src.dackar.anomalies.t_score
============================

.. py:module:: src.dackar.anomalies.t_score

.. autoapi-nested-parse::

   Created on July, 2025

   @author: wangc, mandd



Attributes
----------

.. autoapisummary::

   src.dackar.anomalies.t_score.logger


Functions
---------

.. autoapisummary::

   src.dackar.anomalies.t_score.getL
   src.dackar.anomalies.t_score.omega
   src.dackar.anomalies.t_score.choice
   src.dackar.anomalies.t_score.t_score
   src.dackar.anomalies.t_score.MMD_test
   src.dackar.anomalies.t_score.event2TStest


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

.. py:data:: logger

.. py:function:: getL(loc, window, array, type)

   Method designed to extract a portion of a time series

   :param loc: int, reference location in the time series for the selected portion
   :param window: np.array, size of the selected portion of the time series
   :param array: np.array, original time series
   :param type: string, type of the selected portion of the time series (rear or front)

   :returns: np array, size of the selected portion of the time series
   :rtype: timeseriesPortion


.. py:function:: omega(window, array, Nsamples)

   Method designed to extract set of portions of a time series

   :param window: np.array, size of the selected portion of the time series
   :param array: np.array, original time series
   :param Nsamples: int, number of portions to be selected

   :returns: np.array, set of Nsamples portions of a time series
   :rtype: omegaSet


.. py:function:: choice(array, Nsamples)

   Method designed to randomly choose Nsamples out of an array (replace is set to True)

   :param array: np.array, array of values to be sampled
   :param Nsamples: int, number of elements of array to be selected

   :returns: np.array, set of Nsamples elements randomly chosen from array
   :rtype: sampled


.. py:function:: t_score(array_front, array_rear)

   Method designed to calculate the statistical difference between two arrays using t-test

   :param array_front: np.array, first array
   :param array_rear: np.array, second array

   :returns: float, outcome of t-test
   :rtype: tscore


.. py:function:: MMD_test(test_array, omegaSet, iterations, alphaTest, alphaOmegaset, printFlag=True)

   Method designed to calculate the statistical difference between an array and a set of arrays
   using the Maximum Mean Discrepancy testing method

   :param test_array: np.array, array of values to be tested against omegaSet
   :param omegaSet: np.ndarray or list, population of arrays
   :param iterations: int, number of iterartions required to compute MMD^2_u
   :param alphaTest: float, acceptance value for hypothesis testing (single array testing)
   :param alphaOmegaset: float, acceptance value for hypothesis testing (omegaSet testing)
   :param printFlag: bool, flag to plot MMD distribution

   :returns: float, p-value obtained from MMD testing
             testOutput: bool, logical outcome of MMD testing
   :rtype: p_value


.. py:function:: event2TStest(E_loc, TS, iterations, alphaTest, alphaOmegaset, windowSize, omegaSize, returnMinPval=False)

   Method designed to assess temporal correlation of an event E and a time series TS

   :param E_loc: int, temporal location of event E
   :param TS: np.array, univariate time series
   :param iterations: int, number of iterartions required to compute MMD^2_u
   :param alphaTest: float, acceptance value for hypothesis testing (single array testing)
   :param alphaOmegaset: float, acceptance value for hypothesis testing (omegaSet testing)
   :param windowSize: int, size of time window prior and after event occurence
   :param omegaSize: int, number of samples from time series TS
   :param returnMinPval: bool, flag that indictae whether to return p-value of MMD assessment

   :returns: string, outcome of the event to timeseries temporal analysis
             minPval: float, p-value of MMD assessment
   :rtype: relation