{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "fd96622a",
   "metadata": {},
   "source": [
    "# Plotly Templates and Colors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "87ea7fae",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import plotly.graph_objs as go\n",
    "import plotly.express as px\n",
    "import plotly.io as pio"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1d7655d4",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('Gapminder-data.csv', sep=',')\n",
    "df = df[df['Year'] == 2020]\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9b30ae37",
   "metadata": {},
   "source": [
    "## Templates"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9affbd8e",
   "metadata": {},
   "source": [
    "### All available templates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2d706ca1",
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "for template in ['ggplot2', 'seaborn', 'simple_white', 'plotly', 'plotly_white', 'plotly_dark', \n",
    "                 'presentation', 'xgridoff', 'ygridoff', 'gridon', 'none']:\n",
    "    fig_t = px.scatter(\n",
    "        df,\n",
    "        x='Income', \n",
    "        y='Life expectancy', \n",
    "        size='Population', \n",
    "        color='Region',\n",
    "        log_x=True, \n",
    "        size_max=60,\n",
    "        template=template, title=f'{template} template')\n",
    "    fig_t.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e2c2aa0e",
   "metadata": {},
   "source": [
    "### Setting the default template"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fff00238",
   "metadata": {},
   "outputs": [],
   "source": [
    "pio.templates.default = 'seaborn'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f88c3962",
   "metadata": {},
   "outputs": [],
   "source": [
    "fig = px.scatter(\n",
    "    df,\n",
    "    x='Income', \n",
    "    y='Life expectancy', \n",
    "    size='Population', \n",
    "    color='Region',\n",
    "    log_x=True, \n",
    "    size_max=60)\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "949aa169",
   "metadata": {},
   "source": [
    "### Creating a custom template"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "83d5746d",
   "metadata": {},
   "outputs": [],
   "source": [
    "draft_template = go.layout.Template(\n",
    "    layout=dict(annotations=[\n",
    "        dict(\n",
    "            name='draft watermark',\n",
    "            text='DRAFT',\n",
    "            opacity=0.2,\n",
    "            font=dict(color='black', size=100),\n",
    "            xref='paper',\n",
    "            yref='paper',\n",
    "            x=0.5,\n",
    "            y=0.5,\n",
    "        )\n",
    "    ])\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9b91f019",
   "metadata": {},
   "outputs": [],
   "source": [
    "fig_t = px.scatter(\n",
    "    df,\n",
    "    x='Income', \n",
    "    y='Life expectancy', \n",
    "    size='Population', \n",
    "    color='Region',\n",
    "    log_x=True, \n",
    "    size_max=60,\n",
    "    template=draft_template)\n",
    "fig_t.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "abdaf63c",
   "metadata": {},
   "source": [
    "### Registering a template"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "509c0c73",
   "metadata": {},
   "outputs": [],
   "source": [
    "pio.templates['draft'] = draft_template\n",
    "pio.templates.default = 'draft'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a1b76970",
   "metadata": {},
   "outputs": [],
   "source": [
    "fig = px.scatter(\n",
    "    df,\n",
    "    x='Income', \n",
    "    y='Life expectancy', \n",
    "    size='Population', \n",
    "    color='Region',\n",
    "    log_x=True, \n",
    "    size_max=60)\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3629627e",
   "metadata": {},
   "source": [
    "### Combining templates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "704c58d8",
   "metadata": {},
   "outputs": [],
   "source": [
    "pio.templates.default = 'draft+ggplot2'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cd78d264",
   "metadata": {},
   "outputs": [],
   "source": [
    "fig = px.scatter(\n",
    "    df,\n",
    "    x='Income', \n",
    "    y='Life expectancy', \n",
    "    size='Population', \n",
    "    color='Region',\n",
    "    log_x=True, \n",
    "    size_max=60)\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dd090678",
   "metadata": {},
   "source": [
    "### Template settings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0a1e4f17",
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "pio.templates['plotly'].layout"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1e6238f9",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "pio.templates['plotly'].data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d495a53e",
   "metadata": {},
   "source": [
    "### Template data setting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "15090bea",
   "metadata": {},
   "outputs": [],
   "source": [
    "symbol_template = go.layout.Template(\n",
    "    data=dict(scatter=[\n",
    "        go.Scatter(marker=dict(symbol='diamond', size=10)),\n",
    "    ])\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4aa6bf23",
   "metadata": {},
   "outputs": [],
   "source": [
    "fig = px.scatter(\n",
    "    df,\n",
    "    x='Income', \n",
    "    y='Life expectancy', \n",
    "    size='Population', \n",
    "    color='Region',\n",
    "    log_x=True, \n",
    "    size_max=60,\n",
    "    template=symbol_template\n",
    ")\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f77b8cc4",
   "metadata": {},
   "outputs": [],
   "source": [
    "pio.templates.default = 'plotly_white'"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "50ce9f2f",
   "metadata": {},
   "source": [
    "## Colors"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "65d83039",
   "metadata": {},
   "source": [
    "### Ordinal color maps (continuous color scales)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0aeeb3a1",
   "metadata": {},
   "source": [
    "**Be careful, they are not all perceptually linear!**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "55a0db4f",
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "fig = px.colors.sequential.swatches_continuous()\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4d90653b",
   "metadata": {},
   "outputs": [],
   "source": [
    "fig = px.colors.diverging.swatches_continuous()\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5bfb9746",
   "metadata": {},
   "outputs": [],
   "source": [
    "fig = px.colors.cyclical.swatches_cyclical()\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dbaeb7f5",
   "metadata": {},
   "source": [
    "### Categorical color maps (discrete color sequences)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d14e2134",
   "metadata": {},
   "outputs": [],
   "source": [
    "fig = px.colors.qualitative.swatches()\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "abf83680",
   "metadata": {},
   "source": [
    "### Using color maps in Plotly"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "70936970",
   "metadata": {},
   "outputs": [],
   "source": [
    "fig = px.scatter(\n",
    "    df,\n",
    "    x='Income', \n",
    "    y='Life expectancy', \n",
    "    size='Population', \n",
    "    color='Region',\n",
    "    log_x=True, \n",
    "    size_max=60,\n",
    "    color_discrete_sequence=px.colors.qualitative.Bold\n",
    ")\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "92cbb750",
   "metadata": {},
   "outputs": [],
   "source": [
    "fig = px.scatter(\n",
    "    df,\n",
    "    x='Income', \n",
    "    y='Life expectancy', \n",
    "    size='Population', \n",
    "    color='Child mortality',\n",
    "    log_x=True, \n",
    "    size_max=60,\n",
    "    color_continuous_scale=px.colors.sequential.Viridis\n",
    ")\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cb289097",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.11.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}