flixopt.io ¶

Classes¶

CalculationResultsPaths `dataclass` ¶

Python

CalculationResultsPaths(folder: Path, name: str)

Container for all paths related to saving CalculationResults.

Functions¶

all_paths ¶

Python

all_paths() -> dict[str, pathlib.Path]

Return a dictionary of all paths.

create_folders ¶

Python

create_folders(parents: bool = False) -> None

Ensure the folder exists. Args: parents: Whether to create the parent folders if they do not exist.

update ¶

Python

update(new_name: str | None = None, new_folder: Path | None = None) -> None

Update name and/or folder and refresh all paths.

Functions¶

remove_none_and_empty ¶

Python

remove_none_and_empty(obj)

Recursively removes None and empty dicts and lists values from a dictionary or list.

round_nested_floats ¶

Python

round_nested_floats(obj: dict | list | float | int | Any, decimals: int = 2) -> dict | list | float | int | Any

Recursively round floating point numbers in nested data structures and convert it to python native types.

This function traverses nested data structures (dictionaries, lists) and rounds any floating point numbers to the specified number of decimal places. It handles various data types including NumPy arrays and xarray DataArrays by converting them to lists with rounded values.

Parameters:

Name	Type	Description	Default
`obj`	`dict \| list \| float \| int \| Any`	The object to process. Can be a dict, list, float, int, numpy.ndarray, xarray.DataArray, or any other type.	required
`decimals`	`int`	Number of decimal places to round to. Defaults to 2.	`2`

Returns:

Type	Description
`dict \| list \| float \| int \| Any`	The processed object with the same structure as the input, but with all floating point numbers rounded to the specified precision. NumPy arrays and xarray DataArrays are converted to lists.

Examples:

Python Console Session

>>> data = {'a': 3.14159, 'b': [1.234, 2.678]}
>>> round_nested_floats(data, decimals=2)
{'a': 3.14, 'b': [1.23, 2.68]}

Python Console Session

>>> import numpy as np
>>> arr = np.array([1.234, 5.678])
>>> round_nested_floats(arr, decimals=1)
[1.2, 5.7]

load_json ¶

Python

load_json(path: str | Path) -> dict | list

Load data from a JSON file.

Parameters:

Name	Type	Description	Default
`path`	`str \| Path`	Path to the JSON file.	required

Returns:

Type	Description
`dict \| list`	Loaded data (typically dict or list).

Raises:

Type	Description
`FileNotFoundError`	If the file does not exist.
`JSONDecodeError`	If the file is not valid JSON.

save_json ¶

Python

save_json(data: dict | list, path: str | Path, indent: int = 4, ensure_ascii: bool = False, **kwargs) -> None

Save data to a JSON file with consistent formatting.

Parameters:

Name	Type	Description	Default
`data`	`dict \| list`	Data to save (dict or list).	required
`path`	`str \| Path`	Path to save the JSON file.	required
`indent`	`int`	Number of spaces for indentation (default: 4).	`4`
`ensure_ascii`	`bool`	If False, allow Unicode characters (default: False).	`False`
`**kwargs`		Additional arguments to pass to json.dump().	`{}`

load_yaml ¶

Python

load_yaml(path: str | Path) -> dict | list

Load data from a YAML file.

Parameters:

Name	Type	Description	Default
`path`	`str \| Path`	Path to the YAML file.	required

Returns:

Type	Description
`dict \| list`	Loaded data (typically dict or list), or empty dict if file is empty.

Raises:

Type	Description
`FileNotFoundError`	If the file does not exist.
`YAMLError`	If the file is not valid YAML.
`Note`	Returns {} for empty YAML files instead of None.

save_yaml ¶

Python

save_yaml(data: dict | list, path: str | Path, indent: int = 4, width: int = 1000, allow_unicode: bool = True, sort_keys: bool = False, compact_numeric_lists: bool = False, **kwargs) -> None

Save data to a YAML file with consistent formatting.

Parameters:

Name	Type	Description	Default
`data`	`dict \| list`	Data to save (dict or list).	required
`path`	`str \| Path`	Path to save the YAML file.	required
`indent`	`int`	Number of spaces for indentation (default: 4).	`4`
`width`	`int`	Maximum line width (default: 1000).	`1000`
`allow_unicode`	`bool`	If True, allow Unicode characters (default: True).	`True`
`sort_keys`	`bool`	If True, sort dictionary keys (default: False).	`False`
`compact_numeric_lists`	`bool`	If True, format numeric lists inline for better readability (default: False).	`False`
`**kwargs`		Additional arguments to pass to yaml.dump().	`{}`

format_yaml_string ¶

Python

format_yaml_string(data: dict | list, indent: int = 4, width: int = 1000, allow_unicode: bool = True, sort_keys: bool = False, compact_numeric_lists: bool = False, **kwargs) -> str

Format data as a YAML string with consistent formatting.

This function provides the same formatting as save_yaml() but returns a string instead of writing to a file. Useful for logging or displaying YAML data.

Parameters:

Name	Type	Description	Default
`data`	`dict \| list`	Data to format (dict or list).	required
`indent`	`int`	Number of spaces for indentation (default: 4).	`4`
`width`	`int`	Maximum line width (default: 1000).	`1000`
`allow_unicode`	`bool`	If True, allow Unicode characters (default: True).	`True`
`sort_keys`	`bool`	If True, sort dictionary keys (default: False).	`False`
`compact_numeric_lists`	`bool`	If True, format numeric lists inline for better readability (default: False).	`False`
`**kwargs`		Additional arguments to pass to yaml.dump().	`{}`

Returns:

Type	Description
`str`	Formatted YAML string.

load_config_file ¶

Python

load_config_file(path: str | Path) -> dict

Load a configuration file, automatically detecting JSON or YAML format.

This function intelligently tries to load the file based on its extension, with fallback support if the primary format fails.

Supported extensions: - .json: Tries JSON first, falls back to YAML - .yaml, .yml: Tries YAML first, falls back to JSON - Others: Tries YAML, then JSON

Parameters:

Name	Type	Description	Default
`path`	`str \| Path`	Path to the configuration file.	required

Returns:

Type	Description
`dict`	Loaded configuration as a dictionary.

Raises:

Type	Description
`FileNotFoundError`	If the file does not exist.
`ValueError`	If neither JSON nor YAML parsing succeeds.

document_linopy_model ¶

Python

document_linopy_model(model: Model, path: Path | None = None) -> dict[str, str]

Convert all model variables and constraints to a structured string representation. This can take multiple seconds for large models. The output can be saved to a yaml file with readable formating applied.

Parameters:

Name	Type	Description	Default
`path`	`Path`	Path to save the document. Defaults to None.	`None`

save_dataset_to_netcdf ¶

Python

save_dataset_to_netcdf(ds: Dataset, path: str | Path, compression: int = 0) -> None

Save a dataset to a netcdf file. Store all attrs as JSON strings in 'attrs' attributes.

Parameters:

Name	Type	Description	Default
`ds`	`Dataset`	Dataset to save.	required
`path`	`str \| Path`	Path to save the dataset to.	required
`compression`	`int`	Compression level for the dataset (0-9). 0 means no compression. 5 is a good default.	`0`

Raises:

Type	Description
`ValueError`	If the path has an invalid file extension.

load_dataset_from_netcdf ¶

Python

load_dataset_from_netcdf(path: str | Path) -> xr.Dataset

Load a dataset from a netcdf file. Load all attrs from 'attrs' attributes.

Parameters:

Name	Type	Description	Default
`path`	`str \| Path`	Path to load the dataset from.	required

Returns:

Name	Type	Description
`Dataset`	`Dataset`	Loaded dataset with restored attrs.

numeric_to_str_for_repr ¶

Python

numeric_to_str_for_repr(value: int | float | integer | floating | ndarray | Series | DataFrame | DataArray, precision: int = 1, atol: float = 1e-10) -> str

Format value for display in repr methods.

For single values or uniform arrays, returns the formatted value. For arrays with variation, returns a range showing min-max.

Parameters:

Name	Type	Description	Default
`value`	`int \| float \| integer \| floating \| ndarray \| Series \| DataFrame \| DataArray`	Numeric value or container (DataArray, array, Series, DataFrame)	required
`precision`	`int`	Number of decimal places (default: 1)	`1`
`atol`	`float`	Absolute tolerance for considering values equal (default: 1e-10)	`1e-10`

Returns:

Type	Description
`str`	Formatted string representation:
`str`	Single/uniform values: "100.0"
`str`	Nearly uniform values: "~100.0" (values differ slightly but display similarly)
`str`	Varying values: "50.0-150.0" (shows range from min to max)

Raises:

Type	Description
`TypeError`	If value cannot be converted to numeric format

build_repr_from_init ¶

Python

build_repr_from_init(obj: object, excluded_params: set[str] | None = None, label_as_positional: bool = True, skip_default_size: bool = False) -> str

Build a repr string from init signature, showing non-default parameter values.

This utility function extracts common repr logic used across flixopt classes. It introspects the init method to build a constructor-style repr showing only parameters that differ from their defaults.

Parameters:

Name	Type	Description	Default
`obj`	`object`	The object to create repr for	required
`excluded_params`	`set[str] \| None`	Set of parameter names to exclude (e.g., {'self', 'inputs', 'outputs'}) Default excludes 'self', 'label', and 'kwargs'	`None`
`label_as_positional`	`bool`	If True and 'label' param exists, show it as first positional arg	`True`
`skip_default_size`	`bool`	If True, skip 'size' parameter when it equals CONFIG.Modeling.big	`False`

Returns:

Type	Description
`str`	Formatted repr string like: ClassName("label", param=value)

format_flow_details ¶

Python

format_flow_details(obj, has_inputs: bool = True, has_outputs: bool = True) -> str

Format inputs and outputs as indented bullet list.

Parameters:

Name	Type	Description	Default
`obj`		Object with 'inputs' and/or 'outputs' attributes	required
`has_inputs`	`bool`	Whether to check for inputs	`True`
`has_outputs`	`bool`	Whether to check for outputs	`True`

Returns:

Type	Description
`str`	Formatted string with flow details (including leading newline), or empty string if no flows

format_title_with_underline ¶

Python

format_title_with_underline(title: str, underline_char: str = '-') -> str

Format a title with underline of matching length.

Parameters:

Name	Type	Description	Default
`title`	`str`	The title text	required
`underline_char`	`str`	Character to use for underline (default: '-')	`'-'`

Returns:

Type	Description
`str`	Formatted string: "Title\n-----\n"

format_sections_with_headers ¶

Python

format_sections_with_headers(sections: dict[str, str], underline_char: str = '-') -> list[str]

Format sections with underlined headers.

Parameters:

Name	Type	Description	Default
`sections`	`dict[str, str]`	Dict mapping section headers to content	required
`underline_char`	`str`	Character for underlining headers	`'-'`

Returns:

Type	Description
`list[str]`	List of formatted section strings

build_metadata_info ¶

Python

build_metadata_info(parts: list[str], prefix: str = ' | ') -> str

Build metadata info string from parts.

Parameters:

Name	Type	Description	Default
`parts`	`list[str]`	List of metadata strings (empty strings are filtered out)	required
`prefix`	`str`	Prefix to add if parts is non-empty	`' \| '`

Returns:

Type	Description
`str`	Formatted info string or empty string

suppress_output ¶

Python

suppress_output()

Suppress all console output including C-level output from solvers.

WARNING: Not thread-safe. Modifies global file descriptors. Use only with sequential execution or multiprocessing.

flixopt.io ¶

Classes¶

CalculationResultsPaths dataclass ¶

Functions¶

all_paths ¶

create_folders ¶

update ¶

Functions¶

remove_none_and_empty ¶

round_nested_floats ¶

load_json ¶

save_json ¶

load_yaml ¶

save_yaml ¶

format_yaml_string ¶

load_config_file ¶

document_linopy_model ¶

save_dataset_to_netcdf ¶

load_dataset_from_netcdf ¶

numeric_to_str_for_repr ¶

build_repr_from_init ¶

format_flow_details ¶

format_title_with_underline ¶

format_sections_with_headers ¶

build_metadata_info ¶

suppress_output ¶

CalculationResultsPaths `dataclass` ¶