brain_diffusion.histogram_utils

brain_diffusion.histogram_utils

brain_diffusion.histogram_utils.histogram_by_video(SMfilename, xlabel='Log Diffusion Coefficient Dist', ylabel='Trajectory Count', fps=100.02, y_range=5000, frame_range=range(5, 30, 5), analysis='log', theta='D')

Plots a histogram of mean squared displacements or diffusion coefficients from input data.

SMfilename : string

Filename of particle MSDs. Must be a csv file, comma delimited. Must be organized as frames x particles.

xlabel : string

X label of the output graph.

ylabel : string

Y label of the output graph.

fps : float or int

The frame rate of the video being analyzed. Only required if graphing diffusion coefficients rather than mean squared displacements.

y_range : int

Y range of the output graph.

frame_range : range

Range containing which frames the user wishes to be plotted.

analysis : string

Desired type of data to be plotted. If input is ‘log’, plots the natural logarithm of the data. Any other input will plot the raw input data.

theta : string

Desired type of data to be plotted. If input is ‘D’, plots diffusion coefficients. Any other input will plot the mean squared displacements.

Returns ‘Graph completed successfully’ if function is successful.

>>> nframe = 51
>>> npar = 1000
>>> SMxy = np.zeros((nframe, npar))
>>> for frame in range(0, nframe):
        SMxy[frame, :] = np.random.normal(loc=0.5*frame, scale=0.5, size=npar)
>>> np.savetxt('sample_file.csv', SMxy, delimiter=',')
>>> histogram_by_video('sample_file.csv', y_range=500, analysis="nlog", theta="MSD")
>>> os.remove('sample_file.csv')
>>> os.remove('sample_file_hist.png')