Testing_masking.ipynb

{
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   "cell_type": "markdown",
   "id": "fe021fa2-e6bb-4502-8f6d-ba4586c07b6b",
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   "source": [
    "# Testing masking with xarray"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "41650f15-8dee-4f37-9e8e-f713a347f746",
   "metadata": {},
   "source": [
    "What happens if you apply a mask to data where the mask covers only a subset of the spatial area"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "45ab381c-ad67-437d-97cc-208c7516fffd",
   "metadata": {},
   "outputs": [],
   "source": [
    "import xarray as xr\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3cd9c8ee-8bcd-4d4e-8777-93eae967a491",
   "metadata": {},
   "source": [
    "Load some test data that is part of the `xarray` distribution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "b4521ae5-7e34-437b-b449-ea02875b5a0f",
   "metadata": {},
   "outputs": [],
   "source": [
    "ds = xr.tutorial.open_dataset(\"air_temperature\").load()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "b0f6c617-2493-427a-944b-0c5ef24b6821",
   "metadata": {},
   "outputs": [
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
       "Dimensions:  (lat: 25, time: 2920, lon: 53)\n",
       "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\n",
       "Data variables:\n",
       "    air      (time, lat, lon) float32 241.2 242.5 243.5 ... 296.5 296.2 295.7\n",
       "Attributes:\n",
       "    Conventions:  COARDS\n",
       "    title:        4x daily NMC reanalysis (1948)\n",
       "    description:  Data is from NMC initialized reanalysis\\n(4x/day).  These a...\n",
       "    platform:     Model\n",
       "    references:   http://www.esrl.noaa.gov/psd/data/gridded/data.ncep.reanaly...</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-47b59671-cd97-4958-b80e-897a35f0e51c' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-47b59671-cd97-4958-b80e-897a35f0e51c' 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'>lat</span>: 25</li><li><span class='xr-has-index'>time</span>: 2920</li><li><span class='xr-has-index'>lon</span>: 53</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-e4857f30-3d38-435c-b3ad-1a10449ea39a' class='xr-section-summary-in' type='checkbox'  checked><label for='section-e4857f30-3d38-435c-b3ad-1a10449ea39a' 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'>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-e8123c0e-d015-48be-8d8f-67c062408d9c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e8123c0e-d015-48be-8d8f-67c062408d9c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f45820b3-76e8-4cab-9336-5825d9e8d018' class='xr-var-data-in' type='checkbox'><label for='data-f45820b3-76e8-4cab-9336-5825d9e8d018' 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-d531090d-fa6a-4a08-8cf5-f6344c2c0d39' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d531090d-fa6a-4a08-8cf5-f6344c2c0d39' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-11464d21-9959-4358-8fc5-69bc2f39d492' class='xr-var-data-in' type='checkbox'><label for='data-11464d21-9959-4358-8fc5-69bc2f39d492' 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><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2013-01-01 ... 2014-12-31T18:00:00</div><input id='attrs-c83cf970-23d2-4b9d-b808-848f26d6d44f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c83cf970-23d2-4b9d-b808-848f26d6d44f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-39fafa96-23f3-4299-8d06-20f71b16c078' class='xr-var-data-in' type='checkbox'><label for='data-39fafa96-23f3-4299-8d06-20f71b16c078' 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>time</dd><dt><span>long_name :</span></dt><dd>Time</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;2013-01-01T00:00:00.000000000&#x27;, &#x27;2013-01-01T06:00:00.000000000&#x27;,\n",
       "       &#x27;2013-01-01T12:00:00.000000000&#x27;, ..., &#x27;2014-12-31T06:00:00.000000000&#x27;,\n",
       "       &#x27;2014-12-31T12:00:00.000000000&#x27;, &#x27;2014-12-31T18:00:00.000000000&#x27;],\n",
       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-ef4dcea3-948c-4f32-abbf-f881afb6ba10' class='xr-section-summary-in' type='checkbox'  checked><label for='section-ef4dcea3-948c-4f32-abbf-f881afb6ba10' 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>air</span></div><div class='xr-var-dims'>(time, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>241.2 242.5 243.5 ... 296.2 295.7</div><input id='attrs-59175836-33d6-4034-947c-d9679cab0cb9' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-59175836-33d6-4034-947c-d9679cab0cb9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0707bf0c-7811-4176-95cc-9b6df4cbc6c3' class='xr-var-data-in' type='checkbox'><label for='data-0707bf0c-7811-4176-95cc-9b6df4cbc6c3' 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>long_name :</span></dt><dd>4xDaily Air temperature at sigma level 995</dd><dt><span>units :</span></dt><dd>degK</dd><dt><span>precision :</span></dt><dd>2</dd><dt><span>GRIB_id :</span></dt><dd>11</dd><dt><span>GRIB_name :</span></dt><dd>TMP</dd><dt><span>var_desc :</span></dt><dd>Air temperature</dd><dt><span>dataset :</span></dt><dd>NMC Reanalysis</dd><dt><span>level_desc :</span></dt><dd>Surface</dd><dt><span>statistic :</span></dt><dd>Individual Obs</dd><dt><span>parent_stat :</span></dt><dd>Other</dd><dt><span>actual_range :</span></dt><dd>[185.16 322.1 ]</dd></dl></div><div class='xr-var-data'><pre>array([[[241.2    , 242.5    , 243.5    , ..., 232.79999, 235.5    ,\n",
       "         238.59999],\n",
       "        [243.79999, 244.5    , 244.7    , ..., 232.79999, 235.29999,\n",
       "         239.29999],\n",
       "        [250.     , 249.79999, 248.89   , ..., 233.2    , 236.39   ,\n",
       "         241.7    ],\n",
       "        ...,\n",
       "        [296.6    , 296.19998, 296.4    , ..., 295.4    , 295.1    ,\n",
       "         294.69998],\n",
       "        [295.9    , 296.19998, 296.79   , ..., 295.9    , 295.9    ,\n",
       "         295.19998],\n",
       "        [296.29   , 296.79   , 297.1    , ..., 296.9    , 296.79   ,\n",
       "         296.6    ]],\n",
       "\n",
       "       [[242.09999, 242.7    , 243.09999, ..., 232.     , 233.59999,\n",
       "         235.79999],\n",
       "        [243.59999, 244.09999, 244.2    , ..., 231.     , 232.5    ,\n",
       "         235.7    ],\n",
       "        [253.2    , 252.89   , 252.09999, ..., 230.79999, 233.39   ,\n",
       "         238.5    ],\n",
       "...\n",
       "        [293.69   , 293.88998, 295.38998, ..., 295.09   , 294.69   ,\n",
       "         294.29   ],\n",
       "        [296.29   , 297.19   , 297.59   , ..., 295.29   , 295.09   ,\n",
       "         294.38998],\n",
       "        [297.79   , 298.38998, 298.49   , ..., 295.69   , 295.49   ,\n",
       "         295.19   ]],\n",
       "\n",
       "       [[245.09   , 244.29   , 243.29   , ..., 241.68999, 241.48999,\n",
       "         241.79   ],\n",
       "        [249.89   , 249.29   , 248.39   , ..., 239.59   , 240.29   ,\n",
       "         241.68999],\n",
       "        [262.99   , 262.19   , 261.38998, ..., 239.89   , 242.59   ,\n",
       "         246.29   ],\n",
       "        ...,\n",
       "        [293.79   , 293.69   , 295.09   , ..., 295.29   , 295.09   ,\n",
       "         294.69   ],\n",
       "        [296.09   , 296.88998, 297.19   , ..., 295.69   , 295.69   ,\n",
       "         295.19   ],\n",
       "        [297.69   , 298.09   , 298.09   , ..., 296.49   , 296.19   ,\n",
       "         295.69   ]]], dtype=float32)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-fa4d7f68-b8a2-4b74-9e6c-f57a6945b99e' class='xr-section-summary-in' type='checkbox'  checked><label for='section-fa4d7f68-b8a2-4b74-9e6c-f57a6945b99e' class='xr-section-summary' >Attributes: <span>(5)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>Conventions :</span></dt><dd>COARDS</dd><dt><span>title :</span></dt><dd>4x daily NMC reanalysis (1948)</dd><dt><span>description :</span></dt><dd>Data is from NMC initialized reanalysis\n",
       "(4x/day).  These are the 0.9950 sigma level values.</dd><dt><span>platform :</span></dt><dd>Model</dd><dt><span>references :</span></dt><dd>http://www.esrl.noaa.gov/psd/data/gridded/data.ncep.reanalysis.html</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:  (lat: 25, time: 2920, lon: 53)\n",
       "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\n",
       "Data variables:\n",
       "    air      (time, lat, lon) float32 241.2 242.5 243.5 ... 296.5 296.2 295.7\n",
       "Attributes:\n",
       "    Conventions:  COARDS\n",
       "    title:        4x daily NMC reanalysis (1948)\n",
       "    description:  Data is from NMC initialized reanalysis\\n(4x/day).  These a...\n",
       "    platform:     Model\n",
       "    references:   http://www.esrl.noaa.gov/psd/data/gridded/data.ncep.reanaly..."
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "ab8bdb13-0e04-4644-856e-41b34b0bfed4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.QuadMesh at 0x7f439dc551c0>"
      ]
     },
     "execution_count": 4,
     "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()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ee7da8a6-53a0-4f37-9fb3-e45973fb1641",
   "metadata": {},
   "source": [
    "Select out a \"stripe\" in the middle of the data to see what happens if we a mask that doesn't cover the full extent \n",
    "\n",
    "NB: The slice ordering is from high to low because the latitude coordinate runs from high to low in this data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "4e26b6e0-ef29-4531-b61c-9617422a589b",
   "metadata": {},
   "outputs": [],
   "source": [
    "stripe = ds.air.sel(lat=slice(50.,40.)).isel(time=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "39f5512f-9f2c-4c6c-83ea-d2752e5bb28a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.QuadMesh at 0x7f439dac39d0>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 468x93.6 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "stripe.plot(size=1.3, aspect=5, vmin=230, vmax=300)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bb20e999-6721-480a-8d6f-e7ac86f636ea",
   "metadata": {},
   "source": [
    "Make a mask from the stripe which is (arbitrarily) temperatures less than 270K. In this case the `xarray.where` function is used as this allows the values of the masked and un-masked areas to be defined as 0 and 1 respectively. This makes it obvious when the mask is applied that the data is coming from the original dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "cb563308-bd40-417b-89c6-eeaa66f0d7af",
   "metadata": {},
   "outputs": [],
   "source": [
    "mask = xr.where(stripe<270, 1, 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "95cb5335-bc8e-4a52-9b8e-fc220c5c72a5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.QuadMesh at 0x7f439d9f9520>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAY0AAACPCAYAAAAGGIduAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/YYfK9AAAACXBIWXMAAAsTAAALEwEAmpwYAAAdGUlEQVR4nO3debwcVZ338c83C4kEQswQNAISzAPDiBvIIvKIMApCQFBRBh0QHOdBdGBwQQceHMSdRUdEBETFBwVRXECIQACHVUBJgISEHQwQYVhjCISQ3Nzv88c5fW/l0kvdm+7c7r6/9+t1Xqk6darq153bfbrOqTpHtgkhhBDKGDXcAYQQQugcUWmEEEIoLSqNEEIIpUWlEUIIobSoNEIIIZQWlUYIIYTSotIIIYRQ2ph6GyV9oMQxltu+rEnxhBBCaGOq93CfpGeA3wGqc4xdbE9vdmAhhBDaT90rDeBy2/9Sr4Ck85oYTwghhDZW90ojhBBCKGp0pdFH0tuBacV9bP+0BTGFEEJoU6UqDUk/A6YDdwCrcraBqDRCCGEEKdU8Jelu4PWOtqwQQhjRyj6nMR94dSsDCSGE0P4aPadxKakZan3gLkl/Bl6qbLe9b2vDCyGE0E4a9Wl8a61EEUIIoSOU7dM4yfZ/NMoL3UXSa4G7gA1sr2pUPoTQ/cr2aexeJW+vZgYShp+khZLeXVm3/Yjt9dq1wpA0TtKPJT0saamk2yXtNaDMuyTdI2mZpGskbVbYtlvOWyJpYZXjXyPpKUnPSZorab8G8UzL+yzL53x3YdtUSZdIekySJU0r8fo+kl/bC5IuljS5sO0ASTflc13b6FghNEvdSkPSJyXdCWwlaV4h/QWYt3ZCDKGmMcCjwDuBDYD/BC6sfCFL2hD4bc6fDMwGflnY/wXgHODzNY5/FDDV9kTgMOA8SVPrxHMBcDvwd8BxwK8lTcnbeoErgP3LvDBJWwM/AA4GXgUsA84oFHkWOBU4sczxQmga2zUT6YM4jfRh2KyQJtfbL1LnJeBnpC+2F4HngS/k/3sDY3KZa4GvATflMpeSviDPB54DbgWmFY65FXAV6QvuXuCAtfA65gH75+XDgJsK2ybk17fVgH3eDSxscNwdgOXADjW2b0m6SWT9Qt4NwOEDyo3J7+m0Buf7BvDzwvp0YEXx+Dn/X4Frh/vvJ9LISXWvNGwvAR4B3mj74UJ6tt5+ofPYPpj0f/1epyapk2sUPZD063dj0hfZzcBPSL/k7wa+BCBpAqnC+DmwEfBh4Iz8C/plJJ0h6W81UqmrWkmvIn15L8hZWwNzC6/xBeDBnF+KpJmSlgN/IlWas2sU3Rp4yPbSQt7cwZyryvGKsT9IqjS2HOLxQmiKhn0atnuBublTNISf2H4w/6C4HHjQ9tW2e4BfAdvkcvuQfr3/xHaP7duA3wAfrHZQ25+yPalGelOjoCSNJV3xnGv7npy9HrBkQNElpFvIS7G9Ty4/A5iVPw/VrPG5Wny8EJqi7NhTU4EF+TmNFyqZjuc0RqInCssvVllfLy9vBuwo6W+F7WNIzWBNJWlUPu4K4IjCpueBiQOKTwSWMgi2VwKXSzpK0oO2L5G0gPQaId0UMuRzSXoHqQIGeNj21s2KPYRmK1tpfLmlUYR20cxhYh4FrrNd7c67l5F0FnBQjc2VL9Jq+wn4MamzeEb+gq9YABxSKDuB1KS2gKEZk/dnYDyStgReJ2n9QhPVm0nNc3XZvoH+yrYY+5sLx38dMA64b4ixh9AUpW65tX0dcA/p0nh94O6cF7rLE8DrmnSsmcCWkg6WNDan7SX9Q7XCtg/PfSnVUr1+gTOBfyD1xbw4YNtFwBsk7S9pPHA8MK/SfCVpVM4fm1Y1XtI6edtWkvaS9Ioc+0HALkDVv3vb95EG9PxSPs77gTeRmuTIxxxP+uIHGJfXazkfeK+kd+TK7ivAbysVkqTRef8xwKh8zrF1jhdCU5SqNCQdAPwZ+BBwAPAnSVXbpkNH+ybwxdz5fPSaHCh/ue1B6jh/DPgf4CT6vzTXWH7m4hPAW4D/kfR8Tv+cY3iKdIvr14HFwI45nopdSE1qlwGvzctXVg4PnAA8CTxFuv32n3LfTC0HAtvlc50IfDDHUFG5Mw3Sj7CBlVwf2wuAw0mVx5OkH2ufKhQ5OO9/JvCOvPzDOrGF0BRlnwifC+xu+8m8PgW42vab6+8ZQgihm5R9InxUpcLInhnEviGEELpE2S/+KyTNknSopEOB35Mu6UMIIbSYpHMkPSlpfo3tknSapAfyqB3btiyWMs1TOaj9gZ1Jbb3X276oVUGFEELoJ2kXUn/YT22/ocr2GcCRpOeJdgS+a3vHlsRSttIIIYQwfPKYajNrVBo/IA0nc0FevxfY1fbjzY6j7BzhHyDd+bIR6UpDgJ0Gclsr1tE4j2fC2jpd19ryTcuGO4TQ5u6bt+5wh9C2lrL4adtTGpes7z27TfAzz/YPHj1n3ksLSGObVZxt++xBHHJj0rNRFYty3vBUGsDJpPvg7252AGWNZwI76l3DdfquMWvW3MaFwoj2ntfETZG1XO1fP9yM4zz1bA83XNE/g/Z6r3lkue3t1uCQqpLXkmakspXGE8NZYYQQQjfpxSxzTzMPuQjYtLC+Cen5qKYrW2nMlvRL4GJWnyP8t60IKoQQulkvZnnNsS+H5BLgCEm/IHWEL2lFfwaUrzQmkiaB2aOQZ9IEN3XlGdGWAquAHtvb5RnIfkmar2EhaZ6FxaWjDiGEDtZrsay3WotSdZIuAHYFNpS0iDQFwVgA22eRHoGYATxA+q7+WJND7lOq0rBdNwBJx9r+Zp0iu9l+urB+DPAH2ydKOiavx3zjIYQRoRd4wWV/s4PtDzfYbuDf1jCsUpr1VPeHBll+P+DcvHwu8L4mxRFCCG2vF7HcY/pSJ2lWpVHvOsvAlZLmSDos572q0t6W/92o6kGlwyTNljR7ZX9XSgghdLRej+KF3nF9qZM0q4qrd2vXzrYfk7QRcJWke+qUXf2g6T7lswEmanI8hRhC6ApGLHdnjmTfrEqj5pWG7cfyv09KugjYAXhC0lTbj0uaShr6OYQQRoRexLIOu8KoaFbz1K+qZUqaIGn9yjLp7qv5pNvDKjOqHQL8rklxhBBC21tldWzzVNlJmE6WNDHPYPYHSU/nmcwAsP2NGru+Crgxz8fxZ+D3tq8gTVCzu6T7gd3zegghjAiV5qlK6iRlm6f2sP2FPIXlItLdUtcA59XbyfZDFOY5LuQ/A8SYICGEEamXUSzrXWe4wxiSspVGpSqcAVxg+1mp/IMpIYQQ+vVaLFvVWc1SFWUrjUvzXU8vAp/K070ub7BPaEPVBqOb9VgMYjgSxcCEw6cX8VJvZz2fUVH2ifBjJJ0EPGd7laRlpAf0QgghDFIaRqQzm6fKdoSvS3pE/cyc9RpgTYbxDSGEEcv5SqOSOknZW25/AqwA3p7XFwFfa0lEIYTQ5XotXlw1ti91krKVxnTbJwMrAWy/SP2hQ0IIIdTQi1i+amxf6iRlr4tWSHoFebgQSdMhBoMKIYShsMWKDmuWqigb9ZeAK4BNJZ0P7Awc2qqgQgihm/Uilvd0caVh+ypJtwFvIzVLHTVgfowQQggl2bB8VWdWGmXvnhKwF/BW2zOBdSXt0NLIQgihSxmxsnd0X+okZTvCzwB2AiqzRy0Fvt+SiEIIocvZYkXPmL7UScpGu6PtbSXdDmB7saTOfDIlhBCGmQ0re5s1yPjaVbbSWClpNP13T00hTXMbQghhkIxY2dNZzVIVZau604CLgI0kfR24Eag1HHoIIYQ6bFjZM7ovdZKGVxqSRgF/Ab5AGs5cwPts393i2EIIoSvZYlW3Nk/Z7pX0bds7AaXn9w4hJIMZTXZtjjhc61wx+m3rGVjV05mVRtmor5S0v2ISjRBCWHMWvT2j+lInKdsR/llgAtAjaTmpicq2J7YsshBC6FYGr+rM3+Blnwhfv9WBhBDCiGFg1fBcYeQWo01sPzqU/cs+Eb5tlTRdUqlKR9JoSbdLmpnXT5D0V0l35DRjKMGHEEKn8ir1pUYk7SnpXkkPSDqmyvZdJS0pfKceX/O8toGLhxp32eapM4BtgTvz+huBucDfSTrc9pUN9j8KuBsoNmd9x/a3BhNsCCF0BQv1lGueys/IfR/YnTSX0a2SLrF914CiN9jep2QEt0ja3vatpWPOyl4fLQS2sf1W228F3gLMB94NnFxvR0mbAHsDPxpscCGE0JUM6lFfamAH4AHbD9leAfyCNZ9uezfgZkkPSpon6U5J88rsWPZKYyvbCyortu+StI3th0rcUHUq6RmPgf0iR0j6KDAb+JztxSVjCSGEjqfyY2psDBT7HxYBO1Ypt5OkucBjwNHF7+wq9ip99gHKXmncK+lMSe/M6QzgPknjyLP5VSNpH+BJ23MGbDoTmE66Ynkc+HaN/Q+TNFvS7JUx51MIoVu8/Epjw8p3XU6HFUpX+2XuAeu3AZvZfjPwPWr0WUiqdBEsrZEaKnulcSjwKeDTpBdwI3A0qcLYrc5+OwP75o7u8cBESefZPqjwIn4IzKy2s+2zgbMBJmrywDcphBA6knKlUfC07e1qFF8EbFpY34R0NdHH9nOF5csknSFpwyrzHv0c2AeYw+oVj/L66xrFXvaW2xfz1cVM2/cO2Px8nf2OBY6F1LtPumQ6SNJU24/nYu8n9Y+EEMKIoVWli94KbCFpc+CvwIHAR1Y7lvRq4AnbznMdjQKeGXigSke57c0lTQa2IP2gL63sLbP7AqcA6wCbS3oL8BXb+w7mZAUn52OY1Mn+iSEeJ4QQOo9hVE/JonaPpCOAWcBo4BzbCyQdnrefBXwQ+KSkHuBF4MB8a21Vkv6VdFfrJsAdpFlZbyKNL1jXYOYI3wG4Ngd5h6RpJfcl73NtYf+DB7NvCCF0FQ+qIxzblwGXDcg7q7B8OnD6ICI4CtgeuMX2bpK2Ar5cZseylUaP7SUx9FQIIaw5ASp5pdEiy20vl4SkcbbvkfT3ZXYsW2nMl/QRYLSkLYB/J13KhBBCGKxBNE+1yCJJk0h3WV0laTEDOtdrKVtpHAkcB7wEXEBqW/vqoMMMoUu0avjwGJZ8hPCgOsKbf3r7/XnxBEnXABsAV5TZt+zdU8tIlcZxQ4owhBBCPw9781Qf29cNpnzdSkPSpbz8IZLiyYZ691QIIYxYAkYN45XGmmh0pVEZUPADwKuB8/L6h0m3yoYQQhisYW6eWhN1K43KZYukr9repbDpUknXtzSyEELoVsPfET5kZceemiKp7/Hy/GTilNaEFEII3U2kK41K6iRl7576DHCtpIfy+jTgsNrFQwgh1GQY1dOZw+mVvXvqivx8xlY56x7bMexsCCEMRbc2T0natrJs+yXbc3N6qVqZEEIIjaW7p9yXOkmjK42f5NFp640f8mNgm2YFFEIIXc8wquZMRO2tUaWxAWnc9XqVxlPNCyeEEEYAgzrsCqOi0S2309ZSHCGEMGLI7u6O8BBCCE1kGLUyKo0QQghldGvzVAjdKkaTDcNJhlErBzELUxsp9US4koMkHZ/XX5vnoQ0hhDBYNqNW9valTlJ2GJEzgJ1IAxUCLAW+35KIQghhBNCq3r7USco2T+1oe1tJtwPYXixpnRbGFUIIXUs2Wtlhg05lZa80VkoaTZ5bQ9IUoHT1KGm0pNslzczrkyVdJen+/O8rBx15CCF0KoN6evtSJylbaZwGXARsJOnrwI3ANwZxnqOAuwvrxwB/sL0F8Ie8HkIII4MNK1f1pw5SqtKwfT7wBeCbwOPA+2z/qsy+kjYB9gZ+VMjeDzg3L58LvK9kvCGE0PlstLKnL3WSRtO9Ti6sPglcUNxm+9kS5ziVVOGsX8h7le3HAWw/LmmjGuc/jDwE+3jWLXGqEELoAAZ6OusKo6JRR/gc0ssT8FpgcV6eBDwCbF5vZ0n7AE/anpMHPhwU22cDZwNM1OTOfBImhBAGsmFlZ45Y2Gjsqc0BJJ0FXGL7sry+F/DuEsffGdhX0gxgPDBR0nnAE5Km5quMqaSrmBBCGBk6uNIo2xG+faXCALB9OfDORjvZPtb2JnngwwOB/7Z9EHAJcEgudgjwu0FFHUIInczGPT19qZOUrTSelvRFSdMkbSbpOOCZNTjvicDuku4Hds/rIYQwMtiwYmV/akDSnpLulfSApJfdbZpH7Tgtb5/Xysnxyj7c92HgS6TbbgGup//p8FJsXwtcm5efAd41mP1DCKFr2LjkXVP5Gbnvk35gLwJulXSJ7bsKxfYCtshpR+DM/G/TlZ0j/FnSsxYhhBDWkG16V6woW3wH4AHbDwFI+gXpsYVipbEf8FPbBm6RNKnSb9zMuKFkpSHpGvLT4EW2/7HZAdWylMVPX+1fP5xXNwSeXlvnXkNtH+voqauttn28Awwx3vubHkhJI+T9HRZrI9bNmnGQpSyedXXvhRsWssZLml1YPzvfPQqwMfBoYdsiXn4VUa3MxqTn6pqqbPPU0YXl8cD+wFrtvbE9pbIsabbt7dbm+Yeqk2KFiLfVIt7W6aRYbe85iOLVptse+CO+TJmmKNs8NWdA1h8lXdeCeEIIIaxuEbBpYX0T4LEhlGmKsvNpTC6kDSW9B3h1KwIKIYSwmluBLSRtnkcXP5D02ELRJcBH811UbwOWtKI/A8o3TxWfDO8B/gJ8vBUBlXR24yJto5NihYi31SLe1umkWEuz3SPpCGAWMBo4x/YCSYfn7WcBlwEzgAeAZcDHWhWPUmd7g0LSeNvLB+SNs/1SqwILIYTQfso+3HdTlbybmxlICCGE9tdolNtXk27beoWkbejvoZ8IMexsCCGMNI2uNN4DfIvUE/9fwLdz+izwf1sRkKRNJV0j6W5JCyQdlfNrzvYn6dj8+Py9uZN+rakT7ymS7smP9F8kadJwx1sr1sL2oyVZ0oaFvLZ7b/O2I3NMCySd3M7xSnqLpFsk3SFptqQd2iTe8ZL+LGlujvfLOb9dP2u14m27z1pXs90wAfuXKdeMBEwFts3L6wP3Aa8HTgaOyfnHACfl5dcDc4FxpKHaHwRGt0G8ewBjcv5J7RBvrVjz+qakjraHgQ2HO9YG7+1uwNXAuLxtozaP90pgr5w/A7i2TeIVsF5eHgv8CXhbG3/WasXbdp+1bk51rzQkHZQXp0n67MBUb9+hsv247dvy8lLSNLEbU3u2v/2AX9h+yfZfSHcP7MBaUite21farjwAeQvpam1Y463z3gJ8hzRZVvHOiLZ8b4FPAic634hhuzK0frvGa1KTLsAG9N8/P9zx2vbzeXVsTqZ9P2tV423Hz1o3a9Q8NSH/ux7pl1MxrdfCuACQNA3YhvSLYrXZ/oDKbH+1Hp9f6wbEW/QvwOV5uS3iLcYqaV/gr7bnDijWFrHCy97bLYF3SPqTpOskbZ+LtWu8nwZOkfQoqbn32Fxs2OOVNFrSHaQ5ba6y3daftRrxFrXdZ63bNJqE6Qd58Wrbfyxuk7Rzy6JKx18P+A3wadvPSdWekk9Fq+St9Vn+BsZbyD+O9GzL+ZWsKruv1XiLsZJiO450if+yolXyhv29lTQGeCWpaWJ74EJJr6N94/0a8Bnbv5F0APBj0iRmwx6v7VXAW3I/wEWS3lCneFvGa3s+tOdnrRuVveX2eyXzmkLSWNKH7nzbv83ZTyjN8odWn+1vrT0+X0uNeJF0CLAP8M+2K3+swxpvlVink9p750pamOO5TenOuXZ9bxcBv83NFX8GekmD1bVrvIcAleVf0d9EMuzxVtj+G2nqgj1p489axYB42/Kz1rXqdXgAOwGfI13ifbaQTgDmtqKThfTr4KfAqQPyT2H1zrmT8/LWrN7Z9RBrv3OuWrx7koYunjIgf9jirRXrgDIL6e8Ib9f39nDgK3l5y/z3qTaO925g17z8LmBOm7y/U4BJefkVwA2kL952/azVirftPmvdnBoNI7IOqe9iDKkfo+I54IMN9h2qnYGDgTtz2yWk23tPJDVDfBx4BPgQgNPj9BeS/mh6gH9zuoRdW2rFexrpj/Wq3LR2i+3DhzneqrG6MJVvURu/t+cA50iaD6wADnH6lmjXeP8P8N3crLYcOAza4v2dCpyrNMnPKOBC2zMl3Ux7ftZqxfsA7fdZ61plhxHZzPbDDQuGEELoamUHLFwm6RTS5d74SqbX4iRMIYQQhl/ZjvDzgXtI7YJfJrV739qimEIIIbSpss1Tc2y/VdI822/KedfZfmfLIwwhhNA2yjZPrcz/Pi5pb9Jta5vUKR9CCKELla00viZpA9Ltt98jDYnw6VYFFUIIoT2Vap6quqP0adunNjecEEII7axsR3g1LRmwMLSWpOcbl1qj418maVJOnxrC/rtKmjnI8kskVX3WRNL/k9SqZ4raSn4v3l5Y/4ykRySdPpxxhe6yJpVGzcGgwshle4bTEA+TgEFXGkN0g+0ZrTxBfjCv3e0K9FUatr8DHD9s0YSutCaVRgz81SXUP0lQZRKbV+b8ayWdlCe+uU/SO3L+upIuzOV/mUeb3S5vW6g0idOJwHSliYdOGXgFIel0SYfm5T2VJtG5EfhAocwESedIulXS7ZL2K/FalI99l6Tf0z9CK5LeqjQq7hxJswrjK22fX8vNOdbKAHiHSvqVpEuBK2vFozTy6ik5f56kT+T8qZKuz+/B/Mr7VyPuPfL5b8vnXC/nH5+PO1/S2cqPPEv69/wa50n6hdKouocDn8nnq3muENZIg7FelpKGDBmYlgI9wz0GSqTBJ+D5KnnzgHfm5a+Qx04iDQj37bw8gzTaMcDRwA/y8htIQzRsl9cXkgYPnAbML5xjV2BmYf104FDSw6KPAluQrl4vrJQDvgEclJcnkSY1mjAg9oHH/QBwFTAaeA3wN9KQN2NJc91PyeX+CTgnL88H3p6XT6zEneNbBEyuFw9pWJAv5vxxwGzSM02fA47L+aOB9Wv8n2wIXF95bcB/AMfn5cmFcj8D3puXH6N/EqpJ+d8TgKMHHPtQ4PTh/ruL1D2p0dDo69fbHjpfvituku3rcta5pJFYKyqjs84hVQQA/xv4LoDt+ZLmrUEIWwF/sX1/juc88thMpOHa95V0dF4fD7yWNABgLbsAFziNMfSYpP/O+X9PquAq4xONJt1CPon0ZX5TLvdz0iB4FVfZfrZBPHsAbyr0nWxAqgRvJY2RNRa42PYdNWJ+G2mWuT/m2NYBbs7bdpP0BWBdYDKwALiUVNGfL+li4OI670cITdUJ7bRheL2U/11F/9/LUPqzeli9OXR8YblWU6dIUw3fO8hzVTuegAW2d1otszD/dQ0vNIonNxkdaXvWy04q7QLsDfxM0im2f1ojtqtsf3jAvuOBM0hXcY9KOoH+921vUgW5L/CfkrZu8DpCaIo16dMIXcD2EmBxoQ38YOC6OrsA3AgcACDp9cAbq5RZyuojIz8MvF7SuHx1866cfw+wuaTpeb34xTkLOLLQjr9NiZd0PXBg7meYSppPHOBeYIqknfKxxkra2vZiYKmkt+VyB9Y5dq14ZgGfzFcUSNoy939sBjxp+4ekiZe2rXHcW4CdJf2vvP+6krakv4J4OvdxfDBvHwVsavsa0hS9k0ijUQ98z0NourjSGHnWlbSosP5fpEmCzpK0LmnOgY81OMYZpCGq5wG3k5pKlhQL2H5G0h9zp/Lltj+vNEz1POD+vB+2l0s6DPi9pKdJFVJl9rivAqcC8/IX9UJWbzqq5iLgH4E7SX0O1+XzrMjNR6flSmtMPvYC4OPADyW9QOrHWfLyw9aN50ekprvbcv5TpHm1dwU+L2kl8Dzw0WoHtf2U0k0BF0gal7O/aPs+ST/Mr2Uh/eO9jQbOy69DwHds/y132P86d9AfafuGBu9VCIM25If7wsilNJ/B2PyFPx34A7Cl7RXDEMuupM7fRpVJvWOsZ/v5vHwMMNX2Uc2JcHjlymg720cMdyyhO8SVRhiKdYFrcnOMgE8OR4WRrQDeIOkyD/1Zjb0lHUv6PDxMuuOo40n6DOk23N8Mdyyhe8SVRghrkaQ/kW7LLTrY9p3DEU8IgxWVRgghhNLi7qkQQgilRaURQgihtKg0QgghlBaVRgghhNL+P42HsXBKfxPPAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 468x93.6 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "mask.plot(size=1.3, aspect=5)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f788111f-7463-4331-a67c-9b91026d320e",
   "metadata": {},
   "source": [
    "Mask the original data with the stripe and note that it limits the resulting data to the spatial subset of the mask"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "74630dad-35e8-4aa8-8d9a-cd85534f1eed",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.QuadMesh at 0x7f439d9d5370>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 468x93.6 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ds.air.isel(time=0).where(mask==1).plot(size=1.3, aspect=5, vmin=230, vmax=300)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bec81e65-db00-4668-bbd3-46d9f758ed3f",
   "metadata": {},
   "source": [
    "Intentionally shift the values of the latitudes in the mask by 20 degrees"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "84f9ab8c-eae6-4102-b0dd-880bfa213bfb",
   "metadata": {},
   "outputs": [],
   "source": [
    "mask['lat'] = mask.lat + 20."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "87c3b9c9-7d6a-4af4-aebf-0f79682a166e",
   "metadata": {},
   "source": [
    "So the mask shape remains the same, but it is now masking data from further north, that is colder. The y-axis has also changed, showing the shifted latitudes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "7183e74b-5f92-49f2-bd9f-9d7457d64264",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.QuadMesh at 0x7f439d8d3b50>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 468x93.6 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ds.air.isel(time=0).where(mask==1).plot(size=1.3, aspect=5, vmin=230, vmax=300)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6c1706f1-110b-4073-a2a6-52286f8f221f",
   "metadata": {},
   "source": [
    "Shift it once more so the latitudes not encompass values that are outside the original data and the masking fails"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "84014404-5493-4bd0-9677-1b240eeef7b2",
   "metadata": {},
   "outputs": [],
   "source": [
    "mask['lat'] = mask.lat + 20."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "bcf7be01-42e5-4962-9c36-b76c954fa00e",
   "metadata": {},
   "outputs": [
    {
     "ename": "IndexError",
     "evalue": "index -1 is out of bounds for axis 0 with size 0",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mIndexError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m/local/v45/aph502/tmp/ipykernel_724154/2079345945.py\u001b[0m in \u001b[0;36m<cell line: 1>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mds\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mair\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0misel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtime\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwhere\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmask\u001b[0m\u001b[0;34m==\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msize\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1.3\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maspect\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m5\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvmin\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m230\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvmax\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m300\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m/g/data/hh5/public/apps/miniconda3/envs/analysis3-22.01/lib/python3.9/site-packages/xarray/plot/plot.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, **kwargs)\u001b[0m\n\u001b[1;32m    864\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    865\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__call__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 866\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_da\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    867\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    868\u001b[0m     \u001b[0;31m# we can't use functools.wraps here since that also modifies the name / qualname\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/g/data/hh5/public/apps/miniconda3/envs/analysis3-22.01/lib/python3.9/site-packages/xarray/plot/plot.py\u001b[0m in \u001b[0;36mplot\u001b[0;34m(darray, row, col, col_wrap, ax, hue, rtol, subplot_kws, **kwargs)\u001b[0m\n\u001b[1;32m    330\u001b[0m     \u001b[0mkwargs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"ax\"\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0max\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    331\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 332\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0mplotfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdarray\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    333\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    334\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/g/data/hh5/public/apps/miniconda3/envs/analysis3-22.01/lib/python3.9/site-packages/xarray/plot/plot.py\u001b[0m in \u001b[0;36mnewplotfunc\u001b[0;34m(darray, x, y, figsize, size, aspect, ax, row, col, col_wrap, xincrease, yincrease, add_colorbar, add_labels, vmin, vmax, cmap, center, robust, extend, levels, infer_intervals, colors, subplot_kws, cbar_ax, cbar_kwargs, xscale, yscale, xticks, yticks, xlim, ylim, norm, **kwargs)\u001b[0m\n\u001b[1;32m   1174\u001b[0m         \u001b[0;31m# Replace pd.Intervals if contained in xval or yval.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1175\u001b[0m         \u001b[0mxplt\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mxlab_extra\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_resolve_intervals_2dplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mxval\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mplotfunc\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__name__\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1176\u001b[0;31m         \u001b[0myplt\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mylab_extra\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_resolve_intervals_2dplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0myval\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mplotfunc\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__name__\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1177\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1178\u001b[0m         \u001b[0m_ensure_plottable\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mxplt\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0myplt\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mzval\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/g/data/hh5/public/apps/miniconda3/envs/analysis3-22.01/lib/python3.9/site-packages/xarray/plot/utils.py\u001b[0m in \u001b[0;36m_resolve_intervals_2dplot\u001b[0;34m(val, func_name)\u001b[0m\n\u001b[1;32m    585\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0m_valid_other_type\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mval\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mInterval\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    586\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mfunc_name\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m\"pcolormesh\"\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 587\u001b[0;31m             \u001b[0mval\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_interval_to_bound_points\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mval\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    588\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    589\u001b[0m             \u001b[0mval\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_interval_to_mid_points\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mval\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/g/data/hh5/public/apps/miniconda3/envs/analysis3-22.01/lib/python3.9/site-packages/xarray/plot/utils.py\u001b[0m in \u001b[0;36m_interval_to_bound_points\u001b[0;34m(array)\u001b[0m\n\u001b[1;32m    513\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    514\u001b[0m     \u001b[0marray_boundaries\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mleft\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mx\u001b[0m \u001b[0;32min\u001b[0m \u001b[0marray\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 515\u001b[0;31m     \u001b[0marray_boundaries\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconcatenate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marray_boundaries\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mright\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    516\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    517\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0marray_boundaries\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mIndexError\u001b[0m: index -1 is out of bounds for axis 0 with size 0"
     ]
    }
   ],
   "source": [
    "ds.air.isel(time=0).where(mask==1).plot(size=1.3, aspect=5, vmin=230, vmax=300)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2dc5ba09-2bef-43dd-8cf2-6494bc1d759c",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
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   "file_extension": ".py",
   "mimetype": "text/x-python",
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