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

Author: FBumann

flixopt/clustering.py

@@ -7,7 +7,6 @@
 
 import copy
 import logging
-import pathlib
 import timeit
 from typing import TYPE_CHECKING
 
@@ -29,6 +28,8 @@
 )
 
 if TYPE_CHECKING:
+    import pathlib
+
     import linopy
     import pandas as pd
     import plotly.graph_objects as go
@@ -145,7 +146,7 @@ def use_extreme_periods(self):
         return self.time_series_for_high_peaks or self.time_series_for_low_peaks
 
     def plot(self, colormap: str | None = None, show: bool = True, save: pathlib.Path | None = None) -> go.Figure:
-        from . import plotting
+        import plotly.express as px
 
         df_org = self.original_data.copy().rename(
             columns={col: f'Original - {col}' for col in self.original_data.columns}
@@ -156,10 +157,17 @@ def plot(self, colormap: str | None = None, show: bool = True, save: pathlib.Pat
         colors = list(
             process_colors(colormap or CONFIG.Plotting.default_qualitative_colorscale, list(df_org.columns)).values()
         )
-        fig = plotting.with_plotly(df_org.to_xarray(), 'line', colors=colors, xlabel='Time in h')
+
+        # Create line plot for original data (dashed)
+        index_name = df_org.index.name or 'index'
+        df_org_long = df_org.reset_index().melt(id_vars=index_name, var_name='variable', value_name='value')
+        fig = px.line(df_org_long, x=index_name, y='value', color='variable', color_discrete_sequence=colors)
         for trace in fig.data:
-            trace.update(dict(line=dict(dash='dash')))
-        fig2 = plotting.with_plotly(df_agg.to_xarray(), 'line', colors=colors, xlabel='Time in h')
+            trace.update(line=dict(dash='dash'))
+
+        # Add aggregated data (solid lines)
+        df_agg_long = df_agg.reset_index().melt(id_vars=index_name, var_name='variable', value_name='value')
+        fig2 = px.line(df_agg_long, x=index_name, y='value', color='variable', color_discrete_sequence=colors)
         for trace in fig2.data:
             fig.add_trace(trace)
 
@@ -169,14 +177,10 @@ def plot(self, colormap: str | None = None, show: bool = True, save: pathlib.Pat
             yaxis_title='Value',
         )
 
-        plotting.export_figure(
-            figure_like=fig,
-            default_path=pathlib.Path('aggregated data.html'),
-            default_filetype='.html',
-            user_path=save,
-            show=show,
-            save=save is not None,
-        )
+        if save is not None:
+            fig.write_html(str(save))
+        if show:
+            fig.show()
 
         return fig
 

flixopt/color_processing.py

@@ -15,6 +15,57 @@
 
 logger = logging.getLogger('flixopt')
 
+# Type alias for flexible color input
+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/
+"""
+
 
 def _rgb_string_to_hex(color: str) -> str:
     """Convert Plotly RGB/RGBA string format to hex.

flixopt/plotting.py

@@ -39,7 +39,7 @@
 import plotly.offline
 import xarray as xr
 
-from .color_processing import process_colors
+from .color_processing import ColorType, process_colors
 from .config import CONFIG
 
 if TYPE_CHECKING:
@@ -66,56 +66,6 @@
         plt.register_cmap(name='portland', cmap=mcolors.LinearSegmentedColormap.from_list('portland', _portland_colors))
 
 
-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/
-"""
-
 PlottingEngine = Literal['plotly', 'matplotlib']
 """Identifier for the plotting engine to use."""