solarpy.plotting.two_part_colormap

solarpy.plotting.two_part_colormap#

solarpy.plotting.two_part_colormap(start_color='lightgrey', colormap='viridis', n_gradient=64, n_colormap=64, colormap_start=0.1)#

Create a two-part colormap blending a flat color into a standard colormap.

Parameters:

  • start_color (str or color-like, optional) – The colour at the bottom of the gradient segment. Any Matplotlib color specification is accepted. Default is "lightgrey".

  • colormap (str, optional) – Name of the Matplotlib colormap to use for the upper segment. Default is "viridis".

  • n_gradient (int, optional) – Number of color steps in the gradient segment. Default is 64.

  • n_colormap (int, optional) – Number of color steps sampled from colormap. Default is 64.

  • colormap_start (float, optional) – Starting point within colormap, in [0, 1]. Values above 0 skip the first portion of the colormap, which can improve the visual transition from start_color. Default is 0.1.

Returns:

cm_custom – A colormap of n_gradient + n_colormap discrete colors. Values below the data range are rendered transparent.

Return type:

matplotlib.colors.ListedColormap

Notes

The resulting colormap has two segments: a smooth gradient from start_color to the first color of colormap, followed by the colormap itself. Values below the colormap range (e.g. zero or negative) are rendered as transparent.

This is particularly useful for density plots where low-count bins should appear as a neutral colour before transitioning into the main colormap.

Examples

Use as a drop-in colormap for a density hexbin plot:

>>> import solarpy
>>> import numpy as np
>>> import matplotlib.pyplot as plt
>>> from matplotlib.colors import TwoSlopeNorm
>>> x = np.random.randn(100000)
>>> y = 2 * x + np.random.randn(100000)
>>> cm = solarpy.plotting.two_part_colormap(
...     start_color='lightgrey', colormap='viridis', colormap_start=0.05)
>>> norm = TwoSlopeNorm(vmin=1, vcenter=30, vmax=175)
>>> _ = plt.hexbin(x, y, gridsize=100, cmap=cm, norm=norm, mincnt=1)
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