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+{
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+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### First implementation of RGDR's dbscan-based clustering & dimensionality reduction\n",
+    "This notebook outlines the current status of the RGDR implementation."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "First we will load in some example data, and resample them using the `AdventCalendar`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import xarray as xr\n",
+    "import s2spy.time\n",
+    "import s2spy.rgdr\n",
+    "\n",
+    "file_path = '../tests/test_rgdr/test_data'\n",
+    "field = xr.open_dataset(f'{file_path}/sst_daily_1979-2018_5deg_Pacific_175_240E_25_50N.nc')\n",
+    "target = xr.open_dataset(f'{file_path}/tf5_nc5_dendo_80d77.nc')\n",
+    "\n",
+    "cal = s2spy.time.AdventCalendar((8, 31), freq = \"30d\")\n",
+    "field_resampled = cal.resample(field)\n",
+    "target_resampled = cal.resample(target)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The target timeseries comes from already pre-clustered land surface temperature data."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "target_timeseries = target_resampled.sel(cluster=3).ts"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The precursor field consists of sea surface temperature data, with latitude and longitude dimensions:"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "With `_rgdr.correlation` we can determine correlation coefficient and p-values"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "field_resampled['corr'], field_resampled['p_val'] = (\n",
+    "    s2spy.rgdr._rgdr.correlation(field_resampled.sst,\n",
+    "                                         target_timeseries.sel(i_interval=0),\n",
+    "                                         corr_dim='anchor_year')\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
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+       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
+       "Dimensions:      (anchor_year: 39, i_interval: 12, latitude: 5, longitude: 13)\n",
+       "Coordinates:\n",
+       "    index        (anchor_year, i_interval) int64 0 1 2 3 4 ... 464 465 466 467\n",
+       "    interval     (anchor_year, i_interval) object (1980-08-01, 1980-08-31] .....\n",
+       "  * latitude     (latitude) float64 47.5 42.5 37.5 32.5 27.5\n",
+       "  * longitude    (longitude) float64 177.5 182.5 187.5 ... 227.5 232.5 237.5\n",
+       "  * anchor_year  (anchor_year) int64 1980 1981 1982 1983 ... 2015 2016 2017 2018\n",
+       "  * i_interval   (i_interval) int64 0 1 2 3 4 5 6 7 8 9 10 11\n",
+       "    target       (i_interval) bool True False False False ... False False False\n",
+       "    tfreq        int64 5\n",
+       "    n_clusters   int64 6\n",
+       "    cluster      int64 3\n",
+       "Data variables:\n",
+       "    sst          (anchor_year, i_interval, latitude, longitude) float64 284.2...\n",
+       "    corr         (i_interval, latitude, longitude) float64 -0.08036 ... -0.00...\n",
+       "    p_val        (i_interval, latitude, longitude) float64 0.6267 ... 0.9864</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-dee617b2-7e78-4908-be01-9c57963132c0' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-dee617b2-7e78-4908-be01-9c57963132c0' 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'>anchor_year</span>: 39</li><li><span class='xr-has-index'>i_interval</span>: 12</li><li><span class='xr-has-index'>latitude</span>: 5</li><li><span class='xr-has-index'>longitude</span>: 13</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-a237754c-fff7-4139-9315-b8875c11ab81' class='xr-section-summary-in' type='checkbox'  checked><label for='section-a237754c-fff7-4139-9315-b8875c11ab81' class='xr-section-summary' >Coordinates: <span>(10)</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>index</span></div><div class='xr-var-dims'>(anchor_year, i_interval)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5 ... 463 464 465 466 467</div><input id='attrs-30b59aca-3e8c-48b2-b9e2-3f6982b6a65a' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-30b59aca-3e8c-48b2-b9e2-3f6982b6a65a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f30e0429-543d-483a-a12b-11c08f69cd34' class='xr-var-data-in' type='checkbox'><label for='data-f30e0429-543d-483a-a12b-11c08f69cd34' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[  0,   1,   2,   3,   4,   5,   6,   7,   8,   9,  10,  11],\n",
+       "       [ 12,  13,  14,  15,  16,  17,  18,  19,  20,  21,  22,  23],\n",
+       "       [ 24,  25,  26,  27,  28,  29,  30,  31,  32,  33,  34,  35],\n",
+       "       [ 36,  37,  38,  39,  40,  41,  42,  43,  44,  45,  46,  47],\n",
+       "       [ 48,  49,  50,  51,  52,  53,  54,  55,  56,  57,  58,  59],\n",
+       "       [ 60,  61,  62,  63,  64,  65,  66,  67,  68,  69,  70,  71],\n",
+       "       [ 72,  73,  74,  75,  76,  77,  78,  79,  80,  81,  82,  83],\n",
+       "       [ 84,  85,  86,  87,  88,  89,  90,  91,  92,  93,  94,  95],\n",
+       "       [ 96,  97,  98,  99, 100, 101, 102, 103, 104, 105, 106, 107],\n",
+       "       [108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119],\n",
+       "       [120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 131],\n",
+       "       [132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143],\n",
+       "       [144, 145, 146, 147, 148, 149, 150, 151, 152, 153, 154, 155],\n",
+       "       [156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167],\n",
+       "       [168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179],\n",
+       "       [180, 181, 182, 183, 184, 185, 186, 187, 188, 189, 190, 191],\n",
+       "       [192, 193, 194, 195, 196, 197, 198, 199, 200, 201, 202, 203],\n",
+       "       [204, 205, 206, 207, 208, 209, 210, 211, 212, 213, 214, 215],\n",
+       "       [216, 217, 218, 219, 220, 221, 222, 223, 224, 225, 226, 227],\n",
+       "       [228, 229, 230, 231, 232, 233, 234, 235, 236, 237, 238, 239],\n",
+       "       [240, 241, 242, 243, 244, 245, 246, 247, 248, 249, 250, 251],\n",
+       "       [252, 253, 254, 255, 256, 257, 258, 259, 260, 261, 262, 263],\n",
+       "       [264, 265, 266, 267, 268, 269, 270, 271, 272, 273, 274, 275],\n",
+       "       [276, 277, 278, 279, 280, 281, 282, 283, 284, 285, 286, 287],\n",
+       "       [288, 289, 290, 291, 292, 293, 294, 295, 296, 297, 298, 299],\n",
+       "       [300, 301, 302, 303, 304, 305, 306, 307, 308, 309, 310, 311],\n",
+       "       [312, 313, 314, 315, 316, 317, 318, 319, 320, 321, 322, 323],\n",
+       "       [324, 325, 326, 327, 328, 329, 330, 331, 332, 333, 334, 335],\n",
+       "       [336, 337, 338, 339, 340, 341, 342, 343, 344, 345, 346, 347],\n",
+       "       [348, 349, 350, 351, 352, 353, 354, 355, 356, 357, 358, 359],\n",
+       "       [360, 361, 362, 363, 364, 365, 366, 367, 368, 369, 370, 371],\n",
+       "       [372, 373, 374, 375, 376, 377, 378, 379, 380, 381, 382, 383],\n",
+       "       [384, 385, 386, 387, 388, 389, 390, 391, 392, 393, 394, 395],\n",
+       "       [396, 397, 398, 399, 400, 401, 402, 403, 404, 405, 406, 407],\n",
+       "       [408, 409, 410, 411, 412, 413, 414, 415, 416, 417, 418, 419],\n",
+       "       [420, 421, 422, 423, 424, 425, 426, 427, 428, 429, 430, 431],\n",
+       "       [432, 433, 434, 435, 436, 437, 438, 439, 440, 441, 442, 443],\n",
+       "       [444, 445, 446, 447, 448, 449, 450, 451, 452, 453, 454, 455],\n",
+       "       [456, 457, 458, 459, 460, 461, 462, 463, 464, 465, 466, 467]],\n",
+       "      dtype=int64)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>interval</span></div><div class='xr-var-dims'>(anchor_year, i_interval)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>(1980-08-01, 1980-08-31] ... (20...</div><input id='attrs-a4e7da74-8615-49bb-aa84-8239943da847' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-a4e7da74-8615-49bb-aa84-8239943da847' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a4b53e3f-f535-4160-9d75-cad441361d54' class='xr-var-data-in' type='checkbox'><label for='data-a4b53e3f-f535-4160-9d75-cad441361d54' 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([[Interval(&#x27;1980-08-01&#x27;, &#x27;1980-08-31&#x27;, closed=&#x27;right&#x27;),\n",
+       "        Interval(&#x27;1980-07-02&#x27;, &#x27;1980-08-01&#x27;, closed=&#x27;right&#x27;),\n",
+       "        Interval(&#x27;1980-06-02&#x27;, &#x27;1980-07-02&#x27;, closed=&#x27;right&#x27;),\n",
+       "        Interval(&#x27;1980-05-03&#x27;, &#x27;1980-06-02&#x27;, closed=&#x27;right&#x27;),\n",
+       "        Interval(&#x27;1980-04-03&#x27;, &#x27;1980-05-03&#x27;, closed=&#x27;right&#x27;),\n",
+       "        Interval(&#x27;1980-03-04&#x27;, &#x27;1980-04-03&#x27;, closed=&#x27;right&#x27;),\n",
+       "        Interval(&#x27;1980-02-03&#x27;, &#x27;1980-03-04&#x27;, closed=&#x27;right&#x27;),\n",
+       "        Interval(&#x27;1980-01-04&#x27;, &#x27;1980-02-03&#x27;, closed=&#x27;right&#x27;),\n",
+       "        Interval(&#x27;1979-12-05&#x27;, &#x27;1980-01-04&#x27;, closed=&#x27;right&#x27;),\n",
+       "        Interval(&#x27;1979-11-05&#x27;, &#x27;1979-12-05&#x27;, closed=&#x27;right&#x27;),\n",
+       "        Interval(&#x27;1979-10-06&#x27;, &#x27;1979-11-05&#x27;, closed=&#x27;right&#x27;),\n",
+       "        Interval(&#x27;1979-09-06&#x27;, &#x27;1979-10-06&#x27;, closed=&#x27;right&#x27;)],\n",
+       "       [Interval(&#x27;1981-08-01&#x27;, &#x27;1981-08-31&#x27;, closed=&#x27;right&#x27;),\n",
+       "        Interval(&#x27;1981-07-02&#x27;, &#x27;1981-08-01&#x27;, closed=&#x27;right&#x27;),\n",
+       "        Interval(&#x27;1981-06-02&#x27;, &#x27;1981-07-02&#x27;, closed=&#x27;right&#x27;),\n",
+       "        Interval(&#x27;1981-05-03&#x27;, &#x27;1981-06-02&#x27;, closed=&#x27;right&#x27;),\n",
+       "        Interval(&#x27;1981-04-03&#x27;, &#x27;1981-05-03&#x27;, closed=&#x27;right&#x27;),\n",
+       "        Interval(&#x27;1981-03-04&#x27;, &#x27;1981-04-03&#x27;, closed=&#x27;right&#x27;),\n",
+       "        Interval(&#x27;1981-02-02&#x27;, &#x27;1981-03-04&#x27;, closed=&#x27;right&#x27;),\n",
+       "        Interval(&#x27;1981-01-03&#x27;, &#x27;1981-02-02&#x27;, closed=&#x27;right&#x27;),\n",
+       "...\n",
+       "        Interval(&#x27;2017-03-04&#x27;, &#x27;2017-04-03&#x27;, closed=&#x27;right&#x27;),\n",
+       "        Interval(&#x27;2017-02-02&#x27;, &#x27;2017-03-04&#x27;, closed=&#x27;right&#x27;),\n",
+       "        Interval(&#x27;2017-01-03&#x27;, &#x27;2017-02-02&#x27;, closed=&#x27;right&#x27;),\n",
+       "        Interval(&#x27;2016-12-04&#x27;, &#x27;2017-01-03&#x27;, closed=&#x27;right&#x27;),\n",
+       "        Interval(&#x27;2016-11-04&#x27;, &#x27;2016-12-04&#x27;, closed=&#x27;right&#x27;),\n",
+       "        Interval(&#x27;2016-10-05&#x27;, &#x27;2016-11-04&#x27;, closed=&#x27;right&#x27;),\n",
+       "        Interval(&#x27;2016-09-05&#x27;, &#x27;2016-10-05&#x27;, closed=&#x27;right&#x27;)],\n",
+       "       [Interval(&#x27;2018-08-01&#x27;, &#x27;2018-08-31&#x27;, closed=&#x27;right&#x27;),\n",
+       "        Interval(&#x27;2018-07-02&#x27;, &#x27;2018-08-01&#x27;, closed=&#x27;right&#x27;),\n",
+       "        Interval(&#x27;2018-06-02&#x27;, &#x27;2018-07-02&#x27;, closed=&#x27;right&#x27;),\n",
+       "        Interval(&#x27;2018-05-03&#x27;, &#x27;2018-06-02&#x27;, closed=&#x27;right&#x27;),\n",
+       "        Interval(&#x27;2018-04-03&#x27;, &#x27;2018-05-03&#x27;, closed=&#x27;right&#x27;),\n",
+       "        Interval(&#x27;2018-03-04&#x27;, &#x27;2018-04-03&#x27;, closed=&#x27;right&#x27;),\n",
+       "        Interval(&#x27;2018-02-02&#x27;, &#x27;2018-03-04&#x27;, closed=&#x27;right&#x27;),\n",
+       "        Interval(&#x27;2018-01-03&#x27;, &#x27;2018-02-02&#x27;, closed=&#x27;right&#x27;),\n",
+       "        Interval(&#x27;2017-12-04&#x27;, &#x27;2018-01-03&#x27;, closed=&#x27;right&#x27;),\n",
+       "        Interval(&#x27;2017-11-04&#x27;, &#x27;2017-12-04&#x27;, closed=&#x27;right&#x27;),\n",
+       "        Interval(&#x27;2017-10-05&#x27;, &#x27;2017-11-04&#x27;, closed=&#x27;right&#x27;),\n",
+       "        Interval(&#x27;2017-09-05&#x27;, &#x27;2017-10-05&#x27;, closed=&#x27;right&#x27;)]],\n",
+       "      dtype=object)</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-873b955f-f4bc-4f07-ae9e-2a9814840074' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-873b955f-f4bc-4f07-ae9e-2a9814840074' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9f7db3f1-913a-4397-8af1-aa7456febe87' class='xr-var-data-in' type='checkbox'><label for='data-9f7db3f1-913a-4397-8af1-aa7456febe87' 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 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-6fd897bc-ff11-417b-91c2-9d64963b58cf' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-6fd897bc-ff11-417b-91c2-9d64963b58cf' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a777c786-9208-4577-b16e-27b77d5f7e30' class='xr-var-data-in' type='checkbox'><label for='data-a777c786-9208-4577-b16e-27b77d5f7e30' 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'>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'>1980 1981 1982 ... 2016 2017 2018</div><input id='attrs-f0473a26-f0c5-4a61-aad7-31fc0a9c49a9' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-f0473a26-f0c5-4a61-aad7-31fc0a9c49a9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-dcc0c102-0c3e-4731-b400-785960693b7a' class='xr-var-data-in' type='checkbox'><label for='data-dcc0c102-0c3e-4731-b400-785960693b7a' 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([1980, 1981, 1982, 1983, 1984, 1985, 1986, 1987, 1988, 1989, 1990, 1991,\n",
+       "       1992, 1993, 1994, 1995, 1996, 1997, 1998, 1999, 2000, 2001, 2002, 2003,\n",
+       "       2004, 2005, 2006, 2007, 2008, 2009, 2010, 2011, 2012, 2013, 2014, 2015,\n",
+       "       2016, 2017, 2018], dtype=int64)</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'>0 1 2 3 4 5 6 7 8 9 10 11</div><input id='attrs-aa70aa93-aa91-40bb-a156-7e55f2b3d34b' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-aa70aa93-aa91-40bb-a156-7e55f2b3d34b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-129cb019-ffe2-4723-b815-344756b9e18e' class='xr-var-data-in' type='checkbox'><label for='data-129cb019-ffe2-4723-b815-344756b9e18e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11], dtype=int64)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>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'>True False False ... False False</div><input id='attrs-34b9ebc4-9190-4671-86a7-8f2021ab60dc' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-34b9ebc4-9190-4671-86a7-8f2021ab60dc' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ca73b249-a747-46b0-95fc-bc08c0866784' class='xr-var-data-in' type='checkbox'><label for='data-ca73b249-a747-46b0-95fc-bc08c0866784' 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, False, False, False, False, False, False, False, False,\n",
+       "       False, False, False])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>tfreq</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>5</div><input id='attrs-b62b8364-8438-4c8d-9dbc-5d610b86f147' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-b62b8364-8438-4c8d-9dbc-5d610b86f147' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7ba5c45a-7e95-4430-8d4a-d40611592cc9' class='xr-var-data-in' type='checkbox'><label for='data-7ba5c45a-7e95-4430-8d4a-d40611592cc9' 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(5, dtype=int64)</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-ff6c006a-dd04-4db2-a6bb-b6e581b2aef1' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-ff6c006a-dd04-4db2-a6bb-b6e581b2aef1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1fe9327e-42c0-4b8c-a6f8-a13eefdae4a5' class='xr-var-data-in' type='checkbox'><label for='data-1fe9327e-42c0-4b8c-a6f8-a13eefdae4a5' 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, dtype=int64)</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-604178ea-841f-4f56-8838-b55f096acc4f' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-604178ea-841f-4f56-8838-b55f096acc4f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3179f1fb-e866-4728-8a8e-a5bd05f87cb0' class='xr-var-data-in' type='checkbox'><label for='data-3179f1fb-e866-4728-8a8e-a5bd05f87cb0' 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, dtype=int64)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-814d5889-ef3f-4713-a0b5-ada17adea946' class='xr-section-summary-in' type='checkbox'  checked><label for='section-814d5889-ef3f-4713-a0b5-ada17adea946' 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>sst</span></div><div class='xr-var-dims'>(anchor_year, i_interval, latitude, longitude)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>284.2 284.6 284.9 ... 295.1 294.8</div><input id='attrs-d880bd90-647d-4786-9a97-9dd3807b93b4' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-d880bd90-647d-4786-9a97-9dd3807b93b4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d74a1f60-c39a-47b2-a941-446132659355' class='xr-var-data-in' type='checkbox'><label for='data-d74a1f60-c39a-47b2-a941-446132659355' 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([[[[284.21141968, 284.60114644, 284.9212087 , ..., 288.05801595,\n",
+       "          288.76474915,          nan],\n",
+       "         [290.00809428, 290.47044169, 291.04628601, ..., 290.42871908,\n",
+       "          288.71849976,          nan],\n",
+       "         [296.10618388, 295.56154582, 295.98381551, ..., 291.36407776,\n",
+       "          289.38329061,          nan],\n",
+       "         [298.44655457, 298.28300883, 298.44641724, ..., 292.7240977 ,\n",
+       "          291.84608866, 291.30296122],\n",
+       "         [300.20553284, 299.90429688, 299.52007243, ..., 294.57577108,\n",
+       "          294.11313477, 294.78728739]],\n",
+       "\n",
+       "        [[281.69176839, 282.36223653, 282.63593445, ..., 286.76498108,\n",
+       "          288.20297343,          nan],\n",
+       "         [286.9986674 , 288.24412537, 288.49135132, ..., 288.82235819,\n",
+       "          287.4075887 ,          nan],\n",
+       "         [294.37841899, 294.34992981, 294.10915222, ..., 290.10051982,\n",
+       "          288.7737264 ,          nan],\n",
+       "         [298.23329773, 297.96685791, 297.75141805, ..., 292.21421509,\n",
+       "          291.07962646, 289.92159119],\n",
+       "         [300.49864095, 299.78132528, 299.26354167, ..., 294.05830282,\n",
+       "...\n",
+       "          287.76142985,          nan],\n",
+       "         [288.06269735, 288.01557922, 287.40404765, ..., 290.38401286,\n",
+       "          288.88281352,          nan],\n",
+       "         [294.47946065, 293.63181864, 292.97711385, ..., 291.98397319,\n",
+       "          290.1965861 ,          nan],\n",
+       "         [297.35154012, 297.04137065, 296.45055949, ..., 293.39662374,\n",
+       "          292.07106425, 291.64123535],\n",
+       "         [300.23115845, 299.32375183, 299.15152486, ..., 294.97173767,\n",
+       "          294.37171326, 294.31433614]],\n",
+       "\n",
+       "        [[284.65135396, 284.8360321 , 284.9401123 , ..., 289.74273275,\n",
+       "          290.42215881,          nan],\n",
+       "         [290.35401103, 290.24127604, 289.500236  , ..., 292.27547506,\n",
+       "          290.73305664,          nan],\n",
+       "         [297.37559408, 295.9420756 , 295.05696004, ..., 293.28416138,\n",
+       "          291.18582662,          nan],\n",
+       "         [299.43223368, 298.94459229, 298.43967794, ..., 294.13924052,\n",
+       "          292.84486186, 292.30426534],\n",
+       "         [301.34381409, 300.83740641, 300.74244181, ..., 295.86767782,\n",
+       "          295.12561137, 294.76013896]]]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>corr</span></div><div class='xr-var-dims'>(i_interval, latitude, longitude)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-0.08036 0.03252 ... -0.002827</div><input id='attrs-f0ab1132-082b-48c5-8a09-9af406987599' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-f0ab1132-082b-48c5-8a09-9af406987599' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-636a5efe-b35b-4150-9c5d-503283291cb4' class='xr-var-data-in' type='checkbox'><label for='data-636a5efe-b35b-4150-9c5d-503283291cb4' 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([[[-8.03624058e-02,  3.25224509e-02,  8.07339392e-02,\n",
+       "          1.18414120e-01,  5.79939989e-02, -1.86544044e-02,\n",
+       "         -1.10468066e-01, -1.69258657e-01, -2.66228726e-01,\n",
+       "         -3.68882103e-01, -3.31889589e-01, -1.56756692e-01,\n",
+       "                     nan],\n",
+       "        [-1.04219870e-02,  1.16738892e-01,  1.95767552e-01,\n",
+       "          2.32658108e-01,  1.94821630e-01,  2.06912464e-01,\n",
+       "          1.03087667e-01, -1.92354539e-01, -3.74545416e-01,\n",
+       "         -4.93641994e-01, -5.06832472e-01, -2.51102559e-01,\n",
+       "                     nan],\n",
+       "        [ 1.63693080e-01,  3.05446967e-01,  2.36867530e-01,\n",
+       "          2.62838551e-01,  2.84708361e-01,  2.74349808e-01,\n",
+       "          4.46360985e-02, -2.92314377e-01, -4.26047026e-01,\n",
+       "         -5.03824782e-01, -5.19827322e-01, -4.07955649e-01,\n",
+       "                     nan],\n",
+       "        [ 1.58590136e-01,  1.00311610e-01,  1.33650440e-01,\n",
+       "          8.66783949e-02,  3.90839751e-02, -1.24373026e-01,\n",
+       "         -3.15256560e-01, -4.26628616e-01, -4.97004805e-01,\n",
+       "         -4.64922838e-01, -4.02512644e-01, -3.08245349e-01,\n",
+       "         -1.20452613e-01],\n",
+       "...\n",
+       "        [ 1.43961676e-01,  1.93374631e-01,  9.09830696e-02,\n",
+       "         -7.30494387e-02, -9.95447408e-02, -9.61036046e-02,\n",
+       "         -1.46522330e-01, -1.42475557e-01, -1.97831853e-01,\n",
+       "         -1.55890466e-01,  1.13649920e-02,  2.31563402e-01,\n",
+       "                     nan],\n",
+       "        [ 1.44543141e-01,  1.14853725e-01,  6.24496921e-02,\n",
+       "         -3.57188510e-02, -1.10440045e-01, -1.64700783e-01,\n",
+       "         -1.72777799e-01, -1.62988047e-01, -1.98002395e-01,\n",
+       "         -1.53014575e-01, -6.47078025e-02,  1.20526628e-01,\n",
+       "                     nan],\n",
+       "        [-1.45627969e-01, -1.60053629e-01, -1.48254446e-01,\n",
+       "         -1.00256706e-01, -1.44683760e-01, -1.44035140e-01,\n",
+       "         -7.46974447e-02, -4.02904308e-02,  1.05294147e-02,\n",
+       "          3.38475618e-02,  1.20214457e-02,  1.55450056e-01,\n",
+       "          1.63989199e-01],\n",
+       "        [-3.41839396e-01, -2.87260932e-01, -2.62860434e-01,\n",
+       "         -1.74180935e-01, -1.28772500e-01, -8.60265834e-02,\n",
+       "         -1.57689591e-01, -6.46247148e-02, -3.33061267e-02,\n",
+       "         -4.29547827e-02,  1.89795431e-02,  4.33034647e-02,\n",
+       "         -2.82717708e-03]]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>p_val</span></div><div class='xr-var-dims'>(i_interval, latitude, longitude)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.6267 0.8442 ... 0.7935 0.9864</div><input id='attrs-41131140-a4ff-4516-bc7f-cdd2b3eace25' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-41131140-a4ff-4516-bc7f-cdd2b3eace25' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f216001c-bd05-4638-bc02-76221abad355' class='xr-var-data-in' type='checkbox'><label for='data-f216001c-bd05-4638-bc02-76221abad355' 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([[[6.26738494e-01, 8.44182476e-01, 6.25140283e-01, 4.72776938e-01,\n",
+       "         7.25825179e-01, 9.10255615e-01, 5.03188634e-01, 3.02970021e-01,\n",
+       "         1.01375348e-01, 2.08393255e-02, 3.90054119e-02, 3.40588049e-01,\n",
+       "                    nan],\n",
+       "        [9.49790918e-01, 4.79106601e-01, 2.32325312e-01, 1.54064255e-01,\n",
+       "         2.34634293e-01, 2.06281562e-01, 5.32296748e-01, 2.40729561e-01,\n",
+       "         1.88156222e-02, 1.40560318e-03, 9.92551496e-04, 1.23088688e-01,\n",
+       "                    nan],\n",
+       "        [3.19376287e-01, 5.86345744e-02, 1.46539656e-01, 1.05967028e-01,\n",
+       "         7.89663481e-02, 9.09946465e-02, 7.87300705e-01, 7.09527483e-02,\n",
+       "         6.84437956e-03, 1.07582579e-03, 6.94775111e-04, 9.93631173e-03,\n",
+       "                    nan],\n",
+       "        [3.34899007e-01, 5.43452932e-01, 4.17285471e-01, 5.99802882e-01,\n",
+       "         8.13255547e-01, 4.50624093e-01, 5.05956250e-02, 6.76060706e-03,\n",
+       "         1.28797896e-03, 2.86332520e-03, 1.10732223e-02, 5.62441564e-02,\n",
+       "         4.65134658e-01],\n",
+       "        [4.63326667e-01, 8.92492512e-01, 7.37533857e-01, 5.22082865e-01,\n",
+       "         1.44555795e-01, 9.55703210e-02, 3.36906566e-02, 2.32883638e-02,\n",
+       "         1.00145071e-02, 7.73391856e-03, 4.33575471e-02, 4.06015120e-02,\n",
+       "         1.32048912e-01]],\n",
+       "...\n",
+       "       [[9.02229551e-01, 6.76490238e-01, 6.93118219e-01, 3.92844755e-01,\n",
+       "         3.64171024e-01, 1.69761263e-01, 1.63274356e-01, 1.63418478e-01,\n",
+       "         2.64931701e-01, 6.57455160e-01, 7.17206857e-01, 5.51083878e-01,\n",
+       "                    nan],\n",
+       "        [3.81923922e-01, 2.38196456e-01, 5.81738273e-01, 6.58530887e-01,\n",
+       "         5.46554304e-01, 5.60573501e-01, 3.73424015e-01, 3.86908724e-01,\n",
+       "         2.27340210e-01, 3.43296403e-01, 9.45254445e-01, 1.56066426e-01,\n",
+       "                    nan],\n",
+       "        [3.79983878e-01, 4.86282184e-01, 7.05669489e-01, 8.29084860e-01,\n",
+       "         5.03297599e-01, 3.16365211e-01, 2.92878774e-01, 3.21493619e-01,\n",
+       "         2.26931661e-01, 3.52382441e-01, 6.95528853e-01, 4.64858420e-01,\n",
+       "                    nan],\n",
+       "        [3.76379935e-01, 3.30400293e-01, 3.67738589e-01, 5.43674693e-01,\n",
+       "         3.79515577e-01, 3.81678489e-01, 6.51311930e-01, 8.07597884e-01,\n",
+       "         9.49274039e-01, 8.37916571e-01, 9.42097468e-01, 3.44678440e-01,\n",
+       "         3.18489606e-01],\n",
+       "        [3.31747386e-02, 7.62019433e-02, 1.05936890e-01, 2.88916296e-01,\n",
+       "         4.34637481e-01, 6.02559292e-01, 3.37685955e-01, 6.95901069e-01,\n",
+       "         8.40475627e-01, 7.95137943e-01, 9.08697915e-01, 7.93510950e-01,\n",
+       "         9.86371765e-01]]])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-11f479fe-5155-42b1-b956-f61a2ef8c3ad' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-11f479fe-5155-42b1-b956-f61a2ef8c3ad' 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:      (anchor_year: 39, i_interval: 12, latitude: 5, longitude: 13)\n",
+       "Coordinates:\n",
+       "    index        (anchor_year, i_interval) int64 0 1 2 3 4 ... 464 465 466 467\n",
+       "    interval     (anchor_year, i_interval) object (1980-08-01, 1980-08-31] .....\n",
+       "  * latitude     (latitude) float64 47.5 42.5 37.5 32.5 27.5\n",
+       "  * longitude    (longitude) float64 177.5 182.5 187.5 ... 227.5 232.5 237.5\n",
+       "  * anchor_year  (anchor_year) int64 1980 1981 1982 1983 ... 2015 2016 2017 2018\n",
+       "  * i_interval   (i_interval) int64 0 1 2 3 4 5 6 7 8 9 10 11\n",
+       "    target       (i_interval) bool True False False False ... False False False\n",
+       "    tfreq        int64 5\n",
+       "    n_clusters   int64 6\n",
+       "    cluster      int64 3\n",
+       "Data variables:\n",
+       "    sst          (anchor_year, i_interval, latitude, longitude) float64 284.2...\n",
+       "    corr         (i_interval, latitude, longitude) float64 -0.08036 ... -0.00...\n",
+       "    p_val        (i_interval, latitude, longitude) float64 0.6267 ... 0.9864"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "field_resampled"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Plot `lag 1` correlation map:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.collections.QuadMesh at 0x24e0f102d70>"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 648x216 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "field_resampled.corr.sel(i_interval=1).plot(cmap='viridis', size=3, aspect=3)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Next we can cluster the remaining grid points with `DBSCAN`.\n",
+    "\n",
+    "Compare these results with the [prototype-notebook](https://github.com/AI4S2S/s2spy/blob/6da3233168bc7b083b084a14f7afe5618b8c82c9/notebooks/prototype_RGDR.ipynb)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.collections.QuadMesh at 0x24e0f2e9fc0>"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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2bNuzR3CJdYA7a7bnl30JIiIiIia6FiNTLrQ9a6zyMhYSRERERHSBoVVNRKcWAOvWbM8o+/omDSsjIiK6wWJJk6UL5gDvU2Ur4OF+toeA1ERERER0hYFFnXXx/CGwDVXbifnAocCyALaPBc6k6t55M1UXz/d3luPOJYiIiIjoAkNHNQ6292xx3MBHR32BHkgQERER0QVV74zJ1UogQUREREQ3uLPHGRNRgoiIiIgu6PRxxkSUICIiIqILBuFxhqQpwMq2H2nn/Ild2oiIiHHEVsNlvJJ0sqTnSloJuBa4XtIn20mbICIiIqILXNpENFrGsZml5mFX4CxgfeC97SQc16WKiIiYSCZiTQSwrKRlqYKIObafomri0VLaRERERHSFWDwx20R8C7gduBq4UNKLgLbaRCSIiIiI6IKJ2jvD9jHAMTW77pD0hnbSJoiIiIjoBsPiCRRESPpEi1O+1uo1EkRERER0gSfe44xVOn2BBBERERFd4raaIzYmaQfgv4CpwHG2jxh2fB/gyyydAvzrto8bzbVs/3sHWQXSOyMiIqIrbFiyZErDpRVJU4FvADsCM4E9Jc2sc+qPbG9allEFEMOu+xJJv5Z0bdneWNJn20mbICIiIqJLllgNlzZsAdxs+1bbTwKnALv0NMOVbwOHAE8B2J4H7NFOwgQRERERXbJkiRoubVgHuLNme37ZN9w7JM2TdJqkdbuQ7efYvnTYvkXtJOx5ECFpqqQrJZ1RtteXdImkmyX9SNJyvc5DREREr5nGA02VwaamSZpbs+w7isv8L7Ce7Y2Bc4ETupD1hZI2oAwwJemdwN3tJByLhpUHADcAzy3bRwJH2T5F0rHAB4FvjkE+IiIiesctx4lYaHtWk+MLgNqahRksbUBZXcK+v2bzOOBLI81mHR8FZgMvk7QAuA3Yq52EPa2JkDQDeAtVQZEkYFvgtHLKCVTDbEZEREx4XqKGSxsuAzYqNfbLUbVLmFN7gqS1azZ3pvqR3lmeqzYY2wFrAC+zvbXtO9pJ2+uaiKOBT7G0L+rzgYdsDz1rafS8JyIiYsLppIun7UWSPgb8kqqL5/G2r5N0GDDX9hzg45J2pmqz8ACwT6d5lvR84FBga8CSfgccNqzWo662gghJL6F65DDd9islbQzsbPuLTdK8FbjP9uWStmnnOsPS7wvsCzB1tdVGmjwiImJM2eAOB5uyfSZw5rB9n6tZP4SqJ0U3nQJcCLyjbO8F/AjYrlXCdks7mu4frwF2lnR7yeC2VANorCppKHh51vOeIbZn255le9bUlVdqM5sRERH9YzdexrG1bX/B9m1l+SIwvZ2E7QYRI+7+YfsQ2zNsr0cVcJxney/gfOCd5bS9gZ+3mYeIiIhxrHF7iDbbRPTLOZL2kDSlLO+ieqTSUrtBxKi7f9RxEPAJSTdTtZH4zihfJyIiYnxxk2WckfSopEeADwMnA0+W5RRKc4JW2m1YWa/7x3vazajtC4ALyvqtVKNyRUREDA4z3mscnsH22EzAVb74t5O0EjDF9qOdXjgiImLgTKCpwGtJWg3YCFhhaJ/tC1ulaxpENJprvBruAWy3nGs8IiJi0hiHjy1akfQhqoEhZwBXAVsBF1F1iGiqVZuIVcoyC9ifakyHdYD9gM1HneOIiIhB444Hm+qXA4BXA3fYfgOwGfBQOwmb1kQMzTUu6UJg86HHGJI+D/xi9PmNiIgYQBOwJgL4m+2/SULS8rZvlPTSdhK227ByOlWLzSFP0mYf0oiIiMlC47vGoZH5klYFfgacK+lBoKvDXp8IXCrp9LK9K92ZOSwiImIwjNOunK3YfntZ/byk84HnAWe3k7bd3hmHSzoLeG3Z9X7bV444pxEREQNLMIFqIiStXmf3NeXvylRzczTV7twZLwQWAqfX7rP953bSR0RETApL+p2BEbmcqu6kNvIZ2jbw4lYv0O7jjF+wtJJmRWB94CbgFe3mNCIiYqCZjseJkLQD1TxTU4HjbB8x7PjyVE0MXgXcD7zb9u2jyq69fpt5eoXt6+oda2vYa9t/Z3vjsmxENeLkRe1nNSIiYvBpSeOlZVppKvANYEdgJrCnpJnDTvsg8KDtDYGjgCO7W4K6vt/owKjmLLV9BbDlqLMTERERw20B3Gz7VttDc1jsMuycXVjaseE04I0aGgGydxq+frttImpHrpxCNdDUXR1mKiIiYqC06OI5TdLcmu3ZtmfXbK8D3FmzPZ9n/2B/+hzbiyQ9TDWZ5cJRZ7q1hn1O2m0TUTtJxyKqNhI/6SRHERERA6V1F8+FtmeNTWbGRrtBxPW2f1y7Q9LuwI8bnB8RETHptNP2oYkFwLo12zPKvnrnzJe0DNWYDveP9oLlUcgM23c2Oe3JRgfabRNxSJv7IiIiJq8lTZbWLgM2krS+pOWAPYA5w86ZA+xd1t8JnGd71ENclbRntjhnq0bHWs3iuSOwE7COpGNqDj2X6rFGREREAHK1jFZp4/Ax4JdUXTyPt32dpMOAubbnAN8Bvi/pZqrBoPboPOdcIenVti8bacJWjzPuAuYCO1MNSjHkUeBfRnqxiIiIgdbhiJW2z2RYzYDtz9Ws/w3YvaOLPNuWwF6S7gAepww2ZXvjVglbzeJ5NXC1pJNsp+YhIiKiiU5qIvrozaNN2Opxxqm23wVcKT371rQTpUREREwK7rhhZV/YvkPS1sBGtr8raQ2quTNaavU444Dy962dZDAiImJSmIA1EZIOBWYBLwW+CywL/AB4Tau0TXtn2L67rH7E9h21C/CRzrIdERExWDoZ9rqP3k7V9vFxANt38czxoRpqt4vnm+rs27HNtBEREZODmyzj15Olq6cBJK3UbsJWbSL2p6pxeLGkeTWHVgF+P4qMRkREDKYOu3j20amSvgWsKunDwAeA49pJ2KpNxMnAWcB/AgfX7H/U9gOjyWlERMTAGt+PLeqy/RVJbwIeoWoX8Tnb57aTtlUXz4eBh4E9ASStCawArCxpZdt/bpRW0grAhcDy5Tqn2T5U0veA15fXBdjH9lXtZDYiImK8EhOzJkLSkbYPAs6ts6+pttpESHqbpD8BtwG/AW6nqqFo5glgW9ubAJsCO0gaGjrzk7Y3LctV7eQhIiJiXPOEbVg56naP7Tas/CKwFfBH2+sDbwQubpbAlcfK5rJlmYAxWkRERJsmUMNKSftLugZ4qaR5NcttwLxW6aH9IOIp2/cDUyRNsX0+VZ/SVhmcKukq4D7gXNuXlEOHl4weJWn5NvMQERExrk2wmoiTgbdRTer1tprlVbbf084LtDsV+EOSVqZq43CSpPso/Umbsb0Y2FTSqsDpkl5JNfvnPcBywGzgIOCw4Wkl7QvsCzB1tdXazOZgee1W1/c7C33x24tn9jsLMUZuObrh5IADbYMDm1bkxkRletawUtLqwI+A9aiaFLzL9oN1zlsMXFM2/2x750avOdTuUdJngXtsPyFpG2BjSSfafqhVvtqtidgF+CvVpFtnA7dQRSttKRk5H9jB9t3lUccTVCNjbdEgzWzbs2zPmrpy211WIyIi+mZoJs96S4cOBn5teyPg1zyzx2Stv9a0OWwYQAzzE2CxpA2pftyvS1VL0VJbQYTtx20vtr3I9gm2jymPNxqStEapgUDSilQNN26UtHbZJ2BX4Np28hARETHe9fBxxi7ACWX9BKrvz25ZUibZ3A34b9ufBNZuJ2GrwaYepX5zkKFpQp/bJPnawAmSplIFK6faPkPSeWVyDwFXAfu1k9GIiIhxr3cNKKfXTEVxDzC9wXkrSJoLLAKOsP2zNl77KUl7Au9j6VOGZdvJVKtxItoaO7tB2nnAZnX2bzva14yIiBiv2nhsMa18wQ+ZbXv20+mlXwFr1Un3mdoN2643s3bxItsLJL0YOE/SNbZvaZH191P9oD/c9m2S1ge+3yIN0H7DyoiIiGileRCx0HbDno22t2t0TNK9kta2fXdpFnBfg9dYUP7eKukCqh/zTYMI29cDH6/Zvg04slmaIe02rIyIiIgWetgmYg6wd1nfG/j5s64trTY0bIKkaVRTebfs5ifpNkm3Dl/ayVRqIiIiIrrBPR0P4giqibI+CNwBvAtA0ixgP9sfAl4OfEvSEqpKgiNKLUMrtbUjKwC7A6u3k6kEEREREd3So4aVpUfkG+vsnwt8qKz/Afi7Ub52raMlXQ58rlXaBBERERFdMk5HpmxK0uY1m1Ooaibaig8SRERERHTJRJzFE/hqzfoiyoiY7SRMEBEREdENPRz2updsv2G0aRNEREREdIGYWDURkj7R7Ljtr7V6jQQRERERXaIlEyiKgGYDSrZVkAQRERER3dDbLp5dZ/vfASSdABwwNGunpNV4ZjuJhhJEREREdMuEqoh42sa1037bflDSs6atqCcjVkZERHRJD0es7KUppfYBAEmrky6eERERY6j1BFzj1VeBiyT9uGzvDhzeTsIEEREREV0gxn2NQ122Tyyziw7Nsr1bm8NlJ4iIiIjoGk/MqogSNLQVONRKEBEREdENE6x3RjekYWVERESXaHHjpaPXlXaXdJ2kJWXmzkbn7SDpJkk3Szq4s6u2liAiIiKiS+TGS4euBXYDLmx4bWkq8A1gR2AmsKekmR1fuYk8zoiIiOgG927ESts3AEhqdtoWwM22by3nngLswijaOrQrNRERERHd4iYLTJM0t2bZt8tXXwe4s2Z7ftnXM6mJiIiI6ALZrWoiFtpu1p7hV8BadQ59xvbPO81fLySIiIiI6JJO2j7Y3q7Dyy8A1q3ZnlH29UweZ0RERHRJn4e9vgzYSNL6kpYD9gDm9PKCCSIiIiK6wcBiN146IOntkuYDfw/8QtIvy/4XSDoTwPYi4GPAL4EbgFNtX9fRhVvo2eMMSStQdUVZvlznNNuHSlofOAV4PnA58F7bT/YqHxEREWOlV3Nn2D4dOL3O/ruAnWq2zwTO7E0unq2XNRFPANva3gTYFNhB0lbAkcBRtjcEHgQ+2MM8REREjBktccNlEPUsiHDlsbK5bFlMNcHHaWX/CcCuvcpDRETEmGnWvXMwY4je9s4oo2ddDmxINYrWLcBD5bkNNOnDWvrP7guw0lor8dqtejZWRtTx24t7OshZBAAbHHhxv7Mw6UzWe37bGFxDgDps+zDR9LRhpe3Ftjel6mayBfCyEaSdbXuW7VkrrLpCr7IYERHRNbIbLoNoTMaJsP2QpPOpWpWuKmmZUhvR8z6sERERY8KGAW370EjPaiIkrSFp1bK+IvAmqi4n5wPvLKftDYzLUbgiIiJGarI1rOxlTcTawAmlXcQUqv6qZ0i6HjhF0heBK4Hv9DAPERERY8NjNqjUuNGzIML2PGCzOvtvpWofERERMVgGtMahkcydERER0SWD2oCykQQRERER3TA07PUkkiAiIiKiC8TgduVsJBNwRUREdMuSJY2XDkjaXdJ1kpZImtXkvNslXSPpKklzO7poG1ITERER0Q0Getc741pgN+BbbZz7BtsLe5aTGgkiIiIiukQd1jg0YvsGAEk9ef3RyuOMiIiIbrBbPc6YJmluzbJvL3IBnCPp8h69/jOkJiIiIqJbmldELLTdrD3Dr4C16hz6jO12R3fe2vYCSWsC50q60faFbaYdsQQRERERXdLJ4wzb23V6fdsLyt/7JJ1ONbhjz4KIPM6IiIjoBlONWNlo6TFJK0laZWgd2J6qQWbPJIiIiIjoipZtIkZN0tslzaeaDfsXkn5Z9r9A0pnltOnA7yRdDVwK/ML22R1duIU8zoiIiOiWHg02Zft04PQ6++8CdirrtwKb9CQDDSSIiIiI6AYbFi/udy7GVIKIiIiIbjCweHLNBZ4gIiIiolsm2dwZCSIiIiK6wh03oJxoEkRERER0g0kQEREREaOUICIiIiJGbmwGlRpPEkRERER0g8Hp4hkREREjlnEiIiIiYtTSxTMiIiJGzpPucUbPJuCStK6k8yVdL+k6SQeU/Z+XtEDSVWXZqVd5iIiIGDM9nMVT0pcl3ShpnqTTJa3a4LwdJN0k6WZJB3d00Tb0chbPRcC/2p4JbAV8VNLMcuwo25uW5czGLxERETExmKphZaOlQ+cCr7S9MfBH4JDhJ0iaCnwD2BGYCexZ873bEz0LImzfbfuKsv4ocAOwTq+uFxER0Vc2eEnjpaOX9jm2F5XNi4EZdU7bArjZ9q22nwROAXbp6MIt9LIm4mmS1gM2Ay4puz5WqmSOl7TaWOQhIiKi13pYE1HrA8BZdfavA9xZsz2fHv94l3vcklTSysBvgMNt/1TSdGAhVc3PF4C1bX+gTrp9gX3L5kuBm3qa0camUeV3spmM5Z6MZYaUe7KZrOV+qe1VenkBSWdT3d9GVgD+VrM92/bsmvS/Ataqk+4ztn9ezvkMMAvYzcO+wCW9E9jB9ofK9nuBLW1/bDTlaUdPe2dIWhb4CXCS7Z8C2L635vi3gTPqpS03dna9Y2NJ0lzbs/qdj7E2Gcs9GcsMKXe/8zHWJnO5e30N2zt0mH67Zscl7QO8FXjj8ACiWACsW7M9o+zrmV72zhDwHeAG21+r2b92zWlvB67tVR4iIiIGgaQdgE8BO9v+S4PTLgM2krS+pOWAPYA5vcxXL2siXgO8F7hG0lVl36epWotuSvU443bgn3qYh4iIiEHwdWB54NzqNzoX295P0guA42zvZHuRpI8BvwSmAsfbvq6XmepZEGH7d4DqHJpoXTr7/kilTyZjuSdjmSHlnmxS7gnI9oYN9t8F7FSzfSZj+D3b84aVERERMZjGpItnREREDJ5JH0SUsSruk3Rtzb5NJV1chuWeK2mLsl+SjinDic6TtHn/cj56Dcq8iaSLJF0j6X8lPbfm2CGlzDdJenN/ct25JkOxry7pXEl/Kn9XK/sH5f1uVO7dy/YSSbOGpZnQ73mTMjccOniilxmalvsLpcxXSTqnPEcf+M94zfF/lWRJ08r2QJR7XLA9qRfgdcDmwLU1+84BdizrOwEX1KyfRdXWYyvgkn7nv4tlvgx4fVn/APCFsj4TuJqqQc/6wC3A1H6XYZTlXhvYvKyvQjV07EzgS8DBZf/BwJED9n43KvfLqcZguQCYVXP+hH/Pm5R5e2CZsv/Imvd6wpe5RbmfW3POx4Fjy/pAf8bL9rpUDQ3vAKYNUrnHwzLpayJsXwg8MHw3MPRL/HnAXWV9F+BEVy4GVh3WZXVCaFDmlwAXlvVzgXeU9V2AU2w/Yfs24GaqoVUnHDcein0X4IRy2gnArmV9UN7vuuW2fYPteoO4Tfj3vEmZGw0dPOHLDE3L/UjNaStR/R8HA/4ZL4ePouoaWdsAcCDKPR5M+iCigQOBL0u6E/gKSyc6GfMhRcfQdSwdY313lg5YMpBl1jOHYp9u++5y6B5gelkfuLLr2UPQ1zNQ5W5S5tqhgweqzPDscks6vPyfthfwuXLaQJdb0i7AAttXDztt4MrdLwki6tsf+Bfb6wL/QjVo1qD7APARSZdTVQc+2ef89Iyqodh/Ahw47Bcats0zf7EMjGblHlSNyqxq6OBFwEn9ylsv1Su37c+U/9NOAno2DHI/1Zab6v39NEsDpuiBBBH17Q38tKz/mKXVmmM+pOhYsX2j7e1tvwr4IdUzYRiwMqvOUOzAvUNVmeXvfWX/wJS9QbkbGYhyNyqzlg4dvFcJGmFAygxtvdcnsfRx5SCXewOq9i1XS7qdqmxXSFqLASp3vyWIqO8u4PVlfVvgT2V9DvC+0rJ3K+DhmmrwCU3SmuXvFOCzwLHl0BxgD0nLS1of2Ai4tD+57IxUfyh2qjLuXdb3Bn5es3/Cv99Nyt3IhH/PG5VZjYcOnvBlhqbl3qjmtF2AG8v6wH7GbV9je03b69lej+qRxea272FAyj0u9LtlZ78Xql/ddwNPUX3IPghsDVxO1Vr7EuBV5VwB36D6lX4NNS3aJ9LSoMwHULVo/iNwBGUgsnL+Z0qZb6L0WpmIS3lfDcwDrirLTsDzgV9TBYu/AlYfsPe7UbnfXt7/J4B7gV8OynvepMw3Uz0LH9p37KCUuUW5f0I1T9E84H+pGlsO/Gd82Dm3s7R3xkCUezwsGbEyIiIiRiWPMyIiImJUEkRERETEqCSIiIiIiFFJEBERERGjkiAiIiIiRiVBRMQYkfRYD15zZ0kHl/VdJc0cxWtcoGGzeEZEtCNBRMQEZnuO7SPK5q5UMzZGRIyJBBERY6yMkvdlSddKukbSu8v+bUqtwGmSbpR0UhmJD0k7lX2XSzpG0hll/z6Svi7pH4CdqSaOu0rSBrU1DJKmlaF/kbSipFMk3SDpdGDFmrxtL+kiSVdI+nGZiyAioq5l+p2BiEloN2BTYBNgGnCZpKFp2DcDXkE19PrvgddImgt8C3id7dsk/XD4C9r+g6Q5wBm2TwMo8Uc9+wN/sf1ySRsDV5Tzp1ENeb6d7cclHQR8AjisC2WOiAGUICJi7G0N/ND2YqrJv34DvBp4BLjU9nwASVcB6wGPAbfavq2k/yGwbwfXfx1wDIDteZLmlf1bUT0O+X0JQJYDLurgOhEx4BJERIwvT9SsL6azf6OLWPrIcoU2zhdwru09O7hmREwiaRMRMfZ+C7xb0lRJa1DVDDSbMfIm4MWS1ivb725w3qPAKjXbtwOvKuvvrNl/IfCPAJJeCWxc9l9M9fhkw3JsJUkvaadAETE5JYiIGHunU802eDVwHvApV9MT12X7r8BHgLMlXU4VLDxc59RTgE9KulLSBsBXgP0lXUnV9mLIN4GVJd1A1d7h8nKd/wP2AX5YHnFcBLysk4JGxGDLLJ4RE4CklW0/VnprfAP4k+2j+p2viJjcUhMRMTF8uDS0vA54HlVvjYiIvkpNRERERIxKaiIiIiJiVBJERERExKgkiIiIiIhRSRARERERo5IgIiIiIkYlQURERESMyv8HesaUO01MG/sAAAAASUVORK5CYII=",
+      "text/plain": [
+       "<Figure size 648x216 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import s2spy.rgdr\n",
+    "\n",
+    "clusters = s2spy.rgdr._rgdr.masked_spherical_dbscan(\n",
+    "    field_resampled.sel(i_interval=1),\n",
+    "    eps_km=600,\n",
+    "    alpha=0.05,\n",
+    "    min_area_km2=3000**2\n",
+    ")\n",
+    "\n",
+    "clusters.cluster_labels.plot(cmap='viridis', size=3, aspect=3)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The above plot showing clusters will probably be useful for users to understand what is going on,\n",
+    "and will be required to finetune the parameters `eps_km` and `min_area_km2`. \n",
+    "\n",
+    "(Note: negative labels denote clusters with a negative correlation, positive labels clusters\n",
+    "with a positive correlation, and `0` labelled data is not within a cluster.)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "`_rgdr.reduce_to_clusters` will both perform the clustering and return means per cluster:\n",
+    "\n",
+    "(and thus not providing direct feedback on what geographical region the clusters represent)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
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+       "<defs>\n",
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+       "</symbol>\n",
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+       "</svg>\n",
+       "<style>/* CSS stylesheet for displaying xarray objects in jupyterlab.\n",
+       " *\n",
+       " */\n",
+       "\n",
+       ":root {\n",
+       "  --xr-font-color0: var(--jp-content-font-color0, rgba(0, 0, 0, 1));\n",
+       "  --xr-font-color2: var(--jp-content-font-color2, rgba(0, 0, 0, 0.54));\n",
+       "  --xr-font-color3: var(--jp-content-font-color3, rgba(0, 0, 0, 0.38));\n",
+       "  --xr-border-color: var(--jp-border-color2, #e0e0e0);\n",
+       "  --xr-disabled-color: var(--jp-layout-color3, #bdbdbd);\n",
+       "  --xr-background-color: var(--jp-layout-color0, white);\n",
+       "  --xr-background-color-row-even: var(--jp-layout-color1, white);\n",
+       "  --xr-background-color-row-odd: var(--jp-layout-color2, #eeeeee);\n",
+       "}\n",
+       "\n",
+       "html[theme=dark],\n",
+       "body.vscode-dark {\n",
+       "  --xr-font-color0: rgba(255, 255, 255, 1);\n",
+       "  --xr-font-color2: rgba(255, 255, 255, 0.54);\n",
+       "  --xr-font-color3: rgba(255, 255, 255, 0.38);\n",
+       "  --xr-border-color: #1F1F1F;\n",
+       "  --xr-disabled-color: #515151;\n",
+       "  --xr-background-color: #111111;\n",
+       "  --xr-background-color-row-even: #111111;\n",
+       "  --xr-background-color-row-odd: #313131;\n",
+       "}\n",
+       "\n",
+       ".xr-wrap {\n",
+       "  display: block !important;\n",
+       "  min-width: 300px;\n",
+       "  max-width: 700px;\n",
+       "}\n",
+       "\n",
+       ".xr-text-repr-fallback {\n",
+       "  /* fallback to plain text repr when CSS is not injected (untrusted notebook) */\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-header {\n",
+       "  padding-top: 6px;\n",
+       "  padding-bottom: 6px;\n",
+       "  margin-bottom: 4px;\n",
+       "  border-bottom: solid 1px var(--xr-border-color);\n",
+       "}\n",
+       "\n",
+       ".xr-header > div,\n",
+       ".xr-header > ul {\n",
+       "  display: inline;\n",
+       "  margin-top: 0;\n",
+       "  margin-bottom: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-obj-type,\n",
+       ".xr-array-name {\n",
+       "  margin-left: 2px;\n",
+       "  margin-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-obj-type {\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-sections {\n",
+       "  padding-left: 0 !important;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 150px auto auto 1fr 20px 20px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input + label {\n",
+       "  color: var(--xr-disabled-color);\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input:enabled + label {\n",
+       "  cursor: pointer;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input:enabled + label:hover {\n",
+       "  color: var(--xr-font-color0);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary {\n",
+       "  grid-column: 1;\n",
+       "  color: var(--xr-font-color2);\n",
+       "  font-weight: 500;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary > span {\n",
+       "  display: inline-block;\n",
+       "  padding-left: 0.5em;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:disabled + label {\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in + label:before {\n",
+       "  display: inline-block;\n",
+       "  content: '►';\n",
+       "  font-size: 11px;\n",
+       "  width: 15px;\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:disabled + label:before {\n",
+       "  color: var(--xr-disabled-color);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked + label:before {\n",
+       "  content: '▼';\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked + label > span {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary,\n",
+       ".xr-section-inline-details {\n",
+       "  padding-top: 4px;\n",
+       "  padding-bottom: 4px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-inline-details {\n",
+       "  grid-column: 2 / -1;\n",
+       "}\n",
+       "\n",
+       ".xr-section-details {\n",
+       "  display: none;\n",
+       "  grid-column: 1 / -1;\n",
+       "  margin-bottom: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-array-wrap {\n",
+       "  grid-column: 1 / -1;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 20px auto;\n",
+       "}\n",
+       "\n",
+       ".xr-array-wrap > label {\n",
+       "  grid-column: 1;\n",
+       "  vertical-align: top;\n",
+       "}\n",
+       "\n",
+       ".xr-preview {\n",
+       "  color: var(--xr-font-color3);\n",
+       "}\n",
+       "\n",
+       ".xr-array-preview,\n",
+       ".xr-array-data {\n",
+       "  padding: 0 5px !important;\n",
+       "  grid-column: 2;\n",
+       "}\n",
+       "\n",
+       ".xr-array-data,\n",
+       ".xr-array-in:checked ~ .xr-array-preview {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-array-in:checked ~ .xr-array-data,\n",
+       ".xr-array-preview {\n",
+       "  display: inline-block;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list {\n",
+       "  display: inline-block !important;\n",
+       "  list-style: none;\n",
+       "  padding: 0 !important;\n",
+       "  margin: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list li {\n",
+       "  display: inline-block;\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list:before {\n",
+       "  content: '(';\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list:after {\n",
+       "  content: ')';\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list li:not(:last-child):after {\n",
+       "  content: ',';\n",
+       "  padding-right: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-has-index {\n",
+       "  font-weight: bold;\n",
+       "}\n",
+       "\n",
+       ".xr-var-list,\n",
+       ".xr-var-item {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-var-item > div,\n",
+       ".xr-var-item label,\n",
+       ".xr-var-item > .xr-var-name span {\n",
+       "  background-color: var(--xr-background-color-row-even);\n",
+       "  margin-bottom: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-var-item > .xr-var-name:hover span {\n",
+       "  padding-right: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-list > li:nth-child(odd) > div,\n",
+       ".xr-var-list > li:nth-child(odd) > label,\n",
+       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
+       "  background-color: var(--xr-background-color-row-odd);\n",
+       "}\n",
+       "\n",
+       ".xr-var-name {\n",
+       "  grid-column: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-var-dims {\n",
+       "  grid-column: 2;\n",
+       "}\n",
+       "\n",
+       ".xr-var-dtype {\n",
+       "  grid-column: 3;\n",
+       "  text-align: right;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-var-preview {\n",
+       "  grid-column: 4;\n",
+       "}\n",
+       "\n",
+       ".xr-var-name,\n",
+       ".xr-var-dims,\n",
+       ".xr-var-dtype,\n",
+       ".xr-preview,\n",
+       ".xr-attrs dt {\n",
+       "  white-space: nowrap;\n",
+       "  overflow: hidden;\n",
+       "  text-overflow: ellipsis;\n",
+       "  padding-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-name:hover,\n",
+       ".xr-var-dims:hover,\n",
+       ".xr-var-dtype:hover,\n",
+       ".xr-attrs dt:hover {\n",
+       "  overflow: visible;\n",
+       "  width: auto;\n",
+       "  z-index: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs,\n",
+       ".xr-var-data {\n",
+       "  display: none;\n",
+       "  background-color: var(--xr-background-color) !important;\n",
+       "  padding-bottom: 5px !important;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
+       ".xr-var-data-in:checked ~ .xr-var-data {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       ".xr-var-data > table {\n",
+       "  float: right;\n",
+       "}\n",
+       "\n",
+       ".xr-var-name span,\n",
+       ".xr-var-data,\n",
+       ".xr-attrs {\n",
+       "  padding-left: 25px !important;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs,\n",
+       ".xr-var-attrs,\n",
+       ".xr-var-data {\n",
+       "  grid-column: 1 / -1;\n",
+       "}\n",
+       "\n",
+       "dl.xr-attrs {\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 125px auto;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt,\n",
+       ".xr-attrs dd {\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "  float: left;\n",
+       "  padding-right: 10px;\n",
+       "  width: auto;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt {\n",
+       "  font-weight: normal;\n",
+       "  grid-column: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt:hover span {\n",
+       "  display: inline-block;\n",
+       "  background: var(--xr-background-color);\n",
+       "  padding-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dd {\n",
+       "  grid-column: 2;\n",
+       "  white-space: pre-wrap;\n",
+       "  word-break: break-all;\n",
+       "}\n",
+       "\n",
+       ".xr-icon-database,\n",
+       ".xr-icon-file-text2 {\n",
+       "  display: inline-block;\n",
+       "  vertical-align: middle;\n",
+       "  width: 1em;\n",
+       "  height: 1.5em !important;\n",
+       "  stroke-width: 0;\n",
+       "  stroke: currentColor;\n",
+       "  fill: currentColor;\n",
+       "}\n",
+       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
+       "Dimensions:         (anchor_year: 39, cluster_labels: 3)\n",
+       "Coordinates:\n",
+       "    index           (anchor_year) int64 1 13 25 37 49 61 ... 409 421 433 445 457\n",
+       "    interval        (anchor_year) object (1980-07-02, 1980-08-01] ... (2018-0...\n",
+       "  * anchor_year     (anchor_year) int64 1980 1981 1982 1983 ... 2016 2017 2018\n",
+       "    i_interval      int64 1\n",
+       "    target          bool False\n",
+       "    tfreq           int64 5\n",
+       "    n_clusters      int64 6\n",
+       "    cluster         int64 3\n",
+       "  * cluster_labels  (cluster_labels) float64 -1.0 0.0 1.0\n",
+       "Data variables:\n",
+       "    sst             (cluster_labels, anchor_year) float64 290.8 291.0 ... 298.2\n",
+       "    corr            (cluster_labels) float64 -0.3901 -0.06555 0.3602\n",
+       "    p_val           (cluster_labels) float64 0.01541 0.4659 0.02706\n",
+       "    area            (cluster_labels) float64 3.961e+06 3.869e+06 4.301e+06</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-ae92c4ae-6f32-4cec-9d8b-db7b96234170' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-ae92c4ae-6f32-4cec-9d8b-db7b96234170' 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'>anchor_year</span>: 39</li><li><span class='xr-has-index'>cluster_labels</span>: 3</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-524971b4-5eb2-4e83-9ad2-6434677205b1' class='xr-section-summary-in' type='checkbox'  checked><label for='section-524971b4-5eb2-4e83-9ad2-6434677205b1' class='xr-section-summary' >Coordinates: <span>(9)</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>index</span></div><div class='xr-var-dims'>(anchor_year)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>1 13 25 37 49 ... 421 433 445 457</div><input id='attrs-04543511-517e-4dc7-8b28-2d08fad8ee29' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-04543511-517e-4dc7-8b28-2d08fad8ee29' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8cccd47b-b9ec-445e-ab3f-aea149973c3b' class='xr-var-data-in' type='checkbox'><label for='data-8cccd47b-b9ec-445e-ab3f-aea149973c3b' 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,  13,  25,  37,  49,  61,  73,  85,  97, 109, 121, 133, 145,\n",
+       "       157, 169, 181, 193, 205, 217, 229, 241, 253, 265, 277, 289, 301,\n",
+       "       313, 325, 337, 349, 361, 373, 385, 397, 409, 421, 433, 445, 457],\n",
+       "      dtype=int64)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>interval</span></div><div class='xr-var-dims'>(anchor_year)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>(1980-07-02, 1980-08-01] ... (20...</div><input id='attrs-0dbee58a-24b6-45cb-8d26-1b2bcb46fb78' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-0dbee58a-24b6-45cb-8d26-1b2bcb46fb78' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1edad571-c652-48a0-be57-999c720969af' class='xr-var-data-in' type='checkbox'><label for='data-1edad571-c652-48a0-be57-999c720969af' 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([Interval(&#x27;1980-07-02&#x27;, &#x27;1980-08-01&#x27;, closed=&#x27;right&#x27;),\n",
+       "       Interval(&#x27;1981-07-02&#x27;, &#x27;1981-08-01&#x27;, closed=&#x27;right&#x27;),\n",
+       "       Interval(&#x27;1982-07-02&#x27;, &#x27;1982-08-01&#x27;, closed=&#x27;right&#x27;),\n",
+       "       Interval(&#x27;1983-07-02&#x27;, &#x27;1983-08-01&#x27;, closed=&#x27;right&#x27;),\n",
+       "       Interval(&#x27;1984-07-02&#x27;, &#x27;1984-08-01&#x27;, closed=&#x27;right&#x27;),\n",
+       "       Interval(&#x27;1985-07-02&#x27;, &#x27;1985-08-01&#x27;, closed=&#x27;right&#x27;),\n",
+       "       Interval(&#x27;1986-07-02&#x27;, &#x27;1986-08-01&#x27;, closed=&#x27;right&#x27;),\n",
+       "       Interval(&#x27;1987-07-02&#x27;, &#x27;1987-08-01&#x27;, closed=&#x27;right&#x27;),\n",
+       "       Interval(&#x27;1988-07-02&#x27;, &#x27;1988-08-01&#x27;, closed=&#x27;right&#x27;),\n",
+       "       Interval(&#x27;1989-07-02&#x27;, &#x27;1989-08-01&#x27;, closed=&#x27;right&#x27;),\n",
+       "       Interval(&#x27;1990-07-02&#x27;, &#x27;1990-08-01&#x27;, closed=&#x27;right&#x27;),\n",
+       "       Interval(&#x27;1991-07-02&#x27;, &#x27;1991-08-01&#x27;, closed=&#x27;right&#x27;),\n",
+       "       Interval(&#x27;1992-07-02&#x27;, &#x27;1992-08-01&#x27;, closed=&#x27;right&#x27;),\n",
+       "       Interval(&#x27;1993-07-02&#x27;, &#x27;1993-08-01&#x27;, closed=&#x27;right&#x27;),\n",
+       "       Interval(&#x27;1994-07-02&#x27;, &#x27;1994-08-01&#x27;, closed=&#x27;right&#x27;),\n",
+       "       Interval(&#x27;1995-07-02&#x27;, &#x27;1995-08-01&#x27;, closed=&#x27;right&#x27;),\n",
+       "       Interval(&#x27;1996-07-02&#x27;, &#x27;1996-08-01&#x27;, closed=&#x27;right&#x27;),\n",
+       "       Interval(&#x27;1997-07-02&#x27;, &#x27;1997-08-01&#x27;, closed=&#x27;right&#x27;),\n",
+       "       Interval(&#x27;1998-07-02&#x27;, &#x27;1998-08-01&#x27;, closed=&#x27;right&#x27;),\n",
+       "       Interval(&#x27;1999-07-02&#x27;, &#x27;1999-08-01&#x27;, closed=&#x27;right&#x27;),\n",
+       "       Interval(&#x27;2000-07-02&#x27;, &#x27;2000-08-01&#x27;, closed=&#x27;right&#x27;),\n",
+       "       Interval(&#x27;2001-07-02&#x27;, &#x27;2001-08-01&#x27;, closed=&#x27;right&#x27;),\n",
+       "       Interval(&#x27;2002-07-02&#x27;, &#x27;2002-08-01&#x27;, closed=&#x27;right&#x27;),\n",
+       "       Interval(&#x27;2003-07-02&#x27;, &#x27;2003-08-01&#x27;, closed=&#x27;right&#x27;),\n",
+       "       Interval(&#x27;2004-07-02&#x27;, &#x27;2004-08-01&#x27;, closed=&#x27;right&#x27;),\n",
+       "       Interval(&#x27;2005-07-02&#x27;, &#x27;2005-08-01&#x27;, closed=&#x27;right&#x27;),\n",
+       "       Interval(&#x27;2006-07-02&#x27;, &#x27;2006-08-01&#x27;, closed=&#x27;right&#x27;),\n",
+       "       Interval(&#x27;2007-07-02&#x27;, &#x27;2007-08-01&#x27;, closed=&#x27;right&#x27;),\n",
+       "       Interval(&#x27;2008-07-02&#x27;, &#x27;2008-08-01&#x27;, closed=&#x27;right&#x27;),\n",
+       "       Interval(&#x27;2009-07-02&#x27;, &#x27;2009-08-01&#x27;, closed=&#x27;right&#x27;),\n",
+       "       Interval(&#x27;2010-07-02&#x27;, &#x27;2010-08-01&#x27;, closed=&#x27;right&#x27;),\n",
+       "       Interval(&#x27;2011-07-02&#x27;, &#x27;2011-08-01&#x27;, closed=&#x27;right&#x27;),\n",
+       "       Interval(&#x27;2012-07-02&#x27;, &#x27;2012-08-01&#x27;, closed=&#x27;right&#x27;),\n",
+       "       Interval(&#x27;2013-07-02&#x27;, &#x27;2013-08-01&#x27;, closed=&#x27;right&#x27;),\n",
+       "       Interval(&#x27;2014-07-02&#x27;, &#x27;2014-08-01&#x27;, closed=&#x27;right&#x27;),\n",
+       "       Interval(&#x27;2015-07-02&#x27;, &#x27;2015-08-01&#x27;, closed=&#x27;right&#x27;),\n",
+       "       Interval(&#x27;2016-07-02&#x27;, &#x27;2016-08-01&#x27;, closed=&#x27;right&#x27;),\n",
+       "       Interval(&#x27;2017-07-02&#x27;, &#x27;2017-08-01&#x27;, closed=&#x27;right&#x27;),\n",
+       "       Interval(&#x27;2018-07-02&#x27;, &#x27;2018-08-01&#x27;, closed=&#x27;right&#x27;)], dtype=object)</pre></div></li><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'>1980 1981 1982 ... 2016 2017 2018</div><input id='attrs-941256e9-1f40-4293-aa76-722f7bdc7cf3' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-941256e9-1f40-4293-aa76-722f7bdc7cf3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-db56fcd1-5c9d-4afd-870e-92e355a16290' class='xr-var-data-in' type='checkbox'><label for='data-db56fcd1-5c9d-4afd-870e-92e355a16290' 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([1980, 1981, 1982, 1983, 1984, 1985, 1986, 1987, 1988, 1989, 1990, 1991,\n",
+       "       1992, 1993, 1994, 1995, 1996, 1997, 1998, 1999, 2000, 2001, 2002, 2003,\n",
+       "       2004, 2005, 2006, 2007, 2008, 2009, 2010, 2011, 2012, 2013, 2014, 2015,\n",
+       "       2016, 2017, 2018], dtype=int64)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>i_interval</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>1</div><input id='attrs-d635ff64-f2bb-4edf-a703-b91d9e0cd1ef' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-d635ff64-f2bb-4edf-a703-b91d9e0cd1ef' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e061025e-3a91-47a2-a6b9-08571535c70d' class='xr-var-data-in' type='checkbox'><label for='data-e061025e-3a91-47a2-a6b9-08571535c70d' 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, dtype=int64)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>target</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>bool</div><div class='xr-var-preview xr-preview'>False</div><input id='attrs-68e7fcd1-438e-463e-ae1b-7d11b574b88f' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-68e7fcd1-438e-463e-ae1b-7d11b574b88f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-86e13632-de2c-4170-946f-aaa2616c243f' class='xr-var-data-in' type='checkbox'><label for='data-86e13632-de2c-4170-946f-aaa2616c243f' 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>tfreq</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>5</div><input id='attrs-3627a6de-0a91-4c5a-9bc2-6690e4dbd248' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-3627a6de-0a91-4c5a-9bc2-6690e4dbd248' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4a88802a-02bb-4c2a-8e71-adac6345c7b8' class='xr-var-data-in' type='checkbox'><label for='data-4a88802a-02bb-4c2a-8e71-adac6345c7b8' 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(5, dtype=int64)</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-0c319718-fa11-472f-9926-e28f7ddbfd74' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-0c319718-fa11-472f-9926-e28f7ddbfd74' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4bf4d0ef-3140-4065-95a8-05001e577665' class='xr-var-data-in' type='checkbox'><label for='data-4bf4d0ef-3140-4065-95a8-05001e577665' 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, dtype=int64)</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-b55e4dc1-2a7f-41c9-b6c7-297bcc080ee4' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-b55e4dc1-2a7f-41c9-b6c7-297bcc080ee4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-fe4baa7a-b69c-4223-bcd8-878e9048607b' class='xr-var-data-in' type='checkbox'><label for='data-fe4baa7a-b69c-4223-bcd8-878e9048607b' 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, dtype=int64)</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'>float64</div><div class='xr-var-preview xr-preview'>-1.0 0.0 1.0</div><input id='attrs-0913c760-6fc5-492a-aff2-b4c13c357f23' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-0913c760-6fc5-492a-aff2-b4c13c357f23' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5c9eefab-8ea5-4865-afe8-2f4602650b21' class='xr-var-data-in' type='checkbox'><label for='data-5c9eefab-8ea5-4865-afe8-2f4602650b21' 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.,  0.,  1.])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-65ebcf69-18e1-4012-9d6c-f062c8e5c6cd' class='xr-section-summary-in' type='checkbox'  checked><label for='section-65ebcf69-18e1-4012-9d6c-f062c8e5c6cd' class='xr-section-summary' >Data variables: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>sst</span></div><div class='xr-var-dims'>(cluster_labels, anchor_year)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>290.8 291.0 290.7 ... 299.2 298.2</div><input id='attrs-e98ba505-48cc-461b-8602-00b01b93a204' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-e98ba505-48cc-461b-8602-00b01b93a204' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-defaa9e4-7469-4564-9829-6ce26d206796' class='xr-var-data-in' type='checkbox'><label for='data-defaa9e4-7469-4564-9829-6ce26d206796' 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([[290.79588914, 290.970545  , 290.71731703, 290.0762239 ,\n",
+       "        291.08960917, 291.31511491, 291.11538436, 290.26277142,\n",
+       "        290.80443321, 290.99960169, 291.53446464, 291.36075119,\n",
+       "        291.85483292, 291.09343404, 291.31408735, 291.41374784,\n",
+       "        291.86010891, 292.04141337, 291.10871867, 290.01689114,\n",
+       "        291.222642  , 290.9579155 , 291.22194658, 291.54149051,\n",
+       "        292.30504935, 291.41269426, 291.24292642, 291.02590818,\n",
+       "        291.44962923, 291.91882282, 290.70922506, 290.48853941,\n",
+       "        290.34711093, 291.99974208, 292.3259726 , 293.32741257,\n",
+       "        291.91085874, 291.45966391, 291.37066195],\n",
+       "       [291.41316602, 290.99213881, 290.7072011 , 290.28445664,\n",
+       "        291.24062074, 291.15653517, 290.90280998, 290.26394997,\n",
+       "        291.24222615, 291.51096688, 291.63083812, 292.04367136,\n",
+       "        291.17972744, 291.09824891, 291.49867568, 291.09047242,\n",
+       "        291.78362741, 291.61656609, 291.40476286, 291.22261622,\n",
+       "        291.74089263, 292.15751269, 291.59638891, 291.53674801,\n",
+       "        292.04091404, 292.05970401, 291.54779359, 291.4457052 ,\n",
+       "        291.83593796, 291.9013184 , 291.58505835, 292.59423736,\n",
+       "        291.89844604, 292.16743467, 292.15498931, 292.00892894,\n",
+       "        291.98830915, 291.94098392, 291.62096043],\n",
+       "       [298.94548042, 298.92119198, 297.71694295, 298.98291705,\n",
+       "        299.35080249, 298.06875363, 298.51701671, 298.495829  ,\n",
+       "        299.61375876, 298.35675492, 298.46956715, 299.34460285,\n",
+       "        297.57970076, 298.48196856, 297.85457083, 298.60225261,\n",
+       "        298.95597899, 298.63209096, 298.75689828, 299.10338566,\n",
+       "        298.80111564, 299.39540936, 299.49514094, 298.18805869,\n",
+       "        298.88763028, 299.2529288 , 299.0168395 , 298.84020348,\n",
+       "        298.48441327, 299.30649003, 299.69018872, 299.65241405,\n",
+       "        299.320106  , 299.04325189, 299.48610574, 298.91044985,\n",
+       "        298.89195415, 299.19568083, 298.21053747]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>corr</span></div><div class='xr-var-dims'>(cluster_labels)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-0.3901 -0.06555 0.3602</div><input id='attrs-b42fc85b-6e7e-407d-a73b-50dd08c9c52e' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-b42fc85b-6e7e-407d-a73b-50dd08c9c52e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e201c574-1157-4c68-81a3-9103ef44a075' class='xr-var-data-in' type='checkbox'><label for='data-e201c574-1157-4c68-81a3-9103ef44a075' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([-0.39014765, -0.06554537,  0.36023265])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>p_val</span></div><div class='xr-var-dims'>(cluster_labels)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.01541 0.4659 0.02706</div><input id='attrs-f26d1430-6041-47f6-a028-74b44b4fb0a7' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-f26d1430-6041-47f6-a028-74b44b4fb0a7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a8111e5c-9ab8-4489-894d-fb5430c069d7' class='xr-var-data-in' type='checkbox'><label for='data-a8111e5c-9ab8-4489-894d-fb5430c069d7' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([0.01541483, 0.46594129, 0.02705672])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>area</span></div><div class='xr-var-dims'>(cluster_labels)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>3.961e+06 3.869e+06 4.301e+06</div><input id='attrs-e9e34623-7bb6-4048-9708-f3c9f5a93551' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-e9e34623-7bb6-4048-9708-f3c9f5a93551' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b526fcf8-dd05-44ab-9e71-6785659193f6' class='xr-var-data-in' type='checkbox'><label for='data-b526fcf8-dd05-44ab-9e71-6785659193f6' 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([3961386.47832005, 3869371.43162919, 4301307.66892284])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-3a3b6fc7-2d8f-41ed-9bd9-92532f7d3360' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-3a3b6fc7-2d8f-41ed-9bd9-92532f7d3360' 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:         (anchor_year: 39, cluster_labels: 3)\n",
+       "Coordinates:\n",
+       "    index           (anchor_year) int64 1 13 25 37 49 61 ... 409 421 433 445 457\n",
+       "    interval        (anchor_year) object (1980-07-02, 1980-08-01] ... (2018-0...\n",
+       "  * anchor_year     (anchor_year) int64 1980 1981 1982 1983 ... 2016 2017 2018\n",
+       "    i_interval      int64 1\n",
+       "    target          bool False\n",
+       "    tfreq           int64 5\n",
+       "    n_clusters      int64 6\n",
+       "    cluster         int64 3\n",
+       "  * cluster_labels  (cluster_labels) float64 -1.0 0.0 1.0\n",
+       "Data variables:\n",
+       "    sst             (cluster_labels, anchor_year) float64 290.8 291.0 ... 298.2\n",
+       "    corr            (cluster_labels) float64 -0.3901 -0.06555 0.3602\n",
+       "    p_val           (cluster_labels) float64 0.01541 0.4659 0.02706\n",
+       "    area            (cluster_labels) float64 3.961e+06 3.869e+06 4.301e+06"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "clustered_data = s2spy.rgdr._rgdr.reduce_to_clusters(\n",
+    "    field_resampled.sel(i_interval=1),\n",
+    "    eps_km=600, \n",
+    "    alpha=0.04\n",
+    ")\n",
+    "clustered_data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x24e0f3dc910>]"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "clustered_data['sst'] -= clustered_data['sst'].mean(dim='anchor_year')\n",
+    "\n",
+    "clustered_data['sst'].sel(cluster_labels=1).plot.line(x='anchor_year')\n",
+    "clustered_data['sst'].sel(cluster_labels=-1).plot.line(x='anchor_year')\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Selecting different intervals for the precursor data (i.e. lags) will have a significant impact on the clusters that will be found:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[array([-1.,  0.]),\n",
+       " array([-2., -1.,  0.,  1.]),\n",
+       " array([-1.,  0.,  1.]),\n",
+       " array([-1.,  0.,  1.]),\n",
+       " array([-2., -1.,  0.]),\n",
+       " array([-2., -1.,  0.]),\n",
+       " array([-3., -2., -1.,  0.]),\n",
+       " array([-1.,  0.]),\n",
+       " array([-1.,  0.]),\n",
+       " array([-1.,  0.]),\n",
+       " array([0.]),\n",
+       " array([-1.,  0.])]"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "n_intervals = field_resampled.i_interval.size\n",
+    "cdata = [0]*n_intervals\n",
+    "for interval in range(n_intervals):\n",
+    "    cdata[interval] = s2spy.rgdr._rgdr.reduce_to_clusters(\n",
+    "        field_resampled.sel(i_interval=interval), eps_km=600, alpha=0.05)\n",
+    "\n",
+    "[c.cluster_labels.values for c in cdata]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3.10.5 ('ai4s2s')",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.5"
+  },
+  "orig_nbformat": 4,
+  "vscode": {
+   "interpreter": {
+    "hash": "a87ac8a6238ac8fffcd070769ea0ffc634a0ba702c52b706c3554dda7777b3fd"
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/s2spy/rgdr/__init__.py b/s2spy/rgdr/__init__.py
index e8e3421..76e2a89 100644
--- a/s2spy/rgdr/__init__.py
+++ b/s2spy/rgdr/__init__.py
@@ -1,4 +1,3 @@
 """Response Guided Dimensionality Reduction."""
-from . import _map_analysis
-from . import _map_regions
+from . import _rgdr
 from ._rgdr import RGDR
diff --git a/s2spy/rgdr/_map_analysis.py b/s2spy/rgdr/_map_analysis.py
deleted file mode 100644
index f20194b..0000000
--- a/s2spy/rgdr/_map_analysis.py
+++ /dev/null
@@ -1,88 +0,0 @@
-"""Spatial-temporal data analysis utils.
-
-A toolbox for spatial-temporal data analysis, including regression,
-correlation, auto-correlation and relevant utilities functions.
-"""
-import numpy as np
-import xarray as xr
-from scipy.stats import pearsonr as _pearsonr
-
-
-def _pearsonr_nan(x: np.ndarray, y: np.ndarray):
-    """NaN friendly implementation of scipy.stats.pearsonr. Calculates the correlation
-    coefficient between two arrays, as well as the p-value of this correlation.
-
-    Args:
-        x: 1-D array
-        y: 1-D array
-    Returns:
-        r_coefficient
-        p_value
-
-    """
-    if np.any(np.isnan(x)) or np.any(np.isnan(y)):
-        return np.nan, np.nan
-    return _pearsonr(x, y)
-
-
-def correlation(field: xr.DataArray, target: xr.DataArray, corr_dim: str = "time"):
-    """Calculate correlation maps.
-
-    Args:
-        field: Spatial data with a dimension named `corr_dim`, over which each
-            location should have the Pearson correlation coefficient calculated with the
-            target data.
-        target: Data which has to be correlated with the spatial data. Requires a
-            dimension named `corr_dim`.
-
-    Returns:
-        r_coefficient: DataArray filled with the correlation coefficient for each
-            non-`corr_dim` coordinate.
-        p_value: DataArray filled with the two-tailed p-values for each computed
-            correlation coefficient.
-    """
-    assert (
-        corr_dim in target.dims
-    ), f"input target does not have contain the '{corr_dim}' dimension"
-    assert (
-        corr_dim in field.dims
-    ), f"input field does not have contain the '{corr_dim}' dimension"
-    assert np.all(
-        [dim in field.dims for dim in target.dims]
-    ), "Field and target dims do not match"
-
-    return xr.apply_ufunc(
-        _pearsonr_nan,
-        field,
-        target,
-        input_core_dims=[[corr_dim], [corr_dim]],
-        vectorize=True,
-        output_core_dims=[[], []],
-    )
-
-
-def partial_correlation(field, target, z):
-    """Calculate partial correlation maps."""
-    raise NotImplementedError
-
-
-def regression(field, target):
-    """Regression analysis on entire maps.
-
-    Methods include Linear, Ridge, Lasso.
-    """
-    raise NotImplementedError
-
-
-def save_map():
-    """Save calculated coefficients.
-
-    Store calculated coefficients and significance values, and
-    save them as netcdf files.
-    """
-    raise NotImplementedError
-
-
-def load_map():
-    """Load coefficients from saved netcdf maps."""
-    raise NotImplementedError
diff --git a/s2spy/rgdr/_map_regions.py b/s2spy/rgdr/_map_regions.py
deleted file mode 100644
index 791b101..0000000
--- a/s2spy/rgdr/_map_regions.py
+++ /dev/null
@@ -1,24 +0,0 @@
-"""Regions clustering utils.
-
-A module for clustering regions based on the given correlation
-between spatial fields and target timeseries.
-"""
-
-def spatial_mean():
-    """Calculate 1-d timeseries for each precursor region. Precursor regions 
-    are integer label masks within the np.ndarray labels.
-    """
-    raise NotImplementedError
-
-def cluster_dbscan():
-    """Perform DBSCAN clustering from vector array or distance matrix.
-
-    Density-Based Spatial Clustering of Applications with Noise (DBSCAN).
-    Clusters gridcells together which are of the same sign and in proximity to
-    each other using DBSCAN.
-
-    The input data will be processed in this function to ensure that the distance
-    is free of the impact from spherical curvature. The actual Euclidean distance
-    will be obtained and passed to the DBSCAN clustering function.
-    """
-    raise NotImplementedError
diff --git a/s2spy/rgdr/_rgdr.py b/s2spy/rgdr/_rgdr.py
index 0aa8121..ff89f25 100644
--- a/s2spy/rgdr/_rgdr.py
+++ b/s2spy/rgdr/_rgdr.py
@@ -1,23 +1,22 @@
 """Response Guided Dimensionality Reduction."""
+from typing import Tuple
+import numpy as np
+import xarray as xr
+from scipy.stats import pearsonr as _pearsonr
+from sklearn.cluster import DBSCAN
 
 
 class RGDR:
     """Response Guided Dimensionality Reduction."""
+
     def __init__(self, timeseries, lag_shift: int = 0):
         """Instantiate an RGDR operator."""
         self.timeseries = timeseries
         self.lag_shift = lag_shift
 
-    def lag_shifting(self):
-        """loop through data with lag shift upto the given `lag_shift` value."""
-        # To do: loop through lags
-        # To do: call `map_analysis` and `map_regions` and manage
-        #  inputs and outputs.
-        raise NotImplementedError
-
     def _map_analysis(self):
         """Perform map analysis.
-        
+
         Use chosen method from `map_analysis` and perform map analysis.
         """
         raise NotImplementedError
@@ -33,3 +32,264 @@ def _clustering_regions(self):
     def fit(self, data):
         """Perform RGDR calculations with given data."""
         raise NotImplementedError
+
+    def transform(self, data):
+        """Apply RGDR on data, based on the fit model"""
+        raise NotImplementedError
+
+
+radius_earth_km = 6371
+surface_area_earth_km2 = 5.1e8
+
+
+def spherical_area(latitude: float, dlat: float, dlon: float = None) -> float:
+    """Approximate the area of a square grid cell on a spherical (!) earth.
+    Returns the area in square kilometers of earth surface.
+
+    Args:
+        latitude (float): Latitude at the center of the grid cell (deg)
+        dlat (float): Latitude grid resolution (deg)
+        dlon (float): Longitude grid resolution (deg), optional in case of a square grid.
+
+    Returns:
+        float: Area of the grid cell (km^2)
+    """
+    if dlon is None:
+        dlon = dlat
+    dlon = np.radians(dlon)
+    dlat = np.radians(dlat)
+
+    lat = np.radians(latitude)
+    h = np.sin(lat + dlat / 2) - np.sin(lat - dlat / 2)
+    spherical_area = h * dlon / np.pi * 4
+
+    return spherical_area * surface_area_earth_km2
+
+
+def cluster_area(ds: xr.Dataset, cluster_label: float) -> float:
+    """Determines the total area of a cluster. Requires the input dataset to have the
+    variables `area` and `cluster_labels`.
+
+    Args:
+        ds (xr.Dataset): Dataset containing the variables `area` and `cluster_labels`.
+        cluster_label (float): The label for which the area should be calculated.
+
+    Returns:
+        float: Area of the cluster `cluster_label`.
+    """
+    return (
+        ds["area"].where(ds["cluster_labels"] == cluster_label).sum(skipna=True).values
+    )
+
+
+def remove_small_area_clusters(ds: xr.Dataset, min_area_km2: float) -> xr.Dataset:
+    """Removes the clusters where the area is under the input threshold.
+
+    Args:
+        ds (xr.Dataset): Dataset containing `cluster_labels` and `area`.
+        min_area_km2 (float): The minimum allowed area of each cluster
+
+    Returns:
+        xr.Dataset: The input dataset with the labels of the clusters set to 0 when the
+            area of the cluster is under the `min_area_km2` threshold.
+    """
+    clusters = np.unique(ds["cluster_labels"])
+    areas = [cluster_area(ds, c) for c in clusters]
+    valid_clusters = np.array([c for c, a in zip(clusters, areas) if a > min_area_km2])
+
+    ds["cluster_labels"] = ds["cluster_labels"].where(
+        np.isin(ds["cluster_labels"], valid_clusters), 0
+    )
+
+    return ds
+
+
+def weighted_groupby(ds: xr.Dataset, groupby: str, weight: str, method: str = "mean") -> xr.Dataset:
+    """Apply a weighted reduction after a groupby call. xarray does not currently support
+    combining `weighted` and `groupby`. An open PR adds supports for this functionality
+    (https://github.com/pydata/xarray/pull/5480), but this branch was never merged.
+
+    Args:
+        ds (xr.Dataset): Dataset containing the coordinates or variables specified in
+        the `groupby` and `weight` kwargs.
+        groupby (str): Coordinate which should be used to make the groups.
+        weight (str): Variable in the Dataset containing the weights that should be used.
+        method (str): Method that should be used to reduce the dataset, by default
+            'mean'. Supports any of xarray's builtin methods, e.g. median, min, max.
+
+    Returns:
+        xr.Dataset: Dataset reduced using the `groupby` coordinate, using weights =
+            based on `ds[weight]`.
+    """
+    groups = ds.groupby(groupby)
+
+    # find stacked dim name:
+    group_dims = list(groups)[0][1].dims  # Get ds of first group
+    stacked_dims = [dim for dim in group_dims.keys() if "stacked_" in dim]
+
+    reduced_data = [
+        getattr(g.weighted(g[weight]), method)(dim=stacked_dims) for _, g in groups
+    ]
+    return xr.concat(reduced_data, dim=groupby)
+
+
+def masked_spherical_dbscan(
+    ds: xr.Dataset, alpha: float = 0.05, eps_km: float = 600, min_area_km2: float = None
+) -> xr.Dataset:
+    """Determines the clusters based on sklearn's DBSCAN implementation. Alpha determines
+    the mask based on the minimum p_value. Grouping can be adjusted using the `eps_km`
+    kwarg. Cluster labels are negative for areas with a negative correlation coefficient
+    and positive for areas with a positive correlation coefficient. Areas without any
+    significant correlation are put in the cluster labelled '0'.
+
+    Args:
+        ds (xr.Dataset): Dataset containing 'latitude' and 'longitude' dimensions in
+            degrees. Must also contain 'p_val' and 'corr' to base the groups on.
+        alpha (float): Value below which the correlation is significant enough to be
+            considered
+        eps_km (float): The maximum distance (in km) between two samples for one to be
+            considered as in the neighborhood of the other. This is not a maximum bound
+            on the distances of points within a cluster. This is the most important
+            DBSCAN parameter to choose appropriately.
+        min_area_km2 (float): The minimum area of a cluster. Clusters smaller than this
+            minimum area will be discarded.
+
+    Returns:
+        xr.Dataset: Dataset grouped by the DBSCAN clusters.
+    """
+    ds = ds.stack(coord=["latitude", "longitude"])
+    coords = np.asarray(list(ds["coord"].values))  # turn array of tuples to 2d-array
+    coords = np.radians(coords)
+
+    # Prepare labels, default value is 0 (not in cluster)
+    labels = np.zeros(len(coords))
+
+    for sign, sign_mask in zip([1, -1], [ds["corr"] >= 0, ds["corr"] < 0]):
+        mask = np.logical_and(ds["p_val"] < alpha, sign_mask)
+
+        if np.sum(mask) > 0:  # Check if the mask contains any points to cluster
+            db = DBSCAN(
+                eps=eps_km / radius_earth_km,
+                min_samples=1,
+                algorithm="auto",
+                metric="haversine",
+            ).fit(coords[mask])
+
+            labels[mask] = sign * (db.labels_ + 1)
+
+    ds["cluster_labels"] = ("coord", labels)
+
+    ds = ds.unstack(("coord"))
+
+    dlat = np.abs(ds.latitude.values[1] - ds.latitude.values[0])
+    dlon = np.abs(ds.longitude.values[1] - ds.longitude.values[0])
+    ds["area"] = spherical_area(ds.latitude, dlat, dlon)
+
+    if min_area_km2:
+        ds = remove_small_area_clusters(ds, min_area_km2)
+    return ds
+
+
+def reduce_to_clusters(
+    ds: xr.Dataset, alpha: float = 0.05, eps_km: float = 600, min_area_km2: float = None
+) -> xr.Dataset:
+    """Perform DBSCAN clustering on a prepared Dataset, and then group the data by their
+    determined clusters, taking the weighted mean. The weight is based on the area of
+    each grid cell.
+
+    Density-Based Spatial Clustering of Applications with Noise (DBSCAN).
+    Clusters gridcells together which are of the same sign and in proximity to
+    each other using DBSCAN.
+
+    The input data will be processed in this function to ensure that the distance
+    is free of the impact from spherical curvature. The actual geodesic distance
+    will be obtained and passed to the DBSCAN clustering function.
+
+    Args:
+        ds: Dataset prepared to have p_val and corr.
+        alpha (float): Value below which the correlation is significant enough to be
+            considered
+        eps_km (float): The maximum distance (in km) between two samples for one to be
+            considered as in the neighborhood of the other. This is not a maximum bound
+            on the distances of points within a cluster. This is the most important
+            DBSCAN parameter to choose appropriately.
+
+    """
+    ds = masked_spherical_dbscan(
+        ds, alpha=alpha, eps_km=eps_km, min_area_km2=min_area_km2
+    )
+
+    ds = weighted_groupby(ds, groupby="cluster_labels", weight="area")
+
+    return ds
+
+
+def _pearsonr_nan(x: np.ndarray, y: np.ndarray) -> Tuple[float, float]:
+    """NaN friendly implementation of scipy.stats.pearsonr. Calculates the correlation
+    coefficient between two arrays, as well as the p-value of this correlation. However,
+    instead of raising an error when encountering NaN values, this function will return
+    both the correlation coefficient and the p-value as NaN.
+
+    Args:
+        x: 1-D array
+        y: 1-D array
+    Returns:
+        r_coefficient
+        p_value
+
+    """
+    if np.any(np.isnan(x)) or np.any(np.isnan(y)):
+        return np.nan, np.nan
+    return _pearsonr(x, y)
+
+
+def correlation(
+    field: xr.DataArray, target: xr.DataArray, corr_dim: str = "time"
+) -> Tuple[xr.DataArray, xr.DataArray]:
+    """Calculate correlation maps.
+
+    Args:
+        field: Spatial data with a dimension named `corr_dim`, over which each
+            location should have the Pearson correlation coefficient calculated with the
+            target data.
+        target: Data which has to be correlated with the spatial data. Requires a
+            dimension named `corr_dim`.
+        corr_dim: Dimension over which the correlation coefficient should be calculated.
+
+    Returns:
+        r_coefficient: DataArray filled with the correlation coefficient for each
+            non-`corr_dim` coordinate.
+        p_value: DataArray filled with the two-tailed p-values for each computed
+            correlation coefficient.
+    """
+    assert (
+        corr_dim in target.dims
+    ), f"input target does not have contain the '{corr_dim}' dimension"
+    assert (
+        corr_dim in field.dims
+    ), f"input field does not have contain the '{corr_dim}' dimension"
+    assert np.all(
+        [dim in field.dims for dim in target.dims]
+    ), "Field and target dims do not match"
+
+    return xr.apply_ufunc(
+        _pearsonr_nan,
+        field,
+        target,
+        input_core_dims=[[corr_dim], [corr_dim]],
+        vectorize=True,
+        output_core_dims=[[], []],
+    )
+
+
+def partial_correlation(field, target, z):
+    """Calculate partial correlation maps."""
+    raise NotImplementedError
+
+
+def regression(field, target):
+    """Regression analysis on entire maps.
+
+    Methods include Linear, Ridge, Lasso.
+    """
+    raise NotImplementedError
diff --git a/setup.cfg b/setup.cfg
index f932077..6db6c40 100644
--- a/setup.cfg
+++ b/setup.cfg
@@ -32,6 +32,7 @@ zip_safe = False
 include_package_data = True
 packages = find:
 install_requires =
+    netcdf4
     numpy
     pandas
     scikit-learn
diff --git a/tests/test_rgdr/test_data/sst_daily_1979-2018_5deg_Pacific_175_240E_25_50N.nc b/tests/test_rgdr/test_data/sst_daily_1979-2018_5deg_Pacific_175_240E_25_50N.nc
new file mode 100644
index 0000000..9f02596
Binary files /dev/null and b/tests/test_rgdr/test_data/sst_daily_1979-2018_5deg_Pacific_175_240E_25_50N.nc differ
diff --git a/tests/test_rgdr/test_data/tf5_nc5_dendo_80d77.nc b/tests/test_rgdr/test_data/tf5_nc5_dendo_80d77.nc
new file mode 100644
index 0000000..36a6a2d
Binary files /dev/null and b/tests/test_rgdr/test_data/tf5_nc5_dendo_80d77.nc differ
diff --git a/tests/test_rgdr/test_map_analysis.py b/tests/test_rgdr/test_map_analysis.py
deleted file mode 100644
index 9525dbd..0000000
--- a/tests/test_rgdr/test_map_analysis.py
+++ /dev/null
@@ -1,87 +0,0 @@
-"""Tests for the s2s._RGDR.map_analysis module."""
-import numpy as np
-import pandas as pd
-import pytest
-import xarray as xr
-from s2spy.rgdr import _map_analysis
-
-
-# pylint: disable=protected-access
-
-
-class TestMapAnalysis:
-    @pytest.fixture(autouse=True)
-    def dummy_dataarray(self):
-        time = pd.date_range("20161001", "20211001", freq="60d")
-
-        da = xr.DataArray(
-            np.tile(np.arange(len(time)), (2, 2, 1)),
-            dims=["lat", "lon", "time"],
-            coords={
-                "time": time,
-                "lat": np.arange(0, 2),
-                "lon": np.arange(0, 2),
-            },
-        )
-        return da
-
-    @pytest.fixture(autouse=True)
-    def dummy_timeseries(self, dummy_dataarray):
-        return dummy_dataarray.isel(lat=0, lon=0).drop_vars(["lat", "lon"])
-
-    def test_pearsonr(self):
-        result = _map_analysis._pearsonr_nan([0, 0, 1], [0, 1, 1])
-        expected = (0.5, 0.66666667)
-        np.testing.assert_array_almost_equal(result, expected)
-
-    def test_pearsonr_nan(self):
-        result = _map_analysis._pearsonr_nan([np.nan, 0, 1], [0, 1, 1])
-        expected = (np.nan, np.nan)
-        np.testing.assert_array_almost_equal(result, expected)
-
-    def test_correlation(self, dummy_dataarray, dummy_timeseries):
-        c_val, p_val = _map_analysis.correlation(dummy_dataarray, dummy_timeseries)
-
-        np.testing.assert_equal(c_val.values, 1)
-        np.testing.assert_equal(p_val.values, 0)
-
-    def test_correlation_dim_name(self, dummy_dataarray, dummy_timeseries):
-        da = dummy_dataarray.rename({"time": "i_interval"})
-        ts = dummy_timeseries.rename({"time": "i_interval"})
-        c_val, p_val = _map_analysis.correlation(
-            da, ts, corr_dim="i_interval"
-        )
-
-        np.testing.assert_equal(c_val.values, 1)
-        np.testing.assert_equal(p_val.values, 0)
-
-    def test_correlation_wrong_target_dim_name(self, dummy_dataarray, dummy_timeseries):
-        ts = dummy_timeseries.rename({"time": "dummy"})
-        with pytest.raises(AssertionError):
-            _map_analysis.correlation(dummy_dataarray, ts)
-
-    def test_correlation_wrong_field_dim_name(self, dummy_dataarray, dummy_timeseries):
-        da = dummy_dataarray.rename({"time": "dummy"})
-        with pytest.raises(AssertionError):
-            _map_analysis.correlation(da, dummy_timeseries)
-
-    def test_correlation_wrong_target_dims(self, dummy_dataarray):
-        ts = dummy_dataarray.rename({'lat': 'latitude'})
-        with pytest.raises(AssertionError):
-            _map_analysis.correlation(dummy_dataarray, ts)
-
-    def test_partial_correlation(self):
-        with pytest.raises(NotImplementedError):
-            _map_analysis.partial_correlation("field", "target", "z")
-
-    def test_regression(self):
-        with pytest.raises(NotImplementedError):
-            _map_analysis.regression("field", "target")
-
-    def test_save_map(self):
-        with pytest.raises(NotImplementedError):
-            _map_analysis.save_map()
-
-    def test_load_map(self):
-        with pytest.raises(NotImplementedError):
-            _map_analysis.load_map()
diff --git a/tests/test_rgdr/test_map_regions.py b/tests/test_rgdr/test_map_regions.py
deleted file mode 100644
index d25252a..0000000
--- a/tests/test_rgdr/test_map_regions.py
+++ /dev/null
@@ -1,16 +0,0 @@
-"""Tests for the s2s._RGDR.map_regions module."""
-import pytest
-from s2spy.rgdr import _map_regions
-
-
-# pylint: disable=protected-access
-
-
-class TestMapRegions:
-    def test_spatial_mean(self):
-        with pytest.raises(NotImplementedError):
-            _map_regions.spatial_mean()
-
-    def test_cluster_dbscan(self):
-        with pytest.raises(NotImplementedError):
-            _map_regions.cluster_dbscan()
diff --git a/tests/test_rgdr/test_rgdr.py b/tests/test_rgdr/test_rgdr.py
index 248b4c3..bf646ba 100644
--- a/tests/test_rgdr/test_rgdr.py
+++ b/tests/test_rgdr/test_rgdr.py
@@ -1,6 +1,11 @@
 """Tests for the s2s._RGDR.rgdr module."""
+import numpy as np
+import pandas as pd
 import pytest
+import xarray as xr
 from s2spy import RGDR
+from s2spy.rgdr import _rgdr
+from s2spy.time import AdventCalendar
 
 
 # pylint: disable=protected-access
@@ -8,21 +13,152 @@
 
 class TestRGDR:
     """Test RGDR methods."""
+
     @pytest.fixture(autouse=True)
     def dummy_rgdr(self):
-        rgdr = RGDR('timeseries')
-        return rgdr
+        return RGDR("timeseries")
 
     def test_init(self):
-        rgdr = RGDR('timeseries')
+        rgdr = RGDR("timeseries")
         assert isinstance(rgdr, RGDR)
 
-    def test_lag_shifting(self, dummy_rgdr):
-        with pytest.raises(NotImplementedError):
-            dummy_rgdr.lag_shifting()
-
     def test_fit(self, dummy_rgdr):
         with pytest.raises(NotImplementedError):
             dummy_rgdr.fit("data")
 
-    
+    def test_transform(self, dummy_rgdr):
+        with pytest.raises(NotImplementedError):
+            dummy_rgdr.transform("data")
+
+
+class TestCorrelation:
+    @pytest.fixture(autouse=True)
+    def dummy_dataarray(self):
+        time = pd.date_range("20161001", "20211001", freq="60d")
+
+        return xr.DataArray(
+            np.tile(np.arange(len(time)), (2, 2, 1)),
+            dims=["lat", "lon", "time"],
+            coords={
+                "time": time,
+                "lat": np.arange(0, 2),
+                "lon": np.arange(0, 2),
+            },
+        )
+
+    @pytest.fixture(autouse=True)
+    def dummy_timeseries(self, dummy_dataarray):
+        return dummy_dataarray.isel(lat=0, lon=0).drop_vars(["lat", "lon"])
+
+    def test_pearsonr(self):
+        result = _rgdr._pearsonr_nan([0, 0, 1], [0, 1, 1])
+        expected = (0.5, 0.66666667)
+        np.testing.assert_array_almost_equal(result, expected)
+
+    def test_pearsonr_nan(self):
+        result = _rgdr._pearsonr_nan([np.nan, 0, 1], [0, 1, 1])
+        expected = (np.nan, np.nan)
+        np.testing.assert_array_almost_equal(result, expected)
+
+    def test_correlation(self, dummy_dataarray, dummy_timeseries):
+        c_val, p_val = _rgdr.correlation(dummy_dataarray, dummy_timeseries)
+
+        np.testing.assert_equal(c_val.values, 1)
+        np.testing.assert_equal(p_val.values, 0)
+
+    def test_correlation_dim_name(self, dummy_dataarray, dummy_timeseries):
+        da = dummy_dataarray.rename({"time": "i_interval"})
+        ts = dummy_timeseries.rename({"time": "i_interval"})
+        c_val, p_val = _rgdr.correlation(da, ts, corr_dim="i_interval")
+
+        np.testing.assert_equal(c_val.values, 1)
+        np.testing.assert_equal(p_val.values, 0)
+
+    def test_correlation_wrong_target_dim_name(self, dummy_dataarray, dummy_timeseries):
+        ts = dummy_timeseries.rename({"time": "dummy"})
+        with pytest.raises(AssertionError):
+            _rgdr.correlation(dummy_dataarray, ts)
+
+    def test_correlation_wrong_field_dim_name(self, dummy_dataarray, dummy_timeseries):
+        da = dummy_dataarray.rename({"time": "dummy"})
+        with pytest.raises(AssertionError):
+            _rgdr.correlation(da, dummy_timeseries)
+
+    def test_correlation_wrong_target_dims(self, dummy_dataarray):
+        ts = dummy_dataarray.rename({"lat": "latitude"})
+        with pytest.raises(AssertionError):
+            _rgdr.correlation(dummy_dataarray, ts)
+
+    def test_partial_correlation(self):
+        with pytest.raises(NotImplementedError):
+            _rgdr.partial_correlation("field", "target", "z")
+
+    def test_regression(self):
+        with pytest.raises(NotImplementedError):
+            _rgdr.regression("field", "target")
+
+
+test_file_path = "./tests/test_rgdr/test_data"
+
+
+class TestMapRegions:
+    @pytest.fixture(autouse=True)
+    def dummy_calendar(self):
+        return AdventCalendar(anchor_date=(8, 31), freq="30d")
+
+    @pytest.fixture(autouse=True)
+    def example_target(self):
+        return xr.open_dataset(f"{test_file_path}/tf5_nc5_dendo_80d77.nc").sel(
+            cluster=3
+        )
+
+    @pytest.fixture(autouse=True)
+    def example_field(self):
+        return xr.open_dataset(
+            f"{test_file_path}/sst_daily_1979-2018_5deg_Pacific_175_240E_25_50N.nc"
+        )
+
+    @pytest.fixture(autouse=True)
+    def example_corr(self, dummy_calendar, example_target, example_field):
+        field = dummy_calendar.resample(example_field)
+        target = dummy_calendar.resample(example_target)
+
+        field["corr"], field["p_val"] = _rgdr.correlation(
+            field.sst, target.ts.sel(i_interval=0), corr_dim="anchor_year"
+        )
+
+        return field
+
+    @pytest.fixture(autouse=True)
+    def expected_labels(self):
+        return np.array(
+            [
+                [0.0, 0.0, 0.0, 0.0, -1.0, -1.0, 0.0, -2.0, -2.0, -2.0, 0.0, 0.0, 0.0],
+                [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, -2.0, -2.0, -2.0, 0.0, 0.0],
+                [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, -2.0, -2.0, -2.0, 0.0, 0.0],
+                [0.0, 0.0, 1.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, -2.0, 0.0, 0.0],
+                [1.0, 1.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, -2.0, -2.0, -2.0, -2.0, -2.0],
+            ]
+        )
+
+    def test_dbscan(self, example_corr, expected_labels):
+        clusters = _rgdr.masked_spherical_dbscan(example_corr.sel(i_interval=1))
+
+        np.testing.assert_array_equal(clusters["cluster_labels"], expected_labels)
+
+    def test_dbscan_min_area(self, example_corr, expected_labels):
+        clusters = _rgdr.masked_spherical_dbscan(
+            example_corr.sel(i_interval=1), min_area_km2=3000**2
+        )
+        expected_labels[expected_labels == -1] = 0  # Small -1 cluster is missing
+
+        np.testing.assert_array_equal(clusters["cluster_labels"], expected_labels)
+
+    def test_cluster(self, example_corr):
+        clustered_data = _rgdr.reduce_to_clusters(
+            example_corr.sel(i_interval=1), eps_km=600, alpha=0.04
+        )
+
+        np.testing.assert_array_equal(clustered_data["cluster_labels"], [-1, 0, 1])
+
+        assert set(clustered_data.sst.dims) == {"anchor_year", "cluster_labels"}