flixopt.statistics_accessor ¶

Statistics accessor for FlowSystem.

This module provides a user-friendly API for analyzing optimization results directly from a FlowSystem.

Structure

.statistics - Data/metrics access (cached xarray Datasets)
.statistics.plot - Plotting methods using the statistics data

Example

flow_system.optimize(solver)

Data access¶

flow_system.statistics.flow_rates flow_system.statistics.flow_hours

Plotting¶

flow_system.statistics.plot.balance('ElectricityBus') flow_system.statistics.plot.heatmap('Boiler|on')

Attributes¶

SelectType `module-attribute` ¶

SelectType = dict[str, Any]

xarray-style selection dict: {'time': slice(...), 'scenario': 'base'}

FilterType `module-attribute` ¶

FilterType = str | list[str]

For include/exclude filtering: 'Boiler' or ['Boiler', 'CHP']

FlowSankeySelect `module-attribute` ¶

FlowSankeySelect = dict[Literal['flow', 'bus', 'component', 'carrier', 'time', 'period', 'scenario'], Any]

Select options for flow-based sankey: flow, bus, component, carrier, time, period, scenario.

EffectsSankeySelect `module-attribute` ¶

EffectsSankeySelect = dict[Literal['effect', 'component', 'contributor', 'period', 'scenario'], Any]

Select options for effects sankey: effect, component, contributor, period, scenario.

Classes¶

StatisticsAccessor ¶

StatisticsAccessor(flow_system: FlowSystem)

Statistics accessor for FlowSystem. Access via flow_system.statistics.

This accessor provides cached data properties for optimization results. Use .plot for visualization methods.

Data Properties

flow_rates : xr.Dataset Flow rates for all flows. flow_hours : xr.Dataset Flow hours (energy) for all flows. sizes : xr.Dataset Sizes for all flows. charge_states : xr.Dataset Charge states for all storage components. temporal_effects : xr.Dataset Temporal effects per contributor per timestep. periodic_effects : xr.Dataset Periodic (investment) effects per contributor. total_effects : xr.Dataset Total effects (temporal + periodic) per contributor. effect_share_factors : dict Conversion factors between effects.

Examples:

>>> flow_system.optimize(solver)
>>> flow_system.statistics.flow_rates  # Get data
>>> flow_system.statistics.plot.balance('Bus')  # Plot

Attributes¶

carrier_colors `property` ¶

carrier_colors: dict[str, str]

Cached mapping of carrier name to color.

Delegates to topology accessor for centralized color caching.

Returns:

Type	Description
`dict[str, str]`	Dict mapping carrier names (lowercase) to hex color strings.

component_colors `property` ¶

component_colors: dict[str, str]

Cached mapping of component label to color.

Delegates to topology accessor for centralized color caching.

Returns:

Type	Description
`dict[str, str]`	Dict mapping component labels to hex color strings.

bus_colors `property` ¶

bus_colors: dict[str, str]

Cached mapping of bus label to color (from carrier).

Delegates to topology accessor for centralized color caching.

Returns:

Type	Description
`dict[str, str]`	Dict mapping bus labels to hex color strings.

carrier_units `property` ¶

carrier_units: dict[str, str]

Cached mapping of carrier name to unit string.

Delegates to topology accessor for centralized unit caching.

Returns:

Type	Description
`dict[str, str]`	Dict mapping carrier names (lowercase) to unit strings.

effect_units `property` ¶

effect_units: dict[str, str]

Cached mapping of effect label to unit string.

Delegates to topology accessor for centralized unit caching.

Returns:

Type	Description
`dict[str, str]`	Dict mapping effect labels to unit strings.

plot `property` ¶

plot: StatisticsPlotAccessor

Access plotting methods for statistics.

Returns:

Type	Description
`StatisticsPlotAccessor`	A StatisticsPlotAccessor instance.

Examples:

>>> flow_system.statistics.plot.balance('ElectricityBus')
>>> flow_system.statistics.plot.heatmap('Boiler|on')

flow_rates `property` ¶

flow_rates: Dataset

All flow rates as a Dataset with flow labels as variable names.

Each variable has attributes

'carrier': carrier type (e.g., 'heat', 'electricity', 'gas')
'unit': carrier unit (e.g., 'kW')

flow_hours `property` ¶

flow_hours: Dataset

All flow hours (energy) as a Dataset with flow labels as variable names.

Each variable has attributes

'carrier': carrier type (e.g., 'heat', 'electricity', 'gas')
'unit': energy unit (e.g., 'kWh', 'm3/s*h')

flow_sizes `property` ¶

flow_sizes: Dataset

Flow sizes as a Dataset with flow labels as variable names.

storage_sizes `property` ¶

storage_sizes: Dataset

Storage capacity sizes as a Dataset with storage labels as variable names.

sizes `property` ¶

sizes: Dataset

All investment sizes (flows and storage capacities) as a Dataset.

charge_states `property` ¶

charge_states: Dataset

All storage charge states as a Dataset with storage labels as variable names.

effect_share_factors `property` ¶

effect_share_factors: dict[str, dict]

Effect share factors for temporal and periodic modes.

Returns:

Type	Description
`dict[str, dict]`	Dict with 'temporal' and 'periodic' keys, each containing
`dict[str, dict]`	conversion factors between effects.

temporal_effects `property` ¶

temporal_effects: Dataset

Temporal effects per contributor per timestep.

Returns a Dataset where each effect is a data variable with dimensions [time, contributor] (plus period/scenario if present).

Coordinates

contributor: Individual contributor labels
component: Parent component label for groupby operations
component_type: Component type (e.g., 'Boiler', 'Source', 'Sink')

Examples:

>>> # Get costs per contributor per timestep
>>> statistics.temporal_effects['costs']
>>> # Sum over all contributors to get total costs per timestep
>>> statistics.temporal_effects['costs'].sum('contributor')
>>> # Group by component
>>> statistics.temporal_effects['costs'].groupby('component').sum()

Returns:

Type	Description
`Dataset`	xr.Dataset with effects as variables and contributor dimension.

periodic_effects `property` ¶

periodic_effects: Dataset

Periodic (investment) effects per contributor.

Returns a Dataset where each effect is a data variable with dimensions [contributor] (plus period/scenario if present).

Coordinates

contributor: Individual contributor labels
component: Parent component label for groupby operations
component_type: Component type (e.g., 'Boiler', 'Source', 'Sink')

Examples:

>>> # Get investment costs per contributor
>>> statistics.periodic_effects['costs']
>>> # Sum over all contributors to get total investment costs
>>> statistics.periodic_effects['costs'].sum('contributor')
>>> # Group by component
>>> statistics.periodic_effects['costs'].groupby('component').sum()

Returns:

Type	Description
`Dataset`	xr.Dataset with effects as variables and contributor dimension.

total_effects `property` ¶

total_effects: Dataset

Total effects (temporal + periodic) per contributor.

Returns a Dataset where each effect is a data variable with dimensions [contributor] (plus period/scenario if present).

Coordinates

contributor: Individual contributor labels
component: Parent component label for groupby operations
component_type: Component type (e.g., 'Boiler', 'Source', 'Sink')

Examples:

>>> # Get total costs per contributor
>>> statistics.total_effects['costs']
>>> # Sum over all contributors to get total system costs
>>> statistics.total_effects['costs'].sum('contributor')
>>> # Group by component
>>> statistics.total_effects['costs'].groupby('component').sum()
>>> # Group by component type
>>> statistics.total_effects['costs'].groupby('component_type').sum()

Returns:

Type	Description
`Dataset`	xr.Dataset with effects as variables and contributor dimension.

Functions¶

get_effect_shares ¶

get_effect_shares(element: str, effect: str, mode: Literal['temporal', 'periodic'] | None = None, include_flows: bool = False) -> xr.Dataset

Retrieve individual effect shares for a specific element and effect.

Parameters:

Name	Type	Description	Default
`element`	`str`	The element identifier (component or flow label).	required
`effect`	`str`	The effect identifier.	required
`mode`	`Literal['temporal', 'periodic'] \| None`	'temporal', 'periodic', or None for both.	`None`
`include_flows`	`bool`	Whether to include effects from flows connected to this element.	`False`

Returns:

Type	Description
`Dataset`	xr.Dataset containing the requested effect shares.

Raises:

Type	Description
`ValueError`	If the effect is not available or mode is invalid.

SankeyPlotAccessor ¶

SankeyPlotAccessor(plot_accessor: StatisticsPlotAccessor)

Sankey diagram accessor. Access via flow_system.statistics.plot.sankey.

Provides typed methods for different sankey diagram types.

Examples:

>>> fs.statistics.plot.sankey.flows(select={'bus': 'HeatBus'})
>>> fs.statistics.plot.sankey.effects(select={'effect': 'costs'})
>>> fs.statistics.plot.sankey.sizes(select={'component': 'Boiler'})

Functions¶

flows ¶

flows(*, aggregate: Literal['sum', 'mean'] = 'sum', select: FlowSankeySelect | None = None, colors: ColorType | None = None, show: bool | None = None, **plotly_kwargs: Any) -> PlotResult

Plot Sankey diagram of energy/material flow amounts.

Parameters:

Name	Type	Description	Default
`aggregate`	`Literal['sum', 'mean']`	How to aggregate over time ('sum' or 'mean').	`'sum'`
`select`	`FlowSankeySelect \| None`	Filter options: - flow: filter by flow label (e.g., 'Boiler\|Q_th') - bus: filter by bus label (e.g., 'HeatBus') - component: filter by component label (e.g., 'Boiler') - time: select specific time (e.g., 100 or '2023-01-01') - period, scenario: xarray dimension selection	`None`
`colors`	`ColorType \| None`	Color specification for nodes.	`None`
`show`	`bool \| None`	Whether to display the figure.	`None`
`**plotly_kwargs`	`Any`	Additional arguments passed to Plotly layout.	`{}`

Returns:

Type	Description
`PlotResult`	PlotResult with Sankey flow data and figure.

sizes ¶

sizes(*, select: FlowSankeySelect | None = None, max_size: float | None = None, colors: ColorType | None = None, show: bool | None = None, **plotly_kwargs: Any) -> PlotResult

Plot Sankey diagram of investment sizes/capacities.

Parameters:

Name	Type	Description	Default
`select`	`FlowSankeySelect \| None`	Filter options: - flow: filter by flow label (e.g., 'Boiler\|Q_th') - bus: filter by bus label (e.g., 'HeatBus') - component: filter by component label (e.g., 'Boiler') - period, scenario: xarray dimension selection	`None`
`max_size`	`float \| None`	Filter flows with sizes exceeding this value.	`None`
`colors`	`ColorType \| None`	Color specification for nodes.	`None`
`show`	`bool \| None`	Whether to display the figure.	`None`
`**plotly_kwargs`	`Any`	Additional arguments passed to Plotly layout.	`{}`

Returns:

Type	Description
`PlotResult`	PlotResult with Sankey size data and figure.

peak_flow ¶

peak_flow(*, select: FlowSankeySelect | None = None, colors: ColorType | None = None, show: bool | None = None, **plotly_kwargs: Any) -> PlotResult

Plot Sankey diagram of peak (maximum) flow rates.

Parameters:

Name	Type	Description	Default
`select`	`FlowSankeySelect \| None`	Filter options: - flow: filter by flow label (e.g., 'Boiler\|Q_th') - bus: filter by bus label (e.g., 'HeatBus') - component: filter by component label (e.g., 'Boiler') - time, period, scenario: xarray dimension selection	`None`
`colors`	`ColorType \| None`	Color specification for nodes.	`None`
`show`	`bool \| None`	Whether to display the figure.	`None`
`**plotly_kwargs`	`Any`	Additional arguments passed to Plotly layout.	`{}`

Returns:

Type	Description
`PlotResult`	PlotResult with Sankey peak flow data and figure.

effects ¶

effects(*, select: EffectsSankeySelect | None = None, colors: ColorType | None = None, show: bool | None = None, **plotly_kwargs: Any) -> PlotResult

Plot Sankey diagram of component contributions to effects.

Shows how each component contributes to costs, CO2, and other effects.

Parameters:

Name	Type	Description	Default
`select`	`EffectsSankeySelect \| None`	Filter options: - effect: filter which effects are shown (e.g., 'costs', ['costs', 'CO2']) - component: filter by component label (e.g., 'Boiler') - contributor: filter by contributor label (e.g., 'Boiler\|Q_th') - period, scenario: xarray dimension selection	`None`
`colors`	`ColorType \| None`	Color specification for nodes.	`None`
`show`	`bool \| None`	Whether to display the figure.	`None`
`**plotly_kwargs`	`Any`	Additional arguments passed to Plotly layout.	`{}`

Returns:

Type	Description
`PlotResult`	PlotResult with Sankey effects data and figure.

StatisticsPlotAccessor ¶

StatisticsPlotAccessor(statistics: StatisticsAccessor)

Plot accessor for statistics. Access via flow_system.statistics.plot.

All methods return PlotResult with both data and figure.

Attributes¶

sankey `property` ¶

sankey: SankeyPlotAccessor

Access sankey diagram methods with typed select options.

Returns:

Type	Description
`SankeyPlotAccessor`	SankeyPlotAccessor with methods: flows(), sizes(), peak_flow(), effects()

Examples:

>>> fs.statistics.plot.sankey.flows(select={'bus': 'HeatBus'})
>>> fs.statistics.plot.sankey.effects(select={'effect': 'costs'})

Functions¶

balance ¶

balance(node: str, *, select: SelectType | None = None, include: FilterType | None = None, exclude: FilterType | None = None, unit: Literal['flow_rate', 'flow_hours'] = 'flow_rate', colors: ColorType | None = None, facet_col: str | None = 'period', facet_row: str | None = 'scenario', show: bool | None = None, **plotly_kwargs: Any) -> PlotResult

Plot node balance (inputs vs outputs) for a Bus or Component.

Parameters:

Name	Type	Description	Default
`node`	`str`	Label of the Bus or Component to plot.	required
`select`	`SelectType \| None`	xarray-style selection dict.	`None`
`include`	`FilterType \| None`	Only include flows containing these substrings.	`None`
`exclude`	`FilterType \| None`	Exclude flows containing these substrings.	`None`
`unit`	`Literal['flow_rate', 'flow_hours']`	'flow_rate' (power) or 'flow_hours' (energy).	`'flow_rate'`
`colors`	`ColorType \| None`	Color specification (colorscale name, color list, or label-to-color dict).	`None`
`facet_col`	`str \| None`	Dimension for column facets.	`'period'`
`facet_row`	`str \| None`	Dimension for row facets.	`'scenario'`
`show`	`bool \| None`	Whether to display the plot.	`None`

Returns:

Type	Description
`PlotResult`	PlotResult with .data and .figure.

carrier_balance ¶

carrier_balance(carrier: str, *, select: SelectType | None = None, include: FilterType | None = None, exclude: FilterType | None = None, unit: Literal['flow_rate', 'flow_hours'] = 'flow_rate', colors: ColorType | None = None, facet_col: str | None = 'period', facet_row: str | None = 'scenario', show: bool | None = None, **plotly_kwargs: Any) -> PlotResult

Plot carrier-level balance showing all flows of a carrier type.

Shows production (positive) and consumption (negative) of a carrier across all buses of that carrier type in the system.

Parameters:

Name	Type	Description	Default
`carrier`	`str`	Carrier name (e.g., 'heat', 'electricity', 'gas').	required
`select`	`SelectType \| None`	xarray-style selection dict.	`None`
`include`	`FilterType \| None`	Only include flows containing these substrings.	`None`
`exclude`	`FilterType \| None`	Exclude flows containing these substrings.	`None`
`unit`	`Literal['flow_rate', 'flow_hours']`	'flow_rate' (power) or 'flow_hours' (energy).	`'flow_rate'`
`colors`	`ColorType \| None`	Color specification (colorscale name, color list, or label-to-color dict).	`None`
`facet_col`	`str \| None`	Dimension for column facets.	`'period'`
`facet_row`	`str \| None`	Dimension for row facets.	`'scenario'`
`show`	`bool \| None`	Whether to display the plot.	`None`

Returns:

Type	Description
`PlotResult`	PlotResult with .data and .figure.

Examples:

>>> fs.statistics.plot.carrier_balance('heat')
>>> fs.statistics.plot.carrier_balance('electricity', unit='flow_hours')

Notes

Inputs to carrier buses (from sources/converters) are shown as positive
Outputs from carrier buses (to sinks/converters) are shown as negative
Internal transfers between buses of the same carrier appear on both sides

heatmap ¶

heatmap(variables: str | list[str], *, select: SelectType | None = None, reshape: tuple[str, str] | None = ('D', 'h'), colors: str | list[str] | None = None, facet_col: str | None = 'period', animation_frame: str | None = 'scenario', show: bool | None = None, **plotly_kwargs: Any) -> PlotResult

Plot heatmap of time series data.

Time is reshaped into 2D (e.g., days × hours) when possible. Multiple variables are shown as facets. If too many dimensions exist to display without data loss, reshaping is skipped and variables are shown on the y-axis with time on x-axis.

Parameters:

Name	Type	Description	Default
`variables`	`str \| list[str]`	Flow label(s) or variable name(s). Flow labels like 'Boiler(Q_th)' are automatically resolved to 'Boiler(Q_th)\|flow_rate'. Full variable names like 'Storage\|charge_state' are used as-is.	required
`select`	`SelectType \| None`	xarray-style selection, e.g. {'scenario': 'Base Case'}.	`None`
`reshape`	`tuple[str, str] \| None`	Time reshape frequencies as (outer, inner), e.g. ('D', 'h') for days × hours. Set to None to disable reshaping.	`('D', 'h')`
`colors`	`str \| list[str] \| None`	Colorscale name (str) or list of colors for heatmap coloring. Dicts are not supported for heatmaps (use str or list[str]).	`None`
`facet_col`	`str \| None`	Dimension for subplot columns (default: 'period'). With multiple variables, 'variable' is used instead.	`'period'`
`animation_frame`	`str \| None`	Dimension for animation slider (default: 'scenario').	`'scenario'`
`show`	`bool \| None`	Whether to display the figure.	`None`
`**plotly_kwargs`	`Any`	Additional arguments passed to px.imshow.	`{}`

Returns:

Type	Description
`PlotResult`	PlotResult with processed data and figure.

flows ¶

flows(*, start: str | list[str] | None = None, end: str | list[str] | None = None, component: str | list[str] | None = None, select: SelectType | None = None, unit: Literal['flow_rate', 'flow_hours'] = 'flow_rate', colors: ColorType | None = None, facet_col: str | None = 'period', facet_row: str | None = 'scenario', show: bool | None = None, **plotly_kwargs: Any) -> PlotResult

Plot flow rates filtered by start/end nodes or component.

Parameters:

Name	Type	Description	Default
`start`	`str \| list[str] \| None`	Filter by source node(s).	`None`
`end`	`str \| list[str] \| None`	Filter by destination node(s).	`None`
`component`	`str \| list[str] \| None`	Filter by parent component(s).	`None`
`select`	`SelectType \| None`	xarray-style selection.	`None`
`unit`	`Literal['flow_rate', 'flow_hours']`	'flow_rate' or 'flow_hours'.	`'flow_rate'`
`colors`	`ColorType \| None`	Color specification (colorscale name, color list, or label-to-color dict).	`None`
`facet_col`	`str \| None`	Dimension for column facets.	`'period'`
`facet_row`	`str \| None`	Dimension for row facets.	`'scenario'`
`show`	`bool \| None`	Whether to display.	`None`

Returns:

Type	Description
`PlotResult`	PlotResult with flow data.

sizes ¶

sizes(*, max_size: float | None = 1000000.0, select: SelectType | None = None, colors: ColorType | None = None, facet_col: str | None = 'period', facet_row: str | None = 'scenario', show: bool | None = None, **plotly_kwargs: Any) -> PlotResult

Plot investment sizes (capacities) of flows.

Parameters:

Name	Type	Description	Default
`max_size`	`float \| None`	Maximum size to include (filters defaults).	`1000000.0`
`select`	`SelectType \| None`	xarray-style selection.	`None`
`colors`	`ColorType \| None`	Color specification (colorscale name, color list, or label-to-color dict).	`None`
`facet_col`	`str \| None`	Dimension for column facets.	`'period'`
`facet_row`	`str \| None`	Dimension for row facets.	`'scenario'`
`show`	`bool \| None`	Whether to display.	`None`

Returns:

Type	Description
`PlotResult`	PlotResult with size data.

duration_curve ¶

duration_curve(variables: str | list[str], *, select: SelectType | None = None, normalize: bool = False, colors: ColorType | None = None, facet_col: str | None = 'period', facet_row: str | None = 'scenario', show: bool | None = None, **plotly_kwargs: Any) -> PlotResult

Plot load duration curves (sorted time series).

Parameters:

Name	Type	Description	Default
`variables`	`str \| list[str]`	Flow label(s) or variable name(s). Flow labels like 'Boiler(Q_th)' are looked up in flow_rates. Full variable names like 'Boiler(Q_th)\|flow_rate' are stripped to their flow label. Other variables (e.g., 'Storage\|charge_state') are looked up in the solution directly.	required
`select`	`SelectType \| None`	xarray-style selection.	`None`
`normalize`	`bool`	If True, normalize x-axis to 0-100%.	`False`
`colors`	`ColorType \| None`	Color specification (colorscale name, color list, or label-to-color dict).	`None`
`facet_col`	`str \| None`	Dimension for column facets.	`'period'`
`facet_row`	`str \| None`	Dimension for row facets.	`'scenario'`
`show`	`bool \| None`	Whether to display.	`None`

Returns:

Type	Description
`PlotResult`	PlotResult with sorted duration curve data.

effects ¶

effects(aspect: Literal['total', 'temporal', 'periodic'] = 'total', *, effect: str | None = None, by: Literal['component', 'contributor', 'time'] | None = None, select: SelectType | None = None, colors: ColorType | None = None, facet_col: str | None = 'period', facet_row: str | None = 'scenario', show: bool | None = None, **plotly_kwargs: Any) -> PlotResult

Plot effect (cost, emissions, etc.) breakdown.

Parameters:

Name	Type	Description	Default
`aspect`	`Literal['total', 'temporal', 'periodic']`	Which aspect to plot - 'total', 'temporal', or 'periodic'.	`'total'`
`effect`	`str \| None`	Specific effect name to plot (e.g., 'costs', 'CO2'). If None, plots all effects.	`None`
`by`	`Literal['component', 'contributor', 'time'] \| None`	Group by 'component', 'contributor' (individual flows), 'time', or None to show aggregated totals per effect.	`None`
`select`	`SelectType \| None`	xarray-style selection.	`None`
`colors`	`ColorType \| None`	Color specification (colorscale name, color list, or label-to-color dict).	`None`
`facet_col`	`str \| None`	Dimension for column facets (ignored if not in data).	`'period'`
`facet_row`	`str \| None`	Dimension for row facets (ignored if not in data).	`'scenario'`
`show`	`bool \| None`	Whether to display.	`None`

Returns:

Type	Description
`PlotResult`	PlotResult with effect breakdown data.

Examples:

>>> flow_system.statistics.plot.effects()  # Aggregated totals per effect
>>> flow_system.statistics.plot.effects(effect='costs')  # Just costs
>>> flow_system.statistics.plot.effects(by='component')  # Breakdown by component
>>> flow_system.statistics.plot.effects(by='contributor')  # By individual flows
>>> flow_system.statistics.plot.effects(aspect='temporal', by='time')  # Over time

charge_states ¶

charge_states(storages: str | list[str] | None = None, *, select: SelectType | None = None, colors: ColorType | None = None, facet_col: str | None = 'period', facet_row: str | None = 'scenario', show: bool | None = None, **plotly_kwargs: Any) -> PlotResult

Plot storage charge states over time.

Parameters:

Name	Type	Description	Default
`storages`	`str \| list[str] \| None`	Storage label(s) to plot. If None, plots all storages.	`None`
`select`	`SelectType \| None`	xarray-style selection.	`None`
`colors`	`ColorType \| None`	Color specification (colorscale name, color list, or label-to-color dict).	`None`
`facet_col`	`str \| None`	Dimension for column facets.	`'period'`
`facet_row`	`str \| None`	Dimension for row facets.	`'scenario'`
`show`	`bool \| None`	Whether to display.	`None`

Returns:

Type	Description
`PlotResult`	PlotResult with charge state data.

storage ¶

storage(storage: str, *, select: SelectType | None = None, unit: Literal['flow_rate', 'flow_hours'] = 'flow_rate', colors: ColorType | None = None, charge_state_color: str = 'black', facet_col: str | None = 'period', facet_row: str | None = 'scenario', show: bool | None = None, **plotly_kwargs: Any) -> PlotResult

Plot storage operation: balance and charge state in vertically stacked subplots.

Creates two subplots sharing the x-axis: - Top: Charging/discharging flows as stacked bars (inputs negative, outputs positive) - Bottom: Charge state over time as a line

Parameters:

Name	Type	Description	Default
`storage`	`str`	Storage component label.	required
`select`	`SelectType \| None`	xarray-style selection.	`None`
`unit`	`Literal['flow_rate', 'flow_hours']`	'flow_rate' (power) or 'flow_hours' (energy).	`'flow_rate'`
`colors`	`ColorType \| None`	Color specification for flow bars.	`None`
`charge_state_color`	`str`	Color for the charge state line overlay.	`'black'`
`facet_col`	`str \| None`	Dimension for column facets.	`'period'`
`facet_row`	`str \| None`	Dimension for row facets.	`'scenario'`
`show`	`bool \| None`	Whether to display.	`None`

Returns:

Type	Description
`PlotResult`	PlotResult with combined balance and charge state data.

Raises:

Type	Description
`KeyError`	If storage component not found.
`ValueError`	If component is not a storage.

flixopt.statistics_accessor ¶

Data access¶

Plotting¶

Attributes¶

SelectType module-attribute ¶

FilterType module-attribute ¶

FlowSankeySelect module-attribute ¶

EffectsSankeySelect module-attribute ¶

Classes¶

StatisticsAccessor ¶

Attributes¶

carrier_colors property ¶

component_colors property ¶

bus_colors property ¶

carrier_units property ¶

effect_units property ¶

plot property ¶

flow_rates property ¶

flow_hours property ¶

flow_sizes property ¶

storage_sizes property ¶

sizes property ¶

charge_states property ¶

effect_share_factors property ¶

temporal_effects property ¶

periodic_effects property ¶

total_effects property ¶

Functions¶

get_effect_shares ¶

SankeyPlotAccessor ¶

Functions¶

flows ¶

sizes ¶

peak_flow ¶

effects ¶

StatisticsPlotAccessor ¶

Attributes¶

sankey property ¶

Functions¶

balance ¶

carrier_balance ¶

heatmap ¶

flows ¶

sizes ¶

duration_curve ¶

effects ¶

charge_states ¶

storage ¶

Functions¶

SelectType `module-attribute` ¶

FilterType `module-attribute` ¶

FlowSankeySelect `module-attribute` ¶

EffectsSankeySelect `module-attribute` ¶

carrier_colors `property` ¶

component_colors `property` ¶

bus_colors `property` ¶

carrier_units `property` ¶

effect_units `property` ¶

plot `property` ¶

flow_rates `property` ¶

flow_hours `property` ¶

flow_sizes `property` ¶

storage_sizes `property` ¶

sizes `property` ¶

charge_states `property` ¶

effect_share_factors `property` ¶

temporal_effects `property` ¶

periodic_effects `property` ¶

total_effects `property` ¶

sankey `property` ¶