in the +* LICENSE file in the root directory of this source tree. + +* Unless required by applicable law or agreed to in writing, software +* distributed under the License is distributed on an "AS IS" BASIS, +* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +* See the License for the specific language governing permissions and +* limitations under the License. +* ========================================================================== +*/ +import { ArrayType1D, ArrayType2D, ExcelInputOptionsNode, ExcelOutputOptionsNode } from "../../shared/types" +import { DataFrame, NDframe, Series } from '../../' +import fetch from "node-fetch"; +import XLSX from 'xlsx'; + +/** + * Reads a JSON file from local or remote location into a DataFrame. + * @param filePath URL or local file path to JSON file. + * @param options Configuration object. Supported options: + * - `method`: The HTTP method to use. Defaults to `'GET'`. + * - `headers`: Additional headers to send with the request. Supports the `node-fetch` [HeadersInit] + * @example + * ``` + * import { readExcel } from "danfojs-node" + * const df = await readExcel("https://raw.githubusercontent.com/test.xlsx") + * ``` + * @example + * ``` + * import { readExcel } from "danfojs-node" + * const df = await readExcel("https://raw.githubusercontent.com/test.xlsx", { + * method: "GET", + * headers: { + * Accept: "text/csv", + * Authorization: "Bearer YWRtaW46YWRtaW4=" + * } + * }) + * ``` + * @example + * ``` + * import { readExcel } from "danfojs-node" + * const df = await readExcel("./data/sample.xlsx") + * ``` + */ +const $readExcel = async (filePath: string, options: ExcelInputOptionsNode = {}) => { + const { sheet, method, headers, frameConfig } = { sheet: 0, method: "GET", headers: {}, frameConfig: {}, ...options } + + if (filePath.startsWith("http") || filePath.startsWith("https")) { + + return new Promise(resolve => { + fetch(filePath, { method, headers }).then(response => { + if (response.status !== 200) { + throw new Error(`Failed to load ${filePath}`) + } + response.arrayBuffer().then(arrBuf => { + const arrBufInt8 = new Uint8Array(arrBuf); + const workbook = XLSX.read(arrBufInt8, { type: "array" }) + const worksheet = workbook.Sheets[workbook.SheetNames[sheet]]; + const data = XLSX.utils.sheet_to_json(worksheet); + const df = new DataFrame(data, frameConfig); + resolve(df); + }); + }).catch((err) => { + throw new Error(err) + }) + }) + + } else { + return new Promise(resolve => { + const workbook = XLSX.readFile(filePath); + const worksheet = workbook.Sheets[workbook.SheetNames[sheet]]; + const data = XLSX.utils.sheet_to_json(worksheet); + const df = new DataFrame(data, frameConfig); + resolve(df); + }); + } +}; + +/** + * Converts a DataFrame or Series to Excel Sheet. + * @param df DataFrame or Series to be converted to JSON. + * @param options Configuration object. Supported options: + * - `sheetName`: The sheet name to be written to. Defaults to `'Sheet1'`. + * - `filePath`: The filePath to be written to. Defaults to `'./output.xlsx'`. + * @example + * ``` + * import { toExcel } from "danfojs-node" + * const df = new DataFrame([[1, 2, 3], [4, 5, 6]]) + * toExcel(df, { + * filePath: "./data/sample.xlsx", + * sheetName: "MySheet", + * }) + * ``` + */ +const $toExcel = (df: NDframe | DataFrame | Series, options?: ExcelOutputOptionsNode) => { + let { filePath, sheetName } = { filePath: "./output.xlsx", sheetName: "Sheet1", ...options } + + if (!(filePath.endsWith(".xlsx"))) { + filePath = filePath + ".xlsx" + } + let data; + + if (df.$isSeries) { + const row = df.values as ArrayType1D + const col = df.columns + data = [col, ...(row.map(x => [x]))] + } else { + const row = df.values as ArrayType2D + const cols = df.columns + data = [cols, ...row] + } + + const worksheet = XLSX.utils.aoa_to_sheet(data); + const wb = XLSX.utils.book_new(); + XLSX.utils.book_append_sheet(wb, worksheet, sheetName); + XLSX.writeFile(wb, `${filePath}`) +}; + +export { + $readExcel, + $toExcel +} \ No newline at end of file diff --git a/src/danfojs-base/io/node/io.json.ts b/src/danfojs-base/io/node/io.json.ts new file mode 100644 index 00000000..690974f2 --- /dev/null +++ b/src/danfojs-base/io/node/io.json.ts @@ -0,0 +1,201 @@ +import { ArrayType1D, ArrayType2D, JsonInputOptionsNode, JsonOutputOptionsNode } from '../../shared/types'; +import { streamArray } from "stream-json/streamers/StreamArray" +import { DataFrame, NDframe, Series } from '../../' +import { parser } from "stream-json" +import fetch from "node-fetch"; +import request from "request" +import fs from 'fs' + +/** + * Reads a JSON file from local or remote location into a DataFrame. + * @param filePath URL or local file path to JSON file. + * @param options Configuration object. Supported options: + * - `method`: The HTTP method to use. Defaults to `'GET'`. + * - `headers`: Additional headers to send with the request. Supports the `node-fetch` [HeadersInit] + * @example + * ``` + * import { readJSON } from "danfojs-node" + * const df = await readJSON("https://raw.githubusercontent.com/test.json") + * ``` + * @example + * ``` + * import { readJSON } from "danfojs-node" + * const df = await readJSON("https://raw.githubusercontent.com/test.json", { + * headers: { + * Accept: "text/json", + * Authorization: "Bearer YWRtaW46YWRtaW4=" + * } + * }) + * ``` + * @example + * ``` + * import { readJSON } from "danfojs-node" + * const df = await readJSON("./data/sample.json") + * ``` + */ +const $readJSON = async (filePath: string, options: JsonInputOptionsNode = {}) => { + const { method, headers, frameConfig } = { method: "GET", headers: {}, frameConfig: {}, ...options } + + if (filePath.startsWith("http") || filePath.startsWith("https")) { + + return new Promise(resolve => { + fetch(filePath, { method, headers }).then(response => { + if (response.status !== 200) { + throw new Error(`Failed to load ${filePath}`) + } + response.json().then(json => { + resolve(new DataFrame(json, frameConfig)); + }); + }).catch((err) => { + throw new Error(err) + }) + }) + + } else { + return new Promise(resolve => { + const file = fs.readFileSync(filePath, "utf8") + const df = new DataFrame(JSON.parse(file), frameConfig); + resolve(df); + }); + } +}; + +/** + * Streams a JSON file from local or remote location in chunks. Intermediate chunks is passed as a DataFrame to the callback function. + * @param filePath URL or local file path to CSV file. + * @param callback Callback function to be called once the specifed rows are parsed into DataFrame. + * @param options Configuration object. We use the `request` library for reading remote json files, + * Hence all `request` parameters such as `method`, `headers`, are supported. + * @example + * ``` + * import { streamJSON } from "danfojs-node" + * streamJSON("https://raw.githubusercontent.com/test.json", {}, (dfRow) => { + * const dfModified = dfRow["Names"].map((name) => name.split(",")[0]) + * return dfModified + * }) + * ``` + */ +const $streamJSON = async ( + filePath: string, + callback: (df: DataFrame) => void, + options?: request.RequiredUriUrl & request.CoreOptions, +) => { + const { method, headers, frameConfig } = { method: "GET", headers: {}, frameConfig: {}, ...options } + if (filePath.startsWith("http") || filePath.startsWith("https")) { + return new Promise(resolve => { + let count = -1 + const dataStream = request({ url: filePath, method, headers }) + const pipeline = dataStream.pipe(parser()).pipe(streamArray()); + pipeline.on('data', ({ value }) => { + const df = new DataFrame([value], { ...frameConfig, index: [count++] }); + callback(df); + }); + pipeline.on('end', () => resolve(null)); + + }); + } else { + return new Promise(resolve => { + let count = -1 + const fileStream = fs.createReadStream(filePath) + const pipeline = fileStream.pipe(parser()).pipe(streamArray()); + pipeline.on('data', ({ value }) => { + const df = new DataFrame([value], { ...frameConfig, index: [count++] }); + callback(df); + }); + pipeline.on('end', () => resolve(null)); + }) + } +}; + + +/** + * Converts a DataFrame or Series to JSON. + * @param df DataFrame or Series to be converted to JSON. + * @param options Configuration object. Supported options: + * - `filePath`: The file path to write the JSON to. If not specified, the JSON object is returned. + * - `format`: The format of the JSON. Defaults to `'column'`. E.g for using `column` format: + * ``` + * [{ "a": 1, "b": 2, "c": 3, "d": 4 }, + * { "a": 5, "b": 6, "c": 7, "d": 8 }] + * ``` + * and `row` format: + * ``` + * { "a": [1, 5, 9], + * "b": [2, 6, 10] + * } + * ``` + * @example + * ``` + * import { toJSON } from "danfojs-node" + * const df = new DataFrame([[1, 2, 3], [4, 5, 6]]) + * const json = toJSON(df) + * ``` + * @example + * ``` + * import { toJSON } from "danfojs-node" + * const df = new DataFrame([[1, 2, 3], [4, 5, 6]]) + * toJSON(df, { + * filePath: "./data/sample.json", + * format: "row" + * }) + * ``` + */ +const $toJSON = (df: NDframe | DataFrame | Series, options?: JsonOutputOptionsNode): object | void => { + let { filePath, format } = { filePath: undefined, format: "column", ...options } + + if (df.$isSeries) { + const obj: { [key: string]: ArrayType1D } = {}; + obj[df.columns[0]] = df.values as ArrayType1D; + if (filePath) { + if (!filePath.endsWith(".json")) { + filePath = filePath + ".json" + } + fs.writeFileSync(filePath, JSON.stringify(obj)) + } else { + return obj + } + } else { + const values = df.values as ArrayType2D + const header = df.columns + const jsonArr: any = []; + + if (format === "row") { + const obj: { [key: string]: ArrayType1D } = {}; + for (let i = 0; i < df.columns.length; i++) { + obj[df.columns[i]] = (df as DataFrame).column(df.columns[i]).values as ArrayType1D; + } + if (filePath !== undefined) { + if (!(filePath.endsWith(".json"))) { + filePath = filePath + ".json" + } + + fs.writeFileSync(filePath, JSON.stringify(obj), "utf8") + } else { + return obj + } + } else { + values.forEach((val) => { + const obj: any = {}; + header.forEach((h, i) => { + obj[h] = val[i] + }); + jsonArr.push(obj); + }); + if (filePath) { + if (!filePath.endsWith(".json")) { + filePath = filePath + ".json" + } + fs.writeFileSync(filePath, JSON.stringify(jsonArr)) + } else { + return jsonArr + } + } + } +}; + + +export { + $readJSON, + $toJSON, + $streamJSON +} \ No newline at end of file diff --git a/src/danfojs-base/package.json b/src/danfojs-base/package.json new file mode 100644 index 00000000..6afeeece --- /dev/null +++ b/src/danfojs-base/package.json @@ -0,0 +1,62 @@ +{ + "name": "danfojs-base", + "version": "1.0.0", + "description": "Base package used in danfojs-node and danfojs-browser", + "main": "index.ts", + "scripts": { + "test": "echo \"Error: no test specified\" && exit 1" + }, + "author": "", + "license": "ISC", + "dependencies": { + "@tensorflow/tfjs": "3.6.0", + "@tensorflow/tfjs-node": "3.6.1", + "mathjs": "^10.0.0", + "papaparse": "^5.3.1", + "request": "^2.88.2", + "stream-json": "^1.7.3", + "table": "6.7.1", + "xlsx": "^0.17.2", + "seedrandom": "^2.4.3", + "plotly.js-dist-min": "^2.8.0", + "node-fetch": "^2.6.1" + }, + "devDependencies": { + "@types/papaparse": "^5.3.1", + "@types/plotly.js-dist-min": "^2.3.0", + "dotenv": "^10.0.0", + "@babel/cli": "^7.10.5", + "@babel/core": "^7.10.5", + "@babel/plugin-transform-runtime": "^7.12.10", + "@babel/preset-env": "^7.10.4", + "@types/chai": "^4.2.19", + "@types/mocha": "^8.2.2", + "@types/node": "^15.12.5", + "@types/request": "^2.48.7", + "@types/stream-json": "^1.7.1", + "@types/table": "^6.3.2", + "@typescript-eslint/eslint-plugin": "^4.30.0", + "@typescript-eslint/parser": "^4.30.0", + "awesome-typescript-loader": "^5.2.1", + "babel-loader": "^8.1.0", + "browserify": "16.5.2", + "chai": "^4.2.0", + "coveralls": "^3.1.0", + "eslint": "^7.1.0", + "ify-loader": "^1.1.0", + "install-peers": "^1.0.3", + "karma": "5.2.3", + "karma-browserify": "7.0.0", + "karma-chai": "^0.1.0", + "karma-chrome-launcher": "^3.1.0", + "karma-mocha": "^2.0.1", + "mocha": "^8.3.0", + "nyc": "^15.1.0", + "source-map-loader": "^3.0.0", + "ts-loader": "^9.2.6", + "typescript": "^4.4.2", + "webpack": "5.21.2", + "webpack-cli": "4.5.0", + "yarn": "^1.22.10" + } +} \ No newline at end of file diff --git a/src/danfojs-base/plotting/index.ts b/src/danfojs-base/plotting/index.ts new file mode 100644 index 00000000..ef9db66b --- /dev/null +++ b/src/danfojs-base/plotting/index.ts @@ -0,0 +1,138 @@ +/** +* @license +* Copyright 2022 JsData. All rights reserved. +* +* This source code is licensed under the MIT license found in the +* LICENSE file in the root directory of this source tree. + +* Unless required by applicable law or agreed to in writing, software +* distributed under the License is distributed on an "AS IS" BASIS, +* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +* See the License for the specific language governing permissions and +* limitations under the License. +* ========================================================================== +*/ +import { + linePlot, + barPlot, + scatterPlot, + histPlot, + piePlot, + boxPlot, + violinPlot, + tablePlot, +} from "./plotly/index"; +import Series from "../core/series"; +import DataFrame from "../core/frame"; +import { PlotConfigObject, IPlotlyLib } from "../shared/types" + +let Plotly: IPlotlyLib; + +if (typeof window !== "undefined") { + //check if in browser environment and require "plotly.js-dist-min" module + Plotly = require("plotly.js-dist-min") as IPlotlyLib; + +} + +class PlotlyLib implements IPlotlyLib { + divId: string; + ndframe: DataFrame | Series; + + constructor(ndframe: DataFrame | Series, divId: string) { + this.ndframe = ndframe; + this.divId = divId; + } + + private getPlotConfig(plotConfig?: PlotConfigObject) { + const _plotConfig = { + config: plotConfig && plotConfig.config ? plotConfig.config : {}, + layout: plotConfig && plotConfig.layout ? plotConfig.layout : {} + }; + return _plotConfig; + } + /** + * Plot Series or DataFrame as lines. + * Uses Plotly library as backend, so supports Plotly's configuration parameters + * @param plotConfig configuration options for making Plots, supports Plotly.js Config and Layout parameters. + */ + line(plotConfig?: PlotConfigObject) { + const _plotConfig = this.getPlotConfig(plotConfig); + linePlot(this.ndframe, this.divId, _plotConfig, Plotly); + } + + /** + * Plot Series or DataFrame as bars. + * Uses Plotly library as backend, so supports Plotly's configuration parameters + * @param plotConfig configuration options for making Plots, supports Plotly.js Config and Layout parameters. + */ + bar(plotConfig?: PlotConfigObject) { + const _plotConfig = this.getPlotConfig(plotConfig); + barPlot(this.ndframe, this.divId, _plotConfig, Plotly); + } + + /** + * Plot Series or DataFrame as scatter. + * Uses Plotly library as backend, so supports Plotly's configuration parameters + * @param plotConfig configuration options for making Plots, supports Plotly.js Config and Layout parameters. + */ + scatter(plotConfig?: PlotConfigObject) { + const _plotConfig = this.getPlotConfig(plotConfig); + scatterPlot(this.ndframe, this.divId, _plotConfig, Plotly); + } + + /** + * Plot Series or DataFrame as histogram. + * Uses Plotly library as backend, so supports Plotly's configuration parameters + * @param plotConfig configuration options for making Plots, supports Plotly.js Config and Layout parameters. + */ + hist(plotConfig?: PlotConfigObject) { + const _plotConfig = this.getPlotConfig(plotConfig); + histPlot(this.ndframe, this.divId, _plotConfig, Plotly); + } + + /** + * Plot Series or DataFrame as pie. + * Uses Plotly library as backend, so supports Plotly's configuration parameters + * @param plotConfig configuration options for making Plots, supports Plotly.js Config and Layout parameters. + */ + pie(plotConfig?: PlotConfigObject) { + const _plotConfig = this.getPlotConfig(plotConfig); + piePlot(this.ndframe, this.divId, _plotConfig, Plotly); + } + + /** + * Plot Series or DataFrame as boxplot. + * Uses Plotly library as backend, so supports Plotly's configuration parameters + * @param plotConfig configuration options for making Plots, supports Plotly.js Config and Layout parameters. + */ + box(plotConfig?: PlotConfigObject) { + const _plotConfig = this.getPlotConfig(plotConfig); + boxPlot(this.ndframe, this.divId, _plotConfig, Plotly); + } + + /** + * Plot Series or DataFrame as violinplot. + * Uses Plotly library as backend, so supports Plotly's configuration parameters + * @param plotConfig configuration options for making Plots, supports Plotly.js Config and Layout parameters. + */ + violin(plotConfig?: PlotConfigObject) { + const _plotConfig = this.getPlotConfig(plotConfig); + violinPlot(this.ndframe, this.divId, _plotConfig, Plotly); + } + + /** + * Plot Series or DataFrame as table. + * Uses Plotly library as backend, so supports Plotly's configuration parameters + * @param plotConfig configuration options for making Plots, supports Plotly.js Config and Layout parameters. + */ + table(plotConfig?: PlotConfigObject) { + const _plotConfig = this.getPlotConfig(plotConfig); + tablePlot(this.ndframe, this.divId, _plotConfig, Plotly); + } + +} + + +export { + PlotlyLib +} diff --git a/src/danfojs-base/plotting/plotly/bar.ts b/src/danfojs-base/plotting/plotly/bar.ts new file mode 100644 index 00000000..415ffba6 --- /dev/null +++ b/src/danfojs-base/plotting/plotly/bar.ts @@ -0,0 +1,142 @@ +/** +* @license +* Copyright 2022 JsData. All rights reserved. +* +* This source code is licensed under the MIT license found in the +* LICENSE file in the root directory of this source tree. + +* Unless required by applicable law or agreed to in writing, software +* distributed under the License is distributed on an "AS IS" BASIS, +* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +* See the License for the specific language governing permissions and +* limitations under the License. +* ========================================================================== +*/ +import Series from "../../core/series"; +import DataFrame from "../../core/frame"; +import { Data } from "plotly.js-dist-min" +import { InternalPlotConfigObject } from "../../shared/types" +import { checkIfColsExist, throwErrorOnWrongColName } from "./utils" + + +/** +* Plot Series or DataFrame as bar. +* Uses the Plotly as backend, so supoorts Plotly's configuration parameters, +* Line plot supports different types of parameters, and the behavior will depend on data specified. +* The precedence of columns to plot is: (x and y => x => y => columns). +* @param ndframe Series or DataFrame to plot +* @param divId HTML div id to plot in. +* @param plotConfig configuration options for making Plots, supports Plotly.js Config and Layout parameters. +*/ +export const barPlot = (ndframe: DataFrame | Series, divId: string, plotConfig: InternalPlotConfigObject, Plotly: any) => { + const config = plotConfig["config"] + const layout = plotConfig["layout"] + + if (ndframe instanceof Series) { + let trace: Data = { + x: ndframe.index as any, + y: ndframe.values as any, + type: 'bar', + }; + + Plotly.newPlot(divId, [trace], layout, config); + + } else { + + if (config["x"] && config["y"]) { + //Plotting two columns against each other, when user specifies x and y column names in configuration + throwErrorOnWrongColName(ndframe, config["x"]); + throwErrorOnWrongColName(ndframe, config["y"]); + + const x = ndframe[config.x].values; + const y = ndframe[config.y].values; + + const trace: Data = { + x, + y, + type: 'bar', + }; + + const _layout = { + xaxis: { + title: config.x, + }, + yaxis: { + title: config.y, + }, + ...layout, + }; + + Plotly.newPlot(divId, [trace], _layout, config); + + } else if (config["x"] || config["y"]) { + //plot single column specified in either of param [x | y] against index + if (config["x"]) { + throwErrorOnWrongColName(ndframe, config.x); + + const x = ndframe[config.x].values; + const y = ndframe.index; + + const trace: Data = { + x, + y, + type: 'bar', + }; + const _layout = { + xaxis: { + title: config.x, + }, + yaxis: { + title: "Index", + }, + ...layout, + }; + + Plotly.newPlot(divId, [trace], _layout, config); + } + + if (config["y"]) { + throwErrorOnWrongColName(ndframe, config.y); + + const x = ndframe.index + const y = ndframe[config.y].values; + + const trace: Data = { + x, + y, + type: 'bar', + }; + const _layout = { + xaxis: { + title: "Index", + }, + yaxis: { + title: config.y, + }, + ...layout, + }; + + Plotly.newPlot(divId, [trace], _layout, config); + } + + } else { + //plot specified columns in config param against index + // if columns is not specified in config, then plot all columns + const cols = config["columns"] ? checkIfColsExist(ndframe, config['columns']) : ndframe.columns; + + const traces: Data[] = []; + cols.forEach((col) => { + const x = ndframe.index; + const y = (ndframe as DataFrame)[col].values; + + const trace: Data = { x, y, name: col, type: 'bar' }; + traces.push(trace); + }); + + Plotly.newPlot(divId, traces, layout, config); + + } + + } + +} diff --git a/src/danfojs-base/plotting/plotly/box.ts b/src/danfojs-base/plotting/plotly/box.ts new file mode 100644 index 00000000..6c8e517b --- /dev/null +++ b/src/danfojs-base/plotting/plotly/box.ts @@ -0,0 +1,143 @@ +/** +* @license +* Copyright 2022 JsData. All rights reserved. +* +* This source code is licensed under the MIT license found in the +* LICENSE file in the root directory of this source tree. + +* Unless required by applicable law or agreed to in writing, software +* distributed under the License is distributed on an "AS IS" BASIS, +* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +* See the License for the specific language governing permissions and +* limitations under the License. +* ========================================================================== +*/ +import Series from "../../core/series"; +import DataFrame from "../../core/frame"; +import { Data } from "plotly.js-dist-min" +import { InternalPlotConfigObject } from "../../shared/types" +import { checkIfColsExist, throwErrorOnWrongColName } from "./utils" + + +/** +* Plot Series or DataFrame as box chart. +* Uses the Plotly as backend, so supoorts Plotly's configuration parameters, +* Line plot supports different types of parameters, and the behavior will depend on data specified. +* The precedence of columns to plot is: (x and y => x => y => columns). +* @param ndframe Series or DataFrame to plot +* @param divId HTML div id to plot in. +* @param plotConfig configuration options for making Plots, supports Plotly.js Config and Layout parameters. +*/ +export const boxPlot = (ndframe: DataFrame | Series, divId: string, plotConfig: InternalPlotConfigObject, Plotly: any) => { + const config = plotConfig["config"] + const layout = plotConfig["layout"] + + if (ndframe instanceof Series) { + let trace: Data = { + y: ndframe.values as any, + type: 'box', + }; + + Plotly.newPlot(divId, [trace], layout, config); + + } else { + + if (config["x"] && config["y"]) { + //Plotting two columns against each other, when user specifies x and y column names in configuration + throwErrorOnWrongColName(ndframe, config["x"]); + throwErrorOnWrongColName(ndframe, config["y"]); + + const x = ndframe[config.x].values; + const y = ndframe[config.y].values; + + const trace: Data = { + x, + y, + type: 'box', + }; + const _layout = { + xaxis: { + title: config.x, + }, + yaxis: { + title: config.y, + }, + ...layout, + }; + + Plotly.newPlot(divId, [trace], _layout, config); + + } else if (config["x"] || config["y"]) { + //plot single column specified in either of param [x | y] against index + if (config["x"]) { + throwErrorOnWrongColName(ndframe, config.x); + + const x = ndframe[config.x].values; + const y = ndframe.index; + + const trace: Data = { + x, + y, + type: 'box', + }; + const _layout = { + xaxis: { + title: config.x, + }, + yaxis: { + title: "Index", + }, + ...layout, + }; + + Plotly.newPlot(divId, [trace], _layout, config); + } + + if (config["y"]) { + throwErrorOnWrongColName(ndframe, config.y); + + const x = ndframe.index + const y = ndframe[config.y].values; + + const trace: Data = { + x, + y, + type: 'box', + }; + const _layout = { + xaxis: { + title: "Index", + }, + yaxis: { + title: config.y, + }, + ...layout, + }; + + Plotly.newPlot(divId, [trace], _layout, config); + } + + } else { + //plot specified columns in config param against index + // if columns is not specified in config, then plot all columns + const cols = config["columns"] ? checkIfColsExist(ndframe, config['columns']) : ndframe.columns; + + const traces: Data[] = []; + cols.forEach((col) => { + const y = (ndframe as DataFrame)[col].values; + + const trace: Data = { + y, + name: col, + type: 'box', + }; + traces.push(trace); + }); + + Plotly.newPlot(divId, traces, layout, config); + + } + + } + +} diff --git a/src/danfojs-base/plotting/plotly/hist.ts b/src/danfojs-base/plotting/plotly/hist.ts new file mode 100644 index 00000000..7946438c --- /dev/null +++ b/src/danfojs-base/plotting/plotly/hist.ts @@ -0,0 +1,106 @@ +/** +* @license +* Copyright 2022 JsData. All rights reserved. +* +* This source code is licensed under the MIT license found in the +* LICENSE file in the root directory of this source tree. + +* Unless required by applicable law or agreed to in writing, software +* distributed under the License is distributed on an "AS IS" BASIS, +* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +* See the License for the specific language governing permissions and +* limitations under the License. +* ========================================================================== +*/ +import Series from "../../core/series"; +import DataFrame from "../../core/frame"; +import { Data } from "plotly.js-dist-min" +import { InternalPlotConfigObject } from "../../shared/types" +import { checkIfColsExist, throwErrorOnWrongColName } from "./utils" + + +/** +* Plot Series or DataFrame as histogram. +* Uses the Plotly as backend, so supoorts Plotly's configuration parameters, +* Line plot supports different types of parameters, and the behavior will depend on data specified. +* The precedence of columns to plot is: (x and y => x => y => columns). +* @param ndframe Series or DataFrame to plot +* @param divId HTML div id to plot in. +* @param plotConfig configuration options for making Plots, supports Plotly.js Config and Layout parameters. +*/ +export const histPlot = (ndframe: DataFrame | Series, divId: string, plotConfig: InternalPlotConfigObject, Plotly: any) => { + const config = plotConfig["config"] + const layout = plotConfig["layout"] + + if (ndframe instanceof Series) { + let trace: Data = { + x: ndframe.values as any, + type: 'histogram', + }; + + Plotly.newPlot(divId, [trace], layout, config); + + } else { + + if (config["x"] || config["y"]) { + //plot single column specified in either of param [x | y] against index + if (config["x"]) { + throwErrorOnWrongColName(ndframe, config.x); + + const x = ndframe[config.x].values; + + const trace: Data = { + x, + type: 'histogram', + }; + + const _layout = { + xaxis: { + title: config.x, + }, + ...layout, + }; + + Plotly.newPlot(divId, [trace], _layout, config); + } + + if (config["y"]) { + throwErrorOnWrongColName(ndframe, config.y); + + const y = ndframe[config.y].values; + + const trace: Data = { + y, + type: 'histogram', + }; + const _layout = { + yaxis: { + title: config.y, + }, + ...layout, + }; + + Plotly.newPlot(divId, [trace], _layout, config); + } + + } else { + //plot specified columns in config param against index + // if columns is not specified in config, then plot all columns + const cols = config["columns"] ? checkIfColsExist(ndframe, config['columns']) : ndframe.columns; + + const traces: Data[] = []; + cols.forEach((col) => { + const y = ndframe.index; + const x = (ndframe as DataFrame)[col].values; + + const trace: Data = { x, y, name: col, type: 'histogram' }; + traces.push(trace); + }); + + Plotly.newPlot(divId, traces, layout, config); + + } + + } + +} diff --git a/src/danfojs-base/plotting/plotly/index.ts b/src/danfojs-base/plotting/plotly/index.ts new file mode 100644 index 00000000..67de8e15 --- /dev/null +++ b/src/danfojs-base/plotting/plotly/index.ts @@ -0,0 +1,23 @@ +/** +* @license +* Copyright 2022 JsData. All rights reserved. +* +* This source code is licensed under the MIT license found in the +* LICENSE file in the root directory of this source tree. + +* Unless required by applicable law or agreed to in writing, software +* distributed under the License is distributed on an "AS IS" BASIS, +* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +* See the License for the specific language governing permissions and +* limitations under the License. +* ========================================================================== +*/ + +export { linePlot } from "./line"; +export { barPlot } from "./bar"; +export { scatterPlot } from "./scatter"; +export { histPlot } from "./hist"; +export { piePlot } from "./pie"; +export { boxPlot } from "./box"; +export { violinPlot } from "./violin"; +export { tablePlot } from "./table"; diff --git a/src/danfojs-base/plotting/plotly/line.ts b/src/danfojs-base/plotting/plotly/line.ts new file mode 100644 index 00000000..1d238841 --- /dev/null +++ b/src/danfojs-base/plotting/plotly/line.ts @@ -0,0 +1,132 @@ +/** +* @license +* Copyright 2022 JsData. All rights reserved. +* +* This source code is licensed under the MIT license found in the +* LICENSE file in the root directory of this source tree. + +* Unless required by applicable law or agreed to in writing, software +* distributed under the License is distributed on an "AS IS" BASIS, +* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +* See the License for the specific language governing permissions and +* limitations under the License. +* ========================================================================== +*/ +import Series from "../../core/series"; +import DataFrame from "../../core/frame"; +import { Data } from "plotly.js-dist-min" +import { InternalPlotConfigObject } from "../../shared/types" +import { checkIfColsExist, throwErrorOnWrongColName } from "./utils" + + +/** +* Plot Series or DataFrame as lines. +* Uses the Plotly as backend, so supoorts Plotly's configuration parameters, +* Line plot supports different types of parameters, and the behavior will depend on data specified. +* The precedence of columns to plot is: (x and y => x => y => columns). +* @param ndframe Series or DataFrame to plot +* @param divId HTML div id to plot in. +* @param plotConfig configuration options for making Plots, supports Plotly.js Config and Layout parameters. +* @param Plotly Plotly package passed from the class. +*/ +export const linePlot = (ndframe: DataFrame | Series, divId: string, plotConfig: InternalPlotConfigObject, Plotly: any) => { + const config = plotConfig["config"] + const layout = plotConfig["layout"] + + if (ndframe instanceof Series) { + const y = ndframe.values as any; + let trace: Data = { + x: ndframe.index as any, + y, + type: 'scatter', + mode: 'lines', + }; + + Plotly.newPlot(divId, [trace], layout, config); + + } else { + + if (config["x"] && config["y"]) { + //Plotting two columns against each other, when user specifies x and y column names in configuration + throwErrorOnWrongColName(ndframe, config["x"]); + throwErrorOnWrongColName(ndframe, config["y"]); + + const x = ndframe[config.x].values; + const y = ndframe[config.y].values; + + const trace: Data = { x, y }; + const _layout = { + xaxis: { + title: config.x, + }, + yaxis: { + title: config.y, + }, + ...layout, + }; + + Plotly.newPlot(divId, [trace], _layout, config); + + } else if (config["x"] || config["y"]) { + //plot single column specified in either of param [x | y] against index + if (config["x"]) { + throwErrorOnWrongColName(ndframe, config.x); + + const x = ndframe[config.x].values; + const y = ndframe.index; + + const trace: Data = { x, y }; + const _layout = { + xaxis: { + title: config.x, + }, + yaxis: { + title: "Index", + }, + ...layout, + }; + + Plotly.newPlot(divId, [trace], _layout, config); + } + + if (config["y"]) { + throwErrorOnWrongColName(ndframe, config.y); + + const x = ndframe.index + const y = ndframe[config.y].values; + + const trace: Data = { x, y }; + const _layout = { + xaxis: { + title: "Index", + }, + yaxis: { + title: config.y, + }, + ...layout, + }; + + Plotly.newPlot(divId, [trace], _layout, config); + } + + } else { + //plot specified columns in config param against index + // if columns is not specified in config, then plot all columns + const cols = config["columns"] ? checkIfColsExist(ndframe, config['columns']) : ndframe.columns; + + const traces: Data[] = []; + cols.forEach((col) => { + const x = ndframe.index; + const y = (ndframe as DataFrame)[col].values; + + const trace: Data = { x, y, name: col }; + traces.push(trace); + }); + + Plotly.newPlot(divId, traces, layout, config); + + } + + } + +} diff --git a/src/danfojs-base/plotting/plotly/pie.ts b/src/danfojs-base/plotting/plotly/pie.ts new file mode 100644 index 00000000..0428972d --- /dev/null +++ b/src/danfojs-base/plotting/plotly/pie.ts @@ -0,0 +1,144 @@ +// @ts-nocheck + +/** +* @license +* Copyright 2022 JsData. All rights reserved. +* +* This source code is licensed under the MIT license found in the +* LICENSE file in the root directory of this source tree. + +* Unless required by applicable law or agreed to in writing, software +* distributed under the License is distributed on an "AS IS" BASIS, +* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +* See the License for the specific language governing permissions and +* limitations under the License. +* ========================================================================== +*/ +import Series from "../../core/series"; +import DataFrame from "../../core/frame"; +import { Data } from "plotly.js-dist-min" +import { InternalPlotConfigObject } from "../../shared/types" +import { checkIfColsExist } from "./utils" + + +/** +* Plot Series or DataFrame as pie chart. +* Uses the Plotly as backend, so supoorts Plotly's configuration parameters, +* Line plot supports different types of parameters, and the behavior will depend on data specified. +* @param ndframe Series or DataFrame to plot +* @param divId HTML div id to plot in. +* @param plotConfig configuration options for making Plots, supports Plotly.js Config and Layout parameters. +*/ +export const piePlot = (ndframe: DataFrame | Series, divId: string, plotConfig: InternalPlotConfigObject, Plotly: any) => { + const config = plotConfig["config"] + const layout = plotConfig["layout"] + + if (ndframe instanceof Series) { + let trace: Data = { + values: ndframe.values as any, + labels: config["labels"] || ndframe.index as any, + type: 'pie', + name: config.labels, + hoverinfo: 'label+percent+name', + automargin: true + }; + + Plotly.newPlot(divId, [trace], layout, config); + + } else { + if (config["labels"]) { + + if (!ndframe.columns.includes(config['labels'])) { + throw Error(`Column Error: ${config['labels']} not found in columns. Param "labels" name must be one of [ ${ndframe.columns}]`); + } + + if (config["values"]) { + + if (!ndframe.columns.includes(config['values'])) { + throw Error(`Column Error: ${config['values']} not found in columns. Param "values" name must be one of [ ${ndframe.columns}]`); + } + + let trace: Data = { + values: ndframe[config['values']].values as any, + labels: ndframe[config["labels"]].values as any, + type: 'pie', + name: config.labels, + hoverinfo: 'label+percent+name', + automargin: true + }; + + Plotly.newPlot(divId, [trace], layout, config); + + } else { + // if columns is not specified in config, then plot all columns + const cols = config["columns"] ? checkIfColsExist(ndframe, config['columns']) : ndframe.columns; + + if (config['rowPositions']) { + if (config['rowPositions'].length != cols.length) { + throw Error(`Lenght of rowPositions array must be equal to number of columns. Got ${config['rowPositions'].length}, expected ${cols.length - 1}`); + } + } else { + let tempArr = []; + for (let i = 0; i < cols.length - 1; i++) { + tempArr.push(0); + } + config['rowPositions'] = tempArr; + + } + + if (config['columnPositions']) { + if (config['columnPositions'].length != cols.length) { + throw Error(`Lenght of columnPositions array must be equal to number of columns. Got ${config['columnPositions'].length}, expected ${cols.length - 1}`); + } + } else { + let tempArr = []; + for (let i = 0; i < cols.length - 1; i++) { + tempArr.push(i); + } + config['columnPositions'] = tempArr; + + } + + const traces: Data[] = []; + cols.forEach((col, i) => { + const labels = (ndframe as DataFrame)[config["labels"]].values; + const values = (ndframe as DataFrame)[col].values; + + const trace: Data = { + labels, + values, + name: col, + type: 'pie', + domain: { + row: config['rowPositions'][i], + column: config['columnPositions'][i] + }, + hoverinfo: 'label+percent+name', + automargin: true, + textposition: 'outside' + }; + traces.push(trace); + }); + + const _layout = { + ...layout, + } + + if (!config["grid"]) { + //set default grid + let size = Number((ndframe.shape[1] / 2).toFixed()) + 1; + _layout["grid"] = { rows: size, columns: size }; + }else{ + _layout["grid"] = config["grid"]; + } + + Plotly.newPlot(divId, traces, _layout, config); + + } + } else { + throw new Error(`Param Error: Please provide a column name for "labels" param`) + } + + } + +} diff --git a/src/danfojs-base/plotting/plotly/scatter.ts b/src/danfojs-base/plotting/plotly/scatter.ts new file mode 100644 index 00000000..95e03262 --- /dev/null +++ b/src/danfojs-base/plotting/plotly/scatter.ts @@ -0,0 +1,151 @@ +/** +* @license +* Copyright 2022 JsData. All rights reserved. +* +* This source code is licensed under the MIT license found in the +* LICENSE file in the root directory of this source tree. + +* Unless required by applicable law or agreed to in writing, software +* distributed under the License is distributed on an "AS IS" BASIS, +* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +* See the License for the specific language governing permissions and +* limitations under the License. +* ========================================================================== +*/ +import Series from "../../core/series"; +import DataFrame from "../../core/frame"; +import { Data } from "plotly.js-dist-min" +import { InternalPlotConfigObject } from "../../shared/types" +import { checkIfColsExist, throwErrorOnWrongColName } from "./utils" + +/** +* Plot Series or DataFrame as scatter points. +* Uses the Plotly as backend, so supoorts Plotly's configuration parameters, +* Line plot supports different types of parameters, and the behavior will depend on data specified. +* The precedence of columns to plot is: (x and y => x => y => columns). +* @param ndframe Series or DataFrame to plot +* @param divId HTML div id to plot in. +* @param plotConfig configuration options for making Plots, supports Plotly.js Config and Layout parameters. +*/ +export const scatterPlot = (ndframe: DataFrame | Series, divId: string, plotConfig: InternalPlotConfigObject, Plotly: any) => { + const config = plotConfig["config"] + const layout = plotConfig["layout"] + + if (ndframe instanceof Series) { + const y = ndframe.values as any; + let trace: Data = { + x: ndframe.index as any, + y, + type: 'scatter', + mode: 'markers', + }; + + Plotly.newPlot(divId, [trace], layout, config); + + } else { + + if (config["x"] && config["y"]) { + //Plotting two columns against each other, when user specifies x and y column names in configuration + throwErrorOnWrongColName(ndframe, config["x"]); + throwErrorOnWrongColName(ndframe, config["y"]); + + const x = ndframe[config.x].values; + const y = ndframe[config.y].values; + + const trace: Data = { + x, + y, + type: 'scatter', + mode: 'markers', + }; + const _layout = { + xaxis: { + title: config.x, + }, + yaxis: { + title: config.y, + }, + ...layout, + }; + + Plotly.newPlot(divId, [trace], _layout, config); + + } else if (config["x"] || config["y"]) { + //plot single column specified in either of param [x | y] against index + if (config["x"]) { + throwErrorOnWrongColName(ndframe, config.x); + + const x = ndframe[config.x].values; + const y = ndframe.index; + + const trace: Data = { + x, + y, + type: 'scatter', + mode: 'markers', + }; + const _layout = { + xaxis: { + title: config.x, + }, + yaxis: { + title: "Index", + }, + ...layout, + }; + + Plotly.newPlot(divId, [trace], _layout, config); + } + + if (config["y"]) { + throwErrorOnWrongColName(ndframe, config.y); + + const x = ndframe.index + const y = ndframe[config.y].values; + + const trace: Data = { + x, + y, + type: 'scatter', + mode: 'markers', + }; + const _layout = { + xaxis: { + title: "Index", + }, + yaxis: { + title: config.y, + }, + ...layout, + }; + + Plotly.newPlot(divId, [trace], _layout, config); + } + + } else { + //plot specified columns in config param against index + // if columns is not specified in config, then plot all columns + const cols = config["columns"] ? checkIfColsExist(ndframe, config['columns']) : ndframe.columns; + + const traces: Data[] = []; + cols.forEach((col) => { + const y = ndframe.index; + const x = (ndframe as DataFrame)[col].values; + + const trace: Data = { + x, + y, + name: col, + type: 'scatter', + mode: 'markers', + }; + traces.push(trace); + }); + + Plotly.newPlot(divId, traces, layout, config); + + } + + } + +} diff --git a/src/danfojs-base/plotting/plotly/table.ts b/src/danfojs-base/plotting/plotly/table.ts new file mode 100644 index 00000000..54d220e9 --- /dev/null +++ b/src/danfojs-base/plotting/plotly/table.ts @@ -0,0 +1,77 @@ +/** +* @license +* Copyright 2022 JsData. All rights reserved. +* +* This source code is licensed under the MIT license found in the +* LICENSE file in the root directory of this source tree. + +* Unless required by applicable law or agreed to in writing, software +* distributed under the License is distributed on an "AS IS" BASIS, +* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +* See the License for the specific language governing permissions and +* limitations under the License. +* ========================================================================== +*/ +import Series from "../../core/series"; +import DataFrame from "../../core/frame"; +import { InternalPlotConfigObject } from "../../shared/types" + + +/** +* Display Series or DataFrame as table. +* Uses the Plotly as backend, so supoorts Plotly's configuration parameters, +* @param ndframe Series or DataFrame to plot +* @param divId HTML div id to plot in. +* @param plotConfig configuration options for making Plots, supports Plotly.js Config and Layout parameters. +*/ +export const tablePlot = (ndframe: DataFrame | Series, divId: string, plotConfig: InternalPlotConfigObject, Plotly: any) => { + const config = plotConfig["config"] + const layout = plotConfig["layout"] + let header: any = {}; + let cells: any = {}; + let colsData: any[] = []; + let cols2Show: any[] = [] + + if (config['columns']) { + + config['columns'].forEach((cname) => { + if (!ndframe.columns.includes(cname)) { + throw Error(`Column Error: ${cname} not found in columns. Columns should be one of [ ${ndframe.columns} ]`); + } + + let idx = ndframe.columns.indexOf(cname); + colsData.push(ndframe.getColumnData[idx]); + }); + + cols2Show = config['columns']; + } else { + + cols2Show = ndframe.columns; + colsData = ndframe.getColumnData; + + } + + header['values'] = cols2Show.map((col) => [col]); + cells['values'] = colsData; + + if (config['tableHeaderStyle']) { + Object.keys(config['tableHeaderStyle']).forEach((param) => { + header[param] = config['tableHeaderStyle'][param]; + }); + } + + if (config['tableCellStyle']) { + Object.keys(config['tableCellStyle']).forEach((param) => { + cells[param] = config['tableCellStyle'][param]; + }); + } + + const trace = { + type: 'table', + header, + cells + }; + /* @ts-ignore */ + Plotly.newPlot(divId, [trace], layout, config); + +} diff --git a/src/danfojs-base/plotting/plotly/utils.ts b/src/danfojs-base/plotting/plotly/utils.ts new file mode 100644 index 00000000..116ff9a7 --- /dev/null +++ b/src/danfojs-base/plotting/plotly/utils.ts @@ -0,0 +1,32 @@ +/** +* @license +* Copyright 2022 JsData. All rights reserved. +* +* This source code is licensed under the MIT license found in the +* LICENSE file in the root directory of this source tree. + +* Unless required by applicable law or agreed to in writing, software +* distributed under the License is distributed on an "AS IS" BASIS, +* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +* See the License for the specific language governing permissions and +* limitations under the License. +* ========================================================================== +*/ + +import Series from "../../core/series"; +import DataFrame from "../../core/frame"; + +export const checkIfColsExist = (ndframe: DataFrame | Series, cols: string[]) => { + cols.forEach((col) => { + if (!ndframe.columns.includes(col)) { + throw Error(`Column Error: ${col} not found in columns. Columns should be one of [ ${ndframe.columns} ]`); + } + }); + return cols; +} + +export const throwErrorOnWrongColName = (ndframe: DataFrame, colName: string): void => { + if (!ndframe.columns.includes(colName)) { + throw Error(`ParamError: specified column ${colName} not found in columns`); + } +} diff --git a/src/danfojs-base/plotting/plotly/violin.ts b/src/danfojs-base/plotting/plotly/violin.ts new file mode 100644 index 00000000..c0ca5fcb --- /dev/null +++ b/src/danfojs-base/plotting/plotly/violin.ts @@ -0,0 +1,143 @@ +/** +* @license +* Copyright 2022 JsData. All rights reserved. +* +* This source code is licensed under the MIT license found in the +* LICENSE file in the root directory of this source tree. + +* Unless required by applicable law or agreed to in writing, software +* distributed under the License is distributed on an "AS IS" BASIS, +* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +* See the License for the specific language governing permissions and +* limitations under the License. +* ========================================================================== +*/ +import Series from "../../core/series"; +import DataFrame from "../../core/frame"; +import { Data } from "plotly.js-dist-min" +import { InternalPlotConfigObject } from "../../shared/types" +import { checkIfColsExist, throwErrorOnWrongColName } from "./utils" + + +/** +* Plot Series or DataFrame as violin chart. +* Uses the Plotly as backend, so supoorts Plotly's configuration parameters, +* Line plot supports different types of parameters, and the behavior will depend on data specified. +* The precedence of columns to plot is: (x and y => x => y => columns). +* @param ndframe Series or DataFrame to plot +* @param divId HTML div id to plot in. +* @param plotConfig configuration options for making Plots, supports Plotly.js Config and Layout parameters. +*/ +export const violinPlot = (ndframe: DataFrame | Series, divId: string, plotConfig: InternalPlotConfigObject, Plotly: any) => { + const config = plotConfig["config"] + const layout = plotConfig["layout"] + + if (ndframe instanceof Series) { + let trace: Data = { + y: ndframe.values as any, + type: 'violin', + }; + + Plotly.newPlot(divId, [trace], layout, config); + + } else { + + if (config["x"] && config["y"]) { + //Plotting two columns against each other, when user specifies x and y column names in configuration + throwErrorOnWrongColName(ndframe, config["x"]); + throwErrorOnWrongColName(ndframe, config["y"]); + + const x = ndframe[config.x].values; + const y = ndframe[config.y].values; + + const trace: Data = { + x, + y, + type: 'violin', + }; + const _layout = { + xaxis: { + title: config.x, + }, + yaxis: { + title: config.y, + }, + ...layout, + }; + + Plotly.newPlot(divId, [trace], _layout, config); + + } else if (config["x"] || config["y"]) { + //plot single column specified in either of param [x | y] against index + if (config["x"]) { + throwErrorOnWrongColName(ndframe, config.x); + + const x = ndframe[config.x].values; + const y = ndframe.index; + + const trace: Data = { + x, + y, + type: 'violin', + }; + const _layout = { + xaxis: { + title: config.x, + }, + yaxis: { + title: "Index", + }, + ...layout, + }; + + Plotly.newPlot(divId, [trace], _layout, config); + } + + if (config["y"]) { + throwErrorOnWrongColName(ndframe, config.y); + + const x = ndframe.index + const y = ndframe[config.y].values; + + const trace: Data = { + x, + y, + type: 'violin', + }; + const _layout = { + xaxis: { + title: "Index", + }, + yaxis: { + title: config.y, + }, + ...layout, + }; + + Plotly.newPlot(divId, [trace], _layout, config); + } + + } else { + //plot specified columns in config param against index + // if columns is not specified in config, then plot all columns + const cols = config["columns"] ? checkIfColsExist(ndframe, config['columns']) : ndframe.columns; + + const traces: Data[] = []; + cols.forEach((col) => { + const y = (ndframe as DataFrame)[col].values; + + const trace: Data = { + y, + name: col, + type: 'violin', + }; + traces.push(trace); + }); + + Plotly.newPlot(divId, traces, layout, config); + + } + + } + +} diff --git a/src/danfojs-base/plotting/vega/index.ts b/src/danfojs-base/plotting/vega/index.ts new file mode 100644 index 00000000..480765d3 --- /dev/null +++ b/src/danfojs-base/plotting/vega/index.ts @@ -0,0 +1,16 @@ +/** +* @license +* Copyright 2022 JsData. All rights reserved. +* +* This source code is licensed under the MIT license found in the +* LICENSE file in the root directory of this source tree. + +* Unless required by applicable law or agreed to in writing, software +* distributed under the License is distributed on an "AS IS" BASIS, +* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +* See the License for the specific language governing permissions and +* limitations under the License. +* ========================================================================== +*/ + +//TODO: Add support for vega library plots \ No newline at end of file diff --git a/src/danfojs-base/shared/config.ts b/src/danfojs-base/shared/config.ts new file mode 100644 index 00000000..faffab66 --- /dev/null +++ b/src/danfojs-base/shared/config.ts @@ -0,0 +1,90 @@ +/** +* @license +* Copyright 2022 JsData. All rights reserved. +* +* This source code is licensed under the MIT license found in the +* LICENSE file in the root directory of this source tree. + +* Unless required by applicable law or agreed to in writing, software +* distributed under the License is distributed on an "AS IS" BASIS, +* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +* See the License for the specific language governing permissions and +* limitations under the License. +* ========================================================================== +*/ +import { BaseUserConfig, TableUserConfig } from 'table'; +import { ConfigsType } from './types' + +/** + * Package wide configuration class + */ +export default class Configs { + tableDisplayConfig: BaseUserConfig & TableUserConfig + tableMaxRow: number; + tableMaxColInConsole: number; + dtypeTestLim: number; + lowMemoryMode: boolean; + + constructor(options: ConfigsType) { + const { + tableDisplayConfig, + tableMaxRow, + tableMaxColInConsole, + dtypeTestLim, + lowMemoryMode, + } = { + tableDisplayConfig: {}, + tableMaxRow: 10, + tableMaxColInConsole: 10, + dtypeTestLim: 20, + lowMemoryMode: false, + ...options + } + this.tableDisplayConfig = tableDisplayConfig + this.tableMaxRow = tableMaxRow // The maximum number of rows to display in console + this.tableMaxColInConsole = tableMaxColInConsole // The maximum number of columns to display in console + this.dtypeTestLim = dtypeTestLim // The number of rows to use when inferring data type + this.lowMemoryMode = lowMemoryMode // Whether to use minimal memory or not. + } + + setTableDisplayConfig(config: BaseUserConfig & TableUserConfig) { + this.tableDisplayConfig = config; + } + + get getTableDisplayConfig(): BaseUserConfig & TableUserConfig { + return this.tableDisplayConfig; + } + + setTableMaxColInConsole(val: number) { + this.tableMaxColInConsole = val; + } + + get getTableMaxColInConsole(): number { + return this.tableMaxColInConsole; + } + + setMaxRow(val: number) { + this.tableMaxRow = val; + } + + get getMaxRow(): number { + return this.tableMaxRow; + } + + get getDtypeTestLim(): number { + return this.dtypeTestLim; + } + + setDtypeTestLim(val: number) { + this.dtypeTestLim = val; + } + + get isLowMemoryMode(): boolean { + return this.lowMemoryMode; + } + + setIsLowMemoryMode(val: boolean) { + this.lowMemoryMode = val; + } +} + diff --git a/src/danfojs-base/shared/defaults.ts b/src/danfojs-base/shared/defaults.ts new file mode 100644 index 00000000..5ba467dc --- /dev/null +++ b/src/danfojs-base/shared/defaults.ts @@ -0,0 +1,26 @@ +/** +* @license +* Copyright 2022 JsData. All rights reserved. +* +* This source code is licensed under the MIT license found in the +* LICENSE file in the root directory of this source tree. + +* Unless required by applicable law or agreed to in writing, software +* distributed under the License is distributed on an "AS IS" BASIS, +* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +* See the License for the specific language governing permissions and +* limitations under the License. +* ========================================================================== +*/ + +/** + * Default config object + */ +export const BASE_CONFIG = { + tableMaxRow: 10, + tableMaxColInConsole: 10, + dtypeTestLim: 20, + lowMemoryMode: false, +} + +export const DATA_TYPES = ["float32", "int32", "string", "boolean", 'undefined']; \ No newline at end of file diff --git a/src/danfojs-base/shared/errors.ts b/src/danfojs-base/shared/errors.ts new file mode 100644 index 00000000..1d082c4c --- /dev/null +++ b/src/danfojs-base/shared/errors.ts @@ -0,0 +1,96 @@ +/** +* @license +* Copyright 2022 JsData. All rights reserved. +* +* This source code is licensed under the MIT license found in the +* LICENSE file in the root directory of this source tree. + +* Unless required by applicable law or agreed to in writing, software +* distributed under the License is distributed on an "AS IS" BASIS, +* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +* See the License for the specific language governing permissions and +* limitations under the License. +* ========================================================================== +*/ +import DataFrame from "../core/frame" +import NDframe from "../core/generic" +import { DATA_TYPES } from "./defaults" + +/** + * Package wide error throwing class + */ +class ErrorThrower { + + throwColumnNamesLengthError = (ndframe: NDframe, columns: Array): void => { + const msg = `ParamError: Column names length mismatch. You provided a column of length ${columns.length} but Ndframe columns has lenght of ${ndframe.shape[1]}` + throw new Error(msg) + } + + throwIndexLengthError = (ndframe: NDframe, index: Array): void => { + const msg = `IndexError: You provided an index of length ${index.length} but Ndframe rows has lenght of ${ndframe.shape[0]}` + throw new Error(msg) + } + + throwIndexDuplicateError = (): void => { + const msg = `IndexError: Row index must contain unique values` + throw new Error(msg) + } + + throwColumnDuplicateError = (): void => { + const msg = `ColumnIndexError: Column index must contain unique values` + throw new Error(msg) + } + + throwDtypesLengthError = (ndframe: NDframe, dtypes: Array): void => { + const msg = `DtypeError: You provided a dtype array of length ${dtypes.length} but Ndframe columns has lenght of ${ndframe.shape[1]}` + throw new Error(msg) + } + + throwDtypeNotSupportedError = (dtype: string): void => { + const msg = `DtypeError: Dtype "${dtype}" not supported. dtype must be one of "${DATA_TYPES}"` + throw new Error(msg) + } + + throwColumnLengthError = (ndframe: NDframe | DataFrame, arrLen: number): void => { + const msg = `ParamError: Column data length mismatch. You provided data with length ${arrLen} but Ndframe has column of lenght ${ndframe.shape[1]}` + throw new Error(msg) + } + + throwRowLengthError = (ndframe: NDframe, arrLen: number): void => { + const msg = `ParamError: Row data length mismatch. You provided data with length ${arrLen} but Ndframe has row of lenght ${ndframe.shape[0]}` + throw new Error(msg) + } + + throwColumnNotFoundError = (ndframe: DataFrame | NDframe): void => { + const msg = `ParamError: Column not found!. Column name must be one of ${ndframe.columns}` + throw new Error(msg) + } + + throwNotImplementedError = (): void => { + const msg = `Method not implemented` + throw new Error(msg) + } + + throwIlocRowIndexError = (): void => { + const msg = `ParamError: rows parameter must be a Array. For example: rows: [1,2] or rows: ["0:10"]` + throw new Error(msg) + } + + throwIlocColumnsIndexError = (): void => { + const msg = `ParamError: columns parameter must be a Array. For example: columns: [1,2] or columns: ["0:10"]` + throw new Error(msg) + } + + throwStringDtypeOperationError = (operation: string): void => { + const msg = `DtypeError: String data type does not support ${operation} operation` + throw new Error(msg) + } + + throwSeriesMathOpLengthError = (ndframe: NDframe, other: NDframe): void => { + const msg = `ParamError: Row length mismatch. Length of other (${other.shape[0]}), must be the same as Ndframe (${ndframe.shape[0]})` + throw new Error(msg) + } + +} + +export default new ErrorThrower() \ No newline at end of file diff --git a/src/danfojs-base/shared/tensorflowlib.ts b/src/danfojs-base/shared/tensorflowlib.ts new file mode 100644 index 00000000..47ef99ee --- /dev/null +++ b/src/danfojs-base/shared/tensorflowlib.ts @@ -0,0 +1,5 @@ + + // This file is auto-generated by prebuild.js. Do not edit! + const tf = require("@tensorflow/tfjs") +export default tf + \ No newline at end of file diff --git a/src/danfojs-base/shared/types.ts b/src/danfojs-base/shared/types.ts new file mode 100644 index 00000000..3c3e23fc --- /dev/null +++ b/src/danfojs-base/shared/types.ts @@ -0,0 +1,414 @@ +/** +* @license +* Copyright 2022 JsData. All rights reserved. +* +* This source code is licensed under the MIT license found in the +* LICENSE file in the root directory of this source tree. + +* Unless required by applicable law or agreed to in writing, software +* distributed under the License is distributed on an "AS IS" BASIS, +* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +* See the License for the specific language governing permissions and +* limitations under the License. +* ========================================================================== +*/ + +import { BaseUserConfig, TableUserConfig, } from "table" +import { Config, Layout } from "plotly.js-dist-min" +import { HeadersInit } from "node-fetch"; +import Groupby from '../aggregators/groupby'; +import { ParseConfig } from 'papaparse'; +import DataFrame from '../core/frame'; +import Series from '../core/series'; +import Str from '../core/strings'; +import Dt from '../core/datetime'; + +export type DTYPES = "float32" | "int32" | "string" | "boolean" | "undefined" + +export type ArrayType2D = Array< + number[] + | string[] + | boolean[] + | (number | string | boolean)[]> + +export type ArrayType1D = Array< + number + | string + | boolean + | (number | string | boolean)> + +//Start of Config class types +export type ConfigsType = { + tableDisplayConfig?: BaseUserConfig & TableUserConfig + tableMaxRow?: number; + tableMaxColInConsole?: number; + dtypeTestLim?: number; + lowMemoryMode?: boolean + tfInstance?: any +} +//End of Config class types + +//Start of Generic class types +export interface BaseDataOptionType { + type?: number; + index?: Array + columns?: string[] + dtypes?: Array + config?: ConfigsType; +} +export interface NdframeInputDataType { + data: any + type?: number; + index?: Array + columns?: string[] + dtypes?: Array + config?: ConfigsType; + isSeries: boolean; +} +export interface LoadArrayDataType { + data: ArrayType1D | ArrayType2D + index?: Array + columns?: string[] + dtypes?: Array +} + +export interface LoadObjectDataType { + data: object | Array + type?: number; + index?: Array + columns?: string[] + dtypes?: Array +} + +export type AxisType = { + index: Array + columns: Array +} +export interface NDframeInterface { + config?: ConfigsType; + $setDtypes(dtypes: Array, infer: boolean): void; + $setIndex(index: Array): void; + $resetIndex(): void; + $setColumnNames(columns: string[]): void + get dtypes(): Array; + get ndim(): number; + get axis(): AxisType; + get index(): Array; + get columns(): string[] + get shape(): Array; + get values(): ArrayType1D | ArrayType2D + get tensor(): any + get size(): number; + print(): void; +} +//End of Generic class types + + +//Start of Series class types +type mapFunc = (val: any, i: number) => any[] + +export type mapParam = object | mapFunc +export interface SeriesInterface extends NDframeInterface { + iloc(rows: Array | boolean[]): Series; + head(rows: number): Series + tail(rows: number): Series + sample(num: number, options?: { seed?: number }): Promise; + add(other: Series | number | Array, options?: { inplace?: boolean }): Series | void; + sub(other: Series | number | Array, options?: { inplace?: boolean }): Series | void; + mul(other: Series | number | Array, options?: { inplace?: boolean }): Series | void; + div(other: Series | number | Array, options?: { inplace?: boolean }): Series | void; + pow(other: Series | number | Array, options?: { inplace?: boolean }): Series | void; + mod(other: Series | number | Array, options?: { inplace?: boolean }): Series | void; + mean(): number + median(): number + mode(): any + min(): number + max(): number + sum(): number + count(): number + maximum(other: Series | number | Array): Series + minimum(other: Series | number | Array): Series + round(dp: number, options?: { inplace?: boolean }): Series | void + std(): number + var(): number + isNa(): Series + fillNa(value: number | string | boolean, options?: { inplace?: boolean }): Series | void + sortValues(options?: { ascending?: boolean, inplace?: boolean }): Series | void + copy(): Series + describe(): Series + resetIndex(options?: { inplace?: boolean }): Series | void + setIndex(index: Array, options?: { inplace?: boolean }): Series | void + map( + callable: mapParam, + options?: { inplace?: boolean }) + : Series | void + apply( + callable: (value: any) => any, + options?: { inplace?: boolean }): Series | void + unique(): Series + nUnique(): number + valueCounts(): Series + abs(options?: { inplace?: boolean }): Series | void + cumSum(options?: { inplace?: boolean }): Series | void + cumMin(options?: { inplace?: boolean }): Series | void + cumMax(options?: { inplace?: boolean }): Series | void + cumProd(options?: { inplace?: boolean }): Series | void + lt(other: Series | number | Array | boolean[]): Series + gt(other: Series | number | Array | boolean[]): Series + le(other: Series | number | Array | boolean[]): Series + ge(other: Series | number | Array | boolean[]): Series + ne(other: Series | number | Array | boolean[]): Series + eq(other: Series | number | Array | boolean[]): Series + replace(oldValue: string | number | boolean, newValue: string | number | boolean, options?: { inplace?: boolean }): Series | void + dropNa(options?: { inplace?: boolean }): Series | void + argSort(options?: { ascending: boolean }): Series + argMax(): number + argMin(): number + get dtype(): string + dropDuplicates(options?: { keep?: "first" | "last", inplace?: boolean }): Series | void + asType(dtype: "float32" | "int32" | "string" | "boolean", options?: { inplace?: boolean }): Series | void + get str(): Str + get dt(): Dt + append(values: string | number | boolean | Series | ArrayType1D, + index: Array | number | string, + options?: { inplace?: boolean }): Series | void + toString(): string; + and(other: any): Series + or(other: any): Series + getDummies(options?: { + prefix?: string | Array, + prefixSeparator?: string | Array, + inplace?: boolean + }): DataFrame + plot(divId: string): IPlotlyLib + toCSV(options?: CsvOutputOptionsBrowser): string | void + toJSON(options?: JsonOutputOptionsBrowser): object | void + toExcel(options?: ExcelOutputOptionsBrowser): void +} + +//Start of DataFrame class types +export interface DataFrameInterface extends NDframeInterface { + [key: string]: any + drop( + options: + { + columns?: string | Array, + index?: Array, + inplace?: boolean + } + ): DataFrame | void + loc(options: + { + rows?: Array, + columns?: Array + }): DataFrame; + iloc(options: + { + rows?: Array, + columns?: Array + }): DataFrame; + head(rows?: number): DataFrame + tail(rows?: number): DataFrame + sample(num: number, options?: { seed?: number }): Promise; + add(other: DataFrame | Series | number | number[], options?: { axis?: 0 | 1, inplace?: boolean }): DataFrame | void + sub(other: DataFrame | Series | number | number[], options?: { axis?: 0 | 1, inplace?: boolean }): DataFrame | void + mul(other: DataFrame | Series | number | number[], options?: { axis?: 0 | 1, inplace?: boolean }): DataFrame | void + div(other: DataFrame | Series | number | number[], options?: { axis?: 0 | 1, inplace?: boolean }): DataFrame | void + pow(other: DataFrame | Series | number | number[], options?: { axis?: 0 | 1, inplace?: boolean }): DataFrame | void + mod(other: DataFrame | Series | number | number[], options?: { axis?: 0 | 1, inplace?: boolean }): DataFrame | void + mean(options?: { axis?: 0 | 1 }): Series + median(options?: { axis?: 0 | 1 }): Series + mode(options?: { axis?: 0 | 1, keep?: number }): Series + min(options?: { axis?: 0 | 1 }): Series + max(options?: { axis?: 0 | 1 }): Series + std(options?: { axis?: 0 | 1 }): Series + var(options?: { axis?: 0 | 1 }): Series + sum(options?: { axis?: 0 | 1 }): Series + count(options?: { axis?: 0 | 1 }): Series + round(dp?: number, options?: { inplace: boolean }): DataFrame | void + cumSum(options?: { axis?: 0 | 1 }): DataFrame | void + cumMin(options?: { axis?: 0 | 1 }): DataFrame | void + cumMax(options?: { axis?: 0 | 1 }): DataFrame | void + cumProd(options?: { axis?: 0 | 1 }): DataFrame | void + copy(): DataFrame + resetIndex(options: { inplace?: boolean }): DataFrame | void + setIndex( + options: + { + index: Array, + column?: string, + drop?: boolean, + inplace?: boolean + } + ): DataFrame | void + describe(): DataFrame + selectDtypes(include: Array): DataFrame + abs(options?: { inplace?: boolean }): DataFrame | void + query(condition: Series | Array, options?: { inplace?: boolean }): DataFrame | void + addColumn( + column: string, + values: Series | ArrayType1D, + options?: { + inplace?: boolean, + atIndex?: number | string + } + ): DataFrame | void + groupby(col: Array): Groupby + column(column: string): Series + fillNa(value: ArrayType1D, + options?: + { + columns?: Array, + inplace?: boolean + }): DataFrame | void + isNa(): DataFrame + dropNa(options?: { axis: 0 | 1, inplace?: boolean }): DataFrame | void + apply(callable: any, options?: { axis?: 0 | 1 }): DataFrame | Series + applyMap(callable: any, options?: { inplace?: boolean }): DataFrame | void + lt(other: DataFrame | Series | number, options?: { axis?: 0 | 1 }): DataFrame + gt(other: DataFrame | Series | number, options?: { axis?: 0 | 1 }): DataFrame + le(other: DataFrame | Series | number, options?: { axis?: 0 | 1 }): DataFrame + ge(other: DataFrame | Series | number, options?: { axis?: 0 | 1 }): DataFrame + ne(other: DataFrame | Series | number, options?: { axis?: 0 | 1 }): DataFrame + eq(other: DataFrame | Series | number, options?: { axis?: 0 | 1 }): DataFrame + replace( + oldValue: number | string | boolean, + newValue: number | string | boolean, + options?: { + columns?: Array + inplace?: boolean + } + ): DataFrame | void + transpose(options?: { inplace?: boolean }): DataFrame | void + get T(): DataFrame + get ctypes(): Series + asType( + column: string, + dtype: "float32" | "int32" | "string" | "boolean", + options?: { inplace?: boolean } + ): DataFrame | void + nUnique(axis: 0 | 1): Series + rename( + mapper: object, + options?: { + axis?: 0 | 1 + inplace?: boolean + } + ): DataFrame | void + sortIndex(options?: + { + inplace?: boolean + ascending?: boolean + } + ): DataFrame | void + sortValues( + column: string, + options?: + { + inplace?: boolean + ascending?: boolean + } + ): DataFrame | void + append( + newValues: ArrayType1D | ArrayType2D | Series | DataFrame, + index: Array | number | string, + options?: { + inplace?: boolean, + } + ): DataFrame | void + toString(): string; + getDummies(options?: { + columns?: string | Array, + prefix?: string | Array, + prefixSeparator?: string | Array, + inplace?: boolean + }): DataFrame | void + plot(divId: string): IPlotlyLib + toCSV(options?: CsvOutputOptionsBrowser): string | void + toJSON(options?: JsonOutputOptionsBrowser): object | void + toExcel(options?: ExcelOutputOptionsBrowser): void +} + +export interface DateTime { + month(): Series + dayOfWeek(): Series + year(): Series + monthName(): Series + dayOfMonth(): Series + hours(): Series + seconds(): Series + minutes(): Series +} + +interface CustomConfig extends Config { + x: string + y: string, + values: string, + labels: string, + rowPositions: number[], + columnPositions: number[], + grid: { rows: number, columns: number }, + tableHeaderStyle: any, + tableCellStyle: any, + columns: string[]; +} + +export type PlotConfigObject = { + config?: Partial + layout?: Partial +} + +export type InternalPlotConfigObject = { + config: Partial + layout: Partial +} + +export interface IPlotlyLib { + line(plotConfig?: PlotConfigObject): void + bar(plotConfig?: PlotConfigObject): void + scatter(plotConfig?: PlotConfigObject): void + hist(plotConfig?: PlotConfigObject): void + pie(plotConfig?: PlotConfigObject): void + box(plotConfig?: PlotConfigObject): void + violin(plotConfig?: PlotConfigObject): void + table(plotConfig?: PlotConfigObject): void +} + +export interface CsvInputOptionsBrowser extends ParseConfig { + frameConfig?: BaseDataOptionType +} +export type ExcelInputOptionsBrowser = { + sheet?: number, + method?: string, + headers?: any, + frameConfig?: BaseDataOptionType +} +export type JsonInputOptionsBrowser = { + method?: string, + headers?: any, + frameConfig?: BaseDataOptionType +} + +export interface CsvInputOptionsNode extends ParseConfig { + frameConfig?: BaseDataOptionType +} + +export type ExcelInputOptionsNode = { + sheet?: number, + method?: string, + headers?: HeadersInit + frameConfig?: BaseDataOptionType +} +export type JsonInputOptionsNode = { + method?: string, + headers?: HeadersInit + frameConfig?: BaseDataOptionType +} + +export type CsvOutputOptionsBrowser = { fileName?: string, sep?: string, header?: boolean, download?: boolean }; +export type ExcelOutputOptionsBrowser = { fileName?: string, sheetName?: string }; +export type JsonOutputOptionsBrowser = { fileName?: string, format?: "row" | "column", download?: boolean }; + +export type CsvOutputOptionsNode = { filePath?: string, sep?: string, header?: boolean } +export type JsonOutputOptionsNode = { format?: "row" | "column", filePath?: string } +export type ExcelOutputOptionsNode = { filePath?: string, sheetName?: string } diff --git a/src/danfojs-base/shared/utils.ts b/src/danfojs-base/shared/utils.ts new file mode 100644 index 00000000..16a11df1 --- /dev/null +++ b/src/danfojs-base/shared/utils.ts @@ -0,0 +1,800 @@ +/** +* @license +* Copyright 2022 JsData. All rights reserved. +* +* This source code is licensed under the MIT license found in the +* LICENSE file in the root directory of this source tree. + +* Unless required by applicable law or agreed to in writing, software +* distributed under the License is distributed on an "AS IS" BASIS, +* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +* See the License for the specific language governing permissions and +* limitations under the License. +* ========================================================================== +*/ + +import { BASE_CONFIG } from './defaults' +import Config from './config'; +import { ArrayType1D, ArrayType2D } from './types'; +import { Series } from '../'; +import { DataFrame } from '../'; +import ErrorThrower from '../shared/errors' + +const config = new Config(BASE_CONFIG); + +/** + * General Utility class + */ +export default class Utils { + /** + * Removes an element from a 1D array + * + * ```js + * + * ``` + * @param arr The array to filter. + * @param index The index to filter by. + */ + removeElementFromArray(arr: ArrayType1D, index: number): ArrayType1D { + const newArr = arr.filter((_, i: number) => i != index); + return newArr; + } + + /** + * Check if value is a string. + * @param value The value to check. + * @returns + */ + isString(value: T): boolean { + return typeof value === "string"; + } + + /** + * Checks if value is a number. + * @param value The value to check. + * @returns + */ + isNumber(value: T): boolean { + return typeof value === "number" && isFinite(value); + } + + /** + * Checks if value is an object. + * @param value The value to check. + * @returns + */ + isObject(value: any): boolean { + return value && typeof value === "object" && value.constructor && value.constructor.name === "Object"; + } + + /** + * Checks if a value is null + * @param value The value to check. + * @returns + */ + isNull(value: T): boolean { + return value === null; + } + + /** + * Checks if a value is undefined + * @param value The value to check. + * @returns + */ + isUndefined(value: T): boolean { + return typeof value === "undefined"; + } + + /** + * Checks if a value is empty. Empty means it's either null, undefined or NaN + * @param value The value to check. + * @returns + */ + isEmpty(value: T): boolean { + return value === undefined || value === null || (isNaN(value as any) && typeof value !== "string"); + } + + /** + * Generates an array of integers between specified range + * @param start The starting number. + * @param end The ending number. + */ + range(start: number, end: number): Array { + if (end < start) { + throw new Error("ParamError: end must be greater than start") + } + + if (start === end) { + return [start] + } + + const arr = []; + for (let i = start; i <= end; i++) { + arr.push(i); + } + return arr; + } + + /** + * Checks if object has the specified key + * @param obj The object to check. + * @param key The key to find. + */ + keyInObject(obj: object, key: number | string): boolean { + return Object.prototype.hasOwnProperty.call(obj, key) + } + + /** + * Transposes an array of array + * @param obj The object to check. + * @param key The key to find. + */ + transposeArray(arr: ArrayType1D | ArrayType2D): ArrayType1D | ArrayType2D { //old name: __get_col_values + if (arr.length === 0) return arr + + const rowLen: number = arr.length; + if (Array.isArray(arr[0])) { + const colLen: number = arr[0].length; + const newArr = []; + + for (let i = 0; i <= colLen - 1; i++) { + const temp = []; + for (let j = 0; j < rowLen; j++) { + const _elem = (arr as any)[j][i] + temp.push(_elem); + } + newArr.push(temp); + } + return newArr; + } else { + return arr; + } + } + + /** + * Retrieve row array and column names from an object of the form {a: [1,2,3,4], b: [30,20, 30, 20]} + * @param obj The object to retrieve rows and column names from. + */ + getRowAndColValues(obj: object): [ArrayType1D | ArrayType2D, string[]] { + const colNames = Object.keys(obj); + const colData = Object.values(obj); + const firstColLen = colData[0].length; + + colData.forEach((cdata) => { + if (cdata.length != firstColLen) { + throw Error("Length Error: Length of columns must be the same!"); + } + }); + + const rowsArr = this.transposeArray(colData) + return [rowsArr, colNames]; + } + + /** + * Converts a 2D array of array to 1D array for Series Class + * @param arr The array to convert. + */ + convert2DArrayToSeriesArray(arr: ArrayType2D): Array { + const newArr = arr.map((val) => { + if (this.isObject(val)) { + return JSON.stringify(val) + } else { + return `${val}` + } + }); + return newArr; + } + + /** + * Replaces all missing values with NaN. Missing values are undefined, Null and Infinity + * @param arr The array + * @param isSeries Whether the arr is a series or not + */ + replaceUndefinedWithNaN(arr: ArrayType1D | ArrayType2D, isSeries: boolean): ArrayType1D | ArrayType2D { + if (arr.length === 0) return arr; + + if (isSeries && Array.isArray(arr)) { + const newArr = arr.map((ele) => { + if (typeof ele === "undefined") { + return NaN; + } + if (typeof ele === "number" && (isNaN(ele) || ele == Infinity)) { + return NaN; + } + if (ele == null) { + return NaN; + } + return ele + }); + return newArr as ArrayType1D + } else { + const newArr = [] + if (Array.isArray(arr)) { + for (let i = 0; i < arr.length; i++) { + const innerArr = arr[i] + const temp = (innerArr as unknown as ArrayType2D).map((ele: any) => { + if (typeof ele === "undefined") { + return NaN; + } + if (typeof ele === "number" && (isNaN(ele) || ele == Infinity)) { + return NaN; + } + if (ele == null) { + return NaN; + } + return ele + }); + newArr.push(temp); + } + } + return newArr; + } + } + + /** + * Infer data type from an array or array of arrays + * @param arr An array or array of arrays + */ + inferDtype(arr: ArrayType1D | ArrayType2D) { + const self = this; + if (this.is1DArray(arr)) { + return [this.$typeChecker(arr)]; + } else { + const arrSlice = this.transposeArray(arr.slice(0, config.getDtypeTestLim)) + const dtypes = arrSlice.map((innerArr) => { + return self.$typeChecker(innerArr as any); + }); + return dtypes; + } + } + + /** + * Private type checker used by inferDtype function + * @param arr The array + */ + private $typeChecker(arr: ArrayType1D | ArrayType2D) { + let dtypes: string; + let lim: number; + let intTracker: Array = []; + let floatTracker: Array = []; + let stringTracker: Array = []; + let boolTracker: Array = []; + + if (arr.length < config.getDtypeTestLim) { + lim = arr.length; + } else { + lim = config.getDtypeTestLim; + } + + const arrSlice = arr.slice(0, lim); + + for (let i = 0; i < lim; i++) { + const ele = arrSlice[i]; + if (typeof ele == "boolean") { + floatTracker.push(false); + intTracker.push(false); + stringTracker.push(false); + boolTracker.push(true); + } else if (this.isEmpty(ele)) { + floatTracker.push(true); + intTracker.push(false); + stringTracker.push(false); + boolTracker.push(false); + } else if (!isNaN(Number(ele))) { + if ((ele as unknown as string).toString().includes(".")) { + floatTracker.push(true); + intTracker.push(false); + stringTracker.push(false); + boolTracker.push(false); + } else { + floatTracker.push(false); + intTracker.push(true); + stringTracker.push(false); + boolTracker.push(false); + } + } else { + floatTracker.push(false); + intTracker.push(false); + stringTracker.push(true); + boolTracker.push(false); + } + } + + const even = (ele: number | string | boolean) => ele == true; + + if (stringTracker.some(even)) { + dtypes = "string"; + } else if (floatTracker.some(even)) { + dtypes = "float32"; + } else if (intTracker.some(even)) { + dtypes = "int32"; + } else if (boolTracker.some(even)) { + dtypes = "boolean"; + } else { + dtypes = "undefined"; + } + + return dtypes; + } + + /** + * Returns the unique values in an 1D array + * @param arr The array + */ + unique(arr: ArrayType1D): ArrayType1D { + const uniqueArr = new Set(arr); + return Array.from(uniqueArr); + } + + /** + * Checks if array is 1D + * @param arr The array + */ + is1DArray(arr: ArrayType1D | ArrayType2D): boolean { + if ( + typeof arr[0] == "number" || + typeof arr[0] == "string" || + typeof arr[0] == "boolean" + ) { + return true; + } else { + return false; + } + } + + /** + * Converts an array to an object using array index as object keys + * @param arr The array + */ + convertArrayToObject(arr: ArrayType1D | ArrayType2D) { + const arrObj: any = {}; + for (let i = 0; i < arr.length; i++) { + arrObj[i] = arr[i]; + + } + return arrObj; + } + + /** + * Count the NaN and non-NaN values present in an array + * @param arr Array object + * @param val whether to return the value count instead of the null count + * @param isSeries Whether the array is of type series or not + */ + countNaNs(arr: ArrayType1D | ArrayType2D, returnVal: boolean = true, isSeries: boolean): number | Array { + if (isSeries) { + let nullCount = 0; + let valCount = 0; + for (let i = 0; i < arr.length; i++) { + const ele = arr[i]; + if (Number.isNaN(ele)) { + nullCount = nullCount + 1; + } else { + valCount = valCount + 1; + } + + } + if (returnVal) { + return valCount; + } else { + return nullCount; + } + } else { + const resultArr = []; + for (let i = 0; i < arr.length; i++) { + const innerArr = arr[i]; + let nullCount = 0; + let valCount = 0; + for (let i = 0; i < (innerArr as unknown as ArrayType2D).length; i++) { + const ele = (innerArr as unknown as ArrayType2D)[i]; + if (Number.isNaN(ele)) { + nullCount = nullCount + 1; + } else { + valCount = valCount + 1; + } + } + + if (returnVal) { + resultArr.push(valCount); + } else { + resultArr.push(nullCount); + } + } + return resultArr; + } + } + + /** + * Round elements of an array or array of arrays to specified dp + * @param arr The Array to round + * @param dp The number of dp to round to + * @param isSeries Whether the array is of type Series or not + */ + round(arr: Array, dp: number = 1, isSeries: boolean): ArrayType1D | ArrayType2D { + if (dp < 0) { + dp = 1; + } + + if (isSeries) { + const newArr = []; + for (let i = 0; i < arr.length; i++) { + const ele = arr[i]; + if (typeof ele == "number" && !isNaN(ele) && ele !== undefined && ele !== null) { + newArr.push(Number((ele).toFixed(dp))); + } else { + newArr.push(ele) + } + } + return newArr as ArrayType1D + } else { + const resultArr = []; + for (let i = 0; i < arr.length; i++) { + const innerVal = arr[i]; + const newArr: Array = []; + if (Array.isArray(innerVal)) { + for (let i = 0; i < innerVal.length; i++) { + const ele = innerVal[i]; + if (typeof ele == "number" && !isNaN(ele) && ele !== undefined && ele !== null) { + newArr.push(Number((ele).toFixed(dp))); + } else { + newArr.push(ele) + } + } + resultArr.push(newArr); + } else { + if (typeof innerVal == "number" && !isNaN(innerVal) && innerVal !== undefined && innerVal !== null) { + newArr.push(Number((innerVal).toFixed(dp))); + } else { + newArr.push(innerVal) + } + } + + } + return resultArr; + } + } + + /** + * Checks if a func is a function + * @param func + */ + isFunction(func: object): boolean { + return typeof func == "function"; + } + + /** + * Generates n random numbers between start and end. + * @param start + * @param end + * @param size + */ + randNumberGenerator(start: number, end: number, size: number) { + let genNum: Array = []; + + function randi(a: number, b: number) { + return Math.floor(Math.random() * (b - a) + a); + } + + function recursive(val: number, arr: Array): any { + if (!arr.includes(val)) { + return val; + } + val = randi(start, end); + recursive(val, arr); + } + + for (let i = 0; i < size; i++) { + let genVal = randi(start, end); + let recursiveVal = recursive(genVal, genNum); + genNum.push(recursiveVal); + } + return genNum; + } + + /** + * Throws error when a required parameter is missing. + * @param paramsObject The parameters passed to the function + * @param paramsNeeded The required parameters in the function + */ + throwErrorOnWrongParams(paramsObject: object, paramsNeeded: Array) { + const keys = Object.keys(paramsObject); + const bool = []; + for (let i = 0; i < keys.length; i++) { + if (paramsNeeded.includes(keys[i])) { + bool.push(true); + } else { + bool.push(false); + } + } + const truthy = (element: boolean) => element == false; + if (bool.some(truthy)) { + throw Error( + `Params Error: Required parameter not found. Your params must include the following [${paramsNeeded}]` + ); + } + } + + /** + * Maps integer values (0, 1) to boolean (false, true) + * @param arr The array of integers + * @param dim The dimension of the array + */ + mapIntegersToBooleans(arr: Array, dim: number): Array { + if (dim == 2) { + const newArr: Array = []; + arr.map((innerArr) => { + const temp: Array = []; + (innerArr as Array).map((val) => temp.push(val == 1)); + newArr.push(temp); + }); + return newArr; + } else { + const newArr: Array = []; + arr.map((val) => newArr.push(val == 1)); + return newArr; + } + } + + /** + * Maps boolean values (false, true) to integer equivalent (0, 1) + * @param arr The array of booleans + * @param dim The dimension of the array + */ + mapBooleansToIntegers(arr: Array, dim: number): Array { + if (dim == 2) { + const newArr: Array = []; + arr.map((innerArr) => { + const temp: Array = []; + (innerArr as Array).map((val) => temp.push(val ? 1 : 0)); + newArr.push(temp); + }); + return newArr; + } else { + const newArr: Array = []; + arr.map((val) => newArr.push(val ? 1 : 0)); + return newArr; + } + } + + /** + * Generates an array of dim (row x column) with inner values set to zero + * @param row + * @param column + */ + zeros(row: number, column: number): ArrayType1D | ArrayType2D { + const zeroData = []; + for (let i = 0; i < row; i++) { + const colData = Array(column); + for (let j = 0; j < column; j++) { + colData[j] = 0; + } + zeroData.push(colData); + } + return zeroData; + } + + /** + * Shuffles and returns a random slice of an array + * @param num + * @param array + */ + shuffle(array: ArrayType1D | ArrayType2D, num: number): ArrayType1D | ArrayType2D { + let i = array.length; + let j = 0; + let temp; + + while (i--) { + j = Math.floor(Math.random() * (i + 1)); + temp = array[i]; + array[i] = array[j]; + array[j] = temp; + } + + return array.slice(0, num); + } + + /** + * Sorts an array in specified order + * @param arr + * @param ascending + * @returns + */ + sort(arr: Array, ascending: boolean = true): Array { + const sorted = [...arr] + return sorted.sort((a, b) => { + if (ascending) { + if (typeof a === "string" && typeof b === "string") { + return a.charCodeAt(0) - b.charCodeAt(0); + } else { + return (a as unknown as number) - (b as unknown as number) + } + } else { + if (typeof a === "string" && typeof b === "string") { + return b.charCodeAt(0) - a.charCodeAt(0); + } else { + return (b as unknown as number) - (a as unknown as number) + } + } + }); + } + + /** + * Checks if current environment is Browser + */ + isBrowserEnv() { + const isBrowser = new Function( + "try {return this===window;}catch(e){ return false;}" + ); + return isBrowser(); + } + + /** + * Checks if current environment is Node + */ + isNodeEnv() { + const isNode = new Function( + "try {return this===global;}catch(e){return false;}" + ); + return isNode(); + } + + /** + * Remove NaN values from 1D array + * @param arr + */ + removeMissingValuesFromArray(arr: Array | ArrayType1D) { + const values = arr.filter((val) => { + return !(this.isEmpty(val)) + }) + return values; + } + + + /** + * Replace NaN with null before tensor operations + * @param arr + */ + replaceNanWithNull(arr: ArrayType1D | ArrayType2D) { + const values = arr.map((val) => { + if (isNaN(val as unknown as number)) { + return null; + } else { + return val; + } + }); + return values; + } + + /** + * Get duplicate values in a array + * @param arr + */ + getDuplicate(arr: Array) { + const tempObj: any = {}; + const resultObj: any = {}; + + for (let i = 0; i < arr.length; i++) { + const val = arr[i]; + if (this.keyInObject(tempObj, val as unknown as string | number)) { + tempObj[val]["count"] += 1; + tempObj[val]["index"].push(i); + } else { + tempObj[val] = {}; + tempObj[val]["count"] = 1; + tempObj[val]["index"] = [i]; + } + } + + for (let key in tempObj) { + if (tempObj[key]["count"] >= 2) { + resultObj[key] = {}; + resultObj[key]["count"] = tempObj[key]["count"]; + resultObj[key]["index"] = tempObj[key]["index"]; + } + } + + return resultObj; + } + + /** + * Returns the index of a sorted array + * @param arr1 The first array + * @param arr2 The second array + * @param dtype The data type of the arrays + * + * @returns sorted index + */ + sortArrayByIndex(arr1: ArrayType1D | ArrayType2D, arr2: ArrayType1D | ArrayType2D, dtype: string) { + const sortedIdx = arr1.map((item, index) => { + return [arr2[index], item]; + }); + if (dtype == "string") { + sortedIdx.sort(); + } else { + sortedIdx.sort(([arg1], [arg2]) => (arg2 as unknown as number) - (arg1 as unknown as number)); + } + + return sortedIdx.map(([, item]) => item) as number[] + } + + /** + * Returns a new series with properties of the old series + * + * @param series The series to copy + */ + createNdframeFromNewDataWithOldProps({ ndFrame, newData, isSeries }: { ndFrame: Series, newData: any, isSeries: boolean }) { + if (isSeries) { + return new Series( + newData, + { + index: [...ndFrame.index], + columns: [...ndFrame.columns], + dtypes: [...ndFrame.dtypes], + config: { ...ndFrame.config } + }) + } else { + return new DataFrame(newData, + { + index: [...ndFrame.index], + columns: [...ndFrame.columns], + dtypes: [...ndFrame.dtypes], + config: { ...ndFrame.config } + }) + } + } + + /** + * Checks if two series are compatible for a mathematical operation + * @param object + * + * firstSeries ==> First Series object + * + * secondSeries ==> Second Series object to comapre with + * + * operation ==> The mathematical operation + */ + checkSeriesOpCompactibility({ firstSeries, secondSeries, operation }: { + firstSeries: Series, secondSeries: Series, operation: string + }): void { + + if (firstSeries.shape[0] != secondSeries.shape[0]) { + ErrorThrower.throwSeriesMathOpLengthError(firstSeries, secondSeries) + } + if (firstSeries.dtypes[0] == 'string' || secondSeries.dtypes[0] == 'string') { + ErrorThrower.throwStringDtypeOperationError(operation) + } + } + + /** + * Custom sort for an array of index and values + * @param arr The array of objects to sort + * @param ascending Whether to sort in ascending order or not + */ + sortObj( + arr: Array<{ index: number | string, value: number | string | boolean }>, + ascending: boolean + ) { + return arr.sort((obj1, obj2) => { + const a = obj2.value; + const b = obj1.value; + + if (!ascending) { + if (typeof a === "string" && typeof b === "string") { + return a.charCodeAt(0) - b.charCodeAt(0); + } else if ((typeof a === "number" && typeof b === "number") || (typeof a === "boolean" && typeof b === "boolean")) { + return Number(a) - Number(b); + } else { + throw Error('ParamError: column values must be either numbers or strings'); + } + } else { + if (typeof a === "string" && typeof b === "string") { + return b.charCodeAt(0) - a.charCodeAt(0); + } else if ((typeof a === "number" && typeof b === "number") || (typeof a === "boolean" && typeof b === "boolean")) { + return Number(b) - Number(a); + } else { + throw Error('ParamError: column values must be either numbers or strings'); + } + } + }); + } +} \ No newline at end of file diff --git a/src/danfojs-base/transformers/concat.ts b/src/danfojs-base/transformers/concat.ts new file mode 100644 index 00000000..4a3c66e2 --- /dev/null +++ b/src/danfojs-base/transformers/concat.ts @@ -0,0 +1,147 @@ +/** +* @license +* Copyright 2022 JsData. All rights reserved. +* +* This source code is licensed under the MIT license found in the +* LICENSE file in the root directory of this source tree. + +* Unless required by applicable law or agreed to in writing, software +* distributed under the License is distributed on an "AS IS" BASIS, +* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +* See the License for the specific language governing permissions and +* limitations under the License. +* ========================================================================== +*/ + +import Series from "../core/series" +import DataFrame from "../core/frame" +import { ArrayType1D, ArrayType2D } from "../shared/types" + +/** + * + * @param dfList Array + * @param axis number + * @returns DataFrame + */ +function processColumn(dfList: Array, axis: number ): DataFrame { + let allDf: any = {} + let dublicateColumns: any = {} + let maxLen = 0 + for(let i=0; i < dfList.length; i++) { + let df = dfList[i] + let columnData: ArrayType2D; + if ( df instanceof DataFrame) { + columnData = df.getColumnData as ArrayType2D + } else { + columnData = [df.values] as ArrayType2D + } + let columns = df.columns + for(let j=0; j < columns.length; j++) { + let column = columns[j] + let colData: ArrayType1D = columnData[j] + if (colData.length > maxLen) { + maxLen = colData.length + } + if (!(column in allDf)) { + allDf[column] = colData + dublicateColumns[column] = 0 + } else { + dublicateColumns[column] +=1 + column += dublicateColumns[column] + allDf[column] = colData + } + } + } + Object.keys(allDf).forEach(value => { + let colLength = allDf[value].length + if (colLength < maxLen) { + let residualLen = maxLen - colLength + let nanList = new Array(residualLen).fill(NaN) + allDf[value].push(...nanList) + } + }) + + return new DataFrame(allDf) +} + +/** + * Concat data along rows + * @param dfList Array + * @param axis Array + * @returns DataFrame + */ +function processRow(dfList: Array, axis: number ): DataFrame | Series { + let allDf: any = {} + let maxLen = 0 + for (let i=0; i < dfList.length; i++) { + let df = dfList[i] + let columns = df.columns + let columnData: ArrayType2D; + if ( df instanceof DataFrame) { + columnData = df.getColumnData as ArrayType2D + } else { + columnData = [df.values] as ArrayType2D + } + + + if (i ===0) { + for(let j=0; j < columns.length; j++) { + let column = columns[j] + let colData = columnData[j] + allDf[column] = colData + } + } else { + let nonColumn = Object.keys(allDf).filter( (key:any) =>{ + return !columns.includes(key) + }) + + for(let j=0; j < columns.length; j++) { + let column = columns[j] + let colData = columnData[j] + if (Object.keys(allDf).includes(column)) { + allDf[column].push(...colData) + } + else { + let residualArray = new Array(maxLen).fill(NaN) + residualArray.push(...colData) + allDf[column] = residualArray + } + } + if (nonColumn.length > 0) { + let currentDfLen = columnData[0].length + for( let j=0; j < nonColumn.length; j++) { + let column = nonColumn[j] + let residualArray = new Array(currentDfLen).fill(NaN) + allDf[column].push(...residualArray) + } + } + } + maxLen += columnData[0].length + } + + if (Object.keys(allDf).length === 1) { + return new Series(Object.values(allDf)[0]) + } + return new DataFrame(allDf) +} + +/** +* Concatenate pandas objects along a particular axis. +* @param object +* dfList: Array of DataFrame or Series +* axis: axis of concatenation 1 or 0 +* @returns {DataFrame} +* @example +* concat({dfList: [df1, df2, df3], axis: 1}) +*/ +function concat({dfList, axis}: { + dfList : Array, + axis: 1 | 0 +}): DataFrame | Series { + if (axis === 1) { + return processColumn(dfList, axis) + } + return processRow(dfList, 0) +} + +export default concat \ No newline at end of file diff --git a/src/danfojs-base/transformers/encoders/dummy.encoder.ts b/src/danfojs-base/transformers/encoders/dummy.encoder.ts new file mode 100644 index 00000000..64c13992 --- /dev/null +++ b/src/danfojs-base/transformers/encoders/dummy.encoder.ts @@ -0,0 +1,160 @@ +/** +* @license +* Copyright 2022 JsData. All rights reserved. +* +* This source code is licensed under the MIT license found in the +* LICENSE file in the root directory of this source tree. + +* Unless required by applicable law or agreed to in writing, software +* distributed under the License is distributed on an "AS IS" BASIS, +* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +* See the License for the specific language governing permissions and +* limitations under the License. +* ========================================================================== +*/ +import DataFrame from "../../core/frame" +import Series from "../../core/series" +import Utils from "../../shared/utils" +import { ArrayType1D, ArrayType2D } from "../../shared/types" + +const utils = new Utils() + +/** + * Generate one-hot encoding for categorical columns in an Array, Series or Dataframe. + * @param data Series or Dataframe + * @param columns Columns to encode + * @param prefix Prefix for the new columns + * @param prefixSeparator Separator for the prefix and the column name + * @returns Encoded Dataframe + * @example + * import { DataFrame, DummyEncoder }from 'danfojs'; + * const df = new DataFrame([[1,2,3], [4,5,6]], { columns: ['a', 'b', 'c'] }); + * const df2 = new DummyEncoder({data: df, columns: ['a', 'b'], prefix: 'enc', prefixSeparator: '#'}).encode(); + * df2.print(); + */ +function dummyEncode( + data: Series | DataFrame, + options?: { + columns?: string | Array, + prefix?: string | Array, + prefixSeparator?: string | Array + }) { + let { columns, prefix, prefixSeparator } = { columns: null, prefix: null, prefixSeparator: "_", ...options } + + if (!data) { + throw new Error('ParamError: data must be one of Array, Series or DataFrame') + } + + if (data instanceof Series || data instanceof DataFrame) { + if (!columns) { + const colsWithStringDtype: Array = [] + data.dtypes.forEach((dtype, index) => { + if (dtype === "string") { + colsWithStringDtype.push(data.columns[index]) + } + }) + columns = colsWithStringDtype as string[] + } + } else { + throw new Error('ParamError: data must be one of Array, Series or DataFrame') + } + + + if (typeof columns === "string") { + columns = [columns] + if (Array.isArray(prefix) && prefix.length === 1) { + prefix = prefix + } else if (typeof prefix === "string") { + prefix = [prefix] + } else { + throw new Error('ParamError: prefix must be a string, or an array of same length as columns') + } + + if (Array.isArray(prefixSeparator) && prefixSeparator.length === 1) { + prefixSeparator = prefixSeparator + } else if (typeof prefixSeparator === "string") { + prefixSeparator = [prefixSeparator] + } else { + throw new Error('ParamError: prefix must be a string, or an array of same length as columns') + } + } else if (Array.isArray(columns)) { + if (prefix) { + if (Array.isArray(prefix) && prefix.length !== columns.length) { + throw new Error(`ParamError: prefix and data array must be of the same length. If you need to use the same prefix, then pass a string param instead. e.g {prefix: "${prefix}"}`) + } + + if (typeof prefix === "string") { + prefix = columns.map(_ => prefix as string) + } + } + + if (prefixSeparator) { + if (Array.isArray(prefixSeparator) && prefixSeparator.length !== columns.length) { + throw new Error(`ParamError: prefixSeparator and data array must be of the same length. If you need to use the same prefix separator, then pass a string param instead. e.g {prefixSeparator: "${prefixSeparator}"}`) + } + + if (typeof prefixSeparator === "string") { + prefixSeparator = columns.map(_ => prefixSeparator as string) + } + } + + } else { + throw new Error('ParamError: columns must be a string or an array of strings') + } + + if (data instanceof Series) { + const colData = data.values as ArrayType1D + const newColumnNames = [] + const uniqueValues = Array.from(new Set(colData)) + const oneHotArr: any = utils.zeros(colData.length, uniqueValues.length) + + for (let i = 0; i < colData.length; i++) { + const index = uniqueValues.indexOf(colData[i]) + oneHotArr[i][index] = 1 + } + + for (let i = 0; i < uniqueValues.length; i++) { + const prefixToAdd = prefix ? prefix[0] : i + newColumnNames.push(`${prefixToAdd}${prefixSeparator[0]}${uniqueValues[i]}`) + + } + + return new DataFrame(oneHotArr, { columns: newColumnNames }) + + } else { + + const dfWithSelectedColumnsDropped = data.drop({ columns }) + let newData = dfWithSelectedColumnsDropped?.values as ArrayType2D + const newColumnNames = dfWithSelectedColumnsDropped?.columns as Array + + for (let i = 0; i < columns.length; i++) { + const column = columns[i] + const colData = data.column(column).values as Array + + const uniqueValues = Array.from(new Set(colData)) + const oneHotArr: any = utils.zeros(colData.length, uniqueValues.length) + + for (let j = 0; j < colData.length; j++) { + const index = uniqueValues.indexOf(colData[j]) + oneHotArr[j][index] = 1 + const prefixToAdd = prefix ? prefix[i] : column + const newColName = `${prefixToAdd}${prefixSeparator[i]}${colData[j]}`; + + if (!newColumnNames.includes(newColName)) { + newColumnNames.push(newColName); + } + } + + for (let k = 0; k < newData.length; k++) { + newData[k] = [...newData[k], ...oneHotArr[k]] + + } + + } + + return new DataFrame(newData, { columns: newColumnNames }) + } + +} + +export default dummyEncode diff --git a/src/danfojs-base/transformers/encoders/label.encoder.ts b/src/danfojs-base/transformers/encoders/label.encoder.ts new file mode 100644 index 00000000..3e00121e --- /dev/null +++ b/src/danfojs-base/transformers/encoders/label.encoder.ts @@ -0,0 +1,176 @@ +/** +* @license +* Copyright 2022 JsData. All rights reserved. +* +* This source code is licensed under the MIT license found in the +* LICENSE file in the root directory of this source tree. + +* Unless required by applicable law or agreed to in writing, software +* distributed under the License is distributed on an "AS IS" BASIS, +* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +* See the License for the specific language governing permissions and +* limitations under the License. +* ========================================================================== +*/ + +import tensorflow from '../../shared/tensorflowlib' +import Series from "../../core/series" +import Utils from "../../shared/utils" + +const utils = new Utils() + +/** + * Encode target labels with value between 0 and n_classes-1. + */ +export default class LabelEncoder { + private $labels: { [key: string]: number } + + constructor() { + this.$labels = {} + } + + private $getData(data: Array | typeof tensorflow.Tensor | Series) { + let $data: Array + + if (data instanceof Array) { + if (utils.is1DArray(data)) { + $data = data + } else { + throw new Error("ValueError: data must be a 1D array.") + } + } else if (data instanceof Series) { + $data = data.values as Array + } else if (data instanceof tensorflow.Tensor) { + $data = data.arraySync() as Array + } else { + throw new Error("ParamError: data must be one of Array, 1d Tensor or Series.") + } + return $data + } + + /** + * Maps values to unique integer labels between 0 and n_classes-1. + * @param data 1d array of labels, Tensor, or Series to fit. + * @example + * ``` + * const encoder = new LabelEncoder() + * encoder.fit(["a", "b", "c", "d"]) + * ``` + */ + fit(data: Array | typeof tensorflow.Tensor | Series) { + const $data = this.$getData(data) + const dataSet = Array.from(new Set($data)) + const tempObj: { [key: string | number]: number } = {} + dataSet.forEach((value, index) => { + tempObj[value] = index + }) + this.$labels = tempObj + return this + } + + /** + * Encode labels with value between 0 and n_classes-1. + * @param data 1d array of labels, Tensor, or Series to be encoded. + * @example + * ``` + * const encoder = new LabelEncoder() + * encoder.fit(["a", "b", "c", "d"]) + * console.log(encoder.transform(["a", "b", "c", "d"])) + * // [0, 1, 2, 3] + * ``` + */ + transform(data: Array | typeof tensorflow.Tensor | Series) { + const $data = this.$getData(data) + const encodedData: Array = $data.map(value => { + const label = this.$labels[value] !== undefined ? this.$labels[value] : -1 + return label + }) + + if (data instanceof Array) { + return encodedData + } else if (data instanceof Series) { + return new Series(encodedData) + } else { + return tensorflow.tensor1d(encodedData) + } + } + + /** + * Fit and transform data in one step. + * @param data 1d array of labels, Tensor, or Series to be encoded. + * @example + * ``` + * const encoder = new LabelEncoder() + * encoder.fitTransform(["a", "b", "c", "d"]) + * // [0, 1, 2, 3] + * ``` + */ + fitTransform(data: Array | typeof tensorflow.Tensor | Series) { + this.fit(data) + return this.transform(data) + } + + /** + * Inverse transform values back to original values. + * @param data 1d array of labels, Tensor, or Series to be decoded. + * @example + * ``` + * const encoder = new LabelEncoder() + * encoder.fit(["a", "b", "c", "d"]) + * console.log(encoder.inverseTransform([0, 1, 2, 3])) + * // ["a", "b", "c", "d"] + * ``` + */ + inverseTransform(data: Array | typeof tensorflow.Tensor | Series) { + const $data = this.$getData(data) + const tempData = $data.map(value => { + return Object.keys(this.$labels).find(key => this.$labels[key] === value) + }) + + const decodedData = tempData.map(value => { + if (isNaN(parseInt(value as any))) { + return value + } else { + return Number(value) + } + }) + + if (data instanceof Array) { + return decodedData + } else if (data instanceof Series) { + return new Series(decodedData) + } else { + return tensorflow.tensor1d(decodedData as any) + } + } + + /** + * Get the number of classes. + * @returns number of classes. + * @example + * ``` + * const encoder = new LabelEncoder() + * encoder.fit(["a", "b", "c", "d"]) + * console.log(encoder.nClasses) + * // 4 + * ``` + */ + get nClasses(): number { + return Object.keys(this.$labels).length + } + + /** + * Get the mapping of classes to integers. + * @returns mapping of classes to integers. + * @example + * ``` + * const encoder = new LabelEncoder() + * encoder.fit(["a", "b", "c", "d"]) + * console.log(encoder.classes) + * // {a: 0, b: 1, c: 2, d: 3} + * ``` + */ + get classes(): { [key: string]: number } { + return this.$labels + } +} \ No newline at end of file diff --git a/src/danfojs-base/transformers/encoders/one.hot.encoder.ts b/src/danfojs-base/transformers/encoders/one.hot.encoder.ts new file mode 100644 index 00000000..69b613a3 --- /dev/null +++ b/src/danfojs-base/transformers/encoders/one.hot.encoder.ts @@ -0,0 +1,117 @@ +/** +* @license +* Copyright 2022 JsData. All rights reserved. +* +* This source code is licensed under the MIT license found in the +* LICENSE file in the root directory of this source tree. + +* Unless required by applicable law or agreed to in writing, software +* distributed under the License is distributed on an "AS IS" BASIS, +* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +* See the License for the specific language governing permissions and +* limitations under the License. +* ========================================================================== +*/ + +import DataFrame from "../../core/frame"; +import tensorflow from '../../shared/tensorflowlib' +import Series from "../../core/series" +import Utils from "../../shared/utils" + +const utils = new Utils() + +/** + * Fits a OneHotEncoder to the data. + * @example + * ```js + * const encoder = new OneHotEncoder() + * encoder.fit(["a", "b", "c"]) + * ``` +*/ +export default class OneHotEncoder { + private $labels: Array + + constructor() { + this.$labels = [] + } + + private $getData(data: Array | typeof tensorflow.Tensor | Series) { + let $data: Array + + if (data instanceof Array) { + if (utils.is1DArray(data)) { + $data = data + } else { + throw new Error("ValueError: data must be a 1D array.") + } + } else if (data instanceof Series) { + $data = data.values as Array + } else if (data instanceof tensorflow.Tensor) { + $data = data.arraySync() as Array + } else { + throw new Error("ParamError: data must be one of Array, 1d Tensor or Series.") + } + return $data + } + + /** + * Fits a OneHotEncoder to the data. + * @param data 1d array of labels, Tensor, or Series to be encoded. + * @returns OneHotEncoder + * @example + * ```js + * const encoder = new OneHotEncoder() + * encoder.fit(["a", "b", "c"]) + * ``` + */ + public fit(data: Array | typeof tensorflow.Tensor | Series) { + const $data = this.$getData(data) + const dataSet = Array.from(new Set($data)) + this.$labels = dataSet + return this + } + + /** + * Encodes the data using the fitted OneHotEncoder. + * @param data 1d array of labels, Tensor, or Series to be encoded. + * @example + * ```js + * const encoder = new OneHotEncoder() + * encoder.fit(["a", "b", "c"]) + * encoder.transform(["a", "b", "c"]) + * ``` + */ + public transform(data: Array | typeof tensorflow.Tensor | Series): DataFrame | typeof tensorflow.Tensor | number[][] { + const $data = this.$getData(data) + const oneHotArr: any = utils.zeros($data.length, this.$labels.length) + + for (let i = 0; i < $data.length; i++) { + const index = this.$labels.indexOf($data[i]) + oneHotArr[i][index] = 1 + } + + if (data instanceof Array) { + return oneHotArr + } else if (data instanceof Series) { + return new DataFrame(oneHotArr, { + index: data.index, + }) + } else { + return tensorflow.tensor1d(oneHotArr) + } + } + + /** + * Fit and transform the data using the fitted OneHotEncoder. + * @param data 1d array of labels, Tensor, or Series to be encoded. + * @example + * ```js + * const encoder = new OneHotEncoder() + * encoder.fitTransform(["a", "b", "c"]) + * ``` + */ + public fitTransform(data: Array | typeof tensorflow.Tensor | Series): DataFrame | typeof tensorflow.Tensor | number[][] { + this.fit(data) + return this.transform(data) + } +} \ No newline at end of file diff --git a/src/danfojs-base/transformers/merge.ts b/src/danfojs-base/transformers/merge.ts new file mode 100644 index 00000000..f449557b --- /dev/null +++ b/src/danfojs-base/transformers/merge.ts @@ -0,0 +1,387 @@ +/** +* @license +* Copyright 2022 JsData. All rights reserved. +* +* This source code is licensed under the MIT license found in the +* LICENSE file in the root directory of this source tree. + +* Unless required by applicable law or agreed to in writing, software +* distributed under the License is distributed on an "AS IS" BASIS, +* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +* See the License for the specific language governing permissions and +* limitations under the License. +* ========================================================================== +*/ + +import DataFrame from "../core/frame" +import { ArrayType1D, ArrayType2D } from "../shared/types" +import Utils from "../shared/utils"; +const utils = new Utils(); + +type mergeParam = { + left: DataFrame, + right: DataFrame, + on: Array, + how: "outer" | "inner" | "left" | "right" +} + +type keyComb = { + [key: string] : { + filters: ArrayType2D, + combValues: ArrayType1D + } +} + +class Merge { + left: DataFrame + right: DataFrame + on: Array + how: "outer" | "inner" | "left" | "right" + leftColIndex: ArrayType1D = [] + rightColIndex: ArrayType1D = [] + leftCol?: ArrayType1D + rightCol?: ArrayType1D + columns?: ArrayType1D + + constructor({left, right, on, how}: mergeParam) { + this.left = left + this.right = right + this.on = on + this.how = how + + //Obtain the column index of the column will + //want to merge on for both left and right dataframe + for(let i =0; i < this.on.length; i++) { + let key = this.on[i] + if (this.left.columns.includes(key) && this.right.columns.includes(key)) { + let leftIndex = this.left.columns.indexOf(key) + let rightIndex = this.right.columns.indexOf(key) + this.leftColIndex.push(leftIndex) + this.rightColIndex.push(rightIndex) + } + } + } + + /** + * Generate key combination base on the columns we want to merge on + * e.g df = { + * key1: ["KO", "K0", "K3", "K4"], + * Key2: ["K1", "K1", "K3", "K5"], + * A: [1,2,3,4] + * B: [3,4,5,6] + * } + * keycomb = generateKeyCombination(df.values, [0,1]) + * This should output + * { + * 'k0_k1': { + * filters: [[1,3], [2,4]], # the value of other columns in thesame row with the combination keys + * combValues: ["KO", "k1"] # the combination key from column Key1 (index 2) and key2 (index 1) + * }, + * 'K3_K3 : { + * filters: [[3,5]], + * combValues: ['K3', 'k3'] + * }, + * 'k4_k5' : { + * filters: [[4,6]] + * combValues: ['K4', 'K5'] + * } + * } + * This key combination will be generated for both left and right dataframe + * @param values + * @param colIndex + */ + private generateKeyCombination(values: ArrayType2D, colIndex: ArrayType1D): keyComb { + let colKeyComb: keyComb = {} + + for (let i=0; i < values.length; i++) { + let rowValues = values[i] + let rowKeyCombValues = []; + for (let j =0; j < colIndex.length; j++) { + let index = colIndex[j] as number + rowKeyCombValues.push(rowValues[index]) + } + let rowKeyComb = rowKeyCombValues.join('_') + let otherValues = rowValues.filter((val, index) => { + return !colIndex.includes(index) + }) + if (utils.keyInObject(colKeyComb, rowKeyComb)) { + colKeyComb[rowKeyComb].filters.push(otherValues) + } else { + colKeyComb[rowKeyComb] = { + filters: [otherValues], + combValues: rowKeyCombValues + } + } + } + return colKeyComb + } + + /** + * Generate columns for the newly generated merged DataFrame + * e.g df = { + * key1: ["KO", "K0", "K3", "K4"], + * Key2: ["K1", "K1", "K3", "K5"], + * A: [1,2,3,4] + * B: [3,4,5,6] + * } + * df2 = { + * key1: ["KO", "K0", "K3", "K4"], + * Key2: ["K1", "K1", "K3", "K5"], + * A: [1,2,3,4] + * c: [3,4,5,6] + * } + * And both dataframe are to be merged on `key1` and `key2` + * the newly generated column will be of the form + * columns = ['key1', 'Key2', 'A', 'A_1', 'B', 'C'] + * Notice 'A_1' , this because both DataFrame as column A and 1 is the + * number of duplicate of that column + */ + private createColumns() { + const self = this + this.leftCol = self.left.columns.filter((_, index)=>{ + return !self.leftColIndex.includes(index) + }) + this.rightCol = self.right.columns.filter((_, index) => { + return !self.rightColIndex.includes(index) + }) + this.columns = [...this.on] + const duplicateColumn: { + [key: string] : number + } = {} + const tempColumn = [...this.leftCol] + tempColumn.push(...this.rightCol) + for (let i=0; i< tempColumn.length; i++) { + const col = tempColumn[i] as string + if (utils.keyInObject(duplicateColumn, col)) { + let columnName = `${col}_${duplicateColumn[col]}` + this.columns.push(columnName) + duplicateColumn[col] +=1 + } else { + this.columns.push(col) + duplicateColumn[col] = 1 + } + } + } + + /** + * The basic methos perform the underneath operation of generating + * the merge dataframe; using the combination keys generated from + * bothe left and right DataFrame + * e.g df = { + * key1: ["KO", "K0", "K3", "K4"], + * Key2: ["K1", "K1", "K3", "K5"], + * A: [1,2,3,4] + * B: [3,4,5,6] + * } + * df2 = { + * key1: ["KO", "K0", "K3", "K4"], + * Key2: ["K1", "K2", "K4", "K5"], + * A: [3,6,8,9] + * c: [2,4,6,8] + * } + * Running generatekeyCombination on both left and right data frame + * we should have + * leftKeyDict = { + * 'k0_k1': { + * filters: [[1,3], [2,4]], + * combValues: ["KO", "k1"] + * }, + * 'K3_K3' : { + * filters: [[3,5]], + * combValues: ['K3', 'k3'] + * }, + * 'k4_k5' : { + * filters: [[4,6]] + * combValues: ['K4', 'K5'] + * } + * } + * rightKeyDict = { + * 'k0_k1': { + * filters: [[3,2]], + * combValues: ["KO", "k1"] + * }, + * 'K0_K2': { + * filters: [[6,4]], + * combValues: ['K0', 'K2'] + * }, + * 'K3_K4' : { + * filters: [[8,9]], + * combValues: ['K3', 'k4'] + * }, + * 'k4_k5' : { + * filters: [[9,8]] + * combValues: ['K4', 'K5'] + * } + * } + * The `keys` is generated base on the type of merge operation we want to + * perform. If we assume we are performing `outer` merge (which is a set of the + * key combination from both leftKeyDict and rightKeyDict) then Keys should be + * this + * keys = ['K0_K1', 'K3_K3', 'k4_k5', 'K0_K2', 'k3_k4'] + * The Keys, leftKeyDict and rightKeyDict are used to generated DataFrame data, + * by looping through the Keys and checking if leftKeyDict and rightKeyDict as the + * key if one of them does not the column in that row will be NaN + * e.g Data for each row base on keys + * COLUMNS = ['key1', 'Key2', 'A', 'B', 'A_1', 'C'] + * 'K0_K1': ['K0', 'K1', 1, 3 , 3, 2 ] + * 'K0_K1': ['K0', 'K1', 2, 4, NaN, NaN] + * 'K3_K3': ['k3', 'K3', 3, 5, NaN, NaN] + * 'K4_K5': ['K4', 'K5', 4, 6, 9, 8] + * 'k0_K2': ['k0', 'K2' NaN, NaN, 6, 4] + * 'k3_k4': ['K3', 'K4', NaN, NaN, 8, 6] + * + * @param keys + * @param leftKeyDict + * @param rightKeyDict + */ + private basic(keys: ArrayType1D, leftKeyDict: keyComb, rightKeyDict: keyComb): ArrayType2D { + const data = [] + for (let i=0; i < keys.length; i++) { + const key = keys[i] as string + + if (utils.keyInObject(leftKeyDict, key)) { + const leftRows = leftKeyDict[key].filters + const leftCombValues = leftKeyDict[key].combValues + + for (let lIndex=0; lIndex < leftRows.length; lIndex++) { + const leftRow = leftRows[lIndex] + if (utils.keyInObject(rightKeyDict, key)) { + const rightRows = rightKeyDict[key].filters + for (let rIndex=0; rIndex < rightRows.length; rIndex++) { + const rightRow = rightRows[rIndex] + const combineData = leftCombValues.slice(0) + combineData.push(...leftRow) + combineData.push(...rightRow) + data.push(combineData) + } + } else { + const nanArray = Array(this.rightCol?.length).fill(NaN) + const combineData = leftCombValues.slice(0) + combineData.push(...leftRow) + combineData.push(...nanArray) + data.push(combineData) + } + } + } else { + const rightRows = rightKeyDict[key].filters + const rightCombValues = rightKeyDict[key].combValues + + for (let i =0; i < rightRows.length; i++) { + const rightRow = rightRows[i] + const nanArray = Array(this.leftCol?.length).fill(NaN) + const combineData = rightCombValues.slice(0) + combineData.push(...nanArray) + combineData.push(...rightRow) + data.push(combineData) + + } + } + } + return data + } + + /** + * Generate outer key from leftKeyDict and rightKeyDict + * The Key pass into basic method is the union of + * leftKeyDict and rightKeyDict + * @param leftKeyDict + * @param rightKeyDict + */ + private outer(leftKeyDict: keyComb, rightKeyDict: keyComb): ArrayType2D { + const keys = Object.keys(leftKeyDict) + keys.push(...Object.keys(rightKeyDict)) + + const UniqueKeys = Array.from(new Set(keys)) + const data = this.basic(UniqueKeys, leftKeyDict, rightKeyDict) + return data + } + + /** + * Generate Key for basic method, + * the key geneerated is the intersection of + * leftKeyDict and rightKeyDict + * @param leftKeyDict + * @param rightKeyDict + */ + private inner(leftKeyDict: keyComb, rightKeyDict: keyComb): ArrayType2D { + const leftKey = Object.keys(leftKeyDict) + const rightKey = Object.keys(rightKeyDict) + const keys = leftKey.filter((val) => rightKey.includes(val)) + const data = this.basic(keys, leftKeyDict, rightKeyDict) + return data + } + + + /** + * The key is the leftKeyDict + * @param leftKeyDict + * @param rightKeyDict + */ + private leftMerge(leftKeyDict: keyComb, rightKeyDict: keyComb): ArrayType2D { + const keys = Object.keys(leftKeyDict) + const data = this.basic(keys, leftKeyDict, rightKeyDict) + return data + } + + /** + * The key is the rightKeyDict + * @param leftKeyDict + * @param rightKeyDict + */ + private rightMerge(leftKeyDict: keyComb, rightKeyDict: keyComb): ArrayType2D { + const keys = Object.keys(rightKeyDict) + const data = this.basic(keys, leftKeyDict, rightKeyDict) + return data + } + + /** + * Perform the merge operation + * 1) Obtain both left and right dataframe values + * 2) Generate the leftkeyDict and rightKeyDict + * 3) Generate new merge columns + * 4) check how merge is to be done and apply the + * right methods + */ + operation(): DataFrame { + let leftValues = this.left.values as ArrayType2D + let rightValues = this.right.values as ArrayType2D + let leftKeyDict = this.generateKeyCombination(leftValues, this.leftColIndex) + let rightKeyDict = this.generateKeyCombination(rightValues, this.rightColIndex) + + this.createColumns() + + let data: ArrayType2D= [] + switch (this.how) { + case "outer": + data = this.outer(leftKeyDict, rightKeyDict) + break; + case "inner": + data = this.inner(leftKeyDict, rightKeyDict) + break; + case "left": + data = this.leftMerge(leftKeyDict, rightKeyDict) + break; + case "right": + data = this.rightMerge(leftKeyDict, rightKeyDict) + break; + } + const columns = this.columns as Array + return new DataFrame(data, {columns: [...columns]}) + + } + +} + +/** + * Perform merge operation between two DataFrame + * @param params : { + * left: DataFrame + * right: DataFrame + * on: Array + * how: "outer" | "inner" | "left" | "right" + * } + */ +export default function merge(params: mergeParam): DataFrame { + const mergeClass = new Merge(params) + return mergeClass.operation() +} \ No newline at end of file diff --git a/src/danfojs-base/transformers/scalers/min.max.scaler.ts b/src/danfojs-base/transformers/scalers/min.max.scaler.ts new file mode 100644 index 00000000..c69539bd --- /dev/null +++ b/src/danfojs-base/transformers/scalers/min.max.scaler.ts @@ -0,0 +1,162 @@ +/** +* @license +* Copyright 2022 JsData. All rights reserved. +* +* This source code is licensed under the MIT license found in the +* LICENSE file in the root directory of this source tree. + +* Unless required by applicable law or agreed to in writing, software +* distributed under the License is distributed on an "AS IS" BASIS, +* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +* See the License for the specific language governing permissions and +* limitations under the License. +* ========================================================================== +*/ + +import Series from "../../core/series" +import DataFrame from "../../core/frame" +import Utils from "../../shared/utils" +import tensorflow from '../../shared/tensorflowlib' + +const utils = new Utils() + +/** + * Transform features by scaling each feature to a given range. + * This estimator scales and translates each feature individually such + * that it is in the given range on the training set, e.g. between the maximum and minimum value. +*/ +export default class MinMaxScaler { + private $max: typeof tensorflow.Tensor + private $min: typeof tensorflow.Tensor + + constructor() { + this.$max = tensorflow.tensor1d([]) + this.$min = tensorflow.tensor1d([]) + } + + private $getTensor(data: number[] | number[][] | typeof tensorflow.Tensor | DataFrame | Series) { + let $tensorArray: typeof tensorflow.Tensor + + if (data instanceof Array) { + if (utils.is1DArray(data)) { + $tensorArray = tensorflow.tensor1d(data as number[]) + } else { + $tensorArray = tensorflow.tensor2d(data) + } + } else if (data instanceof DataFrame || data instanceof Series) { + $tensorArray = data.tensor + } else if (data instanceof tensorflow.Tensor) { + $tensorArray = data + } else { + throw new Error("ParamError: data must be one of Array, Tensor, DataFrame or Series") + } + return $tensorArray + } + + /** + * Fits a MinMaxScaler to the data + * @param data Array, Tensor, DataFrame or Series object + * @returns MinMaxScaler + * @example + * const scaler = new MinMaxScaler() + * scaler.fit([1, 2, 3, 4, 5]) + * // MinMaxScaler { + * // $max: [5], + * // $min: [1] + * // } + * + */ + public fit(data: number[] | number[][] | typeof tensorflow.Tensor | DataFrame | Series) { + const tensorArray = this.$getTensor(data) + this.$max = tensorArray.max(0) + this.$min = tensorArray.min(0) + return this + } + + /** + * Transform the data using the fitted scaler + * @param data Array, Tensor, DataFrame or Series object + * @returns Array, Tensor, DataFrame or Series object + * @example + * const scaler = new MinMaxScaler() + * scaler.fit([1, 2, 3, 4, 5]) + * scaler.transform([1, 2, 3, 4, 5]) + * // [0, 0.25, 0.5, 0.75, 1] + * */ + public transform(data: number[] | number[][] | typeof tensorflow.Tensor | DataFrame | Series) { + const tensorArray = this.$getTensor(data) + const outputData = tensorArray + .sub(this.$min) + .div(this.$max.sub(this.$min)) + + if (Array.isArray(data)) { + return outputData.arraySync() + + } else if (data instanceof Series) { + return new Series(outputData, { + index: data.index, + }); + + } else if (data instanceof DataFrame) { + return new DataFrame(outputData, { + index: data.index, + columns: data.columns, + config: { ...data.config }, + }); + } else { + return outputData + } + } + + /** + * Fit the data and transform it + * @param data Array, Tensor, DataFrame or Series object + * @returns Array, Tensor, DataFrame or Series object + * @example + * const scaler = new MinMaxScaler() + * scaler.fitTransform([1, 2, 3, 4, 5]) + * // [0, 0.25, 0.5, 0.75, 1] + * */ + public fitTransform(data: number[] | number[][] | typeof tensorflow.Tensor | DataFrame | Series) { + this.fit(data) + return this.transform(data) + } + + /** + * Inverse transform the data using the fitted scaler + * @param data Array, Tensor, DataFrame or Series object + * @returns Array, Tensor, DataFrame or Series object + * @example + * const scaler = new MinMaxScaler() + * scaler.fit([1, 2, 3, 4, 5]) + * scaler.inverseTransform([0, 0.25, 0.5, 0.75, 1]) + * // [1, 2, 3, 4, 5] + * */ + public inverseTransform(data: number[] | number[][] | typeof tensorflow.Tensor | DataFrame | Series) { + const tensorArray = this.$getTensor(data) + const outputData = tensorArray + .mul(this.$max.sub(this.$min)) + .add(this.$min) + + if (Array.isArray(data)) { + return outputData.arraySync() + + } else if (data instanceof Series) { + return new Series(outputData, { + index: data.index, + }); + + } else if (data instanceof DataFrame) { + return new DataFrame(outputData, { + index: data.index, + columns: data.columns, + config: { ...data.config }, + }); + } else { + return outputData + } + } + +} + + diff --git a/src/danfojs-base/transformers/scalers/standard.scaler.ts b/src/danfojs-base/transformers/scalers/standard.scaler.ts new file mode 100644 index 00000000..fe0c5253 --- /dev/null +++ b/src/danfojs-base/transformers/scalers/standard.scaler.ts @@ -0,0 +1,154 @@ +/** +* @license +* Copyright 2022 JsData. All rights reserved. +* +* This source code is licensed under the MIT license found in the +* LICENSE file in the root directory of this source tree. + +* Unless required by applicable law or agreed to in writing, software +* distributed under the License is distributed on an "AS IS" BASIS, +* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +* See the License for the specific language governing permissions and +* limitations under the License. +* ========================================================================== +*/ + +import tensorflow from '../../shared/tensorflowlib' +import Series from "../../core/series" +import DataFrame from "../../core/frame" +import Utils from "../../shared/utils" + +const utils = new Utils() + +/** + * Standardize features by removing the mean and scaling to unit variance. + * The standard score of a sample x is calculated as: `z = (x - u) / s`, + * where `u` is the mean of the training samples, and `s` is the standard deviation of the training samples. + */ +export default class StandardScaler { + private $std: typeof tensorflow.Tensor + private $mean: typeof tensorflow.Tensor + + constructor() { + this.$std = tensorflow.tensor1d([]) + this.$mean = tensorflow.tensor1d([]) + } + + private $getTensor(data: number[] | number[][] | typeof tensorflow.Tensor | DataFrame | Series) { + let $tensorArray + + if (data instanceof Array) { + if (utils.is1DArray(data)) { + $tensorArray = tensorflow.tensor1d(data as number[]) + } else { + $tensorArray = tensorflow.tensor2d(data) + } + } else if (data instanceof DataFrame || data instanceof Series) { + $tensorArray = data.tensor + } else if (data instanceof tensorflow.Tensor) { + $tensorArray = data + } else { + throw new Error("ParamError: data must be one of Array, DataFrame or Series") + } + return $tensorArray + } + /** + * Fit a StandardScaler to the data. + * @param data Array, Tensor, DataFrame or Series object + * @returns StandardScaler + * @example + * const scaler = new StandardScaler() + * scaler.fit([1, 2, 3, 4, 5]) + */ + public fit(data: number[] | number[][] | typeof tensorflow.Tensor | DataFrame | Series) { + const tensorArray = this.$getTensor(data) + this.$std = tensorflow.moments(tensorArray, 0).variance.sqrt(); + this.$mean = tensorArray.mean(0); + return this + } + + /** + * Transform the data using the fitted scaler + * @param data Array, Tensor, DataFrame or Series object + * @returns Array, Tensor, DataFrame or Series object + * @example + * const scaler = new StandardScaler() + * scaler.fit([1, 2, 3, 4, 5]) + * scaler.transform([1, 2, 3, 4, 5]) + * // [0.0, 0.0, 0.0, 0.0, 0.0] + * */ + public transform(data: number[] | number[][] | typeof tensorflow.Tensor | DataFrame | Series) { + const tensorArray = this.$getTensor(data) + const outputData = tensorArray.sub(this.$mean).div(this.$std) + + if (Array.isArray(data)) { + return outputData.arraySync() + + } else if (data instanceof Series) { + return new Series(outputData, { + index: data.index, + }); + + } else if (data instanceof DataFrame) { + return new DataFrame(outputData, { + index: data.index, + columns: data.columns, + config: { ...data.config }, + }); + } else { + return outputData + } + } + + /** + * Fit and transform the data using the fitted scaler + * @param data Array, Tensor, DataFrame or Series object + * @returns Array, Tensor, DataFrame or Series object + * @example + * const scaler = new StandardScaler() + * scaler.fit([1, 2, 3, 4, 5]) + * scaler.fitTransform([1, 2, 3, 4, 5]) + * // [0.0, 0.0, 0.0, 0.0, 0.0] + * */ + public fitTransform(data: number[] | number[][] | typeof tensorflow.Tensor | DataFrame | Series) { + this.fit(data) + return this.transform(data) + } + + /** + * Inverse transform the data using the fitted scaler + * @param data Array, Tensor, DataFrame or Series object + * @returns Array, Tensor, DataFrame or Series object + * @example + * const scaler = new StandardScaler() + * scaler.fit([1, 2, 3, 4, 5]) + * scaler.transform([1, 2, 3, 4, 5]) + * // [0.0, 0.0, 0.0, 0.0, 0.0] + * scaler.inverseTransform([0.0, 0.0, 0.0, 0.0, 0.0]) + * // [1, 2, 3, 4, 5] + * */ + public inverseTransform(data: number[] | number[][] | typeof tensorflow.Tensor | DataFrame | Series) { + const tensorArray = this.$getTensor(data) + const outputData = tensorArray.mul(this.$std).add(this.$mean) + + if (Array.isArray(data)) { + return outputData.arraySync() + + } else if (data instanceof Series) { + return new Series(outputData, { + index: data.index, + }); + + } else if (data instanceof DataFrame) { + return new DataFrame(outputData, { + index: data.index, + columns: data.columns, + config: { ...data.config }, + }); + } else { + return outputData + } + } +} + + diff --git a/src/danfojs-base/yarn.lock b/src/danfojs-base/yarn.lock new file mode 100644 index 00000000..af8dc9bf --- /dev/null +++ b/src/danfojs-base/yarn.lock @@ -0,0 +1,7165 @@ +# THIS IS AN AUTOGENERATED FILE. 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rename from danfojs-browser/.babelrc rename to src/danfojs-browser/.babelrc diff --git a/danfojs-browser/.coveralls.yml b/src/danfojs-browser/.coveralls.yml similarity index 100% rename from danfojs-browser/.coveralls.yml rename to src/danfojs-browser/.coveralls.yml diff --git a/src/danfojs-browser/.eslintrc b/src/danfojs-browser/.eslintrc new file mode 100644 index 00000000..66ee5d14 --- /dev/null +++ b/src/danfojs-browser/.eslintrc @@ -0,0 +1,3 @@ +{ + "extends": ["plugin:@typescript-eslint/recommended"] + } \ No newline at end of file diff --git a/danfojs-browser/.eslintrc.json b/src/danfojs-browser/.eslintrc.json similarity index 99% rename from danfojs-browser/.eslintrc.json rename to src/danfojs-browser/.eslintrc.json index 89438352..f247ca42 100644 --- a/danfojs-browser/.eslintrc.json +++ b/src/danfojs-browser/.eslintrc.json @@ -58,6 +58,7 @@ ], "comma-style": "error", "no-unused-vars": [ "off"] // Use @typescript-eslint/no-unused-vars instead, otherwise there will be checked twice - } + +} } diff --git a/danfojs-browser/.gitignore b/src/danfojs-browser/.gitignore similarity index 84% rename from danfojs-browser/.gitignore rename to src/danfojs-browser/.gitignore index 0ee6caa9..3e6fe8d0 100644 --- a/danfojs-browser/.gitignore +++ b/src/danfojs-browser/.gitignore @@ -6,4 +6,6 @@ danfojs/data .DS_Store .idea/ -dist/ \ No newline at end of file +dist +build +lib \ No newline at end of file diff --git a/danfojs-browser/.npmignore b/src/danfojs-browser/.npmignore similarity index 100% rename from danfojs-browser/.npmignore rename to src/danfojs-browser/.npmignore diff --git a/danfojs-browser/README.md b/src/danfojs-browser/README.md similarity index 58% rename from danfojs-browser/README.md rename to src/danfojs-browser/README.md index 10c907ac..8f880afe 100644 --- a/danfojs-browser/README.md +++ b/src/danfojs-browser/README.md @@ -47,146 +47,170 @@ easy and intuitive. It is heavily inspired by [Pandas](https://pandas.pydata.org - Robust data preprocessing functions like [OneHotEncoders](https://danfo.jsdata.org/api-reference/general-functions/danfo.onehotencoder), [LabelEncoders](https://danfo.jsdata.org/api-reference/general-functions/danfo.labelencoder), and scalers like [StandardScaler](https://danfo.jsdata.org/api-reference/general-functions/danfo.standardscaler) and [MinMaxScaler](https://danfo.jsdata.org/api-reference/general-functions/danfo.minmaxscaler) are supported on DataFrame and Series +## Installation +There are three ways to install and use Danfo.js in your application +* For Nodejs applications, you can install the [__danfojs-node__]() version via package managers like yarn and/or npm: -To use Danfo.js via script tags, copy and paste the CDN below to the body of your HTML file +```bash +npm install danfojs-node + +or + +yarn add danfojs-node +``` +For client-side applications built with frameworks like React, Vue, Next.js, etc, you can install the [__danfojs__]() version: + +```bash +npm install danfojs + +or + +yarn add danfojs +``` + +For use directly in HTML files, you can add the latest script tag from [JsDelivr](https://www.jsdelivr.com/package/npm/danfojs) to your HTML file: + ```html - + ``` +See all available versions [here](https://www.jsdelivr.com/package/npm/danfojs) ### Example Usage in the Browser -> See the example below in [Code Sandbox](https://codepen.io/risingodegua/pen/bGwPGMG) +> Run in [Code Sandbox](https://codepen.io/risingodegua/pen/bGwPGMG) ```html - - - - - + + + + Document - - - + +
- - - + + ``` Output in Browser: ![](assets/browser-out.gif) -## How to install -Danfo.js is hosted on NPM, and can installed via package managers like npm and yarn - -```sh -npm install danfojs-node -``` - ### Example usage in Nodejs ```javascript const dfd = require("danfojs-node") +const file_url = "https://web.stanford.edu/class/archive/cs/cs109/cs109.1166/stuff/titanic.csv" +dfd.readCSV(file_url) + .then(df => { + //prints the first five columns + df.head().print() + + // Calculate descriptive statistics for all numerical columns + df.describe().print() -dfd.read_csv("https://web.stanford.edu/class/archive/cs/cs109/cs109.1166/stuff/titanic.csv") - .then(df => { - //prints the first five columns - df.head().print() + //prints the shape of the data + console.log(df.shape); - //Calculate descriptive statistics for all numerical columns - df.describe().print() + //prints all column names + console.log(df.columns); - //prints the shape of the data - console.log(df.shape); + // //prints the inferred dtypes of each column + df.ctypes.print() - //prints all column names - console.log(df.column_names); + //selecting a column by subsetting + df['Name'].print() - //prints the inferred dtypes of each column - df.ctypes.print() + //drop columns by names + cols_2_remove = ['Age', 'Pclass'] + df_drop = df.drop({ columns: cols_2_remove, axis: 1 }) + df_drop.print() - //selecting a column by subsetting - df['Name'].print() - //drop columns by names - cols_2_remove = ['Age', 'Pclass'] - df_drop = df.drop({ columns: cols_2_remove, axis: 1 }) - df_drop.print() + //select columns by dtypes + let str_cols = df_drop.selectDtypes(["string"]) + let num_cols = df_drop.selectDtypes(["int32", "float32"]) + str_cols.print() + num_cols.print() - //select columns by dtypes - let str_cols = df_drop.select_dtypes(["string"]) - let num_cols = df_drop.select_dtypes(["int32", "float32"]) - str_cols.print() - num_cols.print() + //add new column to Dataframe + let new_vals = df['Fare'].round(1) + df_drop.addColumn("fare_round", new_vals, { inplace: true }) + df_drop.print() - //add new column to Dataframe - let new_vals = df['Fare'].round().values - df_drop.addColumn({ column: "fare_round", value: new_vals}) - df_drop.print() + df_drop['fare_round'].round(2).print(5) - df_drop['fare_round'].print(5) + //prints the number of occurence each value in the column + df_drop['Survived'].valueCounts().print() - //prints the number of occurence each value in the column - df_drop['Survived'].value_counts().print() + //print the last ten elementa of a DataFrame + df_drop.tail(10).print() - //print the last ten elementa of a DataFrame - df_drop.tail(10).print() + //prints the number of missing values in a DataFrame + df_drop.isNa().sum().print() - //prints the number of missing values in a DataFrame - df_drop.isna().sum().print() + }).catch(err => { + console.log(err); + }) - }).catch(err => { - console.log(err); - }) ``` Output in Node Console: ![](assets/node-rec.gif) - -> If you want to use Danfo in frontend frameworks like React/Vue, read this [guide](https://danfo.jsdata.org/examples/using-danfojs-in-react) - -#### You can play with Danfo.js on Dnotebooks playground [here](https://playnotebook.jsdata.org/demo) +## Notebook support +* You can use Danfo.js on Dnotebooks playground [here](https://playnotebook.jsdata.org/demo) +* VsCode nodejs notebook extension now supports Danfo.js. See guide [here](https://marketplace.visualstudio.com/items?itemName=donjayamanne.typescript-notebook) #### [See the Official Getting Started Guide](https://danfo.jsdata.org/getting-started) ## Documentation The official documentation can be found [here](https://danfo.jsdata.org) +## Danfo.js Official Book + +![image](https://user-images.githubusercontent.com/29900845/134811659-25ff6b05-8e0d-415f-a60c-03ab1d33fd71.jpeg) + + +We recently published a book titled "Building Data Driven Applications with Danfo.js". Read more about it [here](https://danfo.jsdata.org/building-data-driven-applications-with-danfo.js-book) + ## Discussion and Development -Development discussions take place on our [issues](https://github.com/opensource9ja/danfojs/issues) tab. +Development discussions take place [here](https://github.com/opensource9ja/danfojs/discussions). ## Contributing to Danfo All contributions, bug reports, bug fixes, documentation improvements, enhancements, and ideas are welcome. A detailed overview on how to contribute can be found in the [contributing guide](https://danfo.jsdata.org/contributing-guide). @@ -195,4 +219,4 @@ All contributions, bug reports, bug fixes, documentation improvements, enhanceme #### Created by [Rising Odegua](https://github.com/risenW) and [Stephen Oni](https://github.com/steveoni) -Danfo.js - Open Source JavaScript library for manipulating data. | Product Hunt Embed +Danfo.js - Open Source JavaScript library for manipulating data. | Product Hunt Embed \ No newline at end of file diff --git a/danfojs-browser/babel.config.json b/src/danfojs-browser/babel.config.json similarity index 100% rename from danfojs-browser/babel.config.json rename to src/danfojs-browser/babel.config.json diff --git a/danfojs-browser/karma.conf.js b/src/danfojs-browser/karma.conf.js similarity index 94% rename from danfojs-browser/karma.conf.js rename to src/danfojs-browser/karma.conf.js index d4e213dd..922b7d82 100644 --- a/danfojs-browser/karma.conf.js +++ b/src/danfojs-browser/karma.conf.js @@ -8,7 +8,7 @@ module.exports = function (config) { frameworks: [ "mocha", "chai", "browserify" ], // list of files / patterns to load in the browser - files: [ "lib/bundle.js", "tests/**/*.js" ], + files: [ "lib/bundle.js", "tests/**/*.test.js" ], // list of files / patterns to exclude exclude: [], @@ -16,7 +16,7 @@ module.exports = function (config) { // preprocess matching files before serving them to the browser // available preprocessors: https://npmjs.org/browse/keyword/karma-preprocessor preprocessors: { - "tests/**/*.js": [ "browserify" ] + "tests/**/*.test.js": [ "browserify" ] }, // test results reporter to use diff --git a/src/danfojs-browser/nodemon.json b/src/danfojs-browser/nodemon.json new file mode 100644 index 00000000..5131f099 --- /dev/null +++ b/src/danfojs-browser/nodemon.json @@ -0,0 +1,6 @@ +{ + "watch": ["src"], + "ext": ".ts,.js", + "ignore": [], + "exec": "ts-node ./src/index.ts" + } \ No newline at end of file diff --git a/danfojs-browser/package.json b/src/danfojs-browser/package.json similarity index 57% rename from danfojs-browser/package.json rename to src/danfojs-browser/package.json index 7cf9788e..01f7939d 100644 --- a/danfojs-browser/package.json +++ b/src/danfojs-browser/package.json @@ -1,9 +1,9 @@ { "name": "danfojs", - "version": "0.2.7", + "version": "1.0.0", "description": "JavaScript library providing high performance, intuitive, and easy to use data structures for manipulating and processing structured data.", - "main": "lib/bundle.js", - "types": "types/index.d.ts", + "main": "dist/danfojs-browser/src/index.js", + "types": "dist/danfojs-browser/src/index.d.ts", "directories": { "test": "tests" }, @@ -17,20 +17,27 @@ ], "files": [ "lib/", - "src/", - "types" + "dist/" ], "dependencies": { - "@tensorflow/tfjs": "3.6.0", - "mathjs": "7.5.1", - "table": "^5.4.6", - "xlsx": "^0.16.7" + "@tensorflow/tfjs": "^3.11.0", + "mathjs": "9.4.4", + "papaparse": "^5.3.1", + "plotly.js-dist-min": "^2.8.0", + "request": "^2.88.2", + "seedrandom": "^2.4.3", + "stream-json": "^1.7.3", + "table": "6.7.1", + "xlsx": "^0.17.2" }, "scripts": { - "test": "yarn run bundle && karma start --single-run --browsers ChromeHeadless karma.conf.js", - "build": "babel ./src -d ./dist --no-comments", + "test": "karma start --single-run --browsers ChromeHeadless karma.conf.js", + "test:clean": "yarn build:clean && yarn run test", + "build": "node ./scripts/prebuild.js && tsc && yarn run bundle", + "build:clean": "rimraf ./dist && rimraf ./lib && node ./scripts/prebuild.js && yarn run build", + "dev": "nodemon", "lint": "eslint ./src", - "bundle": "webpack --mode production", + "bundle": "webpack --mode development", "coveralls": "cat ./coverage/lcov.info | ./node_modules/.bin/coveralls", "coverage": "nyc report --reporter=text-lcov | coveralls && nyc report --reporter=lcov", "patch": "npm version patch" @@ -62,11 +69,26 @@ "@babel/core": "^7.10.5", "@babel/plugin-transform-runtime": "^7.12.10", "@babel/preset-env": "^7.10.4", + "@types/chai": "^4.2.19", + "@types/mocha": "^8.2.2", + "@types/node": "^15.12.5", + "@types/papaparse": "^5.2.6", + "@types/plotly.js-dist-min": "^2.3.0", + "@types/request": "^2.48.7", + "@types/stream-json": "^1.7.1", + "@types/table": "^6.3.2", + "@typescript-eslint/eslint-plugin": "^4.30.0", + "@typescript-eslint/parser": "^4.30.0", + "awesome-typescript-loader": "^5.2.1", "babel-loader": "^8.1.0", "browserify": "16.5.2", "chai": "^4.2.0", "coveralls": "^3.1.0", + "dotenv": "^10.0.0", "eslint": "^7.1.0", + "ify-loader": "^1.1.0", + "install-peers": "^1.0.3", + "json-loader": "^0.5.7", "karma": "5.2.3", "karma-browserify": "7.0.0", "karma-chai": "^0.1.0", @@ -74,6 +96,9 @@ "karma-mocha": "^2.0.1", "mocha": "^8.3.0", "nyc": "^15.1.0", + "source-map-loader": "^3.0.0", + "ts-loader": "^9.2.6", + "typescript": "^4.4.2", "webpack": "5.21.2", "webpack-cli": "4.5.0", "yarn": "^1.22.10" @@ -85,4 +110,4 @@ ] }, "sideEffects": false -} \ No newline at end of file +} diff --git a/src/danfojs-browser/scripts/prebuild.js b/src/danfojs-browser/scripts/prebuild.js new file mode 100644 index 00000000..2cc91ece --- /dev/null +++ b/src/danfojs-browser/scripts/prebuild.js @@ -0,0 +1,32 @@ +/** +* @license +* Copyright 2022 JsData. All rights reserved. +* +* This source code is licensed under the MIT license found in the +* LICENSE file in the root directory of this source tree. + +* Unless required by applicable law or agreed to in writing, software +* distributed under the License is distributed on an "AS IS" BASIS, +* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +* See the License for the specific language governing permissions and +* limitations under the License. +* ========================================================================== +*/ + +/** + * Danfojs-node version uses Tensorflowjs-node package. Hence on bundling, we set the tensorflow lib + * to use the Tensorflowjs-node package, and vice-versa for browser. + * */ + +/* eslint-disable no-undef */ +const fs = require('fs'); + +function updateTensorflowLib(tensorflowLibPath) { + const importStatement = ` + // This file is auto-generated by prebuild.js. Do not edit! + const tf = require("@tensorflow/tfjs")\nexport default tf + `; + fs.writeFileSync(tensorflowLibPath, importStatement); +} + +updateTensorflowLib('../danfojs-base/shared/tensorflowlib.ts'); diff --git a/src/danfojs-browser/src/core/frame.ts b/src/danfojs-browser/src/core/frame.ts new file mode 100644 index 00000000..5cc0940d --- /dev/null +++ b/src/danfojs-browser/src/core/frame.ts @@ -0,0 +1,34 @@ +/** +* @license +* Copyright 2022 JsData. All rights reserved. +* +* This source code is licensed under the MIT license found in the +* LICENSE file in the root directory of this source tree. + +* Unless required by applicable law or agreed to in writing, software +* distributed under the License is distributed on an "AS IS" BASIS, +* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +* See the License for the specific language governing permissions and +* limitations under the License. +* ========================================================================== +*/ +import { BaseDataOptionType } from "../../../danfojs-base/shared/types"; +import BaseDataFrame from "../../../danfojs-base/core/frame" + + +/** + * Two-dimensional ndarray with axis labels. + * The object supports both integer- and label-based indexing and provides a host of methods for performing operations involving the index. + * Operations between DataFrame (+, -, /, , *) align values based on their associated index values– they need not be the same length. + * @param data 2D Array, JSON, Tensor, Block of data. + * @param options.index Array of numeric or string names for subseting array. If not specified, indexes are auto generated. + * @param options.columns Array of column names. If not specified, column names are auto generated. + * @param options.dtypes Array of data types for each the column. If not specified, dtypes are/is inferred. + * @param options.config General configuration object for extending or setting NDframe behavior. + */ +export default class DataFrame extends BaseDataFrame { + [key: string]: any + constructor(data?: any, options: BaseDataOptionType = {}) { + super(data, options) + } +} \ No newline at end of file diff --git a/src/danfojs-browser/src/core/series.ts b/src/danfojs-browser/src/core/series.ts new file mode 100644 index 00000000..58e3489b --- /dev/null +++ b/src/danfojs-browser/src/core/series.ts @@ -0,0 +1,34 @@ +/** +* @license +* Copyright 2022 JsData. All rights reserved. +* +* This source code is licensed under the MIT license found in the +* LICENSE file in the root directory of this source tree. + +* Unless required by applicable law or agreed to in writing, software +* distributed under the License is distributed on an "AS IS" BASIS, +* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +* See the License for the specific language governing permissions and +* limitations under the License. +* ========================================================================== +*/ +import { BaseDataOptionType } from "../../../danfojs-base/shared/types"; +import BaseSeries from "../../../danfojs-base/core/series" + + +/** + * One-dimensional ndarray with axis labels. + * The object supports both integer- and label-based indexing and provides a host of methods for performing operations involving the index. + * Operations between Series (+, -, /, , *) align values based on their associated index values – they need not be the same length. + * @param data 1D Array, JSON, Tensor, Block of data. + * @param options.index Array of numeric or string index for subseting array. If not specified, indices are auto generated. + * @param options.columns Column name. This is like the name of the Series. If not specified, column name is set to 0. + * @param options.dtypes Data types of the Series data. If not specified, dtypes is inferred. + * @param options.config General configuration object for extending or setting Series behavior. + */ +export default class Series extends BaseSeries { + [key: string]: any + constructor(data?: any, options: BaseDataOptionType = {}) { + super(data, options) + } +} \ No newline at end of file diff --git a/src/danfojs-browser/src/index.ts b/src/danfojs-browser/src/index.ts new file mode 100644 index 00000000..91a0d01e --- /dev/null +++ b/src/danfojs-browser/src/index.ts @@ -0,0 +1,73 @@ +/** +* @license +* Copyright 2022 JsData. All rights reserved. +* +* This source code is licensed under the MIT license found in the +* LICENSE file in the root directory of this source tree. + +* Unless required by applicable law or agreed to in writing, software +* distributed under the License is distributed on an "AS IS" BASIS, +* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +* See the License for the specific language governing permissions and +* limitations under the License. +* ========================================================================== +*/ +import { + NDframe, + Config, + Utils, + Str, + Dt, + MinMaxScaler, + StandardScaler, + LabelEncoder, + OneHotEncoder, + getDummies, + concat, + merge, + toDateTime, + dateRange, + tensorflow, + __version, +} from "../../danfojs-base"; + +import { + readCSVBrowser as readCSV, + streamCSVBrowser as streamCSV, + toCSVBrowser as toCSV, + readJSONBrowser as readJSON, + toJSONBrowser as toJSON, + readExcelBrowser as readExcel, + toExcelBrowser as toExcel, +} from "../../danfojs-base/io/browser" + +import DataFrame from "./core/frame" +import Series from "./core/series" + +export { + NDframe, + Config, + Utils, + Str, + Dt, + toDateTime, + Series, + DataFrame, + readCSV, + streamCSV, + toCSV, + readJSON, + toJSON, + readExcel, + toExcel, + MinMaxScaler, + StandardScaler, + LabelEncoder, + OneHotEncoder, + getDummies, + concat, + merge, + dateRange, + tensorflow, + __version, +} diff --git a/danfojs-browser/tests/core/groupby.js b/src/danfojs-browser/tests/aggregators/groupby.test.js similarity index 58% rename from danfojs-browser/tests/core/groupby.js rename to src/danfojs-browser/tests/aggregators/groupby.test.js index 4bb433db..e56a2aa8 100644 --- a/danfojs-browser/tests/core/groupby.js +++ b/src/danfojs-browser/tests/aggregators/groupby.test.js @@ -1,21 +1,21 @@ +/* eslint-disable no-undef */ describe("groupby", function () { it("Check group by One column data", function () { - let data = [ [ 1, 2, 3 ], [ 4, 5, 6 ], [ 20, 30, 40 ], [ 39, 89, 78 ] ]; let cols = [ "A", "B", "C" ]; let df = new dfd.DataFrame(data, { columns: cols }); - let group_df = df.groupby([ "A" ]); + let groupDf = df.groupby([ "A" ]); - let group_dict = { - '1': [ [ 1, 2, 3 ] ], - '4': [ [ 4, 5, 6 ] ], - '20': [ [ 20, 30, 40 ] ], - '39': [ [ 39, 89, 78 ] ] + let groupDict = { + '1': { A: [ 1 ], B: [ 2 ], C: [ 3 ] }, + '4': { A: [ 4 ], B: [ 5 ], C: [ 6 ] }, + '20': { A: [ 20 ], B: [ 30 ], C: [ 40 ] }, + '39': { A: [ 39 ], B: [ 89 ], C: [ 78 ] } }; - - assert.deepEqual(group_df.col_dict, group_dict); + assert.deepEqual(groupDf.colDict, groupDict); }); + it("Obtain the DataFrame of one of the group", function () { let data = [ [ 1, 2, 3 ], [ 4, 5, 6 ], [ 20, 30, 40 ], [ 39, 89, 78 ] ]; @@ -23,9 +23,9 @@ describe("groupby", function () { let df = new dfd.DataFrame(data, { columns: cols }); let group_df = df.groupby([ "A" ]); let new_data = [ [ 1, 2, 3 ] ]; - - assert.deepEqual(group_df.get_groups([ 1 ]).values, new_data); + assert.deepEqual(group_df.getGroup([ 1 ]).values, new_data); }); + it("Check group by Two column data", function () { let data = [ [ 1, 2, 3 ], [ 4, 5, 6 ], [ 20, 30, 40 ], [ 39, 89, 78 ] ]; @@ -33,13 +33,12 @@ describe("groupby", function () { let df = new dfd.DataFrame(data, { columns: cols }); let group_df = df.groupby([ "A", "B" ]); let new_data = { - '1': { '2': [ [ 1, 2, 3 ] ] }, - '4': { '5': [ [ 4, 5, 6 ] ] }, - '20': { '30': [ [ 20, 30, 40 ] ] }, - '39': { '89': [ [ 39, 89, 78 ] ] } + '1-2': { A: [ 1 ], B: [ 2 ], C: [ 3 ] }, + '4-5': { A: [ 4 ], B: [ 5 ], C: [ 6 ] }, + '20-30': { A: [ 20 ], B: [ 30 ], C: [ 40 ] }, + '39-89': { A: [ 39 ], B: [ 89 ], C: [ 78 ] } }; - - assert.deepEqual(group_df.col_dict, new_data); + assert.deepEqual(group_df.colDict, new_data); }); it("Obtain the DataFrame of one of the group, grouped by two column", function () { @@ -50,7 +49,7 @@ describe("groupby", function () { let group_df = df.groupby([ "A", "B" ]); let new_data = [ [ 1, 2, 3 ] ]; - assert.deepEqual(group_df.get_groups([ 1, 2 ]).values, new_data); + assert.deepEqual(group_df.getGroup([ 1, 2 ]).values, new_data); }); it("Count column in group", function () { @@ -68,6 +67,7 @@ describe("groupby", function () { assert.deepEqual(group_df.col([ "C" ]).count().values, new_data); }); + it("sum column element in group", function () { let data = [ [ 1, 2, 3 ], [ 4, 5, 6 ], [ 20, 30, 40 ], [ 39, 89, 78 ] ]; @@ -83,7 +83,7 @@ describe("groupby", function () { assert.deepEqual(group_df.col([ "C" ]).sum().values, new_data); }); - it("sum column element group by one column", function () { + it("sum column element group by two column", function () { let data = [ [ 1, 2, 3 ], [ 1, 5, 6 ], [ 20, 30, 40 ], [ 39, 89, 78 ] ]; let cols = [ "A", "B", "C" ]; @@ -113,8 +113,9 @@ describe("groupby", function () { [ 39, 89, 89, 1 ] ]; - assert.deepEqual(group_df.agg({ "B": "mean", "C": "count" }).values, new_data); + assert.deepEqual(group_df.agg({ B: "mean", C: "count" }).values, new_data); }); + it("cummulative sum for groupby", function () { let data = [ [ 1, 2, 3 ], [ 4, 5, 6 ], [ 20, 30, 40 ], [ 39, 89, 78 ] ]; @@ -127,8 +128,9 @@ describe("groupby", function () { [ 20, 30, 30, 40 ], [ 39, 89, 89, 78 ] ]; - assert.deepEqual(group_df.col([ "B", "C" ]).cumsum().values, new_data); + assert.deepEqual(group_df.col([ "B", "C" ]).cumSum().values, new_data); }); + it("cummulative max for groupby", function () { let data = [ [ 1, 2, 3 ], [ 4, 5, 6 ], [ 20, 30, 40 ], [ 39, 89, 78 ] ]; @@ -138,8 +140,9 @@ describe("groupby", function () { let new_data = [ [ 1, 3 ], [ 4, 6 ], [ 20, 40 ], [ 39, 78 ] ]; - assert.deepEqual(group_df.col([ "C" ]).cummax().values, new_data); + assert.deepEqual(group_df.col([ "C" ]).cumMax().values, new_data); }); + it("cummulative min for groupby", function () { let data = [ [ 1, 2, 3 ], [ 4, 5, 6 ], [ 20, 30, 40 ], [ 39, 89, 78 ] ]; @@ -148,7 +151,7 @@ describe("groupby", function () { let group_df = df.groupby([ "A" ]); let new_data = [ [ 1, 3 ], [ 4, 6 ], [ 20, 40 ], [ 39, 78 ] ]; - assert.deepEqual(group_df.col([ "C" ]).cummin().values, new_data); + assert.deepEqual(group_df.col([ "C" ]).cumMin().values, new_data); }); it("cummulative prod for groupby", function () { @@ -159,8 +162,9 @@ describe("groupby", function () { let group_df = df.groupby([ "A" ]); let new_data = [ [ 1, 3 ], [ 4, 6 ], [ 20, 40 ], [ 39, 78 ] ]; - assert.deepEqual(group_df.col([ "C" ]).cumprod().values, new_data); + assert.deepEqual(group_df.col([ "C" ]).cumProd().values, new_data); }); + it("mean for groupby", function () { let data = [ [ 1, 2, 3 ], [ 4, 5, 6 ], [ 20, 30, 40 ], [ 39, 89, 78 ] ]; @@ -177,119 +181,176 @@ describe("groupby", function () { assert.deepEqual(group_df.col([ "B", "C" ]).mean().values, new_data); }); - it("printing multiindex table, example with cumsum operation for dataframe group by one column", function(){ + it("should apply grouby operation to all column", function(){ let data = { 'A': [ 'foo', 'bar', 'foo', 'bar', 'foo', 'bar', 'foo', 'foo' ], 'B': [ 'one', 'one', 'two', 'three', 'two', 'two', 'one', 'three' ], 'C': [ 1, 3, 2, 4, 5, 2, 6, 7 ], - 'D': [ 3, 2, 4, 1, 5, 6, 7, 8 ] }; - - + 'D': [ 3, 2, 4, 1, 5, 6, 7, 8 ] + }; let df = new dfd.DataFrame(data); - - let grp = df.groupby([ "A" ]); + let grp = df.groupby([ "A", "B" ]); let rslt = [ - [ 'foo', 1 ], - [ 'foo', 3 ], - [ 'foo', 8 ], - [ 'foo', 14 ], - [ 'foo', 21 ], - [ 'bar', 3 ], - [ 'bar', 7 ], - [ 'bar', 9 ] + [ 'foo', 'one', 2, 2 ], + [ 'foo', 'two', 2, 2 ], + [ 'foo', 'three', 1, 1 ], + [ 'bar', 'one', 1, 1 ], + [ 'bar', 'three', 1, 1 ], + [ 'bar', 'two', 1, 1 ] ]; - assert.deepEqual(grp.col([ "C" ]).cumsum().values, rslt); + assert.deepEqual(grp.count().values, rslt); }); - it("printing multiindex table, example with cumsum operation for dataframe group by one column", function(){ + it("should apply function to specific column", function () { + let data = { 'A': [ 'foo', 'bar', 'foo', 'bar', 'foo', 'bar', 'foo', 'foo' ], 'B': [ 'one', 'one', 'two', 'three', 'two', 'two', 'one', 'three' ], 'C': [ 1, 3, 2, 4, 5, 2, 6, 7 ], - 'D': [ 3, 2, 4, 1, 5, 6, 7, 8 ] }; - + 'D': [ 3, 2, 4, 1, 5, 6, 7, 8 ] + }; let df = new dfd.DataFrame(data); + let group_df = df.groupby([ "A" ]); + let rslt = [ + [ 'foo', 5, 3 ], + [ 'foo', 6, 4 ], + [ 'foo', 7, 7 ], + [ 'foo', 9, 8 ], + [ 'foo', 10, 9 ], + [ 'bar', 4, 5 ], + [ 'bar', 3, 6 ], + [ 'bar', 8, 4 ] + ]; + assert.deepEqual(group_df.col([ 'D', 'C' ]).apply((x) => x.add(2)).values, rslt); + }); - - let grp = df.groupby([ "A", "B" ]); + it("should apply function to group column", function () { + let data = { 'A': [ 'foo', 'bar', 'foo', 'bar', + 'foo', 'bar', 'foo', 'foo' ], + 'B': [ 'one', 'one', 'two', 'three', + 'two', 'two', 'one', 'three' ], + 'C': [ 1, 3, 2, 4, 5, 2, 6, 7 ], + 'D': [ 3, 2, 4, 1, 5, 6, 7, 8 ] + }; + let df = new dfd.DataFrame(data); + let group_df = df.groupby([ "A", "B" ]); let rslt = [ - [ 'foo', 'one', 1, 3 ], - [ 'foo', 'one', 7, 10 ], - [ 'foo', 'two', 2, 4 ], - [ 'foo', 'two', 7, 9 ], - [ 'foo', 'three', 7, 8 ], - [ 'bar', 'one', 3, 2 ], - [ 'bar', 'two', 2, 6 ], - [ 'bar', 'three', 4, 1 ] + [ 'foo', 'one', 2, 2, 2, 2 ], + [ 'foo', 'two', 2, 2, 2, 2 ], + [ 'foo', 'three', 1, 1, 1, 1 ], + [ 'bar', 'one', 1, 1, 1, 1 ], + [ 'bar', 'three', 1, 1, 1, 1 ], + [ 'bar', 'two', 1, 1, 1, 1 ] ]; - assert.deepEqual(grp.col([ "C", "D" ]).cumsum().values, rslt); + assert.deepEqual(group_df.apply((x) => x.count({ axis:0 })).values, rslt); + }); + it("should obtain the number of groups", function () { + let data = { 'A': [ 'foo', 'bar', 'foo', 'bar', + 'foo', 'bar', 'foo', 'foo' ], + 'B': [ 'one', 'one', 'two', 'three', + 'two', 'two', 'one', 'three' ], + 'C': [ 1, 3, 2, 4, 5, 2, 6, 7 ], + 'D': [ 3, 2, 4, 1, 5, 6, 7, 8 ] + }; + let df = new dfd.DataFrame(data); + let group_df = df.groupby([ "A", "B" ]); + let rslt = 6; + assert.equal(group_df.ngroups, rslt); }); - it("should apply grouby operation to all column", function(){ + it("should obtain all groups", function () { let data = { 'A': [ 'foo', 'bar', 'foo', 'bar', 'foo', 'bar', 'foo', 'foo' ], 'B': [ 'one', 'one', 'two', 'three', 'two', 'two', 'one', 'three' ], 'C': [ 1, 3, 2, 4, 5, 2, 6, 7 ], - 'D': [ 3, 2, 4, 1, 5, 6, 7, 8 ] }; - + 'D': [ 3, 2, 4, 1, 5, 6, 7, 8 ] + }; let df = new dfd.DataFrame(data); + let group_df = df.groupby([ "A", "B" ]); + let rslt = { + 'foo-one': { + A: [ 'foo', 'foo' ], + B: [ 'one', 'one' ], + C: [ 1, 6 ], + D: [ 3, 7 ] + }, + 'bar-one': { A: [ 'bar' ], B: [ 'one' ], C: [ 3 ], D: [ 2 ] }, + 'foo-two': { + A: [ 'foo', 'foo' ], + B: [ 'two', 'two' ], + C: [ 2, 5 ], + D: [ 4, 5 ] + }, + 'bar-three': { A: [ 'bar' ], B: [ 'three' ], C: [ 4 ], D: [ 1 ] }, + 'bar-two': { A: [ 'bar' ], B: [ 'two' ], C: [ 2 ], D: [ 6 ] }, + 'foo-three': { A: [ 'foo' ], B: [ 'three' ], C: [ 7 ], D: [ 8 ] } + }; + assert.deepEqual(group_df.groups, rslt); + }); - let grp = df.groupby([ "A", "B" ]); + it("should obtain the first row of all groups", function () { + let data = { 'A': [ 'foo', 'bar', 'foo', 'bar', + 'foo', 'bar', 'foo', 'foo' ], + 'B': [ 'one', 'one', 'one', 'three', + 'two', 'two', 'one', 'three' ], + 'C': [ 1, 3, 2, 4, 5, 2, 6, 7 ], + 'D': [ 3, 2, 4, 1, 5, 6, 7, 8 ] + }; + let df = new dfd.DataFrame(data); + let group_df = df.groupby([ "A", "B" ]); let rslt = [ - [ 'foo', 'one', 2, 2 ], - [ 'foo', 'two', 2, 2 ], - [ 'foo', 'three', 1, 1 ], - [ 'bar', 'one', 1, 1 ], - [ 'bar', 'two', 1, 1 ], - [ 'bar', 'three', 1, 1 ] + [ 'foo', 'one', 'foo', 'one', 1, 3 ], + [ 'foo', 'two', 'foo', 'two', 5, 5 ], + [ 'foo', 'three', 'foo', 'three', 7, 8 ], + [ 'bar', 'one', 'bar', 'one', 3, 2 ], + [ 'bar', 'three', 'bar', 'three', 4, 1 ], + [ 'bar', 'two', 'bar', 'two', 2, 6 ] ]; - - assert.deepEqual(grp.count().values, rslt); + assert.deepEqual(group_df.first().values, rslt); }); - it("should apply function to specific column", function () { + it("should obtain the last row of all groups", function () { let data = { 'A': [ 'foo', 'bar', 'foo', 'bar', 'foo', 'bar', 'foo', 'foo' ], - 'B': [ 'one', 'one', 'two', 'three', + 'B': [ 'one', 'one', 'one', 'three', 'two', 'two', 'one', 'three' ], 'C': [ 1, 3, 2, 4, 5, 2, 6, 7 ], - 'D': [ 3, 2, 4, 1, 5, 6, 7, 8 ] }; + 'D': [ 3, 2, 4, 1, 5, 6, 7, 8 ] + }; let df = new dfd.DataFrame(data); - let group_df = df.groupby([ "A"]); + let group_df = df.groupby([ "A", "B" ]); let rslt = [ - [ 'foo', 5, 3 ], - [ 'foo', 6, 4 ], - [ 'foo', 7, 7 ], - [ 'foo', 9, 8 ], - [ 'foo', 10, 9 ], - [ 'bar', 4, 5 ], - [ 'bar', 3, 6 ], - [ 'bar', 8, 4 ] + [ 'foo', 'one', 'foo', 'one', 6, 7 ], + [ 'foo', 'two', 'foo', 'two', 5, 5 ], + [ 'foo', 'three', 'foo', 'three', 7, 8 ], + [ 'bar', 'one', 'bar', 'one', 3, 2 ], + [ 'bar', 'three', 'bar', 'three', 4, 1 ], + [ 'bar', 'two', 'bar', 'two', 2, 6 ] ]; - assert.deepEqual(group_df.col(['D', 'C']).apply((x) => x.add(2)).values, rslt); + assert.deepEqual(group_df.last().values, rslt); }); - it("should apply function to group column", function () { + it("should obtain the number of rows of each groups", function () { let data = { 'A': [ 'foo', 'bar', 'foo', 'bar', 'foo', 'bar', 'foo', 'foo' ], - 'B': [ 'one', 'one', 'two', 'three', + 'B': [ 'one', 'one', 'one', 'three', 'two', 'two', 'one', 'three' ], 'C': [ 1, 3, 2, 4, 5, 2, 6, 7 ], - 'D': [ 3, 2, 4, 1, 5, 6, 7, 8 ] }; + 'D': [ 3, 2, 4, 1, 5, 6, 7, 8 ] + }; let df = new dfd.DataFrame(data); - let group_df = df.groupby([ "A", "B"]); + let group_df = df.groupby([ "A", "B" ]); let rslt = [ - [ 'foo', 'one', 2, 2 ], - [ 'foo', 'two', 2, 2 ], - [ 'foo', 'three', 1, 1 ], - [ 'bar', 'one', 1, 1 ], - [ 'bar', 'two', 1, 1 ], - [ 'bar', 'three', 1, 1 ] + [ 'foo', 'one', 3 ], + [ 'foo', 'two', 1 ], + [ 'foo', 'three', 1 ], + [ 'bar', 'one', 1 ], + [ 'bar', 'three', 1 ], + [ 'bar', 'two', 1 ] ]; - assert.deepEqual(group_df.apply((x) => x.count()).values, rslt); + assert.deepEqual(group_df.size().values, rslt); }); - }); diff --git a/src/danfojs-browser/tests/core/daterange.test.js b/src/danfojs-browser/tests/core/daterange.test.js new file mode 100644 index 00000000..95b5eef4 --- /dev/null +++ b/src/danfojs-browser/tests/core/daterange.test.js @@ -0,0 +1,87 @@ +describe("dfd.dateRange", function(){ + + // it("Obtain date between start and end specified", function(){ + // let d = dfd.dateRange({start:'2018-04-24',end:'2018-04-27'}) + // let rslt = [ + // '4/24/2018, 1:00:00 AM', + // '4/25/2018, 1:00:00 AM', + // '4/26/2018, 1:00:00 AM', + // '4/27/2018, 1:00:00 AM' + // ] + // assert.deepEqual(d,rslt) + // }); + it("Obtain date between start with end not specified, but period and freq specified", function(){ + let d = dfd.dateRange({ start:'1/1/2018', period:5, freq:'M' }); + let rslt = [ + '1/1/2018, 12:00:00 AM', + '2/1/2018, 12:00:00 AM', + '3/1/2018, 12:00:00 AM', + '4/1/2018, 12:00:00 AM', + '5/1/2018, 12:00:00 AM' + ]; + assert.deepEqual(d, rslt); + }); + it("Obtain date between start with end not specified, but period and freq specified, plus offset", function(){ + let d = dfd.dateRange({ start:'1/1/2018', period:5, freq:'3M' }); + let rslt = [ + '1/1/2018, 12:00:00 AM', + '4/1/2018, 12:00:00 AM', + '7/1/2018, 12:00:00 AM', + '10/1/2018, 12:00:00 AM', + '1/1/2019, 12:00:00 AM' + ]; + assert.deepEqual(d, rslt); + }); + it("Obtain date between start with end not specified, but period and freq specified, plus offset, longer freq", function(){ + let d = dfd.dateRange({ start:'1/1/2018', period:5, freq:'35m' }); + let rslt = [ + "1/1/2018, 12:00:00 AM", + "1/1/2018, 12:35:00 AM", + "1/1/2018, 1:10:00 AM", + "1/1/2018, 1:45:00 AM", + "1/1/2018, 2:20:00 AM" + ]; + assert.deepEqual(d, rslt); + }); + it("Obtain date range with start not specified but end and period is given", function(){ + let d = dfd.dateRange({ end:'1/1/2018', period:8 }); + let rslt = [ + '12/25/2017, 12:00:00 AM', + '12/26/2017, 12:00:00 AM', + '12/27/2017, 12:00:00 AM', + '12/28/2017, 12:00:00 AM', + '12/29/2017, 12:00:00 AM', + '12/30/2017, 12:00:00 AM', + '12/31/2017, 12:00:00 AM', + '1/1/2018, 12:00:00 AM' + ]; + assert.deepEqual(d, rslt); + }); + it("inputing wrong freq", function(){ + assert.throws(function () { dfd.dateRange({ end:'1/1/2018', period:8, freq:"d" }); }, Error, 'invalid freq d'); + }); + it("inputing wrong freq with offset", function(){ + assert.throws(function () { dfd.dateRange({ end:'1/1/2018', period:8, freq:"4d" }); }, Error, 'invalid freq d'); + }); + it("inputing wrong freq offset", function(){ + assert.throws(function () { dfd.dateRange({ end:'1/1/2018', period:8, freq:"abcm" }); }, Error, 'invalid freq offset abc'); + }); + // it("Obtain date between start and end specified with different month", function(){ + + // let d = dfd.dateRange({start:'2018-04-24',end:'2018-05-27'}) + // let rslt = [ + // '4/24/2018, 1:00:00 AM', + // '4/25/2018, 1:00:00 AM', + // '4/26/2018, 1:00:00 AM', + // '4/27/2018, 1:00:00 AM', + // '4/28/2018, 1:00:00 AM', + // '4/29/2018, 1:00:00 AM', + // '4/30/2018, 1:00:00 AM', + // '5/1/2018, 1:00:00 AM', + // '5/2/2018, 1:00:00 AM', + // '5/3/2018, 1:00:00 AM', + // '5/27/2018, 1:00:00 AM' + // ] + // assert.deepEqual(d,rslt) + // }); +}); diff --git a/src/danfojs-browser/tests/core/datetime.test.js b/src/danfojs-browser/tests/core/datetime.test.js new file mode 100644 index 00000000..1562e743 --- /dev/null +++ b/src/danfojs-browser/tests/core/datetime.test.js @@ -0,0 +1,91 @@ +/* eslint-disable no-undef */ + +describe("TimeSeries", function () { + + it("Returns correct month number", function () { + const data = [ "02Sep2019", "03Aug2019", "04July2019" ]; + const dateTime = dfd.toDateTime(data); + const expected = [ 8, 7, 6 ]; + assert.deepEqual(dateTime.month().values, expected); + }); + + it("Returns correct year number", function () { + const data = [ "02Sep2019", "03Aug2029", "04July2020" ]; + const dateTime = dfd.toDateTime(data); + const expected = [ 2019, 2029, 2020 ]; + assert.deepEqual(dateTime.year().values, expected); + }); + + it("Return month name generated", function () { + const data = [ "06-30-02019", "07-29-2019", "08-28-2019" ]; + const dateTime = dfd.toDateTime(data); + const expected = [ "June", "July", "August" ]; + assert.deepEqual(dateTime.monthName().values, expected); + }); + + it("Return day of the week generated", function () { + const data = [ "06-30-02019", "07-29-2019", "08-28-2019" ]; + const dateTime = dfd.toDateTime(data); + const expected = [ "Sunday", "Monday", "Wednesday" ]; + assert.deepEqual(dateTime.dayOfWeekName().values, expected); + }); + + it("Return day of the month generated", function () { + const data = [ "06-30-02019", "07-29-2019", "08-28-2019" ]; + const dateTime = dfd.toDateTime(data); + const expected = [ 30, 29, 28 ]; + assert.deepEqual(dateTime.dayOfMonth().values, expected); + }); + + it("Return seconds generated", function () { + const data = [ "06-30-02019 00:00:12", "07-29-2019 00:30:40", "08-28-2019 00:12:04" ]; + const dateTime = dfd.toDateTime(data); + const expected = [ 12, 40, 4 ]; + assert.deepEqual(dateTime.seconds().values, expected); + }); + + it("Return minutes generated", function () { + const data = [ "06-30-02019 00:00:12", "07-29-2019 00:30:40", "08-28-2019 00:12:04" ]; + const dateTime = dfd.toDateTime(data); + const expected = [ 0, 30, 12 ]; + assert.deepEqual(dateTime.minutes().values, expected); + }); + + it("Return hours generated", function () { + const data = [ "06-30-02019 05:00:12", "07-29-2019 01:30:40", "08-28-2019 06:12:04" ]; + const dateTime = dfd.toDateTime(data); + const expected = [ 5, 1, 6 ]; + assert.deepEqual(dateTime.hours().values, expected); + }); + + it("Return correct date from Series format 1", function () { + const data = new dfd.Series([ "12/30/19 00:01", "12/29/19 07:03", "11/12/20 18:21" ]); + const dateTime = dfd.toDateTime(data); + + const expectedMonth = [ 11, 11, 10 ]; + const expectedYear = [ 2019, 2019, 2020 ]; + const expectedDayOfMonth = [ 30, 29, 12 ]; + const expectedMonthName = [ "December", "December", "November" ]; + + assert.deepEqual(dateTime.month().values, expectedMonth); + assert.deepEqual(dateTime.year().values, expectedYear); + assert.deepEqual(dateTime.dayOfMonth().values, expectedDayOfMonth); + assert.deepEqual(dateTime.monthName().values, expectedMonthName); + }); + + it("Return correct date from Series format 2", function () { + const data = new dfd.Series([ "12.30.19", "12.22.19", "11.01.20" ]); + const dateTime = dfd.toDateTime(data); + + const expectedMonth = [ 11, 11, 10 ]; + const expectedYear = [ 2019, 2019, 2020 ]; + const expectedDay = [ 1, 0, 0 ]; + const expectedMonthName = [ "December", "December", "November" ]; + + assert.deepEqual(dateTime.month().values, expectedMonth); + assert.deepEqual(dateTime.year().values, expectedYear); + assert.deepEqual(dateTime.dayOfWeek().values, expectedDay); + assert.deepEqual(dateTime.monthName().values, expectedMonthName); + + }); +}); diff --git a/src/danfojs-browser/tests/core/frame.test.js b/src/danfojs-browser/tests/core/frame.test.js new file mode 100644 index 00000000..d564bebd --- /dev/null +++ b/src/danfojs-browser/tests/core/frame.test.js @@ -0,0 +1,2906 @@ +/* eslint-disable no-undef */ + +describe("DataFrame", function () { + + describe("Subsetting by column names", function () { + it("retrieves the col data created from an df with two columns", function () { + let data = { alpha: [ "A", "B", "C", "D" ], count: [ 1, 2, 3, 4 ] }; + let df = new dfd.DataFrame(data); + assert.deepEqual(df["alpha"].values, [ "A", "B", "C", "D" ]); + assert.deepEqual(df["count"].values, [ 1, 2, 3, 4 ]); + }); + it("retrieves the column data from an df with three columns", function () { + let data = { alpha: [ "A", "B", "C", "D" ], count: [ 1, 2, 3, 4 ], sum: [ 20.3, 30.456, 40.90, 90.1 ] }; + let df = new dfd.DataFrame(data); + assert.deepEqual(df["alpha"].values, [ "A", "B", "C", "D" ]); + assert.deepEqual(df["count"].values, [ 1, 2, 3, 4 ]); + assert.deepEqual(df["sum"].values, [ 20.3, 30.456, 40.90, 90.1 ]); + }); + + it("Set column count by subseting", function () { + let data = { alpha: [ "A", "B", "C", "D" ], count: [ 1, 2, 3, 4 ], sum: [ 20.3, 30.456, 40.90, 90.1 ] }; + let df = new dfd.DataFrame(data); + df["alpha"] = [ "E", "F", "G", "H" ]; + assert.deepEqual(df["alpha"].values, [ "E", "F", "G", "H" ]); + assert.deepEqual(df.values[0], [ 'E', 1, 20.3 ]); + assert.deepEqual(df.dtypes, [ "string", "int32", "float32" ]); + assert.deepEqual(df["count"].values, [ 1, 2, 3, 4 ]); + assert.deepEqual(df["sum"].values, [ 20.3, 30.456, 40.90, 90.1 ]); + }); + + it("Correct dtype is set after setting a column by subseting", function () { + let data = { alpha: [ "A", "B", "C", "D" ], count: [ 1, 2, 3, 4 ], sum: [ 20.3, 30.456, 40.90, 90.1 ] }; + let df = new dfd.DataFrame(data); + df["alpha"] = [ 2.4, 5.6, 32.5, 1 ]; + + assert.deepEqual(df["alpha"].values, [ 2.4, 5.6, 32.5, 1 ]); + assert.deepEqual(df.values[0], [ 2.4, 1, 20.3 ]); + assert.deepEqual(df.values[1], [ 5.6, 2, 30.456 ]); + assert.deepEqual(df.values[2], [ 32.5, 3, 40.90 ]); + assert.deepEqual(df.values[3], [ 1, 4, 90.1 ]); + + df["count"] = [ "A", "B", "C", "D" ]; + assert.deepEqual(df["count"].values, [ "A", "B", "C", "D" ]); + assert.deepEqual(df.dtypes, [ "float32", "string", "float32" ]); + }); + + it("retrieves the col data created from an df with two columns in low memory mode", function () { + let data = { alpha: [ "A", "B", "C", "D" ], count: [ 1, 2, 3, 4 ] }; + let df = new dfd.DataFrame(data, { lowMemoryMode: true }); + assert.deepEqual(df["alpha"].values, [ "A", "B", "C", "D" ]); + assert.deepEqual(df["count"].values, [ 1, 2, 3, 4 ]); + }); + it("retrieves the column data from an df with threee columns in low memory mode", function () { + let data = { alpha: [ "A", "B", "C", "D" ], count: [ 1, 2, 3, 4 ], sum: [ 20.3, 30.456, 40.90, 90.1 ] }; + let df = new dfd.DataFrame(data, { lowMemoryMode: true }); + assert.deepEqual(df["alpha"].values, [ "A", "B", "C", "D" ]); + assert.deepEqual(df["count"].values, [ 1, 2, 3, 4 ]); + assert.deepEqual(df["sum"].values, [ 20.3, 30.456, 40.90, 90.1 ]); + }); + + it("Set column count by subseting (low memory mode) ", function () { + let data = { alpha: [ "A", "B", "C", "D" ], count: [ 1, 2, 3, 4 ], sum: [ 20.3, 30.456, 40.90, 90.1 ] }; + let df = new dfd.DataFrame(data, { lowMemoryMode: true }); + df["alpha"] = [ "E", "F", "G", "H" ]; + assert.deepEqual(df["alpha"].values, [ "E", "F", "G", "H" ]); + assert.deepEqual(df.values[0], [ 'E', 1, 20.3 ]); + assert.deepEqual(df.dtypes, [ "string", "int32", "float32" ]); + assert.deepEqual(df["count"].values, [ 1, 2, 3, 4 ]); + assert.deepEqual(df["sum"].values, [ 20.3, 30.456, 40.90, 90.1 ]); + }); + + it("Correct dtype is set after setting a column by subseting (low memory mode) ", function () { + let data = { alpha: [ "A", "B", "C", "D" ], count: [ 1, 2, 3, 4 ], sum: [ 20.3, 30.456, 40.90, 90.1 ] }; + let df = new dfd.DataFrame(data, { lowMemoryMode: true }); + df["alpha"] = [ 2.4, 5.6, 32.5, 1 ]; + assert.deepEqual(df["alpha"].values, [ 2.4, 5.6, 32.5, 1 ]); + assert.deepEqual(df.values[0], [ 2.4, 1, 20.3 ]); + assert.deepEqual(df.values[1], [ 5.6, 2, 30.456 ]); + assert.deepEqual(df.values[2], [ 32.5, 3, 40.90 ]); + assert.deepEqual(df.values[3], [ 1, 4, 90.1 ]); + assert.deepEqual(df.dtypes, [ "float32", "int32", "float32" ]); + }); + }); + + describe("addColumn", function () { + it("Add new array values to DataFrame works", function () { + let data = { alpha: [ "A", "B", "C", "D" ], count: [ 1, 2, 3, 4 ], sum: [ 20.3, 30.456, 40.90, 90.1 ] }; + let df = new dfd.DataFrame(data); + const newdf = df.addColumn("new_column", [ "a", "b", "c", "d" ]); + assert.deepEqual(newdf["new_column"].values, [ "a", "b", "c", "d" ]); + assert.deepEqual(newdf.columns, [ "alpha", "count", "sum", "new_column" ]); + assert.deepEqual(newdf.dtypes, [ "string", "int32", "float32", "string" ]); + assert.deepEqual(newdf.index, [ 0, 1, 2, 3 ]); + }); + it("Add new array values to DataFrame inplace works", function () { + let data = { alpha: [ "A", "B", "C", "D" ], count: [ 1, 2, 3, 4 ], sum: [ 20.3, 30.456, 40.90, 90.1 ] }; + let df = new dfd.DataFrame(data); + df.addColumn("new_column", [ "a", "b", "c", "d" ], { inplace: true }); + assert.deepEqual(df["new_column"].values, [ "a", "b", "c", "d" ]); + assert.deepEqual(df.columns, [ "alpha", "count", "sum", "new_column" ]); + assert.deepEqual(df.dtypes, [ "string", "int32", "float32", "string" ]); + assert.deepEqual(df.index, [ 0, 1, 2, 3 ]); + }); + it("Add new Series to DataFrame works", function () { + let data = { alpha: [ "A", "B", "C", "D" ], count: [ 1, 2, 3, 4 ], sum: [ 20.3, 30.456, 40.90, 90.1 ] }; + let df = new dfd.DataFrame(data); + const newdf = df.addColumn("new_column", new dfd.Series([ "a", "b", "c", "d" ])); + assert.deepEqual(newdf["new_column"].values, [ "a", "b", "c", "d" ]); + assert.deepEqual(newdf.columns, [ "alpha", "count", "sum", "new_column" ]); + assert.deepEqual(newdf.dtypes, [ "string", "int32", "float32", "string" ]); + assert.deepEqual(newdf.index, [ 0, 1, 2, 3 ]); + }); + it("Correct column data is set", function () { + let data = { alpha: [ "A", "B", "C", "D" ], count: [ 1, 2, 3, 4 ], sum: [ 20.3, 30.456, 40.90, 90.1 ] }; + let df = new dfd.DataFrame(data); + df.addColumn("new_column", [ "a", "b", "c", "d" ], { inplace: true }); + assert.deepEqual(df["new_column"].values, [ "a", "b", "c", "d" ]); + assert.deepEqual(df["alpha"].values, [ "A", "B", "C", "D" ]); + assert.deepEqual(df["count"].values, [ 1, 2, 3, 4 ]); + assert.deepEqual(df["sum"].values, [ 20.3, 30.456, 40.90, 90.1 ]); + }); + it("throw error for wrong column lenght", function () { + const data = { alpha: [ "A", "B", "C", "D" ], count: [ 1, 2, 3, 4 ], sum: [ 20.3, 30.456, 40.90, 90.1 ] }; + const df = new dfd.DataFrame(data); + + assert.throws(function () { + df.addColumn("new_column", new dfd.Series([ "a", "b", "c" ])), + Error, + 'ParamError: Column data length mismatch. You provided data with length 3 but Ndframe has column of lenght 4'; + }); + + }); + it("Ensure add column does not mutate parent when not in place", function () { + const data = { alpha: [ "A", "B", "C", "D" ], count: [ 1, 2, 3, 4 ], sum: [ 20.3, 30.456, 40.90, 90.1 ] }; + const df = new dfd.DataFrame(data); + const dfNew = df.addColumn("new_column", [ "a", "b", "c", "d" ]); + assert.notDeepEqual(df, dfNew); + assert.deepEqual(dfNew["new_column"].values, [ "a", "b", "c", "d" ]); + assert.deepEqual(df["alpha"].values, [ "A", "B", "C", "D" ]); + assert.deepEqual(df["count"].values, [ 1, 2, 3, 4 ]); + assert.deepEqual(df["sum"].values, [ 20.3, 30.456, 40.90, 90.1 ]); + }); + + it("Add new array values to DataFrame works", function () { + let data = { alpha: [ "A", "B", "C", "D" ], val_count: [ 1, 2, 3, 4 ], val_sum: [ 20.3, 30.456, 40.90, 90.1 ] }; + let df = new dfd.DataFrame(data); + const newdf = df.addColumn("new_column", [ "a", "b", "c", "d" ], { atIndex: 0 }); + assert.deepEqual(newdf["new_column"].values, [ "a", "b", "c", "d" ]); + assert.deepEqual(newdf.columns, [ "new_column", "alpha", "val_count", "val_sum" ]); + assert.deepEqual(newdf.dtypes, [ "string", "int32", "float32", "string" ]); + assert.deepEqual(newdf.index, [ 0, 1, 2, 3 ]); + }); + + it("Add new array values to specific column index", function () { + let data = { alpha: [ "A", "B", "C", "D" ], val_count: [ 1, 2, 3, 4 ], val_sum: [ 20.3, 30.456, 40.90, 90.1 ] }; + let df = new dfd.DataFrame(data); + const newdf = df.addColumn("new_column", [ "a", "b", "c", "d" ], { atIndex: 'alpha' }); + assert.deepEqual(newdf["new_column"].values, [ "a", "b", "c", "d" ]); + assert.deepEqual(newdf.columns, [ "new_column", "alpha", "val_count", "val_sum" ]); + assert.deepEqual(newdf.dtypes, [ "string", "int32", "float32", "string" ]); + assert.deepEqual(newdf.index, [ 0, 1, 2, 3 ]); + }); + }); + + describe("drop", function () { + it("throw error for wrong column name", function () { + const data = [ [ 1, 2, 3 ], [ 4, 5, 6 ] ]; + const cols = [ "A", "B", "C" ]; + const df = new dfd.DataFrame(data, { columns: cols }); + assert.throws(function () { + df.drop({ columns: [ 3 ] }); + }, + Error, + 'ParamError: specified column "3" not found in columns'); + }); + it("throw error for wrong row index", function () { + const data = [ [ 1, 2, 3 ], [ 4, 5, 6 ] ]; + const cols = [ "A", "B", "C" ]; + const df = new dfd.DataFrame(data, { columns: cols }); + assert.throws(function () { df.drop({ index: [ 10 ] }); }, + Error, 'ParamError: specified index "10" not found in indices'); + }); + + it("drop a column inplace", function () { + const data = [ [ 1, 2, 3 ], [ 4, 5, 6 ] ]; + const cols = [ "A", "B", "C" ]; + const df = new dfd.DataFrame(data, { columns: cols }); + df.drop({ columns: [ "C", "B" ], inplace: true }); + assert.deepEqual(df.columns, [ "A" ]); + assert.deepEqual(df.values, [ [ 1 ], [ 4 ] ]); + assert.deepEqual(df["A"].values, [ 1, 4 ]); + assert.deepEqual(df.dtypes, [ "int32" ]); + }); + it("drop a column inplace in low memory mode", function () { + const data = [ [ 1, 2, 3 ], [ 4, 5, 6 ] ]; + const cols = [ "A", "B", "C" ]; + const df = new dfd.DataFrame(data, { columns: cols, config: { lowMemoryMode: true } }); + df.drop({ columns: [ "C", "B" ], inplace: true }); + assert.deepEqual(df.columns, [ "A" ]); + assert.deepEqual(df.values, [ [ 1 ], [ 4 ] ]); + assert.deepEqual(df["A"].values, [ 1, 4 ]); + assert.deepEqual(df.dtypes, [ "int32" ]); + }); + it("drop a scalar column inplace", function () { + const data = [ [ 1, 2, 3 ], [ 4, 5, 6 ] ]; + const cols = [ "A", "B", "C" ]; + const df = new dfd.DataFrame(data, { columns: cols }); + df.drop({ columns: "C", inplace: true }); + assert.deepEqual(df.columns, [ "A", "B" ]); + assert.deepEqual(df.values, [ [ 1, 2 ], [ 4, 5 ] ]); + assert.deepEqual(df["A"].values, [ 1, 4 ]); + assert.deepEqual(df["B"].values, [ 2, 5 ]); + }); + it("check if data is updated after column is dropped", function () { + const data = [ [ 1, 2, 3 ], [ 4, 5, 6 ] ]; + const cols = [ "A", "B", "C" ]; + const df = new dfd.DataFrame(data, { columns: cols }); + df.drop({ columns: [ "C" ], axis: 1, inplace: true }); + const new_data = [ [ 1, 2 ], [ 4, 5 ] ]; + assert.deepEqual(df.values, new_data); + assert.deepEqual(df.dtypes.length, 2); + + }); + + it("check if data is updated after row is dropped", function () { + const data = [ [ 1, 2, 3 ], [ 4, 5, 6 ] ]; + const cols = [ "A", "B", "C" ]; + const dfNew = new dfd.DataFrame(data, { columns: cols }); + const df = dfNew.drop({ index: [ 0 ] }); + const new_data = [ [ 4, 5, 6 ] ]; + assert.deepEqual(df.values, new_data); + assert.deepEqual(df.dtypes, [ "int32", "int32", "int32" ]); + assert.deepEqual(df["A"].values, [ 4 ]); + assert.deepEqual(df["B"].values, [ 5 ]); + assert.deepEqual(df["C"].values, [ 6 ]); + assert.deepEqual(df.columns, cols); + assert.notDeepEqual(dfNew, df); + }); + it("check if data is updated after row is dropped (inplace)", function () { + const data = [ [ 1, 2, 3 ], [ 4, 5, 6 ] ]; + const cols = [ "A", "B", "C" ]; + const df = new dfd.DataFrame(data, { columns: cols }); + df.drop({ index: [ 0 ], inplace: true }); + const new_data = [ [ 4, 5, 6 ] ]; + assert.deepEqual(df.values, new_data); + assert.deepEqual(df.dtypes, [ "int32", "int32", "int32" ]); + assert.deepEqual(df["A"].values, [ 4 ]); + assert.deepEqual(df["B"].values, [ 5 ]); + assert.deepEqual(df["C"].values, [ 6 ]); + assert.deepEqual(df.columns, cols); + }); + it("check if new dfd.dataframe is properly created after column is dropped (not-in-inplace)", function () { + const data = [ [ 1, 2, 3 ], [ 4, 5, 6 ] ]; + const cols = [ "A", "B", "C" ]; + const df = new dfd.DataFrame(data, { columns: cols }); + const df_drop = df.drop({ columns: [ "C" ], axis: 1, inplace: false }); + + const expected_data = [ [ 1, 2 ], [ 4, 5 ] ]; + const expected_cols = [ "A", "B" ]; + const expected_df = new dfd.DataFrame(expected_data, { columns: expected_cols }); + assert.deepEqual(df_drop.values, expected_df.values); + }); + it("check that the dtype is updated after column drop", function () { + const data = [ [ 1, 2, 3 ], [ 4, 5, 6 ] ]; + const cols = [ "A", "B", "C" ]; + const df = new dfd.DataFrame(data, { columns: cols }); + df.drop({ columns: [ "A" ], axis: 1, inplace: true }); + const dtype = [ 'int32', 'int32' ]; + assert.deepEqual(df.dtypes, dtype); + }); + it("drop row by single string labels", function () { + const data = [ [ 1, 2, 3 ], [ 4, 5, 6 ], [ 20, 34, 5 ] ]; + const cols = [ "A", "B", "C" ]; + const df = new dfd.DataFrame(data, { columns: cols, index: [ "a", "b", "c" ] }); + df.drop({ index: [ "a" ], inplace: true }); + const new_data = [ [ 4, 5, 6 ], [ 20, 34, 5 ] ]; + assert.deepEqual(df.values, new_data); + }); + it("drop row by two or more string labels", function () { + const data = [ [ 1, 2, 3 ], [ 4, 5, 6 ], [ 20, 34, 5 ], [ 2, 3.4, 5 ], [ 2.0, 340, 5 ] ]; + const cols = [ "A", "B", "C" ]; + const df = new dfd.DataFrame(data, { columns: cols, index: [ "a", "b", "c", "d", "e" ] }); + df.drop({ index: [ "a", "b" ], inplace: true }); + const new_data = [ [ 20, 34, 5 ], [ 2, 3.4, 5 ], [ 2.0, 340, 5 ] ]; + assert.deepEqual(df.values, new_data); + + }); + it("drop row by two or more string labels with numeric index", function () { + const data = [ [ 1, 2, 3 ], [ 4, 5, 6 ], [ 20, 34, 5 ], [ 2, 3.4, 5 ], [ 2.0, 340, 5 ] ]; + const cols = [ "A", "B", "C" ]; + const df = new dfd.DataFrame(data, { columns: cols, index: [ "a", "b", 1, "d", "e" ] }); + df.drop({ index: [ 1, "b" ], inplace: true }); + const new_data = [ [ 1, 2, 3 ], [ 2, 3.4, 5 ], [ 2.0, 340, 5 ] ]; + assert.deepEqual(df.values, new_data); + assert.deepEqual(df.index, [ "a", "d", "e" ]); + + }); + }); + + describe("head", function () { + it("Gets the first n rows in a DataFrame", function () { + const data = [ [ 1, 2, 3 ], [ 4, 5, 6 ], [ 20, 30, 40 ], [ 39, 89, 78 ] ]; + const cols = [ "A", "B", "C" ]; + const df = new dfd.DataFrame(data, { columns: cols }); + assert.deepEqual(df.head(2).values, [ [ 1, 2, 3 ], [ 4, 5, 6 ] ]); + }); + it("Throws error if row specified is greater than values", function () { + const data = [ [ 1, 2, 3 ], [ 4, 5, 6 ], [ 20, 30, 40 ], [ 39, 89, 78 ] ]; + const cols = [ "A", "B", "C" ]; + const df = new dfd.DataFrame(data, { columns: cols }); + assert.throws(() => df.head(10), Error, + "ParamError: Number of rows cannot be greater than available rows in data"); + }); + it("Throws error if row specified is less than 0", function () { + const data = [ [ 1, 2, 3 ], [ 4, 5, 6 ], [ 20, 30, 40 ], [ 39, 89, 78 ] ]; + const cols = [ "A", "B", "C" ]; + const df = new dfd.DataFrame(data, { columns: cols }); + assert.throws(() => df.head(-1), Error, + "ParamError: Number of rows cannot be less than 1"); + }); + + }); + + describe("tail", function () { + it("Prints the last n rows of a DataFrame", function () { + const data = [ [ 1, 2, 3 ], [ 4, 5, 6 ], [ 20, 30, 40 ], [ 39, 89, 78 ] ]; + const cols = [ "A", "B", "C" ]; + const df = new dfd.DataFrame(data, { columns: cols }); + assert.deepEqual(df.tail(2).values, [ [ 20, 30, 40 ], [ 39, 89, 78 ] ]); + }); + it("Throws error if row specified is greater than values", function () { + const data = [ [ 1, 2, 3 ], [ 4, 5, 6 ], [ 20, 30, 40 ], [ 39, 89, 78 ] ]; + const cols = [ "A", "B", "C" ]; + const df = new dfd.DataFrame(data, { columns: cols }); + assert.throws(() => df.tail(10), Error, + "ParamError: Number of rows cannot be greater than available rows in data"); + }); + it("Throws error if row specified is less than 0", function () { + const data = [ [ 1, 2, 3 ], [ 4, 5, 6 ], [ 20, 30, 40 ], [ 39, 89, 78 ] ]; + const cols = [ "A", "B", "C" ]; + const df = new dfd.DataFrame(data, { columns: cols }); + assert.throws(() => df.tail(-1), Error, + "ParamError: Number of rows cannot be less than 1"); + }); + it("Return last 3 row index in a DataFrame", function () { + const data = [ [ 1, 2, 3 ], [ 4, 5, 6 ], [ 20, 30, 40 ], [ 39, 89, 78 ] ]; + const cols = [ "A", "B", "C" ]; + const df = new dfd.DataFrame(data, { columns: cols }); + assert.deepEqual(df.tail(2).index, [ 2, 3 ]); + }); + it("Check print format on head call", function () { + const data = [ [ 1, 2, 34, 5, 0, 6, 4, 5, 6, 7 ], [ 20, 30, 40, 39, 89, 78, 45, 56, 56, 45 ] ]; + const df = new dfd.DataFrame(data); + assert.deepEqual(df.tail(2).values, [ [ 1, 2, 34, 5, 0, 6, 4, 5, 6, 7 ], [ 20, 30, 40, 39, 89, 78, 45, 56, 56, 45 ] ]); + }); + }); + + describe("sample", function () { + it("Samples n number of random elements from a DataFrame", async function () { + const data = [ [ 1, 2, 3 ], [ 4, 5, 6 ], [ 20, 30, 40 ], [ 39, 89, 78 ], [ 100, 200, 300 ] ]; + const cols = [ "A", "B", "C" ]; + const df = new dfd.DataFrame(data, { columns: cols }); + const expected = [ [ 1, 2, 3 ], [ 20, 30, 40 ] ]; + const values = (await df.sample(2)).values; + assert.deepEqual(values, expected); + }); + it("Throw error if n is greater than lenght of Dataframe", async function () { + const data = [ [ 1, 2, 3 ], [ 4, 5, 6 ], [ 20, 30, 40 ], [ 39, 89, 78 ], [ 100, 200, 300 ] ]; + const cols = [ "A", "B", "C" ]; + const df = new dfd.DataFrame(data, { columns: cols }); + try { + await df.sample(100); + } catch (e) { + expect(e).to.be.instanceOf(Error); + expect(e.message).to.eql('ParamError: Sample size cannot be bigger than number of rows'); + } + }); + it("Throw error if n is less than 0", async function () { + const data = [ [ 1, 2, 3 ], [ 4, 5, 6 ], [ 20, 30, 40 ], [ 39, 89, 78 ], [ 100, 200, 300 ] ]; + const cols = [ "A", "B", "C" ]; + const df = new dfd.DataFrame(data, { columns: cols }); + try { + await df.sample(-2); + } catch (e) { + expect(e).to.be.instanceOf(Error); + expect(e.message).to.eql('ParamError: Sample size cannot be less than 1'); + } + }); + it("Throw error if n is 0", async function () { + const data = [ [ 1, 2, 3 ], [ 4, 5, 6 ], [ 20, 30, 40 ], [ 39, 89, 78 ], [ 100, 200, 300 ] ]; + const cols = [ "A", "B", "C" ]; + const df = new dfd.DataFrame(data, { columns: cols }); + try { + await df.sample(0); + } catch (e) { + expect(e).to.be.instanceOf(Error); + expect(e.message).to.eql('ParamError: Sample size cannot be less than 1'); + } + }); + it("Seed works and random number is reproducible", async function () { + const data = [ [ 1, 2, 3 ], [ 4, 5, 6 ], [ 20, 30, 40 ], [ 39, 89, 78 ], [ 100, 200, 300 ] ]; + const cols = [ "A", "B", "C" ]; + const df = new dfd.DataFrame(data, { columns: cols }); + const values1 = (await df.sample(2, { seed: 453 })).values; + const values2 = (await df.sample(2, { seed: 453 })).values; + const values3 = (await df.sample(2, { seed: 1 })).values; + + assert.deepEqual(values1, values2); + assert.notDeepEqual(values1, values3); + + }); + }); + + describe("loc", function () { + + it("throw error for wrong column name", function () { + const data = [ [ 1, 2, 3 ], [ 4, 5, 6 ] ]; + const cols = [ "A", "B", "C" ]; + const df = new dfd.DataFrame(data, { columns: cols }); + assert.throws(function () { + df.loc({ "rows": [ 0, 1 ], "columns": [ "A", "D" ] }); + }, + Error, + "IndexError: Specified column (D) not found"); + }); + + it(`check data after selecting { "rows": ["0", "1"], "columns": ["B", "C"] }`, function () { + const data = [ [ 1, 2, 3 ], [ 4, 5, 6 ] ]; + const cols = [ "A", "B", "C" ]; + const df = new dfd.DataFrame(data, { columns: cols, index: [ "0", "1" ] }); + + const colDf = df.loc({ "rows": [ "0", "1" ], "columns": [ "B", "C" ] }); + const expected = [ [ 2, 3 ], [ 5, 6 ] ]; + + assert.deepEqual(colDf.values, expected); + + }); + it("check data after selecting row index", function () { + const data = [ [ 1, 2, 3 ], [ 4, 5, 6 ] ]; + const cols = [ "A", "B", "C" ]; + const df = new dfd.DataFrame(data, { columns: cols, index: [ 0, 1 ] }); + + const colDf = df.loc({ "rows": [ "1" ], "columns": [ "B" ] }); + const expected = [ [ 5 ] ]; + + assert.deepEqual(colDf.values, expected); + + }); + it("check data after selecting with single row index", function () { + const data = [ [ 1, 2, 3 ], [ 4, 5, 6 ] ]; + const cols = [ "A", "B", "C" ]; + const df = new dfd.DataFrame(data, { columns: cols, index: [ "0", "1" ] }); + + const colDf = df.loc({ "rows": [ `"1"` ], "columns": [ "B", "C" ] }); + const expected = [ [ 5, 6 ] ]; + + assert.deepEqual(colDf.values, expected); + + }); + it("check data after selecting with single column index", function () { + const data = [ [ 1, 2, 3 ], [ 4, 5, 6 ] ]; + const cols = [ "A", "B", "C" ]; + const df = new dfd.DataFrame(data, { columns: cols, index: [ "0", "1" ] }); + + const colDf = df.loc({ "rows": [ `"0"` ], "columns": [ "A" ] }); + const expected = [ [ 1 ] ]; + + assert.deepEqual(colDf.values, expected); + + }); + it("check data after row and column slice", function () { + const data = [ [ 1, 2, 3 ], [ 4, 5, 6 ], [ 20, 30, 40 ], [ 39, 89, 78 ] ]; + const cols = [ "A", "B", "C" ]; + const df = new dfd.DataFrame(data, { columns: cols, index: [ "0", "1", "2", "3" ] }); + + const colDf = df.loc({ "rows": [ `'0':'2'` ], "columns": [ "B:C" ] }); + const expected = [ [ 2 ], [ 5 ] ]; + + assert.deepEqual(colDf.values, expected); + + }); + it("check data after row slice", function () { + const data = [ [ 1, 2, 3 ], [ 4, 5, 6 ], [ 20, 30, 40 ], [ 39, 89, 78 ] ]; + const cols = [ "A", "B", "C" ]; + const df = new dfd.DataFrame(data, { columns: cols }); + + const colDf = df.loc({ "rows": [ `0:2` ], "columns": [ "B", "C" ] }); + const expected = [ [ 2, 3 ], [ 5, 6 ] ]; + + assert.deepEqual(colDf.values, expected); + + }); + it(`check data after column slice ["A:C"]`, function () { + const data = [ [ 1, 2, 3 ], [ 4, 5, 6 ], [ 20, 30, 40 ], [ 39, 89, 78 ] ]; + const cols = [ "A", "B", "C" ]; + const df = new dfd.DataFrame(data, { columns: cols, index: [ "0", "1", "2", "3" ] }); + + const colDf = df.loc({ "rows": [ "0", "1" ], "columns": [ "A:C" ] }); + const expected = [ [ 1, 2 ], [ 4, 5 ] ]; + assert.deepEqual(colDf.values, expected); + + }); + it("check data after numeric row slice", function () { + const data = [ [ 1, 2, 3 ], [ 4, 5, 6 ], [ 20, 30, 40 ], [ 39, 89, 78 ] ]; + const cols = [ "A", "B", "C" ]; + const df = new dfd.DataFrame(data, { columns: cols }); + + const colDf = df.loc({ "rows": [ 0, 1 ], "columns": [ "A:C" ] }); + const expected = [ [ 1, 2 ], [ 4, 5 ] ]; + assert.deepEqual(colDf.values, expected); + + }); + it("loc by single string index", function () { + const data = { + "Name": [ "Apples", "Mango", "Banana", "Pear" ], + "Count": [ 21, 5, 30, 10 ], + "Price": [ 200, 300, 40, 250 ] + }; + + const df = new dfd.DataFrame(data, { index: [ "a", "b", "c", "d" ] }); + const subDf = df.loc({ rows: [ `"a"` ], columns: [ "Name", "Count" ] }); + const expected = [ [ 'Apples', 21 ] ]; + assert.deepEqual(subDf.values, expected); + + }); + + it("loc by slice string index", function () { + const data = { + "Name": [ "Apples", "Mango", "Banana", "Pear" ], + "Count": [ 21, 5, 30, 10 ], + "Price": [ 200, 300, 40, 250 ] + }; + + const df = new dfd.DataFrame(data, { index: [ "a", "b", "c", "d" ] }); + const subDf = df.loc({ rows: [ `"a":"c"` ], columns: [ "Name", "Count" ] }); + const expected = [ [ "Apples", 21 ], [ "Mango", 5 ] ]; + assert.deepEqual(subDf.values, expected); + + }); + + + }); + + describe("DataFrame iloc", function () { + + it("throw error for wrong row index (array format)", function () { + const data = [ [ 1, 2, 3 ], [ 4, 5, 6 ] ]; + const cols = [ "A", "B", "C" ]; + const df = new dfd.DataFrame(data, { columns: cols }); + assert.throws(function () { + df.iloc({ "rows": [ 0, 1, 3 ] }); + }, + Error, + "Invalid row parameter: Specified index 3 cannot be bigger than index length 2"); + }); + + it("throw error for wrong row index (string slice format)", function () { + const data = [ [ 1, 2, 3 ], [ 4, 5, 6 ] ]; + const cols = [ "A", "B", "C" ]; + const df = new dfd.DataFrame(data, { columns: cols }); + assert.throws(function () { + df.iloc({ "rows": [ "1:5" ] }); + }, + Error, + "row slice [end] index cannot be bigger than 2"); + }); + + it("throw error for wrong column index (array format)", function () { + const data = [ [ 1, 2, 3 ], [ 4, 5, 6 ] ]; + const cols = [ "A", "B", "C" ]; + const df = new dfd.DataFrame(data, { columns: cols }); + assert.throws(function () { + df.iloc({ "columns": [ 1, 4 ] }); + }, + Error, + "Invalid column parameter: Specified index 4 cannot be bigger than index length 3"); + }); + + it("throw error for wrong column index (string slice format)", function () { + const data = [ [ 1, 2, 3 ], [ 4, 5, 6 ] ]; + const cols = [ "A", "B", "C" ]; + const df = new dfd.DataFrame(data, { columns: cols }); + assert.throws(function () { + df.iloc({ "columns": [ "A:C" ] }); + }, + Error, + "Invalid column split parameter. Split parameter must be a number"); + }); + + it("iloc works for {row: [0, 1], column: [1, 2]}", function () { + const data = [ [ 1, 2, 3 ], [ 4, 5, 6 ] ]; + const cols = [ "A", "B", "C" ]; + const df = new dfd.DataFrame(data, { columns: cols }); + + const colDf = df.iloc({ "rows": [ 0, 1 ], "columns": [ 1, 2 ] }); + const expected = [ [ 2, 3 ], [ 5, 6 ] ]; + + assert.deepEqual(colDf.values, expected); + + }); + + it(`iloc works for { "rows": [1], "columns": [1, 2] }`, function () { + const data = [ [ 1, 2, 3 ], [ 4, 5, 6 ] ]; + const cols = [ "A", "B", "C" ]; + const df = new dfd.DataFrame(data, { columns: cols }); + const colDf = df.iloc({ "rows": [ 1 ], "columns": [ 1, 2 ] }); + const expected = [ [ 5, 6 ] ]; + assert.deepEqual(colDf.values, expected); + + }); + it("check data after row and column slice", function () { + const data = [ [ 1, 2, 3 ], [ 4, 5, 6 ], [ 20, 30, 40 ], [ 39, 89, 78 ] ]; + const cols = [ "A", "B", "C" ]; + const df = new dfd.DataFrame(data, { columns: cols }); + + const colDf = df.iloc({ "rows": [ "0:2" ], "columns": [ "1:2" ] }); + const expected = [ [ 2 ], [ 5 ] ]; + + assert.deepEqual(colDf.values, expected); + + }); + it("check data after row slice", function () { + const data = [ [ 1, 2, 3 ], [ 4, 5, 6 ], [ 20, 30, 40 ], [ 39, 89, 78 ] ]; + const cols = [ "A", "B", "C" ]; + const df = new dfd.DataFrame(data, { columns: cols }); + + const colDf = df.iloc({ "rows": [ "0:2" ], "columns": [ 1, 2 ] }); + const expected = [ [ 2, 3 ], [ 5, 6 ] ]; + + assert.deepEqual(colDf.values, expected); + + }); + it("check data after column slice", function () { + const data = [ [ 1, 2, 3 ], [ 4, 5, 6 ], [ 20, 30, 40 ], [ 39, 89, 78 ] ]; + const cols = [ "A", "B", "C" ]; + const df = new dfd.DataFrame(data, { columns: cols }); + + const colDf = df.iloc({ "rows": [ 0, 1, 2 ], "columns": [ "1:2" ] }); + const expected = [ [ 2 ], [ 5 ], [ 30 ] ]; + assert.deepEqual(colDf.values, expected); + + }); + it("Return all columns if columns parameter is not specified", function () { + const data = [ [ 1, 2, 3 ], [ 4, 5, 6 ], [ 20, 30, 40 ], [ 39, 89, 78 ] ]; + const cols = [ "A", "B", "C" ]; + const df = new dfd.DataFrame(data, { columns: cols }); + + const colDf = df.iloc({ "rows": [ 0, 1, 2 ] }); + const expected = [ [ 1, 2, 3 ], [ 4, 5, 6 ], [ 20, 30, 40 ] ]; + assert.deepEqual(colDf.values, expected); + + }); + it("Return all rows if rows parameter is not specified", function () { + const data = [ [ 1, 2, 3 ], [ 4, 5, 6 ], [ 20, 30, 40 ], [ 39, 89, 78 ] ]; + const cols = [ "A", "B", "C" ]; + const df = new dfd.DataFrame(data, { columns: cols }); + + const colDf = df.iloc({ "columns": [ "1:2" ] }); + const expected = [ [ 2 ], [ 5 ], [ 30 ], [ 89 ] ]; + assert.deepEqual(colDf.values, expected); + + }); + it("column slice starting with 0 and returning a single result works", function () { + const data = { + "Name": [ "Apples", "Mango", "Banana", "Pear" ], + "Count": [ 21, 5, 30, 10 ], + "Price": [ 200, 300, 40, 250 ], + "index": [ 1, 2, 3, 4 ] + }; + const df = new dfd.DataFrame(data); + const subDf = df.iloc({ rows: [ "2:3" ], columns: [ "0:1" ] }); + const result = [ [ "Banana" ] ]; + assert.deepEqual(subDf.values, result); + + }); + it("column slice with format '0:' works", function () { + const data = { + "Name": [ "Apples", "Mango", "Banana", "Pear" ], + "Count": [ 21, 5, 30, 10 ], + "Price": [ 200, 300, 40, 250 ] + }; + const df = new dfd.DataFrame(data); + const subDf = df.iloc({ rows: [ "2:3" ], columns: [ "0:" ] }); + const result = [ [ "Banana", 30, 40 ] ]; + assert.deepEqual(subDf.values, result); + + }); + it("column slice with format ':2' works", function () { + const data = { + "Name": [ "Apples", "Mango", "Banana", "Pear" ], + "Count": [ 21, 5, 30, 10 ], + "Price": [ 200, 300, 40, 250 ] + }; + const df = new dfd.DataFrame(data); + const subDf = df.iloc({ rows: [ "2:3" ], columns: [ ":2" ] }); + const result = [ [ "Banana", 30 ] ]; + assert.deepEqual(subDf.values, result); + + }); + it("row slice with format ':2' works", function () { + const data = { + "Name": [ "Apples", "Mango", "Banana", "Pear" ], + "Count": [ 21, 5, 30, 10 ], + "Price": [ 200, 300, 40, 250 ] + }; + const df = new dfd.DataFrame(data); + const subDf = df.iloc({ rows: [ ":2" ], columns: [ ":1" ] }); + const result = [ [ 'Apples' ], [ 'Mango' ] ]; + assert.deepEqual(subDf.values, result); + + }); + it("row slice with format '1:' works", function () { + const data = { + "Name": [ "Apples", "Mango", "Banana", "Pear" ], + "Count": [ 21, 5, 30, 10 ], + "Price": [ 200, 300, 40, 250 ] + }; + const df = new dfd.DataFrame(data); + const subDf = df.iloc({ rows: [ ":2" ], columns: [ ":2" ] }); + const result = [ [ 'Apples', 21 ], [ 'Mango', 5 ] ]; + assert.deepEqual(subDf.values, result); + + }); + + }); + + describe("toString", function () { + it("Prints a DataFrame to console", function () { + const data = { + "Name": [ "Apples", "Mango", "Banana", "Pear" ], + "Count": [ 21, 5, 30, 10 ], + "Price": [ 200, 300, 40, 250 ] + }; + const df = new dfd.DataFrame(data); + df.print(); + }); + it("User config works when printing a DataFrame to console", function () { + const data = { + "Name": [ "Apples", "Mango", "Banana", "Pear" ], + "Count": [ 21, 5, 30, 10 ], + "Price": [ 200, 300, 40, 250 ] + }; + const df = new dfd.DataFrame(data, { + config: { + tableDisplayConfig: { + header: { + alignment: 'center', + content: 'THE HEADER\nThis is the table about something' + } + } + } + }); + df.print(); + }); + it("Long columns are properly truncated before printing", function () { + const data = { + "Name": [ "Apples", "Mango", "Banana", "Pear" ], + "Count": [ 21, 5, 30, 10 ], + "Price": [ 200, 300, 40, 250 ], + "Name2": [ "Apples", "Mango", "Banana", "Pear" ], + "Count2": [ 21, 5, 30, 10 ], + "Price2": [ 200, 300, 40, 250 ], + "Name3": [ "Apples", "Mango", "Banana", "Pear" ], + "Count3": [ 21, 5, 30, 10 ], + "Price3": [ 200, 300, 40, 250 ], + "Name4": [ "Apples", "Mango", "Banana", "Pear" ], + "Count4": [ 21, 5, 30, 10 ], + "Price4": [ 200, 300, 40, 250 ], + "Name5": [ "Apples", "Mango", "Banana", "Pear" ], + "Count5": [ 21, 5, 30, 10 ], + "Price6": [ 200, 300, 40, 250 ], + "Name7": [ "Apples", "Mango", "Banana", "Pear" ], + "Count7": [ 21, 5, 30, 10 ], + "Price7": [ 200, 300, 40, 250 ], + "Name8": [ "Apples", "Mango", "Banana", "Pear" ], + "Count8": [ 21, 5, 30, 10 ], + "Price8": [ 200, 300, 40, 250 ], + "Name9": [ "Apples", "Mango", "Banana", "Pear" ], + "Count9": [ 21, 5, 30, 10 ], + "Price9": [ 200, 300, 40, 250 ], + "Name10": [ "Apples", "Mango", "Banana", "Pear" ], + "Count10": [ 21, 5, 30, 10 ], + "Price10": [ 200, 300, 40, 250 ] + }; + const df = new dfd.DataFrame(data); + df.print(); + }); + + it("Long rows are automatically truncated", function () { + const data = { + "Name": [ "Apples", "Mango", "Banana", "Pear", "Apples", "Mango", "Banana", "Pear", "Apples", "Mango", "Banana", "Pear" ], + "Count": [ 21, 5, 30, 10, 21, 5, 30, 10, 21, 5, 30, 10 ], + "Price": [ 200, 300, 40, 250, 200, 300, 40, 250, 200, 300, 40, 250 ] + }; + const df = new dfd.DataFrame(data); + df.print(); + }); + }); + + describe("add", function () { + it("Return Addition of DataFrame with a single Number", function () { + const data = [ [ 0, 2, 4 ], [ 360, 180, 360 ] ]; + const df = new dfd.DataFrame(data); + assert.deepEqual(df.add(2).values, [ [ 2, 4, 6 ], [ 362, 182, 362 ] ]); + }); + it("Return addition of a DataFrame with a Series along default axis 1", function () { + const data = [ [ 0, 2, 4 ], [ 360, 180, 360 ] ]; + const sf = new dfd.Series([ 1, 2, 1 ]); + const df = new dfd.DataFrame(data); + assert.deepEqual(df.add(sf).values, [ [ 1, 4, 5 ], [ 361, 182, 361 ] ]); + }); + it("Return addition of a DataFrame with a Series along default axis 1", function () { + const data = [ [ 0, 2, 4 ], [ 360, 180, 360 ] ]; + const sf = [ 1, 2, 1 ]; + const df = new dfd.DataFrame(data); + assert.deepEqual(df.add(sf).values, [ [ 1, 4, 5 ], [ 361, 182, 361 ] ]); + }); + it("Return addition of a DataFrame with a Series along axis 0", function () { + const data = [ [ 0, 2, 4 ], + [ 360, 180, 360 ] ]; + const sf = new dfd.Series([ 1, 2 ]); + const df = new dfd.DataFrame(data); + assert.deepEqual(df.add(sf, { axis: 0 }).values, [ [ 1, 3, 5 ], [ 362, 182, 362 ] ]); + }); + it("Return addition of a DataFrame with a Array along axis 0", function () { + const data = [ [ 0, 2, 4 ], + [ 360, 180, 360 ] ]; + const sf = [ 1, 2 ]; + const df = new dfd.DataFrame(data); + assert.deepEqual(df.add(sf, { axis: 0 }).values, [ [ 1, 3, 5 ], [ 362, 182, 362 ] ]); + }); + it("Return addition of a DataFrame with a DataFrame along default axis 1", function () { + const df1 = new dfd.DataFrame([ [ 0, 2, 4 ], [ 360, 180, 360 ] ]); + const df2 = new dfd.DataFrame([ [ 1, 2, 4 ], [ 10, 5, 0 ] ]); + assert.deepEqual(df1.add(df2).values, [ [ 1, 4, 8 ], [ 370, 185, 360 ] ]); + }); + it("Return addition of a DataFrame with a DataFrame along axis 0", function () { + const df1 = new dfd.DataFrame([ [ 0, 2, 4 ], [ 360, 180, 360 ] ]); + const df2 = new dfd.DataFrame([ [ 1, 2, 4 ], [ 10, 5, 0 ] ]); + assert.deepEqual(df1.add(df2).values, [ [ 1, 4, 8 ], [ 370, 185, 360 ] ]); + }); + it("Return addition of a DataFrame with a Series along default axis 1 (inplace)", function () { + const data = [ [ 0, 2, 4 ], [ 360, 180, 360 ] ]; + const sf = new dfd.Series([ 1, 2, 1 ]); + const df = new dfd.DataFrame(data); + df.add(sf, { axis: 1, inplace: true }); + assert.deepEqual(df.values, [ [ 1, 4, 5 ], [ 361, 182, 361 ] ]); + }); + + it("Return addition of a DataFrame with a DataFrame along axis 0 (inplace)", function () { + const df1 = new dfd.DataFrame([ [ 0, 2, 4 ], [ 360, 180, 360 ] ]); + const df2 = new dfd.DataFrame([ [ 1, 2, 4 ], [ 10, 5, 0 ] ]); + df1.add(df2, { axis: 0, inplace: true }); + assert.deepEqual(df1.values, [ [ 1, 4, 8 ], [ 370, 185, 360 ] ]); + }); + it("Adds work for DataFrame with undefined and null values", function () { + const df1 = new dfd.DataFrame([ [ undefined, 2, 4 ], [ 360, NaN, 360 ] ]); + const df2 = new dfd.DataFrame([ [ 1, 2, 4 ], [ 10, 5, 0 ] ]); + df1.add(df2, { axis: 0, inplace: true }); + assert.deepEqual(df1.values, [ [ NaN, 4, 8 ], [ 370, NaN, 360 ] ]); + }); + + }); + + describe("sub", function () { + it("Return subtraction of DataFrame with a single Number", function () { + const data = [ [ 0, 2, 4 ], [ 360, 180, 360 ] ]; + const df = new dfd.DataFrame(data); + assert.deepEqual(df.sub(2).values, [ [ -2, 0, 2 ], [ 358, 178, 358 ] ]); + }); + it("Return subtraction of a DataFrame with a Series along default axis 1", function () { + const data = [ [ 0, 2, 4 ], [ 360, 180, 360 ] ]; + const sf = new dfd.Series([ 1, 2, 1 ]); + const df = new dfd.DataFrame(data); + assert.deepEqual(df.sub(sf).values, [ [ -1, 0, 3 ], [ 359, 178, 359 ] ]); + }); + it("Return subtraction of a DataFrame with a Series along axis 0", function () { + const data = [ [ 0, 2, 4 ], [ 360, 180, 360 ] ]; + const sf = new dfd.Series([ 1, 2 ]); + const df = new dfd.DataFrame(data); + assert.deepEqual(df.sub(sf, { axis: 0 }).values, [ [ -1, 1, 3 ], [ 358, 178, 358 ] ]); + }); + it("Return subtraction of a DataFrame with a DataFrame along default axis 1", function () { + const df1 = new dfd.DataFrame([ [ 0, 2, 4 ], [ 360, 180, 360 ] ]); + const df2 = new dfd.DataFrame([ [ 1, 2, 4 ], [ 10, 5, 0 ] ]); + assert.deepEqual(df1.sub(df2).values, [ [ -1, 0, 0 ], [ 350, 175, 360 ] ]); + }); + it("Return subtraction of a DataFrame with a DataFrame along axis 0", function () { + const df1 = new dfd.DataFrame([ [ 0, 2, 4 ], [ 360, 180, 360 ] ]); + const df2 = new dfd.DataFrame([ [ 1, 2, 4 ], [ 10, 5, 0 ] ]); + assert.deepEqual(df1.sub(df2).values, [ [ -1, 0, 0 ], [ 350, 175, 360 ] ]); + }); + + }); + + describe("mul", function () { + it("Return multiplication of DataFrame with a single Number", function () { + const data = [ [ 0, 2, 4 ], [ 360, 180, 360 ] ]; + const df = new dfd.DataFrame(data); + assert.deepEqual(df.mul(2).values, [ [ 0, 4, 8 ], [ 720, 360, 720 ] ]); + }); + it("Return multiplication of a DataFrame with a Series along default axis 1", function () { + const data = [ [ 0, 2, 4 ], [ 360, 180, 360 ] ]; + const sf = new dfd.Series([ 1, 2, 1 ]); + const df = new dfd.DataFrame(data); + assert.deepEqual(df.mul(sf).values, [ [ 0, 4, 4 ], [ 360, 360, 360 ] ]); + }); + it("Return multiplication of a DataFrame with a Series along axis 0", function () { + const data = [ [ 0, 2, 4 ], [ 360, 180, 360 ] ]; + const sf = new dfd.Series([ 1, 2 ]); + const df = new dfd.DataFrame(data); + assert.deepEqual(df.mul(sf, { axis: 0 }).values, [ [ 0, 2, 4 ], [ 720, 360, 720 ] ]); + }); + it("Return multiplication of a DataFrame with a DataFrame along default axis 1", function () { + const df1 = new dfd.DataFrame([ [ 0, 2, 4 ], [ 360, 180, 360 ] ]); + const df2 = new dfd.DataFrame([ [ 1, 2, 4 ], [ 10, 5, 0 ] ]); + assert.deepEqual(df1.mul(df2).values, [ [ 0, 4, 16 ], [ 3600, 900, 0 ] ]); + }); + it("Return multiplication of a DataFrame with a DataFrame along axis 0", function () { + const df1 = new dfd.DataFrame([ [ 0, 2, 4 ], [ 360, 180, 360 ] ]); + const df2 = new dfd.DataFrame([ [ 1, 2, 4 ], [ 10, 5, 0 ] ]); + assert.deepEqual(df1.mul(df2, { axis: 0 }).values, [ [ 0, 4, 16 ], [ 3600, 900, 0 ] ]); + }); + + }); + + describe("div", function () { + it("Return division of DataFrame with a single Number", function () { + const data = [ [ 0, 2, 4 ], [ 360, 180, 360 ] ]; + const df = new dfd.DataFrame(data); + assert.deepEqual(df.div(2).values, [ [ 0, 1, 2 ], [ 180, 90, 180 ] ]); + }); + it("Return division of a DataFrame with a Series along default axis 1", function () { + const data = [ [ 0, 2, 4 ], [ 360, 180, 360 ] ]; + const sf = new dfd.Series([ 1, 2, 1 ]); + const df = new dfd.DataFrame(data); + assert.deepEqual(df.div(sf).values, [ [ 0, 1, 4 ], [ 360, 90, 360 ] ]); + }); + it("Return division of a DataFrame with a Series along axis 0", function () { + const data = [ [ 0, 2, 4 ], [ 360, 180, 360 ] ]; + const sf = new dfd.Series([ 1, 2 ]); + const df = new dfd.DataFrame(data); + assert.deepEqual(df.div(sf, { axis: 0 }).values, [ [ 0, 2, 4 ], [ 180, 90, 180 ] ]); + }); + it("Return division of a DataFrame with a DataFrame along default axis 1", function () { + const df1 = new dfd.DataFrame([ [ 0, 2, 4 ], [ 360, 180, 360 ] ]); + const df2 = new dfd.DataFrame([ [ 1, 2, 4 ], [ 10, 5, 0 ] ]); + assert.deepEqual(df1.div(df2).values, [ [ 0, 1, 1 ], [ 36, 36, Infinity ] ]); + }); + it("Return division of a DataFrame with a DataFrame along axis 0", function () { + const df1 = new dfd.DataFrame([ [ 0, 2, 4 ], [ 360, 180, 360 ] ]); + const df2 = new dfd.DataFrame([ [ 1, 2, 4 ], [ 10, 5, 0 ] ]); + assert.deepEqual(df1.div(df2).values, [ [ 0, 1, 1 ], [ 36, 36, Infinity ] ]); + }); + + }); + + //So CI test result varies depending on where it is run. This is difficult to test. + //so I'm commenting it out. See https://github.com/javascriptdata/danfojs/issues/329 + // describe("pow", function () { + // it("Return exponential of DataFrame with a single Number", function () { + // const data = [ [ 0, 2, 4 ], [ 360, 180, 360 ] ]; + // const df = new dfd.DataFrame(data); + // assert.deepEqual(df.pow(2).values, [ [ 0, 4, 16 ], [ 129600, 32400, 129600 ] ]); + // }); + // it("Return exponential of a DataFrame with a Series along default axis 1", function () { + // const data = [ [ 0, 2, 4 ], [ 360, 180, 360 ] ]; + // const sf = new dfd.Series([ 1, 2, 1 ]); + // const df = new dfd.DataFrame(data); + // assert.deepEqual(df.pow(sf).values, [ [ 0, 4, 4 ], [ 360, 32400, 360 ] ]); + // }); + // it("Return exponential of a DataFrame with a Series along axis 0", function () { + // const data = [ [ 0, 2, 4 ], [ 360, 180, 360 ] ]; + // const sf = new dfd.Series([ 1, 2 ]); + // const df = new dfd.DataFrame(data); + // assert.deepEqual(df.pow(sf, { axis: 0 }).values, [ [ 0, 2, 4 ], [ 129600, 32400, 129600 ] ]); + // }); + // it("Return exponential of a DataFrame with another DataFrame along default axis 1", function () { + // const df1 = new dfd.DataFrame([ [ 0, 2, 4 ], [ 3, 10, 4 ] ]); + // const df2 = new dfd.DataFrame([ [ 1, 2, 4 ], [ 10, 5, 0 ] ]); + // assert.deepEqual(df1.pow(df2).values, [ [ 0, 4, 256 ], [ 59049, 100000, 1 ] ]); + // }); + // it("Return exponential of a DataFrame with another DataFrame along axis 0", function () { + // const df1 = new dfd.DataFrame([ [ 0, 2, 4 ], [ 3, 10, 4 ] ]); + // const df2 = new dfd.DataFrame([ [ 1, 2, 4 ], [ 10, 5, 0 ] ]); + // assert.deepEqual(df1.pow(df2, { axis: 0 }).values, [ [ 0, 4, 256 ], [ 59049, 100000, 1 ] ]); + // }); + + // }); + + describe("mod", function () { + it("Return modulus of DataFrame with a single Number", function () { + const data = [ [ 0, 2, 4 ], [ 360, 180, 360 ] ]; + const df = new dfd.DataFrame(data); + assert.deepEqual(df.mod(2).values, [ [ 0, 0, 0 ], [ 0, 0, 0 ] ]); + }); + it("Return modulus of a DataFrame with a Series along default axis 1", function () { + const data = [ [ 0, 2, 4 ], [ 31, 15, 360 ] ]; + const sf = new dfd.Series([ 1, 2, 1 ]); + const df = new dfd.DataFrame(data); + assert.deepEqual(df.mod(sf).values, [ [ 0, 0, 0 ], [ 0, 1, 0 ] ]); + }); + it("Return modulus of a DataFrame with a Series along axis 0", function () { + const data = [ [ 0, 2, 4 ], [ 31, 15, 360 ] ]; + const sf = new dfd.Series([ 1, 2 ]); + const df = new dfd.DataFrame(data); + assert.deepEqual(df.mod(sf, { axis: 0 }).values, [ [ 0, 0, 0 ], [ 1, 1, 0 ] ]); + }); + it("Return modulus of a DataFrame with a DataFrame along default axis 1", function () { + const df1 = new dfd.DataFrame([ [ 0, 2, 4 ], [ 31, 15, 360 ] ]); + const df2 = new dfd.DataFrame([ [ 1, 2, 4 ], [ 10, 5, 0 ] ]); + assert.deepEqual(df1.mod(df2).values, [ [ 0, 0, 0 ], [ 1, 0, NaN ] ]); + }); + it("Return modulus of a DataFrame with a DataFrame along axis 0", function () { + const df1 = new dfd.DataFrame([ [ 0, 2, 4 ], [ 360, 180, 360 ] ]); + const df2 = new dfd.DataFrame([ [ 1, 2, 4 ], [ 10, 5, 0 ] ]); + assert.deepEqual(df1.mod(df2).values, [ [ 0, 0, 0 ], [ 0, 0, NaN ] ]); + }); + + }); + + describe("mean", function () { + it("Returns the mean of a DataFrame (Default axis is [1:column])", function () { + const data = [ [ 0, 2, 4 ], + [ 360, 180, 360 ] ]; + const df = new dfd.DataFrame(data, { columns: [ "col1", "col2", "col3" ], index: [ "row1", "row2" ] }); + assert.deepEqual(df.mean().values, [ 2, 300 ]); + assert.deepEqual(df.mean().index, [ "row1", "row2" ]); + }); + it("Return mean of a DataFrame along axis 1 (column)", function () { + const data = [ [ 0, 2, 4 ], [ 360, 180, 360 ] ]; + const df = new dfd.DataFrame(data); + assert.deepEqual(df.mean({ "axis": 1 }).values, [ 2, 300 ]); + }); + it("Removes NaN before calculating mean of a DataFrame", function () { + const data = [ [ 11, 20, 3 ], + [ NaN, 15, 6 ], + [ 2, 30, 40 ], + [ 2, 89, 78 ] ]; + const df = new dfd.DataFrame(data); + assert.deepEqual(df.mean({ "axis": 1 }).values, [ 11.333333333333334, 10.5, 24, 56.333333333333336 ]); + }); + it("Return mean of a DataFrame along axis 0 (column)", function () { + const data = [ [ 0, 2, 4 ], [ 360, 180, 360 ] ]; + const df = new dfd.DataFrame(data); + assert.deepEqual(df.mean({ "axis": 0 }).values, [ 180, 91, 182 ]); + }); + it("Removes NaN before calculating mean of a DataFrame along axis 0 (column)", function () { + const data = [ [ 11, 20, 3 ], + [ NaN, 15, 6 ], + [ 2, 30, 40 ], + [ 2, 89, 78 ] ]; + const df = new dfd.DataFrame(data); + assert.deepEqual(df.mean({ "axis": 0 }).values, [ 5, 38.5, 31.75 ]); + }); + it("Throws error on wrong axis specified", function () { + const data = [ [ 0, 2, 4 ], [ 360, 180, 360 ] ]; + const df = new dfd.DataFrame(data); + assert.throws(() => df.mean({ "axis": 2 }), Error, "ParamError: Axis must be 0 or 1"); + }); + }); + + describe("median", function () { + it("Returns the median of a DataFrame (Default axis is [1:column])", function () { + const data = [ [ 0, 2, 4 ], [ 360, 180, 360 ] ]; + const df = new dfd.DataFrame(data); + assert.deepEqual(df.median().values, [ 2, 360 ]); + }); + it("Return median of a DataFrame along axis 0 (row)", function () { + const data = [ [ 0, 2, 4 ], [ 360, 180, 360 ] ]; + const df = new dfd.DataFrame(data); + assert.deepEqual(df.median({ "axis": 0 }).values, [ 180, 91, 182 ]); + }); + + }); + + describe("mode", function () { + it("Returns the mode of a DataFrame (Default axis is [1:column])", function () { + const data = [ [ 0, 2, 4, 2 ], [ 360, 180, 360 ] ]; + const df = new dfd.DataFrame(data); + assert.deepEqual(df.mode().values, [ 2, 360 ]); + }); + it("Returns the mode of a DataFrame with keep set to 1", function () { + const data = [ [ 0, 2, 4, 2, 4 ], [ 360, 180, 360, 360 ] ]; + const df = new dfd.DataFrame(data); + assert.deepEqual(df.mode({ keep: 1 }).values, [ 4, 360 ]); + }); + it("Returns mode of a DataFrame along axis 0 (row)", function () { + const data = [ [ 0, 2, 4 ], + [ 360, 180, 360 ], + [ 0, 2, 360 ] ]; + const df = new dfd.DataFrame(data); + assert.deepEqual(df.mode({ "axis": 0 }).values, [ 0, 2, 360 ]); + }); + it("Returns mode of a DataFrame along axis 0 for objects", function () { + const data = { "col1": [ 0, 2, 4, 0 ], "col2": [ 360, 180, 360, 360 ] }; + const df = new dfd.DataFrame(data); + assert.deepEqual(df.mode({ "axis": 0 }).values, [ 0, 360 ]); + }); + + }); + + describe("min", function () { + it("Returns the minimum values in a DataFrame (Default axis is [1:column])", function () { + const data = [ [ 0, 2, 4 ], [ 360, 180, 360 ] ]; + const df = new dfd.DataFrame(data); + assert.deepEqual(df.min().values, [ 0, 180 ]); + }); + it("Returns the minimum values of a DataFrame along axis 0 (row)", function () { + const data = [ [ 0, 2, 4 ], [ 360, 180, 360 ] ]; + const df = new dfd.DataFrame(data); + assert.deepEqual(df.min({ "axis": 0 }).values, [ 0, 2, 4 ]); + }); + it("Returns the minimum values of a DataFrame along axis 0 (row) using TFJS", function () { + const data = [ [ 0, 2, 4 ], [ 360, 180, 360 ] ]; + const df = new dfd.DataFrame(data, { config: { useTfjsMathFunctions: true } }); + assert.deepEqual(df.min({ "axis": 0 }).values, [ 0, 2, 4 ]); + }); + it("Returns the minimum values in a DataFrame-Default axis 1 using TFJS", function () { + const data = [ [ 0, 2, 4 ], [ 360, 180, 360 ] ]; + const df = new dfd.DataFrame(data, { config: { useTfjsMathFunctions: true } }); + assert.deepEqual(df.min().values, [ 0, 180 ]); + }); + + }); + + describe("max", function () { + it("Returns the maximum values in a DataFrame (Default axis is [1:column])", function () { + const data = [ [ 0, 2, 4 ], [ 360, 180, 360 ] ]; + const df = new dfd.DataFrame(data); + assert.deepEqual(df.max().values, [ 4, 360 ]); + }); + it("Returns the maximum values of a DataFrame along axis 0 (row)", function () { + const data = [ [ 0, 2, 4 ], [ 360, 180, 360 ] ]; + const df = new dfd.DataFrame(data); + assert.deepEqual(df.max({ "axis": 0 }).values, [ 360, 180, 360 ]); + }); + + }); + + describe("std", function () { + it("Returns the standard deviations of values in a DataFrame (Default axis is [1:column])", function () { + const data = [ [ 0, 2, 4 ], [ 360, 180, 360 ] ]; + const df = new dfd.DataFrame(data); + assert.deepEqual(df.std().values, [ 2, 103.92304845413264 ]); + }); + it("Return the standard deviations of values of a DataFrame along axis 0 (row)", function () { + const data = [ [ 0, 2, 4 ], [ 360, 180, 360 ] ]; + const df = new dfd.DataFrame(data); + assert.deepEqual(df.std({ axis: 0 }).values, [ 254.55844122715712, 125.86500705120545, 251.7300141024109 ]); + }); + + + }); + + describe("var", function () { + it("Returns the variance of values in a DataFrame (Default axis is [1:column])", function () { + const data = [ [ 0, 2, 4 ], [ 360, 180, 360 ] ]; + const df = new dfd.DataFrame(data); + assert.deepEqual(df.var().values, [ 4, 10800 ]); + }); + it("Return the variance of values of a DataFrame along axis 0 (row)", function () { + const data = [ [ 0, 2, 4 ], [ 360, 180, 360 ] ]; + const df = new dfd.DataFrame(data); + assert.deepEqual(df.var({ axis: 0 }).values, [ 64800, 15842, 63368 ]); + }); + + + }); + + describe("describe", function () { + it("Returns descriptive statistics of columns in a DataFrame created from an array", function () { + const data = [ [ 0, 2, 4, "a" ], + [ 360, 180, 360, "b" ], + [ 2, 4, 6, "c" ] ]; + + const df = new dfd.DataFrame(data); + const res = [ [ 3, 3, 3 ], [ 120.66666666666667, 62, 123.33333333333333 ], + [ 207.27115895206774, 102.19589032832974, 204.961785055979 ], + [ 0, 2, 4 ], [ 2, 4, 6 ], + [ 360, 180, 360 ], + [ 42961.33333333333, 10444, 42009.333333333336 ] ]; + assert.deepEqual(df.describe().values, res); + }); + it("Returns descriptive statistics of columns in a DataFrame created from an Object", function () { + const data = { + "col1": [ 0, 2, 4 ], + "col2": [ 360, 180, 360 ], + "col3": [ 2, 4, 6 ], + "col4": [ "boy", "girl", "man" ], + "col5": [ "apple", "car", "bee" ] + }; + const df = new dfd.DataFrame(data); + + const res = [ [ 3, 3, 3 ], [ 2, 300, 4 ], + [ 2, 103.92304845413264, 2 ], + [ 0, 180, 2 ], [ 2, 360, 4 ], + [ 4, 360, 6 ], + [ 4, 10800, 4 ] ]; + assert.deepEqual(df.describe().values, res); + }); + + }); + + describe("count", function () { + it("Returns the count of non-nan values in a DataFrame (Default axis is [1:column])", function () { + const data = [ [ 0, 2, 4 ], + [ 360, 180.1, 360.11 ], + [ NaN, 2, 4 ], + [ 360, undefined, 360 ] ]; + const df = new dfd.DataFrame(data); + assert.deepEqual(df.count().values, [ 3, 3, 2, 2 ]); + }); + it("Return the count of non NaN values of a DataFrame along axis 0", function () { + const data = [ [ 0, 2, 4, NaN ], + [ 360, undefined, 360, 70 ] ]; + const df = new dfd.DataFrame(data); + assert.deepEqual(df.count({ axis: 0 }).values, [ 2, 1, 2, 1 ]); + }); + + }); + + describe("round", function () { + it("Rounds values in a DataFrame to 3dp", function () { + const data = [ [ 10.1, 2.092, 4.23 ], [ 360.232244, 180.0190290, 36.902612 ] ]; + const df = new dfd.DataFrame(data); + const expected = [ [ 10.1, 2.092, 4.23 ], [ 360.232, 180.0190, 36.903 ] ]; + assert.deepEqual(df.round(3).values, expected); + }); + it("Rounds values in a DataFrame to 1dp, inplace", function () { + const data = [ [ 10.1, 2.092, 4.23 ], [ 360.232244, 180.0190290, 36.902612 ] ]; + const df = new dfd.DataFrame(data); + const expected = [ [ 10.1, 2.1, 4.2 ], [ 360.2, 180.0, 36.9 ] ]; + df.round(1, { inplace: true }); + assert.deepEqual(df.values, expected); + }); + it("Rounds values in a DataFrame to 3dp with missing values", function () { + const data = [ [ 10.1, 2.092, NaN ], [ 360.232244, undefined, 36.902612 ] ]; + const df = new dfd.DataFrame(data); + const expected = [ [ 10.1, 2.092, NaN ], [ 360.232, undefined, 36.903 ] ]; + assert.deepEqual(df.round(3, { axis: 0 }).values, expected); + }); + + }); + + describe("sortValues", function () { + it("Sort values in DataFrame by specified column in ascending order (Default)", function () { + const data = [ [ 0, 2, 4, "a" ], + [ 360, 180, 360, "b" ], + [ 2, 4, 6, "c" ] ]; + + const df = new dfd.DataFrame(data, { "columns": [ "col1", "col2", "col3", "col4" ], index: [ "a", 1, "c" ] }); + df.sortValues("col1", { inplace: true }); + const expected = [ [ 0, 2, 4, "a" ], [ 2, 4, 6, "c" ], [ 360, 180, 360, "b" ] ]; + assert.deepEqual(df.values, expected); + assert.deepEqual(df.index, [ "a", "c", 1 ]); + + }); + + it("Sort values in DataFrame by specified column in ascending order (Default)", function () { + const data = [ [ 0, 2, 4, "a" ], + [ 360, 180, 1, "b" ], + [ 2, 4, 6, "c" ] ]; + + const df = new dfd.DataFrame(data, { "columns": [ "col1", "col2", "col3", "col4" ] }); + const df_sort = df.sortValues("col3"); + const expected = [ [ 360, 180, 1, "b" ], [ 0, 2, 4, "a" ], [ 2, 4, 6, "c" ] ]; + assert.deepEqual(df_sort.values, expected); + assert.deepEqual(df_sort.index, [ 1, 0, 2 ]); + + }); + it("Sort values in DataFrame by specified column in descending order", function () { + const data = [ [ 0, 2, 4, "a" ], + [ 360, 180, 360, "b" ], + [ 2, 4, 6, "c" ] ]; + + const df = new dfd.DataFrame(data, { "columns": [ "col1", "col2", "col3", "col4" ] }); + const expected = [ [ 360, 180, 360, "b" ], [ 2, 4, 6, "c" ], [ 0, 2, 4, "a" ] ]; + assert.deepEqual(df.sortValues("col1", { "ascending": false }).values, expected); + }); + + it("Sort values in DataFrame by specified column in descending order (second col)", function () { + const data = [ [ 0, 2, 4, "a" ], + [ 360, 180, 1, "b" ], + [ 2, 4, 6, "c" ] ]; + + const df = new dfd.DataFrame(data, { "columns": [ "col1", "col2", "col3", "col4" ] }); + const expected = [ [ 2, 4, 6, "c" ], [ 0, 2, 4, "a" ], [ 360, 180, 1, "b" ] ]; + assert.deepEqual(df.sortValues("col3", { "ascending": false }).values, expected); + }); + it("Sort values in DataFrame by specified column containing alpha(numeric) values", function () { + const data = [ [ 0, 2, 4, "a" ], + [ 360, 180, 1, "b" ], + [ 2, 4, 6, "c" ] ]; + + const df = new dfd.DataFrame(data, { "columns": [ "col1", "col2", "col3", "col4" ] }); + const expected = [ [ 2, 4, 6, 'c' ], [ 360, 180, 1, 'b' ], [ 0, 2, 4, 'a' ] ]; + assert.deepEqual(df.sortValues("col4", { "ascending": false }).values, expected); + }); + it("Sort duplicate DataFrame with duplicate columns", function () { + + const data = { + "A": [ 1, 2, 3, 4, 5, 3, 5, 6, 4, 5, 3, 4 ], + "B": [ 2, 3, 4, 5, 6, 7, 8, 9, 1, 2, 3, 4 ] + }; + + const df = new dfd.DataFrame(data); + const expected = [ [ 1, 2 ], + [ 2, 3 ], + [ 3, 4 ], + [ 3, 7 ], + [ 3, 3 ], + [ 4, 5 ], + [ 4, 1 ], + [ 4, 4 ], + [ 5, 6 ], + [ 5, 8 ], + [ 5, 2 ], + [ 6, 9 ] ]; + assert.deepEqual(df.sortValues("A", { "ascending": true }).values, expected); + }); + }); + + describe("copy", function () { + it("Makes a deep copy of DataFrame", function () { + const data = [ [ 0, 2, 4 ], [ 360, 180, 360 ] ]; + const df = new dfd.DataFrame(data); + const df_copy = df.copy(); + assert.deepEqual(df_copy.values, [ [ 0, 2, 4 ], [ 360, 180, 360 ] ]); + }); + it("Confirms child copy modification does not affect parent DataFrame", function () { + const data = [ [ 0, 2, 4 ], [ 360, 180, 360 ] ]; + const df = new dfd.DataFrame(data); + const df_copy = df.copy(); + df_copy.addColumn("col_new", [ "boy", "girl" ], { inplace: true }); + assert.notDeepEqual(df_copy.values, df.values); + assert.notDeepEqual(df_copy, df); + }); + + }); + + + describe("setIndex", function () { + it("Sets the index of a DataFrame created from an Object inplace", function () { + const data = [ { alpha: "A", count: 1 }, { alpha: "B", count: 2 }, { alpha: "C", count: 3 } ]; + const df = new dfd.DataFrame(data); + df.setIndex({ index: [ "one", "two", "three" ], inplace: true }); + assert.deepEqual(df.index, [ "one", "two", "three" ]); + }); + it("Sets the index of a DataFrame from column name", function () { + const data = [ { alpha: "A", count: 1 }, { alpha: "B", count: 2 }, { alpha: "C", count: 3 } ]; + const df = new dfd.DataFrame(data); + const df_new = df.setIndex({ column: "alpha" }); + assert.deepEqual(df_new.index, [ "A", "B", "C" ]); + }); + it("Sets the index of a DataFrame from column name", function () { + const data = [ { alpha: "A", count: 1 }, { alpha: "B", count: 2 }, { alpha: "C", count: 3 } ]; + const df = new dfd.DataFrame(data); + const df_new = df.setIndex({ column: "alpha" }); + assert.deepEqual(df_new.index, [ "A", "B", "C" ]); + }); + it("Sets the index of a DataFrame from column name and drop column data (inplace)", function () { + const data = [ { alpha: "A", count: 1 }, { alpha: "B", count: 2 }, { alpha: "C", count: 3 } ]; + const df = new dfd.DataFrame(data); + df.setIndex({ column: "alpha", drop: true, inplace: true }); + assert.deepEqual(df.index, [ "A", "B", "C" ]); + assert.deepEqual(df.columns, [ "count" ]); + assert.deepEqual(df.values, [ [ 1 ], [ 2 ], [ 3 ] ]); + assert.throws(() => df["alpha"], Error, + "ParamError: Column not found!. Column name must be one of count" + ); + }); + it("Throw error if index and column not passed", function () { + const data = [ { alpha: "A", count: 1 }, { alpha: "B", count: 2 }, { alpha: "C", count: 3 } ]; + const df = new dfd.DataFrame(data); + assert.throws(() => df.setIndex({ drop: true, inplace: true }), Error, + "ParamError: must specify either index or column" + ); + }); + + it("Sets the index of a DataFrame from column name and drop column data", function () { + const data = [ { alpha: "A", count: 1 }, { alpha: "B", count: 2 }, { alpha: "C", count: 3 } ]; + const df = new dfd.DataFrame(data); + const df_new = df.setIndex({ column: "alpha", drop: true }); + assert.deepEqual(df_new.index, [ "A", "B", "C" ]); + assert.deepEqual(df_new.columns, [ "count" ]); + assert.deepEqual(df_new.values, [ [ 1 ], [ 2 ], [ 3 ] ]); + assert.deepEqual(df_new["alpha"], undefined); + }); + it("Sets the index of a DataFrame created from an Array inplace", function () { + const data = [ [ 0, 2, 4 ], [ 360, 180, 360 ], [ 0, 2, 4 ], [ 360, 180, 360 ], [ 0, 2, 4 ] ]; + const df = new dfd.DataFrame(data); + df.setIndex({ index: [ "one", "two", "three", "four", "five" ], "inplace": true }); + assert.deepEqual(df.index, [ "one", "two", "three", "four", "five" ]); + }); + it("Throws error on wrong row length", function () { + const data = [ [ 0, 2, 4 ], [ 360, 180, 360 ] ]; + const df = new dfd.DataFrame(data); + assert.throws(() => df.setIndex({ index: [ "one", "two", "three", "four", "five" ], "inplace": true }), Error, + "ParamError: index must be the same length as the number of rows" + ); + }); + it("Throws error on column not found", function () { + const data = [ [ 0, 2, 4 ], [ 360, 180, 360 ] ]; + const df = new dfd.DataFrame(data); + assert.throws(() => df.setIndex({ column: "alpha", inplace: true }), Error, + "ParamError: column not found in column names" + ); + }); + + }); + + describe("resetIndex", function () { + it("Resets the index of a DataFrame created from an Object", function () { + const data = [ { alpha: "A", count: 1 }, { alpha: "B", count: 2 }, { alpha: "C", count: 3 } ]; + const df = new dfd.DataFrame(data, { index: [ "one", "two", "three" ] }); + const df_reset = df.resetIndex(); + assert.deepEqual(df_reset.index, [ 0, 1, 2 ]); + }); + it("Resets the index of a DataFrame created from an Array inplace", function () { + const data = [ [ 0, 2, 4 ], [ 360, 180, 360 ], [ 0, 2, 4 ], [ 360, 180, 360 ], [ 0, 2, 4 ] ]; + const df = new dfd.DataFrame(data, { index: [ "one", "two", "three", "four", "five" ] }); + df.resetIndex({ inplace: true }); + assert.deepEqual(df.index, [ 0, 1, 2, 3, 4 ]); + }); + }); + + + describe("apply", function () { + + it("Apply an aggregation function to a DataFrame axis = 1", function () { + const data = [ [ 0, 2, 4 ], + [ 3, 2, 2 ], + [ 0, 2, 1 ] ]; + const df = new dfd.DataFrame(data); + + const sum = (x) => { + return x.reduce((a, b) => a + b, 0); + }; + const expected = [ 6, 7, 3 ]; + const dfApply = df.apply(sum, { axis: 1 }); + assert.deepEqual(dfApply.values, expected); + assert.deepEqual(dfApply.index, [ 0, 1, 2 ]); + + }); + it("Apply an aggregation function to a DataFrame axis = 0", function () { + const data = [ [ 0, 2, 4 ], + [ 3, 2, 2 ], + [ 0, 2, 1 ] ]; + const df = new dfd.DataFrame(data, { columns: [ "col1", "col2", "col3" ] }); + + const sum = (x) => { + return x.reduce((a, b) => a + b, 0); + }; + const expected = [ 3, 6, 7 ]; + const dfApply = df.apply(sum, { axis: 0 }); + assert.deepEqual(dfApply.values, expected); + assert.deepEqual(dfApply.index, [ "col1", "col2", "col3" ]); + }); + + }); + + describe("applyMap", function () { + it("Apply an element-wise function to a DataFrame", function () { + const data = [ [ 0, 2, 4 ], + [ 360, 180, 360 ], + [ 0, 2, 4 ] ]; + const df = new dfd.DataFrame(data); + + const add = (x) => { + return x + 1000; + }; + const expected = [ [ 1000, 1002, 1004 ], [ 1360, 1180, 1360 ], [ 1000, 1002, 1004 ] ]; + const dfApply = df.applyMap(add); + assert.deepEqual(dfApply.values, expected); + }); + + it("Apply an element-wise function to a DataFrame", function () { + const data = [ [ 0, 2, 4 ], + [ 360, 180, 360 ], + [ 0, 2, 4 ], + [ 0, 2, 4 ] ]; + const df = new dfd.DataFrame(data); + const add = (x) => { + return x + 1000; + }; + const expected = [ [ 1000, 1002, 1004 ], [ 1360, 1180, 1360 ], [ 1000, 1002, 1004 ], [ 1000, 1002, 1004 ] ]; + const dfApply = df.applyMap(add); + assert.deepEqual(dfApply.values, expected); + }); + + it("Apply an element-wise function to a DataFrame inplace", function () { + const data = [ [ 0, 2, 4 ], + [ 360, 180, 360 ], + [ 0, 2, 4 ] ]; + const df = new dfd.DataFrame(data); + + const add = (x) => { + return x + 1000; + }; + const expected = [ [ 1000, 1002, 1004 ], [ 1360, 1180, 1360 ], [ 1000, 1002, 1004 ] ]; + df.applyMap(add, { inplace: true }); + assert.deepEqual(df.values, expected); + }); + + it("Apply an element-wise function to a DataFrame inplace", function () { + const data = [ [ 0, 2, 4 ], + [ 360, 180, 360 ], + [ 0, 2, 4 ], + [ 0, 2, 4 ] ]; + const df = new dfd.DataFrame(data); + + const add = (x) => { + return x + 1000; + }; + const expected = [ [ 1000, 1002, 1004 ], [ 1360, 1180, 1360 ], [ 1000, 1002, 1004 ], [ 1000, 1002, 1004 ] ]; + df.applyMap(add, { inplace: true }); + assert.deepEqual(df.values, expected); + }); + }); + + describe("column", function () { + it("Obtain a column from a dataframe created from object", function () { + const data = [ { alpha: "A", count: 1 }, { alpha: "B", count: 2 }, { alpha: "C", count: 3 } ]; + const options = { columns: [ "Gender", "count" ] }; + const df = new dfd.DataFrame(data, options); + const expected = df.column("count"); + const rslt_data = [ 1, 2, 3 ]; + assert.deepEqual(expected.values, rslt_data); + }); + it("Obtain a column from a dataframe", function () { + const data = [ [ 1, 2, 3 ], [ 4, 5, 6 ], [ 20, 30, 40 ], [ 39, 89, 78 ] ]; + const cols = [ "A", "B", "C" ]; + const df = new dfd.DataFrame(data, { columns: cols }); + const expected = df.column("C"); + const rslt_data = [ 3, 6, 40, 78 ]; + assert.deepEqual(expected.values, rslt_data); + }); + it("Throw Error for wrong column", function () { + const data = [ [ 1, 2, 3 ], [ 4, 5, 6 ], [ 20, 30, 40 ], [ 39, 89, 78 ] ]; + const cols = [ "A", "B", "C" ]; + const df = new dfd.DataFrame(data, { columns: cols }); + assert.throws(() => { df.column("D"); }, Error, "ParamError: Column not found!. Column name must be one of A,B,C"); + + }); + }); + + + describe("dropNa", function () { + it("drop NaNs along axis 0", function () { + const data = [ [ 0, 2, 4 ], + [ 360, 180, 360 ], + [ NaN, 180, 360 ] ]; + const column = [ "A", "B", "C" ]; + const df = new dfd.DataFrame(data, { columns: column }); + const df_val = [ [ 2, 4 ], + [ 180, 360 ], + [ 180, 360 ] ]; + assert.deepEqual(df.dropNa({ axis: 0 }).values, df_val); + + }); + it("drop NaNs along axis 1", function () { + const data = [ [ 0, 2, 4 ], + [ 360, 180, 360 ], + [ NaN, 180, 360 ] ]; + const column = [ "A", "B", "C" ]; + const df = new dfd.DataFrame(data, { columns: column }); + const df_val = [ [ 0, 2, 4 ], + [ 360, 180, 360 ] ]; + assert.deepEqual(df.dropNa({ axis: 1 }).values, df_val); + + }); + it("drop NaNs along axis 1", function () { + const data = [ [ NaN, 1, 2, 3 ], [ 3, 4, NaN, 9 ], [ 5, 6, 7, 8 ] ]; + const column = [ "A", "B", "C", "D" ]; + const df = new dfd.DataFrame(data, { columns: column }); + const df_val = [ [ 5, 6, 7, 8 ] ]; + assert.deepEqual(df.dropNa({ axis: 1 }).values, df_val); + + }); + it("drop inplace at axis 0, inplace false ", function () { + const data = [ [ NaN, 1, 2, 3 ], [ 3, 4, NaN, 9 ], [ 5, 6, 7, 8 ] ]; + const column = [ "A", "B", "C", "D" ]; + const df = new dfd.DataFrame(data, { columns: column }); + + const df_val = [ [ 1, 3 ], [ 4, 9 ], [ 6, 8 ] ]; + + assert.deepEqual(df.dropNa({ axis: 0 }).values, df_val); + + }); + it("drop inplace at axis 0, inplace true ", function () { + const data = [ [ NaN, 1, 2, 3 ], [ 3, 4, NaN, 9 ], [ 5, 6, 7, 8 ] ]; + const column = [ "A", "B", "C", "D" ]; + const df = new dfd.DataFrame(data, { columns: column }); + + const df_val = [ [ 1, 3 ], [ 4, 9 ], [ 6, 8 ] ]; + df.dropNa({ axis: 0, inplace: true }); + assert.deepEqual(df.values, df_val); + + }); + it("drop inplace at axis 1 at inplace true", function () { + const data = [ [ NaN, 1, 2, 3 ], [ 3, 4, NaN, 9 ], [ 5, 6, 7, 8 ] ]; + const column = [ "A", "B", "C", "D" ]; + const df = new dfd.DataFrame(data, { columns: column }); + + const df_val = [ [ 5, 6, 7, 8 ] ]; + + df.dropNa({ axis: 1, inplace: true }); + assert.deepEqual(df.values, df_val); + + }); + + it("drop works for undefined values", function () { + let data = [ [ null, 1, 2, 3 ], [ 3, 4, undefined, 9 ], [ 5, 6, 7, 8 ] ]; + let column = [ "A", "B", "C", "D" ]; + let df = new dfd.DataFrame(data, { columns: column }); + + let df_val = [ [ 5, 6, 7, 8 ] ]; + + df.dropNa({ axis: 1, inplace: true }); + assert.deepEqual(df.values, df_val); + + }); + }); + + describe("isNa", function () { + + it("check if values are empty (element-wise", function () { + const data = [ [ NaN, 1, 2, 3 ], [ 3, 4, undefined, 9 ], [ 5, 6, 7, 8 ] ]; + const column = [ "A", "B", "C", "D" ]; + const df = new dfd.DataFrame(data, { columns: column }); + + const df_val = [ + [ true, false, false, false ], + [ false, false, true, false ], + [ false, false, false, false ] + ]; + const dfNew = df.isNa(); + assert.deepEqual(dfNew.values, df_val); + assert.deepEqual(dfNew.dtypes, [ "boolean", "boolean", "boolean", "boolean" ]); + assert.deepEqual(dfNew.columns, column); + }); + }); + + describe("fillNa", function () { + + it("replace all NaN value inplace", function () { + const data = [ [ NaN, 1, 2, 3 ], [ 3, 4, NaN, 9 ], [ 5, 6, 7, 8 ] ]; + const columns = [ "A", "B", "C", "D" ]; + const df = new dfd.DataFrame(data, { columns: columns }); + + const expected = [ [ -999, 1, 2, 3 ], [ 3, 4, -999, 9 ], [ 5, 6, 7, 8 ] ]; + df.fillNa(-999, { inplace: true }); + assert.deepEqual(df.values, expected); + }); + it("replace all undefined value", function () { + const data = [ [ undefined, 1, 2, 3 ], [ 3, 4, undefined, 9 ], [ 5, 6, 7, 8 ] ]; + const columns = [ "A", "B", "C", "D" ]; + const df = new dfd.DataFrame(data, { columns: columns }); + + const expected = [ [ -999, 1, 2, 3 ], [ 3, 4, -999, 9 ], [ 5, 6, 7, 8 ] ]; + + const df_filled = df.fillNa(-999); + assert.deepEqual(df_filled.values, expected); + }); + it("replace all undefined value with 0 works", function () { + const data = [ [ undefined, 1, 2, 3 ], [ 3, 4, null, 9 ], [ 5, NaN, 7, 8 ] ]; + const columns = [ "A", "B", "C", "D" ]; + const df = new dfd.DataFrame(data, { columns: columns }); + + const expected = [ [ 0, 1, 2, 3 ], [ 3, 4, 0, 9 ], [ 5, 0, 7, 8 ] ]; + + const df_filled = df.fillNa(0); + assert.deepEqual(df_filled.values, expected); + }); + it("Fills only a specified column", function () { + const data = [ [ 1, 2, 3 ], + [ 4, 5, 6 ], + [ 20, NaN, 40 ], + [ 39, NaN, NaN ] ]; + const cols = [ "A", "B", "C" ]; + const df = new dfd.DataFrame(data, { columns: cols }); + const expected = [ [ 1, 2, 3 ], [ 4, 5, 6 ], [ 20, 2, 40 ], [ 39, 2, NaN ] ]; + const df_filled = df.fillNa([ 2 ], { columns: [ "B" ] }); + + assert.deepEqual(df_filled.values, expected); + }); + it("Fills column with specified values not in place", function () { + const data = [ [ 1, 2, 3 ], [ 4, 5, 6 ], [ NaN, 20, 40 ], [ NaN, -1, 78 ] ]; + const cols = [ "A", "B", "C" ]; + const df = new dfd.DataFrame(data, { columns: cols }); + const new_vals = [ [ 1, 2, 3 ], [ 4, 5, 6 ], [ -2, 20, 40 ], [ -2, -1, 78 ] ]; + const df_filled = df.fillNa([ -2 ], { columns: [ "A" ] }); + + assert.deepEqual(df_filled.values, new_vals); + }); + + it("Fills a list of columns with specified values", function () { + const data = [ [ 1, undefined, 3 ], [ 4, undefined, 6 ], [ NaN, "boy", 40 ], [ NaN, "girl", NaN ] ]; + const cols = [ "A", "B", "C" ]; + const df = new dfd.DataFrame(data, { columns: cols }); + const new_vals = [ [ 1, "girl", 3 ], [ 4, "girl", 6 ], [ 200, "boy", 40 ], [ 200, "girl", NaN ] ]; + const df_filled = df.fillNa([ 200, "girl" ], { columns: [ "A", "B" ] }); + assert.deepEqual(df_filled.values, new_vals); + }); + it("Fills a list of columns with specified values inplace", function () { + const data = [ [ 1, undefined, 3 ], [ 4, undefined, 6 ], [ NaN, "boy", 40 ], [ NaN, "girl", 78 ] ]; + const cols = [ "A", "B", "C" ]; + const df = new dfd.DataFrame(data, { columns: cols }); + const new_vals = [ [ 1, "girl", 3 ], [ 4, "girl", 6 ], [ 200, "boy", 40 ], [ 200, "girl", 78 ] ]; + df.fillNa([ 200, "girl" ], { columns: [ "A", "B" ], inplace: true }); + assert.deepEqual(df.values, new_vals); + }); + }); + + describe("selectDtypes", function () { + + it("Returns float columns in a DataFrame", function () { + const data = [ [ 30, 1, 2, "boy" ], [ 3.2, 4, 30, "girl" ], [ 5.09, 6, 7, "cat" ] ]; + const column = [ "A", "B", "C", "D" ]; + const df = new dfd.DataFrame(data, { columns: column }); + const df_sub = df.selectDtypes([ 'float32' ]); + assert.deepEqual(df_sub.values, [ [ 30 ], [ 3.2 ], [ 5.09 ] ]); + }); + + it("Returns int columns in a DataFrame", function () { + const data = [ [ 30, 1, 2, "boy" ], + [ 3.2, 4, 30, "girl" ], + [ 5.09, 6, 7, "cat" ] ]; + const column = [ "A", "B", "C", "D" ]; + const df = new dfd.DataFrame(data, { columns: column }); + const df_sub = df.selectDtypes([ 'int32' ]); + assert.deepEqual(df_sub.values, [ [ 1, 2 ], [ 4, 30 ], [ 6, 7 ] ]); + }); + + it("Returns string columns in a DataFrame", function () { + const data = [ [ 30, 1, 2, "boy" ], + [ 3.2, 4, 30, "girl" ], + [ 5.09, 6, 7, "cat" ] ]; + const column = [ "A", "B", "C", "D" ]; + const df = new dfd.DataFrame(data, { columns: column }); + const df_sub = df.selectDtypes([ 'string' ]); + assert.deepEqual(df_sub.values, [ [ "boy" ], [ "girl" ], [ "cat" ] ]); + }); + + it("Returns string and float columns in a DataFrame", function () { + const data = [ [ 30, 1, 2, "boy" ], + [ 3.2, 4, 30, "girl" ], + [ 5.09, 6, 7, "cat" ] ]; + const column = [ "A", "B", "C", "D" ]; + const df = new dfd.DataFrame(data, { columns: column }); + const df_sub = df.selectDtypes([ 'string', 'float32' ]); + assert.deepEqual(df_sub.values, [ [ 30, "boy" ], [ 3.2, "girl" ], [ 5.09, "cat" ] ]); + }); + + it("Returns int and float columns in a DataFrame", function () { + const data = [ [ 30, 1, 2, "boy" ], + [ 3.2, 4, 30, "girl" ], + [ 5.09, 6, 7, "cat" ] ]; + const column = [ "A", "B", "C", "D" ]; + const df = new dfd.DataFrame(data, { columns: column }); + const df_sub = df.selectDtypes([ 'int32', 'float32' ]); + assert.deepEqual(df_sub.values, [ [ 30, 1, 2 ], [ 3.2, 4, 30 ], [ 5.09, 6, 7 ] ]); + }); + }); + + + describe("lt", function () { + it("Returns Less than of DataFrame and other DataFrame (element-wise)", function () { + const data1 = [ [ 10, 45, 56, 10 ], [ 25, 23, 20, 10 ] ]; + const data2 = [ [ 100, 450, 590, 5 ], [ 25, 2, 0, 10 ] ]; + + const df = new dfd.DataFrame(data1); + const df2 = new dfd.DataFrame(data2); + const expected = [ [ true, true, true, false ], + [ false, false, false, false ] ]; + assert.deepEqual(df.lt(df2).values, expected); + }); + + it("Return Less than of series and scalar (element-wise)", function () { + const data1 = [ [ 10, 45, 56, 10 ], [ 25, 23, 20, 10 ] ]; + const sf = new dfd.DataFrame(data1); + const expected = [ [ true, false, false, true ], + [ true, true, true, true ] ]; + assert.deepEqual(sf.lt(30).values, expected); + }); + it("Return Less than of series and DataFrame along axis 1", function () { + const data1 = [ [ 10, 45, 56, 10 ], + [ 23, 20, 10, 10 ] ]; + const sf = new dfd.Series([ 10, 23, 56, 100 ]); + const df = new dfd.DataFrame(data1); + const expected = [ [ false, false, false, true ], [ false, true, true, true ] ]; + assert.deepEqual(df.lt(sf, { axis: 1 }).values, expected); + }); + + it("Return Less than of Array and DataFrame along axis 1", function () { + const data1 = [ [ 10, 45, 56, 10 ], [ 23, 20, 10, 10 ] ]; + const sf = [ 10, 23, 56, 100 ]; + const df = new dfd.DataFrame(data1); + const expected = [ [ false, false, false, true ], [ false, true, true, true ] ]; + assert.deepEqual(df.lt(sf, { axis: 1 }).values, expected); + }); + it("Return Less than of series and DataFrame along axis 0", function () { + const data1 = [ [ 10, 45, 56, 10 ], + [ 23, 20, 10, 10 ] ]; + const sf = new dfd.Series([ 10, 23 ]); + const df = new dfd.DataFrame(data1); + const expected = [ [ false, false, false, false ], [ false, true, true, true ] ]; + const result = df.lt(sf, { axis: 0 }); + assert.deepEqual(result.values, expected); + }); + + it("Return Less than of Array and DataFrame along axis 0", function () { + const data1 = [ [ 10, 45, 56, 10 ], [ 23, 20, 10, 10 ] ]; + const sf = [ 10, 23 ]; + const df = new dfd.DataFrame(data1); + const expected = [ [ false, false, false, false ], [ false, true, true, true ] ]; + const result = df.lt(sf, { axis: 0 }); + assert.deepEqual(result.values, expected); + }); + + }); + + describe("gt", function () { + it("Return Greater than of series and other series (element-wise)", function () { + const data1 = [ [ 10, 45, 56, 10 ], [ 25, 23, 20, 10 ] ]; + const data2 = [ [ 100, 450, 590, 5 ], [ 25, 2, 0, 10 ] ]; + + const df = new dfd.DataFrame(data1); + const df2 = new dfd.DataFrame(data2); + const expected = [ [ false, false, false, true ], [ false, true, true, false ] ]; + assert.deepEqual(df.gt(df2).values, expected); + }); + + it("Return Greater than of series scalar (element-wise)", function () { + const data1 = [ [ 10, 45, 56, 10 ], [ 25, 23, 20, 10 ] ]; + const sf = new dfd.DataFrame(data1); + const expected = [ [ false, true, true, false ], [ false, false, false, false ] ]; + assert.deepEqual(sf.gt(30).values, expected); + }); + + it("Return Less than of Array and DataFrame scalar along axis 1", function () { + const data1 = [ [ 10, 45, 56, 10 ], [ 23, 20, 10, 10 ] ]; + const sf = [ 10, 23, 56, 100 ]; + const df = new dfd.DataFrame(data1); + const expected = [ [ false, true, false, false ], [ true, false, false, false ] ]; + assert.deepEqual(df.gt(sf, { axis: 1 }).values, expected); + }); + it("Return Less than of Array and DataFrame scalar along axis 0", function () { + const data1 = [ [ 10, 45, 56, 10 ], [ 23, 20, 10, 10 ] ]; + const sf = [ 10, 23 ]; + const df = new dfd.DataFrame(data1); + const expected = [ [ false, true, true, false ], [ false, false, false, false ] ]; + assert.deepEqual(df.gt(sf, { axis: 0 }).values, expected); + }); + + }); + + describe("le", function () { + it("Return Less than or Equal to of series and other series (element-wise)", function () { + const data1 = [ [ 10, 45, 56, 10 ], [ 25, 23, 20, 10 ] ]; + const data2 = [ [ 100, 450, 590, 5 ], [ 25, 2, 0, 10 ] ]; + + const df = new dfd.DataFrame(data1); + const df2 = new dfd.DataFrame(data2); + const expected = [ [ true, true, true, false ], [ true, false, false, true ] ]; + assert.deepEqual(df.le(df2).values, expected); + }); + + it("Return Less than or Equal to of series scalar (element-wise)", function () { + const data1 = [ [ 10, 45, 56, 10 ], [ 25, 23, 30, 10 ] ]; + const sf = new dfd.DataFrame(data1); + const expected = [ [ true, false, false, true ], [ true, true, true, true ] ]; + assert.deepEqual(sf.le(30).values, expected); + }); + + }); + + describe("ge", function () { + it("Return Greater than or Equal to of series and other series (element-wise)", function () { + const data1 = [ [ 10, 45, 56, 10 ], [ 25, 23, 20, 10 ] ]; + const data2 = [ [ 100, 450, 590, 5 ], [ 25, 2, 0, 10 ] ]; + + const df = new dfd.DataFrame(data1); + const df2 = new dfd.DataFrame(data2); + const expected = [ [ false, false, false, true ], [ true, true, true, true ] ]; + assert.deepEqual(df.ge(df2).values, expected); + }); + + it("Return Greater than or Equal to of series scalar (element-wise)", function () { + const data1 = [ [ 10, 45, 56, 10 ], [ 25, 23, 30, 10 ] ]; + const sf = new dfd.DataFrame(data1); + const expected = [ [ false, true, true, false ], [ false, false, true, false ] ]; + assert.deepEqual(sf.ge(30).values, expected); + }); + + }); + + describe("ne", function () { + it("Return Not Equal to of series and other series (element-wise)", function () { + const data1 = [ [ 10, 45, 56, 10 ], [ 25, 23, 20, 10 ] ]; + const data2 = [ [ 100, 450, 590, 5 ], [ 25, 2, 0, 10 ] ]; + + const df = new dfd.DataFrame(data1); + const df2 = new dfd.DataFrame(data2); + const expected = [ [ true, true, true, true ], [ false, true, true, false ] ]; + assert.deepEqual(df.ne(df2).values, expected); + }); + + it("Return Not Equal to of series scalar (element-wise)", function () { + const data1 = [ [ 10, 45, 56, 10 ], [ 25, 23, 30, 10 ] ]; + const sf = new dfd.DataFrame(data1); + const expected = [ [ true, true, true, true ], [ true, true, false, true ] ]; + assert.deepEqual(sf.ne(30).values, expected); + }); + it("Return Less than of Array and DataFrame along axis 1 (column)", function () { + const data = { + 'cost': [ 250, 150, 100 ], + 'revenue': [ 100, 250, 300 ] + }; + const sf = [ 100, 300 ]; + const df = new dfd.DataFrame(data, { index: [ 'A', 'B', 'C' ] }); + const expected = [ [ true, true ], [ true, true ], [ false, false ] ]; + const result = df.ne(sf, { axis: 1 }); + assert.deepEqual(result.values, expected); + }); + + }); + + describe("eq", function () { + it("Return Equal to of DataFrame and other DataFrame (element-wise)", function () { + const data1 = [ [ 10, 45, 56, 10 ], [ 25, 23, 20, 10 ] ]; + const data2 = [ [ 100, 450, 590, 5 ], [ 25, 2, 0, 10 ] ]; + + const df = new dfd.DataFrame(data1); + const df2 = new dfd.DataFrame(data2); + const expected = [ [ false, false, false, false ], [ true, false, false, true ] ]; + assert.deepEqual(df.eq(df2).values, expected); + }); + + it("Return Equal to of DataFrame with scalar (element-wise)", function () { + const data1 = [ [ 10, 45, 56, 10 ], [ 25, 23, 30, 10 ] ]; + const sf = new dfd.DataFrame(data1); + const expected = [ [ false, false, false, false ], [ false, false, true, false ] ]; + assert.deepEqual(sf.eq(30).values, expected); + }); + it("Return Equal to of series and DataFrame scalar along axis 1", function () { + const data1 = { "Col1": [ 10, 45, 56, 10 ], "Col2": [ 23, 20, 10, 10 ] }; + const sf = new dfd.Series([ 10, 23 ]); + const df = new dfd.DataFrame(data1); + const expected = [ [ true, true ], [ false, false ], [ false, false ], [ true, false ] ]; + assert.deepEqual(df.eq(sf, { axis: 1 }).values, expected); + }); + it("Return Less than of Array and DataFrame along axis 0", function () { + const data = { + 'cost': [ 250, 150, 100 ], + 'revenue': [ 100, 250, 300 ] + }; + const sf = [ 250, 250, 100 ]; + const df = new dfd.DataFrame(data, { index: [ 'A', 'B', 'C' ] }); + const expected = [ [ true, false ], [ false, true ], [ true, false ] ]; + const result = df.eq(sf, { axis: 0 }); + assert.deepEqual(result.values, expected); + }); + + }); + + describe("replace", function () { + it("Replace across all columns", function () { + const data1 = [ [ 10, 45, 56, 25 ], [ 23, 20, 10, 24 ] ]; + const df = new dfd.DataFrame(data1); + const expected = [ [ -999, 45, 56, 25 ], [ 23, 20, -999, 24 ] ]; + const df_rep = df.replace(10, -999); + assert.deepEqual(df_rep.values, expected); + }); + + it("Replace accross all columns inplace", function () { + const data1 = [ [ "A", "A", "A", "B" ], [ "B", "C", "C", "D" ] ]; + const df = new dfd.DataFrame(data1); + const expected = [ [ "boy", "boy", "boy", "B" ], [ "B", "C", "C", "D" ] ]; + df.replace("A", "boy", { inplace: true }); + assert.deepEqual(df.values, expected); + }); + it("Replace values in specified two column(s)", function () { + const data1 = [ [ "A", "A", 1, "girl" ], + [ "B", "A", 2, "woman" ], + [ "A", "B", 3, "man" ] ]; + const df = new dfd.DataFrame(data1, { columns: [ "col1", "col2", "col3", "col4" ] }); + const expected = [ [ "boy", "boy", 1, "girl" ], + [ "B", "boy", 2, "woman" ], + [ "boy", "B", 3, "man" ] ]; + const df_rep = df.replace("A", "boy", { columns: [ "col1", "col2" ] }); + assert.deepEqual(df_rep.values, expected); + }); + + it("Replace values in specified single column(s)", function () { + const data1 = [ [ 2, "A", 1, "girl" ], + [ 3, "A", 2, "woman" ], + [ 4, "B", 3, "man" ] ]; + const df = new dfd.DataFrame(data1, { columns: [ "col1", "col2", "col3", "col4" ] }); + const expected = [ [ 2, "A", 1, "girl" ], + [ 100, "A", 2, "woman" ], + [ 4, "B", 3, "man" ] ]; + const df_rep = df.replace(3, 100, { columns: [ "col1" ] }); + assert.deepEqual(df_rep.values, expected); + assert.notDeepEqual(df_rep, df); + assert.notDeepEqual(df_rep.values, df.values); + }); + + it("Replace values in specified two column(s) inplace", function () { + const data1 = [ [ "A", "A", 1, "girl" ], + [ "B", "A", 2, "woman" ], + [ "A", "B", 3, "man" ] ]; + const df = new dfd.DataFrame(data1, { columns: [ "col1", "col2", "col3", "col4" ] }); + const expected = [ [ "boy", "boy", 1, "girl" ], + [ "B", "boy", 2, "woman" ], + [ "boy", "B", 3, "man" ] ]; + df.replace("A", "boy", { columns: [ "col1", "col2" ], inplace: true }); + assert.deepEqual(df.values, expected); + }); + + it("Replace values in specified single column(s) inplace", function () { + const data1 = [ [ 2, "A", 1, "girl" ], + [ 3, "A", 2, "woman" ], + [ 4, "B", 3, "man" ] ]; + const df = new dfd.DataFrame(data1, { columns: [ "col1", "col2", "col3", "col4" ] }); + const expected = [ [ 2, "A", 1, "girl" ], + [ 100, "A", 2, "woman" ], + [ 4, "B", 3, "man" ] ]; + df.replace(3, 100, { columns: [ "col1" ], inplace: true }); + assert.deepEqual(df.values, expected); + }); + + }); + + describe("sum", function () { + it("Sum values of a DataFrame by Default axis column (axis=1)", function () { + const data1 = [ [ 30, 40, 3.1 ], + [ 5, 5, 5.1 ], + [ 5, 5, 3.2 ] ]; + const sf = new dfd.DataFrame(data1); + const res = [ 73.1, 15.1, 13.2 ]; + assert.deepEqual(sf.sum().values, res); + }); + it("Sum values of a DataFrame along row axis (axis=0)", function () { + const data1 = [ [ 30, 40, 3.1 ], + [ 5, 5, 5.1 ], + [ 5, 5, 3.2 ] ]; + const df = new dfd.DataFrame(data1); + const res = [ 40, 50, 11.399999999999999 ]; + assert.deepEqual(df.sum({ axis: 0 }).values, res); + }); + it("Sum values of a mixed DataFrame along row axis (axis=0)", function () { + const data1 = [ [ 30, 40, 3.1, true ], + [ 5, 5, 5.1, true ], + [ 5, 5, 3.2, true ] ]; + const df = new dfd.DataFrame(data1); + const res = [ 40, 50, 11.399999999999999, 3 ]; + assert.deepEqual(df.sum({ axis: 0 }).values, res); + }); + it("Sum values of a boolean DataFrame along row axis (axis=0)", function () { + const data1 = [ [ true, true, false, true ], + [ false, false, false, false ], + [ false, true, true, false ] ]; + const df = new dfd.DataFrame(data1); + const res = [ 1, 2, 1, 1 ]; + assert.deepEqual(df.sum({ axis: 0 }).values, res); + }); + it("Sum values of a boolean DataFrame along default column axis (axis=1)", function () { + const data1 = [ [ true, true, false, true ], + [ false, false, false, false ], + [ false, true, true, false ] ]; + const df = new dfd.DataFrame(data1); + const res = [ 3, 0, 2 ]; + assert.deepEqual(df.sum().values, res); + }); + it("Sum values of a df with missing values", function () { + const data1 = [ [ 11, 20, 3 ], [ null, 15, 6 ], [ 2, 30, 40 ], [ 2, 89, 78 ] ]; + const df = new dfd.DataFrame(data1); + const res = [ 34, 21, 72, 169 ]; + assert.deepEqual(df.sum({ axis: 1 }).values, res); + }); + + }); + + describe("abs", function () { + it("Returns the absolute values in DataFrame of ints", function () { + const data1 = [ [ -10, 45, 56, 10 ], [ -25, 23, 20, -10 ] ]; + const df = new dfd.DataFrame(data1); + const expected = [ [ 10, 45, 56, 10 ], [ 25, 23, 20, 10 ] ]; + assert.deepEqual(df.abs().values, expected); + }); + + it("Returns the absolute values in mixed DataFrame", function () { + const data1 = [ [ -10, -45.1, 56, 10 ], [ -25, -23.2, 20, -10 ] ]; + const df = new dfd.DataFrame(data1); + const expected = [ [ 10, 45.1, 56, 10 ], [ 25, 23.2, 20, 10 ] ]; + assert.deepEqual(df.abs().values, expected); + }); + }); + + describe("T", function () { + it("Return the Transpose of a DataFrame", function () { + const data1 = [ [ 10, 45, 56, 10 ], + [ 25, 23, 20, 10 ] ]; + + const cols = [ "a", "b", "c", "d" ]; + const df = new dfd.DataFrame(data1, { columns: cols }); + const df_trans = df.T; + const expected_vals = [ [ 10, 25 ], [ 45, 23 ], [ 56, 20 ], [ 10, 10 ] ]; + const expected_index = cols; + const expected_col_names = [ "0", "1" ]; + assert.deepEqual(df_trans.index, expected_index); + assert.deepEqual(df_trans.values, expected_vals); + assert.deepEqual(df_trans.columns, expected_col_names); + + }); + }); + + describe("transpose", function () { + it("Return the Transpose of a DataFrame", function () { + const data1 = [ [ 10, 45, 56, 10 ], + [ 25, 23, 20, 10 ] ]; + + const cols = [ "a", "b", "c", "d" ]; + const df = new dfd.DataFrame(data1, { columns: cols }); + const df_trans = df.T; + const expected_vals = [ [ 10, 25 ], [ 45, 23 ], [ 56, 20 ], [ 10, 10 ] ]; + const expected_index = cols; + const expected_col_names = [ "0", "1" ]; + assert.deepEqual(df_trans.index, expected_index); + assert.deepEqual(df_trans.values, expected_vals); + assert.deepEqual(df_trans.columns, expected_col_names); + + }); + it("Transpose a DataFrame inplace", function () { + const data1 = [ [ 10, 45, 56, 10 ], + [ 25, 23, 20, 10 ] ]; + + const cols = [ "a", "b", "c", "d" ]; + const df = new dfd.DataFrame(data1, { columns: cols }); + df.transpose({ inplace: true }); + const expected_vals = [ [ 10, 25 ], [ 45, 23 ], [ 56, 20 ], [ 10, 10 ] ]; + const expected_index = cols; + const expected_col_names = [ "0", "1" ]; + assert.deepEqual(df.index, expected_index); + assert.deepEqual(df.values, expected_vals); + assert.deepEqual(df.columns, expected_col_names); + + }); + }); + + + describe("asType", function () { + it("set type of float column to int", function () { + const data = { + "A": [ -20.1, 30, 47.3, -20 ], + "B": [ 34, -4, 5, 6 ], + "C": [ 20.1, -20.23, 30.3, 40.11 ], + "D": [ "a", "b", "c", "c" ] + }; + const df = new dfd.DataFrame(data); + const dfNew = df.asType("A", "int32"); + dfNew["D"] = [ "a", "b", "c", "F" ]; + assert.deepEqual(dfNew.dtypes, [ 'int32', 'int32', 'float32', 'string' ]); + assert.deepEqual(dfNew['A'].values, [ -20, 30, 47, -20 ]); + assert.notDeepEqual(dfNew["D"].values, df["D"].values); + + }); + it("set type of int column to float", function () { + const data = { + "A": [ -20.1, 30, 47.3, -20 ], + "B": [ 34, -4, 5, 6 ], + "C": [ 20.1, -20.23, 30.3, 40.11 ], + "D": [ "a", "b", "c", "c" ] + }; + const df = new dfd.DataFrame(data); + const dfNew = df.asType("B", "float32"); + + assert.deepEqual(dfNew.dtypes, [ 'float32', 'float32', 'float32', 'string' ]); + assert.deepEqual(dfNew['B'].values, [ 34, -4, 5, 6 ]); + + }); + it("set type of string column to int", function () { + const data = { + "A": [ -20.1, 30, 47.3, -20 ], + "B": [ 34, -4, 5, 6 ], + "C": [ 20.1, -20.23, 30.3, 40.11 ], + "D": [ "20.1", "21", "23.4", "50.78" ] + }; + const df = new dfd.DataFrame(data); + const dfNew = df.asType("D", "int32"); + + assert.deepEqual(dfNew.dtypes, [ 'float32', 'int32', 'float32', 'int32' ]); + assert.deepEqual(dfNew['D'].values, [ 20, 21, 23, 50 ]); + + }); + it("set type of string column to float", function () { + const data = { + "A": [ -20.1, 30, 47.3, -20 ], + "B": [ 34, -4, 5, 6 ], + "C": [ 20.1, -20.23, 30.3, 40.11 ], + "D": [ "20.1", "21", "23.4", "50.78" ] + }; + const df = new dfd.DataFrame(data); + const dfNew = df.asType("D", "float32"); + + assert.deepEqual(dfNew.dtypes, [ 'float32', 'int32', 'float32', 'float32' ]); + assert.deepEqual(dfNew['D'].values, [ 20.1, 21, 23.4, 50.78 ]); + + }); + }); + + describe("nUnique", function () { + it("Returns the number of unique elements along axis 1", function () { + const data = { + "A": [ -20, 30, 47.3, -20, 2 ], + "B": [ 34, -4, 5, 6, 2 ], + "C": [ 20, 20, 30, 30, 2 ], + "D": [ "a", "b", "c", "c", "d" ] + }; + + const df = new dfd.DataFrame(data); + const res = [ 4, 4, 4, 4, 2 ]; + assert.deepEqual(df.nUnique(1).values, res); + + }); + it("Returns the number of unique elements along axis 0", function () { + const data = { + "A": [ 20, 30, 47.3, 30 ], + "B": [ 34, -4, 5, 30 ], + "C": [ 20, 20, 30, 30 ], + "D": [ "a", "b", "c", "c" ] + }; + + const df = new dfd.DataFrame(data); + const res = [ 3, 4, 2, 3 ]; + assert.deepEqual(df.nUnique(0).values, res); + + }); + + }); + + + describe("rename", function () { + it("Rename columns along axis 1", function () { + const data = { + "A": [ -20, 30, 47.3, -20 ], + "B": [ 34, -4, 5, 6 ], + "C": [ 20, 20, 30, 30 ] + }; + + const df = new dfd.DataFrame(data); + const dfNew = df.rename({ "A": "a1", "B": "b1" }); + const res = [ "a1", "b1", "C" ]; + assert.deepEqual(dfNew.columns, res); + + }); + it("confirms original column name is not modified along axis 1", function () { + const data = { + "A": [ -20, 30, 47.3, -20 ], + "B": [ 34, -4, 5, 6 ], + "D": [ "a", "b", "c", "c" ] + }; + + const df = new dfd.DataFrame(data); + const dfNew = df.rename({ "A": "a1", "B": "b1" }, { axis: 1 }); + const res = [ "A", "B", "D" ]; + assert.deepEqual(df.columns, res); + + }); + it("Rename columns along axis 1 inplace", function () { + const data = { + "A": [ -20, 30, 47.3, -20 ], + "B": [ 34, -4, 5, 6 ], + "C": [ 20, 20, 30, 30 ] + }; + + const df = new dfd.DataFrame(data); + df.rename({ "A": "a1", "B": "b1" }, { inplace: true }); + const res = [ "a1", "b1", "C" ]; + assert.deepEqual(df.columns, res); + + }); + it("Rename string index along axis 0", function () { + const data = { + "A": [ -20, 30, 47.3, -20 ], + "B": [ 34, -4, 5, 6 ], + "C": [ 20, 20, 30, 30 ] + }; + + const df = new dfd.DataFrame(data, { index: [ "a", "b", "c", "d" ] }); + const dfNew = df.rename({ "a": 0, "b": 1 }, { axis: 0 }); + const res = [ 0, 1, "c", "d" ]; + assert.deepEqual(dfNew.index, res); + + }); + it("Rename string index along axis 0 inplace", function () { + const data = { + "A": [ -20, 30, 47.3, -20 ], + "B": [ 34, -4, 5, 6 ], + "C": [ 20, 20, 30, 30 ] + }; + + const df = new dfd.DataFrame(data, { index: [ "a", "b", "c", "d" ] }); + df.rename({ "a": 0, "b": 1 }, { axis: 0, inplace: true }); + const res = [ 0, 1, "c", "d" ]; + assert.deepEqual(df.index, res); + + }); + + it("Get new column via subseting works after rename (inplace)", function () { + let data = { + "A": [ -20, 30, 47.3 ], + "B": [ 34, -4, 5 ], + "C": [ 20, 2, 30 ] + }; + let df = new dfd.DataFrame(data); + df.rename({ "A": "new_name" }, { inplace: true }); + df["new_name"].print(); + assert.deepEqual(df["new_name"].values, data["A"]); + }); + + it("Get new column via subseting works after rename (not-inplace)", function () { + let data = { + "A": [ -20, 30, 47.3 ], + "B": [ 34, -4, 5 ], + "C": [ 20, 2, 30 ] + }; + let df = new dfd.DataFrame(data); + let new_df = df.rename({ "A": "new_name" }); + assert.deepEqual(new_df["new_name"].values, data["A"]); + }); + }); + + describe("sortIndex", function () { + + it("sort index in ascending order", function () { + const data = [ [ 0, 2, 4, "b" ], + [ 360, 180, 360, "a" ], + [ 2, 4, 6, "c" ] ]; + + const df = new dfd.DataFrame(data, { "columns": [ "col1", "col2", "col3", "col4" ], index: [ "b", "a", "c" ] }); + const df2 = df.sortIndex(); + const rslt = [ [ 360, 180, 360, 'a' ], [ 0, 2, 4, 'b' ], [ 2, 4, 6, 'c' ] ]; + + assert.deepEqual(df2.values, rslt); + }); + it("sort index in ascending order - inplace", function () { + const data = [ [ 0, 2, 4, "b" ], + [ 360, 180, 360, "a" ], + [ 2, 4, 6, "c" ] ]; + + const df = new dfd.DataFrame(data, { "columns": [ "col1", "col2", "col3", "col4" ], index: [ "b", "a", "c" ] }); + df.sortIndex({ inplace: true }); + const rslt = [ [ 360, 180, 360, 'a' ], [ 0, 2, 4, 'b' ], [ 2, 4, 6, 'c' ] ]; + assert.deepEqual(df.values, rslt); + }); + it("sort index in descending order", function () { + const data = [ [ 0, 2, 4, "b" ], + [ 360, 180, 360, "a" ], + [ 2, 4, 6, "c" ] ]; + + const df = new dfd.DataFrame(data, { "columns": [ "col1", "col2", "col3", "col4" ], index: [ "b", "a", "c" ] }); + const df2 = df.sortIndex({ ascending: false }); + const rslt = [ [ 2, 4, 6, 'c' ], [ 0, 2, 4, 'b' ], [ 360, 180, 360, 'a' ] ]; + + assert.deepEqual(df2.values, rslt); + }); + it("sort index in descending order with inplace set to true", function () { + const data = [ [ 0, 2, 4, "b" ], + [ 360, 180, 360, "a" ], + [ 2, 4, 6, "c" ] ]; + + const df = new dfd.DataFrame(data, { "columns": [ "col1", "col2", "col3", "col4" ], index: [ 4, 2, 5 ] }); + df.sortIndex({ ascending: false, inplace: true }); + const rslt = [ [ 2, 4, 6, 'c' ], [ 0, 2, 4, 'b' ], [ 360, 180, 360, 'a' ] ]; + assert.deepEqual(df.values, rslt); + }); + + it("sort index in descending order and retains index", function () { + let data = [ [ 0, 2, 4, "b" ], + [ 360, 180, 360, "a" ], + [ 2, 4, 6, "c" ] ]; + + let df = new dfd.DataFrame(data, { "columns": [ "col1", "col2", "col3", "col4" ], index: [ "b", "a", "c" ] }); + let df2 = df.sortIndex({ ascending: false }); + let rslt = [ "c", "b", "a" ]; + + assert.deepEqual(df2.index, rslt); + }); + }); + + describe("append", function () { + + it("Append works for an array", function () { + const data = [ [ 0, 2, 4, "b" ], + [ 360, 180, 360, "a" ], + [ 2, 4, 6, "c" ] ]; + + const df = new dfd.DataFrame(data); + const expected_val = [ [ 0, 2, 4, "b" ], + [ 360, 180, 360, "a" ], + [ 2, 4, 6, "c" ], + [ 20, 40, 60, "d" ] ]; + + const newDf = df.append([ 20, 40, 60, "d" ], [ "n1" ]); + assert.deepEqual(newDf.values, expected_val); + assert.deepEqual(newDf.index, [ 0, 1, 2, "n1" ]); + }); + it("Append works for an array of arrays", function () { + const data = [ [ 0, 2, 4, "b" ], + [ 360, 180, 360, "a" ], + [ 2, 4, 6, "c" ] ]; + + const df = new dfd.DataFrame(data); + const expected_val = [ [ 0, 2, 4, "b" ], + [ 360, 180, 360, "a" ], + [ 2, 4, 6, "c" ], + [ 20, 40, 60, "d" ], + [ 21, 42, 61, "y" ] ]; + + const newDf = df.append([ [ 20, 40, 60, "d" ], [ 21, 42, 61, "y" ] ], [ "n1", "n2" ]); + assert.deepEqual(newDf.values, expected_val); + assert.deepEqual(newDf.index, [ 0, 1, 2, "n1", "n2" ]); + }); + + it("Append works for DataFrame", function () { + const data = [ [ 0, 2, 4, "b" ], + [ 360, 180, 360, "a" ], + [ 2, 4, 6, "c" ] ]; + + const df = new dfd.DataFrame(data, { "columns": [ "col1", "col2", "col3", "col4" ] }); + const df2 = new dfd.DataFrame([ [ 20, 40, 60, "d" ] ], { "columns": [ "col1", "col2", "col3", "col4" ] }); + + const expected_val = [ [ 0, 2, 4, "b" ], + [ 360, 180, 360, "a" ], + [ 2, 4, 6, "c" ], + [ 20, 40, 60, "d" ] ]; + + const newDf = df.append(df2, [ "n1" ]); + assert.deepEqual(newDf.values, expected_val); + + }); + it("Append works for Series", function () { + const data = [ [ 0, 2, 4, "b" ], + [ 360, 180, 360, "a" ], + [ 2, 4, 6, "c" ] ]; + + const df = new dfd.DataFrame(data, { "columns": [ "col1", "col2", "col3", "col4" ] }); + const sf = new dfd.Series([ 20, 40, 60, "d" ]); + + const expected_val = [ [ 0, 2, 4, "b" ], + [ 360, 180, 360, "a" ], + [ 2, 4, 6, "c" ], + [ 20, 40, 60, "d" ] ]; + + const newDf = df.append(sf, [ "n1" ]); + assert.deepEqual(newDf.values, expected_val); + + }); + }); + + describe("cumProd", function () { + + it("cumProd works for axis 1", function () { + const data = [ [ 2, 1, 2, 3 ], [ 3, 4, 11, 9 ], [ 5, 6, 7, 8 ] ]; + const column = [ "A", "B", "C", "D" ]; + const df = new dfd.DataFrame(data, { columns: column }); + const rslt = [ [ 2, 2, 4, 12 ], + [ 3, 12, 132, 1188 ], + [ 5, 30, 210, 1680 ] ]; + + const newDf = df.cumProd(); + assert.deepEqual(newDf.values, rslt); + }); + it("cumProd axis 0 works", function () { + const data = [ [ 2, 1, 2, 3 ], [ 3, 4, 11, 9 ], [ 5, 6, 7, 8 ] ]; + const column = [ "A", "B", "C", "D" ]; + const df = new dfd.DataFrame(data, { columns: column }); + const rslt = [ [ 2, 1, 2, 3 ], + [ 6, 4, 22, 27 ], + [ 30, 24, 154, 216 ] ]; + + assert.deepEqual(df.cumProd({ axis: 0 }).values, rslt); + }); + + it("cumProd works for axis 1 inplace", function () { + const data = [ [ 2, 1, 2, 3 ], [ 3, 4, 11, 9 ], [ 5, 6, 7, 8 ] ]; + const column = [ "A", "B", "C", "D" ]; + const df = new dfd.DataFrame(data, { columns: column }); + const rslt = [ [ 2, 2, 4, 12 ], + [ 3, 12, 132, 1188 ], + [ 5, 30, 210, 1680 ] ]; + + df.cumProd({ inplace: true }); + assert.deepEqual(df.values, rslt); + }); + it("cumProd axis 0 works inplace", function () { + const data = [ [ 2, 1, 2, 3 ], [ 3, 4, 11, 9 ], [ 5, 6, 7, 8 ] ]; + const column = [ "A", "B", "C", "D" ]; + const df = new dfd.DataFrame(data, { columns: column }); + const rslt = [ [ 2, 1, 2, 3 ], + [ 6, 4, 22, 27 ], + [ 30, 24, 154, 216 ] ]; + df.cumProd({ axis: 0, inplace: true }); + assert.deepEqual(df.values, rslt); + }); + }); + + describe("cumSum", function () { + + it("cumSum works for axis 1", function () { + const data = [ [ 2, 1, 2, 3 ], [ 3, 4, 11, 9 ], [ 5, 6, 7, 8 ] ]; + const column = [ "A", "B", "C", "D" ]; + const df = new dfd.DataFrame(data, { columns: column }); + const rslt = [ [ 2, 3, 5, 8 ], + [ 3, 7, 18, 27 ], + [ 5, 11, 18, 26 ] ]; + + const newDf = df.cumSum(); + assert.deepEqual(newDf.values, rslt); + }); + it("cumSum axis 0 works", function () { + const data = [ [ 2, 1, 2, 3 ], [ 3, 4, 11, 9 ], [ 5, 6, 7, 8 ] ]; + const column = [ "A", "B", "C", "D" ]; + const df = new dfd.DataFrame(data, { columns: column }); + const rslt = [ [ 2, 1, 2, 3 ], + [ 5, 5, 13, 12 ], + [ 10, 11, 20, 20 ] ]; + + assert.deepEqual(df.cumSum({ axis: 0 }).values, rslt); + }); + + it("cumSum works for axis 1 inplace", function () { + const data = [ [ 2, 1, 2, 3 ], [ 3, 4, 11, 9 ], [ 5, 6, 7, 8 ] ]; + const column = [ "A", "B", "C", "D" ]; + const df = new dfd.DataFrame(data, { columns: column }); + const rslt = [ [ 2, 3, 5, 8 ], + [ 3, 7, 18, 27 ], + [ 5, 11, 18, 26 ] ]; + + df.cumSum({ inplace: true }); + assert.deepEqual(df.values, rslt); + }); + it("cumSum axis 0 works inplace", function () { + const data = [ [ 2, 1, 2, 3 ], [ 3, 4, 11, 9 ], [ 5, 6, 7, 8 ] ]; + const column = [ "A", "B", "C", "D" ]; + const df = new dfd.DataFrame(data, { columns: column }); + const rslt = [ [ 2, 1, 2, 3 ], + [ 5, 5, 13, 12 ], + [ 10, 11, 20, 20 ] ]; + df.cumSum({ axis: 0, inplace: true }); + assert.deepEqual(df.values, rslt); + }); + }); + + describe("cumMin", function () { + + it("cumMin works for axis 1", function () { + const data = [ [ 2, 1, 2, 3 ], [ 3, 4, 11, 9 ], [ 5, 6, 7, 8 ] ]; + const column = [ "A", "B", "C", "D" ]; + const df = new dfd.DataFrame(data, { columns: column }); + const rslt = [ [ 2, 1, 1, 1 ], + [ 3, 3, 3, 3 ], + [ 5, 5, 5, 5 ] ]; + + const newDf = df.cumMin(); + assert.deepEqual(newDf.values, rslt); + }); + it("cumMin axis 0 works", function () { + const data = [ [ 2, 1, 2, 3 ], [ 3, 4, 11, 9 ], [ 5, 6, 7, 8 ] ]; + const column = [ "A", "B", "C", "D" ]; + const df = new dfd.DataFrame(data, { columns: column }); + const rslt = [ [ 2, 1, 2, 3 ], + [ 2, 1, 2, 3 ], + [ 2, 1, 2, 3 ] ]; + + assert.deepEqual(df.cumMin({ axis: 0 }).values, rslt); + }); + + it("cumMin works for axis 1 inplace", function () { + const data = [ [ 2, 1, 2, 3 ], [ 3, 4, 11, 9 ], [ 5, 6, 7, 8 ] ]; + const column = [ "A", "B", "C", "D" ]; + const df = new dfd.DataFrame(data, { columns: column }); + const rslt = [ [ 2, 1, 1, 1 ], + [ 3, 3, 3, 3 ], + [ 5, 5, 5, 5 ] ]; + + df.cumMin({ inplace: true }); + assert.deepEqual(df.values, rslt); + }); + it("cumMin axis 0 works inplace", function () { + const data = [ [ 2, 1, 2, 3 ], [ 3, 4, 11, 9 ], [ 5, 6, 7, 8 ] ]; + const column = [ "A", "B", "C", "D" ]; + const df = new dfd.DataFrame(data, { columns: column }); + const rslt = [ [ 2, 1, 2, 3 ], + [ 2, 1, 2, 3 ], + [ 2, 1, 2, 3 ] ]; + df.cumMin({ axis: 0, inplace: true }); + assert.deepEqual(df.values, rslt); + }); + }); + + describe("cumMax", function () { + + it("cumMax works for axis 1", function () { + const data = [ [ 2, 1, 2, 3 ], [ 3, 4, 11, 9 ], [ 5, 6, 7, 8 ] ]; + const column = [ "A", "B", "C", "D" ]; + const df = new dfd.DataFrame(data, { columns: column }); + const rslt = [ [ 2, 2, 2, 3 ], + [ 3, 4, 11, 11 ], + [ 5, 6, 7, 8 ] ]; + + const newDf = df.cumMax(); + assert.deepEqual(newDf.values, rslt); + }); + it("cumMax axis 0 works", function () { + const data = [ [ 2, 1, 2, 3 ], [ 3, 4, 11, 9 ], [ 5, 6, 7, 8 ] ]; + const column = [ "A", "B", "C", "D" ]; + const df = new dfd.DataFrame(data, { columns: column }); + const rslt = [ [ 2, 1, 2, 3 ], + [ 3, 4, 11, 9 ], + [ 5, 6, 11, 9 ] ]; + + assert.deepEqual(df.cumMax({ axis: 0 }).values, rslt); + }); + + it("cumMax works for axis 1 inplace", function () { + const data = [ [ 2, 1, 2, 3 ], [ 3, 4, 11, 9 ], [ 5, 6, 7, 8 ] ]; + const column = [ "A", "B", "C", "D" ]; + const df = new dfd.DataFrame(data, { columns: column }); + const rslt = [ [ 2, 2, 2, 3 ], + [ 3, 4, 11, 11 ], + [ 5, 6, 7, 8 ] ]; + + df.cumMax({ inplace: true }); + assert.deepEqual(df.values, rslt); + }); + it("cumMax axis 0 works inplace", function () { + const data = [ [ 2, 1, 2, 3 ], [ 3, 4, 11, 9 ], [ 5, 6, 7, 8 ] ]; + const column = [ "A", "B", "C", "D" ]; + const df = new dfd.DataFrame(data, { columns: column }); + const rslt = [ [ 2, 1, 2, 3 ], + [ 3, 4, 11, 9 ], + [ 5, 6, 11, 9 ] ]; + df.cumMax({ axis: 0, inplace: true }); + assert.deepEqual(df.values, rslt); + }); + }); + + + describe("query", function () { + + it("Get the DataFrame containing rows with the filtered column", function () { + const data = [ [ 1, 2, 3 ], [ 4, 5, 6 ], [ 20, 30, 40 ], [ 39, 89, 78 ] ]; + const cols = [ "A", "B", "C" ]; + const df = new dfd.DataFrame(data, { columns: cols }); + const query_df = df.query(df["B"].ge(5)); + const query_data = [ [ 4, 5, 6 ], [ 20, 30, 40 ], [ 39, 89, 78 ] ]; + assert.deepEqual(query_df.values, query_data); + }); + it("Get the Dataframe containing rows with the filtered column in String values", function () { + const data = { "Abs": [ 20, 30, 47 ], "Count": [ 34, 4, 5 ], "country code": [ "NG", "FR", "GH" ] }; + const cols = [ "Abs", "Count", "country code" ]; + const df = new dfd.DataFrame(data, { columns: cols }); + const query_df = df.query(df["country code"].str.includes("NG")); + + const query_data = [ [ 20, 34, "NG" ] ]; + assert.deepEqual(query_df.values, query_data); + }); + it("Get the Dataframe containing rows with the filtered column in String values inplace", function () { + const data = { "Abs": [ 20, 30, 47 ], "Count": [ 34, 4, 5 ], "country code": [ "NG", "FR", "GH" ] }; + const cols = [ "Abs", "Count", "country code" ]; + const df = new dfd.DataFrame(data, { columns: cols }); + df.query(df["country code"].eq("NG"), { inplace: true }); + const query_data = [ [ 20, 34, "NG" ] ]; + assert.deepEqual(df.values, query_data); + }); + it("Confirms that query index are updated", function () { + + const data = [ [ 1, 2, 3 ], [ 4, 5, 6 ], [ 20, 30, 40 ], [ 39, 89, 78 ] ]; + const cols = [ "A", "B", "C" ]; + const df = new dfd.DataFrame(data, { columns: cols }); + const query_df = df.query(df["B"].ge(5)); + assert.deepEqual(query_df.index, [ 1, 2, 3 ]); + }); + + it("Confirms chaining boolean queries work", function () { + + const data = [ [ 1, 2, 3 ], + [ 4, 5, 60 ], + [ 20, 30, 4 ], + [ 39, 89, 7 ] ]; + const cols = [ "A", "B", "C" ]; + const df = new dfd.DataFrame(data, { columns: cols }); + + const query_df = df.query( + df["B"].ge(5).and(df["C"].lt(10)) + ); + const query_data = [ [ 20, 30, 4 ], [ 39, 89, 7 ] ]; + assert.deepEqual(query_df.values, query_data); + assert.deepEqual(query_df.index, [ 2, 3 ]); + }); + + it("Confirms chaining boolean queries work and returns empty DF", function () { + + const data = [ [ 1, 2, 3 ], + [ 4, 5, 60 ], + [ 20, 30, 40 ], + [ 39, 89, 70 ] ]; + const cols = [ "A", "B", "C" ]; + const df = new dfd.DataFrame(data, { columns: cols }); + + const query_df = df.query( + df["B"].ge(5).and(df["C"].lt(10)) + ); + assert.deepEqual(query_df.values, []); + assert.deepEqual(query_df.index, []); + }); + + it("Confirms that column names are not changed", function () { + + let data = [ [ 1, 2, 3 ], [ 4, 5, 6 ], [ 20, 30, 40 ], [ 39, 89, 78 ] ]; + let cols = [ "A", "B", "C" ]; + let df = new dfd.DataFrame(data, { columns: cols }); + let df_query = df.query( df["B"].ge(5)); + assert.deepEqual(df_query.index, [ 1, 2, 3 ]); + assert.deepEqual(df_query.columns, [ "A", "B", "C" ]); + }); + + }); + + describe("cTypes", function () { + + it("Returns the correct dtype in a DataFrame", function () { + const data = [ [ "boy", 1.2, 2, 3 ], [ "girl", 4.32, 11, 9 ], [ '4', 6.1, 7, 8 ] ]; + const column = [ "A", "B", "C", "D" ]; + const df = new dfd.DataFrame(data, { columns: column }); + const rslt = [ "string", "float32", "int32", "int32" ]; + assert.deepEqual(df.ctypes.values, rslt); + }); + }); + + // describe("IO outputs", function () { + // it("toExcel works", async function () { + // const data = [[1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12]] + // const df = new dfd.DataFrame(data, { columns: ["a", "b", "c", "d"] }); + + // const filePath = path.join(process.cwd(), "test", "samples", "test.xlsx"); + // df.toExcel({ filePath }) + + // const dfNew = await readExcel(filePath, {}); + // assert.equal(fs.existsSync(filePath), true) + // assert.deepEqual(dfNew.columns, [ + // 'a', + // 'b', + // 'c', + // 'd', + // ]); + // assert.deepEqual(dfNew.dtypes, [ + // 'int32', 'int32', + // 'int32', 'int32', + // ]); + // assert.deepEqual(dfNew.shape, [3, 4]) + // }); + + // it("toCSV works for specified seperator", async function () { + // const data = [[1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12]] + // let df = new dfd.DataFrame(data, { columns: ["a", "b", "c", "d"] }); + // assert.deepEqual(df.toCSV({ sep: "+" }), `a+b+c+d\n1+2+3+4\n5+6+7+8\n9+10+11+12\n`); + // }); + // it("toCSV write to local file works", async function () { + // const data = [[1, 2, 3, "4"], [5, 6, 7, "8"], [9, 10, 11, "12"]] + // let df = new dfd.DataFrame(data, { columns: ["a", "b", "c", "d"] }); + + // const filePath = path.join(process.cwd(), "test", "samples", "test_write.csv"); + + // df.toCSV({ sep: ",", filePath }); + // assert.equal(fs.existsSync(filePath), true); + // }); + // it("toJSON works for row format", async function () { + // const data = [[1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12]] + // const df = new dfd.DataFrame(data, { columns: ["a", "b", "c", "d"] }); + // const expected = { + // "a": [1, 5, 9], + // "b": [2, 6, 10], + // "c": [3, 7, 11], + // "d": [4, 8, 12], + // } + // const json = df.toJSON({ format: "row" }) + // assert.deepEqual(json, expected); + // }); + // it("toJSON writes file to local path", async function () { + // const data = [[1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12]] + // const df = new dfd.DataFrame(data, { columns: ["a", "b", "c", "d"] }); + + // const rowfilePath = path.join(process.cwd(), "test", "samples", "test_row_write.json"); + // const colfilePath = path.join(process.cwd(), "test", "samples", "test_col_write.json"); + + // df.toJSON({ format: "row", filePath: rowfilePath }) + // df.toJSON({ format: "column", filePath: colfilePath }) + // assert.equal(fs.existsSync(rowfilePath), true); + // assert.equal(fs.existsSync(colfilePath), true); + // }); + // }) + + describe("getDummies", function () { + it("getDummies works on DataFrame", function () { + + const data = [ [ 1, "dog", 1.0, "fat" ], [ 3, "fog", 2.0, "good" ], [ 4, "gof", 3.0, "best" ] ]; + const columns = [ "A", "B", "C", "d" ]; + const df = new dfd.DataFrame(data, { columns: columns }); + + const df1 = df.getDummies({ prefixSeparator: [ "_", "#" ], columns: [ "A", "d" ], prefix: "test" }); + const expectedColumns = [ 'B', 'C', 'test_1', 'test_3', 'test_4', 'test#fat', 'test#good', 'test#best' ]; + const expected = [ [ 'dog', 1.0, 1, 0, 0, 1, 0, 0 ], + [ 'fog', 2.0, 0, 1, 0, 0, 1, 0 ], + [ 'gof', 3.0, 0, 0, 1, 0, 0, 1 ] ]; + assert.deepEqual(df1.values, expected); + assert.deepEqual(df1.columns, expectedColumns); + + }); + it("Throw error if the prefix specified is not equal to the column specified", function () { + + const data = [ [ 1, "dog", 1.0, "fat" ], [ 3, "fog", 2.0, "good" ], [ 4, "gof", 3.0, "best" ] ]; + const columns = [ "A", "B", "C", "d" ]; + const df = new dfd.DataFrame(data, { columns: columns }); + + assert.throws(function () { df.getDummies({ prefix: [ "fg" ], prefixSeparator: "_", columns: [ "A", "d" ] }); }, Error, + `ParamError: prefix and data array must be of the same length. If you need to use the same prefix, then pass a string param instead. e.g {prefix: "fg"}`); + + }); + it("replace column sepecified with prefix", function () { + + const data = [ [ 1, "dog", 1.0, "fat" ], [ 3, "fog", 2.0, "good" ], [ 4, "gof", 3.0, "best" ] ]; + const columns = [ "A", "B", "C", "d" ]; + const df = new dfd.DataFrame(data, { columns: columns }); + + const df1 = df.getDummies({ prefix: [ "F", "G" ], prefixSeparator: "_", columns: [ "A", "d" ] }); + const expectedColumns = [ + 'B', 'C', + 'F_1', 'F_3', + 'F_4', 'G_fat', + 'G_good', 'G_best' + ]; + + const expected = [ [ 'dog', 1.0, 1, 0, 0, 1, 0, 0 ], + [ 'fog', 2.0, 0, 1, 0, 0, 1, 0 ], + [ 'gof', 3.0, 0, 0, 1, 0, 0, 1 ] ]; + + assert.deepEqual(df1.values, expected); + assert.deepEqual(df1.columns, expectedColumns); + + }); + + it("getDummies auto infers and encode columns with string dtype", function () { + + const data = [ [ 1, "dog", 1.0, "fat" ], [ 3, "fog", 2.0, "good" ], [ 4, "gof", 3.0, "best" ] ]; + const columns = [ "A", "B", "C", "d" ]; + const df = new dfd.DataFrame(data, { columns: columns }); + + const df1 = df.getDummies({ prefixSeparator: "_" }); + const expectedColumns = [ + 'A', 'C', + 'B_dog', 'B_fog', + 'B_gof', 'd_fat', + 'd_good', 'd_best' + ]; + const expected = [ + [ + 1, 1, 1, 0, + 0, 1, 0, 0 + ], + [ + 3, 2, 0, 1, + 0, 0, 1, 0 + ], + [ + 4, 3, 0, 0, + 1, 0, 0, 1 + ] + ]; + assert.deepEqual(df1.values, expected); + assert.deepEqual(df1.columns, expectedColumns); + + }); + + it("should one hot encode all other columns", function () { + + const data = [ [ 1, "dog", 1.0, "fat" ], [ 3, "fog", 2.0, "good" ], [ 4, "gof", 3.0, "best" ] ]; + const columns = [ "A", "B", "C", "d" ]; + const df = new dfd.DataFrame(data, { columns: columns }); + const rslt = [ + [ 1, 'dog', 1, 1, 0, 0 ], + [ 3, 'fog', 2, 0, 1, 0 ], + [ 4, 'gof', 3, 0, 0, 1 ] + ]; + + assert.deepEqual(df.getDummies({ columns: [ "d" ] }).values, rslt); + + }); + }); + + +}); diff --git a/src/danfojs-browser/tests/core/generic.test.js b/src/danfojs-browser/tests/core/generic.test.js new file mode 100644 index 00000000..554d8408 --- /dev/null +++ b/src/danfojs-browser/tests/core/generic.test.js @@ -0,0 +1,421 @@ +/* eslint-disable no-undef */ +const tensor = require('@tensorflow/tfjs').tensor; + +describe("Generic (NDFrame)", function () { + describe("NDframe Created from Array", function () { + it("prints the shape of a 1D array", function () { + let data = [ 1, 2, 3, "Boy", "Girl" ]; + let ndframe = new dfd.NDframe({ data, isSeries: true }); + assert.deepEqual(ndframe.shape, [ 5, 1 ]); + }); + it("prints the default assigned column name in a series", function () { + let data = [ "Boy", 20, 25 ]; + let ndframe = new dfd.NDframe({ data, isSeries: true }); + assert.deepEqual(ndframe.columns, [ "0" ]); + }); + it("prints the assigned column name in a series", function () { + let data = [ "Boy", 20, 25 ]; + let ndframe = new dfd.NDframe({ data, columns: [ "Records" ], isSeries: true }); + assert.deepEqual(ndframe.columns, [ "Records" ]); + }); + it("prints the shape of a 2D array", function () { + let data = [ [ "Boy", 20 ], [ "Girl", 25 ] ]; + let ndframe = new dfd.NDframe({ data, isSeries: false }); + assert.deepEqual(ndframe.shape, [ 2, 2 ]); + }); + it("prints the default assigned column names in 2D frame", function () { + let data = [ [ "Boy", 20 ], [ "Girl", 25 ] ]; + let ndframe = new dfd.NDframe({ data, isSeries: false }); + assert.deepEqual(ndframe.columns, [ "0", "1" ]); + }); + it("prints the assigned column names", function () { + let data = [ [ "Boy", 20 ], [ "Girl", 25 ] ]; + let options = { "columns": [ "Gender", "Age" ], "isSeries": false }; + let ndframe = new dfd.NDframe({ data, ...options }); + assert.deepEqual(ndframe.columns, [ "Gender", "Age" ]); + }); + it("prints the size of a frame", function () { + let data = [ [ "Boy", 20, 1 ], [ "Girl", 25, 3 ] ]; + let options = { "columns": [ "Gender", "Age", "count" ], "isSeries": false }; + let ndframe = new dfd.NDframe({ data, ...options }); + assert.deepEqual(ndframe.size, 6); + }); + it("prints the dimension of a frame", function () { + let data = [ [ "Boy", 20, 1 ], [ "Girl", 25, 3 ] ]; + let options = { "columns": [ "Gender", "Age", "count" ], "isSeries": false }; + let ndframe = new dfd.NDframe({ data, ...options }); + assert.deepEqual(ndframe.ndim, 2); + }); + it("prints the values of a frame", function () { + let data = [ [ 21, 20, 1 ], [ 20, 25, 3 ] ]; + let ndframe = new dfd.NDframe({ data, isSeries: false }); + assert.deepEqual(ndframe.values, data); + }); + it("prints the values of a frame", function () { + let data = [ [ 21, 20, 1 ], [ 20, 25, 3 ] ]; + let ndframe = new dfd.NDframe({ data, isSeries: false }); + assert.deepEqual(ndframe.values, data); + }); + + it("Throws error on duplicate column name", function () { + let data = [ [ 21, 20, 1 ], [ 20, 25, 3 ] ]; + assert.throws(() => { + new dfd.NDframe({ data, isSeries: false, columns: [ "A", "A", "C" ] }), + Error, + "ColumnIndexError: Column index must contain unique values"; + }); + }); + + it("Throws error on duplicate index", function () { + let data = [ [ 21, 20, 1 ], [ 20, 25, 3 ] ]; + assert.throws(() => { + new dfd.NDframe({ data, isSeries: false, index: [ 1, 1, 2 ] }), + Error, + "IndexError: Row index must contain unique values"; + }); + }); + }); + + describe("NDframe Created from JavaScript Object", function () { + + it("prints the shape of a 2D frame created from an Object", function () { + let data = [ { alpha: "A", count: 1 }, { alpha: "B", count: 2 }, { alpha: "C", count: 3 } ]; + let ndframe = new dfd.NDframe({ data, isSeries: false }); + assert.deepEqual(ndframe.shape, [ 3, 2 ]); + }); + it("prints the column names of frame created from an Object", function () { + let data = [ { alpha: "A", count: 1 }, { alpha: "B", count: 2 }, { alpha: "C", count: 3 } ]; + let ndframe = new dfd.NDframe({ data, isSeries: false }); + assert.deepEqual(ndframe.columns, [ "alpha", "count" ]); + }); + it("prints the shape of a 1D frame created from an Object", function () { + let data = [ { alpha: "A", count: 1 } ]; + let ndframe = new dfd.NDframe({ data, isSeries: false }); + assert.deepEqual(ndframe.shape, [ 1, 2 ]); + }); + + it("prints the size of a frame created from an Object", function () { + let data = [ { alpha: "A", count: 1 }, { alpha: "B", count: 2 }, { alpha: "C", count: 3 } ]; + let ndframe = new dfd.NDframe({ data, isSeries: false }); + assert.deepEqual(ndframe.size, 6); + }); + it("prints the dimension of a frame created from an Object", function () { + let data = [ { alpha: "A", count: 1 }, { alpha: "B", count: 2 } ]; + let ndframe = new dfd.NDframe({ data, isSeries: false }); + assert.deepEqual(ndframe.ndim, 2); + }); + it("prints the values of a frame created from an Object", function () { + let data = [ { alpha: "A", count: 1 }, { alpha: "B", count: 2 } ]; + let ndframe = new dfd.NDframe({ data, isSeries: false }); + assert.deepEqual(ndframe.values, [ [ "A", 1 ], [ "B", 2 ] ]); + }); + it("prints the values of a frame created from an Object with undefined values", function () { + let data = [ { alpha: "A", count: undefined }, { alpha: undefined, count: 2 } ]; + let ndframe = new dfd.NDframe({ data, isSeries: false }); + assert.deepEqual(ndframe.values, [ [ "A", undefined ], [ undefined, 2 ] ]); + }); + + it("prints the values of a frame created from an Object with NaN values", function () { + let data = [ { alpha: "A", count: NaN }, { alpha: NaN, count: 2 } ]; + let ndframe = new dfd.NDframe({ data, isSeries: false }); + assert.deepEqual(ndframe.values, [ [ "A", NaN ], [ NaN, 2 ] ]); + }); + + // it("NDframe created from json takes key position into consideration", function () { + // let json_data = [ { A: "A", B: "B", C: "C" }, + // { A: "A", B: "B", C: "C" }, + // { C: "C", B: "B", A: "A" }, + // { A: "A", C: "C", B: "B" } ]; + + // let output = [ + // [ + // 'A', + // 'B', + // 'C' + // ], + // [ + // 'A', + // 'B', + // 'C' + // ], + // [ + // 'A', + // 'B', + // 'C' + // ], + // [ + // 'A', + // 'B', + // 'C' + // ] + // ]; + // let ndframe = new dfd.NDframe({ json_data, isSeries: false }); + // assert.deepEqual(ndframe.values, output); + // }); + + // it("NDframe created from json sets value to NaN if not present", function () { + // let json_data = [ { A: "A", B: "B", C: "C" }, + // { A: "A", B: "B", C: "C" }, + // { C: "C", B: "B", A: "A" }, + // { A: "A", C: "C" } ]; + + // let output = [ + // [ + // 'A', + // 'B', + // 'C' + // ], + // [ + // 'A', + // 'B', + // 'C' + // ], + // [ + // 'A', + // 'B', + // 'C' + // ], + // [ + // 'A', + // 'B', + // NaN + // ] + // ]; + // let ndframe = new dfd.NDframe({ json_data, isSeries: false }); + // assert.deepEqual(ndframe.values, output); + // }); + + }); + + describe("Replacing row data", function () { + it("retrieves the col data after row data is replaced in a DataFrame", function () { + let data = { alpha: [ "A", "B" ], count: [ 1, 2 ] }; + let ndframe = new dfd.NDframe({ data, isSeries: false }); + ndframe.$setValues([ [ "A", 20 ], [ "D", 211 ] ]); + assert.deepEqual(ndframe.values, [ [ "A", 20 ], [ "D", 211 ] ]); + + }); + it("Correct dtypes is inferred after row data is replaced in a DataFrame", function () { + let data = { alpha: [ "A", "B" ], count: [ 1, 2 ] }; + let ndframe = new dfd.NDframe({ data, isSeries: false }); + ndframe.$setValues([ [ 20, 20 ], [ 11.4, 211 ] ]); + assert.deepEqual(ndframe.dtypes, [ "float32", "int32" ]); + + }); + it("Throws row length error on invalid data length in DataFrame", function () { + let data = { alpha: [ "A", "B" ], count: [ 1, 2 ] }; + let ndframe = new dfd.NDframe({ data, isSeries: false }); + assert.throws( + () => { + ndframe.$setValues([ [ 20, 20 ], [ 11.4, 211 ], [ 11.4, 211 ] ]); + }, + Error, + "Row data length mismatch. You provided data with length 3 but Ndframe has row of lenght 2" + ); + }); + it("Throws column length error on invalid data length in DataFrame", function () { + let data = { alpha: [ "A", "B" ], count: [ 1, 2 ] }; + let ndframe = new dfd.NDframe({ data, isSeries: false }); + assert.throws( + () => { + ndframe.$setValues([ [ 20, 211 ], [ 20, 20, 11.4, 211 ] ]); + }, + Error, + "Column data length mismatch. You provided data with length 2 but Ndframe has column of lenght 2" + ); + }); + it("retrieves the col data after row data is replaced in a Series", function () { + let data = [ "A", "B", 1, 2 ]; + let ndframe = new dfd.NDframe({ data, isSeries: true }); + ndframe.$setValues([ "A", "D", "E", "O" ]); + assert.deepEqual(ndframe.values, [ "A", "D", "E", "O" ]); + assert.deepEqual(ndframe.$dataIncolumnFormat, [ "A", "D", "E", "O" ]); + + }); + it("Correct dtypes is inferred after updating values", function () { + let data = [ "A", "D", "E", "O" ]; + let ndframe = new dfd.NDframe({ data, isSeries: true }); + ndframe.$setValues([ 1, 2, 3, 4 ]); + assert.deepEqual(ndframe.values, [ 1, 2, 3, 4 ]); + assert.deepEqual(ndframe.dtypes[0], "int32"); + }); + it("Correct dtypes is inferred after updating values in low memory mode", function () { + let data = [ "A", "D", "E", "O" ]; + let ndframe = new dfd.NDframe({ data, isSeries: true, config: { lowMemoryMode: true } }); + ndframe.$setValues([ 1, 2, 3, 4 ]); + assert.deepEqual(ndframe.values, [ 1, 2, 3, 4 ]); + assert.deepEqual(ndframe.dtypes[0], "int32"); + }); + it("Throws row length error on invalid data length", function () { + let data = [ "A", "D", "E", "O" ]; + let ndframe = new dfd.NDframe({ data, isSeries: true, config: { lowMemoryMode: true } }); + assert.throws( + () => { + ndframe.$setValues([ 1, 2, 3, 4, 1, 3 ]); + }, + Error, + "Row data length mismatch. You provided data with length 6 but Ndframe has row of lenght 4" + ); + }); + + }); + + describe("NDframe Created from a Tensor", function () { + + it("prints the shape of a 2D frame created from a 2D tensor", function () { + let data = tensor([ 1, 2, 3, 4 ]); + let ndframe = new dfd.NDframe({ data, isSeries: true }); + assert.deepEqual(ndframe.ndim, 1); + assert.deepEqual(ndframe.values, [ 1, 2, 3, 4 ]); + + }); + it("prints the shape of a 2D frame created from a 1D tensor", function () { + let data = tensor([ [ 2, 3, 4 ], [ 4, 5, 6 ] ]); + let ndframe = new dfd.NDframe({ data, columns: [ "alpha", "count", "sum" ], isSeries: false }); + assert.deepEqual(ndframe.columns, [ "alpha", "count", "sum" ]); + }); + + }); + + describe("index", function () { + it("Returns the index of an NDframe", function () { + let data = [ { alpha: "A", count: 1 }, { alpha: "B", count: 2 }, { alpha: "C", count: 3 } ]; + let df = new dfd.NDframe({ data, isSeries: false }); + assert.deepEqual(df.index, [ 0, 1, 2 ]); + }); + it("Returns the index of an NDframe created from an Array", function () { + let data = [ [ 12, 2, 20 ], [ 90, 5, 23 ], [ 45, 56, 70 ], [ 9, 10, 19 ] ]; + let df = new dfd.NDframe({ data, isSeries: false }); + assert.deepEqual(df.index, [ 0, 1, 2, 3 ]); + }); + }); + + describe("NDframe Created from JavaScript Object of Arrays", function () { + + it("retrieves the col data created from OA ", function () { + let data = { alpha: [ "A", "B", "C", "D" ], count: [ 1, 2, 3, 4 ] }; + let ndframe = new dfd.NDframe({ data, isSeries: false }); + assert.deepEqual(ndframe.values, [ [ 'A', 1 ], [ 'B', 2 ], [ 'C', 3 ], [ 'D', 4 ] ]); + }); + it("retrieves the data 2", function () { + let data = { alpha: [ "A", "B", "C", "D" ], count: [ 1, 2, 3, 4 ], sum: [ 20.3, 30.456, 40.90, 90.1 ] }; + let ndframe = new dfd.NDframe({ data, isSeries: false }); + let res = [ + [ 'A', 1, 20.3 ], + [ 'B', 2, 30.456 ], + [ 'C', 3, 40.9 ], + [ 'D', 4, 90.1 ] + ]; + assert.deepEqual(ndframe.values, res); + }); + it("retrieves the row data created from OA ", function () { + let data = { alpha: [ "A", "B" ], count: [ 1, 2 ] }; + let ndframe = new dfd.NDframe({ data, isSeries: false }); + assert.deepEqual(ndframe.values, [ [ "A", 1 ], [ "B", 2 ] ]); + }); + }); + + describe("setIndex", function () { + it("sets the index of an NDframe", function () { + let data = [ { alpha: "A", count: 1 }, { alpha: "B", count: 2 }, { alpha: "C", count: 3 } ]; + let df = new dfd.NDframe({ data, isSeries: false }); + df.$setIndex([ "A", "B", "C" ]); + assert.deepEqual(df.index, [ "A", "B", "C" ]); + }); + it("Returns the index of an NDframe created from an Array", function () { + let data = [ [ 12, 2, 20 ], [ 90, 5, 23 ], [ 45, 56, 70 ], [ 9, 10, 19 ] ]; + let df = new dfd.NDframe({ data, isSeries: false }); + df.$setIndex([ 10, 20, 30, 40 ]); + assert.deepEqual(df.index, [ 10, 20, 30, 40 ]); + }); + }); + + + describe("dtype", function () { + it("Returns int dtype set during creation of 1DFrame (Series) from an Object", function () { + let data = [ 1, 2, 3, 4, 5, 6, 7, 8, 9 ]; + let options = { dtypes: [ 'int32' ], isSeries: true }; + let ndframe = new dfd.NDframe({ data, ...options }); + assert.deepEqual(ndframe.dtypes, [ 'int32' ]); + }); + + it("Returns string dtype set during creation of 1DFrame (Series) from an Array", function () { + let data = [ "Alice", "Yemi", "Rising", "Mark" ]; + let options = { dtypes: [ 'string' ], isSeries: true }; + let ndframe = new dfd.NDframe({ data, ...options }); + assert.deepEqual(ndframe.dtypes, [ "string" ]); + }); + + it("Returns string dtype automatically inferred from 1DFrame (Series)", function () { + let data = [ "Alice", "Yemi", "Rising", "Mark" ]; + let options = { columns: 'Names', isSeries: true }; + let ndframe = new dfd.NDframe({ data, ...options }); + assert.deepEqual(ndframe.dtypes, [ "string" ]); + }); + + it("Returns int dtype automatically inferred from 1DFrame (Series)", function () { + let data = [ 20, 30, 20, 20 ]; + let options = { columns: 'Size', isSeries: true }; + let ndframe = new dfd.NDframe({ data, ...options }); + assert.deepEqual(ndframe.dtypes, [ "int32" ]); + }); + it("Returns float dtype automatically inferred from 1DFrame (Series)", function () { + let data = [ 20.1, 30.4, 20.2, 4.23, 20.1 ]; + let options = { columns: 'Size', isSeries: true }; + let ndframe = new dfd.NDframe({ data, ...options }); + assert.deepEqual(ndframe.dtypes, [ "float32" ]); + }); + + + it("Returns dtype set during creation of 2DFrame from an Object", function () { + let data = [ { alpha: "A", count: 1 }, { alpha: "B", count: 2 } ]; + let options = { dtypes: [ 'string', 'int32' ] }; + let ndframe = new dfd.NDframe({ data, ...options }); + assert.deepEqual(ndframe.dtypes, [ 'string', 'int32' ]); + }); + it("Returns dtype set during creation of 2DFrame from an Array", function () { + let data = [ [ "Alice", 2, 3.0 ], [ "Boy", 5, 6.1 ], [ "Girl", 30, 40 ], [ 39, 89, 78.2 ] ]; + let cols = [ "Name", "Count", "Score" ]; + let options = { columns: cols, dtypes: [ 'string', 'int32', 'float32' ] }; + let ndframe = new dfd.NDframe({ data, ...options }); + assert.deepEqual(ndframe.dtypes, [ "string", "int32", "float32" ]); + }); + + it("Returns dtype automatically inferred from 2DFrame", function () { + let data = [ [ "Alice", 2, 3.1 ], [ "Boy", 5, 6.1 ], [ "Girl", 30, 40.2 ], [ 39, 89, 78.2 ] ]; + let cols = [ "Name", "Count", "Score" ]; + let options = { columns: cols }; + let ndframe = new dfd.NDframe({ data, ...options }); + assert.deepEqual(ndframe.dtypes, [ "string", "int32", "float32" ]); + }); + + }); + + describe("Empty NDFrame", function () { + it("Can successfully create an empty NDframe from empty array", function () { + let data = []; + let sf = new dfd.NDframe({ data, isSeries: false }); + assert.deepEqual(sf.shape, [ 0, 0 ]); + assert.deepEqual(sf.columns, []); + assert.deepEqual(sf.dtypes, []); + assert.deepEqual(sf.values, []); + }); + it("Can successfully create an empty NDframe from undefined data", function () { + let data = undefined; + let sf = new dfd.NDframe({ data, isSeries: true }); + assert.deepEqual(sf.shape, [ 0, 0 ]); + assert.deepEqual(sf.columns, []); + assert.deepEqual(sf.dtypes, []); + assert.deepEqual(sf.values, []); + }); + + it("Can successfully create an empty NDframe", function () { + let sf = new dfd.NDframe({ isSeries: true }); + assert.deepEqual(sf.shape, [ 0, 0 ]); + assert.deepEqual(sf.columns, []); + assert.deepEqual(sf.dtypes, []); + assert.deepEqual(sf.values, []); + }); + }); + +}); diff --git a/src/danfojs-browser/tests/core/indexing.test.js b/src/danfojs-browser/tests/core/indexing.test.js new file mode 100644 index 00000000..48602c6c --- /dev/null +++ b/src/danfojs-browser/tests/core/indexing.test.js @@ -0,0 +1,413 @@ +/* eslint-disable no-undef */ + +describe("Iloc and Loc based Indexing", function () { + describe("Iloc Index", function () { + it("throw error for wrong row index value", function () { + let data = [ 1, 2, 34, 5, 6 ]; + let df = new dfd.Series(data); + assert.throws(function () { df.iloc(0); }, Error, `rows parameter must be an Array. For example: rows: [1,2] or rows: ["0:10"]`); + }); + + it("throw error for wrong string split parameter", function () { + let data = [ 1, 2, 34, 5, 6 ]; + let df = new dfd.Series(data); + assert.throws(function () { df.iloc([ "0;1" ]); }, Error, `Invalid row split parameter: If using row split string, it must be of the form; rows: ["start:end"]`); + }); + + it("throw error for wrong string split value", function () { + let data = [ 1, 2, 34, 5, 6 ]; + let df = new dfd.Series(data); + assert.throws(function () { df.iloc([ "0:a" ]); }, Error, `Invalid row split parameter. Split parameter must be a number`); + }); + + it("throw error for string split values greater than 2", function () { + let data = [ 1, 2, 34, 5, 6 ]; + let df = new dfd.Series(data); + assert.throws(function () { df.iloc([ "0:4:2" ]); }, Error, `Invalid row split parameter: If using row split string, it must be of the form; rows: ["start:end"]`); + }); + + it("throw error for row index larger than series length", function () { + let data = [ 1, 2, 34, 5, 6 ]; + + let df = new dfd.Series(data); + assert.throws(function () { df.iloc([ 0, 8 ]); }, Error, "Invalid row parameter: Specified index 8 cannot be bigger than index length 5"); + }); + + it("throw error for non-numeric row index", function () { + let data = [ 1, 2, 34, 5, 6 ]; + let df = new dfd.Series(data); + assert.throws(function () { df.iloc([ 0, "4" ]); }, Error, "Invalid row parameter: row index 4 must be a number"); + }); + + + it("df.iloc works for rows:[0,1]", function () { + let data = [ 1, 2, 34, 5, 6 ]; + + let df = new dfd.Series(data); + + let sf = df.iloc([ 0, 1 ]); + let expected = [ 1, 2 ]; + + assert.deepEqual(sf.values, expected); + }); + + it("df.iloc works for rows:[1]", function () { + let data = [ 1, 2, 34, 5, 6 ]; + + let df = new dfd.Series(data); + + let sf = df.iloc([ 1 ]); + let expected = [ 2 ]; + + assert.deepEqual(sf.values, expected); + }); + + it("correct index is returned for df.iloc rows:[1, 2]", function () { + let data = [ 1, 2, 34, 5, 6 ]; + let df = new dfd.Series(data, { index: [ "a", "b", "c", "d", "e" ] }); + + let sf = df.iloc([ 1, 4 ]); + let expected = [ "b", "e" ]; + + assert.deepEqual(sf.index, expected); + }); + + + it("row slice with string param works [0:2]", function () { + let data = [ 1, 2, 4, 5, 6, 20, 30, 40, 39, 89, 78 ]; + let df = new dfd.Series(data); + + let sf = df.iloc([ "0:2" ]); + let expected = [ 1, 2 ]; + + assert.deepEqual(sf.values, expected); + + }); + it("row slice with string param works [1:]", function () { + let data = [ 1, 2, 34, 5, 620, 30, 409, 89, 78 ]; + let df = new dfd.Series(data); + + let sf = df.iloc([ "1:" ]); + let expected = [ 2, 34, 5, 620, 30, 409, 89, 78 ]; + + assert.deepEqual(sf.values, expected); + + }); + it("row slice with string param works [:2]", function () { + let data = [ 1, 2, 34, 5, 6, 20, 30, 40, 39, 89, 78 ]; + let df = new dfd.Series(data); + let sf = df.iloc([ ":2" ]); + let expected = [ 1, 2 ]; + + assert.deepEqual(sf.values, expected); + + }); + it("row slice with string param works [:]", function () { + let data = [ 1, 2, 3, 5, 20, 30, 4039, 89, 78 ]; + let df = new dfd.Series(data); + let expected = [ 1, 2, 3, 5, 20, 30, 4039, 89, 78 ]; + + let sf = df.iloc([ ":" ]); + assert.deepEqual(sf.values, expected); + + }); + + it(`throw error for wrong start index size ["0:20"]`, function () { + let data = [ 1, 2, 34, 5, 6 ]; + let df = new dfd.Series(data); + assert.throws(function () { df.iloc([ "0:20" ]); }, Error, `row slice [end] index cannot be bigger than 5`); + }); + + it(`throw error for wrong start index size ["-1:2"]`, function () { + let data = [ 1, 2, 34, 5, 6 ]; + let df = new dfd.Series(data); + assert.throws(function () { df.iloc([ "-1:2" ]); }, Error, `row slice [start] index cannot be less than 0`); + }); + + it("check data after column slice", function () { + let data = [ 1, 2, 34, 5, 620, 30, 4039, 89, 78 ]; + let df = new dfd.Series(data); + + let sf = df.iloc([ 0, 1, 6 ]); + let expected = [ 1, 2, 4039 ]; + assert.deepEqual(sf.values, expected); + + }); + + it("iloc works for boolean array", function () { + let data = [ 1, 2, 34, 5, 620 ]; + let df = new dfd.Series(data); + + let sf = df.iloc([ true, true, false, true, false ]); + let expected = [ 1, 2, 5 ]; + assert.deepEqual(sf.values, expected); + + }); + + it("iloc works for boolean array (all true)", function () { + let data = [ 1, 2, 34, 5, 620 ]; + let df = new dfd.Series(data); + + let sf = df.iloc([ true, true, true, true, true ]); + let expected = [ 1, 2, 34, 5, 620 ]; + assert.deepEqual(sf.values, expected); + + }); + it("iloc works for boolean array (all false)", function () { + let data = [ 1, 2, 34, 5, 620 ]; + let df = new dfd.Series(data); + + let sf = df.iloc([ false, false, false, false, false ]); + let expected = []; + assert.deepEqual(sf.values, expected); + + }); + + it("boolean iloc works for DataFrame", function () { + const data = { + "Name": [ "Apples", "Mango", "Banana", "Pear" ], + "Count": [ 21, 5, 30, 10 ], + "Price": [ 200, 300, 40, 250 ] + }; + const df = new dfd.DataFrame(data); + const subDf = df.iloc({ rows: [ false, false, false, true ] }); + const result = [ [ 'Pear', 10, 250 ] ]; + assert.deepEqual(subDf.values, result); + + }); + + + it("boolean iloc works for DataFrame with specified columns", function () { + const data = { + "Name": [ "Apples", "Mango", "Banana", "Pear" ], + "Count": [ 21, 5, 30, 10 ], + "Price": [ 200, 300, 40, 250 ] + }; + const df = new dfd.DataFrame(data); + const subDf = df.iloc({ rows: [ false, false, false, true ], columns: [ 0, 2 ] }); + const result = [ [ 'Pear', 250 ] ]; + assert.deepEqual(subDf.values, result); + + }); + + it("boolean iloc works for DataFrame with Series bool selector", function () { + const data = { + "Name": [ "Apples", "Mango", "Banana", "Pear" ], + "Count": [ 21, 5, 30, 10 ], + "Price": [ 200, 300, 40, 250 ] + }; + const df = new dfd.DataFrame(data); + const subDf = df.iloc({ rows: df["Count"].gt(10), columns: [ 0, 2 ] }); + const result = [ [ 'Apples', 200 ], [ 'Banana', 40 ] ]; + assert.deepEqual(subDf.values, result); + + }); + }); + + describe("Loc Index", function () { + it("throw error for wrong row index value", function () { + let data = [ 1, 2, 34, 5, 6 ]; + let df = new dfd.Series(data); + assert.throws(function () { df.loc(0); }, Error, `rows parameter must be an Array. For example: rows: [1,2] or rows: ["0:10"]`); + }); + + it("throw error for wrong string split parameter", function () { + let data = [ 1, 2, 34, 5, 6 ]; + let df = new dfd.Series(data); + assert.throws(function () { df.loc([ "a;1" ]); }, Error, `IndexError: Specified index (a;1) not found`); + }); + it(`throw error for wrong string split parameter ("0;1")`, function () { + let data = [ 1, 2, 34, 5, 6 ]; + let df = new dfd.Series(data); + assert.throws(function () { df.loc([ "0;1" ]); }, Error, `IndexError: Specified index (0;1) not found`); + }); + + it("throw error for wrong string split end value", function () { + let data = [ 1, 2, 34, 5, 6 ]; + let df = new dfd.Series(data); + assert.throws(function () { df.loc([ "0:a" ]); }, Error, `IndexError: Specified end index not found`); + }); + + it("throw error for string split values greater than 2", function () { + let data = [ 1, 2, 34, 5, 6 ]; + let df = new dfd.Series(data); + assert.throws(function () { df.loc([ "0:4:2" ]); }, Error, `Invalid row split parameter: If using row split string, it must be of the form; rows: ["start:end"]`); + }); + + it("throw error for row index larger than series length", function () { + let data = [ 1, 2, 34, 5, 6 ]; + + let df = new dfd.Series(data); + assert.throws(function () { df.loc([ 0, 8 ]); }, Error, "IndexError: Specified index (8) not found"); + }); + + it("throw error for non-numeric row index not found", function () { + let data = [ 1, 2, 34, 5, 6 ]; + let df = new dfd.Series(data); + assert.throws(function () { df.loc([ 0, "4" ]); }, Error, "IndexError: Specified index (4) not found"); + }); + + + it("df.iloc works for rows:[0,1]", function () { + let data = [ 1, 2, 34, 5, 6 ]; + + let df = new dfd.Series(data); + + let sf = df.loc([ 0, 1 ]); + let expected = [ 1, 2 ]; + + assert.deepEqual(sf.values, expected); + }); + + it("df.iloc works for rows:[1]", function () { + let data = [ 1, 2, 34, 5, 6 ]; + + let df = new dfd.Series(data); + + let sf = df.loc([ 1 ]); + let expected = [ 2 ]; + + assert.deepEqual(sf.values, expected); + }); + + it("correct index is returned for df.loc([1, 4])", function () { + let data = [ 1, 2, 34, 5, 6 ]; + let df = new dfd.Series(data, { index: [ "a", "b", "c", "d", "e" ] }); + + let sf = df.loc([ "b", "e" ]); + let expected = [ "b", "e" ]; + assert.deepEqual(sf.index, expected); + }); + + it("row slice with string param works [0:2]", function () { + let data = [ 1, 2, 4, 5, 6, 20, 30, 40, 39, 89, 78 ]; + let df = new dfd.Series(data); + + let sf = df.loc([ "0:2" ]); + let expected = [ 1, 2 ]; + + assert.deepEqual(sf.values, expected); + + }); + it("row slice with string param works [1:]", function () { + let data = [ 1, 2, 34, 5, 620, 30, 409, 89, 78 ]; + let df = new dfd.Series(data); + + let sf = df.loc([ "1:" ]); + let expected = [ 2, 34, 5, 620, 30, 409, 89, 78 ]; + + assert.deepEqual(sf.values, expected); + + }); + it("row slice with string param works [:2]", function () { + let data = [ 1, 2, 34, 5, 6, 20, 30, 40, 39, 89, 78 ]; + let df = new dfd.Series(data); + let sf = df.loc([ ":2" ]); + let expected = [ 1, 2 ]; + + assert.deepEqual(sf.values, expected); + + }); + it("row slice with string param works [:]", function () { + let data = [ 1, 2, 3, 5, 20, 30, 4039, 89, 78 ]; + let df = new dfd.Series(data); + let expected = [ 1, 2, 3, 5, 20, 30, 4039, 89, 78 ]; + + let sf = df.loc([ ":" ]); + assert.deepEqual(sf.values, expected); + + }); + + it(`throw error for wrong start index size ["0:20"]`, function () { + let data = [ 1, 2, 34, 5, 6 ]; + let df = new dfd.Series(data); + assert.throws(function () { df.iloc([ "0:20" ]); }, Error, `row slice [end] index cannot be bigger than 5`); + }); + + it(`throw error for wrong start index size ["-1:2"]`, function () { + let data = [ 1, 2, 34, 5, 6 ]; + let df = new dfd.Series(data); + assert.throws(function () { df.iloc([ "-1:2" ]); }, Error, `row slice [start] index cannot be less than 0`); + }); + + it("check data after column slice", function () { + let data = [ 1, 2, 34, 5, 620, 30, 4039, 89, 78 ]; + let df = new dfd.Series(data); + + let sf = df.loc([ 0, 1, 6 ]); + let expected = [ 1, 2, 4039 ]; + assert.deepEqual(sf.values, expected); + + }); + + it("loc works for boolean array", function () { + let data = [ 1, 2, 34, 5, 620 ]; + let df = new dfd.Series(data); + + let sf = df.loc([ true, true, false, true, false ]); + let expected = [ 1, 2, 5 ]; + assert.deepEqual(sf.values, expected); + + }); + + it("loc works for boolean array (all true)", function () { + let data = [ 1, 2, 34, 5, 620 ]; + let df = new dfd.Series(data); + + let sf = df.loc([ true, true, true, true, true ]); + let expected = [ 1, 2, 34, 5, 620 ]; + assert.deepEqual(sf.values, expected); + + }); + it("loc works for boolean array (all false)", function () { + let data = [ 1, 2, 34, 5, 620 ]; + let df = new dfd.Series(data); + + let sf = df.loc([ false, false, false, false, false ]); + let expected = []; + assert.deepEqual(sf.values, expected); + + }); + + it("boolean loc works for DataFrame", function () { + const data = { + "Name": [ "Apples", "Mango", "Banana", "Pear" ], + "Count": [ 21, 5, 30, 10 ], + "Price": [ 200, 300, 40, 250 ] + }; + const df = new dfd.DataFrame(data); + const subDf = df.loc({ rows: [ false, false, false, true ] }); + const result = [ [ 'Pear', 10, 250 ] ]; + assert.deepEqual(subDf.values, result); + + }); + + + it("boolean loc works for DataFrame with specified columns", function () { + const data = { + "Name": [ "Apples", "Mango", "Banana", "Pear" ], + "Count": [ 21, 5, 30, 10 ], + "Price": [ 200, 300, 40, 250 ] + }; + const df = new dfd.DataFrame(data); + const subDf = df.loc({ rows: [ false, false, false, true ], columns: [ "Name", "Price" ] }); + const result = [ [ 'Pear', 250 ] ]; + assert.deepEqual(subDf.values, result); + + }); + + it("boolean loc works for DataFrame with Series bool selector", function () { + const data = { + "Name": [ "Apples", "Mango", "Banana", "Pear" ], + "Count": [ 21, 5, 30, 10 ], + "Price": [ 200, 300, 40, 250 ] + }; + const df = new dfd.DataFrame(data); + const subDf = df.loc({ rows: df["Count"].gt(10), columns: [ "Name", "Price" ] }); + const result = [ [ 'Apples', 200 ], [ 'Banana', 40 ] ]; + assert.deepEqual(subDf.values, result); + + }); + }); + +}); diff --git a/src/danfojs-browser/tests/core/series.test.js b/src/danfojs-browser/tests/core/series.test.js new file mode 100644 index 00000000..2a5f4680 --- /dev/null +++ b/src/danfojs-browser/tests/core/series.test.js @@ -0,0 +1,1586 @@ +/* eslint-disable no-undef */ + +describe("Series Functions", () => { + + describe("head", function () { + it("Gets the first 2 rows in a Series", function () { + const data = [ 1, 2, 3, 4, 5, 620, 30, 40, 39, 89, 78 ]; + const cols = [ "A" ]; + const sf = new dfd.Series(data, { columns: cols }); + assert.deepEqual(sf.head(2).values, [ 1, 2 ]); + assert.deepEqual(sf.head(5).values, [ 1, 2, 3, 4, 5 ]); + }); + it("throw error when row specified is greater than values", function () { + const data = [ "Boy", "Girl", "Man", "Woman", "Tall" ]; + const cols = [ "Items" ]; + const sf = new dfd.Series(data, { columns: cols }); + assert.throws(function () { assert.deepEqual(sf.head(10).values, data); }, Error, `row slice [end] index cannot be bigger than 5`); + }); + + it("throw error when row specified is less than 0", function () { + const data = [ 1, 2, 3, 4, 5, 620, 30, 40, 39, 89, 78 ]; + const sf = new dfd.Series(data); + assert.throws(function () { assert.deepEqual(sf.head(-1).values, data); }, Error, `ParamError: end must be greater than start`); + }); + }); + + + describe("tail", function () { + it("Prints the last n rows of a Series", function () { + const data = [ 1, 2, 3, 4, 5, 620, 30, 40, 39, 89, 78 ]; + const sf = new dfd.Series(data); + assert.deepEqual(sf.tail(2).values, [ 89, 78 ]); + assert.deepEqual(sf.tail(4).values, [ 40, 39, 89, 78 ]); + + }); + it("throw error when row specified is greater than values", function () { + const data = [ "Boy", "Girl", "Man", "Woman", "Tall" ]; + const cols = [ "Items" ]; + const sf = new dfd.Series(data, { columns: cols }); + assert.throws(function () { assert.deepEqual(sf.tail(15).values, data); }, Error, `row slice [start] index cannot be less than 0`); + }); + + it("throw error when row specified is less than 0", function () { + const data = [ "Boy", "Girl", "Man", "Woman", "Tall" ]; + const cols = [ "Items" ]; + const sf = new dfd.Series(data, { columns: cols }); + assert.throws(function () { assert.deepEqual(sf.tail(-1).values, data); }, Error, `ParamError: end must be greater than start`); + }); + }); + + describe("sample", function () { + it("Samples n number of random elements from a DataFrame", async function () { + const data = [ 1, 2, 3, 4, 5, 620, 30, 40, 39, 89, 78 ]; + const sf = new dfd.Series(data); + assert.deepEqual((await sf.sample(7)).values.length, 7); + }); + it("Return all values if n of sample -1", async function () { + const data = [ 1, 2, 3, 4, 5, 620, 30, 40, 39, 89, 78 ]; + const sf = new dfd.Series(data); + assert.deepEqual((await sf.sample(-1)).values.length, data.length); + }); + + it("Throw error if n is greater than lenght of Series", async function () { + const data = [ 1, 2, 3, 4, 5, 620, 30, 40, 39, 89, 78 ]; + const sf = new dfd.Series(data); + try { + await sf.sample(100); + } catch (e) { + expect(e.message).to.eql('Sample size n cannot be bigger than size of dataset'); + } + }); + }); + + describe("add", function () { + it("Return Addition of series with another series", function () { + const data = [ 1, 2, 3, 4, 5, 6 ]; + const data2 = [ 30, 40, 39, 1, 2, 1 ]; + const sf = new dfd.Series(data); + const sf2 = new dfd.Series(data2); + assert.deepEqual(sf.add(sf2).values, [ 31, 42, 42, 5, 7, 7 ]); + }); + it("Return Addition of series with a single value (Broadcasting)", function () { + const data = [ 1, 2, 3, 4, 5 ]; + const sf = new dfd.Series(data); + assert.deepEqual(sf.add(1).values, [ 2, 3, 4, 5, 6 ]); + }); + it("Return Addition of series with another series inplace", function () { + const data = [ 1, 2, 3, 4, 5, 6 ]; + const data2 = [ 30, 40, 39, 1, 2, 1 ]; + const sf = new dfd.Series(data); + const sf2 = new dfd.Series(data2); + sf.add(sf2, { inplace: true }); + assert.deepEqual(sf.values, [ 31, 42, 42, 5, 7, 7 ]); + }); + it("Return Addition of series with a single value (Broadcasting) inplace", function () { + const data = [ 1, 2, 3, 4, 5 ]; + const sf = new dfd.Series(data); + sf.add(1, { inplace: true }); + assert.deepEqual(sf.values, [ 2, 3, 4, 5, 6 ]); + }); + it("Dtype is properly updated on addition of series with a single float value inplace", function () { + const data = [ 1, 2, 3, 4, 5 ]; + const sf = new dfd.Series(data); + sf.add(1.23, { inplace: true }); + assert.deepEqual(sf.dtypes[0], "float32"); + assert.deepEqual(sf.values, [ 2.23, 3.23, 4.23, 5.23, 6.23 ]); + }); + it("Add works properly when using tfjs add function", function () { + const data = [ 1, 2, 3, 4, 5 ]; + const sf = new dfd.Series(data); + sf.add(1.23, { inplace: true }); + assert.deepEqual(sf.dtypes[0], "float32"); + assert.deepEqual(sf.values, [ 2.23, 3.23, 4.23, 5.23, 6.23 ]); + }); + it("Add works properly when using tfjs add function on Series", function () { + const data = [ 1, 2, 3, 4, 5 ]; + const sf = new dfd.Series(data); + const sf2 = new dfd.Series([ 1.23, 1.23, 1.23, 1.23, 1.23 ]); + sf.add(sf2, { inplace: true }); + assert.deepEqual(sf.values, [ 2.23, 3.23, 4.23, 5.23, 6.23 ]); + }); + it("Throws type error on addition of string type", function () { + const data = [ 1, 2, 3, 4 ]; + const data2 = [ "A", "B", "C", "d" ]; + const sf = new dfd.Series(data); + const sf2 = new dfd.Series(data2); + assert.throws( + () => { + sf.add(sf2); + }, + Error, + "DtypeError: String data type does not support add operation" + ); + }); + it("Throws length error if series lenght mixmatch", function () { + const data = [ 1, 2, 3, 4 ]; + const data2 = [ 1, 2, 3, 4, 5, 6 ]; + const sf = new dfd.Series(data); + const sf2 = new dfd.Series(data2); + assert.throws(() => { sf.add(sf2); }, Error, "Row length mismatch. Length of other (6), must be the same as Ndframe (4)"); + }); + }); + + describe("sub", function () { + it("Return Subtraction of series with another series", function () { + const data1 = [ 30, 40, 39, 1, 2, 1 ]; + const data2 = [ 1, 2, 3, 4, 5, 6 ]; + const sf1 = new dfd.Series(data1); + const sf2 = new dfd.Series(data2); + assert.deepEqual(sf1.sub(sf2).values, [ 29, 38, 36, -3, -3, -5 ]); + }); + it("Return Subtraction of series with a single value (Broadcasting)", function () { + const data = [ 1, 2, 3, 4, 5 ]; + const sf = new dfd.Series(data); + assert.deepEqual(sf.sub(1).values, [ 0, 1, 2, 3, 4 ]); + }); + it("Throws type error on Subtraction of string type", function () { + const data = [ 1, 2, 3, 4 ]; + const data2 = [ "A", "B", "C", "d" ]; + const sf = new dfd.Series(data); + const sf2 = new dfd.Series(data2); + assert.throws( + () => { + sf.sub(sf2); + }, + Error, + "DtypeError: String data type does not support sub operation" + ); + }); + it("Throws length error if series lenght mixmatch", function () { + const data = [ 1, 2, 3, 4 ]; + const data2 = [ 1, 2, 3, 4, 5, 6 ]; + const sf = new dfd.Series(data); + const sf2 = new dfd.Series(data2); + assert.throws(() => { sf.sub(sf2); }, Error, "Row length mismatch. Length of other (6), must be the same as Ndframe (4)"); + }); + }); + + describe("mul", function () { + it("Return multiplication of series with another series", function () { + const data1 = [ 30, 40, 3, 5 ]; + const data2 = [ 1, 2, 3, 4 ]; + const sf1 = new dfd.Series(data1); + const sf2 = new dfd.Series(data2); + assert.deepEqual(sf1.mul(sf2).values, [ 30, 80, 9, 20 ]); + }); + it("Return multiplication of series with a single value (Broadcasting)", function () { + const data = [ 1, 2, 3, 4, 5 ]; + const sf = new dfd.Series(data); + assert.deepEqual(sf.mul(1).values, [ 1, 2, 3, 4, 5 ]); + }); + it("Throws type error on multiplication of string type", function () { + const data = [ 1, 2, 3, 4 ]; + const data2 = [ "A", "B", "C", "d" ]; + const sf = new dfd.Series(data); + const sf2 = new dfd.Series(data2); + assert.throws(() => { sf.mul(sf2); }, Error, "DtypeError: String data type does not support mul operation"); + }); + it("Throws length error if series lenght mixmatch", function () { + const data = [ 1, 2, 3, 4 ]; + const data2 = [ 1, 2, 3, 4, 5, 6 ]; + const sf = new dfd.Series(data); + const sf2 = new dfd.Series(data2); + assert.throws(() => { sf.mul(sf2); }, Error, "ParamError: Row length mismatch. Length of other (6), must be the same as Ndframe (4)"); + }); + }); + + + describe("div", function () { + it("Return float division of series with another series", function () { + const data1 = [ 30, 40, 3, 5 ]; + const data2 = [ 1, 2, 3, 4 ]; + const sf1 = new dfd.Series(data1); + const sf2 = new dfd.Series(data2); + assert.deepEqual(sf1.div(sf2).values, [ 30, 20, 1, 1.25 ]); + }); + it("Return integer division of series with another series", function () { + const data1 = [ 30, 40, 3, 5 ]; + const data2 = [ 1, 2, 3, 4 ]; + const sf1 = new dfd.Series(data1); + const sf2 = new dfd.Series(data2); + assert.deepEqual(sf1.div(sf2, false).values, [ 30, 20, 1, 1.25 ]); + }); + it("Return division of series with a single value (Broadcasting)", function () { + const data = [ 10, 2, 3, 90 ]; + const sf = new dfd.Series(data); + assert.deepEqual(sf.div(2).values, [ 5, 1, 1.5, 45 ]); + }); + it("Throws type error on division of string type", function () { + const data = [ 1, 2, 3, 4 ]; + const data2 = [ "A", "B", "C", "d" ]; + const sf = new dfd.Series(data); + const sf2 = new dfd.Series(data2); + assert.throws(() => { sf.div(sf2); }, Error, `DtypeError: String data type does not support div operation`); + }); + it("Throws length error if series lenght mixmatch", function () { + const data = [ 1, 2, 3, 4 ]; + const data2 = [ 1, 2, 3, 4, 5, 6 ]; + const sf = new dfd.Series(data); + const sf2 = new dfd.Series(data2); + assert.throws(() => { sf.div(sf2); }, Error, "ParamError: Row length mismatch. Length of other (6), must be the same as Ndframe (4)"); + }); + }); + + describe("pow", function () { + it("Return Exponetial power of series with another series", function () { + const data1 = [ 2, 3, 4, 5 ]; + const data2 = [ 1, 2, 3, 0 ]; + const sf1 = new dfd.Series(data1); + const sf2 = new dfd.Series(data2); + assert.deepEqual(sf1.pow(sf2).values, [ 2, 9, 64, 1 ]); + }); + it("Return Exponetial power of series with a single value (Broadcasting)", function () { + const data = [ 1, 2, 3, 4, 5 ]; + const sf = new dfd.Series(data); + assert.deepEqual(sf.pow(2).values, [ 1, 4, 9, 16, 25 ]); + }); + }); + + describe("mod", function () { + it("Return modulo of series with another float series", function () { + const data1 = [ 2, 30, 4, 5 ]; + const data2 = [ 1.1, 2.2, 3.3, 2.4 ]; + const sf1 = new dfd.Series(data1); + const sf2 = new dfd.Series(data2); + const expected = [ + 0.8999999999999999, + 1.3999999999999977, + 0.7000000000000002, + 0.20000000000000018 + ]; + assert.deepEqual(sf1.mod(sf2).values, expected); + }); + it("Return modulo of series with another int series", function () { + const data1 = [ 2, 30, 4, 5 ]; + const data2 = [ 1, 2, 3, 1 ]; + const sf1 = new dfd.Series(data1); + const sf2 = new dfd.Series(data2); + assert.deepEqual(sf1.mod(sf2).values, [ 0, 0, 1, 0 ]); + }); + it("Return modulo power of series with a single value (Broadcasting)", function () { + const data = [ 1, 2, 3, 4, 5 ]; + const sf = new dfd.Series(data); + assert.deepEqual(sf.mod(2).values, [ 1, 0, 1, 0, 1 ]); + }); + }); + + describe("toString", function () { + it("Prints a series to the console", async function () { + const data = [ 1, 2, 3, 4, 5, 620, 30, 40, 39, 89, 78 ]; + const sf = new dfd.Series(data); + sf.print(); + }); + it("Prints a series to the console", async function () { + const data = [ "This is a long text group and I want it to print in full", + "This is a long text group and I want it to print in full" ]; + const sf = new dfd.Series(data); + sf.print(); + }); + }); + + describe("Empty Series", function () { + it("Can successfully create an empty Series from empty array", function () { + const data = []; + const sf = new dfd.Series(data); + assert.deepEqual(sf.shape, [ 0, 0 ]); + assert.deepEqual(sf.columns, []); + assert.deepEqual(sf.dtypes, []); + assert.deepEqual(sf.values, []); + }); + + it("Can successfully create an empty Series", function () { + const sf = new dfd.Series(); + assert.deepEqual(sf.shape, [ 0, 0 ]); + assert.deepEqual(sf.columns, []); + assert.deepEqual(sf.dtypes, []); + assert.deepEqual(sf.values, []); + }); + }); + + describe("mean", function () { + it("Computes the mean of elements in a int series", function () { + const data1 = [ 30, 40, 3, 5, NaN ]; + const sf = new dfd.Series(data1); + assert.deepEqual(sf.mean(), 19.5); + }); + it("Computes the mean of elements in a int series using TFJS", function () { + const data1 = [ 30, 40, 3, 5, NaN ]; + const sf = new dfd.Series(data1, { config: { useTfjsMathFunctions: true } }); + assert.deepEqual(sf.mean(), 19.5); + }); + it("Computes the mean of elements in a float series", function () { + const data1 = [ 30.1, 40.2, 3.1, 5.1 ]; + const sf = new dfd.Series(data1); + assert.deepEqual(sf.mean(), 19.625); + }); + it("Computes the mean of elements in a float series with NaN", function () { + const data1 = [ 30.1, 40.2, 3.1, 5.1, NaN ]; + const sf = new dfd.Series(data1); + assert.deepEqual(sf.mean(), 19.625); + }); + it("Computes the mean of a boolean series", function () { + const data1 = [ true, false, false, false, true, true, false, true ]; + const sf = new dfd.Series(data1); + assert.deepEqual(sf.mean(), 0.5); + }); + it("Throws error if dtype is string", function () { + const data1 = [ "boy", "girl", "Man" ]; + const sf = new dfd.Series(data1); + assert.throws( + () => { + sf.mean(); + }, + Error, + "DtypeError: String data type does not support mean operation" + ); + }); + }); + + describe("median", function () { + it("Computes the median value of elements across int Series", function () { + const data1 = [ 30, 40, 3, 5 ]; + const sf = new dfd.Series(data1); + assert.deepEqual(sf.median(), 17.5); + }); + it("Computes the median value of elements across float Series", function () { + const data1 = [ 30.1, 40.2, 3.1, 5.1, NaN ]; + const sf = new dfd.Series(data1); + assert.deepEqual(sf.median(), 17.6); + }); + }); + + describe("sum", function () { + it("Sum values of a Int Series", function () { + const data1 = [ 30, 40, 3, 5, 5, 5, 5, 5, 3, 3, 3, 21, 3 ]; + const sf = new dfd.Series(data1); + assert.deepEqual(sf.sum(), 131); + }); + it("Sum values of a Float Series", function () { + const data1 = [ 30.1, 3.1, 40.2, 3.1, 5.1 ]; + const sf = new dfd.Series(data1); + assert.deepEqual(sf.sum(), 81.6); + }); + it("Sum values of a bool Series", function () { + const data1 = [ true, true, false, false, false ]; + const sf = new dfd.Series(data1); + assert.deepEqual(sf.sum(), 2); + }); + it("Sum values a Series with missing values", function () { + const data1 = [ 11, NaN, 2, 2 ]; + const sf = new dfd.Series(data1); + assert.deepEqual(sf.sum(), 15); + }); + }); + + describe("mode", function () { + it("Computes the multi-modal values of a Series", function () { + const data1 = [ 30, 40, 3, 5, 5, 5, 5, 5, 3, 3, 3, 21, 3 ]; + const sf = new dfd.Series(data1); + assert.deepEqual(sf.mode(), [ 5, 3 ]); + }); + it("Computes the modal value of a Series", function () { + const data1 = [ 30.1, 3.1, 40.2, 3.1, 5.1 ]; + const sf = new dfd.Series(data1); + assert.deepEqual(sf.mode(), [ 3.1 ]); + }); + }); + + describe("min", function () { + it("Returns the single smallest elementin a Series", function () { + const data = [ 30, 40, 3, 5 ]; + const sf = new dfd.Series(data); + assert.deepEqual(sf.min(), 3); + }); + it("Computes the minimum of elements across an float Series", function () { + const data1 = [ 30.1, 40.2, 3.12, 5.1 ]; + const sf = new dfd.Series(data1, { dtypes: [ "float32" ] }); + assert.deepEqual(Number(sf.min().toFixed(2)), 3.12); + }); + }); + + describe("max", function () { + it("Computes the maximum of elements across dimensions of a Series", function () { + const data1 = [ 30, 40, 3, 5 ]; + const sf = new dfd.Series(data1); + assert.deepEqual(sf.max(), 40); + }); + it("Return sum of float values in a series", function () { + const data1 = [ 30.1, 40.21, 3.1, 5.1 ]; + const sf = new dfd.Series(data1); + assert.deepEqual(Number(sf.max().toFixed(2)), 40.21); + }); + it("Throws error on addition of string Series", function () { + const data1 = [ "boy", "gitl", "woman", "man" ]; + const sf = new dfd.Series(data1); + assert.throws( + () => { + sf.max(); + }, + Error, + "DtypeError: String data type does not support max operation" + ); + }); + }); + + describe("count", function () { + it("Returns the count of non NaN values in a string Series", function () { + const data = [ "boy", "gitl", "woman", NaN ]; + const sf = new dfd.Series(data); + assert.deepEqual(sf.count(), 3); + }); + it("Returns the count of values in a string Series without NaN", function () { + const data = [ "boy", "gitl", "woman", "Man" ]; + const sf = new dfd.Series(data); + assert.deepEqual(sf.count(), 4); + }); + it("Returns the count of non NaN values in a int Series", function () { + const data = [ 20, 30, NaN, 2, NaN, 30, 21 ]; + const sf = new dfd.Series(data); + assert.deepEqual(sf.count(), 5); + }); + it("Returns the count of non NaN values in a float Series", function () { + const data = [ 20.1, 30.4, NaN, 2.1, NaN, 30.0, 21.3 ]; + const sf = new dfd.Series(data); + assert.deepEqual(sf.count(), 5); + }); + }); + + describe("std", function () { + it("Computes the standard of elements in a int series", function () { + const data1 = [ 30, 40, 3, 5 ]; + const sf = new dfd.Series(data1); + assert.deepEqual(sf.std(), 18.375708603116962); + }); + it("Computes the standard deviation of elements in a float series", function () { + const data1 = [ 30.1, 40.2, 3.1, 5.1 ]; + const sf = new dfd.Series(data1); + assert.deepEqual(sf.std(), 18.412925713566906); + }); + it("Computes the standard deviation of elements in a float series with missing values", function () { + const data1 = [ 30, 40, 3, 5, undefined ]; + const sf = new dfd.Series(data1); + assert.deepEqual(sf.std(), 18.375708603116962); + }); + }); + + describe("var", function () { + it("Computes the variance of elements in a int series", function () { + const data1 = [ 30, 40, 3, 5 ]; + const sf = new dfd.Series(data1); + assert.deepEqual(sf.var(), 337.6666666666667); + }); + it("Computes the variance of elements in a float series", function () { + const data1 = [ 30.1, 40.2, 3.1, 5.1 ]; + const sf = new dfd.Series(data1); + assert.deepEqual(sf.var(), 339.03583333333336); + }); + it("Computes the variance of elements in a int series with missing values", function () { + const data1 = [ 30, undefined, 40, 3, 5 ]; + const sf = new dfd.Series(data1); + assert.deepEqual(sf.var(), 337.6666666666667); + }); + }); + + describe("round", function () { + it("Rounds elements in a Series to nearest whole number", function () { + const data1 = [ 30.21091, 40.190901, 3.564, 5.0212 ]; + const sf = new dfd.Series(data1); + assert.deepEqual(sf.round().values, [ 30.2, 40.2, 3.6, 5 ]); + }); + it("Rounds elements in a Series to 1dp", function () { + const data1 = [ 30.21091, 40.190901, 3.564, 5.0212 ]; + const sf = new dfd.Series(data1); + assert.deepEqual(sf.round(1).values, [ 30.2, 40.2, 3.6, 5.0 ]); + }); + it("Rounds elements in a Series to 2dp", function () { + const data1 = [ 30.2191, 40.190901, 3.564, 5.0212 ]; + const sf = new dfd.Series(data1); + assert.deepEqual(sf.round(2).values, [ 30.22, 40.19, 3.56, 5.02 ]); + }); + + it("Rounds elements in a Series to 2dp inplace", function () { + const data1 = [ 30.2191, 40.190901, 3.564, 5.0212 ]; + const sf = new dfd.Series(data1); + sf.round(2, { inplace: true }); + assert.deepEqual(sf.values, [ 30.22, 40.19, 3.56, 5.02 ]); + }); + + it("Rounds elements in a Series with missing values to 2dp", function () { + const data1 = [ 30.2191, undefined, 3.564, NaN ]; + const sf = new dfd.Series(data1); + sf.round(2, { inplace: true }); + assert.deepEqual(sf.values, [ 30.22, undefined, 3.56, NaN ]); + }); + + }); + + describe("maximum", function () { + it("Returns the maximum of two series", function () { + const data1 = [ 30, 40, 3, 5 ]; + const data2 = [ 10, 41, 2, 0 ]; + const sf1 = new dfd.Series(data1); + const sf2 = new dfd.Series(data2); + assert.deepEqual(sf1.maximum(sf2).values, [ 30, 41, 3, 5 ]); + }); + it("Returns the maximum of series and Array", function () { + const data1 = [ 30, 40, 3, 5 ]; + const data2 = [ 10, 41, 2, 0 ]; + const sf1 = new dfd.Series(data1); + assert.deepEqual(sf1.maximum([ 10, 41, 2, 0 ]).values, [ 30, 41, 3, 5 ]); + }); + it("Returns the maximum of series and scalar", function () { + const data1 = [ 30, 40, 3, 5 ]; + const sf1 = new dfd.Series(data1); + assert.deepEqual(sf1.maximum(10).values, [ 30, 40, 10, 10 ]); + }); + it("Throws error on checking maximum of incompatible Series", function () { + const data1 = [ 30, 40, 3, 5 ]; + const data2 = [ 10, 41, 2 ]; + const sf1 = new dfd.Series(data1); + const sf2 = new dfd.Series(data2); + assert.throws( + () => { + sf1.maximum(sf2); + }, + Error, + "ParamError: Row length mismatch. Length of other (3), must be the same as Ndframe (4)" + ); + }); + }); + + describe("minimum", function () { + it("Returns the minimum of two series", function () { + const data1 = [ 30, 40, 3, 5 ]; + const data2 = [ 10, 41, 2, 0 ]; + const sf1 = new dfd.Series(data1); + const sf2 = new dfd.Series(data2); + assert.deepEqual(sf1.minimum(sf2).values, [ 10, 40, 2, 0 ]); + }); + it("Returns the minimum of series and array", function () { + const data1 = [ 30, 40, 3, 5 ]; + const data2 = [ 10, 41, 2, 0 ]; + const sf1 = new dfd.Series(data1); + assert.deepEqual(sf1.minimum(data2).values, [ 10, 40, 2, 0 ]); + }); + it("Returns the minimum of two series", function () { + const data1 = [ 30, 40, 3, 5 ]; + const sf1 = new dfd.Series(data1); + assert.deepEqual(sf1.minimum(10).values, [ 10, 10, 3, 5 ]); + }); + }); + + describe("isNa", function () { + it("Return a boolean same-sized object indicating if string Series contain NaN", function () { + const data1 = [ NaN, undefined, "girl", "Man" ]; + const sf = new dfd.Series(data1); + assert.deepEqual(sf.isNa().values, [ true, true, false, false ]); + }); + it("Return a boolean same-sized object indicating if float Series values are NaN", function () { + const data1 = [ 30.21091, NaN, 3.564, undefined ]; + const sf = new dfd.Series(data1); + assert.deepEqual(sf.isNa().values, [ false, true, false, true ]); + }); + it("Return a boolean same-sized object indicating if int Series values are NaN", function () { + const data1 = [ 30, 40, 3, 5, undefined, null ]; + const sf = new dfd.Series(data1); + assert.deepEqual(sf.isNa().values, [ + false, + false, + false, + false, + true, + true + ]); + }); + }); + + describe("fillNa", function () { + it("replace all NaN value with specified value", function () { + const data = [ NaN, 1, 2, 33, 4, null, 5, 6, 7, 8 ]; + const sf = new dfd.Series(data); + const sfVal = [ -999, 1, 2, 33, 4, -999, 5, 6, 7, 8 ]; + sf.fillNa(-999, { inplace: true }); + assert.deepEqual(sf.values, sfVal); + }); + it("replace all NaN value in string Series with specified value", function () { + const data = [ NaN, "boy", NaN, "hey", "Man", undefined ]; + const sf = new dfd.Series(data); + const sfVal = [ "filled", "boy", "filled", "hey", "Man", "filled" ]; + const sfFill = sf.fillNa("filled"); + assert.deepEqual(sfFill.values, sfVal); + }); + it("Data is in right format after filling", function () { + const data = [ NaN, "boy", NaN, "hey", "Man", undefined ]; + const sf = new dfd.Series(data); + const sfVal = [ "filled", "boy", "filled", "hey", "Man", "filled" ]; + const sfFill = sf.fillNa("filled"); + assert.deepEqual(sfFill.values, sfVal); + assert.deepEqual(sfFill.$dataIncolumnFormat, sfVal); + + }); + }); + + describe("sortValues", function () { + it("Sort values in a Series in ascending order (not inplace)", function () { + const sf = new dfd.Series([ 20, 30, 1, 2, 4, 57, 89, 0, 4 ]); + const result = [ 0, 1, 2, 4, 4, 20, 30, 57, 89 ]; + const sortedSf = sf.sortValues(); + assert.deepEqual(sortedSf.values, result); + assert.deepEqual(sortedSf.$dataIncolumnFormat, result); + + }); + it("confirms that sortValues in ascending order does not happen inplace", function () { + const sf = new dfd.Series([ 20, 30, 1, 2, 4, 57, 89, 0, 4 ]); + const result = [ 0, 1, 2, 4, 4, 20, 30, 57, 89 ]; + const expectedIndex = [ 7, 2, 3, 8, 4, 0, 1, 5, 6 ]; + sf.sortValues({ ascending:true, inplace: true }); + assert.deepEqual(sf.values, result); + assert.deepEqual(sf.index, expectedIndex); + }); + it("Sort values in a Series in Descending order", function () { + const sf = new dfd.Series([ 20, 30, 1, 2, 4, 57, 89, 0, 4 ]); + const result = [ 89, 57, 30, 20, 4, 4, 2, 1, 0 ]; + const sortedSf = sf.sortValues({ ascending: false }); + assert.deepEqual(sortedSf.values, result); + }); + it("confirms that sortValues in descending order happens inplace", function () { + const sf = new dfd.Series([ 20, 30, 1, 2, 4, 57, 89, 0, 4 ]); + const result = [ 89, 57, 30, 20, 4, 4, 2, 1, 0 ]; + sf.sortValues({ ascending: false, inplace: true }); + assert.deepEqual(sf.values, result); + }); + it("Confirms that series index is sorted in ascending order (not in inplace)", function () { + const sf = new dfd.Series([ 20, 30, 1, 2, 4, 57, 89, 0, 4 ]); + const result = [ 7, 2, 3, 8, 4, 0, 1, 5, 6 ]; + const sortedSf = sf.sortValues(); + assert.deepEqual(sortedSf.index, result); + }); + it("Confirms that series index is sorted in descending order (not in inplace)", function () { + const sf = new dfd.Series([ 20, 30, 1, 2, 4, 57, 89, 0, 4 ]); + const result = [ 6, 5, 1, 0, 4, 8, 3, 2, 7 ]; + const sortedSf = sf.sortValues({ ascending: false }); + assert.deepEqual(sortedSf.index, result); + }); + it("Sort string values in a Series", function () { + const sf = new dfd.Series([ "boy", "zebra", "girl", "man" ]); + const result = [ "boy", "girl", "man", "zebra" ]; + const sortedSf = sf.sortValues({ ascending: false }); + assert.deepEqual(sortedSf.values, result); + }); + }); + + describe("describe", function () { + it("Computes the descriptive statistics on an int Series", function () { + const data1 = [ 10, 45, 56, 25, 23, 20, 10 ]; + const sf = new dfd.Series(data1); + assert.deepEqual(sf.describe().round().values, [ + 7, + 27, + 17.4, + 10, + 23, + 56, + 302 + ]); + }); + it("Computes the descriptive statistics on a float Series", function () { + const data1 = [ 30.1, 40.2, 3.1, 5.1 ]; + const sf = new dfd.Series(data1); + assert.deepEqual(sf.describe().round().values, [ + 4, + 19.6, + 18.4, + 3.1, + 17.6, + 40.2, + 339 + ]); + }); + it("Computes the descriptive statistics on a float Series", function () { + const data1 = [ 30.1, 40.2, 3.1, 5.1 ]; + const sf = new dfd.Series(data1); + assert.deepEqual(sf.describe().index, [ + "count", + "mean", + "std", + "min", + "median", + "max", + "variance" + ]); + }); + }); + + describe("resetIndex", function () { + it("resets the index of a Series", function () { + const data = [ + { alpha: "A", count: 1 }, + { alpha: "B", count: 2 }, + { alpha: "C", count: 3 } + ]; + const df = new dfd.Series(data, { index: [ "one", "two", "three" ] }); + const dfReset = df.resetIndex(); + assert.deepEqual(dfReset.index, [ 0, 1, 2 ]); + }); + it("Reset the index of a Series created from an Array", function () { + const data = [ 1, 2, 3, 4, 5, 6 ]; + const df = new dfd.Series(data, { index: [ "one", "two", "three", "four", "five", "six" ] }); + const dfNew = df.resetIndex(); + assert.deepEqual(dfNew.index, [ 0, 1, 2, 3, 4, 5 ]); + }); + it("checks that the original series changed after reseting new index inplace", function () { + const data = [ + { index: "A", count: 1 }, + { index: "B", count: 2 }, + { index: "C", count: 3 } + ]; + const df = new dfd.Series(data, { index: [ "one", "two", "three" ] }); + df.resetIndex({ inplace: true }); + assert.deepEqual(df.index, [ 0, 1, 2 ]); + }); + }); + + describe("setIndex", function () { + it("sets the index of an Series", function () { + const data = [ + { alpha: "A", count: 1 }, + { alpha: "B", count: 2 }, + { alpha: "C", count: 3 } + ]; + const df = new dfd.Series(data); + const dfNew = df.setIndex([ "one", "two", "three" ]); + assert.deepEqual(dfNew.index, [ "one", "two", "three" ]); + assert.notDeepEqual(df.index, dfNew.index); + }); + it("checks that the original series is not modified after setting new index not-inplace", function () { + const data = [ + { alpha: "A", count: 1 }, + { alpha: "B", count: 2 }, + { alpha: "C", count: 3 } + ]; + const df = new dfd.Series(data); + const dfNew = df.setIndex([ "one", "two", "three" ]); + assert.notDeepEqual(df.index, dfNew.index); + }); + it("sets the index of an Series inplace", function () { + const data = [ 12, 2, 20, 50 ]; + const df = new dfd.Series(data); + df.setIndex([ "one", "two", "three", "four" ], { inplace: true }); + assert.deepEqual(df.index, [ "one", "two", "three", "four" ]); + }); + it("Throws index not found error", function () { + const data = [ 12, 2, 20, 50 ]; + const df = new dfd.Series(data); + assert.throws(() => { + df.setIndex(); + }, Error, + "Param Error: Must specify index array" + ); + }); + }); + + describe("Map", function () { + it("map series element to object keys", function () { + const sf = new dfd.Series([ 1, 2, 3, 4 ]); + const map = { 1: "ok", 2: "okie", 3: "frit", 4: "gop" }; + const rslt = [ "ok", "okie", "frit", "gop" ]; + assert.deepEqual(sf.map(map).values, rslt); + }); + it("map series element to object keys inplace", function () { + const sf = new dfd.Series([ 1, 2, 3, 4 ]); + const map = { 1: "ok", 2: "okie", 3: "frit", 4: "gop" }; + const rslt = [ "ok", "okie", "frit", "gop" ]; + sf.map(map, { inplace: true }); + assert.deepEqual(sf.values, rslt); + }); + it("map series element to incomplete object keys", function () { + const sf = new dfd.Series([ 1, 2, 3, 4 ]); + const map = { 1: "ok", 2: "okie", 4: "gop" }; + const rslt = [ "ok", "okie", 3, "gop" ]; + sf.map(map, { inplace: true }); + assert.deepEqual(sf.values, rslt); + }); + it("map series element to a function statement", function () { + const sf = new dfd.Series([ 1, 2, 3, 4 ]); + const func_map = (x) => { + return x + 1; + }; + const rslt = [ 2, 3, 4, 5 ]; + assert.deepEqual(sf.map(func_map).values, rslt); + }); + + it("map series element to a function statement inplace", function () { + const sf = new dfd.Series([ 1, 2, 3, 4 ]); + const func_map = (x) => { + return x + 1; + }; + const rslt = [ 2, 3, 4, 5 ]; + sf.map(func_map, { inplace: true }); + assert.deepEqual(sf.values, rslt); + }); + + it("map passes along the index", function () { + const sf = new dfd.Series([ 1, 2, 3, 4 ]); + const func_map = (x, i) => { + return x + i; + }; + const rslt = [ 1, 3, 5, 7 ]; + sf.map(func_map, { inplace: true }); + assert.deepEqual(sf.values, rslt); + }); + }); + + describe("Apply", function () { + it("apply a function to a series element", function () { + const sf = new dfd.Series([ 1, 2, 3, 4, 5, 6, 7, 8 ]); + const applyFunc = (x) => { + return x + x; + }; + + const rslt = [ 2, 4, 6, 8, 10, 12, 14, 16 ]; + assert.deepEqual(sf.apply(applyFunc).values, rslt); + }); + + it("apply a function to a series element inplace", function () { + const sf = new dfd.Series([ 1, 2, 3, 4, 5, 6, 7, 8 ]); + const applyFunc = (x) => { + return x + x; + }; + + const rslt = [ 2, 4, 6, 8, 10, 12, 14, 16 ]; + sf.apply(applyFunc, { inplace: true }); + assert.deepEqual(sf.values, rslt); + }); + }); + + describe("unique", function () { + it("returns the unique values in a Series of type int", function () { + const sf = new dfd.Series([ 1, 2, 3, 4, 5, 6, 7, 8, 1, 1, 22, 8, 5, 5, 5 ]); + const expected = [ 1, 2, 3, 4, 5, 6, 7, 8, 22 ]; + assert.deepEqual(sf.unique().values, expected); + }); + it("returns the unique values in a Series of type string", function () { + const sf = new dfd.Series([ "a", "a", "b", "c", "c", "d", "e", "d", "d", "e" ]); + const expected = [ "a", "b", "c", "d", "e" ]; + assert.deepEqual(sf.unique().values, expected); + }); + it("returns the unique values in a Series of type string", function () { + const sf = new dfd.Series([ "a", "a", "b", "c", "c", "d", "e", "d", "d", "e" ]); + const expected = [ "a", "b", "c", "d", "e" ]; + assert.deepEqual(sf.unique().values, expected); + }); + }); + + describe("nUnique", function () { + it("returns the number of unique values in a Series of type string", function () { + const sf = new dfd.Series([ "a", "a", "b", "c", "c", "d", "e", "d", "d", "e" ]); + assert.deepEqual(sf.nUnique(), 5); + }); + it("returns the number of unique values in a Series of type int32", function () { + const sf = new dfd.Series([ 1, 2, 3, 4, 3, 4, 3, 50, 4, 4, 4, 1 ]); + assert.deepEqual(sf.nUnique(), 5); + }); + }); + + describe("valueCounts", function () { + it("returns the unique values and their counts in a Series of type int", function () { + const sf = new dfd.Series([ 1, 2, 3, 4, 5, 6, 7, 8, 1, 1, 22, 8, 5, 5, 5 ]); + const expectedIndex = [ 1, 2, 3, 4, 5, 6, 7, 8, 22 ]; + const expectedVals = [ 3, 1, 1, 1, 4, 1, 1, 2, 1 ]; + assert.deepEqual(sf.valueCounts().values, expectedVals); + assert.deepEqual(sf.valueCounts().index, expectedIndex); + }); + it("returns the unique values and their counts in a Series of type string", function () { + const sf = new dfd.Series([ "a", "a", "b", "c", "c", "d", "e", "d", "d", "e" ]); + const expectedVals = [ 2, 1, 2, 3, 2 ]; + const expectedIndex = [ "a", "b", "c", "d", "e" ]; + assert.deepEqual(sf.valueCounts().values, expectedVals); + assert.deepEqual(sf.valueCounts().index, expectedIndex); + }); + it("returns the unique values and their counts in a Series of type boolean", function () { + const sf = new dfd.Series([ true, false, false, true, true ]); + const expectedVals = [ 3, 2 ]; + const expectedIndex = [ "true", "false" ]; + assert.deepEqual(sf.valueCounts().values, expectedVals); + assert.deepEqual(sf.valueCounts().index, expectedIndex); + }); + }); + + describe("abs", function () { + it("Returns the absolute values in Series", function () { + const data1 = [ -10, 45, 56, -25, 23, -20, 10 ]; + const sf = new dfd.Series(data1); + assert.deepEqual(sf.abs().values, [ 10, 45, 56, 25, 23, 20, 10 ]); + }); + it("Computes the descriptive statistics on a float Series", function () { + const data1 = [ -30.1, -40.2, -3.1, -5.1 ]; + const sf = new dfd.Series(data1); + assert.deepEqual(sf.abs().values, [ 30.1, 40.2, 3.1, 5.1 ]); + }); + }); + + describe("cumSum", function () { + it("Return cumulative sum over a Series", function () { + const data1 = [ 10, 45, 56, 25, 23, 20, 10 ]; + const sf = new dfd.Series(data1); + assert.deepEqual(sf.cumSum().values, [ 10, 55, 111, 136, 159, 179, 189 ]); + }); + it("Return cumulative sum over a Series inplace", function () { + const data1 = [ 10, 45, 56, 25, 23, 20, 10 ]; + const sf = new dfd.Series(data1); + sf.cumSum({ inplace: true }); + assert.deepEqual(sf.values, [ 10, 55, 111, 136, 159, 179, 189 ]); + }); + }); + + describe("cumMax", function () { + it("Return cumulative maximum over a Series", function () { + const data1 = [ 10, 45, 56, 25, 23, 20, 10 ]; + const sf = new dfd.Series(data1); + assert.deepEqual(sf.cumMax().values, [ 10, 45, 56, 56, 56, 56, 56 ]); + }); + it("Return cumulative maximum over a Series inplace", function () { + const data1 = [ 10, 45, 56, 25, 23, 20, 10 ]; + const sf = new dfd.Series(data1); + sf.cumMax({ inplace: true }); + assert.deepEqual(sf.values, [ 10, 45, 56, 56, 56, 56, 56 ]); + }); + }); + + describe("cumMin", function () { + it("Return cumulative minimum over a Series", function () { + const data1 = [ 10, 45, 56, 25, 23, 20, 10 ]; + const sf = new dfd.Series(data1); + assert.deepEqual(sf.cumMin().values, [ 10, 10, 10, 10, 10, 10, 10 ]); + }); + }); + + describe("cumProd", function () { + it("Return cumulative product over a Series", function () { + const data1 = [ 1, 2, 10, 3, 12, 14, 1 ]; + const sf = new dfd.Series(data1); + const rslt = [ 1, 2, 20, 60, 720, 10080, 10080 ]; + assert.deepEqual(sf.cumProd().values, rslt); + }); + }); + + describe("lt", function () { + it("Return Less than of series and other series (element-wise)", function () { + const data1 = [ 10, 45, 56, 25, 23, 20, 10 ]; + const data2 = [ 100, 450, 590, 5, 25, 2, 0 ]; + + const sf = new dfd.Series(data1); + const sf2 = new dfd.Series(data2); + const expected = [ true, true, true, false, true, false, false ]; + assert.deepEqual(sf.lt(sf2).values, expected); + }); + + it("Return Less than of series scalar (element-wise)", function () { + const data1 = [ 10, 45, 56, 25, 23, 20, 10 ]; + const sf = new dfd.Series(data1); + const expected = [ true, false, false, true, true, true, true ]; + assert.deepEqual(sf.lt(30).values, expected); + }); + + it("Correct index is returned after operation", function () { + const data1 = [ true, true, true, false, false ]; + const data2 = [ true, false, true, true, false ]; + const sf = new dfd.Series(data1, { index: [ "one", "two", "three", "four", "five" ] }); + + const expected = [ "one", "two", "three", "four", "five" ]; + assert.deepEqual(sf.lt(data2).index, expected); + }); + }); + + describe("gt", function () { + it("Return Greater than of series and other series (element-wise)", function () { + const data1 = [ 10, 45, 56, 25, 23, 20, 10 ]; + const data2 = [ 100, 450, 590, 5, 25, 2, 0 ]; + + const sf = new dfd.Series(data1); + const sf2 = new dfd.Series(data2); + const expected = [ false, false, false, true, false, true, true ]; + assert.deepEqual(sf.gt(sf2).values, expected); + }); + + it("Return Greater than of series scalar (element-wise)", function () { + const data1 = [ 10, 45, 56, 25, 23, 20, 10 ]; + const sf = new dfd.Series(data1); + const expected = [ false, true, true, false, false, false, false ]; + assert.deepEqual(sf.gt(30).values, expected); + }); + + it("Correct index is returned after operation", function () { + const data1 = [ true, true, true, false, false ]; + const data2 = [ true, false, true, true, false ]; + const sf = new dfd.Series(data1, { index: [ "one", "two", "three", "four", "five" ] }); + + const expected = [ "one", "two", "three", "four", "five" ]; + assert.deepEqual(sf.and(data2).index, expected); + }); + }); + + describe("le", function () { + it("Return Less than or Equal to of series and other series (element-wise)", function () { + const data1 = [ 10, 45, 56, 25, 23, 20, 10 ]; + const data2 = [ 100, 450, 590, 5, 25, 2, 0 ]; + + const sf = new dfd.Series(data1); + const sf2 = new dfd.Series(data2); + const expected = [ true, true, true, false, true, false, false ]; + assert.deepEqual(sf.le(sf2).values, expected); + }); + + it("Return Less than or Equal to of series scalar (element-wise)", function () { + const data1 = [ 10, 45, 56, 25, 23, 20, 10 ]; + const sf = new dfd.Series(data1); + const expected = [ true, false, false, true, true, true, true ]; + assert.deepEqual(sf.le(30).values, expected); + }); + }); + + describe("ge", function () { + it("Return Greater than or Equal to of series and other series (element-wise)", function () { + const data1 = [ 10, 45, 56, 25, 23, 20, 10 ]; + const data2 = [ 100, 450, 56, 5, 25, 20, 0 ]; + + const sf = new dfd.Series(data1); + const sf2 = new dfd.Series(data2); + const expected = [ false, false, true, true, false, true, true ]; + assert.deepEqual(sf.ge(sf2).values, expected); + }); + + it("Return Greater than or Equal to of series scalar (element-wise)", function () { + const data1 = [ 30, 45, 56, 25, 23, 20, 10 ]; + const sf = new dfd.Series(data1); + const expected = [ true, true, true, false, false, false, false ]; + assert.deepEqual(sf.ge(30).values, expected); + }); + }); + + describe("ne", function () { + it("Return Not Equal to of series and other series (element-wise)", function () { + const data1 = [ 10, 45, 56, 25, 23, 20, 10 ]; + const data2 = [ 10, 450, 56, 5, 25, 2, 0 ]; + + const sf = new dfd.Series(data1); + const sf2 = new dfd.Series(data2); + const expected = [ false, true, false, true, true, true, true ]; + assert.deepEqual(sf.ne(sf2).values, expected); + }); + + it("Return Not Equal to of series scalar (element-wise)", function () { + const data1 = [ 10, 30, 56, 30, 23, 20, 10 ]; + const sf = new dfd.Series(data1); + const expected = [ true, false, true, false, true, true, true ]; + assert.deepEqual(sf.ne(30).values, expected); + }); + }); + + describe("eq", function () { + it("Return Equal to of series and other series (element-wise)", function () { + const data1 = [ 10, 45, 56, 25, 23, 20, 10 ]; + const data2 = [ 100, 450, 590, 25, 25, 2, 0 ]; + + const sf = new dfd.Series(data1); + const sf2 = new dfd.Series(data2); + const expected = [ false, false, false, true, false, false, false ]; + assert.deepEqual(sf.eq(sf2).values, expected); + }); + + it("Return Equal to of series scalar (element-wise)", function () { + const data1 = [ 10, 45, 56, 25, 23, 20, 30 ]; + const sf = new dfd.Series(data1); + const expected = [ false, false, false, false, false, false, true ]; + assert.deepEqual(sf.eq(30).values, expected); + }); + }); + + describe("replace", function () { + it("Replace values with new value", function () { + const data1 = [ 10, 45, 56, 25, 23, 20, 10 ]; + const sf = new dfd.Series(data1); + const expected = [ -50, 45, 56, 25, 23, 20, -50 ]; + const dfRep = sf.replace(10, -50); + assert.deepEqual(dfRep.values, expected); + }); + + it("Replace values given in replace param with value (String type)", function () { + const data1 = [ "A", "A", "A", "B", "B", "C", "C", "D" ]; + const sf = new dfd.Series(data1); + const expected = [ "boy", "boy", "boy", "B", "B", "C", "C", "D" ]; + sf.replace("A", "boy", { inplace: true }); + assert.deepEqual(sf.values, expected); + }); + it("Replace values given in replace param with value (boolean type)", function () { + const data1 = [ true, true, false, false ]; + const sf = new dfd.Series(data1); + const expected = [ false, false, false, false ]; + sf.replace(true, false, { inplace: true }); + assert.deepEqual(sf.values, expected); + }); + it("Throw error on wrong param passed", function () { + const data1 = [ "A", "A", "A", "B", "B", "C", "C", "D" ]; + const sf = new dfd.Series(data1); + assert.throws( + () => { + sf.replace(null, "boy", { inplace: true }); + }, + Error, + "Params Error: Must specify param 'oldValue' to replace" + ); + }); + }); + + describe("dropDuplicates", function () { + it("Return Series with duplicate values removed (Default, first values kept)", function () { + const data1 = [ 10, 45, 56, 10, 23, 20, 10, 10 ]; + const sf = new dfd.Series(data1); + const expected = [ 10, 45, 56, 23, 20 ]; + const expectedIndex = [ 0, 1, 2, 4, 5 ]; + const df_drop = sf.dropDuplicates(); + assert.deepEqual(df_drop.values, expected); + assert.deepEqual(df_drop.index, expectedIndex); + }); + + it("Return Series with duplicate values removed (last values kept)", function () { + const data1 = [ 10, 45, 56, 10, 23, 20, 10, 10 ]; + const sf = new dfd.Series(data1); + const expected = [ 45, 56, 23, 20, 10 ]; + const expectedIndex = [ 1, 2, 4, 5, 7 ]; + const df_drop = sf.dropDuplicates({ keep: "last" }); + assert.deepEqual(df_drop.values, expected); + assert.deepEqual(df_drop.index, expectedIndex); + }); + + it("Return Series with duplicate values removed (String)", function () { + const data1 = [ "A", "A", "A", "B", "B", "C", "C", "D" ]; + const sf = new dfd.Series(data1); + const expected = [ "A", "B", "C", "D" ]; + const expectedIndex = [ 0, 3, 5, 7 ]; + sf.dropDuplicates({ keep: "first", inplace: true }); + assert.deepEqual(sf.values, expected); + assert.deepEqual(sf.index, expectedIndex); + }); + }); + + describe("dropNa", function () { + it("Return a new dfd.Series with missing values removed (Int)", function () { + const data1 = [ 10, 45, undefined, 10, 23, 20, undefined, 10 ]; + const sf = new dfd.Series(data1); + const expected = [ 10, 45, 10, 23, 20, 10 ]; + const expectedIndex = [ 0, 1, 3, 4, 5, 7 ]; + const sfDrop = sf.dropNa(); + assert.deepEqual(sfDrop.values, expected); + assert.deepEqual(sfDrop.index, expectedIndex); + assert.deepEqual(sfDrop.shape, [ 6, 1 ]); + }); + + it("Return a new dfd.Series with missing values removed (String)", function () { + const data1 = [ "A", NaN, "A", "B", "B", NaN, "C", undefined ]; + const sf = new dfd.Series(data1); + const expected = [ "A", "A", "B", "B", "C" ]; + const expectedIndex = [ 0, 2, 3, 4, 6 ]; + + sf.dropNa({ inplace: true }); + assert.deepEqual(sf.values, expected); + assert.deepEqual(sf.index, expectedIndex); + assert.deepEqual(sf.shape, [ 5, 1 ]); + }); + }); + + describe("argSort", function () { + it("Return the integer indices that would sort the Series values", function () { + const data1 = [ 10, 45, 20, 10, 23, 20, 30, 11 ]; + const sf = new dfd.Series(data1); + const expected = [ 3, 0, 7, 5, 2, 4, 6, 1 ]; + const sf_sort = sf.argSort(); + assert.deepEqual(sf_sort.values, expected); + }); + + it("Return the integer indices that would sort the Series values", function () { + const data1 = [ 10.22, 4.5, 2.0, 10, 23.23, 20.1, 30, 11 ]; + const sf = new dfd.Series(data1); + const expected = [ 6, 4, 5, 7, 0, 3, 1, 2 ]; + const sf_sort = sf.argSort({ ascending: false }); + assert.deepEqual(sf_sort.values, expected); + }); + }); + + describe("argMax", function () { + it("Return int position of the largest value in the Series.", function () { + const data1 = [ 10, 45, 20, 10, 23, 20, 30, 11 ]; + const sf = new dfd.Series(data1); + const expected = 1; + const argMax = sf.argMax(); + assert.deepEqual(argMax, expected); + }); + + it("Return int position of the largest value in the Float Series.", function () { + const data1 = [ 10.22, 4.5, 2.0, 10, 23.23, 20.1, 30, 11 ]; + const sf = new dfd.Series(data1); + const expected = 6; + const argMax = sf.argMax(); + assert.deepEqual(argMax, expected); + }); + }); + + describe("argMin", function () { + it("Return int position of the smallest value in the Series", function () { + const data1 = [ 10, 45, 20, 122, 23, 20, 30, 11 ]; + const sf = new dfd.Series(data1); + const expected = 0; + const argMin = sf.argMin(); + assert.deepEqual(argMin, expected); + }); + + it("Return int position of the smallest value in a Float Series", function () { + const data1 = [ 10.22, 4.5, 2.0, 10, 23.23, 20.1, 30, 11 ]; + const sf = new dfd.Series(data1); + const expected = 2; + const argMin = sf.argMin(); + assert.deepEqual(argMin, expected); + }); + }); + + describe("Str", function () { + it("Converts all characters to lowercase inplace", function () { + const data = [ "lower", "CAPITALS", "this is a sentence", "SwApCaSe" ]; + const res = [ "lower", "capitals", "this is a sentence", "swapcase" ]; + const sf = new dfd.Series(data); + sf.str.toLowerCase({ inplace: true }); + assert.deepEqual(sf.values, res); + }); + it("Converts all characters to lowercase", function () { + const data = [ "lower", "CAPITALS", "this is a sentence", "SwApCaSe" ]; + const res = [ "lower", "capitals", "this is a sentence", "swapcase" ]; + const sf = new dfd.Series(data); + assert.deepEqual(sf.str.toLowerCase().values, res); + }); + it("Converts all characters to capital case.", function () { + const data = [ "lower", "CAPITALS", "this is a sentence", "SwApCaSe" ]; + const res = [ "Lower", "Capitals", "This is a sentence", "Swapcase" ]; + const sf = new dfd.Series(data); + assert.deepEqual(sf.str.capitalize().values, res); + }); + + it("Returns the character at the specified index (position)", function () { + const data = [ "lower", "CAPITALS", "this is a sentence", "SwApCaSe" ]; + const res = [ "w", "P", "i", "A" ]; + const sf = new dfd.Series(data); + assert.deepEqual(sf.str.charAt(2).values, res); + }); + + it("Throws error on concat of numeric series", function () { + const data = [ 1, 2, 3, 4, 5, 6 ]; + const sf = new dfd.Series(data); + assert.throws( + () => { + sf.str.concat("20", 1); + }, + Error, + "Cannot call accessor str on non-string type" + ); + + }); + }); + + describe("dt", function () { + it("check month generated", function () { + const data = [ "02Sep2019", "03Dec2019", "04Jan2019" ]; + const sf = new dfd.Series(data); + const expected = [ 8, 11, 0 ]; + assert.deepEqual(sf.dt.month().values, expected); + }); + + it("check month Name generated", function () { + const data = [ "06-30-2019", "07-29-2019", "08-28-2019" ]; + const sf = new dfd.Series(data); + const expected = [ "June", "July", "August" ]; + assert.deepEqual(sf.dt.monthName().values, expected); + }); + + it("check days of the weeks generated", function () { + const data = [ "06-30-2019", "07-29-2019", "08-28-2019" ]; + const sf = new dfd.Series(data); + const expected = [ "Sunday", "Monday", "Wednesday" ]; + assert.deepEqual(sf.dt.dayOfWeekName().values, expected); + }); + + it("check day of the month generated", function () { + const data = [ "06-30-2019", "07-29-2019", "08-28-2019" ]; + const sf = new dfd.Series(data); + const expected = [ 30, 29, 28 ]; + assert.deepEqual(sf.dt.dayOfMonth().values, expected); + }); + }); + + describe("astype", function () { + it("set type of float column to int", function () { + const data = [ -20.1, 30, 47.3, -20 ]; + const ndframe = new dfd.Series(data); + const df = ndframe.asType("int32"); + + assert.deepEqual(df.dtypes[0], "int32"); + assert.deepEqual(df.values, [ -20, 30, 47, -20 ]); + }); + it("set type of int column to float", function () { + const data = [ 34, -4, 5, 6 ]; + const ndframe = new dfd.Series(data); + const df = ndframe.asType("float32"); + assert.deepEqual(df.dtypes[0], "float32"); + assert.deepEqual(df.values, [ 34, -4, 5, 6 ]); + }); + it("set type of string column to int", function () { + const data = [ "20.1", "21", "23.4", "50.78" ]; + const ndframe = new dfd.Series(data); + const df = ndframe.asType("int32"); + + assert.deepEqual(df.dtypes[0], "int32"); + assert.deepEqual(df.values, [ 20, 21, 23, 50 ]); + }); + it("set type of string column to float", function () { + const data = [ "20.1", "21", "23.4", "50.78" ]; + const ndframe = new dfd.Series(data); + const df = ndframe.asType("float32"); + + assert.deepEqual(df.dtypes[0], "float32"); + assert.deepEqual(df.values, [ 20.1, 21, 23.4, 50.78 ]); + }); + it("set type of float column to string", function () { + const data = [ -20.1, 30, 47.3, -20 ]; + const ndframe = new dfd.Series(data); + const df = ndframe.asType("string"); + assert.deepEqual(df.dtypes[0], "string"); + assert.deepEqual(df.values, [ "-20.1", "30", "47.3", "-20" ]); + }); + it("set type of int column to string", function () { + const data = [ 34, -4, 5, 6 ]; + const ndframe = new dfd.Series(data); + const df = ndframe.asType("string"); + assert.deepEqual(df.dtypes[0], "string"); + assert.deepEqual(df.values, [ "34", "-4", "5", "6" ]); + }); + it("set type of int column to string inplace", function () { + const data = [ 34, -4, 5, 6 ]; + const ndframe = new dfd.Series(data); + ndframe.asType("string", { inplace: true }); + assert.deepEqual(ndframe.dtypes[0], "string"); + assert.deepEqual(ndframe.values, [ "34", "-4", "5", "6" ]); + }); + }); + + describe("append", function () { + it("Add a new single value to the end of a Series inplace", function () { + const data = [ 1, 2, 3, 4, "a", "b", "c" ]; + const sf = new dfd.Series(data); + const expected_val = [ 1, 2, 3, 4, "a", "b", "c", "d" ]; + sf.append("d", 7, { inplace: true }); + assert.deepEqual(sf.values, expected_val); + }); + it("Add a new array of values to the end of a Series inplace", function () { + const data = [ 1, 2, 3, 4 ]; + const to_add = [ "a", "b", "c" ]; + const index = [ 4, 5, 6 ]; + const sf = new dfd.Series(data); + const expected_val = [ 1, 2, 3, 4, "a", "b", "c" ]; + sf.append(to_add, index, { inplace: true }); + assert.deepEqual(sf.values, expected_val); + }); + it("Add a Series to the end of another Series inplace", function () { + const sf1 = new dfd.Series([ 1, 2, 3, 4 ]); + const sf2 = new dfd.Series([ "a", "b", "c" ]); + const index = [ 4, 5, 6 ]; + const expected_val = [ 1, 2, 3, 4, "a", "b", "c" ]; + sf1.append(sf2, index, { inplace: true }); + assert.deepEqual(sf1.values, expected_val); + }); + it("Add a new single value to the end of a Series", function () { + const data = [ 1, 2, 3, 4, "a", "b", "c" ]; + const sf = new dfd.Series(data); + const expected_val = [ 1, 2, 3, 4, "a", "b", "c", "d" ]; + const sf2 = sf.append("d", 7); + assert.deepEqual(sf2.values, expected_val); + }); + it("Add a new array of values to the end of a Series", function () { + const data = [ 1, 2, 3, 4 ]; + const to_add = [ "a", "b", "c" ]; + const index = [ 4, 5, 6 ]; + const sf = new dfd.Series(data); + const expected_val = [ 1, 2, 3, 4, "a", "b", "c" ]; + const sf2 = sf.append(to_add, index); + assert.deepEqual(sf2.values, expected_val); + }); + it("Add a Series to the end of another Series", function () { + const sf1 = new dfd.Series([ 1, 2, 3, 4 ]); + const sf2 = new dfd.Series([ "a", "b", "c" ]); + const index = [ 4, 5, 6 ]; + const expected_val = [ 1, 2, 3, 4, "a", "b", "c" ]; + const sf3 = sf1.append(sf2, index); + assert.deepEqual(sf3.values, expected_val); + }); + it("Confirm index Change after append", function () { + const sf1 = new dfd.Series([ 1, 2, 3, 4 ]); + const sf2 = new dfd.Series([ "a", "b", "c" ]); + const index = [ 4, 5, 6 ]; + const sf3 = sf1.append(sf2, index); + assert.deepEqual(sf3.index, [ 0, 1, 2, 3, 4, 5, 6 ]); + }); + it("Confirm index Change after append inplace", function () { + const sf1 = new dfd.Series([ 1, 2, 3, 4 ]); + const sf2 = new dfd.Series([ "a", "b", "c" ]); + const index = [ 4, 5, 6 ]; + sf1.append(sf2, index, { inplace: true }); + assert.deepEqual(sf1.index, [ 0, 1, 2, 3, 4, 5, 6 ]); + }); + }); + + describe("and", function () { + it("Return logical AND of series and other series (element-wise)", function () { + const data1 = [ true, true, true, false, false ]; + const data2 = [ true, false, true, true, false ]; + const sf = new dfd.Series(data1); + const sf2 = new dfd.Series(data2); + + const expected = [ true, false, true, false, false ]; + assert.deepEqual(sf.and(sf2).values, expected); + }); + + it("Return logical AND of series and other scalar", function () { + const data1 = [ true, true, true, false, false ]; + const sf = new dfd.Series(data1); + + const expected = [ true, true, true, false, false ]; + assert.deepEqual(sf.and(true).values, expected); + }); + + it("Return logical AND of series and other array (element-wise)", function () { + const data1 = [ true, true, true, false, false ]; + const data2 = [ true, false, true, true, false ]; + const sf = new dfd.Series(data1); + + const expected = [ true, false, true, false, false ]; + assert.deepEqual(sf.and(data2).values, expected); + }); + + it("Chaining works for logical AND of series and other array (element-wise)", function () { + const data1 = [ true, true, true, false, false ]; + const data2 = [ true, false, true, true, false ]; + const data3 = [ true, false, false, true, false ]; + + const sf = new dfd.Series(data1); + const expected = [ true, false, false, false, false ]; + assert.deepEqual(sf.and(data2).and(data3).values, expected); + }); + + it("Chaining works for logical AND and OR combined", function () { + const data1 = [ true, true, true, false, false ]; + const data2 = [ true, false, true, true, false ]; + const data3 = [ true, false, false, true, false ]; + + const sf = new dfd.Series(data1); + const expected = [ true, false, true, true, false ]; + assert.deepEqual(sf.and(data2).or(data3).values, expected); + }); + it("Correct index is returned after operation", function () { + const data1 = [ true, true, true, false, false ]; + const data2 = [ true, false, true, true, false ]; + const sf = new dfd.Series(data1, { index: [ "one", "two", "three", "four", "five" ] }); + + const expected = [ "one", "two", "three", "four", "five" ]; + assert.deepEqual(sf.and(data2).index, expected); + }); + }); + + describe("or", function () { + it("Return logical OR of series and other series (element-wise)", function () { + const data1 = [ true, true, true, false, false ]; + const data2 = [ true, false, true, true, false ]; + const sf = new dfd.Series(data1); + const sf2 = new dfd.Series(data2); + + const expected = [ true, true, true, true, false ]; + assert.deepEqual(sf.or(sf2).values, expected); + }); + + it("Return logical OR of series and other scalar", function () { + const data1 = [ true, true, true, false, false ]; + const sf = new dfd.Series(data1); + + const expected = [ true, true, true, true, true ]; + assert.deepEqual(sf.or(true).values, expected); + }); + + it("Return logical OR of series and other array (element-wise)", function () { + const data1 = [ true, true, true, false, false ]; + const data2 = [ true, false, true, true, false ]; + const sf = new dfd.Series(data1); + + const expected = [ true, true, true, true, false ]; + assert.deepEqual(sf.or(data2).values, expected); + }); + }); + + describe("getDummies", function () { + it("getDummies works on Series", function () { + + const data = [ "dog", "male", "female", "male", "female", "male", "dog" ]; + const sf = new dfd.Series(data); + const df = sf.getDummies({ prefix: "test", prefixSeparator: "/" }); + + const dfValues = [ + [ 1, 0, 0 ], + [ 0, 1, 0 ], + [ 0, 0, 1 ], + [ 0, 1, 0 ], + [ 0, 0, 1 ], + [ 0, 1, 0 ], + [ 1, 0, 0 ] + ]; + const dfColumns = [ 'test/dog', 'test/male', 'test/female' ]; + assert.deepEqual(df.values, dfValues); + assert.deepEqual(df.columns, dfColumns); + }); + it("getDummies works on Series with default prefix and prefixSeperator", function () { + + const data = [ "dog", "male", "female", "male", "female", "male", "dog" ]; + const sf = new dfd.Series(data); + const df = sf.getDummies(); + + const dfValues = [ + [ 1, 0, 0 ], + [ 0, 1, 0 ], + [ 0, 0, 1 ], + [ 0, 1, 0 ], + [ 0, 0, 1 ], + [ 0, 1, 0 ], + [ 1, 0, 0 ] + ]; + const dfColumns = [ '0_dog', '1_male', '2_female' ]; + assert.deepEqual(df.values, dfValues); + assert.deepEqual(df.columns, dfColumns); + }); + + }); +}); diff --git a/danfojs-browser/tests/core/strings.js b/src/danfojs-browser/tests/core/strings.test.js similarity index 85% rename from danfojs-browser/tests/core/strings.js rename to src/danfojs-browser/tests/core/strings.test.js index 96738afe..74c16108 100644 --- a/danfojs-browser/tests/core/strings.js +++ b/src/danfojs-browser/tests/core/strings.test.js @@ -1,29 +1,29 @@ - +/* eslint-disable no-undef */ describe("Str", function () { it("Converts all characters to lowercase.", function () { let data = [ 'lower', 'CAPITALS', 'this is a sentence', 'SwApCaSe' ]; let res = [ 'lower', 'capitals', 'this is a sentence', 'swapcase' ]; - let str = new dfd.Str(new dfd.Series(data)); + let str = new dfd.Series(data).str; assert.deepEqual(str.toLowerCase().values, res); }); it("Converts all characters to uppercase.", function () { let data = [ 'lower', 'CAPITALS', 'this is a sentence', 'SwApCaSe' ]; let res = [ 'LOWER', 'CAPITALS', 'THIS IS A SENTENCE', 'SWAPCASE' ]; - let str = new dfd.Str(new dfd.Series(data)); + let str = new dfd.Series(data).str; assert.deepEqual(str.toUpperCase().values, res); }); it("Converts all characters to capital case.", function () { let data = [ 'lower', 'CAPITALS', 'this is a sentence', 'SwApCaSe' ]; let res = [ 'Lower', 'Capitals', 'This is a sentence', 'Swapcase' ]; - let str = new dfd.Str(new dfd.Series(data)); + let str = new dfd.Series(data).str; assert.deepEqual(str.capitalize().values, res); }); it("Returns the character at the specified index (position)", function () { let data = [ 'lower', 'CAPITALS', 'this is a sentence', 'SwApCaSe' ]; let res = [ "w", "P", "i", "A" ]; - let str = new dfd.Str(new dfd.Series(data)); + let str = new dfd.Series(data).str; assert.deepEqual(str.charAt(2).values, res); }); it("Joins two or more strings. 0 joins from the start", function () { @@ -31,7 +31,7 @@ describe("Str", function () { let data2 = [ 'XX', 'YY', 'BB', '01' ]; let res = [ 'XXlower', 'YYCAPITALS', 'BBsentence', '01SwApCaSe' ]; - let str = new dfd.Str(new dfd.Series(data)); + let str = new dfd.Series(data).str; assert.deepEqual(str.concat(data2, 0).values, res); }); @@ -40,125 +40,125 @@ describe("Str", function () { let data2 = [ 'XX', 'YY', 'BB', '01' ]; let res = [ 'lowerXX', 'CAPITALSYY', 'sentenceBB', 'SwApCaSe01' ]; - let str = new dfd.Str(new dfd.Series(data)); + let str = new dfd.Series(data).str; assert.deepEqual(str.concat(data2, 1).values, res); }); it("Joins two arrays of strings. 0 joins from the start", function () { let data = [ 'lower', 'CAPITALS', 'sentence', 'SwApCaSe' ]; let res = [ 'prelower', 'preCAPITALS', 'presentence', 'preSwApCaSe' ]; - let str = new dfd.Str(new dfd.Series(data)); + let str = new dfd.Series(data).str; assert.deepEqual(str.concat("pre", 0).values, res); }); it("Joins two or more strings. 1 joins from the end", function () { let data = [ 'lower', 'CAPITALS', 'this is a sentence', 'SwApCaSe' ]; let res = [ 'lowerpost', 'CAPITALSpost', 'this is a sentencepost', 'SwApCaSepost' ]; - let str = new dfd.Str(new dfd.Series(data)); + let str = new dfd.Series(data).str; assert.deepEqual(str.concat("post", 1).values, res); }); it("Checks whether a string begins with specified characters", function () { let data = [ 'lower', 'CAPITALS', 'this is a sentence', 'SwApCaSe' ]; let res = [ true, false, false, false ]; - let str = new dfd.Str(new dfd.Series(data)); + let str = new dfd.Series(data).str; assert.deepEqual(str.startsWith("l").values, res); }); it("Checks whether a string ends with specified characters", function () { let data = [ 'lower', 'CAPITALS', 'this is a sentence', 'SwApCaSe' ]; let res = [ false, false, true, true ]; - let str = new dfd.Str(new dfd.Series(data)); + let str = new dfd.Series(data).str; assert.deepEqual(str.endsWith("e").values, res); }); it("Checks whether a string contains the specified string/characters", function () { let data = [ 'lower', 'CAPITALS', 'this is a sentence', 'SwApCaSe' ]; let res = [ false, false, true, false ]; - let str = new dfd.Str(new dfd.Series(data)); + let str = new dfd.Series(data).str; assert.deepEqual(str.includes("sentence").values, res); }); it("Returns the position of the first found occurrence of a specified value in a string", function () { let data = [ 'lower', 'CAPITALS', 'this is a sentence', 'SwApCaSe' ]; let res = [ -1, 0, -1, 4 ]; - let str = new dfd.Str(new dfd.Series(data)); + let str = new dfd.Series(data).str; assert.deepEqual(str.indexOf("C").values, res); }); it("Returns the position of the last found occurrence of a specified value in a string", function () { let data = [ 'lower', 'CAPITALS', 'this is a sentence', 'SwApCaSe' ]; let res = [ 4, -1, -1, -1 ]; - let str = new dfd.Str(new dfd.Series(data)); + let str = new dfd.Series(data).str; assert.deepEqual(str.lastIndexOf("r").values, res); }); it("Returns a new string with a specified number of copies of an existing string", function () { let data = [ 'a', 'b', 'c', 'd' ]; let res = [ 'aaa', 'bbb', 'ccc', 'ddd' ]; - let str = new dfd.Str(new dfd.Series(data)); + let str = new dfd.Series(data).str; assert.deepEqual(str.repeat(3).values, res); }); it("Searches a string for a specified value, or a regular expression, and returns a new string where the specified values are replaced", function () { let data = [ 'lower', 'CAPITALS', 'this is a sentence', 'SwApCaSe' ]; let res = [ 'lower', 'CXXXPITALS', 'this is a sentence', 'SwXXXpCaSe' ]; - let str = new dfd.Str(new dfd.Series(data)); + let str = new dfd.Series(data).str; assert.deepEqual(str.replace("A", "XXX").values, res); }); it("Searches a string for a specified value, or regular expression, and returns the position of the match", function () { let data = [ 'lower', 'CAPITALS', 'this is a sentence', 'SwApCaSe' ]; let res = [ -1, 1, -1, 2 ]; - let str = new dfd.Str(new dfd.Series(data)); + let str = new dfd.Series(data).str; assert.deepEqual(str.search("A").values, res); }); it("Extracts a part of a string and returns a new string", function () { let data = [ 'lower', 'CAPITALS', 'this is a sentence', 'SwApCaSe' ]; let res = [ 'ow', 'AP', 'hi', 'wA' ]; - let str = new dfd.Str(new dfd.Series(data)); + let str = new dfd.Series(data).str; assert.deepEqual(str.slice(1, 3).values, res); }); it("Splits a string into an array of substrings", function () { let data = [ 'lower part', 'CAPITALS city', 'is a sentence', 'SwAp CaSe' ]; let res = [ "lower,part", "CAPITALS,city", "is,a,sentence", "SwAp,CaSe" ]; - let str = new dfd.Str(new dfd.Series(data)); + let str = new dfd.Series(data).str; assert.deepEqual(str.split(" ").values, res); }); it("Extracts the characters from a string, beginning at a specified start position, and through the specified number of character", function () { let data = [ 'lower part', 'CAPITALS city', 'this is a sentence', 'SwAp CaSe' ]; let res = [ " p", "AL", "is", "Ca" ]; - let str = new dfd.Str(new dfd.Series(data)); + let str = new dfd.Series(data).str; assert.deepEqual(str.substr(5, 2).values, res); }); it("Extracts the characters from a string, between two specified indices", function () { let data = [ 'lower part', 'CAPITALS city', 'this is a sentence', 'SwAp CaSe' ]; let res = [ "w", "P", "i", "A" ]; - let str = new dfd.Str(new dfd.Series(data)); + let str = new dfd.Series(data).str; assert.deepEqual(str.substring(2, 3).values, res); }); it("Removes whitespace from both ends of a string", function () { let data = [ 'lower part ', ' CAPITALS city', ' this is a sentence', ' SwAp CaSe' ]; let res = [ 'lower part', 'CAPITALS city', 'this is a sentence', 'SwAp CaSe' ]; - let str = new dfd.Str(new dfd.Series(data)); + let str = new dfd.Series(data).str; assert.deepEqual(str.trim().values, res); }); it("Joins strings to specified value", function () { let data = [ 'lower part', 'CAPITALS city', 'this is a sentence', 'SwAp CaSe' ]; let res = [ 'lower part,new', 'CAPITALS city,new', 'this is a sentence,new', 'SwAp CaSe,new' ]; - let str = new dfd.Str(new dfd.Series(data)); + let str = new dfd.Series(data).str; assert.deepEqual(str.join("new", ",").values, res); }); it("Counts the number of characters in string", function () { let data = [ 'lower part', 'CAPITALS', 'this is a sentence', 'SwApCaSe' ]; let res = [ 10, 8, 18, 8 ]; - let str = new dfd.Str(new dfd.Series(data)); + let str = new dfd.Series(data).str; assert.deepEqual(str.len().values, res); }); diff --git a/src/danfojs-browser/tests/io/csv.reader.test.js b/src/danfojs-browser/tests/io/csv.reader.test.js new file mode 100644 index 00000000..34aad9f7 --- /dev/null +++ b/src/danfojs-browser/tests/io/csv.reader.test.js @@ -0,0 +1,108 @@ +/* eslint-disable no-undef */ + +describe("readCSV", function () { + this.timeout(10000); + it("Read remote csv file works", async function () { + const remoteFile = "https://raw.githubusercontent.com/opensource9ja/danfojs/dev/danfojs-node/tests/samples/titanic.csv"; + let df = await dfd.readCSV(remoteFile, { header: true, preview: 5 }); + assert.deepEqual(df.shape, [ 5, 8 ]); + assert.deepEqual(df.columns, [ + 'Survived', + 'Pclass', + 'Name', + 'Sex', + 'Age', + 'Siblings/Spouses Aboard', + 'Parents/Children Aboard', + 'Fare' + ]); + assert.deepEqual(df.dtypes, [ + 'int32', 'int32', + 'string', 'string', + 'int32', 'int32', + 'int32', 'float32' + ]); + }); + + it("Read remote csv file with config works", async function () { + const remoteFile = "https://raw.githubusercontent.com/opensource9ja/danfojs/dev/danfojs-node/tests/samples/titanic.csv"; + const frameConfig = { + columns: [ + 'A', + 'B', + 'C', + 'D', + 'E', + 'F', + 'G', + 'H' + ] + }; + let df = await dfd.readCSV(remoteFile, { header: true, preview: 5, frameConfig }); + assert.deepEqual(df.shape, [ 5, 8 ]); + assert.deepEqual(df.columns, [ + 'A', + 'B', + 'C', + 'D', + 'E', + 'F', + 'G', + 'H' + ]); + assert.deepEqual(df.dtypes, [ + 'int32', 'int32', + 'string', 'string', + 'int32', 'int32', + 'int32', 'float32' + ]); + }); + +}); + +// describe("streamCSV", function () { +// this.timeout(100000); + +// it("Streaming remote csv file with callback works", async function () { +// const remoteFile = "https://raw.githubusercontent.com/opensource9ja/danfojs/dev/danfojs-node/tests/samples/titanic.csv"; +// await dfd.streamCSV(remoteFile, (df) => { +// if (df) { +// assert.deepEqual(df.shape, [ 1, 8 ]); +// assert.deepEqual(df.columns, [ +// 'Survived', +// 'Pclass', +// 'Name', +// 'Sex', +// 'Age', +// 'Siblings/Spouses Aboard', +// 'Parents/Children Aboard', +// 'Fare' +// ]); +// } else { +// assert.deepEqual(df, null); +// } +// }, { header: true, preview: 3 }); + +// }); + +// }); + + +describe("toCSV", function () { + it("toCSV works", async function () { + const data = [ [ 1, 2, 3, 4 ], [ 5, 6, 7, 8 ], [ 9, 10, 11, 12 ] ]; + let df = new dfd.DataFrame(data, { columns: [ "a", "b", "c", "d" ] }); + assert.deepEqual(dfd.toCSV(df, { download: false }), `a,b,c,d\n1,2,3,4\n5,6,7,8\n9,10,11,12\n`); + }); + it("toCSV works for specified seperator", async function () { + const data = [ [ 1, 2, 3, 4 ], [ 5, 6, 7, 8 ], [ 9, 10, 11, 12 ] ]; + let df = new dfd.DataFrame(data, { columns: [ "a", "b", "c", "d" ] }); + assert.deepEqual(dfd.toCSV(df, { sep: "+", download: false }), `a+b+c+d\n1+2+3+4\n5+6+7+8\n9+10+11+12\n`); + }); + it("toCSV works for series", async function () { + const data = [ 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12 ]; + let df = new dfd.Series(data); + assert.deepEqual(dfd.toCSV(df, { sep: "+", download: false }), `1+2+3+4+5+6+7+8+9+10+11+12`); + }); + +}); diff --git a/src/danfojs-browser/tests/io/excel.reader.test.js b/src/danfojs-browser/tests/io/excel.reader.test.js new file mode 100644 index 00000000..4c4735e9 --- /dev/null +++ b/src/danfojs-browser/tests/io/excel.reader.test.js @@ -0,0 +1,45 @@ +/* eslint-disable no-undef */ + +describe("readExcel", function () { + this.timeout(100000); + it("Read remote excel file works", async function () { + const remoteFile = "https://raw.githubusercontent.com/opensource9ja/danfojs/dev/danfojs-node/tests/samples/SampleData.xlsx"; + let df = await dfd.readExcel(remoteFile); + assert.deepEqual(df.columns, [ + 'Year', + 'Stocks', + 'T.Bills', + 'T.Bonds' + ]); + assert.deepEqual(df.dtypes, [ + 'int32', 'float32', + 'float32', 'float32' + ]); + assert.deepEqual(df.shape, [ 82, 4 ]); + }); + + it("Read remote excel file with config works", async function () { + const remoteFile = "https://raw.githubusercontent.com/opensource9ja/danfojs/dev/danfojs-node/tests/samples/SampleData.xlsx"; + const frameConfig = { + columns: [ + 'A', + 'B', + 'C', + 'D' + ] + }; + let df = await dfd.readExcel(remoteFile, { frameConfig }); + assert.deepEqual(df.columns, [ + 'A', + 'B', + 'C', + 'D' + ]); + assert.deepEqual(df.dtypes, [ + 'int32', 'float32', + 'float32', 'float32' + ]); + assert.deepEqual(df.shape, [ 82, 4 ]); + }); +}); + diff --git a/src/danfojs-browser/tests/io/json.reader.test.js b/src/danfojs-browser/tests/io/json.reader.test.js new file mode 100644 index 00000000..203b84f4 --- /dev/null +++ b/src/danfojs-browser/tests/io/json.reader.test.js @@ -0,0 +1,76 @@ +/* eslint-disable no-undef */ + +describe("readJSON", function () { + this.timeout(100000); + + it("Read remote csv file works", async function () { + const remoteFile = "https://raw.githubusercontent.com/opensource9ja/danfojs/dev/danfojs-node/tests/samples/book.json"; + const df = await dfd.readJSON(remoteFile); + assert.deepEqual(df.columns, [ + 'book_id', + 'title', + 'image_url', + 'authors' + ]); + assert.deepEqual(df.dtypes, [ + 'int32', 'string', + 'string', 'string' + ]); + }); + + it("Read remote csv file with config works", async function () { + const remoteFile = "https://raw.githubusercontent.com/opensource9ja/danfojs/dev/danfojs-node/tests/samples/book.json"; + const frameConfig = { + columns: [ + 'A', + 'B', + 'C', + 'D' + ] + }; + const df = await dfd.readJSON(remoteFile, { frameConfig }); + assert.deepEqual(df.columns, [ + 'A', + 'B', + 'C', + 'D' + ]); + assert.deepEqual(df.dtypes, [ + 'int32', 'string', + 'string', 'string' + ]); + }); +}); + +describe("toJSON", function () { + it("toJSON works", async function () { + const data = [ [ 1, 2, 3, 4 ], [ 5, 6, 7, 8 ], [ 9, 10, 11, 12 ] ]; + const df = new dfd.DataFrame(data, { columns: [ "a", "b", "c", "d" ] }); + const expected = [ + { "a": 1, "b": 2, "c": 3, "d": 4 }, + { "a": 5, "b": 6, "c": 7, "d": 8 }, + { "a": 9, "b": 10, "c": 11, "d": 12 } + ]; + const json = dfd.toJSON(df, { download: false }); + assert.deepEqual(json, expected); + }); + it("toJSON works for row format", async function () { + const data = [ [ 1, 2, 3, 4 ], [ 5, 6, 7, 8 ], [ 9, 10, 11, 12 ] ]; + const df = new dfd.DataFrame(data, { columns: [ "a", "b", "c", "d" ] }); + const expected = { + "a": [ 1, 5, 9 ], + "b": [ 2, 6, 10 ], + "c": [ 3, 7, 11 ], + "d": [ 4, 8, 12 ] + }; + const json = dfd.toJSON(df, { format: "row", download: false }); + assert.deepEqual(json, expected); + }); + + it("toJSON works for series", async function () { + const data = [ 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12 ]; + const df = new dfd.Series(data); + assert.deepEqual(dfd.toJSON(df, { download: false }), { "0": [ 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12 ] }); + }); + +}); diff --git a/danfojs-browser/tests/core/concat.js b/src/danfojs-browser/tests/transformers/concat.test.js similarity index 65% rename from danfojs-browser/tests/core/concat.js rename to src/danfojs-browser/tests/transformers/concat.test.js index cc7f396b..1d3aca5a 100644 --- a/danfojs-browser/tests/core/concat.js +++ b/src/danfojs-browser/tests/transformers/concat.test.js @@ -1,5 +1,6 @@ -describe("Concatenate", function () { +/* eslint-disable no-undef */ +describe("Concat", () => { it("Check the axis 0 concatenation", function () { let data = [ [ 1, 2, 3 ], [ 4, 5, 6 ], [ 20, 30, 40 ], [ 39, 89, 78 ] ]; let cols = [ "A", "B", "C" ]; @@ -13,7 +14,7 @@ describe("Concatenate", function () { let cols2 = [ "A", "B", "C", "D" ]; let df2 = new dfd.DataFrame(data2, { columns: cols2 }); - let new_df = dfd.concat({ "df_list": [ df, df1, df2 ], "axis": 0 }); + let new_df = dfd.concat({ dfList: [ df, df1, df2 ], axis: 0 }); let data_values = [ [ 1, 2, 3, NaN ], [ 4, 5, 6, NaN ], [ 20, 30, 40, NaN ], [ 39, 89, 78, NaN ], [ 1, 2, 3, NaN ], [ 4, 5, 6, NaN ], [ 20, 30, 40, NaN ], [ 39, 89, 78, NaN ], @@ -35,7 +36,7 @@ describe("Concatenate", function () { let cols2 = [ "A", "B", "C", "D" ]; let df2 = new dfd.DataFrame(data2, { columns: cols2 }); - let new_df = dfd.concat({ "df_list": [ df, df1, df2 ], "axis": 1 }); + let new_df = dfd.concat({ dfList: [ df, df1, df2 ], axis: 1 }); let data_values = [ [ 1, 2, 3, 1, 2, 3, 1, 2, 3, 5 ], [ 4, 5, 6, 4, 5, 6, 4, 5, 6, 8 ], [ 20, 30, 40, 20, 30, 40, 20, 30, 40, 10 ], [ 39, 89, 78, 39, 89, 78, NaN, @@ -53,20 +54,18 @@ describe("Concatenate", function () { let df = new dfd.DataFrame(data, { columns: cols }); let s1 = new dfd.Series(data1); - let s2 = new dfd.Series(data2); let rslt = [ - [ 1, 2, 3, NaN ], - [ 4, 5, 6, NaN ], - [ 20, 30, 40, NaN ], - [ 39, 89, 78, NaN ], - [ NaN, NaN, NaN, 1 ], - [ NaN, NaN, NaN, 2 ], - [ NaN, NaN, NaN, 3 ], - [ NaN, NaN, NaN, 4 ] + [ NaN, 1, 2, 3 ], + [ NaN, 4, 5, 6 ], + [ NaN, 20, 30, 40 ], + [ NaN, 39, 89, 78 ], + [ 1, NaN, NaN, NaN ], + [ 2, NaN, NaN, NaN ], + [ 3, NaN, NaN, NaN ], + [ 4, NaN, NaN, NaN ] ]; - - let con = dfd.concat({ "df_list": [ df, s1 ], "axis": 0 }); + let con = dfd.concat({ dfList: [ df, s1 ], axis: 0 }); assert.deepEqual(con.values, rslt); @@ -81,14 +80,14 @@ describe("Concatenate", function () { let data1 = [ 1, 2, 3, 4 ]; let s1 = new dfd.Series(data1); let rslt = [ - [ 1, 2, 3, 1 ], - [ 4, 5, 6, 2 ], - [ 20, 30, 40, 3 ], - [ 39, 89, 78, 4 ] + [ 1, 1, 2, 3 ], + [ 2, 4, 5, 6 ], + [ 3, 20, 30, 40 ], + [ 4, 39, 89, 78 ] ]; - let con = dfd.concat({ "df_list": [ df, s1 ], "axis": 1 }); + let con = dfd.concat({ dfList: [ df, s1 ], axis: 1 }); assert.deepEqual(con.values, rslt); @@ -103,7 +102,7 @@ describe("Concatenate", function () { let rslt = [ [ 1, 3 ], [ 2, 4 ], [ 3, 5 ], [ 4, 6 ] ]; - let con = dfd.concat({ "df_list": [ s1, s2 ], "axis": 1 }); + let con = dfd.concat({ dfList: [ s1, s2 ], axis: 1 }); assert.deepEqual(con.values, rslt); @@ -120,38 +119,10 @@ describe("Concatenate", function () { 3, 4, 5, 6 ]; - let con = dfd.concat({ "df_list": [ s1, s2 ], "axis": 0 }); - - assert.deepEqual(con.values, rslt); - - }); - - it("test if df_list is an array", function(){ - - assert.throws(function () { dfd.concat({ "df_list":23, "axis":0 }); }, Error, 'df_list must be an Array of dataFrames/Series'); - - }); - it("assign default axis for concating", function(){ - let data1 = [ 1, 2, 3, 4 ]; - let data2 = [ 3, 4, 5, 6 ]; - - let s1 = new dfd.Series(data1); - let s2 = new dfd.Series(data2); - let rslt = [ [ 1, 3 ], [ 2, 4 ], [ 3, 5 ], [ 4, 6 ] ]; - - - let con = dfd.concat({ "df_list": [ s1, s2 ], "axis": 12 }); + let con = dfd.concat({ dfList: [ s1, s2 ], axis: 0 }); assert.deepEqual(con.values, rslt); - }); - it("ensure axis is a number", function(){ - let data1 = [ 1, 2, 3, 4 ]; - let data2 = [ 3, 4, 5, 6 ]; - - let s1 = new dfd.Series(data1); - let s2 = new dfd.Series(data2); - assert.throws(function () { dfd.concat({ "df_list":[ s1, s2 ], "axis":"r" }); }, Error, 'axis must be a number'); }); }); diff --git a/src/danfojs-browser/tests/transformers/dummy.encoder.test.js b/src/danfojs-browser/tests/transformers/dummy.encoder.test.js new file mode 100644 index 00000000..a15d79d6 --- /dev/null +++ b/src/danfojs-browser/tests/transformers/dummy.encoder.test.js @@ -0,0 +1,173 @@ +/* eslint-disable no-undef */ + +describe("DummyEncoder", function () { + it("getDummies works on Series", function () { + + const data = [ "dog", "male", "female", "male", "female", "male", "dog" ]; + const series = new dfd.Series(data); + const df = dfd.getDummies(series, { prefix: "test", prefixSeparator: "/" }); + + const dfValues = [ + [ 1, 0, 0 ], + [ 0, 1, 0 ], + [ 0, 0, 1 ], + [ 0, 1, 0 ], + [ 0, 0, 1 ], + [ 0, 1, 0 ], + [ 1, 0, 0 ] + ]; + const dfColumns = [ 'test/dog', 'test/male', 'test/female' ]; + assert.deepEqual(df.values, dfValues); + assert.deepEqual(df.columns, dfColumns); + }); + it("getDummies works on Series with default prefix and prefixSeperator", function () { + + const data = [ "dog", "male", "female", "male", "female", "male", "dog" ]; + const series = new dfd.Series(data); + const df = dfd.getDummies(series); + + const dfValues = [ + [ 1, 0, 0 ], + [ 0, 1, 0 ], + [ 0, 0, 1 ], + [ 0, 1, 0 ], + [ 0, 0, 1 ], + [ 0, 1, 0 ], + [ 1, 0, 0 ] + ]; + const dfColumns = [ '0_dog', '1_male', '2_female' ]; + assert.deepEqual(df.values, dfValues); + assert.deepEqual(df.columns, dfColumns); + }); + + it("getDummies works on DataFrame", function () { + + const data = [ [ 1, "dog", 1.0, "fat" ], [ 3, "fog", 2.0, "good" ], [ 4, "gof", 3.0, "best" ] ]; + const columns = [ "A", "B", "C", "d" ]; + const df = new dfd.DataFrame(data, { columns: columns }); + + const df1 = dfd.getDummies(df, { prefixSeparator: [ "_", "#" ], columns: [ "A", "d" ], prefix: "test" }); + const expectedColumns = [ 'B', 'C', 'test_1', 'test_3', 'test_4', 'test#fat', 'test#good', 'test#best' ]; + const expected = [ [ 'dog', 1.0, 1, 0, 0, 1, 0, 0 ], + [ 'fog', 2.0, 0, 1, 0, 0, 1, 0 ], + [ 'gof', 3.0, 0, 0, 1, 0, 0, 1 ] ]; + assert.deepEqual(df1.values, expected); + assert.deepEqual(df1.columns, expectedColumns); + + }); + it("Throw error if the prefix specified is not equal to the column specified", function () { + + const data = [ [ 1, "dog", 1.0, "fat" ], [ 3, "fog", 2.0, "good" ], [ 4, "gof", 3.0, "best" ] ]; + const columns = [ "A", "B", "C", "d" ]; + const df = new dfd.DataFrame(data, { columns: columns }); + + assert.throws(function () { dfd.getDummies(df, { prefix: [ "fg" ], prefixSeparator: "_", columns: [ "A", "d" ] }); }, Error, + `ParamError: prefix and data array must be of the same length. If you need to use the same prefix, then pass a string param instead. e.g {prefix: "fg"}`); + + }); + it("replace column sepecified with prefix", function () { + + const data = [ [ 1, "dog", 1.0, "fat" ], [ 3, "fog", 2.0, "good" ], [ 4, "gof", 3.0, "best" ] ]; + const columns = [ "A", "B", "C", "d" ]; + const df = new dfd.DataFrame(data, { columns: columns }); + + const df1 = dfd.getDummies(df, { prefix: [ "F", "G" ], prefixSeparator: "_", columns: [ "A", "d" ] }); + const expectedColumns = [ + 'B', 'C', + 'F_1', 'F_3', + 'F_4', 'G_fat', + 'G_good', 'G_best' + ]; + + const expected = [ [ 'dog', 1.0, 1, 0, 0, 1, 0, 0 ], + [ 'fog', 2.0, 0, 1, 0, 0, 1, 0 ], + [ 'gof', 3.0, 0, 0, 1, 0, 0, 1 ] ]; + + assert.deepEqual(df1.values, expected); + assert.deepEqual(df1.columns, expectedColumns); + + }); + + it("getDummies auto infers and encode columns with string dtype", function () { + + const data = [ [ 1, "dog", 1.0, "fat" ], [ 3, "fog", 2.0, "good" ], [ 4, "gof", 3.0, "best" ] ]; + const columns = [ "A", "B", "C", "d" ]; + const df = new dfd.DataFrame(data, { columns: columns }); + + const df1 = dfd.getDummies(df, { prefixSeparator: "_" }); + const expectedColumns = [ + 'A', 'C', + 'B_dog', 'B_fog', + 'B_gof', 'd_fat', + 'd_good', 'd_best' + ]; + const expected = [ + [ + 1, 1, 1, 0, + 0, 1, 0, 0 + ], + [ + 3, 2, 0, 1, + 0, 0, 1, 0 + ], + [ + 4, 3, 0, 0, + 1, 0, 0, 1 + ] + ]; + assert.deepEqual(df1.values, expected); + assert.deepEqual(df1.columns, expectedColumns); + + }); + + it("should one hot encode all other columns", function () { + + const data = [ [ 1, "dog", 1.0, "fat" ], [ 3, "fog", 2.0, "good" ], [ 4, "gof", 3.0, "best" ] ]; + const columns = [ "A", "B", "C", "d" ]; + const df = new dfd.DataFrame(data, { columns: columns }); + const rslt = [ + [ 1, 'dog', 1, 1, 0, 0 ], + [ 3, 'fog', 2, 0, 1, 0 ], + [ 4, 'gof', 3, 0, 0, 1 ] + ]; + + assert.deepEqual(dfd.getDummies(df, { columns: [ "d" ] }).values, rslt); + + }); + it("Dummification works for object DF", function () { + + let data = { + fruits: [ 'pear', 'mango', "pawpaw", "mango", "bean" ], + Count: [ 20, 30, 89, 12, 30 ], + Country: [ "NG", "NG", "GH", "RU", "RU" ] + }; + + let df = new dfd.DataFrame(data); + const expected = [ + [ + 20, 1, 0, 0, + 0, 1, 0, 0 + ], + [ + 30, 0, 1, 0, + 0, 1, 0, 0 + ], + [ + 89, 0, 0, 1, + 0, 0, 1, 0 + ], + [ + 12, 0, 1, 0, + 0, 0, 0, 1 + ], + [ + 30, 0, 0, 0, + 1, 0, 0, 1 + ] + ]; + + let dum_df = dfd.getDummies(df, { prefixSeparator: "_" }); + assert.deepEqual(dum_df.values, expected); + + }); +}); diff --git a/src/danfojs-browser/tests/transformers/label.encoder.test.js b/src/danfojs-browser/tests/transformers/label.encoder.test.js new file mode 100644 index 00000000..a1a63e60 --- /dev/null +++ b/src/danfojs-browser/tests/transformers/label.encoder.test.js @@ -0,0 +1,51 @@ +/* eslint-disable no-undef */ + +describe("LabelEncoder", function () { + + it("LabelEncoder works for Series", function () { + const sf = new dfd.Series([ 1, 2, 2, 6 ]); + const scaler = new dfd.LabelEncoder(); + scaler.fit(sf); + const expected = [ 0, 1, 1, 2 ]; + assert.deepEqual(scaler.transform(sf).values, expected); + }); + it("LabelEncoder works for 1D array", function () { + const sf = [ 1, 2, 2, "boy", "git", "git" ]; + const scaler = new dfd.LabelEncoder(); + scaler.fit(sf); + const expected = [ 0, 1, 1, 2, 3, 3 ]; + assert.deepEqual(scaler.transform(sf), expected); + }); + it("LabelEncoder works for values not found", function () { + const sf = new dfd.Series([ 1, 2, 2, 6 ]); + const scaler = new dfd.LabelEncoder(); + scaler.fit(sf); + + const expected = [ 0, 1, 1, 2, -1 ]; + assert.deepEqual((scaler.transform(new dfd.Series([ 1, 2, 2, 6, 7 ]))).values, expected); + }); + it("fitTransform works for 1D array", function () { + const sf = [ 1, 2, 2, "boy", "git", "git" ]; + const scaler = new dfd.LabelEncoder(); + const result = scaler.fitTransform(sf); + const expected = [ 0, 1, 1, 2, 3, 3 ]; + assert.deepEqual(result, expected); + }); + it("inverseTransform works for 1D array", function () { + const sf = [ 1, 2, 2, "boy", "git", "git" ]; + const scaler = new dfd.LabelEncoder(); + scaler.fit(sf); + const result = scaler.inverseTransform([ 0, 1, 1, 2, 3, 3 ]); + assert.deepEqual(result, [ 1, 2, 2, "boy", "git", "git" ]); + }); + it("Get properties from LabelEncoder", function () { + const sf = [ 1, 2, 2, "boy", "git", "git" ]; + const scaler = new dfd.LabelEncoder(); + scaler.fit(sf); + const classes = scaler.classes; + const nClasses = scaler.nClasses; + + assert.deepEqual(classes, { 1: 0, 2: 1, boy: 2, git: 3 }); + assert.equal(nClasses, 4); + }); +}); diff --git a/danfojs-browser/tests/core/merge.js b/src/danfojs-browser/tests/transformers/merge.test.js similarity index 89% rename from danfojs-browser/tests/core/merge.js rename to src/danfojs-browser/tests/transformers/merge.test.js index 2acd35da..d44a97dd 100644 --- a/danfojs-browser/tests/core/merge.js +++ b/src/danfojs-browser/tests/transformers/merge.test.js @@ -1,3 +1,4 @@ +/* eslint-disable no-undef */ describe("Merge", function () { @@ -13,7 +14,7 @@ describe("Merge", function () { let df1 = new dfd.DataFrame(data, { columns: colum1 }); let df2 = new dfd.DataFrame(data2, { columns: colum2 }); - let merge_df = dfd.merge({ "left": df1, "right": df2, "on": [ "Key1", "Key2" ], "how": "outer" }); + let merge_df = dfd.merge({ left: df1, right: df2, on: [ "Key1", "Key2" ], how: "outer" }); let output_data = [ [ 'K0', 'k0', 'A0', 'B0', 'C0', 'D0' ], @@ -24,7 +25,6 @@ describe("Merge", function () { [ 'K2', 'K0', NaN, NaN, 'C3', 'D3' ] ]; - assert.deepEqual(merge_df.values, output_data); }); @@ -40,7 +40,7 @@ describe("Merge", function () { let df1 = new dfd.DataFrame(data, { columns: colum1 }); let df2 = new dfd.DataFrame(data2, { columns: colum2 }); - let merge_df = dfd.merge({ "left": df1, "right": df2, "on": [ "Key1", "Key2" ], "how": "inner" }); + let merge_df = dfd.merge({ left: df1, right: df2, on: [ "Key1", "Key2" ], how: "inner" }); let output_data = [ [ 'K0', 'k0', 'A0', 'B0', 'C0', 'D0' ], @@ -63,7 +63,7 @@ describe("Merge", function () { let df1 = new dfd.DataFrame(data, { columns: colum1 }); let df2 = new dfd.DataFrame(data2, { columns: colum2 }); - let merge_df = dfd.merge({ "left": df1, "right": df2, "on": [ "Key1", "Key2" ], "how": "left" }); + let merge_df = dfd.merge({ left: df1, right: df2, on: [ "Key1", "Key2" ], how: "left" }); let output_data = [ [ 'K0', 'k0', 'A0', 'B0', 'C0', 'D0' ], @@ -88,7 +88,7 @@ describe("Merge", function () { let df1 = new dfd.DataFrame(data, { columns: colum1 }); let df2 = new dfd.DataFrame(data2, { columns: colum2 }); - let merge_df = dfd.merge({ "left": df1, "right": df2, "on": [ "Key1", "Key2" ], "how": "right" }); + let merge_df = dfd.merge({ left: df1, right: df2, on: [ "Key1", "Key2" ], how: "right" }); let output_data = [ [ 'K0', 'k0', 'A0', 'B0', 'C0', 'D0' ], diff --git a/src/danfojs-browser/tests/transformers/min.max.scaler.test.js b/src/danfojs-browser/tests/transformers/min.max.scaler.test.js new file mode 100644 index 00000000..4ae1071f --- /dev/null +++ b/src/danfojs-browser/tests/transformers/min.max.scaler.test.js @@ -0,0 +1,54 @@ +/* eslint-disable no-undef */ + +describe("MinMaxscaler", function () { + + it("Standardize values in a DataFrame using a MinMaxScaler", function () { + const data = [ [ -1, 2 ], [ -0.5, 6 ], [ 0, 10 ], [ 1, 18 ] ]; + const scaler = new dfd.MinMaxScaler(); + + const expected = [ [ 0, 0 ], [ 0.25, 0.25 ], [ 0.5, 0.5 ], [ 1, 1 ] ]; + const transformedData = [ [ 1.5, 0. ] ]; + + scaler.fit(new dfd.DataFrame(data)); + const resultDf = scaler.transform(new dfd.DataFrame(data)); + assert.deepEqual(resultDf.values, expected); + assert.deepEqual(scaler.transform([ [ 2, 2 ] ]), transformedData); + }); + it("fitTransform using a MinMaxScaler", function () { + const data = [ [ -1, 2 ], [ -0.5, 6 ], [ 0, 10 ], [ 1, 18 ] ]; + const scaler = new dfd.MinMaxScaler(); + const resultDf = scaler.fitTransform(new dfd.DataFrame(data)); + + const expected = [ [ 0, 0 ], [ 0.25, 0.25 ], [ 0.5, 0.5 ], [ 1, 1 ] ]; + assert.deepEqual(resultDf.values, expected); + }); + it("InverseTransform with MinMaxScaler", function () { + const scaler = new dfd.MinMaxScaler(); + scaler.fit([ 1, 2, 3, 4, 5 ]); + const resultTransform = scaler.transform([ 1, 2, 3, 4, 5 ]); + const resultInverse = scaler.inverseTransform([ 0, 0.25, 0.5, 0.75, 1 ]); + + assert.deepEqual(resultTransform, [ 0, 0.25, 0.5, 0.75, 1 ]); + assert.deepEqual([ 1, 2, 3, 4, 5 ], resultInverse); + }); + it("Index and columns are kept after transformation", function () { + const data = [ [ -1, 2 ], [ -0.5, 6 ], [ 0, 10 ], [ 1, 18 ] ]; + const df = new dfd.DataFrame(data, { index: [ 1, 2, 3, 4 ], columns: [ "a", "b" ] }); + + const scaler = new dfd.MinMaxScaler(); + scaler.fit(df); + const resultDf = scaler.transform(df); + + assert.deepEqual(resultDf.index, [ 1, 2, 3, 4 ]); + assert.deepEqual(resultDf.columns, [ "a", "b" ]); + }); + it("Standardize values in a Series using a MinMaxScaler", function () { + const data = [ -1, 2, -0.5, 60, 101, 18 ]; + const scaler = new dfd.MinMaxScaler(); + const result = [ 0, 0.029411764815449715, 0.0049019609577953815, 0.5980392098426819, 1, 0.18627451360225677 ]; + const transformedData = [ 0.029411764815449715, 0.029411764815449715 ]; + scaler.fit(new dfd.Series(data)); + assert.deepEqual(scaler.transform(new dfd.Series(data)).values, result); + assert.deepEqual(scaler.transform([ 2, 2 ]), transformedData); + }); +}); diff --git a/src/danfojs-browser/tests/transformers/one.hot.encoder.test.js b/src/danfojs-browser/tests/transformers/one.hot.encoder.test.js new file mode 100644 index 00000000..a8b001fc --- /dev/null +++ b/src/danfojs-browser/tests/transformers/one.hot.encoder.test.js @@ -0,0 +1,82 @@ +/* eslint-disable no-undef */ + +describe("OneHotEncoder", function () { + it("OneHotEncoder works on array", function () { + const data = [ "dog", "cat", "man", "dog", "cat", "man", "man", "cat" ]; + const encode = new dfd.OneHotEncoder(); + encode.fit(data); + + const expected = [ + [ 1, 0, 0 ], + [ 0, 1, 0 ], + [ 0, 0, 1 ], + [ 1, 0, 0 ], + [ 0, 1, 0 ], + [ 0, 0, 1 ], + [ 0, 0, 1 ], + [ 0, 1, 0 ] + ]; + assert.deepEqual(encode.transform(data), expected); + assert.deepEqual(encode.transform([ "man", "cat" ]), [ [ 0, 0, 1 ], [ 0, 1, 0 ] ]); + }); + it("OneHotEncoder works on Series", function () { + const data = [ "dog", "cat", "man", "dog", "cat", "man", "man", "cat" ]; + const series = new dfd.Series(data); + const encoder = new dfd.OneHotEncoder(); + encoder.fit(series); + + const expected = [ + [ 1, 0, 0 ], + [ 0, 1, 0 ], + [ 0, 0, 1 ], + [ 1, 0, 0 ], + [ 0, 1, 0 ], + [ 0, 0, 1 ], + [ 0, 0, 1 ], + [ 0, 1, 0 ] + ]; + assert.deepEqual(encoder.transform(series).values, expected); + }); + + it("fitTransform works on OneHotEncoder", function () { + const data = [ "dog", "cat", "man", "dog", "cat", "man", "man", "cat" ]; + const series = new dfd.Series(data); + const encoder = new dfd.OneHotEncoder(); + const result = encoder.fitTransform(series); + + const expected = [ + [ 1, 0, 0 ], + [ 0, 1, 0 ], + [ 0, 0, 1 ], + [ 1, 0, 0 ], + [ 0, 1, 0 ], + [ 0, 0, 1 ], + [ 0, 0, 1 ], + [ 0, 1, 0 ] + ]; + assert.deepEqual(result.values, expected); + }); + + it("Correct index is returned after transformation", function () { + const data = [ "dog", "cat", "man", "dog", "cat", "man", "man", "cat" ]; + const series = new dfd.Series(data, + { index: [ "a", "b", "c", "d", "e", "f", "g", "h" ] + }); + const encoder = new dfd.OneHotEncoder(); + const result = encoder.fitTransform(series); + + const expected = [ + [ 1, 0, 0 ], + [ 0, 1, 0 ], + [ 0, 0, 1 ], + [ 1, 0, 0 ], + [ 0, 1, 0 ], + [ 0, 0, 1 ], + [ 0, 0, 1 ], + [ 0, 1, 0 ] + ]; + assert.deepEqual(result.values, expected); + assert.deepEqual(result.index, [ "a", "b", "c", "d", "e", "f", "g", "h" ]); + assert.deepEqual(result.columns, [ "0", "1", "2" ]); + }); +}); diff --git a/src/danfojs-browser/tests/transformers/standard.scaler.test.js b/src/danfojs-browser/tests/transformers/standard.scaler.test.js new file mode 100644 index 00000000..1363fe71 --- /dev/null +++ b/src/danfojs-browser/tests/transformers/standard.scaler.test.js @@ -0,0 +1,55 @@ +/* eslint-disable no-undef */ + +describe("StandardScaler", function () { + + it("StandardScaler works for DataFrame", function () { + const data = [ [ 0, 0 ], [ 0, 0 ], [ 1, 1 ], [ 1, 1 ] ]; + + const scaler = new dfd.StandardScaler(); + scaler.fit(new dfd.DataFrame(data)); + + const expected = [ [ -1, -1 ], [ -1, -1 ], [ 1, 1 ], [ 1, 1 ] ]; + const resultDf = scaler.transform(new dfd.DataFrame(data)); + assert.deepEqual(resultDf.values, expected); + assert.deepEqual(scaler.transform([ [ 2, 2 ] ]), [ [ 3, 3 ] ]); + }); + it("fitTransform works for StandardScaler", function () { + const data = [ [ 0, 0 ], [ 0, 0 ], [ 1, 1 ], [ 1, 1 ] ]; + + const scaler = new dfd.StandardScaler(); + const resultDf = scaler.fitTransform(new dfd.DataFrame(data)); + + const expected = [ [ -1, -1 ], [ -1, -1 ], [ 1, 1 ], [ 1, 1 ] ]; + assert.deepEqual(resultDf.values, expected); + }); + it("inverseTransform works for StandardScaler", function () { + const data = [ [ 0, 0 ], [ 0, 0 ], [ 1, 1 ], [ 1, 1 ] ]; + + const scaler = new dfd.StandardScaler(); + scaler.fit(new dfd.DataFrame(data)); + const resultDf = scaler.inverseTransform([ [ -1, -1 ], [ -1, -1 ], [ 1, 1 ], [ 1, 1 ] ]); + + assert.deepEqual(resultDf, data); + }); + it("StandardScaler works for Array", function () { + const data = [ [ 0, 0 ], [ 0, 0 ], [ 1, 1 ], [ 1, 1 ] ]; + + const scaler = new dfd.StandardScaler(); + scaler.fit(data); + const expected = [ [ -1, -1 ], [ -1, -1 ], [ 1, 1 ], [ 1, 1 ] ]; + + assert.deepEqual(scaler.transform(data), expected); + assert.deepEqual(scaler.transform([ [ 2, 2 ] ]), [ [ 3, 3 ] ]); + }); + + it("StandardScaler works for Series", function () { + const data = [ 0, 0, 0, 0, 1, 1, 1, 1 ]; + + const scaler = new dfd.StandardScaler(); + scaler.fit(new dfd.Series(data)); + const expected = [ -1, -1, -1, -1, 1, 1, 1, 1 ]; + + assert.deepEqual(scaler.transform(new dfd.Series(data)).values, expected); + assert.deepEqual(scaler.transform([ 2, 2 ]), [ 3, 3 ]); + }); +}); diff --git a/src/danfojs-browser/tests/utils.test.js b/src/danfojs-browser/tests/utils.test.js new file mode 100644 index 00000000..d29ecf42 --- /dev/null +++ b/src/danfojs-browser/tests/utils.test.js @@ -0,0 +1,218 @@ +/* eslint-disable no-undef */ + +const utils = new dfd.Utils(); + +describe("Utils", function () { + it("removes an element from an array", function () { + let arr = [ 1, 2, 3, 4 ]; + assert.deepEqual(utils.removeElementFromArray(arr, 2), [ 1, 2, 4 ]); + }); + + it("Checks if variable is a string", function () { + let arr = [ "1", "2" ]; + assert.isTrue(utils.isString(arr[0])); + }); + + it("Checks if variable is a number", function () { + let arr = [ 1, 2, 3, 4 ]; + assert.isTrue(utils.isNumber(arr[0])); + }); + + it("Checks if value is null", function () { + let val = null; + let val2 = 1; + assert.isTrue(utils.isNull(val)); + assert.isFalse(utils.isNull(val2)); + }); + + it("Checks if value is undefined", function () { + let arr; + assert.isTrue(utils.isUndefined(arr)); + }); + + it("Generate numbers between two set of values [both inclusive]", function () { + let start = 0; + let end = 5; + let data = [ 0, 1, 2, 3, 4, 5 ]; + assert.deepEqual(utils.range(start, end), data); + }); + + it("transposes an array", function () { + let data = [ [ 1, 2, 3 ], [ 4, 5, 6 ], [ 20, 30, 40 ] ]; + let result = [ [ 1, 4, 20 ], [ 2, 5, 30 ], [ 3, 6, 40 ] ]; + assert.deepEqual(utils.transposeArray(data), result); + }); + + describe("inferDtype", function () { + it("Returns string type present in an 1D array", function () { + let data = [ 'Alice', 'Boy', 'Girl', "39" ]; + let result = [ 'string' ]; + assert.deepEqual(utils.inferDtype(data), result); + }); + + it("Returns float type present in an 1D array", function () { + let data = [ 1.1, 2.1, 3.2, 4.4 ]; + let result = [ 'float32' ]; + assert.deepEqual(utils.inferDtype(data), result); + }); + + it("Returns int type present in an 1D array", function () { + let data = [ 1, 2, 3, 45 ]; + let result = [ 'int32' ]; + assert.deepEqual(utils.inferDtype(data), result); + }); + it("Returns float when there's a mixture of int and float in a 1D array", function () { + let data = [ 1, 2.1, 3, 45 ]; + let result = [ 'float32' ]; + assert.deepEqual(utils.inferDtype(data), result); + }); + + it("Returns float type when NaN is present in an 1D array", function () { + let data = [ 1, 2, 3, 45, NaN ]; + let result = [ 'float32' ]; + assert.deepEqual(utils.inferDtype(data), result); + }); + + it("Returns correct dtype if NaN present in data", function () { + let data = utils.transposeArray([ + [ 18.7, 17.4, 18, NaN, 19.3 ], + [ 20, NaN, 19, 18, 20 ] ]); + let result = [ 'float32', 'float32' ]; + assert.deepEqual(utils.inferDtype(data), result); + }); + + it("Returns the data type present in an 2D array", function () { + let data = utils.transposeArray([ [ 'Alice', 'Boy', 'Girl', "39" ], [ 2, 5, 30, 89 ], [ 3.1, 6.1, 40.1, 78.2 ] ]); + let result = [ 'string', 'int32', 'float32' ]; + assert.deepEqual(utils.inferDtype(data), result); + }); + + it("Returns the string dtype when there's a mixture of dtypes in a 2D array", function () { + let data = utils.transposeArray([ [ 'Alice', 'Boy', 'Girl', 21 ], [ 2, 5, 30, "hey" ], [ 3, 6, 40.1, 78.2 ] ]); + let result = [ 'string', 'string', 'float32' ]; + assert.deepEqual(utils.inferDtype(data), result); + }); + + it("Returns bool type in a 1D array", function () { + let data = [ true, true, false, false, false, true ]; + let result = [ 'boolean' ]; + assert.deepEqual(utils.inferDtype(data), result); + }); + it("Returns bool type in a 2D array", function () { + let data = utils.transposeArray([ [ true, false, true ], [ "boy", "girl", "man" ], [ 20, 30, 24 ] ]); + let result = [ 'boolean', 'string', 'int32' ]; + assert.deepEqual(utils.inferDtype(data), result); + }); + + it("Returns string type if values are all NaN", function () { + let data = utils.transposeArray([ [ true, false, true ], [ "boy", "girl", "boy" ], [ NaN, NaN, NaN ] ]); + let result = [ 'boolean', 'string', 'float32' ]; + assert.deepEqual(utils.inferDtype(data), result); + }); + + }); + + describe("mapIntegersToBooleans", function () { + it("map ints to bools in array of arrays", function () { + let data = [ [ 1, 0, 1 ], [ 1, 1, 0 ] ]; + assert.deepEqual(utils.mapIntegersToBooleans(data, 2), [ [ true, false, true ], [ true, true, false ] ]); + }); + + it("map ints to bools in array", function () { + let data = [ 1, 0, 0, 1, 1 ]; + assert.deepEqual(utils.mapIntegersToBooleans(data, 1), [ true, false, false, true, true ]); + }); + }); + + describe("round", function () { + it("round elements in array to 1 dp", function () { + let data = [ 10.01, 2.2, 3.11, 20.505, 20.22, 40.0909 ]; + assert.deepEqual(utils.round(data, 1, true), [ 10.0, 2.2, 3.1, 20.5, 20.2, 40.1 ]); + }); + + it("round elements in array to 2 dp", function () { + let data = [ 10.019, 2.2099, 3.1145, 20.506, 20.22, 40.0909 ]; + assert.deepEqual(utils.round(data, 2, true), [ 10.02, 2.21, 3.11, 20.51, 20.22, 40.09 ]); + }); + }); + + describe("replaceUndefinedWithNaN", function () { + it("replace undefined in Series with NaN", function () { + let data = [ 10.01, 2.2, undefined, 20.505, 20.22, undefined ]; + assert.deepEqual(utils.replaceUndefinedWithNaN(data, true), [ 10.01, 2.2, NaN, 20.505, 20.22, NaN ]); + }); + + it("replace undefined in DataFrame with NaN", function () { + let data = [ [ 10.01, 2.2, undefined, 20.505, 20.22, undefined ], + [ 10.01, undefined, undefined, 20.505, 20, undefined ] ]; + + let result = [ [ 10.01, 2.2, NaN, 20.505, 20.22, NaN ], + [ 10.01, NaN, NaN, 20.505, 20, NaN ] ]; + assert.deepEqual(utils.replaceUndefinedWithNaN(data, false), result); + }); + + it("replace null in Series with NaN", function () { + let data = [ 10.01, 2.2, null, 20.505, 20.22, null ]; + assert.deepEqual(utils.replaceUndefinedWithNaN(data, true), [ 10.01, 2.2, NaN, 20.505, 20.22, NaN ]); + }); + + it("replace null in DataFrame with NaN", function () { + let data = [ [ 10.01, 2.2, null, 20.505, 20.22, null ], + [ 10.01, null, null, 20.505, 20, null ] ]; + + let result = [ [ 10.01, 2.2, NaN, 20.505, 20.22, NaN ], + [ 10.01, NaN, NaN, 20.505, 20, NaN ] ]; + assert.deepEqual(utils.replaceUndefinedWithNaN(data, false), result); + }); + }); + + describe("convert2DArrayToSeriesArray", function () { + it("convert 2D array of array to 1D of string values", function () { + let data = [ [ 10.01, 2.2, "a" ], [ 20.505, 20.22, "boy" ] ]; + assert.deepEqual(utils.convert2DArrayToSeriesArray(data), [ "10.01,2.2,a", "20.505,20.22,boy" ]); + }); + + }); + + describe("throwErrorOnWrongParams", function () { + it("check if the right params are passed to a function", function () { + let paramsNeeded = [ "replace", "with", "inplace" ]; + let kwargs = { "replae": 2, "with": 12, "inplace": true }; + + assert.throws(() => { + utils.throwErrorOnWrongParams(kwargs, paramsNeeded); + }, Error, `Params Error: Required parameter not found. Your params must include the following [${paramsNeeded}]`); + }); + + it("check if the right params are passed to a function 2", function () { + let paramsNeeded = [ "replace", "with", "inplace" ]; + let kwargs = { "replace": 2, "with": 12, "inplace": true }; + utils.throwErrorOnWrongParams(kwargs, paramsNeeded); + }); + + }); + + describe("getRowAndColValues", function () { + it("retreive rows and labels from column object", function () { + let data = { "Alpha": [ "A", "B", "C", "D" ], count: [ 1, 2, 3, 4 ], sum: [ 20.3, 30.456, 40.90, 90.1 ] }; + let res = [ [ "A", 1, 20.3 ], [ "B", 2, 30.456 ], [ "C", 3, 40.90 ], [ "D", 4, 90.1 ] ]; + assert.deepEqual(utils.getRowAndColValues(data)[0], res); + assert.deepEqual(utils.getRowAndColValues(data)[1], [ "Alpha", "count", "sum" ]); + }); + + }); + + + describe("getDuplicate", function () { + it("obtain duplicates and their index", function () { + let data = [ 1, 2, 3, 4, 5, 3, 4, 6, 4, 5 ]; + let res = { + '3': { count: 2, index: [ 2, 5 ] }, + '4': { count: 3, index: [ 3, 6, 8 ] }, + '5': { count: 2, index: [ 4, 9 ] } + }; + assert.deepEqual(utils.getDuplicate(data), res); + }); + }); + +}); diff --git a/src/danfojs-browser/tsconfig.json b/src/danfojs-browser/tsconfig.json new file mode 100644 index 00000000..3e83d96e --- /dev/null +++ b/src/danfojs-browser/tsconfig.json @@ -0,0 +1,23 @@ +{ + "compilerOptions": { + "target": "es5", /* Specify ECMAScript target version: 'ES3' (default), 'ES5', 'ES2015', 'ES2016', 'ES2017', 'ES2018', 'ES2019', 'ES2020', or 'ESNEXT'. */ + "module": "commonjs", /* Specify module code generation: 'none', 'commonjs', 'amd', 'system', 'umd', 'es2015', 'es2020', or 'ESNext'. */ + "lib": ["es6", "dom"], /* Specify library files to be included in the compilation. */ + "allowJs": true, /* Allow javascript files to be compiled. */ + "outDir": "./dist", /* Redirect output structure to the directory. */ + "strict": true, /* Enable all strict type-checking options. */ + "noImplicitAny": true, /* Raise error on expressions and declarations with an implied 'any' type. */ + // "noUnusedLocals": true, /* Report errors on unused locals. */ + // "noUnusedParameters": true, /* Report errors on unused parameters. */ + "noFallthroughCasesInSwitch": true, /* Report errors for fallthrough cases in switch statement. */ + "esModuleInterop": true, /* Enables emit interoperability between CommonJS and ES Modules via creation of namespace objects for all imports. Implies 'allowSyntheticDefaultImports'. */ + "resolveJsonModule": true, /* Include modules imported with '.json' extension */ + "skipLibCheck": true, /* Skip type checking of declaration files. */ + "forceConsistentCasingInFileNames": true, /* Disallow inconsistently-cased references to the same file. */ + "declaration": true, + "baseUrl": "./src", + }, + "include": [ + "./src/**/*" +], +} diff --git a/src/danfojs-browser/webpack.config.js b/src/danfojs-browser/webpack.config.js new file mode 100644 index 00000000..3fb037b9 --- /dev/null +++ b/src/danfojs-browser/webpack.config.js @@ -0,0 +1,58 @@ +/* eslint-disable no-undef */ +const path = require("path"); +const webpack = require("webpack"); + +const createConfig = (target) => { + return { + mode: "production", + devtool: "source-map", + context: path.resolve(__dirname), + entry: { + index: `./src/index.ts` + }, + target: target, + output: { + path: path.resolve(__dirname, "lib"), + filename: `bundle.js`, + library: "dfd" + }, + plugins: [ + // Work around for Buffer is undefined: + // https://github.com/webpack/changelog-v5/issues/10 + new webpack.ProvidePlugin({ + Buffer: [ 'buffer', 'Buffer' ] + }), + new webpack.ProvidePlugin({ + process: 'process/browser' + }) + ], + module: { + rules: [ + { + test: /\.tsx?$/, + use: [ 'ts-loader', 'ify-loader' ], + exclude: /node_modules/ + } + ] + }, + resolve: { + extensions: [ '.tsx', '.ts', '.js' ], + fallback: { + "fs": false, + "path": require.resolve("path-browserify"), + "dotenv": require.resolve('dotenv'), + "os": require.resolve('os-browserify/browser'), + "stream": require.resolve('stream-browserify'), + "buffer": require.resolve('buffer'), + "crypto": require.resolve('crypto-browserify'), + "http": require.resolve('stream-http'), + "https": require.resolve("https-browserify"), + "zlib": require.resolve("browserify-zlib"), + "net": false, + "tls": false + } + } + }; +}; + +module.exports = [ createConfig("web") ]; diff --git a/danfojs-browser/yarn-error.log b/src/danfojs-browser/yarn-error.log similarity index 100% rename from danfojs-browser/yarn-error.log rename to src/danfojs-browser/yarn-error.log diff --git a/danfojs-browser/yarn.lock b/src/danfojs-browser/yarn.lock similarity index 83% rename from danfojs-browser/yarn.lock rename to src/danfojs-browser/yarn.lock index 61f11255..79804963 100644 --- a/danfojs-browser/yarn.lock +++ b/src/danfojs-browser/yarn.lock @@ -800,6 +800,13 @@ "@babel/types" "^7.4.4" esutils "^2.0.2" +"@babel/runtime@^7.14.6": + version "7.15.4" + resolved "https://registry.yarnpkg.com/@babel/runtime/-/runtime-7.15.4.tgz#fd17d16bfdf878e6dd02d19753a39fa8a8d9c84a" + integrity sha512-99catp6bHCaxr4sJ/DbTGgHS4+Rs2RVd2g7iOap6SLGPDknRK9ztKNsE/Fg6QhSeh1FGE5f6gHGQmvvn3I3xhw== + dependencies: + regenerator-runtime "^0.13.4" + "@babel/runtime@^7.8.4": version "7.12.5" resolved "https://registry.yarnpkg.com/@babel/runtime/-/runtime-7.12.5.tgz#410e7e487441e1b360c29be715d870d9b985882e" @@ 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tsutils "^3.21.0" + +"@typescript-eslint/visitor-keys@4.30.0": + version "4.30.0" + resolved "https://registry.yarnpkg.com/@typescript-eslint/visitor-keys/-/visitor-keys-4.30.0.tgz#a47c6272fc71b0c627d1691f68eaecf4ad71445e" + integrity sha512-pNaaxDt/Ol/+JZwzP7MqWc8PJQTUhZwoee/PVlQ+iYoYhagccvoHnC9e4l+C/krQYYkENxznhVSDwClIbZVxRw== + dependencies: + "@typescript-eslint/types" "4.30.0" + eslint-visitor-keys "^2.0.0" "@ungap/promise-all-settled@1.1.2": version "1.1.2" @@ -1180,6 +1377,11 @@ JSONStream@^1.0.3: jsonparse "^1.2.0" through ">=2.2.7 <3" +abab@^2.0.3, abab@^2.0.5: + version "2.0.5" + resolved "https://registry.yarnpkg.com/abab/-/abab-2.0.5.tgz#c0b678fb32d60fc1219c784d6a826fe385aeb79a" + integrity sha512-9IK9EadsbHo6jLWIpxpR6pL0sazTXV6+SQv25ZB+F7Bj9mJNaOc4nCRabwd5M/JwmUa8idz6Eci6eKfJryPs6Q== + accepts@~1.3.4: version "1.3.7" resolved "https://registry.yarnpkg.com/accepts/-/accepts-1.3.7.tgz#531bc726517a3b2b41f850021c6cc15eaab507cd" @@ -1188,6 +1390,14 @@ accepts@~1.3.4: mime-types "~2.1.24" negotiator "0.6.2" +acorn-globals@^6.0.0: + version "6.0.0" + resolved "https://registry.yarnpkg.com/acorn-globals/-/acorn-globals-6.0.0.tgz#46cdd39f0f8ff08a876619b55f5ac8a6dc770b45" + integrity sha512-ZQl7LOWaF5ePqqcX4hLuv/bLXYQNfNWw2c0/yX/TsPRKamzHcTGQnlCjHT3TsmkOUVEPS3crCxiPfdzE/Trlhg== + dependencies: + acorn "^7.1.1" + acorn-walk "^7.1.1" + acorn-jsx@^5.3.1: version "5.3.1" resolved "https://registry.yarnpkg.com/acorn-jsx/-/acorn-jsx-5.3.1.tgz#fc8661e11b7ac1539c47dbfea2e72b3af34d267b" @@ -1202,12 +1412,12 @@ acorn-node@^1.2.0, acorn-node@^1.3.0, acorn-node@^1.5.2, acorn-node@^1.6.1: acorn-walk "^7.0.0" xtend "^4.0.2" -acorn-walk@^7.0.0: +acorn-walk@^7.0.0, acorn-walk@^7.1.1: version "7.2.0" resolved "https://registry.yarnpkg.com/acorn-walk/-/acorn-walk-7.2.0.tgz#0de889a601203909b0fbe07b8938dc21d2e967bc" integrity sha512-OPdCF6GsMIP+Az+aWfAAOEt2/+iVDKE7oy6lJ098aoe59oAmK76qV6Gw60SbZ8jHuG2wH058GF4pLFbYamYrVA== -acorn@^7.0.0, acorn@^7.4.0: +acorn@^7.0.0, acorn@^7.1.1, 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resolved "https://registry.yarnpkg.com/adler-32/-/adler-32-1.3.0.tgz#3cad1b71cdfa69f6c8a91f3e3615d31a4fdedc72" + integrity sha512-f5nltvjl+PRUh6YNfUstRaXwJxtfnKEWhAWWlmKvh+Y3J2+98a0KKVYDEhz6NdKGqswLhjNGznxfSsZGOvOd9g== + dependencies: + printj "~1.2.2" + after@0.8.2: version "0.8.2" resolved "https://registry.yarnpkg.com/after/-/after-0.8.2.tgz#fedb394f9f0e02aa9768e702bda23b505fae7e1f" integrity sha1-/ts5T58OAqqXaOcCvaI7UF+ufh8= +agent-base@6: + version "6.0.2" + resolved "https://registry.yarnpkg.com/agent-base/-/agent-base-6.0.2.tgz#49fff58577cfee3f37176feab4c22e00f86d7f77" + integrity sha512-RZNwNclF7+MS/8bDg70amg32dyeZGZxiDuQmZxKLAlQjr3jGyLx+4Kkk58UO7D2QdgFIQCovuSuZESne6RG6XQ== + dependencies: + debug "4" + aggregate-error@^3.0.0: version "3.1.0" resolved "https://registry.yarnpkg.com/aggregate-error/-/aggregate-error-3.1.0.tgz#92670ff50f5359bdb7a3e0d40d0ec30c5737687a" @@ -1243,7 +1472,7 @@ ajv-keywords@^3.5.2: resolved 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"^4.2.2" + ansi-colors@4.1.1, ansi-colors@^4.1.1: version "4.1.1" resolved "https://registry.yarnpkg.com/ansi-colors/-/ansi-colors-4.1.1.tgz#cbb9ae256bf750af1eab344f229aa27fe94ba348" @@ -1273,17 +1512,12 @@ ansi-regex@^3.0.0: resolved "https://registry.yarnpkg.com/ansi-regex/-/ansi-regex-3.0.0.tgz#ed0317c322064f79466c02966bddb605ab37d998" integrity sha1-7QMXwyIGT3lGbAKWa922Bas32Zg= -ansi-regex@^4.1.0: - version "4.1.0" - resolved "https://registry.yarnpkg.com/ansi-regex/-/ansi-regex-4.1.0.tgz#8b9f8f08cf1acb843756a839ca8c7e3168c51997" - integrity sha512-1apePfXM1UOSqw0o9IiFAovVz9M5S1Dg+4TrDwfMewQ6p/rmMueb7tWZjQ1rx4Loy1ArBggoqGpfqqdI4rondg== - ansi-regex@^5.0.0: version "5.0.0" resolved "https://registry.yarnpkg.com/ansi-regex/-/ansi-regex-5.0.0.tgz#388539f55179bf39339c81af30a654d69f87cb75" integrity sha512-bY6fj56OUQ0hU1KjFNDQuJFezqKdrAyFdIevADiqrWHwSlbmBNMHp5ak2f40Pm8JTFyM2mqxkG6ngkHO11f/lg== -ansi-styles@^3.2.0, ansi-styles@^3.2.1: +ansi-styles@^3.2.1: version "3.2.1" resolved "https://registry.yarnpkg.com/ansi-styles/-/ansi-styles-3.2.1.tgz#41fbb20243e50b12be0f04b8dedbf07520ce841d" integrity sha512-VT0ZI6kZRdTh8YyJw3SMbYm/u+NqfsAxEpWO0Pf9sq8/e94WxxOpPKx9FR1FlyCtOVDNOQ+8ntlqFxiRc+r5qA== @@ -1352,6 +1586,11 @@ arr-union@^3.1.0: resolved "https://registry.yarnpkg.com/arr-union/-/arr-union-3.1.0.tgz#e39b09aea9def866a8f206e288af63919bae39c4" integrity sha1-45sJrqne+Gao8gbiiK9jkZuuOcQ= +array-union@^2.1.0: + version "2.1.0" + resolved "https://registry.yarnpkg.com/array-union/-/array-union-2.1.0.tgz#b798420adbeb1de828d84acd8a2e23d3efe85e8d" + integrity sha512-HGyxoOTYUyCM6stUe6EJgnd4EoewAI7zMdfqO+kGjnlZmBDz/cR5pf8r/cR4Wq60sL/p0IkcjUEEPwS3GFrIyw== + array-unique@^0.3.2: version "0.3.2" resolved "https://registry.yarnpkg.com/array-unique/-/array-unique-0.3.2.tgz#a894b75d4bc4f6cd679ef3244a9fd8f46ae2d428" @@ -1402,11 +1641,6 @@ assign-symbols@^1.0.0: resolved "https://registry.yarnpkg.com/assign-symbols/-/assign-symbols-1.0.0.tgz#59667f41fadd4f20ccbc2bb96b8d4f7f78ec0367" integrity sha1-WWZ/QfrdTyDMvCu5a41Pf3jsA2c= -astral-regex@^1.0.0: - version "1.0.0" - resolved "https://registry.yarnpkg.com/astral-regex/-/astral-regex-1.0.0.tgz#6c8c3fb827dd43ee3918f27b82782ab7658a6fd9" - integrity sha512-+Ryf6g3BKoRc7jfp7ad8tM4TtMiaWvbF/1/sQcZPkkS7ag3D5nMBCe2UfOTONtAkaG0tO0ij3C5Lwmf1EiyjHg== - astral-regex@^2.0.0: version "2.0.0" resolved "https://registry.yarnpkg.com/astral-regex/-/astral-regex-2.0.0.tgz#483143c567aeed4785759c0865786dc77d7d2e31" @@ -1427,6 +1661,20 @@ atob@^2.1.2: resolved "https://registry.yarnpkg.com/atob/-/atob-2.1.2.tgz#6d9517eb9e030d2436666651e86bd9f6f13533c9" integrity sha512-Wm6ukoaOGJi/73p/cl2GvLjTI5JM1k/O14isD73YML8StrH/7/lRFgmg8nICZgD3bZZvjwCGxtMOD3wWNAu8cg== +awesome-typescript-loader@^5.2.1: + version "5.2.1" + resolved "https://registry.yarnpkg.com/awesome-typescript-loader/-/awesome-typescript-loader-5.2.1.tgz#a41daf7847515f4925cdbaa3075d61f289e913fc" + integrity sha512-slv66OAJB8orL+UUaTI3pKlLorwIvS4ARZzYR9iJJyGsEgOqueMfOMdKySWzZ73vIkEe3fcwFgsKMg4d8zyb1g== + dependencies: + chalk "^2.4.1" + enhanced-resolve "^4.0.0" + loader-utils "^1.1.0" + lodash "^4.17.5" + micromatch "^3.1.9" + mkdirp "^0.5.1" + source-map-support "^0.5.3" + webpack-log "^1.2.0" + aws-sign2@~0.7.0: version "0.7.0" resolved "https://registry.yarnpkg.com/aws-sign2/-/aws-sign2-0.7.0.tgz#b46e890934a9591f2d2f6f86d7e6a9f1b3fe76a8" @@ -1514,6 +1762,14 @@ binary-extensions@^2.0.0: resolved "https://registry.yarnpkg.com/binary-extensions/-/binary-extensions-2.2.0.tgz#75f502eeaf9ffde42fc98829645be4ea76bd9e2d" integrity sha512-jDctJ/IVQbZoJykoeHbhXpOlNBqGNcwXJKJog42E5HDPUwQTSdjCHdihjj0DlnheQ7blbT6dHOafNAiS8ooQKA== +bl@^1.0.0: + version "1.2.3" + resolved "https://registry.yarnpkg.com/bl/-/bl-1.2.3.tgz#1e8dd80142eac80d7158c9dccc047fb620e035e7" + integrity sha512-pvcNpa0UU69UT341rO6AYy4FVAIkUHuZXRIWbq+zHnsVcRzDDjIAhGuuYoi0d//cwIwtt4pkpKycWEfjdV+vww== + dependencies: + readable-stream "^2.3.5" + safe-buffer "^5.1.1" + blob@0.0.5: version "0.0.5" resolved "https://registry.yarnpkg.com/blob/-/blob-0.0.5.tgz#d680eeef25f8cd91ad533f5b01eed48e64caf683" @@ -1569,7 +1825,7 @@ braces@^2.3.1, braces@^2.3.2: split-string "^3.0.2" to-regex "^3.0.1" -braces@^3.0.2, braces@~3.0.2: +braces@^3.0.1, braces@^3.0.2, braces@~3.0.2: version "3.0.2" resolved "https://registry.yarnpkg.com/braces/-/braces-3.0.2.tgz#3454e1a462ee8d599e236df336cd9ea4f8afe107" integrity sha512-b8um+L1RzM3WDSzvhm6gIz1yfTbBt6YTlcEKAvsmqCZZFw46z626lVj9j1yEPW33H5H+lBQpZMP1k8l+78Ha0A== @@ -1593,6 +1849,11 @@ browser-pack@^6.0.1: through2 "^2.0.0" umd "^3.0.0" +browser-process-hrtime@^1.0.0: + version "1.0.0" + resolved "https://registry.yarnpkg.com/browser-process-hrtime/-/browser-process-hrtime-1.0.0.tgz#3c9b4b7d782c8121e56f10106d84c0d0ffc94626" + integrity sha512-9o5UecI3GhkpM6DrXr69PblIuWxPKk9Y0jHBRhdocZ2y7YECBFCsHm79Pr3OyR2AvjhDkabFJaDJMYRazHgsow== + browser-resolve@^2.0.0: version "2.0.0" resolved "https://registry.yarnpkg.com/browser-resolve/-/browser-resolve-2.0.0.tgz#99b7304cb392f8d73dba741bb2d7da28c6d7842b" @@ -1822,14 +2083,14 @@ caseless@~0.12.0: resolved "https://registry.yarnpkg.com/caseless/-/caseless-0.12.0.tgz#1b681c21ff84033c826543090689420d187151dc" integrity sha1-G2gcIf+EAzyCZUMJBolCDRhxUdw= -cfb@^1.1.4: - version "1.2.0" - resolved "https://registry.yarnpkg.com/cfb/-/cfb-1.2.0.tgz#6a4d0872b525ed60349e1ef51fb4b0bf73eca9a8" - integrity sha512-sXMvHsKCICVR3Naq+J556K+ExBo9n50iKl6LGarlnvuA2035uMlGA/qVrc0wQtow5P1vJEw9UyrKLCbtIKz+TQ== +cfb@^1.1.4, cfb@^1.2.0: + version "1.2.1" + resolved "https://registry.yarnpkg.com/cfb/-/cfb-1.2.1.tgz#209429e4c68efd30641f6fc74b2d6028bd202402" + integrity sha512-wT2ScPAFGSVy7CY+aauMezZBnNrfnaLSrxHUHdea+Td/86vrk6ZquggV+ssBR88zNs0OnBkL2+lf9q0K+zVGzQ== dependencies: - adler-32 "~1.2.0" + adler-32 "~1.3.0" crc-32 "~1.2.0" - printj "~1.1.2" + printj "~1.3.0" chai@^4.2.0: version "4.2.0" @@ -1843,7 +2104,7 @@ chai@^4.2.0: pathval "^1.1.0" type-detect "^4.0.5" -chalk@^2.0.0: +chalk@^2.0.0, chalk@^2.0.1, chalk@^2.1.0, chalk@^2.4.1: version "2.4.2" resolved "https://registry.yarnpkg.com/chalk/-/chalk-2.4.2.tgz#cd42541677a54333cf541a49108c1432b44c9424" integrity sha512-Mti+f9lpJNcwF4tWV8/OrTTtF1gZi+f8FqlyAdouralcFWFQWF2+NgCHShjkCb+IFBLq9buZwE1xckQU4peSuQ== @@ -1937,13 +2198,10 @@ clone-deep@^4.0.1: kind-of "^6.0.2" shallow-clone "^3.0.0" -codepage@~1.14.0: - version "1.14.0" - resolved "https://registry.yarnpkg.com/codepage/-/codepage-1.14.0.tgz#8cbe25481323559d7d307571b0fff91e7a1d2f99" - integrity sha1-jL4lSBMjVZ19MHVxsP/5HnodL5k= - dependencies: - commander "~2.14.1" - exit-on-epipe "~1.0.1" +codepage@~1.15.0: + version "1.15.0" + resolved "https://registry.yarnpkg.com/codepage/-/codepage-1.15.0.tgz#2e00519024b39424ec66eeb3ec07227e692618ab" + integrity sha512-3g6NUTPd/YtuuGrhMnOMRjFc+LJw/bnMp3+0r/Wcz3IXUuCosKRJvMphm5+Q+bvTVGcJJuRvVLuYba+WojaFaA== collection-visit@^1.0.0: version "1.0.0" @@ -1987,6 +2245,11 @@ colors@^1.4.0: resolved "https://registry.yarnpkg.com/colors/-/colors-1.4.0.tgz#c50491479d4c1bdaed2c9ced32cf7c7dc2360f78" integrity sha512-a+UqTh4kgZg/SlGvfbzDHpgRu7AAQOmmqRHJnxhRZICKFUT91brVhNNt58CMWU9PsBbv3PDCZUHbVxuDiH2mtA== +colors@~0.6.0-1: + version "0.6.2" + resolved "https://registry.yarnpkg.com/colors/-/colors-0.6.2.tgz#2423fe6678ac0c5dae8852e5d0e5be08c997abcc" + integrity sha1-JCP+ZnisDF2uiFLl0OW+CMmXq8w= + combine-source-map@^0.8.0, combine-source-map@~0.8.0: version "0.8.0" resolved "https://registry.yarnpkg.com/combine-source-map/-/combine-source-map-0.8.0.tgz#a58d0df042c186fcf822a8e8015f5450d2d79a8b" @@ -2019,10 +2282,10 @@ commander@^7.0.0: resolved "https://registry.yarnpkg.com/commander/-/commander-7.0.0.tgz#3e2bbfd8bb6724760980988fb5b22b7ee6b71ab2" integrity sha512-ovx/7NkTrnPuIV8sqk/GjUIIM1+iUQeqA3ye2VNpq9sVoiZsooObWlQy+OPWGI17GDaEoybuAGJm6U8yC077BA== -commander@~2.14.1: - version "2.14.1" - resolved "https://registry.yarnpkg.com/commander/-/commander-2.14.1.tgz#2235123e37af8ca3c65df45b026dbd357b01b9aa" - integrity sha512-+YR16o3rK53SmWHU3rEM3tPAh2rwb1yPcQX5irVn7mb0gXbwuCCrnkbV5+PBfETdfg1vui07nM6PCG1zndcjQw== +commander@~2.1.0: + version "2.1.0" + resolved "https://registry.yarnpkg.com/commander/-/commander-2.1.0.tgz#d121bbae860d9992a3d517ba96f56588e47c6781" + integrity sha1-0SG7roYNmZKj1Re6lvVliOR8Z4E= commander@~2.17.1: version "2.17.1" @@ -2034,10 +2297,10 @@ commondir@^1.0.1: resolved "https://registry.yarnpkg.com/commondir/-/commondir-1.0.1.tgz#ddd800da0c66127393cca5950ea968a3aaf1253b" integrity sha1-3dgA2gxmEnOTzKWVDqloo6rxJTs= -complex.js@^2.0.11: - version "2.0.11" - resolved "https://registry.yarnpkg.com/complex.js/-/complex.js-2.0.11.tgz#09a873fbf15ffd8c18c9c2201ccef425c32b8bf1" - integrity sha512-6IArJLApNtdg1P1dFtn3dnyzoZBEF0MwMnrfF1exSBRpZYoy4yieMkpZhQDC0uwctw48vii0CFVyHfpgZ/DfGw== +complex.js@^2.0.15: + version "2.0.15" + resolved "https://registry.yarnpkg.com/complex.js/-/complex.js-2.0.15.tgz#7add6848b4c1d12aa9262f7df925ebe7a51a7406" + integrity sha512-gDBvQU8IG139ZBQTSo2qvDFP+lANMGluM779csXOr6ny1NUtA3wkUnCFjlDNH/moAVfXtvClYt6G0zarFbtz5w== component-bind@1.0.0: version "1.0.0" @@ -2215,11 +2478,36 @@ crypto-browserify@^3.0.0: randombytes "^2.0.0" randomfill "^1.0.3" +cssom@^0.4.4: + version "0.4.4" + resolved "https://registry.yarnpkg.com/cssom/-/cssom-0.4.4.tgz#5a66cf93d2d0b661d80bf6a44fb65f5c2e4e0a10" + integrity sha512-p3pvU7r1MyyqbTk+WbNJIgJjG2VmTIaB10rI93LzVPrmDJKkzKYMtxxyAvQXR/NS6otuzveI7+7BBq3SjBS2mw== + +cssom@~0.3.6: + version "0.3.8" + resolved "https://registry.yarnpkg.com/cssom/-/cssom-0.3.8.tgz#9f1276f5b2b463f2114d3f2c75250af8c1a36f4a" + integrity sha512-b0tGHbfegbhPJpxpiBPU2sCkigAqtM9O121le6bbOlgyV+NyGyCmVfJ6QW9eRjz8CpNfWEOYBIMIGRYkLwsIYg== + +cssstyle@^2.3.0: + version "2.3.0" + resolved "https://registry.yarnpkg.com/cssstyle/-/cssstyle-2.3.0.tgz#ff665a0ddbdc31864b09647f34163443d90b0852" + integrity sha512-AZL67abkUzIuvcHqk7c09cezpGNcxUxU4Ioi/05xHk4DQeTkWmGYftIE6ctU6AEt+Gn4n1lDStOtj7FKycP71A== + dependencies: + cssom "~0.3.6" + custom-event@~1.0.0: version "1.0.1" resolved "https://registry.yarnpkg.com/custom-event/-/custom-event-1.0.1.tgz#5d02a46850adf1b4a317946a3928fccb5bfd0425" integrity sha1-XQKkaFCt8bSjF5RqOSj8y1v9BCU= +d@1, d@^1.0.1: + version "1.0.1" + resolved "https://registry.yarnpkg.com/d/-/d-1.0.1.tgz#8698095372d58dbee346ffd0c7093f99f8f9eb5a" + integrity sha512-m62ShEObQ39CfralilEQRjH6oAMtNCV1xJyEx5LpRYUVN+EviphDgUc/F3hnYbADmkiNs67Y+3ylmlG7Lnu+FA== + dependencies: + es5-ext "^0.10.50" + type "^1.0.1" + dash-ast@^1.0.0: version "1.0.0" resolved "https://registry.yarnpkg.com/dash-ast/-/dash-ast-1.0.0.tgz#12029ba5fb2f8aa6f0a861795b23c1b4b6c27d37" @@ -2232,6 +2520,15 @@ dashdash@^1.12.0: dependencies: assert-plus "^1.0.0" +data-urls@^2.0.0: + version "2.0.0" + resolved "https://registry.yarnpkg.com/data-urls/-/data-urls-2.0.0.tgz#156485a72963a970f5d5821aaf642bef2bf2db9b" + integrity sha512-X5eWTSXO/BJmpdIKCRuKUgSCgAN0OwliVK3yPKbwIWU1Tdw5BRajxlzMidvh+gwko9AfQ9zIj52pzF91Q3YAvQ== + dependencies: + abab "^2.0.3" + whatwg-mimetype "^2.3.0" + whatwg-url "^8.0.0" + date-format@^2.1.0: version "2.1.0" resolved "https://registry.yarnpkg.com/date-format/-/date-format-2.1.0.tgz#31d5b5ea211cf5fd764cd38baf9d033df7e125cf" @@ -2249,6 +2546,13 @@ debug@2.6.9, debug@^2.2.0, debug@^2.3.3: dependencies: ms "2.0.0" +debug@4, debug@^4.3.1: + version "4.3.2" + resolved "https://registry.yarnpkg.com/debug/-/debug-4.3.2.tgz#f0a49c18ac8779e31d4a0c6029dfb76873c7428b" + integrity sha512-mOp8wKcvj7XxC78zLgw/ZA+6TSgkoE2C/ienthhRD298T7UNwAg9diBpLRxC0mOezLl4B0xV7M0cCO6P/O0Xhw== + dependencies: + ms "2.1.2" + debug@4.3.1, debug@^4.0.1, debug@^4.1.0, debug@^4.1.1: version "4.3.1" resolved "https://registry.yarnpkg.com/debug/-/debug-4.3.1.tgz#f0d229c505e0c6d8c49ac553d1b13dc183f6b2ee" @@ -2280,10 +2584,10 @@ decamelize@^4.0.0: resolved "https://registry.yarnpkg.com/decamelize/-/decamelize-4.0.0.tgz#aa472d7bf660eb15f3494efd531cab7f2a709837" integrity sha512-9iE1PgSik9HeIIw2JO94IidnE3eBoQrFJ3w7sFuzSX4DpmZ3v5sZpUiV5Swcf6mQEF+Y0ru8Neo+p+nyh2J+hQ== -decimal.js@^10.2.1: - version "10.2.1" - resolved "https://registry.yarnpkg.com/decimal.js/-/decimal.js-10.2.1.tgz#238ae7b0f0c793d3e3cea410108b35a2c01426a3" - integrity sha512-KaL7+6Fw6i5A2XSnsbhm/6B+NuEA7TZ4vqxnd5tXz9sbKtrN9Srj8ab4vKVdK8YAqZO9P1kg45Y6YLoduPf+kw== +decimal.js@^10.2.1, decimal.js@^10.3.1: + version "10.3.1" + resolved "https://registry.yarnpkg.com/decimal.js/-/decimal.js-10.3.1.tgz#d8c3a444a9c6774ba60ca6ad7261c3a94fd5e783" + integrity sha512-V0pfhfr8suzyPGOx3nmq4aHqabehUZn6Ch9kyFpV79TGDTWFmHqUqXdabR7QHqxzrYolF4+tVmJhUG4OURg5dQ== decode-uri-component@^0.2.0: version "0.2.0" @@ -2302,6 +2606,11 @@ deep-is@^0.1.3: resolved "https://registry.yarnpkg.com/deep-is/-/deep-is-0.1.3.tgz#b369d6fb5dbc13eecf524f91b070feedc357cf34" integrity sha1-s2nW+128E+7PUk+RsHD+7cNXzzQ= +deep-is@~0.1.3: + version "0.1.4" + resolved "https://registry.yarnpkg.com/deep-is/-/deep-is-0.1.4.tgz#a6f2dce612fadd2ef1f519b73551f17e85199831" + integrity sha512-oIPzksmTg4/MriiaYGO+okXDT7ztn/w3Eptv/+gSIdMdKsJo0u4CfYNFJPy+4SKMuCqGw2wxnA+URMg3t8a/bQ== + default-require-extensions@^3.0.0: version "3.0.0" resolved "https://registry.yarnpkg.com/default-require-extensions/-/default-require-extensions-3.0.0.tgz#e03f93aac9b2b6443fc52e5e4a37b3ad9ad8df96" @@ -2399,6 +2708,13 @@ diffie-hellman@^5.0.0: miller-rabin "^4.0.0" randombytes "^2.0.0" +dir-glob@^3.0.1: + version "3.0.1" + resolved "https://registry.yarnpkg.com/dir-glob/-/dir-glob-3.0.1.tgz#56dbf73d992a4a93ba1584f4534063fd2e41717f" + integrity sha512-WkrWp9GR4KXfKGYzOLmTuGVi1UWFfws377n9cc55/tb6DuqyF6pcQ5AbiHEshaDpY9v6oaSr2XCDidGmMwdzIA== + dependencies: + path-type "^4.0.0" + doctrine@^3.0.0: version "3.0.0" resolved "https://registry.yarnpkg.com/doctrine/-/doctrine-3.0.0.tgz#addebead72a6574db783639dc87a121773973961" @@ -2421,6 +2737,18 @@ domain-browser@^1.2.0: resolved "https://registry.yarnpkg.com/domain-browser/-/domain-browser-1.2.0.tgz#3d31f50191a6749dd1375a7f522e823d42e54eda" integrity sha512-jnjyiM6eRyZl2H+W8Q/zLMA481hzi0eszAaBUzIVnmYVDBbnLxVNnfu1HgEBvCbL+71FrxMl3E6lpKH7Ge3OXA== +domexception@^2.0.1: + version "2.0.1" + resolved "https://registry.yarnpkg.com/domexception/-/domexception-2.0.1.tgz#fb44aefba793e1574b0af6aed2801d057529f304" + integrity sha512-yxJ2mFy/sibVQlu5qHjOkf9J3K6zgmCxgJ94u2EdvDOV09H+32LtRswEcUsmUWN72pVLOEnTSRaIVVzVQgS0dg== + dependencies: + webidl-conversions "^5.0.0" + +dotenv@^10.0.0: + version "10.0.0" + resolved "https://registry.yarnpkg.com/dotenv/-/dotenv-10.0.0.tgz#3d4227b8fb95f81096cdd2b66653fb2c7085ba81" + integrity sha512-rlBi9d8jpv9Sf1klPjNfFAuWDjKLwTIJJ/VxtoTwIR6hnZxcEOQCZg2oIL3MWBYw5GpUDKOEnND7LXTbIpQ03Q== + duplexer2@^0.1.2, duplexer2@~0.1.0, duplexer2@~0.1.2: version "0.1.4" resolved "https://registry.yarnpkg.com/duplexer2/-/duplexer2-0.1.4.tgz#8b12dab878c0d69e3e7891051662a32fc6bddcc1" @@ -2459,11 +2787,6 @@ elliptic@^6.5.3: minimalistic-assert "^1.0.1" minimalistic-crypto-utils "^1.0.1" -emoji-regex@^7.0.1: - version "7.0.3" - resolved "https://registry.yarnpkg.com/emoji-regex/-/emoji-regex-7.0.3.tgz#933a04052860c85e83c122479c4748a8e4c72156" - integrity sha512-CwBLREIQ7LvYFB0WyRvwhq5N5qPhc6PMjD6bYggFlI5YyDgl+0vxq5VHbMOFqLg7hfWzmu8T5Z1QofhmTIhItA== - emoji-regex@^8.0.0: version "8.0.0" resolved "https://registry.yarnpkg.com/emoji-regex/-/emoji-regex-8.0.0.tgz#e818fd69ce5ccfcb404594f842963bf53164cc37" @@ -2519,6 +2842,23 @@ engine.io@~3.5.0: engine.io-parser "~2.2.0" ws "~7.4.2" +enhanced-resolve@^4.0.0: + version "4.5.0" + resolved "https://registry.yarnpkg.com/enhanced-resolve/-/enhanced-resolve-4.5.0.tgz#2f3cfd84dbe3b487f18f2db2ef1e064a571ca5ec" + integrity sha512-Nv9m36S/vxpsI+Hc4/ZGRs0n9mXqSWGGq49zxb/cJfPAQMbUtttJAlNPS4AQzaBdw/pKskw5bMbekT/Y7W/Wlg== + dependencies: + graceful-fs "^4.1.2" + memory-fs "^0.5.0" + tapable "^1.0.0" + +enhanced-resolve@^5.0.0: + version "5.8.3" + resolved "https://registry.yarnpkg.com/enhanced-resolve/-/enhanced-resolve-5.8.3.tgz#6d552d465cce0423f5b3d718511ea53826a7b2f0" + integrity sha512-EGAbGvH7j7Xt2nc0E7D99La1OiEs8LnyimkRgwExpUMScN6O+3x9tIWs7PLQZVNx4YD+00skHXPXi1yQHpAmZA== + dependencies: + graceful-fs "^4.2.4" + tapable "^2.2.0" + enhanced-resolve@^5.7.0: version "5.7.0" resolved "https://registry.yarnpkg.com/enhanced-resolve/-/enhanced-resolve-5.7.0.tgz#525c5d856680fbd5052de453ac83e32049958b5c" @@ -2544,16 +2884,49 @@ envinfo@^7.7.3: resolved "https://registry.yarnpkg.com/envinfo/-/envinfo-7.7.4.tgz#c6311cdd38a0e86808c1c9343f667e4267c4a320" integrity sha512-TQXTYFVVwwluWSFis6K2XKxgrD22jEv0FTuLCQI+OjH7rn93+iY0fSSFM5lrSxFY+H1+B0/cvvlamr3UsBivdQ== +errno@^0.1.3: + version "0.1.8" + resolved "https://registry.yarnpkg.com/errno/-/errno-0.1.8.tgz#8bb3e9c7d463be4976ff888f76b4809ebc2e811f" + integrity sha512-dJ6oBr5SQ1VSd9qkk7ByRgb/1SH4JZjCHSW/mr63/QcXO9zLVxvJ6Oy13nio03rxpSnVDDjFor75SjVeZWPW/A== + dependencies: + prr "~1.0.1" + es-module-lexer@^0.3.26: version "0.3.26" resolved "https://registry.yarnpkg.com/es-module-lexer/-/es-module-lexer-0.3.26.tgz#7b507044e97d5b03b01d4392c74ffeb9c177a83b" integrity sha512-Va0Q/xqtrss45hWzP8CZJwzGSZJjDM5/MJRE3IXXnUCcVLElR9BRaE9F62BopysASyc4nM3uwhSW7FFB9nlWAA== +es5-ext@^0.10.35, es5-ext@^0.10.50: + version "0.10.53" + resolved "https://registry.yarnpkg.com/es5-ext/-/es5-ext-0.10.53.tgz#93c5a3acfdbef275220ad72644ad02ee18368de1" + integrity sha512-Xs2Stw6NiNHWypzRTY1MtaG/uJlwCk8kH81920ma8mvN8Xq1gsfhZvpkImLQArw8AHnv8MT2I45J3c0R8slE+Q== + dependencies: + es6-iterator "~2.0.3" + es6-symbol "~3.1.3" + next-tick "~1.0.0" + es6-error@^4.0.1: version "4.1.1" resolved "https://registry.yarnpkg.com/es6-error/-/es6-error-4.1.1.tgz#9e3af407459deed47e9a91f9b885a84eb05c561d" integrity sha512-Um/+FxMr9CISWh0bi5Zv0iOD+4cFh5qLeks1qhAopKVAJw3drgKbKySikp7wGhDL0HPeaja0P5ULZrxLkniUVg== +es6-iterator@~2.0.3: + version "2.0.3" + resolved "https://registry.yarnpkg.com/es6-iterator/-/es6-iterator-2.0.3.tgz#a7de889141a05a94b0854403b2d0a0fbfa98f3b7" + integrity sha1-p96IkUGgWpSwhUQDstCg+/qY87c= + dependencies: + d "1" + es5-ext "^0.10.35" + es6-symbol "^3.1.1" + +es6-symbol@^3.1.1, es6-symbol@~3.1.3: + version "3.1.3" + resolved "https://registry.yarnpkg.com/es6-symbol/-/es6-symbol-3.1.3.tgz#bad5d3c1bcdac28269f4cb331e431c78ac705d18" + integrity sha512-NJ6Yn3FuDinBaBRWl/q5X/s4koRHBrgKAu+yGI6JCBeiu3qrcbJhwT2GeR/EXVfylRk8dpQVJoLEFhK+Mu31NA== + dependencies: + d "^1.0.1" + ext "^1.1.2" + escalade@^3.1.1: version "3.1.1" resolved "https://registry.yarnpkg.com/escalade/-/escalade-3.1.1.tgz#d8cfdc7000965c5a0174b4a82eaa5c0552742e40" @@ -2579,6 +2952,18 @@ escape-string-regexp@^1.0.5: resolved "https://registry.yarnpkg.com/escape-string-regexp/-/escape-string-regexp-1.0.5.tgz#1b61c0562190a8dff6ae3bb2cf0200ca130b86d4" integrity sha1-G2HAViGQqN/2rjuyzwIAyhMLhtQ= +escodegen@^2.0.0: + version "2.0.0" + resolved "https://registry.yarnpkg.com/escodegen/-/escodegen-2.0.0.tgz#5e32b12833e8aa8fa35e1bf0befa89380484c7dd" + integrity sha512-mmHKys/C8BFUGI+MAWNcSYoORYLMdPzjrknd2Vc+bUsjN5bXcr8EhrNB+UTqfL1y3I9c4fw2ihgtMPQLBRiQxw== + dependencies: + esprima "^4.0.1" + estraverse "^5.2.0" + esutils "^2.0.2" + optionator "^0.8.1" + optionalDependencies: + source-map "~0.6.1" + eslint-scope@^5.1.1: version "5.1.1" resolved "https://registry.yarnpkg.com/eslint-scope/-/eslint-scope-5.1.1.tgz#e786e59a66cb92b3f6c1fb0d508aab174848f48c" @@ -2594,6 +2979,13 @@ eslint-utils@^2.1.0: dependencies: eslint-visitor-keys "^1.1.0" +eslint-utils@^3.0.0: + version "3.0.0" + resolved "https://registry.yarnpkg.com/eslint-utils/-/eslint-utils-3.0.0.tgz#8aebaface7345bb33559db0a1f13a1d2d48c3672" + integrity sha512-uuQC43IGctw68pJA1RgbQS8/NP7rch6Cwd4j3ZBtgo4/8Flj4eGE7ZYSZRN3iq5pVUv6GPdW5Z1RFleo84uLDA== + dependencies: + eslint-visitor-keys "^2.0.0" + eslint-visitor-keys@^1.1.0, eslint-visitor-keys@^1.3.0: version "1.3.0" resolved "https://registry.yarnpkg.com/eslint-visitor-keys/-/eslint-visitor-keys-1.3.0.tgz#30ebd1ef7c2fdff01c3a4f151044af25fab0523e" @@ -2656,7 +3048,7 @@ espree@^7.3.0, espree@^7.3.1: acorn-jsx "^5.3.1" eslint-visitor-keys "^1.3.0" -esprima@^4.0.0: +esprima@^4.0.0, esprima@^4.0.1: version "4.0.1" resolved "https://registry.yarnpkg.com/esprima/-/esprima-4.0.1.tgz#13b04cdb3e6c5d19df91ab6987a8695619b0aa71" integrity sha512-eGuFFw7Upda+g4p+QHvnW0RyTX/SVeJBDM/gCtMARO0cLuT2HcEKnTPvhjV6aGeqrCB/sbNop0Kszm0jsaWU4A== @@ -2728,6 +3120,13 @@ execa@^5.0.0: signal-exit "^3.0.3" strip-final-newline "^2.0.0" +executioner@^2.0.1: + version "2.0.1" + resolved "https://registry.yarnpkg.com/executioner/-/executioner-2.0.1.tgz#add328e03bc45dd598f358fbb529fc0be0ec6fcd" + integrity sha1-rdMo4DvEXdWY81j7tSn8C+Dsb80= + dependencies: + mixly "^1.0.0" + exit-on-epipe@~1.0.1: version "1.0.1" resolved "https://registry.yarnpkg.com/exit-on-epipe/-/exit-on-epipe-1.0.1.tgz#0bdd92e87d5285d267daa8171d0eb06159689692" @@ -2746,6 +3145,13 @@ expand-brackets@^2.1.4: snapdragon "^0.8.1" to-regex "^3.0.1" +ext@^1.1.2: + version "1.4.0" + resolved "https://registry.yarnpkg.com/ext/-/ext-1.4.0.tgz#89ae7a07158f79d35517882904324077e4379244" + integrity sha512-Key5NIsUxdqKg3vIsdw9dSuXpPCQ297y6wBjL30edxwPgt2E44WcWBZey/ZvUc6sERLTxKdyCu4gZFmUbk1Q7A== + dependencies: + type "^2.0.0" + extend-shallow@^2.0.1: version "2.0.1" resolved "https://registry.yarnpkg.com/extend-shallow/-/extend-shallow-2.0.1.tgz#51af7d614ad9a9f610ea1bafbb989d6b1c56890f" @@ -2795,12 +3201,23 @@ fast-deep-equal@^3.1.1: resolved "https://registry.yarnpkg.com/fast-deep-equal/-/fast-deep-equal-3.1.3.tgz#3a7d56b559d6cbc3eb512325244e619a65c6c525" integrity sha512-f3qQ9oQy9j2AhBe/H9VC91wLmKBCCU/gDOnKNAYG5hswO7BLKj09Hc5HYNz9cGI++xlpDCIgDaitVs03ATR84Q== +fast-glob@^3.1.1: + version "3.2.7" + resolved "https://registry.yarnpkg.com/fast-glob/-/fast-glob-3.2.7.tgz#fd6cb7a2d7e9aa7a7846111e85a196d6b2f766a1" + integrity sha512-rYGMRwip6lUMvYD3BTScMwT1HtAs2d71SMv66Vrxs0IekGZEjhM0pcMfjQPnknBt2zeCwQMEupiN02ZP4DiT1Q== + dependencies: + "@nodelib/fs.stat" "^2.0.2" + "@nodelib/fs.walk" "^1.2.3" + glob-parent "^5.1.2" + merge2 "^1.3.0" + micromatch "^4.0.4" + fast-json-stable-stringify@^2.0.0: version "2.1.0" resolved "https://registry.yarnpkg.com/fast-json-stable-stringify/-/fast-json-stable-stringify-2.1.0.tgz#874bf69c6f404c2b5d99c481341399fd55892633" integrity sha512-lhd/wF+Lk98HZoTCtlVraHtfh5XYijIjalXck7saUtuanSDyLMxnHhSXEDJqHxD7msR8D0uCmqlkwjCV8xvwHw== -fast-levenshtein@^2.0.6: +fast-levenshtein@^2.0.6, fast-levenshtein@~2.0.6: version "2.0.6" resolved "https://registry.yarnpkg.com/fast-levenshtein/-/fast-levenshtein-2.0.6.tgz#3d8a5c66883a16a30ca8643e851f19baa7797917" integrity sha1-PYpcZog6FqMMqGQ+hR8Zuqd5eRc= @@ -2815,6 +3232,13 @@ fastest-levenshtein@^1.0.12: resolved "https://registry.yarnpkg.com/fastest-levenshtein/-/fastest-levenshtein-1.0.12.tgz#9990f7d3a88cc5a9ffd1f1745745251700d497e2" integrity sha512-On2N+BpYJ15xIC974QNVuYGMOlEVt4s0EOI3wwMqOmK1fdDY+FN/zltPV8vosq4ad4c/gJ1KHScUn/6AWIgiow== +fastq@^1.6.0: + version "1.12.0" + resolved "https://registry.yarnpkg.com/fastq/-/fastq-1.12.0.tgz#ed7b6ab5d62393fb2cc591c853652a5c318bf794" + integrity sha512-VNX0QkHK3RsXVKr9KrlUv/FoTa0NdbYoHHl7uXHv2rzyHSlxjdNAKug2twd9luJxpcyNeAgf5iPPMutJO67Dfg== + dependencies: + reusify "^1.0.4" + fflate@^0.3.8: version "0.3.11" resolved "https://registry.yarnpkg.com/fflate/-/fflate-0.3.11.tgz#2c440d7180fdeb819e64898d8858af327b042a5d" @@ -2882,6 +3306,14 @@ find-up@^4.0.0, find-up@^4.1.0: locate-path "^5.0.0" path-exists "^4.0.0" +findup@^0.1.5: + version "0.1.5" + resolved "https://registry.yarnpkg.com/findup/-/findup-0.1.5.tgz#8ad929a3393bac627957a7e5de4623b06b0e2ceb" + integrity sha1-itkpozk7rGJ5V6fl3kYjsGsOLOs= + dependencies: + colors "~0.6.0-1" + commander "~2.1.0" + flat-cache@^3.0.4: version "3.0.4" resolved "https://registry.yarnpkg.com/flat-cache/-/flat-cache-3.0.4.tgz#61b0338302b2fe9f957dcc32fc2a87f1c3048b11" @@ -2928,6 +3360,15 @@ forever-agent@~0.6.1: resolved "https://registry.yarnpkg.com/forever-agent/-/forever-agent-0.6.1.tgz#fbc71f0c41adeb37f96c577ad1ed42d8fdacca91" integrity sha1-+8cfDEGt6zf5bFd60e1C2P2sypE= +form-data@^2.5.0: + version "2.5.1" + resolved "https://registry.yarnpkg.com/form-data/-/form-data-2.5.1.tgz#f2cbec57b5e59e23716e128fe44d4e5dd23895f4" + integrity sha512-m21N3WOmEEURgk6B9GLOE4RuWOFf28Lhh9qGYeNlGq4VDXUlJy2th2slBNU8Gp8EzloYZOibZJ7t5ecIrFSjVA== + dependencies: + asynckit "^0.4.0" + combined-stream "^1.0.6" + mime-types "^2.1.12" + form-data@^3.0.0: version "3.0.0" resolved "https://registry.yarnpkg.com/form-data/-/form-data-3.0.0.tgz#31b7e39c85f1355b7139ee0c647cf0de7f83c682" @@ -2951,10 +3392,10 @@ frac@~1.1.2: resolved "https://registry.yarnpkg.com/frac/-/frac-1.1.2.tgz#3d74f7f6478c88a1b5020306d747dc6313c74d0b" integrity sha512-w/XBfkibaTl3YDqASwfDUqkna4Z2p9cFSr1aHDt0WoMTECnRfBOv2WArlZILlqgWlmdIlALXGpM2AOhEk5W3IA== -fraction.js@^4.0.12: - version "4.0.13" - resolved "https://registry.yarnpkg.com/fraction.js/-/fraction.js-4.0.13.tgz#3c1c315fa16b35c85fffa95725a36fa729c69dfe" - integrity sha512-E1fz2Xs9ltlUp+qbiyx9wmt2n9dRzPsS11Jtdb8D2o+cC7wr9xkkKsVKJuBX0ST+LVS+LhLO+SbLJNtfWcJvXA== +fraction.js@^4.1.1: + version "4.1.1" + resolved "https://registry.yarnpkg.com/fraction.js/-/fraction.js-4.1.1.tgz#ac4e520473dae67012d618aab91eda09bcb400ff" + integrity sha512-MHOhvvxHTfRFpF1geTK9czMIZ6xclsEor2wkIGYYq+PxcQqT7vStJqjhe6S1TenZrMZzo+wlqOufBDVepUEgPg== fragment-cache@^0.2.1: version "0.2.1" @@ -2963,6 +3404,21 @@ fragment-cache@^0.2.1: dependencies: map-cache "^0.2.2" +from2-array@0.0.4: + version "0.0.4" + resolved "https://registry.yarnpkg.com/from2-array/-/from2-array-0.0.4.tgz#eafc16b65f6e2719bcd57fdc1869005ac1332cd6" + integrity sha1-6vwWtl9uJxm81X/cGGkAWsEzLNY= + dependencies: + from2 "^2.0.3" + +from2@^2.0.3: + version "2.3.0" + resolved "https://registry.yarnpkg.com/from2/-/from2-2.3.0.tgz#8bfb5502bde4a4d36cfdeea007fcca21d7e382af" + integrity sha1-i/tVAr3kpNNs/e6gB/zKIdfjgq8= + dependencies: + inherits "^2.0.1" + readable-stream "^2.0.0" + fromentries@^1.2.0: version "1.3.2" resolved "https://registry.yarnpkg.com/fromentries/-/fromentries-1.3.2.tgz#e4bca6808816bf8f93b52750f1127f5a6fd86e3a" @@ -2992,6 +3448,11 @@ fsevents@~2.3.1: resolved "https://registry.yarnpkg.com/fsevents/-/fsevents-2.3.1.tgz#b209ab14c61012636c8863507edf7fb68cc54e9f" integrity sha512-YR47Eg4hChJGAB1O3yEAOkGO+rlzutoICGqGo9EZ4lKWokzZRSyIW1QmTzqjtw8MJdj9srP869CuWw/hyzSiBw== +fulcon@^1.0.1: + version "1.0.2" + resolved "https://registry.yarnpkg.com/fulcon/-/fulcon-1.0.2.tgz#8a4dfda4c73fcd9cc62a79d5045c392b45547320" + integrity sha1-ik39pMc/zZzGKnnVBFw5K0VUcyA= + function-bind@^1.1.1: version "1.1.1" resolved "https://registry.yarnpkg.com/function-bind/-/function-bind-1.1.1.tgz#a56899d3ea3c9bab874bb9773b7c5ede92f4895d" @@ -3068,6 +3529,13 @@ glob-parent@^5.0.0, glob-parent@~5.1.0: dependencies: is-glob "^4.0.1" +glob-parent@^5.1.2: + version "5.1.2" + resolved "https://registry.yarnpkg.com/glob-parent/-/glob-parent-5.1.2.tgz#869832c58034fe68a4093c17dc15e8340d8401c4" + integrity sha512-AOIgSQCepiJYwP3ARnGx+5VnTu2HBYdzbGP45eLw1vr3zB3vZLeyed1sC9hnbcOc9/SrMyM5RPQrkGz4aS9Zow== + dependencies: + is-glob "^4.0.1" + glob-to-regexp@^0.4.1: version "0.4.1" resolved "https://registry.yarnpkg.com/glob-to-regexp/-/glob-to-regexp-0.4.1.tgz#c75297087c851b9a578bd217dd59a92f59fe546e" @@ -3085,6 +3553,18 @@ glob@7.1.6, glob@^7.0.0, glob@^7.1.0, glob@^7.1.3, glob@^7.1.4, glob@^7.1.6: once "^1.3.0" path-is-absolute "^1.0.0" +glob@^7.1.1: + version "7.2.0" + resolved "https://registry.yarnpkg.com/glob/-/glob-7.2.0.tgz#d15535af7732e02e948f4c41628bd910293f6023" + integrity sha512-lmLf6gtyrPq8tTjSmrO94wBeQbFR3HbLHbuyD69wuyQkImp2hWqMGB47OX65FBkPffO641IP9jWa1z4ivqG26Q== + dependencies: + fs.realpath "^1.0.0" + inflight "^1.0.4" + inherits "2" + minimatch "^3.0.4" + once "^1.3.0" + path-is-absolute "^1.0.0" + globals@^11.1.0: version "11.12.0" resolved "https://registry.yarnpkg.com/globals/-/globals-11.12.0.tgz#ab8795338868a0babd8525758018c2a7eb95c42e" @@ -3097,6 +3577,18 @@ globals@^12.1.0: dependencies: type-fest "^0.8.1" +globby@^11.0.3: + version "11.0.4" + resolved "https://registry.yarnpkg.com/globby/-/globby-11.0.4.tgz#2cbaff77c2f2a62e71e9b2813a67b97a3a3001a5" + integrity sha512-9O4MVG9ioZJ08ffbcyVYyLOJLk5JQ688pJ4eMGLpdWLHq/Wr1D9BlriLQyL0E+jbkuePVZXYFj47QM/v093wHg== + dependencies: + array-union "^2.1.0" + dir-glob "^3.0.1" + fast-glob "^3.1.1" + ignore "^5.1.4" + merge2 "^1.3.0" + slash "^3.0.0" + graceful-fs@^4.1.11, graceful-fs@^4.1.15, graceful-fs@^4.1.2, graceful-fs@^4.1.6, graceful-fs@^4.2.0, graceful-fs@^4.2.4: version "4.2.4" resolved "https://registry.yarnpkg.com/graceful-fs/-/graceful-fs-4.2.4.tgz#2256bde14d3632958c465ebc96dc467ca07a29fb" @@ -3229,6 +3721,18 @@ hmac-drbg@^1.0.1: minimalistic-assert "^1.0.0" minimalistic-crypto-utils "^1.0.1" +hosted-git-info@^2.1.4: + version "2.8.9" + resolved "https://registry.yarnpkg.com/hosted-git-info/-/hosted-git-info-2.8.9.tgz#dffc0bf9a21c02209090f2aa69429e1414daf3f9" + integrity sha512-mxIDAb9Lsm6DoOJ7xH+5+X4y1LU/4Hi50L9C5sIswK3JzULS4bwk1FvjdBgvYR4bzT4tuUQiC15FE2f5HbLvYw== + +html-encoding-sniffer@^2.0.1: + version "2.0.1" + resolved "https://registry.yarnpkg.com/html-encoding-sniffer/-/html-encoding-sniffer-2.0.1.tgz#42a6dc4fd33f00281176e8b23759ca4e4fa185f3" + integrity sha512-D5JbOMBIR/TVZkubHT+OyT2705QvogUW4IBn6nHd756OwieSF9aDYFj4dv6HHEVGYbHaLETa3WggZYWWMyy3ZQ== + dependencies: + whatwg-encoding "^1.0.5" + html-escaper@^2.0.0: version "2.0.2" resolved "https://registry.yarnpkg.com/html-escaper/-/html-escaper-2.0.2.tgz#dfd60027da36a36dfcbe236262c00a5822681453" @@ -3250,6 +3754,15 @@ http-errors@1.7.2: statuses ">= 1.5.0 < 2" toidentifier "1.0.0" +http-proxy-agent@^4.0.1: + version "4.0.1" + resolved "https://registry.yarnpkg.com/http-proxy-agent/-/http-proxy-agent-4.0.1.tgz#8a8c8ef7f5932ccf953c296ca8291b95aa74aa3a" + integrity sha512-k0zdNgqWTGA6aeIRVpvfVob4fL52dTfaehylg0Y4UvSySvOq/Y+BOyPrgpUrA7HylqvU8vIZGsRuXmspskV0Tg== + dependencies: + "@tootallnate/once" "1" + agent-base "6" + debug "4" + http-proxy@^1.18.1: version "1.18.1" resolved "https://registry.yarnpkg.com/http-proxy/-/http-proxy-1.18.1.tgz#401541f0534884bbf95260334e72f88ee3976549" @@ -3273,6 +3786,14 @@ https-browserify@^1.0.0: resolved "https://registry.yarnpkg.com/https-browserify/-/https-browserify-1.0.0.tgz#ec06c10e0a34c0f2faf199f7fd7fc78fffd03c73" integrity sha1-7AbBDgo0wPL68Zn3/X/Hj//QPHM= +https-proxy-agent@^5.0.0: + version "5.0.0" + resolved "https://registry.yarnpkg.com/https-proxy-agent/-/https-proxy-agent-5.0.0.tgz#e2a90542abb68a762e0a0850f6c9edadfd8506b2" + integrity sha512-EkYm5BcKUGiduxzSt3Eppko+PiNWNEpa4ySk9vTC6wDsQJW9rHSa+UhGNJoRYp7bz6Ht1eaRIa6QaJqO5rCFbA== + dependencies: + agent-base "6" + debug "4" + human-signals@^2.1.0: version "2.1.0" resolved "https://registry.yarnpkg.com/human-signals/-/human-signals-2.1.0.tgz#dc91fcba42e4d06e4abaed33b3e7a3c02f514ea0" @@ -3285,16 +3806,41 @@ iconv-lite@0.4.24: dependencies: safer-buffer ">= 2.1.2 < 3" +iconv-lite@^0.6.2: + version "0.6.3" + resolved "https://registry.yarnpkg.com/iconv-lite/-/iconv-lite-0.6.3.tgz#a52f80bf38da1952eb5c681790719871a1a72501" + integrity sha512-4fCk79wshMdzMp2rH06qWrJE4iolqLhCUH+OiuIgU++RB0+94NlDL81atO7GX55uUKueo0txHNtvEyI6D7WdMw== + dependencies: + safer-buffer ">= 2.1.2 < 3.0.0" + ieee754@^1.1.4: version "1.2.1" resolved "https://registry.yarnpkg.com/ieee754/-/ieee754-1.2.1.tgz#8eb7a10a63fff25d15a57b001586d177d1b0d352" integrity sha512-dcyqhDvX1C46lXZcVqCpK+FtMRQVdIMN6/Df5js2zouUsqG7I6sFxitIC+7KYK29KdXOLHdu9zL4sFnoVQnqaA== +ify-loader@^1.1.0: + version "1.1.0" + resolved "https://registry.yarnpkg.com/ify-loader/-/ify-loader-1.1.0.tgz#abe0654c919b48483cf075fe4a0ed2aad2da6a8b" + integrity sha512-EiyC45FRIs+z4g98+jBzuYCfoM6TKG9p7Ek5YZUeM7rucNucaMZIseRj/5Q3I4ypkZXyC2wnU1RcYrVmshe2xw== + dependencies: + bl "^1.0.0" + findup "^0.1.5" + from2-array "0.0.4" + map-limit "0.0.1" + multipipe "^0.3.0" + read-package-json "^2.0.2" + resolve "^1.1.6" + ignore@^4.0.6: version "4.0.6" resolved "https://registry.yarnpkg.com/ignore/-/ignore-4.0.6.tgz#750e3db5862087b4737ebac8207ffd1ef27b25fc" integrity sha512-cyFDKrqc/YdcWFniJhzI42+AzS+gNwmUzOSFcRCQYwySuBBBy/KjuxWLZ/FHEH6Moq1NizMOBWyTcv8O4OZIMg== +ignore@^5.1.4: + version "5.1.8" + resolved "https://registry.yarnpkg.com/ignore/-/ignore-5.1.8.tgz#f150a8b50a34289b33e22f5889abd4d8016f0e57" + integrity sha512-BMpfD7PpiETpBl/A6S498BaIJ6Y/ABT93ETbby2fP00v4EbvPBXWEoaR1UBPKs3iR53pJY7EtZk5KACI57i1Uw== + import-fresh@^3.0.0, import-fresh@^3.2.1: version "3.3.0" resolved "https://registry.yarnpkg.com/import-fresh/-/import-fresh-3.3.0.tgz#37162c25fcb9ebaa2e6e53d5b4d88ce17d9e0c2b" @@ -3372,6 +3918,13 @@ insert-module-globals@^7.0.0: undeclared-identifiers "^1.1.2" xtend "^4.0.0" +install-peers@^1.0.3: + version "1.0.3" + resolved "https://registry.yarnpkg.com/install-peers/-/install-peers-1.0.3.tgz#6348f8f67e6bc23c19ee78adb819c43f8d1dd7d7" + integrity sha512-MAlSHlrn4p+g3fhx8ZVxQZXX+MkeinKLu/ThfAmrVnN5c2L8Vof7myb0UsgowJEiGcFNHYnTvo37r3uap5asYA== + dependencies: + executioner "^2.0.1" + interpret@^2.2.0: version "2.2.0" resolved "https://registry.yarnpkg.com/interpret/-/interpret-2.2.0.tgz#1a78a0b5965c40a5416d007ad6f50ad27c417df9" @@ -3514,6 +4067,11 @@ is-plain-object@^2.0.3, is-plain-object@^2.0.4: dependencies: isobject "^3.0.1" +is-potential-custom-element-name@^1.0.1: + version "1.0.1" + resolved "https://registry.yarnpkg.com/is-potential-custom-element-name/-/is-potential-custom-element-name-1.0.1.tgz#171ed6f19e3ac554394edf78caa05784a45bebb5" + integrity sha512-bCYeRA2rVibKZd+s2625gGnGF/t7DSqDs4dP7CrLA1m7jKWz6pps0LpYLJN8Q64HtmPKJ1hrN3nzPNKFEKOUiQ== + is-stream@^2.0.0: version "2.0.0" resolved "https://registry.yarnpkg.com/is-stream/-/is-stream-2.0.0.tgz#bde9c32680d6fae04129d6ac9d921ce7815f78e3" @@ -3671,6 +4229,39 @@ jsbn@~0.1.0: resolved "https://registry.yarnpkg.com/jsbn/-/jsbn-0.1.1.tgz#a5e654c2e5a2deb5f201d96cefbca80c0ef2f513" integrity sha1-peZUwuWi3rXyAdls77yoDA7y9RM= +jsdom@^16.2.2: + version "16.7.0" + resolved "https://registry.yarnpkg.com/jsdom/-/jsdom-16.7.0.tgz#918ae71965424b197c819f8183a754e18977b710" + integrity sha512-u9Smc2G1USStM+s/x1ru5Sxrl6mPYCbByG1U/hUmqaVsm4tbNyS7CicOSRyuGQYZhTu0h84qkZZQ/I+dzizSVw== + dependencies: + abab "^2.0.5" + acorn "^8.2.4" + acorn-globals "^6.0.0" + cssom "^0.4.4" + cssstyle "^2.3.0" + data-urls "^2.0.0" + decimal.js "^10.2.1" + domexception "^2.0.1" + escodegen "^2.0.0" + form-data "^3.0.0" + html-encoding-sniffer "^2.0.1" + http-proxy-agent "^4.0.1" + https-proxy-agent "^5.0.0" + is-potential-custom-element-name "^1.0.1" + nwsapi "^2.2.0" + parse5 "6.0.1" + saxes "^5.0.1" + symbol-tree "^3.2.4" + tough-cookie "^4.0.0" + w3c-hr-time "^1.0.2" + w3c-xmlserializer "^2.0.0" + webidl-conversions "^6.1.0" + whatwg-encoding "^1.0.5" + whatwg-mimetype "^2.3.0" + whatwg-url "^8.5.0" + ws "^7.4.6" + xml-name-validator "^3.0.0" + jsesc@^2.5.1: version "2.5.2" resolved "https://registry.yarnpkg.com/jsesc/-/jsesc-2.5.2.tgz#80564d2e483dacf6e8ef209650a67df3f0c283a4" @@ -3681,11 +4272,21 @@ jsesc@~0.5.0: resolved "https://registry.yarnpkg.com/jsesc/-/jsesc-0.5.0.tgz#e7dee66e35d6fc16f710fe91d5cf69f70f08911d" integrity sha1-597mbjXW/Bb3EP6R1c9p9w8IkR0= +json-loader@^0.5.7: + version "0.5.7" + resolved "https://registry.yarnpkg.com/json-loader/-/json-loader-0.5.7.tgz#dca14a70235ff82f0ac9a3abeb60d337a365185d" + integrity sha512-QLPs8Dj7lnf3e3QYS1zkCo+4ZwqOiF9d/nZnYozTISxXWCfNs9yuky5rJw4/W34s7POaNlbZmQGaB5NiXCbP4w== + json-parse-better-errors@^1.0.2: version "1.0.2" resolved "https://registry.yarnpkg.com/json-parse-better-errors/-/json-parse-better-errors-1.0.2.tgz#bb867cfb3450e69107c131d1c514bab3dc8bcaa9" integrity sha512-mrqyZKfX5EhL7hvqcV6WG1yYjnjeuYDzDhhcAAUrq8Po85NBQBJP+ZDUT75qZQ98IkUoBqdkExkukOU7Ts2wrw== +json-parse-even-better-errors@^2.3.0: + version "2.3.1" + resolved "https://registry.yarnpkg.com/json-parse-even-better-errors/-/json-parse-even-better-errors-2.3.1.tgz#7c47805a94319928e05777405dc12e1f7a4ee02d" + integrity sha512-xyFwyhro/JEof6Ghe2iz2NcXoj2sloNsWr/XsERDK/oiPCfaNhl5ONfp+jQdAZRQQ0IJWNzH9zIZF7li91kh2w== + json-schema-traverse@^0.4.1: version "0.4.1" resolved "https://registry.yarnpkg.com/json-schema-traverse/-/json-schema-traverse-0.4.1.tgz#69f6a87d9513ab8bb8fe63bdb0979c448e684660" @@ -3864,12 +4465,20 @@ levn@^0.4.1: prelude-ls "^1.2.1" type-check "~0.4.0" +levn@~0.3.0: + version "0.3.0" + resolved "https://registry.yarnpkg.com/levn/-/levn-0.3.0.tgz#3b09924edf9f083c0490fdd4c0bc4421e04764ee" + integrity sha1-OwmSTt+fCDwEkP3UwLxEIeBHZO4= + dependencies: + prelude-ls "~1.1.2" + type-check "~0.3.2" + loader-runner@^4.2.0: version "4.2.0" resolved "https://registry.yarnpkg.com/loader-runner/-/loader-runner-4.2.0.tgz#d7022380d66d14c5fb1d496b89864ebcfd478384" integrity sha512-92+huvxMvYlMzMt0iIOukcwYBFpkYJdpl2xsZ7LrlayO7E8SOv+JJUEK17B/dJIHAOLMfh2dZZ/Y18WgmGtYNw== -loader-utils@^1.4.0: +loader-utils@^1.1.0, loader-utils@^1.4.0: version "1.4.0" resolved "https://registry.yarnpkg.com/loader-utils/-/loader-utils-1.4.0.tgz#c579b5e34cb34b1a74edc6c1fb36bfa371d5a613" integrity sha512-qH0WSMBtn/oHuwjy/NucEgbx5dbxxnxup9s4PVXJUDHZBQY+s0NWA9rJf53RBnQZxfch7euUui7hpoAPvALZdA== @@ -3892,6 +4501,11 @@ locate-path@^6.0.0: dependencies: p-locate "^5.0.0" +lodash.clonedeep@^4.5.0: + version "4.5.0" + resolved "https://registry.yarnpkg.com/lodash.clonedeep/-/lodash.clonedeep-4.5.0.tgz#e23f3f9c4f8fbdde872529c1071857a086e5ccef" + integrity sha1-4j8/nE+Pvd6HJSnBBxhXoIblzO8= + lodash.flattendeep@^4.4.0: version "4.4.0" resolved "https://registry.yarnpkg.com/lodash.flattendeep/-/lodash.flattendeep-4.4.0.tgz#fb030917f86a3134e5bc9bec0d69e0013ddfedb2" @@ -3902,7 +4516,12 @@ lodash.memoize@~3.0.3: resolved "https://registry.yarnpkg.com/lodash.memoize/-/lodash.memoize-3.0.4.tgz#2dcbd2c287cbc0a55cc42328bd0c736150d53e3f" integrity sha1-LcvSwofLwKVcxCMovQxzYVDVPj8= -lodash@^4.17.14, lodash@^4.17.19, lodash@^4.17.20: +lodash.truncate@^4.4.2: + version "4.4.2" + resolved "https://registry.yarnpkg.com/lodash.truncate/-/lodash.truncate-4.4.2.tgz#5a350da0b1113b837ecfffd5812cbe58d6eae193" + integrity sha1-WjUNoLERO4N+z//VgSy+WNbq4ZM= + +lodash@^4.17.14, lodash@^4.17.19, lodash@^4.17.20, lodash@^4.17.5, lodash@^4.7.0: version "4.17.21" resolved "https://registry.yarnpkg.com/lodash/-/lodash-4.17.21.tgz#679591c564c3bffaae8454cf0b3df370c3d6911c" integrity sha512-v2kDEe57lecTulaDIuNTPy3Ry4gLGJ6Z1O3vE1krgXZNrsQ+LFTGHVxVjcXPs17LhbZVGedAJv8XZ1tvj5FvSg== @@ -3919,6 +4538,13 @@ log-symbols@4.0.0: dependencies: chalk "^4.0.0" +log-symbols@^2.1.0: + version "2.2.0" + resolved "https://registry.yarnpkg.com/log-symbols/-/log-symbols-2.2.0.tgz#5740e1c5d6f0dfda4ad9323b5332107ef6b4c40a" + integrity sha512-VeIAFslyIerEJLXHziedo2basKbMKtTw3vfn5IzG0XTjhAVEJyNHnL2p7vc+wBDSdQuUpNw3M2u6xb9QsAY5Eg== + dependencies: + chalk "^2.0.1" + log4js@^6.2.1: version "6.3.0" resolved "https://registry.yarnpkg.com/log4js/-/log4js-6.3.0.tgz#10dfafbb434351a3e30277a00b9879446f715bcb" @@ -3930,6 +4556,19 @@ log4js@^6.2.1: rfdc "^1.1.4" streamroller "^2.2.4" +loglevelnext@^1.0.1: + version "1.0.5" + resolved "https://registry.yarnpkg.com/loglevelnext/-/loglevelnext-1.0.5.tgz#36fc4f5996d6640f539ff203ba819641680d75a2" + integrity sha512-V/73qkPuJmx4BcBF19xPBr+0ZRVBhc4POxvZTZdMeXpJ4NItXSJ/MSwuFT0kQJlCbXvdlZoQQ/418bS1y9Jh6A== + dependencies: + es6-symbol "^3.1.1" + object.assign "^4.1.0" + +long@4.0.0: + version "4.0.0" + resolved "https://registry.yarnpkg.com/long/-/long-4.0.0.tgz#9a7b71cfb7d361a194ea555241c92f7468d5bf28" + integrity sha512-XsP+KhQif4bjX1kbuSiySJFNAehNxgLb6hPRGJ9QsUr8ajHkuXGdrHmFUTUUXhDwVX2R5bY4JNZEwbUiMhV+MA== + lru-cache@^6.0.0: version "6.0.0" resolved "https://registry.yarnpkg.com/lru-cache/-/lru-cache-6.0.0.tgz#6d6fe6570ebd96aaf90fcad1dafa3b2566db3a94" @@ -3957,6 +4596,13 @@ map-cache@^0.2.2: resolved "https://registry.yarnpkg.com/map-cache/-/map-cache-0.2.2.tgz#c32abd0bd6525d9b051645bb4f26ac5dc98a0dbf" integrity sha1-wyq9C9ZSXZsFFkW7TyasXcmKDb8= +map-limit@0.0.1: + version "0.0.1" + resolved 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complex.js "^2.0.15" + decimal.js "^10.3.1" escape-latex "^1.2.0" - fraction.js "^4.0.12" + fraction.js "^4.1.1" javascript-natural-sort "^0.7.1" - seed-random "^2.2.0" + seedrandom "^3.0.5" tiny-emitter "^2.1.0" typed-function "^2.0.0" @@ -3992,12 +4639,25 @@ media-typer@0.3.0: resolved "https://registry.yarnpkg.com/media-typer/-/media-typer-0.3.0.tgz#8710d7af0aa626f8fffa1ce00168545263255748" integrity sha1-hxDXrwqmJvj/+hzgAWhUUmMlV0g= +memory-fs@^0.5.0: + version "0.5.0" + resolved "https://registry.yarnpkg.com/memory-fs/-/memory-fs-0.5.0.tgz#324c01288b88652966d161db77838720845a8e3c" + integrity sha512-jA0rdU5KoQMC0e6ppoNRtpp6vjFq6+NY7r8hywnC7V+1Xj/MtHwGIbB1QaK/dunyjWteJzmkpd7ooeWg10T7GA== + dependencies: + errno "^0.1.3" + readable-stream "^2.0.1" + merge-stream@^2.0.0: version "2.0.0" resolved "https://registry.yarnpkg.com/merge-stream/-/merge-stream-2.0.0.tgz#52823629a14dd00c9770fb6ad47dc6310f2c1f60" integrity sha512-abv/qOcuPfk3URPfDzmZU1LKmuw8kT+0nIHvKrKgFrwifol/doWcdA4ZqsWQ8ENrFKkd67Mfpo/LovbIUsbt3w== -micromatch@^3.1.10, micromatch@^3.1.4: +merge2@^1.3.0: + version "1.4.1" + resolved "https://registry.yarnpkg.com/merge2/-/merge2-1.4.1.tgz#4368892f885e907455a6fd7dc55c0c9d404990ae" + integrity sha512-8q7VEgMJW4J8tcfVPy8g09NcQwZdbwFEqhe/WZkoIzjn/3TGDwtOCYtXGxA3O8tPzpczCCDgv+P2P5y00ZJOOg== + +micromatch@^3.1.10, micromatch@^3.1.4, micromatch@^3.1.9: version "3.1.10" resolved "https://registry.yarnpkg.com/micromatch/-/micromatch-3.1.10.tgz#70859bc95c9840952f359a068a3fc49f9ecfac23" integrity sha512-MWikgl9n9M3w+bpsY3He8L+w9eF9338xRl8IAO5viDizwSzziFEyUzo2xrrloB64ADbTf8uA8vRqqttDTOmccg== @@ -4016,6 +4676,14 @@ micromatch@^3.1.10, micromatch@^3.1.4: snapdragon "^0.8.1" to-regex "^3.0.2" +micromatch@^4.0.0, micromatch@^4.0.4: + version "4.0.4" + resolved "https://registry.yarnpkg.com/micromatch/-/micromatch-4.0.4.tgz#896d519dfe9db25fce94ceb7a500919bf881ebf9" + integrity sha512-pRmzw/XUcwXGpD9aI9q/0XOwLNygjETJ8y0ao0wdqprrzDa4YnxLcz7fQRZr8voh8V10kGhABbNcHVk5wHgWwg== + dependencies: + braces "^3.0.1" + picomatch "^2.2.3" + miller-rabin@^4.0.0: version "4.0.1" resolved "https://registry.yarnpkg.com/miller-rabin/-/miller-rabin-4.0.1.tgz#f080351c865b0dc562a8462966daa53543c78a4d" @@ -4076,11 +4744,25 @@ mixin-deep@^1.2.0: for-in "^1.0.2" is-extendable "^1.0.1" +mixly@^1.0.0: + version "1.0.0" + resolved "https://registry.yarnpkg.com/mixly/-/mixly-1.0.0.tgz#9b5a2e1f63e6dfba0d30e6797ffae62ab1dc24ef" + integrity sha1-m1ouH2Pm37oNMOZ5f/rmKrHcJO8= + dependencies: + fulcon "^1.0.1" + mkdirp-classic@^0.5.2: version "0.5.3" resolved "https://registry.yarnpkg.com/mkdirp-classic/-/mkdirp-classic-0.5.3.tgz#fa10c9115cc6d8865be221ba47ee9bed78601113" integrity sha512-gKLcREMhtuZRwRAfqP3RFW+TK4JqApVBtOIftVgjuABpAtpxhPGaDcfvbhNvD0B8iD1oUr/txX35NjcaY6Ns/A== +mkdirp@^0.5.1: + version "0.5.5" + resolved "https://registry.yarnpkg.com/mkdirp/-/mkdirp-0.5.5.tgz#d91cefd62d1436ca0f41620e251288d420099def" + integrity sha512-NKmAlESf6jMGym1++R0Ra7wvhV+wFW63FaSOFPwRahvea0gMUcGUhVeAg/0BC0wiv9ih5NYPB1Wn1UEI1/L+xQ== + dependencies: + minimist "^1.2.5" + mocha@^8.3.0: version "8.3.1" resolved "https://registry.yarnpkg.com/mocha/-/mocha-8.3.1.tgz#b9eda6da1eb8cb8d29860a9c2205de5b8a076560" @@ -4148,6 +4830,13 @@ ms@2.1.3, ms@^2.1.1: resolved "https://registry.yarnpkg.com/ms/-/ms-2.1.3.tgz#574c8138ce1d2b5861f0b44579dbadd60c6615b2" integrity sha512-6FlzubTLZG3J2a/NVCAleEhjzq5oxgHyaCU9yYXvcLsvoVaHJq/s5xXI6/XXP6tz7R9xAOtHnSO/tXtF3WRTlA== +multipipe@^0.3.0: + version "0.3.1" + resolved "https://registry.yarnpkg.com/multipipe/-/multipipe-0.3.1.tgz#92625525761ba04feaa09605b6382bce2c91f11f" + integrity sha1-kmJVJXYboE/qoJYFtjgrziyR8R8= + dependencies: + duplexer2 "^0.1.2" + nanoid@3.1.20: version "3.1.20" resolved "https://registry.yarnpkg.com/nanoid/-/nanoid-3.1.20.tgz#badc263c6b1dcf14b71efaa85f6ab4c1d6cfc788" @@ -4185,6 +4874,11 @@ neo-async@^2.6.2: resolved "https://registry.yarnpkg.com/neo-async/-/neo-async-2.6.2.tgz#b4aafb93e3aeb2d8174ca53cf163ab7d7308305f" integrity sha512-Yd3UES5mWCSqR+qNT93S3UoYUkqAZ9lLg8a7g9rimsWmYGK8cVToA4/sF3RrshdyV3sAGMXVUmpMYOw+dLpOuw== +next-tick@~1.0.0: + version "1.0.0" + resolved "https://registry.yarnpkg.com/next-tick/-/next-tick-1.0.0.tgz#ca86d1fe8828169b0120208e3dc8424b9db8342c" + integrity sha1-yobR/ogoFpsBICCOPchCS524NCw= + node-fetch@~2.6.1: version "2.6.1" resolved "https://registry.yarnpkg.com/node-fetch/-/node-fetch-2.6.1.tgz#045bd323631f76ed2e2b55573394416b639a0052" @@ -4202,6 +4896,16 @@ node-releases@^1.1.71: resolved "https://registry.yarnpkg.com/node-releases/-/node-releases-1.1.72.tgz#14802ab6b1039a79a0c7d662b610a5bbd76eacbe" integrity sha512-LLUo+PpH3dU6XizX3iVoubUNheF/owjXCZZ5yACDxNnPtgFuludV1ZL3ayK1kVep42Rmm0+R9/Y60NQbZ2bifw== +normalize-package-data@^2.0.0: + version "2.5.0" + resolved "https://registry.yarnpkg.com/normalize-package-data/-/normalize-package-data-2.5.0.tgz#e66db1838b200c1dfc233225d12cb36520e234a8" + integrity sha512-/5CMN3T0R4XTj4DcGaexo+roZSdSFW/0AOOTROrjxzCG1wrWXEsGbRKevjlIL+ZDE4sZlJr5ED4YW0yqmkK+eA== + dependencies: + hosted-git-info "^2.1.4" + resolve "^1.10.0" + semver "2 || 3 || 4 || 5" + validate-npm-package-license "^3.0.1" + normalize-path@^2.1.1: version "2.1.1" resolved "https://registry.yarnpkg.com/normalize-path/-/normalize-path-2.1.1.tgz#1ab28b556e198363a8c1a6f7e6fa20137fe6aed9" @@ -4214,6 +4918,11 @@ normalize-path@^3.0.0, normalize-path@~3.0.0: resolved "https://registry.yarnpkg.com/normalize-path/-/normalize-path-3.0.0.tgz#0dcd69ff23a1c9b11fd0978316644a0388216a65" integrity sha512-6eZs5Ls3WtCisHWp9S2GUy8dqkpGi4BVSz3GaqiE6ezub0512ESztXUwUB6C6IKbQkY2Pnb/mD4WYojCRwcwLA== +npm-normalize-package-bin@^1.0.0: + version "1.0.1" + resolved "https://registry.yarnpkg.com/npm-normalize-package-bin/-/npm-normalize-package-bin-1.0.1.tgz#6e79a41f23fd235c0623218228da7d9c23b8f6e2" + integrity sha512-EPfafl6JL5/rU+ot6P3gRSCpPDW5VmIzX959Ob1+ySFUuuYHWHekXpwdUZcKP5C+DS4GEtdJluwBjnsNDl+fSA== + npm-run-path@^4.0.1: version "4.0.1" resolved "https://registry.yarnpkg.com/npm-run-path/-/npm-run-path-4.0.1.tgz#b7ecd1e5ed53da8e37a55e1c2269e0b97ed748ea" @@ -4221,6 +4930,11 @@ npm-run-path@^4.0.1: dependencies: path-key "^3.0.0" +nwsapi@^2.2.0: + version "2.2.0" + resolved "https://registry.yarnpkg.com/nwsapi/-/nwsapi-2.2.0.tgz#204879a9e3d068ff2a55139c2c772780681a38b7" + integrity sha512-h2AatdwYH+JHiZpv7pt/gSX1XoRGb7L/qSIeuqA6GwYoF9w1vP1cw42TO0aI2pNyshRK5893hNSl+1//vHK7hQ== + nyc@^15.1.0: version "15.1.0" resolved "https://registry.yarnpkg.com/nyc/-/nyc-15.1.0.tgz#1335dae12ddc87b6e249d5a1994ca4bdaea75f02" @@ -4316,6 +5030,13 @@ once@^1.3.0: dependencies: wrappy "1" +once@~1.3.0: + version "1.3.3" + resolved "https://registry.yarnpkg.com/once/-/once-1.3.3.tgz#b2e261557ce4c314ec8304f3fa82663e4297ca20" + integrity sha1-suJhVXzkwxTsgwTz+oJmPkKXyiA= + dependencies: + wrappy "1" + onetime@^5.1.2: version "5.1.2" resolved "https://registry.yarnpkg.com/onetime/-/onetime-5.1.2.tgz#d0e96ebb56b07476df1dd9c4806e5237985ca45e" @@ -4323,6 +5044,18 @@ onetime@^5.1.2: dependencies: mimic-fn "^2.1.0" +optionator@^0.8.1: + version "0.8.3" + resolved "https://registry.yarnpkg.com/optionator/-/optionator-0.8.3.tgz#84fa1d036fe9d3c7e21d99884b601167ec8fb495" + integrity sha512-+IW9pACdk3XWmmTXG8m3upGUJst5XRGzxMRjXzAuJ1XnIFNvfhjjIuYkDvysnPQ7qzqVzLt78BCruntqRhWQbA== + dependencies: + deep-is "~0.1.3" + fast-levenshtein "~2.0.6" + levn "~0.3.0" + prelude-ls "~1.1.2" + type-check "~0.3.2" + word-wrap "~1.2.3" + optionator@^0.9.1: version "0.9.1" resolved "https://registry.yarnpkg.com/optionator/-/optionator-0.9.1.tgz#4f236a6373dae0566a6d43e1326674f50c291499" @@ -4400,6 +5133,11 @@ pako@~1.0.5: resolved "https://registry.yarnpkg.com/pako/-/pako-1.0.11.tgz#6c9599d340d54dfd3946380252a35705a6b992bf" integrity sha512-4hLB8Py4zZce5s4yd9XzopqwVv/yGNhV1Bl8NTmCq1763HeK2+EwVTv+leGeL13Dnh2wfbqowVPXCIO0z4taYw== +papaparse@^5.3.1: + version "5.3.1" + resolved "https://registry.yarnpkg.com/papaparse/-/papaparse-5.3.1.tgz#770b7a9124d821d4b2132132b7bd7dce7194b5b1" + integrity sha512-Dbt2yjLJrCwH2sRqKFFJaN5XgIASO9YOFeFP8rIBRG2Ain8mqk5r1M6DkfvqEVozVcz3r3HaUGw253hA1nLIcA== + parent-module@^1.0.0: version "1.0.1" resolved "https://registry.yarnpkg.com/parent-module/-/parent-module-1.0.1.tgz#691d2709e78c79fae3a156622452d00762caaaa2" @@ -4425,6 +5163,11 @@ parse-asn1@^5.0.0, parse-asn1@^5.1.5: pbkdf2 "^3.0.3" safe-buffer "^5.1.1" +parse5@6.0.1: + version "6.0.1" + resolved "https://registry.yarnpkg.com/parse5/-/parse5-6.0.1.tgz#e1a1c085c569b3dc08321184f19a39cc27f7c30b" + integrity sha512-Ofn/CTFzRGTTxwpNEs9PP93gXShHcTq255nzRYSKe8AkVpZY7e1fpmTfOyoIvjP5HG7Z2ZM7VS9PPhQGW2pOpw== + parseqs@0.0.6: version "0.0.6" resolved "https://registry.yarnpkg.com/parseqs/-/parseqs-0.0.6.tgz#8e4bb5a19d1cdc844a08ac974d34e273afa670d5" @@ -4480,6 +5223,11 @@ path-platform@~0.11.15: resolved "https://registry.yarnpkg.com/path-platform/-/path-platform-0.11.15.tgz#e864217f74c36850f0852b78dc7bf7d4a5721bf2" integrity sha1-6GQhf3TDaFDwhSt43Hv31KVyG/I= +path-type@^4.0.0: + version "4.0.0" + resolved "https://registry.yarnpkg.com/path-type/-/path-type-4.0.0.tgz#84ed01c0a7ba380afe09d90a8c180dcd9d03043b" + integrity sha512-gDKb8aZMDeD/tZWs9P6+q0J9Mwkdl6xMV8TjnGP3qJVJ06bdMgkbBlLU8IdfOsIsFz2BW1rNVT3XuNEl8zPAvw== + pathval@^1.1.0: version "1.1.0" resolved "https://registry.yarnpkg.com/pathval/-/pathval-1.1.0.tgz#b942e6d4bde653005ef6b71361def8727d0645e0" @@ -4506,6 +5254,11 @@ picomatch@^2.0.4, picomatch@^2.2.1: resolved "https://registry.yarnpkg.com/picomatch/-/picomatch-2.2.2.tgz#21f333e9b6b8eaff02468f5146ea406d345f4dad" integrity sha512-q0M/9eZHzmr0AulXyPwNfZjtwZ/RBZlbN3K3CErVrk50T2ASYI7Bye0EvekFY3IP1Nt2DHu0re+V2ZHIpMkuWg== +picomatch@^2.2.3: + version "2.3.0" + resolved "https://registry.yarnpkg.com/picomatch/-/picomatch-2.3.0.tgz#f1f061de8f6a4bf022892e2d128234fb98302972" + integrity sha512-lY1Q/PiJGC2zOv/z391WOTD+Z02bCgsFfvxoXXf6h7kv9o+WmsmzYqrAwY63sNgOxE4xEdq0WyUnXfKeBrSvYw== + pify@^4.0.1: version "4.0.1" resolved "https://registry.yarnpkg.com/pify/-/pify-4.0.1.tgz#4b2cd25c50d598735c50292224fd8c6df41e3231" @@ -4518,6 +5271,11 @@ pkg-dir@^4.1.0, pkg-dir@^4.2.0: dependencies: find-up "^4.0.0" +plotly.js-dist-min@^2.8.0: + version "2.8.3" + resolved "https://registry.yarnpkg.com/plotly.js-dist-min/-/plotly.js-dist-min-2.8.3.tgz#0ffa317de10dd48f0c32d15c83c41f60230f50b3" + integrity sha512-TjovBrqIbedUfjGWNMMs++az3lF4hsIAB8EjqM/mC0cPrO7Wg86lBkHAsRbMra5aWyXA+M52yiQUUKBfThqwzg== + posix-character-classes@^0.1.0: version "0.1.1" resolved "https://registry.yarnpkg.com/posix-character-classes/-/posix-character-classes-0.1.1.tgz#01eac0fe3b5af71a2a6c02feabb8c1fef7e00eab" @@ -4528,11 +5286,26 @@ prelude-ls@^1.2.1: resolved "https://registry.yarnpkg.com/prelude-ls/-/prelude-ls-1.2.1.tgz#debc6489d7a6e6b0e7611888cec880337d316396" integrity sha512-vkcDPrRZo1QZLbn5RLGPpg/WmIQ65qoWWhcGKf/b5eplkkarX0m9z8ppCat4mlOqUsWpyNuYgO3VRyrYHSzX5g== -printj@~1.1.0, printj@~1.1.2: +prelude-ls@~1.1.2: + version "1.1.2" + resolved "https://registry.yarnpkg.com/prelude-ls/-/prelude-ls-1.1.2.tgz#21932a549f5e52ffd9a827f570e04be62a97da54" + integrity sha1-IZMqVJ9eUv/ZqCf1cOBL5iqX2lQ= + +printj@~1.1.0: version "1.1.2" resolved "https://registry.yarnpkg.com/printj/-/printj-1.1.2.tgz#d90deb2975a8b9f600fb3a1c94e3f4c53c78a222" integrity sha512-zA2SmoLaxZyArQTOPj5LXecR+RagfPSU5Kw1qP+jkWeNlrq+eJZyY2oS68SU1Z/7/myXM4lo9716laOFAVStCQ== +printj@~1.2.2: + version "1.2.3" + resolved "https://registry.yarnpkg.com/printj/-/printj-1.2.3.tgz#2cfb2b192a1e5385dbbe5b46658ac34aa828508a" + integrity sha512-sanczS6xOJOg7IKDvi4sGOUOe7c1tsEzjwlLFH/zgwx/uyImVM9/rgBkc8AfiQa/Vg54nRd8mkm9yI7WV/O+WA== + +printj@~1.3.0: + version "1.3.0" + resolved "https://registry.yarnpkg.com/printj/-/printj-1.3.0.tgz#9018a918a790e43707f10625d6e10187a367cff6" + integrity sha512-017o8YIaz8gLhaNxRB9eBv2mWXI2CtzhPJALnQTP+OPpuUfP0RMWqr/mHCzqVeu1AQxfzSfAtAq66vKB8y7Lzg== + process-nextick-args@~2.0.0: version "2.0.1" resolved "https://registry.yarnpkg.com/process-nextick-args/-/process-nextick-args-2.0.1.tgz#7820d9b16120cc55ca9ae7792680ae7dba6d7fe2" @@ -4555,7 +5328,12 @@ progress@^2.0.0: resolved "https://registry.yarnpkg.com/progress/-/progress-2.0.3.tgz#7e8cf8d8f5b8f239c1bc68beb4eb78567d572ef8" integrity sha512-7PiHtLll5LdnKIMw100I+8xJXR5gW2QwWYkT6iJva0bXitZKa/XMrSbdmg3r2Xnaidz9Qumd0VPaMrZlF9V9sA== -psl@^1.1.28: +prr@~1.0.1: + version "1.0.1" + resolved "https://registry.yarnpkg.com/prr/-/prr-1.0.1.tgz#d3fc114ba06995a45ec6893f484ceb1d78f5f476" + integrity sha1-0/wRS6BplaRexok/SEzrHXj19HY= + +psl@^1.1.28, psl@^1.1.33: version "1.8.0" resolved "https://registry.yarnpkg.com/psl/-/psl-1.8.0.tgz#9326f8bcfb013adcc005fdff056acce020e51c24" integrity sha512-RIdOzyoavK+hA18OGGWDqUTsCLhtA7IcZ/6NCs4fFJaHBDab+pDDmDIByWFRQJq2Cd7r1OoQxBGKOaztq+hjIQ== @@ -4612,6 +5390,11 @@ querystring@0.2.0: resolved "https://registry.yarnpkg.com/querystring/-/querystring-0.2.0.tgz#b209849203bb25df820da756e747005878521620" integrity sha1-sgmEkgO7Jd+CDadW50cAWHhSFiA= +queue-microtask@^1.2.2: + version "1.2.3" + resolved "https://registry.yarnpkg.com/queue-microtask/-/queue-microtask-1.2.3.tgz#4929228bbc724dfac43e0efb058caf7b6cfb6243" + integrity sha512-NuaNSa6flKT5JaSYQzJok04JzTL1CA6aGhv5rfLW3PgqA+M2ChpZQnAC8h8i4ZFkBS8X5RqkDBHA7r4hej3K9A== + randombytes@^2.0.0, randombytes@^2.0.1, randombytes@^2.0.5, randombytes@^2.1.0: version "2.1.0" resolved "https://registry.yarnpkg.com/randombytes/-/randombytes-2.1.0.tgz#df6f84372f0270dc65cdf6291349ab7a473d4f2a" @@ -4649,7 +5432,17 @@ read-only-stream@^2.0.0: dependencies: readable-stream "^2.0.2" -readable-stream@^2.0.2, readable-stream@^2.2.2, readable-stream@~2.3.6: +read-package-json@^2.0.2: + version "2.1.2" + resolved "https://registry.yarnpkg.com/read-package-json/-/read-package-json-2.1.2.tgz#6992b2b66c7177259feb8eaac73c3acd28b9222a" + integrity sha512-D1KmuLQr6ZSJS0tW8hf3WGpRlwszJOXZ3E8Yd/DNRaM5d+1wVRZdHlpGBLAuovjr28LbWvjpWkBHMxpRGGjzNA== + dependencies: + glob "^7.1.1" + json-parse-even-better-errors "^2.3.0" + normalize-package-data "^2.0.0" + npm-normalize-package-bin "^1.0.0" + +readable-stream@^2.0.0, readable-stream@^2.0.1, readable-stream@^2.0.2, readable-stream@^2.2.2, readable-stream@^2.3.5, readable-stream@~2.3.6: version "2.3.7" resolved "https://registry.yarnpkg.com/readable-stream/-/readable-stream-2.3.7.tgz#1eca1cf711aef814c04f62252a36a62f6cb23b57" integrity sha512-Ebho8K4jIbHAxnuxi7o42OrZgF/ZTNcsZj6nRKyUmkhLFq8CHItp/fy6hQZuZmP/n3yZ9VBUbp4zz/mX8hmYPw== @@ -4853,7 +5646,7 @@ resolve@^1.1.4, resolve@^1.17.0, resolve@^1.4.0: is-core-module "^2.1.0" path-parse "^1.0.6" -resolve@^1.9.0: +resolve@^1.1.6, resolve@^1.10.0, resolve@^1.9.0: version "1.20.0" resolved "https://registry.yarnpkg.com/resolve/-/resolve-1.20.0.tgz#629a013fb3f70755d6f0b7935cc1c2c5378b1975" integrity sha512-wENBPt4ySzg4ybFQW2TT1zMQucPK95HSh/nq2CFTZVOGut2+pQvSsgtda4d26YrYcr067wjbmzOG8byDPBX63A== @@ -4866,6 +5659,11 @@ ret@~0.1.10: resolved "https://registry.yarnpkg.com/ret/-/ret-0.1.15.tgz#b8a4825d5bdb1fc3f6f53c2bc33f81388681c7bc" integrity sha512-TTlYpa+OL+vMMNG24xSlQGEJ3B/RzEfUlLct7b5G/ytav+wPrplCpVMFuwzXbkecJrb6IYo1iFb0S9v37754mg== +reusify@^1.0.4: + version "1.0.4" + resolved "https://registry.yarnpkg.com/reusify/-/reusify-1.0.4.tgz#90da382b1e126efc02146e90845a88db12925d76" + integrity sha512-U9nH88a3fc/ekCF1l0/UP1IosiuIjyTh7hBvXVMHYgVcfGvt897Xguj2UOLDeI5BG2m7/uwyaLVT6fbtCwTyzw== + rfdc@^1.1.4: version "1.2.0" resolved "https://registry.yarnpkg.com/rfdc/-/rfdc-1.2.0.tgz#9e9894258f48f284b43c3143c68070a4f373b949" @@ -4886,6 +5684,13 @@ ripemd160@^2.0.0, ripemd160@^2.0.1: hash-base "^3.0.0" inherits "^2.0.1" +run-parallel@^1.1.9: + version "1.2.0" + resolved "https://registry.yarnpkg.com/run-parallel/-/run-parallel-1.2.0.tgz#66d1368da7bdf921eb9d95bd1a9229e7f21a43ee" + integrity sha512-5l4VyZR86LZ/lDxZTR6jqL8AFE2S0IFLMP26AbjsLVADxHdhB/c0GUsH+y39UfCi3dzz8OlQuPmnaJOMoDHQBA== + dependencies: + queue-microtask "^1.2.2" + safe-buffer@^5.0.1, safe-buffer@^5.1.0, safe-buffer@^5.1.1, safe-buffer@^5.1.2, safe-buffer@^5.2.0, safe-buffer@~5.2.0: version "5.2.1" resolved "https://registry.yarnpkg.com/safe-buffer/-/safe-buffer-5.2.1.tgz#1eaf9fa9bdb1fdd4ec75f58f9cdb4e6b7827eec6" @@ -4903,11 +5708,18 @@ safe-regex@^1.1.0: dependencies: ret "~0.1.10" -"safer-buffer@>= 2.1.2 < 3", safer-buffer@^2.0.2, safer-buffer@^2.1.0, safer-buffer@~2.1.0: +"safer-buffer@>= 2.1.2 < 3", "safer-buffer@>= 2.1.2 < 3.0.0", safer-buffer@^2.0.2, safer-buffer@^2.1.0, safer-buffer@~2.1.0: version "2.1.2" resolved "https://registry.yarnpkg.com/safer-buffer/-/safer-buffer-2.1.2.tgz#44fa161b0187b9549dd84bb91802f9bd8385cd6a" integrity sha512-YZo3K82SD7Riyi0E1EQPojLz7kpepnSQI9IyPbHHg1XXXevb5dJI7tpyN2ADxGcQbHG7vcyRHk0cbwqcQriUtg== +saxes@^5.0.1: + version "5.0.1" + resolved "https://registry.yarnpkg.com/saxes/-/saxes-5.0.1.tgz#eebab953fa3b7608dbe94e5dadb15c888fa6696d" + integrity sha512-5LBh1Tls8c9xgGjw3QrMwETmTMVk0oFgvrFSvWx62llR2hcEInrKNZ2GZCCuuy2lvWrdl5jhbpeqc5hRYKFOcw== + dependencies: + xmlchars "^2.2.0" + schema-utils@^2.6.5: version "2.7.1" resolved "https://registry.yarnpkg.com/schema-utils/-/schema-utils-2.7.1.tgz#1ca4f32d1b24c590c203b8e7a50bf0ea4cd394d7" @@ -4926,26 +5738,31 @@ schema-utils@^3.0.0: ajv "^6.12.5" ajv-keywords "^3.5.2" -seed-random@^2.2.0: - version "2.2.0" - resolved "https://registry.yarnpkg.com/seed-random/-/seed-random-2.2.0.tgz#2a9b19e250a817099231a5b99a4daf80b7fbed54" - integrity sha1-KpsZ4lCoFwmSMaW5mk2vgLf77VQ= - seedrandom@2.4.3: version "2.4.3" resolved "https://registry.yarnpkg.com/seedrandom/-/seedrandom-2.4.3.tgz#2438504dad33917314bff18ac4d794f16d6aaecc" integrity sha1-JDhQTa0zkXMUv/GKxNeU8W1qrsw= -semver@7.0.0: - version "7.0.0" - resolved "https://registry.yarnpkg.com/semver/-/semver-7.0.0.tgz#5f3ca35761e47e05b206c6daff2cf814f0316b8e" - integrity sha512-+GB6zVA9LWh6zovYQLALHwv5rb2PHGlJi3lfiqIHxR0uuwCgefcOJc59v9fv1w8GbStwxuuqqAjI9NMAOOgq1A== +seedrandom@^2.4.3: + version "2.4.4" + resolved "https://registry.yarnpkg.com/seedrandom/-/seedrandom-2.4.4.tgz#b25ea98632c73e45f58b77cfaa931678df01f9ba" + integrity sha512-9A+PDmgm+2du77B5i0Ip2cxOqqHjgNxnBgglxLcX78A2D6c2rTo61z4jnVABpF4cKeDMDG+cmXXvdnqse2VqMA== + +seedrandom@^3.0.5: + version "3.0.5" + resolved "https://registry.yarnpkg.com/seedrandom/-/seedrandom-3.0.5.tgz#54edc85c95222525b0c7a6f6b3543d8e0b3aa0a7" + integrity sha512-8OwmbklUNzwezjGInmZ+2clQmExQPvomqjL7LFqOYqtmuxRgQYqOD3mHaU+MvZn5FLUeVxVfQjwLZW/n/JFuqg== -semver@^5.4.1, semver@^5.5.0, semver@^5.5.1, semver@^5.6.0: +"semver@2 || 3 || 4 || 5", semver@^5.4.1, semver@^5.5.0, semver@^5.5.1, semver@^5.6.0: version "5.7.1" resolved "https://registry.yarnpkg.com/semver/-/semver-5.7.1.tgz#a954f931aeba508d307bbf069eff0c01c96116f7" integrity sha512-sauaDf/PZdVgrLTNYHRtpXa1iRiKcaebiKQ1BJdpQlWH2lCvexQdX55snPFyK7QzpudqbCI0qXFfOasHdyNDGQ== +semver@7.0.0: + version "7.0.0" + resolved "https://registry.yarnpkg.com/semver/-/semver-7.0.0.tgz#5f3ca35761e47e05b206c6daff2cf814f0316b8e" + integrity sha512-+GB6zVA9LWh6zovYQLALHwv5rb2PHGlJi3lfiqIHxR0uuwCgefcOJc59v9fv1w8GbStwxuuqqAjI9NMAOOgq1A== + semver@^6.0.0, semver@^6.3.0: version "6.3.0" resolved "https://registry.yarnpkg.com/semver/-/semver-6.3.0.tgz#ee0a64c8af5e8ceea67687b133761e1becbd1d3d" @@ -4958,6 +5775,13 @@ semver@^7.2.1: dependencies: lru-cache "^6.0.0" +semver@^7.3.4, semver@^7.3.5: + version "7.3.5" + resolved "https://registry.yarnpkg.com/semver/-/semver-7.3.5.tgz#0b621c879348d8998e4b0e4be94b3f12e6018ef7" + integrity sha512-PoeGJYh8HK4BTO/a9Tf6ZG3veo/A7ZVsYrSA6J8ny9nb3B1VrpkuN+z9OE5wfE5p6H4LchYZsegiQgbJD94ZFQ== + dependencies: + lru-cache "^6.0.0" + serialize-javascript@5.0.1, serialize-javascript@^5.0.1: version "5.0.1" resolved "https://registry.yarnpkg.com/serialize-javascript/-/serialize-javascript-5.0.1.tgz#7886ec848049a462467a97d3d918ebb2aaf934f4" @@ -5047,14 +5871,10 @@ slash@^2.0.0: resolved "https://registry.yarnpkg.com/slash/-/slash-2.0.0.tgz#de552851a1759df3a8f206535442f5ec4ddeab44" integrity sha512-ZYKh3Wh2z1PpEXWr0MpSBZ0V6mZHAQfYevttO11c51CaWjGTaadiKZ+wVt1PbMlDV5qhMFslpZCemhwOK7C89A== -slice-ansi@^2.1.0: - version "2.1.0" - resolved "https://registry.yarnpkg.com/slice-ansi/-/slice-ansi-2.1.0.tgz#cacd7693461a637a5788d92a7dd4fba068e81636" - integrity sha512-Qu+VC3EwYLldKa1fCxuuvULvSJOKEgk9pi8dZeCVK7TqBfUNTH4sFkk4joj8afVSfAYgJoSOetjx9QWOJ5mYoQ== - dependencies: - ansi-styles "^3.2.0" - astral-regex "^1.0.0" - is-fullwidth-code-point "^2.0.0" +slash@^3.0.0: + version "3.0.0" + resolved "https://registry.yarnpkg.com/slash/-/slash-3.0.0.tgz#6539be870c165adbd5240220dbe361f1bc4d4634" + integrity sha512-g9Q1haeby36OSStwb4ntCGGGaKsaVSjQ68fBxoQcutl5fS1vuY18H3wSt3jFyFtrkx+Kz0V1G85A4MyAdDMi2Q== slice-ansi@^4.0.0: version "4.0.0" @@ -5152,6 +5972,20 @@ source-list-map@^2.0.1: resolved "https://registry.yarnpkg.com/source-list-map/-/source-list-map-2.0.1.tgz#3993bd873bfc48479cca9ea3a547835c7c154b34" integrity sha512-qnQ7gVMxGNxsiL4lEuJwe/To8UnK7fAnmbGEEH8RpLouuKbeEm0lhbQVFIrNSuB+G7tVrAlVsZgETT5nljf+Iw== +source-map-js@^0.6.2: + version "0.6.2" + resolved "https://registry.yarnpkg.com/source-map-js/-/source-map-js-0.6.2.tgz#0bb5de631b41cfbda6cfba8bd05a80efdfd2385e" + integrity sha512-/3GptzWzu0+0MBQFrDKzw/DvvMTUORvgY6k6jd/VS6iCR4RDTKWH6v6WPwQoUO8667uQEf9Oe38DxAYWY5F/Ug== + +source-map-loader@^3.0.0: + version "3.0.0" + resolved "https://registry.yarnpkg.com/source-map-loader/-/source-map-loader-3.0.0.tgz#f2a04ee2808ad01c774dea6b7d2639839f3b3049" + integrity sha512-GKGWqWvYr04M7tn8dryIWvb0s8YM41z82iQv01yBtIylgxax0CwvSy6gc2Y02iuXwEfGWRlMicH0nvms9UZphw== + dependencies: + abab "^2.0.5" + iconv-lite "^0.6.2" + source-map-js "^0.6.2" + source-map-resolve@^0.5.0: version "0.5.3" resolved "https://registry.yarnpkg.com/source-map-resolve/-/source-map-resolve-0.5.3.tgz#190866bece7553e1f8f267a2ee82c606b5509a1a" @@ -5163,7 +5997,7 @@ source-map-resolve@^0.5.0: source-map-url "^0.4.0" urix "^0.1.0" -source-map-support@~0.5.19: +source-map-support@^0.5.3, source-map-support@~0.5.19: version "0.5.19" resolved "https://registry.yarnpkg.com/source-map-support/-/source-map-support-0.5.19.tgz#a98b62f86dcaf4f67399648c085291ab9e8fed61" integrity sha512-Wonm7zOCIJzBGQdB+thsPar0kYuCIzYvxZwlBa87yi/Mdjv7Tip2cyVbLj5o0cFPN4EVkuTwb3GDDyUx2DGnGw== @@ -5181,7 +6015,7 @@ source-map@^0.5.0, source-map@^0.5.6, source-map@~0.5.3: resolved "https://registry.yarnpkg.com/source-map/-/source-map-0.5.7.tgz#8a039d2d1021d22d1ea14c80d8ea468ba2ef3fcc" integrity sha1-igOdLRAh0i0eoUyA2OpGi6LvP8w= -source-map@^0.6.0, source-map@^0.6.1: +source-map@^0.6.0, source-map@^0.6.1, source-map@~0.6.1: version "0.6.1" resolved "https://registry.yarnpkg.com/source-map/-/source-map-0.6.1.tgz#74722af32e9614e9c287a8d0bbde48b5e2f1a263" integrity sha512-UjgapumWlbMhkBgzT7Ykc5YXUT46F0iKu8SGXq0bcwP5dz/h0Plj6enJqjz1Zbq2l5WaqYnrVbwWOWMyF3F47g== @@ -5203,6 +6037,32 @@ spawn-wrap@^2.0.0: signal-exit "^3.0.2" which "^2.0.1" +spdx-correct@^3.0.0: + version "3.1.1" + resolved "https://registry.yarnpkg.com/spdx-correct/-/spdx-correct-3.1.1.tgz#dece81ac9c1e6713e5f7d1b6f17d468fa53d89a9" + integrity sha512-cOYcUWwhCuHCXi49RhFRCyJEK3iPj1Ziz9DpViV3tbZOwXD49QzIN3MpOLJNxh2qwq2lJJZaKMVw9qNi4jTC0w== + dependencies: + spdx-expression-parse "^3.0.0" + spdx-license-ids "^3.0.0" + +spdx-exceptions@^2.1.0: + version "2.3.0" + resolved "https://registry.yarnpkg.com/spdx-exceptions/-/spdx-exceptions-2.3.0.tgz#3f28ce1a77a00372683eade4a433183527a2163d" + integrity sha512-/tTrYOC7PPI1nUAgx34hUpqXuyJG+DTHJTnIULG4rDygi4xu/tfgmq1e1cIRwRzwZgo4NLySi+ricLkZkw4i5A== + +spdx-expression-parse@^3.0.0: + version "3.0.1" + resolved "https://registry.yarnpkg.com/spdx-expression-parse/-/spdx-expression-parse-3.0.1.tgz#cf70f50482eefdc98e3ce0a6833e4a53ceeba679" + integrity sha512-cbqHunsQWnJNE6KhVSMsMeH5H/L9EpymbzqTQ3uLwNCLZ1Q481oWaofqH7nO6V07xlXwY6PhQdQ2IedWx/ZK4Q== + dependencies: + spdx-exceptions "^2.1.0" + spdx-license-ids "^3.0.0" + +spdx-license-ids@^3.0.0: + version "3.0.11" + resolved "https://registry.yarnpkg.com/spdx-license-ids/-/spdx-license-ids-3.0.11.tgz#50c0d8c40a14ec1bf449bae69a0ea4685a9d9f95" + integrity sha512-Ctl2BrFiM0X3MANYgj3CkygxhRmr9mi6xhejbdO960nF6EDJApTYpn0BQnDKlnNBULKiCN1n3w9EBkHK8ZWg+g== + split-string@^3.0.1, split-string@^3.0.2: version "3.1.0" resolved "https://registry.yarnpkg.com/split-string/-/split-string-3.1.0.tgz#7cb09dda3a86585705c64b39a6466038682e8fe2" @@ -5258,6 +6118,11 @@ stream-browserify@^2.0.0: inherits "~2.0.1" readable-stream "^2.0.2" +stream-chain@^2.2.4: + version "2.2.4" + resolved "https://registry.yarnpkg.com/stream-chain/-/stream-chain-2.2.4.tgz#2e5e9858e00d25ebaf7e4339e8328ddcab5facad" + integrity sha512-9lsl3YM53V5N/I1C2uJtc3Kavyi3kNYN83VkKb/bMWRk7D9imiFyUPYa0PoZbLohSVOX1mYE9YsmwObZUsth6Q== + stream-combiner2@^1.1.1: version "1.1.1" resolved "https://registry.yarnpkg.com/stream-combiner2/-/stream-combiner2-1.1.1.tgz#fb4d8a1420ea362764e21ad4780397bebcb41cbe" @@ -5276,6 +6141,13 @@ stream-http@^3.0.0: readable-stream "^3.6.0" xtend "^4.0.2" +stream-json@^1.7.3: + version "1.7.3" + resolved "https://registry.yarnpkg.com/stream-json/-/stream-json-1.7.3.tgz#8a2abe8b51a2e84ffbc8d09bca9d01dc70facae3" + integrity sha512-Y6dXn9KKWSwxOqnvHGcdZy1PK+J+7alBwHCeU3W9oRqm4ilLRA0XSPmd1tWwhg7tv9EIxJTMWh7KF15tYelKJg== + dependencies: + stream-chain "^2.2.4" + stream-splicer@^2.0.0: version "2.0.1" resolved "https://registry.yarnpkg.com/stream-splicer/-/stream-splicer-2.0.1.tgz#0b13b7ee2b5ac7e0609a7463d83899589a363fcd" @@ -5301,15 +6173,6 @@ streamroller@^2.2.4: is-fullwidth-code-point "^2.0.0" strip-ansi "^4.0.0" -string-width@^3.0.0: - version "3.1.0" - resolved "https://registry.yarnpkg.com/string-width/-/string-width-3.1.0.tgz#22767be21b62af1081574306f69ac51b62203961" - integrity sha512-vafcv6KjVZKSgz06oM/H6GDBrAtz8vdhQakGjFIvNrHA6y3HCF1CInLy+QLq8dTJPQ1b+KDUqDFctkdRW44e1w== - dependencies: - emoji-regex "^7.0.1" - is-fullwidth-code-point "^2.0.0" - strip-ansi "^5.1.0" - string-width@^4.1.0, string-width@^4.2.0: version "4.2.0" resolved 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"^1.2.1" +type-check@~0.3.2: + version "0.3.2" + resolved "https://registry.yarnpkg.com/type-check/-/type-check-0.3.2.tgz#5884cab512cf1d355e3fb784f30804b2b520db72" + integrity sha1-WITKtRLPHTVeP7eE8wgEsrUg23I= + dependencies: + prelude-ls "~1.1.2" + type-detect@^4.0.0, type-detect@^4.0.5: version "4.0.8" resolved "https://registry.yarnpkg.com/type-detect/-/type-detect-4.0.8.tgz#7646fb5f18871cfbb7749e69bd39a6388eb7450c" @@ -5598,6 +6506,16 @@ type-is@~1.6.17: media-typer "0.3.0" mime-types "~2.1.24" +type@^1.0.1: + version "1.2.0" + resolved "https://registry.yarnpkg.com/type/-/type-1.2.0.tgz#848dd7698dafa3e54a6c479e759c4bc3f18847a0" + integrity sha512-+5nt5AAniqsCnu2cEQQdpzCAh33kVx8n0VoFidKpB1dVVLAN/F+bgVOqOJqOnEnrhp222clB5p3vUlD+1QAnfg== + +type@^2.0.0: + version "2.5.0" + resolved "https://registry.yarnpkg.com/type/-/type-2.5.0.tgz#0a2e78c2e77907b252abe5f298c1b01c63f0db3d" + integrity sha512-180WMDQaIMm3+7hGXWf12GtdniDEy7nYcyFMKJn/eZz/6tSLXrUN9V0wKSbMjej0I1WHWbpREDEKHtqPQa9NNw== + typed-function@^2.0.0: version "2.0.0" resolved "https://registry.yarnpkg.com/typed-function/-/typed-function-2.0.0.tgz#15ab3825845138a8b1113bd89e60cd6a435739e8" @@ -5615,6 +6533,11 @@ typedarray@^0.0.6: resolved "https://registry.yarnpkg.com/typedarray/-/typedarray-0.0.6.tgz#867ac74e3864187b1d3d47d996a78ec5c8830777" integrity sha1-hnrHTjhkGHsdPUfZlqeOxciDB3c= +typescript@^4.4.2: + version "4.4.2" + resolved "https://registry.yarnpkg.com/typescript/-/typescript-4.4.2.tgz#6d618640d430e3569a1dfb44f7d7e600ced3ee86" + integrity sha512-gzP+t5W4hdy4c+68bfcv0t400HVJMMd2+H9B7gae1nQlBzCqvrXX+6GL/b3GAgyTH966pzrZ70/fRjwAtZksSQ== + ua-parser-js@0.7.22: version "0.7.22" resolved "https://registry.yarnpkg.com/ua-parser-js/-/ua-parser-js-0.7.22.tgz#960df60a5f911ea8f1c818f3747b99c6e177eae3" @@ -5669,7 +6592,7 @@ union-value@^1.0.0: is-extendable "^0.1.1" set-value "^2.0.1" -universalify@^0.1.0: +universalify@^0.1.0, universalify@^0.1.2: version "0.1.2" resolved 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webpack-merge@^5.7.3: version "5.7.3" resolved "https://registry.yarnpkg.com/webpack-merge/-/webpack-merge-5.7.3.tgz#2a0754e1877a25a8bbab3d2475ca70a052708213" @@ -5843,6 +6808,27 @@ webpack@5.21.2: watchpack "^2.0.0" webpack-sources "^2.1.1" +whatwg-encoding@^1.0.5: + version "1.0.5" + resolved "https://registry.yarnpkg.com/whatwg-encoding/-/whatwg-encoding-1.0.5.tgz#5abacf777c32166a51d085d6b4f3e7d27113ddb0" + integrity sha512-b5lim54JOPN9HtzvK9HFXvBma/rnfFeqsic0hSpjtDbVxR3dJKLc+KB4V6GgiGOvl7CY/KNh8rxSo9DKQrnUEw== + dependencies: + iconv-lite "0.4.24" + +whatwg-mimetype@^2.3.0: + version "2.3.0" + resolved "https://registry.yarnpkg.com/whatwg-mimetype/-/whatwg-mimetype-2.3.0.tgz#3d4b1e0312d2079879f826aff18dbeeca5960fbf" + integrity sha512-M4yMwr6mAnQz76TbJm914+gPpB/nCwvZbJU28cUD6dR004SAxDLOOSUaB1JDRqLtaOV/vi0IC5lEAGFgrjGv/g== + +whatwg-url@^8.0.0, whatwg-url@^8.5.0: + version "8.7.0" + resolved 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"https://registry.yarnpkg.com/xml-name-validator/-/xml-name-validator-3.0.0.tgz#6ae73e06de4d8c6e47f9fb181f78d648ad457c6a" + integrity sha512-A5CUptxDsvxKJEU3yO6DuWBSJz/qizqzJKOMIfUJHETbBw/sFaDxgd6fxm1ewUaM0jZ444Fc5vC5ROYurg/4Pw== + +xmlchars@^2.2.0: + version "2.2.0" + resolved "https://registry.yarnpkg.com/xmlchars/-/xmlchars-2.2.0.tgz#060fe1bcb7f9c76fe2a17db86a9bc3ab894210cb" + integrity sha512-JZnDKK8B0RCDw84FNdDAIpZK+JuJw+s7Lz8nksI7SIuU3UXJJslUthsi+uWBUYOwPFwW7W7PRLRfUKpxjtjFCw== xmlhttprequest-ssl@~1.5.4: version "1.5.5" @@ -6032,9 +7036,9 @@ yargs@^15.0.2, yargs@^15.3.1: yargs-parser "^18.1.2" yarn@^1.22.10: - version "1.22.10" - resolved "https://registry.yarnpkg.com/yarn/-/yarn-1.22.10.tgz#c99daa06257c80f8fa2c3f1490724e394c26b18c" - integrity sha512-IanQGI9RRPAN87VGTF7zs2uxkSyQSrSPsju0COgbsKQOOXr5LtcVPeyXWgwVa0ywG3d8dg6kSYKGBuYK021qeA== + version "1.22.11" + resolved "https://registry.yarnpkg.com/yarn/-/yarn-1.22.11.tgz#d0104043e7349046e0e2aec977c24be106925ed6" + integrity sha512-AWje4bzqO9RUn3sdnM5N8n4ZJ0BqCc/kqFJvpOI5/EVkINXui0yuvU7NDCEF//+WaxHuNay2uOHxA4+tq1P3cg== yeast@0.1.2: version "0.1.2" diff --git a/danfojs-node/.babelrc b/src/danfojs-node/.babelrc similarity index 100% rename from danfojs-node/.babelrc rename to src/danfojs-node/.babelrc diff --git a/danfojs-node/.coveralls.yml b/src/danfojs-node/.coveralls.yml similarity index 100% rename from danfojs-node/.coveralls.yml rename to src/danfojs-node/.coveralls.yml diff --git a/danfojs-node/.eslintrc.json b/src/danfojs-node/.eslintrc.json similarity index 100% rename from danfojs-node/.eslintrc.json rename to src/danfojs-node/.eslintrc.json diff --git a/danfojs-node/.gitignore b/src/danfojs-node/.gitignore similarity index 64% rename from danfojs-node/.gitignore rename to src/danfojs-node/.gitignore index f013b3e4..c2db4c55 100644 --- a/danfojs-node/.gitignore +++ b/src/danfojs-node/.gitignore @@ -5,3 +5,8 @@ coverage danfojs/data .DS_Store .idea/ +build +lib/* +dist +*.xlsx +tensorflowlib.ts diff --git a/danfojs-node/.npmignore b/src/danfojs-node/.npmignore similarity index 100% rename from danfojs-node/.npmignore rename to src/danfojs-node/.npmignore diff --git a/src/danfojs-node/.prettierrc b/src/danfojs-node/.prettierrc new file mode 100644 index 00000000..ed799517 --- /dev/null +++ b/src/danfojs-node/.prettierrc @@ -0,0 +1,7 @@ +{ + "trailingComma": "es5", + "tabWidth": 2, + "semi": true, + "singleQuote": true, + "printWidth": 79 +} \ No newline at end of file diff --git a/danfojs-node/README.md b/src/danfojs-node/README.md similarity index 58% rename from danfojs-node/README.md rename to src/danfojs-node/README.md index 10c907ac..8f880afe 100644 --- a/danfojs-node/README.md +++ b/src/danfojs-node/README.md @@ -47,146 +47,170 @@ easy and intuitive. It is heavily inspired by [Pandas](https://pandas.pydata.org - Robust data preprocessing functions like [OneHotEncoders](https://danfo.jsdata.org/api-reference/general-functions/danfo.onehotencoder), [LabelEncoders](https://danfo.jsdata.org/api-reference/general-functions/danfo.labelencoder), and scalers like [StandardScaler](https://danfo.jsdata.org/api-reference/general-functions/danfo.standardscaler) and [MinMaxScaler](https://danfo.jsdata.org/api-reference/general-functions/danfo.minmaxscaler) are supported on DataFrame and Series +## Installation +There are three ways to install and use Danfo.js in your application +* For Nodejs applications, you can install the [__danfojs-node__]() version via package managers like yarn and/or npm: -To use Danfo.js via script tags, copy and paste the CDN below to the body of your HTML file +```bash +npm install danfojs-node + +or + +yarn add danfojs-node +``` +For client-side applications built with frameworks like React, Vue, Next.js, etc, you can install the [__danfojs__]() version: + +```bash +npm install danfojs + +or + +yarn add danfojs +``` + +For use directly in HTML files, you can add the latest script tag from [JsDelivr](https://www.jsdelivr.com/package/npm/danfojs) to your HTML file: + ```html - + ``` +See all available versions [here](https://www.jsdelivr.com/package/npm/danfojs) ### Example Usage in the Browser -> See the example below in [Code Sandbox](https://codepen.io/risingodegua/pen/bGwPGMG) +> Run in [Code Sandbox](https://codepen.io/risingodegua/pen/bGwPGMG) ```html - - - - - + + + + Document - - - + +
- - - + + ``` Output in Browser: ![](assets/browser-out.gif) -## How to install -Danfo.js is hosted on NPM, and can installed via package managers like npm and yarn - -```sh -npm install danfojs-node -``` - ### Example usage in Nodejs ```javascript const dfd = require("danfojs-node") +const file_url = "https://web.stanford.edu/class/archive/cs/cs109/cs109.1166/stuff/titanic.csv" +dfd.readCSV(file_url) + .then(df => { + //prints the first five columns + df.head().print() + + // Calculate descriptive statistics for all numerical columns + df.describe().print() -dfd.read_csv("https://web.stanford.edu/class/archive/cs/cs109/cs109.1166/stuff/titanic.csv") - .then(df => { - //prints the first five columns - df.head().print() + //prints the shape of the data + console.log(df.shape); - //Calculate descriptive statistics for all numerical columns - df.describe().print() + //prints all column names + console.log(df.columns); - //prints the shape of the data - console.log(df.shape); + // //prints the inferred dtypes of each column + df.ctypes.print() - //prints all column names - console.log(df.column_names); + //selecting a column by subsetting + df['Name'].print() - //prints the inferred dtypes of each column - df.ctypes.print() + //drop columns by names + cols_2_remove = ['Age', 'Pclass'] + df_drop = df.drop({ columns: cols_2_remove, axis: 1 }) + df_drop.print() - //selecting a column by subsetting - df['Name'].print() - //drop columns by names - cols_2_remove = ['Age', 'Pclass'] - df_drop = df.drop({ columns: cols_2_remove, axis: 1 }) - df_drop.print() + //select columns by dtypes + let str_cols = df_drop.selectDtypes(["string"]) + let num_cols = df_drop.selectDtypes(["int32", "float32"]) + str_cols.print() + num_cols.print() - //select columns by dtypes - let str_cols = df_drop.select_dtypes(["string"]) - let num_cols = df_drop.select_dtypes(["int32", "float32"]) - str_cols.print() - num_cols.print() + //add new column to Dataframe + let new_vals = df['Fare'].round(1) + df_drop.addColumn("fare_round", new_vals, { inplace: true }) + df_drop.print() - //add new column to Dataframe - let new_vals = df['Fare'].round().values - df_drop.addColumn({ column: "fare_round", value: new_vals}) - df_drop.print() + df_drop['fare_round'].round(2).print(5) - df_drop['fare_round'].print(5) + //prints the number of occurence each value in the column + df_drop['Survived'].valueCounts().print() - //prints the number of occurence each value in the column - df_drop['Survived'].value_counts().print() + //print the last ten elementa of a DataFrame + df_drop.tail(10).print() - //print the last ten elementa of a DataFrame - df_drop.tail(10).print() + //prints the number of missing values in a DataFrame + df_drop.isNa().sum().print() - //prints the number of missing values in a DataFrame - df_drop.isna().sum().print() + }).catch(err => { + console.log(err); + }) - }).catch(err => { - console.log(err); - }) ``` Output in Node Console: ![](assets/node-rec.gif) - -> If you want to use Danfo in frontend frameworks like React/Vue, read this [guide](https://danfo.jsdata.org/examples/using-danfojs-in-react) - -#### You can play with Danfo.js on Dnotebooks playground [here](https://playnotebook.jsdata.org/demo) +## Notebook support +* You can use Danfo.js on Dnotebooks playground [here](https://playnotebook.jsdata.org/demo) +* VsCode nodejs notebook extension now supports Danfo.js. See guide [here](https://marketplace.visualstudio.com/items?itemName=donjayamanne.typescript-notebook) #### [See the Official Getting Started Guide](https://danfo.jsdata.org/getting-started) ## Documentation The official documentation can be found [here](https://danfo.jsdata.org) +## Danfo.js Official Book + +![image](https://user-images.githubusercontent.com/29900845/134811659-25ff6b05-8e0d-415f-a60c-03ab1d33fd71.jpeg) + + +We recently published a book titled "Building Data Driven Applications with Danfo.js". Read more about it [here](https://danfo.jsdata.org/building-data-driven-applications-with-danfo.js-book) + ## Discussion and Development -Development discussions take place on our [issues](https://github.com/opensource9ja/danfojs/issues) tab. +Development discussions take place [here](https://github.com/opensource9ja/danfojs/discussions). ## Contributing to Danfo All contributions, bug reports, bug fixes, documentation improvements, enhancements, and ideas are welcome. A detailed overview on how to contribute can be found in the [contributing guide](https://danfo.jsdata.org/contributing-guide). @@ -195,4 +219,4 @@ All contributions, bug reports, bug fixes, documentation improvements, enhanceme #### Created by [Rising Odegua](https://github.com/risenW) and [Stephen Oni](https://github.com/steveoni) -Danfo.js - Open Source JavaScript library for manipulating data. | Product Hunt Embed +Danfo.js - Open Source JavaScript library for manipulating data. | Product Hunt Embed \ No newline at end of file diff --git a/danfojs-node/babel.config.json b/src/danfojs-node/babel.config.json similarity index 100% rename from danfojs-node/babel.config.json rename to src/danfojs-node/babel.config.json diff --git a/src/danfojs-node/nodemon.json b/src/danfojs-node/nodemon.json new file mode 100644 index 00000000..0c84bd97 --- /dev/null +++ b/src/danfojs-node/nodemon.json @@ -0,0 +1,6 @@ +{ + "watch": ["src"], + "ext": ".ts,.js", + "ignore": [], + "exec": "yarn test" + } \ No newline at end of file diff --git a/danfojs-node/package.json b/src/danfojs-node/package.json similarity index 61% rename from danfojs-node/package.json rename to src/danfojs-node/package.json index 57663645..aa24f2aa 100644 --- a/danfojs-node/package.json +++ b/src/danfojs-node/package.json @@ -1,11 +1,11 @@ { "name": "danfojs-node", - "version": "0.2.7", + "version": "1.0.0", "description": "JavaScript library providing high performance, intuitive, and easy to use data structures for manipulating and processing structured data.", - "main": "dist/index.js", - "types": "types/index.d.ts", + "main": "dist/danfojs-node/src/index.js", + "types": "dist/danfojs-node/src/index.d.ts", "directories": { - "test": "tests" + "test": "test" }, "contributors": [ { @@ -16,23 +16,27 @@ } ], "files": [ - "dist/", - "src/", - "types" + "dist/" ], "dependencies": { "@tensorflow/tfjs-node": "3.6.1", - "frictionless.js": "0.13.4", - "mathjs": "7.5.1", + "mathjs": "^9.4.4", "node-fetch": "^2.6.1", - "stream-to-array": "^2.3.0", - "table": "^5.4.6", - "@babel/runtime": "^7.13.10" + "papaparse": "^5.3.1", + "request": "^2.88.2", + "seedrandom": "^2.4.3", + "stream-json": "^1.7.3", + "table": "6.7.1", + "xlsx": "^0.17.2", + "plotly.js-dist-min": "^2.8.0" + }, "scripts": { - "test": "yarn run build && nyc mocha --require @babel/register tests/*", - "dev": "yarn run lint && babel ./src -d dist --no-comments", - "build": "babel ./src -d ./dist --no-comments", + "test": "nyc mocha --require ts-node/register test/**/*.test.ts", + "test:clean": "yarn build:clean && yarn test", + "dev": "nodemon", + "build": "tsc", + "build:clean": "rimraf ./build && node ./scripts/prebuild.js && tsc", "lint": "eslint ./src", "coveralls": "cat ./coverage/lcov.info | ./node_modules/.bin/coveralls", "coverage": "nyc report --reporter=text-lcov | coveralls && nyc report --reporter=lcov", @@ -66,11 +70,24 @@ "@babel/plugin-transform-runtime": "^7.12.10", "@babel/preset-env": "^7.10.4", "@babel/register": "^7.10.1", + "@types/chai": "^4.2.19", + "@types/mocha": "^9.0.0", + "@types/node": "^16.9.6", + "@types/papaparse": "^5.2.6", + "@types/request": "^2.48.7", + "@types/stream-json": "^1.7.1", + "@types/table": "^6.3.2", "chai": "^4.2.0", "coveralls": "^3.1.0", + "dotenv": "^10.0.0", + "dts-bundle-generator": "^5.9.0", "eslint": "^7.1.0", "mocha": "^7.2.0", + "nodemon": "^2.0.7", "nyc": "^15.1.0", + "rimraf": "^3.0.2", + "ts-node": "^10.0.0", + "typescript": "^4.4.2", "yarn": "^1.22.10" }, "nyc": { @@ -79,4 +96,4 @@ "text" ] } -} \ No newline at end of file +} diff --git a/src/danfojs-node/scripts/prebuild.js b/src/danfojs-node/scripts/prebuild.js new file mode 100644 index 00000000..222ba6c8 --- /dev/null +++ b/src/danfojs-node/scripts/prebuild.js @@ -0,0 +1,29 @@ +/** +* @license +* Copyright 2022 JsData. All rights reserved. +* +* This source code is licensed under the MIT license found in the +* LICENSE file in the root directory of this source tree. + +* Unless required by applicable law or agreed to in writing, software +* distributed under the License is distributed on an "AS IS" BASIS, +* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +* See the License for the specific language governing permissions and +* limitations under the License. +* ========================================================================== +*/ + +/** + * Danfojs-node version uses Tensorflowjs-node package. Hence on bundling, we set the tensorflow lib + * to use the Tensorflowjs-node package. + * */ + +/* eslint-disable no-undef */ +const fs = require('fs'); + +function updateTensorflowLib(tensorflowLibPath) { + const importStatement = `const tf = require("@tensorflow/tfjs-node")\nexport default tf`; + fs.writeFileSync(tensorflowLibPath, importStatement); +} + +updateTensorflowLib('../danfojs-base/shared/tensorflowlib.ts'); diff --git a/src/danfojs-node/src/core/frame.ts b/src/danfojs-node/src/core/frame.ts new file mode 100644 index 00000000..0a6e6891 --- /dev/null +++ b/src/danfojs-node/src/core/frame.ts @@ -0,0 +1,36 @@ +/** +* @license +* Copyright 2022 JsData. All rights reserved. +* +* This source code is licensed under the MIT license found in the +* LICENSE file in the root directory of this source tree. + +* Unless required by applicable law or agreed to in writing, software +* distributed under the License is distributed on an "AS IS" BASIS, +* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +* See the License for the specific language governing permissions and +* limitations under the License. +* ========================================================================== +*/ +import BaseDataFrame from "../../../danfojs-base/core/frame" +import { + BaseDataOptionType +} from "../../../danfojs-base/shared/types"; + + +/** + * Two-dimensional ndarray with axis labels. + * The object supports both integer- and label-based indexing and provides a host of methods for performing operations involving the index. + * Operations between DataFrame (+, -, /, , *) align values based on their associated index values– they need not be the same length. + * @param data 2D Array, JSON, Tensor, Block of data. + * @param options.index Array of numeric or string names for subseting array. If not specified, indices are auto generated. + * @param options.columns Array of column names. If not specified, column names are auto generated. + * @param options.dtypes Array of data types for each the column. If not specified, dtypes are/is inferred. + * @param options.config General configuration object for extending or setting NDframe behavior. + */ +export default class DataFrame extends BaseDataFrame { + [key: string]: any + constructor(data?: any, options: BaseDataOptionType = {}) { + super(data, options) + } +} \ No newline at end of file diff --git a/src/danfojs-node/src/core/series.ts b/src/danfojs-node/src/core/series.ts new file mode 100644 index 00000000..931b8941 --- /dev/null +++ b/src/danfojs-node/src/core/series.ts @@ -0,0 +1,36 @@ +/** +* @license +* Copyright 2022 JsData. All rights reserved. +* +* This source code is licensed under the MIT license found in the +* LICENSE file in the root directory of this source tree. + +* Unless required by applicable law or agreed to in writing, software +* distributed under the License is distributed on an "AS IS" BASIS, +* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +* See the License for the specific language governing permissions and +* limitations under the License. +* ========================================================================== +*/ + +import { BaseDataOptionType } from "../../../danfojs-base/shared/types"; +import BaseSeries from "../../../danfojs-base/core/series" + + + +/** + * One-dimensional ndarray with axis labels. + * The object supports both integer- and label-based indexing and provides a host of methods for performing operations involving the index. + * Operations between Series (+, -, /, , *) align values based on their associated index values – they need not be the same length. + * @param data 1D Array, JSON, Tensor, Block of data. + * @param options.index Array of numeric or string index for subseting array. If not specified, indices are auto generated. + * @param options.columns Column name. This is like the name of the Series. If not specified, column name is set to 0. + * @param options.dtypes Data types of the Series data. If not specified, dtypes is inferred. + * @param options.config General configuration object for extending or setting Series behavior. + */ +export default class Series extends BaseSeries { + [key: string]: any + constructor(data?: any, options: BaseDataOptionType = {}) { + super(data, options) + } +} \ No newline at end of file diff --git a/src/danfojs-node/src/index.ts b/src/danfojs-node/src/index.ts new file mode 100644 index 00000000..8a40e1e5 --- /dev/null +++ b/src/danfojs-node/src/index.ts @@ -0,0 +1,80 @@ +/** +* @license +* Copyright 2022 JsData. All rights reserved. +* +* This source code is licensed under the MIT license found in the +* LICENSE file in the root directory of this source tree. + +* Unless required by applicable law or agreed to in writing, software +* distributed under the License is distributed on an "AS IS" BASIS, +* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +* See the License for the specific language governing permissions and +* limitations under the License. +* ========================================================================== +*/ +import { + NDframe, + Config, + Utils, + Str, + Dt, + MinMaxScaler, + StandardScaler, + LabelEncoder, + OneHotEncoder, + getDummies, + concat, + merge, + toDateTime, + dateRange, + tensorflow, + __version +} from "../../danfojs-base"; +import { + streamCsvTransformer, + convertFunctionTotransformer +} from "./streams/csv.stream.transformer" +import { + readCSVNode as readCSV, + streamCSVNode as streamCSV, + toCSVNode as toCSV, + readJSONNode as readJSON, + toJSONNode as toJSON, + streamJSONNode as streamJSON, + readExcelNode as readExcel, + toExcelNode as toExcel +} from "../../danfojs-base/io/node" + +import DataFrame from "./core/frame" +import Series from "./core/series" + +export { + NDframe, + Config, + Utils, + Str, + Dt, + toDateTime, + Series, + DataFrame, + readCSV, + streamCSV, + toCSV, + streamCsvTransformer, + convertFunctionTotransformer, + readJSON, + toJSON, + streamJSON, + readExcel, + toExcel, + MinMaxScaler, + StandardScaler, + LabelEncoder, + OneHotEncoder, + getDummies, + concat, + merge, + dateRange, + tensorflow, + __version +} diff --git a/src/danfojs-node/src/streams/csv.stream.transformer.ts b/src/danfojs-node/src/streams/csv.stream.transformer.ts new file mode 100644 index 00000000..8e8bd736 --- /dev/null +++ b/src/danfojs-node/src/streams/csv.stream.transformer.ts @@ -0,0 +1,114 @@ +/** +* @license +* Copyright 2022 JsData. All rights reserved. +* +* This source code is licensed under the MIT license found in the +* LICENSE file in the root directory of this source tree. + +* Unless required by applicable law or agreed to in writing, software +* distributed under the License is distributed on an "AS IS" BASIS, +* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +* See the License for the specific language governing permissions and +* limitations under the License. +* ========================================================================== +*/ +import { CsvInputOptionsNode, CsvOutputOptionsNode } from "../../../danfojs-base/shared/types" +import { writeCsvOutputStreamNode, openCsvInputStreamNode } from "../../../danfojs-base/io/node" +import DataFrame from "../core/frame" +import stream from "stream" + +/** + * Converts a function to a pipe transformer. + * @param func The function to convert to a pipe transformer. + * @returns A pipe transformer that applies the function to each row of object. + * @example + * ``` + * import { convertFunctionTotransformer } from "danfojs-node" + * + * const renamer = (dfRow) => { + * const dfModified = dfRow["Names"].map((name) => name.split(",")[0]) + * return dfModified + * } + * const transformer = convertFunctionTotransformer(renamer) + * ``` + * +*/ +const convertFunctionTotransformer = (func: (df: DataFrame) => DataFrame) => { + const transformStream = new stream.Transform({ objectMode: true }) + transformStream._transform = (chunk: any, encoding, callback) => { + const outputChunk = func(chunk) + transformStream.push(outputChunk) + callback() + } + return transformStream +} + + +/** + * A pipeline transformer to stream a CSV file from local storage, + * transform it with custom transformer, and write to the output stream. + * @param inputFilePath The path to the CSV file to stream from. + * @param transformer The transformer function to apply to each row. Note that each row + * of the CSV file is passed as a DataFrame with a single row to the transformer function, and + * the transformer function is expected to return a transformed DataFrame. + * @param options Configuration options for the pipeline. Includes: + * - `outputFilePath` The local file path to write the transformed CSV file to. + * - `customCSVStreamWriter` A custom CSV stream writer function. This is applied at the end of each transform. + * If not provided, the default CSV stream writer is used, and this writes to local storage. + * - `inputStreamOptions` Configuration options for the input stream. Supports all Papaparse csv reader config options. + * - `outputStreamOptions` Configuration options for the output stream. This only applies when + * using the default CSV stream writer. Supports all `toCSV` options. + * @returns A promise that resolves when the pipeline is complete. + * @example + * ``` + * import { streamCsvTransformer } from "danfojs-node" + * + * const transformer = (dfRow) => { + * const dfModified = dfRow["Names"].map((name) => name.split(",")[0]) + * return dfModified + * } + * const inputFilePath = "./data/input.csv" + * const outputFilePath = "./data/output.csv" + * + * streamCsvTransformer(inputFilePath, transformer, { outputFilePath }) + * ``` +*/ +const streamCsvTransformer = ( + inputFilePath: string, + transformer: (df: DataFrame) => DataFrame, + options: { + outputFilePath?: string, + customCSVStreamWriter?: any, + inputStreamOptions?: CsvInputOptionsNode, + outputStreamOptions?: CsvOutputOptionsNode + }) => { + const { outputFilePath, customCSVStreamWriter, inputStreamOptions, outputStreamOptions } = { + outputFilePath: "./", + inputStreamOptions: {}, + outputStreamOptions: {}, + ...options + } + + if (customCSVStreamWriter) { + openCsvInputStreamNode(inputFilePath, inputStreamOptions) + .pipe(convertFunctionTotransformer(transformer)) + .pipe(customCSVStreamWriter()) + .on("error", (err: any) => { + console.error("An error occurred while transforming the CSV file") + console.error(err) + }) + } else { + openCsvInputStreamNode(inputFilePath, inputStreamOptions) + .pipe(convertFunctionTotransformer(transformer)) + .pipe(writeCsvOutputStreamNode(outputFilePath, outputStreamOptions)) + .on("error", (err: any) => { + console.error("An error occurred while transforming the CSV file") + console.error(err) + }) + } +} + +export { + streamCsvTransformer, + convertFunctionTotransformer +} \ No newline at end of file diff --git a/src/danfojs-node/test/aggregators/groupby.test.ts b/src/danfojs-node/test/aggregators/groupby.test.ts new file mode 100644 index 00000000..ad7953f6 --- /dev/null +++ b/src/danfojs-node/test/aggregators/groupby.test.ts @@ -0,0 +1,367 @@ +import { assert } from "chai"; +import { describe, it } from "mocha"; +import { DataFrame } from '../../dist/danfojs-node/src'; + + +describe("groupby", function () { + it("Check group by One column data", function () { + let data = [[1, 2, 3], [4, 5, 6], [20, 30, 40], [39, 89, 78]]; + let cols = ["A", "B", "C"]; + let df = new DataFrame(data, { columns: cols }); + let groupDf = df.groupby(["A"]); + + let groupDict = { + '1': { A: [1], B: [2], C: [3] }, + '4': { A: [4], B: [5], C: [6] }, + '20': { A: [20], B: [30], C: [40] }, + '39': { A: [39], B: [89], C: [78] } + } + assert.deepEqual(groupDf.colDict, groupDict); + }); + + it("Obtain the DataFrame of one of the group", function () { + + let data = [[1, 2, 3], [4, 5, 6], [20, 30, 40], [39, 89, 78]]; + let cols = ["A", "B", "C"]; + let df = new DataFrame(data, { columns: cols }); + let group_df = df.groupby(["A"]); + let new_data = [[1, 2, 3]]; + assert.deepEqual(group_df.getGroup([1]).values, new_data); + }); + + it("Check group by Two column data", function () { + + let data = [[1, 2, 3], [4, 5, 6], [20, 30, 40], [39, 89, 78]]; + let cols = ["A", "B", "C"]; + let df = new DataFrame(data, { columns: cols }); + let group_df = df.groupby(["A", "B"]); + let new_data = { + '1-2': { A: [1], B: [2], C: [3] }, + '4-5': { A: [4], B: [5], C: [6] }, + '20-30': { A: [20], B: [30], C: [40] }, + '39-89': { A: [39], B: [89], C: [78] } + } + assert.deepEqual(group_df.colDict, new_data); + }); + + it("Obtain the DataFrame of one of the group, grouped by two column", function () { + + let data = [[1, 2, 3], [4, 5, 6], [20, 30, 40], [39, 89, 78]]; + let cols = ["A", "B", "C"]; + let df = new DataFrame(data, { columns: cols }); + let group_df = df.groupby(["A", "B"]); + let new_data = [[1, 2, 3]]; + + assert.deepEqual(group_df.getGroup([1, 2]).values, new_data); + }); + + it("Count column in group", function () { + + let data = [[1, 2, 3], [4, 5, 6], [20, 30, 40], [39, 89, 78]]; + let cols = ["A", "B", "C"]; + let df = new DataFrame(data, { columns: cols }); + let group_df = df.groupby(["A", "B"]); + let new_data = [ + [1, 2, 1], + [4, 5, 1], + [20, 30, 1], + [39, 89, 1] + ]; + + assert.deepEqual(group_df.col(["C"]).count().values, new_data); + }); + + it("sum column element in group", function () { + + let data = [[1, 2, 3], [4, 5, 6], [20, 30, 40], [39, 89, 78]]; + let cols = ["A", "B", "C"]; + let df = new DataFrame(data, { columns: cols }); + let group_df = df.groupby(["A", "B"]); + let new_data = [ + [1, 2, 3], + [4, 5, 6], + [20, 30, 40], + [39, 89, 78] + ]; + assert.deepEqual(group_df.col(["C"]).sum().values, new_data); + }); + + it("sum column element group by two column", function () { + + let data = [[1, 2, 3], [1, 5, 6], [20, 30, 40], [39, 89, 78]]; + let cols = ["A", "B", "C"]; + let df = new DataFrame(data, { columns: cols }); + let group_df = df.groupby(["A", "B"]); + + let new_data = [ + [1, 2, 2, 3], + [1, 5, 5, 6], + [20, 30, 30, 40], + [39, 89, 89, 78] + ]; + + assert.deepEqual(group_df.col(["B", "C"]).sum().values, new_data); + }); + + it("Perform aggregate on column for groupby", function () { + + let data = [[1, 2, 3], [4, 5, 6], [20, 30, 40], [39, 89, 78]]; + let cols = ["A", "B", "C"]; + let df = new DataFrame(data, { columns: cols }); + let group_df = df.groupby(["A", "B"]); + let new_data = [ + [1, 2, 2, 1], + [4, 5, 5, 1], + [20, 30, 30, 1], + [39, 89, 89, 1] + ]; + + assert.deepEqual(group_df.agg({ B: "mean", C: "count" }).values, new_data); + }); + + it("cummulative sum for groupby", function () { + + let data = [[1, 2, 3], [4, 5, 6], [20, 30, 40], [39, 89, 78]]; + let cols = ["A", "B", "C"]; + let df = new DataFrame(data, { columns: cols }); + let group_df = df.groupby(["A", "B"]); + let new_data = [ + [1, 2, 2, 3], + [4, 5, 5, 6], + [20, 30, 30, 40], + [39, 89, 89, 78] + ]; + assert.deepEqual(group_df.col(["B", "C"]).cumSum().values, new_data); + }); + + it("cummulative max for groupby", function () { + + let data = [[1, 2, 3], [4, 5, 6], [20, 30, 40], [39, 89, 78]]; + let cols = ["A", "B", "C"]; + let df = new DataFrame(data, { columns: cols }); + let group_df = df.groupby(["A"]); + let new_data = [[1, 3], [4, 6], [20, 40], [39, 78]]; + + + assert.deepEqual(group_df.col(["C"]).cumMax().values, new_data); + }); + + it("cummulative min for groupby", function () { + + let data = [[1, 2, 3], [4, 5, 6], [20, 30, 40], [39, 89, 78]]; + let cols = ["A", "B", "C"]; + let df = new DataFrame(data, { columns: cols }); + let group_df = df.groupby(["A"]); + let new_data = [[1, 3], [4, 6], [20, 40], [39, 78]]; + + assert.deepEqual(group_df.col(["C"]).cumMin().values, new_data); + }); + + it("cummulative prod for groupby", function () { + + let data = [[1, 2, 3], [4, 5, 6], [20, 30, 40], [39, 89, 78]]; + let cols = ["A", "B", "C"]; + let df = new DataFrame(data, { columns: cols }); + let group_df = df.groupby(["A"]); + let new_data = [[1, 3], [4, 6], [20, 40], [39, 78]]; + + assert.deepEqual(group_df.col(["C"]).cumProd().values, new_data); + }); + + it("mean for groupby", function () { + + let data = [[1, 2, 3], [4, 5, 6], [20, 30, 40], [39, 89, 78]]; + let cols = ["A", "B", "C"]; + let df = new DataFrame(data, { columns: cols }); + let group_df = df.groupby(["A", "B"]); + let new_data = [ + [1, 2, 2, 3], + [4, 5, 5, 6], + [20, 30, 30, 40], + [39, 89, 89, 78] + ]; + + assert.deepEqual(group_df.col(["B", "C"]).mean().values, new_data); + }); + + it("should apply grouby operation to all column", function () { + let data = { + 'A': ['foo', 'bar', 'foo', 'bar', + 'foo', 'bar', 'foo', 'foo'], + 'B': ['one', 'one', 'two', 'three', + 'two', 'two', 'one', 'three'], + 'C': [1, 3, 2, 4, 5, 2, 6, 7], + 'D': [3, 2, 4, 1, 5, 6, 7, 8] + }; + let df = new DataFrame(data); + let grp = df.groupby(["A", "B"]); + let rslt = [ + ['foo', 'one', 2, 2], + ['foo', 'two', 2, 2], + ['foo', 'three', 1, 1], + ['bar', 'one', 1, 1], + ['bar', 'three', 1, 1], + ['bar', 'two', 1, 1] + ]; + + assert.deepEqual(grp.count().values, rslt); + }); + it("should apply function to specific column", function () { + + let data = { + 'A': ['foo', 'bar', 'foo', 'bar', + 'foo', 'bar', 'foo', 'foo'], + 'B': ['one', 'one', 'two', 'three', + 'two', 'two', 'one', 'three'], + 'C': [1, 3, 2, 4, 5, 2, 6, 7], + 'D': [3, 2, 4, 1, 5, 6, 7, 8] + }; + let df = new DataFrame(data); + let group_df = df.groupby(["A"]); + let rslt = [ + ['foo', 5, 3], + ['foo', 6, 4], + ['foo', 7, 7], + ['foo', 9, 8], + ['foo', 10, 9], + ['bar', 4, 5], + ['bar', 3, 6], + ['bar', 8, 4] + ] + assert.deepEqual(group_df.col(['D', 'C']).apply((x) => x.add(2)).values, rslt); + }); + + it("should apply function to group column", function () { + let data = { + 'A': ['foo', 'bar', 'foo', 'bar', + 'foo', 'bar', 'foo', 'foo'], + 'B': ['one', 'one', 'two', 'three', + 'two', 'two', 'one', 'three'], + 'C': [1, 3, 2, 4, 5, 2, 6, 7], + 'D': [3, 2, 4, 1, 5, 6, 7, 8] + }; + let df = new DataFrame(data); + let group_df = df.groupby(["A", "B"]); + let rslt = [ + ['foo', 'one', 2, 2, 2, 2], + ['foo', 'two', 2, 2, 2, 2], + ['foo', 'three', 1, 1, 1, 1], + ['bar', 'one', 1, 1, 1, 1], + ['bar', 'three', 1, 1, 1, 1], + ['bar', 'two', 1, 1, 1, 1] + ] + assert.deepEqual(group_df.apply((x) => x.count({ axis: 0 })).values, rslt); + }); + + it("should obtain the number of groups", function () { + let data = { + 'A': ['foo', 'bar', 'foo', 'bar', + 'foo', 'bar', 'foo', 'foo'], + 'B': ['one', 'one', 'two', 'three', + 'two', 'two', 'one', 'three'], + 'C': [1, 3, 2, 4, 5, 2, 6, 7], + 'D': [3, 2, 4, 1, 5, 6, 7, 8] + }; + let df = new DataFrame(data); + let group_df = df.groupby(["A", "B"]); + let rslt = 6 + assert.equal(group_df.ngroups, rslt); + }); + it("should obtain all groups", function () { + let data = { + 'A': ['foo', 'bar', 'foo', 'bar', + 'foo', 'bar', 'foo', 'foo'], + 'B': ['one', 'one', 'two', 'three', + 'two', 'two', 'one', 'three'], + 'C': [1, 3, 2, 4, 5, 2, 6, 7], + 'D': [3, 2, 4, 1, 5, 6, 7, 8] + }; + let df = new DataFrame(data); + let group_df = df.groupby(["A", "B"]); + let rslt = { + 'foo-one': { + A: ['foo', 'foo'], + B: ['one', 'one'], + C: [1, 6], + D: [3, 7] + }, + 'bar-one': { A: ['bar'], B: ['one'], C: [3], D: [2] }, + 'foo-two': { + A: ['foo', 'foo'], + B: ['two', 'two'], + C: [2, 5], + D: [4, 5] + }, + 'bar-three': { A: ['bar'], B: ['three'], C: [4], D: [1] }, + 'bar-two': { A: ['bar'], B: ['two'], C: [2], D: [6] }, + 'foo-three': { A: ['foo'], B: ['three'], C: [7], D: [8] } + } + assert.deepEqual(group_df.groups, rslt); + }); + + it("should obtain the first row of all groups", function () { + let data = { + 'A': ['foo', 'bar', 'foo', 'bar', + 'foo', 'bar', 'foo', 'foo'], + 'B': ['one', 'one', 'one', 'three', + 'two', 'two', 'one', 'three'], + 'C': [1, 3, 2, 4, 5, 2, 6, 7], + 'D': [3, 2, 4, 1, 5, 6, 7, 8] + }; + let df = new DataFrame(data); + let group_df = df.groupby(["A", "B"]); + let rslt = [ + ['foo', 'one', 'foo', 'one', 1, 3], + ['foo', 'two', 'foo', 'two', 5, 5], + ['foo', 'three', 'foo', 'three', 7, 8], + ['bar', 'one', 'bar', 'one', 3, 2], + ['bar', 'three', 'bar', 'three', 4, 1], + ['bar', 'two', 'bar', 'two', 2, 6] + ] + assert.deepEqual(group_df.first().values, rslt); + }); + + it("should obtain the last row of all groups", function () { + let data = { + 'A': ['foo', 'bar', 'foo', 'bar', + 'foo', 'bar', 'foo', 'foo'], + 'B': ['one', 'one', 'one', 'three', + 'two', 'two', 'one', 'three'], + 'C': [1, 3, 2, 4, 5, 2, 6, 7], + 'D': [3, 2, 4, 1, 5, 6, 7, 8] + }; + let df = new DataFrame(data); + let group_df = df.groupby(["A", "B"]); + let rslt = [ + ['foo', 'one', 'foo', 'one', 6, 7], + ['foo', 'two', 'foo', 'two', 5, 5], + ['foo', 'three', 'foo', 'three', 7, 8], + ['bar', 'one', 'bar', 'one', 3, 2], + ['bar', 'three', 'bar', 'three', 4, 1], + ['bar', 'two', 'bar', 'two', 2, 6] + ] + assert.deepEqual(group_df.last().values, rslt); + }); + + it("should obtain the number of rows of each groups", function () { + let data = { + 'A': ['foo', 'bar', 'foo', 'bar', + 'foo', 'bar', 'foo', 'foo'], + 'B': ['one', 'one', 'one', 'three', + 'two', 'two', 'one', 'three'], + 'C': [1, 3, 2, 4, 5, 2, 6, 7], + 'D': [3, 2, 4, 1, 5, 6, 7, 8] + }; + let df = new DataFrame(data); + let group_df = df.groupby(["A", "B"]); + let rslt = [ + ['foo', 'one', 3], + ['foo', 'two', 1], + ['foo', 'three', 1], + ['bar', 'one', 1], + ['bar', 'three', 1], + ['bar', 'two', 1] + ] + assert.deepEqual(group_df.size().values, rslt); + }); +}) \ No newline at end of file diff --git a/src/danfojs-node/test/core/daterange.test.ts b/src/danfojs-node/test/core/daterange.test.ts new file mode 100644 index 00000000..ba7edd17 --- /dev/null +++ b/src/danfojs-node/test/core/daterange.test.ts @@ -0,0 +1,92 @@ +import { assert } from "chai"; +import { describe, it } from "mocha"; +import { dateRange } from '../../dist/danfojs-node/src'; + + +describe("dateRange", function(){ + + // it("Obtain date between start and end specified", function(){ + // let d = dateRange({start:'2018-04-24',end:'2018-04-27'}) + // let rslt = [ + // '4/24/2018, 1:00:00 AM', + // '4/25/2018, 1:00:00 AM', + // '4/26/2018, 1:00:00 AM', + // '4/27/2018, 1:00:00 AM' + // ] + // assert.deepEqual(d,rslt) + // }); + it("Obtain date between start with end not specified, but period and freq specified", function(){ + let d = dateRange({ start:'1/1/2018', period:5, freq:'M' }); + let rslt = [ + '1/1/2018, 12:00:00 AM', + '2/1/2018, 12:00:00 AM', + '3/1/2018, 12:00:00 AM', + '4/1/2018, 12:00:00 AM', + '5/1/2018, 12:00:00 AM' + ]; + assert.deepEqual(d, rslt); + }); + it("Obtain date between start with end not specified, but period and freq specified, plus offset", function(){ + let d = dateRange({ start:'1/1/2018', period:5, freq:'3M' }); + let rslt = [ + '1/1/2018, 12:00:00 AM', + '4/1/2018, 12:00:00 AM', + '7/1/2018, 12:00:00 AM', + '10/1/2018, 12:00:00 AM', + '1/1/2019, 12:00:00 AM' + ]; + assert.deepEqual(d, rslt); + }); + it("Obtain date between start with end not specified, but period and freq specified, plus offset, longer freq", function(){ + let d = dateRange({ start:'1/1/2018', period:5, freq:'35m' }); + let rslt = [ + "1/1/2018, 12:00:00 AM", + "1/1/2018, 12:35:00 AM", + "1/1/2018, 1:10:00 AM", + "1/1/2018, 1:45:00 AM", + "1/1/2018, 2:20:00 AM" + ]; + assert.deepEqual(d, rslt); + }); + it("Obtain date range with start not specified but end and period is given", function(){ + let d = dateRange({ end:'1/1/2018', period:8 }); + let rslt = [ + '12/25/2017, 12:00:00 AM', + '12/26/2017, 12:00:00 AM', + '12/27/2017, 12:00:00 AM', + '12/28/2017, 12:00:00 AM', + '12/29/2017, 12:00:00 AM', + '12/30/2017, 12:00:00 AM', + '12/31/2017, 12:00:00 AM', + '1/1/2018, 12:00:00 AM' + ]; + assert.deepEqual(d, rslt); + }); + it("inputing wrong freq", function(){ + assert.throws(function () { dateRange({ end:'1/1/2018', period:8, freq:"d" }); }, Error, 'invalid freq d'); + }); + it("inputing wrong freq with offset", function(){ + assert.throws(function () { dateRange({ end:'1/1/2018', period:8, freq:"4d" }); }, Error, 'invalid freq d'); + }); + it("inputing wrong freq offset", function(){ + assert.throws(function () { dateRange({ end:'1/1/2018', period:8, freq:"abcm" }); }, Error, 'invalid freq offset abc'); + }); + // it("Obtain date between start and end specified with different month", function(){ + + // let d = dateRange({start:'2018-04-24',end:'2018-05-27'}) + // let rslt = [ + // '4/24/2018, 1:00:00 AM', + // '4/25/2018, 1:00:00 AM', + // '4/26/2018, 1:00:00 AM', + // '4/27/2018, 1:00:00 AM', + // '4/28/2018, 1:00:00 AM', + // '4/29/2018, 1:00:00 AM', + // '4/30/2018, 1:00:00 AM', + // '5/1/2018, 1:00:00 AM', + // '5/2/2018, 1:00:00 AM', + // '5/3/2018, 1:00:00 AM', + // '5/27/2018, 1:00:00 AM' + // ] + // assert.deepEqual(d,rslt) + // }); +}); diff --git a/src/danfojs-node/test/core/datetime.test.ts b/src/danfojs-node/test/core/datetime.test.ts new file mode 100644 index 00000000..94d0c122 --- /dev/null +++ b/src/danfojs-node/test/core/datetime.test.ts @@ -0,0 +1,94 @@ +import { assert } from "chai"; +import { describe, it } from "mocha"; +import { Series } from "../../dist/danfojs-node/src"; +import { toDateTime } from '../../dist/danfojs-node/src'; + +describe("TimeSeries", function () { + + it("Returns correct month number", function () { + const data = ["02Sep2019", "03Aug2019", "04July2019"]; + const dateTime = toDateTime(data); + const expected = [8, 7, 6]; + assert.deepEqual(dateTime.month().values, expected); + }); + + it("Returns correct year number", function () { + const data = ["02Sep2019", "03Aug2029", "04July2020"]; + const dateTime = toDateTime(data); + const expected = [2019, 2029, 2020]; + assert.deepEqual(dateTime.year().values, expected); + }); + + it("Return month name generated", function () { + const data = ["06-30-02019", "07-29-2019", "08-28-2019"]; + const dateTime = toDateTime(data); + const expected = ["June", "July", "August"]; + assert.deepEqual(dateTime.monthName().values, expected); + }); + + it("Return day of the week generated", function () { + const data = ["06-30-02019", "07-29-2019", "08-28-2019"]; + const dateTime = toDateTime(data); + const expected = ["Sunday", "Monday", "Wednesday"]; + assert.deepEqual(dateTime.dayOfWeekName().values, expected); + }); + + it("Return day of the month generated", function () { + const data = ["06-30-02019", "07-29-2019", "08-28-2019"]; + const dateTime = toDateTime(data); + const expected = [30, 29, 28]; + assert.deepEqual(dateTime.dayOfMonth().values, expected); + }); + + it("Return seconds generated", function () { + const data = ["06-30-02019 00:00:12", "07-29-2019 00:30:40", "08-28-2019 00:12:04"]; + const dateTime = toDateTime(data); + const expected = [12, 40, 4]; + assert.deepEqual(dateTime.seconds().values, expected); + }); + + it("Return minutes generated", function () { + const data = ["06-30-02019 00:00:12", "07-29-2019 00:30:40", "08-28-2019 00:12:04"]; + const dateTime = toDateTime(data); + const expected = [0, 30, 12]; + assert.deepEqual(dateTime.minutes().values, expected); + }); + + it("Return hours generated", function () { + const data = ["06-30-02019 05:00:12", "07-29-2019 01:30:40", "08-28-2019 06:12:04"]; + const dateTime = toDateTime(data); + const expected = [5, 1, 6]; + assert.deepEqual(dateTime.hours().values, expected); + }); + + it("Return correct date from Series format 1", function () { + const data = new Series(["12/30/19 00:01", "12/29/19 07:03", "11/12/20 18:21"]); + const dateTime = toDateTime(data); + + const expectedMonth = [11, 11, 10]; + const expectedYear = [2019, 2019, 2020]; + const expectedDayOfMonth = [30, 29, 12]; + const expectedMonthName = ["December", "December", "November"]; + + assert.deepEqual(dateTime.month().values, expectedMonth); + assert.deepEqual(dateTime.year().values, expectedYear); + assert.deepEqual(dateTime.dayOfMonth().values, expectedDayOfMonth); + assert.deepEqual(dateTime.monthName().values, expectedMonthName); + }); + + it("Return correct date from Series format 2", function () { + const data = new Series(["12.30.19", "12.22.19", "11.01.20"]); + const dateTime = toDateTime(data); + + const expectedMonth = [11, 11, 10]; + const expectedYear = [2019, 2019, 2020]; + const expectedDay = [1, 0, 0]; + const expectedMonthName = ["December", "December", "November"]; + + assert.deepEqual(dateTime.month().values, expectedMonth); + assert.deepEqual(dateTime.year().values, expectedYear); + assert.deepEqual(dateTime.dayOfWeek().values, expectedDay); + assert.deepEqual(dateTime.monthName().values, expectedMonthName); + + }); +}); diff --git a/src/danfojs-node/test/core/frame.test.ts b/src/danfojs-node/test/core/frame.test.ts new file mode 100644 index 00000000..a6bf5108 --- /dev/null +++ b/src/danfojs-node/test/core/frame.test.ts @@ -0,0 +1,2909 @@ +import { assert, expect } from "chai"; +import { describe, it } from "mocha"; +import fs from "fs"; +import path from "path"; +import { DataFrame, readExcel, Series } from '../../dist/danfojs-node/src'; + + +describe("DataFrame", function () { + + describe("Subsetting by column names", function () { + it("retrieves the col data created from an df with two columns", function () { + let data = { alpha: ["A", "B", "C", "D"], val_count: [1, 2, 3, 4] }; + let df = new DataFrame(data); + assert.deepEqual(df["alpha"].values, ["A", "B", "C", "D"]); + assert.deepEqual(df["val_count"].values, [1, 2, 3, 4]); + }); + it("retrieves the column data from an df with three columns", function () { + let data = { alpha: ["A", "B", "C", "D"], val_count: [1, 2, 3, 4], val_sum: [20.3, 30.456, 40.90, 90.1] }; + let df = new DataFrame(data); + assert.deepEqual(df["alpha"].values, ["A", "B", "C", "D"]); + assert.deepEqual(df["val_count"].values, [1, 2, 3, 4]); + assert.deepEqual(df["val_sum"].values, [20.3, 30.456, 40.90, 90.1]); + }); + + it("Set column count by subseting", function () { + let data = { alpha: ["A", "B", "C", "D"], val_count: [1, 2, 3, 4], val_sum: [20.3, 30.456, 40.90, 90.1] }; + let df = new DataFrame(data); + df["alpha"] = ["E", "F", "G", "H"] + assert.deepEqual(df["alpha"].values, ["E", "F", "G", "H"]); + assert.deepEqual(df.values[0], ['E', 1, 20.3]); + assert.deepEqual(df.dtypes, ["string", "int32", "float32",]); + assert.deepEqual(df["val_count"].values, [1, 2, 3, 4]); + assert.deepEqual(df["val_sum"].values, [20.3, 30.456, 40.90, 90.1]); + }); + + it("Correct dtype is set after setting a column by subseting", function () { + let data = { alpha: ["A", "B", "C", "D"], val_count: [1, 2, 3, 4], val_sum: [20.3, 30.456, 40.90, 90.1] }; + let df = new DataFrame(data); + df["alpha"] = [2.4, 5.6, 32.5, 1] + + assert.deepEqual(df["alpha"].values, [2.4, 5.6, 32.5, 1]); + assert.deepEqual(df.values[0], [2.4, 1, 20.3]); + assert.deepEqual(df.values[1], [5.6, 2, 30.456]); + assert.deepEqual(df.values[2], [32.5, 3, 40.90]); + assert.deepEqual(df.values[3], [1, 4, 90.1]); + + df["val_count"] = ["A", "B", "C", "D"] + assert.deepEqual(df["val_count"].values, ["A", "B", "C", "D"]); + assert.deepEqual(df.dtypes, ["float32", "string", "float32",]); + }); + + it("retrieves the col data created from an df with two columns in low memory mode", function () { + let data = { alpha: ["A", "B", "C", "D"], val_count: [1, 2, 3, 4] }; + let df = new DataFrame(data, { config: { lowMemoryMode: true } }); + assert.deepEqual(df["alpha"].values, ["A", "B", "C", "D"]); + assert.deepEqual(df["val_count"].values, [1, 2, 3, 4]); + }); + it("retrieves the column data from an df with threee columns in low memory mode", function () { + let data = { alpha: ["A", "B", "C", "D"], val_count: [1, 2, 3, 4], val_sum: [20.3, 30.456, 40.90, 90.1] }; + let df = new DataFrame(data, { config: { lowMemoryMode: true } }); + assert.deepEqual(df["alpha"].values, ["A", "B", "C", "D"]); + assert.deepEqual(df["val_count"].values, [1, 2, 3, 4]); + assert.deepEqual(df["val_sum"].values, [20.3, 30.456, 40.90, 90.1]); + }); + + it("Set column count by subseting (low memory mode) ", function () { + let data = { alpha: ["A", "B", "C", "D"], val_count: [1, 2, 3, 4], val_sum: [20.3, 30.456, 40.90, 90.1] }; + let df = new DataFrame(data, { config: { lowMemoryMode: true } }); + df["alpha"] = ["E", "F", "G", "H"] + assert.deepEqual(df["alpha"].values, ["E", "F", "G", "H"]); + assert.deepEqual(df.values[0], ['E', 1, 20.3]); + assert.deepEqual(df.dtypes, ["string", "int32", "float32",]); + assert.deepEqual(df["val_count"].values, [1, 2, 3, 4]); + assert.deepEqual(df["val_sum"].values, [20.3, 30.456, 40.90, 90.1]); + }); + + it("Correct dtype is set after setting a column by subseting (low memory mode) ", function () { + let data = { alpha: ["A", "B", "C", "D"], val_count: [1, 2, 3, 4], val_sum: [20.3, 30.456, 40.90, 90.1] }; + let df = new DataFrame(data, { config: { lowMemoryMode: true } }); + df["alpha"] = [2.4, 5.6, 32.5, 1] + assert.deepEqual(df["alpha"].values, [2.4, 5.6, 32.5, 1]); + assert.deepEqual(df.values[0], [2.4, 1, 20.3]); + assert.deepEqual(df.values[1], [5.6, 2, 30.456]); + assert.deepEqual(df.values[2], [32.5, 3, 40.90]); + assert.deepEqual(df.values[3], [1, 4, 90.1]); + assert.deepEqual(df.dtypes, ["float32", "int32", "float32",]); + }); + }) + + describe("addColumn", function () { + it("Add new array values to DataFrame works", function () { + let data = { alpha: ["A", "B", "C", "D"], val_count: [1, 2, 3, 4], val_sum: [20.3, 30.456, 40.90, 90.1] }; + let df = new DataFrame(data); + const newdf = df.addColumn("new_column", ["a", "b", "c", "d"]) as DataFrame; + assert.deepEqual(newdf["new_column"].values, ["a", "b", "c", "d"]); + assert.deepEqual(newdf.columns, ["alpha", "val_count", "val_sum", "new_column"]); + assert.deepEqual(newdf.dtypes, ["string", "int32", "float32", "string"]); + assert.deepEqual(newdf.index, [0, 1, 2, 3]); + }); + it("Add new array values to DataFrame inplace works", function () { + let data = { alpha: ["A", "B", "C", "D"], val_count: [1, 2, 3, 4], val_sum: [20.3, 30.456, 40.90, 90.1] }; + let df = new DataFrame(data); + df.addColumn("new_column", ["a", "b", "c", "d"], { inplace: true }); + assert.deepEqual(df["new_column"].values, ["a", "b", "c", "d"]); + assert.deepEqual(df.columns, ["alpha", "val_count", "val_sum", "new_column"]); + assert.deepEqual(df.dtypes, ["string", "int32", "float32", "string"]); + assert.deepEqual(df.index, [0, 1, 2, 3]); + }); + it("Add new Series to DataFrame works", function () { + let data = { alpha: ["A", "B", "C", "D"], val_count: [1, 2, 3, 4], val_sum: [20.3, 30.456, 40.90, 90.1] }; + let df = new DataFrame(data); + const newdf = df.addColumn("new_column", new Series(["a", "b", "c", "d"])) as DataFrame; + assert.deepEqual(newdf["new_column"].values, ["a", "b", "c", "d"]); + assert.deepEqual(newdf.columns, ["alpha", "val_count", "val_sum", "new_column"]); + assert.deepEqual(newdf.dtypes, ["string", "int32", "float32", "string"]); + assert.deepEqual(newdf.index, [0, 1, 2, 3]); + }); + it("Correct column data is set", function () { + let data = { alpha: ["A", "B", "C", "D"], val_count: [1, 2, 3, 4], val_sum: [20.3, 30.456, 40.90, 90.1] }; + let df = new DataFrame(data); + df.addColumn("new_column", ["a", "b", "c", "d"], { inplace: true }); + assert.deepEqual(df["new_column"].values, ["a", "b", "c", "d"]); + assert.deepEqual(df["alpha"].values, ["A", "B", "C", "D"]); + assert.deepEqual(df["val_count"].values, [1, 2, 3, 4]); + assert.deepEqual(df["val_sum"].values, [20.3, 30.456, 40.90, 90.1]); + }); + it("throw error for wrong column lenght", function () { + const data = { alpha: ["A", "B", "C", "D"], val_count: [1, 2, 3, 4], val_sum: [20.3, 30.456, 40.90, 90.1] }; + const df = new DataFrame(data); + + assert.throws(function () { + df.addColumn("new_column", new Series(["a", "b", "c"])), + Error, + 'ParamError: Column data length mismatch. You provided data with length 3 but Ndframe has column of lenght 4' + }) + + }); + it("Ensure add column does not mutate parent when not in place", function () { + const data = { alpha: ["A", "B", "C", "D"], val_count: [1, 2, 3, 4], val_sum: [20.3, 30.456, 40.90, 90.1] }; + const df = new DataFrame(data); + const dfNew = df.addColumn("new_column", ["a", "b", "c", "d"]) as DataFrame + assert.notDeepEqual(df, dfNew) + assert.deepEqual(dfNew["new_column"].values, ["a", "b", "c", "d"]); + assert.deepEqual(df["alpha"].values, ["A", "B", "C", "D"]); + assert.deepEqual(df["val_count"].values, [1, 2, 3, 4]); + assert.deepEqual(df["val_sum"].values, [20.3, 30.456, 40.90, 90.1]); + }); + it("Add new array values to DataFrame works", function () { + let data = { alpha: ["A", "B", "C", "D"], val_count: [1, 2, 3, 4], val_sum: [20.3, 30.456, 40.90, 90.1] }; + let df = new DataFrame(data); + const newdf = df.addColumn("new_column", ["a", "b", "c", "d"], { atIndex: 0 }) as DataFrame; + assert.deepEqual(newdf["new_column"].values, ["a", "b", "c", "d"]); + assert.deepEqual(newdf.columns, ["new_column", "alpha", "val_count", "val_sum"]); + assert.deepEqual(newdf.dtypes, ["string", "int32", "float32", "string"]); + assert.deepEqual(newdf.index, [0, 1, 2, 3]); + }); + + it("Add new array values to specific column index", function () { + let data = { alpha: ["A", "B", "C", "D"], val_count: [1, 2, 3, 4], val_sum: [20.3, 30.456, 40.90, 90.1] }; + let df = new DataFrame(data); + const newdf = df.addColumn("new_column", ["a", "b", "c", "d"], { atIndex: 'alpha' }) as DataFrame; + assert.deepEqual(newdf["new_column"].values, ["a", "b", "c", "d"]); + assert.deepEqual(newdf.columns, ["new_column", "alpha", "val_count", "val_sum"]); + assert.deepEqual(newdf.dtypes, ["string", "int32", "float32", "string"]); + assert.deepEqual(newdf.index, [0, 1, 2, 3]); + }); + }) + + describe("drop", function () { + it("throw error for wrong column name", function () { + const data = [[1, 2, 3], [4, 5, 6]]; + const cols = ["A", "B", "C"]; + const df = new DataFrame(data, { columns: cols }); + assert.throws(function () { + df.drop({ columns: ["3"] }); + }, + Error, + 'ParamError: specified column "3" not found in columns'); + }); + it("throw error for wrong row index", function () { + const data = [[1, 2, 3], [4, 5, 6]]; + const cols = ["A", "B", "C"]; + const df = new DataFrame(data, { columns: cols }); + assert.throws(function () { df.drop({ index: [10] }); }, + Error, 'ParamError: specified index "10" not found in indices'); + }); + + it("drop a column inplace", function () { + const data = [[1, 2, 3], [4, 5, 6]]; + const cols = ["A", "B", "C"]; + const df = new DataFrame(data, { columns: cols }); + df.drop({ columns: ["C", "B"], inplace: true }); + assert.deepEqual(df.columns, ["A"]); + assert.deepEqual(df.values, [[1], [4]]); + assert.deepEqual(df["A"].values, [1, 4]); + assert.deepEqual(df.dtypes, ["int32"]); + }); + it("drop a column inplace in low memory mode", function () { + const data = [[1, 2, 3], [4, 5, 6]]; + const cols = ["A", "B", "C"]; + const df = new DataFrame(data, { columns: cols, config: { lowMemoryMode: true } }); + df.drop({ columns: ["C", "B"], inplace: true }); + assert.deepEqual(df.columns, ["A"]); + assert.deepEqual(df.values, [[1], [4]]); + assert.deepEqual(df["A"].values, [1, 4]); + assert.deepEqual(df.dtypes, ["int32"]); + }); + it("drop a scalar column inplace", function () { + const data = [[1, 2, 3], [4, 5, 6]]; + const cols = ["A", "B", "C"]; + const df = new DataFrame(data, { columns: cols }); + df.drop({ columns: "C", inplace: true }); + assert.deepEqual(df.columns, ["A", "B"]); + assert.deepEqual(df.values, [[1, 2], [4, 5]]); + assert.deepEqual(df["A"].values, [1, 4]); + assert.deepEqual(df["B"].values, [2, 5]); + }); + it("check if data is updated after column is dropped", function () { + const data = [[1, 2, 3], [4, 5, 6]]; + const cols = ["A", "B", "C"]; + const df = new DataFrame(data, { columns: cols }); + df.drop({ columns: ["C"], inplace: true }); + const new_data = [[1, 2], [4, 5]]; + assert.deepEqual(df.values, new_data); + assert.deepEqual(df.dtypes.length, 2); + + }); + + it("check if data is updated after row is dropped", function () { + const data = [[1, 2, 3], [4, 5, 6]]; + const cols = ["A", "B", "C"]; + const dfNew = new DataFrame(data, { columns: cols }); + const df = dfNew.drop({ index: [0] }) as DataFrame; + const new_data = [[4, 5, 6]]; + assert.deepEqual(df.values, new_data); + assert.deepEqual(df.dtypes, ["int32", "int32", "int32"]); + assert.deepEqual(df["A"].values, [4]); + assert.deepEqual(df["B"].values, [5]); + assert.deepEqual(df["C"].values, [6]); + assert.deepEqual(df.columns, cols); + assert.notDeepEqual(dfNew, df) + }); + it("check if data is updated after row is dropped (inplace)", function () { + const data = [[1, 2, 3], [4, 5, 6]]; + const cols = ["A", "B", "C"]; + const df = new DataFrame(data, { columns: cols }); + df.drop({ index: [0], inplace: true }); + const new_data = [[4, 5, 6]]; + assert.deepEqual(df.values, new_data); + assert.deepEqual(df.dtypes, ["int32", "int32", "int32"]); + assert.deepEqual(df["A"].values, [4]); + assert.deepEqual(df["B"].values, [5]); + assert.deepEqual(df["C"].values, [6]); + assert.deepEqual(df.columns, cols); + }); + it("check if new dataframe is properly created after column is dropped (not-in-inplace)", function () { + const data = [[1, 2, 3], [4, 5, 6]]; + const cols = ["A", "B", "C"]; + const df = new DataFrame(data, { columns: cols }); + const df_drop = df.drop({ columns: ["C"], inplace: false }) as DataFrame; + + const expected_data = [[1, 2], [4, 5]]; + const expected_cols = ["A", "B"]; + const expected_df = new DataFrame(expected_data, { columns: expected_cols }); + assert.deepEqual(df_drop.values, expected_df.values); + }); + it("check that the dtype is updated after column drop", function () { + const data = [[1, 2, 3], [4, 5, 6]]; + const cols = ["A", "B", "C"]; + const df = new DataFrame(data, { columns: cols }); + df.drop({ columns: ["A"], inplace: true }); + const dtype = ['int32', 'int32']; + assert.deepEqual(df.dtypes, dtype); + }); + it("drop row by single string labels", function () { + const data = [[1, 2, 3], [4, 5, 6], [20, 34, 5]]; + const cols = ["A", "B", "C"]; + const df = new DataFrame(data, { columns: cols, index: ["a", "b", "c"] }); + df.drop({ index: ["a"], inplace: true }); + const new_data = [[4, 5, 6], [20, 34, 5]]; + assert.deepEqual(df.values, new_data); + }); + it("drop row by two or more string labels", function () { + const data = [[1, 2, 3], [4, 5, 6], [20, 34, 5], [2, 3.4, 5], [2.0, 340, 5]]; + const cols = ["A", "B", "C"]; + const df = new DataFrame(data, { columns: cols, index: ["a", "b", "c", "d", "e"] }); + df.drop({ index: ["a", "b"], inplace: true }); + const new_data = [[20, 34, 5], [2, 3.4, 5], [2.0, 340, 5]]; + assert.deepEqual(df.values, new_data); + + }); + it("drop row by two or more string labels with numeric index", function () { + const data = [[1, 2, 3], [4, 5, 6], [20, 34, 5], [2, 3.4, 5], [2.0, 340, 5]]; + const cols = ["A", "B", "C"]; + const df = new DataFrame(data, { columns: cols, index: ["a", "b", 1, "d", "e"] }); + df.drop({ index: [1, "b"], inplace: true }); + const new_data = [[1, 2, 3], [2, 3.4, 5], [2.0, 340, 5]] + assert.deepEqual(df.values, new_data); + assert.deepEqual(df.index, ["a", "d", "e"]); + + }); + }); + + describe("head", function () { + it("Gets the first n rows in a DataFrame", function () { + const data = [[1, 2, 3], [4, 5, 6], [20, 30, 40], [39, 89, 78]]; + const cols = ["A", "B", "C"]; + const df = new DataFrame(data, { columns: cols }); + assert.deepEqual(df.head(2).values, [[1, 2, 3], [4, 5, 6]]); + }); + it("Throws error if row specified is greater than values", function () { + const data = [[1, 2, 3], [4, 5, 6], [20, 30, 40], [39, 89, 78]]; + const cols = ["A", "B", "C"]; + const df = new DataFrame(data, { columns: cols }); + assert.throws(() => df.head(10), Error, + "ParamError: Number of rows cannot be greater than available rows in data"); + }); + it("Throws error if row specified is less than 0", function () { + const data = [[1, 2, 3], [4, 5, 6], [20, 30, 40], [39, 89, 78]]; + const cols = ["A", "B", "C"]; + const df = new DataFrame(data, { columns: cols }); + assert.throws(() => df.head(-1), Error, + "ParamError: Number of rows cannot be less than 1"); + }); + + }); + + describe("tail", function () { + it("Prints the last n rows of a DataFrame", function () { + const data = [[1, 2, 3], [4, 5, 6], [20, 30, 40], [39, 89, 78]]; + const cols = ["A", "B", "C"]; + const df = new DataFrame(data, { columns: cols }); + assert.deepEqual(df.tail(2).values, [[20, 30, 40], [39, 89, 78]]); + }); + it("Throws error if row specified is greater than values", function () { + const data = [[1, 2, 3], [4, 5, 6], [20, 30, 40], [39, 89, 78]]; + const cols = ["A", "B", "C"]; + const df = new DataFrame(data, { columns: cols }); + assert.throws(() => df.tail(10), Error, + "ParamError: Number of rows cannot be greater than available rows in data"); + }); + it("Throws error if row specified is less than 0", function () { + const data = [[1, 2, 3], [4, 5, 6], [20, 30, 40], [39, 89, 78]]; + const cols = ["A", "B", "C"]; + const df = new DataFrame(data, { columns: cols }); + assert.throws(() => df.tail(-1), Error, + "ParamError: Number of rows cannot be less than 1"); + }); + it("Return last 3 row index in a DataFrame", function () { + const data = [[1, 2, 3], [4, 5, 6], [20, 30, 40], [39, 89, 78]]; + const cols = ["A", "B", "C"]; + const df = new DataFrame(data, { columns: cols }); + assert.deepEqual(df.tail(2).index, [2, 3]); + }); + it("Check print format on head call", function () { + const data = [[1, 2, 34, 5, 0, 6, 4, 5, 6, 7], [20, 30, 40, 39, 89, 78, 45, 56, 56, 45]]; + const df = new DataFrame(data); + assert.deepEqual(df.tail(2).values, [[1, 2, 34, 5, 0, 6, 4, 5, 6, 7], [20, 30, 40, 39, 89, 78, 45, 56, 56, 45]]); + }); + }); + + describe("sample", function () { + it("Samples n number of random elements from a DataFrame", async function () { + const data = [[1, 2, 3], [4, 5, 6], [20, 30, 40], [39, 89, 78], [100, 200, 300]]; + const cols = ["A", "B", "C"]; + const df = new DataFrame(data, { columns: cols }); + const expected = [[1, 2, 3], [20, 30, 40]]; + const values = (await df.sample(2)).values; + assert.deepEqual(values, expected); + }); + it("Throw error if n is greater than lenght of Dataframe", async function () { + const data = [[1, 2, 3], [4, 5, 6], [20, 30, 40], [39, 89, 78], [100, 200, 300]]; + const cols = ["A", "B", "C"]; + const df = new DataFrame(data, { columns: cols }); + try { + await df.sample(100); + } catch (e: any) { + expect(e).to.be.instanceOf(Error); + expect(e.message).to.eql('ParamError: Sample size cannot be bigger than number of rows'); + } + }); + it("Throw error if n is less than 0", async function () { + const data = [[1, 2, 3], [4, 5, 6], [20, 30, 40], [39, 89, 78], [100, 200, 300]]; + const cols = ["A", "B", "C"]; + const df = new DataFrame(data, { columns: cols }); + try { + await df.sample(-2); + } catch (e: any) { + expect(e).to.be.instanceOf(Error); + expect(e.message).to.eql('ParamError: Sample size cannot be less than 1'); + } + }); + it("Throw error if n is 0", async function () { + const data = [[1, 2, 3], [4, 5, 6], [20, 30, 40], [39, 89, 78], [100, 200, 300]]; + const cols = ["A", "B", "C"]; + const df = new DataFrame(data, { columns: cols }); + try { + await df.sample(0); + } catch (e: any) { + expect(e).to.be.instanceOf(Error); + expect(e.message).to.eql('ParamError: Sample size cannot be less than 1'); + } + }); + it("Seed works and random number is reproducible", async function () { + const data = [[1, 2, 3], [4, 5, 6], [20, 30, 40], [39, 89, 78], [100, 200, 300]]; + const cols = ["A", "B", "C"]; + const df = new DataFrame(data, { columns: cols }); + const values1 = (await df.sample(2, { seed: 453 })).values; + const values2 = (await df.sample(2, { seed: 453 })).values; + const values3 = (await df.sample(2, { seed: 1 })).values; + + assert.deepEqual(values1, values2); + assert.notDeepEqual(values1, values3); + + }); + }); + + describe("loc", function () { + + it("throw error for wrong column name", function () { + const data = [[1, 2, 3], [4, 5, 6]]; + const cols = ["A", "B", "C"]; + const df = new DataFrame(data, { columns: cols }); + assert.throws(function () { + df.loc({ "rows": [0, 1], "columns": ["A", "D"] }); + }, + Error, + "IndexError: Specified column (D) not found"); + }); + + it(`check data after selecting { "rows": ["0", "1"], "columns": ["B", "C"] }`, function () { + const data = [[1, 2, 3], [4, 5, 6]]; + const cols = ["A", "B", "C"]; + const df = new DataFrame(data, { columns: cols, index: ["0", "1"] }); + + const colDf = df.loc({ "rows": ["0", "1"], "columns": ["B", "C"] }); + const expected = [[2, 3], [5, 6]]; + + assert.deepEqual(colDf.values, expected); + + }); + it("check data after selecting row index", function () { + const data = [[1, 2, 3], [4, 5, 6]]; + const cols = ["A", "B", "C"]; + const df = new DataFrame(data, { columns: cols, index: [0, 1] }); + + const colDf = df.loc({ "rows": ["1"], "columns": ["B"] }); + const expected = [[5]]; + + assert.deepEqual(colDf.values, expected); + + }); + it("check data after selecting with single row index", function () { + const data = [[1, 2, 3], [4, 5, 6]]; + const cols = ["A", "B", "C"]; + const df = new DataFrame(data, { columns: cols, index: ["0", "1"] }); + + const colDf = df.loc({ "rows": [`"1"`], "columns": ["B", "C"] }); + const expected = [[5, 6]]; + + assert.deepEqual(colDf.values, expected); + + }); + it("check data after selecting with single column index", function () { + const data = [[1, 2, 3], [4, 5, 6]]; + const cols = ["A", "B", "C"]; + const df = new DataFrame(data, { columns: cols, index: ["0", "1"] }); + + const colDf = df.loc({ "rows": [`"0"`], "columns": ["A"] }); + const expected = [[1]]; + + assert.deepEqual(colDf.values, expected); + + }); + it("check data after row and column slice", function () { + const data = [[1, 2, 3], [4, 5, 6], [20, 30, 40], [39, 89, 78]]; + const cols = ["A", "B", "C"]; + const df = new DataFrame(data, { columns: cols, index: ["0", "1", "2", "3"] }); + + const colDf = df.loc({ "rows": [`'0':'2'`], "columns": ["B:C"] }); + const expected = [[2], [5]]; + + assert.deepEqual(colDf.values, expected); + + }); + it("check data after row slice", function () { + const data = [[1, 2, 3], [4, 5, 6], [20, 30, 40], [39, 89, 78]]; + const cols = ["A", "B", "C"]; + const df = new DataFrame(data, { columns: cols }); + + const colDf = df.loc({ "rows": [`0:2`], "columns": ["B", "C"] }); + const expected = [[2, 3], [5, 6]]; + + assert.deepEqual(colDf.values, expected); + + }); + it(`check data after column slice ["A:C"]`, function () { + const data = [[1, 2, 3], [4, 5, 6], [20, 30, 40], [39, 89, 78]]; + const cols = ["A", "B", "C"]; + const df = new DataFrame(data, { columns: cols, index: ["0", "1", "2", "3"] }); + + const colDf = df.loc({ "rows": ["0", "1"], "columns": ["A:C"] }); + const expected = [[1, 2], [4, 5]]; + assert.deepEqual(colDf.values, expected); + + }); + it("check data after numeric row slice", function () { + const data = [[1, 2, 3], [4, 5, 6], [20, 30, 40], [39, 89, 78]]; + const cols = ["A", "B", "C"]; + const df = new DataFrame(data, { columns: cols }); + + const colDf = df.loc({ "rows": [0, 1], "columns": ["A:C"] }); + const expected = [[1, 2], [4, 5]]; + assert.deepEqual(colDf.values, expected); + + }); + it("loc by single string index", function () { + const data = { + "Name": ["Apples", "Mango", "Banana", "Pear"], + "Count": [21, 5, 30, 10], + "Price": [200, 300, 40, 250] + }; + + const df = new DataFrame(data, { index: ["a", "b", "c", "d"] }); + const subDf = df.loc({ rows: [`"a"`], columns: ["Name", "Count"] }); + const expected = [['Apples', 21]]; + assert.deepEqual(subDf.values, expected); + + }); + + it("loc by slice string index", function () { + const data = { + "Name": ["Apples", "Mango", "Banana", "Pear"], + "Count": [21, 5, 30, 10], + "Price": [200, 300, 40, 250] + }; + + const df = new DataFrame(data, { index: ["a", "b", "c", "d"] }); + const subDf = df.loc({ rows: [`"a":"c"`], columns: ["Name", "Count"] }); + const expected = [["Apples", 21], ["Mango", 5]]; + assert.deepEqual(subDf.values, expected); + + }); + + + }); + + describe("DataFrame iloc", function () { + + it("throw error for wrong row index (array format)", function () { + const data = [[1, 2, 3], [4, 5, 6]]; + const cols = ["A", "B", "C"]; + const df = new DataFrame(data, { columns: cols }); + assert.throws(function () { + df.iloc({ "rows": [0, 1, 3] }); + }, + Error, + "Invalid row parameter: Specified index 3 cannot be bigger than index length 2"); + }); + + it("throw error for wrong row index (string slice format)", function () { + const data = [[1, 2, 3], [4, 5, 6]]; + const cols = ["A", "B", "C"]; + const df = new DataFrame(data, { columns: cols }); + assert.throws(function () { + df.iloc({ "rows": ["1:5"] }); + }, + Error, + "row slice [end] index cannot be bigger than 2"); + }); + + it("throw error for wrong column index (array format)", function () { + const data = [[1, 2, 3], [4, 5, 6]]; + const cols = ["A", "B", "C"]; + const df = new DataFrame(data, { columns: cols }); + assert.throws(function () { + df.iloc({ "columns": [1, 4] }); + }, + Error, + "Invalid column parameter: Specified index 4 cannot be bigger than index length 3"); + }); + + it("throw error for wrong column index (string slice format)", function () { + const data = [[1, 2, 3], [4, 5, 6]]; + const cols = ["A", "B", "C"]; + const df = new DataFrame(data, { columns: cols }); + assert.throws(function () { + df.iloc({ "columns": ["A:C"] }); + }, + Error, + "Invalid column split parameter. Split parameter must be a number"); + }); + + it("iloc works for {row: [0, 1], column: [1, 2]}", function () { + const data = [[1, 2, 3], [4, 5, 6]]; + const cols = ["A", "B", "C"]; + const df = new DataFrame(data, { columns: cols }); + + const colDf = df.iloc({ "rows": [0, 1], "columns": [1, 2] }); + const expected = [[2, 3], [5, 6]]; + + assert.deepEqual(colDf.values, expected); + + }); + + it(`iloc works for { "rows": [1], "columns": [1, 2] }`, function () { + const data = [[1, 2, 3], [4, 5, 6]]; + const cols = ["A", "B", "C"]; + const df = new DataFrame(data, { columns: cols }); + const colDf = df.iloc({ "rows": [1], "columns": [1, 2] }); + const expected = [[5, 6]]; + assert.deepEqual(colDf.values, expected); + + }); + it("check data after row and column slice", function () { + const data = [[1, 2, 3], [4, 5, 6], [20, 30, 40], [39, 89, 78]]; + const cols = ["A", "B", "C"]; + const df = new DataFrame(data, { columns: cols }); + + const colDf = df.iloc({ "rows": ["0:2"], "columns": ["1:2"] }); + const expected = [[2], [5]]; + + assert.deepEqual(colDf.values, expected); + + }); + it("check data after row slice", function () { + const data = [[1, 2, 3], [4, 5, 6], [20, 30, 40], [39, 89, 78]]; + const cols = ["A", "B", "C"]; + const df = new DataFrame(data, { columns: cols }); + + const colDf = df.iloc({ "rows": ["0:2"], "columns": [1, 2] }); + const expected = [[2, 3], [5, 6]]; + + assert.deepEqual(colDf.values, expected); + + }); + it("check data after column slice", function () { + const data = [[1, 2, 3], [4, 5, 6], [20, 30, 40], [39, 89, 78]]; + const cols = ["A", "B", "C"]; + const df = new DataFrame(data, { columns: cols }); + + const colDf = df.iloc({ "rows": [0, 1, 2], "columns": ["1:2"] }); + const expected = [[2], [5], [30]]; + assert.deepEqual(colDf.values, expected); + + }); + it("Return all columns if columns parameter is not specified", function () { + const data = [[1, 2, 3], [4, 5, 6], [20, 30, 40], [39, 89, 78]]; + const cols = ["A", "B", "C"]; + const df = new DataFrame(data, { columns: cols }); + + const colDf = df.iloc({ "rows": [0, 1, 2] }); + const expected = [[1, 2, 3], [4, 5, 6], [20, 30, 40]]; + assert.deepEqual(colDf.values, expected); + + }); + it("Return all rows if rows parameter is not specified", function () { + const data = [[1, 2, 3], [4, 5, 6], [20, 30, 40], [39, 89, 78]]; + const cols = ["A", "B", "C"]; + const df = new DataFrame(data, { columns: cols }); + + const colDf = df.iloc({ "columns": ["1:2"] }); + const expected = [[2], [5], [30], [89]]; + assert.deepEqual(colDf.values, expected); + + }); + it("column slice starting with 0 and returning a single result works", function () { + const data = { + "Name": ["Apples", "Mango", "Banana", "Pear"], + "Count": [21, 5, 30, 10], + "Price": [200, 300, 40, 250], + "index": [1, 2, 3, 4] + }; + const df = new DataFrame(data); + const subDf = df.iloc({ rows: ["2:3"], columns: ["0:1"] }); + const result = [["Banana"]]; + assert.deepEqual(subDf.values, result); + + }); + it("column slice with format '0:' works", function () { + const data = { + "Name": ["Apples", "Mango", "Banana", "Pear"], + "Count": [21, 5, 30, 10], + "Price": [200, 300, 40, 250] + }; + const df = new DataFrame(data); + const subDf = df.iloc({ rows: ["2:3"], columns: ["0:"] }); + const result = [["Banana", 30, 40]]; + assert.deepEqual(subDf.values, result); + + }); + it("column slice with format ':2' works", function () { + const data = { + "Name": ["Apples", "Mango", "Banana", "Pear"], + "Count": [21, 5, 30, 10], + "Price": [200, 300, 40, 250] + }; + const df = new DataFrame(data); + const subDf = df.iloc({ rows: ["2:3"], columns: [":2"] }); + const result = [["Banana", 30]]; + assert.deepEqual(subDf.values, result); + + }); + it("row slice with format ':2' works", function () { + const data = { + "Name": ["Apples", "Mango", "Banana", "Pear"], + "Count": [21, 5, 30, 10], + "Price": [200, 300, 40, 250] + }; + const df = new DataFrame(data); + const subDf = df.iloc({ rows: [":2"], columns: [":1"] }); + const result = [['Apples'], ['Mango']]; + assert.deepEqual(subDf.values, result); + + }); + it("row slice with format '1:' works", function () { + const data = { + "Name": ["Apples", "Mango", "Banana", "Pear"], + "Count": [21, 5, 30, 10], + "Price": [200, 300, 40, 250] + }; + const df = new DataFrame(data); + const subDf = df.iloc({ rows: [":2"], columns: [":2"] }); + const result = [['Apples', 21], ['Mango', 5]]; + assert.deepEqual(subDf.values, result); + + }); + + }); + + describe("toString", function () { + it("Prints a DataFrame to console", function () { + const data = { + "Name": ["Apples", "Mango", "Banana", "Pear"], + "Count": [21, 5, 30, 10], + "Price": [200, 300, 40, 250] + }; + const df = new DataFrame(data); + df.print() + }) + it("User config works when printing a DataFrame to console", function () { + const data = { + "Name": ["Apples", "Mango", "Banana", "Pear"], + "Count": [21, 5, 30, 10], + "Price": [200, 300, 40, 250] + }; + const df = new DataFrame(data, { + config: { + tableDisplayConfig: { + header: { + alignment: 'center', + content: 'THE HEADER\nThis is the table about something', + }, + }, + } + }); + df.print() + }) + it("Long columns are properly truncated before printing", function () { + const data = { + "Name": ["Apples", "Mango", "Banana", "Pear"], + "Count": [21, 5, 30, 10], + "Price": [200, 300, 40, 250], + "Name2": ["Apples", "Mango", "Banana", "Pear"], + "Count2": [21, 5, 30, 10], + "Price2": [200, 300, 40, 250], + "Name3": ["Apples", "Mango", "Banana", "Pear"], + "Count3": [21, 5, 30, 10], + "Price3": [200, 300, 40, 250], + "Name4": ["Apples", "Mango", "Banana", "Pear"], + "Count4": [21, 5, 30, 10], + "Price4": [200, 300, 40, 250], + "Name5": ["Apples", "Mango", "Banana", "Pear"], + "Count5": [21, 5, 30, 10], + "Price6": [200, 300, 40, 250], + "Name7": ["Apples", "Mango", "Banana", "Pear"], + "Count7": [21, 5, 30, 10], + "Price7": [200, 300, 40, 250], + "Name8": ["Apples", "Mango", "Banana", "Pear"], + "Count8": [21, 5, 30, 10], + "Price8": [200, 300, 40, 250], + "Name9": ["Apples", "Mango", "Banana", "Pear"], + "Count9": [21, 5, 30, 10], + "Price9": [200, 300, 40, 250], + "Name10": ["Apples", "Mango", "Banana", "Pear"], + "Count10": [21, 5, 30, 10], + "Price10": [200, 300, 40, 250] + }; + const df = new DataFrame(data); + df.print() + }) + + it("Long rows are automatically truncated", function () { + const data = { + "Name": ["Apples", "Mango", "Banana", "Pear", "Apples", "Mango", "Banana", "Pear", "Apples", "Mango", "Banana", "Pear"], + "Count": [21, 5, 30, 10, 21, 5, 30, 10, 21, 5, 30, 10], + "Price": [200, 300, 40, 250, 200, 300, 40, 250, 200, 300, 40, 250] + }; + const df = new DataFrame(data); + df.print() + }) + }) + + describe("add", function () { + it("Return Addition of DataFrame with a single Number", function () { + const data = [[0, 2, 4], [360, 180, 360]]; + const df = new DataFrame(data) + assert.deepEqual(df.add(2).values, [[2, 4, 6], [362, 182, 362]]); + }); + it("Return addition of a DataFrame with a Series along default axis 1", function () { + const data = [[0, 2, 4], [360, 180, 360]]; + const sf = new Series([1, 2, 1]); + const df = new DataFrame(data); + assert.deepEqual((df.add(sf) as DataFrame).values, [[1, 4, 5], [361, 182, 361]]); + }); + it("Return addition of a DataFrame with a Series along default axis 1", function () { + const data = [[0, 2, 4], [360, 180, 360]]; + const sf = [1, 2, 1] + const df = new DataFrame(data); + assert.deepEqual((df.add(sf) as DataFrame).values, [[1, 4, 5], [361, 182, 361]]); + }); + it("Return addition of a DataFrame with a Series along axis 0", function () { + const data = [[0, 2, 4], + [360, 180, 360]]; + const sf = new Series([1, 2]); + const df = new DataFrame(data); + assert.deepEqual((df.add(sf, { axis: 0 }) as DataFrame).values, [[1, 3, 5], [362, 182, 362]]); + }); + it("Return addition of a DataFrame with a Array along axis 0", function () { + const data = [[0, 2, 4], + [360, 180, 360]]; + const sf = [1, 2] + const df = new DataFrame(data); + assert.deepEqual((df.add(sf, { axis: 0 }) as DataFrame).values, [[1, 3, 5], [362, 182, 362]]); + }); + it("Return addition of a DataFrame with a DataFrame along default axis 1", function () { + const df1 = new DataFrame([[0, 2, 4], [360, 180, 360]]); + const df2 = new DataFrame([[1, 2, 4], [10, 5, 0]]); + assert.deepEqual((df1.add(df2) as DataFrame).values, [[1, 4, 8], [370, 185, 360]]); + }); + it("Return addition of a DataFrame with a DataFrame along axis 0", function () { + const df1 = new DataFrame([[0, 2, 4], [360, 180, 360]]); + const df2 = new DataFrame([[1, 2, 4], [10, 5, 0]]); + assert.deepEqual((df1.add(df2) as DataFrame).values, [[1, 4, 8], [370, 185, 360]]); + }); + it("Return addition of a DataFrame with a Series along default axis 1 (inplace)", function () { + const data = [[0, 2, 4], [360, 180, 360]]; + const sf = new Series([1, 2, 1]); + const df = new DataFrame(data); + df.add(sf, { axis: 1, inplace: true }) + assert.deepEqual(df.values, [[1, 4, 5], [361, 182, 361]]); + }); + + it("Return addition of a DataFrame with a DataFrame along axis 0 (inplace)", function () { + const df1 = new DataFrame([[0, 2, 4], [360, 180, 360]]); + const df2 = new DataFrame([[1, 2, 4], [10, 5, 0]]); + df1.add(df2, { axis: 0, inplace: true }) + assert.deepEqual(df1.values, [[1, 4, 8], [370, 185, 360]]); + }); + it("Adds work for DataFrame with undefined and null values", function () { + const df1 = new DataFrame([[undefined, 2, 4], [360, NaN, 360]]); + const df2 = new DataFrame([[1, 2, 4], [10, 5, 0]]); + df1.add(df2, { axis: 0, inplace: true }) + assert.deepEqual(df1.values, [[NaN, 4, 8], [370, NaN, 360]]); + }); + + }); + + describe("sub", function () { + it("Return subtraction of DataFrame with a single Number", function () { + const data = [[0, 2, 4], [360, 180, 360]]; + const df = new DataFrame(data); + assert.deepEqual((df.sub(2) as DataFrame).values, [[-2, 0, 2], [358, 178, 358]]); + }); + it("Return subtraction of a DataFrame with a Series along default axis 1", function () { + const data = [[0, 2, 4], [360, 180, 360]]; + const sf = new Series([1, 2, 1]); + const df = new DataFrame(data); + assert.deepEqual((df.sub(sf) as DataFrame).values, [[-1, 0, 3], [359, 178, 359]]); + }); + it("Return subtraction of a DataFrame with a Series along axis 0", function () { + const data = [[0, 2, 4], [360, 180, 360]]; + const sf = new Series([1, 2]); + const df = new DataFrame(data); + assert.deepEqual((df.sub(sf, { axis: 0 }) as DataFrame).values, [[-1, 1, 3], [358, 178, 358]]); + }); + it("Return subtraction of a DataFrame with a DataFrame along default axis 1", function () { + const df1 = new DataFrame([[0, 2, 4], [360, 180, 360]]); + const df2 = new DataFrame([[1, 2, 4], [10, 5, 0]]); + assert.deepEqual((df1.sub(df2) as DataFrame).values, [[-1, 0, 0], [350, 175, 360]]); + }); + it("Return subtraction of a DataFrame with a DataFrame along axis 0", function () { + const df1 = new DataFrame([[0, 2, 4], [360, 180, 360]]); + const df2 = new DataFrame([[1, 2, 4], [10, 5, 0]]); + assert.deepEqual((df1.sub(df2) as DataFrame).values, [[-1, 0, 0], [350, 175, 360]]); + }); + + }); + + describe("mul", function () { + it("Return multiplication of DataFrame with a single Number", function () { + const data = [[0, 2, 4], [360, 180, 360]]; + const df = new DataFrame(data); + assert.deepEqual((df.mul(2) as DataFrame).values, [[0, 4, 8], [720, 360, 720]]); + }); + it("Return multiplication of a DataFrame with a Series along default axis 1", function () { + const data = [[0, 2, 4], [360, 180, 360]]; + const sf = new Series([1, 2, 1]); + const df = new DataFrame(data); + assert.deepEqual((df.mul(sf) as DataFrame).values, [[0, 4, 4], [360, 360, 360]]); + }); + it("Return multiplication of a DataFrame with a Series along axis 0", function () { + const data = [[0, 2, 4], [360, 180, 360]]; + const sf = new Series([1, 2]); + const df = new DataFrame(data); + assert.deepEqual((df.mul(sf, { axis: 0 }) as DataFrame).values, [[0, 2, 4], [720, 360, 720]]); + }); + it("Return multiplication of a DataFrame with a DataFrame along default axis 1", function () { + const df1 = new DataFrame([[0, 2, 4], [360, 180, 360]]); + const df2 = new DataFrame([[1, 2, 4], [10, 5, 0]]); + assert.deepEqual((df1.mul(df2) as DataFrame).values, [[0, 4, 16], [3600, 900, 0]]); + }); + it("Return multiplication of a DataFrame with a DataFrame along axis 0", function () { + const df1 = new DataFrame([[0, 2, 4], [360, 180, 360]]); + const df2 = new DataFrame([[1, 2, 4], [10, 5, 0]]); + assert.deepEqual((df1.mul(df2, { axis: 0 }) as DataFrame).values, [[0, 4, 16], [3600, 900, 0]]); + }); + + }); + + describe("div", function () { + it("Return division of DataFrame with a single Number", function () { + const data = [[0, 2, 4], [360, 180, 360]]; + const df = new DataFrame(data); + assert.deepEqual((df.div(2) as DataFrame).values, [[0, 1, 2], [180, 90, 180]]); + }); + it("Return division of a DataFrame with a Series along default axis 1", function () { + const data = [[0, 2, 4], [360, 180, 360]]; + const sf = new Series([1, 2, 1]); + const df = new DataFrame(data); + assert.deepEqual((df.div(sf) as DataFrame).values, [[0, 1, 4], [360, 90, 360]]); + }); + it("Return division of a DataFrame with a Series along axis 0", function () { + const data = [[0, 2, 4], [360, 180, 360]]; + const sf = new Series([1, 2]); + const df = new DataFrame(data); + assert.deepEqual((df.div(sf, { axis: 0 }) as DataFrame).values, [[0, 2, 4], [180, 90, 180]]); + }); + it("Return division of a DataFrame with a DataFrame along default axis 1", function () { + const df1 = new DataFrame([[0, 2, 4], [360, 180, 360]]); + const df2 = new DataFrame([[1, 2, 4], [10, 5, 0]]); + assert.deepEqual((df1.div(df2) as DataFrame).values, [[0, 1, 1], [36, 36, Infinity]]); + }); + it("Return division of same DataFrame along axis 0", function () { + const df1 = new DataFrame([[0, 2, 4], [360, 180, 360]]); + assert.deepEqual((df1.div(df1) as DataFrame).values, [[NaN, 1, 1], [1, 1, 1]]); + }); + it("Return division of a DataFrame with a DataFrame along axis 0", function () { + const df1 = new DataFrame([[0, 2, 4], [360, 180, 360]]); + const df2 = new DataFrame([[1, 2, 4], [10, 5, 0]]); + assert.deepEqual((df1.div(df2) as DataFrame).values, [[0, 1, 1], [36, 36, Infinity]]); + }); + + }); + + describe("pow", function () { + it("Return exponential of DataFrame with a single Number", function () { + const data = [[0, 2, 4], [360, 180, 360]]; + const df = new DataFrame(data); + assert.deepEqual((df.pow(2) as DataFrame).values, [[0, 4, 16], [129600, 32400, 129600]]); + }); + it("Return exponential of a DataFrame with a Series along default axis 1", function () { + const data = [[0, 2, 4], [360, 180, 360]]; + const sf = new Series([1, 2, 1]); + const df = new DataFrame(data); + assert.deepEqual((df.pow(sf) as DataFrame).values, [[0, 4, 4], [360, 32400, 360]]); + }); + it("Return exponential of a DataFrame with a Series along axis 0", function () { + const data = [[0, 2, 4], [360, 180, 360]]; + const sf = new Series([1, 2]); + const df = new DataFrame(data); + assert.deepEqual((df.pow(sf, { axis: 0 }) as DataFrame).values, [[0, 2, 4], [129600, 32400, 129600]]); + }); + it("Return exponential of a DataFrame with another DataFrame along default axis 1", function () { + const df1 = new DataFrame([[0, 2, 4], [3, 10, 4]]); + const df2 = new DataFrame([[1, 2, 4], [10, 5, 0]]); + assert.deepEqual((df1.pow(df2) as DataFrame).values, [[0, 4, 256], [59049, 100000, 1]]); + }); + it("Return exponential of a DataFrame with another DataFrame along axis 0", function () { + const df1 = new DataFrame([[0, 2, 4], [3, 10, 4]]); + const df2 = new DataFrame([[1, 2, 4], [10, 5, 0]]); + assert.deepEqual((df1.pow(df2, { axis: 0 }) as DataFrame).values, [[0, 4, 256], [59049, 100000, 1]]); + }); + + }); + + describe("mod", function () { + it("Return modulus of DataFrame with a single Number", function () { + const data = [[0, 2, 4], [360, 180, 360]]; + const df = new DataFrame(data); + assert.deepEqual((df.mod(2) as DataFrame).values, [[0, 0, 0], [0, 0, 0]]); + }); + it("Return modulus of a DataFrame with a Series along default axis 1", function () { + const data = [[0, 2, 4], [31, 15, 360]]; + const sf = new Series([1, 2, 1]); + const df = new DataFrame(data); + assert.deepEqual((df.mod(sf) as DataFrame).values, [[0, 0, 0], [0, 1, 0]]); + }); + it("Return modulus of a DataFrame with a Series along axis 0", function () { + const data = [[0, 2, 4], [31, 15, 360]]; + const sf = new Series([1, 2]); + const df = new DataFrame(data); + assert.deepEqual((df.mod(sf, { axis: 0 }) as DataFrame).values, [[0, 0, 0], [1, 1, 0]]); + }); + it("Return modulus of a DataFrame with a DataFrame along default axis 1", function () { + const df1 = new DataFrame([[0, 2, 4], [31, 15, 360]]); + const df2 = new DataFrame([[1, 2, 4], [10, 5, 0]]); + assert.deepEqual((df1.mod(df2) as DataFrame).values, [[0, 0, 0], [1, 0, NaN]]); + }); + it("Return modulus of a DataFrame with a DataFrame along axis 0", function () { + const df1 = new DataFrame([[0, 2, 4], [360, 180, 360]]); + const df2 = new DataFrame([[1, 2, 4], [10, 5, 0]]); + assert.deepEqual((df1.mod(df2) as DataFrame).values, [[0, 0, 0], [0, 0, NaN]]); + }); + + }); + + describe("mean", function () { + it("Returns the mean of a DataFrame (Default axis is [1:column])", function () { + const data = [[0, 2, 4], + [360, 180, 360]]; + const df = new DataFrame(data, { columns: ["col1", "col2", "col3"], index: ["row1", "row2"] }); + assert.deepEqual(df.mean().values, [2, 300]); + assert.deepEqual(df.mean().index, ["row1", "row2"]); + }); + it("Return mean of a DataFrame along axis 1 (column)", function () { + const data = [[0, 2, 4], [360, 180, 360]]; + const df = new DataFrame(data); + assert.deepEqual(df.mean({ "axis": 1 }).values, [2, 300]); + }); + it("Removes NaN before calculating mean of a DataFrame", function () { + const data = [[11, 20, 3], + [NaN, 15, 6], + [2, 30, 40], + [2, 89, 78]]; + const df = new DataFrame(data); + assert.deepEqual(df.mean({ "axis": 1 }).values, [11.333333333333334, 10.5, 24, 56.333333333333336]); + }); + it("Return mean of a DataFrame along axis 0 (column)", function () { + const data = [[0, 2, 4], [360, 180, 360]]; + const df = new DataFrame(data, { columns: ["col1", "col2", "col3"] }); + assert.deepEqual(df.mean({ "axis": 0 }).values, [180, 91, 182]); + assert.deepEqual(df.mean({ "axis": 0 }).index, ["col1", "col2", "col3"]); + + }); + it("Removes NaN before calculating mean of a DataFrame along axis 0 (column)", function () { + const data = [[11, 20, 3], + [NaN, 15, 6], + [2, 30, 40], + [2, 89, 78]]; + const df = new DataFrame(data); + assert.deepEqual(df.mean({ "axis": 0 }).values, [5, 38.5, 31.75]); + }); + }); + + describe("median", function () { + it("Returns the median of a DataFrame (Default axis is [1:column])", function () { + const data = [[0, 2, 4], [360, 180, 360]]; + const df = new DataFrame(data); + assert.deepEqual(df.median().values, [2, 360]); + }); + it("Return median of a DataFrame along axis 0 (row)", function () { + const data = [[0, 2, 4], [360, 180, 360]]; + const df = new DataFrame(data); + assert.deepEqual(df.median({ "axis": 0 }).values, [180, 91, 182]); + }); + + }); + + describe("mode", function () { + it("Returns the mode of a DataFrame (Default axis is [1:column])", function () { + const data = [[0, 2, 4, 2], [360, 180, 360]] + const df = new DataFrame(data) + assert.deepEqual(df.mode().values, [2, 360]) + }) + it("Returns the mode of a DataFrame with keep set to 1", function () { + const data = [[0, 2, 4, 2, 4], [360, 180, 360, 360]] + const df = new DataFrame(data) + assert.deepEqual(df.mode({ keep: 1 }).values, [4, 360]) + }) + it("Returns mode of a DataFrame along axis 0 (row)", function () { + const data = [[0, 2, 4], + [360, 180, 360], + [0, 2, 360]] + const df = new DataFrame(data) + assert.deepEqual(df.mode({ "axis": 0 }).values, [0, 2, 360]) + }) + it("Returns mode of a DataFrame along axis 0 for objects", function () { + const data = { "col1": [0, 2, 4, 0], "col2": [360, 180, 360, 360] } + const df = new DataFrame(data) + assert.deepEqual(df.mode({ "axis": 0 }).values, [0, 360]) + }) + + }) + + describe("min", function () { + it("Returns the minimum values in a DataFrame (Default axis is [1:column])", function () { + const data = [[0, 2, 4], [360, 180, 360]]; + const df = new DataFrame(data); + assert.deepEqual(df.min().values, [0, 180]); + }); + it("Returns the minimum values of a DataFrame along axis 0 (row)", function () { + const data = [[0, 2, 4], [360, 180, 360]]; + const df = new DataFrame(data); + assert.deepEqual(df.min({ "axis": 0 }).values, [0, 2, 4]); + }); + it("Returns the minimum values of a DataFrame along axis 0 (row)", function () { + const data = [[0, 2, 4], [360, 180, 360]]; + const df = new DataFrame(data); + assert.deepEqual(df.min({ "axis": 0 }).values, [0, 2, 4]); + }); + it("Returns the minimum values in a DataFrame-Default axis 1", function () { + const data = [[0, 2, 4], [360, 180, 360]]; + const df = new DataFrame(data); + assert.deepEqual(df.min().values, [0, 180]); + }); + + }); + + describe("max", function () { + it("Returns the maximum values in a DataFrame (Default axis is [1:column])", function () { + const data = [[0, 2, 4], [360, 180, 360]]; + const df = new DataFrame(data); + assert.deepEqual(df.max().values, [4, 360]); + }); + it("Returns the maximum values of a DataFrame along axis 0 (row)", function () { + const data = [[0, 2, 4], [360, 180, 360]]; + const df = new DataFrame(data); + assert.deepEqual(df.max({ "axis": 0 }).values, [360, 180, 360]); + }); + + }); + + describe("std", function () { + it("Returns the standard deviations of values in a DataFrame (Default axis is [1:column])", function () { + const data = [[0, 2, 4], [360, 180, 360]]; + const df = new DataFrame(data); + assert.deepEqual(df.std().values, [2, 103.92304845413264]); + }); + it("Return the standard deviations of values of a DataFrame along axis 0 (row)", function () { + const data = [[0, 2, 4], [360, 180, 360]]; + const df = new DataFrame(data); + assert.deepEqual(df.std({ axis: 0 }).values, [254.55844122715712, 125.86500705120545, 251.7300141024109]); + }); + + + }); + + describe("var", function () { + it("Returns the variance of values in a DataFrame (Default axis is [1:column])", function () { + const data = [[0, 2, 4], [360, 180, 360]]; + const df = new DataFrame(data); + assert.deepEqual(df.var().values, [4, 10800]); + }); + it("Return the variance of values of a DataFrame along axis 0 (row)", function () { + const data = [[0, 2, 4], [360, 180, 360]]; + const df = new DataFrame(data); + assert.deepEqual(df.var({ axis: 0 }).values, [64800, 15842, 63368]); + }); + + + }); + + describe("describe", function () { + it("Returns descriptive statistics of columns in a DataFrame created from an array", function () { + const data = [[0, 2, 4, "a"], + [360, 180, 360, "b"], + [2, 4, 6, "c"]]; + + const df = new DataFrame(data); + const res = [[3, 3, 3], [120.66666666666667, 62, 123.33333333333333], + [207.27115895206774, 102.19589032832974, 204.961785055979], + [0, 2, 4], [2, 4, 6], + [360, 180, 360], + [42961.33333333333, 10444, 42009.333333333336]]; + assert.deepEqual(df.describe().values, res); + }); + it("Returns descriptive statistics of columns in a DataFrame created from an Object", function () { + const data = { + "col1": [0, 2, 4], + "col2": [360, 180, 360], + "col3": [2, 4, 6], + "col4": ["boy", "girl", "man"], + "col5": ["apple", "car", "bee"] + }; + const df = new DataFrame(data); + + const res = [[3, 3, 3], [2, 300, 4], + [2, 103.92304845413264, 2], + [0, 180, 2], [2, 360, 4], + [4, 360, 6], + [4, 10800, 4]]; + assert.deepEqual(df.describe().values, res); + }); + + }); + + describe("count", function () { + it("Returns the count of non-nan values in a DataFrame (Default axis is [1:column])", function () { + const data = [[0, 2, 4], + [360, 180.1, 360.11], + [NaN, 2, 4], + [360, undefined, 360]]; + const df = new DataFrame(data); + assert.deepEqual(df.count().values, [3, 3, 2, 2]); + }); + it("Return the count of non NaN values of a DataFrame along axis 0", function () { + const data = [[0, 2, 4, NaN], + [360, undefined, 360, 70]]; + const df = new DataFrame(data); + assert.deepEqual(df.count({ axis: 0 }).values, [2, 1, 2, 1]); + }); + + }); + + describe("round", function () { + it("Rounds values in a DataFrame to 3dp", function () { + const data = [[10.1, 2.092, 4.23], [360.232244, 180.0190290, 36.902612]]; + const df = new DataFrame(data); + const expected = [[10.1, 2.092, 4.23], [360.232, 180.0190, 36.903]]; + assert.deepEqual((df.round(3) as DataFrame).values as number[][], expected); + }); + it("Rounds values in a DataFrame to 1dp, inplace", function () { + const data = [[10.1, 2.092, 4.23], [360.232244, 180.0190290, 36.902612]]; + const df = new DataFrame(data); + const expected = [[10.1, 2.1, 4.2], [360.2, 180.0, 36.9]]; + df.round(1, { inplace: true }) as DataFrame + assert.deepEqual(df.values as number[][], expected); + }); + it("Rounds values in a DataFrame to 3dp with missing values", function () { + const data = [[10.1, 2.092, null], [360.232244, null, 36.902612]]; + const df = new DataFrame(data); + const expected = [[10.1, 2.092, null], [360.232, null, 36.903]]; + assert.deepEqual((df.round(3) as DataFrame).values as number[][], expected); + }); + + }); + + describe("sortValues", function () { + it("Sort values in DataFrame by specified column in ascending order (Default)", function () { + const data = [[0, 2, 4, "a"], + [360, 180, 360, "b"], + [2, 4, 6, "c"]]; + + const df = new DataFrame(data, { "columns": ["col1", "col2", "col3", "col4"], index: ["a", 1, "c"] }); + df.sortValues("col1", { inplace: true }); + const expected = [[0, 2, 4, "a"], [2, 4, 6, "c"], [360, 180, 360, "b"]]; + assert.deepEqual(df.values, expected); + assert.deepEqual(df.index, ["a", "c", 1]); + + }); + + it("Sort values in DataFrame by specified column in ascending order (Default)", function () { + const data = [[0, 2, 4, "a"], + [360, 180, 1, "b"], + [2, 4, 6, "c"]]; + + const df = new DataFrame(data, { "columns": ["col1", "col2", "col3", "col4"] }); + const df_sort = df.sortValues("col3") as DataFrame + const expected = [[360, 180, 1, "b"], [0, 2, 4, "a"], [2, 4, 6, "c"]]; + assert.deepEqual(df_sort.values, expected); + assert.deepEqual(df_sort.index, [1, 0, 2]); + + }); + it("Sort values in DataFrame by specified column in descending order", function () { + const data = [[0, 2, 4, "a"], + [360, 180, 360, "b"], + [2, 4, 6, "c"]]; + + const df = new DataFrame(data, { "columns": ["col1", "col2", "col3", "col4"] }); + const expected = [[360, 180, 360, "b"], [2, 4, 6, "c"], [0, 2, 4, "a"]]; + assert.deepEqual((df.sortValues("col1", { "ascending": false }) as DataFrame).values, expected); + }); + + it("Sort values in DataFrame by specified column in descending order (second col)", function () { + const data = [[0, 2, 4, "a"], + [360, 180, 1, "b"], + [2, 4, 6, "c"]]; + + const df = new DataFrame(data, { "columns": ["col1", "col2", "col3", "col4"] }); + const expected = [[2, 4, 6, "c"], [0, 2, 4, "a"], [360, 180, 1, "b"]]; + assert.deepEqual((df.sortValues("col3", { "ascending": false }) as DataFrame).values, expected); + }); + it("Sort values in DataFrame by specified column containing alpha(numeric) values", function () { + const data = [[0, 2, 4, "a"], + [360, 180, 1, "b"], + [2, 4, 6, "c"]]; + + const df = new DataFrame(data, { "columns": ["col1", "col2", "col3", "col4"] }); + const expected = [[2, 4, 6, 'c'], [360, 180, 1, 'b'], [0, 2, 4, 'a']]; + assert.deepEqual((df.sortValues("col4", { "ascending": false }) as DataFrame).values, expected); + }); + it("Sort duplicate DataFrame with duplicate columns", function () { + + const data = { + "A": [1, 2, 3, 4, 5, 3, 5, 6, 4, 5, 3, 4], + "B": [2, 3, 4, 5, 6, 7, 8, 9, 1, 2, 3, 4] + }; + + const df = new DataFrame(data); + const expected = [[1, 2], + [2, 3], + [3, 4], + [3, 7], + [3, 3], + [4, 5], + [4, 1], + [4, 4], + [5, 6], + [5, 8], + [5, 2], + [6, 9]]; + assert.deepEqual((df.sortValues("A", { "ascending": true }) as DataFrame).values, expected); + }); + it("sort index in descending order and retains index", function () { + let data = [[0, 2, 4, "b"], + [360, 180, 360, "a"], + [2, 4, 6, "c"]]; + + let df = new DataFrame(data, { "columns": ["col1", "col2", "col3", "col4"], index: ["b", "a", "c"] }); + let df2 = df.sortIndex({ ascending: false }); + let rslt = ["c", "b", "a"]; + + assert.deepEqual(df2.index, rslt); + }); + }); + + describe("copy", function () { + it("Makes a deep copy of DataFrame", function () { + const data = [[0, 2, 4], [360, 180, 360]]; + const df = new DataFrame(data); + const df_copy = df.copy(); + assert.deepEqual(df_copy.values, [[0, 2, 4], [360, 180, 360]]); + }); + it("Confirms child copy modification does not affect parent DataFrame", function () { + const data = [[0, 2, 4], [360, 180, 360]]; + const df = new DataFrame(data); + const df_copy = df.copy(); + df_copy.addColumn("col_new", ["boy", "girl"], { inplace: true }); + assert.notDeepEqual(df_copy.values, df.values); + assert.notDeepEqual(df_copy, df); + }); + + }); + + + describe("setIndex", function () { + it("Sets the index of a DataFrame created from an Object inplace", function () { + const data = [{ alpha: "A", val_count: 1 }, { alpha: "B", val_count: 2 }, { alpha: "C", val_count: 3 }]; + const df = new DataFrame(data); + df.setIndex({ index: ["one", "two", "three"], inplace: true }) + assert.deepEqual(df.index, ["one", "two", "three"]); + }); + it("Sets the index of a DataFrame from column name", function () { + const data = [{ alpha: "A", val_count: 1 }, { alpha: "B", val_count: 2 }, { alpha: "C", val_count: 3 }]; + const df = new DataFrame(data); + const df_new = df.setIndex({ column: "alpha" }) as DataFrame + assert.deepEqual(df_new.index, ["A", "B", "C"]); + }); + it("Sets the index of a DataFrame from column name", function () { + const data = [{ alpha: "A", val_count: 1 }, { alpha: "B", val_count: 2 }, { alpha: "C", val_count: 3 }]; + const df = new DataFrame(data); + const df_new = df.setIndex({ column: "alpha" }) as DataFrame + assert.deepEqual(df_new.index, ["A", "B", "C"]); + }); + it("Sets the index of a DataFrame from column name and drop column data (inplace)", function () { + const data = [{ alpha: "A", val_count: 1 }, { alpha: "B", val_count: 2 }, { alpha: "C", val_count: 3 }]; + const df = new DataFrame(data); + df.setIndex({ column: "alpha", drop: true, inplace: true }); + assert.deepEqual(df.index, ["A", "B", "C"]); + assert.deepEqual(df.columns, ["val_count"]); + assert.deepEqual(df.values, [[1], [2], [3]]); + assert.throws(() => df["alpha"], Error, + "ParamError: Column not found!. Column name must be one of val_count" + ); + }); + it("Throw error if index and column not passed", function () { + const data = [{ alpha: "A", val_count: 1 }, { alpha: "B", val_count: 2 }, { alpha: "C", val_count: 3 }]; + const df = new DataFrame(data); + assert.throws(() => df.setIndex({ drop: true, inplace: true }), Error, + "ParamError: must specify either index or column" + ); + }); + + it("Sets the index of a DataFrame from column name and drop column data", function () { + const data = [{ alpha: "A", val_count: 1 }, { alpha: "B", val_count: 2 }, { alpha: "C", val_count: 3 }]; + const df = new DataFrame(data); + const df_new = df.setIndex({ column: "alpha", drop: true }) as DataFrame + assert.deepEqual(df_new.index, ["A", "B", "C"]); + assert.deepEqual(df_new.columns, ["val_count"]); + assert.deepEqual(df_new.values, [[1], [2], [3]]); + assert.deepEqual(df_new["alpha"], undefined); + }); + it("Sets the index of a DataFrame created from an Array inplace", function () { + const data = [[0, 2, 4], [360, 180, 360], [0, 2, 4], [360, 180, 360], [0, 2, 4]]; + const df = new DataFrame(data); + df.setIndex({ index: ["one", "two", "three", "four", "five"], "inplace": true }); + assert.deepEqual(df.index, ["one", "two", "three", "four", "five"]); + }); + it("Throws error on wrong row length", function () { + const data = [[0, 2, 4], [360, 180, 360]]; + const df = new DataFrame(data); + assert.throws(() => df.setIndex({ index: ["one", "two", "three", "four", "five"], "inplace": true }), Error, + "ParamError: index must be the same length as the number of rows" + ); + }) + it("Throws error on column not found", function () { + const data = [[0, 2, 4], [360, 180, 360]]; + const df = new DataFrame(data); + assert.throws(() => df.setIndex({ column: "alpha", inplace: true }), Error, + "ParamError: column not found in column names" + ); + }) + + }); + + describe("resetIndex", function () { + it("Resets the index of a DataFrame created from an Object", function () { + const data = [{ alpha: "A", val_count: 1 }, { alpha: "B", val_count: 2 }, { alpha: "C", val_count: 3 }]; + const df = new DataFrame(data, { index: ["one", "two", "three"] }); + const df_reset = df.resetIndex() as DataFrame + assert.deepEqual(df_reset.index, [0, 1, 2]); + }); + it("Resets the index of a DataFrame created from an Array inplace", function () { + const data = [[0, 2, 4], [360, 180, 360], [0, 2, 4], [360, 180, 360], [0, 2, 4]]; + const df = new DataFrame(data, { index: ["one", "two", "three", "four", "five"] }); + df.resetIndex({ inplace: true }); + assert.deepEqual(df.index, [0, 1, 2, 3, 4]); + }); + }); + + + describe("apply", function () { + + it("Apply an aggregation function to a DataFrame axis = 1", function () { + const data = [[0, 2, 4], + [3, 2, 2], + [0, 2, 1]] + const df = new DataFrame(data) + + const sum = (x: any) => { + return x.reduce((a: any, b: any) => a + b, 0) + } + const expected = [6, 7, 3] + const dfApply = df.apply(sum, { axis: 1 }) + assert.deepEqual(dfApply.values, expected) + assert.deepEqual(dfApply.index, [0, 1, 2]) + + }); + it("Apply an aggregation function to a DataFrame axis = 0", function () { + const data = [[0, 2, 4], + [3, 2, 2], + [0, 2, 1]] + const df = new DataFrame(data, { columns: ["col1", "col2", "col3"] }) + + const sum = (x: any) => { + return x.reduce((a: any, b: any) => a + b, 0) + } + const expected = [3, 6, 7] + const dfApply = df.apply(sum, { axis: 0 }) + assert.deepEqual(dfApply.values, expected) + assert.deepEqual(dfApply.index, ["col1", "col2", "col3"]) + }); + + }); + + describe("applyMap", function () { + it("Apply an element-wise function to a DataFrame", function () { + const data = [[0, 2, 4], + [360, 180, 360], + [0, 2, 4]] + const df = new DataFrame(data) + + const add = (x: any) => { + return x + 1000 + } + const expected = [[1000, 1002, 1004], [1360, 1180, 1360], [1000, 1002, 1004]] + const dfApply = df.applyMap(add) as DataFrame + assert.deepEqual(dfApply.values, expected) + }); + + it("Apply an element-wise function to a DataFrame", function () { + const data = [[0, 2, 4], + [360, 180, 360], + [0, 2, 4], + [0, 2, 4]] + const df = new DataFrame(data) + + const add = (x: any) => { + return x + 1000 + } + const expected = [[1000, 1002, 1004], [1360, 1180, 1360], [1000, 1002, 1004], [1000, 1002, 1004]] + const dfApply = df.applyMap(add) as DataFrame + assert.deepEqual(dfApply.values, expected) + }); + + it("Apply an element-wise function to a DataFrame inplace", function () { + const data = [[0, 2, 4], + [360, 180, 360], + [0, 2, 4]] + const df = new DataFrame(data) + + const add = (x: any) => { + return x + 1000 + } + const expected = [[1000, 1002, 1004], [1360, 1180, 1360], [1000, 1002, 1004]] + df.applyMap(add, { inplace: true }) + assert.deepEqual(df.values, expected) + }); + + it("Apply an element-wise function to a DataFrame inplace", function () { + const data = [[0, 2, 4], + [360, 180, 360], + [0, 2, 4], + [0, 2, 4]] + const df = new DataFrame(data) + + const add = (x: any) => { + return x + 1000 + } + const expected = [[1000, 1002, 1004], [1360, 1180, 1360], [1000, 1002, 1004], [1000, 1002, 1004]] + df.applyMap(add, { inplace: true }) + assert.deepEqual(df.values, expected) + }); + }); + + describe("column", function () { + it("Obtain a column from a dataframe created from object", function () { + const data = [{ alpha: "A", val_count: 1 }, { alpha: "B", val_count: 2 }, { alpha: "C", val_count: 3 }]; + const options = { columns: ["Gender", "count"] }; + const df = new DataFrame(data, options); + const expected = df.column("count"); + const rslt_data = [1, 2, 3]; + assert.deepEqual(expected.values, rslt_data); + }); + it("Obtain a column from a dataframe", function () { + const data = [[1, 2, 3], [4, 5, 6], [20, 30, 40], [39, 89, 78]]; + const cols = ["A", "B", "C"]; + const df = new DataFrame(data, { columns: cols }); + const expected = df.column("C"); + const rslt_data = [3, 6, 40, 78]; + assert.deepEqual(expected.values, rslt_data); + }); + it("Throw Error for wrong column", function () { + const data = [[1, 2, 3], [4, 5, 6], [20, 30, 40], [39, 89, 78]]; + const cols = ["A", "B", "C"]; + const df = new DataFrame(data, { columns: cols }); + assert.throws(() => { df.column("D"); }, Error, "ParamError: Column not found!. Column name must be one of A,B,C"); + + }); + }); + + + describe("dropNa", function () { + it("drop NaNs along axis 0", function () { + const data = [[0, 2, 4], + [360, 180, 360], + [NaN, 180, 360]] + const column = ["A", "B", "C"]; + const df = new DataFrame(data, { columns: column }); + const df_val = [[2, 4], + [180, 360], + [180, 360]] + assert.deepEqual((df.dropNa({ axis: 0 }) as DataFrame).values, df_val); + + }); + it("drop NaNs along axis 1", function () { + const data = [[0, 2, 4], + [360, 180, 360], + [NaN, 180, 360]] + const column = ["A", "B", "C"]; + const df = new DataFrame(data, { columns: column }); + const df_val = [[0, 2, 4], + [360, 180, 360]] + assert.deepEqual((df.dropNa({ axis: 1 }) as DataFrame).values, df_val); + + }); + it("drop NaNs along axis 1", function () { + const data = [[NaN, 1, 2, 3], [3, 4, NaN, 9], [5, 6, 7, 8]]; + const column = ["A", "B", "C", "D"]; + const df = new DataFrame(data, { columns: column }); + const df_val = [[5, 6, 7, 8]]; + assert.deepEqual((df.dropNa({ axis: 1 }) as DataFrame).values, df_val); + + }); + it("drop inplace at axis 0, inplace false ", function () { + const data = [[NaN, 1, 2, 3], [3, 4, NaN, 9], [5, 6, 7, 8]]; + const column = ["A", "B", "C", "D"]; + const df = new DataFrame(data, { columns: column }); + + const df_val = [[1, 3], [4, 9], [6, 8]]; + + assert.deepEqual((df.dropNa({ axis: 0 }) as DataFrame).values, df_val); + + }); + it("drop inplace at axis 0, inplace true ", function () { + const data = [[NaN, 1, 2, 3], [3, 4, NaN, 9], [5, 6, 7, 8]]; + const column = ["A", "B", "C", "D"]; + const df = new DataFrame(data, { columns: column }); + + const df_val = [[1, 3], [4, 9], [6, 8]]; + df.dropNa({ axis: 0, inplace: true }); + assert.deepEqual(df.values, df_val); + + }); + it("drop inplace at axis 1 at inplace true", function () { + const data = [[NaN, 1, 2, 3], [3, 4, NaN, 9], [5, 6, 7, 8]]; + const column = ["A", "B", "C", "D"]; + const df = new DataFrame(data, { columns: column }); + + const df_val = [[5, 6, 7, 8]]; + + df.dropNa({ axis: 1, inplace: true }); + assert.deepEqual(df.values, df_val); + + }); + it("drop works for undefined values", function () { + let data = [[null, 1, 2, 3], [3, 4, undefined, 9], [5, 6, 7, 8]]; + let column = ["A", "B", "C", "D"]; + let df = new DataFrame(data, { columns: column }); + + let df_val = [[5, 6, 7, 8]]; + + df.dropNa({ axis: 1, inplace: true }); + assert.deepEqual(df.values, df_val); + + }); + }); + + describe("isNa", function () { + + it("check if values are empty (element-wise", function () { + const data = [[NaN, 1, 2, 3], [3, 4, undefined, 9], [5, 6, 7, 8]]; + const column = ["A", "B", "C", "D"]; + const df = new DataFrame(data, { columns: column }); + + const df_val = [ + [true, false, false, false], + [false, false, true, false], + [false, false, false, false] + ]; + const dfNew = df.isNa() + assert.deepEqual(dfNew.values, df_val); + assert.deepEqual(dfNew.dtypes, ["boolean", "boolean", "boolean", "boolean"]); + assert.deepEqual(dfNew.columns, column); + }); + }); + + describe("fillNa", function () { + + it("replace all NaN value inplace", function () { + const data = [[NaN, 1, 2, 3], [3, 4, NaN, 9], [5, 6, 7, 8]]; + const columns = ["A", "B", "C", "D"]; + const df = new DataFrame(data, { columns: columns }); + + const expected = [[-999, 1, 2, 3], [3, 4, -999, 9], [5, 6, 7, 8]]; + df.fillNa(-999, { inplace: true }); + assert.deepEqual(df.values, expected); + }); + it("replace all undefined value", function () { + const data = [[undefined, 1, 2, 3], [3, 4, null, 9], [5, 6, 7, 8]]; + const columns = ["A", "B", "C", "D"]; + const df = new DataFrame(data, { columns: columns }); + + const expected = [[-999, 1, 2, 3], [3, 4, -999, 9], [5, 6, 7, 8]]; + + const df_filled = df.fillNa(-999) as DataFrame + assert.deepEqual(df_filled.values, expected); + }); + it("replace all undefined value with 0 works", function () { + const data = [[undefined, 1, 2, 3], [3, 4, null, 9], [5, NaN, 7, 8]]; + const columns = ["A", "B", "C", "D"]; + const df = new DataFrame(data, { columns: columns }); + + const expected = [[0, 1, 2, 3], [3, 4, 0, 9], [5, 0, 7, 8]]; + + const df_filled = df.fillNa(0) as DataFrame + assert.deepEqual(df_filled.values, expected); + }); + + + it("Fills only a specified column", function () { + const data = [[1, 2, 3], + [4, 5, 6], + [20, NaN, 40], + [39, NaN, NaN]]; + const cols = ["A", "B", "C"]; + const df = new DataFrame(data, { columns: cols }); + const expected = [[1, 2, 3], [4, 5, 6], [20, 2, 40], [39, 2, NaN]]; + const df_filled = df.fillNa([2], { columns: ["B"] }) as DataFrame; + + assert.deepEqual(df_filled.values, expected); + }); + it("Fills column with specified values not in place", function () { + const data = [[1, 2, 3], [4, 5, 6], [NaN, 20, 40], [NaN, -1, 78]]; + const cols = ["A", "B", "C"]; + const df = new DataFrame(data, { columns: cols }); + const new_vals = [[1, 2, 3], [4, 5, 6], [-2, 20, 40], [-2, -1, 78]]; + const df_filled = df.fillNa([-2], { columns: ["A"] }) as DataFrame + + assert.deepEqual(df_filled.values, new_vals); + }); + + it("Fills a list of columns with specified values", function () { + const data = [[1, undefined, 3], [4, undefined, 6], [NaN, "boy", 40], [NaN, "girl", NaN]]; + const cols = ["A", "B", "C"]; + const df = new DataFrame(data, { columns: cols }); + const new_vals = [[1, "girl", 3], [4, "girl", 6], [200, "boy", 40], [200, "girl", NaN]]; + const df_filled = df.fillNa([200, "girl"], { columns: ["A", "B"] }) as DataFrame + assert.deepEqual(df_filled.values, new_vals); + }); + it("Fills a list of columns with specified values inplace", function () { + const data = [[1, undefined, 3], [4, undefined, 6], [NaN, "boy", 40], [NaN, "girl", 78]]; + const cols = ["A", "B", "C"]; + const df = new DataFrame(data, { columns: cols }); + const new_vals = [[1, "girl", 3], [4, "girl", 6], [200, "boy", 40], [200, "girl", 78]]; + df.fillNa([200, "girl"], { columns: ["A", "B"], inplace: true }); + assert.deepEqual(df.values, new_vals); + }); + }); + + describe("selectDtypes", function () { + + it("Returns float columns in a DataFrame", function () { + const data = [[30, 1, 2, "boy"], [3.2, 4, 30, "girl"], [5.09, 6, 7, "cat"]]; + const column = ["A", "B", "C", "D"]; + const df = new DataFrame(data, { columns: column }); + const df_sub = df.selectDtypes(['float32']); + assert.deepEqual(df_sub.values, [[30], [3.2], [5.09]]); + }); + + it("Returns int columns in a DataFrame", function () { + const data = [[30, 1, 2, "boy"], + [3.2, 4, 30, "girl"], + [5.09, 6, 7, "cat"]]; + const column = ["A", "B", "C", "D"]; + const df = new DataFrame(data, { columns: column }); + const df_sub = df.selectDtypes(['int32']); + assert.deepEqual(df_sub.values, [[1, 2], [4, 30], [6, 7]]); + }); + + it("Returns string columns in a DataFrame", function () { + const data = [[30, 1, 2, "boy"], + [3.2, 4, 30, "girl"], + [5.09, 6, 7, "cat"]]; + const column = ["A", "B", "C", "D"]; + const df = new DataFrame(data, { columns: column }); + const df_sub = df.selectDtypes(['string']); + assert.deepEqual(df_sub.values, [["boy"], ["girl"], ["cat"]]); + }); + + it("Returns string and float columns in a DataFrame", function () { + const data = [[30, 1, 2, "boy"], + [3.2, 4, 30, "girl"], + [5.09, 6, 7, "cat"]]; + const column = ["A", "B", "C", "D"]; + const df = new DataFrame(data, { columns: column }); + const df_sub = df.selectDtypes(['string', 'float32']); + assert.deepEqual(df_sub.values, [[30, "boy"], [3.2, "girl"], [5.09, "cat"]]); + }); + + it("Returns int and float columns in a DataFrame", function () { + const data = [[30, 1, 2, "boy"], + [3.2, 4, 30, "girl"], + [5.09, 6, 7, "cat"]]; + const column = ["A", "B", "C", "D"]; + const df = new DataFrame(data, { columns: column }); + const df_sub = df.selectDtypes(['int32', 'float32']); + assert.deepEqual(df_sub.values, [[30, 1, 2], [3.2, 4, 30], [5.09, 6, 7]]); + }); + }); + + + describe("lt", function () { + it("Returns Less than of DataFrame and other DataFrame (element-wise)", function () { + const data1 = [[10, 45, 56, 10], [25, 23, 20, 10]]; + const data2 = [[100, 450, 590, 5], [25, 2, 0, 10]]; + + const df = new DataFrame(data1); + const df2 = new DataFrame(data2); + const expected = [[true, true, true, false], + [false, false, false, false]]; + assert.deepEqual(df.lt(df2).values, expected); + }); + + it("Return Less than of series and scalar (element-wise)", function () { + const data1 = [[10, 45, 56, 10], [25, 23, 20, 10]]; + const sf = new DataFrame(data1); + const expected = [[true, false, false, true], + [true, true, true, true]]; + assert.deepEqual(sf.lt(30).values, expected); + }); + it("Return Less than of series and DataFrame along axis 1", function () { + const data1 = [[10, 45, 56, 10], + [23, 20, 10, 10]]; + const sf = new Series([10, 23, 56, 100]); + const df = new DataFrame(data1); + const expected = [[false, false, false, true], [false, true, true, true]]; + assert.deepEqual(df.lt(sf, { axis: 1 }).values, expected); + }); + + it("Return Less than of Array and DataFrame along axis 1", function () { + const data1 = [[10, 45, 56, 10], [23, 20, 10, 10]]; + const sf = [10, 23, 56, 100]; + const df = new DataFrame(data1); + const expected = [[false, false, false, true], [false, true, true, true]]; + assert.deepEqual(df.lt(sf, { axis: 1 }).values, expected); + }); + it("Return Less than of series and DataFrame along axis 0", function () { + const data1 = [[10, 45, 56, 10], + [23, 20, 10, 10]]; + const sf = new Series([10, 23]); + const df = new DataFrame(data1); + const expected = [[false, false, false, false], [false, true, true, true]]; + const result = df.lt(sf, { axis: 0 }) + assert.deepEqual(result.values, expected); + }); + + it("Return Less than of Array and DataFrame along axis 0", function () { + const data1 = [[10, 45, 56, 10], [23, 20, 10, 10]]; + const sf = [10, 23]; + const df = new DataFrame(data1); + const expected = [[false, false, false, false], [false, true, true, true]]; + const result = df.lt(sf, { axis: 0 }) + assert.deepEqual(result.values, expected); + }); + + }); + + describe("gt", function () { + it("Return Greater than of series and other series (element-wise)", function () { + const data1 = [[10, 45, 56, 10], [25, 23, 20, 10]]; + const data2 = [[100, 450, 590, 5], [25, 2, 0, 10]]; + + const df = new DataFrame(data1); + const df2 = new DataFrame(data2); + const expected = [[false, false, false, true], [false, true, true, false]]; + assert.deepEqual(df.gt(df2).values, expected); + }); + + it("Return Greater than of series scalar (element-wise)", function () { + const data1 = [[10, 45, 56, 10], [25, 23, 20, 10]]; + const sf = new DataFrame(data1); + const expected = [[false, true, true, false], [false, false, false, false]]; + assert.deepEqual(sf.gt(30).values, expected); + }); + + it("Return Less than of Array and DataFrame scalar along axis 1", function () { + const data1 = [[10, 45, 56, 10], [23, 20, 10, 10]]; + const sf = [10, 23, 56, 100]; + const df = new DataFrame(data1); + const expected = [[false, true, false, false], [true, false, false, false]]; + assert.deepEqual(df.gt(sf, { axis: 1 }).values, expected); + }); + it("Return Less than of Array and DataFrame scalar along axis 0", function () { + const data1 = [[10, 45, 56, 10], [23, 20, 10, 10]]; + const sf = [10, 23]; + const df = new DataFrame(data1); + const expected = [[false, true, true, false], [false, false, false, false]]; + assert.deepEqual(df.gt(sf, { axis: 0 }).values, expected); + }); + + }); + + describe("le", function () { + it("Return Less than or Equal to of series and other series (element-wise)", function () { + const data1 = [[10, 45, 56, 10], [25, 23, 20, 10]]; + const data2 = [[100, 450, 590, 5], [25, 2, 0, 10]]; + + const df = new DataFrame(data1); + const df2 = new DataFrame(data2); + const expected = [[true, true, true, false], [true, false, false, true]]; + assert.deepEqual(df.le(df2).values, expected); + }); + + it("Return Less than or Equal to of series scalar (element-wise)", function () { + const data1 = [[10, 45, 56, 10], [25, 23, 30, 10]]; + const sf = new DataFrame(data1); + const expected = [[true, false, false, true], [true, true, true, true]]; + assert.deepEqual(sf.le(30).values, expected); + }); + + }); + + describe("ge", function () { + it("Return Greater than or Equal to of series and other series (element-wise)", function () { + const data1 = [[10, 45, 56, 10], [25, 23, 20, 10]]; + const data2 = [[100, 450, 590, 5], [25, 2, 0, 10]]; + + const df = new DataFrame(data1); + const df2 = new DataFrame(data2); + const expected = [[false, false, false, true], [true, true, true, true]]; + assert.deepEqual(df.ge(df2).values, expected); + }); + + it("Return Greater than or Equal to of series scalar (element-wise)", function () { + const data1 = [[10, 45, 56, 10], [25, 23, 30, 10]]; + const sf = new DataFrame(data1); + const expected = [[false, true, true, false], [false, false, true, false]]; + assert.deepEqual(sf.ge(30).values, expected); + }); + + }); + + describe("ne", function () { + it("Return Not Equal to of series and other series (element-wise)", function () { + const data1 = [[10, 45, 56, 10], [25, 23, 20, 10]]; + const data2 = [[100, 450, 590, 5], [25, 2, 0, 10]]; + + const df = new DataFrame(data1); + const df2 = new DataFrame(data2); + const expected = [[true, true, true, true], [false, true, true, false]]; + assert.deepEqual(df.ne(df2).values, expected); + }); + + it("Return Not Equal to of series scalar (element-wise)", function () { + const data1 = [[10, 45, 56, 10], [25, 23, 30, 10]]; + const sf = new DataFrame(data1); + const expected = [[true, true, true, true], [true, true, false, true]]; + assert.deepEqual(sf.ne(30).values, expected); + }); + it("Return Less than of Array and DataFrame along axis 1 (column)", function () { + const data = { + 'cost': [250, 150, 100], + 'revenue': [100, 250, 300] + } + const sf = [100, 300] + const df = new DataFrame(data, { index: ['A', 'B', 'C'] }) + const expected = [[true, true], [true, true], [false, false]]; + const result = df.ne(sf, { axis: 1 }) + assert.deepEqual(result.values, expected); + }); + + }); + + describe("eq", function () { + it("Return Equal to of DataFrame and other DataFrame (element-wise)", function () { + const data1 = [[10, 45, 56, 10], [25, 23, 20, 10]]; + const data2 = [[100, 450, 590, 5], [25, 2, 0, 10]]; + + const df = new DataFrame(data1); + const df2 = new DataFrame(data2); + const expected = [[false, false, false, false], [true, false, false, true]]; + assert.deepEqual(df.eq(df2).values, expected); + }); + + it("Return Equal to of DataFrame with scalar (element-wise)", function () { + const data1 = [[10, 45, 56, 10], [25, 23, 30, 10]]; + const sf = new DataFrame(data1); + const expected = [[false, false, false, false], [false, false, true, false]]; + assert.deepEqual(sf.eq(30).values, expected); + }); + it("Return Equal to of series and DataFrame scalar along axis 1", function () { + const data1 = { "Col1": [10, 45, 56, 10], "Col2": [23, 20, 10, 10] }; + const sf = new Series([10, 23]); + const df = new DataFrame(data1); + const expected = [[true, true], [false, false], [false, false], [true, false]]; + assert.deepEqual(df.eq(sf, { axis: 1 }).values, expected); + }); + it("Return Less than of Array and DataFrame along axis 0", function () { + const data = { + 'cost': [250, 150, 100], + 'revenue': [100, 250, 300] + } + const sf = [250, 250, 100] + const df = new DataFrame(data, { index: ['A', 'B', 'C'] }) + const expected = [[true, false], [false, true], [true, false]]; + const result = df.eq(sf, { axis: 0 }) + assert.deepEqual(result.values, expected); + }); + + }); + + describe("replace", function () { + it("Replace across all columns", function () { + const data1 = [[10, 45, 56, 25], [23, 20, 10, 24]]; + const df = new DataFrame(data1); + const expected = [[-999, 45, 56, 25], [23, 20, -999, 24]]; + const df_rep = df.replace(10, -999) as DataFrame + assert.deepEqual(df_rep.values, expected); + }); + + it("Replace accross all columns inplace", function () { + const data1 = [["A", "A", "A", "B"], ["B", "C", "C", "D"]]; + const df = new DataFrame(data1); + const expected = [["boy", "boy", "boy", "B"], ["B", "C", "C", "D"]]; + df.replace("A", "boy", { inplace: true }); + assert.deepEqual(df.values, expected); + }); + it("Replace values in specified two column(s)", function () { + const data1 = [["A", "A", 1, "girl"], + ["B", "A", 2, "woman"], + ["A", "B", 3, "man"]]; + const df = new DataFrame(data1, { columns: ["col1", "col2", "col3", "col4"] }); + const expected = [["boy", "boy", 1, "girl"], + ["B", "boy", 2, "woman"], + ["boy", "B", 3, "man"]]; + const df_rep = df.replace("A", "boy", { columns: ["col1", "col2"] }) as DataFrame + assert.deepEqual(df_rep.values, expected); + }); + + it("Replace values in specified single column(s)", function () { + const data1 = [[2, "A", 1, "girl"], + [3, "A", 2, "woman"], + [4, "B", 3, "man"]]; + const df = new DataFrame(data1, { columns: ["col1", "col2", "col3", "col4"] }); + const expected = [[2, "A", 1, "girl"], + [100, "A", 2, "woman"], + [4, "B", 3, "man"]]; + const df_rep = df.replace(3, 100, { columns: ["col1"] }) as DataFrame + assert.deepEqual(df_rep.values, expected); + assert.notDeepEqual(df_rep, df); + assert.notDeepEqual(df_rep.values, df.values); + }); + + it("Replace values in specified two column(s) inplace", function () { + const data1 = [["A", "A", 1, "girl"], + ["B", "A", 2, "woman"], + ["A", "B", 3, "man"]]; + const df = new DataFrame(data1, { columns: ["col1", "col2", "col3", "col4"] }); + const expected = [["boy", "boy", 1, "girl"], + ["B", "boy", 2, "woman"], + ["boy", "B", 3, "man"]]; + df.replace("A", "boy", { columns: ["col1", "col2"], inplace: true }); + assert.deepEqual(df.values, expected); + }); + + it("Replace values in specified single column(s) inplace", function () { + const data1 = [[2, "A", 1, "girl"], + [3, "A", 2, "woman"], + [4, "B", 3, "man"]]; + const df = new DataFrame(data1, { columns: ["col1", "col2", "col3", "col4"] }); + const expected = [[2, "A", 1, "girl"], + [100, "A", 2, "woman"], + [4, "B", 3, "man"]]; + df.replace(3, 100, { columns: ["col1"], inplace: true }); + assert.deepEqual(df.values, expected); + }); + + }); + + describe("sum", function () { + it("Sum values of a DataFrame by Default axis column (axis=1)", function () { + const data1 = [[30, 40, 3.1], + [5, 5, 5.1], + [5, 5, 3.2]]; + const sf = new DataFrame(data1); + const res = [73.1, 15.1, 13.2]; + assert.deepEqual(sf.sum().values, res); + }); + it("Sum values of a DataFrame along row axis (axis=0)", function () { + const data1 = [[30, 40, 3.1], + [5, 5, 5.1], + [5, 5, 3.2]]; + const df = new DataFrame(data1); + const res = [40, 50, 11.399999999999999]; + assert.deepEqual(df.sum({ axis: 0 }).values, res); + }); + it("Sum values of a mixed DataFrame along row axis (axis=0)", function () { + const data1 = [[30, 40, 3.1, true], + [5, 5, 5.1, true], + [5, 5, 3.2, true]]; + const df = new DataFrame(data1); + const res = [40, 50, 11.399999999999999, 3]; + assert.deepEqual(df.sum({ axis: 0 }).values, res); + }); + it("Sum values of a boolean DataFrame along row axis (axis=0)", function () { + const data1 = [[true, true, false, true], + [false, false, false, false], + [false, true, true, false]]; + const df = new DataFrame(data1); + const res = [1, 2, 1, 1]; + assert.deepEqual(df.sum({ axis: 0 }).values, res); + }); + it("Sum values of a boolean DataFrame along default column axis (axis=1)", function () { + const data1 = [[true, true, false, true], + [false, false, false, false], + [false, true, true, false]]; + const df = new DataFrame(data1); + const res = [3, 0, 2]; + assert.deepEqual(df.sum().values, res); + }); + it("Sum values of a df with missing values", function () { + const data1 = [[11, 20, 3], [null, 15, 6], [2, 30, 40], [2, 89, 78]]; + const df = new DataFrame(data1); + const res = [34, 21, 72, 169] + assert.deepEqual(df.sum({ axis: 1 }).values, res); + }); + + }); + + describe("abs", function () { + it("Returns the absolute values in DataFrame of ints", function () { + const data1 = [[-10, 45, 56, 10], [-25, 23, 20, -10]]; + const df = new DataFrame(data1); + const expected = [[10, 45, 56, 10], [25, 23, 20, 10]]; + assert.deepEqual((df.abs() as DataFrame).values, expected); + }); + + it("Returns the absolute values in mixed DataFrame", function () { + const data1 = [[-10, -45.1, 56, 10], [-25, -23.2, 20, -10]]; + const df = new DataFrame(data1); + const expected = [[10, 45.1, 56, 10], [25, 23.2, 20, 10]]; + assert.deepEqual((df.abs() as DataFrame).values, expected); + }); + }); + + describe("T", function () { + it("Return the Transpose of a DataFrame", function () { + const data1 = [[10, 45, 56, 10], + [25, 23, 20, 10]]; + + const cols = ["a", "b", "c", "d"]; + const df = new DataFrame(data1, { columns: cols }); + const df_trans = df.T; + const expected_vals = [[10, 25], [45, 23], [56, 20], [10, 10]]; + const expected_index = cols; + const expected_col_names = ["0", "1"]; + assert.deepEqual(df_trans.index, expected_index); + assert.deepEqual(df_trans.values, expected_vals); + assert.deepEqual(df_trans.columns, expected_col_names); + + }); + }); + + describe("transpose", function () { + it("Return the Transpose of a DataFrame", function () { + const data1 = [[10, 45, 56, 10], + [25, 23, 20, 10]]; + + const cols = ["a", "b", "c", "d"]; + const df = new DataFrame(data1, { columns: cols }); + const df_trans = df.T; + const expected_vals = [[10, 25], [45, 23], [56, 20], [10, 10]]; + const expected_index = cols; + const expected_col_names = ["0", "1"]; + assert.deepEqual(df_trans.index, expected_index); + assert.deepEqual(df_trans.values, expected_vals); + assert.deepEqual(df_trans.columns, expected_col_names); + + }); + it("Transpose a DataFrame inplace", function () { + const data1 = [[10, 45, 56, 10], + [25, 23, 20, 10]]; + + const cols = ["a", "b", "c", "d"]; + const df = new DataFrame(data1, { columns: cols }); + df.transpose({ inplace: true }); + const expected_vals = [[10, 25], [45, 23], [56, 20], [10, 10]]; + const expected_index = cols; + const expected_col_names = ["0", "1"]; + assert.deepEqual(df.index, expected_index); + assert.deepEqual(df.values, expected_vals); + assert.deepEqual(df.columns, expected_col_names); + + }); + }); + + + describe("asType", function () { + it("set type of float column to int", function () { + const data = { + "A": [-20.1, 30, 47.3, -20], + "B": [34, -4, 5, 6], + "C": [20.1, -20.23, 30.3, 40.11], + "D": ["a", "b", "c", "c"] + }; + const df = new DataFrame(data); + const dfNew = df.asType("A", "int32") as DataFrame + dfNew["D"] = ["a", "b", "c", "F"]; + assert.deepEqual(dfNew.dtypes, ['int32', 'int32', 'float32', 'string']); + assert.deepEqual(dfNew['A'].values, [-20, 30, 47, -20]); + assert.notDeepEqual(dfNew["D"].values, df["D"].values); + + }); + it("set type of int column to float", function () { + const data = { + "A": [-20.1, 30, 47.3, -20], + "B": [34, -4, 5, 6], + "C": [20.1, -20.23, 30.3, 40.11], + "D": ["a", "b", "c", "c"] + }; + const df = new DataFrame(data); + const dfNew = df.asType("B", "float32") as DataFrame + + assert.deepEqual(dfNew.dtypes, ['float32', 'float32', 'float32', 'string']); + assert.deepEqual(dfNew['B'].values, [34, -4, 5, 6]); + + }); + it("set type of string column to int", function () { + const data = { + "A": [-20.1, 30, 47.3, -20], + "B": [34, -4, 5, 6], + "C": [20.1, -20.23, 30.3, 40.11], + "D": ["20.1", "21", "23.4", "50.78"] + }; + const df = new DataFrame(data); + const dfNew = df.asType("D", "int32") as DataFrame + + assert.deepEqual(dfNew.dtypes, ['float32', 'int32', 'float32', 'int32']); + assert.deepEqual(dfNew['D'].values, [20, 21, 23, 50]); + + }); + it("set type of string column to float", function () { + const data = { + "A": [-20.1, 30, 47.3, -20], + "B": [34, -4, 5, 6], + "C": [20.1, -20.23, 30.3, 40.11], + "D": ["20.1", "21", "23.4", "50.78"] + }; + const df = new DataFrame(data); + const dfNew = df.asType("D", "float32") as DataFrame + + assert.deepEqual(dfNew.dtypes, ['float32', 'int32', 'float32', 'float32']); + assert.deepEqual(dfNew['D'].values, [20.1, 21, 23.4, 50.78]); + + }); + }); + + describe("nUnique", function () { + it("Returns the number of unique elements along axis 1", function () { + const data = { + "A": [-20, 30, 47.3, -20, 2], + "B": [34, -4, 5, 6, 2], + "C": [20, 20, 30, 30, 2], + "D": ["a", "b", "c", "c", "d"] + }; + + const df = new DataFrame(data); + const res = [4, 4, 4, 4, 2]; + assert.deepEqual(df.nUnique(1).values, res); + + }); + it("Returns the number of unique elements along axis 0", function () { + const data = { + "A": [20, 30, 47.3, 30], + "B": [34, -4, 5, 30], + "C": [20, 20, 30, 30], + "D": ["a", "b", "c", "c"] + }; + + const df = new DataFrame(data); + const res = [3, 4, 2, 3]; + assert.deepEqual(df.nUnique(0).values, res); + + }); + + }); + + + describe("rename", function () { + it("Rename columns along axis 1", function () { + const data = { + "A": [-20, 30, 47.3, -20], + "B": [34, -4, 5, 6], + "C": [20, 20, 30, 30], + }; + + const df = new DataFrame(data); + const dfNew = df.rename({ "A": "a1", "B": "b1" }) as DataFrame + const res = ["a1", "b1", "C",]; + assert.deepEqual(dfNew.columns, res); + + }); + it("confirms original column name is not modified along axis 1", function () { + const data = { + "A": [-20, 30, 47.3, -20], + "B": [34, -4, 5, 6], + "D": ["a", "b", "c", "c"] + }; + + const df = new DataFrame(data); + const dfNew = df.rename({ "A": "a1", "B": "b1" }, { axis: 1 }) + const res = ["A", "B", "D"]; + assert.deepEqual(df.columns, res); + + }); + it("Rename columns along axis 1 inplace", function () { + const data = { + "A": [-20, 30, 47.3, -20], + "B": [34, -4, 5, 6], + "C": [20, 20, 30, 30], + }; + + const df = new DataFrame(data); + df.rename({ "A": "a1", "B": "b1" }, { inplace: true }); + const res = ["a1", "b1", "C"]; + assert.deepEqual(df.columns, res); + + }); + it("Rename string index along axis 0", function () { + const data = { + "A": [-20, 30, 47.3, -20], + "B": [34, -4, 5, 6], + "C": [20, 20, 30, 30], + }; + + const df = new DataFrame(data, { index: ["a", "b", "c", "d"] }); + const dfNew = df.rename({ "a": 0, "b": 1 }, { axis: 0 }) as DataFrame + const res = [0, 1, "c", "d"]; + assert.deepEqual(dfNew.index, res); + + }); + it("Rename string index along axis 0 inplace", function () { + const data = { + "A": [-20, 30, 47.3, -20], + "B": [34, -4, 5, 6], + "C": [20, 20, 30, 30], + }; + + const df = new DataFrame(data, { index: ["a", "b", "c", "d"] }); + df.rename({ "a": 0, "b": 1 }, { axis: 0, inplace: true }); + const res = [0, 1, "c", "d"]; + assert.deepEqual(df.index, res); + + }); + it("Get new column via subseting works after rename (inplace)", function () { + let data = { + "A": [-20, 30, 47.3], + "B": [34, -4, 5], + "C": [20, 2, 30] + }; + let df = new DataFrame(data); + df.rename({ "A": "new_name" }, { inplace: true }); + df["new_name"].print(); + assert.deepEqual(df["new_name"].values, data["A"]); + }); + + it("Get new column via subseting works after rename (not-inplace)", function () { + let data = { + "A": [-20, 30, 47.3], + "B": [34, -4, 5], + "C": [20, 2, 30] + }; + let df = new DataFrame(data); + let new_df = df.rename({ "A": "new_name" }); + assert.deepEqual(new_df["new_name"].values, data["A"]); + }); + }); + + describe("sortIndex", function () { + + it("sort index in ascending order", function () { + const data = [[0, 2, 4, "b"], + [360, 180, 360, "a"], + [2, 4, 6, "c"]]; + + const df = new DataFrame(data, { "columns": ["col1", "col2", "col3", "col4"], index: ["b", "a", "c"] }); + const df2 = df.sortIndex() as DataFrame + const rslt = [[360, 180, 360, 'a'], [0, 2, 4, 'b'], [2, 4, 6, 'c']]; + + assert.deepEqual(df2.values, rslt); + }); + it("sort index in ascending order - inplace", function () { + const data = [[0, 2, 4, "b"], + [360, 180, 360, "a"], + [2, 4, 6, "c"]]; + + const df = new DataFrame(data, { "columns": ["col1", "col2", "col3", "col4"], index: ["b", "a", "c"] }); + df.sortIndex({ inplace: true }); + const rslt = [[360, 180, 360, 'a'], [0, 2, 4, 'b'], [2, 4, 6, 'c']]; + assert.deepEqual(df.values, rslt); + }); + it("sort index in descending order", function () { + const data = [[0, 2, 4, "b"], + [360, 180, 360, "a"], + [2, 4, 6, "c"]]; + + const df = new DataFrame(data, { "columns": ["col1", "col2", "col3", "col4"], index: ["b", "a", "c"] }); + const df2 = df.sortIndex({ ascending: false }) as DataFrame + const rslt = [[2, 4, 6, 'c'], [0, 2, 4, 'b'], [360, 180, 360, 'a']]; + + assert.deepEqual(df2.values, rslt); + }); + it("sort index in descending order with inplace set to true", function () { + const data = [[0, 2, 4, "b"], + [360, 180, 360, "a"], + [2, 4, 6, "c"]]; + + const df = new DataFrame(data, { "columns": ["col1", "col2", "col3", "col4"], index: [4, 2, 5] }); + df.sortIndex({ ascending: false, inplace: true }); + const rslt = [[2, 4, 6, 'c'], [0, 2, 4, 'b'], [360, 180, 360, 'a']]; + assert.deepEqual(df.values, rslt); + }); + }); + + describe("append", function () { + + it("Append works for an array", function () { + const data = [[0, 2, 4, "b"], + [360, 180, 360, "a"], + [2, 4, 6, "c"]]; + + const df = new DataFrame(data); + const expected_val = [[0, 2, 4, "b"], + [360, 180, 360, "a"], + [2, 4, 6, "c"], + [20, 40, 60, "d"]]; + + const newDf = df.append([20, 40, 60, "d"], ["n1"]) as DataFrame + assert.deepEqual(newDf.values, expected_val); + assert.deepEqual(newDf.index, [0, 1, 2, "n1"]); + }); + it("Append works for an array of arrays", function () { + const data = [[0, 2, 4, "b"], + [360, 180, 360, "a"], + [2, 4, 6, "c"]]; + + const df = new DataFrame(data); + const expected_val = [[0, 2, 4, "b"], + [360, 180, 360, "a"], + [2, 4, 6, "c"], + [20, 40, 60, "d"], + [21, 42, 61, "y"]]; + + const newDf = df.append([[20, 40, 60, "d"], [21, 42, 61, "y"]], ["n1", "n2"]) as DataFrame + assert.deepEqual(newDf.values, expected_val); + assert.deepEqual(newDf.index, [0, 1, 2, "n1", "n2"]); + }); + + it("Append works for DataFrame", function () { + const data = [[0, 2, 4, "b"], + [360, 180, 360, "a"], + [2, 4, 6, "c"]]; + + const df = new DataFrame(data, { "columns": ["col1", "col2", "col3", "col4"] }); + const df2 = new DataFrame([[20, 40, 60, "d"]], { "columns": ["col1", "col2", "col3", "col4"] }); + + const expected_val = [[0, 2, 4, "b"], + [360, 180, 360, "a"], + [2, 4, 6, "c"], + [20, 40, 60, "d"]]; + + const newDf = df.append(df2, ["n1"]) as DataFrame + assert.deepEqual(newDf.values, expected_val); + + }); + it("Append works for Series", function () { + const data = [[0, 2, 4, "b"], + [360, 180, 360, "a"], + [2, 4, 6, "c"]]; + + const df = new DataFrame(data, { "columns": ["col1", "col2", "col3", "col4"] }); + const sf = new Series([20, 40, 60, "d"]); + + const expected_val = [[0, 2, 4, "b"], + [360, 180, 360, "a"], + [2, 4, 6, "c"], + [20, 40, 60, "d"]]; + + const newDf = df.append(sf, ["n1"]) as DataFrame + assert.deepEqual(newDf.values, expected_val); + + }); + }); + + describe("cumProd", function () { + + it("cumProd works for axis 1", function () { + const data = [[2, 1, 2, 3], [3, 4, 11, 9], [5, 6, 7, 8]]; + const column = ["A", "B", "C", "D"]; + const df = new DataFrame(data, { columns: column }); + const rslt = [[2, 2, 4, 12], + [3, 12, 132, 1188], + [5, 30, 210, 1680]] + + const newDf = df.cumProd() as DataFrame + assert.deepEqual(newDf.values, rslt); + }); + it("cumProd axis 0 works", function () { + const data = [[2, 1, 2, 3], [3, 4, 11, 9], [5, 6, 7, 8]]; + const column = ["A", "B", "C", "D"]; + const df = new DataFrame(data, { columns: column }); + const rslt = [[2, 1, 2, 3], + [6, 4, 22, 27], + [30, 24, 154, 216]] + + assert.deepEqual((df.cumProd({ axis: 0 }) as DataFrame).values, rslt); + }); + + it("cumProd works for axis 1 inplace", function () { + const data = [[2, 1, 2, 3], [3, 4, 11, 9], [5, 6, 7, 8]]; + const column = ["A", "B", "C", "D"]; + const df = new DataFrame(data, { columns: column }); + const rslt = [[2, 2, 4, 12], + [3, 12, 132, 1188], + [5, 30, 210, 1680]] + + df.cumProd({ inplace: true }) + assert.deepEqual(df.values, rslt); + }); + it("cumProd axis 0 works inplace", function () { + const data = [[2, 1, 2, 3], [3, 4, 11, 9], [5, 6, 7, 8]]; + const column = ["A", "B", "C", "D"]; + const df = new DataFrame(data, { columns: column }); + const rslt = [[2, 1, 2, 3], + [6, 4, 22, 27], + [30, 24, 154, 216]] + df.cumProd({ axis: 0, inplace: true }) + assert.deepEqual(df.values, rslt); + }); + }); + + describe("cumSum", function () { + + it("cumSum works for axis 1", function () { + const data = [[2, 1, 2, 3], [3, 4, 11, 9], [5, 6, 7, 8]]; + const column = ["A", "B", "C", "D"]; + const df = new DataFrame(data, { columns: column }); + const rslt = [[2, 3, 5, 8], + [3, 7, 18, 27], + [5, 11, 18, 26]] + + const newDf = df.cumSum() as DataFrame + assert.deepEqual(newDf.values, rslt); + }); + it("cumSum axis 0 works", function () { + const data = [[2, 1, 2, 3], [3, 4, 11, 9], [5, 6, 7, 8]]; + const column = ["A", "B", "C", "D"]; + const df = new DataFrame(data, { columns: column }); + const rslt = [[2, 1, 2, 3], + [5, 5, 13, 12], + [10, 11, 20, 20]] + + assert.deepEqual((df.cumSum({ axis: 0 }) as DataFrame).values, rslt); + }); + + it("cumSum works for axis 1 inplace", function () { + const data = [[2, 1, 2, 3], [3, 4, 11, 9], [5, 6, 7, 8]]; + const column = ["A", "B", "C", "D"]; + const df = new DataFrame(data, { columns: column }); + const rslt = [[2, 3, 5, 8], + [3, 7, 18, 27], + [5, 11, 18, 26]] + + df.cumSum({ inplace: true }) + assert.deepEqual(df.values, rslt); + }); + it("cumSum axis 0 works inplace", function () { + const data = [[2, 1, 2, 3], [3, 4, 11, 9], [5, 6, 7, 8]]; + const column = ["A", "B", "C", "D"]; + const df = new DataFrame(data, { columns: column }); + const rslt = [[2, 1, 2, 3], + [5, 5, 13, 12], + [10, 11, 20, 20]] + df.cumSum({ axis: 0, inplace: true }) + assert.deepEqual(df.values, rslt); + }); + }); + + describe("cumMin", function () { + + it("cumMin works for axis 1", function () { + const data = [[2, 1, 2, 3], [3, 4, 11, 9], [5, 6, 7, 8]]; + const column = ["A", "B", "C", "D"]; + const df = new DataFrame(data, { columns: column }); + const rslt = [[2, 1, 1, 1], + [3, 3, 3, 3], + [5, 5, 5, 5]] + + const newDf = df.cumMin() as DataFrame + assert.deepEqual(newDf.values, rslt); + }); + it("cumMin axis 0 works", function () { + const data = [[2, 1, 2, 3], [3, 4, 11, 9], [5, 6, 7, 8]]; + const column = ["A", "B", "C", "D"]; + const df = new DataFrame(data, { columns: column }); + const rslt = [[2, 1, 2, 3], + [2, 1, 2, 3], + [2, 1, 2, 3]] + + assert.deepEqual((df.cumMin({ axis: 0 }) as DataFrame).values, rslt); + }); + + it("cumMin works for axis 1 inplace", function () { + const data = [[2, 1, 2, 3], [3, 4, 11, 9], [5, 6, 7, 8]]; + const column = ["A", "B", "C", "D"]; + const df = new DataFrame(data, { columns: column }); + const rslt = [[2, 1, 1, 1], + [3, 3, 3, 3], + [5, 5, 5, 5]] + + df.cumMin({ inplace: true }) + assert.deepEqual(df.values, rslt); + }); + it("cumMin axis 0 works inplace", function () { + const data = [[2, 1, 2, 3], [3, 4, 11, 9], [5, 6, 7, 8]]; + const column = ["A", "B", "C", "D"]; + const df = new DataFrame(data, { columns: column }); + const rslt = [[2, 1, 2, 3], + [2, 1, 2, 3], + [2, 1, 2, 3]] + df.cumMin({ axis: 0, inplace: true }) + assert.deepEqual(df.values, rslt); + }); + }); + + describe("cumMax", function () { + + it("cumMax works for axis 1", function () { + const data = [[2, 1, 2, 3], [3, 4, 11, 9], [5, 6, 7, 8]]; + const column = ["A", "B", "C", "D"]; + const df = new DataFrame(data, { columns: column }); + const rslt = [[2, 2, 2, 3], + [3, 4, 11, 11], + [5, 6, 7, 8]] + + const newDf = df.cumMax() as DataFrame + assert.deepEqual(newDf.values, rslt); + }); + it("cumMax axis 0 works", function () { + const data = [[2, 1, 2, 3], [3, 4, 11, 9], [5, 6, 7, 8]]; + const column = ["A", "B", "C", "D"]; + const df = new DataFrame(data, { columns: column }); + const rslt = [[2, 1, 2, 3], + [3, 4, 11, 9], + [5, 6, 11, 9]] + + assert.deepEqual((df.cumMax({ axis: 0 }) as DataFrame).values, rslt); + }); + + it("cumMax works for axis 1 inplace", function () { + const data = [[2, 1, 2, 3], [3, 4, 11, 9], [5, 6, 7, 8]]; + const column = ["A", "B", "C", "D"]; + const df = new DataFrame(data, { columns: column }); + const rslt = [[2, 2, 2, 3], + [3, 4, 11, 11], + [5, 6, 7, 8]] + + df.cumMax({ inplace: true }) + assert.deepEqual(df.values, rslt); + }); + it("cumMax axis 0 works inplace", function () { + const data = [[2, 1, 2, 3], [3, 4, 11, 9], [5, 6, 7, 8]]; + const column = ["A", "B", "C", "D"]; + const df = new DataFrame(data, { columns: column }); + const rslt = [[2, 1, 2, 3], + [3, 4, 11, 9], + [5, 6, 11, 9]] + df.cumMax({ axis: 0, inplace: true }) + assert.deepEqual(df.values, rslt); + }); + }); + + + describe("query", function () { + + it("Get the DataFrame containing rows with the filtered column", function () { + const data = [[1, 2, 3], [4, 5, 6], [20, 30, 40], [39, 89, 78]]; + const cols = ["A", "B", "C"]; + const df = new DataFrame(data, { columns: cols }); + const query_df = df.query(df["B"].ge(5)) as DataFrame + const query_data = [[4, 5, 6], [20, 30, 40], [39, 89, 78]] + assert.deepEqual(query_df.values, query_data); + }); + it("Get the Dataframe containing rows with the filtered column in String values", function () { + const data = { "Abs": [20, 30, 47], "Count": [34, 4, 5], "country code": ["NG", "FR", "GH"] }; + const cols = ["Abs", "Count", "country code"]; + const df = new DataFrame(data, { columns: cols }); + const query_df = df.query(df["country code"].str.includes("NG")) as DataFrame + + const query_data = [[20, 34, "NG"]]; + assert.deepEqual(query_df.values, query_data); + }); + it("Get the Dataframe containing rows with the filtered column in String values inplace", function () { + const data = { "Abs": [20, 30, 47], "Count": [34, 4, 5], "country code": ["NG", "FR", "GH"] }; + const cols = ["Abs", "Count", "country code"]; + const df = new DataFrame(data, { columns: cols }); + df.query(df["country code"].eq("NG"), { inplace: true }); + const query_data = [[20, 34, "NG"]]; + assert.deepEqual(df.values, query_data); + }); + it("Confirms that query index are updated", function () { + + const data = [[1, 2, 3], [4, 5, 6], [20, 30, 40], [39, 89, 78]]; + const cols = ["A", "B", "C"]; + const df = new DataFrame(data, { columns: cols }); + const query_df = df.query(df["B"].ge(5)) as DataFrame + assert.deepEqual(query_df.index, [1, 2, 3]); + }); + + it("Confirms chaining boolean queries work", function () { + + const data = [[1, 2, 3], + [4, 5, 60], + [20, 30, 4], + [39, 89, 7]]; + const cols = ["A", "B", "C"]; + const df = new DataFrame(data, { columns: cols }); + + const query_df = df.query( + df["B"].ge(5).and(df["C"].lt(10)) + ) as DataFrame + const query_data = [[20, 30, 4], [39, 89, 7]]; + assert.deepEqual(query_df.values, query_data) + assert.deepEqual(query_df.index, [2, 3]); + }); + + it("Confirms chaining boolean queries work and returns empty DF", function () { + + const data = [[1, 2, 3], + [4, 5, 60], + [20, 30, 40], + [39, 89, 70]]; + const cols = ["A", "B", "C"]; + const df = new DataFrame(data, { columns: cols }); + + const query_df = df.query( + df["B"].ge(5).and(df["C"].lt(10)) + ) as DataFrame + assert.deepEqual(query_df.values, []); + assert.deepEqual(query_df.index, []); + }); + + it("Confirms that column names are not changed", function () { + + let data = [[1, 2, 3], [4, 5, 6], [20, 30, 40], [39, 89, 78]]; + let cols = ["A", "B", "C"]; + let df = new DataFrame(data, { columns: cols }); + let df_query = df.query(df["B"].ge(5)); + assert.deepEqual(df_query.index, [1, 2, 3]); + assert.deepEqual(df_query.columns, ["A", "B", "C"]); + }); + + }); + + describe("cTypes", function () { + + it("Returns the correct dtype in a DataFrame", function () { + const data = [["boy", 1.2, 2, 3], ["girl", 4.32, 11, 9], ['4', 6.1, 7, 8]]; + const column = ["A", "B", "C", "D"]; + const df = new DataFrame(data, { columns: column }); + const rslt = ["string", "float32", "int32", "int32"] + assert.deepEqual(df.ctypes.values, rslt); + }); + }); + + describe("IO outputs", function () { + it("toExcel works", async function () { + const data = [[1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12]] + const df: any = new DataFrame(data, { columns: ["a", "b", "c", "d"] }); + + const filePath = path.join(process.cwd(), "test", "samples", "test.xlsx"); + df.toExcel({ filePath }) + + const dfNew: any = await readExcel(filePath, {}); + assert.equal(fs.existsSync(filePath), true) + assert.deepEqual(dfNew.columns, [ + 'a', + 'b', + 'c', + 'd', + ]); + assert.deepEqual(dfNew.dtypes, [ + 'int32', 'int32', + 'int32', 'int32', + ]); + assert.deepEqual(dfNew.shape, [3, 4]) + }); + + it("toCSV works for specified seperator", async function () { + const data = [[1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12]] + let df: any = new DataFrame(data, { columns: ["a", "b", "c", "d"] }); + assert.deepEqual(df.toCSV({ sep: "+" }), `a+b+c+d\n1+2+3+4\n5+6+7+8\n9+10+11+12\n`); + }); + it("toCSV write to local file works", async function () { + const data = [[1, 2, 3, "4"], [5, 6, 7, "8"], [9, 10, 11, "12"]] + let df: any = new DataFrame(data, { columns: ["a", "b", "c", "d"] }); + + const filePath = path.join(process.cwd(), "test", "samples", "test_write.csv"); + + df.toCSV({ sep: ",", filePath }); + assert.equal(fs.existsSync(filePath), true); + }); + it("toJSON works for row format", async function () { + const data = [[1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12]] + const df: any = new DataFrame(data, { columns: ["a", "b", "c", "d"] }); + const expected: any = { + "a": [1, 5, 9], + "b": [2, 6, 10], + "c": [3, 7, 11], + "d": [4, 8, 12], + } + const json = df.toJSON({ format: "row" }) + assert.deepEqual(json, expected); + }); + it("toJSON writes file to local path", async function () { + const data = [[1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12]] + const df: any = new DataFrame(data, { columns: ["a", "b", "c", "d"] }); + + const rowfilePath = path.join(process.cwd(), "test", "samples", "test_row_write.json"); + const colfilePath = path.join(process.cwd(), "test", "samples", "test_col_write.json"); + + df.toJSON({ format: "row", filePath: rowfilePath }) + df.toJSON({ format: "column", filePath: colfilePath }) + assert.equal(fs.existsSync(rowfilePath), true); + assert.equal(fs.existsSync(colfilePath), true); + }); + }) + + describe("getDummies", function () { + it("getDummies works on DataFrame", function () { + + const data = [[1, "dog", 1.0, "fat"], [3, "fog", 2.0, "good"], [4, "gof", 3.0, "best"]]; + const columns = ["A", "B", "C", "d"]; + const df = new DataFrame(data, { columns: columns }); + + const df1 = df.getDummies({ prefixSeparator: ["_", "#"], columns: ["A", "d"], prefix: "test" }) as DataFrame + const expectedColumns = ['B', 'C', 'test_1', 'test_3', 'test_4', 'test#fat', 'test#good', 'test#best'] + const expected = [['dog', 1.0, 1, 0, 0, 1, 0, 0], + ['fog', 2.0, 0, 1, 0, 0, 1, 0], + ['gof', 3.0, 0, 0, 1, 0, 0, 1]] + assert.deepEqual(df1.values, expected); + assert.deepEqual(df1.columns, expectedColumns); + + }); + it("Throw error if the prefix specified is not equal to the column specified", function () { + + const data = [[1, "dog", 1.0, "fat"], [3, "fog", 2.0, "good"], [4, "gof", 3.0, "best"]]; + const columns = ["A", "B", "C", "d"]; + const df = new DataFrame(data, { columns: columns }); + + assert.throws(function () { df.getDummies({ prefix: ["fg"], prefixSeparator: "_", columns: ["A", "d"] }); }, Error, + `ParamError: prefix and data array must be of the same length. If you need to use the same prefix, then pass a string param instead. e.g {prefix: "fg"}`); + + }); + it("replace column sepecified with prefix", function () { + + const data = [[1, "dog", 1.0, "fat"], [3, "fog", 2.0, "good"], [4, "gof", 3.0, "best"]]; + const columns = ["A", "B", "C", "d"]; + const df = new DataFrame(data, { columns: columns }); + + const df1 = df.getDummies({ prefix: ["F", "G"], prefixSeparator: "_", columns: ["A", "d"] }) as DataFrame + const expectedColumns = [ + 'B', 'C', + 'F_1', 'F_3', + 'F_4', 'G_fat', + 'G_good', 'G_best' + ]; + + const expected = [['dog', 1.0, 1, 0, 0, 1, 0, 0], + ['fog', 2.0, 0, 1, 0, 0, 1, 0], + ['gof', 3.0, 0, 0, 1, 0, 0, 1]] + + assert.deepEqual(df1.values, expected); + assert.deepEqual(df1.columns, expectedColumns); + + }); + + it("getDummies auto infers and encode columns with string dtype", function () { + + const data = [[1, "dog", 1.0, "fat"], [3, "fog", 2.0, "good"], [4, "gof", 3.0, "best"]]; + const columns = ["A", "B", "C", "d"]; + const df = new DataFrame(data, { columns: columns }); + + const df1 = df.getDummies({ prefixSeparator: "_" }) as DataFrame + const expectedColumns = [ + 'A', 'C', + 'B_dog', 'B_fog', + 'B_gof', 'd_fat', + 'd_good', 'd_best' + ]; + const expected = [ + [ + 1, 1, 1, 0, + 0, 1, 0, 0 + ], + [ + 3, 2, 0, 1, + 0, 0, 1, 0 + ], + [ + 4, 3, 0, 0, + 1, 0, 0, 1 + ] + ]; + assert.deepEqual(df1.values, expected); + assert.deepEqual(df1.columns, expectedColumns); + + }); + + it("should one hot encode all other columns", function () { + + const data = [[1, "dog", 1.0, "fat"], [3, "fog", 2.0, "good"], [4, "gof", 3.0, "best"]]; + const columns = ["A", "B", "C", "d"]; + const df = new DataFrame(data, { columns: columns }) + const rslt = [ + [1, 'dog', 1, 1, 0, 0], + [3, 'fog', 2, 0, 1, 0], + [4, 'gof', 3, 0, 0, 1] + ] + + assert.deepEqual((df.getDummies({ columns: ["d"] }) as DataFrame).values, rslt) + + }); + }); + + +}); diff --git a/src/danfojs-node/test/core/generic.test.ts b/src/danfojs-node/test/core/generic.test.ts new file mode 100644 index 00000000..ddd09a37 --- /dev/null +++ b/src/danfojs-node/test/core/generic.test.ts @@ -0,0 +1,421 @@ +import { assert } from "chai"; +import { describe, it } from "mocha"; +import { NDframe } from '../../dist/danfojs-node/src'; +import { tensor } from '@tensorflow/tfjs-node'; + + +describe("Generic (NDFrame)", function () { + describe("NDframe Created from Array", function () { + it("prints the shape of a 1D array", function () { + let data = [1, 2, 3, "Boy", "Girl"]; + let ndframe = new NDframe({ data, isSeries: true }); + assert.deepEqual(ndframe.shape, [5, 1]); + }); + it("prints the default assigned column name in a series", function () { + let data = ["Boy", 20, 25]; + let ndframe = new NDframe({ data, isSeries: true }); + assert.deepEqual(ndframe.columns, ["0"]); + }); + it("prints the assigned column name in a series", function () { + let data = ["Boy", 20, 25]; + let ndframe = new NDframe({ data, columns: ["Records"], isSeries: true }); + assert.deepEqual(ndframe.columns, ["Records"]); + }); + it("prints the shape of a 2D array", function () { + let data = [["Boy", 20], ["Girl", 25]]; + let ndframe = new NDframe({ data, isSeries: false }); + assert.deepEqual(ndframe.shape, [2, 2]); + }); + it("prints the default assigned column names in 2D frame", function () { + let data = [["Boy", 20], ["Girl", 25]]; + let ndframe = new NDframe({ data, isSeries: false }); + assert.deepEqual(ndframe.columns, ["0", "1"]); + }); + it("prints the assigned column names", function () { + let data = [["Boy", 20], ["Girl", 25]]; + let options = { "columns": ["Gender", "Age"], "isSeries": false }; + let ndframe = new NDframe({ data, ...options }); + assert.deepEqual(ndframe.columns, ["Gender", "Age"]); + }); + it("prints the size of a frame", function () { + let data = [["Boy", 20, 1], ["Girl", 25, 3]]; + let options = { "columns": ["Gender", "Age", "count"], "isSeries": false }; + let ndframe = new NDframe({ data, ...options }); + assert.deepEqual(ndframe.size, 6); + }); + it("prints the dimension of a frame", function () { + let data = [["Boy", 20, 1], ["Girl", 25, 3]]; + let options = { "columns": ["Gender", "Age", "count"], "isSeries": false }; + let ndframe = new NDframe({ data, ...options }); + assert.deepEqual(ndframe.ndim, 2); + }); + it("prints the values of a frame", function () { + let data = [[21, 20, 1], [20, 25, 3]]; + let ndframe = new NDframe({ data, isSeries: false }); + assert.deepEqual(ndframe.values, data); + }); + it("prints the values of a frame", function () { + let data = [[21, 20, 1], [20, 25, 3]]; + let ndframe = new NDframe({ data, isSeries: false }); + assert.deepEqual(ndframe.values, data); + }); + + it("Throws error on duplicate column name", function () { + let data = [[21, 20, 1], [20, 25, 3]]; + assert.throws(() => { + new NDframe({ data, isSeries: false, columns: ["A", "A", "C"] }), + Error, + "ColumnIndexError: Column index must contain unique values" + }); + }); + + it("Throws error on duplicate index", function () { + let data = [[21, 20, 1], [20, 25, 3]]; + assert.throws(() => { + new NDframe({ data, isSeries: false, index: [1, 1, 2] }), + Error, + "IndexError: Row index must contain unique values" + }); + }); + }) + + describe("NDframe Created from JavaScript Object", function () { + + it("prints the shape of a 2D frame created from an Object", function () { + let data = [{ alpha: "A", count: 1 }, { alpha: "B", count: 2 }, { alpha: "C", count: 3 }]; + let ndframe = new NDframe({ data, isSeries: false }); + assert.deepEqual(ndframe.shape, [3, 2]); + }); + it("prints the column names of frame created from an Object", function () { + let data = [{ alpha: "A", count: 1 }, { alpha: "B", count: 2 }, { alpha: "C", count: 3 }]; + let ndframe = new NDframe({ data, isSeries: false }); + assert.deepEqual(ndframe.columns, ["alpha", "count"]); + }); + it("prints the shape of a 1D frame created from an Object", function () { + let data = [{ alpha: "A", count: 1 }]; + let ndframe = new NDframe({ data, isSeries: false }); + assert.deepEqual(ndframe.shape, [1, 2]); + }); + + it("prints the size of a frame created from an Object", function () { + let data = [{ alpha: "A", count: 1 }, { alpha: "B", count: 2 }, { alpha: "C", count: 3 }]; + let ndframe = new NDframe({ data, isSeries: false }); + assert.deepEqual(ndframe.size, 6); + }); + it("prints the dimension of a frame created from an Object", function () { + let data = [{ alpha: "A", count: 1 }, { alpha: "B", count: 2 }]; + let ndframe = new NDframe({ data, isSeries: false }); + assert.deepEqual(ndframe.ndim, 2); + }); + it("prints the values of a frame created from an Object", function () { + let data = [{ alpha: "A", count: 1 }, { alpha: "B", count: 2 }]; + let ndframe = new NDframe({ data, isSeries: false }); + assert.deepEqual(ndframe.values as any, [["A", 1], ["B", 2]]); + }); + it("prints the values of a frame created from an Object with undefined values", function () { + let data = [{ alpha: "A", count: undefined }, { alpha: undefined, count: 2 }]; + let ndframe = new NDframe({ data, isSeries: false }); + assert.deepEqual(ndframe.values as any, [["A", undefined], [undefined, 2]]); + }); + + it("prints the values of a frame created from an Object with NaN values", function () { + let data = [{ alpha: "A", count: NaN }, { alpha: NaN, count: 2 }]; + let ndframe = new NDframe({ data, isSeries: false }); + assert.deepEqual(ndframe.values as any, [["A", NaN], [NaN, 2]]); + }); + // it("NDframe created from json takes key position into consideration", function () { + // let json_data = [{ A: "A", B: "B", C: "C" }, + // { A: "A", B: "B", C: "C" }, + // { C: "C", B: "B", A: "A" }, + // { A: "A", C: "C", B: "B" }]; + + // let output = [ + // [ + // 'A', + // 'B', + // 'C' + // ], + // [ + // 'A', + // 'B', + // 'C' + // ], + // [ + // 'A', + // 'B', + // 'C' + // ], + // [ + // 'A', + // 'B', + // 'C' + // ] + // ]; + // let ndframe = new NDframe({ data: json_data, isSeries: false }); + // assert.deepEqual(ndframe.values, output); + // }); + + // it("NDframe created from json sets value to NaN if not present", function () { + // let json_data = [{ A: "A", B: "B", C: "C" }, + // { A: "A", B: "B", C: "C" }, + // { C: "C", B: "B", A: "A" }, + // { A: "A", C: "C" }]; + + // let output = [ + // [ + // 'A', + // 'B', + // 'C' + // ], + // [ + // 'A', + // 'B', + // 'C' + // ], + // [ + // 'A', + // 'B', + // 'C' + // ], + // [ + // 'A', + // 'B', + // NaN + // ] + // ]; + // let ndframe = new NDframe({ data: json_data, isSeries: false }); + // assert.deepEqual(ndframe.values, output); + // }); + + }); + + describe("Replacing row data", function () { + it("retrieves the col data after row data is replaced in a DataFrame", function () { + let data = { alpha: ["A", "B"], count: [1, 2] }; + let ndframe = new NDframe({ data, isSeries: false }); + ndframe.$setValues([["A", 20], ["D", 211]]) + assert.deepEqual(ndframe.values, [["A", 20], ["D", 211]]); + + }); + it("Correct dtypes is inferred after row data is replaced in a DataFrame", function () { + let data = { alpha: ["A", "B"], count: [1, 2] }; + let ndframe = new NDframe({ data, isSeries: false }); + ndframe.$setValues([[20, 20], [11.4, 211]]) + assert.deepEqual(ndframe.dtypes, ["float32", "int32"]); + + }); + it("Throws row length error on invalid data length in DataFrame", function () { + let data = { alpha: ["A", "B"], count: [1, 2] }; + let ndframe = new NDframe({ data, isSeries: false }); + assert.throws( + () => { + ndframe.$setValues([[20, 20], [11.4, 211], [11.4, 211]]) + }, + Error, + "Row data length mismatch. You provided data with length 3 but Ndframe has row of lenght 2" + ); + }); + it("Throws column length error on invalid data length in DataFrame", function () { + let data = { alpha: ["A", "B"], count: [1, 2] }; + let ndframe = new NDframe({ data, isSeries: false }); + assert.throws( + () => { + ndframe.$setValues([[20, 211], [20, 20, 11.4, 211]]) + }, + Error, + "Column data length mismatch. You provided data with length 2 but Ndframe has column of lenght 2" + ); + }); + it("retrieves the col data after row data is replaced in a Series", function () { + let data = ["A", "B", 1, 2] + let ndframe = new NDframe({ data, isSeries: true }); + ndframe.$setValues(["A", "D", "E", "O"]) + assert.deepEqual(ndframe.values, ["A", "D", "E", "O"]); + + }); + it("Correct dtypes is inferred after updating values", function () { + let data = ["A", "D", "E", "O"] + let ndframe = new NDframe({ data, isSeries: true }); + ndframe.$setValues([1, 2, 3, 4]) + assert.deepEqual(ndframe.values, [1, 2, 3, 4]); + assert.deepEqual(ndframe.dtypes[0], "int32"); + }); + it("Correct dtypes is inferred after updating values in low memory mode", function () { + let data = ["A", "D", "E", "O"] + let ndframe = new NDframe({ data, isSeries: true, config: { lowMemoryMode: true } }); + ndframe.$setValues([1, 2, 3, 4]) + assert.deepEqual(ndframe.values, [1, 2, 3, 4]); + assert.deepEqual(ndframe.dtypes[0], "int32"); + }); + it("Throws row length error on invalid data length", function () { + let data = ["A", "D", "E", "O"] + let ndframe = new NDframe({ data, isSeries: true, config: { lowMemoryMode: true } }); + assert.throws( + () => { + ndframe.$setValues([1, 2, 3, 4, 1, 3]) + }, + Error, + "Row data length mismatch. You provided data with length 6 but Ndframe has row of lenght 4" + ); + }); + + }) + + describe("NDframe Created from a Tensor", function () { + + it("prints the shape of a 2D frame created from a 2D tensor", function () { + let data = tensor([1, 2, 3, 4]); + let ndframe = new NDframe({ data, isSeries: true }); + assert.deepEqual(ndframe.ndim, 1); + assert.deepEqual(ndframe.values, [1, 2, 3, 4]); + + }); + it("prints the shape of a 2D frame created from a 1D tensor", function () { + let data = tensor([[2, 3, 4], [4, 5, 6]]); + let ndframe = new NDframe({ data, columns: ["alpha", "count", "sum"], isSeries: false }); + assert.deepEqual(ndframe.columns, ["alpha", "count", "sum"]); + }); + + }); + + describe("index", function () { + it("Returns the index of an NDframe", function () { + let data = [{ alpha: "A", count: 1 }, { alpha: "B", count: 2 }, { alpha: "C", count: 3 }]; + let df = new NDframe({ data, isSeries: false }); + assert.deepEqual(df.index, [0, 1, 2]); + }); + it("Returns the index of an NDframe created from an Array", function () { + let data = [[12, 2, 20], [90, 5, 23], [45, 56, 70], [9, 10, 19]]; + let df = new NDframe({ data, isSeries: false }); + assert.deepEqual(df.index, [0, 1, 2, 3]); + }); + }); + + describe("NDframe Created from JavaScript Object of Arrays", function () { + + it("retrieves the col data created from OA ", function () { + let data = { alpha: ["A", "B", "C", "D"], count: [1, 2, 3, 4] }; + let ndframe = new NDframe({ data, isSeries: false }); + assert.deepEqual(ndframe.values, [['A', 1], ['B', 2], ['C', 3], ['D', 4]]); + }); + it("retrieves the data 2", function () { + let data = { alpha: ["A", "B", "C", "D"], count: [1, 2, 3, 4], sum: [20.3, 30.456, 40.90, 90.1] }; + let ndframe = new NDframe({ data, isSeries: false }); + let res = [ + ['A', 1, 20.3], + ['B', 2, 30.456], + ['C', 3, 40.9], + ['D', 4, 90.1] + ] + assert.deepEqual(ndframe.values, res); + }); + it("retrieves the row data created from OA ", function () { + let data = { alpha: ["A", "B"], count: [1, 2] }; + let ndframe = new NDframe({ data, isSeries: false }); + assert.deepEqual(ndframe.values, [["A", 1], ["B", 2]]); + }); + }); + + describe("setIndex", function () { + it("sets the index of an NDframe", function () { + let data = [{ alpha: "A", count: 1 }, { alpha: "B", count: 2 }, { alpha: "C", count: 3 }]; + let df = new NDframe({ data, isSeries: false }); + df.$setIndex(["A", "B", "C"]); + assert.deepEqual(df.index, ["A", "B", "C"]); + }); + it("Returns the index of an NDframe created from an Array", function () { + let data = [[12, 2, 20], [90, 5, 23], [45, 56, 70], [9, 10, 19]]; + let df = new NDframe({ data, isSeries: false }); + df.$setIndex([10, 20, 30, 40]); + assert.deepEqual(df.index, [10, 20, 30, 40]); + }); + }); + + + describe("dtype", function () { + it("Returns int dtype set during creation of 1DFrame (Series) from an Object", function () { + let data = [1, 2, 3, 4, 5, 6, 7, 8, 9]; + let options = { dtypes: ['int32'], isSeries: true }; + let ndframe = new NDframe({ data, ...options }); + assert.deepEqual(ndframe.dtypes, ['int32']); + }); + + it("Returns string dtype set during creation of 1DFrame (Series) from an Array", function () { + let data = ["Alice", "Yemi", "Rising", "Mark"]; + let options = { dtypes: ['string'], isSeries: true }; + let ndframe = new NDframe({ data, ...options }); + assert.deepEqual(ndframe.dtypes, ["string"]); + }); + + it("Returns string dtype automatically inferred from 1DFrame (Series)", function () { + let data = ["Alice", "Yemi", "Rising", "Mark"]; + let options = { columns: ['Names'], isSeries: true }; + let ndframe = new NDframe({ data, ...options }); + assert.deepEqual(ndframe.dtypes, ["string"]); + }); + + it("Returns int dtype automatically inferred from 1DFrame (Series)", function () { + let data = [20, 30, 20, 20]; + let options = { columns: ['Size'], isSeries: true }; + let ndframe = new NDframe({ data, ...options }); + assert.deepEqual(ndframe.dtypes, ["int32"]); + }); + it("Returns float dtype automatically inferred from 1DFrame (Series)", function () { + let data = [20.1, 30.4, 20.2, 4.23, 20.1]; + let options = { columns: ['Size'], isSeries: true }; + let ndframe = new NDframe({ data, ...options }); + assert.deepEqual(ndframe.dtypes, ["float32"]); + }); + + + it("Returns dtype set during creation of 2DFrame from an Object", function () { + let data = [{ alpha: "A", count: 1 }, { alpha: "B", count: 2 }]; + let options = { dtypes: ['string', 'int32'] }; + let ndframe = new NDframe({ data, ...options, isSeries: false }); + assert.deepEqual(ndframe.dtypes, ['string', 'int32']); + }); + it("Returns dtype set during creation of 2DFrame from an Array", function () { + let data = [["Alice", 2, 3.0], ["Boy", 5, 6.1], ["Girl", 30, 40], [39, 89, 78.2]]; + let cols = ["Name", "Count", "Score"]; + let options = { columns: cols, dtypes: ['string', 'int32', 'float32'] }; + let ndframe = new NDframe({ data, ...options, isSeries: false }); + assert.deepEqual(ndframe.dtypes, ["string", "int32", "float32"]); + }); + + it("Returns dtype automatically inferred from 2DFrame", function () { + let data = [["Alice", 2, 3.1], ["Boy", 5, 6.1], ["Girl", 30, 40.2], [39, 89, 78.2]]; + let cols = ["Name", "Count", "Score"]; + let options = { columns: cols, isSeries: false }; + let ndframe = new NDframe({ data, ...options }); + assert.deepEqual(ndframe.dtypes, ["string", "int32", "float32"]); + }); + + }); + + describe("Empty NDFrame", function () { + it("Can successfully create an empty NDframe from empty array", function () { + let data: any = []; + let sf = new NDframe({ data, isSeries: false }); + assert.deepEqual(sf.shape, [0, 0]); + assert.deepEqual(sf.columns, []); + assert.deepEqual(sf.dtypes, []); + assert.deepEqual(sf.values, []); + }); + it("Can successfully create an empty NDframe from undefined data", function () { + let data = undefined; + let sf = new NDframe({ data, isSeries: true }); + assert.deepEqual(sf.shape, [0, 0]); + assert.deepEqual(sf.columns, []); + assert.deepEqual(sf.dtypes, []); + assert.deepEqual(sf.values, []); + }); + + it("Can successfully create an empty NDframe", function () { + let sf = new NDframe({ data: [], isSeries: true }); + assert.deepEqual(sf.shape, [0, 0]); + assert.deepEqual(sf.columns, []); + assert.deepEqual(sf.dtypes, []); + assert.deepEqual(sf.values, []); + }); + }); +}); diff --git a/src/danfojs-node/test/core/indexing.test.ts b/src/danfojs-node/test/core/indexing.test.ts new file mode 100644 index 00000000..70327449 --- /dev/null +++ b/src/danfojs-node/test/core/indexing.test.ts @@ -0,0 +1,416 @@ +import { assert } from "chai"; +import { describe, it } from "mocha"; +import { DataFrame, Series } from "../../dist/danfojs-node/src"; + +describe("Iloc and Loc based Indexing", function () { + describe("Iloc Index", function () { + it("throw error for wrong row index value", function () { + let data = [1, 2, 34, 5, 6]; + let df = new Series(data); + + assert.throws(function () { df.iloc(0 as any) }, Error, `rows parameter must be an Array. For example: rows: [1,2] or rows: ["0:10"]`); + }); + + it("throw error for wrong string split parameter", function () { + let data = [1, 2, 34, 5, 6]; + let df = new Series(data); + assert.throws(function () { df.iloc(["0;1"]); }, Error, `Invalid row split parameter: If using row split string, it must be of the form; rows: ["start:end"]`); + }); + + it("throw error for wrong string split value", function () { + let data = [1, 2, 34, 5, 6]; + let df = new Series(data); + assert.throws(function () { df.iloc(["0:a"]); }, Error, `Invalid row split parameter. Split parameter must be a number`); + }); + + it("throw error for string split values greater than 2", function () { + let data = [1, 2, 34, 5, 6]; + let df = new Series(data); + assert.throws(function () { df.iloc(["0:4:2"]); }, Error, `Invalid row split parameter: If using row split string, it must be of the form; rows: ["start:end"]`); + }); + + it("throw error for row index larger than series length", function () { + let data = [1, 2, 34, 5, 6]; + + let df = new Series(data); + assert.throws(function () { df.iloc([0, 8]); }, Error, "Invalid row parameter: Specified index 8 cannot be bigger than index length 5"); + }); + + it("throw error for non-numeric row index", function () { + let data = [1, 2, 34, 5, 6]; + let df = new Series(data); + assert.throws(function () { df.iloc([0, "4"]); }, Error, "Invalid row parameter: row index 4 must be a number"); + }); + + + it("df.iloc works for rows:[0,1]", function () { + let data = [1, 2, 34, 5, 6]; + + let df = new Series(data); + + let sf = df.iloc([0, 1]); + let expected = [1, 2]; + + assert.deepEqual(sf.values, expected); + }); + + it("df.iloc works for rows:[1]", function () { + let data = [1, 2, 34, 5, 6]; + + let df = new Series(data); + + let sf = df.iloc([1]); + let expected = [2]; + + assert.deepEqual(sf.values, expected); + }); + + it("correct index is returned for df.iloc rows:[1, 2]", function () { + let data = [1, 2, 34, 5, 6]; + let df = new Series(data, { index: ["a", "b", "c", "d", "e"] }); + + let sf = df.iloc([1, 4]); + let expected = ["b", "e"]; + + assert.deepEqual(sf.index, expected); + }); + + + it("row slice with string param works [0:2]", function () { + let data = [1, 2, 4, 5, 6, 20, 30, 40, 39, 89, 78]; + let df = new Series(data); + + let sf = df.iloc(["0:2"]); + let expected = [1, 2]; + + assert.deepEqual(sf.values, expected); + + }); + it("row slice with string param works [1:]", function () { + let data = [1, 2, 34, 5, 620, 30, 409, 89, 78]; + let df = new Series(data); + + let sf = df.iloc(["1:"]); + let expected = [2, 34, 5, 620, 30, 409, 89, 78]; + + assert.deepEqual(sf.values, expected); + + }); + it("row slice with string param works [:2]", function () { + let data = [1, 2, 34, 5, 6, 20, 30, 40, 39, 89, 78]; + let df = new Series(data); + let sf = df.iloc([":2"]); + let expected = [1, 2]; + + assert.deepEqual(sf.values, expected); + + }); + it("row slice with string param works [:]", function () { + let data = [1, 2, 3, 5, 20, 30, 4039, 89, 78]; + let df = new Series(data); + let expected = [1, 2, 3, 5, 20, 30, 4039, 89, 78]; + + let sf = df.iloc([":"]); + assert.deepEqual(sf.values, expected); + + }); + + it(`throw error for wrong start index size ["0:20"]`, function () { + let data = [1, 2, 34, 5, 6]; + let df = new Series(data); + assert.throws(function () { df.iloc(["0:20"]); }, Error, `row slice [end] index cannot be bigger than 5`); + }); + + it(`throw error for wrong start index size ["-1:2"]`, function () { + let data = [1, 2, 34, 5, 6]; + let df = new Series(data); + assert.throws(function () { df.iloc(["-1:2"]); }, Error, `row slice [start] index cannot be less than 0`); + }); + + it("check data after column slice", function () { + let data = [1, 2, 34, 5, 620, 30, 4039, 89, 78]; + let df = new Series(data); + + let sf = df.iloc([0, 1, 6]); + let expected = [1, 2, 4039]; + assert.deepEqual(sf.values, expected); + + }); + + it("iloc works for boolean array", function () { + let data = [1, 2, 34, 5, 620]; + let df = new Series(data); + + let sf = df.iloc([true, true, false, true, false]); + let expected = [1, 2, 5]; + assert.deepEqual(sf.values, expected); + + }); + + it("iloc works for boolean array (all true)", function () { + let data = [1, 2, 34, 5, 620]; + let df = new Series(data); + + let sf = df.iloc([true, true, true, true, true]); + let expected = [1, 2, 34, 5, 620]; + assert.deepEqual(sf.values, expected); + + }); + it("iloc works for boolean array (all false)", function () { + let data = [1, 2, 34, 5, 620]; + let df = new Series(data); + + let sf = df.iloc([false, false, false, false, false]); + let expected: any = []; + assert.deepEqual(sf.values, expected); + + }); + + it("boolean iloc works for DataFrame", function () { + const data = { + "Name": ["Apples", "Mango", "Banana", "Pear"], + "Count": [21, 5, 30, 10], + "Price": [200, 300, 40, 250] + }; + const df = new DataFrame(data); + const subDf = df.iloc({ rows: [false, false, false, true] }); + const result = [['Pear', 10, 250]]; + assert.deepEqual(subDf.values, result); + + }); + + + it("boolean iloc works for DataFrame with specified columns", function () { + const data = { + "Name": ["Apples", "Mango", "Banana", "Pear"], + "Count": [21, 5, 30, 10], + "Price": [200, 300, 40, 250] + }; + const df = new DataFrame(data); + const subDf = df.iloc({ rows: [false, false, false, true], columns: [0, 2] }); + const result = [['Pear', 250]]; + assert.deepEqual(subDf.values, result); + + }); + + it("boolean iloc works for DataFrame with Series bool selector", function () { + const data = { + "Name": ["Apples", "Mango", "Banana", "Pear"], + "Count": [21, 5, 30, 10], + "Price": [200, 300, 40, 250] + }; + const df = new DataFrame(data); + const subDf = df.iloc({ rows: df["Count"].gt(10), columns: [0, 2] }); + const result = [['Apples', 200], ['Banana', 40]] + assert.deepEqual(subDf.values, result); + + }); + }) + + describe("Loc Index", function () { + it("throw error for wrong row index value", function () { + let data = [1, 2, 34, 5, 6]; + let df = new Series(data); + assert.throws(function () { df.loc(0 as any) }, Error, `rows parameter must be an Array. For example: rows: [1,2] or rows: ["0:10"]`); + }); + + it("throw error for wrong string split parameter", function () { + let data = [1, 2, 34, 5, 6]; + let df = new Series(data); + assert.throws(function () { df.loc(["a;1"]); }, Error, `IndexError: Specified index (a;1) not found`); + }); + it(`throw error for wrong string split parameter ("0;1")`, function () { + let data = [1, 2, 34, 5, 6]; + let df = new Series(data); + assert.throws(function () { df.loc(["0;1"]); }, Error, `IndexError: Specified index (0;1) not found`); + }); + + it("throw error for wrong string split end value", function () { + let data = [1, 2, 34, 5, 6]; + let df = new Series(data); + assert.throws(function () { df.loc(["0:a"]); }, Error, `IndexError: Specified end index not found`); + }); + + it("throw error for string split values greater than 2", function () { + let data = [1, 2, 34, 5, 6]; + let df = new Series(data); + assert.throws(function () { df.loc(["0:4:2"]); }, Error, `Invalid row split parameter: If using row split string, it must be of the form; rows: ["start:end"]`); + }); + + it("throw error for row index larger than series length", function () { + let data = [1, 2, 34, 5, 6]; + + let df = new Series(data); + assert.throws(function () { df.loc([0, 8]); }, Error, "IndexError: Specified index (8) not found"); + }); + + it("throw error for non-numeric row index not found", function () { + let data = [1, 2, 34, 5, 6]; + let df = new Series(data); + assert.throws(function () { df.loc([0, "4"]); }, Error, "IndexError: Specified index (4) not found"); + }); + + + it("df.iloc works for rows:[0,1]", function () { + let data = [1, 2, 34, 5, 6]; + + let df = new Series(data); + + let sf = df.loc([0, 1]); + let expected = [1, 2]; + + assert.deepEqual(sf.values, expected); + }); + + it("df.iloc works for rows:[1]", function () { + let data = [1, 2, 34, 5, 6]; + + let df = new Series(data); + + let sf = df.loc([1]); + let expected = [2]; + + assert.deepEqual(sf.values, expected); + }); + + it("correct index is returned for df.loc([1, 4])", function () { + let data = [1, 2, 34, 5, 6]; + let df = new Series(data, { index: ["a", "b", "c", "d", "e"] }); + + let sf = df.loc(["b", "e"]); + let expected = ["b", "e"]; + assert.deepEqual(sf.index, expected); + }); + + it("row slice with string param works [0:2]", function () { + let data = [1, 2, 4, 5, 6, 20, 30, 40, 39, 89, 78]; + let df = new Series(data); + + let sf = df.loc(["0:2"]); + let expected = [1, 2]; + + assert.deepEqual(sf.values, expected); + + }); + it("row slice with string param works [1:]", function () { + let data = [1, 2, 34, 5, 620, 30, 409, 89, 78]; + let df = new Series(data); + + let sf = df.loc(["1:"]); + let expected = [2, 34, 5, 620, 30, 409, 89, 78]; + + assert.deepEqual(sf.values, expected); + + }); + it("row slice with string param works [:2]", function () { + let data = [1, 2, 34, 5, 6, 20, 30, 40, 39, 89, 78]; + let df = new Series(data); + let sf = df.loc([":2"]); + let expected = [1, 2]; + + assert.deepEqual(sf.values, expected); + + }); + it("row slice with string param works [:]", function () { + let data = [1, 2, 3, 5, 20, 30, 4039, 89, 78]; + let df = new Series(data); + let expected = [1, 2, 3, 5, 20, 30, 4039, 89, 78]; + + let sf = df.loc([":"]); + assert.deepEqual(sf.values, expected); + + }); + + it(`throw error for wrong start index size ["0:20"]`, function () { + let data = [1, 2, 34, 5, 6]; + let df = new Series(data); + assert.throws(function () { df.iloc(["0:20"]); }, Error, `row slice [end] index cannot be bigger than 5`); + }); + + it(`throw error for wrong start index size ["-1:2"]`, function () { + let data = [1, 2, 34, 5, 6]; + let df = new Series(data); + assert.throws(function () { df.iloc(["-1:2"]); }, Error, `row slice [start] index cannot be less than 0`); + }); + + it("check data after column slice", function () { + let data = [1, 2, 34, 5, 620, 30, 4039, 89, 78]; + let df = new Series(data); + + let sf = df.loc([0, 1, 6]); + let expected = [1, 2, 4039]; + assert.deepEqual(sf.values, expected); + + }); + + it("loc works for boolean array", function () { + let data = [1, 2, 34, 5, 620]; + let df = new Series(data); + + let sf = df.loc([true, true, false, true, false]); + let expected = [1, 2, 5]; + assert.deepEqual(sf.values, expected); + + }); + + it("loc works for boolean array (all true)", function () { + let data = [1, 2, 34, 5, 620]; + let df = new Series(data); + + let sf = df.loc([true, true, true, true, true]); + let expected = [1, 2, 34, 5, 620]; + assert.deepEqual(sf.values, expected); + + }); + it("loc works for boolean array (all false)", function () { + let data = [1, 2, 34, 5, 620]; + let df = new Series(data); + + let sf = df.loc([false, false, false, false, false]); + let expected: any = []; + assert.deepEqual(sf.values, expected); + + }); + + it("boolean loc works for DataFrame", function () { + const data = { + "Name": ["Apples", "Mango", "Banana", "Pear"], + "Count": [21, 5, 30, 10], + "Price": [200, 300, 40, 250] + }; + const df = new DataFrame(data); + const subDf = df.loc({ rows: [false, false, false, true] }); + const result = [['Pear', 10, 250]]; + assert.deepEqual(subDf.values, result); + + }); + + + it("boolean loc works for DataFrame with specified columns", function () { + const data = { + "Name": ["Apples", "Mango", "Banana", "Pear"], + "Count": [21, 5, 30, 10], + "Price": [200, 300, 40, 250] + }; + const df = new DataFrame(data); + const subDf = df.loc({ rows: [false, false, false, true], columns: ["Name", "Price"] }); + const result = [['Pear', 250]]; + assert.deepEqual(subDf.values, result); + + }); + + it("boolean loc works for DataFrame with Series bool selector", function () { + const data = { + "Name": ["Apples", "Mango", "Banana", "Pear"], + "Count": [21, 5, 30, 10], + "Price": [200, 300, 40, 250] + }; + const df = new DataFrame(data); + const subDf = df.loc({ rows: df["Count"].gt(10), columns: ["Name", "Price"] }); + const result = [['Apples', 200], ['Banana', 40]] + assert.deepEqual(subDf.values, result); + + }); + }) + +}); \ No newline at end of file diff --git a/src/danfojs-node/test/core/series.test.ts b/src/danfojs-node/test/core/series.test.ts new file mode 100644 index 00000000..9ec573cc --- /dev/null +++ b/src/danfojs-node/test/core/series.test.ts @@ -0,0 +1,1594 @@ +import { assert, expect } from "chai"; +import { describe, it } from "mocha"; +import { Series } from "../../dist/danfojs-node/src"; + +describe("Series Functions", () => { + + describe("head", function () { + it("Gets the first 2 rows in a Series", function () { + const data = [1, 2, 3, 4, 5, 620, 30, 40, 39, 89, 78]; + const cols = ["A"]; + const sf = new Series(data, { columns: cols }); + assert.deepEqual(sf.head(2).values, [1, 2]); + assert.deepEqual(sf.head(5).values, [1, 2, 3, 4, 5]); + }); + it("throw error when row specified is greater than values", function () { + const data = ["Boy", "Girl", "Man", "Woman", "Tall"]; + const cols = ["Items"]; + const sf = new Series(data, { columns: cols }); + assert.throws(function () { assert.deepEqual(sf.head(10).values, data) }, Error, `row slice [end] index cannot be bigger than 5`); + }); + + it("throw error when row specified is less than 0", function () { + const data = [1, 2, 3, 4, 5, 620, 30, 40, 39, 89, 78]; + const sf = new Series(data); + assert.throws(function () { assert.deepEqual(sf.head(-1).values, data) }, Error, `ParamError: end must be greater than start`); + }); + }); + + + describe("tail", function () { + it("Prints the last n rows of a Series", function () { + const data = [1, 2, 3, 4, 5, 620, 30, 40, 39, 89, 78]; + const sf = new Series(data); + assert.deepEqual(sf.tail(2).values, [89, 78]); + assert.deepEqual(sf.tail(4).values, [40, 39, 89, 78]); + + }); + it("throw error when row specified is greater than values", function () { + const data = ["Boy", "Girl", "Man", "Woman", "Tall"]; + const cols = ["Items"]; + const sf = new Series(data, { columns: cols }); + assert.throws(function () { assert.deepEqual(sf.tail(15).values, data) }, Error, `row slice [start] index cannot be less than 0`); + }); + + it("throw error when row specified is less than 0", function () { + const data = ["Boy", "Girl", "Man", "Woman", "Tall"]; + const cols = ["Items"]; + const sf = new Series(data, { columns: cols }); + assert.throws(function () { assert.deepEqual(sf.tail(-1).values, data) }, Error, `ParamError: end must be greater than start`); + }); + }); + + describe("sample", function () { + it("Samples n number of random elements from a DataFrame", async function () { + const data = [1, 2, 3, 4, 5, 620, 30, 40, 39, 89, 78]; + const sf = new Series(data); + assert.deepEqual((await sf.sample(7)).values.length, 7); + }); + it("Return all values if n of sample -1", async function () { + const data = [1, 2, 3, 4, 5, 620, 30, 40, 39, 89, 78]; + const sf = new Series(data); + assert.deepEqual((await sf.sample(-1)).values.length, data.length); + }); + + it("Throw error if n is greater than lenght of Series", async function () { + const data = [1, 2, 3, 4, 5, 620, 30, 40, 39, 89, 78]; + const sf = new Series(data); + try { + await sf.sample(100); + } catch (e: any) { + expect(e.message).to.eql('Sample size n cannot be bigger than size of dataset'); + } + }); + }); + + describe("add", function () { + it("Return Addition of series with another series", function () { + const data = [1, 2, 3, 4, 5, 6]; + const data2 = [30, 40, 39, 1, 2, 1]; + const sf = new Series(data); + const sf2 = new Series(data2); + assert.deepEqual((sf.add(sf2) as Series).values, [31, 42, 42, 5, 7, 7]); + }); + it("Return Addition of series with a single value (Broadcasting)", function () { + const data = [1, 2, 3, 4, 5]; + const sf = new Series(data); + assert.deepEqual((sf.add(1) as Series).values, [2, 3, 4, 5, 6]); + }); + it("Return Addition of series with another series inplace", function () { + const data = [1, 2, 3, 4, 5, 6]; + const data2 = [30, 40, 39, 1, 2, 1]; + const sf = new Series(data); + const sf2 = new Series(data2); + sf.add(sf2, { inplace: true }) + assert.deepEqual(sf.values, [31, 42, 42, 5, 7, 7]); + }); + it("Return Addition of series with a single value (Broadcasting) inplace", function () { + const data = [1, 2, 3, 4, 5]; + const sf = new Series(data); + sf.add(1, { inplace: true }) + assert.deepEqual(sf.values, [2, 3, 4, 5, 6]); + }); + it("Dtype is properly updated on addition of series with a single float value inplace", function () { + const data = [1, 2, 3, 4, 5]; + const sf = new Series(data); + sf.add(1.23, { inplace: true }) + assert.deepEqual(sf.dtypes[0], "float32"); + assert.deepEqual(sf.values, [2.23, 3.23, 4.23, 5.23, 6.23]); + }); + it("Add works properly when using tfjs add function", function () { + const data = [1, 2, 3, 4, 5]; + const sf = new Series(data); + sf.add(1.23, { inplace: true }) + assert.deepEqual(sf.dtypes[0], "float32"); + assert.deepEqual(sf.values, [2.23, 3.23, 4.23, 5.23, 6.23]); + }); + it("Add works properly when using tfjs add function on Series", function () { + const data = [1, 2, 3, 4, 5]; + const sf = new Series(data); + const sf2 = new Series([1.23, 1.23, 1.23, 1.23, 1.23]); + sf.add(sf2, { inplace: true }) + assert.deepEqual(sf.values, [2.23, 3.23, 4.23, 5.23, 6.23]); + }); + it("Throws type error on addition of string type", function () { + const data = [1, 2, 3, 4]; + const data2 = ["A", "B", "C", "d"]; + const sf = new Series(data); + const sf2 = new Series(data2); + assert.throws( + () => { + sf.add(sf2); + }, + Error, + "DtypeError: String data type does not support add operation" + ); + }); + it("Throws length error if series lenght mixmatch", function () { + const data = [1, 2, 3, 4] + const data2 = [1, 2, 3, 4, 5, 6] + const sf = new Series(data) + const sf2 = new Series(data2) + assert.throws(() => { sf.add(sf2) }, Error, "Row length mismatch. Length of other (6), must be the same as Ndframe (4)") + }) + }); + + describe("sub", function () { + it("Return Subtraction of series with another series", function () { + const data1 = [30, 40, 39, 1, 2, 1]; + const data2 = [1, 2, 3, 4, 5, 6]; + const sf1 = new Series(data1); + const sf2 = new Series(data2); + assert.deepEqual((sf1.sub(sf2) as Series).values, [29, 38, 36, -3, -3, -5]); + }); + it("Return Subtraction of series with a single value (Broadcasting)", function () { + const data = [1, 2, 3, 4, 5]; + const sf = new Series(data); + assert.deepEqual((sf.sub(1) as Series).values, [0, 1, 2, 3, 4]); + }); + it("Throws type error on Subtraction of string type", function () { + const data = [1, 2, 3, 4]; + const data2 = ["A", "B", "C", "d"]; + const sf = new Series(data); + const sf2 = new Series(data2); + assert.throws( + () => { + sf.sub(sf2); + }, + Error, + "DtypeError: String data type does not support sub operation" + ); + }); + it("Throws length error if series lenght mixmatch", function () { + const data = [1, 2, 3, 4] + const data2 = [1, 2, 3, 4, 5, 6] + const sf = new Series(data) + const sf2 = new Series(data2) + assert.throws(() => { sf.sub(sf2) }, Error, "Row length mismatch. Length of other (6), must be the same as Ndframe (4)") + }) + }); + + describe("mul", function () { + it("Return multiplication of series with another series", function () { + const data1 = [30, 40, 3, 5]; + const data2 = [1, 2, 3, 4]; + const sf1 = new Series(data1); + const sf2 = new Series(data2); + assert.deepEqual((sf1.mul(sf2) as Series).values, [30, 80, 9, 20]); + }); + it("Return multiplication of series with a single value (Broadcasting)", function () { + const data = [1, 2, 3, 4, 5]; + const sf = new Series(data); + assert.deepEqual((sf.mul(1) as Series).values, [1, 2, 3, 4, 5]); + }); + it("Throws type error on multiplication of string type", function () { + const data = [1, 2, 3, 4] + const data2 = ["A", "B", "C", "d"] + const sf = new Series(data) + const sf2 = new Series(data2) + assert.throws(() => { sf.mul(sf2) }, Error, "DtypeError: String data type does not support mul operation") + }) + it("Throws length error if series lenght mixmatch", function () { + const data = [1, 2, 3, 4] + const data2 = [1, 2, 3, 4, 5, 6] + const sf = new Series(data) + const sf2 = new Series(data2) + assert.throws(() => { sf.mul(sf2) }, Error, "ParamError: Row length mismatch. Length of other (6), must be the same as Ndframe (4)") + }) + }); + + + describe("div", function () { + it("Return float division of series with another series", function () { + const data1 = [30, 40, 3, 5]; + const data2 = [1, 2, 3, 4]; + const sf1 = new Series(data1); + const sf2 = new Series(data2); + assert.deepEqual((sf1.div(sf2) as Series).values, [30, 20, 1, 1.25]); + }); + it("Return integer division of series with another series", function () { + const data1 = [30, 40, 3, 5]; + const data2 = [1, 2, 3, 4]; + const sf1 = new Series(data1); + const sf2 = new Series(data2); + assert.deepEqual((sf1.div(sf2) as Series).values, [30, 20, 1, 1.25]); + }); + it("Return division of series with a single value (Broadcasting)", function () { + const data = [10, 2, 3, 90]; + const sf = new Series(data); + assert.deepEqual((sf.div(2) as Series).values, [5, 1, 1.5, 45]); + }); + it("Throws type error on division of string type", function () { + const data = [1, 2, 3, 4] + const data2 = ["A", "B", "C", "d"] + const sf = new Series(data) + const sf2 = new Series(data2) + assert.throws(() => { sf.div(sf2) }, Error, `DtypeError: String data type does not support div operation`) + }) + it("Throws length error if series lenght mixmatch", function () { + const data = [1, 2, 3, 4] + const data2 = [1, 2, 3, 4, 5, 6] + const sf = new Series(data) + const sf2 = new Series(data2) + assert.throws(() => { sf.div(sf2) }, Error, "ParamError: Row length mismatch. Length of other (6), must be the same as Ndframe (4)") + }) + }); + + describe("pow", function () { + it("Return Exponetial power of series with another series", function () { + const data1 = [2, 3, 4, 5]; + const data2 = [1, 2, 3, 0]; + const sf1 = new Series(data1); + const sf2 = new Series(data2); + assert.deepEqual((sf1.pow(sf2) as Series).values, [2, 9, 64, 1]); + }); + it("Return Exponetial power of series with a single value (Broadcasting)", function () { + const data = [1, 2, 3, 4, 5]; + const sf = new Series(data); + assert.deepEqual((sf.pow(2) as Series).values, [1, 4, 9, 16, 25]); + }); + }); + + describe("mod", function () { + it("Return modulo of series with another float series", function () { + const data1 = [2, 30, 4, 5]; + const data2 = [1.1, 2.2, 3.3, 2.4]; + const sf1 = new Series(data1); + const sf2 = new Series(data2); + const expected = [ + 0.8999999999999999, + 1.3999999999999977, + 0.7000000000000002, + 0.20000000000000018 + ]; + assert.deepEqual((sf1.mod(sf2) as Series).values, expected); + }); + it("Return modulo of series with another int series", function () { + const data1 = [2, 30, 4, 5]; + const data2 = [1, 2, 3, 1]; + const sf1 = new Series(data1); + const sf2 = new Series(data2); + assert.deepEqual((sf1.mod(sf2) as Series).values, [0, 0, 1, 0]); + }); + it("Return modulo power of series with a single value (Broadcasting)", function () { + const data = [1, 2, 3, 4, 5]; + const sf = new Series(data); + assert.deepEqual((sf.mod(2) as Series).values, [1, 0, 1, 0, 1]); + }); + }); + + describe("toString", function () { + it("Prints a series to the console", async function () { + const data = [1, 2, 3, 4, 5, 620, 30, 40, 39, 89, 78]; + const sf = new Series(data); + sf.print() + }); + it("Prints a series to the console", async function () { + const data = ["This is a long text group and I want it to print in full", + "This is a long text group and I want it to print in full"]; + const sf = new Series(data); + sf.print() + }); + }); + + describe("Empty Series", function () { + it("Can successfully create an empty Series from empty array", function () { + const data: any = []; + const sf = new Series(data); + assert.deepEqual(sf.shape, [0, 0]); + assert.deepEqual(sf.columns, []); + assert.deepEqual(sf.dtypes, []); + assert.deepEqual(sf.values, []); + }); + + it("Can successfully create an empty Series", function () { + const sf = new Series(); + assert.deepEqual(sf.shape, [0, 0]); + assert.deepEqual(sf.columns, []); + assert.deepEqual(sf.dtypes, []); + assert.deepEqual(sf.values, []); + }); + }); + + describe("mean", function () { + it("Computes the mean of elements in a int series", function () { + const data1 = [30, 40, 3, 5, NaN]; + const sf = new Series(data1); + assert.deepEqual(sf.mean(), 19.5); + }); + it("Computes the mean of elements in a int series", function () { + const data1 = [30, 40, 3, 5, NaN]; + const sf = new Series(data1); + assert.deepEqual(sf.mean(), 19.5); + }); + it("Computes the mean of elements in a float series", function () { + const data1 = [30.1, 40.2, 3.1, 5.1]; + const sf = new Series(data1); + assert.deepEqual(sf.mean(), 19.625); + }); + it("Computes the mean of elements in a float series with NaN", function () { + const data1 = [30.1, 40.2, 3.1, 5.1, NaN]; + const sf = new Series(data1); + assert.deepEqual(sf.mean(), 19.625); + }); + it("Computes the mean of a boolean series", function () { + const data1 = [true, false, false, false, true, true, false, true]; + const sf = new Series(data1); + assert.deepEqual(sf.mean(), 0.5); + }); + it("Throws error if dtype is string", function () { + const data1 = ["boy", "girl", "Man"]; + const sf = new Series(data1); + assert.throws( + () => { + sf.mean(); + }, + Error, + "DtypeError: String data type does not support mean operation" + ); + }); + }); + + describe("median", function () { + it("Computes the median value of elements across int Series", function () { + const data1 = [30, 40, 3, 5]; + const sf = new Series(data1); + assert.deepEqual(sf.median(), 17.5); + }); + it("Computes the median value of elements across float Series", function () { + const data1 = [30.1, 40.2, 3.1, 5.1, NaN]; + const sf = new Series(data1); + assert.deepEqual(sf.median(), 17.6); + }); + }); + + describe("sum", function () { + it("Sum values of a Int Series", function () { + const data1 = [30, 40, 3, 5, 5, 5, 5, 5, 3, 3, 3, 21, 3]; + const sf = new Series(data1); + assert.deepEqual(sf.sum(), 131); + }); + it("Sum values of a Float Series", function () { + const data1 = [30.1, 3.1, 40.2, 3.1, 5.1]; + const sf = new Series(data1); + assert.deepEqual(sf.sum(), 81.6); + }); + it("Sum values of a bool Series", function () { + const data1 = [true, true, false, false, false]; + const sf = new Series(data1); + assert.deepEqual(sf.sum(), 2); + }); + it("Sum values a Series with missing values", function () { + const data1 = [11, NaN, 2, 2]; + const sf = new Series(data1); + assert.deepEqual(sf.sum(), 15); + }); + }); + + describe("mode", function () { + it("Computes the multi-modal values of a Series", function () { + const data1 = [30, 40, 3, 5, 5, 5, 5, 5, 3, 3, 3, 21, 3]; + const sf = new Series(data1); + assert.deepEqual(sf.mode(), [5, 3]); + }); + it("Computes the modal value of a Series", function () { + const data1 = [30.1, 3.1, 40.2, 3.1, 5.1]; + const sf = new Series(data1); + assert.deepEqual(sf.mode(), [3.1]); + }); + }); + + describe("min", function () { + it("Returns the single smallest elementin a Series", function () { + const data = [30, 40, 3, 5]; + const sf = new Series(data); + assert.deepEqual(sf.min(), 3); + }); + it("Computes the minimum of elements across an float Series", function () { + const data1 = [30.1, 40.2, 3.12, 5.1]; + const sf = new Series(data1, { dtypes: ["float32"] }); + assert.deepEqual(Number(sf.min().toFixed(2)), 3.12); + }); + }); + + describe("max", function () { + it("Computes the maximum of elements across dimensions of a Series", function () { + const data1 = [30, 40, 3, 5]; + const sf = new Series(data1); + assert.deepEqual(sf.max(), 40); + }); + it("Return sum of float values in a series", function () { + const data1 = [30.1, 40.21, 3.1, 5.1]; + const sf = new Series(data1); + assert.deepEqual(Number(sf.max().toFixed(2)), 40.21); + }); + it("Throws error on addition of string Series", function () { + const data1 = ["boy", "gitl", "woman", "man"]; + const sf = new Series(data1); + assert.throws( + () => { + sf.max(); + }, + Error, + "DtypeError: String data type does not support max operation" + ); + }); + }); + + describe("count", function () { + it("Returns the count of non NaN values in a string Series", function () { + const data = ["boy", "gitl", "woman", NaN]; + const sf = new Series(data); + assert.deepEqual(sf.count(), 3); + }); + it("Returns the count of values in a string Series without NaN", function () { + const data = ["boy", "gitl", "woman", "Man"]; + const sf = new Series(data); + assert.deepEqual(sf.count(), 4); + }); + it("Returns the count of non NaN values in a int Series", function () { + const data = [20, 30, NaN, 2, NaN, 30, 21]; + const sf = new Series(data); + assert.deepEqual(sf.count(), 5); + }); + it("Returns the count of non NaN values in a float Series", function () { + const data = [20.1, 30.4, NaN, 2.1, NaN, 30.0, 21.3]; + const sf = new Series(data); + assert.deepEqual(sf.count(), 5); + }); + }); + + describe("std", function () { + it("Computes the standard of elements in a int series", function () { + const data1 = [30, 40, 3, 5]; + const sf = new Series(data1); + assert.deepEqual(sf.std(), 18.375708603116962); + }); + it("Computes the standard deviation of elements in a float series", function () { + const data1 = [30.1, 40.2, 3.1, 5.1]; + const sf = new Series(data1); + assert.deepEqual(sf.std(), 18.412925713566906); + }); + it("Computes the standard deviation of elements in a float series with missing values", function () { + const data1 = [30, 40, 3, 5, undefined]; + const sf = new Series(data1); + assert.deepEqual(sf.std(), 18.375708603116962); + }); + }); + + describe("var", function () { + it("Computes the variance of elements in a int series", function () { + const data1 = [30, 40, 3, 5]; + const sf = new Series(data1); + assert.deepEqual(sf.var(), 337.6666666666667); + }); + it("Computes the variance of elements in a float series", function () { + const data1 = [30.1, 40.2, 3.1, 5.1]; + const sf = new Series(data1); + assert.deepEqual(sf.var(), 339.03583333333336); + }); + it("Computes the variance of elements in a int series with missing values", function () { + const data1 = [30, undefined, 40, 3, 5]; + const sf = new Series(data1); + assert.deepEqual(sf.var(), 337.6666666666667); + }); + }); + + describe("round", function () { + it("Rounds elements in a Series to nearest whole number", function () { + const data1 = [30.21091, 40.190901, 3.564, 5.0212]; + const sf = new Series(data1); + assert.deepEqual((sf.round() as Series).values, [30.2, 40.2, 3.6, 5]); + }); + it("Rounds elements in a Series to 1dp", function () { + const data1 = [30.21091, 40.190901, 3.564, 5.0212]; + const sf = new Series(data1); + assert.deepEqual((sf.round(1) as Series).values, [30.2, 40.2, 3.6, 5.0]); + }); + it("Rounds elements in a Series to 2dp", function () { + const data1 = [30.2191, 40.190901, 3.564, 5.0212]; + const sf = new Series(data1); + assert.deepEqual((sf.round(2) as Series).values, [30.22, 40.19, 3.56, 5.02]); + }); + + it("Rounds elements in a Series to 2dp inplace", function () { + const data1 = [30.2191, 40.190901, 3.564, 5.0212]; + const sf = new Series(data1); + sf.round(2, { inplace: true }) + assert.deepEqual(sf.values, [30.22, 40.19, 3.56, 5.02]); + }); + + it("Rounds elements in a Series with missing values to 2dp", function () { + const data1 = [30.2191, undefined, 3.564, NaN]; + const sf = new Series(data1); + sf.round(2, { inplace: true }) + assert.deepEqual(sf.values as number[], [30.22, undefined, 3.56, NaN]); + }); + + }); + + describe("maximum", function () { + it("Returns the maximum of two series", function () { + const data1 = [30, 40, 3, 5]; + const data2 = [10, 41, 2, 0]; + const sf1 = new Series(data1); + const sf2 = new Series(data2); + assert.deepEqual(sf1.maximum(sf2).values, [30, 41, 3, 5]); + }); + it("Returns the maximum of series and Array", function () { + const data1 = [30, 40, 3, 5]; + const data2 = [10, 41, 2, 0]; + const sf1 = new Series(data1); + assert.deepEqual(sf1.maximum([10, 41, 2, 0]).values, [30, 41, 3, 5]); + }); + it("Returns the maximum of series and scalar", function () { + const data1 = [30, 40, 3, 5]; + const sf1 = new Series(data1); + assert.deepEqual(sf1.maximum(10).values, [30, 40, 10, 10]); + }); + it("Throws error on checking maximum of incompatible Series", function () { + const data1 = [30, 40, 3, 5]; + const data2 = [10, 41, 2]; + const sf1 = new Series(data1); + const sf2 = new Series(data2); + assert.throws( + () => { + sf1.maximum(sf2); + }, + Error, + "ParamError: Row length mismatch. Length of other (3), must be the same as Ndframe (4)" + ); + }); + }); + + describe("minimum", function () { + it("Returns the minimum of two series", function () { + const data1 = [30, 40, 3, 5]; + const data2 = [10, 41, 2, 0]; + const sf1 = new Series(data1); + const sf2 = new Series(data2); + assert.deepEqual(sf1.minimum(sf2).values, [10, 40, 2, 0]); + }); + it("Returns the minimum of series and array", function () { + const data1 = [30, 40, 3, 5]; + const data2 = [10, 41, 2, 0]; + const sf1 = new Series(data1); + assert.deepEqual(sf1.minimum(data2).values, [10, 40, 2, 0]); + }); + it("Returns the minimum of two series", function () { + const data1 = [30, 40, 3, 5]; + const sf1 = new Series(data1); + assert.deepEqual(sf1.minimum(10).values, [10, 10, 3, 5]); + }); + }); + + describe("isNa", function () { + it("Return a boolean same-sized object indicating if string Series contain NaN", function () { + const data1 = [NaN, undefined, "girl", "Man"]; + const sf = new Series(data1); + assert.deepEqual(sf.isNa().values, [true, true, false, false]); + }); + it("Return a boolean same-sized object indicating if float Series values are NaN", function () { + const data1 = [30.21091, NaN, 3.564, undefined]; + const sf = new Series(data1); + assert.deepEqual(sf.isNa().values, [false, true, false, true]); + }); + it("Return a boolean same-sized object indicating if int Series values are NaN", function () { + const data1 = [30, 40, 3, 5, null, undefined]; + const sf = new Series(data1); + assert.deepEqual(sf.isNa().values, [ + false, + false, + false, + false, + true, + true + ]); + }); + }); + + describe("fillNa", function () { + it("replace all NaN value with specified value", function () { + const data = [NaN, 1, 2, 33, 4, NaN, 5, 6, 7, 8]; + const sf = new Series(data); + const sfVal = [-999, 1, 2, 33, 4, -999, 5, 6, 7, 8]; + sf.fillNa(-999, { inplace: true }); + assert.deepEqual(sf.values, sfVal); + }); + it("replace all NaN value in string Series with specified value", function () { + const data = [NaN, "boy", null, "hey", "Man", undefined]; + const sf = new Series(data); + const sfVal = ["filled", "boy", "filled", "hey", "Man", "filled"]; + const sfFill = sf.fillNa("filled"); + assert.deepEqual((sfFill as Series).values, sfVal); + }); + it("Data is in right format after filling", function () { + const data = [NaN, "boy", NaN, "hey", "Man", undefined]; + const sf = new Series(data); + const sfVal = ["filled", "boy", "filled", "hey", "Man", "filled"]; + const sfFill = sf.fillNa("filled"); + assert.deepEqual((sfFill as Series).values, sfVal); + }); + }); + + describe("sortValues", function () { + it("Sort values in a Series in ascending order (not inplace)", function () { + const sf = new Series([20, 30, 1, 2, 4, 57, 89, 0, 4]); + const result = [0, 1, 2, 4, 4, 20, 30, 57, 89]; + const sortedSf = sf.sortValues(); + assert.deepEqual((sortedSf as Series).values, result); + + }); + it("confirms that sortValues in ascending order does not happen inplace", function () { + const sf = new Series([20, 30, 1, 2, 4, 57, 89, 0, 4]); + const result = [0, 1, 2, 4, 4, 20, 30, 57, 89]; + const expectedIndex = [7, 2, 3, 8, 4, 0, 1, 5, 6]; + sf.sortValues({ ascending: true, inplace: true }); + assert.deepEqual(sf.values, result); + assert.deepEqual(sf.index, expectedIndex); + }); + it("Sort values in a Series in Descending order", function () { + const sf = new Series([20, 30, 1, 2, 4, 57, 89, 0, 4]); + const result = [89, 57, 30, 20, 4, 4, 2, 1, 0]; + const sortedSf = sf.sortValues({ascending: false}); + assert.deepEqual((sortedSf as Series).values, result); + }); + it("confirms that sortValues in descending order happens inplace", function () { + const sf = new Series([20, 30, 1, 2, 4, 57, 89, 0, 4]); + const result = [89, 57, 30, 20, 4, 4, 2, 1, 0]; + sf.sortValues({ ascending: false, inplace: true }); + assert.deepEqual(sf.values, result); + }); + it("Confirms that series index is sorted in ascending order (not in inplace)", function () { + const sf = new Series([20, 30, 1, 2, 4, 57, 89, 0, 4]); + const result = [7, 2, 3, 8, 4, 0, 1, 5, 6]; + const sortedSf = sf.sortValues() as Series + assert.deepEqual(sortedSf.index, result); + }); + it("Confirms that series index is sorted in descending order (not in inplace)", function () { + const sf = new Series([20, 30, 1, 2, 4, 57, 89, 0, 4]); + const result = [6, 5, 1, 0, 4, 8, 3, 2, 7]; + const sortedSf = sf.sortValues({ ascending: false}) as Series + assert.deepEqual(sortedSf.index, result); + }); + it("Sort string values in a Series", function () { + const sf = new Series(["boy", "zebra", "girl", "man"]); + const result = ["boy", "girl", "man", "zebra"]; + const sortedSf = sf.sortValues({ ascending: false}) as Series + assert.deepEqual(sortedSf.values, result); + }); + }); + + describe("describe", function () { + it("Computes the descriptive statistics on an int Series", function () { + const data1 = [10, 45, 56, 25, 23, 20, 10]; + const sf = new Series(data1) + assert.deepEqual((sf.describe().round() as Series).values, [ + 7, + 27, + 17.4, + 10, + 23, + 56, + 302 + ]); + }); + it("Computes the descriptive statistics on a float Series", function () { + const data1 = [30.1, 40.2, 3.1, 5.1]; + const sf = new Series(data1); + assert.deepEqual((sf.describe().round() as Series).values, [ + 4, + 19.6, + 18.4, + 3.1, + 17.6, + 40.2, + 339 + ]); + }); + it("Computes the descriptive statistics on a float Series", function () { + const data1 = [30.1, 40.2, 3.1, 5.1]; + const sf = new Series(data1); + assert.deepEqual(sf.describe().index, [ + "count", + "mean", + "std", + "min", + "median", + "max", + "variance" + ]); + }); + }); + + describe("resetIndex", function () { + it("resets the index of a Series", function () { + const data = [ + { alpha: "A", count: 1 }, + { alpha: "B", count: 2 }, + { alpha: "C", count: 3 } + ]; + const df = new Series(data, { index: ["one", "two", "three"] }); + const dfReset = df.resetIndex() as Series + assert.deepEqual(dfReset.index, [0, 1, 2]); + }); + it("Reset the index of a Series created from an Array", function () { + const data = [1, 2, 3, 4, 5, 6]; + const df = new Series(data, { index: ["one", "two", "three", "four", "five", "six"] }); + const dfNew = df.resetIndex() as Series + assert.deepEqual(dfNew.index, [0, 1, 2, 3, 4, 5]); + }); + it("checks that the original series changed after reseting new index inplace", function () { + const data = [ + { index: "A", count: 1 }, + { index: "B", count: 2 }, + { index: "C", count: 3 } + ]; + const df = new Series(data, { index: ["one", "two", "three"] }); + df.resetIndex({ inplace: true }); + assert.deepEqual(df.index, [0, 1, 2]); + }); + }); + + describe("setIndex", function () { + it("sets the index of an Series", function () { + const data = [ + { alpha: "A", count: 1 }, + { alpha: "B", count: 2 }, + { alpha: "C", count: 3 } + ]; + const df = new Series(data); + const dfNew = df.setIndex(["one", "two", "three"]) as Series + assert.deepEqual(dfNew.index, ["one", "two", "three"]); + assert.notDeepEqual(df.index, dfNew.index); + }); + it("checks that the original series is not modified after setting new index not-inplace", function () { + const data = [ + { alpha: "A", count: 1 }, + { alpha: "B", count: 2 }, + { alpha: "C", count: 3 } + ]; + const df = new Series(data); + const dfNew = df.setIndex(["one", "two", "three"]) as Series + assert.notDeepEqual(df.index, dfNew.index); + }); + it("sets the index of an Series inplace", function () { + const data = [12, 2, 20, 50]; + const df = new Series(data); + df.setIndex(["one", "two", "three", "four"], { inplace: true }); + assert.deepEqual(df.index, ["one", "two", "three", "four"]); + }); + it("Throws index not found error", function () { + const data = [12, 2, 20, 50]; + const df = new Series(data); + assert.throws(() => { + // @ts-ignore: + df.setIndex() + }, Error, + "Param Error: Must specify index array" + ) + }); + }); + + describe("Map", function () { + it("map series element to object keys", function () { + const sf = new Series([1, 2, 3, 4]); + const map = { 1: "ok", 2: "okie", 3: "frit", 4: "gop" }; + const rslt = ["ok", "okie", "frit", "gop"]; + assert.deepEqual((sf.map(map) as Series).values, rslt); + }); + it("map series element to object keys inplace", function () { + const sf = new Series([1, 2, 3, 4]); + const map = { 1: "ok", 2: "okie", 3: "frit", 4: "gop" }; + const rslt = ["ok", "okie", "frit", "gop"]; + sf.map(map, { inplace: true }) + assert.deepEqual(sf.values, rslt); + }); + it("map series element to incomplete object keys", function () { + const sf = new Series([1, 2, 3, 4]); + const map = { 1: "ok", 2: "okie", 4: "gop" }; + const rslt = ["ok", "okie", 3, "gop"]; + sf.map(map, { inplace: true }) + assert.deepEqual(sf.values, rslt); + }); + it("map series element to a function statement", function () { + const sf = new Series([1, 2, 3, 4]); + const func_map = (x: any) => { + return x + 1; + }; + const rslt = [2, 3, 4, 5]; + assert.deepEqual((sf.map(func_map) as Series).values, rslt); + }); + + it("map series element to a function statement inplace", function () { + const sf = new Series([1, 2, 3, 4]); + const func_map = (x: any) => { + return x + 1; + }; + const rslt = [2, 3, 4, 5]; + sf.map(func_map, { inplace: true }) + assert.deepEqual(sf.values, rslt); + }); + + it("map passes along the index", function () { + const sf = new Series([ 1, 2, 3, 4 ]); + const func_map = (x: any, i: any) => { + return x + i; + }; + const rslt = [ 1, 3, 5, 7 ]; + sf.map(func_map, { inplace: true }); + assert.deepEqual(sf.values, rslt); + }); + }); + + describe("Apply", function () { + it("apply a function to a series element", function () { + const sf = new Series([1, 2, 3, 4, 5, 6, 7, 8]); + const applyFunc = (x: any) => { + return x + x; + }; + + const rslt = [2, 4, 6, 8, 10, 12, 14, 16]; + assert.deepEqual((sf.apply(applyFunc) as Series).values, rslt); + }); + + it("apply a function to a series element inplace", function () { + const sf = new Series([1, 2, 3, 4, 5, 6, 7, 8]); + const applyFunc = (x: any) => { + return x + x; + }; + + const rslt = [2, 4, 6, 8, 10, 12, 14, 16]; + sf.apply(applyFunc, { inplace: true }) + assert.deepEqual(sf.values, rslt); + }); + }); + + describe("unique", function () { + it("returns the unique values in a Series of type int", function () { + const sf = new Series([1, 2, 3, 4, 5, 6, 7, 8, 1, 1, 22, 8, 5, 5, 5]); + const expected = [1, 2, 3, 4, 5, 6, 7, 8, 22]; + assert.deepEqual(sf.unique().values, expected); + }); + it("returns the unique values in a Series of type string", function () { + const sf = new Series(["a", "a", "b", "c", "c", "d", "e", "d", "d", "e"]); + const expected = ["a", "b", "c", "d", "e"]; + assert.deepEqual(sf.unique().values, expected); + }); + it("returns the unique values in a Series of type string", function () { + const sf = new Series(["a", "a", "b", "c", "c", "d", "e", "d", "d", "e"]); + const expected = ["a", "b", "c", "d", "e"]; + assert.deepEqual(sf.unique().values, expected); + }); + }); + + describe("nUnique", function () { + it("returns the number of unique values in a Series of type string", function () { + const sf = new Series(["a", "a", "b", "c", "c", "d", "e", "d", "d", "e"]); + assert.deepEqual(sf.nUnique(), 5); + }); + it("returns the number of unique values in a Series of type int32", function () { + const sf = new Series([1, 2, 3, 4, 3, 4, 3, 50, 4, 4, 4, 1]); + assert.deepEqual(sf.nUnique(), 5); + }); + }); + + describe("valueCounts", function () { + it("returns the unique values and their counts in a Series of type int", function () { + const sf = new Series([1, 2, 3, 4, 5, 6, 7, 8, 1, 1, 22, 8, 5, 5, 5]); + const expectedIndex = [1, 2, 3, 4, 5, 6, 7, 8, 22]; + const expectedVals = [3, 1, 1, 1, 4, 1, 1, 2, 1]; + assert.deepEqual(sf.valueCounts().values, expectedVals); + assert.deepEqual(sf.valueCounts().index, expectedIndex); + }); + it("returns the unique values and their counts in a Series of type string", function () { + const sf = new Series(["a", "a", "b", "c", "c", "d", "e", "d", "d", "e"]); + const expectedVals = [2, 1, 2, 3, 2]; + const expectedIndex = ["a", "b", "c", "d", "e"]; + assert.deepEqual(sf.valueCounts().values, expectedVals); + assert.deepEqual(sf.valueCounts().index, expectedIndex); + }); + it("returns the unique values and their counts in a Series of type boolean", function () { + const sf = new Series([true, false, false, true, true]); + const expectedVals = [3, 2]; + const expectedIndex = ["true", "false"]; + assert.deepEqual(sf.valueCounts().values, expectedVals); + assert.deepEqual(sf.valueCounts().index, expectedIndex); + }); + }); + + describe("abs", function () { + it("Returns the absolute values in Series", function () { + const data1 = [-10, 45, 56, -25, 23, -20, 10]; + const sf = new Series(data1); + assert.deepEqual((sf.abs() as Series).values, [10, 45, 56, 25, 23, 20, 10]); + }); + it("Computes the descriptive statistics on a float Series", function () { + const data1 = [-30.1, -40.2, -3.1, -5.1]; + const sf = new Series(data1); + assert.deepEqual((sf.abs() as Series).values, [30.1, 40.2, 3.1, 5.1]); + }); + }); + + describe("cumSum", function () { + it("Return cumulative sum over a Series", function () { + const data1 = [10, 45, 56, 25, 23, 20, 10]; + const sf = new Series(data1); + assert.deepEqual((sf.cumSum() as Series).values, [10, 55, 111, 136, 159, 179, 189]); + }); + it("Return cumulative sum over a Series inplace", function () { + const data1 = [10, 45, 56, 25, 23, 20, 10]; + const sf = new Series(data1); + sf.cumSum({ inplace: true }); + assert.deepEqual(sf.values, [10, 55, 111, 136, 159, 179, 189]); + }); + }); + + describe("cumMax", function () { + it("Return cumulative maximum over a Series", function () { + const data1 = [10, 45, 56, 25, 23, 20, 10]; + const sf = new Series(data1); + assert.deepEqual((sf.cumMax() as Series).values, [10, 45, 56, 56, 56, 56, 56]); + }); + it("Return cumulative maximum over a Series inplace", function () { + const data1 = [10, 45, 56, 25, 23, 20, 10]; + const sf = new Series(data1); + sf.cumMax({ inplace: true }); + assert.deepEqual(sf.values, [10, 45, 56, 56, 56, 56, 56]); + }); + }); + + describe("cumMin", function () { + it("Return cumulative minimum over a Series", function () { + const data1 = [10, 45, 56, 25, 23, 20, 10]; + const sf = new Series(data1); + assert.deepEqual((sf.cumMin() as Series).values, [10, 10, 10, 10, 10, 10, 10]); + }); + }); + + describe("cumProd", function () { + it("Return cumulative product over a Series", function () { + const data1 = [1, 2, 10, 3, 12, 14, 1]; + const sf = new Series(data1); + const rslt = [1, 2, 20, 60, 720, 10080, 10080]; + assert.deepEqual((sf.cumProd() as Series).values, rslt); + }); + }); + + describe("lt", function () { + it("Return Less than of series and other series (element-wise)", function () { + const data1 = [10, 45, 56, 25, 23, 20, 10]; + const data2 = [100, 450, 590, 5, 25, 2, 0]; + + const sf = new Series(data1); + const sf2 = new Series(data2); + const expected = [true, true, true, false, true, false, false]; + assert.deepEqual(sf.lt(sf2).values, expected); + }); + + it("Return Less than of series scalar (element-wise)", function () { + const data1 = [10, 45, 56, 25, 23, 20, 10]; + const sf = new Series(data1); + const expected = [true, false, false, true, true, true, true]; + assert.deepEqual(sf.lt(30).values, expected); + }); + + it("Correct index is returned after operation", function () { + const data1 = [true, true, true, false, false]; + const data2 = [true, false, true, true, false]; + const sf = new Series(data1, { index: ["one", "two", "three", "four", "five"] }); + + const expected = ["one", "two", "three", "four", "five"]; + assert.deepEqual(sf.lt(data2).index, expected); + }); + }); + + describe("gt", function () { + it("Return Greater than of series and other series (element-wise)", function () { + const data1 = [10, 45, 56, 25, 23, 20, 10]; + const data2 = [100, 450, 590, 5, 25, 2, 0]; + + const sf = new Series(data1); + const sf2 = new Series(data2); + const expected = [false, false, false, true, false, true, true]; + assert.deepEqual(sf.gt(sf2).values, expected); + }); + + it("Return Greater than of series scalar (element-wise)", function () { + const data1 = [10, 45, 56, 25, 23, 20, 10]; + const sf = new Series(data1); + const expected = [false, true, true, false, false, false, false]; + assert.deepEqual(sf.gt(30).values, expected); + }); + + it("Correct index is returned after operation", function () { + const data1 = [true, true, true, false, false]; + const data2 = [true, false, true, true, false]; + const sf = new Series(data1, { index: ["one", "two", "three", "four", "five"] }); + + const expected = ["one", "two", "three", "four", "five"]; + assert.deepEqual(sf.and(data2).index, expected); + }); + }); + + describe("le", function () { + it("Return Less than or Equal to of series and other series (element-wise)", function () { + const data1 = [10, 45, 56, 25, 23, 20, 10]; + const data2 = [100, 450, 590, 5, 25, 2, 0]; + + const sf = new Series(data1); + const sf2 = new Series(data2); + const expected = [true, true, true, false, true, false, false]; + assert.deepEqual(sf.le(sf2).values, expected); + }); + + it("Return Less than or Equal to of series scalar (element-wise)", function () { + const data1 = [10, 45, 56, 25, 23, 20, 10]; + const sf = new Series(data1); + const expected = [true, false, false, true, true, true, true]; + assert.deepEqual(sf.le(30).values, expected); + }); + }); + + describe("ge", function () { + it("Return Greater than or Equal to of series and other series (element-wise)", function () { + const data1 = [10, 45, 56, 25, 23, 20, 10]; + const data2 = [100, 450, 56, 5, 25, 20, 0]; + + const sf = new Series(data1); + const sf2 = new Series(data2); + const expected = [false, false, true, true, false, true, true]; + assert.deepEqual(sf.ge(sf2).values, expected); + }); + + it("Return Greater than or Equal to of series scalar (element-wise)", function () { + const data1 = [30, 45, 56, 25, 23, 20, 10]; + const sf = new Series(data1); + const expected = [true, true, true, false, false, false, false]; + assert.deepEqual(sf.ge(30).values, expected); + }); + }); + + describe("ne", function () { + it("Return Not Equal to of series and other series (element-wise)", function () { + const data1 = [10, 45, 56, 25, 23, 20, 10]; + const data2 = [10, 450, 56, 5, 25, 2, 0]; + + const sf = new Series(data1); + const sf2 = new Series(data2); + const expected = [false, true, false, true, true, true, true]; + assert.deepEqual(sf.ne(sf2).values, expected); + }); + + it("Return Not Equal to of series scalar (element-wise)", function () { + const data1 = [10, 30, 56, 30, 23, 20, 10]; + const sf = new Series(data1); + const expected = [true, false, true, false, true, true, true]; + assert.deepEqual(sf.ne(30).values, expected); + }); + }); + + describe("eq", function () { + it("Return Equal to of series and other series (element-wise)", function () { + const data1 = [10, 45, 56, 25, 23, 20, 10]; + const data2 = [100, 450, 590, 25, 25, 2, 0]; + + const sf = new Series(data1); + const sf2 = new Series(data2); + const expected = [false, false, false, true, false, false, false]; + assert.deepEqual(sf.eq(sf2).values, expected); + }); + + it("Return Equal to of series scalar (element-wise)", function () { + const data1 = [10, 45, 56, 25, 23, 20, 30]; + const sf = new Series(data1); + const expected = [false, false, false, false, false, false, true]; + assert.deepEqual(sf.eq(30).values, expected); + }); + }); + + describe("replace", function () { + it("Replace values with new value", function () { + const data1 = [10, 45, 56, 25, 23, 20, 10]; + const sf = new Series(data1); + const expected = [-50, 45, 56, 25, 23, 20, -50]; + const dfRep = sf.replace(10, -50) as Series + assert.deepEqual(dfRep.values, expected); + }); + + it("Replace values given in replace param with value (String type)", function () { + const data1 = ["A", "A", "A", "B", "B", "C", "C", "D"]; + const sf = new Series(data1); + const expected = ["boy", "boy", "boy", "B", "B", "C", "C", "D"]; + sf.replace("A", "boy", { inplace: true }); + assert.deepEqual(sf.values, expected); + }); + it("Replace values given in replace param with value (boolean type)", function () { + const data1 = [true, true, false, false]; + const sf = new Series(data1); + const expected = [false, false, false, false]; + sf.replace(true, false, { inplace: true }); + assert.deepEqual(sf.values, expected); + }); + // it("Throw error on wrong param passed", function () { + // const data1 = ["A", "A", "A", "B", "B", "C", "C", "D"]; + // const sf = new Series(data1); + // assert.throws( + // () => { + // sf.replace(null, "boy", { inplace: true }); + // }, + // Error, + // "Params Error: Must specify param 'oldValue' to replace" + // ); + // }); + }); + + describe("dropDuplicates", function () { + it("Return Series with duplicate values removed (Default, first values kept)", function () { + const data1 = [10, 45, 56, 10, 23, 20, 10, 10]; + const sf = new Series(data1); + const expected = [10, 45, 56, 23, 20]; + const expectedIndex = [0, 1, 2, 4, 5]; + const df_drop = sf.dropDuplicates() as Series + assert.deepEqual(df_drop.values, expected); + assert.deepEqual(df_drop.index, expectedIndex); + }); + + it("Return Series with duplicate values removed (last values kept)", function () { + const data1 = [10, 45, 56, 10, 23, 20, 10, 10]; + const sf = new Series(data1); + const expected = [45, 56, 23, 20, 10]; + const expectedIndex = [1, 2, 4, 5, 7]; + const df_drop = sf.dropDuplicates({ keep: "last" }) as Series + assert.deepEqual(df_drop.values, expected); + assert.deepEqual(df_drop.index, expectedIndex); + }); + + it("Return Series with duplicate values removed (String)", function () { + const data1 = ["A", "A", "A", "B", "B", "C", "C", "D"]; + const sf = new Series(data1); + const expected = ["A", "B", "C", "D"]; + const expectedIndex = [0, 3, 5, 7]; + sf.dropDuplicates({ keep: "first", inplace: true }); + assert.deepEqual(sf.values, expected); + assert.deepEqual(sf.index, expectedIndex); + }); + }); + + describe("dropNa", function () { + it("Return a new Series with missing values removed (Int)", function () { + const data1 = [10, 45, undefined, 10, 23, 20, undefined, 10]; + const sf = new Series(data1); + const expected = [10, 45, 10, 23, 20, 10]; + const expectedIndex = [0, 1, 3, 4, 5, 7]; + const sfDrop = sf.dropNa() as Series + assert.deepEqual(sfDrop.values, expected); + assert.deepEqual(sfDrop.index, expectedIndex); + assert.deepEqual(sfDrop.shape, [6, 1]); + }); + + it("Return a new Series with missing values removed (String)", function () { + const data1 = ["A", NaN, "A", "B", "B", NaN, "C", undefined]; + const sf = new Series(data1); + const expected = ["A", "A", "B", "B", "C"]; + const expectedIndex = [0, 2, 3, 4, 6]; + + sf.dropNa({ inplace: true }) as Series + assert.deepEqual(sf.values, expected); + assert.deepEqual(sf.index, expectedIndex); + assert.deepEqual(sf.shape, [5, 1]); + }); + }); + + describe("argSort", function () { + it("Return the integer indices that would sort the Series values", function () { + const data1 = [10, 45, 20, 10, 23, 20, 30, 11]; + const sf = new Series(data1); + const expected = [3, 0, 7, 5, 2, 4, 6, 1]; + const sf_sort = sf.argSort(); + assert.deepEqual(sf_sort.values, expected); + }); + + it("Return the integer indices that would sort the Series values", function () { + const data1 = [10.22, 4.5, 2.0, 10, 23.23, 20.1, 30, 11]; + const sf = new Series(data1); + const expected = [6, 4, 5, 7, 0, 3, 1, 2]; + const sf_sort = sf.argSort({ ascending: false}); + assert.deepEqual(sf_sort.values, expected); + }); + }); + + describe("argMax", function () { + it("Return int position of the largest value in the Series.", function () { + const data1 = [10, 45, 20, 10, 23, 20, 30, 11]; + const sf = new Series(data1); + const expected = 1; + const argMax = sf.argMax(); + assert.deepEqual(argMax, expected); + }); + + it("Return int position of the largest value in the Float Series.", function () { + const data1 = [10.22, 4.5, 2.0, 10, 23.23, 20.1, 30, 11]; + const sf = new Series(data1); + const expected = 6; + const argMax = sf.argMax(); + assert.deepEqual(argMax, expected); + }); + }); + + describe("argMin", function () { + it("Return int position of the smallest value in the Series", function () { + const data1 = [10, 45, 20, 122, 23, 20, 30, 11]; + const sf = new Series(data1); + const expected = 0; + const argMin = sf.argMin(); + assert.deepEqual(argMin, expected); + }); + + it("Return int position of the smallest value in a Float Series", function () { + const data1 = [10.22, 4.5, 2.0, 10, 23.23, 20.1, 30, 11]; + const sf = new Series(data1); + const expected = 2; + const argMin = sf.argMin(); + assert.deepEqual(argMin, expected); + }); + }); + + describe("Str", function () { + it("Converts all characters to lowercase inplace", function () { + const data = ["lower", "CAPITALS", "this is a sentence", "SwApCaSe"]; + const res = ["lower", "capitals", "this is a sentence", "swapcase"]; + const sf = new Series(data); + sf.str.toLowerCase({ inplace: true }) + assert.deepEqual(sf.values, res); + }); + it("Converts all characters to lowercase", function () { + const data = ["lower", "CAPITALS", "this is a sentence", "SwApCaSe"]; + const res = ["lower", "capitals", "this is a sentence", "swapcase"]; + const sf = new Series(data); + assert.deepEqual((sf.str.toLowerCase() as Series).values, res); + }); + it("Converts all characters to capital case.", function () { + const data = ["lower", "CAPITALS", "this is a sentence", "SwApCaSe"]; + const res = ["Lower", "Capitals", "This is a sentence", "Swapcase"]; + const sf = new Series(data); + assert.deepEqual((sf.str.capitalize() as Series).values, res); + }); + + it("Returns the character at the specified index (position)", function () { + const data = ["lower", "CAPITALS", "this is a sentence", "SwApCaSe"]; + const res = ["w", "P", "i", "A"]; + const sf = new Series(data); + assert.deepEqual((sf.str.charAt(2) as Series).values, res); + }); + + it("Throws error on concat of numeric series", function () { + const data = [1, 2, 3, 4, 5, 6]; + const sf = new Series(data); + assert.throws( + () => { + sf.str.concat("20", 1); + }, + Error, + "Cannot call accessor str on non-string type" + ); + + }); + }); + + describe("dt", function () { + it("check month generated", function () { + const data = ["02Sep2019", "03Dec2019", "04Jan2019"]; + const sf = new Series(data); + const expected = [8, 11, 0]; + assert.deepEqual(sf.dt.month().values, expected); + }); + + it("check month Name generated", function () { + const data = ["06-30-2019", "07-29-2019", "08-28-2019"]; + const sf = new Series(data); + const expected = ["June", "July", "August"]; + assert.deepEqual(sf.dt.monthName().values, expected); + }); + + it("check days of the weeks generated", function () { + const data = ["06-30-2019", "07-29-2019", "08-28-2019"]; + const sf = new Series(data); + const expected = ["Sunday", "Monday", "Wednesday"]; + assert.deepEqual(sf.dt.dayOfWeekName().values, expected); + }); + + it("check day of the month generated", function () { + const data = ["06-30-2019", "07-29-2019", "08-28-2019"]; + const sf = new Series(data); + const expected = [30, 29, 28]; + assert.deepEqual(sf.dt.dayOfMonth().values, expected); + }); + }); + + describe("astype", function () { + it("set type of float column to int", function () { + const data = [-20.1, 30, 47.3, -20]; + const ndframe = new Series(data); + const df = ndframe.asType("int32") as Series + + assert.deepEqual(df.dtypes[0], "int32"); + assert.deepEqual(df.values, [-20, 30, 47, -20]); + }); + it("set type of int column to float", function () { + const data = [34, -4, 5, 6]; + const ndframe = new Series(data); + const df = ndframe.asType("float32") as Series + assert.deepEqual(df.dtypes[0], "float32"); + assert.deepEqual(df.values, [34, -4, 5, 6]); + }); + it("set type of string column to int", function () { + const data = ["20.1", "21", "23.4", "50.78"]; + const ndframe = new Series(data); + const df = ndframe.asType("int32") as Series + + assert.deepEqual(df.dtypes[0], "int32"); + assert.deepEqual(df.values, [20, 21, 23, 50]); + }); + it("set type of string column to float", function () { + const data = ["20.1", "21", "23.4", "50.78"]; + const ndframe = new Series(data); + const df = ndframe.asType("float32") as Series + + assert.deepEqual(df.dtypes[0], "float32"); + assert.deepEqual(df.values, [20.1, 21, 23.4, 50.78]); + }); + it("set type of float column to string", function () { + const data = [-20.1, 30, 47.3, -20]; + const ndframe = new Series(data); + const df = ndframe.asType("string") as Series + assert.deepEqual(df.dtypes[0], "string"); + assert.deepEqual(df.values, ["-20.1", "30", "47.3", "-20"]); + }); + it("set type of int column to string", function () { + const data = [34, -4, 5, 6]; + const ndframe = new Series(data); + const df = ndframe.asType("string") as Series + assert.deepEqual(df.dtypes[0], "string"); + assert.deepEqual(df.values, ["34", "-4", "5", "6"]); + }); + it("set type of int column to string inplace", function () { + const data = [34, -4, 5, 6]; + const ndframe = new Series(data); + ndframe.asType("string", { inplace: true }); + assert.deepEqual(ndframe.dtypes[0], "string"); + assert.deepEqual(ndframe.values, ["34", "-4", "5", "6"]); + }); + }); + + describe("append", function () { + it("Add a new single value to the end of a Series inplace", function () { + const data = [1, 2, 3, 4, "a", "b", "c"]; + const sf = new Series(data); + const expected_val = [1, 2, 3, 4, "a", "b", "c", "d"]; + sf.append("d", 7, { inplace: true }); + assert.deepEqual(sf.values, expected_val); + }); + it("Add a new array of values to the end of a Series inplace", function () { + const data = [1, 2, 3, 4]; + const to_add = ["a", "b", "c"]; + const index = [4, 5, 6]; + const sf = new Series(data); + const expected_val = [1, 2, 3, 4, "a", "b", "c"]; + sf.append(to_add, index, { inplace: true }); + assert.deepEqual(sf.values, expected_val); + }); + it("Add a Series to the end of another Series inplace", function () { + const sf1 = new Series([1, 2, 3, 4]); + const sf2 = new Series(["a", "b", "c"]); + const index = [4, 5, 6]; + const expected_val = [1, 2, 3, 4, "a", "b", "c"]; + sf1.append(sf2, index, { inplace: true }); + assert.deepEqual(sf1.values, expected_val); + }); + it("Add a new single value to the end of a Series", function () { + const data = [1, 2, 3, 4, "a", "b", "c"]; + const sf = new Series(data); + const expected_val = [1, 2, 3, 4, "a", "b", "c", "d"]; + const sf2 = sf.append("d", 7) as Series + assert.deepEqual(sf2.values, expected_val); + }); + it("Add a new array of values to the end of a Series", function () { + const data = [1, 2, 3, 4]; + const to_add = ["a", "b", "c"]; + const index = [4, 5, 6]; + const sf = new Series(data); + const expected_val = [1, 2, 3, 4, "a", "b", "c"]; + const sf2 = sf.append(to_add, index) as Series; + assert.deepEqual(sf2.values, expected_val); + }); + it("Add a Series to the end of another Series", function () { + const sf1 = new Series([1, 2, 3, 4]); + const sf2 = new Series(["a", "b", "c"]); + const index = [4, 5, 6]; + const expected_val = [1, 2, 3, 4, "a", "b", "c"]; + const sf3 = sf1.append(sf2, index) as Series; + assert.deepEqual(sf3.values, expected_val); + }); + it("Confirm index Change after append", function () { + const sf1 = new Series([1, 2, 3, 4]); + const sf2 = new Series(["a", "b", "c"]); + const index = [4, 5, 6]; + const sf3 = sf1.append(sf2, index) as Series; + assert.deepEqual(sf3.index, [0, 1, 2, 3, 4, 5, 6]); + }); + it("Confirm index Change after append inplace", function () { + const sf1 = new Series([1, 2, 3, 4]); + const sf2 = new Series(["a", "b", "c"]); + const index = [4, 5, 6]; + sf1.append(sf2, index, { inplace: true }); + assert.deepEqual(sf1.index, [0, 1, 2, 3, 4, 5, 6]); + }); + }); + + describe("and", function () { + it("Return logical AND of series and other series (element-wise)", function () { + const data1 = [true, true, true, false, false]; + const data2 = [true, false, true, true, false]; + const sf = new Series(data1); + const sf2 = new Series(data2); + + const expected = [true, false, true, false, false]; + assert.deepEqual(sf.and(sf2).values, expected); + }); + + it("Return logical AND of series and other scalar", function () { + const data1 = [true, true, true, false, false]; + const sf = new Series(data1); + + const expected = [true, true, true, false, false]; + assert.deepEqual(sf.and(true).values, expected); + }); + + it("Return logical AND of series and other array (element-wise)", function () { + const data1 = [true, true, true, false, false]; + const data2 = [true, false, true, true, false]; + const sf = new Series(data1); + + const expected = [true, false, true, false, false]; + assert.deepEqual(sf.and(data2).values, expected); + }); + + it("Chaining works for logical AND of series and other array (element-wise)", function () { + const data1 = [true, true, true, false, false]; + const data2 = [true, false, true, true, false]; + const data3 = [true, false, false, true, false]; + + const sf = new Series(data1); + const expected = [true, false, false, false, false]; + assert.deepEqual(sf.and(data2).and(data3).values, expected); + }); + + it("Chaining works for logical AND and OR combined", function () { + const data1 = [true, true, true, false, false]; + const data2 = [true, false, true, true, false]; + const data3 = [true, false, false, true, false]; + + const sf = new Series(data1); + const expected = [true, false, true, true, false]; + assert.deepEqual(sf.and(data2).or(data3).values, expected); + }); + }); + + describe("or", function () { + it("Return logical OR of series and other series (element-wise)", function () { + const data1 = [true, true, true, false, false]; + const data2 = [true, false, true, true, false]; + const sf = new Series(data1); + const sf2 = new Series(data2); + + const expected = [true, true, true, true, false]; + assert.deepEqual(sf.or(sf2).values, expected); + }); + + it("Return logical OR of series and other scalar", function () { + const data1 = [true, true, true, false, false]; + const sf = new Series(data1); + + const expected = [true, true, true, true, true]; + assert.deepEqual(sf.or(true).values, expected); + }); + + it("Return logical OR of series and other array (element-wise)", function () { + const data1 = [true, true, true, false, false]; + const data2 = [true, false, true, true, false]; + const sf = new Series(data1); + + const expected = [true, true, true, true, false]; + assert.deepEqual(sf.or(data2).values, expected); + }); + }); + + describe("and", function () { + it("Return logical AND of series and other series (element-wise)", function () { + const data1 = [true, true, true, false, false]; + const data2 = [true, false, true, true, false]; + const sf = new Series(data1); + const sf2 = new Series(data2); + + const expected = [true, false, true, false, false]; + assert.deepEqual(sf.and(sf2).values, expected); + }); + + it("Return logical AND of series and other scalar", function () { + const data1 = [true, true, true, false, false]; + const sf = new Series(data1); + + const expected = [true, true, true, false, false]; + assert.deepEqual(sf.and(true).values, expected); + }); + + it("Return logical AND of series and other array (element-wise)", function () { + const data1 = [true, true, true, false, false]; + const data2 = [true, false, true, true, false]; + const sf = new Series(data1); + + const expected = [true, false, true, false, false]; + assert.deepEqual(sf.and(data2).values, expected); + }); + + it("Chaining works for logical AND of series and other array (element-wise)", function () { + const data1 = [true, true, true, false, false]; + const data2 = [true, false, true, true, false]; + const data3 = [true, false, false, true, false]; + + const sf = new Series(data1); + const expected = [true, false, false, false, false]; + assert.deepEqual(sf.and(data2).and(data3).values, expected); + }); + + it("Chaining works for logical AND and OR combined", function () { + const data1 = [true, true, true, false, false]; + const data2 = [true, false, true, true, false]; + const data3 = [true, false, false, true, false]; + + const sf = new Series(data1); + const expected = [true, false, true, true, false]; + assert.deepEqual(sf.and(data2).or(data3).values, expected); + }); + + it("Correct index is returned after operation", function () { + const data1 = [true, true, true, false, false]; + const data2 = [true, false, true, true, false]; + const sf = new Series(data1, { index: ["one", "two", "three", "four", "five"] }); + + const expected = ["one", "two", "three", "four", "five"]; + assert.deepEqual(sf.and(data2).index, expected); + }); + }); +}) \ No newline at end of file diff --git a/src/danfojs-node/test/core/strings.test.ts b/src/danfojs-node/test/core/strings.test.ts new file mode 100644 index 00000000..55b57182 --- /dev/null +++ b/src/danfojs-node/test/core/strings.test.ts @@ -0,0 +1,167 @@ +import { assert, expect } from "chai"; +import { describe, it } from "mocha"; +import { Series} from "../../dist/danfojs-node/src"; + +describe("Str", function () { + it("Converts all characters to lowercase.", function () { + let data = ['lower', 'CAPITALS', 'this is a sentence', 'SwApCaSe']; + let res = ['lower', 'capitals', 'this is a sentence', 'swapcase']; + let str = new Series(data).str + assert.deepEqual((str.toLowerCase()as Series).values, res); + }); + it("Converts all characters to uppercase.", function () { + let data = ['lower', 'CAPITALS', 'this is a sentence', 'SwApCaSe']; + let res = ['LOWER', 'CAPITALS', 'THIS IS A SENTENCE', 'SWAPCASE']; + let str = new Series(data).str; + assert.deepEqual((str.toUpperCase()as Series).values, res); + }); + it("Converts all characters to capital case.", function () { + let data = ['lower', 'CAPITALS', 'this is a sentence', 'SwApCaSe']; + let res = ['Lower', 'Capitals', 'This is a sentence', 'Swapcase']; + let str = new Series(data).str; + assert.deepEqual((str.capitalize()as Series).values, res); + }); + + it("Returns the character at the specified index (position)", function () { + let data = ['lower', 'CAPITALS', 'this is a sentence', 'SwApCaSe']; + let res = ["w", "P", "i", "A"]; + let str = new Series(data).str; + assert.deepEqual((str.charAt(2)as Series).values, res); + }); + it("Joins two or more strings. 0 joins from the start", function () { + let data = ['lower', 'CAPITALS', 'sentence', 'SwApCaSe']; + let data2 = ['XX', 'YY', 'BB', '01']; + + let res = ['XXlower', 'YYCAPITALS', 'BBsentence', '01SwApCaSe']; + let str = new Series(data).str; + assert.deepEqual((str.concat(data2, 0)as Series).values, res); + }); + + it("Joins two or more strings. 1 joins from the end", function () { + let data = ['lower', 'CAPITALS', 'sentence', 'SwApCaSe']; + let data2 = ['XX', 'YY', 'BB', '01']; + + let res = ['lowerXX', 'CAPITALSYY', 'sentenceBB', 'SwApCaSe01']; + let str = new Series(data).str; + assert.deepEqual((str.concat(data2, 1)as Series).values, res); + }); + + it("Joins two arrays of strings. 0 joins from the start", function () { + let data = ['lower', 'CAPITALS', 'sentence', 'SwApCaSe']; + let res = ['prelower', 'preCAPITALS', 'presentence', 'preSwApCaSe']; + let str = new Series(data).str; + assert.deepEqual((str.concat("pre", 0)as Series).values, res); + }); + + it("Joins two or more strings. 1 joins from the end", function () { + let data = ['lower', 'CAPITALS', 'this is a sentence', 'SwApCaSe']; + let res = ['lowerpost', 'CAPITALSpost', 'this is a sentencepost', 'SwApCaSepost']; + let str = new Series(data).str; + assert.deepEqual((str.concat("post", 1)as Series).values, res); + }); + + it("Checks whether a string begins with specified characters", function () { + let data = ['lower', 'CAPITALS', 'this is a sentence', 'SwApCaSe']; + let res = [true, false, false, false]; + let str = new Series(data).str; + assert.deepEqual((str.startsWith("l")as Series).values, res); + }); + it("Checks whether a string ends with specified characters", function () { + let data = ['lower', 'CAPITALS', 'this is a sentence', 'SwApCaSe']; + let res = [false, false, true, true]; + let str = new Series(data).str; + assert.deepEqual((str.endsWith("e")as Series).values, res); + }); + + it("Checks whether a string contains the specified string/characters", function () { + let data = ['lower', 'CAPITALS', 'this is a sentence', 'SwApCaSe']; + let res = [false, false, true, false]; + let str = new Series(data).str; + assert.deepEqual((str.includes("sentence")as Series).values, res); + }); + + it("Returns the position of the first found occurrence of a specified value in a string", function () { + let data = ['lower', 'CAPITALS', 'this is a sentence', 'SwApCaSe']; + let res = [-1, 0, -1, 4]; + let str = new Series(data).str; + assert.deepEqual((str.indexOf("C")as Series).values, res); + }); + + it("Returns the position of the last found occurrence of a specified value in a string", function () { + let data = ['lower', 'CAPITALS', 'this is a sentence', 'SwApCaSe']; + let res = [4, -1, -1, -1]; + let str = new Series(data).str; + assert.deepEqual((str.lastIndexOf("r")as Series).values, res); + }); + + it("Returns a new string with a specified number of copies of an existing string", function () { + let data = ['a', 'b', 'c', 'd']; + let res = ['aaa', 'bbb', 'ccc', 'ddd']; + let str = new Series(data).str; + assert.deepEqual((str.repeat(3)as Series).values, res); + }); + + it("Searches a string for a specified value, or a regular expression, and returns a new string where the specified values are replaced", function () { + let data = ['lower', 'CAPITALS', 'this is a sentence', 'SwApCaSe']; + let res = ['lower', 'CXXXPITALS', 'this is a sentence', 'SwXXXpCaSe']; + let str = new Series(data).str; + assert.deepEqual((str.replace("A", "XXX")as Series).values, res); + }); + + it("Searches a string for a specified value, or regular expression, and returns the position of the match", function () { + let data = ['lower', 'CAPITALS', 'this is a sentence', 'SwApCaSe']; + let res = [-1, 1, -1, 2]; + let str = new Series(data).str; + assert.deepEqual((str.search("A")as Series).values, res); + }); + + it("Extracts a part of a string and returns a new string", function () { + let data = ['lower', 'CAPITALS', 'this is a sentence', 'SwApCaSe']; + let res = ['ow', 'AP', 'hi', 'wA']; + let str = new Series(data).str; + assert.deepEqual((str.slice(1, 3)as Series).values, res); + }); + + it("Splits a string into an array of substrings", function () { + let data = ['lower part', 'CAPITALS city', 'is a sentence', 'SwAp CaSe']; + let res = ["lower,part", "CAPITALS,city", "is,a,sentence", "SwAp,CaSe"]; + let str = new Series(data).str; + assert.deepEqual((str.split(" ")as Series).values, res); + }); + + it("Extracts the characters from a string, beginning at a specified start position, and through the specified number of character", function () { + let data = ['lower part', 'CAPITALS city', 'this is a sentence', 'SwAp CaSe']; + let res = [" p", "AL", "is", "Ca"]; + let str = new Series(data).str; + assert.deepEqual((str.substr(5, 2)as Series).values, res); + }); + + it("Extracts the characters from a string, between two specified indices", function () { + let data = ['lower part', 'CAPITALS city', 'this is a sentence', 'SwAp CaSe']; + let res = ["w", "P", "i", "A"]; + let str = new Series(data).str; + assert.deepEqual((str.substring(2, 3)as Series).values, res); + }); + + it("Removes whitespace from both ends of a string", function () { + let data = ['lower part ', ' CAPITALS city', ' this is a sentence', ' SwAp CaSe']; + let res = ['lower part', 'CAPITALS city', 'this is a sentence', 'SwAp CaSe']; + let str = new Series(data).str; + assert.deepEqual((str.trim()as Series).values, res); + }); + + it("Joins strings to specified value", function () { + let data = ['lower part', 'CAPITALS city', 'this is a sentence', 'SwAp CaSe']; + let res = ['lower part,new', 'CAPITALS city,new', 'this is a sentence,new', 'SwAp CaSe,new']; + let str = new Series(data).str; + assert.deepEqual((str.join("new", ",")as Series).values, res); + }); + + it("Counts the number of characters in string", function () { + let data = ['lower part', 'CAPITALS', 'this is a sentence', 'SwApCaSe']; + let res = [10, 8, 18, 8]; + let str = new Series(data).str; + assert.deepEqual((str.len()as Series).values, res); + }); + +}); diff --git a/src/danfojs-node/test/io/csv.reader.test.ts b/src/danfojs-node/test/io/csv.reader.test.ts new file mode 100644 index 00000000..752f7ed2 --- /dev/null +++ b/src/danfojs-node/test/io/csv.reader.test.ts @@ -0,0 +1,164 @@ +import path from "path" +import { assert } from "chai"; +import { describe, it } from "mocha"; +import { DataFrame, readCSV, Series, streamCSV, toCSV } from "../../dist/danfojs-node/src"; + +describe("readCSV", function () { + this.timeout(10000); + it("Read local csv file works", async function () { + const filePath = path.join(process.cwd(), "test", "samples", "titanic.csv"); + let df: any = await readCSV(filePath, { header: true, preview: 5 }); + assert.deepEqual(df.shape, [5, 8]); + assert.deepEqual(df.columns, [ + 'Survived', + 'Pclass', + 'Name', + 'Sex', + 'Age', + 'Siblings/Spouses Aboard', + 'Parents/Children Aboard', + 'Fare' + ]); + assert.deepEqual(df.dtypes, [ + 'int32', 'int32', + 'string', 'string', + 'int32', 'int32', + 'int32', 'float32' + ]); + }); + it("Read local CSV file with config works", async function () { + const filePath = path.join(process.cwd(), "test", "samples", "titanic.csv"); + const frameConfig = { + columns: [ + 'A', + 'B', + 'C', + 'D', + 'E', + 'F', + 'G', + 'H' + ] + } + let df: any = await readCSV(filePath, { frameConfig, header: true, preview: 5 }); + assert.deepEqual(df.shape, [5, 8]); + assert.deepEqual(df.columns, [ + 'A', + 'B', + 'C', + 'D', + 'E', + 'F', + 'G', + 'H' + ]); + assert.deepEqual(df.dtypes, [ + 'int32', 'int32', + 'string', 'string', + 'int32', 'int32', + 'int32', 'float32' + ]); + }); + + // it("Read remote csv file works", async function () { + // const remoteFile = "https://raw.githubusercontent.com/opensource9ja/danfojs/dev/danfojs-node/tests/samples/titanic.csv" + // let df: any = await readCSV(remoteFile, { header: true, preview: 5 }); + // assert.deepEqual(df.shape, [5, 8]); + // assert.deepEqual(df.columns, [ + // 'Survived', + // 'Pclass', + // 'Name', + // 'Sex', + // 'Age', + // 'Siblings/Spouses Aboard', + // 'Parents/Children Aboard', + // 'Fare' + // ]); + // assert.deepEqual(df.dtypes, [ + // 'int32', 'int32', + // 'string', 'string', + // 'int32', 'int32', + // 'int32', 'float32' + // ]); + // }); + +}) + +describe("streamCSV", function () { + this.timeout(100000); + it("Streaming local csv file with callback works", async function () { + const filePath = path.join(process.cwd(), "test", "samples", "titanic.csv"); + await streamCSV(filePath, (df: any) => { + if (df) { + assert.deepEqual(df.shape, [1, 8]) + assert.deepEqual(df.columns, [ + 'Survived', + 'Pclass', + 'Name', + 'Sex', + 'Age', + 'Siblings/Spouses Aboard', + 'Parents/Children Aboard', + 'Fare' + ]); + } else { + assert.deepEqual(df, null); + } + }, { header: true }); + + }); + + // it("Streaming remote csv file with callback works", async function () { + // const remoteFile = "https://raw.githubusercontent.com/opensource9ja/danfojs/dev/danfojs-node/tests/samples/titanic.csv" + // await streamCSV(remoteFile, { header: true }, (df: any) => { + // if (df) { + // assert.deepEqual(df.shape, [1, 8]) + // assert.deepEqual(df.columns, [ + // 'Survived', + // 'Pclass', + // 'Name', + // 'Sex', + // 'Age', + // 'Siblings/Spouses Aboard', + // 'Parents/Children Aboard', + // 'Fare' + // ]); + // } else { + // assert.deepEqual(df, null); + // } + // }); + + // }); + +}) + + +describe("toCSV", function () { + it("toCSV works", async function () { + const data = [[1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12]] + let df: any = new DataFrame(data, { columns: ["a", "b", "c", "d"] }); + assert.deepEqual(toCSV(df, {}), `a,b,c,d\n1,2,3,4\n5,6,7,8\n9,10,11,12\n`); + }); + it("toCSV works for specified seperator", async function () { + const data = [[1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12]] + let df: any = new DataFrame(data, { columns: ["a", "b", "c", "d"] }); + assert.deepEqual(toCSV(df, { sep: "+" }), `a+b+c+d\n1+2+3+4\n5+6+7+8\n9+10+11+12\n`); + }); + it("toCSV write to local file works", async function () { + const data = [[1, 2, 3, "4"], [5, 6, 7, "8"], [9, 10, 11, "12"]] + let df: any = new DataFrame(data, { columns: ["a", "b", "c", "d"] }); + const filePath = path.join(process.cwd(), "test", "samples", "test_write.csv"); + toCSV(df, { sep: ",", filePath }); + }); + it("toCSV works for series", async function () { + const data = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12] + let df: any = new Series(data); + assert.deepEqual(toCSV(df, { sep: "+" }), `1+2+3+4+5+6+7+8+9+10+11+12`); + }); + it("calling df.toCSV works", async function () { + const data = [[1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12]] + let df: any = new DataFrame(data, { columns: ["a", "b", "c", "d"] }); + assert.deepEqual(df.toCSV(), `a,b,c,d\n1,2,3,4\n5,6,7,8\n9,10,11,12\n`); + }); + +}) \ No newline at end of file diff --git a/src/danfojs-node/test/io/excel.reader.test.ts b/src/danfojs-node/test/io/excel.reader.test.ts new file mode 100644 index 00000000..04160837 --- /dev/null +++ b/src/danfojs-node/test/io/excel.reader.test.ts @@ -0,0 +1,109 @@ +import fs from "fs"; +import path from "path" +import { assert } from "chai"; +import { describe, it } from "mocha"; +import { DataFrame, readExcel, Series, toExcel } from "../../dist/danfojs-node/src"; + +describe("readExcel", function () { + this.timeout(100000); + it("Read local excel file works", async function () { + const filePath = path.join(process.cwd(), "test", "samples", "sample.xlsx"); + let df: any = await readExcel(filePath, {}); + assert.deepEqual(df.columns, [ + 'Year', + 'Stocks', + 'T.Bills', + 'T.Bonds', + ]); + assert.deepEqual(df.dtypes, [ + 'int32', 'float32', + 'float32', 'float32', + ]); + assert.deepEqual(df.shape, [82, 4]) + }); + it("Read local excel file with config works", async function () { + const filePath = path.join(process.cwd(), "test", "samples", "sample.xlsx"); + const frameConfig = { + columns: [ + 'A', + 'B', + 'C', + 'D', + ] + } + let df: any = await readExcel(filePath, { frameConfig }); + assert.deepEqual(df.columns, [ + 'A', + 'B', + 'C', + 'D', + ]); + assert.deepEqual(df.dtypes, [ + 'int32', 'float32', + 'float32', 'float32', + ]); + assert.deepEqual(df.shape, [82, 4]) + }); + it("Read local excel file works for selected sheet", async function () { + const filePath = path.join(process.cwd(), "test", "samples", "sample.xlsx"); + let df: any = await readExcel(filePath, { sheet: 1 }); + assert.deepEqual(df.columns, []); + assert.deepEqual(df.shape, [0, 0]) + }); + + it("Read remote excel file works", async function () { + const remoteFile = "https://raw.githubusercontent.com/opensource9ja/danfojs/dev/danfojs-node/tests/samples/SampleData.xlsx" + let df: any = await readExcel(remoteFile, {}); + assert.deepEqual(df.columns, [ + 'Year', + 'Stocks', + 'T.Bills', + 'T.Bonds', + ]); + assert.deepEqual(df.dtypes, [ + 'int32', 'float32', + 'float32', 'float32', + ]); + assert.deepEqual(df.shape, [82, 4]) + }); +}); + + +describe("toExcel", function () { + it("toExcel works", async function () { + const data = [[1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12]] + const df: any = new DataFrame(data, { columns: ["a", "b", "c", "d"] }); + const filePath = path.join(process.cwd(), "test", "samples", "sampleOut.xlsx"); + toExcel(df, { filePath }) + + const dfNew: any = await readExcel(filePath, {}); + + assert.equal(fs.existsSync(filePath), true) + assert.deepEqual(dfNew.columns, [ + 'a', + 'b', + 'c', + 'd', + ]); + assert.deepEqual(dfNew.dtypes, [ + 'int32', 'int32', + 'int32', 'int32', + ]); + assert.deepEqual(dfNew.shape, [3, 4]) + }); + it("toExcel works for series", async function () { + const data = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12] + const df: any = new Series(data); + const filePath = path.join(process.cwd(), "test", "samples", "testSeries.xlsx"); + toExcel(df, { filePath }) + assert.equal(fs.existsSync(filePath), true) + }); + it("calling df.toExcel works", async function () { + const data = [[1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12]] + const df: any = new DataFrame(data, { columns: ["a", "b", "c", "d"] }); + const filePath = path.join(process.cwd(), "test", "samples", "sampleOut.xlsx"); + df.toExcel({ filePath }) + assert.equal(fs.existsSync(filePath), true) + }); + +}) diff --git a/src/danfojs-node/test/io/json.reader.test.ts b/src/danfojs-node/test/io/json.reader.test.ts new file mode 100644 index 00000000..2a716d19 --- /dev/null +++ b/src/danfojs-node/test/io/json.reader.test.ts @@ -0,0 +1,154 @@ +import fs from "fs"; +import path from "path" +import { assert } from "chai"; +import { describe, it } from "mocha"; +import { DataFrame, Series, readJSON, toJSON, streamJSON } from "../../dist/danfojs-node/src"; + +describe("readJSON", function () { + this.timeout(100000); + it("Read local json file works", async function () { + const filePath = path.join(process.cwd(), "test", "samples", "book.json"); + const df: any = await readJSON(filePath, {}); + assert.deepEqual(df.columns, [ + 'book_id', + 'title', + 'image_url', + 'authors', + ]); + assert.deepEqual(df.dtypes, [ + 'int32', 'string', + 'string', 'string', + ]); + }); + + it("Read local json file with config works", async function () { + const filePath = path.join(process.cwd(), "test", "samples", "book.json"); + const frameConfig = { + columns: ["A", "B", "C", "D"] + } + const df: any = await readJSON(filePath, { frameConfig }); + assert.deepEqual(df.columns, ["A", "B", "C", "D"]); + assert.deepEqual(df.dtypes, [ + 'int32', 'string', + 'string', 'string', + ]); + }); + // it("Read remote json file works", async function () { + // const remoteFile = "https://raw.githubusercontent.com/opensource9ja/danfojs/dev/danfojs-node/tests/samples/book.json" + // const df: any = await readJSON(remoteFile, {}); + // assert.deepEqual(df.columns, [ + // 'book_id', + // 'title', + // 'image_url', + // 'authors', + // ]); + // assert.deepEqual(df.dtypes, [ + // 'int32', 'string', + // 'string', 'string', + // ]); + // }); +}); + + +describe("streamJSON", function () { + this.timeout(100000); + it("Streaming local csv file with callback works", async function () { + const filePath = path.join(process.cwd(), "test", "samples", "book_small.json"); + await streamJSON(filePath, (df: any) => { + if (df) { + df.print(); + assert.deepEqual(df.shape, [1, 4]) + assert.deepEqual(df.columns, [ + 'book_id', + 'title', + 'image_url', + 'authors', + ]); + assert.deepEqual(df.dtypes, [ + 'int32', 'string', + 'string', 'string', + ]); + } else { + assert.deepEqual(df, null); + } + }); + }); + + // it("Streaming remote json file with callback works", async function () { + // const remoteFile = "https://raw.githubusercontent.com/opensource9ja/danfojs/dev/danfojs-node/tests/samples/book.json" + // await streamJSON(remoteFile, { header: true }, (df: any) => { + // if (df) { + // df.print(); + // assert.deepEqual(df.shape, [1, 4]) + // assert.deepEqual(df.columns, [ + // 'book_id', + // 'title', + // 'image_url', + // 'authors', + // ]); + // } else { + // assert.deepEqual(df, null); + // } + // }); + + // }); + +}) + + +describe("toJSON", function () { + it("toJSON works", async function () { + const data = [[1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12]] + const df: any = new DataFrame(data, { columns: ["a", "b", "c", "d"] }); + const expected: any = [ + { "a": 1, "b": 2, "c": 3, "d": 4 }, + { "a": 5, "b": 6, "c": 7, "d": 8 }, + { "a": 9, "b": 10, "c": 11, "d": 12 }, + ] + const json = toJSON(df, {}) + assert.deepEqual(json, expected); + }); + it("toJSON works for row format", async function () { + const data = [[1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12]] + const df: any = new DataFrame(data, { columns: ["a", "b", "c", "d"] }); + const expected: any = { + "a": [1, 5, 9], + "b": [2, 6, 10], + "c": [3, 7, 11], + "d": [4, 8, 12], + } + const json = toJSON(df, { format: "row" }) + assert.deepEqual(json, expected); + }); + it("toJSON writes file to local path", async function () { + const data = [[1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12]] + const df: any = new DataFrame(data, { columns: ["a", "b", "c", "d"] }); + + const colFilePath = path.join(process.cwd(), "test", "samples", "test_col_write.json"); + const rowFilePath = path.join(process.cwd(), "test", "samples", "test_row_write.json"); + + toJSON(df, { format: "row", filePath: rowFilePath }) + toJSON(df, { format: "column", filePath: colFilePath }) + + assert.equal(fs.existsSync(rowFilePath), true); + assert.equal(fs.existsSync(colFilePath), true); + }); + it("toJSON works for series", async function () { + const data = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12] + const df: any = new Series(data); + assert.deepEqual(toJSON(df, {}), { "0": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12] }); + }); + it("df.toJSON works", async function () { + const data = [[1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12]] + const df: any = new DataFrame(data, { columns: ["a", "b", "c", "d"] }); + const expected: any = { + "a": [1, 5, 9], + "b": [2, 6, 10], + "c": [3, 7, 11], + "d": [4, 8, 12], + } + const json = df.toJSON({ format: "row" }) + assert.deepEqual(json, expected); + }); + +}) diff --git a/danfojs-browser/tests/samples/book.json b/src/danfojs-node/test/samples/book.json similarity index 100% rename from danfojs-browser/tests/samples/book.json rename to src/danfojs-node/test/samples/book.json diff --git a/src/danfojs-node/test/samples/book_small.json b/src/danfojs-node/test/samples/book_small.json new file mode 100644 index 00000000..e56276db --- /dev/null +++ b/src/danfojs-node/test/samples/book_small.json @@ -0,0 +1,80 @@ +[ + { + "book_id": 30555488, + "title": "The Underground Railroad", + "image_url": "https:\/\/images.gr-assets.com\/books\/1493178362m\/30555488.jpg", + "authors": "Colson Whitehead" + }, + { + "book_id": 30831912, + "title": "Norse Mythology", + "image_url": "https:\/\/images.gr-assets.com\/books\/1473864212m\/30831912.jpg", + "authors": "Neil Gaiman" + }, + { + "book_id": 30839185, + "title": "Vicious (Sinners of Saint, #1)", + "image_url": "https:\/\/images.gr-assets.com\/books\/1481945407m\/30839185.jpg", + "authors": "L.J. Shen" + }, + { + "book_id": 31140847, + "title": "Bossman", + "image_url": "https:\/\/s.gr-assets.com\/assets\/nophoto\/book\/111x148-bcc042a9c91a29c1d680899eff700a03.png", + "authors": "Vi Keeland" + }, + { + "book_id": 31176886, + "title": "The Chemist", + "image_url": "https:\/\/images.gr-assets.com\/books\/1468944057m\/31176886.jpg", + "authors": "Stephenie Meyer" + }, + { + "book_id": 31194270, + "title": "Evelyn, After", + "image_url": "https:\/\/s.gr-assets.com\/assets\/nophoto\/book\/111x148-bcc042a9c91a29c1d680899eff700a03.png", + "authors": "Victoria Helen Stone" + }, + { + "book_id": 31538614, + "title": "Short Stories from Hogwarts of Power, Politics and Pesky Poltergeists (Pottermore Presents, #2)", + "image_url": "https:\/\/images.gr-assets.com\/books\/1471436601m\/31538614.jpg", + "authors": "J.K. Rowling, MinaLima" + }, + { + "book_id": 31538635, + "title": "Short Stories from Hogwarts of Heroism, Hardship and Dangerous Hobbies (Pottermore Presents, #1)", + "image_url": "https:\/\/images.gr-assets.com\/books\/1471436733m\/31538635.jpg", + "authors": "J.K. Rowling, MinaLima" + }, + { + "book_id": 31538647, + "title": "Hogwarts: An Incomplete and Unreliable Guide (Pottermore Presents, #3)", + "image_url": "https:\/\/images.gr-assets.com\/books\/1502156557m\/31538647.jpg", + "authors": "J.K. Rowling" + }, + { + "book_id": 31845516, + "title": "Love Warrior", + "image_url": "https:\/\/images.gr-assets.com\/books\/1473164102m\/31845516.jpg", + "authors": "Glennon Doyle Melton" + }, + { + "book_id": 32075671, + "title": "The Hate U Give", + "image_url": "https:\/\/images.gr-assets.com\/books\/1476284759m\/32075671.jpg", + "authors": "Angie Thomas" + }, + { + "book_id": 32848471, + "title": "Egomaniac", + "image_url": "https:\/\/images.gr-assets.com\/books\/1479387807m\/32848471.jpg", + "authors": "Vi Keeland" + }, + { + "book_id": 33288638, + "title": "Wait for It", + "image_url": "https:\/\/s.gr-assets.com\/assets\/nophoto\/book\/111x148-bcc042a9c91a29c1d680899eff700a03.png", + "authors": "Mariana Zapata" + } +] \ No newline at end of file diff --git a/danfojs-browser/tests/samples/SampleData.xlsx b/src/danfojs-node/test/samples/sample.xlsx similarity index 100% rename from danfojs-browser/tests/samples/SampleData.xlsx rename to src/danfojs-node/test/samples/sample.xlsx diff --git a/src/danfojs-node/test/samples/sampleOut.xlsx b/src/danfojs-node/test/samples/sampleOut.xlsx new file mode 100644 index 00000000..f2136730 Binary files /dev/null and b/src/danfojs-node/test/samples/sampleOut.xlsx differ diff --git a/src/danfojs-node/test/samples/test.xlsx b/src/danfojs-node/test/samples/test.xlsx new file mode 100644 index 00000000..e657c177 Binary files /dev/null and b/src/danfojs-node/test/samples/test.xlsx differ diff --git a/src/danfojs-node/test/samples/testSeries.xlsx b/src/danfojs-node/test/samples/testSeries.xlsx new file mode 100644 index 00000000..2a5fbf3b Binary files /dev/null and b/src/danfojs-node/test/samples/testSeries.xlsx differ diff --git a/src/danfojs-node/test/samples/test_col_write.json b/src/danfojs-node/test/samples/test_col_write.json new file mode 100644 index 00000000..1e0a6dec --- /dev/null +++ b/src/danfojs-node/test/samples/test_col_write.json @@ -0,0 +1 @@ +[{"a":1,"b":2,"c":3,"d":4},{"a":5,"b":6,"c":7,"d":8},{"a":9,"b":10,"c":11,"d":12}] \ No newline at end of file diff --git a/src/danfojs-node/test/samples/test_row_write.json b/src/danfojs-node/test/samples/test_row_write.json new file mode 100644 index 00000000..d4df0231 --- /dev/null +++ b/src/danfojs-node/test/samples/test_row_write.json @@ -0,0 +1 @@ +{"a":[1,5,9],"b":[2,6,10],"c":[3,7,11],"d":[4,8,12]} \ No newline at end of file diff --git a/src/danfojs-node/test/samples/test_write.csv b/src/danfojs-node/test/samples/test_write.csv new file mode 100644 index 00000000..0932371e --- /dev/null +++ b/src/danfojs-node/test/samples/test_write.csv @@ -0,0 +1,4 @@ +a,b,c,d +1,2,3,4 +5,6,7,8 +9,10,11,12 diff --git a/danfojs-browser/tests/samples/titanic.csv b/src/danfojs-node/test/samples/titanic.csv similarity index 100% rename from danfojs-browser/tests/samples/titanic.csv rename to src/danfojs-node/test/samples/titanic.csv diff --git a/src/danfojs-node/test/samples/titanicOutLocal.csv b/src/danfojs-node/test/samples/titanicOutLocal.csv new file mode 100644 index 00000000..489234db --- /dev/null +++ b/src/danfojs-node/test/samples/titanicOutLocal.csv @@ -0,0 +1,888 @@ +Survived,Pclass,Name,Sex,Age,Siblings/Spouses Aboard,Parents/Children Aboard,Fare,titles +0,3, Owen Harris Braund,male,22,1,0,7.25,Mr +1,1, John Bradley (Florence Briggs Thayer) Cumings,female,38,1,0,71.2833,Mrs +1,3, Laina Heikkinen,female,26,0,0,7.925,Miss +1,1, Jacques Heath (Lily May Peel) Futrelle,female,35,1,0,53.1,Mrs +0,3, William Henry Allen,male,35,0,0,8.05,Mr +0,3, James Moran,male,27,0,0,8.4583,Mr +0,1, Timothy J McCarthy,male,54,0,0,51.8625,Mr +0,3, Gosta Leonard Palsson,male,2,3,1,21.075,Master +1,3, Oscar W (Elisabeth Vilhelmina Berg) Johnson,female,27,0,2,11.1333,Mrs +1,2, Nicholas (Adele Achem) Nasser,female,14,1,0,30.0708,Mrs +1,3, Marguerite Rut Sandstrom,female,4,1,1,16.7,Miss +1,1, Elizabeth Bonnell,female,58,0,0,26.55,Miss +0,3, William Henry Saundercock,male,20,0,0,8.05,Mr +0,3, Anders Johan Andersson,male,39,1,5,31.275,Mr +0,3, Hulda Amanda Adolfina Vestrom,female,14,0,0,7.8542,Miss +1,2, (Mary D Kingcome) Hewlett,female,55,0,0,16,Mrs +0,3, Eugene Rice,male,2,4,1,29.125,Master +1,2, Charles Eugene Williams,male,23,0,0,13,Mr +0,3, Julius (Emelia Maria Vandemoortele) Vander Planke,female,31,1,0,18,Mrs +1,3, Fatima Masselmani,female,22,0,0,7.225,Mrs +0,2, Joseph J Fynney,male,35,0,0,26,Mr +1,2, Lawrence Beesley,male,34,0,0,13,Mr +1,3, Anna McGowan,female,15,0,0,8.0292,Miss +1,1, William Thompson Sloper,male,28,0,0,35.5,Mr +0,3, Torborg Danira Palsson,female,8,3,1,21.075,Miss +1,3, Carl Oscar (Selma Augusta Emilia Johansson) Asplund,female,38,1,5,31.3875,Mrs +0,3, Farred Chehab Emir,male,26,0,0,7.225,Mr +0,1, Charles Alexander Fortune,male,19,3,2,263,Mr +1,3, Ellen O'Dwyer,female,24,0,0,7.8792,Miss +0,3, Lalio Todoroff,male,23,0,0,7.8958,Mr +0,1, Manuel E Uruchurtu,male,40,0,0,27.7208,Don +1,1, William Augustus (Marie Eugenie) Spencer,female,48,1,0,146.5208,Mrs +1,3, Mary Agatha Glynn,female,18,0,0,7.75,Miss +0,2, Edward H Wheadon,male,66,0,0,10.5,Mr +0,1, Edgar Joseph Meyer,male,28,1,0,82.1708,Mr +0,1, Alexander Oskar Holverson,male,42,1,0,52,Mr +1,3, Hanna Mamee,male,18,0,0,7.2292,Mr +0,3, Ernest Charles Cann,male,21,0,0,8.05,Mr +0,3, Augusta Maria Vander Planke,female,18,2,0,18,Miss +1,3, Jamila Nicola-Yarred,female,14,1,0,11.2417,Miss +0,3, Johan (Johanna Persdotter Larsson) Ahlin,female,40,1,0,9.475,Mrs +0,2, William John Robert (Dorothy Ann Wonnacott) Turpin,female,27,1,0,21,Mrs +1,2, Simonne Marie Anne Andree Laroche,female,3,1,2,41.5792,Miss +1,3, Margaret Delia Devaney,female,19,0,0,7.8792,Miss +0,3, William John Rogers,male,30,0,0,8.05,Mr +0,3, Denis Lennon,male,20,1,0,15.5,Mr +1,3, Bridget O'Driscoll,female,27,0,0,7.75,Miss +0,3, Youssef Samaan,male,16,2,0,21.6792,Mr +0,3, Josef (Josefine Franchi) Arnold-Franchi,female,18,1,0,17.8,Mrs +0,3, Juha Niilo Panula,male,7,4,1,39.6875,Master +0,3, Richard Cater Nosworthy,male,21,0,0,7.8,Mr +1,1, Henry Sleeper (Myna Haxtun) Harper,female,49,1,0,76.7292,Mrs +1,2, Lizzie (Elizabeth Anne Wilkinson) Faunthorpe,female,29,1,0,26,Mrs +0,1, Engelhart Cornelius Ostby,male,65,0,1,61.9792,Mr +1,1, Hugh Woolner,male,46,0,0,35.5,Mr +1,2, Emily Rugg,female,21,0,0,10.5,Miss +0,3, Mansouer Novel,male,28.5,0,0,7.2292,Mr +1,2, Constance Mirium West,female,5,1,2,27.75,Miss +0,3, William Frederick Goodwin,male,11,5,2,46.9,Master +0,3, Orsen Sirayanian,male,22,0,0,7.2292,Mr +1,1, Amelie Icard,female,38,0,0,80,Miss +0,1, Henry Birkhardt Harris,male,45,1,0,83.475,Mr +0,3, Harald Skoog,male,4,3,2,27.9,Master +0,1, Albert A Stewart,male,64,0,0,27.7208,Mr +1,3, Gerios Moubarek,male,7,1,1,15.2458,Master +1,2, (Elizabeth Ramell) Nye,female,29,0,0,10.5,Mrs +0,3, Ernest James Crease,male,19,0,0,8.1583,Mr +1,3, Erna Alexandra Andersson,female,17,4,2,7.925,Miss +0,3, Vincenz Kink,male,26,2,0,8.6625,Mr +0,2, Stephen Curnow Jenkin,male,32,0,0,10.5,Mr +0,3, Lillian Amy Goodwin,female,16,5,2,46.9,Miss +0,2, Ambrose Jr Hood,male,21,0,0,73.5,Mr +0,3, Apostolos Chronopoulos,male,26,1,0,14.4542,Mr +1,3, Lee Bing,male,32,0,0,56.4958,Mr +0,3, Sigurd Hansen Moen,male,25,0,0,7.65,Mr +0,3, Ivan Staneff,male,23,0,0,7.8958,Mr +0,3, Rahamin Haim Moutal,male,28,0,0,8.05,Mr +1,2, Alden Gates Caldwell,male,0.83,0,2,29,Master +1,3, Elizabeth Dowdell,female,30,0,0,12.475,Miss +0,3, Achille Waelens,male,22,0,0,9,Mr +1,3, Jan Baptist Sheerlinck,male,29,0,0,9.5,Mr +1,3, Brigdet Delia McDermott,female,31,0,0,7.7875,Miss +0,1, Francisco M Carrau,male,28,0,0,47.1,Mr +1,2, Bertha Ilett,female,17,0,0,10.5,Miss +1,3, Karl Alfred (Maria Mathilda Gustafsson) Backstrom,female,33,3,0,15.85,Mrs +0,3, William Neal Ford,male,16,1,3,34.375,Mr +0,3, Selman Francis Slocovski,male,20,0,0,8.05,Mr +1,1, Mabel Helen Fortune,female,23,3,2,263,Miss +0,3, Francesco Celotti,male,24,0,0,8.05,Mr +0,3, Emil Christmann,male,29,0,0,8.05,Mr +0,3, Paul Edvin Andreasson,male,20,0,0,7.8542,Mr +0,1, Herbert Fuller Chaffee,male,46,1,0,61.175,Mr +0,3, Bertram Frank Dean,male,26,1,2,20.575,Mr +0,3, Daniel Coxon,male,59,0,0,7.25,Mr +0,3, Charles Joseph Shorney,male,22,0,0,8.05,Mr +0,1, George B Goldschmidt,male,71,0,0,34.6542,Mr +1,1, William Bertram Greenfield,male,23,0,1,63.3583,Mr +1,2, John T (Ada Julia Bone) Doling,female,34,0,1,23,Mrs +0,2, Sinai Kantor,male,34,1,0,26,Mr +0,3, Matilda Petranec,female,28,0,0,7.8958,Miss +0,3, Pastcho Petroff,male,29,0,0,7.8958,Mr +0,1, Richard Frasar White,male,21,0,1,77.2875,Mr +0,3, Gustaf Joel Johansson,male,33,0,0,8.6542,Mr +0,3, Anders Vilhelm Gustafsson,male,37,2,0,7.925,Mr +0,3, Stoytcho Mionoff,male,28,0,0,7.8958,Mr +1,3, Anna Kristine Salkjelsvik,female,21,0,0,7.65,Miss +1,3, Albert Johan Moss,male,29,0,0,7.775,Mr +0,3, Tido Rekic,male,38,0,0,7.8958,Mr +1,3, Bertha Moran,female,28,1,0,24.15,Miss +0,1, Walter Chamberlain Porter,male,47,0,0,52,Mr +0,3, Hileni Zabour,female,14.5,1,0,14.4542,Miss +0,3, David John Barton,male,22,0,0,8.05,Mr +0,3, Katriina Jussila,female,20,1,0,9.825,Miss +0,3, Malake Attalah,female,17,0,0,14.4583,Miss +0,3, Edvard Pekoniemi,male,21,0,0,7.925,Mr +0,3, Patrick Connors,male,70.5,0,0,7.75,Mr +0,2, William John Robert Turpin,male,29,1,0,21,Mr +0,1, Quigg Edmond Baxter,male,24,0,1,247.5208,Mr +0,3, Ellis Anna Maria Andersson,female,2,4,2,31.275,Miss +0,2, Stanley George Hickman,male,21,2,0,73.5,Mr +0,3, Leonard Charles Moore,male,19,0,0,8.05,Mr +0,2, Nicholas Nasser,male,32.5,1,0,30.0708,Mr +1,2, Susan Webber,female,32.5,0,0,13,Miss +0,1, Percival Wayland White,male,54,0,1,77.2875,Mr +1,3, Elias Nicola-Yarred,male,12,1,0,11.2417,Master +0,3, Martin McMahon,male,19,0,0,7.75,Mr +1,3, Fridtjof Arne Madsen,male,24,0,0,7.1417,Mr +1,3, Anna Peter,female,2,1,1,22.3583,Miss +0,3, Johan Ekstrom,male,45,0,0,6.975,Mr +0,3, Jozef Drazenoic,male,33,0,0,7.8958,Mr +0,3, Domingos Fernandeo Coelho,male,20,0,0,7.05,Mr +0,3, Alexander A (Grace Charity Laury) Robins,female,47,1,0,14.5,Mrs +1,2, Leopold (Mathilde Francoise Pede) Weisz,female,29,1,0,26,Mrs +0,2, Samuel James Hayden Sobey,male,25,0,0,13,Mr +0,2, Emile Richard,male,23,0,0,15.0458,Mr +1,1, Helen Monypeny Newsom,female,19,0,2,26.2833,Miss +0,1, Jacques Heath Futrelle,male,37,1,0,53.1,Mr +0,3, Olaf Elon Osen,male,16,0,0,9.2167,Mr +0,1, Victor Giglio,male,24,0,0,79.2,Mr +0,3, Joseph (Sultana) Boulos,female,40,0,2,15.2458,Mrs +1,3, Anna Sofia Nysten,female,22,0,0,7.75,Miss +1,3, Pekka Pietari (Elin Matilda Dolck) Hakkarainen,female,24,1,0,15.85,Mrs +0,3, Jeremiah Burke,male,19,0,0,6.75,Mr +0,2, Edgardo Samuel Andrew,male,18,0,0,11.5,Mr +0,2, Joseph Charles Nicholls,male,19,1,1,36.75,Mr +1,3, August Edvard Andersson,male,27,0,0,7.7958,Mr +0,3, Robina Maggie Ford,female,9,2,2,34.375,Miss +0,2, Michel Navratil,male,36.5,0,2,26,Mr +0,2, Thomas Roussel Davids Byles,male,42,0,0,13,Rev +0,2, Robert James Bateman,male,51,0,0,12.525,Rev +1,1, Thomas (Edith Wearne) Pears,female,22,1,0,66.6,Mrs +0,3, Alfonzo Meo,male,55.5,0,0,8.05,Mr +0,3, Austin Blyler van Billiard,male,40.5,0,2,14.5,Mr +0,3, Ole Martin Olsen,male,27,0,0,7.3125,Mr +0,1, Charles Duane Williams,male,51,0,1,61.3792,Mr +1,3, Katherine Gilnagh,female,16,0,0,7.7333,Miss +0,3, Harry Corn,male,30,0,0,8.05,Mr +0,3, Mile Smiljanic,male,37,0,0,8.6625,Mr +0,3, Thomas Henry Sage,male,5,8,2,69.55,Master +0,3, John Hatfield Cribb,male,44,0,1,16.1,Mr +1,2, James (Elizabeth Inglis Milne) Watt,female,40,0,0,15.75,Mrs +0,3, John Viktor Bengtsson,male,26,0,0,7.775,Mr +0,3, Jovo Calic,male,17,0,0,8.6625,Mr +0,3, Eino Viljami Panula,male,1,4,1,39.6875,Master +1,3, Frank John William Goldsmith,male,9,0,2,20.525,Master +1,1, (Edith Martha Bowerman) Chibnall,female,48,0,1,55,Mrs +0,3, William (Anna Bernhardina Karlsson) Skoog,female,45,1,4,27.9,Mrs +0,1, John D Baumann,male,60,0,0,25.925,Mr +0,3, Lee Ling,male,28,0,0,56.4958,Mr +0,1, Wyckoff Van der hoef,male,61,0,0,33.5,Mr +0,3, Arthur Rice,male,4,4,1,29.125,Master +1,3, Eleanor Ileen Johnson,female,1,1,1,11.1333,Miss +0,3, Antti Wilhelm Sivola,male,21,0,0,7.925,Mr +0,1, James Clinch Smith,male,56,0,0,30.6958,Mr +0,3, Klas Albin Klasen,male,18,1,1,7.8542,Mr +0,3, Henry Forbes Lefebre,male,5,3,1,25.4667,Master +0,1, Ann Elizabeth Isham,female,50,0,0,28.7125,Miss +0,2, Reginald Hale,male,30,0,0,13,Mr +0,3, Lionel Leonard,male,36,0,0,0,Mr +0,3, Constance Gladys Sage,female,8,8,2,69.55,Miss +0,2, Rene Pernot,male,39,0,0,15.05,Mr +0,3, Clarence Gustaf Hugo Asplund,male,9,4,2,31.3875,Master +1,2, Richard F Becker,male,1,2,1,39,Master +1,3, Luise Gretchen Kink-Heilmann,female,4,0,2,22.025,Miss +0,1, Hugh Roscoe Rood,male,39,0,0,50,Mr +1,3, Thomas (Johanna Godfrey) O'Brien,female,26,1,0,15.5,Mrs +1,1, Charles Hallace Romaine,male,45,0,0,26.55,Mr +0,3, John Bourke,male,40,1,1,15.5,Mr +0,3, Stjepan Turcin,male,36,0,0,7.8958,Mr +1,2, (Rosa) Pinsky,female,32,0,0,13,Mrs +0,2, William Carbines,male,19,0,0,13,Mr +1,3, Carla Christine Nielsine Andersen-Jensen,female,19,1,0,7.8542,Miss +1,2, Michel M Navratil,male,3,1,1,26,Master +1,1, James Joseph (Margaret Tobin) Brown,female,44,0,0,27.7208,Mrs +1,1, Elise Lurette,female,58,0,0,146.5208,Miss +0,3, Robert Mernagh,male,28,0,0,7.75,Mr +0,3, Karl Siegwart Andreas Olsen,male,42,0,1,8.4042,Mr +1,3, Margaret Madigan,female,21,0,0,7.75,Miss +0,2, Henriette Yrois,female,24,0,0,13,Miss +0,3, Nestor Cyriel Vande Walle,male,28,0,0,9.5,Mr +0,3, Frederick Sage,male,17,8,2,69.55,Mr +0,3, Jakob Alfred Johanson,male,34,0,0,6.4958,Mr +0,3, Gerious Youseff,male,45.5,0,0,7.225,Mr +1,3, Gurshon Cohen,male,18,0,0,8.05,Mr +0,3, Telma Matilda Strom,female,2,0,1,10.4625,Miss +0,3, Karl Alfred Backstrom,male,32,1,0,15.85,Mr +1,3, Nassef Cassem Albimona,male,26,0,0,18.7875,Mr +1,3, Helen Carr,female,16,0,0,7.75,Miss +1,1, Henry Blank,male,40,0,0,31,Mr +0,3, Ahmed Ali,male,24,0,0,7.05,Mr +1,2, Clear Annie Cameron,female,35,0,0,21,Miss +0,3, John Henry Perkin,male,22,0,0,7.25,Mr +0,2, Hans Kristensen Givard,male,30,0,0,13,Mr +0,3, Philip Kiernan,male,22,1,0,7.75,Mr +1,1, Madeleine Newell,female,31,1,0,113.275,Miss +1,3, Eliina Honkanen,female,27,0,0,7.925,Miss +0,2, Sidney Samuel Jacobsohn,male,42,1,0,27,Mr +1,1, Albina Bazzani,female,32,0,0,76.2917,Miss +0,2, Walter Harris,male,30,0,0,10.5,Mr +1,3, Victor Francis Sunderland,male,16,0,0,8.05,Mr +0,2, James H Bracken,male,27,0,0,13,Mr +0,3, George Henry Green,male,51,0,0,8.05,Mr +0,3, Christo Nenkoff,male,22,0,0,7.8958,Mr +1,1, Frederick Maxfield Hoyt,male,38,1,0,90,Mr +0,3, Karl Ivar Sven Berglund,male,22,0,0,9.35,Mr +1,2, William John Mellors,male,19,0,0,10.5,Mr +0,3, John Hall Lovell,male,20.5,0,0,7.25,Mr +0,2, Arne Jonas Fahlstrom,male,18,0,0,13,Mr +0,3, Mathilde Lefebre,female,12,3,1,25.4667,Miss +1,1, Henry Birkhardt (Irene Wallach) Harris,female,35,1,0,83.475,Mrs +0,3, Bengt Edvin Larsson,male,29,0,0,7.775,Mr +0,2, Ernst Adolf Sjostedt,male,59,0,0,13.5,Mr +1,3, Lillian Gertrud Asplund,female,5,4,2,31.3875,Miss +0,2, Robert William Norman Leyson,male,24,0,0,10.5,Mr +0,3, Alice Phoebe Harknett,female,21,0,0,7.55,Miss +0,2, Stephen Hold,male,44,1,0,26,Mr +1,2, Marjorie Collyer,female,8,0,2,26.25,Miss +0,2, Frederick William Pengelly,male,19,0,0,10.5,Mr +0,2, George Henry Hunt,male,33,0,0,12.275,Mr +0,3, Thamine Zabour,female,19,1,0,14.4542,Miss +1,3, Katherine Murphy,female,18,1,0,15.5,Miss +0,2, Reginald Charles Coleridge,male,29,0,0,10.5,Mr +0,3, Matti Alexanteri Maenpaa,male,22,0,0,7.125,Mr +0,3, Sleiman Attalah,male,30,0,0,7.225,Mr +0,1, William Edward Minahan,male,44,2,0,90,Dr +0,3, Agda Thorilda Viktoria Lindahl,female,25,0,0,7.775,Miss +1,2, William (Anna) Hamalainen,female,24,0,2,14.5,Mrs +1,1, Richard Leonard Beckwith,male,37,1,1,52.5542,Mr +0,2, Ernest Courtenay Carter,male,54,1,0,26,Rev +0,3, James George Reed,male,18,0,0,7.25,Mr +0,3, Wilhelm (Elna Matilda Persson) Strom,female,29,1,1,10.4625,Mrs +0,1, William Thomas Stead,male,62,0,0,26.55,Mr +0,3, William Arthur Lobb,male,30,1,0,16.1,Mr +0,3, Viktor (Helena Wilhelmina) Rosblom,female,41,0,2,20.2125,Mrs +1,3, Darwis (Hanne Youssef Razi) Touma,female,29,0,2,15.2458,Mrs +1,1, Gertrude Maybelle Thorne,female,38,0,0,79.2,Mrs +1,1, Gladys Cherry,female,30,0,0,86.5,Miss +1,1, Anna Ward,female,35,0,0,512.3292,Miss +1,2, (Lutie Davis) Parrish,female,50,0,1,26,Mrs +1,3, Edvin Rojj Felix Asplund,male,3,4,2,31.3875,Master +0,1, Emil Taussig,male,52,1,1,79.65,Mr +0,1, William Harrison,male,40,0,0,0,Mr +0,3, Delia Henry,female,21,0,0,7.75,Miss +0,2, David Reeves,male,36,0,0,10.5,Mr +0,3, Ernesti Arvid Panula,male,16,4,1,39.6875,Mr +1,3, Ernst Ulrik Persson,male,25,1,0,7.775,Mr +1,1, William Thompson (Edith Junkins) Graham,female,58,0,1,153.4625,Mrs +1,1, Amelia Bissette,female,35,0,0,135.6333,Miss +0,1, Alexander Cairns,male,28,0,0,31,Mr +1,3, William Henry Tornquist,male,25,0,0,0,Mr +1,2, (Elizabeth Anne Maidment) Mellinger,female,41,0,1,19.5,Mrs +0,1, Charles H Natsch,male,37,0,1,29.7,Mr +1,3, Hanora Healy,female,33,0,0,7.75,Miss +1,1, Kornelia Theodosia Andrews,female,63,1,0,77.9583,Miss +0,3, Augusta Charlotta Lindblom,female,45,0,0,7.75,Miss +0,2, Francis Parkes,male,21,0,0,0,Mr +0,3, Eric Rice,male,7,4,1,29.125,Master +1,3, Stanton (Rosa Hunt) Abbott,female,35,1,1,20.25,Mrs +0,3, Frank Duane,male,65,0,0,7.75,Mr +0,3, Nils Johan Goransson Olsson,male,28,0,0,7.8542,Mr +0,3, Alfons de Pelsmaeker,male,16,0,0,9.5,Mr +1,3, Edward Arthur Dorking,male,19,0,0,8.05,Mr +0,1, Richard William Smith,male,57,0,0,26,Mr +0,3, Ivan Stankovic,male,33,0,0,8.6625,Mr +1,3, Theodore de Mulder,male,30,0,0,9.5,Mr +0,3, Penko Naidenoff,male,22,0,0,7.8958,Mr +1,2, Masabumi Hosono,male,42,0,0,13,Mr +1,3, Kate Connolly,female,22,0,0,7.75,Miss +1,1, Ellen Barber,female,26,0,0,78.85,Miss +1,1, Dickinson H (Helen Walton) Bishop,female,19,1,0,91.0792,Mrs +0,2, Rene Jacques Levy,male,36,0,0,12.875,Mr +0,3, Aloisia Haas,female,24,0,0,8.85,Miss +0,3, Ivan Mineff,male,24,0,0,7.8958,Mr +0,1, Ervin G Lewy,male,30,0,0,27.7208,Mr +0,3, Mansour Hanna,male,23.5,0,0,7.2292,Mr +0,1, Helen Loraine Allison,female,2,1,2,151.55,Miss +1,1, Adolphe Saalfeld,male,47,0,0,30.5,Mr +1,1, James (Helene DeLaudeniere Chaput) Baxter,female,50,0,1,247.5208,Mrs +1,3, Anna Katherine Kelly,female,20,0,0,7.75,Miss +1,3, Bernard McCoy,male,24,2,0,23.25,Mr +0,3, William Cahoone Jr Johnson,male,19,0,0,0,Mr +1,2, Nora A Keane,female,46,0,0,12.35,Miss +0,3, Howard Hugh Williams,male,28,0,0,8.05,Mr +1,1, Hudson Trevor Allison,male,0.92,1,2,151.55,Master +1,1, Margaret Fleming,female,42,0,0,110.8833,Miss +1,1, Victor de Satode (Maria Josefa Perez de Soto y Vallejo) Penasco y Castellana,female,17,1,0,108.9,Mrs +0,2, Samuel Abelson,male,30,1,0,24,Mr +1,1, Laura Mabel Francatelli,female,30,0,0,56.9292,Miss +1,1, Margaret Bechstein Hays,female,24,0,0,83.1583,Miss +1,1, Emily Borie Ryerson,female,18,2,2,262.375,Miss +0,2, William (Anna Sylfven) Lahtinen,female,26,1,1,26,Mrs +0,3, Ignjac Hendekovic,male,28,0,0,7.8958,Mr +0,2, Benjamin Hart,male,43,1,1,26.25,Mr +1,3, Helmina Josefina 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Miriam Hart,female,7,0,2,26.25,Miss +0,1, Archibald Willingham Butt,male,45,0,0,26.55,Major +1,1, Bertha LeRoy,female,30,0,0,106.425,Miss +0,3, Samuel Beard Risien,male,69,0,0,14.5,Mr +1,1, Hedwig Margaritha Frolicher,female,22,0,2,49.5,Miss +1,1, Harriet R Crosby,female,36,0,2,71,Miss +0,3, Ingeborg Constanzia Andersson,female,9,4,2,31.275,Miss +0,3, Sigrid Elisabeth Andersson,female,11,4,2,31.275,Miss +1,2, Edward Beane,male,32,1,0,26,Mr +0,1, Walter Donald Douglas,male,50,1,0,106.425,Mr +0,1, Arthur Ernest Nicholson,male,64,0,0,26,Mr +1,2, Edward (Ethel Clarke) Beane,female,19,1,0,26,Mrs +1,2, Julian Padro y Manent,male,27,0,0,13.8625,Mr +0,3, Frank John Goldsmith,male,33,1,1,20.525,Mr +1,2, John Morgan Jr Davies,male,8,1,1,36.75,Master +1,1, John Borland Jr Thayer,male,17,0,2,110.8833,Mr +0,2, Percival James R Sharp,male,27,0,0,26,Mr +0,3, Timothy O'Brien,male,21,0,0,7.8292,Mr +1,3, Fahim Leeni,male,22,0,0,7.225,Mr +1,3, Velin Ohman,female,22,0,0,7.775,Miss +0,1, George 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Chapman,male,52,0,0,13.5,Mr +0,3, James Kelly,male,44,0,0,8.05,Mr +1,3, Katherine Mullens,female,19,0,0,7.7333,Miss +0,1, John Borland Thayer,male,49,1,1,110.8833,Mr +0,3, Adolf Mathias Nicolai Olsen Humblen,male,42,0,0,7.65,Mr +1,1, John Jacob (Madeleine Talmadge Force) Astor,female,18,1,0,227.525,Mrs +1,1, Spencer Victor Silverthorne,male,35,0,0,26.2875,Mr +0,3, Saiide Barbara,female,18,0,1,14.4542,Miss +0,3, Martin Gallagher,male,25,0,0,7.7417,Mr +0,3, Henrik Juul Hansen,male,26,1,0,7.8542,Mr +0,2, Henry Samuel Morley,male,39,0,0,26,Mr +1,2, Florence Kelly,female,45,0,0,13.5,Mrs +1,1, Edward Pennington Calderhead,male,42,0,0,26.2875,Mr +1,1, Alice Cleaver,female,22,0,0,151.55,Miss +1,3, Halim Gonios Moubarek,male,4,1,1,15.2458,Master +1,1, Berthe Antonine Mayne,female,24,0,0,49.5042,Mlle +0,1, Herman Klaber,male,41,0,0,26.55,Mr +1,1, Elmer Zebley Taylor,male,48,1,0,52,Mr +0,3, August Viktor Larsson,male,29,0,0,9.4833,Mr +0,2, Samuel Greenberg,male,52,0,0,13,Mr +0,3, Peter Andreas Lauritz Andersen Soholt,male,19,0,0,7.65,Mr +1,1, Caroline Louise Endres,female,38,0,0,227.525,Miss +1,2, Edwina Celia Troutt,female,27,0,0,10.5,Miss +0,3, Malkolm Joackim Johnson,male,33,0,0,7.775,Mr +1,2, Annie Jessie Harper,female,6,0,1,33,Miss +0,3, Svend Lauritz Jensen,male,17,1,0,7.0542,Mr +0,2, William Henry Gillespie,male,34,0,0,13,Mr +0,2, Henry Price Hodges,male,50,0,0,13,Mr +1,1, Norman Campbell Chambers,male,27,1,0,53.1,Mr +0,3, Luka Oreskovic,male,20,0,0,8.6625,Mr +1,2, Peter Henry (Lillian Jefferys) Renouf,female,30,3,0,21,Mrs +1,3, Margareth Mannion,female,28,0,0,7.7375,Miss +0,2, Kurt Arnold Gottfrid Bryhl,male,25,1,0,26,Mr +0,3, Pieta Sofia Ilmakangas,female,25,1,0,7.925,Miss +1,1, Elisabeth Walton Allen,female,29,0,0,211.3375,Miss +0,3, Houssein G N Hassan,male,11,0,0,18.7875,Mr +0,2, Robert J Knight,male,41,0,0,0,Mr +0,2, William John Berriman,male,23,0,0,13,Mr +0,2, Moses Aaron Troupiansky,male,23,0,0,13,Mr +0,3, Leslie Williams,male,28.5,0,0,16.1,Mr +0,3, Edward 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Bailey,male,18,0,0,11.5,Mr +0,3, Thomas Leonard Theobald,male,34,0,0,8.05,Mr +1,1, of (Lucy Noel Martha Dyer-Edwards) Rothes,female,33,0,0,86.5,the Countess +0,3, John Garfirth,male,23,0,0,14.5,Mr +0,3, Iisakki Antino Aijo Nirva,male,41,0,0,7.125,Mr +1,3, Hanna Assi Barah,male,20,0,0,7.2292,Mr +1,1, William Ernest (Lucile Polk) Carter,female,36,1,2,120,Mrs +0,3, Hans Linus Eklund,male,16,0,0,7.775,Mr +1,1, John C (Anna Andrews) Hogeboom,female,51,1,0,77.9583,Mrs +0,1, Arthur Jackson Brewe,male,46,0,0,39.6,Dr +0,3, Mary Mangan,female,30.5,0,0,7.75,Miss +0,3, Daniel J Moran,male,28,1,0,24.15,Mr +0,3, Daniel Danielsen Gronnestad,male,32,0,0,8.3625,Mr +0,3, Rene Aime Lievens,male,24,0,0,9.5,Mr +0,3, Niels Peder Jensen,male,48,0,0,7.8542,Mr +0,2, (Mary) Mack,female,57,0,0,10.5,Mrs +0,3, Dibo Elias,male,29,0,0,7.225,Mr +1,2, Elizabeth (Eliza Needs) Hocking,female,54,1,3,23,Mrs +0,3, Pehr Fabian Oliver Malkolm Myhrman,male,18,0,0,7.75,Mr +0,3, Roger Tobin,male,20,0,0,7.75,Mr +1,3, Virginia Ethel Emanuel,female,5,0,0,12.475,Miss +0,3, Thomas J Kilgannon,male,22,0,0,7.7375,Mr +1,1, Edward Scott (Elisabeth Walton McMillan) Robert,female,43,0,1,211.3375,Mrs +1,3, Banoura Ayoub,female,13,0,0,7.2292,Miss +1,1, Albert Adrian (Vera Gillespie) Dick,female,17,1,0,57,Mrs +0,1, Milton Clyde Long,male,29,0,0,30,Mr +0,3, Andrew G Johnston,male,35,1,2,23.45,Mr +0,3, William Ali,male,25,0,0,7.05,Mr +0,3, Abraham (David Lishin) Harmer,male,25,0,0,7.25,Mr +1,3, Anna Sofia Sjoblom,female,18,0,0,7.4958,Miss +0,3, George Hugh Rice,male,8,4,1,29.125,Master +1,3, Bertram Vere Dean,male,1,1,2,20.575,Master +0,1, Benjamin Guggenheim,male,46,0,0,79.2,Mr +0,3, Andrew Keane,male,20,0,0,7.75,Mr +0,2, Alfred Gaskell,male,16,0,0,26,Mr +0,3, Stella Anna Sage,female,21,8,2,69.55,Miss +0,1, William Fisher Hoyt,male,43,0,0,30.6958,Mr +0,3, Ristiu Dantcheff,male,25,0,0,7.8958,Mr +0,2, Richard Otter,male,39,0,0,13,Mr +1,1, Alice (Farnham) Leader,female,49,0,0,25.9292,Dr +1,3, Mara 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Heininen,female,23,0,0,7.925,Miss +0,2, Albert Mallet,male,31,1,1,37.0042,Mr +0,3, John Fredrik Alexander Holm,male,43,0,0,6.45,Mr +0,3, Karl Thorsten Skoog,male,10,3,2,27.9,Master +1,1, Charles Melville (Clara Jennings Gregg) Hays,female,52,1,1,93.5,Mrs +1,3, Nikola Lulic,male,27,0,0,8.6625,Mr +0,1, John George Reuchlin,male,38,0,0,0,Jonkheer +1,3, (Beila) Moor,female,27,0,1,12.475,Mrs +0,3, Urho Abraham Panula,male,2,4,1,39.6875,Master +0,3, John Flynn,male,36,0,0,6.95,Mr +0,3, Len Lam,male,23,0,0,56.4958,Mr +1,2, Andre Mallet,male,1,0,2,37.0042,Master +1,3, Thomas Joseph McCormack,male,19,0,0,7.75,Mr +1,1, George Nelson (Martha Evelyn) Stone,female,62,0,0,80,Mrs +1,3, Antoni (Selini Alexander) Yasbeck,female,15,1,0,14.4542,Mrs +1,2, George Sibley Richards,male,0.83,1,1,18.75,Master +0,3, Amin Saad,male,30,0,0,7.2292,Mr +0,3, Albert Augustsson,male,23,0,0,7.8542,Mr +0,3, Owen George Allum,male,18,0,0,8.3,Mr +1,1, Sara Rebecca Compton,female,39,1,1,83.1583,Miss +0,3, Jakob 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George B Goldschmidt,male,71,0,0,34.6542,Mr +1,1, William Bertram Greenfield,male,23,0,1,63.3583,Mr +1,2, John T (Ada Julia Bone) Doling,female,34,0,1,23,Mrs +0,2, Sinai Kantor,male,34,1,0,26,Mr +0,3, Matilda Petranec,female,28,0,0,7.8958,Miss +0,3, Pastcho Petroff,male,29,0,0,7.8958,Mr +0,1, Richard Frasar White,male,21,0,1,77.2875,Mr +0,3, Gustaf Joel Johansson,male,33,0,0,8.6542,Mr +0,3, Anders Vilhelm Gustafsson,male,37,2,0,7.925,Mr +0,3, Stoytcho Mionoff,male,28,0,0,7.8958,Mr +1,3, Anna Kristine Salkjelsvik,female,21,0,0,7.65,Miss +1,3, Albert Johan Moss,male,29,0,0,7.775,Mr +0,3, Tido Rekic,male,38,0,0,7.8958,Mr +1,3, Bertha Moran,female,28,1,0,24.15,Miss +0,1, Walter Chamberlain Porter,male,47,0,0,52,Mr +0,3, Hileni Zabour,female,14.5,1,0,14.4542,Miss +0,3, David John Barton,male,22,0,0,8.05,Mr +0,3, Katriina Jussila,female,20,1,0,9.825,Miss +0,3, Malake Attalah,female,17,0,0,14.4583,Miss +0,3, Edvard Pekoniemi,male,21,0,0,7.925,Mr +0,3, Patrick Connors,male,70.5,0,0,7.75,Mr 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Gilnagh,female,16,0,0,7.7333,Miss +0,3, Harry Corn,male,30,0,0,8.05,Mr +0,3, Mile Smiljanic,male,37,0,0,8.6625,Mr +0,3, Thomas Henry Sage,male,5,8,2,69.55,Master +0,3, John Hatfield Cribb,male,44,0,1,16.1,Mr +1,2, James (Elizabeth Inglis Milne) Watt,female,40,0,0,15.75,Mrs +0,3, John Viktor Bengtsson,male,26,0,0,7.775,Mr +0,3, Jovo Calic,male,17,0,0,8.6625,Mr +0,3, Eino Viljami Panula,male,1,4,1,39.6875,Master +1,3, Frank John William Goldsmith,male,9,0,2,20.525,Master +1,1, (Edith Martha Bowerman) Chibnall,female,48,0,1,55,Mrs +0,3, William (Anna Bernhardina Karlsson) Skoog,female,45,1,4,27.9,Mrs +0,1, John D Baumann,male,60,0,0,25.925,Mr +0,3, Lee Ling,male,28,0,0,56.4958,Mr +0,1, Wyckoff Van der hoef,male,61,0,0,33.5,Mr +0,3, Arthur Rice,male,4,4,1,29.125,Master +1,3, Eleanor Ileen Johnson,female,1,1,1,11.1333,Miss +0,3, Antti Wilhelm Sivola,male,21,0,0,7.925,Mr +0,1, James Clinch Smith,male,56,0,0,30.6958,Mr +0,3, Klas Albin Klasen,male,18,1,1,7.8542,Mr +0,3, Henry Forbes 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Mernagh,male,28,0,0,7.75,Mr +0,3, Karl Siegwart Andreas Olsen,male,42,0,1,8.4042,Mr +1,3, Margaret Madigan,female,21,0,0,7.75,Miss +0,2, Henriette Yrois,female,24,0,0,13,Miss +0,3, Nestor Cyriel Vande Walle,male,28,0,0,9.5,Mr +0,3, Frederick Sage,male,17,8,2,69.55,Mr +0,3, Jakob Alfred Johanson,male,34,0,0,6.4958,Mr +0,3, Gerious Youseff,male,45.5,0,0,7.225,Mr +1,3, Gurshon Cohen,male,18,0,0,8.05,Mr +0,3, Telma Matilda Strom,female,2,0,1,10.4625,Miss +0,3, Karl Alfred Backstrom,male,32,1,0,15.85,Mr +1,3, Nassef Cassem Albimona,male,26,0,0,18.7875,Mr +1,3, Helen Carr,female,16,0,0,7.75,Miss +1,1, Henry Blank,male,40,0,0,31,Mr +0,3, Ahmed Ali,male,24,0,0,7.05,Mr +1,2, Clear Annie Cameron,female,35,0,0,21,Miss +0,3, John Henry Perkin,male,22,0,0,7.25,Mr +0,2, Hans Kristensen Givard,male,30,0,0,13,Mr +0,3, Philip Kiernan,male,22,1,0,7.75,Mr +1,1, Madeleine Newell,female,31,1,0,113.275,Miss +1,3, Eliina Honkanen,female,27,0,0,7.925,Miss +0,2, Sidney Samuel Jacobsohn,male,42,1,0,27,Mr +1,1, 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Baxter,female,50,0,1,247.5208,Mrs +1,3, Anna Katherine Kelly,female,20,0,0,7.75,Miss +1,3, Bernard McCoy,male,24,2,0,23.25,Mr +0,3, William Cahoone Jr Johnson,male,19,0,0,0,Mr +1,2, Nora A Keane,female,46,0,0,12.35,Miss +0,3, Howard Hugh Williams,male,28,0,0,8.05,Mr +1,1, Hudson Trevor Allison,male,0.92,1,2,151.55,Master +1,1, Margaret Fleming,female,42,0,0,110.8833,Miss +1,1, Victor de Satode (Maria Josefa Perez de Soto y Vallejo) Penasco y Castellana,female,17,1,0,108.9,Mrs +0,2, Samuel Abelson,male,30,1,0,24,Mr +1,1, Laura Mabel Francatelli,female,30,0,0,56.9292,Miss +1,1, Margaret Bechstein Hays,female,24,0,0,83.1583,Miss +1,1, Emily Borie Ryerson,female,18,2,2,262.375,Miss +0,2, William (Anna Sylfven) Lahtinen,female,26,1,1,26,Mrs +0,3, Ignjac Hendekovic,male,28,0,0,7.8958,Mr +0,2, Benjamin Hart,male,43,1,1,26.25,Mr +1,3, Helmina Josefina Nilsson,female,26,0,0,7.8542,Miss +1,2, Sinai (Miriam Sternin) Kantor,female,24,1,0,26,Mrs +0,2, Ernest Moraweck,male,54,0,0,14,Dr +1,1, Mary Natalie Wick,female,31,0,2,164.8667,Miss +1,1, Frederic Oakley (Margaretta Corning Stone) Spedden,female,40,1,1,134.5,Mrs +0,3, Samuel Dennis,male,22,0,0,7.25,Mr +0,3, Yoto Danoff,male,27,0,0,7.8958,Mr +1,2, Hilda Mary Slayter,female,30,0,0,12.35,Miss +1,2, Albert Francis (Sylvia Mae Harbaugh) Caldwell,female,22,1,1,29,Mrs +0,3, George John Jr Sage,male,20,8,2,69.55,Mr +1,1, Marie Grice Young,female,36,0,0,135.6333,Miss +0,3, Johan Hansen Nysveen,male,61,0,0,6.2375,Mr +1,2, (Ada E Hall) Ball,female,36,0,0,13,Mrs +1,3, Frank John (Emily Alice Brown) Goldsmith,female,31,1,1,20.525,Mrs +1,1, Jean Gertrude Hippach,female,16,0,1,57.9792,Miss +1,3, Agnes McCoy,female,28,2,0,23.25,Miss +0,1, Austen Partner,male,45.5,0,0,28.5,Mr +0,1, George Edward Graham,male,38,0,1,153.4625,Mr +0,3, Leo Edmondus Vander Planke,male,16,2,0,18,Mr +1,1, Henry William (Clara Heinsheimer) Frauenthal,female,42,1,0,133.65,Mrs +0,3, Mitto Denkoff,male,30,0,0,7.8958,Mr +0,1, Thomas Clinton Pears,male,29,1,0,66.6,Mr 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Harry Elkins Widener,male,27,0,2,211.5,Mr +0,3, Tannous Betros,male,20,0,0,4.0125,Mr +0,3, Karl Gideon Gustafsson,male,19,0,0,7.775,Mr +1,1, Rosalie Bidois,female,42,0,0,227.525,Miss +1,3, Maria Nakid,female,1,0,2,15.7417,Miss +0,3, Juho Tikkanen,male,32,0,0,7.925,Mr +1,1, Alexander Oskar (Mary Aline Towner) Holverson,female,35,1,0,52,Mrs +0,3, Vasil Plotcharsky,male,27,0,0,7.8958,Mr +0,2, Charles Henry Davies,male,18,0,0,73.5,Mr +0,3, Sidney Leonard Goodwin,male,1,5,2,46.9,Master +1,2, Kate Buss,female,36,0,0,13,Miss +0,3, Matthew Sadlier,male,19,0,0,7.7292,Mr +1,2, Bertha Lehmann,female,17,0,0,12,Miss +1,1, William Ernest Carter,male,36,1,2,120,Mr +1,3, Carl Olof Jansson,male,21,0,0,7.7958,Mr +0,3, Johan Birger Gustafsson,male,28,2,0,7.925,Mr +1,1, Marjorie Newell,female,23,1,0,113.275,Miss +1,3, Hjalmar (Agnes Charlotta Bengtsson) Sandstrom,female,24,0,2,16.7,Mrs +0,3, Erik Johansson,male,22,0,0,7.7958,Mr +0,3, Elina Olsson,female,31,0,0,7.8542,Miss +0,2, Peter David McKane,male,46,0,0,26,Mr +0,2, Alfred Pain,male,23,0,0,10.5,Dr +1,2, William H (Jessie L) Trout,female,28,0,0,12.65,Mrs +1,3, Juha Niskanen,male,39,0,0,7.925,Mr +0,3, John Adams,male,26,0,0,8.05,Mr +0,3, Mari Aina Jussila,female,21,1,0,9.825,Miss +0,3, Pekka Pietari Hakkarainen,male,28,1,0,15.85,Mr +0,3, Marija Oreskovic,female,20,0,0,8.6625,Miss +0,2, Shadrach Gale,male,34,1,0,21,Mr +0,3, Carl/Charles Peter Widegren,male,51,0,0,7.75,Mr +1,2, William Rowe Richards,male,3,1,1,18.75,Master +0,3, Hans Martin Monsen Birkeland,male,21,0,0,7.775,Mr +0,3, Ida Lefebre,female,3,3,1,25.4667,Miss +0,3, Todor Sdycoff,male,42,0,0,7.8958,Mr +0,3, Henry Hart,male,27,0,0,6.8583,Mr +1,1, Daisy E Minahan,female,33,1,0,90,Miss +0,2, Alfred Fleming Cunningham,male,22,0,0,0,Mr +1,3, Johan Julian Sundman,male,44,0,0,7.925,Mr +0,3, Thomas (Annie Louise Rowley) Meek,female,32,0,0,8.05,Mrs +1,2, James Vivian (Lulu Thorne Christian) Drew,female,34,1,1,32.5,Mrs +1,2, Lyyli Karoliina Silven,female,18,0,2,13,Miss 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Richards,female,24,2,3,18.75,Mrs +0,1, Mark Fortune,male,64,1,4,263,Mr +0,2, Johan Henrik Johannesson Kvillner,male,31,0,0,10.5,Mr +1,2, Benjamin (Esther Ada Bloomfield) Hart,female,45,1,1,26.25,Mrs +0,3, Leon Hampe,male,20,0,0,9.5,Mr +0,3, Johan Emil Petterson,male,25,1,0,7.775,Mr +1,2, Encarnacion Reynaldo,female,28,0,0,13,Ms +1,3, Bernt Johannesen-Bratthammer,male,29,0,0,8.1125,Mr +1,1, Washington Dodge,male,4,0,2,81.8583,Master +1,2, Madeleine Violet Mellinger,female,13,0,1,19.5,Miss +1,1, Frederic Kimber Seward,male,34,0,0,26.55,Mr +1,3, Marie Catherine Baclini,female,5,2,1,19.2583,Miss +1,1, Arthur Godfrey Peuchen,male,52,0,0,30.5,Major +0,2, Edwy Arthur West,male,36,1,2,27.75,Mr +0,3, Ingvald Olai Olsen Hagland,male,28,1,0,19.9667,Mr +0,1, Benjamin Laventall Foreman,male,30,0,0,27.75,Mr +1,1, Samuel L Goldenberg,male,49,1,0,89.1042,Mr +0,3, Joseph Peduzzi,male,24,0,0,8.05,Mr +1,3, Ivan Jalsevac,male,29,0,0,7.8958,Mr +0,1, Francis Davis Millet,male,65,0,0,26.55,Mr +1,1, Frederick R (Marion) Kenyon,female,41,1,0,51.8625,Mrs +1,2, Ellen Toomey,female,50,0,0,10.5,Miss +0,3, Maurice O'Connor,male,17,0,0,7.75,Mr +1,1, Harry Anderson,male,48,0,0,26.55,Mr +0,3, William Morley,male,34,0,0,8.05,Mr +0,1, Arthur H Gee,male,47,0,0,38.5,Mr +0,2, Jacob Christian Milling,male,48,0,0,13,Mr +0,3, Simon Maisner,male,34,0,0,8.05,Mr +0,3, Manuel Estanslas Goncalves,male,38,0,0,7.05,Mr +0,2, William Campbell,male,21,0,0,0,Mr +0,1, John Montgomery Smart,male,56,0,0,26.55,Mr +0,3, James Scanlan,male,22,0,0,7.725,Mr +1,3, Helene Barbara Baclini,female,0.75,2,1,19.2583,Miss +0,3, Arthur Keefe,male,39,0,0,7.25,Mr +0,3, Luka Cacic,male,38,0,0,8.6625,Mr +1,2, Edwy Arthur (Ada Mary Worth) West,female,33,1,2,27.75,Mrs +1,2, Amin S (Marie Marthe Thuillard) Jerwan,female,23,0,0,13.7917,Mrs +0,3, Ida Sofia Strandberg,female,22,0,0,9.8375,Miss +0,1, George Quincy Clifford,male,40,0,0,52,Mr +0,2, Peter Henry Renouf,male,34,1,0,21,Mr +0,3, Lewis Richard Braund,male,29,1,0,7.0458,Mr +0,3, Nils August Karlsson,male,22,0,0,7.5208,Mr +1,3, Hildur E Hirvonen,female,2,0,1,12.2875,Miss +0,3, Harold Victor Goodwin,male,9,5,2,46.9,Master +0,2, Anthony Wood Frost,male,37,0,0,0,Mr +0,3, Richard Henry Rouse,male,50,0,0,8.05,Mr +1,3, (Hedwig) Turkula,female,63,0,0,9.5875,Mrs +1,1, Dickinson H Bishop,male,25,1,0,91.0792,Mr +0,3, Jeannie Lefebre,female,8,3,1,25.4667,Miss +1,1, Frederick Maxfield (Jane Anne Forby) Hoyt,female,35,1,0,90,Mrs +0,1, Edward Austin Kent,male,58,0,0,29.7,Mr +0,3, Francis William Somerton,male,30,0,0,8.05,Mr +1,3, Eden Leslie Coutts,male,9,1,1,15.9,Master +0,3, Konrad Mathias Reiersen Hagland,male,19,1,0,19.9667,Mr +0,3, Einar Windelov,male,21,0,0,7.25,Mr +0,1, Harry Markland Molson,male,55,0,0,30.5,Mr +0,1, Ramon Artagaveytia,male,71,0,0,49.5042,Mr +0,3, Edward Roland Stanley,male,21,0,0,8.05,Mr +0,3, Gerious Yousseff,male,26,0,0,14.4583,Mr +1,1, Elizabeth Mussey Eustis,female,54,1,0,78.2667,Miss +0,3, Frederick William Shellard,male,55,0,0,15.1,Mr +0,1, Hudson J C (Bessie Waldo Daniels) Allison,female,25,1,2,151.55,Mrs +0,3, Olof Svensson,male,24,0,0,7.7958,Mr +0,3, Petar Calic,male,17,0,0,8.6625,Mr +0,3, Mary Canavan,female,21,0,0,7.75,Miss +0,3, Bridget Mary O'Sullivan,female,21,0,0,7.6292,Miss +0,3, Kristina Sofia Laitinen,female,37,0,0,9.5875,Miss +1,1, Roberta Maioni,female,16,0,0,86.5,Miss +0,1, Victor de Satode Penasco y Castellana,male,18,1,0,108.9,Mr +1,2, Frederick Charles (Jane Richards) Quick,female,33,0,2,26,Mrs +1,1, George Bradley,male,37,0,0,26.55,Mr +0,3, Henry Margido Olsen,male,28,0,0,22.525,Mr +1,3, Fang Lang,male,26,0,0,56.4958,Mr +1,3, Eugene Patrick Daly,male,29,0,0,7.75,Mr +0,3, James Webber,male,66,0,0,8.05,Mr +1,1, James Robert McGough,male,36,0,0,26.2875,Mr +1,1, Martin (Elizabeth L,female,54,1,0,59.4,Mrs +0,3, Satio Coleff,male,24,0,0,7.4958,Mr +0,1, William Anderson Walker,male,47,0,0,34.0208,Mr +1,2, (Amelia Milley) Lemore,female,34,0,0,10.5,Mrs +0,3, Patrick Ryan,male,30,0,0,24.15,Mr +1,2, William A (Florence Agnes Hughes) Angle,female,36,1,0,26,Mrs +0,3, Stefo Pavlovic,male,32,0,0,7.8958,Mr +1,1, Anne Perreault,female,30,0,0,93.5,Miss +0,3, Janko Vovk,male,22,0,0,7.8958,Mr +0,3, Sarkis Lahoud,male,35,0,0,7.225,Mr +1,1, Louis Albert (Ida Sophia Fischer) Hippach,female,44,0,1,57.9792,Mrs +0,3, Fared Kassem,male,18,0,0,7.2292,Mr +0,3, James Farrell,male,40.5,0,0,7.75,Mr +1,2, Lucy Ridsdale,female,50,0,0,10.5,Miss +0,1, John Farthing,male,49,0,0,221.7792,Mr +0,3, Johan Werner Salonen,male,39,0,0,7.925,Mr +0,2, Richard George Hocking,male,23,2,1,11.5,Mr +1,2, Phyllis May Quick,female,2,1,1,26,Miss +0,3, Nakli Toufik,male,17,0,0,7.2292,Mr +0,3, Joseph Jr Elias,male,17,1,1,7.2292,Mr +1,3, Catherine (Catherine Rizk) Peter,female,24,0,2,22.3583,Mrs +0,3, Marija Cacic,female,30,0,0,8.6625,Miss +1,2, Eva Miriam Hart,female,7,0,2,26.25,Miss +0,1, Archibald Willingham Butt,male,45,0,0,26.55,Major +1,1, Bertha LeRoy,female,30,0,0,106.425,Miss +0,3, Samuel Beard Risien,male,69,0,0,14.5,Mr +1,1, Hedwig Margaritha Frolicher,female,22,0,2,49.5,Miss +1,1, Harriet R Crosby,female,36,0,2,71,Miss +0,3, Ingeborg Constanzia Andersson,female,9,4,2,31.275,Miss +0,3, Sigrid Elisabeth Andersson,female,11,4,2,31.275,Miss +1,2, Edward Beane,male,32,1,0,26,Mr +0,1, Walter Donald Douglas,male,50,1,0,106.425,Mr +0,1, Arthur Ernest Nicholson,male,64,0,0,26,Mr +1,2, Edward (Ethel Clarke) Beane,female,19,1,0,26,Mrs +1,2, Julian Padro y Manent,male,27,0,0,13.8625,Mr +0,3, Frank John Goldsmith,male,33,1,1,20.525,Mr +1,2, John Morgan Jr Davies,male,8,1,1,36.75,Master +1,1, John Borland Jr Thayer,male,17,0,2,110.8833,Mr +0,2, Percival James R Sharp,male,27,0,0,26,Mr +0,3, Timothy O'Brien,male,21,0,0,7.8292,Mr +1,3, Fahim Leeni,male,22,0,0,7.225,Mr +1,3, Velin Ohman,female,22,0,0,7.775,Miss +0,1, George Wright,male,62,0,0,26.55,Mr +1,1, (Lucille Christiana Sutherland)Duff Gordon,female,48,1,0,39.6,Lady +0,1, Victor Robbins,male,45,0,0,227.525,Mr +1,1, Emil (Tillie Mandelbaum) Taussig,female,39,1,1,79.65,Mrs 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Eiriik Jussila,male,32,0,0,7.925,Mr +1,2, Julie Rachel Christy,female,25,1,1,30,Miss +1,1, John Borland (Marian Longstreth Morris) Thayer,female,39,1,1,110.8833,Mrs +0,2, William James Downton,male,54,0,0,26,Mr +0,1, John Hugo Ross,male,36,0,0,40.125,Mr +0,3, Uscher Paulner,male,16,0,0,8.7125,Mr +1,1, Ruth Taussig,female,18,0,2,79.65,Miss +0,2, John Denzil Jarvis,male,47,0,0,15,Mr +1,1, Maxmillian Frolicher-Stehli,male,60,1,1,79.2,Mr +0,3, Eliezer Gilinski,male,22,0,0,8.05,Mr +0,3, Joseph Murdlin,male,22,0,0,8.05,Mr +0,3, Matti Rintamaki,male,35,0,0,7.125,Mr +1,1, Walter Bertram (Martha Eustis) Stephenson,female,52,1,0,78.2667,Mrs +0,3, William James Elsbury,male,47,0,0,7.25,Mr +0,3, Mary Bourke,female,40,0,2,7.75,Miss +0,2, John Henry Chapman,male,37,1,0,26,Mr +0,3, Jean Baptiste Van Impe,male,36,1,1,24.15,Mr +1,2, Jessie Wills Leitch,female,31,0,0,33,Miss +0,3, Alfred Johnson,male,49,0,0,0,Mr +0,3, Hanna Boulos,male,18,0,0,7.225,Mr +1,1, Cosmo Edmund Duff Gordon,male,49,1,0,56.9292,Sir +1,2, Sidney Samuel (Amy Frances Christy) Jacobsohn,female,24,2,1,27,Mrs +0,3, Petco Slabenoff,male,42,0,0,7.8958,Mr +0,1, Charles H Harrington,male,37,0,0,42.4,Mr +0,3, Ernst William Torber,male,44,0,0,8.05,Mr +1,1, Harry Homer,male,35,0,0,26.55,Mr +0,3, Edvard Bengtsson Lindell,male,36,1,0,15.55,Mr +0,3, Milan Karaic,male,30,0,0,7.8958,Mr +1,1, Robert Williams Daniel,male,27,0,0,30.5,Mr +1,2, Joseph (Juliette Marie Louise Lafargue) Laroche,female,22,1,2,41.5792,Mrs +1,1, Elizabeth W Shutes,female,40,0,0,153.4625,Miss +0,3, Anders Johan (Alfrida Konstantia Brogren) Andersson,female,39,1,5,31.275,Mrs +0,3, Jose Neto Jardin,male,21,0,0,7.05,Mr +1,3, Margaret Jane Murphy,female,18,1,0,15.5,Miss +0,3, John Horgan,male,22,0,0,7.75,Mr +0,3, William Alfred Brocklebank,male,35,0,0,8.05,Mr +1,2, Alice Herman,female,24,1,2,65,Miss +0,3, Ernst Gilbert Danbom,male,34,1,1,14.4,Mr +0,3, William Arthur (Cordelia K Stanlick) Lobb,female,26,1,0,16.1,Mrs +1,2, Marion Louise Becker,female,4,2,1,39,Miss +0,2, Lawrence Gavey,male,26,0,0,10.5,Mr +0,3, Antoni Yasbeck,male,27,1,0,14.4542,Mr +1,1, Edwin Nelson Jr Kimball,male,42,1,0,52.5542,Mr +1,3, Sahid Nakid,male,20,1,1,15.7417,Mr +0,3, Henry Damsgaard Hansen,male,21,0,0,7.8542,Mr +0,3, David John Bowen,male,21,0,0,16.1,Mr +0,1, Frederick Sutton,male,61,0,0,32.3208,Mr +0,2, Charles Leonard Kirkland,male,57,0,0,12.35,Rev +1,1, Gretchen Fiske Longley,female,21,0,0,77.9583,Miss +0,3, Guentcho Bostandyeff,male,26,0,0,7.8958,Mr +0,3, Patrick D O'Connell,male,18,0,0,7.7333,Mr +1,1, Algernon Henry Wilson Barkworth,male,80,0,0,30,Mr +0,3, Johan Svensson Lundahl,male,51,0,0,7.0542,Mr +1,1, Max Stahelin-Maeglin,male,32,0,0,30.5,Dr +0,1, William Henry Marsh Parr,male,30,0,0,0,Mr +0,3, Mabel Skoog,female,9,3,2,27.9,Miss +1,2, Mary Davis,female,28,0,0,13,Miss +0,3, Antti Gustaf Leinonen,male,32,0,0,7.925,Mr +0,2, Harvey Collyer,male,31,1,1,26.25,Mr +0,3, Juha (Maria Emilia Ojala) Panula,female,41,0,5,39.6875,Mrs 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Cardeza,male,36,0,1,512.3292,Mr +0,3, Katie Peters,female,28,0,0,8.1375,Miss +1,1, Hammad Hassab,male,27,0,0,76.7292,Mr +0,3, Thor Anderson Olsvigen,male,20,0,0,9.225,Mr +0,3, Charles Edward Goodwin,male,14,5,2,46.9,Mr +0,2, Thomas William Solomon Brown,male,60,1,1,39,Mr +0,2, Joseph Philippe Lemercier Laroche,male,25,1,2,41.5792,Mr +0,3, Jaako Arnold Panula,male,14,4,1,39.6875,Mr +0,3, Branko Dakic,male,19,0,0,10.1708,Mr +0,3, Eberhard Thelander Fischer,male,18,0,0,7.7958,Mr +1,1, Georgette Alexandra Madill,female,15,0,1,211.3375,Miss +1,1, Albert Adrian Dick,male,31,1,0,57,Mr +1,3, Manca Karun,female,4,0,1,13.4167,Miss +1,3, Ali Lam,male,37,0,0,56.4958,Mr +0,3, Khalil Saad,male,25,0,0,7.225,Mr +0,1, John Weir,male,60,0,0,26.55,Col +0,2, Charles Henry Chapman,male,52,0,0,13.5,Mr +0,3, James Kelly,male,44,0,0,8.05,Mr +1,3, Katherine Mullens,female,19,0,0,7.7333,Miss +0,1, John Borland Thayer,male,49,1,1,110.8833,Mr +0,3, Adolf Mathias Nicolai Olsen Humblen,male,42,0,0,7.65,Mr +1,1, John Jacob (Madeleine Talmadge Force) Astor,female,18,1,0,227.525,Mrs +1,1, Spencer Victor Silverthorne,male,35,0,0,26.2875,Mr +0,3, Saiide Barbara,female,18,0,1,14.4542,Miss +0,3, Martin Gallagher,male,25,0,0,7.7417,Mr +0,3, Henrik Juul Hansen,male,26,1,0,7.8542,Mr +0,2, Henry Samuel Morley,male,39,0,0,26,Mr +1,2, Florence Kelly,female,45,0,0,13.5,Mrs +1,1, Edward Pennington Calderhead,male,42,0,0,26.2875,Mr +1,1, Alice Cleaver,female,22,0,0,151.55,Miss +1,3, Halim Gonios Moubarek,male,4,1,1,15.2458,Master +1,1, Berthe Antonine Mayne,female,24,0,0,49.5042,Mlle +0,1, Herman Klaber,male,41,0,0,26.55,Mr +1,1, Elmer Zebley Taylor,male,48,1,0,52,Mr +0,3, August Viktor Larsson,male,29,0,0,9.4833,Mr +0,2, Samuel Greenberg,male,52,0,0,13,Mr +0,3, Peter Andreas Lauritz Andersen Soholt,male,19,0,0,7.65,Mr +1,1, Caroline Louise Endres,female,38,0,0,227.525,Miss +1,2, Edwina Celia Troutt,female,27,0,0,10.5,Miss +0,3, Malkolm Joackim Johnson,male,33,0,0,7.775,Mr +1,2, Annie Jessie Harper,female,6,0,1,33,Miss +0,3, Svend Lauritz Jensen,male,17,1,0,7.0542,Mr +0,2, William Henry Gillespie,male,34,0,0,13,Mr +0,2, Henry Price Hodges,male,50,0,0,13,Mr +1,1, Norman Campbell Chambers,male,27,1,0,53.1,Mr +0,3, Luka Oreskovic,male,20,0,0,8.6625,Mr +1,2, Peter Henry (Lillian Jefferys) Renouf,female,30,3,0,21,Mrs +1,3, Margareth Mannion,female,28,0,0,7.7375,Miss +0,2, Kurt Arnold Gottfrid Bryhl,male,25,1,0,26,Mr +0,3, Pieta Sofia Ilmakangas,female,25,1,0,7.925,Miss +1,1, Elisabeth Walton Allen,female,29,0,0,211.3375,Miss +0,3, Houssein G N Hassan,male,11,0,0,18.7875,Mr +0,2, Robert J Knight,male,41,0,0,0,Mr +0,2, William John Berriman,male,23,0,0,13,Mr +0,2, Moses Aaron Troupiansky,male,23,0,0,13,Mr +0,3, Leslie Williams,male,28.5,0,0,16.1,Mr +0,3, Edward (Margaret Ann Watson) Ford,female,48,1,3,34.375,Mrs +1,1, Gustave J Lesurer,male,35,0,0,512.3292,Mr +0,3, Kanio Ivanoff,male,20,0,0,7.8958,Mr +0,3, Minko Nankoff,male,32,0,0,7.8958,Mr +1,1, Walter James Hawksford,male,45,0,0,30,Mr +0,1, Tyrell William Cavendish,male,36,1,0,78.85,Mr +1,1, Susan Parker Ryerson,female,21,2,2,262.375,Miss +0,3, Neal McNamee,male,24,1,0,16.1,Mr +1,3, Juho Stranden,male,31,0,0,7.925,Mr +0,1, Edward Gifford Crosby,male,70,1,1,71,Capt +0,3, Rossmore Edward Abbott,male,16,1,1,20.25,Mr +1,2, Anna Sinkkonen,female,30,0,0,13,Miss +0,1, Daniel Warner Marvin,male,19,1,0,53.1,Mr +0,3, Michael Connaghton,male,31,0,0,7.75,Mr +1,2, Joan Wells,female,4,1,1,23,Miss +1,3, Meier Moor,male,6,0,1,12.475,Master +0,3, Johannes Joseph Vande Velde,male,33,0,0,9.5,Mr +0,3, Lalio Jonkoff,male,23,0,0,7.8958,Mr +1,2, Samuel (Jane Laver) Herman,female,48,1,2,65,Mrs +1,2, Viljo Hamalainen,male,0.67,1,1,14.5,Master +0,3, August Sigfrid Carlsson,male,28,0,0,7.7958,Mr +0,2, Percy Andrew Bailey,male,18,0,0,11.5,Mr +0,3, Thomas Leonard Theobald,male,34,0,0,8.05,Mr +1,1, of (Lucy Noel Martha Dyer-Edwards) Rothes,female,33,0,0,86.5,the Countess +0,3, John Garfirth,male,23,0,0,14.5,Mr +0,3, Iisakki Antino Aijo Nirva,male,41,0,0,7.125,Mr +1,3, Hanna Assi Barah,male,20,0,0,7.2292,Mr +1,1, William Ernest (Lucile Polk) Carter,female,36,1,2,120,Mrs +0,3, Hans Linus Eklund,male,16,0,0,7.775,Mr +1,1, John C (Anna Andrews) Hogeboom,female,51,1,0,77.9583,Mrs +0,1, Arthur Jackson Brewe,male,46,0,0,39.6,Dr +0,3, Mary Mangan,female,30.5,0,0,7.75,Miss +0,3, Daniel J Moran,male,28,1,0,24.15,Mr +0,3, Daniel Danielsen Gronnestad,male,32,0,0,8.3625,Mr +0,3, Rene Aime Lievens,male,24,0,0,9.5,Mr +0,3, Niels Peder Jensen,male,48,0,0,7.8542,Mr +0,2, (Mary) Mack,female,57,0,0,10.5,Mrs +0,3, Dibo Elias,male,29,0,0,7.225,Mr +1,2, Elizabeth (Eliza Needs) Hocking,female,54,1,3,23,Mrs +0,3, Pehr Fabian Oliver Malkolm Myhrman,male,18,0,0,7.75,Mr +0,3, Roger Tobin,male,20,0,0,7.75,Mr +1,3, Virginia Ethel Emanuel,female,5,0,0,12.475,Miss +0,3, Thomas J Kilgannon,male,22,0,0,7.7375,Mr +1,1, Edward Scott (Elisabeth Walton McMillan) Robert,female,43,0,1,211.3375,Mrs +1,3, Banoura Ayoub,female,13,0,0,7.2292,Miss +1,1, Albert Adrian (Vera Gillespie) Dick,female,17,1,0,57,Mrs +0,1, Milton Clyde Long,male,29,0,0,30,Mr +0,3, Andrew G Johnston,male,35,1,2,23.45,Mr +0,3, William Ali,male,25,0,0,7.05,Mr +0,3, Abraham (David Lishin) Harmer,male,25,0,0,7.25,Mr +1,3, Anna Sofia Sjoblom,female,18,0,0,7.4958,Miss +0,3, George Hugh Rice,male,8,4,1,29.125,Master +1,3, Bertram Vere Dean,male,1,1,2,20.575,Master +0,1, Benjamin Guggenheim,male,46,0,0,79.2,Mr +0,3, Andrew Keane,male,20,0,0,7.75,Mr +0,2, Alfred Gaskell,male,16,0,0,26,Mr +0,3, Stella Anna Sage,female,21,8,2,69.55,Miss +0,1, William Fisher Hoyt,male,43,0,0,30.6958,Mr +0,3, Ristiu Dantcheff,male,25,0,0,7.8958,Mr +0,2, Richard Otter,male,39,0,0,13,Mr +1,1, Alice (Farnham) Leader,female,49,0,0,25.9292,Dr +1,3, Mara Osman,female,31,0,0,8.6833,Mrs +0,3, Yousseff Ibrahim Shawah,male,30,0,0,7.2292,Mr +0,3, Jean Baptiste (Rosalie Paula Govaert) Van Impe,female,30,1,1,24.15,Mrs +0,2, Martin Ponesell,male,34,0,0,13,Mr +1,2, Harvey (Charlotte Annie Tate) Collyer,female,31,1,1,26.25,Mrs +1,1, William Thornton II Carter,male,11,1,2,120,Master +1,3, Assad Alexander Thomas,male,0.42,0,1,8.5167,Master +1,3, Oskar Arvid Hedman,male,27,0,0,6.975,Mr +0,3, Karl Johan Johansson,male,31,0,0,7.775,Mr +0,1, Thomas Jr Andrews,male,39,0,0,0,Mr +0,3, Ellen Natalia Pettersson,female,18,0,0,7.775,Miss +0,2, August Meyer,male,39,0,0,13,Mr +1,1, Norman Campbell (Bertha Griggs) Chambers,female,33,1,0,53.1,Mrs +0,3, William Alexander,male,26,0,0,7.8875,Mr +0,3, James Lester,male,39,0,0,24.15,Mr +0,2, Richard James Slemen,male,35,0,0,10.5,Mr +0,3, Ebba Iris Alfrida Andersson,female,6,4,2,31.275,Miss +0,3, Ernest Portage Tomlin,male,30.5,0,0,8.05,Mr +0,1, Richard Fry,male,39,0,0,0,Mr +0,3, Wendla Maria Heininen,female,23,0,0,7.925,Miss +0,2, Albert Mallet,male,31,1,1,37.0042,Mr +0,3, John Fredrik Alexander Holm,male,43,0,0,6.45,Mr +0,3, Karl Thorsten Skoog,male,10,3,2,27.9,Master +1,1, Charles Melville (Clara Jennings Gregg) Hays,female,52,1,1,93.5,Mrs +1,3, Nikola Lulic,male,27,0,0,8.6625,Mr +0,1, John George Reuchlin,male,38,0,0,0,Jonkheer +1,3, (Beila) Moor,female,27,0,1,12.475,Mrs +0,3, Urho Abraham Panula,male,2,4,1,39.6875,Master +0,3, John Flynn,male,36,0,0,6.95,Mr +0,3, Len Lam,male,23,0,0,56.4958,Mr +1,2, Andre Mallet,male,1,0,2,37.0042,Master +1,3, Thomas Joseph McCormack,male,19,0,0,7.75,Mr +1,1, George Nelson (Martha Evelyn) Stone,female,62,0,0,80,Mrs +1,3, Antoni (Selini Alexander) Yasbeck,female,15,1,0,14.4542,Mrs +1,2, George Sibley Richards,male,0.83,1,1,18.75,Master +0,3, Amin Saad,male,30,0,0,7.2292,Mr +0,3, Albert Augustsson,male,23,0,0,7.8542,Mr +0,3, Owen George Allum,male,18,0,0,8.3,Mr +1,1, Sara Rebecca Compton,female,39,1,1,83.1583,Miss +0,3, Jakob Pasic,male,21,0,0,8.6625,Mr +0,3, Maurice Sirota,male,20,0,0,8.05,Mr +1,3, Chang Chip,male,32,0,0,56.4958,Mr +1,1, Pierre Marechal,male,29,0,0,29.7,Mr +0,3, Ilmari Rudolf Alhomaki,male,20,0,0,7.925,Mr +0,2, Thomas Charles Mudd,male,16,0,0,10.5,Mr +1,1, Augusta Serepeca,female,30,0,0,31,Miss +0,3, Peter L Lemberopolous,male,34.5,0,0,6.4375,Mr +0,3, Jeso Culumovic,male,17,0,0,8.6625,Mr +0,3, Anthony Abbing,male,42,0,0,7.55,Mr +0,3, Douglas Bullen Sage,male,18,8,2,69.55,Mr +0,3, Marin Markoff,male,35,0,0,7.8958,Mr +0,2, John Harper,male,28,0,1,33,Rev +1,1, Samuel L (Edwiga Grabowska) Goldenberg,female,40,1,0,89.1042,Mrs +0,3, Sigvard Harald Elias Andersson,male,4,4,2,31.275,Master +0,3, Johan Svensson,male,74,0,0,7.775,Mr +0,3, Nourelain Boulos,female,9,1,1,15.2458,Miss +1,1, Mary Conover Lines,female,16,0,1,39.4,Miss +0,2, Ernest Courtenay (Lilian Hughes) Carter,female,44,1,0,26,Mrs +1,3, Sam (Leah Rosen) Aks,female,18,0,1,9.35,Mrs +1,1, George Dennick (Mary Hitchcock) Wick,female,45,1,1,164.8667,Mrs +1,1, Peter Denis Daly,male,51,0,0,26.55,Mr +1,3, Solomon (Latifa Qurban) Baclini,female,24,0,3,19.2583,Mrs +0,3, Raihed Razi,male,30,0,0,7.2292,Mr +0,3, Claus Peter Hansen,male,41,2,0,14.1083,Mr +0,2, Frederick Edward Giles,male,21,1,0,11.5,Mr +1,1, Frederick Joel (Margaret Welles Barron) Swift,female,48,0,0,25.9292,Mrs +0,3, Dorothy Edith Sage,female,14,8,2,69.55,Miss +0,2, John William Gill,male,24,0,0,13,Mr +1,2, (Karolina) Bystrom,female,42,0,0,13,Mrs +1,2, Asuncion Duran y More,female,27,1,0,13.8583,Miss +0,1, Washington Augustus II Roebling,male,31,0,0,50.4958,Mr +0,3, Philemon van Melkebeke,male,23,0,0,9.5,Mr +1,3, Harold Theodor Johnson,male,4,1,1,11.1333,Master +0,3, Cerin Balkic,male,26,0,0,7.8958,Mr +1,1, Richard Leonard (Sallie Monypeny) Beckwith,female,47,1,1,52.5542,Mrs +0,1, Frans Olof Carlsson,male,33,0,0,5,Mr +0,3, Victor Vander Cruyssen,male,47,0,0,9,Mr +1,2, Samuel (Hannah Wizosky) Abelson,female,28,1,0,24,Mrs +1,3, Adele Kiamie Najib,female,15,0,0,7.225,Miss +0,3, Alfred Ossian Gustafsson,male,20,0,0,9.8458,Mr +0,3, Nedelio Petroff,male,19,0,0,7.8958,Mr +0,3, Kristo Laleff,male,23,0,0,7.8958,Mr +1,1, Thomas Jr (Lily Alexenia Wilson) Potter,female,56,0,1,83.1583,Mrs +1,2, William (Imanita Parrish Hall) Shelley,female,25,0,1,26,Mrs +0,3, Johann Markun,male,33,0,0,7.8958,Mr +0,3, Gerda Ulrika Dahlberg,female,22,0,0,10.5167,Miss +0,2, Frederick James Banfield,male,28,0,0,10.5,Mr +0,3, Henry Jr Sutehall,male,25,0,0,7.05,Mr +0,3, William (Margaret Norton) Rice,female,39,0,5,29.125,Mrs +0,2, Juozas Montvila,male,27,0,0,13,Rev +1,1, Margaret Edith Graham,female,19,0,0,30,Miss +0,3, Catherine Helen Johnston,female,7,1,2,23.45,Miss +1,1, Karl Howell Behr,male,26,0,0,30,Mr +0,3, Patrick Dooley,male,32,0,0,7.75,Mr diff --git a/danfojs-node/tests/core/concat.js b/src/danfojs-node/test/transformers/concat.test.ts similarity index 63% rename from danfojs-node/tests/core/concat.js rename to src/danfojs-node/test/transformers/concat.test.ts index ee4d6010..0dd84947 100644 --- a/danfojs-node/tests/core/concat.js +++ b/src/danfojs-node/test/transformers/concat.test.ts @@ -1,11 +1,8 @@ import { assert } from "chai"; -import { DataFrame } from '../../src/core/frame'; -import { concat } from '../../src/core/concat'; -import { Series } from "../../src/core/series"; - - -describe("Concatenate", function () { +import { describe, it } from "mocha"; +import { DataFrame, concat , Series } from "../../dist/danfojs-node/src"; +describe("Concat", ()=>{ it("Check the axis 0 concatenation", function () { let data = [ [ 1, 2, 3 ], [ 4, 5, 6 ], [ 20, 30, 40 ], [ 39, 89, 78 ] ]; let cols = [ "A", "B", "C" ]; @@ -19,7 +16,7 @@ describe("Concatenate", function () { let cols2 = [ "A", "B", "C", "D" ]; let df2 = new DataFrame(data2, { columns: cols2 }); - let new_df = concat({ "df_list": [ df, df1, df2 ], "axis": 0 }); + let new_df = concat({ dfList: [ df, df1, df2 ], axis: 0 }); let data_values = [ [ 1, 2, 3, NaN ], [ 4, 5, 6, NaN ], [ 20, 30, 40, NaN ], [ 39, 89, 78, NaN ], [ 1, 2, 3, NaN ], [ 4, 5, 6, NaN ], [ 20, 30, 40, NaN ], [ 39, 89, 78, NaN ], @@ -41,7 +38,7 @@ describe("Concatenate", function () { let cols2 = [ "A", "B", "C", "D" ]; let df2 = new DataFrame(data2, { columns: cols2 }); - let new_df = concat({ "df_list": [ df, df1, df2 ], "axis": 1 }); + let new_df = concat({ dfList: [ df, df1, df2 ], axis: 1 }); let data_values = [ [ 1, 2, 3, 1, 2, 3, 1, 2, 3, 5 ], [ 4, 5, 6, 4, 5, 6, 4, 5, 6, 8 ], [ 20, 30, 40, 20, 30, 40, 20, 30, 40, 10 ], [ 39, 89, 78, 39, 89, 78, NaN, @@ -59,20 +56,18 @@ describe("Concatenate", function () { let df = new DataFrame(data, { columns: cols }); let s1 = new Series(data1); - let s2 = new Series(data2); let rslt = [ - [ 1, 2, 3, NaN ], - [ 4, 5, 6, NaN ], - [ 20, 30, 40, NaN ], - [ 39, 89, 78, NaN ], - [ NaN, NaN, NaN, 1 ], - [ NaN, NaN, NaN, 2 ], - [ NaN, NaN, NaN, 3 ], - [ NaN, NaN, NaN, 4 ] + [ NaN, 1, 2, 3 ], + [ NaN, 4, 5, 6 ], + [ NaN, 20, 30, 40], + [ NaN, 39, 89, 78 ], + [ 1, NaN, NaN, NaN ], + [ 2, NaN, NaN, NaN ], + [ 3, NaN, NaN, NaN], + [ 4, NaN, NaN, NaN ] ]; - - let con = concat({ "df_list": [ df, s1 ], "axis": 0 }); + let con = concat({ dfList: [ df, s1 ], axis: 0 }); assert.deepEqual(con.values, rslt); @@ -87,14 +82,14 @@ describe("Concatenate", function () { let data1 = [ 1, 2, 3, 4 ]; let s1 = new Series(data1); let rslt = [ - [ 1, 2, 3, 1 ], - [ 4, 5, 6, 2 ], - [ 20, 30, 40, 3 ], - [ 39, 89, 78, 4 ] + [ 1, 1, 2, 3 ], + [ 2, 4, 5, 6 ], + [ 3, 20, 30, 40 ], + [ 4, 39, 89, 78] ]; - let con = concat({ "df_list": [ df, s1 ], "axis": 1 }); + let con = concat({ dfList: [ df, s1 ], axis: 1 }); assert.deepEqual(con.values, rslt); @@ -109,7 +104,7 @@ describe("Concatenate", function () { let rslt = [ [ 1, 3 ], [ 2, 4 ], [ 3, 5 ], [ 4, 6 ] ]; - let con = concat({ "df_list": [ s1, s2 ], "axis": 1 }); + let con = concat({ dfList: [ s1, s2 ], axis: 1 }); assert.deepEqual(con.values, rslt); @@ -126,38 +121,10 @@ describe("Concatenate", function () { 3, 4, 5, 6 ]; - let con = concat({ "df_list": [ s1, s2 ], "axis": 0 }); - - assert.deepEqual(con.values, rslt); - - }); - - it("test if df_list is an array", function(){ - - assert.throws(function () { concat({ "df_list":23, "axis":0 }); }, Error, 'df_list must be an Array of dataFrames/Series'); - - }); - it("assign default axis for concating", function(){ - let data1 = [ 1, 2, 3, 4 ]; - let data2 = [ 3, 4, 5, 6 ]; - - let s1 = new Series(data1); - let s2 = new Series(data2); - let rslt = [ [ 1, 3 ], [ 2, 4 ], [ 3, 5 ], [ 4, 6 ] ]; - - - let con = concat({ "df_list": [ s1, s2 ], "axis": 12 }); + let con = concat({ dfList: [ s1, s2 ], axis: 0 }); assert.deepEqual(con.values, rslt); - }); - it("ensure axis is a number", function(){ - let data1 = [ 1, 2, 3, 4 ]; - let data2 = [ 3, 4, 5, 6 ]; - - let s1 = new Series(data1); - let s2 = new Series(data2); - assert.throws(function () { concat({ "df_list":[ s1, s2 ], "axis":"r" }); }, Error, 'axis must be a number'); }); -}); +}) \ No newline at end of file diff --git a/src/danfojs-node/test/transformers/csv.stream.transformer.test.ts b/src/danfojs-node/test/transformers/csv.stream.transformer.test.ts new file mode 100644 index 00000000..2d56b080 --- /dev/null +++ b/src/danfojs-node/test/transformers/csv.stream.transformer.test.ts @@ -0,0 +1,63 @@ +import { DataFrame, Series, streamCsvTransformer } from "../../dist/danfojs-node/src"; +import stream from "stream" +import path from "path" +import { describe, it } from "mocha"; + +describe("pipeCsvTransform", function () { + it("streamCsvTransformer works for local files", async function () { + const inputFilePath = path.join(process.cwd(), "test", "samples", "titanic.csv"); + const outputFilePath = path.join(process.cwd(), "test", "samples", "titanicOutLocal.csv"); + + const transformer = (df: DataFrame) => { + const titles = df["Name"].map((name: string) => name.split(".")[0]); + const names = df["Name"].map((name: string) => name.split(".")[1]); + df["Name"] = names + df.addColumn("titles", titles, { inplace: true }) + return df + } + streamCsvTransformer(inputFilePath, transformer, { outputFilePath, inputStreamOptions: { header: false } }) + }); + + it("streamCsvTransformer works for remote files", async function () { + const inputFilePath = "https://raw.githubusercontent.com/opensource9ja/danfojs/dev/danfojs-node/tests/samples/titanic.csv" + const outputFilePath = path.join(process.cwd(), "test", "samples", "titanicOutRemote.csv"); + const transformer = (df: DataFrame) => { + const titles = df["Name"].map((name: string) => name.split(".")[0]); + const names = df["Name"].map((name: string) => name.split(".")[1]); + df["Name"] = names + df.addColumn("titles", titles, { inplace: true }) + return df + } + streamCsvTransformer(inputFilePath, transformer, { outputFilePath, inputStreamOptions: { header: true } }) + }); + + it("streamCsvTransformer works for custom writers", async function () { + const inputFilePath = "https://raw.githubusercontent.com/opensource9ja/danfojs/dev/danfojs-node/tests/samples/titanic.csv" + const transformer = (df: DataFrame) => { + const titles = df["Name"].map((name: string) => name.split(".")[0]); + const names = df["Name"].map((name: string) => name.split(".")[1]); + df["Name"] = names + df.addColumn("titles", titles, { inplace: true }) + return df + } + let count = 0 + + const customWriter = function () { + const csvOutputStream = new stream.Writable({ objectMode: true }) + csvOutputStream._write = (chunk: DataFrame | Series, encoding, callback) => { + count += 1 + callback() + + } + return csvOutputStream + } + + streamCsvTransformer( + inputFilePath, + transformer, + { + customCSVStreamWriter: customWriter, + inputStreamOptions: { header: true } + }) + }); +}) \ No newline at end of file diff --git a/src/danfojs-node/test/transformers/dummy.encoder.test.ts b/src/danfojs-node/test/transformers/dummy.encoder.test.ts new file mode 100644 index 00000000..3ed921a3 --- /dev/null +++ b/src/danfojs-node/test/transformers/dummy.encoder.test.ts @@ -0,0 +1,177 @@ +import { assert } from "chai"; +import { describe, it } from "mocha"; +import { DataFrame, Series, getDummies } from "../../dist/danfojs-node/src"; + +describe("DummyEncoder", function () { + it("getDummies works on Series", function () { + + const data = ["dog", "male", "female", "male", "female", "male", "dog"]; + const series = new Series(data); + const df = getDummies(series, { prefix: "test", prefixSeparator: "/" }); + + const dfValues = [ + [1, 0, 0], + [0, 1, 0], + [0, 0, 1], + [0, 1, 0], + [0, 0, 1], + [0, 1, 0], + [1, 0, 0] + ]; + const dfColumns = ['test/dog', 'test/male', 'test/female']; + assert.deepEqual(df.values, dfValues); + assert.deepEqual(df.columns, dfColumns); + }); + it("getDummies works on Series with default prefix and prefixSeperator", function () { + + const data = ["dog", "male", "female", "male", "female", "male", "dog"]; + const series = new Series(data); + const df = getDummies(series); + + const dfValues = [ + [1, 0, 0], + [0, 1, 0], + [0, 0, 1], + [0, 1, 0], + [0, 0, 1], + [0, 1, 0], + [1, 0, 0] + ]; + const dfColumns = ['0_dog', '1_male', '2_female']; + assert.deepEqual(df.values, dfValues); + assert.deepEqual(df.columns, dfColumns); + }); + + it("getDummies works on DataFrame", function () { + + const data = [[1, "dog", 1.0, "fat"], [3, "fog", 2.0, "good"], [4, "gof", 3.0, "best"]]; + const columns = ["A", "B", "C", "d"]; + const df = new DataFrame(data, { columns: columns }); + + const df1 = getDummies(df, { prefixSeparator: ["_", "#"], columns: ["A", "d"], prefix: "test" }); + const expectedColumns = ['B', 'C', 'test_1', 'test_3', 'test_4', 'test#fat', 'test#good', 'test#best'] + const expected = [['dog', 1.0, 1, 0, 0, 1, 0, 0], + ['fog', 2.0, 0, 1, 0, 0, 1, 0], + ['gof', 3.0, 0, 0, 1, 0, 0, 1]] + assert.deepEqual(df1.values, expected); + assert.deepEqual(df1.columns, expectedColumns); + + }); + it("Throw error if the prefix specified is not equal to the column specified", function () { + + const data = [[1, "dog", 1.0, "fat"], [3, "fog", 2.0, "good"], [4, "gof", 3.0, "best"]]; + const columns = ["A", "B", "C", "d"]; + const df = new DataFrame(data, { columns: columns }); + + assert.throws(function () { getDummies(df, { prefix: ["fg"], prefixSeparator: "_", columns: ["A", "d"] }); }, Error, + `ParamError: prefix and data array must be of the same length. If you need to use the same prefix, then pass a string param instead. e.g {prefix: "fg"}`); + + }); + it("replace column sepecified with prefix", function () { + + const data = [[1, "dog", 1.0, "fat"], [3, "fog", 2.0, "good"], [4, "gof", 3.0, "best"]]; + const columns = ["A", "B", "C", "d"]; + const df = new DataFrame(data, { columns: columns }); + + const df1 = getDummies(df, { prefix: ["F", "G"], prefixSeparator: "_", columns: ["A", "d"] }); + const expectedColumns = [ + 'B', 'C', + 'F_1', 'F_3', + 'F_4', 'G_fat', + 'G_good', 'G_best' + ]; + + const expected = [['dog', 1.0, 1, 0, 0, 1, 0, 0], + ['fog', 2.0, 0, 1, 0, 0, 1, 0], + ['gof', 3.0, 0, 0, 1, 0, 0, 1]] + + assert.deepEqual(df1.values, expected); + assert.deepEqual(df1.columns, expectedColumns); + + }); + + it("getDummies auto infers and encode columns with string dtype", function () { + + const data = [[1, "dog", 1.0, "fat"], [3, "fog", 2.0, "good"], [4, "gof", 3.0, "best"]]; + const columns = ["A", "B", "C", "d"]; + const df = new DataFrame(data, { columns: columns }); + + const df1 = getDummies(df, { prefixSeparator: "_" }); + const expectedColumns = [ + 'A', 'C', + 'B_dog', 'B_fog', + 'B_gof', 'd_fat', + 'd_good', 'd_best' + ]; + const expected = [ + [ + 1, 1, 1, 0, + 0, 1, 0, 0 + ], + [ + 3, 2, 0, 1, + 0, 0, 1, 0 + ], + [ + 4, 3, 0, 0, + 1, 0, 0, 1 + ] + ]; + assert.deepEqual(df1.values, expected); + assert.deepEqual(df1.columns, expectedColumns); + + }); + + it("should one hot encode all other columns", function () { + + const data = [[1, "dog", 1.0, "fat"], [3, "fog", 2.0, "good"], [4, "gof", 3.0, "best"]]; + const columns = ["A", "B", "C", "d"]; + const df = new DataFrame(data, { columns: columns }); + const rslt = [ + [1, 'dog', 1, 1, 0, 0], + [3, 'fog', 2, 0, 1, 0], + [4, 'gof', 3, 0, 0, 1] + ] + + assert.deepEqual(getDummies(df, { columns: ["d"] }).values, rslt) + + }); + + + it("Dummification works for object DF", function () { + + let data = { + fruits: ['pear', 'mango', "pawpaw", "mango", "bean"], + Count: [20, 30, 89, 12, 30], + Country: ["NG", "NG", "GH", "RU", "RU"] + }; + + let df = new DataFrame(data); + const expected = [ + [ + 20, 1, 0, 0, + 0, 1, 0, 0 + ], + [ + 30, 0, 1, 0, + 0, 1, 0, 0 + ], + [ + 89, 0, 0, 1, + 0, 0, 1, 0 + ], + [ + 12, 0, 1, 0, + 0, 0, 0, 1 + ], + [ + 30, 0, 0, 0, + 1, 0, 0, 1 + ] + ]; + + let dum_df = getDummies(df, { prefixSeparator: "_" }); + assert.deepEqual(dum_df.values, expected); + + }); +}); diff --git a/src/danfojs-node/test/transformers/label.encoder.test.ts b/src/danfojs-node/test/transformers/label.encoder.test.ts new file mode 100644 index 00000000..1eda8862 --- /dev/null +++ b/src/danfojs-node/test/transformers/label.encoder.test.ts @@ -0,0 +1,53 @@ +import { assert } from "chai"; +import { describe, it } from "mocha"; +import { DataFrame, Series, LabelEncoder } from "../../dist/danfojs-node/src"; + +describe("LabelEncoder", function () { + + it("LabelEncoder works for Series", function () { + const sf = new Series([1, 2, 2, 6]) + const scaler = new LabelEncoder(); + scaler.fit(sf); + const expected = [0, 1, 1, 2]; + assert.deepEqual((scaler.transform(sf) as Series).values, expected); + }); + it("LabelEncoder works for values not found", function () { + const sf = new Series([1, 2, 2, 6]) + const scaler = new LabelEncoder(); + scaler.fit(sf); + + const expected = [0, 1, 1, 2, -1]; + assert.deepEqual((scaler.transform(new Series([1, 2, 2, 6, 7])) as Series).values, expected); + }); + it("LabelEncoder works for 1D array", function () { + const sf = [1, 2, 2, "boy", "git", "git"] + const scaler = new LabelEncoder(); + scaler.fit(sf); + const expected = [0, 1, 1, 2, 3, 3] + assert.deepEqual(scaler.transform(sf), expected); + }); + it("fitTransform works for 1D array", function () { + const sf = [1, 2, 2, "boy", "git", "git"] + const scaler = new LabelEncoder(); + const result = scaler.fitTransform(sf) + const expected = [0, 1, 1, 2, 3, 3] + assert.deepEqual(result, expected); + }); + it("inverseTransform works for 1D array", function () { + const sf = [1, 2, 2, "boy", "git", "git"] + const scaler = new LabelEncoder(); + scaler.fit(sf); + const result = scaler.inverseTransform([0, 1, 1, 2, 3, 3]) + assert.deepEqual(result, [1, 2, 2, "boy", "git", "git"]); + }); + it("Get properties from LabelEncoder", function () { + const sf = [1, 2, 2, "boy", "git", "git"] + const scaler = new LabelEncoder(); + scaler.fit(sf); + const classes = scaler.classes + const nClasses = scaler.nClasses + + assert.deepEqual(classes, { 1: 0, 2: 1, boy: 2, git: 3 }); + assert.equal(nClasses, 4) + }); +}); diff --git a/danfojs-node/tests/core/merge.js b/src/danfojs-node/test/transformers/merge.test.ts similarity index 87% rename from danfojs-node/tests/core/merge.js rename to src/danfojs-node/test/transformers/merge.test.ts index ab82527f..809701e3 100644 --- a/danfojs-node/tests/core/merge.js +++ b/src/danfojs-node/test/transformers/merge.test.ts @@ -1,6 +1,7 @@ import { assert } from "chai"; -import { DataFrame } from '../../src/core/frame'; -import { merge } from '../../src/core/merge'; +import { describe, it } from "mocha"; +import { DataFrame, merge } from "../../dist/danfojs-node/src"; + describe("Merge", function () { @@ -16,7 +17,7 @@ describe("Merge", function () { let df1 = new DataFrame(data, { columns: colum1 }); let df2 = new DataFrame(data2, { columns: colum2 }); - let merge_df = merge({ "left": df1, "right": df2, "on": [ "Key1", "Key2" ], "how": "outer" }); + let merge_df = merge({ left: df1, right: df2, on: [ "Key1", "Key2" ], how: "outer" }); let output_data = [ [ 'K0', 'k0', 'A0', 'B0', 'C0', 'D0' ], @@ -27,7 +28,6 @@ describe("Merge", function () { [ 'K2', 'K0', NaN, NaN, 'C3', 'D3' ] ]; - assert.deepEqual(merge_df.values, output_data); }); @@ -43,7 +43,7 @@ describe("Merge", function () { let df1 = new DataFrame(data, { columns: colum1 }); let df2 = new DataFrame(data2, { columns: colum2 }); - let merge_df = merge({ "left": df1, "right": df2, "on": [ "Key1", "Key2" ], "how": "inner" }); + let merge_df = merge({ left: df1, right: df2, on: [ "Key1", "Key2" ], how: "inner" }); let output_data = [ [ 'K0', 'k0', 'A0', 'B0', 'C0', 'D0' ], @@ -66,7 +66,7 @@ describe("Merge", function () { let df1 = new DataFrame(data, { columns: colum1 }); let df2 = new DataFrame(data2, { columns: colum2 }); - let merge_df = merge({ "left": df1, "right": df2, "on": [ "Key1", "Key2" ], "how": "left" }); + let merge_df = merge({ left: df1, right: df2, on: [ "Key1", "Key2" ], how: "left" }); let output_data = [ [ 'K0', 'k0', 'A0', 'B0', 'C0', 'D0' ], @@ -91,7 +91,7 @@ describe("Merge", function () { let df1 = new DataFrame(data, { columns: colum1 }); let df2 = new DataFrame(data2, { columns: colum2 }); - let merge_df = merge({ "left": df1, "right": df2, "on": [ "Key1", "Key2" ], "how": "right" }); + let merge_df = merge({ left: df1, right: df2, on: [ "Key1", "Key2" ], how: "right" }); let output_data = [ [ 'K0', 'k0', 'A0', 'B0', 'C0', 'D0' ], @@ -102,4 +102,5 @@ describe("Merge", function () { assert.deepEqual(merge_df.values, output_data); }); -}); +}) + diff --git a/src/danfojs-node/test/transformers/min.max.scaler.test.ts b/src/danfojs-node/test/transformers/min.max.scaler.test.ts new file mode 100644 index 00000000..8c32340b --- /dev/null +++ b/src/danfojs-node/test/transformers/min.max.scaler.test.ts @@ -0,0 +1,56 @@ +import { assert } from "chai"; +import { describe, it } from "mocha"; +import { DataFrame, Series, MinMaxScaler } from "../../dist/danfojs-node/src"; + +describe("MinMaxscaler", function () { + + it("Standardize values in a DataFrame using a MinMaxScaler", function () { + const data = [[-1, 2], [-0.5, 6], [0, 10], [1, 18]]; + const scaler = new MinMaxScaler(); + + const expected = [[0, 0], [0.25, 0.25], [0.5, 0.5], [1, 1]]; + const transformedData = [[1.5, 0.]]; + + scaler.fit(new DataFrame(data)); + const resultDf = scaler.transform(new DataFrame(data)) as DataFrame; + assert.deepEqual(resultDf.values, expected); + assert.deepEqual(scaler.transform([[2, 2]]) as any, transformedData); + }); + it("fitTransform using a MinMaxScaler", function () { + const data = [[-1, 2], [-0.5, 6], [0, 10], [1, 18]]; + const scaler = new MinMaxScaler(); + const resultDf = scaler.fitTransform(new DataFrame(data)) as DataFrame; + + const expected = [[0, 0], [0.25, 0.25], [0.5, 0.5], [1, 1]]; + assert.deepEqual(resultDf.values, expected); + }); + it("InverseTransform with MinMaxScaler", function () { + const scaler = new MinMaxScaler(); + scaler.fit([1, 2, 3, 4, 5]) + const resultTransform = scaler.transform([1, 2, 3, 4, 5]) + const resultInverse = scaler.inverseTransform([0, 0.25, 0.5, 0.75, 1]) + + assert.deepEqual(resultTransform, [0, 0.25, 0.5, 0.75, 1]); + assert.deepEqual([1, 2, 3, 4, 5], resultInverse); + }); + it("Index and columns are kept after transformation", function () { + const data = [[-1, 2], [-0.5, 6], [0, 10], [1, 18]]; + const df = new DataFrame(data, { index: [1, 2, 3, 4], columns: ["a", "b"] }); + + const scaler = new MinMaxScaler(); + scaler.fit(df); + const resultDf = scaler.transform(df) as DataFrame + + assert.deepEqual(resultDf.index, [1, 2, 3, 4]); + assert.deepEqual(resultDf.columns, ["a", "b"]); + }); + it("Standardize values in a Series using a MinMaxScaler", function () { + const data = [-1, 2, -0.5, 60, 101, 18]; + const scaler = new MinMaxScaler(); + const result = [0, 0.029411764815449715, 0.0049019609577953815, 0.5980392098426819, 1, 0.18627451360225677]; + const transformedData = [0.029411764815449715, 0.029411764815449715]; + scaler.fit(new Series(data)) + assert.deepEqual((scaler.transform(new Series(data)) as Series).values, result); + assert.deepEqual(scaler.transform([2, 2]), transformedData); + }); +}); diff --git a/src/danfojs-node/test/transformers/one.hot.encoder.test.ts b/src/danfojs-node/test/transformers/one.hot.encoder.test.ts new file mode 100644 index 00000000..9885a89a --- /dev/null +++ b/src/danfojs-node/test/transformers/one.hot.encoder.test.ts @@ -0,0 +1,84 @@ +import { assert } from "chai"; +import { describe, it } from "mocha"; +import { Series, OneHotEncoder, DataFrame } from "../../dist/danfojs-node/src"; + +describe("OneHotEncoder", function () { + it("OneHotEncoder works on array", function () { + const data = ["dog", "cat", "man", "dog", "cat", "man", "man", "cat"]; + const encode = new OneHotEncoder(); + encode.fit(data) + + const expected = [ + [1, 0, 0], + [0, 1, 0], + [0, 0, 1], + [1, 0, 0], + [0, 1, 0], + [0, 0, 1], + [0, 0, 1], + [0, 1, 0] + ]; + assert.deepEqual(encode.transform(data), expected); + assert.deepEqual(encode.transform(["man", "cat"]), [[0, 0, 1], [0, 1, 0]]); + }); + it("OneHotEncoder works on Series", function () { + const data = ["dog", "cat", "man", "dog", "cat", "man", "man", "cat"]; + const series = new Series(data); + const encoder = new OneHotEncoder(); + encoder.fit(series); + + const expected = [ + [1, 0, 0], + [0, 1, 0], + [0, 0, 1], + [1, 0, 0], + [0, 1, 0], + [0, 0, 1], + [0, 0, 1], + [0, 1, 0] + ]; + assert.deepEqual((encoder.transform(series) as DataFrame).values, expected); + }); + + it("fitTransform works on OneHotEncoder", function () { + const data = ["dog", "cat", "man", "dog", "cat", "man", "man", "cat"]; + const series = new Series(data); + const encoder = new OneHotEncoder(); + const result = encoder.fitTransform(series) as DataFrame + + const expected = [ + [1, 0, 0], + [0, 1, 0], + [0, 0, 1], + [1, 0, 0], + [0, 1, 0], + [0, 0, 1], + [0, 0, 1], + [0, 1, 0] + ]; + assert.deepEqual(result.values, expected); + }); + + it("Correct index is returned after transformation", function () { + const data = ["dog", "cat", "man", "dog", "cat", "man", "man", "cat"]; + const series = new Series(data, + { index: ["a", "b", "c", "d", "e", "f", "g", "h"], + }); + const encoder = new OneHotEncoder(); + const result = encoder.fitTransform(series) as DataFrame + + const expected = [ + [1, 0, 0], + [0, 1, 0], + [0, 0, 1], + [1, 0, 0], + [0, 1, 0], + [0, 0, 1], + [0, 0, 1], + [0, 1, 0] + ]; + assert.deepEqual(result.values, expected); + assert.deepEqual(result.index, ["a", "b", "c", "d", "e", "f", "g", "h"]); + assert.deepEqual(result.columns, ["0", "1", "2"]); + }); +}); diff --git a/src/danfojs-node/test/transformers/standard.scaler.test.ts b/src/danfojs-node/test/transformers/standard.scaler.test.ts new file mode 100644 index 00000000..ffe2d955 --- /dev/null +++ b/src/danfojs-node/test/transformers/standard.scaler.test.ts @@ -0,0 +1,58 @@ +import { assert } from "chai"; +import { describe, it } from "mocha"; +import { DataFrame, Series, StandardScaler } from "../../dist/danfojs-node/src"; + + +describe("StandardScaler", function () { + + it("StandardScaler works for DataFrame", function () { + const data = [[0, 0], [0, 0], [1, 1], [1, 1]]; + + const scaler = new StandardScaler(); + scaler.fit(new DataFrame(data)) + + const expected = [[-1, -1], [-1, -1], [1, 1], [1, 1]]; + const resultDf = scaler.transform(new DataFrame(data)) as DataFrame + assert.deepEqual(resultDf.values, expected); + assert.deepEqual(scaler.transform([[2, 2]]) as any, [[3, 3]]); + }); + it("fitTransform works for StandardScaler", function () { + const data = [[0, 0], [0, 0], [1, 1], [1, 1]]; + + const scaler = new StandardScaler(); + const resultDf = scaler.fitTransform(new DataFrame(data)) as DataFrame; + + const expected = [[-1, -1], [-1, -1], [1, 1], [1, 1]]; + assert.deepEqual(resultDf.values, expected); + }); + it("inverseTransform works for StandardScaler", function () { + const data = [[0, 0], [0, 0], [1, 1], [1, 1]]; + + const scaler = new StandardScaler(); + scaler.fit(new DataFrame(data)) + const resultDf = scaler.inverseTransform([[-1, -1], [-1, -1], [1, 1], [1, 1]]) as any + + assert.deepEqual(resultDf, data); + }); + it("StandardScaler works for Array", function () { + const data = [[0, 0], [0, 0], [1, 1], [1, 1]]; + + const scaler = new StandardScaler(); + scaler.fit(data) + const expected = [[-1, -1], [-1, -1], [1, 1], [1, 1]]; + + assert.deepEqual(scaler.transform(data) as any, expected); + assert.deepEqual(scaler.transform([[2, 2]]) as any, [[3, 3]]); + }); + + it("StandardScaler works for Series", function () { + const data = [0, 0, 0, 0, 1, 1, 1, 1] + + const scaler = new StandardScaler(); + scaler.fit(new Series(data)) + const expected = [-1, -1, -1, -1, 1, 1, 1, 1] + + assert.deepEqual((scaler.transform(new Series(data)) as Series).values, expected); + assert.deepEqual(scaler.transform([2, 2]), [3, 3]); + }); +}); diff --git a/src/danfojs-node/test/utils.test.ts b/src/danfojs-node/test/utils.test.ts new file mode 100644 index 00000000..a9d1ab39 --- /dev/null +++ b/src/danfojs-node/test/utils.test.ts @@ -0,0 +1,227 @@ +import { assert } from "chai"; +import { describe, it } from "mocha"; +import { Utils, __version } from "../dist/danfojs-node/src"; +import packagejson from "../package.json"; + +const pversion = packagejson.version; + +const utils = new Utils(); + +describe("Utils", function () { + it("should have a version", function () { + assert.equal(__version, pversion); + }); + + it("removes an element from an array", function () { + let arr = [1, 2, 3, 4]; + assert.deepEqual(utils.removeElementFromArray(arr, 2), [1, 2, 4]); + }); + + it("Checks if variable is a string", function () { + let arr = ["1", "2"]; + assert.isTrue(utils.isString(arr[0])); + }); + + it("Checks if variable is a number", function () { + let arr = [1, 2, 3, 4]; + assert.isTrue(utils.isNumber(arr[0])); + }); + + it("Checks if value is null", function () { + let val = null; + let val2 = 1; + assert.isTrue(utils.isNull(val)); + assert.isFalse(utils.isNull(val2)); + }); + + it("Checks if value is undefined", function () { + let arr; + assert.isTrue(utils.isUndefined(arr)); + }); + + it("Generate numbers between two set of values [both inclusive]", function () { + let start = 0; + let end = 5; + let data = [0, 1, 2, 3, 4, 5]; + assert.deepEqual(utils.range(start, end), data); + }); + + it("transposes an array", function () { + let data = [[1, 2, 3], [4, 5, 6], [20, 30, 40]]; + let result = [[1, 4, 20], [2, 5, 30], [3, 6, 40]]; + assert.deepEqual(utils.transposeArray(data), result); + }); + + describe("inferDtype", function () { + it("Returns string type present in an 1D array", function () { + let data = ['Alice', 'Boy', 'Girl', "39"]; + let result = ['string']; + assert.deepEqual(utils.inferDtype(data), result); + }); + + it("Returns float type present in an 1D array", function () { + let data = [1.1, 2.1, 3.2, 4.4]; + let result = ['float32']; + assert.deepEqual(utils.inferDtype(data), result); + }); + + it("Returns int type present in an 1D array", function () { + let data = [1, 2, 3, 45]; + let result = ['int32']; + assert.deepEqual(utils.inferDtype(data), result); + }); + it("Returns float when there's a mixture of int and float in a 1D array", function () { + let data = [1, 2.1, 3, 45]; + let result = ['float32']; + assert.deepEqual(utils.inferDtype(data), result); + }); + + it("Returns float type when NaN is present in an 1D array", function () { + let data = [1, 2, 3, 45, NaN]; + let result = ['float32']; + assert.deepEqual(utils.inferDtype(data), result); + }); + + it("Returns correct dtype if NaN present in data", function () { + let data = utils.transposeArray([ + [18.7, 17.4, 18, NaN, 19.3], + [20, NaN, 19, 18, 20]]) + let result = ['float32', 'float32']; + assert.deepEqual(utils.inferDtype(data), result); + }); + + it("Returns the data type present in an 2D array", function () { + let data = utils.transposeArray([['Alice', 'Boy', 'Girl', "39"], [2, 5, 30, 89], [3.1, 6.1, 40.1, 78.2]]) + let result = ['string', 'int32', 'float32']; + assert.deepEqual(utils.inferDtype(data), result); + }); + + it("Returns the string dtype when there's a mixture of dtypes in a 2D array", function () { + let data = utils.transposeArray([['Alice', 'Boy', 'Girl', 21], [2, 5, 30, "hey"], [3, 6, 40.1, 78.2]]) + let result = ['string', 'string', 'float32']; + assert.deepEqual(utils.inferDtype(data), result); + }); + + it("Returns bool type in a 1D array", function () { + let data = [true, true, false, false, false, true]; + let result = ['boolean']; + assert.deepEqual(utils.inferDtype(data), result); + }); + it("Returns bool type in a 2D array", function () { + let data = utils.transposeArray([[true, false, true], ["boy", "girl", "man"], [20, 30, 24]]) + let result = ['boolean', 'string', 'int32']; + assert.deepEqual(utils.inferDtype(data), result); + }); + + it("Returns string type if values are all NaN", function () { + let data = utils.transposeArray([[true, false, true], ["boy", "girl", "boy"], [NaN, NaN, NaN]]) + let result = ['boolean', 'string', 'float32']; + assert.deepEqual(utils.inferDtype(data), result); + }); + + }); + + describe("mapIntegersToBooleans", function () { + it("map ints to bools in array of arrays", function () { + let data = [[1, 0, 1], [1, 1, 0]]; + assert.deepEqual(utils.mapIntegersToBooleans(data, 2), [[true, false, true], [true, true, false]]); + }); + + it("map ints to bools in array", function () { + let data = [1, 0, 0, 1, 1]; + assert.deepEqual(utils.mapIntegersToBooleans(data, 1), [true, false, false, true, true]); + }); + }); + + describe("round", function () { + it("round elements in array to 1 dp", function () { + let data = [10.01, 2.2, 3.11, 20.505, 20.22, 40.0909]; + assert.deepEqual(utils.round(data, 1, true), [10.0, 2.2, 3.1, 20.5, 20.2, 40.1]); + }); + + it("round elements in array to 2 dp", function () { + let data = [10.019, 2.2099, 3.1145, 20.506, 20.22, 40.0909]; + assert.deepEqual(utils.round(data, 2, true), [10.02, 2.21, 3.11, 20.51, 20.22, 40.09]); + }); + }); + + describe("replaceUndefinedWithNaN", function () { + it("replace undefined in Series with NaN", function () { + let data = [10.01, 2.2, undefined, 20.505, 20.22, undefined]; + assert.deepEqual(utils.replaceUndefinedWithNaN(data, true), [10.01, 2.2, NaN, 20.505, 20.22, NaN]); + }); + + it("replace undefined in DataFrame with NaN", function () { + let data = [[10.01, 2.2, undefined, 20.505, 20.22, undefined], + [10.01, undefined, undefined, 20.505, 20, undefined]]; + + let result = [[10.01, 2.2, NaN, 20.505, 20.22, NaN], + [10.01, NaN, NaN, 20.505, 20, NaN]]; + assert.deepEqual(utils.replaceUndefinedWithNaN(data, false), result); + }); + + it("replace null in Series with NaN", function () { + let data = [10.01, 2.2, null, 20.505, 20.22, null]; + assert.deepEqual(utils.replaceUndefinedWithNaN(data, true), [10.01, 2.2, NaN, 20.505, 20.22, NaN]); + }); + + it("replace null in DataFrame with NaN", function () { + let data = [[10.01, 2.2, null, 20.505, 20.22, null], + [10.01, null, null, 20.505, 20, null]]; + + let result = [[10.01, 2.2, NaN, 20.505, 20.22, NaN], + [10.01, NaN, NaN, 20.505, 20, NaN]]; + assert.deepEqual(utils.replaceUndefinedWithNaN(data, false), result); + }); + }); + + describe("convert2DArrayToSeriesArray", function () { + it("convert 2D array of array to 1D of string values", function () { + let data = [[10.01, 2.2, "a"], [20.505, 20.22, "boy"]]; + assert.deepEqual(utils.convert2DArrayToSeriesArray(data), ["10.01,2.2,a", "20.505,20.22,boy"]); + }); + + }); + + describe("throwErrorOnWrongParams", function () { + it("check if the right params are passed to a function", function () { + let paramsNeeded = ["replace", "with", "inplace"] + let kwargs = { "replae": 2, "with": 12, "inplace": true } + + assert.throws(() => { + utils.throwErrorOnWrongParams(kwargs, paramsNeeded) + }, Error, `Params Error: Required parameter not found. Your params must include the following [${paramsNeeded}]`); + }) + + it("check if the right params are passed to a function 2", function () { + let paramsNeeded = ["replace", "with", "inplace"] + let kwargs = { "replace": 2, "with": 12, "inplace": true } + utils.throwErrorOnWrongParams(kwargs, paramsNeeded) + }) + + }) + + describe("getRowAndColValues", function () { + it("retreive rows and labels from column object", function () { + let data = { "Alpha": ["A", "B", "C", "D"], count: [1, 2, 3, 4], sum: [20.3, 30.456, 40.90, 90.1] }; + let res = [["A", 1, 20.3], ["B", 2, 30.456], ["C", 3, 40.90], ["D", 4, 90.1]]; + assert.deepEqual(utils.getRowAndColValues(data)[0], res as any); + assert.deepEqual(utils.getRowAndColValues(data)[1], ["Alpha", "count", "sum"]); + }); + + }); + + + describe("getDuplicate", function () { + it("obtain duplicates and their index", function () { + let data = [1, 2, 3, 4, 5, 3, 4, 6, 4, 5]; + let res = { + '3': { count: 2, index: [2, 5] }, + '4': { count: 3, index: [3, 6, 8] }, + '5': { count: 2, index: [4, 9] } + }; + assert.deepEqual(utils.getDuplicate(data), res); + }); + }); + +}); diff --git a/src/danfojs-node/tsconfig.json b/src/danfojs-node/tsconfig.json new file mode 100644 index 00000000..3c52c2a7 --- /dev/null +++ b/src/danfojs-node/tsconfig.json @@ -0,0 +1,27 @@ +{ + "compilerOptions": { + "target": "es5", /* Specify ECMAScript target version: 'ES3' (default), 'ES5', 'ES2015', 'ES2016', 'ES2017', 'ES2018', 'ES2019', 'ES2020', or 'ESNEXT'. */ + "module": "commonjs", /* Specify module code generation: 'none', 'commonjs', 'amd', 'system', 'umd', 'es2015', 'es2020', or 'ESNext'. */ + "lib": ["es6", "DOM"], /* Specify library files to be included in the compilation. */ + "allowJs": true, /* Allow javascript files to be compiled. */ + "outDir": "./dist", /* Redirect output structure to the directory. */ + "strict": true, /* Enable all strict type-checking options. */ + "noImplicitAny": true, /* Raise error on expressions and declarations with an implied 'any' type. */ + // "noUnusedLocals": true, /* Report errors on unused locals. */ + // "noUnusedParameters": true, /* Report errors on unused parameters. */ + "noFallthroughCasesInSwitch": true, /* Report errors for fallthrough cases in switch statement. */ + "esModuleInterop": true, /* Enables emit interoperability between CommonJS and ES Modules via creation of namespace objects for all imports. Implies 'allowSyntheticDefaultImports'. */ + "resolveJsonModule": true, /* Include modules imported with '.json' extension */ + "skipLibCheck": true, /* Skip type checking of declaration files. */ + "forceConsistentCasingInFileNames": true, /* Disallow inconsistently-cased references to the same file. */ + "declaration": true, + "baseUrl": "./src", + }, + "include": [ + "./src/**/*" +], +"exclude": [ + "test.ts", + "**/*.spec.ts" +] +} diff --git a/src/danfojs-node/yarn-error.log b/src/danfojs-node/yarn-error.log new file mode 100644 index 00000000..eecc46ae --- /dev/null +++ b/src/danfojs-node/yarn-error.log @@ -0,0 +1,5663 @@ +Arguments: + /usr/local/bin/node /usr/local/Cellar/yarn/1.22.5/libexec/bin/yarn.js test + +PATH: + /Users/mac/.poetry/bin:/Users/mac/Downloads/google-cloud-sdk/bin:/Users/mac/opt/anaconda3/condabin:/usr/local/bin:/usr/bin:/bin:/usr/sbin:/sbin:/Library/Apple/usr/bin + +Yarn version: + 1.22.5 + +Node version: + 14.17.0 + +Platform: + darwin x64 + +Trace: + SyntaxError: /Users/mac/Documents/Opensource/danfojs/src/danfojs-node/package.json: Unexpected token y in JSON at position 750 + at JSON.parse () + at /usr/local/Cellar/yarn/1.22.5/libexec/lib/cli.js:1625:59 + at Generator.next () + at step (/usr/local/Cellar/yarn/1.22.5/libexec/lib/cli.js:310:30) + at /usr/local/Cellar/yarn/1.22.5/libexec/lib/cli.js:321:13 + +npm manifest: + { + "name": "danfojs-node", + "version": "1.0.0", + "description": "JavaScript library providing high performance, intuitive, and easy to use data structures for manipulating and processing structured data.", + "main": "dist/danfojs-node/src/index.js", + "types": "dist/danfojs-node/src/index.d.ts", + "directories": { + "test": "test" + }, + "contributors": [ + { + "name": "Rising Odegua" + }, + { + "name": "Stephen Oni" + } + ], + "files": [ + "dist/" + ], + "dependencies": { + "@tensorflow/tfjs-node": "3.6.1", + "mathjs": "^9.4.4", + "node-fetch": "^2.6.1", + "papaparse": "^5.3.1", + "request": "^2.88.2", + "stream-json": "^1.7.3", + "table": "6.7.1", + "xlsx": "^0.17.2", + "seedrandom": "^2.4.3" + }, + yarn "scripts": { + "test": "nyc mocha --require ts-node/register test/**/*.test.ts", + "test:clean": "yarn build:clean && yarn test", + "dev": "nodemon", + "build": "tsc", + "build:clean": "rimraf ./build && node ./scripts/prebuild.js && tsc", + "lint": "eslint ./src", + "coveralls": "cat ./coverage/lcov.info | ./node_modules/.bin/coveralls", + "coverage": "nyc report --reporter=text-lcov | coveralls && nyc report --reporter=lcov", + "patch": "npm version patch" + }, + "publishConfig": { + "access": "public", + "branches": [ + "master" + ] + }, + "repository": { + "type": "git", + "url": "git+https://github.com/opensource9ja/danfojs.git" + }, + "keywords": [ + "pandas", + "data-analysis", + "data-manipulation", + "analysis" + ], + "author": "Rising Odegua ", + "license": "MIT", + "bugs": { + "url": "https://github.com/opensource9ja/danfojs/issues" + }, + "homepage": "https://github.com/opensource9ja/danfojs#readme", + "devDependencies": { + "@babel/cli": "^7.10.5", + "@babel/core": "^7.10.5", + "@babel/plugin-transform-runtime": "^7.12.10", + "@babel/preset-env": "^7.10.4", + "@babel/register": "^7.10.1", + "@types/chai": "^4.2.19", + "@types/mocha": "^8.2.2", + "@types/node": "^16.9.6", + "@types/papaparse": "^5.2.6", + "@types/request": "^2.48.7", + "@types/stream-json": "^1.7.1", + "@types/table": "^6.3.2", + "chai": "^4.2.0", + "coveralls": "^3.1.0", + "dotenv": "^10.0.0", + "dts-bundle-generator": "^5.9.0", + "eslint": "^7.1.0", + "mocha": "^7.2.0", + "nodemon": "^2.0.7", + "nyc": "^15.1.0", + "rimraf": "^3.0.2", + "ts-node": "^10.0.0", + "typescript": "^4.4.2", + "yarn": "^1.22.10" + }, + "nyc": { + "reporter": [ + "lcov", + "text" + ] + } + } + +yarn manifest: + No manifest + +Lockfile: + # THIS IS AN AUTOGENERATED FILE. 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ajv@^6.12.4: version "6.12.6" resolved "https://registry.yarnpkg.com/ajv/-/ajv-6.12.6.tgz#baf5a62e802b07d977034586f8c3baf5adf26df4" integrity sha512-j3fVLgvTo527anyYyJOGTYJbG+vnnQYvE0m5mmkc1TK+nxAppkCLMIL0aZ4dblVCNoGShhm+kzE4ZUykBoMg4g== @@ -1090,6 +1221,23 @@ ajv@^7.0.2: require-from-string "^2.0.2" uri-js "^4.2.2" +ajv@^8.0.1: + version "8.6.1" + resolved "https://registry.yarnpkg.com/ajv/-/ajv-8.6.1.tgz#ae65764bf1edde8cd861281cda5057852364a295" + integrity sha512-42VLtQUOLefAvKFAQIxIZDaThq6om/PrfP0CYk3/vn+y4BMNkKnbli8ON2QCiHov4KkzOSJ/xSoBJdayiiYvVQ== + dependencies: + fast-deep-equal "^3.1.1" + json-schema-traverse "^1.0.0" + require-from-string "^2.0.2" + uri-js "^4.2.2" + +ansi-align@^3.0.0: + version "3.0.0" + resolved "https://registry.yarnpkg.com/ansi-align/-/ansi-align-3.0.0.tgz#b536b371cf687caaef236c18d3e21fe3797467cb" + integrity sha512-ZpClVKqXN3RGBmKibdfWzqCY4lnjEuoNzU5T0oEFpfd/z5qJHVarukridD4juLO2FXMiwUQxr9WqQtaYa8XRYw== + dependencies: + string-width "^3.0.0" + ansi-colors@3.2.3: version "3.2.3" resolved "https://registry.yarnpkg.com/ansi-colors/-/ansi-colors-3.2.3.tgz#57d35b8686e851e2cc04c403f1c00203976a1813" @@ -1134,11 +1282,6 @@ ansi-styles@^4.0.0, ansi-styles@^4.1.0: dependencies: color-convert "^2.0.1" -any-promise@^1.1.0: - version "1.3.0" - resolved "https://registry.yarnpkg.com/any-promise/-/any-promise-1.3.0.tgz#abc6afeedcea52e809cdc0376aed3ce39635d17f" - integrity sha1-q8av7tzqUugJzcA3au0845Y10X8= - anymatch@^2.0.0: version "2.0.0" resolved "https://registry.yarnpkg.com/anymatch/-/anymatch-2.0.0.tgz#bcb24b4f37934d9aa7ac17b4adaf89e7c76ef2eb" @@ -1155,6 +1298,14 @@ anymatch@~3.1.1: normalize-path "^3.0.0" picomatch "^2.0.4" +anymatch@~3.1.2: + version "3.1.2" + resolved "https://registry.yarnpkg.com/anymatch/-/anymatch-3.1.2.tgz#c0557c096af32f106198f4f4e2a383537e378716" + integrity sha512-P43ePfOAIupkguHUycrc4qJ9kz8ZiuOUijaETwX7THt0Y/GNK7v0aa8rY816xWjZ7rJdA5XdMcpVFTKMq+RvWg== + dependencies: + normalize-path "^3.0.0" + picomatch "^2.0.4" + append-transform@^2.0.0: version "2.0.0" resolved "https://registry.yarnpkg.com/append-transform/-/append-transform-2.0.0.tgz#99d9d29c7b38391e6f428d28ce136551f0b77e12" @@ -1180,6 +1331,11 @@ are-we-there-yet@~1.1.2: delegates "^1.0.0" readable-stream "^2.0.6" +arg@^4.1.0: + version "4.1.3" + resolved "https://registry.yarnpkg.com/arg/-/arg-4.1.3.tgz#269fc7ad5b8e42cb63c896d5666017261c144089" + integrity sha512-58S9QDqG0Xx27YwPSt9fJxivjYl432YCwfDMfZ+71RAqUrZef7LrKQZ3LHLOwCS4FLNBplP533Zx895SeOCHvA== + argparse@^1.0.10, argparse@^1.0.7: version "1.0.10" resolved "https://registry.yarnpkg.com/argparse/-/argparse-1.0.10.tgz#bcd6791ea5ae09725e17e5ad988134cd40b3d911" @@ -1229,11 +1385,6 @@ assign-symbols@^1.0.0: resolved "https://registry.yarnpkg.com/assign-symbols/-/assign-symbols-1.0.0.tgz#59667f41fadd4f20ccbc2bb96b8d4f7f78ec0367" integrity sha1-WWZ/QfrdTyDMvCu5a41Pf3jsA2c= -astral-regex@^1.0.0: - version "1.0.0" - resolved 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"https://registry.yarnpkg.com/babel-plugin-dynamic-import-node/-/babel-plugin-dynamic-import-node-2.3.3.tgz#84fda19c976ec5c6defef57f9427b3def66e17a3" @@ -1313,6 +1457,20 @@ binary-extensions@^2.0.0: resolved "https://registry.yarnpkg.com/binary-extensions/-/binary-extensions-2.2.0.tgz#75f502eeaf9ffde42fc98829645be4ea76bd9e2d" integrity sha512-jDctJ/IVQbZoJykoeHbhXpOlNBqGNcwXJKJog42E5HDPUwQTSdjCHdihjj0DlnheQ7blbT6dHOafNAiS8ooQKA== +boxen@^4.2.0: + version "4.2.0" + resolved "https://registry.yarnpkg.com/boxen/-/boxen-4.2.0.tgz#e411b62357d6d6d36587c8ac3d5d974daa070e64" + integrity sha512-eB4uT9RGzg2odpER62bBwSLvUeGC+WbRjjyyFhGsKnc8wp/m0+hQsMUvUe3H2V0D5vw0nBdO1hCJoZo5mKeuIQ== + dependencies: + ansi-align "^3.0.0" + camelcase "^5.3.1" + chalk "^3.0.0" + cli-boxes "^2.2.0" + string-width "^4.1.0" + term-size "^2.1.0" + type-fest "^0.8.1" + widest-line "^3.1.0" + brace-expansion@^1.1.7: version "1.1.11" resolved "https://registry.yarnpkg.com/brace-expansion/-/brace-expansion-1.1.11.tgz#3c7fcbf529d87226f3d2f52b966ff5271eb441dd" @@ -1344,6 +1502,11 @@ braces@~3.0.2: dependencies: fill-range "^7.0.1" +browser-process-hrtime@^1.0.0: + version "1.0.0" + resolved "https://registry.yarnpkg.com/browser-process-hrtime/-/browser-process-hrtime-1.0.0.tgz#3c9b4b7d782c8121e56f10106d84c0d0ffc94626" + integrity sha512-9o5UecI3GhkpM6DrXr69PblIuWxPKk9Y0jHBRhdocZ2y7YECBFCsHm79Pr3OyR2AvjhDkabFJaDJMYRazHgsow== + browser-stdout@1.3.1: version "1.3.1" resolved "https://registry.yarnpkg.com/browser-stdout/-/browser-stdout-1.3.1.tgz#baa559ee14ced73452229bad7326467c61fabd60" @@ -1380,6 +1543,19 @@ cache-base@^1.0.1: union-value "^1.0.0" unset-value "^1.0.0" +cacheable-request@^6.0.0: + version "6.1.0" + resolved "https://registry.yarnpkg.com/cacheable-request/-/cacheable-request-6.1.0.tgz#20ffb8bd162ba4be11e9567d823db651052ca912" + integrity sha512-Oj3cAGPCqOZX7Rz64Uny2GYAZNliQSqfbePrgAQ1wKAihYmCUnraBtJtKcGR4xz7wF+LoJC+ssFZvv5BgF9Igg== + dependencies: + clone-response "^1.0.2" + get-stream "^5.1.0" + http-cache-semantics "^4.0.0" + keyv "^3.0.0" + lowercase-keys "^2.0.0" + normalize-url "^4.1.0" + responselike "^1.0.2" + caching-transform@^4.0.0: version "4.0.0" resolved "https://registry.yarnpkg.com/caching-transform/-/caching-transform-4.0.0.tgz#00d297a4206d71e2163c39eaffa8157ac0651f0f" @@ -1418,14 +1594,14 @@ caseless@~0.12.0: resolved "https://registry.yarnpkg.com/caseless/-/caseless-0.12.0.tgz#1b681c21ff84033c826543090689420d187151dc" integrity sha1-G2gcIf+EAzyCZUMJBolCDRhxUdw= -cfb@^1.1.4: - version "1.2.0" - resolved "https://registry.yarnpkg.com/cfb/-/cfb-1.2.0.tgz#6a4d0872b525ed60349e1ef51fb4b0bf73eca9a8" - integrity sha512-sXMvHsKCICVR3Naq+J556K+ExBo9n50iKl6LGarlnvuA2035uMlGA/qVrc0wQtow5P1vJEw9UyrKLCbtIKz+TQ== +cfb@^1.1.4, cfb@^1.2.0: + version "1.2.1" + resolved "https://registry.yarnpkg.com/cfb/-/cfb-1.2.1.tgz#209429e4c68efd30641f6fc74b2d6028bd202402" + integrity sha512-wT2ScPAFGSVy7CY+aauMezZBnNrfnaLSrxHUHdea+Td/86vrk6ZquggV+ssBR88zNs0OnBkL2+lf9q0K+zVGzQ== dependencies: - adler-32 "~1.2.0" + adler-32 "~1.3.0" crc-32 "~1.2.0" - printj "~1.1.2" + printj "~1.3.0" chai@^4.2.0: version "4.2.0" @@ -1448,6 +1624,14 @@ chalk@^2.0.0, chalk@^2.4.2: escape-string-regexp "^1.0.5" supports-color "^5.3.0" +chalk@^3.0.0: + version "3.0.0" + resolved "https://registry.yarnpkg.com/chalk/-/chalk-3.0.0.tgz#3f73c2bf526591f574cc492c51e2456349f844e4" + integrity sha512-4D3B6Wf41KOYRFdszmDqMCGq5VV/uMAB273JILmO+3jAlh8X4qDtdtgCR3fxtbLEMzSx22QdhnDcJvu2u1fVwg== + dependencies: + ansi-styles "^4.1.0" + supports-color "^7.1.0" + chalk@^4.0.0, chalk@^4.1.0: version "4.1.0" resolved "https://registry.yarnpkg.com/chalk/-/chalk-4.1.0.tgz#4e14870a618d9e2edd97dd8345fd9d9dc315646a" @@ -1456,11 +1640,6 @@ chalk@^4.0.0, chalk@^4.1.0: ansi-styles "^4.1.0" supports-color "^7.1.0" -chardet@1.3.0: - version "1.3.0" - resolved "https://registry.yarnpkg.com/chardet/-/chardet-1.3.0.tgz#a56ed2d9e4517a7128721340a0cb9a10a8fac238" - integrity sha512-cyTQGGptIjIT+CMGT5J/0l9c6Fb+565GCFjjeUTKxUO7w3oR+FcNCMEKTn5xtVKaLFmladN7QF68IiQsv5Fbdw== - check-error@^1.0.2: version "1.0.2" resolved "https://registry.yarnpkg.com/check-error/-/check-error-1.0.2.tgz#574d312edd88bb5dd8912e9286dd6c0aed4aac82" @@ -1481,6 +1660,21 @@ chokidar@3.3.0: optionalDependencies: fsevents "~2.1.1" +chokidar@^3.2.2: + version "3.5.2" + resolved "https://registry.yarnpkg.com/chokidar/-/chokidar-3.5.2.tgz#dba3976fcadb016f66fd365021d91600d01c1e75" + integrity sha512-ekGhOnNVPgT77r4K/U3GDhu+FQ2S8TnK/s2KbIGXi0SZWuwkZ2QNyfWdZW+TVfn84DpEP7rLeCt2UI6bJ8GwbQ== + dependencies: + anymatch "~3.1.2" + braces "~3.0.2" + glob-parent "~5.1.2" + is-binary-path "~2.1.0" + is-glob "~4.0.1" + normalize-path "~3.0.0" + readdirp "~3.6.0" + optionalDependencies: + fsevents "~2.3.2" + chokidar@^3.4.0: version "3.5.1" resolved "https://registry.yarnpkg.com/chokidar/-/chokidar-3.5.1.tgz#ee9ce7bbebd2b79f49f304799d5468e31e14e68a" @@ -1501,6 +1695,11 @@ chownr@^1.1.1: resolved "https://registry.yarnpkg.com/chownr/-/chownr-1.1.4.tgz#6fc9d7b42d32a583596337666e7d08084da2cc6b" integrity sha512-jJ0bqzaylmJtVnNgzTeSOs8DPavpbYgEr/b0YL8/2GO3xJEhInFmhKMUnEJQjZumK7KXGFhUy89PrsJWlakBVg== +ci-info@^2.0.0: + version "2.0.0" + resolved "https://registry.yarnpkg.com/ci-info/-/ci-info-2.0.0.tgz#67a9e964be31a51e15e5010d58e6f12834002f46" + integrity sha512-5tK7EtrZ0N+OLFMthtqOj4fI2Jeb88C4CAZPu25LDVUgXJ0A3Js4PMGqrn0JU1W0Mh1/Z8wZzYPxqUrXeBboCQ== + class-utils@^0.3.5: version "0.3.6" resolved "https://registry.yarnpkg.com/class-utils/-/class-utils-0.3.6.tgz#f93369ae8b9a7ce02fd41faad0ca83033190c463" @@ -1516,6 +1715,11 @@ clean-stack@^2.0.0: resolved "https://registry.yarnpkg.com/clean-stack/-/clean-stack-2.2.0.tgz#ee8472dbb129e727b31e8a10a427dee9dfe4008b" integrity 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sha512-gDBvQU8IG139ZBQTSo2qvDFP+lANMGluM779csXOr6ny1NUtA3wkUnCFjlDNH/moAVfXtvClYt6G0zarFbtz5w== component-emitter@^1.2.1: version "1.3.0" @@ -1635,6 +1838,18 @@ concat-map@0.0.1: resolved "https://registry.yarnpkg.com/concat-map/-/concat-map-0.0.1.tgz#d8a96bd77fd68df7793a73036a3ba0d5405d477b" integrity sha1-2Klr13/Wjfd5OnMDajug1UBdR3s= +configstore@^5.0.1: + version "5.0.1" + resolved "https://registry.yarnpkg.com/configstore/-/configstore-5.0.1.tgz#d365021b5df4b98cdd187d6a3b0e3f6a7cc5ed96" + integrity sha512-aMKprgk5YhBNyH25hj8wGt2+D52Sw1DRRIzqBwLp2Ya9mFmY8KPvvtvmna8SxVR9JMZ4kzMD68N22vlaRpkeFA== + dependencies: + dot-prop "^5.2.0" + graceful-fs "^4.1.2" + make-dir "^3.0.0" + unique-string "^2.0.0" + write-file-atomic "^3.0.0" + xdg-basedir "^4.0.0" + console-control-strings@^1.0.0, console-control-strings@~1.1.0: version "1.1.0" resolved "https://registry.yarnpkg.com/console-control-strings/-/console-control-strings-1.1.0.tgz#3d7cf4464db6446ea644bf4b39507f9851008e8e" @@ -1689,6 +1904,11 @@ crc-32@~1.2.0: exit-on-epipe "~1.0.1" printj "~1.1.0" +create-require@^1.1.0: + version "1.1.1" + resolved "https://registry.yarnpkg.com/create-require/-/create-require-1.1.1.tgz#c1d7e8f1e5f6cfc9ff65f9cd352d37348756c333" + integrity sha512-dcKFX3jn0MpIaXjisoRvexIJVEKzaq7z2rZKxf+MSr9TkdmHmsU4m2lcLojrj/FHl8mk5VxMmYA+ftRkP/3oKQ== + cross-spawn@^7.0.0, cross-spawn@^7.0.2: version "7.0.3" resolved "https://registry.yarnpkg.com/cross-spawn/-/cross-spawn-7.0.3.tgz#f73a85b9d5d41d045551c177e2882d4ac85728a6" @@ -1698,25 +1918,27 @@ cross-spawn@^7.0.0, cross-spawn@^7.0.2: shebang-command "^2.0.0" which "^2.0.1" -csv-parse@*: - version "4.15.0" - resolved "https://registry.yarnpkg.com/csv-parse/-/csv-parse-4.15.0.tgz#d86d447e88d5f9a539e8768874f89a8d86d8fd78" - integrity sha512-y2wGeU/ybvUlyw6F+eanM6lxxE4JthCuHuaoTgPXdw6ImmfYXqtP0nrCLqd6Ew/a0FgPEz36y5HznI0W5oJ+cg== +crypto-random-string@^2.0.0: + version "2.0.0" + resolved 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"2.0.0" + resolved "https://registry.yarnpkg.com/data-urls/-/data-urls-2.0.0.tgz#156485a72963a970f5d5821aaf642bef2bf2db9b" + integrity sha512-X5eWTSXO/BJmpdIKCRuKUgSCgAN0OwliVK3yPKbwIWU1Tdw5BRajxlzMidvh+gwko9AfQ9zIj52pzF91Q3YAvQ== + dependencies: + abab "^2.0.3" + whatwg-mimetype "^2.3.0" + whatwg-url "^8.0.0" + debug@3.2.6: version "3.2.6" resolved "https://registry.yarnpkg.com/debug/-/debug-3.2.6.tgz#e83d17de16d8a7efb7717edbe5fb10135eee629b" @@ -1732,12 +1963,12 @@ debug@3.2.6: dependencies: ms "^2.1.1" -debug@=3.1.0: - version "3.1.0" - resolved "https://registry.yarnpkg.com/debug/-/debug-3.1.0.tgz#5bb5a0672628b64149566ba16819e61518c67261" - integrity sha512-OX8XqP7/1a9cqkxYw2yXss15f26NKWBpDXQd0/uK/KPqdQhxbPa994hnzjcE2VqQpDslf55723cKPUOGSmMY3g== +debug@4: + version "4.3.2" + resolved "https://registry.yarnpkg.com/debug/-/debug-4.3.2.tgz#f0a49c18ac8779e31d4a0c6029dfb76873c7428b" + integrity 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"https://registry.yarnpkg.com/enquirer/-/enquirer-2.3.6.tgz#2a7fe5dd634a1e4125a975ec994ff5456dc3734d" @@ -1914,7 +2206,7 @@ es-to-primitive@^1.2.1: is-date-object "^1.0.1" is-symbol "^1.0.2" -es6-error@^4.0.1, es6-error@^4.1.1: +es6-error@^4.0.1: version "4.1.1" resolved "https://registry.yarnpkg.com/es6-error/-/es6-error-4.1.1.tgz#9e3af407459deed47e9a91f9b885a84eb05c561d" integrity sha512-Um/+FxMr9CISWh0bi5Zv0iOD+4cFh5qLeks1qhAopKVAJw3drgKbKySikp7wGhDL0HPeaja0P5ULZrxLkniUVg== @@ -1936,6 +2228,11 @@ escalade@^3.1.1: resolved "https://registry.yarnpkg.com/escalade/-/escalade-3.1.1.tgz#d8cfdc7000965c5a0174b4a82eaa5c0552742e40" integrity sha512-k0er2gUkLf8O0zKJiAhmkTnJlTvINGv7ygDNPbeIsX/TJjGJZHuh9B2UxbsaEkmlEo9MfhrSzmhIlhRlI2GXnw== +escape-goat@^2.0.0: + version "2.1.1" + resolved "https://registry.yarnpkg.com/escape-goat/-/escape-goat-2.1.1.tgz#1b2dc77003676c457ec760b2dc68edb648188675" + integrity 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"https://registry.yarnpkg.com/eslint-scope/-/eslint-scope-5.1.1.tgz#e786e59a66cb92b3f6c1fb0d508aab174848f48c" @@ -2023,7 +2332,7 @@ espree@^7.3.0, espree@^7.3.1: acorn-jsx "^5.3.1" eslint-visitor-keys "^1.3.0" -esprima@^4.0.0: +esprima@^4.0.0, esprima@^4.0.1: version "4.0.1" resolved "https://registry.yarnpkg.com/esprima/-/esprima-4.0.1.tgz#13b04cdb3e6c5d19df91ab6987a8695619b0aa71" integrity sha512-eGuFFw7Upda+g4p+QHvnW0RyTX/SVeJBDM/gCtMARO0cLuT2HcEKnTPvhjV6aGeqrCB/sbNop0Kszm0jsaWU4A== @@ -2129,11 +2438,16 @@ fast-json-stable-stringify@^2.0.0: resolved "https://registry.yarnpkg.com/fast-json-stable-stringify/-/fast-json-stable-stringify-2.1.0.tgz#874bf69c6f404c2b5d99c481341399fd55892633" integrity sha512-lhd/wF+Lk98HZoTCtlVraHtfh5XYijIjalXck7saUtuanSDyLMxnHhSXEDJqHxD7msR8D0uCmqlkwjCV8xvwHw== -fast-levenshtein@^2.0.6: +fast-levenshtein@^2.0.6, fast-levenshtein@~2.0.6: version "2.0.6" resolved 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"https://registry.yarnpkg.com/form-data/-/form-data-3.0.0.tgz#31b7e39c85f1355b7139ee0c647cf0de7f83c682" @@ -2259,10 +2575,10 @@ frac@~1.1.2: resolved "https://registry.yarnpkg.com/frac/-/frac-1.1.2.tgz#3d74f7f6478c88a1b5020306d747dc6313c74d0b" integrity sha512-w/XBfkibaTl3YDqASwfDUqkna4Z2p9cFSr1aHDt0WoMTECnRfBOv2WArlZILlqgWlmdIlALXGpM2AOhEk5W3IA== -fraction.js@^4.0.12: - version "4.0.13" - resolved "https://registry.yarnpkg.com/fraction.js/-/fraction.js-4.0.13.tgz#3c1c315fa16b35c85fffa95725a36fa729c69dfe" - integrity sha512-E1fz2Xs9ltlUp+qbiyx9wmt2n9dRzPsS11Jtdb8D2o+cC7wr9xkkKsVKJuBX0ST+LVS+LhLO+SbLJNtfWcJvXA== +fraction.js@^4.1.1: + version "4.1.1" + resolved "https://registry.yarnpkg.com/fraction.js/-/fraction.js-4.1.1.tgz#ac4e520473dae67012d618aab91eda09bcb400ff" + integrity sha512-MHOhvvxHTfRFpF1geTK9czMIZ6xclsEor2wkIGYYq+PxcQqT7vStJqjhe6S1TenZrMZzo+wlqOufBDVepUEgPg== fragment-cache@^0.2.1: version "0.2.1" @@ -2271,25 +2587,6 @@ fragment-cache@^0.2.1: dependencies: map-cache "^0.2.2" -frictionless.js@0.13.4: - version "0.13.4" - resolved "https://registry.yarnpkg.com/frictionless.js/-/frictionless.js-0.13.4.tgz#4570b788073bad928fc2166afaa693e333c42f62" - integrity sha512-hB2noLCOl0pQZjSopcjwg+IeGcIg2FAQDO5EdJ9IsYZuoj45rzgM9ZLVMacTXLtEnftfML21e8ERQl38F4KpqA== - dependencies: - chardet "1.3.0" - csv-parse "4.12.0" - csv-sniffer "0.1.1" - iconv-lite "0.6.2" - lodash "4.17.20" - mime-types "2.1.27" - node-fetch "2.6.1" - readable-web-to-node-stream "^2.0.0" - stream-to-array "2.3.0" - stream-to-string "1.2.0" - tableschema "1.12.3" - url-join "4.0.1" - xlsx "0.16.7" - fromentries@^1.2.0: version "1.3.2" resolved "https://registry.yarnpkg.com/fromentries/-/fromentries-1.3.2.tgz#e4bca6808816bf8f93b52750f1127f5a6fd86e3a" @@ -2322,6 +2619,11 @@ fsevents@~2.3.1: resolved "https://registry.yarnpkg.com/fsevents/-/fsevents-2.3.1.tgz#b209ab14c61012636c8863507edf7fb68cc54e9f" integrity sha512-YR47Eg4hChJGAB1O3yEAOkGO+rlzutoICGqGo9EZ4lKWokzZRSyIW1QmTzqjtw8MJdj9srP869CuWw/hyzSiBw== +fsevents@~2.3.2: + version "2.3.2" + resolved "https://registry.yarnpkg.com/fsevents/-/fsevents-2.3.2.tgz#8a526f78b8fdf4623b709e0b975c52c24c02fd1a" + integrity sha512-xiqMQR4xAeHTuB9uWm+fFRcIOgKBMiOBP+eXiyT7jsgVCq1bkVygt00oASowB7EdtpOHaaPgKt812P9ab+DDKA== + function-bind@^1.1.1: version "1.1.1" resolved "https://registry.yarnpkg.com/function-bind/-/function-bind-1.1.1.tgz#a56899d3ea3c9bab874bb9773b7c5ede92f4895d" @@ -2375,6 +2677,20 @@ get-package-type@^0.1.0: resolved "https://registry.yarnpkg.com/get-package-type/-/get-package-type-0.1.0.tgz#8de2d803cff44df3bc6c456e6668b36c3926e11a" integrity sha512-pjzuKtY64GYfWizNAJ0fr9VqttZkNiK2iS430LtIHzjBEr6bX8Am2zm4sW4Ro5wjWW5cAlRL1qAMTcXbjNAO2Q== +get-stream@^4.1.0: + version "4.1.0" + resolved "https://registry.yarnpkg.com/get-stream/-/get-stream-4.1.0.tgz#c1b255575f3dc21d59bfc79cd3d2b46b1c3a54b5" + integrity sha512-GMat4EJ5161kIy2HevLlr4luNjBgvmj413KaQA7jt4V8B4RDsfpHk7WQ9GVqfYyyx8OS/L66Kox+rJRNklLK7w== + dependencies: + pump "^3.0.0" + +get-stream@^5.1.0: + version "5.2.0" + resolved "https://registry.yarnpkg.com/get-stream/-/get-stream-5.2.0.tgz#4966a1795ee5ace65e706c4b7beb71257d6e22d3" + integrity sha512-nBF+F1rAZVCu/p7rjzgA+Yb4lfYXrpl7a6VmJrU8wF9I1CKvP/QwPNZHnOlwbTkY6dvtFIzFMSyQXbLoTQPRpA== + dependencies: + pump "^3.0.0" + get-value@^2.0.3, get-value@^2.0.6: version "2.0.6" resolved "https://registry.yarnpkg.com/get-value/-/get-value-2.0.6.tgz#dc15ca1c672387ca76bd37ac0a395ba2042a2c28" @@ -2402,6 +2718,13 @@ glob-parent@^5.0.0, glob-parent@~5.1.0: dependencies: is-glob "^4.0.1" +glob-parent@~5.1.2: + version "5.1.2" + resolved "https://registry.yarnpkg.com/glob-parent/-/glob-parent-5.1.2.tgz#869832c58034fe68a4093c17dc15e8340d8401c4" + integrity sha512-AOIgSQCepiJYwP3ARnGx+5VnTu2HBYdzbGP45eLw1vr3zB3vZLeyed1sC9hnbcOc9/SrMyM5RPQrkGz4aS9Zow== + dependencies: + is-glob "^4.0.1" + glob@7.1.3: version "7.1.3" resolved "https://registry.yarnpkg.com/glob/-/glob-7.1.3.tgz#3960832d3f1574108342dafd3a67b332c0969df1" @@ -2426,6 +2749,13 @@ glob@^7.0.0, glob@^7.1.3, glob@^7.1.4, glob@^7.1.6: once "^1.3.0" path-is-absolute "^1.0.0" +global-dirs@^2.0.1: + version "2.1.0" + resolved "https://registry.yarnpkg.com/global-dirs/-/global-dirs-2.1.0.tgz#e9046a49c806ff04d6c1825e196c8f0091e8df4d" + integrity sha512-MG6kdOUh/xBnyo9cJFeIKkLEc1AyFq42QTU4XiX51i2NEdxLxLWXIjEjmqKeSuKR7pAZjTqUVoT2b2huxVLgYQ== + dependencies: + ini "1.3.7" + globals@^11.1.0: version "11.12.0" resolved "https://registry.yarnpkg.com/globals/-/globals-11.12.0.tgz#ab8795338868a0babd8525758018c2a7eb95c42e" @@ -2443,11 +2773,33 @@ google-protobuf@^3.9.2: resolved "https://registry.yarnpkg.com/google-protobuf/-/google-protobuf-3.14.0.tgz#20373d22046e63831a5110e11a84f713cc43651e" integrity sha512-bwa8dBuMpOxg7COyqkW6muQuvNnWgVN8TX/epDRGW5m0jcrmq2QJyCyiV8ZE2/6LaIIqJtiv9bYokFhfpy/o6w== +got@^9.6.0: + version "9.6.0" + resolved 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sha512-nTnJ528pbqxYanhpDYsi4Rd8MAeaBA67+RZ10CM1m3bTAVFEDcd5AuA4a6W5YkGZ1iNXHzZz8T6TBKLeBuNriQ== + growl@1.10.5: version "1.10.5" resolved "https://registry.yarnpkg.com/growl/-/growl-1.10.5.tgz#f2735dc2283674fa67478b10181059355c369e5e" @@ -2517,6 +2869,11 @@ has-values@^1.0.0: is-number "^3.0.0" kind-of "^4.0.0" +has-yarn@^2.1.0: + version "2.1.0" + resolved "https://registry.yarnpkg.com/has-yarn/-/has-yarn-2.1.0.tgz#137e11354a7b5bf11aa5cb649cf0c6f3ff2b2e77" + integrity sha512-UqBRqi4ju7T+TqGNdqAO0PaSVGsDGJUBQvk9eUWNGRY1CFGDzYhLWoM7JQEemnlvVcv/YEmc2wNW8BC24EnUsw== + has@^1.0.3: version "1.0.3" resolved "https://registry.yarnpkg.com/has/-/has-1.0.3.tgz#722d7cbfc1f6aa8241f16dd814e011e1f41e8796" @@ -2537,11 +2894,32 @@ he@1.2.0: resolved "https://registry.yarnpkg.com/he/-/he-1.2.0.tgz#84ae65fa7eafb165fddb61566ae14baf05664f0f" integrity sha512-F/1DnUGPopORZi0ni+CvrCgHQ5FyEAHRLSApuYWMmrbSwoN2Mn/7k+Gl38gJnR7yyDZk6WLXwiGod1JOWNDKGw== +html-encoding-sniffer@^2.0.1: + version "2.0.1" + resolved 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"https://registry.yarnpkg.com/http-proxy-agent/-/http-proxy-agent-4.0.1.tgz#8a8c8ef7f5932ccf953c296ca8291b95aa74aa3a" + integrity sha512-k0zdNgqWTGA6aeIRVpvfVob4fL52dTfaehylg0Y4UvSySvOq/Y+BOyPrgpUrA7HylqvU8vIZGsRuXmspskV0Tg== + dependencies: + "@tootallnate/once" "1" + agent-base "6" + debug "4" + http-signature@~1.2.0: version "1.2.0" resolved "https://registry.yarnpkg.com/http-signature/-/http-signature-1.2.0.tgz#9aecd925114772f3d95b65a60abb8f7c18fbace1" @@ -2559,20 +2937,26 @@ https-proxy-agent@^2.2.1: agent-base "^4.3.0" debug "^3.1.0" -iconv-lite@0.6.2: - version "0.6.2" - resolved "https://registry.yarnpkg.com/iconv-lite/-/iconv-lite-0.6.2.tgz#ce13d1875b0c3a674bd6a04b7f76b01b1b6ded01" - integrity sha512-2y91h5OpQlolefMPmUlivelittSWy0rP+oYVpn6A7GwVHNE8AWzoYOBNmlwks3LobaJxgHCYZAnyNo2GgpNRNQ== +https-proxy-agent@^5.0.0: + version "5.0.0" + resolved "https://registry.yarnpkg.com/https-proxy-agent/-/https-proxy-agent-5.0.0.tgz#e2a90542abb68a762e0a0850f6c9edadfd8506b2" + integrity 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+import-lazy@^2.1.0: + version "2.1.0" + resolved "https://registry.yarnpkg.com/import-lazy/-/import-lazy-2.1.0.tgz#05698e3d45c88e8d7e9d92cb0584e77f096f3e43" + integrity sha1-BWmOPUXIjo1+nZLLBYTnfwlvPkM= + imurmurhash@^0.1.4: version "0.1.4" resolved "https://registry.yarnpkg.com/imurmurhash/-/imurmurhash-0.1.4.tgz#9218b9b2b928a238b13dc4fb6b6d576f231453ea" @@ -2611,11 +3000,16 @@ inflight@^1.0.4: once "^1.3.0" wrappy "1" -inherits@2, inherits@^2.0.3, inherits@^2.0.4, inherits@~2.0.3: +inherits@2, inherits@^2.0.3, inherits@~2.0.3: version "2.0.4" resolved "https://registry.yarnpkg.com/inherits/-/inherits-2.0.4.tgz#0fa2c64f932917c3433a0ded55363aae37416b7c" integrity sha512-k/vGaX4/Yla3WzyMCvTQOXYeIHvqOKtnqBduzTHpzpQZzAskKMhZ2K+EnBiSM9zGSoIFeMpXKxa4dYeZIQqewQ== +ini@1.3.7: + version "1.3.7" + resolved "https://registry.yarnpkg.com/ini/-/ini-1.3.7.tgz#a09363e1911972ea16d7a8851005d84cf09a9a84" + integrity 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is-glob@^4.0.0, is-glob@^4.0.1, is-glob@~4.0.1: dependencies: is-extglob "^2.1.1" +is-installed-globally@^0.3.1: + version "0.3.2" + resolved "https://registry.yarnpkg.com/is-installed-globally/-/is-installed-globally-0.3.2.tgz#fd3efa79ee670d1187233182d5b0a1dd00313141" + integrity sha512-wZ8x1js7Ia0kecP/CHM/3ABkAmujX7WPvQk6uu3Fly/Mk44pySulQpnHG46OMjHGXApINnV4QhY3SWnECO2z5g== + dependencies: + global-dirs "^2.0.1" + is-path-inside "^3.0.1" + is-negative-zero@^2.0.1: version "2.0.1" resolved "https://registry.yarnpkg.com/is-negative-zero/-/is-negative-zero-2.0.1.tgz#3de746c18dda2319241a53675908d8f766f11c24" integrity sha512-2z6JzQvZRa9A2Y7xC6dQQm4FSTSTNWjKIYYTt4246eMTJmIo0Q+ZyOsU66X8lxK1AbB92dFeglPLrhwpeRKO6w== +is-npm@^4.0.0: + version "4.0.0" + resolved "https://registry.yarnpkg.com/is-npm/-/is-npm-4.0.0.tgz#c90dd8380696df87a7a6d823c20d0b12bbe3c84d" + integrity sha512-96ECIfh9xtDDlPylNPXhzjsykHsMJZ18ASpaWzQyBr4YRTcVjUvzaHayDAES2oU/3KpljhHUjtSRNiDwi0F0ig== + is-number@^3.0.0: version 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"https://registry.yarnpkg.com/is-plain-object/-/is-plain-object-2.0.4.tgz#2c163b3fafb1b606d9d17928f05c2a1c38e07677" @@ -2773,6 +3197,11 @@ is-plain-object@^2.0.3, is-plain-object@^2.0.4: dependencies: isobject "^3.0.1" +is-potential-custom-element-name@^1.0.1: + version "1.0.1" + resolved "https://registry.yarnpkg.com/is-potential-custom-element-name/-/is-potential-custom-element-name-1.0.1.tgz#171ed6f19e3ac554394edf78caa05784a45bebb5" + integrity sha512-bCYeRA2rVibKZd+s2625gGnGF/t7DSqDs4dP7CrLA1m7jKWz6pps0LpYLJN8Q64HtmPKJ1hrN3nzPNKFEKOUiQ== + is-regex@^1.1.1: version "1.1.1" resolved "https://registry.yarnpkg.com/is-regex/-/is-regex-1.1.1.tgz#c6f98aacc546f6cec5468a07b7b153ab564a57b9" @@ -2802,6 +3231,11 @@ is-windows@^1.0.2: resolved "https://registry.yarnpkg.com/is-windows/-/is-windows-1.0.2.tgz#d1850eb9791ecd18e6182ce12a30f396634bb19d" integrity sha512-eXK1UInq2bPmjyX6e3VHIzMLobc4J94i4AWn+Hpq3OU5KkrRC96OAcR3PRJ/pGu6m8TRnBHP9dkXQVsT/COVIA== +is-yarn-global@^0.3.0: + version "0.3.0" + resolved "https://registry.yarnpkg.com/is-yarn-global/-/is-yarn-global-0.3.0.tgz#d502d3382590ea3004893746754c89139973e232" + integrity sha512-VjSeb/lHmkoyd8ryPVIKvOCn4D1koMqY+vqyjjUfc3xyKtP4dYOxM44sZrnqQSzSds3xyOrUTLTC9LVCVgLngw== + isarray@1.0.0, isarray@~1.0.0: version "1.0.0" resolved "https://registry.yarnpkg.com/isarray/-/isarray-1.0.0.tgz#bb935d48582cba168c06834957a54a3e07124f11" @@ -2921,6 +3355,39 @@ jsbn@~0.1.0: resolved "https://registry.yarnpkg.com/jsbn/-/jsbn-0.1.1.tgz#a5e654c2e5a2deb5f201d96cefbca80c0ef2f513" integrity sha1-peZUwuWi3rXyAdls77yoDA7y9RM= +jsdom@^16.2.2: + version "16.7.0" + resolved "https://registry.yarnpkg.com/jsdom/-/jsdom-16.7.0.tgz#918ae71965424b197c819f8183a754e18977b710" + integrity sha512-u9Smc2G1USStM+s/x1ru5Sxrl6mPYCbByG1U/hUmqaVsm4tbNyS7CicOSRyuGQYZhTu0h84qkZZQ/I+dzizSVw== + dependencies: + abab "^2.0.5" + acorn "^8.2.4" + acorn-globals "^6.0.0" + cssom "^0.4.4" + cssstyle "^2.3.0" + data-urls "^2.0.0" + decimal.js "^10.2.1" + domexception "^2.0.1" + escodegen "^2.0.0" + form-data "^3.0.0" + html-encoding-sniffer "^2.0.1" + http-proxy-agent "^4.0.1" + https-proxy-agent "^5.0.0" + is-potential-custom-element-name "^1.0.1" + nwsapi "^2.2.0" + parse5 "6.0.1" + saxes "^5.0.1" + symbol-tree "^3.2.4" + tough-cookie "^4.0.0" + w3c-hr-time "^1.0.2" + w3c-xmlserializer "^2.0.0" + webidl-conversions "^6.1.0" + whatwg-encoding "^1.0.5" + whatwg-mimetype "^2.3.0" + whatwg-url "^8.5.0" + ws "^7.4.6" + xml-name-validator "^3.0.0" + jsesc@^2.5.1: version "2.5.2" resolved "https://registry.yarnpkg.com/jsesc/-/jsesc-2.5.2.tgz#80564d2e483dacf6e8ef209650a67df3f0c283a4" @@ -2931,6 +3398,11 @@ jsesc@~0.5.0: resolved "https://registry.yarnpkg.com/jsesc/-/jsesc-0.5.0.tgz#e7dee66e35d6fc16f710fe91d5cf69f70f08911d" integrity sha1-597mbjXW/Bb3EP6R1c9p9w8IkR0= +json-buffer@3.0.0: + version "3.0.0" + resolved "https://registry.yarnpkg.com/json-buffer/-/json-buffer-3.0.0.tgz#5b1f397afc75d677bde8bcfc0e47e1f9a3d9a898" + integrity sha1-Wx85evx11ne96Lz8Dkfh+aPZqJg= + json-schema-traverse@^0.4.1: version "0.4.1" resolved "https://registry.yarnpkg.com/json-schema-traverse/-/json-schema-traverse-0.4.1.tgz#69f6a87d9513ab8bb8fe63bdb0979c448e684660" @@ -2973,6 +3445,13 @@ jsprim@^1.2.2: json-schema "0.2.3" verror "1.10.0" +keyv@^3.0.0: + version "3.1.0" + resolved "https://registry.yarnpkg.com/keyv/-/keyv-3.1.0.tgz#ecc228486f69991e49e9476485a5be1e8fc5c4d9" + integrity sha512-9ykJ/46SN/9KPM/sichzQ7OvXyGDYKGTaDlKMGCAlg2UK8KRy4jb0d8sFc+0Tt0YYnThq8X2RZgCg74RPxgcVA== + dependencies: + json-buffer "3.0.0" + kind-of@^3.0.2, kind-of@^3.0.3, kind-of@^3.2.0: version "3.2.2" resolved "https://registry.yarnpkg.com/kind-of/-/kind-of-3.2.2.tgz#31ea21a734bab9bbb0f32466d893aea51e4a3c64" @@ -2997,6 +3476,13 @@ kind-of@^6.0.0, kind-of@^6.0.2: resolved "https://registry.yarnpkg.com/kind-of/-/kind-of-6.0.3.tgz#07c05034a6c349fa06e24fa35aa76db4580ce4dd" integrity sha512-dcS1ul+9tmeD95T+x28/ehLgd9mENa3LsvDTtzm3vyBEO7RPptvAD+t44WVXaUjTBRcrpFeFlC8WCruUR456hw== +latest-version@^5.0.0: + version "5.1.0" + resolved "https://registry.yarnpkg.com/latest-version/-/latest-version-5.1.0.tgz#119dfe908fe38d15dfa43ecd13fa12ec8832face" + integrity sha512-weT+r0kTkRQdCdYCNtkMwWXQTMEswKrFBkm4ckQOMVhhqhIMI1UT2hMj+1iigIhgSZm5gTmrRXBNoGUgaTY1xA== + dependencies: + package-json "^6.3.0" + lcov-parse@^1.0.0: version "1.0.0" resolved "https://registry.yarnpkg.com/lcov-parse/-/lcov-parse-1.0.0.tgz#eb0d46b54111ebc561acb4c408ef9363bdc8f7e0" @@ -3010,6 +3496,14 @@ levn@^0.4.1: prelude-ls "^1.2.1" type-check "~0.4.0" +levn@~0.3.0: + version "0.3.0" + resolved "https://registry.yarnpkg.com/levn/-/levn-0.3.0.tgz#3b09924edf9f083c0490fdd4c0bc4421e04764ee" + integrity sha1-OwmSTt+fCDwEkP3UwLxEIeBHZO4= + dependencies: + prelude-ls "~1.1.2" + type-check "~0.3.2" + locate-path@^3.0.0: version "3.0.0" resolved "https://registry.yarnpkg.com/locate-path/-/locate-path-3.0.0.tgz#dbec3b3ab759758071b58fe59fc41871af21400e" @@ -3025,16 +3519,31 @@ locate-path@^5.0.0: dependencies: p-locate "^4.1.0" +lodash.clonedeep@^4.5.0: + version "4.5.0" + resolved "https://registry.yarnpkg.com/lodash.clonedeep/-/lodash.clonedeep-4.5.0.tgz#e23f3f9c4f8fbdde872529c1071857a086e5ccef" + integrity sha1-4j8/nE+Pvd6HJSnBBxhXoIblzO8= + lodash.flattendeep@^4.4.0: version "4.4.0" resolved "https://registry.yarnpkg.com/lodash.flattendeep/-/lodash.flattendeep-4.4.0.tgz#fb030917f86a3134e5bc9bec0d69e0013ddfedb2" integrity sha1-+wMJF/hqMTTlvJvsDWngAT3f7bI= -lodash@4.17.20, lodash@^4.17.14, lodash@^4.17.15, lodash@^4.17.19, lodash@^4.17.20: +lodash.truncate@^4.4.2: + version "4.4.2" + resolved "https://registry.yarnpkg.com/lodash.truncate/-/lodash.truncate-4.4.2.tgz#5a350da0b1113b837ecfffd5812cbe58d6eae193" + integrity sha1-WjUNoLERO4N+z//VgSy+WNbq4ZM= + +lodash@^4.17.15, lodash@^4.17.19, lodash@^4.17.20: version "4.17.20" resolved "https://registry.yarnpkg.com/lodash/-/lodash-4.17.20.tgz#b44a9b6297bcb698f1c51a3545a2b3b368d59c52" integrity sha512-PlhdFcillOINfeV7Ni6oF1TAEayyZBoZ8bcshTHqOYJYlrqzRK5hagpagky5o4HfCzzd1TRkXPMFq6cKk9rGmA== +lodash@^4.7.0: + version "4.17.21" + resolved "https://registry.yarnpkg.com/lodash/-/lodash-4.17.21.tgz#679591c564c3bffaae8454cf0b3df370c3d6911c" + integrity sha512-v2kDEe57lecTulaDIuNTPy3Ry4gLGJ6Z1O3vE1krgXZNrsQ+LFTGHVxVjcXPs17LhbZVGedAJv8XZ1tvj5FvSg== + log-driver@^1.2.7: version "1.2.7" resolved "https://registry.yarnpkg.com/log-driver/-/log-driver-1.2.7.tgz#63b95021f0702fedfa2c9bb0a24e7797d71871d8" @@ -3047,6 +3556,16 @@ log-symbols@3.0.0: dependencies: chalk "^2.4.2" +lowercase-keys@^1.0.0, lowercase-keys@^1.0.1: + version "1.0.1" + resolved "https://registry.yarnpkg.com/lowercase-keys/-/lowercase-keys-1.0.1.tgz#6f9e30b47084d971a7c820ff15a6c5167b74c26f" + integrity sha512-G2Lj61tXDnVFFOi8VZds+SoQjtQC3dgokKdDG2mTm1tx4m50NUHBOZSBwQQHyy0V12A0JTG4icfZQH+xPyh8VA== + +lowercase-keys@^2.0.0: + version "2.0.0" + resolved "https://registry.yarnpkg.com/lowercase-keys/-/lowercase-keys-2.0.0.tgz#2603e78b7b4b0006cbca2fbcc8a3202558ac9479" + integrity sha512-tqNXrS78oMOE73NMxK4EMLQsQowWf8jKooH9g7xPavRT706R6bkQJ6DY2Te7QukaZsulxa30wQ7bk0pm4XiHmA== + lru-cache@^6.0.0: version "6.0.0" resolved "https://registry.yarnpkg.com/lru-cache/-/lru-cache-6.0.0.tgz#6d6fe6570ebd96aaf90fcad1dafa3b2566db3a94" @@ -3069,6 +3588,11 @@ make-dir@^3.0.0, make-dir@^3.0.2: dependencies: semver "^6.0.0" +make-error@^1.1.1: + version "1.3.6" + resolved "https://registry.yarnpkg.com/make-error/-/make-error-1.3.6.tgz#2eb2e37ea9b67c4891f684a1394799af484cf7a2" + integrity sha512-s8UhlNe7vPKomQhC1qFelMokr/Sc3AgNbso3n74mVPA5LTZwkB9NlXf4XPamLxJE8h0gh73rM94xvwRT2CVInw== + map-cache@^0.2.2: version "0.2.2" resolved "https://registry.yarnpkg.com/map-cache/-/map-cache-0.2.2.tgz#c32abd0bd6525d9b051645bb4f26ac5dc98a0dbf" @@ -3081,17 +3605,18 @@ map-visit@^1.0.0: dependencies: object-visit "^1.0.0" -mathjs@7.5.1: - version "7.5.1" - resolved "https://registry.yarnpkg.com/mathjs/-/mathjs-7.5.1.tgz#eb125295310a99ddcaf6145c47b09aab36e48274" - integrity sha512-H2q/Dq0qxBLMw+G84SSXmGqo/znihuxviGgAQwAcyeFLwK2HksvSGNx4f3dllZF51bWOnu2op60VZxH2Sb51Pw== +mathjs@^9.4.4: + version "9.4.4" + resolved "https://registry.yarnpkg.com/mathjs/-/mathjs-9.4.4.tgz#29acd67563c1e720910213062824c3faf61bc858" + integrity sha512-5EEJXnWOzLDgMHSFyw623nH+MTBZxquWwXtrzTsingOouJJ6UZG2VNO1lwH31IMt9aMno1axO6TYleIP4YSDaQ== dependencies: - complex.js "^2.0.11" - decimal.js "^10.2.1" + "@babel/runtime" "^7.14.6" + complex.js "^2.0.15" + decimal.js "^10.3.1" escape-latex "^1.2.0" - fraction.js "^4.0.12" + fraction.js "^4.1.1" javascript-natural-sort "^0.7.1" - seed-random "^2.2.0" + seedrandom "^3.0.5" tiny-emitter "^2.1.0" typed-function "^2.0.0" @@ -3114,23 +3639,11 @@ micromatch@^3.1.10, micromatch@^3.1.4: snapdragon "^0.8.1" to-regex "^3.0.2" -mime-db@1.44.0: - version "1.44.0" - resolved 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dependencies: mime-db "1.45.0" +mimic-response@^1.0.0, mimic-response@^1.0.1: + version "1.0.1" + resolved "https://registry.yarnpkg.com/mimic-response/-/mimic-response-1.0.1.tgz#4923538878eef42063cb8a3e3b0798781487ab1b" + integrity sha512-j5EctnkH7amfV/q5Hgmoal1g2QHFJRraOtmx0JpIqkxhBhI/lJSl1nMpQ45hVarwNETOoWEimndZ4QK0RHxuxQ== + minimatch@3.0.4, minimatch@^3.0.4: version "3.0.4" resolved "https://registry.yarnpkg.com/minimatch/-/minimatch-3.0.4.tgz#5166e286457f03306064be5497e8dbb0c3d32083" @@ -3210,11 +3728,6 @@ mocha@^7.2.0: yargs-parser "13.1.2" yargs-unparser "1.6.0" -moment@~2.24.0: - version "2.24.0" - resolved "https://registry.yarnpkg.com/moment/-/moment-2.24.0.tgz#0d055d53f5052aa653c9f6eb68bb5d12bf5c2b5b" - integrity sha512-bV7f+6l2QigeBBZSM/6yTNq4P2fNpSWj/0e7jQcy87A8e7o2nAfP/34/2ky5Vw4B9S446EtIhodAzkFCcR4dQg== - ms@2.0.0: version "2.0.0" resolved "https://registry.yarnpkg.com/ms/-/ms-2.0.0.tgz#5608aeadfc00be6c2901df5f9861788de0d597c8" @@ -3274,7 +3787,7 @@ node-environment-flags@1.0.6: object.getownpropertydescriptors "^2.0.3" semver "^5.7.0" -node-fetch@2.6.1, node-fetch@^2.6.1, node-fetch@~2.6.1: +node-fetch@^2.6.1, node-fetch@~2.6.1: version "2.6.1" resolved "https://registry.yarnpkg.com/node-fetch/-/node-fetch-2.6.1.tgz#045bd323631f76ed2e2b55573394416b639a0052" integrity sha512-V4aYg89jEoVRxRb2fJdAg8FHvI7cEyYdVAh94HH0UIK8oJxUfkjlDQN9RbMx+bEjP7+ggMiFRprSti032Oipxw== @@ -3312,6 +3825,22 @@ node-releases@^1.1.71: resolved "https://registry.yarnpkg.com/node-releases/-/node-releases-1.1.72.tgz#14802ab6b1039a79a0c7d662b610a5bbd76eacbe" integrity sha512-LLUo+PpH3dU6XizX3iVoubUNheF/owjXCZZ5yACDxNnPtgFuludV1ZL3ayK1kVep42Rmm0+R9/Y60NQbZ2bifw== +nodemon@^2.0.7: + version "2.0.7" + resolved "https://registry.yarnpkg.com/nodemon/-/nodemon-2.0.7.tgz#6f030a0a0ebe3ea1ba2a38f71bf9bab4841ced32" + integrity sha512-XHzK69Awgnec9UzHr1kc8EomQh4sjTQ8oRf8TsGrSmHDx9/UmiGG9E/mM3BuTfNeFwdNBvrqQq/RHL0xIeyFOA== + dependencies: + chokidar "^3.2.2" + debug "^3.2.6" + ignore-by-default "^1.0.1" + minimatch "^3.0.4" + pstree.remy "^1.1.7" + semver "^5.7.1" + supports-color "^5.5.0" + touch "^3.1.0" + undefsafe "^2.0.3" + update-notifier "^4.1.0" + nopt@^4.0.1: version "4.0.3" resolved "https://registry.yarnpkg.com/nopt/-/nopt-4.0.3.tgz#a375cad9d02fd921278d954c2254d5aa57e15e48" @@ -3320,6 +3849,13 @@ nopt@^4.0.1: abbrev "1" osenv "^0.1.4" +nopt@~1.0.10: + version "1.0.10" + resolved "https://registry.yarnpkg.com/nopt/-/nopt-1.0.10.tgz#6ddd21bd2a31417b92727dd585f8a6f37608ebee" + integrity sha1-bd0hvSoxQXuScn3Vhfim83YI6+4= + dependencies: + abbrev "1" + normalize-path@^2.1.1: version "2.1.1" resolved "https://registry.yarnpkg.com/normalize-path/-/normalize-path-2.1.1.tgz#1ab28b556e198363a8c1a6f7e6fa20137fe6aed9" @@ -3332,6 +3868,11 @@ normalize-path@^3.0.0, normalize-path@~3.0.0: resolved "https://registry.yarnpkg.com/normalize-path/-/normalize-path-3.0.0.tgz#0dcd69ff23a1c9b11fd0978316644a0388216a65" integrity sha512-6eZs5Ls3WtCisHWp9S2GUy8dqkpGi4BVSz3GaqiE6ezub0512ESztXUwUB6C6IKbQkY2Pnb/mD4WYojCRwcwLA== +normalize-url@^4.1.0: + version "4.5.1" + resolved "https://registry.yarnpkg.com/normalize-url/-/normalize-url-4.5.1.tgz#0dd90cf1288ee1d1313b87081c9a5932ee48518a" + integrity sha512-9UZCFRHQdNrfTpGg8+1INIg93B6zE0aXMVFkw1WFwvO4SlZywU6aLg5Of0Ap/PgcbSw4LNxvMWXMeugwMCX0AA== + npm-bundled@^1.0.1: version "1.1.1" resolved "https://registry.yarnpkg.com/npm-bundled/-/npm-bundled-1.1.1.tgz#1edd570865a94cdb1bc8220775e29466c9fb234b" @@ -3368,6 +3909,11 @@ number-is-nan@^1.0.0: resolved "https://registry.yarnpkg.com/number-is-nan/-/number-is-nan-1.0.1.tgz#097b602b53422a522c1afb8790318336941a011d" integrity sha1-CXtgK1NCKlIsGvuHkDGDNpQaAR0= +nwsapi@^2.2.0: + version "2.2.0" + resolved "https://registry.yarnpkg.com/nwsapi/-/nwsapi-2.2.0.tgz#204879a9e3d068ff2a55139c2c772780681a38b7" + integrity sha512-h2AatdwYH+JHiZpv7pt/gSX1XoRGb7L/qSIeuqA6GwYoF9w1vP1cw42TO0aI2pNyshRK5893hNSl+1//vHK7hQ== + nyc@^15.1.0: version "15.1.0" resolved "https://registry.yarnpkg.com/nyc/-/nyc-15.1.0.tgz#1335dae12ddc87b6e249d5a1994ca4bdaea75f02" @@ -3473,13 +4019,25 @@ object.pick@^1.3.0: dependencies: isobject "^3.0.1" -once@^1.3.0: +once@^1.3.0, once@^1.3.1, once@^1.4.0: version "1.4.0" resolved "https://registry.yarnpkg.com/once/-/once-1.4.0.tgz#583b1aa775961d4b113ac17d9c50baef9dd76bd1" integrity sha1-WDsap3WWHUsROsF9nFC6753Xa9E= dependencies: wrappy "1" +optionator@^0.8.1: + version "0.8.3" + resolved "https://registry.yarnpkg.com/optionator/-/optionator-0.8.3.tgz#84fa1d036fe9d3c7e21d99884b601167ec8fb495" + integrity sha512-+IW9pACdk3XWmmTXG8m3upGUJst5XRGzxMRjXzAuJ1XnIFNvfhjjIuYkDvysnPQ7qzqVzLt78BCruntqRhWQbA== + dependencies: + deep-is "~0.1.3" + fast-levenshtein "~2.0.6" + levn "~0.3.0" + prelude-ls "~1.1.2" + type-check "~0.3.2" + word-wrap "~1.2.3" + optionator@^0.9.1: version "0.9.1" resolved "https://registry.yarnpkg.com/optionator/-/optionator-0.9.1.tgz#4f236a6373dae0566a6d43e1326674f50c291499" @@ -3510,6 +4068,11 @@ osenv@^0.1.4: os-homedir "^1.0.0" os-tmpdir "^1.0.0" +p-cancelable@^1.0.0: + version "1.1.0" + resolved "https://registry.yarnpkg.com/p-cancelable/-/p-cancelable-1.1.0.tgz#d078d15a3af409220c886f1d9a0ca2e441ab26cc" + integrity sha512-s73XxOZ4zpt1edZYZzvhqFa6uvQc1vwUa0K0BdtIZgQMAJj9IbebH+JkgKZc9h+B05PKHLOTl4ajG1BmNrVZlw== + p-limit@^2.0.0, p-limit@^2.2.0: version "2.3.0" resolved "https://registry.yarnpkg.com/p-limit/-/p-limit-2.3.0.tgz#3dd33c647a214fdfffd835933eb086da0dc21db1" @@ -3553,6 +4116,21 @@ package-hash@^4.0.0: lodash.flattendeep "^4.4.0" release-zalgo "^1.0.0" +package-json@^6.3.0: + version "6.5.0" + resolved "https://registry.yarnpkg.com/package-json/-/package-json-6.5.0.tgz#6feedaca35e75725876d0b0e64974697fed145b0" + integrity sha512-k3bdm2n25tkyxcjSKzB5x8kfVxlMdgsbPr0GkZcwHsLpba6cBjqCt1KlcChKEvxHIcTB1FVMuwoijZ26xex5MQ== + dependencies: + got "^9.6.0" + registry-auth-token "^4.0.0" + registry-url "^5.0.0" + semver "^6.2.0" + +papaparse@^5.3.1: + version "5.3.1" + resolved "https://registry.yarnpkg.com/papaparse/-/papaparse-5.3.1.tgz#770b7a9124d821d4b2132132b7bd7dce7194b5b1" + integrity sha512-Dbt2yjLJrCwH2sRqKFFJaN5XgIASO9YOFeFP8rIBRG2Ain8mqk5r1M6DkfvqEVozVcz3r3HaUGw253hA1nLIcA== + parent-module@^1.0.0: version "1.0.1" resolved "https://registry.yarnpkg.com/parent-module/-/parent-module-1.0.1.tgz#691d2709e78c79fae3a156622452d00762caaaa2" @@ -3560,6 +4138,11 @@ parent-module@^1.0.0: dependencies: callsites "^3.0.0" +parse5@6.0.1: + version "6.0.1" + resolved "https://registry.yarnpkg.com/parse5/-/parse5-6.0.1.tgz#e1a1c085c569b3dc08321184f19a39cc27f7c30b" + integrity sha512-Ofn/CTFzRGTTxwpNEs9PP93gXShHcTq255nzRYSKe8AkVpZY7e1fpmTfOyoIvjP5HG7Z2ZM7VS9PPhQGW2pOpw== + pascalcase@^0.1.1: version "0.1.1" resolved "https://registry.yarnpkg.com/pascalcase/-/pascalcase-0.1.1.tgz#b363e55e8006ca6fe21784d2db22bd15d7917f14" @@ -3631,6 +4214,11 @@ pkg-dir@^4.1.0: dependencies: find-up "^4.0.0" +plotly.js-dist-min@^2.8.0: + version "2.8.3" + resolved "https://registry.yarnpkg.com/plotly.js-dist-min/-/plotly.js-dist-min-2.8.3.tgz#0ffa317de10dd48f0c32d15c83c41f60230f50b3" + integrity sha512-TjovBrqIbedUfjGWNMMs++az3lF4hsIAB8EjqM/mC0cPrO7Wg86lBkHAsRbMra5aWyXA+M52yiQUUKBfThqwzg== + posix-character-classes@^0.1.0: version "0.1.1" resolved "https://registry.yarnpkg.com/posix-character-classes/-/posix-character-classes-0.1.1.tgz#01eac0fe3b5af71a2a6c02feabb8c1fef7e00eab" @@ -3641,11 +4229,31 @@ prelude-ls@^1.2.1: resolved "https://registry.yarnpkg.com/prelude-ls/-/prelude-ls-1.2.1.tgz#debc6489d7a6e6b0e7611888cec880337d316396" integrity sha512-vkcDPrRZo1QZLbn5RLGPpg/WmIQ65qoWWhcGKf/b5eplkkarX0m9z8ppCat4mlOqUsWpyNuYgO3VRyrYHSzX5g== -printj@~1.1.0, printj@~1.1.2: +prelude-ls@~1.1.2: + version "1.1.2" + resolved "https://registry.yarnpkg.com/prelude-ls/-/prelude-ls-1.1.2.tgz#21932a549f5e52ffd9a827f570e04be62a97da54" + integrity sha1-IZMqVJ9eUv/ZqCf1cOBL5iqX2lQ= + +prepend-http@^2.0.0: + version "2.0.0" + resolved 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"https://registry.yarnpkg.com/process-nextick-args/-/process-nextick-args-2.0.1.tgz#7820d9b16120cc55ca9ae7792680ae7dba6d7fe2" @@ -3663,27 +4271,42 @@ progress@^2.0.0: resolved "https://registry.yarnpkg.com/progress/-/progress-2.0.3.tgz#7e8cf8d8f5b8f239c1bc68beb4eb78567d572ef8" integrity sha512-7PiHtLll5LdnKIMw100I+8xJXR5gW2QwWYkT6iJva0bXitZKa/XMrSbdmg3r2Xnaidz9Qumd0VPaMrZlF9V9sA== -promise-polyfill@^1.1.6: - version "1.1.6" - resolved "https://registry.yarnpkg.com/promise-polyfill/-/promise-polyfill-1.1.6.tgz#cd04eff46f5c95c3a7d045591d79b5e3e01f12d7" - integrity sha1-zQTv9G9clcOn0EVZHXm14+AfEtc= - -psl@^1.1.28: +psl@^1.1.28, psl@^1.1.33: version "1.8.0" resolved "https://registry.yarnpkg.com/psl/-/psl-1.8.0.tgz#9326f8bcfb013adcc005fdff056acce020e51c24" integrity sha512-RIdOzyoavK+hA18OGGWDqUTsCLhtA7IcZ/6NCs4fFJaHBDab+pDDmDIByWFRQJq2Cd7r1OoQxBGKOaztq+hjIQ== +pstree.remy@^1.1.7: + version "1.1.8" + resolved 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dependencies: - inherits "^2.0.3" - string_decoder "^1.1.1" - util-deprecate "^1.0.1" - readable-stream@^2.0.2, readable-stream@^2.0.6: version "2.3.7" resolved "https://registry.yarnpkg.com/readable-stream/-/readable-stream-2.3.7.tgz#1eca1cf711aef814c04f62252a36a62f6cb23b57" @@ -3715,11 +4329,6 @@ readable-stream@^2.0.2, readable-stream@^2.0.6: string_decoder "~1.1.1" util-deprecate "~1.0.1" -readable-web-to-node-stream@^2.0.0: - version "2.0.0" - resolved "https://registry.yarnpkg.com/readable-web-to-node-stream/-/readable-web-to-node-stream-2.0.0.tgz#751e632f466552ac0d5c440cc01470352f93c4b7" - integrity sha512-+oZJurc4hXpaaqsN68GoZGQAQIA3qr09Or4fqEsargABnbe5Aau8hFn6ISVleT3cpY/0n/8drn7huyyEvTbghA== - readdirp@^2.2.1: version "2.2.1" resolved "https://registry.yarnpkg.com/readdirp/-/readdirp-2.2.1.tgz#0e87622a3325aa33e892285caf8b4e846529a525" @@ -3743,6 +4352,13 @@ readdirp@~3.5.0: dependencies: picomatch "^2.2.1" +readdirp@~3.6.0: + version "3.6.0" + resolved "https://registry.yarnpkg.com/readdirp/-/readdirp-3.6.0.tgz#74a370bd857116e245b29cc97340cd431a02a6c7" + integrity sha512-hOS089on8RduqdbhvQ5Z37A0ESjsqz6qnRcffsMU3495FuTdqSm+7bhJ29JvIOsBDEEnan5DPu9t3To9VRlMzA== + dependencies: + picomatch "^2.2.1" + regenerate-unicode-properties@^8.2.0: version "8.2.0" resolved "https://registry.yarnpkg.com/regenerate-unicode-properties/-/regenerate-unicode-properties-8.2.0.tgz#e5de7111d655e7ba60c057dbe9ff37c87e65cdec" @@ -3792,6 +4408,20 @@ regexpu-core@^4.7.1: unicode-match-property-ecmascript "^1.0.4" unicode-match-property-value-ecmascript "^1.2.0" +registry-auth-token@^4.0.0: + version "4.2.1" + resolved "https://registry.yarnpkg.com/registry-auth-token/-/registry-auth-token-4.2.1.tgz#6d7b4006441918972ccd5fedcd41dc322c79b250" + integrity sha512-6gkSb4U6aWJB4SF2ZvLb76yCBjcvufXBqvvEx1HbmKPkutswjW1xNVRY0+daljIYRbogN7O0etYSlbiaEQyMyw== + dependencies: + rc "^1.2.8" + +registry-url@^5.0.0: + version "5.1.0" + resolved 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@@ rimraf@^3.0.0, rimraf@^3.0.2: dependencies: glob "^7.1.3" -safe-buffer@^5.0.1, safe-buffer@^5.1.2, safe-buffer@~5.2.0: +safe-buffer@^5.0.1, safe-buffer@^5.1.2: version "5.2.1" resolved "https://registry.yarnpkg.com/safe-buffer/-/safe-buffer-5.2.1.tgz#1eaf9fa9bdb1fdd4ec75f58f9cdb4e6b7827eec6" integrity sha512-rp3So07KcdmmKbGvgaNxQSJr7bGVSVk5S9Eq1F+ppbRo70+YeaDxkw5Dd8NPN+GD6bjnYm2VuPuCXmpuYvmCXQ== @@ -3918,7 +4555,7 @@ safe-regex@^1.1.0: dependencies: ret "~0.1.10" -"safer-buffer@>= 2.1.2 < 3", "safer-buffer@>= 2.1.2 < 3.0.0", safer-buffer@^2.0.2, safer-buffer@^2.1.0, safer-buffer@~2.1.0: +"safer-buffer@>= 2.1.2 < 3", safer-buffer@^2.0.2, safer-buffer@^2.1.0, safer-buffer@~2.1.0: version "2.1.2" resolved "https://registry.yarnpkg.com/safer-buffer/-/safer-buffer-2.1.2.tgz#44fa161b0187b9549dd84bb91802f9bd8385cd6a" integrity sha512-YZo3K82SD7Riyi0E1EQPojLz7kpepnSQI9IyPbHHg1XXXevb5dJI7tpyN2ADxGcQbHG7vcyRHk0cbwqcQriUtg== @@ -3928,27 +4565,46 @@ sax@^1.2.4: resolved 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sha512-+GB6zVA9LWh6zovYQLALHwv5rb2PHGlJi3lfiqIHxR0uuwCgefcOJc59v9fv1w8GbStwxuuqqAjI9NMAOOgq1A== -semver@^5.3.0, semver@^5.4.1, semver@^5.5.0, semver@^5.5.1, semver@^5.6.0, semver@^5.7.0: +semver@^5.3.0, semver@^5.4.1, semver@^5.5.0, semver@^5.5.1, semver@^5.6.0, semver@^5.7.0, semver@^5.7.1: version "5.7.1" resolved "https://registry.yarnpkg.com/semver/-/semver-5.7.1.tgz#a954f931aeba508d307bbf069eff0c01c96116f7" integrity sha512-sauaDf/PZdVgrLTNYHRtpXa1iRiKcaebiKQ1BJdpQlWH2lCvexQdX55snPFyK7QzpudqbCI0qXFfOasHdyNDGQ== -semver@^6.0.0, semver@^6.3.0: +semver@^6.0.0, semver@^6.2.0, semver@^6.3.0: version "6.3.0" resolved "https://registry.yarnpkg.com/semver/-/semver-6.3.0.tgz#ee0a64c8af5e8ceea67687b133761e1becbd1d3d" integrity sha512-b39TBaTSfV6yBrapU89p5fKekE2m/NwnDocOVruQFS1/veMgdzuPcnOM34M6CwxW8jH/lxEa5rBoDeUwu5HHTw== @@ -3997,15 +4653,6 @@ slash@^2.0.0: resolved "https://registry.yarnpkg.com/slash/-/slash-2.0.0.tgz#de552851a1759df3a8f206535442f5ec4ddeab44" integrity sha512-ZYKh3Wh2z1PpEXWr0MpSBZ0V6mZHAQfYevttO11c51CaWjGTaadiKZ+wVt1PbMlDV5qhMFslpZCemhwOK7C89A== -slice-ansi@^2.1.0: - version "2.1.0" - resolved "https://registry.yarnpkg.com/slice-ansi/-/slice-ansi-2.1.0.tgz#cacd7693461a637a5788d92a7dd4fba068e81636" - integrity sha512-Qu+VC3EwYLldKa1fCxuuvULvSJOKEgk9pi8dZeCVK7TqBfUNTH4sFkk4joj8afVSfAYgJoSOetjx9QWOJ5mYoQ== - dependencies: - ansi-styles "^3.2.0" - astral-regex "^1.0.0" - is-fullwidth-code-point "^2.0.0" - slice-ansi@^4.0.0: version "4.0.0" resolved "https://registry.yarnpkg.com/slice-ansi/-/slice-ansi-4.0.0.tgz#500e8dd0fd55b05815086255b3195adf2a45fe6b" @@ -4056,7 +4703,7 @@ source-map-resolve@^0.5.0: source-map-url "^0.4.0" urix "^0.1.0" -source-map-support@^0.5.16: +source-map-support@^0.5.16, source-map-support@^0.5.17: version "0.5.19" resolved "https://registry.yarnpkg.com/source-map-support/-/source-map-support-0.5.19.tgz#a98b62f86dcaf4f67399648c085291ab9e8fed61" integrity sha512-Wonm7zOCIJzBGQdB+thsPar0kYuCIzYvxZwlBa87yi/Mdjv7Tip2cyVbLj5o0cFPN4EVkuTwb3GDDyUx2DGnGw== @@ -4074,7 +4721,7 @@ source-map@^0.5.0, source-map@^0.5.6: resolved "https://registry.yarnpkg.com/source-map/-/source-map-0.5.7.tgz#8a039d2d1021d22d1ea14c80d8ea468ba2ef3fcc" integrity sha1-igOdLRAh0i0eoUyA2OpGi6LvP8w= -source-map@^0.6.0, source-map@^0.6.1: +source-map@^0.6.0, source-map@^0.6.1, source-map@~0.6.1: version "0.6.1" resolved "https://registry.yarnpkg.com/source-map/-/source-map-0.6.1.tgz#74722af32e9614e9c287a8d0bbde48b5e2f1a263" integrity sha512-UjgapumWlbMhkBgzT7Ykc5YXUT46F0iKu8SGXq0bcwP5dz/h0Plj6enJqjz1Zbq2l5WaqYnrVbwWOWMyF3F47g== @@ -4133,24 +4780,17 @@ static-extend@^0.1.1: define-property "^0.2.5" object-copy "^0.1.0" -stream-to-array@2.3.0, stream-to-array@^2.3.0: - version "2.3.0" - resolved "https://registry.yarnpkg.com/stream-to-array/-/stream-to-array-2.3.0.tgz#bbf6b39f5f43ec30bc71babcb37557acecf34353" - integrity sha1-u/azn19D7DC8cbq8s3VXrOzzQ1M= - dependencies: - any-promise "^1.1.0" - -stream-to-async-iterator@^0.2.0: - version "0.2.0" - resolved "https://registry.yarnpkg.com/stream-to-async-iterator/-/stream-to-async-iterator-0.2.0.tgz#bef5c885e9524f98b2fa5effecc357bd58483780" - integrity sha1-vvXIhelST5iy+l7/7MNXvVhIN4A= +stream-chain@^2.2.4: + version "2.2.4" + resolved "https://registry.yarnpkg.com/stream-chain/-/stream-chain-2.2.4.tgz#2e5e9858e00d25ebaf7e4339e8328ddcab5facad" + integrity sha512-9lsl3YM53V5N/I1C2uJtc3Kavyi3kNYN83VkKb/bMWRk7D9imiFyUPYa0PoZbLohSVOX1mYE9YsmwObZUsth6Q== -stream-to-string@1.2.0: - version "1.2.0" - resolved "https://registry.yarnpkg.com/stream-to-string/-/stream-to-string-1.2.0.tgz#3ca506a097ecbf78b0e0aee0b6fa5c4565412a15" - integrity sha512-8drZlFIKBHSMdX9GCWv8V9AAWnQcTqw0iAI6/GC7UJ0H0SwKeFKjOoZfGY1tOU00GGU7FYZQoJ/ZCUEoXhD7yQ== +stream-json@^1.7.3: + version "1.7.3" + resolved "https://registry.yarnpkg.com/stream-json/-/stream-json-1.7.3.tgz#8a2abe8b51a2e84ffbc8d09bca9d01dc70facae3" + integrity sha512-Y6dXn9KKWSwxOqnvHGcdZy1PK+J+7alBwHCeU3W9oRqm4ilLRA0XSPmd1tWwhg7tv9EIxJTMWh7KF15tYelKJg== dependencies: - promise-polyfill "^1.1.6" + stream-chain "^2.2.4" string-width@^1.0.1: version "1.0.2" @@ -4178,6 +4818,15 @@ string-width@^3.0.0, string-width@^3.1.0: is-fullwidth-code-point "^2.0.0" strip-ansi "^5.1.0" +string-width@^4.0.0: + version "4.2.2" + resolved "https://registry.yarnpkg.com/string-width/-/string-width-4.2.2.tgz#dafd4f9559a7585cfba529c6a0a4f73488ebd4c5" + integrity sha512-XBJbT3N4JhVumXE0eoLU9DCjcaF92KLNqTmFCnG1pf8duUxFGwtP6AD6nkjw9a3IdiRtL3E2w3JDiE/xi3vOeA== + dependencies: + emoji-regex "^8.0.0" + is-fullwidth-code-point "^3.0.0" + strip-ansi "^6.0.0" + string-width@^4.1.0, string-width@^4.2.0: version "4.2.0" resolved "https://registry.yarnpkg.com/string-width/-/string-width-4.2.0.tgz#952182c46cc7b2c313d1596e623992bd163b72b5" @@ -4203,13 +4852,6 @@ string.prototype.trimstart@^1.0.3: call-bind "^1.0.0" define-properties "^1.1.3" -string_decoder@^1.1.1: - version "1.3.0" - resolved "https://registry.yarnpkg.com/string_decoder/-/string_decoder-1.3.0.tgz#42f114594a46cf1a8e30b0a84f56c78c3edac21e" - integrity sha512-hkRX8U1WjJFd8LsDJ2yQ/wWWxaopEsABU1XfkM8A+j0+85JAGppt16cr1Whg6KIbb4okU6Mql6BOj+uup/wKeA== - dependencies: - safe-buffer "~5.2.0" - string_decoder@~1.1.1: version "1.1.1" resolved "https://registry.yarnpkg.com/string_decoder/-/string_decoder-1.1.1.tgz#9cf1611ba62685d7030ae9e4ba34149c3af03fc8" @@ -4267,7 +4909,7 @@ supports-color@6.0.0: dependencies: has-flag "^3.0.0" -supports-color@^5.3.0: +supports-color@^5.3.0, supports-color@^5.5.0: version "5.5.0" resolved "https://registry.yarnpkg.com/supports-color/-/supports-color-5.5.0.tgz#e2e69a44ac8772f78a1ec0b35b689df6530efc8f" integrity sha512-QjVjwdXIt408MIiAqCX4oUKsgU2EqAGzs2Ppkm4aQYbjm+ZEWEcW4SfFNTr4uMNZma0ey4f5lgLrkB0aX0QMow== @@ -4281,15 +4923,22 @@ supports-color@^7.1.0: dependencies: has-flag "^4.0.0" -table@^5.4.6: - version "5.4.6" - resolved "https://registry.yarnpkg.com/table/-/table-5.4.6.tgz#1292d19500ce3f86053b05f0e8e7e4a3bb21079e" - integrity sha512-wmEc8m4fjnob4gt5riFRtTu/6+4rSe12TpAELNSqHMfF3IqnA+CH37USM6/YR3qRZv7e56kAEAtd6nKZaxe0Ug== +symbol-tree@^3.2.4: + version "3.2.4" + resolved "https://registry.yarnpkg.com/symbol-tree/-/symbol-tree-3.2.4.tgz#430637d248ba77e078883951fb9aa0eed7c63fa2" + integrity sha512-9QNk5KwDF+Bvz+PyObkmSYjI5ksVUYtjW7AU22r2NKcfLJcXp96hkDWU3+XndOsUb+AQ9QhfzfCT2O+CNWT5Tw== + +table@*, table@6.7.1: + version "6.7.1" + resolved "https://registry.yarnpkg.com/table/-/table-6.7.1.tgz#ee05592b7143831a8c94f3cee6aae4c1ccef33e2" + integrity sha512-ZGum47Yi6KOOFDE8m223td53ath2enHcYLgOCjGr5ngu8bdIARQk6mN/wRMv4yMRcHnCSnHbCEha4sobQx5yWg== dependencies: - ajv "^6.10.2" - lodash "^4.17.14" - slice-ansi "^2.1.0" - string-width "^3.0.0" + ajv "^8.0.1" + lodash.clonedeep "^4.5.0" + lodash.truncate "^4.4.2" + slice-ansi "^4.0.0" + string-width "^4.2.0" + strip-ansi "^6.0.0" table@^6.0.4: version "6.0.7" @@ -4301,24 +4950,6 @@ table@^6.0.4: slice-ansi "^4.0.0" string-width "^4.2.0" -tableschema@1.12.3: - version "1.12.3" - resolved "https://registry.yarnpkg.com/tableschema/-/tableschema-1.12.3.tgz#624f2919d98382099831a3fd86dcea4fcdb08c49" - integrity sha512-957Zzq8NKVAzv3fp33NXbmDOp+oj5NaGjoyb9ZAh4LLwNYRJZSaYvo5zkLO+3St/Tc7ECbeEdfX11cAYZZKgLw== - dependencies: - "@types/csv-parse" "^1.2.2" - axios "^0.19.2" - csv-parse "~4.8.8" - csv-sniffer "^0.1.1" - es6-error "^4.1.1" - lodash "^4.17.15" - moment "~2.24.0" - regenerator-runtime "^0.13.5" - stream-to-async-iterator "^0.2.0" - through2 "^3.0.1" - tv4 "^1.3.0" - validator "^13.0.0" - tar@^4.4.2, tar@^4.4.6: version "4.4.13" resolved "https://registry.yarnpkg.com/tar/-/tar-4.4.13.tgz#43b364bc52888d555298637b10d60790254ab525" @@ -4332,6 +4963,11 @@ tar@^4.4.2, tar@^4.4.6: safe-buffer "^5.1.2" yallist "^3.0.3" +term-size@^2.1.0: + version "2.2.1" + resolved "https://registry.yarnpkg.com/term-size/-/term-size-2.2.1.tgz#2a6a54840432c2fb6320fea0f415531e90189f54" + integrity sha512-wK0Ri4fOGjv/XPy8SBHZChl8CM7uMc5VML7SqiQ0zG7+J5Vr+RMQDoHa2CNT6KHUnTGIXH34UDMkPzAUyapBZg== + test-exclude@^6.0.0: version "6.0.0" resolved "https://registry.yarnpkg.com/test-exclude/-/test-exclude-6.0.0.tgz#04a8698661d805ea6fa293b6cb9e63ac044ef15e" @@ -4346,14 +4982,6 @@ text-table@^0.2.0: resolved "https://registry.yarnpkg.com/text-table/-/text-table-0.2.0.tgz#7f5ee823ae805207c00af2df4a84ec3fcfa570b4" integrity sha1-f17oI66AUgfACvLfSoTsP8+lcLQ= -through2@^3.0.1: - version "3.0.2" - resolved "https://registry.yarnpkg.com/through2/-/through2-3.0.2.tgz#99f88931cfc761ec7678b41d5d7336b5b6a07bf4" - integrity sha512-enaDQ4MUyP2W6ZyT6EsMzqBPZaM/avg8iuo+l2d3QCs0J+6RaqkHV/2/lOwDTueBHeJ/2LG9lrLW3d5rWPucuQ== - dependencies: - inherits "^2.0.4" - readable-stream "2 || 3" - tiny-emitter@^2.1.0: version "2.1.0" resolved "https://registry.yarnpkg.com/tiny-emitter/-/tiny-emitter-2.1.0.tgz#1d1a56edfc51c43e863cbb5382a72330e3555423" @@ -4371,6 +4999,11 @@ to-object-path@^0.3.0: dependencies: kind-of "^3.0.2" +to-readable-stream@^1.0.0: + version "1.0.0" + resolved "https://registry.yarnpkg.com/to-readable-stream/-/to-readable-stream-1.0.0.tgz#ce0aa0c2f3df6adf852efb404a783e77c0475771" + integrity sha512-Iq25XBt6zD5npPhlLVXGFN3/gyR2/qODcKNNyTMd4vbm39HUaOiAM4PMq0eMVC/Tkxz+Zjdsc55g9yyz+Yq00Q== + to-regex-range@^2.1.0: version "2.1.1" resolved "https://registry.yarnpkg.com/to-regex-range/-/to-regex-range-2.1.1.tgz#7c80c17b9dfebe599e27367e0d4dd5590141db38" @@ -4396,6 +5029,22 @@ to-regex@^3.0.1, to-regex@^3.0.2: regex-not "^1.0.2" safe-regex "^1.1.0" +touch@^3.1.0: + version "3.1.0" + resolved "https://registry.yarnpkg.com/touch/-/touch-3.1.0.tgz#fe365f5f75ec9ed4e56825e0bb76d24ab74af83b" + integrity sha512-WBx8Uy5TLtOSRtIq+M03/sKDrXCLHxwDcquSP2c43Le03/9serjQBIztjRz6FkJez9D/hleyAXTBGLwwZUw9lA== + dependencies: + nopt "~1.0.10" + +tough-cookie@^4.0.0: + version "4.0.0" + resolved "https://registry.yarnpkg.com/tough-cookie/-/tough-cookie-4.0.0.tgz#d822234eeca882f991f0f908824ad2622ddbece4" + integrity sha512-tHdtEpQCMrc1YLrMaqXXcj6AxhYi/xgit6mZu1+EDWUn+qhUf8wMQoFIy9NXuq23zAwtcB0t/MjACGR18pcRbg== + dependencies: + psl "^1.1.33" + punycode "^2.1.1" + universalify "^0.1.2" + tough-cookie@~2.5.0: version "2.5.0" resolved "https://registry.yarnpkg.com/tough-cookie/-/tough-cookie-2.5.0.tgz#cd9fb2a0aa1d5a12b473bd9fb96fa3dcff65ade2" @@ -4404,6 +5053,29 @@ tough-cookie@~2.5.0: psl "^1.1.28" punycode "^2.1.1" +tr46@^2.1.0: + version "2.1.0" + resolved "https://registry.yarnpkg.com/tr46/-/tr46-2.1.0.tgz#fa87aa81ca5d5941da8cbf1f9b749dc969a4e240" + integrity sha512-15Ih7phfcdP5YxqiB+iDtLoaTz4Nd35+IiAv0kQ5FNKHzXgdWqPoTIqEDDJmXceQt4JZk6lVPT8lnDlPpGDppw== + dependencies: + punycode "^2.1.1" + +ts-node@^10.0.0: + version "10.0.0" + resolved "https://registry.yarnpkg.com/ts-node/-/ts-node-10.0.0.tgz#05f10b9a716b0b624129ad44f0ea05dac84ba3be" + integrity sha512-ROWeOIUvfFbPZkoDis0L/55Fk+6gFQNZwwKPLinacRl6tsxstTF1DbAcLKkovwnpKMVvOMHP1TIbnwXwtLg1gg== + dependencies: + "@tsconfig/node10" "^1.0.7" + "@tsconfig/node12" "^1.0.7" + "@tsconfig/node14" "^1.0.0" + "@tsconfig/node16" "^1.0.1" + arg "^4.1.0" + create-require "^1.1.0" + diff "^4.0.1" + make-error "^1.1.1" + source-map-support "^0.5.17" + yn "3.1.1" + tunnel-agent@^0.6.0: version "0.6.0" resolved "https://registry.yarnpkg.com/tunnel-agent/-/tunnel-agent-0.6.0.tgz#27a5dea06b36b04a0a9966774b290868f0fc40fd" @@ -4411,11 +5083,6 @@ tunnel-agent@^0.6.0: dependencies: safe-buffer "^5.0.1" -tv4@^1.3.0: - version "1.3.0" - resolved "https://registry.yarnpkg.com/tv4/-/tv4-1.3.0.tgz#d020c846fadd50c855abb25ebaecc68fc10f7963" - integrity sha1-0CDIRvrdUMhVq7JeuuzGj8EPeWM= - tweetnacl@^0.14.3, tweetnacl@~0.14.0: version "0.14.5" resolved "https://registry.yarnpkg.com/tweetnacl/-/tweetnacl-0.14.5.tgz#5ae68177f192d4456269d108afa93ff8743f4f64" @@ -4428,6 +5095,13 @@ type-check@^0.4.0, type-check@~0.4.0: dependencies: prelude-ls "^1.2.1" +type-check@~0.3.2: + version "0.3.2" + resolved "https://registry.yarnpkg.com/type-check/-/type-check-0.3.2.tgz#5884cab512cf1d355e3fb784f30804b2b520db72" + integrity sha1-WITKtRLPHTVeP7eE8wgEsrUg23I= + dependencies: + prelude-ls "~1.1.2" + type-detect@^4.0.0, type-detect@^4.0.5: version "4.0.8" resolved "https://registry.yarnpkg.com/type-detect/-/type-detect-4.0.8.tgz#7646fb5f18871cfbb7749e69bd39a6388eb7450c" @@ -4450,6 +5124,23 @@ typedarray-to-buffer@^3.1.5: dependencies: is-typedarray "^1.0.0" +typescript@>=3.0.1: + version "4.4.3" + resolved "https://registry.yarnpkg.com/typescript/-/typescript-4.4.3.tgz#bdc5407caa2b109efd4f82fe130656f977a29324" + integrity sha512-4xfscpisVgqqDfPaJo5vkd+Qd/ItkoagnHpufr+i2QCHBsNYp+G7UAoyFl8aPtx879u38wPV65rZ8qbGZijalA== + +typescript@^4.4.2: + version "4.4.2" + resolved "https://registry.yarnpkg.com/typescript/-/typescript-4.4.2.tgz#6d618640d430e3569a1dfb44f7d7e600ced3ee86" + integrity sha512-gzP+t5W4hdy4c+68bfcv0t400HVJMMd2+H9B7gae1nQlBzCqvrXX+6GL/b3GAgyTH966pzrZ70/fRjwAtZksSQ== + +undefsafe@^2.0.3: + version "2.0.3" + resolved "https://registry.yarnpkg.com/undefsafe/-/undefsafe-2.0.3.tgz#6b166e7094ad46313b2202da7ecc2cd7cc6e7aae" + integrity sha512-nrXZwwXrD/T/JXeygJqdCO6NZZ1L66HrxM/Z7mIq2oPanoN0F1nLx3lwJMu6AwJY69hdixaFQOuoYsMjE5/C2A== + dependencies: + debug "^2.2.0" + unicode-canonical-property-names-ecmascript@^1.0.4: version "1.0.4" resolved "https://registry.yarnpkg.com/unicode-canonical-property-names-ecmascript/-/unicode-canonical-property-names-ecmascript-1.0.4.tgz#2619800c4c825800efdd8343af7dd9933cbe2818" @@ -4483,6 +5174,18 @@ union-value@^1.0.0: is-extendable "^0.1.1" set-value "^2.0.1" +unique-string@^2.0.0: + version "2.0.0" + resolved "https://registry.yarnpkg.com/unique-string/-/unique-string-2.0.0.tgz#39c6451f81afb2749de2b233e3f7c5e8843bd89d" + integrity sha512-uNaeirEPvpZWSgzwsPGtU2zVSTrn/8L5q/IexZmH0eH6SA73CmAA5U4GwORTxQAZs95TAXLNqeLoPPNO5gZfWg== + dependencies: + crypto-random-string "^2.0.0" + +universalify@^0.1.2: + version "0.1.2" + resolved "https://registry.yarnpkg.com/universalify/-/universalify-0.1.2.tgz#b646f69be3942dabcecc9d6639c80dc105efaa66" + integrity sha512-rBJeI5CXAlmy1pV+617WB9J63U6XcazHHF2f2dbJix4XzpUF0RS3Zbj0FGIOCAva5P/d/GBOYaACQ1w+0azUkg== + unset-value@^1.0.0: version "1.0.0" resolved "https://registry.yarnpkg.com/unset-value/-/unset-value-1.0.0.tgz#8376873f7d2335179ffb1e6fc3a8ed0dfc8ab559" @@ -4496,6 +5199,25 @@ upath@^1.1.1: resolved "https://registry.yarnpkg.com/upath/-/upath-1.2.0.tgz#8f66dbcd55a883acdae4408af8b035a5044c1894" integrity sha512-aZwGpamFO61g3OlfT7OQCHqhGnW43ieH9WZeP7QxN/G/jS4jfqUkZxoryvJgVPEcrl5NL/ggHsSmLMHuH64Lhg== +update-notifier@^4.1.0: + version "4.1.3" + resolved 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integrity sha512-jk1+QP6ZJqyOiuEI9AEWQfju/nB2Pw466kbA0LEZljHwKeMgd9WrAEgEGxjPDD2+TNbbb37rTyhEfrCXfuKXnA== +url-parse-lax@^3.0.0: + version "3.0.0" + resolved "https://registry.yarnpkg.com/url-parse-lax/-/url-parse-lax-3.0.0.tgz#16b5cafc07dbe3676c1b1999177823d6503acb0c" + integrity sha1-FrXK/Afb42dsGxmZF3gj1lA6yww= + dependencies: + prepend-http "^2.0.0" use@^3.1.0: version "3.1.1" resolved "https://registry.yarnpkg.com/use/-/use-3.1.1.tgz#d50c8cac79a19fbc20f2911f56eb973f4e10070f" integrity sha512-cwESVXlO3url9YWlFW/TA9cshCEhtu7IKJ/p5soJ/gGpj7vbvFrAY/eIioQ6Dw23KjZhYgiIo8HOs1nQ2vr/oQ== -util-deprecate@^1.0.1, util-deprecate@~1.0.1: +util-deprecate@~1.0.1: version "1.0.2" resolved "https://registry.yarnpkg.com/util-deprecate/-/util-deprecate-1.0.2.tgz#450d4dc9fa70de732762fbd2d4a28981419a0ccf" integrity sha1-RQ1Nyfpw3nMnYvvS1KKJgUGaDM8= @@ -4533,11 +5257,6 @@ v8-compile-cache@^2.0.3: resolved 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browser-process-hrtime "^1.0.0" + +w3c-xmlserializer@^2.0.0: + version "2.0.0" + resolved "https://registry.yarnpkg.com/w3c-xmlserializer/-/w3c-xmlserializer-2.0.0.tgz#3e7104a05b75146cc60f564380b7f683acf1020a" + integrity sha512-4tzD0mF8iSiMiNs30BiLO3EpfGLZUT2MSX/G+o7ZywDzliWQ3OPtTZ0PTC3B3ca1UAf4cJMHB+2Bf56EriJuRA== + dependencies: + xml-name-validator "^3.0.0" + +webidl-conversions@^5.0.0: + version "5.0.0" + resolved "https://registry.yarnpkg.com/webidl-conversions/-/webidl-conversions-5.0.0.tgz#ae59c8a00b121543a2acc65c0434f57b0fc11aff" + integrity sha512-VlZwKPCkYKxQgeSbH5EyngOmRp7Ww7I9rQLERETtf5ofd9pGeswWiOtogpEO850jziPRarreGxn5QIiTqpb2wA== + +webidl-conversions@^6.1.0: + version "6.1.0" + resolved "https://registry.yarnpkg.com/webidl-conversions/-/webidl-conversions-6.1.0.tgz#9111b4d7ea80acd40f5270d666621afa78b69514" + integrity sha512-qBIvFLGiBpLjfwmYAaHPXsn+ho5xZnGvyGvsarywGNc8VyQJUMHJ8OBKGGrPER0okBeMDaan4mNBlgBROxuI8w== + +whatwg-encoding@^1.0.5: + version "1.0.5" + resolved 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"https://registry.yarnpkg.com/which-module/-/which-module-2.0.0.tgz#d9ef07dce77b9902b8a3a8fa4b31c3e3f7e6e87a" @@ -4573,20 +5337,30 @@ wide-align@1.1.3, wide-align@^1.1.0: dependencies: string-width "^1.0.2 || 2" +widest-line@^3.1.0: + version "3.1.0" + resolved "https://registry.yarnpkg.com/widest-line/-/widest-line-3.1.0.tgz#8292333bbf66cb45ff0de1603b136b7ae1496eca" + integrity sha512-NsmoXalsWVDMGupxZ5R08ka9flZjjiLvHVAWYOKtiKM8ujtZWr9cRffak+uSE48+Ob8ObalXpwyeUiyDD6QFgg== + dependencies: + string-width "^4.0.0" + wmf@~1.0.1: version "1.0.2" resolved "https://registry.yarnpkg.com/wmf/-/wmf-1.0.2.tgz#7d19d621071a08c2bdc6b7e688a9c435298cc2da" integrity sha512-/p9K7bEh0Dj6WbXg4JG0xvLQmIadrner1bi45VMJTfnbVHsc7yIajZyoSoK60/dtVBs12Fm6WkUI5/3WAVsNMw== -word-wrap@^1.2.3: +word-wrap@^1.2.3, word-wrap@~1.2.3: version "1.2.3" resolved "https://registry.yarnpkg.com/word-wrap/-/word-wrap-1.2.3.tgz#610636f6b1f703891bd34771ccb17fb93b47079c" integrity sha512-Hz/mrNwitNRh/HUAtM/VT/5VH+ygD6DV7mYKZAtHOrbs8U7lvPS6xf7EJKMF0uW1KJCl0H701g3ZGus+muE5vQ== -word@~0.3.0: - version "0.3.0" - resolved "https://registry.yarnpkg.com/word/-/word-0.3.0.tgz#8542157e4f8e849f4a363a288992d47612db9961" - integrity sha512-OELeY0Q61OXpdUfTp+oweA/vtLVg5VDOXh+3he3PNzLGG/y0oylSOC1xRVj0+l4vQ3tj/bB1HVHv1ocXkQceFA== +word@~0.4.0: + version "0.4.0" + resolved "https://registry.yarnpkg.com/word/-/word-0.4.0.tgz#691272aaecb9da96388fc873d2ecb48a17cbee54" + integrity sha512-Aq84KjS7Z9HSU14qf4l/NEouaqfJAZtE9zEz7TIvw9V/3oJeUbjQwhz7ggqbL7I7REt4Bz+9HuCWsBO5N7xChw== + dependencies: + cfb "^1.2.0" + jsdom "^16.2.2" wrap-ansi@^5.1.0: version "5.1.0" @@ -4630,20 +5404,41 @@ write-file-atomic@^3.0.0: signal-exit "^3.0.2" typedarray-to-buffer "^3.1.5" -xlsx@0.16.7: - version "0.16.7" - resolved "https://registry.yarnpkg.com/xlsx/-/xlsx-0.16.7.tgz#62fd6590addac7c4419daaaa2b0c5388015d5f69" - integrity sha512-Xc4NRjci2Grbh9NDk/XoaWycJurxEug1wwn0aJCmB0NvIMyQuHYq2muWLWGidYNZPf94aUbqm6K8Fbjd7gKTZg== +ws@^7.4.6: + version "7.5.5" + resolved "https://registry.yarnpkg.com/ws/-/ws-7.5.5.tgz#8b4bc4af518cfabd0473ae4f99144287b33eb881" + integrity sha512-BAkMFcAzl8as1G/hArkxOxq3G7pjUqQ3gzYbLL0/5zNkph70e+lCoxBGnm6AW1+/aiNeV4fnKqZ8m4GZewmH2w== + +xdg-basedir@^4.0.0: + version "4.0.0" + resolved "https://registry.yarnpkg.com/xdg-basedir/-/xdg-basedir-4.0.0.tgz#4bc8d9984403696225ef83a1573cbbcb4e79db13" + integrity sha512-PSNhEJDejZYV7h50BohL09Er9VaIefr2LMAf3OEmpCkjOi34eYyQYAXUTjEQtZJTKcF0E2UKTh+osDLsgNim9Q== + +xlsx@^0.17.2: + version "0.17.2" + resolved "https://registry.yarnpkg.com/xlsx/-/xlsx-0.17.2.tgz#032bcca02ca01b5ebf7da93dcb035891f2c092af" + integrity sha512-RIhN6/oc/ZqYZqY4jz4AX92yNfULhtNrcZP1lknIcsyR+Ra8Zu/9F1lAZWncYbDex95iYQX/XNNNzNFXZjlNOQ== dependencies: adler-32 "~1.2.0" cfb "^1.1.4" - codepage "~1.14.0" + codepage "~1.15.0" commander "~2.17.1" crc-32 "~1.2.0" exit-on-epipe "~1.0.1" + fflate "^0.3.8" ssf "~0.11.2" wmf "~1.0.1" - word "~0.3.0" + word "~0.4.0" + +xml-name-validator@^3.0.0: + version "3.0.0" + resolved "https://registry.yarnpkg.com/xml-name-validator/-/xml-name-validator-3.0.0.tgz#6ae73e06de4d8c6e47f9fb181f78d648ad457c6a" + integrity sha512-A5CUptxDsvxKJEU3yO6DuWBSJz/qizqzJKOMIfUJHETbBw/sFaDxgd6fxm1ewUaM0jZ444Fc5vC5ROYurg/4Pw== + +xmlchars@^2.2.0: + version "2.2.0" + resolved "https://registry.yarnpkg.com/xmlchars/-/xmlchars-2.2.0.tgz#060fe1bcb7f9c76fe2a17db86a9bc3ab894210cb" + integrity sha512-JZnDKK8B0RCDw84FNdDAIpZK+JuJw+s7Lz8nksI7SIuU3UXJJslUthsi+uWBUYOwPFwW7W7PRLRfUKpxjtjFCw== y18n@^4.0.0: version "4.0.1" @@ -4711,7 +5506,7 @@ yargs@13.3.2, yargs@^13.3.0: y18n "^4.0.0" yargs-parser "^13.1.2" -yargs@^15.0.2: +yargs@^15.0.2, yargs@^15.3.1: version "15.4.1" resolved "https://registry.yarnpkg.com/yargs/-/yargs-15.4.1.tgz#0d87a16de01aee9d8bec2bfbf74f67851730f4f8" integrity sha512-aePbxDmcYW++PaqBsJ+HYUFwCdv4LVvdnhBy78E57PIor8/OVvhMrADFFEDh8DHDFRv/O9i3lPhsENjO7QX0+A== @@ -4745,3 +5540,8 @@ yarn@^1.22.10: version "1.22.10" resolved "https://registry.yarnpkg.com/yarn/-/yarn-1.22.10.tgz#c99daa06257c80f8fa2c3f1490724e394c26b18c" integrity sha512-IanQGI9RRPAN87VGTF7zs2uxkSyQSrSPsju0COgbsKQOOXr5LtcVPeyXWgwVa0ywG3d8dg6kSYKGBuYK021qeA== + +yn@3.1.1: + version "3.1.1" + resolved "https://registry.yarnpkg.com/yn/-/yn-3.1.1.tgz#1e87401a09d767c1d5eab26a6e4c185182d2eb50" + integrity sha512-Ux4ygGWsu2c7isFWe8Yu1YluJmqVhxqK2cLXNQA5AcC3QfbGNpM7fu0Y8b/z16pXLnFxZYvWhd3fhBY9DLmC6Q== diff --git a/yarn.lock b/yarn.lock new file mode 100644 index 00000000..fb57ccd1 --- /dev/null +++ b/yarn.lock @@ -0,0 +1,4 @@ +# THIS IS AN AUTOGENERATED FILE. DO NOT EDIT THIS FILE DIRECTLY. +# yarn lockfile v1 + +