{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Download Data from Copernicus Open Access Hub\n",
    "\n",
    "These notebooks contain information about querying and downloading data from the Sentinel-2 mission run by the Copernicus program.\n",
    "\n",
    "```{note}\n",
    "In order to run these notebooks, a free account for the Copernicus Open Access Hub is needed.\n",
    "Please create an account first:\n",
    "\n",
    "https://scihub.copernicus.eu/dhus/#/self-registration\n",
    "\n",
    "Consult the [README](../README.md) for information on setting up the environment variables.\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "from sentinelsat import SentinelAPI, read_geojson\n",
    "from tqdm.notebook import tqdm\n",
    "\n",
    "api = SentinelAPI(os.getenv('SCIHUB_USERNAME'), os.getenv('SCIHUB_PASSWORD'))\n",
    "api._tqdm = tqdm # ← gives us better progress bars"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Defining the Region of Interest\n",
    "\n",
    "The Copernicus Open Access Hub API expects a point or area that designates the region of interest.\n",
    "We use the [OpenStreetMap Nominatim API](https://nominatim.org/) to query for the administrative boundaries of Berlin using the `search_osm` function defined in `sentinel_helpers.py`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\u001b[0;31mSignature:\u001b[0m \u001b[0msearch_osm\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mplace\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
       "\u001b[0;31mSource:\u001b[0m   \n",
       "\u001b[0;32mdef\u001b[0m \u001b[0msearch_osm\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mplace\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0;34m'''\u001b[0m\n",
       "\u001b[0;34m    Returns a GeoDataFrame with results from OpenStreetMap Nominatim for the given search string.\u001b[0m\n",
       "\u001b[0;34m    '''\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0murlescaped_place\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0murllib\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mparse\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mquote\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mplace\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0msearch_url\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;34m'https://nominatim.openstreetmap.org/search/?q={}'\u001b[0m \u001b[0;34m+\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m                  \u001b[0;34m'&format=geojson&polygon_geojson=1'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0murlescaped_place\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0;32mreturn\u001b[0m \u001b[0mgpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread_file\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msearch_url\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
       "\u001b[0;31mFile:\u001b[0m      ~/sources/sentinel_helpers.py\n",
       "\u001b[0;31mType:\u001b[0m      function\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sentinel_helpers import search_osm\n",
    "??search_osm"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The function is mostly concerned with constructing the correct URL and ensuring that the response is returned as GeoJSON so it can be parsed by the `GeoPandas` library.\n",
    "\n",
    "A query returns a `GeoPandas` `GeoDataFrame` which contains a geometry and additional information that can be used to narrow down the area we are interested in:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>place_id</th>\n",
       "      <th>osm_type</th>\n",
       "      <th>osm_id</th>\n",
       "      <th>display_name</th>\n",
       "      <th>place_rank</th>\n",
       "      <th>category</th>\n",
       "      <th>type</th>\n",
       "      <th>importance</th>\n",
       "      <th>icon</th>\n",
       "      <th>geometry</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>574401</td>\n",
       "      <td>node</td>\n",
       "      <td>240109189</td>\n",
       "      <td>Berlin, 10117, Deutschland</td>\n",
       "      <td>15</td>\n",
       "      <td>place</td>\n",
       "      <td>city</td>\n",
       "      <td>0.897539</td>\n",
       "      <td>https://nominatim.openstreetmap.org/ui/mapicon...</td>\n",
       "      <td>POINT (13.38886 52.51704)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>256375666</td>\n",
       "      <td>relation</td>\n",
       "      <td>62422</td>\n",
       "      <td>Berlin, Deutschland</td>\n",
       "      <td>8</td>\n",
       "      <td>boundary</td>\n",
       "      <td>administrative</td>\n",
       "      <td>0.897539</td>\n",
       "      <td>https://nominatim.openstreetmap.org/ui/mapicon...</td>\n",
       "      <td>MULTIPOLYGON (((13.08835 52.41963, 13.09021 52...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1253688</td>\n",
       "      <td>node</td>\n",
       "      <td>313826576</td>\n",
       "      <td>Berlin, Tauentzienstraße, Charlottenburg, Char...</td>\n",
       "      <td>30</td>\n",
       "      <td>tourism</td>\n",
       "      <td>artwork</td>\n",
       "      <td>0.315372</td>\n",
       "      <td>https://nominatim.openstreetmap.org/ui/mapicon...</td>\n",
       "      <td>POINT (13.33864 52.50340)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>225671</td>\n",
       "      <td>node</td>\n",
       "      <td>50798110</td>\n",
       "      <td>Berlin, Seedorf, Trave-Land, Segeberg, Schlesw...</td>\n",
       "      <td>19</td>\n",
       "      <td>place</td>\n",
       "      <td>village</td>\n",
       "      <td>0.272651</td>\n",
       "      <td>https://nominatim.openstreetmap.org/ui/mapicon...</td>\n",
       "      <td>POINT (10.44613 54.03636)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>107621118</td>\n",
       "      <td>way</td>\n",
       "      <td>68276545</td>\n",
       "      <td>Berlin, Potsdam, Klein Bennebek, Kropp-Stapelh...</td>\n",
       "      <td>26</td>\n",
       "      <td>highway</td>\n",
       "      <td>unclassified</td>\n",
       "      <td>0.210000</td>\n",
       "      <td>None</td>\n",
       "      <td>LINESTRING (9.41929 54.40283, 9.42072 54.40343...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    place_id  osm_type     osm_id  \\\n",
       "0     574401      node  240109189   \n",
       "1  256375666  relation      62422   \n",
       "2    1253688      node  313826576   \n",
       "3     225671      node   50798110   \n",
       "4  107621118       way   68276545   \n",
       "\n",
       "                                        display_name  place_rank  category  \\\n",
       "0                         Berlin, 10117, Deutschland          15     place   \n",
       "1                                Berlin, Deutschland           8  boundary   \n",
       "2  Berlin, Tauentzienstraße, Charlottenburg, Char...          30   tourism   \n",
       "3  Berlin, Seedorf, Trave-Land, Segeberg, Schlesw...          19     place   \n",
       "4  Berlin, Potsdam, Klein Bennebek, Kropp-Stapelh...          26   highway   \n",
       "\n",
       "             type  importance  \\\n",
       "0            city    0.897539   \n",
       "1  administrative    0.897539   \n",
       "2         artwork    0.315372   \n",
       "3         village    0.272651   \n",
       "4    unclassified    0.210000   \n",
       "\n",
       "                                                icon  \\\n",
       "0  https://nominatim.openstreetmap.org/ui/mapicon...   \n",
       "1  https://nominatim.openstreetmap.org/ui/mapicon...   \n",
       "2  https://nominatim.openstreetmap.org/ui/mapicon...   \n",
       "3  https://nominatim.openstreetmap.org/ui/mapicon...   \n",
       "4                                               None   \n",
       "\n",
       "                                            geometry  \n",
       "0                          POINT (13.38886 52.51704)  \n",
       "1  MULTIPOLYGON (((13.08835 52.41963, 13.09021 52...  \n",
       "2                          POINT (13.33864 52.50340)  \n",
       "3                          POINT (10.44613 54.03636)  \n",
       "4  LINESTRING (9.41929 54.40283, 9.42072 54.40343...  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results = search_osm('Berlin, Germany')\n",
    "results.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "OpenStreetMap contains geoinformation at vastly different scales and of very different types.\n",
    "We can use it to query outlines of parks or entire countries.\n",
    "The [OpenStreetMap wiki](https://wiki.openstreetmap.org/wiki/Main_Page) contains exhaustive information about the architectural design of OpenStreetMap.\n",
    "Using the `search_osm` function we are very flexible in the type of query information we can retrieve.\n",
    "\n",
    "The first result is the city's centroid.\n",
    "We use the `type` to select the administrative boundaries."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "berlin = results[results['type'] == 'administrative']\n",
    "berlin.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "… and save this result for later. All downloaded files as well as all the artifacts generated by the various notebooks will live inside the `resources` directory:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pathlib import Path\n",
    "\n",
    "resources = Path('resources')\n",
    "resources.mkdir(exist_ok=True)\n",
    "berlin.to_file(resources / 'berlin.geojson', driver='GeoJSON')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "OpenStreetMap provides detailed geometries not only for cities, but also for larger areas such as federal states:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "brandenburg = search_osm('Brandenburg, Germany')\n",
    "brandenburg.iloc[:1].plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "… or smaller areas such as parks:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "tiergarten = search_osm('Tiergarten, Berlin, Germany')\n",
    "tiergarten.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In addition to the [OpenStreetMap Wiki](https://wiki.openstreetmap.org/) the [OpenStreetMap Website](https://openstreetmap.org/) is useful to interactively test out different search queries.\n",
    "\n",
    "We continue with `berlin`."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Defining a Request to the Copernicus Open Access Hub\n",
    "\n",
    "An API request additionally contains the time span for which to search for results - we might be interested in very recent images or in imagery over a long time span - and the maximum cloud coverage.\n",
    "The maximum cloud coverage is needed because Sentinel-2 is equipped with optical instruments.\n",
    "If there are too many clouds the products will not contain a lot of useful information."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "from datetime import date\n",
    "import datetime\n",
    "\n",
    "# note that start_date are python date objects this means they can be\n",
    "# manipulated using python functions\n",
    "start_date = date(2020, 5, 22) \n",
    "end_date = date(2020, 6, 22) \n",
    "cloud_coverage = (0,30) # between 0 and 30 percent"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The region of interest is given as the `footprint` parameter.\n",
    "We use a simplified version of the geometry retrieved from OpenStreetMap - its convex hull - due to restrictions in URL lengths that don't allow us to query for arbitrarily detailed geometries:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/svg+xml": [
       "<svg xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\" width=\"100.0\" height=\"100.0\" viewBox=\"13.061432364 52.311332164 0.7266411720000008 0.39108917200000093\" preserveAspectRatio=\"xMinYMin meet\"><g transform=\"matrix(1,0,0,-1,0,105.0137535)\"><path fill-rule=\"evenodd\" fill=\"#66cc99\" stroke=\"#555555\" stroke-width=\"0.014532823440000016\" opacity=\"0.6\" d=\"M 13.6477328,52.3382448 L 13.6474433,52.338271 L 13.131305,52.3872254 L 13.1310825,52.3872512 L 13.1307559,52.3873763 L 13.1298482,52.3878739 L 13.1267317,52.3895836 L 13.0907635,52.4115602 L 13.090275,52.4122142 L 13.088345,52.4196325 L 13.1284297,52.5860175 L 13.1289619,52.5873048 L 13.2827678,52.6607411 L 13.4794873,52.6755087 L 13.4884252,52.6707899 L 13.523022,52.6450365 L 13.7564462,52.4461766 L 13.7611609,52.4377097 L 13.76095,52.4373221 L 13.7390343,52.4073353 L 13.7342706,52.4021367 L 13.7341193,52.4019835 L 13.6922028,52.3672074 L 13.6513711,52.3392255 L 13.6509462,52.3389451 L 13.6506935,52.3388565 L 13.6488471,52.3383381 L 13.6484243,52.3382725 L 13.6477328,52.3382448 z\" /></g></svg>"
      ],
      "text/plain": [
       "<shapely.geometry.polygon.Polygon at 0x7fe238d62cd0>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "footprint = berlin.iloc[0]['geometry'].convex_hull\n",
    "footprint"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 16 results\n"
     ]
    }
   ],
   "source": [
    "products = api.query(footprint,\n",
    "                     platformname='Sentinel-2',\n",
    "                     processinglevel='Level-2A',\n",
    "                     date=(start_date, end_date),\n",
    "                     cloudcoverpercentage=cloud_coverage)\n",
    "\n",
    "print(f'Found {len(products)} results')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Selecting Products from the Result Set\n",
    "\n",
    "The criteria for selecting a product depends on the specific use case.\n",
    "The first use case is to plot an image of Berlin, so want to make sure that as much of the city as possible is visible in the data we download.\n",
    "\n",
    "We convert the list of products to a `GeoDataFrame`, for which `gdf` is an acronym:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.8/site-packages/pyproj/crs/crs.py:53: FutureWarning: '+init=<authority>:<code>' syntax is deprecated. '<authority>:<code>' is the preferred initialization method. When making the change, be mindful of axis order changes: https://pyproj4.github.io/pyproj/stable/gotchas.html#axis-order-changes-in-proj-6\n",
      "  return _prepare_from_string(\" \".join(pjargs))\n"
     ]
    }
   ],
   "source": [
    "gdf = api.to_geodataframe(products)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The `plot_all` function is a helper that allows us to draw several geometries on top of each other in a single plot.\n",
    "We can use it to get a quick visual impression of the result:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Area of Interest and Products Returned from the API')"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x864 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sentinel_helpers import plot_all\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "\n",
    "plot_all([berlin, gdf, gdf],\n",
    "         [{'facecolor': '#777777', 'figsize': (12, 12)},\n",
    "          {'facecolor': '#988ED5', 'alpha': 0.1},\n",
    "          {'facecolor': 'none', 'edgecolor': '#988ED5', 'alpha': 0.4}])\n",
    "plt.title('Area of Interest and Products Returned from the API')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "```{note}\n",
    "The geometrical shape of the captured data depends on where the Sentinel-2 satellite was in orbit when it crossed the area of interest.\n",
    "For an in-depth analysis see [](01c Coverage Analysis.ipynb)</code>.\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Because products are are large (roughly 1GB), we want to skip unnecessary downloads wherever possible.\n",
    "We are selecting products based on two criteria:\n",
    "\n",
    "- How much of the area of interest is visible in the product (i.e. area of the intersection of a product's tile and our area of interest)\n",
    "- Cloud coverage (the less the better)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# we are using a metric coordinate reference system for europe in order to\n",
    "# safely compare areas \n",
    "gdf = gdf.to_crs('EPSG:4839')\n",
    "berlin = berlin.to_crs('EPSG:4839')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>title</th>\n",
       "      <th>link</th>\n",
       "      <th>link_alternative</th>\n",
       "      <th>link_icon</th>\n",
       "      <th>summary</th>\n",
       "      <th>ondemand</th>\n",
       "      <th>beginposition</th>\n",
       "      <th>endposition</th>\n",
       "      <th>ingestiondate</th>\n",
       "      <th>orbitnumber</th>\n",
       "      <th>...</th>\n",
       "      <th>osm_type</th>\n",
       "      <th>osm_id</th>\n",
       "      <th>display_name</th>\n",
       "      <th>place_rank</th>\n",
       "      <th>category</th>\n",
       "      <th>type</th>\n",
       "      <th>importance</th>\n",
       "      <th>icon</th>\n",
       "      <th>geometry</th>\n",
       "      <th>area</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>S2A_MSIL2A_20200617T101031_N0214_R022_T33UUU_2...</td>\n",
       "      <td>https://scihub.copernicus.eu/apihub/odata/v1/P...</td>\n",
       "      <td>https://scihub.copernicus.eu/apihub/odata/v1/P...</td>\n",
       "      <td>https://scihub.copernicus.eu/apihub/odata/v1/P...</td>\n",
       "      <td>Date: 2020-06-17T10:10:31.024Z, Instrument: MS...</td>\n",
       "      <td>false</td>\n",
       "      <td>2020-06-17 10:10:31.024</td>\n",
       "      <td>2020-06-17 10:10:31.024</td>\n",
       "      <td>2020-06-17 21:35:56.836</td>\n",
       "      <td>26045</td>\n",
       "      <td>...</td>\n",
       "      <td>relation</td>\n",
       "      <td>62422</td>\n",
       "      <td>Berlin, Deutschland</td>\n",
       "      <td>8</td>\n",
       "      <td>boundary</td>\n",
       "      <td>administrative</td>\n",
       "      <td>0.897539</td>\n",
       "      <td>https://nominatim.openstreetmap.org/ui/mapicon...</td>\n",
       "      <td>MULTIPOLYGON (((215331.937 168288.512, 216047....</td>\n",
       "      <td>8.535962e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>S2B_MSIL2A_20200615T101559_N0214_R065_T33UUU_2...</td>\n",
       "      <td>https://scihub.copernicus.eu/apihub/odata/v1/P...</td>\n",
       "      <td>https://scihub.copernicus.eu/apihub/odata/v1/P...</td>\n",
       "      <td>https://scihub.copernicus.eu/apihub/odata/v1/P...</td>\n",
       "      <td>Date: 2020-06-15T10:15:59.024Z, Instrument: MS...</td>\n",
       "      <td>false</td>\n",
       "      <td>2020-06-15 10:15:59.024</td>\n",
       "      <td>2020-06-15 10:15:59.024</td>\n",
       "      <td>2020-06-16 01:21:09.378</td>\n",
       "      <td>17108</td>\n",
       "      <td>...</td>\n",
       "      <td>relation</td>\n",
       "      <td>62422</td>\n",
       "      <td>Berlin, Deutschland</td>\n",
       "      <td>8</td>\n",
       "      <td>boundary</td>\n",
       "      <td>administrative</td>\n",
       "      <td>0.897539</td>\n",
       "      <td>https://nominatim.openstreetmap.org/ui/mapicon...</td>\n",
       "      <td>MULTIPOLYGON (((215331.937 168288.512, 216047....</td>\n",
       "      <td>8.535962e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>S2B_MSIL2A_20200602T100559_N0214_R022_T33UUU_2...</td>\n",
       "      <td>https://scihub.copernicus.eu/apihub/odata/v1/P...</td>\n",
       "      <td>https://scihub.copernicus.eu/apihub/odata/v1/P...</td>\n",
       "      <td>https://scihub.copernicus.eu/apihub/odata/v1/P...</td>\n",
       "      <td>Date: 2020-06-02T10:05:59.024Z, Instrument: MS...</td>\n",
       "      <td>false</td>\n",
       "      <td>2020-06-02 10:05:59.024</td>\n",
       "      <td>2020-06-02 10:05:59.024</td>\n",
       "      <td>2020-06-02 18:40:43.331</td>\n",
       "      <td>16922</td>\n",
       "      <td>...</td>\n",
       "      <td>relation</td>\n",
       "      <td>62422</td>\n",
       "      <td>Berlin, Deutschland</td>\n",
       "      <td>8</td>\n",
       "      <td>boundary</td>\n",
       "      <td>administrative</td>\n",
       "      <td>0.897539</td>\n",
       "      <td>https://nominatim.openstreetmap.org/ui/mapicon...</td>\n",
       "      <td>MULTIPOLYGON (((215331.937 168288.512, 216047....</td>\n",
       "      <td>8.535962e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>S2A_MSIL2A_20200531T102031_N0214_R065_T33UUU_2...</td>\n",
       "      <td>https://scihub.copernicus.eu/apihub/odata/v1/P...</td>\n",
       "      <td>https://scihub.copernicus.eu/apihub/odata/v1/P...</td>\n",
       "      <td>https://scihub.copernicus.eu/apihub/odata/v1/P...</td>\n",
       "      <td>Date: 2020-05-31T10:20:31.024Z, Instrument: MS...</td>\n",
       "      <td>false</td>\n",
       "      <td>2020-05-31 10:20:31.024</td>\n",
       "      <td>2020-05-31 10:20:31.024</td>\n",
       "      <td>2020-05-31 20:21:27.279</td>\n",
       "      <td>25802</td>\n",
       "      <td>...</td>\n",
       "      <td>relation</td>\n",
       "      <td>62422</td>\n",
       "      <td>Berlin, Deutschland</td>\n",
       "      <td>8</td>\n",
       "      <td>boundary</td>\n",
       "      <td>administrative</td>\n",
       "      <td>0.897539</td>\n",
       "      <td>https://nominatim.openstreetmap.org/ui/mapicon...</td>\n",
       "      <td>MULTIPOLYGON (((215331.937 168288.512, 216047....</td>\n",
       "      <td>8.535962e+08</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4 rows × 46 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                title  \\\n",
       "1   S2A_MSIL2A_20200617T101031_N0214_R022_T33UUU_2...   \n",
       "3   S2B_MSIL2A_20200615T101559_N0214_R065_T33UUU_2...   \n",
       "10  S2B_MSIL2A_20200602T100559_N0214_R022_T33UUU_2...   \n",
       "11  S2A_MSIL2A_20200531T102031_N0214_R065_T33UUU_2...   \n",
       "\n",
       "                                                 link  \\\n",
       "1   https://scihub.copernicus.eu/apihub/odata/v1/P...   \n",
       "3   https://scihub.copernicus.eu/apihub/odata/v1/P...   \n",
       "10  https://scihub.copernicus.eu/apihub/odata/v1/P...   \n",
       "11  https://scihub.copernicus.eu/apihub/odata/v1/P...   \n",
       "\n",
       "                                     link_alternative  \\\n",
       "1   https://scihub.copernicus.eu/apihub/odata/v1/P...   \n",
       "3   https://scihub.copernicus.eu/apihub/odata/v1/P...   \n",
       "10  https://scihub.copernicus.eu/apihub/odata/v1/P...   \n",
       "11  https://scihub.copernicus.eu/apihub/odata/v1/P...   \n",
       "\n",
       "                                            link_icon  \\\n",
       "1   https://scihub.copernicus.eu/apihub/odata/v1/P...   \n",
       "3   https://scihub.copernicus.eu/apihub/odata/v1/P...   \n",
       "10  https://scihub.copernicus.eu/apihub/odata/v1/P...   \n",
       "11  https://scihub.copernicus.eu/apihub/odata/v1/P...   \n",
       "\n",
       "                                              summary ondemand  \\\n",
       "1   Date: 2020-06-17T10:10:31.024Z, Instrument: MS...    false   \n",
       "3   Date: 2020-06-15T10:15:59.024Z, Instrument: MS...    false   \n",
       "10  Date: 2020-06-02T10:05:59.024Z, Instrument: MS...    false   \n",
       "11  Date: 2020-05-31T10:20:31.024Z, Instrument: MS...    false   \n",
       "\n",
       "             beginposition             endposition           ingestiondate  \\\n",
       "1  2020-06-17 10:10:31.024 2020-06-17 10:10:31.024 2020-06-17 21:35:56.836   \n",
       "3  2020-06-15 10:15:59.024 2020-06-15 10:15:59.024 2020-06-16 01:21:09.378   \n",
       "10 2020-06-02 10:05:59.024 2020-06-02 10:05:59.024 2020-06-02 18:40:43.331   \n",
       "11 2020-05-31 10:20:31.024 2020-05-31 10:20:31.024 2020-05-31 20:21:27.279   \n",
       "\n",
       "    orbitnumber  ...  osm_type  osm_id         display_name  place_rank  \\\n",
       "1         26045  ...  relation   62422  Berlin, Deutschland           8   \n",
       "3         17108  ...  relation   62422  Berlin, Deutschland           8   \n",
       "10        16922  ...  relation   62422  Berlin, Deutschland           8   \n",
       "11        25802  ...  relation   62422  Berlin, Deutschland           8   \n",
       "\n",
       "    category            type  importance  \\\n",
       "1   boundary  administrative    0.897539   \n",
       "3   boundary  administrative    0.897539   \n",
       "10  boundary  administrative    0.897539   \n",
       "11  boundary  administrative    0.897539   \n",
       "\n",
       "                                                 icon  \\\n",
       "1   https://nominatim.openstreetmap.org/ui/mapicon...   \n",
       "3   https://nominatim.openstreetmap.org/ui/mapicon...   \n",
       "10  https://nominatim.openstreetmap.org/ui/mapicon...   \n",
       "11  https://nominatim.openstreetmap.org/ui/mapicon...   \n",
       "\n",
       "                                             geometry          area  \n",
       "1   MULTIPOLYGON (((215331.937 168288.512, 216047....  8.535962e+08  \n",
       "3   MULTIPOLYGON (((215331.937 168288.512, 216047....  8.535962e+08  \n",
       "10  MULTIPOLYGON (((215331.937 168288.512, 216047....  8.535962e+08  \n",
       "11  MULTIPOLYGON (((215331.937 168288.512, 216047....  8.535962e+08  \n",
       "\n",
       "[4 rows x 46 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import geopandas as gpd\n",
    "\n",
    "intersection = gpd.overlay(\n",
    "    gdf,\n",
    "    berlin,\n",
    "    how=\"intersection\"\n",
    ")\n",
    "intersection['area'] = intersection['geometry'].area\n",
    "max_area = max(*intersection['area'])\n",
    "\n",
    "largest_intersections = intersection[intersection['area'] == max_area]\n",
    "largest_intersections"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>title</th>\n",
       "      <th>cloudcoverpercentage</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>S2A_MSIL2A_20200617T101031_N0214_R022_T33UUU_2...</td>\n",
       "      <td>24.374344</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>S2B_MSIL2A_20200615T101559_N0214_R065_T33UUU_2...</td>\n",
       "      <td>3.626466</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>S2B_MSIL2A_20200602T100559_N0214_R022_T33UUU_2...</td>\n",
       "      <td>0.345483</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>S2A_MSIL2A_20200531T102031_N0214_R065_T33UUU_2...</td>\n",
       "      <td>16.512980</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                title  cloudcoverpercentage\n",
       "1   S2A_MSIL2A_20200617T101031_N0214_R022_T33UUU_2...             24.374344\n",
       "3   S2B_MSIL2A_20200615T101559_N0214_R065_T33UUU_2...              3.626466\n",
       "10  S2B_MSIL2A_20200602T100559_N0214_R022_T33UUU_2...              0.345483\n",
       "11  S2A_MSIL2A_20200531T102031_N0214_R065_T33UUU_2...             16.512980"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "largest_intersections[['title', 'cloudcoverpercentage']]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "These files will all be downloaded to `resources/true_color_image`.\n",
    "Repeated calls to `download` or `download_all` will skip the download process if a product has previously been downloaded."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'958ef7fc-815e-47c6-b323-219de6a91339': {'id': '958ef7fc-815e-47c6-b323-219de6a91339',\n",
       "  'title': 'S2A_MSIL2A_20200617T101031_N0214_R022_T33UUU_20200617T161854',\n",
       "  'size': 854267363,\n",
       "  'md5': 'E8E78FEEBF25D95E50A7E5E0C971C061',\n",
       "  'date': datetime.datetime(2020, 6, 17, 10, 10, 31, 24000),\n",
       "  'footprint': 'POLYGON((12.352510548540328 52.23126993496904,12.408603762795623 52.36271559980042,12.470993716931147 52.50836314366547,12.533637922787934 52.653880456978825,12.596456783078388 52.79937966494399,12.659608391506955 52.944876790325615,12.72334134983589 53.09033052896263,12.783072077326212 53.2261774473976,13.648341514492438 53.24196354764364,13.678544065009483 52.25515074878083,12.352510548540328 52.23126993496904))',\n",
       "  'url': \"https://scihub.copernicus.eu/apihub/odata/v1/Products('958ef7fc-815e-47c6-b323-219de6a91339')/$value\",\n",
       "  'Online': True,\n",
       "  'Creation Date': datetime.datetime(2020, 6, 17, 21, 36, 46, 539000),\n",
       "  'Ingestion Date': datetime.datetime(2020, 6, 17, 21, 35, 56, 836000),\n",
       "  'path': 'resources/true_color_image/S2A_MSIL2A_20200617T101031_N0214_R022_T33UUU_20200617T161854.zip',\n",
       "  'downloaded_bytes': 0},\n",
       " 'b64d4450-d999-41dc-9ecb-6d263f322dd3': {'id': 'b64d4450-d999-41dc-9ecb-6d263f322dd3',\n",
       "  'title': 'S2B_MSIL2A_20200615T101559_N0214_R065_T33UUU_20200615T141117',\n",
       "  'size': 1176239030,\n",
       "  'md5': '86D9DB1D0C6C087D66AE28E4A106EF04',\n",
       "  'date': datetime.datetime(2020, 6, 15, 10, 15, 59, 24000),\n",
       "  'footprint': 'POLYGON((12.004760925069467 53.21197782662703,13.648341514492438 53.24196354764364,13.678544065009483 52.25515074878083,12.071595116334858 52.22621087043558,12.004760925069467 53.21197782662703))',\n",
       "  'url': \"https://scihub.copernicus.eu/apihub/odata/v1/Products('b64d4450-d999-41dc-9ecb-6d263f322dd3')/$value\",\n",
       "  'Online': True,\n",
       "  'Creation Date': datetime.datetime(2020, 6, 16, 1, 22, 2, 573000),\n",
       "  'Ingestion Date': datetime.datetime(2020, 6, 16, 1, 21, 9, 378000),\n",
       "  'path': 'resources/true_color_image/S2B_MSIL2A_20200615T101559_N0214_R065_T33UUU_20200615T141117.zip',\n",
       "  'downloaded_bytes': 0},\n",
       " '10190cf1-5690-48a3-b71e-861629703a3f': {'id': '10190cf1-5690-48a3-b71e-861629703a3f',\n",
       "  'title': 'S2B_MSIL2A_20200602T100559_N0214_R022_T33UUU_20200602T135324',\n",
       "  'size': 862966648,\n",
       "  'md5': 'A93C8F7AA12C2396CF57CFD02ADF21A2',\n",
       "  'date': datetime.datetime(2020, 6, 2, 10, 5, 59, 24000),\n",
       "  'footprint': 'POLYGON((12.358321903656456 52.23137459287384,12.415699786858166 52.36633012201262,12.47808001229512 52.51194199175493,12.5407946185535 52.65754848189568,12.603698138951865 52.80313660845604,12.667166015291048 52.94869104851679,12.731188091140115 53.09422646557383,12.789415151489028 53.226293171356296,13.648341514492438 53.24196354764364,13.678544065009483 52.25515074878083,12.358321903656456 52.23137459287384))',\n",
       "  'url': \"https://scihub.copernicus.eu/apihub/odata/v1/Products('10190cf1-5690-48a3-b71e-861629703a3f')/$value\",\n",
       "  'Online': True,\n",
       "  'Creation Date': datetime.datetime(2020, 6, 2, 18, 42, 4, 900000),\n",
       "  'Ingestion Date': datetime.datetime(2020, 6, 2, 18, 40, 43, 331000),\n",
       "  'path': 'resources/true_color_image/S2B_MSIL2A_20200602T100559_N0214_R022_T33UUU_20200602T135324.zip',\n",
       "  'downloaded_bytes': 0},\n",
       " 'e385d397-4d1b-4fa2-a1ce-917f420c86dd': {'id': 'e385d397-4d1b-4fa2-a1ce-917f420c86dd',\n",
       "  'title': 'S2A_MSIL2A_20200531T102031_N0214_R065_T33UUU_20200531T134031',\n",
       "  'size': 1162711104,\n",
       "  'md5': '7E74A83DA5EA1176CFB68517E53A343D',\n",
       "  'date': datetime.datetime(2020, 5, 31, 10, 20, 31, 24000),\n",
       "  'footprint': 'POLYGON((12.004760925069467 53.21197782662703,13.648341514492438 53.24196354764364,13.678544065009483 52.25515074878083,12.071595116334858 52.22621087043558,12.004760925069467 53.21197782662703))',\n",
       "  'url': \"https://scihub.copernicus.eu/apihub/odata/v1/Products('e385d397-4d1b-4fa2-a1ce-917f420c86dd')/$value\",\n",
       "  'Online': True,\n",
       "  'Creation Date': datetime.datetime(2020, 5, 31, 20, 22, 5, 475000),\n",
       "  'Ingestion Date': datetime.datetime(2020, 5, 31, 20, 21, 27, 279000),\n",
       "  'path': 'resources/true_color_image/S2A_MSIL2A_20200531T102031_N0214_R065_T33UUU_20200531T134031.zip',\n",
       "  'downloaded_bytes': 0}}"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dst_path = resources / 'true_color_image'\n",
    "# we make sure to create the directory at dst_path so the notebook can run\n",
    "# without interruptions\n",
    "dst_path.mkdir(exist_ok=True, parents=True)\n",
    "\n",
    "downloads, _, _ = api.download_all(largest_intersections['uuid'].values, dst_path)\n",
    "downloads"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`downloads` contains a dictionary, mapping each product's UUID to detailed information about the downloaded product.\n",
    "We can use it to calculate the total download size:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Downloaded 0.00GB\n"
     ]
    }
   ],
   "source": [
    "downloaded_bytes_total = sum(p['downloaded_bytes'] for p in downloads.values())\n",
    "print(f'Downloaded {(downloaded_bytes_total / 1024 ** 3):.2f}GB')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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