Tutorial_Part_3_WFS_WCS.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Part 3: Subset and download data using OGC Web Feature and Coverage Services (WFS/WCS)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The OWSLib library is a python library that makes accessing data and metadata through OGC services possible. The library abstracts all of the traditional HTTP API calls into programmatic function calls. We will be using this library throughout the tutorial to interact with the EMODnet OGC services.\n",
    "\n",
    "The library is available from GitHub @  https://github.com/geopython/OWSLib"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from owslib.wfs import WebFeatureService\n",
    "from owslib.wcs import WebCoverageService\n",
    "from owslib.wms import WebMapService\n",
    "from owslib.etree import etree\n",
    "import owslib.fes as fes \n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.image as mpimg\n",
    "import os\n",
    "import rasterio\n",
    "import cartopy\n",
    "import cartopy.crs as ccrs\n",
    "import rioxarray\n",
    "import geopandas as gpd\n",
    "import warnings"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3.1 Get vector data with WFS\n",
    "#### Working with EMODnet Human Activities datasets"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Create a WebFeatureService (WFS) object\n",
    "Note that we use version 1.1.0 as in OWSLib this version has support for [OGC Filter Encoding](https://www.ogc.org/standards/filter)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "wfs = WebFeatureService('https://ows.emodnet-humanactivities.eu/wfs', version = '1.1.0')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Find out what the WFS has to offer. Service metadata:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WFS\n",
      "1.1.0\n",
      "GeoServer Web Feature Service\n",
      "This is the reference implementation of WFS 1.0.0 and WFS 1.1.0, supports all WFS operations including Transaction.\n"
     ]
    }
   ],
   "source": [
    "print(wfs.identification.type)\n",
    "print(wfs.identification.version)\n",
    "print(wfs.identification.title)\n",
    "print(wfs.identification.abstract)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### See available methods"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['GetCapabilities',\n",
       " 'DescribeFeatureType',\n",
       " 'GetFeature',\n",
       " 'GetGmlObject',\n",
       " 'LockFeature',\n",
       " 'GetFeatureWithLock',\n",
       " 'Transaction']"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "[operation.name for operation in wfs.operations]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Get available formats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'resultType': {'values': ['results', 'hits']},\n",
       " 'outputFormat': {'values': ['text/xml; subtype=gml/3.1.1',\n",
       "   'GML2',\n",
       "   'KML',\n",
       "   'SHAPE-ZIP',\n",
       "   'application/gml+xml; version=3.2',\n",
       "   'application/json',\n",
       "   'application/vnd.google-earth.kml xml',\n",
       "   'application/vnd.google-earth.kml+xml',\n",
       "   'csv',\n",
       "   'gml3',\n",
       "   'gml32',\n",
       "   'json',\n",
       "   'text/csv',\n",
       "   'text/xml; subtype=gml/2.1.2',\n",
       "   'text/xml; subtype=gml/3.2']}}"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wfs.getOperationByName('GetFeature').parameters"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Find out what vector datasets (FeatureTypes) are available\n",
    "Note that only vector datasets that are uploaded trhough the Human Activities portal and are publicly available are shown"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['emodnet:activelicenses',\n",
       " 'emodnet:advisorycouncils',\n",
       " 'emodnet:aquaculture',\n",
       " 'emodnet:baltic',\n",
       " 'emodnet:blacksea',\n",
       " 'emodnet:longdistancefleet',\n",
       " 'emodnet:market',\n",
       " 'emodnet:mediterranean',\n",
       " 'emodnet:northsea',\n",
       " 'emodnet:northwesternwaters',\n",
       " 'emodnet:outermostregions',\n",
       " 'emodnet:pelagicstocks',\n",
       " 'emodnet:southwesternwaters',\n",
       " 'emodnet:aggregateareas',\n",
       " 'emodnet:aggregates',\n",
       " 'emodnet:fishingbeamtrawls',\n",
       " 'emodnet:fishingbottomottertrawls',\n",
       " 'emodnet:fishingbottomseines',\n",
       " 'emodnet:fishingdredges',\n",
       " 'emodnet:fishingpelagic',\n",
       " 'emodnet:fishingstaticgears',\n",
       " 'emodnet:fishingsubsurface',\n",
       " 'emodnet:fishingsurface',\n",
       " 'emodnet:barcelona',\n",
       " 'emodnet:hydrocarbons',\n",
       " 'emodnet:bucharest',\n",
       " 'emodnet:desalination',\n",
       " 'emodnet:dischargepoints',\n",
       " 'emodnet:dredgespoil',\n",
       " 'emodnet:dredgespoilpoly',\n",
       " 'emodnet:dredging',\n",
       " 'emodnet:munitions',\n",
       " 'emodnet:munitionspoly',\n",
       " 'emodnet:emeraldnetwork',\n",
       " 'emodnet:faoareas_old',\n",
       " 'emodnet:fao_areas',\n",
       " 'emodnet:finfish',\n",
       " 'emodnet:finfishV1',\n",
       " 'emodnet:fishsales',\n",
       " 'emodnet:fishsalesbyspecies',\n",
       " 'emodnet:divisioncatches',\n",
       " 'emodnet:majorcatches',\n",
       " 'emodnet:subareacatches',\n",
       " 'emodnet:subdivisioncatches',\n",
       " 'emodnet:subunitcatches',\n",
       " 'emodnet:divisioncatchesbygroup',\n",
       " 'emodnet:majorcatchesbygroup',\n",
       " 'emodnet:subareacatchesbygroup',\n",
       " 'emodnet:subdivisioncatchesbygroup',\n",
       " 'emodnet:subunitcatchesbygroup',\n",
       " 'emodnet:divisioncatchesbycountry',\n",
       " 'emodnet:majorcatchesbycountry',\n",
       " 'emodnet:subareacatchesbycountry',\n",
       " 'emodnet:subdivisioncatchesbycountry',\n",
       " 'emodnet:subunitcatchesbycountry',\n",
       " 'emodnet:divisioneffortbycountry',\n",
       " 'emodnet:gfcmsubareaeffortbycountry',\n",
       " 'emodnet:majoreffortbycountry',\n",
       " 'emodnet:subareaeffortbycountry',\n",
       " 'emodnet:subdivisioneffortbycountry',\n",
       " 'emodnet:subuniteffortbycountry',\n",
       " 'emodnet:divisioneffortbyvesseltype',\n",
       " 'emodnet:gfcmsubareaeffortbyvesseltype',\n",
       " 'emodnet:majoreffortbyvesseltype',\n",
       " 'emodnet:subareaeffortbyvesseltype',\n",
       " 'emodnet:subdivisioneffortbyvesseltype',\n",
       " 'emodnet:subuniteffortbyvesseltype',\n",
       " 'emodnet:divisioneffort',\n",
       " 'emodnet:gfcmsubareaeffort',\n",
       " 'emodnet:majoreffort',\n",
       " 'emodnet:subareaeffort',\n",
       " 'emodnet:subdivisioneffort',\n",
       " 'emodnet:subuniteffort',\n",
       " 'emodnet:fishquotas',\n",
       " 'emodnet:fishquotas240408',\n",
       " 'emodnet:fishquotas240408bylevel',\n",
       " 'emodnet:fishquotasbylevel',\n",
       " 'emodnet:fishquotas240408byspecies',\n",
       " 'emodnet:fishquotasbyspecies',\n",
       " 'emodnet:freshwater',\n",
       " 'emodnet:helcom',\n",
       " 'emodnet:heritageshipwrecks',\n",
       " 'emodnet:icesareas',\n",
       " 'emodnet:intconventions',\n",
       " 'emodnet:lighthouses',\n",
       " 'emodnet:reportingunits',\n",
       " 'emodnet:mspspatialplan',\n",
       " 'emodnet:mspzoningpoly',\n",
       " 'emodnet:mspzoningline',\n",
       " 'emodnet:mspzoninglocs',\n",
       " 'emodnet:macroalgae',\n",
       " 'emodnet:portgoods',\n",
       " 'emodnet:portlocations',\n",
       " 'emodnet:portpassengers',\n",
       " 'emodnet:portvessels',\n",
       " 'emodnet:portvesselsbytonnage',\n",
       " 'emodnet:portvesselsbytype',\n",
       " 'emodnet:microalgae',\n",
       " 'emodnet:militaryareas',\n",
       " 'emodnet:militaryareaspoly',\n",
       " 'emodnet:cddaareas',\n",
       " 'emodnet:cddalocations',\n",
       " 'emodnet:natura2000areas',\n",
       " 'emodnet:nuclear',\n",
       " 'emodnet:ospar',\n",
       " 'emodnet:oenergy',\n",
       " 'emodnet:oenergytests',\n",
       " 'emodnet:platforms',\n",
       " 'emodnet:pipelines',\n",
       " 'emodnet:pcablesbshcontis',\n",
       " 'emodnet:pcablesshom',\n",
       " 'emodnet:pcablesrijks',\n",
       " 'emodnet:pcablesnve',\n",
       " 'emodnet:marineprotectedareas',\n",
       " 'emodnet:shellfishlics',\n",
       " 'emodnet:shellfish',\n",
       " 'emodnet:shellfishwfdareas',\n",
       " 'emodnet:spirulina',\n",
       " 'emodnet:bathingwaters',\n",
       " 'emodnet:bshcontiscables',\n",
       " 'emodnet:cicacables',\n",
       " 'emodnet:shomcables',\n",
       " 'emodnet:sigcables',\n",
       " 'emodnet:maltacables',\n",
       " 'emodnet:rijkscables',\n",
       " 'emodnet:ukfibrecables',\n",
       " 'emodnet:treatmentplants',\n",
       " 'emodnet:unescowhl',\n",
       " 'emodnet:wasteatports',\n",
       " 'emodnet:windfarms',\n",
       " 'emodnet:windfarmspoly',\n",
       " 'emodnet:wdpaareas',\n",
       " 'emodnet:wdpalocations',\n",
       " 'emodnet:wwshipwrecks']"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(wfs.contents)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Select the desired dataset among the list returned above"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Here we chose the European windfarms dataset presented as georeferenced polygons\n",
    "dataset = 'emodnet:windfarmspoly'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Get the FeatureType Metadata an Bounding Box:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Wind Farms (Polygons)\n",
      "The dataset on offshore wind farms in the European seas was created in 2014 by CETMAR for the European Marine Observation and Data Network (EMODnet). It is the result of the aggregation and harmonization of datasets provided by several sources. It is updated every year and it is available for viewing and download on EMODnet web portal (Human Activities, https://emodnet.ec.europa.eu/en/human-activities). The dataset contains points and/or (where available) polygons representing offshore wind farms in the following countries: Belgium, Denmark, Estonia, Finland, France, Germany, Greece, Ireland, Italy, Latvia, Lithuania, Netherlands, Norway, Poland, Portugal, Spain, Sweden and United Kingdom. Each point and polygon has the following attributes (where available): Name, Nº of turbines, Status (Approved, Planned, Dismantled, Construction, Production, Test site), Country, Year, Power (MW), Distance to coast (metres) and Area (square kilometres). The distance to coast (EEA coastline shapefile) has been calculated using the UTM WGS84 Zone projected coordinate system where data fall in.\n",
      "(-10.32376766204834, 39.712947845458984, 24.912221908569336, 65.59857177734375)\n"
     ]
    }
   ],
   "source": [
    "print(wfs.contents[dataset].title)\n",
    "print(wfs.contents[dataset].abstract)\n",
    "bbox1 = wfs.contents[dataset].boundingBoxWGS84\n",
    "print (bbox1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Get the FeatureType’s schema via `DescribeFeatureType`.\n",
    "The dictionary returned is compatible with a [Fiona schema object](https://fiona.readthedocs.io/en/latest/fiona.html#fiona.collection.Collection.schema)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'properties': {'country': 'string',\n",
       "  'n_turbines': 'int',\n",
       "  'power_mw': 'decimal',\n",
       "  'status': 'string',\n",
       "  'year': 'string',\n",
       "  'dist_coast': 'decimal',\n",
       "  'area_sqkm': 'decimal',\n",
       "  'notes': 'string'},\n",
       " 'required': [],\n",
       " 'geometry': 'GeometryCollection',\n",
       " 'geometry_column': 'the_geom'}"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wfs.get_schema(dataset)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Subset data with a bounding box"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "        vertical-align: middle;\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>country</th>\n",
       "      <th>name</th>\n",
       "      <th>n_turbines</th>\n",
       "      <th>power_mw</th>\n",
       "      <th>status</th>\n",
       "      <th>year</th>\n",
       "      <th>dist_coast</th>\n",
       "      <th>area_sqkm</th>\n",
       "      <th>notes</th>\n",
       "      <th>geometry</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>windfarmspoly.1</td>\n",
       "      <td>Sweden</td>\n",
       "      <td>Stora Middelgrund</td>\n",
       "      <td>108.0</td>\n",
       "      <td>864.0</td>\n",
       "      <td>Planned</td>\n",
       "      <td>NaN</td>\n",
       "      <td>24805.731113</td>\n",
       "      <td>59.826131</td>\n",
       "      <td>NaN</td>\n",
       "      <td>MULTIPOLYGON (((12.03532 56.65295, 12.06134 56...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>windfarmspoly.2</td>\n",
       "      <td>Sweden</td>\n",
       "      <td>Lovstaviken</td>\n",
       "      <td>5.0</td>\n",
       "      <td>11.5</td>\n",
       "      <td>Production</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.193854</td>\n",
       "      <td>NaN</td>\n",
       "      <td>MULTIPOLYGON (((12.46710 56.89015, 12.47244 56...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>windfarmspoly.3</td>\n",
       "      <td>Sweden</td>\n",
       "      <td>Kattegatt Offshore</td>\n",
       "      <td>50.0</td>\n",
       "      <td>282.0</td>\n",
       "      <td>Approved</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6381.649369</td>\n",
       "      <td>22.111037</td>\n",
       "      <td>NaN</td>\n",
       "      <td>MULTIPOLYGON (((12.33361 56.86036, 12.34738 56...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>windfarmspoly.5</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>Islay Demo Zone</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>Approved</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1122.942248</td>\n",
       "      <td>27.611537</td>\n",
       "      <td>NaN</td>\n",
       "      <td>MULTIPOLYGON (((-6.52572 55.65146, -6.55948 55...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>windfarmspoly.6</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>North West Lewis (30MW)</td>\n",
       "      <td>30.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>Planned</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>51.062081</td>\n",
       "      <td>NaN</td>\n",
       "      <td>MULTIPOLYGON (((-6.58016 58.36416, -6.58043 58...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>557</th>\n",
       "      <td>windfarmspoly.551</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>Culzean</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.0</td>\n",
       "      <td>Planned</td>\n",
       "      <td>NaN</td>\n",
       "      <td>221021.383044</td>\n",
       "      <td>2.001045</td>\n",
       "      <td>NaN</td>\n",
       "      <td>MULTIPOLYGON (((1.87627 57.19864, 1.87668 57.1...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>558</th>\n",
       "      <td>windfarmspoly.552</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>Judy</td>\n",
       "      <td>NaN</td>\n",
       "      <td>15.0</td>\n",
       "      <td>Planned</td>\n",
       "      <td>NaN</td>\n",
       "      <td>259624.252434</td>\n",
       "      <td>8.045883</td>\n",
       "      <td>NaN</td>\n",
       "      <td>MULTIPOLYGON (((2.27045 56.67040, 2.27103 56.6...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>559</th>\n",
       "      <td>windfarmspoly.568</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>Offshore Wind Leasing Round 5 - PDA 1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Planned</td>\n",
       "      <td>NaN</td>\n",
       "      <td>35824.936586</td>\n",
       "      <td>369.050704</td>\n",
       "      <td>NaN</td>\n",
       "      <td>MULTIPOLYGON (((-5.61768 51.23197, -5.61767 51...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>560</th>\n",
       "      <td>windfarmspoly.569</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>Offshore Wind Leasing Round 5 - PDA 2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Planned</td>\n",
       "      <td>NaN</td>\n",
       "      <td>58291.392216</td>\n",
       "      <td>357.532494</td>\n",
       "      <td>NaN</td>\n",
       "      <td>MULTIPOLYGON (((-5.61767 51.21310, -5.61767 51...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>561</th>\n",
       "      <td>windfarmspoly.570</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>Sinclair</td>\n",
       "      <td>NaN</td>\n",
       "      <td>100.0</td>\n",
       "      <td>Planned</td>\n",
       "      <td>NaN</td>\n",
       "      <td>58700.697705</td>\n",
       "      <td>24.914015</td>\n",
       "      <td>NaN</td>\n",
       "      <td>MULTIPOLYGON (((-1.77221 58.24662, -1.77068 58...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>562 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                    id         country                                   name  \\\n",
       "0      windfarmspoly.1          Sweden                      Stora Middelgrund   \n",
       "1      windfarmspoly.2          Sweden                            Lovstaviken   \n",
       "2      windfarmspoly.3          Sweden                     Kattegatt Offshore   \n",
       "3      windfarmspoly.5  United Kingdom                        Islay Demo Zone   \n",
       "4      windfarmspoly.6  United Kingdom                North West Lewis (30MW)   \n",
       "..                 ...             ...                                    ...   \n",
       "557  windfarmspoly.551  United Kingdom                                Culzean   \n",
       "558  windfarmspoly.552  United Kingdom                                   Judy   \n",
       "559  windfarmspoly.568  United Kingdom  Offshore Wind Leasing Round 5 - PDA 1   \n",
       "560  windfarmspoly.569  United Kingdom  Offshore Wind Leasing Round 5 - PDA 2   \n",
       "561  windfarmspoly.570  United Kingdom                               Sinclair   \n",
       "\n",
       "     n_turbines  power_mw      status year     dist_coast   area_sqkm notes  \\\n",
       "0         108.0     864.0     Planned  NaN   24805.731113   59.826131   NaN   \n",
       "1           5.0      11.5  Production  NaN       0.000000    0.193854   NaN   \n",
       "2          50.0     282.0    Approved  NaN    6381.649369   22.111037   NaN   \n",
       "3          10.0      10.0    Approved  NaN    1122.942248   27.611537   NaN   \n",
       "4          30.0      30.0     Planned  NaN       0.000000   51.062081   NaN   \n",
       "..          ...       ...         ...  ...            ...         ...   ...   \n",
       "557         NaN       3.0     Planned  NaN  221021.383044    2.001045   NaN   \n",
       "558         NaN      15.0     Planned  NaN  259624.252434    8.045883   NaN   \n",
       "559         NaN       0.0     Planned  NaN   35824.936586  369.050704   NaN   \n",
       "560         NaN       0.0     Planned  NaN   58291.392216  357.532494   NaN   \n",
       "561         NaN     100.0     Planned  NaN   58700.697705   24.914015   NaN   \n",
       "\n",
       "                                              geometry  \n",
       "0    MULTIPOLYGON (((12.03532 56.65295, 12.06134 56...  \n",
       "1    MULTIPOLYGON (((12.46710 56.89015, 12.47244 56...  \n",
       "2    MULTIPOLYGON (((12.33361 56.86036, 12.34738 56...  \n",
       "3    MULTIPOLYGON (((-6.52572 55.65146, -6.55948 55...  \n",
       "4    MULTIPOLYGON (((-6.58016 58.36416, -6.58043 58...  \n",
       "..                                                 ...  \n",
       "557  MULTIPOLYGON (((1.87627 57.19864, 1.87668 57.1...  \n",
       "558  MULTIPOLYGON (((2.27045 56.67040, 2.27103 56.6...  \n",
       "559  MULTIPOLYGON (((-5.61768 51.23197, -5.61767 51...  \n",
       "560  MULTIPOLYGON (((-5.61767 51.21310, -5.61767 51...  \n",
       "561  MULTIPOLYGON (((-1.77221 58.24662, -1.77068 58...  \n",
       "\n",
       "[562 rows x 11 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Subset data with a bounding box\n",
    "response = wfs.getfeature(typename=dataset,\n",
    "                          bbox=(bbox1), #subset to the Study area ( based on bbox params)\n",
    "                          outputFormat='application/json')\n",
    "\n",
    "# Write it to a GeoJson file\n",
    "with open(f'data/{dataset.replace(\":\",\"_\")}.json', 'wb') as outfile:\n",
    "    outfile.write(response.read())\n",
    "response.close()\n",
    "\n",
    "# Read the GeoJson file using GeoPandas\n",
    "gdf = gpd.read_file(f'data/{dataset.replace(\":\",\"_\")}.json')\n",
    "gdf"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### SETUP basemap"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "Bathy = WebMapService('https://ows.emodnet-bathymetry.eu/ows', version= '1.3.0')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['GetCapabilities', 'GetMap', 'GetFeatureInfo']"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "[op.name for op in Bathy.operations]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['image/png',\n",
       " 'application/atom+xml',\n",
       " 'application/json;type=utfgrid',\n",
       " 'application/pdf',\n",
       " 'application/rss+xml',\n",
       " 'application/vnd.google-earth.kml+xml',\n",
       " 'application/vnd.google-earth.kml+xml;mode=networklink',\n",
       " 'application/vnd.google-earth.kmz',\n",
       " 'image/geotiff',\n",
       " 'image/geotiff8',\n",
       " 'image/gif',\n",
       " 'image/jpeg',\n",
       " 'image/png; mode=8bit',\n",
       " 'image/svg+xml',\n",
       " 'image/tiff',\n",
       " 'image/tiff8',\n",
       " 'image/vnd.jpeg-png',\n",
       " 'image/vnd.jpeg-png8',\n",
       " 'text/html; subtype=openlayers',\n",
       " 'text/html; subtype=openlayers2',\n",
       " 'text/html; subtype=openlayers3']"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Bathy.getOperationByName('GetMap').formatOptions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Get a coastlines basemap for plotting:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "Bmap = Bathy['coastlines']\n",
    "save_path = 'data/'\n",
    "def getMap(filename, bbox, layerName):\n",
    "    Bathy.getOperationByName('GetMap').formatOptions\n",
    "    img = Bathy.getmap(layers= layerName,\n",
    "                 size=(1200,600), \n",
    "                 srs='EPSG:4326',\n",
    "                 bbox=bbox,\n",
    "                 format='image/png',\n",
    "                 style='contour',\n",
    "                 transparent=True)\n",
    "\n",
    "# Write the image to a temporary file\n",
    "    out = open(os.path.join(save_path, filename), 'wb')\n",
    "    out.write(img.read())\n",
    "    out.close()  \n",
    "\n",
    "# Define the layers to be used for the image, \n",
    "# here we use the multicolor bathymery dataset.\n",
    "getMap(\n",
    "    layerName=['coastlines'],\n",
    "    bbox=(bbox1),\n",
    "    filename = 'Basemap.png'\n",
    ")\n",
    "#read the image\n",
    "image=mpimg.imread('data/Basemap.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\matteo.mikos\\AppData\\Local\\anaconda3\\envs\\ogc_workshop\\Lib\\site-packages\\geopandas\\plotting.py:732: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(values.dtype):\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 900x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Load the image representing the basemap\n",
    "basemap_img = mpimg.imread('data/Basemap.png')\n",
    "\n",
    "# Create a figure and axis object\n",
    "fig, ax = plt.subplots(figsize=(9, 8))\n",
    "\n",
    "# Plot the basemap image using imshow\n",
    "ax.imshow(basemap_img, extent=[bbox1[0],  bbox1[2], bbox1[1], bbox1[3]]) \n",
    "\n",
    "# Plot the data on top of the basemap\n",
    "gdf.plot(column='status',                                       # Specify the column in which the data you need to plot is located\n",
    "         categorical=False,\n",
    "         cmap='RdYlGn',\n",
    "         legend=True,\n",
    "         legend_kwds={'loc': 'lower right'},\n",
    "         alpha=1,\n",
    "         ax=ax) \n",
    "\n",
    "# Set title and limits\n",
    "ax.set_title('Offshore Wind Farm Projects in European waters', fontsize=20)\n",
    "ax.set_xlim(bbox1[0], bbox1[2]) \n",
    "ax.set_ylim(bbox1[1], bbox1[3]) \n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Subset data by attributes using [OGC Filter Encoding](https://www.ogc.org/standards/filter)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "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>id</th>\n",
       "      <th>country</th>\n",
       "      <th>name</th>\n",
       "      <th>n_turbines</th>\n",
       "      <th>power_mw</th>\n",
       "      <th>status</th>\n",
       "      <th>year</th>\n",
       "      <th>dist_coast</th>\n",
       "      <th>area_sqkm</th>\n",
       "      <th>notes</th>\n",
       "      <th>geometry</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>windfarmspoly.286</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>N3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Planned</td>\n",
       "      <td>2020</td>\n",
       "      <td>9457.333545</td>\n",
       "      <td>1106.354044</td>\n",
       "      <td>NaN</td>\n",
       "      <td>MULTIPOLYGON (((-6.32441 59.05853, -6.31226 59...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>windfarmspoly.287</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>N1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Planned</td>\n",
       "      <td>2020</td>\n",
       "      <td>3171.943570</td>\n",
       "      <td>1162.427300</td>\n",
       "      <td>NaN</td>\n",
       "      <td>MULTIPOLYGON (((-4.10739 59.13575, -4.09759 59...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>windfarmspoly.289</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>E2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Planned</td>\n",
       "      <td>2020</td>\n",
       "      <td>59706.165817</td>\n",
       "      <td>1291.429348</td>\n",
       "      <td>NaN</td>\n",
       "      <td>MULTIPOLYGON (((-0.50129 57.47805, -0.48542 57...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>windfarmspoly.290</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>E1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Planned</td>\n",
       "      <td>2020</td>\n",
       "      <td>56455.635331</td>\n",
       "      <td>3743.866036</td>\n",
       "      <td>NaN</td>\n",
       "      <td>MULTIPOLYGON (((-0.47392 56.91518, -0.45823 56...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>windfarmspoly.440</td>\n",
       "      <td>Denmark</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Planned</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5000.551370</td>\n",
       "      <td>10079.276602</td>\n",
       "      <td>NaN</td>\n",
       "      <td>MULTIPOLYGON (((14.60980 55.11517, 14.60971 55...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>windfarmspoly.40</td>\n",
       "      <td>Denmark</td>\n",
       "      <td>Nordsren II</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Planned</td>\n",
       "      <td>NaN</td>\n",
       "      <td>49194.172947</td>\n",
       "      <td>1872.941410</td>\n",
       "      <td>NaN</td>\n",
       "      <td>MULTIPOLYGON (((6.41926 55.80585, 6.41538 55.8...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>windfarmspoly.48</td>\n",
       "      <td>Denmark</td>\n",
       "      <td>Nordsren I</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Planned</td>\n",
       "      <td>NaN</td>\n",
       "      <td>19964.419331</td>\n",
       "      <td>3176.148975</td>\n",
       "      <td>NaN</td>\n",
       "      <td>MULTIPOLYGON (((7.79172 56.37805, 7.79165 56.3...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>windfarmspoly.36</td>\n",
       "      <td>Denmark</td>\n",
       "      <td>Nordsren III vest</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Planned</td>\n",
       "      <td>NaN</td>\n",
       "      <td>109379.688310</td>\n",
       "      <td>4484.323143</td>\n",
       "      <td>NaN</td>\n",
       "      <td>MULTIPOLYGON (((6.33084 56.44456, 6.32952 56.4...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>windfarmspoly.44</td>\n",
       "      <td>Denmark</td>\n",
       "      <td>Nordsren III</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Planned</td>\n",
       "      <td>NaN</td>\n",
       "      <td>73863.316263</td>\n",
       "      <td>1642.587692</td>\n",
       "      <td>NaN</td>\n",
       "      <td>MULTIPOLYGON (((6.51131 56.88091, 6.54150 56.8...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>windfarmspoly.81</td>\n",
       "      <td>Netherlands</td>\n",
       "      <td>IJmuiden Ver</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4000.0</td>\n",
       "      <td>Planned</td>\n",
       "      <td>NaN</td>\n",
       "      <td>49466.898142</td>\n",
       "      <td>1172.060155</td>\n",
       "      <td>NaN</td>\n",
       "      <td>MULTIPOLYGON (((3.74863 53.16913, 3.74864 53.0...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>windfarmspoly.133</td>\n",
       "      <td>Ireland</td>\n",
       "      <td>Inis Ealge Marine Energy Park - DP Ireland</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Planned</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1009.408557</td>\n",
       "      <td>NaN</td>\n",
       "      <td>MULTIPOLYGON (((-7.49680 52.06100, -7.49660 52...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>windfarmspoly.438</td>\n",
       "      <td>Finland</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Planned</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6150.131138</td>\n",
       "      <td>1007.154648</td>\n",
       "      <td>NaN</td>\n",
       "      <td>MULTIPOLYGON (((20.60960 59.50261, 20.38395 59...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>windfarmspoly.439</td>\n",
       "      <td>Finland</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Planned</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6150.131138</td>\n",
       "      <td>1007.154648</td>\n",
       "      <td>NaN</td>\n",
       "      <td>MULTIPOLYGON (((20.60960 59.50261, 20.38395 59...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>windfarmspoly.441</td>\n",
       "      <td>Denmark</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Planned</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>5713.909591</td>\n",
       "      <td>NaN</td>\n",
       "      <td>MULTIPOLYGON (((11.76105 54.53160, 11.76105 54...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>windfarmspoly.507</td>\n",
       "      <td>Spain</td>\n",
       "      <td>ZAPER_NOR_2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Planned</td>\n",
       "      <td>NaN</td>\n",
       "      <td>28752.000636</td>\n",
       "      <td>1807.819544</td>\n",
       "      <td>NaN</td>\n",
       "      <td>MULTIPOLYGON (((-8.67734 43.76666, -8.99647 43...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>windfarmspoly.519</td>\n",
       "      <td>Estonia</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Planned</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9903.634178</td>\n",
       "      <td>1217.896092</td>\n",
       "      <td>NaN</td>\n",
       "      <td>MULTIPOLYGON (((21.69848 57.84691, 21.65391 57...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   id         country  \\\n",
       "0   windfarmspoly.286  United Kingdom   \n",
       "1   windfarmspoly.287  United Kingdom   \n",
       "2   windfarmspoly.289  United Kingdom   \n",
       "3   windfarmspoly.290  United Kingdom   \n",
       "4   windfarmspoly.440         Denmark   \n",
       "5    windfarmspoly.40         Denmark   \n",
       "6    windfarmspoly.48         Denmark   \n",
       "7    windfarmspoly.36         Denmark   \n",
       "8    windfarmspoly.44         Denmark   \n",
       "9    windfarmspoly.81     Netherlands   \n",
       "10  windfarmspoly.133         Ireland   \n",
       "11  windfarmspoly.438         Finland   \n",
       "12  windfarmspoly.439         Finland   \n",
       "13  windfarmspoly.441         Denmark   \n",
       "14  windfarmspoly.507           Spain   \n",
       "15  windfarmspoly.519         Estonia   \n",
       "\n",
       "                                          name  n_turbines  power_mw   status  \\\n",
       "0                                           N3         NaN       0.0  Planned   \n",
       "1                                           N1         NaN       0.0  Planned   \n",
       "2                                           E2         NaN       0.0  Planned   \n",
       "3                                           E1         NaN       0.0  Planned   \n",
       "4                                          NaN         NaN       0.0  Planned   \n",
       "5                                  Nordsren II         NaN       0.0  Planned   \n",
       "6                                   Nordsren I         NaN       0.0  Planned   \n",
       "7                            Nordsren III vest         NaN       0.0  Planned   \n",
       "8                                 Nordsren III         NaN       0.0  Planned   \n",
       "9                                 IJmuiden Ver         NaN    4000.0  Planned   \n",
       "10  Inis Ealge Marine Energy Park - DP Ireland         NaN       0.0  Planned   \n",
       "11                                         NaN         NaN       0.0  Planned   \n",
       "12                                         NaN         NaN       0.0  Planned   \n",
       "13                                         NaN         NaN       0.0  Planned   \n",
       "14                                 ZAPER_NOR_2         NaN       0.0  Planned   \n",
       "15                                         NaN         NaN       0.0  Planned   \n",
       "\n",
       "    year     dist_coast     area_sqkm  notes  \\\n",
       "0   2020    9457.333545   1106.354044    NaN   \n",
       "1   2020    3171.943570   1162.427300    NaN   \n",
       "2   2020   59706.165817   1291.429348    NaN   \n",
       "3   2020   56455.635331   3743.866036    NaN   \n",
       "4    NaN    5000.551370  10079.276602    NaN   \n",
       "5    NaN   49194.172947   1872.941410    NaN   \n",
       "6    NaN   19964.419331   3176.148975    NaN   \n",
       "7    NaN  109379.688310   4484.323143    NaN   \n",
       "8    NaN   73863.316263   1642.587692    NaN   \n",
       "9    NaN   49466.898142   1172.060155    NaN   \n",
       "10   NaN       0.000000   1009.408557    NaN   \n",
       "11   NaN    6150.131138   1007.154648    NaN   \n",
       "12   NaN    6150.131138   1007.154648    NaN   \n",
       "13   NaN       0.000000   5713.909591    NaN   \n",
       "14   NaN   28752.000636   1807.819544    NaN   \n",
       "15   NaN    9903.634178   1217.896092    NaN   \n",
       "\n",
       "                                             geometry  \n",
       "0   MULTIPOLYGON (((-6.32441 59.05853, -6.31226 59...  \n",
       "1   MULTIPOLYGON (((-4.10739 59.13575, -4.09759 59...  \n",
       "2   MULTIPOLYGON (((-0.50129 57.47805, -0.48542 57...  \n",
       "3   MULTIPOLYGON (((-0.47392 56.91518, -0.45823 56...  \n",
       "4   MULTIPOLYGON (((14.60980 55.11517, 14.60971 55...  \n",
       "5   MULTIPOLYGON (((6.41926 55.80585, 6.41538 55.8...  \n",
       "6   MULTIPOLYGON (((7.79172 56.37805, 7.79165 56.3...  \n",
       "7   MULTIPOLYGON (((6.33084 56.44456, 6.32952 56.4...  \n",
       "8   MULTIPOLYGON (((6.51131 56.88091, 6.54150 56.8...  \n",
       "9   MULTIPOLYGON (((3.74863 53.16913, 3.74864 53.0...  \n",
       "10  MULTIPOLYGON (((-7.49680 52.06100, -7.49660 52...  \n",
       "11  MULTIPOLYGON (((20.60960 59.50261, 20.38395 59...  \n",
       "12  MULTIPOLYGON (((20.60960 59.50261, 20.38395 59...  \n",
       "13  MULTIPOLYGON (((11.76105 54.53160, 11.76105 54...  \n",
       "14  MULTIPOLYGON (((-8.67734 43.76666, -8.99647 43...  \n",
       "15  MULTIPOLYGON (((21.69848 57.84691, 21.65391 57...  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Build a filter\n",
    "sa_filter = fes.PropertyIsEqualTo(propertyname='status', literal='Planned') # Planned Windfarm projects\n",
    "fid_filter = fes.PropertyIsBetween(propertyname='area_sqkm', lower = 1000, upper= 100000) # filter for projects bigger than 1000Km²\n",
    "filter = fes.And([sa_filter,fid_filter]) # combine both filtering parameters using \"AND\"\n",
    "\n",
    "# Get an xml representation of the filter\n",
    "filterxml = etree.tostring(filter.toXML()).decode(\"utf-8\")\n",
    "\n",
    "# Pass it to getFeature\n",
    "response = wfs.getfeature(typename=dataset,\n",
    "                        outputFormat='application/json',\n",
    "                        filter=filterxml)\n",
    "\n",
    "# Write it to a GeoJson file\n",
    "with open(f'data/{dataset.replace(\":\",\"_\")}_filtered.json', 'wb') as outfile:\n",
    "    outfile.write(response.read())\n",
    "response.close()\n",
    "\n",
    "# Read the GeoJson file using GeoPandas\n",
    "gdf = gpd.read_file(f'data/{dataset.replace(\":\",\"_\")}_filtered.json')\n",
    "gdf"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Change basemap to emodnet streetmap from bathy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\matteo.mikos\\AppData\\Local\\anaconda3\\envs\\ogc_workshop\\Lib\\site-packages\\geopandas\\plotting.py:732: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(values.dtype):\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(39.712947845458984, 65.59857177734375)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1, figsize=(8,6))\n",
    "\n",
    "# Plot the basemap image using imshow\n",
    "ax.imshow(basemap_img, extent=[bbox1[0],  bbox1[2], bbox1[1], bbox1[3]])\n",
    "\n",
    "#Plot the dataset\n",
    "gdf.plot(column='status',\n",
    "         categorical = False,\n",
    "         cmap='RdYlGn',\n",
    "         legend= True,\n",
    "         legend_kwds={'loc': 'lower right'},\n",
    "         alpha = 1,\n",
    "         ax=ax)\n",
    "ax.set_title('Planned offshore wind farm projects\\n larger than 1000 km²',fontsize=18)\n",
    "\n",
    "#Set the map limits based on the bbox params to zoom in on your dataset\n",
    "ax.set_xlim(bbox1[0],bbox1[2]) \n",
    "ax.set_ylim(bbox1[1],bbox1[3])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Example 2: Combining Subset of WFS request with a WCS (Web Coverage Services) request\n",
    "### 1. Define a new study area"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this example, we will be looking to subset the _\"Modelled occurrence probability for coralligenous habitats of the Mediterranean sea\"_ dataset obtained from Seabed Habitats thematic of EMODnet, to the marine region of the Western Mediterranean sea (as defined by ICES Ecoregions)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 1.1 Connect to the MarineRegions.org WebFeatureService \n",
    "Marine Regions is an integration of the VLIMAR Gazetteer and the VLIZ Maritime Boundaries Geodatabase. The VLIMAR Gazetteer is a database with geographic, mainly marine names such as seas, sandbanks, seamounts, ridges, bays or even standard sampling stations used in marine research. It is accessible at: https://www.marineregions.org/gazetteer.php?p=search"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WFS\n",
      "1.1.0\n",
      "Flanders Marine Institute (VLIZ) - WFS Service\n",
      "This is the reference implementation of OGC WFS 1.0.0 and WFS 1.1.0, supports all WFS operations including Transaction.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "['MarineRegions:eez_archipelagic_waters',\n",
       " 'MarineRegions:arcticmarineareas',\n",
       " 'MarineRegions:brasdorlakebiospherereserve',\n",
       " 'MarineRegions:isa_ccz_managementarea',\n",
       " 'MarineRegions:coasts_subnational',\n",
       " 'MarineRegions:coasts_per_ocean',\n",
       " 'MarineRegions:eez_24nm',\n",
       " 'MarineRegions:comarge',\n",
       " 'MarineRegions:cross_dateline_polygons',\n",
       " 'MarineRegions:eca_reg13_nox',\n",
       " 'MarineRegions:eca_reg14_sox_pm',\n",
       " 'MarineRegions:eez',\n",
       " 'MarineRegions:ecs',\n",
       " 'MarineRegions:ecs_boundaries',\n",
       " 'MarineRegions:fadaregions',\n",
       " 'MarineRegions:fao',\n",
       " 'MarineRegions:tnc_wwf_feow_hydrosheds',\n",
       " 'MarineRegions:gsas_mediterraneanfishingzones',\n",
       " 'MarineRegions:longhurst',\n",
       " 'MarineRegions:goas',\n",
       " 'MarineRegions:haedatgrids_polygon',\n",
       " 'MarineRegions:high_seas',\n",
       " 'MarineRegions:ices_ecoregions',\n",
       " 'MarineRegions:ices_areas',\n",
       " 'MarineRegions:ices_statistical_rectangles',\n",
       " 'MarineRegions:iho',\n",
       " 'MarineRegions:iho_quadrants_20150810',\n",
       " 'MarineRegions:eez_internal_waters',\n",
       " 'MarineRegions:itmarbiogeogsector_bianchi',\n",
       " 'MarineRegions:eez_land',\n",
       " 'MarineRegions:eez_boundaries',\n",
       " 'MarineRegions:mesopelagiczones',\n",
       " 'MarineRegions:gazetteer_line',\n",
       " 'MarineRegions:gazetteer_polygon',\n",
       " 'MarineRegions:world_countries_coasts',\n",
       " 'MarineRegions:na_coastalwaterbodies',\n",
       " 'MarineRegions:ospar_regions',\n",
       " 'MarineRegions:ospar_boundaries',\n",
       " 'MarineRegions:mra_nor_blocks',\n",
       " 'MarineRegions:mra_uk_blocks',\n",
       " 'MarineRegions:seavox_v16',\n",
       " 'MarineRegions:seavox_v17',\n",
       " 'MarineRegions:seavox_v18',\n",
       " 'MarineRegions:seavox_v19',\n",
       " 'MarineRegions:eez_12nm',\n",
       " 'MarineRegions:lme',\n",
       " 'MarineRegions:eez_iho',\n",
       " 'MarineRegions:worldheritagemarineprogramme_v1',\n",
       " 'MarineRegions:worldheritagemarineprogramme',\n",
       " 'MarineRegions:world_bay_gulf',\n",
       " 'MarineRegions:world_estuary_delta',\n",
       " 'MarineRegions:worldcountries_esri_2014',\n",
       " 'MarineRegions:distritos_ecologicos',\n",
       " 'MarineRegions:land_v9']"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wfs2 = WebFeatureService('https://geo.vliz.be/geoserver/MarineRegions/wfs', version= '1.1.0')\n",
    "\n",
    "#Inspect available datasets\n",
    "print(wfs2.identification.type)\n",
    "print(wfs2.identification.version)\n",
    "print(wfs2.identification.title)\n",
    "print(wfs2.identification.abstract)\n",
    "list(wfs2.contents)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['GetCapabilities',\n",
       " 'DescribeFeatureType',\n",
       " 'GetFeature',\n",
       " 'GetGmlObject',\n",
       " 'LockFeature',\n",
       " 'GetFeatureWithLock',\n",
       " 'Transaction']"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Define desired regional classification, here ICES Ecoregions\n",
    "dataset2 = 'MarineRegions:ices_ecoregions'\n",
    "\n",
    "# See available methods\n",
    "[operation.name for operation in wfs2.operations]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ICES Ecoregions (2015)\n",
      "Delineation of the ICES Ecoregions was originally described in ICES Advice ACFM/ACE report(2004).\n",
      "\n",
      "In the present updated version, the delineation has been updated to account for additional policy objectives and reconciling of legislation (eg the MSFD and the EU water and habitats directives)\n",
      "\n",
      "The differences are based on the four principles:\n",
      "1. Within the EU accept the MSFD regional and sub-regional definitions.\n",
      "2. Separate Icelandic Seas from those around Greenland.\n",
      "3. Use Large Marine Ecosystem units to define the Arctic Ocean.\n",
      "4. Use the accepted Norwegian regional management plans definitions of the Norwegian and Barents Seas.\n",
      "\n",
      "The Ecoregions were proposed to assist the implementation of the ecosystem approach in European waters\n",
      "(-44.0000000000998, 30.2670495513959, 68.5000001347984, 90.0000000001001)\n"
     ]
    }
   ],
   "source": [
    "#Inspect ICES Ecoregions dataset\n",
    "print(wfs2.contents[dataset2].title)\n",
    "print(wfs2.contents[dataset2].abstract)\n",
    "bbox = wfs2.contents[dataset2].boundingBoxWGS84\n",
    "print (bbox)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'properties': {'objectid': 'int',\n",
       "  'ecoregion': 'string',\n",
       "  'mrgid': 'int',\n",
       "  'longitude': 'decimal',\n",
       "  'latitude': 'decimal',\n",
       "  'area_km2': 'decimal',\n",
       "  'placetype': 'string',\n",
       "  'placetypei': 'int',\n",
       "  'parent': 'string',\n",
       "  'parentid': 'int',\n",
       "  'source': 'string',\n",
       "  'sourceid': 'int',\n",
       "  'lon_min': 'decimal',\n",
       "  'lon_max': 'decimal',\n",
       "  'lat_min': 'decimal',\n",
       "  'lat_max': 'decimal'},\n",
       " 'required': [],\n",
       " 'geometry': 'MultiPolygon',\n",
       " 'geometry_column': 'the_geom'}"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get the FeatureType’s schema via `DescribeFeatureType`\n",
    "wfs2.get_schema(dataset2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 1.2 Inspect the ICES Ecoregions datasets\n",
    "Listing all available regional delimitations based on the ICES Ecoregions classification"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "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>id</th>\n",
       "      <th>objectid</th>\n",
       "      <th>ecoregion</th>\n",
       "      <th>mrgid</th>\n",
       "      <th>longitude</th>\n",
       "      <th>latitude</th>\n",
       "      <th>area_km2</th>\n",
       "      <th>placetype</th>\n",
       "      <th>placetypei</th>\n",
       "      <th>parent</th>\n",
       "      <th>parentid</th>\n",
       "      <th>source</th>\n",
       "      <th>sourceid</th>\n",
       "      <th>lon_min</th>\n",
       "      <th>lon_max</th>\n",
       "      <th>lat_min</th>\n",
       "      <th>lat_max</th>\n",
       "      <th>geometry</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ices_ecoregions.1</td>\n",
       "      <td>1</td>\n",
       "      <td>Greenland Sea</td>\n",
       "      <td>36310</td>\n",
       "      <td>-28.070627</td>\n",
       "      <td>69.670326</td>\n",
       "      <td>1.065385e+06</td>\n",
       "      <td>ICES Ecoregion</td>\n",
       "      <td>246</td>\n",
       "      <td>ICES Ecoregions</td>\n",
       "      <td>22540</td>\n",
       "      <td>ICES</td>\n",
       "      <td>378</td>\n",
       "      <td>-44.000000</td>\n",
       "      <td>5.677908</td>\n",
       "      <td>56.560460</td>\n",
       "      <td>82.304253</td>\n",
       "      <td>MULTIPOLYGON (((-43.99177 60.29694, -44.00000 ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ices_ecoregions.2</td>\n",
       "      <td>2</td>\n",
       "      <td>Bay of Biscay and the Iberian Coast</td>\n",
       "      <td>36311</td>\n",
       "      <td>-8.886815</td>\n",
       "      <td>42.662908</td>\n",
       "      <td>7.557796e+05</td>\n",
       "      <td>ICES Ecoregion</td>\n",
       "      <td>246</td>\n",
       "      <td>ICES Ecoregions</td>\n",
       "      <td>22540</td>\n",
       "      <td>ICES</td>\n",
       "      <td>378</td>\n",
       "      <td>-13.861560</td>\n",
       "      <td>-0.146245</td>\n",
       "      <td>36.000000</td>\n",
       "      <td>48.000002</td>\n",
       "      <td>MULTIPOLYGON (((-5.60000 36.01104, -5.60000 36...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ices_ecoregions.3</td>\n",
       "      <td>3</td>\n",
       "      <td>Azores</td>\n",
       "      <td>36312</td>\n",
       "      <td>-28.438624</td>\n",
       "      <td>39.154203</td>\n",
       "      <td>7.928794e+05</td>\n",
       "      <td>ICES Ecoregion</td>\n",
       "      <td>246</td>\n",
       "      <td>ICES Ecoregions</td>\n",
       "      <td>22540</td>\n",
       "      <td>ICES</td>\n",
       "      <td>378</td>\n",
       "      <td>-35.579300</td>\n",
       "      <td>-20.855320</td>\n",
       "      <td>36.000000</td>\n",
       "      <td>43.067630</td>\n",
       "      <td>MULTIPOLYGON (((-21.05878 36.00000, -23.49448 ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ices_ecoregions.4</td>\n",
       "      <td>4</td>\n",
       "      <td>Western Mediterranean Sea</td>\n",
       "      <td>22167</td>\n",
       "      <td>6.109270</td>\n",
       "      <td>39.531134</td>\n",
       "      <td>8.444074e+05</td>\n",
       "      <td>ICES Ecoregion</td>\n",
       "      <td>246</td>\n",
       "      <td>ICES Ecoregions</td>\n",
       "      <td>22540</td>\n",
       "      <td>ICES</td>\n",
       "      <td>378</td>\n",
       "      <td>-5.600000</td>\n",
       "      <td>16.223122</td>\n",
       "      <td>35.070668</td>\n",
       "      <td>44.427203</td>\n",
       "      <td>MULTIPOLYGON (((2.99963 43.14220, 2.99973 43.1...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ices_ecoregions.5</td>\n",
       "      <td>5</td>\n",
       "      <td>Ionian Sea and the Central Mediterranean Sea</td>\n",
       "      <td>36313</td>\n",
       "      <td>17.320194</td>\n",
       "      <td>35.190387</td>\n",
       "      <td>7.726428e+05</td>\n",
       "      <td>ICES Ecoregion</td>\n",
       "      <td>246</td>\n",
       "      <td>ICES Ecoregions</td>\n",
       "      <td>22540</td>\n",
       "      <td>ICES</td>\n",
       "      <td>378</td>\n",
       "      <td>10.015849</td>\n",
       "      <td>24.122811</td>\n",
       "      <td>30.267050</td>\n",
       "      <td>40.520659</td>\n",
       "      <td>MULTIPOLYGON (((24.12281 34.80180, 24.12272 34...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>ices_ecoregions.6</td>\n",
       "      <td>6</td>\n",
       "      <td>Black Sea</td>\n",
       "      <td>22172</td>\n",
       "      <td>34.262870</td>\n",
       "      <td>43.464598</td>\n",
       "      <td>4.747685e+05</td>\n",
       "      <td>ICES Ecoregion</td>\n",
       "      <td>246</td>\n",
       "      <td>ICES Ecoregions</td>\n",
       "      <td>22540</td>\n",
       "      <td>ICES</td>\n",
       "      <td>378</td>\n",
       "      <td>26.175552</td>\n",
       "      <td>42.354960</td>\n",
       "      <td>39.998909</td>\n",
       "      <td>47.376395</td>\n",
       "      <td>MULTIPOLYGON (((41.58356 42.40528, 41.57035 42...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>ices_ecoregions.7</td>\n",
       "      <td>7</td>\n",
       "      <td>Adriatic Sea</td>\n",
       "      <td>36314</td>\n",
       "      <td>16.118553</td>\n",
       "      <td>42.714787</td>\n",
       "      <td>1.392988e+05</td>\n",
       "      <td>ICES Ecoregion</td>\n",
       "      <td>246</td>\n",
       "      <td>ICES Ecoregions</td>\n",
       "      <td>22540</td>\n",
       "      <td>ICES</td>\n",
       "      <td>378</td>\n",
       "      <td>12.065226</td>\n",
       "      <td>20.020828</td>\n",
       "      <td>39.744192</td>\n",
       "      <td>45.808914</td>\n",
       "      <td>MULTIPOLYGON (((19.99367 39.79384, 19.99293 39...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>ices_ecoregions.8</td>\n",
       "      <td>8</td>\n",
       "      <td>Aegean-Levantine Sea</td>\n",
       "      <td>22169</td>\n",
       "      <td>28.686066</td>\n",
       "      <td>34.822184</td>\n",
       "      <td>7.567145e+05</td>\n",
       "      <td>ICES Ecoregion</td>\n",
       "      <td>246</td>\n",
       "      <td>ICES Ecoregions</td>\n",
       "      <td>22540</td>\n",
       "      <td>ICES</td>\n",
       "      <td>378</td>\n",
       "      <td>22.149885</td>\n",
       "      <td>36.215729</td>\n",
       "      <td>30.756594</td>\n",
       "      <td>41.091326</td>\n",
       "      <td>MULTIPOLYGON (((25.74529 36.79303, 25.74350 36...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>ices_ecoregions.9</td>\n",
       "      <td>9</td>\n",
       "      <td>Celtic Seas</td>\n",
       "      <td>22173</td>\n",
       "      <td>-9.264609</td>\n",
       "      <td>54.591504</td>\n",
       "      <td>9.177512e+05</td>\n",
       "      <td>ICES Ecoregion</td>\n",
       "      <td>246</td>\n",
       "      <td>ICES Ecoregions</td>\n",
       "      <td>22540</td>\n",
       "      <td>ICES</td>\n",
       "      <td>378</td>\n",
       "      <td>-16.090850</td>\n",
       "      <td>1.806284</td>\n",
       "      <td>48.000002</td>\n",
       "      <td>63.890844</td>\n",
       "      <td>MULTIPOLYGON (((1.37123 62.00000, 1.37123 62.0...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>ices_ecoregions.10</td>\n",
       "      <td>10</td>\n",
       "      <td>Baltic Sea</td>\n",
       "      <td>22170</td>\n",
       "      <td>19.497017</td>\n",
       "      <td>58.713587</td>\n",
       "      <td>3.999565e+05</td>\n",
       "      <td>ICES Ecoregion</td>\n",
       "      <td>246</td>\n",
       "      <td>ICES Ecoregions</td>\n",
       "      <td>22540</td>\n",
       "      <td>ICES</td>\n",
       "      <td>378</td>\n",
       "      <td>9.419914</td>\n",
       "      <td>31.685704</td>\n",
       "      <td>52.871122</td>\n",
       "      <td>67.080588</td>\n",
       "      <td>MULTIPOLYGON (((11.07890 53.14186, 11.09182 53...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>ices_ecoregions.12</td>\n",
       "      <td>12</td>\n",
       "      <td>Arctic Ocean</td>\n",
       "      <td>36315</td>\n",
       "      <td>11.178360</td>\n",
       "      <td>85.665237</td>\n",
       "      <td>6.359402e+05</td>\n",
       "      <td>ICES Ecoregion</td>\n",
       "      <td>246</td>\n",
       "      <td>ICES Ecoregions</td>\n",
       "      <td>22540</td>\n",
       "      <td>ICES</td>\n",
       "      <td>378</td>\n",
       "      <td>-40.000000</td>\n",
       "      <td>68.500000</td>\n",
       "      <td>80.573675</td>\n",
       "      <td>90.000000</td>\n",
       "      <td>MULTIPOLYGON (((68.50000 82.97005, 68.05861 82...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>ices_ecoregions.13</td>\n",
       "      <td>13</td>\n",
       "      <td>Iceland Sea</td>\n",
       "      <td>36316</td>\n",
       "      <td>-18.921173</td>\n",
       "      <td>64.634624</td>\n",
       "      <td>7.563617e+05</td>\n",
       "      <td>ICES Ecoregion</td>\n",
       "      <td>246</td>\n",
       "      <td>ICES Ecoregions</td>\n",
       "      <td>22540</td>\n",
       "      <td>ICES</td>\n",
       "      <td>378</td>\n",
       "      <td>-30.863334</td>\n",
       "      <td>-5.557446</td>\n",
       "      <td>59.963399</td>\n",
       "      <td>69.583333</td>\n",
       "      <td>MULTIPOLYGON (((-21.72000 63.84772, -21.71718 ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>ices_ecoregions.14</td>\n",
       "      <td>14</td>\n",
       "      <td>Barents Sea</td>\n",
       "      <td>22162</td>\n",
       "      <td>36.343739</td>\n",
       "      <td>75.260031</td>\n",
       "      <td>2.126450e+06</td>\n",
       "      <td>ICES Ecoregion</td>\n",
       "      <td>246</td>\n",
       "      <td>ICES Ecoregions</td>\n",
       "      <td>22540</td>\n",
       "      <td>ICES</td>\n",
       "      <td>378</td>\n",
       "      <td>0.833790</td>\n",
       "      <td>68.500000</td>\n",
       "      <td>61.941305</td>\n",
       "      <td>82.970055</td>\n",
       "      <td>MULTIPOLYGON (((39.12844 61.95031, 39.13307 61...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>ices_ecoregions.15</td>\n",
       "      <td>15</td>\n",
       "      <td>Faroes</td>\n",
       "      <td>22161</td>\n",
       "      <td>-7.183476</td>\n",
       "      <td>62.578992</td>\n",
       "      <td>2.672277e+05</td>\n",
       "      <td>ICES Ecoregion</td>\n",
       "      <td>246</td>\n",
       "      <td>ICES Ecoregions</td>\n",
       "      <td>22540</td>\n",
       "      <td>ICES</td>\n",
       "      <td>378</td>\n",
       "      <td>-13.909697</td>\n",
       "      <td>-0.484345</td>\n",
       "      <td>59.944713</td>\n",
       "      <td>65.694080</td>\n",
       "      <td>MULTIPOLYGON (((-0.48933 63.89084, -0.49057 63...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>ices_ecoregions.16</td>\n",
       "      <td>16</td>\n",
       "      <td>Norwegian Sea</td>\n",
       "      <td>22164</td>\n",
       "      <td>2.389611</td>\n",
       "      <td>70.232395</td>\n",
       "      <td>1.206188e+06</td>\n",
       "      <td>ICES Ecoregion</td>\n",
       "      <td>246</td>\n",
       "      <td>ICES Ecoregions</td>\n",
       "      <td>22540</td>\n",
       "      <td>ICES</td>\n",
       "      <td>378</td>\n",
       "      <td>-13.629556</td>\n",
       "      <td>17.900159</td>\n",
       "      <td>62.000001</td>\n",
       "      <td>80.833862</td>\n",
       "      <td>MULTIPOLYGON (((9.84627 63.31005, 9.84375 63.3...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>ices_ecoregions.17</td>\n",
       "      <td>17</td>\n",
       "      <td>Oceanic Northeast Atlantic</td>\n",
       "      <td>22166</td>\n",
       "      <td>-27.561575</td>\n",
       "      <td>48.708945</td>\n",
       "      <td>4.753194e+06</td>\n",
       "      <td>ICES Ecoregion</td>\n",
       "      <td>246</td>\n",
       "      <td>ICES Ecoregions</td>\n",
       "      <td>22540</td>\n",
       "      <td>ICES</td>\n",
       "      <td>378</td>\n",
       "      <td>-42.000000</td>\n",
       "      <td>-9.368772</td>\n",
       "      <td>36.000000</td>\n",
       "      <td>63.313333</td>\n",
       "      <td>MULTIPOLYGON (((-13.90970 60.82639, -13.27398 ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>ices_ecoregions.11</td>\n",
       "      <td>11</td>\n",
       "      <td>Greater North Sea</td>\n",
       "      <td>36317</td>\n",
       "      <td>2.932991</td>\n",
       "      <td>56.011900</td>\n",
       "      <td>6.708331e+05</td>\n",
       "      <td>ICES Ecoregion</td>\n",
       "      <td>246</td>\n",
       "      <td>ICES Ecoregions</td>\n",
       "      <td>22540</td>\n",
       "      <td>ICES</td>\n",
       "      <td>378</td>\n",
       "      <td>-5.000000</td>\n",
       "      <td>13.062911</td>\n",
       "      <td>48.000002</td>\n",
       "      <td>62.000003</td>\n",
       "      <td>MULTIPOLYGON (((-4.56139 48.00000, -4.56354 48...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    id  objectid  \\\n",
       "0    ices_ecoregions.1         1   \n",
       "1    ices_ecoregions.2         2   \n",
       "2    ices_ecoregions.3         3   \n",
       "3    ices_ecoregions.4         4   \n",
       "4    ices_ecoregions.5         5   \n",
       "5    ices_ecoregions.6         6   \n",
       "6    ices_ecoregions.7         7   \n",
       "7    ices_ecoregions.8         8   \n",
       "8    ices_ecoregions.9         9   \n",
       "9   ices_ecoregions.10        10   \n",
       "10  ices_ecoregions.12        12   \n",
       "11  ices_ecoregions.13        13   \n",
       "12  ices_ecoregions.14        14   \n",
       "13  ices_ecoregions.15        15   \n",
       "14  ices_ecoregions.16        16   \n",
       "15  ices_ecoregions.17        17   \n",
       "16  ices_ecoregions.11        11   \n",
       "\n",
       "                                       ecoregion  mrgid  longitude   latitude  \\\n",
       "0                                  Greenland Sea  36310 -28.070627  69.670326   \n",
       "1            Bay of Biscay and the Iberian Coast  36311  -8.886815  42.662908   \n",
       "2                                         Azores  36312 -28.438624  39.154203   \n",
       "3                      Western Mediterranean Sea  22167   6.109270  39.531134   \n",
       "4   Ionian Sea and the Central Mediterranean Sea  36313  17.320194  35.190387   \n",
       "5                                      Black Sea  22172  34.262870  43.464598   \n",
       "6                                   Adriatic Sea  36314  16.118553  42.714787   \n",
       "7                           Aegean-Levantine Sea  22169  28.686066  34.822184   \n",
       "8                                    Celtic Seas  22173  -9.264609  54.591504   \n",
       "9                                     Baltic Sea  22170  19.497017  58.713587   \n",
       "10                                  Arctic Ocean  36315  11.178360  85.665237   \n",
       "11                                   Iceland Sea  36316 -18.921173  64.634624   \n",
       "12                                   Barents Sea  22162  36.343739  75.260031   \n",
       "13                                        Faroes  22161  -7.183476  62.578992   \n",
       "14                                 Norwegian Sea  22164   2.389611  70.232395   \n",
       "15                    Oceanic Northeast Atlantic  22166 -27.561575  48.708945   \n",
       "16                             Greater North Sea  36317   2.932991  56.011900   \n",
       "\n",
       "        area_km2       placetype  placetypei           parent  parentid  \\\n",
       "0   1.065385e+06  ICES Ecoregion         246  ICES Ecoregions     22540   \n",
       "1   7.557796e+05  ICES Ecoregion         246  ICES Ecoregions     22540   \n",
       "2   7.928794e+05  ICES Ecoregion         246  ICES Ecoregions     22540   \n",
       "3   8.444074e+05  ICES Ecoregion         246  ICES Ecoregions     22540   \n",
       "4   7.726428e+05  ICES Ecoregion         246  ICES Ecoregions     22540   \n",
       "5   4.747685e+05  ICES Ecoregion         246  ICES Ecoregions     22540   \n",
       "6   1.392988e+05  ICES Ecoregion         246  ICES Ecoregions     22540   \n",
       "7   7.567145e+05  ICES Ecoregion         246  ICES Ecoregions     22540   \n",
       "8   9.177512e+05  ICES Ecoregion         246  ICES Ecoregions     22540   \n",
       "9   3.999565e+05  ICES Ecoregion         246  ICES Ecoregions     22540   \n",
       "10  6.359402e+05  ICES Ecoregion         246  ICES Ecoregions     22540   \n",
       "11  7.563617e+05  ICES Ecoregion         246  ICES Ecoregions     22540   \n",
       "12  2.126450e+06  ICES Ecoregion         246  ICES Ecoregions     22540   \n",
       "13  2.672277e+05  ICES Ecoregion         246  ICES Ecoregions     22540   \n",
       "14  1.206188e+06  ICES Ecoregion         246  ICES Ecoregions     22540   \n",
       "15  4.753194e+06  ICES Ecoregion         246  ICES Ecoregions     22540   \n",
       "16  6.708331e+05  ICES Ecoregion         246  ICES Ecoregions     22540   \n",
       "\n",
       "   source  sourceid    lon_min    lon_max    lat_min    lat_max  \\\n",
       "0    ICES       378 -44.000000   5.677908  56.560460  82.304253   \n",
       "1    ICES       378 -13.861560  -0.146245  36.000000  48.000002   \n",
       "2    ICES       378 -35.579300 -20.855320  36.000000  43.067630   \n",
       "3    ICES       378  -5.600000  16.223122  35.070668  44.427203   \n",
       "4    ICES       378  10.015849  24.122811  30.267050  40.520659   \n",
       "5    ICES       378  26.175552  42.354960  39.998909  47.376395   \n",
       "6    ICES       378  12.065226  20.020828  39.744192  45.808914   \n",
       "7    ICES       378  22.149885  36.215729  30.756594  41.091326   \n",
       "8    ICES       378 -16.090850   1.806284  48.000002  63.890844   \n",
       "9    ICES       378   9.419914  31.685704  52.871122  67.080588   \n",
       "10   ICES       378 -40.000000  68.500000  80.573675  90.000000   \n",
       "11   ICES       378 -30.863334  -5.557446  59.963399  69.583333   \n",
       "12   ICES       378   0.833790  68.500000  61.941305  82.970055   \n",
       "13   ICES       378 -13.909697  -0.484345  59.944713  65.694080   \n",
       "14   ICES       378 -13.629556  17.900159  62.000001  80.833862   \n",
       "15   ICES       378 -42.000000  -9.368772  36.000000  63.313333   \n",
       "16   ICES       378  -5.000000  13.062911  48.000002  62.000003   \n",
       "\n",
       "                                             geometry  \n",
       "0   MULTIPOLYGON (((-43.99177 60.29694, -44.00000 ...  \n",
       "1   MULTIPOLYGON (((-5.60000 36.01104, -5.60000 36...  \n",
       "2   MULTIPOLYGON (((-21.05878 36.00000, -23.49448 ...  \n",
       "3   MULTIPOLYGON (((2.99963 43.14220, 2.99973 43.1...  \n",
       "4   MULTIPOLYGON (((24.12281 34.80180, 24.12272 34...  \n",
       "5   MULTIPOLYGON (((41.58356 42.40528, 41.57035 42...  \n",
       "6   MULTIPOLYGON (((19.99367 39.79384, 19.99293 39...  \n",
       "7   MULTIPOLYGON (((25.74529 36.79303, 25.74350 36...  \n",
       "8   MULTIPOLYGON (((1.37123 62.00000, 1.37123 62.0...  \n",
       "9   MULTIPOLYGON (((11.07890 53.14186, 11.09182 53...  \n",
       "10  MULTIPOLYGON (((68.50000 82.97005, 68.05861 82...  \n",
       "11  MULTIPOLYGON (((-21.72000 63.84772, -21.71718 ...  \n",
       "12  MULTIPOLYGON (((39.12844 61.95031, 39.13307 61...  \n",
       "13  MULTIPOLYGON (((-0.48933 63.89084, -0.49057 63...  \n",
       "14  MULTIPOLYGON (((9.84627 63.31005, 9.84375 63.3...  \n",
       "15  MULTIPOLYGON (((-13.90970 60.82639, -13.27398 ...  \n",
       "16  MULTIPOLYGON (((-4.56139 48.00000, -4.56354 48...  "
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "response = wfs2.getfeature(typename=dataset2,\n",
    "                          bbox=(bbox), \n",
    "                          outputFormat='application/json')\n",
    "\n",
    "# Write it to a GeoJson file\n",
    "with open(f'data/{dataset2.replace(\":\",\"_\")}.json', 'wb') as outfile:\n",
    "    outfile.write(response.read())\n",
    "response.close()\n",
    "\n",
    "# Read the GeoJson file using GeoPandas\n",
    "import geopandas as gpd\n",
    "gdf = gpd.read_file(f'data/{dataset2.replace(\":\",\"_\")}.json')\n",
    "gdf"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 1.3 Write a filter to select the desired region (Greater North Sea)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "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>id</th>\n",
       "      <th>objectid</th>\n",
       "      <th>ecoregion</th>\n",
       "      <th>mrgid</th>\n",
       "      <th>longitude</th>\n",
       "      <th>latitude</th>\n",
       "      <th>area_km2</th>\n",
       "      <th>placetype</th>\n",
       "      <th>placetypei</th>\n",
       "      <th>parent</th>\n",
       "      <th>parentid</th>\n",
       "      <th>source</th>\n",
       "      <th>sourceid</th>\n",
       "      <th>lon_min</th>\n",
       "      <th>lon_max</th>\n",
       "      <th>lat_min</th>\n",
       "      <th>lat_max</th>\n",
       "      <th>geometry</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ices_ecoregions.4</td>\n",
       "      <td>4</td>\n",
       "      <td>Western Mediterranean Sea</td>\n",
       "      <td>22167</td>\n",
       "      <td>6.10927</td>\n",
       "      <td>39.531134</td>\n",
       "      <td>844407.444602</td>\n",
       "      <td>ICES Ecoregion</td>\n",
       "      <td>246</td>\n",
       "      <td>ICES Ecoregions</td>\n",
       "      <td>22540</td>\n",
       "      <td>ICES</td>\n",
       "      <td>378</td>\n",
       "      <td>-5.6</td>\n",
       "      <td>16.223122</td>\n",
       "      <td>35.070668</td>\n",
       "      <td>44.427203</td>\n",
       "      <td>MULTIPOLYGON (((2.99963 43.14220, 2.99973 43.1...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  id  objectid                  ecoregion  mrgid  longitude  \\\n",
       "0  ices_ecoregions.4         4  Western Mediterranean Sea  22167    6.10927   \n",
       "\n",
       "    latitude       area_km2       placetype  placetypei           parent  \\\n",
       "0  39.531134  844407.444602  ICES Ecoregion         246  ICES Ecoregions   \n",
       "\n",
       "   parentid source  sourceid  lon_min    lon_max    lat_min    lat_max  \\\n",
       "0     22540   ICES       378     -5.6  16.223122  35.070668  44.427203   \n",
       "\n",
       "                                            geometry  \n",
       "0  MULTIPOLYGON (((2.99963 43.14220, 2.99973 43.1...  "
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Build a filter\n",
    "filter = fes.PropertyIsEqualTo(propertyname='ecoregion', literal='Western Mediterranean Sea') # Specifying the needed Ecoregion\n",
    "\n",
    "# Get an xml representation of the filter\n",
    "filterxml = etree.tostring(filter.toXML()).decode(\"utf-8\")\n",
    "\n",
    "# Pass it to getFeature\n",
    "response = wfs2.getfeature(typename = dataset2,\n",
    "                        outputFormat='application/json',\n",
    "                        filter=filterxml)\n",
    "\n",
    "# Write it to a GeoJson file\n",
    "with open(f'data/{dataset2.replace(\":\",\"_\")}_filtered.json', 'wb') as outfile:\n",
    "    outfile.write(response.read())\n",
    "response.close()\n",
    "\n",
    "# Read the GeoJson file using GeoPandas\n",
    "Study_area = gpd.read_file(f'data/{dataset2.replace(\":\",\"_\")}_filtered.json')\n",
    "\n",
    "# Plot and inspect the study area dataset\n",
    "Study_area.plot()\n",
    "Study_area"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.2 Get raster data with WCS connecting to the Seabed Habitats Thematic of EMODnet"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### <i>Create a WebCoverageService (WCS) object</i>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "wcs = WebCoverageService('https://ows.emodnet-seabedhabitats.eu/geoserver/emodnet_open_maplibrary/wcs', version = '2.0.1')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### <i>Get a list of available coverages</i>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "emodnet_open_maplibrary__Corine_2018_Coastal_Wetlands_WGS84\n",
      "emodnet_open_maplibrary__Modiolus_habitatsuitabilitymodel_uk_50\n",
      "emodnet_open_maplibrary__Sabellaria_habitatsuitabilitymodel_uk_50\n",
      "emodnet_open_maplibrary__Zostera_habitatsuitabilitymodel_uk_50\n",
      "emodnet_open_maplibrary__LAMpred_Nordic\n",
      "emodnet_open_maplibrary__SACpred_Nordic\n",
      "emodnet_open_maplibrary__mediseh_cora\n",
      "emodnet_open_maplibrary__mediseh_maerl\n",
      "emodnet_open_maplibrary__mediseh_posidonia\n",
      "emodnet_open_maplibrary__DK004008_EFH_Cod_Spawning grounds\n",
      "emodnet_open_maplibrary__DK004009_EFH_Cod_Feeding grounds\n",
      "emodnet_open_maplibrary__DK004010_EFH_Cod_Nursery grounds\n",
      "emodnet_open_maplibrary__DK004011_EFH_Plaice_Spawning grounds\n",
      "emodnet_open_maplibrary__DK004012_EFH_Plaice_Feeding grounds\n",
      "emodnet_open_maplibrary__DK004013_EFH_Plaice_Nursery grounds\n",
      "emodnet_open_maplibrary__DK004014_EFH_Sole_Feeding grounds\n",
      "emodnet_open_maplibrary__DK004015_EFH_Sole_Nursery grounds\n",
      "emodnet_open_maplibrary__DK004016_EFH_Turbot_Feeding grounds\n",
      "emodnet_open_maplibrary__DK004017_EFH_Flounder_Spawning grounds\n",
      "emodnet_open_maplibrary__DK004018_EFH_Flounder_Feeding grounds\n",
      "emodnet_open_maplibrary__DK004019_EFH_Flounder_Nursery grounds\n",
      "emodnet_open_maplibrary__DK004020_EFH_Herring_Feeding grounds\n",
      "emodnet_open_maplibrary__DK004021_EFH_Herring_Nursery grounds\n",
      "emodnet_open_maplibrary__DK004022_EFH_Sprat_Feeding grounds\n",
      "emodnet_open_maplibrary__ES005001\n",
      "emodnet_open_maplibrary__ES005002\n",
      "emodnet_open_maplibrary__ES005003\n",
      "emodnet_open_maplibrary__ES005004\n",
      "emodnet_open_maplibrary__ES005005\n",
      "emodnet_open_maplibrary__ES005006\n",
      "emodnet_open_maplibrary__ES005007\n",
      "emodnet_open_maplibrary__ES005008\n",
      "emodnet_open_maplibrary__fi004001\n",
      "emodnet_open_maplibrary__fi004002\n",
      "emodnet_open_maplibrary__fi004003\n",
      "emodnet_open_maplibrary__fi004004\n",
      "emodnet_open_maplibrary__fi004005\n",
      "emodnet_open_maplibrary__fi004006\n",
      "emodnet_open_maplibrary__fi004007\n",
      "emodnet_open_maplibrary__fi004008\n",
      "emodnet_open_maplibrary__FR005109_EFH_Seabass_SpawningGrounds\n",
      "emodnet_open_maplibrary__FR005110_EFH_Dragonets_SpawningGrounds\n",
      "emodnet_open_maplibrary__FR005111_EFH_Cod_SpawningGrounds\n",
      "emodnet_open_maplibrary__FR005112_EFH_Dab_SpawningGrounds\n",
      "emodnet_open_maplibrary__FR005113_EFH_Rocklings_SpawningGrounds\n",
      "emodnet_open_maplibrary__FR005114_EFH_Whiting_SpawningGrounds\n",
      "emodnet_open_maplibrary__FR005115_EFH_Flounder_SpawningGrounds\n",
      "emodnet_open_maplibrary__FR005116_EFH_Plaice_SpawningGrounds\n",
      "emodnet_open_maplibrary__FR005117_EFH_Sole_SpawningGrounds\n",
      "emodnet_open_maplibrary__GB000050_EFH_Whiting_SpawningGrounds\n",
      "emodnet_open_maplibrary__GB000051_EFH_Haddock_SpawningGrounds\n",
      "emodnet_open_maplibrary__GB300015_model\n",
      "emodnet_open_maplibrary__GB300016_model\n",
      "emodnet_open_maplibrary__GB300017_model\n",
      "emodnet_open_maplibrary__GB300018_model\n",
      "emodnet_open_maplibrary__GB300019_model\n",
      "emodnet_open_maplibrary__GB300020_model\n",
      "emodnet_open_maplibrary__GB300021_model\n",
      "emodnet_open_maplibrary__GB300022_model\n",
      "emodnet_open_maplibrary__PT004002\n",
      "emodnet_open_maplibrary__PT004003\n",
      "emodnet_open_maplibrary__PT004004\n",
      "emodnet_open_maplibrary__PT004005\n",
      "emodnet_open_maplibrary__PT005001_EFH_Sardine_SpawningGrounds\n",
      "emodnet_open_maplibrary__PT005002_EFH_Sardine_SpawningGrounds\n",
      "emodnet_open_maplibrary__PT005003_EFH_Sardine_SpawningGrounds\n",
      "emodnet_open_maplibrary__PT005004_EFH_Sardine_SpawningGrounds\n",
      "emodnet_open_maplibrary__PT005005_EFH_Sardine_SpawningGrounds\n",
      "emodnet_open_maplibrary__PT005006_EFH_Sardine_SpawningGrounds\n"
     ]
    }
   ],
   "source": [
    "for coverage_name in wcs.contents.keys():\n",
    "    print(coverage_name)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### <i>Define the dataset</i>\n",
    "Here we select the \"modelled occurrence probability for coralligenous habitats of the Mediterranean sea\" dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset = 'emodnet_open_maplibrary__mediseh_cora'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### <i>Retrieve a list of available properties of a coverage</i>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "abstract\n",
      "axisDescriptions\n",
      "boundingBox\n",
      "boundingBoxWGS84\n",
      "boundingboxes\n",
      "crsOptions\n",
      "defaulttimeposition\n",
      "grid\n",
      "id\n",
      "keywords\n",
      "styles\n",
      "supportedCRS\n",
      "supportedFormats\n",
      "timelimits\n",
      "timepositions\n",
      "title\n"
     ]
    }
   ],
   "source": [
    "for item in dir(wcs.contents[dataset]):\n",
    "    if \"_\" not in item:\n",
    "        print(item)\n",
    "        "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### <i>Retrieve grid property information</i>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "axislabels: ['i', 'j']\n",
      "dimension: 2\n",
      "highlimits: ['13781', '6398']\n",
      "lowlimits: ['0', '0']\n",
      "offsetvectors: [['400.0', '0.0'], ['0.0', '-400.0']]\n",
      "origin: ['-729757.059693927', '5991106.766843']\n"
     ]
    }
   ],
   "source": [
    "for item in dir(wcs.contents[dataset].grid):\n",
    "    if \"_\" not in item:\n",
    "        print(item + \": \" + str(wcs.contents[dataset].grid.__dict__[item]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Get geo-bounding boxes and native CRS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[{'nativeSrs': 'http://www.opengis.net/def/crs/EPSG/0/3857', 'bbox': (-729957.059693927, 3431706.7668430004, 4782842.940306073, 5991306.766843)}]\n",
      "[]\n",
      "['application/gml+xml', 'image/jpeg', 'image/png', 'image/tiff', 'image/tiff;application=geotiff', 'text/plain']\n",
      "None\n",
      "None\n"
     ]
    }
   ],
   "source": [
    "print(wcs.contents[dataset].boundingboxes)\n",
    "print(wcs.contents[dataset].supportedCRS)\n",
    "print(wcs.contents[dataset].supportedFormats)\n",
    "print(wcs.contents[dataset].boundingBox)\n",
    "print(wcs.contents[dataset].boundingBoxWGS84)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This output shows us that the native CRS of the dataset we are accessing is EPSG 3857 and there are no other supported CRS ('print(wcs.contents[dataset].supportedCRS)')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Retrieve supported formats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['application/gml+xml',\n",
       " 'image/jpeg',\n",
       " 'image/png',\n",
       " 'image/tiff',\n",
       " 'image/tiff;application=geotiff',\n",
       " 'text/plain']"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wcs.contents[dataset].supportedFormats"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Inspect then subset and download raster data using a GetCoverage request"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "# get parameters for grid\n",
    "bbox2 = Study_area.bounds.values[0].tolist() # get bbox for the Western Mediterranean Sea study area \n",
    "crs2 = Study_area.crs.srs # get crs of the Study Area file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[-7.6, 33.07066846, 18.22312212, 46.42720294]\n"
     ]
    }
   ],
   "source": [
    "#Adding 2 decimal degrees to each side of the bbox\n",
    "\n",
    "bbox2 = [bbox2[0]-2,bbox2[1]-2,bbox2[2]+2,bbox2[3]+2]\n",
    "print(bbox2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "https://ows.emodnet-seabedhabitats.eu/geoserver/emodnet_open_maplibrary/wcs?version=2.0.1&request=GetCoverage&service=WCS&CoverageID=emodnet_open_maplibrary__mediseh_cora&crs=EPSG%3A4326&format=image%2Ftiff\n"
     ]
    }
   ],
   "source": [
    "# GetCoverage request\n",
    "response = wcs.getCoverage(identifier=[dataset],\n",
    "                           bbox=bbox2,\n",
    "                           format='image/tiff',\n",
    "                           crs=crs2,\n",
    "                           resx=0.00833333,resy=0.00833333,\n",
    "                           timeout = 120\n",
    "                           )\n",
    "print(response.geturl())\n",
    "with open(f'data/{dataset.replace(\":\",\"_\")}.tif','wb') as outfile:\n",
    "    outfile.write(response.read())\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Inspect then Clip the raster dataset to the study area using rasterio"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Compare the Study area and Raster CRS and Bounding boxes:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Raster CRS: EPSG:3857\n",
      "Study Area CRS: EPSG:4326\n",
      "Raster Bbox: (-44.0000000000998, 30.2670495513959, 68.5000001347984, 90.0000000001001)\n",
      "Study Area Bbox: [-7.6, 33.07066846, 18.22312212, 46.42720294]\n",
      "Need to reproject raster to match raster CRS.\n"
     ]
    }
   ],
   "source": [
    "# Open the raster file\n",
    "with rasterio.open(f'data/{dataset.replace(\":\", \"_\")}.tif') as src:\n",
    "    raster_crs = src.crs  # Get raster CRS\n",
    "\n",
    "print(f\"Raster CRS: {raster_crs}\")\n",
    "print(f\"Study Area CRS: {crs2}\")\n",
    "print(f\"Raster Bbox: {bbox}\")\n",
    "print(f\"Study Area Bbox: {bbox2}\")\n",
    "\n",
    "if raster_crs != crs2:\n",
    "    print(\"Need to reproject raster to match raster CRS.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Reproject the Raster data to match the Study Area CRS:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Reprojected raster saved at: data/emodnet_open_maplibrary__mediseh_cora_reprojected.tif\n"
     ]
    }
   ],
   "source": [
    "from rasterio.warp import calculate_default_transform, reproject, Resampling\n",
    "\n",
    "# Define input and output file paths\n",
    "input_raster = f'data/{dataset.replace(\":\", \"_\")}.tif'\n",
    "output_raster = f'data/{dataset.replace(\":\", \"_\")}_reprojected.tif'\n",
    "\n",
    "# Open the original raster\n",
    "with rasterio.open(input_raster) as src:\n",
    "    # Define target CRS (Study Area CRS)\n",
    "    dst_crs = \"EPSG:4326\"\n",
    "\n",
    "    # Calculate new transform, width, and height\n",
    "    transform, width, height = calculate_default_transform(\n",
    "        src.crs, dst_crs, src.width, src.height, *src.bounds\n",
    "    )\n",
    "\n",
    "    # Update metadata for the new raster\n",
    "    kwargs = src.meta.copy()\n",
    "    kwargs.update({\n",
    "        'crs': dst_crs,\n",
    "        'transform': transform,\n",
    "        'width': width,\n",
    "        'height': height\n",
    "    })\n",
    "\n",
    "    # Perform the reprojection\n",
    "    with rasterio.open(output_raster, 'w', **kwargs) as dst:\n",
    "        for i in range(1, src.count + 1):  # Loop through raster bands\n",
    "            reproject(\n",
    "                source=rasterio.band(src, i),\n",
    "                destination=rasterio.band(dst, i),\n",
    "                src_transform=src.transform,\n",
    "                src_crs=src.crs,\n",
    "                dst_transform=transform,\n",
    "                dst_crs=dst_crs,\n",
    "                resampling=Resampling.nearest\n",
    "            )\n",
    "\n",
    "print(f\"Reprojected raster saved at: {output_raster}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Clip the reprojected raster data to match the study area"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'driver': 'GTiff', 'dtype': 'float32', 'nodata': -3.4028234663852886e+38, 'width': 14294, 'height': 5155, 'count': 1, 'crs': CRS.from_epsg(4326), 'transform': Affine(0.0034645994105386834, 0.0, -6.557315834740004,\n",
      "       0.0, -0.0034645994105386834, 47.300772718499964)}\n"
     ]
    }
   ],
   "source": [
    "with rasterio.open(f'data/{dataset.replace(\":\",\"_\")}_reprojected.tif') as src:\n",
    "    # Clip the raster using the study area geometry\n",
    "    out_image, out_transform = rasterio.mask.mask(src, Study_area.geometry, crop=True)\n",
    "    out_meta = src.meta\n",
    "    print(out_meta)\n",
    "\n",
    "out_meta.update({\"driver\": \"GTiff\",\n",
    "                 \"height\": out_image.shape[1],\n",
    "                 \"width\": out_image.shape[2],\n",
    "                 \"transform\": out_transform})\n",
    "\n",
    "# Create a new raster file containing the clipped data\n",
    "clipped_raster_path = f'data/{dataset.replace(\":\",\"_\")}_clipped.tif'\n",
    "\n",
    "with rasterio.open(clipped_raster_path, \"w\", **out_meta) as dest:\n",
    "    dest.write(out_image)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Open the clipped rasterdata in an xarray object using the rasterio library"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
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       "}\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 (band: 1, y: 2702, x: 6300)&gt;\n",
       "[17022600 values with dtype=float32]\n",
       "Coordinates:\n",
       "  * band         (band) int32 1\n",
       "  * x            (x) float64 -5.599 -5.596 -5.592 -5.589 ... 16.22 16.22 16.22\n",
       "  * y            (y) float64 44.43 44.42 44.42 44.42 ... 35.08 35.08 35.07 35.07\n",
       "    spatial_ref  int32 0\n",
       "Attributes:\n",
       "    AREA_OR_POINT:  Area\n",
       "    _FillValue:     -3.4028235e+38\n",
       "    scale_factor:   1.0\n",
       "    add_offset:     0.0</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'></div><ul class='xr-dim-list'><li><span class='xr-has-index'>band</span>: 1</li><li><span class='xr-has-index'>y</span>: 2702</li><li><span class='xr-has-index'>x</span>: 6300</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-fca7a985-3dee-4b38-8992-e9ec50f59ab1' class='xr-array-in' type='checkbox' checked><label for='section-fca7a985-3dee-4b38-8992-e9ec50f59ab1' 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>...</span></div><div class='xr-array-data'><pre>[17022600 values with dtype=float32]</pre></div></div></li><li class='xr-section-item'><input id='section-00d1e918-a2b9-4cc9-9855-1a07056b1db9' class='xr-section-summary-in' type='checkbox'  checked><label for='section-00d1e918-a2b9-4cc9-9855-1a07056b1db9' 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'>band</span></div><div class='xr-var-dims'>(band)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>1</div><input id='attrs-0dcc8352-1fe4-4144-a115-09f03ce7ad7c' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-0dcc8352-1fe4-4144-a115-09f03ce7ad7c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c2b939d2-71a9-4300-9f8f-9659827c8b04' class='xr-var-data-in' type='checkbox'><label for='data-c2b939d2-71a9-4300-9f8f-9659827c8b04' 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([1])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>x</span></div><div class='xr-var-dims'>(x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-5.599 -5.596 ... 16.22 16.22</div><input id='attrs-ac5fa095-a4df-477f-901b-b59aee6da6af' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-ac5fa095-a4df-477f-901b-b59aee6da6af' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-53344362-98d6-41f3-aa1b-0232e8908aa1' class='xr-var-data-in' type='checkbox'><label for='data-53344362-98d6-41f3-aa1b-0232e8908aa1' 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([-5.599354, -5.595889, -5.592425, ..., 16.217228, 16.220693, 16.224158])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>y</span></div><div class='xr-var-dims'>(y)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>44.43 44.42 44.42 ... 35.07 35.07</div><input id='attrs-984ab533-d3d3-4c91-b7b6-b59454704192' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-984ab533-d3d3-4c91-b7b6-b59454704192' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d73ba2d9-9cdf-490f-8549-cb062b65d3ec' class='xr-var-data-in' type='checkbox'><label for='data-d73ba2d9-9cdf-490f-8549-cb062b65d3ec' 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([44.426888, 44.423423, 44.419958, ..., 35.075934, 35.072469, 35.069004])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>spatial_ref</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>0</div><input id='attrs-9a74c8a1-4e7f-48b3-998a-395e3101f830' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9a74c8a1-4e7f-48b3-998a-395e3101f830' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-26dcd83c-9a8d-4ecd-93e4-e18939689ec3' class='xr-var-data-in' type='checkbox'><label for='data-26dcd83c-9a8d-4ecd-93e4-e18939689ec3' 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'><dt><span>crs_wkt :</span></dt><dd>GEOGCS[&quot;WGS 84&quot;,DATUM[&quot;WGS_1984&quot;,SPHEROID[&quot;WGS 84&quot;,6378137,298.257223563,AUTHORITY[&quot;EPSG&quot;,&quot;7030&quot;]],AUTHORITY[&quot;EPSG&quot;,&quot;6326&quot;]],PRIMEM[&quot;Greenwich&quot;,0,AUTHORITY[&quot;EPSG&quot;,&quot;8901&quot;]],UNIT[&quot;degree&quot;,0.0174532925199433,AUTHORITY[&quot;EPSG&quot;,&quot;9122&quot;]],AXIS[&quot;Latitude&quot;,NORTH],AXIS[&quot;Longitude&quot;,EAST],AUTHORITY[&quot;EPSG&quot;,&quot;4326&quot;]]</dd><dt><span>semi_major_axis :</span></dt><dd>6378137.0</dd><dt><span>semi_minor_axis :</span></dt><dd>6356752.314245179</dd><dt><span>inverse_flattening :</span></dt><dd>298.257223563</dd><dt><span>reference_ellipsoid_name :</span></dt><dd>WGS 84</dd><dt><span>longitude_of_prime_meridian :</span></dt><dd>0.0</dd><dt><span>prime_meridian_name :</span></dt><dd>Greenwich</dd><dt><span>geographic_crs_name :</span></dt><dd>WGS 84</dd><dt><span>horizontal_datum_name :</span></dt><dd>World Geodetic System 1984</dd><dt><span>grid_mapping_name :</span></dt><dd>latitude_longitude</dd><dt><span>spatial_ref :</span></dt><dd>GEOGCS[&quot;WGS 84&quot;,DATUM[&quot;WGS_1984&quot;,SPHEROID[&quot;WGS 84&quot;,6378137,298.257223563,AUTHORITY[&quot;EPSG&quot;,&quot;7030&quot;]],AUTHORITY[&quot;EPSG&quot;,&quot;6326&quot;]],PRIMEM[&quot;Greenwich&quot;,0,AUTHORITY[&quot;EPSG&quot;,&quot;8901&quot;]],UNIT[&quot;degree&quot;,0.0174532925199433,AUTHORITY[&quot;EPSG&quot;,&quot;9122&quot;]],AXIS[&quot;Latitude&quot;,NORTH],AXIS[&quot;Longitude&quot;,EAST],AUTHORITY[&quot;EPSG&quot;,&quot;4326&quot;]]</dd><dt><span>GeoTransform :</span></dt><dd>-5.601086397431327 0.0034645994105386834 0.0 44.42861980716339 0.0 -0.0034645994105386834</dd></dl></div><div class='xr-var-data'><pre>array(0)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-148d06e7-89fa-430c-b855-6b0d4a750328' class='xr-section-summary-in' type='checkbox'  ><label for='section-148d06e7-89fa-430c-b855-6b0d4a750328' 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>band</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-02cff89e-c6aa-409a-a5c7-61470eb543b9' class='xr-index-data-in' type='checkbox'/><label for='index-02cff89e-c6aa-409a-a5c7-61470eb543b9' 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([1], dtype=&#x27;int32&#x27;, name=&#x27;band&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>x</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-b7f0253e-ab81-4b3b-9c3c-678739765491' class='xr-index-data-in' type='checkbox'/><label for='index-b7f0253e-ab81-4b3b-9c3c-678739765491' 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([ -5.599354097726058,  -5.595889498315519,  -5.592424898904981,\n",
       "        -5.588960299494442,  -5.585495700083904, -5.5820311006733645,\n",
       "        -5.578566501262826,  -5.575101901852287,  -5.571637302441749,\n",
       "         -5.56817270303121,\n",
       "       ...\n",
       "        16.192976194562263,  16.196440793972798,   16.19990539338334,\n",
       "        16.203369992793874,  16.206834592204416,   16.21029919161495,\n",
       "        16.213763791025492,  16.217228390436034,   16.22069298984657,\n",
       "         16.22415758925711],\n",
       "      dtype=&#x27;float64&#x27;, name=&#x27;x&#x27;, length=6300))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>y</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-93e30a1a-fa5b-4156-98b4-ce6116bba197' class='xr-index-data-in' type='checkbox'/><label for='index-93e30a1a-fa5b-4156-98b4-ce6116bba197' 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([44.426887507458126, 44.423422908047584,  44.41995830863705,\n",
       "        44.41649370922651,  44.41302910981597,  44.40956451040543,\n",
       "         44.4060999109949, 44.402635311584355,  44.39917071217381,\n",
       "        44.39570611276328,\n",
       "       ...\n",
       "        35.10018589428799, 35.096721294877455,  35.09325669546691,\n",
       "        35.08979209605637, 35.086327496645836, 35.082862897235294,\n",
       "        35.07939829782476,  35.07593369841422,  35.07246909900368,\n",
       "        35.06900449959314],\n",
       "      dtype=&#x27;float64&#x27;, name=&#x27;y&#x27;, length=2702))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-de162cef-8741-492d-a40f-2e88018b8c06' class='xr-section-summary-in' type='checkbox'  checked><label for='section-de162cef-8741-492d-a40f-2e88018b8c06' class='xr-section-summary' >Attributes: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>AREA_OR_POINT :</span></dt><dd>Area</dd><dt><span>_FillValue :</span></dt><dd>-3.4028235e+38</dd><dt><span>scale_factor :</span></dt><dd>1.0</dd><dt><span>add_offset :</span></dt><dd>0.0</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.DataArray (band: 1, y: 2702, x: 6300)>\n",
       "[17022600 values with dtype=float32]\n",
       "Coordinates:\n",
       "  * band         (band) int32 1\n",
       "  * x            (x) float64 -5.599 -5.596 -5.592 -5.589 ... 16.22 16.22 16.22\n",
       "  * y            (y) float64 44.43 44.42 44.42 44.42 ... 35.08 35.08 35.07 35.07\n",
       "    spatial_ref  int32 0\n",
       "Attributes:\n",
       "    AREA_OR_POINT:  Area\n",
       "    _FillValue:     -3.4028235e+38\n",
       "    scale_factor:   1.0\n",
       "    add_offset:     0.0"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rasterdata_clipped = rioxarray.open_rasterio(f'data/{dataset.replace(\":\",\"_\")}_clipped.tif')\n",
    "rasterdata_clipped"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Plot a map of the clipped rasterdata overlaid with the study area\n",
    "\n",
    "This time we will use Cartopy features to plot the basemap."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x1000 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "warnings.simplefilter(\"ignore\", RuntimeWarning)\n",
    "\n",
    "fig = plt.figure(figsize=(8,10))# Increase the figure size\n",
    "ax = fig.add_subplot(111, projection=ccrs.PlateCarree())\n",
    "\n",
    "#add Basemap features from Cartopy\n",
    "ax.add_feature(cartopy.feature.OCEAN, )\n",
    "ax.add_feature(cartopy.feature.LAND)\n",
    "ax.add_feature(cartopy.feature.BORDERS, linestyle=':')\n",
    "ax.coastlines(resolution='10m')\n",
    "\n",
    "#plot the data\n",
    "cbar_kwargs = {'shrink':0.5, 'extend': 'neither'}                                                             \n",
    "rasterdata_clipped.plot(ax=ax, cmap = 'Greens', vmin = 0 , vmax = 1, cbar_kwargs = cbar_kwargs) # plot raster data\n",
    "Study_area.boundary.plot(ax=ax, color=None, edgecolor = 'red',linewidth=0.8 , label = 'Study Area')\n",
    "\n",
    "ax.set_title('Modelled occurrence probability for coralligenous \\n habitats in the Western Mediterranean Sea', fontsize=15)\n",
    "\n",
    "# Close the raster file after plotting\n",
    "rasterdata_clipped.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### [>> Next: Tutorial Part 4: Web Map Tiling Services and interactive maps](Tutorial_Part_4_WMTS.ipynb)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<hr>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a href=\"https://emodnet.ec.europa.eu/\"><img style=\"float:left;border-width:0;width:200px\" src=\"https://emodnet.ec.europa.eu/sites/emodnet.ec.europa.eu/files/public/emodnet_logos/web/EMODnet_standard_colour.png\" /></a><a rel=\"license\" href=\"http://creativecommons.org/licenses/by/4.0/\"><img style=\"float: right\" alt=\"Creative Commons Lizenzvertrag\" style=\"border-width:0\" src=\"https://i.creativecommons.org/l/by/4.0/88x31.png\" /></a>"
   ]
  }
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