Discrete Color Palette.ipynb

discrete-color-palette.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Discrete Color Palette.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/gist/ekellbuch/868ac0e483bed5b68261458a85373204/discrete-color-palette.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vhkmFJT_03je",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import matplotlib as mpl\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "import numpy as np\n",
        "\n",
        "sns.set_style(\"white\")\n",
        "sns.set_context(\"talk\")\n",
        "\n",
        "color_names = [\n",
        "    \"windows blue\",\n",
        "    \"red\",\n",
        "    \"amber\",\n",
        "    \"faded green\",\n",
        "    \"dusty purple\",\n",
        "    #\"orange\",\n",
        "    #\"clay\",\n",
        "    #\"pink\",\n",
        "    #\"greyish\",\n",
        "    #\"mint\",\n",
        "    #\"light cyan\",\n",
        "    #\"steel blue\",\n",
        "    #\"forest green\",\n",
        "    #\"pastel purple\",\n",
        "    #\"salmon\",\n",
        "    #\"dark brown\",\n",
        "]\n",
        "\n",
        "num_colors = 8\n",
        "\n",
        "colors = sns.xkcd_palette(color_names[:num_colors])\n",
        "cmap = mpl.colors.LinearSegmentedColormap.from_list(\n",
        "    'Custom cmap', colors, num_colors)\n",
        "bounds = np.linspace(0, num_colors, num_colors+1)\n",
        "norm = mpl.colors.BoundaryNorm(bounds, num_colors +1)\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SB8HJgmy1ZMi",
        "colab_type": "code",
        "outputId": "7be81883-a171-42ff-9e7e-d6f3b63d7826",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 162
        }
      },
      "source": [
        "a = np.arange(num_colors)[None,:]\n",
        "\n",
        "plt.imshow(a, cmap=cmap, norm=norm)\n",
        "plt.xticks([])\n",
        "plt.yticks([])\n",
        "\n",
        "cbar = plt.colorbar(orientation='horizontal')\n",
        "tick_locs = (np.arange(num_colors +1) + 0.5) * (num_colors) / num_colors\n",
        "cbar.set_ticks(tick_locs)\n",
        "cbar.set_ticklabels(np.arange(1, num_colors+1))\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    }
  ]
}