flixopt.calculation ¶

This module provides backwards-compatible aliases for the renamed Optimization classes.

DEPRECATED: This module is deprecated. Use the optimization module instead. The following classes have been renamed: - Calculation -> Optimization - FullCalculation -> Optimization (now the standard, no "Full" prefix) - AggregatedCalculation -> ClusteredOptimization - SegmentedCalculation -> SegmentedOptimization

Import from flixopt.optimization or use the new names from flixopt directly.

Classes¶

Calculation ¶

Calculation(name: str, flow_system: FlowSystem, active_timesteps: Annotated[DatetimeIndex | None, 'DEPRECATED: Use flow_system.sel(time=...) or flow_system.isel(time=...) instead'] = None, folder: Path | None = None, normalize_weights: bool = True)

Bases: Optimization

DEPRECATED: Use Optimization instead.

class for defined way of solving a flow_system optimization

Parameters:

Name	Type	Description	Default
`name`	`str`	name of calculation	required
`flow_system`	`FlowSystem`	flow_system which should be calculated	required
`folder`	`Path \| None`	folder where results should be saved. If None, then the current working directory is used.	`None`
`normalize_weights`	`bool`	Whether to automatically normalize the weights of scenarios to sum up to 1 when solving.	`True`
`active_timesteps`	`Annotated[DatetimeIndex \| None, 'DEPRECATED: Use flow_system.sel(time=...) or flow_system.isel(time=...) instead']`	Deprecated. Use FlowSystem.sel(time=...) or FlowSystem.isel(time=...) instead.	`None`

Functions¶

fix_sizes ¶

fix_sizes(ds: Dataset | None = None, decimal_rounding: int | None = 5) -> Optimization

Fix the sizes of the optimizations to specified values.

Parameters:

Name	Type	Description	Default
`ds`	`Dataset \| None`	The dataset that contains the variable names mapped to their sizes. If None, the dataset is loaded from the results.	`None`
`decimal_rounding`	`int \| None`	The number of decimal places to round the sizes to. If no rounding is applied, numerical errors might lead to infeasibility.	`5`

FullCalculation ¶

FullCalculation(name: str, flow_system: FlowSystem, active_timesteps: Annotated[DatetimeIndex | None, 'DEPRECATED: Use flow_system.sel(time=...) or flow_system.isel(time=...) instead'] = None, folder: Path | None = None, normalize_weights: bool = True)

Bases: Optimization

DEPRECATED: Use Optimization instead (the "Full" prefix has been removed).

FullCalculation solves the complete optimization problem using all time steps.

This is the most comprehensive calculation type that considers every time step in the optimization, providing the most accurate but computationally intensive solution.

Parameters:

Name	Type	Description	Default
`name`	`str`	name of calculation	required
`flow_system`	`FlowSystem`	flow_system which should be calculated	required
`folder`	`Path \| None`	folder where results should be saved. If None, then the current working directory is used.	`None`
`normalize_weights`	`bool`	Whether to automatically normalize the weights of scenarios to sum up to 1 when solving.	`True`
`active_timesteps`	`Annotated[DatetimeIndex \| None, 'DEPRECATED: Use flow_system.sel(time=...) or flow_system.isel(time=...) instead']`	Deprecated. Use FlowSystem.sel(time=...) or FlowSystem.isel(time=...) instead.	`None`

Functions¶

fix_sizes ¶

fix_sizes(ds: Dataset | None = None, decimal_rounding: int | None = 5) -> Optimization

Fix the sizes of the optimizations to specified values.

Parameters:

Name	Type	Description	Default
`ds`	`Dataset \| None`	The dataset that contains the variable names mapped to their sizes. If None, the dataset is loaded from the results.	`None`
`decimal_rounding`	`int \| None`	The number of decimal places to round the sizes to. If no rounding is applied, numerical errors might lead to infeasibility.	`5`

AggregatedCalculation ¶

AggregatedCalculation(name: str, flow_system: FlowSystem, aggregation_parameters: AggregationParameters, components_to_clusterize: list[Component] | None = None, active_timesteps: Annotated[DatetimeIndex | None, 'DEPRECATED: Use flow_system.sel(time=...) or flow_system.isel(time=...) instead'] = None, folder: Path | None = None)

Bases: ClusteredOptimization

DEPRECATED: Use ClusteredOptimization instead.

AggregatedCalculation reduces computational complexity by clustering time series into typical periods.

This calculation approach aggregates time series data using clustering techniques (tsam) to identify representative time periods, significantly reducing computation time while maintaining solution accuracy.

Parameters:

Name	Type	Description	Default
`name`	`str`	Name of the calculation	required
`flow_system`	`FlowSystem`	FlowSystem to be optimized	required
`aggregation_parameters`	`AggregationParameters`	Parameters for aggregation. See AggregationParameters class documentation	required
`components_to_clusterize`	`list[Component] \| None`	list of Components to perform aggregation on. If None, all components are aggregated. This equalizes variables in the components according to the typical periods computed in the aggregation	`None`
`active_timesteps`	`Annotated[DatetimeIndex \| None, 'DEPRECATED: Use flow_system.sel(time=...) or flow_system.isel(time=...) instead']`	DatetimeIndex of timesteps to use for optimization. If None, all timesteps are used	`None`
`folder`	`Path \| None`	Folder where results should be saved. If None, current working directory is used	`None`

Functions¶

fix_sizes ¶

fix_sizes(ds: Dataset | None = None, decimal_rounding: int | None = 5) -> Optimization

Fix the sizes of the optimizations to specified values.

Parameters:

Name	Type	Description	Default
`ds`	`Dataset \| None`	The dataset that contains the variable names mapped to their sizes. If None, the dataset is loaded from the results.	`None`
`decimal_rounding`	`int \| None`	The number of decimal places to round the sizes to. If no rounding is applied, numerical errors might lead to infeasibility.	`5`

calculate_clustering_weights `classmethod` ¶

calculate_clustering_weights(ds: Dataset) -> dict[str, float]

Calculate weights for all datavars in the dataset. Weights are pulled from the attrs of the datavars.

calculate_aggregation_weights `classmethod` ¶

calculate_aggregation_weights(ds: Dataset) -> dict[str, float]

Deprecated: Use calculate_clustering_weights instead.

SegmentedCalculation ¶

SegmentedCalculation(name: str, flow_system: FlowSystem, timesteps_per_segment: int, overlap_timesteps: int, nr_of_previous_values: int = 1, folder: Path | None = None)

Bases: SegmentedOptimization

DEPRECATED: Use SegmentedOptimization instead.

Solve large optimization problems by dividing time horizon into (overlapping) segments.

Parameters:

Name	Type	Description	Default
`name`	`str`	Unique identifier for the calculation, used in result files and logging.	required
`flow_system`	`FlowSystem`	The FlowSystem to optimize, containing all components, flows, and buses.	required
`timesteps_per_segment`	`int`	Number of timesteps in each segment (excluding overlap).	required
`overlap_timesteps`	`int`	Number of additional timesteps added to each segment.	required
`nr_of_previous_values`	`int`	Number of previous timestep values to transfer between segments for initialization.	`1`
`folder`	`Path \| None`	Directory for saving results. Defaults to current working directory + 'results'.	`None`

Attributes¶

start_values_of_segments `property` ¶

start_values_of_segments: list[dict[str, Any]]

Gives an overview of the start values of all Segments

modeled `property` ¶

modeled: bool

Returns True if all segments have been modeled.

main_results `property` ¶

main_results: dict[str, int | float | dict]

Aggregated main results from all segments.

Note

For SegmentedOptimization, results are aggregated from SegmentedResults which handles the overlapping segments properly. Individual segment results should not be summed directly as they contain overlapping timesteps.

The objective value shown is the sum of all segment objectives and includes double-counting from overlapping regions. It does not represent a true full-horizon objective value.

summary `property` ¶

summary

Summary of the segmented optimization with aggregated information from all segments.

Functions¶

do_modeling_and_solve ¶

do_modeling_and_solve(solver: _Solver, log_file: Path | None = None, log_main_results: bool = False, show_individual_solves: bool = False) -> SegmentedOptimization

Model and solve all segments of the segmented optimization.

This method creates sub-optimizations for each time segment, then iteratively models and solves each segment. It supports two output modes: a progress bar for compact output, or detailed individual solve information.

Parameters:

Name	Type	Description	Default
`solver`	`_Solver`	The solver instance to use for optimization (e.g., Gurobi, HiGHS).	required
`log_file`	`Path \| None`	Optional path to the solver log file. If None, defaults to folder/name.log.	`None`
`log_main_results`	`bool`	Whether to log main results (objective, effects, etc.) after each segment solve. Defaults to False.	`False`
`show_individual_solves`	`bool`	If True, shows detailed output for each segment solve with logger messages. If False (default), shows a compact progress bar with suppressed solver output for cleaner display.	`False`

Returns:

Type	Description
`SegmentedOptimization`	Self, for method chaining.

Note

The method automatically transfers all start values between segments to ensure continuity of storage states and flow rates across segment boundaries.

flixopt.calculation ¶

Classes¶

Calculation ¶

Functions¶

fix_sizes ¶

FullCalculation ¶

Functions¶

fix_sizes ¶

AggregatedCalculation ¶

Functions¶

fix_sizes ¶

calculate_clustering_weights classmethod ¶

calculate_aggregation_weights classmethod ¶

SegmentedCalculation ¶

Attributes¶

start_values_of_segments property ¶

modeled property ¶

main_results property ¶

summary property ¶

Functions¶

do_modeling_and_solve ¶

calculate_clustering_weights `classmethod` ¶

calculate_aggregation_weights `classmethod` ¶

start_values_of_segments `property` ¶

modeled `property` ¶

main_results `property` ¶

summary `property` ¶