stripes-from-haduk-grid.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "1de667bf-caf8-4565-923b-fad4c386a6e9",
   "metadata": {},
   "source": [
    "# Climate stripes demonstrated in a notebook"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "410368f0-89e3-496a-98ca-e90947889453",
   "metadata": {},
   "source": [
    "If you already have these packages installed, then you should be able to get started without any installations:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "26774c61-4e69-4e48-81e2-d63a22edf502",
   "metadata": {},
   "outputs": [],
   "source": [
    "!cat requirements.txt"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "15f5c65e-ca08-4750-83c2-bbbc0e244003",
   "metadata": {},
   "source": [
    "If not, then you'll need to install them, usually with `pip`, as follows:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "cef666eb-5958-4975-b1a6-db9ccad8d7e1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "\u001b[?25hInstalling collected packages: commonmark, PyMuPDFb, convertbng, pymupdf, mdpdf, kerchunk\n",
      "\u001b[33m  WARNING: The script cmark is installed in '/home/users/astephen/.local/bin' which is not on PATH.\n",
      "  Consider adding this directory to PATH or, if you prefer to suppress this warning, use --no-warn-script-location.\u001b[0m\u001b[33m\n",
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      "\u001b[0m\u001b[33m  WARNING: The script pymupdf is installed in '/home/users/astephen/.local/bin' which is not on PATH.\n",
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      "\u001b[0m\u001b[33m  WARNING: The script mdpdf is installed in '/home/users/astephen/.local/bin' which is not on PATH.\n",
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      "\u001b[0mSuccessfully installed PyMuPDFb-1.24.3 commonmark-0.9.1 convertbng-0.7.2 kerchunk-0.2.5 mdpdf-0.0.18 pymupdf-1.24.4\n"
     ]
    }
   ],
   "source": [
    "!pip install -r requirements.txt"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "43f47353-a18a-4e3f-aa55-8d542ff73879",
   "metadata": {},
   "source": [
    "If you had to install the dependencies, then you should restart the kernel (with the button/option above, or by running this cell)..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "a2fd23c3-37ae-4e14-940f-a4dc53f224ca",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'status': 'ok', 'restart': True}"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import IPython\n",
    "IPython.Application.instance().kernel.do_shutdown(True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a9cc7e07-74fb-4fdb-8d61-27236fa2a367",
   "metadata": {},
   "source": [
    "We are ready to import the required libraries:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "2889400f-bdf2-488d-8e61-1119892dc1ff",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import xarray as xr\n",
    "import numpy as np\n",
    "import fsspec\n",
    "from convertbng.util import convert_bng\n",
    "import IPython\n",
    "from IPython.display import Image\n",
    "from xhtml2pdf import pisa\n",
    "from io import BytesIO\n",
    "import os"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6ca5c1b2-a48c-4b0f-a21f-9c1a658203ec",
   "metadata": {},
   "source": [
    "Since getting the data can take some time (approx. 20 seconds), we use a **global cache** to speed up making repeat requests when testing and editing the code."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "e01653cb-6891-47e6-a3f4-f74eec5f1617",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define the cache as a dictionary\n",
    "CACHE = {}"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "576d1d1b-777b-4bd1-8ba1-0a2f0e3f379c",
   "metadata": {
    "jp-MarkdownHeadingCollapsed": true
   },
   "source": [
    "Create some functions to deal with colour maps."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "768aec03-74ef-41cd-a903-32f2566dadd6",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define some global constants for the colour maps\n",
    "DEFAULT_CMAP = \"RdBu_r\"\n",
    "N_COLOURS = 20\n",
    "\n",
    "\n",
    "def get_colour_map(cmap_name=DEFAULT_CMAP, n_colours=-1):\n",
    "    \"\"\"\n",
    "    Return a colour map object based on colour map name and the number of colour bins.\n",
    "    Set n_colours to -1 to get a continuous colour map.\n",
    "    \"\"\"\n",
    "    args = [cmap_name]\n",
    "    if n_colours > 1: \n",
    "        args.append(n_colours)\n",
    "\n",
    "    cmap = plt.get_cmap(*args)\n",
    "    cmap._init()\n",
    "    return cmap\n",
    "\n",
    "\n",
    "def rgba_to_hex(r, g, b, a=None):\n",
    "    \"\"\"\n",
    "    Convert (red, green, blue, alpha) tuples and convert to a Hex string to represent the colour.\n",
    "    \"\"\"\n",
    "    r, g, b = [int(i * 255) for i in (r, g, b)]\n",
    "    return \"#\" + \"\".join([(\"0\" + hex(i).split(\"x\")[1])[-2:] for i in (r, g, b)])\n",
    "\n",
    "\n",
    "def get_colours_lookup(cmap_name=DEFAULT_CMAP, n_colours=N_COLOURS): \n",
    "    \"\"\"\n",
    "    Based on a colour map and a number of requested colours, return a lookup dictionary of:\n",
    "        - (red, green, blue): colour name\n",
    "    \"\"\"\n",
    "    cmap = get_colour_map(cmap_name, n_colours)\n",
    "    assert cmap.N == n_colours\n",
    "\n",
    "    cols = [((r, g, b), f\"colour_{(i + 1):02d}\") for i, (r, g, b, a) in sorted(enumerate(cmap._lut))]\n",
    "    print(\"NOTE: The last three rows are the colours for data low and high out-of-range values and for masked values.\")\n",
    "    return dict(cols)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "10973b2b-9084-450a-929f-cdcc481dc677",
   "metadata": {},
   "source": [
    "Now let's define a class to create a stripes dataset and plot."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "id": "2a0ada67-5d30-42d6-801c-caf737846b55",
   "metadata": {},
   "outputs": [],
   "source": [
    "# First define some global constants\n",
    "KERCHUNK_PATH = \"haduk-grid1.json\"\n",
    "SPATIAL_PROXIMITY_THRESHOLD = 0.05\n",
    "DEFAULT_REFERENCE_PERIOD = (1901, 2000)\n",
    "\n",
    "\n",
    "class HadUKStripesMaker:\n",
    "    \"\"\"\n",
    "    A class to create climate stripes for different locations.\n",
    "    You can tweak:\n",
    "       - the location\n",
    "       - the time range\n",
    "       - the time range of the reference period (used for the average value)\n",
    "       - the colour map used\n",
    "       - the number of colours used\n",
    "       - the spatial threshold used for checking a grid box centre is near the requested location\n",
    "\n",
    "    Use as follows:\n",
    "    >>> stripes_maker = HadUKStripesMaker()\n",
    "    >>> df = stripes_maker.create(51.23, -1.23, )\n",
    "    >>> stripes_maker.show_plot()\n",
    "    >>> strips_maker.show_table()\n",
    "\n",
    "    To specify a time range and a different number of colours and a blue-green colour map:\n",
    "    >>> stripes_maker.create(51.23, -1.23, n_colours=10, cmap_name=\"winter\", time_range=(1950, 2010), \n",
    "               output_file=\"new-stripes.png\")\n",
    "    \"\"\"\n",
    "\n",
    "    def __init__(self, kerchunk_path=KERCHUNK_PATH, \n",
    "                spatial_threshold=SPATIAL_PROXIMITY_THRESHOLD,\n",
    "                reference_period=DEFAULT_REFERENCE_PERIOD,\n",
    "                cmap_name=DEFAULT_CMAP,\n",
    "                n_colours=N_COLOURS):\n",
    "\n",
    "        self.kerchunk_path = kerchunk_path\n",
    "        self.spatial_threshold = spatial_threshold\n",
    "        self.reference_period = reference_period\n",
    "        self.cmap_name = cmap_name\n",
    "        self.n_colours = n_colours\n",
    "\n",
    "        self.latest_df = None\n",
    "        self.latest_plot = None\n",
    "        self.latest_request = None\n",
    "\n",
    "    def _check_location_is_near(self, lat, lon, point_ds, ds):\n",
    "        \"\"\"\n",
    "        Checks that selected eastings and northings (in BNG coordinates) are within the lat/lon threshold of\n",
    "        the `lat` and `lon` requested by the user.\n",
    "    \n",
    "        Raises an exception if outside the acceptable threshold.\n",
    "    \n",
    "        Returns tuple of: (eastings, northings)\n",
    "        \"\"\"\n",
    "        y = float(point_ds.projection_y_coordinate.values)\n",
    "        x = float(point_ds.projection_x_coordinate.values)\n",
    "        \n",
    "        lat_diff = abs(float(ds.latitude.sel(projection_y_coordinate=y, projection_x_coordinate=x)) - lat)\n",
    "        lon_diff = abs(float(ds.longitude.sel(projection_y_coordinate=y, projection_x_coordinate=x)) - lon)\n",
    "    \n",
    "        assert lat_diff < self.spatial_threshold, f\"Lat diff is too big: {lat_diff}\"\n",
    "        assert lon_diff < self.spatial_threshold, f\"Lon diff is too big: {lon_diff}\"\n",
    "    \n",
    "        return (x, y)\n",
    "        \n",
    "    def _extract_time_series_at_location(self, lat, lon, years=None, ref_period=DEFAULT_REFERENCE_PERIOD):\n",
    "        \"\"\"\n",
    "        Read the data from the data files.\n",
    "        Return a dictionary containing keys:\n",
    "            - temp_series: the extracted temperature series\n",
    "            - demeaned_temp_series: the temperature series with the mean of the reference period subtracted\n",
    "            - eastings: actual easting of grid box centre (British National Grid)\n",
    "            - northings: actual northing of grid box centre (British National Grid)\n",
    "            - lat: actual latitude of grid box centre\n",
    "            - lon: actual longitude of grid box centre\n",
    "        \"\"\"\n",
    "        # Create a mapper to load the data from Kerchunk\n",
    "        compression = \"zstd\" if self.kerchunk_path.split(\".\")[-1].startswith(\"zst\") else None\n",
    "        mapper = fsspec.get_mapper(\"reference://\", fo=self.kerchunk_path, target_options={\"compression\": compression})\n",
    "\n",
    "        # Create an Xarray dataset that will read from the NetCDF data files\n",
    "        print(\"opening kerchunk...need bigger arrays and specify duplicate coords and lat lon from each\")\n",
    "        ds = xr.open_zarr(mapper, consolidated=False, use_cftime=True, decode_timedelta=False)\n",
    "    \n",
    "        print(\"convert to northings, eastings...\")\n",
    "        requested_eastings, requested_northings = [i[0] for i in convert_bng(lon, lat)] \n",
    "     \n",
    "        print(\"extract nearest grid point (with time subset if specified)...\")\n",
    "        start_year, end_year = (str(years[0]), str(years[1])) if years \\\n",
    "                                else (str(ds.time.min().dt.year.values), str(ds.time.max().dt.year.values))\n",
    "        temp_series = ds.tas.sel(projection_y_coordinate=requested_northings, \n",
    "                                 projection_x_coordinate=requested_eastings,\n",
    "                                 method=\"nearest\").sel(time=slice(start_year, end_year))\n",
    "    \n",
    "        # Check the chosen location is near the requested location\n",
    "        print(\"check data point is close enough to the requested location (within spatial threshold)...\")\n",
    "        actual_eastings, actual_northings = self._check_location_is_near(lat, lon, temp_series, ds)\n",
    "    \n",
    "        # Get mean over reference period\n",
    "        print(\"calculate the mean over the reference period...\")\n",
    "        reference_mean = temp_series.sel(time=slice(str(ref_period[0]), str(ref_period[1]))).mean()\n",
    "    \n",
    "        # Construct content to return\n",
    "        response = {\n",
    "            \"temp_series\": temp_series.squeeze().compute(),\n",
    "            \"demeaned_temp_series\": (temp_series - reference_mean).squeeze().compute(),\n",
    "            \"eastings\": actual_eastings, \"northings\": actual_northings,\n",
    "            \"lat\": float(ds.latitude.sel(projection_y_coordinate=actual_northings, projection_x_coordinate=actual_eastings)),\n",
    "            \"lon\": float(ds.longitude.sel(projection_y_coordinate=actual_northings, projection_x_coordinate=actual_eastings))\n",
    "        }\n",
    "        print(\"Returning data objects...\")\n",
    "        return response\n",
    "\n",
    "    def create(self, lat, lon, n_colours=N_COLOURS, cmap_name=DEFAULT_CMAP, time_range=None,\n",
    "               output_file=\"climate-stripes.png\", range_buffer=0.2):\n",
    "        \"\"\"\n",
    "        Creates both a plot and a dataset (as a `pandas DataFrame`) based on input requirements.\n",
    "        \n",
    "        NOTE: range_buffer can be modified to ensure that the colours are all within range of the cmap.\n",
    "        Returns a `pandas.DataFrame` object.\n",
    "        \"\"\"\n",
    "        self.cmap_name = cmap_name or self.cmap_name\n",
    "        time_range = tuple(time_range) if time_range else time_range\n",
    "\n",
    "        n_colours = n_colours if n_colours > 0 else self.n_colours\n",
    "        args = (lat, lon, n_colours, cmap_name, range_buffer, time_range)\n",
    "\n",
    "        # Use the cache if the request has already been made\n",
    "        if args in CACHE.keys():\n",
    "            print(\"Loading from cache...\")\n",
    "            data = CACHE[args]\n",
    "        else:\n",
    "            print(\"Loading from file...\")\n",
    "            resp = self._extract_time_series_at_location(lat, lon, time_range)\n",
    "            years = resp[\"temp_series\"].time.dt.year\n",
    "        \n",
    "            actual_values = resp[\"temp_series\"].values\n",
    "            stripes_data = resp[\"demeaned_temp_series\"].values\n",
    "            data = {\"years\": years, \"actual_values\": actual_values, \"stripes_data\": stripes_data}\n",
    "            print(\"Saving to cache...\")\n",
    "            CACHE[args] = data\n",
    "\n",
    "        self.latest_request = {\n",
    "            \"lat\": lat, \"lon\": lon, \"n_colours\": n_colours,\n",
    "            \"cmap_name\": cmap_name, \"time_range\": (time_range or (int(data[\"years\"].min()), int(data[\"years\"].max())))\n",
    "        }\n",
    "        \n",
    "        print(\"Downloaded data...\")\n",
    "        # Unpack dictionary\n",
    "        years, actual_values, stripes_data = data[\"years\"], data[\"actual_values\"], data[\"stripes_data\"]\n",
    "    \n",
    "        print(\"Min and max:\", stripes_data.min(), stripes_data.max())\n",
    "    \n",
    "        # Add a buffer around the lower and upper boundaries - to use only values within the colourmap\n",
    "        normalised_data = plt.Normalize(stripes_data.min() - range_buffer, stripes_data.max() + range_buffer)\n",
    "        cmap = get_colour_map(self.cmap_name, n_colours)\n",
    "        \n",
    "        fig, ax = plt.subplots(figsize=(10, 2))\n",
    "        \n",
    "        print(\"Starting plot\")\n",
    "    \n",
    "        colours = []\n",
    "        \n",
    "        for i in range(stripes_data.shape[0]):\n",
    "            actual_value = actual_values[i]\n",
    "            year = years[i]\n",
    "            normalised_value = normalised_data(stripes_data[i])\n",
    "            rgba_colour = cmap(normalised_value)\n",
    "            colours.append(rgba_colour)\n",
    "    \n",
    "            # collected.append([actual_value, normalised_value, colour])\n",
    "            ax.axvspan(\n",
    "                xmin=i - 0.5, xmax=i + 0.5, color=rgba_colour\n",
    "            )\n",
    "        \n",
    "        ax.axis(\"off\")\n",
    "        plt.savefig(output_file)\n",
    "        print(f\"Saved image file: {output_file}\")\n",
    "        self.latest_plot = output_file\n",
    "\n",
    "        df = pd.DataFrame({\n",
    "            \"years\": years,\n",
    "            \"temp_value\": actual_values,\n",
    "            \"temp_demeaned\": stripes_data,\n",
    "            \"hex_colour\": [rgba_to_hex(r, g, b) for (r, g, b, a) in colours],\n",
    "            \"red\": [col[0] for col in colours],\n",
    "            \"green\": [col[1] for col in colours],\n",
    "            \"blue\": [col[2] for col in colours]\n",
    "        })\n",
    "\n",
    "        self.latest_df = self._extend_dataframe(df, cmap_name, n_colours)\n",
    "        return self.latest_df\n",
    "\n",
    "    def _extend_dataframe(self, df, cmap_name, n_colours):\n",
    "        \"\"\"\n",
    "        Extends and returns the DataFrame with \"colour_block\" and \"colour\" columns.\n",
    "        \"colour_block\" is empty - ready for highlighted rendering with `.show_table()`.\n",
    "        \"\"\"\n",
    "        col_lookup = get_colours_lookup(cmap_name, n_colours)\n",
    "        df[\"colour_block\"] = \"\"\n",
    "        df.loc[:, \"colour\"] = df.apply(lambda row: col_lookup[(row[\"red\"], row[\"green\"], row[\"blue\"])], axis=1)\n",
    "        return df\n",
    "\n",
    "    def _get_colour_mapping(self, df):\n",
    "        tmp_df = df[[\"colour\", \"hex_colour\"]].drop_duplicates().sort_values(\"colour\")\n",
    "        dct = pd.Series(tmp_df.hex_colour.values, index=tmp_df.colour).to_dict()\n",
    "        return {key: f\"background-color: {colour}\" for key, colour in dct.items()}\n",
    "    \n",
    "    def show_table(self, full=True):\n",
    "        \"\"\"\n",
    "        Show the latest data table created.\n",
    "        \"\"\"\n",
    "        print(f\"Request details: {self.latest_request}\")\n",
    "\n",
    "        if full:\n",
    "            df2 = self.latest_df.copy()\n",
    "            df2[\"colour_block\"] = \"\"\n",
    "        else:\n",
    "            df2 = pd.DataFrame(self.latest_df[[\"colour\"]].value_counts().sort_values().sort_index())\n",
    "            df2[\"colour_block\"] = \"\"\n",
    "            df2 = df2.reset_index()\n",
    "                \n",
    "        def _highlight_cols(df, full_df=self.latest_df):\n",
    "            style_df = df.copy().astype(str)\n",
    "            style_df.loc[:,:] = 'background-color: none'\n",
    "            colour_dict = self._get_colour_mapping(full_df)\n",
    "            style_df[\"colour_block\"] = df.colour.map(colour_dict)\n",
    "            return style_df\n",
    "        \n",
    "        return df2.style.apply(_highlight_cols, axis=None)\n",
    "\n",
    "    def show_plot(self):\n",
    "        \"Show the latest plot.\"\n",
    "        print(f\"Image location: {self.latest_plot}\")\n",
    "        return Image(self.latest_plot)\n",
    "    \n",
    "    def clear_cache(self):\n",
    "        \"Empties global cache dictionary.\"\n",
    "        keys = list(CACHE.keys())\n",
    "\n",
    "        for key in keys:\n",
    "            del CACHE[key]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "id": "c85ef27f-06d1-4907-8c78-db64de1beeee",
   "metadata": {},
   "outputs": [],
   "source": [
    "HTML_TEMPLATE = \"\"\"<html>\n",
    "<head>\n",
    "\n",
    "<style>\n",
    "    body {{font-family: \"Times New Roman\", Times, serif; font-size: 12px;}}\n",
    "    table, tr, th, td {{border: 1px solid black; padding: 2px;}}\n",
    "    td {{min-width: 100px; text-align: right;}}\n",
    "</style>\n",
    "\n",
    "</head>\n",
    "<body>\n",
    "    <h1>Climate Stripes for:</h1>\n",
    "    {project}\n",
    "    <p>Latitude: {lat}&deg;,  Longitude: {lon}&deg;.</p>\n",
    "    <p>Time period: {time_range}</p>\n",
    "    <p>Using: {n_colours} colours</p>\n",
    "    </br/>\n",
    "    <img src=\"{png_file}\" />\n",
    "    <br/>\n",
    "    <h1>Table 1: Temperature variations from the average, and colours per year.</h1>\n",
    "\n",
    "{table_1}\n",
    "\n",
    "    <h1>Table 2: Colour table</h1>\n",
    "\n",
    "{table_2}\n",
    "\n",
    "<h1>Additional information</h1>\n",
    "<p>If you like this, then please do something nice...</p>\n",
    "\n",
    "</body>\n",
    "</html>\"\"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "id": "0a1e2af0-a1c9-48a2-ae2e-279baed566e0",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create an extension to write to HTML and PDF outputs\n",
    "\n",
    "class HadUKStripesRenderer(HadUKStripesMaker):\n",
    "    \n",
    "    def _get_table(self, table):\n",
    "        ldf = self.latest_df\n",
    "        colour_dict = self._get_colour_mapping(ldf)\n",
    "\n",
    "        if table == 1:\n",
    "            df = ldf.copy()\n",
    "            row_vars = \"years temp_value temp_demeaned hex_colour red green blue colour\".split()\n",
    "        elif table == 2:\n",
    "            df = pd.DataFrame(ldf[[\"colour\"]].value_counts().sort_values().sort_index())\n",
    "            df[\"colour_block\"] = \"\"\n",
    "            df = df.reset_index()\n",
    "            df = df.rename(columns={\"count\": \"colour_count\"})\n",
    "            row_vars = [\"colour\", \"colour_count\"]\n",
    "\n",
    "        df[\"colour_block\"] = df[\"colour\"].map(colour_dict)\n",
    "\n",
    "        normal_rows = \"\\n\".join([f\"        <td>{{{row_var}}}</td>\\n\" for row_var in row_vars])\n",
    "\n",
    "        row_template = f\"\"\"\n",
    "    <tr>\n",
    "{normal_rows}\n",
    "      <td style=\"{{colour_block}}\">         </td>\n",
    "    </tr>\"\"\"\n",
    "\n",
    "        table_rows = \"\"\n",
    "\n",
    "        for idx, row in df.round(5).iterrows():\n",
    "            html_row = row_template.format(**row)\n",
    "            table_rows += html_row\n",
    "\n",
    "        col_names = row_vars + [\"actual_colour\"]\n",
    "        head_rows = \"\\n\".join([f\"      <th>{col_name}</th>\" for col_name in col_names])\n",
    "\n",
    "        html_table = f\"\"\"<table>\n",
    "  <thead>\n",
    "{head_rows}\n",
    "  </thead>\n",
    "  <tbody>\n",
    "    {table_rows}\n",
    "  </tbody>\n",
    "</table>\n",
    "\"\"\"\n",
    "        return html_table\n",
    "    \n",
    "    def to_html(self, html_file=None, project_name=None):\n",
    "        \"\"\"\n",
    "        Return HTML content as a string (or write to `html_file` if defined).\n",
    "        \"\"\"\n",
    "        if project_name:\n",
    "            project = f\"<p><b>{project_name}</b></p>\"\n",
    "        else:\n",
    "            project = \"\"\n",
    "\n",
    "        content = self.latest_request.copy()\n",
    "        content[\"project\"] = project\n",
    "        content[\"png_file\"] = self.latest_plot\n",
    "\n",
    "        content[\"table_1\"] = self._get_table(table=1)\n",
    "        content[\"table_2\"] = self._get_table(table=2)\n",
    "        \n",
    "        html = HTML_TEMPLATE.format(**content)\n",
    "\n",
    "        if html_file:\n",
    "            with open(html_file, \"w\", encoding=\"utf-8\") as file:\n",
    "                file.write(html)\n",
    "\n",
    "        return html\n",
    "\n",
    "    def to_pdf(self, pdf_file, project_name=None):\n",
    "        html_content = self.to_html(project_name=project_name)\n",
    "        \n",
    "        # Create a BytesIO object to store the PDF output\n",
    "        pdf_output = BytesIO()\n",
    "        \n",
    "        # Convert the HTML content to a PDF document\n",
    "        pisa.CreatePDF(html_content, dest=pdf_output, encoding='utf-8')\n",
    "        \n",
    "        # Open a PDF file for writing in binary mode\n",
    "        with open(pdf_file, \"wb\") as pdf_writer:\n",
    "            pdf_writer.write(pdf_output.getvalue())\n",
    "\n",
    "        return pdf_file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "id": "d71ecfc9-56c7-4678-aa20-ad897b07c5cb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loading from cache...\n",
      "Downloaded data...\n",
      "Min and max: -1.4422028892180654 1.9585098211170298\n",
      "Starting plot\n",
      "Saved image file: climate-stripes.png\n",
      "NOTE: The last three rows are the colours for data low and high out-of-range values and for masked values.\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1000x200 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "stripes_maker = HadUKStripesRenderer()\n",
    "df = stripes_maker.create(51.570664384, -1.308832098)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "id": "7ad46e5b-78b1-4ef0-aefc-c1dc42521345",
   "metadata": {},
   "outputs": [],
   "source": [
    "html = stripes_maker.to_html(html_file=\"output.html\", project_name=\"My great project\")\n",
    "pdf_file = stripes_maker.to_pdf(\"output.pdf\", project_name=\"Another project\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "id": "542f5839-3335-40dc-bae9-3979f7033abf",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Image location: climate-stripes.png\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 175,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stripes_maker.show_plot()"
   ]
  },
  {
   "cell_type": "raw",
   "id": "115c127a-a99c-4059-9047-bde257b16207",
   "metadata": {},
   "source": [
    "If you ever want to clear the cache, uncomment and run the following cell:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 177,
   "id": "0f00c618-7d72-4806-997c-4ec574bb1f7c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Request details: {'lat': 51.570664384, 'lon': -1.308832098, 'n_colours': 20, 'cmap_name': 'RdBu_r', 'time_range': (1884, 2022)}\n"
     ]
    },
    {
     "data": {
      "text/html": [
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       "  background-color: none;\n",
       "}\n",
       "#T_7fe80_row0_col2 {\n",
       "  background-color: #134c88;\n",
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       "#T_7fe80_row1_col2 {\n",
       "  background-color: #2268ad;\n",
       "}\n",
       "#T_7fe80_row2_col2 {\n",
       "  background-color: #3480b9;\n",
       "}\n",
       "#T_7fe80_row3_col2 {\n",
       "  background-color: #4b98c5;\n",
       "}\n",
       "#T_7fe80_row4_col2 {\n",
       "  background-color: #74b2d4;\n",
       "}\n",
       "#T_7fe80_row5_col2 {\n",
       "  background-color: #9bcae0;\n",
       "}\n",
       "#T_7fe80_row6_col2 {\n",
       "  background-color: #bddaea;\n",
       "}\n",
       "#T_7fe80_row7_col2 {\n",
       "  background-color: #d9e8f1;\n",
       "}\n",
       "#T_7fe80_row8_col2 {\n",
       "  background-color: #edf2f5;\n",
       "}\n",
       "#T_7fe80_row9_col2 {\n",
       "  background-color: #f8efea;\n",
       "}\n",
       "#T_7fe80_row10_col2 {\n",
       "  background-color: #fbe0d1;\n",
       "}\n",
       "#T_7fe80_row11_col2 {\n",
       "  background-color: #fac9b1;\n",
       "}\n",
       "#T_7fe80_row12_col2 {\n",
       "  background-color: #f5ad8c;\n",
       "}\n",
       "#T_7fe80_row13_col2 {\n",
       "  background-color: #e88b6e;\n",
       "}\n",
       "#T_7fe80_row14_col2 {\n",
       "  background-color: #d96752;\n",
       "}\n",
       "#T_7fe80_row15_col2 {\n",
       "  background-color: #c6413e;\n",
       "}\n",
       "#T_7fe80_row16_col2 {\n",
       "  background-color: #b31b2c;\n",
       "}\n",
       "#T_7fe80_row17_col2 {\n",
       "  background-color: #8e0c25;\n",
       "}\n",
       "</style>\n",
       "<table id=\"T_7fe80\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_7fe80_level0_col0\" class=\"col_heading level0 col0\" >colour</th>\n",
       "      <th id=\"T_7fe80_level0_col1\" class=\"col_heading level0 col1\" >count</th>\n",
       "      <th id=\"T_7fe80_level0_col2\" class=\"col_heading level0 col2\" >colour_block</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_7fe80_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
       "      <td id=\"T_7fe80_row0_col0\" class=\"data row0 col0\" >colour_02</td>\n",
       "      <td id=\"T_7fe80_row0_col1\" class=\"data row0 col1\" >3</td>\n",
       "      <td id=\"T_7fe80_row0_col2\" class=\"data row0 col2\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7fe80_level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
       "      <td id=\"T_7fe80_row1_col0\" class=\"data row1 col0\" >colour_03</td>\n",
       "      <td id=\"T_7fe80_row1_col1\" class=\"data row1 col1\" >3</td>\n",
       "      <td id=\"T_7fe80_row1_col2\" class=\"data row1 col2\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7fe80_level0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
       "      <td id=\"T_7fe80_row2_col0\" class=\"data row2 col0\" >colour_04</td>\n",
       "      <td id=\"T_7fe80_row2_col1\" class=\"data row2 col1\" >4</td>\n",
       "      <td id=\"T_7fe80_row2_col2\" class=\"data row2 col2\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7fe80_level0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
       "      <td id=\"T_7fe80_row3_col0\" class=\"data row3 col0\" >colour_05</td>\n",
       "      <td id=\"T_7fe80_row3_col1\" class=\"data row3 col1\" >7</td>\n",
       "      <td id=\"T_7fe80_row3_col2\" class=\"data row3 col2\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7fe80_level0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
       "      <td id=\"T_7fe80_row4_col0\" class=\"data row4 col0\" >colour_06</td>\n",
       "      <td id=\"T_7fe80_row4_col1\" class=\"data row4 col1\" >9</td>\n",
       "      <td id=\"T_7fe80_row4_col2\" class=\"data row4 col2\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7fe80_level0_row5\" class=\"row_heading level0 row5\" >5</th>\n",
       "      <td id=\"T_7fe80_row5_col0\" class=\"data row5 col0\" >colour_07</td>\n",
       "      <td id=\"T_7fe80_row5_col1\" class=\"data row5 col1\" >14</td>\n",
       "      <td id=\"T_7fe80_row5_col2\" class=\"data row5 col2\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7fe80_level0_row6\" class=\"row_heading level0 row6\" >6</th>\n",
       "      <td id=\"T_7fe80_row6_col0\" class=\"data row6 col0\" >colour_08</td>\n",
       "      <td id=\"T_7fe80_row6_col1\" class=\"data row6 col1\" >18</td>\n",
       "      <td id=\"T_7fe80_row6_col2\" class=\"data row6 col2\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7fe80_level0_row7\" class=\"row_heading level0 row7\" >7</th>\n",
       "      <td id=\"T_7fe80_row7_col0\" class=\"data row7 col0\" >colour_09</td>\n",
       "      <td id=\"T_7fe80_row7_col1\" class=\"data row7 col1\" >16</td>\n",
       "      <td id=\"T_7fe80_row7_col2\" class=\"data row7 col2\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7fe80_level0_row8\" class=\"row_heading level0 row8\" >8</th>\n",
       "      <td id=\"T_7fe80_row8_col0\" class=\"data row8 col0\" >colour_10</td>\n",
       "      <td id=\"T_7fe80_row8_col1\" class=\"data row8 col1\" >14</td>\n",
       "      <td id=\"T_7fe80_row8_col2\" class=\"data row8 col2\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7fe80_level0_row9\" class=\"row_heading level0 row9\" >9</th>\n",
       "      <td id=\"T_7fe80_row9_col0\" class=\"data row9 col0\" >colour_11</td>\n",
       "      <td id=\"T_7fe80_row9_col1\" class=\"data row9 col1\" >10</td>\n",
       "      <td id=\"T_7fe80_row9_col2\" class=\"data row9 col2\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7fe80_level0_row10\" class=\"row_heading level0 row10\" >10</th>\n",
       "      <td id=\"T_7fe80_row10_col0\" class=\"data row10 col0\" >colour_12</td>\n",
       "      <td id=\"T_7fe80_row10_col1\" class=\"data row10 col1\" >8</td>\n",
       "      <td id=\"T_7fe80_row10_col2\" class=\"data row10 col2\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7fe80_level0_row11\" class=\"row_heading level0 row11\" >11</th>\n",
       "      <td id=\"T_7fe80_row11_col0\" class=\"data row11 col0\" >colour_13</td>\n",
       "      <td id=\"T_7fe80_row11_col1\" class=\"data row11 col1\" >6</td>\n",
       "      <td id=\"T_7fe80_row11_col2\" class=\"data row11 col2\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7fe80_level0_row12\" class=\"row_heading level0 row12\" >12</th>\n",
       "      <td id=\"T_7fe80_row12_col0\" class=\"data row12 col0\" >colour_14</td>\n",
       "      <td id=\"T_7fe80_row12_col1\" class=\"data row12 col1\" >4</td>\n",
       "      <td id=\"T_7fe80_row12_col2\" class=\"data row12 col2\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7fe80_level0_row13\" class=\"row_heading level0 row13\" >13</th>\n",
       "      <td id=\"T_7fe80_row13_col0\" class=\"data row13 col0\" >colour_15</td>\n",
       "      <td id=\"T_7fe80_row13_col1\" class=\"data row13 col1\" >12</td>\n",
       "      <td id=\"T_7fe80_row13_col2\" class=\"data row13 col2\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7fe80_level0_row14\" class=\"row_heading level0 row14\" >14</th>\n",
       "      <td id=\"T_7fe80_row14_col0\" class=\"data row14 col0\" >colour_16</td>\n",
       "      <td id=\"T_7fe80_row14_col1\" class=\"data row14 col1\" >5</td>\n",
       "      <td id=\"T_7fe80_row14_col2\" class=\"data row14 col2\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7fe80_level0_row15\" class=\"row_heading level0 row15\" >15</th>\n",
       "      <td id=\"T_7fe80_row15_col0\" class=\"data row15 col0\" >colour_17</td>\n",
       "      <td id=\"T_7fe80_row15_col1\" class=\"data row15 col1\" >2</td>\n",
       "      <td id=\"T_7fe80_row15_col2\" class=\"data row15 col2\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7fe80_level0_row16\" class=\"row_heading level0 row16\" >16</th>\n",
       "      <td id=\"T_7fe80_row16_col0\" class=\"data row16 col0\" >colour_18</td>\n",
       "      <td id=\"T_7fe80_row16_col1\" class=\"data row16 col1\" >3</td>\n",
       "      <td id=\"T_7fe80_row16_col2\" class=\"data row16 col2\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7fe80_level0_row17\" class=\"row_heading level0 row17\" >17</th>\n",
       "      <td id=\"T_7fe80_row17_col0\" class=\"data row17 col0\" >colour_19</td>\n",
       "      <td id=\"T_7fe80_row17_col1\" class=\"data row17 col1\" >1</td>\n",
       "      <td id=\"T_7fe80_row17_col2\" class=\"data row17 col2\" ></td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x7f0619b196d0>"
      ]
     },
     "execution_count": 177,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stripes_maker.show_table(full=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "43f9f040-ec86-4cf7-97ca-0362a2f082db",
   "metadata": {},
   "outputs": [],
   "source": [
    "# stripes_maker = HadUKStripesMaker()\n",
    "# stripes_maker.clear_cache()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2c40d9c2-be6c-4d5c-9571-e215e5a014e0",
   "metadata": {},
   "source": [
    "## Start up the stripes maker\n",
    "\n",
    "You can create many stripes datasets from this maker object."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "e74ced34-3ed1-4930-8093-0ca0a1f00ef2",
   "metadata": {},
   "outputs": [],
   "source": [
    "stripes_maker = HadUKStripesMaker()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4606b951-f12b-4392-96b2-d7790d9992ba",
   "metadata": {},
   "source": [
    "## Create stripes for the RAL location\n",
    "\n",
    "```\n",
    "RAL_LAT, RAL_LON = 51.570664384, -1.308832098\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "9bf74b4c-cf7a-40b0-b3e9-f76f38669462",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loading from cache...\n",
      "Downloaded data...\n",
      "Min and max: -1.4422028892180654 1.9585098211170298\n",
      "Starting plot\n",
      "Saved image file: climate-stripes.png\n",
      "NOTE: The last three rows are the colours for data low and high out-of-range values and for masked values.\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1000x200 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df = stripes_maker.create(51.570664384, -1.308832098)\n",
    "#df = stripes_maker.create(51.23, -1.23, n_colours=10, cmap_name=\"winter\", time_range=(1950, 2010), output_file=\"new-stripes.png\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a5fc3f54-e900-4f08-8c6b-ccae479f07a2",
   "metadata": {},
   "source": [
    "## View the latest table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "id": "acb43c27-ba24-4e4c-86cf-7cb6b98f5fbc",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<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>colour</th>\n",
       "      <th>colour_count</th>\n",
       "      <th>colour_block</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>colour_02</td>\n",
       "      <td>3</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>colour_03</td>\n",
       "      <td>3</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>colour_04</td>\n",
       "      <td>4</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>colour_05</td>\n",
       "      <td>7</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>colour_06</td>\n",
       "      <td>9</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      colour  colour_count colour_block\n",
       "0  colour_02             3             \n",
       "1  colour_03             3             \n",
       "2  colour_04             4             \n",
       "3  colour_05             7             \n",
       "4  colour_06             9             "
      ]
     },
     "execution_count": 180,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#stripes_maker.show_table()\n",
    "# df2 = pd.DataFrame(stripes_maker.latest_df[[\"colour\"]].value_counts().sort_values().sort_index())\n",
    "# df2[\"colour_block\"] = \"\"\n",
    "# df2 = df2.reset_index()\n",
    "# df2 = df2.rename(columns={\"count\": \"colour_count\"})\n",
    "# df2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "f87d2f3c-d6fd-40eb-9419-c8bf97f23277",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Request details: {'lat': 51.570664384, 'lon': -1.308832098, 'n_colours': 20, 'cmap_name': 'RdBu_r', 'time_range': (1884, 2022)}\n"
     ]
    },
    {
     "data": {
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       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_6d692_level0_col0\" class=\"col_heading level0 col0\" >colour</th>\n",
       "      <th id=\"T_6d692_level0_col1\" class=\"col_heading level0 col1\" >count</th>\n",
       "      <th id=\"T_6d692_level0_col2\" class=\"col_heading level0 col2\" >colour_block</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_6d692_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
       "      <td id=\"T_6d692_row0_col0\" class=\"data row0 col0\" >colour_02</td>\n",
       "      <td id=\"T_6d692_row0_col1\" class=\"data row0 col1\" >3</td>\n",
       "      <td id=\"T_6d692_row0_col2\" class=\"data row0 col2\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_6d692_level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
       "      <td id=\"T_6d692_row1_col0\" class=\"data row1 col0\" >colour_03</td>\n",
       "      <td id=\"T_6d692_row1_col1\" class=\"data row1 col1\" >3</td>\n",
       "      <td id=\"T_6d692_row1_col2\" class=\"data row1 col2\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_6d692_level0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
       "      <td id=\"T_6d692_row2_col0\" class=\"data row2 col0\" >colour_04</td>\n",
       "      <td id=\"T_6d692_row2_col1\" class=\"data row2 col1\" >4</td>\n",
       "      <td id=\"T_6d692_row2_col2\" class=\"data row2 col2\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_6d692_level0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
       "      <td id=\"T_6d692_row3_col0\" class=\"data row3 col0\" >colour_05</td>\n",
       "      <td id=\"T_6d692_row3_col1\" class=\"data row3 col1\" >7</td>\n",
       "      <td id=\"T_6d692_row3_col2\" class=\"data row3 col2\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_6d692_level0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
       "      <td id=\"T_6d692_row4_col0\" class=\"data row4 col0\" >colour_06</td>\n",
       "      <td id=\"T_6d692_row4_col1\" class=\"data row4 col1\" >9</td>\n",
       "      <td id=\"T_6d692_row4_col2\" class=\"data row4 col2\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_6d692_level0_row5\" class=\"row_heading level0 row5\" >5</th>\n",
       "      <td id=\"T_6d692_row5_col0\" class=\"data row5 col0\" >colour_07</td>\n",
       "      <td id=\"T_6d692_row5_col1\" class=\"data row5 col1\" >14</td>\n",
       "      <td id=\"T_6d692_row5_col2\" class=\"data row5 col2\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_6d692_level0_row6\" class=\"row_heading level0 row6\" >6</th>\n",
       "      <td id=\"T_6d692_row6_col0\" class=\"data row6 col0\" >colour_08</td>\n",
       "      <td id=\"T_6d692_row6_col1\" class=\"data row6 col1\" >18</td>\n",
       "      <td id=\"T_6d692_row6_col2\" class=\"data row6 col2\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_6d692_level0_row7\" class=\"row_heading level0 row7\" >7</th>\n",
       "      <td id=\"T_6d692_row7_col0\" class=\"data row7 col0\" >colour_09</td>\n",
       "      <td id=\"T_6d692_row7_col1\" class=\"data row7 col1\" >16</td>\n",
       "      <td id=\"T_6d692_row7_col2\" class=\"data row7 col2\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_6d692_level0_row8\" class=\"row_heading level0 row8\" >8</th>\n",
       "      <td id=\"T_6d692_row8_col0\" class=\"data row8 col0\" >colour_10</td>\n",
       "      <td id=\"T_6d692_row8_col1\" class=\"data row8 col1\" >14</td>\n",
       "      <td id=\"T_6d692_row8_col2\" class=\"data row8 col2\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_6d692_level0_row9\" class=\"row_heading level0 row9\" >9</th>\n",
       "      <td id=\"T_6d692_row9_col0\" class=\"data row9 col0\" >colour_11</td>\n",
       "      <td id=\"T_6d692_row9_col1\" class=\"data row9 col1\" >10</td>\n",
       "      <td id=\"T_6d692_row9_col2\" class=\"data row9 col2\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_6d692_level0_row10\" class=\"row_heading level0 row10\" >10</th>\n",
       "      <td id=\"T_6d692_row10_col0\" class=\"data row10 col0\" >colour_12</td>\n",
       "      <td id=\"T_6d692_row10_col1\" class=\"data row10 col1\" >8</td>\n",
       "      <td id=\"T_6d692_row10_col2\" class=\"data row10 col2\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_6d692_level0_row11\" class=\"row_heading level0 row11\" >11</th>\n",
       "      <td id=\"T_6d692_row11_col0\" class=\"data row11 col0\" >colour_13</td>\n",
       "      <td id=\"T_6d692_row11_col1\" class=\"data row11 col1\" >6</td>\n",
       "      <td id=\"T_6d692_row11_col2\" class=\"data row11 col2\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_6d692_level0_row12\" class=\"row_heading level0 row12\" >12</th>\n",
       "      <td id=\"T_6d692_row12_col0\" class=\"data row12 col0\" >colour_14</td>\n",
       "      <td id=\"T_6d692_row12_col1\" class=\"data row12 col1\" >4</td>\n",
       "      <td id=\"T_6d692_row12_col2\" class=\"data row12 col2\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_6d692_level0_row13\" class=\"row_heading level0 row13\" >13</th>\n",
       "      <td id=\"T_6d692_row13_col0\" class=\"data row13 col0\" >colour_15</td>\n",
       "      <td id=\"T_6d692_row13_col1\" class=\"data row13 col1\" >12</td>\n",
       "      <td id=\"T_6d692_row13_col2\" class=\"data row13 col2\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_6d692_level0_row14\" class=\"row_heading level0 row14\" >14</th>\n",
       "      <td id=\"T_6d692_row14_col0\" class=\"data row14 col0\" >colour_16</td>\n",
       "      <td id=\"T_6d692_row14_col1\" class=\"data row14 col1\" >5</td>\n",
       "      <td id=\"T_6d692_row14_col2\" class=\"data row14 col2\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_6d692_level0_row15\" class=\"row_heading level0 row15\" >15</th>\n",
       "      <td id=\"T_6d692_row15_col0\" class=\"data row15 col0\" >colour_17</td>\n",
       "      <td id=\"T_6d692_row15_col1\" class=\"data row15 col1\" >2</td>\n",
       "      <td id=\"T_6d692_row15_col2\" class=\"data row15 col2\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_6d692_level0_row16\" class=\"row_heading level0 row16\" >16</th>\n",
       "      <td id=\"T_6d692_row16_col0\" class=\"data row16 col0\" >colour_18</td>\n",
       "      <td id=\"T_6d692_row16_col1\" class=\"data row16 col1\" >3</td>\n",
       "      <td id=\"T_6d692_row16_col2\" class=\"data row16 col2\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_6d692_level0_row17\" class=\"row_heading level0 row17\" >17</th>\n",
       "      <td id=\"T_6d692_row17_col0\" class=\"data row17 col0\" >colour_19</td>\n",
       "      <td id=\"T_6d692_row17_col1\" class=\"data row17 col1\" >1</td>\n",
       "      <td id=\"T_6d692_row17_col2\" class=\"data row17 col2\" ></td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x7efdf8ddea50>"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stripes_maker.show_table(full=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "865c705f-e359-4ff0-9a46-f9d260d49b93",
   "metadata": {},
   "source": [
    "## View the latest plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "id": "ce0e6f10-4bd3-4e8a-a2e6-277192234d70",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Image location: climate-stripes.png\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 151,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stripes_maker.show_plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0571a886-6ac9-4a04-9bb5-9430b48deded",
   "metadata": {},
   "source": [
    "## Appendices\n",
    "\n",
    "### Appendix 1: Colour map definitions\n",
    "\n",
    "You can define a colour map with unlimited (maybe 256 is the limit) colours, or specify your own limit, e.g.:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "ea894d36-941b-4e12-8f14-14225118094f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/html": [
       "<div style=\"vertical-align: middle;\"><strong>RdBu_r</strong> </div><div class=\"cmap\"><img alt=\"RdBu_r colormap\" title=\"RdBu_r\" style=\"border: 1px solid #555;\" src=\"data:image/png;base64,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\"></div><div style=\"vertical-align: middle; max-width: 514px; display: flex; justify-content: space-between;\"><div style=\"float: left;\"><div title=\"#053061ff\" style=\"display: inline-block; width: 1em; height: 1em; margin: 0; vertical-align: middle; border: 1px solid #555; background-color: #053061ff;\"></div> under</div><div style=\"margin: 0 auto; display: inline-block;\">bad <div title=\"#00000000\" style=\"display: inline-block; width: 1em; height: 1em; margin: 0; vertical-align: middle; border: 1px solid #555; background-color: #00000000;\"></div></div><div style=\"float: right;\">over <div title=\"#67001fff\" style=\"display: inline-block; width: 1em; height: 1em; margin: 0; vertical-align: middle; border: 1px solid #555; background-color: #67001fff;\"></div></div>"
      ],
      "text/plain": [
       "<matplotlib.colors.LinearSegmentedColormap at 0x7fa1eb6243d0>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cmap = plt.get_cmap(\"RdBu_r\")\n",
    "cmap"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "d140c43d-d080-4bb3-93d3-2acaaa1005fd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/html": [
       "<div style=\"vertical-align: middle;\"><strong>RdBu_r</strong> </div><div class=\"cmap\"><img alt=\"RdBu_r colormap\" title=\"RdBu_r\" style=\"border: 1px solid #555;\" src=\"data:image/png;base64,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\"></div><div style=\"vertical-align: middle; max-width: 514px; display: flex; justify-content: space-between;\"><div style=\"float: left;\"><div title=\"#053061ff\" style=\"display: inline-block; width: 1em; height: 1em; margin: 0; vertical-align: middle; border: 1px solid #555; background-color: #053061ff;\"></div> under</div><div style=\"margin: 0 auto; display: inline-block;\">bad <div title=\"#00000000\" style=\"display: inline-block; width: 1em; height: 1em; margin: 0; vertical-align: middle; border: 1px solid #555; background-color: #00000000;\"></div></div><div style=\"float: right;\">over <div title=\"#67001fff\" style=\"display: inline-block; width: 1em; height: 1em; margin: 0; vertical-align: middle; border: 1px solid #555; background-color: #67001fff;\"></div></div>"
      ],
      "text/plain": [
       "<matplotlib.colors.LinearSegmentedColormap at 0x7fa1eb69ca10>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cmap = plt.get_cmap(\"RdBu_r\", 20)\n",
    "cmap"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c7cb54eb-33a8-4591-beda-f09757f6d4a7",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
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