src.pydasa.workflows.influence
==============================

.. py:module:: src.pydasa.workflows.influence

.. autoapi-nested-parse::

   Module influence.py
   ===========================================

   Module for **SensitivityAnalysis** to manage sensitivity analysis in *PyDASA*

   This module provides the SensitivityAnalysis class for coordinating multiple sensitivity analyses and generating reports on which variables have the most significant impact on dimensionless coefficients.

   Classes:
       **SensitivityAnalysis**: Manages sensitivity analyses for multiple coefficients, processes results, and generates reports on variable impacts.

   *IMPORTANT:* Based on the theory from:

       # H.Gorter, *Dimensionalanalyse: Eine Theoririe der physikalischen Dimensionen mit Anwendungen*



Classes
-------

.. autoapisummary::

   src.pydasa.workflows.influence.SensitivityAnalysis


Module Contents
---------------

.. py:class:: SensitivityAnalysis

   Bases: :py:obj:`pydasa.core.basic.Foundation`, :py:obj:`pydasa.workflows.basic.WorkflowBase`


   **SensitivityAnalysis** class for managing multiple sensitivity analyses in *PyDASA*.

   Coordinates sensitivity analyses for multiple coefficients, processes their results, and generates comprehensive reports on variable impacts.

   :param Foundation: Inherits common validation logic.
   :type Foundation: Foundation
   :param WorkflowBase: Inherits workflow basic functionalities.
   :type WorkflowBase: WorkflowBase

   .. attribute:: # From Foundation

      name (str): User-friendly name of the sensitivity analysis.
      description (str): Brief summary of the sensitivity analysis.
      _idx (int): Index/precedence of the sensitivity analysis.
      _sym (str): Symbol representation (LaTeX or alphanumeric).
      _alias (str): Python-compatible alias for use in code.
      _fwk (str): Frameworks context (PHYSICAL, COMPUTATION, SOFTWARE, CUSTOM).

      :type: Identification and Classification

   .. attribute:: # From WorkflowBase

      _variables (Dict[str, Variable]): All available parameters/variables in the model (*Variable*). Accepts Variable instances or dicts.
      _schema (Optional[Schema]): Dimensional framework schema for the workflow. After __post_init__, this will always be a Schema instance.
      _coefficients (Dict[str, Coefficient]): All available coefficients in the model (*Coefficient*). Accepts Coefficient instances or dicts.
      _results (Dict[str, Dict[str, Any]]): Consolidated results from sensitivity analyses.
      _is_solved (bool): Flag indicating if the sensitivity analysis workflow has been completed.

      :type: Common Workflow Components

   .. attribute:: # Specific to SensitivityAnalysis

      _cat (str): Category of sensitivity analysis (SYM, NUM, HYB).
      _analyses (Dict[str, Sensitivity]): All sensitivity analyses performed for each coefficient.


   .. py:attribute:: _cat
      :type:  str
      :value: 'SYM'


      Category of sensitivity analysis workflow (SYM, NUM).


   .. py:attribute:: _analyses
      :type:  Dict[str, pydasa.analysis.scenario.Sensitivity]

      Dictionary of sensitivity analyses performed in the workflow.


   .. py:method:: _create_analyses()

      *_create_analyses()* Creates sensitivity analyses for each coefficient.

      Sets up Sensitivity objects for each coefficient to be analyzed.



   .. py:method:: _get_variable_value(var_sym, val_type = 'mean')

      *_get_variable_value()* Gets a value for a variable based on value type.

      :param var_sym: Symbol of the variable.
      :type var_sym: str
      :param val_type: Type of value to return (mean, min, max). Defaults to "mean".
      :type val_type: str, optional

      :returns: Variable value.
      :rtype: float

      :raises ValueError: If the variable is not found.
      :raises ValueError: If the value type is invalid.



   .. py:method:: analyze_symbolic(val_type = 'mean')

      *analyze_symbolic()* Performs symbolic sensitivity analysis.

      Analyzes each coefficient using symbolic differentiation at specified values.

      :param val_type: Type of value to use (mean, min, max). Defaults to "mean".
      :type val_type: str, optional

      :returns: Sensitivity results by coefficient and variable.
      :rtype: Dict[str, Dict[str, float]]



   .. py:method:: analyze_numeric(n_samples = 1000)

      *analyze_numeric()* Performs numerical sensitivity analysis.

      Analyzes each coefficient using Fourier Amplitude Sensitivity Test (FAST).

      :param n_samples: Number of samples to use. Defaults to 1000.
      :type n_samples: int, optional

      :returns: Sensitivity results by coefficient.
      :rtype: Dict[str, Dict[str, Any]]



   .. py:property:: cat
      :type: str


      *cat* Get the analysis category.

      :returns: Category (SYM, NUM, HYB).
      :rtype: str


   .. py:property:: analyses
      :type: Dict[str, pydasa.analysis.scenario.Sensitivity]


      *analyses* Get the dictionary of sensitivity analyses.

      :returns: Dictionary of sensitivity analyses.
      :rtype: Dict[str, Sensitivity]


   .. py:method:: reset()

      *reset()* Reset the analysis state while preserving input configuration.

      Clears coefficients, results, analyses, and solved state while keeping variables and schema.



   .. py:method:: clear()

      *clear()* Reset all attributes to default values.

      Resets all analysis properties to their initial state, including variables, coefficients, and results from WorkflowBase.



   .. py:method:: to_dict()

      *to_dict()* Convert sensitivity analysis to dictionary representation.

      :returns: Dictionary representation of sensitivity analysis.
      :rtype: Dict[str, Any]



   .. py:method:: from_dict(data)
      :classmethod:


      *from_dict()* Create sensitivity analysis from dictionary representation.

      :param data: Dictionary representation of sensitivity analysis.
      :type data: Dict[str, Any]

      :returns: New sensitivity analysis instance.
      :rtype: SensitivityAnalysis