wholecell.utils.plotting_tools

Reusable plotting functions and tools

wholecell.utils.plotting_tools.export_figure(plt, plotOutDir, plotOutFileName, metadata=None, transparent=False, dpi=120, extension=None)

wholecell.utils.plotting_tools.heatmap(ax, mask, data, completion_data, xticklabels, yticklabels, xlabel='', ylabel='', title='', box_text_size='medium', ax_font_size=9, title_font_size=9, percent_completion_threshold=0.88)

Parameters:

• ax – Axes object

• mask – Only plot values where mask is true, must match dimensions of

• data

• data – 2-dimensional numpy array of data to plot

• completion_data – Percent of seeds that successfully completed all

• value (generations that contributed to this)

• of (must match dimensions)

• data

• xticklabels – tick values for x-axis

• yticklabels – tick values for y-axis

• xlabel – x-axis label for plot

• ylabel – y-axis label for plot

• title – plot title

• box_text_size – size of text value to be printed in box

• ax_font_size – font size for labeling axes

• title_font_size – font size for title

• percent_completion_threshold – If the percent completion for this

• threshold (parameter combination is lower than the)

• the (the number in)

• 0 (box will be colored red. If the threshold is)

• be (no numbers will)

• red. (colored)

Returns:

heatmap of data, where squares are colored by value and numbers are colored by the percent of simulations that successfuly completed, for data corresponding to different parameter values in 2 dimensions

wholecell.utils.plotting_tools.labeled_indexable_hist(ax, data, gen_data, gen_start, gen_end, colors, xlabel, bin_width=1.0, xlim=None, sf=1, font_size=9)

Creates a histogram of (subset of) data, with label for mean and standard deviation of data for each variant

Parameters:

• ax – Axes object

• data – data to plot

• gen_data – generation index corresponding to each data point

• gen_start – index of generation to start from (inclusive)

• gen_end – index of generation to end at (exclusive)

• colors – list of colors to use for each variant

• xlabel – x-axis label for plot

• bin_width – used in conjunction with xlim to determine number of bins

• xlim – specify x-axis plotting region

• sf – scale factor

• font_size – font size for labeling axes

Returns:

histogram of data, colored by variant, for data corresponding to

generation indexes in [gen_start:gen_end]

wholecell.utils.plotting_tools.labeled_indexable_scatter(ax, xdata, ydata, gen_data, gen_start, gen_end, colors, xlabel, ylabel, xlim=None, ylim=None, sf=1, font_size=9)

Creates a scatterplot of (subset of) data, with label for mean and standard deviation of data for each variant

Parameters:

• ax – Axes object

• xdata – data to plot on x axes

• ydata – data to plot on y axes

• gen_data – generation index corresponding to each data point

• gen_start – index of generation to start from (inclusive)

• gen_end – index of generation to end at (exclusive)

• colors – list of colors to use for each variant

• xlabel – x-axis label for plot

• ylabel – y-axis label for plot

• xlim – specify x-axis plotting region

• ylim – specify y-axis plotting region

• sf – scale factor

• font_size – font size for labeling axes

Returns:

scatterplot of data, colored by variant, for data corresponding

to generation indexes in [gen_start:gen_end]

wholecell.utils.plotting_tools.plot_splom(arrayOfdataArrays, nameArray='', stdArrays=None, labels=None, fig=None, plotCorrCoef=True, formatString='o')

Plot a scatterplot matrix (Splom) of data contained in arrayOfdataArrays, with labels in the same order held within nameArray.

wholecell.utils.plotting_tools.remove_border(ax=None, bottom=False)