multi_locus_analysis.plotting

For plotting multi_locus_analysis results

multi_locus_analysis.plotting.cvv_plot_sized(cvvs, analytical_deltas=[], delta_col='delta', t_col='t', cvv_col='cvv_normed', max_t_over_delta=4, data_deltas=None, A=1, beta=0.5, tDeltaN=None, cmap_name='viridis', fig=None, alpha_map=None, size_map=None, theory_linewidth=2, include_errorbar=True, include_lines=False, include_points=False, data_line_alpha=1, data_linewidth=1, BASE_DOT_SIZE=10000, **kwargs)

One pretty version of the Velocity-Velocity correlation plots for the experimental data, with some theory overlaid.

multi_locus_analysis.plotting.make_all_disps_hist(displacements, centering='mean,std', cmap=None, reverse=False, alpha=1, cmap_log=False, factor_by=None, xlim=None, ylim=None, include_theory_data=False, include_theory_exact=True, vxcol='vx', vycol='vy', max_plots=inf, laplace=True, normal=True, axs=None, no_tick_labels=False, cbar=True, xbins=None, frames_to_sec=None, yscale='log', omit_title=True)

Make a manual factor plot of displacement histograms.

Parameters

• factor_by (List<str>) – list of level numbers into the displacements heirarchical index telling which set of levels to factor on.

• centering (str) – controls how to center the data so that it fits on a single plot. one of “none”, “mean”, “mean,std” for nothing, mean subtraction, and mean subtraction+dividing by std deviation (respectively).

• reverse (bool) – True plots smallest deltas on top, False the opposite