BioSANS2020.analysis.numeric.transform_data

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

normalize(vect)

returns the normalized form of the input vector v

calc_cross_corr(edata, items)

This function calculates the cross correlation of edata[0] and

calc_covariance2(edata)

This function calculates the covariance of edata[0] and prints

calc_covariance(edata, items, points=100)

This function calculates the covariance of edata[0] and prints

fano_factor(edata, items, points=100)

This function calculates the fano-factor of edata[0] and prints

prob_density_calc(edata, items)

This function calculates the probability density of edata[0] and

prob_density_calc2(edata, items)

This function calculates the probability density of edata[0] and

prob_density_calc3(edata, items, bins=50)

This function calculates the probability density of edata[0] per

ave_traj_calc(edata, items)

This function calculates the average trajectory of edata[0] and
BioSANS2020.analysis.numeric.transform_data.normalize(vect)

returns the normalized form of the input vector v

BioSANS2020.analysis.numeric.transform_data.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:

edatatwo 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]

BioSANS2020.analysis.numeric.transform_data.calc_covariance2(edata)

This function calculates the covariance of edata[0] and prints the result in a terminal window. Args:

edatatwo 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.

BioSANS2020.analysis.numeric.transform_data.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:

edatatwo 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.

BioSANS2020.analysis.numeric.transform_data.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:

edatatwo 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)

BioSANS2020.analysis.numeric.transform_data.prob_density_calc(edata, items)

This function calculates the probability density of edata[0] and returns a plot of the probability density. Args:

edatatwo 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]

BioSANS2020.analysis.numeric.transform_data.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:

edatatwo 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]

BioSANS2020.analysis.numeric.transform_data.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:

edatatwo 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

BioSANS2020.analysis.numeric.transform_data.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:

edatatwo 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]