:py:mod:`HAlphaAnomalyzer._cell_range_calculator`
=================================================

.. py:module:: HAlphaAnomalyzer._cell_range_calculator


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


Functions
~~~~~~~~~

.. autoapisummary::

   HAlphaAnomalyzer._cell_range_calculator._calculate_range_values
   HAlphaAnomalyzer._cell_range_calculator._calculate_cell_wise_ranges



.. py:function:: _calculate_range_values(data_cells, lower_range=2, upper_range=98)

   Calculate the lower and upper percentage values of a grid cell pixel 
   averages from the training image data.

   Parameters
   ----------
   data_cells : pd.DataFrame
       The DataFrame containing the grid cell pixel averages from the 
       training image data.
   lower_range : float, optional
       The lower percentage to calculate, by default 2.
   upper_range : float, optional
       The upper percentage to calculate, by default 98.

   Returns
   -------
   lower_range_val : float
       The lower percentage value of the grid cell pixel averages from the 
       training image data.
   upper_range_val : float
       The upper percentage value of the grid cell pixel averages from the 
       training image data.


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

   Calculate candidate upper and lower percentage values for each grid cell 
   of the training images data for the One-way ANOVA F-test.

   Parameters
   ----------
   images_data : pd.DataFrame
       The DataFrame containing 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_all_ranges : pd.DataFrame
       A DataFrame with candidate ranges for each grid cell of the training 
       images data.