:py:mod:`BioSANS2020.analysis.numeric.transform_data`
=====================================================

.. py:module:: BioSANS2020.analysis.numeric.transform_data

.. autoapi-nested-parse::

                          This is the transform_data module

   This module process numeric trajectories and either prints the result or
   display the output into a text area embedded in a canvas or plots it

   The following is the list of function for this module:

   1. normalize
   2. calc_cross_corr
   3. calc_covariance2
   4. calc_covariance
   5. fano_factor
   6. prob_density_calc
   7. prob_density_calc2
   8. prob_density_calc3
   9. ave_traj_calc



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


Functions
~~~~~~~~~

.. autoapisummary::

   BioSANS2020.analysis.numeric.transform_data.normalize
   BioSANS2020.analysis.numeric.transform_data.calc_cross_corr
   BioSANS2020.analysis.numeric.transform_data.calc_covariance2
   BioSANS2020.analysis.numeric.transform_data.calc_covariance
   BioSANS2020.analysis.numeric.transform_data.fano_factor
   BioSANS2020.analysis.numeric.transform_data.prob_density_calc
   BioSANS2020.analysis.numeric.transform_data.prob_density_calc2
   BioSANS2020.analysis.numeric.transform_data.prob_density_calc3
   BioSANS2020.analysis.numeric.transform_data.ave_traj_calc



.. py:function:: normalize(vect)

   returns the normalized form of the input vector v 


.. py:function:: calc_cross_corr(edata, items)

   This function calculates the cross correlation of edata[0] and
   returns a plot of the correlation as a function of lags.
   Args:
       edata : two dimensional array of data & labels (data, label).
               data is a 3D array where each row are the individual
               trajectories. Each trajectory is a 2D numpy array where
               the first column is time and the remaining columns are
               the corresponding components.
       items : 3 item list of [canvas, scroll_x, scroll_y]


.. py:function:: calc_covariance2(edata)

   This function calculates the covariance of edata[0] and prints
   the result in a terminal window.
   Args:
       edata : two dimensional array of data & labels (data, label).
               data is a 3D array where each row are the individual
               trajectories. Each trajectory is a 2D numpy array where
               the first column is time and the remaining columns are
               the corresponding components.


.. py:function:: calc_covariance(edata, items, points=100)

   This function calculates the covariance of edata[0] and prints
   the result in a text area embedded in a canvas.
   Args:
       edata : two dimensional array of data & labels (data, label).
               data is a 3D array where each row are the individual
               trajectories. Each trajectory is a 2D numpy array where
               the first column is time and the remaining columns are
               the corresponding components.
       items : 3 item list of [canvas, scroll_x, scroll_y]
       points : last number of points considered in covariance
                calculation from -points to the end of array
                or equivalent to [-points:] slice.


.. py:function:: fano_factor(edata, items, points=100)

   This function calculates the fano-factor of edata[0] and prints
   the result in a text area embedded in a canvas.
   Args:
       edata : two dimensional array of data & labels (data, label).
               data is a 3D array where each row are the individual
               trajectories. Each trajectory is a 2D numpy array where
               the first column is time and the remaining columns are
               the corresponding components.
       items : 3 item list of [canvas, scroll_x, scroll_y]
       points : last number of points considered in fano-factor
        calculation from -points to the end of array ([-points:] slice)


.. py:function:: prob_density_calc(edata, items)

   This function calculates the probability density of edata[0] and
   returns a plot of the probability density.
   Args:
       edata : two dimensional array of data & labels (data, label).
               data is a 3D array where each row are the individual
               trajectories. Each trajectory is a 2D numpy array where
               the first column is time and the remaining columns are
               the corresponding components.
       items : 3 item list of [canvas, scroll_x, scroll_y]


.. py:function:: prob_density_calc2(edata, items)

   This function calculates the probability density of edata[0] and
   returns a plot of the probability density with time.
   Args:
       edata : two dimensional array of data & labels (data, label).
               data is a 3D array where each row are the individual
               trajectories. Each trajectory is a 2D numpy array where
               the first column is time and the remaining columns are
               the corresponding components.
       items : 3 item list of [canvas, scroll_x, scroll_y]


.. py:function:: prob_density_calc3(edata, items, bins=50)

   This function calculates the probability density of edata[0] per
   bins and returns a plot of the probability density (time slice).
   Args:
       edata : two dimensional array of data & labels (data, label).
               data is a 3D array where each row are the individual
               trajectories. Each trajectory is a 2D numpy array where
               the first column is time and the remaining columns are
               the corresponding components.
       items : 3 item list of [canvas, scroll_x, scroll_y]
       bins : number of bins an entire trajectory will be discretized


.. py:function:: ave_traj_calc(edata, items)

   This function calculates the average trajectory of edata[0] and
   returns a plot of the average trajectory as a function of time.
   Args:
       edata : two dimensional array of data & labels (data, label).
               data is a 3D array where each row are the individual
               trajectories. Each trajectory is a 2D numpy array where
               the first column is time and the remaining columns are
               the corresponding components.
       items : 3 item list of [canvas, scroll_x, scroll_y]