scratchwork/useOcetrac.ipynb

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   "cell_type": "markdown",
   "id": "b4b9ae29-4c4e-47ee-b1c6-6b721cf391f9",
   "metadata": {},
   "source": [
    "### LOADING IN PACKAGES"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "7cb52c74-8e9d-4205-942e-cde1f08db369",
   "metadata": {},
   "outputs": [],
   "source": [
    "import s3fs; import xarray as xr; import numpy as np\n",
    "import pandas as pd; \n",
    "import dask.array as da\n",
    "import ocetrac\n",
    "\n",
    "import matplotlib.pyplot as plt; import cartopy.crs as ccrs\n",
    "\n",
    "import warnings; import expectexception\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "import netCDF4 as nc; import datetime as dt\n",
    "import scipy\n",
    "\n",
    "import intake; import pprint\n",
    "# Allow multiple lines per cell to be displayed without print (default is just last line)\n",
    "from IPython.core.interactiveshell import InteractiveShell\n",
    "InteractiveShell.ast_node_interactivity = \"all\"\n",
    "# Enable more explicit control of DataFrame display (e.g., to omit annoying line numbers)\n",
    "# from IPython.display import HTML"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dc093fa4-fc2a-48b7-822c-03bdcd3e865f",
   "metadata": {},
   "source": [
    "# Dask "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2b4c4426-ff9c-4925-8813-d6c825bfb737",
   "metadata": {},
   "outputs": [],
   "source": [
    "# import dask"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "74c6adc0-fbc7-4a80-abed-cfdeb9a0c529",
   "metadata": {},
   "outputs": [],
   "source": [
    "# # ONLY RUN THIS CELL ONCE\n",
    "# # NOTE: you may need to change the project number\n",
    "# from dask_jobqueue import PBSCluster\n",
    "# from dask.distributed import Client\n",
    "# proj = 'UWIS0040'\n",
    "\n",
    "# # Setup your PBSCluster\n",
    "# cluster = PBSCluster(\n",
    "#     cores=1, # The number of cores you want\n",
    "#     memory='40GB', # Amount of memory\n",
    "#     processes=1, # How many processes\n",
    "#     queue='casper', # The type of queue to utilize (/glade/u/apps/dav/opt/usr/bin/execcasper)\n",
    "#     local_directory='$TMPDIR', # Use your local directory\n",
    "#     resource_spec='select=1:ncpus=1:mem=20GB', # Specify resources\n",
    "#     project=proj, # Input your project ID here\n",
    "#     walltime='02:00:00', # Amount of wall time\n",
    "#     interface='ib0', # Interface to use\n",
    "# )\n",
    "\n",
    "# # Scale up\n",
    "# #cluster.scale(18)\n",
    "# cluster.adapt(minimum=1,maximum=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4248c019-4667-4c0a-a2aa-284580909ee1",
   "metadata": {},
   "outputs": [],
   "source": [
    "# # Change your url to the dask dashboard so you can see it\n",
    "# dask.config.set({'distributed.dashboard.link':'https://jupyterhub.hpc.ucar.edu/stable/user/{USER}/proxy/{port}/status'})\n",
    "\n",
    "# # Setup your client\n",
    "# client = Client(cluster)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "efb1995f-9f45-4842-bc92-da7bafd1bb7e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# client"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "197bcc55-19ae-42f8-a4c3-b61fbaa66fac",
   "metadata": {},
   "outputs": [],
   "source": [
    "# cluster.close()\n",
    "# client.shutdown()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "be983795-11c8-4af5-9199-5c13f4f5c2b7",
   "metadata": {},
   "source": [
    "### Open original collection description file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "c1d1eba9-7ed3-4e35-b974-2dc393446611",
   "metadata": {},
   "outputs": [],
   "source": [
    "cat_url_orig = '/glade/collections/cmip/catalog/intake-esm-datastore/catalogs/glade-cesm2-le.json'\n",
    "coll_orig = intake.open_esm_datastore(cat_url_orig)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "6c3e1f2f-541b-41ff-9aa6-e5ad7514650a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "100\n"
     ]
    }
   ],
   "source": [
    "subset = coll_orig.search(component='atm',variable='SST',frequency='month_1',experiment='historical')\n",
    "subset_as_df = subset.df\n",
    "grouped_df_subset = subset_as_df.groupby('member_id')\n",
    "print(len(grouped_df_subset))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f7ab67f8-51db-4881-89aa-7f2e38bffd06",
   "metadata": {},
   "source": [
    "## Load ensemble mean file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "729defd7-5afb-4496-a6c7-d9399112a921",
   "metadata": {},
   "outputs": [],
   "source": [
    "ensemble_mean = xr.open_dataset('/glade/work/cassiacai/ensemble_mean.nc')\n",
    "# ensemble_mean = xr.open_dataset('/glade/work/cassiacai/ensemble_mean.nc', chunks = {})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "5183fe56-2008-49d5-beb6-fedab8f10123",
   "metadata": {},
   "outputs": [
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
       "Dimensions:  (lat: 192, lon: 288, time: 1980)\n",
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       "  * time     (time) object 1850-02-01 00:00:00 ... 2015-01-01 00:00:00\n",
       "Data variables:\n",
       "    SST      (time, lat, lon) float32 ...</pre><div class='xr-wrap' hidden><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-b28ea6c3-2b7d-47c4-a955-acc0e5958ee3' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-b28ea6c3-2b7d-47c4-a955-acc0e5958ee3' 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>: 192</li><li><span class='xr-has-index'>lon</span>: 288</li><li><span class='xr-has-index'>time</span>: 1980</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-3f372bc7-968d-421d-bcd0-a8b9fe2df6dd' class='xr-section-summary-in' type='checkbox'  checked><label for='section-3f372bc7-968d-421d-bcd0-a8b9fe2df6dd' 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'>float64</div><div class='xr-var-preview xr-preview'>-90.0 -89.06 -88.12 ... 89.06 90.0</div><input id='attrs-fb200cc4-e14a-4276-9fda-b3b57d0fbaf9' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-fb200cc4-e14a-4276-9fda-b3b57d0fbaf9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e3e2ccaf-a995-47f6-8136-9f4300ed3e0f' class='xr-var-data-in' type='checkbox'><label for='data-e3e2ccaf-a995-47f6-8136-9f4300ed3e0f' 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>latitude</dd><dt><span>units :</span></dt><dd>degrees_north</dd></dl></div><div class='xr-var-data'><pre>array([-90.      , -89.057592, -88.115183, -87.172775, -86.230366, -85.287958,\n",
       "       -84.34555 , -83.403141, -82.460733, -81.518325, -80.575916, -79.633508,\n",
       "       -78.691099, -77.748691, -76.806283, -75.863874, -74.921466, -73.979058,\n",
       "       -73.036649, -72.094241, -71.151832, -70.209424, -69.267016, -68.324607,\n",
       "       -67.382199, -66.439791, -65.497382, -64.554974, -63.612565, -62.670157,\n",
       "       -61.727749, -60.78534 , -59.842932, -58.900524, -57.958115, -57.015707,\n",
       "       -56.073298, -55.13089 , -54.188482, -53.246073, -52.303665, -51.361257,\n",
       "       -50.418848, -49.47644 , -48.534031, -47.591623, -46.649215, -45.706806,\n",
       "       -44.764398, -43.82199 , -42.879581, -41.937173, -40.994764, -40.052356,\n",
       "       -39.109948, -38.167539, -37.225131, -36.282723, -35.340314, -34.397906,\n",
       "       -33.455497, -32.513089, -31.570681, -30.628272, -29.685864, -28.743455,\n",
       "       -27.801047, -26.858639, -25.91623 , -24.973822, -24.031414, -23.089005,\n",
       "       -22.146597, -21.204188, -20.26178 , -19.319372, -18.376963, -17.434555,\n",
       "       -16.492147, -15.549738, -14.60733 , -13.664921, -12.722513, -11.780105,\n",
       "       -10.837696,  -9.895288,  -8.95288 ,  -8.010471,  -7.068063,  -6.125654,\n",
       "        -5.183246,  -4.240838,  -3.298429,  -2.356021,  -1.413613,  -0.471204,\n",
       "         0.471204,   1.413613,   2.356021,   3.298429,   4.240838,   5.183246,\n",
       "         6.125654,   7.068063,   8.010471,   8.95288 ,   9.895288,  10.837696,\n",
       "        11.780105,  12.722513,  13.664921,  14.60733 ,  15.549738,  16.492147,\n",
       "        17.434555,  18.376963,  19.319372,  20.26178 ,  21.204188,  22.146597,\n",
       "        23.089005,  24.031414,  24.973822,  25.91623 ,  26.858639,  27.801047,\n",
       "        28.743455,  29.685864,  30.628272,  31.570681,  32.513089,  33.455497,\n",
       "        34.397906,  35.340314,  36.282723,  37.225131,  38.167539,  39.109948,\n",
       "        40.052356,  40.994764,  41.937173,  42.879581,  43.82199 ,  44.764398,\n",
       "        45.706806,  46.649215,  47.591623,  48.534031,  49.47644 ,  50.418848,\n",
       "        51.361257,  52.303665,  53.246073,  54.188482,  55.13089 ,  56.073298,\n",
       "        57.015707,  57.958115,  58.900524,  59.842932,  60.78534 ,  61.727749,\n",
       "        62.670157,  63.612565,  64.554974,  65.497382,  66.439791,  67.382199,\n",
       "        68.324607,  69.267016,  70.209424,  71.151832,  72.094241,  73.036649,\n",
       "        73.979058,  74.921466,  75.863874,  76.806283,  77.748691,  78.691099,\n",
       "        79.633508,  80.575916,  81.518325,  82.460733,  83.403141,  84.34555 ,\n",
       "        85.287958,  86.230366,  87.172775,  88.115183,  89.057592,  90.      ])</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'>float64</div><div class='xr-var-preview xr-preview'>0.0 1.25 2.5 ... 356.2 357.5 358.8</div><input id='attrs-78576f71-c5d3-457f-9527-d63a565c22ad' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-78576f71-c5d3-457f-9527-d63a565c22ad' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7d774512-f7ca-4da6-81a5-ad8533a40118' class='xr-var-data-in' type='checkbox'><label for='data-7d774512-f7ca-4da6-81a5-ad8533a40118' 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>longitude</dd><dt><span>units :</span></dt><dd>degrees_east</dd></dl></div><div class='xr-var-data'><pre>array([  0.  ,   1.25,   2.5 , ..., 356.25, 357.5 , 358.75])</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'>object</div><div class='xr-var-preview xr-preview'>1850-02-01 00:00:00 ... 2015-01-...</div><input id='attrs-2d0dccab-b822-4ac8-8766-44ae5f98e22e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-2d0dccab-b822-4ac8-8766-44ae5f98e22e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f8af3e97-74cd-403c-8530-754878fd0e94' class='xr-var-data-in' type='checkbox'><label for='data-f8af3e97-74cd-403c-8530-754878fd0e94' 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>time</dd><dt><span>bounds :</span></dt><dd>time_bnds</dd></dl></div><div class='xr-var-data'><pre>array([cftime.DatetimeNoLeap(1850, 2, 1, 0, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(1850, 3, 1, 0, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(1850, 4, 1, 0, 0, 0, 0, has_year_zero=True), ...,\n",
       "       cftime.DatetimeNoLeap(2014, 11, 1, 0, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(2014, 12, 1, 0, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(2015, 1, 1, 0, 0, 0, 0, has_year_zero=True)],\n",
       "      dtype=object)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-6be29a4a-2bf0-42d7-8ace-92e0272b81a5' class='xr-section-summary-in' type='checkbox'  checked><label for='section-6be29a4a-2bf0-42d7-8ace-92e0272b81a5' 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'>...</div><input id='attrs-1ffe108a-86d9-4e28-b994-fe4688fe7e6e' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-1ffe108a-86d9-4e28-b994-fe4688fe7e6e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9d34777f-caf6-4e70-9fc2-a2caf779ccc6' class='xr-var-data-in' type='checkbox'><label for='data-9d34777f-caf6-4e70-9fc2-a2caf779ccc6' 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>[109486080 values with dtype=float32]</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-087c8be8-44ad-46b3-a00f-b9e74071b2ef' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-087c8be8-44ad-46b3-a00f-b9e74071b2ef' 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.Dataset>\n",
       "Dimensions:  (lat: 192, lon: 288, time: 1980)\n",
       "Coordinates:\n",
       "  * lat      (lat) float64 -90.0 -89.06 -88.12 -87.17 ... 87.17 88.12 89.06 90.0\n",
       "  * lon      (lon) float64 0.0 1.25 2.5 3.75 5.0 ... 355.0 356.2 357.5 358.8\n",
       "  * time     (time) object 1850-02-01 00:00:00 ... 2015-01-01 00:00:00\n",
       "Data variables:\n",
       "    SST      (time, lat, lon) float32 ..."
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ensemble_mean"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "adb08dfa-2826-437f-81c6-eeb329e101fe",
   "metadata": {},
   "outputs": [],
   "source": [
    "# ensemble_mean.SST.shape\n",
    "# ensemble_mean.SST[1979,:,:].plot()\n",
    "# np.nanmean(ensemble_mean.SST[1979,:,:])\n",
    "# np.nanmean(ensemble_mean.SST[0,:,:])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "16c72a17-bb28-4213-b8f3-acaaf34aae9c",
   "metadata": {},
   "source": [
    "## Make new netCDF files for SST and SSTA "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "3b00f3d2-5199-4872-9b23-99ab7147251f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# SST_full = []; SSTA_full = []\n",
    "\n",
    "# i = 1\n",
    "# for l, ent in grouped_df_subset:\n",
    "#     if i < 11:\n",
    "#         print(i, l)\n",
    "#         subset = coll_orig.search(component='atm',variable='SST',frequency='month_1',experiment='historical',member_id=l)\n",
    "#     #     subset.df\n",
    "#         dsets = subset.to_dataset_dict(zarr_kwargs={\"consolidated\": True}, storage_options={\"anon\": True})\n",
    "#         key = list(dsets.keys())[0]\n",
    "#         ds = dsets[str(key)]\n",
    "#         SSTA_now = ds.SST.isel(member_id=0) - ensemble_mean; SSTA_full.append(SSTA_now.load())\n",
    "#         SST_now = ds.SST.isel(member_id=0); SST_full.append(SSTA_now.load())\n",
    "#         i+= 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "c56aaebf-991e-4de5-811f-a1b002561f39",
   "metadata": {},
   "outputs": [],
   "source": [
    "# %%time\n",
    "# ensemble_mean = sum(SST_full)/100\n",
    "# ensemble_mean\n",
    "\n",
    "# ensemble_mean.to_netcdf('ensemble_mean.nc')\n",
    "# ensemble_mean.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d392ba00-097c-4f7f-853c-4c24e27bfc95",
   "metadata": {},
   "outputs": [],
   "source": [
    "# %%time\n",
    "# newfile = xr.concat([SST_full[0], SST_full[17], SST_full[34], SST_full[51],\n",
    "#                     SST_full[1], SST_full[18], SST_full[35], SST_full[52],\n",
    "#                     SST_full[2], SST_full[19], SST_full[36], SST_full[53],\n",
    "#                     SST_full[3], SST_full[20], SST_full[37], SST_full[54], \n",
    "#                     SST_full[4], SST_full[21], SST_full[38], SST_full[55],\n",
    "#                     SST_full[5], SST_full[22], SST_full[39], SST_full[56], \n",
    "#                     SST_full[6], SST_full[23], SST_full[40], SST_full[57], \n",
    "#                     SST_full[7], SST_full[24], SST_full[41], SST_full[58],\n",
    "#                     SST_full[8], SST_full[25], SST_full[42], SST_full[59],\n",
    "#                     SST_full[9], SST_full[26], SST_full[43], SST_full[60],\n",
    "#                     SST_full[10], SST_full[27], SST_full[44], SST_full[61],\n",
    "#                     SST_full[11], SST_full[28], SST_full[45], SST_full[62],\n",
    "#                     SST_full[12], SST_full[29], SST_full[46], SST_full[63], \n",
    "#                     SST_full[13], SST_full[30], SST_full[47], SST_full[64],\n",
    "#                     SST_full[14], SST_full[31], SST_full[48], SST_full[65],\n",
    "#                     SST_full[15], SST_full[32], SST_full[49], SST_full[66], \n",
    "#                     SST_full[16], SST_full[33], SST_full[50], SST_full[67],\n",
    "#                     SST_full[68], SST_full[69], SST_full[70], SST_full[71],\n",
    "#                     SST_full[72], SST_full[73], SST_full[74], SST_full[75],\n",
    "#                     SST_full[76], SST_full[77], SST_full[78], SST_full[79],\n",
    "#                     SST_full[80], SST_full[81], SST_full[82], SST_full[83],\n",
    "#                     SST_full[84], SST_full[85], SST_full[86], SST_full[87],\n",
    "#                     SST_full[88], SST_full[89], SST_full[90], SST_full[91],\n",
    "#                     SST_full[92], SST_full[93], SST_full[94], SST_full[95],\n",
    "#                     SST_full[96], SST_full[97], SST_full[98], SST_full[99],\n",
    "#                     ],dim='member_id')\n",
    "# print(newfile)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "29b77dce-ec63-46c6-bc0f-4314e8d68680",
   "metadata": {},
   "outputs": [],
   "source": [
    "# newfile = xr.concat([SST_full[0], SST_full[1], SST_full[2], SST_full[3],\n",
    "#                     SST_full[4], SST_full[5], SST_full[6], SST_full[7],\n",
    "#                     SST_full[8], SST_full[9]],dim='member_id')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "63d309ee-aafe-4d67-a72b-1ba1e6a6d79d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# newfile.to_netcdf('SST_first10.nc')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "687af474-b28f-41cb-89eb-669dff17bef1",
   "metadata": {},
   "outputs": [],
   "source": [
    "# newfile.to_netcdf('SSTA_full.nc')\n",
    "# newfile.to_netcdf('SST_full.nc')\n",
    "# newfile.close()\n",
    "# print ('finished saving')\n",
    "\n",
    "# newfile.to_netcdf('SSTA_full.nc', encoding={'dis': {'zlib': True}})"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "54f6ed2c-fa82-47ca-b6e8-0afbc0c4607a",
   "metadata": {},
   "source": [
    "## Load SST and SSTA files"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8acb14c3-6209-415b-be97-4040e6114c78",
   "metadata": {},
   "outputs": [],
   "source": [
    "# M = np.array(1,101,1,dtype='int')\n",
    "# M = xr.DataArray(M, dims=['M'], coords={'M':M})\n",
    "# ds_mf = xr.open_mfdataset('/glade/work/cassiacai/SSTA_full.nc', concat_dim = M, combine='nested')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "f97ce9e1-a78b-4a98-b7a5-6311c1198bcf",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<xarray.Dataset>\n",
      "Dimensions:    (lat: 192, lon: 288, time: 1980, member_id: 10)\n",
      "Coordinates:\n",
      "  * lat        (lat) float64 -90.0 -89.06 -88.12 -87.17 ... 88.12 89.06 90.0\n",
      "  * lon        (lon) float64 0.0 1.25 2.5 3.75 5.0 ... 355.0 356.2 357.5 358.8\n",
      "  * time       (time) object 1850-02-01 00:00:00 ... 2015-01-01 00:00:00\n",
      "  * member_id  (member_id) object 'r10i1181p1f1' ... 'r11i1301p1f2'\n",
      "Data variables:\n",
      "    SST        (member_id, time, lat, lon) float32 ...\n",
      "\n",
      "<xarray.Dataset>\n",
      "Dimensions:    (lat: 192, lon: 288, time: 1980, member_id: 100)\n",
      "Coordinates:\n",
      "  * lat        (lat) float64 -90.0 -89.06 -88.12 -87.17 ... 88.12 89.06 90.0\n",
      "  * lon        (lon) float64 0.0 1.25 2.5 3.75 5.0 ... 355.0 356.2 357.5 358.8\n",
      "  * time       (time) object 1850-02-01 00:00:00 ... 2015-01-01 00:00:00\n",
      "  * member_id  (member_id) object 'r10i1181p1f1' ... 'r9i1301p1f1'\n",
      "Data variables:\n",
      "    SST        (member_id, time, lat, lon) float32 ...\n"
     ]
    }
   ],
   "source": [
    "# SST_full = xr.open_dataset('/glade/work/cassiacai/SST_full.nc')\n",
    "# print(SST_full)\n",
    "SST_full = xr.open_dataset('SST_first10.nc')\n",
    "print(SST_full)\n",
    "print('')\n",
    "SSTA_full = xr.open_dataset('/glade/work/cassiacai/SSTA_full.nc')\n",
    "print(SSTA_full)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "73e3d1ac-8e74-40c0-9d49-061c5d68aab7",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
       "Dimensions:    (lat: 192, lon: 288, time: 1980, member_id: 10)\n",
       "Coordinates:\n",
       "  * lat        (lat) float64 -90.0 -89.06 -88.12 -87.17 ... 88.12 89.06 90.0\n",
       "  * lon        (lon) float64 0.0 1.25 2.5 3.75 5.0 ... 355.0 356.2 357.5 358.8\n",
       "  * time       (time) object 1850-02-01 00:00:00 ... 2015-01-01 00:00:00\n",
       "  * member_id  (member_id) object &#x27;r10i1181p1f1&#x27; ... &#x27;r14i1251p1f2&#x27;\n",
       "Data variables:\n",
       "    SST        (member_id, time, lat, lon) float32 ...</pre><div class='xr-wrap' hidden><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-1ae4db59-3f3a-4649-aa62-47bcea84963a' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-1ae4db59-3f3a-4649-aa62-47bcea84963a' 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>: 192</li><li><span class='xr-has-index'>lon</span>: 288</li><li><span class='xr-has-index'>time</span>: 1980</li><li><span class='xr-has-index'>member_id</span>: 10</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-d06ac3cb-601a-41bf-a88e-11d716c723e3' class='xr-section-summary-in' type='checkbox'  checked><label for='section-d06ac3cb-601a-41bf-a88e-11d716c723e3' class='xr-section-summary' >Coordinates: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lat</span></div><div class='xr-var-dims'>(lat)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-90.0 -89.06 -88.12 ... 89.06 90.0</div><input id='attrs-355f4d02-568f-4ece-8720-52aba4e6a78f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-355f4d02-568f-4ece-8720-52aba4e6a78f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-704f6441-a50b-46d5-8c00-9d8b03ac09bc' class='xr-var-data-in' type='checkbox'><label for='data-704f6441-a50b-46d5-8c00-9d8b03ac09bc' 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>latitude</dd><dt><span>units :</span></dt><dd>degrees_north</dd></dl></div><div class='xr-var-data'><pre>array([-90.      , -89.057592, -88.115183, -87.172775, -86.230366, -85.287958,\n",
       "       -84.34555 , -83.403141, -82.460733, -81.518325, -80.575916, -79.633508,\n",
       "       -78.691099, -77.748691, -76.806283, -75.863874, -74.921466, -73.979058,\n",
       "       -73.036649, -72.094241, -71.151832, -70.209424, -69.267016, -68.324607,\n",
       "       -67.382199, -66.439791, -65.497382, -64.554974, -63.612565, -62.670157,\n",
       "       -61.727749, -60.78534 , -59.842932, -58.900524, -57.958115, -57.015707,\n",
       "       -56.073298, -55.13089 , -54.188482, -53.246073, -52.303665, -51.361257,\n",
       "       -50.418848, -49.47644 , -48.534031, -47.591623, -46.649215, -45.706806,\n",
       "       -44.764398, -43.82199 , -42.879581, -41.937173, -40.994764, -40.052356,\n",
       "       -39.109948, -38.167539, -37.225131, -36.282723, -35.340314, -34.397906,\n",
       "       -33.455497, -32.513089, -31.570681, -30.628272, -29.685864, -28.743455,\n",
       "       -27.801047, -26.858639, -25.91623 , -24.973822, -24.031414, -23.089005,\n",
       "       -22.146597, -21.204188, -20.26178 , -19.319372, -18.376963, -17.434555,\n",
       "       -16.492147, -15.549738, -14.60733 , -13.664921, -12.722513, -11.780105,\n",
       "       -10.837696,  -9.895288,  -8.95288 ,  -8.010471,  -7.068063,  -6.125654,\n",
       "        -5.183246,  -4.240838,  -3.298429,  -2.356021,  -1.413613,  -0.471204,\n",
       "         0.471204,   1.413613,   2.356021,   3.298429,   4.240838,   5.183246,\n",
       "         6.125654,   7.068063,   8.010471,   8.95288 ,   9.895288,  10.837696,\n",
       "        11.780105,  12.722513,  13.664921,  14.60733 ,  15.549738,  16.492147,\n",
       "        17.434555,  18.376963,  19.319372,  20.26178 ,  21.204188,  22.146597,\n",
       "        23.089005,  24.031414,  24.973822,  25.91623 ,  26.858639,  27.801047,\n",
       "        28.743455,  29.685864,  30.628272,  31.570681,  32.513089,  33.455497,\n",
       "        34.397906,  35.340314,  36.282723,  37.225131,  38.167539,  39.109948,\n",
       "        40.052356,  40.994764,  41.937173,  42.879581,  43.82199 ,  44.764398,\n",
       "        45.706806,  46.649215,  47.591623,  48.534031,  49.47644 ,  50.418848,\n",
       "        51.361257,  52.303665,  53.246073,  54.188482,  55.13089 ,  56.073298,\n",
       "        57.015707,  57.958115,  58.900524,  59.842932,  60.78534 ,  61.727749,\n",
       "        62.670157,  63.612565,  64.554974,  65.497382,  66.439791,  67.382199,\n",
       "        68.324607,  69.267016,  70.209424,  71.151832,  72.094241,  73.036649,\n",
       "        73.979058,  74.921466,  75.863874,  76.806283,  77.748691,  78.691099,\n",
       "        79.633508,  80.575916,  81.518325,  82.460733,  83.403141,  84.34555 ,\n",
       "        85.287958,  86.230366,  87.172775,  88.115183,  89.057592,  90.      ])</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'>float64</div><div class='xr-var-preview xr-preview'>0.0 1.25 2.5 ... 356.2 357.5 358.8</div><input id='attrs-be4a06d7-4398-4dbb-921b-9a770dfc569f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-be4a06d7-4398-4dbb-921b-9a770dfc569f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2bfb832e-4a07-4fe2-9d7b-feb4d8a6b822' class='xr-var-data-in' type='checkbox'><label for='data-2bfb832e-4a07-4fe2-9d7b-feb4d8a6b822' 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>longitude</dd><dt><span>units :</span></dt><dd>degrees_east</dd></dl></div><div class='xr-var-data'><pre>array([  0.  ,   1.25,   2.5 , ..., 356.25, 357.5 , 358.75])</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'>object</div><div class='xr-var-preview xr-preview'>1850-02-01 00:00:00 ... 2015-01-...</div><input id='attrs-38f8b4b3-a55a-4cb7-b41e-d5b1004f9c94' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-38f8b4b3-a55a-4cb7-b41e-d5b1004f9c94' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3a320d2f-6349-46fc-8285-c3b3466de6fc' class='xr-var-data-in' type='checkbox'><label for='data-3a320d2f-6349-46fc-8285-c3b3466de6fc' 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>time</dd><dt><span>bounds :</span></dt><dd>time_bnds</dd></dl></div><div class='xr-var-data'><pre>array([cftime.DatetimeNoLeap(1850, 2, 1, 0, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(1850, 3, 1, 0, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(1850, 4, 1, 0, 0, 0, 0, has_year_zero=True), ...,\n",
       "       cftime.DatetimeNoLeap(2014, 11, 1, 0, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(2014, 12, 1, 0, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(2015, 1, 1, 0, 0, 0, 0, has_year_zero=True)],\n",
       "      dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>member_id</span></div><div class='xr-var-dims'>(member_id)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>&#x27;r10i1181p1f1&#x27; ... &#x27;r14i1251p1f2&#x27;</div><input id='attrs-e32af8d5-a20f-45f5-89b0-02051e394cd3' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-e32af8d5-a20f-45f5-89b0-02051e394cd3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b394ee3c-fcc1-4c1e-bebc-fbaa302ca203' class='xr-var-data-in' type='checkbox'><label for='data-b394ee3c-fcc1-4c1e-bebc-fbaa302ca203' 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;r10i1181p1f1&#x27;, &#x27;r13i1301p1f2&#x27;, &#x27;r18i1231p1f2&#x27;, &#x27;r20i1301p1f2&#x27;,\n",
       "       &#x27;r10i1191p1f2&#x27;, &#x27;r14i1231p1f2&#x27;, &#x27;r18i1251p1f2&#x27;, &#x27;r2i1021p1f1&#x27;,\n",
       "       &#x27;r10i1231p1f1&#x27;, &#x27;r14i1251p1f2&#x27;], dtype=object)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-6dbef785-ede4-4fe2-b2b2-68a49d441a50' class='xr-section-summary-in' type='checkbox'  checked><label for='section-6dbef785-ede4-4fe2-b2b2-68a49d441a50' 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'>(member_id, time, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-304ade28-fd65-4484-8bf9-d8039f571193' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-304ade28-fd65-4484-8bf9-d8039f571193' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4eb4003d-ff97-4851-9f74-f5dd855b5a45' class='xr-var-data-in' type='checkbox'><label for='data-4eb4003d-ff97-4851-9f74-f5dd855b5a45' 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>[1094860800 values with dtype=float32]</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-85bcb4c2-9f9c-4d05-800c-4ef7f74ad0c7' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-85bcb4c2-9f9c-4d05-800c-4ef7f74ad0c7' 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.Dataset>\n",
       "Dimensions:    (lat: 192, lon: 288, time: 1980, member_id: 10)\n",
       "Coordinates:\n",
       "  * lat        (lat) float64 -90.0 -89.06 -88.12 -87.17 ... 88.12 89.06 90.0\n",
       "  * lon        (lon) float64 0.0 1.25 2.5 3.75 5.0 ... 355.0 356.2 357.5 358.8\n",
       "  * time       (time) object 1850-02-01 00:00:00 ... 2015-01-01 00:00:00\n",
       "  * member_id  (member_id) object 'r10i1181p1f1' ... 'r14i1251p1f2'\n",
       "Data variables:\n",
       "    SST        (member_id, time, lat, lon) float32 ..."
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "first_10_members = SSTA_full.isel(member_id = [0,1,2,3,4,5,6,7,8,9])\n",
    "first_10_members"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "bbe35a40-f5c9-4b00-96f2-cf0b99741321",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(10, 1980, 192, 288)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.QuadMesh at 0x2b2e3c6e4100>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "first_10_members.SST.shape\n",
    "first_10_members.SST[0,1950,:,:].plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "cafa5982-8477-4b4a-bbfd-efd5296f79a6",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  --xr-background-color-row-even: var(--jp-layout-color1, white);\n",
       "  --xr-background-color-row-odd: var(--jp-layout-color2, #eeeeee);\n",
       "}\n",
       "\n",
       "html[theme=dark],\n",
       "body.vscode-dark {\n",
       "  --xr-font-color0: rgba(255, 255, 255, 1);\n",
       "  --xr-font-color2: rgba(255, 255, 255, 0.54);\n",
       "  --xr-font-color3: rgba(255, 255, 255, 0.38);\n",
       "  --xr-border-color: #1F1F1F;\n",
       "  --xr-disabled-color: #515151;\n",
       "  --xr-background-color: #111111;\n",
       "  --xr-background-color-row-even: #111111;\n",
       "  --xr-background-color-row-odd: #313131;\n",
       "}\n",
       "\n",
       ".xr-wrap {\n",
       "  display: block;\n",
       "  min-width: 300px;\n",
       "  max-width: 700px;\n",
       "}\n",
       "\n",
       ".xr-text-repr-fallback {\n",
       "  /* fallback to plain text repr when CSS is not injected (untrusted notebook) */\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-header {\n",
       "  padding-top: 6px;\n",
       "  padding-bottom: 6px;\n",
       "  margin-bottom: 4px;\n",
       "  border-bottom: solid 1px var(--xr-border-color);\n",
       "}\n",
       "\n",
       ".xr-header > div,\n",
       ".xr-header > ul {\n",
       "  display: inline;\n",
       "  margin-top: 0;\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-obj-type,\n",
       ".xr-array-name {\n",
       "  margin-left: 2px;\n",
       "  margin-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-obj-type {\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-sections {\n",
       "  padding-left: 0 !important;\n",
       "  display: grid;\n",
       "  grid-template-columns: 150px auto auto 1fr 20px 20px;\n",
       "}\n",
       "\n",
       ".xr-section-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-section-item input {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-section-item input + label {\n",
       "  color: var(--xr-disabled-color);\n",
       "}\n",
       "\n",
       ".xr-section-item input:enabled + label {\n",
       "  cursor: pointer;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-section-item input:enabled + label:hover {\n",
       "  color: var(--xr-font-color0);\n",
       "}\n",
       "\n",
       ".xr-section-summary {\n",
       "  grid-column: 1;\n",
       "  color: var(--xr-font-color2);\n",
       "  font-weight: 500;\n",
       "}\n",
       "\n",
       ".xr-section-summary > span {\n",
       "  display: inline-block;\n",
       "  padding-left: 0.5em;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label {\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in + label:before {\n",
       "  display: inline-block;\n",
       "  content: '►';\n",
       "  font-size: 11px;\n",
       "  width: 15px;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label:before {\n",
       "  color: var(--xr-disabled-color);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label:before {\n",
       "  content: '▼';\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label > span {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-section-summary,\n",
       ".xr-section-inline-details {\n",
       "  padding-top: 4px;\n",
       "  padding-bottom: 4px;\n",
       "}\n",
       "\n",
       ".xr-section-inline-details {\n",
       "  grid-column: 2 / -1;\n",
       "}\n",
       "\n",
       ".xr-section-details {\n",
       "  display: none;\n",
       "  grid-column: 1 / -1;\n",
       "  margin-bottom: 5px;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-array-wrap {\n",
       "  grid-column: 1 / -1;\n",
       "  display: grid;\n",
       "  grid-template-columns: 20px auto;\n",
       "}\n",
       "\n",
       ".xr-array-wrap > label {\n",
       "  grid-column: 1;\n",
       "  vertical-align: top;\n",
       "}\n",
       "\n",
       ".xr-preview {\n",
       "  color: var(--xr-font-color3);\n",
       "}\n",
       "\n",
       ".xr-array-preview,\n",
       ".xr-array-data {\n",
       "  padding: 0 5px !important;\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-array-data,\n",
       ".xr-array-in:checked ~ .xr-array-preview {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-array-in:checked ~ .xr-array-data,\n",
       ".xr-array-preview {\n",
       "  display: inline-block;\n",
       "}\n",
       "\n",
       ".xr-dim-list {\n",
       "  display: inline-block !important;\n",
       "  list-style: none;\n",
       "  padding: 0 !important;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list li {\n",
       "  display: inline-block;\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list:before {\n",
       "  content: '(';\n",
       "}\n",
       "\n",
       ".xr-dim-list:after {\n",
       "  content: ')';\n",
       "}\n",
       "\n",
       ".xr-dim-list li:not(:last-child):after {\n",
       "  content: ',';\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-has-index {\n",
       "  font-weight: bold;\n",
       "}\n",
       "\n",
       ".xr-var-list,\n",
       ".xr-var-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-var-item > div,\n",
       ".xr-var-item label,\n",
       ".xr-var-item > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-even);\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-var-item > .xr-var-name:hover span {\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-var-list > li:nth-child(odd) > div,\n",
       ".xr-var-list > li:nth-child(odd) > label,\n",
       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-odd);\n",
       "}\n",
       "\n",
       ".xr-var-name {\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-var-dims {\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-var-dtype {\n",
       "  grid-column: 3;\n",
       "  text-align: right;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-preview {\n",
       "  grid-column: 4;\n",
       "}\n",
       "\n",
       ".xr-var-name,\n",
       ".xr-var-dims,\n",
       ".xr-var-dtype,\n",
       ".xr-preview,\n",
       ".xr-attrs dt {\n",
       "  white-space: nowrap;\n",
       "  overflow: hidden;\n",
       "  text-overflow: ellipsis;\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-var-name:hover,\n",
       ".xr-var-dims:hover,\n",
       ".xr-var-dtype:hover,\n",
       ".xr-attrs dt:hover {\n",
       "  overflow: visible;\n",
       "  width: auto;\n",
       "  z-index: 1;\n",
       "}\n",
       "\n",
       ".xr-var-attrs,\n",
       ".xr-var-data {\n",
       "  display: none;\n",
       "  background-color: var(--xr-background-color) !important;\n",
       "  padding-bottom: 5px !important;\n",
       "}\n",
       "\n",
       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
       ".xr-var-data-in:checked ~ .xr-var-data {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       ".xr-var-data > table {\n",
       "  float: right;\n",
       "}\n",
       "\n",
       ".xr-var-name span,\n",
       ".xr-var-data,\n",
       ".xr-attrs {\n",
       "  padding-left: 25px !important;\n",
       "}\n",
       "\n",
       ".xr-attrs,\n",
       ".xr-var-attrs,\n",
       ".xr-var-data {\n",
       "  grid-column: 1 / -1;\n",
       "}\n",
       "\n",
       "dl.xr-attrs {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  display: grid;\n",
       "  grid-template-columns: 125px auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt,\n",
       ".xr-attrs dd {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  float: left;\n",
       "  padding-right: 10px;\n",
       "  width: auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt {\n",
       "  font-weight: normal;\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-attrs dt:hover span {\n",
       "  display: inline-block;\n",
       "  background: var(--xr-background-color);\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-attrs dd {\n",
       "  grid-column: 2;\n",
       "  white-space: pre-wrap;\n",
       "  word-break: break-all;\n",
       "}\n",
       "\n",
       ".xr-icon-database,\n",
       ".xr-icon-file-text2 {\n",
       "  display: inline-block;\n",
       "  vertical-align: middle;\n",
       "  width: 1em;\n",
       "  height: 1.5em !important;\n",
       "  stroke-width: 0;\n",
       "  stroke: currentColor;\n",
       "  fill: currentColor;\n",
       "}\n",
       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
       "Dimensions:    (lat: 192, lon: 288, time: 1980, member_id: 10)\n",
       "Coordinates:\n",
       "  * lat        (lat) float64 -90.0 -89.06 -88.12 -87.17 ... 88.12 89.06 90.0\n",
       "  * lon        (lon) float64 0.0 1.25 2.5 3.75 5.0 ... 355.0 356.2 357.5 358.8\n",
       "  * time       (time) object 1850-02-01 00:00:00 ... 2015-01-01 00:00:00\n",
       "  * member_id  (member_id) object &#x27;r10i1181p1f1&#x27; ... &#x27;r11i1301p1f2&#x27;\n",
       "Data variables:\n",
       "    SST        (member_id, time, lat, lon) float32 ...</pre><div class='xr-wrap' hidden><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-5a79139e-2d94-4cc2-8910-39df63dfd9f4' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-5a79139e-2d94-4cc2-8910-39df63dfd9f4' 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>: 192</li><li><span class='xr-has-index'>lon</span>: 288</li><li><span class='xr-has-index'>time</span>: 1980</li><li><span class='xr-has-index'>member_id</span>: 10</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-90dadb7d-fecc-4a81-a2fa-0939306f707e' class='xr-section-summary-in' type='checkbox'  checked><label for='section-90dadb7d-fecc-4a81-a2fa-0939306f707e' class='xr-section-summary' >Coordinates: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lat</span></div><div class='xr-var-dims'>(lat)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-90.0 -89.06 -88.12 ... 89.06 90.0</div><input id='attrs-eee0b610-0b57-40a5-a205-12e9c95c4e28' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-eee0b610-0b57-40a5-a205-12e9c95c4e28' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-42228fb5-45ef-4ef4-8219-705ac22fbb4c' class='xr-var-data-in' type='checkbox'><label for='data-42228fb5-45ef-4ef4-8219-705ac22fbb4c' 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>latitude</dd><dt><span>units :</span></dt><dd>degrees_north</dd></dl></div><div class='xr-var-data'><pre>array([-90.      , -89.057592, -88.115183, -87.172775, -86.230366, -85.287958,\n",
       "       -84.34555 , -83.403141, -82.460733, -81.518325, -80.575916, -79.633508,\n",
       "       -78.691099, -77.748691, -76.806283, -75.863874, -74.921466, -73.979058,\n",
       "       -73.036649, -72.094241, -71.151832, -70.209424, -69.267016, -68.324607,\n",
       "       -67.382199, -66.439791, -65.497382, -64.554974, -63.612565, -62.670157,\n",
       "       -61.727749, -60.78534 , -59.842932, -58.900524, -57.958115, -57.015707,\n",
       "       -56.073298, -55.13089 , -54.188482, -53.246073, -52.303665, -51.361257,\n",
       "       -50.418848, -49.47644 , -48.534031, -47.591623, -46.649215, -45.706806,\n",
       "       -44.764398, -43.82199 , -42.879581, -41.937173, -40.994764, -40.052356,\n",
       "       -39.109948, -38.167539, -37.225131, -36.282723, -35.340314, -34.397906,\n",
       "       -33.455497, -32.513089, -31.570681, -30.628272, -29.685864, -28.743455,\n",
       "       -27.801047, -26.858639, -25.91623 , -24.973822, -24.031414, -23.089005,\n",
       "       -22.146597, -21.204188, -20.26178 , -19.319372, -18.376963, -17.434555,\n",
       "       -16.492147, -15.549738, -14.60733 , -13.664921, -12.722513, -11.780105,\n",
       "       -10.837696,  -9.895288,  -8.95288 ,  -8.010471,  -7.068063,  -6.125654,\n",
       "        -5.183246,  -4.240838,  -3.298429,  -2.356021,  -1.413613,  -0.471204,\n",
       "         0.471204,   1.413613,   2.356021,   3.298429,   4.240838,   5.183246,\n",
       "         6.125654,   7.068063,   8.010471,   8.95288 ,   9.895288,  10.837696,\n",
       "        11.780105,  12.722513,  13.664921,  14.60733 ,  15.549738,  16.492147,\n",
       "        17.434555,  18.376963,  19.319372,  20.26178 ,  21.204188,  22.146597,\n",
       "        23.089005,  24.031414,  24.973822,  25.91623 ,  26.858639,  27.801047,\n",
       "        28.743455,  29.685864,  30.628272,  31.570681,  32.513089,  33.455497,\n",
       "        34.397906,  35.340314,  36.282723,  37.225131,  38.167539,  39.109948,\n",
       "        40.052356,  40.994764,  41.937173,  42.879581,  43.82199 ,  44.764398,\n",
       "        45.706806,  46.649215,  47.591623,  48.534031,  49.47644 ,  50.418848,\n",
       "        51.361257,  52.303665,  53.246073,  54.188482,  55.13089 ,  56.073298,\n",
       "        57.015707,  57.958115,  58.900524,  59.842932,  60.78534 ,  61.727749,\n",
       "        62.670157,  63.612565,  64.554974,  65.497382,  66.439791,  67.382199,\n",
       "        68.324607,  69.267016,  70.209424,  71.151832,  72.094241,  73.036649,\n",
       "        73.979058,  74.921466,  75.863874,  76.806283,  77.748691,  78.691099,\n",
       "        79.633508,  80.575916,  81.518325,  82.460733,  83.403141,  84.34555 ,\n",
       "        85.287958,  86.230366,  87.172775,  88.115183,  89.057592,  90.      ])</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'>float64</div><div class='xr-var-preview xr-preview'>0.0 1.25 2.5 ... 356.2 357.5 358.8</div><input id='attrs-bde3da12-cd3e-4941-89c7-8429854adb78' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-bde3da12-cd3e-4941-89c7-8429854adb78' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9ccc5004-357c-496b-baab-f4c702b96895' class='xr-var-data-in' type='checkbox'><label for='data-9ccc5004-357c-496b-baab-f4c702b96895' 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>longitude</dd><dt><span>units :</span></dt><dd>degrees_east</dd></dl></div><div class='xr-var-data'><pre>array([  0.  ,   1.25,   2.5 , ..., 356.25, 357.5 , 358.75])</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'>object</div><div class='xr-var-preview xr-preview'>1850-02-01 00:00:00 ... 2015-01-...</div><input id='attrs-d071c331-6e25-49b2-8352-28a349b1daab' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d071c331-6e25-49b2-8352-28a349b1daab' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7dfe7a97-8c5c-4b6e-9ebc-fb830f5b2aea' class='xr-var-data-in' type='checkbox'><label for='data-7dfe7a97-8c5c-4b6e-9ebc-fb830f5b2aea' 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>time</dd><dt><span>bounds :</span></dt><dd>time_bnds</dd></dl></div><div class='xr-var-data'><pre>array([cftime.DatetimeNoLeap(1850, 2, 1, 0, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(1850, 3, 1, 0, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(1850, 4, 1, 0, 0, 0, 0, has_year_zero=True), ...,\n",
       "       cftime.DatetimeNoLeap(2014, 11, 1, 0, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(2014, 12, 1, 0, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(2015, 1, 1, 0, 0, 0, 0, has_year_zero=True)],\n",
       "      dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>member_id</span></div><div class='xr-var-dims'>(member_id)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>&#x27;r10i1181p1f1&#x27; ... &#x27;r11i1301p1f2&#x27;</div><input id='attrs-73b9bb89-bc1e-40a7-9b4e-9047db3eddd0' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-73b9bb89-bc1e-40a7-9b4e-9047db3eddd0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-42bd6e00-53b0-4e6b-a372-55184f93b0d2' class='xr-var-data-in' type='checkbox'><label for='data-42bd6e00-53b0-4e6b-a372-55184f93b0d2' 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;r10i1181p1f1&#x27;, &#x27;r10i1191p1f2&#x27;, &#x27;r10i1231p1f1&#x27;, &#x27;r10i1251p1f1&#x27;,\n",
       "       &#x27;r10i1281p1f1&#x27;, &#x27;r10i1301p1f1&#x27;, &#x27;r11i1231p1f2&#x27;, &#x27;r11i1251p1f2&#x27;,\n",
       "       &#x27;r11i1281p1f2&#x27;, &#x27;r11i1301p1f2&#x27;], dtype=object)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-7e52c7c1-9456-40c2-8a75-5a07fd8f5103' class='xr-section-summary-in' type='checkbox'  checked><label for='section-7e52c7c1-9456-40c2-8a75-5a07fd8f5103' 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'>(member_id, time, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-af9aca73-e468-44b6-adc9-b06e93c33e87' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-af9aca73-e468-44b6-adc9-b06e93c33e87' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e588a6b1-46de-483e-804f-b47526302f08' class='xr-var-data-in' type='checkbox'><label for='data-e588a6b1-46de-483e-804f-b47526302f08' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>K</dd><dt><span>long_name :</span></dt><dd>sea surface temperature</dd><dt><span>cell_methods :</span></dt><dd>time: mean</dd></dl></div><div class='xr-var-data'><pre>[1094860800 values with dtype=float32]</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-61fc42ca-5432-4be9-bd85-5a15ff1af3ff' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-61fc42ca-5432-4be9-bd85-5a15ff1af3ff' 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.Dataset>\n",
       "Dimensions:    (lat: 192, lon: 288, time: 1980, member_id: 10)\n",
       "Coordinates:\n",
       "  * lat        (lat) float64 -90.0 -89.06 -88.12 -87.17 ... 88.12 89.06 90.0\n",
       "  * lon        (lon) float64 0.0 1.25 2.5 3.75 5.0 ... 355.0 356.2 357.5 358.8\n",
       "  * time       (time) object 1850-02-01 00:00:00 ... 2015-01-01 00:00:00\n",
       "  * member_id  (member_id) object 'r10i1181p1f1' ... 'r11i1301p1f2'\n",
       "Data variables:\n",
       "    SST        (member_id, time, lat, lon) float32 ..."
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "first_10_members_SST = SST_full.isel(member_id = [0,1,2,3,4,5,6,7,8,9])\n",
    "first_10_members_SST"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "18aaa652-5677-40a1-9fc9-9d941a75d741",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(10, 1980, 192, 288)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.QuadMesh at 0x2b2e4484b940>"
      ]
     },
     "execution_count": 11,
     "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": [
    "first_10_members_SST.SST.shape\n",
    "first_10_members_SST.SST[0,1950,:,:].plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "6e82f8d7-697c-4659-af51-0c3a68ca5933",
   "metadata": {},
   "outputs": [],
   "source": [
    "## Trying to crop (we can crop later)\n",
    "# %%time\n",
    "# SSTA_full_new = SSTA_full.where((SSTA_full.lat >= 39.109948) & (SSTA_full.lat <= 50.418848) \n",
    "#                         &(SSTA_full.lon >= 200.0) & (SSTA_full.lon <= 236.),drop=True)\n",
    "\n",
    "# SST_full_new = SST_full.where((SST_full.lat >= 39.109948) & (SST_full.lat <= 50.418848) \n",
    "#                         &(SST_full.lon >= 200.0) & (SST_full.lon <= 236.),drop=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "f6500c52-d82c-485f-a928-3856dda3979c",
   "metadata": {},
   "outputs": [],
   "source": [
    "detrended = first_10_members.SST\n",
    "\n",
    "if detrended.chunks:\n",
    "    detrended = detrended.chunk({'time': -1})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "ecaadc81-6fd3-4fa6-91a6-21d4a3a95d48",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 6min, sys: 8.39 s, total: 6min 9s\n",
      "Wall time: 6min 27s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "threshold = detrended.groupby('time.month').quantile(0.9,dim=('time')) \n",
    "features_ssta = detrended.where(detrended.groupby('time.month')>=threshold, other=np.nan)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "83605648-a20d-4c04-b664-422639969358",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 881 µs, sys: 0 ns, total: 881 µs\n",
      "Wall time: 886 µs\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "features_ssta= features_ssta[:,:,:].load()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "74c35d5e-e847-4e65-a968-75c8196fc061",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 2.58 s, sys: 2 s, total: 4.58 s\n",
      "Wall time: 4.66 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "full_mask_land = features_ssta\n",
    "full_masked = full_mask_land.where(full_mask_land != 0)\n",
    "binary_out_afterlandmask=np.isfinite(full_masked)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "d1cc4528-3882-4912-9209-d47c7c0253e4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 2.18 s, sys: 1.99 s, total: 4.17 s\n",
      "Wall time: 4.28 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "full_mask_land = features_ssta\n",
    "full_masked = full_mask_land.where(full_mask_land != 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "42889c0d-2432-48c4-8d14-c531d23ede34",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 17.2 s, sys: 4.04 s, total: 21.2 s\n",
      "Wall time: 28.6 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "mask = np.isfinite(first_10_members_SST.SST) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "1ebddca2-bee1-4bc7-849c-8e40faa380b8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.QuadMesh at 0x2b2e44941be0>"
      ]
     },
     "execution_count": 19,
     "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": [
    "binary_out_afterlandmask[6,1960,:,:].plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "ff13c855-8077-479c-a0e9-ea144201603d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.QuadMesh at 0x2b2eca4e5e20>"
      ]
     },
     "execution_count": 20,
     "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[0,1960,:,:].plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "57a1b4dc-3cc1-4737-99a4-1857bf0a4f4b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.QuadMesh at 0x2b2eca5b6250>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(192, 288)\n",
      "197.66042\n"
     ]
    },
    {
     "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": [
    "first_10_members_SST.SST[0,1960,:,:].plot()\n",
    "print(first_10_members_SST.SST[0,1960,:,:].shape)\n",
    "print(np.nanmean(first_10_members_SST.SST[0,1960,:,:]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "31158b44-c9b9-41e4-a665-5b424f6b9271",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "minimum area: 40.0\n",
      "inital objects identified \t 17762\n",
      "final objects tracked \t 4443\n",
      "CPU times: user 4min 34s, sys: 2min 54s, total: 7min 29s\n",
      "Wall time: 7min 39s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "Tracker = ocetrac.Tracker(binary_out_afterlandmask[9,:,:,:], mask[9,:,:,:], radius=4, min_size_quartile=0., timedim = 'time', xdim = 'lon', ydim='lat', positive=True)\n",
    "blobs = Tracker.track()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "eab196ab-8851-4dbd-8a58-297e4c5d78e2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'inital objects identified': 17762,\n",
       " 'final objects tracked': 4443,\n",
       " 'radius': 4,\n",
       " 'size quantile threshold': 0.0,\n",
       " 'min area': 40.0,\n",
       " 'percent area reject': 8.753668334136274e-05,\n",
       " 'percent area accept': 0.9999124633166586}"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "blobs.attrs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "6bc9eada-12cc-4b3d-b20a-832de26b3417",
   "metadata": {},
   "outputs": [],
   "source": [
    "blobs.to_netcdf(\"blobs_ens_9_rad4.nc\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cfcde004-faba-40f5-b935-3bb4b453de8f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# full_masked.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "a0bb7cae-05dc-43dc-8380-92a4ba0b5e4b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 1152x216 with 0 Axes>"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.QuadMesh at 0x2ba481675940>"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.QuadMesh at 0x2ba481701c70>"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x216 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib.colors import ListedColormap\n",
    "maxl = int(np.nanmax(blobs.values))\n",
    "cm = ListedColormap(np.random.random(size=(maxl, 3)).tolist())\n",
    "\n",
    "timed = 1760\n",
    "plt.figure(figsize=(16,3))\n",
    "ax1 = plt.subplot(121)\n",
    "blobs.isel(time=timed).plot(cmap= cm)\n",
    "# features_ssta.isel(time=timed).where(features_ssta.isel(time=timed)==0.0).plot.contourf(colors='k', add_colorbar=False)\n",
    "\n",
    "ax2 = plt.subplot(122); \n",
    "full_masked[0,timed,:,:].plot(levels=[0.0,0.25,0.50,0.75,1.0,1.25,1.50,1.75,2.0,2.25,2.5],cmap='Reds')\n",
    "# features_ssta.isel(time=timed).where(features_ssta.isel(time=timed)==0.0).plot.contourf(colors='k', add_colorbar=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cfd0e171-983b-44ef-b392-8b9b43854020",
   "metadata": {},
   "source": [
    "## Code from before "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "00feb386-7162-47ad-9a25-508a1df617cd",
   "metadata": {},
   "outputs": [],
   "source": [
    "detrended = SSTA_full.SST.isel(member_id=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f0ad99fb-abfc-4441-95c6-b5290a04e434",
   "metadata": {},
   "outputs": [],
   "source": [
    "if detrended.chunks:\n",
    "    detrended = detrended.chunk({'time': -1})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "05a210a3-ce67-4f4f-8f31-52c83bc30720",
   "metadata": {},
   "outputs": [],
   "source": [
    "threshold = detrended.groupby('time.month').quantile(0.9,dim=('time')) \n",
    "features_ssta = detrended.where(detrended.groupby('time.month')>=threshold, other=np.nan)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a9863360-f374-48aa-b4e2-0e33a491780a",
   "metadata": {},
   "outputs": [],
   "source": [
    "%%time\n",
    "features_ssta= features_ssta[:,:,:].load()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d2ab3bb9-845e-4cf2-82df-177839d882e9",
   "metadata": {},
   "outputs": [],
   "source": [
    "timed = 300\n",
    "full_mask_land = features_ssta\n",
    "full_masked = full_mask_land.where(full_mask_land != 0)\n",
    "binary_out_afterlandmask=np.isfinite(full_masked)\n",
    "\n",
    "binary_out_afterlandmask[timed,:,:].plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "46b81ffb-b02d-478c-ad8a-181191f7c53f",
   "metadata": {},
   "outputs": [],
   "source": [
    "full_mask_land = features_ssta\n",
    "full_masked = full_mask_land.where(full_mask_land != 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ae3d801f-3706-4a25-86ff-f753d8e819bf",
   "metadata": {},
   "outputs": [],
   "source": [
    "full_masked[300,:,:].plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e93b2a33-2b1b-41b8-acce-6777de316022",
   "metadata": {},
   "outputs": [],
   "source": [
    "SST_full.SST.isel(time=0, member_id=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "39f79f44-8761-468c-b142-53d3527595bb",
   "metadata": {},
   "outputs": [],
   "source": [
    "mask = np.isfinite(SST_full.SST.isel(time=0, member_id=0)) \n",
    "mask.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "62cf2e78-0a01-4a13-9662-b8c5f2307c74",
   "metadata": {},
   "outputs": [],
   "source": [
    "mask"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1e92e50c-6b53-4502-a024-431c0f49a526",
   "metadata": {},
   "outputs": [],
   "source": [
    "newmask_1 = SST_full.SST.isel(member_id=0)\n",
    "newmask = newmask_1.where(newmask_1 != 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "601c7c4c-250c-4c3a-9119-f528665a162c",
   "metadata": {},
   "outputs": [],
   "source": [
    "newmask[timed,:,:].plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "972d71ed-a1d1-45ad-b9b2-6fef8c4ed82e",
   "metadata": {},
   "outputs": [],
   "source": [
    "binary_out_afterlandmask.shape\n",
    "mask.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "31eef29b-de3c-4f0a-9432-53d7dca2fbec",
   "metadata": {},
   "outputs": [],
   "source": [
    "# %%time\n",
    "Tracker = ocetrac.Tracker(binary_out_afterlandmask[:,:,:], mask, radius=6, min_size_quartile=0., timedim = 'time', xdim = 'lon', ydim='lat', positive=True)\n",
    "blobs = Tracker.track()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5185614d-a7a8-4efc-91e6-99e796d72dc3",
   "metadata": {},
   "outputs": [],
   "source": [
    "blobs.attrs"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "test-env",
   "language": "python",
   "name": "test-env"
  },
  "language_info": {
   "codemirror_mode": {
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