{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Colormaps in `matplotlib`\n", "\n", "[back to overview page](index.ipynb)\n", "\n", "* [matplotlib colormaps documentation](https://matplotlib.org/stable/api/pyplot_summary.html#colormaps)\n", "\n", "* [display of all matplotlib colormaps](https://matplotlib.org/stable/tutorials/colors/colormaps.html)\n", "\n", "* tool for analyzing and creating colormaps: [viscm](https://github.com/BIDS/viscm)\n", "\n", "* tool for colorspace conversions: [colorspacious](http://colorspacious.readthedocs.org/)\n", "\n", "* another tool [colour](http://colour-science.org/), [repo](https://github.com/colour-science/colour)" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import matplotlib.pyplot as plt\n", "import numpy as np" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "plt.rcParams['figure.figsize'] = 10, 2" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false, "jupyter": { "outputs_hidden": false } }, "outputs": [], "source": [ "import colorspacious\n", "\n", "def show_colormap_with_lightness(cmap, colors=256, ax=None):\n", " cmap = plt.cm.get_cmap(cmap)\n", " C = np.arange(colors)\n", " rgb = cmap(C)[:, :3] # ignore alpha values\n", " cam02ucs = colorspacious.cspace_convert(rgb, \"sRGB1\", \"CAM02-UCS\")\n", " lightness, chroma, hue = cam02ucs.T\n", " X = np.arange(len(C) + 1)\n", " Y = np.array([0, 100])\n", " xmiddle = X[:-1] + np.diff(X) / 2\n", " if ax is None:\n", " ax = plt.gca()\n", " ax.pcolormesh(X, Y, C.reshape(1, -1), cmap=cmap, rasterized=True)\n", " ax.plot(xmiddle, lightness, '-w', xmiddle, lightness, '--k')\n", " ax.axis([0, colors, 0, 100])\n", " ax.tick_params(axis='x', bottom=False, top=False, labelbottom=False)\n", " ax.set_title(cmap.name)\n", " ax.set_ylabel(\"CAM02-UCS Lightness\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Reversed Colormaps\n", "\n", "For most (all?) of `matplotlib`'s colormaps, there is also a reversed version.\n", "Just append \"`_r`\" to the name of the colormap." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Sequential Colormaps\n", "\n", "The `cividis` colormap is available since matplotlib 2.2.\n", "It further improves on `viridis` with regards to color blindness.\n", "See Nuñez et al. (2018), \"Optimizing colormaps with consideration for color vision deficiency to enable accurate interpretation of scientific data\", https://doi.org/10.1371/journal.pone.0199239." ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false, "jupyter": { "outputs_hidden": false } }, "outputs": [ { "data": { "image/svg+xml": [ "\n", "\n", "\n" ], "text/plain": [ "