make-your-own-colormaps.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Simple way to make your own colormaps"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from matplotlib.colors import ListedColormap\n",
    "import matplotlib as mpl\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I often start off by using www.color-hex.com to choose a color that I like. This site helps you see the details of the color, rgb and hex codes, shades, complimentary colors etc. \n",
    "\n",
    "For this example, let's use a color described by the hex code \"#407294\" (https://www.color-hex.com/color/407294). The rgb (red-green-blue) composition of this color is: 64 114 148 according to the above website (these values can range between 0 and 256)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The matplotlib function that I use to create a colormap (matplotlib.colors.ListedColormap) takes an array of floating points between 0 and 1 as input. So, I convert the rgb values from above to a range from 0 to 1 to create a colormap that goes from blue (RGB: 64, 114, 148) to white (RGB: 255, 255, 255)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "N = 256"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "vals = np.ones((N, 4))\n",
    "vals[:, 0] = np.linspace(64/N, 1, N) \n",
    "vals[:, 1] = np.linspace(114/N, 1, N)\n",
    "vals[:, 2] = np.linspace(148/N, 1, N)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create the colormaps:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "blue_cmap   = ListedColormap(vals)\n",
    "blue_cmap_r = ListedColormap(vals).reversed() # reverse direction colormap in case you want that"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Visualize the continuous colorbar: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.colorbar.ColorbarBase at 0x7fcfb022aa90>"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x36 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1,1, figsize=(6, 0.5))\n",
    "mpl.colorbar.ColorbarBase(ax, cmap=blue_cmap, orientation='horizontal')"
   ]
  }
 ],
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