{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "\n# Sector color\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Sectors are colored with the `colors` argument.\n\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Color sectors with numerical values.\n\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": false
      },
      "outputs": [],
      "source": [
        "import plotly\nimport ptvis\n\n\nelements = list(ptvis.Element) * 2\natomic_numbers = (\n    [element.value for element in ptvis.Element]\n    + [element.value + 50 for element in ptvis.Element]\n)\n\nfig = plotly.graph_objects.Figure()\nptvis.attach_pie_cells(fig, elements, colors=atomic_numbers)\n\nplotly.io.show(fig)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Color sectors with non-numerical values.\n\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": false
      },
      "outputs": [],
      "source": [
        "import plotly\nimport ptvis\n\n\nelements = list(ptvis.Element) * 2\ninitials = (\n    [element.name[0] for element in ptvis.Element]\n    + [element.name[0].lower() for element in ptvis.Element]\n)\n\nfig = plotly.graph_objects.Figure()\nptvis.attach_pie_cells(fig, elements, colors=initials)\n\nplotly.io.show(fig)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Conversion from values to colors\n\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "By default, a conversion from the `colors` argument to colors is defined by\nthe :class:`ptvis.color.ContinuousColorConversion` class for numerical values\nand by the :class:`ptvis.color.CategoricalColorConversion` class for\nnon-numerical values. The conversion can be changed by the `color_conversion`\nargument. For example, numerical values can be treated as categories.\n\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": false
      },
      "outputs": [],
      "source": [
        "import plotly\nimport ptvis.color\n\n\nelements = list(ptvis.Element) * 2\natomic_numbers = (\n    [element.value for element in ptvis.Element]\n    + [element.value + 55 for element in ptvis.Element]\n)\n\nfig = plotly.graph_objects.Figure()\nptvis.attach_pie_cells(\n    fig,\n    elements,\n    colors=atomic_numbers,\n    color_conversion=ptvis.color.CategoricalColorConversion(),\n)\n\nplotly.io.show(fig)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "See the :doc:`tutorial on the color conversion <../color_conversion/index>`\nfor more details.\n\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Color guide\n\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "A color guide is shown by giving its attributes to the `color_guide`\nargument. The kind of color guide is determined by the color conversion.\n\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "A color scale is shown for a conversion from numerical values. Attributes of\nthe :class:`plotly.graph_objects.scatter.marker.ColorBar` are valid.\n\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": false
      },
      "outputs": [],
      "source": [
        "import plotly\nimport ptvis\n\n\nelements = list(ptvis.Element) * 2\natomic_numbers = (\n    [element.value for element in ptvis.Element]\n    + [element.value + 55 for element in ptvis.Element]\n)\n\nfig = plotly.graph_objects.Figure()\nptvis.attach_pie_cells(\n    fig,\n    elements,\n    colors=atomic_numbers,\n    color_guide={\n        \"outlinewidth\": 0,\n        \"ticks\": \"outside\",\n    },\n)\n\nplotly.io.show(fig)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "A legend is shown for a conversion from categories. Attributes of the\n:class:`plotly.graph_objects.scatter.Marker` are valid.\n\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": false
      },
      "outputs": [],
      "source": [
        "import plotly\nimport ptvis\n\n\nelements = list(ptvis.Element) * 2\ninitials = (\n    [element.name[0] for element in ptvis.Element]\n    + [element.name[0].lower() for element in ptvis.Element]\n)\n\nfig = plotly.graph_objects.Figure()\nptvis.attach_pie_cells(\n    fig,\n    elements,\n    colors=initials,\n    color_guide={\n        \"size\": 12,\n        \"symbol\": \"square\",\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
}