docs/notebooks/core_subset.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "keep_output": true
   },
   "source": [
    "Subsetting utilities\n",
    "================\n",
    "\n",
    "`clisops` comes with some utilities to perform common tasks that are either not implemented in xarray, or that are implemented but do not have the generality needed for climate science work. Here we show examples of the `clisops.core.subset` submodule."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "keep_output": true
   },
   "outputs": [],
   "source": [
    "import xarray as xr\n",
    "from clisops.core import subset\n",
    "xr.set_options(display_style='html')\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "plt.style.use('seaborn')\n",
    "plt.rcParams['figure.figsize'] = (13, 5)\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "keep_output": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Coordinates:\n",
       "  * lat      (lat) float32 75.0 72.5 70.0 67.5 65.0 ... 25.0 22.5 20.0 17.5 15.0\n",
       "  * lon      (lon) float32 200.0 202.5 205.0 207.5 ... 322.5 325.0 327.5 330.0\n",
       "  * time     (time) datetime64[ns] 2013-01-01 ... 2014-12-31T18:00:00"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds = xr.tutorial.open_dataset('air_temperature')\n",
    "ds.coords"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "keep_output": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.QuadMesh at 0x7fbce02e6760>"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ds.air.isel(time=0).plot()  # Simple index-selection with xarray"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "keep_output": true
   },
   "source": [
    "## subset_bbox : using a latitude-longitude bounding box\n",
    "\n",
    "In the previous example notebook, we used xarray's `.sel()` to cut a lat-lon subset of our data. clisops offers the same utility, but with more generality. For example, if we mindlessly try xarray's method on our dataset:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "keep_output": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Coordinates:\n",
       "  * lat      (lat) float32 \n",
       "  * lon      (lon) float32 \n",
       "  * time     (time) datetime64[ns] 2013-01-01 ... 2014-12-31T18:00:00"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds.sel(lat=slice(45, 50), lon=slice(-60, -55)).coords"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "keep_output": true
   },
   "source": [
    "As you can see, `lat` and `lon` are empty. In this dataset, the lats are defined in descending order and lons are in the range \\[0, 360[ instead of [-180, 180[, which is why xarray's method did not return the expected result. clisops understands these nuances:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "keep_output": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Coordinates:\n",
       "  * lat      (lat) float32 50.0 47.5 45.0\n",
       "  * lon      (lon) float32 300.0 302.5 305.0\n",
       "  * time     (time) datetime64[ns] 2013-01-01 ... 2014-12-31T18:00:00"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "subset.subset_bbox(ds, lat_bnds=[45, 50], lon_bnds=[-60, -55]).coords"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "keep_output": true
   },
   "source": [
    "### When lat and lon are 2-D\n",
    "`subset_bbox` also manages cases where the lat-lon coordinates are not sorted 1D vectors, for example with this more complex dataset:\n",
    "\n",
    "<div class=\"alert alert-warning\">\n",
    "\n",
    "Most `subset` methods expect the input dataset / dataarray to have `lat` and `lon` as variables. It may be able to understand your data if other common names are used (like `latitude`, or `lons`), but we recommend renaming the variables before using the tool (like in this example).\n",
    "\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "keep_output": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-6-e7e8689dc693>:7: MatplotlibDeprecationWarning: shading='flat' when X and Y have the same dimensions as C is deprecated since 3.3.  Either specify the corners of the quadrilaterals with X and Y, or pass shading='auto', 'nearest' or 'gouraud', or set rcParams['pcolor.shading'].  This will become an error two minor releases later.\n",
      "  axLatLon.pcolormesh(salt.lon, salt.lat, salt)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'latitude of RHO-points')"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ds_roms = xr.tutorial.open_dataset('ROMS_example').rename(lon_rho='lon', lat_rho='lat')\n",
    "salt = ds_roms.salt.isel(ocean_time=0, s_rho=0)\n",
    "\n",
    "#matplotlib-based plotting\n",
    "fig, (axEtaXi, axLatLon) = plt.subplots(1, 2)\n",
    "salt.plot(cmap=plt.cm.gray_r, ax=axEtaXi, add_colorbar=False)\n",
    "axLatLon.pcolormesh(salt.lon, salt.lat, salt)\n",
    "axLatLon.set_xlabel(salt.lon.long_name)\n",
    "axLatLon.set_ylabel(salt.lat.long_name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "keep_output": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/phobos/Python/clisops/clisops/core/subset.py:272: UserWarning: lat and lon-like dimensions are not found among arg `<xarray.DataArray 'salt' (eta_rho: 191, xi_rho: 371)>\n",
      "array([[34.953022, 34.955223, 34.957733, ..., 34.91684 , 34.913895, 34.913876],\n",
      "       [34.95414 , 34.957577, 34.961086, ..., 34.916145, 34.91495 , 34.913864],\n",
      "       [34.959286, 34.963936, 34.968594, ..., 34.923023, 34.92564 , 34.926853],\n",
      "       ...,\n",
      "       [      nan,       nan,       nan, ...,       nan,       nan,       nan],\n",
      "       [      nan,       nan,       nan, ...,       nan,       nan,       nan],\n",
      "       [      nan,       nan,       nan, ...,       nan,       nan,       nan]],\n",
      "      dtype=float32)\n",
      "Coordinates:\n",
      "    Cs_r        float64 ...\n",
      "    lon         (eta_rho, xi_rho) float64 -93.6 -93.58 -93.57 ... -88.88 -88.87\n",
      "    hc          float64 ...\n",
      "    h           (eta_rho, xi_rho) float64 ...\n",
      "    lat         (eta_rho, xi_rho) float64 27.45 27.45 27.45 ... 30.85 30.86\n",
      "    Vtransform  int32 ...\n",
      "    ocean_time  datetime64[ns] 2001-08-01\n",
      "    s_rho       float64 -0.9833\n",
      "Dimensions without coordinates: eta_rho, xi_rho\n",
      "Attributes:\n",
      "    long_name:  salinity\n",
      "    time:       ocean_time\n",
      "    field:      salinity, scalar, series` dimensions: ['eta_rho', 'xi_rho'].\n",
      "  warnings.warn(\n",
      "<ipython-input-7-67bc17b9a9a6>:5: MatplotlibDeprecationWarning: shading='flat' when X and Y have the same dimensions as C is deprecated since 3.3.  Either specify the corners of the quadrilaterals with X and Y, or pass shading='auto', 'nearest' or 'gouraud', or set rcParams['pcolor.shading'].  This will become an error two minor releases later.\n",
      "  axLatLon.pcolormesh(salt_bb.lon, salt_bb.lat, salt_bb)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'latitude of RHO-points')"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "salt_bb = subset.subset_bbox(salt, lat_bnds=[28, 30], lon_bnds=[-91, -88])\n",
    "\n",
    "fig, (axEtaXi, axLatLon) = plt.subplots(1, 2)\n",
    "salt_bb.plot(cmap=plt.cm.gray_r, ax=axEtaXi, add_colorbar=False)\n",
    "axLatLon.pcolormesh(salt_bb.lon, salt_bb.lat, salt_bb)\n",
    "axLatLon.set_xlabel(salt_bb.lon.long_name)\n",
    "axLatLon.set_ylabel(salt_bb.lat.long_name)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "keep_output": true
   },
   "source": [
    "### Add time subsetting\n",
    "`subset_bbox` and other methods of the submodule give options to also give time bounds. These options are mostly for convenience as only some basics sanity checks are performed."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "keep_output": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Coordinates:\n",
       "  * lat      (lat) float32 50.0 47.5 45.0\n",
       "  * lon      (lon) float32 300.0 302.5 305.0\n",
       "  * time     (time) datetime64[ns] 2013-02-01 ... 2013-08-31T18:00:00"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "subset.subset_bbox(ds, lat_bnds=[45, 50], lon_bnds=[-60, -55],\n",
    "                       start_date='2013-02', end_date='2013-08').coords"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "keep_output": true
   },
   "source": [
    "## subset_gridpoint :  Selecting grid points\n",
    "`subset_gridpoint` can be used for selecting single locations. Compared to `.sel()`, this method adds a `tolerance` parameter in *meters* so that it finds the nearest point from the given coordinate within this distance, or else it returns `NaN`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "keep_output": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Coordinates:\n",
       "    lat      float32 50.0\n",
       "    lon      float32 290.0\n",
       "  * time     (time) datetime64[ns] 2013-01-01 ... 2014-12-31T18:00:00"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lon_pt = -70.2\n",
    "lat_pt = 50.1\n",
    "\n",
    "# Get timeseries for point within 100 km of (-70.2, 50.1)\n",
    "ds3 = subset.subset_gridpoint(ds, lon=lon_pt, lat=lat_pt, tolerance=100 * 1000)\n",
    "ds3.coords"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "keep_output": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7fbcdfc62b20>]"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ds3.air.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This also works with a list of coordinates. In that case, a new dimension `site` concatenates the selected gridpoints. Moreover, passing `add_distance = True` allows us to inspect how close the selected gridpoints were from the requested coordinates. In this example, the third point (-90, 54.1) is a little bit further than 100 km from the nearest gridpoint, so the coordinate of this gridpoint and the distance are returned, but the values are masked with NaN (and thus they do not appear on the graph)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Coordinates:\n",
      "    lat       (site) float32 45.0 50.0 55.0\n",
      "    lon       (site) float32 295.0 290.0 270.0\n",
      "  * time      (time) datetime64[ns] 2013-01-01 ... 2014-12-31T18:00:00\n",
      "    distance  (site) float64 0.0 1.814e+04 1.002e+05\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7fbcdfb2fa60>,\n",
       " <matplotlib.lines.Line2D at 0x7fbcdfb66490>,\n",
       " <matplotlib.lines.Line2D at 0x7fbcdfb66310>]"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "lon_pt = [-65, -70.2, -90]\n",
    "lat_pt = [45, 50.1, 54.1]\n",
    "\n",
    "# Get timeseries for point within 100 km of each points\n",
    "ds3 = subset.subset_gridpoint(ds, lon=lon_pt, lat=lat_pt, tolerance=100 * 1000, add_distance=True)\n",
    "print(ds3.coords)\n",
    "ds3.air.plot(hue='site')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "keep_output": true
   },
   "source": [
    "## subset_shape : Selecting a region from a polygon\n",
    "If the region we want to keep in our dataset is complex, we can use `subset_shape` and pass a polygon. The input shape can be any georeferenced data file understood by [geopandas](http://geopandas.org/) and [fiona](https://fiona.readthedocs.io/en/latest/) that run under the hood. For example here with a geojson file of Canada available online:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "keep_output": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2021-01-26 14:44:38,120 - root - INFO - No file or no dimensions found in arg `https://raw.githubusercontent.com/johan/world.geo.json/master/countries/CAN.geo.json`.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/phobos/Python/clisops/clisops/core/subset.py:279: UserWarning: CRS definitions are similar but raster lon values must be wrapped.\n",
      "  final = func(*formatted_args, **kwargs)\n",
      "/home/phobos/Python/clisops/clisops/core/subset.py:463: UserWarning: Wrapping longitudes at 180 degrees.\n",
      "  warnings.warn(\"Wrapping longitudes at 180 degrees.\")\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.QuadMesh at 0x7fbcdf949310>"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ds_can = subset.subset_shape(ds, 'https://raw.githubusercontent.com/johan/world.geo.json/master/countries/CAN.geo.json')\n",
    "\n",
    "fig, (axall, axcan) = plt.subplots(1, 2)\n",
    "ds.air.isel(time=0).plot(ax=axall)\n",
    "ds_can.air.isel(time=0).plot(ax=axcan)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "keep_output": true
   },
   "source": [
    "## create_mask : Creating a mask from polygons\n",
    "\n",
    "We can also create a mask to divide the data in regions. In the example, we load the shapes for Canada, Mexico and the US, merge them with pandas and then create the mask. `create_mask` is a bit more restrictive than the other methods showed here and specifically asks for the `x` and `y` dims. Also, we need to pass `wrap_lons=True` since our longitudes go from 0 to 360 instead of -180 to 180."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "keep_output": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>geometry</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>CAN</th>\n",
       "      <td>Canada</td>\n",
       "      <td>MULTIPOLYGON (((-63.66450 46.55001, -62.93930 ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>USA</th>\n",
       "      <td>United States of America</td>\n",
       "      <td>MULTIPOLYGON (((-155.54211 19.08348, -155.6881...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MEX</th>\n",
       "      <td>Mexico</td>\n",
       "      <td>POLYGON ((-97.14001 25.87000, -97.52807 24.992...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                         name  \\\n",
       "id                              \n",
       "CAN                    Canada   \n",
       "USA  United States of America   \n",
       "MEX                    Mexico   \n",
       "\n",
       "                                              geometry  \n",
       "id                                                      \n",
       "CAN  MULTIPOLYGON (((-63.66450 46.55001, -62.93930 ...  \n",
       "USA  MULTIPOLYGON (((-155.54211 19.08348, -155.6881...  \n",
       "MEX  POLYGON ((-97.14001 25.87000, -97.52807 24.992...  "
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import geopandas as gpd\n",
    "can = gpd.read_file('https://raw.githubusercontent.com/johan/world.geo.json/master/countries/CAN.geo.json')\n",
    "usa = gpd.read_file('https://raw.githubusercontent.com/johan/world.geo.json/master/countries/USA.geo.json')\n",
    "mex = gpd.read_file('https://raw.githubusercontent.com/johan/world.geo.json/master/countries/MEX.geo.json')\n",
    "northam = pd.concat([can, usa, mex]).set_index('id')\n",
    "northam"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "keep_output": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/phobos/Python/clisops/clisops/core/subset.py:463: UserWarning: Wrapping longitudes at 180 degrees.\n",
      "  warnings.warn(\"Wrapping longitudes at 180 degrees.\")\n"
     ]
    }
   ],
   "source": [
    "mask = subset.create_mask(x_dim=ds.lon, y_dim=ds.lat, poly=northam, wrap_lons=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "keep_output": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.QuadMesh at 0x7fbcdf82ad00>"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "mask.T.plot() # The transpose is needed because create_mask returns lon, lat instead the input lat, lon."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "keep_output": true
   },
   "source": [
    "The same can be done with 2D lat/lon coordinates. Here get the regions of `salt` that are within the boundaries of the US as defined in our geojson data. As our data comes from a small region in the Gulf of Mexico and our USA mask is quite coarse, the following isn't really helpful. In any case, it illustrates what can be done with clisops."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "keep_output": true
   },
   "outputs": [],
   "source": [
    "usa_mask = subset.create_mask(x_dim=salt.lon, y_dim=salt.lat, poly=usa)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "keep_output": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.QuadMesh at 0x7fbcdf778df0>"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "salt.plot()\n",
    "usa_mask.plot(cmap=plt.cm.Blues_r, add_colorbar=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## create_weight_masks : Creating exact weights masks from polygons\n",
    "\n",
    "While `create_mask` (and `subset_shape`) only check if the center of a grid cell lies inside of a polygon or not, `create_weight_masks` (and `average_shape`, see the Average utilities notebook) consider the partial overlap of grid cells and polygons. Thus, instead of creating an integer mask for the data, this method creates one mask for each polygon. The weights are the fraction of the grid cells-polygon overlap over the whole polygon. As such, the sum along spatial dimensions gives 1 for each geometry.\n",
    "\n",
    "However, this feature of clisops requires the optional xESMF dependency (version 0.6.2 or later), which is not currently available of pypi (`pip`) and must be installed either through `conda` or from the source.\n",
    "\n",
    "The call signature is different: a dataset or dataarray containing the grid information is passed directly. The [same restrictions as for xESMF](https://pangeo-xesmf.readthedocs.io/en/latest/user_api.html#xesmf.frontend.SpatialAverager) apply."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
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       "  font-size: 11px;\n",
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       "  text-align: center;\n",
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       "\n",
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       "\n",
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       "\n",
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       "\n",
       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
       "  display: contents;\n",
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       "\n",
       ".xr-array-wrap {\n",
       "  grid-column: 1 / -1;\n",
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       "\n",
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       "  grid-column: 1;\n",
       "  vertical-align: top;\n",
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       "\n",
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       "  color: var(--xr-font-color3);\n",
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       "\n",
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       "  display: none;\n",
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       "\n",
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       "\n",
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       "\n",
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       ".xr-var-dtype:hover,\n",
       ".xr-attrs dt:hover {\n",
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       "\n",
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       "\n",
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       "  float: right;\n",
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       "\n",
       ".xr-var-name span,\n",
       ".xr-var-data,\n",
       ".xr-attrs {\n",
       "  padding-left: 25px !important;\n",
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       "\n",
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       "\n",
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       "\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",
       "  display: inline-block;\n",
       "  vertical-align: middle;\n",
       "  width: 1em;\n",
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       "  stroke-width: 0;\n",
       "  stroke: currentColor;\n",
       "  fill: currentColor;\n",
       "}\n",
       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray (geom: 3, lat: 25, lon: 53)&gt;\n",
       "array([[[0.00000000e+00, 0.00000000e+00, 0.00000000e+00, ...,\n",
       "         0.00000000e+00, 0.00000000e+00, 0.00000000e+00],\n",
       "        [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, ...,\n",
       "         0.00000000e+00, 0.00000000e+00, 0.00000000e+00],\n",
       "        [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, ...,\n",
       "         0.00000000e+00, 0.00000000e+00, 0.00000000e+00],\n",
       "        ...,\n",
       "        [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, ...,\n",
       "         0.00000000e+00, 0.00000000e+00, 0.00000000e+00],\n",
       "        [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, ...,\n",
       "         0.00000000e+00, 0.00000000e+00, 0.00000000e+00],\n",
       "        [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, ...,\n",
       "         0.00000000e+00, 0.00000000e+00, 0.00000000e+00]],\n",
       "\n",
       "       [[0.00000000e+00, 0.00000000e+00, 0.00000000e+00, ...,\n",
       "         0.00000000e+00, 0.00000000e+00, 0.00000000e+00],\n",
       "        [0.00000000e+00, 4.38326936e-05, 1.38383820e-05, ...,\n",
       "         0.00000000e+00, 0.00000000e+00, 0.00000000e+00],\n",
       "        [4.47662669e-03, 5.31807679e-03, 5.29956131e-03, ...,\n",
       "         0.00000000e+00, 0.00000000e+00, 0.00000000e+00],\n",
       "...\n",
       "        [0.00000000e+00, 1.73510030e-04, 9.35640399e-04, ...,\n",
       "         0.00000000e+00, 0.00000000e+00, 0.00000000e+00],\n",
       "        [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, ...,\n",
       "         0.00000000e+00, 0.00000000e+00, 0.00000000e+00],\n",
       "        [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, ...,\n",
       "         0.00000000e+00, 0.00000000e+00, 0.00000000e+00]],\n",
       "\n",
       "       [[0.00000000e+00, 0.00000000e+00, 0.00000000e+00, ...,\n",
       "         0.00000000e+00, 0.00000000e+00, 0.00000000e+00],\n",
       "        [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, ...,\n",
       "         0.00000000e+00, 0.00000000e+00, 0.00000000e+00],\n",
       "        [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, ...,\n",
       "         0.00000000e+00, 0.00000000e+00, 0.00000000e+00],\n",
       "        ...,\n",
       "        [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, ...,\n",
       "         0.00000000e+00, 0.00000000e+00, 0.00000000e+00],\n",
       "        [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, ...,\n",
       "         0.00000000e+00, 0.00000000e+00, 0.00000000e+00],\n",
       "        [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, ...,\n",
       "         0.00000000e+00, 0.00000000e+00, 0.00000000e+00]]])\n",
       "Coordinates:\n",
       "    name     (geom) object &#x27;Canada&#x27; &#x27;United States of America&#x27; &#x27;Mexico&#x27;\n",
       "  * geom     (geom) object &#x27;CAN&#x27; &#x27;USA&#x27; &#x27;MEX&#x27;\n",
       "  * lat      (lat) float32 75.0 72.5 70.0 67.5 65.0 ... 25.0 22.5 20.0 17.5 15.0\n",
       "  * lon      (lon) float32 200.0 202.5 205.0 207.5 ... 322.5 325.0 327.5 330.0</pre><div class='xr-wrap' hidden><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'>geom</span>: 3</li><li><span class='xr-has-index'>lat</span>: 25</li><li><span class='xr-has-index'>lon</span>: 53</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-8dc55348-49ad-4d88-80c3-8f4895265f9a' class='xr-array-in' type='checkbox' checked><label for='section-8dc55348-49ad-4d88-80c3-8f4895265f9a' 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>0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0</span></div><div class='xr-array-data'><pre>array([[[0.00000000e+00, 0.00000000e+00, 0.00000000e+00, ...,\n",
       "         0.00000000e+00, 0.00000000e+00, 0.00000000e+00],\n",
       "        [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, ...,\n",
       "         0.00000000e+00, 0.00000000e+00, 0.00000000e+00],\n",
       "        [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, ...,\n",
       "         0.00000000e+00, 0.00000000e+00, 0.00000000e+00],\n",
       "        ...,\n",
       "        [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, ...,\n",
       "         0.00000000e+00, 0.00000000e+00, 0.00000000e+00],\n",
       "        [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, ...,\n",
       "         0.00000000e+00, 0.00000000e+00, 0.00000000e+00],\n",
       "        [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, ...,\n",
       "         0.00000000e+00, 0.00000000e+00, 0.00000000e+00]],\n",
       "\n",
       "       [[0.00000000e+00, 0.00000000e+00, 0.00000000e+00, ...,\n",
       "         0.00000000e+00, 0.00000000e+00, 0.00000000e+00],\n",
       "        [0.00000000e+00, 4.38326936e-05, 1.38383820e-05, ...,\n",
       "         0.00000000e+00, 0.00000000e+00, 0.00000000e+00],\n",
       "        [4.47662669e-03, 5.31807679e-03, 5.29956131e-03, ...,\n",
       "         0.00000000e+00, 0.00000000e+00, 0.00000000e+00],\n",
       "...\n",
       "        [0.00000000e+00, 1.73510030e-04, 9.35640399e-04, ...,\n",
       "         0.00000000e+00, 0.00000000e+00, 0.00000000e+00],\n",
       "        [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, ...,\n",
       "         0.00000000e+00, 0.00000000e+00, 0.00000000e+00],\n",
       "        [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, ...,\n",
       "         0.00000000e+00, 0.00000000e+00, 0.00000000e+00]],\n",
       "\n",
       "       [[0.00000000e+00, 0.00000000e+00, 0.00000000e+00, ...,\n",
       "         0.00000000e+00, 0.00000000e+00, 0.00000000e+00],\n",
       "        [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, ...,\n",
       "         0.00000000e+00, 0.00000000e+00, 0.00000000e+00],\n",
       "        [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, ...,\n",
       "         0.00000000e+00, 0.00000000e+00, 0.00000000e+00],\n",
       "        ...,\n",
       "        [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, ...,\n",
       "         0.00000000e+00, 0.00000000e+00, 0.00000000e+00],\n",
       "        [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, ...,\n",
       "         0.00000000e+00, 0.00000000e+00, 0.00000000e+00],\n",
       "        [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, ...,\n",
       "         0.00000000e+00, 0.00000000e+00, 0.00000000e+00]]])</pre></div></div></li><li class='xr-section-item'><input id='section-3600d562-03c5-4bda-9d68-8fa99d36f7ac' class='xr-section-summary-in' type='checkbox'  checked><label for='section-3600d562-03c5-4bda-9d68-8fa99d36f7ac' 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>name</span></div><div class='xr-var-dims'>(geom)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>&#x27;Canada&#x27; ... &#x27;Mexico&#x27;</div><input id='attrs-6a6e8198-3e53-47cb-bf73-1b5f2e6bed84' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-6a6e8198-3e53-47cb-bf73-1b5f2e6bed84' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-fb2aef16-9c7f-459c-8f15-97326a6f1070' class='xr-var-data-in' type='checkbox'><label for='data-fb2aef16-9c7f-459c-8f15-97326a6f1070' 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([&#x27;Canada&#x27;, &#x27;United States of America&#x27;, &#x27;Mexico&#x27;], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>geom</span></div><div class='xr-var-dims'>(geom)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>&#x27;CAN&#x27; &#x27;USA&#x27; &#x27;MEX&#x27;</div><input id='attrs-91bc82c2-69a4-44a8-850b-c184f5c4738d' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-91bc82c2-69a4-44a8-850b-c184f5c4738d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a5ef20e4-2fca-4454-9bc4-ac53f0772052' class='xr-var-data-in' type='checkbox'><label for='data-a5ef20e4-2fca-4454-9bc4-ac53f0772052' 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([&#x27;CAN&#x27;, &#x27;USA&#x27;, &#x27;MEX&#x27;], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lat</span></div><div class='xr-var-dims'>(lat)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>75.0 72.5 70.0 ... 20.0 17.5 15.0</div><input id='attrs-2d9c7fff-9927-432a-9d4e-b9d6264079a4' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-2d9c7fff-9927-432a-9d4e-b9d6264079a4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-46218b22-9b67-4b14-a67a-880da8b90505' class='xr-var-data-in' type='checkbox'><label for='data-46218b22-9b67-4b14-a67a-880da8b90505' 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>standard_name :</span></dt><dd>latitude</dd><dt><span>long_name :</span></dt><dd>Latitude</dd><dt><span>units :</span></dt><dd>degrees_north</dd><dt><span>axis :</span></dt><dd>Y</dd></dl></div><div class='xr-var-data'><pre>array([75. , 72.5, 70. , 67.5, 65. , 62.5, 60. , 57.5, 55. , 52.5, 50. , 47.5,\n",
       "       45. , 42.5, 40. , 37.5, 35. , 32.5, 30. , 27.5, 25. , 22.5, 20. , 17.5,\n",
       "       15. ], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lon</span></div><div class='xr-var-dims'>(lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>200.0 202.5 205.0 ... 327.5 330.0</div><input id='attrs-b39b27c4-67b9-4ae5-bb6e-14f032bd2128' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b39b27c4-67b9-4ae5-bb6e-14f032bd2128' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8fa76aea-6254-4175-b6a0-a6f5b8155a64' class='xr-var-data-in' type='checkbox'><label for='data-8fa76aea-6254-4175-b6a0-a6f5b8155a64' 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>standard_name :</span></dt><dd>longitude</dd><dt><span>long_name :</span></dt><dd>Longitude</dd><dt><span>units :</span></dt><dd>degrees_east</dd><dt><span>axis :</span></dt><dd>X</dd></dl></div><div class='xr-var-data'><pre>array([200. , 202.5, 205. , 207.5, 210. , 212.5, 215. , 217.5, 220. , 222.5,\n",
       "       225. , 227.5, 230. , 232.5, 235. , 237.5, 240. , 242.5, 245. , 247.5,\n",
       "       250. , 252.5, 255. , 257.5, 260. , 262.5, 265. , 267.5, 270. , 272.5,\n",
       "       275. , 277.5, 280. , 282.5, 285. , 287.5, 290. , 292.5, 295. , 297.5,\n",
       "       300. , 302.5, 305. , 307.5, 310. , 312.5, 315. , 317.5, 320. , 322.5,\n",
       "       325. , 327.5, 330. ], dtype=float32)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-f459ae30-e8ed-48ce-af24-88ba1dea9e04' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-f459ae30-e8ed-48ce-af24-88ba1dea9e04' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.DataArray (geom: 3, lat: 25, lon: 53)>\n",
       "array([[[0.00000000e+00, 0.00000000e+00, 0.00000000e+00, ...,\n",
       "         0.00000000e+00, 0.00000000e+00, 0.00000000e+00],\n",
       "        [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, ...,\n",
       "         0.00000000e+00, 0.00000000e+00, 0.00000000e+00],\n",
       "        [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, ...,\n",
       "         0.00000000e+00, 0.00000000e+00, 0.00000000e+00],\n",
       "        ...,\n",
       "        [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, ...,\n",
       "         0.00000000e+00, 0.00000000e+00, 0.00000000e+00],\n",
       "        [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, ...,\n",
       "         0.00000000e+00, 0.00000000e+00, 0.00000000e+00],\n",
       "        [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, ...,\n",
       "         0.00000000e+00, 0.00000000e+00, 0.00000000e+00]],\n",
       "\n",
       "       [[0.00000000e+00, 0.00000000e+00, 0.00000000e+00, ...,\n",
       "         0.00000000e+00, 0.00000000e+00, 0.00000000e+00],\n",
       "        [0.00000000e+00, 4.38326936e-05, 1.38383820e-05, ...,\n",
       "         0.00000000e+00, 0.00000000e+00, 0.00000000e+00],\n",
       "        [4.47662669e-03, 5.31807679e-03, 5.29956131e-03, ...,\n",
       "         0.00000000e+00, 0.00000000e+00, 0.00000000e+00],\n",
       "...\n",
       "        [0.00000000e+00, 1.73510030e-04, 9.35640399e-04, ...,\n",
       "         0.00000000e+00, 0.00000000e+00, 0.00000000e+00],\n",
       "        [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, ...,\n",
       "         0.00000000e+00, 0.00000000e+00, 0.00000000e+00],\n",
       "        [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, ...,\n",
       "         0.00000000e+00, 0.00000000e+00, 0.00000000e+00]],\n",
       "\n",
       "       [[0.00000000e+00, 0.00000000e+00, 0.00000000e+00, ...,\n",
       "         0.00000000e+00, 0.00000000e+00, 0.00000000e+00],\n",
       "        [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, ...,\n",
       "         0.00000000e+00, 0.00000000e+00, 0.00000000e+00],\n",
       "        [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, ...,\n",
       "         0.00000000e+00, 0.00000000e+00, 0.00000000e+00],\n",
       "        ...,\n",
       "        [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, ...,\n",
       "         0.00000000e+00, 0.00000000e+00, 0.00000000e+00],\n",
       "        [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, ...,\n",
       "         0.00000000e+00, 0.00000000e+00, 0.00000000e+00],\n",
       "        [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, ...,\n",
       "         0.00000000e+00, 0.00000000e+00, 0.00000000e+00]]])\n",
       "Coordinates:\n",
       "    name     (geom) object 'Canada' 'United States of America' 'Mexico'\n",
       "  * geom     (geom) object 'CAN' 'USA' 'MEX'\n",
       "  * lat      (lat) float32 75.0 72.5 70.0 67.5 65.0 ... 25.0 22.5 20.0 17.5 15.0\n",
       "  * lon      (lon) float32 200.0 202.5 205.0 207.5 ... 322.5 325.0 327.5 330.0"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "weights = subset.create_weight_masks(ds, northam)\n",
    "weights"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<xarray.plot.facetgrid.FacetGrid at 0x7fbcddd3f640>"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x216 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "weights.plot(col='geom', cmap='pink')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div><svg style=\"position: absolute; width: 0; height: 0; overflow: hidden\">\n",
       "<defs>\n",
       "<symbol id=\"icon-database\" viewBox=\"0 0 32 32\">\n",
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray (geom: 3)&gt;\n",
       "array([1., 1., 1.])\n",
       "Coordinates:\n",
       "    name     (geom) object &#x27;Canada&#x27; &#x27;United States of America&#x27; &#x27;Mexico&#x27;\n",
       "  * geom     (geom) object &#x27;CAN&#x27; &#x27;USA&#x27; &#x27;MEX&#x27;</pre><div class='xr-wrap' hidden><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'>geom</span>: 3</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-1af0cadb-02b0-4874-b838-5394b7105ead' class='xr-array-in' type='checkbox' checked><label for='section-1af0cadb-02b0-4874-b838-5394b7105ead' 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>1.0 1.0 1.0</span></div><div class='xr-array-data'><pre>array([1., 1., 1.])</pre></div></div></li><li class='xr-section-item'><input id='section-90419ba0-6470-439d-8a4c-8f6bc02cd57b' class='xr-section-summary-in' type='checkbox'  checked><label for='section-90419ba0-6470-439d-8a4c-8f6bc02cd57b' class='xr-section-summary' >Coordinates: <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-var-name'><span>name</span></div><div class='xr-var-dims'>(geom)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>&#x27;Canada&#x27; ... &#x27;Mexico&#x27;</div><input id='attrs-3a7f2e36-3376-4dbb-b5fc-1563b1fa586d' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-3a7f2e36-3376-4dbb-b5fc-1563b1fa586d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-815d1ed5-c6f8-4e95-baf5-7952e50c0afd' class='xr-var-data-in' type='checkbox'><label for='data-815d1ed5-c6f8-4e95-baf5-7952e50c0afd' 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([&#x27;Canada&#x27;, &#x27;United States of America&#x27;, &#x27;Mexico&#x27;], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>geom</span></div><div class='xr-var-dims'>(geom)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>&#x27;CAN&#x27; &#x27;USA&#x27; &#x27;MEX&#x27;</div><input id='attrs-41a1995e-ac6b-4f3a-8aa3-14e039799e52' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-41a1995e-ac6b-4f3a-8aa3-14e039799e52' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-97059b21-d148-4cbf-a749-bc987aa79dab' class='xr-var-data-in' type='checkbox'><label for='data-97059b21-d148-4cbf-a749-bc987aa79dab' 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([&#x27;CAN&#x27;, &#x27;USA&#x27;, &#x27;MEX&#x27;], dtype=object)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-1e533d64-d0ed-496c-a2c2-81a1cac16769' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-1e533d64-d0ed-496c-a2c2-81a1cac16769' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.DataArray (geom: 3)>\n",
       "array([1., 1., 1.])\n",
       "Coordinates:\n",
       "    name     (geom) object 'Canada' 'United States of America' 'Mexico'\n",
       "  * geom     (geom) object 'CAN' 'USA' 'MEX'"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "weights.sum(['lon', 'lat'])"
   ]
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