{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "\n# From categories\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "The :class:`ptvis.color.CategoricalColorConversion` class defines a\nconversion from categories to colors.\n\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": false
      },
      "outputs": [],
      "source": [
        "import plotly\nimport ptvis.color\n\n\nelements = list(ptvis.Element)\nvalues = [str(i % 3) for i in range(len(elements))]\n\nfig = plotly.graph_objects.Figure()\nptvis.attach_plain_cells(\n    fig,\n    elements,\n    colors=values,\n    color_conversion=ptvis.color.CategoricalColorConversion(),\n)\n\nplotly.io.show(fig)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Defining a conversion explicitly\n\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "The `mapping` argument determines an explicit mapping from categories to\ncolors.\n\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": false
      },
      "outputs": [],
      "source": [
        "import plotly\nimport ptvis.color\n\n\nelements = list(ptvis.Element)\nvalues = [str(i % 3) for i in range(len(elements))]\n\nfig = plotly.graph_objects.Figure()\nptvis.attach_plain_cells(\n    fig,\n    elements,\n    colors=values,\n    color_conversion=ptvis.color.CategoricalColorConversion(\n        mapping={\"0\": \"red\", \"1\": \"green\", \"2\": \"blue\"},\n    ),\n)\n\nplotly.io.show(fig)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Colors for categories missing from the mapping\n\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Categories missing from the `mapping` argument are converted using a preset\ncolorway.\n\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": false
      },
      "outputs": [],
      "source": [
        "import plotly\nimport ptvis.color\n\n\nelements = list(ptvis.Element)\nvalues = [str(i % 3) for i in range(len(elements))]\n\nfig = plotly.graph_objects.Figure()\nptvis.attach_plain_cells(\n    fig,\n    elements,\n    colors=values,\n    color_conversion=ptvis.color.CategoricalColorConversion(\n        mapping={\"0\": \"red\"},\n    ),\n)\n\nplotly.io.show(fig)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "The colorway can be changed by the `missing_colors` argument.\n\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": false
      },
      "outputs": [],
      "source": [
        "import plotly\nimport ptvis.color\n\n\nelements = list(ptvis.Element)\nvalues = [str(i % 3) for i in range(len(elements))]\n\nfig = plotly.graph_objects.Figure()\nptvis.attach_plain_cells(\n    fig,\n    elements,\n    colors=values,\n    color_conversion=ptvis.color.CategoricalColorConversion(\n        mapping={\"0\": \"red\"},\n        missing_colors=[\"cyan\", \"magenta\", \"yellow\"],\n    ),\n)\n\nplotly.io.show(fig)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Color for N/A\n\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "A color for N/A is given by the `na_color` argument.\n\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": false
      },
      "outputs": [],
      "source": [
        "import plotly\nimport ptvis.color\n\n\nelements = list(ptvis.Element)\nvalues = [str(i % 3) if i % 10 else None for i in range(len(elements))]\n\nfig = plotly.graph_objects.Figure()\nptvis.attach_plain_cells(\n    fig,\n    elements,\n    colors=values,\n    color_conversion=ptvis.color.CategoricalColorConversion(\n        mapping={\"0\": \"red\", \"1\": \"green\", \"2\": \"blue\"},\n        na_color=\"white\",\n    ),\n)\n\nplotly.io.show(fig)"
      ]
    }
  ],
  "metadata": {
    "kernelspec": {
      "display_name": "Python 3",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.13.3"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}