scratchwork/try-ocetrac-on-datasets.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "9c9ef2d0-7a7f-48f4-bb15-034bccc72977",
   "metadata": {},
   "source": [
    "## Loading in packages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "957e9bfe-1c93-4b72-80b8-5537cf4c4330",
   "metadata": {},
   "outputs": [],
   "source": [
    "##### LOADING IN PACKAGES #--------------------------------------------------------------\n",
    "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": "ff1271c6-b6ef-4bc2-a962-76eda3ce9d8f",
   "metadata": {},
   "source": [
    "## Loading CESM LE dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "6c16f729-ee01-45e8-be59-32d97f7526eb",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Open original collection description file #----------------------------------------------\n",
    "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": "15c648ce-1f9c-453e-988e-81edb6d76c04",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['r1i1001p1f1', 'r2i1021p1f1', 'r3i1041p1f1', 'r4i1061p1f1',\n",
       "       'r5i1081p1f1', 'r6i1101p1f1', 'r7i1121p1f1', 'r8i1141p1f1',\n",
       "       'r9i1161p1f1', 'r10i1181p1f1', 'r1i1231p1f1', 'r2i1231p1f1',\n",
       "       'r3i1231p1f1', 'r4i1231p1f1', 'r5i1231p1f1', 'r6i1231p1f1',\n",
       "       'r7i1231p1f1', 'r8i1231p1f1', 'r9i1231p1f1', 'r10i1231p1f1',\n",
       "       'r1i1251p1f1', 'r2i1251p1f1', 'r3i1251p1f1', 'r4i1251p1f1',\n",
       "       'r5i1251p1f1', 'r6i1251p1f1', 'r7i1251p1f1', 'r8i1251p1f1',\n",
       "       'r9i1251p1f1', 'r10i1251p1f1', 'r1i1281p1f1', 'r2i1281p1f1',\n",
       "       'r3i1281p1f1', 'r4i1281p1f1', 'r5i1281p1f1', 'r6i1281p1f1',\n",
       "       'r7i1281p1f1', 'r8i1281p1f1', 'r9i1281p1f1', 'r10i1281p1f1',\n",
       "       'r1i1301p1f1', 'r2i1301p1f1', 'r3i1301p1f1', 'r4i1301p1f1',\n",
       "       'r5i1301p1f1', 'r6i1301p1f1', 'r7i1301p1f1', 'r8i1301p1f1',\n",
       "       'r9i1301p1f1', 'r10i1301p1f1', 'r1i1011p1f2', 'r2i1031p1f2',\n",
       "       'r3i1051p1f2', 'r4i1071p1f2', 'r5i1091p1f2', 'r6i1111p1f2',\n",
       "       'r7i1131p1f2', 'r8i1151p1f2', 'r9i1171p1f2', 'r10i1191p1f2',\n",
       "       'r11i1231p1f2', 'r12i1231p1f2', 'r13i1231p1f2', 'r14i1231p1f2',\n",
       "       'r15i1231p1f2', 'r16i1231p1f2', 'r17i1231p1f2', 'r18i1231p1f2',\n",
       "       'r19i1231p1f2', 'r20i1231p1f2', 'r11i1251p1f2', 'r12i1251p1f2',\n",
       "       'r13i1251p1f2', 'r14i1251p1f2', 'r15i1251p1f2', 'r16i1251p1f2',\n",
       "       'r17i1251p1f2', 'r18i1251p1f2', 'r19i1251p1f2', 'r20i1251p1f2',\n",
       "       'r11i1281p1f2', 'r12i1281p1f2', 'r13i1281p1f2', 'r14i1281p1f2',\n",
       "       'r15i1281p1f2', 'r16i1281p1f2', 'r17i1281p1f2', 'r18i1281p1f2',\n",
       "       'r19i1281p1f2', 'r20i1281p1f2', 'r11i1301p1f2', 'r12i1301p1f2',\n",
       "       'r13i1301p1f2', 'r14i1301p1f2', 'r15i1301p1f2', 'r16i1301p1f2',\n",
       "       'r17i1301p1f2', 'r18i1301p1f2', 'r19i1301p1f2', 'r20i1301p1f2'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "100\n"
     ]
    }
   ],
   "source": [
    "subset = coll_orig.search(component='atm',variable='SST',frequency='month_1',experiment='historical')\n",
    "subset.df.member_id.unique()\n",
    "print(len(subset.df.member_id.unique()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "5fe62058-5080-4805-935c-bf80e1840bb9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>component</th>\n",
       "      <th>stream</th>\n",
       "      <th>case</th>\n",
       "      <th>member_id</th>\n",
       "      <th>variable</th>\n",
       "      <th>start_time</th>\n",
       "      <th>end_time</th>\n",
       "      <th>time_range</th>\n",
       "      <th>long_name</th>\n",
       "      <th>units</th>\n",
       "      <th>vertical_levels</th>\n",
       "      <th>frequency</th>\n",
       "      <th>path</th>\n",
       "      <th>experiment</th>\n",
       "      <th>forcing_variant</th>\n",
       "      <th>cesm_member_id</th>\n",
       "      <th>control_branch_year</th>\n",
       "      <th>cmip_experiment_id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>atm</td>\n",
       "      <td>cam.h0</td>\n",
       "      <td>b.e21.BHISTcmip6.f09_g17.LE2-1001.001</td>\n",
       "      <td>r1i1001p1f1</td>\n",
       "      <td>SST</td>\n",
       "      <td>1850-01</td>\n",
       "      <td>1859-12</td>\n",
       "      <td>185001-185912</td>\n",
       "      <td>sea surface temperature</td>\n",
       "      <td>K</td>\n",
       "      <td>1.0</td>\n",
       "      <td>month_1</td>\n",
       "      <td>/glade/campaign/cgd/cesm/CESM2-LE/timeseries/a...</td>\n",
       "      <td>historical</td>\n",
       "      <td>cmip6</td>\n",
       "      <td>1001.001</td>\n",
       "      <td>1001</td>\n",
       "      <td>CESM2_historical_r1i1001p1f1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>atm</td>\n",
       "      <td>cam.h0</td>\n",
       "      <td>b.e21.BHISTcmip6.f09_g17.LE2-1001.001</td>\n",
       "      <td>r1i1001p1f1</td>\n",
       "      <td>SST</td>\n",
       "      <td>1860-01</td>\n",
       "      <td>1869-12</td>\n",
       "      <td>186001-186912</td>\n",
       "      <td>sea surface temperature</td>\n",
       "      <td>K</td>\n",
       "      <td>1.0</td>\n",
       "      <td>month_1</td>\n",
       "      <td>/glade/campaign/cgd/cesm/CESM2-LE/timeseries/a...</td>\n",
       "      <td>historical</td>\n",
       "      <td>cmip6</td>\n",
       "      <td>1001.001</td>\n",
       "      <td>1001</td>\n",
       "      <td>CESM2_historical_r1i1001p1f1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>atm</td>\n",
       "      <td>cam.h0</td>\n",
       "      <td>b.e21.BHISTcmip6.f09_g17.LE2-1001.001</td>\n",
       "      <td>r1i1001p1f1</td>\n",
       "      <td>SST</td>\n",
       "      <td>1870-01</td>\n",
       "      <td>1879-12</td>\n",
       "      <td>187001-187912</td>\n",
       "      <td>sea surface temperature</td>\n",
       "      <td>K</td>\n",
       "      <td>1.0</td>\n",
       "      <td>month_1</td>\n",
       "      <td>/glade/campaign/cgd/cesm/CESM2-LE/timeseries/a...</td>\n",
       "      <td>historical</td>\n",
       "      <td>cmip6</td>\n",
       "      <td>1001.001</td>\n",
       "      <td>1001</td>\n",
       "      <td>CESM2_historical_r1i1001p1f1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>atm</td>\n",
       "      <td>cam.h0</td>\n",
       "      <td>b.e21.BHISTcmip6.f09_g17.LE2-1001.001</td>\n",
       "      <td>r1i1001p1f1</td>\n",
       "      <td>SST</td>\n",
       "      <td>1880-01</td>\n",
       "      <td>1889-12</td>\n",
       "      <td>188001-188912</td>\n",
       "      <td>sea surface temperature</td>\n",
       "      <td>K</td>\n",
       "      <td>1.0</td>\n",
       "      <td>month_1</td>\n",
       "      <td>/glade/campaign/cgd/cesm/CESM2-LE/timeseries/a...</td>\n",
       "      <td>historical</td>\n",
       "      <td>cmip6</td>\n",
       "      <td>1001.001</td>\n",
       "      <td>1001</td>\n",
       "      <td>CESM2_historical_r1i1001p1f1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>atm</td>\n",
       "      <td>cam.h0</td>\n",
       "      <td>b.e21.BHISTcmip6.f09_g17.LE2-1001.001</td>\n",
       "      <td>r1i1001p1f1</td>\n",
       "      <td>SST</td>\n",
       "      <td>1890-01</td>\n",
       "      <td>1899-12</td>\n",
       "      <td>189001-189912</td>\n",
       "      <td>sea surface temperature</td>\n",
       "      <td>K</td>\n",
       "      <td>1.0</td>\n",
       "      <td>month_1</td>\n",
       "      <td>/glade/campaign/cgd/cesm/CESM2-LE/timeseries/a...</td>\n",
       "      <td>historical</td>\n",
       "      <td>cmip6</td>\n",
       "      <td>1001.001</td>\n",
       "      <td>1001</td>\n",
       "      <td>CESM2_historical_r1i1001p1f1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>atm</td>\n",
       "      <td>cam.h0</td>\n",
       "      <td>b.e21.BHISTcmip6.f09_g17.LE2-1001.001</td>\n",
       "      <td>r1i1001p1f1</td>\n",
       "      <td>SST</td>\n",
       "      <td>1900-01</td>\n",
       "      <td>1909-12</td>\n",
       "      <td>190001-190912</td>\n",
       "      <td>sea surface temperature</td>\n",
       "      <td>K</td>\n",
       "      <td>1.0</td>\n",
       "      <td>month_1</td>\n",
       "      <td>/glade/campaign/cgd/cesm/CESM2-LE/timeseries/a...</td>\n",
       "      <td>historical</td>\n",
       "      <td>cmip6</td>\n",
       "      <td>1001.001</td>\n",
       "      <td>1001</td>\n",
       "      <td>CESM2_historical_r1i1001p1f1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>atm</td>\n",
       "      <td>cam.h0</td>\n",
       "      <td>b.e21.BHISTcmip6.f09_g17.LE2-1001.001</td>\n",
       "      <td>r1i1001p1f1</td>\n",
       "      <td>SST</td>\n",
       "      <td>1910-01</td>\n",
       "      <td>1919-12</td>\n",
       "      <td>191001-191912</td>\n",
       "      <td>sea surface temperature</td>\n",
       "      <td>K</td>\n",
       "      <td>1.0</td>\n",
       "      <td>month_1</td>\n",
       "      <td>/glade/campaign/cgd/cesm/CESM2-LE/timeseries/a...</td>\n",
       "      <td>historical</td>\n",
       "      <td>cmip6</td>\n",
       "      <td>1001.001</td>\n",
       "      <td>1001</td>\n",
       "      <td>CESM2_historical_r1i1001p1f1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>atm</td>\n",
       "      <td>cam.h0</td>\n",
       "      <td>b.e21.BHISTcmip6.f09_g17.LE2-1001.001</td>\n",
       "      <td>r1i1001p1f1</td>\n",
       "      <td>SST</td>\n",
       "      <td>1920-01</td>\n",
       "      <td>1929-12</td>\n",
       "      <td>192001-192912</td>\n",
       "      <td>sea surface temperature</td>\n",
       "      <td>K</td>\n",
       "      <td>1.0</td>\n",
       "      <td>month_1</td>\n",
       "      <td>/glade/campaign/cgd/cesm/CESM2-LE/timeseries/a...</td>\n",
       "      <td>historical</td>\n",
       "      <td>cmip6</td>\n",
       "      <td>1001.001</td>\n",
       "      <td>1001</td>\n",
       "      <td>CESM2_historical_r1i1001p1f1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>atm</td>\n",
       "      <td>cam.h0</td>\n",
       "      <td>b.e21.BHISTcmip6.f09_g17.LE2-1001.001</td>\n",
       "      <td>r1i1001p1f1</td>\n",
       "      <td>SST</td>\n",
       "      <td>1930-01</td>\n",
       "      <td>1939-12</td>\n",
       "      <td>193001-193912</td>\n",
       "      <td>sea surface temperature</td>\n",
       "      <td>K</td>\n",
       "      <td>1.0</td>\n",
       "      <td>month_1</td>\n",
       "      <td>/glade/campaign/cgd/cesm/CESM2-LE/timeseries/a...</td>\n",
       "      <td>historical</td>\n",
       "      <td>cmip6</td>\n",
       "      <td>1001.001</td>\n",
       "      <td>1001</td>\n",
       "      <td>CESM2_historical_r1i1001p1f1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>atm</td>\n",
       "      <td>cam.h0</td>\n",
       "      <td>b.e21.BHISTcmip6.f09_g17.LE2-1001.001</td>\n",
       "      <td>r1i1001p1f1</td>\n",
       "      <td>SST</td>\n",
       "      <td>1940-01</td>\n",
       "      <td>1949-12</td>\n",
       "      <td>194001-194912</td>\n",
       "      <td>sea surface temperature</td>\n",
       "      <td>K</td>\n",
       "      <td>1.0</td>\n",
       "      <td>month_1</td>\n",
       "      <td>/glade/campaign/cgd/cesm/CESM2-LE/timeseries/a...</td>\n",
       "      <td>historical</td>\n",
       "      <td>cmip6</td>\n",
       "      <td>1001.001</td>\n",
       "      <td>1001</td>\n",
       "      <td>CESM2_historical_r1i1001p1f1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>atm</td>\n",
       "      <td>cam.h0</td>\n",
       "      <td>b.e21.BHISTcmip6.f09_g17.LE2-1001.001</td>\n",
       "      <td>r1i1001p1f1</td>\n",
       "      <td>SST</td>\n",
       "      <td>1950-01</td>\n",
       "      <td>1959-12</td>\n",
       "      <td>195001-195912</td>\n",
       "      <td>sea surface temperature</td>\n",
       "      <td>K</td>\n",
       "      <td>1.0</td>\n",
       "      <td>month_1</td>\n",
       "      <td>/glade/campaign/cgd/cesm/CESM2-LE/timeseries/a...</td>\n",
       "      <td>historical</td>\n",
       "      <td>cmip6</td>\n",
       "      <td>1001.001</td>\n",
       "      <td>1001</td>\n",
       "      <td>CESM2_historical_r1i1001p1f1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>atm</td>\n",
       "      <td>cam.h0</td>\n",
       "      <td>b.e21.BHISTcmip6.f09_g17.LE2-1001.001</td>\n",
       "      <td>r1i1001p1f1</td>\n",
       "      <td>SST</td>\n",
       "      <td>1960-01</td>\n",
       "      <td>1969-12</td>\n",
       "      <td>196001-196912</td>\n",
       "      <td>sea surface temperature</td>\n",
       "      <td>K</td>\n",
       "      <td>1.0</td>\n",
       "      <td>month_1</td>\n",
       "      <td>/glade/campaign/cgd/cesm/CESM2-LE/timeseries/a...</td>\n",
       "      <td>historical</td>\n",
       "      <td>cmip6</td>\n",
       "      <td>1001.001</td>\n",
       "      <td>1001</td>\n",
       "      <td>CESM2_historical_r1i1001p1f1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>atm</td>\n",
       "      <td>cam.h0</td>\n",
       "      <td>b.e21.BHISTcmip6.f09_g17.LE2-1001.001</td>\n",
       "      <td>r1i1001p1f1</td>\n",
       "      <td>SST</td>\n",
       "      <td>1970-01</td>\n",
       "      <td>1979-12</td>\n",
       "      <td>197001-197912</td>\n",
       "      <td>sea surface temperature</td>\n",
       "      <td>K</td>\n",
       "      <td>1.0</td>\n",
       "      <td>month_1</td>\n",
       "      <td>/glade/campaign/cgd/cesm/CESM2-LE/timeseries/a...</td>\n",
       "      <td>historical</td>\n",
       "      <td>cmip6</td>\n",
       "      <td>1001.001</td>\n",
       "      <td>1001</td>\n",
       "      <td>CESM2_historical_r1i1001p1f1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>atm</td>\n",
       "      <td>cam.h0</td>\n",
       "      <td>b.e21.BHISTcmip6.f09_g17.LE2-1001.001</td>\n",
       "      <td>r1i1001p1f1</td>\n",
       "      <td>SST</td>\n",
       "      <td>1980-01</td>\n",
       "      <td>1989-12</td>\n",
       "      <td>198001-198912</td>\n",
       "      <td>sea surface temperature</td>\n",
       "      <td>K</td>\n",
       "      <td>1.0</td>\n",
       "      <td>month_1</td>\n",
       "      <td>/glade/campaign/cgd/cesm/CESM2-LE/timeseries/a...</td>\n",
       "      <td>historical</td>\n",
       "      <td>cmip6</td>\n",
       "      <td>1001.001</td>\n",
       "      <td>1001</td>\n",
       "      <td>CESM2_historical_r1i1001p1f1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>atm</td>\n",
       "      <td>cam.h0</td>\n",
       "      <td>b.e21.BHISTcmip6.f09_g17.LE2-1001.001</td>\n",
       "      <td>r1i1001p1f1</td>\n",
       "      <td>SST</td>\n",
       "      <td>1990-01</td>\n",
       "      <td>1999-12</td>\n",
       "      <td>199001-199912</td>\n",
       "      <td>sea surface temperature</td>\n",
       "      <td>K</td>\n",
       "      <td>1.0</td>\n",
       "      <td>month_1</td>\n",
       "      <td>/glade/campaign/cgd/cesm/CESM2-LE/timeseries/a...</td>\n",
       "      <td>historical</td>\n",
       "      <td>cmip6</td>\n",
       "      <td>1001.001</td>\n",
       "      <td>1001</td>\n",
       "      <td>CESM2_historical_r1i1001p1f1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>atm</td>\n",
       "      <td>cam.h0</td>\n",
       "      <td>b.e21.BHISTcmip6.f09_g17.LE2-1001.001</td>\n",
       "      <td>r1i1001p1f1</td>\n",
       "      <td>SST</td>\n",
       "      <td>2000-01</td>\n",
       "      <td>2009-12</td>\n",
       "      <td>200001-200912</td>\n",
       "      <td>sea surface temperature</td>\n",
       "      <td>K</td>\n",
       "      <td>1.0</td>\n",
       "      <td>month_1</td>\n",
       "      <td>/glade/campaign/cgd/cesm/CESM2-LE/timeseries/a...</td>\n",
       "      <td>historical</td>\n",
       "      <td>cmip6</td>\n",
       "      <td>1001.001</td>\n",
       "      <td>1001</td>\n",
       "      <td>CESM2_historical_r1i1001p1f1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>atm</td>\n",
       "      <td>cam.h0</td>\n",
       "      <td>b.e21.BHISTcmip6.f09_g17.LE2-1001.001</td>\n",
       "      <td>r1i1001p1f1</td>\n",
       "      <td>SST</td>\n",
       "      <td>2010-01</td>\n",
       "      <td>2014-12</td>\n",
       "      <td>201001-201412</td>\n",
       "      <td>sea surface temperature</td>\n",
       "      <td>K</td>\n",
       "      <td>1.0</td>\n",
       "      <td>month_1</td>\n",
       "      <td>/glade/campaign/cgd/cesm/CESM2-LE/timeseries/a...</td>\n",
       "      <td>historical</td>\n",
       "      <td>cmip6</td>\n",
       "      <td>1001.001</td>\n",
       "      <td>1001</td>\n",
       "      <td>CESM2_historical_r1i1001p1f1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   component  stream                                   case    member_id  \\\n",
       "0        atm  cam.h0  b.e21.BHISTcmip6.f09_g17.LE2-1001.001  r1i1001p1f1   \n",
       "1        atm  cam.h0  b.e21.BHISTcmip6.f09_g17.LE2-1001.001  r1i1001p1f1   \n",
       "2        atm  cam.h0  b.e21.BHISTcmip6.f09_g17.LE2-1001.001  r1i1001p1f1   \n",
       "3        atm  cam.h0  b.e21.BHISTcmip6.f09_g17.LE2-1001.001  r1i1001p1f1   \n",
       "4        atm  cam.h0  b.e21.BHISTcmip6.f09_g17.LE2-1001.001  r1i1001p1f1   \n",
       "5        atm  cam.h0  b.e21.BHISTcmip6.f09_g17.LE2-1001.001  r1i1001p1f1   \n",
       "6        atm  cam.h0  b.e21.BHISTcmip6.f09_g17.LE2-1001.001  r1i1001p1f1   \n",
       "7        atm  cam.h0  b.e21.BHISTcmip6.f09_g17.LE2-1001.001  r1i1001p1f1   \n",
       "8        atm  cam.h0  b.e21.BHISTcmip6.f09_g17.LE2-1001.001  r1i1001p1f1   \n",
       "9        atm  cam.h0  b.e21.BHISTcmip6.f09_g17.LE2-1001.001  r1i1001p1f1   \n",
       "10       atm  cam.h0  b.e21.BHISTcmip6.f09_g17.LE2-1001.001  r1i1001p1f1   \n",
       "11       atm  cam.h0  b.e21.BHISTcmip6.f09_g17.LE2-1001.001  r1i1001p1f1   \n",
       "12       atm  cam.h0  b.e21.BHISTcmip6.f09_g17.LE2-1001.001  r1i1001p1f1   \n",
       "13       atm  cam.h0  b.e21.BHISTcmip6.f09_g17.LE2-1001.001  r1i1001p1f1   \n",
       "14       atm  cam.h0  b.e21.BHISTcmip6.f09_g17.LE2-1001.001  r1i1001p1f1   \n",
       "15       atm  cam.h0  b.e21.BHISTcmip6.f09_g17.LE2-1001.001  r1i1001p1f1   \n",
       "16       atm  cam.h0  b.e21.BHISTcmip6.f09_g17.LE2-1001.001  r1i1001p1f1   \n",
       "\n",
       "   variable start_time end_time     time_range                long_name units  \\\n",
       "0       SST    1850-01  1859-12  185001-185912  sea surface temperature     K   \n",
       "1       SST    1860-01  1869-12  186001-186912  sea surface temperature     K   \n",
       "2       SST    1870-01  1879-12  187001-187912  sea surface temperature     K   \n",
       "3       SST    1880-01  1889-12  188001-188912  sea surface temperature     K   \n",
       "4       SST    1890-01  1899-12  189001-189912  sea surface temperature     K   \n",
       "5       SST    1900-01  1909-12  190001-190912  sea surface temperature     K   \n",
       "6       SST    1910-01  1919-12  191001-191912  sea surface temperature     K   \n",
       "7       SST    1920-01  1929-12  192001-192912  sea surface temperature     K   \n",
       "8       SST    1930-01  1939-12  193001-193912  sea surface temperature     K   \n",
       "9       SST    1940-01  1949-12  194001-194912  sea surface temperature     K   \n",
       "10      SST    1950-01  1959-12  195001-195912  sea surface temperature     K   \n",
       "11      SST    1960-01  1969-12  196001-196912  sea surface temperature     K   \n",
       "12      SST    1970-01  1979-12  197001-197912  sea surface temperature     K   \n",
       "13      SST    1980-01  1989-12  198001-198912  sea surface temperature     K   \n",
       "14      SST    1990-01  1999-12  199001-199912  sea surface temperature     K   \n",
       "15      SST    2000-01  2009-12  200001-200912  sea surface temperature     K   \n",
       "16      SST    2010-01  2014-12  201001-201412  sea surface temperature     K   \n",
       "\n",
       "    vertical_levels frequency  \\\n",
       "0               1.0   month_1   \n",
       "1               1.0   month_1   \n",
       "2               1.0   month_1   \n",
       "3               1.0   month_1   \n",
       "4               1.0   month_1   \n",
       "5               1.0   month_1   \n",
       "6               1.0   month_1   \n",
       "7               1.0   month_1   \n",
       "8               1.0   month_1   \n",
       "9               1.0   month_1   \n",
       "10              1.0   month_1   \n",
       "11              1.0   month_1   \n",
       "12              1.0   month_1   \n",
       "13              1.0   month_1   \n",
       "14              1.0   month_1   \n",
       "15              1.0   month_1   \n",
       "16              1.0   month_1   \n",
       "\n",
       "                                                 path  experiment  \\\n",
       "0   /glade/campaign/cgd/cesm/CESM2-LE/timeseries/a...  historical   \n",
       "1   /glade/campaign/cgd/cesm/CESM2-LE/timeseries/a...  historical   \n",
       "2   /glade/campaign/cgd/cesm/CESM2-LE/timeseries/a...  historical   \n",
       "3   /glade/campaign/cgd/cesm/CESM2-LE/timeseries/a...  historical   \n",
       "4   /glade/campaign/cgd/cesm/CESM2-LE/timeseries/a...  historical   \n",
       "5   /glade/campaign/cgd/cesm/CESM2-LE/timeseries/a...  historical   \n",
       "6   /glade/campaign/cgd/cesm/CESM2-LE/timeseries/a...  historical   \n",
       "7   /glade/campaign/cgd/cesm/CESM2-LE/timeseries/a...  historical   \n",
       "8   /glade/campaign/cgd/cesm/CESM2-LE/timeseries/a...  historical   \n",
       "9   /glade/campaign/cgd/cesm/CESM2-LE/timeseries/a...  historical   \n",
       "10  /glade/campaign/cgd/cesm/CESM2-LE/timeseries/a...  historical   \n",
       "11  /glade/campaign/cgd/cesm/CESM2-LE/timeseries/a...  historical   \n",
       "12  /glade/campaign/cgd/cesm/CESM2-LE/timeseries/a...  historical   \n",
       "13  /glade/campaign/cgd/cesm/CESM2-LE/timeseries/a...  historical   \n",
       "14  /glade/campaign/cgd/cesm/CESM2-LE/timeseries/a...  historical   \n",
       "15  /glade/campaign/cgd/cesm/CESM2-LE/timeseries/a...  historical   \n",
       "16  /glade/campaign/cgd/cesm/CESM2-LE/timeseries/a...  historical   \n",
       "\n",
       "   forcing_variant  cesm_member_id  control_branch_year  \\\n",
       "0            cmip6        1001.001                 1001   \n",
       "1            cmip6        1001.001                 1001   \n",
       "2            cmip6        1001.001                 1001   \n",
       "3            cmip6        1001.001                 1001   \n",
       "4            cmip6        1001.001                 1001   \n",
       "5            cmip6        1001.001                 1001   \n",
       "6            cmip6        1001.001                 1001   \n",
       "7            cmip6        1001.001                 1001   \n",
       "8            cmip6        1001.001                 1001   \n",
       "9            cmip6        1001.001                 1001   \n",
       "10           cmip6        1001.001                 1001   \n",
       "11           cmip6        1001.001                 1001   \n",
       "12           cmip6        1001.001                 1001   \n",
       "13           cmip6        1001.001                 1001   \n",
       "14           cmip6        1001.001                 1001   \n",
       "15           cmip6        1001.001                 1001   \n",
       "16           cmip6        1001.001                 1001   \n",
       "\n",
       "              cmip_experiment_id  \n",
       "0   CESM2_historical_r1i1001p1f1  \n",
       "1   CESM2_historical_r1i1001p1f1  \n",
       "2   CESM2_historical_r1i1001p1f1  \n",
       "3   CESM2_historical_r1i1001p1f1  \n",
       "4   CESM2_historical_r1i1001p1f1  \n",
       "5   CESM2_historical_r1i1001p1f1  \n",
       "6   CESM2_historical_r1i1001p1f1  \n",
       "7   CESM2_historical_r1i1001p1f1  \n",
       "8   CESM2_historical_r1i1001p1f1  \n",
       "9   CESM2_historical_r1i1001p1f1  \n",
       "10  CESM2_historical_r1i1001p1f1  \n",
       "11  CESM2_historical_r1i1001p1f1  \n",
       "12  CESM2_historical_r1i1001p1f1  \n",
       "13  CESM2_historical_r1i1001p1f1  \n",
       "14  CESM2_historical_r1i1001p1f1  \n",
       "15  CESM2_historical_r1i1001p1f1  \n",
       "16  CESM2_historical_r1i1001p1f1  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "subset = coll_orig.search(component='atm',variable='SST',frequency='month_1',experiment='historical',member_id= 'r1i1001p1f1')\n",
    "subset.df\n",
    "## control"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "733a55af-efc3-4db2-8004-a7afb515d8fb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "--> The keys in the returned dictionary of datasets are constructed as follows:\n",
      "\t'component.experiment.stream.forcing_variant.variable'\n"
     ]
    },
    {
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     "text": [
      "\n",
      "Dataset dictionary keys:\n",
      " dict_keys(['atm.historical.cam.h0.cmip6.SST'])\n"
     ]
    }
   ],
   "source": [
    "# Load catalog entries for subset into a dictionary of xarray datasets\n",
    "dsets = subset.to_dataset_dict(zarr_kwargs={\"consolidated\": True}, storage_options={\"anon\": True})\n",
    "print(f\"\\nDataset dictionary keys:\\n {dsets.keys()}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "bd91d3c0-8127-4eec-823b-0da0d8db4174",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'atm.historical.cam.h0.cmip6.SST': <xarray.Dataset>\n",
      "Dimensions:       (lat: 192, zlon: 1, nbnd: 2, lon: 288, lev: 32, ilev: 33, time: 1980, member_id: 1)\n",
      "Coordinates:\n",
      "  * lat           (lat) float64 -90.0 -89.06 -88.12 -87.17 ... 88.12 89.06 90.0\n",
      "  * zlon          (zlon) float64 0.0\n",
      "  * lon           (lon) float64 0.0 1.25 2.5 3.75 ... 355.0 356.2 357.5 358.8\n",
      "  * lev           (lev) float64 3.643 7.595 14.36 24.61 ... 957.5 976.3 992.6\n",
      "  * ilev          (ilev) float64 2.255 5.032 10.16 18.56 ... 967.5 985.1 1e+03\n",
      "  * time          (time) object 1850-02-01 00:00:00 ... 2015-01-01 00:00:00\n",
      "  * member_id     (member_id) <U11 'r1i1001p1f1'\n",
      "Dimensions without coordinates: nbnd\n",
      "Data variables: (12/27)\n",
      "    zlon_bnds     (zlon, nbnd) float64 dask.array<chunksize=(1, 2), meta=np.ndarray>\n",
      "    gw            (lat) float64 dask.array<chunksize=(192,), meta=np.ndarray>\n",
      "    hyam          (lev) float64 dask.array<chunksize=(32,), meta=np.ndarray>\n",
      "    hybm          (lev) float64 dask.array<chunksize=(32,), meta=np.ndarray>\n",
      "    P0            float64 ...\n",
      "    hyai          (ilev) float64 dask.array<chunksize=(33,), meta=np.ndarray>\n",
      "    ...            ...\n",
      "    n2ovmr        (time) float64 dask.array<chunksize=(120,), meta=np.ndarray>\n",
      "    f11vmr        (time) float64 dask.array<chunksize=(120,), meta=np.ndarray>\n",
      "    f12vmr        (time) float64 dask.array<chunksize=(120,), meta=np.ndarray>\n",
      "    sol_tsi       (time) float64 dask.array<chunksize=(120,), meta=np.ndarray>\n",
      "    nsteph        (time) float64 dask.array<chunksize=(120,), meta=np.ndarray>\n",
      "    SST           (member_id, time, lat, lon) float32 dask.array<chunksize=(1, 120, 192, 288), meta=np.ndarray>\n",
      "Attributes:\n",
      "    intake_esm_varname:      ['SST']\n",
      "    case:                    b.e21.BHISTcmip6.f09_g17.LE2-1001.001\n",
      "    initial_file:            b.e21.B1850.f09_g17.CMIP6-piControl.001.cam.i.10...\n",
      "    source:                  CAM\n",
      "    time_period_freq:        month_1\n",
      "    topography_file:         /mnt/lustre/share/CESM/cesm_input/atm/cam/topo/f...\n",
      "    logname:                 sunseon\n",
      "    Conventions:             CF-1.0\n",
      "    model_doi_url:           https://doi.org/10.5065/D67H1H0V\n",
      "    intake_esm_dataset_key:  atm.historical.cam.h0.cmip6.SST}\n"
     ]
    }
   ],
   "source": [
    "print(dsets)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "e24d3d3e-12b3-45d3-ad69-d686ebeeb105",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<xarray.DataArray 'SST' (time: 1980, lat: 192, lon: 288)>\n",
      "dask.array<getitem, shape=(1980, 192, 288), dtype=float32, chunksize=(120, 192, 288), chunktype=numpy.ndarray>\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  <U11 'r1i1001p1f1'\n",
      "Attributes:\n",
      "    units:         K\n",
      "    long_name:     sea surface temperature\n",
      "    cell_methods:  time: mean\n"
     ]
    }
   ],
   "source": [
    "ds = dsets['atm.historical.cam.h0.cmip6.SST']\n",
    "SST = ds.SST.isel(member_id=0)\n",
    "print(SST)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "c1cfe842-545d-40b1-9782-0c070c5006ab",
   "metadata": {},
   "outputs": [
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       "  --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.DataArray &#x27;SST&#x27; (time: 1980, lat: 192, lon: 288)&gt;\n",
       "array([[[  0.     ,   0.     ,   0.     , ...,   0.     ,   0.     ,\n",
       "           0.     ],\n",
       "        [  0.     ,   0.     ,   0.     , ...,   0.     ,   0.     ,\n",
       "           0.     ],\n",
       "        [  0.     ,   0.     ,   0.     , ...,   0.     ,   0.     ,\n",
       "           0.     ],\n",
       "        ...,\n",
       "        [271.33502, 271.335  , 271.33496, ..., 271.33514, 271.3351 ,\n",
       "         271.33508],\n",
       "        [271.3414 , 271.3414 , 271.34137, ..., 271.34146, 271.34143,\n",
       "         271.3414 ],\n",
       "        [271.34573, 271.34573, 271.34573, ..., 271.34576, 271.34573,\n",
       "         271.34573]],\n",
       "\n",
       "       [[  0.     ,   0.     ,   0.     , ...,   0.     ,   0.     ,\n",
       "           0.     ],\n",
       "        [  0.     ,   0.     ,   0.     , ...,   0.     ,   0.     ,\n",
       "           0.     ],\n",
       "        [  0.     ,   0.     ,   0.     , ...,   0.     ,   0.     ,\n",
       "           0.     ],\n",
       "...\n",
       "        [271.39133, 271.39127, 271.39124, ..., 271.39154, 271.39145,\n",
       "         271.39142],\n",
       "        [271.3968 , 271.39676, 271.39673, ..., 271.39688, 271.39685,\n",
       "         271.39682],\n",
       "        [271.40018, 271.40015, 271.40015, ..., 271.40018, 271.40018,\n",
       "         271.40018]],\n",
       "\n",
       "       [[  0.     ,   0.     ,   0.     , ...,   0.     ,   0.     ,\n",
       "           0.     ],\n",
       "        [  0.     ,   0.     ,   0.     , ...,   0.     ,   0.     ,\n",
       "           0.     ],\n",
       "        [  0.     ,   0.     ,   0.     , ...,   0.     ,   0.     ,\n",
       "           0.     ],\n",
       "        ...,\n",
       "        [271.3778 , 271.37772, 271.37762, ..., 271.37802, 271.37796,\n",
       "         271.3779 ],\n",
       "        [271.38275, 271.38272, 271.38266, ..., 271.38287, 271.38284,\n",
       "         271.38278],\n",
       "        [271.38614, 271.38614, 271.38614, ..., 271.38617, 271.38617,\n",
       "         271.38617]]], dtype=float32)\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  &lt;U11 &#x27;r1i1001p1f1&#x27;\n",
       "Attributes:\n",
       "    units:         K\n",
       "    long_name:     sea surface temperature\n",
       "    cell_methods:  time: mean</pre><div class='xr-wrap' hidden><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'SST'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 1980</li><li><span class='xr-has-index'>lat</span>: 192</li><li><span class='xr-has-index'>lon</span>: 288</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-246a95b8-acc5-4a28-9b7c-22637808c3c7' class='xr-array-in' type='checkbox' checked><label for='section-246a95b8-acc5-4a28-9b7c-22637808c3c7' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... 271.4 271.4 271.4 271.4 271.4 271.4</span></div><div class='xr-array-data'><pre>array([[[  0.     ,   0.     ,   0.     , ...,   0.     ,   0.     ,\n",
       "           0.     ],\n",
       "        [  0.     ,   0.     ,   0.     , ...,   0.     ,   0.     ,\n",
       "           0.     ],\n",
       "        [  0.     ,   0.     ,   0.     , ...,   0.     ,   0.     ,\n",
       "           0.     ],\n",
       "        ...,\n",
       "        [271.33502, 271.335  , 271.33496, ..., 271.33514, 271.3351 ,\n",
       "         271.33508],\n",
       "        [271.3414 , 271.3414 , 271.34137, ..., 271.34146, 271.34143,\n",
       "         271.3414 ],\n",
       "        [271.34573, 271.34573, 271.34573, ..., 271.34576, 271.34573,\n",
       "         271.34573]],\n",
       "\n",
       "       [[  0.     ,   0.     ,   0.     , ...,   0.     ,   0.     ,\n",
       "           0.     ],\n",
       "        [  0.     ,   0.     ,   0.     , ...,   0.     ,   0.     ,\n",
       "           0.     ],\n",
       "        [  0.     ,   0.     ,   0.     , ...,   0.     ,   0.     ,\n",
       "           0.     ],\n",
       "...\n",
       "        [271.39133, 271.39127, 271.39124, ..., 271.39154, 271.39145,\n",
       "         271.39142],\n",
       "        [271.3968 , 271.39676, 271.39673, ..., 271.39688, 271.39685,\n",
       "         271.39682],\n",
       "        [271.40018, 271.40015, 271.40015, ..., 271.40018, 271.40018,\n",
       "         271.40018]],\n",
       "\n",
       "       [[  0.     ,   0.     ,   0.     , ...,   0.     ,   0.     ,\n",
       "           0.     ],\n",
       "        [  0.     ,   0.     ,   0.     , ...,   0.     ,   0.     ,\n",
       "           0.     ],\n",
       "        [  0.     ,   0.     ,   0.     , ...,   0.     ,   0.     ,\n",
       "           0.     ],\n",
       "        ...,\n",
       "        [271.3778 , 271.37772, 271.37762, ..., 271.37802, 271.37796,\n",
       "         271.3779 ],\n",
       "        [271.38275, 271.38272, 271.38266, ..., 271.38287, 271.38284,\n",
       "         271.38278],\n",
       "        [271.38614, 271.38614, 271.38614, ..., 271.38617, 271.38617,\n",
       "         271.38617]]], dtype=float32)</pre></div></div></li><li class='xr-section-item'><input id='section-36d4c0f9-fee6-44cd-8017-eb7da10ec9af' class='xr-section-summary-in' type='checkbox'  checked><label for='section-36d4c0f9-fee6-44cd-8017-eb7da10ec9af' 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-d9d780b1-1d5e-4a40-a197-e8fbcb51751f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d9d780b1-1d5e-4a40-a197-e8fbcb51751f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-20a8e48f-094d-4db6-897f-44f6a187db1d' class='xr-var-data-in' type='checkbox'><label for='data-20a8e48f-094d-4db6-897f-44f6a187db1d' 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-cb4970de-7ddd-4a3c-8a27-7b4c036f867e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-cb4970de-7ddd-4a3c-8a27-7b4c036f867e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b76594cd-779e-4708-a7ea-0ca30d012617' class='xr-var-data-in' type='checkbox'><label for='data-b76594cd-779e-4708-a7ea-0ca30d012617' 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-296b4726-2db0-4a38-8f85-85803ab68d5c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-296b4726-2db0-4a38-8f85-85803ab68d5c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ec9bdc72-1886-40ec-8bb0-cbddfa029c22' class='xr-var-data-in' type='checkbox'><label for='data-ec9bdc72-1886-40ec-8bb0-cbddfa029c22' 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>member_id</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>&lt;U11</div><div class='xr-var-preview xr-preview'>&#x27;r1i1001p1f1&#x27;</div><input id='attrs-23ce3e45-e2ba-4443-ab54-7366db3db9d1' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-23ce3e45-e2ba-4443-ab54-7366db3db9d1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-87b08ca0-771e-484d-a24c-1c9c36ea1613' class='xr-var-data-in' type='checkbox'><label for='data-87b08ca0-771e-484d-a24c-1c9c36ea1613' 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;r1i1001p1f1&#x27;, dtype=&#x27;&lt;U11&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-4d6e72f7-33d3-4e7d-8b9e-e09a30308284' class='xr-section-summary-in' type='checkbox'  checked><label for='section-4d6e72f7-33d3-4e7d-8b9e-e09a30308284' class='xr-section-summary' >Attributes: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><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></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.DataArray 'SST' (time: 1980, lat: 192, lon: 288)>\n",
       "array([[[  0.     ,   0.     ,   0.     , ...,   0.     ,   0.     ,\n",
       "           0.     ],\n",
       "        [  0.     ,   0.     ,   0.     , ...,   0.     ,   0.     ,\n",
       "           0.     ],\n",
       "        [  0.     ,   0.     ,   0.     , ...,   0.     ,   0.     ,\n",
       "           0.     ],\n",
       "        ...,\n",
       "        [271.33502, 271.335  , 271.33496, ..., 271.33514, 271.3351 ,\n",
       "         271.33508],\n",
       "        [271.3414 , 271.3414 , 271.34137, ..., 271.34146, 271.34143,\n",
       "         271.3414 ],\n",
       "        [271.34573, 271.34573, 271.34573, ..., 271.34576, 271.34573,\n",
       "         271.34573]],\n",
       "\n",
       "       [[  0.     ,   0.     ,   0.     , ...,   0.     ,   0.     ,\n",
       "           0.     ],\n",
       "        [  0.     ,   0.     ,   0.     , ...,   0.     ,   0.     ,\n",
       "           0.     ],\n",
       "        [  0.     ,   0.     ,   0.     , ...,   0.     ,   0.     ,\n",
       "           0.     ],\n",
       "...\n",
       "        [271.39133, 271.39127, 271.39124, ..., 271.39154, 271.39145,\n",
       "         271.39142],\n",
       "        [271.3968 , 271.39676, 271.39673, ..., 271.39688, 271.39685,\n",
       "         271.39682],\n",
       "        [271.40018, 271.40015, 271.40015, ..., 271.40018, 271.40018,\n",
       "         271.40018]],\n",
       "\n",
       "       [[  0.     ,   0.     ,   0.     , ...,   0.     ,   0.     ,\n",
       "           0.     ],\n",
       "        [  0.     ,   0.     ,   0.     , ...,   0.     ,   0.     ,\n",
       "           0.     ],\n",
       "        [  0.     ,   0.     ,   0.     , ...,   0.     ,   0.     ,\n",
       "           0.     ],\n",
       "        ...,\n",
       "        [271.3778 , 271.37772, 271.37762, ..., 271.37802, 271.37796,\n",
       "         271.3779 ],\n",
       "        [271.38275, 271.38272, 271.38266, ..., 271.38287, 271.38284,\n",
       "         271.38278],\n",
       "        [271.38614, 271.38614, 271.38614, ..., 271.38617, 271.38617,\n",
       "         271.38617]]], dtype=float32)\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  <U11 'r1i1001p1f1'\n",
       "Attributes:\n",
       "    units:         K\n",
       "    long_name:     sea surface temperature\n",
       "    cell_methods:  time: mean"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "SST.load()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "f7202a08-375d-43fc-bf68-bb63b794a75d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.contour.QuadContourSet at 0x2b5c3aa331c0>"
      ]
     },
     "execution_count": 10,
     "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": [
    "plt.contourf(SST[0,:,:])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "0946f276-865e-4363-9b4b-54362268a338",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'xarray.core.dataarray.DataArray'>\n"
     ]
    }
   ],
   "source": [
    "SST_land = SST[0,:,:]\n",
    "print(type(SST_land))\n",
    "np.save('SST_land', SST_land)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a0fa1665-f14b-4e85-b3f2-de268d211395",
   "metadata": {},
   "source": [
    "## Linearly detrend SST \n",
    "#### Last 40 years (satellite period)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "82d7ec09-467c-43d6-8c1b-862032e3dfa2",
   "metadata": {},
   "outputs": [],
   "source": [
    "### Decompose SST maps into mean, trend, annual, and semi-annual harmonics\n",
    "## Use least-squares regression and solve for model coefficients\n",
    "dyr = SST.time.dt.year + (SST.time.dt.month-0.5)/12\n",
    "dyr = dyr[-481:] # can remove this line if you want to detrend across the entire period"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "ee51151c-8696-470d-ad3d-0ae2b12082f6",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Our 6 coefficient model is composed of the mean, trend, annual sine and cosine harmonics, & semi-annual sine and cosine harmonics\n",
    "model = np.array([np.ones(len(dyr))] + [dyr-np.mean(dyr)] + [np.sin(2*np.pi*dyr)] + [np.cos(2*np.pi*dyr)] + [np.sin(4*np.pi*dyr)] + [np.cos(4*np.pi*dyr)])\n",
    "\n",
    "# Take the pseudo-inverse of model to 'solve' least-squares problem\n",
    "pmodel = np.linalg.pinv(model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "40b6388f-416f-45a7-93a2-aed6e7f5d516",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Convert model and pmodel to xaray DataArray\n",
    "model_da = xr.DataArray(model.T, dims=['time','coeff'], coords={'time':SST.time.values[-481:], 'coeff':np.arange(1,7,1)}) \n",
    "pmodel_da = xr.DataArray(pmodel.T, dims=['coeff','time'], coords={'coeff':np.arange(1,7,1), 'time':SST.time.values[-481:]})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "4333d3fc-0437-44aa-b5de-4f9c69031d78",
   "metadata": {},
   "outputs": [],
   "source": [
    "# resulting coefficients of the model\n",
    "sst_mod = xr.DataArray(pmodel_da.dot(SST), dims=['coeff','lat','lon'], coords={'coeff':np.arange(1,7,1), 'lat':SST.lat.values, 'lon':SST.lon.values})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "d5746dd0-2a03-4078-9d3f-1d143b24b740",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Construct mean, trend, and seasonal cycle\n",
    "mean = model_da[:,0].dot(sst_mod[0,:,:])\n",
    "trend = model_da[:,1].dot(sst_mod[1,:,:])\n",
    "seas = model_da[:,2:].dot(sst_mod[2:,:,:])\n",
    "\n",
    "# compute anomalies by removing all  the model coefficients \n",
    "ssta_notrend = SST-model_da.dot(sst_mod) #this is anomalies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "f0aeb698-84cd-41fd-84f0-80e7e27197e8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(6, 192, 288)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(481, 192, 288)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(481, 192, 288)\n"
     ]
    }
   ],
   "source": [
    "print(pmodel_da.dot(SST).shape)\n",
    "ssta_notrend.shape\n",
    "print(model_da.dot(sst_mod).shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "4991d1c7-5e4c-42b9-a105-5aee85a2c059",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.QuadMesh at 0x2b46b2f744c0>"
      ]
     },
     "execution_count": 15,
     "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": [
    "mean[400,:,:].plot(levels=[262.5,265,267.2,270,272.5,275,277.5,280,282.5,285,287.5,290,292.5,295,297.5,300,302.5,305,307.5],cmap='Reds')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "3e8de815-e2fe-4ed1-8a20-228a2404db9f",
   "metadata": {},
   "outputs": [],
   "source": [
    "detrended = ssta_notrend"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "15422bb9-2106-44bb-810f-b677b1ccb5f2",
   "metadata": {},
   "outputs": [],
   "source": [
    "# detrended.to_netcdf('detrended_ens0_last40years.nc')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c4c0fd2a-3b71-4e6b-8bb9-307321556621",
   "metadata": {},
   "source": [
    "## Thresholding and Features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "1b48b218-c4eb-4532-9726-6cd1f041498d",
   "metadata": {},
   "outputs": [],
   "source": [
    "###### THRESHOLD and FEATURES\n",
    "if detrended.chunks:\n",
    "    detrended = detrended.chunk({'time': -1})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "ec7024c7-c6a8-423c-8cd5-071b7ad19f99",
   "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": 20,
   "id": "e08dbec3-922c-456e-866b-11c688bb512d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 727 µs, sys: 0 ns, total: 727 µs\n",
      "Wall time: 736 µs\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "features_ssta= features_ssta[:,:,:].load()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "fbe967d0-d4d3-4aa6-bae0-b636a8cab8e2",
   "metadata": {},
   "outputs": [],
   "source": [
    "##### MASKING\n",
    "full_mask_land = features_ssta\n",
    "full_masked = full_mask_land.where(full_mask_land != 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "85016d67-7da9-4d5b-abc1-afbe73e4b3f8",
   "metadata": {},
   "outputs": [],
   "source": [
    "binary_out_afterlandmask=np.isfinite(full_masked)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "4f974072-64c2-4a00-9a42-f29c02fbb375",
   "metadata": {},
   "outputs": [],
   "source": [
    "newmask_1 = ds.SST.isel(member_id=0)[-481:]\n",
    "newmask = newmask_1.where(newmask_1 != 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "2c0b7970-70fa-49d8-9f53-558b820e0411",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(481, 192, 288)\n"
     ]
    }
   ],
   "source": [
    "full_mask_land = features_ssta\n",
    "full_masked = full_mask_land.where(full_mask_land != 0)\n",
    "print(full_masked.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "446f912a-6cf9-4c3d-b21f-25d7216c026d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.QuadMesh at 0x2b46bbb95520>"
      ]
     },
     "execution_count": 25,
     "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": [
    "full_masked[0,:,:].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') # trying this"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "04981740-d777-425c-87f8-175339408602",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.QuadMesh at 0x2b46bb018160>"
      ]
     },
     "execution_count": 26,
     "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": [
    "full_masked[480,:,:].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') # trying this"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "4073a727-8f36-49fd-8b43-79e0f0c72f29",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(481, 192, 288)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.QuadMesh at 0x2b46b30a5ac0>"
      ]
     },
     "execution_count": 27,
     "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=np.isfinite(full_masked)\n",
    "print(binary_out_afterlandmask.shape)\n",
    "\n",
    "binary_out_afterlandmask[3,:,:].plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "39f59c9e-1e53-4df1-a949-8e78ff620277",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(481, 192, 288)\n",
      "(481, 192, 288)\n"
     ]
    }
   ],
   "source": [
    "print(binary_out_afterlandmask.shape)\n",
    "newmask = np.isfinite(ds.SST[0,:,:,:][-481:]) #------------------------------------------------------------------------- Not really what I'm looking for...\n",
    "print(newmask.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "c1e9ac77-0883-48d9-be4d-76560a7731e2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "minimum area: 314.0\n",
      "inital objects identified \t 5750\n",
      "final objects tracked \t 370\n",
      "CPU times: user 26.5 s, sys: 6.5 s, total: 33 s\n",
      "Wall time: 1min 7s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "Tracker = ocetrac.Tracker(binary_out_afterlandmask[:,:,:], newmask, radius=3, min_size_quartile=0.75, timedim = 'time', xdim = 'lon', ydim='lat', positive=True)\n",
    "blobs = Tracker.track()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "f89ee194-4ed7-41b4-b9ca-475d1fd76dd1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'inital objects identified': 5750,\n",
       " 'final objects tracked': 370,\n",
       " 'radius': 3,\n",
       " 'size quantile threshold': 0.75,\n",
       " 'min area': 314.0,\n",
       " 'percent area reject': 0.2797669497236017,\n",
       " 'percent area accept': 0.7202330502763983}"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "blobs.attrs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "6e904fe5-db6f-4bf5-acae-c2fe2fd0ba36",
   "metadata": {},
   "outputs": [],
   "source": [
    "# blobs.to_netcdf(\"blobs_ens_0_rad4_rel_itself.nc\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "b6a8e09d-94f1-4b97-be36-c0b1a7822261",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 1152x216 with 0 Axes>"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.QuadMesh at 0x2b46ba442670>"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.contour.QuadContourSet at 0x2b46ba40ad60>"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.QuadMesh at 0x2b46ba102f40>"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.contour.QuadContourSet at 0x2b46b9ef2550>"
      ]
     },
     "execution_count": 58,
     "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 = 22\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[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": "code",
   "execution_count": 47,
   "id": "310efbea-6b17-4053-b3e0-befdd33ccd66",
   "metadata": {},
   "outputs": [],
   "source": [
    "mo = Tracker._morphological_operations()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "8c7bc86a-07b9-41ea-b86a-8ea1ac434d07",
   "metadata": {},
   "outputs": [],
   "source": [
    "newname = 'sdnjk'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "8b8c6452-cd18-451d-87f2-0262bc21e120",
   "metadata": {},
   "outputs": [],
   "source": [
    "# blobs.to_netcdf(\"blobs_detrended_last40years.nc\")\n",
    "blobs.to_netcdf('/glade/work/cassiacai/'+newname+'.nc')\n",
    "\n",
    "#detrended.to_netcdf(\"detrended_sample.nc\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2805d7aa-e13a-49ca-949b-baa56abafb3b",
   "metadata": {},
   "source": [
    "# Blob attributes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "a99b35ab-4f05-4e6b-8185-6602d6bef0a2",
   "metadata": {},
   "outputs": [
    {
     "ename": "AttributeError",
     "evalue": "'DatetimeIndex' object has no attribute 'to_datetimeindex'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[0;32m/glade/scratch/cassiacai/ipykernel_178524/2774282741.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mdatetimeindex_blobs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mblobs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindexes\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'time'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto_datetimeindex\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdatetimeindex_blobs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mAttributeError\u001b[0m: 'DatetimeIndex' object has no attribute 'to_datetimeindex'"
     ]
    }
   ],
   "source": [
    "datetimeindex_blobs = blobs.indexes['time'].to_datetimeindex()\n",
    "print(datetimeindex_blobs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "fe7aa2cc-703b-4cd4-889e-db16c7313beb",
   "metadata": {},
   "outputs": [],
   "source": [
    "blobs['time'] = datetimeindex_blobs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "ba458353-0d18-4c2d-8896-224fddffd23d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<xarray.plot.facetgrid.FacetGrid at 0x2b9d7c287b20>"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 936x432 with 9 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "blobs.sel(time=slice('02-01-1850', '06-01-1850')).plot(col='time', col_wrap=4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "195f138f-1ad0-433e-8a1d-c73a3c3c7be8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1, 6, 11)"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<xarray.DataArray 'labels' (time: 1, lat: 6, lon: 11)>\n",
      "array([[[nan, nan,  1.,  1.,  1.,  1.,  1., nan, nan, nan, nan],\n",
      "        [ 1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.,  1., nan],\n",
      "        [ 1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.],\n",
      "        [ 1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.],\n",
      "        [ 1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.],\n",
      "        [nan, nan, nan,  1.,  1.,  1.,  1.,  1.,  1., nan, nan]]])\n",
      "Coordinates:\n",
      "  * lat        (lat) float64 -63.61 -62.67 -61.73 -60.79 -59.84 -58.9\n",
      "  * lon        (lon) float64 217.5 218.8 220.0 221.2 ... 226.2 227.5 228.8 230.0\n",
      "  * time       (time) datetime64[ns] 1850-02-01\n",
      "    member_id  <U11 'r1i1001p1f1'\n",
      "    quantile   float64 0.9\n",
      "    month      (time) int64 2\n",
      "Attributes:\n",
      "    inital objects identified:  37264\n",
      "    final objects tracked:      9105\n",
      "    radius:                     2\n",
      "    size quantile threshold:    0.0\n",
      "    min area:                   1.0\n",
      "    percent area reject:        3.91754142664971e-06\n",
      "    percent area accept:        0.9999960824585733\n"
     ]
    }
   ],
   "source": [
    "id = 1\n",
    "event = blobs.where(blobs==id, drop=True)\n",
    "event.shape\n",
    "print(event)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "d674ec28-e3b7-4e4b-a044-33dc4a13c565",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[2]\n",
      "[3]\n",
      "[array(['1850-02', '1850-03'], dtype='datetime64[M]')]\n"
     ]
    }
   ],
   "source": [
    "duration = [] # number of months\n",
    "duration.append(list(event.shape)[0])\n",
    "print(duration)\n",
    "\n",
    "id_stored = []\n",
    "id_stored.append(int(np.nanmedian(event.values)))\n",
    "print(id_stored)\n",
    "\n",
    "date = []\n",
    "date.append(event.time.values.astype('datetime64[M]'))\n",
    "print(date)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "0e47717b-ebdc-4df6-8e24-451f769a7e6b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[array([1.39851163, 1.4381957 ])]\n",
      "[array([0.80345107, 0.80785385])]\n",
      "[array([1.13404981, 1.15048679])]\n",
      "[277.7930961273208]\n",
      "<xarray.DataArray 'labels' (time: 2, z: 288)>\n",
      "array([[nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,  3.,  3.,  3.,\n",
      "         3.,  3.,  3.,  3.,  3.,  3., nan, nan, nan, nan, nan, nan, nan,\n",
      "        nan, nan, nan, nan, nan, nan,  3.,  3.,  3.,  3.,  3.,  3.,  3.,\n",
      "         3.,  3.,  3.,  3., nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
      "        nan, nan, nan, nan,  3.,  3.,  3.,  3.,  3.,  3.,  3.,  3.,  3.,\n",
      "         3.,  3., nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
      "        nan, nan,  3.,  3.,  3.,  3.,  3.,  3.,  3.,  3.,  3.,  3.,  3.,\n",
      "         3., nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
      "        nan, nan, nan,  3.,  3.,  3.,  3.,  3.,  3.,  3.,  3.,  3.,  3.,\n",
      "         3., nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
      "        nan,  3.,  3.,  3.,  3.,  3.,  3.,  3.,  3.,  3.,  3.,  3.,  3.,\n",
      "         3., nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,  3.,\n",
      "         3.,  3.,  3.,  3.,  3.,  3.,  3.,  3.,  3.,  3.,  3.,  3., nan,\n",
      "        nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,  3.,  3.,  3.,\n",
      "         3.,  3.,  3.,  3.,  3.,  3.,  3.,  3.,  3.,  3., nan, nan, nan,\n",
      "        nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
      "        nan,  3.,  3.,  3.,  3.,  3.,  3., nan, nan, nan, nan, nan, nan,\n",
      "        nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,  3.,\n",
      "         3.,  3.,  3.,  3.,  3., nan, nan, nan, nan, nan, nan, nan, nan,\n",
      "        nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,  3.,\n",
      "...\n",
      "        nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,  3.,\n",
      "         3.,  3.,  3.,  3.,  3.,  3.,  3.,  3.,  3., nan, nan, nan, nan,\n",
      "        nan, nan, nan, nan, nan, nan, nan, nan,  3.,  3.,  3.,  3.,  3.,\n",
      "         3.,  3.,  3.,  3.,  3.,  3.,  3.,  3.,  3.,  3.,  3., nan, nan,\n",
      "        nan, nan, nan, nan, nan,  3.,  3.,  3.,  3.,  3.,  3.,  3.,  3.,\n",
      "         3.,  3.,  3.,  3.,  3.,  3.,  3.,  3.,  3., nan, nan, nan, nan,\n",
      "        nan, nan, nan,  3.,  3.,  3.,  3.,  3.,  3.,  3.,  3.,  3.,  3.,\n",
      "         3.,  3.,  3.,  3.,  3.,  3.,  3., nan, nan, nan, nan, nan, nan,\n",
      "        nan,  3.,  3.,  3.,  3.,  3.,  3.,  3.,  3.,  3.,  3.,  3.,  3.,\n",
      "         3.,  3.,  3.,  3., nan, nan, nan, nan, nan, nan, nan, nan,  3.,\n",
      "         3.,  3.,  3.,  3.,  3.,  3.,  3.,  3.,  3.,  3.,  3.,  3.,  3.,\n",
      "         3.,  3., nan, nan, nan, nan, nan, nan, nan, nan,  3.,  3.,  3.,\n",
      "         3.,  3.,  3.,  3.,  3.,  3.,  3.,  3.,  3.,  3.,  3.,  3., nan,\n",
      "        nan, nan, nan, nan, nan, nan, nan, nan,  3.,  3.,  3.,  3., nan,\n",
      "        nan, nan,  3.,  3.,  3.,  3.,  3.,  3.,  3.,  3., nan, nan, nan,\n",
      "        nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
      "         3.,  3.,  3.,  3.,  3.,  3.,  3., nan, nan, nan, nan, nan, nan,\n",
      "        nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,  3.,\n",
      "         3.,  3.,  3.,  3., nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
      "        nan, nan]])\n",
      "Coordinates:\n",
      "  * time       (time) datetime64[ns] 1850-02-01 1850-03-01\n",
      "    member_id  <U11 'r1i1001p1f1'\n",
      "    quantile   float64 0.9\n",
      "    month      (time) int64 2 3\n",
      "  * z          (z) MultiIndex\n",
      "  - lat        (z) float64 -43.82 -43.82 -43.82 -43.82 ... -33.46 -33.46 -33.46\n",
      "  - lon        (z) float64 326.2 327.5 328.8 330.0 ... 351.2 352.5 353.8 355.0\n",
      "Attributes:\n",
      "    inital objects identified:  37264\n",
      "    final objects tracked:      9105\n",
      "    radius:                     2\n",
      "    size quantile threshold:    0.0\n",
      "    min area:                   1.0\n",
      "    percent area reject:        3.91754142664971e-06\n",
      "    percent area accept:        0.9999960824585733\n"
     ]
    }
   ],
   "source": [
    "event_ssta = detrended.where(event>0,drop=True)\n",
    "intensity_max = []; intensity_min = []; intensity_mean = []; intensity_cumulative = []; coordinates = []\n",
    "intensity_mean.append(event_ssta.mean(('lat','lon')).values)\n",
    "intensity_max.append(event_ssta.max(('lat','lon')).values)\n",
    "intensity_min.append(event_ssta.min(('lat','lon')).values)\n",
    "intensity_cumulative.append(np.nansum(event_ssta))\n",
    "\n",
    "coords = event.stack(z=('lat','lon'))\n",
    "coord_pairs = [(coords.isel(time=t[0]).dropna(dim='z', how='any').z.lat.values, \n",
    "                coords.isel(time=t[0]).dropna(dim='z', how='any').z.lon.values) for t in enumerate(event.time)]\n",
    "coordinates.append(coord_pairs)   \n",
    "print(intensity_max)\n",
    "print(intensity_min)\n",
    "print(intensity_mean)\n",
    "print(intensity_cumulative)\n",
    "print(coords)\n",
    "\n",
    "\n",
    "# event_intensity = detrended.where((ds.time==event.time) & (ds.lat==event.lat) & (ds.lon==event.lon), drop=True).load();\n",
    "# event_intensity = event_intensity.expand_dims(dim='intensity', axis=3)\n",
    "# events_contour = event.fillna(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "4005507b-c6c0-45bb-aa8e-58f05c4c85a8",
   "metadata": {},
   "outputs": [],
   "source": [
    "def _get_intensity_area(event, ssta, mhw):\n",
    "    '''Calculates event intensities and area at each time interval using anomaly data and coordinates \n",
    "    cooresponding to the event.'''\n",
    "    \n",
    "    event_ssta = ssta.where(event>0, drop=True)        \n",
    "    mhw['intensity_mean'].append(event_ssta.mean(('lat','lon')).values)\n",
    "    mhw['intensity_max'].append(event_ssta.max(('lat','lon')).values) \n",
    "    mhw['intensity_min'].append(event_ssta.min(('lat','lon')).values)\n",
    "    mhw['intensity_cumulative'].append(np.nansum(event_ssta))\n",
    "    coords = event.stack(z=('lat','lon'))\n",
    "    coord_pairs = [(coords.isel(time=t[0]).dropna(dim='z', how='any').z.lat.values, \n",
    "                      coords.isel(time=t[0]).dropna(dim='z', how='any').z.lon.values) for t in enumerate(event.time)]\n",
    "\n",
    "    mhw['coords'].append(coord_pairs)\n",
    "\n",
    "    # Calculate weighted cell area assuming 0.25º resolution data and 111 km per degree of latitude\n",
    "    y, x = zip(*coord_pairs)\n",
    "    dlon = [np.cos(y[c]*np.pi/180)*(111*.25) for c in np.arange(0, len(coord_pairs))]\n",
    "    dlat = (111*.25) * np.ones(len(dlon))\n",
    "    cell_area = [np.sum(dlon[c]*dlat[c]) for c in np.arange(0, len(coord_pairs))]\n",
    "    mhw['area'].append(cell_area)\n",
    "\n",
    "    return mhw"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "6607f403-0a31-4222-b58b-a78a1a843e1b",
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'mhw' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m/glade/scratch/cassiacai/ipykernel_178524/3359264710.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mintensity_area\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_get_intensity_area\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mevent\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdetrended\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmhw\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m: name 'mhw' is not defined"
     ]
    }
   ],
   "source": [
    "intensity_area = _get_intensity_area(event, detrended, mhw)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "71f6af72-1efb-4b84-bbf3-351008a7f534",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<xarray.DataArray 'time' ()>\n",
      "array('1850-02-01T00:00:00.000000000', dtype='datetime64[ns]')\n",
      "Coordinates:\n",
      "    time       datetime64[ns] 1850-02-01\n",
      "    member_id  <U11 'r1i1001p1f1'\n",
      "    quantile   float64 0.9\n",
      "    month      int64 2\n"
     ]
    }
   ],
   "source": [
    "print(event.time[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "d36b33d4-ae20-4035-b7d4-a837034b4cc4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "DatetimeIndex(['1850-02-01'], dtype='datetime64[ns]', name='time', freq=None)\n",
      "<xarray.DataArray 'time' (time: 1980)>\n",
      "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)\n",
      "Coordinates:\n",
      "  * time     (time) object 1850-02-01 00:00:00 ... 2015-01-01 00:00:00\n",
      "Attributes:\n",
      "    long_name:  time\n",
      "    bounds:     time_bnds\n"
     ]
    }
   ],
   "source": [
    "datetimeindex_event = event.indexes['time']\n",
    "print(datetimeindex_event)\n",
    "print(ds.time)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "fce581e4-3db6-4582-81ca-3cb4ac76a465",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "DatetimeIndex(['1850-02-01', '1850-03-01', '1850-04-01', '1850-05-01',\n",
      "               '1850-06-01', '1850-07-01', '1850-08-01', '1850-09-01',\n",
      "               '1850-10-01', '1850-11-01',\n",
      "               ...\n",
      "               '2014-04-01', '2014-05-01', '2014-06-01', '2014-07-01',\n",
      "               '2014-08-01', '2014-09-01', '2014-10-01', '2014-11-01',\n",
      "               '2014-12-01', '2015-01-01'],\n",
      "              dtype='datetime64[ns]', length=1980, freq=None)\n"
     ]
    }
   ],
   "source": [
    "datetimeindex_ds_init = ds.indexes['time'].to_datetimeindex()\n",
    "print(datetimeindex_ds_init)\n",
    "ds['time'] = datetimeindex_ds_init"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "69eeb8b9-edae-4560-9225-1dfb22e8ba9c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "DatetimeIndex(['1850-02-01', '1850-03-01', '1850-04-01', '1850-05-01',\n",
      "               '1850-06-01', '1850-07-01', '1850-08-01', '1850-09-01',\n",
      "               '1850-10-01', '1850-11-01',\n",
      "               ...\n",
      "               '2014-04-01', '2014-05-01', '2014-06-01', '2014-07-01',\n",
      "               '2014-08-01', '2014-09-01', '2014-10-01', '2014-11-01',\n",
      "               '2014-12-01', '2015-01-01'],\n",
      "              dtype='datetime64[ns]', length=1980, freq=None)\n"
     ]
    }
   ],
   "source": [
    "datetimeindex_detrended = detrended.indexes['time'].to_datetimeindex()\n",
    "print(datetimeindex_detrended)\n",
    "detrended['time'] = datetimeindex_detrended"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "3bce215c-5f42-4ac9-b5ad-1e9a209bdfb5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<xarray.DataArray 'labels' (time: 1, lat: 6, lon: 11)>\n",
      "array([[[nan, nan,  1.,  1.,  1.,  1.,  1., nan, nan, nan, nan],\n",
      "        [ 1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.,  1., nan],\n",
      "        [ 1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.],\n",
      "        [ 1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.],\n",
      "        [ 1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.],\n",
      "        [nan, nan, nan,  1.,  1.,  1.,  1.,  1.,  1., nan, nan]]])\n",
      "Coordinates:\n",
      "  * lat        (lat) float64 -63.61 -62.67 -61.73 -60.79 -59.84 -58.9\n",
      "  * lon        (lon) float64 217.5 218.8 220.0 221.2 ... 226.2 227.5 228.8 230.0\n",
      "  * time       (time) datetime64[ns] 1850-02-01\n",
      "    member_id  <U11 'r1i1001p1f1'\n",
      "    quantile   float64 0.9\n",
      "    month      (time) int64 2\n",
      "Attributes:\n",
      "    inital objects identified:  37264\n",
      "    final objects tracked:      9105\n",
      "    radius:                     2\n",
      "    size quantile threshold:    0.0\n",
      "    min area:                   1.0\n",
      "    percent area reject:        3.91754142664971e-06\n",
      "    percent area accept:        0.9999960824585733\n"
     ]
    }
   ],
   "source": [
    "print(event.sel(time=slice('02-01-1850', '05-01-1850')))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "bd102d94-8471-4166-8025-04b7fcf10136",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1, 192, 288)\n"
     ]
    }
   ],
   "source": [
    "event_intensity = detrended.where((ds.time==event.time), drop=True).load()\n",
    "print(event_intensity.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "4daafdb8-5f76-4147-8223-db389d61577d",
   "metadata": {},
   "outputs": [],
   "source": [
    "event_intensity = detrended.where((ds.time==event.time) & (ds.lat==event.lat) & (ds.lon==event.lon), drop=True).load();\n",
    "event_intensity = event_intensity.expand_dims(dim='intensity', axis=3)\n",
    "events_contour = event.fillna(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "762d5195-625a-4c88-aec4-818921441fc6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1, 6, 11)\n",
      "(1, 6, 11, 1)\n"
     ]
    }
   ],
   "source": [
    "print(events_contour.shape)\n",
    "print(event_intensity.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "c3053c3a-1d68-47c8-bb70-693382b9b83f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.QuadMesh at 0x2b9d78603ee0>"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.8083315473909352\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "event_intensity.plot()\n",
    "print(np.nanmax(event_intensity))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "d94f28b2-e311-4fe4-b3c4-a9f7e939f2c5",
   "metadata": {},
   "outputs": [],
   "source": [
    "event = blobs\n",
    "event = blobs.where(blobs==id, drop=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "bf3bd03b-cff7-4b64-937c-dcf31a7182cb",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<defs>\n",
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       "<style>/* CSS stylesheet for displaying xarray objects in jupyterlab.\n",
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       "  --xr-font-color2: var(--jp-content-font-color2, rgba(0, 0, 0, 0.54));\n",
       "  --xr-font-color3: var(--jp-content-font-color3, rgba(0, 0, 0, 0.38));\n",
       "  --xr-border-color: var(--jp-border-color2, #e0e0e0);\n",
       "  --xr-disabled-color: var(--jp-layout-color3, #bdbdbd);\n",
       "  --xr-background-color: var(--jp-layout-color0, white);\n",
       "  --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",
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       "\n",
       ".xr-section-item input {\n",
       "  display: none;\n",
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       "\n",
       ".xr-section-item input + label {\n",
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       "\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",
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       "\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",
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       "\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.DataArray &#x27;labels&#x27; (time: 1980, lat: 192, lon: 288)&gt;\n",
       "array([[[ nan,  nan,  nan, ...,  nan,  nan,  nan],\n",
       "        [ nan,  nan,  nan, ...,  nan,  nan,  nan],\n",
       "        [ nan,  nan,  nan, ...,  nan,  nan,  nan],\n",
       "        ...,\n",
       "        [152.,  nan,  nan, ..., 152., 152., 152.],\n",
       "        [152.,  nan,  nan, ..., 152., 152., 152.],\n",
       "        [152.,  nan,  nan, ..., 152., 152., 152.]],\n",
       "\n",
       "       [[ nan,  nan,  nan, ...,  nan,  nan,  nan],\n",
       "        [ nan,  nan,  nan, ...,  nan,  nan,  nan],\n",
       "        [ nan,  nan,  nan, ...,  nan,  nan,  nan],\n",
       "        ...,\n",
       "        [152., 152., 152., ..., 152., 152., 152.],\n",
       "        [152., 152., 152., ..., 152., 152., 152.],\n",
       "        [152., 152., 152., ..., 152., 152., 152.]],\n",
       "\n",
       "       [[ nan,  nan,  nan, ...,  nan,  nan,  nan],\n",
       "        [ nan,  nan,  nan, ...,  nan,  nan,  nan],\n",
       "        [ nan,  nan,  nan, ...,  nan,  nan,  nan],\n",
       "        ...,\n",
       "...\n",
       "        ...,\n",
       "        [ nan,  nan,  nan, ...,  nan,  nan,  nan],\n",
       "        [ nan,  nan,  nan, ...,  nan,  nan,  nan],\n",
       "        [ nan,  nan,  nan, ...,  nan,  nan,  nan]],\n",
       "\n",
       "       [[ nan,  nan,  nan, ...,  nan,  nan,  nan],\n",
       "        [ nan,  nan,  nan, ...,  nan,  nan,  nan],\n",
       "        [ nan,  nan,  nan, ...,  nan,  nan,  nan],\n",
       "        ...,\n",
       "        [ nan,  nan,  nan, ...,  nan,  nan,  nan],\n",
       "        [ nan,  nan,  nan, ...,  nan,  nan,  nan],\n",
       "        [ nan,  nan,  nan, ...,  nan,  nan,  nan]],\n",
       "\n",
       "       [[ nan,  nan,  nan, ...,  nan,  nan,  nan],\n",
       "        [ nan,  nan,  nan, ...,  nan,  nan,  nan],\n",
       "        [ nan,  nan,  nan, ...,  nan,  nan,  nan],\n",
       "        ...,\n",
       "        [ nan,  nan,  nan, ...,  nan,  nan,  nan],\n",
       "        [ nan,  nan,  nan, ...,  nan,  nan,  nan],\n",
       "        [ nan,  nan,  nan, ...,  nan,  nan,  nan]]])\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) datetime64[ns] 1850-02-01 1850-03-01 ... 2015-01-01\n",
       "    member_id  &lt;U11 &#x27;r1i1001p1f1&#x27;\n",
       "    quantile   float64 0.9\n",
       "    month      (time) int64 2 3 4 5 6 7 8 9 10 11 12 ... 4 5 6 7 8 9 10 11 12 1\n",
       "Attributes:\n",
       "    inital objects identified:  37264\n",
       "    final objects tracked:      9105\n",
       "    radius:                     2\n",
       "    size quantile threshold:    0.0\n",
       "    min area:                   1.0\n",
       "    percent area reject:        3.91754142664971e-06\n",
       "    percent area accept:        0.9999960824585733</pre><div class='xr-wrap' hidden><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'labels'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 1980</li><li><span class='xr-has-index'>lat</span>: 192</li><li><span class='xr-has-index'>lon</span>: 288</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-02691e69-fff7-4077-a0e3-015e5fb0d211' class='xr-array-in' type='checkbox' checked><label for='section-02691e69-fff7-4077-a0e3-015e5fb0d211' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>nan nan nan nan nan nan nan nan ... nan nan nan nan nan nan nan nan</span></div><div class='xr-array-data'><pre>array([[[ nan,  nan,  nan, ...,  nan,  nan,  nan],\n",
       "        [ nan,  nan,  nan, ...,  nan,  nan,  nan],\n",
       "        [ nan,  nan,  nan, ...,  nan,  nan,  nan],\n",
       "        ...,\n",
       "        [152.,  nan,  nan, ..., 152., 152., 152.],\n",
       "        [152.,  nan,  nan, ..., 152., 152., 152.],\n",
       "        [152.,  nan,  nan, ..., 152., 152., 152.]],\n",
       "\n",
       "       [[ nan,  nan,  nan, ...,  nan,  nan,  nan],\n",
       "        [ nan,  nan,  nan, ...,  nan,  nan,  nan],\n",
       "        [ nan,  nan,  nan, ...,  nan,  nan,  nan],\n",
       "        ...,\n",
       "        [152., 152., 152., ..., 152., 152., 152.],\n",
       "        [152., 152., 152., ..., 152., 152., 152.],\n",
       "        [152., 152., 152., ..., 152., 152., 152.]],\n",
       "\n",
       "       [[ nan,  nan,  nan, ...,  nan,  nan,  nan],\n",
       "        [ nan,  nan,  nan, ...,  nan,  nan,  nan],\n",
       "        [ nan,  nan,  nan, ...,  nan,  nan,  nan],\n",
       "        ...,\n",
       "...\n",
       "        ...,\n",
       "        [ nan,  nan,  nan, ...,  nan,  nan,  nan],\n",
       "        [ nan,  nan,  nan, ...,  nan,  nan,  nan],\n",
       "        [ nan,  nan,  nan, ...,  nan,  nan,  nan]],\n",
       "\n",
       "       [[ nan,  nan,  nan, ...,  nan,  nan,  nan],\n",
       "        [ nan,  nan,  nan, ...,  nan,  nan,  nan],\n",
       "        [ nan,  nan,  nan, ...,  nan,  nan,  nan],\n",
       "        ...,\n",
       "        [ nan,  nan,  nan, ...,  nan,  nan,  nan],\n",
       "        [ nan,  nan,  nan, ...,  nan,  nan,  nan],\n",
       "        [ nan,  nan,  nan, ...,  nan,  nan,  nan]],\n",
       "\n",
       "       [[ nan,  nan,  nan, ...,  nan,  nan,  nan],\n",
       "        [ nan,  nan,  nan, ...,  nan,  nan,  nan],\n",
       "        [ nan,  nan,  nan, ...,  nan,  nan,  nan],\n",
       "        ...,\n",
       "        [ nan,  nan,  nan, ...,  nan,  nan,  nan],\n",
       "        [ nan,  nan,  nan, ...,  nan,  nan,  nan],\n",
       "        [ nan,  nan,  nan, ...,  nan,  nan,  nan]]])</pre></div></div></li><li class='xr-section-item'><input id='section-bee93d78-fa4e-49b0-95b2-24540d174a32' class='xr-section-summary-in' type='checkbox'  checked><label for='section-bee93d78-fa4e-49b0-95b2-24540d174a32' class='xr-section-summary' >Coordinates: <span>(6)</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-f39f4fea-e466-4758-b79e-1fa240a3570b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f39f4fea-e466-4758-b79e-1fa240a3570b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-822d48e8-26ce-4f40-87d5-0a3c0361561c' class='xr-var-data-in' type='checkbox'><label for='data-822d48e8-26ce-4f40-87d5-0a3c0361561c' 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-bf0557e5-f4cd-42df-9c05-85bd72f9d01c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-bf0557e5-f4cd-42df-9c05-85bd72f9d01c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c2b77b1d-f44c-4d80-81eb-e875a30f74b8' class='xr-var-data-in' type='checkbox'><label for='data-c2b77b1d-f44c-4d80-81eb-e875a30f74b8' 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'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>1850-02-01 ... 2015-01-01</div><input id='attrs-7519b3c2-89b9-4f17-943c-76b471255cdd' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-7519b3c2-89b9-4f17-943c-76b471255cdd' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-907cc354-cf2d-48a9-b823-9aea072b556d' class='xr-var-data-in' type='checkbox'><label for='data-907cc354-cf2d-48a9-b823-9aea072b556d' 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;1850-02-01T00:00:00.000000000&#x27;, &#x27;1850-03-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;1850-04-01T00:00:00.000000000&#x27;, ..., &#x27;2014-11-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2014-12-01T00:00:00.000000000&#x27;, &#x27;2015-01-01T00:00:00.000000000&#x27;],\n",
       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>member_id</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>&lt;U11</div><div class='xr-var-preview xr-preview'>&#x27;r1i1001p1f1&#x27;</div><input id='attrs-c5792d91-c62a-4281-b4ec-6a2250e90ef7' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-c5792d91-c62a-4281-b4ec-6a2250e90ef7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8c197279-7ba5-4c36-93b4-4075ae24ac9c' class='xr-var-data-in' type='checkbox'><label for='data-8c197279-7ba5-4c36-93b4-4075ae24ac9c' 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;r1i1001p1f1&#x27;, dtype=&#x27;&lt;U11&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>quantile</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.9</div><input id='attrs-4b1f2b38-c2bf-4baa-8a7c-de9216c48011' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-4b1f2b38-c2bf-4baa-8a7c-de9216c48011' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2fa904d3-3e22-41a4-a332-2cd355ca3a88' class='xr-var-data-in' type='checkbox'><label for='data-2fa904d3-3e22-41a4-a332-2cd355ca3a88' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array(0.9)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>month</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>2 3 4 5 6 7 8 ... 7 8 9 10 11 12 1</div><input id='attrs-54e0f2bd-e0f3-4967-9653-f2ce6695495e' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-54e0f2bd-e0f3-4967-9653-f2ce6695495e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1b1e77e3-cd67-4f9f-ae42-4925929c04d0' class='xr-var-data-in' type='checkbox'><label for='data-1b1e77e3-cd67-4f9f-ae42-4925929c04d0' 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([ 2,  3,  4, ..., 11, 12,  1])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-c0353796-d4ec-480b-8707-445543e59b74' class='xr-section-summary-in' type='checkbox'  checked><label for='section-c0353796-d4ec-480b-8707-445543e59b74' class='xr-section-summary' >Attributes: <span>(7)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>inital objects identified :</span></dt><dd>37264</dd><dt><span>final objects tracked :</span></dt><dd>9105</dd><dt><span>radius :</span></dt><dd>2</dd><dt><span>size quantile threshold :</span></dt><dd>0.0</dd><dt><span>min area :</span></dt><dd>1.0</dd><dt><span>percent area reject :</span></dt><dd>3.91754142664971e-06</dd><dt><span>percent area accept :</span></dt><dd>0.9999960824585733</dd></dl></div></li></ul></div></div>"
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       "\n",
       "       [[ nan,  nan,  nan, ...,  nan,  nan,  nan],\n",
       "        [ nan,  nan,  nan, ...,  nan,  nan,  nan],\n",
       "        [ nan,  nan,  nan, ...,  nan,  nan,  nan],\n",
       "        ...,\n",
       "        [ nan,  nan,  nan, ...,  nan,  nan,  nan],\n",
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       "\n",
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       "        [ nan,  nan,  nan, ...,  nan,  nan,  nan],\n",
       "        [ nan,  nan,  nan, ...,  nan,  nan,  nan],\n",
       "        ...,\n",
       "        [ nan,  nan,  nan, ...,  nan,  nan,  nan],\n",
       "        [ nan,  nan,  nan, ...,  nan,  nan,  nan],\n",
       "        [ nan,  nan,  nan, ...,  nan,  nan,  nan]]])\n",
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       "  * lat        (lat) float64 -90.0 -89.06 -88.12 -87.17 ... 88.12 89.06 90.0\n",
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       "  * time       (time) datetime64[ns] 1850-02-01 1850-03-01 ... 2015-01-01\n",
       "    member_id  <U11 'r1i1001p1f1'\n",
       "    quantile   float64 0.9\n",
       "    month      (time) int64 2 3 4 5 6 7 8 9 10 11 12 ... 4 5 6 7 8 9 10 11 12 1\n",
       "Attributes:\n",
       "    inital objects identified:  37264\n",
       "    final objects tracked:      9105\n",
       "    radius:                     2\n",
       "    size quantile threshold:    0.0\n",
       "    min area:                   1.0\n",
       "    percent area reject:        3.91754142664971e-06\n",
       "    percent area accept:        0.9999960824585733"
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     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "event.where(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "id": "d394f385-d842-42ea-8f27-a88740e82158",
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       "}\n",
       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray (time: 1980, lat: 192, lon: 288)&gt;\n",
       "array([[[0.        , 0.        , 0.        , ..., 0.        ,\n",
       "         0.        , 0.        ],\n",
       "        [0.        , 0.        , 0.        , ..., 0.        ,\n",
       "         0.        , 0.        ],\n",
       "        [0.        , 0.        , 0.        , ..., 0.        ,\n",
       "         0.        , 0.        ],\n",
       "        ...,\n",
       "        [0.00204387, 0.00202101, 0.00198073, ..., 0.00214502,\n",
       "         0.0021168 , 0.00208237],\n",
       "        [0.00379115, 0.00376729, 0.00371629, ..., 0.00389063,\n",
       "         0.00385792, 0.00381125],\n",
       "        [0.00548383, 0.00548433, 0.00548465, ..., 0.00551038,\n",
       "         0.00548165, 0.00548196]],\n",
       "\n",
       "       [[0.        , 0.        , 0.        , ..., 0.        ,\n",
       "         0.        , 0.        ],\n",
       "        [0.        , 0.        , 0.        , ..., 0.        ,\n",
       "         0.        , 0.        ],\n",
       "        [0.        , 0.        , 0.        , ..., 0.        ,\n",
       "         0.        , 0.        ],\n",
       "...\n",
       "        [0.01422885, 0.01409479, 0.01397243, ..., 0.01463456,\n",
       "         0.0144768 , 0.01437459],\n",
       "        [0.01453887, 0.01446966, 0.01440458, ..., 0.01470859,\n",
       "         0.01466107, 0.01460187],\n",
       "        [0.01526279, 0.01523316, 0.01523337, ..., 0.01526084,\n",
       "         0.01526212, 0.01526233]],\n",
       "\n",
       "       [[0.        , 0.        , 0.        , ..., 0.        ,\n",
       "         0.        , 0.        ],\n",
       "        [0.        , 0.        , 0.        , ..., 0.        ,\n",
       "         0.        , 0.        ],\n",
       "        [0.        , 0.        , 0.        , ..., 0.        ,\n",
       "         0.        , 0.        ],\n",
       "        ...,\n",
       "        [0.00135659, 0.00123092, 0.00108443, ..., 0.00166409,\n",
       "         0.00156793, 0.00146543],\n",
       "        [0.00099742, 0.00093717, 0.0008516 , ..., 0.00116708,\n",
       "         0.00113073, 0.00105066],\n",
       "        [0.00152555, 0.00152735, 0.00152793, ..., 0.0015502 ,\n",
       "         0.00155327, 0.00155428]]])\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) datetime64[ns] 1850-02-01 1850-03-01 ... 2015-01-01\n",
       "    member_id  &lt;U11 &#x27;r1i1001p1f1&#x27;</pre><div class='xr-wrap' hidden><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'></div><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 1980</li><li><span class='xr-has-index'>lat</span>: 192</li><li><span class='xr-has-index'>lon</span>: 288</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-cf56269a-3c04-487c-906e-c28a4739e14d' class='xr-array-in' type='checkbox' checked><label for='section-cf56269a-3c04-487c-906e-c28a4739e14d' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>0.0 0.0 0.0 0.0 0.0 ... 0.001574 0.001578 0.00155 0.001553 0.001554</span></div><div class='xr-array-data'><pre>array([[[0.        , 0.        , 0.        , ..., 0.        ,\n",
       "         0.        , 0.        ],\n",
       "        [0.        , 0.        , 0.        , ..., 0.        ,\n",
       "         0.        , 0.        ],\n",
       "        [0.        , 0.        , 0.        , ..., 0.        ,\n",
       "         0.        , 0.        ],\n",
       "        ...,\n",
       "        [0.00204387, 0.00202101, 0.00198073, ..., 0.00214502,\n",
       "         0.0021168 , 0.00208237],\n",
       "        [0.00379115, 0.00376729, 0.00371629, ..., 0.00389063,\n",
       "         0.00385792, 0.00381125],\n",
       "        [0.00548383, 0.00548433, 0.00548465, ..., 0.00551038,\n",
       "         0.00548165, 0.00548196]],\n",
       "\n",
       "       [[0.        , 0.        , 0.        , ..., 0.        ,\n",
       "         0.        , 0.        ],\n",
       "        [0.        , 0.        , 0.        , ..., 0.        ,\n",
       "         0.        , 0.        ],\n",
       "        [0.        , 0.        , 0.        , ..., 0.        ,\n",
       "         0.        , 0.        ],\n",
       "...\n",
       "        [0.01422885, 0.01409479, 0.01397243, ..., 0.01463456,\n",
       "         0.0144768 , 0.01437459],\n",
       "        [0.01453887, 0.01446966, 0.01440458, ..., 0.01470859,\n",
       "         0.01466107, 0.01460187],\n",
       "        [0.01526279, 0.01523316, 0.01523337, ..., 0.01526084,\n",
       "         0.01526212, 0.01526233]],\n",
       "\n",
       "       [[0.        , 0.        , 0.        , ..., 0.        ,\n",
       "         0.        , 0.        ],\n",
       "        [0.        , 0.        , 0.        , ..., 0.        ,\n",
       "         0.        , 0.        ],\n",
       "        [0.        , 0.        , 0.        , ..., 0.        ,\n",
       "         0.        , 0.        ],\n",
       "        ...,\n",
       "        [0.00135659, 0.00123092, 0.00108443, ..., 0.00166409,\n",
       "         0.00156793, 0.00146543],\n",
       "        [0.00099742, 0.00093717, 0.0008516 , ..., 0.00116708,\n",
       "         0.00113073, 0.00105066],\n",
       "        [0.00152555, 0.00152735, 0.00152793, ..., 0.0015502 ,\n",
       "         0.00155327, 0.00155428]]])</pre></div></div></li><li class='xr-section-item'><input id='section-e6843308-6206-4f8b-ae60-7a59d08d32bf' class='xr-section-summary-in' type='checkbox'  checked><label for='section-e6843308-6206-4f8b-ae60-7a59d08d32bf' 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-bd807ced-580d-48ff-a6f5-3258e47d0cfd' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-bd807ced-580d-48ff-a6f5-3258e47d0cfd' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-453faf63-6b07-4e9c-bcce-f1b0d549261e' class='xr-var-data-in' type='checkbox'><label for='data-453faf63-6b07-4e9c-bcce-f1b0d549261e' 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-a0b7b92f-4afa-4af2-8dd6-82a9edb11760' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a0b7b92f-4afa-4af2-8dd6-82a9edb11760' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-14a414ba-0ee2-44c8-a077-1fb23c40ceb9' class='xr-var-data-in' type='checkbox'><label for='data-14a414ba-0ee2-44c8-a077-1fb23c40ceb9' 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'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>1850-02-01 ... 2015-01-01</div><input id='attrs-b8ff3e76-f3cf-4e23-96e2-7f2509be1456' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-b8ff3e76-f3cf-4e23-96e2-7f2509be1456' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-de6b1637-d395-48c1-baae-3e2cf8550a2d' class='xr-var-data-in' type='checkbox'><label for='data-de6b1637-d395-48c1-baae-3e2cf8550a2d' 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;1850-02-01T00:00:00.000000000&#x27;, &#x27;1850-03-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;1850-04-01T00:00:00.000000000&#x27;, ..., &#x27;2014-11-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2014-12-01T00:00:00.000000000&#x27;, &#x27;2015-01-01T00:00:00.000000000&#x27;],\n",
       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>member_id</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>&lt;U11</div><div class='xr-var-preview xr-preview'>&#x27;r1i1001p1f1&#x27;</div><input id='attrs-a355373c-a1c1-427b-bf29-55dacbc72a06' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-a355373c-a1c1-427b-bf29-55dacbc72a06' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-03b6a789-5321-4485-b6bc-d546b1b644e9' class='xr-var-data-in' type='checkbox'><label for='data-03b6a789-5321-4485-b6bc-d546b1b644e9' 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;r1i1001p1f1&#x27;, dtype=&#x27;&lt;U11&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-1771392c-5791-45b9-91fb-1a126eaa0c29' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-1771392c-5791-45b9-91fb-1a126eaa0c29' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.DataArray (time: 1980, lat: 192, lon: 288)>\n",
       "array([[[0.        , 0.        , 0.        , ..., 0.        ,\n",
       "         0.        , 0.        ],\n",
       "        [0.        , 0.        , 0.        , ..., 0.        ,\n",
       "         0.        , 0.        ],\n",
       "        [0.        , 0.        , 0.        , ..., 0.        ,\n",
       "         0.        , 0.        ],\n",
       "        ...,\n",
       "        [0.00204387, 0.00202101, 0.00198073, ..., 0.00214502,\n",
       "         0.0021168 , 0.00208237],\n",
       "        [0.00379115, 0.00376729, 0.00371629, ..., 0.00389063,\n",
       "         0.00385792, 0.00381125],\n",
       "        [0.00548383, 0.00548433, 0.00548465, ..., 0.00551038,\n",
       "         0.00548165, 0.00548196]],\n",
       "\n",
       "       [[0.        , 0.        , 0.        , ..., 0.        ,\n",
       "         0.        , 0.        ],\n",
       "        [0.        , 0.        , 0.        , ..., 0.        ,\n",
       "         0.        , 0.        ],\n",
       "        [0.        , 0.        , 0.        , ..., 0.        ,\n",
       "         0.        , 0.        ],\n",
       "...\n",
       "        [0.01422885, 0.01409479, 0.01397243, ..., 0.01463456,\n",
       "         0.0144768 , 0.01437459],\n",
       "        [0.01453887, 0.01446966, 0.01440458, ..., 0.01470859,\n",
       "         0.01466107, 0.01460187],\n",
       "        [0.01526279, 0.01523316, 0.01523337, ..., 0.01526084,\n",
       "         0.01526212, 0.01526233]],\n",
       "\n",
       "       [[0.        , 0.        , 0.        , ..., 0.        ,\n",
       "         0.        , 0.        ],\n",
       "        [0.        , 0.        , 0.        , ..., 0.        ,\n",
       "         0.        , 0.        ],\n",
       "        [0.        , 0.        , 0.        , ..., 0.        ,\n",
       "         0.        , 0.        ],\n",
       "        ...,\n",
       "        [0.00135659, 0.00123092, 0.00108443, ..., 0.00166409,\n",
       "         0.00156793, 0.00146543],\n",
       "        [0.00099742, 0.00093717, 0.0008516 , ..., 0.00116708,\n",
       "         0.00113073, 0.00105066],\n",
       "        [0.00152555, 0.00152735, 0.00152793, ..., 0.0015502 ,\n",
       "         0.00155327, 0.00155428]]])\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) datetime64[ns] 1850-02-01 1850-03-01 ... 2015-01-01\n",
       "    member_id  <U11 'r1i1001p1f1'"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "detrended"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8f33fee3-097d-44a8-a0db-bfe3748e3df9",
   "metadata": {},
   "source": [
    "# ANALYSIS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "0fbf17a2-e08b-4ef4-9bda-d64ccdf68621",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'inital objects identified': 37264,\n",
       " 'final objects tracked': 9105,\n",
       " 'radius': 2,\n",
       " 'size quantile threshold': 0.0,\n",
       " 'min area': 1.0,\n",
       " 'percent area reject': 3.91754142664971e-06,\n",
       " 'percent area accept': 0.9999960824585733}"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "blobs.attrs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "id": "772be8d7-57c8-4a8d-9de1-73e09c403d86",
   "metadata": {},
   "outputs": [],
   "source": [
    "id_list = []\n",
    "length_list = []\n",
    "lat_list = []\n",
    "lon_list = []\n",
    "year_start_list = []\n",
    "for i in range(1,9105):\n",
    "    id = i; id_list.append(i)\n",
    "    event = blobs.where(blobs==id, drop=True)\n",
    "    characteristics = list(event.shape)\n",
    "    length = characteristics[0]; length_list.append(length)\n",
    "    lat = characteristics[1]; lat_list.append(lat)\n",
    "    lon = characteristics[0]; lon_list.append(lon)\n",
    "    year_start = int(event.time.dt.year[0])\n",
    "    year_start_list.append(year_start)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "id": "0c5dba67-3bcc-443e-ae7f-607bb83bb7ef",
   "metadata": {},
   "outputs": [],
   "source": [
    "id_list1 = []\n",
    "length_list1 = []\n",
    "lat_list1 = []\n",
    "lon_list1 = []\n",
    "year_start_list1 = []\n",
    "for i in range(941,1037):\n",
    "    id = i; id_list.append(i)\n",
    "    event = blobs.where(blobs==id, drop=True)\n",
    "    characteristics = list(event.shape)\n",
    "    length = characteristics[0]; length_list1.append(length)\n",
    "    lat = characteristics[1]; lat_list1.append(lat)\n",
    "    lon = characteristics[0]; lon_list1.append(lon)\n",
    "    year_start = int(event.time.dt.year[0])\n",
    "    year_start_list1.append(year_start)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "id": "e58f9f2f-9aed-41fd-a021-da901614f8d0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1890\n"
     ]
    }
   ],
   "source": [
    "print(np.nanmax(year_start_list))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "id": "2898094b-cd02-4bc6-bba4-14da2901ed88",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1895\n"
     ]
    }
   ],
   "source": [
    "print(np.nanmax(year_start_list1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 211,
   "id": "b90d08d8-5a7c-4fb5-9a42-624a2b6cfff6",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.DataFrame(list(zip(id_list, length_list, lat_list, lon_list, year_start_list)),\n",
    "               columns =['id', 'length', 'lat', 'lon', 'year_start'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 208,
   "id": "7fa936dc-6514-4d50-b1fc-b359044250e1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x2ab06246b2b0>"
      ]
     },
     "execution_count": 208,
     "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": [
    "plt.scatter(year_start_list, length_list,alpha=0.3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 212,
   "id": "b3867842-94d1-474a-a37a-21a458b0de3c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 212,
     "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": [
    "df['year_start'].hist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 205,
   "id": "5867a0a1-c56d-4fbd-8b84-fb3e268ca214",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1850]\n",
      "[1]\n",
      "[21]\n"
     ]
    }
   ],
   "source": [
    "print(year_start_list)\n",
    "print(id_list)\n",
    "print(lon_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "55aeb116-ca08-4ec7-886f-a09024ff2171",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2\n",
      "[2, 12, 24]\n",
      "****\n",
      "<xarray.DataArray 'time' ()>\n",
      "array('1850-02-01T00:00:00.000000000', dtype='datetime64[ns]')\n",
      "Coordinates:\n",
      "    time       datetime64[ns] 1850-02-01\n",
      "    member_id  <U11 'r1i1001p1f1'\n",
      "    quantile   float64 0.9\n",
      "    month      int64 2\n",
      "****\n",
      "<xarray.DataArray 'time' ()>\n",
      "array('1850-02-01T00:00:00.000000000', dtype='datetime64[ns]')\n",
      "Coordinates:\n",
      "    time       datetime64[ns] 1850-02-01\n",
      "    member_id  <U11 'r1i1001p1f1'\n",
      "    quantile   float64 0.9\n",
      "    month      int64 2\n"
     ]
    }
   ],
   "source": [
    "id = 3\n",
    "event = blobs.where(blobs==id, drop=True)\n",
    "shaped = list(event.shape)\n",
    "print(shaped[0])\n",
    "print(list(shaped))\n",
    "time = event[0]['time']\n",
    "print('****')\n",
    "print(time.time)\n",
    "print('****')\n",
    "print(event[0]['time']['time'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 181,
   "id": "fcde82a0-c0c4-44e2-ad60-66b1fe471893",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2\n"
     ]
    }
   ],
   "source": [
    "time = event.time[0]['month']\n",
    "print(int(time))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 183,
   "id": "e8ece975-cbc8-45dc-af3a-e91373767df9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<xarray.DataArray 'time' ()>\n",
      "array('1850-02-01T00:00:00.000000000', dtype='datetime64[ns]')\n",
      "Coordinates:\n",
      "    time       datetime64[ns] 1850-02-01\n",
      "    member_id  <U11 'r1i1001p1f1'\n",
      "    quantile   float64 0.9\n",
      "    month      int64 2\n"
     ]
    }
   ],
   "source": [
    "time = event.time[0]\n",
    "print((time))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 197,
   "id": "76937f82-d9c6-40c3-9943-c0acc2ecebf0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[\"<xarray.DataArray 'time' ()>\\narray('1850-02-01T00:00:00.000000000', dtype='datetime64[ns]')\\nCoordinates:\\n    time       datetime64[ns] 1850-02-01\\n    member_id  <U11 'r1i1001p1f1'\\n    quantile   float64 0.9\\n    month      int64 2\", \"<xarray.DataArray 'time' ()>\\narray('1850-03-01T00:00:00.000000000', dtype='datetime64[ns]')\\nCoordinates:\\n    time       datetime64[ns] 1850-03-01\\n    member_id  <U11 'r1i1001p1f1'\\n    quantile   float64 0.9\\n    month      int64 3\"]\n"
     ]
    }
   ],
   "source": [
    "import datetime\n",
    "lists = []\n",
    "lists.append(str(event[0,:,:]['time']))\n",
    "lists.append(str(event[1,:,:]['time']))\n",
    "print(lists)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 202,
   "id": "5ca4a787-5268-46f8-a52c-f042093b14f9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1850"
      ]
     },
     "execution_count": 202,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "int(event.time.dt.year[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "450995b0-d072-48d1-b598-e5d5a74f0791",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "test-env",
   "language": "python",
   "name": "test-env"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}