Clustering.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Clustering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "import os, inspect, sys\n",
    "main_dir = os.path.dirname(os.path.abspath(inspect.getfile(inspect.currentframe()))) # script directory\n",
    "RGCPD_func = os.path.join(main_dir, 'RGCPD')\n",
    "cluster_func = os.path.join(main_dir, 'clustering')\n",
    "if RGCPD_func not in sys.path:\n",
    "    sys.path.append(RGCPD_func)\n",
    "    sys.path.append(cluster_func)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The autoreload extension is already loaded. To reload it, use:\n",
      "  %reload_ext autoreload\n"
     ]
    }
   ],
   "source": [
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "import os\n",
    "from RGCPD.clustering import clustering_spatial as cl\n",
    "from RGCPD import RGCPD\n",
    "from RGCPD import plot_maps\n",
    "# define input\n",
    "list_of_name_path = [('fake', 'by conventions: first entry links to filepath of 1-d target variable'),\n",
    "                     ('sst', './data/sst_daily_1979-2018_5deg_Pacific_175_240E_25_50N.nc')]\n",
    "# use RGCPD to pre-process data\n",
    "rg = RGCPD(list_of_name_path=list_of_name_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loaded pre-processed data of sst\n",
      "\n"
     ]
    }
   ],
   "source": [
    "rg.pp_precursors()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('sst',\n",
       "  './data/preprocessed/sst_daily_1979-2018_5deg_Pacific_175_240E_25_50N.nc')]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# list_precur_pp contains name and filepath to pre-processed data \n",
    "rg.list_precur_pp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "clustering fake: 0, n_clusters: 2\n",
      "clustering fake: 0, n_clusters: 3\n",
      "\n",
      "\n",
      "Plotting Corr maps sst, split 0, n_clusters 2\n",
      "Plotting Corr maps sst, split 0, n_clusters 3\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1660x300 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "var_filename = rg.list_precur_pp[0][1]\n",
    "mask = [185.0, 230.0, 20.0, 50.0]\n",
    "q = 85\n",
    "n_clusters = [2, 3]\n",
    "xrclusteredall, results = cl.dendogram_clustering(var_filename, mask=mask, kwrgs_clust={'q':q, 'n_clusters':n_clusters}, \n",
    "                                                  n_cpu=1)\n",
    "fig = plot_maps.plot_labels(xrclusteredall, {'col_dim':'n_clusters', 'title':'Hierarchical Clustering'})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For more information on Dendogram clustering see:\n",
    "1. McKinnon, K. A., Rhines, A., Tingley, M. P. & Huybers, P. Long-lead predictions of eastern United States hot days from Pacific sea surface temperatures. Nat. Geosci. 9, 389–394 (2016).\n",
    "2. Vijverberg, S., Schmeits, M., van der Wiel, K. & Coumou, D. Sub-seasonal statistical forecasts of eastern United States hot temperature events. Mon. Weather Rev. (in print) (2020)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For other sklearn supported clustering methods, change the clustermethodkey (see sklearn.cluster.__dict__ for options) and change the setting via kwrgs_clust. \n",
    "\n",
    "https://scikit-learn.org/stable/modules/clustering.html"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Plotting Corr maps sst, split 0, eps 90\n",
      "Plotting Corr maps sst, split 0, eps 100\n"
     ]
    },
    {
     "data": {
      "image/png": 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2VqtXrx4f13RwcHDB80DHaZHV1NTEnQUUyNLVcWcBKBuVtH2JQDmhlIzdrl9ZWRl3VhYMcWIy0FYC0aE+JQdlhVJRUVEx/v9CPxhKouO06Orr6+POAgqUquLEAESlpo62LwkoJ5SKVCqluro6pVIpdXV1qb+/P+4sLQjixGSgrQSiQ31KDsoKpaK2tlZmJskPc7TQ45zScVpkO3bsiDsLKFCmvzvuLABlo6eDti8JKCeUkt12203pdFqZTEa33nrrLhFD7QqfsRzQVgLRoT4lB2WFUtHW1jb+A/u2bdu0fv16DQ8PL9j26TgFAABA7Pbaay81NTWpqqpKQ0NDuu++++LOEgAAAGJmZtpvv/1UXV2tiooK7dixQ08++eSCbZ+O0yJrbm6OOwsoUKqmIe4sAGWjrrkl7iygAJQTSklVVZWOPPJIVVZWysw0ODiobDYbd7aKijgxGWgrgehQn5KDskIpWbFihVatWjU+xun27dsXbNt0nBYZT0tNkDL/cgYspGxmNO4soACUE0rN2PhVZqZsNqvHH3885hwVF3FiMtBWAtGhPiUHZYVSU1dXNz6+aU9Pj3p7exdku3ScFtlCFSTmLzu8azyIAlgIg7R9iUA5odRUVVXpoIMOUnV1tcxMGzdu1Oho+X5xI05MBtpKIDrUp+SgrFBqVq5cqWXLlqmqqkqZTEYbN25ckO3ScQoAAICSsXLlSq1atUoVFRUaGRlRe3t73FkCAABAzCoqKnT44YerpqZGqVRKW7duXZBhneg4LbLly5fHnQUUqKJhUdxZAMpGyzLaviSgnFCqqqqqlEr5MHVkZCTm3BQPcWIy0FYC0aE+JQdlhVJUUVGhqqoqmZkymQwdp+WAqySSI9vfFXcWgLKxcwdtXxJQTihVTz31lEZHR5VKpdTW1hZ3doqGODEZaCuB6FCfkoOyQinq7OxUb2+vstmsWltbxx8WVUx0nBZZOY/LVW5clgc0AFHJ0vYlAuWEUjQ6OjoeEDc3N6u+vj7uLBUNcWIy0FYC0aE+JQdlhVLU09MzfpXpihUrFmSbdJwWWXV1ddxZQIEsXRV3FoCyUUnblwiUE0rR8PDw+P9jt+uXK+LEZKCtBKJDfUoOygqlaGBgYPz/hYoTyzsaLQFNTU1xZwEFSlWX7xUtwEKrbaTtSwLKCaUmk8noxhtvHO88bW5ujjlHxUWcmAy0lUB0qE/JQVmh1Gzfvl2PP/74+HBOjY2NC7JdOk6LbPv27XFnAQXK9HXGnQWgbOxsp+1LAsoJpeaee+7R0NCQstmszEy777573FkqKuLEZKCtBKJDfUoOygqlJJPJ6I477tDQ0JAymYxSqZQaGhoWZNvz7jg1s3eYmTOz3jzzDjezP5hZr5l1mdnVZrZ3nnQHmtktZtZvZr8zs91C89cF2/h9nmVXBfPOnu9nAQAAQDR+8IMfaLfddtM+++wzad7dd9+tU045Rfvss4+e9axn6YwzztADDzygzZs3jz8htaWlRf/85z918skn6+CDD9YZZ5yhrVu3TljPaaedpn333Vdvf/vbJ21j06ZNOvzww4v2+QAAALAwNm7cqJGREY2MjKiiokIHHHDAgm17Xh2nZra7pIskPZVn3rMkrZNUJekUSW+X9ExJN5rZklDyn0m6UdLLJLVL+vYUm3yJmf3LfPK80Mr9FrNykqpemF8rgF1BXRNtXxJQTiiWzZs36/Of/7yWL18+ad7DDz+s1772tRoZGdFll12mr371q3rsscd0yimnqKurS845NTU16TnPeY4+8IEP6LnPfa6+853vqLW1Veeee27e7d14443629/+VuyPFTnixGSgrQSiQ31KDsoKpeSpp57S6Oio0um0Dj744AV7MJQkpee5/H9L+rOkDkmvDc27QNKQpFc453ZKkpmtl/SwpLMlnRO8t1jScufc2cHrv0v6Z55tPRTk90tm9lznnJtn3hfE2NO+kACOsgKiQtuXDJQTiuWcc87RkUceqdbWVv3617+eMO+iiy5SVVWV/u///m98bKpDDjlExxxzjK699lq98pWvVENDg7q6utTe3q5PfOITkqQDDjhAxx133KRtrV69WqOjo/rSl76kq6++WmZW/A8YEepgMlBOQHSoT8lBWaGUpNNPd19WVlYu6LbnfMWpmb1J0vGS3pdnXlrSKyRdNdZpKknOuY2Srpf06pzkHZKcmb3JzJqC9T2YZ5Mjkj4t6TmSXj/XfC+0np6euLOAAmWH++POAlA2Bntp+5KAckIxXHXVVbr55pv1xS9+cdK80dFRXXfddTrxxBMnDOi/cuVKHXXUUbrzzjtlZtq8ebPuv/9+Oed0zTXXqKenRz/84Q+1evXqSetMp9P6yEc+onvvvVe/+c1vivrZokacmAy0lUB0qE/JQVmhlCxevFjpdFrZbFZ33HGH/vGPf2hoaGhBtj2njlMzWyrp65I+4ZzblCfJMyTVSro7z7y7Je1jZjWS5JzLSnqHpEsldUv6gKT3TLHpn0haL+lCM1vYLmYAAABMq729XZ/97Gf1qU99Srvtttuk+Rs2bNDg4KD233//SfMOOuggbdu2TRUVFUqn0+rr69Opp56qz3zmMzr88MP1/e9/XxdccEHe7Z544ok66KCD9LWvfU0jIyORfy4AAADEZ6+99lJra6uqq6sl+bHsb731Vu3cuXOGJedvrrfqf0v+qtBLp5i/KPjbkWdehyST1CppsyQ5535uZssl7SHpMefccL6VOuecmZ0j6Q+S3i3pkkIya2bVkqpz3mqcKm3Uli1btlCbwjxVNLTFnQWgbDQvpe1LAsoJUfvkJz+pZzzjGXrrW9+ad35nZ6ckqaWlZdK81tZWOee0//77q7OzU9u3b9ezn/1s7bfffkqn0zrhhBNUW1ubd71mpo997GN661vfqh//+Md685vfXFB+44wRJeLEpKCtBKJDfUoOygqlpLKyUkcffbQ2bdqkRx55RL29verv79dtt92mAw44oKhjns6649TMTpb0SkmHFTDO6HTzJ8xzzvVJemCm7Tvn/mhm10r6rJl9b6b0gU9KOi/85tatW5XJZFRbW6uODt/H29LSopGREfX19UmSVqxYoW3btimTyaimpkb19fXasWOHJD+gfyaTUW9vryRp+fLlam9v1+joqKqrq9XU1KSHHnpIra2tam5uVjabHb8la9myZers7NTw8LCqqqrU0tKibdu2SZKampokabznfOnSperq6hpP29raOv5U2cbGRqVSKXV3d0uSlixZop07d2poaEjpdFqLFy/Wli1bJEkNDQ2qqKgYT7to0SL19fVpcHBQFRUVWrp0qTZv3ixJqq+vV2Vlpbq6uiRJbW1tGhgY0MDAgEZ7O5RuaNNob4fkskpV1sjS1coM+PVW1DbJjQ4rOzIoyZRuXKRMb6ecy8gqa5SqrFam36dN1TRK2VFlhwckSenGxcr0dcplM7J0tVJVtcr0dwVpG6RsdvyW+oqGRcr2d8tlR2UVlUrVNCjT57+QpaobJJeTtr5V2cFeucxInrT1kkyjXZuVqm2cmDaVVqquSZlef3ykquqkVErZQV/mFXUtyg73y40Oy1IVStW1KNO7IydthbKDPUHaZmWHB+VGh2RWoYqGVo327JDklKqqlVVUKjOwM9iHzcqODsmNDEqWmrC/rbJGqfD+zowE+zC0v9PVSlXVhPZ3JrQPu4L9XaVUVd00+7tN2f6dU+9vOWWH+mbe31V1kqWUHeqdnDaVVqquObQPw/t7wO/DVIUq6ls12tMepK2VUumJ+3vE78NJ+7uyRpaumrC/3eiQP2Zn2N95j9kp93e+YzZnf1fXK9PXqc98/brJx2xDm7IDPdMcs5p6f9c2Tjxmw/t7qG+aYzZ3f+c7ZqfY36Fj1lIVctlMWbURZ716lSSpafES9e/cqdHhIVVUVqqhtU3d23ybXNPQqFRFhfq7fZ4aFy3WYG+PRoaGlEqn1bRosbq2bgnSNqginVZf0M42ti3SYH+fRgYHlaqoUPOSpercukVyTtX19aqsqlJv0OnU0Nam4YEBDQ8MyFIptSxdpq5tW+WyWVXV1qqqtla9wXmtobVVI8PDGurrk8zUumy5urdvUzaT0eDAgJbsvlI9Hf4YqG9pUWZ0VIPBea1l2XLt3NGu7OioKqurVdPQqJ4d/hioa27x6whu42peuky9nR3KjIwoXVWtuqYm7Wzf7tM2Ncs5p4EeX+ealyxVb3eXMsPDqqiqUkNzi7q3b3t6H6ZS6t/ZnXd/N7YtmrAPUxXpifu7r9fvw3RazYuXqHOLP69V19crXVk5YX8PDfRreGBg8v6uq1NldfWE/T0yOKih/v4Z93d9S4tGR0b8/pbUunzF+P6urKlRTV39+P72+3Dq/V3b6Pfh5npLRBxx22236dprr9UPf/hD9fT0qLKyUv39/XLOaWhoSAMDA+MxlJlp69atymazqqurU01NzXh+a2pqtN9++8k5p0cffVR1dXXq6urS+vXrdfDBB0+I27LZrLLZrLZs2aK9995bz3/+8/WNb3xDxx57rAYHB1WAvDFif3+/zGx8f4f3YXh/m9mEfdjd3a3h4WFVVlaqra1tyv1tZqqqqppyf6fT6fFYLF/ctmXLFjnnVF9fr6qqqvGO6dy4LZVKadmyZeP7u7a2dsI+bG1t1fDwsPr6+mRmWr58+ZSxb0tLi0ZHR6eMfRsbG/WZr18vqbziiOxgn6qW7FWyccT4PiyjOKIcv2tMfcxO/V3jvDPXlHQcET6vFRJHPPXow2psaSWOWMA4YmwfZrPZifu7q1OZ4WGlq6pV39w8vg9rG30cse3JjWpsaVXzkqXq6+72+7CqSg0trRNj39D+HujZOeUxO2F/5ztmp9jf0x2z1XV1qqypmXjMDg1pqL9/0jFbVVur6tq6CcfspP3dvt3vw5oa1dQ3hGLfifu7p2PHlMfshP09zTFb2+jjiKmO2Zm+a3R09JZVHNHe7vd3vti3o6NDlZWV2n///bVhwwY98sgjGhkZ0fr163X44YcrlfI31S9ZskRdXV0aGRlRZWWlWlpatH379vH9kkqlxuO4QthsnrFkZg2SHpF0haQLc2Z9S9K/SVopPxbpSvlO0DOdc98KrePLkj4qqc45V1BEa2brJC12zh0UvD5U0h2SPi/pckmPS/qYc+6iKZbPdzXBpgcffHDC+FrFsHnz5gV92tdCOPriO+LOQlGM9rQr3bg47mwAZaEc69N5ZxwWdxYi17lls1qXl9c5qhy9tKH0H3bU19enY445Rq95zWt01llnjb//yU9+Utdee63Wr1+vyspKPfXUU3rBC16gL3zhCzr99NMnrOOCCy7QZZddpkcffVQ1NTWSpO3bt+vOO+8c/0H7+c9//oSrTk877TR1dnbqt7/9rSTpvvvu00knnaQzzzxTJ598sk444YSxpM254+6PiTNGlIgTk6Icz2lIBmIPxImySoYkxInFkM1mdf/992vDhg0aGhrSkiVLdPjhhxe8fE9Pz1j6vDFirtmOcbpY0jL5js/OnOkNkuqD/38g6VFJA5IOzrOOgyU9UminaT7OuTsl/UjSR4L8zJR+yDm3c2yStGCjHFdVVS3UpjBPVsGwuUBUqE/JkK6qnjkRUICOjg5t375dl112mfbff//x6Re/+IX6+/u1//7768wzz9SqVatUU1OjBx6YfJPRAw88oNWrV493mkr+ioG99957/GEAM10dcMABB+gVr3iFLr/88vErFqYTZ4woEScmBec0IDrEHslBWaGUpVIpHXDAAWpqalIqldKOHTs0PJx31M95m+2t+lsknZDn/U9IOl7SyyS1O+dGzexXkl5jZh93zvVIkpntGSz/tXnkecy5kl6rPLdXlZJ8Y3ihNKVqGuLOAlA2qE/JUN/cHHcWUCaWLFmiK6+8ctL7l1xyiW6++WZdccUVamtrUzqd1otf/GL99re/1bnnnquGBt9WbNq0STfddJPe+c53TlpHXV2dzPzVFKOjozPm5cMf/rB+//vf65JLChoKP1bEicnAOQ2IDrFHclBWKHVmppqaGpmZstmsRkdHi/Kj9Kw6ToOrRNeF3zez0yVlnHO5886TdJukX5vZf0iqkXSBpHZJX5lbdifk5XEzu1TSh+a7rmLatm1b2d2CVa4yfZ3chgVEhPqUDN3bt3ELFiJRU1OjY445ZtL7P/3pT5VKpSbMO/vss/Xyl79cb3nLW/T+979fQ0ND+vKXv6y2tja95z3vmbSOjo4OZTIZSVI6PXPouscee+iNb3yjvve9QofCjw9xYjJwTgOiQ+yRHJQVSp1zTh0dHcpms0qlUlM+RHS+ZnurfsGccw9IWiM/5umVktbKj4/6Aufc9og2c6GkacciAAAAQOnYd999ddVVVymdTuud73ynzjrrLK1atUpXX321Fi1aNCl9fX39+GD/Yw+Qmsn73ve+8atZAQAAUH7MTPX19eNXnI49sCpqs71VPy/n3OmSTs/z/npJL4pg/WumeL9dUklfPz72ZFuUvrEnjAKYP+pTMow9NRUolq9//ev6+te/Pun9Qw45RD/96U8LWsfKlSv12GOPaXR0VFu2bNEzn/lMVVf7cdd+8IMf5F2mra1Nf//733MH/i9JxInJwDkNiA6xR3JQVkiCffbZRzt37tTg4KCefPLJvD/Cz1fRrjgFAAAA5iudTmv33XdXKpWSc67gq04BAABQ3pYtW6aqqiqZmXp6esaHd4oSHadFRnCfHNmhvrizAJQN6lMyDPRwjkIy1NXVjXec3n333dqwYYOy2Wzc2Zo34sRk4JwGRIfYIzkoKyRB7u36/f39uvnmm9XV1RXpNug4BQAAQElbunSpmpubVVVVpUwmowcffFC33367BgYG4s4aAAAAYrR69WrV1tYqnU6rt7dXt912mx5++GE55yJZPx2nRbZ06dK4s4ACVdS3xp0FoGxQn5KheQnnKCRDdXW1jjnmGO23337jgXFnZ6duueUWPfnkkxoZGYk7i3NCnJgMnNOA6BB7JAdlhaRYvny5jj32WC1btmx8HPzHHntMd9xxh3bs2DHvDlQ6Toss6kuEUTzZwd64swCUDepTMvR1d8edBaBgFRUV2nfffXX00UertbVVlZWVGhoa0n333ae//vWviYy5kpjnXRHnNCA6xB7JQVkhSRoaGvS85z1PBx10kGpqalRRUaH29natX79ed9xxx7zGPqXjtMiGh4fjzgIK5DLJvFoFKEXUp2QYHR6KOwvArDU3N+uoo47SypUrVVtbO96Bevvtt2vHjh1xZ29WiBOTgXMaEB1ij+SgrJA0Zqa99tpLRx55pFpaWlRTU6NUKqX29nbddtttGhqa2zFNx2mRVVVVxZ0FFMgqKuPOAlA2qE/JUME5CglVWVmpww47TMcdd5za2tqUTqeVyWS0fv16Pfzww+Pp+vv7Y8zlzIgTk4FzGhAdYo/koKyQVK2trTruuOP07Gc/W9XV1UqlUurs7NSNN944p/Hx6TgtstZWxkRKilRtY9xZAMoG9SkZGlo4RyHZGhoadPjhh48HxdlsVhs2bJBzTp2dnbr11lvjzuK0iBOTgXMaEB1ij+SgrJBkqVRKu+++uw444ABVVlYqnU5rZGREDz74oCSN/y1oXcXKJLytW7fGnQUUKNPbEXcWgLJBfUqG7m2co5B8tbW1MjNJ/hatbDarW2+9Vffee++8xrNaCMSJycA5DYgOsUdyUFYoBw0NDZI0/oCo7du36/7779dTTz1V8DrSRckZAAAAsEBqa2s1MuLHoRwdHR1/6NJ8n6IKAACA5EqlUkqlUqqqqpJzTqOjo3riiSdm9eM6HadF1tjIrT1JkaqqizsLQNmgPiVDTQPnKJSHww8/XNu2bVNFRYWefPJJdXd3K5PJqKKiIu6sTYs4MRk4pwHRIfZIDsoK5aClpUWHHXaYhoaGNDw8rI0bN47/Xyg6TosslWI0hMQwygqIDPUpEThHoVzU19dr9erVkqQ999xTXV1duuWWW2LO1cyogwnBOQ2IDO1eclBWKBfLly8f/3/16tXatGmT7rjjjoKXpyYUWXd3d9xZQIGyQ71xZwEoG9SnZOjfyTkK5amlpUUrVqwYH/u0VBEnJgPnNCA6xB7JQVmhHKXTae2xxx6zWoaOUwAAAJSdpqamkr9VHwAAAAuroqJC9fX1Baen47TIlixZEncWUKCK+ta4swCUDepTMjQt5hyF8rXnnnuW/BiixInJwDkNiA6xR3JQVihnz3rWswpOS8dpke3cuTPuLKBA2aG+uLMAlA3qUzIM9HCOQvlKpVJavHhx3NmYFnFiMnBOA6JD7JEclBXKWVtbW8Fp6TgtsqGhobizgAK50cKfqgZgetSnZBjhHIUyt3Tp0rizMC3ixGTgnAZEh9gjOSgrwKPjtMjS6XTcWUCBLMU4aEBUqE/JkOIchTLX2lrat1gTJyYD5zQgOsQeyUFZAR4dp0VW6reI4Wmpupa4swCUDepTMjQt4hwFxIk4MRk4pwHRIfZIDsoK8Og4LbItW7bEnQUUKNO7I+4sAGWD+pQMXVs5RwFxIk5MBs5pQHSIPZKDsgI8Ok4BAAAAAAAAIISO0yJraGiIOwsoUKqqLu4sAGWD+pQMNZyjgFgRJyYD5zQgOsQeyUFZAR4dp0VWUcFg8omRojoAkaE+JUKqgkH/gTgRJyYE5zQgMsQeyUFZAR5RQJF1d3fHnQUUKDvYG3cWgLJBfUqG/u6uuLMA7NKIE5OBcxoQHWKP5KCsAI+OUwAAAAAAAAAIoeO0yBYtWhR3FlCgirrmuLMAlA3qUzI0tnGOAuJEnJgMnNOA6BB7JAdlBXh0nBZZX19f3FlAgbLDg3FnASgb1KdkGOznHAXEiTgxGTinAdEh9kgOygrw6DgtssFBAq2kcKNDcWcBKBvUp2QY4RwFxIo4MRk4pwHRIfZIDsoK8Og4LTKelpocZpQVEBXqUzKkOEcBsSJOTAbOaUB0iD2Sg7ICPDpOi2zp0qVxZwEFqmhojTsLQNmgPiVD8xLOUUCciBOTgXMaEB1ij+SgrACPjtMi27x5c9xZQIFGe9rjzgJQNqhPydC5hXMUECfixGTgnAZEh9gjOSgrwKPjFAAAAAAAAABC6Dgtsvr6+rizgAKlqmrjzgJQNqhPyVDNOQqIFXFiMnBOA6JD7JEclBXg0XFaZJWVlXFnAYVKpePOAVA+qE+JkOYcBcSKODEhOKcBkSH2SA7KCvDoOC2yrq6uuLOAAmUHe+LOAlA2qE/J0Mc5CogVcWIycE4DokPskRyUFeDx8ylm7bwzDos7C0XRuWWzWpeviDsbQFmgPgHArqkc40TOaQAA7Lq44rTI2tra4s4CCtRAWQGRoT4lA+UExIs4MRloK4HoUJ+Sg7ICPDpOi2xgYCDuLKBAw5QVEBnqUzJQTkC8iBOTgbYSiA71KTkoK8Cj47TICIiTgxMDEB3qUzJQTkC8iBOTgbYSiA71KTkoK8Cj47TIUil2cVIYZQVEhvqUDJQTEC/ixGSgrQSiQ31KDsoK8KgJRbZs2bK4s4ACtSylrICoUJ+SgXIC4kWcmAy0lUB0qE/JQVkBHh2nRbZ169a4s4ACdW2jrICoUJ+SgXIC4kWcmAy0lUB0qE/JQVkBHh2nRZbNZuPOAgrkKCsgMtSnZKCcgHgRJyYDbSUQHepTclBWgEfHaZHV1dXFnQUUqJqyAiJDfUoGygmIF3FiMtBWAtGhPiUHZQV4dJwWWU1NTdxZQIEqKSsgMtSnZKCcgHgRJyYDbSUQHepTclBWgEfHaZF1dHTEnQUUqJeyAiJDfUoGygmIF3FiMtBWAtGhPiUHZQV4dJwCAAAAAAAAQAgdp0XW2toadxZQoAbKCogM9SkZKCcgXsSJyUBbCUSH+pQclBXg0XFaZENDQ3FnAQUaoayAyFCfkoFyAuJFnJgMtJVAdKhPyUFZAR4dp0XW398fdxZQoCHKCogM9SkZKCcgXsSJyUBbCUSH+pQclBXg0XFaZGYWdxZ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      "text/plain": [
       "<Figure size 1660x300 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "var_filename = rg.list_precur_pp[0][1]\n",
    "mask = [185.0, 230.0, 20.0, 50.0]\n",
    "\n",
    "xrclusteredall, results = cl.sklearn_clustering(var_filename, mask=mask, clustermethodkey='DBSCAN', kwrgs_clust={'eps':[90, 100]})\n",
    "xrclusteredall += 1\n",
    "fig = plot_maps.plot_labels(xrclusteredall, {'col_dim':'eps', 'title':'Density-Based Spatial Clustering of Applications with Noise'})\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Adjustment the data that is loaded and used for the clustering by e.g. timeslicing or time-aggregation.\n",
    "\n",
    "See core_pp.import_ds_lazy() for full functionality on loading Netcdf data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Plotting Corr maps sst, split 0, eps 8\n",
      "Plotting Corr maps sst, split 0, eps 10\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1660x300 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "var_filename = rg.list_precur_pp[0][1]\n",
    "mask = [185.0, 230.0, 20.0, 50.0]\n",
    "\n",
    "xrclusteredall, results = cl.sklearn_clustering(var_filename, mask=mask, kwrgs_load={'tfreq':15,\n",
    "                                                                                  'start_end_date':('06-01','08-31')},\n",
    "                                             clustermethodkey='DBSCAN', kwrgs_clust={'eps':[8, 10]})\n",
    "xrclusteredall += 1\n",
    "fig = plot_maps.plot_labels(xrclusteredall, {'col_dim':'eps', 'title':'Density-Based Spatial Clustering of Applications with Noise (JJA, 15-day means)'})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### To store the results (to be used by RGCPD format)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "('eps', 'latitude', 'longitude')"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xrclusteredall.dims"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# select parameter(s)\n",
    "eps = 8\n",
    "xrclustered = xrclusteredall.sel(eps=eps).copy()\n",
    "# spatial_mean_clusters will calculate an area-weighted spatial mean timeseries on the timescale and the dates \n",
    "# of the input dataset, i.e. in this case on daily timescale and year-round. \n",
    "ds = cl.spatial_mean_clusters(var_filename,\n",
    "                              xrclustered,\n",
    "                              kwrgs_load={'selbox':None})\n",
    "f_name = 'eps{}'.format(eps)\n",
    "ds['xrclusteredall'] = xrclustered\n",
    "filepath = os.path.join(rg.path_outmain, f_name)\n",
    "# To store, uncomment line below\n",
    "# cl.store_netcdf(ds, filepath=filepath, append_hash='dendo_'+xrclustered.attrs['hash']) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "ds now contains the original clustering output with all parameter in xrclusteredall, and the clustering output that was selected to construct the 1-timeseries (named xrclusterd). ts contains the timeseries on dims (cluster, time), with cluster referring to the cluster label. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div><svg style=\"position: absolute; width: 0; height: 0; overflow: hidden\">\n",
       "<defs>\n",
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       "\n",
       ".xr-array-in:checked ~ .xr-array-data,\n",
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       "\n",
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       "  margin: 0;\n",
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       "\n",
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       "\n",
       ".xr-dim-list:after {\n",
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       "\n",
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       "\n",
       ".xr-has-index {\n",
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       "\n",
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       "\n",
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       "\n",
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       ".xr-var-dims,\n",
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       ".xr-attrs dt {\n",
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       "\n",
       ".xr-var-name:hover,\n",
       ".xr-var-dims:hover,\n",
       ".xr-var-dtype:hover,\n",
       ".xr-attrs dt:hover {\n",
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       "  width: auto;\n",
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       "\n",
       ".xr-var-attrs,\n",
       ".xr-var-data {\n",
       "  display: none;\n",
       "  background-color: var(--xr-background-color) !important;\n",
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       "\n",
       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
       ".xr-var-data-in:checked ~ .xr-var-data {\n",
       "  display: block;\n",
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       "\n",
       ".xr-var-data > table {\n",
       "  float: right;\n",
       "}\n",
       "\n",
       ".xr-var-name span,\n",
       ".xr-var-data,\n",
       ".xr-attrs {\n",
       "  padding-left: 25px !important;\n",
       "}\n",
       "\n",
       ".xr-attrs,\n",
       ".xr-var-attrs,\n",
       ".xr-var-data {\n",
       "  grid-column: 1 / -1;\n",
       "}\n",
       "\n",
       "dl.xr-attrs {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  display: grid;\n",
       "  grid-template-columns: 125px auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt,\n",
       ".xr-attrs dd {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  float: left;\n",
       "  padding-right: 10px;\n",
       "  width: auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt {\n",
       "  font-weight: normal;\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-attrs dt:hover span {\n",
       "  display: inline-block;\n",
       "  background: var(--xr-background-color);\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-attrs dd {\n",
       "  grid-column: 2;\n",
       "  white-space: pre-wrap;\n",
       "  word-break: break-all;\n",
       "}\n",
       "\n",
       ".xr-icon-database,\n",
       ".xr-icon-file-text2 {\n",
       "  display: inline-block;\n",
       "  vertical-align: middle;\n",
       "  width: 1em;\n",
       "  height: 1.5em !important;\n",
       "  stroke-width: 0;\n",
       "  stroke: currentColor;\n",
       "  fill: currentColor;\n",
       "}\n",
       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
       "Dimensions:         (latitude: 5, longitude: 13, cluster: 2, time: 14600)\n",
       "Coordinates:\n",
       "  * latitude        (latitude) float64 27.5 32.5 37.5 42.5 47.5\n",
       "  * longitude       (longitude) float64 177.5 182.5 187.5 ... 227.5 232.5 237.5\n",
       "    mask            (latitude, longitude) bool True True True ... True False\n",
       "  * cluster         (cluster) int64 1 2\n",
       "  * time            (time) datetime64[ns] 1979-01-01 1979-01-02 ... 2018-12-31\n",
       "    eps             int64 8\n",
       "Data variables:\n",
       "    xrclustered     (latitude, longitude) float64 nan nan 2.0 ... 1.0 nan nan\n",
       "    ts              (cluster, time) float64 0.5922 0.6283 ... -0.1208 -0.01669\n",
       "    xrclusteredall  (latitude, longitude) float64 nan nan 2.0 ... 1.0 nan nan</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-a7088f3b-d3ec-4e1a-8c1b-eb3e6fc478d6' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-a7088f3b-d3ec-4e1a-8c1b-eb3e6fc478d6' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>latitude</span>: 5</li><li><span class='xr-has-index'>longitude</span>: 13</li><li><span class='xr-has-index'>cluster</span>: 2</li><li><span class='xr-has-index'>time</span>: 14600</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-b029c64b-c966-479b-ab24-ebb110feb192' class='xr-section-summary-in' type='checkbox'  checked><label for='section-b029c64b-c966-479b-ab24-ebb110feb192' 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'>latitude</span></div><div class='xr-var-dims'>(latitude)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>27.5 32.5 37.5 42.5 47.5</div><input id='attrs-d9995fe8-18ed-4f09-b535-a58fafc1d16e' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-d9995fe8-18ed-4f09-b535-a58fafc1d16e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a13715dc-fa7a-4cdb-a0f0-1b5019ecb756' class='xr-var-data-in' type='checkbox'><label for='data-a13715dc-fa7a-4cdb-a0f0-1b5019ecb756' 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([27.5, 32.5, 37.5, 42.5, 47.5])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>longitude</span></div><div class='xr-var-dims'>(longitude)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>177.5 182.5 187.5 ... 232.5 237.5</div><input id='attrs-3f55ec89-e1f8-4d47-8a44-645a467f2410' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-3f55ec89-e1f8-4d47-8a44-645a467f2410' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8ec64ce1-a993-4122-a18c-97efb6fb69b5' class='xr-var-data-in' type='checkbox'><label for='data-8ec64ce1-a993-4122-a18c-97efb6fb69b5' 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([177.5, 182.5, 187.5, 192.5, 197.5, 202.5, 207.5, 212.5, 217.5, 222.5,\n",
       "       227.5, 232.5, 237.5])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>mask</span></div><div class='xr-var-dims'>(latitude, longitude)</div><div class='xr-var-dtype'>bool</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-35080e80-248b-4a2d-b3f5-32c213fd048e' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-35080e80-248b-4a2d-b3f5-32c213fd048e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-dd9d1782-eb68-4c0f-b092-76f4256e64e3' class='xr-var-data-in' type='checkbox'><label for='data-dd9d1782-eb68-4c0f-b092-76f4256e64e3' 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([[ True,  True,  True,  True,  True,  True,  True,  True,  True,  True,\n",
       "         True,  True,  True],\n",
       "       [ True,  True,  True,  True,  True,  True,  True,  True,  True,  True,\n",
       "         True,  True,  True],\n",
       "       [ True,  True,  True,  True,  True,  True,  True,  True,  True,  True,\n",
       "         True,  True, False],\n",
       "       [ True,  True,  True,  True,  True,  True,  True,  True,  True,  True,\n",
       "         True,  True, False],\n",
       "       [ True,  True,  True,  True,  True,  True,  True,  True,  True,  True,\n",
       "         True,  True, False]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>cluster</span></div><div class='xr-var-dims'>(cluster)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>1 2</div><input id='attrs-3aeae3ea-4caa-45dd-85f0-f818105a0afe' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-3aeae3ea-4caa-45dd-85f0-f818105a0afe' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ff228b87-026b-401a-a84a-e1076ecae299' class='xr-var-data-in' type='checkbox'><label for='data-ff228b87-026b-401a-a84a-e1076ecae299' 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])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>1979-01-01 ... 2018-12-31</div><input id='attrs-c497d774-f14a-48e0-b729-10041d89bf85' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-c497d774-f14a-48e0-b729-10041d89bf85' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9ea8efca-464c-4690-8beb-bf593f1e08fa' class='xr-var-data-in' type='checkbox'><label for='data-9ea8efca-464c-4690-8beb-bf593f1e08fa' 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;1979-01-01T00:00:00.000000000&#x27;, &#x27;1979-01-02T00:00:00.000000000&#x27;,\n",
       "       &#x27;1979-01-03T00:00:00.000000000&#x27;, ..., &#x27;2018-12-29T00:00:00.000000000&#x27;,\n",
       "       &#x27;2018-12-30T00:00:00.000000000&#x27;, &#x27;2018-12-31T00:00:00.000000000&#x27;],\n",
       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>eps</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>8</div><input id='attrs-df8a7733-251f-48d2-9265-cc5459a14072' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-df8a7733-251f-48d2-9265-cc5459a14072' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9b6a0ac5-af50-4640-a2bd-f823af3b470d' class='xr-var-data-in' type='checkbox'><label for='data-9b6a0ac5-af50-4640-a2bd-f823af3b470d' 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(8)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-694e61ec-8da5-4aa0-97f0-e532fb647c0c' class='xr-section-summary-in' type='checkbox'  checked><label for='section-694e61ec-8da5-4aa0-97f0-e532fb647c0c' class='xr-section-summary' >Data variables: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>xrclustered</span></div><div class='xr-var-dims'>(latitude, longitude)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>nan nan 2.0 2.0 ... 1.0 1.0 nan nan</div><input id='attrs-513a402b-ef23-4999-a1b6-c6af9a13fad7' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-513a402b-ef23-4999-a1b6-c6af9a13fad7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-30e5ab6f-d1da-408a-9063-b939669c8bab' class='xr-var-data-in' type='checkbox'><label for='data-30e5ab6f-d1da-408a-9063-b939669c8bab' 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>Sea surface temperature</dd><dt><span>units :</span></dt><dd>K</dd><dt><span>regridded :</span></dt><dd>bilinear_2.5d_to_5d</dd><dt><span>anomaly :</span></dt><dd>True</dd><dt><span>fft :</span></dt><dd>True</dd><dt><span>n_harmonics :</span></dt><dd>6</dd><dt><span>detrend :</span></dt><dd>True</dd><dt><span>method :</span></dt><dd>DBSCAN</dd><dt><span>kwrgs :</span></dt><dd>{&#x27;eps&#x27;: [8, 10]}</dd><dt><span>target :</span></dt><dd>sst</dd><dt><span>hash :</span></dt><dd>629fb</dd><dt><span>eps :</span></dt><dd>&lt;xarray.DataArray &#x27;eps&#x27; ()&gt;\n",
       "array(8)\n",
       "Coordinates:\n",
       "    eps      int64 8</dd></dl></div><div class='xr-var-data'><pre>array([[nan, nan,  2.,  2.,  2.,  2.,  2.,  2.,  2.,  2.,  2., nan, nan],\n",
       "       [nan, nan,  1.,  1.,  1.,  1.,  1.,  1.,  2.,  2.,  2., nan, nan],\n",
       "       [nan, nan,  1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.,  1., nan, nan],\n",
       "       [nan, nan,  1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.,  1., nan, nan],\n",
       "       [nan, nan,  1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.,  1., nan, nan]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ts</span></div><div class='xr-var-dims'>(cluster, time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.5922 0.6283 ... -0.1208 -0.01669</div><input id='attrs-3f78ede6-d6f6-42c5-92bc-9279c3457d29' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3f78ede6-d6f6-42c5-92bc-9279c3457d29' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b7cd29b1-0dcf-4afd-b69a-05972bfed5f8' class='xr-var-data-in' type='checkbox'><label for='data-b7cd29b1-0dcf-4afd-b69a-05972bfed5f8' 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>eps :</span></dt><dd>&lt;xarray.DataArray &#x27;eps&#x27; ()&gt;\n",
       "array(8)\n",
       "Coordinates:\n",
       "    eps      int64 8</dd></dl></div><div class='xr-var-data'><pre>array([[ 0.59221665,  0.62825671,  0.70662874, ...,  0.08307002,\n",
       "         0.00458086, -0.05585605],\n",
       "       [-0.13907497, -0.17558731, -0.13146717, ..., -0.06580795,\n",
       "        -0.12080677, -0.01668592]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>xrclusteredall</span></div><div class='xr-var-dims'>(latitude, longitude)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>nan nan 2.0 2.0 ... 1.0 1.0 nan nan</div><input id='attrs-13d27e0e-9622-4203-aec6-2c4ed9e5a240' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-13d27e0e-9622-4203-aec6-2c4ed9e5a240' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-70153b38-78cb-4fca-80b0-5e4928d5d4d3' class='xr-var-data-in' type='checkbox'><label for='data-70153b38-78cb-4fca-80b0-5e4928d5d4d3' 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>Sea surface temperature</dd><dt><span>units :</span></dt><dd>K</dd><dt><span>regridded :</span></dt><dd>bilinear_2.5d_to_5d</dd><dt><span>anomaly :</span></dt><dd>True</dd><dt><span>fft :</span></dt><dd>True</dd><dt><span>n_harmonics :</span></dt><dd>6</dd><dt><span>detrend :</span></dt><dd>True</dd><dt><span>method :</span></dt><dd>DBSCAN</dd><dt><span>kwrgs :</span></dt><dd>{&#x27;eps&#x27;: [8, 10]}</dd><dt><span>target :</span></dt><dd>sst</dd><dt><span>hash :</span></dt><dd>629fb</dd><dt><span>eps :</span></dt><dd>&lt;xarray.DataArray &#x27;eps&#x27; ()&gt;\n",
       "array(8)\n",
       "Coordinates:\n",
       "    eps      int64 8</dd></dl></div><div class='xr-var-data'><pre>array([[nan, nan,  2.,  2.,  2.,  2.,  2.,  2.,  2.,  2.,  2., nan, nan],\n",
       "       [nan, nan,  1.,  1.,  1.,  1.,  1.,  1.,  2.,  2.,  2., nan, nan],\n",
       "       [nan, nan,  1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.,  1., nan, nan],\n",
       "       [nan, nan,  1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.,  1., nan, nan],\n",
       "       [nan, nan,  1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.,  1., nan, nan]])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-db2ef32f-bfd2-4937-a0c6-8dbf68322b03' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-db2ef32f-bfd2-4937-a0c6-8dbf68322b03' 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:         (latitude: 5, longitude: 13, cluster: 2, time: 14600)\n",
       "Coordinates:\n",
       "  * latitude        (latitude) float64 27.5 32.5 37.5 42.5 47.5\n",
       "  * longitude       (longitude) float64 177.5 182.5 187.5 ... 227.5 232.5 237.5\n",
       "    mask            (latitude, longitude) bool ...\n",
       "  * cluster         (cluster) int64 1 2\n",
       "  * time            (time) datetime64[ns] 1979-01-01 1979-01-02 ... 2018-12-31\n",
       "    eps             int64 8\n",
       "Data variables:\n",
       "    xrclustered     (latitude, longitude) float64 nan nan 2.0 ... 1.0 nan nan\n",
       "    ts              (cluster, time) float64 0.5922 0.6283 ... -0.1208 -0.01669\n",
       "    xrclusteredall  (latitude, longitude) float64 nan nan 2.0 ... 1.0 nan nan"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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