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    "# Applying the Ocean function to geometries and generating grids\n",
    "\n",
    "A common application is to test whether a certain geometry lies over the ocean and/or to generate an rasterized ocean mask.\n",
    "\n",
    "The [natural earth collection](https://www.naturalearthdata.com) contains ocean geometries. However, naively testing for inclusion of a geometry can take a very long time since the polygon associated with Asia a has many points and is not optimized for inclusion tests. Furthermore, for most applications, the isolated Capsian Sea may also not be considered to belong to the ocean.\n",
    "\n",
    "For these reasons a dedicated table can be generated in geoslurp, which will speed up inclusion searches and raster generation. This will be described below.\n",
    "\n",
    "The idea is that the coastal polygons are subdivided in smaller manageable pieces with PostGIS ([ST_subdivide](https://postgis.net/docs/ST_Subdivide.html)) and a geospatial index is generated to speed up queries."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
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   "source": [
    "%load_ext autoreload\n",
    "%autoreload 2"
   ]
  },
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   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Geoslurp-INFO: Reading CF convention defaults from /home/roelof/.cf-conventions.yaml\n"
     ]
    }
   ],
   "source": [
    "from geoslurp.config.catalogue import DatasetCatalogue\n",
    "from geoslurp.db import Settings\n",
    "from geoslurp.config import setInfoLevel\n",
    "\n",
    "from geoslurp.db import geoslurpConnect\n",
    "from geoslurp.tools.shapelytools import gdal2rastio\n",
    "from geoslurp.tools.pandas import *\n",
    "from geoslurp.tools.cf import *\n",
    "from math import floor\n",
    "import xarray as xr\n",
    "import numpy as np\n",
    "setInfoLevel()\n",
    "\n",
    "gpcon=geoslurpConnect(readonly_user=False) # this will be a connection based on the readonly userfrom geoslurp.db.geo\n",
    "\n",
    "#Some datasets need info from the server side settings so we need to load these\n",
    "conf=Settings(gpcon)\n",
    "\n",
    "#catalogue\n",
    "gscat=DatasetCatalogue()\n",
    "\n",
    "tocea=\"natearth.ne_10m_oceanfunc\"\n",
    "#original table\n",
    "tocea_orig=\"natearth.ne_10m_ocean\"\n",
    "\n"
   ]
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   "source": [
    "Let's pull and register the dedicated ocean function table\n",
    "```\n",
    "geoslurper.py -v --pull --register \"natearth.ne_10m_oceanfunc\"\n",
    "```\n",
    "\n",
    "Or alternatively in Python:"
   ]
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    {
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     "text": [
      "Geoslurp-INFO: using cached github catalogue /home/data/geoslurp_cache/githubcache/strawpants_geoshapes.yaml\n",
      "Geoslurp-INFO: using cached github catalogue /home/data/geoslurp_cache/githubcache/nvkelso_natural-earth-vector.yaml\n",
      "/usr/lib/python3.12/site-packages/osgeo/osr.py:410: FutureWarning: Neither osr.UseExceptions() nor osr.DontUseExceptions() has been explicitly called. In GDAL 4.0, exceptions will be enabled by default.\n",
      "  warnings.warn(\n",
      "Geoslurp-INFO: creating table natearth.ne_10m_oceanfunc and index, this can take a while..\n",
      "Geoslurp-INFO: Done..\n"
     ]
    }
   ],
   "source": [
    "dsoceanf=gscat.getDsetClass(conf, tocea)(gpcon)\n",
    "dsoceanf.pull()\n",
    "dsoceanf.register()"
   ]
  },
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Example 1: Generate a global 0.125 degree equidistant raster\n",
    "\n",
    "Note: The generated grid can also be found on [this github repository](https://github.com/strawpants/geoshapes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [],
   "source": [
    "resolution=0.125\n",
    "\n",
    "east=180\n",
    "west=-180\n",
    "south=-90\n",
    "north=90\n",
    "width=floor((east-west)/resolution)\n",
    "height=floor((north-south)/resolution)\n",
    "\n",
    "# create a query which creates a reference raster object and only selects those pixel longitude/lattiudes which lie within the polygons\n",
    "qry=f\"\"\"\n",
    "    CREATE TEMPORARY TABLE oceanrast AS \n",
    "    (SELECT ST_addband(ST_MakeEmptyRaster({width},{height},{west},{north},{resolution},-{resolution},0,0,srid=>4326),'8BUI'::text,1.0) as rast);\n",
    "    SELECT ST_x(pnts.geom) as longitude ,ST_y(pnts.geom) as latitude from {tocea} as ocean INNER JOIN (SELECT (ST_PixelAsCentroids(rast, 1)).* from oceanrast ) pnts ON ST_Within(pnts.geom,ocean.geom);\n",
    "\"\"\"\n",
    "\n",
    "#load the valid lon latitudes in a pandas dataframe\n",
    "dfocean=pd.DataFrame.gslrp.load(gpcon,qry)\n",
    "\n",
    "#create an zero values grid in xaray\n",
    "lat=np.arange(south+resolution/2,north,resolution)\n",
    "lon=np.arange(west+resolution/2,east,resolution)\n",
    "\n",
    "daocean=xr.DataArray(data=0.0,dims=[\"latitude\",\"longitude\"],coords={\"longitude\":(\"longitude\",lon),\"latitude\":(\"latitude\",lat)}).stack(lonlat=[\"longitude\",\"latitude\"])\n",
    "#assign 1 at ocean points\n",
    "daocean.loc[pd.MultiIndex.from_frame(dfocean)]=1.0\n",
    "daocean=daocean.unstack('lonlat').T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {},
   "outputs": [
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
       "Dimensions:      (longitude: 2880, latitude: 1440)\n",
       "Coordinates:\n",
       "  * longitude    (longitude) float64 -179.9 -179.8 -179.7 ... 179.7 179.8 179.9\n",
       "  * latitude     (latitude) float64 -89.94 -89.81 -89.69 ... 89.69 89.81 89.94\n",
       "    spatial_ref  int64 0\n",
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       "    oceanfunc    (latitude, longitude) float64 0.0 0.0 0.0 0.0 ... 1.0 1.0 1.0\n",
       "Attributes:\n",
       "    Conventions:  CF-1.9\n",
       "    title:        Gridded Ocean mask generated from natural earth 10m dataset\n",
       "    institution:  Roelof Rietbroek &lt;r.rietbroek@utwente.nl&gt;, Faculty of Geoin...\n",
       "    source:       geoslurp\n",
       "    history:      2024-11-26 14:53:20.439413 geoslurp\n",
       "    references:   https://geoslurp.wobbly.earth/en/latest/notebooks/OceanFunc...\n",
       "    comment:      Auto generated</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-f5756478-5297-46fc-ab32-42dc775c45cd' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-f5756478-5297-46fc-ab32-42dc775c45cd' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>longitude</span>: 2880</li><li><span class='xr-has-index'>latitude</span>: 1440</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-c0e10c1d-971c-4713-9dae-3f238a5cae5b' class='xr-section-summary-in' type='checkbox'  checked><label for='section-c0e10c1d-971c-4713-9dae-3f238a5cae5b' class='xr-section-summary' >Coordinates: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>longitude</span></div><div class='xr-var-dims'>(longitude)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-179.9 -179.8 ... 179.8 179.9</div><input id='attrs-8e0257de-a364-418d-b1b4-58b23b78e3a7' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8e0257de-a364-418d-b1b4-58b23b78e3a7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1d342e1d-0135-408c-817a-9faf41f84aa4' class='xr-var-data-in' type='checkbox'><label for='data-1d342e1d-0135-408c-817a-9faf41f84aa4' 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>units :</span></dt><dd>degree_east</dd><dt><span>standard_name :</span></dt><dd>longitude</dd></dl></div><div class='xr-var-data'><pre>array([-179.9375, -179.8125, -179.6875, ...,  179.6875,  179.8125,  179.9375])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>latitude</span></div><div class='xr-var-dims'>(latitude)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-89.94 -89.81 ... 89.81 89.94</div><input id='attrs-1346ce6c-21f3-486d-82a9-7ad9648dd871' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1346ce6c-21f3-486d-82a9-7ad9648dd871' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7533b7d4-cfd3-4e90-b0f2-73c3457ca133' class='xr-var-data-in' type='checkbox'><label for='data-7533b7d4-cfd3-4e90-b0f2-73c3457ca133' 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>units :</span></dt><dd>degree_north</dd><dt><span>standard_name :</span></dt><dd>latitude</dd></dl></div><div class='xr-var-data'><pre>array([-89.9375, -89.8125, -89.6875, ...,  89.6875,  89.8125,  89.9375])</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'>int64</div><div class='xr-var-preview xr-preview'>0</div><input id='attrs-8007a0fb-5e64-4b04-83f9-0932f0e80c9a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8007a0fb-5e64-4b04-83f9-0932f0e80c9a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-23c485c4-e6fa-4fd3-8863-6c3df7804d02' class='xr-var-data-in' type='checkbox'><label for='data-23c485c4-e6fa-4fd3-8863-6c3df7804d02' 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>GEOGCRS[&quot;WGS 84&quot;,ENSEMBLE[&quot;World Geodetic System 1984 ensemble&quot;,MEMBER[&quot;World Geodetic System 1984 (Transit)&quot;],MEMBER[&quot;World Geodetic System 1984 (G730)&quot;],MEMBER[&quot;World Geodetic System 1984 (G873)&quot;],MEMBER[&quot;World Geodetic System 1984 (G1150)&quot;],MEMBER[&quot;World Geodetic System 1984 (G1674)&quot;],MEMBER[&quot;World Geodetic System 1984 (G1762)&quot;],MEMBER[&quot;World Geodetic System 1984 (G2139)&quot;],MEMBER[&quot;World Geodetic System 1984 (G2296)&quot;],ELLIPSOID[&quot;WGS 84&quot;,6378137,298.257223563,LENGTHUNIT[&quot;metre&quot;,1]],ENSEMBLEACCURACY[2.0]],PRIMEM[&quot;Greenwich&quot;,0,ANGLEUNIT[&quot;degree&quot;,0.0174532925199433]],CS[ellipsoidal,2],AXIS[&quot;geodetic latitude (Lat)&quot;,north,ORDER[1],ANGLEUNIT[&quot;degree&quot;,0.0174532925199433]],AXIS[&quot;geodetic longitude (Lon)&quot;,east,ORDER[2],ANGLEUNIT[&quot;degree&quot;,0.0174532925199433]],USAGE[SCOPE[&quot;Horizontal component of 3D system.&quot;],AREA[&quot;World.&quot;],BBOX[-90,-180,90,180]],ID[&quot;EPSG&quot;,4326]]</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 ensemble</dd><dt><span>grid_mapping_name :</span></dt><dd>latitude_longitude</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-fdd92b3a-585e-46f2-8788-a8f92231d3d0' class='xr-section-summary-in' type='checkbox'  checked><label for='section-fdd92b3a-585e-46f2-8788-a8f92231d3d0' class='xr-section-summary' >Data variables: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>oceanfunc</span></div><div class='xr-var-dims'>(latitude, longitude)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0 0.0 0.0 0.0 ... 1.0 1.0 1.0 1.0</div><input id='attrs-813bba19-cf79-4398-8c90-2525e18ce030' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-813bba19-cf79-4398-8c90-2525e18ce030' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ed639a51-0863-4c08-8a4c-aa7b1afa8ac2' class='xr-var-data-in' type='checkbox'><label for='data-ed639a51-0863-4c08-8a4c-aa7b1afa8ac2' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[0., 0., 0., ..., 0., 0., 0.],\n",
       "       [0., 0., 0., ..., 0., 0., 0.],\n",
       "       [0., 0., 0., ..., 0., 0., 0.],\n",
       "       ...,\n",
       "       [1., 1., 1., ..., 1., 1., 1.],\n",
       "       [1., 1., 1., ..., 1., 1., 1.],\n",
       "       [1., 1., 1., ..., 1., 1., 1.]])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-a01ff63d-8ab0-466b-9faf-7fe9ed025224' class='xr-section-summary-in' type='checkbox'  ><label for='section-a01ff63d-8ab0-466b-9faf-7fe9ed025224' class='xr-section-summary' >Indexes: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>longitude</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-32dcd95f-3c10-42a2-a909-a9ebe4214fdf' class='xr-index-data-in' type='checkbox'/><label for='index-32dcd95f-3c10-42a2-a909-a9ebe4214fdf' 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([-179.9375, -179.8125, -179.6875, -179.5625, -179.4375, -179.3125,\n",
       "       -179.1875, -179.0625, -178.9375, -178.8125,\n",
       "       ...\n",
       "        178.8125,  178.9375,  179.0625,  179.1875,  179.3125,  179.4375,\n",
       "        179.5625,  179.6875,  179.8125,  179.9375],\n",
       "      dtype=&#x27;float64&#x27;, name=&#x27;longitude&#x27;, length=2880))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>latitude</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-91737ec7-ab43-445c-9454-5197f41d94c8' class='xr-index-data-in' type='checkbox'/><label for='index-91737ec7-ab43-445c-9454-5197f41d94c8' 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([-89.9375, -89.8125, -89.6875, -89.5625, -89.4375, -89.3125, -89.1875,\n",
       "       -89.0625, -88.9375, -88.8125,\n",
       "       ...\n",
       "        88.8125,  88.9375,  89.0625,  89.1875,  89.3125,  89.4375,  89.5625,\n",
       "        89.6875,  89.8125,  89.9375],\n",
       "      dtype=&#x27;float64&#x27;, name=&#x27;latitude&#x27;, length=1440))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-cce85d26-e87e-479a-b86d-702b92b4fc67' class='xr-section-summary-in' type='checkbox'  checked><label for='section-cce85d26-e87e-479a-b86d-702b92b4fc67' class='xr-section-summary' >Attributes: <span>(7)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>Conventions :</span></dt><dd>CF-1.9</dd><dt><span>title :</span></dt><dd>Gridded Ocean mask generated from natural earth 10m dataset</dd><dt><span>institution :</span></dt><dd>Roelof Rietbroek &lt;r.rietbroek@utwente.nl&gt;, Faculty of Geoinformation Science and Earth Observation, University of Twente, Netherlands</dd><dt><span>source :</span></dt><dd>geoslurp</dd><dt><span>history :</span></dt><dd>2024-11-26 14:53:20.439413 geoslurp</dd><dt><span>references :</span></dt><dd>https://geoslurp.wobbly.earth/en/latest/notebooks/OceanFunction.html</dd><dt><span>comment :</span></dt><dd>Auto generated</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:      (longitude: 2880, latitude: 1440)\n",
       "Coordinates:\n",
       "  * longitude    (longitude) float64 -179.9 -179.8 -179.7 ... 179.7 179.8 179.9\n",
       "  * latitude     (latitude) float64 -89.94 -89.81 -89.69 ... 89.69 89.81 89.94\n",
       "    spatial_ref  int64 0\n",
       "Data variables:\n",
       "    oceanfunc    (latitude, longitude) float64 0.0 0.0 0.0 0.0 ... 1.0 1.0 1.0\n",
       "Attributes:\n",
       "    Conventions:  CF-1.9\n",
       "    title:        Gridded Ocean mask generated from natural earth 10m dataset\n",
       "    institution:  Roelof Rietbroek <r.rietbroek@utwente.nl>, Faculty of Geoin...\n",
       "    source:       geoslurp\n",
       "    history:      2024-11-26 14:53:20.439413 geoslurp\n",
       "    references:   https://geoslurp.wobbly.earth/en/latest/notebooks/OceanFunc...\n",
       "    comment:      Auto generated"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "daocean.plot(cmap=\"Blues\")\n",
    "fout=f\"/tmp/ne_10m_oceangrid_{resolution}.nc\"\n",
    "daocean.name='oceanfunc'\n",
    "daocean=daocean.to_dataset()\n",
    "cfadd_global(daocean,title=\"Gridded Ocean mask generated from natural earth 10m dataset\",references=\"https://geoslurp.wobbly.earth/en/latest/notebooks/OceanFunction.html\",crs=4326)\n",
    "cfadd_standard_name(daocean.longitude,'longitude')\n",
    "cfadd_standard_name(daocean.latitude,'latitude')\n",
    "\n",
    "display(daocean)\n",
    "\n",
    "daocean.to_netcdf(fout)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Example 2: Use the optimized table for other queries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [],
   "source": [
    "wkttest=\"Polygon ((-15.19112857609935929 54.39984018637987617, 33.96385536287604623 45.7254312559724525, 60.4001492460224938 -5.63257717326514751, -83.34719874358626157 4.00565497163198359, -15.19112857609935929 54.39984018637987617))\"\n",
    "from shapely import from_wkt\n",
    "area=from_wkt(wkttest)\n",
    "seed=1000\n",
    "npoints=2000\n",
    "\n",
    "# generate two queries\n",
    "\n",
    "# Fast version using the index\n",
    "qryfast=f\"\"\"CREATE TEMPORARY TABLE testpnts AS SELECT (ST_dump(ST_generatePoints(geom,{npoints},{seed}))).geom AS geom FROM (SELECT ST_GeomfromText('{wkttest}',4326) AS geom) AS s;\n",
    "    SELECT tst.geom AS geom from testpnts AS tst INNER JOIN {tocea} as oce ON ST_within(tst.geom,oce.geom);\n",
    "    \"\"\"\n",
    "#slow version using the original input table\n",
    "qryslow=f\"\"\"CREATE TEMPORARY TABLE testpnts AS SELECT (ST_dump(ST_generatePoints(geom,{npoints},{seed}))).geom AS geom FROM (SELECT ST_GeomfromText('{wkttest}',4326) AS geom) AS s;\n",
    "    SELECT tst.geom AS geom from testpnts AS tst INNER JOIN {tocea_orig} as oce ON ST_within(tst.geom,oce.geom::geometry);\n",
    "    \"\"\"\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 55.5 ms, sys: 3.06 ms, total: 58.6 ms\n",
      "Wall time: 43.3 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "pntsslow=pd.DataFrame.gslrp.load(gpcon,qryslow)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 55.7 ms, sys: 94 μs, total: 55.8 ms\n",
      "Wall time: 165 ms\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "pntsfast=pd.DataFrame.gslrp.load(gpcon,qryfast)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Conclusion: An indexed and subdivided geometry table speeds up the ocean queries considerably\n",
    "For this particular example and database, quering 2000 points was sped up to 165 ms from 43 seconds (factor 260x)\n",
    "\n",
    "Let's visualize the results:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-10.0, 60.0)"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from shapely.plotting import plot_polygon\n",
    "qry=f\"SELECT geom as geom FROM {tocea_orig};\"\n",
    "#load the query result in a pandas dataframe\n",
    "dfoce=pd.DataFrame.gslrp.load(gpcon,qry)\n",
    "\n",
    "ax=dfoce.plot()\n",
    "plot_polygon(area,ax=ax,color='orange')\n",
    "\n",
    "pntsslow.plot(ax=ax,color='red',markersize=0.1)\n",
    "ax.set_xlim([-100,75])\n",
    "ax.set_ylim([-10,60])\n",
    "\n"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "pygeoslurp",
   "language": "python",
   "name": "pygeoslurp"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.12.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}