EarthDataHub/tutorial-hybrid-world-population.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "d2926c6a-46ff-4a1e-b6e6-fa498d61814e",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-09-10T14:02:56.576524Z",
     "iopub.status.busy": "2024-09-10T14:02:56.574392Z",
     "iopub.status.idle": "2024-09-10T14:02:56.595201Z",
     "shell.execute_reply": "2024-09-10T14:02:56.593471Z",
     "shell.execute_reply.started": "2024-09-10T14:02:56.576450Z"
    }
   },
   "source": [
    "## How to work with Hybrid gridded demographic data for the world, 1950-2020, on Earth Data Hub"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "93543b78-9096-4a3f-9c6a-156bf5cab430",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-09-10T14:09:11.982294Z",
     "iopub.status.busy": "2024-09-10T14:09:11.981058Z",
     "iopub.status.idle": "2024-09-10T14:09:11.994741Z",
     "shell.execute_reply": "2024-09-10T14:09:11.993243Z",
     "shell.execute_reply.started": "2024-09-10T14:09:11.982237Z"
    }
   },
   "source": [
    "[Earth Data Hub](https://earthdatahub.com) (EDH) provides an innovative access to earth related data. This notebook will provide you guidance on how to access and use the [`https://data.earthdatahub.destine.eu/derived-GPWv4-Histsoc/demographics-hybrid-1950-2020-15-min-v0`](https://earthdatahub.destine.eu/collections/derived-GPWv4-Histsoc/datasets/demographics-hybrid-1950-2020-15-min) dataset on Earth Data Hub.\n",
    "\n",
    "\n",
    "This dataset combines the NASA SEDAC Gridded Population of the World version 4 (GPWv4) with the ISIMIP Histsoc gridded population data and the United Nations World Population Program (WPP) demographic modelling data. The data is supplied in 5-years population bands with 0.25° spatial resolution. For pre-2000 population data, the ISIMIP Histsoc data was upscaled from it's native 0.5˚ resolution to  0.25° resolution. The dataset grid is explicitly designed to match with the one of ERA5 single levels climate reanalysis dataset. \n",
    "\n",
    "### Goal of this tutorial\n",
    "\n",
    "In this tutorial our goal is to compute and visualize the European population distribution in 2020.\n",
    "\n",
    "### What you will learn:\n",
    "* how to access the data\n",
    "* select and reduce the data\n",
    "* plot the results\n",
    "***\n",
    "\n",
    "In order to access datasets on Earth Data Hub you need to instruct your tools (xarray, Zarr, etc.) to use EDH personal access token when downloading the data.\n",
    "\n",
    "To obtain a personal access token you first need to register to the [Destination Earth platform](https://platform.destine.eu) platform. Then, you can go to [Earth Data Hub account settings](https://earthdatahub.destine.eu/account-settings) where you can find your default personal access token or create others. After retrieving your personal access token, please cut and paste it below: ⤵"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "7ec379a8-16a9-4309-bf2b-76027b95b69e",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-10-23T14:58:46.044978Z",
     "iopub.status.busy": "2024-10-23T14:58:46.044682Z",
     "iopub.status.idle": "2024-10-23T14:58:46.053369Z",
     "shell.execute_reply": "2024-10-23T14:58:46.052027Z",
     "shell.execute_reply.started": "2024-10-23T14:58:46.044949Z"
    }
   },
   "outputs": [],
   "source": [
    "PAT = \"your_personal_access_token\"\n",
    "\n",
    "# e.g. PAT=\"edh_pat_44bbb7e9192a4c6bb47ddf07d07564eee5d17de8dfc48f7118f88e3bc4a4157f8fe2403f5aa0a2d53441b6922ea9a33a\""
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5e8cd434-c978-4240-9fb9-15ff51da2bde",
   "metadata": {},
   "source": [
    "### Working with EDH data\n",
    "Datasets on EDH are often very large and remotely hosted. Typical use imply a selection of the data followed by one or more reduction steps to be performed in a local or distributed Dask environment.\n",
    "\n",
    "The structure of a workflow that uses EDH data tipically looks like this:\n",
    "\n",
    "* data access \n",
    "* data selection\n",
    "* (optional) data reduction\n",
    "* data download\n",
    "* further operations and visualization\n",
    "\n",
    "[Xarray](https://docs.xarray.dev/en/stable) and [Dask](https://www.dask.org) work together following a lazy principle. This means that when you access and manipulate a Zarr store the data is in not immediately downloaded and loaded in memory. Instead, Dask constructs a task graph that represents the operations to be performed. \n",
    "A smart user will first reduce the amount of data that needs to be downloaded and explicitly call `compute()`\n",
    " on it. Once the `compute()`\n",
    " operation is complete the data is loaded into memory and available for subsequent fast processing."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "719ee8cb-a6de-44f3-a563-4f884526b627",
   "metadata": {
    "editable": true,
    "execution": {
     "iopub.execute_input": "2024-09-06T09:32:56.278545Z",
     "iopub.status.busy": "2024-09-06T09:32:56.277838Z",
     "iopub.status.idle": "2024-09-06T09:32:56.285658Z",
     "shell.execute_reply": "2024-09-06T09:32:56.284013Z",
     "shell.execute_reply.started": "2024-09-06T09:32:56.278495Z"
    },
    "slideshow": {
     "slide_type": ""
    },
    "tags": []
   },
   "source": [
    "#### 1. Data access\n",
    "\n",
    "To preview the data, only the dataset metadata must be downloaded. Xarray does this automatically when you access a Zarr dataset:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "43e8cc7e-7785-46f4-a830-dd7fa70dcb46",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-10-23T14:58:46.055514Z",
     "iopub.status.busy": "2024-10-23T14:58:46.055159Z",
     "iopub.status.idle": "2024-10-23T14:58:48.148547Z",
     "shell.execute_reply": "2024-10-23T14:58:48.147430Z",
     "shell.execute_reply.started": "2024-10-23T14:58:46.055488Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div><svg style=\"position: absolute; width: 0; height: 0; overflow: hidden\">\n",
       "<defs>\n",
       "<symbol id=\"icon-database\" viewBox=\"0 0 32 32\">\n",
       "<path d=\"M16 0c-8.837 0-16 2.239-16 5v4c0 2.761 7.163 5 16 5s16-2.239 16-5v-4c0-2.761-7.163-5-16-5z\"></path>\n",
       "<path d=\"M16 17c-8.837 0-16-2.239-16-5v6c0 2.761 7.163 5 16 5s16-2.239 16-5v-6c0 2.761-7.163 5-16 5z\"></path>\n",
       "<path d=\"M16 26c-8.837 0-16-2.239-16-5v6c0 2.761 7.163 5 16 5s16-2.239 16-5v-6c0 2.761-7.163 5-16 5z\"></path>\n",
       "</symbol>\n",
       "<symbol id=\"icon-file-text2\" viewBox=\"0 0 32 32\">\n",
       "<path d=\"M28.681 7.159c-0.694-0.947-1.662-2.053-2.724-3.116s-2.169-2.030-3.116-2.724c-1.612-1.182-2.393-1.319-2.841-1.319h-15.5c-1.378 0-2.5 1.121-2.5 2.5v27c0 1.378 1.122 2.5 2.5 2.5h23c1.378 0 2.5-1.122 2.5-2.5v-19.5c0-0.448-0.137-1.23-1.319-2.841zM24.543 5.457c0.959 0.959 1.712 1.825 2.268 2.543h-4.811v-4.811c0.718 0.556 1.584 1.309 2.543 2.268zM28 29.5c0 0.271-0.229 0.5-0.5 0.5h-23c-0.271 0-0.5-0.229-0.5-0.5v-27c0-0.271 0.229-0.5 0.5-0.5 0 0 15.499-0 15.5 0v7c0 0.552 0.448 1 1 1h7v19.5z\"></path>\n",
       "<path d=\"M23 26h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
       "<path d=\"M23 22h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
       "<path d=\"M23 18h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
       "</symbol>\n",
       "</defs>\n",
       "</svg>\n",
       "<style>/* CSS stylesheet for displaying xarray objects in jupyterlab.\n",
       " *\n",
       " */\n",
       "\n",
       ":root {\n",
       "  --xr-font-color0: var(--jp-content-font-color0, rgba(0, 0, 0, 1));\n",
       "  --xr-font-color2: var(--jp-content-font-color2, rgba(0, 0, 0, 0.54));\n",
       "  --xr-font-color3: var(--jp-content-font-color3, rgba(0, 0, 0, 0.38));\n",
       "  --xr-border-color: var(--jp-border-color2, #e0e0e0);\n",
       "  --xr-disabled-color: var(--jp-layout-color3, #bdbdbd);\n",
       "  --xr-background-color: var(--jp-layout-color0, white);\n",
       "  --xr-background-color-row-even: var(--jp-layout-color1, white);\n",
       "  --xr-background-color-row-odd: var(--jp-layout-color2, #eeeeee);\n",
       "}\n",
       "\n",
       "html[theme=dark],\n",
       "html[data-theme=dark],\n",
       "body[data-theme=dark],\n",
       "body.vscode-dark {\n",
       "  --xr-font-color0: rgba(255, 255, 255, 1);\n",
       "  --xr-font-color2: rgba(255, 255, 255, 0.54);\n",
       "  --xr-font-color3: rgba(255, 255, 255, 0.38);\n",
       "  --xr-border-color: #1F1F1F;\n",
       "  --xr-disabled-color: #515151;\n",
       "  --xr-background-color: #111111;\n",
       "  --xr-background-color-row-even: #111111;\n",
       "  --xr-background-color-row-odd: #313131;\n",
       "}\n",
       "\n",
       ".xr-wrap {\n",
       "  display: block !important;\n",
       "  min-width: 300px;\n",
       "  max-width: 700px;\n",
       "}\n",
       "\n",
       ".xr-text-repr-fallback {\n",
       "  /* fallback to plain text repr when CSS is not injected (untrusted notebook) */\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-header {\n",
       "  padding-top: 6px;\n",
       "  padding-bottom: 6px;\n",
       "  margin-bottom: 4px;\n",
       "  border-bottom: solid 1px var(--xr-border-color);\n",
       "}\n",
       "\n",
       ".xr-header > div,\n",
       ".xr-header > ul {\n",
       "  display: inline;\n",
       "  margin-top: 0;\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-obj-type,\n",
       ".xr-array-name {\n",
       "  margin-left: 2px;\n",
       "  margin-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-obj-type {\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-sections {\n",
       "  padding-left: 0 !important;\n",
       "  display: grid;\n",
       "  grid-template-columns: 150px auto auto 1fr 0 20px 0 20px;\n",
       "}\n",
       "\n",
       ".xr-section-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-section-item input {\n",
       "  display: inline-block;\n",
       "  opacity: 0;\n",
       "}\n",
       "\n",
       ".xr-section-item input + label {\n",
       "  color: var(--xr-disabled-color);\n",
       "}\n",
       "\n",
       ".xr-section-item input:enabled + label {\n",
       "  cursor: pointer;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-section-item input:focus + label {\n",
       "  border: 2px solid var(--xr-font-color0);\n",
       "}\n",
       "\n",
       ".xr-section-item input:enabled + label:hover {\n",
       "  color: var(--xr-font-color0);\n",
       "}\n",
       "\n",
       ".xr-section-summary {\n",
       "  grid-column: 1;\n",
       "  color: var(--xr-font-color2);\n",
       "  font-weight: 500;\n",
       "}\n",
       "\n",
       ".xr-section-summary > span {\n",
       "  display: inline-block;\n",
       "  padding-left: 0.5em;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label {\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in + label:before {\n",
       "  display: inline-block;\n",
       "  content: '►';\n",
       "  font-size: 11px;\n",
       "  width: 15px;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label:before {\n",
       "  color: var(--xr-disabled-color);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label:before {\n",
       "  content: '▼';\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label > span {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-section-summary,\n",
       ".xr-section-inline-details {\n",
       "  padding-top: 4px;\n",
       "  padding-bottom: 4px;\n",
       "}\n",
       "\n",
       ".xr-section-inline-details {\n",
       "  grid-column: 2 / -1;\n",
       "}\n",
       "\n",
       ".xr-section-details {\n",
       "  display: none;\n",
       "  grid-column: 1 / -1;\n",
       "  margin-bottom: 5px;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-array-wrap {\n",
       "  grid-column: 1 / -1;\n",
       "  display: grid;\n",
       "  grid-template-columns: 20px auto;\n",
       "}\n",
       "\n",
       ".xr-array-wrap > label {\n",
       "  grid-column: 1;\n",
       "  vertical-align: top;\n",
       "}\n",
       "\n",
       ".xr-preview {\n",
       "  color: var(--xr-font-color3);\n",
       "}\n",
       "\n",
       ".xr-array-preview,\n",
       ".xr-array-data {\n",
       "  padding: 0 5px !important;\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-array-data,\n",
       ".xr-array-in:checked ~ .xr-array-preview {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-array-in:checked ~ .xr-array-data,\n",
       ".xr-array-preview {\n",
       "  display: inline-block;\n",
       "}\n",
       "\n",
       ".xr-dim-list {\n",
       "  display: inline-block !important;\n",
       "  list-style: none;\n",
       "  padding: 0 !important;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list li {\n",
       "  display: inline-block;\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list:before {\n",
       "  content: '(';\n",
       "}\n",
       "\n",
       ".xr-dim-list:after {\n",
       "  content: ')';\n",
       "}\n",
       "\n",
       ".xr-dim-list li:not(:last-child):after {\n",
       "  content: ',';\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-has-index {\n",
       "  font-weight: bold;\n",
       "}\n",
       "\n",
       ".xr-var-list,\n",
       ".xr-var-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-var-item > div,\n",
       ".xr-var-item label,\n",
       ".xr-var-item > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-even);\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-var-item > .xr-var-name:hover span {\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-var-list > li:nth-child(odd) > div,\n",
       ".xr-var-list > li:nth-child(odd) > label,\n",
       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-odd);\n",
       "}\n",
       "\n",
       ".xr-var-name {\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-var-dims {\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-var-dtype {\n",
       "  grid-column: 3;\n",
       "  text-align: right;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-preview {\n",
       "  grid-column: 4;\n",
       "}\n",
       "\n",
       ".xr-index-preview {\n",
       "  grid-column: 2 / 5;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-name,\n",
       ".xr-var-dims,\n",
       ".xr-var-dtype,\n",
       ".xr-preview,\n",
       ".xr-attrs dt {\n",
       "  white-space: nowrap;\n",
       "  overflow: hidden;\n",
       "  text-overflow: ellipsis;\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-var-name:hover,\n",
       ".xr-var-dims:hover,\n",
       ".xr-var-dtype:hover,\n",
       ".xr-attrs dt:hover {\n",
       "  overflow: visible;\n",
       "  width: auto;\n",
       "  z-index: 1;\n",
       "}\n",
       "\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-data {\n",
       "  display: none;\n",
       "  background-color: var(--xr-background-color) !important;\n",
       "  padding-bottom: 5px !important;\n",
       "}\n",
       "\n",
       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
       ".xr-var-data-in:checked ~ .xr-var-data,\n",
       ".xr-index-data-in:checked ~ .xr-index-data {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       ".xr-var-data > table {\n",
       "  float: right;\n",
       "}\n",
       "\n",
       ".xr-var-name span,\n",
       ".xr-var-data,\n",
       ".xr-index-name div,\n",
       ".xr-index-data,\n",
       ".xr-attrs {\n",
       "  padding-left: 25px !important;\n",
       "}\n",
       "\n",
       ".xr-attrs,\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-data {\n",
       "  grid-column: 1 / -1;\n",
       "}\n",
       "\n",
       "dl.xr-attrs {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  display: grid;\n",
       "  grid-template-columns: 125px auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt,\n",
       ".xr-attrs dd {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  float: left;\n",
       "  padding-right: 10px;\n",
       "  width: auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt {\n",
       "  font-weight: normal;\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-attrs dt:hover span {\n",
       "  display: inline-block;\n",
       "  background: var(--xr-background-color);\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-attrs dd {\n",
       "  grid-column: 2;\n",
       "  white-space: pre-wrap;\n",
       "  word-break: break-all;\n",
       "}\n",
       "\n",
       ".xr-icon-database,\n",
       ".xr-icon-file-text2,\n",
       ".xr-no-icon {\n",
       "  display: inline-block;\n",
       "  vertical-align: middle;\n",
       "  width: 1em;\n",
       "  height: 1.5em !important;\n",
       "  stroke-width: 0;\n",
       "  stroke: currentColor;\n",
       "  fill: currentColor;\n",
       "}\n",
       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt; Size: 4GB\n",
       "Dimensions:               (age_band_lower_bound: 14, latitude: 720,\n",
       "                           longitude: 1440, year: 71)\n",
       "Coordinates:\n",
       "  * age_band_lower_bound  (age_band_lower_bound) int64 112B 0 5 10 ... 55 60 65\n",
       "  * latitude              (latitude) float64 6kB 90.0 89.75 ... -89.5 -89.75\n",
       "  * longitude             (longitude) float64 12kB 0.0 0.25 0.5 ... 359.5 359.8\n",
       "  * year                  (year) int64 568B 1950 1951 1952 ... 2018 2019 2020\n",
       "Data variables:\n",
       "    demographic_totals    (latitude, longitude, age_band_lower_bound, year) float32 4GB dask.array&lt;chunksize=(64, 64, 14, 71), meta=np.ndarray&gt;</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-fc8ac2de-e1ba-4b27-9f06-5963e1c7ce5b' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-fc8ac2de-e1ba-4b27-9f06-5963e1c7ce5b' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>age_band_lower_bound</span>: 14</li><li><span class='xr-has-index'>latitude</span>: 720</li><li><span class='xr-has-index'>longitude</span>: 1440</li><li><span class='xr-has-index'>year</span>: 71</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-5ab86e69-e537-42cb-b1e2-f75c5b62ea8e' class='xr-section-summary-in' type='checkbox'  checked><label for='section-5ab86e69-e537-42cb-b1e2-f75c5b62ea8e' class='xr-section-summary' >Coordinates: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>age_band_lower_bound</span></div><div class='xr-var-dims'>(age_band_lower_bound)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 5 10 15 20 25 ... 45 50 55 60 65</div><input id='attrs-c31c09cb-b9fd-46a9-85da-c89711712191' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-c31c09cb-b9fd-46a9-85da-c89711712191' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c0c0d3c9-9dd7-41d1-b70b-c2c7c6f55a99' class='xr-var-data-in' type='checkbox'><label for='data-c0c0d3c9-9dd7-41d1-b70b-c2c7c6f55a99' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([ 0,  5, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>latitude</span></div><div class='xr-var-dims'>(latitude)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>90.0 89.75 89.5 ... -89.5 -89.75</div><input id='attrs-fd305328-4617-4bf4-a089-54d4de6be57c' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-fd305328-4617-4bf4-a089-54d4de6be57c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a40b2f6e-a31e-4478-aab7-9dcf591d68c2' class='xr-var-data-in' type='checkbox'><label for='data-a40b2f6e-a31e-4478-aab7-9dcf591d68c2' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([ 90.  ,  89.75,  89.5 , ..., -89.25, -89.5 , -89.75])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>longitude</span></div><div class='xr-var-dims'>(longitude)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0 0.25 0.5 ... 359.2 359.5 359.8</div><input id='attrs-61591e70-0398-4882-9703-88cfb9d55628' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-61591e70-0398-4882-9703-88cfb9d55628' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e96ec165-b901-4f11-a1ae-774064f6891b' class='xr-var-data-in' type='checkbox'><label for='data-e96ec165-b901-4f11-a1ae-774064f6891b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([0.0000e+00, 2.5000e-01, 5.0000e-01, ..., 3.5925e+02, 3.5950e+02,\n",
       "       3.5975e+02])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>year</span></div><div class='xr-var-dims'>(year)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>1950 1951 1952 ... 2018 2019 2020</div><input id='attrs-c3fe8fc0-df9c-4537-bb55-b34b8e3b5b52' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-c3fe8fc0-df9c-4537-bb55-b34b8e3b5b52' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d0a2c55c-c8df-40b2-a2ce-bd2a23cd3197' class='xr-var-data-in' type='checkbox'><label for='data-d0a2c55c-c8df-40b2-a2ce-bd2a23cd3197' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([1950, 1951, 1952, 1953, 1954, 1955, 1956, 1957, 1958, 1959, 1960, 1961,\n",
       "       1962, 1963, 1964, 1965, 1966, 1967, 1968, 1969, 1970, 1971, 1972, 1973,\n",
       "       1974, 1975, 1976, 1977, 1978, 1979, 1980, 1981, 1982, 1983, 1984, 1985,\n",
       "       1986, 1987, 1988, 1989, 1990, 1991, 1992, 1993, 1994, 1995, 1996, 1997,\n",
       "       1998, 1999, 2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009,\n",
       "       2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-4516c94f-1763-4c10-9be5-afd581519e5f' class='xr-section-summary-in' type='checkbox'  checked><label for='section-4516c94f-1763-4c10-9be5-afd581519e5f' class='xr-section-summary' >Data variables: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>demographic_totals</span></div><div class='xr-var-dims'>(latitude, longitude, age_band_lower_bound, year)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(64, 64, 14, 71), meta=np.ndarray&gt;</div><input id='attrs-407454f7-30c8-42d1-a90b-b3860ce7d504' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-407454f7-30c8-42d1-a90b-b3860ce7d504' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2e929a36-2d13-441c-8b9a-00203ac4305e' class='xr-var-data-in' type='checkbox'><label for='data-2e929a36-2d13-441c-8b9a-00203ac4305e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><table>\n",
       "    <tr>\n",
       "        <td>\n",
       "            <table style=\"border-collapse: collapse;\">\n",
       "                <thead>\n",
       "                    <tr>\n",
       "                        <td> </td>\n",
       "                        <th> Array </th>\n",
       "                        <th> Chunk </th>\n",
       "                    </tr>\n",
       "                </thead>\n",
       "                <tbody>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 3.84 GiB </td>\n",
       "                        <td> 15.53 MiB </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (720, 1440, 14, 71) </td>\n",
       "                        <td> (64, 64, 14, 71) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 276 chunks in 2 graph layers </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
       "                    </tr>\n",
       "                </tbody>\n",
       "            </table>\n",
       "        </td>\n",
       "        <td>\n",
       "        <svg width=\"415\" height=\"146\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
       "\n",
       "  <!-- Horizontal lines -->\n",
       "  <line x1=\"0\" y1=\"0\" x2=\"60\" y2=\"0\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"0\" y1=\"25\" x2=\"60\" y2=\"25\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Vertical lines -->\n",
       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"5\" y1=\"0\" x2=\"5\" y2=\"25\" />\n",
       "  <line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"25\" />\n",
       "  <line x1=\"16\" y1=\"0\" x2=\"16\" y2=\"25\" />\n",
       "  <line x1=\"21\" y1=\"0\" x2=\"21\" y2=\"25\" />\n",
       "  <line x1=\"26\" y1=\"0\" x2=\"26\" y2=\"25\" />\n",
       "  <line x1=\"32\" y1=\"0\" x2=\"32\" y2=\"25\" />\n",
       "  <line x1=\"37\" y1=\"0\" x2=\"37\" y2=\"25\" />\n",
       "  <line x1=\"42\" y1=\"0\" x2=\"42\" y2=\"25\" />\n",
       "  <line x1=\"48\" y1=\"0\" x2=\"48\" y2=\"25\" />\n",
       "  <line x1=\"53\" y1=\"0\" x2=\"53\" y2=\"25\" />\n",
       "  <line x1=\"58\" y1=\"0\" x2=\"58\" y2=\"25\" />\n",
       "  <line x1=\"60\" y1=\"0\" x2=\"60\" y2=\"25\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Colored Rectangle -->\n",
       "  <polygon points=\"0.0,0.0 60.0,0.0 60.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
       "\n",
       "  <!-- Text -->\n",
       "  <text x=\"30.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >720</text>\n",
       "  <text x=\"80.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,80.000000,12.706308)\">1</text>\n",
       "\n",
       "\n",
       "  <!-- Horizontal lines -->\n",
       "  <line x1=\"130\" y1=\"0\" x2=\"200\" y2=\"70\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"130\" y1=\"25\" x2=\"200\" y2=\"96\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Vertical lines -->\n",
       "  <line x1=\"130\" y1=\"0\" x2=\"130\" y2=\"25\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"133\" y1=\"3\" x2=\"133\" y2=\"28\" />\n",
       "  <line x1=\"136\" y1=\"6\" x2=\"136\" y2=\"31\" />\n",
       "  <line x1=\"139\" y1=\"9\" x2=\"139\" y2=\"34\" />\n",
       "  <line x1=\"142\" y1=\"12\" x2=\"142\" y2=\"37\" />\n",
       "  <line x1=\"145\" y1=\"15\" x2=\"145\" y2=\"41\" />\n",
       "  <line x1=\"148\" y1=\"18\" x2=\"148\" y2=\"44\" />\n",
       "  <line x1=\"151\" y1=\"21\" x2=\"151\" y2=\"47\" />\n",
       "  <line x1=\"155\" y1=\"25\" x2=\"155\" y2=\"50\" />\n",
       "  <line x1=\"158\" y1=\"28\" x2=\"158\" y2=\"53\" />\n",
       "  <line x1=\"161\" y1=\"31\" x2=\"161\" y2=\"56\" />\n",
       "  <line x1=\"164\" y1=\"34\" x2=\"164\" y2=\"59\" />\n",
       "  <line x1=\"167\" y1=\"37\" x2=\"167\" y2=\"63\" />\n",
       "  <line x1=\"170\" y1=\"40\" x2=\"170\" y2=\"66\" />\n",
       "  <line x1=\"173\" y1=\"43\" x2=\"173\" y2=\"69\" />\n",
       "  <line x1=\"177\" y1=\"47\" x2=\"177\" y2=\"72\" />\n",
       "  <line x1=\"180\" y1=\"50\" x2=\"180\" y2=\"75\" />\n",
       "  <line x1=\"183\" y1=\"53\" x2=\"183\" y2=\"78\" />\n",
       "  <line x1=\"186\" y1=\"56\" x2=\"186\" y2=\"81\" />\n",
       "  <line x1=\"189\" y1=\"59\" x2=\"189\" y2=\"85\" />\n",
       "  <line x1=\"192\" y1=\"62\" x2=\"192\" y2=\"88\" />\n",
       "  <line x1=\"195\" y1=\"65\" x2=\"195\" y2=\"91\" />\n",
       "  <line x1=\"199\" y1=\"69\" x2=\"199\" y2=\"94\" />\n",
       "  <line x1=\"200\" y1=\"70\" x2=\"200\" y2=\"96\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Colored Rectangle -->\n",
       "  <polygon points=\"130.0,0.0 200.58823529411765,70.58823529411765 200.58823529411765,96.00085180870013 130.0,25.412616514582485\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
       "\n",
       "  <!-- Horizontal lines -->\n",
       "  <line x1=\"130\" y1=\"0\" x2=\"164\" y2=\"0\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"133\" y1=\"3\" x2=\"167\" y2=\"3\" />\n",
       "  <line x1=\"136\" y1=\"6\" x2=\"170\" y2=\"6\" />\n",
       "  <line x1=\"139\" y1=\"9\" x2=\"173\" y2=\"9\" />\n",
       "  <line x1=\"142\" y1=\"12\" x2=\"176\" y2=\"12\" />\n",
       "  <line x1=\"145\" y1=\"15\" x2=\"180\" y2=\"15\" />\n",
       "  <line x1=\"148\" y1=\"18\" x2=\"183\" y2=\"18\" />\n",
       "  <line x1=\"151\" y1=\"21\" x2=\"186\" y2=\"21\" />\n",
       "  <line x1=\"155\" y1=\"25\" x2=\"189\" y2=\"25\" />\n",
       "  <line x1=\"158\" y1=\"28\" x2=\"192\" y2=\"28\" />\n",
       "  <line x1=\"161\" y1=\"31\" x2=\"195\" y2=\"31\" />\n",
       "  <line x1=\"164\" y1=\"34\" x2=\"198\" y2=\"34\" />\n",
       "  <line x1=\"167\" y1=\"37\" x2=\"202\" y2=\"37\" />\n",
       "  <line x1=\"170\" y1=\"40\" x2=\"205\" y2=\"40\" />\n",
       "  <line x1=\"173\" y1=\"43\" x2=\"208\" y2=\"43\" />\n",
       "  <line x1=\"177\" y1=\"47\" x2=\"211\" y2=\"47\" />\n",
       "  <line x1=\"180\" y1=\"50\" x2=\"214\" y2=\"50\" />\n",
       "  <line x1=\"183\" y1=\"53\" x2=\"217\" y2=\"53\" />\n",
       "  <line x1=\"186\" y1=\"56\" x2=\"220\" y2=\"56\" />\n",
       "  <line x1=\"189\" y1=\"59\" x2=\"224\" y2=\"59\" />\n",
       "  <line x1=\"192\" y1=\"62\" x2=\"227\" y2=\"62\" />\n",
       "  <line x1=\"195\" y1=\"65\" x2=\"230\" y2=\"65\" />\n",
       "  <line x1=\"199\" y1=\"69\" x2=\"233\" y2=\"69\" />\n",
       "  <line x1=\"200\" y1=\"70\" x2=\"235\" y2=\"70\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Vertical lines -->\n",
       "  <line x1=\"130\" y1=\"0\" x2=\"200\" y2=\"70\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"164\" y1=\"0\" x2=\"235\" y2=\"70\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Colored Rectangle -->\n",
       "  <polygon points=\"130.0,0.0 164.41536200310776,0.0 235.0035972972254,70.58823529411765 200.58823529411765,70.58823529411765\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
       "\n",
       "  <!-- Horizontal lines -->\n",
       "  <line x1=\"200\" y1=\"70\" x2=\"235\" y2=\"70\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"200\" y1=\"96\" x2=\"235\" y2=\"96\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Vertical lines -->\n",
       "  <line x1=\"200\" y1=\"70\" x2=\"200\" y2=\"96\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"235\" y1=\"70\" x2=\"235\" y2=\"96\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Colored Rectangle -->\n",
       "  <polygon points=\"200.58823529411765,70.58823529411765 235.0035972972254,70.58823529411765 235.0035972972254,96.00085180870013 200.58823529411765,96.00085180870013\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
       "\n",
       "  <!-- Text -->\n",
       "  <text x=\"217.795916\" y=\"116.000852\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >71</text>\n",
       "  <text x=\"255.003597\" y=\"83.294544\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,255.003597,83.294544)\">14</text>\n",
       "  <text x=\"155.294118\" y=\"80.706734\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,155.294118,80.706734)\">1440</text>\n",
       "</svg>\n",
       "        </td>\n",
       "    </tr>\n",
       "</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-cc264494-2e84-4f91-a4ac-63f407ab1119' class='xr-section-summary-in' type='checkbox'  ><label for='section-cc264494-2e84-4f91-a4ac-63f407ab1119' class='xr-section-summary' >Indexes: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>age_band_lower_bound</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-5a0e4e2a-3384-4ca0-b811-cd54428843b9' class='xr-index-data-in' type='checkbox'/><label for='index-5a0e4e2a-3384-4ca0-b811-cd54428843b9' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([0, 5, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65], dtype=&#x27;int64&#x27;, name=&#x27;age_band_lower_bound&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>latitude</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-3ce8439b-06cf-4ef1-8608-82b3143717cf' class='xr-index-data-in' type='checkbox'/><label for='index-3ce8439b-06cf-4ef1-8608-82b3143717cf' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([  90.0,  89.75,   89.5,  89.25,   89.0,  88.75,   88.5,  88.25,   88.0,\n",
       "        87.75,\n",
       "       ...\n",
       "        -87.5, -87.75,  -88.0, -88.25,  -88.5, -88.75,  -89.0, -89.25,  -89.5,\n",
       "       -89.75],\n",
       "      dtype=&#x27;float64&#x27;, name=&#x27;latitude&#x27;, length=720))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>longitude</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-c959a2c1-0b29-453f-a730-a825ffc0f131' class='xr-index-data-in' type='checkbox'/><label for='index-c959a2c1-0b29-453f-a730-a825ffc0f131' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([   0.0,   0.25,    0.5,   0.75,    1.0,   1.25,    1.5,   1.75,    2.0,\n",
       "         2.25,\n",
       "       ...\n",
       "        357.5, 357.75,  358.0, 358.25,  358.5, 358.75,  359.0, 359.25,  359.5,\n",
       "       359.75],\n",
       "      dtype=&#x27;float64&#x27;, name=&#x27;longitude&#x27;, length=1440))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>year</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-72757fdb-681a-4bb6-a4d7-85328545ad78' class='xr-index-data-in' type='checkbox'/><label for='index-72757fdb-681a-4bb6-a4d7-85328545ad78' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([1950, 1951, 1952, 1953, 1954, 1955, 1956, 1957, 1958, 1959, 1960, 1961,\n",
       "       1962, 1963, 1964, 1965, 1966, 1967, 1968, 1969, 1970, 1971, 1972, 1973,\n",
       "       1974, 1975, 1976, 1977, 1978, 1979, 1980, 1981, 1982, 1983, 1984, 1985,\n",
       "       1986, 1987, 1988, 1989, 1990, 1991, 1992, 1993, 1994, 1995, 1996, 1997,\n",
       "       1998, 1999, 2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009,\n",
       "       2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020],\n",
       "      dtype=&#x27;int64&#x27;, name=&#x27;year&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-c6e2289a-afc8-4dff-8cb7-92ff05f2fe27' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-c6e2289a-afc8-4dff-8cb7-92ff05f2fe27' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset> Size: 4GB\n",
       "Dimensions:               (age_band_lower_bound: 14, latitude: 720,\n",
       "                           longitude: 1440, year: 71)\n",
       "Coordinates:\n",
       "  * age_band_lower_bound  (age_band_lower_bound) int64 112B 0 5 10 ... 55 60 65\n",
       "  * latitude              (latitude) float64 6kB 90.0 89.75 ... -89.5 -89.75\n",
       "  * longitude             (longitude) float64 12kB 0.0 0.25 0.5 ... 359.5 359.8\n",
       "  * year                  (year) int64 568B 1950 1951 1952 ... 2018 2019 2020\n",
       "Data variables:\n",
       "    demographic_totals    (latitude, longitude, age_band_lower_bound, year) float32 4GB dask.array<chunksize=(64, 64, 14, 71), meta=np.ndarray>"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import xarray as xr\n",
    "\n",
    "ds = xr.open_dataset(\n",
    "    f\"https://edh:{PAT}@data.earthdatahub.destine.eu/derived-GPWv4-Histsoc/demographics-hybrid-1950-2020-15-min-v0\",\n",
    "    storage_options={\"client_kwargs\":{\"trust_env\":True}},\n",
    "    chunks={},\n",
    "    engine=\"zarr\",\n",
    ")\n",
    "ds"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c3998588-da22-4c82-9904-5bf9870f30cf",
   "metadata": {},
   "source": [
    "The longitude of the dataset is set from 0 to 360 degrees, which makes it uncomfortable to deal with European data when using xarray:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "1c4d96df-3c76-423b-b247-787eac261d98",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-10-23T14:58:48.150700Z",
     "iopub.status.busy": "2024-10-23T14:58:48.149698Z",
     "iopub.status.idle": "2024-10-23T14:58:56.837365Z",
     "shell.execute_reply": "2024-10-23T14:58:56.836230Z",
     "shell.execute_reply.started": "2024-10-23T14:58:48.150657Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.QuadMesh at 0x7f745ec685d0>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ds.demographic_totals.sel(age_band_lower_bound=0, year=2020).plot(vmax=50_000, cmap=\"YlGnBu_r\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "67d94b80-e13b-4bb5-a5da-42e5ee071d0b",
   "metadata": {},
   "source": [
    "We can roll the longitude to a -180 to 180 extent:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "5ced786b-00cd-4dd6-b632-e36f8546c44b",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-10-23T14:58:56.839118Z",
     "iopub.status.busy": "2024-10-23T14:58:56.838015Z",
     "iopub.status.idle": "2024-10-23T14:58:56.871510Z",
     "shell.execute_reply": "2024-10-23T14:58:56.870111Z",
     "shell.execute_reply.started": "2024-10-23T14:58:56.839094Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div><svg style=\"position: absolute; width: 0; height: 0; overflow: hidden\">\n",
       "<defs>\n",
       "<symbol id=\"icon-database\" viewBox=\"0 0 32 32\">\n",
       "<path d=\"M16 0c-8.837 0-16 2.239-16 5v4c0 2.761 7.163 5 16 5s16-2.239 16-5v-4c0-2.761-7.163-5-16-5z\"></path>\n",
       "<path d=\"M16 17c-8.837 0-16-2.239-16-5v6c0 2.761 7.163 5 16 5s16-2.239 16-5v-6c0 2.761-7.163 5-16 5z\"></path>\n",
       "<path d=\"M16 26c-8.837 0-16-2.239-16-5v6c0 2.761 7.163 5 16 5s16-2.239 16-5v-6c0 2.761-7.163 5-16 5z\"></path>\n",
       "</symbol>\n",
       "<symbol id=\"icon-file-text2\" viewBox=\"0 0 32 32\">\n",
       "<path d=\"M28.681 7.159c-0.694-0.947-1.662-2.053-2.724-3.116s-2.169-2.030-3.116-2.724c-1.612-1.182-2.393-1.319-2.841-1.319h-15.5c-1.378 0-2.5 1.121-2.5 2.5v27c0 1.378 1.122 2.5 2.5 2.5h23c1.378 0 2.5-1.122 2.5-2.5v-19.5c0-0.448-0.137-1.23-1.319-2.841zM24.543 5.457c0.959 0.959 1.712 1.825 2.268 2.543h-4.811v-4.811c0.718 0.556 1.584 1.309 2.543 2.268zM28 29.5c0 0.271-0.229 0.5-0.5 0.5h-23c-0.271 0-0.5-0.229-0.5-0.5v-27c0-0.271 0.229-0.5 0.5-0.5 0 0 15.499-0 15.5 0v7c0 0.552 0.448 1 1 1h7v19.5z\"></path>\n",
       "<path d=\"M23 26h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
       "<path d=\"M23 22h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
       "<path d=\"M23 18h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
       "</symbol>\n",
       "</defs>\n",
       "</svg>\n",
       "<style>/* CSS stylesheet for displaying xarray objects in jupyterlab.\n",
       " *\n",
       " */\n",
       "\n",
       ":root {\n",
       "  --xr-font-color0: var(--jp-content-font-color0, rgba(0, 0, 0, 1));\n",
       "  --xr-font-color2: var(--jp-content-font-color2, rgba(0, 0, 0, 0.54));\n",
       "  --xr-font-color3: var(--jp-content-font-color3, rgba(0, 0, 0, 0.38));\n",
       "  --xr-border-color: var(--jp-border-color2, #e0e0e0);\n",
       "  --xr-disabled-color: var(--jp-layout-color3, #bdbdbd);\n",
       "  --xr-background-color: var(--jp-layout-color0, white);\n",
       "  --xr-background-color-row-even: var(--jp-layout-color1, white);\n",
       "  --xr-background-color-row-odd: var(--jp-layout-color2, #eeeeee);\n",
       "}\n",
       "\n",
       "html[theme=dark],\n",
       "html[data-theme=dark],\n",
       "body[data-theme=dark],\n",
       "body.vscode-dark {\n",
       "  --xr-font-color0: rgba(255, 255, 255, 1);\n",
       "  --xr-font-color2: rgba(255, 255, 255, 0.54);\n",
       "  --xr-font-color3: rgba(255, 255, 255, 0.38);\n",
       "  --xr-border-color: #1F1F1F;\n",
       "  --xr-disabled-color: #515151;\n",
       "  --xr-background-color: #111111;\n",
       "  --xr-background-color-row-even: #111111;\n",
       "  --xr-background-color-row-odd: #313131;\n",
       "}\n",
       "\n",
       ".xr-wrap {\n",
       "  display: block !important;\n",
       "  min-width: 300px;\n",
       "  max-width: 700px;\n",
       "}\n",
       "\n",
       ".xr-text-repr-fallback {\n",
       "  /* fallback to plain text repr when CSS is not injected (untrusted notebook) */\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-header {\n",
       "  padding-top: 6px;\n",
       "  padding-bottom: 6px;\n",
       "  margin-bottom: 4px;\n",
       "  border-bottom: solid 1px var(--xr-border-color);\n",
       "}\n",
       "\n",
       ".xr-header > div,\n",
       ".xr-header > ul {\n",
       "  display: inline;\n",
       "  margin-top: 0;\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-obj-type,\n",
       ".xr-array-name {\n",
       "  margin-left: 2px;\n",
       "  margin-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-obj-type {\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-sections {\n",
       "  padding-left: 0 !important;\n",
       "  display: grid;\n",
       "  grid-template-columns: 150px auto auto 1fr 0 20px 0 20px;\n",
       "}\n",
       "\n",
       ".xr-section-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-section-item input {\n",
       "  display: inline-block;\n",
       "  opacity: 0;\n",
       "}\n",
       "\n",
       ".xr-section-item input + label {\n",
       "  color: var(--xr-disabled-color);\n",
       "}\n",
       "\n",
       ".xr-section-item input:enabled + label {\n",
       "  cursor: pointer;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-section-item input:focus + label {\n",
       "  border: 2px solid var(--xr-font-color0);\n",
       "}\n",
       "\n",
       ".xr-section-item input:enabled + label:hover {\n",
       "  color: var(--xr-font-color0);\n",
       "}\n",
       "\n",
       ".xr-section-summary {\n",
       "  grid-column: 1;\n",
       "  color: var(--xr-font-color2);\n",
       "  font-weight: 500;\n",
       "}\n",
       "\n",
       ".xr-section-summary > span {\n",
       "  display: inline-block;\n",
       "  padding-left: 0.5em;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label {\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in + label:before {\n",
       "  display: inline-block;\n",
       "  content: '►';\n",
       "  font-size: 11px;\n",
       "  width: 15px;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label:before {\n",
       "  color: var(--xr-disabled-color);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label:before {\n",
       "  content: '▼';\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label > span {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-section-summary,\n",
       ".xr-section-inline-details {\n",
       "  padding-top: 4px;\n",
       "  padding-bottom: 4px;\n",
       "}\n",
       "\n",
       ".xr-section-inline-details {\n",
       "  grid-column: 2 / -1;\n",
       "}\n",
       "\n",
       ".xr-section-details {\n",
       "  display: none;\n",
       "  grid-column: 1 / -1;\n",
       "  margin-bottom: 5px;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-array-wrap {\n",
       "  grid-column: 1 / -1;\n",
       "  display: grid;\n",
       "  grid-template-columns: 20px auto;\n",
       "}\n",
       "\n",
       ".xr-array-wrap > label {\n",
       "  grid-column: 1;\n",
       "  vertical-align: top;\n",
       "}\n",
       "\n",
       ".xr-preview {\n",
       "  color: var(--xr-font-color3);\n",
       "}\n",
       "\n",
       ".xr-array-preview,\n",
       ".xr-array-data {\n",
       "  padding: 0 5px !important;\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-array-data,\n",
       ".xr-array-in:checked ~ .xr-array-preview {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-array-in:checked ~ .xr-array-data,\n",
       ".xr-array-preview {\n",
       "  display: inline-block;\n",
       "}\n",
       "\n",
       ".xr-dim-list {\n",
       "  display: inline-block !important;\n",
       "  list-style: none;\n",
       "  padding: 0 !important;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list li {\n",
       "  display: inline-block;\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list:before {\n",
       "  content: '(';\n",
       "}\n",
       "\n",
       ".xr-dim-list:after {\n",
       "  content: ')';\n",
       "}\n",
       "\n",
       ".xr-dim-list li:not(:last-child):after {\n",
       "  content: ',';\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-has-index {\n",
       "  font-weight: bold;\n",
       "}\n",
       "\n",
       ".xr-var-list,\n",
       ".xr-var-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-var-item > div,\n",
       ".xr-var-item label,\n",
       ".xr-var-item > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-even);\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-var-item > .xr-var-name:hover span {\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-var-list > li:nth-child(odd) > div,\n",
       ".xr-var-list > li:nth-child(odd) > label,\n",
       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-odd);\n",
       "}\n",
       "\n",
       ".xr-var-name {\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-var-dims {\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-var-dtype {\n",
       "  grid-column: 3;\n",
       "  text-align: right;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-preview {\n",
       "  grid-column: 4;\n",
       "}\n",
       "\n",
       ".xr-index-preview {\n",
       "  grid-column: 2 / 5;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-name,\n",
       ".xr-var-dims,\n",
       ".xr-var-dtype,\n",
       ".xr-preview,\n",
       ".xr-attrs dt {\n",
       "  white-space: nowrap;\n",
       "  overflow: hidden;\n",
       "  text-overflow: ellipsis;\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-var-name:hover,\n",
       ".xr-var-dims:hover,\n",
       ".xr-var-dtype:hover,\n",
       ".xr-attrs dt:hover {\n",
       "  overflow: visible;\n",
       "  width: auto;\n",
       "  z-index: 1;\n",
       "}\n",
       "\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-data {\n",
       "  display: none;\n",
       "  background-color: var(--xr-background-color) !important;\n",
       "  padding-bottom: 5px !important;\n",
       "}\n",
       "\n",
       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
       ".xr-var-data-in:checked ~ .xr-var-data,\n",
       ".xr-index-data-in:checked ~ .xr-index-data {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       ".xr-var-data > table {\n",
       "  float: right;\n",
       "}\n",
       "\n",
       ".xr-var-name span,\n",
       ".xr-var-data,\n",
       ".xr-index-name div,\n",
       ".xr-index-data,\n",
       ".xr-attrs {\n",
       "  padding-left: 25px !important;\n",
       "}\n",
       "\n",
       ".xr-attrs,\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-data {\n",
       "  grid-column: 1 / -1;\n",
       "}\n",
       "\n",
       "dl.xr-attrs {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  display: grid;\n",
       "  grid-template-columns: 125px auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt,\n",
       ".xr-attrs dd {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  float: left;\n",
       "  padding-right: 10px;\n",
       "  width: auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt {\n",
       "  font-weight: normal;\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-attrs dt:hover span {\n",
       "  display: inline-block;\n",
       "  background: var(--xr-background-color);\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-attrs dd {\n",
       "  grid-column: 2;\n",
       "  white-space: pre-wrap;\n",
       "  word-break: break-all;\n",
       "}\n",
       "\n",
       ".xr-icon-database,\n",
       ".xr-icon-file-text2,\n",
       ".xr-no-icon {\n",
       "  display: inline-block;\n",
       "  vertical-align: middle;\n",
       "  width: 1em;\n",
       "  height: 1.5em !important;\n",
       "  stroke-width: 0;\n",
       "  stroke: currentColor;\n",
       "  fill: currentColor;\n",
       "}\n",
       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt; Size: 4GB\n",
       "Dimensions:               (age_band_lower_bound: 14, latitude: 720,\n",
       "                           longitude: 1440, year: 71)\n",
       "Coordinates:\n",
       "  * age_band_lower_bound  (age_band_lower_bound) int64 112B 0 5 10 ... 55 60 65\n",
       "  * latitude              (latitude) float64 6kB 90.0 89.75 ... -89.5 -89.75\n",
       "  * year                  (year) int64 568B 1950 1951 1952 ... 2018 2019 2020\n",
       "  * longitude             (longitude) float64 12kB -180.0 -179.8 ... 179.5 179.8\n",
       "Data variables:\n",
       "    demographic_totals    (latitude, longitude, age_band_lower_bound, year) float32 4GB dask.array&lt;chunksize=(64, 64, 14, 71), meta=np.ndarray&gt;</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-df696a8f-2693-4890-a857-2fff527c55a3' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-df696a8f-2693-4890-a857-2fff527c55a3' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>age_band_lower_bound</span>: 14</li><li><span class='xr-has-index'>latitude</span>: 720</li><li><span class='xr-has-index'>longitude</span>: 1440</li><li><span class='xr-has-index'>year</span>: 71</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-6dc87e85-ad56-41b6-ac8a-d42d907a1edd' class='xr-section-summary-in' type='checkbox'  checked><label for='section-6dc87e85-ad56-41b6-ac8a-d42d907a1edd' class='xr-section-summary' >Coordinates: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>age_band_lower_bound</span></div><div class='xr-var-dims'>(age_band_lower_bound)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 5 10 15 20 25 ... 45 50 55 60 65</div><input id='attrs-8d102693-672f-4090-8226-cca7cdb3f6e5' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-8d102693-672f-4090-8226-cca7cdb3f6e5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c82a8935-3a4b-4c50-8dae-9801c1395e03' class='xr-var-data-in' type='checkbox'><label for='data-c82a8935-3a4b-4c50-8dae-9801c1395e03' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([ 0,  5, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>latitude</span></div><div class='xr-var-dims'>(latitude)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>90.0 89.75 89.5 ... -89.5 -89.75</div><input id='attrs-e68590a1-e034-4396-be4c-a4d69bc620b0' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-e68590a1-e034-4396-be4c-a4d69bc620b0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5d2f3638-d6eb-42af-b138-275a42cae54c' class='xr-var-data-in' type='checkbox'><label for='data-5d2f3638-d6eb-42af-b138-275a42cae54c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([ 90.  ,  89.75,  89.5 , ..., -89.25, -89.5 , -89.75])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>year</span></div><div class='xr-var-dims'>(year)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>1950 1951 1952 ... 2018 2019 2020</div><input id='attrs-fca37e7f-a80f-4d20-876b-3cd05bcf9f75' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-fca37e7f-a80f-4d20-876b-3cd05bcf9f75' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e1e96a93-b21e-4ec7-b30d-38573ecccaa2' class='xr-var-data-in' type='checkbox'><label for='data-e1e96a93-b21e-4ec7-b30d-38573ecccaa2' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([1950, 1951, 1952, 1953, 1954, 1955, 1956, 1957, 1958, 1959, 1960, 1961,\n",
       "       1962, 1963, 1964, 1965, 1966, 1967, 1968, 1969, 1970, 1971, 1972, 1973,\n",
       "       1974, 1975, 1976, 1977, 1978, 1979, 1980, 1981, 1982, 1983, 1984, 1985,\n",
       "       1986, 1987, 1988, 1989, 1990, 1991, 1992, 1993, 1994, 1995, 1996, 1997,\n",
       "       1998, 1999, 2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009,\n",
       "       2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>longitude</span></div><div class='xr-var-dims'>(longitude)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-180.0 -179.8 ... 179.5 179.8</div><input id='attrs-2992b302-0554-4e2a-8615-0c5d5153e13c' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-2992b302-0554-4e2a-8615-0c5d5153e13c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d213e1be-2387-49b4-aad5-dfd4f3830531' class='xr-var-data-in' type='checkbox'><label for='data-d213e1be-2387-49b4-aad5-dfd4f3830531' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([-180.  , -179.75, -179.5 , ...,  179.25,  179.5 ,  179.75])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-7c0d4cbc-454e-4182-a6c7-84deea9cbf0f' class='xr-section-summary-in' type='checkbox'  checked><label for='section-7c0d4cbc-454e-4182-a6c7-84deea9cbf0f' class='xr-section-summary' >Data variables: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>demographic_totals</span></div><div class='xr-var-dims'>(latitude, longitude, age_band_lower_bound, year)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(64, 64, 14, 71), meta=np.ndarray&gt;</div><input id='attrs-5ab4cf9f-f5a7-43d4-bfba-f295f5e80d81' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-5ab4cf9f-f5a7-43d4-bfba-f295f5e80d81' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-489c814e-1e5d-4d7b-a7da-0907552ff013' class='xr-var-data-in' type='checkbox'><label for='data-489c814e-1e5d-4d7b-a7da-0907552ff013' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><table>\n",
       "    <tr>\n",
       "        <td>\n",
       "            <table style=\"border-collapse: collapse;\">\n",
       "                <thead>\n",
       "                    <tr>\n",
       "                        <td> </td>\n",
       "                        <th> Array </th>\n",
       "                        <th> Chunk </th>\n",
       "                    </tr>\n",
       "                </thead>\n",
       "                <tbody>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 3.84 GiB </td>\n",
       "                        <td> 15.53 MiB </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (720, 1440, 14, 71) </td>\n",
       "                        <td> (64, 64, 14, 71) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 276 chunks in 6 graph layers </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
       "                    </tr>\n",
       "                </tbody>\n",
       "            </table>\n",
       "        </td>\n",
       "        <td>\n",
       "        <svg width=\"415\" height=\"146\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
       "\n",
       "  <!-- Horizontal lines -->\n",
       "  <line x1=\"0\" y1=\"0\" x2=\"60\" y2=\"0\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"0\" y1=\"25\" x2=\"60\" y2=\"25\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Vertical lines -->\n",
       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"5\" y1=\"0\" x2=\"5\" y2=\"25\" />\n",
       "  <line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"25\" />\n",
       "  <line x1=\"16\" y1=\"0\" x2=\"16\" y2=\"25\" />\n",
       "  <line x1=\"21\" y1=\"0\" x2=\"21\" y2=\"25\" />\n",
       "  <line x1=\"26\" y1=\"0\" x2=\"26\" y2=\"25\" />\n",
       "  <line x1=\"32\" y1=\"0\" x2=\"32\" y2=\"25\" />\n",
       "  <line x1=\"37\" y1=\"0\" x2=\"37\" y2=\"25\" />\n",
       "  <line x1=\"42\" y1=\"0\" x2=\"42\" y2=\"25\" />\n",
       "  <line x1=\"48\" y1=\"0\" x2=\"48\" y2=\"25\" />\n",
       "  <line x1=\"53\" y1=\"0\" x2=\"53\" y2=\"25\" />\n",
       "  <line x1=\"58\" y1=\"0\" x2=\"58\" y2=\"25\" />\n",
       "  <line x1=\"60\" y1=\"0\" x2=\"60\" y2=\"25\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Colored Rectangle -->\n",
       "  <polygon points=\"0.0,0.0 60.0,0.0 60.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
       "\n",
       "  <!-- Text -->\n",
       "  <text x=\"30.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >720</text>\n",
       "  <text x=\"80.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,80.000000,12.706308)\">1</text>\n",
       "\n",
       "\n",
       "  <!-- Horizontal lines -->\n",
       "  <line x1=\"130\" y1=\"0\" x2=\"200\" y2=\"70\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"130\" y1=\"25\" x2=\"200\" y2=\"96\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Vertical lines -->\n",
       "  <line x1=\"130\" y1=\"0\" x2=\"130\" y2=\"25\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"133\" y1=\"3\" x2=\"133\" y2=\"28\" />\n",
       "  <line x1=\"136\" y1=\"6\" x2=\"136\" y2=\"31\" />\n",
       "  <line x1=\"139\" y1=\"9\" x2=\"139\" y2=\"34\" />\n",
       "  <line x1=\"142\" y1=\"12\" x2=\"142\" y2=\"37\" />\n",
       "  <line x1=\"145\" y1=\"15\" x2=\"145\" y2=\"41\" />\n",
       "  <line x1=\"148\" y1=\"18\" x2=\"148\" y2=\"44\" />\n",
       "  <line x1=\"151\" y1=\"21\" x2=\"151\" y2=\"47\" />\n",
       "  <line x1=\"155\" y1=\"25\" x2=\"155\" y2=\"50\" />\n",
       "  <line x1=\"158\" y1=\"28\" x2=\"158\" y2=\"53\" />\n",
       "  <line x1=\"161\" y1=\"31\" x2=\"161\" y2=\"56\" />\n",
       "  <line x1=\"164\" y1=\"34\" x2=\"164\" y2=\"59\" />\n",
       "  <line x1=\"167\" y1=\"37\" x2=\"167\" y2=\"63\" />\n",
       "  <line x1=\"170\" y1=\"40\" x2=\"170\" y2=\"66\" />\n",
       "  <line x1=\"173\" y1=\"43\" x2=\"173\" y2=\"69\" />\n",
       "  <line x1=\"177\" y1=\"47\" x2=\"177\" y2=\"72\" />\n",
       "  <line x1=\"180\" y1=\"50\" x2=\"180\" y2=\"75\" />\n",
       "  <line x1=\"183\" y1=\"53\" x2=\"183\" y2=\"78\" />\n",
       "  <line x1=\"186\" y1=\"56\" x2=\"186\" y2=\"81\" />\n",
       "  <line x1=\"189\" y1=\"59\" x2=\"189\" y2=\"85\" />\n",
       "  <line x1=\"192\" y1=\"62\" x2=\"192\" y2=\"88\" />\n",
       "  <line x1=\"195\" y1=\"65\" x2=\"195\" y2=\"91\" />\n",
       "  <line x1=\"199\" y1=\"69\" x2=\"199\" y2=\"94\" />\n",
       "  <line x1=\"200\" y1=\"70\" x2=\"200\" y2=\"96\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Colored Rectangle -->\n",
       "  <polygon points=\"130.0,0.0 200.58823529411765,70.58823529411765 200.58823529411765,96.00085180870013 130.0,25.412616514582485\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
       "\n",
       "  <!-- Horizontal lines -->\n",
       "  <line x1=\"130\" y1=\"0\" x2=\"164\" y2=\"0\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"133\" y1=\"3\" x2=\"167\" y2=\"3\" />\n",
       "  <line x1=\"136\" y1=\"6\" x2=\"170\" y2=\"6\" />\n",
       "  <line x1=\"139\" y1=\"9\" x2=\"173\" y2=\"9\" />\n",
       "  <line x1=\"142\" y1=\"12\" x2=\"176\" y2=\"12\" />\n",
       "  <line x1=\"145\" y1=\"15\" x2=\"180\" y2=\"15\" />\n",
       "  <line x1=\"148\" y1=\"18\" x2=\"183\" y2=\"18\" />\n",
       "  <line x1=\"151\" y1=\"21\" x2=\"186\" y2=\"21\" />\n",
       "  <line x1=\"155\" y1=\"25\" x2=\"189\" y2=\"25\" />\n",
       "  <line x1=\"158\" y1=\"28\" x2=\"192\" y2=\"28\" />\n",
       "  <line x1=\"161\" y1=\"31\" x2=\"195\" y2=\"31\" />\n",
       "  <line x1=\"164\" y1=\"34\" x2=\"198\" y2=\"34\" />\n",
       "  <line x1=\"167\" y1=\"37\" x2=\"202\" y2=\"37\" />\n",
       "  <line x1=\"170\" y1=\"40\" x2=\"205\" y2=\"40\" />\n",
       "  <line x1=\"173\" y1=\"43\" x2=\"208\" y2=\"43\" />\n",
       "  <line x1=\"177\" y1=\"47\" x2=\"211\" y2=\"47\" />\n",
       "  <line x1=\"180\" y1=\"50\" x2=\"214\" y2=\"50\" />\n",
       "  <line x1=\"183\" y1=\"53\" x2=\"217\" y2=\"53\" />\n",
       "  <line x1=\"186\" y1=\"56\" x2=\"220\" y2=\"56\" />\n",
       "  <line x1=\"189\" y1=\"59\" x2=\"224\" y2=\"59\" />\n",
       "  <line x1=\"192\" y1=\"62\" x2=\"227\" y2=\"62\" />\n",
       "  <line x1=\"195\" y1=\"65\" x2=\"230\" y2=\"65\" />\n",
       "  <line x1=\"199\" y1=\"69\" x2=\"233\" y2=\"69\" />\n",
       "  <line x1=\"200\" y1=\"70\" x2=\"235\" y2=\"70\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Vertical lines -->\n",
       "  <line x1=\"130\" y1=\"0\" x2=\"200\" y2=\"70\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"164\" y1=\"0\" x2=\"235\" y2=\"70\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Colored Rectangle -->\n",
       "  <polygon points=\"130.0,0.0 164.41536200310776,0.0 235.0035972972254,70.58823529411765 200.58823529411765,70.58823529411765\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
       "\n",
       "  <!-- Horizontal lines -->\n",
       "  <line x1=\"200\" y1=\"70\" x2=\"235\" y2=\"70\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"200\" y1=\"96\" x2=\"235\" y2=\"96\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Vertical lines -->\n",
       "  <line x1=\"200\" y1=\"70\" x2=\"200\" y2=\"96\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"235\" y1=\"70\" x2=\"235\" y2=\"96\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Colored Rectangle -->\n",
       "  <polygon points=\"200.58823529411765,70.58823529411765 235.0035972972254,70.58823529411765 235.0035972972254,96.00085180870013 200.58823529411765,96.00085180870013\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
       "\n",
       "  <!-- Text -->\n",
       "  <text x=\"217.795916\" y=\"116.000852\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >71</text>\n",
       "  <text x=\"255.003597\" y=\"83.294544\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,255.003597,83.294544)\">14</text>\n",
       "  <text x=\"155.294118\" y=\"80.706734\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,155.294118,80.706734)\">1440</text>\n",
       "</svg>\n",
       "        </td>\n",
       "    </tr>\n",
       "</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-69480227-ad12-411a-b89e-bda0e629d923' class='xr-section-summary-in' type='checkbox'  ><label for='section-69480227-ad12-411a-b89e-bda0e629d923' class='xr-section-summary' >Indexes: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>age_band_lower_bound</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-5624bc9a-0ad8-44a4-ac4d-bb7626de2597' class='xr-index-data-in' type='checkbox'/><label for='index-5624bc9a-0ad8-44a4-ac4d-bb7626de2597' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([0, 5, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65], dtype=&#x27;int64&#x27;, name=&#x27;age_band_lower_bound&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>latitude</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-ce266a71-b956-48d5-af47-3e4e91e79ff1' class='xr-index-data-in' type='checkbox'/><label for='index-ce266a71-b956-48d5-af47-3e4e91e79ff1' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([  90.0,  89.75,   89.5,  89.25,   89.0,  88.75,   88.5,  88.25,   88.0,\n",
       "        87.75,\n",
       "       ...\n",
       "        -87.5, -87.75,  -88.0, -88.25,  -88.5, -88.75,  -89.0, -89.25,  -89.5,\n",
       "       -89.75],\n",
       "      dtype=&#x27;float64&#x27;, name=&#x27;latitude&#x27;, length=720))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>year</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-75646443-8ed4-42c7-b12a-8d181ccd8c19' class='xr-index-data-in' type='checkbox'/><label for='index-75646443-8ed4-42c7-b12a-8d181ccd8c19' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([1950, 1951, 1952, 1953, 1954, 1955, 1956, 1957, 1958, 1959, 1960, 1961,\n",
       "       1962, 1963, 1964, 1965, 1966, 1967, 1968, 1969, 1970, 1971, 1972, 1973,\n",
       "       1974, 1975, 1976, 1977, 1978, 1979, 1980, 1981, 1982, 1983, 1984, 1985,\n",
       "       1986, 1987, 1988, 1989, 1990, 1991, 1992, 1993, 1994, 1995, 1996, 1997,\n",
       "       1998, 1999, 2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009,\n",
       "       2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020],\n",
       "      dtype=&#x27;int64&#x27;, name=&#x27;year&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>longitude</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-1f89d297-25d5-4e00-b988-a0337f60b1b2' class='xr-index-data-in' type='checkbox'/><label for='index-1f89d297-25d5-4e00-b988-a0337f60b1b2' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([ -180.0, -179.75,  -179.5, -179.25,  -179.0, -178.75,  -178.5, -178.25,\n",
       "        -178.0, -177.75,\n",
       "       ...\n",
       "         177.5,  177.75,   178.0,  178.25,   178.5,  178.75,   179.0,  179.25,\n",
       "         179.5,  179.75],\n",
       "      dtype=&#x27;float64&#x27;, name=&#x27;longitude&#x27;, length=1440))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-0866a1fe-631e-4aaa-a5b5-3ee91493e43b' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-0866a1fe-631e-4aaa-a5b5-3ee91493e43b' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset> Size: 4GB\n",
       "Dimensions:               (age_band_lower_bound: 14, latitude: 720,\n",
       "                           longitude: 1440, year: 71)\n",
       "Coordinates:\n",
       "  * age_band_lower_bound  (age_band_lower_bound) int64 112B 0 5 10 ... 55 60 65\n",
       "  * latitude              (latitude) float64 6kB 90.0 89.75 ... -89.5 -89.75\n",
       "  * year                  (year) int64 568B 1950 1951 1952 ... 2018 2019 2020\n",
       "  * longitude             (longitude) float64 12kB -180.0 -179.8 ... 179.5 179.8\n",
       "Data variables:\n",
       "    demographic_totals    (latitude, longitude, age_band_lower_bound, year) float32 4GB dask.array<chunksize=(64, 64, 14, 71), meta=np.ndarray>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xr.set_options(keep_attrs=True)\n",
    "\n",
    "ds = ds.assign_coords(longitude=(((ds.longitude + 180) % 360) - 180))\n",
    "ds = ds.roll(longitude=int(len(ds.longitude) / 2), roll_coords=True)\n",
    "ds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "8eb7cbb2-c08d-4f29-8a40-2a27eb3f89a0",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-10-23T14:58:56.873215Z",
     "iopub.status.busy": "2024-10-23T14:58:56.872993Z",
     "iopub.status.idle": "2024-10-23T14:59:05.355320Z",
     "shell.execute_reply": "2024-10-23T14:59:05.354024Z",
     "shell.execute_reply.started": "2024-10-23T14:58:56.873197Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.QuadMesh at 0x7f745c1cb350>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ds.demographic_totals.sel(age_band_lower_bound=0, year=2020).plot(vmax=50_000, cmap=\"YlGnBu_r\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "618241f4-0f99-47ca-8576-ca507eb703c5",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-09-20T12:54:26.997894Z",
     "iopub.status.busy": "2024-09-20T12:54:26.997521Z",
     "iopub.status.idle": "2024-09-20T12:54:27.006825Z",
     "shell.execute_reply": "2024-09-20T12:54:27.004090Z",
     "shell.execute_reply.started": "2024-09-20T12:54:26.997863Z"
    }
   },
   "source": [
    "#### 2. Data selection\n",
    "\n",
    "First, we perform a geographical selection corresponding to the European area:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "24724925-81ad-4bdd-ade2-3ee9844deb83",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-10-23T14:59:05.356439Z",
     "iopub.status.busy": "2024-10-23T14:59:05.356193Z",
     "iopub.status.idle": "2024-10-23T14:59:05.362406Z",
     "shell.execute_reply": "2024-10-23T14:59:05.361623Z",
     "shell.execute_reply.started": "2024-10-23T14:59:05.356418Z"
    }
   },
   "outputs": [],
   "source": [
    "europe_population = ds.demographic_totals.sel(latitude=slice(70, 35), longitude=slice(-10,41))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "976a89f7-6d69-4d2a-8af1-3ae15baeef07",
   "metadata": {},
   "source": [
    "Next, we select year 2020"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "9f866830-b5ad-4916-88b5-140a6d2ec7ea",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-10-23T14:59:05.363599Z",
     "iopub.status.busy": "2024-10-23T14:59:05.363371Z",
     "iopub.status.idle": "2024-10-23T14:59:05.383478Z",
     "shell.execute_reply": "2024-10-23T14:59:05.381877Z",
     "shell.execute_reply.started": "2024-10-23T14:59:05.363579Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div><svg style=\"position: absolute; width: 0; height: 0; overflow: hidden\">\n",
       "<defs>\n",
       "<symbol id=\"icon-database\" viewBox=\"0 0 32 32\">\n",
       "<path d=\"M16 0c-8.837 0-16 2.239-16 5v4c0 2.761 7.163 5 16 5s16-2.239 16-5v-4c0-2.761-7.163-5-16-5z\"></path>\n",
       "<path d=\"M16 17c-8.837 0-16-2.239-16-5v6c0 2.761 7.163 5 16 5s16-2.239 16-5v-6c0 2.761-7.163 5-16 5z\"></path>\n",
       "<path d=\"M16 26c-8.837 0-16-2.239-16-5v6c0 2.761 7.163 5 16 5s16-2.239 16-5v-6c0 2.761-7.163 5-16 5z\"></path>\n",
       "</symbol>\n",
       "<symbol id=\"icon-file-text2\" viewBox=\"0 0 32 32\">\n",
       "<path d=\"M28.681 7.159c-0.694-0.947-1.662-2.053-2.724-3.116s-2.169-2.030-3.116-2.724c-1.612-1.182-2.393-1.319-2.841-1.319h-15.5c-1.378 0-2.5 1.121-2.5 2.5v27c0 1.378 1.122 2.5 2.5 2.5h23c1.378 0 2.5-1.122 2.5-2.5v-19.5c0-0.448-0.137-1.23-1.319-2.841zM24.543 5.457c0.959 0.959 1.712 1.825 2.268 2.543h-4.811v-4.811c0.718 0.556 1.584 1.309 2.543 2.268zM28 29.5c0 0.271-0.229 0.5-0.5 0.5h-23c-0.271 0-0.5-0.229-0.5-0.5v-27c0-0.271 0.229-0.5 0.5-0.5 0 0 15.499-0 15.5 0v7c0 0.552 0.448 1 1 1h7v19.5z\"></path>\n",
       "<path d=\"M23 26h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
       "<path d=\"M23 22h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
       "<path d=\"M23 18h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
       "</symbol>\n",
       "</defs>\n",
       "</svg>\n",
       "<style>/* CSS stylesheet for displaying xarray objects in jupyterlab.\n",
       " *\n",
       " */\n",
       "\n",
       ":root {\n",
       "  --xr-font-color0: var(--jp-content-font-color0, rgba(0, 0, 0, 1));\n",
       "  --xr-font-color2: var(--jp-content-font-color2, rgba(0, 0, 0, 0.54));\n",
       "  --xr-font-color3: var(--jp-content-font-color3, rgba(0, 0, 0, 0.38));\n",
       "  --xr-border-color: var(--jp-border-color2, #e0e0e0);\n",
       "  --xr-disabled-color: var(--jp-layout-color3, #bdbdbd);\n",
       "  --xr-background-color: var(--jp-layout-color0, white);\n",
       "  --xr-background-color-row-even: var(--jp-layout-color1, white);\n",
       "  --xr-background-color-row-odd: var(--jp-layout-color2, #eeeeee);\n",
       "}\n",
       "\n",
       "html[theme=dark],\n",
       "html[data-theme=dark],\n",
       "body[data-theme=dark],\n",
       "body.vscode-dark {\n",
       "  --xr-font-color0: rgba(255, 255, 255, 1);\n",
       "  --xr-font-color2: rgba(255, 255, 255, 0.54);\n",
       "  --xr-font-color3: rgba(255, 255, 255, 0.38);\n",
       "  --xr-border-color: #1F1F1F;\n",
       "  --xr-disabled-color: #515151;\n",
       "  --xr-background-color: #111111;\n",
       "  --xr-background-color-row-even: #111111;\n",
       "  --xr-background-color-row-odd: #313131;\n",
       "}\n",
       "\n",
       ".xr-wrap {\n",
       "  display: block !important;\n",
       "  min-width: 300px;\n",
       "  max-width: 700px;\n",
       "}\n",
       "\n",
       ".xr-text-repr-fallback {\n",
       "  /* fallback to plain text repr when CSS is not injected (untrusted notebook) */\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-header {\n",
       "  padding-top: 6px;\n",
       "  padding-bottom: 6px;\n",
       "  margin-bottom: 4px;\n",
       "  border-bottom: solid 1px var(--xr-border-color);\n",
       "}\n",
       "\n",
       ".xr-header > div,\n",
       ".xr-header > ul {\n",
       "  display: inline;\n",
       "  margin-top: 0;\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-obj-type,\n",
       ".xr-array-name {\n",
       "  margin-left: 2px;\n",
       "  margin-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-obj-type {\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-sections {\n",
       "  padding-left: 0 !important;\n",
       "  display: grid;\n",
       "  grid-template-columns: 150px auto auto 1fr 0 20px 0 20px;\n",
       "}\n",
       "\n",
       ".xr-section-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-section-item input {\n",
       "  display: inline-block;\n",
       "  opacity: 0;\n",
       "}\n",
       "\n",
       ".xr-section-item input + label {\n",
       "  color: var(--xr-disabled-color);\n",
       "}\n",
       "\n",
       ".xr-section-item input:enabled + label {\n",
       "  cursor: pointer;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-section-item input:focus + label {\n",
       "  border: 2px solid var(--xr-font-color0);\n",
       "}\n",
       "\n",
       ".xr-section-item input:enabled + label:hover {\n",
       "  color: var(--xr-font-color0);\n",
       "}\n",
       "\n",
       ".xr-section-summary {\n",
       "  grid-column: 1;\n",
       "  color: var(--xr-font-color2);\n",
       "  font-weight: 500;\n",
       "}\n",
       "\n",
       ".xr-section-summary > span {\n",
       "  display: inline-block;\n",
       "  padding-left: 0.5em;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label {\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in + label:before {\n",
       "  display: inline-block;\n",
       "  content: '►';\n",
       "  font-size: 11px;\n",
       "  width: 15px;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label:before {\n",
       "  color: var(--xr-disabled-color);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label:before {\n",
       "  content: '▼';\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label > span {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-section-summary,\n",
       ".xr-section-inline-details {\n",
       "  padding-top: 4px;\n",
       "  padding-bottom: 4px;\n",
       "}\n",
       "\n",
       ".xr-section-inline-details {\n",
       "  grid-column: 2 / -1;\n",
       "}\n",
       "\n",
       ".xr-section-details {\n",
       "  display: none;\n",
       "  grid-column: 1 / -1;\n",
       "  margin-bottom: 5px;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-array-wrap {\n",
       "  grid-column: 1 / -1;\n",
       "  display: grid;\n",
       "  grid-template-columns: 20px auto;\n",
       "}\n",
       "\n",
       ".xr-array-wrap > label {\n",
       "  grid-column: 1;\n",
       "  vertical-align: top;\n",
       "}\n",
       "\n",
       ".xr-preview {\n",
       "  color: var(--xr-font-color3);\n",
       "}\n",
       "\n",
       ".xr-array-preview,\n",
       ".xr-array-data {\n",
       "  padding: 0 5px !important;\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-array-data,\n",
       ".xr-array-in:checked ~ .xr-array-preview {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-array-in:checked ~ .xr-array-data,\n",
       ".xr-array-preview {\n",
       "  display: inline-block;\n",
       "}\n",
       "\n",
       ".xr-dim-list {\n",
       "  display: inline-block !important;\n",
       "  list-style: none;\n",
       "  padding: 0 !important;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list li {\n",
       "  display: inline-block;\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list:before {\n",
       "  content: '(';\n",
       "}\n",
       "\n",
       ".xr-dim-list:after {\n",
       "  content: ')';\n",
       "}\n",
       "\n",
       ".xr-dim-list li:not(:last-child):after {\n",
       "  content: ',';\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-has-index {\n",
       "  font-weight: bold;\n",
       "}\n",
       "\n",
       ".xr-var-list,\n",
       ".xr-var-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-var-item > div,\n",
       ".xr-var-item label,\n",
       ".xr-var-item > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-even);\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-var-item > .xr-var-name:hover span {\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-var-list > li:nth-child(odd) > div,\n",
       ".xr-var-list > li:nth-child(odd) > label,\n",
       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-odd);\n",
       "}\n",
       "\n",
       ".xr-var-name {\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-var-dims {\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-var-dtype {\n",
       "  grid-column: 3;\n",
       "  text-align: right;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-preview {\n",
       "  grid-column: 4;\n",
       "}\n",
       "\n",
       ".xr-index-preview {\n",
       "  grid-column: 2 / 5;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-name,\n",
       ".xr-var-dims,\n",
       ".xr-var-dtype,\n",
       ".xr-preview,\n",
       ".xr-attrs dt {\n",
       "  white-space: nowrap;\n",
       "  overflow: hidden;\n",
       "  text-overflow: ellipsis;\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-var-name:hover,\n",
       ".xr-var-dims:hover,\n",
       ".xr-var-dtype:hover,\n",
       ".xr-attrs dt:hover {\n",
       "  overflow: visible;\n",
       "  width: auto;\n",
       "  z-index: 1;\n",
       "}\n",
       "\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-data {\n",
       "  display: none;\n",
       "  background-color: var(--xr-background-color) !important;\n",
       "  padding-bottom: 5px !important;\n",
       "}\n",
       "\n",
       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
       ".xr-var-data-in:checked ~ .xr-var-data,\n",
       ".xr-index-data-in:checked ~ .xr-index-data {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       ".xr-var-data > table {\n",
       "  float: right;\n",
       "}\n",
       "\n",
       ".xr-var-name span,\n",
       ".xr-var-data,\n",
       ".xr-index-name div,\n",
       ".xr-index-data,\n",
       ".xr-attrs {\n",
       "  padding-left: 25px !important;\n",
       "}\n",
       "\n",
       ".xr-attrs,\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-data {\n",
       "  grid-column: 1 / -1;\n",
       "}\n",
       "\n",
       "dl.xr-attrs {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  display: grid;\n",
       "  grid-template-columns: 125px auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt,\n",
       ".xr-attrs dd {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  float: left;\n",
       "  padding-right: 10px;\n",
       "  width: auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt {\n",
       "  font-weight: normal;\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-attrs dt:hover span {\n",
       "  display: inline-block;\n",
       "  background: var(--xr-background-color);\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-attrs dd {\n",
       "  grid-column: 2;\n",
       "  white-space: pre-wrap;\n",
       "  word-break: break-all;\n",
       "}\n",
       "\n",
       ".xr-icon-database,\n",
       ".xr-icon-file-text2,\n",
       ".xr-no-icon {\n",
       "  display: inline-block;\n",
       "  vertical-align: middle;\n",
       "  width: 1em;\n",
       "  height: 1.5em !important;\n",
       "  stroke-width: 0;\n",
       "  stroke: currentColor;\n",
       "  fill: currentColor;\n",
       "}\n",
       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;demographic_totals&#x27; (latitude: 141, longitude: 205,\n",
       "                                        age_band_lower_bound: 14)&gt; Size: 2MB\n",
       "dask.array&lt;getitem, shape=(141, 205, 14), dtype=float32, chunksize=(64, 64, 14), chunktype=numpy.ndarray&gt;\n",
       "Coordinates:\n",
       "  * age_band_lower_bound  (age_band_lower_bound) int64 112B 0 5 10 ... 55 60 65\n",
       "  * latitude              (latitude) float64 1kB 70.0 69.75 69.5 ... 35.25 35.0\n",
       "    year                  int64 8B 2020\n",
       "  * longitude             (longitude) float64 2kB -10.0 -9.75 ... 40.75 41.0</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'demographic_totals'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>latitude</span>: 141</li><li><span class='xr-has-index'>longitude</span>: 205</li><li><span class='xr-has-index'>age_band_lower_bound</span>: 14</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-0b2903e8-0356-4882-a7ae-323e6c96a1d5' class='xr-array-in' type='checkbox' checked><label for='section-0b2903e8-0356-4882-a7ae-323e6c96a1d5' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>dask.array&lt;chunksize=(48, 24, 14), meta=np.ndarray&gt;</span></div><div class='xr-array-data'><table>\n",
       "    <tr>\n",
       "        <td>\n",
       "            <table style=\"border-collapse: collapse;\">\n",
       "                <thead>\n",
       "                    <tr>\n",
       "                        <td> </td>\n",
       "                        <th> Array </th>\n",
       "                        <th> Chunk </th>\n",
       "                    </tr>\n",
       "                </thead>\n",
       "                <tbody>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 1.54 MiB </td>\n",
       "                        <td> 224.00 kiB </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (141, 205, 14) </td>\n",
       "                        <td> (64, 64, 14) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 12 chunks in 8 graph layers </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
       "                    </tr>\n",
       "                </tbody>\n",
       "            </table>\n",
       "        </td>\n",
       "        <td>\n",
       "        <svg width=\"144\" height=\"218\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
       "\n",
       "  <!-- Horizontal lines -->\n",
       "  <line x1=\"10\" y1=\"0\" x2=\"58\" y2=\"48\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"10\" y1=\"14\" x2=\"58\" y2=\"62\" />\n",
       "  <line x1=\"10\" y1=\"51\" x2=\"58\" y2=\"100\" />\n",
       "  <line x1=\"10\" y1=\"88\" x2=\"58\" y2=\"137\" />\n",
       "  <line x1=\"10\" y1=\"120\" x2=\"58\" y2=\"168\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Vertical lines -->\n",
       "  <line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"120\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"26\" y1=\"16\" x2=\"26\" y2=\"136\" />\n",
       "  <line x1=\"48\" y1=\"38\" x2=\"48\" y2=\"158\" />\n",
       "  <line x1=\"58\" y1=\"48\" x2=\"58\" y2=\"168\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Colored Rectangle -->\n",
       "  <polygon points=\"10.0,0.0 58.55093256814921,48.55093256814921 58.55093256814921,168.55093256814922 10.0,120.0\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
       "\n",
       "  <!-- Horizontal lines -->\n",
       "  <line x1=\"10\" y1=\"0\" x2=\"46\" y2=\"0\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"26\" y1=\"16\" x2=\"62\" y2=\"16\" />\n",
       "  <line x1=\"48\" y1=\"38\" x2=\"84\" y2=\"38\" />\n",
       "  <line x1=\"58\" y1=\"48\" x2=\"94\" y2=\"48\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Vertical lines -->\n",
       "  <line x1=\"10\" y1=\"0\" x2=\"58\" y2=\"48\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"46\" y1=\"0\" x2=\"94\" y2=\"48\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Colored Rectangle -->\n",
       "  <polygon points=\"10.0,0.0 46.39205625297564,0.0 94.94298882112486,48.55093256814921 58.55093256814921,48.55093256814921\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
       "\n",
       "  <!-- Horizontal lines -->\n",
       "  <line x1=\"58\" y1=\"48\" x2=\"94\" y2=\"48\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"58\" y1=\"62\" x2=\"94\" y2=\"62\" />\n",
       "  <line x1=\"58\" y1=\"100\" x2=\"94\" y2=\"100\" />\n",
       "  <line x1=\"58\" y1=\"137\" x2=\"94\" y2=\"137\" />\n",
       "  <line x1=\"58\" y1=\"168\" x2=\"94\" y2=\"168\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Vertical lines -->\n",
       "  <line x1=\"58\" y1=\"48\" x2=\"58\" y2=\"168\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"94\" y1=\"48\" x2=\"94\" y2=\"168\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Colored Rectangle -->\n",
       "  <polygon points=\"58.55093256814921,48.55093256814921 94.94298882112486,48.55093256814921 94.94298882112486,168.55093256814922 58.55093256814921,168.55093256814922\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
       "\n",
       "  <!-- Text -->\n",
       "  <text x=\"76.746961\" y=\"188.550933\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >14</text>\n",
       "  <text x=\"114.942989\" y=\"108.550933\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,114.942989,108.550933)\">205</text>\n",
       "  <text x=\"24.275466\" y=\"164.275466\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,24.275466,164.275466)\">141</text>\n",
       "</svg>\n",
       "        </td>\n",
       "    </tr>\n",
       "</table></div></div></li><li class='xr-section-item'><input id='section-a5a5b402-9034-4d34-9c44-c3e61b799fb0' class='xr-section-summary-in' type='checkbox'  checked><label for='section-a5a5b402-9034-4d34-9c44-c3e61b799fb0' class='xr-section-summary' >Coordinates: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>age_band_lower_bound</span></div><div class='xr-var-dims'>(age_band_lower_bound)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 5 10 15 20 25 ... 45 50 55 60 65</div><input id='attrs-4570484e-5c12-4c25-be57-a06fe3d94a37' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-4570484e-5c12-4c25-be57-a06fe3d94a37' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1a424c1a-1974-46e3-b7d9-cbe13b490d34' class='xr-var-data-in' type='checkbox'><label for='data-1a424c1a-1974-46e3-b7d9-cbe13b490d34' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([ 0,  5, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>latitude</span></div><div class='xr-var-dims'>(latitude)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>70.0 69.75 69.5 ... 35.5 35.25 35.0</div><input id='attrs-0db5d76d-c12e-4ba2-9783-2546488acf4d' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-0db5d76d-c12e-4ba2-9783-2546488acf4d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8de27715-c158-43db-bf93-e69cca2fb536' class='xr-var-data-in' type='checkbox'><label for='data-8de27715-c158-43db-bf93-e69cca2fb536' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([70.  , 69.75, 69.5 , 69.25, 69.  , 68.75, 68.5 , 68.25, 68.  , 67.75,\n",
       "       67.5 , 67.25, 67.  , 66.75, 66.5 , 66.25, 66.  , 65.75, 65.5 , 65.25,\n",
       "       65.  , 64.75, 64.5 , 64.25, 64.  , 63.75, 63.5 , 63.25, 63.  , 62.75,\n",
       "       62.5 , 62.25, 62.  , 61.75, 61.5 , 61.25, 61.  , 60.75, 60.5 , 60.25,\n",
       "       60.  , 59.75, 59.5 , 59.25, 59.  , 58.75, 58.5 , 58.25, 58.  , 57.75,\n",
       "       57.5 , 57.25, 57.  , 56.75, 56.5 , 56.25, 56.  , 55.75, 55.5 , 55.25,\n",
       "       55.  , 54.75, 54.5 , 54.25, 54.  , 53.75, 53.5 , 53.25, 53.  , 52.75,\n",
       "       52.5 , 52.25, 52.  , 51.75, 51.5 , 51.25, 51.  , 50.75, 50.5 , 50.25,\n",
       "       50.  , 49.75, 49.5 , 49.25, 49.  , 48.75, 48.5 , 48.25, 48.  , 47.75,\n",
       "       47.5 , 47.25, 47.  , 46.75, 46.5 , 46.25, 46.  , 45.75, 45.5 , 45.25,\n",
       "       45.  , 44.75, 44.5 , 44.25, 44.  , 43.75, 43.5 , 43.25, 43.  , 42.75,\n",
       "       42.5 , 42.25, 42.  , 41.75, 41.5 , 41.25, 41.  , 40.75, 40.5 , 40.25,\n",
       "       40.  , 39.75, 39.5 , 39.25, 39.  , 38.75, 38.5 , 38.25, 38.  , 37.75,\n",
       "       37.5 , 37.25, 37.  , 36.75, 36.5 , 36.25, 36.  , 35.75, 35.5 , 35.25,\n",
       "       35.  ])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>year</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>2020</div><input id='attrs-ef7e7ffe-588c-4975-9344-f1423b47036a' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-ef7e7ffe-588c-4975-9344-f1423b47036a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1baefa92-e7c6-4e8b-8c75-cffacb8e522d' class='xr-var-data-in' type='checkbox'><label for='data-1baefa92-e7c6-4e8b-8c75-cffacb8e522d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array(2020)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>longitude</span></div><div class='xr-var-dims'>(longitude)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-10.0 -9.75 -9.5 ... 40.75 41.0</div><input id='attrs-9e7e63b9-8cdf-4d62-b085-1aea80061858' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-9e7e63b9-8cdf-4d62-b085-1aea80061858' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-53ec1ddf-23d1-4faa-8d89-280ce9af2299' class='xr-var-data-in' type='checkbox'><label for='data-53ec1ddf-23d1-4faa-8d89-280ce9af2299' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([-10.  ,  -9.75,  -9.5 , ...,  40.5 ,  40.75,  41.  ])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-145f2669-8636-4f09-a564-a8004292a8ff' class='xr-section-summary-in' type='checkbox'  ><label for='section-145f2669-8636-4f09-a564-a8004292a8ff' class='xr-section-summary' >Indexes: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>age_band_lower_bound</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-48000e36-8173-40ca-a494-66bdc8bccdcd' class='xr-index-data-in' type='checkbox'/><label for='index-48000e36-8173-40ca-a494-66bdc8bccdcd' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([0, 5, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65], dtype=&#x27;int64&#x27;, name=&#x27;age_band_lower_bound&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>latitude</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-44746c77-4019-4dc8-8e1a-6b12796b87da' class='xr-index-data-in' type='checkbox'/><label for='index-44746c77-4019-4dc8-8e1a-6b12796b87da' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([ 70.0, 69.75,  69.5, 69.25,  69.0, 68.75,  68.5, 68.25,  68.0, 67.75,\n",
       "       ...\n",
       "       37.25,  37.0, 36.75,  36.5, 36.25,  36.0, 35.75,  35.5, 35.25,  35.0],\n",
       "      dtype=&#x27;float64&#x27;, name=&#x27;latitude&#x27;, length=141))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>longitude</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-34571bb4-69bc-4130-a04f-9349b888a7d1' class='xr-index-data-in' type='checkbox'/><label for='index-34571bb4-69bc-4130-a04f-9349b888a7d1' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([-10.0, -9.75,  -9.5, -9.25,  -9.0, -8.75,  -8.5, -8.25,  -8.0, -7.75,\n",
       "       ...\n",
       "       38.75,  39.0, 39.25,  39.5, 39.75,  40.0, 40.25,  40.5, 40.75,  41.0],\n",
       "      dtype=&#x27;float64&#x27;, name=&#x27;longitude&#x27;, length=205))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-601c6973-d9d4-41c4-8e53-867c9d512a21' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-601c6973-d9d4-41c4-8e53-867c9d512a21' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.DataArray 'demographic_totals' (latitude: 141, longitude: 205,\n",
       "                                        age_band_lower_bound: 14)> Size: 2MB\n",
       "dask.array<getitem, shape=(141, 205, 14), dtype=float32, chunksize=(64, 64, 14), chunktype=numpy.ndarray>\n",
       "Coordinates:\n",
       "  * age_band_lower_bound  (age_band_lower_bound) int64 112B 0 5 10 ... 55 60 65\n",
       "  * latitude              (latitude) float64 1kB 70.0 69.75 69.5 ... 35.25 35.0\n",
       "    year                  int64 8B 2020\n",
       "  * longitude             (longitude) float64 2kB -10.0 -9.75 ... 40.75 41.0"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "europe_population_2020 = europe_population.sel(year = 2020)\n",
    "europe_population_2020"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5d190097-b82d-4467-ab92-847429bb8b9b",
   "metadata": {},
   "source": [
    "This sequence of selections greatly reduces the amount of data that will be downloaded. \n",
    "For the moment, the bahaviour is still lazy."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c548bf86-0f67-4aaa-91a6-8455a0c555b7",
   "metadata": {},
   "source": [
    "To estimate the size of the download, we can use the `costing.py` module. This estimate must be done before we apply any reduction operation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "d30a9ae1-0a8b-4802-919c-5b776fc62a25",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-10-23T14:59:05.412825Z",
     "iopub.status.busy": "2024-10-23T14:59:05.412353Z",
     "iopub.status.idle": "2024-10-23T14:59:05.419984Z",
     "shell.execute_reply": "2024-10-23T14:59:05.419205Z",
     "shell.execute_reply.started": "2024-10-23T14:59:05.412804Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "estimated_needed_chunks: 12\n",
      "estimated_memory_size: 0.195 GB\n",
      "estimated_download_size: 0.02 GB\n"
     ]
    }
   ],
   "source": [
    "import costing\n",
    "\n",
    "costing.estimate_download_size(ds.demographic_totals, europe_population_2020)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ecc94c40-04e2-4548-861f-c6b7a791342f",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-09-20T12:57:04.785773Z",
     "iopub.status.busy": "2024-09-20T12:57:04.778619Z",
     "iopub.status.idle": "2024-09-20T12:57:04.793070Z",
     "shell.execute_reply": "2024-09-20T12:57:04.791669Z",
     "shell.execute_reply.started": "2024-09-20T12:57:04.785737Z"
    }
   },
   "source": [
    "#### 3. Data reduction\n",
    "For the selected year, we compute the sum of all population bands:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "a499ed8c-ef61-4dae-9f85-c8095e81b01b",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-10-23T14:59:05.385789Z",
     "iopub.status.busy": "2024-10-23T14:59:05.385533Z",
     "iopub.status.idle": "2024-10-23T14:59:05.411759Z",
     "shell.execute_reply": "2024-10-23T14:59:05.410896Z",
     "shell.execute_reply.started": "2024-10-23T14:59:05.385771Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div><svg style=\"position: absolute; width: 0; height: 0; overflow: hidden\">\n",
       "<defs>\n",
       "<symbol id=\"icon-database\" viewBox=\"0 0 32 32\">\n",
       "<path d=\"M16 0c-8.837 0-16 2.239-16 5v4c0 2.761 7.163 5 16 5s16-2.239 16-5v-4c0-2.761-7.163-5-16-5z\"></path>\n",
       "<path d=\"M16 17c-8.837 0-16-2.239-16-5v6c0 2.761 7.163 5 16 5s16-2.239 16-5v-6c0 2.761-7.163 5-16 5z\"></path>\n",
       "<path d=\"M16 26c-8.837 0-16-2.239-16-5v6c0 2.761 7.163 5 16 5s16-2.239 16-5v-6c0 2.761-7.163 5-16 5z\"></path>\n",
       "</symbol>\n",
       "<symbol id=\"icon-file-text2\" viewBox=\"0 0 32 32\">\n",
       "<path d=\"M28.681 7.159c-0.694-0.947-1.662-2.053-2.724-3.116s-2.169-2.030-3.116-2.724c-1.612-1.182-2.393-1.319-2.841-1.319h-15.5c-1.378 0-2.5 1.121-2.5 2.5v27c0 1.378 1.122 2.5 2.5 2.5h23c1.378 0 2.5-1.122 2.5-2.5v-19.5c0-0.448-0.137-1.23-1.319-2.841zM24.543 5.457c0.959 0.959 1.712 1.825 2.268 2.543h-4.811v-4.811c0.718 0.556 1.584 1.309 2.543 2.268zM28 29.5c0 0.271-0.229 0.5-0.5 0.5h-23c-0.271 0-0.5-0.229-0.5-0.5v-27c0-0.271 0.229-0.5 0.5-0.5 0 0 15.499-0 15.5 0v7c0 0.552 0.448 1 1 1h7v19.5z\"></path>\n",
       "<path d=\"M23 26h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
       "<path d=\"M23 22h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
       "<path d=\"M23 18h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
       "</symbol>\n",
       "</defs>\n",
       "</svg>\n",
       "<style>/* CSS stylesheet for displaying xarray objects in jupyterlab.\n",
       " *\n",
       " */\n",
       "\n",
       ":root {\n",
       "  --xr-font-color0: var(--jp-content-font-color0, rgba(0, 0, 0, 1));\n",
       "  --xr-font-color2: var(--jp-content-font-color2, rgba(0, 0, 0, 0.54));\n",
       "  --xr-font-color3: var(--jp-content-font-color3, rgba(0, 0, 0, 0.38));\n",
       "  --xr-border-color: var(--jp-border-color2, #e0e0e0);\n",
       "  --xr-disabled-color: var(--jp-layout-color3, #bdbdbd);\n",
       "  --xr-background-color: var(--jp-layout-color0, white);\n",
       "  --xr-background-color-row-even: var(--jp-layout-color1, white);\n",
       "  --xr-background-color-row-odd: var(--jp-layout-color2, #eeeeee);\n",
       "}\n",
       "\n",
       "html[theme=dark],\n",
       "html[data-theme=dark],\n",
       "body[data-theme=dark],\n",
       "body.vscode-dark {\n",
       "  --xr-font-color0: rgba(255, 255, 255, 1);\n",
       "  --xr-font-color2: rgba(255, 255, 255, 0.54);\n",
       "  --xr-font-color3: rgba(255, 255, 255, 0.38);\n",
       "  --xr-border-color: #1F1F1F;\n",
       "  --xr-disabled-color: #515151;\n",
       "  --xr-background-color: #111111;\n",
       "  --xr-background-color-row-even: #111111;\n",
       "  --xr-background-color-row-odd: #313131;\n",
       "}\n",
       "\n",
       ".xr-wrap {\n",
       "  display: block !important;\n",
       "  min-width: 300px;\n",
       "  max-width: 700px;\n",
       "}\n",
       "\n",
       ".xr-text-repr-fallback {\n",
       "  /* fallback to plain text repr when CSS is not injected (untrusted notebook) */\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-header {\n",
       "  padding-top: 6px;\n",
       "  padding-bottom: 6px;\n",
       "  margin-bottom: 4px;\n",
       "  border-bottom: solid 1px var(--xr-border-color);\n",
       "}\n",
       "\n",
       ".xr-header > div,\n",
       ".xr-header > ul {\n",
       "  display: inline;\n",
       "  margin-top: 0;\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-obj-type,\n",
       ".xr-array-name {\n",
       "  margin-left: 2px;\n",
       "  margin-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-obj-type {\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-sections {\n",
       "  padding-left: 0 !important;\n",
       "  display: grid;\n",
       "  grid-template-columns: 150px auto auto 1fr 0 20px 0 20px;\n",
       "}\n",
       "\n",
       ".xr-section-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-section-item input {\n",
       "  display: inline-block;\n",
       "  opacity: 0;\n",
       "}\n",
       "\n",
       ".xr-section-item input + label {\n",
       "  color: var(--xr-disabled-color);\n",
       "}\n",
       "\n",
       ".xr-section-item input:enabled + label {\n",
       "  cursor: pointer;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-section-item input:focus + label {\n",
       "  border: 2px solid var(--xr-font-color0);\n",
       "}\n",
       "\n",
       ".xr-section-item input:enabled + label:hover {\n",
       "  color: var(--xr-font-color0);\n",
       "}\n",
       "\n",
       ".xr-section-summary {\n",
       "  grid-column: 1;\n",
       "  color: var(--xr-font-color2);\n",
       "  font-weight: 500;\n",
       "}\n",
       "\n",
       ".xr-section-summary > span {\n",
       "  display: inline-block;\n",
       "  padding-left: 0.5em;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label {\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in + label:before {\n",
       "  display: inline-block;\n",
       "  content: '►';\n",
       "  font-size: 11px;\n",
       "  width: 15px;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label:before {\n",
       "  color: var(--xr-disabled-color);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label:before {\n",
       "  content: '▼';\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label > span {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-section-summary,\n",
       ".xr-section-inline-details {\n",
       "  padding-top: 4px;\n",
       "  padding-bottom: 4px;\n",
       "}\n",
       "\n",
       ".xr-section-inline-details {\n",
       "  grid-column: 2 / -1;\n",
       "}\n",
       "\n",
       ".xr-section-details {\n",
       "  display: none;\n",
       "  grid-column: 1 / -1;\n",
       "  margin-bottom: 5px;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-array-wrap {\n",
       "  grid-column: 1 / -1;\n",
       "  display: grid;\n",
       "  grid-template-columns: 20px auto;\n",
       "}\n",
       "\n",
       ".xr-array-wrap > label {\n",
       "  grid-column: 1;\n",
       "  vertical-align: top;\n",
       "}\n",
       "\n",
       ".xr-preview {\n",
       "  color: var(--xr-font-color3);\n",
       "}\n",
       "\n",
       ".xr-array-preview,\n",
       ".xr-array-data {\n",
       "  padding: 0 5px !important;\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-array-data,\n",
       ".xr-array-in:checked ~ .xr-array-preview {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-array-in:checked ~ .xr-array-data,\n",
       ".xr-array-preview {\n",
       "  display: inline-block;\n",
       "}\n",
       "\n",
       ".xr-dim-list {\n",
       "  display: inline-block !important;\n",
       "  list-style: none;\n",
       "  padding: 0 !important;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list li {\n",
       "  display: inline-block;\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list:before {\n",
       "  content: '(';\n",
       "}\n",
       "\n",
       ".xr-dim-list:after {\n",
       "  content: ')';\n",
       "}\n",
       "\n",
       ".xr-dim-list li:not(:last-child):after {\n",
       "  content: ',';\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-has-index {\n",
       "  font-weight: bold;\n",
       "}\n",
       "\n",
       ".xr-var-list,\n",
       ".xr-var-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-var-item > div,\n",
       ".xr-var-item label,\n",
       ".xr-var-item > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-even);\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-var-item > .xr-var-name:hover span {\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-var-list > li:nth-child(odd) > div,\n",
       ".xr-var-list > li:nth-child(odd) > label,\n",
       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-odd);\n",
       "}\n",
       "\n",
       ".xr-var-name {\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-var-dims {\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-var-dtype {\n",
       "  grid-column: 3;\n",
       "  text-align: right;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-preview {\n",
       "  grid-column: 4;\n",
       "}\n",
       "\n",
       ".xr-index-preview {\n",
       "  grid-column: 2 / 5;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-name,\n",
       ".xr-var-dims,\n",
       ".xr-var-dtype,\n",
       ".xr-preview,\n",
       ".xr-attrs dt {\n",
       "  white-space: nowrap;\n",
       "  overflow: hidden;\n",
       "  text-overflow: ellipsis;\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-var-name:hover,\n",
       ".xr-var-dims:hover,\n",
       ".xr-var-dtype:hover,\n",
       ".xr-attrs dt:hover {\n",
       "  overflow: visible;\n",
       "  width: auto;\n",
       "  z-index: 1;\n",
       "}\n",
       "\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-data {\n",
       "  display: none;\n",
       "  background-color: var(--xr-background-color) !important;\n",
       "  padding-bottom: 5px !important;\n",
       "}\n",
       "\n",
       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
       ".xr-var-data-in:checked ~ .xr-var-data,\n",
       ".xr-index-data-in:checked ~ .xr-index-data {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       ".xr-var-data > table {\n",
       "  float: right;\n",
       "}\n",
       "\n",
       ".xr-var-name span,\n",
       ".xr-var-data,\n",
       ".xr-index-name div,\n",
       ".xr-index-data,\n",
       ".xr-attrs {\n",
       "  padding-left: 25px !important;\n",
       "}\n",
       "\n",
       ".xr-attrs,\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-data {\n",
       "  grid-column: 1 / -1;\n",
       "}\n",
       "\n",
       "dl.xr-attrs {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  display: grid;\n",
       "  grid-template-columns: 125px auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt,\n",
       ".xr-attrs dd {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  float: left;\n",
       "  padding-right: 10px;\n",
       "  width: auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt {\n",
       "  font-weight: normal;\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-attrs dt:hover span {\n",
       "  display: inline-block;\n",
       "  background: var(--xr-background-color);\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-attrs dd {\n",
       "  grid-column: 2;\n",
       "  white-space: pre-wrap;\n",
       "  word-break: break-all;\n",
       "}\n",
       "\n",
       ".xr-icon-database,\n",
       ".xr-icon-file-text2,\n",
       ".xr-no-icon {\n",
       "  display: inline-block;\n",
       "  vertical-align: middle;\n",
       "  width: 1em;\n",
       "  height: 1.5em !important;\n",
       "  stroke-width: 0;\n",
       "  stroke: currentColor;\n",
       "  fill: currentColor;\n",
       "}\n",
       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;demographic_totals&#x27; (latitude: 141, longitude: 205)&gt; Size: 116kB\n",
       "dask.array&lt;sum-aggregate, shape=(141, 205), dtype=float32, chunksize=(64, 64), chunktype=numpy.ndarray&gt;\n",
       "Coordinates:\n",
       "  * latitude   (latitude) float64 1kB 70.0 69.75 69.5 69.25 ... 35.5 35.25 35.0\n",
       "    year       int64 8B 2020\n",
       "  * longitude  (longitude) float64 2kB -10.0 -9.75 -9.5 ... 40.5 40.75 41.0</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'demographic_totals'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>latitude</span>: 141</li><li><span class='xr-has-index'>longitude</span>: 205</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-9121381a-6d5d-4bd8-a93c-41d81c04fb86' class='xr-array-in' type='checkbox' checked><label for='section-9121381a-6d5d-4bd8-a93c-41d81c04fb86' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>dask.array&lt;chunksize=(48, 24), meta=np.ndarray&gt;</span></div><div class='xr-array-data'><table>\n",
       "    <tr>\n",
       "        <td>\n",
       "            <table style=\"border-collapse: collapse;\">\n",
       "                <thead>\n",
       "                    <tr>\n",
       "                        <td> </td>\n",
       "                        <th> Array </th>\n",
       "                        <th> Chunk </th>\n",
       "                    </tr>\n",
       "                </thead>\n",
       "                <tbody>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 112.91 kiB </td>\n",
       "                        <td> 16.00 kiB </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (141, 205) </td>\n",
       "                        <td> (64, 64) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 12 chunks in 13 graph layers </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
       "                    </tr>\n",
       "                </tbody>\n",
       "            </table>\n",
       "        </td>\n",
       "        <td>\n",
       "        <svg width=\"170\" height=\"132\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
       "\n",
       "  <!-- Horizontal lines -->\n",
       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"0\" y1=\"28\" x2=\"120\" y2=\"28\" />\n",
       "  <line x1=\"0\" y1=\"65\" x2=\"120\" y2=\"65\" />\n",
       "  <line x1=\"0\" y1=\"82\" x2=\"120\" y2=\"82\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Vertical lines -->\n",
       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"82\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"14\" y1=\"0\" x2=\"14\" y2=\"82\" />\n",
       "  <line x1=\"51\" y1=\"0\" x2=\"51\" y2=\"82\" />\n",
       "  <line x1=\"88\" y1=\"0\" x2=\"88\" y2=\"82\" />\n",
       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"82\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Colored Rectangle -->\n",
       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,82.53658536585365 0.0,82.53658536585365\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
       "\n",
       "  <!-- Text -->\n",
       "  <text x=\"60.000000\" y=\"102.536585\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >205</text>\n",
       "  <text x=\"140.000000\" y=\"41.268293\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,140.000000,41.268293)\">141</text>\n",
       "</svg>\n",
       "        </td>\n",
       "    </tr>\n",
       "</table></div></div></li><li class='xr-section-item'><input id='section-4e4d1459-d6f6-4cb6-b621-458a0f841f9b' class='xr-section-summary-in' type='checkbox'  checked><label for='section-4e4d1459-d6f6-4cb6-b621-458a0f841f9b' class='xr-section-summary' >Coordinates: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>latitude</span></div><div class='xr-var-dims'>(latitude)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>70.0 69.75 69.5 ... 35.5 35.25 35.0</div><input id='attrs-8dee67de-b2fe-4643-b9aa-f9a306caef4d' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-8dee67de-b2fe-4643-b9aa-f9a306caef4d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a85dcace-7622-4a35-af20-5c50bc4cd69e' class='xr-var-data-in' type='checkbox'><label for='data-a85dcace-7622-4a35-af20-5c50bc4cd69e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([70.  , 69.75, 69.5 , 69.25, 69.  , 68.75, 68.5 , 68.25, 68.  , 67.75,\n",
       "       67.5 , 67.25, 67.  , 66.75, 66.5 , 66.25, 66.  , 65.75, 65.5 , 65.25,\n",
       "       65.  , 64.75, 64.5 , 64.25, 64.  , 63.75, 63.5 , 63.25, 63.  , 62.75,\n",
       "       62.5 , 62.25, 62.  , 61.75, 61.5 , 61.25, 61.  , 60.75, 60.5 , 60.25,\n",
       "       60.  , 59.75, 59.5 , 59.25, 59.  , 58.75, 58.5 , 58.25, 58.  , 57.75,\n",
       "       57.5 , 57.25, 57.  , 56.75, 56.5 , 56.25, 56.  , 55.75, 55.5 , 55.25,\n",
       "       55.  , 54.75, 54.5 , 54.25, 54.  , 53.75, 53.5 , 53.25, 53.  , 52.75,\n",
       "       52.5 , 52.25, 52.  , 51.75, 51.5 , 51.25, 51.  , 50.75, 50.5 , 50.25,\n",
       "       50.  , 49.75, 49.5 , 49.25, 49.  , 48.75, 48.5 , 48.25, 48.  , 47.75,\n",
       "       47.5 , 47.25, 47.  , 46.75, 46.5 , 46.25, 46.  , 45.75, 45.5 , 45.25,\n",
       "       45.  , 44.75, 44.5 , 44.25, 44.  , 43.75, 43.5 , 43.25, 43.  , 42.75,\n",
       "       42.5 , 42.25, 42.  , 41.75, 41.5 , 41.25, 41.  , 40.75, 40.5 , 40.25,\n",
       "       40.  , 39.75, 39.5 , 39.25, 39.  , 38.75, 38.5 , 38.25, 38.  , 37.75,\n",
       "       37.5 , 37.25, 37.  , 36.75, 36.5 , 36.25, 36.  , 35.75, 35.5 , 35.25,\n",
       "       35.  ])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>year</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>2020</div><input id='attrs-54998d8d-b972-4e83-8601-6db229f94abf' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-54998d8d-b972-4e83-8601-6db229f94abf' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9a132c35-d673-4dc6-9bff-e859d2eda8c5' class='xr-var-data-in' type='checkbox'><label for='data-9a132c35-d673-4dc6-9bff-e859d2eda8c5' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array(2020)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>longitude</span></div><div class='xr-var-dims'>(longitude)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-10.0 -9.75 -9.5 ... 40.75 41.0</div><input id='attrs-4c7ecac4-39dd-4b35-b72a-b5e8b24b388b' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-4c7ecac4-39dd-4b35-b72a-b5e8b24b388b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8e2638ec-aecc-422d-83a6-80f73da0290c' class='xr-var-data-in' type='checkbox'><label for='data-8e2638ec-aecc-422d-83a6-80f73da0290c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([-10.  ,  -9.75,  -9.5 , ...,  40.5 ,  40.75,  41.  ])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-fbc51ebb-b37f-4cc1-a55f-1526e605cb7b' class='xr-section-summary-in' type='checkbox'  ><label for='section-fbc51ebb-b37f-4cc1-a55f-1526e605cb7b' class='xr-section-summary' >Indexes: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>latitude</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-5eead2ea-884d-4801-bb8a-9809aec5bcd9' class='xr-index-data-in' type='checkbox'/><label for='index-5eead2ea-884d-4801-bb8a-9809aec5bcd9' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([ 70.0, 69.75,  69.5, 69.25,  69.0, 68.75,  68.5, 68.25,  68.0, 67.75,\n",
       "       ...\n",
       "       37.25,  37.0, 36.75,  36.5, 36.25,  36.0, 35.75,  35.5, 35.25,  35.0],\n",
       "      dtype=&#x27;float64&#x27;, name=&#x27;latitude&#x27;, length=141))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>longitude</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-e0b8a648-c673-42ec-ba18-a375a5ec8aff' class='xr-index-data-in' type='checkbox'/><label for='index-e0b8a648-c673-42ec-ba18-a375a5ec8aff' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([-10.0, -9.75,  -9.5, -9.25,  -9.0, -8.75,  -8.5, -8.25,  -8.0, -7.75,\n",
       "       ...\n",
       "       38.75,  39.0, 39.25,  39.5, 39.75,  40.0, 40.25,  40.5, 40.75,  41.0],\n",
       "      dtype=&#x27;float64&#x27;, name=&#x27;longitude&#x27;, length=205))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-29597edc-030c-4228-9ff7-3600ad3f4c7c' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-29597edc-030c-4228-9ff7-3600ad3f4c7c' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.DataArray 'demographic_totals' (latitude: 141, longitude: 205)> Size: 116kB\n",
       "dask.array<sum-aggregate, shape=(141, 205), dtype=float32, chunksize=(64, 64), chunktype=numpy.ndarray>\n",
       "Coordinates:\n",
       "  * latitude   (latitude) float64 1kB 70.0 69.75 69.5 69.25 ... 35.5 35.25 35.0\n",
       "    year       int64 8B 2020\n",
       "  * longitude  (longitude) float64 2kB -10.0 -9.75 -9.5 ... 40.5 40.75 41.0"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "europe_total_population_2020 = europe_population_2020.sum(\"age_band_lower_bound\")\n",
    "europe_total_population_2020"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "74359fcd-2fd8-4bc0-b0b7-e5beee5fb44a",
   "metadata": {},
   "source": [
    "#### 4. Data downalod"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5d5a96d9-d0c4-4ddc-b521-74b506f6aadf",
   "metadata": {},
   "source": [
    "This is the phase where we explicitly trigger the download of the data. Remember to assign the return of the `compute()` function to a new variable, so that the data is kept in memory."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "85b6c064-1808-4d2b-a003-d852bda4bce6",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-10-23T14:59:05.421170Z",
     "iopub.status.busy": "2024-10-23T14:59:05.420933Z",
     "iopub.status.idle": "2024-10-23T14:59:06.301478Z",
     "shell.execute_reply": "2024-10-23T14:59:06.300406Z",
     "shell.execute_reply.started": "2024-10-23T14:59:05.421150Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 839 ms, sys: 184 ms, total: 1.02 s\n",
      "Wall time: 875 ms\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "europe_total_population_2020_computed = europe_total_population_2020.compute();"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ab6a4149-6afd-48a5-b518-5dfaacefd3ac",
   "metadata": {},
   "source": [
    "#### 5. Visualization"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cd3d4560-9c6d-4b4b-8f2a-8eb7a3fc5774",
   "metadata": {},
   "source": [
    "Finally, we can easily plot the total population in Europe for the year 2020:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "8d536861-348d-411b-a6c6-e244089c1209",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-10-23T14:59:06.302645Z",
     "iopub.status.busy": "2024-10-23T14:59:06.302376Z",
     "iopub.status.idle": "2024-10-23T14:59:06.628643Z",
     "shell.execute_reply": "2024-10-23T14:59:06.627769Z",
     "shell.execute_reply.started": "2024-10-23T14:59:06.302621Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.QuadMesh at 0x7f7447fda2d0>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "europe_total_population_2020_computed.plot(vmax=300_000, cmap=\"YlGnBu_r\")"
   ]
  }
 ],
 "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": "3.11.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}