ctrappy.gui_cpa_figs

Functions for calculating and plotting useful quantities from Experiments using Change Point Analysis (CPA).

ctrappy.gui_cpa_figs.get_anom_diff_fit_log(experiment, sigma_error=0.066, threshold=0.0025, min_segment_size=3, penalty=0.3, jump=1)

Get anomalous diffusion fit using log transformation method from https://doi.org/10.1371/journal.pone.0117722.

ctrappy.gui_cpa_figs.get_velocity(experiment, penalty=0.5, min_segment_size=3, fit_type='piecewise_linear', jump=1)

Get trace locations over time; do Kalman filtering and change-point analysis with given values for the penalty and minimum segment size. References: - Kalman filter: https://pykalman.github.io/ - Changepoint detection: https://centre-borelli.github.io/ruptures-docs/user-guide/detection/kernelcpd/ - See also: http://www.laurentoudre.fr/publis/TOG-SP-19.pdf / https://arxiv.org/pdf/1801.00826.pdf

ctrappy.gui_cpa_figs.plot_anom_diff_fit_log(data)

Plot anomalous diffusion data.

ctrappy.gui_cpa_figs.plot_velocity(data, vel_bin_width_bp=0.5)

Plot the result of get_velocity(); use bin width vel_bin_width_bp for the velocity histogram.