plorts package¶

Submodules¶

plorts.legend module¶

plorts.legend.label_line(line, x, label=None, **kwargs)¶

plorts.legend.legend(*args, **kwargs)¶

plt.legend with a few extra location options:

inline: place legends on top of line
end: place legend at the right end of line
max: place legend slightly up and right of the max value of each line

Parameters:	loc (string or int) – location to place the legend

plorts.legend.legend_end(xoff=0.05, yoff=0, *args, **kwargs)¶

plorts.legend.legend_inline(xvals=None, xoffset=None, **kwargs)¶

plorts.legend.legend_max(xoff=0.05, yoff=0.01, *args, **kwargs)¶

plorts.palettes module¶

Styles for plots.

Mainly adds the cool, warn, and neon themes from this post

plorts.palettes.colorblind = ['#0072B2', '#009E73', '#D55E00', '#CC79A7', '#F0E442', '#56B4E9']¶: colorblind-friendly colors for use in axes.prop_cycle

plorts.palettes.gradient_to_colormap(name, gradient)¶

Generates a matplotlib colormap from a gradient.

Plain white to black gradient

>>> plorts.palettes.gradient_to_colormap('bw', [(0, "white"), (1, "black")])
<matplotlib.colors.LinearSegmentedColormap object at ...>

First 25% of gradient is white to blue, second 75% is blue to black

>>> plorts.palettes.gradient_to_colormap('bw', [(0, "white"), (0.25, "#0000ff"), (1, "black")])
<matplotlib.colors.LinearSegmentedColormap object at ...>

Parameters:	name – name of the colormap to register with matplotlib gradient – list of tuples specifying gradient
Returns:
Return type:	matplotlib.colors.LinearSegmentedColormap

plorts.palettes.solarized = ['#6c71c4', '#268bd2', '#2aa198', '#859900', '#b58900', '#cb4b16', '#dc322f', '#d33682']¶: solarized colorsheme for axes.prop_cycle

plorts.plotting module¶

plorts.plotting.cdf(data, x, *args, **kwargs)¶

Plot a cdf from a dataframe column.

If hue is provided, plot many overlayed cdfs, one per value of the data[hue] column.

Keyword Arguments:
Parameters:	data (pandas.DataFrame) – dataframe to plot x (string) – column of data to plot
	hue (string) – column of dataframe to split on

plorts.plotting.colors_from_hue(data, hue, cmap)¶

plorts.plotting.hist(data, x, alpha=0.5, rwidth=0.92, *args, **kwargs)¶

Plot a histogram from a dataframe column.

If hue is provided, plot many overlayed histograms, one per value of the data[hue] column.

Keyword Arguments:
Parameters:	data (pandas.DataFrame) – dataframe to plot x (string) – column of data to plot
	hue (string) – column of dataframe to split on alpha (float) – opacity of histogram (default: 0.5) rwidth (float) – The relative width of the bars as a fraction of the bin width (default: 0.92)

plorts.plotting.hueize(data, hue=None, cmap=<matplotlib.colors.LinearSegmentedColormap object>, *args, **kwargs)¶

Groups a dataframe by hues, if any, and generates plot settings for each hue.

Keyword Arguments:
Returns:
Return type:	Generator of (pd.DataFrame, kwargs), where the kwargs can be used in matplotlib functions.
Parameters:	data (pandas.DataFrame) – dataframe to plot x (string) – column of data to plot
	hue (string) – column of dataframe to split on

plorts.plotting.pdf(data, x, bins=10, normalize=True, *args, **kwargs)¶

Plot a probability density function (pdf) from a dataframe column.

If hue is provided, plot many overlayed pdf, one per value of the data[hue] column.

Keyword Arguments:
Parameters:	data (pandas.DataFrame) – dataframe to plot x (string) – column of data to plot
	hue (string) – column of dataframe to split on bins (int or sequence of scalars or str, optional) – Bins to use for the function. See numpy.histogram <https://numpy.org/doc/stable/reference/generated/numpy.histogram.html> normalize (boolean) – Normalize to a probability density (default). If false, returns a histogram.

plorts.plotting.plot(data, x, y, error=None, *args, **kwargs)¶

plorts.plotting.savefig(filename, **kwargs)¶: Saves a figure, but also creates the directory and calls tight_layout before saving

plorts.plotting.scatter(data, x, y, markers=['o'], linestyles=[''], **kwargs)¶

plorts.plotting.stackplot(data, x, y, hue, cmap=<matplotlib.colors.LinearSegmentedColormap object>)¶

plorts.style module¶

plorts.style.style_axis(show_xaxis=False, label_fontweight='medium', label_fontstyle='italic', tight_layout=True)¶