ENH: Update Build Software (jb==1.04) and anaconda=2025.05

.github/workflows/cache.yml

@@ -14,7 +14,7 @@ jobs:
           auto-update-conda: true
           auto-activate-base: true
           miniconda-version: 'latest'
-          python-version: "3.12"
+          python-version: "3.13"
           environment-file: environment.yml
           activate-environment: quantecon
       - name: Build HTML

.github/workflows/ci.yml

@@ -11,7 +11,7 @@ jobs:
           auto-update-conda: true
           auto-activate-base: true
           miniconda-version: 'latest'
-          python-version: "3.12"
+          python-version: "3.13"
           environment-file: environment.yml
           activate-environment: quantecon
       - name: Install latex dependencies

.github/workflows/execution-linux.yml

@@ -11,7 +11,7 @@ jobs:
       fail-fast: false
       matrix:
         os: ["ubuntu-latest"]
-        python-version: ["3.12"]
+        python-version: ["3.13"]
     steps:
       - name: Checkout
         uses: actions/checkout@v4

.github/workflows/execution-osx.yml

@@ -11,7 +11,7 @@ jobs:
       fail-fast: false
       matrix:
         os: ["macos-latest"]
-        python-version: ["3.12"]
+        python-version: ["3.13"]
     steps:
       - name: Checkout
         uses: actions/checkout@v4

.github/workflows/execution-win.yml

@@ -11,7 +11,7 @@ jobs:
       fail-fast: false
       matrix:
         os: ["windows-latest"]
-        python-version: ["3.12"]
+        python-version: ["3.13"]
     steps:
       - name: Checkout
         uses: actions/checkout@v4

.github/workflows/publish.yml

@@ -16,7 +16,7 @@ jobs:
           auto-update-conda: true
           auto-activate-base: true
           miniconda-version: 'latest'
-          python-version: "3.12"
+          python-version: "3.13"
           environment-file: environment.yml
           activate-environment: quantecon
       - name: Install latex dependencies

.gitignore

@@ -7,6 +7,7 @@ dask-worker-space
 .ipynb_checkpoints/
 .virtual_documents/
 
+lectures/sine_wave.py
 lectures/mathfoo.py
 lectures/mod.py
 lectures/test.py

environment.yml

@@ -2,17 +2,16 @@ name: quantecon
 channels:
   - default
 dependencies:
-  - python=3.12
-  - anaconda=2024.10
+  - python=3.13
+  - anaconda=2025.06
   - pip
   - pip:
-    - jupyter-book==1.0.3
-    - quantecon-book-theme==0.8.2
-    - sphinx-tojupyter==0.3.0
+    - jupyter-book==1.0.4post1
+    - quantecon-book-theme==0.8.3
+    - sphinx-tojupyter==0.3.1
     - sphinxext-rediraffe==0.2.7
     - sphinx-exercise==1.0.1
-    - ghp-import==2.1.0
-    - sphinxcontrib-youtube==1.3.0  #Version 1.3.0 is required as quantecon-book-theme is only compatible with sphinx<=5
+    - sphinxcontrib-youtube==1.4.1
     - sphinx-togglebutton==0.3.2
 
 

lectures/getting_started.md

@@ -376,7 +376,7 @@ You will spend a lot of time debugging code, so it is important to [learn how to
 If you are using a newer version of Jupyter, you should see a bug icon on the right end of the toolbar.
 
 ```{figure} /_static/lecture_specific/getting_started/debug.png
-:scale: 80%
+:scale: 50%
 :figclass: auto
 ```
 

lectures/matplotlib.md

@@ -122,7 +122,7 @@ If everything is properly configured, then adding LaTeX is trivial
 
 ```{code-cell} python3
 fig, ax = plt.subplots()
-ax.plot(x, y, 'r-', linewidth=2, label='$y=\sin(x)$', alpha=0.6)
+ax.plot(x, y, 'r-', linewidth=2, label=r'$y=\sin(x)$', alpha=0.6)
 ax.legend(loc='upper center')
 plt.show()
 ```
@@ -131,7 +131,7 @@ Controlling the ticks, adding titles and so on is also straightforward
 
 ```{code-cell} python3
 fig, ax = plt.subplots()
-ax.plot(x, y, 'r-', linewidth=2, label='$y=\sin(x)$', alpha=0.6)
+ax.plot(x, y, 'r-', linewidth=2, label=r'$y=\sin(x)$', alpha=0.6)
 ax.legend(loc='upper center')
 ax.set_yticks([-1, 0, 1])
 ax.set_title('Test plot')
@@ -163,7 +163,7 @@ x = np.linspace(-4, 4, 150)
 for i in range(3):
     m, s = uniform(-1, 1), uniform(1, 2)
     y = norm.pdf(x, loc=m, scale=s)
-    current_label = f'$\mu = {m:.2}$'
+    current_label = rf'$\mu = {m:.2}$'
     ax.plot(x, y, linewidth=2, alpha=0.6, label=current_label)
 ax.legend()
 plt.show()
@@ -186,7 +186,7 @@ for i in range(num_rows):
         m, s = uniform(-1, 1), uniform(1, 2)
         x = norm.rvs(loc=m, scale=s, size=100)
         axes[i, j].hist(x, alpha=0.6, bins=20)
-        t = f'$\mu = {m:.2}, \quad \sigma = {s:.2}$'
+        t = rf'$\mu = {m:.2}, \quad \sigma = {s:.2}$'
         axes[i, j].set(title=t, xticks=[-4, 0, 4], yticks=[])
 plt.show()
 ```
@@ -419,7 +419,7 @@ x = np.linspace(-4, 4, 150)
 for i in range(3):
     m, s = uniform(-1, 1), uniform(1, 2)
     y = norm.pdf(x, loc=m, scale=s)
-    current_label = f'$\mu = {m:.2}$'
+    current_label = rf'$\mu = {m:.2}$'
     ax.plot(x, y, linewidth=2, alpha=0.6, label=current_label)
 ax.legend()
 plt.show()
@@ -462,8 +462,9 @@ Place all the curves in the same figure.
 
 The output should look like this
 
-```{figure} /_static/lecture_specific/matplotlib/matplotlib_ex1.png
+```{image} /_static/lecture_specific/matplotlib/matplotlib_ex1.png
 :scale: 130
+:align: center
 ```
 
 ```{exercise-end}

lectures/numba.md

@@ -563,7 +563,8 @@ Suppose that the volatility of returns on an asset can be in one of two regimes
 
 The transition probabilities across states are as follows
 
-```{figure} /_static/lecture_specific/sci_libs/nfs_ex1.png
+```{image} /_static/lecture_specific/sci_libs/nfs_ex1.png
+:align: center
 ```
 
 For example, let the period length be one day, and suppose the current state is high.

lectures/oop_intro.md

@@ -61,6 +61,7 @@ We'll make use of the following third party library
 
 
 ```{code-cell} python3
+:tags: [hide-output]
 !pip install rich
 ```
 

lectures/pandas.md

@@ -682,8 +682,9 @@ ticker = read_data(ticker_list)
 
 Complete the program to plot the result as a bar graph like this one:
 
-```{figure} /_static/lecture_specific/pandas/pandas_share_prices.png
+```{image} /_static/lecture_specific/pandas/pandas_share_prices.png
 :scale: 80
+:align: center
 ```
 
 ```{exercise-end}
@@ -748,8 +749,9 @@ indices_list = {'^GSPC': 'S&P 500',
 
 Complete the program to show summary statistics and plot the result as a time series graph like this one:
 
-```{figure} /_static/lecture_specific/pandas/pandas_indices_pctchange.png
+```{image} /_static/lecture_specific/pandas/pandas_indices_pctchange.png
 :scale: 80
+:align: center
 ```
 
 ```{exercise-end}

lectures/pandas_panel.md

@@ -150,7 +150,7 @@ the row index (`.unstack()` works in the opposite direction - try it
 out)
 
 ```{code-cell} ipython3
-realwage.stack().head()
+realwage.stack(future_stack=True).head()
 ```
 
 We can also pass in an argument to select the level we would like to
@@ -432,7 +432,7 @@ plt.show()
 summary statistics
 
 ```{code-cell} ipython3
-merged.stack().describe()
+merged.stack(future_stack=True).describe()
 ```
 
 This is a simplified way to use `groupby`.

lectures/workspace.md

@@ -3,10 +3,12 @@ jupytext:
   text_representation:
     extension: .md
     format_name: myst
+    format_version: 0.13
+    jupytext_version: 1.17.2
 kernelspec:
-  display_name: Python 3
-  language: python
   name: python3
+  display_name: Python 3 (ipykernel)
+  language: python
 ---
 
 (workspace)=
@@ -51,7 +53,7 @@ Python files are used when writing long, reusable blocks of code - by convention
 
 Let us begin by working with the following example.
 
-```{code-block} python
+```{code-cell} ipython3
 :caption: sine_wave.py
 :lineno-start: 1
 
@@ -68,17 +70,18 @@ plt.title('Sine Wave')
 plt.show()
 ```
 
-The code is first saved locally on the computer before it is executed. 
-
 As there are various ways to execute the code, we will explore them in the context of different development environments.
 
 One major advantage of using Python scripts lies in the fact that you can "import" functionality from other scripts into your current script or Jupyter Notebook. 
 
-Let's rewrite the earlier code into a function.
+Let's rewrite the earlier code into a function and write to to a file called `sine_wave.py`.
 
-```{code-block} python
+```{code-cell} ipython3
 :caption: sine_wave.py
 :lineno-start: 1
+
+%%writefile sine_wave.py
+
 import matplotlib.pyplot as plt
 import numpy as np
 
@@ -94,7 +97,7 @@ def plot_wave(title : str = 'Sine Wave'):
   plt.show()
 ```
 
-```{code-block} python
+```{code-cell} ipython3
 :caption: second_script.py
 :lineno-start: 1
 
@@ -345,9 +348,7 @@ In case you already haven't, try
 
 For example, if you've installed the command line version, open up a terminal and enter.
 
-```{code-block} bash
-:class: no-execute
-
+```bash
 git clone https://github.com/QuantEcon/QuantEcon.py
 ```
 (This is just `git clone` in front of the URL for the repository)