:py:mod:`HAlphaAnomalyzer._anova_analysis`
==========================================

.. py:module:: HAlphaAnomalyzer._anova_analysis


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


Functions
~~~~~~~~~

.. autoapisummary::

   HAlphaAnomalyzer._anova_analysis._preprocess_and_filter_data
   HAlphaAnomalyzer._anova_analysis._anova_ftest



.. py:function:: _preprocess_and_filter_data(data_with_ranges)

   Computes the S statistic and separates data by anomaly labels.

   This function calculates the S statistic as the sum of absolute deviations 
   between candidate range values and the average pixel values for each grid 
   cell of the training images data. It then separates the data into two 
   DataFrames based on the anomaly label: one for non-anomalous (label 0) and 
   one for anomalous (label 1) data.

   Parameters
   ----------
   data_with_ranges : pd.DataFrame
       The DataFrame with candidate ranges for each grid cell of the training 
       images data.

   Returns
   -------
   df_label_0 : pd.DataFrame
       A DataFrame with computed S statistics for non-anomalous data (label 
       0).
   df_label_1 : pd.DataFrame
       A DataFrame with computed S statistics for anomalous data (label 1).


.. py:function:: _anova_ftest(data_with_ranges, grid_size=8, lower_range_end=20, upper_range_start=80, step_size=2)

   Performs One-way ANOVA F-test on S statistics across grid cells and
   candidate ranges.

   This function calculates the One-way ANOVA F-test statistic between
   anomalous and non-anomalous images for each combination of grid cell and
   candidate range of the training images data using the S statistics.

   Parameters
   ----------
   data_with_ranges : pd.DataFrame
       The DataFrame with candidate ranges for each grid cell of the training
       images data.
   grid_size : int, optional
       The number of rows and columns to divide each image into, by default 8.
   lower_range_end : int, optional
       The end of candidate lower ranges, by default 20.
   upper_range_start : int, optional
       The start of candidate upper ranges, by default 80.
   step_size : int, optional
       The step size for candidate ranges, by default 2.

   Returns
   -------
   df_anova_results : pd.DataFrame
       A DataFrame with computed F-statistic for each combination of grid
       cell and candidate range of the training images data.