scratchwork/Untitled4 (1).ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "0357a3a9-7a39-46eb-8d29-7c3f1796f61c",
   "metadata": {},
   "source": [
    "<code> \n",
    "<strong><font size=\"+3\">COMPARING ALL BLOB-LIKE EVENTS</font></strong>\n",
    "</code>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "d5e399c4-ce5d-4816-a8da-fa5a19b76684",
   "metadata": {},
   "outputs": [],
   "source": [
    "# import packages\n",
    "import xarray as xr; import numpy as np; import pandas as pd; import netCDF4 as nc; import scipy\n",
    "# for plotting purposes\n",
    "import matplotlib.pyplot as plt; import cmocean; import seaborn as sns\n",
    "# using sklearn\n",
    "from sklearn import preprocessing\n",
    "from sklearn.decomposition import PCA\n",
    "from sklearn.preprocessing import StandardScaler"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "363aea4f-a7cd-4ee7-8409-2e3c3e70ff01",
   "metadata": {},
   "source": [
    "### Area of interest (larger area in the North Pacific Ocean and by western US coast)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "ce7b1801-fb79-4a1b-bb00-e64297d3ed5f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# latitude and longitude range for the North Pacific Ocean\n",
    "lon_lim_less = 0.; lon_lim_great = 67.5 # x\n",
    "lat_lim_less = 20.; lat_lim_great = 60. # y\n",
    "\n",
    "# rescale\n",
    "x_list = np.array([-180, lon_lim_less, lon_lim_great, 180]).reshape(-1,1)\n",
    "x_scaler = preprocessing.MinMaxScaler(feature_range=(0, 287))\n",
    "x_normalizedlist=x_scaler.fit_transform(x_list)\n",
    "\n",
    "y_list = np.array([-90, lat_lim_less, lat_lim_great, 90]).reshape(-1,1)\n",
    "y_scaler = preprocessing.MinMaxScaler(feature_range=(0, 191))\n",
    "y_normalizedlist=y_scaler.fit_transform(y_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "647f6922-33ca-4681-b679-acfb5979794a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "117 159\n",
      "144 197\n"
     ]
    }
   ],
   "source": [
    "f_row = int(np.round(y_normalizedlist[1].item())); l_row = int(np.round(y_normalizedlist[2].item()))\n",
    "f_col = int(np.round(x_normalizedlist[1].item())); l_col = int(np.round(x_normalizedlist[2].item()))\n",
    "print(f_row, l_row)\n",
    "print(f_col, l_col)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "4a954cca-3679-4952-a00f-cd12b68b55f9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "117 159\n",
      "170 197\n"
     ]
    }
   ],
   "source": [
    "f_row_small = int(np.round(y_normalizedlist[1].item())); l_row_small = int(np.round(y_normalizedlist[2].item()))\n",
    "f_col_small = int(x_normalizedlist[1].item()+((x_normalizedlist[2].item()-x_normalizedlist[1].item())/2))\n",
    "l_col_small = int(np.round(x_normalizedlist[2].item()))\n",
    "print(f_row_small, l_row_small)\n",
    "print(f_col_small, l_col_small)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "abdbfa01-48a8-4bcd-be43-f12f799b60be",
   "metadata": {},
   "outputs": [
    {
     "data": {
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zL7ywklizZN32ymj3Yt0Wqog3HmGsqn2Mfhh/bPzgmvHfABBSm4bzGbqPT2eS/bTJ5L/oM4PBoNDOATsW7cB6QghtSvgIJ0T706ph/CrMe6LZvEfVjk0eFkAcuMEOumJZe4dwQi37zif7scmGPs3CD7lxcEc+AFnMazOy3PUTcensMP4saYb2Q2PjBOJS9VA+bQNmoWcPABEgyWMRkj2QoLFEaGUdO6futksdI8gk+wqVcRJf1uNsTTsM0RY0lmgCHpgVv7rWL8fsKzKZPMq4OUWeuEiHZYYmoT7Gq84dOZJ9IBsbG8E2YhoLAMAkhvFbIus14Gk+y3AhO0ZoJobzUTZ69i1S9qWBXU90XZ9/NBv1E2Ui2VdkXlIuewMuugOwsrIS9BBDE3RtftFe1NN41D1SQ7KvQVkn7E1WnrorUlvQeALoIpJ9hapMwLOm3cQ7ADYJiR5AHZrQLpu7112G1MxsU9Lmvn37Dhw6dChIzOFwqF6vV2gas55PPBgMgsabZ/IZ1/PKVNRkeULEm4yZ9RneRcUcb3d3V2tra9q7d2+QeFLO+Tt2TLrzTumMM7LH6/W0Mhxm/l5Wt335y/K1NZ2dYVku2l7SLKOY62aT45XVzm1ubu66+0V5v9+qs/Hd/RpJ1+zfv/9AqJVaRgWa16OctbdXZbx5ZZj8fNl7oLPuEFZFvEXLOGSPPuZ4/X5fW1tbuvTSS4PEk3LO38GD0nXXSRdfnD3eWWdp47bbMn8vqz+94god39rS8zIsy0XbStqrb2Ktm02O14RevcQwfq2qqJhFpzk+ea1N9wRgeB4AFmtVzz5GZV5PW3bSK1q2tOUpEodEH56ZBYu1tbWlfr+f6TsHJD1Vkt75zszx9mxt6U+vuCLz9+oWuleL9iHZB9DFDbFr84tm+duc33uKpA+XWZAlsbJYtlPcxXYG6ZHsI9C0DTxPebhjWPO5u3Z2dhTypN464r0oULw89X28bbGtICuO2QdS9OSaKr5bxfSbPC9ALOadV7OonRkMBuwkdBg9+4CWPa627RsiyRqoX9vbEVSDZN9STUmsVZwUKKVvsDhOiS6r8rJZxIVk3yAbGxs6fPjwwvebpMryZBnpIOED2e7nge7hmH1LdDGZZb05EICTzbqxFbqHZB9YkTNw61Jn/LrnHYjBrIQ//YO4MYxfg0XDzuNHzjZNG04gZDgfmG+6bWn69oxy0bOvCXvTAOoU8rbYqB89+5qNEz4b3XwccwSAYujZAwBQkaZ05FIlexu5zMx+I/n/XDN7UrVFQ9NMVtqmVOBJTSwT0HRsN9VrwjK2NPedNrP/Ium4pKe5+6PM7MGS/sbdn1h1AafKsSlpc9++fQcOHToUJOZwOFSv16s8zvgZ9mU8zz6LtsYbDAYn/D9vmm2dP+LFHy+2eZvcJldWVqKbvzLiTbdbWWxubu66+0V5v5/2mP2PuvsTzOzjkuTu3zaz++cNmpe7XyPpmv379x+IdYNcpIq9w9BnsBOPeMQLH6uKeNPnzmRpJ7vcltV1zlHaY/bfN7OeJJckMztHo54+AACZdPkk27qG9NMm+/8o6T2SHmJmvy3pQ5J+p7JS4SRNOOYDAFI5ybrLCb8OqYbx3f2dZrYr6emSTNJz3P0zlZYMANAoZSdoboQVzsKevZmdNf6R9E1J75K0LenW5DUEwMYAIFb08MNYNoy/K+mG5PffS/qcpM8nf+9WWzRIJHoAzVFVe9S1hF9Hu75wGN/dz5MkM/sDSe919/cl/z9T0k9UX7zZTjnllJkLq2sVBgBi0bUh/fG8hspbaS+9e6K7/8vxP+7+V2b2horKlFsbHtYCAJitawlfCtfLT5vsj5nZv5X0Do0uv7tM0rcqK1UBMSX8rlX6ECbrBssXaJ5Z7TfbanFpk/0LJP2mRpffSdJ1yWuNxBA/ZiHRA+3UxR5/2VJdZ+/ut7n7K9z98cnPK9z9tqoL12VU7BOxswZ0G21AMal69mZ2RMnd8ya5+9NKLxFI9FPGG3mRvXsaCgBdlnYY/4qJv0+T9DxJ95RVCDN7hqS3SupJ+kN3f2NZ026bptyHv6myJvzBYECiB9B5aYfxdyd+Puzur5b0o2UUILnn/tskPVPSBZJeYGYXlDHttqFHvxzLCKhP3TvOdcdvs7TD+JN3yztF0oWS9pVUhidJ+oK7fzGJ9W5Jz5b0dyVNvxVIYvMxdA8AxaQdxt/V6Ji9aTR8/yVJLympDA+T9NWJ/7+mJaMGX/3qVzMngNtvvz1ruSSFe579gx70IEmjco7/DiHmeLfffvvc9VdVGWJensRrb6wy4mVtQ6tsO2fNR9uWZ2hpk/2j3P2uyRfM7AEllcFmvHbSyYBmdrmkyyXpfve7X66Kl4e75/5uWr1e7975GQ6HuXdM8og53nA4nLv+qipDzMuTeO2NVTRenjawyrbzW9/61kk7Em1annVIm+z/j6QnTL32kRmv5fE1SQ+f+H9N0jemP+TuByUdlKT19XW/8cYbMwXJO6Q7GAwqPWlueoQi9PWkMcfb2dmZuf6qjB/z8iRee2MVjZen/ay67ZRO3JbbtDzzMJvVL05vYbI3s30aDbP/gJk9Xvf1wh8o6fRCke/zMUn7zew8SV+X9HxJ/7ykaTcax+mzGzc6eZcdyxyIB+flpLfsbPyfkrSlUW/7Skm/l/y8WtKvlVEAd79H0q9Ier+kz0i62t0/Xca0JzWtkW9aedpmZ2dn6YbOMgbK0YZtaTAY1F2ERlv21LurJF1lZs9z9z+vqhDJ0/TeV9X0x5py3/w2bDixWFlZqWV5c39vxKYp7eci3BJ7voU9ezO7LPnzEWb26umfAOUDTtDmDbjpDSUwT5qRtKZpW3mrtuwEvT3J71lnWZx0xjyWa3OyQnrzekH0+IFwpre3Lm9ry4bx/2vy5/9y9w9PvmdmT6msVMACbdlg0w578kQvIIwub2upbpcr6T+lfA3AhLQNC0OOQBhd3daWXXr3ZEk/LumcqWP0D9TooTWt04aTTBAX6hzQLEUv4W2jZT37+2t0vP5USWdM/HxH0s9WWzSgO7rU6KB9qJ/tt+yY/bWSrjWzP3b3LwcqU9S6fMyoy2b17qkHaBNGqNot7e1yv2dmb5b0aI2eZy9JcvenVVIqIEIkdwB1SXuC3jslfVbSeZJeL+lmjW5zCwAAGi5tsj/b3f9I0vfd/Vp3f7GkH6uwXJWihwUA3da1PJA22X8/+X2Lmf108lCctYrKFETXVjQAYKSL7X/aZP/vzexMSa+RdIWkP5T0yqoKBQBonhiSZAzzkIe557vrrZm90t3fUm5xlsbclLS5urp6YHt7u/D00jwlaTgcqtcr/5YC857zHOIZ0MQjHvHqj9fWeUv7dLmq2s6i8cpa5qHXX7/f33X3i/J+v0iy/4q7n5s3cBHr6+t+9OjRwtNJcxlJlSt01h5m6EvziEc84tUTr83zVnfbmTdemcs79Pozs0LJPu0w/szYBb5bO64XRRnGTwOjPqFL2jgU3sYyl6lIsm/tU+9omFGG6XpEvQLQVMvujX+nZid1k/QDlZQIAACUamHP3t3PcPcHzvg5w93T3n2vUdrW+2KIuJmKrBPWKWLQ9WHxtmllwo5Bmg2FhFC/KtdBF5+8hbhwv/z2KHLMHjmR6NshzzpIe1kSEIuNjQ12WFuAZB9Q3o2CDSm8osP0QNfQTjUbyT6QvBsCG1A7cUkeuoj2qrk6d8y+jmNMbADdRsJHlzTxOD5tMD37ylHJAAB1I9lXiETfTqF7JU3rBQGID8m+IiT6dqor8XJ8H0CVOnfMXmrmMSXUo2n1IPTDNYDYsT2NdLZnX3UFKOPSLSpptZqW6MeaWi6gbWhD79PJnn2ThX4sZNeQSIFuINGfKPfz7OtgZpuSNldXVw9sb28Xnl6au50Nh0P1er1c08+TtO+4445744VI+k185nRV8ULc3a5IfZmUdhl1af3FFi/2eStrW0hrOBzqzDPPDBYv9Prr9/uFnmffqmQ/tr6+7kePHi08nTS9vCIrNM+e5eHDh++NF2LPNPQx4jrjhejVl9kApL2tclfWX2zxYp+3kMlwfB5WrMtTksysULLv7DF7qZnDPCsrK40sF8LjDH1gMe7Ln16nk32VilZAKjDGSPjAyWgjsyHZAy1AwgdQBMkeAIDIkewBAIhc55N9Fcd9OJYEAGiSzid7qdzkTKIH0GWcX9JMJHt0BjtiALqKZJ8oIxGQTJqPdQS0H9txdiT7klD5AKB6tLX5kOxLQOUDgOrR1ubHU+8m5HnOPZWvO9Ks68OHD9cWG4gZ20AxJPspWRI+lS9+WddxFQ/+oJ6h69gGiiPZZ0Sl64686zrPCBEAVIlj9hmQ6LujKeu6KeUA6sI2UI5WPc/ezDYlba6urh7Y3t4OEjPkM5mJFybeYDC49++yy7IsXlbLyteE5Um85scKGW9c34fDoXq9Xq5p5ClnrMtzrN/vF3qefauS/dj6+rofPXo0SKydnZ2ge5bEizdeFSd/Nmn+iNfcWKHiTdbxvMkwbxljXJ6TzKxQsmcYHwgkS8PA0CXahvNUmo1kDwBA5Ej2AIBC6NU3H8keAIDIcZ09EBDH4oHZ2DaqRbIHAARHcg+LYXwAQFAk+vBI9gAARI5hfABA5ejN14uePQAAkSPZAwAKodfefEGTvZk90cyGZvazE6/dbGafMrMbzeyGkOUBAKALgh2zN7OepDdJev+Mt/vufixUWQAA4dDzr1/Inv3LJf25pG8GjAkAqBGJvhmCJHsze5ik50r6gxlvu6S/MbNdM7s8RHkAAOiSIM+zN7M/k/R77v5RM/tjSYfd/b8n7z3U3b9hZg+R9AFJL3f362ZM43JJl0vSOeecc+HVV19debml/M9kJh7xiEe8psSqOt5gMDjpteFwqDPPPLOSePPKEMvynKXf7xd6nr3cvZIfSS+TdGPy8yVJNyc/A42G8p8z4zu/JemKZdM+//zzPZQjR44Ei0U84hGvO/FimrcjR47M/Akp9niSbvACObmyE/Tc/W2S3jb9+kTP/n+a2R5Jp7j7ncnfPynp31VVJgBAGLN6+6hP3dfZ75X0ITP7hKTrJf2lu/91zWUCAJSA59w3R/Db5br7L0z8/UVJjw1dBgBAOUjo7VB3zx4A0FLLEj2X3TUHyR4AULqQZ6pjOZI9ACAXeu7tQbIHAOQ2K+GzE9A8JHsAACJHsgcAFEJPvvlI9gAARI5kDwBA5ILfVAcAEB+G8puNnj0AAJEj2QMAEDmSPQAAkbPRY3Lbwcw2JW2urq4e2N7eDhJzMBgEve0j8YhHvG7Ei3neiFe+fr+/6+4X5Z5A2gffN+nn/PPP91COHDkSLBbxiEe87sSLed6IVz5JN3iBvMkwPgAAkSPZAwAQOZI9AACRI9kDABA5kj0AAJEj2QMAEDmSPQAAkSPZAwAQOZI9AACRI9kDABA5nmcPAAhqZ2fn3r83NjZqK0eX0LMHACByJHsAQDCTvXqEwzB+S01vMDEMhaVtBGKYV6BLSPD1I9m3xLKNZWdnp9YkmHZjHgwGhTf8LN8vI14WeeOxAwOgSjZ6TG47mNmmpM3V1dUD29vbQWIOBgOtrKwEiTUv3mAwSP39rGUtY/6ylG84HKrX6xWKl0Wb4uVZD02on8Rrfqy64y1qI8oqU+zLs9/v77r7RXm/36pkP7a+vu5Hjx4NEitUj3ncG6yzwuaZz6y92Ng3yLLipV0XoUd0iNfOWHXHW9ROlFWm2JenmRVK9gzj16zNx7LaXPamC9E4AiFQl5uhk8m+7JPbYkl6scxH7CbX0+Q5AjScqNN0+7Hs/BXqa1idu/RuVuUrkuRIkGgK6iLqkqfuUV/DamXP/vjx46VXlHnTC302N1AEly8ipDKurOl6XQyVX1qZ7AEUQyMbD2492z51dCA7N4wPALGYThohkwgjnvnUtdzo2QMdRA+w3RYljKyHJLNc5onFmryMSPZA5EjsWCTNIZ0mJ7GmaPoyYhgfiBiJHmnkGSnAfdqwjOjZA5Ei0ecXovGeNayeZp1VVba6ElYbTxbd2dlp3ZVaJHsAUcrTENfdgLcpeZSp7BudVaXN64dkDyAKbW6IcaJ56zL0TkBMdYpj9gBaL6ZGGfNxaWF+9OyBSLXxWOgssTW6KKaqZ0HEXs9I9kDEym7AyjimneUyr9CPKEZ7ZHnwzrw6F3uCn9Sq59mb2aakzX379h04dOhQkJjD4VC9Xi9ILOIRryvx5iXwwWBQSby0QsaLed6aGG+6zk3XtbLjlW1zc7M7z7N392skXbN///4Dofb2Q/csiEe8Lseb1SDHNH91xSLeyYqWrW2jTpygBwBA5Ej2AABEjmQPAEDkSPYAAESOZA8AQORI9gAARI5kDwBA5Ej2AABEjmQPAEDkSPYAAESOZA8AQOSCJHsze7CZvcfMPmlm15vZYybee4aZHTWzL5jZa0OUBwCALgnVs/81STe6+49IeqGkt0qSmfUkvU3SMyVdIOkFZnZBoDIBANAJoZL9BZI+KEnu/llJjzCzvZKeJOkL7v5Fd/8HSe+W9OxAZQIAoBNCJftPSPoZSTKzJ0n6IUlrkh4m6asTn/ta8hoAAChJqOfZv1HSW83sRkmfkvRxSfdIshmf9VkTMLPLJV2e/Ht3v9+/qYJyznKmpDsCxepCvB+UdCxgvNiXJ+uvvfFYd8TLYr3Qt929kh9JL5N0Y/Lz0InXTdLNkh4o6cmS3j/x3uskvS7FtG+oqtwzYh0MFasj8YKtu44sT9ZfS+Ox7ogXcv1VNozv7m9z98e5++Mkfc/M7p+89UuSrnP370j6mKT9ZnZe8v7zJb23qjLldA3xWi325cn6a2881h3xgrFkj6HaIGZPlvQnkoaS/k7SS9z928l7z5L0Fkk9SW93999OMb0b3P2i6kqMqrDu2o31116su3Yruv6CHLN3949I2j/nvfdJel/GSR4sXCjUhXXXbqy/9mLdtVuh9RekZw8AAOrD7XIBAIhcI5O9mf2wmX3EzO42syum3nuVmX3azG4ys3eZ2WnJ62eZ2QfM7PPJ7wfXU3qMcZvkeLC+2sfMXpG0k582s1cmr9FONpSZvd3MvmlmN0289jgz+6iZ3WhmNyT3qRm/97pkezxqZj+1bPqNTPaSbpP0ryRtTb5oZg9LXr/I3R+j0Ul9z0/efq2kD7r7fo3u1keDVD9ukxwB1lf7JDvWBzS6S+ljJV1iZvtFO9lkfyzpGVOv/a6k1ydXtf1G8r+S7e/5kh6dfOf3k+10rkYme3f/prt/TNL3Z7x9qqQfMLNTJZ0u6RvJ68+WdFXy91WSnlN1ObEUt0mOA+urfR4l6aPu/j13v0fStZKeK9rJxnL36zTq6J7wskb3pJFGN/GZzHfvdve73f1Lkr6g0XY6VyOT/Tzu/nWNevtfkXSLpDvc/W+St/e6+y3J526R9JB6SokJ3CY5Dqyv9rlJ0sVmdraZnS7pWZIeLtrJtnmlpDeb2Vc1yn2vS17PvE22Ktknx5eeLek8SQ+VtMfMLqu3VFjgjZIenNwm+eXKcZtkNALrq2Xc/TOS3iTpA5L+WqMd73tqLRTy+GVJr3L3h0t6laQ/Sl7PvE02Jtmb2cuSkxBuNLOHzvnYT0j6krv/vbt/X9L/kPTjyXu3mtlqMq1VSd+svtSYNrkeJa24+y8mx5teKOkcSV/SaC/04RNfW9N9w1NoHtZXC7n7H7n7E9z9Yo2Ghz8v2sm2eZFGeU6S/kz3DdVn3iYbk+wnb6/r7vMK/RVJP2Zmp5uZSXq6pM8k771XowWj5PdfVFtizBLRbZJxH9ZXC5nZQ5Lf52p0OO1dop1sm29I+ifJ30/TaIdNGq3H55vZA8zsPI1uWnf9ogk18qY6ZrZP0g0anZhwXNJA0gXu/h0ze72kSzUakvq4pF9y97vN7GxJV0s6V6Odgp9z9+mTHRBQ2bdJRn1YX+1jZn8r6WyNTnR+tbt/kHayuczsXZI2NHo64a2SflPSUY2uYjpV0l2SXuruu8nnf13SizXKha90979aOP0mJnsAAFCexgzjAwCAapDsAQCIHMkeAIDIkewBAIgcyR4AgMiR7IGImNmg4um/z8welPy8NMf3N8zscBVlAzAfyR5Aau7+LHe/XdKDJGVO9gDqQbIHIjfxTOxPmtl7xs8wN7MdM3uTmV1vZp8zs6cmr59uZlcnn/9vZvZ/zeyi5L2bzewHNXruwSOTWyO/ebrHbmb/2cx+Ifn7GWb2WTP7kJIHIyWv70me4f0xM/u4mfEkPaAiJHsgfn8i6d+4+49I+pRGd+YaO9Xdn6TR07XGr79U0reTz79B0oUzpvlaSf8vuTXyr84LbGanSTokaVPSUyXtm3j71yX9b3d/oqS+Rk/32pNj/gAsQbIHImZmZ0p6kLtfm7x0laSLJz4yfsjGrqRHJH//Y42eWS93v0nSJwsU4Yc1enjV5310u853TLz3k5Jemzw0aUfSaRrdxhVAyU6tuwAAanV38nuo+9qDWY/PXOYendh5OG3i73n35DZJz3P3ozniAciAnj0QMXe/Q9K3x8fjJf28pGsXfEWSPiTpn0mSmV0g6R/N+Mydks6Y+P/Lki5InsJ1pkZPpJSkz0o6z8wemfz/gonvvF/Sy5MnWMrMHp9urgBkRc8eiMvpZva1if+v1OhRpn9gZqdL+qKkX1wyjd+XdJWZfVKjJ0t+UtIdkx9w92+Z2YfN7CZJf+Xuv2pmVyef/XzyPbn7XWZ2uaS/NLNjGu1IPCaZzBs0epLeJ5OEf7OkS/LNNoBFeOodgBOYWU/S/ZJE/UhJH5R0vrv/Q81FA5ATPXsA006XdMTM7qfRcfVfJtED7UbPHgCAyHGCHgAAkSPZAwAQOZI9AACRI9kDABA5kj0AAJEj2QMAELn/D6iLjLhTsyc3AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 576x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib import patches # for the purpose of plotting\n",
    "\n",
    "NPO_patch = patches.Rectangle((x_normalizedlist[1].item(), y_normalizedlist[1].item()), x_normalizedlist[2].item()-x_normalizedlist[1].item(), y_normalizedlist[2].item()-y_normalizedlist[1].item(), linewidth=2, edgecolor='k', facecolor='none',alpha=1.) \n",
    "NPO_smaller_patch_rotated = patches.Rectangle((x_normalizedlist[1].item()+((x_normalizedlist[2].item()-x_normalizedlist[1].item())/2), y_normalizedlist[1].item()), (x_normalizedlist[2].item()-x_normalizedlist[1].item())/2, y_normalizedlist[2].item()-y_normalizedlist[1].item(), linewidth=2, edgecolor='red', facecolor='red',alpha=.4) \n",
    "\n",
    "# landmask with relabeled axes\n",
    "land_mask = np.load('SST_land.npy')\n",
    "land_mask[land_mask > 0] = np.nan\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(8,5))\n",
    "ax.contourf(land_mask, cmap='binary')\n",
    "ax.minorticks_on()\n",
    "ax.axhline(y=95.5, c='k', linestyle='-') # this is the equator line\n",
    "ax.set_xticks([0, 71.75, 143.5, 215.25, 287]);ax.set_xticklabels([-180, -90, 0, 90, 180])\n",
    "ax.set_yticks([0, 47.75, 95.5, 143.25, 191]);ax.set_yticklabels([-90, -45, 0, 45, 90])\n",
    "ax.set_xlabel('Longitude'); ax.set_ylabel('Latitude')\n",
    "ax.grid(which='both')\n",
    "ax.add_patch(NPO_patch)\n",
    "ax.add_patch(NPO_smaller_patch_rotated)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f38f1511-40e5-4c42-b2bb-ca321c2f2edc",
   "metadata": {},
   "source": [
    "### Combining saved data files"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "cb04b74d-e49a-4269-b36c-722c55e01433",
   "metadata": {},
   "outputs": [],
   "source": [
    "# let's get our saved data files. We have a total of 1131 heatwaves.\n",
    "file0 = '/glade/work/cassiacai/member0_9_bloblike_events_new_stuff.nc' # 118 heatwave events\n",
    "file1 = '/glade/work/cassiacai/member10_19_bloblike_events_new_stuff.nc' # 99 heatwave events\n",
    "file2 = '/glade/work/cassiacai/member20_29_bloblike_events_new_stuff.nc' # 121 heatwave events\n",
    "file3 = '/glade/work/cassiacai/member30_39_bloblike_events_new_stuff.nc' # 116 heatwave events\n",
    "file4 = '/glade/work/cassiacai/member40_49_bloblike_events_new_stuff.nc' # 116 heatwave events\n",
    "file5 = '/glade/work/cassiacai/member50_59_bloblike_events_new_stuff.nc' # 123 heatwave events\n",
    "file6 = '/glade/work/cassiacai/member60_69_bloblike_events_new_stuff.nc' # 112 heatwave events\n",
    "file7 = '/glade/work/cassiacai/member70_79_bloblike_events_new_stuff_.nc' # 105 heatwave events\n",
    "file8 = '/glade/work/cassiacai/member80_89_bloblike_events_new_stuff_.nc' # 99 heatwave events\n",
    "file9 = '/glade/work/cassiacai/member90_99_bloblike_events_new_stuff_.nc' # 122 heatwave events"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "3bc6c8b3-cbd8-41d3-866c-d0a425f947aa",
   "metadata": {},
   "outputs": [],
   "source": [
    "files = [file0, file1, file2, file3, file4, file5, file6, file7, file8, file9]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "cc2b0b45-59c6-454c-bb84-8cb6e567e708",
   "metadata": {},
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   "source": [
    "blob_files = []\n",
    "for i in range(len(files)):\n",
    "    blob_file_name = xr.open_dataset(files[i])\n",
    "    blob_files.append(blob_file_name)"
   ]
  },
  {
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   "execution_count": 9,
   "id": "ccefc4a8-5346-4b66-98e3-59f6d7192e4d",
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       "\n",
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
       "Dimensions:    (heatwave_label: 118, time: 481, lat: 192, lon: 288)\n",
       "Coordinates:\n",
       "  * time       (time) object 1975-01-01 00:00:00 ... 2015-01-01 00:00:00\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",
       "    member_id  (heatwave_label) object &#x27;r1i1001p1f1&#x27; ... &#x27;r10i1181p1f1&#x27;\n",
       "    quantile   float64 0.9\n",
       "    month      (time) int64 1 2 3 4 5 6 7 8 9 10 11 ... 3 4 5 6 7 8 9 10 11 12 1\n",
       "Dimensions without coordinates: heatwave_label\n",
       "Data variables:\n",
       "    labels     (heatwave_label, time, lat, lon) float64 ...\n",
       "    SSTA       (heatwave_label, time, lat, lon) float64 ...</pre><div class='xr-wrap' hidden><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-fe63263a-5404-482d-be3e-cd826e568c74' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-fe63263a-5404-482d-be3e-cd826e568c74' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span>heatwave_label</span>: 118</li><li><span class='xr-has-index'>time</span>: 481</li><li><span class='xr-has-index'>lat</span>: 192</li><li><span class='xr-has-index'>lon</span>: 288</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-f2991a50-8b73-46a7-9082-161fb55e1d94' class='xr-section-summary-in' type='checkbox'  checked><label for='section-f2991a50-8b73-46a7-9082-161fb55e1d94' 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'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>1975-01-01 00:00:00 ... 2015-01-...</div><input id='attrs-26d55282-2b87-4ffb-ac63-db5fb41053dc' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-26d55282-2b87-4ffb-ac63-db5fb41053dc' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5e98c486-23b8-4738-861e-7a60f6fa6ac3' class='xr-var-data-in' type='checkbox'><label for='data-5e98c486-23b8-4738-861e-7a60f6fa6ac3' 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(1975, 1, 1, 0, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(1975, 2, 1, 0, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(1975, 3, 1, 0, 0, 0, 0, has_year_zero=True), ...,\n",
       "       cftime.DatetimeNoLeap(2014, 11, 1, 0, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(2014, 12, 1, 0, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(2015, 1, 1, 0, 0, 0, 0, has_year_zero=True)],\n",
       "      dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>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-831e5922-5c7c-4544-918e-c530b21055b7' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-831e5922-5c7c-4544-918e-c530b21055b7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f676900c-6d8e-4086-9209-3a3cb38fe9b9' class='xr-var-data-in' type='checkbox'><label for='data-f676900c-6d8e-4086-9209-3a3cb38fe9b9' 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-e0d375b4-e917-4da8-82d5-68aa48b074c7' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e0d375b4-e917-4da8-82d5-68aa48b074c7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-86273cb8-cb26-4e25-9548-932600214699' class='xr-var-data-in' type='checkbox'><label for='data-86273cb8-cb26-4e25-9548-932600214699' 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>member_id</span></div><div class='xr-var-dims'>(heatwave_label)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-7a0ede65-a7c4-4d1a-8fa7-32cfbaed44b8' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-7a0ede65-a7c4-4d1a-8fa7-32cfbaed44b8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9a15e259-dd1b-4f33-a942-547daeb7fa67' class='xr-var-data-in' type='checkbox'><label for='data-9a15e259-dd1b-4f33-a942-547daeb7fa67' 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;, &#x27;r1i1001p1f1&#x27;, &#x27;r1i1001p1f1&#x27;, &#x27;r1i1001p1f1&#x27;,\n",
       "       &#x27;r1i1001p1f1&#x27;, &#x27;r1i1001p1f1&#x27;, &#x27;r1i1001p1f1&#x27;, &#x27;r1i1001p1f1&#x27;,\n",
       "       &#x27;r1i1001p1f1&#x27;, &#x27;r1i1001p1f1&#x27;, &#x27;r1i1001p1f1&#x27;, &#x27;r1i1001p1f1&#x27;,\n",
       "       &#x27;r1i1001p1f1&#x27;, &#x27;r2i1021p1f1&#x27;, &#x27;r2i1021p1f1&#x27;, &#x27;r2i1021p1f1&#x27;,\n",
       "       &#x27;r2i1021p1f1&#x27;, &#x27;r2i1021p1f1&#x27;, &#x27;r2i1021p1f1&#x27;, &#x27;r2i1021p1f1&#x27;,\n",
       "       &#x27;r2i1021p1f1&#x27;, &#x27;r2i1021p1f1&#x27;, &#x27;r2i1021p1f1&#x27;, &#x27;r2i1021p1f1&#x27;,\n",
       "       &#x27;r3i1041p1f1&#x27;, &#x27;r3i1041p1f1&#x27;, &#x27;r3i1041p1f1&#x27;, &#x27;r3i1041p1f1&#x27;,\n",
       "       &#x27;r3i1041p1f1&#x27;, &#x27;r3i1041p1f1&#x27;, &#x27;r3i1041p1f1&#x27;, &#x27;r3i1041p1f1&#x27;,\n",
       "       &#x27;r3i1041p1f1&#x27;, &#x27;r3i1041p1f1&#x27;, &#x27;r3i1041p1f1&#x27;, &#x27;r3i1041p1f1&#x27;,\n",
       "       &#x27;r3i1041p1f1&#x27;, &#x27;r3i1041p1f1&#x27;, &#x27;r3i1041p1f1&#x27;, &#x27;r4i1061p1f1&#x27;,\n",
       "       &#x27;r4i1061p1f1&#x27;, &#x27;r4i1061p1f1&#x27;, &#x27;r4i1061p1f1&#x27;, &#x27;r4i1061p1f1&#x27;,\n",
       "       &#x27;r4i1061p1f1&#x27;, &#x27;r4i1061p1f1&#x27;, &#x27;r4i1061p1f1&#x27;, &#x27;r4i1061p1f1&#x27;,\n",
       "       &#x27;r4i1061p1f1&#x27;, &#x27;r4i1061p1f1&#x27;, &#x27;r5i1081p1f1&#x27;, &#x27;r5i1081p1f1&#x27;,\n",
       "       &#x27;r5i1081p1f1&#x27;, &#x27;r5i1081p1f1&#x27;, &#x27;r5i1081p1f1&#x27;, &#x27;r5i1081p1f1&#x27;,\n",
       "       &#x27;r5i1081p1f1&#x27;, &#x27;r5i1081p1f1&#x27;, &#x27;r5i1081p1f1&#x27;, &#x27;r5i1081p1f1&#x27;,\n",
       "       &#x27;r5i1081p1f1&#x27;, &#x27;r5i1081p1f1&#x27;, &#x27;r5i1081p1f1&#x27;, &#x27;r6i1101p1f1&#x27;,\n",
       "       &#x27;r6i1101p1f1&#x27;, &#x27;r6i1101p1f1&#x27;, &#x27;r6i1101p1f1&#x27;, &#x27;r6i1101p1f1&#x27;,\n",
       "       &#x27;r6i1101p1f1&#x27;, &#x27;r6i1101p1f1&#x27;, &#x27;r6i1101p1f1&#x27;, &#x27;r6i1101p1f1&#x27;,\n",
       "       &#x27;r6i1101p1f1&#x27;, &#x27;r6i1101p1f1&#x27;, &#x27;r6i1101p1f1&#x27;, &#x27;r7i1121p1f1&#x27;,\n",
       "       &#x27;r7i1121p1f1&#x27;, &#x27;r7i1121p1f1&#x27;, &#x27;r7i1121p1f1&#x27;, &#x27;r7i1121p1f1&#x27;,\n",
       "       &#x27;r7i1121p1f1&#x27;, &#x27;r7i1121p1f1&#x27;, &#x27;r7i1121p1f1&#x27;, &#x27;r7i1121p1f1&#x27;,\n",
       "       &#x27;r7i1121p1f1&#x27;, &#x27;r7i1121p1f1&#x27;, &#x27;r8i1141p1f1&#x27;, &#x27;r8i1141p1f1&#x27;,\n",
       "       &#x27;r8i1141p1f1&#x27;, &#x27;r8i1141p1f1&#x27;, &#x27;r8i1141p1f1&#x27;, &#x27;r8i1141p1f1&#x27;,\n",
       "       &#x27;r8i1141p1f1&#x27;, &#x27;r8i1141p1f1&#x27;, &#x27;r8i1141p1f1&#x27;, &#x27;r8i1141p1f1&#x27;,\n",
       "       &#x27;r8i1141p1f1&#x27;, &#x27;r9i1161p1f1&#x27;, &#x27;r9i1161p1f1&#x27;, &#x27;r9i1161p1f1&#x27;,\n",
       "       &#x27;r9i1161p1f1&#x27;, &#x27;r9i1161p1f1&#x27;, &#x27;r9i1161p1f1&#x27;, &#x27;r9i1161p1f1&#x27;,\n",
       "       &#x27;r9i1161p1f1&#x27;, &#x27;r9i1161p1f1&#x27;, &#x27;r9i1161p1f1&#x27;, &#x27;r9i1161p1f1&#x27;,\n",
       "       &#x27;r9i1161p1f1&#x27;, &#x27;r9i1161p1f1&#x27;, &#x27;r10i1181p1f1&#x27;, &#x27;r10i1181p1f1&#x27;,\n",
       "       &#x27;r10i1181p1f1&#x27;, &#x27;r10i1181p1f1&#x27;, &#x27;r10i1181p1f1&#x27;, &#x27;r10i1181p1f1&#x27;,\n",
       "       &#x27;r10i1181p1f1&#x27;, &#x27;r10i1181p1f1&#x27;], dtype=object)</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'>...</div><input id='attrs-6fe89209-af6e-46aa-bda3-59c84be74d02' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-6fe89209-af6e-46aa-bda3-59c84be74d02' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4ed186db-15c7-4427-a37f-b80a2960ca8c' class='xr-var-data-in' type='checkbox'><label for='data-4ed186db-15c7-4427-a37f-b80a2960ca8c' 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'>...</div><input id='attrs-d9b3258a-4248-4d67-b589-b7ec02cfc379' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-d9b3258a-4248-4d67-b589-b7ec02cfc379' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6b5d90d8-4822-46aa-aec7-64d408c7b4b5' class='xr-var-data-in' type='checkbox'><label for='data-6b5d90d8-4822-46aa-aec7-64d408c7b4b5' 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([ 1,  2,  3, ..., 11, 12,  1])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-b7fb81da-1e54-466f-b895-a9b835498701' class='xr-section-summary-in' type='checkbox'  checked><label for='section-b7fb81da-1e54-466f-b895-a9b835498701' class='xr-section-summary' >Data variables: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>labels</span></div><div class='xr-var-dims'>(heatwave_label, time, lat, lon)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-9e44134b-209f-45cf-9019-7b118f573828' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9e44134b-209f-45cf-9019-7b118f573828' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-62f92133-a7fa-4098-b99a-ec93718e91be' class='xr-var-data-in' type='checkbox'><label for='data-62f92133-a7fa-4098-b99a-ec93718e91be' 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>inital objects identified :</span></dt><dd>5750</dd><dt><span>final objects tracked :</span></dt><dd>370</dd><dt><span>radius :</span></dt><dd>3</dd><dt><span>size quantile threshold :</span></dt><dd>0.75</dd><dt><span>min area :</span></dt><dd>314.0</dd><dt><span>percent area reject :</span></dt><dd>0.2797669497236017</dd><dt><span>percent area accept :</span></dt><dd>0.7202330502763983</dd></dl></div><div class='xr-var-data'><pre>[3138490368 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>SSTA</span></div><div class='xr-var-dims'>(heatwave_label, time, lat, lon)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-1ccade0d-42fb-4453-b0f9-73bf6f7b9858' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-1ccade0d-42fb-4453-b0f9-73bf6f7b9858' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3f145950-46a1-44b5-8090-c5485b9845f1' class='xr-var-data-in' type='checkbox'><label for='data-3f145950-46a1-44b5-8090-c5485b9845f1' 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>[3138490368 values with dtype=float64]</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-6475b752-3382-4e01-a76c-46b933791dc2' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-6475b752-3382-4e01-a76c-46b933791dc2' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:    (heatwave_label: 118, time: 481, lat: 192, lon: 288)\n",
       "Coordinates:\n",
       "  * time       (time) object 1975-01-01 00:00:00 ... 2015-01-01 00:00:00\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",
       "    member_id  (heatwave_label) object ...\n",
       "    quantile   float64 ...\n",
       "    month      (time) int64 ...\n",
       "Dimensions without coordinates: heatwave_label\n",
       "Data variables:\n",
       "    labels     (heatwave_label, time, lat, lon) float64 ...\n",
       "    SSTA       (heatwave_label, time, lat, lon) float64 ..."
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "blob_files[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "06426299-18a0-4cbc-9950-0cc7da9b4b3c",
   "metadata": {},
   "source": [
    "### Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "758bb5e6-6589-41cc-a451-6d1122d12e1e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 5.2 s, sys: 11.7 s, total: 16.9 s\n",
      "Wall time: 18.4 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "# Arctic circle is approximately 66°N, which is why our lat is 0:160\n",
    "file_number = 0\n",
    "cumulative_SSTA_shapes = blob_files[file_number].SSTA[:,:,0:160,:].sum(dim='time')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "5a81b798-9625-40a4-bd00-aab3bd7103af",
   "metadata": {},
   "outputs": [
    {
     "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(land_mask[0:160,:])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ec65073f-5002-4d38-bb0c-97a0c02220cc",
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.contourf(cumulative_SSTA_shapes[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "35d6e7bd-e709-4db6-b47e-3bbee4cd1852",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import MinMaxScaler\n",
    "\n",
    "# mhw_id = 0\n",
    "\n",
    "# data = cumulative_SSTA_shapes[mhw_id]\n",
    "# scaler = MinMaxScaler(feature_range=(-1, 1))\n",
    "# scaler.fit(data)\n",
    "# scaled_data = scaler.transform(data)\n",
    "\n",
    "# plt.contourf(scaled_data, cmap='seismic'); plt.colorbar(); plt.contourf(land_mask, cmap='binary'); plt.show() # where are the hotspots? what is the line?\n",
    "# plt.contourf(cumulative_SSTA_shapes[mhw_id]); plt.colorbar(); plt.contourf(land_mask, cmap='binary'); plt.show()\n",
    "\n",
    "scaled_data_full = []\n",
    "for i in range(len(cumulative_SSTA_shapes)):\n",
    "    data = cumulative_SSTA_shapes[i]\n",
    "    scaler = MinMaxScaler(feature_range=(-1, 1))\n",
    "    scaler.fit(data)\n",
    "    scaled_data = scaler.transform(data)\n",
    "    scaled_data_full.append(scaled_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "afdb90a0-48a0-4444-90d4-70545db02060",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "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": [
    "plt.contourf(scaled_data_full[0]); plt.colorbar(); plt.show()\n",
    "plt.contourf(scaled_data_full[1]); plt.colorbar(); plt.show()\n",
    "plt.contourf(scaled_data_full[2]); plt.colorbar(); plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "00daa6b8-ea72-401c-a069-3606a0b1b591",
   "metadata": {},
   "outputs": [],
   "source": [
    "# cumulative_duration_shapes = blob_files[file_number].labels[:,:,0:160,:].sum(dim='time')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "9bda1e3c-3429-455e-aa32-dd8f3884f8a1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(118, 160, 288)\n",
      "0.2966101694915254\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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68+W9ERXBuHBMf+5+IjIBVVqQ49ODb+5i8pyWSBcjILW1KfzyltOorDzSim0YCcuWlJKSsZHZ35oSwdKJRNfU0iIBVQh3qqsjXQIRgKgIxiL6xFR3CrMJ7A7+3/M7KRhqJbufjYxsh8+rSUnz/TnB4u61X3p5InV1qdz7uyUMGdJAktnBhx+O4L8vTOWT9/aSmpXM5FPHRqC0TjFzfgSZ9DMWQvhiwYgRLNmzJ9LFABKnb3FPvnyIj4lgHMoLj7QWx4Yx01q58rbybjenbds5kA8WT6S+MY20NCsZRfsZNLzZ79e4/relnPytehx2eOI3RRzckxKEkrt36ndqOemiesB5493EY47u3lFfn0LBwGaOmVV2eNnll2/FbHLwzHPTee+ptYyYWUx6blrIyumJhEEhok/MdKdwbVHNz49cOcIoGsJxTJwbUSAmgnFX62WwWzElFMeOsy6v4ZiTmgDnjXLPPjeVV96awLBh9Vx62TbeeGMcv/r1Av773NukpPg3pFlWPzuzFjhfY/u6ep5/qCBo5Qc45eJaxs9sJSPbzvzz6jG6vPscDnj9jXFs2ZpPVWU6zc1JlFdkMGZMDZ9sGMlbK8dT1ZBBflYLv//+Ip7/32QsaalU7K5hxKzwBuNEDcVSX/imK6QFO6xJ940Y5q6LQXV1fIRjT9vWJT8/ouE4UUOxP/VFTARjOHJR6u2OcLlbPD6MmtzKlOOO9A82mTXHnt4IgM2m+NfT03n7nbGcecYubvzJakwmjVKaxx6fRVNzEikpbX6/9ptvjWXhohEU9G/g29fvxeFQLHk7h9oKs8fnJKc4OOt7NRiMntdbUGwlb1wKT/1rLmjFq59aufSSLQwc0MLqNUVs25bPmrWFDB7cSEFBM0OGNDBjRhnZwzq454VTKc6vZ2RhDcu3DeH2f5/JKSft4cOFY3j9oWXc9PhFpGaFrnXblby3hC+6Lkqe7giXWdkSSCj73XatO1QB21MLd2/b1PNvEfwAIO8v38RMMO6pt9bjmOofG8XCuR+Hjm2jcKiVnHwb1/669HAf4erqVP719HRWrE5n0qRKHn54Ntu+6c+FF3zDtT9ah6FziprcnHYADh7MIiO9g5QUm8cxgT1ZtGg4Tz41A4B9+3J447VVpKbaGDmpjQd+NuSoxyelODjmpEbOv7qaacf3fqNfVXUqv77nGJqbk5h73EF27urHH++fB4DB4CA3t53vf28jl1+2pVu5//7ucSSZbPznl6+RbLbz2Iez+c/iGTx873t8uHAMHcpE9f5aiicX+baxXpAbU4+Q/RAcnlqPvW1VltbivkVld4pw3YwWivDpLuAGINStxjI83BH+7oc+g7FS6hngXKBSaz2pc9kDwHmAFdgN/FBrXa+UGgZsA7Z3Pn2l1vp6v0rmBdfg1vPCJRey2PLwe7tITXeG4fLydB7/12Rq61LYty+HurpUPv9iKAAZGRbuvGMZ808o6fb8iROryMy0cNfdJwOQm9PGfX9YwvDh9V6XYfmKweTltXLrLSu5+1cLuPW2U5k6tYLjZ+52+/h7/rWPY05u6nO977w7mscenwXArbes4JST99HRYWDlykEog2bSpCpysi1HPU9rKK3NJCPFSrLZ2T1kyrByAJ5YOIehQ+rYb8vh/X8s4wcPXUBKRrLX29oXGfc7tkVzve3axaLnchEnfA2PgfQ5DkdLdIzo+kAk4Tgw3rQYPws8AvzHZdki4E6ttU0p9SfgTuD2zr/t1lpPC2YhexOui3bPllMJC8Fz1wvVPPn0TNotJjqsRrZs7U9zs5mRI+sYNbKW88/byeNPzmDc2Bp+9KN1bkNkv37t/OPhj1mxchAWi4lnn5vKJ58O49ofrfeqDJu35LNh40AmT65k2tQK/u+2Ffz1b3PYszeX0aNr+bh0g9fbc+hQBgcPZbHq6yJ27erH7t25jB5dw89/uoqRI+ux2gyU1GRjyFeU1mSzdWUBbRYzORntJJns1LeksHFvASXV2dQ0pTN+VDlvbx9DgyWF/mktnD3zG177ciL/vf1lbrn5VOrI5fm7PmTmGWMwmgyMOX64TyG5r28F5NuXmPQsUVxvh+Oi7allWoRItITIQLtVBLIdfT23s0U7GK3GfX0rEHXfGsSQPoOx1vqLzhYF12ULXX5dCXw7yOUSceaEc+u585/7u91wBvDqa+O5/8H5ZKRbyci0kJxsZ+jQem64fg1DhzQefty/Zr3f52sMHNjChRfsAOCVV8djtxuOeozW8Nlnwygrz0ApjcnkYP2GgaxbV0hqagdX/2A9ACfOP4BSmj/eP49+uZ77LB88mElJSZZzAo5WM19/XcSatYWAs4vEhPHVHHfsQX7x81W02c38/qWTWLh+FDZ79w7JqUkdtFmd/ZiNBgcjC2uYNuYQG2z9WdFSwPIvB6JsCuVQXDh0Ow5t4D/LZ/L3hxdyyy2nUFWfzoevbgetyXtrCz948DyS05N63V8SeL0Tix+Cpd4WIRUNITiYZfAm0Aa6vh5B3d9wLIE39ILRx/hq4GWX34crpdYBjcCvtNZL3T1JKXUdcB3AwIEDg1AMEWnX/eYQ889rOGq5waDpN/DoPr8HDmTxzL+nMe/4A9x22wqSzMEZR7i93UhraxLZ2e1H/a2kJIsH/nJct2X9+7dw1ZUbOPusXWRlWbs9FuCOu04hJ6edoqImDAaNw6743vc2M3VKBTf97EwsliNvo/z8Fq74/kYmTKjm4MFM7HYDF5y/g4r6dH7y2AVUN6Zx/uxtTB1eTlG/RoryGslOs2A0aFotJmx2A2aznQON2fzs4zM5aEpFjWjFok0UJjczrbWJ90pGcflxW3h7xUS+v2A9DzywmPv/NJempiRqatOosXTwyq8/4LL7z8WUFLO3EURcLAZiH8R9vR2VfW3DIG63OZjh1Jd1+fu65eVHlhUUHOn/7NIPesGIEYcfEumh3OJJoN8MBXTVVErdDdiAFzoXlQFDtNY1SqmZwFtKqYla68aez9VaPwk8CTB27FgdSDn84alvsic9b0RLhK4VPbfxN8/sJSPb/VBoWbl2ho07Ooj2prYuFYDzztsRtFAMUF7unClu/foCqqrS2b8/G7tDOcNnq7NV9q47lnHccQexWo2kpNgO38Tn6jvf3saI4fWUHMzi0KFMysszcGhFbW0qv7v3BJ564n0sFhMTJlRx680rSE21kZlpxWjUtLcbufMuZ3/nE046wM+fOpfG1mT+ecM7TBxS6bbc+5py+H/L5rGhYiA2FCrfSnum5vjcCjJNVj6uHMYFY1fx2f5hDB1SCyugsS2FGSNLeeyfHwGw6utCfvPbBRzYW8mq1zYw9/KZQduvIj7Ear3dFfgCuejF+7TScRWKQ9Eq7evNed6WoefjXEOxl6JhnON4EIz3td/BWCl1Fc6bO07RWmsArbUFsHT+f41SajcwBlgdcEmDqGfADUaojbeblNx9zT73zKOukwExGp1h2OFwk0q91NZmYseOflRUplNU1MSkidUMGdLA5Zdt4r33x7B3Xw5DhzZgSnGwv6MfHVlGik9sYWdbf5L3ODCbHDS3JWF3KEDRP7uFCcWVKAVJSQ6OO+4Qx3Go22su/mwoDzw4l5tvPQ2ArVv7YzJpcnIsOBzwyHvHYjA4uOvOZYwcUcvf3jmekqpsHrn+3W6hWGv4urSIDZUD+aY6j4V7h5OVYuWCyVv5pH0QlbY0vl+8jXvGruSJfZP5qHI4Uwc6n7+lpr9zHxq6f6AYPszZYm9OTaJ0eyVaa5Svw3OIuBXL9XYXX24s6q3VWG5QilKh7qbhaf09Q7M/odiPQCyij1/BWCl1Js6bNk7UWre6LO8P1Gqt7UqpEcBoIOo/AnkbavsaJ7m34eNEdw0NSaxf7/wqtqXF8xjBXRwO+Nvf59DeZiIzy0JzUxJlZRns3JV3+DGpqR28/uprGAxwxfc3M2pWAy8vnUJFcy51zanUWlPJSWunpDWFTYs8D2+WkWIhN6ONjBQrJqODtGQrx4w+RG5GG6lJHWQUWJh3wn6sFhOTJlYxbFj94W4bLy2dwgufTwPgvV8/x1srJ7Bo/Wh+cMoapo8ow+5QbK4aQHVrKov3DePN7eNxmB2k5LZjGdZBGUb+1zyabJOFf8/4mBPzu4fy9CRnV4+GNue4xQNyus/0V1fvXD5nQQXLXqxm7btbmHn+pD73r4h/8VRvBzMc9/Y8ESHBCMf+3HznbYtyiMKwfFiLDt4M1/YisADIV0odBH6D827mZGBRZ2tU1/A+84F7lVI2wA5cr7WuDVHZ/RbucY597bYRSaHeLy2tJrZs6c8/HjmG6up0MjIsjBlT0+fzNmwcyKJFIzz+PSurnXnzSg73Y35u8XQe/3AOhbmNjCiopX92C5eesJFjRjuDZkNLEiv3DCHL7AzBRoPG5lA8v3EKayoKqLaZqXQk49AKe7uBlR8MQbl8cZyd1kZ2uoU2qwmrPsRXO1vosBvZsv9Iv8u/vT2XRRtHc8K0vTj62/n1kgWsKy9gT30uDpPGntdB9qgGah0pWDFy9sC9TMuuIs3YwYn5BxmUemRs5ApLOkblwNF5Q2FFTSb5WS0MzO4ejEsPZQJw2Q2HWPbiEMp2uO+2IXoXC+/V3sRjvR1In2F/nhuMrhvhErYuFMEKrL212gZLX+vqLQD3NXZxCFuGJRz7L1j7zZtRKS5zs/hpD499HXg90EJFgi9dIVynqO56brBfIxJCHYrr65P5+S9Pp7IyA0NnF4D2dhP/d/sp9Mtt5zf3fNHt5jdXZrP7vs3FxQ08/NeFpKbaAGf3hCc+ms1zi2dw+vSd3P3dz0gyHeluoDV8uHsU/1w9iz31uYzLq+K7E7ZisZl4a8dYttfkU5zVQG5mGzaHAa0Vu+r6cdrJuzln+C5sNgMNzSns2tsfq82IBhZuHskHa8eiNGggPaudurQkXm8aBSM1H7QOxbZ1KGmpFtIzrEwqLmG3JYt2h5HxOTWc0r+EMwbuozCl9ajt0xpeODiO/5WM5fi8UtZXOqeprqtN5bgRhw73jV6+YhCffTaM9esLGNC/heLi4HZ7EbElEeptfwKE6+PjpT9uWLYjHIHVm9dwN95xb8/zJfx2LettfcEIxPEw/XWcS9hb1gNtNXYNuPEw014oy9/ebuSBB49j/YaB2O0Gvv+9jTz/whROO20PHR0GliwZRnl5JtXVaR6D8aSJ1bz68mvs2t2P7Ox2ksx2tFYMGNhy+MY9rZ0zxb20dCrnHPMNd1z8BabOfsxNliRKGrN4ZdsEXtk6kcGZjdw462ue2ziFe5eeCEBeZjNnH7OVlFQrKUY7VxRvY2haE39YOo//bZnMon0jD5fnsombmVt4gMfWzqRmsIkUYwdpZhv1lmQadAo6rwOVbcOKM7lqFA0YaTOYwZ7GmMw67hm3kvGZdR73m10rfr11Li8dGsuJ+SU8MGEp1717LrnJbTTtTmPUTGdLe0lJJr//w3yys9uZObOM712+ieRk9x8kRN+i+cOr8J+7CUViPRyHvPzBGtnBn9fp6+++BmnwruzBbg0uKOj7MRKWAxbMVvaEDcaxKNgjYYQrzH+9uojlK4qZM/sgF16wnVdfnwBAa4uZL5cXYzA4uP+Pixkxor7X9WijYtKkKowGBzsO5ZOa3EGS2YHW8NziGby6bBK1zWmccMweKrKSufaDc0Ar6i3JbK85UvFcMXkjt8/9EoOCH05dT01bKi+Xj+bpA5N4o2YkueZ2WmxmXjk4hkenLeaO47/k5OF7sTsM7G3L4rmSCfynfCz/KRsHWTbsOVY60DQpACsO7QzDpw8oYUyGc5tOzi9hRHoDmSarV1NVaw13bJnH66WjuaRgO02lGZz09VXYtYH0cjtZSVb2fZ1JxaQ0Nmx0dt/4ywOLGDSoe9eKDR9s49xbT/bqOAkR7WItzPYsazAu3hHf/nAFYm9fx12Q9RRG3Q2lFio9y+Ap/LpZLt0pIkuCcZgFszuFP+uKRMt2XZ3zhrCf/fRr9u7LYW3nBBhfLi/m+LklXHjBdvIHtXHvSyexasdgbHYDSWY7mSlWmtqTyEy1YFCa3eV5pJhtpCVbqWlKJ8XcwdWnraGsNpM3V05kzpgSjh+/lod3zaKt0cT4fGcl2D+tldNHrCI3o5VjCw6xqTWPn248CZvDQJLBztf1BVRa0jhr4F7uGrOKQaktlLanc+XqM7hl03yWzHuNuYMPUWlJ5aYv59PkSCY110Kbw8yxuaUcn1fGHlsme0jHgaK/aufEjDKuKNxBqTWNr5oHsNKSz3Z7JvmmdpocSRjQnJx1iAyjze0+e3DXTF4vHc1lBdv5aNV4DEozzNhI2YFszhq7m5SydpZ+NgSszhA9YEAzRUXNbtclRLwKR4AI5mv4u66IhGF/hyrzhruQ6Ot6fAm3vT020Jny3HENxV3rlVbhmJHQwdjd2MTh+BrVn5vxehsJw9v1RCIUb9/Rj48/HklGupXc3Haysyv45S9WsmTJMMaNq+Z7l2/GaNRc8/eL2F3ejwWT9pKeYqW1w8Se5lwyTTb62S2YHQ5mjjtIdUs6zVYzJ/XbzRebh/HIwmMBmDimnNMWbGdDxUBq2lO4dOZGhg6oZ2dLDrlmC6Oyq/jx+lNhv7NcBcktZJstWBxGpmZVccWQbczLK6XZbuKbtmySDXam5FTyVukYZn9+Gc/N/JjFVcU02ZxTLV8/fCPLagZxYn4pg3Pr+aiyiPEpdYxMaeSzxiJeaRrB+y3FtDlMOFCkGzpocXQffWO3JYtfFmw6ap89d2A8j+2dyvkDdvPF+pHkpLSTVWqnqiaDC2d+w83nLeWmn54FwIqVgwG46MJvvGqJFiLehCNsBvNGPF/LG/EW4t7423rsy/P8ad3tek5f3Rhcy+FtmfoKuH2FYtebDyUsR6WEDsbuRGMrbLT0X7ZaFEnJ3o/p39SUxC9+eQYAM2eUUV+fQklJFnOPO8jpp+09/LhdZf3YWjKQX5z/JZec4AyK33vzQtZ1FECH8zHJRhuWA8NcVg5kdv4AK+2FrPy0EKPBRs6IBp6rGwd1kGtup9GWhL2ze4NSmj+M/5JLinZ0m9Sjw6FosRu5bu98mhzOqZTNyXZuHbOKV0vG8ZP1J/P6nPfY2pjH5zWDee7ABN4/7k0WNw/i8cqJzE6v5I6idZiV5sr8Hfzm4Cy2t2dzds4BLs7dQ77ZQrvDyPrWPF6sGckeSzYnZpYdtc/eLhvB7745jpPyDrB7ewEWm4kpqoK1VcXcMH85pmY7N950FuXlmdx6ywpKSzMwGOD883Z4PA6ezul46BsvEk8ku1P4Emp7K6O364lYKA5H4O0pkO4NnvoSl5c7g2pBwdHrD8V4yd62FHsRiD2dI7HWnSgWSTAOUCDBwpsQHk3B5bzhU/i4dIPXj09N7eCEeQdYumwIGzYO4PLvXwRA8eAG/vzQYkqqsnFoxbOfzsBktJMxoI2HVs6hqjWddRWFzCs+wN3zlvLSlkmApiizmfzUVjKSrLTZzLR2mHFoaLMbaVZmSuwZLK8vYF9bNr8cuYazC/aSZu7gw9ohLGsspJYk7Bj4X+sIFu4rJNdoodlhptGeRIvDjBEHaYYjXRvGp9Rz7eAtFCW3cvOmBcxf+l0ADMrB8YNK+F35TEqsmRyXUc4tBRsxd47nlmqwc+/g1VgcBrJNzmS/uz2Td+uH8lnjIJINdn46cBMz0rtXzEtrirh98wnMzikjucrIvvoc8musrKsbTFpVKy8+PhGAKZMruO5H6zjuuO5jHPfGl28pJDCLeBaOUJEQwSWYwdJTKA7GawQzEHvbwuspFPvYQuzLNxUSmIMn4YOxuxDgbatxtLQUh3MYOK3x+it7k0lz151f8vgTbbz9zljn8xU0DMzgnN9defgmNYNyMGNOCbd9dhpmg53+aS1MyK/iF7O/Ymh2I7fPXU55exp/3z2dDVUjaOpIokMbcGhFssFOpTUVq8NIXlYLRfmNTDc2stBeyMJDhYfLkm7o4OSMUgqTWjGg+aYtB6s2UJjUSpaxg2yjlTaHkZXNAw+3GO+1ZLK3PYsT8g/x54lfsL6hP0UZTXxhHcgeRwajVT3/V7ieeRnlR+2TFIOdFIOdPe2Z/Lt6LOtb80lWNs7J2c8lebvJNnZ026fvlo/g9i3zGJHewHey9/DrtSczsK2NPGWhMKWMstZ07vnjUgYNaiIvr83v49fzXOl5/suIDCIW9AwB4bxZqa/X8jacRPUNVu7GGvZ3ZAjouzU41LPdBfoa3gTartbinqE4CN0lep4rPc//qD2PYlTCB2OIzhayaCtPl5f+MYDLfubdpBFaw/9enMjb74xl5sxDGJNgs30QJS25XDZ/AzNHlmIyOhiQ3cSjm2ZBFay6+mmSTd2HGvu0sphr15+GUTk4Ie8QYzPqSDLYMShNGSkUGQ1g0LRoM6NT6hmS1ER/czvpBhtm5WBsai2tOMg22tEakpTmvNx9JKmju4X8IH8HB6wZfNOew/PVo7ml5DgUmnNyDnD+kF28UDMKs3Lw0JDljE7xPFZwu8PIk5Xj+bSxiAyjjR/mf8Pp2Qe73WxX1p7GV3UFvFc2gsXVQ5iRXcG1A7Zy+yenMjyzjprdOVxzyZc8+5fJHDvnEFOmeD9Zh/a+x4uEYZEwQt2iFuz1R6wV0JtQ7A1vbnoLhZ59eP19LW9DracRKELYh1jCcOhIMPagt1bYUIZWf9ftzVflwfgA8Oz9hWRk2znvqt5nqysty+DDD0fy2usTOOXkvXz7+9u4/bmzqK5L5w/fX8TJU5wzzjZbzdz2yal8fmAYI3LqMBuPHn93Y6OzcrmwcDcPTFqK1vBW3TDeqx9CtS2J8Sl1DEpqYXRKA2dml2BQzmBY5zBSYktmpTUVO92bdBWa/kYbBcYORprbyeiccEQpGJrczNDkZqal1bC8aSC7LVm8Vz+U9+qHAvCzgZt6DcUAB6zpLGoczJiUen43aDUZRhtNNjP/3j+Br+oK2dCQT4UlHYBMk4WbR64ht62D2xadxuCMJjp2JlOY20SBuYHGxmRmzSrttv6WRgOfvJaL1WJw9/Ic3J3ssWzSQiziUbBacv197UCe11u5wx6OvQ3FfYXNQLtH+Dtxhz+v5e/6vR2SLQikhTh8JBj7IFpbcV311a0iGOH4kTsH095iYMb8JkZOaj/q758tGcoDDx6H1opj5x2kJDOPy/9yKZmpFv5+3XuMHFTDK1vH83XpIJbsH0pLRxK3H/cll0/ajMElvzZ2mNnbms3QtCZyzO00diRhdRj4a/lkljUXMim1lusGbGNORlW317doxfL2DA52jiAx1GRhalIr7VphUhqLNlBhN1NqS2KjNY39tiTmJDfT4DBh0Qqj0ow0WxhobuOifvtwOOCUnH00OIwoZceoHNTZjeS6CfFdyqzO0Htl/g5SDXZeOjiGv+ycSU1HKkPTGjg2t4wsbaO6JoORafW8tHQ6te2p9FPt1G/KJC+tjT9d8SF/+9NsUlM7mDXzyI167a0G7v7eCLatSQ/kMAKex8aOxm9RhPBHLPS7jIpuFcEKxF163vDmz4QcvT0uEiM69DbKRRiHZfM0Prb0Mw4OCcad+goCoQoJ0T5NtCf/+kMRAB+XbqC0NIPX3xxHQ0MyDfUpbN4ygBGT6hg2vYmVe4dQvTedK05axwVztlHUr4lfLDydhXtGMiCtmWMHHeT0EXs4b8zObut/6eAYfrPtODq0EYA8cyuzBpZye8kcdlmy+UH+dr6Vu/eovr0tDgOftmXR5DAyPamFYrOVbIMzwGYffpSdIlMH05Nb2deRxNL2LBa25XRbzzfWVMYntVFqS8IBlNud/Y6TUFhxBuv5qU309zAO8aqW/gAMNrfw8O7pPLJnGsfklPPk6E9obkrhkdXH8HHlQMwGO585jGRhIa1Uk59i5dR5W8ltb+aPv51LZWU69/7uczIyOrDboWRXMk/dWxSUUOyO6/noel5KSBbRxpsQEC8hIeSBJ1j9iQNpJfa1ddfT4931j+75d1/5E4jDGNxdP1jF47Tn4SbB2IW7cY3jUbBaA3/11D4A/vTAXHbsyKO4uIHsLAsLTtvHO6WTWbvGwPjiSu78zufMGnWkK8C68gJOG76Hv53+sdsb+cra07hr6zzm9ivlyiFbGZLahMWg+NWh2eSZ2rm1YAMnZh091JldwydtWbQ5DJyS2kiBqePolfcwzGzFQRM2DYNMHSQpB7V2EwvbclhtySBFObBrmJXczEizhSSlqbEb+aA1l49aczg3rc5ty/F5OQf4oqmI12uH8/S+iZzefx9zVDU3vHUujZYUMk0WCltbaSlPJdmkSUdzTNEuMhraWPNGf8rLRzB2TDXX/WgdM2c4LzZvPtWfp+4t8vLo+E+6WohY4e4mvHCIihbeQAVyM12grxPK1/Vmeml/u0qAdwE4Aq3Z0tUieCQY99AVBGI5FIcr2JxwTgMABw5kccEF27n+urUArNlVxFtPTOUP31/IKVP3HPW8/LRWFu0dwYL/Xkl2soVR/Wq57bjlFGa0AJDa2QqbYbJyYv5Bkg0OPqp3TmTx5+KVDDAf3X0D4IAtiUaHiRNTvAvFXUaYLd1+H2iyMS2phZ0dKZyXXocJZ99jraHMZmazNdW5HYaOw63RADat+KB+CJvbctneloNCs6hkOBaHkYZDmTxwcCzZdgupVRrVksSA3BoyUmo5sCETg02zYUN/8vJaGTe2hku+u5XTT9tzeKxlrWHbmjSvt0mIRNEVAmK9dUyCTZj4E1qjNAyL0JBgHAVC2Z3C3bpDEfoVR4ZBaO9wnlYFue6nKL77+GV8tn8Y9e0pNFiS+Xz/UJYfHMxfTl3E8cUHyTFbuXXUah7cNYsb15/MU9M/odXhXGeG0XPgzTHYAc22jlQGmawYfZgJrtRmpsVhYLDJSorSjElqJ9tgp00bsGgDJbYk9nck06yNpCo7U5NaGG9u48vmAr5oLORQRzrVHSm0aRMDTG2kaAdVNVnUN6eSVQdb6grIa7Sg6gxcPGEj27/K5eDWLGwZZi771mbOPWcnjg4HV8wcR9UnsJRM/spU7zcgRGK1q48QIsh6Br9wDLEWbH2FV9e/exp+zdt1RVBcfKMRQRKMo0S4WqhD8TqDBjWxa3e/w7+nJTvDa1WD+xbOGYXlzCg80hdtf0MWP/v4TH78wTn8eMZaThyyn0sLd5BkcPD/dszmnBUXcsFw58xuVoeRNIP7m95yjXZmJLey1pLO4rYspiW3kmewHR6lQgOVdjMGpUlGU2Y30+Aw0uwwUtrZhxgLZCkbDhTNnf2bwRn8C40dTDK1ouxJlFqyuatiCt+055KMHbNdY7WZqW7KYEuzs7tDaocdXZaKxQKZB+0YG8Bc0canG4fSL7cVx+4DNDY28+JSxYt3jvF7/4ear+dMrH/rIh8GhDfC2UIdla3h7vryupthzt91BUtvAdbT31xDcQwFYle+njOx/s1LMD8MKO3LgKchMnbsWP3EE09EuhhHidULe7h8dGgDSsF/n5/E/16cTF5eK8fPLWHK9Ep++95paOD7CzZw6tRdDM7vfXizlg4Tv/rsJD7eMwpwBtEfTV9L5oAW/rF3GgPSWkjp106BuZWpaTUUmFvJMlrJM1kYlNTCAFPb4REt1lrS2NWRgkUbSMJBjtFOld2EAhw9hm1LwkGqwUGrLRWbLZmxadXstiXRgYFR5nYGGDswoclSmjfqRvB1c38OdWQA4LAryuqyaGxOJdtoJdPQgaXNTE1dBoZ2haldkVwN5gYHY1rKOW76QaZOraBgYAsdjRZ+dnb0hmF/hePbiXDxNhzfd/KjfKJfW6O1nhXiIkWVaKy3Y/WiHhHB6mPs6TldATkYw7N54hpSe45d7O3zwPOwa+PG+V6mGNQzUMby+8jbcHzSSSd5rLMlGPsoli/0wfatH1dy3T1ldHQYeP+D0Wze3J+vVxfR0WFk5IRaanOy2FufB8DowmpOnLyXQf0amTK8nKJ+TUetT2vYVZdLSWM2n+wZzls7xnFC8X7GFFfxz/JJDM5uYERuLa3KSKs2d3tuirIxJLmZ4qRmRiQ3MTfzEPUOExvbM2nSBgwOIzkmK4UmK6nKQQewvy2HMmsmJdYMNrblHV7X6JRaxqfVkKmcM+YZlObThkHssmSTi5VtNfk0W5IxWcBanoZqM6A6A3eS3Y6qN2Bu1STX28ioamXmuFLuvONL0tOOjGCxc2MqN50ZP8HYlxbWWHoPeTsDpgTj6BXLF/mwCNZkHu6e62s47rmuYLXO9hWEez4mjEOvRZIvLayx9D7yZrt6C8bSlcIHsXRBD4c3nhgAWjFmais5VHDm3FJu/uVKPv9iKP99fgr1W00U5VYxanYjVTqTfy08BoAkk40rT1rHt+dtJivVgs1uQCmoakxjeHY9o/vVcfKwfYzPr+ZPK+aytGQoEwdVkmFo5fN9IwDon9xCltnCoPRmijKaSDbbsCoD61ry+bRxME9VjSdF2WjXJlKUDas2HtVaDM7gm2dq55jkKtZUFFKrktjZodjTnoOdI5NnKK2prMlic3MmWVYbjvpkzFY7Zwzcw8r1Q9EWKM5uJMNgpfxAGlTaOWXBPq7+43qysqzhOSAR4mu3AxkjWYgo0rPV1V0rrC/rcn1OV9eK3gJmb8OuefN63q7HNQz31j0izsNwF1+7HSTSGMkSjEVA3niyf7ffz72qmtMvKeW+uw9BajKPPzGTjR8PZM7sgzzyh3coq8vkmUWz+NeiY3j6k1kkm2xYbUbMJjuWDjNmo51RhTUsmLyHS07YxLmjd/LpvuHc8/kCFpj28fox77KisZCStkyabElsaszni4ohAPxq7EpuLt7E0vpCHCZNRUcqx2VUMiWtBrtW7LRkU2tLpsySxtf1BZQ0ZtNiTafRkcmHzf0YaG4ly95BRUMOtY4U0o1WrBixA2aHxtymSKoxk6NsDKCOukPpLNs2irzUZjIPtlC/JoV6TOTltfL7vy5h+DDnqB12O+zZkorDcWQ/LXq5H/HA3764Eo5FOCTKhTxoek6h7E9I7vkcd62zrv2QgxFEextn2NtAHKyyxAB/++ImSjiWYOwluYh7573n8nnvOWflcsalNfzpL4t5+ZUJPPefqfz67gVMnlzJ+eO3cMncDXy9p5jG1mTSkjtotSQxdEAdpTVZbNhbwGMfHsvryydx6rRdfPf4TdwwczWPrZlFeXM6b3zntW6vWd+RxB1b5nHf9jlsbcxjTcMA3jn2HTJNHbQ6jBiUs8+y1WJmfW0Bj+yZSofDyPjMWiwWE5Ut6ZiajNTX5+AwW2npSMIMdJCCoutNohmfX4HV0c6hHf2oTzYwobCS0vI0mraZGT61njkXHmLwoEYGFdSTltZBY50R7YAHfzmEVZ9khflIRD8Jx0JEKXc3w/UVGn1pXe4Kq/7cqOf6/L6WgYThIIuFcBzojXh99jFWSj0DnAtUaq0ndS7rB7wMDAP2Ad/VWtd1/u1O4BrADvxMa/1xX4XIUv30HHVKt2XRdle4XMD9c+dj+znh3Ab+8tCxfLZk2OHlE8ZX8ZcHP/H4vJXbB/PQW/Moqc6hqF8Dx564j39vnMaw7Ho+uOzFox5fY03hhC++S7vDxBXFWxmS1kQLRhZZijjeWMXnFcVsb3a20ubarBS1tlPRkEVzRzKmdgfGVoWpFYwWcCSBPRm0AqXB2AYGBygHKKW54ayvmFlQwm3/dyrZ2RZuuXnl4Uk4ziiK/BBr4RboezXa31t9bV809jEOR709a8YMvXrZssO/R9vwUNF+8Y4Zgc5I19fzvQ3H3ky20RsJxN0E+n6NhfdXb9sYaB/jZ4FHgP+4LLsD+FRrfb9S6o7O329XSk0ALgUmAkXAJ0qpMVpr9+NriagSimG26qpMrPxqEEs+HwpAWpqV9PQOLrxwu9vH2x2KXaV5LN82lIM1zkmcB4+t498bpzGrsJSrp613+7y8pHbuHb+c+7bP4ZVDY7A4TBiUg6z0dp5qLsRkBVODCWOzwtJmZp8jA4MN0ps1IzLquePSL8hOa0cpaGhJpsWShMI5xJvDobB0mMhKszB8YC3JysZ9/28eqak2Hv/nB4f7EFstPgycLA6L9qmno7FMXniWMNfbMnZqZIRsmK1AR6gI1vBrvnSF8FaCh+JgiIWpp/0tV5/BWGv9hVJqWI/FFwALOv//HLAEuL1z+UtaawuwVym1C5gNrPC1YD0vRpFsQY7RC6NPQrF/s3JtzDm1kUefnkRKio0nH3+f/Pw2t49tbE3iv59N551V42lsTUEpzUXHbuGSEzdywRuXYChoY1lqHsu2n8KJ1SX8e+YiAFYcHMRHu0dR155CfmorV6XvIMPcQUlLJm/sGIfFkURWhwOD1YChxsCsAYeYUFRJRqaFDqsBk9YMy62ludTEwuXj2LmzHwajc9Djjg4jNrvCaNSYzQ7a241YrSZaW03Y7QZuuvFrsrKsdFgV5w6bEvT9l4ike0VwRKrejpbZ26L1Qh1sUTH2bG8B2LU12FNXh976B4didAgJxUEXC90rfOFvH+OBWusyAK11mVJqQOfyQcBKl8cd7Fx2FKXUdcB1ACnIVLfRIlihJDXdzu+f38Pr701h7dpCvv+9TUeF4rrmFN5cMYGG1hTW7S5id3k/Tpy0lxMn7WP6yFIGZLdgtRuYVVjKkpYiSHM2YK0+MIhv7fk2VruRPfX9MCs7RrvGpgzYlAIUCk1yq4PcRisT+1dhR1HXnMTetRnsI8NtmVNSOpg+vRyjQdNYZ2TDsnTnGHJKOX8cHeCwgMOBrm3g4WsyeDgKZqeLNxKOQyao9faQ4uJeX0xakGNYsMYe7i30euJPGJawG3HxFI6DffOdu++S3XZi1lo/CTwJzj7GQS5H0MTqBdqXbhGhaC2+84kS/vnsCezclcdFF37D5Zdt7vb3JZuGc/9r82loTT287NeXLObsWTu6PS7J6OCh0xbx8NezKW3KpCiziUaVzMrdxdS3pJDcrslscjBzVClWm5G6phTqm1JwOBR2qwH22NjZ5rzxzaza0QfK0O3tJGeZmHNyLSlJHSilMShNWoqFJJMNhx2+fisf3dIQ9P0iRBTyq96eNWNGVNbbsXpx9vWr6ZB96Ogr7PozOkVvj3G3PukPLCLI32BcoZQq7Gx1KAQqO5cfBFybEQYDpYEUMJJiPRQH+7HeuPOx/Sy4oJ4PPxrJzl153PyLlZx66l5U56W3sj6dFz6fxivLJjO+uJJHb3iHdquZrSX9OWvmDrfrzEy28qt5zpt8Wi0m7vrPGTj2mhlQ3QrNGmObneoyE2azg4YKA9phQwFJ2LCX1eFobgGbHUu75fA6LS3wxQtmwHWiEPctycKzaLtJVvQq7uvtWA3FvopIKA5Wn2FvQq8E4ZAK1fkTL63G/gbjd4CrgPs7/33bZfn/lFIP4byJYzQQk9+lxWooBmfZvQksoQg1BUOc4XPnzn5kZlq6heK9Fbn85LHzaWhN4eK5m/n5ecsxm5yD+04cUulpld18sXk4X+0oZnbBfnZszuHxx95n06YBfPNNPg6H4oR5JeToch6/uwBH36sTUUq6U4REXNfbsX5B7up60td2RKR7SrBDsQRfEcX6DMZKqRdx3rCRr5Q6CPwGZ8X6ilLqGuAA8B0ArfUWpdQrwFbABtwoI1JERqRDhcnkwOE48g3tvsocbnr8PIwGB8/+/DXGDKpx+7yq1lQqmjOY2L/qcKB2lZfVCsCYgdXsIIfMDCvnnrOLc8/Zdfgx7z6bd/QTRdBJa3H0kno7NsV1KBZRQfr9982bUSku8/CnU9wt1FrfB9wXSKEiLdKhMh4UFjbT0pJEfX0Kubnt3PvSyWjg0R+/y7CB9QB02A08sXYG+xtyuHveUv62ag6vbRuPQxuYXXSIETl19Ett45IJW+mf7gzEBTnNANR2OG/YrK1NpV+/9khsoggxaTX2XyLW2yIEPIVi16mjXR8jLcEJLx66UxgiXYBoE28X4ki16g0pdt64tnmzc8roDpuB/tktFOQ2HynbkpP455pjeH/XaOY+ezWvbp3ApRO2cMPM1ZQ0ZvHRnpE8vnYmV75zAY7O23wG5zcwe3QJn+0chQZKDsqMcvHs7sU3Ssu06FOsX4hd+dqiF9Rt9ybYdgXh6mr3k3m4W97zuSKuLVm1KqZbpmMmGIcjsMZrKPY2XARj+1sajABMmlTF0KH1/P0fs6mtTeF7J25gZ2k+Nzx2PhX16ZQ1p/P+ztEcP/gA84fsB+APCz7jVycs46fHfM2n33+e5T94lmunr2N/Qw6bKgdgtRtQCgpym7HajWijIsks3/gmAgnHwpN4CsVdPAWLmN9WaVFOKLEajoM9XJuIIn3dhBeKDwJ3XT7yyC9ptRjG5vDrexYwaFATl09fy1tbJ/Gjf3yLG85bQXFWI2vKC7lvwWfcPnc5w3PqWVFbwHXrTuP5WR8yOr2e3NwWlMnOpW9+iySDg0smbCGpnxW7CVoGp7H0y2IGD25k6NDGoG+L6J23N3kGi3StiD2hHss45oOiH0K+za5DqLnrLuFJeXn3cYvddbFw7YIhIiLc44vHYtcKCcZxLtzhpXhyIZYW5xTJTTUtWFtbKTuUysGSDKzLzBx7wl42WAbz+xdOZeCARrIGWLjlk9P54dT1XD5xM3XWFCZnVXH12tNotCVj1wYYYgOgQ2ueqR6Hoc2AYYQiqcHEG5WTWPi7MVw2fwP9c1tJT+vAnNvhnJBDR+Uwq3EjEq24oZi2XIhoEomJUdy2To8YceSXnmG5Z1DumuHOdaa73nQFZAnJYReJVtyomKHRBxKMO8mFNjhKNpUdDi8N5Y28fPf71G9oxG5zoIoGsOmr/nRY7RQPraahIYP2yhSyB1v594Zp/HvDNKYOLGf+pN2srCsC4O6xXwHQ2JHEfls6X1UVUdGWAXl27AOgbbCR5uoUHqk8FtM+TVqZjZTqdtJmWxk5pJItbzaBTbpbxBvXUC7v3cQUKxfZWOAujC/Zs+fI30eMOLrV1zUcFxT0Hopd/9bVsuzaeuwakCUsxy1fJ7GJlJjpYyz8d9/Jj4Y1PHS9VnZBFtc9fRn/98GPuenFK8nsaIVt+7n2yq8oTG5ErbcwoL6e4uZmMvZrzijYzYGGbP63Yhq3DV9NisHGZ1XFXD1kCz8buZ7dhgy+M2oby+e/xG2jVzNzYBn2HDvpw5ox5VppKzDQMs5I3cRMOiaksLZ9BMa5Y1FFAyA5KWzbL8JL+h+LeLNg9uywB4feXm/Jnj3dQ2zPH3CGXXdTQLvrhtEVlF1v1uv5I+JaNPc/jqlgLC1DsaPnscrMS+eMn82nvdHCk7/M5G8PLeSPv1/MsPRaGpcbmNC/ispdOTx7/ttYbSbW7xjMXWNXsby2iD/vnIUBzTujP+KXBZsoSGnlhuEbeWn2h1wzdDM1pmQGTa7mB7PW4kjXtBYpKLZjGgD1xVk4hg3APHkEF9/WFqG9IUJNRq8QIgxcg7C3rbye/ubaiuwahiUgJ4xoHb0ipoKxiF0NFU18+NclpOem8syX21AKpk+v4Ibr16CAfsYW9lT0472l4zkmp4ylJUP5eO1YzsrfxxP7pnDNutOosaYcNenH3WNX8cS0T9jXksWHbcWsvPoZzhm1g31kc/45m+mf3ULWTAdDR9Tz0ZdTuOz2+khsvhAJK5q/MhXdeX2segvC7lqNPenZ/cI1CLsGZSHCSIJxJ2ltCr77Tn6UR7/3X+475VEeuew/2NsbeODl9Qwabj38mLFjapg1q5Ttn2czZ+QBXv1yMqtXDiWtzs6e2ly+WD2SKwduY0VtIdeuOw2r4+hT9rQBB7hm2Gb2t2WBgvtPXswJxft5bvNUrr9gBeX1mRjHmUhN62D51onh3AVxTb7BEZEUjS1N8ajXbh09W4376lbhTm/hWASdfFDtm9x8J0LK0V7L5T+vQSnNvHMaGDGh+yx1BgPccvNKfv6L0yn7PJmfnP8lw8Y08Lu3TqWwuYmafDNvr5rEDfNW87eS6Zy09Nss6H+QHLMFk3IAoIDFVcUANHQkMTCljXvmf8HFr36Xf22fzo3nruDv7xzPRadv4rMXh3DK9aV0HKrji3dzwrw3hBAiuvnUatxz+LWeN+b1dVNeX1zXJzfliTCJuWAc7uHHRGC+e2Ml3/lJVa+Pycm28Pvffc6j/5zFC89PBmD6SQdZeWgYGY3tZBZZeWXVFH6/YDmLawbzYcUwmm1J2HT31uPJWVXkJTmD96DMZu4/5VN+8uHZ7BhQwZwxB3hn0wQmTz7E11tG8JMb1jB0fAUHdybRWGdkzRKZQS/WSQu2EGHmbui2nsv7CseebthzNwqGhGMRBjEXjEHCcSwxmr0bS3jIkEb+dP9iamtTeOnlibz73hgmTzpIXVIm1bv60VwM//1yOrcdt4KnZ3wCOIcpVgocGmzagFk5uvVBXjB0P1dPXcczG6bz42lrKKnJpsyWi8HQzp8fmMvQofXceceXDClu5H9/G8B/HigMxS4QYSChOLpFYmxeESbuJu9w13Lsq97WJwE5bkRj1w7pYyyiSr9+7dxw/Rp+8fOvKNudTscaG4MNDWRWOLBYTfz047O46u3z+e+myWyrdlaOBgVJBsdRN+YB3HzsSs4bvZ0n1s8kbXQb1a1ptIzJ5Nwf7KKiMp3rbziHX9x8OsPmJPHRoQ2cc6X0b/NFNATSaCiDEAnP3YgVgQZYdzfjdf1f+iL7LVrCaLSUoycJxiLqKAVnnL6Hfzz8ERPGV2PZ7CC51k7blnQGtLSxszqPP345j2+//h2uefdc1pZ1b41YVTuQR/ZMxaGdofmPJy/mlmNXsKFmAMNmVTE4v4HnV82kdUIGA06wUNqRzR/un8fCRSO4/t5SFlxQF6EtF0KIGOcuHAcSkHsLxBKORQjEZDAOVTcK6Z7hu67xY3vbd421RqwWN825fRg0qJnf/eYL/vXP9zgmaR/pJS1wwIDjm2SmtNVw/bTV7Kzrx1XvXMDLWydgczhf49LV5/DQrplsb+4HOMPxNdPW86t5S1lbWciM4w5wz6WfcsLEfdR2pHMwNY/2sWk8+MRxXHPteQyem8XUBTLmsRDBEqpuFNI9wz9d48eGbP+5mwCk5+89lZd77ovcMwDLhCABi3RrbaRfvzcx18dYwmvs+c6kSeT27+ClDVv7fKzdBg5H9xCdn9fK/fctZuOmAbz08iTW7S/igM7h4JvTOWnabvZmZfC7L07k3R1j+McZH/Hl/JeosaYyPrO2eznGb2N1WRGPrZnFK996nbNm7kRrWL1rEL954RTqRmTSbuzghZcmk5Jk44xf7mHYkFo2fexg+Uc5wdwlcSWSff6lG4UQvgnrh4meIdi1j3DPMNtXP2RPN99J/2MRZDHZYhwJEsj9s211OgB1VWZ0H/fhvfFUPmcPmcq5w6a4/bnrvAI2Pl/NzKxNZO9vIqO5jU/XjqJhUyan5u1jc1V/zn/lEj7fPZwRqQ1HrV8puGPul6SbO/juGxfzwuZJKAXHjD7EC7e+wkXHbuGgvR8/uHkTM2eWsWjJGJ76z7FYioZz+T0tHHNKYyh2kRAiBKQ1OTBB3X/uWov96WLhqWVY+h+LIIqpFuNQh1NPrU8Sip382Q+uYwVfPPk4WmpbAy7H+vc1pO0hd1p/bAawD1F8vWoo44bWUJ9s5t6l8/nT8rkMyW7gnFE7uWrKRpJNdg40ZJGT0s684gN8uHs09y07gZzkds4ZvYvcjHZuuWgZ2w/l84+P5zJucBWn/Wg/BbYGXn9lHOvWFZKb28aZN+ygfFM765dlBrwd8URGihGeRCKgSih2CnQ/dD0/aF979xzzuGuZL0G2t5Zj19dwXae0Irslo8W4F1PBOJQkFAfu7sU3ut2PVz/+XQ5uLqNidzUbPtwWnBdrbadueQnGfo04mgrpV2Sj1phOoz2F9BRNR7qR/a25/K32WN7eMZZUk42t1f0PP31CfhXJRhsjc4/caGc0aB744Ue8+uUkNu4r4I2Vk0hL7mD2+SXkGFvZ8VUOC1dO5ZLvbGbklGo2LU9jx/q04GxPHJBwLMLNXWCTC71vlqxaFd7+nu66V7gLzN4+vyd3XSpkDGSPJBwfze9grJQaC7zssmgEcA+QA1wLdM3qcJfW+gN/XyccpJ9i37wNPO7CceGY/hSOcYbSoAXjLrUNaGsHgwemcmDTQObNLmH2KRVYO0x8smEkm8v7U2HPICPDyvUzVrO3Pofzx+zgpGH73a6uX2YbPz7zawB2lubx/JJprNtTSFVDBiTBxNmlvPTqJBQwZUo5fNMC7ZbgblMMk3Ac3eKp3o7mm3eiQdSHHX8Cri96thhLC7JHEo678zsYa623A9MAlFJG4BDwJvBD4K9a6weDUcBokcjh2deg0/X4sO2z5lb2LWzlukdK+dfT0xmY3sLpp+1h6qkHWbFnKC+unMaYQQ2cP2wnja3JPPzOXO4tP5VTp+3i9ouXYjI6Dq/qy5LB/HnFXDrsRsbk1TBxfBW/uOhLWlqS+NfCWXy8bgwzzjjIMTn7+fjjUaTPyKNlWyXUHd2nOVFJOI5e8VJvexOKJTh7r2eXiYiGpECDcG836Hn6XUg4dhGsrhSnALu11vuVu1kWgiQUF9xEDryhFu5wdPG3vqG6OpW33h7H4s+GH16eNbqVjfsKueyBSwHITG3nhIn7eO/r8bRYkrj38k8wGZ13Bq48NJidtXkA2LVi4Z6RPLl2BjfMXM09ly6mKK+Rf38yi5R0O3f9bhlP/3Ma29sGMWR+Biccv4//3pEMDsfRhUsw4QjH8t4NWFjq7WBfcCXwhk5EgpGvfYzd8TS2cW9hWEaxOEq4wnG0v4eDFYwvBV50+f0mpdSVwGrgFq31UTMmKKWuA64DSCEy/TTlwhp+nvohB8PGFelcd+06zjt3J5VV6dg6DNTWpfDSKxNprTfy81tXYTZrZo8pITejnfHFVfz93bk8O7COH52+GoCfHbOKRksSr26bSH5qK7fPXc5LWyby4Mq5DMlu5NrTV5NitvHMJzNZu/t8HrjhQyp3pbLk86G88OJUDFNh2tRyfnHTcv5842A2f5URkm0VIggCqreHFBeHpZCuov2CGq9C3g/Zl3DqLkR7CtfehGHpYhFWsfAeDni4NqVUEnA+8GrnoseAkTi/risD/uLueVrrJ7XWs7TWs8wkB1oMn0ko9k4oWv1C1ZJ428Wj2LUplaKiZqZNrWDWrDJOP20vF5y3A1OrnRMn7OOMGTvJzWgH4LL5G5k/cS9vLJ94eB1mo4PfzP+CPyxYzO66ftz00VlUtDiHnPts31CUgitPXs/L//cSKUk2Xvh8GmeftZs/37+YB/70CZd8dwubt/Tnmf/M5E+v7ubJJd/wxGdHfk75dq3bsgsRTsGot/uHOUTEwgU1WsTFV+Kezq/elkuwFUEQjBbjs4C1WusKgK5/AZRSTwHvBeE1DgvGV7S+huJEC9Hh6AIRqpbjm84cw7+++IbiUUduiHPYnV8TK3X0QMqji2r4Ystw7A6F0eD8u0HBt8Zt5/jBB3l352hWlQ5ieE49P56x9vDzBua08O3jN/Ovhcfw6pcTmT68jPETqpk0qYqKynSWLBmGzWbgphtXk5vbfvh5tzxUwqnfruPDF/K6DWUnRJiFtd4WoRfqMBz20St601v3C2/CcV+ThYiEFowJPi7D5es4pVShy98uAjYH4TW6CTRQ+TLCQiLpa2rnULxeKPxo/rhu3XwNnf2H7faj+1G2WU0kmWyHQ7GrgRkt/Gj6ep48533+dvpChmR3n+Dj4rlbSEu28tBbJ3DFX7/L/a/NB+DnP13FlVdsYNXXRVxz7bks+mT44clNjCaYMb+Z2x/Zz80PHWD2qTJpiD8S7cNqCIS13g5GoPIm+AV93N0YENKpnd28VlQIxgQeniYCkQlCQiZW3pcBtRgrpdKA04Afuyz+s1JqGqCBfT3+JqJUvH0IaKwzkpNnByAl2QZAu8VENlYAtIZPN4zk47WjSU+x+vUaZqOdu7+7hK0HBvDC59Mor3NO+pGSYufUU/cycmQdr746gYf+eiyfLh7Gfb9fgrEzpJvMcMaldZz8rXp+84NhrFmSFeAWJwYJxIGTejt+RE1QjUdyc17QxEog7hJQMNZatwJ5PZZdEVCJhAiCSyZP4qNDG1AKzGZn83FFeQYDBzhn3nvswzn897PpZKa28+3jvW8ca7eaWLl9MJ9sGMXnm4djsxsP/23SUOe30Q///Rg++ngUAAP6twCwYUMB9fUp5OW1dVufOUlz12P7uXj8ZP83NgFIIA4eqbdFICLapUJacmNKrAXiLjE78124hoOKt5bUaBPKUSr27Mzkf69MZd36AlJSOsjOPtLX9+2vxjNvwj7+dNVHGLzoUFRSlc0Ln0/l47Wjae8wk53WxkXHbmXO2BLyM1vpn9PMhtUD+OP9c/li6VDGjKnh9NP28OXywbS2msnLbyUltcPtupNSju7GIY6QUBwfwjEUlIzFGh4hD8ehDMC9tQC7m05a+CVWQzHEcDAOhC8XWrkoh16owvHqNUUsX1HM8XNLuPqH6ykqaj78t5z0NirqM9AonN8ee7blwAB+9I9vYTLaOXPGDk6btosZI8u6TQyiNfzjkdm0tZk55eS9/OLnX2Eyac45e1fQtyuRyPtPdPH2QhvLF+RYElU34/XFm+4Qro+R7hMBiZnzwoNg3HwnRMBC0TKfkeHsOzxlSkW3UAzwo9NXs7M0n0ffn3P4xjhP9lfmAGCzG/neiRuYPeYQJqMDq9XA+x+M4r/PT+Ivfz2WtjYzv/zFSm69ZSUmk7QCCyFEUIUqsEoQFi4SssVYJIbj5pSwak0xTzw5g8LCZo6ZVXb4b6dO3c2aXYN48YtpLFo/mouO3cIPT12LuwnAzpixE4PS/OGVk3h9xURuufBLAP734iRefsU5BrLJZOfMM3Yxa2oJ9dXOt1VGtg2Tufcytrca+HpxZnA2WAghhG8kFIseJBiLuGUwwO3/t5zb7ziF+/7fPP70x08ZO9Y5wYZScOtFS5k8tILFG0fw1MLZLNs2jAHZzQzp38CPTv8as9GB3aEwGTVnztzJX96aR3Nb0uH1b9o0gNGjanj4bwupqzJx2bSJfHD/xG5l+N/aLeQV2NyWb9Wnmfz6ihGh2wFCxIlY/2pW+MibcYoD6QcsYVj0Iqa7UvjT/1D6LCaWtDQb9/7uc3Jz27jntws4dOjIFM0mo+acY7bz4NUf8sNTV2PpMLK/Mof/fjadb99/OQvu+hFn/+4H1DalAnDylD0sWj+KslpnC2///q3UN6TQ0mjgsmkT3b7+5TPcL9/8VXpch2K5aVW4IwFXeK2v8BrINNJyc51bcuOqU0wHYxE88fiBoaXReXrn5rZz3++XAJp7fruAhoakbo9TCq47YzUv3PIqL972Cr///iIGZLdgtZloaktmb0UuAFectA67w8iyrUMByMltp7XV3OdQa9b27v0zdm9O4ZaLRgVlG4WIdxKmPYv7feNNOHb9ESIIYj4YywgTwpOr543H1jlCWlFRM7+95wuqqtL40wNz3c6C1+XUqbv510/f5OX/c04MVlHvbGUu7/w3Ndm50tqaVLIyLe5X4uK8EVM4o2jq4Z+fnD42kM1KGPJ+jU++hLm4D36ib74EXm8DsoTokImH92zMB2NvyUU2MZUfONI6PH58DTfesJp16wp5+plpfT63ILcJo8FBSXU2Le1m/t8rCyjMbeTUqbuxWg1s2DgQ1d7W53qE7+T9KuLhAiuCRIJsTIiX92xc3HzX10QccpENLk/7M5B+paE6Rv++v5C7n9h/eBKPM87Yw569Obz51jgqq9L49re+Ydy4GrfPTTI5GJTXyOqdgzhQlUNFfQaP/eRtUpJsvPnWWBobk6nf2RqScicyeb/GP5mII7w8BZZAjkFMhSAJ1iEXU+dDHxKmxVgE7r6TH+01tERjoFn2fg77t6d0W3bdteu44PztbNgwkLt+dRK7d+d4fP6E4ko2Hyhg8caRXHXKWqYMc077/Mmnwxk/rgqaJRgLEWzxdJGNpAWzZ/e6L2NqP/sbbns+T0Ky6ENctBi7E40hLZaFcn+G+lgtez+b4eOPTAdtNGqu//FaLr54Gzffchq/vmcBDz7wyVGTgADcfvEXnDd7G42tKcyfuBeAbd/ksXdvDhdd+A1bQlryxCPv28QWU0EtyvkyU2DMtN57O1SbhN+wirf3rdJ9TfsVBlmqn56jTol0MQRHukMEI6B427UiHGHopj8e5Lyrju4yUVKSya3/dyopyXb+/KdPGTiwpdf1rFs/kD8/MJeUlA7+9tBCvjt+fKiKHNN87VYT64H4E/3aGq31rEiXI5xmzZihVy9bFuliJLyuUBuMcOJtQI6KIOTvkGsSmj3y9QNSVJwHflLp6R7rbAnGIqT6CkjhDkS/fLCEMy+v7bZs165c7rz7JJKT7Vz9w/WMHVNDUrIdu81AbW0qBw9lsmdPLhs3DWDv3lyKipr47T2fU1zcxBlFU8Na/ljgT19zCcaxR4JxfPImHEVNIPI1HEso9sifbw2i5jzwQ2/BOG67Ugjhzl9vLSYt08788xoOLxs1qo4/37+YB/5yLA88ONft85KTbYwdU8P1P17NmWfsITnZHq4iCyFE2PTVtSKqwlBvM+T1fJwQXpJgLBLOfT8eRnrmbmYuONKnePjweh75+0esXlNIU1MyVqsRk9FBdk47g4qaKChowWjs/u2K1eJ5LOREJTPeCSHCSkJvwGKmj3mYSDAWCel31wzj/pf3MGHWkVElDAaYfUyZ1+t44GdDQlG0hBPr3SiEEELEDwnGIqSiNfRY2oz8+orhPPjG7m4jVnirrcXAF+/mBL9gCSZazw8hEllUdZcQUSfezw8JxiJhNTeYuOuyETz09i4Kh1p9em4U3LMasyQMCyFEbIn3MOxKJvgQCa220swdl4ygpkI+I4aDhGIhhIgtiRSKIcBgrJTap5TapJRar5Ra3bmsn1JqkVJqZ+e/ucEpqhChUX4gmcunT+zzcQ4H2O3On62r08NQsvgioTg6SL0thPBWooViCE5XipO01q7jpdwBfKq1vl8pdUfn77cH4XWECCmrRZGU7L6PxJK3c/jjDUPDXKL4IaE46ki9LYToVSKGYghNV4oLgOc6//8ccGEIXkOIoDtv+BTOKJp61DBsXy/OlFDsJQnAMUvqbSESVKIGYE8CbTHWwEKllAae0Fo/CQzUWpcBaK3LlFID3D1RKXUdcB1ACmkBFkOI4Dlv+JRIF0GIUApKvT2kuDhc5RVCiLAJNBgfr7Uu7axEFymlvvH2iZ2V8ZPgnBI6wHIIIaKUtCJHnaDU27NmzJB6W4g4lcityAF1pdBal3b+Wwm8CcwGKpRShQCd/1YGWkghRGyQme+in9TbQghXMvNdd34HY6VUulIqs+v/wOnAZuAd4KrOh10FvB1oIYUQsUlai6OL1NtCiL4kcmsxBNZiPBBYppTaAKwC3tdafwTcD5ymlNoJnNb5uxAiAUkLctSRelsI0atEb0H2u4+x1noPMNXN8hrglEAKJYQQIvik3hZCiN7JzHdCiKCQ1mEhhIgtid467I4EYyFEyEgfYyGEiC3Sx1gIIYQQQgghwVgIERw9W4eltVgIIaJbz9bhRG8tBgnGQgghhBBCABKMhRBBJK3EQggRW6SVuDsJxkIIIYQQQhDAOMZCCOGOtBoLIURskVbjI6TFWAghhBBCCCQYCyGEEEIIAUgwFkIIIYQQApBgLIQQQgghBCDBWAghhBBCCECCsRBCCCGEEIAEYyGEEEIIIQAJxkIIIYQQQgASjIUQQgghhAAkGAshhBBCCAFIMBZCCCGEEAIIIBgrpYqVUp8ppbYppbYopX7eufy3SqlDSqn1nT9nB6+4Qggh/CX1thBC9M4UwHNtwC1a67VKqUxgjVJqUeff/qq1fjDw4gkh4sndi28E4L6TH41wSRKW1NtCCJ8sWbXq8P8XzJ4dwZKEh9/BWGtdBpR1/r9JKbUNGBSsggkhhAguqbeFEKJ3gbQYH6aUGgZMB74CjgduUkpdCazG2TpR5+Y51wHXAaSQFoxihFVXyxdER+uXa3l6iobyicTW2/kpIiPQentIcXH4ChsE0dbq5Voed6KhjCKx9XWOxiultQ5sBUplAJ8D92mt31BKDQSqAQ38HijUWl/d2zqyVD89R50SUDnCIZzhM56DhAT1xOLuXI63c+AT/doarfWsSJfDW8Got2fNmKFXL1sW+sIGoLcLeyiCZ7wGCQnpicfTuRwv54JKT/dYZwfUYqyUMgOvAy9ord8A0FpXuPz9KeC9QF4jUnwNpt4+vq9AEM+BuIv0MxUicuK13vY1lHr7eG+CQLwG4i5d2xcvoUiI3vgdjJVSCnga2Ka1fshleWFnPzaAi4DNgRUx/BIhnEaDRGhJFCKaxGu9HcpgumTVKgmEneK9FVH0LlGOs99dKZRS84ClwCbA0bn4LuAyYBrOr+T2AT92qXDd8qUrRc8w5W2QkrAbmyQox56+3mvxeExjpStFMOttX7pS+NO/N95bYeNZogSoeJJofd5760rh9zjGWutlWmultZ6itZ7W+fOB1voKrfXkzuXn91W5BsqbwCuhOHbJsYst8n6MbpGot3tecCXwxj85xrHFm+OVSMc04JvvgmHs2LH6iSeeiHQxRAyKx9bHWOVL4I2l4+ZNC3istBgHk9Tbwl/x1voYy3wNvLFy7PrarpNOOik0N98JIWJPKIYajMdW4HjcJiFEbArFcIPx2AocjG2SYCxEAukZ9rwJf57Cc7wEx3jZDiFiQay0OEYTf7ojedrP8RKGQ7kdEoxFzImlr+Hjwd2Lbzxqn8dLmIyX7RAimkkYDj93o6lIKPaOBGMRU+IxFAcznPW2fwJ5nWAHSHdhO1wkDAsRPvEaioMZznrbR4G8TrwEYQjvtkgwFjEjlkJxpMJXLIW+SEyrHkv7RwgRXpEKkrEUYCMxtXq4948EYyGCREKX//wdn9yfdQshRJdYCqXRpue+C2ZQjuRx8XscYyHEERK+gitY+1OOixDCEwnFwRWs/Rnp4yItxiJmRLJfqmsZRHh07Wtfj7kcIyGiRzRMqR3poJVIuva1r8c8mo6RBGMRU8IReuJ1BIZY1dv+dz1WcpyEiE7hCD3xOgJDrOpt/7seq2g8ThKMhehBAlbskGMlhIDoDFjCvWg/VtLHWAghhBBCCCQYCyGEEEIIAUgwFkIIIYQQApBgLIQQQgghBCDBWAghhBBCCECCsRBCCCGEEIAEYyGEEEIIIQAJxkIIIYQQQgASjIUQQgghhABCGIyVUmcqpbYrpXYppe4I1esIIYQInNTZQggRomCslDICjwJnAROAy5RSE0LxWkIIIQIjdbYQQjiFqsV4NrBLa71Ha20FXgIuCNFrCSGECIzU2UIIAZhCtN5BQInL7weBOa4PUEpdB1zX+avlpJNO2hyiskRKPlAd6UIEmWxTbJBtCr+hkS5AgPqss0Hq7Rgl2xQbZJvCy2OdHapgrNws091+0fpJ4EkApdRqrfWsEJUlImSbYoNsU2yIx22KMn3W2SD1diySbYoNsk3RI1RdKQ4CxS6/DwZKQ/RaQgghAiN1thBCELpg/DUwWik1XCmVBFwKvBOi1xJCCBEYqbOFEIIQdaXQWtuUUjcBHwNG4Bmt9ZZenvJkKMoRYbJNsUG2KTbE4zZFDT/qbIjPYyLbFBtkm2JDTG6T0vqobmRCCCGEEEIkHJn5TgghhBBCCCQYCyGEEEIIAURBMI6XaUiVUvuUUpuUUuuVUqs7l/VTSi1SSu3s/Dc30uXsjVLqGaVUpVJqs8syj9uglLqz87htV0qdEZlSe+Zhe36rlDrUeZzWK6XOdvlbVG8PgFKqWCn1mVJqm1Jqi1Lq553LY/k4edqmmD5W8Urq7OgRb3U2SL3t8pyo3q64rre11hH7wXmTx25gBJAEbAAmRLJMAWzLPiC/x7I/A3d0/v8O4E+RLmcf2zAfmAFs7msbcE4buwFIBoZ3HkdjpLfBi+35LXCrm8dG/fZ0lrMQmNH5/0xgR2fZY/k4edqmmD5W8fgjdXZ0/cRbnd3LNsV0XSD1dmxsU9dPpFuM430a0guA5zr//xxwYeSK0jet9RdAbY/FnrbhAuAlrbVFa70X2IXzeEYND9vjSdRvD4DWukxrvbbz/03ANpyzlsXycfK0TZ5E/TbFMamzo0i81dkg9Xbn/6N+u+K53o50MHY3DWlvOzaaaWChUmqNck6bCjBQa10GzpMIGBCx0vnP0zbE8rG7SSm1sfMru66vrmJue5RSw4DpwFfEyXHqsU0QJ8cqjsTTvpc6O7aOXVzUBVJvHxa12xTpYOzVNKQx4nit9QzgLOBGpdT8SBcoxGL12D0GjASmAWXAXzqXx9T2KKUygNeBX2itG3t7qJtlUbldbrYpLo5VnImnfS91duwcu7ioC6TePkpUblOkg3HcTEOqtS7t/LcSeBPnVwQVSqlCgM5/KyNXQr952oaYPHZa6wqttV1r7QCe4shXOTGzPUopM86K6AWt9Rudi2P6OLnbpng4VnEobva91Nmxc+zioS6Qejs2tgkiH4zjYhpSpVS6Uiqz6//A6cBmnNtyVefDrgLejkwJA+JpG94BLlVKJSulhgOjgVURKJ9PuiqhThfhPE4QI9ujlFLA08A2rfVDLn+K2ePkaZti/VjFKamzo1/M1gWexHpdIPU2EAPbdFik7/4DzsZ5N+Nu4O5Il8fPbRiB827LDcCWru0A8oBPgZ2d//aLdFn72I4XcX710YHz0901vW0DcHfncdsOnBXp8nu5Pf8FNgEbcb5RC2NlezrLOA/n108bgfWdP2fH+HHytE0xfazi9Ufq7Oj5ibc6u5dtium6QOrt2Nimrh+ZEloIIYQQQggi35VCCCGEEEKIqCDBWAghhBBCCCQYCyGEEEIIAUgwFkIIIYQQApBgLIQQQgghBCDBWAghhBBCCECCsRBCCCGEEAD8f/2yl6zjA5F9AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 864x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# ------ Group by cumulative area\n",
    "\n",
    "copy_cumulative_SSTA_shapes = np.copy(cumulative_SSTA_shapes)\n",
    "print(copy_cumulative_SSTA_shapes.shape)\n",
    "\n",
    "copy_cumulative_SSTA_shapes[:][copy_cumulative_SSTA_shapes[:] != 0] = 1\n",
    "# plt.contourf(copy_cumulative_SSTA_shapes[0])\n",
    "# plt.show()\n",
    "\n",
    "cumulative_area = []\n",
    "for i in range(len(copy_cumulative_SSTA_shapes)):\n",
    "    cumulative_area.append(copy_cumulative_SSTA_shapes[i].sum())\n",
    "\n",
    "cumulative_area_ind = np.flip(np.argsort(cumulative_area))\n",
    "sorted_cumulative_area = np.flip(np.sort(cumulative_area))\n",
    "\n",
    "# plt.hist(sorted_cumulative_area)\n",
    "# plt.xlabel('area'); plt.ylabel('frequency')\n",
    "# plt.show()\n",
    "\n",
    "df_area = pd.DataFrame(list(zip(cumulative_area_ind, sorted_cumulative_area)), columns =['ind', 'cumul_area'])\n",
    "area_above_mean = df_area[df_area[\"cumul_area\"] > df_area.cumul_area.mean()]\n",
    "# print(list(area_above_mean.ind))\n",
    "print(len(area_above_mean)/len(df_area))\n",
    "\n",
    "fig, (ax1, ax2) = plt.subplots(1, 2,figsize=(12,4))\n",
    "ax1.pcolor(copy_cumulative_SSTA_shapes[list(area_above_mean.ind)[0]]); ax1.contourf(land_mask, cmap='binary')\n",
    "ax2.contourf(cumulative_SSTA_shapes[list(area_above_mean.ind)[0]],cmap='seismic',vmin=-50, vmax=50); ax2.contourf(land_mask, cmap='binary')\n",
    "ax1.contour(cumulative_SSTA_shapes[list(area_above_mean.ind)[0]])\n",
    "plt.show()\n",
    "\n",
    "fig, (ax1, ax2) = plt.subplots(1, 2,figsize=(12,4))\n",
    "ax1.pcolor(copy_cumulative_SSTA_shapes[list(area_above_mean.ind)[-1]]); ax1.contourf(land_mask, cmap='binary')\n",
    "ax2.contourf(cumulative_SSTA_shapes[list(area_above_mean.ind)[-1]],cmap='seismic',vmin=-50, vmax=50); ax2.contourf(land_mask, cmap='binary')\n",
    "ax1.contour(cumulative_SSTA_shapes[list(area_above_mean.ind)[-1]])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "79d3053a-baa1-4784-8515-b67856d0f184",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x288 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "mhw_number = 2\n",
    "# copy_cumulative_SSTA_shapes, cumulative_SSTA_shapes, scaled_data_full\n",
    "\n",
    "fig, (ax1, ax2, ax3) = plt.subplots(1, 3,figsize=(20,4))\n",
    "ax1.pcolor(copy_cumulative_SSTA_shapes[mhw_number]); ax1.contourf(land_mask[0:160,:], cmap='binary')\n",
    "ax2.contourf(cumulative_SSTA_shapes[mhw_number],cmap='seismic',vmin=-50, vmax=50); ax2.contourf(land_mask[0:160,:], cmap='binary')\n",
    "ax3.contourf(scaled_data_full[mhw_number]); ax3.contourf(land_mask[0:160,:], cmap='binary')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "470c512a-121a-40b1-baac-9cfb456933db",
   "metadata": {},
   "outputs": [],
   "source": [
    "# how many have an ENSO signal?\n",
    "cropped_to_enso_signal_region = cumulative_SSTA_shapes.sel(lat=slice(-10,10), lon=slice(120,290))\n",
    "# cropped_to_enso_signal_region[0,:,:].values\n",
    "\n",
    "cropped_to_enso_signal_region[:,:,:].values[cropped_to_enso_signal_region[:,:,:].values != 0] = 1\n",
    "enso_area = []\n",
    "for i in range(len(cropped_to_enso_signal_region)):\n",
    "    enso_area.append(cropped_to_enso_signal_region[i,:,:].sum().item())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "0a341f44-91f7-41f5-ab63-9c03147c4970",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# are the heatwaves with an ENSO signal our long heatwaves?\n",
    "\n",
    "length_of_heatwave = []\n",
    "first_month = []\n",
    "last_month = []\n",
    "for i in range(blob_files[file_number].SSTA[:,:,0:160,:].shape[0]):\n",
    "    shortened= blob_files[file_number].SSTA[i,:,0:160,:].mean(axis=(1,2)).dropna(dim=\"time\", how=\"any\")\n",
    "    length_of_heatwave.append(shortened.shape[0])\n",
    "    first_month.append(shortened[0].time.month.values)\n",
    "    last_month.append(shortened[-1].time.month.values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "acbd27be-1f80-4093-bd97-de8979b5dda8",
   "metadata": {},
   "outputs": [],
   "source": [
    "first_month = np.array(first_month).flatten()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "9ce0ec05-1943-4ac5-80f8-befdff27bfc4",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXAAAAD4CAYAAAD1jb0+AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjQuMywgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/MnkTPAAAACXBIWXMAAAsTAAALEwEAmpwYAAALxElEQVR4nO3dX2id933H8fdndkbLOmjcyMbE2bQLMxrK4oDIAtlFFzfDw6H2TUYLHboI+KaDFDqK25vRXXk3pTe7MW2ooF07Q9vZJLDNqA3doKSV23RNcIpL8bIQY6npSpObjqTfXejJpshydCzp6Ogrv19gnj86yvPND/Lm4fE5J6kqJEn9/NakB5AkbYwBl6SmDLgkNWXAJakpAy5JTe3dzovdddddNT09vZ2XlKT2Ll269POqmlp9flsDPj09zcLCwnZeUpLaS/Kfa533EYokNWXAJakpAy5JTRlwSWrKgEtSUwZckpoy4JLUlAGXpKYMuCQ1ta2fxNyM6dNPT+zaV88cn9i1JelmvAOXpKYMuCQ1ZcAlqSkDLklNGXBJasqAS1JTBlySmhrpfeBJrgKvAW8Cb1TVTJJ9wD8C08BV4C+q6r/HM6YkabVbuQP/06o6UlUzw/FpYL6qDgPzw7EkaZts5hHKCWBu2J8DTm56GknSyEYNeAH/muRSklPDuQNVdQ1g2O5f6xeTnEqykGRhaWlp8xNLkoDRvwvloap6Jcl+4GKSF0e9QFWdBc4CzMzM1AZmlCStYaQ78Kp6ZdguAt8EHgCuJzkIMGwXxzWkJOlG6wY8ye8k+d239oE/A54HLgCzw8tmgfPjGlKSdKNRHqEcAL6Z5K3X/0NV/XOS7wPnkjwOvAQ8Nr4xJUmrrRvwqvoZcN8a518Fjo5jKEnS+vwkpiQ1ZcAlqSkDLklNGXBJasqAS1JTBlySmjLgktSUAZekpgy4JDVlwCWpKQMuSU0ZcElqyoBLUlMGXJKaMuCS1JQBl6SmDLgkNWXAJakpAy5JTRlwSWrKgEtSUwZckpoy4JLUlAGXpKYMuCQ1ZcAlqSkDLklNGXBJasqAS1JTBlySmho54En2JPlhkqeG431JLia5MmzvHN+YkqTVbuUO/Ang8orj08B8VR0G5odjSdI2GSngSQ4Bx4EvrDh9Apgb9ueAk1s6mSTpHY16B/554FPAb1acO1BV1wCG7f6tHU2S9E7WDXiSR4HFqrq0kQskOZVkIcnC0tLSRv4RkqQ1jHIH/hDw4SRXga8BDyf5MnA9yUGAYbu41i9X1dmqmqmqmampqS0aW5K0bsCr6tNVdaiqpoGPAN+qqo8BF4DZ4WWzwPmxTSlJusFm3gd+BngkyRXgkeFYkrRN9t7Ki6vqGeCZYf9V4OjWjyRJGoWfxJSkpgy4JDVlwCWpKQMuSU0ZcElqyoBLUlMGXJKaMuCS1JQBl6SmDLgkNWXAJakpAy5JTRlwSWrKgEtSUwZckpoy4JLUlAGXpKYMuCQ1ZcAlqSkDLklNGXBJasqAS1JTBlySmjLgktSUAZekpgy4JDVlwCWpKQMuSU0ZcElqyoBLUlPrBjzJu5J8L8mPkryQ5LPD+X1JLia5MmzvHP+4kqS3jHIH/mvg4aq6DzgCHEvyIHAamK+qw8D8cCxJ2ibrBryWvT4c3jH8KeAEMDecnwNOjmNASdLaRnoGnmRPkueAReBiVT0LHKiqawDDdv/YppQk3WCkgFfVm1V1BDgEPJDkA6NeIMmpJAtJFpaWljY4piRptVt6F0pV/RJ4BjgGXE9yEGDYLt7kd85W1UxVzUxNTW1uWknS/xnlXShTSd477L8b+BDwInABmB1eNgucH9OMkqQ17B3hNQeBuSR7WA7+uap6Ksl3gXNJHgdeAh4b45ySpFXWDXhV/Qdw/xrnXwWOjmMoSdL6/CSmJDVlwCWpKQMuSU0ZcElqyoBLUlMGXJKaMuCS1JQBl6SmDLgkNWXAJakpAy5JTRlwSWrKgEtSUwZckpoy4JLUlAGXpKYMuCQ1ZcAlqSkDLklNGXBJasqAS1JTBlySmjLgktSUAZekpgy4JDVlwCWpKQMuSU0ZcElqyoBLUlMGXJKaWjfgSe5J8u0kl5O8kOSJ4fy+JBeTXBm2d45/XEnSW0a5A38D+GRVvR94EPh4knuB08B8VR0G5odjSdI2WTfgVXWtqn4w7L8GXAbuBk4Ac8PL5oCTY5pRkrSGW3oGnmQauB94FjhQVddgOfLA/i2fTpJ0UyMHPMl7gK8Dn6iqX93C751KspBkYWlpaSMzSpLWMFLAk9zBcry/UlXfGE5fT3Jw+PlBYHGt362qs1U1U1UzU1NTWzGzJInR3oUS4IvA5ar63IofXQBmh/1Z4PzWjydJupm9I7zmIeAvgR8neW449xngDHAuyePAS8BjY5lQkrSmdQNeVf8O5CY/Prq14+xM06efnsh1r545PpHrSurBT2JKUlMGXJKaMuCS1JQBl6SmDLgkNWXAJakpAy5JTRlwSWrKgEtSUwZckpoy4JLUlAGXpKYMuCQ1ZcAlqSkDLklNGXBJasqAS1JTBlySmjLgktSUAZekpgy4JDVlwCWpKQMuSU0ZcElqyoBLUlMGXJKaMuCS1JQBl6SmDLgkNWXAJampvZMeQDvP9OmnJz3Ctrt65vikR5Bu2bp34EmeTLKY5PkV5/YluZjkyrC9c7xjSpJWG+URypeAY6vOnQbmq+owMD8cS5K20boBr6rvAL9YdfoEMDfszwEnt3YsSdJ6NvqXmAeq6hrAsN1/sxcmOZVkIcnC0tLSBi8nSVpt7O9CqaqzVTVTVTNTU1Pjvpwk3TY2GvDrSQ4CDNvFrRtJkjSKjQb8AjA77M8C57dmHEnSqEZ5G+FXge8Cf5jk5SSPA2eAR5JcAR4ZjiVJ22jdD/JU1Udv8qOjWzyLJOkW+FF6SWrKgEtSU34Xyg52O34niaTReQcuSU0ZcElqykcoEpN9XOVX2WqjvAOXpKYMuCQ1ZcAlqSkDLklNGXBJasqAS1JTBlySmjLgktSUAZekpgy4JDVlwCWpKQMuSU0ZcElqyoBLUlMGXJKa8vvApQmb1HeRT/J7yG/Hf+dx8A5ckpoy4JLUlAGXpKZ8Bi7ptrHb/t+n3oFLUlMGXJKaMuCS1JTPwKXb1CSfB2trbOoOPMmxJD9J8tMkp7dqKEnS+jYc8CR7gL8H/hy4F/hoknu3ajBJ0jvbzB34A8BPq+pnVfU/wNeAE1szliRpPZt5Bn438F8rjl8G/nj1i5KcAk4Nh68n+ckmrrmb3AX8fNJDNOA6jc61Gs1E1il/t6lf//21Tm4m4FnjXN1wouoscHYT19mVkixU1cyk59jpXKfRuVaj2U3rtJlHKC8D96w4PgS8srlxJEmj2kzAvw8cTvIHSX4b+AhwYWvGkiStZ8OPUKrqjSR/BfwLsAd4sqpe2LLJdj8fK43GdRqdazWaXbNOqbrhsbUkqQE/Si9JTRlwSWrKgG+DJE8mWUzy/Ipz+5JcTHJl2N45yRl3giT3JPl2kstJXkjyxHDetVohybuSfC/Jj4Z1+uxw3nVaQ5I9SX6Y5KnheNeskwHfHl8Cjq06dxqYr6rDwPxwfLt7A/hkVb0feBD4+PD1DK7V2/0aeLiq7gOOAMeSPIjrdDNPAJdXHO+adTLg26CqvgP8YtXpE8DcsD8HnNzOmXaiqrpWVT8Y9l9j+T+6u3Gt3qaWvT4c3jH8KVynGyQ5BBwHvrDi9K5ZJwM+OQeq6hoshwvYP+F5dpQk08D9wLO4VjcYHgs8BywCF6vKdVrb54FPAb9ZcW7XrJMB146T5D3A14FPVNWvJj3PTlRVb1bVEZY/Af1Akg9MeKQdJ8mjwGJVXZr0LONiwCfnepKDAMN2ccLz7AhJ7mA53l+pqm8Mp12rm6iqXwLPsPx3LK7T2z0EfDjJVZa/LfXhJF9mF62TAZ+cC8DssD8LnJ/gLDtCkgBfBC5X1edW/Mi1WiHJVJL3DvvvBj4EvIjr9DZV9emqOlRV0yx/1ce3qupj7KJ18pOY2yDJV4EPsvw1lteBvwH+CTgH/B7wEvBYVa3+i87bSpI/Af4N+DH//8zyMyw/B3etBkn+iOW/fNvD8k3Yuar62yTvw3VaU5IPAn9dVY/upnUy4JLUlI9QJKkpAy5JTRlwSWrKgEtSUwZckpoy4JLUlAGXpKb+Fzw54n3uLHfUAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3\n"
     ]
    },
    {
     "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.hist(length_of_heatwave)\n",
    "plt.show()\n",
    "print(np.nanmin(length_of_heatwave))\n",
    "\n",
    "plt.hist(first_month)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "dd104aa0-0e62-4482-80d9-d74d7b847d04",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_full = pd.DataFrame(list(zip(cumulative_area, enso_area, length_of_heatwave, first_month, last_month)), \n",
    "                       columns =['cumul_area', 'enso_area', 'length_of_heatwave', 'first_month', 'last_month'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "13c6b6e3-10f7-4b5f-b0e5-f8c403d39d3b",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_full['enso_cumularea'] = (df_full.enso_area / df_full.cumul_area)*100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "4a3c34f3-2120-429a-9f10-5cb01b7203ac",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>cumul_area</th>\n",
       "      <th>enso_area</th>\n",
       "      <th>length_of_heatwave</th>\n",
       "      <th>first_month</th>\n",
       "      <th>last_month</th>\n",
       "      <th>enso_cumularea</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5589.0</td>\n",
       "      <td>324.0</td>\n",
       "      <td>17</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>5.797101</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>15028.0</td>\n",
       "      <td>1836.0</td>\n",
       "      <td>18</td>\n",
       "      <td>7</td>\n",
       "      <td>12</td>\n",
       "      <td>12.217195</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1317.0</td>\n",
       "      <td>242.0</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>18.375095</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1392.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7</td>\n",
       "      <td>12</td>\n",
       "      <td>6</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1447.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>9</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   cumul_area  enso_area  length_of_heatwave  first_month last_month  \\\n",
       "0      5589.0      324.0                  17            1          5   \n",
       "1     15028.0     1836.0                  18            7         12   \n",
       "2      1317.0      242.0                   3            3          5   \n",
       "3      1392.0        0.0                   7           12          6   \n",
       "4      1447.0        0.0                   4            6          9   \n",
       "\n",
       "   enso_cumularea  \n",
       "0        5.797101  \n",
       "1       12.217195  \n",
       "2       18.375095  \n",
       "3        0.000000  \n",
       "4        0.000000  "
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_full.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "83428af2-ae88-479b-8403-d0a9f503ba12",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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Kul6uU/tSRai1eBG/eb2EsrSyzgO3TfLZ249ub9t0jkvLazx073SELau07chYum7baDrJpeW1rvx+H6oIe91HEemM1zNwn68o53PbumUQ+igSsn1n4Gb2DeCLwEXn3L3VbRPAU8AJ4MfALzjnLne7cSdnxvnGn13grcVlVkobjKYS3DE5xt//q7ft+p6tpNs7iyvkV9c5NJLitsmxriXftn7/hcVl3lu6xl1TBzl2eCSy26n1ki+3jBORxpqZgf8n4KEd2x4Hzjrn7gTOVl933cWrRS58dI3ypmMkOUR503Hho2tcvFpsuP9W0i2XL/Le0jWW18q8v7TKh9vbO0u+1Sb17jia5RNTWd5YKPD2xeXI1+R7wZe1eBFpbN8ZuHPuBTM7sWPzI8Dnq8+fBJ4HvtbNhgE8ez7Hxw6lOXzg+jrs5WtrPHs+x8lbDt+w/1bS7c2LBQ6kE4ykKsnPS4V17pwa6/h2Wjtv03XLxCiHDwwzMpyIfE2+V3xYixeRxtpdA59yzuUAqo9Hd9vRzB4zszkzm1tcXGzpQxauFjlUcy40wKFMioVdZuBbBTCFYplMMgFAJjVEYa3UlUIYFdiIiE96nsR0zj3hnJt1zs1OTk629N6pgxmuFEt1264US0wdzDTc/3oBTJJieQOAYmmTbDrVleSbknoi4pN2TyNcMLNp51zOzKaBi91s1JaH753mt585z0JhjfWyYzhpTGXT/ObP3Vu3X23i8t2la3zsYJrLKyWKpQ02N+HY4bGuJN8aJfXml65xJJvmWy++G6tKxUYVmICqMkU80u4M/BngdPX5aeDp7jSn3qVCkcurG2w6SAw5Nh1cXt3gUuH6EkptYvH2o2PcNZXlJ1eLjI8kGUsnOT4xws3VZFynwWZnUq9YKsOQkUklYlWp2KgC87s/nOe7c++rKlPEI82cRvhtKgnLI2Y2D/wm8DvAd8zsq8B7wJd60bin5uaZHk9z+MDB7W2Xr63x1Nw8D36ykjTcmVg8PnGA8QOpniUWa5N6z53PkUld/+ytx06TpVHb+Z1mMymWlvMA3H3zoe1tW/uG3FeRkDVzFspXdvmnB7vclhssFIrcvGO9+1A6yYc1ScwoqwXjWqnYqF+ljU3A6rbFoa8iIfO6EnMqm+HKjqThlbUyU9nrQT3KxGJck5qN+pVKDJFK1AfwOPRVJGReXwvl787O8M+efYN3FldwbhOzITKpJP/04RNAZa328soa37+wxE2jw9z1sYOkk0N9qxZstlIxtEuyNurXxFgaNitXRlRVpogfvJ6BH8lmODySYMhgY9MYMjg8kuBINrOdaMukknz29koQ+fO3LlEsbfStWrCZSsUQL8naqF+P3j/Do7PHVZUp4hGvZ+DPns9x97FDfLZBJeZ9txzeTrRlSXHkjgyFYomR4URfg8p+lYqNEoJb230Ofrv1y+c2iwwar2fge1VihlIVGUo7RSQ8XgfwvSoxQ0kghtJOEQmP10soD987zT/+Ly/zQb5IaXOT1NAQx8Yz/IsvfYqjBzNBVEXqkqwi0itez8B/cGGRD/JFNp0jYcamc3yQL/KDC4vBVEXqkqwi0itez8C/+eL7jNUkAAEKxRLffPF9/sHnPxFMVaQuySoiveD1DPxKscSBVH0TD6SGblgXByULRWTweD0DP5RJkbtapFSGTSr/tUklqavE3HL9UrLXZ+vdTBbuVoyzc/v0wQy56lkyjV77sC4vIvHg9Qz8tpsyFMuwUX29ARTLle07nZwZp1AsUyiW2HSVisFCsbx9GdRO7FaMc+69y3Xbc/kiT7zwDh/mVzkylubD/CpPvPAOuXzRq3V5EYkHrwP4hY+KjKaMZPUSHEmD0ZRx4aMb78jTy2RhbTHOkFmleCiT5Nnzubrti8tFxkeTXCqsM2TGpcI646NJFpeLde87N5/vuE0iIl4voVwpljiaTZNIJLa3bWxscHn1xjVw6F2ycLerDi5cLfLpWye2txWKZQ5lUhTWKu0rrJUqr4vluvfpCn4i0g1ez8APZVJcK23WbbtW2ryhOrPXdivGmTqYqduezSS5UiyRTVfPhEmnKq8zybr3qYhHRLrB6xn4L/6V4/zuf3+ThcL1M0lSBr/2s3fW7dfK1f7a2ffC4jLvLV3jrqmDHDs8sl2M85lbD3PmLxbZ2HTcNDpMJjVEfqXM7ZNjbDrHkeww7350jTsms2w6pyIeaSi0q1WKP7yegV9dLVFy9dtKrrJ9SytX+2t33zuOZvnEVJY3Fgq8fXGZkeEEJ2cO8cGVNe6aynJkLMVHK2v85GqRv3PfNDePj3BpeY2bx0d47HMfZ3o8oyIeaSjEq1WKP7yegX977gMOZhKMDtcsQayX+fbcB/zqFz4JtHa1v072vWVilMMHhrdv1fZcTQLz+MQBoFJkVHLccCu3k135NiSOQr1apfjB6xn4ylqZzI67wGQSVrfu3EoBTzf3VeGQdIOOI+mE1wF8NJ2kuFG/hlLccHUHfCtX++vmvrrKoHSDjiPphNcB/Cuzx7ha3CB3dW3752pxg6/MHtvep5UCnv32zeVXee58jm+9+C6XV9aZX7q26769LBySwaHjSDrhdQDPXWmcyKnd3koBz1777kwmZVIJGDKKpXLD36urDEo36DiSTnidxPze+QWSBumaC1qtlTb53vkF/mXNfq0U8Oy2b6Nk0sw420nLVn6XSCt0HEm7vJ6Br5chuaOFyaHK9m5TMklEQuN1AB9OQrm+EJPyZmV7tymZJCKh8XoJ5Yv3TvHdlxcor9dH8Vuzaf79n77JbZNjdVVre1W07VftdnJmnO/+cJ6l5TyljU1SiSEmxtI8ev/M9j6DVjE3aP0VCY3XM/APLl9ruD2/us77S6t8WE085vKre1a0NV3ttrl1yqLteD14FXOD1l+REHk9A//+uwVgO5yyFU4/KjoODFcu23rn1Nj25Vl3q2jb69+2ZpTn5vPMTBzg7psPbX9+oVja3mfQKuYGrb8iIfJ6Br6XTGqIwlppO9G4VxKymQSlKi/rDVp/RUIUbAAvljbJplPbica9kpDNJChVeVlv0PorEiKvA/hP3ZoFKksntQX1N2WMa+tljmSHt6vW9qpoa6babb99Bq1ibtD6KxIic87tv1eXzM7Ourm5uZbe85Xfe2F7LRzg5mySv3XfcQ6NpLp6Fkoz+wzaWRmD1l8RX5nZS8652Ru2+x7ARUQG3W4B3OslFBER2Z0CuIhIoBTARUQCpQAuIhIoBXARkUD19SwUM1sE3m3z7UeAS11sjm/Uv3DFuW+g/vngVufc5M6NfQ3gnTCzuUan0cSF+heuOPcN1D+faQlFRCRQCuAiIoEKKYA/EXUDekz9C1ec+wbqn7eCWQMXEZF6Ic3ARUSkhgK4iEigggjgZvaQmb1hZm+Z2eNRt6cdZvZjM/uRmb1sZnPVbRNmdsbM3qw+Hq7Z/9er/X3DzH42upY3ZmbfMLOLZna+ZlvL/TGzT1e/l7fM7N+ame38rCjs0r/fMrMPqmP4spk9XPNvwfTPzI6b2Z+a2etm9qqZ/XJ1eyzGb4/+xWL86jjnvP4BEsDbwG3AMHAOuCfqdrXRjx8DR3Zs+13g8erzx4F/Xn1+T7WfaeDj1f4nou7DjrZ/DrgfON9Jf4AfAD9F5dan/w34QtR926N/vwX8owb7BtU/YBq4v/o8C/y/ah9iMX579C8W41f7E8IM/DPAW865C865deCPgEciblO3PAI8WX3+JPDzNdv/yDm35px7B3iLyvfgDefcC8DSjs0t9cfMpoGDzrnvu8pfyx/UvCdSu/RvN0H1zzmXc879sPq8ALwOHCMm47dH/3YTVP9qhRDAjwHv17yeZ+/B8JUD/oeZvWRmj1W3TTnnclA56ICj1e2h9rnV/hyrPt+53Wf/0MxeqS6xbC0xBNs/MzsB3Ae8SAzHb0f/IGbjF0IAb7TmFOK5jz/tnLsf+ALwS2b2uT32jUuft+zWn9D6+XvA7cCngBzwr6rbg+yfmY0Bfwz8inPu6l67NtgWYv9iNX4QRgCfB47XvJ4BPoyoLW1zzn1YfbwI/FcqSyIL1f9No/p4sbp7qH1utT/z1ec7t3vJObfgnNtwzm0C/4Hry1rB9c/MUlSC2x865/6kujk249eof3Eavy0hBPD/A9xpZh83s2Hgy8AzEbepJWY2ambZrefA3wDOU+nH6epup4Gnq8+fAb5sZmkz+zhwJ5Vkiu9a6k/1f9MLZvZANbv/92re452t4Fb1t6mMIQTWv2pbfh943Tn39Zp/isX47da/uIxfnaizqM38AA9TySS/DfxG1O1po/23UclynwNe3eoDcBNwFniz+jhR857fqPb3DTzLfFfb920q/xtaojJT+Wo7/QFmqfwhvQ38O6rVwVH/7NK//wz8CHiFyh/9dIj9A36GylLAK8DL1Z+H4zJ+e/QvFuNX+6NSehGRQIWwhCIiIg0ogIuIBEoBXEQkUArgIiKBUgAXEQmUAriISKAUwEVEAvX/ASbyjCfJ63MaAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "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(df_full.cumul_area, df_full.length_of_heatwave,alpha=0.3)\n",
    "plt.show()\n",
    "plt.scatter(df_full.enso_area, df_full.length_of_heatwave,alpha=0.3)\n",
    "plt.show()\n",
    "plt.scatter(df_full.enso_cumularea, df_full.length_of_heatwave,alpha=0.3)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "baa75ecd-92a8-40a1-8672-02f723ff6df0",
   "metadata": {},
   "outputs": [],
   "source": [
    "# where do the large heatwaves begin? \n",
    "sortedbycumularea_df = df_full.sort_values(by=['cumul_area'])\n",
    "indices = np.flip(list(df_full.sort_values(by=['cumul_area']).index))\n",
    "\n",
    "for i in indices[0:30]:\n",
    "    new = blob_files[file_number].SSTA[i,:,0:160,:].mean(axis=(1,2)).values\n",
    "    mask = np.isnan(new)\n",
    "    leading_nans = mask.argmin()\n",
    "    # print(sortedbycumularea_df.iloc[i].length_of_heatwave)\n",
    "    # plt.contourf(blob_files[file_number].SSTA[i,leading_nans,0:160,:])\n",
    "    # plt.show()\n",
    "    \n",
    "# where do the long heatwaves begin?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "f3eadff2-2426-4433-aca3-8af6043a2bf1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n"
     ]
    }
   ],
   "source": [
    "new = blob_files[file_number].SSTA[i,:,0:160,:].mean(axis=(1,2)).values\n",
    "leading_nans = mask.argmin()\n",
    "print(leading_nans)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "55695622-f068-4043-bfec-9a5eaf339d65",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "## Picking out characteristics\n",
    "\n",
    "# related to duration\n",
    "length_of_heatwave = []\n",
    "first_month = []\n",
    "last_month = []\n",
    "\n",
    "# related to intensity\n",
    "mean_SSTa_timeseries = []\n",
    "max_SSTa_timeseries = []\n",
    "\n",
    "for i in range(blob_files[file_number].SSTA[:,:,0:160,:].shape[0]):\n",
    "    shortened_mean= blob_files[file_number].SSTA[i,:,0:160,:].mean(axis=(1,2)).dropna(dim=\"time\", how=\"any\")\n",
    "    shortened_max= blob_files[file_number].SSTA[i,:,0:160,:].max(axis=(1,2)).dropna(dim=\"time\", how=\"any\")\n",
    "    \n",
    "    mean_SSTa_timeseries.append(shortened_mean.values)\n",
    "    max_SSTa_timeseries.append(shortened_max.values)\n",
    "    \n",
    "    length_of_heatwave.append(shortened_mean.shape[0])\n",
    "    first_month.append(shortened_mean[0].time.month.values)\n",
    "    last_month.append(shortened_mean[-1].time.month.values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "e818b93b-f377-4cba-ab3e-6da4afd87b32",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>cumul_area</th>\n",
       "      <th>enso_area</th>\n",
       "      <th>length_of_heatwave</th>\n",
       "      <th>first_month</th>\n",
       "      <th>last_month</th>\n",
       "      <th>enso_cumularea</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>117</th>\n",
       "      <td>15039.0</td>\n",
       "      <td>2042.0</td>\n",
       "      <td>35</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>13.578030</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>15118.0</td>\n",
       "      <td>2620.0</td>\n",
       "      <td>27</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>17.330335</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80</th>\n",
       "      <td>15222.0</td>\n",
       "      <td>2495.0</td>\n",
       "      <td>27</td>\n",
       "      <td>8</td>\n",
       "      <td>10</td>\n",
       "      <td>16.390750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>16375.0</td>\n",
       "      <td>2729.0</td>\n",
       "      <td>44</td>\n",
       "      <td>9</td>\n",
       "      <td>4</td>\n",
       "      <td>16.665649</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>113</th>\n",
       "      <td>18566.0</td>\n",
       "      <td>2084.0</td>\n",
       "      <td>38</td>\n",
       "      <td>10</td>\n",
       "      <td>11</td>\n",
       "      <td>11.224820</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     cumul_area  enso_area  length_of_heatwave  first_month last_month  \\\n",
       "117     15039.0     2042.0                  35            3          1   \n",
       "50      15118.0     2620.0                  27            1          3   \n",
       "80      15222.0     2495.0                  27            8         10   \n",
       "22      16375.0     2729.0                  44            9          4   \n",
       "113     18566.0     2084.0                  38           10         11   \n",
       "\n",
       "     enso_cumularea  \n",
       "117       13.578030  \n",
       "50        17.330335  \n",
       "80        16.390750  \n",
       "22        16.665649  \n",
       "113       11.224820  "
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sortedbycumularea_df.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "ddcd3afa-dfa9-4662-ad49-448647920192",
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "446\n",
      "480\n",
      "46077.0 ----- 0\n",
      "46080.0 ----- 1\n",
      "46080.0 ----- 2\n",
      "46078.0 ----- 3\n",
      "46057.0 ----- 4\n",
      "46054.0 ----- 5\n",
      "46068.0 ----- 6\n",
      "46063.0 ----- 7\n",
      "46061.0 ----- 8\n",
      "46049.0 ----- 9\n",
      "46031.0 ----- 10\n",
      "46046.0 ----- 11\n",
      "46049.0 ----- 12\n",
      "46029.0 ----- 13\n",
      "46047.0 ----- 14\n",
      "46002.0 ----- 15\n",
      "46043.0 ----- 16\n",
      "46062.0 ----- 17\n",
      "46021.0 ----- 18\n",
      "46012.0 ----- 19\n",
      "46008.0 ----- 20\n",
      "46040.0 ----- 21\n",
      "46080.0 ----- 22\n",
      "46080.0 ----- 23\n",
      "46080.0 ----- 24\n",
      "46080.0 ----- 25\n",
      "46062.0 ----- 26\n",
      "46072.0 ----- 27\n",
      "46072.0 ----- 28\n",
      "46069.0 ----- 29\n",
      "46077.0 ----- 30\n",
      "46079.0 ----- 31\n",
      "46080.0 ----- 32\n",
      "46080.0 ----- 33\n"
     ]
    }
   ],
   "source": [
    "# feeding first, max_extent, and last image\n",
    "mhw_ind = 117\n",
    "\n",
    "firstind = np.argwhere(np.isfinite(blob_files[file_number].SSTA[mhw_ind,:,0:160,:].mean(axis=(1,2)).values)).flatten()[0]\n",
    "lastind = np.argwhere(np.isfinite(blob_files[file_number].SSTA[mhw_ind,:,0:160,:].mean(axis=(1,2)).values)).flatten()[-1]\n",
    "print(firstind)\n",
    "print(lastind)\n",
    "first_image = blob_files[file_number].SSTA[mhw_ind,firstind:lastind,0:160,:][0]\n",
    "last_image = blob_files[file_number].SSTA[mhw_ind,firstind:lastind,0:160,:][-1]\n",
    "tot_len = lastind - firstind\n",
    "\n",
    "# first_image.plot(); plt.show()\n",
    "# last_image.plot(); plt.show()\n",
    "total_area_ls = []\n",
    "\n",
    "for i in range(tot_len):\n",
    "    # new = blob_files[file_number].SSTA[mhw_ind,firstind:lastind,0:160,:][i][blob_files[file_number].SSTA[mhw_ind,firstind:lastind,0:160,:][i] != 0] = 1\n",
    "    # print(new)\n",
    "    new = np.copy(blob_files[file_number].SSTA[mhw_ind,firstind:lastind,0:160,:][i].values)\n",
    "    new[:][new[:] == np.nan] = 0\n",
    "    new[:][new[:] != 0] = 1\n",
    "    print(np.sum(new), '-----',i)\n",
    "    total_area_ls.append(np.sum(new))\n",
    "    # print(blob_files[file_number].SSTA[mhw_ind,firstind:lastind,0:160,:][i].values)\n",
    "    # print(new[new != 0] == 1)\n",
    "    # blob_files[file_number].SSTA[mhw_ind,firstind:lastind,0:160,:][i].plot(vmin=-10,vmax=10); plt.show() \n",
    "    # print(firstind + i)\n",
    "# could I get groups if I only fed in the first_image\n",
    "# first_image.plot(); plt.show()\n",
    "# last_image.plot(); plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "a03c1387-b267-4a4b-9a8d-38fbfbde3897",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1\n",
      "[46077.0, 46080.0, 46080.0, 46078.0, 46057.0, 46054.0, 46068.0, 46063.0, 46061.0, 46049.0, 46031.0, 46046.0, 46049.0, 46029.0, 46047.0, 46002.0, 46043.0, 46062.0, 46021.0, 46012.0, 46008.0, 46040.0, 46080.0, 46080.0, 46080.0, 46080.0, 46062.0, 46072.0, 46072.0, 46069.0, 46077.0, 46079.0, 46080.0, 46080.0]\n"
     ]
    }
   ],
   "source": [
    "print(np.argmax(total_area_ls))\n",
    "print(total_area_ls)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3aa23b6d-0059-40af-996e-3b72b36571b7",
   "metadata": {},
   "outputs": [],
   "source": [
    "print('yes')\n",
    "print('maybe')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "d792747a-740b-411f-b611-77bd597359bf",
   "metadata": {},
   "outputs": [],
   "source": [
    "blob_files[file_number].SSTA[0,:,0:160,:].mean(axis=(1,2)).values\n",
    "\n",
    "firstind = np.argwhere(np.isfinite(blob_files[file_number].SSTA[0,:,0:160,:].mean(axis=(1,2)).values)).flatten()[0]\n",
    "lastind = np.argwhere(np.isfinite(blob_files[file_number].SSTA[0,:,0:160,:].mean(axis=(1,2)).values)).flatten()[-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "7687d332-6b78-4729-bb3a-467072f75c50",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "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(cumulative_SSTA_shapes[0],vmin=-50, vmax= 50, cmap='bwr'); plt.colorbar()\n",
    "plt.contour(cumulative_SSTA_shapes[0])\n",
    "plt.show()\n",
    "\n",
    "plt.plot(range(len(mean_SSTa_timeseries[0])), mean_SSTa_timeseries[0])\n",
    "plt.plot(range(len(mean_SSTa_timeseries[0])), max_SSTa_timeseries[0])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "261ca67a-a971-4189-b1a2-51fee342cb3d",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8148822c-eb24-454d-ba30-51209d27933b",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "53eafd0d-b2ad-4171-8d1c-d26c1a418549",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Arctic circle is approximately 66°N\n",
    "file_number = 0\n",
    "SSTA_blob_file0 = blob_files[file_number].SSTA[:,:,0:160,:]\n",
    "label_blob_file0 = blob_files[file_number].labels[:,:,0:160,:]\n",
    "\n",
    "cumulative_SST_shapes = []\n",
    "\n",
    "for i in range((np.shape(np.asarray(blob_files[file_number].SSTA))[0])):\n",
    "    x = blob_files[file_number].SSTA[i,:,0:160,:] # looping through each marine heatwave event\n",
    "    mhw_as_one_im = x.sum(axis=(0))\n",
    "    cumulative_SST_shapes.append(mhw_as_one_im)\n",
    "    # no_nans_x = np.nan_to_num(mhw_as_one_im)\n",
    "        # no_nans_x[no_nans_x != 0] = 1\n",
    "        # no_nans_x_full.append(no_nans_x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "id": "d2548ec0-e5af-4985-a5d6-a95879466ca2",
   "metadata": {},
   "outputs": [
    {
     "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(cumulative_SST_shapes[0])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "9652a92c-0298-4878-b585-0f87825d76bb",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "cumulative_SSTA_shapes[0].values[cumulative_SSTA_shapes[0].values != 0] = 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "3f508a53-d6ff-4f8e-a97e-bf774143622d",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "ename": "AttributeError",
     "evalue": "'numpy.ndarray' object has no attribute 'unique'",
     "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_126282/2280406593.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0mc\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mblob_files\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mfile_number\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mSSTA\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwhere\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mblob_files\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mfile_number\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mSSTA\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mother\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mc\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0munique\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mAttributeError\u001b[0m: 'numpy.ndarray' object has no attribute 'unique'"
     ]
    }
   ],
   "source": [
    "c = blob_files[file_number].SSTA[0,:,:,:].where(blob_files[file_number].SSTA[0,:,:,:] != 0, other=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "5fe4a0ff-d5e6-4077-bc06-8a51b59d86cd",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "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(cumulative_SSTA_shapes[0,:,:])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c4746bbf-2bcd-4319-afc9-a3c2ab386829",
   "metadata": {},
   "outputs": [],
   "source": [
    "# %%timeit\n",
    "## Note that this requires a lot of GPU\n",
    "# combined_file_0_to_5 = xr.concat(blob_files[0:5],\"heatwave_label\")\n",
    "# combined_file_5_to_10 = xr.concat(blob_files[5:10],\"heatwave_label\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "53af5cb8-b1ef-40fa-ba80-27e7219d9d1b",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# combined_file0_to_5.to_netcdf('/glade/work/cassiacai/combined_file0_to_5.nc')\n",
    "# combined_file5_to_10.to_netcdf('/glade/work/cassiacai/combined_file5_to_10.nc')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9e8c5025-b1b6-46b1-9f96-c4f5b4daf12e",
   "metadata": {},
   "source": [
    "## Data Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "1a920730-1d70-43dc-a4bf-638cdffa150d",
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n"
     ]
    }
   ],
   "source": [
    "no_nans_x_full = []\n",
    "\n",
    "for file_num in range(len(blob_files)):\n",
    "    print(file_num)\n",
    "    \n",
    "    for i in range((np.shape(np.asarray(blob_files[file_num].SSTA))[0])):\n",
    "        \n",
    "        x = blob_files[file_num].SSTA[i,:,:,:] # looping through each marine heatwave event \n",
    "        \n",
    "        # similar to MNIST dataset where we group images of numbers.\n",
    "        mhw_as_one_im = x.sum(axis=(0)) \n",
    "        no_nans_x = np.nan_to_num(mhw_as_one_im)\n",
    "        no_nans_x[no_nans_x != 0] = 1\n",
    "        no_nans_x_full.append(no_nans_x)\n",
    "\n",
    "# np.save('no_nans_x_full.npy', no_nans_x_full, allow_pickle=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "1ca63971-fdf7-4f62-b96b-d7ed8c2a0dfe",
   "metadata": {},
   "outputs": [],
   "source": [
    "no_nans_x_full = np.load('no_nans_x_full.npy', allow_pickle=True)\n",
    "print(len(no_nans_x_full))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "9b090d5a-73f2-44fc-b3ad-b447f6a75240",
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Using torch 1.12.1+cu102\n",
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    }
   ],
   "source": [
    "# try pixel similarity\n",
    "import torch.nn.functional as F\n",
    "import torch\n",
    "print(\"Using torch\", torch.__version__)\n",
    "\n",
    "'''\n",
    "1. Take the mean of the absolute value of differences (absolute value is the function \n",
    "  that replaces negative values with positive values). This is called the mean absolute \n",
    "  difference or L1 norm\n",
    "2. Take the mean of the square of differences (which makes everything positive) and then \n",
    "  take the square root (which undoes the squaring). This is called the root mean squared \n",
    "  error (RMSE) or L2 norm.\n",
    "'''\n",
    "npcopyof_no_nans_x_full = np.copy(no_nans_x_full)\n",
    "\n",
    "l1_loss_full = []; MSE_loss_full = []\n",
    "\n",
    "for i in range(len(no_nans_x_full)):\n",
    "    print(i)\n",
    "    l1_loss_i = []; MSE_loss_i = []\n",
    "    first = torch.tensor(no_nans_x_full[i])\n",
    "    for j in range(len(npcopyof_no_nans_x_full)):\n",
    "        second = torch.tensor(npcopyof_no_nans_x_full[j])\n",
    "        l1_loss_i.append(F.l1_loss(first,second))\n",
    "        MSE_loss_i.append(F.mse_loss(first,second).sqrt())\n",
    "    l1_loss_full.append(l1_loss_i)\n",
    "    MSE_loss_full.append(MSE_loss_i)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "dbdd896f-4f59-4fa1-bf96-f774da6f4695",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1131"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(MSE_loss_full)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "6e813d30-39af-41bc-a4d9-3ff0dfb1e0f1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1131"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(MSE_loss_full[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "212723b7-cecd-4918-a5a6-a35f90345c40",
   "metadata": {},
   "outputs": [],
   "source": [
    "# np.save('no_nans_x_l1_loss_full.npy', l1_loss_full, allow_pickle=True)\n",
    "# np.save('no_nans_x_MSE_loss_full.npy', MSE_loss_full, allow_pickle=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a54dbf6d-4c05-4d53-84a7-a59526b83b99",
   "metadata": {},
   "outputs": [],
   "source": [
    "no_nans_x_l1_loss_full = np.load('no_nans_x_l1_loss_full.npy', allow_pickle=True)\n",
    "no_nans_x_MSE_loss_full = np.load('no_nans_x_MSE_loss_full.npy', allow_pickle=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "3344cc0a-7091-44e7-9689-fb877521af53",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "yes\n"
     ]
    }
   ],
   "source": [
    "print('yes')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "1aef94bb-32cf-46d0-9969-78ba70018a03",
   "metadata": {},
   "outputs": [],
   "source": [
    "first = torch.tensor(no_nans_x_full[0])\n",
    "second = torch.tensor(no_nans_x_full[1])\n",
    "l1loss = F.l1_loss(first,second)\n",
    "mseloss = F.mse_loss(first,second).sqrt()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "4cbb9f65-eaab-41d4-9241-662a890b75de",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.45679615162037035\n"
     ]
    }
   ],
   "source": [
    "print(l1loss.item())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "10e97c9b-9090-439e-b32d-31edc7690f84",
   "metadata": {},
   "outputs": [
    {
     "ename": "AttributeError",
     "evalue": "'torch.dtype' object has no attribute 'type'",
     "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_6870/969078141.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0;31m# Compute SSIM between two images\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 6\u001b[0;31m \u001b[0mscore\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdiff\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mstructural_similarity\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfirst\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msecond\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfull\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\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      7\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Similarity Score: {:.3f}%\"\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mscore\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0;36m100\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      8\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/miniconda3/envs/test-env/lib/python3.9/site-packages/skimage/metrics/_structural_similarity.py\u001b[0m in \u001b[0;36mstructural_similarity\u001b[0;34m(im1, im2, win_size, gradient, data_range, multichannel, gaussian_weights, full, **kwargs)\u001b[0m\n\u001b[1;32m    161\u001b[0m             warn(\"Inputs have mismatched dtype.  Setting data_range based on \"\n\u001b[1;32m    162\u001b[0m                  \"im1.dtype.\", stacklevel=2)\n\u001b[0;32m--> 163\u001b[0;31m         \u001b[0mdmin\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdmax\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdtype_range\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mim1\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtype\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    164\u001b[0m         \u001b[0mdata_range\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdmax\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0mdmin\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    165\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mAttributeError\u001b[0m: 'torch.dtype' object has no attribute 'type'"
     ]
    }
   ],
   "source": [
    "from skimage.metrics import structural_similarity\n",
    "import cv2\n",
    "import numpy as np\n",
    "\n",
    "# Compute SSIM between two images\n",
    "score, diff = structural_similarity(first, second, full=True)\n",
    "print(\"Similarity Score: {:.3f}%\".format(score * 100))\n",
    "\n",
    "# The diff image contains the actual image differences between the two images\n",
    "# and is represented as a floating point data type so we must convert the array \n",
    "# to 8-bit unsigned integers in the range [0,255] before we can use it with OpenCV\n",
    "diff = (diff * 255).astype(\"uint8\")\n",
    "\n",
    "# Threshold the difference image, followed by finding contours to\n",
    "# obtain the regions that differ between the two images\n",
    "thresh = cv2.threshold(diff, 0, 255, cv2.THRESH_BINARY_INV | cv2.THRESH_OTSU)[1]\n",
    "contours = cv2.findContours(thresh, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)\n",
    "contours = contours[0] if len(contours) == 2 else contours[1]\n",
    "\n",
    "# Highlight differences\n",
    "mask = np.zeros(first.shape, dtype='uint8')\n",
    "filled = second.copy()\n",
    "\n",
    "for c in contours:\n",
    "    area = cv2.contourArea(c)\n",
    "    if area > 100:\n",
    "        x,y,w,h = cv2.boundingRect(c)\n",
    "        cv2.rectangle(first, (x, y), (x + w, y + h), (36,255,12), 2)\n",
    "        cv2.rectangle(second, (x, y), (x + w, y + h), (36,255,12), 2)\n",
    "        cv2.drawContours(mask, [c], 0, (0,255,0), -1)\n",
    "        cv2.drawContours(filled, [c], 0, (0,255,0), -1)\n",
    "\n",
    "cv2.imshow('first', first)\n",
    "cv2.imshow('second', second)\n",
    "cv2.imshow('diff', diff)\n",
    "cv2.imshow('mask', mask)\n",
    "cv2.imshow('filled', filled)\n",
    "cv2.waitKey()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b1890bee-4f31-45b6-80bb-66f3d5036e11",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c61e0aa9-dce2-45ce-9244-dd9baffdd5fc",
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.contourf(first - second)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "fa09d75a-2fd1-4023-996b-693e288924f0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     0    1    2    3    4    5    6    7    8    9    ...  278  279  280  \\\n",
      "0    0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  ...  0.0  0.0  0.0   \n",
      "1    0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  ...  0.0  0.0  0.0   \n",
      "2    0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  ...  0.0  0.0  0.0   \n",
      "3    0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  ...  0.0  0.0  0.0   \n",
      "4    0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  ...  0.0  0.0  0.0   \n",
      "..   ...  ...  ...  ...  ...  ...  ...  ...  ...  ...  ...  ...  ...  ...   \n",
      "187  1.0  1.0  1.0  1.0  1.0  1.0  1.0  1.0  1.0  1.0  ...  1.0  1.0  1.0   \n",
      "188  1.0  1.0  1.0  1.0  1.0  1.0  1.0  1.0  1.0  1.0  ...  1.0  1.0  1.0   \n",
      "189  1.0  1.0  1.0  1.0  1.0  1.0  1.0  1.0  1.0  1.0  ...  1.0  1.0  1.0   \n",
      "190  1.0  1.0  1.0  1.0  1.0  1.0  1.0  1.0  1.0  1.0  ...  1.0  1.0  1.0   \n",
      "191  1.0  1.0  1.0  1.0  1.0  1.0  1.0  1.0  1.0  1.0  ...  1.0  1.0  1.0   \n",
      "\n",
      "     281  282  283  284  285  286  287  \n",
      "0    0.0  0.0  0.0  0.0  0.0  0.0  0.0  \n",
      "1    0.0  0.0  0.0  0.0  0.0  0.0  0.0  \n",
      "2    0.0  0.0  0.0  0.0  0.0  0.0  0.0  \n",
      "3    0.0  0.0  0.0  0.0  0.0  0.0  0.0  \n",
      "4    0.0  0.0  0.0  0.0  0.0  0.0  0.0  \n",
      "..   ...  ...  ...  ...  ...  ...  ...  \n",
      "187  1.0  1.0  1.0  1.0  1.0  1.0  1.0  \n",
      "188  1.0  1.0  1.0  1.0  1.0  1.0  1.0  \n",
      "189  1.0  1.0  1.0  1.0  1.0  1.0  1.0  \n",
      "190  1.0  1.0  1.0  1.0  1.0  1.0  1.0  \n",
      "191  1.0  1.0  1.0  1.0  1.0  1.0  1.0  \n",
      "\n",
      "[192 rows x 288 columns]\n"
     ]
    }
   ],
   "source": [
    "im3_t = torch.tensor(no_nans_x_full[0])\n",
    "df = pd.DataFrame(im3_t)\n",
    "print(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0a63a0a8-2be7-4c42-916f-66d6e3e5e90d",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "cumulative_SSTA = [] # adding up SSTAs\n",
    "area_filled_of_region_with_days = [] \n",
    "area_filled_of_region_wo_days = []\n",
    "length_of_event = []\n",
    "\n",
    "area_filled_of_region_with_days_small = [] # this is right by the coastline\n",
    "area_filled_of_region_wo_days_small = []\n",
    "\n",
    "for file_num in range(len(blob_files)):\n",
    "    print(file_num)\n",
    "    \n",
    "    for i in range((np.shape(np.asarray(blob_files[file_num].SSTA))[0])):\n",
    "        x = blob_files[file_num].SSTA[i,:,:,:]\n",
    "\n",
    "        # cumulative SSTA (summing SSTAs across all days)\n",
    "        cumulative_SSTA.append(x.sum(axis=(0)))\n",
    "\n",
    "        # collecting events that fill up X percentage of our area of interest\n",
    "        no_nans_x = np.nan_to_num(x)\n",
    "        no_nans_x[no_nans_x != 0] = 1\n",
    "        sum_of_one = no_nans_x.sum(axis=(0))\n",
    "        array_of_interest = np.copy(sum_of_one[f_row:l_row,f_col:l_col])\n",
    "\n",
    "        area_filled_of_region_with_days.append(100*np.nansum(array_of_interest) / (42*53)) # with days might not make much sense\n",
    "        length_of_event.append(np.nanmax(array_of_interest))\n",
    "        array_of_interest[array_of_interest != 0] = 1\n",
    "        area_filled_of_region_wo_days.append(100*np.nansum(array_of_interest)/(42*53))    \n",
    "\n",
    "        array_of_interest_small = np.copy(sum_of_one[f_row_small:l_row_small,f_col_small:l_col_small])\n",
    "        area_filled_of_region_with_days_small.append(100*np.nansum(array_of_interest_small) / (42*53)) \n",
    "        array_of_interest_small[array_of_interest_small != 0] = 1\n",
    "        area_filled_of_region_wo_days_small.append(100*np.nansum(array_of_interest_small)/(42*53)) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "08ab002e-0d78-4bc2-a84c-20b046499b9d",
   "metadata": {},
   "outputs": [],
   "source": [
    "print(len(cumulative_SSTA))\n",
    "print(len(area_filled_of_region_with_days))\n",
    "print(len(area_filled_of_region_wo_days))\n",
    "print(len(length_of_event))\n",
    "print('')\n",
    "print(len(area_filled_of_region_with_days_small))\n",
    "print(len(area_filled_of_region_wo_days_small))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e637684e-b40f-473d-a501-90b22942fc54",
   "metadata": {},
   "outputs": [],
   "source": [
    "print(np.nanmax(length_of_event))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a592d704-5393-4497-ae8b-94d4d705ad5f",
   "metadata": {},
   "outputs": [],
   "source": [
    "sns.histplot(data=length_of_event, binwidth=1, kde=True)\n",
    "\n",
    "plt.title('MHW duration')\n",
    "plt.show()\n",
    "\n",
    "print('The longest event lasts', np.nanmax(length_of_event), 'months or', np.around(np.nanmax(length_of_event)/12, decimals=2), 'years.') \n",
    "print('The shortest collected event lasts', np.nanmin(length_of_event), 'months or', np.around(np.nanmin(length_of_event)/12, decimals=2), 'years.')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fe3ddfab-73d8-4108-9df4-0add3051b762",
   "metadata": {},
   "source": [
    "### Data processing\n",
    "\n",
    "- Timeseries analysis --> \n",
    "- Compare shapes (don't use SSTA but make all SSTA values equal to some constant)\n",
    "\n",
    "- Visualize as a widget (timeseries) *code is up on Github\n",
    " ----- save slices, stitch as a GIF --> visualize and see\n",
    "- Patterns of evolution (look the same in space but different in time) --> acted differently maybe\n",
    "\n",
    "- variance --> build --> sum, variance, moments --> can start describing the distribution --> skewness\n",
    "- maybe make feature --> distances\n",
    "\n",
    "- how you sample --> 2 cluster within 2 groups --> some popular thing --> hides the details of the other groups\n",
    "- clustering on the other stuff\n",
    "- Statistics of lots of stuff\n",
    "\n",
    "- try hierarchical clustering --> level of connection between hard to differentiate clusters --> don't have \n",
    "- to decide the number of clusters until later --> link --> scikit-learn --> dendogram"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8c496250-8811-434c-88b9-a90ffcef1e2b",
   "metadata": {},
   "source": [
    "Clustering is a processof grouping data with similar spatial attributes, temporal attributes, or both, from which many significant events and regular phenomena can be discovered. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b463c5cf-8743-4722-bfd6-d26ce21251e0",
   "metadata": {},
   "outputs": [],
   "source": [
    "%%time\n",
    "\n",
    "cumulative_SSTA = [] # adding up SSTAs\n",
    "area_filled_of_region_with_days = [] \n",
    "area_filled_of_region_wo_days = []\n",
    "length_of_event = []\n",
    "\n",
    "area_filled_of_region_with_days_small = [] # this is right by the coastline\n",
    "area_filled_of_region_wo_days_small = []\n",
    "\n",
    "for i in range((np.shape(np.asarray(combined_file.SSTA))[0])):\n",
    "    x = combined_file.SSTA[i,:,:,:]\n",
    "\n",
    "    # cumulative SSTA (summing SSTAs across all days)\n",
    "    cumulative_SSTA.append(x.sum(axis=(0)))\n",
    "\n",
    "    # collecting events that fill up X percentage of our area of interest\n",
    "    no_nans_x = np.nan_to_num(x)\n",
    "    no_nans_x[no_nans_x != 0] = 1\n",
    "    sum_of_one = no_nans_x.sum(axis=(0))\n",
    "    array_of_interest = np.copy(sum_of_one[f_row:l_row,f_col:l_col])\n",
    "        \n",
    "    area_filled_of_region_with_days.append(100*np.nansum(array_of_interest) / (42*53)) # with days might not make much sense\n",
    "    length_of_event.append(np.nanmax(array_of_interest))\n",
    "    array_of_interest[array_of_interest != 0] = 1\n",
    "    area_filled_of_region_wo_days.append(100*np.nansum(array_of_interest)/(42*53))    \n",
    "    \n",
    "    array_of_interest_small = np.copy(sum_of_one[f_row_small:l_row_small,f_col_small:l_col_small])\n",
    "    area_filled_of_region_with_days_small.append(100*np.nansum(array_of_interest_small) / (42*53)) \n",
    "    array_of_interest_small[array_of_interest_small != 0] = 1\n",
    "    area_filled_of_region_wo_days_small.append(100*np.nansum(array_of_interest_small)/(42*53)) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "25574c5a-a218-4f57-aa08-c55e6587b00b",
   "metadata": {},
   "outputs": [],
   "source": [
    "print(len(cumulative_SSTA))\n",
    "print(len(area_filled_of_region_with_days))\n",
    "print(len(area_filled_of_region_wo_days))\n",
    "print(len(length_of_event))\n",
    "print('')\n",
    "print(len(area_filled_of_region_with_days_small))\n",
    "print(len(area_filled_of_region_wo_days_small))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9cc991b9-4a40-4205-a4b6-8036b5f3f7f5",
   "metadata": {},
   "outputs": [],
   "source": [
    "sns.histplot(data=length_of_event, binwidth=1, kde=True)\n",
    "plt.ylim(0, 50)\n",
    "plt.title('MHW duration')\n",
    "plt.show()\n",
    "\n",
    "print('The longest event lasts', np.nanmax(length_of_event), 'months or', np.around(np.nanmax(length_of_event)/12, decimals=2), 'years.') \n",
    "print('The shortest collected event lasts', np.nanmin(length_of_event), 'months or', np.around(np.nanmin(length_of_event)/12, decimals=2), 'years.')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "208bb9a5-44cc-41ba-a837-9bfdfb2b0558",
   "metadata": {},
   "outputs": [],
   "source": [
    "print(len(cumulative_SSTA))\n",
    "print(len(area_filled_of_region_with_days))\n",
    "print(len(area_filled_of_region_wo_days))\n",
    "print(len(length_of_event))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "bea7c56f-23dd-47df-ac87-188774805690",
   "metadata": {},
   "outputs": [],
   "source": [
    "print(np.nanmax(area_filled_of_region_wo_days))\n",
    "print(np.nanmin(area_filled_of_region_wo_days))\n",
    "\n",
    "print(np.nanmax(area_filled_of_region_wo_days_small))\n",
    "print(np.nanmin(area_filled_of_region_wo_days_small))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "104bcb88-b4be-4219-8075-054d341c7b60",
   "metadata": {},
   "outputs": [],
   "source": [
    "sns.histplot(data=area_filled_of_region_wo_days, binwidth=5, kde=True)\n",
    "plt.xlim(20, 100)\n",
    "plt.show()\n",
    "\n",
    "print(np.nanmin(area_filled_of_region_wo_days))\n",
    "print(np.nanmax(area_filled_of_region_wo_days))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9dd7ee41-4778-40c4-bc90-bbe6ec4688de",
   "metadata": {},
   "outputs": [],
   "source": [
    "area_filled_pd = pd.DataFrame(\n",
    "    {'full_reg': area_filled_of_region_wo_days,\n",
    "     'coast_reg': area_filled_of_region_wo_days_small,\n",
    "     'event_len': length_of_event\n",
    "    })"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1dbcd769-9ed8-4ab9-945e-31f7065b4e55",
   "metadata": {},
   "outputs": [],
   "source": [
    "area_filled_pd.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e97b8353-6279-4213-a7e6-72ad303ecdb2",
   "metadata": {},
   "outputs": [],
   "source": [
    "sns.scatterplot(x=area_filled_pd['full_reg'],y=area_filled_pd['coast_reg'],alpha=1.0, s=40)\n",
    "plt.xlim(20,100);plt.ylim(-10,50)\n",
    "plt.axhline(y=0, c='k')\n",
    "plt.xlabel('% area of full region filled')\n",
    "plt.ylabel('% area of coast region filled')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5808035a-22b8-4b94-9460-393fd18eb231",
   "metadata": {},
   "outputs": [],
   "source": [
    "print(len(area_filled_pd))\n",
    "rslt_df = area_filled_pd[area_filled_pd['coast_reg'] > 0].reset_index()\n",
    "rslt_df = rslt_df.sort_values(by=['coast_reg']).reset_index()\n",
    "print(rslt_df.head())\n",
    "print('')\n",
    "print(rslt_df.tail())\n",
    "print('')\n",
    "print(len(area_filled_pd) - len(rslt_df)) # number of MHWs without a footprint by the coast"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d88b908c-7f6f-4c98-888e-694188cc9423",
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.scatter(x=area_filled_pd['full_reg'],y=area_filled_pd['coast_reg'],c=area_filled_pd['event_len'], cmap='Reds',edgecolors= \"black\", s = 60, alpha=0.8)\n",
    "plt.colorbar()\n",
    "plt.xlim(20,100);plt.ylim(-5,45)\n",
    "plt.axhline(y=0, c='k')\n",
    "plt.xlabel('% area of full region filled')\n",
    "plt.ylabel('% area of coast region filled')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4d1bde94-14fd-4179-9c72-4b8ab49b955c",
   "metadata": {},
   "outputs": [],
   "source": [
    "area_filled_pd_with_days = pd.DataFrame(\n",
    "    {'full_reg': area_filled_of_region_with_days,\n",
    "     'coast_reg': area_filled_of_region_with_days_small,\n",
    "     'event_len': length_of_event\n",
    "    })"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2c7bdf73-1bf0-483d-8cd1-e9e3c07926d8",
   "metadata": {},
   "outputs": [],
   "source": [
    "area_filled_pd_with_days.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8423bf97-bff5-497b-8ef8-6d74fe8c8902",
   "metadata": {},
   "outputs": [],
   "source": [
    "sns.scatterplot(x=area_filled_pd_with_days['full_reg'],y=area_filled_pd_with_days['coast_reg'],alpha=0.7, s=60)\n",
    "# plt.xlim(20,100);plt.ylim(-10,50)\n",
    "plt.axhline(y=0, c='k')\n",
    "plt.xlabel('% area of full region filled with days')\n",
    "plt.ylabel('% area of coast region filled with days')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1ae37fd4-c59d-456b-9b92-b8e4fdb1e3b3",
   "metadata": {},
   "outputs": [],
   "source": [
    "print(len(area_filled_pd_with_days))\n",
    "rslt_df_with_days = area_filled_pd_with_days[area_filled_pd_with_days['coast_reg'] > 0].reset_index()\n",
    "rslt_df_with_days = rslt_df_with_days.sort_values(by=['coast_reg']).reset_index()\n",
    "print(rslt_df_with_days.head())\n",
    "print('')\n",
    "print(rslt_df_with_days.tail())\n",
    "print('')\n",
    "print(len(area_filled_pd_with_days) - len(rslt_df_with_days)) # number of MHWs without a footprint by the coast"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "15d44230-2af7-40b9-9991-286117a5f685",
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.scatter(x=area_filled_pd_with_days['full_reg'],y=area_filled_pd_with_days['coast_reg'],c=area_filled_pd_with_days['event_len'], cmap='Reds',edgecolors= \"black\", s = 60, alpha=0.7)\n",
    "plt.colorbar()\n",
    "plt.axhline(y=0, c='k')\n",
    "plt.xlabel('% area of full region filled with days')\n",
    "plt.ylabel('% area of coast region filled with days')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a1b6b4f8-27ec-4a88-99a7-b28dc545b3a9",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Cumulative SSTAs\n",
    "\n",
    "X = np.reshape(np.asarray(cumulative_SSTA), (1131, 192*288))\n",
    "print(X.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "83a565dd-a825-45b4-8f7c-4a26fa9c2616",
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_grid(XX, N):  \n",
    "    \"\"\"Small helper function to plot\"\"\"\n",
    "    fig, ax = plt.subplots(N, N, figsize=(16, 16))\n",
    "    for i in range(N):\n",
    "        for j in range(N):\n",
    "            ax[i,j].imshow(XX[(N)*i+j,:].reshape((192, 288)), cmap=\"seismic\")\n",
    "            ax[i,j].axis(\"off\")\n",
    "plot_grid(X, 8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d919df5d-9b92-4d4c-bda6-3d6bfc450766",
   "metadata": {},
   "outputs": [],
   "source": [
    "mean_data = np.mean(X,0)\n",
    "centered_X = X - mean_data\n",
    "\n",
    "pca = PCA(n_components = 100)\n",
    "pca.fit(centered_X) #X_train_standard\n",
    "\n",
    "components = pca.components_\n",
    "print(components[:16,:].shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "08259366-662b-4de6-86f2-31aa15b57b18",
   "metadata": {},
   "outputs": [],
   "source": [
    "#### Explained variance, explained variance ratio, singular values\n",
    "# -------------------------------------------\n",
    "# From documentation:\n",
    "\n",
    "# explained_variance_ : array, shape (n_components,) The amount \n",
    "# of variance explained by each of the selected components.\n",
    "\n",
    "# explained_variance_ratio_ : array, shape (n_components,) Percentage \n",
    "# of variance explained by each of the selected components.\n",
    "\n",
    "# singular_values_ndarray of shape (n_components,)\n",
    "# The singular values corresponding to each of the selected components. \n",
    "# The singular values are equal to the 2-norms of the n_components \n",
    "# variables in the lower-dimensional space.\n",
    "\n",
    "# -------------------------------------------\n",
    "\n",
    "nuclear_norm = np.linalg.norm(pca.singular_values_) # is the sum of singular singulars\n",
    "sv_scaled = pca.singular_values_**2/(nuclear_norm**2) \n",
    "# sv_scaled = pca.explained_variance_ratio_\n",
    "sv_total = np.cumsum(pca.singular_values_**2)/(nuclear_norm**2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9d779c05-38f6-4902-92f1-da7c7f874da2",
   "metadata": {},
   "outputs": [],
   "source": [
    "# -- Plot the singular values\n",
    "plt.rcParams[\"figure.figsize\"] = (25,6)\n",
    "\n",
    "plt.subplot(1, 4, 1)\n",
    "plt.plot(sv_scaled, 'blue') # equivalent to pca.explained_variance_ratio_\n",
    "plt.xticks(fontsize=15); plt.yticks(fontsize=15)\n",
    "plt.xlabel('PCA component', fontsize=15)\n",
    "plt.ylabel('explained variance ratio', fontsize=15)\n",
    "\n",
    "plt.subplot(1, 4, 2)\n",
    "plt.plot(sv_total, 'blue')\n",
    "plt.xticks(fontsize=15); plt.yticks(fontsize=15)\n",
    "\n",
    "plt.xlabel('PCA component', fontsize=15)\n",
    "plt.ylabel('cumulatiave explained variance ratio', fontsize=15)\n",
    "\n",
    "plt.subplot(1, 4, 3)\n",
    "plt.plot(sv_total[:30], 'blue')\n",
    "plt.xticks(fontsize=15); plt.yticks(fontsize=15)\n",
    "\n",
    "plt.xlabel('PCA component', fontsize=15)\n",
    "plt.ylabel('cumulatiave explained variance ratio', fontsize=15)\n",
    "\n",
    "plt.subplot(1, 4, 4)\n",
    "plt.plot(np.log(sv_scaled),'blue') # equivalent to np.log(pca.singular_values_)\n",
    "# plt.plot(np.log(pca.singular_values_))\n",
    "plt.xticks(fontsize=15); plt.yticks(fontsize=15)\n",
    "plt.xlabel('PCA component', fontsize=15)\n",
    "plt.ylabel('log(explained variance ratio)', fontsize=15)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "39a3784e-69ba-44cd-afa4-9c3c25aead70",
   "metadata": {},
   "outputs": [],
   "source": [
    "pca_total = PCA(n_components=100)\n",
    "pca_total.fit(X)\n",
    "frobenius_norm = np.sqrt(sum(pca_total.singular_values_**2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f1634db4-b55b-4e6e-8ed4-31682461455e",
   "metadata": {},
   "outputs": [],
   "source": [
    "def no_pca_modes(ratio):\n",
    "    pca_test = PCA(n_components=1)\n",
    "    pca_test.fit(X)\n",
    "    np.sqrt(sum(pca_test.singular_values_**2))\n",
    "    i = 1\n",
    "    while np.sqrt(sum(pca_test.singular_values_**2)) < ratio*frobenius_norm:\n",
    "        i += 1\n",
    "        pca_test = PCA(n_components=i)\n",
    "        pca_test.fit(X)\n",
    "    return i\n",
    "\n",
    "print('Number of PCA modes to keep to approximate X_train up to 60%: ', no_pca_modes(0.6))\n",
    "print('Number of PCA modes to keep to approximate X_train up to 80%: ',no_pca_modes(0.8))\n",
    "print('Number of PCA modes to keep to approximate X_train up to 90%: ',no_pca_modes(0.9))\n",
    "print('Number of PCA modes to keep to approximate X_train up to 95%: ',no_pca_modes(0.95))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "18e42f7d-2ed4-4aaa-9cc9-b96bab32c194",
   "metadata": {},
   "outputs": [],
   "source": [
    "plot_grid(components[:19,:], 4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "723df5ee-8000-4a6c-93d5-5616324ff0cb",
   "metadata": {},
   "outputs": [],
   "source": [
    "reshape_comp = np.reshape(components, (100,192,288))\n",
    "print(reshape_comp.shape)\n",
    "reshape_comp[0,:,:].shape\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(8,5))\n",
    "plt.contourf(reshape_comp[0,:,:])\n",
    "plt.colorbar()\n",
    "plt.contourf(land_mask, cmap='binary')\n",
    "plt.show()\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(8,5))\n",
    "plt.contourf(reshape_comp[1,:,:])\n",
    "plt.colorbar()\n",
    "plt.contourf(land_mask, cmap='binary')\n",
    "plt.show()\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(8,5))\n",
    "plt.contourf(reshape_comp[2,:,:])\n",
    "plt.colorbar()\n",
    "plt.contourf(land_mask, cmap='binary')\n",
    "plt.show()\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(8,5))\n",
    "plt.contourf(reshape_comp[3,:,:])\n",
    "plt.colorbar()\n",
    "plt.contourf(land_mask, cmap='binary')\n",
    "plt.show()\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(8,5))\n",
    "plt.contourf(reshape_comp[4,:,:])\n",
    "plt.colorbar()\n",
    "plt.contourf(land_mask, cmap='binary')\n",
    "plt.show()\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(8,5))\n",
    "plt.contourf(reshape_comp[5,:,:])\n",
    "plt.colorbar()\n",
    "plt.contourf(land_mask, cmap='binary')\n",
    "plt.show()\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(8,5))\n",
    "plt.contourf(reshape_comp[6,:,:])\n",
    "plt.colorbar()\n",
    "plt.contourf(land_mask, cmap='binary')\n",
    "plt.show()\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(8,5))\n",
    "plt.contourf(reshape_comp[7,:,:])\n",
    "plt.colorbar()\n",
    "plt.contourf(land_mask, cmap='binary')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a9cb9e00-ac3e-466e-ab2e-0dd3972ec9e4",
   "metadata": {},
   "outputs": [],
   "source": [
    "PCs = pca.transform(X)\n",
    "PCs.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5c66390a-536a-48fa-921f-963f3d827e02",
   "metadata": {},
   "outputs": [],
   "source": [
    "PCs[0,:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f45f3804-1be7-4a75-b0ac-b82e8ba31073",
   "metadata": {},
   "outputs": [],
   "source": [
    "number_of_PCA_modes = 8"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5441fb6d-db1b-4062-9f13-e2a177da5766",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Let's just check what this looks like. Note to self: This is basically EOFs. \n",
    "print(PCs[0,:8]) \n",
    "print(PCs[1,:8])\n",
    "print(PCs[2,:8])\n",
    "print(PCs[3,:8])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3fcd52b0-83d5-44ab-a08c-95417df8d7ce",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import decomposition\n",
    "\n",
    "grid = X\n",
    "n_samples, n_features = grid.shape\n",
    "estimator_3 = decomposition.PCA(n_components=3)\n",
    "grid_recons_3 = estimator_3.inverse_transform(estimator_3.fit_transform(grid))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "60451ffd-ca08-4471-a736-862e78c385ed",
   "metadata": {},
   "outputs": [],
   "source": [
    "indices = np.random.choice(n_samples, 5, replace=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a8c7229b-6f72-4d98-8fef-de3d8afa24d5",
   "metadata": {},
   "outputs": [],
   "source": [
    "image_shape = (192, 288)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "83a5f3e5-119c-43af-a51b-85595e90d27c",
   "metadata": {},
   "outputs": [],
   "source": [
    "n_components = 8"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6b0f5c17-63c6-4807-b95b-a6296917acad",
   "metadata": {},
   "source": [
    "Show 5 randomly chosen grids and their PCA reconstructions with 8 dominant eigenvectors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b3f20917-2512-4bd4-b49b-029d94ccae91",
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.figure(figsize=(10,5))\n",
    "for i in range(len(indices)):\n",
    "    plt.subplot(1,5,i+1), plt.imshow(np.reshape(grid[indices[i],:], image_shape), cmap=\"Greys\"), plt.axis('on')\n",
    "plt.show()\n",
    "\n",
    "plt.figure(figsize=(10,5))\n",
    "for i in range(len(indices)):\n",
    "    plt.subplot(1,5,i+1), plt.imshow(np.reshape(grid_recons_3[indices[i],:], image_shape),cmap=\"Greys\"), plt.axis('on')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ef88dabe-a69b-4553-8576-1c0dd53e1013",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.cluster import KMeans\n",
    "\n",
    "kmeans = KMeans(init=\"random\",\n",
    "                n_clusters=8, # elbow plot\n",
    "                n_init=10,\n",
    "                max_iter=300,\n",
    "                random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8892caf3-4c56-4b12-9892-76b04a3f0600",
   "metadata": {},
   "outputs": [],
   "source": [
    "kmeans.fit(PCs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "369095e6-189b-42cd-9060-74d2b99c2a82",
   "metadata": {},
   "outputs": [],
   "source": [
    "# The lowest SSE value --> What does this mean? Algorithm is trying to minimize this.\n",
    "kmeans.inertia_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "456e51f3-8a91-4fd3-87d7-7971088deb3b",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Final locations of the centroid\n",
    "kmeans.cluster_centers_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ffcb10b6-c51c-452b-a6df-f34a80d7bccb",
   "metadata": {},
   "outputs": [],
   "source": [
    "# The number of iterations required to converge\n",
    "kmeans.n_iter_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9fc519bc-d751-405e-af30-95bba823e36b",
   "metadata": {},
   "outputs": [],
   "source": [
    "kmeans.labels_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "65b066de-de53-45af-b77d-58db66d4e4de",
   "metadata": {},
   "outputs": [],
   "source": [
    "ind_0 = []; ind_1 = []; ind_2 = []; ind_3 = []\n",
    "ind_4 = []; ind_5 = []; ind_6 = []; ind_7 = []\n",
    "\n",
    "for i in range(len(kmeans.labels_)):\n",
    "    if kmeans.labels_[i] == 0:\n",
    "        ind_0.append(i)\n",
    "    elif kmeans.labels_[i] == 1:\n",
    "        ind_1.append(i)\n",
    "    elif kmeans.labels_[i] == 2:\n",
    "        ind_2.append(i)\n",
    "    elif kmeans.labels_[i] == 3:\n",
    "        ind_3.append(i)\n",
    "    elif kmeans.labels_[i] == 4:\n",
    "        ind_4.append(i)\n",
    "    elif kmeans.labels_[i] == 5:\n",
    "        ind_5.append(i)\n",
    "    elif kmeans.labels_[i] == 6:\n",
    "        ind_6.append(i)\n",
    "    elif kmeans.labels_[i] == 7:\n",
    "        ind_7.append(i)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "99093cda-5f69-4326-a594-2fdb999d8779",
   "metadata": {},
   "outputs": [],
   "source": [
    "print(len(ind_0))\n",
    "print(len(ind_1))\n",
    "print(len(ind_2))\n",
    "print(len(ind_3))\n",
    "print(len(ind_4))\n",
    "print(len(ind_5))\n",
    "print(len(ind_6))\n",
    "print(len(ind_7))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "57edef48-81f1-486d-b9b8-25127887ffb3",
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.hist(kmeans.labels_)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "171c86a0-324e-4e70-a29a-76153ee1f769",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "ind_2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fe05d338-03f4-4091-9510-3efd36768b93",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "for i in range(len(ind_2)):\n",
    "    combined_file.SSTA[i,:,:,:].sum(axis=0).plot(cmap='Reds')\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6e8153c2-29ef-43e3-b2b9-a9562f4c9dc8",
   "metadata": {},
   "outputs": [],
   "source": [
    "combined_file.SSTA[1,:,:,:].sum(axis=0).plot(cmap='Reds')\n",
    "plt.show()\n",
    "combined_file.SSTA[9,:,:,:].sum(axis=0).plot(cmap='Reds')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "16908b7d-e855-40c4-b6a4-a03601a0969a",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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