Styling_Plotly_Themes_Templates.ipynb

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  {
   "cell_type": "markdown",
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   "metadata": {},
   "source": [
    "# Customizing Visual Styles in Plotly\n",
    "\n",
    "Ever have that feeling that a lot of data viz you see screams the tool it was made in? Using the [Plotly Open Source](https://github.com/plotly/plotly.py) Python graphing library, we will take a look under the hood of:\n",
    "\n",
    "- the style themes available, \n",
    "- understand the visual elements like figure and chart backgrounds, and \n",
    "- build our own default theme script inspired by 1980's computers. \n",
    "\n",
    "See documentation for further reference:<br>\n",
    "[Python > Fundamentals > Theming and templates](https://plotly.com/python/templates/)<br>\n",
    "[Python > Getting Started with Plotly in Python](https://plotly.com/python/getting-started/)\n",
    "\n",
    "### **For style inspiration, first watch the movie trailer for** [<span style=\"color:Magenta\">**Electric Dreams**</span>](https://www.imdb.com/title/tt0087197/)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "aa9abbcd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: plotly==5.5.0 in ./plotlystyle_env/lib/python3.9/site-packages (5.5.0)\n",
      "Requirement already satisfied: tenacity>=6.2.0 in ./plotlystyle_env/lib/python3.9/site-packages (from plotly==5.5.0) (8.0.1)\n",
      "Requirement already satisfied: six in ./plotlystyle_env/lib/python3.9/site-packages (from plotly==5.5.0) (1.16.0)\n"
     ]
    }
   ],
   "source": [
    "# install the packages we'll use\n",
    "# In Jupyter, the console commands can be executed by the ‘!’ sign before the command within the cell.\n",
    "# you can check the most up to date version on https://pypi.org/project/plotly/\n",
    "# alternately install using ! conda install -c plotly plotly=5.5.0\n",
    "# ! pip install plotly==5.5.0  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "c3f3b3d1",
   "metadata": {},
   "outputs": [],
   "source": [
    "# !pip install --upgrade nbformat"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "e878af62",
   "metadata": {},
   "outputs": [],
   "source": [
    "# load the packages we'll use\n",
    "import plotly.express as px\n",
    "import plotly.io as pio\n",
    "\n",
    "pio.templates.default = \"simple_white\""
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b60d3ca6",
   "metadata": {},
   "source": [
    "### Data \n",
    "\n",
    "To keep this part simple, we'll make a dataframe of **stocks** from the [Plotly Express built in datasets](https://plotly.com/python-api-reference/generated/plotly.express.data.html).\n",
    "\n",
    "By indexing the dataframe object, we can select from the column names, just the two big ones brawling in the early to mid 1980's computer market race."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "ecbce4b4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['date', 'GOOG', 'AAPL', 'AMZN', 'FB', 'NFLX', 'MSFT'], dtype='object')\n"
     ]
    },
    {
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       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>GOOG</th>\n",
       "      <th>AAPL</th>\n",
       "      <th>AMZN</th>\n",
       "      <th>FB</th>\n",
       "      <th>NFLX</th>\n",
       "      <th>MSFT</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2018-01-01</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2018-01-08</td>\n",
       "      <td>1.018172</td>\n",
       "      <td>1.011943</td>\n",
       "      <td>1.061881</td>\n",
       "      <td>0.959968</td>\n",
       "      <td>1.053526</td>\n",
       "      <td>1.015988</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2018-01-15</td>\n",
       "      <td>1.032008</td>\n",
       "      <td>1.019771</td>\n",
       "      <td>1.053240</td>\n",
       "      <td>0.970243</td>\n",
       "      <td>1.049860</td>\n",
       "      <td>1.020524</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
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       "         date      GOOG      AAPL      AMZN        FB      NFLX      MSFT\n",
       "0  2018-01-01  1.000000  1.000000  1.000000  1.000000  1.000000  1.000000\n",
       "1  2018-01-08  1.018172  1.011943  1.061881  0.959968  1.053526  1.015988\n",
       "2  2018-01-15  1.032008  1.019771  1.053240  0.970243  1.049860  1.020524"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = px.data.stocks()\n",
    "print(df.columns)\n",
    "\n",
    "# review the top 3 rows of data with the head function\n",
    "df.head(3)"
   ]
  },
  {
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   "execution_count": 5,
   "id": "6dde5ae6",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>stock</th>\n",
       "      <th>value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>105</th>\n",
       "      <td>2018-01-01</td>\n",
       "      <td>AAPL</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>106</th>\n",
       "      <td>2018-01-08</td>\n",
       "      <td>AAPL</td>\n",
       "      <td>1.011943</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>107</th>\n",
       "      <td>2018-01-15</td>\n",
       "      <td>AAPL</td>\n",
       "      <td>1.019771</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>108</th>\n",
       "      <td>2018-01-22</td>\n",
       "      <td>AAPL</td>\n",
       "      <td>0.980057</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>109</th>\n",
       "      <td>2018-01-29</td>\n",
       "      <td>AAPL</td>\n",
       "      <td>0.917143</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>625</th>\n",
       "      <td>2019-12-02</td>\n",
       "      <td>MSFT</td>\n",
       "      <td>1.720717</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>626</th>\n",
       "      <td>2019-12-09</td>\n",
       "      <td>MSFT</td>\n",
       "      <td>1.752239</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>627</th>\n",
       "      <td>2019-12-16</td>\n",
       "      <td>MSFT</td>\n",
       "      <td>1.784896</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>628</th>\n",
       "      <td>2019-12-23</td>\n",
       "      <td>MSFT</td>\n",
       "      <td>1.802472</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>629</th>\n",
       "      <td>2019-12-30</td>\n",
       "      <td>MSFT</td>\n",
       "      <td>1.788185</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>210 rows × 3 columns</p>\n",
       "</div>"
      ],
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       "           date stock     value\n",
       "105  2018-01-01  AAPL  1.000000\n",
       "106  2018-01-08  AAPL  1.011943\n",
       "107  2018-01-15  AAPL  1.019771\n",
       "108  2018-01-22  AAPL  0.980057\n",
       "109  2018-01-29  AAPL  0.917143\n",
       "..          ...   ...       ...\n",
       "625  2019-12-02  MSFT  1.720717\n",
       "626  2019-12-09  MSFT  1.752239\n",
       "627  2019-12-16  MSFT  1.784896\n",
       "628  2019-12-23  MSFT  1.802472\n",
       "629  2019-12-30  MSFT  1.788185\n",
       "\n",
       "[210 rows x 3 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Reshape this from wide format to long format data, with one \"stock\" column\n",
    "df1 = df.melt(id_vars=[\"date\"], var_name=\"stock\")\n",
    "# select only Apple and Microsoft stock tickers using locate\n",
    "df1 = df1.loc[df1['stock'].isin(['AAPL','MSFT'])]\n",
    "df1"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4d8cf793",
   "metadata": {},
   "source": [
    "### Simple plotting with default styling\n",
    "\n",
    "Plot the simplest line chart on a figure with all the defaults for style. We're focusing on the highest level Plotly Express, which makes it quick to spin up interactive charts with few lines of code.\n",
    "\n",
    "All of this is also possible plus more with the Plotly Graph Objects, which is a bigger learning curve than Express but has even more customization possibilities.\n",
    "\n",
    "See docs for more options and examples of line charts [Python > Basic Charts > Line Charts](https://plotly.com/python/line-charts/)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "a9e17198",
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        "plotlyServerURL": "https://plot.ly"
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   "source": [
    "fig = px.line(df1, x=\"date\", y=\"value\", color='stock', title='ComputerWorld Race: default style theming')\n",
    "fig.show()"
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   "cell_type": "markdown",
   "id": "7b95d680",
   "metadata": {},
   "source": [
    "### Using built-in themes\n",
    "\n",
    "You can style your individual plots piece by piece, but if you find yourself doing that over and over to meet your company's, a client's, or just your individual preference, themes allow you to change more all at once.\n",
    "\n",
    "Print a list of the built-in themes, and your current default theme like this. \n",
    "\n",
    "It's worth noting you probably would not generally import plotly.io, which gives access to this kind of deeper styling, under typical circumstances, so remember that's needed for this part. To make the simplot plot we just made, only Plotly Express would typically need to be imported."
   ]
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  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "5f7df4de",
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   "outputs": [
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       "Templates configuration\n",
       "-----------------------\n",
       "    Default template: 'plotly'\n",
       "    Available templates:\n",
       "        ['ggplot2', 'seaborn', 'simple_white', 'plotly',\n",
       "         'plotly_white', 'plotly_dark', 'presentation', 'xgridoff',\n",
       "         'ygridoff', 'gridon', 'none']"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pio.templates"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2bb4d00d",
   "metadata": {},
   "source": [
    "Unless you've altered it, 'plotly' is the name of the default theme when you install the plotly.py library.\n",
    "\n",
    "Chances are you recognize some of the other names for themes that mirror tried and true styles from other popular foundational data visualization libraries like Python's Seaborn or the loved ggplot2 package native to R. Other theme names sound more descriptive.\n",
    "\n",
    "Let's take a peek at each by restyling our plot, figure and all, with one simple addition of a single template attribute passed to the line chart.\n",
    "\n",
    "The __'plotly_dark'__ template makes it easy to see how far the full figure ground extends, i.e. outside the plot background. The colors are still the same as the default 'plotly' template."
   ]
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   "id": "49e1de56",
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    "fig = px.line(df1, x=\"date\", y=\"value\", color='stock', title=\"ComputerWorld Race: 'plotly_dark' theme\", template='plotly_dark')\n",
    "fig.show()"
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  {
   "cell_type": "markdown",
   "id": "121fdf00",
   "metadata": {},
   "source": [
    "The __'ggplot2'__ has a nice warm plot background, and a change in colors, with centered title."
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  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "01b4aa01",
   "metadata": {},
   "outputs": [
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Race: 'ggplot2' theme\"}},                        {\"responsive\": true}                    ).then(function(){\n",
       "                            \n",
       "var gd = document.getElementById('7ff84c3f-e62b-4a58-a22f-ec5b6310e46c');\n",
       "var x = new MutationObserver(function (mutations, observer) {{\n",
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       "            Plotly.purge(gd);\n",
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       "}});\n",
       "\n",
       "// Listen for the removal of the full notebook cells\n",
       "var notebookContainer = gd.closest('#notebook-container');\n",
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = px.line(df1, x=\"date\", y=\"value\", color='stock', title=\"ComputerWorld Race: 'ggplot2' theme\", template='ggplot2')\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "242e8ca9",
   "metadata": {},
   "source": [
    "The [Theming and templates doc](https://plotly.com/python/templates/) shows a great round robin of them all on different chart types."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3ed9be1e",
   "metadata": {},
   "source": [
    "### Switching your default template\n",
    "\n",
    "This one's easy from here. Simply assign the templates default attribute. \n",
    "\n",
    "As the docs note, you'll need to declare this once during each Python session, or if you have to restart your iPython kernel, like here in Jupyter Notebooks. The default will revert to 'plotly' with each new session.\n",
    "\n",
    "It's super quick, like this:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "3d3288e3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.plotly.v1+json": {
       "config": {
        "plotlyServerURL": "https://plot.ly"
       },
       "data": [
        {
         "hovertemplate": "stock=AAPL<br>date=%{x}<br>value=%{y}<extra></extra>",
         "legendgroup": "AAPL",
         "line": {
          "color": "#636efa",
          "dash": "solid"
         },
         "marker": {
          "symbol": "circle"
         },
         "mode": "lines",
         "name": "AAPL",
         "orientation": "v",
         "showlegend": true,
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          "2018-01-08",
          "2018-01-15",
          "2018-01-22",
          "2018-01-29",
          "2018-02-05",
          "2018-02-12",
          "2018-02-19",
          "2018-02-26",
          "2018-03-05",
          "2018-03-12",
          "2018-03-19",
          "2018-03-26",
          "2018-04-02",
          "2018-04-09",
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          "2018-07-16",
          "2018-07-23",
          "2018-07-30",
          "2018-08-06",
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   ],
   "source": [
    "pio.templates.default = \"plotly_white\"\n",
    "\n",
    "# Now I don't need to declar this theme in my individual plots.\n",
    "fig = px.line(df1, x=\"date\", y=\"value\", color='stock', title=\"ComputerWorld Race: 'simple_white' theme is now defaulted\")\n",
    "fig.show()"
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  {
   "cell_type": "markdown",
   "id": "30711a0d",
   "metadata": {},
   "source": [
    "Plotting the stocks as a horizontal bar chart instead, without declaring a template, and the updated default carries over."
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  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "eeee52de",
   "metadata": {},
   "outputs": [
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   "source": [
    "fig = px.bar(df1, x=\"value\", y=\"stock\", color='stock', orientation='h',\n",
    "             title=\"ComputerWorld Race: 'simple_white' theme is still defaulted\", template='simple_white')\n",
    "fig.show()"
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  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "44d4eba4",
   "metadata": {},
   "outputs": [
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     "metadata": {},
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   ],
   "source": [
    "fig = px.bar(df1, x=\"value\", y=\"stock\", color='stock')\n",
    "\n",
    "fig.show()"
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  {
   "cell_type": "markdown",
   "id": "d4e16b73",
   "metadata": {},
   "source": [
    "### Disabling all default theming"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "b2e30380",
   "metadata": {},
   "outputs": [
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       "    x.observe(notebookContainer, {childList: true});\n",
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       "    x.observe(outputEl, {childList: true});\n",
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       "\n",
       "                        })                };                });            </script>        </div>"
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     "metadata": {},
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   ],
   "source": [
    "pio.templates.default = \"none\"\n",
    "\n",
    "fig = px.bar(df1, x=\"value\", y=\"stock\", color='stock', orientation='h',\n",
    "             title=\"ComputerWorld Race: 'none' theme is now defaulted\")\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "92b568b7",
   "metadata": {},
   "source": [
    "## Time to reinvent the wheel (theme)\n",
    "\n",
    "This is one instance worth reading the docs word for word to understand the groundwork...\n",
    "\n",
    "> > Themes in plotly.py are represented by instances of the Template class from the plotly.graph_objects.layout module. A Template is a graph object that contains two top-level properties: layout and data.\n",
    "\n",
    "That's right folks style has data. You heard it here!"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ef308d5d",
   "metadata": {},
   "source": [
    "### <span style=\"color:Teal\">Template Layout</span>\n",
    "\n",
    "This has the same structure as the layout property of any Plotly figure. It populates the default values of attributes when it (the template) is applied as a default or to any figure. Fonts, margins, etc. \n",
    "\n",
    "Even annotations! (Has you boss been forcing you to add that pesky _*footer about why the numbers are the way they are_ at the bottom of every chart?\n",
    "\n",
    "See [Python > Figure Reference > layout](https://plotly.com/python/reference/layout/)."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dcd24dea",
   "metadata": {},
   "source": [
    "### <span style=\"color:Teal\">Template Data</span>\n",
    "\n",
    "This part sets the default values of the properties of traces that are added to a figure. In Plotly Express, you don't have to write code to add 'traces' but it happens in the background, e.g. when you assign data columns to the x and y axis values in the charts above. Think of the lines and bars as data traces generally speaking. What color, what marker shape, etc.\n",
    "\n",
    "You might find this reference helpful which describes how data and layout work more: [Figures as Trees of Attributes](https://plotly.com/python/figure-structure/#figures-as-trees-of-attributes) "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9a7ce1dc",
   "metadata": {},
   "source": [
    "### When you just want to change a few things\n",
    "\n",
    "To use the 'plotly' or another available theme for most of the style, you can combine it with a custom theme that contains only the parts you specify differently.\n",
    "\n",
    "By default a theme applied replaces the default theme wholesale (i.e. entirely). To change that behavior, and have the styling of a custom template applied on top of the default styling, then you use a combined string with multiple template names joined on \"+\" characters, like this.\n",
    "\n",
    "_Note that at this point Graph Objects is required to make your own template._"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "bf1cac34",
   "metadata": {},
   "outputs": [],
   "source": [
    "import plotly.graph_objects as go\n",
    "\n",
    "# assign the color to a variable name\n",
    "NTSCgreen = 'rgb(12, 234, 97)'\n",
    "\n",
    "# create a custom template with a NTSC green plot background mimicing the 1980's\n",
    "pio.templates[\"NTSCgreenPlot\"] = go.layout.Template(layout_plot_bgcolor='rgb(12, 234, 97)')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "05dd6cec",
   "metadata": {},
   "source": [
    "See what that new template named 'NTSCgreenPlot' looks like on its own"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "8f3e3eca",
   "metadata": {},
   "outputs": [
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    "fig = px.bar(df1, x=\"value\", y=\"stock\", color='stock', orientation='h',\n",
    "             title=\"ComputerWorld Race: 'NTSCgreenPlot' theme by itself\",\n",
    "             template='NTSCgreenPlot')\n",
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  {
   "cell_type": "markdown",
   "id": "40310348",
   "metadata": {},
   "source": [
    "#### Yikes! Our AppleIIGS first color computer palette is starting to materialize!\n",
    "\n",
    "Let's combine it with the dark theme, like this."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "369bc55b",
   "metadata": {},
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    "pio.templates.default = \"plotly_dark+NTSCgreenPlot\"\n",
    "\n",
    "# now let's just show a figure without data to see how it looks...\n",
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    "#### We're getting close\n",
    "\n",
    "The bar chart seems more appropriate for the decade, BUT we need to respect some best data viz practices here, so we'll go with the line chart.\n",
    "- The font's all wrong for '84\n",
    "- Those lines need to be thicker\n",
    "- Absolutely need Magents\n",
    "\n",
    "_Note that combining themes is supported with Plotly Express or Graph Objects plots._ We did still need to use go to make the custom template, but could use it with our basic Express chart code."
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       "                            \n",
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   ],
   "source": [
    "fig = px.line(df1, x=\"date\", y=\"value\", color='stock', title=\"ComputerWorld Race: 'plotly_dark+NTSCgreenPlot' is defaulted\")\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a974d808",
   "metadata": {},
   "source": [
    "## A little bit of finetuning on the spot\n",
    "\n",
    "Style the remaining changes in the figure object itself.\n",
    "\n",
    "The fig.update_layout and fig.update_traces make it easy to layer adjustment right on the figure and plot you've already got set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "fb69e5ef",
   "metadata": {},
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    "# NTSCgreen = 'rgb(12, 234, 97)'\n",
    "# purple3 = 'rgb(255, 68, 253)' or #ff44fd\n",
    "# dkBlue2 = 'rgb(96, 78, 189)' or #604ebd\n",
    "\n",
    "fig = px.line(df1, x=\"date\", y=\"value\", color='stock', title=\"ComputerWorld Race: Like it's 1984\", \n",
    "              # add just a two color sequence for our plot\n",
    "              color_discrete_sequence=['rgb(255, 68, 253)', 'rgb(96, 78, 189)'])\n",
    "\n",
    "# using a Thai font that comes with Mac OX systems, which \n",
    "# has the same Latin letters as the old '80's Chicago font --not perfect but a similar feel\n",
    "fig.update_layout(font={'family': \"Krungthep\",\n",
    "                        'size': 14,\n",
    "                        'color': 'RGB(208, 221, 141)'},\n",
    "                  title={'font_size': 36})\n",
    "\n",
    "# now widen the data lines\n",
    "fig.update_traces(line={'width': 10.0})\n",
    "\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4e830c57",
   "metadata": {},
   "source": [
    "Source for most of these color values: (https://mrob.com/pub/xapple2/colors.html)\n",
    "\n",
    "Here's what they looked like courtest of [Wikipedia](https://en.wikipedia.org/wiki/Apple_II_graphics).\n",
    "\n",
    "![image of AppleII Low-resolution colors 0-15](https://upload.wikimedia.org/wikipedia/commons/9/98/Apple_II_Low_Res_-_no_scanlines.png)\n",
    "<br><br>\n",
    "\n",
    "- Just missing Apple II shell Beige for the box in hex color #bfca87"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "217b509e",
   "metadata": {},
   "source": [
    "## Time to really look under the hood of Templates\n",
    "\n",
    "Pick a built-in template that has the most of what you want, leaving you the least attributes to change. You can load its graph object by name from the templates configuration object, and see its layout.\n",
    "\n",
    "Let's have a look at plotly_dark!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "18fd5e91",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Layout({\n",
       "    'annotationdefaults': {'arrowcolor': '#f2f5fa', 'arrowhead': 0, 'arrowwidth': 1},\n",
       "    'autotypenumbers': 'strict',\n",
       "    'coloraxis': {'colorbar': {'outlinewidth': 0, 'ticks': ''}},\n",
       "    'colorscale': {'diverging': [[0, '#8e0152'], [0.1, '#c51b7d'], [0.2,\n",
       "                                 '#de77ae'], [0.3, '#f1b6da'], [0.4, '#fde0ef'],\n",
       "                                 [0.5, '#f7f7f7'], [0.6, '#e6f5d0'], [0.7,\n",
       "                                 '#b8e186'], [0.8, '#7fbc41'], [0.9, '#4d9221'],\n",
       "                                 [1, '#276419']],\n",
       "                   'sequential': [[0.0, '#0d0887'], [0.1111111111111111,\n",
       "                                  '#46039f'], [0.2222222222222222, '#7201a8'],\n",
       "                                  [0.3333333333333333, '#9c179e'],\n",
       "                                  [0.4444444444444444, '#bd3786'],\n",
       "                                  [0.5555555555555556, '#d8576b'],\n",
       "                                  [0.6666666666666666, '#ed7953'],\n",
       "                                  [0.7777777777777778, '#fb9f3a'],\n",
       "                                  [0.8888888888888888, '#fdca26'], [1.0,\n",
       "                                  '#f0f921']],\n",
       "                   'sequentialminus': [[0.0, '#0d0887'], [0.1111111111111111,\n",
       "                                       '#46039f'], [0.2222222222222222, '#7201a8'],\n",
       "                                       [0.3333333333333333, '#9c179e'],\n",
       "                                       [0.4444444444444444, '#bd3786'],\n",
       "                                       [0.5555555555555556, '#d8576b'],\n",
       "                                       [0.6666666666666666, '#ed7953'],\n",
       "                                       [0.7777777777777778, '#fb9f3a'],\n",
       "                                       [0.8888888888888888, '#fdca26'], [1.0,\n",
       "                                       '#f0f921']]},\n",
       "    'colorway': [#636efa, #EF553B, #00cc96, #ab63fa, #FFA15A, #19d3f3, #FF6692,\n",
       "                 #B6E880, #FF97FF, #FECB52],\n",
       "    'font': {'color': '#f2f5fa'},\n",
       "    'geo': {'bgcolor': 'rgb(17,17,17)',\n",
       "            'lakecolor': 'rgb(17,17,17)',\n",
       "            'landcolor': 'rgb(17,17,17)',\n",
       "            'showlakes': True,\n",
       "            'showland': True,\n",
       "            'subunitcolor': '#506784'},\n",
       "    'hoverlabel': {'align': 'left'},\n",
       "    'hovermode': 'closest',\n",
       "    'mapbox': {'style': 'dark'},\n",
       "    'paper_bgcolor': 'rgb(17,17,17)',\n",
       "    'plot_bgcolor': 'rgb(17,17,17)',\n",
       "    'polar': {'angularaxis': {'gridcolor': '#506784', 'linecolor': '#506784', 'ticks': ''},\n",
       "              'bgcolor': 'rgb(17,17,17)',\n",
       "              'radialaxis': {'gridcolor': '#506784', 'linecolor': '#506784', 'ticks': ''}},\n",
       "    'scene': {'xaxis': {'backgroundcolor': 'rgb(17,17,17)',\n",
       "                        'gridcolor': '#506784',\n",
       "                        'gridwidth': 2,\n",
       "                        'linecolor': '#506784',\n",
       "                        'showbackground': True,\n",
       "                        'ticks': '',\n",
       "                        'zerolinecolor': '#C8D4E3'},\n",
       "              'yaxis': {'backgroundcolor': 'rgb(17,17,17)',\n",
       "                        'gridcolor': '#506784',\n",
       "                        'gridwidth': 2,\n",
       "                        'linecolor': '#506784',\n",
       "                        'showbackground': True,\n",
       "                        'ticks': '',\n",
       "                        'zerolinecolor': '#C8D4E3'},\n",
       "              'zaxis': {'backgroundcolor': 'rgb(17,17,17)',\n",
       "                        'gridcolor': '#506784',\n",
       "                        'gridwidth': 2,\n",
       "                        'linecolor': '#506784',\n",
       "                        'showbackground': True,\n",
       "                        'ticks': '',\n",
       "                        'zerolinecolor': '#C8D4E3'}},\n",
       "    'shapedefaults': {'line': {'color': '#f2f5fa'}},\n",
       "    'sliderdefaults': {'bgcolor': '#C8D4E3', 'bordercolor': 'rgb(17,17,17)', 'borderwidth': 1, 'tickwidth': 0},\n",
       "    'ternary': {'aaxis': {'gridcolor': '#506784', 'linecolor': '#506784', 'ticks': ''},\n",
       "                'baxis': {'gridcolor': '#506784', 'linecolor': '#506784', 'ticks': ''},\n",
       "                'bgcolor': 'rgb(17,17,17)',\n",
       "                'caxis': {'gridcolor': '#506784', 'linecolor': '#506784', 'ticks': ''}},\n",
       "    'title': {'x': 0.05},\n",
       "    'updatemenudefaults': {'bgcolor': '#506784', 'borderwidth': 0},\n",
       "    'xaxis': {'automargin': True,\n",
       "              'gridcolor': '#283442',\n",
       "              'linecolor': '#506784',\n",
       "              'ticks': '',\n",
       "              'title': {'standoff': 15},\n",
       "              'zerolinecolor': '#283442',\n",
       "              'zerolinewidth': 2},\n",
       "    'yaxis': {'automargin': True,\n",
       "              'gridcolor': '#283442',\n",
       "              'linecolor': '#506784',\n",
       "              'ticks': '',\n",
       "              'title': {'standoff': 15},\n",
       "              'zerolinecolor': '#283442',\n",
       "              'zerolinewidth': 2}\n",
       "})"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "plotly_template = pio.templates[\"plotly_dark\"]\n",
    "plotly_template.layout"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7d91d5bd",
   "metadata": {},
   "source": [
    "## Wow, a moment of respect for whoever wrote that, tested it..."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fe1d1f4c",
   "metadata": {},
   "source": [
    "See the [Theming and Templates in Plotly docs](https://plotly.com/python/templates/) for more detail, and how to save and distribute custom themes you make as Python script files!"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python (plotlystyle_env)",
   "language": "python",
   "name": "plotlystyle_env"
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  "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"
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