flixOpt.structure
This module contains the core structure of the flixOpt framework. These classes are not directly used by the end user, but are used by other modules.
Classes
SystemModel
SystemModel(label: str, modeling_language: Literal['pyomo', 'cvxpy'], flow_system: FlowSystem, time_indices: Optional[Union[List[int], range]])
Bases: MathModel
Hier kommen die ModellingLanguage-spezifischen Sachen rein
Attributes
Functions
solve
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
solver
|
Solver
|
An Instance of the class Solver. Choose from flixOpt.solvers |
required |
excess_threshold
|
float, positive!
|
threshold for excess: If sum(Excess)>excess_threshold a warning is raised, that an excess occurs |
0.1
|
Interface
This class is used to collect arguments about a Model.
Functions
infos
Generate a dictionary representation of the object's constructor arguments. Excludes default values and empty dictionaries and lists. Converts data to be compatible with JSON.
Parameters:
use_numpy bool:
Whether to convert NumPy arrays to lists. Defaults to True.
If True, numeric numpy arrays (np.ndarray) are preserved as-is.
If False, they are converted to lists.
use_element_label bool:
Whether to use the element label instead of the infos of the element. Defaults to False.
Note that Elements used as keys in dictionaries are always converted to their labels.
Returns: Dict: A dictionary representation of the object's constructor arguments.
Element
Bases: Interface
Basic Element of flixOpt
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
label
|
str
|
label of the element |
required |
meta_data
|
Optional[Dict]
|
used to store more information about the element. Is not used internally, but saved in the results |
None
|
Functions
infos
Generate a dictionary representation of the object's constructor arguments. Excludes default values and empty dictionaries and lists. Converts data to be compatible with JSON.
Parameters:
use_numpy bool:
Whether to convert NumPy arrays to lists. Defaults to True.
If True, numeric numpy arrays (np.ndarray) are preserved as-is.
If False, they are converted to lists.
use_element_label bool:
Whether to use the element label instead of the infos of the element. Defaults to False.
Note that Elements used as keys in dictionaries are always converted to their labels.
Returns: Dict: A dictionary representation of the object's constructor arguments.
ElementModel
Interface to create the mathematical Models for Elements
Functions
create_equation
create_equation(label: str, element_model: ElementModel, eq_type: Literal['eq', 'ineq'] = 'eq') -> Union[Equation, Inequation]
Creates an Equation and adds it to the model of the Element
create_variable
create_variable(label: str, element_model: ElementModel, length: int, is_binary: bool = False, fixed_value: Optional[Numeric] = None, lower_bound: Optional[Numeric] = None, upper_bound: Optional[Numeric] = None, previous_values: Optional[Numeric] = None, avoid_use_of_variable_ts: bool = False) -> VariableTS
Creates a VariableTS and adds it to the model of the Element
copy_and_convert_datatypes
copy_and_convert_datatypes(data: Any, use_numpy: bool = True, use_element_label: bool = False) -> Any
Converts values in a nested data structure into JSON-compatible types while preserving or transforming numpy arrays
and custom Element objects based on the specified options.
The function handles various data types and transforms them into a consistent, readable format:
- Primitive types (int, float, str, bool, None) are returned as-is.
- Numpy scalars are converted to their corresponding Python scalar types.
- Collections (list, tuple, set, dict) are recursively processed to ensure all elements are compatible.
- Numpy arrays are preserved or converted to lists, depending on use_numpy.
- Custom Element objects can be represented either by their label or their initialization parameters as a dictionary.
- Timestamps (datetime) are converted to ISO 8601 strings.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
data
|
Any
|
The input data to process, which may be deeply nested and contain a mix of types. |
required |
use_numpy
|
bool
|
If |
True
|
use_element_label
|
bool
|
If |
False
|
Returns:
| Type | Description |
|---|---|
Any
|
A transformed version of the input data, containing only JSON-compatible types:
- |
Raises:
| Type | Description |
|---|---|
TypeError
|
If the data cannot be converted to the specified types. |
Examples:
>>> copy_and_convert_datatypes({'a': np.array([1, 2, 3]), 'b': Element(label='example')})
{'a': array([1, 2, 3]), 'b': {'class': 'Element', 'label': 'example'}}
>>> copy_and_convert_datatypes({'a': np.array([1, 2, 3]), 'b': Element(label='example')}, use_numpy=False)
{'a': [1, 2, 3], 'b': {'class': 'Element', 'label': 'example'}}
Notes
- The function gracefully handles unexpected types by issuing a warning and returning a deep copy of the data.
- Empty collections (lists, dictionaries) and default parameter values in
Elementobjects are omitted from the output. - Numpy arrays with non-numeric data types are automatically converted to lists.
get_compact_representation
Generate a compact json serializable representation of deeply nested data. Numpy arrays are statistically described if they exceed a threshold and converted to lists.
Args: data (Any): The data to format and represent. array_threshold (int): Maximum length of NumPy arrays to display. Longer arrays are statistically described. decimals (int): Number of decimal places in which to describe the arrays.
Returns: Dict: A dictionary representation of the data
get_str_representation
Generate a string representation of deeply nested data using rich.print.
NumPy arrays are shortened to the specified length and converted to strings.
Args: data (Any): The data to format and represent. array_threshold (int): Maximum length of NumPy arrays to display. Longer arrays are statistically described. decimals (int): Number of decimal places in which to describe the arrays.
Returns: str: The formatted string representation of the data.