mhw_pipeline/working-oisst-mhw-example.ipynb

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    "import os\n",
    "import sys\n",
    "\n",
    "sys.path.append(\"/home/ec2-user/marineHeatWaves\")\n",
    "import marineHeatWaves as mh\n",
    "\n",
    "import xarray as xr\n",
    "import numpy as np\n",
    "\n",
    "from dask.distributed import Client\n",
    "\n",
    "from datetime import date, datetime\n",
    "\n",
    "from functools import partial\n",
    "\n",
    "import gcsfs\n",
    "\n",
    "import cartopy.crs as ccrs\n",
    "import cartopy.feature as cf\n",
    "\n",
    "from pandas import Timestamp\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "from multiprocessing import Pool\n",
    "\n",
    "from numba import float64, guvectorize, jit\n",
    "\n",
    "GCP_PROJECT_ID = '170771369993'\n",
    "OISST_GCP = 'oisst/oisst.zarr'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from importlib import reload"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "client = Client()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Marine Heatwaves in OISST Data\n",
    "\n",
    "via  [`marineHeatWaves.py`](https://github.com/ecjoliver/marineHeatWaves/blob/master/marineHeatWaves.py)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "fs = gcsfs.GCSFileSystem(project=GCP_PROJECT_ID, token=\"../gc-pangeo.json\")\n",
    "oisst = xr.open_zarr(fs.get_mapper(OISST_GCP))\n",
    "oisst = oisst.assign_coords(lon=(((oisst.lon + 180) % 360) - 180)).sortby('lon')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
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       "</style><div class='xr-wrap'><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-dd47d981-6b9d-4b79-9bf0-2ee1207140aa' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-dd47d981-6b9d-4b79-9bf0-2ee1207140aa' 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>: 720</li><li><span class='xr-has-index'>lon</span>: 1440</li><li><span class='xr-has-index'>time</span>: 13636</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-84aba6eb-3e37-4610-ab47-bd7672899d5c' class='xr-section-summary-in' type='checkbox'  checked><label for='section-84aba6eb-3e37-4610-ab47-bd7672899d5c' 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'>-89.875 -89.625 ... 89.625 89.875</div><input id='attrs-be45e5e6-2a7b-4b4a-9ce4-be3d5a3a8e8a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-be45e5e6-2a7b-4b4a-9ce4-be3d5a3a8e8a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8aa1475a-4447-4687-a577-1b024351b53c' class='xr-var-data-in' type='checkbox'><label for='data-8aa1475a-4447-4687-a577-1b024351b53c' 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>actual_range :</span></dt><dd>[-89.875, 89.875]</dd><dt><span>axis :</span></dt><dd>Y</dd><dt><span>long_name :</span></dt><dd>Latitude</dd><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>units :</span></dt><dd>degrees_north</dd></dl></div><pre class='xr-var-data'>array([-89.875, -89.625, -89.375, ...,  89.375,  89.625,  89.875],\n",
       "      dtype=float32)</pre></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'>-179.875 -179.625 ... 179.875</div><input id='attrs-efb990de-a406-481c-837d-69abccd6649e' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-efb990de-a406-481c-837d-69abccd6649e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b7bd5669-df96-4ba6-9a9b-48065100943a' class='xr-var-data-in' type='checkbox'><label for='data-b7bd5669-df96-4ba6-9a9b-48065100943a' 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><pre class='xr-var-data'>array([-179.875, -179.625, -179.375, ...,  179.375,  179.625,  179.875],\n",
       "      dtype=float32)</pre></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'>1981-09-01 ... 2018-12-31</div><input id='attrs-c46a4f48-326c-4872-b4d3-e0bc47625896' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c46a4f48-326c-4872-b4d3-e0bc47625896' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a8fa44a9-30ac-410c-9742-8507c97e55b2' class='xr-var-data-in' type='checkbox'><label for='data-a8fa44a9-30ac-410c-9742-8507c97e55b2' 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>avg_period :</span></dt><dd>0000-00-01 00:00:00</dd><dt><span>axis :</span></dt><dd>T</dd><dt><span>delta_t :</span></dt><dd>0000-00-01 00:00:00</dd><dt><span>long_name :</span></dt><dd>Time</dd></dl></div><pre class='xr-var-data'>array([&#x27;1981-09-01T00:00:00.000000000&#x27;, &#x27;1981-09-02T00:00:00.000000000&#x27;,\n",
       "       &#x27;1981-09-03T00:00:00.000000000&#x27;, ..., &#x27;2018-12-29T00:00:00.000000000&#x27;,\n",
       "       &#x27;2018-12-30T00:00:00.000000000&#x27;, &#x27;2018-12-31T00:00:00.000000000&#x27;],\n",
       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></li></ul></div></li><li class='xr-section-item'><input id='section-00414ebd-ad70-490d-b70f-010a945354b4' class='xr-section-summary-in' type='checkbox'  checked><label for='section-00414ebd-ad70-490d-b70f-010a945354b4' 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>sst</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'>dask.array&lt;chunksize=(100, 50, 280), meta=np.ndarray&gt;</div><input id='attrs-a1ceb2d7-76f0-461c-b5f3-a9fbcd80ca68' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a1ceb2d7-76f0-461c-b5f3-a9fbcd80ca68' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-cbb04ab3-8d30-4cb2-b6b8-76a56a8de416' class='xr-var-data-in' type='checkbox'><label for='data-cbb04ab3-8d30-4cb2-b6b8-76a56a8de416' 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>dataset :</span></dt><dd>NOAA High-resolution Blended Analysis</dd><dt><span>level_desc :</span></dt><dd>Surface</dd><dt><span>long_name :</span></dt><dd>Daily Sea Surface Temperature</dd><dt><span>parent_stat :</span></dt><dd>Individual Observations</dd><dt><span>precision :</span></dt><dd>2.0</dd><dt><span>statistic :</span></dt><dd>Mean</dd><dt><span>units :</span></dt><dd>degC</dd><dt><span>valid_range :</span></dt><dd>[-3.0, 45.0]</dd><dt><span>var_desc :</span></dt><dd>Sea Surface Temperature</dd></dl></div><pre class='xr-var-data'><table>\n",
       "<tr>\n",
       "<td>\n",
       "<table>\n",
       "  <thead>\n",
       "    <tr><td> </td><th> Array </th><th> Chunk </th></tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr><th> Bytes </th><td> 56.55 GB </td> <td> 10.00 MB </td></tr>\n",
       "    <tr><th> Shape </th><td> (13636, 720, 1440) </td> <td> (100, 50, 500) </td></tr>\n",
       "    <tr><th> Count </th><td> 14386 Tasks </td><td> 8220 Chunks </td></tr>\n",
       "    <tr><th> Type </th><td> float32 </td><td> numpy.ndarray </td></tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</td>\n",
       "<td>\n",
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       "</table></pre></li></ul></div></li><li class='xr-section-item'><input id='section-ff83bd23-174d-4b23-a9e5-c4659111f507' class='xr-section-summary-in' type='checkbox'  checked><label for='section-ff83bd23-174d-4b23-a9e5-c4659111f507' class='xr-section-summary' >Attributes: <span>(8)</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-1.5</dd><dt><span>comment :</span></dt><dd>Reynolds, et al., 2007: Daily High-Resolution-Blended Analyses for Sea Surface Temperature. J. Climate, 20, 5473-5496.  Climatology is based on 1971-2000 OI.v2 SST, Satellite data: Navy NOAA17 NOAA18 AVHRR, Ice data: NCEP ice.</dd><dt><span>dataset_title :</span></dt><dd>NOAA Daily Optimum Interpolation Sea Surface Temperature</dd><dt><span>history :</span></dt><dd>Thu Aug 24 13:34:17 2017: ncatted -O -a References,global,d,, sst.day.mean.1981.v2.nc\n",
       "Version 1.0</dd><dt><span>institution :</span></dt><dd>NOAA/NCDC</dd><dt><span>references :</span></dt><dd>https://www.esrl.noaa.gov/psd/data/gridded/data.noaa.oisst.v2.highres.html</dd><dt><span>source :</span></dt><dd>NOAA/NCDC  ftp://eclipse.ncdc.noaa.gov/pub/OI-daily-v2/</dd><dt><span>title :</span></dt><dd>NOAA High-resolution Blended Analysis: Daily Values using AVHRR only</dd></dl></div></li></ul></div></div>"
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       "<xarray.Dataset>\n",
       "Dimensions:  (lat: 720, lon: 1440, time: 13636)\n",
       "Coordinates:\n",
       "  * lat      (lat) float32 -89.875 -89.625 -89.375 ... 89.375 89.625 89.875\n",
       "  * lon      (lon) float32 -179.875 -179.625 -179.375 ... 179.625 179.875\n",
       "  * time     (time) datetime64[ns] 1981-09-01 1981-09-02 ... 2018-12-31\n",
       "Data variables:\n",
       "    sst      (time, lat, lon) float32 dask.array<chunksize=(100, 50, 280), meta=np.ndarray>\n",
       "Attributes:\n",
       "    Conventions:    CF-1.5\n",
       "    comment:        Reynolds, et al., 2007: Daily High-Resolution-Blended Ana...\n",
       "    dataset_title:  NOAA Daily Optimum Interpolation Sea Surface Temperature\n",
       "    history:        Thu Aug 24 13:34:17 2017: ncatted -O -a References,global...\n",
       "    institution:    NOAA/NCDC\n",
       "    references:     https://www.esrl.noaa.gov/psd/data/gridded/data.noaa.oiss...\n",
       "    source:         NOAA/NCDC  ftp://eclipse.ncdc.noaa.gov/pub/OI-daily-v2/\n",
       "    title:          NOAA High-resolution Blended Analysis: Daily Values using..."
      ]
     },
     "execution_count": 6,
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   ],
   "source": [
    "oisst"
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  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
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   "execution_count": 7,
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   "outputs": [
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       "</style><div class='xr-wrap'><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-ca247224-6fd1-4bec-be55-b294423bd133' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-ca247224-6fd1-4bec-be55-b294423bd133' 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>: 720</li><li><span class='xr-has-index'>lon</span>: 1440</li><li><span class='xr-has-index'>time</span>: 13636</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-ce3ad0be-efa0-4d1e-bf96-f7e1cc322a04' class='xr-section-summary-in' type='checkbox'  checked><label for='section-ce3ad0be-efa0-4d1e-bf96-f7e1cc322a04' 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'>-89.875 -89.625 ... 89.625 89.875</div><input id='attrs-f6be033a-c920-4312-adb6-1d8a064c407f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f6be033a-c920-4312-adb6-1d8a064c407f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-42d3a84a-e6b9-4064-95ed-bf95e9b196bb' class='xr-var-data-in' type='checkbox'><label for='data-42d3a84a-e6b9-4064-95ed-bf95e9b196bb' 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>actual_range :</span></dt><dd>[-89.875, 89.875]</dd><dt><span>axis :</span></dt><dd>Y</dd><dt><span>long_name :</span></dt><dd>Latitude</dd><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>units :</span></dt><dd>degrees_north</dd></dl></div><pre class='xr-var-data'>array([-89.875, -89.625, -89.375, ...,  89.375,  89.625,  89.875],\n",
       "      dtype=float32)</pre></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'>-179.875 -179.625 ... 179.875</div><input id='attrs-634c54fd-924d-470f-9015-0d5613fe8bca' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-634c54fd-924d-470f-9015-0d5613fe8bca' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5ea1edce-19f5-46ca-a998-8da34b7b47c4' class='xr-var-data-in' type='checkbox'><label for='data-5ea1edce-19f5-46ca-a998-8da34b7b47c4' 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><pre class='xr-var-data'>array([-179.875, -179.625, -179.375, ...,  179.375,  179.625,  179.875],\n",
       "      dtype=float32)</pre></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'>1981-09-01 ... 2018-12-31</div><input id='attrs-82d985a8-a01d-419b-99db-7b6a18bb1217' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-82d985a8-a01d-419b-99db-7b6a18bb1217' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-df60e344-52b9-4171-876e-4d2d7716e6c9' class='xr-var-data-in' type='checkbox'><label for='data-df60e344-52b9-4171-876e-4d2d7716e6c9' 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>avg_period :</span></dt><dd>0000-00-01 00:00:00</dd><dt><span>axis :</span></dt><dd>T</dd><dt><span>delta_t :</span></dt><dd>0000-00-01 00:00:00</dd><dt><span>long_name :</span></dt><dd>Time</dd></dl></div><pre class='xr-var-data'>array([&#x27;1981-09-01T00:00:00.000000000&#x27;, &#x27;1981-09-02T00:00:00.000000000&#x27;,\n",
       "       &#x27;1981-09-03T00:00:00.000000000&#x27;, ..., &#x27;2018-12-29T00:00:00.000000000&#x27;,\n",
       "       &#x27;2018-12-30T00:00:00.000000000&#x27;, &#x27;2018-12-31T00:00:00.000000000&#x27;],\n",
       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></li></ul></div></li><li class='xr-section-item'><input id='section-88348c4e-428c-4509-b8db-3c723283ac91' class='xr-section-summary-in' type='checkbox'  checked><label for='section-88348c4e-428c-4509-b8db-3c723283ac91' 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>sst</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'>dask.array&lt;chunksize=(100, 50, 280), meta=np.ndarray&gt;</div><input id='attrs-a9235119-802b-471e-93d9-362f3a8ae7a7' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a9235119-802b-471e-93d9-362f3a8ae7a7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0f6fb92d-0a8a-4d6c-a507-a596ee52c232' class='xr-var-data-in' type='checkbox'><label for='data-0f6fb92d-0a8a-4d6c-a507-a596ee52c232' 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>dataset :</span></dt><dd>NOAA High-resolution Blended Analysis</dd><dt><span>level_desc :</span></dt><dd>Surface</dd><dt><span>long_name :</span></dt><dd>Daily Sea Surface Temperature</dd><dt><span>parent_stat :</span></dt><dd>Individual Observations</dd><dt><span>precision :</span></dt><dd>2.0</dd><dt><span>statistic :</span></dt><dd>Mean</dd><dt><span>units :</span></dt><dd>degC</dd><dt><span>valid_range :</span></dt><dd>[-3.0, 45.0]</dd><dt><span>var_desc :</span></dt><dd>Sea Surface Temperature</dd></dl></div><pre class='xr-var-data'><table>\n",
       "<tr>\n",
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       "    <tr><th> Shape </th><td> (13636, 720, 1440) </td> <td> (100, 50, 500) </td></tr>\n",
       "    <tr><th> Count </th><td> 14386 Tasks </td><td> 8220 Chunks </td></tr>\n",
       "    <tr><th> Type </th><td> float32 </td><td> numpy.ndarray </td></tr>\n",
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       "</table></pre></li></ul></div></li><li class='xr-section-item'><input id='section-2f0b5fd2-44d3-4869-88a3-22de5e59af6e' class='xr-section-summary-in' type='checkbox'  checked><label for='section-2f0b5fd2-44d3-4869-88a3-22de5e59af6e' class='xr-section-summary' >Attributes: <span>(8)</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-1.5</dd><dt><span>comment :</span></dt><dd>Reynolds, et al., 2007: Daily High-Resolution-Blended Analyses for Sea Surface Temperature. J. Climate, 20, 5473-5496.  Climatology is based on 1971-2000 OI.v2 SST, Satellite data: Navy NOAA17 NOAA18 AVHRR, Ice data: NCEP ice.</dd><dt><span>dataset_title :</span></dt><dd>NOAA Daily Optimum Interpolation Sea Surface Temperature</dd><dt><span>history :</span></dt><dd>Thu Aug 24 13:34:17 2017: ncatted -O -a References,global,d,, sst.day.mean.1981.v2.nc\n",
       "Version 1.0</dd><dt><span>institution :</span></dt><dd>NOAA/NCDC</dd><dt><span>references :</span></dt><dd>https://www.esrl.noaa.gov/psd/data/gridded/data.noaa.oisst.v2.highres.html</dd><dt><span>source :</span></dt><dd>NOAA/NCDC  ftp://eclipse.ncdc.noaa.gov/pub/OI-daily-v2/</dd><dt><span>title :</span></dt><dd>NOAA High-resolution Blended Analysis: Daily Values using AVHRR only</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:  (lat: 720, lon: 1440, time: 13636)\n",
       "Coordinates:\n",
       "  * lat      (lat) float32 -89.875 -89.625 -89.375 ... 89.375 89.625 89.875\n",
       "  * lon      (lon) float32 -179.875 -179.625 -179.375 ... 179.625 179.875\n",
       "  * time     (time) datetime64[ns] 1981-09-01 1981-09-02 ... 2018-12-31\n",
       "Data variables:\n",
       "    sst      (time, lat, lon) float32 dask.array<chunksize=(100, 50, 280), meta=np.ndarray>\n",
       "Attributes:\n",
       "    Conventions:    CF-1.5\n",
       "    comment:        Reynolds, et al., 2007: Daily High-Resolution-Blended Ana...\n",
       "    dataset_title:  NOAA Daily Optimum Interpolation Sea Surface Temperature\n",
       "    history:        Thu Aug 24 13:34:17 2017: ncatted -O -a References,global...\n",
       "    institution:    NOAA/NCDC\n",
       "    references:     https://www.esrl.noaa.gov/psd/data/gridded/data.noaa.oiss...\n",
       "    source:         NOAA/NCDC  ftp://eclipse.ncdc.noaa.gov/pub/OI-daily-v2/\n",
       "    title:          NOAA High-resolution Blended Analysis: Daily Values using..."
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "oisst"
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  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# s\n",
    "PNW_LAT = slice(40, 50)\n",
    "PNW_LON = slice(-165.9, -121.9)\n",
    "oisst_pnw = oisst.sel(lat = PNW_LAT, lon = PNW_LON).compute()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
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     "text": [
      "/home/ec2-user/miniconda3/envs/notebook/lib/python3.7/site-packages/xarray/core/nanops.py:142: RuntimeWarning: Mean of empty slice\n",
      "  return np.nanmean(a, axis=axis, dtype=dtype)\n"
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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "oisst_pnw.sst.mean('time').plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7fb6c67ff590>]"
      ]
     },
     "execution_count": 14,
     "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": [
    "oisst_pnw.sst.mean(['lat', 'lon']).plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "def mhw_1d(temps, time):\n",
    "    if(np.isnan(temps).any()): return xr.DataArray(\n",
    "        np.full_like(temps, np.nan), \n",
    "        name = 'mhw', \n",
    "        dims = 'time', \n",
    "        coords ={\n",
    "            'time': time\n",
    "        }\n",
    "    )\n",
    "\n",
    "    ordinals =  np.array([Timestamp(t).toordinal() for t in time])\n",
    "    dets = mh.detect(ordinals, temps.copy())\n",
    "    events = dets[0]['n_events']\n",
    "    del dets[0]['n_events']\n",
    "    \n",
    "    arrays = {\n",
    "        key: xr.DataArray(np.full_like(time, 0., dtype='float64'),\n",
    "                          name = key, dims='time',\n",
    "                          coords={\n",
    "                              'time' : time,\n",
    "                          })\n",
    "        for key in ['mhw_cum_intensity']\n",
    "    }\n",
    "    \n",
    "    for event in range(events):\n",
    "        start_date = dets[0]['date_start'][event]\n",
    "        end_date = dets[0]['date_end'][event]\n",
    "        cum_intense = dets[0]['intensity_cumulative'][event]\n",
    "\n",
    "#         arrays['mhw'].loc[dict(time=slice(start_date, end_date))] = True\n",
    "        arrays['mhw_cum_intensity'].loc[dict(time=slice(start_date, end_date))] = cum_intense\n",
    "        \n",
    "#     clim = xr.DataArray(dets[1]['thresh'], name='clim_thresh', dims='time', coords={'time': time})\n",
    "        \n",
    "    return (arrays['mhw_cum_intensity'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "a = xr.apply_ufunc(\n",
    "    mhw_1d, \n",
    "    oisst_pnw.sst.chunk({'lat': 1, 'lon': 1, 'time': -1}), \n",
    "    oisst_pnw.time, \n",
    "    input_core_dims = [['time'], ['time']],\n",
    "    output_core_dims=[[\"time\"]], \n",
    "    vectorize=True, \n",
    "    dask='parallelized', \n",
    "    output_dtypes=['float64']\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table>\n",
       "  <thead>\n",
       "    <tr><td> </td><th> Array </th><th> Chunk </th></tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr><th> Bytes </th><td> 767.98 MB </td> <td> 109.09 kB </td></tr>\n",
       "    <tr><th> Shape </th><td> (40, 176, 13636) </td> <td> (1, 1, 13636) </td></tr>\n",
       "    <tr><th> Count </th><td> 21122 Tasks </td><td> 7040 Chunks </td></tr>\n",
       "    <tr><th> Type </th><td> float64 </td><td> numpy.ndarray </td></tr>\n",
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      "text/plain": [
       "dask.array<vectorize_mhw_1d, shape=(40, 176, 13636), dtype=float64, chunksize=(1, 1, 13636), chunktype=numpy.ndarray>"
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     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a.data\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
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      "\u001b[0;32m<ipython-input-20-ac6b0e907208>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mans\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0ma\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcompute\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[0;32m~/miniconda3/envs/notebook/lib/python3.7/site-packages/xarray/core/dataarray.py\u001b[0m in \u001b[0;36mcompute\u001b[0;34m(self, **kwargs)\u001b[0m\n\u001b[1;32m    826\u001b[0m         \"\"\"\n\u001b[1;32m    827\u001b[0m         \u001b[0mnew\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcopy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdeep\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 828\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mnew\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mload\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    829\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    830\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mpersist\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\"DataArray\"\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/miniconda3/envs/notebook/lib/python3.7/site-packages/xarray/core/dataarray.py\u001b[0m in \u001b[0;36mload\u001b[0;34m(self, **kwargs)\u001b[0m\n\u001b[1;32m    800\u001b[0m         \u001b[0mdask\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcompute\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    801\u001b[0m         \"\"\"\n\u001b[0;32m--> 802\u001b[0;31m         \u001b[0mds\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_to_temp_dataset\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mload\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    803\u001b[0m         \u001b[0mnew\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_from_temp_dataset\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    804\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_variable\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnew\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_variable\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/miniconda3/envs/notebook/lib/python3.7/site-packages/xarray/core/dataset.py\u001b[0m in \u001b[0;36mload\u001b[0;34m(self, **kwargs)\u001b[0m\n\u001b[1;32m    652\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    653\u001b[0m             \u001b[0;31m# evaluate all the dask arrays simultaneously\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 654\u001b[0;31m             \u001b[0mevaluated_data\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mda\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcompute\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mlazy_data\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\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    655\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    656\u001b[0m             \u001b[0;32mfor\u001b[0m \u001b[0mk\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdata\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mzip\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlazy_data\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mevaluated_data\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~/miniconda3/envs/notebook/lib/python3.7/site-packages/dask/base.py\u001b[0m in \u001b[0;36mcompute\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    434\u001b[0m     \u001b[0mkeys\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__dask_keys__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mx\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mcollections\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    435\u001b[0m     \u001b[0mpostcomputes\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__dask_postcompute__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mx\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mcollections\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 436\u001b[0;31m     \u001b[0mresults\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mschedule\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdsk\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkeys\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    437\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mrepack\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0ma\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mzip\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mresults\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpostcomputes\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    438\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/miniconda3/envs/notebook/lib/python3.7/site-packages/distributed/client.py\u001b[0m in \u001b[0;36mget\u001b[0;34m(self, dsk, keys, restrictions, loose_restrictions, resources, sync, asynchronous, direct, retries, priority, fifo_timeout, actors, **kwargs)\u001b[0m\n\u001b[1;32m   2570\u001b[0m                     \u001b[0mshould_rejoin\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2571\u001b[0m             \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2572\u001b[0;31m                 \u001b[0mresults\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgather\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpacked\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0masynchronous\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0masynchronous\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdirect\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdirect\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   2573\u001b[0m             \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2574\u001b[0m                 \u001b[0;32mfor\u001b[0m \u001b[0mf\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mfutures\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\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[0;32m~/miniconda3/envs/notebook/lib/python3.7/site-packages/distributed/client.py\u001b[0m in \u001b[0;36mgather\u001b[0;34m(self, futures, errors, direct, asynchronous)\u001b[0m\n\u001b[1;32m   1870\u001b[0m                 \u001b[0mdirect\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdirect\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1871\u001b[0m                 \u001b[0mlocal_worker\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlocal_worker\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1872\u001b[0;31m                 \u001b[0masynchronous\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0masynchronous\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   1873\u001b[0m             )\n\u001b[1;32m   1874\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/miniconda3/envs/notebook/lib/python3.7/site-packages/distributed/client.py\u001b[0m in \u001b[0;36msync\u001b[0;34m(self, func, asynchronous, callback_timeout, *args, **kwargs)\u001b[0m\n\u001b[1;32m    765\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    766\u001b[0m             return sync(\n\u001b[0;32m--> 767\u001b[0;31m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mloop\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcallback_timeout\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcallback_timeout\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[0m\n\u001b[0m\u001b[1;32m    768\u001b[0m             )\n\u001b[1;32m    769\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/miniconda3/envs/notebook/lib/python3.7/site-packages/distributed/utils.py\u001b[0m in \u001b[0;36msync\u001b[0;34m(loop, func, callback_timeout, *args, **kwargs)\u001b[0m\n\u001b[1;32m    329\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    330\u001b[0m         \u001b[0;32mwhile\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mis_set\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[0;32m--> 331\u001b[0;31m             \u001b[0me\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwait\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m10\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    332\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0merror\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\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    333\u001b[0m         \u001b[0mtyp\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mexc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtb\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0merror\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/miniconda3/envs/notebook/lib/python3.7/threading.py\u001b[0m in \u001b[0;36mwait\u001b[0;34m(self, timeout)\u001b[0m\n\u001b[1;32m    550\u001b[0m             \u001b[0msignaled\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_flag\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    551\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0msignaled\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 552\u001b[0;31m                 \u001b[0msignaled\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_cond\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwait\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtimeout\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    553\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0msignaled\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    554\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/miniconda3/envs/notebook/lib/python3.7/threading.py\u001b[0m in \u001b[0;36mwait\u001b[0;34m(self, timeout)\u001b[0m\n\u001b[1;32m    298\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    299\u001b[0m                 \u001b[0;32mif\u001b[0m \u001b[0mtimeout\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 300\u001b[0;31m                     \u001b[0mgotit\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mwaiter\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0macquire\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtimeout\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    301\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    302\u001b[0m                     \u001b[0mgotit\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mwaiter\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0macquire\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "distributed.comm.tcp - WARNING - Closing dangling stream in <TCP  local=tcp://127.0.0.1:38874 remote=tcp://127.0.0.1:39661>\n",
      "distributed.comm.tcp - WARNING - Closing dangling stream in <TCP  local=tcp://127.0.0.1:38876 remote=tcp://127.0.0.1:39661>\n",
      "distributed.comm.tcp - WARNING - Closing dangling stream in <TCP  local=tcp://127.0.0.1:38878 remote=tcp://127.0.0.1:39661>\n",
      "distributed.comm.tcp - WARNING - Closing dangling stream in <TCP  local=tcp://127.0.0.1:38880 remote=tcp://127.0.0.1:39661>\n",
      "distributed.nanny - WARNING - Restarting worker\n",
      "distributed.nanny - WARNING - Restarting worker\n",
      "distributed.nanny - WARNING - Restarting worker\n",
      "distributed.nanny - WARNING - Restarting worker\n"
     ]
    }
   ],
   "source": [
    "ans = a.compute()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "ans.to_netcdf(\"pnw_mhw_intensity.nc\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "sels = ans.isel(lat=25, lon=20)\n",
    "fig = plt.figure(figsize=(15, 5))\n",
    "\n",
    "plt.vlines(sels.time[sels].values, 0, 10, color='red', )\n",
    "plt.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "sels = ans.isel(lat=5, lon=20)\n",
    "\n",
    "import matplotlib.transforms as mt\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(15, 5))\n",
    "sst = oisst_pnw.sel(lat=sels.lat.values, lon=sels.lon.values).persist().sst \n",
    "ax.vlines(sels.time[sels].values, 0, 10, color='red', alpha=0.2,  transform = trans)\n",
    "\n",
    "ax.plot(sst.time, sst)\n",
    "trans = mt.blended_transform_factory(ax.transData, ax.transAxes)\n",
    "\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "sst = oisst_pnw.sel(lat=sels.lat.values, lon=sels.lon.values).sst \n",
    "plt.plot(sst.time, sst)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "se"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "sels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "sel"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# s\n",
    "PNW_LAT = slice(44.5, 46.5)\n",
    "PNW_LON = slice(165.5, 166)\n",
    "oisst_pnw = oisst.sel(lat = PNW_LAT, lon = PNW_LON).load()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "oisst_pnw"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "a = xr.apply_ufunc(\n",
    "    mhw_1d, \n",
    "    oisst_pnw.sst.chunk({'lat': 5, 'lon': 5, 'time': -1}).load(), \n",
    "    oisst_pnw.time.load(), \n",
    "    input_core_dims = [['time'], ['time']],\n",
    "    output_core_dims=[[\"time\"], [\"time\"]], \n",
    "    vectorize=True, \n",
    "    output_dtypes=['bool']\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "ans = a.compute()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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