Custom colourmaps for matplotlib

Custom_colourmaps.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Custom colormaps\n",
    "\n",
    "Let's look at a matplotlib colormap:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/html": [
       "<div style=\"vertical-align: middle;\"><strong>viridis</strong> </div><div class=\"cmap\"><img alt=\"viridis colormap\" title=\"viridis\" style=\"border: 1px solid #555;\" src=\"data:image/png;base64,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\"></div><div style=\"vertical-align: middle; max-width: 514px; display: flex; justify-content: space-between;\"><div style=\"float: left;\"><div title=\"#440154ff\" style=\"display: inline-block; width: 1em; height: 1em; margin: 0; vertical-align: middle; border: 1px solid #555; background-color: #440154ff;\"></div> under</div><div style=\"margin: 0 auto; display: inline-block;\">bad <div title=\"#00000000\" style=\"display: inline-block; width: 1em; height: 1em; margin: 0; vertical-align: middle; border: 1px solid #555; background-color: #00000000;\"></div></div><div style=\"float: right;\">over <div title=\"#fde725ff\" style=\"display: inline-block; width: 1em; height: 1em; margin: 0; vertical-align: middle; border: 1px solid #555; background-color: #fde725ff;\"></div></div>"
      ],
      "text/plain": [
       "<matplotlib.colors.ListedColormap at 0x7fdea020e250>"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib import cm\n",
    "from matplotlib.colors import ListedColormap, LinearSegmentedColormap\n",
    "from collections import OrderedDict\n",
    "\n",
    "viridis = cm.get_cmap('viridis', 12)\n",
    "viridis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can get an RGB color for a particular value &mdash; based on the range (0, 1):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.119512, 0.607464, 0.540218, 1.0)"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "viridis(0.56)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Or look at all the colours:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.267004, 0.004874, 0.329415, 1.      ],\n",
       "       [0.283072, 0.130895, 0.449241, 1.      ],\n",
       "       [0.262138, 0.242286, 0.520837, 1.      ],\n",
       "       [0.220057, 0.343307, 0.549413, 1.      ],\n",
       "       [0.177423, 0.437527, 0.557565, 1.      ],\n",
       "       [0.143343, 0.522773, 0.556295, 1.      ],\n",
       "       [0.119512, 0.607464, 0.540218, 1.      ],\n",
       "       [0.166383, 0.690856, 0.496502, 1.      ],\n",
       "       [0.319809, 0.770914, 0.411152, 1.      ],\n",
       "       [0.525776, 0.833491, 0.288127, 1.      ],\n",
       "       [0.762373, 0.876424, 0.137064, 1.      ],\n",
       "       [0.993248, 0.906157, 0.143936, 1.      ]])"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "viridis.colors"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Use `under`, `bad` and `over`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/html": [
       "<div style=\"vertical-align: middle;\"><strong>viridis</strong> </div><div class=\"cmap\"><img alt=\"viridis colormap\" title=\"viridis\" style=\"border: 1px solid #555;\" src=\"data:image/png;base64,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\"></div><div style=\"vertical-align: middle; max-width: 514px; display: flex; justify-content: space-between;\"><div style=\"float: left;\"><div title=\"#ff00ffff\" style=\"display: inline-block; width: 1em; height: 1em; margin: 0; vertical-align: middle; border: 1px solid #555; background-color: #ff00ffff;\"></div> under</div><div style=\"margin: 0 auto; display: inline-block;\">bad <div title=\"#00ffffff\" style=\"display: inline-block; width: 1em; height: 1em; margin: 0; vertical-align: middle; border: 1px solid #555; background-color: #00ffffff;\"></div></div><div style=\"float: right;\">over <div title=\"#ff8000ff\" style=\"display: inline-block; width: 1em; height: 1em; margin: 0; vertical-align: middle; border: 1px solid #555; background-color: #ff8000ff;\"></div></div>"
      ],
      "text/plain": [
       "<matplotlib.colors.ListedColormap at 0x7fde6b0a29d0>"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "magenta = np.array([256/256, 0/256, 256/256, 1])\n",
    "cyan  = np.array([0/256, 256/256, 256/256, 1])\n",
    "orange = np.array([256/256, 128/256, 0/256, 1])\n",
    "\n",
    "viridis.set_under(color=magenta)\n",
    "viridis.set_bad(color=cyan)\n",
    "viridis.set_over(color=orange)\n",
    "\n",
    "viridis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.colorbar.Colorbar at 0x7fde69065d60>"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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ZVvL4v08xtU6Dt5tat/S9pjZi6SXOOu//se3yce9U+951p9k5adPX2D8TAMj6YZ6pld9u/8yKp9rf8/Oxdzvr7Dfvt57nQz57NoMQKEoH934SQpoKqkGGnzRqhJAmQggBXDoYeogQ0lQI0lMLsqMgXtCoEUKi4JwaISSpCKmg3GfBgYlXCCFNB/XviTGcNyGkyRDOUZDERk1EZgEYC6BIVY+MnOsI4B8AsgHkAbhAVW2nrgihgynYv8HbHy3U1h0ypV3XfaZ286Fvm9pTD5xlag/Oe8JZ56W/sf3NWt280dQO9igzteYFLZ11VmTafmz9ehaZ2lnj7axZj88b46xTim0frbOy7WeQ8l6WqQFA3iN2OJ8+T91jaoM62t9z0qEfO+u86ZMLTE1Pst+xzgtTbW2Z+9XemJptauVj7Rn13DPssESTPrvU1ACg6yBvH7e/t7L9FYNSXzsK4kUQT73ZqBno7TYA76rqAADvRv5PCEkCVAUVoWbOI5Hn1HyNmqp+AGBHtdPjAMyJfJ4D4Jz6bRYhJF4owuG6/Y5Epa5zat0qd9OraoGIdLUuFJErEQ7Vi5QOdtJXQkhiEGhHQVOeU4uVSNC4mQDQMisrgUfihBAgbLDqI5x3vKjr7tdCEckEgMi/9swuIaRJoRGXDufQM4GHn3U1aq8AmBz5PBnAy/XTHEJIvAmSIi+R834GcenwCvQ2HcA8EbkMwCYA5weprEWbMvQ+yjtkz87nezjvferWJ03th6/ZrgN5n/7S1Pq8lOasc+TPVpva3z/8gam17FJiam0W264DAFDS13YHcW0yXrm3p6m1/r4d5gcA9q22M0atmdLG1P495j5nuWN+/JWpdZ3Z29R2LmhlalOvcEeP1t6lptasJMXUOq20wwB1+Is7a9bOR+3sV98Mdd5q8vHGPk69dK93OKSdxZ/UrcKqaACXjQQefvoaNUegt1PruS2EkARA1T8um1+2qUpEZDSAhwGkAHhCVadX00chPNKr/Av4gqpOq12Lo+GOAkJIFME2tPuXIyIpAP4M4HSEk6wsFpFXVHVNtUsXqurYurW2Jky8QgiJRisXC+wjIMcAyFXVDapaCuBZhH1cGxQaNUJIFKqCUKiZz/Ft3s8lVY4rqxVlJSuuzggR+VxE5ovIEbG2n8NPQkgUisDrAH55P4MkK14GoLeq7hORMQBeAjAgWPXesKdGCKmB7zapYDsKfJMVq+oeVd0X+fw6gFQR6RxL2xu1p1ZakoqNy7t7ar3X2kvxAHDubNs1o+t622vmwTWnm9oohxsEAKx4fLCpZW+1XS9aFpab2rZhziqR/la6qWmendXo/R96P1cA6LDa/QJOum6Bqc199WRTm/hzO4IHAGwab/9c5Hi7L3Cwve3OUNHa3Ydovt12mekxxM6T+9X4TFNbu9LdcehZbLdJu9nv9fd/+qCpbRthu7UAwMb8WzzPD3P/GgVCVaAhH6MVzPl2MYABItIHwFYAFwK4qOoFInIIgEJVVRE5BuGOltsHyQcOPwkhNfBbDAiyWKCq5SJyDYA3EXbpmKWqq0Xk6oj+GIDzAPxMRMoB7AdwYSQfaJ2hUSOERFG5TcrvmmBl6esIZ2Kveu6xKp9nAJhR60Y6oFEjhFQjyN7OxN37SaNGCIlGAfXb3NmUt0kRQr5bBBt+sqdGCGlKJPOG9nolNQTpfsBTKrrWvRZdtsFeqt+TbbvbbT1oR9td/M/vuet05BUZf+NCU3v6aXuvf4973VEUch881hbFdulIz7P/cuq4b5x1Lrx8uKm1O8px39HebgWVnPGXqaZW3Mt2Xdk50P4ufV+wo2kAQNGv7feopMx+h048bYWpffyy4yEA2H7xHlPLfMaOBFNixosGTjjqC2edI7K9n/0XT8/xPF8rArh0JHLiFfbUCCEecKGAEJIsBNknxZ4aIaTJoAL47ihonKbUBRo1QkgNEnnOzA8aNUJINBx+EkKSDj8/NPqpEUKaDAoIdxQEo7RjJjZceIendv+aM533rsy0s0199rIdIuiNZ0eY2r4j3L5xGctsv7CFl9hpgvSHdpm35Nr+UABw+Xt2bKLW39j+eHuOsbXcxfc46xxy9B9M7ZYbnzW1k66/11lus3sLTa3sb21NLa3A/o159LlHnXWee6/tO1dsJ33CO30yTG3AgmJnnedNfM/U1t/RzdQeHDLP1HouvNZZ5/c7b/Y8/2VzOyRWcLj3kxCSTHBOjRCSVCjgm604gY0aw3kTQmqi4nMEK0ZERovIWhHJFZHbPHQRkUci+goROTrWptOoEUKiUUD8jgDFVMn7eRaAwwFMEJHDq112FsKJVgYAuBKAe9I0ADRqhJCaaIDDnyB5P8cBmKthFgHIEBE7YUQAaNQIIVH49tIiRwCC5P0Mmhs0ML4LBSIyC8BYAEWqemTk3J0ArgCwLXLZ7ZFY5E5a7spH9gt3eWr/am+7ZQBA/qd2tqTuy2zXDE2xO8pSYbtsAEDGuK2mtnaA/cek1xv2snqa+KT7KbPbuzcrxa7zLe+QTgBwUovpzirbtPWbFfam9dI8p752ZT9T63/pFlMreK2XqU3MmeSsc18v+7etxW772V4/8m1Te3/QIGed9712tqn1f3afqR013M4mNfRqb5eNSt4p8G5TSbkdXqlWBHPpGCkil1Y5OVNVZ1a/qHrJXgX5XFMrgqx+zkY4McLcaucfUtUHYqmcEJKABHfp8Etm7Jv3M+A1tcJ3+KmqHwDYEUslhJAmRGRHgesI2Jf6Nu+niLRAOO/nK9WueQXApMgq6HEAdquqnaA1ALHMqV0TWYKdJSJ2eFlCSNOjHhYKVLUcQGXezxwA8yrzflbm/kQ4fd4GALkA/grg57E2va7Ot48C+C3CX+23AP4A4FKvC0XkSoSXatG8s2OfCiEkcagn59oAeT8VwC/qp7YwdeqpqWqhqlaoaghh63qM49qZqjpMVYeltG9T13YSQhoJUYGE/I54t9KmTkatmh/JeACr6qc5hJC4Uz8+anEjiEvHMwBGAegsIlsATAUwSkSGIPz18gBcFaSy1JQQMjO8M++UVdjuCgBwzg/tLEwr7rUjPuwcd4Sptd3s/nPTt+12U0s7ynbNKOhrt+f4Phucda7caqewGppxtamlHGhtavscWbEAoN1Xtjb1uQtNbeK7C5zlrn+jv6mtW2+76MARPaVkRztnnS2KbVeEsvb2b+NN/3jH1B7ufZqzzlZ77L7Bl+fZ78Kok+2ILW/ed4KzztBF3u9mfeXj9PNDC+inFhd8jZqqTvA4/WQDtIUQkgg0gd6YC0bpIIREIUGCRCbwnBqNGiGkJuypEUKShgB7O5v0nBoh5DsII98SQpKFWkThSEho1AghNWFPLRiZLXbj1uz5ntoTX5/ovPf1Z443tVbn2U+49c4KU8s/3515Z9sCOxxS6kBvfzsAOLDZ9k0adOdDzjoPvmdvfWv/WStT67DeDj20/LEpzjoH3G2HwHG9vO/e4val+nL+TaY28lw7wEvZFbtMrbTc7c+4s4/9SvexE2Nh6v12+KD0DSXOOku/tsP9uHo8i547ytTaH7DfWwD4eIx3hrBheMt5XyDo0kEISTb8XDoSeZsUjRohJIogc2qJm/WTRo0Q4gXn1AghSUOQHQU0aoSQJgMztBNCko4GNloi0hHAPwBkIxzp5wJV3elxXR6AvQAqAJSr6jC/shvVqLXbfgCj5+Z4amWT3Ev1vxxuh6rZXmQHn0zLs7/i2EOXOev86J3hpjb/kpmmduymm02t+8KDzjo7nb7R1JaKnWWp7Y92mVqvmfc767ztgpdM7dKnPza1fqf8wVnu6I8vM7W9PY80tX1rOptaF/ePDNk7bFeIr861p7fzlh9nau0zfFw6HEM11zDu1As/s+tsvt9Z59nX5Xmez71ik/O+IATZ0F4Pzrm3AXhXVadHMrffBuBW49qTVfWboAUz7ychJJogOT9jN2rjAMyJfJ4D4JyYS4xAo0YIqUnDR7/tVpk1KvJvV0dL3hKRpZF8J75wTo0QEk1wo+VMZiwi7wA4xOO+O2rRmpGqmi8iXQG8LSJfRNJ2mtCoEUKiEAQOPeRMZqyqZhx0ESkUkUxVLYjkPCkyysiP/FskIi8inOTJadQ4/CSERBNgTq0eFgpeATA58nkygJerXyAiaSLStvIzgDMQIMkTjRohpCYNn01qOoDTRWQ9gNMj/4eIdBeRyjyh3QB8KCKfA/gMwGuq+oZfwY06/Fxb3AMnLr7OU2t36mbnvX1+Y0ehyLnezqRU0t1em/7gr7bLBgCUj9tlaqNm/MrU0hzBP1r+usBZ538+HGhqGUfY2a0+/Mh22xhzyrXOOmfddo6pPZZ+rqkNWPS1s9x5a+yIESfebWf5+uom2yWm46WOiCIAym6wn9FhN9mve9GITqa242g7CgcANLODp6BZua3Nzz3c1P4y7ClnnWUhbxeoxSnuyDOBaIQdBaq6HcCpHufzAYyJfN4AwA5lYsA5NUJITbijgBCSLASK0kGjRghpUiSw0fKDRo0QEk3jbJNqMGjUCCHRMEoHISSZCOJ826SNmohkAZiL8HaHEMJbIR4OGjqkKqUZwMYfeUdK2Hjin5ztWLmxp6kNnrXV1IYW2pEkylu5gxLv22VH/2jlWOVvO6rQ1Eru6+Gss222rRWX2m4HA5vb3/PgOodfAYDVM2aY2j3ffN/UFl891Fnu9xxJZFKz7PtGnT7d1L651h0xY89n3UwtfYYdweLUDutM7f4Xn3PW+ewl9jO69f0LTC1lU5qprR/stbvov6SneEd7aVYf1kYVEnKXk8jDzyDOt+UAblbVwwAcB+AXInI4/hs6ZACAdyP/J4Q0dRp+M3uD4mvUVLVAVZdFPu8FkAOgBxowdAghJH5UDj8bOPRQg1GrOTURyQYwFMCnqBY6JLKLnhDS1PmurH6KSDqA5wHcoKp7RIIlyYrEQLoSAFI6ZtShiYSQRqWJr34G2tAuIqkIG7SnVPWFyOnCSMgQ+IQOmamqw1R1WEq6PTFKCEkMggw/E7mn5mvUJNwlexJAjqpW3U3sGzqEENJEacILBUGGnyMBTASwUkSWR87djnCokHkichmATQDOb5AWEkIaF4WvSwc0cS2br1FT1Q9hZ5mvETrEhTRXtOrinSWn3/3ukDLZX9r+PgdH2l9jx2Y7FMtRA9zhjnql7TC1GRueMbVRD9l+Vjv7u8PYtBzrOYoP35ufYWqpRXa5Fwxf7KzzyJfs0ETaxs7O1OqMFs5yO71vv/gdL7af/eYDdtasIzvnOetcoR1MrWhfuqkNzLTDKJ31pHe4rEr2rLSd7npctc3UPj7zXlPr3ckdQijzLe93fvfXnzvvCwI3tBNCko9kXygghHyHCAFS4T5iNWoicr6IrBaRkIiYCYpFZLSIrBWR3Eh+UF9o1AghUTTS6ucqAOfCkURFRFIA/BnAWQAOBzAhspvJCYefhJBoFP4LAbGH884BAB9/12MA5EbCekNEnkV4J9Ma103sqRFCoonsKHAeYaM2UkSWVDkCJRuuBT0AVF1R2hI554Q9NUJIFLUIPeTM++lKZqyqQfxavbpxvn3ERjVqLbeG0OcO79AxX9zh4+qQYofPKdxrL9W3/sp2Ozjw587OOhcf2sfUji892tSum/Gsqf163kXOOvet6mJqp520wtQWfPA9U3tjzvHOOiXb3uin5fbw4EB3d0ijTsd/Y2rr1nU3tbRSu8zmPpsS+z602tTSX7Vf97sXnG1q2Ue66xx8u+1G8candjIkl+E4ZYEdvgoArpjmPRV1+0rbDSkwqgH80PzHn65kxgHZAqCqv0xPAPl+N3H4SQipQYJsk1oMYICI9BGRFgAuRHgnkxMaNUJIFBJgTq0eXDrGi8gWACMAvCYib0bOf5vMWFXLAVwD4E2EQ57NU1W7Kx6Bc2qEkGgUQANvk1LVFwG86HH+22TGkf+/DuD16te5oFEjhNSkCe8ooFEjhETTxHMU0KgRQqLhhvbglGY0x+azvbP96AF3VIL1hbarQ+bslqbWZ+pKU1t3gl0mAJS+2M7Uhl1mL+NPm2W67qD58N3OOvt0spfkP3nBdg8Y+HyBqW24x3Z5AYCec1ubWpsFtvP2rnGDneUW7rEzO6GdHf2juK/9LixaPtBZZ9extgtKO7EzRnVenGJqmya6383N6w4ztT+NnmNqw7fakWmOzb3BWecdheM9z28ttt+DWpHARssP9tQIIVGEXTaSOJ4aIeQ7hiqkwmdOzScxSzyhUSOERNMEQna7oFEjhNSEw09CSLIggVw6aNQIIU2FAMmME3l42qhGTRRIOeittd7ijtIBh/6rP80ytetf/ompVaS5f3It7PwfeOvzI02tle1hgrsGv+qs8/FNJ5lacX/btWD6O3YimCG93QlmTjj7flPb+pT9EHreakfhAID261qZ2o67jBcBQMlC29WmdMg+Z52ljtyyi7/qbWotu9uuIHkX3e6sc+iVtmvG7/71E1PbNtSus4U7AArKyo1t2xX1tJ27gYNENiTsqRFCouDwkxCSXAQK502jRghpKigAzqkRQpIHDbCjoHFaUhcYJJIQEo0qEAq5jxiHn7XI+5knIitFZLmILAlSNntqhJBoGmf4WZn38/EA156squ6l9ir49tREJEtEFohITsSyXh85f6eIbI1Y0OUiMsavLEJI4lO5od3viAVVzVHVtfXU5CiC9NTKAdysqstEpC2ApSLydkR7SFUfCFpZZtcd+PW1/+epPT7ZO5TKtzSzfXq2XtTR1CocIW7gyJQEAKUd7XtT0+2UR+WDbH+yu2bbYYkAoCTLdlCSMru9F8+4ydR69fuVs84tJ3tlMQuju+2wRC1mujMXdWu91dTOSd9kaj8f876p+cXxOuHv95na0P7rTW3rw3bmsNTih5x1Dr58laktKcgytcn9lpnanPknO+uUdsb7l1IfO80jw0/nJY02qaYA3hIRBfC4qs70u8HXqKlqAYCCyOe9IpKDAAlFCSFNlOAuHSNF5NIqZ2dWNTr1kPcTAEaqar6IdAXwtoh8oare+QEj1GpOTUSyAQwF8CmAkQCuEZFJAJYg3JvbWZvyCCEJSPA5NWcy43rI+1mZiAWqWiQiLwI4BoDTqAVe/RSRdADPA7hBVfcAeBRAPwBDEO7J/cG478rKtPR7dvjs/SCEJAD+82nuiZv6QUTSIlNeEJE0AGcgvMDgJJBRE5FUhA3aU6r6AgCoaqGqVqhqCMBfEbagNVDVmao6TFWHtevIxVZCEp6QAhUh9+E35+ZDkLyfALoB+FBEPgfwGYDXVPUNv7J9rYyICIAnAeSo6oNVzmdG5tsAYDwCWFBCSBOhgZ1vg+T9VNUNAOzEHAZBuk4jAUwEsFJElkfO3Q5ggogMQfjr5QG4qraVE0ISENUAq5uJu6UgyOrnh4DnELpWWZMBYOv2jvj13Es8tdaD3Q+p2dnbTe2f+d83taGD8kztBx2/dNb5p49PNbW+3WxfwC+X2OF6yo9yh85pvcrO/JRywL6vuI/tflK+Ic9ZJ7p0sLWQIyTPc/2cxa4ZaX/XJWm2q8PfL7C9hEZnucMo5U3KNrUPjv2bqZ30zi9Nbd8qO5wRAKyYZYehKjnMfq8/vNt+b1PHuGetbhn+muf5aW3c2coCoZHhpwu/DO5xhJNchJBoVAFNGD+1WkOjRgipCUMPEUKShiDDTxo1QkiTIchCAY0aIaRJ0YTjqdGoEUKiqYyn5rwmcVO0N6pR02ZAWbq3ib/uolec987OG2FqF/f41NS2lNoRPN4qOsxZ54C/2ZE4tpxoZyZqu9v+M7a7eRtnnc2H2Evyq8+909SOvtzOaHTwrOHOOrt2tqNtfLOyq6llLtzlLHftcDub1AVH/MfUZl9wnKnNfWCes87zP7/M1E5/70ZT27Sip6l1WO12rwg5EqGF2tiuNmuvsF1FDvm322jkl3q74ZRpPfxKBzJqidtVY0+NEBKNwt8PLXFtGo0aIaQaqlCf4aWfHk9o1Agh0XBHASEkqeCcGiEkuaCfGiEkiVBVqE9PTWnUwjRrVYFWg7xdFubdcJbz3sILbG1WykhTu6SX7e6xdb7tlgEAWV/kmFqLKbarw28Pt8OvX/PeRGedbV9oa2oDp9kJQKZNeNrU5px6grPOn/dbaGrTX7nI1HYd1s5ZbvtFdgzSfy60Iz2ve9xOInPOy45EOgDS59ltKjmYYWrNj7bdNnaccNBZZ69M2yWm9RN2Upt93e3n8/A9DzvrvHHt/3ie310WKDWmmxCa9JwakxkTQqoRidLhOmL06RCR+0XkCxFZISIvikiGcd1oEVkrIrkicluQsmnUCCHRqEJD7qMe5tTeBnCkqn4PwDoAU6pfICIpAP4M4CwAhyMcmPZwv4Jp1AghUWhIoRUV7iPGHAWq+paqVmZiWgTAa0vHMQByVXWDqpYCeBbAOL+yadQIIdXwGXpqqL6dby8FMN/jfA8AVUMdb0GAnMNc/SSEVGVZR6Rhvj6D5g7zcCbOxBdANxGpujJR62TGInIHgHIAT3lc57V64zvupVEjhHyLqm6fIlPwNJ7GJEzyvOYjfIR2aAdVnQpgqqMsZzJjEZkMYCyAU9XbR2QLgKrJLHoCyPf7Dhx+EkKiuHnbzfgj/ohyeCcfn4Zp+M2K38RUh4iMBnArgLNVtcS4bDGAASLSR0RaALgQgDucDxq5p9ZiSwhZt3inRNpwSYbz3o2X2WFjzlhwg6k9knOyqV3zU9ufDAAeaWHPSfaaaocI+tGylbbmrBHoPXu6qWW9nGJqf/nUduTbfKN9HwAc0eJrU1v260dN7bDHf+4stzTDnneRbnZqLHVE+hG9y1ln7z2/d9xsS4feW2xquRPt8FUAcKCL/WvUPs8uN/+U1qZ2yT+uc9bZ/QNvgyNFsf9Kd+7cGWfiTM/eWmUvbfDgwbFWMwNASwBvh1MLY5GqXi0i3QE8oapjVLVcRK4B8CaAFACzVHW1X8EcfhJCanDztptxRpczcBEuippbm4ZpeGCFncIwKKra3zj/bTLjyP9fRy3TcXL4SQipQdXeWiX12EtrUGjUCCGeVJ9bq4+5tMaAw09CiCdVe2v90K9J9NIAGjVCiIPKubVO6IQHV9h5MBIJDj8JISadO3fGaIxGB3RoEr00AJDGjIskItsAbKxyqjOAbxqtAf6wPW4SrT1A4rUp3u3prapd4lh/3GlUo1ajcpElqjosbg2oBtvjJtHaAyRemxKtPd9FOPwkhCQVNGqEkKQi3kZtpv8ljQrb4ybR2gMkXpsSrT3fOeI6p0YIIfVNvHtqhBBSr8TFqNUlmUIDtydPRFaKyPJqQe8asw2zRKRIRFZVOddRRN4WkfWRfzvEuT13isjWyHNaLiJjXGXUc3uyRGSBiOSIyGoRuT5yPi7PyNGeuD0jEqbRh5+RZArrAJyOcBC4xQAmqOqaRm1IdJvyAAxT1bj5F4nIiQD2AZirqkdGzt0HYIeqTo8Y/w6qemsc23MngH2qGnuYhtq3JxNApqouE5G2AJYCOAfATxCHZ+RozwWI0zMiYeLRU6tTMoVkR1U/AFA9geQ4AHMin+cg/EsTz/bEDVUtUNVlkc97AeQgHK8+Ls/I0R4SZ+Jh1OqUTKGBUQBvichSEbkyzm2pSjdVLQDCv0QA7AzKjcc1kVyNsxpzOFwVEckGMBTAp0iAZ1StPUACPKPvMvEwanVKptDAjFTVoxHOL/iLyNCL1ORRAP0ADAFQAOAPjd0AEUkH8DyAG1R1T2PXH6A9cX9G33XiYdTqlEyhIYlE24SqFgF4EeEhciJQGJm7qZzDKYpnY1S1UFUrNJwf7a9o5OckIqkIG5CnVPWFyOm4PSOv9sT7GZH4GLU6JVNoKEQkLTLRCxFJA3AGgFXuuxqNVwBMjnyeDMCdVKGBqTQeEcajEZ+ThAPZPwkgR1WrxsCJyzOy2hPPZ0TCxMX5NrLM/Uf8N5nC7xq9Ef9tS1+Ee2dAOL7c0/Foj4g8A2AUwlEeChFOPfYSgHkAegHYBOB8VW2UyXujPaMQHlYpgDwAV1XOZzVCe34AYCGAlQAqM7rcjvA8VqM/I0d7JiBOz4iE4Y4CQkhSwR0FhJCkgkaNEJJU0KgRQpIKGjVCSFJBo0YISSpo1AghSQWNGiEkqaBRI4QkFf8PP1j/v63iaW0AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data = np.random.standard_normal(size=900)\n",
    "data[np.random.randint(0, 900, size=20)] = np.nan\n",
    "data = data.reshape(30, 30)\n",
    "\n",
    "plt.imshow(data, cmap=viridis, vmin=-2, vmax=2)\n",
    "plt.colorbar(extend='both')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Modify viridis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/html": [
       "<div style=\"vertical-align: middle;\"><strong>from_list</strong> </div><div class=\"cmap\"><img alt=\"from_list colormap\" title=\"from_list\" style=\"border: 1px solid #555;\" src=\"data:image/png;base64,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\"></div><div style=\"vertical-align: middle; max-width: 514px; display: flex; justify-content: space-between;\"><div style=\"float: left;\"><div title=\"#f71893ff\" style=\"display: inline-block; width: 1em; height: 1em; margin: 0; vertical-align: middle; border: 1px solid #555; background-color: #f71893ff;\"></div> under</div><div style=\"margin: 0 auto; display: inline-block;\">bad <div title=\"#00000000\" style=\"display: inline-block; width: 1em; height: 1em; margin: 0; vertical-align: middle; border: 1px solid #555; background-color: #00000000;\"></div></div><div style=\"float: right;\">over <div title=\"#fde725ff\" style=\"display: inline-block; width: 1em; height: 1em; margin: 0; vertical-align: middle; border: 1px solid #555; background-color: #fde725ff;\"></div></div>"
      ],
      "text/plain": [
       "<matplotlib.colors.ListedColormap at 0x7fde69027730>"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "viridis = cm.get_cmap('viridis', 256)\n",
    "\n",
    "newcolors = viridis(np.linspace(0, 1, 256))\n",
    "\n",
    "pink = np.array([248/256, 24/256, 148/256, 1])\n",
    "newcolors[:25, :] = pink\n",
    "newcmp = ListedColormap(newcolors)\n",
    "\n",
    "newcmp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x216 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "cms = [viridis, newcmp]\n",
    "\n",
    "data = np.random.randn(30, 30)\n",
    "\n",
    "fig, axs = plt.subplots(1, 2, figsize=(8, 3), constrained_layout=True)\n",
    "for ax, cmap in zip(axs, cms):\n",
    "    psm = ax.pcolormesh(data, cmap=cmap, rasterized=True, vmin=-3, vmax=3)\n",
    "    fig.colorbar(psm, ax=ax)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Make a new ramp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Channel:  RED    GREEN     BLUE  ALPHA\n",
    "\n",
    "red =   [100/256,   0/256,   0/256, 1.0]\n",
    "white = [256/256, 256/256, 256/256, 1.0]\n",
    "blue =  [  0/256,  50/256, 100/256, 1.0]\n",
    "\n",
    "newcmp = LinearSegmentedColormap.from_list(\"foo\", [red, white, blue])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x216 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "cms = [viridis, newcmp]\n",
    "\n",
    "data = np.random.randn(30, 30)\n",
    "\n",
    "fig, axs = plt.subplots(1, 2, figsize=(8, 3), constrained_layout=True)\n",
    "for ax, cmap in zip(axs, cms):\n",
    "    psm = ax.pcolormesh(data, cmap=cmap, rasterized=True, vmin=-4, vmax=4)\n",
    "    fig.colorbar(psm, ax=ax)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "py39",
   "language": "python",
   "name": "py39"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}