Repository: AI4S2S/s2spy

Issue:
Merge the plotting functionalities in RGDR module
Currently in RGDR module, there are two ways to plot the clusters:
- Preview the clusters by `RGDR(...).plot_clusters(precursor_field, target_timeseries)`
- Plotting clusters after fitting and transforming `cluster_map.cluster_labels[0].plot()`

The first option was designed to let the user have a quick check and preview the clusters. But it can cause confusion. We can merge these two functionalities and ask the user to `fit/transform` first and then `plot` the clusters.

Discussion:

Patch:
diff --git a/notebooks/tutorial_RGDR.ipynb b/notebooks/tutorial_RGDR.ipynb
index 84f5228..0f8213a 100644
--- a/notebooks/tutorial_RGDR.ipynb
+++ b/notebooks/tutorial_RGDR.ipynb
@@ -76,14 +76,27 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Using `.plot_correlation` we can see the correlation and p-value map of the target timeseries and precursor field:"
+    "Using `.preview_correlation` we can visualize the correlation and p-value map of the target timeseries and precursor field, for a single lag.\n",
+    "\n",
+    "In this case we use lag `1`."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[<matplotlib.collections.QuadMesh at 0x1a0501cb6d0>,\n",
+       " <matplotlib.collections.QuadMesh at 0x1a063a29270>]"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
     {
      "data": {
       "image/png": 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",
@@ -99,23 +112,33 @@
    ],
    "source": [
     "fig, (ax1, ax2) = plt.subplots(ncols=2, figsize=(12, 2))\n",
-    "_ = rgdr.plot_correlation(precursor_field, target_timeseries, lag=1, ax1=ax1, ax2=ax2)"
+    "rgdr.preview_correlation(precursor_field, target_timeseries, lag=1, ax1=ax1, ax2=ax2)"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "We can compare two RGDR initializations to preview what the effect on the generated clusters will be.\n",
+    "Using the preview utilities we can compare two RGDR setups, and see what the effect on the generated clusters will be.\n",
     "\n",
     "Note that the second figure has an extra cluster, due to the minimum area argument having a smaller value."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.collections.QuadMesh at 0x1a063c76b60>"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
     {
      "data": {
       "image/png": 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",
@@ -132,17 +155,21 @@
    "source": [
     "fig, (ax1, ax2) = plt.subplots(ncols=2, figsize=(12, 2))\n",
     "\n",
-    "_ = rgdr.plot_clusters(precursor_field, target_timeseries, lag=1, ax=ax1)\n",
+    "RGDR(eps_km=600, min_area_km2=3000**2).preview_clusters(precursor_field,\n",
+    "                                                        target_timeseries,\n",
+    "                                                        lag=1, ax=ax1)\n",
     "\n",
-    "_ = RGDR(eps_km=600, min_area_km2=None).plot_clusters(precursor_field, target_timeseries,\n",
-    "                                                      lag=1, ax=ax2)"
+    "RGDR(eps_km=600, min_area_km2=None).preview_clusters(precursor_field,\n",
+    "                                                     target_timeseries,\n",
+    "                                                     lag=1, ax=ax2)"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "With `.fit` the RGDR clustering can be fit to a precursor field.\n",
+    "Once you are satisfied with the selected paramters, the `.fit` method can be used to fit the RGDR clustering to a precursor field.\n",
+    "\n",
     "`.transform` can then be used to return the data reduced to clusters:"
    ]
   },
@@ -515,7 +542,7 @@
        "    longitude       (cluster_labels) float64 223.9 185.4 221.8 190.2 217.8 219.3\n",
        "Attributes:\n",
        "    data:         Clustered data with Response Guided Dimensionality Reduction.\n",
-       "    coordinates:  Latitudes and longitudes are geographical centers associate...</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'sst'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>anchor_year</span>: 39</li><li><span class='xr-has-index'>cluster_labels</span>: 6</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-ebb62979-6703-4d4f-9e66-216747b50a2f' class='xr-array-in' type='checkbox' ><label for='section-ebb62979-6703-4d4f-9e66-216747b50a2f' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>290.8 298.9 287.8 296.6 286.0 284.9 ... 298.2 288.4 296.6 286.9 285.1</span></div><div class='xr-array-data'><pre>array([[290.79588914, 298.94548042, 287.83356654, 296.5913755 ,\n",
+       "    coordinates:  Latitudes and longitudes are geographical centers associate...</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'sst'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>anchor_year</span>: 39</li><li><span class='xr-has-index'>cluster_labels</span>: 6</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-b9814065-77d7-499d-bf20-524974fa3294' class='xr-array-in' type='checkbox' ><label for='section-b9814065-77d7-499d-bf20-524974fa3294' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>290.8 298.9 287.8 296.6 286.0 284.9 ... 298.2 288.4 296.6 286.9 285.1</span></div><div class='xr-array-data'><pre>array([[290.79588914, 298.94548042, 287.83356654, 296.5913755 ,\n",
        "        286.03044129, 284.87776786],\n",
        "       [290.970545  , 298.92119198, 288.55306112, 296.90258837,\n",
        "        286.41065088, 285.21587978],\n",
@@ -555,13 +582,13 @@
        "       [291.45966391, 299.19568083, 288.59732323, 296.99780806,\n",
        "        286.61949986, 284.98540926],\n",
        "       [291.37066195, 298.21053747, 288.43352188, 296.60848274,\n",
-       "        286.90046576, 285.06801918]])</pre></div></div></li><li class='xr-section-item'><input id='section-3a5d483f-c7dd-4c8e-89c9-63da712b7ba6' class='xr-section-summary-in' type='checkbox'  checked><label for='section-3a5d483f-c7dd-4c8e-89c9-63da712b7ba6' class='xr-section-summary' >Coordinates: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>anchor_year</span></div><div class='xr-var-dims'>(anchor_year)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>1980 1981 1982 ... 2016 2017 2018</div><input id='attrs-8e70b916-84d8-40d9-918a-209ea4c71272' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-8e70b916-84d8-40d9-918a-209ea4c71272' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a088dd2e-4ea3-49c5-b26a-bec2841de5d3' class='xr-var-data-in' type='checkbox'><label for='data-a088dd2e-4ea3-49c5-b26a-bec2841de5d3' 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",
+       "        286.90046576, 285.06801918]])</pre></div></div></li><li class='xr-section-item'><input id='section-8ab02218-59be-4b93-a221-54e4749a4c78' class='xr-section-summary-in' type='checkbox'  checked><label for='section-8ab02218-59be-4b93-a221-54e4749a4c78' class='xr-section-summary' >Coordinates: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>anchor_year</span></div><div class='xr-var-dims'>(anchor_year)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>1980 1981 1982 ... 2016 2017 2018</div><input id='attrs-e7d9a151-f6d5-4bdc-8098-860833f05ce3' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-e7d9a151-f6d5-4bdc-8098-860833f05ce3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4933660f-689b-4cff-bf5a-9f595f3ddc32' class='xr-var-data-in' type='checkbox'><label for='data-4933660f-689b-4cff-bf5a-9f595f3ddc32' 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])</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'>&lt;U20</div><div class='xr-var-preview xr-preview'>&#x27;lag:1_cluster:-2&#x27; ... &#x27;lag:4_cl...</div><input id='attrs-3951033c-4bfd-451e-9fb2-0927e2b3c321' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-3951033c-4bfd-451e-9fb2-0927e2b3c321' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d6752cfe-c010-420c-855e-8b2ebd984b5f' class='xr-var-data-in' type='checkbox'><label for='data-d6752cfe-c010-420c-855e-8b2ebd984b5f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;lag:1_cluster:-2&#x27;, &#x27;lag:1_cluster:1&#x27;, &#x27;lag:2_cluster:-1&#x27;,\n",
-       "       &#x27;lag:2_cluster:1&#x27;, &#x27;lag:3_cluster:-1&#x27;, &#x27;lag:4_cluster:-2&#x27;], dtype=&#x27;&lt;U20&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>latitude</span></div><div class='xr-var-dims'>(cluster_labels)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>36.05 29.44 37.33 29.58 38.14 39.78</div><input id='attrs-1e9d0a47-a70a-4a4a-a32d-6fdabbe7af38' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-1e9d0a47-a70a-4a4a-a32d-6fdabbe7af38' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d7b33e7f-ab59-4715-8dac-97820938005b' class='xr-var-data-in' type='checkbox'><label for='data-d7b33e7f-ab59-4715-8dac-97820938005b' 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([36.0508552 , 29.4398051 , 37.33257702, 29.58134561, 38.13773082,\n",
-       "       39.78162825])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>longitude</span></div><div class='xr-var-dims'>(cluster_labels)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>223.9 185.4 221.8 190.2 217.8 219.3</div><input id='attrs-140e577b-192d-4ef3-aa46-3cacf76cf2b5' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-140e577b-192d-4ef3-aa46-3cacf76cf2b5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1a127e1f-5fae-4c9a-b627-bda596ae2de7' class='xr-var-data-in' type='checkbox'><label for='data-1a127e1f-5fae-4c9a-b627-bda596ae2de7' 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([223.86658208, 185.40970765, 221.82516648, 190.20336403,\n",
-       "       217.7810629 , 219.30300121])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-52ed9bea-fa67-4733-a041-2bd5da86e1af' class='xr-section-summary-in' type='checkbox'  checked><label for='section-52ed9bea-fa67-4733-a041-2bd5da86e1af' class='xr-section-summary' >Attributes: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>data :</span></dt><dd>Clustered data with Response Guided Dimensionality Reduction.</dd><dt><span>coordinates :</span></dt><dd>Latitudes and longitudes are geographical centers associated with clusters.</dd></dl></div></li></ul></div></div>"
+       "       2016, 2017, 2018])</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'>&lt;U20</div><div class='xr-var-preview xr-preview'>&#x27;lag:1_cluster:-2&#x27; ... &#x27;lag:4_cl...</div><input id='attrs-a3775c40-6ebc-4a42-bf0e-e3037a18ad11' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-a3775c40-6ebc-4a42-bf0e-e3037a18ad11' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2c65aa63-0d9f-479a-9a52-6235fdbe351c' class='xr-var-data-in' type='checkbox'><label for='data-2c65aa63-0d9f-479a-9a52-6235fdbe351c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;lag:1_cluster:-2&#x27;, &#x27;lag:1_cluster:1&#x27;, &#x27;lag:2_cluster:-1&#x27;,\n",
+       "       &#x27;lag:2_cluster:1&#x27;, &#x27;lag:3_cluster:-1&#x27;, &#x27;lag:4_cluster:-2&#x27;], dtype=&#x27;&lt;U20&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>latitude</span></div><div class='xr-var-dims'>(cluster_labels)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>36.05 29.44 37.33 29.58 38.14 39.78</div><input id='attrs-5ec3591f-c5c0-42d9-a37e-83f52120d27f' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-5ec3591f-c5c0-42d9-a37e-83f52120d27f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b117a3f9-382c-430a-a83c-273c78962a08' class='xr-var-data-in' type='checkbox'><label for='data-b117a3f9-382c-430a-a83c-273c78962a08' 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([36.0508552 , 29.4398051 , 37.33257702, 29.58134561, 38.13773082,\n",
+       "       39.78162825])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>longitude</span></div><div class='xr-var-dims'>(cluster_labels)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>223.9 185.4 221.8 190.2 217.8 219.3</div><input id='attrs-b30326c1-962c-4973-abcb-0fffdd76751c' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-b30326c1-962c-4973-abcb-0fffdd76751c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ca106886-bf06-4ac2-a980-c3df7cff353c' class='xr-var-data-in' type='checkbox'><label for='data-ca106886-bf06-4ac2-a980-c3df7cff353c' 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([223.86658208, 185.40970765, 221.82516648, 190.20336403,\n",
+       "       217.7810629 , 219.30300121])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-df1be5c8-b32f-4819-812f-7c3936f2b85e' class='xr-section-summary-in' type='checkbox'  checked><label for='section-df1be5c8-b32f-4819-812f-7c3936f2b85e' class='xr-section-summary' >Attributes: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>data :</span></dt><dd>Clustered data with Response Guided Dimensionality Reduction.</dd><dt><span>coordinates :</span></dt><dd>Latitudes and longitudes are geographical centers associated with clusters.</dd></dl></div></li></ul></div></div>"
       ],
       "text/plain": [
        "<xarray.DataArray 'sst' (anchor_year: 39, cluster_labels: 6)>\n",
@@ -632,59 +659,471 @@
     "plt.legend()"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The `RGDR` object will also contain the correlation maps, p-values, and cluster maps.\n",
+    "\n",
+    "These are all `xr.DataArray` objects, and can easily be plot using the builtin methods:"
+   ]
+  },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 18,
    "metadata": {},
    "outputs": [
     {
      "data": {
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+       "  display: none;\n",
+       "  background-color: var(--xr-background-color) !important;\n",
+       "  padding-bottom: 5px !important;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
+       ".xr-var-data-in:checked ~ .xr-var-data {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       ".xr-var-data > table {\n",
+       "  float: right;\n",
+       "}\n",
+       "\n",
+       ".xr-var-name span,\n",
+       ".xr-var-data,\n",
+       ".xr-attrs {\n",
+       "  padding-left: 25px !important;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs,\n",
+       ".xr-var-attrs,\n",
+       ".xr-var-data {\n",
+       "  grid-column: 1 / -1;\n",
+       "}\n",
+       "\n",
+       "dl.xr-attrs {\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 125px auto;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt,\n",
+       ".xr-attrs dd {\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "  float: left;\n",
+       "  padding-right: 10px;\n",
+       "  width: auto;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt {\n",
+       "  font-weight: normal;\n",
+       "  grid-column: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt:hover span {\n",
+       "  display: inline-block;\n",
+       "  background: var(--xr-background-color);\n",
+       "  padding-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dd {\n",
+       "  grid-column: 2;\n",
+       "  white-space: pre-wrap;\n",
+       "  word-break: break-all;\n",
+       "}\n",
+       "\n",
+       ".xr-icon-database,\n",
+       ".xr-icon-file-text2 {\n",
+       "  display: inline-block;\n",
+       "  vertical-align: middle;\n",
+       "  width: 1em;\n",
+       "  height: 1.5em !important;\n",
+       "  stroke-width: 0;\n",
+       "  stroke: currentColor;\n",
+       "  fill: currentColor;\n",
+       "}\n",
+       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray (latitude: 5, longitude: 13, i_interval: 4)&gt;\n",
+       "0.01618 -0.2187 -0.2194 -0.2663 0.02414 ... -0.3984 -0.38 -0.3063 -0.1126\n",
+       "Coordinates:\n",
+       "  * i_interval  (i_interval) int64 1 2 3 4\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",
+       "    area        (latitude) float64 3.34e+06 3.645e+06 ... 4.169e+06 4.385e+06\n",
+       "    tfreq       int64 5\n",
+       "    n_clusters  int64 6\n",
+       "    cluster     int64 3</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'></div><ul class='xr-dim-list'><li><span class='xr-has-index'>latitude</span>: 5</li><li><span class='xr-has-index'>longitude</span>: 13</li><li><span class='xr-has-index'>i_interval</span>: 4</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-420d8bed-40b7-4777-abcf-2c09c8b76ead' class='xr-array-in' type='checkbox' ><label for='section-420d8bed-40b7-4777-abcf-2c09c8b76ead' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>0.01618 -0.2187 -0.2194 -0.2663 ... -0.3984 -0.38 -0.3063 -0.1126</span></div><div class='xr-array-data'><pre>array([[[ 0.01617516, -0.21868534, -0.21941745, -0.26630719],\n",
+       "        [ 0.02414245, -0.12315571, -0.21464621, -0.18191947],\n",
+       "        [-0.1105048 , -0.23253951, -0.4007449 , -0.25372747],\n",
+       "        [-0.24520611, -0.35037825, -0.46371192, -0.35772739],\n",
+       "        [-0.32824171, -0.4534247 , -0.46975605, -0.3473753 ],\n",
+       "        [-0.323078  , -0.43326205, -0.41748415, -0.29461981],\n",
+       "        [-0.30660624, -0.34313136, -0.34457092, -0.28702518],\n",
+       "        [-0.38616188, -0.34666184, -0.37666874, -0.37721962],\n",
+       "        [-0.3822271 , -0.33402403, -0.30240485, -0.32951966],\n",
+       "        [-0.36827501, -0.41554246, -0.28493204, -0.33065629],\n",
+       "        [-0.28047128, -0.27858467, -0.19205284, -0.27627975],\n",
+       "        [-0.10757776, -0.2545805 , -0.15956299, -0.12823108],\n",
+       "        [        nan,         nan,         nan,         nan]],\n",
+       "\n",
+       "       [[-0.02842247, -0.12801465, -0.1379688 , -0.19425886],\n",
+       "        [ 0.00598683, -0.0124359 , -0.0967947 , -0.10557213],\n",
+       "        [ 0.00479765, -0.09888618, -0.13544553, -0.1265711 ],\n",
+       "        [ 0.01930277, -0.06881219, -0.16586661, -0.2325628 ],\n",
+       "        [-0.01405595, -0.1865748 , -0.22187587, -0.25983436],\n",
+       "        [ 0.01204043, -0.26110484, -0.28542316, -0.29141342],\n",
+       "...\n",
+       "        [-0.18490648, -0.12178649, -0.12912278, -0.29769152],\n",
+       "        [-0.24043321, -0.27218421, -0.25308925, -0.3604698 ],\n",
+       "        [-0.29061517, -0.38322784, -0.34714351, -0.39152915],\n",
+       "        [-0.35707131, -0.54341488, -0.46870146, -0.3338985 ],\n",
+       "        [-0.26357851, -0.48241304, -0.36081808, -0.155657  ],\n",
+       "        [-0.27768413, -0.32115578, -0.26949376, -0.10772449]],\n",
+       "\n",
+       "       [[ 0.42487447,  0.50036829,  0.20242345,  0.01770287],\n",
+       "        [ 0.34211447,  0.4839066 ,  0.2784725 ,  0.00806344],\n",
+       "        [ 0.3433933 ,  0.43694834,  0.3199787 ,  0.14065339],\n",
+       "        [ 0.18648733,  0.35050515,  0.33449607,  0.13369711],\n",
+       "        [ 0.04340644,  0.25401412,  0.29487164,  0.1233588 ],\n",
+       "        [-0.10334886,  0.09830805,  0.26352384,  0.0517264 ],\n",
+       "        [-0.17111999, -0.08694884,  0.16013368, -0.01373979],\n",
+       "        [-0.24750864, -0.25987983, -0.08067427, -0.06523411],\n",
+       "        [-0.35825522, -0.39384288, -0.31606223, -0.1921587 ],\n",
+       "        [-0.41354082, -0.4202639 , -0.34898835, -0.11634853],\n",
+       "        [-0.37029449, -0.43487533, -0.35672934, -0.1791453 ],\n",
+       "        [-0.40753351, -0.42768064, -0.34349675, -0.21013388],\n",
+       "        [-0.39836497, -0.37996126, -0.3062787 , -0.11258559]]])</pre></div></div></li><li class='xr-section-item'><input id='section-fa2fda83-c99e-417a-8f36-df966d53fc7f' class='xr-section-summary-in' type='checkbox'  checked><label for='section-fa2fda83-c99e-417a-8f36-df966d53fc7f' class='xr-section-summary' >Coordinates: <span>(7)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>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'>1 2 3 4</div><input id='attrs-05b3da1b-4dbc-4156-9810-fc5c2536ae1b' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-05b3da1b-4dbc-4156-9810-fc5c2536ae1b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4750df8e-ff1d-49cf-8c3a-73e1781348ff' class='xr-var-data-in' type='checkbox'><label for='data-4750df8e-ff1d-49cf-8c3a-73e1781348ff' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([1, 2, 3, 4], dtype=int64)</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-5dc96415-86e8-47db-83c9-2ba4a1c1daa6' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-5dc96415-86e8-47db-83c9-2ba4a1c1daa6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2edfc74c-d2b8-42bd-a888-fb5bc152816c' class='xr-var-data-in' type='checkbox'><label for='data-2edfc74c-d2b8-42bd-a888-fb5bc152816c' 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-eb1cc3e1-d454-4e45-8b8e-147c60eab798' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-eb1cc3e1-d454-4e45-8b8e-147c60eab798' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6243d329-5917-4487-90b6-680c4621ee68' class='xr-var-data-in' type='checkbox'><label for='data-6243d329-5917-4487-90b6-680c4621ee68' 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>area</span></div><div class='xr-var-dims'>(latitude)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>3.34e+06 3.645e+06 ... 4.385e+06</div><input id='attrs-61c0ee69-fbcc-47fe-8498-3f24bf3a405a' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-61c0ee69-fbcc-47fe-8498-3f24bf3a405a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-00f507f0-341a-4c02-8721-40b9b0ab92f8' class='xr-var-data-in' type='checkbox'><label for='data-00f507f0-341a-4c02-8721-40b9b0ab92f8' 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([3339800.84283772, 3644753.05166713, 3921966.48901128,\n",
+       "       4169331.39322594, 4384965.16802703])</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-8c0f5aac-772d-4c8e-8e28-d05843c168d0' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-8c0f5aac-772d-4c8e-8e28-d05843c168d0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-98695b91-dd7e-401b-9167-a67d74ab8e62' class='xr-var-data-in' type='checkbox'><label for='data-98695b91-dd7e-401b-9167-a67d74ab8e62' 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-ccf5d767-d518-4df4-8e1b-47e31111af9c' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-ccf5d767-d518-4df4-8e1b-47e31111af9c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e11d594d-5e70-4bfb-b766-84db0639c9db' class='xr-var-data-in' type='checkbox'><label for='data-e11d594d-5e70-4bfb-b766-84db0639c9db' 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-56288bf7-23ee-4a56-a121-e561a5fb98f1' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-56288bf7-23ee-4a56-a121-e561a5fb98f1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-19ac81fa-703f-4610-80e3-f932c99d5df1' class='xr-var-data-in' type='checkbox'><label for='data-19ac81fa-703f-4610-80e3-f932c99d5df1' 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-60650f8e-73b5-4cda-adda-8dc4aed5eb47' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-60650f8e-73b5-4cda-adda-8dc4aed5eb47' 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": [
-       "<matplotlib.collections.QuadMesh at 0x1c0c3f197b0>"
+       "<xarray.DataArray (latitude: 5, longitude: 13, i_interval: 4)>\n",
+       "0.01618 -0.2187 -0.2194 -0.2663 0.02414 ... -0.3984 -0.38 -0.3063 -0.1126\n",
+       "Coordinates:\n",
+       "  * i_interval  (i_interval) int64 1 2 3 4\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",
+       "    area        (latitude) float64 3.34e+06 3.645e+06 ... 4.169e+06 4.385e+06\n",
+       "    tfreq       int64 5\n",
+       "    n_clusters  int64 6\n",
+       "    cluster     int64 3"
       ]
      },
-     "execution_count": 7,
+     "execution_count": 18,
      "metadata": {},
      "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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BTZyweqX17Ty10uoAOP1dM5oXGoa/7b99pfUBrOlnK6/zzserre9Ll0yutkLgl5feWWl9i6+/uNL6AJ5+ZOvK65z0/h0rr7MrjfMnKLXtK0fS24EHbV9b99JRwMuB1wIbAkc2er/tubZn2p650UbV/2NGRAxKom/11ZpO3aqdkb0B2FfSPsCawHqSvm/7/eXrz0j6DvCJNsYQETFsUstDCnSltkVu+yjb02xvQXFR/qW23y9pKoAkAfsBN7crhoiIkVFVNyh1RCeOJU6TtDHFXbs3AId2IIaIiMFVeB17J4xJYrd9OXB5OT/qkcsiItpLqC+JPSKiZ0iir+Ir58ZSEntERD1BX1rsERG9JX3sERG9REpij4joJYKcPI2I6ClpsUdE9BhB/4Txmx7H71dSRESbSMV17M2mFuvaS9IdkhZImtPg9Y9JulXSTZIukbT5aONPYo+IaKCKIQUk9QMnAXsD2wAHSNqmrtj1wEzb2wNnAV8abexJ7BER9VTZWDE7AQts3237WeAMYFZtAduX2X6qXLwSmDba8MdvJ1JERBu12NUyWdL8muW5tufWLG8G3FezvBDYeYj6DgHOaznIQSSxR0TUkURff38rRZfYnlnRNt8PzAR2G21dSewREfUEfdVcFbMImF6zPK1ct+LmpD2Ao4HdGj1RbriS2CMiVlLZ6I7XADMkbUmR0N8LHLjClqRXAycDe9l+sIqNJrFHRNRRReOx214q6XDgAqAf+LbtWyQdB8y3PQ/4MrAu8KPi+UP8yfa+o9luEntERL0K7zy1fS5wbt26Y2rm96hkQzWS2CMiGshYMRERvURCq03odBQjlsQeEbESQVrsERE9RKDWrmPvSknsERErEfQlsUdE9A6RxB4R0UtU3Q1KHZHEHhFRT4JcFRMR0VvSYo+I6CXKydOIiB6TxB4R0VtyHXtERK/JnacREb0lY8VERPSgcdxiH7+RR0S0i4T6+ptOrVWlvSTdIWmBpDkNXt9V0nWSlkp6VxXhJ7FHRKykvCqm2dSsFqkfOAnYG9gGOEDSNnXF/gQcDJxeVfTpiomIqCeq6orZCVhg+24ASWcAs4BbBwrYvrd8bXkVG4QxaLFL6pd0vaRzyuUtJV1VHpb8UNL4PUMRET1JElp9QtMJmCxpfs00u66qzYD7apYXluvaaiy6Yo4AbqtZ/iJwou2tgIeBQ8YghoiIYWi5K2aJ7Zk109xORw5tTuySpgFvA04plwW8GTirLHIqsF87Y4iIGAn19TWdWrAImF6zPK1c11bt7mP/GvApYGK5vBHwiO2l5fKghyXlIc1sgBdPn8b6fqrayG68otr6ZuxcbX0Ad11VaXXPXfPbSusDWOfdh1Ze5/SJ1R6p3veXJyutD+CZR/9SeZ1Va/WqjeE466bFldb3yqnrV1pfZaobK+YaYIakLSkS+nuBA6uoeChta7FLejvwoO1rR/J+23MHDm8mb7RRxdFFRDShvuZTE2Uj9nDgAoou6TNt3yLpOEn7Akh6raSFwLuBkyXdMtrQ29lifwOwr6R9gDWB9YB/ByZJWq38wGNyWBIRMTxqKXG3wva5wLl1646pmb+GIhdWpm0tdttH2Z5mewuKw49Lbb8PuAwYuAj/IOBn7YohImJEBO5brenUrTpxg9KRwMckLaDoc/9WB2KIiBiCin72ZlOXGpOvHNuXA5eX83dTXLQfEdG9en2sGElbS7pE0s3l8vaSPtPe0CIiOsOA1dd06latRvbfwFHAcwC2b6LoN4+I6D1SJVfFdEqrXTFr275aK/YpLR2scETE+Cbo4pOjzbQa+RJJL6U4QqEcWrLaOxUiIrpIN3e1NNNqYv8nYC7wckmLgHuA97ctqoiITuv1xF5eybKHpHWAPtuPtzesiIgO6vLLGZsZMrFL+tgg6wGw/dU2xBQR0Xk93GIfGLzrZcBrgXnl8juAq9sVVEREp/VsH7vtzwJI+iWw40AXjKRjgV+0PbqIiE6QoL/3r4qZAjxbs/xsuS4iogdVNwhYJ7Sa2P8HuFrS2eXyfhQPyYiI6E29nthtHy/pPOCN5ap/sH19+8KKiOisnu1jHyDpxcAS4Ozadbb/1K7AIiI6RqtGV8wvKO86BdYCtgTuALZtR1ARER1X0XXskvaieMhQP3CK7RPqXl+Dorv7NcBDwP627x3NNlvtinllXSA7AoeNZsMREd1LlTxIQ1I/cBKwJ8Uznq+RNM/2rTXFDgEetr2VpPcCXwT2H812R3SsYfs6oA1Pb46I6BLVjO64E7DA9t22nwXOAGbVlZnFCxejnAXsLo3ucKHVPvbaO1D7gB2B+0ez4YiIbmUJt5ZbJ0uaX7M81/bcmuXNgPtqlheycqP4+TK2l0p6lOLpckuGHXip1WONiTXzSyn63H880o1GRHQ1g928GLDE9sw2RzNsrSb2W23/qHaFpHcDPxqkfETEOGaWt5jZm1gETK9Znlaua1RmoaTVgPUpTqKOWKt97Ee1uC4iYtwzsMzNpxZcA8yQtKWkCRRPnptXV2YecFA5/y7gUnt03yrNRnfcG9gH2EzS12teWo88QSkietgoc+tAHUslHQ5cQHG547dt3yLpOGC+7XnAt4DvSVoA/JUKHjvarCvmfmA+sC9wbc36x4GPjnbjERHdyMDySnpiwPa5wLl1646pmf8b8O5qtlZoNrrjjcCNkk6znRZ6RKwyKsrrHdGsK+ZM2+8Brpe00ue0vX3bIouI6BRX12LvhGZdMUeUP9/e7kAiIrpJFX3snTLkVTG2F5ezh9n+Y+1EhhSIiB5V4VUxHdHq5Y57Nli3d5WBRER0k+VuPnWrZn3sH6Fomb9E0k01L00EftPOwCIiOsUe310xzfrYTwfOA/4VmFOz/nHbf21bVBERHba80wGMQrPLHR8FHgUOAJD0ImBNYF1J6+ZBGxHRq8Zxg721PnZJ75B0F3APcAVwL0VLPiKi5xQ3KLnp1K1aPXn6eeB1wJ22twR2B65sW1QRER22KlwV85zth4A+SX22LwO6bqjKiIiq2M2nbtXqsL2PSFoX+CVwmqQHgSfbF1ZEROcYs3wcDyrQaot9FvA0xcBf5wN/AN7RrqAiIjqqhdb6uG+x265tnZ86aMEaktakaOGvUW7nLNv/Ium7wG4UV9sAHGz7hlYDjogYC918A1IzzW5QepzGg5wJsO31hnj7M8CbbT8haXXg15IGrqT5pO2zRhRxRESbFUMKjN/M3uw69olDvd7kvQaeKBdXL6fxu6ciYpUyjvN6yydPR0RSP8UDOrYCTrJ9VTlMwfGSjgEuAebYfqbBe2cDswFevOkUVnvgjkpjW/SLc5sXGoabv39cpfUBLK/4eqqX7LFFpfUBTNrtnsrrfGyNqZXW97YdN6u0PoBNN1q70vruWbhdpfUBLL57xA+5H9Rv7qq2zt1uvrzS+qoycB17u0naEPghsAXF/UHvsf1wg3LnU1xy/mvbTUfbbfXk6YjYXmZ7B4oHuO4kaTuKZ6W+HHgtsCFw5CDvnWt7pu2ZG28wqZ1hRkSsyLBsefOpAnOAS2zPoGzoDlLuy8AHWq20rYl9gO1HgMuAvWwvduEZ4DvATmMRQ0REq8bwztNZvHBByqnAfg3jsS+heCRpS9qW2CVtLGlSOb8WxdC/t0uaWq4TxYe4uV0xRESMjFnm5hMwWdL8mmn2MDc0pea5F38GplQRfTv72KcCp5b97H3AmbbPkXSppI0prqy5ATi0jTFERAybDc+1do5rie0h78KXdDGwSYOXjl5xm3ajR5CORNsSu+2bgFc3WP/mdm0zIqIKVZ48tb3HYK9JekDSVNuLy96MB6vY5pj0sUdEjDctdsWM1jzgoHL+IOBnVVSaxB4RUadosY/Jo/FOAPYsh0Xfo1xG0kxJpwwUkvQr4EfA7pIWSnrrUJW29Tr2iIhxybBsDMYUKEfN3b3B+vnAh2uW3zicepPYIyLqmO5+kEYzSewREXUMPDeORwFLYo+IqDdGXTHtksQeEVFnrMaKaZck9oiIBrr5mabNJLFHRNRJiz0iosfYbnVIga6UxB4R0UBa7BERPaSnH40XEbFKMizP5Y4REb2jaLF3OoqRS2KPiGggfewRET3ENs9W9FDTTkhij4ioYzKkQERET/E4HysmD9qIiGhg2XI3nUZL0oaSLpJ0V/lzgwZldpD0O0m3SLpJ0v7N6k1ij4ioY5on9Ypa9HOAS2zPAC4pl+s9BXzQ9rbAXsDXJE0aqtJ0xURE1LHh2aVjcvJ0FvCmcv5U4HLgyBVj8Z018/dLehDYGHhksEqT2CMi6gyjj32ypPk1y3Ntzx3GpqbYXlzO/xmYMlRhSTsBE4A/DFUuiT0iooEWE/sS2zOHKiDpYmCTBi8dXbtg25IG3aikqcD3gINsD3k4kcQeEVFnoI+9krrsPQZ7TdIDkqbaXlwm7gcHKbce8AvgaNtXNttmTp5GRNSxYelyN50qMA84qJw/CPhZfQFJE4Czgf+xfVYrlSaxR0Q0MEZXxZwA7CnpLmCPchlJMyWdUpZ5D7ArcLCkG8pph6EqTVdMREQdmzEZUsD2Q8DuDdbPBz5czn8f+P5w6k1ij4ioU2UfeycksUdE1BnvQwoksUdENJDEHhHRQ4rRHTNsb0RE73D62CMiespywzNjM1ZMWySxR0TUGe8P2mjbDUqS1pR0taQby3GEP1uu31LSVZIWSPpheVdVRET38JjdoNQW7bzz9BngzbZfBewA7CXpdcAXgRNtbwU8DBzSxhgiIoZtDMdjb4u2JXYXnigXVy8nA28GBsY7OBXYr10xRESM1HhO7G3tY5fUD1wLbAWcRDGG8CO2l5ZFFgKbDfLe2cBsgIn086ltP1BpbLtvNrHS+tphl2P3rbS+td/x4UrrA7j6qer34413Lqm0viv/8FCl9QHM//WQw2EP28N331hpfQDrbbZ15XXO/+n5ldbXP2GtSuurig1Lc/K0MdvLgB3KxzidDbx8GO+dC8wF2ERrdO9XY0T0HBuWd3GLvJkxuSrG9iOSLgNeD0yStFrZap8GLBqLGCIiWmfs8ZvY23lVzMYDD1yVtBawJ3AbcBnwrrJYw/GHIyI6zcvddOpW7WyxTwVOLfvZ+4AzbZ8j6VbgDEmfB64HvtXGGCIihi9dMY3Zvgl4dYP1dwM7tWu7ERGjZWDop4p2tzxBKSKinmHZsuVNp9GStKGkiyTdVf7coEGZzSVdVz456RZJhzarN4k9ImIlzfvXK+pjnwNcYnsGcEm5XG8x8HrbOwA7A3MkbTpUpUnsERF1iq6YMUnssyhu1IRBbti0/aztZ8rFNWghb2cQsIiIeoblrV3uOFnS/JrlueU9OK2aYntxOf9nYEqjQpKmA7+guNnzk7bvH6rSJPaIiAZabJEvsT1zqAKSLgY2afDS0Stsz7akhhu1fR+wfdkF81NJZ9l+YLBtJrFHRDRQ1XXqtvcY7DVJD0iaanuxpKnAg03qul/SzcAbeWHMrZWkjz0ioo7tMbkqBphHcaMmDHLDpqRp5U2elFfN7ALcMVSlSewREQ14efOpAicAe0q6C9ijXEbSTEmnlGVeAVwl6UbgCuArtn8/VKXpiomIqDNWg4DZfgjYvcH6+cCHy/mLgO2HU28Se0REA908FkwzSewREfWcxB4R0VOMqzo52hFJ7BER9dJij4joPRm2NyKix4znJyglsUdE1LG7+wlJzSSxR0Q0kK6YiIheYrN86bOdjmLEktgjIuoY4+XLOh3GiCWxR0TUM3hZEntERA9Jiz0iorc4iT0ioucksUdE9BDnqpiIiF5jlo/jFnueoBQRUa/sY282jZakDSVdJOmu8ucGQ5RdT9JCSf/ZrN4k9oiIOoYxSezAHOAS2zOAS8rlwXwO+GUrlSaxR0TUs/GyZU2nCswCTi3nTwX2a1RI0muAKcCFrVSaPvaIiHqtnzydLGl+zfJc23OHsaUptheX83+mSN4rkNQH/BvwfooHXjeVxB4RsZKWr2NfYnvmUAUkXQxs0uClo1fYom1JjUYeOww41/ZCSa3ElMQeEVGv6GOv5tF4tgdtZUt6QNJU24slTQUebFDs9cAbJR0GrAtMkPSE7UH745PYIyLqjd2dp/OAg4ATyp8/WzkUv29gXtLBwMyhkjrk5GlERENjdFXMCcCeku6i6D8/AUDSTEmnjLTStNgjIup5bG5Qsv0QsHuD9fOBDzdY/13gu83qTWKPiKhjm+XPjd8hBdrWFSNpuqTLJN0q6RZJR5Trj5W0SNIN5bRPu2KIiBiZsbnztF3a2WJfCnzc9nWSJgLXSrqofO1E219p47YjIkalmxN3M21L7OVF94vL+ccl3QZs1q7tRURUZpyPxy67/U/ilrQFxRgH2wEfAw4GHgPmU7TqH27wntnA7HLxZcAdFYc1GVhScZ1VS4zVSIzVGQ9xvsz2xNFUIOl8is/azBLbe41mW+3Q9sQuaV3gCuB42z+RNIXiD8MUg9pMtf2htgbROK75ze4Y67TEWI3EWJ3xEOd4iLHd2nodu6TVgR8Dp9n+CYDtB2wvs70c+G9gp3bGEBGxqmnnVTECvgXcZvurNeun1hT7e+DmdsUQEbEqaudVMW8APgD8XtIN5bpPAwdI2oGiK+Ze4B/bGMNQhjMCW6ckxmokxuqMhzjHQ4xtNSYnTyMiYuxkrJiIiB6TxB4R0WN6MrFL+rakByXdXLNuB0lXlsMYzJe0U7lekr4uaYGkmyTt2MEYXyXpd5J+L+nnktaree2oMsY7JL11jGIcbFiIhg/g7cS+HCLGd5fLyyXNrHtPN+3LL0u6vdxfZ0ua1Kk4h4jxc2V8N0i6UNKm5fqu+X3XvP5xSZY0uVMxdgXbPTcBuwI7AjfXrLsQ2Luc3we4vGb+PEDA64CrOhjjNcBu5fyHgM+V89sANwJrAFsCfwD6xyDGqcCO5fxE4M4yli8Bc8r1c4AvdmpfDhHjKyhubLucYvzqgfLdti/fAqxWrv9izb4c8ziHiHG9mjL/D/hmt/2+y+XpwAXAH4HJnYqxG6aebLHb/iXw1/rVwEALeH3g/nJ+FvA/LlwJTKq7JHMsY9yaF55CfhHwzpoYz7D9jO17gAWMwfX/thfbvq6cfxwYGBZisAfwjvm+HCxG27fZbnS3clftS9sX2l5aFrsSmNapOIeI8bGaYutQ/C8NxNgVv+/y5ROBT9XE15EYu0FPJvZB/DPwZUn3AV8BjirXbwbcV1NuIZ0b0+YWij9EgHdTtECgC2JUMSzEq4GrGPwBvB2Nsy7GwXTbvqz1IYrWJXTZvpR0fPm/8z7gmG6LUdIsYJHtG+uKdfz33QmrUmL/CPBR29OBj1LcPNVtPgQcJulaisPMrhgQWsWwED8G/rmu9YaL492OXzM7VIzdZLA4JR1NMSLqaZ2KrSaWlWK0fXT5v3MacHgn44MVY6TYb5/mhS+cVd6qlNgPAn5Szv+IFw5rF/FCyxiKQ+FFYxjX82zfbvsttl8D/ICiXxU6GKMaDAsBPDBwOKsVH8DbkTgHiXEw3bYvB55j+XbgfeUXZcfibGFfnsYLXYTdEuNLKc5D3Cjp3jKO6yRt0qkYO21VSuz3A7uV828G7irn5wEfLM+evw54tKabYUxJelH5sw/4DPDNmhjfK2kNSVsCM4CrxyCehsNC8MIDeGHFB/CO+b4cIsbBdNW+lLQXRb/wvraf6mScQ8Q4o6bYLOD2mhg7/vu2/XvbL7K9he0tKLpbdrT9507E2BU6ffa2HRNFa3cx8BzFL/kQYBfgWoorDa4CXlOWFXASRev499RcQdGBGI+gOMt/J8VDbVVT/ugyxjsor+4Zgxh3oehmuQm4oZz2ATYCLqH4crwY2LBT+3KIGP++3K/PAA8AF3TpvlxA0Qc8sO6bnYpziBh/TDGm003AzylOqHbV77uuzL28cFVMR/6/Oz1lSIGIiB6zKnXFRESsEpLYIyJ6TBJ7RESPSWKPiOgxSewRET0miT3aStITbahzX0lzyvn9JG0zgjouV92ojxG9Iok9xh3b82yfUC7uRzECYUSUkthjTJR3/n1Z0s0qxpvfv1z/prL1fJaKcclPK+8uRNI+5bpryzG1zynXHyzpPyX9HbAvxeBuN0h6aW1LXNLk8hZzJK0l6QxJt0k6G1irJra3qBgH/zpJPyrHIYkYt9r5MOuIWv8b2AF4FTAZuEbSwBDFrwa2pRj24TfAGyTNB04GdrV9j6Qf1Fdo+7eS5gHn2D4LoPxOaOQjwFO2XyFpe+C6svxkiuEb9rD9pKQjgY8Bx1XwmSM6Iok9xsouwA9sL6MYROwK4LXAY8DVthcCSLoB2AJ4ArjbxVjkUAzBMHsU298V+DqA7Zsk3VSufx1FV85vyi+FCcDvRrGdiI5LYo9u8EzN/DJG93e5lBe6GNdsobyAi2wfMIptRnSV9LHHWPkVsL+kfkkbU7Sghxqt8A7gJeXDFAD2H6Tc4xRj1w+4F3hNOf+umvW/BA4EkLQdsH25/kqKrp+tytfWkbR1Kx8oolslscdYOZtiRL4bgUuBT7kYVrUh208DhwHnlw8eeRx4tEHRM4BPSrpe0kspno71EUnXU/TlD/gvYF1Jt1H0n19bbucvwMHAD8rumd8BLx/NB43otIzuGF1L0rq2nyivkjkJuMv2iZ2OK6LbpcUe3ez/lCdTb6F4APnJnQ0nYnxIiz0iosekxR4R0WOS2CMiekwSe0REj0lij4joMUnsERE95v8D1NwfwBbM8aAAAAAASUVORK5CYII=",
-      "text/plain": [
-       "<Figure size 432x288 with 2 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
     }
    ],
    "source": [
-    "# visualize correlation map after RGDR().fit(precursor_field, target_timeseries)\n",
-    "rgdr.corr_map[0].plot()"
+    "# View the contents of the corr_map DataArray:\n",
+    "rgdr.corr_map"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 27,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "<matplotlib.collections.QuadMesh at 0x1c0c3e0e620>"
+       "<matplotlib.collections.QuadMesh at 0x1a068ef16f0>"
       ]
      },
-     "execution_count": 8,
+     "execution_count": 27,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
-       "<Figure size 432x288 with 2 Axes>"
+       "<Figure size 720x216 with 4 Axes>"
       ]
      },
      "metadata": {
@@ -694,30 +1133,32 @@
     }
    ],
    "source": [
-    "# visualize p-values map\n",
-    "rgdr.pval_map[0].plot()"
+    "fig, (ax1, ax2) = plt.subplots(figsize=(10, 3), ncols=2)\n",
+    "\n",
+    "# Visualize correlation map after RGDR().fit(precursor_field, target_timeseries)\n",
+    "rgdr.corr_map.sel(i_interval=1).plot(ax=ax1)\n",
+    "\n",
+    "# Visualize p-values map\n",
+    "rgdr.pval_map.sel(i_interval=1).plot(ax=ax2)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The cluster maps can also be visualized. Note that clusters can differ between lags."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 28,
    "metadata": {},
    "outputs": [
     {
      "data": {
+      "image/png": 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",
       "text/plain": [
-       "<matplotlib.collections.QuadMesh at 0x1c0c298fdc0>"
-      ]
-     },
-     "execution_count": 9,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 432x288 with 2 Axes>"
+       "<Figure size 1296x144 with 8 Axes>"
       ]
      },
      "metadata": {
@@ -727,11 +1168,22 @@
     }
    ],
    "source": [
-    "# visualize clusters\n",
     "from s2spy.rgdr.utils import cluster_labels_to_ints\n",
     "cluster_map = cluster_labels_to_ints(rgdr.cluster_map.copy())\n",
-    "cluster_map.cluster_labels[0].plot()"
+    "\n",
+    "fig, axes = plt.subplots(figsize=(18, 2), ncols=4)\n",
+    "\n",
+    "# Loop through lag 1 -> 4\n",
+    "for i, ax in zip((1, 2, 3, 4), axes):\n",
+    "    cluster_map.cluster_labels.sel(i_interval=i).plot(ax=ax, cmap='RdYlBu', vmin=-2, vmax=2)"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {
diff --git a/s2spy/rgdr/rgdr.py b/s2spy/rgdr/rgdr.py
index 239f73c..605add3 100644
--- a/s2spy/rgdr/rgdr.py
+++ b/s2spy/rgdr/rgdr.py
@@ -392,7 +392,7 @@ class RGDR:
         corr, p_val = self.get_correlation(precursor, timeseries)
         return masked_spherical_dbscan(precursor, corr, p_val, self._dbscan_params)
 
-    def plot_correlation(  # pylint: disable=too-many-arguments
+    def preview_correlation(  # pylint: disable=too-many-arguments
         self,
         precursor: xr.DataArray,
         timeseries: xr.DataArray,
@@ -444,7 +444,7 @@ class RGDR:
 
         return [plot1, plot2]
 
-    def plot_clusters(
+    def preview_clusters(
         self,
         precursor: xr.DataArray,
         timeseries: xr.DataArray,


Test patch:
diff --git a/tests/test_rgdr/test_rgdr.py b/tests/test_rgdr/test_rgdr.py
index 4c719eb..fcdf93b 100644
--- a/tests/test_rgdr/test_rgdr.py
+++ b/tests/test_rgdr/test_rgdr.py
@@ -221,38 +221,38 @@ class TestRGDR:
              "lag:2_cluster:1", "lag:3_cluster:-1"])
         np.testing.assert_array_equal(clustered_data["cluster_labels"], cluster_labels)
 
-    def test_corr_plot(self, dummy_rgdr, example_field, example_target):
-        dummy_rgdr.plot_correlation(example_field, example_target)
+    def test_corr_preview(self, dummy_rgdr, example_field, example_target):
+        dummy_rgdr.preview_correlation(example_field, example_target)
 
-    def test_corr_plot_multiple_lags(
+    def test_corr_preview_multiple_lags(
         self, dummy_rgdr, example_field_multiple_lags, example_target
         ):
-        dummy_rgdr.plot_correlation(example_field_multiple_lags, example_target, lag=1)
+        dummy_rgdr.preview_correlation(example_field_multiple_lags, example_target, lag=1)
 
-    def test_corr_plot_multiple_lags_fail(
+    def test_corr_preview_multiple_lags_fail(
         self, dummy_rgdr, example_field_multiple_lags, example_target
         ):
         with pytest.raises(ValueError):
-            dummy_rgdr.plot_correlation(example_field_multiple_lags, example_target)
+            dummy_rgdr.preview_correlation(example_field_multiple_lags, example_target)
 
     def test_corr_plot_ax(self, dummy_rgdr, example_field, example_target):
         _, (ax1, ax2) = plt.subplots(ncols=2)
-        dummy_rgdr.plot_correlation(example_field, example_target, ax1=ax1, ax2=ax2)
+        dummy_rgdr.preview_correlation(example_field, example_target, ax1=ax1, ax2=ax2)
 
     def test_cluster_plot(self, dummy_rgdr, example_field, example_target):
-        dummy_rgdr.plot_clusters(example_field, example_target)
+        dummy_rgdr.preview_clusters(example_field, example_target)
 
-    def test_cluster_plot_multiple_lags(
+    def test_cluster_preview_multiple_lags(
         self, dummy_rgdr, example_field_multiple_lags, example_target
         ):
-        dummy_rgdr.plot_clusters(example_field_multiple_lags, example_target, lag=1)
+        dummy_rgdr.preview_clusters(example_field_multiple_lags, example_target, lag=1)
 
-    def test_cluster_plot_multiple_lags_fail(
+    def test_cluster_preview_multiple_lags_fail(
         self, dummy_rgdr, example_field_multiple_lags, example_target
         ):
         with pytest.raises(ValueError):
-            dummy_rgdr.plot_clusters(example_field_multiple_lags, example_target)
+            dummy_rgdr.preview_clusters(example_field_multiple_lags, example_target)
 
     def test_cluster_plot_ax(self, dummy_rgdr, example_field, example_target):
         _, ax = plt.subplots()
-        dummy_rgdr.plot_clusters(example_field, example_target, ax=ax)
+        dummy_rgdr.preview_clusters(example_field, example_target, ax=ax)