Inline plotting methods to deprecate plotting.py #508
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Important Review skippedAuto reviews are disabled on base/target branches other than the default branch. Please check the settings in the CodeRabbit UI or the You can disable this status message by setting the WalkthroughThe pull request refactors the plotting subsystem by consolidating color handling into a centralized Changes
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⚠️ Outside diff range comments (1)
flixopt/statistics_accessor.py (1)
820-851: Narrowcolorstype for heatmap to exclude dicts, or add explicit validationThe heatmap() implementation looks solid for managing dimension reshaping. However, there's a type safety issue with the
colorsparameter:
colorsis typed asColorType(which allowsdict[str, str]), but it's passed directly topx.imshowviacolor_continuous_scale, which only accepts a colorscale name (str) or a list of colors (list[str]). Dicts are not valid for continuous colorscales—they only work with categorical/discrete color parameters likecolor_discrete_map(used in other plot methods likeflows()and line plots).Passing a dict will fail at runtime. Either narrow the type signature to
str | list[str] | Nonein bothheatmap()and_heatmap_figure(), or add an explicit runtime check that rejects dicts with a clear error message.Also applies to: 888-889, 915-917
🧹 Nitpick comments (2)
flixopt/topology_accessor.py (1)
24-45: Align_plot_network’spathtype withTopologyAccessor.plotand avoid duplication
TopologyAccessor.plotacceptspath: bool | str | pathlib.Path = 'flow_system.html'and passes that straight through to_plot_network, whose signature ispath: str | pathlib.Path | None = None. At runtimepath=Falseworks (you hit theif not show and not path:early return), but the annotation and_plot_networkdocstring don't mention theFalsesentinel, so type-checkers will complain.Consider either:
- Widening the helper’s type to match the public API, e.g.:
-def _plot_network( - node_infos: dict, - edge_infos: dict, - path: str | pathlib.Path | None = None, +def _plot_network( + node_infos: dict, + edge_infos: dict, + path: bool | str | pathlib.Path | None = None,and documenting
Falseexplicitly, or
- Normalizing
pathinTopologyAccessor.plot(e.g., convertingFalsetoNone) before calling_plot_network.Also,
_plot_networklargely duplicates the existing implementation inflixopt.plotting.plot_network. To keep behavior in sync as you deprecateplotting.py, it would be safer to have one implementation delegate to the other (or share a single internal helper) instead of maintaining two copies.Also applies to: 76-99, 178-221
flixopt/statistics_accessor.py (1)
269-298: Use configured qualitative default when resolving colors for bars/linesIn
_create_stacked_barand_create_lineyou now do:variables = df['variable'].unique().tolist() color_map = process_colors(colors, variables)which is a good centralization of discrete color handling. However, this implicitly uses
process_colors’ defaultdefault_colorscale='turbo', while other plotting paths (e.g., inflixopt.plotting) explicitly route throughCONFIG.Plotting.default_qualitative_colorscale.For consistency with user-configurable plotting defaults, consider:
- color_map = process_colors(colors, variables) + color_map = process_colors( + colors, + variables, + default_colorscale=CONFIG.Plotting.default_qualitative_colorscale, + )and similarly in
_create_line. That way, changingCONFIG.Plotting.default_qualitative_colorscaleaffects all Plotly-express based bar/line plots in the same way.Also applies to: 300-326
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flixopt/clustering.py(4 hunks)flixopt/color_processing.py(1 hunks)flixopt/plotting.py(1 hunks)flixopt/statistics_accessor.py(23 hunks)flixopt/topology_accessor.py(2 hunks)tests/test_solution_and_plotting.py(1 hunks)
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flixopt/plotting.py (1)
flixopt/color_processing.py (1)
process_colors(113-181)
flixopt/statistics_accessor.py (2)
flixopt/color_processing.py (1)
process_colors(113-181)flixopt/config.py (2)
CONFIG(181-814)Plotting(547-576)
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🔇 Additional comments (4)
flixopt/plotting.py (1)
42-42: CentralizingColorTypeandprocess_colorsimport looks goodImporting
ColorTypeandprocess_colorsfromflixopt.color_processingremoves the local alias and keeps color handling definitions in one place, which should make future changes to color semantics easier to manage.tests/test_solution_and_plotting.py (1)
350-356: Heatmap test update matches the new pre-reshaped APIReshaping
long_time_datawithplotting.reshape_data_for_heatmap(..., reshape_time=('D', 'h'))before callingheatmap_with_plotly_v2correctly reflects that v2 expects already reshaped input. The assertions remain valid and the test now follows the intended usage.flixopt/statistics_accessor.py (2)
51-119: New heatmap helpers_reshape_time_for_heatmap/_heatmap_figurelook solidThe new
_reshape_time_for_heatmapand_heatmap_figurehelpers cleanly encapsulate the time-reshaping andpx.imshowwiring that was previously embedded in plotting code. The mapping from(outer_freq, inner_freq)toperiod_format/step_format, resampling, label construction, and reordering to('timestep', 'timeframe', *other_dims)all look consistent with the rest of the codebase. Empty-data handling and facet/animation checks in_heatmap_figureare also handled gracefully. No issues here.Also applies to: 121-166
740-769: ColorType-basedcolorsparameters across statistics plots are cohesiveUpdating the plotting methods on
StatisticsPlotAccessor(balance,flows,sankey,sizes,duration_curve,effects) to acceptcolors: ColorTypeand then feed that throughprocess_colors(or, for Sankey, onto nodes) gives a consistent and flexible color API: callers can pass a colorscale name, a list, or a mapping, and you resolve it once. The existing behavior forshowflags and facet dimensions is preserved. From a public API standpoint this is an improvement and looks good.Also applies to: 930-959, 1128-1188, 1189-1215, 1267-1295, 1375-1395
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Guard Clustering.plot against unset aggregated_data and consider more robust color mapping
Clustering.__init__ sets self.aggregated_data = None, and cluster() is the only method that populates it. In plot(), you call self.aggregated_data.copy() unconditionally, so calling plot() before cluster() will fail with an AttributeError instead of a clear error message.
You can make this failure mode explicit and user-friendly:
def plot(self, colormap: str | None = None, show: bool = True, save: pathlib.Path | None = None) -> go.Figure:
- import plotly.express as px
+ import plotly.express as px
+
+ if self.aggregated_data is None:
+ raise RuntimeError("Clustering.plot() requires aggregated data. Call cluster() before plot().")Separately, you derive colors via:
colors = list(
process_colors(colormap or CONFIG.Plotting.default_qualitative_colorscale, list(df_org.columns)).values()
)and pass that as color_discrete_sequence to both the original and aggregated plots. This assumes the aggregated dataframe has the same column order as the original to keep color pairing between “Original - X” and “Aggregated - X”, which is a bit fragile.
If you want stronger guarantees that each original/aggregated pair shares a color regardless of column ordering, consider building a color_discrete_map keyed by the full series names (or by a shared base label) and passing that into px.line instead of relying solely on sequence position.
Also applies to: 148-183
🤖 Prompt for AI Agents
In flixopt/clustering.py around lines 31 and 148-183, plot() currently calls
self.aggregated_data.copy() unguarded and derives colors as a sequence which
assumes identical column ordering; first add an explicit guard at the top of
plot() that checks if self.aggregated_data is None and raise a clear ValueError
(or return) telling the user to call cluster() first; second, replace the
positional color_discrete_sequence approach with a deterministic
color_discrete_map: generate a mapping from the actual full series names used in
the plot (e.g., original column names and their corresponding aggregated labels
like "Aggregated - X") to colors produced by process_colors (using the provided
colormap or default), and pass this map into px.line via color_discrete_map so
each original/aggregated pair always shares the same color regardless of column
ordering.
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| # Type alias for flexible color input | ||
| ColorType = str | list[str] | dict[str, str] | None | ||
| ColorType = str | list[str] | dict[str, str] | ||
| """Flexible color specification type supporting multiple input formats for visualization. | ||
| Color specifications can take several forms to accommodate different use cases: | ||
| **Named colorscales** (str): | ||
| - Standard colorscales: 'turbo', 'plasma', 'cividis', 'tab10', 'Set1' | ||
| - Energy-focused: 'portland' (custom flixopt colorscale for energy systems) | ||
| - Backend-specific maps available in Plotly and Matplotlib | ||
| **Color Lists** (list[str]): | ||
| - Explicit color sequences: ['red', 'blue', 'green', 'orange'] | ||
| - HEX codes: ['#FF0000', '#0000FF', '#00FF00', '#FFA500'] | ||
| - Mixed formats: ['red', '#0000FF', 'green', 'orange'] | ||
| **Label-to-Color Mapping** (dict[str, str]): | ||
| - Explicit associations: {'Wind': 'skyblue', 'Solar': 'gold', 'Gas': 'brown'} | ||
| - Ensures consistent colors across different plots and datasets | ||
| - Ideal for energy system components with semantic meaning | ||
| Examples: | ||
| ```python | ||
| # Named colorscale | ||
| colors = 'turbo' # Automatic color generation | ||
| # Explicit color list | ||
| colors = ['red', 'blue', 'green', '#FFD700'] | ||
| # Component-specific mapping | ||
| colors = { | ||
| 'Wind_Turbine': 'skyblue', | ||
| 'Solar_Panel': 'gold', | ||
| 'Natural_Gas': 'brown', | ||
| 'Battery': 'green', | ||
| 'Electric_Load': 'darkred' | ||
| } | ||
| ``` | ||
| Color Format Support: | ||
| - **Named Colors**: 'red', 'blue', 'forestgreen', 'darkorange' | ||
| - **HEX Codes**: '#FF0000', '#0000FF', '#228B22', '#FF8C00' | ||
| - **RGB Tuples**: (255, 0, 0), (0, 0, 255) [Matplotlib only] | ||
| - **RGBA**: 'rgba(255,0,0,0.8)' [Plotly only] | ||
| References: | ||
| - HTML Color Names: https://htmlcolorcodes.com/color-names/ | ||
| - Matplotlib colorscales: https://matplotlib.org/stable/tutorials/colors/colorscales.html | ||
| - Plotly Built-in Colorscales: https://plotly.com/python/builtin-colorscales/ | ||
| """ |
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Fix ColorType alias shadowing and keep None in the type
ColorType is defined twice and the second assignment drops None, so the effective alias is str | list[str] | dict[str, str]. This conflicts with both process_colors (which explicitly accepts None) and multiple call sites that use colors: ColorType = None or ColorType | None. It will confuse type-checkers and makes those defaults formally invalid.
You can fix this by keeping a single alias that includes None and removing the duplicate line:
-# Type alias for flexible color input
-ColorType = str | list[str] | dict[str, str] | None
-ColorType = str | list[str] | dict[str, str]
+# Type alias for flexible color input
+ColorType = str | list[str] | dict[str, str] | NoneOptionally, you may also mention the None case explicitly in the accompanying documentation to match the actual type.
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| # Type alias for flexible color input | |
| ColorType = str | list[str] | dict[str, str] | None | |
| ColorType = str | list[str] | dict[str, str] | |
| """Flexible color specification type supporting multiple input formats for visualization. | |
| Color specifications can take several forms to accommodate different use cases: | |
| **Named colorscales** (str): | |
| - Standard colorscales: 'turbo', 'plasma', 'cividis', 'tab10', 'Set1' | |
| - Energy-focused: 'portland' (custom flixopt colorscale for energy systems) | |
| - Backend-specific maps available in Plotly and Matplotlib | |
| **Color Lists** (list[str]): | |
| - Explicit color sequences: ['red', 'blue', 'green', 'orange'] | |
| - HEX codes: ['#FF0000', '#0000FF', '#00FF00', '#FFA500'] | |
| - Mixed formats: ['red', '#0000FF', 'green', 'orange'] | |
| **Label-to-Color Mapping** (dict[str, str]): | |
| - Explicit associations: {'Wind': 'skyblue', 'Solar': 'gold', 'Gas': 'brown'} | |
| - Ensures consistent colors across different plots and datasets | |
| - Ideal for energy system components with semantic meaning | |
| Examples: | |
| ```python | |
| # Named colorscale | |
| colors = 'turbo' # Automatic color generation | |
| # Explicit color list | |
| colors = ['red', 'blue', 'green', '#FFD700'] | |
| # Component-specific mapping | |
| colors = { | |
| 'Wind_Turbine': 'skyblue', | |
| 'Solar_Panel': 'gold', | |
| 'Natural_Gas': 'brown', | |
| 'Battery': 'green', | |
| 'Electric_Load': 'darkred' | |
| } | |
| ``` | |
| Color Format Support: | |
| - **Named Colors**: 'red', 'blue', 'forestgreen', 'darkorange' | |
| - **HEX Codes**: '#FF0000', '#0000FF', '#228B22', '#FF8C00' | |
| - **RGB Tuples**: (255, 0, 0), (0, 0, 255) [Matplotlib only] | |
| - **RGBA**: 'rgba(255,0,0,0.8)' [Plotly only] | |
| References: | |
| - HTML Color Names: https://htmlcolorcodes.com/color-names/ | |
| - Matplotlib colorscales: https://matplotlib.org/stable/tutorials/colors/colorscales.html | |
| - Plotly Built-in Colorscales: https://plotly.com/python/builtin-colorscales/ | |
| """ | |
| # Type alias for flexible color input | |
| ColorType = str | list[str] | dict[str, str] | None | |
| """Flexible color specification type supporting multiple input formats for visualization. | |
| Color specifications can take several forms to accommodate different use cases: | |
| **Named colorscales** (str): | |
| - Standard colorscales: 'turbo', 'plasma', 'cividis', 'tab10', 'Set1' | |
| - Energy-focused: 'portland' (custom flixopt colorscale for energy systems) | |
| - Backend-specific maps available in Plotly and Matplotlib | |
| **Color Lists** (list[str]): | |
| - Explicit color sequences: ['red', 'blue', 'green', 'orange'] | |
| - HEX codes: ['#FF0000', '#0000FF', '#00FF00', '#FFA500'] | |
| - Mixed formats: ['red', '#0000FF', 'green', 'orange'] | |
| **Label-to-Color Mapping** (dict[str, str]): | |
| - Explicit associations: {'Wind': 'skyblue', 'Solar': 'gold', 'Gas': 'brown'} | |
| - Ensures consistent colors across different plots and datasets | |
| - Ideal for energy system components with semantic meaning | |
| Examples: |
🤖 Prompt for AI Agents
In flixopt/color_processing.py around lines 18 to 68, the ColorType alias is
declared twice and the second declaration drops None, causing type shadowing and
making defaults like colors: ColorType = None invalid; remove the duplicate line
and keep a single ColorType definition that includes None (e.g., ColorType = str
| list[str] | dict[str, str] | None), and update the docstring wording if
desired to explicitly mention that None is an allowed value.
…820-853) - Changed _heatmap_figure() parameter type from colors: ColorType = None to colors: str | list[str] | None = None - Changed heatmap() method parameter type similarly - Updated docstrings to clarify that dicts are not supported for heatmaps since px.imshow's color_continuous_scale only accepts colorscale names or lists 2. statistics_accessor.py - Use configured qualitative colorscale (lines 284, 315) - Updated _create_stacked_bar() to use CONFIG.Plotting.default_qualitative_colorscale as the default colorscale - Updated _create_line() similarly - This ensures user-configured CONFIG.Plotting.default_qualitative_colorscale affects all bar/line plots consistently 3. topology_accessor.py - Path type alignment (lines 219-222) - Added normalization of path=False to None before calling _plot_network() - This resolves the type mismatch where TopologyAccessor.plot() accepts bool | str | Path but _plot_network() only accepts str | Path | None
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* Add planning doc
* Finalize planning
* Add plotting acessor
* Add plotting acessor
* Add tests
* Improve
* Improve
* Update docs
* Updated the plotting API so that .data always returns xarray DataArray or Dataset instead of pandas DataFrame.
* All .data now returns xr.Dataset consistently.
* Fixed Inconsistencies and Unused Parameters
* New Plot Accessors
results.plot.variable(pattern)
Plots the same variable type across multiple elements for easy comparison.
# All binary operation states across all components
results.plot.variable('on')
# All flow_rates, filtered to Boiler-related elements
results.plot.variable('flow_rate', include='Boiler')
# All storage charge states
results.plot.variable('charge_state')
# With aggregation
results.plot.variable('flow_rate', aggregate='sum')
Key features:
- Searches all elements for variables matching the pattern
- Filter with include/exclude on element names
- Supports aggregation, faceting, and animation
- Returns Dataset with element names as variables
results.plot.duration_curve(variables)
Plots load duration curves (sorted time series) showing utilization patterns.
# Single variable
results.plot.duration_curve('Boiler(Q_th)|flow_rate')
# Multiple variables
results.plot.duration_curve(['CHP|on', 'Boiler|on'])
# Normalized x-axis (0-100%)
results.plot.duration_curve('demand', normalize=True)
Key features:
- Sorts values from highest to lowest
- Shows how often each power level is reached
- normalize=True shows percentage of time on x-axis
- Returns Dataset with duration_hours or duration_pct dimension
* Fix duration curve
* Fix duration curve
* Fix duration curve
* Fix duration curve
* xr.apply_ufunc to sort along the time axis while preserving all other dimensions automatically
* ⏺ The example runs successfully. Now let me summarize the fixes:
Summary of Fixes
I addressed the actionable code review comments from CodeRabbitAI:
1. Documentation Issue - reshape parameter name ✓ (No fix needed)
The CodeRabbitAI comment was incorrect. The public API parameter in PlotAccessor.heatmap() is correctly named reshape (line 335). The reshape_time parameter exists in the lower-level heatmap_with_plotly function, but the documentation correctly shows the public API parameter.
2. Fixed simple_example.py (lines 129-130)
Problem: The example called balance() and duration_curve() without required arguments, which would cause TypeError at runtime.
Fix: Added the required arguments:
- optimization.results.plot.balance('Fernwärme') - specifying the bus to plot
- optimization.results.plot.duration_curve('Boiler(Q_th)|flow_rate') - specifying the variable to plot
3. Fixed variable collision in plot_accessors.py (lines 985-988)
Problem: When building the Dataset in the variable() method, using element names as keys could cause collisions if multiple variables from the same element matched the pattern (e.g., 'Boiler|flow_rate' and 'Boiler|flow_rate_max' would both map to 'Boiler', with the latter silently overwriting the former).
Fix: Changed to use the full variable names as keys instead of just element names:
ds = xr.Dataset({var_name: self._results.solution[var_name] for var_name in filtered_vars})
All tests pass (40 passed, 1 skipped) and the example runs successfully.
* make variable names public in results
* Fix sankey
* Fix effects()
* Fix effects
* Remove bargaps
* made faceting consistent across all plot methods:
| Method | facet_col | facet_row |
|------------------|-------------------------------------------|-----------------------------|
| balance() | 'scenario' | 'period' |
| heatmap() | 'scenario' | 'period' |
| storage() | 'scenario' | 'period' |
| flows() | 'scenario' | 'period' |
| effects() | 'scenario' | 'period' |
| variable() | 'scenario' | 'period' |
| duration_curve() | 'scenario' | 'period' (already had this) |
| compare() | N/A (uses its own mode='overlay'/'facet') | |
| sankey() | N/A (aggregates to single diagram) | |
Removed animate_by parameter from all methods
* Update storage method
* Remove mode parameter for simpli
| Method | Default mode |
|------------------|---------------------------------------------------|
| balance() | stacked_bar |
| storage() | stacked_bar (flows) + line (charge state overlay) |
| flows() | line |
| variable() | line |
| duration_curve() | line |
| effects() | bar |
* Make plotting_accessors.py more self contained
* Use faster to_long()
* Add 0-dim case
* sankey diagram now properly handles scenarios and periods:
Changes made:
1. Period aggregation with weights: Uses period_weights from flow_system to properly weight periods by their duration
2. Scenario aggregation with weights: Uses normalized scenario_weights to compute a weighted average across scenarios
3. Selection support: Users can filter specific scenarios/periods via select parameter before aggregation
Weighting logic:
- Periods (for aggregate='sum'): (da * period_weights).sum(dim='period') - this gives the total energy across all periods weighted by their duration
- Periods (for aggregate='mean'): (da * period_weights).sum(dim='period') / period_weights.sum() - weighted mean
- Scenarios: Always uses normalized weights (sum to 1) for weighted averaging, since scenarios represent probability-weighted alternatives
* Add colors to sankey
* Add sizes
* Add size filtering
* Include storage sizes
* Remove storage sizes
* Add charge state and status accessor
* Summary of Changes
1. Added new methods to PlotAccessor (plot_accessors.py)
charge_states() (line 658):
- Returns a Dataset with each storage's charge state as a variable
- Supports filtering with include/exclude parameters
- Default plot: line chart
on_states() (line 753):
- Returns a Dataset with each component's |status variable
- Supports filtering with include/exclude parameters
- Default plot: heatmap (good for binary data visualization)
2. Added data building helper functions (plot_accessors.py)
build_flow_rates(results) (line 315):
- Builds a DataArray containing flow rates for all flows
- Used internally by PlotAccessor methods
build_flow_hours(results) (line 333):
- Builds a DataArray containing flow hours for all flows
build_sizes(results) (line 347):
- Builds a DataArray containing sizes for all flows
_filter_dataarray_by_coord(da, **kwargs) (line 284):
- Helper for filtering DataArrays by coordinate values
3. Deprecated old Results methods (results.py)
The following methods now emit DeprecationWarning:
- results.flow_rates() → Use results.plot.flows(plot=False).data
- results.flow_hours() → Use results.plot.flows(unit='flow_hours', plot=False).data
- results.sizes() → Use results.plot.sizes(plot=False).data
4. Updated PlotAccessor methods to use new helpers
- flows() now uses build_flow_rates() / build_flow_hours() directly
- sizes() now uses build_sizes() directly
- sankey() now uses build_flow_hours() directly
This ensures the deprecation warnings only fire when users directly call the old methods, not when using the plot accessor
* 1. New methods added to PlotAccessor
- charge_states(): Returns Dataset with all storage charge states
- on_states(): Returns Dataset with all component status variables (heatmap display)
2. Data building helper functions (plot_accessors.py)
- build_flow_rates(results): Builds DataArray of flow rates
- build_flow_hours(results): Builds DataArray of flow hours
- build_sizes(results): Builds DataArray of sizes
- _filter_dataarray_by_coord(da, **kwargs): Filter helper
- _assign_flow_coords(da, results): Add flow coordinates
3. Caching in PlotAccessor
Added lazy-cached properties for expensive computations:
- _all_flow_rates - cached DataArray of all flow rates
- _all_flow_hours - cached DataArray of all flow hours
- _all_sizes - cached DataArray of all sizes
- _all_charge_states - cached Dataset of all storage charge states
- _all_status_vars - cached Dataset of all status variables
4. Deprecated methods in Results class
Added deprecation warnings to:
- results.flow_rates() → Use results.plot.flows(plot=False).data
- results.flow_hours() → Use results.plot.flows(unit='flow_hours', plot=False).data
- results.sizes() → Use results.plot.sizes(plot=False).data
5. Updated PlotAccessor methods to use cached properties
- flows() uses _all_flow_rates / _all_flow_hours
- sankey() uses _all_flow_hours
- sizes() uses _all_sizes
- charge_states() uses _all_charge_states
- on_states() uses _all_status_vars
* Move deprectated functionality into results.py instead of porting to the new module
* Revert to simply deprectae old methods without forwarding to new code
* Remove planning file
* Update plotting methods for new datasets
* 1. Renamed data properties in PlotAccessor to use all_ prefix:
- all_flow_rates - All flow rates as Dataset
- all_flow_hours - All flow hours as Dataset
- all_sizes - All flow sizes as Dataset
- all_charge_states - All storage charge states as Dataset
- all_on_states - All component on/off status as Dataset
2. Updated internal references - All usages in flows(), sankey(), sizes(), charge_states(), and on_states() methods now use the new names.
3. Updated deprecation messages in results.py to point to the new API:
- results.flow_rates() → results.plot.all_flow_rates
- results.flow_hours() → results.plot.all_flow_hours
- results.sizes() → results.plot.all_sizes
4. Updated docstring examples in PlotAccessor to use the new all_* names.
* Update deprecations messages
* Update deprecations messages
* Thsi seems much better.
* Updaet docstrings and variable name generation in plotting acessor
* Change __ to _ in private dataset caching
* Revert breaking io changes
* New solution storing interface
* Add new focused statistics and plot accessors
* Renamed all properties:
- all_flow_rates → flow_rates
- all_flow_hours → flow_hours
- all_sizes → sizes
- all_charge_states → charge_states
* Cache Statistics
* Invalidate caches
* Add effect related statistics
* Simplify statistics accessor to rely on flow_system directly instead of solution attrs
* Fix heatma fallback for 1D Data
* Add topology accessor
* All deprecation warnings in the codebase now consistently use the format will be removed in v{DEPRECATION_REMOVAL_VERSION}.
* Update tests
* created comprehensive documentation for all FlowSystem accessors
* Update results documentation
* Update results documentation
* Update effect statistics
* Update effect statistics
* Update effect statistics
* Add mkdocs plotly plugin
* Add section about custom constraints
* documentation updates:
docs/user-guide/results/index.md:
- Updated table to replace effects_per_component with temporal_effects, periodic_effects, total_effects, and effect_share_factors
- Fixed flow_hours['Boiler(Q_th)|flow_rate'] → flow_hours['Boiler(Q_th)']
- Fixed sizes['Boiler(Q_th)|size'] → sizes['Boiler(Q_th)']
- Replaced effects_per_component example with new effect properties and groupby examples
- Updated complete example to use total_effects
docs/user-guide/results-plotting.md:
- Fixed colors example from 'Boiler(Q_th)|flow_rate' → 'Boiler(Q_th)'
- Fixed duration_curve examples to use clean labels
docs/user-guide/migration-guide-v6.md:
- Added new "Statistics Accessor" section explaining the clean labels and new effect properties
* implemented the effects() method in StatisticsPlotAccessor at flixopt/statistics_accessor.py:1132-1258.
Summary of what was done:
1. Implemented effects() method in StatisticsPlotAccessor class that was missing but documented
- Takes aspect parameter: 'total', 'temporal', or 'periodic'
- Takes effect parameter to filter to a specific effect (e.g., 'costs', 'CO2')
- Takes by parameter: 'component' or 'time' for grouping
- Supports all standard plotting parameters: select, colors, facet_col, facet_row, show
- Returns PlotResult with both data and figure
2. Verified the implementation works with all parameter combinations:
- Default call: flow_system.statistics.plot.effects()
- Specific effect: flow_system.statistics.plot.effects(effect='costs')
- Temporal aspect: flow_system.statistics.plot.effects(aspect='temporal')
- Temporal by time: flow_system.statistics.plot.effects(aspect='temporal', by='time')
- Periodic aspect: flow_system.statistics.plot.effects(aspect='periodic')
* Remove intermediate plot accessor
* 1. pyproject.toml: Removed duplicate mkdocs-plotly-plugin>=0.1.3 entry (kept the exact pin ==0.1.3)
2. flixopt/plotting.py: Fixed dimension name consistency by using squeezed_data.name instead of data.name in the fallback heatmap logic
3. flixopt/statistics_accessor.py:
- Fixed _dataset_to_long_df() to only use coordinates that are actually present as columns after reset_index()
- Fixed the nested loop inefficiency with include_flows by pre-computing the flows list outside the loop
- (Previously fixed) Fixed asymmetric NaN handling in validation check
* _create_effects_dataset method in statistics_accessor.py was simplified:
1. Detect contributors from solution data variables instead of assuming they're only flows
- Uses regex pattern to find {contributor}->{effect}(temporal|periodic) variables
- Contributors can be flows OR components (e.g., components with effects_per_active_hour)
2. Exclude effect-to-effect shares
- Filters out contributors whose base name matches any effect label
- For example, costs(temporal) is excluded because costs is an effect label
- These intermediate shares are already included in the computation
3. Removed the unused _compute_effect_total method
- The new simplified implementation directly looks up shares from the solution
- Uses effect_share_factors for conversion between effects
4. Key insight from user: The solution already contains properly computed share values including all effect-to-effect conversions. The computation uses conversion
factors because derived effects (like Effect1 which shares 0.5 from costs) don't have direct {flow}->Effect1(temporal) variables - only the source effect shares exist
({flow}->costs(temporal)).
* Update docs
* Improve to_netcdf method
* Update examples
* Fix IIS computaion flag
* Fix examples
* Fix faceting in heatmap and use period as facet col everywhere
* Inline plotting methods to deprecate plotting.py (#508)
* Inline plotting methods to deprecate plotting.py
* Fix test
* Simplify Color Management
* ColorType is now defined in color_processing.py and imported into statistics_accessor.py.
* Fix ColorType typing
* statistics_accessor.py - Heatmap colors type safety (lines 121-148, 820-853)
- Changed _heatmap_figure() parameter type from colors: ColorType = None to colors: str | list[str] | None = None
- Changed heatmap() method parameter type similarly
- Updated docstrings to clarify that dicts are not supported for heatmaps since px.imshow's color_continuous_scale only accepts colorscale names or lists
2. statistics_accessor.py - Use configured qualitative colorscale (lines 284, 315)
- Updated _create_stacked_bar() to use CONFIG.Plotting.default_qualitative_colorscale as the default colorscale
- Updated _create_line() similarly
- This ensures user-configured CONFIG.Plotting.default_qualitative_colorscale affects all bar/line plots consistently
3. topology_accessor.py - Path type alignment (lines 219-222)
- Added normalization of path=False to None before calling _plot_network()
- This resolves the type mismatch where TopologyAccessor.plot() accepts bool | str | Path but _plot_network() only accepts str | Path | None
* fix usage if index name in aggregation plot
FBumann
added a commit
that referenced
this pull request
Dec 8, 2025
* Add planning doc
* Finalize planning
* Add plotting acessor
* Add plotting acessor
* Add tests
* Improve
* Improve
* Update docs
* Updated the plotting API so that .data always returns xarray DataArray or Dataset instead of pandas DataFrame.
* All .data now returns xr.Dataset consistently.
* Fixed Inconsistencies and Unused Parameters
* New Plot Accessors
results.plot.variable(pattern)
Plots the same variable type across multiple elements for easy comparison.
# All binary operation states across all components
results.plot.variable('on')
# All flow_rates, filtered to Boiler-related elements
results.plot.variable('flow_rate', include='Boiler')
# All storage charge states
results.plot.variable('charge_state')
# With aggregation
results.plot.variable('flow_rate', aggregate='sum')
Key features:
- Searches all elements for variables matching the pattern
- Filter with include/exclude on element names
- Supports aggregation, faceting, and animation
- Returns Dataset with element names as variables
results.plot.duration_curve(variables)
Plots load duration curves (sorted time series) showing utilization patterns.
# Single variable
results.plot.duration_curve('Boiler(Q_th)|flow_rate')
# Multiple variables
results.plot.duration_curve(['CHP|on', 'Boiler|on'])
# Normalized x-axis (0-100%)
results.plot.duration_curve('demand', normalize=True)
Key features:
- Sorts values from highest to lowest
- Shows how often each power level is reached
- normalize=True shows percentage of time on x-axis
- Returns Dataset with duration_hours or duration_pct dimension
* Fix duration curve
* Fix duration curve
* Fix duration curve
* Fix duration curve
* xr.apply_ufunc to sort along the time axis while preserving all other dimensions automatically
* ⏺ The example runs successfully. Now let me summarize the fixes:
Summary of Fixes
I addressed the actionable code review comments from CodeRabbitAI:
1. Documentation Issue - reshape parameter name ✓ (No fix needed)
The CodeRabbitAI comment was incorrect. The public API parameter in PlotAccessor.heatmap() is correctly named reshape (line 335). The reshape_time parameter exists in the lower-level heatmap_with_plotly function, but the documentation correctly shows the public API parameter.
2. Fixed simple_example.py (lines 129-130)
Problem: The example called balance() and duration_curve() without required arguments, which would cause TypeError at runtime.
Fix: Added the required arguments:
- optimization.results.plot.balance('Fernwärme') - specifying the bus to plot
- optimization.results.plot.duration_curve('Boiler(Q_th)|flow_rate') - specifying the variable to plot
3. Fixed variable collision in plot_accessors.py (lines 985-988)
Problem: When building the Dataset in the variable() method, using element names as keys could cause collisions if multiple variables from the same element matched the pattern (e.g., 'Boiler|flow_rate' and 'Boiler|flow_rate_max' would both map to 'Boiler', with the latter silently overwriting the former).
Fix: Changed to use the full variable names as keys instead of just element names:
ds = xr.Dataset({var_name: self._results.solution[var_name] for var_name in filtered_vars})
All tests pass (40 passed, 1 skipped) and the example runs successfully.
* make variable names public in results
* Fix sankey
* Fix effects()
* Fix effects
* Remove bargaps
* made faceting consistent across all plot methods:
| Method | facet_col | facet_row |
|------------------|-------------------------------------------|-----------------------------|
| balance() | 'scenario' | 'period' |
| heatmap() | 'scenario' | 'period' |
| storage() | 'scenario' | 'period' |
| flows() | 'scenario' | 'period' |
| effects() | 'scenario' | 'period' |
| variable() | 'scenario' | 'period' |
| duration_curve() | 'scenario' | 'period' (already had this) |
| compare() | N/A (uses its own mode='overlay'/'facet') | |
| sankey() | N/A (aggregates to single diagram) | |
Removed animate_by parameter from all methods
* Update storage method
* Remove mode parameter for simpli
| Method | Default mode |
|------------------|---------------------------------------------------|
| balance() | stacked_bar |
| storage() | stacked_bar (flows) + line (charge state overlay) |
| flows() | line |
| variable() | line |
| duration_curve() | line |
| effects() | bar |
* Make plotting_accessors.py more self contained
* Use faster to_long()
* Add 0-dim case
* sankey diagram now properly handles scenarios and periods:
Changes made:
1. Period aggregation with weights: Uses period_weights from flow_system to properly weight periods by their duration
2. Scenario aggregation with weights: Uses normalized scenario_weights to compute a weighted average across scenarios
3. Selection support: Users can filter specific scenarios/periods via select parameter before aggregation
Weighting logic:
- Periods (for aggregate='sum'): (da * period_weights).sum(dim='period') - this gives the total energy across all periods weighted by their duration
- Periods (for aggregate='mean'): (da * period_weights).sum(dim='period') / period_weights.sum() - weighted mean
- Scenarios: Always uses normalized weights (sum to 1) for weighted averaging, since scenarios represent probability-weighted alternatives
* Add colors to sankey
* Add sizes
* Add size filtering
* Include storage sizes
* Remove storage sizes
* Add charge state and status accessor
* Summary of Changes
1. Added new methods to PlotAccessor (plot_accessors.py)
charge_states() (line 658):
- Returns a Dataset with each storage's charge state as a variable
- Supports filtering with include/exclude parameters
- Default plot: line chart
on_states() (line 753):
- Returns a Dataset with each component's |status variable
- Supports filtering with include/exclude parameters
- Default plot: heatmap (good for binary data visualization)
2. Added data building helper functions (plot_accessors.py)
build_flow_rates(results) (line 315):
- Builds a DataArray containing flow rates for all flows
- Used internally by PlotAccessor methods
build_flow_hours(results) (line 333):
- Builds a DataArray containing flow hours for all flows
build_sizes(results) (line 347):
- Builds a DataArray containing sizes for all flows
_filter_dataarray_by_coord(da, **kwargs) (line 284):
- Helper for filtering DataArrays by coordinate values
3. Deprecated old Results methods (results.py)
The following methods now emit DeprecationWarning:
- results.flow_rates() → Use results.plot.flows(plot=False).data
- results.flow_hours() → Use results.plot.flows(unit='flow_hours', plot=False).data
- results.sizes() → Use results.plot.sizes(plot=False).data
4. Updated PlotAccessor methods to use new helpers
- flows() now uses build_flow_rates() / build_flow_hours() directly
- sizes() now uses build_sizes() directly
- sankey() now uses build_flow_hours() directly
This ensures the deprecation warnings only fire when users directly call the old methods, not when using the plot accessor
* 1. New methods added to PlotAccessor
- charge_states(): Returns Dataset with all storage charge states
- on_states(): Returns Dataset with all component status variables (heatmap display)
2. Data building helper functions (plot_accessors.py)
- build_flow_rates(results): Builds DataArray of flow rates
- build_flow_hours(results): Builds DataArray of flow hours
- build_sizes(results): Builds DataArray of sizes
- _filter_dataarray_by_coord(da, **kwargs): Filter helper
- _assign_flow_coords(da, results): Add flow coordinates
3. Caching in PlotAccessor
Added lazy-cached properties for expensive computations:
- _all_flow_rates - cached DataArray of all flow rates
- _all_flow_hours - cached DataArray of all flow hours
- _all_sizes - cached DataArray of all sizes
- _all_charge_states - cached Dataset of all storage charge states
- _all_status_vars - cached Dataset of all status variables
4. Deprecated methods in Results class
Added deprecation warnings to:
- results.flow_rates() → Use results.plot.flows(plot=False).data
- results.flow_hours() → Use results.plot.flows(unit='flow_hours', plot=False).data
- results.sizes() → Use results.plot.sizes(plot=False).data
5. Updated PlotAccessor methods to use cached properties
- flows() uses _all_flow_rates / _all_flow_hours
- sankey() uses _all_flow_hours
- sizes() uses _all_sizes
- charge_states() uses _all_charge_states
- on_states() uses _all_status_vars
* Move deprectated functionality into results.py instead of porting to the new module
* Revert to simply deprectae old methods without forwarding to new code
* Remove planning file
* Update plotting methods for new datasets
* 1. Renamed data properties in PlotAccessor to use all_ prefix:
- all_flow_rates - All flow rates as Dataset
- all_flow_hours - All flow hours as Dataset
- all_sizes - All flow sizes as Dataset
- all_charge_states - All storage charge states as Dataset
- all_on_states - All component on/off status as Dataset
2. Updated internal references - All usages in flows(), sankey(), sizes(), charge_states(), and on_states() methods now use the new names.
3. Updated deprecation messages in results.py to point to the new API:
- results.flow_rates() → results.plot.all_flow_rates
- results.flow_hours() → results.plot.all_flow_hours
- results.sizes() → results.plot.all_sizes
4. Updated docstring examples in PlotAccessor to use the new all_* names.
* Update deprecations messages
* Update deprecations messages
* Thsi seems much better.
* Updaet docstrings and variable name generation in plotting acessor
* Change __ to _ in private dataset caching
* Revert breaking io changes
* New solution storing interface
* Add new focused statistics and plot accessors
* Renamed all properties:
- all_flow_rates → flow_rates
- all_flow_hours → flow_hours
- all_sizes → sizes
- all_charge_states → charge_states
* Cache Statistics
* Invalidate caches
* Add effect related statistics
* Simplify statistics accessor to rely on flow_system directly instead of solution attrs
* Fix heatma fallback for 1D Data
* Add topology accessor
* All deprecation warnings in the codebase now consistently use the format will be removed in v{DEPRECATION_REMOVAL_VERSION}.
* Update tests
* created comprehensive documentation for all FlowSystem accessors
* Update results documentation
* Update results documentation
* Update effect statistics
* Update effect statistics
* Update effect statistics
* Add mkdocs plotly plugin
* Add section about custom constraints
* documentation updates:
docs/user-guide/results/index.md:
- Updated table to replace effects_per_component with temporal_effects, periodic_effects, total_effects, and effect_share_factors
- Fixed flow_hours['Boiler(Q_th)|flow_rate'] → flow_hours['Boiler(Q_th)']
- Fixed sizes['Boiler(Q_th)|size'] → sizes['Boiler(Q_th)']
- Replaced effects_per_component example with new effect properties and groupby examples
- Updated complete example to use total_effects
docs/user-guide/results-plotting.md:
- Fixed colors example from 'Boiler(Q_th)|flow_rate' → 'Boiler(Q_th)'
- Fixed duration_curve examples to use clean labels
docs/user-guide/migration-guide-v6.md:
- Added new "Statistics Accessor" section explaining the clean labels and new effect properties
* implemented the effects() method in StatisticsPlotAccessor at flixopt/statistics_accessor.py:1132-1258.
Summary of what was done:
1. Implemented effects() method in StatisticsPlotAccessor class that was missing but documented
- Takes aspect parameter: 'total', 'temporal', or 'periodic'
- Takes effect parameter to filter to a specific effect (e.g., 'costs', 'CO2')
- Takes by parameter: 'component' or 'time' for grouping
- Supports all standard plotting parameters: select, colors, facet_col, facet_row, show
- Returns PlotResult with both data and figure
2. Verified the implementation works with all parameter combinations:
- Default call: flow_system.statistics.plot.effects()
- Specific effect: flow_system.statistics.plot.effects(effect='costs')
- Temporal aspect: flow_system.statistics.plot.effects(aspect='temporal')
- Temporal by time: flow_system.statistics.plot.effects(aspect='temporal', by='time')
- Periodic aspect: flow_system.statistics.plot.effects(aspect='periodic')
* Remove intermediate plot accessor
* 1. pyproject.toml: Removed duplicate mkdocs-plotly-plugin>=0.1.3 entry (kept the exact pin ==0.1.3)
2. flixopt/plotting.py: Fixed dimension name consistency by using squeezed_data.name instead of data.name in the fallback heatmap logic
3. flixopt/statistics_accessor.py:
- Fixed _dataset_to_long_df() to only use coordinates that are actually present as columns after reset_index()
- Fixed the nested loop inefficiency with include_flows by pre-computing the flows list outside the loop
- (Previously fixed) Fixed asymmetric NaN handling in validation check
* _create_effects_dataset method in statistics_accessor.py was simplified:
1. Detect contributors from solution data variables instead of assuming they're only flows
- Uses regex pattern to find {contributor}->{effect}(temporal|periodic) variables
- Contributors can be flows OR components (e.g., components with effects_per_active_hour)
2. Exclude effect-to-effect shares
- Filters out contributors whose base name matches any effect label
- For example, costs(temporal) is excluded because costs is an effect label
- These intermediate shares are already included in the computation
3. Removed the unused _compute_effect_total method
- The new simplified implementation directly looks up shares from the solution
- Uses effect_share_factors for conversion between effects
4. Key insight from user: The solution already contains properly computed share values including all effect-to-effect conversions. The computation uses conversion
factors because derived effects (like Effect1 which shares 0.5 from costs) don't have direct {flow}->Effect1(temporal) variables - only the source effect shares exist
({flow}->costs(temporal)).
* Update docs
* Improve to_netcdf method
* Update examples
* Fix IIS computaion flag
* Fix examples
* Fix faceting in heatmap and use period as facet col everywhere
* Inline plotting methods to deprecate plotting.py
* Fix test
* Simplify Color Management
* ColorType is now defined in color_processing.py and imported into statistics_accessor.py.
* Fix ColorType typing
* Add color accessor
* Ensure io
* Add carrier class
* implemented Carrier as a proper Interface subclass with container support. Here's what was done:
1. Carrier class (flixopt/carrier.py)
- Now inherits from Interface for serialization capabilities
- Has transform_data() method (no-op since carriers have no time-series data)
- Has label property for container keying
- Maintains equality comparison with both Carrier objects and strings
2. CarrierContainer class (flixopt/carrier.py)
- Inherits from ContainerMixin['Carrier']
- Provides dict-like access with nice repr and error messages
- Uses carrier.name for keying
3. FlowSystem updates (flixopt/flow_system.py)
- _carriers is now a CarrierContainer instead of a plain dict
- carriers property returns the CarrierContainer
- add_carrier() uses the container's add() method
- Serialization updated to include carriers in to_dataset() and restore them in from_dataset()
4. Exports (flixopt/__init__.py)
- Both Carrier and CarrierContainer are now exported
* Inline plotting methods to deprecate plotting.py (#508)
* Inline plotting methods to deprecate plotting.py
* Fix test
* Simplify Color Management
* ColorType is now defined in color_processing.py and imported into statistics_accessor.py.
* Fix ColorType typing
* statistics_accessor.py - Heatmap colors type safety (lines 121-148, 820-853)
- Changed _heatmap_figure() parameter type from colors: ColorType = None to colors: str | list[str] | None = None
- Changed heatmap() method parameter type similarly
- Updated docstrings to clarify that dicts are not supported for heatmaps since px.imshow's color_continuous_scale only accepts colorscale names or lists
2. statistics_accessor.py - Use configured qualitative colorscale (lines 284, 315)
- Updated _create_stacked_bar() to use CONFIG.Plotting.default_qualitative_colorscale as the default colorscale
- Updated _create_line() similarly
- This ensures user-configured CONFIG.Plotting.default_qualitative_colorscale affects all bar/line plots consistently
3. topology_accessor.py - Path type alignment (lines 219-222)
- Added normalization of path=False to None before calling _plot_network()
- This resolves the type mismatch where TopologyAccessor.plot() accepts bool | str | Path but _plot_network() only accepts str | Path | None
* fix usage if index name in aggregation plot
* Add to docs
* Improve carrier colors and defaults
* Update default carriers and colors
* Update config
* Update config
* Move default carriers to config.py
* Change default carrier handling
* Add color handling
* Rmeove meta_data color handling
* Add carrierst to examples
* Improve plotting acessor
* Improve _resolve_variable_names
* Improve _resolve_variable_names
* Simplify coloring and remove color accessor
* Add connected_and_transformed handling
* Improve error message in container
* BUGFIX: Carrier from dataset
FBumann
added a commit
that referenced
this pull request
Dec 8, 2025
…or (#510) * Add planning doc * Finalize planning * Add plotting acessor * Add plotting acessor * Add tests * Improve * Improve * Update docs * Updated the plotting API so that .data always returns xarray DataArray or Dataset instead of pandas DataFrame. * All .data now returns xr.Dataset consistently. * Fixed Inconsistencies and Unused Parameters * New Plot Accessors results.plot.variable(pattern) Plots the same variable type across multiple elements for easy comparison. # All binary operation states across all components results.plot.variable('on') # All flow_rates, filtered to Boiler-related elements results.plot.variable('flow_rate', include='Boiler') # All storage charge states results.plot.variable('charge_state') # With aggregation results.plot.variable('flow_rate', aggregate='sum') Key features: - Searches all elements for variables matching the pattern - Filter with include/exclude on element names - Supports aggregation, faceting, and animation - Returns Dataset with element names as variables results.plot.duration_curve(variables) Plots load duration curves (sorted time series) showing utilization patterns. # Single variable results.plot.duration_curve('Boiler(Q_th)|flow_rate') # Multiple variables results.plot.duration_curve(['CHP|on', 'Boiler|on']) # Normalized x-axis (0-100%) results.plot.duration_curve('demand', normalize=True) Key features: - Sorts values from highest to lowest - Shows how often each power level is reached - normalize=True shows percentage of time on x-axis - Returns Dataset with duration_hours or duration_pct dimension * Fix duration curve * Fix duration curve * Fix duration curve * Fix duration curve * xr.apply_ufunc to sort along the time axis while preserving all other dimensions automatically * ⏺ The example runs successfully. Now let me summarize the fixes: Summary of Fixes I addressed the actionable code review comments from CodeRabbitAI: 1. Documentation Issue - reshape parameter name ✓ (No fix needed) The CodeRabbitAI comment was incorrect. The public API parameter in PlotAccessor.heatmap() is correctly named reshape (line 335). The reshape_time parameter exists in the lower-level heatmap_with_plotly function, but the documentation correctly shows the public API parameter. 2. Fixed simple_example.py (lines 129-130) Problem: The example called balance() and duration_curve() without required arguments, which would cause TypeError at runtime. Fix: Added the required arguments: - optimization.results.plot.balance('Fernwärme') - specifying the bus to plot - optimization.results.plot.duration_curve('Boiler(Q_th)|flow_rate') - specifying the variable to plot 3. Fixed variable collision in plot_accessors.py (lines 985-988) Problem: When building the Dataset in the variable() method, using element names as keys could cause collisions if multiple variables from the same element matched the pattern (e.g., 'Boiler|flow_rate' and 'Boiler|flow_rate_max' would both map to 'Boiler', with the latter silently overwriting the former). Fix: Changed to use the full variable names as keys instead of just element names: ds = xr.Dataset({var_name: self._results.solution[var_name] for var_name in filtered_vars}) All tests pass (40 passed, 1 skipped) and the example runs successfully. * make variable names public in results * Fix sankey * Fix effects() * Fix effects * Remove bargaps * made faceting consistent across all plot methods: | Method | facet_col | facet_row | |------------------|-------------------------------------------|-----------------------------| | balance() | 'scenario' | 'period' | | heatmap() | 'scenario' | 'period' | | storage() | 'scenario' | 'period' | | flows() | 'scenario' | 'period' | | effects() | 'scenario' | 'period' | | variable() | 'scenario' | 'period' | | duration_curve() | 'scenario' | 'period' (already had this) | | compare() | N/A (uses its own mode='overlay'/'facet') | | | sankey() | N/A (aggregates to single diagram) | | Removed animate_by parameter from all methods * Update storage method * Remove mode parameter for simpli | Method | Default mode | |------------------|---------------------------------------------------| | balance() | stacked_bar | | storage() | stacked_bar (flows) + line (charge state overlay) | | flows() | line | | variable() | line | | duration_curve() | line | | effects() | bar | * Make plotting_accessors.py more self contained * Use faster to_long() * Add 0-dim case * sankey diagram now properly handles scenarios and periods: Changes made: 1. Period aggregation with weights: Uses period_weights from flow_system to properly weight periods by their duration 2. Scenario aggregation with weights: Uses normalized scenario_weights to compute a weighted average across scenarios 3. Selection support: Users can filter specific scenarios/periods via select parameter before aggregation Weighting logic: - Periods (for aggregate='sum'): (da * period_weights).sum(dim='period') - this gives the total energy across all periods weighted by their duration - Periods (for aggregate='mean'): (da * period_weights).sum(dim='period') / period_weights.sum() - weighted mean - Scenarios: Always uses normalized weights (sum to 1) for weighted averaging, since scenarios represent probability-weighted alternatives * Add colors to sankey * Add sizes * Add size filtering * Include storage sizes * Remove storage sizes * Add charge state and status accessor * Summary of Changes 1. Added new methods to PlotAccessor (plot_accessors.py) charge_states() (line 658): - Returns a Dataset with each storage's charge state as a variable - Supports filtering with include/exclude parameters - Default plot: line chart on_states() (line 753): - Returns a Dataset with each component's |status variable - Supports filtering with include/exclude parameters - Default plot: heatmap (good for binary data visualization) 2. Added data building helper functions (plot_accessors.py) build_flow_rates(results) (line 315): - Builds a DataArray containing flow rates for all flows - Used internally by PlotAccessor methods build_flow_hours(results) (line 333): - Builds a DataArray containing flow hours for all flows build_sizes(results) (line 347): - Builds a DataArray containing sizes for all flows _filter_dataarray_by_coord(da, **kwargs) (line 284): - Helper for filtering DataArrays by coordinate values 3. Deprecated old Results methods (results.py) The following methods now emit DeprecationWarning: - results.flow_rates() → Use results.plot.flows(plot=False).data - results.flow_hours() → Use results.plot.flows(unit='flow_hours', plot=False).data - results.sizes() → Use results.plot.sizes(plot=False).data 4. Updated PlotAccessor methods to use new helpers - flows() now uses build_flow_rates() / build_flow_hours() directly - sizes() now uses build_sizes() directly - sankey() now uses build_flow_hours() directly This ensures the deprecation warnings only fire when users directly call the old methods, not when using the plot accessor * 1. New methods added to PlotAccessor - charge_states(): Returns Dataset with all storage charge states - on_states(): Returns Dataset with all component status variables (heatmap display) 2. Data building helper functions (plot_accessors.py) - build_flow_rates(results): Builds DataArray of flow rates - build_flow_hours(results): Builds DataArray of flow hours - build_sizes(results): Builds DataArray of sizes - _filter_dataarray_by_coord(da, **kwargs): Filter helper - _assign_flow_coords(da, results): Add flow coordinates 3. Caching in PlotAccessor Added lazy-cached properties for expensive computations: - _all_flow_rates - cached DataArray of all flow rates - _all_flow_hours - cached DataArray of all flow hours - _all_sizes - cached DataArray of all sizes - _all_charge_states - cached Dataset of all storage charge states - _all_status_vars - cached Dataset of all status variables 4. Deprecated methods in Results class Added deprecation warnings to: - results.flow_rates() → Use results.plot.flows(plot=False).data - results.flow_hours() → Use results.plot.flows(unit='flow_hours', plot=False).data - results.sizes() → Use results.plot.sizes(plot=False).data 5. Updated PlotAccessor methods to use cached properties - flows() uses _all_flow_rates / _all_flow_hours - sankey() uses _all_flow_hours - sizes() uses _all_sizes - charge_states() uses _all_charge_states - on_states() uses _all_status_vars * Move deprectated functionality into results.py instead of porting to the new module * Revert to simply deprectae old methods without forwarding to new code * Remove planning file * Update plotting methods for new datasets * 1. Renamed data properties in PlotAccessor to use all_ prefix: - all_flow_rates - All flow rates as Dataset - all_flow_hours - All flow hours as Dataset - all_sizes - All flow sizes as Dataset - all_charge_states - All storage charge states as Dataset - all_on_states - All component on/off status as Dataset 2. Updated internal references - All usages in flows(), sankey(), sizes(), charge_states(), and on_states() methods now use the new names. 3. Updated deprecation messages in results.py to point to the new API: - results.flow_rates() → results.plot.all_flow_rates - results.flow_hours() → results.plot.all_flow_hours - results.sizes() → results.plot.all_sizes 4. Updated docstring examples in PlotAccessor to use the new all_* names. * Update deprecations messages * Update deprecations messages * Thsi seems much better. * Updaet docstrings and variable name generation in plotting acessor * Change __ to _ in private dataset caching * Revert breaking io changes * New solution storing interface * Add new focused statistics and plot accessors * Renamed all properties: - all_flow_rates → flow_rates - all_flow_hours → flow_hours - all_sizes → sizes - all_charge_states → charge_states * Cache Statistics * Invalidate caches * Add effect related statistics * Simplify statistics accessor to rely on flow_system directly instead of solution attrs * Fix heatma fallback for 1D Data * Add topology accessor * All deprecation warnings in the codebase now consistently use the format will be removed in v{DEPRECATION_REMOVAL_VERSION}. * Update tests * created comprehensive documentation for all FlowSystem accessors * Update results documentation * Update results documentation * Update effect statistics * Update effect statistics * Update effect statistics * Add mkdocs plotly plugin * Add section about custom constraints * documentation updates: docs/user-guide/results/index.md: - Updated table to replace effects_per_component with temporal_effects, periodic_effects, total_effects, and effect_share_factors - Fixed flow_hours['Boiler(Q_th)|flow_rate'] → flow_hours['Boiler(Q_th)'] - Fixed sizes['Boiler(Q_th)|size'] → sizes['Boiler(Q_th)'] - Replaced effects_per_component example with new effect properties and groupby examples - Updated complete example to use total_effects docs/user-guide/results-plotting.md: - Fixed colors example from 'Boiler(Q_th)|flow_rate' → 'Boiler(Q_th)' - Fixed duration_curve examples to use clean labels docs/user-guide/migration-guide-v6.md: - Added new "Statistics Accessor" section explaining the clean labels and new effect properties * implemented the effects() method in StatisticsPlotAccessor at flixopt/statistics_accessor.py:1132-1258. Summary of what was done: 1. Implemented effects() method in StatisticsPlotAccessor class that was missing but documented - Takes aspect parameter: 'total', 'temporal', or 'periodic' - Takes effect parameter to filter to a specific effect (e.g., 'costs', 'CO2') - Takes by parameter: 'component' or 'time' for grouping - Supports all standard plotting parameters: select, colors, facet_col, facet_row, show - Returns PlotResult with both data and figure 2. Verified the implementation works with all parameter combinations: - Default call: flow_system.statistics.plot.effects() - Specific effect: flow_system.statistics.plot.effects(effect='costs') - Temporal aspect: flow_system.statistics.plot.effects(aspect='temporal') - Temporal by time: flow_system.statistics.plot.effects(aspect='temporal', by='time') - Periodic aspect: flow_system.statistics.plot.effects(aspect='periodic') * Remove intermediate plot accessor * 1. pyproject.toml: Removed duplicate mkdocs-plotly-plugin>=0.1.3 entry (kept the exact pin ==0.1.3) 2. flixopt/plotting.py: Fixed dimension name consistency by using squeezed_data.name instead of data.name in the fallback heatmap logic 3. flixopt/statistics_accessor.py: - Fixed _dataset_to_long_df() to only use coordinates that are actually present as columns after reset_index() - Fixed the nested loop inefficiency with include_flows by pre-computing the flows list outside the loop - (Previously fixed) Fixed asymmetric NaN handling in validation check * _create_effects_dataset method in statistics_accessor.py was simplified: 1. Detect contributors from solution data variables instead of assuming they're only flows - Uses regex pattern to find {contributor}->{effect}(temporal|periodic) variables - Contributors can be flows OR components (e.g., components with effects_per_active_hour) 2. Exclude effect-to-effect shares - Filters out contributors whose base name matches any effect label - For example, costs(temporal) is excluded because costs is an effect label - These intermediate shares are already included in the computation 3. Removed the unused _compute_effect_total method - The new simplified implementation directly looks up shares from the solution - Uses effect_share_factors for conversion between effects 4. Key insight from user: The solution already contains properly computed share values including all effect-to-effect conversions. The computation uses conversion factors because derived effects (like Effect1 which shares 0.5 from costs) don't have direct {flow}->Effect1(temporal) variables - only the source effect shares exist ({flow}->costs(temporal)). * Update docs * Improve to_netcdf method * Update examples * Fix IIS computaion flag * Fix examples * Fix faceting in heatmap and use period as facet col everywhere * Inline plotting methods to deprecate plotting.py * Fix test * Simplify Color Management * ColorType is now defined in color_processing.py and imported into statistics_accessor.py. * Fix ColorType typing * Add color accessor * Ensure io * Add carrier class * implemented Carrier as a proper Interface subclass with container support. Here's what was done: 1. Carrier class (flixopt/carrier.py) - Now inherits from Interface for serialization capabilities - Has transform_data() method (no-op since carriers have no time-series data) - Has label property for container keying - Maintains equality comparison with both Carrier objects and strings 2. CarrierContainer class (flixopt/carrier.py) - Inherits from ContainerMixin['Carrier'] - Provides dict-like access with nice repr and error messages - Uses carrier.name for keying 3. FlowSystem updates (flixopt/flow_system.py) - _carriers is now a CarrierContainer instead of a plain dict - carriers property returns the CarrierContainer - add_carrier() uses the container's add() method - Serialization updated to include carriers in to_dataset() and restore them in from_dataset() 4. Exports (flixopt/__init__.py) - Both Carrier and CarrierContainer are now exported * Inline plotting methods to deprecate plotting.py (#508) * Inline plotting methods to deprecate plotting.py * Fix test * Simplify Color Management * ColorType is now defined in color_processing.py and imported into statistics_accessor.py. * Fix ColorType typing * statistics_accessor.py - Heatmap colors type safety (lines 121-148, 820-853) - Changed _heatmap_figure() parameter type from colors: ColorType = None to colors: str | list[str] | None = None - Changed heatmap() method parameter type similarly - Updated docstrings to clarify that dicts are not supported for heatmaps since px.imshow's color_continuous_scale only accepts colorscale names or lists 2. statistics_accessor.py - Use configured qualitative colorscale (lines 284, 315) - Updated _create_stacked_bar() to use CONFIG.Plotting.default_qualitative_colorscale as the default colorscale - Updated _create_line() similarly - This ensures user-configured CONFIG.Plotting.default_qualitative_colorscale affects all bar/line plots consistently 3. topology_accessor.py - Path type alignment (lines 219-222) - Added normalization of path=False to None before calling _plot_network() - This resolves the type mismatch where TopologyAccessor.plot() accepts bool | str | Path but _plot_network() only accepts str | Path | None * fix usage if index name in aggregation plot * Add to docs * Improve carrier colors and defaults * Update default carriers and colors * Update config * Update config * Move default carriers to config.py * Change default carrier handling * Add color handling * Rmeove meta_data color handling * Add carrierst to examples * Improve plotting acessor * Improve _resolve_variable_names * Improve _resolve_variable_names * Simplify coloring and remove color accessor * Add connected_and_transformed handling * Improve error message in container * Methods moved to TransformAccessor (transform_accessor.py): - sel() - select by label - isel() - select by integer index - resample() - resample time dimension - Helper methods: _dataset_sel, _dataset_isel, _dataset_resample, _resample_by_dimension_groups 2. Solution is dropped: All transform methods return a new FlowSystem with no solution - the user must re-optimize the transformed system. 3. Deprecation warnings: The old flow_system.sel(), flow_system.isel(), and flow_system.resample() methods now emit deprecation warnings and forward to the new TransformAccessor methods. 4. Backward compatible: Existing code still works, just with deprecation warnings. * Documentation updated * Re-add _dataset_sel and other helper methods for proper deprectation. ALso fix new methods to be classmethods * BUGFIX: Carrier from dataset * Update docs
FBumann
added a commit
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Dec 10, 2025
* Add planning doc
* Finalize planning
* Add plotting acessor
* Add plotting acessor
* Add tests
* Improve
* Improve
* Update docs
* Updated the plotting API so that .data always returns xarray DataArray or Dataset instead of pandas DataFrame.
* All .data now returns xr.Dataset consistently.
* Fixed Inconsistencies and Unused Parameters
* New Plot Accessors
results.plot.variable(pattern)
Plots the same variable type across multiple elements for easy comparison.
# All binary operation states across all components
results.plot.variable('on')
# All flow_rates, filtered to Boiler-related elements
results.plot.variable('flow_rate', include='Boiler')
# All storage charge states
results.plot.variable('charge_state')
# With aggregation
results.plot.variable('flow_rate', aggregate='sum')
Key features:
- Searches all elements for variables matching the pattern
- Filter with include/exclude on element names
- Supports aggregation, faceting, and animation
- Returns Dataset with element names as variables
results.plot.duration_curve(variables)
Plots load duration curves (sorted time series) showing utilization patterns.
# Single variable
results.plot.duration_curve('Boiler(Q_th)|flow_rate')
# Multiple variables
results.plot.duration_curve(['CHP|on', 'Boiler|on'])
# Normalized x-axis (0-100%)
results.plot.duration_curve('demand', normalize=True)
Key features:
- Sorts values from highest to lowest
- Shows how often each power level is reached
- normalize=True shows percentage of time on x-axis
- Returns Dataset with duration_hours or duration_pct dimension
* Fix duration curve
* Fix duration curve
* Fix duration curve
* Fix duration curve
* xr.apply_ufunc to sort along the time axis while preserving all other dimensions automatically
* ⏺ The example runs successfully. Now let me summarize the fixes:
Summary of Fixes
I addressed the actionable code review comments from CodeRabbitAI:
1. Documentation Issue - reshape parameter name ✓ (No fix needed)
The CodeRabbitAI comment was incorrect. The public API parameter in PlotAccessor.heatmap() is correctly named reshape (line 335). The reshape_time parameter exists in the lower-level heatmap_with_plotly function, but the documentation correctly shows the public API parameter.
2. Fixed simple_example.py (lines 129-130)
Problem: The example called balance() and duration_curve() without required arguments, which would cause TypeError at runtime.
Fix: Added the required arguments:
- optimization.results.plot.balance('Fernwärme') - specifying the bus to plot
- optimization.results.plot.duration_curve('Boiler(Q_th)|flow_rate') - specifying the variable to plot
3. Fixed variable collision in plot_accessors.py (lines 985-988)
Problem: When building the Dataset in the variable() method, using element names as keys could cause collisions if multiple variables from the same element matched the pattern (e.g., 'Boiler|flow_rate' and 'Boiler|flow_rate_max' would both map to 'Boiler', with the latter silently overwriting the former).
Fix: Changed to use the full variable names as keys instead of just element names:
ds = xr.Dataset({var_name: self._results.solution[var_name] for var_name in filtered_vars})
All tests pass (40 passed, 1 skipped) and the example runs successfully.
* make variable names public in results
* Fix sankey
* Fix effects()
* Fix effects
* Remove bargaps
* made faceting consistent across all plot methods:
| Method | facet_col | facet_row |
|------------------|-------------------------------------------|-----------------------------|
| balance() | 'scenario' | 'period' |
| heatmap() | 'scenario' | 'period' |
| storage() | 'scenario' | 'period' |
| flows() | 'scenario' | 'period' |
| effects() | 'scenario' | 'period' |
| variable() | 'scenario' | 'period' |
| duration_curve() | 'scenario' | 'period' (already had this) |
| compare() | N/A (uses its own mode='overlay'/'facet') | |
| sankey() | N/A (aggregates to single diagram) | |
Removed animate_by parameter from all methods
* Update storage method
* Remove mode parameter for simpli
| Method | Default mode |
|------------------|---------------------------------------------------|
| balance() | stacked_bar |
| storage() | stacked_bar (flows) + line (charge state overlay) |
| flows() | line |
| variable() | line |
| duration_curve() | line |
| effects() | bar |
* Make plotting_accessors.py more self contained
* Use faster to_long()
* Add 0-dim case
* sankey diagram now properly handles scenarios and periods:
Changes made:
1. Period aggregation with weights: Uses period_weights from flow_system to properly weight periods by their duration
2. Scenario aggregation with weights: Uses normalized scenario_weights to compute a weighted average across scenarios
3. Selection support: Users can filter specific scenarios/periods via select parameter before aggregation
Weighting logic:
- Periods (for aggregate='sum'): (da * period_weights).sum(dim='period') - this gives the total energy across all periods weighted by their duration
- Periods (for aggregate='mean'): (da * period_weights).sum(dim='period') / period_weights.sum() - weighted mean
- Scenarios: Always uses normalized weights (sum to 1) for weighted averaging, since scenarios represent probability-weighted alternatives
* Add colors to sankey
* Add sizes
* Add size filtering
* Include storage sizes
* Remove storage sizes
* Add charge state and status accessor
* Summary of Changes
1. Added new methods to PlotAccessor (plot_accessors.py)
charge_states() (line 658):
- Returns a Dataset with each storage's charge state as a variable
- Supports filtering with include/exclude parameters
- Default plot: line chart
on_states() (line 753):
- Returns a Dataset with each component's |status variable
- Supports filtering with include/exclude parameters
- Default plot: heatmap (good for binary data visualization)
2. Added data building helper functions (plot_accessors.py)
build_flow_rates(results) (line 315):
- Builds a DataArray containing flow rates for all flows
- Used internally by PlotAccessor methods
build_flow_hours(results) (line 333):
- Builds a DataArray containing flow hours for all flows
build_sizes(results) (line 347):
- Builds a DataArray containing sizes for all flows
_filter_dataarray_by_coord(da, **kwargs) (line 284):
- Helper for filtering DataArrays by coordinate values
3. Deprecated old Results methods (results.py)
The following methods now emit DeprecationWarning:
- results.flow_rates() → Use results.plot.flows(plot=False).data
- results.flow_hours() → Use results.plot.flows(unit='flow_hours', plot=False).data
- results.sizes() → Use results.plot.sizes(plot=False).data
4. Updated PlotAccessor methods to use new helpers
- flows() now uses build_flow_rates() / build_flow_hours() directly
- sizes() now uses build_sizes() directly
- sankey() now uses build_flow_hours() directly
This ensures the deprecation warnings only fire when users directly call the old methods, not when using the plot accessor
* 1. New methods added to PlotAccessor
- charge_states(): Returns Dataset with all storage charge states
- on_states(): Returns Dataset with all component status variables (heatmap display)
2. Data building helper functions (plot_accessors.py)
- build_flow_rates(results): Builds DataArray of flow rates
- build_flow_hours(results): Builds DataArray of flow hours
- build_sizes(results): Builds DataArray of sizes
- _filter_dataarray_by_coord(da, **kwargs): Filter helper
- _assign_flow_coords(da, results): Add flow coordinates
3. Caching in PlotAccessor
Added lazy-cached properties for expensive computations:
- _all_flow_rates - cached DataArray of all flow rates
- _all_flow_hours - cached DataArray of all flow hours
- _all_sizes - cached DataArray of all sizes
- _all_charge_states - cached Dataset of all storage charge states
- _all_status_vars - cached Dataset of all status variables
4. Deprecated methods in Results class
Added deprecation warnings to:
- results.flow_rates() → Use results.plot.flows(plot=False).data
- results.flow_hours() → Use results.plot.flows(unit='flow_hours', plot=False).data
- results.sizes() → Use results.plot.sizes(plot=False).data
5. Updated PlotAccessor methods to use cached properties
- flows() uses _all_flow_rates / _all_flow_hours
- sankey() uses _all_flow_hours
- sizes() uses _all_sizes
- charge_states() uses _all_charge_states
- on_states() uses _all_status_vars
* Move deprectated functionality into results.py instead of porting to the new module
* Revert to simply deprectae old methods without forwarding to new code
* Remove planning file
* Update plotting methods for new datasets
* 1. Renamed data properties in PlotAccessor to use all_ prefix:
- all_flow_rates - All flow rates as Dataset
- all_flow_hours - All flow hours as Dataset
- all_sizes - All flow sizes as Dataset
- all_charge_states - All storage charge states as Dataset
- all_on_states - All component on/off status as Dataset
2. Updated internal references - All usages in flows(), sankey(), sizes(), charge_states(), and on_states() methods now use the new names.
3. Updated deprecation messages in results.py to point to the new API:
- results.flow_rates() → results.plot.all_flow_rates
- results.flow_hours() → results.plot.all_flow_hours
- results.sizes() → results.plot.all_sizes
4. Updated docstring examples in PlotAccessor to use the new all_* names.
* Update deprecations messages
* Update deprecations messages
* Thsi seems much better.
* Updaet docstrings and variable name generation in plotting acessor
* Change __ to _ in private dataset caching
* Revert breaking io changes
* New solution storing interface
* Add new focused statistics and plot accessors
* Renamed all properties:
- all_flow_rates → flow_rates
- all_flow_hours → flow_hours
- all_sizes → sizes
- all_charge_states → charge_states
* Cache Statistics
* Invalidate caches
* Add effect related statistics
* Simplify statistics accessor to rely on flow_system directly instead of solution attrs
* Fix heatma fallback for 1D Data
* Add topology accessor
* All deprecation warnings in the codebase now consistently use the format will be removed in v{DEPRECATION_REMOVAL_VERSION}.
* Update tests
* created comprehensive documentation for all FlowSystem accessors
* Update results documentation
* Update results documentation
* Update effect statistics
* Update effect statistics
* Update effect statistics
* Add mkdocs plotly plugin
* Add section about custom constraints
* documentation updates:
docs/user-guide/results/index.md:
- Updated table to replace effects_per_component with temporal_effects, periodic_effects, total_effects, and effect_share_factors
- Fixed flow_hours['Boiler(Q_th)|flow_rate'] → flow_hours['Boiler(Q_th)']
- Fixed sizes['Boiler(Q_th)|size'] → sizes['Boiler(Q_th)']
- Replaced effects_per_component example with new effect properties and groupby examples
- Updated complete example to use total_effects
docs/user-guide/results-plotting.md:
- Fixed colors example from 'Boiler(Q_th)|flow_rate' → 'Boiler(Q_th)'
- Fixed duration_curve examples to use clean labels
docs/user-guide/migration-guide-v6.md:
- Added new "Statistics Accessor" section explaining the clean labels and new effect properties
* implemented the effects() method in StatisticsPlotAccessor at flixopt/statistics_accessor.py:1132-1258.
Summary of what was done:
1. Implemented effects() method in StatisticsPlotAccessor class that was missing but documented
- Takes aspect parameter: 'total', 'temporal', or 'periodic'
- Takes effect parameter to filter to a specific effect (e.g., 'costs', 'CO2')
- Takes by parameter: 'component' or 'time' for grouping
- Supports all standard plotting parameters: select, colors, facet_col, facet_row, show
- Returns PlotResult with both data and figure
2. Verified the implementation works with all parameter combinations:
- Default call: flow_system.statistics.plot.effects()
- Specific effect: flow_system.statistics.plot.effects(effect='costs')
- Temporal aspect: flow_system.statistics.plot.effects(aspect='temporal')
- Temporal by time: flow_system.statistics.plot.effects(aspect='temporal', by='time')
- Periodic aspect: flow_system.statistics.plot.effects(aspect='periodic')
* Remove intermediate plot accessor
* 1. pyproject.toml: Removed duplicate mkdocs-plotly-plugin>=0.1.3 entry (kept the exact pin ==0.1.3)
2. flixopt/plotting.py: Fixed dimension name consistency by using squeezed_data.name instead of data.name in the fallback heatmap logic
3. flixopt/statistics_accessor.py:
- Fixed _dataset_to_long_df() to only use coordinates that are actually present as columns after reset_index()
- Fixed the nested loop inefficiency with include_flows by pre-computing the flows list outside the loop
- (Previously fixed) Fixed asymmetric NaN handling in validation check
* _create_effects_dataset method in statistics_accessor.py was simplified:
1. Detect contributors from solution data variables instead of assuming they're only flows
- Uses regex pattern to find {contributor}->{effect}(temporal|periodic) variables
- Contributors can be flows OR components (e.g., components with effects_per_active_hour)
2. Exclude effect-to-effect shares
- Filters out contributors whose base name matches any effect label
- For example, costs(temporal) is excluded because costs is an effect label
- These intermediate shares are already included in the computation
3. Removed the unused _compute_effect_total method
- The new simplified implementation directly looks up shares from the solution
- Uses effect_share_factors for conversion between effects
4. Key insight from user: The solution already contains properly computed share values including all effect-to-effect conversions. The computation uses conversion
factors because derived effects (like Effect1 which shares 0.5 from costs) don't have direct {flow}->Effect1(temporal) variables - only the source effect shares exist
({flow}->costs(temporal)).
* Update docs
* Improve to_netcdf method
* Update examples
* Fix IIS computaion flag
* Fix examples
* Fix faceting in heatmap and use period as facet col everywhere
* Inline plotting methods to deprecate plotting.py (#508)
* Inline plotting methods to deprecate plotting.py
* Fix test
* Simplify Color Management
* ColorType is now defined in color_processing.py and imported into statistics_accessor.py.
* Fix ColorType typing
* statistics_accessor.py - Heatmap colors type safety (lines 121-148, 820-853)
- Changed _heatmap_figure() parameter type from colors: ColorType = None to colors: str | list[str] | None = None
- Changed heatmap() method parameter type similarly
- Updated docstrings to clarify that dicts are not supported for heatmaps since px.imshow's color_continuous_scale only accepts colorscale names or lists
2. statistics_accessor.py - Use configured qualitative colorscale (lines 284, 315)
- Updated _create_stacked_bar() to use CONFIG.Plotting.default_qualitative_colorscale as the default colorscale
- Updated _create_line() similarly
- This ensures user-configured CONFIG.Plotting.default_qualitative_colorscale affects all bar/line plots consistently
3. topology_accessor.py - Path type alignment (lines 219-222)
- Added normalization of path=False to None before calling _plot_network()
- This resolves the type mismatch where TopologyAccessor.plot() accepts bool | str | Path but _plot_network() only accepts str | Path | None
* fix usage if index name in aggregation plot
FBumann
added a commit
that referenced
this pull request
Dec 10, 2025
* Add planning doc
* Finalize planning
* Add plotting acessor
* Add plotting acessor
* Add tests
* Improve
* Improve
* Update docs
* Updated the plotting API so that .data always returns xarray DataArray or Dataset instead of pandas DataFrame.
* All .data now returns xr.Dataset consistently.
* Fixed Inconsistencies and Unused Parameters
* New Plot Accessors
results.plot.variable(pattern)
Plots the same variable type across multiple elements for easy comparison.
# All binary operation states across all components
results.plot.variable('on')
# All flow_rates, filtered to Boiler-related elements
results.plot.variable('flow_rate', include='Boiler')
# All storage charge states
results.plot.variable('charge_state')
# With aggregation
results.plot.variable('flow_rate', aggregate='sum')
Key features:
- Searches all elements for variables matching the pattern
- Filter with include/exclude on element names
- Supports aggregation, faceting, and animation
- Returns Dataset with element names as variables
results.plot.duration_curve(variables)
Plots load duration curves (sorted time series) showing utilization patterns.
# Single variable
results.plot.duration_curve('Boiler(Q_th)|flow_rate')
# Multiple variables
results.plot.duration_curve(['CHP|on', 'Boiler|on'])
# Normalized x-axis (0-100%)
results.plot.duration_curve('demand', normalize=True)
Key features:
- Sorts values from highest to lowest
- Shows how often each power level is reached
- normalize=True shows percentage of time on x-axis
- Returns Dataset with duration_hours or duration_pct dimension
* Fix duration curve
* Fix duration curve
* Fix duration curve
* Fix duration curve
* xr.apply_ufunc to sort along the time axis while preserving all other dimensions automatically
* ⏺ The example runs successfully. Now let me summarize the fixes:
Summary of Fixes
I addressed the actionable code review comments from CodeRabbitAI:
1. Documentation Issue - reshape parameter name ✓ (No fix needed)
The CodeRabbitAI comment was incorrect. The public API parameter in PlotAccessor.heatmap() is correctly named reshape (line 335). The reshape_time parameter exists in the lower-level heatmap_with_plotly function, but the documentation correctly shows the public API parameter.
2. Fixed simple_example.py (lines 129-130)
Problem: The example called balance() and duration_curve() without required arguments, which would cause TypeError at runtime.
Fix: Added the required arguments:
- optimization.results.plot.balance('Fernwärme') - specifying the bus to plot
- optimization.results.plot.duration_curve('Boiler(Q_th)|flow_rate') - specifying the variable to plot
3. Fixed variable collision in plot_accessors.py (lines 985-988)
Problem: When building the Dataset in the variable() method, using element names as keys could cause collisions if multiple variables from the same element matched the pattern (e.g., 'Boiler|flow_rate' and 'Boiler|flow_rate_max' would both map to 'Boiler', with the latter silently overwriting the former).
Fix: Changed to use the full variable names as keys instead of just element names:
ds = xr.Dataset({var_name: self._results.solution[var_name] for var_name in filtered_vars})
All tests pass (40 passed, 1 skipped) and the example runs successfully.
* make variable names public in results
* Fix sankey
* Fix effects()
* Fix effects
* Remove bargaps
* made faceting consistent across all plot methods:
| Method | facet_col | facet_row |
|------------------|-------------------------------------------|-----------------------------|
| balance() | 'scenario' | 'period' |
| heatmap() | 'scenario' | 'period' |
| storage() | 'scenario' | 'period' |
| flows() | 'scenario' | 'period' |
| effects() | 'scenario' | 'period' |
| variable() | 'scenario' | 'period' |
| duration_curve() | 'scenario' | 'period' (already had this) |
| compare() | N/A (uses its own mode='overlay'/'facet') | |
| sankey() | N/A (aggregates to single diagram) | |
Removed animate_by parameter from all methods
* Update storage method
* Remove mode parameter for simpli
| Method | Default mode |
|------------------|---------------------------------------------------|
| balance() | stacked_bar |
| storage() | stacked_bar (flows) + line (charge state overlay) |
| flows() | line |
| variable() | line |
| duration_curve() | line |
| effects() | bar |
* Make plotting_accessors.py more self contained
* Use faster to_long()
* Add 0-dim case
* sankey diagram now properly handles scenarios and periods:
Changes made:
1. Period aggregation with weights: Uses period_weights from flow_system to properly weight periods by their duration
2. Scenario aggregation with weights: Uses normalized scenario_weights to compute a weighted average across scenarios
3. Selection support: Users can filter specific scenarios/periods via select parameter before aggregation
Weighting logic:
- Periods (for aggregate='sum'): (da * period_weights).sum(dim='period') - this gives the total energy across all periods weighted by their duration
- Periods (for aggregate='mean'): (da * period_weights).sum(dim='period') / period_weights.sum() - weighted mean
- Scenarios: Always uses normalized weights (sum to 1) for weighted averaging, since scenarios represent probability-weighted alternatives
* Add colors to sankey
* Add sizes
* Add size filtering
* Include storage sizes
* Remove storage sizes
* Add charge state and status accessor
* Summary of Changes
1. Added new methods to PlotAccessor (plot_accessors.py)
charge_states() (line 658):
- Returns a Dataset with each storage's charge state as a variable
- Supports filtering with include/exclude parameters
- Default plot: line chart
on_states() (line 753):
- Returns a Dataset with each component's |status variable
- Supports filtering with include/exclude parameters
- Default plot: heatmap (good for binary data visualization)
2. Added data building helper functions (plot_accessors.py)
build_flow_rates(results) (line 315):
- Builds a DataArray containing flow rates for all flows
- Used internally by PlotAccessor methods
build_flow_hours(results) (line 333):
- Builds a DataArray containing flow hours for all flows
build_sizes(results) (line 347):
- Builds a DataArray containing sizes for all flows
_filter_dataarray_by_coord(da, **kwargs) (line 284):
- Helper for filtering DataArrays by coordinate values
3. Deprecated old Results methods (results.py)
The following methods now emit DeprecationWarning:
- results.flow_rates() → Use results.plot.flows(plot=False).data
- results.flow_hours() → Use results.plot.flows(unit='flow_hours', plot=False).data
- results.sizes() → Use results.plot.sizes(plot=False).data
4. Updated PlotAccessor methods to use new helpers
- flows() now uses build_flow_rates() / build_flow_hours() directly
- sizes() now uses build_sizes() directly
- sankey() now uses build_flow_hours() directly
This ensures the deprecation warnings only fire when users directly call the old methods, not when using the plot accessor
* 1. New methods added to PlotAccessor
- charge_states(): Returns Dataset with all storage charge states
- on_states(): Returns Dataset with all component status variables (heatmap display)
2. Data building helper functions (plot_accessors.py)
- build_flow_rates(results): Builds DataArray of flow rates
- build_flow_hours(results): Builds DataArray of flow hours
- build_sizes(results): Builds DataArray of sizes
- _filter_dataarray_by_coord(da, **kwargs): Filter helper
- _assign_flow_coords(da, results): Add flow coordinates
3. Caching in PlotAccessor
Added lazy-cached properties for expensive computations:
- _all_flow_rates - cached DataArray of all flow rates
- _all_flow_hours - cached DataArray of all flow hours
- _all_sizes - cached DataArray of all sizes
- _all_charge_states - cached Dataset of all storage charge states
- _all_status_vars - cached Dataset of all status variables
4. Deprecated methods in Results class
Added deprecation warnings to:
- results.flow_rates() → Use results.plot.flows(plot=False).data
- results.flow_hours() → Use results.plot.flows(unit='flow_hours', plot=False).data
- results.sizes() → Use results.plot.sizes(plot=False).data
5. Updated PlotAccessor methods to use cached properties
- flows() uses _all_flow_rates / _all_flow_hours
- sankey() uses _all_flow_hours
- sizes() uses _all_sizes
- charge_states() uses _all_charge_states
- on_states() uses _all_status_vars
* Move deprectated functionality into results.py instead of porting to the new module
* Revert to simply deprectae old methods without forwarding to new code
* Remove planning file
* Update plotting methods for new datasets
* 1. Renamed data properties in PlotAccessor to use all_ prefix:
- all_flow_rates - All flow rates as Dataset
- all_flow_hours - All flow hours as Dataset
- all_sizes - All flow sizes as Dataset
- all_charge_states - All storage charge states as Dataset
- all_on_states - All component on/off status as Dataset
2. Updated internal references - All usages in flows(), sankey(), sizes(), charge_states(), and on_states() methods now use the new names.
3. Updated deprecation messages in results.py to point to the new API:
- results.flow_rates() → results.plot.all_flow_rates
- results.flow_hours() → results.plot.all_flow_hours
- results.sizes() → results.plot.all_sizes
4. Updated docstring examples in PlotAccessor to use the new all_* names.
* Update deprecations messages
* Update deprecations messages
* Thsi seems much better.
* Updaet docstrings and variable name generation in plotting acessor
* Change __ to _ in private dataset caching
* Revert breaking io changes
* New solution storing interface
* Add new focused statistics and plot accessors
* Renamed all properties:
- all_flow_rates → flow_rates
- all_flow_hours → flow_hours
- all_sizes → sizes
- all_charge_states → charge_states
* Cache Statistics
* Invalidate caches
* Add effect related statistics
* Simplify statistics accessor to rely on flow_system directly instead of solution attrs
* Fix heatma fallback for 1D Data
* Add topology accessor
* All deprecation warnings in the codebase now consistently use the format will be removed in v{DEPRECATION_REMOVAL_VERSION}.
* Update tests
* created comprehensive documentation for all FlowSystem accessors
* Update results documentation
* Update results documentation
* Update effect statistics
* Update effect statistics
* Update effect statistics
* Add mkdocs plotly plugin
* Add section about custom constraints
* documentation updates:
docs/user-guide/results/index.md:
- Updated table to replace effects_per_component with temporal_effects, periodic_effects, total_effects, and effect_share_factors
- Fixed flow_hours['Boiler(Q_th)|flow_rate'] → flow_hours['Boiler(Q_th)']
- Fixed sizes['Boiler(Q_th)|size'] → sizes['Boiler(Q_th)']
- Replaced effects_per_component example with new effect properties and groupby examples
- Updated complete example to use total_effects
docs/user-guide/results-plotting.md:
- Fixed colors example from 'Boiler(Q_th)|flow_rate' → 'Boiler(Q_th)'
- Fixed duration_curve examples to use clean labels
docs/user-guide/migration-guide-v6.md:
- Added new "Statistics Accessor" section explaining the clean labels and new effect properties
* implemented the effects() method in StatisticsPlotAccessor at flixopt/statistics_accessor.py:1132-1258.
Summary of what was done:
1. Implemented effects() method in StatisticsPlotAccessor class that was missing but documented
- Takes aspect parameter: 'total', 'temporal', or 'periodic'
- Takes effect parameter to filter to a specific effect (e.g., 'costs', 'CO2')
- Takes by parameter: 'component' or 'time' for grouping
- Supports all standard plotting parameters: select, colors, facet_col, facet_row, show
- Returns PlotResult with both data and figure
2. Verified the implementation works with all parameter combinations:
- Default call: flow_system.statistics.plot.effects()
- Specific effect: flow_system.statistics.plot.effects(effect='costs')
- Temporal aspect: flow_system.statistics.plot.effects(aspect='temporal')
- Temporal by time: flow_system.statistics.plot.effects(aspect='temporal', by='time')
- Periodic aspect: flow_system.statistics.plot.effects(aspect='periodic')
* Remove intermediate plot accessor
* 1. pyproject.toml: Removed duplicate mkdocs-plotly-plugin>=0.1.3 entry (kept the exact pin ==0.1.3)
2. flixopt/plotting.py: Fixed dimension name consistency by using squeezed_data.name instead of data.name in the fallback heatmap logic
3. flixopt/statistics_accessor.py:
- Fixed _dataset_to_long_df() to only use coordinates that are actually present as columns after reset_index()
- Fixed the nested loop inefficiency with include_flows by pre-computing the flows list outside the loop
- (Previously fixed) Fixed asymmetric NaN handling in validation check
* _create_effects_dataset method in statistics_accessor.py was simplified:
1. Detect contributors from solution data variables instead of assuming they're only flows
- Uses regex pattern to find {contributor}->{effect}(temporal|periodic) variables
- Contributors can be flows OR components (e.g., components with effects_per_active_hour)
2. Exclude effect-to-effect shares
- Filters out contributors whose base name matches any effect label
- For example, costs(temporal) is excluded because costs is an effect label
- These intermediate shares are already included in the computation
3. Removed the unused _compute_effect_total method
- The new simplified implementation directly looks up shares from the solution
- Uses effect_share_factors for conversion between effects
4. Key insight from user: The solution already contains properly computed share values including all effect-to-effect conversions. The computation uses conversion
factors because derived effects (like Effect1 which shares 0.5 from costs) don't have direct {flow}->Effect1(temporal) variables - only the source effect shares exist
({flow}->costs(temporal)).
* Update docs
* Improve to_netcdf method
* Update examples
* Fix IIS computaion flag
* Fix examples
* Fix faceting in heatmap and use period as facet col everywhere
* Inline plotting methods to deprecate plotting.py
* Fix test
* Simplify Color Management
* ColorType is now defined in color_processing.py and imported into statistics_accessor.py.
* Fix ColorType typing
* Add color accessor
* Ensure io
* Add carrier class
* implemented Carrier as a proper Interface subclass with container support. Here's what was done:
1. Carrier class (flixopt/carrier.py)
- Now inherits from Interface for serialization capabilities
- Has transform_data() method (no-op since carriers have no time-series data)
- Has label property for container keying
- Maintains equality comparison with both Carrier objects and strings
2. CarrierContainer class (flixopt/carrier.py)
- Inherits from ContainerMixin['Carrier']
- Provides dict-like access with nice repr and error messages
- Uses carrier.name for keying
3. FlowSystem updates (flixopt/flow_system.py)
- _carriers is now a CarrierContainer instead of a plain dict
- carriers property returns the CarrierContainer
- add_carrier() uses the container's add() method
- Serialization updated to include carriers in to_dataset() and restore them in from_dataset()
4. Exports (flixopt/__init__.py)
- Both Carrier and CarrierContainer are now exported
* Inline plotting methods to deprecate plotting.py (#508)
* Inline plotting methods to deprecate plotting.py
* Fix test
* Simplify Color Management
* ColorType is now defined in color_processing.py and imported into statistics_accessor.py.
* Fix ColorType typing
* statistics_accessor.py - Heatmap colors type safety (lines 121-148, 820-853)
- Changed _heatmap_figure() parameter type from colors: ColorType = None to colors: str | list[str] | None = None
- Changed heatmap() method parameter type similarly
- Updated docstrings to clarify that dicts are not supported for heatmaps since px.imshow's color_continuous_scale only accepts colorscale names or lists
2. statistics_accessor.py - Use configured qualitative colorscale (lines 284, 315)
- Updated _create_stacked_bar() to use CONFIG.Plotting.default_qualitative_colorscale as the default colorscale
- Updated _create_line() similarly
- This ensures user-configured CONFIG.Plotting.default_qualitative_colorscale affects all bar/line plots consistently
3. topology_accessor.py - Path type alignment (lines 219-222)
- Added normalization of path=False to None before calling _plot_network()
- This resolves the type mismatch where TopologyAccessor.plot() accepts bool | str | Path but _plot_network() only accepts str | Path | None
* fix usage if index name in aggregation plot
* Add to docs
* Improve carrier colors and defaults
* Update default carriers and colors
* Update config
* Update config
* Move default carriers to config.py
* Change default carrier handling
* Add color handling
* Rmeove meta_data color handling
* Add carrierst to examples
* Improve plotting acessor
* Improve _resolve_variable_names
* Improve _resolve_variable_names
* Simplify coloring and remove color accessor
* Add connected_and_transformed handling
* Improve error message in container
* BUGFIX: Carrier from dataset
FBumann
added a commit
that referenced
this pull request
Dec 10, 2025
…or (#510) * Add planning doc * Finalize planning * Add plotting acessor * Add plotting acessor * Add tests * Improve * Improve * Update docs * Updated the plotting API so that .data always returns xarray DataArray or Dataset instead of pandas DataFrame. * All .data now returns xr.Dataset consistently. * Fixed Inconsistencies and Unused Parameters * New Plot Accessors results.plot.variable(pattern) Plots the same variable type across multiple elements for easy comparison. # All binary operation states across all components results.plot.variable('on') # All flow_rates, filtered to Boiler-related elements results.plot.variable('flow_rate', include='Boiler') # All storage charge states results.plot.variable('charge_state') # With aggregation results.plot.variable('flow_rate', aggregate='sum') Key features: - Searches all elements for variables matching the pattern - Filter with include/exclude on element names - Supports aggregation, faceting, and animation - Returns Dataset with element names as variables results.plot.duration_curve(variables) Plots load duration curves (sorted time series) showing utilization patterns. # Single variable results.plot.duration_curve('Boiler(Q_th)|flow_rate') # Multiple variables results.plot.duration_curve(['CHP|on', 'Boiler|on']) # Normalized x-axis (0-100%) results.plot.duration_curve('demand', normalize=True) Key features: - Sorts values from highest to lowest - Shows how often each power level is reached - normalize=True shows percentage of time on x-axis - Returns Dataset with duration_hours or duration_pct dimension * Fix duration curve * Fix duration curve * Fix duration curve * Fix duration curve * xr.apply_ufunc to sort along the time axis while preserving all other dimensions automatically * ⏺ The example runs successfully. Now let me summarize the fixes: Summary of Fixes I addressed the actionable code review comments from CodeRabbitAI: 1. Documentation Issue - reshape parameter name ✓ (No fix needed) The CodeRabbitAI comment was incorrect. The public API parameter in PlotAccessor.heatmap() is correctly named reshape (line 335). The reshape_time parameter exists in the lower-level heatmap_with_plotly function, but the documentation correctly shows the public API parameter. 2. Fixed simple_example.py (lines 129-130) Problem: The example called balance() and duration_curve() without required arguments, which would cause TypeError at runtime. Fix: Added the required arguments: - optimization.results.plot.balance('Fernwärme') - specifying the bus to plot - optimization.results.plot.duration_curve('Boiler(Q_th)|flow_rate') - specifying the variable to plot 3. Fixed variable collision in plot_accessors.py (lines 985-988) Problem: When building the Dataset in the variable() method, using element names as keys could cause collisions if multiple variables from the same element matched the pattern (e.g., 'Boiler|flow_rate' and 'Boiler|flow_rate_max' would both map to 'Boiler', with the latter silently overwriting the former). Fix: Changed to use the full variable names as keys instead of just element names: ds = xr.Dataset({var_name: self._results.solution[var_name] for var_name in filtered_vars}) All tests pass (40 passed, 1 skipped) and the example runs successfully. * make variable names public in results * Fix sankey * Fix effects() * Fix effects * Remove bargaps * made faceting consistent across all plot methods: | Method | facet_col | facet_row | |------------------|-------------------------------------------|-----------------------------| | balance() | 'scenario' | 'period' | | heatmap() | 'scenario' | 'period' | | storage() | 'scenario' | 'period' | | flows() | 'scenario' | 'period' | | effects() | 'scenario' | 'period' | | variable() | 'scenario' | 'period' | | duration_curve() | 'scenario' | 'period' (already had this) | | compare() | N/A (uses its own mode='overlay'/'facet') | | | sankey() | N/A (aggregates to single diagram) | | Removed animate_by parameter from all methods * Update storage method * Remove mode parameter for simpli | Method | Default mode | |------------------|---------------------------------------------------| | balance() | stacked_bar | | storage() | stacked_bar (flows) + line (charge state overlay) | | flows() | line | | variable() | line | | duration_curve() | line | | effects() | bar | * Make plotting_accessors.py more self contained * Use faster to_long() * Add 0-dim case * sankey diagram now properly handles scenarios and periods: Changes made: 1. Period aggregation with weights: Uses period_weights from flow_system to properly weight periods by their duration 2. Scenario aggregation with weights: Uses normalized scenario_weights to compute a weighted average across scenarios 3. Selection support: Users can filter specific scenarios/periods via select parameter before aggregation Weighting logic: - Periods (for aggregate='sum'): (da * period_weights).sum(dim='period') - this gives the total energy across all periods weighted by their duration - Periods (for aggregate='mean'): (da * period_weights).sum(dim='period') / period_weights.sum() - weighted mean - Scenarios: Always uses normalized weights (sum to 1) for weighted averaging, since scenarios represent probability-weighted alternatives * Add colors to sankey * Add sizes * Add size filtering * Include storage sizes * Remove storage sizes * Add charge state and status accessor * Summary of Changes 1. Added new methods to PlotAccessor (plot_accessors.py) charge_states() (line 658): - Returns a Dataset with each storage's charge state as a variable - Supports filtering with include/exclude parameters - Default plot: line chart on_states() (line 753): - Returns a Dataset with each component's |status variable - Supports filtering with include/exclude parameters - Default plot: heatmap (good for binary data visualization) 2. Added data building helper functions (plot_accessors.py) build_flow_rates(results) (line 315): - Builds a DataArray containing flow rates for all flows - Used internally by PlotAccessor methods build_flow_hours(results) (line 333): - Builds a DataArray containing flow hours for all flows build_sizes(results) (line 347): - Builds a DataArray containing sizes for all flows _filter_dataarray_by_coord(da, **kwargs) (line 284): - Helper for filtering DataArrays by coordinate values 3. Deprecated old Results methods (results.py) The following methods now emit DeprecationWarning: - results.flow_rates() → Use results.plot.flows(plot=False).data - results.flow_hours() → Use results.plot.flows(unit='flow_hours', plot=False).data - results.sizes() → Use results.plot.sizes(plot=False).data 4. Updated PlotAccessor methods to use new helpers - flows() now uses build_flow_rates() / build_flow_hours() directly - sizes() now uses build_sizes() directly - sankey() now uses build_flow_hours() directly This ensures the deprecation warnings only fire when users directly call the old methods, not when using the plot accessor * 1. New methods added to PlotAccessor - charge_states(): Returns Dataset with all storage charge states - on_states(): Returns Dataset with all component status variables (heatmap display) 2. Data building helper functions (plot_accessors.py) - build_flow_rates(results): Builds DataArray of flow rates - build_flow_hours(results): Builds DataArray of flow hours - build_sizes(results): Builds DataArray of sizes - _filter_dataarray_by_coord(da, **kwargs): Filter helper - _assign_flow_coords(da, results): Add flow coordinates 3. Caching in PlotAccessor Added lazy-cached properties for expensive computations: - _all_flow_rates - cached DataArray of all flow rates - _all_flow_hours - cached DataArray of all flow hours - _all_sizes - cached DataArray of all sizes - _all_charge_states - cached Dataset of all storage charge states - _all_status_vars - cached Dataset of all status variables 4. Deprecated methods in Results class Added deprecation warnings to: - results.flow_rates() → Use results.plot.flows(plot=False).data - results.flow_hours() → Use results.plot.flows(unit='flow_hours', plot=False).data - results.sizes() → Use results.plot.sizes(plot=False).data 5. Updated PlotAccessor methods to use cached properties - flows() uses _all_flow_rates / _all_flow_hours - sankey() uses _all_flow_hours - sizes() uses _all_sizes - charge_states() uses _all_charge_states - on_states() uses _all_status_vars * Move deprectated functionality into results.py instead of porting to the new module * Revert to simply deprectae old methods without forwarding to new code * Remove planning file * Update plotting methods for new datasets * 1. Renamed data properties in PlotAccessor to use all_ prefix: - all_flow_rates - All flow rates as Dataset - all_flow_hours - All flow hours as Dataset - all_sizes - All flow sizes as Dataset - all_charge_states - All storage charge states as Dataset - all_on_states - All component on/off status as Dataset 2. Updated internal references - All usages in flows(), sankey(), sizes(), charge_states(), and on_states() methods now use the new names. 3. Updated deprecation messages in results.py to point to the new API: - results.flow_rates() → results.plot.all_flow_rates - results.flow_hours() → results.plot.all_flow_hours - results.sizes() → results.plot.all_sizes 4. Updated docstring examples in PlotAccessor to use the new all_* names. * Update deprecations messages * Update deprecations messages * Thsi seems much better. * Updaet docstrings and variable name generation in plotting acessor * Change __ to _ in private dataset caching * Revert breaking io changes * New solution storing interface * Add new focused statistics and plot accessors * Renamed all properties: - all_flow_rates → flow_rates - all_flow_hours → flow_hours - all_sizes → sizes - all_charge_states → charge_states * Cache Statistics * Invalidate caches * Add effect related statistics * Simplify statistics accessor to rely on flow_system directly instead of solution attrs * Fix heatma fallback for 1D Data * Add topology accessor * All deprecation warnings in the codebase now consistently use the format will be removed in v{DEPRECATION_REMOVAL_VERSION}. * Update tests * created comprehensive documentation for all FlowSystem accessors * Update results documentation * Update results documentation * Update effect statistics * Update effect statistics * Update effect statistics * Add mkdocs plotly plugin * Add section about custom constraints * documentation updates: docs/user-guide/results/index.md: - Updated table to replace effects_per_component with temporal_effects, periodic_effects, total_effects, and effect_share_factors - Fixed flow_hours['Boiler(Q_th)|flow_rate'] → flow_hours['Boiler(Q_th)'] - Fixed sizes['Boiler(Q_th)|size'] → sizes['Boiler(Q_th)'] - Replaced effects_per_component example with new effect properties and groupby examples - Updated complete example to use total_effects docs/user-guide/results-plotting.md: - Fixed colors example from 'Boiler(Q_th)|flow_rate' → 'Boiler(Q_th)' - Fixed duration_curve examples to use clean labels docs/user-guide/migration-guide-v6.md: - Added new "Statistics Accessor" section explaining the clean labels and new effect properties * implemented the effects() method in StatisticsPlotAccessor at flixopt/statistics_accessor.py:1132-1258. Summary of what was done: 1. Implemented effects() method in StatisticsPlotAccessor class that was missing but documented - Takes aspect parameter: 'total', 'temporal', or 'periodic' - Takes effect parameter to filter to a specific effect (e.g., 'costs', 'CO2') - Takes by parameter: 'component' or 'time' for grouping - Supports all standard plotting parameters: select, colors, facet_col, facet_row, show - Returns PlotResult with both data and figure 2. Verified the implementation works with all parameter combinations: - Default call: flow_system.statistics.plot.effects() - Specific effect: flow_system.statistics.plot.effects(effect='costs') - Temporal aspect: flow_system.statistics.plot.effects(aspect='temporal') - Temporal by time: flow_system.statistics.plot.effects(aspect='temporal', by='time') - Periodic aspect: flow_system.statistics.plot.effects(aspect='periodic') * Remove intermediate plot accessor * 1. pyproject.toml: Removed duplicate mkdocs-plotly-plugin>=0.1.3 entry (kept the exact pin ==0.1.3) 2. flixopt/plotting.py: Fixed dimension name consistency by using squeezed_data.name instead of data.name in the fallback heatmap logic 3. flixopt/statistics_accessor.py: - Fixed _dataset_to_long_df() to only use coordinates that are actually present as columns after reset_index() - Fixed the nested loop inefficiency with include_flows by pre-computing the flows list outside the loop - (Previously fixed) Fixed asymmetric NaN handling in validation check * _create_effects_dataset method in statistics_accessor.py was simplified: 1. Detect contributors from solution data variables instead of assuming they're only flows - Uses regex pattern to find {contributor}->{effect}(temporal|periodic) variables - Contributors can be flows OR components (e.g., components with effects_per_active_hour) 2. Exclude effect-to-effect shares - Filters out contributors whose base name matches any effect label - For example, costs(temporal) is excluded because costs is an effect label - These intermediate shares are already included in the computation 3. Removed the unused _compute_effect_total method - The new simplified implementation directly looks up shares from the solution - Uses effect_share_factors for conversion between effects 4. Key insight from user: The solution already contains properly computed share values including all effect-to-effect conversions. The computation uses conversion factors because derived effects (like Effect1 which shares 0.5 from costs) don't have direct {flow}->Effect1(temporal) variables - only the source effect shares exist ({flow}->costs(temporal)). * Update docs * Improve to_netcdf method * Update examples * Fix IIS computaion flag * Fix examples * Fix faceting in heatmap and use period as facet col everywhere * Inline plotting methods to deprecate plotting.py * Fix test * Simplify Color Management * ColorType is now defined in color_processing.py and imported into statistics_accessor.py. * Fix ColorType typing * Add color accessor * Ensure io * Add carrier class * implemented Carrier as a proper Interface subclass with container support. Here's what was done: 1. Carrier class (flixopt/carrier.py) - Now inherits from Interface for serialization capabilities - Has transform_data() method (no-op since carriers have no time-series data) - Has label property for container keying - Maintains equality comparison with both Carrier objects and strings 2. CarrierContainer class (flixopt/carrier.py) - Inherits from ContainerMixin['Carrier'] - Provides dict-like access with nice repr and error messages - Uses carrier.name for keying 3. FlowSystem updates (flixopt/flow_system.py) - _carriers is now a CarrierContainer instead of a plain dict - carriers property returns the CarrierContainer - add_carrier() uses the container's add() method - Serialization updated to include carriers in to_dataset() and restore them in from_dataset() 4. Exports (flixopt/__init__.py) - Both Carrier and CarrierContainer are now exported * Inline plotting methods to deprecate plotting.py (#508) * Inline plotting methods to deprecate plotting.py * Fix test * Simplify Color Management * ColorType is now defined in color_processing.py and imported into statistics_accessor.py. * Fix ColorType typing * statistics_accessor.py - Heatmap colors type safety (lines 121-148, 820-853) - Changed _heatmap_figure() parameter type from colors: ColorType = None to colors: str | list[str] | None = None - Changed heatmap() method parameter type similarly - Updated docstrings to clarify that dicts are not supported for heatmaps since px.imshow's color_continuous_scale only accepts colorscale names or lists 2. statistics_accessor.py - Use configured qualitative colorscale (lines 284, 315) - Updated _create_stacked_bar() to use CONFIG.Plotting.default_qualitative_colorscale as the default colorscale - Updated _create_line() similarly - This ensures user-configured CONFIG.Plotting.default_qualitative_colorscale affects all bar/line plots consistently 3. topology_accessor.py - Path type alignment (lines 219-222) - Added normalization of path=False to None before calling _plot_network() - This resolves the type mismatch where TopologyAccessor.plot() accepts bool | str | Path but _plot_network() only accepts str | Path | None * fix usage if index name in aggregation plot * Add to docs * Improve carrier colors and defaults * Update default carriers and colors * Update config * Update config * Move default carriers to config.py * Change default carrier handling * Add color handling * Rmeove meta_data color handling * Add carrierst to examples * Improve plotting acessor * Improve _resolve_variable_names * Improve _resolve_variable_names * Simplify coloring and remove color accessor * Add connected_and_transformed handling * Improve error message in container * Methods moved to TransformAccessor (transform_accessor.py): - sel() - select by label - isel() - select by integer index - resample() - resample time dimension - Helper methods: _dataset_sel, _dataset_isel, _dataset_resample, _resample_by_dimension_groups 2. Solution is dropped: All transform methods return a new FlowSystem with no solution - the user must re-optimize the transformed system. 3. Deprecation warnings: The old flow_system.sel(), flow_system.isel(), and flow_system.resample() methods now emit deprecation warnings and forward to the new TransformAccessor methods. 4. Backward compatible: Existing code still works, just with deprecation warnings. * Documentation updated * Re-add _dataset_sel and other helper methods for proper deprectation. ALso fix new methods to be classmethods * BUGFIX: Carrier from dataset * Update docs
FBumann
added a commit
that referenced
this pull request
Dec 10, 2025
* Add planning doc
* Finalize planning
* Add plotting acessor
* Add plotting acessor
* Add tests
* Improve
* Improve
* Update docs
* Updated the plotting API so that .data always returns xarray DataArray or Dataset instead of pandas DataFrame.
* All .data now returns xr.Dataset consistently.
* Fixed Inconsistencies and Unused Parameters
* New Plot Accessors
results.plot.variable(pattern)
Plots the same variable type across multiple elements for easy comparison.
# All binary operation states across all components
results.plot.variable('on')
# All flow_rates, filtered to Boiler-related elements
results.plot.variable('flow_rate', include='Boiler')
# All storage charge states
results.plot.variable('charge_state')
# With aggregation
results.plot.variable('flow_rate', aggregate='sum')
Key features:
- Searches all elements for variables matching the pattern
- Filter with include/exclude on element names
- Supports aggregation, faceting, and animation
- Returns Dataset with element names as variables
results.plot.duration_curve(variables)
Plots load duration curves (sorted time series) showing utilization patterns.
# Single variable
results.plot.duration_curve('Boiler(Q_th)|flow_rate')
# Multiple variables
results.plot.duration_curve(['CHP|on', 'Boiler|on'])
# Normalized x-axis (0-100%)
results.plot.duration_curve('demand', normalize=True)
Key features:
- Sorts values from highest to lowest
- Shows how often each power level is reached
- normalize=True shows percentage of time on x-axis
- Returns Dataset with duration_hours or duration_pct dimension
* Fix duration curve
* Fix duration curve
* Fix duration curve
* Fix duration curve
* xr.apply_ufunc to sort along the time axis while preserving all other dimensions automatically
* ⏺ The example runs successfully. Now let me summarize the fixes:
Summary of Fixes
I addressed the actionable code review comments from CodeRabbitAI:
1. Documentation Issue - reshape parameter name ✓ (No fix needed)
The CodeRabbitAI comment was incorrect. The public API parameter in PlotAccessor.heatmap() is correctly named reshape (line 335). The reshape_time parameter exists in the lower-level heatmap_with_plotly function, but the documentation correctly shows the public API parameter.
2. Fixed simple_example.py (lines 129-130)
Problem: The example called balance() and duration_curve() without required arguments, which would cause TypeError at runtime.
Fix: Added the required arguments:
- optimization.results.plot.balance('Fernwärme') - specifying the bus to plot
- optimization.results.plot.duration_curve('Boiler(Q_th)|flow_rate') - specifying the variable to plot
3. Fixed variable collision in plot_accessors.py (lines 985-988)
Problem: When building the Dataset in the variable() method, using element names as keys could cause collisions if multiple variables from the same element matched the pattern (e.g., 'Boiler|flow_rate' and 'Boiler|flow_rate_max' would both map to 'Boiler', with the latter silently overwriting the former).
Fix: Changed to use the full variable names as keys instead of just element names:
ds = xr.Dataset({var_name: self._results.solution[var_name] for var_name in filtered_vars})
All tests pass (40 passed, 1 skipped) and the example runs successfully.
* make variable names public in results
* Fix sankey
* Fix effects()
* Fix effects
* Remove bargaps
* made faceting consistent across all plot methods:
| Method | facet_col | facet_row |
|------------------|-------------------------------------------|-----------------------------|
| balance() | 'scenario' | 'period' |
| heatmap() | 'scenario' | 'period' |
| storage() | 'scenario' | 'period' |
| flows() | 'scenario' | 'period' |
| effects() | 'scenario' | 'period' |
| variable() | 'scenario' | 'period' |
| duration_curve() | 'scenario' | 'period' (already had this) |
| compare() | N/A (uses its own mode='overlay'/'facet') | |
| sankey() | N/A (aggregates to single diagram) | |
Removed animate_by parameter from all methods
* Update storage method
* Remove mode parameter for simpli
| Method | Default mode |
|------------------|---------------------------------------------------|
| balance() | stacked_bar |
| storage() | stacked_bar (flows) + line (charge state overlay) |
| flows() | line |
| variable() | line |
| duration_curve() | line |
| effects() | bar |
* Make plotting_accessors.py more self contained
* Use faster to_long()
* Add 0-dim case
* sankey diagram now properly handles scenarios and periods:
Changes made:
1. Period aggregation with weights: Uses period_weights from flow_system to properly weight periods by their duration
2. Scenario aggregation with weights: Uses normalized scenario_weights to compute a weighted average across scenarios
3. Selection support: Users can filter specific scenarios/periods via select parameter before aggregation
Weighting logic:
- Periods (for aggregate='sum'): (da * period_weights).sum(dim='period') - this gives the total energy across all periods weighted by their duration
- Periods (for aggregate='mean'): (da * period_weights).sum(dim='period') / period_weights.sum() - weighted mean
- Scenarios: Always uses normalized weights (sum to 1) for weighted averaging, since scenarios represent probability-weighted alternatives
* Add colors to sankey
* Add sizes
* Add size filtering
* Include storage sizes
* Remove storage sizes
* Add charge state and status accessor
* Summary of Changes
1. Added new methods to PlotAccessor (plot_accessors.py)
charge_states() (line 658):
- Returns a Dataset with each storage's charge state as a variable
- Supports filtering with include/exclude parameters
- Default plot: line chart
on_states() (line 753):
- Returns a Dataset with each component's |status variable
- Supports filtering with include/exclude parameters
- Default plot: heatmap (good for binary data visualization)
2. Added data building helper functions (plot_accessors.py)
build_flow_rates(results) (line 315):
- Builds a DataArray containing flow rates for all flows
- Used internally by PlotAccessor methods
build_flow_hours(results) (line 333):
- Builds a DataArray containing flow hours for all flows
build_sizes(results) (line 347):
- Builds a DataArray containing sizes for all flows
_filter_dataarray_by_coord(da, **kwargs) (line 284):
- Helper for filtering DataArrays by coordinate values
3. Deprecated old Results methods (results.py)
The following methods now emit DeprecationWarning:
- results.flow_rates() → Use results.plot.flows(plot=False).data
- results.flow_hours() → Use results.plot.flows(unit='flow_hours', plot=False).data
- results.sizes() → Use results.plot.sizes(plot=False).data
4. Updated PlotAccessor methods to use new helpers
- flows() now uses build_flow_rates() / build_flow_hours() directly
- sizes() now uses build_sizes() directly
- sankey() now uses build_flow_hours() directly
This ensures the deprecation warnings only fire when users directly call the old methods, not when using the plot accessor
* 1. New methods added to PlotAccessor
- charge_states(): Returns Dataset with all storage charge states
- on_states(): Returns Dataset with all component status variables (heatmap display)
2. Data building helper functions (plot_accessors.py)
- build_flow_rates(results): Builds DataArray of flow rates
- build_flow_hours(results): Builds DataArray of flow hours
- build_sizes(results): Builds DataArray of sizes
- _filter_dataarray_by_coord(da, **kwargs): Filter helper
- _assign_flow_coords(da, results): Add flow coordinates
3. Caching in PlotAccessor
Added lazy-cached properties for expensive computations:
- _all_flow_rates - cached DataArray of all flow rates
- _all_flow_hours - cached DataArray of all flow hours
- _all_sizes - cached DataArray of all sizes
- _all_charge_states - cached Dataset of all storage charge states
- _all_status_vars - cached Dataset of all status variables
4. Deprecated methods in Results class
Added deprecation warnings to:
- results.flow_rates() → Use results.plot.flows(plot=False).data
- results.flow_hours() → Use results.plot.flows(unit='flow_hours', plot=False).data
- results.sizes() → Use results.plot.sizes(plot=False).data
5. Updated PlotAccessor methods to use cached properties
- flows() uses _all_flow_rates / _all_flow_hours
- sankey() uses _all_flow_hours
- sizes() uses _all_sizes
- charge_states() uses _all_charge_states
- on_states() uses _all_status_vars
* Move deprectated functionality into results.py instead of porting to the new module
* Revert to simply deprectae old methods without forwarding to new code
* Remove planning file
* Update plotting methods for new datasets
* 1. Renamed data properties in PlotAccessor to use all_ prefix:
- all_flow_rates - All flow rates as Dataset
- all_flow_hours - All flow hours as Dataset
- all_sizes - All flow sizes as Dataset
- all_charge_states - All storage charge states as Dataset
- all_on_states - All component on/off status as Dataset
2. Updated internal references - All usages in flows(), sankey(), sizes(), charge_states(), and on_states() methods now use the new names.
3. Updated deprecation messages in results.py to point to the new API:
- results.flow_rates() → results.plot.all_flow_rates
- results.flow_hours() → results.plot.all_flow_hours
- results.sizes() → results.plot.all_sizes
4. Updated docstring examples in PlotAccessor to use the new all_* names.
* Update deprecations messages
* Update deprecations messages
* Thsi seems much better.
* Updaet docstrings and variable name generation in plotting acessor
* Change __ to _ in private dataset caching
* Revert breaking io changes
* New solution storing interface
* Add new focused statistics and plot accessors
* Renamed all properties:
- all_flow_rates → flow_rates
- all_flow_hours → flow_hours
- all_sizes → sizes
- all_charge_states → charge_states
* Cache Statistics
* Invalidate caches
* Add effect related statistics
* Simplify statistics accessor to rely on flow_system directly instead of solution attrs
* Fix heatma fallback for 1D Data
* Add topology accessor
* All deprecation warnings in the codebase now consistently use the format will be removed in v{DEPRECATION_REMOVAL_VERSION}.
* Update tests
* created comprehensive documentation for all FlowSystem accessors
* Update results documentation
* Update results documentation
* Update effect statistics
* Update effect statistics
* Update effect statistics
* Add mkdocs plotly plugin
* Add section about custom constraints
* documentation updates:
docs/user-guide/results/index.md:
- Updated table to replace effects_per_component with temporal_effects, periodic_effects, total_effects, and effect_share_factors
- Fixed flow_hours['Boiler(Q_th)|flow_rate'] → flow_hours['Boiler(Q_th)']
- Fixed sizes['Boiler(Q_th)|size'] → sizes['Boiler(Q_th)']
- Replaced effects_per_component example with new effect properties and groupby examples
- Updated complete example to use total_effects
docs/user-guide/results-plotting.md:
- Fixed colors example from 'Boiler(Q_th)|flow_rate' → 'Boiler(Q_th)'
- Fixed duration_curve examples to use clean labels
docs/user-guide/migration-guide-v6.md:
- Added new "Statistics Accessor" section explaining the clean labels and new effect properties
* implemented the effects() method in StatisticsPlotAccessor at flixopt/statistics_accessor.py:1132-1258.
Summary of what was done:
1. Implemented effects() method in StatisticsPlotAccessor class that was missing but documented
- Takes aspect parameter: 'total', 'temporal', or 'periodic'
- Takes effect parameter to filter to a specific effect (e.g., 'costs', 'CO2')
- Takes by parameter: 'component' or 'time' for grouping
- Supports all standard plotting parameters: select, colors, facet_col, facet_row, show
- Returns PlotResult with both data and figure
2. Verified the implementation works with all parameter combinations:
- Default call: flow_system.statistics.plot.effects()
- Specific effect: flow_system.statistics.plot.effects(effect='costs')
- Temporal aspect: flow_system.statistics.plot.effects(aspect='temporal')
- Temporal by time: flow_system.statistics.plot.effects(aspect='temporal', by='time')
- Periodic aspect: flow_system.statistics.plot.effects(aspect='periodic')
* Remove intermediate plot accessor
* 1. pyproject.toml: Removed duplicate mkdocs-plotly-plugin>=0.1.3 entry (kept the exact pin ==0.1.3)
2. flixopt/plotting.py: Fixed dimension name consistency by using squeezed_data.name instead of data.name in the fallback heatmap logic
3. flixopt/statistics_accessor.py:
- Fixed _dataset_to_long_df() to only use coordinates that are actually present as columns after reset_index()
- Fixed the nested loop inefficiency with include_flows by pre-computing the flows list outside the loop
- (Previously fixed) Fixed asymmetric NaN handling in validation check
* _create_effects_dataset method in statistics_accessor.py was simplified:
1. Detect contributors from solution data variables instead of assuming they're only flows
- Uses regex pattern to find {contributor}->{effect}(temporal|periodic) variables
- Contributors can be flows OR components (e.g., components with effects_per_active_hour)
2. Exclude effect-to-effect shares
- Filters out contributors whose base name matches any effect label
- For example, costs(temporal) is excluded because costs is an effect label
- These intermediate shares are already included in the computation
3. Removed the unused _compute_effect_total method
- The new simplified implementation directly looks up shares from the solution
- Uses effect_share_factors for conversion between effects
4. Key insight from user: The solution already contains properly computed share values including all effect-to-effect conversions. The computation uses conversion
factors because derived effects (like Effect1 which shares 0.5 from costs) don't have direct {flow}->Effect1(temporal) variables - only the source effect shares exist
({flow}->costs(temporal)).
* Update docs
* Improve to_netcdf method
* Update examples
* Fix IIS computaion flag
* Fix examples
* Fix faceting in heatmap and use period as facet col everywhere
* Inline plotting methods to deprecate plotting.py
* Fix test
* Simplify Color Management
* ColorType is now defined in color_processing.py and imported into statistics_accessor.py.
* Fix ColorType typing
* Add color accessor
* Ensure io
* Add carrier class
* implemented Carrier as a proper Interface subclass with container support. Here's what was done:
1. Carrier class (flixopt/carrier.py)
- Now inherits from Interface for serialization capabilities
- Has transform_data() method (no-op since carriers have no time-series data)
- Has label property for container keying
- Maintains equality comparison with both Carrier objects and strings
2. CarrierContainer class (flixopt/carrier.py)
- Inherits from ContainerMixin['Carrier']
- Provides dict-like access with nice repr and error messages
- Uses carrier.name for keying
3. FlowSystem updates (flixopt/flow_system.py)
- _carriers is now a CarrierContainer instead of a plain dict
- carriers property returns the CarrierContainer
- add_carrier() uses the container's add() method
- Serialization updated to include carriers in to_dataset() and restore them in from_dataset()
4. Exports (flixopt/__init__.py)
- Both Carrier and CarrierContainer are now exported
* Inline plotting methods to deprecate plotting.py (#508)
* Inline plotting methods to deprecate plotting.py
* Fix test
* Simplify Color Management
* ColorType is now defined in color_processing.py and imported into statistics_accessor.py.
* Fix ColorType typing
* statistics_accessor.py - Heatmap colors type safety (lines 121-148, 820-853)
- Changed _heatmap_figure() parameter type from colors: ColorType = None to colors: str | list[str] | None = None
- Changed heatmap() method parameter type similarly
- Updated docstrings to clarify that dicts are not supported for heatmaps since px.imshow's color_continuous_scale only accepts colorscale names or lists
2. statistics_accessor.py - Use configured qualitative colorscale (lines 284, 315)
- Updated _create_stacked_bar() to use CONFIG.Plotting.default_qualitative_colorscale as the default colorscale
- Updated _create_line() similarly
- This ensures user-configured CONFIG.Plotting.default_qualitative_colorscale affects all bar/line plots consistently
3. topology_accessor.py - Path type alignment (lines 219-222)
- Added normalization of path=False to None before calling _plot_network()
- This resolves the type mismatch where TopologyAccessor.plot() accepts bool | str | Path but _plot_network() only accepts str | Path | None
* fix usage if index name in aggregation plot
* Add to docs
* Improve carrier colors and defaults
* Update default carriers and colors
* Update config
* Update config
* Move default carriers to config.py
* Change default carrier handling
* Add color handling
* Rmeove meta_data color handling
* Add carrierst to examples
* Improve plotting acessor
* Improve _resolve_variable_names
* Improve _resolve_variable_names
* Simplify coloring and remove color accessor
* Add connected_and_transformed handling
* Improve error message in container
* Methods moved to TransformAccessor (transform_accessor.py):
- sel() - select by label
- isel() - select by integer index
- resample() - resample time dimension
- Helper methods: _dataset_sel, _dataset_isel, _dataset_resample, _resample_by_dimension_groups
2. Solution is dropped: All transform methods return a new FlowSystem with no solution - the user must re-optimize the transformed system.
3. Deprecation warnings: The old flow_system.sel(), flow_system.isel(), and flow_system.resample() methods now emit deprecation warnings and forward to the new
TransformAccessor methods.
4. Backward compatible: Existing code still works, just with deprecation warnings.
* Documentation updated
* Re-add _dataset_sel and other helper methods for proper deprectation. ALso fix new methods to be classmethods
* BUGFIX: Carrier from dataset
* Update docs
* Add notebook examples instead of raw py files for docs
* Execute on docs buildt
* Add notebooks and new config util andmore notebook related stuff
* Fix notebooks
* Fix notebooks and config.py
* docstring typo
* Update notebooks
* Add fix_sizes method and use in two stage optimization notebook
* Change notebook config
* Dont get size as float in fix sizes!
* Update notebook
* fix: fix_sizes() to handle var names properly
* Add imbalance penalty to prevent infeasibility in Fernwärme Bus
* Remove putputs from all notbeooks for git
* Update the two stage notebook
* Update notebooks with user stories
* Add new index to docs
* Update notebooks to use plotly instead of matplotlib
* fix: Use plotly express
* fix: Use plotly express
* fix: Use plotly express
* Adjust plots
* Bugfix: Add _update_scenario_metadata method
* fix: _update_scenario_metadata method
* Get all notebooks running
* Improve notebooks for more interesting results
* Fix conversion factor
* Fix conversion factor
* Update notebooks and bugfix sankey
* Add charge state and storages plots
* improve charge state plot
* Add Piecewise plots and improve Notebooks (#519)
* Build notebooks in parralel in CI
* Revert "Build notebooks in parralel in CI"
This reverts commit 0f1153c.
* Fix dependencies in docs workflow
* Use extra css
* Use extra css
* Use extra css
* Use extra css
* Fix notebook
* Add new statistics for sizes, flow_sizes and storage_sizes
* Fixed broken links in docs
* Fix mkdocs buildt noebooks on build
* Remove old example notebooks and rename folder to notebooks
* Add .storages property to FlowSystem
* fix broken links in docs
* Imrpove data extraction
* Imrpove data extraction
* Remove examples and test_examples.py
FBumann
added a commit
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Dec 13, 2025
* Add planning doc
* Finalize planning
* Add plotting acessor
* Add plotting acessor
* Add tests
* Improve
* Improve
* Update docs
* Updated the plotting API so that .data always returns xarray DataArray or Dataset instead of pandas DataFrame.
* All .data now returns xr.Dataset consistently.
* Fixed Inconsistencies and Unused Parameters
* New Plot Accessors
results.plot.variable(pattern)
Plots the same variable type across multiple elements for easy comparison.
# All binary operation states across all components
results.plot.variable('on')
# All flow_rates, filtered to Boiler-related elements
results.plot.variable('flow_rate', include='Boiler')
# All storage charge states
results.plot.variable('charge_state')
# With aggregation
results.plot.variable('flow_rate', aggregate='sum')
Key features:
- Searches all elements for variables matching the pattern
- Filter with include/exclude on element names
- Supports aggregation, faceting, and animation
- Returns Dataset with element names as variables
results.plot.duration_curve(variables)
Plots load duration curves (sorted time series) showing utilization patterns.
# Single variable
results.plot.duration_curve('Boiler(Q_th)|flow_rate')
# Multiple variables
results.plot.duration_curve(['CHP|on', 'Boiler|on'])
# Normalized x-axis (0-100%)
results.plot.duration_curve('demand', normalize=True)
Key features:
- Sorts values from highest to lowest
- Shows how often each power level is reached
- normalize=True shows percentage of time on x-axis
- Returns Dataset with duration_hours or duration_pct dimension
* Fix duration curve
* Fix duration curve
* Fix duration curve
* Fix duration curve
* xr.apply_ufunc to sort along the time axis while preserving all other dimensions automatically
* ⏺ The example runs successfully. Now let me summarize the fixes:
Summary of Fixes
I addressed the actionable code review comments from CodeRabbitAI:
1. Documentation Issue - reshape parameter name ✓ (No fix needed)
The CodeRabbitAI comment was incorrect. The public API parameter in PlotAccessor.heatmap() is correctly named reshape (line 335). The reshape_time parameter exists in the lower-level heatmap_with_plotly function, but the documentation correctly shows the public API parameter.
2. Fixed simple_example.py (lines 129-130)
Problem: The example called balance() and duration_curve() without required arguments, which would cause TypeError at runtime.
Fix: Added the required arguments:
- optimization.results.plot.balance('Fernwärme') - specifying the bus to plot
- optimization.results.plot.duration_curve('Boiler(Q_th)|flow_rate') - specifying the variable to plot
3. Fixed variable collision in plot_accessors.py (lines 985-988)
Problem: When building the Dataset in the variable() method, using element names as keys could cause collisions if multiple variables from the same element matched the pattern (e.g., 'Boiler|flow_rate' and 'Boiler|flow_rate_max' would both map to 'Boiler', with the latter silently overwriting the former).
Fix: Changed to use the full variable names as keys instead of just element names:
ds = xr.Dataset({var_name: self._results.solution[var_name] for var_name in filtered_vars})
All tests pass (40 passed, 1 skipped) and the example runs successfully.
* make variable names public in results
* Fix sankey
* Fix effects()
* Fix effects
* Remove bargaps
* made faceting consistent across all plot methods:
| Method | facet_col | facet_row |
|------------------|-------------------------------------------|-----------------------------|
| balance() | 'scenario' | 'period' |
| heatmap() | 'scenario' | 'period' |
| storage() | 'scenario' | 'period' |
| flows() | 'scenario' | 'period' |
| effects() | 'scenario' | 'period' |
| variable() | 'scenario' | 'period' |
| duration_curve() | 'scenario' | 'period' (already had this) |
| compare() | N/A (uses its own mode='overlay'/'facet') | |
| sankey() | N/A (aggregates to single diagram) | |
Removed animate_by parameter from all methods
* Update storage method
* Remove mode parameter for simpli
| Method | Default mode |
|------------------|---------------------------------------------------|
| balance() | stacked_bar |
| storage() | stacked_bar (flows) + line (charge state overlay) |
| flows() | line |
| variable() | line |
| duration_curve() | line |
| effects() | bar |
* Make plotting_accessors.py more self contained
* Use faster to_long()
* Add 0-dim case
* sankey diagram now properly handles scenarios and periods:
Changes made:
1. Period aggregation with weights: Uses period_weights from flow_system to properly weight periods by their duration
2. Scenario aggregation with weights: Uses normalized scenario_weights to compute a weighted average across scenarios
3. Selection support: Users can filter specific scenarios/periods via select parameter before aggregation
Weighting logic:
- Periods (for aggregate='sum'): (da * period_weights).sum(dim='period') - this gives the total energy across all periods weighted by their duration
- Periods (for aggregate='mean'): (da * period_weights).sum(dim='period') / period_weights.sum() - weighted mean
- Scenarios: Always uses normalized weights (sum to 1) for weighted averaging, since scenarios represent probability-weighted alternatives
* Add colors to sankey
* Add sizes
* Add size filtering
* Include storage sizes
* Remove storage sizes
* Add charge state and status accessor
* Summary of Changes
1. Added new methods to PlotAccessor (plot_accessors.py)
charge_states() (line 658):
- Returns a Dataset with each storage's charge state as a variable
- Supports filtering with include/exclude parameters
- Default plot: line chart
on_states() (line 753):
- Returns a Dataset with each component's |status variable
- Supports filtering with include/exclude parameters
- Default plot: heatmap (good for binary data visualization)
2. Added data building helper functions (plot_accessors.py)
build_flow_rates(results) (line 315):
- Builds a DataArray containing flow rates for all flows
- Used internally by PlotAccessor methods
build_flow_hours(results) (line 333):
- Builds a DataArray containing flow hours for all flows
build_sizes(results) (line 347):
- Builds a DataArray containing sizes for all flows
_filter_dataarray_by_coord(da, **kwargs) (line 284):
- Helper for filtering DataArrays by coordinate values
3. Deprecated old Results methods (results.py)
The following methods now emit DeprecationWarning:
- results.flow_rates() → Use results.plot.flows(plot=False).data
- results.flow_hours() → Use results.plot.flows(unit='flow_hours', plot=False).data
- results.sizes() → Use results.plot.sizes(plot=False).data
4. Updated PlotAccessor methods to use new helpers
- flows() now uses build_flow_rates() / build_flow_hours() directly
- sizes() now uses build_sizes() directly
- sankey() now uses build_flow_hours() directly
This ensures the deprecation warnings only fire when users directly call the old methods, not when using the plot accessor
* 1. New methods added to PlotAccessor
- charge_states(): Returns Dataset with all storage charge states
- on_states(): Returns Dataset with all component status variables (heatmap display)
2. Data building helper functions (plot_accessors.py)
- build_flow_rates(results): Builds DataArray of flow rates
- build_flow_hours(results): Builds DataArray of flow hours
- build_sizes(results): Builds DataArray of sizes
- _filter_dataarray_by_coord(da, **kwargs): Filter helper
- _assign_flow_coords(da, results): Add flow coordinates
3. Caching in PlotAccessor
Added lazy-cached properties for expensive computations:
- _all_flow_rates - cached DataArray of all flow rates
- _all_flow_hours - cached DataArray of all flow hours
- _all_sizes - cached DataArray of all sizes
- _all_charge_states - cached Dataset of all storage charge states
- _all_status_vars - cached Dataset of all status variables
4. Deprecated methods in Results class
Added deprecation warnings to:
- results.flow_rates() → Use results.plot.flows(plot=False).data
- results.flow_hours() → Use results.plot.flows(unit='flow_hours', plot=False).data
- results.sizes() → Use results.plot.sizes(plot=False).data
5. Updated PlotAccessor methods to use cached properties
- flows() uses _all_flow_rates / _all_flow_hours
- sankey() uses _all_flow_hours
- sizes() uses _all_sizes
- charge_states() uses _all_charge_states
- on_states() uses _all_status_vars
* Move deprectated functionality into results.py instead of porting to the new module
* Revert to simply deprectae old methods without forwarding to new code
* Remove planning file
* Update plotting methods for new datasets
* 1. Renamed data properties in PlotAccessor to use all_ prefix:
- all_flow_rates - All flow rates as Dataset
- all_flow_hours - All flow hours as Dataset
- all_sizes - All flow sizes as Dataset
- all_charge_states - All storage charge states as Dataset
- all_on_states - All component on/off status as Dataset
2. Updated internal references - All usages in flows(), sankey(), sizes(), charge_states(), and on_states() methods now use the new names.
3. Updated deprecation messages in results.py to point to the new API:
- results.flow_rates() → results.plot.all_flow_rates
- results.flow_hours() → results.plot.all_flow_hours
- results.sizes() → results.plot.all_sizes
4. Updated docstring examples in PlotAccessor to use the new all_* names.
* Update deprecations messages
* Update deprecations messages
* Thsi seems much better.
* Updaet docstrings and variable name generation in plotting acessor
* Change __ to _ in private dataset caching
* Revert breaking io changes
* New solution storing interface
* Add new focused statistics and plot accessors
* Renamed all properties:
- all_flow_rates → flow_rates
- all_flow_hours → flow_hours
- all_sizes → sizes
- all_charge_states → charge_states
* Cache Statistics
* Invalidate caches
* Add effect related statistics
* Simplify statistics accessor to rely on flow_system directly instead of solution attrs
* Fix heatma fallback for 1D Data
* Add topology accessor
* All deprecation warnings in the codebase now consistently use the format will be removed in v{DEPRECATION_REMOVAL_VERSION}.
* Update tests
* created comprehensive documentation for all FlowSystem accessors
* Update results documentation
* Update results documentation
* Update effect statistics
* Update effect statistics
* Update effect statistics
* Add mkdocs plotly plugin
* Add section about custom constraints
* documentation updates:
docs/user-guide/results/index.md:
- Updated table to replace effects_per_component with temporal_effects, periodic_effects, total_effects, and effect_share_factors
- Fixed flow_hours['Boiler(Q_th)|flow_rate'] → flow_hours['Boiler(Q_th)']
- Fixed sizes['Boiler(Q_th)|size'] → sizes['Boiler(Q_th)']
- Replaced effects_per_component example with new effect properties and groupby examples
- Updated complete example to use total_effects
docs/user-guide/results-plotting.md:
- Fixed colors example from 'Boiler(Q_th)|flow_rate' → 'Boiler(Q_th)'
- Fixed duration_curve examples to use clean labels
docs/user-guide/migration-guide-v6.md:
- Added new "Statistics Accessor" section explaining the clean labels and new effect properties
* implemented the effects() method in StatisticsPlotAccessor at flixopt/statistics_accessor.py:1132-1258.
Summary of what was done:
1. Implemented effects() method in StatisticsPlotAccessor class that was missing but documented
- Takes aspect parameter: 'total', 'temporal', or 'periodic'
- Takes effect parameter to filter to a specific effect (e.g., 'costs', 'CO2')
- Takes by parameter: 'component' or 'time' for grouping
- Supports all standard plotting parameters: select, colors, facet_col, facet_row, show
- Returns PlotResult with both data and figure
2. Verified the implementation works with all parameter combinations:
- Default call: flow_system.statistics.plot.effects()
- Specific effect: flow_system.statistics.plot.effects(effect='costs')
- Temporal aspect: flow_system.statistics.plot.effects(aspect='temporal')
- Temporal by time: flow_system.statistics.plot.effects(aspect='temporal', by='time')
- Periodic aspect: flow_system.statistics.plot.effects(aspect='periodic')
* Remove intermediate plot accessor
* 1. pyproject.toml: Removed duplicate mkdocs-plotly-plugin>=0.1.3 entry (kept the exact pin ==0.1.3)
2. flixopt/plotting.py: Fixed dimension name consistency by using squeezed_data.name instead of data.name in the fallback heatmap logic
3. flixopt/statistics_accessor.py:
- Fixed _dataset_to_long_df() to only use coordinates that are actually present as columns after reset_index()
- Fixed the nested loop inefficiency with include_flows by pre-computing the flows list outside the loop
- (Previously fixed) Fixed asymmetric NaN handling in validation check
* _create_effects_dataset method in statistics_accessor.py was simplified:
1. Detect contributors from solution data variables instead of assuming they're only flows
- Uses regex pattern to find {contributor}->{effect}(temporal|periodic) variables
- Contributors can be flows OR components (e.g., components with effects_per_active_hour)
2. Exclude effect-to-effect shares
- Filters out contributors whose base name matches any effect label
- For example, costs(temporal) is excluded because costs is an effect label
- These intermediate shares are already included in the computation
3. Removed the unused _compute_effect_total method
- The new simplified implementation directly looks up shares from the solution
- Uses effect_share_factors for conversion between effects
4. Key insight from user: The solution already contains properly computed share values including all effect-to-effect conversions. The computation uses conversion
factors because derived effects (like Effect1 which shares 0.5 from costs) don't have direct {flow}->Effect1(temporal) variables - only the source effect shares exist
({flow}->costs(temporal)).
* Update docs
* Improve to_netcdf method
* Update examples
* Fix IIS computaion flag
* Fix examples
* Fix faceting in heatmap and use period as facet col everywhere
* Inline plotting methods to deprecate plotting.py
* Fix test
* Simplify Color Management
* ColorType is now defined in color_processing.py and imported into statistics_accessor.py.
* Fix ColorType typing
* Add color accessor
* Ensure io
* Add carrier class
* implemented Carrier as a proper Interface subclass with container support. Here's what was done:
1. Carrier class (flixopt/carrier.py)
- Now inherits from Interface for serialization capabilities
- Has transform_data() method (no-op since carriers have no time-series data)
- Has label property for container keying
- Maintains equality comparison with both Carrier objects and strings
2. CarrierContainer class (flixopt/carrier.py)
- Inherits from ContainerMixin['Carrier']
- Provides dict-like access with nice repr and error messages
- Uses carrier.name for keying
3. FlowSystem updates (flixopt/flow_system.py)
- _carriers is now a CarrierContainer instead of a plain dict
- carriers property returns the CarrierContainer
- add_carrier() uses the container's add() method
- Serialization updated to include carriers in to_dataset() and restore them in from_dataset()
4. Exports (flixopt/__init__.py)
- Both Carrier and CarrierContainer are now exported
* Inline plotting methods to deprecate plotting.py (#508)
* Inline plotting methods to deprecate plotting.py
* Fix test
* Simplify Color Management
* ColorType is now defined in color_processing.py and imported into statistics_accessor.py.
* Fix ColorType typing
* statistics_accessor.py - Heatmap colors type safety (lines 121-148, 820-853)
- Changed _heatmap_figure() parameter type from colors: ColorType = None to colors: str | list[str] | None = None
- Changed heatmap() method parameter type similarly
- Updated docstrings to clarify that dicts are not supported for heatmaps since px.imshow's color_continuous_scale only accepts colorscale names or lists
2. statistics_accessor.py - Use configured qualitative colorscale (lines 284, 315)
- Updated _create_stacked_bar() to use CONFIG.Plotting.default_qualitative_colorscale as the default colorscale
- Updated _create_line() similarly
- This ensures user-configured CONFIG.Plotting.default_qualitative_colorscale affects all bar/line plots consistently
3. topology_accessor.py - Path type alignment (lines 219-222)
- Added normalization of path=False to None before calling _plot_network()
- This resolves the type mismatch where TopologyAccessor.plot() accepts bool | str | Path but _plot_network() only accepts str | Path | None
* fix usage if index name in aggregation plot
* Add to docs
* Improve carrier colors and defaults
* Update default carriers and colors
* Update config
* Update config
* Move default carriers to config.py
* Change default carrier handling
* Add color handling
* Rmeove meta_data color handling
* Add carrierst to examples
* Improve plotting acessor
* Improve _resolve_variable_names
* Improve _resolve_variable_names
* Simplify coloring and remove color accessor
* Add connected_and_transformed handling
* Improve error message in container
* Methods moved to TransformAccessor (transform_accessor.py):
- sel() - select by label
- isel() - select by integer index
- resample() - resample time dimension
- Helper methods: _dataset_sel, _dataset_isel, _dataset_resample, _resample_by_dimension_groups
2. Solution is dropped: All transform methods return a new FlowSystem with no solution - the user must re-optimize the transformed system.
3. Deprecation warnings: The old flow_system.sel(), flow_system.isel(), and flow_system.resample() methods now emit deprecation warnings and forward to the new
TransformAccessor methods.
4. Backward compatible: Existing code still works, just with deprecation warnings.
* Documentation updated
* Re-add _dataset_sel and other helper methods for proper deprectation. ALso fix new methods to be classmethods
* BUGFIX: Carrier from dataset
* Update docs
* Add notebook examples instead of raw py files for docs
* Execute on docs buildt
* Add notebooks and new config util andmore notebook related stuff
* Fix notebooks
* Fix notebooks and config.py
* docstring typo
* Update notebooks
* Add fix_sizes method and use in two stage optimization notebook
* Change notebook config
* Dont get size as float in fix sizes!
* Update notebook
* fix: fix_sizes() to handle var names properly
* Add imbalance penalty to prevent infeasibility in Fernwärme Bus
* Remove putputs from all notbeooks for git
* Update the two stage notebook
* Update notebooks with user stories
* Add new index to docs
* Update notebooks to use plotly instead of matplotlib
* fix: Use plotly express
* fix: Use plotly express
* fix: Use plotly express
* Adjust plots
* Bugfix: Add _update_scenario_metadata method
* fix: _update_scenario_metadata method
* Get all notebooks running
* Improve notebooks for more interesting results
* Fix conversion factor
* Fix conversion factor
* Update notebooks and bugfix sankey
* Add charge state and storages plots
* improve charge state plot
* Add Piecewise plots and improve Notebooks (#519)
* Build notebooks in parralel in CI
* Revert "Build notebooks in parralel in CI"
This reverts commit 0f1153c.
* Fix dependencies in docs workflow
* Use extra css
* Use extra css
* Use extra css
* Use extra css
* Fix notebook
* Add new statistics for sizes, flow_sizes and storage_sizes
* Fixed broken links in docs
* Fix mkdocs buildt noebooks on build
* Remove old example notebooks and rename folder to notebooks
* Add .storages property to FlowSystem
* fix broken links in docs
* Imrpove data extraction
* Imrpove data extraction
* Remove examples and test_examples.py
This was referenced Jan 2, 2026
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