docs/notebooks/tutorial_RGDR.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Using RGDR\n",
    "This notebook outlines the current status of the RGDR implementation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import urllib\n",
    "import xarray as xr\n",
    "import matplotlib.pyplot as plt\n",
    "import lilio\n",
    "from s2spy import RGDR"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First we will load in some example data  (more details about the example data can be found [here](https://github.com/AI4S2S/cookbook/tree/main/data)), <br>\n",
    "and resample them using `lilio`'s `Calendar`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "('t2m_daily_1959-2021_2deg_clustered_226_300E_30_70N.nc',\n",
       " <http.client.HTTPMessage at 0x7fa5870dd210>)"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# URL of the dataset from zenodo\n",
    "sst_url = \"https://zenodo.org/record/8186914/files/sst_daily_1959-2021_5deg_Pacific_175_240E_25_50N.nc\"\n",
    "t2m_url = \"https://zenodo.org/record/8186914/files/t2m_daily_1959-2021_2deg_clustered_226_300E_30_70N.nc\"\n",
    "sst_field = \"sst_daily_1959-2021_5deg_Pacific_175_240E_25_50N.nc\"\n",
    "t2m_field = \"t2m_daily_1959-2021_2deg_clustered_226_300E_30_70N.nc\"\n",
    "\n",
    "urllib.request.urlretrieve(sst_url, sst_field)\n",
    "urllib.request.urlretrieve(t2m_url, t2m_field)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "field = xr.open_dataset(sst_field)\n",
    "target = xr.open_dataset(t2m_field)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create calendar, map it to dataset and visualize it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 700x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cal = lilio.Calendar(anchor=\"08-01\")\n",
    "cal.add_intervals(\"target\", length=\"1W\", n=4)\n",
    "cal.add_intervals(\"precursor\", length=\"1W\", n=4)\n",
    "\n",
    "cal = cal.map_to_data(field)\n",
    "cal.visualize(n_years=3, relative_dates=True)\n",
    "_ = plt.title(\"Calendar used in this tutorial\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Resample data with calendar."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "field_resampled: xr.Dataset = lilio.resample(cal, field)\n",
    "target_resampled: xr.Dataset = lilio.resample(cal, target)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### The target timeseries comes from already pre-clustered land surface temperature data.\n",
    "RGDR requires `DataArray`s as input, as otherwise it is ambiguous which variable should be used.\n",
    "\n",
    "When initializing RGDR, the target interval(s) and lag have to be specified as well.\n",
    "The precursor interval(s) will be determined from the target intervals and lag.\n",
    "The maximum possible lag will depend on the number of available precursor intervals.\n",
    "\n",
    "In the illustration you see the algorithm that is used to calculate the correlation in RGDR, when you specify a single target interval:\n",
    "\n",
    "![Single target](../notebooks/single_target_RGDR.svg)\n",
    "\n",
    "Below we use the first target interval, and a lag of `4`. This makes the precursor interval index `-4`. An example with multiple targets follows later."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[-4]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "target_timeseries = target_resampled.sel(cluster=3)[\"t2m\"]\n",
    "precursor_field = field_resampled[\"sst\"]\n",
    "\n",
    "target_intervals = 1\n",
    "lag = 4\n",
    "rgdr = RGDR(\n",
    "    target_intervals=target_intervals,\n",
    "    lag=lag,\n",
    "    eps_km=500,\n",
    "    alpha=0.05,\n",
    "    min_area_km2=0\n",
    ")\n",
    "rgdr.precursor_intervals"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Using `.preview_correlation` we can visualize the correlation and p-value map of the target timeseries and precursor field.\n",
    "\n",
    "The red hashing shows where p-value `<` alpha"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x200 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, (ax1, ax2) = plt.subplots(ncols=2, figsize=(12, 2))\n",
    "_ = rgdr.preview_correlation(precursor_field, target_timeseries, ax1=ax1, ax2=ax2)\n",
    "rgdr.fit(precursor_field, target_timeseries)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Using the preview utilities we can compare two RGDR setups, and see what the effect on the generated clusters will be.\n",
    "\n",
    "Note that the second figure has an extra cluster, due to the minimum area argument having a smaller value."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.QuadMesh at 0x7f1d2e4af310>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1200x200 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, (ax1, ax2) = plt.subplots(ncols=2, figsize=(12, 2))\n",
    "\n",
    "RGDR(\n",
    "    target_intervals, lag,\n",
    "    eps_km=500, alpha=0.05, min_area_km2=600**2\n",
    ").preview_clusters(precursor_field, target_timeseries, ax=ax1, vmin=-2, vmax=2)\n",
    "\n",
    "RGDR(\n",
    "    target_intervals, lag,\n",
    "    eps_km=500, alpha=0.05, min_area_km2=None\n",
    ").preview_clusters(precursor_field, target_timeseries, ax=ax2, vmin=-2, vmax=2)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Once you are satisfied with the selected parameters, the `.fit` method can be used to fit the RGDR clustering to a precursor field.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "rgdr.fit(precursor_field, target_timeseries)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`.transform` can then be used to return the data reduced to clusters.\n",
    "\n",
    "Note that only the data for the specified precursor interval index (`-4`) is returned:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
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       ".xr-section-summary-in:disabled + label:before {\n",
       "  color: var(--xr-disabled-color);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label:before {\n",
       "  content: '▼';\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label > span {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-section-summary,\n",
       ".xr-section-inline-details {\n",
       "  padding-top: 4px;\n",
       "  padding-bottom: 4px;\n",
       "}\n",
       "\n",
       ".xr-section-inline-details {\n",
       "  grid-column: 2 / -1;\n",
       "}\n",
       "\n",
       ".xr-section-details {\n",
       "  display: none;\n",
       "  grid-column: 1 / -1;\n",
       "  margin-bottom: 5px;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-array-wrap {\n",
       "  grid-column: 1 / -1;\n",
       "  display: grid;\n",
       "  grid-template-columns: 20px auto;\n",
       "}\n",
       "\n",
       ".xr-array-wrap > label {\n",
       "  grid-column: 1;\n",
       "  vertical-align: top;\n",
       "}\n",
       "\n",
       ".xr-preview {\n",
       "  color: var(--xr-font-color3);\n",
       "}\n",
       "\n",
       ".xr-array-preview,\n",
       ".xr-array-data {\n",
       "  padding: 0 5px !important;\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-array-data,\n",
       ".xr-array-in:checked ~ .xr-array-preview {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-array-in:checked ~ .xr-array-data,\n",
       ".xr-array-preview {\n",
       "  display: inline-block;\n",
       "}\n",
       "\n",
       ".xr-dim-list {\n",
       "  display: inline-block !important;\n",
       "  list-style: none;\n",
       "  padding: 0 !important;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list li {\n",
       "  display: inline-block;\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list:before {\n",
       "  content: '(';\n",
       "}\n",
       "\n",
       ".xr-dim-list:after {\n",
       "  content: ')';\n",
       "}\n",
       "\n",
       ".xr-dim-list li:not(:last-child):after {\n",
       "  content: ',';\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-has-index {\n",
       "  font-weight: bold;\n",
       "}\n",
       "\n",
       ".xr-var-list,\n",
       ".xr-var-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-var-item > div,\n",
       ".xr-var-item label,\n",
       ".xr-var-item > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-even);\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-var-item > .xr-var-name:hover span {\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-var-list > li:nth-child(odd) > div,\n",
       ".xr-var-list > li:nth-child(odd) > label,\n",
       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-odd);\n",
       "}\n",
       "\n",
       ".xr-var-name {\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-var-dims {\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-var-dtype {\n",
       "  grid-column: 3;\n",
       "  text-align: right;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-preview {\n",
       "  grid-column: 4;\n",
       "}\n",
       "\n",
       ".xr-index-preview {\n",
       "  grid-column: 2 / 5;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-name,\n",
       ".xr-var-dims,\n",
       ".xr-var-dtype,\n",
       ".xr-preview,\n",
       ".xr-attrs dt {\n",
       "  white-space: nowrap;\n",
       "  overflow: hidden;\n",
       "  text-overflow: ellipsis;\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-var-name:hover,\n",
       ".xr-var-dims:hover,\n",
       ".xr-var-dtype:hover,\n",
       ".xr-attrs dt:hover {\n",
       "  overflow: visible;\n",
       "  width: auto;\n",
       "  z-index: 1;\n",
       "}\n",
       "\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-data {\n",
       "  display: none;\n",
       "  background-color: var(--xr-background-color) !important;\n",
       "  padding-bottom: 5px !important;\n",
       "}\n",
       "\n",
       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
       ".xr-var-data-in:checked ~ .xr-var-data,\n",
       ".xr-index-data-in:checked ~ .xr-index-data {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       ".xr-var-data > table {\n",
       "  float: right;\n",
       "}\n",
       "\n",
       ".xr-var-name span,\n",
       ".xr-var-data,\n",
       ".xr-index-name div,\n",
       ".xr-index-data,\n",
       ".xr-attrs {\n",
       "  padding-left: 25px !important;\n",
       "}\n",
       "\n",
       ".xr-attrs,\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-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",
       ".xr-no-icon {\n",
       "  display: inline-block;\n",
       "  vertical-align: middle;\n",
       "  width: 1em;\n",
       "  height: 1.5em !important;\n",
       "  stroke-width: 0;\n",
       "  stroke: currentColor;\n",
       "  fill: currentColor;\n",
       "}\n",
       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;sst&#x27; (anchor_year: 63, i_interval: 1, cluster_labels: 4)&gt;\n",
       "287.1 283.4 291.7 296.4 287.0 283.2 ... 292.1 296.0 289.6 284.9 293.2 297.1\n",
       "Coordinates:\n",
       "  * anchor_year     (anchor_year) int64 1959 1960 1961 1962 ... 2019 2020 2021\n",
       "  * i_interval      (i_interval) int64 -4\n",
       "    left_bound      (anchor_year, i_interval) datetime64[ns] 1959-07-04 ... 2...\n",
       "    right_bound     (anchor_year, i_interval) datetime64[ns] 1959-07-11 ... 2...\n",
       "    is_target       (i_interval) bool False\n",
       "  * cluster_labels  (cluster_labels) int16 -2 -1 1 2\n",
       "    latitude        (cluster_labels) float64 42.5 47.5 37.5 32.5\n",
       "    longitude       (cluster_labels) float64 215.0 212.5 182.5 182.5\n",
       "Attributes:\n",
       "    data:         Clustered data with Response Guided Dimensionality Reduction.\n",
       "    coordinates:  Latitudes and longitudes are geographical centers associate...</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'sst'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>anchor_year</span>: 63</li><li><span class='xr-has-index'>i_interval</span>: 1</li><li><span class='xr-has-index'>cluster_labels</span>: 4</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-f33f5615-4ef6-4e65-beae-12a447b834a5' class='xr-array-in' type='checkbox' ><label for='section-f33f5615-4ef6-4e65-beae-12a447b834a5' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>287.1 283.4 291.7 296.4 287.0 283.2 ... 296.0 289.6 284.9 293.2 297.1</span></div><div class='xr-array-data'><pre>array([[[287.10886383, 283.38946533, 291.7381897 , 296.43683879]],\n",
       "\n",
       "       [[287.02774811, 283.16993408, 292.3805542 , 296.64592489]],\n",
       "\n",
       "       [[286.22309113, 283.39697266, 295.16619873, 297.66360474]],\n",
       "\n",
       "       [[288.62598419, 284.74578247, 292.56723022, 296.76961263]],\n",
       "\n",
       "       [[289.36114502, 285.19439087, 292.79199219, 296.85743205]],\n",
       "\n",
       "       [[287.08869171, 283.6881958 , 293.57376099, 297.57780965]],\n",
       "\n",
       "       [[290.38078308, 285.16781006, 291.56219482, 296.89009603]],\n",
       "\n",
       "       [[288.27313232, 284.36610718, 291.74249268, 296.71409098]],\n",
       "\n",
       "       [[289.04909515, 284.9065979 , 290.99459839, 295.92515055]],\n",
       "\n",
       "       [[287.56668854, 284.13991699, 291.86239624, 297.34043376]],\n",
       "\n",
       "...\n",
       "\n",
       "       [[286.10547638, 282.63609009, 294.25064087, 297.99043783]],\n",
       "\n",
       "       [[287.987854  , 284.90361328, 294.47198486, 297.70853678]],\n",
       "\n",
       "       [[288.77849579, 285.1822876 , 292.16293335, 297.77416992]],\n",
       "\n",
       "       [[290.07991028, 285.859552  , 291.62518311, 297.09870402]],\n",
       "\n",
       "       [[288.14543152, 284.45953369, 293.6194458 , 297.37908936]],\n",
       "\n",
       "       [[286.77661896, 284.25762939, 293.193573  , 298.33200073]],\n",
       "\n",
       "       [[287.53064728, 284.25801392, 292.81326294, 296.39140828]],\n",
       "\n",
       "       [[289.86067963, 285.63557129, 292.07577515, 295.68477376]],\n",
       "\n",
       "       [[290.30373383, 285.80601807, 292.12728882, 296.01493327]],\n",
       "\n",
       "       [[289.62393951, 284.8852356 , 293.16723633, 297.06682332]]])</pre></div></div></li><li class='xr-section-item'><input id='section-07cb604d-2e6f-40db-a2f4-877fa7452cf0' class='xr-section-summary-in' type='checkbox'  checked><label for='section-07cb604d-2e6f-40db-a2f4-877fa7452cf0' class='xr-section-summary' >Coordinates: <span>(8)</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'>anchor_year</span></div><div class='xr-var-dims'>(anchor_year)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>1959 1960 1961 ... 2019 2020 2021</div><input id='attrs-58a61eb0-3b6b-4eb2-87de-3400c315a93b' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-58a61eb0-3b6b-4eb2-87de-3400c315a93b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4db35b3b-62a1-43d0-a235-7827b1d4b767' class='xr-var-data-in' type='checkbox'><label for='data-4db35b3b-62a1-43d0-a235-7827b1d4b767' 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([1959, 1960, 1961, 1962, 1963, 1964, 1965, 1966, 1967, 1968, 1969, 1970,\n",
       "       1971, 1972, 1973, 1974, 1975, 1976, 1977, 1978, 1979, 1980, 1981, 1982,\n",
       "       1983, 1984, 1985, 1986, 1987, 1988, 1989, 1990, 1991, 1992, 1993, 1994,\n",
       "       1995, 1996, 1997, 1998, 1999, 2000, 2001, 2002, 2003, 2004, 2005, 2006,\n",
       "       2007, 2008, 2009, 2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018,\n",
       "       2019, 2020, 2021])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>i_interval</span></div><div class='xr-var-dims'>(i_interval)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>-4</div><input id='attrs-130a4d10-83b4-4e69-8499-3f40474bdd60' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-130a4d10-83b4-4e69-8499-3f40474bdd60' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-730c135a-c437-4025-bf1e-efb338e0a1bd' class='xr-var-data-in' type='checkbox'><label for='data-730c135a-c437-4025-bf1e-efb338e0a1bd' 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([-4])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>left_bound</span></div><div class='xr-var-dims'>(anchor_year, i_interval)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>1959-07-04 ... 2021-07-04</div><input id='attrs-d9883782-ba22-4a9f-8a57-e86d8c0827c3' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-d9883782-ba22-4a9f-8a57-e86d8c0827c3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-afdc8f3b-b033-48fd-8af8-314292e9cb81' class='xr-var-data-in' type='checkbox'><label for='data-afdc8f3b-b033-48fd-8af8-314292e9cb81' 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;1959-07-04T00:00:00.000000000&#x27;],\n",
       "       [&#x27;1960-07-04T00:00:00.000000000&#x27;],\n",
       "       [&#x27;1961-07-04T00:00:00.000000000&#x27;],\n",
       "       [&#x27;1962-07-04T00:00:00.000000000&#x27;],\n",
       "       [&#x27;1963-07-04T00:00:00.000000000&#x27;],\n",
       "       [&#x27;1964-07-04T00:00:00.000000000&#x27;],\n",
       "       [&#x27;1965-07-04T00:00:00.000000000&#x27;],\n",
       "       [&#x27;1966-07-04T00:00:00.000000000&#x27;],\n",
       "       [&#x27;1967-07-04T00:00:00.000000000&#x27;],\n",
       "       [&#x27;1968-07-04T00:00:00.000000000&#x27;],\n",
       "       [&#x27;1969-07-04T00:00:00.000000000&#x27;],\n",
       "       [&#x27;1970-07-04T00:00:00.000000000&#x27;],\n",
       "       [&#x27;1971-07-04T00:00:00.000000000&#x27;],\n",
       "       [&#x27;1972-07-04T00:00:00.000000000&#x27;],\n",
       "       [&#x27;1973-07-04T00:00:00.000000000&#x27;],\n",
       "       [&#x27;1974-07-04T00:00:00.000000000&#x27;],\n",
       "       [&#x27;1975-07-04T00:00:00.000000000&#x27;],\n",
       "       [&#x27;1976-07-04T00:00:00.000000000&#x27;],\n",
       "       [&#x27;1977-07-04T00:00:00.000000000&#x27;],\n",
       "       [&#x27;1978-07-04T00:00:00.000000000&#x27;],\n",
       "...\n",
       "       [&#x27;2002-07-04T00:00:00.000000000&#x27;],\n",
       "       [&#x27;2003-07-04T00:00:00.000000000&#x27;],\n",
       "       [&#x27;2004-07-04T00:00:00.000000000&#x27;],\n",
       "       [&#x27;2005-07-04T00:00:00.000000000&#x27;],\n",
       "       [&#x27;2006-07-04T00:00:00.000000000&#x27;],\n",
       "       [&#x27;2007-07-04T00:00:00.000000000&#x27;],\n",
       "       [&#x27;2008-07-04T00:00:00.000000000&#x27;],\n",
       "       [&#x27;2009-07-04T00:00:00.000000000&#x27;],\n",
       "       [&#x27;2010-07-04T00:00:00.000000000&#x27;],\n",
       "       [&#x27;2011-07-04T00:00:00.000000000&#x27;],\n",
       "       [&#x27;2012-07-04T00:00:00.000000000&#x27;],\n",
       "       [&#x27;2013-07-04T00:00:00.000000000&#x27;],\n",
       "       [&#x27;2014-07-04T00:00:00.000000000&#x27;],\n",
       "       [&#x27;2015-07-04T00:00:00.000000000&#x27;],\n",
       "       [&#x27;2016-07-04T00:00:00.000000000&#x27;],\n",
       "       [&#x27;2017-07-04T00:00:00.000000000&#x27;],\n",
       "       [&#x27;2018-07-04T00:00:00.000000000&#x27;],\n",
       "       [&#x27;2019-07-04T00:00:00.000000000&#x27;],\n",
       "       [&#x27;2020-07-04T00:00:00.000000000&#x27;],\n",
       "       [&#x27;2021-07-04T00:00:00.000000000&#x27;]], dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>right_bound</span></div><div class='xr-var-dims'>(anchor_year, i_interval)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>1959-07-11 ... 2021-07-11</div><input id='attrs-ff015fb7-afc3-43e8-a725-d8040eb3e4e3' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-ff015fb7-afc3-43e8-a725-d8040eb3e4e3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-59a44dff-112c-488b-be3f-29569b6ee68d' class='xr-var-data-in' type='checkbox'><label for='data-59a44dff-112c-488b-be3f-29569b6ee68d' 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;1959-07-11T00:00:00.000000000&#x27;],\n",
       "       [&#x27;1960-07-11T00:00:00.000000000&#x27;],\n",
       "       [&#x27;1961-07-11T00:00:00.000000000&#x27;],\n",
       "       [&#x27;1962-07-11T00:00:00.000000000&#x27;],\n",
       "       [&#x27;1963-07-11T00:00:00.000000000&#x27;],\n",
       "       [&#x27;1964-07-11T00:00:00.000000000&#x27;],\n",
       "       [&#x27;1965-07-11T00:00:00.000000000&#x27;],\n",
       "       [&#x27;1966-07-11T00:00:00.000000000&#x27;],\n",
       "       [&#x27;1967-07-11T00:00:00.000000000&#x27;],\n",
       "       [&#x27;1968-07-11T00:00:00.000000000&#x27;],\n",
       "       [&#x27;1969-07-11T00:00:00.000000000&#x27;],\n",
       "       [&#x27;1970-07-11T00:00:00.000000000&#x27;],\n",
       "       [&#x27;1971-07-11T00:00:00.000000000&#x27;],\n",
       "       [&#x27;1972-07-11T00:00:00.000000000&#x27;],\n",
       "       [&#x27;1973-07-11T00:00:00.000000000&#x27;],\n",
       "       [&#x27;1974-07-11T00:00:00.000000000&#x27;],\n",
       "       [&#x27;1975-07-11T00:00:00.000000000&#x27;],\n",
       "       [&#x27;1976-07-11T00:00:00.000000000&#x27;],\n",
       "       [&#x27;1977-07-11T00:00:00.000000000&#x27;],\n",
       "       [&#x27;1978-07-11T00:00:00.000000000&#x27;],\n",
       "...\n",
       "       [&#x27;2002-07-11T00:00:00.000000000&#x27;],\n",
       "       [&#x27;2003-07-11T00:00:00.000000000&#x27;],\n",
       "       [&#x27;2004-07-11T00:00:00.000000000&#x27;],\n",
       "       [&#x27;2005-07-11T00:00:00.000000000&#x27;],\n",
       "       [&#x27;2006-07-11T00:00:00.000000000&#x27;],\n",
       "       [&#x27;2007-07-11T00:00:00.000000000&#x27;],\n",
       "       [&#x27;2008-07-11T00:00:00.000000000&#x27;],\n",
       "       [&#x27;2009-07-11T00:00:00.000000000&#x27;],\n",
       "       [&#x27;2010-07-11T00:00:00.000000000&#x27;],\n",
       "       [&#x27;2011-07-11T00:00:00.000000000&#x27;],\n",
       "       [&#x27;2012-07-11T00:00:00.000000000&#x27;],\n",
       "       [&#x27;2013-07-11T00:00:00.000000000&#x27;],\n",
       "       [&#x27;2014-07-11T00:00:00.000000000&#x27;],\n",
       "       [&#x27;2015-07-11T00:00:00.000000000&#x27;],\n",
       "       [&#x27;2016-07-11T00:00:00.000000000&#x27;],\n",
       "       [&#x27;2017-07-11T00:00:00.000000000&#x27;],\n",
       "       [&#x27;2018-07-11T00:00:00.000000000&#x27;],\n",
       "       [&#x27;2019-07-11T00:00:00.000000000&#x27;],\n",
       "       [&#x27;2020-07-11T00:00:00.000000000&#x27;],\n",
       "       [&#x27;2021-07-11T00:00:00.000000000&#x27;]], dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>is_target</span></div><div class='xr-var-dims'>(i_interval)</div><div class='xr-var-dtype'>bool</div><div class='xr-var-preview xr-preview'>False</div><input id='attrs-9776a6b8-2ffc-49d3-9281-cebfce501acb' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-9776a6b8-2ffc-49d3-9281-cebfce501acb' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-79c8209c-154b-4909-a6f6-e19bd413ab80' class='xr-var-data-in' type='checkbox'><label for='data-79c8209c-154b-4909-a6f6-e19bd413ab80' 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([False])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>cluster_labels</span></div><div class='xr-var-dims'>(cluster_labels)</div><div class='xr-var-dtype'>int16</div><div class='xr-var-preview xr-preview'>-2 -1 1 2</div><input id='attrs-d9ae2d54-7394-478c-96a3-b24048128268' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-d9ae2d54-7394-478c-96a3-b24048128268' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4e9c310a-50aa-4f53-8930-b53a1441cd24' class='xr-var-data-in' type='checkbox'><label for='data-4e9c310a-50aa-4f53-8930-b53a1441cd24' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([-2, -1,  1,  2], dtype=int16)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>latitude</span></div><div class='xr-var-dims'>(cluster_labels)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>42.5 47.5 37.5 32.5</div><input id='attrs-60939af5-7aaf-4efa-8974-64edff02187d' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-60939af5-7aaf-4efa-8974-64edff02187d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5c1f1714-363c-4550-8c31-f8fc431d21d0' class='xr-var-data-in' type='checkbox'><label for='data-5c1f1714-363c-4550-8c31-f8fc431d21d0' 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([42.5, 47.5, 37.5, 32.5])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>longitude</span></div><div class='xr-var-dims'>(cluster_labels)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>215.0 212.5 182.5 182.5</div><input id='attrs-155ebe07-f19e-4502-a7ae-21af9ffebdf4' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-155ebe07-f19e-4502-a7ae-21af9ffebdf4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8dc23f15-eab3-4a3e-aa0c-bab93991e274' class='xr-var-data-in' type='checkbox'><label for='data-8dc23f15-eab3-4a3e-aa0c-bab93991e274' 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([215. , 212.5, 182.5, 182.5])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-e914c595-381d-49b0-b25b-026043cc2fed' class='xr-section-summary-in' type='checkbox'  ><label for='section-e914c595-381d-49b0-b25b-026043cc2fed' class='xr-section-summary' >Indexes: <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-index-name'><div>anchor_year</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-2cf98cd2-c0da-4092-9d37-26840666abb5' class='xr-index-data-in' type='checkbox'/><label for='index-2cf98cd2-c0da-4092-9d37-26840666abb5' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Int64Index([1959, 1960, 1961, 1962, 1963, 1964, 1965, 1966, 1967, 1968, 1969,\n",
       "            1970, 1971, 1972, 1973, 1974, 1975, 1976, 1977, 1978, 1979, 1980,\n",
       "            1981, 1982, 1983, 1984, 1985, 1986, 1987, 1988, 1989, 1990, 1991,\n",
       "            1992, 1993, 1994, 1995, 1996, 1997, 1998, 1999, 2000, 2001, 2002,\n",
       "            2003, 2004, 2005, 2006, 2007, 2008, 2009, 2010, 2011, 2012, 2013,\n",
       "            2014, 2015, 2016, 2017, 2018, 2019, 2020, 2021],\n",
       "           dtype=&#x27;int64&#x27;, name=&#x27;anchor_year&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>i_interval</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-6128b03a-42e1-40de-8453-b5c790b18df3' class='xr-index-data-in' type='checkbox'/><label for='index-6128b03a-42e1-40de-8453-b5c790b18df3' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Int64Index([-4], dtype=&#x27;int64&#x27;, name=&#x27;i_interval&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>cluster_labels</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-9f58cf27-676c-42f3-95f1-eb0eee713182' class='xr-index-data-in' type='checkbox'/><label for='index-9f58cf27-676c-42f3-95f1-eb0eee713182' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Int64Index([-2, -1, 1, 2], dtype=&#x27;int64&#x27;, name=&#x27;cluster_labels&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-295413cb-c4fe-489c-8095-2d16fe78105d' class='xr-section-summary-in' type='checkbox'  checked><label for='section-295413cb-c4fe-489c-8095-2d16fe78105d' class='xr-section-summary' >Attributes: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>data :</span></dt><dd>Clustered data with Response Guided Dimensionality Reduction.</dd><dt><span>coordinates :</span></dt><dd>Latitudes and longitudes are geographical centers associated with clusters.</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.DataArray 'sst' (anchor_year: 63, i_interval: 1, cluster_labels: 4)>\n",
       "287.1 283.4 291.7 296.4 287.0 283.2 ... 292.1 296.0 289.6 284.9 293.2 297.1\n",
       "Coordinates:\n",
       "  * anchor_year     (anchor_year) int64 1959 1960 1961 1962 ... 2019 2020 2021\n",
       "  * i_interval      (i_interval) int64 -4\n",
       "    left_bound      (anchor_year, i_interval) datetime64[ns] 1959-07-04 ... 2...\n",
       "    right_bound     (anchor_year, i_interval) datetime64[ns] 1959-07-11 ... 2...\n",
       "    is_target       (i_interval) bool False\n",
       "  * cluster_labels  (cluster_labels) int16 -2 -1 1 2\n",
       "    latitude        (cluster_labels) float64 42.5 47.5 37.5 32.5\n",
       "    longitude       (cluster_labels) float64 215.0 212.5 182.5 182.5\n",
       "Attributes:\n",
       "    data:         Clustered data with Response Guided Dimensionality Reduction.\n",
       "    coordinates:  Latitudes and longitudes are geographical centers associate..."
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clustered_data = rgdr.transform(precursor_field)\n",
    "xr.set_options(display_expand_data=False) # Hide the full data repr\n",
    "clustered_data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we can plot the data for each cluster (blue for positively correlated lcusters, red for negatively correlated ones):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f1d2e39aa70>]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "clustered_data = clustered_data  - clustered_data.mean(dim=\"anchor_year\")\n",
    "kwargs = {'x': 'anchor_year', 'add_legend': False}\n",
    "clustered_data.sel(cluster_labels=1).plot.line(c='tab:blue', **kwargs)\n",
    "clustered_data.sel(cluster_labels=2).plot.line(c='tab:blue', **kwargs)\n",
    "clustered_data.sel(cluster_labels=-1).plot.line(c='tab:red', **kwargs)\n",
    "clustered_data.sel(cluster_labels=-2).plot.line(c='tab:red', **kwargs)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The `RGDR` object will also contain the correlation maps, p-values, and cluster maps.\n",
    "\n",
    "These are all `xr.DataArray` objects, and can easily be plot using the builtin methods:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "}\n",
       "\n",
       ".xr-obj-type,\n",
       ".xr-array-name {\n",
       "  margin-left: 2px;\n",
       "  margin-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-obj-type {\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-sections {\n",
       "  padding-left: 0 !important;\n",
       "  display: grid;\n",
       "  grid-template-columns: 150px auto auto 1fr 20px 20px;\n",
       "}\n",
       "\n",
       ".xr-section-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-section-item input {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-section-item input + label {\n",
       "  color: var(--xr-disabled-color);\n",
       "}\n",
       "\n",
       ".xr-section-item input:enabled + label {\n",
       "  cursor: pointer;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-section-item input:enabled + label:hover {\n",
       "  color: var(--xr-font-color0);\n",
       "}\n",
       "\n",
       ".xr-section-summary {\n",
       "  grid-column: 1;\n",
       "  color: var(--xr-font-color2);\n",
       "  font-weight: 500;\n",
       "}\n",
       "\n",
       ".xr-section-summary > span {\n",
       "  display: inline-block;\n",
       "  padding-left: 0.5em;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label {\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in + label:before {\n",
       "  display: inline-block;\n",
       "  content: '►';\n",
       "  font-size: 11px;\n",
       "  width: 15px;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label:before {\n",
       "  color: var(--xr-disabled-color);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label:before {\n",
       "  content: '▼';\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label > span {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-section-summary,\n",
       ".xr-section-inline-details {\n",
       "  padding-top: 4px;\n",
       "  padding-bottom: 4px;\n",
       "}\n",
       "\n",
       ".xr-section-inline-details {\n",
       "  grid-column: 2 / -1;\n",
       "}\n",
       "\n",
       ".xr-section-details {\n",
       "  display: none;\n",
       "  grid-column: 1 / -1;\n",
       "  margin-bottom: 5px;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-array-wrap {\n",
       "  grid-column: 1 / -1;\n",
       "  display: grid;\n",
       "  grid-template-columns: 20px auto;\n",
       "}\n",
       "\n",
       ".xr-array-wrap > label {\n",
       "  grid-column: 1;\n",
       "  vertical-align: top;\n",
       "}\n",
       "\n",
       ".xr-preview {\n",
       "  color: var(--xr-font-color3);\n",
       "}\n",
       "\n",
       ".xr-array-preview,\n",
       ".xr-array-data {\n",
       "  padding: 0 5px !important;\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-array-data,\n",
       ".xr-array-in:checked ~ .xr-array-preview {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-array-in:checked ~ .xr-array-data,\n",
       ".xr-array-preview {\n",
       "  display: inline-block;\n",
       "}\n",
       "\n",
       ".xr-dim-list {\n",
       "  display: inline-block !important;\n",
       "  list-style: none;\n",
       "  padding: 0 !important;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list li {\n",
       "  display: inline-block;\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list:before {\n",
       "  content: '(';\n",
       "}\n",
       "\n",
       ".xr-dim-list:after {\n",
       "  content: ')';\n",
       "}\n",
       "\n",
       ".xr-dim-list li:not(:last-child):after {\n",
       "  content: ',';\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-has-index {\n",
       "  font-weight: bold;\n",
       "}\n",
       "\n",
       ".xr-var-list,\n",
       ".xr-var-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-var-item > div,\n",
       ".xr-var-item label,\n",
       ".xr-var-item > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-even);\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-var-item > .xr-var-name:hover span {\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-var-list > li:nth-child(odd) > div,\n",
       ".xr-var-list > li:nth-child(odd) > label,\n",
       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-odd);\n",
       "}\n",
       "\n",
       ".xr-var-name {\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-var-dims {\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-var-dtype {\n",
       "  grid-column: 3;\n",
       "  text-align: right;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-preview {\n",
       "  grid-column: 4;\n",
       "}\n",
       "\n",
       ".xr-index-preview {\n",
       "  grid-column: 2 / 5;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-name,\n",
       ".xr-var-dims,\n",
       ".xr-var-dtype,\n",
       ".xr-preview,\n",
       ".xr-attrs dt {\n",
       "  white-space: nowrap;\n",
       "  overflow: hidden;\n",
       "  text-overflow: ellipsis;\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-var-name:hover,\n",
       ".xr-var-dims:hover,\n",
       ".xr-var-dtype:hover,\n",
       ".xr-attrs dt:hover {\n",
       "  overflow: visible;\n",
       "  width: auto;\n",
       "  z-index: 1;\n",
       "}\n",
       "\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-data {\n",
       "  display: none;\n",
       "  background-color: var(--xr-background-color) !important;\n",
       "  padding-bottom: 5px !important;\n",
       "}\n",
       "\n",
       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
       ".xr-var-data-in:checked ~ .xr-var-data,\n",
       ".xr-index-data-in:checked ~ .xr-index-data {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       ".xr-var-data > table {\n",
       "  float: right;\n",
       "}\n",
       "\n",
       ".xr-var-name span,\n",
       ".xr-var-data,\n",
       ".xr-index-name div,\n",
       ".xr-index-data,\n",
       ".xr-attrs {\n",
       "  padding-left: 25px !important;\n",
       "}\n",
       "\n",
       ".xr-attrs,\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-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",
       ".xr-no-icon {\n",
       "  display: inline-block;\n",
       "  vertical-align: middle;\n",
       "  width: 1em;\n",
       "  height: 1.5em !important;\n",
       "  stroke-width: 0;\n",
       "  stroke: currentColor;\n",
       "  fill: currentColor;\n",
       "}\n",
       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray (latitude: 5, longitude: 13)&gt;\n",
       "0.2078 0.1499 0.03607 -0.1006 -0.1741 ... -0.01505 0.0007811 -0.0216 -0.08173\n",
       "Coordinates:\n",
       "  * longitude   (longitude) float64 177.5 182.5 187.5 ... 227.5 232.5 237.5\n",
       "  * latitude    (latitude) float64 47.5 42.5 37.5 32.5 27.5\n",
       "    area        (latitude) float64 2.088e+05 2.278e+05 ... 2.606e+05 2.741e+05\n",
       "    n_clusters  int64 6\n",
       "    cluster     int64 3</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'></div><ul class='xr-dim-list'><li><span class='xr-has-index'>latitude</span>: 5</li><li><span class='xr-has-index'>longitude</span>: 13</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-02c169d0-a83c-45e4-ae7c-acb64d844386' class='xr-array-in' type='checkbox' ><label for='section-02c169d0-a83c-45e4-ae7c-acb64d844386' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>0.2078 0.1499 0.03607 -0.1006 ... -0.01505 0.0007811 -0.0216 -0.08173</span></div><div class='xr-array-data'><pre>array([[ 2.07801245e-01,  1.49876690e-01,  3.60677103e-02,\n",
       "        -1.00593305e-01, -1.74138804e-01, -2.80001213e-01,\n",
       "        -3.68613672e-01, -3.74115170e-01, -3.76554565e-01,\n",
       "        -3.44061011e-01, -1.76121425e-01,  5.16835951e-02,\n",
       "                    nan],\n",
       "       [ 1.25512287e-01,  1.59305509e-01,  1.32107407e-01,\n",
       "        -3.04594400e-02, -1.41662888e-01, -2.46024110e-01,\n",
       "        -2.85223654e-01, -3.40309707e-01, -3.72617156e-01,\n",
       "        -3.34648564e-01, -1.43553876e-01,  3.06695090e-02,\n",
       "                    nan],\n",
       "       [ 2.15055403e-01,  2.70901517e-01,  2.12365380e-01,\n",
       "         1.25010661e-01, -9.10190514e-06, -7.10910453e-02,\n",
       "        -6.82844239e-02, -1.34341829e-01, -2.10828202e-01,\n",
       "        -1.16932784e-01, -7.29768294e-02, -9.80702831e-02,\n",
       "                    nan],\n",
       "       [ 2.66895499e-01,  2.79349490e-01,  2.49905561e-01,\n",
       "         2.32558238e-01,  1.91967358e-01,  8.23171236e-02,\n",
       "         3.91414662e-02,  3.26824365e-02, -7.70895590e-03,\n",
       "        -1.97170891e-02, -7.74471065e-02, -5.97727979e-02,\n",
       "        -1.10933868e-01],\n",
       "       [ 1.89446477e-01,  1.28754408e-01,  1.04125014e-01,\n",
       "         3.22591558e-02,  3.71697132e-02,  3.35812931e-02,\n",
       "         3.60161482e-02,  2.39827905e-02, -7.25709166e-03,\n",
       "        -1.50487228e-02,  7.81073707e-04, -2.15976630e-02,\n",
       "        -8.17294301e-02]])</pre></div></div></li><li class='xr-section-item'><input id='section-9b130ae4-94a7-467b-b9e9-ef9386c0f8ef' class='xr-section-summary-in' type='checkbox'  checked><label for='section-9b130ae4-94a7-467b-b9e9-ef9386c0f8ef' class='xr-section-summary' >Coordinates: <span>(5)</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'>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-8f02069e-103a-452e-8478-8bdedc3d406c' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-8f02069e-103a-452e-8478-8bdedc3d406c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-de0f49fd-36cf-4866-9466-9422631c608b' class='xr-var-data-in' type='checkbox'><label for='data-de0f49fd-36cf-4866-9466-9422631c608b' 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 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'>47.5 42.5 37.5 32.5 27.5</div><input id='attrs-0065b0ad-3a38-42ce-8d7d-1d3bf945780f' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-0065b0ad-3a38-42ce-8d7d-1d3bf945780f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2ca856f2-a572-4a5a-9fef-bee72278be0f' class='xr-var-data-in' type='checkbox'><label for='data-2ca856f2-a572-4a5a-9fef-bee72278be0f' 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([47.5, 42.5, 37.5, 32.5, 27.5])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>area</span></div><div class='xr-var-dims'>(latitude)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>2.088e+05 2.278e+05 ... 2.741e+05</div><input id='attrs-6a04ba43-54d3-4dd0-b520-a8282c74bbac' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-6a04ba43-54d3-4dd0-b520-a8282c74bbac' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ccaaa6f5-957d-4657-a670-9ac1125283b1' class='xr-var-data-in' type='checkbox'><label for='data-ccaaa6f5-957d-4657-a670-9ac1125283b1' 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([208767.02150832, 227829.22531495, 245157.51114987, 260620.0002948 ,\n",
       "       274099.01387082])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>n_clusters</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>6</div><input id='attrs-1dbb690f-d393-4c6c-aeb6-2f7311df7e02' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-1dbb690f-d393-4c6c-aeb6-2f7311df7e02' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-501ecc7e-fc75-4661-ae6d-fb0bcac5eb92' class='xr-var-data-in' type='checkbox'><label for='data-501ecc7e-fc75-4661-ae6d-fb0bcac5eb92' 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(6)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>cluster</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>3</div><input id='attrs-29369ad3-dd5b-4ed0-b078-b7b670e382fa' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-29369ad3-dd5b-4ed0-b078-b7b670e382fa' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-08d39872-ff84-48ad-b110-ffbc90058322' class='xr-var-data-in' type='checkbox'><label for='data-08d39872-ff84-48ad-b110-ffbc90058322' 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(3)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-171b4a95-18f9-4959-9d3a-5d9da0dfdbd0' class='xr-section-summary-in' type='checkbox'  ><label for='section-171b4a95-18f9-4959-9d3a-5d9da0dfdbd0' class='xr-section-summary' >Indexes: <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-index-name'><div>longitude</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-bbb7999b-3eb8-4eaa-9b35-80426759a685' class='xr-index-data-in' type='checkbox'/><label for='index-bbb7999b-3eb8-4eaa-9b35-80426759a685' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Float64Index([177.5, 182.5, 187.5, 192.5, 197.5, 202.5, 207.5, 212.5, 217.5,\n",
       "              222.5, 227.5, 232.5, 237.5],\n",
       "             dtype=&#x27;float64&#x27;, name=&#x27;longitude&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>latitude</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-b76333e4-64a9-4545-adb8-6818c88f435c' class='xr-index-data-in' type='checkbox'/><label for='index-b76333e4-64a9-4545-adb8-6818c88f435c' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Float64Index([47.5, 42.5, 37.5, 32.5, 27.5], dtype=&#x27;float64&#x27;, name=&#x27;latitude&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-a2ccaba3-af2b-42bb-aaf3-fd7492b07aab' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-a2ccaba3-af2b-42bb-aaf3-fd7492b07aab' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.DataArray (latitude: 5, longitude: 13)>\n",
       "0.2078 0.1499 0.03607 -0.1006 -0.1741 ... -0.01505 0.0007811 -0.0216 -0.08173\n",
       "Coordinates:\n",
       "  * longitude   (longitude) float64 177.5 182.5 187.5 ... 227.5 232.5 237.5\n",
       "  * latitude    (latitude) float64 47.5 42.5 37.5 32.5 27.5\n",
       "    area        (latitude) float64 2.088e+05 2.278e+05 ... 2.606e+05 2.741e+05\n",
       "    n_clusters  int64 6\n",
       "    cluster     int64 3"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# View the contents of the corr_map DataArray:\n",
    "rgdr.corr_map"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.QuadMesh at 0x7f1d292a4940>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x300 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, (ax1, ax2) = plt.subplots(figsize=(10, 3), ncols=2)\n",
    "\n",
    "# Visualize correlation map after RGDR().fit(precursor_field, target_timeseries)\n",
    "rgdr.corr_map.plot(ax=ax1)\n",
    "\n",
    "# Visualize p-values map\n",
    "rgdr.pval_map.plot(ax=ax2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The cluster maps can also be visualized:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.QuadMesh at 0x7f1d2e7a76d0>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "rgdr.cluster_map.plot(cmap='RdYlBu', vmin=-2, vmax=2)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Multiple target intervals\n",
    "\n",
    "As mentioned earlier, with RGDR you can also analyse the correlation of multiple intervals at once.\n",
    "\n",
    "Instead of a simple correlation, this is more akin to a cross-correlation operation.\n",
    "\n",
    "When you select multiple target intervals, these will be correlated with the same number of precursor intervals, shifted by the `lag` value.\n",
    "\n",
    "As an illustration, see the image below:\n",
    "\n",
    "![Multiple Targets](../notebooks/multiple_targets_RGDR.svg)\n",
    "\n",
    "Now let's see it in action:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[-4, -3, -2, -1]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "target_intervals = [1, 2, 3, 4]\n",
    "lag = 4\n",
    "rgdr = RGDR(\n",
    "    target_intervals=target_intervals,\n",
    "    lag=lag,\n",
    "    eps_km=600,\n",
    "    alpha=0.05,\n",
    "    min_area_km2=0\n",
    ")\n",
    "rgdr.precursor_intervals"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x200 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, (ax1, ax2) = plt.subplots(ncols=2, figsize=(12, 2))\n",
    "_ = rgdr.preview_correlation(precursor_field, target_timeseries, ax1=ax1, ax2=ax2)\n",
    "rgdr.fit(precursor_field, target_timeseries)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note that the transformed data now has all four precursor intervals:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div><svg style=\"position: absolute; width: 0; height: 0; overflow: hidden\">\n",
       "<defs>\n",
       "<symbol id=\"icon-database\" viewBox=\"0 0 32 32\">\n",
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       "<path d=\"M16 17c-8.837 0-16-2.239-16-5v6c0 2.761 7.163 5 16 5s16-2.239 16-5v-6c0 2.761-7.163 5-16 5z\"></path>\n",
       "<path d=\"M16 26c-8.837 0-16-2.239-16-5v6c0 2.761 7.163 5 16 5s16-2.239 16-5v-6c0 2.761-7.163 5-16 5z\"></path>\n",
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       "\n",
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       "\n",
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       "\n",
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       "  fill: currentColor;\n",
       "}\n",
       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;sst&#x27; (anchor_year: 63, i_interval: 4, cluster_labels: 2)&gt;\n",
       "293.0 288.1 293.4 288.7 293.6 289.3 ... 292.7 289.8 292.8 290.7 293.2 291.4\n",
       "Coordinates:\n",
       "  * anchor_year     (anchor_year) int64 1959 1960 1961 1962 ... 2019 2020 2021\n",
       "  * i_interval      (i_interval) int64 -4 -3 -2 -1\n",
       "    left_bound      (anchor_year, i_interval) datetime64[ns] 1959-07-04 ... 2...\n",
       "    right_bound     (anchor_year, i_interval) datetime64[ns] 1959-07-11 ... 2...\n",
       "    is_target       (i_interval) bool False False False False\n",
       "  * cluster_labels  (cluster_labels) int16 -2 -1\n",
       "    latitude        (cluster_labels) float64 27.5 40.89\n",
       "    longitude       (cluster_labels) float64 235.0 206.2\n",
       "Attributes:\n",
       "    data:         Clustered data with Response Guided Dimensionality Reduction.\n",
       "    coordinates:  Latitudes and longitudes are geographical centers associate...</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'sst'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>anchor_year</span>: 63</li><li><span class='xr-has-index'>i_interval</span>: 4</li><li><span class='xr-has-index'>cluster_labels</span>: 2</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-e89b8094-51ef-4b85-a90e-622471f7fd14' class='xr-array-in' type='checkbox' ><label for='section-e89b8094-51ef-4b85-a90e-622471f7fd14' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>293.0 288.1 293.4 288.7 293.6 289.3 ... 289.8 292.8 290.7 293.2 291.4</span></div><div class='xr-array-data'><pre>array([[[293.04328918, 288.0610336 ],\n",
       "        [293.37278748, 288.66074909],\n",
       "        [293.56242371, 289.30481948],\n",
       "        [293.57678223, 289.98418378]],\n",
       "\n",
       "       [[293.02174377, 287.80927727],\n",
       "        [293.33534241, 288.47373641],\n",
       "        [293.52182007, 289.08839689],\n",
       "        [293.53269958, 289.68154052]],\n",
       "\n",
       "       [[292.53941345, 288.0344477 ],\n",
       "        [292.8681488 , 288.55887356],\n",
       "        [293.30213928, 289.44859854],\n",
       "        [294.00375366, 290.25388907]],\n",
       "\n",
       "       [[292.01747131, 288.92730724],\n",
       "        [291.71492004, 289.56299806],\n",
       "        [292.11482239, 290.3419696 ],\n",
       "        [292.32862854, 290.92454857]],\n",
       "\n",
       "...\n",
       "\n",
       "       [[293.28085327, 288.20771851],\n",
       "        [293.35533142, 289.03824444],\n",
       "        [293.87088013, 289.88097386],\n",
       "        [294.19723511, 290.37095394]],\n",
       "\n",
       "       [[292.77754211, 288.75796434],\n",
       "        [293.03834534, 289.66435041],\n",
       "        [293.48588562, 290.32514764],\n",
       "        [293.82594299, 291.21448261]],\n",
       "\n",
       "       [[293.30586243, 289.42332277],\n",
       "        [293.50587463, 290.15175922],\n",
       "        [293.92388916, 290.72784622],\n",
       "        [293.85119629, 291.57498362]],\n",
       "\n",
       "       [[292.11256409, 289.03012793],\n",
       "        [292.66850281, 289.82898954],\n",
       "        [292.84553528, 290.67736464],\n",
       "        [293.19741821, 291.41449098]]])</pre></div></div></li><li class='xr-section-item'><input id='section-32149faf-9193-4136-a262-bbaf094bcc3c' class='xr-section-summary-in' type='checkbox'  checked><label for='section-32149faf-9193-4136-a262-bbaf094bcc3c' class='xr-section-summary' >Coordinates: <span>(8)</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'>anchor_year</span></div><div class='xr-var-dims'>(anchor_year)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>1959 1960 1961 ... 2019 2020 2021</div><input id='attrs-5c19a229-1d2b-4b86-94c2-8ba49f1dcaba' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-5c19a229-1d2b-4b86-94c2-8ba49f1dcaba' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0946e61c-2207-4b23-99a3-0df7bae506a7' class='xr-var-data-in' type='checkbox'><label for='data-0946e61c-2207-4b23-99a3-0df7bae506a7' 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([1959, 1960, 1961, 1962, 1963, 1964, 1965, 1966, 1967, 1968, 1969, 1970,\n",
       "       1971, 1972, 1973, 1974, 1975, 1976, 1977, 1978, 1979, 1980, 1981, 1982,\n",
       "       1983, 1984, 1985, 1986, 1987, 1988, 1989, 1990, 1991, 1992, 1993, 1994,\n",
       "       1995, 1996, 1997, 1998, 1999, 2000, 2001, 2002, 2003, 2004, 2005, 2006,\n",
       "       2007, 2008, 2009, 2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018,\n",
       "       2019, 2020, 2021])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>i_interval</span></div><div class='xr-var-dims'>(i_interval)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>-4 -3 -2 -1</div><input id='attrs-9a1110cd-dcbc-4daf-96de-666cb9a93d97' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-9a1110cd-dcbc-4daf-96de-666cb9a93d97' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-fc458fea-e2cc-4c80-8c72-6fa7e86a69c4' class='xr-var-data-in' type='checkbox'><label for='data-fc458fea-e2cc-4c80-8c72-6fa7e86a69c4' 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([-4, -3, -2, -1])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>left_bound</span></div><div class='xr-var-dims'>(anchor_year, i_interval)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>1959-07-04 ... 2021-07-25</div><input id='attrs-8e5a1ed5-5a2f-4ecd-9fae-dc542780fc7f' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-8e5a1ed5-5a2f-4ecd-9fae-dc542780fc7f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b0916ca8-b49d-4291-90b7-2034266eedde' class='xr-var-data-in' type='checkbox'><label for='data-b0916ca8-b49d-4291-90b7-2034266eedde' 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;1959-07-04T00:00:00.000000000&#x27;, &#x27;1959-07-11T00:00:00.000000000&#x27;,\n",
       "        &#x27;1959-07-18T00:00:00.000000000&#x27;, &#x27;1959-07-25T00:00:00.000000000&#x27;],\n",
       "       [&#x27;1960-07-04T00:00:00.000000000&#x27;, &#x27;1960-07-11T00:00:00.000000000&#x27;,\n",
       "        &#x27;1960-07-18T00:00:00.000000000&#x27;, &#x27;1960-07-25T00:00:00.000000000&#x27;],\n",
       "       [&#x27;1961-07-04T00:00:00.000000000&#x27;, &#x27;1961-07-11T00:00:00.000000000&#x27;,\n",
       "        &#x27;1961-07-18T00:00:00.000000000&#x27;, &#x27;1961-07-25T00:00:00.000000000&#x27;],\n",
       "       [&#x27;1962-07-04T00:00:00.000000000&#x27;, &#x27;1962-07-11T00:00:00.000000000&#x27;,\n",
       "        &#x27;1962-07-18T00:00:00.000000000&#x27;, &#x27;1962-07-25T00:00:00.000000000&#x27;],\n",
       "       [&#x27;1963-07-04T00:00:00.000000000&#x27;, &#x27;1963-07-11T00:00:00.000000000&#x27;,\n",
       "        &#x27;1963-07-18T00:00:00.000000000&#x27;, &#x27;1963-07-25T00:00:00.000000000&#x27;],\n",
       "       [&#x27;1964-07-04T00:00:00.000000000&#x27;, &#x27;1964-07-11T00:00:00.000000000&#x27;,\n",
       "        &#x27;1964-07-18T00:00:00.000000000&#x27;, &#x27;1964-07-25T00:00:00.000000000&#x27;],\n",
       "       [&#x27;1965-07-04T00:00:00.000000000&#x27;, &#x27;1965-07-11T00:00:00.000000000&#x27;,\n",
       "        &#x27;1965-07-18T00:00:00.000000000&#x27;, &#x27;1965-07-25T00:00:00.000000000&#x27;],\n",
       "       [&#x27;1966-07-04T00:00:00.000000000&#x27;, &#x27;1966-07-11T00:00:00.000000000&#x27;,\n",
       "        &#x27;1966-07-18T00:00:00.000000000&#x27;, &#x27;1966-07-25T00:00:00.000000000&#x27;],\n",
       "       [&#x27;1967-07-04T00:00:00.000000000&#x27;, &#x27;1967-07-11T00:00:00.000000000&#x27;,\n",
       "        &#x27;1967-07-18T00:00:00.000000000&#x27;, &#x27;1967-07-25T00:00:00.000000000&#x27;],\n",
       "       [&#x27;1968-07-04T00:00:00.000000000&#x27;, &#x27;1968-07-11T00:00:00.000000000&#x27;,\n",
       "        &#x27;1968-07-18T00:00:00.000000000&#x27;, &#x27;1968-07-25T00:00:00.000000000&#x27;],\n",
       "...\n",
       "        &#x27;2012-07-18T00:00:00.000000000&#x27;, &#x27;2012-07-25T00:00:00.000000000&#x27;],\n",
       "       [&#x27;2013-07-04T00:00:00.000000000&#x27;, &#x27;2013-07-11T00:00:00.000000000&#x27;,\n",
       "        &#x27;2013-07-18T00:00:00.000000000&#x27;, &#x27;2013-07-25T00:00:00.000000000&#x27;],\n",
       "       [&#x27;2014-07-04T00:00:00.000000000&#x27;, &#x27;2014-07-11T00:00:00.000000000&#x27;,\n",
       "        &#x27;2014-07-18T00:00:00.000000000&#x27;, &#x27;2014-07-25T00:00:00.000000000&#x27;],\n",
       "       [&#x27;2015-07-04T00:00:00.000000000&#x27;, &#x27;2015-07-11T00:00:00.000000000&#x27;,\n",
       "        &#x27;2015-07-18T00:00:00.000000000&#x27;, &#x27;2015-07-25T00:00:00.000000000&#x27;],\n",
       "       [&#x27;2016-07-04T00:00:00.000000000&#x27;, &#x27;2016-07-11T00:00:00.000000000&#x27;,\n",
       "        &#x27;2016-07-18T00:00:00.000000000&#x27;, &#x27;2016-07-25T00:00:00.000000000&#x27;],\n",
       "       [&#x27;2017-07-04T00:00:00.000000000&#x27;, &#x27;2017-07-11T00:00:00.000000000&#x27;,\n",
       "        &#x27;2017-07-18T00:00:00.000000000&#x27;, &#x27;2017-07-25T00:00:00.000000000&#x27;],\n",
       "       [&#x27;2018-07-04T00:00:00.000000000&#x27;, &#x27;2018-07-11T00:00:00.000000000&#x27;,\n",
       "        &#x27;2018-07-18T00:00:00.000000000&#x27;, &#x27;2018-07-25T00:00:00.000000000&#x27;],\n",
       "       [&#x27;2019-07-04T00:00:00.000000000&#x27;, &#x27;2019-07-11T00:00:00.000000000&#x27;,\n",
       "        &#x27;2019-07-18T00:00:00.000000000&#x27;, &#x27;2019-07-25T00:00:00.000000000&#x27;],\n",
       "       [&#x27;2020-07-04T00:00:00.000000000&#x27;, &#x27;2020-07-11T00:00:00.000000000&#x27;,\n",
       "        &#x27;2020-07-18T00:00:00.000000000&#x27;, &#x27;2020-07-25T00:00:00.000000000&#x27;],\n",
       "       [&#x27;2021-07-04T00:00:00.000000000&#x27;, &#x27;2021-07-11T00:00:00.000000000&#x27;,\n",
       "        &#x27;2021-07-18T00:00:00.000000000&#x27;, &#x27;2021-07-25T00:00:00.000000000&#x27;]],\n",
       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>right_bound</span></div><div class='xr-var-dims'>(anchor_year, i_interval)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>1959-07-11 ... 2021-08-01</div><input id='attrs-b8c11f15-df0e-4c2f-a711-e059e4bc46e2' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-b8c11f15-df0e-4c2f-a711-e059e4bc46e2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8a31c6e7-2cd5-4b5d-8ac7-64e73f0134c9' class='xr-var-data-in' type='checkbox'><label for='data-8a31c6e7-2cd5-4b5d-8ac7-64e73f0134c9' 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;1959-07-11T00:00:00.000000000&#x27;, &#x27;1959-07-18T00:00:00.000000000&#x27;,\n",
       "        &#x27;1959-07-25T00:00:00.000000000&#x27;, &#x27;1959-08-01T00:00:00.000000000&#x27;],\n",
       "       [&#x27;1960-07-11T00:00:00.000000000&#x27;, &#x27;1960-07-18T00:00:00.000000000&#x27;,\n",
       "        &#x27;1960-07-25T00:00:00.000000000&#x27;, &#x27;1960-08-01T00:00:00.000000000&#x27;],\n",
       "       [&#x27;1961-07-11T00:00:00.000000000&#x27;, &#x27;1961-07-18T00:00:00.000000000&#x27;,\n",
       "        &#x27;1961-07-25T00:00:00.000000000&#x27;, &#x27;1961-08-01T00:00:00.000000000&#x27;],\n",
       "       [&#x27;1962-07-11T00:00:00.000000000&#x27;, &#x27;1962-07-18T00:00:00.000000000&#x27;,\n",
       "        &#x27;1962-07-25T00:00:00.000000000&#x27;, &#x27;1962-08-01T00:00:00.000000000&#x27;],\n",
       "       [&#x27;1963-07-11T00:00:00.000000000&#x27;, &#x27;1963-07-18T00:00:00.000000000&#x27;,\n",
       "        &#x27;1963-07-25T00:00:00.000000000&#x27;, &#x27;1963-08-01T00:00:00.000000000&#x27;],\n",
       "       [&#x27;1964-07-11T00:00:00.000000000&#x27;, &#x27;1964-07-18T00:00:00.000000000&#x27;,\n",
       "        &#x27;1964-07-25T00:00:00.000000000&#x27;, &#x27;1964-08-01T00:00:00.000000000&#x27;],\n",
       "       [&#x27;1965-07-11T00:00:00.000000000&#x27;, &#x27;1965-07-18T00:00:00.000000000&#x27;,\n",
       "        &#x27;1965-07-25T00:00:00.000000000&#x27;, &#x27;1965-08-01T00:00:00.000000000&#x27;],\n",
       "       [&#x27;1966-07-11T00:00:00.000000000&#x27;, &#x27;1966-07-18T00:00:00.000000000&#x27;,\n",
       "        &#x27;1966-07-25T00:00:00.000000000&#x27;, &#x27;1966-08-01T00:00:00.000000000&#x27;],\n",
       "       [&#x27;1967-07-11T00:00:00.000000000&#x27;, &#x27;1967-07-18T00:00:00.000000000&#x27;,\n",
       "        &#x27;1967-07-25T00:00:00.000000000&#x27;, &#x27;1967-08-01T00:00:00.000000000&#x27;],\n",
       "       [&#x27;1968-07-11T00:00:00.000000000&#x27;, &#x27;1968-07-18T00:00:00.000000000&#x27;,\n",
       "        &#x27;1968-07-25T00:00:00.000000000&#x27;, &#x27;1968-08-01T00:00:00.000000000&#x27;],\n",
       "...\n",
       "        &#x27;2012-07-25T00:00:00.000000000&#x27;, &#x27;2012-08-01T00:00:00.000000000&#x27;],\n",
       "       [&#x27;2013-07-11T00:00:00.000000000&#x27;, &#x27;2013-07-18T00:00:00.000000000&#x27;,\n",
       "        &#x27;2013-07-25T00:00:00.000000000&#x27;, &#x27;2013-08-01T00:00:00.000000000&#x27;],\n",
       "       [&#x27;2014-07-11T00:00:00.000000000&#x27;, &#x27;2014-07-18T00:00:00.000000000&#x27;,\n",
       "        &#x27;2014-07-25T00:00:00.000000000&#x27;, &#x27;2014-08-01T00:00:00.000000000&#x27;],\n",
       "       [&#x27;2015-07-11T00:00:00.000000000&#x27;, &#x27;2015-07-18T00:00:00.000000000&#x27;,\n",
       "        &#x27;2015-07-25T00:00:00.000000000&#x27;, &#x27;2015-08-01T00:00:00.000000000&#x27;],\n",
       "       [&#x27;2016-07-11T00:00:00.000000000&#x27;, &#x27;2016-07-18T00:00:00.000000000&#x27;,\n",
       "        &#x27;2016-07-25T00:00:00.000000000&#x27;, &#x27;2016-08-01T00:00:00.000000000&#x27;],\n",
       "       [&#x27;2017-07-11T00:00:00.000000000&#x27;, &#x27;2017-07-18T00:00:00.000000000&#x27;,\n",
       "        &#x27;2017-07-25T00:00:00.000000000&#x27;, &#x27;2017-08-01T00:00:00.000000000&#x27;],\n",
       "       [&#x27;2018-07-11T00:00:00.000000000&#x27;, &#x27;2018-07-18T00:00:00.000000000&#x27;,\n",
       "        &#x27;2018-07-25T00:00:00.000000000&#x27;, &#x27;2018-08-01T00:00:00.000000000&#x27;],\n",
       "       [&#x27;2019-07-11T00:00:00.000000000&#x27;, &#x27;2019-07-18T00:00:00.000000000&#x27;,\n",
       "        &#x27;2019-07-25T00:00:00.000000000&#x27;, &#x27;2019-08-01T00:00:00.000000000&#x27;],\n",
       "       [&#x27;2020-07-11T00:00:00.000000000&#x27;, &#x27;2020-07-18T00:00:00.000000000&#x27;,\n",
       "        &#x27;2020-07-25T00:00:00.000000000&#x27;, &#x27;2020-08-01T00:00:00.000000000&#x27;],\n",
       "       [&#x27;2021-07-11T00:00:00.000000000&#x27;, &#x27;2021-07-18T00:00:00.000000000&#x27;,\n",
       "        &#x27;2021-07-25T00:00:00.000000000&#x27;, &#x27;2021-08-01T00:00:00.000000000&#x27;]],\n",
       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>is_target</span></div><div class='xr-var-dims'>(i_interval)</div><div class='xr-var-dtype'>bool</div><div class='xr-var-preview xr-preview'>False False False False</div><input id='attrs-20d1177b-41fb-40a6-8e7c-2c9a85b13fa6' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-20d1177b-41fb-40a6-8e7c-2c9a85b13fa6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ee81922a-f2e5-4735-8ad1-d4ecb4fa1fd8' class='xr-var-data-in' type='checkbox'><label for='data-ee81922a-f2e5-4735-8ad1-d4ecb4fa1fd8' 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([False, False, False, False])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>cluster_labels</span></div><div class='xr-var-dims'>(cluster_labels)</div><div class='xr-var-dtype'>int16</div><div class='xr-var-preview xr-preview'>-2 -1</div><input id='attrs-5a33f0ec-749e-48a8-bce4-2288d347ee15' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-5a33f0ec-749e-48a8-bce4-2288d347ee15' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9bc274cd-fe6e-4069-99b6-faed9dacb060' class='xr-var-data-in' type='checkbox'><label for='data-9bc274cd-fe6e-4069-99b6-faed9dacb060' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([-2, -1], dtype=int16)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>latitude</span></div><div class='xr-var-dims'>(cluster_labels)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>27.5 40.89</div><input id='attrs-72fe7f1d-3eb8-418b-9ec0-369d3e2148dc' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-72fe7f1d-3eb8-418b-9ec0-369d3e2148dc' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5cbdb7ed-8c77-4fbe-9b60-d00616102357' class='xr-var-data-in' type='checkbox'><label for='data-5cbdb7ed-8c77-4fbe-9b60-d00616102357' 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       , 40.89259641])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>longitude</span></div><div class='xr-var-dims'>(cluster_labels)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>235.0 206.2</div><input id='attrs-8185216a-3a5b-47e1-8868-2a76a03ad63a' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-8185216a-3a5b-47e1-8868-2a76a03ad63a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-51b080d4-6d07-4a97-ac7b-773b722df2bb' class='xr-var-data-in' type='checkbox'><label for='data-51b080d4-6d07-4a97-ac7b-773b722df2bb' 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([235.        , 206.20145391])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-26321d65-5f93-470b-9fbc-05544d357894' class='xr-section-summary-in' type='checkbox'  ><label for='section-26321d65-5f93-470b-9fbc-05544d357894' class='xr-section-summary' >Indexes: <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-index-name'><div>anchor_year</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-d2076795-02e5-4bae-8a65-e327141e07dc' class='xr-index-data-in' type='checkbox'/><label for='index-d2076795-02e5-4bae-8a65-e327141e07dc' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Int64Index([1959, 1960, 1961, 1962, 1963, 1964, 1965, 1966, 1967, 1968, 1969,\n",
       "            1970, 1971, 1972, 1973, 1974, 1975, 1976, 1977, 1978, 1979, 1980,\n",
       "            1981, 1982, 1983, 1984, 1985, 1986, 1987, 1988, 1989, 1990, 1991,\n",
       "            1992, 1993, 1994, 1995, 1996, 1997, 1998, 1999, 2000, 2001, 2002,\n",
       "            2003, 2004, 2005, 2006, 2007, 2008, 2009, 2010, 2011, 2012, 2013,\n",
       "            2014, 2015, 2016, 2017, 2018, 2019, 2020, 2021],\n",
       "           dtype=&#x27;int64&#x27;, name=&#x27;anchor_year&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>i_interval</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-c1b316ce-08cd-4adf-bfdd-e0dd2f82add8' class='xr-index-data-in' type='checkbox'/><label for='index-c1b316ce-08cd-4adf-bfdd-e0dd2f82add8' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Int64Index([-4, -3, -2, -1], dtype=&#x27;int64&#x27;, name=&#x27;i_interval&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>cluster_labels</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-5dbfaa5c-5672-4db1-bed8-350b8749fd64' class='xr-index-data-in' type='checkbox'/><label for='index-5dbfaa5c-5672-4db1-bed8-350b8749fd64' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Int64Index([-2, -1], dtype=&#x27;int64&#x27;, name=&#x27;cluster_labels&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-aa98aa90-3946-43ee-861d-ceca76f425d3' class='xr-section-summary-in' type='checkbox'  checked><label for='section-aa98aa90-3946-43ee-861d-ceca76f425d3' class='xr-section-summary' >Attributes: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>data :</span></dt><dd>Clustered data with Response Guided Dimensionality Reduction.</dd><dt><span>coordinates :</span></dt><dd>Latitudes and longitudes are geographical centers associated with clusters.</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.DataArray 'sst' (anchor_year: 63, i_interval: 4, cluster_labels: 2)>\n",
       "293.0 288.1 293.4 288.7 293.6 289.3 ... 292.7 289.8 292.8 290.7 293.2 291.4\n",
       "Coordinates:\n",
       "  * anchor_year     (anchor_year) int64 1959 1960 1961 1962 ... 2019 2020 2021\n",
       "  * i_interval      (i_interval) int64 -4 -3 -2 -1\n",
       "    left_bound      (anchor_year, i_interval) datetime64[ns] 1959-07-04 ... 2...\n",
       "    right_bound     (anchor_year, i_interval) datetime64[ns] 1959-07-11 ... 2...\n",
       "    is_target       (i_interval) bool False False False False\n",
       "  * cluster_labels  (cluster_labels) int16 -2 -1\n",
       "    latitude        (cluster_labels) float64 27.5 40.89\n",
       "    longitude       (cluster_labels) float64 235.0 206.2\n",
       "Attributes:\n",
       "    data:         Clustered data with Response Guided Dimensionality Reduction.\n",
       "    coordinates:  Latitudes and longitudes are geographical centers associate..."
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clustered_data = rgdr.transform(precursor_field)\n",
    "clustered_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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