asi_core.visualization.density_scatter

Functions

plot_density(x, y[, pairs, ax, xlabel, ylabel, title, ...])

Create density plot with defined grid size allowing to interpret color code quantitatively.

Module Contents

asi_core.visualization.density_scatter.plot_density(x, y, pairs=False, ax=None, xlabel=None, ylabel=None, title='', xlim=(None, None), ylim=(None, None), cbar_scale='linear', cbar_lim=(None, None), metrics=None, quantiles=False, quan_bin_num=10, save=False, cut_bins=True, print_perc_legend=True, print_metrics_box=True)

Create density plot with defined grid size allowing to interpret color code quantitatively.

Parameters:

  • x – (list, DataFrame column) x-axis values

  • y – (list, DataFrame column) y-axis values

  • pairs – (bool) If True, parity plot will be produced (equal aspect and angle bisecting)

  • ax – (matplotlib axis) Here a subplots axis can be passed. If None, a single plot will be returned

  • xlabel – (str) Custom xlabel

  • ylabel – (str) Custom ylabel

  • title – (str) Desired plot title

  • xlim – (tuple) x-axis limits

  • ylim – (tuple) y-axis limits

  • cbar_scale – (string) use ‘linear’ or ‘log’ color coding and colorbar

  • cbar_lim – (tuple) Limits of colorbar

  • metrics – (str) Choose between rel for relative or abs for absolute or both

  • quantiles – (bool) If True, 5%, 25%, 50%, 75%, and 95% quantiles will be plotted

  • quan_bin_num – (int) Number of bins for quantiles

  • save – (bool or str) If Truthy, plot is saved as pickle. If truthy str, file is named to the value of save

  • cut_bins – If False, bin edges will not cut the x values

  • print_perc_legend – If True, print a legend describing plotted percentiles

  • print_metrics_box – If True, print deviation metrics in plot