Remove fxplot accessor in favor of external ploty accessor (#560)

* Remove fxplot accessor in favor of external ploty accessor

* Moved stats accessor to new module name

* Reuse fxstats.duration_curve

* Update notebooks

* Update notebooks

* Remove auto

* Set x='time' for most plots

* Remove slot params

* Improve color handling

* 1. statistics_accessor.py

  - Removed explicit slot parameters (facet_col, facet_row, animation_frame) from all plotting methods
  - Added _build_color_kwargs() helper to consistently handle dict, list, and string colors
  - Refactored all methods to use _build_color_kwargs for consistent color handling
  - Updated _prepare_for_heatmap() to support reshape='auto' (default)
  - Slot parameters are now passed through via **plotly_kwargs

  2. clustering/base.py

  - Removed explicit slot parameters from compare() and heatmap() methods
  - Parameters passed through via **plotly_kwargs

  3. comparison.py

  - Completely refactored ComparisonStatisticsPlot:
    - Removed the complex generic _plot() method
    - Each method now has explicit parameters with proper docstrings
    - Uses _build_color_kwargs consistently
    - Changed ds.fxplot to ds.plotly
    - Changed stacked_bar to bar with barmode='relative'

  Key Design Patterns

  1. Color handling: All methods use _build_color_kwargs(colors, labels) which handles:
    - dict → color_discrete_map
    - list → color_discrete_sequence
    - str (colorscale name) → converted to color_discrete_map via process_colors()
  2. Dimension slots: Let xarray-plotly handle assignments by default, only set explicitly when needed (e.g., x='time' for time series)
  3. Consistent API: All methods now have explicit parameters instead of **kwargs everywhere

* ⏺ All done! Here's a summary of the changes:

  Changes to flixopt/comparison.py:

  1. Added import: from xarray_plotly import SLOT_ORDERS
  2. Added helper function:
  _CASE_SLOTS = frozenset(slot for slots in SLOT_ORDERS.values() for slot in slots)

  def _set_case_default(kwargs: dict, slot: str) -> None:
      """Set default slot for 'case' dimension if not already assigned elsewhere."""
      if not any(kwargs.get(s) == 'case' for s in _CASE_SLOTS):
          kwargs.setdefault(slot, 'case')
  3. Updated all 9 plotting methods with sensible defaults:

  | Method            | Default Slot                   |
  |-------------------|--------------------------------|
  | balance()         | facet_col='case'               |
  | carrier_balance() | facet_col='case'               |
  | storage()         | facet_col='case'               |
  | flows()           | line_dash='case'               |
  | charge_states()   | line_dash='case'               |
  | duration_curve()  | line_dash='case'               |
  | sizes()           | color='case' + barmode='group' |
  | effects()         | color='case' + barmode='group' |
  | heatmap()         | facet_col='case'               |

  The defaults are "safe" - if the user explicitly assigns 'case' to any slot, the default won't override it.

* ⏺ Add sensible slot defaults for comparison plots and block unused slots

  Comparison plots now automatically assign the 'case' dimension to
  appropriate slots based on plot type:
  - Stacked bars (balance, carrier_balance, storage): facet_col='case'
  - Line charts (flows, charge_states, duration_curve): line_dash='case'
  - Grouped bars (sizes, effects): color='case' with barmode='group'
  - Heatmaps: facet_col='case'

  Uses _set_case_default() helper that checks if 'case' is already
  assigned elsewhere before setting the default, preventing conflicts.

  Also blocks unused slots in StatisticsPlotAccessor to prevent
  xarray_plotly from auto-assigning dimensions to visually unhelpful
  slots:
  - Time-series bars: pattern_shape=None
  - Line charts: symbol=None

  All defaults use setdefault() so users can override when needed.

* Add sensible slot defaults for plotting methods

  Use setdefault() for all dimension-to-slot assignments, allowing user
  overrides while providing sensible defaults.

  Comparison plots (ComparisonStatisticsPlot):
  - Stacked bars: x='time', facet_col='case'
  - Line charts: x='time', line_dash='case'
  - Grouped bars: x='variable', color='case', barmode='group'
  - Duration curve: x='duration_pct'/'duration'
  - Heatmaps: facet_col='case'

  Statistics plots (StatisticsPlotAccessor):
  - Time-series bars: x='time', pattern_shape=None (blocked)
  - Line charts: x='time', symbol=None (blocked)
  - Duration curve: x='duration_pct'/'duration'
  - Sizes: x='variable'
  - Effects: x computed from 'by' parameter

  Uses SLOT_ORDERS from xarray_plotly to dynamically detect all valid
  slots when checking for 'case' dimension conflicts.

  All defaults use setdefault() so users can override when needed.

* Updated _SLOT_DEFAULTS dict with proper plotly kwargs (no custom keys):
  _SLOT_DEFAULTS: dict[str, dict[str, str | None]] = {
      'balance': {'x': 'time', 'color': 'variable', 'pattern_shape': None, 'facet_col': 'case'},
      'carrier_balance': {'x': 'time', 'color': 'variable', 'pattern_shape': None, 'facet_col': 'case'},
      'flows': {'x': 'time', 'color': 'variable', 'symbol': None, 'line_dash': 'case'},
      'charge_states': {'x': 'time', 'color': 'variable', 'symbol': None, 'line_dash': 'case'},
      'storage': {'x': 'time', 'color': 'variable', 'pattern_shape': None, 'facet_col': 'case'},
      'sizes': {'x': 'variable', 'color': 'case'},
      'duration_curve': {'symbol': None, 'line_dash': 'case'},
      'effects': {'color': 'case'},
      'heatmap': {'facet_col': 'case'},
  }

  Updated _apply_slot_defaults() helper to detect if 'case' is already assigned:
  - If user has already set 'case' to any slot, skips the default case assignment
  - Otherwise applies all defaults via setdefault() (user overridable)

  Updated all 9 methods (balance, carrier_balance, flows, charge_states, storage, duration_curve, sizes, effects, heatmap) to use:
  _apply_slot_defaults(plotly_kwargs, 'method_name')

* Move slot defaults inline into each comparison plot method

Improves code locality by defining defaults dict directly in each method
rather than in a module-level lookup table.

* Build job (PRs & pushes):
  - Only runs fast notebooks (~1 min total)
  - Slow notebooks are skipped

  Deploy job (releases only):
  - Runs fast notebooks first
  - Then runs slow notebooks (~10 min extra)
  - Full documentation with all notebooks executed

  Files changed:
  - .github/workflows/docs.yaml - updated both execution steps
  - docs/notebooks/slow_notebooks.txt - created with the 2 slow notebooks

  To add/remove slow notebooks in the future, just edit slow_notebooks.txt.

* Fix storage plot passing invalid kwargs to add_line_overlay

Filter plotly_kwargs to only pass faceting-related kwargs (x, facet_col,
facet_row, animation_frame) to add_line_overlay, avoiding TypeError from
bar-chart-specific kwargs like pattern_shape.

Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>

* Fix storage plot passing invalid kwargs to add_line_overlay

Filter plotly_kwargs to only pass faceting-related kwargs (x, facet_col,
facet_row, animation_frame) to add_line_overlay, avoiding TypeError from
bar-chart-specific kwargs like pattern_shape.

Applied to both statistics_accessor.py and comparison.py.

Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>

* 1. Identified slow notebooks - 08b-rolling-horizon (5 min) and 08c2-clustering-storage-modes (5 min)
  2. Created slow_notebooks.txt - config file to exclude them from regular CI
  3. Updated CI - fast notebooks run on every build, slow ones only on release
  4. Switched to Plotly CDN - notebooks now ~98% smaller (4.8MB → 79KB each)

* - Type: tuple[str, str] | Literal['auto'] | None
  - Docstring: "Use 'auto' to reshape to ('D', 'h') if data has a time index"

* Apply slot defaults pattern to clustering and fix color handling

- Add _apply_slot_defaults helper for clustering plots
- Use inline defaults dict in compare() and clusters() methods
- Remove explicit slot parameters (color, line_dash, facet_col) from signatures
- Pass all slot assignments through **plotly_kwargs with sensible defaults
- Fix color handling: use _build_color_kwargs for proper ColorType support
- Apply default colorscale for heatmap when colors=None

Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>

* Add other plot to notebook

* Add plotly template

* Add plotly template

* Fix notebook

* Add docs about ploty express

* More about plotly templates

* Fixed. Now n is calculated explicitly from the first entry in sorted_vars rather than relying on the implicit loop variable from the last iteration.

* Template System

  - 'flixopt' template registered on import (no side effects)
  - CONFIG.use_theme() to explicitly activate
  - Fixed .title() for colorscale names + fallback handling

  Color Handling Simplification

  - process_colors() reads CONFIG internally
  - Removed 8+ explicit default_colorscale=CONFIG... calls
  - Heatmaps use template default when colors not specified

  Heatmap Default

  - Changed reshape=None → reshape=('D', 'h')
  - More useful out-of-the-box daily pattern visualization

  Documentation

  - Added "Plotly Express Internals" section covering slots, colorscales, templates
  - Fixed markdown code block language specifiers

* All fixes have been applied and verified. Here's a summary of the changes:

  Changes Made

  1. CHANGELOG.md (line 145)

  - Before: #### FXPlot Accessor (#548)
  - After: #### Plotly Accessor (#548)

  2. CHANGELOG.md (lines 169-170)

  - Before: Two duplicate .plotly.bar() entries (grouped/stacked)
  - After: Single entry: .plotly.bar() | Bar charts (use barmode='group' or 'relative' for stacked)

  3. flixopt/stats_accessor.py - to_duration_curve() (lines 43-66)

  - Before: Used da.values (breaks dask laziness) and tuple assignment (loses attrs)
  - After: Uses xr.apply_ufunc() with dask='parallelized' to preserve:
    - Dask lazy evaluation
    - DataArray attributes
    - Dataset attributes
    - Variables without time dimension (unchanged)

  4. docs/user-guide/recipes/plotting-custom-data.md

  - Added note explaining that .plotly and .fxstats accessors are automatically registered on import

* 1. flixopt/stats_accessor.py (line 67-69) - Preserve non-time coordinates

  # Before
  sorted_ds = xr.Dataset(sorted_vars, attrs=self._ds.attrs)

  # After
  non_time_coords = {k: v for k, v in self._ds.coords.items() if k != 'time'}
  sorted_ds = xr.Dataset(sorted_vars, coords=non_time_coords, attrs=self._ds.attrs)

  2. flixopt/config.py (line 820-821) - Re-register template on use_theme()

  # Re-register template to pick up any config changes made after import
  _register_flixopt_template()
  pio.templates.default = 'plotly_white+flixopt'

  Now if a user modifies CONFIG.Plotting.default_qualitative_colorscale after import but before calling use_theme(), the template will use the updated value.

  3. flixopt/config.py (line 958-963) - Log warning on colorscale fallback

  if colorway is None:
      logging.getLogger(__name__).warning(
          f"Colorscale '{CONFIG.Plotting.default_qualitative_colorscale}' not found in "
          f"plotly.express.colors.qualitative, falling back to 'Plotly'. "
          f"Available: {[n for n in dir(colors.qualitative) if not n.startswith('_')]}"
      )
      colorway = colors.qualitative.Plotly

* ⏺ Much cleaner. The simplified implementation:
  - Preserves DataArray attrs via attrs=da.attrs
  - Preserves variable-level coords via coords={k: v for k, v in da.coords.items() if k != 'time'}
  - Preserves dataset-level coords and attrs separately

---------

Co-authored-by: Claude Opus 4.5 <noreply@anthropic.com>

Author: FBumann

CHANGELOG.md

@@ -53,15 +53,15 @@ Until here -->
 
 ## [6.0.0] - Upcoming
 
-**Summary**: Major release featuring a complete rewrite of the clustering/aggregation system with tsam integration, new `fxplot` plotting accessor, FlowSystem comparison tools, and removal of deprecated v5.0 classes.
+**Summary**: Major release featuring a complete rewrite of the clustering/aggregation system with tsam integration, new `plotly` plotting accessor, FlowSystem comparison tools, and removal of deprecated v5.0 classes.
 
 !!! warning "Breaking Changes"
     This release removes `ClusteredOptimization` and `ClusteringParameters` which were deprecated in v5.0.0. Use `flow_system.transform.cluster()` instead. See [Migration](#migration-from-clusteredoptimization) below.
 
 ### Key Features
 
 - **Clustering/Aggregation Rework** (#549, #552) - Complete rewrite with tsam integration, inter-cluster storage linking, and 4 storage modes
-- **fxplot Plotting Accessor** (#548) - Universal xarray plotting with automatic faceting
+- **plotly Plotting Accessor** (#548) - Universal xarray plotting with automatic faceting
 - **Comparison Module** (#550) - Compare multiple FlowSystems side-by-side
 - **Improved Notebooks** (#542, #551) - Better tutorial data and faster CI execution
 
@@ -142,21 +142,21 @@ charge_state = fs_expanded.solution['SeasonalPit|charge_state']
     Use `'cyclic'` for short-term storage like batteries or hot water tanks where only daily patterns matter.
     Use `'independent'` for quick estimates when storage behavior isn't critical.
 
-#### FXPlot Accessor (#548)
+#### Plotly Accessor (#548)
 
 New global xarray accessors for universal plotting with automatic faceting and smart dimension handling. Works on any xarray Dataset, not just flixopt results.
 
 ```python
 import flixopt as fx  # Registers accessors automatically
 
 # Plot any xarray Dataset with automatic faceting
-dataset.fxplot.bar(x='component')
-dataset.fxplot.area(x='time')
-dataset.fxplot.heatmap(x='time', y='component')
-dataset.fxplot.line(x='time', facet_col='scenario')
+dataset.plotly.bar(x='component')
+dataset.plotly.area(x='time')
+dataset.plotly.imshow(x='time', y='component')
+dataset.plotly.line(x='time', facet_col='scenario')
 
 # DataArray support
-data_array.fxplot.line()
+data_array.plotly.line()
 
 # Statistics transformations
 dataset.fxstats.to_duration_curve()
@@ -166,13 +166,12 @@ dataset.fxstats.to_duration_curve()
 
 | Method | Description |
 |--------|-------------|
-| `.fxplot.bar()` | Grouped bar charts |
-| `.fxplot.stacked_bar()` | Stacked bar charts |
-| `.fxplot.line()` | Line charts with faceting |
-| `.fxplot.area()` | Stacked area charts |
-| `.fxplot.heatmap()` | Heatmap visualizations |
-| `.fxplot.scatter()` | Scatter plots |
-| `.fxplot.pie()` | Pie charts with faceting |
+| `.plotly.bar()` | Bar charts (use `barmode='group'` or `'relative'` for stacked) |
+| `.plotly.line()` | Line charts with faceting |
+| `.plotly.area()` | Stacked area charts |
+| `.plotly.imshow()` | Heatmap visualizations |
+| `.plotly.scatter()` | Scatter plots |
+| `.plotly.pie()` | Pie charts with faceting |
 | `.fxstats.to_duration_curve()` | Transform to duration curve format |
 
 **Key Features**:
@@ -195,7 +194,7 @@ comp = fx.Comparison([fs1, fs2, fs3], names=['baseline', 'low_cost', 'high_eff']
 
 # Side-by-side plots (auto-facets by 'case' dimension)
 comp.statistics.plot.balance('Heat')
-comp.statistics.flow_rates.fxplot.line()
+comp.statistics.flow_rates.plotly.line()
 
 # Access combined data with 'case' dimension
 comp.solution  # xr.Dataset
@@ -310,7 +309,6 @@ Note: `topology.plot()` now renders a Sankey diagram. The old PyVis visualizatio
 - **08c2-clustering-storage-modes.ipynb** - Comparison of all 4 storage cluster modes
 - **08d-clustering-multiperiod.ipynb** - Clustering with periods and scenarios
 - **08e-clustering-internals.ipynb** - Understanding clustering internals
-- **fxplot_accessor_demo.ipynb** - Demo of the new fxplot accessor
 
 **Improved Tutorials:**
 

docs/notebooks/02-heat-system.ipynb

@@ -72,13 +72,13 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "# Visualize the demand pattern with fxplot\n",
+    "# Visualize the demand pattern with plotly\n",
     "demand_ds = xr.Dataset(\n",
     "    {\n",
     "        'Heat Demand': xr.DataArray(heat_demand, dims=['time'], coords={'time': timesteps}),\n",
     "    }\n",
     ")\n",
-    "demand_ds.fxplot.line(title='Office Heat Demand Profile')"
+    "demand_ds.plotly.line(x='time', title='Office Heat Demand Profile')"
    ]
   },
   {
@@ -98,13 +98,13 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "# Visualize time-of-use gas prices with fxplot\n",
+    "# Visualize time-of-use gas prices with plotly\n",
     "price_ds = xr.Dataset(\n",
     "    {\n",
     "        'Gas Price': xr.DataArray(gas_price, dims=['time'], coords={'time': timesteps}),\n",
     "    }\n",
     ")\n",
-    "price_ds.fxplot.line(title='Gas Price [€/kWh]')"
+    "price_ds.plotly.line(x='time', title='Gas Price [€/kWh]')"
    ]
   },
   {
@@ -375,7 +375,13 @@
    ]
   }
  ],
- "metadata": {},
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  }
+ },
  "nbformat": 4,
  "nbformat_minor": 5
 }

docs/notebooks/03-investment-optimization.ipynb

@@ -100,14 +100,14 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "# Visualize profiles with fxplot\n",
+    "# Visualize profiles with plotly\n",
     "profiles = xr.Dataset(\n",
     "    {\n",
     "        'Solar Profile [kW/kW]': xr.DataArray(solar_profile, dims=['time'], coords={'time': timesteps}),\n",
     "        'Pool Demand [kW]': xr.DataArray(pool_demand, dims=['time'], coords={'time': timesteps}),\n",
     "    }\n",
     ")\n",
-    "profiles.fxplot.line(title='Solar and Pool Profiles', height=300)"
+    "profiles.plotly.line(x='time', title='Solar and Pool Profiles', height=300)"
    ]
   },
   {
@@ -424,7 +424,13 @@
    ]
   }
  ],
- "metadata": {},
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  }
+ },
  "nbformat": 4,
  "nbformat_minor": 5
 }

docs/notebooks/04-operational-constraints.ipynb

@@ -85,13 +85,13 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "# Visualize the demand with fxplot\n",
+    "# Visualize the demand with plotly\n",
     "demand_ds = xr.Dataset(\n",
     "    {\n",
     "        'Steam Demand [kW]': xr.DataArray(steam_demand, dims=['time'], coords={'time': timesteps}),\n",
     "    }\n",
     ")\n",
-    "demand_ds.fxplot.line(title='Factory Steam Demand')"
+    "demand_ds.plotly.line(x='time', title='Factory Steam Demand')"
    ]
   },
   {
@@ -250,7 +250,7 @@
     "    }\n",
     ")\n",
     "\n",
-    "status_ds.fxplot.line(title='Main Boiler Operation', height=300)"
+    "status_ds.plotly.line(x='time', title='Main Boiler Operation', height=300)"
    ]
   },
   {

docs/notebooks/05-multi-carrier-system.ipynb

@@ -101,15 +101,15 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "# Visualize demands and prices with fxplot\n",
+    "# Visualize demands and prices with plotly\n",
     "profiles = xr.Dataset(\n",
     "    {\n",
     "        'Electricity Demand [kW]': xr.DataArray(electricity_demand, dims=['time'], coords={'time': timesteps}),\n",
     "        'Heat Demand [kW]': xr.DataArray(heat_demand, dims=['time'], coords={'time': timesteps}),\n",
     "        'Elec. Buy Price [EUR/kWh]': xr.DataArray(elec_buy_price, dims=['time'], coords={'time': timesteps}),\n",
     "    }\n",
     ")\n",
-    "profiles.fxplot.line(title='Hospital Energy Profiles', height=300)"
+    "profiles.plotly.line(x='time', title='Hospital Energy Profiles', height=300)"
    ]
   },
   {

docs/notebooks/06a-time-varying-parameters.ipynb

@@ -93,14 +93,14 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "# Visualize input profiles with fxplot\n",
+    "# Visualize input profiles with plotly\n",
     "profiles = xr.Dataset(\n",
     "    {\n",
     "        'Outdoor Temp [°C]': xr.DataArray(outdoor_temp, dims=['time'], coords={'time': timesteps}),\n",
     "        'Heat Demand [kW]': xr.DataArray(heat_demand, dims=['time'], coords={'time': timesteps}),\n",
     "    }\n",
     ")\n",
-    "profiles.fxplot.line(title='Temperature and Heat Demand Profiles', height=300)"
+    "profiles.plotly.line(x='time', title='Temperature and Heat Demand Profiles', height=300)"
    ]
   },
   {

docs/notebooks/07-scenarios-and-periods.ipynb

@@ -100,7 +100,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "# Visualize demand scenarios with fxplot\n",
+    "# Visualize demand scenarios with plotly\n",
     "demand_ds = xr.Dataset(\n",
     "    {\n",
     "        scenario: xr.DataArray(\n",
@@ -111,7 +111,7 @@
     "        for scenario in scenarios\n",
     "    }\n",
     ")\n",
-    "demand_ds.fxplot.line(title='Heat Demand by Scenario')"
+    "demand_ds.plotly.line(x='time', title='Heat Demand by Scenario')"
    ]
   },
   {

docs/notebooks/08c-clustering.ipynb

@@ -77,7 +77,7 @@
     "        'Electricity Price': flow_system.components['GridBuy'].outputs[0].effects_per_flow_hour['costs'],\n",
     "    }\n",
     ")\n",
-    "input_ds.fxplot.line(facet_row='variable', title='One Month of Input Data')"
+    "input_ds.plotly.line(x='time', facet_row='variable', title='One Month of Input Data')"
    ]
   },
   {
@@ -461,7 +461,7 @@
     "    }\n",
     ")\n",
     "\n",
-    "comparison_ds.fxplot.line(facet_col='variable', color='method', title='Heat Production Comparison')"
+    "comparison_ds.plotly.line(x='time', facet_col='variable', color='method', title='Heat Production Comparison')"
    ]
   },
   {

docs/notebooks/08e-clustering-internals.ipynb

@@ -120,6 +120,27 @@
    "id": "9",
    "metadata": {},
    "outputs": [],
+   "source": [
+    "# Use a different approach of visualizing the data using normalize heatmaps\n",
+    "ds = fs_clustered.clustering.plot.compare(data_only=True).data\n",
+    "\n",
+    "ds_normalized = (ds - ds.min()) / (ds.max() - ds.min())\n",
+    "ds_normalized.to_array().plotly.imshow(\n",
+    "    x='time',\n",
+    "    animation_frame='representation',\n",
+    "    zmin=0,\n",
+    "    zmax=1,\n",
+    "    color_continuous_scale='viridis',\n",
+    "    title='Normalized Comparison',\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "10",
+   "metadata": {},
+   "outputs": [],
    "source": [
     "# Compare specific variables only\n",
     "fs_clustered.clustering.plot.compare(variables='HeatDemand(Q_th)|fixed_relative_profile')"
@@ -128,7 +149,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "10",
+   "id": "11",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -139,19 +160,19 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "11",
+   "id": "12",
    "metadata": {},
    "outputs": [],
    "source": [
     "# View typical period profiles for each cluster\n",
     "# Each line represents a cluster's representative day\n",
-    "fs_clustered.clustering.plot.clusters(variables='HeatDemand(Q_th)|fixed_relative_profile')"
+    "fs_clustered.clustering.plot.clusters(variables='HeatDemand(Q_th)|fixed_relative_profile', color='cluster')"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "12",
+   "id": "13",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -161,7 +182,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "13",
+   "id": "14",
    "metadata": {},
    "source": [
     "## Expanding Aggregated Data\n",
@@ -173,7 +194,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "14",
+   "id": "15",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -192,7 +213,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "15",
+   "id": "16",
    "metadata": {},
    "source": [
     "## Summary\n",
@@ -236,7 +257,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "16",
+   "id": "17",
    "metadata": {},
    "source": [
     "## Cluster Weights\n",
@@ -252,7 +273,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "17",
+   "id": "18",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -262,7 +283,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "18",
+   "id": "19",
    "metadata": {},
    "source": [
     "## Solution Expansion\n",
@@ -273,7 +294,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "19",
+   "id": "20",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -287,7 +308,13 @@
    ]
   }
  ],
- "metadata": {},
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  }
+ },
  "nbformat": 4,
  "nbformat_minor": 5
 }