:py:mod:`BioSANS2020.cli_functs.ssl_calls`
==========================================

.. py:module:: BioSANS2020.cli_functs.ssl_calls

.. autoapi-nested-parse::

                          This is the ssl_calls module

   This module interacts with BioSSL.py by fulfilling its requests to pro-
   vide a console interface that supports some features of BioSANS.

   The following is the list of function for this module:

   1. load_data_traj
   2. calc_average_conc_at_tend
   3. calc_covariance
   4. calc_covariance_per_traj
   5. calc_covariance_bootsrap
   6. prob_density_calc_wtime
   7. prob_density_calc_tslice
   8. prob_density_calc



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


Functions
~~~~~~~~~

.. autoapisummary::

   BioSANS2020.cli_functs.ssl_calls.load_data_traj
   BioSANS2020.cli_functs.ssl_calls.calc_average_conc_at_tend
   BioSANS2020.cli_functs.ssl_calls.calc_covariance
   BioSANS2020.cli_functs.ssl_calls.calc_covariance_per_traj
   BioSANS2020.cli_functs.ssl_calls.calc_covariance_bootsrap
   BioSANS2020.cli_functs.ssl_calls.prob_density_calc_wtime
   BioSANS2020.cli_functs.ssl_calls.prob_density_calc_tslice
   BioSANS2020.cli_functs.ssl_calls.prob_density_calc



.. py:function:: load_data_traj(file_name)

   This function loads trajectory data from a tab delimited file.
   The first column in the file is time, the remaining columns are
   species or components. All sampled trajectories are concatenated
   in the file.
   Args:
       file_name : name of trajectory file generated in BioSANS
                   simulations (either deterministic or stochastic).
   Return:
       current_data : two dimensional array of [data, slabels].
                      slabels are the names in the header of file_name.
                      data is a list of trajectory data w/o header



.. py:function:: calc_average_conc_at_tend(edata, points=100)

   This function calculates the average or mean of edata[0] using
   the last number of points in the trajectory. If the simulation is
   long enought, this is the steady state mean concentration.
   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.
       points : number of data points to slice at end of trajectory


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

   This function calculates the covariance of edata[0] and prints
   the result in a console.
   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.
       points : last number of points considered in covariance
                calculation from -points to the end of array
                or equivalent to [-points:] slice.


.. py:function:: calc_covariance_per_traj(edata, points=100, fname='', mname='')

   This function calculates the covariance of edata[0] per rows,
   prints the result in a console, and plots data into image.
   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.
       points : last number of points considered in covariance
        calculation from -points to the end of array ([-points:] slice)
       fname : prepended name to plots fname_mname*
       mname : prepended name to plots fname_mname*


.. py:function:: calc_covariance_bootsrap(edata, points=100, msamp=1000, fname='', mname='')

   This function calculates the covariance of edata[0] with sampling
   , prints the result in a console, and plots data into image.
   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.
       points : last number of points considered in covariance
                calculation from -points to the end of array
                or equivalent to [-points:] slice.
       msamp : number of randomly chosen trajectories
       fname : prepended name to plots fname_mname*
       mname : prepended name to plots fname_mname*


.. py:function:: prob_density_calc_wtime(edata, fname, mname)

   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.
       fname : prepended name to plots fname_mname*
       mname : prepended name to plots fname_mname*


.. py:function:: prob_density_calc_tslice(edata, bins=50, fname='')

   This function calculates the probability density of edata[0] per
   bins 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.
       bins : number of bins an entire trajectory will be discretized
       fname : name prepended to plot name


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

   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]