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    "# Xarray Interpolation, Groupby, Resample, Rolling, and Coarsen\n",
    "\n",
    "**Attribution**: This notebook is a revision of the [Xarray Interpolation, Groupby, Resample, Rolling, and Coarsen notebook](https://earth-env-data-science.github.io/lectures/xarray/xarray-part2.html) by Ryan Abernathey from [An Introduction to Earth and Environmental Data Science](https://earth-env-data-science.github.io/intro.html). Thanks to Aiyin Zhang for preparing this notebook. \n",
    "\n",
    "In this lesson, we cover some more advanced aspects of xarray."
   ]
  },
  {
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   "metadata": {
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   "source": [
    "import numpy as np\n",
    "import xarray as xr\n",
    "from matplotlib import pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Interpolation\n",
    "\n",
    "In the previous lesson on Xarray, we learned how to select data based on its dimension coordinates and align data with dimension different coordinates.\n",
    "But what if we want to estimate the value of the data variables at _different coordinates_.\n",
    "This is where interpolation comes in."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "tags": []
   },
   "outputs": [
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray (x: 11)&gt;\n",
       "array([  0,   1,   4,   9,  16,  25,  36,  49,  64,  81, 100])\n",
       "Coordinates:\n",
       "  * x        (x) int64 0 1 2 3 4 5 6 7 8 9 10</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'>x</span>: 11</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-35ff8b68-3add-4edd-b3cc-ac341cdbf820' class='xr-array-in' type='checkbox' checked><label for='section-35ff8b68-3add-4edd-b3cc-ac341cdbf820' 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 1 4 9 16 25 36 49 64 81 100</span></div><div class='xr-array-data'><pre>array([  0,   1,   4,   9,  16,  25,  36,  49,  64,  81, 100])</pre></div></div></li><li class='xr-section-item'><input id='section-847f1970-cec6-46b5-9163-050a253630d4' class='xr-section-summary-in' type='checkbox'  checked><label for='section-847f1970-cec6-46b5-9163-050a253630d4' class='xr-section-summary' >Coordinates: <span>(1)</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'>x</span></div><div class='xr-var-dims'>(x)</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</div><input id='attrs-fa5be0d9-5c2b-4c1c-b0fd-e6af48f0858a' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-fa5be0d9-5c2b-4c1c-b0fd-e6af48f0858a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9e2908d9-2838-448d-9dee-785f33400ca8' class='xr-var-data-in' type='checkbox'><label for='data-9e2908d9-2838-448d-9dee-785f33400ca8' 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])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-ebb33d2a-3c18-40ee-9a72-e3a053c9d153' class='xr-section-summary-in' type='checkbox'  ><label for='section-ebb33d2a-3c18-40ee-9a72-e3a053c9d153' class='xr-section-summary' >Indexes: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>x</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-2d4820ba-3fd4-4d1a-8d07-3989113acdb0' class='xr-index-data-in' type='checkbox'/><label for='index-2d4820ba-3fd4-4d1a-8d07-3989113acdb0' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10], dtype=&#x27;int64&#x27;, name=&#x27;x&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-6f19d2d2-3d12-4128-9071-274302b8473d' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-6f19d2d2-3d12-4128-9071-274302b8473d' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.DataArray (x: 11)>\n",
       "array([  0,   1,   4,   9,  16,  25,  36,  49,  64,  81, 100])\n",
       "Coordinates:\n",
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    "# we write it out explicitly so we can see each point.\n",
    "x_data = np.array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10])\n",
    "f = xr.DataArray(x_data**2, dims=['x'], coords={'x': x_data})\n",
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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "f.plot(marker='o')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
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       "<defs>\n",
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       "</defs>\n",
       "</svg>\n",
       "<style>/* CSS stylesheet for displaying xarray objects in jupyterlab.\n",
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       " */\n",
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       "  --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[data-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",
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       "\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",
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       "\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",
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       "\n",
       ".xr-section-summary {\n",
       "  grid-column: 1;\n",
       "  color: var(--xr-font-color2);\n",
       "  font-weight: 500;\n",
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       "\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",
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       "}\n",
       "\n",
       ".xr-section-summary,\n",
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       "  padding-bottom: 4px;\n",
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       "\n",
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       "\n",
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       "  margin-bottom: 5px;\n",
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       "\n",
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       "\n",
       ".xr-array-wrap {\n",
       "  grid-column: 1 / -1;\n",
       "  display: grid;\n",
       "  grid-template-columns: 20px auto;\n",
       "}\n",
       "\n",
       ".xr-array-wrap > label {\n",
       "  grid-column: 1;\n",
       "  vertical-align: top;\n",
       "}\n",
       "\n",
       ".xr-preview {\n",
       "  color: var(--xr-font-color3);\n",
       "}\n",
       "\n",
       ".xr-array-preview,\n",
       ".xr-array-data {\n",
       "  padding: 0 5px !important;\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-array-data,\n",
       ".xr-array-in:checked ~ .xr-array-preview {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-array-in:checked ~ .xr-array-data,\n",
       ".xr-array-preview {\n",
       "  display: inline-block;\n",
       "}\n",
       "\n",
       ".xr-dim-list {\n",
       "  display: inline-block !important;\n",
       "  list-style: none;\n",
       "  padding: 0 !important;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list li {\n",
       "  display: inline-block;\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list:before {\n",
       "  content: '(';\n",
       "}\n",
       "\n",
       ".xr-dim-list:after {\n",
       "  content: ')';\n",
       "}\n",
       "\n",
       ".xr-dim-list li:not(:last-child):after {\n",
       "  content: ',';\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-has-index {\n",
       "  font-weight: bold;\n",
       "}\n",
       "\n",
       ".xr-var-list,\n",
       ".xr-var-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-var-item > div,\n",
       ".xr-var-item label,\n",
       ".xr-var-item > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-even);\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-var-item > .xr-var-name:hover span {\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-var-list > li:nth-child(odd) > div,\n",
       ".xr-var-list > li:nth-child(odd) > label,\n",
       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-odd);\n",
       "}\n",
       "\n",
       ".xr-var-name {\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-var-dims {\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-var-dtype {\n",
       "  grid-column: 3;\n",
       "  text-align: right;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-preview {\n",
       "  grid-column: 4;\n",
       "}\n",
       "\n",
       ".xr-index-preview {\n",
       "  grid-column: 2 / 5;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-name,\n",
       ".xr-var-dims,\n",
       ".xr-var-dtype,\n",
       ".xr-preview,\n",
       ".xr-attrs dt {\n",
       "  white-space: nowrap;\n",
       "  overflow: hidden;\n",
       "  text-overflow: ellipsis;\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-var-name:hover,\n",
       ".xr-var-dims:hover,\n",
       ".xr-var-dtype:hover,\n",
       ".xr-attrs dt:hover {\n",
       "  overflow: visible;\n",
       "  width: auto;\n",
       "  z-index: 1;\n",
       "}\n",
       "\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-data {\n",
       "  display: none;\n",
       "  background-color: var(--xr-background-color) !important;\n",
       "  padding-bottom: 5px !important;\n",
       "}\n",
       "\n",
       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
       ".xr-var-data-in:checked ~ .xr-var-data,\n",
       ".xr-index-data-in:checked ~ .xr-index-data {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       ".xr-var-data > table {\n",
       "  float: right;\n",
       "}\n",
       "\n",
       ".xr-var-name span,\n",
       ".xr-var-data,\n",
       ".xr-index-name div,\n",
       ".xr-index-data,\n",
       ".xr-attrs {\n",
       "  padding-left: 25px !important;\n",
       "}\n",
       "\n",
       ".xr-attrs,\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-data {\n",
       "  grid-column: 1 / -1;\n",
       "}\n",
       "\n",
       "dl.xr-attrs {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  display: grid;\n",
       "  grid-template-columns: 125px auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt,\n",
       ".xr-attrs dd {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  float: left;\n",
       "  padding-right: 10px;\n",
       "  width: auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt {\n",
       "  font-weight: normal;\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-attrs dt:hover span {\n",
       "  display: inline-block;\n",
       "  background: var(--xr-background-color);\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-attrs dd {\n",
       "  grid-column: 2;\n",
       "  white-space: pre-wrap;\n",
       "  word-break: break-all;\n",
       "}\n",
       "\n",
       ".xr-icon-database,\n",
       ".xr-icon-file-text2,\n",
       ".xr-no-icon {\n",
       "  display: inline-block;\n",
       "  vertical-align: middle;\n",
       "  width: 1em;\n",
       "  height: 1.5em !important;\n",
       "  stroke-width: 0;\n",
       "  stroke: currentColor;\n",
       "  fill: currentColor;\n",
       "}\n",
       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray ()&gt;\n",
       "array(9)\n",
       "Coordinates:\n",
       "    x        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></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-34644819-cdb5-4564-9f13-0e4aec866223' class='xr-array-in' type='checkbox' checked><label for='section-34644819-cdb5-4564-9f13-0e4aec866223' 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>9</span></div><div class='xr-array-data'><pre>array(9)</pre></div></div></li><li class='xr-section-item'><input id='section-b49f9763-1610-4e23-be81-0b6088bb94f0' class='xr-section-summary-in' type='checkbox'  checked><label for='section-b49f9763-1610-4e23-be81-0b6088bb94f0' class='xr-section-summary' >Coordinates: <span>(1)</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>x</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-36ae5e3c-019b-4e7b-9e94-a20c64a1d0be' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-36ae5e3c-019b-4e7b-9e94-a20c64a1d0be' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b4803e04-5f13-403e-911e-177a03c0d78d' class='xr-var-data-in' type='checkbox'><label for='data-b4803e04-5f13-403e-911e-177a03c0d78d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array(3)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-acaf5751-1eb2-46d2-8d91-4f041f5cebe0' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-acaf5751-1eb2-46d2-8d91-4f041f5cebe0' class='xr-section-summary'  title='Expand/collapse section'>Indexes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'></ul></div></li><li class='xr-section-item'><input id='section-a90a61f7-ee98-4c05-9e33-a8f657304bd8' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-a90a61f7-ee98-4c05-9e33-a8f657304bd8' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.DataArray ()>\n",
       "array(9)\n",
       "Coordinates:\n",
       "    x        int64 3"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "f.sel(x=3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We only have data on the integer points in x.\n",
    "But what if we wanted to estimate the value at, say, 4.5?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "ename": "KeyError",
     "evalue": "\"not all values found in index 'x'. Try setting the `method` keyword argument (example: method='nearest').\"",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "File \u001b[0;32m/srv/conda/envs/notebook/lib/python3.11/site-packages/pandas/core/indexes/base.py:3652\u001b[0m, in \u001b[0;36mIndex.get_loc\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m   3651\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m-> 3652\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_engine\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_loc\u001b[49m\u001b[43m(\u001b[49m\u001b[43mcasted_key\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   3653\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m err:\n",
      "File \u001b[0;32m/srv/conda/envs/notebook/lib/python3.11/site-packages/pandas/_libs/index.pyx:147\u001b[0m, in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
      "File \u001b[0;32m/srv/conda/envs/notebook/lib/python3.11/site-packages/pandas/_libs/index.pyx:155\u001b[0m, in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
      "File \u001b[0;32mpandas/_libs/index_class_helper.pxi:70\u001b[0m, in \u001b[0;36mpandas._libs.index.Int64Engine._check_type\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;31mKeyError\u001b[0m: 4.5",
      "\nThe above exception was the direct cause of the following exception:\n",
      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "File \u001b[0;32m/srv/conda/envs/notebook/lib/python3.11/site-packages/xarray/core/indexes.py:486\u001b[0m, in \u001b[0;36mPandasIndex.sel\u001b[0;34m(self, labels, method, tolerance)\u001b[0m\n\u001b[1;32m    485\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 486\u001b[0m     indexer \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mindex\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_loc\u001b[49m\u001b[43m(\u001b[49m\u001b[43mlabel_value\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    487\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m e:\n",
      "File \u001b[0;32m/srv/conda/envs/notebook/lib/python3.11/site-packages/pandas/core/indexes/base.py:3654\u001b[0m, in \u001b[0;36mIndex.get_loc\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m   3653\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m err:\n\u001b[0;32m-> 3654\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m(key) \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01merr\u001b[39;00m\n\u001b[1;32m   3655\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m:\n\u001b[1;32m   3656\u001b[0m     \u001b[38;5;66;03m# If we have a listlike key, _check_indexing_error will raise\u001b[39;00m\n\u001b[1;32m   3657\u001b[0m     \u001b[38;5;66;03m#  InvalidIndexError. Otherwise we fall through and re-raise\u001b[39;00m\n\u001b[1;32m   3658\u001b[0m     \u001b[38;5;66;03m#  the TypeError.\u001b[39;00m\n",
      "\u001b[0;31mKeyError\u001b[0m: 4.5",
      "\nThe above exception was the direct cause of the following exception:\n",
      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[5], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mf\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msel\u001b[49m\u001b[43m(\u001b[49m\u001b[43mx\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m4.5\u001b[39;49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m/srv/conda/envs/notebook/lib/python3.11/site-packages/xarray/core/dataarray.py:1549\u001b[0m, in \u001b[0;36mDataArray.sel\u001b[0;34m(self, indexers, method, tolerance, drop, **indexers_kwargs)\u001b[0m\n\u001b[1;32m   1439\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21msel\u001b[39m(\n\u001b[1;32m   1440\u001b[0m     \u001b[38;5;28mself\u001b[39m: T_DataArray,\n\u001b[1;32m   1441\u001b[0m     indexers: Mapping[Any, Any] \u001b[38;5;241m|\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m   1445\u001b[0m     \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mindexers_kwargs: Any,\n\u001b[1;32m   1446\u001b[0m ) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m T_DataArray:\n\u001b[1;32m   1447\u001b[0m \u001b[38;5;250m    \u001b[39m\u001b[38;5;124;03m\"\"\"Return a new DataArray whose data is given by selecting index\u001b[39;00m\n\u001b[1;32m   1448\u001b[0m \u001b[38;5;124;03m    labels along the specified dimension(s).\u001b[39;00m\n\u001b[1;32m   1449\u001b[0m \n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m   1547\u001b[0m \u001b[38;5;124;03m    Dimensions without coordinates: points\u001b[39;00m\n\u001b[1;32m   1548\u001b[0m \u001b[38;5;124;03m    \"\"\"\u001b[39;00m\n\u001b[0;32m-> 1549\u001b[0m     ds \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_to_temp_dataset\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msel\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m   1550\u001b[0m \u001b[43m        \u001b[49m\u001b[43mindexers\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mindexers\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1551\u001b[0m \u001b[43m        \u001b[49m\u001b[43mdrop\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdrop\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1552\u001b[0m \u001b[43m        \u001b[49m\u001b[43mmethod\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmethod\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1553\u001b[0m \u001b[43m        \u001b[49m\u001b[43mtolerance\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtolerance\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1554\u001b[0m \u001b[43m        \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mindexers_kwargs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1555\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1556\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_from_temp_dataset(ds)\n",
      "File \u001b[0;32m/srv/conda/envs/notebook/lib/python3.11/site-packages/xarray/core/dataset.py:2642\u001b[0m, in \u001b[0;36mDataset.sel\u001b[0;34m(self, indexers, method, tolerance, drop, **indexers_kwargs)\u001b[0m\n\u001b[1;32m   2581\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"Returns a new dataset with each array indexed by tick labels\u001b[39;00m\n\u001b[1;32m   2582\u001b[0m \u001b[38;5;124;03malong the specified dimension(s).\u001b[39;00m\n\u001b[1;32m   2583\u001b[0m \n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m   2639\u001b[0m \u001b[38;5;124;03mDataArray.sel\u001b[39;00m\n\u001b[1;32m   2640\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m   2641\u001b[0m indexers \u001b[38;5;241m=\u001b[39m either_dict_or_kwargs(indexers, indexers_kwargs, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124msel\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m-> 2642\u001b[0m query_results \u001b[38;5;241m=\u001b[39m \u001b[43mmap_index_queries\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m   2643\u001b[0m \u001b[43m    \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mindexers\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mindexers\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmethod\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmethod\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtolerance\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtolerance\u001b[49m\n\u001b[1;32m   2644\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   2646\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m drop:\n\u001b[1;32m   2647\u001b[0m     no_scalar_variables \u001b[38;5;241m=\u001b[39m {}\n",
      "File \u001b[0;32m/srv/conda/envs/notebook/lib/python3.11/site-packages/xarray/core/indexing.py:190\u001b[0m, in \u001b[0;36mmap_index_queries\u001b[0;34m(obj, indexers, method, tolerance, **indexers_kwargs)\u001b[0m\n\u001b[1;32m    188\u001b[0m         results\u001b[38;5;241m.\u001b[39mappend(IndexSelResult(labels))\n\u001b[1;32m    189\u001b[0m     \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m--> 190\u001b[0m         results\u001b[38;5;241m.\u001b[39mappend(\u001b[43mindex\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msel\u001b[49m\u001b[43m(\u001b[49m\u001b[43mlabels\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43moptions\u001b[49m\u001b[43m)\u001b[49m)\n\u001b[1;32m    192\u001b[0m merged \u001b[38;5;241m=\u001b[39m merge_sel_results(results)\n\u001b[1;32m    194\u001b[0m \u001b[38;5;66;03m# drop dimension coordinates found in dimension indexers\u001b[39;00m\n\u001b[1;32m    195\u001b[0m \u001b[38;5;66;03m# (also drop multi-index if any)\u001b[39;00m\n\u001b[1;32m    196\u001b[0m \u001b[38;5;66;03m# (.sel() already ensures alignment)\u001b[39;00m\n",
      "File \u001b[0;32m/srv/conda/envs/notebook/lib/python3.11/site-packages/xarray/core/indexes.py:488\u001b[0m, in \u001b[0;36mPandasIndex.sel\u001b[0;34m(self, labels, method, tolerance)\u001b[0m\n\u001b[1;32m    486\u001b[0m                 indexer \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mindex\u001b[38;5;241m.\u001b[39mget_loc(label_value)\n\u001b[1;32m    487\u001b[0m             \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[0;32m--> 488\u001b[0m                 \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m(\n\u001b[1;32m    489\u001b[0m                     \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mnot all values found in index \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mcoord_name\u001b[38;5;132;01m!r}\u001b[39;00m\u001b[38;5;124m. \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m    490\u001b[0m                     \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mTry setting the `method` keyword argument (example: method=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mnearest\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m).\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m    491\u001b[0m                 ) \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01me\u001b[39;00m\n\u001b[1;32m    493\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m label_array\u001b[38;5;241m.\u001b[39mdtype\u001b[38;5;241m.\u001b[39mkind \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mb\u001b[39m\u001b[38;5;124m\"\u001b[39m:\n\u001b[1;32m    494\u001b[0m     indexer \u001b[38;5;241m=\u001b[39m label_array\n",
      "\u001b[0;31mKeyError\u001b[0m: \"not all values found in index 'x'. Try setting the `method` keyword argument (example: method='nearest').\""
     ]
    }
   ],
   "source": [
    "f.sel(x=4.5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Interpolation to the rescue!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "tags": []
   },
   "outputs": [
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       ".xr-var-item > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-even);\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-var-item > .xr-var-name:hover span {\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-var-list > li:nth-child(odd) > div,\n",
       ".xr-var-list > li:nth-child(odd) > label,\n",
       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-odd);\n",
       "}\n",
       "\n",
       ".xr-var-name {\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-var-dims {\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-var-dtype {\n",
       "  grid-column: 3;\n",
       "  text-align: right;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-preview {\n",
       "  grid-column: 4;\n",
       "}\n",
       "\n",
       ".xr-index-preview {\n",
       "  grid-column: 2 / 5;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-name,\n",
       ".xr-var-dims,\n",
       ".xr-var-dtype,\n",
       ".xr-preview,\n",
       ".xr-attrs dt {\n",
       "  white-space: nowrap;\n",
       "  overflow: hidden;\n",
       "  text-overflow: ellipsis;\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-var-name:hover,\n",
       ".xr-var-dims:hover,\n",
       ".xr-var-dtype:hover,\n",
       ".xr-attrs dt:hover {\n",
       "  overflow: visible;\n",
       "  width: auto;\n",
       "  z-index: 1;\n",
       "}\n",
       "\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-data {\n",
       "  display: none;\n",
       "  background-color: var(--xr-background-color) !important;\n",
       "  padding-bottom: 5px !important;\n",
       "}\n",
       "\n",
       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
       ".xr-var-data-in:checked ~ .xr-var-data,\n",
       ".xr-index-data-in:checked ~ .xr-index-data {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       ".xr-var-data > table {\n",
       "  float: right;\n",
       "}\n",
       "\n",
       ".xr-var-name span,\n",
       ".xr-var-data,\n",
       ".xr-index-name div,\n",
       ".xr-index-data,\n",
       ".xr-attrs {\n",
       "  padding-left: 25px !important;\n",
       "}\n",
       "\n",
       ".xr-attrs,\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-data {\n",
       "  grid-column: 1 / -1;\n",
       "}\n",
       "\n",
       "dl.xr-attrs {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  display: grid;\n",
       "  grid-template-columns: 125px auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt,\n",
       ".xr-attrs dd {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  float: left;\n",
       "  padding-right: 10px;\n",
       "  width: auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt {\n",
       "  font-weight: normal;\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-attrs dt:hover span {\n",
       "  display: inline-block;\n",
       "  background: var(--xr-background-color);\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-attrs dd {\n",
       "  grid-column: 2;\n",
       "  white-space: pre-wrap;\n",
       "  word-break: break-all;\n",
       "}\n",
       "\n",
       ".xr-icon-database,\n",
       ".xr-icon-file-text2,\n",
       ".xr-no-icon {\n",
       "  display: inline-block;\n",
       "  vertical-align: middle;\n",
       "  width: 1em;\n",
       "  height: 1.5em !important;\n",
       "  stroke-width: 0;\n",
       "  stroke: currentColor;\n",
       "  fill: currentColor;\n",
       "}\n",
       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray ()&gt;\n",
       "array(20.5)\n",
       "Coordinates:\n",
       "    x        float64 4.5</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></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-73619173-361a-4d44-bd11-e6070f9fd9f6' class='xr-array-in' type='checkbox' checked><label for='section-73619173-361a-4d44-bd11-e6070f9fd9f6' 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>20.5</span></div><div class='xr-array-data'><pre>array(20.5)</pre></div></div></li><li class='xr-section-item'><input id='section-25c358de-51c2-430a-b806-e3d6ec841f93' class='xr-section-summary-in' type='checkbox'  checked><label for='section-25c358de-51c2-430a-b806-e3d6ec841f93' class='xr-section-summary' >Coordinates: <span>(1)</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>x</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>4.5</div><input id='attrs-61cc9353-1399-405d-a67e-ff5c27bfcba1' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-61cc9353-1399-405d-a67e-ff5c27bfcba1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-eb062146-c420-4f98-82b7-972e0c10745f' class='xr-var-data-in' type='checkbox'><label for='data-eb062146-c420-4f98-82b7-972e0c10745f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array(4.5)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-8a3a1146-d572-4629-bc5e-d8222c15b406' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-8a3a1146-d572-4629-bc5e-d8222c15b406' class='xr-section-summary'  title='Expand/collapse section'>Indexes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'></ul></div></li><li class='xr-section-item'><input id='section-dcdc3f97-69a2-4fb0-8c25-1bc189c05969' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-dcdc3f97-69a2-4fb0-8c25-1bc189c05969' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.DataArray ()>\n",
       "array(20.5)\n",
       "Coordinates:\n",
       "    x        float64 4.5"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "f.interp(x=4.5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Interpolation uses [scipy.interpolate](https://docs.scipy.org/doc/scipy/reference/interpolate.html) under the hood.\n",
    "There are different modes of interpolation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(20.5)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# default\n",
    "f.interp(x=4.5, method='linear').values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(16.)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "f.interp(x=4.5, method='nearest').values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(20.25)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "f.interp(x=4.5, method='cubic').values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can interpolate to a whole new coordinate at once:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x782661d96250>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x_new = x_data + 0.5\n",
    "f_interp_linear = f.interp(x=x_new, method='linear')\n",
    "f_interp_cubic = f.interp(x=x_new, method='cubic')\n",
    "f.plot(marker='o', label='original')\n",
    "f_interp_linear.plot(marker='o', label='linear')\n",
    "f_interp_cubic.plot(marker='o', label='cubic')\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note that values outside of the original range are not supported:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.25,  2.25,  6.25, 12.25, 20.25, 30.25, 42.25, 56.25, 72.25,\n",
       "       90.25,   nan])"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "f_interp_cubic.values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "```{note}\n",
    "You can apply interpolation to any dimension, and even to multiple dimensions at a time.\n",
    "(Multidimensional interpolation only supports `mode='nearest'` and `mode='linear'`.)\n",
    "But keep in mind that _Xarray has no built-in understanding of geography_.\n",
    "If you use `interp` on lat / lon coordinates, it will just perform naive interpolation of the lat / lon values.\n",
    "More sophisticated treatment of spherical geometry requires another package such as [xesmf](https://xesmf.readthedocs.io/).\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "## Groupby\n",
    "\n",
    "Xarray copies Pandas' very useful groupby functionality, enabling the \"split / apply / combine\" workflow on xarray DataArrays and Datasets. In the first part of the lesson, we will learn to use groupby by analyzing sea-surface temperature data.\n",
    "\n",
    "\n",
    "First we load a dataset. We will use the [NOAA Extended Reconstructed Sea Surface Temperature (ERSST) v5](https://www.ncdc.noaa.gov/data-access/marineocean-data/extended-reconstructed-sea-surface-temperature-ersst-v5) product, a widely used and trusted gridded compilation of historical data going back to 1854.\n",
    "\n",
    "Since the data is provided via an [OPeNDAP](https://en.wikipedia.org/wiki/OPeNDAP) server, we can load it directly without downloading anything:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "url = 'http://www.esrl.noaa.gov/psd/thredds/dodsC/Datasets/noaa.ersst.v5/sst.mnmean.nc'\n",
    "ds = xr.open_dataset(url, drop_variables=['time_bnds'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "tags": []
   },
   "outputs": [
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
       "Dimensions:  (lat: 89, lon: 180, time: 756)\n",
       "Coordinates:\n",
       "  * lat      (lat) float32 88.0 86.0 84.0 82.0 80.0 ... -82.0 -84.0 -86.0 -88.0\n",
       "  * lon      (lon) float32 0.0 2.0 4.0 6.0 8.0 ... 350.0 352.0 354.0 356.0 358.0\n",
       "  * time     (time) datetime64[ns] 1960-01-01 1960-02-01 ... 2022-12-01\n",
       "Data variables:\n",
       "    sst      (time, lat, lon) float32 ...\n",
       "Attributes: (12/39)\n",
       "    climatology:                     Climatology is based on 1971-2000 SST, X...\n",
       "    description:                     In situ data: ICOADS2.5 before 2007 and ...\n",
       "    keywords_vocabulary:             NASA Global Change Master Directory (GCM...\n",
       "    keywords:                        Earth Science &gt; Oceans &gt; Ocean Temperatu...\n",
       "    instrument:                      Conventional thermometers\n",
       "    source_comment:                  SSTs were observed by conventional therm...\n",
       "    ...                              ...\n",
       "    comment:                         SSTs were observed by conventional therm...\n",
       "    summary:                         ERSST.v5 is developed based on v4 after ...\n",
       "    dataset_title:                   NOAA Extended Reconstructed SST V5\n",
       "    _NCProperties:                   version=2,netcdf=4.6.3,hdf5=1.10.5\n",
       "    data_modified:                   2023-11-03\n",
       "    DODS_EXTRA.Unlimited_Dimension:  time</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-27a55b14-be79-4858-85cf-41f1e480d828' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-27a55b14-be79-4858-85cf-41f1e480d828' 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'>lat</span>: 89</li><li><span class='xr-has-index'>lon</span>: 180</li><li><span class='xr-has-index'>time</span>: 756</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-e1111471-51ad-4f01-80dc-eb258981ba87' class='xr-section-summary-in' type='checkbox'  checked><label for='section-e1111471-51ad-4f01-80dc-eb258981ba87' class='xr-section-summary' >Coordinates: <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 class='xr-has-index'>lat</span></div><div class='xr-var-dims'>(lat)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>88.0 86.0 84.0 ... -86.0 -88.0</div><input id='attrs-4f9d14ba-f96f-4944-9f50-f9308a61857b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4f9d14ba-f96f-4944-9f50-f9308a61857b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c5196121-c8f5-48b4-b11f-676eca846413' class='xr-var-data-in' type='checkbox'><label for='data-c5196121-c8f5-48b4-b11f-676eca846413' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degrees_north</dd><dt><span>long_name :</span></dt><dd>Latitude</dd><dt><span>actual_range :</span></dt><dd>[ 88. -88.]</dd><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>axis :</span></dt><dd>Y</dd><dt><span>coordinate_defines :</span></dt><dd>center</dd></dl></div><div class='xr-var-data'><pre>array([ 88.,  86.,  84.,  82.,  80.,  78.,  76.,  74.,  72.,  70.,  68.,  66.,\n",
       "        64.,  62.,  60.,  58.,  56.,  54.,  52.,  50.,  48.,  46.,  44.,  42.,\n",
       "        40.,  38.,  36.,  34.,  32.,  30.,  28.,  26.,  24.,  22.,  20.,  18.,\n",
       "        16.,  14.,  12.,  10.,   8.,   6.,   4.,   2.,   0.,  -2.,  -4.,  -6.,\n",
       "        -8., -10., -12., -14., -16., -18., -20., -22., -24., -26., -28., -30.,\n",
       "       -32., -34., -36., -38., -40., -42., -44., -46., -48., -50., -52., -54.,\n",
       "       -56., -58., -60., -62., -64., -66., -68., -70., -72., -74., -76., -78.,\n",
       "       -80., -82., -84., -86., -88.], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lon</span></div><div class='xr-var-dims'>(lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.0 2.0 4.0 ... 354.0 356.0 358.0</div><input id='attrs-ec8b665f-0b06-4a2c-be0f-d9135c2a42c9' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ec8b665f-0b06-4a2c-be0f-d9135c2a42c9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c6631101-5816-419d-a2c4-e2d00c7fd691' class='xr-var-data-in' type='checkbox'><label for='data-c6631101-5816-419d-a2c4-e2d00c7fd691' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degrees_east</dd><dt><span>long_name :</span></dt><dd>Longitude</dd><dt><span>actual_range :</span></dt><dd>[  0. 358.]</dd><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>axis :</span></dt><dd>X</dd><dt><span>coordinate_defines :</span></dt><dd>center</dd></dl></div><div class='xr-var-data'><pre>array([  0.,   2.,   4.,   6.,   8.,  10.,  12.,  14.,  16.,  18.,  20.,  22.,\n",
       "        24.,  26.,  28.,  30.,  32.,  34.,  36.,  38.,  40.,  42.,  44.,  46.,\n",
       "        48.,  50.,  52.,  54.,  56.,  58.,  60.,  62.,  64.,  66.,  68.,  70.,\n",
       "        72.,  74.,  76.,  78.,  80.,  82.,  84.,  86.,  88.,  90.,  92.,  94.,\n",
       "        96.,  98., 100., 102., 104., 106., 108., 110., 112., 114., 116., 118.,\n",
       "       120., 122., 124., 126., 128., 130., 132., 134., 136., 138., 140., 142.,\n",
       "       144., 146., 148., 150., 152., 154., 156., 158., 160., 162., 164., 166.,\n",
       "       168., 170., 172., 174., 176., 178., 180., 182., 184., 186., 188., 190.,\n",
       "       192., 194., 196., 198., 200., 202., 204., 206., 208., 210., 212., 214.,\n",
       "       216., 218., 220., 222., 224., 226., 228., 230., 232., 234., 236., 238.,\n",
       "       240., 242., 244., 246., 248., 250., 252., 254., 256., 258., 260., 262.,\n",
       "       264., 266., 268., 270., 272., 274., 276., 278., 280., 282., 284., 286.,\n",
       "       288., 290., 292., 294., 296., 298., 300., 302., 304., 306., 308., 310.,\n",
       "       312., 314., 316., 318., 320., 322., 324., 326., 328., 330., 332., 334.,\n",
       "       336., 338., 340., 342., 344., 346., 348., 350., 352., 354., 356., 358.],\n",
       "      dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>1960-01-01 ... 2022-12-01</div><input id='attrs-5159bc0c-c812-42f2-b755-e2d88a41b24f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5159bc0c-c812-42f2-b755-e2d88a41b24f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-802fc375-89f7-4cb9-8bfa-18a6792947a9' class='xr-var-data-in' type='checkbox'><label for='data-802fc375-89f7-4cb9-8bfa-18a6792947a9' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Time</dd><dt><span>delta_t :</span></dt><dd>0000-01-00 00:00:00</dd><dt><span>avg_period :</span></dt><dd>0000-01-00 00:00:00</dd><dt><span>prev_avg_period :</span></dt><dd>0000-00-07 00:00:00</dd><dt><span>standard_name :</span></dt><dd>time</dd><dt><span>axis :</span></dt><dd>T</dd><dt><span>actual_range :</span></dt><dd>[19723. 81722.]</dd><dt><span>_ChunkSizes :</span></dt><dd>1</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;1960-01-01T00:00:00.000000000&#x27;, &#x27;1960-02-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;1960-03-01T00:00:00.000000000&#x27;, ..., &#x27;2022-10-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2022-11-01T00:00:00.000000000&#x27;, &#x27;2022-12-01T00:00:00.000000000&#x27;],\n",
       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-19dd971b-3d62-46f2-9d16-a5083f81632a' class='xr-section-summary-in' type='checkbox'  checked><label for='section-19dd971b-3d62-46f2-9d16-a5083f81632a' class='xr-section-summary' >Data variables: <span>(1)</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'>(time, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-07635f6e-c70a-4b40-81b6-ce13329228b4' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-07635f6e-c70a-4b40-81b6-ce13329228b4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a2f21b46-7ce8-4bbc-ba5f-25b671b116f0' class='xr-var-data-in' type='checkbox'><label for='data-a2f21b46-7ce8-4bbc-ba5f-25b671b116f0' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Monthly Means of Sea Surface Temperature</dd><dt><span>units :</span></dt><dd>degC</dd><dt><span>var_desc :</span></dt><dd>Sea Surface Temperature</dd><dt><span>level_desc :</span></dt><dd>Surface</dd><dt><span>statistic :</span></dt><dd>Mean</dd><dt><span>dataset :</span></dt><dd>NOAA Extended Reconstructed SST V5</dd><dt><span>parent_stat :</span></dt><dd>Individual Values</dd><dt><span>actual_range :</span></dt><dd>[-1.8     42.32636]</dd><dt><span>valid_range :</span></dt><dd>[-1.8 45. ]</dd><dt><span>_ChunkSizes :</span></dt><dd>[  1  89 180]</dd></dl></div><div class='xr-var-data'><pre>[12111120 values with dtype=float32]</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-9f6cf8bf-ed00-4145-8a7d-3be2a578ecb5' class='xr-section-summary-in' type='checkbox'  ><label for='section-9f6cf8bf-ed00-4145-8a7d-3be2a578ecb5' class='xr-section-summary' >Indexes: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>lat</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-ecb74c09-c726-4873-bc0e-e3c842e1dd68' class='xr-index-data-in' type='checkbox'/><label for='index-ecb74c09-c726-4873-bc0e-e3c842e1dd68' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([ 88.0,  86.0,  84.0,  82.0,  80.0,  78.0,  76.0,  74.0,  72.0,  70.0,\n",
       "        68.0,  66.0,  64.0,  62.0,  60.0,  58.0,  56.0,  54.0,  52.0,  50.0,\n",
       "        48.0,  46.0,  44.0,  42.0,  40.0,  38.0,  36.0,  34.0,  32.0,  30.0,\n",
       "        28.0,  26.0,  24.0,  22.0,  20.0,  18.0,  16.0,  14.0,  12.0,  10.0,\n",
       "         8.0,   6.0,   4.0,   2.0,   0.0,  -2.0,  -4.0,  -6.0,  -8.0, -10.0,\n",
       "       -12.0, -14.0, -16.0, -18.0, -20.0, -22.0, -24.0, -26.0, -28.0, -30.0,\n",
       "       -32.0, -34.0, -36.0, -38.0, -40.0, -42.0, -44.0, -46.0, -48.0, -50.0,\n",
       "       -52.0, -54.0, -56.0, -58.0, -60.0, -62.0, -64.0, -66.0, -68.0, -70.0,\n",
       "       -72.0, -74.0, -76.0, -78.0, -80.0, -82.0, -84.0, -86.0, -88.0],\n",
       "      dtype=&#x27;float32&#x27;, name=&#x27;lat&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>lon</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-62b365da-a36f-436b-afc4-a40299c113a8' class='xr-index-data-in' type='checkbox'/><label for='index-62b365da-a36f-436b-afc4-a40299c113a8' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([  0.0,   2.0,   4.0,   6.0,   8.0,  10.0,  12.0,  14.0,  16.0,  18.0,\n",
       "       ...\n",
       "       340.0, 342.0, 344.0, 346.0, 348.0, 350.0, 352.0, 354.0, 356.0, 358.0],\n",
       "      dtype=&#x27;float32&#x27;, name=&#x27;lon&#x27;, length=180))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-ec903978-4198-4d88-8769-697218450411' class='xr-index-data-in' type='checkbox'/><label for='index-ec903978-4198-4d88-8769-697218450411' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(DatetimeIndex([&#x27;1960-01-01&#x27;, &#x27;1960-02-01&#x27;, &#x27;1960-03-01&#x27;, &#x27;1960-04-01&#x27;,\n",
       "               &#x27;1960-05-01&#x27;, &#x27;1960-06-01&#x27;, &#x27;1960-07-01&#x27;, &#x27;1960-08-01&#x27;,\n",
       "               &#x27;1960-09-01&#x27;, &#x27;1960-10-01&#x27;,\n",
       "               ...\n",
       "               &#x27;2022-03-01&#x27;, &#x27;2022-04-01&#x27;, &#x27;2022-05-01&#x27;, &#x27;2022-06-01&#x27;,\n",
       "               &#x27;2022-07-01&#x27;, &#x27;2022-08-01&#x27;, &#x27;2022-09-01&#x27;, &#x27;2022-10-01&#x27;,\n",
       "               &#x27;2022-11-01&#x27;, &#x27;2022-12-01&#x27;],\n",
       "              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, length=756, freq=None))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-061f7fac-15da-4b33-a935-69f39d5b70ad' class='xr-section-summary-in' type='checkbox'  ><label for='section-061f7fac-15da-4b33-a935-69f39d5b70ad' class='xr-section-summary' >Attributes: <span>(39)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>climatology :</span></dt><dd>Climatology is based on 1971-2000 SST, Xue, Y., T. M. Smith, and R. W. Reynolds, 2003: Interdecadal changes of 30-yr SST normals during 1871.2000. Journal of Climate, 16, 1601-1612.</dd><dt><span>description :</span></dt><dd>In situ data: ICOADS2.5 before 2007 and NCEP in situ data from 2008 to present. Ice data: HadISST ice before 2010 and NCEP ice after 2010.</dd><dt><span>keywords_vocabulary :</span></dt><dd>NASA Global Change Master Directory (GCMD) Science Keywords</dd><dt><span>keywords :</span></dt><dd>Earth Science &gt; Oceans &gt; Ocean Temperature &gt; Sea Surface Temperature &gt;</dd><dt><span>instrument :</span></dt><dd>Conventional thermometers</dd><dt><span>source_comment :</span></dt><dd>SSTs were observed by conventional thermometers in Buckets (insulated or un-insulated canvas and wooded buckets) or Engine Room Intaker</dd><dt><span>geospatial_lon_min :</span></dt><dd>-1.0</dd><dt><span>geospatial_lon_max :</span></dt><dd>359.0</dd><dt><span>geospatial_laty_max :</span></dt><dd>89.0</dd><dt><span>geospatial_laty_min :</span></dt><dd>-89.0</dd><dt><span>geospatial_lat_max :</span></dt><dd>89.0</dd><dt><span>geospatial_lat_min :</span></dt><dd>-89.0</dd><dt><span>geospatial_lat_units :</span></dt><dd>degrees_north</dd><dt><span>geospatial_lon_units :</span></dt><dd>degrees_east</dd><dt><span>cdm_data_type :</span></dt><dd>Grid</dd><dt><span>project :</span></dt><dd>NOAA Extended Reconstructed Sea Surface Temperature (ERSST)</dd><dt><span>original_publisher_url :</span></dt><dd>http://www.ncdc.noaa.gov</dd><dt><span>References :</span></dt><dd>https://www.ncdc.noaa.gov/data-access/marineocean-data/extended-reconstructed-sea-surface-temperature-ersst-v5 at NCEI and http://www.esrl.noaa.gov/psd/data/gridded/data.noaa.ersst.v5.html</dd><dt><span>source :</span></dt><dd>In situ data: ICOADS R3.0 before 2015, NCEP in situ GTS from 2016 to present, and Argo SST from 1999 to present. Ice data: HadISST2 ice before 2015, and NCEP ice after 2015</dd><dt><span>title :</span></dt><dd>NOAA ERSSTv5 (in situ only)</dd><dt><span>history :</span></dt><dd>created 07/2017 by PSD data using NCEI&#x27;s ERSST V5 NetCDF values</dd><dt><span>institution :</span></dt><dd>This version written at NOAA/ESRL PSD: obtained from NOAA/NESDIS/National Centers for Environmental Information and time aggregated. Original Full Source: NOAA/NESDIS/NCEI/CCOG</dd><dt><span>citation :</span></dt><dd>Huang et al, 2017: Extended Reconstructed Sea Surface Temperatures Version 5 (ERSSTv5): Upgrades, Validations, and Intercomparisons. Journal of Climate, https://doi.org/10.1175/JCLI-D-16-0836.1</dd><dt><span>platform :</span></dt><dd>Ship and Buoy SSTs from ICOADS R3.0 and NCEP GTS</dd><dt><span>standard_name_vocabulary :</span></dt><dd>CF Standard Name Table (v40, 25 January 2017)</dd><dt><span>processing_level :</span></dt><dd>NOAA Level 4</dd><dt><span>Conventions :</span></dt><dd>CF-1.6, ACDD-1.3</dd><dt><span>metadata_link :</span></dt><dd>:metadata_link = https://doi.org/10.7289/V5T72FNM (original format)</dd><dt><span>creator_name :</span></dt><dd>Boyin Huang (original)</dd><dt><span>date_created :</span></dt><dd>2017-06-30T12:18:00Z (original)</dd><dt><span>product_version :</span></dt><dd>Version 5</dd><dt><span>creator_url_original :</span></dt><dd>https://www.ncei.noaa.gov</dd><dt><span>license :</span></dt><dd>No constraints on data access or use</dd><dt><span>comment :</span></dt><dd>SSTs were observed by conventional thermometers in Buckets (insulated or un-insulated canvas and wooded buckets), Engine Room Intakers, or floats and drifters</dd><dt><span>summary :</span></dt><dd>ERSST.v5 is developed based on v4 after revisions of 8 parameters using updated data sets and advanced knowledge of ERSST analysis</dd><dt><span>dataset_title :</span></dt><dd>NOAA Extended Reconstructed SST V5</dd><dt><span>_NCProperties :</span></dt><dd>version=2,netcdf=4.6.3,hdf5=1.10.5</dd><dt><span>data_modified :</span></dt><dd>2023-11-03</dd><dt><span>DODS_EXTRA.Unlimited_Dimension :</span></dt><dd>time</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:  (lat: 89, lon: 180, time: 756)\n",
       "Coordinates:\n",
       "  * lat      (lat) float32 88.0 86.0 84.0 82.0 80.0 ... -82.0 -84.0 -86.0 -88.0\n",
       "  * lon      (lon) float32 0.0 2.0 4.0 6.0 8.0 ... 350.0 352.0 354.0 356.0 358.0\n",
       "  * time     (time) datetime64[ns] 1960-01-01 1960-02-01 ... 2022-12-01\n",
       "Data variables:\n",
       "    sst      (time, lat, lon) float32 ...\n",
       "Attributes: (12/39)\n",
       "    climatology:                     Climatology is based on 1971-2000 SST, X...\n",
       "    description:                     In situ data: ICOADS2.5 before 2007 and ...\n",
       "    keywords_vocabulary:             NASA Global Change Master Directory (GCM...\n",
       "    keywords:                        Earth Science > Oceans > Ocean Temperatu...\n",
       "    instrument:                      Conventional thermometers\n",
       "    source_comment:                  SSTs were observed by conventional therm...\n",
       "    ...                              ...\n",
       "    comment:                         SSTs were observed by conventional therm...\n",
       "    summary:                         ERSST.v5 is developed based on v4 after ...\n",
       "    dataset_title:                   NOAA Extended Reconstructed SST V5\n",
       "    _NCProperties:                   version=2,netcdf=4.6.3,hdf5=1.10.5\n",
       "    data_modified:                   2023-11-03\n",
       "    DODS_EXTRA.Unlimited_Dimension:  time"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds = ds.sel(time=slice('1960', '2022'))\n",
    "ds"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's do some basic visualizations of the data, just to make sure it looks reasonable."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.QuadMesh at 0x782654dd3f50>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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RqFq1KkaNGgWXy6XUKamjV4IgCOLG5a+//sKib69gxOD4gNo/82hpRIQDn7xfya92HTt2xP33348aNWqgRo0aGDNmDEqVKoWtW7dCFEVMmTIFw4cPR6dOnVCnTh3Mnz8fOTk5WLhwYUBxhgrFZmD07rvvYtasWfjggw/w119/4b333sOECRMwffp0pU5JHb0SBEEQNy5vDrkDPZ6Mxc1VIgJqHxYmYNRr5TD6/fPIyckJqA+n04lFixbh6tWraNKkCY4ePYqsrCy0adNGqWOz2dC8eXNs3rw5oHWECsVGfL1lyxY89NBD6NChAwCgSpUq+PLLL7F9+3YA0I1eAWD+/PlITEzEwoUL8eKLLxZZ7ETBYAmXTl+X3VHEkRBEwSJYJL8aTwLoQAhEoByKQmsjPMVZnITZx48fx/err+DotrR89dOpQymMfv88Pv/8czz55JOaZTabDTab8RTdnj170KRJE1y/fh2lSpXCsmXLkJ6ergx+EhMTNfUTExNx/PjxfMVa1BSbO0Z33XUX1q5diwMHDgAAfv/9d2zatAn3338/AAQ8es3NzUV2drbmRRAEQRChwNWrVxFpE1AhKX/3MQRBQFpKOJYtW4a4uDjNa9y4cabtatasid27d2Pr1q146aWX0K1bN+zbt0/TrxpRFD0aTxYHis0do9deew2XLl1CrVq1YLVa4XQ6MWbMGDz11FMAJH8HwP/R67hx4zBy5MiCC5wgCIIg8okLLu+VfKB169ZYvFh7N83sbhEAREREoFq1agCARo0aYdu2bZg6dSpee+01ANLf3uTkZKX+mTNndH+HixvF5o7R4sWL8fnnn2PhwoXYuXMn5s+fj4kTJ2L+/Pmaev6OXocNG4ZLly4pr5MnTxZI/ARBEAQRKE7Rle+XCBFhYWHK4/fs5WlgxCOKInJzc5GWloakpCRkZmYqy/Ly8rBhwwY0bdq0IHZBoVFs7hgNGTIEr7/+ujI3WrduXRw/fhzjxo1Dt27dkJSUBMD/0avZ3GpYQjzCbHKCvvBwudDAHIs9FccPvhxu3YvjxClvm0cEAGmLiBuFYGmL/NHWFBctUWFjiZAE0EKk9HdDFPOAS0UZUcHxxhtvoH379qhcuTIuX76MRYsW4aeffsKqVasgCAIGDBiAsWPHonr16qhevTrGjh2L6OhodOnSpahDzxfFZmCUk5MDi0V7g8tqtSqP66tHrw0aNADgHr2+++67hR4vQRAEQQQLF/I/OPa3h//7v/9D165dcfr0acTFxaFevXpYtWoVWrduDQAYOnQorl27hj59+uDChQto3Lgx1qxZg9KlS+c71qKk2AyMOnbsiDFjxiAlJQW1a9fGrl27MHnyZPTs2RMASvTolSAIgrixCYbGSPRzaPTpp596XC4IAjIyMpCRkZGPqEKPYjMwmj59Ot566y306dMHZ86cQYUKFfDiiy/i7bffVuqU1NErQRAEcWPjFINr1UCYU2wGRqVLl8aUKVMwZcoU0zoFNnqVPURgVU3lMU2RKJWJYfIyNt3nCleqhqWlSHXOnJXemS17mLT7BYu+X9HplL6ypH/yj0J0GfzXIC8T2Nx3hLxulS5BFKV24vVc6T1Xfg+yL0pBU1C6h+Lka0IQngjWuVySPY7MYH5Rhsvka7Eg+6e5daXF+9F0Qk+xGRgRBEEQxI1KcDRGxesf4aKCBkYEQRAEEcKIAJxFIL6+USk2PkYEQRAEQRAFDd0xMsNqdWuK5LllMVzlYyTrgkSroFkmGngdCTGS34UQFy29O0VNW5dV3S9rJM9bO1yadyHP7d0jOCQdEmQ9kqIpYvPkKv2QIGuMBNmzyXX+ohQD0zsBCIsvK63q/AXdNhQ2xV2rQJQ8ijL3Fr/uUNXEsbhC6ffraV9ZZC8iph/SaDiZ2Fm+FitaUKY1Yu8uA3+7AqAoHte/UaGBEUEQBEGEOMF4Kk2kJ9t8ggZGBEEQBBHiBCdTGuELpDEiCIIgCIKQoTtGJohxpSFGSpogV4S0m8Rw9zjSFc40RszHSNYaGfhgCC7Zr0jW/CgaI6YjMhiesjpMJ2Sxyxohlc5JsMteR/K8OPuu4FB9d0nrcp75V1q33C/TFQH50xb5o4EIJf0BI1gxFbT3S6hqS24Egn1OB3IsjdoE49wN9nnF+gtLKg8AWHlqelD794X2N/U2XWaNiwUACLYITbmg1hjxmk3Zdw7Mx4jl0HTlAufzHa5X6Km0woMGRgRBEAQR4jiDMKqhgZFv0MCIIAiCIEKcYGiMaGDkG6QxIgiCIAiCkKE7RibYb4qBKzIKAOBiXkVWt35I+cxLipj1hWpoLlsIYcOK13xef6t7x8vda700oI4Bst6ISYmY1xHTFtnt7g6d8rIwaV6c+RcVhmdRKGqKCoqC3taC0iOFkkdPsMjv9vvTnq8b7P3pqY3Zun1ZD6sb7HgtUZHyB+l/7/Zpg5RlK49O9ru/QGDXNkuEW0dkqZQsfRC4CzfTFjkcqjL52ivnnhSjpH7ESOndZQuTm1wHjgQzcmOcQcjJRneMfIMGRgRBEAQRwojQ+PUSBQxNpREEQRAEQcjQHSOCIAiCCHGCM5WW/z5uBGhgRBAEQRAhDmmMCg8aGJlgLx0GZ6Rs7MgndgUgMu01M2CU9c7MmHHD8iH5Wv+6ta/73aZ9NXmdV3MAAM6z55RlIjdBfSMJom9UgnWMfemnoI0tAyG//efHtNEfM0hPiVf9ScoaiAjbH9G4JwTZBNGaKBk6Qk6mvfLY+wH15w113NaycVIMgmy2Kz9YEpZaGQDgSC6j1FU8G5k5LjPFZeJrp1uoLch5xViCcFe0tMwZJYuuI6VyB2+sW0C4RBoYFRakMSIIgiAIgpChO0YEQRAEEeKQxqjwoIERQRAEQYQ4ziBM8NBUmm/QwMgEh80Ca6Rs7GgwyFYMHB1yHXny+ufvvWuL8jOvX1ySs4ZSLERw8ZTI1B9TwZJIINvmqU1hJafN7zGxJpSTPkTa5AJJf9P+lmEAgJV/jfOrv3b13wYAOGNkU0XZ2Db8H8m0UWxWT6lrD5MGDCyxtytCfmf5ttVmjrJu6OdvtdfpVq0lQ11LnjvxhiAb5rL2rF+nTdYc2eT1WApHkRIMjZHhHzNCB2mMCIIgCIIgZOiOEUEQBEGEMCLocf3ChAZGBEEQBBHiOMVgaIxoKs0XaGBkgitCAMKlk0jRE6mH28wQQ57H3vTVYK99skSKLMHi6ivz/Y6rJOsziNDGHz8cf/qxhEuXodXXvwgssBuQYCf29ccviaFOzoqYaADuRKsseSzzB2qf/oZSdeW+sV77tpeVEniLYbK+R9YR2WsnAgCc4e4/8C5WR8r1quiRFDmNWmIkf270nJTIll3brYlS3JY890Xe4pA+C5wHnHt90rvDSoONkgYNjAiCIAgixHHRU2mFBg2MCIIgCCLEIY1R4UEDI4IgCIIIaQTSGBUiNDAywRkBCMwHg+VBUw23t37xqs99tas9XGove3xYKiYHI0SC0BAsD6H2N/WWPpQuJb07HACAsPIJ0neX2+vFJeflc1277nOcpJMLnGBpi8zytfnSP9OEWRITlDLRJvsNRch/UpjGSPECcucTa1fvTQDAqj9Ga/plXkKA2yNIZN5ETNcjX5Ndao1RONMhybHIqxZl/yHNeII1426dOGV9khDl7pflShO46z/TKbF+nXk02Chp0MCIIAiCIEIcF93tKTSKlcHjqVOn8Mwzz6BcuXKIjo5G/fr1sWPHDmW5KIrIyMhAhQoVEBUVhRYtWmDv3r1FGDFBEARB5B8nLPl+0VSabxSbgdGFCxfQrFkzhIeHY+XKldi3bx8mTZqEMmXKKHXee+89TJ48GR988AG2bduGpKQktG7dGpcvXy66wAmCIAginzhFS75fIqUE8YliM5X27rvvonLlypg7d65SVqVKFeWzKIqYMmUKhg8fjk6dOgEA5s+fj8TERCxcuBAvvviiX+sTLYIyD83mlnk/C5/7kufdWWtB1my0rz5UKrh8xV035xoAYFW2ezuJGwem8bCWipEKZK2GM1s/uPeWn0ytI2G6ofaV+ksFsk6E5bTSEFdas26EyXVYHixR5fUSI8Vpkc9p8VK29O506uq6rucaxuuL5qh5h/ek7phnjIfru1sDIn3YtNS7x1ioE2zfIh6jY2C2TktcrPRBMDgITNcjHydB/t9bo/ORZUdtG2VIXyMlgY8Y7f5zxI6dWy8ETb+iyjtIV4e1tWrL5YDkQj5uOTRBX+i+/nNt5XeXiwYbJY1ic8fo+++/R6NGjfD444+jfPnyaNCgAT7++GNl+dGjR5GVlYU2bdooZTabDc2bN8fmzZtN+83NzUV2drbmRRAEQRChggjJxyi/L3pc3zeKzcDoyJEjmDlzJqpXr47Vq1ejd+/e6N+/PxYsWAAAyMrKAgAkJiZq2iUmJirLjBg3bhzi4uKUV+XKlQtuIwiCIAgiAJyikO8XaYx8o9gMjFwuF2677TaMHTsWDRo0wIsvvohevXph5syZmnoCd3tXFEVdmZphw4bh0qVLyuvkyZMFEj9BEARBEKFPsdEYJScnIz09XVN2yy234JtvvgEAJCUlAZDuHCUnu32Czpw5o7uLpMZms8Fms+nKLU4RFja37JQ+WOzu5fc8OAEAEH5F0lZYrkuaiszNb+r6ckVJc+iCyv8FAIRwqd+VWTNM4yNKLp50I84rVzXfBVk3YY0vq5S1T+4LwO0vtPL0h+YrY/ogphcKk3/67J8Go38erLI+hC3z8G+U4JR8bAQ5D6DANEYqXZ7SXN4WyN5H7ZP6SN/Dw5W6edWk37NT9rNhbdhv0RALF6dctdljE1WBarUrmxf77kdWlHjTkxXkOnnaRnUFoM2VJtil6yDCuJNEOXdU5UwTZNUeC0FUH1ttvjNFR8R8jFTSOGUZr0uy6OvqbpiYaI00VeQ6Fp3mVPte0DgpJUihUWzuGDVr1gz79+/XlB04cACpqakAgLS0NCQlJSEzM1NZnpeXhw0bNqBp06aFGitBEARBBBOXaMn3y+NTC4RCsbljNHDgQDRt2hRjx45F586d8dtvv2H27NmYPXs2AGkKbcCAARg7diyqV6+O6tWrY+zYsYiOjkaXLl2KOHqCIAiCCJzg3DGigZEvFJuB0e23345ly5Zh2LBhGDVqFNLS0jBlyhQ8/fTTSp2hQ4fi2rVr6NOnDy5cuIDGjRtjzZo1KF26dBFGThAEQRBEcaHYDIwA4IEHHsADDzxgulwQBGRkZCAjIyPf6xKcqlw5ylyySi9hl3VHuU75u/Tepsk7AABXuGp0zz472HfpbeWukfmOkyh+BKITEQx0cIpuw2UscmhfZaD7i5zLSvEtYvohq4EGxDQI/X+bSj4qJjfhQzHqlvVTtpQmBleEWwzC9CJKfkIfPMQUiYqHhy14rcqdT08C4F/uw6LEF8+ngtYhrb72GQCgTYT7Trw1vgwAQHBJvlZChOxNxDzcwt3H1iV/FsOY3xCnZQPcMz6cBo5pjtR5K9k5J8iFitaI9aE6B5Xjz22T4RnDtEXydVvg0r7t+Fj6fWVnZyNuwRtGPQQVZxDMGUUSGflEsRoYEQRBEMSNiIvE14VGsRFfEwRBEMSNiCgGKSWInxqjcePG4fbbb0fp0qVRvnx5PPzww7qHoLp37w5BEDSvO++8M5ibX+jQwIggCIIgCB0bNmxA3759sXXrVmRmZsLhcKBNmza4elVrJ9KuXTucPn1aef34449FFHFwoKk0giAIgghpBLiC8ESZv1Npq1at0nyfO3cuypcvjx07duCee+5Rym02m+IlWBKggZEJ6z/qhzZ9PgHgFl2rjbwsDulL2FkpAezK/eMLN0DihkBJJhvOGTKqP6uMEQGgfbUh+vIwJniVbxLLImy/xNcGiLzQWV6lyPrTJPDUGvDxglrFoFHdhP32uPWJGkGtXKaIeXmjP3e/rjCuTH67o9tkpc5v8wfp4ihOeDKD9EW87Str8hbqytqW6gYAsCTEAwAERAHgxNcRcmLkSO05yI4f4D4+zJzRbQIpf1f/hTdRUvNGj2Zl6i40Zo3y593TJZH1bb3fN2xbWDiDsOL8Pq5/6dIlAEB8fLym/KeffkL58uVRpkwZNG/eHGPGjEH58uXzta6ihAZGBEEQBHGD4HA4dMnSzTJAqBFFEYMGDcJdd92FOnXqKOXt27fH448/jtTUVBw9ehRvvfUWWrVqhR07dnjtM1QhjRFBEARBhDhOWPL9AgRkZmZqEqfHxcVh3LhxXtffr18//PHHH/jyyy815U888QQ6dOiAOnXqoGPHjli5ciUOHDiAFStWFNCeKHjojhFBEARBhDiuIPkYtW7dGosXa6dUvd3Zefnll/H9999j48aNqFSpkse6ycnJSE1NxcGDB/Mdb1FBAyMT7un/IWwW6WRhZnCbFg8uwoiIkgKv8/BkyCfmSZmLlYSdVlVGTKbjkQ3y2leVz0+LXjekaH6sWq2Ru9z3i65OV6SGM+YTVf2K3DJdGx/W6TZoVPUbxvQoxhojl+oqp5hJst3I9CiqGBr2krQkzMCvuFKYiWf5dQgXJC2KYJF3tE2ld5PPT2ekrDWSEwU7I1THlGmLTM4NjW6IHVP5OPPvoub469tLBXLcTtU2yOuuN0g6HwT5J8h0TvVflsqdedeNgwwywUoJEhYWhtjYWN/qiyJefvllLFu2DD/99BPS0tK8tjl37hxOnjypSeZe3KCpNIIgCIIgdPTt2xeff/45Fi5ciNKlSyMrKwtZWVm4du0aAODKlSsYPHgwtmzZgmPHjuGnn35Cx44dkZCQgEceeaSIow8cumNEEARBECGOKyhPpfnHzJkzAQAtWrTQlM+dOxfdu3eH1WrFnj17sGDBAly8eBHJyclo2bIlFi9eXKxzlNLAiCAIgiBCHGdQfIz860P0klwtKioKq1evzk9IIQkNjEzYOK2vz/OwBBFsBJZENUbygYGclFPxMwIAixWGMHGE2heIaXPM9EHqC6DiL6T1+vEcsBePInVciq6HxWkQDq8p4r2KjDQmih8OtN81dU2WaeKU3phvDfs3e+dHxVNzlF/vIjONkmDgO6XAztO8PACA5bpbY8Q+W6Kkg2EvJWjeAcAhn/ZK0liX9l19vHhvIp22zMjHiJe5ifq6kPVGTrlMkey5tG1cKl1SQSEiOHeMCN+gPU0QBEEQBCFDd4wIgiAIIsQpiqm0GxUaGBEEQRBEiBMU8bW/6usbFBoYEUQR0b7iywAAa5xby+a6ImWttsREAwCEyEhpAfMxCjPRFamRfY3U+alE5n/EaYAUVN/decSMdUNG8PnOjPRDvGaJtRGNtCpsmc6TSNC10WmKFB2Rvn/Fx4bzszHyy9HpWkKItg1HuL+w+DgvqtXbM4KyLjM/JCFMpRuKks5TgWnh+HMl1+6uK38WnFJdpgWyR7ur2+UHmthxUo6FwyBA3otKMClXL+OR+9eMPbh1i9z5wDyPXCF4fhD5gwZGBEEQBBHihEIS2RsFGhgRBEEQRIjjokFNoUEDI4IgCIIIaQS6Y1SI0MAoyNR8R/I+2f9W/jxP7nxmMgD3nPevCwYBAG7vOVmpw5Zt/2SQ1/5aNxsNALDkSRPjln/OSH0kllPqrNo9Kl8xE77R/pZh0odSMQAAQZXA0cprNJi2yKbXbuh0PBattkijMTLTBzEdRpgqr5ou55iBJ5HSXluo03mo9T1m2g9DzyNo1q3kveJznUGvF+K1RqKHurp4AXfeLFH7PZRYvWOkrqzNndLvV3BIope2jTKkBSrFrVE7fxFkvZqljMrnTdbEKX5bbJ1OKRZR5b/lipLO6bw4qex6vLTzc8u6u3NGSe3ZcWJ6HsEp+3upj4nJ8RF8OW5yHUONmaip4taacVqjQrAxIgoZGhgRBEEQRIjjMsuo6wf0VJpv0MCIIAiCIEIcZxD8mGkqzTfI+ZogCIIgQhgpJYiQ71co3jD6+eef8cwzz6BJkyY4deoUAOCzzz7Dpk2biiwmumPkA9XHyTmTDPLzsInoQ0MknY9LnmKv9t77St1DQ/3XGznZVL28noYvSP2J4Xr/llsHSMt+n2K+HmekVNleWupYTKoCANiwfIjfsRGB0a7emwAAsZTk+eKyMeGM+3KV+ctEAED79DekRbL2R/EhUmuB5M+ucIvxdyN/IO5eOp+DTFqXVsej6ydI/3Tyeco0fkN83itFL6TVD2njlN/DjNsCcOeM43OkqVPF8VqSUPxr4gHmX9S2wQjdMr5s9S6t5ujeluPcX+RzxZor6xIb3CIVy9q13Gi3j5EzWipj+9oiC28sedJOdKnOr+vlpAN0JVkqu15elPtQGQLxecmc7CSR6gou9cVYruPSftdpxdTw01IGdXiNmc7XivkbGXkrET7xzTffoGvXrnj66aexa9cu5ObmAgAuX76MsWPH4scffyySuOiOEUEQBEGEOC5Y8v0K2n81QWL06NGYNWsWPv74Y4SHuwfaTZs2xc6dO4ssLrpjRBAEQRAhjjMY4usgxBFM9u/fj3vuuUdXHhsbi4sXLxZ+QDJ0x4ggCIIgiEInOTkZhw4d0pVv2rQJVatWLYKIJOiOkQ8oegSLqC+TB/FV35f8hQTZdubwILe3UNUpkzXtj/R/1es63TmhpO87P5L0Q/X7ubVL7hxR0nvdwdKyPRP1WqN1a18HANzRXYrFESn1z/RJgGeNEuEb97aSNBrMS0ZwqM6ZspLXiz1GOnDOKP3/Jc07TgAAWCrFacqV80GlMXPKn10RTBPE5zZTdSBqy9x5yvR1zXKaGd6F5/4F1ekyPDwfrFuPOq8apwUyy3EGqPVH3usa5s+CNh+a4pkTwrnSjFiz9W3Nd8Egideq398xbMvOO5RS+Q3J+88p+2zllZJ2mr20/B7jbu9kVlzskMq6G2ue9K4+Bg65XV5ZeUfHSJUF9fXVKevkZG2RKGuK3HnLVAfQqV3GUHyIDO+0cCZFnjRGcr+HBxv7xWVnZyNu/BuGy4JJUB7XD0IcweTFF1/EK6+8gjlz5kAQBPz777/YsmULBg8ejLffftt7BwUEDYwIgiAIIsRxBcH52tiltegYOnQoLl26hJYtW+L69eu45557YLPZMHjwYPTr16/I4qKBEUEQBEGEOM4gCKdD7Y4RAIwZMwbDhw/Hvn374HK5kJ6ejlKlShVpTMVWYzRu3DgIgoABAwYoZaIoIiMjAxUqVEBUVBRatGiBvXv3Fl2QBEEQBEEYcunSJZw/fx7R0dFo1KgR7rjjDpQqVQrnz59HdnZ2kcVVLAdG27Ztw+zZs1GvXj1N+XvvvYfJkyfjgw8+wLZt25CUlITWrVvj8uXLRRQpQRAEQeSf4Bg8htZU2pNPPolFixbpyr/66is8+eSTRRCRRLGbSrty5QqefvppfPzxxxg9erRSLooipkyZguHDh6NTp04AgPnz5yMxMRELFy7Eiy++6Nd66k/9AEKUZMQHzmwRgE6IKsrCQXarMm36JPdCIxEogLQPpDrqad9jfSVh9o6PjYXQuz/In0D6mpywkRlRisXuDCh8mCmeK0raaY7SKmO7SOmgbvxOMsq8miyr7w2Stipi1gjtMdg1w31MG/SRxPCKyFRu406i6u7PLBGqRxkBb3CodOb+aCY69tSvXnRt0MjsPj6/Her4OPG1YtZoJNTmjR6toj5ufhtYvE73AossHC5u4mseM6E1ALRuKl07r5eXVNM5ydIJ5ohy12GCantp6T2vjJwQNs4OALDa3M6GVqu8TN7ZLlk8nesQNOUAYJHrhoXJDyjIi1wqQbVyPbVqxdfKwVTXZcdOMXhk61Rq6LZfJ7o2OLnZOXy0n/eHZQqDYGiMQm0q7ddff8XkyZN15S1atMDw4cOLICIJn/4ssoGGP8yaNQvly5f3u503+vbtiw4dOuC+++7TDIyOHj2KrKwstGnTRimz2Wxo3rw5Nm/ebDowys3NVdw2ARTp7TuCIAiCMMIVYnd7gkFubi4cDr11uN1ux7Vr14ogIgmfhqDffvstIiIiEBcX59NrxYoVuHLlStCDXbRoEXbu3Ilx48bplmVlZQEAEhMTNeWJiYnKMiPGjRunib1y5crBDZogCIIgCB233347Zs+erSufNWsWGjZsWAQRSfg8kTJt2jSf7wAtWbIk4IDMOHnyJF555RWsWbMGkZGRpvUEbgpDFEVdmZphw4ZhkMpzKDs7mwZHBEEQRMggIkjO1yH2uP6YMWNw33334ffff8e9994LAFi7di22bduGNWvWFFlcPg2M1q9fj/j4eJ87XblyJSpWrBhwUEbs2LEDZ86c0YwinU4nNm7ciA8++AD79+8HIN05Sk5OVuqcOXNGdxdJjc1mg81m05WLVih7h2kVDFHM6UTNdxjpGuQ6VT6WzdQiWFt31Srz3gUAHOv+mvk684FDfgqSaVZK4N3ZoOOSk2WyBLyOGLfQx2nT7sDsNDmRqxU6WGJNdrz/GiVpi25Wz7HLZq+KOZ2ZJgh6AzqPmOiQjPpgCToVo0NfhAm+JO7k6/I6PYPfjJluSGNIqdTRLvPlbwCLU3CpzAXtUkOLXbsfaoyR9F8Hhhd/I9TLaZKY6EoFacfmVJQTuca6pzUsEdKGh0dKZWWjJclBdITk2mhRHWSny8K9S/vOKR84tTkh0xKxuvx3wJ1QmOmERHmZyLR36oPL64/488swiSzrl2tjoIlj1+tjvYo24XZJ1Bg1a9YMW7ZswYQJE/DVV18hKioK9erVw6efforq1asXWVw+DYyaN2/uV6d33XVXQMF44t5778WePXs0ZT169ECtWrXw2muvoWrVqkhKSkJmZiYaNGgAAMjLy8OGDRvw7rvvBj0egiAIgiDyR/369fHFF18UdRgaAnomyeVy4dChQzhz5gxcnO28UUK4YFC6dGnUqVNHUxYTE4Ny5cop5QMGDMDYsWNRvXp1VK9eHWPHjkV0dDS6dOlSIDERBEEQRMEjBCUlSChPEVy7dg12u11TFhsbWySx+D0w2rp1K7p06YLjx49D5PIgCYIAp9MZtOD8ZejQobh27Rr69OmDCxcuoHHjxlizZg1Kly5dZDERBEEQRH4JxlNpoTaVlpOTg6FDh+Krr77CuXPndMuLajzh98Cod+/eaNSoEVasWIHk5GSPwuaC5qefftJ8FwQBGRkZyMjIyHffv/fr59doNfUTWTck6DUQul3E1VEvFyzSHbiqX44F4Pb8sMjaCvW8/t+d/E+yx3QthO9k/vImAKBxV0kLZI92HzBHjLZuTgX5h2wkB5Bvrh7rMxgAkPa5nHA2SaVvkf8rFOTjLHK6CU3yTPmz4uOiy4yqNifiYuE0FRpNEFuHi/tuAO9fZLRqbxj+I8xr9/hEzhqNkVTmy6VI5OJV9rfD3VjgdF3M1yjU8kzlh/9rIr2LcZJuKL6c9BRxKVuurq5VNnIKs2hnB+xOldbOZD2srfrvhEX+bGFeRfKxcKk0Ri7Oi4gtM5QLsd+Bzr/IqK5FU4fXGGl+X9xvT9GGKtoz2bPp2nXzFQaRoCSRDbGR0ZAhQ7B+/XrMmDEDzz77LD788EOcOnUKH330EcaPH19kcfk9MDp48CCWLFmCatWqFUQ8BEEQBEHcACxfvhwLFixAixYt0LNnT9x9992oVq0aUlNT8cUXX+Dpp58ukrj8lrk3btwYhw4dKohYCIIgCIIwwCVa8v0KNY3R+fPnkZaWBkDSE50/fx6A9ADXxo0biywun+4Y/fHHH8rnl19+Ga+++iqysrJQt25dhIeHa+ry+csIgiAIgsgHYpCm0oIQSjCpWrUqjh07htTUVKSnp+Orr77CHXfcgeXLl6NMmTJFFpdPA6P69etDEASN2Lpnz57KZ7asqMXXRYkg5/1hGgjBoj8FeXMtpiMRVD5JrB1bxubhLf6INogCIbeMdPzsKl1RrmzvVWXuewCAsATZ48XA+4od09rfZQAAYsuY/1bcegnm8SLrGdQ6DKVMW1cwOFeYxEPktDX6vFLQ6S14nZMmTnDLRK6q+pzn49Jp77Q9A6rfA6ct0uiJzH4bvmwb05RYVR46LD8X89IJk/er/BNX50EUw6UOj/UebBxDiNFhY38AQJV06R/aCKt0DkZaJSGV+o+vg/Mg4t+NYOeelZuLCPMh4Zz6+qjTGHEni6e6/G9HHS+r41R8kbjfkPr35dSeI8e6G/sYZWdnI+61N71uX34Jjvg6tO4Y9ejRA7///juaN2+OYcOGoUOHDpg+fTocDodhDrXCwqeB0dGjRws6DoIgCIIgbiAGDnQ/DNSyZUv8/fff2L59O26++WbceuutRRaXTwOj1NRU5fPGjRvRtGlThIVpmzocDmzevFlTlyAIgiCI/BMUH6MQmHiIj4/HgQMHkJCQgJ49e2Lq1KmKpU5KSgpSUlKKOMIAxNctW7ZUBFJqLl26hJYtWwYlKIIgCIIgJERIA6P8vkJgXIS8vDxkZ2cDAObPn4/r1wvH7sAf/H5c3ywp67lz5xATE2PQ4saAz21286KxujpMo3XkqTc05dW/Hq185rVJTFvE5u7/emRE/oMlAsIhpZeCXeUXao+XtBnRZa4BAMrESO9W2fPF36SNLhN9hJMrVy/zB390GHwdw/50/XuPiddAsRYWi16HYuU0d0b6KX7dHnVZolY34nLKOhqnyktKFsgwrREc3H4Q1HXl/Ifz39UsO/bs66ZxFjbPb++ufK4Yrd1/V51SwsbrTjkPoGpfhTEjK+6QKlojq3tBuMupWcaj1kiyz+zdqI2Znon/fQBuLZT7uFu4uu72LHebw6l9V9poZH9S2eGnhxluExEYTZo0wcMPP4yGDRtCFEX0798fUVFRhnXnzJlTyNFJ+Dww6tSpEwBJaN29e3dN4lWn04k//vgDTZs2DX6EBEEQBHGDE5yn0opefP3555/j/fffx+HDhwFIs02hdtfI54FRXFwcAOmuR+nSpTUjvIiICNx5553o1atX8CMkCIIgiBuckqIxSkxMVFyt09LS8Nlnn6FcuXJFHJUWnwdGc+fOhSiKEEUR06dPp/xjBEEQBFFIlLRcaXa7HVWqVMG5c+eK78AIkO4WLVy4EMOHD6eBkRcOP/mG90oyBx8veA8MRrNMSQv1S+t3C22dJYW8MuzdLUSwlZVuAd8UK+WaKmuTNEbsvzumfwDc+g1P//nxywLR7PjShl1kjTQbTpfnZzI86Xx8gffkEjjNCQBYYFzHaNvYtrC43e9Mn6U6Bry2xMq0JWr/Gln7wvyMwtg62bvKd4zJjmQt1FFZj1J1oaQxPNLF9+uAJ5j3FdPLhIepzkH5M9O1sf3IfuMxVnf+syirlL081yVd+tm+U/a3O/2ZKUZ/oN2aIONzx+KDj5G6rdIfd54aaY/Yb4z/fbHzQPMblPO7mZ3DmvPLFUrDiJJHeHg4/vzzzyLNt2qGX0+lWSwWVK9e3TALLkEQBEEQBUMwnkoLtZQgzz77LD799NOiDkOH30+lvffeexgyZAhmzpyJOnXqFERMBEEQBEGoKIkpQfLy8vDJJ58gMzMTjRo10j3ZXlTu134PjJ555hnk5OTg1ltvRUREhO4xOyOPI4IgCIIgAicoAyM/R0bjxo3D0qVL8ffffyMqKgpNmzbFu+++i5o1a6r6FDFy5EjMnj0bFy5cQOPGjfHhhx+idu3aXvv/888/cdtttwEADhw4oFlWlFNsfg+MpkyZUgBhEARBEAQRSmzYsAF9+/bF7bffDofDgeHDh6NNmzbYt2+fcnfnvffew+TJkzFv3jzUqFEDo0ePRuvWrbF//36vWuT169cXxmb4jd8Do27duhVEHIQXXt75NACtiHFqgy/97ifcIgk1O/78MgBg+d3TgxDdjUFeBSlBbGSsW8yaXOYSACAhMgcAECbvX4dLEnnmudxqVj6RZhgnllXDxLC80FUtSnYb5Un98MJX9X+YZkmIjUTivpj0+YqnbfOnXzMxLuAW3bJtyJMFtkx8a1cdA7tF+uywckZ/Fvf2M0E2n0TX6L9tRXwtb0ONb94BAITJNm+1lo5S6v7d6W2v28nTYq2UnLZcKa1IOircrtQpFZanacP2Q7ffngMApES5PWKiZSF2rksydAwXpPPVHibtF6uHY+Hkzgt1XadOHO1dvurUCatV5yAT1LPzUz6GrNyhOqZm4mv+fABU55pD+tPnlI87+2keePQtr3EXFUF5XN9PVq1apfk+d+5clC9fHjt27MA999wDURQxZcoUDB8+XPE6nD9/PhITE7Fw4UK8+OKLhR5zMPB7YARIho7ffvst/vrrLwiCgPT0dDz44IOwWn14pIEgCIIgCL/w10XfsI98iq8vXZL+EYyPjwcgJZjPyspCmzZtlDo2mw3NmzfH5s2bvQ6MWrZs6XHKbN26dbqyadOm+R13jx49/HqS3u+B0aFDh3D//ffj1KlTqFmzJkRRxIEDB1C5cmWsWLECN998s79dEgRBEARRCDgcDiVXGcNms2myWRghiiIGDRqEu+66S3nwKisrC4Bk2qgmMTERx48f9xpL/fr1Nd/tdjt2796NP//803R2asCAAahUqZLPN2JOnjyJBx54oGAHRv3798fNN9+MrVu3KqPGc+fO4ZlnnkH//v2xYsUKf7skCIIgCMIDwTB4BIDMzEy8+uqrmrIRI0YgIyPDY7t+/frhjz/+wKZNm3TL+Ls+ZjlVed5//33D8oyMDFy5csW03fbt21G+fHmv/QMIyHPR74HRhg0bNIMiAChXrhzGjx+PZs2a+R0A4RvTb/sCAPDWnkfy1c9P904EADy19QUA2gSTzAgunGlf5CSS7936NQDg9T8e0/U3vt6SfMVTHLhl2UgAQLlESctRqfQlZVm87SoAIJzpfOSLF9O1RBrofJgmiGk0wgS3WR/rxyIY64/80YD40o7XhgCAXfT8n5jV4KFfZz4u2qw/oz54/Yld0ZPoNSZ5smkh03KxcqvTre1iJoh2J9PUSHUsFnd/ouiU380T7fL9WWSDR6bhY6j/NtyzdggAYOO9E3T9mJEYLf1xiLBIyYqZho0ZNQJA2bAczbpz5MSw7NwpbVVpjCzSbzxSkPorZfU/R5Un/RA7d5ipplsrpNewOaHVBLlUtnp2RS8kH3cL05HJ/cv7A3CfC0wnZqTvY/Dmpf4+pVVUiAje4/qtW7fG4sWLNeXe7ha9/PLL+P7777Fx40ZUqlRJKU9KSgIg3TlKTk5Wys+cOaO7i+QPzzzzDO644w5MnDhRt2zEiBEoVaqUz3298cYbmvGKL/hl8AhIO/Dy5cu68itXriAiIsLf7giCIAiC8IIoCvl+QRQQFhaG2NhYzctsYCSKIvr164elS5di3bp1SEtL0yxPS0tDUlISMjMzlbK8vDxs2LAhX0nlt2zZgsjISMNlI0aMQHR0tM99DRs2DGXKlPFr/X7fMXrggQfwwgsv4NNPP8Udd9wBAPj111/Ru3dvPPjgg/52RxAEQRBECNK3b18sXLgQ3333HUqXLq1oiuLi4hAVFQVBEDBgwACMHTsW1atXR/Xq1TF27FhER0ejS5cuXvtnT7IxRFHE6dOnsX37drz1lvkTgteuXUNmZiZatmypmyrLzs7GTz/9hLZt23q9E2aG3wOjadOmoVu3bmjSpAnCw6VHPh0OBx588EFMnTo1oCAIgiAIgjBDKBLn65kzZwIAWrRooSmfO3cuunfvDgAYOnQorl27hj59+igGj2vWrPFJ2xMXF6f5brFYULNmTYwaNUrzpBvP7Nmz8f333xvejImNjcW0adNw8uRJ9O3b12sMRgiiGNgs68GDB/H3339DFEWkp6ejWrVqAQUQamRnZyMuLg6XLl1CbGxsUYejY+JfbZXPkYKkM+hXS/9IYzBgmiIr9Akgee3LO3WXFUgMRQnThDDtRlK0NIVc3uaeSmY+MC6TWWmLat+xfcb2J+s3XKMxcsp1ZR0StLokT/jiHcNwe8hYNN+N+nH60K8vwlCzfowu+Ex/omhKOD8blgQVcOuOmMaI15iofWx0Xkds+116DYwvj0czjVFkmPRbjLQyLZD+eLGySFkf9EXjj732/8qupwC4dX9Mj6Y+Z+LDJJ1baes1TVu2bVYP5w7zRfJUh4fXD6nL2HFjWiO3Nsx9DHTnnkEdd3tB850dW7s6May8zM4fd/l8yHGEK3Uv50l3EHLyJNmH3SHV3fPgSN823oCC/puxb98+1G3UALcvG5Tvvg6M+gYZT/RD//79gxBZ0XHHHXfgrbfeQseOHQ2X//DDDxg1ahR+++23gPoPyMcIgHLbjCAIgiCIgiU4KUFCK4msmuvXr2Px4sW4evUqWrdu7XF8cfDgQdx6662my+vVq4eDBw8GHIvfAyOn04l58+Zh7dq1OHPmDFwu7X8aRoZMBEEQBEEQADBkyBDk5eUp8pu8vDzceeed2LdvH6KjozF06FBkZmaiSZMmhu0dDgf+++8/pKSkGC7/77//4HA4DJf5gt9Ppb3yyit45ZVX4HQ6UadOHdx6662aF0EQBEEQwUUU8/8KFVauXIl7771X+f7FF1/gxIkTOHjwIC5cuIDHH38co0ePNm1fu3Zt/O9//zNdnpmZ6VMSWzP8vmO0aNEifPXVV7j//vsDXikROINvWa18/uzgnQW6rkiLXfNdnePKyvntMO2TOr78wPpj2gKmtRmavjJf/bbdMEDznXneRFjdmo0IWfuTVlra/jLhkk9MWfndptovdlnHwO8PpgFRe/6Ey94xTGsUwb4b1LFyeiSzfGhqnOC1QQa+QNDqOoy0QbxehL+Fz6/HCCPtEr+MR6ND4XQnynkg6DVXFuUYSMvs8jLm+RNh0WuMlFxpLvP9YIb6eLHzhumGYuS8Zcx3yMjzKYzzOvJEKcVbTKstUm8/8yaKt0qeR+x36z7Gqjxw3LFUa5W84T53mO7H/eeD1xbxfkbqc8ZMf2QVVX5T8memG1L0eez4q/2I5GZO2TTKInA501R1HbK2LBjaosImGAaPoTI2OnHiBNLT05Xva9aswWOPPYbU1FQA0g0YT2OMnj17YtCgQahduzYeeOABzbLly5dj9OjRmDx5csDx+T0wioiIKDFCa4IgCIIgCheLxQL1c19bt27VPJ5fpkwZXLhwwbT9Cy+8gI0bN+LBBx9ErVq1ULNmTQiCgL/++gsHDhxA586d8cILLwQen78NXn31VUydOhUBPswWMOPGjcPtt9+O0qVLo3z58nj44Yexf/9+TR1RFJGRkYEKFSogKioKLVq0wN69ews1ToIgCIIINkExeAwRatWqheXLlwMA9u7dixMnTqBly5bK8uPHj3t1zv7888+xaNEi1KhRAwcOHMDff/+NmjVr4ssvv8SXX36Zr/j8vmO0adMmrF+/HitXrkTt2rUVLyPG0qVL8xWQGRs2bEDfvn1x++23w+FwYPjw4WjTpg327duHmJgYAMB7772HyZMnY968eahRowZGjx6N1q1bY//+/QHlSyEIgiCIUKAkPZU2ZMgQPPXUU1ixYgX27t2L+++/X+Oq/eOPPyoG0p7o3LkzOnfuHPT4/B4YlSlTBo88kr98XYGwatUqzfe5c+eifPny2LFjB+655x6IoogpU6Zg+PDhipvm/PnzkZiYiIULF+LFF18s9JgLmlg5z9HyI/UAAB2r/hGUfsftk+Z2bdz9RCO/HfbO9DJzDtwFAOhZQ59o0BdY+9KyLoRpE5jWZv5Bt818t+qb/e6/TIS0z5jOg2mMoqx5Sh2WhyouTPKFKSv7xDDfKI3GxsretNoiXiukrsP0IRFML6LyOrIq/kW++8ownJx+SA2vD3Hrh/R1nVxdfR9G/XM6JANtiVLXRLMULrrPL1534tacsNxkbt0Q05+4RGmfuyzavGrqbXTn7tJ66Xj6o2OW4w5wa4mi5XMmyiKdR0zno9YCMb2RP7oepi1iujbWNtriPl9frLkBALDuWE2pDuT+PfwN5H2LDPVIJsfULkj73vB4yX9SWJy85kiqI63byfLUycfWotIYKWXyPmO+Vez4WVRJ6Hj9kS84XVL7Gt+8AwA48Ki5y3KoEEri6fzy6KOP4scff8SKFSvQpk0bvPzyy5rl0dHR6NOnj9d+srOzDcsFQYDNZgs4TZnfA6O5c+f6VO+XX35Bo0aNArbk9salS1IiT5Yc7ujRo8jKytK4ZdpsNjRv3hybN282HRjl5uYiNzdX+W62owmCIAiCCA733Xcf7rvvPsNlI0aM8KmPMmXKQBDM/wOoVKkSunfvjhEjRsBi8V055LfGyFfat2+PU6dOFUjfoihi0KBBuOuuu1CnTh0AUHK48POSiYmJyjIjxo0bh7i4OOVVuXLlAomZIAiCIAKlJGmMgsW8efNQoUIFvPHGG/j222+xbNkyvPHGG6hYsSJmzpyJF154AdOmTcP48eP96jdg52tvFKQ4u1+/fvjjjz+waZN+uoYfPYqi6HFEOWzYMAwa5LZaz87OpsERQRAEETqIwdEHlaTpOECSy0yaNEmjM3rwwQdRt25dfPTRR1i7di1SUlIwZswYvPHGGz73W2ADo4Li5Zdfxvfff4+NGzeiUqVKSnlSUhIA6c5RcnKyUn7mzBmP6nabzVZg030FzUNVdwMAvjtSHwCw9HADAECnm3cF1B/zDgrnfn9ubYTbSZRpKJjWIVKQ3v2Z52d8fbih8jnGy+1OdQyBkBQpTcHaFG1IntyvWy/B9BylLZIeqbSs5epS7VddvAymm2Dbr2iMjPLMcfmp1F43lgC0RYr2B1r9kLaOvEzgdEKCtg9pmaBZptMjqc4PUz0Svx7NMkGzjO0Pl2FOPq3+hNeuGK3DJXrYDya6KSMsiiZM6ynFzh0ACJc/R8rHmx33SE4TpP7MfitME1TGImnZbks5oYsho853pvExNhyrAQCIETjfMS5u6bPx7zNP0OYmA1Tnk7x/3d5cst4LKt0QO5flv7x5quMDaM9rO/uzw0Lx8PeenUdMa8W6UR83JZ8g9654lKl8o8Jk36mIcN91XqFCMMTXHnd2MWTLli2YNWuWrrxBgwbYsmULAOCuu+7CiRP635YnCmwqLdiIooh+/fph6dKlWLdunUbBDgBpaWlISkpCZmamUpaXl4cNGzagadOmfHcEQRAEQRRjKlWqhE8//VRX/umnnyozP+fOnUPZsmX96rfY3DHq27cvFi5ciO+++w6lS5dWdENxcXGIioqCIAgYMGAAxo4dqyS4HTt2LKKjo9GlS5cijp4gCIIgAicY02AlbCYNEydOxOOPP46VK1fi9ttvhyAI2LZtG/7++28sWbIEALBt2zY88cQTfvVbYAMjT7qeQJg5cyYAoEWLFpryuXPnonv37gCAoUOH4tq1a+jTpw8uXLiAxo0bY82aNeRhRBAEQRRrgiKeDsGR0ZIlS/DVV1/hxIkTyMvL0yzbuXOnx7YPPvgg9u/fj1mzZuHAgQMQRRHt27fHt99+iypVqgAAXnrpJb9jKjbia1/6EwQBGRkZyMjICOq6Q53rLslkk5/X98QHf7cCoNVh6LVFWo1FhErfY5P1DEwvESPna2Jai1+Pu6c6G6ceNYyBaaMiVOu1cDoJJRaWgwxubQDTVjBdR9PUI4Zt1UxtIDmiTv37PjlevRaE6UPc28RrQvQz0LxeyEgrxHvHGOk9LF40WkYaENYNi4rpnDQ+NIqex2kYi/o8sHAz7Eqc8j5S64lYXXbuuHOkme8Hpg9RtFbM10Ydg+JNJMj9O7n+3THwXkxG+dn8QTmGXN473rPLaBk7FyO474D73GXnXIysz4uR6xz5x62NvC5v00WXpH9kOp+7qxzSxdu8ygHD7dh5IkWzPUbw+0pz/skfrey4c+ebuqmFXZ9Nd736Tw2nE5Sbqr2wXNDqmtwrkuNW50ozuey5/afMf2d5YcVm0qREMm3aNAwfPhzdunXDd999hx49euDw4cPYtm0b+vbt61MfVapU8fupM2/4rTG6du0acnJylO/Hjx/HlClTsGbNGk29y5cvo2rVqvmPkCAIgiBuYESUzMf1Z8yYgdmzZ+ODDz5AREQEhg4diszMTPTv31/xKvTGzz//jGeeeQZNmzZVLII+++wzw6fWfcXvgdFDDz2EBQsWAAAuXryIxo0bY9KkSXjooYeU6S6CIAiCIIKHGKRXKHHixAnl4aioqChcvnwZANC1a1ef8p198803aNu2LaKiorBz507FrPny5csYO3ZswHH5PTDauXMn7r77bgDS3GBiYiKOHz+OBQsWYNq0aQEHQhAEQRCEMcG5YxRad42SkpJw7tw5AEBqaiq2bt0KQMpk4Yt8ZvTo0Zg1axY+/vhjTd7Wpk2betUnecLvgVFOTo4iZl6zZg06deoEi8WCO++8E8ePHw84EIIgCIIgbhxatWqF5cuXAwCee+45DBw4EK1bt8YTTzzhU07W/fv345577tGVx8bG4uLFiwHH5bfyrFq1avj222/xyCOPYPXq1Rg4cCAAyUgxNjY24ECIwHmi2jbN908OSHf0LAY3TpkxnkXQinHVuM0KtYkcjQweedE1M3yMMOiXx8isMdxEHGxkMhku+G+GyChjlXRyTAgbbXHny4tUhOV2TSxMAKs2sXQbOWqFnoEkgfUEEyYbiWPBxecWPKtM8HQCWv5/In0iWz6Br9K/6p9Ott1MkO0+Z7SibEAtkpZjUUwg5YSugkFCWyW5qdZsUC3U9sf4jt8mhsUg2a+VE1+7hfp68TX/cAD7Hql6mICdr+y3wb6z/akWQvOiaCbm3neyotyvO34m1E6vbJyCyUiM7jbtFEzr6Npw54z6QQiluQ+mjezPDjP05BMQe8LCXZMA9zUtTN5HNjmZcK5TWo86QXSknOw3Okx6dxgYkIYswZgHC7G5tNmzZ8MlJ4bu3bs34uPjsWnTJnTs2BG9e/f22j45ORmHDh1SnkBjbNq0KV8aZ78HRm+//Ta6dOmCgQMHolWrVmjSpAkA6e5RgwYNAg6EIAiCIAhjQlE8nV8sFosmuWvnzp016T288eKLL+KVV17BnDlzIAgC/v33X2zZsgWDBw/G22+/HXBcfg+MHnvsMdx11104ffo0br31VqX83nvv9enWF0EQBEEQ/lHS8pwxfv75Z3z00Uc4fPgwlixZgooVK+Kzzz5DWloa7rrrLo9thw4dikuXLqFly5a4fv067rnnHthsNgwePBj9+vULOKaA7iMmJSWhdOnSyMzMxLVrUp6f22+/HbVq1Qo4EIIgCIIgbhzUT5Xt2rUroKfKxowZg7Nnz+K3337D1q1b8d9//+Gdd97JV1x+3zE6d+4cOnfujPXr10MQBBw8eBBVq1bF888/jzJlymDSpEn5CojIP0baIjaP7zYi1M/VM9w6EVkTwTQWFrdegpkeMm0R01Iw/YRas8F0EbwGwigG3hjPk3aHN677/URlzfdbU07qts0dv1ZbFCsnjFUv400GmaJCbVrI60bccRqYLPoAr5dh7Q01S0yTY2LEp27j1h+xuiJXVw/TkPBJRI00IU4DfZC6rRQv5PacIaNB0lc+YS2frNYo+atRwlpfUWva+PPewumFjAweeWNPd1u9dkmJV9FNydukOvZ53BFh/djlbbyuMnNlGhtmqsoMVY3OQTNtETNHZaapfDs1RoaRbhNQl3Zb8pn0mW13uKjtxy64/3SxONl+sLtkDZNVq0tTx2WmNQtlgjGVFmo3ndhTZc8++ywWLVqklDdt2hSjRo3yuZ/o6Gg0atQoaHH5PTAaOHAgwsPDceLECdxyyy1K+RNPPIGBAwfSwIggCIIggk0JTAkSyFNlnTp18rn/pUuXBhSX3wOjNWvWYPXq1ahUqZKmvHr16vS4PkEQBEEQPhHIU2VxcXHKZ1EUsWzZMsTFxSl3jHbs2IGLFy/6NYDi8XtgdPXqVURHR+vKz549C5vNFnAgBEEQBEEYUxLF14E8VTZ37lzl82uvvYbOnTtj1qxZsFqlqVSn04k+ffrkyz7I74HRPffcgwULFijiJkEQ4HK5MGHCBLRs2TLgQIjg0bOG9xwxnx28E4DWk4fNtyv6Hja/z+mIALf+JIJLommUsJHXKriTvwqavtT98ZoiXmukLuP7ZzqMzcfd/3EwDQXb7hiLdpuMvGkUBK0eQa2xcO8zPk65L1Vdlw8XNqeg3Rbm5Wqk97ByiTtZnXBdTb0GyB//GqV/HzQ8Ov2Q4L1/3qtIisuiWcb37zSYVnAG9iwJAE67Jmj1QmYeRUZlRucpH5+dxS7yy1Xbb7Kvr4th8rv7KLN1x8i6P6bpswhaDZMaFl/jFG2CZ7OEtEao9Ug8ym9I8TUy1xq5tYYqvY+X00Z9njFtEdtndotWn6X+PZt5U/14tA4A4P60Pz2vuCgpgQOj/D5VNmfOHGzatEkZFAGA1WrFoEGD0LRpU0yYMCGguPweGE2YMAEtWrTA9u3bkZeXh6FDh2Lv3r04f/48fvnll4CCIAiCIAjCnKD4GIWgF9KYMWMwfPhw7Nu3Dy6XC+np6ShVqpRPbR0OB/766y/UrFlTU/7XX38pxpGB4PfAKD09HX/88QdmzJgBq9WKq1evolOnTujbty+Sk5MDDoQgCIIgiBsP9lTZ8ePHceLECdSqVUtj/GhGjx490LNnTxw6dAh33inNBmzduhXjx49Hjx49Ao7H74ERIPkY+fMoHUEQBEEQ+aAETaXNnz8fFy5cwIABA5SyF154AZ9++ikAoGbNmli9ejUqV65s0oPExIkTkZSUhPfffx+nT58GIAm6hw4dildffTXg+AIaGDGnyiNHjuDrr7/2y6mSKFq+PtwQABApD8atGk8ephtyaL7zeiL1Mr3PkF73w/Qs205UkdvKvjiApo2nfmFQly9TfGHAvF7cOoylhxvI2xDGbat2fUb9Wk10JIDbK2n/yQpyvNq2mv95FB8fc8JNrn5GWiCzfox1Q1yuNBaLP3nGfNELedD5mK3LqWhh9G1Z/jS354+sR/IQCx+DL141Rn5eSnuD484vc6/bXNdjppfi2xrB62jUeqwIOa5I5XerzV9nV3ke5cmf765yyHRdvqLWI607VtOwjqI9VCmH3T5p8p8feVMiPZw7Fu4Yqvcd74PlUnL6aT2lpHikfRSj+LAxjaFUZ89J99PWdSv/YxpPUVCSfIxmzZqFF154Qfm+atUqzJ07FwsWLMAtt9yCfv36YeTIkfjkk0889mOxWDB06FAMHToU2dnZABCUnK1+qxXVTpU7d+4MyKmSIAiCIAg/EIP0CgEOHDigMWT87rvv8OCDD+Lpp5/GbbfdhrFjx2Lt2rV+9RkbGxu0RPZ+D4yYU+XHH3+M8HD3f+RNmzbFzp07gxIUQRAEQRAlk2vXrmkGMZs3b9YYPVatWhVZWVmGbW+77TZcuHDB53XdddddOHXqlPeKKvyeSgvEqZIgCIIgiPwQek+UBUpqaip27NiB1NRUnD17Fnv37tXIcLKysjRGjmp2796N33//HfHx8T6ta/fu3crMlq/4PTAKxKmyOHLwnxqIjWVaGFlrojovK1c8XRRhBQ23/47aQ4h5+zCtAnvX5gMD9Joif9bJ6zLUfZj1y/sDGfYv6xjqphwDAGQedaesCef8lnj/IiPfGaYfYr4wkQbb6pO2iMWn1GHvfuh7jPaz4i8kvbO9ys5XI80Kr2EyUt+YaV0sHuJl+iErr2VSwbZfp0NSvG6McruxMoumjlFuNyXOIDvhsVjY2a8+f/NcRWNqq/7d8rn9mE8S8zy6KkYoddV6o2DSqsp+w/Kfj1UDoI2X6YN0fmEGmF1ffPF8MtKGsX0VyV3bjK4vR/6RnrKuWilErvUhMg0WDJ599ln07dsXe/fuxbp161CrVi00bNhQWb5582bUqVPHtP29994L0cffueCDNpLH74FRIE6VBEEQBEEQgORYnZOTg6VLlyIpKQlff/21Zvkvv/yCp556yrDt0aNHDcs9wacw84bfA6P8OlUSBEEQBOEnJeiOkcViwTvvvKNk0ODhB0pqUlNTCyosBb8GRk6nE5s2bcKrr74asFMlQRAEQRB+ICI4rtUh6Hwdivg1MLJarWjbti3++usvxMfHax63K2lYBP38tlP1lc0/M0JmHtqExYduB6DX6Kj1EhZFH6PN/8W0ReGqut7m/jXLBW3/nvrwpiXS6JHk9yryvj/4TwVN3QiDHEmMcINtYtyWcgKAWz8UaaA/YnjLNabWpfGaImMdknF/4Yo/jir3Gvug6LLkmETWv0FdHnl9vuRxM4L3JuK9pIy8i/h95vbm0e8RnX+RD7osf3Kmecr/pvMZ8iE/G++h4wtGPktW7pzj87ap9+tVSBqiPKdWP8R8vNR+XoUN80v69XiaqtS7toiH1wBqzisuVyB/XbEY5IPU968vY/2x6wBfztZz5XLgqSf8oSQmkQ1V/H5cv27dujhy5EhBxEIQBEEQBFGk+D0wGjNmDAYPHowffvgBp0+fRnZ2tuZFEARBEESQKSHmjsVhnOC3+Lpdu3YAgAcffFDzGJwoihAEAU6n/7dJCYIgCILwQFA0RvnvIr+ULVsWp0+fRvny5dGqVSssXboUZcqUCbi/ixcvYsmSJTh8+DCGDBmC+Ph47Ny5E4mJiahYsWJAffo9MFq/fn1AKyIIgiAIIjA8SB0LjI0bN2LChAnYsWMHTp8+jWXLluHhhx9Wlnfv3h3z58/XtGncuDG2bt1q2mepUqVw7tw5lC9fHj/99BPsdnvA8f3xxx+47777EBcXh2PHjqFXr16Ij4/HsmXLcPz4cSxYsCCgfv0eGDVv3jygFRU3XKLbKM1ImBeoWLUwYYJrI6yKYNctHIzQJVaVkz16MHNk+4btD0+CajOM9q9OQKnEra978pQkhK8ui7CZIWO0xS06dSnHUur3dtkE8vcTUvZmZuaopmblfwEAxzihvRZjQ0ejOBn6uoH9J+juhwmetUak6ocFWF1/ZKJMZOop0SwvtrYzM0QDQbW56Nq7qNmjQNssOa1BXRcntmZ1jPpwgd8WfbJbTyJzwNg41Az1b5F/WMCiHFuWgFVdV/9bltbt0pV3unmXz/EEk8apbu8Z9pvTnQ8Gx8tMLK3+fbF2bB/xx0L93c79yWOmsBaDfWh23eKPqbcHMIozV69exa233ooePXrg0UcfNazTrl07zJ07V/keERFhWI9x3333oWXLlrjlFsmA95FHHjFts27dOo99DRo0CN27d8d7772H0qVLK+Xt27dHly5dPLb1hN8Doz/++MOwXBAEREZGIiUlBTZb0bjBEgRBEESJpAj+GW/fvj3at2/vsY7NZkNSUpLPfX7++eeYP38+Dh8+jA0bNqB27dqIjo4OKL5t27bho48+0pVXrFjRNNeaL/g9MKpfv75Hi+3w8HA88cQT+OijjxAZGRlwYPlhxowZmDBhAk6fPo3atWtjypQpuPvuu4skFoIgCILIH0LIaox++uknlC9fHmXKlEHz5s0xZswYlC9f3rR+VFQUevfuDQDYvn073n333YA1RpGRkYZi7v379+Omm24KqE8ggKfSli1bhurVq2P27NnYvXs3du3ahdmzZ6NmzZpYuHAhPv30U6xbtw5vvvlmwEHlh8WLF2PAgAEYPnw4du3ahbvvvhvt27fHiRMniiQegiAIgggVHA6H7mlyf5OsMtq3b48vvvgC69atw6RJk7Bt2za0atXK5/7Wr1+vDIpEUfQ5/xnjoYcewqhRoxSdkiAIOHHiBF5//XXTqT9fEEQ/I7njjjvwzjvvoG3btpry1atX46233sJvv/2Gb7/9Fq+++ioOHz4ccGCB0rhxY9x2222YOXOmUnbLLbfg4Ycfxrhx47y2z87ORlxcHNb9WRlx8pSlJ40Mm19mpoChwNLDDXRlTIfAtAkRShJFfTJKZnrIEi2y7dcme/UcgycNls5k0sP+9Ue7w2AJfpmWQbtuaV0R8n5gOiIj/j2lNXYz+i/Cm2bH038eLh/qMDzpkJzcT5j1q0486+R2sbuOucFjns68kOmJVAk8OY1NnpykVNHnGCT75DVFTk7Lo40zcJNFI/2Qvj99TKyMJVx1b6N+m/gyXsNkBL+dvJ4IMDeIdBugmmth2LIYi/THySa4xa1dq5uLYgsLpgFk+8zOHS9f8GQcyuvR8lSJc70ZhPImtIA+Ga37GipdO69eduHeuidw6dIlxMbG+rwNvrJv3z7Uue02pEwZne++zsyah+bJlbFq1SpN+YgRI5CRkeGxrSAIOvE1z+nTp5GamopFixahU6dOPsW0YMECTJgwAQcPHgQA1KhRA0OGDEHXrl29ts3Ozsb999+PvXv34vLly6hQoQKysrLQpEkT/Pjjj4iJifEpBh6/p9L27NljmKskNTUVe/bsASBNt50+XfhO0Hl5edixYwdef/11TXmbNm2wefNmwza5ubma0W1x8FggCIIgbjCCNA3WunVrLF68WFMWLF1wcnIyUlNTlUGONyZPnoy33noL/fr1Q7NmzSCKIn755Rf07t0bZ8+excCBAz22j42NxaZNm7Bu3Trs3LkTLpcLt912G+677758bYffA6NatWph/PjxmD17tqIkt9vtGD9+PGrVqgUAOHXqFBITE/MVWCCcPXsWTqdTt+7ExERTIda4ceMwcuTIwgiPIAiCIAIjSAOjsLCwArmzBQDnzp3DyZMnkZzs6UleN9OnT8fMmTPx7LPPKmUPPfQQateujYyMDI8DI4fDgcjISOzevRutWrVCq1at8h0/w++B0YcffogHH3wQlSpVQr169SAIAv744w84nU788MMPAIAjR46gT58+QQvSX3hxODOfNGLYsGEYNGiQ8j07OxuVK+unYAiCIAjiRuLKlSs4dOiQ8v3o0aPYvXs34uPjER8fj4yMDDz66KNITk7GsWPH8MYbbyAhIQGPPPKIT/2fPn0aTZs21ZU3bdrU66xTWFgYUlNTC8RU2u+BUdOmTXHs2DF8/vnnOHDgAERRxGOPPYYuXbooPgK+zA0WBAkJCbBarbq7Q2fOnDG9g2Wz2QxvI15yRsIiT+PzCRzVeEpCWdhkHpV8IWJkEY96vpyfJw/n9ENSmVNTZqQt8oYv/k6sDtMaqTUb3tal1srweiM+Savam4jpGrwlqTXC7aHE9qtas2LidWKgZWB1b6pwCoDbf8noZ81viz8ZJHl/I0DlbaQcU4lw+bs6Brf2iSUs1Xan1RjJdaD1ifKoR+J1PpwXkhqdHknQ65FYQmC3rol5/VjkbVX7Dsnbq/haSXXtoupSKJcp8YjyHlH8obTJWqV1iJq2Rlojs2uFUblZXZfiY6T25oFmnaxtruzjFW0JTFhbUKRXPqX5vvNECgCth5CZP5SRZxCvKXJrl7QaMcO6uv7ddXkPJeX6yK6l8nlx1VVI2R6C8lSaf31s374dLVu2VL6zmwjdunXDzJkzsWfPHixYsAAXL15EcnIyWrZsicWLF2s8hTxRrVo1fPXVV3jjjTc05YsXL0b16tW9tn/zzTcxbNgwfP7554iPj/djyzzj98AIkJwr2eN2oURERAQaNmyIzMxMzYg1MzMTDz30UBFGRhAEQRCBEwzna3+HVi1atPD4pNjq1avzFc/IkSPxxBNPYOPGjWjWrBkEQcCmTZuwdu1afPXVV17bT5s2DYcOHUKFChWQmpqqE1vv3LkzoLgCGhh99tln+Oijj3DkyBFs2bIFqampeP/991G1atUiH4AMGjQIXbt2RaNGjdCkSRPMnj0bJ06cCMmBHEEQBEH4RDHItuAvjz76KH799Ve8//77+PbbbyGKItLT0/Hbb7+hQQP909U8np6Qyw9+D4xmzpyJt99+GwMGDMDo0aOV+b2yZctiypQpRT4weuKJJ3Du3DmMGjUKp0+fRp06dfDjjz8aPklHEARBEETR0bBhQ3z++ecBtR0xYkSQo5Hwe2A0ffp0fPzxx3j44Ycxfvx4pbxRo0YYPHhwUIMLlD59+uRb/H1NtCFCvoXI/H0MdQPyzcnVR9MBAG3T9uVrvYHA5uhjLVrtgzqnDz9f7tE7yCQXkLqNmZbIr7xBol7vw6/LH68fph9h2h3mZwS4dQ1HuLxnLA+aWq/E63tcSjn77sl3icWv3w98mTo+HrYNRv2YofM6MrgFzmuflG0zWI3ig6TLvaY6D1hded2RolPzXev541kvotZ38BolXnPjEsx/i7rcZj5oTJxwe/0wvZFNtGu++5Irjfc88rS9geDpd2uXz1CmrTnvkKYVhtZbme/1FiS+eMBtPl4VgGevK/74G/ljmXtT6fvlU5vaRZY7UpuDLqewNEZEoeH3wOjo0aOGt7hsNhuuXr0alKAIgiAIgnATDI1RSZuOs1gsHlOUBfrEmt8Do7S0NOzevVs3NbVy5Uqkp6cHFARBEARBEIQ/LFu2TPPdbrdj165dmD9/fr78Cf0eGA0ZMgR9+/bF9evXIYoifvvtN3z55ZcYN24cPvnkk4ADIQiCIAjChGA8rl/CMNI0P/bYY6hduzYWL16M5557LqB+/R4Y9ejRAw6HA0OHDkVOTg66dOmCihUrYurUqXjyyScDCiIUueqyIUwW0jDvlEhVziHmZcHPZxcFkbK/RqTg+21DI70D00koWhD5zap43Zj7Dbm9aMx/vGb6CLVeifc2CkhrJPfHdDpqqlY6bbpMaW92v5n5L6mKjLyNvJH9r6wJq2CurWD6Iz5fm6ecaQwWi0VV10wXpXfkcede47cpXGB9meNU7vczvyD3MpeBD5Ia9XnAa5SMfJGUul5yuqk1JkybxHvd5Kl8jFgOLJZjK0LUflfrmxTNksDpWww0UEb5vQKF1wECgEVk/kvSeq44IwEAL2zvptSZ3Wh+0GIoTJqmHgEA/HysGgC9FxZgnovPBSNNmDaPmstAj+QN9jfgmujwuU2+KGHTYEZkZ2dj3bp1qFmzJm655ZaA+2ncuDF69eoVcPuAHtfv1asXevXqhbNnz8LlcqF8+fIBB0AQBEEQhBdKoMaoc+fOuOeee9CvXz9cu3YNjRo1wrFjxyCKIhYtWoRHH33U7z6vXbuG6dOno1KlSgHHFdDAiJGQkJCf5gRBEARB3KBs3LgRw4cPByDphURRxMWLFzF//nyMHj3a68CobNmyGvG1KIq4fPkyoqOjA7YAAHwcGDVo0MCj8ltNoE6TBEEQBEEYE5Sn0kKMS5cuKak8Vq1ahUcffRTR0dHo0KEDhgwZ4rX9+++/rxmbWCwW3HTTTWjcuDHKli0bcFw+DYzU7pLXr1/HjBkzkJ6ejiZNmgAAtm7dir179xZp4thgc8EZrQgkrsvaohhVzqFIi9bloihzpvHaHabTMfIa4rU7as0Gr1vw5Jmi96AJjn5CF7NuQC4afjTCKK8a0+wwfyB21Ix0M2ZHVK3zCZdrKf5FXLxOD3b6Rlqji/9KCYyZzqlCxX8BAP/9W9EgPuN9blX0XqJpXU9eTN50TP7okoz8kXRtOb8kdX/KceH0SZ7ObaZPUrRGqu1x+9hIZSzPWrhKn8fqWOTLI9MUMQ2P2qMoD1qNioLIYvJ+XQhEn6g5t6H11YmU8yDa5OuWRYjwu/9QRclXqdau6XLvafVC6nxo7NhdF8M1dYzyqvniTQUA1x2kMQqUypUrY8uWLYiPj8eqVauwaNEiAMCFCxcQGRnptX2rVq1QuXJlwxs3J06cQEpKSkBx+TQwUrtLPv/88+jfvz/eeecdXZ2TJ0/yTQmCIAiCyC8lUGM0YMAAPP300yhVqhRSU1PRokULANIUW926db22T0tLw+nTp3U653PnziEtLa3wfIy+/vprbN++XVf+zDPPoFGjRpgzZ05AgRAEQRAEcePQp08f3HHHHTh58iRat24Ni0W6K1e1alWMHj3aa3uzBLdXrlzx6Y6TGX4PjKKiorBp0yZUr15dU75p06Z8BUIQBEEQhAFicDRGoeiE1KhRIzRq1EhT1qFDB49tBg0aBAAQBAFvv/02oqOjlWVOpxO//vor6tevH3BMfg+MBgwYgJdeegk7duzAnXfeCUDSGM2ZMwdvv/12wIGEGucdpZBnl0avNllPVNpyXVkeLeuNeK3RwkONAQBdqv1aGGECAGpWlnQo+09K+hm7B78UXguk1hEFohPypQ2vVfJLu8RVNdQCcX5LSrlRP/LVhc9BFq4W8HH5zvR+PlZ9XaZDYXoEOVI7vN/KvXra7SLPr+vcv+aPnDKdEN/G1IcJnrVFPGb6KF+8lBi+qGeMtFK8ZxLbJouo1yOxOuGcbs7dVu+/pfgYyRohdS5Bq7JfRU0di3JM9SqrPMXjSt5izudMjT5Pl5GnmP+6o3DOxyzamgcAuOK0KWVdf30eAPBZ4+Jlxsv8i3zRrDGY9siu8qhivkW8f5WdK1d/Vo4Tn4NP/p7r4LOqFRAl0ODR6XRi3rx5WLt2Lc6cOQOXS3uFX7dunWG7Xbt2AZDuGO3ZswcREW4dXUREBG699dZ85W71e2D0+uuvo2rVqpg6dSoWLlwIALjlllswb948dO7cOeBACIIgCIK4cXjllVcwb948dOjQAXXq1PH56ff169cDkAynp06ditjY2KDGFZCPUefOnWkQRBAEQRCFRQkUXy9atAhfffUV7r///oDaz507N8gRSeTL4JEgCIIgiIKnJPoYRUREoFq1avnqY9u2bfj6669x4sQJ5OXlaZYtXbo0oD59msiOj4/H2bNnfe40JSUFx48fDygggiAIgiA4xCC8QoxXX30VU6dONX26zBuLFi1Cs2bNsG/fPixbtgx2ux379u3DunXrEBcXF3BcPt0xunjxIlauXOnzis6dOxewf0CocMEejet2afcwUeMVq/upu2hLnvyeq6nDCyALkzx5nGskbjYTSXsST3taxifE9CTq5rvxpV9/RN1MJOtJ5M0E2lDEu8bCZcBt2shE1qxOuOD7DVYmwo1KPqRb5sqqof2uEv7mQhJy2kXjVK2GgmhfTBS5dp4SwXrDperLk0GmenmwYOJbtShfvw52jOXK6m2XRdH8cbdoeuEM+xThvvSmTUrKzBW1onuXqBd1u8CJeblEtmrBNS/a5gW/aqzKNkmBhlu08Ydb1DFI/XTY2B8AsOKeabr+QgkmumYYi9mZ2F67j/IUgbVbUJ0rGzted0nvTJh9nYmwXe7feK78mYnlHcwEkku2neswsjwlfGHTpk1Yv349Vq5cidq1ayM8PFyz3Nsdn7Fjx+L9999H3759Ubp0aUydOhVpaWl48cUXkZxsnijcGz5f6bt16+a9EkEQBEEQQScoU2khdteoTJkyeOSRRwJuf/jwYeXRfpvNhqtXr0IQBAwcOBCtWrXCyJEjA+rXp4ER/wgdQRAEQRCFSIgNaoJBfsXT8fHxuHz5MgCgYsWK+PPPP1G3bl1cvHgROTk5AfdL4muCIAiCCHVK4MAov9x9993IzMxE3bp10blzZ7zyyitYt24dMjMzce+99wbcLw2MTLjssCHXLplGWeV7mNkOt8YoXJ7XD7NotUXv3fp1YYbpM0xvk175FADg9xOVvbbRJ4rVz+/zWiOG2vLM4scvmtcY+ZOcl2ktFO2R6t5zhMiOk1THbQYovas92118Ml3BewyWpANSW1k/xL77glrfEpMsPbSQwyWN1SVVVcfrg3DRk+mjaRs/mpgZ76kVd7yppk8xcHF7islU56Rerag1eHTr0PQ6HEU/xCel1ZhBMn2LS9MvO6aeFIe8iaP6XOf1Mm4dEluf/pxkBpThLu1lXZ1Elf0WwywlbxZAMc7kDDS1Bo9hmjKmLcp1abVHUpm0zOHSJh5m39kxynOWPOPFwmTJkiX46quvDJ8q27lzp8e2H3zwAa5fl4yXhw0bhvDwcGzatAmdOnXCW2+9FXBMRZcSniAIgiAInxDE/L9CjWnTpqFHjx4oX748du3ahTvuuAPlypXDkSNH0L59e49tHQ4Hli9fruRXs1gsGDp0KL7//ntMnjwZZcuWDTguGhgRBEEQBFHozJgxA7Nnz8YHH3yAiIgIDB06FJmZmejfvz8uXbrksW1YWBheeukl5ObmBj0uGhgRBEEQBFHonDhxAk2bNgUgJahnQuquXbviyy+/9Nq+cePGSt60YBKQxujw4cOYO3cuDh8+jKlTp6J8+fJYtWoVKleujNq1awc7xiLBLobB5Qw3Xc7mmR3yPLbDVXRjzG0nqgAAIpSEplo9kRFGPkG8hsgXvY8LWk8WQ/y4hcsnauRj8xQDg2ktIgSHqkyUyyTVR6QgqaCi5STALo0axCm30SaRtRhojXgtkS/aIl6PZASvKWJaG7XGJhgqEV/8oly+HD+ujsWgW09Jfr3GwH337IVl3k8giZIZil+WoNYjsfPfxM9ItT52DrPz06L8rqy6fiEaa5R4rZH0Wbsnr7F4BZduuc0q/SaiRUnL8ejmPpr+ljX7ULfdRYmL22d8OaC/JjiV65a5JlLxkHIx/yJ2PXd7ErFrvJ1d41miWZf2GOQV1lPbJfBx/aSkJJw7dw6pqalITU3F1q1bceutt+Lo0aM+mT726dMHr776Kv755x80bNgQMTExmuX16tULKC6/B0YbNmxA+/bt0axZM2zcuBFjxoxB+fLl8ccff+CTTz7BkiVLAgqEIAiCIAhjgqERCjWZeKtWrbB8+XLcdttteO655zBw4EAsWbIE27dvR6dOnby2f+KJJwAA/fv3V8oEQYAoihAEIWCjab8HRq+//jpGjx6NQYMGoXTp0kp5y5YtMXXq1ICCIAiCIAjCAyF2tycYzJ49W/FJ7N27N+Lj47Fp0yZ07NgRvXv39tr+6NGjBRKX3wOjPXv2YOHChbrym266CefOnQtKUARBEARBlGwsFovyVBkAdO7cGZ07d/a5fWpqakGE5f/AqEyZMjh9+jTS0tI05bt27ULFihVNWhU/HC4rRM6vQq0juu6Udt1V2eso1174llCbj1cFAIT7cX+U6ZGgeH2oNRBaLRGvHzL0WTHR/hj5rCjr0eVZM9ICWAzrqNsyLxJej8RQ+yeFy3ojpjFiOe5iRckDw2W55m4n55riPY+U5X54FHnCUz9JFf8FAJw8JeX7scshqDUydi/56vzBzI8q0P6tBvoAC+8P5eFfYLNlgeT2M9o2J3f+Gv0OgoH6HLRyuiPlNyKfk2rvL/Z7Yroju6JDkvpj+hd1vIrHkQfvLxZPTJikMQpzSv0zreTjm19S6n7ddKb3DSxg8pTt9qApC8DzzB+U3HZcjjSHaHztKzBKoMYIAH7++Wd89NFHOHz4MJYsWYKKFSvis88+Q1paGu666y6v7T/77DPMmjULR48exZYtW5CamoopU6YgLS0NDz30UEAx+X1Eu3Tpgtdeew1ZWVkQBAEulwu//PILBg8ejGeffTagIAiCIAiCMEZAyfQx+uabb9C2bVtERUVh165dyqP3ly9fxtixY722nzlzJgYNGoT7778fFy9eVDRFZcqUwZQpUwKOy++B0ZgxY5CSkoKKFSviypUrSE9Pxz333IOmTZvizTffDDgQTxw7dgzPPfcc0tLSEBUVhZtvvhkjRozQuWSeOHECHTt2RExMDBISEtC/f39dHYIgCIIodohBeIUYo0ePxqxZs/Dxxx8jPNz9FHjTpk29ul4DwPTp0/Hxxx9j+PDhsFrdd1EbNWqEPXv2BByX3/M/4eHh+OKLLzBq1Cjs2rULLpcLDRo0QPXq1QMOwht///03XC4XPvroI1SrVg1//vknevXqhatXr2LixIkAAKfTiQ4dOuCmm27Cpk2bcO7cOXTr1g2iKGL69OkFFhtBEARBEP6zf/9+3HPPPbry2NhYXLx40Wv7o0ePokGDBrpym82Gq1evBhxXwMKYm2++GTfffHPAK/aHdu3aoV27dsr3qlWrYv/+/Zg5c6YyMFqzZg327duHkydPokKFCgCASZMmoXv37hgzZgxiY2P9Xi+vLVJrjJQypzRKzcmTtEY1lrwDADjwWOB5Wjyx4Zjb+4blY1L0GD5IIzx5E5lping9kXoZ0z64tRoWg7rG+Z2MPYkEwzqsP7vKZ8Tt6aJfxsNy2dlk36IYWWNkt+rbRApXpHdR0l9EVSiYJx98oXLF0wCAg/9I57Rdo7GStjsPWl8VTxoZXrvjS0463XEz6F/JT8f5zai9eYx0R4Bee6Tt13+PGDM/Lm0d4/MM8OF34IOmhO0HdSxsX/B+RlD6d+8Hl6Bdxs5fti0sRyMAOGUvHvfvQPtb1MTF8gnK+5z5GllcVtM2RcHXhxsCACIF6S6Cp/NAr4n0oLGSt5udi+yYsHKL6FLV1ebDdLnk6xira5CTsSAJympC7K5RcnIyDh06hCpVqmjKN23ahKpVq3ptn5aWht27d+tE2CtXrkR6enrAcfk0MBo0aJDPHU6ePDngYPzh0qVLiI+PV75v2bIFderUUQZFANC2bVvk5uZix44daNmypWE/ubm5Gkvx7OzsgguaIAiCIAIhxAY1weDFF1/EK6+8gjlz5kAQBPz777/YsmULBg8ejLfffttr+yFDhqBv3764fv06RFHEb7/9hi+//BLjxo3DJ598EnBcPg2MeMvtHTt2wOl0ombNmgCAAwcOwGq1omHDhgEH4g+HDx/G9OnTMWnSJKUsKysLiYmJmnply5ZFREQEsrKyTPsaN24cRo4cWWCxEgRBEAShZ+jQobh06RJatmyJ69ev45577oHNZsPgwYPRr18/r+179OgBh8OBoUOHIicnB126dEHFihUxdepUPPnkkwHH5dPAaP369crnyZMno3Tp0pg/f76SvfbChQvo0aMH7r77br9WnpGR4XVQsm3bNjRq1Ej5/u+//6Jdu3Z4/PHH8fzzz2vqCgaPdDIHTDOGDRumuSOWnZ2NypUr+7oJBEEQBFHwlMA7RoD0QNfw4cOxb98+uFwupKeno1SpUj6379WrF3r16oWzZ8/C5XKhfPny+Y7Jb43RpEmTsGbNGmVQBEh3ZkaPHo02bdrg1Vdf9bmvfv36eR3Vqece//33X7Rs2RJNmjTB7NmzNfWSkpLw66+/asouXLgAu92uu5OkxmazwWaz6cotgkt3Hpr55ahxOgt2jj5Po6Mx1tQ4dRmW3FwXw+U6vnsTuQw8hHhNEYvLZaAF4nVCLo+5jLT6Dv47y22kXgd7Z7mMeJ8kQKWpkHOklQq7Lsct9XddjFDqNq8SHJ+iYMLyf6m1MUxbdJ3le4J2f/iDkX7MzIvK0/5luhlFW6T6EfE6EQv3C9PkClPamOiS/NAeefTU8uF3oNQ10LLxvwNf1sn7GbE3i+rYWrhrDavLtEaaZYKgeVc0S4q2T9WXyPRNWn2MI0S0RQzFo4zFa3BumMH7DqnRnYPMq4xpuFT/RLuY8aDchC1zyu8WuX+hkEYsQUkJEqKDq+joaM0NEH85c+YM9u/fD0EQIAgCbrrppnzF4/fAKDs7G//3f/+nSxZ75swZJTOuryQkJCAhIcGnuqdOnULLli3RsGFDzJ07V+OWCQBNmjTBmDFjcPr0aSQnS6Z4a9asgc1mK7QpPoIgCIIoEEJ0UBMIPXv29KnenDlzPC7Pzs5G37598eWXXyqpRaxWK5544gl8+OGHiIuLCyg+v/9NeOSRR9CjRw8sWbIE//zzD/755x8sWbIEzz33nE9J3wLh33//RYsWLVC5cmVMnDgR//33H7KysjTaoTZt2iA9PR1du3bFrl27sHbtWgwePBi9evUK6Ik0giAIgiCCz7x587B+/XpcvHgRFy5cMH154/nnn8evv/6KFStW4OLFi7h06RJ++OEHbN++Hb169Qo4Pr/vGM2aNQuDBw/GM888A7tdmpYICwvDc889hwkTJgQciCfWrFmDQ4cO4dChQ6hUqZJmmcgeu7RasWLFCvTp0wfNmjVDVFQUunTpojzOTxAEQRDFkhA1aAyU3r17Y9GiRThy5Ah69uyJZ555RvOUua+sWLECq1ev1qQOadu2LT7++GONxY+/+H3HKDo6GjNmzMC5c+ewa9cu7Ny5E+fPn8eMGTMQExMTcCCe6N69O0RRNHypSUlJwQ8//ICcnBycO3cO06dPN9QPEQRBEERxIigpQUJkcDVjxgycPn0ar732GpYvX47KlSujc+fOWL16te7vuifKlStnOF0WFxen0UH7S8AGjzExMahXr17AKw51LIII0YezyGrRGoQVNDmifqDHBNURcqJUXtQKAN8dqS8vi9SUuzSiW8/JXdUCUz6pKxNL8oaPUjs+YSdvDqkyLXRpE8M6OSGpkcFjrtyGJda0exCS2uQEsTkuSWydY5X25wVLwQzqg0XNyv/qyn4+Vg2A+/jz70bHyxf8MeRk8MJpI3G0lfuNmAlhjZbxbdX9G4m2/cXQ4NHk9+Dpd+BuqzWFVJeZHQu10Fx59ENJZCwjN3WqzGYVc0JmXsgE1awv1Xa4uN8Tjy8PmASLOQfuMl2nRd5iIxG/rxgeA2auyYwu5T/CzFAzXGOcaWHBSHVk80fFfJOVGwjiC4QiGNRs3LgREyZMwI4dO3D69GksW7YMDz/8sDskUcTIkSMxe/ZsXLhwAY0bN8aHH36o0yAbYbPZ8NRTT+Gpp57C8ePHMW/ePPTp0wd2ux379u3z6cm0N998E4MGDcKCBQsUbXFWVhaGDBmCt94K3GTZ74FRy5YtPT7+vm7duoCDIQiCIAgiNLh69SpuvfVW9OjRA48++qhu+XvvvYfJkydj3rx5qFGjBkaPHo3WrVtj//79KF26tM/rYU+TiaKoiKh9YebMmTh06BBSU1ORkpICQMqZarPZ8N9//+Gjjz5S6vqSe43h98Cofv36mu92ux27d+/Gn3/+iW7duvnbHUEQBEEQXiiKR+3bt2+P9u3bGy4TRRFTpkzB8OHDlQev5s+fj8TERCxcuBAvvviix75zc3OxdOlSzJkzB5s2bcIDDzyADz74AO3atdM9dW6G+u5VMPF7YPT+++8blmdkZODKlSv5DoggCIIgCI4Qy5V29OhRZGVloU2bNkqZzWZD8+bNsXnzZo8Doz59+mDRokVISUlBjx49sGjRIpQrV87vGEaMGBFQ7N4IWGPE88wzz+COO+4oMU+BWeEC5PlmB2eKpkZg8/pWOeFgeMHMN3928E75k1sjxGtAePM3dxttOzWejOgYRnP1/DLevJFpjqRlxmaNRslf7S6tRomZNtpd+rpMU6RojESLptxIR3FdPqbXnJLG6JpV0jKEB0GnUtjkyKaUzJwy2ykdY17vBfiWWNW8bnDM/3iTPr3GSDRdpiT7hDb5p6a9Yi4pGvZhFAPDkx7LTD+kac9p4vjfg6Y/E1NIT8l/lfih3zZef+UJ92/Es9aoIGHXJavAYmDGrO79kcf2kcs4PiOdFtsn7DzytO99MUHljThZMlnl+iXH5s/+DwUcDocuJ6iZ0bEnmF0Ob6CcmJiI48ePe2w7a9YspKSkIC0tDRs2bMCGDRsM6y1dutTneK5cuaKbhgvUqidodqdbtmxBZKTxH1+CIAiCIPKBGIQXgMzMTMTFxWle48aNCzgsXnPsLQ0XADz77LNo2bIlypQpo4tF/fLG0aNH0aFDB8TExChPopUtWxZlypQp3KfSeBNHURRx+vRpbN++PV8qcIIgCIIgjAnGvT0BQOvWrbF48WJNeSC2NklJSQCkO0fsiTBAyoLhKQ0XIBk8BoOnn34agOSQnZiY6HVA5it+D4xiY2M1K7dYLKhZsyZGjRqlmWskCIIgCCJIBGnGLiwsLCjZINLS0pCUlITMzEw0aNAAAJCXl4cNGzbg3XffzXf/vvDHH39gx44dqFmzZlD79XtgFKyRXkmBzWdHWOX55zBpdrLW0lFKnb87vZ3v9eS4pBG9J38gT/D6Hl/gE3gatTXTSWg0G7oEm8YeRYBBYljOmyhXlUSWLcvTaYy07+p1Mo0GO145DkmfE2YpJC+SIHJVPifYew73rj5epsl5PejHGLrjpzpevH+XP5oVvq2xbshYU6T1PBI1Zex7OPP1MtAuefI+MvMbMvq9eTv/Pf3ePGm3vP1mtEl/Pe9z9XrYbyLPZdV8Z9u2tuVkj30FA14Dx96Z/5a6zOw8NdJ5+aJDY5j1Z3QtcnnQi5V0rly5gkOHDinfjx49it27dyM+Ph4pKSkYMGAAxo4di+rVq6N69eoYO3YsoqOj0aVLl0KJ7/bbb8fJkyeLfmBUtWpVbNu2Tacgv3jxIm677TYcOXIkaMERBEEQBFE0j+tv374dLVu2VL4PGjQIANCtWzfMmzcPQ4cOxbVr19CnTx/F4HHNmjV+eRjlh08++QS9e/fGqVOnUKdOHYSHh2uWB2pC7ffA6NixY3A69f9d5+bm4tSpUwEFQRAEQRCEB4rgcf0WLVp4TNEhCAIyMjKQkZGRv7gC5L///sPhw4fRo0cPTUxMAG40VvEFnwdG33//vfJ59erVGsW40+nE2rVrUaVKlYCCIAiCIAjCA8XLFcAnrl69mq8cqz179kSDBg3w5ZdfFo34mjlMCoKgc7gODw9HlSpVMGnSpKAEFQrYRStElrdL8TExyukj6xlkzYoYrqui6I0C0RqN2fuAtB5Bf/J4m39Xz4nzvkBGMK0C8+Xg810Z5YMLZN5dn4PL3YfDpc2fxvRDdkVPpPcx4vUS/HcAELljaHXIvlMW7XtxwkxbxPLA+eMP5VHvouhm9Pox3hfI5YNmicfIH8ysfyUfmKoNq8OWMT8v9q7RGPmhQ2H44uPF0OcF9L4ffPESc69PeywAvR7PyZ3rDgONEXu3O626OgXB1L/vU32TbF1yXXJuP/ldrR/MlS+k/PnJn4vqZfx5wGvPfMHod8DWyesd3def4nftCBUSExPRuXNn9OzZE3fddZff7Y8fP47vv/8e1apVC2pcPv8aXC4XXC4XUlJScObMGeW7y+VCbm4u9u/fjwceeCCowREEQRAEIWmM8vsKNb788ktcunQJ9957L2rUqIHx48fj33/1CbPNaNWqFX7//fegx+W3xujo0aNBD4IgCIIgCA+EWEqQYNCxY0d07NgR586dw4IFCzBv3jy89dZbaNu2LXr27IkHH3wQYWHmw5SOHTti4MCB2LNnD+rWrasTXz/44IMBxeXTwGjatGl44YUXEBkZiWnTpnms279//4ACIQiCIAjixqNcuXIYOHAgBg4ciOnTp2PIkCH48ccfkZCQgN69e+P1119HdHS0rl3v3r0BAKNGjdItK3Dx9fvvv4+nn34akZGRpklkWSAlZWCU44iAlZt/z3Oqck+5tLoDljMtIsypKVcv84fBvz8hf9Jqi5yauXWWl0yrMXBrjfTz8LxeypMGIoybs/c0V5+ffEGe4lT2PfMqUumGeC0R00soehq1j5HcH/+AhdUiFYRZ3T+gu/43FACw6b73vMZ+249vSv2Y5eBSnQc72o/x2p8/mGmLcpx6jRHTb7jzysl55pzmlwD+HOHPL8M2Iq9HCtzXyNe67LP7fHVpvqs9qtz6Oe/ndCAeTbzGyshjKJDccyxO/veh6ZfTwhhdt1gZ+62w4+9wFozGyK2RjFDKeG3RNSd7V9fxnP/QBf11kMfofHIfd+Pj70lHaXYNtReSBVowpsJC1YkpKysLCxYswNy5c3HixAk89thjeO655/Dvv/9i/Pjx2Lp1K9asWaNrx+dGCxY+DYzU02c0lUYQBEEQhUyITYMFg6VLl2Lu3LlYvXo10tPT0bdvXzzzzDMoU6aMUqd+/fqKs7Ynrl+/HrR8rX7/mzBq1Cjk5OToyq9du2Z4O4sgCIIgiPwRFPF1iA2uevTogQoVKuCXX37B7t270a9fP82gCJBMpYcPH27Y3ul04p133kHFihVRqlQpxWD6rbfewqeffhpwXH4PjEaOHIkrV67oynNycjBy5MiAAyEIgiAI4sbh9OnT+Oijj3D77beb1omKisKIESMMl40ZMwbz5s3De++9h4gI91Rs3bp18cknnwQcl99PpTFHSZ7ff/8d8fHxAQcSamTn2mAJk27LufUp5vPabG6a6YkiwxzKMpaX6561QwAAMeF5AIBSYdJ7pNWu1HXrIaLl9RjPa6vLmP6GaQk8+S659SLmWgX3/Lus3bBovV/Uy8zaesKTdoPXpvD5z+wuvc6L6SWYnkPRf7kEXV0zjZHV6p6r5uNjPlQCd4yl9rKeQ/D+P8atP7wlt5e+7+7wjtc2PO//5U7U7JT9YHKcksboivzOtBoaXxh2jvD55Vz6uNn28+cKfyzU8L8RVtfTb8YfRK6t+hjwvz2mLbJatBo59Wfl3eBfaL4OX+4JTxorb95OnnLQKeU+/Mvv1hrpjxfzXcpzaK8ZTldw1Sev//EYAMAKvd6NnZ/XOI1RnkrvlmfiUcbe1f5RTu5a5s+5ongeCfpzhYc/f5Vz/Zq5N1xQCbG7PYGSnZ3t8bsab8luFyxYgNmzZ+Pee+9VhNiAlArk77//DjhGnwdGZcuWhSAIEAQBNWrU0AyOnE4nrly5ogmMIAiCIIggUUIGRmXKlPHqUO1rSo9Tp04Zmju6XC7Y7XaDFr7h88BoypQpEEURPXv2xMiRIzUpQSIiIlClShU0adIk4EAIgiAIgjAmKAaNITC4Wr9+fdD6ql27Nn7++WekpqZqyr/++mufBNtm+DwwYmlA0tLS0LRpU52REkEQBEEQhCeaN2+e7z569uyJqVOnYsSIEejatStOnToFl8uFpUuXYv/+/ViwYAF++OGHgPv3W2Ok3qhr167pbld5mxMsLpzPiYEVkmbDkzZCyc8k+xcxPZHaF8cm64ZYPjWmE8hz6fMUmWkUjHyHeN0Nm2t3etSASN+N8qnx28Rg8+++5Ofj5/D9ReS2m+mGmM+KS7VtShmvLRC1fUnt2DJOf8B0KBZVvHKV6l+PlvvR7k/1fjC7Watsv6quol2R1xlIDr2z9lLudcu6DabZuCK/uzVnes0Gn0/OF40Za+P2vHFrKvjjxOu7jDRGvM6L14T4i1v7pY3fSBNmVsdIu8Of776c2/z5q95W9hs2216jfvm4+Pg9YXRs+d+T3SEdy30PZ3jtzxee/rUXACDGKvmvMZ8gdY7Gy3ZZG+eQtUVMT6Q6r9i5q+gIuWub06nWGGnPPdGTXkpepJwbcjH7TfqTg5QdA2eO339GAyME7vYUBBcvXsRvv/2mpBpT8+yzzxq2mT9/PsaPH4+OHTti8eLFGDt2LARBwNtvv43bbrsNy5cvR+vWrQOOye8jmpOTg6FDh+Krr77CuXPndMsDdZokCIIgCMIYgf+PIpA+ghBHMFm+fDmefvppXL16FaVLl9ZojwRBMB0Yiap90bZtW7Rt2zaocfn9uP6QIUOwbt06zJgxAzabDZ988glGjhyJChUqYMGCBUENjiAIgiCIksmrr76Knj174vLly7h48SIuXLigvM6fP++xrTcBd37w+47R8uXLsWDBArRo0QI9e/bE3XffjWrVqiE1NRVffPEFnn766YKIkyAIgiBuTIJlzhhi03GnTp1C//79DfOgeYN/Ot4Ib4MrM/weGJ0/fx5paWkAJD0RW/Fdd92Fl156KaAgCIIgCIIwJyhPpYUYbdu2xfbt21G1alW/2/JPxwcTvwdGVatWxbFjx5Camor09HR89dVXuOOOO7B8+XKdlXdx5sqlKFjybPI381EpE+9aIyRtlc0mGTtGR+QpdcQwre7KziWnVYulzcSbRsJNJjp06QzOoKvL9xvIfw6eRLJGYuOA4GJXRLzcOwCITvZZfufzCWq233h1yj8cBuLrwNDuB0HVL/usrFPeZzcvGqvUOfzkGwCAesslQXaMTTqPEqKuAgDKRLhNVNk5cV02yLvu0Jo42g2Sh+rPL/ON3dp2nOZ7w5WSLb864SgTYjt5IbxLe/ykQk6QbHCemsIfP1UTs3OP38/aZdpj4dPDAh7q6rbB8DdoXNeoP11cum1TnVdcmaf+2WeXgQlqfrh9pXTe2sLLAgCiwqTHEqLld7UQ/LJ8Xb3OxNcGJpNMFK6I+Z3aeF0O1TWTXQfYOlzG55kGZf+y7x6uX2anhhyC61rBJOD1OY5ixvfff6987tChA4YMGYJ9+/ahbt26uqfdH3zwQdN+nnzySZQvX75AYvR7YNSjRw/8/vvvaN68OYYNG4YOHTpg+vTpcDgcmDx5ckHESBAEQRBECeDhhx/WlRnlWfVk8FiQ+iIgAPH1wIED0b9/fwBAy5Yt8ffff+PLL7/Ezp078corrwQ9QJ7c3FzUr18fgiBg9+7dmmUnTpxAx44dERMTg4SEBPTv3x95eXnGHREEQRBEMaGkJJF1uVw+vTw94S4G4Qk9T+T7HmBKSgo6deqE+Ph49OzZMxgxeWTo0KGoUKGCrtzpdKJDhw64evUqNm3ahEWLFuGbb77Bq6++WuAxEQRBEESBIgbhFWIsWLAAubm5uvK8vDyPT7m7XK4Cm0YDAphKM+P8+fOYP38+5syZE6wudaxcuRJr1qzBN998g5UrV2qWrVmzBvv27cPJkyeVgdOkSZPQvXt3jBkzxm/jSeu5cAhRkmGeJ/kB8y1zRMhGZtHSKPe6zZ3pNyxC0h2xm3+KaaOTvavGp7z5HW+Up9YP8HPo/N1FAz2O4NLWFdUaGKvJPLuyHnPNTiB3Ng0H/dy6lHgVg0ZVXblM4PcDi8nTcfMUr7EXpEldbp9xmiWN1ybTGLF9zpapAmV6o7AI6afJjCfPC9JTG5cV3ZtbY6Qk0eUTbaqPv0kCTCNdCmunT54brlkPADgdWg0IO89EI0NNRScGzbL8ikp1p6cHvYjuPA1k5b7oUEzMLA3rGvRrqn1R6viuiVPr3CxW7cpFXpfnA/VXSMmQmQ4IAK5fj5E7lIIJl6950ZHSHXu1zularqwtypNNHO1SP6L6Oujk9p+Lux44VH43/DJFayQvV2+yiVZN1O1fd12lPb+v5HCF64Xj3RcM8XWo+Rj16NED7dq10w1yLl++jB49epj6GBU0haQayz//93//h169euGzzz4zfLRvy5YtqFOnjuZuUtu2bZGbm4sdO3aY9pubm4vs7GzNiyAIgiCIgoUli+X5559/CuyJM18oJC/z/CGKIrp3747evXujUaNGOHbsmK5OVlYWEhMTNWVly5ZFREQEsrKyTPseN24cRo4cGeyQCYIgCCJ4hOBUWKA0aNAAgiBAEATce++9CAtzD0WcTieOHj2Kdu3aFVl8RTowysjI8Doo2bZtGzZv3ozs7GwMGzbMY12jkafZiJQxbNgwDBo0SPmenZ2NypUre4mcIAiCIAqPoPgYhcjgij2Ztnv3brRt2xalSrlzQEZERKBKlSp49NFHiyg6PwZGnTp18rj84sWLfq+8X79+ePLJJz3WqVKlCkaPHo2tW7fCZrNpljVq1AhPP/005s+fj6SkJPz666+a5RcuXIDdbtfdSVJjs9l0/QJAxHkBVptW12J0YjKNkStc9iaKlJNz2tyV87h5fWVO3GneL1unhZs/F5z6OjyKLEE1Jy5w0+AsL6pLZRvhlGMWw1kd47ilhvxKuXfNyrTvHn/gfF2Xh2PgZfsN+1UqycVG8XrSHZisjK+r5J1VHXtFJsbKrNx3AE5ZW+G0Sx3slxPMMr2PGmVXcd5BZn45gFvrYeGOrTopp9sfSrtNijePWrvE9EZME8K0bKwP9Xkit7Pw2o98JpHlD7hf3Xmq64MWyNsfGsPTi9cR+qRl4+qqO+HPU05zqD6/HFZO5yZTZe57AIBjPYaahlDjm3fkDuVkxTmqi8e1MM26nTbpe65cR+PnlCedM0Ku9G6R9UIWu1o3JH/gdITucujqujVG0LTRyLG86IWMjoVZv6yuMzfUlDuhz4gRIwBIf9+feOIJREZGFnFEWnweGHmb74uLi/NbKJWQkICEhASv9aZNm4bRo0cr3//991+0bdsWixcvRuPGjQEATZo0wZgxY3D69GkkJycDkATZNpsNDRs29CsugiAIgggpCvgR9aKgW7duAKSn0M6cOQOXSztiTUlJKYqwfB8YzZ07tyDj8Ai/c9htt5tvvhmVKlUCALRp0wbp6eno2rUrJkyYgPPnz2Pw4MHo1auX30+kEQRBEEQoURJTghw8eBA9e/bE5s2bNeVMAuPJy6ggKRbia1+wWq1YsWIF+vTpg2bNmiEqKgpdunTBxIkTizo0giAIgsgfJUhjxOjevTvCwsLwww8/IDk5ucAdrX2lWA6MqlSpYuh8mZKSgh9++CEo64g8D1jkaU9Dnxz2mWl1wrTvosXAlMTD3LcZ/Jy4xYPGSF9X5VHE6ZnkdFpw2txxOiJlfUuUXCeMy7nkQbOkzNEbzNXr9AG8BkK9GoGrq9OjqOry/k2++A+Z6IYC1hrJx1anMVL8jNyNXJymiOnT1Dokl1XegWFSWeqnkvZDsNp0gXrMBWWyLby25Fj31wAAVT7y4R8ID8dA4LUgvG8W4D6WOt8ZaNp6DMHDMfCprtLG+7rcHflRl3Xvz7Ex7IALwcM5qGgBdR5N8nKrqq5y7mm9tI69ONhrSHlXZb2QrH+z5Lg7tjCdDbseMr2QINWxqHRrgpQ+DdY8pi2C5h1QXV+4379OT6Sqq7u+sOWG56u+H8BEY2R2LJnGiJIrBMzu3buxY8cO1KpVq6hD0VAsB0YEQRAEcSPBD+JKAunp6Th79mxRh6Gj2Bg8EgRBEMQNixikVwjx7rvvYujQofjpp59w7ty5kDFbpjtGBEEQBBHilMSUIPfddx8A4N5779WUk/g6RIk874I1Qnvv0tDHiNfWKPmvDDxkXMwAg32H5jvg1gOIVl7fI/uPODzHIzWWvWocKo0RN1eveHBEqHJkRcq5tpichY/Bqe+P9+1h2ipRfS+S3xUsBn6f8e1goC0w0liZ6HuMYnBrf7jvqhh1u5XvT12Xb2+yHnV7tl/574BKo6ZoQVhCJoPg+O33VM5pi5jvULUJkwEAYWohipnRjlG/fF3ed8vAb0b33ZPfDBeCuwL0mO0PX/DDm8gj/ugGfenOTFukPgctxhsscvpHABDZ+RXGzkHvwVSZOVHT2Cr7D4Vdda/X4tCuUzmn+Wsd3L9h1saSp/2urq/UdfJt3XGzduz65EnfxZ+X+jaqaxzXD3+NY8fAYS+aP94lgfXr1xd1CIbQwIggCIIgQp2g+BiF1lxa8+bNizoEQ2hgRBAEQRAhTklKCaLm4sWL+PTTT/HXX39BEASkp6ejZ8+eRZpElsTXBEEQBEEUOtu3b8fNN9+M999/H+fPn8fZs2cxefJk3Hzzzdi5c2eRxUV3jEyIyHYgLMxhXsEXHxy+iTLfzsyE9HUU/QmnQwHfFgaPb3I6FEE9X+40/lfBGuYeG7uuSR1GMP0Bm0o30Ano4tblClP57ej0PNIHt/eTvi6/f439S0TDuBSdlkYLo90mvSZMvy28lkDp12JUV9uPYb98fAb9KT4wTKPhIYeTP1oakdO8mXpLwe0zZOYvpNl+q3a/uvsw6td8mW49wfzP1l/NEb/u/PgY+bMeI48is9g1GiPj9qzcorrKs9+aW8vmPUxrjpzbjPmjyZ5FYdfddSy5cggm+04wOg+Yxoj5GBn4rrm1RUw3ydqoNEbsOuDgrgcGU0+8bsh9DdG+S/25NHWZJxl/jbY6VAZMBUkI3u3JLwMHDsSDDz6Ijz/+GGFh0knpcDjw/PPPY8CAAdi4cWORxEUDI4IgCIIIYQQEaSotxNi+fbtmUAQAYWFhGDp0KBo1alRkcdFUGkEQBEGEMqIYpFdRb4iW2NhYnDhxQld+8uRJlC5duggikqCBEUEQBEEQhc4TTzyB5557DosXL8bJkyfxzz//YNGiRXj++efx1FNPFVlcNJVGEARBECFOSTR4nDhxIgRBwLPPPguHQxKQhYeH46WXXsL48eOLLC4aGJkQceE62LQnL1wF4L7XFkD+GsHFRH0GZ7pF6liURdFuAbD8rhYHsvbs3UNmYmUbeJGsw70B1lw+ISqv5vT+y/T04+UTq7rCpW1Ui69d4Ux0zsVrID5XEuqaCMt9Mu1jOmONAFyOz6r9LhokCHZxAmozEzjAgxmk6r6tixdmmxlSqtt5SDCqWzeryvaHByNGneiavauTkrIY+HvPRoJ9EzG34bHhhdkGVfKDpzNZty5uW3gTUqAAH6U22XDR6Lzi2ijnouoqL8ifhXBWx3zP1hz1PgAgDFrTTkUsrUqeas3jljHTRZdWNA24hdXWPK0RLfsOuIXU7JqjM2R0uk8st3DaZENc+h3LXzuVh1Ncqn7ZtdHFdcxdo+HMNVlxkAmxabBgEBERgalTp2LcuHE4fPgwRFFEtWrVEB0dXaRx0VQaQRAEQYQ4gpj/l7+Dq4yMDAiCoHklJSUFfduio6NRt25d1KtXr8gHRQDdMSIIgiAIwoTatWvjf//7n/LdavXB48ELPXv29KnenDlz8r2uQKCBEUEQBEGEOgZTgoVBWFhY0O8SzZs3D6mpqWjQoAHEoKQ6CS40MDLBkn0NljD5gPE6H0Cvt3GJxuV8O8A9Z22kDQqTRuPK/DVbZjGY9eTX5ekEYwaHrB8r9x0wn1j1oF3SrVvRp6hiYdvLpuqVRJPytka4/wNhuiNF+8DHpNHCcDoBnXbFQFtgokNQJ3Jln11M58UnfVVrokwSwirJOQ20IJ4SpLrXLffPzDb9MJdUlvuQyJfXGgHGZprq/jSmgCwGHww5TTVGRvB12bsPOipf+tcZkqr6NU/O7L1f04S+npr48XfBMOmx2baw76ocp0wD5JLfeb1U+vD3lc/sJ6EYMSq6IW05AFiZtkg2XmTrsbLvKv1QWK5cluuSlznld72+h9djKlogo6zrTPvDXSs11yJel8lft9R6IodTW4etU5ANL+VrlMVVsjVGBw8eRIUKFWCz2dC4cWOMHTsWVatWzVefvXv3xqJFi3DkyBH07NkTzzzzDOLj44MUcf4hjRFBEARB3CA4HA5kZ2drXrm5xoO7xo0bY8GCBVi9ejU+/vhjZGVloWnTpjh37ly+YpgxYwZOnz6N1157DcuXL0flypXRuXNnrF69OiTuINHAiCAIgiBCnGCJrzMzMxEXF6d5jRs3znCd7du3x6OPPoq6devivvvuw4oVKwAA8+fPz/f22Gw2PPXUU8jMzMS+fftQu3Zt9OnTB6mpqbhy5Uq++88PNJVGEARBEKFOUO6kiGjdujUWL16sKbXZbD61jomJQd26dXHw4MEgxOKGPfEmiiJcvD1CEUADIzOuXwd48b2R5wcviPPl5OW1RVbVjTvZn0NwWIzrGOmceH2TkXZJ/izI/YjKd30dfo7e8L6imceR4Vy9S1+mWo9od5+GljBtfB7vafJ6Jv7dCD5uts0ajZGsIZDjduud9HXdGiN2nHgvKIMYdPoeVbzMQymMew/Xrk9ap/bdvU0GdcO4upw3j7EWSLsfFT8nD8fE3Z/eW8bUZ8ZIlsf3w2uNNJXlRZz/lqfEznrfKX1//mDmE6atpP2q2zaP/bNG5uvWHRcj7yv5msY0QKy/eoMkbZH6crBn0kAAwK39pWUWndZI5TvEJX1l3kRMW8R0RQBgvS5rinLld/m7YHfrhgSm51GSvMonBNP92FWJW1kc7HfLDOiUY2JwzTTTZ6q1S0qZiZ+R4i2Xh8IgWAaPYWFhiI2NDah9bm4u/vrrL9x99935jiU3NxdLly7FnDlzsGnTJjzwwAP44IMP0K5dO1iMNLWFCA2MCIIgCILQMXjwYHTs2BEpKSk4c+YMRo8ejezsbHTr1i1f/fbp0weLFi1CSkoKevTogUWLFqFcuXJBijr/0MCIIAiCIEKd4Myk+cU///yDp556CmfPnsVNN92EO++8E1u3bkVqamq+wpg1axZSUlKQlpaGDRs2YMOGDYb1li5dmq/1BAoNjAiCIAgixOGntguDRYsWFUi/zz77LARfbGCKCBoYmeFwAuC8MgysMxTY/LMvc6OeTgjRpV0X064Y6YZ0XhzsOxNoqD2K2Hy4rPdh/aim6pU5ekWjwel8/PAzEtT5y0zm6AVZSKL+wYtOOQbva9Ktk9ccaXLc8dvA6akEtRBDycsErq5eYyTw+qOAtFEG28Kvk9McAe68cvxxYZuiruu0af2R9Dno3O1N9TxGIgelvVZTxPaduonbd4pfj4Eeyak9jyzcd01s7CfCPKCs3HFSaQN57ye3T5Rqvyr7SKtV8pwHMPA/Wvx+MK7EHWONn5WoK5PasOVGZZwuyUCPdNuLkrYonOmFZG0R8yYSVNdD93Hivst1rbnuysyvyJIrdahoi9QaI95DSNEYyUE4VCtX9EgW7TJBf/x1mOkzjdZt2oenPwxBJBia5KJ/Eh6AZPAYytDj+gRBEARBEDJ0x4ggCIIgQpyimEq7UaGBEUEQBEGEOjQuKjRoYGSGKHqfW84v/miNmF5INIjJ9D8JVV2+H8PqvAaI07BoNDtMyGLiB6KOyWmyH1kfqpxLAqvrSRegxOvZQ0owipfTTwkWpn9S67G4fhULKQOfJ97HyIMWRNHQePrPj+lvHNw+U/KUuTtkeeX062TeRyrdTIRFU6b2OOJhmhdeE8QvN6rDa4CM6irvTuN36TPz82LaEvm7k9OeACotieyBFS6Z9bjCWd5BA68upkeS96HW88nEF8jg96rzSgq2nlTUfZC+qTVGivbNOBatxoid//xB1a+aHQ+rbNNjlbVBAst/5nD3YVGOj3ws2Xc7e9d7FLmPrbxMff1RdEOcPtGT3xDrh/d8M9J98td2o9+ktzs0rH+6k1PioIERQRAEQYQ6wRiA0SDOJ4qV+HrFihVo3LgxoqKikJCQgE6dOmmWnzhxAh07dkRMTAwSEhLQv39/5OUVjispQRAEQRQUwciVFroPyIcWxeaO0TfffINevXph7NixaNWqFURRxJ49e5TlTqcTHTp0wE033YRNmzbh3Llz6NatG0RRxPTp04swcoIgCILIJ3S3p9AoFgMjh8OBV155BRMmTMBzzz2nlNesWVP5vGbNGuzbtw8nT55EhQoVAACTJk1C9+7dMWbMmMByw/iTc4thpEsy8zZS+jfwJtLVNfDK4DU2uvUalDkdBoV8O+N8Yoa+SHwsRhooM9gmGeQKg8tk//pycTA6Nk5tO6WGkotOI4aRYzHuV61dUnyQTHySNLBzg99F6vXwHkdm+ZkAdz4hq3adikdNmLtjUdbSKNoalpNOyRWm8mYy0Rgp+hGN14u2rltjpNUTacqc2m1TvjvVOhRON+IpDx6nJRHy5He2X6xu8Y2oeBxJZSw3n8brSKcb48o1OQi1oXjKz8bLejzV9apfUZ+DTBPFijzFZHLdMsxtJ+uDmLaI+Q8xjyG1Dk75zPRD7PjZ5euNWmfIfH9c3Dmu1g0pvxVOWyUalPP7yKn1LxI86Sr5fg3QtfdF/0gUa4rFVNrOnTtx6tQpWCwWNGjQAMnJyWjfvj327t2r1NmyZQvq1KmjDIoAoG3btsjNzcWOHTtM+87NzUV2drbmRRAEQRAhgygNWvP7oifbfKNYDIyOHDkCAMjIyMCbb76JH374AWXLlkXz5s1x/vx5AEBWVhYSExM17cqWLYuIiAhkZWWZ9j1u3DjExcUpr8qVKxfchhAEQRBEIIhi/l+ETxTpwCgjIwOCIHh8bd++HS75turw4cPx6KOPomHDhpg7dy4EQcDXX3+t9GeUe0UURY85WYYNG4ZLly4pr5MnTwZ/QwmCIAgiP4hBehFeKVKNUb9+/fDkk096rFOlShVcvnwZAJCenq6U22w2VK1aFSdOnAAAJCUl4ddff9W0vXDhAux2u+5OkhqbzQabzeZ/8Ea5ywDfcqXxfRj5/PiRl8wUp8FyX/5r4MUPSiyqOHkfo0Dm3RWdgJEuy0zDpYrfTH/jS44kXjflFFSLTNoZ6IcEE38kZblh7iVunxmtT5f/Tn/cBH47dTnTVN+VvGFarZHy3aiuLhYP26R4EWk1QZqceWbbZPQ74HUoPOpjyx9L2ZNKcLBylXbJyraXJf7Se93o9o2Jhksq08bAex+pNTs+++IYwefVU1VV/KzM+lFLGJlOjjsWOo0Y9B5EgpynTMhjHkNG+co43yGngUeRvC6dXkjVn8hyonnTUaoQwuU/Z4o+ix1rX655Btdz0hLdsBTpwCghIQEJCQle6zVs2BA2mw379+/HXXfdBQCw2+04duwYUlNTAQBNmjTBmDFjcPr0aSQnJwOQBNk2mw0NGzYsuI0gCIIgiAKGUoIUHsXiqbTY2Fj07t0bI0aMQOXKlZGamooJEyYAAB5//HEAQJs2bZCeno6uXbtiwoQJOH/+PAYPHoxevXoF9kQaQRAEQYQKQRkY0eDKF4rFwAgAJkyYgLCwMHTt2hXXrl1D48aNsW7dOpQtWxYAYLVasWLFCvTp0wfNmjVDVFQUunTpgokTJxZx5ARBEARBFBeKzcAoPDwcEydO9DjQSUlJwQ8//FCIUREEQRBEIRCM1J10w8gnis3AqNAxEkJ6EkcGIrrObx1vbdSCR17o6smIkOFpm5gBoyLi9EMs7uIEmkYCSzNhskqgK8rpXlzXrktVI8Ll9wjpXWXsp+uPLTM0g5RFoGz7ebM9D/tMEUTziSzV7dk2GInE+b59EGGbmvZ5ECjzonFNH9w2+CQoNxPdquuabYMvCTx5ob363OSThfLnombbTLZf1R9bZmreadS32Tnhy7b5cq3xcD1QTCoDSWrqwVBUSfzKTBrz7PJ3+d2hF0uL13Pl73Zt+H6IqANF4NbpuTIzAZWvA/K5LoSHu+uwvLVyommBv2b4cg0NIsHQGJGc3DdoYEQQBEEQoQ6JrwuNYmHwSBAEQRAEURjQHSOCIAiCCHWCcceI7jr5BA2MTBDtdkBnGObhpDLSVDC8zUGr25ity4c5el0iRKNEi3L/q85/bNpP+5t6Sx8snBbG88q13z2YFoqyJki0GyS05bZBlPerkUbBEikZc67JWwgAaBvVFYDxtrW2PqENT9aRrLEv8lo3EDKdi02XtU98SQ5Cn+RUgdNhKftKbarHa8A4zZJglPTXTBdhpNnhk+YaGd7xiUDNkt+ql/mCt7pG/euMDfX6IdM2BhornQ5JWe4hmbIvmJmiBqhVUVr5onnxlIwX0Jps2rWaIjFX0vQp2j5ZTxQq+KdjYtdrpomT9XR5bp2Scvzl36dijqnokuRyMS+wgP0lGOJrwidoYEQQBEEQIU5QDB7phpFPkMaIIAiCIAhChu4YEQRBEESoQ/qgQoMGRmY4HBAh6zo8aSt8OVl5jQbf1kgvwWC6HF7DoemHaYGkG4Crr33mPSYPrPxvlt9t2iW8oPmu1rcoHieyNmH11QX5iM4cT9vtSfOTn7qBsPL/ZvrdJhDdk9rHSOftxOub1HXZsfNFA8P5LOl0br7EKehvXHvUy/mLp9+vHwhGWrBA1u0tSbG/8NcgHzy6mF6IfXfl5EjlBvvFGhMt1WEeRWqdWzHHvb1yolzV8RJdzBfLoVkmyjosIZxdfw20kgUBpQQpNGgqjSAIgiAIQobuGBEEQRBEqEPi60KDBkYEQRAEEerQ4/qFBg2MTFj6zww8ntxP+qL4mqh0M7Lmh5Up8+4GHiW8pqZdbA9tGxUFpb8paFadnV3UIZRoAtE9qXVJLIcV00kwvQzTHiHMfSlguaHgi5SEaYvytF4uPulx5P+AXUZ+Vrq6xv5W0iLP/wZrc8Zx6gE/NFGG28T1p8Ql9+spNsGDB1Jh5BYzQn2esfNnVfZcw7rB8PsKaTjtJjsmgvz7UDzWxMLRXNHj+oUHaYwIgiAIgiBk6I4RQRAEQYQ69FRaoUEDI4IgCIIIZUTkz7JC3Q/hFRoYeaCg9D5mc/YEEUx80SW1i+upKzPyFQJU3kKqC7SYK3vbyDqhUPS40ep1Ao9PdAXXr6aodERGWOS8kCVeN+QHRnkUjcjOzkZcXMF6nwEiGTwWIqQxIgiCIAiCkKE7RgRBEAQR6pDGqNCggRFBEARBhDo0lVZo0MCIIG4AFO8sWQskyJoSo1xpPEquO5Yry2F3LwshnUxJoqDz9d2oWGTfrvzmkyRKNjQwIgiCIIhQh55KKzRoYEQQBEEQoY4fLu0eOglCHyUfGhgRBEEQRKhDKUEKDXpcnyAIgiAIQobuGBHEDYDr2jXpA2feKMgJklmiZACArMdWzBtd2qSZoQoJlguHNhFdALhNIQEfEwEXEiX2PKAHHQoNGhgRBEEQRKgTlKk0Glz5Ak2lEQRBEESoI4r5fwXAjBkzkJaWhsjISDRs2BA///xzkDcs9KCBEUEQBEEQOhYvXowBAwZg+PDh2LVrF+6++260b98eJ06cKOrQChSaSiOIGwBfE2IShDfW5C0s6hBuTIpgGmzy5Ml47rnn8PzzzwMApkyZgtWrV2PmzJkYN25cocdTWNAdI4IgCIIIdVyu/L/8GFzl5eVhx44daNOmjaa8TZs22Lx5c7C3LqQoNgOjAwcO4KGHHkJCQgJiY2PRrFkzrF+/XlPnxIkT6NixI2JiYpCQkID+/fsjLy+viCImCIIgiNDC4XAgOztb88qVn0BVc/bsWTidTiQmJmrKExMTkZWVVVjhFgnFZmDUoUMHOBwOrFu3Djt27ED9+vXxwAMPKAfI6XSiQ4cOuHr1KjZt2oRFixbhm2++wauvvlrEkRMEQRBEPgmS+DozMxNxcXGal6dpMUHQ5lEURVFXVtIoFhqjs2fP4tChQ5gzZw7q1asHABg/fjxmzJiBvXv3IikpCWvWrMG+fftw8uRJVKhQAQAwadIkdO/eHWPGjEFsbGxRbgJBEARBBE6QHtdv3bo1Fi/Wej3ZbDZd1YSEBFitVt3doTNnzujuIpU0isUdo3LlyuGWW27BggULcPXqVTgcDnz00UdITExEw4YNAQBbtmxBnTp1lEERALRt2xa5ubnYsWOHad+5ubm624oEQRAEEVK4xPy/AISFhSE2NlbzMhoYRUREoGHDhsjMzNSUZ2ZmomnTpoWyyUVFsbhjJAgCMjMz8dBDD6F06dKwWCxITEzEqlWrUKZMGQBAVlaWbhRbtmxZREREeJwPHTduHEaOHFmQ4RMEQRBEsWPQoEHo2rUrGjVqhCZNmmD27Nk4ceIEevfuXdShFShFescoIyMDgiB4fG3fvh2iKKJPnz4oX748fv75Z/z222946KGH8MADD+D06dNKf0bznt7mQ4cNG4ZLly4pr5MnTxbIthIEQRBEQIiAKLry/fJ3Ou6JJ57AlClTMGrUKNSvXx8bN27Ejz/+iNTU1ALa0NCgSO8Y9evXD08++aTHOlWqVMG6devwww8/4MKFC4pWaMaMGcjMzMT8+fPx+uuvIykpCb/++qum7YULF2C32z3Oh9psNsPbiARBEAQRMhRRrrQ+ffqgT58+RbLuoqJIB0YJCQlISEjwWi8nJwcAYLFob3BZLBa45ASXTZo0wZgxY3D69GkkJycDANasWQObzabokAiCIAiiWEJ5zgqNYiG+btKkCcqWLYtu3brh999/x4EDBzBkyBAcPXoUHTp0ACCZTqWnp6Nr167YtWsX1q5di8GDB6NXr170RBpBEARBED5RLAZGCQkJWLVqFa5cuYJWrVqhUaNG2LRpE7777jvceuutAACr1YoVK1YgMjISzZo1Q+fOnfHwww9j4sSJRRw9QRAEQeQHsdCdr29kisVTaQDQqFEjrF692mOdlJQU/PDDD4UUEUEQBEEUEjSoKTSKxR0jgiAIgiCIwqDY3DEiCIIgiBsVUX7QKH+d0F0nX6CBEUEQBEGEOjSoKTRoYEQQBEEQoU4wfIxobOUTpDEiCIIgCIKQoTtGBEEQBBHqiEHQGNEtI5+ggRFBEARBhDhiEKbSSKbkGzQwIgiCIIhQh+4YFRqkMSIIgiAIgpChO0YEQRAEEeIEYyqNbhj5Bg2MCIIgCCLUoam0QoMGRhyirE7Lzs4u4kgIgiCIUIf9rRALSNkcGxsLES6sFZfmuy8RLpQvXz4IUZVsaGDEcfnyZQBA5cqVizgSgiAIorhw+fJlxMXFBb3fSpUq4dTpU7h69Wq++7JYLKhSpUr+gyrhCGJBDXOLKS6XC/v/v727j4qqTuMA/h1wZhgZYUHAmeFlBA1ZknAlIQRSUUhNBW1b16PLsC+WlSRHNy01oT1uma0v29tmZb6dTthJp7YSDVpAkjQlDDQkUAhkhyiPwvgCKDz7R8NdL8PAaOhw8fmcM+fI/d259/e9jy+Pd34zU1GB0NBQ1NXVwc3NzdFT6lPNzc3w9/fnbBLD2aRpIGcDBnY+e7MREcxmM3Q6HZyc+P1MAwHfMerCyckJvr6+AH6+hTnQ/rB34mzSxNmkaSBnAwZ2Pnuy3Yo7RcxxuL1ljDHGGLPgxogxxhhjzIIbo24olUpkZGRAqVQ6eip9jrNJE2eTpoGcDRjY+QZyNtYzXnzNGGOMMWbBd4wYY4wxxiy4MWKMMcYYs+DGiDHGGGPMghsjxhhjjDELboy6eP311xEYGAgXFxdERESgsLDQ0VO6YZmZmZDJZKKHRqMRxokImZmZ0Ol0UKlUmDhxIk6ePOnAGdt28OBBzJw5EzqdDjKZDB9++KFo3J4sra2tSEtLg5eXF1xdXTFr1iycPXv2NqboXm/ZUlNTrep43333ifbpr9leeOEFjBs3DkOGDIGPjw+Sk5NRUVEh2keqtbMnm1Rr969//Qv33HOP8KGG0dHRyM7OFsalWjOg92xSrRnre9wYXWf37t1IT0/HqlWrUFJSgri4OEybNg21tbWOntoNu/vuu2EymYRHWVmZMLZ+/Xps3LgRr776Ko4ePQqNRoOEhAThe+L6k0uXLiE8PByvvvpqt+P2ZElPT4fRaERWVha++OILXLx4ETNmzEB7e/vtitGt3rIBwNSpU0V13Ldvn2i8v2YrKCjAE088gcOHDyMnJwfXrl1DYmKi6PuepFo7e7IB0qydn58f1q1bh2PHjuHYsWOIj49HUlKS0PxItWZA79kAadaM3QLEBJGRkbRo0SLRtpCQEHr66acdNKObk5GRQeHh4d2OdXR0kEajoXXr1gnbWlpayN3dnd54443bNMObA4CMRqPwsz1ZLly4QHK5nLKysoR96uvrycnJifbv33/b5t6brtmIiAwGAyUlJdl8jlSyERE1NjYSACooKCCigVW7rtmIBlbtPDw86O233x5QNevUmY1oYNWM/TJ8x8iira0NxcXFSExMFG1PTExEUVGRg2Z18yorK6HT6RAYGIjf//73OHPmDACguroaDQ0NopxKpRITJkyQXE57shQXF+Pq1auifXQ6HUaPHi2JvPn5+fDx8UFwcDAWLlyIxsZGYUxK2ZqamgAAnp6eAAZW7bpm6yT12rW3tyMrKwuXLl1CdHT0gKpZ12ydpF4z1jf4S2QtfvrpJ7S3t2PYsGGi7cOGDUNDQ4ODZnVzoqKisHPnTgQHB+OHH37A2rVrMX78eJw8eVLI0l3O77//3hHTvWn2ZGloaIBCoYCHh4fVPv29rtOmTcPDDz8MvV6P6upqPPvss4iPj0dxcTGUSqVkshERli5ditjYWIwePRrAwKldd9kAadeurKwM0dHRaGlpgVqthtFoRGhoqPCPv5RrZisbIO2asb7FjVEXMplM9DMRWW3r76ZNmyb8OiwsDNHR0RgxYgR27NghLCYcCDk73UwWKeSdO3eu8OvRo0fj3nvvhV6vx6effoo5c+bYfF5/y7Z48WKUlpbiiy++sBqTeu1sZZNy7UaNGoXjx4/jwoUL2LNnDwwGAwoKCoRxKdfMVrbQ0FBJ14z1LX4pzcLLywvOzs5WnX9jY6PV/5CkxtXVFWFhYaisrBTenTYQctqTRaPRoK2tDefPn7e5j1RotVro9XpUVlYCkEa2tLQ0/Pvf/0ZeXh78/PyE7QOhdraydUdKtVMoFBg5ciTuvfdevPDCCwgPD8c///nPAVEzW9m6I6Wasb7FjZGFQqFAREQEcnJyRNtzcnIwfvx4B82qb7S2tqK8vBxarRaBgYHQaDSinG1tbSgoKJBcTnuyREREQC6Xi/YxmUw4ceKE5PKeO3cOdXV10Gq1APp3NiLC4sWLsXfvXvznP/9BYGCgaFzKtestW3ekVLuuiAitra2Srpktndm6I+WasV/oti/37seysrJILpfT1q1b6dtvv6X09HRydXWlmpoaR0/thixbtozy8/PpzJkzdPjwYZoxYwYNGTJEyLFu3Tpyd3envXv3UllZGc2bN4+0Wi01Nzc7eObWzGYzlZSUUElJCQGgjRs3UklJCX3//fdEZF+WRYsWkZ+fH+Xm5tLXX39N8fHxFB4eTteuXXNULCLqOZvZbKZly5ZRUVERVVdXU15eHkVHR5Ovr68ksj322GPk7u5O+fn5ZDKZhMfly5eFfaRau96ySbl2zzzzDB08eJCqq6uptLSUVq5cSU5OTvTZZ58RkXRrRtRzNinXjPU9boy6eO2110iv15NCoaCxY8eK3oIrFXPnziWtVktyuZx0Oh3NmTOHTp48KYx3dHRQRkYGaTQaUiqVdP/991NZWZkDZ2xbXl4eAbB6GAwGIrIvy5UrV2jx4sXk6elJKpWKZsyYQbW1tQ5II9ZTtsuXL1NiYiJ5e3uTXC6ngIAAMhgMVvPur9m6ywWAtm3bJuwj1dr1lk3KtfvTn/4k/P3n7e1NkydPFpoiIunWjKjnbFKuGet7MiKi23d/ijHGGGOs/+I1RowxxhhjFtwYMcYYY4xZcGPEGGOMMWbBjRFjjDHGmAU3RowxxhhjFtwYMcYYY4xZcGPEGGOMMWbBjRFjfaympgYymQzHjx+/JceXyWT48MMPb/r5+fn5kMlkkMlkSE5O7nHfiRMnIj09/abPxXrWWYdf/epXjp4KY8yCGyM2oKSmpvb6j/2t5u/vD5PJhNGjRwP4fyNy4cIFh86rq4qKCmzfvt3R07gj2Pp9aTKZsHnz5ts+H8aYbdwYMdbHnJ2dodFoMGjQIEdPpUc+Pj794k7F1atXHT0Fh9FoNHB3d3f0NBhj1+HGiN1RCgoKEBkZCaVSCa1Wi6effhrXrl0TxidOnIgnn3wSy5cvh6enJzQaDTIzM0XHOHXqFGJjY+Hi4oLQ0FDk5uaKXt66/qW0mpoaTJo0CQDg4eEBmUyG1NRUAMDw4cOt7haMGTNGdL7Kykrcf//9wrmu/2bvTvX19Zg7dy48PDwwdOhQJCUloaam5oavzaVLl5CSkgK1Wg2tVosNGzZY7dPW1obly5fD19cXrq6uiIqKQn5+vmift956C/7+/hg8eDBmz56NjRs3ihqwzMxMjBkzBu+88w6CgoKgVCpBRGhqasIjjzwCHx8fuLm5IT4+Ht98843o2B9//DEiIiLg4uKCoKAgPPfcc6L6ZWZmIiAgAEqlEjqdDk8++aRd2XvLde7cOcybNw9+fn4YPHgwwsLC8N5774mO8cEHHyAsLAwqlQpDhw7FlClTcOnSJWRmZmLHjh346KOPhJfOul4zxlj/0b//S8tYH6qvr8f06dORmpqKnTt34tSpU1i4cCFcXFxEzciOHTuwdOlSHDlyBF9++SVSU1MRExODhIQEdHR0IDk5GQEBAThy5AjMZjOWLVtm85z+/v7Ys2cPHnroIVRUVMDNzQ0qlcqu+XZ0dGDOnDnw8vLC4cOH0dzcbLXe5/Lly5g0aRLi4uJw8OBBDBo0CGvXrsXUqVNRWloKhUJh9/V56qmnkJeXB6PRCI1Gg5UrV6K4uBhjxowR9vnjH/+ImpoaZGVlQafTwWg0YurUqSgrK8Ndd92FQ4cOYdGiRXjxxRcxa9Ys5Obm4tlnn7U6V1VVFd5//33s2bMHzs7OAIAHH3wQnp6e2LdvH9zd3bFlyxZMnjwZ3333HTw9PXHgwAEsWLAAL7/8MuLi4nD69Gk88sgjAICMjAx88MEH2LRpE7KysnD33XejoaHBqrGypbdcLS0tiIiIwIoVK+Dm5oZPP/0Uf/jDHxAUFISoqCiYTCbMmzcP69evx+zZs2E2m1FYWAgiwl//+leUl5ejubkZ27ZtAwB4enraXRfG2G3m2O+wZaxvGQwGSkpK6nZs5cqVNGrUKOro6BC2vfbaa6RWq6m9vZ2IiCZMmECxsbGi540bN45WrFhBRETZ2dk0aNAgMplMwnhOTg4BIKPRSERE1dXVBIBKSkqIiCgvL48A0Pnz50XH1ev1tGnTJtG28PBwysjIICKiAwcOkLOzM9XV1Qnj2dnZonNt3brVKlNrayupVCo6cOBAt9ehu/mYzWZSKBSUlZUlbDt37hypVCpasmQJERFVVVWRTCaj+vp60fEmT55MzzzzDBERzZ07lx588EHR+Pz588nd3V34OSMjg+RyOTU2NgrbPv/8c3Jzc6OWlhbRc0eMGEFbtmwhIqK4uDh6/vnnReO7du0irVZLREQbNmyg4OBgamtr6za3Lfbk6s706dNp2bJlRERUXFxMAKimpqbbfXv6fblt2zbR9WGMORbfMWJ3jPLyckRHR0MmkwnbYmJicPHiRZw9exYBAQEAgHvuuUf0PK1Wi8bGRgA/L1j29/eHRqMRxiMjI2/ZfAMCAuDn5ydsi46OFu1TXFyMqqoqDBkyRLS9paUFp0+ftvtcp0+fRltbm+j4np6eGDVqlPDz119/DSJCcHCw6Lmtra0YOnQogJ+vz+zZs0XjkZGR+OSTT0Tb9Ho9vL29RTkuXrwoHKfTlStXhBzFxcU4evQo/v73vwvj7e3taGlpweXLl/Hwww9j8+bNCAoKwtSpUzF9+nTMnDmz17Ve9uRqb2/HunXrsHv3btTX16O1tRWtra1wdXUFAISHh2Py5MkICwvDAw88gMTERPz2t7+Fh4dHj+dmjPU/3BixOwYRiZqizm0ARNvlcrloH5lMho6ODpvHuFlOTk7C+TtdvxC561jXeQI/v9wWERGBd99912rf6xuP3nR3rq46Ojrg7OyM4uJi4eWvTmq1WjiOrWt8vc6G4vpja7XabtfedK5P6ujowHPPPYc5c+ZY7ePi4gJ/f39UVFQgJycHubm5ePzxx/HSSy+hoKDAqqY3mmvDhg3YtGkTNm/ejLCwMLi6uiI9PR1tbW0Afl5wn5OTg6KiInz22Wd45ZVXsGrVKhw5cgSBgYE2z80Y63+4MWJ3jNDQUOzZs0f0j3dRURGGDBkCX19fu44REhKC2tpa/PDDDxg2bBgA4OjRoz0+p3OdT3t7u2i7t7c3TCaT8HNzczOqq6tF862trcV///tf6HQ6AMCXX34pOsbYsWOxe/duYcHyzRo5ciTkcjkOHz4s3Dk7f/48vvvuO0yYMAEA8Jvf/Abt7e1obGxEXFxct8cJCQnBV199Jdp27NixXs8/duxYNDQ0YNCgQRg+fLjNfSoqKjBy5Eibx1GpVJg1axZmzZqFJ554AiEhISgrK8PYsWNtPseeXIWFhUhKSsKCBQsA/NxMVVZW4te//rWwj0wmQ0xMDGJiYrBmzRro9XoYjUYsXboUCoXCqv6Msf6J35XGBpympiYcP35c9KitrcXjjz+Ouro6pKWl4dSpU/joo4+QkZGBpUuXwsnJvj8KCQkJGDFiBAwGA0pLS3Ho0CGsWrUKgPXdnE56vR4ymQyffPIJfvzxR1y8eBEAEB8fj127dqGwsBAnTpyAwWAQ3bGYMmUKRo0ahZSUFHzzzTcoLCwUztVp/vz58PLyQlJSEgoLC1FdXY2CggIsWbIEZ8+etfuaqdVq/PnPf8ZTTz2Fzz//HCdOnEBqaqrougQHB2P+/PlISUnB3r17UV1djaNHj+LFF1/Evn37AABpaWnYt28fNm7ciMrKSmzZsgXZ2dm93mWbMmUKoqOjkZycjAMHDqCmpgZFRUVYvXq10FitWbMGO3fuRGZmJk6ePIny8nLs3r0bq1evBgBs374dW7duxYkTJ3DmzBns2rULKpUKer2+x3Pbk2vkyJHCHaHy8nI8+uijaGhoEI5x5MgRPP/88zh27Bhqa2uxd+9e/Pjjj0LjNHz4cJSWlqKiogI//fTTHf0RBYz1ew5a28TYLWEwGAiA1cNgMBARUX5+Po0bN44UCgVpNBpasWIFXb16VXj+hAkThMXGnZKSkoTnExGVl5dTTEwMKRQKCgkJoY8//pgA0P79+4nIevE1EdHf/vY30mg0JJPJhGM1NTXR7373O3JzcyN/f3/avn27aPE1EVFFRQXFxsaSQqGg4OBg2r9/v2jxNRGRyWSilJQU8vLyIqVSSUFBQbRw4UJqamrq9hrZWgxuNptpwYIFNHjwYBo2bBitX7/e6nq0tbXRmjVraPjw4SSXy0mj0dDs2bOptLRU2OfNN98kX19fUqlUlJycTGvXriWNRiOMZ2RkUHh4uNW8mpubKS0tjXQ6HcnlcvL396f58+dTbW2tsM/+/ftp/PjxpFKpyM3NjSIjI+nNN98kIiKj0UhRUVHk5uZGrq6udN9991Fubm6316Cr3nKdO3eOkpKSSK1Wk4+PD61evZpSUlKEBdXffvstPfDAA+Tt7U1KpZKCg4PplVdeEY7f2NhICQkJpFarCQDl5eUJY7z4mrH+RUZkx+ICxphNhw4dQmxsLKqqqjBixAhHT6dX+fn5mDRpEs6fP39bPuBx4cKFOHXqFAoLC2/5uaRo+/btSE9P73efjM7YnYrXGDF2g4xGI9RqNe666y5UVVVhyZIliImJkURTdD0/Pz/MnDnT6oMKf6l//OMfSEhIgKurK7Kzs7Fjxw68/vrrfXqOgUKtVuPatWtwcXFx9FQYYxbcGDF2g8xmM5YvX466ujp4eXlhypQp3X5KdH8VFRWFyspKAP9/11Vf+uqrr7B+/XqYzWYEBQXh5Zdfxl/+8pc+P4+9CgsLMW3aNJvjnWu+HKHzi4a7vhuOMeY4/FIaY2xAu3LlCurr622O9/QuN8bYnYcbI8YYY4wxC367PmOMMcaYBTdGjDHGGGMW3BgxxhhjjFlwY8QYY4wxZsGNEWOMMcaYBTdGjDHGGGMW3BgxxhhjjFlwY8QYY4wxZvE/sUoihYF1d7QAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ds.sst[0].plot(vmin=-2, vmax=30)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Other ways"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.QuadMesh at 0x78265439fa90>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ds.sst.sel(time = '1960-01-01').plot(vmin=-2, vmax=30)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.QuadMesh at 0x78265427b990>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ds.sst.isel(time = 0).plot(vmin=-2, vmax=30)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note that xarray correctly parsed the time index, resulting in a Pandas datetime index on the time dimension."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;time&#x27; (time: 756)&gt;\n",
       "array([&#x27;1960-01-01T00:00:00.000000000&#x27;, &#x27;1960-02-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;1960-03-01T00:00:00.000000000&#x27;, ..., &#x27;2022-10-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2022-11-01T00:00:00.000000000&#x27;, &#x27;2022-12-01T00:00:00.000000000&#x27;],\n",
       "      dtype=&#x27;datetime64[ns]&#x27;)\n",
       "Coordinates:\n",
       "  * time     (time) datetime64[ns] 1960-01-01 1960-02-01 ... 2022-12-01\n",
       "Attributes:\n",
       "    long_name:        Time\n",
       "    delta_t:          0000-01-00 00:00:00\n",
       "    avg_period:       0000-01-00 00:00:00\n",
       "    prev_avg_period:  0000-00-07 00:00:00\n",
       "    standard_name:    time\n",
       "    axis:             T\n",
       "    actual_range:     [19723. 81722.]\n",
       "    _ChunkSizes:      1</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'>'time'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 756</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-18d19e21-a425-470a-af2e-b16ee8928b61' class='xr-array-in' type='checkbox' checked><label for='section-18d19e21-a425-470a-af2e-b16ee8928b61' 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>1960-01-01 1960-02-01 1960-03-01 ... 2022-10-01 2022-11-01 2022-12-01</span></div><div class='xr-array-data'><pre>array([&#x27;1960-01-01T00:00:00.000000000&#x27;, &#x27;1960-02-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;1960-03-01T00:00:00.000000000&#x27;, ..., &#x27;2022-10-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2022-11-01T00:00:00.000000000&#x27;, &#x27;2022-12-01T00:00:00.000000000&#x27;],\n",
       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></div></li><li class='xr-section-item'><input id='section-776eecf0-7de0-44eb-a0cd-57a439e66f23' class='xr-section-summary-in' type='checkbox'  checked><label for='section-776eecf0-7de0-44eb-a0cd-57a439e66f23' class='xr-section-summary' >Coordinates: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>1960-01-01 ... 2022-12-01</div><input id='attrs-912fa8fb-dc34-4db5-98bb-380353ea7e34' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-912fa8fb-dc34-4db5-98bb-380353ea7e34' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1ff4676a-78f0-4a6a-b19f-75bfdb2d008f' class='xr-var-data-in' type='checkbox'><label for='data-1ff4676a-78f0-4a6a-b19f-75bfdb2d008f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Time</dd><dt><span>delta_t :</span></dt><dd>0000-01-00 00:00:00</dd><dt><span>avg_period :</span></dt><dd>0000-01-00 00:00:00</dd><dt><span>prev_avg_period :</span></dt><dd>0000-00-07 00:00:00</dd><dt><span>standard_name :</span></dt><dd>time</dd><dt><span>axis :</span></dt><dd>T</dd><dt><span>actual_range :</span></dt><dd>[19723. 81722.]</dd><dt><span>_ChunkSizes :</span></dt><dd>1</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;1960-01-01T00:00:00.000000000&#x27;, &#x27;1960-02-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;1960-03-01T00:00:00.000000000&#x27;, ..., &#x27;2022-10-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2022-11-01T00:00:00.000000000&#x27;, &#x27;2022-12-01T00:00:00.000000000&#x27;],\n",
       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-b12d72fe-f26a-4c3a-8669-b29c355f676d' class='xr-section-summary-in' type='checkbox'  ><label for='section-b12d72fe-f26a-4c3a-8669-b29c355f676d' class='xr-section-summary' >Indexes: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-27f30a9f-5fcc-451f-8270-3cdd3047d257' class='xr-index-data-in' type='checkbox'/><label for='index-27f30a9f-5fcc-451f-8270-3cdd3047d257' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(DatetimeIndex([&#x27;1960-01-01&#x27;, &#x27;1960-02-01&#x27;, &#x27;1960-03-01&#x27;, &#x27;1960-04-01&#x27;,\n",
       "               &#x27;1960-05-01&#x27;, &#x27;1960-06-01&#x27;, &#x27;1960-07-01&#x27;, &#x27;1960-08-01&#x27;,\n",
       "               &#x27;1960-09-01&#x27;, &#x27;1960-10-01&#x27;,\n",
       "               ...\n",
       "               &#x27;2022-03-01&#x27;, &#x27;2022-04-01&#x27;, &#x27;2022-05-01&#x27;, &#x27;2022-06-01&#x27;,\n",
       "               &#x27;2022-07-01&#x27;, &#x27;2022-08-01&#x27;, &#x27;2022-09-01&#x27;, &#x27;2022-10-01&#x27;,\n",
       "               &#x27;2022-11-01&#x27;, &#x27;2022-12-01&#x27;],\n",
       "              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, length=756, freq=None))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-2246417f-ad09-480f-832b-8b296b623b61' class='xr-section-summary-in' type='checkbox'  checked><label for='section-2246417f-ad09-480f-832b-8b296b623b61' class='xr-section-summary' >Attributes: <span>(8)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Time</dd><dt><span>delta_t :</span></dt><dd>0000-01-00 00:00:00</dd><dt><span>avg_period :</span></dt><dd>0000-01-00 00:00:00</dd><dt><span>prev_avg_period :</span></dt><dd>0000-00-07 00:00:00</dd><dt><span>standard_name :</span></dt><dd>time</dd><dt><span>axis :</span></dt><dd>T</dd><dt><span>actual_range :</span></dt><dd>[19723. 81722.]</dd><dt><span>_ChunkSizes :</span></dt><dd>1</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.DataArray 'time' (time: 756)>\n",
       "array(['1960-01-01T00:00:00.000000000', '1960-02-01T00:00:00.000000000',\n",
       "       '1960-03-01T00:00:00.000000000', ..., '2022-10-01T00:00:00.000000000',\n",
       "       '2022-11-01T00:00:00.000000000', '2022-12-01T00:00:00.000000000'],\n",
       "      dtype='datetime64[ns]')\n",
       "Coordinates:\n",
       "  * time     (time) datetime64[ns] 1960-01-01 1960-02-01 ... 2022-12-01\n",
       "Attributes:\n",
       "    long_name:        Time\n",
       "    delta_t:          0000-01-00 00:00:00\n",
       "    avg_period:       0000-01-00 00:00:00\n",
       "    prev_avg_period:  0000-00-07 00:00:00\n",
       "    standard_name:    time\n",
       "    axis:             T\n",
       "    actual_range:     [19723. 81722.]\n",
       "    _ChunkSizes:      1"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds.time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x78265413b310>]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ds.sst.sel(lon=300, lat=50).plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As we can see from the plot, the timeseries at any one point is totally dominated by the seasonal cycle. We would like to remove this seasonal cycle (called the \"climatology\") in order to better see the long-term variaitions in temperature. We will accomplish this using **groupby**.\n",
    "\n",
    "The syntax of Xarray's groupby is almost identical to Pandas.\n",
    "We will first apply groupby to a single DataArray."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\u001b[0;31mSignature:\u001b[0m\n",
       "\u001b[0mds\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msst\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgroupby\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mgroup\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;34m'Hashable | DataArray | IndexVariable'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0msqueeze\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;34m'bool'\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mrestore_coord_dims\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;34m'bool'\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0;34m'DataArrayGroupBy'\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
       "\u001b[0;31mDocstring:\u001b[0m\n",
       "Returns a DataArrayGroupBy object for performing grouped operations.\n",
       "\n",
       "Parameters\n",
       "----------\n",
       "group : Hashable, DataArray or IndexVariable\n",
       "    Array whose unique values should be used to group this array. If a\n",
       "    Hashable, must be the name of a coordinate contained in this dataarray.\n",
       "squeeze : bool, default: True\n",
       "    If \"group\" is a dimension of any arrays in this dataset, `squeeze`\n",
       "    controls whether the subarrays have a dimension of length 1 along\n",
       "    that dimension or if the dimension is squeezed out.\n",
       "restore_coord_dims : bool, default: False\n",
       "    If True, also restore the dimension order of multi-dimensional\n",
       "    coordinates.\n",
       "\n",
       "Returns\n",
       "-------\n",
       "grouped : DataArrayGroupBy\n",
       "    A `DataArrayGroupBy` object patterned after `pandas.GroupBy` that can be\n",
       "    iterated over in the form of `(unique_value, grouped_array)` pairs.\n",
       "\n",
       "Examples\n",
       "--------\n",
       "Calculate daily anomalies for daily data:\n",
       "\n",
       ">>> da = xr.DataArray(\n",
       "...     np.linspace(0, 1826, num=1827),\n",
       "...     coords=[pd.date_range(\"2000-01-01\", \"2004-12-31\", freq=\"D\")],\n",
       "...     dims=\"time\",\n",
       "... )\n",
       ">>> da\n",
       "<xarray.DataArray (time: 1827)>\n",
       "array([0.000e+00, 1.000e+00, 2.000e+00, ..., 1.824e+03, 1.825e+03,\n",
       "       1.826e+03])\n",
       "Coordinates:\n",
       "  * time     (time) datetime64[ns] 2000-01-01 2000-01-02 ... 2004-12-31\n",
       ">>> da.groupby(\"time.dayofyear\") - da.groupby(\"time.dayofyear\").mean(\"time\")\n",
       "<xarray.DataArray (time: 1827)>\n",
       "array([-730.8, -730.8, -730.8, ...,  730.2,  730.2,  730.5])\n",
       "Coordinates:\n",
       "  * time       (time) datetime64[ns] 2000-01-01 2000-01-02 ... 2004-12-31\n",
       "    dayofyear  (time) int64 1 2 3 4 5 6 7 8 ... 359 360 361 362 363 364 365 366\n",
       "\n",
       "See Also\n",
       "--------\n",
       ":ref:`groupby`\n",
       "    Users guide explanation of how to group and bin data.\n",
       "DataArray.groupby_bins\n",
       "Dataset.groupby\n",
       "core.groupby.DataArrayGroupBy\n",
       "pandas.DataFrame.groupby\n",
       "\u001b[0;31mFile:\u001b[0m      /srv/conda/envs/notebook/lib/python3.11/site-packages/xarray/core/dataarray.py\n",
       "\u001b[0;31mType:\u001b[0m      method"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ds.sst.groupby?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Split Step\n",
    "\n",
    "The most important argument is `group`: this defines the unique values we will use to \"split\" the data for grouped analysis. We can pass either a DataArray or a name of a variable in the dataset. Lets first use a DataArray. Just like with Pandas, we can use the time indexe to extract specific components of dates and times. Xarray uses a special syntax for this `.dt`, called the `DatetimeAccessor`.\n",
    "\n",
    "See a list of datatime properties you can access through .dt [here](https://pandas.pydata.org/docs/user_guide/timeseries.html#time-date-components)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<xarray.core.accessor_dt.DatetimeAccessor at 0x782654284490>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;month&#x27; (time: 756)&gt;\n",
       "array([ 1,  2,  3, ..., 10, 11, 12])\n",
       "Coordinates:\n",
       "  * time     (time) datetime64[ns] 1960-01-01 1960-02-01 ... 2022-12-01\n",
       "Attributes:\n",
       "    long_name:        Time\n",
       "    delta_t:          0000-01-00 00:00:00\n",
       "    avg_period:       0000-01-00 00:00:00\n",
       "    prev_avg_period:  0000-00-07 00:00:00\n",
       "    standard_name:    time\n",
       "    axis:             T\n",
       "    actual_range:     [19723. 81722.]\n",
       "    _ChunkSizes:      1</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'>'month'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 756</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-6840535d-3fe1-4996-831d-655a498838a8' class='xr-array-in' type='checkbox' checked><label for='section-6840535d-3fe1-4996-831d-655a498838a8' 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>1 2 3 4 5 6 7 8 9 10 11 12 1 2 3 ... 11 12 1 2 3 4 5 6 7 8 9 10 11 12</span></div><div class='xr-array-data'><pre>array([ 1,  2,  3, ..., 10, 11, 12])</pre></div></div></li><li class='xr-section-item'><input id='section-9e2d382a-0b63-48b7-80b9-c8f41880d1ac' class='xr-section-summary-in' type='checkbox'  checked><label for='section-9e2d382a-0b63-48b7-80b9-c8f41880d1ac' class='xr-section-summary' >Coordinates: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>1960-01-01 ... 2022-12-01</div><input id='attrs-1cec331e-7305-4238-be4f-daaba917a6ba' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1cec331e-7305-4238-be4f-daaba917a6ba' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-fc7f0111-d0f7-4745-953d-9138053b5583' class='xr-var-data-in' type='checkbox'><label for='data-fc7f0111-d0f7-4745-953d-9138053b5583' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Time</dd><dt><span>delta_t :</span></dt><dd>0000-01-00 00:00:00</dd><dt><span>avg_period :</span></dt><dd>0000-01-00 00:00:00</dd><dt><span>prev_avg_period :</span></dt><dd>0000-00-07 00:00:00</dd><dt><span>standard_name :</span></dt><dd>time</dd><dt><span>axis :</span></dt><dd>T</dd><dt><span>actual_range :</span></dt><dd>[19723. 81722.]</dd><dt><span>_ChunkSizes :</span></dt><dd>1</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;1960-01-01T00:00:00.000000000&#x27;, &#x27;1960-02-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;1960-03-01T00:00:00.000000000&#x27;, ..., &#x27;2022-10-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2022-11-01T00:00:00.000000000&#x27;, &#x27;2022-12-01T00:00:00.000000000&#x27;],\n",
       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-725e8656-cc1d-4d89-94dc-5077fd1f32a7' class='xr-section-summary-in' type='checkbox'  ><label for='section-725e8656-cc1d-4d89-94dc-5077fd1f32a7' class='xr-section-summary' >Indexes: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-2f8455c8-c276-44f8-b07a-59ef875756dc' class='xr-index-data-in' type='checkbox'/><label for='index-2f8455c8-c276-44f8-b07a-59ef875756dc' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(DatetimeIndex([&#x27;1960-01-01&#x27;, &#x27;1960-02-01&#x27;, &#x27;1960-03-01&#x27;, &#x27;1960-04-01&#x27;,\n",
       "               &#x27;1960-05-01&#x27;, &#x27;1960-06-01&#x27;, &#x27;1960-07-01&#x27;, &#x27;1960-08-01&#x27;,\n",
       "               &#x27;1960-09-01&#x27;, &#x27;1960-10-01&#x27;,\n",
       "               ...\n",
       "               &#x27;2022-03-01&#x27;, &#x27;2022-04-01&#x27;, &#x27;2022-05-01&#x27;, &#x27;2022-06-01&#x27;,\n",
       "               &#x27;2022-07-01&#x27;, &#x27;2022-08-01&#x27;, &#x27;2022-09-01&#x27;, &#x27;2022-10-01&#x27;,\n",
       "               &#x27;2022-11-01&#x27;, &#x27;2022-12-01&#x27;],\n",
       "              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, length=756, freq=None))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-c9e5cffc-35c4-467c-b1be-242d3986c5d3' class='xr-section-summary-in' type='checkbox'  checked><label for='section-c9e5cffc-35c4-467c-b1be-242d3986c5d3' class='xr-section-summary' >Attributes: <span>(8)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Time</dd><dt><span>delta_t :</span></dt><dd>0000-01-00 00:00:00</dd><dt><span>avg_period :</span></dt><dd>0000-01-00 00:00:00</dd><dt><span>prev_avg_period :</span></dt><dd>0000-00-07 00:00:00</dd><dt><span>standard_name :</span></dt><dd>time</dd><dt><span>axis :</span></dt><dd>T</dd><dt><span>actual_range :</span></dt><dd>[19723. 81722.]</dd><dt><span>_ChunkSizes :</span></dt><dd>1</dd></dl></div></li></ul></div></div>"
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       "    prev_avg_period:  0000-00-07 00:00:00\n",
       "    standard_name:    time\n",
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       "    actual_range:     [19723. 81722.]\n",
       "    _ChunkSizes:      1"
      ]
     },
     "execution_count": 21,
     "metadata": {},
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    }
   ],
   "source": [
    "ds.time.dt.month"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
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   },
   "outputs": [
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;year&#x27; (time: 756)&gt;\n",
       "array([1960, 1960, 1960, ..., 2022, 2022, 2022])\n",
       "Coordinates:\n",
       "  * time     (time) datetime64[ns] 1960-01-01 1960-02-01 ... 2022-12-01\n",
       "Attributes:\n",
       "    long_name:        Time\n",
       "    delta_t:          0000-01-00 00:00:00\n",
       "    avg_period:       0000-01-00 00:00:00\n",
       "    prev_avg_period:  0000-00-07 00:00:00\n",
       "    standard_name:    time\n",
       "    axis:             T\n",
       "    actual_range:     [19723. 81722.]\n",
       "    _ChunkSizes:      1</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'>'year'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 756</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-902add09-786a-4249-9e11-b6bfcf3bf242' class='xr-array-in' type='checkbox' checked><label for='section-902add09-786a-4249-9e11-b6bfcf3bf242' 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>1960 1960 1960 1960 1960 1960 1960 ... 2022 2022 2022 2022 2022 2022</span></div><div class='xr-array-data'><pre>array([1960, 1960, 1960, ..., 2022, 2022, 2022])</pre></div></div></li><li class='xr-section-item'><input id='section-7f2e87b3-6ae9-4afa-a2c8-8b4bb3436c56' class='xr-section-summary-in' type='checkbox'  checked><label for='section-7f2e87b3-6ae9-4afa-a2c8-8b4bb3436c56' class='xr-section-summary' >Coordinates: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>1960-01-01 ... 2022-12-01</div><input id='attrs-2f1ef66d-8da6-42ca-9f14-09f2c7e6488f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-2f1ef66d-8da6-42ca-9f14-09f2c7e6488f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f01da170-d181-4d5c-876f-6cce26f3ebea' class='xr-var-data-in' type='checkbox'><label for='data-f01da170-d181-4d5c-876f-6cce26f3ebea' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Time</dd><dt><span>delta_t :</span></dt><dd>0000-01-00 00:00:00</dd><dt><span>avg_period :</span></dt><dd>0000-01-00 00:00:00</dd><dt><span>prev_avg_period :</span></dt><dd>0000-00-07 00:00:00</dd><dt><span>standard_name :</span></dt><dd>time</dd><dt><span>axis :</span></dt><dd>T</dd><dt><span>actual_range :</span></dt><dd>[19723. 81722.]</dd><dt><span>_ChunkSizes :</span></dt><dd>1</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;1960-01-01T00:00:00.000000000&#x27;, &#x27;1960-02-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;1960-03-01T00:00:00.000000000&#x27;, ..., &#x27;2022-10-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2022-11-01T00:00:00.000000000&#x27;, &#x27;2022-12-01T00:00:00.000000000&#x27;],\n",
       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-4c8d29a4-5b77-494c-949c-61245445c6f3' class='xr-section-summary-in' type='checkbox'  ><label for='section-4c8d29a4-5b77-494c-949c-61245445c6f3' class='xr-section-summary' >Indexes: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-1f5abc49-c5bc-4a5e-b2cf-1c93a2ac5091' class='xr-index-data-in' type='checkbox'/><label for='index-1f5abc49-c5bc-4a5e-b2cf-1c93a2ac5091' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(DatetimeIndex([&#x27;1960-01-01&#x27;, &#x27;1960-02-01&#x27;, &#x27;1960-03-01&#x27;, &#x27;1960-04-01&#x27;,\n",
       "               &#x27;1960-05-01&#x27;, &#x27;1960-06-01&#x27;, &#x27;1960-07-01&#x27;, &#x27;1960-08-01&#x27;,\n",
       "               &#x27;1960-09-01&#x27;, &#x27;1960-10-01&#x27;,\n",
       "               ...\n",
       "               &#x27;2022-03-01&#x27;, &#x27;2022-04-01&#x27;, &#x27;2022-05-01&#x27;, &#x27;2022-06-01&#x27;,\n",
       "               &#x27;2022-07-01&#x27;, &#x27;2022-08-01&#x27;, &#x27;2022-09-01&#x27;, &#x27;2022-10-01&#x27;,\n",
       "               &#x27;2022-11-01&#x27;, &#x27;2022-12-01&#x27;],\n",
       "              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, length=756, freq=None))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-b3427ff1-1d28-4ada-ae80-3a5d8ec85de9' class='xr-section-summary-in' type='checkbox'  checked><label for='section-b3427ff1-1d28-4ada-ae80-3a5d8ec85de9' class='xr-section-summary' >Attributes: <span>(8)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Time</dd><dt><span>delta_t :</span></dt><dd>0000-01-00 00:00:00</dd><dt><span>avg_period :</span></dt><dd>0000-01-00 00:00:00</dd><dt><span>prev_avg_period :</span></dt><dd>0000-00-07 00:00:00</dd><dt><span>standard_name :</span></dt><dd>time</dd><dt><span>axis :</span></dt><dd>T</dd><dt><span>actual_range :</span></dt><dd>[19723. 81722.]</dd><dt><span>_ChunkSizes :</span></dt><dd>1</dd></dl></div></li></ul></div></div>"
      ],
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       "<xarray.DataArray 'year' (time: 756)>\n",
       "array([1960, 1960, 1960, ..., 2022, 2022, 2022])\n",
       "Coordinates:\n",
       "  * time     (time) datetime64[ns] 1960-01-01 1960-02-01 ... 2022-12-01\n",
       "Attributes:\n",
       "    long_name:        Time\n",
       "    delta_t:          0000-01-00 00:00:00\n",
       "    avg_period:       0000-01-00 00:00:00\n",
       "    prev_avg_period:  0000-00-07 00:00:00\n",
       "    standard_name:    time\n",
       "    axis:             T\n",
       "    actual_range:     [19723. 81722.]\n",
       "    _ChunkSizes:      1"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds.time.dt.year"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can use these arrays in a groupby operation:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DataArrayGroupBy, grouped over 'month'\n",
       "12 groups with labels 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12."
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gb = ds.sst.groupby(ds.time.dt.month)\n",
    "gb"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Xarray also offers a more concise syntax when the variable you're grouping on is already present in the dataset. This is identical to the previous line:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DataArrayGroupBy, grouped over 'month'\n",
       "12 groups with labels 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12."
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gb = ds.sst.groupby('time.month')\n",
    "gb"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now that the data are split, we can manually iterate over the group. The iterator returns the key (group name) and the value (the actual dataset corresponding to that group) for each group."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1\n"
     ]
    },
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;sst&#x27; (time: 63, lat: 89, lon: 180)&gt;\n",
       "[1009260 values with dtype=float32]\n",
       "Coordinates:\n",
       "  * lat      (lat) float32 88.0 86.0 84.0 82.0 80.0 ... -82.0 -84.0 -86.0 -88.0\n",
       "  * lon      (lon) float32 0.0 2.0 4.0 6.0 8.0 ... 350.0 352.0 354.0 356.0 358.0\n",
       "  * time     (time) datetime64[ns] 1960-01-01 1961-01-01 ... 2022-01-01\n",
       "Attributes:\n",
       "    long_name:     Monthly Means of Sea Surface Temperature\n",
       "    units:         degC\n",
       "    var_desc:      Sea Surface Temperature\n",
       "    level_desc:    Surface\n",
       "    statistic:     Mean\n",
       "    dataset:       NOAA Extended Reconstructed SST V5\n",
       "    parent_stat:   Individual Values\n",
       "    actual_range:  [-1.8     42.32636]\n",
       "    valid_range:   [-1.8 45. ]\n",
       "    _ChunkSizes:   [  1  89 180]</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'>time</span>: 63</li><li><span class='xr-has-index'>lat</span>: 89</li><li><span class='xr-has-index'>lon</span>: 180</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-cd5d363f-64ee-4cca-8761-1bbe53bbe9d1' class='xr-array-in' type='checkbox' checked><label for='section-cd5d363f-64ee-4cca-8761-1bbe53bbe9d1' 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>...</span></div><div class='xr-array-data'><pre>[1009260 values with dtype=float32]</pre></div></div></li><li class='xr-section-item'><input id='section-3349195b-cd0c-4ee6-a8d8-494e31c21ce4' class='xr-section-summary-in' type='checkbox'  checked><label for='section-3349195b-cd0c-4ee6-a8d8-494e31c21ce4' class='xr-section-summary' >Coordinates: <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 class='xr-has-index'>lat</span></div><div class='xr-var-dims'>(lat)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>88.0 86.0 84.0 ... -86.0 -88.0</div><input id='attrs-aa38431e-7a92-41fd-940e-387d13b0f9da' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-aa38431e-7a92-41fd-940e-387d13b0f9da' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-963b7cd3-f76f-4e6e-a401-b84437bba1af' class='xr-var-data-in' type='checkbox'><label for='data-963b7cd3-f76f-4e6e-a401-b84437bba1af' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degrees_north</dd><dt><span>long_name :</span></dt><dd>Latitude</dd><dt><span>actual_range :</span></dt><dd>[ 88. -88.]</dd><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>axis :</span></dt><dd>Y</dd><dt><span>coordinate_defines :</span></dt><dd>center</dd></dl></div><div class='xr-var-data'><pre>array([ 88.,  86.,  84.,  82.,  80.,  78.,  76.,  74.,  72.,  70.,  68.,  66.,\n",
       "        64.,  62.,  60.,  58.,  56.,  54.,  52.,  50.,  48.,  46.,  44.,  42.,\n",
       "        40.,  38.,  36.,  34.,  32.,  30.,  28.,  26.,  24.,  22.,  20.,  18.,\n",
       "        16.,  14.,  12.,  10.,   8.,   6.,   4.,   2.,   0.,  -2.,  -4.,  -6.,\n",
       "        -8., -10., -12., -14., -16., -18., -20., -22., -24., -26., -28., -30.,\n",
       "       -32., -34., -36., -38., -40., -42., -44., -46., -48., -50., -52., -54.,\n",
       "       -56., -58., -60., -62., -64., -66., -68., -70., -72., -74., -76., -78.,\n",
       "       -80., -82., -84., -86., -88.], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lon</span></div><div class='xr-var-dims'>(lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.0 2.0 4.0 ... 354.0 356.0 358.0</div><input id='attrs-364f6ad5-102e-4847-adf8-db6d30c1947e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-364f6ad5-102e-4847-adf8-db6d30c1947e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2bb99ee9-5e5d-46e0-90e8-446dc02193ff' class='xr-var-data-in' type='checkbox'><label for='data-2bb99ee9-5e5d-46e0-90e8-446dc02193ff' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degrees_east</dd><dt><span>long_name :</span></dt><dd>Longitude</dd><dt><span>actual_range :</span></dt><dd>[  0. 358.]</dd><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>axis :</span></dt><dd>X</dd><dt><span>coordinate_defines :</span></dt><dd>center</dd></dl></div><div class='xr-var-data'><pre>array([  0.,   2.,   4.,   6.,   8.,  10.,  12.,  14.,  16.,  18.,  20.,  22.,\n",
       "        24.,  26.,  28.,  30.,  32.,  34.,  36.,  38.,  40.,  42.,  44.,  46.,\n",
       "        48.,  50.,  52.,  54.,  56.,  58.,  60.,  62.,  64.,  66.,  68.,  70.,\n",
       "        72.,  74.,  76.,  78.,  80.,  82.,  84.,  86.,  88.,  90.,  92.,  94.,\n",
       "        96.,  98., 100., 102., 104., 106., 108., 110., 112., 114., 116., 118.,\n",
       "       120., 122., 124., 126., 128., 130., 132., 134., 136., 138., 140., 142.,\n",
       "       144., 146., 148., 150., 152., 154., 156., 158., 160., 162., 164., 166.,\n",
       "       168., 170., 172., 174., 176., 178., 180., 182., 184., 186., 188., 190.,\n",
       "       192., 194., 196., 198., 200., 202., 204., 206., 208., 210., 212., 214.,\n",
       "       216., 218., 220., 222., 224., 226., 228., 230., 232., 234., 236., 238.,\n",
       "       240., 242., 244., 246., 248., 250., 252., 254., 256., 258., 260., 262.,\n",
       "       264., 266., 268., 270., 272., 274., 276., 278., 280., 282., 284., 286.,\n",
       "       288., 290., 292., 294., 296., 298., 300., 302., 304., 306., 308., 310.,\n",
       "       312., 314., 316., 318., 320., 322., 324., 326., 328., 330., 332., 334.,\n",
       "       336., 338., 340., 342., 344., 346., 348., 350., 352., 354., 356., 358.],\n",
       "      dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>1960-01-01 ... 2022-01-01</div><input id='attrs-fbe30f13-e7ea-4a33-8b65-61c718f99807' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-fbe30f13-e7ea-4a33-8b65-61c718f99807' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-81a920ac-1756-4e0a-ab6c-febd5a979016' class='xr-var-data-in' type='checkbox'><label for='data-81a920ac-1756-4e0a-ab6c-febd5a979016' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Time</dd><dt><span>delta_t :</span></dt><dd>0000-01-00 00:00:00</dd><dt><span>avg_period :</span></dt><dd>0000-01-00 00:00:00</dd><dt><span>prev_avg_period :</span></dt><dd>0000-00-07 00:00:00</dd><dt><span>standard_name :</span></dt><dd>time</dd><dt><span>axis :</span></dt><dd>T</dd><dt><span>actual_range :</span></dt><dd>[19723. 81722.]</dd><dt><span>_ChunkSizes :</span></dt><dd>1</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;1960-01-01T00:00:00.000000000&#x27;, &#x27;1961-01-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;1962-01-01T00:00:00.000000000&#x27;, &#x27;1963-01-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;1964-01-01T00:00:00.000000000&#x27;, &#x27;1965-01-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;1966-01-01T00:00:00.000000000&#x27;, &#x27;1967-01-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;1968-01-01T00:00:00.000000000&#x27;, &#x27;1969-01-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;1970-01-01T00:00:00.000000000&#x27;, &#x27;1971-01-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;1972-01-01T00:00:00.000000000&#x27;, &#x27;1973-01-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;1974-01-01T00:00:00.000000000&#x27;, &#x27;1975-01-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;1976-01-01T00:00:00.000000000&#x27;, &#x27;1977-01-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;1978-01-01T00:00:00.000000000&#x27;, &#x27;1979-01-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;1980-01-01T00:00:00.000000000&#x27;, &#x27;1981-01-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;1982-01-01T00:00:00.000000000&#x27;, &#x27;1983-01-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;1984-01-01T00:00:00.000000000&#x27;, &#x27;1985-01-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;1986-01-01T00:00:00.000000000&#x27;, &#x27;1987-01-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;1988-01-01T00:00:00.000000000&#x27;, &#x27;1989-01-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;1990-01-01T00:00:00.000000000&#x27;, &#x27;1991-01-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;1992-01-01T00:00:00.000000000&#x27;, &#x27;1993-01-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;1994-01-01T00:00:00.000000000&#x27;, &#x27;1995-01-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;1996-01-01T00:00:00.000000000&#x27;, &#x27;1997-01-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;1998-01-01T00:00:00.000000000&#x27;, &#x27;1999-01-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2000-01-01T00:00:00.000000000&#x27;, &#x27;2001-01-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2002-01-01T00:00:00.000000000&#x27;, &#x27;2003-01-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2004-01-01T00:00:00.000000000&#x27;, &#x27;2005-01-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2006-01-01T00:00:00.000000000&#x27;, &#x27;2007-01-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2008-01-01T00:00:00.000000000&#x27;, &#x27;2009-01-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2010-01-01T00:00:00.000000000&#x27;, &#x27;2011-01-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2012-01-01T00:00:00.000000000&#x27;, &#x27;2013-01-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2014-01-01T00:00:00.000000000&#x27;, &#x27;2015-01-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2016-01-01T00:00:00.000000000&#x27;, &#x27;2017-01-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2018-01-01T00:00:00.000000000&#x27;, &#x27;2019-01-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2020-01-01T00:00:00.000000000&#x27;, &#x27;2021-01-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2022-01-01T00:00:00.000000000&#x27;], dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-6b81d7fe-011f-47e4-a432-4a9705e4f981' class='xr-section-summary-in' type='checkbox'  ><label for='section-6b81d7fe-011f-47e4-a432-4a9705e4f981' class='xr-section-summary' >Indexes: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>lat</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-6aafd6f8-9dc2-41f9-bc57-269410b70f9f' class='xr-index-data-in' type='checkbox'/><label for='index-6aafd6f8-9dc2-41f9-bc57-269410b70f9f' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([ 88.0,  86.0,  84.0,  82.0,  80.0,  78.0,  76.0,  74.0,  72.0,  70.0,\n",
       "        68.0,  66.0,  64.0,  62.0,  60.0,  58.0,  56.0,  54.0,  52.0,  50.0,\n",
       "        48.0,  46.0,  44.0,  42.0,  40.0,  38.0,  36.0,  34.0,  32.0,  30.0,\n",
       "        28.0,  26.0,  24.0,  22.0,  20.0,  18.0,  16.0,  14.0,  12.0,  10.0,\n",
       "         8.0,   6.0,   4.0,   2.0,   0.0,  -2.0,  -4.0,  -6.0,  -8.0, -10.0,\n",
       "       -12.0, -14.0, -16.0, -18.0, -20.0, -22.0, -24.0, -26.0, -28.0, -30.0,\n",
       "       -32.0, -34.0, -36.0, -38.0, -40.0, -42.0, -44.0, -46.0, -48.0, -50.0,\n",
       "       -52.0, -54.0, -56.0, -58.0, -60.0, -62.0, -64.0, -66.0, -68.0, -70.0,\n",
       "       -72.0, -74.0, -76.0, -78.0, -80.0, -82.0, -84.0, -86.0, -88.0],\n",
       "      dtype=&#x27;float32&#x27;, name=&#x27;lat&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>lon</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-49b77813-846d-47ff-aa90-13902d8b17a1' class='xr-index-data-in' type='checkbox'/><label for='index-49b77813-846d-47ff-aa90-13902d8b17a1' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([  0.0,   2.0,   4.0,   6.0,   8.0,  10.0,  12.0,  14.0,  16.0,  18.0,\n",
       "       ...\n",
       "       340.0, 342.0, 344.0, 346.0, 348.0, 350.0, 352.0, 354.0, 356.0, 358.0],\n",
       "      dtype=&#x27;float32&#x27;, name=&#x27;lon&#x27;, length=180))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-092b632f-bed4-4bef-9097-835003b72bb2' class='xr-index-data-in' type='checkbox'/><label for='index-092b632f-bed4-4bef-9097-835003b72bb2' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(DatetimeIndex([&#x27;1960-01-01&#x27;, &#x27;1961-01-01&#x27;, &#x27;1962-01-01&#x27;, &#x27;1963-01-01&#x27;,\n",
       "               &#x27;1964-01-01&#x27;, &#x27;1965-01-01&#x27;, &#x27;1966-01-01&#x27;, &#x27;1967-01-01&#x27;,\n",
       "               &#x27;1968-01-01&#x27;, &#x27;1969-01-01&#x27;, &#x27;1970-01-01&#x27;, &#x27;1971-01-01&#x27;,\n",
       "               &#x27;1972-01-01&#x27;, &#x27;1973-01-01&#x27;, &#x27;1974-01-01&#x27;, &#x27;1975-01-01&#x27;,\n",
       "               &#x27;1976-01-01&#x27;, &#x27;1977-01-01&#x27;, &#x27;1978-01-01&#x27;, &#x27;1979-01-01&#x27;,\n",
       "               &#x27;1980-01-01&#x27;, &#x27;1981-01-01&#x27;, &#x27;1982-01-01&#x27;, &#x27;1983-01-01&#x27;,\n",
       "               &#x27;1984-01-01&#x27;, &#x27;1985-01-01&#x27;, &#x27;1986-01-01&#x27;, &#x27;1987-01-01&#x27;,\n",
       "               &#x27;1988-01-01&#x27;, &#x27;1989-01-01&#x27;, &#x27;1990-01-01&#x27;, &#x27;1991-01-01&#x27;,\n",
       "               &#x27;1992-01-01&#x27;, &#x27;1993-01-01&#x27;, &#x27;1994-01-01&#x27;, &#x27;1995-01-01&#x27;,\n",
       "               &#x27;1996-01-01&#x27;, &#x27;1997-01-01&#x27;, &#x27;1998-01-01&#x27;, &#x27;1999-01-01&#x27;,\n",
       "               &#x27;2000-01-01&#x27;, &#x27;2001-01-01&#x27;, &#x27;2002-01-01&#x27;, &#x27;2003-01-01&#x27;,\n",
       "               &#x27;2004-01-01&#x27;, &#x27;2005-01-01&#x27;, &#x27;2006-01-01&#x27;, &#x27;2007-01-01&#x27;,\n",
       "               &#x27;2008-01-01&#x27;, &#x27;2009-01-01&#x27;, &#x27;2010-01-01&#x27;, &#x27;2011-01-01&#x27;,\n",
       "               &#x27;2012-01-01&#x27;, &#x27;2013-01-01&#x27;, &#x27;2014-01-01&#x27;, &#x27;2015-01-01&#x27;,\n",
       "               &#x27;2016-01-01&#x27;, &#x27;2017-01-01&#x27;, &#x27;2018-01-01&#x27;, &#x27;2019-01-01&#x27;,\n",
       "               &#x27;2020-01-01&#x27;, &#x27;2021-01-01&#x27;, &#x27;2022-01-01&#x27;],\n",
       "              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, freq=None))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-7be6488c-5299-4274-a252-37bde8b13a12' class='xr-section-summary-in' type='checkbox'  ><label for='section-7be6488c-5299-4274-a252-37bde8b13a12' class='xr-section-summary' >Attributes: <span>(10)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Monthly Means of Sea Surface Temperature</dd><dt><span>units :</span></dt><dd>degC</dd><dt><span>var_desc :</span></dt><dd>Sea Surface Temperature</dd><dt><span>level_desc :</span></dt><dd>Surface</dd><dt><span>statistic :</span></dt><dd>Mean</dd><dt><span>dataset :</span></dt><dd>NOAA Extended Reconstructed SST V5</dd><dt><span>parent_stat :</span></dt><dd>Individual Values</dd><dt><span>actual_range :</span></dt><dd>[-1.8     42.32636]</dd><dt><span>valid_range :</span></dt><dd>[-1.8 45. ]</dd><dt><span>_ChunkSizes :</span></dt><dd>[  1  89 180]</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.DataArray 'sst' (time: 63, lat: 89, lon: 180)>\n",
       "[1009260 values with dtype=float32]\n",
       "Coordinates:\n",
       "  * lat      (lat) float32 88.0 86.0 84.0 82.0 80.0 ... -82.0 -84.0 -86.0 -88.0\n",
       "  * lon      (lon) float32 0.0 2.0 4.0 6.0 8.0 ... 350.0 352.0 354.0 356.0 358.0\n",
       "  * time     (time) datetime64[ns] 1960-01-01 1961-01-01 ... 2022-01-01\n",
       "Attributes:\n",
       "    long_name:     Monthly Means of Sea Surface Temperature\n",
       "    units:         degC\n",
       "    var_desc:      Sea Surface Temperature\n",
       "    level_desc:    Surface\n",
       "    statistic:     Mean\n",
       "    dataset:       NOAA Extended Reconstructed SST V5\n",
       "    parent_stat:   Individual Values\n",
       "    actual_range:  [-1.8     42.32636]\n",
       "    valid_range:   [-1.8 45. ]\n",
       "    _ChunkSizes:   [  1  89 180]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "for group_name, group_da in gb:\n",
    "    # stop iterating after the first loop\n",
    "    break\n",
    "\n",
    "print(group_name)\n",
    "group_da"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Map & Combine\n",
    "\n",
    "Now that we have groups defined, it's time to \"apply\" a calculation to the group. Like in Pandas, these calculations can either be:\n",
    "- _aggregation_: reduces the size of the group\n",
    "- _transformation_: preserves the group's full size\n",
    "\n",
    "At then end of the apply step, xarray will automatically combine the aggregated / transformed groups back into a single object."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "```{warning}\n",
    "Xarray calls the \"apply\" step `map`. This is different from Pandas!\n",
    "```\n",
    "\n",
    "The most fundamental way to apply is with the `.map` method."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\u001b[0;31mSignature:\u001b[0m\n",
       "\u001b[0mgb\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmap\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mfunc\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;34m'Callable[..., DataArray]'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0margs\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;34m'tuple[Any, ...]'\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mshortcut\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;34m'bool | None'\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;34m'Any'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0;34m'DataArray'\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
       "\u001b[0;31mDocstring:\u001b[0m\n",
       "Apply a function to each array in the group and concatenate them\n",
       "together into a new array.\n",
       "\n",
       "`func` is called like `func(ar, *args, **kwargs)` for each array `ar`\n",
       "in this group.\n",
       "\n",
       "Apply uses heuristics (like `pandas.GroupBy.apply`) to figure out how\n",
       "to stack together the array. The rule is:\n",
       "\n",
       "1. If the dimension along which the group coordinate is defined is\n",
       "   still in the first grouped array after applying `func`, then stack\n",
       "   over this dimension.\n",
       "2. Otherwise, stack over the new dimension given by name of this\n",
       "   grouping (the argument to the `groupby` function).\n",
       "\n",
       "Parameters\n",
       "----------\n",
       "func : callable\n",
       "    Callable to apply to each array.\n",
       "shortcut : bool, optional\n",
       "    Whether or not to shortcut evaluation under the assumptions that:\n",
       "\n",
       "    (1) The action of `func` does not depend on any of the array\n",
       "        metadata (attributes or coordinates) but only on the data and\n",
       "        dimensions.\n",
       "    (2) The action of `func` creates arrays with homogeneous metadata,\n",
       "        that is, with the same dimensions and attributes.\n",
       "\n",
       "    If these conditions are satisfied `shortcut` provides significant\n",
       "    speedup. This should be the case for many common groupby operations\n",
       "    (e.g., applying numpy ufuncs).\n",
       "*args : tuple, optional\n",
       "    Positional arguments passed to `func`.\n",
       "**kwargs\n",
       "    Used to call `func(ar, **kwargs)` for each array `ar`.\n",
       "\n",
       "Returns\n",
       "-------\n",
       "applied : DataArray\n",
       "    The result of splitting, applying and combining this array.\n",
       "\u001b[0;31mFile:\u001b[0m      /srv/conda/envs/notebook/lib/python3.11/site-packages/xarray/core/groupby.py\n",
       "\u001b[0;31mType:\u001b[0m      method"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "gb.map?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Aggregations"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Like Pandas, xarray's groupby object has many built-in aggregation operations (e.g. `mean`, `min`, `max`, `std`, etc):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;sst&#x27; (month: 12, lat: 89, lon: 180)&gt;\n",
       "array([[[-1.8      , -1.8      , -1.8      , ..., -1.8      ,\n",
       "         -1.8      , -1.8      ],\n",
       "        [-1.8      , -1.8      , -1.8      , ..., -1.8      ,\n",
       "         -1.8      , -1.8      ],\n",
       "        [-1.8      , -1.8      , -1.8      , ..., -1.8      ,\n",
       "         -1.8      , -1.8      ],\n",
       "        ...,\n",
       "        [       nan,        nan,        nan, ...,        nan,\n",
       "                nan,        nan],\n",
       "        [       nan,        nan,        nan, ...,        nan,\n",
       "                nan,        nan],\n",
       "        [       nan,        nan,        nan, ...,        nan,\n",
       "                nan,        nan]],\n",
       "\n",
       "       [[-1.8      , -1.8      , -1.8      , ..., -1.8      ,\n",
       "         -1.8      , -1.8      ],\n",
       "        [-1.8      , -1.8      , -1.8      , ..., -1.8      ,\n",
       "         -1.8      , -1.8      ],\n",
       "        [-1.8      , -1.8      , -1.8      , ..., -1.8      ,\n",
       "         -1.8      , -1.8      ],\n",
       "...\n",
       "        [       nan,        nan,        nan, ...,        nan,\n",
       "                nan,        nan],\n",
       "        [       nan,        nan,        nan, ...,        nan,\n",
       "                nan,        nan],\n",
       "        [       nan,        nan,        nan, ...,        nan,\n",
       "                nan,        nan]],\n",
       "\n",
       "       [[-1.7995342, -1.7996206, -1.7998532, ..., -1.7998041,\n",
       "         -1.7996737, -1.7995361],\n",
       "        [-1.7995963, -1.799773 , -1.8      , ..., -1.8      ,\n",
       "         -1.7998328, -1.7996292],\n",
       "        [-1.8      , -1.8      , -1.8      , ..., -1.8      ,\n",
       "         -1.8      , -1.8      ],\n",
       "        ...,\n",
       "        [       nan,        nan,        nan, ...,        nan,\n",
       "                nan,        nan],\n",
       "        [       nan,        nan,        nan, ...,        nan,\n",
       "                nan,        nan],\n",
       "        [       nan,        nan,        nan, ...,        nan,\n",
       "                nan,        nan]]], dtype=float32)\n",
       "Coordinates:\n",
       "  * lat      (lat) float32 88.0 86.0 84.0 82.0 80.0 ... -82.0 -84.0 -86.0 -88.0\n",
       "  * lon      (lon) float32 0.0 2.0 4.0 6.0 8.0 ... 350.0 352.0 354.0 356.0 358.0\n",
       "  * month    (month) int64 1 2 3 4 5 6 7 8 9 10 11 12\n",
       "Attributes:\n",
       "    long_name:     Monthly Means of Sea Surface Temperature\n",
       "    units:         degC\n",
       "    var_desc:      Sea Surface Temperature\n",
       "    level_desc:    Surface\n",
       "    statistic:     Mean\n",
       "    dataset:       NOAA Extended Reconstructed SST V5\n",
       "    parent_stat:   Individual Values\n",
       "    actual_range:  [-1.8     42.32636]\n",
       "    valid_range:   [-1.8 45. ]\n",
       "    _ChunkSizes:   [  1  89 180]</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'>month</span>: 12</li><li><span class='xr-has-index'>lat</span>: 89</li><li><span class='xr-has-index'>lon</span>: 180</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-8802492a-f2f9-4490-bb65-b999e9fe9ee3' class='xr-array-in' type='checkbox' checked><label for='section-8802492a-f2f9-4490-bb65-b999e9fe9ee3' 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>-1.8 -1.8 -1.8 -1.8 -1.8 -1.8 -1.8 ... nan nan nan nan nan nan nan</span></div><div class='xr-array-data'><pre>array([[[-1.8      , -1.8      , -1.8      , ..., -1.8      ,\n",
       "         -1.8      , -1.8      ],\n",
       "        [-1.8      , -1.8      , -1.8      , ..., -1.8      ,\n",
       "         -1.8      , -1.8      ],\n",
       "        [-1.8      , -1.8      , -1.8      , ..., -1.8      ,\n",
       "         -1.8      , -1.8      ],\n",
       "        ...,\n",
       "        [       nan,        nan,        nan, ...,        nan,\n",
       "                nan,        nan],\n",
       "        [       nan,        nan,        nan, ...,        nan,\n",
       "                nan,        nan],\n",
       "        [       nan,        nan,        nan, ...,        nan,\n",
       "                nan,        nan]],\n",
       "\n",
       "       [[-1.8      , -1.8      , -1.8      , ..., -1.8      ,\n",
       "         -1.8      , -1.8      ],\n",
       "        [-1.8      , -1.8      , -1.8      , ..., -1.8      ,\n",
       "         -1.8      , -1.8      ],\n",
       "        [-1.8      , -1.8      , -1.8      , ..., -1.8      ,\n",
       "         -1.8      , -1.8      ],\n",
       "...\n",
       "        [       nan,        nan,        nan, ...,        nan,\n",
       "                nan,        nan],\n",
       "        [       nan,        nan,        nan, ...,        nan,\n",
       "                nan,        nan],\n",
       "        [       nan,        nan,        nan, ...,        nan,\n",
       "                nan,        nan]],\n",
       "\n",
       "       [[-1.7995342, -1.7996206, -1.7998532, ..., -1.7998041,\n",
       "         -1.7996737, -1.7995361],\n",
       "        [-1.7995963, -1.799773 , -1.8      , ..., -1.8      ,\n",
       "         -1.7998328, -1.7996292],\n",
       "        [-1.8      , -1.8      , -1.8      , ..., -1.8      ,\n",
       "         -1.8      , -1.8      ],\n",
       "        ...,\n",
       "        [       nan,        nan,        nan, ...,        nan,\n",
       "                nan,        nan],\n",
       "        [       nan,        nan,        nan, ...,        nan,\n",
       "                nan,        nan],\n",
       "        [       nan,        nan,        nan, ...,        nan,\n",
       "                nan,        nan]]], dtype=float32)</pre></div></div></li><li class='xr-section-item'><input id='section-5c8e3fd3-5ab1-4426-a25f-5bcdffdeed09' class='xr-section-summary-in' type='checkbox'  checked><label for='section-5c8e3fd3-5ab1-4426-a25f-5bcdffdeed09' class='xr-section-summary' >Coordinates: <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 class='xr-has-index'>lat</span></div><div class='xr-var-dims'>(lat)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>88.0 86.0 84.0 ... -86.0 -88.0</div><input id='attrs-d7727d3e-fb9a-4bf7-994a-06d7513c3236' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d7727d3e-fb9a-4bf7-994a-06d7513c3236' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-746e14d6-fd11-4009-bc8a-ed51bd8ede72' class='xr-var-data-in' type='checkbox'><label for='data-746e14d6-fd11-4009-bc8a-ed51bd8ede72' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degrees_north</dd><dt><span>long_name :</span></dt><dd>Latitude</dd><dt><span>actual_range :</span></dt><dd>[ 88. -88.]</dd><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>axis :</span></dt><dd>Y</dd><dt><span>coordinate_defines :</span></dt><dd>center</dd></dl></div><div class='xr-var-data'><pre>array([ 88.,  86.,  84.,  82.,  80.,  78.,  76.,  74.,  72.,  70.,  68.,  66.,\n",
       "        64.,  62.,  60.,  58.,  56.,  54.,  52.,  50.,  48.,  46.,  44.,  42.,\n",
       "        40.,  38.,  36.,  34.,  32.,  30.,  28.,  26.,  24.,  22.,  20.,  18.,\n",
       "        16.,  14.,  12.,  10.,   8.,   6.,   4.,   2.,   0.,  -2.,  -4.,  -6.,\n",
       "        -8., -10., -12., -14., -16., -18., -20., -22., -24., -26., -28., -30.,\n",
       "       -32., -34., -36., -38., -40., -42., -44., -46., -48., -50., -52., -54.,\n",
       "       -56., -58., -60., -62., -64., -66., -68., -70., -72., -74., -76., -78.,\n",
       "       -80., -82., -84., -86., -88.], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lon</span></div><div class='xr-var-dims'>(lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.0 2.0 4.0 ... 354.0 356.0 358.0</div><input id='attrs-66c73666-2846-4961-92f9-2a045de32393' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-66c73666-2846-4961-92f9-2a045de32393' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b1ea0716-4005-4030-bd37-5be80084b7f1' class='xr-var-data-in' type='checkbox'><label for='data-b1ea0716-4005-4030-bd37-5be80084b7f1' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degrees_east</dd><dt><span>long_name :</span></dt><dd>Longitude</dd><dt><span>actual_range :</span></dt><dd>[  0. 358.]</dd><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>axis :</span></dt><dd>X</dd><dt><span>coordinate_defines :</span></dt><dd>center</dd></dl></div><div class='xr-var-data'><pre>array([  0.,   2.,   4.,   6.,   8.,  10.,  12.,  14.,  16.,  18.,  20.,  22.,\n",
       "        24.,  26.,  28.,  30.,  32.,  34.,  36.,  38.,  40.,  42.,  44.,  46.,\n",
       "        48.,  50.,  52.,  54.,  56.,  58.,  60.,  62.,  64.,  66.,  68.,  70.,\n",
       "        72.,  74.,  76.,  78.,  80.,  82.,  84.,  86.,  88.,  90.,  92.,  94.,\n",
       "        96.,  98., 100., 102., 104., 106., 108., 110., 112., 114., 116., 118.,\n",
       "       120., 122., 124., 126., 128., 130., 132., 134., 136., 138., 140., 142.,\n",
       "       144., 146., 148., 150., 152., 154., 156., 158., 160., 162., 164., 166.,\n",
       "       168., 170., 172., 174., 176., 178., 180., 182., 184., 186., 188., 190.,\n",
       "       192., 194., 196., 198., 200., 202., 204., 206., 208., 210., 212., 214.,\n",
       "       216., 218., 220., 222., 224., 226., 228., 230., 232., 234., 236., 238.,\n",
       "       240., 242., 244., 246., 248., 250., 252., 254., 256., 258., 260., 262.,\n",
       "       264., 266., 268., 270., 272., 274., 276., 278., 280., 282., 284., 286.,\n",
       "       288., 290., 292., 294., 296., 298., 300., 302., 304., 306., 308., 310.,\n",
       "       312., 314., 316., 318., 320., 322., 324., 326., 328., 330., 332., 334.,\n",
       "       336., 338., 340., 342., 344., 346., 348., 350., 352., 354., 356., 358.],\n",
       "      dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>month</span></div><div class='xr-var-dims'>(month)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>1 2 3 4 5 6 7 8 9 10 11 12</div><input id='attrs-af7f4860-5aee-47fc-a249-7a78585e6d5d' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-af7f4860-5aee-47fc-a249-7a78585e6d5d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-16c7646f-6ece-4511-af05-1d0f4f450b8f' class='xr-var-data-in' type='checkbox'><label for='data-16c7646f-6ece-4511-af05-1d0f4f450b8f' 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,  5,  6,  7,  8,  9, 10, 11, 12])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-f3c533b2-6da6-449e-9a93-ba021ed2d872' class='xr-section-summary-in' type='checkbox'  ><label for='section-f3c533b2-6da6-449e-9a93-ba021ed2d872' class='xr-section-summary' >Indexes: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>lat</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-f8cbb683-6180-4027-ac4e-412522a39b7a' class='xr-index-data-in' type='checkbox'/><label for='index-f8cbb683-6180-4027-ac4e-412522a39b7a' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([ 88.0,  86.0,  84.0,  82.0,  80.0,  78.0,  76.0,  74.0,  72.0,  70.0,\n",
       "        68.0,  66.0,  64.0,  62.0,  60.0,  58.0,  56.0,  54.0,  52.0,  50.0,\n",
       "        48.0,  46.0,  44.0,  42.0,  40.0,  38.0,  36.0,  34.0,  32.0,  30.0,\n",
       "        28.0,  26.0,  24.0,  22.0,  20.0,  18.0,  16.0,  14.0,  12.0,  10.0,\n",
       "         8.0,   6.0,   4.0,   2.0,   0.0,  -2.0,  -4.0,  -6.0,  -8.0, -10.0,\n",
       "       -12.0, -14.0, -16.0, -18.0, -20.0, -22.0, -24.0, -26.0, -28.0, -30.0,\n",
       "       -32.0, -34.0, -36.0, -38.0, -40.0, -42.0, -44.0, -46.0, -48.0, -50.0,\n",
       "       -52.0, -54.0, -56.0, -58.0, -60.0, -62.0, -64.0, -66.0, -68.0, -70.0,\n",
       "       -72.0, -74.0, -76.0, -78.0, -80.0, -82.0, -84.0, -86.0, -88.0],\n",
       "      dtype=&#x27;float32&#x27;, name=&#x27;lat&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>lon</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-51e3e3ad-7e8a-4741-931d-a08c3b2bde1e' class='xr-index-data-in' type='checkbox'/><label for='index-51e3e3ad-7e8a-4741-931d-a08c3b2bde1e' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([  0.0,   2.0,   4.0,   6.0,   8.0,  10.0,  12.0,  14.0,  16.0,  18.0,\n",
       "       ...\n",
       "       340.0, 342.0, 344.0, 346.0, 348.0, 350.0, 352.0, 354.0, 356.0, 358.0],\n",
       "      dtype=&#x27;float32&#x27;, name=&#x27;lon&#x27;, length=180))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>month</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-3112cdf7-8902-47f1-a572-9fed71555ab5' class='xr-index-data-in' type='checkbox'/><label for='index-3112cdf7-8902-47f1-a572-9fed71555ab5' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12], dtype=&#x27;int64&#x27;, name=&#x27;month&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-733d23a6-9b8f-4b10-8284-f62ed091f21f' class='xr-section-summary-in' type='checkbox'  ><label for='section-733d23a6-9b8f-4b10-8284-f62ed091f21f' class='xr-section-summary' >Attributes: <span>(10)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Monthly Means of Sea Surface Temperature</dd><dt><span>units :</span></dt><dd>degC</dd><dt><span>var_desc :</span></dt><dd>Sea Surface Temperature</dd><dt><span>level_desc :</span></dt><dd>Surface</dd><dt><span>statistic :</span></dt><dd>Mean</dd><dt><span>dataset :</span></dt><dd>NOAA Extended Reconstructed SST V5</dd><dt><span>parent_stat :</span></dt><dd>Individual Values</dd><dt><span>actual_range :</span></dt><dd>[-1.8     42.32636]</dd><dt><span>valid_range :</span></dt><dd>[-1.8 45. ]</dd><dt><span>_ChunkSizes :</span></dt><dd>[  1  89 180]</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.DataArray 'sst' (month: 12, lat: 89, lon: 180)>\n",
       "array([[[-1.8      , -1.8      , -1.8      , ..., -1.8      ,\n",
       "         -1.8      , -1.8      ],\n",
       "        [-1.8      , -1.8      , -1.8      , ..., -1.8      ,\n",
       "         -1.8      , -1.8      ],\n",
       "        [-1.8      , -1.8      , -1.8      , ..., -1.8      ,\n",
       "         -1.8      , -1.8      ],\n",
       "        ...,\n",
       "        [       nan,        nan,        nan, ...,        nan,\n",
       "                nan,        nan],\n",
       "        [       nan,        nan,        nan, ...,        nan,\n",
       "                nan,        nan],\n",
       "        [       nan,        nan,        nan, ...,        nan,\n",
       "                nan,        nan]],\n",
       "\n",
       "       [[-1.8      , -1.8      , -1.8      , ..., -1.8      ,\n",
       "         -1.8      , -1.8      ],\n",
       "        [-1.8      , -1.8      , -1.8      , ..., -1.8      ,\n",
       "         -1.8      , -1.8      ],\n",
       "        [-1.8      , -1.8      , -1.8      , ..., -1.8      ,\n",
       "         -1.8      , -1.8      ],\n",
       "...\n",
       "        [       nan,        nan,        nan, ...,        nan,\n",
       "                nan,        nan],\n",
       "        [       nan,        nan,        nan, ...,        nan,\n",
       "                nan,        nan],\n",
       "        [       nan,        nan,        nan, ...,        nan,\n",
       "                nan,        nan]],\n",
       "\n",
       "       [[-1.7995342, -1.7996206, -1.7998532, ..., -1.7998041,\n",
       "         -1.7996737, -1.7995361],\n",
       "        [-1.7995963, -1.799773 , -1.8      , ..., -1.8      ,\n",
       "         -1.7998328, -1.7996292],\n",
       "        [-1.8      , -1.8      , -1.8      , ..., -1.8      ,\n",
       "         -1.8      , -1.8      ],\n",
       "        ...,\n",
       "        [       nan,        nan,        nan, ...,        nan,\n",
       "                nan,        nan],\n",
       "        [       nan,        nan,        nan, ...,        nan,\n",
       "                nan,        nan],\n",
       "        [       nan,        nan,        nan, ...,        nan,\n",
       "                nan,        nan]]], dtype=float32)\n",
       "Coordinates:\n",
       "  * lat      (lat) float32 88.0 86.0 84.0 82.0 80.0 ... -82.0 -84.0 -86.0 -88.0\n",
       "  * lon      (lon) float32 0.0 2.0 4.0 6.0 8.0 ... 350.0 352.0 354.0 356.0 358.0\n",
       "  * month    (month) int64 1 2 3 4 5 6 7 8 9 10 11 12\n",
       "Attributes:\n",
       "    long_name:     Monthly Means of Sea Surface Temperature\n",
       "    units:         degC\n",
       "    var_desc:      Sea Surface Temperature\n",
       "    level_desc:    Surface\n",
       "    statistic:     Mean\n",
       "    dataset:       NOAA Extended Reconstructed SST V5\n",
       "    parent_stat:   Individual Values\n",
       "    actual_range:  [-1.8     42.32636]\n",
       "    valid_range:   [-1.8 45. ]\n",
       "    _ChunkSizes:   [  1  89 180]"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sst_mm = gb.mean(dim='time')\n",
    "sst_mm"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`.map` accepts as its argument a function. We can pass an existing function:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;sst&#x27; (month: 12)&gt;\n",
       "array([13.67924 , 13.787482, 13.784192, 13.703959, 13.662183, 13.736521,\n",
       "       13.950218, 14.123172, 14.008661, 13.715476, 13.52839 , 13.548249],\n",
       "      dtype=float32)\n",
       "Coordinates:\n",
       "  * month    (month) int64 1 2 3 4 5 6 7 8 9 10 11 12</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'>month</span>: 12</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-c0562631-4635-41de-95c0-237eec9960ca' class='xr-array-in' type='checkbox' checked><label for='section-c0562631-4635-41de-95c0-237eec9960ca' 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>13.68 13.79 13.78 13.7 13.66 13.74 13.95 14.12 14.01 13.72 13.53 13.55</span></div><div class='xr-array-data'><pre>array([13.67924 , 13.787482, 13.784192, 13.703959, 13.662183, 13.736521,\n",
       "       13.950218, 14.123172, 14.008661, 13.715476, 13.52839 , 13.548249],\n",
       "      dtype=float32)</pre></div></div></li><li class='xr-section-item'><input id='section-204b9df4-7d98-4549-bab7-656aa86fcb2b' class='xr-section-summary-in' type='checkbox'  checked><label for='section-204b9df4-7d98-4549-bab7-656aa86fcb2b' class='xr-section-summary' >Coordinates: <span>(1)</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'>month</span></div><div class='xr-var-dims'>(month)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>1 2 3 4 5 6 7 8 9 10 11 12</div><input id='attrs-dc805b60-4bcb-42f9-a879-2e72e7496538' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-dc805b60-4bcb-42f9-a879-2e72e7496538' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e88348f6-bd25-4b2c-a946-dcfbe56809a5' class='xr-var-data-in' type='checkbox'><label for='data-e88348f6-bd25-4b2c-a946-dcfbe56809a5' 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,  5,  6,  7,  8,  9, 10, 11, 12])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-4d8076cd-d075-4d2d-954f-8a0b02f226d6' class='xr-section-summary-in' type='checkbox'  ><label for='section-4d8076cd-d075-4d2d-954f-8a0b02f226d6' class='xr-section-summary' >Indexes: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>month</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-eaa8afb3-4bfd-4167-9e35-2f860e2c21be' class='xr-index-data-in' type='checkbox'/><label for='index-eaa8afb3-4bfd-4167-9e35-2f860e2c21be' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12], dtype=&#x27;int64&#x27;, name=&#x27;month&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-48aa3e56-e2de-43a2-84fc-d63cd97203f4' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-48aa3e56-e2de-43a2-84fc-d63cd97203f4' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.DataArray 'sst' (month: 12)>\n",
       "array([13.67924 , 13.787482, 13.784192, 13.703959, 13.662183, 13.736521,\n",
       "       13.950218, 14.123172, 14.008661, 13.715476, 13.52839 , 13.548249],\n",
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       "Coordinates:\n",
       "  * month    (month) int64 1 2 3 4 5 6 7 8 9 10 11 12"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gb.map(np.mean)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Because we specified no extra arguments (like `axis`) the function was applied over all space and time dimensions. This is not what we wanted. Instead, we could define a custom function. This function takes a single argument--the group dataset--and returns a new dataset to be combined:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "tags": []
   },
   "outputs": [
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       "\n",
       ".xr-dim-list {\n",
       "  display: inline-block !important;\n",
       "  list-style: none;\n",
       "  padding: 0 !important;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list li {\n",
       "  display: inline-block;\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list:before {\n",
       "  content: '(';\n",
       "}\n",
       "\n",
       ".xr-dim-list:after {\n",
       "  content: ')';\n",
       "}\n",
       "\n",
       ".xr-dim-list li:not(:last-child):after {\n",
       "  content: ',';\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-has-index {\n",
       "  font-weight: bold;\n",
       "}\n",
       "\n",
       ".xr-var-list,\n",
       ".xr-var-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-var-item > div,\n",
       ".xr-var-item label,\n",
       ".xr-var-item > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-even);\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-var-item > .xr-var-name:hover span {\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-var-list > li:nth-child(odd) > div,\n",
       ".xr-var-list > li:nth-child(odd) > label,\n",
       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-odd);\n",
       "}\n",
       "\n",
       ".xr-var-name {\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-var-dims {\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-var-dtype {\n",
       "  grid-column: 3;\n",
       "  text-align: right;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-preview {\n",
       "  grid-column: 4;\n",
       "}\n",
       "\n",
       ".xr-index-preview {\n",
       "  grid-column: 2 / 5;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-name,\n",
       ".xr-var-dims,\n",
       ".xr-var-dtype,\n",
       ".xr-preview,\n",
       ".xr-attrs dt {\n",
       "  white-space: nowrap;\n",
       "  overflow: hidden;\n",
       "  text-overflow: ellipsis;\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-var-name:hover,\n",
       ".xr-var-dims:hover,\n",
       ".xr-var-dtype:hover,\n",
       ".xr-attrs dt:hover {\n",
       "  overflow: visible;\n",
       "  width: auto;\n",
       "  z-index: 1;\n",
       "}\n",
       "\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-data {\n",
       "  display: none;\n",
       "  background-color: var(--xr-background-color) !important;\n",
       "  padding-bottom: 5px !important;\n",
       "}\n",
       "\n",
       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
       ".xr-var-data-in:checked ~ .xr-var-data,\n",
       ".xr-index-data-in:checked ~ .xr-index-data {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       ".xr-var-data > table {\n",
       "  float: right;\n",
       "}\n",
       "\n",
       ".xr-var-name span,\n",
       ".xr-var-data,\n",
       ".xr-index-name div,\n",
       ".xr-index-data,\n",
       ".xr-attrs {\n",
       "  padding-left: 25px !important;\n",
       "}\n",
       "\n",
       ".xr-attrs,\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-data {\n",
       "  grid-column: 1 / -1;\n",
       "}\n",
       "\n",
       "dl.xr-attrs {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  display: grid;\n",
       "  grid-template-columns: 125px auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt,\n",
       ".xr-attrs dd {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  float: left;\n",
       "  padding-right: 10px;\n",
       "  width: auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt {\n",
       "  font-weight: normal;\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-attrs dt:hover span {\n",
       "  display: inline-block;\n",
       "  background: var(--xr-background-color);\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-attrs dd {\n",
       "  grid-column: 2;\n",
       "  white-space: pre-wrap;\n",
       "  word-break: break-all;\n",
       "}\n",
       "\n",
       ".xr-icon-database,\n",
       ".xr-icon-file-text2,\n",
       ".xr-no-icon {\n",
       "  display: inline-block;\n",
       "  vertical-align: middle;\n",
       "  width: 1em;\n",
       "  height: 1.5em !important;\n",
       "  stroke-width: 0;\n",
       "  stroke: currentColor;\n",
       "  fill: currentColor;\n",
       "}\n",
       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;sst&#x27; (month: 12, lat: 89, lon: 180)&gt;\n",
       "array([[[-1.800001 , -1.800001 , -1.800001 , ..., -1.800001 ,\n",
       "         -1.800001 , -1.800001 ],\n",
       "        [-1.800001 , -1.800001 , -1.800001 , ..., -1.800001 ,\n",
       "         -1.800001 , -1.800001 ],\n",
       "        [-1.800001 , -1.800001 , -1.800001 , ..., -1.800001 ,\n",
       "         -1.800001 , -1.800001 ],\n",
       "        ...,\n",
       "        [       nan,        nan,        nan, ...,        nan,\n",
       "                nan,        nan],\n",
       "        [       nan,        nan,        nan, ...,        nan,\n",
       "                nan,        nan],\n",
       "        [       nan,        nan,        nan, ...,        nan,\n",
       "                nan,        nan]],\n",
       "\n",
       "       [[-1.800001 , -1.800001 , -1.800001 , ..., -1.800001 ,\n",
       "         -1.800001 , -1.800001 ],\n",
       "        [-1.800001 , -1.800001 , -1.800001 , ..., -1.800001 ,\n",
       "         -1.800001 , -1.800001 ],\n",
       "        [-1.800001 , -1.800001 , -1.800001 , ..., -1.800001 ,\n",
       "         -1.800001 , -1.800001 ],\n",
       "...\n",
       "        [       nan,        nan,        nan, ...,        nan,\n",
       "                nan,        nan],\n",
       "        [       nan,        nan,        nan, ...,        nan,\n",
       "                nan,        nan],\n",
       "        [       nan,        nan,        nan, ...,        nan,\n",
       "                nan,        nan]],\n",
       "\n",
       "       [[-1.7995352, -1.7996216, -1.7998542, ..., -1.799805 ,\n",
       "         -1.7996746, -1.7995371],\n",
       "        [-1.7995974, -1.799774 , -1.800001 , ..., -1.800001 ,\n",
       "         -1.7998339, -1.7996302],\n",
       "        [-1.800001 , -1.800001 , -1.800001 , ..., -1.800001 ,\n",
       "         -1.800001 , -1.800001 ],\n",
       "        ...,\n",
       "        [       nan,        nan,        nan, ...,        nan,\n",
       "                nan,        nan],\n",
       "        [       nan,        nan,        nan, ...,        nan,\n",
       "                nan,        nan],\n",
       "        [       nan,        nan,        nan, ...,        nan,\n",
       "                nan,        nan]]], dtype=float32)\n",
       "Coordinates:\n",
       "  * lat      (lat) float32 88.0 86.0 84.0 82.0 80.0 ... -82.0 -84.0 -86.0 -88.0\n",
       "  * lon      (lon) float32 0.0 2.0 4.0 6.0 8.0 ... 350.0 352.0 354.0 356.0 358.0\n",
       "  * month    (month) int64 1 2 3 4 5 6 7 8 9 10 11 12</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'>month</span>: 12</li><li><span class='xr-has-index'>lat</span>: 89</li><li><span class='xr-has-index'>lon</span>: 180</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-0349d246-c3e8-4685-9e96-3822aedf1d52' class='xr-array-in' type='checkbox' checked><label for='section-0349d246-c3e8-4685-9e96-3822aedf1d52' 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>-1.8 -1.8 -1.8 -1.8 -1.8 -1.8 -1.8 ... nan nan nan nan nan nan nan</span></div><div class='xr-array-data'><pre>array([[[-1.800001 , -1.800001 , -1.800001 , ..., -1.800001 ,\n",
       "         -1.800001 , -1.800001 ],\n",
       "        [-1.800001 , -1.800001 , -1.800001 , ..., -1.800001 ,\n",
       "         -1.800001 , -1.800001 ],\n",
       "        [-1.800001 , -1.800001 , -1.800001 , ..., -1.800001 ,\n",
       "         -1.800001 , -1.800001 ],\n",
       "        ...,\n",
       "        [       nan,        nan,        nan, ...,        nan,\n",
       "                nan,        nan],\n",
       "        [       nan,        nan,        nan, ...,        nan,\n",
       "                nan,        nan],\n",
       "        [       nan,        nan,        nan, ...,        nan,\n",
       "                nan,        nan]],\n",
       "\n",
       "       [[-1.800001 , -1.800001 , -1.800001 , ..., -1.800001 ,\n",
       "         -1.800001 , -1.800001 ],\n",
       "        [-1.800001 , -1.800001 , -1.800001 , ..., -1.800001 ,\n",
       "         -1.800001 , -1.800001 ],\n",
       "        [-1.800001 , -1.800001 , -1.800001 , ..., -1.800001 ,\n",
       "         -1.800001 , -1.800001 ],\n",
       "...\n",
       "        [       nan,        nan,        nan, ...,        nan,\n",
       "                nan,        nan],\n",
       "        [       nan,        nan,        nan, ...,        nan,\n",
       "                nan,        nan],\n",
       "        [       nan,        nan,        nan, ...,        nan,\n",
       "                nan,        nan]],\n",
       "\n",
       "       [[-1.7995352, -1.7996216, -1.7998542, ..., -1.799805 ,\n",
       "         -1.7996746, -1.7995371],\n",
       "        [-1.7995974, -1.799774 , -1.800001 , ..., -1.800001 ,\n",
       "         -1.7998339, -1.7996302],\n",
       "        [-1.800001 , -1.800001 , -1.800001 , ..., -1.800001 ,\n",
       "         -1.800001 , -1.800001 ],\n",
       "        ...,\n",
       "        [       nan,        nan,        nan, ...,        nan,\n",
       "                nan,        nan],\n",
       "        [       nan,        nan,        nan, ...,        nan,\n",
       "                nan,        nan],\n",
       "        [       nan,        nan,        nan, ...,        nan,\n",
       "                nan,        nan]]], dtype=float32)</pre></div></div></li><li class='xr-section-item'><input id='section-8dc03ab0-c05f-45df-95ae-00caac66c397' class='xr-section-summary-in' type='checkbox'  checked><label for='section-8dc03ab0-c05f-45df-95ae-00caac66c397' class='xr-section-summary' >Coordinates: <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 class='xr-has-index'>lat</span></div><div class='xr-var-dims'>(lat)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>88.0 86.0 84.0 ... -86.0 -88.0</div><input id='attrs-fa0652f0-939f-4d4d-ab16-40ca38eb1562' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-fa0652f0-939f-4d4d-ab16-40ca38eb1562' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e865ffc5-be7b-4f97-87e4-57982920a95b' class='xr-var-data-in' type='checkbox'><label for='data-e865ffc5-be7b-4f97-87e4-57982920a95b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degrees_north</dd><dt><span>long_name :</span></dt><dd>Latitude</dd><dt><span>actual_range :</span></dt><dd>[ 88. -88.]</dd><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>axis :</span></dt><dd>Y</dd><dt><span>coordinate_defines :</span></dt><dd>center</dd></dl></div><div class='xr-var-data'><pre>array([ 88.,  86.,  84.,  82.,  80.,  78.,  76.,  74.,  72.,  70.,  68.,  66.,\n",
       "        64.,  62.,  60.,  58.,  56.,  54.,  52.,  50.,  48.,  46.,  44.,  42.,\n",
       "        40.,  38.,  36.,  34.,  32.,  30.,  28.,  26.,  24.,  22.,  20.,  18.,\n",
       "        16.,  14.,  12.,  10.,   8.,   6.,   4.,   2.,   0.,  -2.,  -4.,  -6.,\n",
       "        -8., -10., -12., -14., -16., -18., -20., -22., -24., -26., -28., -30.,\n",
       "       -32., -34., -36., -38., -40., -42., -44., -46., -48., -50., -52., -54.,\n",
       "       -56., -58., -60., -62., -64., -66., -68., -70., -72., -74., -76., -78.,\n",
       "       -80., -82., -84., -86., -88.], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lon</span></div><div class='xr-var-dims'>(lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.0 2.0 4.0 ... 354.0 356.0 358.0</div><input id='attrs-fd3c78bf-907f-4c51-a9b7-0ba441a8ebbe' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-fd3c78bf-907f-4c51-a9b7-0ba441a8ebbe' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4e7b0bfd-a9b3-416d-872f-6956bd1de3ef' class='xr-var-data-in' type='checkbox'><label for='data-4e7b0bfd-a9b3-416d-872f-6956bd1de3ef' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degrees_east</dd><dt><span>long_name :</span></dt><dd>Longitude</dd><dt><span>actual_range :</span></dt><dd>[  0. 358.]</dd><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>axis :</span></dt><dd>X</dd><dt><span>coordinate_defines :</span></dt><dd>center</dd></dl></div><div class='xr-var-data'><pre>array([  0.,   2.,   4.,   6.,   8.,  10.,  12.,  14.,  16.,  18.,  20.,  22.,\n",
       "        24.,  26.,  28.,  30.,  32.,  34.,  36.,  38.,  40.,  42.,  44.,  46.,\n",
       "        48.,  50.,  52.,  54.,  56.,  58.,  60.,  62.,  64.,  66.,  68.,  70.,\n",
       "        72.,  74.,  76.,  78.,  80.,  82.,  84.,  86.,  88.,  90.,  92.,  94.,\n",
       "        96.,  98., 100., 102., 104., 106., 108., 110., 112., 114., 116., 118.,\n",
       "       120., 122., 124., 126., 128., 130., 132., 134., 136., 138., 140., 142.,\n",
       "       144., 146., 148., 150., 152., 154., 156., 158., 160., 162., 164., 166.,\n",
       "       168., 170., 172., 174., 176., 178., 180., 182., 184., 186., 188., 190.,\n",
       "       192., 194., 196., 198., 200., 202., 204., 206., 208., 210., 212., 214.,\n",
       "       216., 218., 220., 222., 224., 226., 228., 230., 232., 234., 236., 238.,\n",
       "       240., 242., 244., 246., 248., 250., 252., 254., 256., 258., 260., 262.,\n",
       "       264., 266., 268., 270., 272., 274., 276., 278., 280., 282., 284., 286.,\n",
       "       288., 290., 292., 294., 296., 298., 300., 302., 304., 306., 308., 310.,\n",
       "       312., 314., 316., 318., 320., 322., 324., 326., 328., 330., 332., 334.,\n",
       "       336., 338., 340., 342., 344., 346., 348., 350., 352., 354., 356., 358.],\n",
       "      dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>month</span></div><div class='xr-var-dims'>(month)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>1 2 3 4 5 6 7 8 9 10 11 12</div><input id='attrs-0b71d987-55c9-4090-a8c7-c3651568285a' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-0b71d987-55c9-4090-a8c7-c3651568285a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3f6e36a1-ca71-4ad5-9ca2-6d1674901bea' class='xr-var-data-in' type='checkbox'><label for='data-3f6e36a1-ca71-4ad5-9ca2-6d1674901bea' 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,  5,  6,  7,  8,  9, 10, 11, 12])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-f7bd5437-4f8d-4873-a39f-a9fda243cf69' class='xr-section-summary-in' type='checkbox'  ><label for='section-f7bd5437-4f8d-4873-a39f-a9fda243cf69' class='xr-section-summary' >Indexes: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>lat</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-c64dcbc4-954a-42cc-b594-e37906ae62d3' class='xr-index-data-in' type='checkbox'/><label for='index-c64dcbc4-954a-42cc-b594-e37906ae62d3' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([ 88.0,  86.0,  84.0,  82.0,  80.0,  78.0,  76.0,  74.0,  72.0,  70.0,\n",
       "        68.0,  66.0,  64.0,  62.0,  60.0,  58.0,  56.0,  54.0,  52.0,  50.0,\n",
       "        48.0,  46.0,  44.0,  42.0,  40.0,  38.0,  36.0,  34.0,  32.0,  30.0,\n",
       "        28.0,  26.0,  24.0,  22.0,  20.0,  18.0,  16.0,  14.0,  12.0,  10.0,\n",
       "         8.0,   6.0,   4.0,   2.0,   0.0,  -2.0,  -4.0,  -6.0,  -8.0, -10.0,\n",
       "       -12.0, -14.0, -16.0, -18.0, -20.0, -22.0, -24.0, -26.0, -28.0, -30.0,\n",
       "       -32.0, -34.0, -36.0, -38.0, -40.0, -42.0, -44.0, -46.0, -48.0, -50.0,\n",
       "       -52.0, -54.0, -56.0, -58.0, -60.0, -62.0, -64.0, -66.0, -68.0, -70.0,\n",
       "       -72.0, -74.0, -76.0, -78.0, -80.0, -82.0, -84.0, -86.0, -88.0],\n",
       "      dtype=&#x27;float32&#x27;, name=&#x27;lat&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>lon</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-2b4b3db4-991a-44a0-b694-722ac5696c85' class='xr-index-data-in' type='checkbox'/><label for='index-2b4b3db4-991a-44a0-b694-722ac5696c85' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([  0.0,   2.0,   4.0,   6.0,   8.0,  10.0,  12.0,  14.0,  16.0,  18.0,\n",
       "       ...\n",
       "       340.0, 342.0, 344.0, 346.0, 348.0, 350.0, 352.0, 354.0, 356.0, 358.0],\n",
       "      dtype=&#x27;float32&#x27;, name=&#x27;lon&#x27;, length=180))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>month</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-af35c238-5c3a-4607-84ab-774a7d93c41e' class='xr-index-data-in' type='checkbox'/><label for='index-af35c238-5c3a-4607-84ab-774a7d93c41e' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12], dtype=&#x27;int64&#x27;, name=&#x27;month&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-02cc21f9-4951-4979-836d-1e47cac778e6' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-02cc21f9-4951-4979-836d-1e47cac778e6' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.DataArray 'sst' (month: 12, lat: 89, lon: 180)>\n",
       "array([[[-1.800001 , -1.800001 , -1.800001 , ..., -1.800001 ,\n",
       "         -1.800001 , -1.800001 ],\n",
       "        [-1.800001 , -1.800001 , -1.800001 , ..., -1.800001 ,\n",
       "         -1.800001 , -1.800001 ],\n",
       "        [-1.800001 , -1.800001 , -1.800001 , ..., -1.800001 ,\n",
       "         -1.800001 , -1.800001 ],\n",
       "        ...,\n",
       "        [       nan,        nan,        nan, ...,        nan,\n",
       "                nan,        nan],\n",
       "        [       nan,        nan,        nan, ...,        nan,\n",
       "                nan,        nan],\n",
       "        [       nan,        nan,        nan, ...,        nan,\n",
       "                nan,        nan]],\n",
       "\n",
       "       [[-1.800001 , -1.800001 , -1.800001 , ..., -1.800001 ,\n",
       "         -1.800001 , -1.800001 ],\n",
       "        [-1.800001 , -1.800001 , -1.800001 , ..., -1.800001 ,\n",
       "         -1.800001 , -1.800001 ],\n",
       "        [-1.800001 , -1.800001 , -1.800001 , ..., -1.800001 ,\n",
       "         -1.800001 , -1.800001 ],\n",
       "...\n",
       "        [       nan,        nan,        nan, ...,        nan,\n",
       "                nan,        nan],\n",
       "        [       nan,        nan,        nan, ...,        nan,\n",
       "                nan,        nan],\n",
       "        [       nan,        nan,        nan, ...,        nan,\n",
       "                nan,        nan]],\n",
       "\n",
       "       [[-1.7995352, -1.7996216, -1.7998542, ..., -1.799805 ,\n",
       "         -1.7996746, -1.7995371],\n",
       "        [-1.7995974, -1.799774 , -1.800001 , ..., -1.800001 ,\n",
       "         -1.7998339, -1.7996302],\n",
       "        [-1.800001 , -1.800001 , -1.800001 , ..., -1.800001 ,\n",
       "         -1.800001 , -1.800001 ],\n",
       "        ...,\n",
       "        [       nan,        nan,        nan, ...,        nan,\n",
       "                nan,        nan],\n",
       "        [       nan,        nan,        nan, ...,        nan,\n",
       "                nan,        nan],\n",
       "        [       nan,        nan,        nan, ...,        nan,\n",
       "                nan,        nan]]], dtype=float32)\n",
       "Coordinates:\n",
       "  * lat      (lat) float32 88.0 86.0 84.0 82.0 80.0 ... -82.0 -84.0 -86.0 -88.0\n",
       "  * lon      (lon) float32 0.0 2.0 4.0 6.0 8.0 ... 350.0 352.0 354.0 356.0 358.0\n",
       "  * month    (month) int64 1 2 3 4 5 6 7 8 9 10 11 12"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def time_mean(a):\n",
    "    return a.mean(dim='time')\n",
    "\n",
    "sst_mm = gb.map(time_mean)\n",
    "sst_mm"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So we did what we wanted to do: calculate the climatology at every point in the dataset. Let's look at the data a bit.\n",
    "\n",
    "_Climatlogy at a specific point in the North Atlantic_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x78264c002c10>]"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sst_mm.sel(lon=300, lat=-50).plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "_Zonal Mean Climatology_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.contour.QuadContourSet at 0x78264bee8910>"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sst_mm.mean(dim='lon').transpose().plot.contourf(levels=12, vmin=-2, vmax=30)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "_Difference between January and July Climatology_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.QuadMesh at 0x78264bf24690>"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "(sst_mm.sel(month=1) - sst_mm.sel(month=7)).plot(vmax=10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Transformations\n",
    "\n",
    "Now we want to _remove_ this climatology from the dataset, to examine the residual, called the _anomaly_, which is the interesting part from a climate perspective.\n",
    "Removing the seasonal climatology is a perfect example of a transformation: it operates over a group, but doesn't change the size of the dataset. Here is one way to code it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
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       "  --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[data-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-index-preview {\n",
       "  grid-column: 2 / 5;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-name,\n",
       ".xr-var-dims,\n",
       ".xr-var-dtype,\n",
       ".xr-preview,\n",
       ".xr-attrs dt {\n",
       "  white-space: nowrap;\n",
       "  overflow: hidden;\n",
       "  text-overflow: ellipsis;\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-var-name:hover,\n",
       ".xr-var-dims:hover,\n",
       ".xr-var-dtype:hover,\n",
       ".xr-attrs dt:hover {\n",
       "  overflow: visible;\n",
       "  width: auto;\n",
       "  z-index: 1;\n",
       "}\n",
       "\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-data {\n",
       "  display: none;\n",
       "  background-color: var(--xr-background-color) !important;\n",
       "  padding-bottom: 5px !important;\n",
       "}\n",
       "\n",
       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
       ".xr-var-data-in:checked ~ .xr-var-data,\n",
       ".xr-index-data-in:checked ~ .xr-index-data {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       ".xr-var-data > table {\n",
       "  float: right;\n",
       "}\n",
       "\n",
       ".xr-var-name span,\n",
       ".xr-var-data,\n",
       ".xr-index-name div,\n",
       ".xr-index-data,\n",
       ".xr-attrs {\n",
       "  padding-left: 25px !important;\n",
       "}\n",
       "\n",
       ".xr-attrs,\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-data {\n",
       "  grid-column: 1 / -1;\n",
       "}\n",
       "\n",
       "dl.xr-attrs {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  display: grid;\n",
       "  grid-template-columns: 125px auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt,\n",
       ".xr-attrs dd {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  float: left;\n",
       "  padding-right: 10px;\n",
       "  width: auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt {\n",
       "  font-weight: normal;\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-attrs dt:hover span {\n",
       "  display: inline-block;\n",
       "  background: var(--xr-background-color);\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-attrs dd {\n",
       "  grid-column: 2;\n",
       "  white-space: pre-wrap;\n",
       "  word-break: break-all;\n",
       "}\n",
       "\n",
       ".xr-icon-database,\n",
       ".xr-icon-file-text2,\n",
       ".xr-no-icon {\n",
       "  display: inline-block;\n",
       "  vertical-align: middle;\n",
       "  width: 1em;\n",
       "  height: 1.5em !important;\n",
       "  stroke-width: 0;\n",
       "  stroke: currentColor;\n",
       "  fill: currentColor;\n",
       "}\n",
       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
       "Dimensions:  (lat: 89, lon: 180, time: 756)\n",
       "Coordinates:\n",
       "  * lat      (lat) float32 88.0 86.0 84.0 82.0 80.0 ... -82.0 -84.0 -86.0 -88.0\n",
       "  * lon      (lon) float32 0.0 2.0 4.0 6.0 8.0 ... 350.0 352.0 354.0 356.0 358.0\n",
       "  * time     (time) datetime64[ns] 1960-01-01 1960-02-01 ... 2022-12-01\n",
       "Data variables:\n",
       "    sst      (time, lat, lon) float32 1.073e-06 1.073e-06 1.073e-06 ... nan nan</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-66473408-46af-4235-915d-d043c6523e73' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-66473408-46af-4235-915d-d043c6523e73' 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'>lat</span>: 89</li><li><span class='xr-has-index'>lon</span>: 180</li><li><span class='xr-has-index'>time</span>: 756</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-28d9e3ef-8747-4ee2-ac10-f800686ae681' class='xr-section-summary-in' type='checkbox'  checked><label for='section-28d9e3ef-8747-4ee2-ac10-f800686ae681' class='xr-section-summary' >Coordinates: <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 class='xr-has-index'>lat</span></div><div class='xr-var-dims'>(lat)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>88.0 86.0 84.0 ... -86.0 -88.0</div><input id='attrs-d5219da3-b34c-4847-ade4-92b6cc89a7ef' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d5219da3-b34c-4847-ade4-92b6cc89a7ef' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c7752cb4-ce4e-4329-b53a-21fae964ac71' class='xr-var-data-in' type='checkbox'><label for='data-c7752cb4-ce4e-4329-b53a-21fae964ac71' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degrees_north</dd><dt><span>long_name :</span></dt><dd>Latitude</dd><dt><span>actual_range :</span></dt><dd>[ 88. -88.]</dd><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>axis :</span></dt><dd>Y</dd><dt><span>coordinate_defines :</span></dt><dd>center</dd></dl></div><div class='xr-var-data'><pre>array([ 88.,  86.,  84.,  82.,  80.,  78.,  76.,  74.,  72.,  70.,  68.,  66.,\n",
       "        64.,  62.,  60.,  58.,  56.,  54.,  52.,  50.,  48.,  46.,  44.,  42.,\n",
       "        40.,  38.,  36.,  34.,  32.,  30.,  28.,  26.,  24.,  22.,  20.,  18.,\n",
       "        16.,  14.,  12.,  10.,   8.,   6.,   4.,   2.,   0.,  -2.,  -4.,  -6.,\n",
       "        -8., -10., -12., -14., -16., -18., -20., -22., -24., -26., -28., -30.,\n",
       "       -32., -34., -36., -38., -40., -42., -44., -46., -48., -50., -52., -54.,\n",
       "       -56., -58., -60., -62., -64., -66., -68., -70., -72., -74., -76., -78.,\n",
       "       -80., -82., -84., -86., -88.], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lon</span></div><div class='xr-var-dims'>(lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.0 2.0 4.0 ... 354.0 356.0 358.0</div><input id='attrs-32ef669e-a59c-4f2c-9a22-65db4c528b59' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-32ef669e-a59c-4f2c-9a22-65db4c528b59' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0fba50bd-2af0-4486-a01f-78e81fa9d9c1' class='xr-var-data-in' type='checkbox'><label for='data-0fba50bd-2af0-4486-a01f-78e81fa9d9c1' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degrees_east</dd><dt><span>long_name :</span></dt><dd>Longitude</dd><dt><span>actual_range :</span></dt><dd>[  0. 358.]</dd><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>axis :</span></dt><dd>X</dd><dt><span>coordinate_defines :</span></dt><dd>center</dd></dl></div><div class='xr-var-data'><pre>array([  0.,   2.,   4.,   6.,   8.,  10.,  12.,  14.,  16.,  18.,  20.,  22.,\n",
       "        24.,  26.,  28.,  30.,  32.,  34.,  36.,  38.,  40.,  42.,  44.,  46.,\n",
       "        48.,  50.,  52.,  54.,  56.,  58.,  60.,  62.,  64.,  66.,  68.,  70.,\n",
       "        72.,  74.,  76.,  78.,  80.,  82.,  84.,  86.,  88.,  90.,  92.,  94.,\n",
       "        96.,  98., 100., 102., 104., 106., 108., 110., 112., 114., 116., 118.,\n",
       "       120., 122., 124., 126., 128., 130., 132., 134., 136., 138., 140., 142.,\n",
       "       144., 146., 148., 150., 152., 154., 156., 158., 160., 162., 164., 166.,\n",
       "       168., 170., 172., 174., 176., 178., 180., 182., 184., 186., 188., 190.,\n",
       "       192., 194., 196., 198., 200., 202., 204., 206., 208., 210., 212., 214.,\n",
       "       216., 218., 220., 222., 224., 226., 228., 230., 232., 234., 236., 238.,\n",
       "       240., 242., 244., 246., 248., 250., 252., 254., 256., 258., 260., 262.,\n",
       "       264., 266., 268., 270., 272., 274., 276., 278., 280., 282., 284., 286.,\n",
       "       288., 290., 292., 294., 296., 298., 300., 302., 304., 306., 308., 310.,\n",
       "       312., 314., 316., 318., 320., 322., 324., 326., 328., 330., 332., 334.,\n",
       "       336., 338., 340., 342., 344., 346., 348., 350., 352., 354., 356., 358.],\n",
       "      dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>1960-01-01 ... 2022-12-01</div><input id='attrs-1407a976-064e-47c9-9e17-e9e72fcff29b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1407a976-064e-47c9-9e17-e9e72fcff29b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-63fa7b19-93ab-4b29-aaf6-699400b8b403' class='xr-var-data-in' type='checkbox'><label for='data-63fa7b19-93ab-4b29-aaf6-699400b8b403' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Time</dd><dt><span>delta_t :</span></dt><dd>0000-01-00 00:00:00</dd><dt><span>avg_period :</span></dt><dd>0000-01-00 00:00:00</dd><dt><span>prev_avg_period :</span></dt><dd>0000-00-07 00:00:00</dd><dt><span>standard_name :</span></dt><dd>time</dd><dt><span>axis :</span></dt><dd>T</dd><dt><span>actual_range :</span></dt><dd>[19723. 81722.]</dd><dt><span>_ChunkSizes :</span></dt><dd>1</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;1960-01-01T00:00:00.000000000&#x27;, &#x27;1960-02-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;1960-03-01T00:00:00.000000000&#x27;, ..., &#x27;2022-10-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2022-11-01T00:00:00.000000000&#x27;, &#x27;2022-12-01T00:00:00.000000000&#x27;],\n",
       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-7d2a0004-2440-4782-9467-7c9cc4723e93' class='xr-section-summary-in' type='checkbox'  checked><label for='section-7d2a0004-2440-4782-9467-7c9cc4723e93' class='xr-section-summary' >Data variables: <span>(1)</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'>(time, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>1.073e-06 1.073e-06 ... nan nan</div><input id='attrs-3771ebb9-40e8-4c1e-9b86-3706f7d1c1f6' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-3771ebb9-40e8-4c1e-9b86-3706f7d1c1f6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4c2d2e3e-0ca6-4318-a07d-31e64c4d4db9' class='xr-var-data-in' type='checkbox'><label for='data-4c2d2e3e-0ca6-4318-a07d-31e64c4d4db9' 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([[[ 1.0728836e-06,  1.0728836e-06,  1.0728836e-06, ...,\n",
       "          1.0728836e-06,  1.0728836e-06,  1.0728836e-06],\n",
       "        [ 1.0728836e-06,  1.0728836e-06,  1.0728836e-06, ...,\n",
       "          1.0728836e-06,  1.0728836e-06,  1.0728836e-06],\n",
       "        [ 1.0728836e-06,  1.0728836e-06,  1.0728836e-06, ...,\n",
       "          1.0728836e-06,  1.0728836e-06,  1.0728836e-06],\n",
       "        ...,\n",
       "        [           nan,            nan,            nan, ...,\n",
       "                    nan,            nan,            nan],\n",
       "        [           nan,            nan,            nan, ...,\n",
       "                    nan,            nan,            nan],\n",
       "        [           nan,            nan,            nan, ...,\n",
       "                    nan,            nan,            nan]],\n",
       "\n",
       "       [[ 1.0728836e-06,  1.0728836e-06,  1.0728836e-06, ...,\n",
       "          1.0728836e-06,  1.0728836e-06,  1.0728836e-06],\n",
       "        [ 1.0728836e-06,  1.0728836e-06,  1.0728836e-06, ...,\n",
       "          1.0728836e-06,  1.0728836e-06,  1.0728836e-06],\n",
       "        [ 1.0728836e-06,  1.0728836e-06,  1.0728836e-06, ...,\n",
       "          1.0728836e-06,  1.0728836e-06,  1.0728836e-06],\n",
       "...\n",
       "        [           nan,            nan,            nan, ...,\n",
       "                    nan,            nan,            nan],\n",
       "        [           nan,            nan,            nan, ...,\n",
       "                    nan,            nan,            nan],\n",
       "        [           nan,            nan,            nan, ...,\n",
       "                    nan,            nan,            nan]],\n",
       "\n",
       "       [[-4.6479702e-04, -3.7837029e-04, -1.4579296e-04, ...,\n",
       "         -1.9490719e-04, -3.2532215e-04, -4.6288967e-04],\n",
       "        [-4.0256977e-04, -2.2590160e-04,  1.0728836e-06, ...,\n",
       "          1.0728836e-06, -1.6605854e-04, -3.6978722e-04],\n",
       "        [ 1.0728836e-06,  1.0728836e-06,  1.0728836e-06, ...,\n",
       "          1.0728836e-06,  1.0728836e-06,  1.0728836e-06],\n",
       "        ...,\n",
       "        [           nan,            nan,            nan, ...,\n",
       "                    nan,            nan,            nan],\n",
       "        [           nan,            nan,            nan, ...,\n",
       "                    nan,            nan,            nan],\n",
       "        [           nan,            nan,            nan, ...,\n",
       "                    nan,            nan,            nan]]], dtype=float32)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-36c3a940-40d9-4c45-a09b-6e9c11fddd8b' class='xr-section-summary-in' type='checkbox'  ><label for='section-36c3a940-40d9-4c45-a09b-6e9c11fddd8b' class='xr-section-summary' >Indexes: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>lat</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-7d60bd65-2f40-4fcc-85b6-30334e51e515' class='xr-index-data-in' type='checkbox'/><label for='index-7d60bd65-2f40-4fcc-85b6-30334e51e515' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([ 88.0,  86.0,  84.0,  82.0,  80.0,  78.0,  76.0,  74.0,  72.0,  70.0,\n",
       "        68.0,  66.0,  64.0,  62.0,  60.0,  58.0,  56.0,  54.0,  52.0,  50.0,\n",
       "        48.0,  46.0,  44.0,  42.0,  40.0,  38.0,  36.0,  34.0,  32.0,  30.0,\n",
       "        28.0,  26.0,  24.0,  22.0,  20.0,  18.0,  16.0,  14.0,  12.0,  10.0,\n",
       "         8.0,   6.0,   4.0,   2.0,   0.0,  -2.0,  -4.0,  -6.0,  -8.0, -10.0,\n",
       "       -12.0, -14.0, -16.0, -18.0, -20.0, -22.0, -24.0, -26.0, -28.0, -30.0,\n",
       "       -32.0, -34.0, -36.0, -38.0, -40.0, -42.0, -44.0, -46.0, -48.0, -50.0,\n",
       "       -52.0, -54.0, -56.0, -58.0, -60.0, -62.0, -64.0, -66.0, -68.0, -70.0,\n",
       "       -72.0, -74.0, -76.0, -78.0, -80.0, -82.0, -84.0, -86.0, -88.0],\n",
       "      dtype=&#x27;float32&#x27;, name=&#x27;lat&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>lon</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-00153c65-3cb5-4788-bbe9-8ce9bbf16010' class='xr-index-data-in' type='checkbox'/><label for='index-00153c65-3cb5-4788-bbe9-8ce9bbf16010' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([  0.0,   2.0,   4.0,   6.0,   8.0,  10.0,  12.0,  14.0,  16.0,  18.0,\n",
       "       ...\n",
       "       340.0, 342.0, 344.0, 346.0, 348.0, 350.0, 352.0, 354.0, 356.0, 358.0],\n",
       "      dtype=&#x27;float32&#x27;, name=&#x27;lon&#x27;, length=180))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-036f2544-e51a-46e5-a142-f6e57227d76a' class='xr-index-data-in' type='checkbox'/><label for='index-036f2544-e51a-46e5-a142-f6e57227d76a' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(DatetimeIndex([&#x27;1960-01-01&#x27;, &#x27;1960-02-01&#x27;, &#x27;1960-03-01&#x27;, &#x27;1960-04-01&#x27;,\n",
       "               &#x27;1960-05-01&#x27;, &#x27;1960-06-01&#x27;, &#x27;1960-07-01&#x27;, &#x27;1960-08-01&#x27;,\n",
       "               &#x27;1960-09-01&#x27;, &#x27;1960-10-01&#x27;,\n",
       "               ...\n",
       "               &#x27;2022-03-01&#x27;, &#x27;2022-04-01&#x27;, &#x27;2022-05-01&#x27;, &#x27;2022-06-01&#x27;,\n",
       "               &#x27;2022-07-01&#x27;, &#x27;2022-08-01&#x27;, &#x27;2022-09-01&#x27;, &#x27;2022-10-01&#x27;,\n",
       "               &#x27;2022-11-01&#x27;, &#x27;2022-12-01&#x27;],\n",
       "              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, length=756, freq=None))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-d83d6ac6-8c27-4d3b-9227-1da654dd7d9c' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-d83d6ac6-8c27-4d3b-9227-1da654dd7d9c' 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:  (lat: 89, lon: 180, time: 756)\n",
       "Coordinates:\n",
       "  * lat      (lat) float32 88.0 86.0 84.0 82.0 80.0 ... -82.0 -84.0 -86.0 -88.0\n",
       "  * lon      (lon) float32 0.0 2.0 4.0 6.0 8.0 ... 350.0 352.0 354.0 356.0 358.0\n",
       "  * time     (time) datetime64[ns] 1960-01-01 1960-02-01 ... 2022-12-01\n",
       "Data variables:\n",
       "    sst      (time, lat, lon) float32 1.073e-06 1.073e-06 1.073e-06 ... nan nan"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def remove_time_mean(x):\n",
    "    return x - x.mean(dim='time')\n",
    "\n",
    "ds_anom = ds.groupby('time.month').map(remove_time_mean)\n",
    "ds_anom"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "```{note}\n",
    "In the above example, we applied `groupby` to a `Dataset` instead of a `DataArray`.\n",
    "```\n",
    "\n",
    "Xarray makes these sorts of transformations easy by supporting _groupby arithmetic_.\n",
    "This concept is easiest explained with an example:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
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       "}\n",
       "\n",
       ".xr-var-preview {\n",
       "  grid-column: 4;\n",
       "}\n",
       "\n",
       ".xr-index-preview {\n",
       "  grid-column: 2 / 5;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-name,\n",
       ".xr-var-dims,\n",
       ".xr-var-dtype,\n",
       ".xr-preview,\n",
       ".xr-attrs dt {\n",
       "  white-space: nowrap;\n",
       "  overflow: hidden;\n",
       "  text-overflow: ellipsis;\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-var-name:hover,\n",
       ".xr-var-dims:hover,\n",
       ".xr-var-dtype:hover,\n",
       ".xr-attrs dt:hover {\n",
       "  overflow: visible;\n",
       "  width: auto;\n",
       "  z-index: 1;\n",
       "}\n",
       "\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-data {\n",
       "  display: none;\n",
       "  background-color: var(--xr-background-color) !important;\n",
       "  padding-bottom: 5px !important;\n",
       "}\n",
       "\n",
       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
       ".xr-var-data-in:checked ~ .xr-var-data,\n",
       ".xr-index-data-in:checked ~ .xr-index-data {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       ".xr-var-data > table {\n",
       "  float: right;\n",
       "}\n",
       "\n",
       ".xr-var-name span,\n",
       ".xr-var-data,\n",
       ".xr-index-name div,\n",
       ".xr-index-data,\n",
       ".xr-attrs {\n",
       "  padding-left: 25px !important;\n",
       "}\n",
       "\n",
       ".xr-attrs,\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-data {\n",
       "  grid-column: 1 / -1;\n",
       "}\n",
       "\n",
       "dl.xr-attrs {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  display: grid;\n",
       "  grid-template-columns: 125px auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt,\n",
       ".xr-attrs dd {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  float: left;\n",
       "  padding-right: 10px;\n",
       "  width: auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt {\n",
       "  font-weight: normal;\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-attrs dt:hover span {\n",
       "  display: inline-block;\n",
       "  background: var(--xr-background-color);\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-attrs dd {\n",
       "  grid-column: 2;\n",
       "  white-space: pre-wrap;\n",
       "  word-break: break-all;\n",
       "}\n",
       "\n",
       ".xr-icon-database,\n",
       ".xr-icon-file-text2,\n",
       ".xr-no-icon {\n",
       "  display: inline-block;\n",
       "  vertical-align: middle;\n",
       "  width: 1em;\n",
       "  height: 1.5em !important;\n",
       "  stroke-width: 0;\n",
       "  stroke: currentColor;\n",
       "  fill: currentColor;\n",
       "}\n",
       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
       "Dimensions:  (lat: 89, lon: 180, time: 756)\n",
       "Coordinates:\n",
       "  * lat      (lat) float32 88.0 86.0 84.0 82.0 80.0 ... -82.0 -84.0 -86.0 -88.0\n",
       "  * lon      (lon) float32 0.0 2.0 4.0 6.0 8.0 ... 350.0 352.0 354.0 356.0 358.0\n",
       "  * time     (time) datetime64[ns] 1960-01-01 1960-02-01 ... 2022-12-01\n",
       "    month    (time) int64 1 2 3 4 5 6 7 8 9 10 11 ... 2 3 4 5 6 7 8 9 10 11 12\n",
       "Data variables:\n",
       "    sst      (time, lat, lon) float32 0.0 0.0 0.0 0.0 0.0 ... nan nan nan nan</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-3028045a-1d38-4a34-9c6b-86e349597eda' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-3028045a-1d38-4a34-9c6b-86e349597eda' 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'>lat</span>: 89</li><li><span class='xr-has-index'>lon</span>: 180</li><li><span class='xr-has-index'>time</span>: 756</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-6d1eaa3c-6f23-465c-98ce-e4ef36b6857b' class='xr-section-summary-in' type='checkbox'  checked><label for='section-6d1eaa3c-6f23-465c-98ce-e4ef36b6857b' 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'>lat</span></div><div class='xr-var-dims'>(lat)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>88.0 86.0 84.0 ... -86.0 -88.0</div><input id='attrs-0f1a8531-83aa-432d-8151-51eb820e26b3' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0f1a8531-83aa-432d-8151-51eb820e26b3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-aa10dd16-fc29-4e11-ab98-e8644cbde833' class='xr-var-data-in' type='checkbox'><label for='data-aa10dd16-fc29-4e11-ab98-e8644cbde833' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degrees_north</dd><dt><span>long_name :</span></dt><dd>Latitude</dd><dt><span>actual_range :</span></dt><dd>[ 88. -88.]</dd><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>axis :</span></dt><dd>Y</dd><dt><span>coordinate_defines :</span></dt><dd>center</dd></dl></div><div class='xr-var-data'><pre>array([ 88.,  86.,  84.,  82.,  80.,  78.,  76.,  74.,  72.,  70.,  68.,  66.,\n",
       "        64.,  62.,  60.,  58.,  56.,  54.,  52.,  50.,  48.,  46.,  44.,  42.,\n",
       "        40.,  38.,  36.,  34.,  32.,  30.,  28.,  26.,  24.,  22.,  20.,  18.,\n",
       "        16.,  14.,  12.,  10.,   8.,   6.,   4.,   2.,   0.,  -2.,  -4.,  -6.,\n",
       "        -8., -10., -12., -14., -16., -18., -20., -22., -24., -26., -28., -30.,\n",
       "       -32., -34., -36., -38., -40., -42., -44., -46., -48., -50., -52., -54.,\n",
       "       -56., -58., -60., -62., -64., -66., -68., -70., -72., -74., -76., -78.,\n",
       "       -80., -82., -84., -86., -88.], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lon</span></div><div class='xr-var-dims'>(lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.0 2.0 4.0 ... 354.0 356.0 358.0</div><input id='attrs-9d6c9e1d-bed0-4562-b71d-b2234847eaca' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9d6c9e1d-bed0-4562-b71d-b2234847eaca' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ee275d7a-10b9-4469-b74c-62f4830aabaa' class='xr-var-data-in' type='checkbox'><label for='data-ee275d7a-10b9-4469-b74c-62f4830aabaa' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degrees_east</dd><dt><span>long_name :</span></dt><dd>Longitude</dd><dt><span>actual_range :</span></dt><dd>[  0. 358.]</dd><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>axis :</span></dt><dd>X</dd><dt><span>coordinate_defines :</span></dt><dd>center</dd></dl></div><div class='xr-var-data'><pre>array([  0.,   2.,   4.,   6.,   8.,  10.,  12.,  14.,  16.,  18.,  20.,  22.,\n",
       "        24.,  26.,  28.,  30.,  32.,  34.,  36.,  38.,  40.,  42.,  44.,  46.,\n",
       "        48.,  50.,  52.,  54.,  56.,  58.,  60.,  62.,  64.,  66.,  68.,  70.,\n",
       "        72.,  74.,  76.,  78.,  80.,  82.,  84.,  86.,  88.,  90.,  92.,  94.,\n",
       "        96.,  98., 100., 102., 104., 106., 108., 110., 112., 114., 116., 118.,\n",
       "       120., 122., 124., 126., 128., 130., 132., 134., 136., 138., 140., 142.,\n",
       "       144., 146., 148., 150., 152., 154., 156., 158., 160., 162., 164., 166.,\n",
       "       168., 170., 172., 174., 176., 178., 180., 182., 184., 186., 188., 190.,\n",
       "       192., 194., 196., 198., 200., 202., 204., 206., 208., 210., 212., 214.,\n",
       "       216., 218., 220., 222., 224., 226., 228., 230., 232., 234., 236., 238.,\n",
       "       240., 242., 244., 246., 248., 250., 252., 254., 256., 258., 260., 262.,\n",
       "       264., 266., 268., 270., 272., 274., 276., 278., 280., 282., 284., 286.,\n",
       "       288., 290., 292., 294., 296., 298., 300., 302., 304., 306., 308., 310.,\n",
       "       312., 314., 316., 318., 320., 322., 324., 326., 328., 330., 332., 334.,\n",
       "       336., 338., 340., 342., 344., 346., 348., 350., 352., 354., 356., 358.],\n",
       "      dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>1960-01-01 ... 2022-12-01</div><input id='attrs-0a6be7cc-c092-456a-9e6a-6a368acfc4ad' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0a6be7cc-c092-456a-9e6a-6a368acfc4ad' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9bf3f9ee-1bf3-483a-9f89-e9120aa600b7' class='xr-var-data-in' type='checkbox'><label for='data-9bf3f9ee-1bf3-483a-9f89-e9120aa600b7' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Time</dd><dt><span>delta_t :</span></dt><dd>0000-01-00 00:00:00</dd><dt><span>avg_period :</span></dt><dd>0000-01-00 00:00:00</dd><dt><span>prev_avg_period :</span></dt><dd>0000-00-07 00:00:00</dd><dt><span>standard_name :</span></dt><dd>time</dd><dt><span>axis :</span></dt><dd>T</dd><dt><span>actual_range :</span></dt><dd>[19723. 81722.]</dd><dt><span>_ChunkSizes :</span></dt><dd>1</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;1960-01-01T00:00:00.000000000&#x27;, &#x27;1960-02-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;1960-03-01T00:00:00.000000000&#x27;, ..., &#x27;2022-10-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2022-11-01T00:00:00.000000000&#x27;, &#x27;2022-12-01T00:00:00.000000000&#x27;],\n",
       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>month</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>1 2 3 4 5 6 7 ... 6 7 8 9 10 11 12</div><input id='attrs-73f93ba9-0515-4a64-964d-c53e76bde434' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-73f93ba9-0515-4a64-964d-c53e76bde434' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3073807b-6e84-41b0-9809-63e021c78ab0' class='xr-var-data-in' type='checkbox'><label for='data-3073807b-6e84-41b0-9809-63e021c78ab0' 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, ..., 10, 11, 12])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-6d4bda6a-4d08-41e6-bf53-009f439a39e9' class='xr-section-summary-in' type='checkbox'  checked><label for='section-6d4bda6a-4d08-41e6-bf53-009f439a39e9' class='xr-section-summary' >Data variables: <span>(1)</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'>(time, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.0 0.0 0.0 0.0 ... nan nan nan nan</div><input id='attrs-bd13e68e-78aa-4b51-89b6-e6862a0544bf' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-bd13e68e-78aa-4b51-89b6-e6862a0544bf' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8d90868d-6516-4d46-891c-cb31db6e3a09' class='xr-var-data-in' type='checkbox'><label for='data-8d90868d-6516-4d46-891c-cb31db6e3a09' 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.        ,  0.        ,  0.        , ...,  0.        ,\n",
       "          0.        ,  0.        ],\n",
       "        [ 0.        ,  0.        ,  0.        , ...,  0.        ,\n",
       "          0.        ,  0.        ],\n",
       "        [ 0.        ,  0.        ,  0.        , ...,  0.        ,\n",
       "          0.        ,  0.        ],\n",
       "        ...,\n",
       "        [        nan,         nan,         nan, ...,         nan,\n",
       "                 nan,         nan],\n",
       "        [        nan,         nan,         nan, ...,         nan,\n",
       "                 nan,         nan],\n",
       "        [        nan,         nan,         nan, ...,         nan,\n",
       "                 nan,         nan]],\n",
       "\n",
       "       [[ 0.        ,  0.        ,  0.        , ...,  0.        ,\n",
       "          0.        ,  0.        ],\n",
       "        [ 0.        ,  0.        ,  0.        , ...,  0.        ,\n",
       "          0.        ,  0.        ],\n",
       "        [ 0.        ,  0.        ,  0.        , ...,  0.        ,\n",
       "          0.        ,  0.        ],\n",
       "...\n",
       "        [        nan,         nan,         nan, ...,         nan,\n",
       "                 nan,         nan],\n",
       "        [        nan,         nan,         nan, ...,         nan,\n",
       "                 nan,         nan],\n",
       "        [        nan,         nan,         nan, ...,         nan,\n",
       "                 nan,         nan]],\n",
       "\n",
       "       [[-0.00046575, -0.00037932, -0.00014675, ..., -0.00019586,\n",
       "         -0.00032628, -0.00046384],\n",
       "        [-0.00040364, -0.00022697,  0.        , ...,  0.        ,\n",
       "         -0.00016713, -0.00037074],\n",
       "        [ 0.        ,  0.        ,  0.        , ...,  0.        ,\n",
       "          0.        ,  0.        ],\n",
       "        ...,\n",
       "        [        nan,         nan,         nan, ...,         nan,\n",
       "                 nan,         nan],\n",
       "        [        nan,         nan,         nan, ...,         nan,\n",
       "                 nan,         nan],\n",
       "        [        nan,         nan,         nan, ...,         nan,\n",
       "                 nan,         nan]]], dtype=float32)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-aceeee19-6209-4267-b4b9-98af27fdabed' class='xr-section-summary-in' type='checkbox'  ><label for='section-aceeee19-6209-4267-b4b9-98af27fdabed' class='xr-section-summary' >Indexes: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>lat</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-d66e24a1-7de1-4f5c-97de-fb4485d11cbb' class='xr-index-data-in' type='checkbox'/><label for='index-d66e24a1-7de1-4f5c-97de-fb4485d11cbb' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([ 88.0,  86.0,  84.0,  82.0,  80.0,  78.0,  76.0,  74.0,  72.0,  70.0,\n",
       "        68.0,  66.0,  64.0,  62.0,  60.0,  58.0,  56.0,  54.0,  52.0,  50.0,\n",
       "        48.0,  46.0,  44.0,  42.0,  40.0,  38.0,  36.0,  34.0,  32.0,  30.0,\n",
       "        28.0,  26.0,  24.0,  22.0,  20.0,  18.0,  16.0,  14.0,  12.0,  10.0,\n",
       "         8.0,   6.0,   4.0,   2.0,   0.0,  -2.0,  -4.0,  -6.0,  -8.0, -10.0,\n",
       "       -12.0, -14.0, -16.0, -18.0, -20.0, -22.0, -24.0, -26.0, -28.0, -30.0,\n",
       "       -32.0, -34.0, -36.0, -38.0, -40.0, -42.0, -44.0, -46.0, -48.0, -50.0,\n",
       "       -52.0, -54.0, -56.0, -58.0, -60.0, -62.0, -64.0, -66.0, -68.0, -70.0,\n",
       "       -72.0, -74.0, -76.0, -78.0, -80.0, -82.0, -84.0, -86.0, -88.0],\n",
       "      dtype=&#x27;float32&#x27;, name=&#x27;lat&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>lon</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-74bd062a-014a-4d34-907b-59e2502d170c' class='xr-index-data-in' type='checkbox'/><label for='index-74bd062a-014a-4d34-907b-59e2502d170c' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([  0.0,   2.0,   4.0,   6.0,   8.0,  10.0,  12.0,  14.0,  16.0,  18.0,\n",
       "       ...\n",
       "       340.0, 342.0, 344.0, 346.0, 348.0, 350.0, 352.0, 354.0, 356.0, 358.0],\n",
       "      dtype=&#x27;float32&#x27;, name=&#x27;lon&#x27;, length=180))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-8333fc31-0bd5-43e6-8368-5e328fd25666' class='xr-index-data-in' type='checkbox'/><label for='index-8333fc31-0bd5-43e6-8368-5e328fd25666' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(DatetimeIndex([&#x27;1960-01-01&#x27;, &#x27;1960-02-01&#x27;, &#x27;1960-03-01&#x27;, &#x27;1960-04-01&#x27;,\n",
       "               &#x27;1960-05-01&#x27;, &#x27;1960-06-01&#x27;, &#x27;1960-07-01&#x27;, &#x27;1960-08-01&#x27;,\n",
       "               &#x27;1960-09-01&#x27;, &#x27;1960-10-01&#x27;,\n",
       "               ...\n",
       "               &#x27;2022-03-01&#x27;, &#x27;2022-04-01&#x27;, &#x27;2022-05-01&#x27;, &#x27;2022-06-01&#x27;,\n",
       "               &#x27;2022-07-01&#x27;, &#x27;2022-08-01&#x27;, &#x27;2022-09-01&#x27;, &#x27;2022-10-01&#x27;,\n",
       "               &#x27;2022-11-01&#x27;, &#x27;2022-12-01&#x27;],\n",
       "              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, length=756, freq=None))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-67726cd2-f14f-44c4-bb9b-c0a25eafd2d3' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-67726cd2-f14f-44c4-bb9b-c0a25eafd2d3' 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:  (lat: 89, lon: 180, time: 756)\n",
       "Coordinates:\n",
       "  * lat      (lat) float32 88.0 86.0 84.0 82.0 80.0 ... -82.0 -84.0 -86.0 -88.0\n",
       "  * lon      (lon) float32 0.0 2.0 4.0 6.0 8.0 ... 350.0 352.0 354.0 356.0 358.0\n",
       "  * time     (time) datetime64[ns] 1960-01-01 1960-02-01 ... 2022-12-01\n",
       "    month    (time) int64 1 2 3 4 5 6 7 8 9 10 11 ... 2 3 4 5 6 7 8 9 10 11 12\n",
       "Data variables:\n",
       "    sst      (time, lat, lon) float32 0.0 0.0 0.0 0.0 0.0 ... nan nan nan nan"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gb = ds.groupby('time.month')\n",
    "ds_anom = gb - gb.mean(dim='time')\n",
    "ds_anom"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we can view the climate signal without the overwhelming influence of the seasonal cycle.\n",
    "\n",
    "_Timeseries at a single point in the North Atlantic_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x78264be4f090>]"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ds_anom.sst.sel(lon=300, lat=50).plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "_Difference between Jan. 1 2018 and Jan. 1 1960_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.QuadMesh at 0x78264bcc95d0>"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "(ds_anom.sel(time='2022-01-01') - ds_anom.sel(time='1960-01-01')).sst.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Grouby-Related: Resample, Rolling, Coarsen\n",
    "\n",
    "\n",
    "### Resample\n",
    "\n",
    "[Resample](https://docs.xarray.dev/en/v2023.10.1/generated/xarray.Dataset.resample.html) in xarray is nearly identical to Pandas.\n",
    "**It can be applied only to time-index dimensions.** Here we compute the five-year mean.\n",
    "It is effectively a group-by operation, and uses the same basic syntax.\n",
    "Note that resampling changes the length of the the output arrays.\n",
    "\n",
    "See the [list](https://pandas.pydata.org/docs/user_guide/timeseries.html#offset-aliases) of string aliases for time series frequencies "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
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       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-section-item input + label {\n",
       "  color: var(--xr-disabled-color);\n",
       "}\n",
       "\n",
       ".xr-section-item input:enabled + label {\n",
       "  cursor: pointer;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-section-item input:enabled + label:hover {\n",
       "  color: var(--xr-font-color0);\n",
       "}\n",
       "\n",
       ".xr-section-summary {\n",
       "  grid-column: 1;\n",
       "  color: var(--xr-font-color2);\n",
       "  font-weight: 500;\n",
       "}\n",
       "\n",
       ".xr-section-summary > span {\n",
       "  display: inline-block;\n",
       "  padding-left: 0.5em;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label {\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in + label:before {\n",
       "  display: inline-block;\n",
       "  content: '►';\n",
       "  font-size: 11px;\n",
       "  width: 15px;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label:before {\n",
       "  color: var(--xr-disabled-color);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label:before {\n",
       "  content: '▼';\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label > span {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-section-summary,\n",
       ".xr-section-inline-details {\n",
       "  padding-top: 4px;\n",
       "  padding-bottom: 4px;\n",
       "}\n",
       "\n",
       ".xr-section-inline-details {\n",
       "  grid-column: 2 / -1;\n",
       "}\n",
       "\n",
       ".xr-section-details {\n",
       "  display: none;\n",
       "  grid-column: 1 / -1;\n",
       "  margin-bottom: 5px;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-array-wrap {\n",
       "  grid-column: 1 / -1;\n",
       "  display: grid;\n",
       "  grid-template-columns: 20px auto;\n",
       "}\n",
       "\n",
       ".xr-array-wrap > label {\n",
       "  grid-column: 1;\n",
       "  vertical-align: top;\n",
       "}\n",
       "\n",
       ".xr-preview {\n",
       "  color: var(--xr-font-color3);\n",
       "}\n",
       "\n",
       ".xr-array-preview,\n",
       ".xr-array-data {\n",
       "  padding: 0 5px !important;\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-array-data,\n",
       ".xr-array-in:checked ~ .xr-array-preview {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-array-in:checked ~ .xr-array-data,\n",
       ".xr-array-preview {\n",
       "  display: inline-block;\n",
       "}\n",
       "\n",
       ".xr-dim-list {\n",
       "  display: inline-block !important;\n",
       "  list-style: none;\n",
       "  padding: 0 !important;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list li {\n",
       "  display: inline-block;\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list:before {\n",
       "  content: '(';\n",
       "}\n",
       "\n",
       ".xr-dim-list:after {\n",
       "  content: ')';\n",
       "}\n",
       "\n",
       ".xr-dim-list li:not(:last-child):after {\n",
       "  content: ',';\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-has-index {\n",
       "  font-weight: bold;\n",
       "}\n",
       "\n",
       ".xr-var-list,\n",
       ".xr-var-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-var-item > div,\n",
       ".xr-var-item label,\n",
       ".xr-var-item > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-even);\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-var-item > .xr-var-name:hover span {\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-var-list > li:nth-child(odd) > div,\n",
       ".xr-var-list > li:nth-child(odd) > label,\n",
       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-odd);\n",
       "}\n",
       "\n",
       ".xr-var-name {\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-var-dims {\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-var-dtype {\n",
       "  grid-column: 3;\n",
       "  text-align: right;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-preview {\n",
       "  grid-column: 4;\n",
       "}\n",
       "\n",
       ".xr-index-preview {\n",
       "  grid-column: 2 / 5;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-name,\n",
       ".xr-var-dims,\n",
       ".xr-var-dtype,\n",
       ".xr-preview,\n",
       ".xr-attrs dt {\n",
       "  white-space: nowrap;\n",
       "  overflow: hidden;\n",
       "  text-overflow: ellipsis;\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-var-name:hover,\n",
       ".xr-var-dims:hover,\n",
       ".xr-var-dtype:hover,\n",
       ".xr-attrs dt:hover {\n",
       "  overflow: visible;\n",
       "  width: auto;\n",
       "  z-index: 1;\n",
       "}\n",
       "\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-data {\n",
       "  display: none;\n",
       "  background-color: var(--xr-background-color) !important;\n",
       "  padding-bottom: 5px !important;\n",
       "}\n",
       "\n",
       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
       ".xr-var-data-in:checked ~ .xr-var-data,\n",
       ".xr-index-data-in:checked ~ .xr-index-data {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       ".xr-var-data > table {\n",
       "  float: right;\n",
       "}\n",
       "\n",
       ".xr-var-name span,\n",
       ".xr-var-data,\n",
       ".xr-index-name div,\n",
       ".xr-index-data,\n",
       ".xr-attrs {\n",
       "  padding-left: 25px !important;\n",
       "}\n",
       "\n",
       ".xr-attrs,\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-data {\n",
       "  grid-column: 1 / -1;\n",
       "}\n",
       "\n",
       "dl.xr-attrs {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  display: grid;\n",
       "  grid-template-columns: 125px auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt,\n",
       ".xr-attrs dd {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  float: left;\n",
       "  padding-right: 10px;\n",
       "  width: auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt {\n",
       "  font-weight: normal;\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-attrs dt:hover span {\n",
       "  display: inline-block;\n",
       "  background: var(--xr-background-color);\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-attrs dd {\n",
       "  grid-column: 2;\n",
       "  white-space: pre-wrap;\n",
       "  word-break: break-all;\n",
       "}\n",
       "\n",
       ".xr-icon-database,\n",
       ".xr-icon-file-text2,\n",
       ".xr-no-icon {\n",
       "  display: inline-block;\n",
       "  vertical-align: middle;\n",
       "  width: 1em;\n",
       "  height: 1.5em !important;\n",
       "  stroke-width: 0;\n",
       "  stroke: currentColor;\n",
       "  fill: currentColor;\n",
       "}\n",
       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
       "Dimensions:  (time: 14, lat: 89, lon: 180)\n",
       "Coordinates:\n",
       "  * lat      (lat) float32 88.0 86.0 84.0 82.0 80.0 ... -82.0 -84.0 -86.0 -88.0\n",
       "  * lon      (lon) float32 0.0 2.0 4.0 6.0 8.0 ... 350.0 352.0 354.0 356.0 358.0\n",
       "  * time     (time) datetime64[ns] 1960-12-31 1965-12-31 ... 2025-12-31\n",
       "Data variables:\n",
       "    sst      (time, lat, lon) float32 -0.0005343 -0.000517 ... nan nan</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-f0c9fefb-1ab9-4bb4-9d77-08e271573979' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-f0c9fefb-1ab9-4bb4-9d77-08e271573979' 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'>time</span>: 14</li><li><span class='xr-has-index'>lat</span>: 89</li><li><span class='xr-has-index'>lon</span>: 180</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-66e06d4b-8c4f-4929-b542-cf04168f0a7c' class='xr-section-summary-in' type='checkbox'  checked><label for='section-66e06d4b-8c4f-4929-b542-cf04168f0a7c' class='xr-section-summary' >Coordinates: <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 class='xr-has-index'>lat</span></div><div class='xr-var-dims'>(lat)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>88.0 86.0 84.0 ... -86.0 -88.0</div><input id='attrs-66bbe2ef-49e9-486b-bba6-3cad9459992d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-66bbe2ef-49e9-486b-bba6-3cad9459992d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-eb473f22-6279-4e44-8680-16f07c45f8a6' class='xr-var-data-in' type='checkbox'><label for='data-eb473f22-6279-4e44-8680-16f07c45f8a6' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degrees_north</dd><dt><span>long_name :</span></dt><dd>Latitude</dd><dt><span>actual_range :</span></dt><dd>[ 88. -88.]</dd><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>axis :</span></dt><dd>Y</dd><dt><span>coordinate_defines :</span></dt><dd>center</dd></dl></div><div class='xr-var-data'><pre>array([ 88.,  86.,  84.,  82.,  80.,  78.,  76.,  74.,  72.,  70.,  68.,  66.,\n",
       "        64.,  62.,  60.,  58.,  56.,  54.,  52.,  50.,  48.,  46.,  44.,  42.,\n",
       "        40.,  38.,  36.,  34.,  32.,  30.,  28.,  26.,  24.,  22.,  20.,  18.,\n",
       "        16.,  14.,  12.,  10.,   8.,   6.,   4.,   2.,   0.,  -2.,  -4.,  -6.,\n",
       "        -8., -10., -12., -14., -16., -18., -20., -22., -24., -26., -28., -30.,\n",
       "       -32., -34., -36., -38., -40., -42., -44., -46., -48., -50., -52., -54.,\n",
       "       -56., -58., -60., -62., -64., -66., -68., -70., -72., -74., -76., -78.,\n",
       "       -80., -82., -84., -86., -88.], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lon</span></div><div class='xr-var-dims'>(lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.0 2.0 4.0 ... 354.0 356.0 358.0</div><input id='attrs-3d42636b-ea51-41f4-8927-1398c7b087f3' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3d42636b-ea51-41f4-8927-1398c7b087f3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ac700343-42ef-4d05-8df9-75bb8306381f' class='xr-var-data-in' type='checkbox'><label for='data-ac700343-42ef-4d05-8df9-75bb8306381f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degrees_east</dd><dt><span>long_name :</span></dt><dd>Longitude</dd><dt><span>actual_range :</span></dt><dd>[  0. 358.]</dd><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>axis :</span></dt><dd>X</dd><dt><span>coordinate_defines :</span></dt><dd>center</dd></dl></div><div class='xr-var-data'><pre>array([  0.,   2.,   4.,   6.,   8.,  10.,  12.,  14.,  16.,  18.,  20.,  22.,\n",
       "        24.,  26.,  28.,  30.,  32.,  34.,  36.,  38.,  40.,  42.,  44.,  46.,\n",
       "        48.,  50.,  52.,  54.,  56.,  58.,  60.,  62.,  64.,  66.,  68.,  70.,\n",
       "        72.,  74.,  76.,  78.,  80.,  82.,  84.,  86.,  88.,  90.,  92.,  94.,\n",
       "        96.,  98., 100., 102., 104., 106., 108., 110., 112., 114., 116., 118.,\n",
       "       120., 122., 124., 126., 128., 130., 132., 134., 136., 138., 140., 142.,\n",
       "       144., 146., 148., 150., 152., 154., 156., 158., 160., 162., 164., 166.,\n",
       "       168., 170., 172., 174., 176., 178., 180., 182., 184., 186., 188., 190.,\n",
       "       192., 194., 196., 198., 200., 202., 204., 206., 208., 210., 212., 214.,\n",
       "       216., 218., 220., 222., 224., 226., 228., 230., 232., 234., 236., 238.,\n",
       "       240., 242., 244., 246., 248., 250., 252., 254., 256., 258., 260., 262.,\n",
       "       264., 266., 268., 270., 272., 274., 276., 278., 280., 282., 284., 286.,\n",
       "       288., 290., 292., 294., 296., 298., 300., 302., 304., 306., 308., 310.,\n",
       "       312., 314., 316., 318., 320., 322., 324., 326., 328., 330., 332., 334.,\n",
       "       336., 338., 340., 342., 344., 346., 348., 350., 352., 354., 356., 358.],\n",
       "      dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>1960-12-31 ... 2025-12-31</div><input id='attrs-80da14c2-d4be-40fb-8739-804d90582d25' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-80da14c2-d4be-40fb-8739-804d90582d25' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b46aff32-1000-4051-be69-1217f157a928' class='xr-var-data-in' type='checkbox'><label for='data-b46aff32-1000-4051-be69-1217f157a928' 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;1960-12-31T00:00:00.000000000&#x27;, &#x27;1965-12-31T00:00:00.000000000&#x27;,\n",
       "       &#x27;1970-12-31T00:00:00.000000000&#x27;, &#x27;1975-12-31T00:00:00.000000000&#x27;,\n",
       "       &#x27;1980-12-31T00:00:00.000000000&#x27;, &#x27;1985-12-31T00:00:00.000000000&#x27;,\n",
       "       &#x27;1990-12-31T00:00:00.000000000&#x27;, &#x27;1995-12-31T00:00:00.000000000&#x27;,\n",
       "       &#x27;2000-12-31T00:00:00.000000000&#x27;, &#x27;2005-12-31T00:00:00.000000000&#x27;,\n",
       "       &#x27;2010-12-31T00:00:00.000000000&#x27;, &#x27;2015-12-31T00:00:00.000000000&#x27;,\n",
       "       &#x27;2020-12-31T00:00:00.000000000&#x27;, &#x27;2025-12-31T00:00:00.000000000&#x27;],\n",
       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-4e3fa6d3-71e0-426c-aead-bda05da99832' class='xr-section-summary-in' type='checkbox'  checked><label for='section-4e3fa6d3-71e0-426c-aead-bda05da99832' class='xr-section-summary' >Data variables: <span>(1)</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'>(time, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>-0.0005343 -0.000517 ... nan nan</div><input id='attrs-8e6a5c4a-11e8-44a5-b34a-24a2eb2867da' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-8e6a5c4a-11e8-44a5-b34a-24a2eb2867da' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f565af6f-cb62-4c84-9b4a-97c5f689acb9' class='xr-var-data-in' type='checkbox'><label for='data-f565af6f-cb62-4c84-9b4a-97c5f689acb9' 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([[[-5.3429604e-04, -5.1703054e-04, -4.9649674e-04, ...,\n",
       "         -4.0187439e-04, -4.8724809e-04, -5.4604810e-04],\n",
       "        [-5.0445396e-04, -5.6445599e-04, -6.0094398e-04, ...,\n",
       "         -1.4868379e-04, -3.3017001e-04, -4.2128563e-04],\n",
       "        [-1.4215708e-05, -1.7046928e-04, -2.7028719e-04, ...,\n",
       "         -1.1832515e-04, -7.5548887e-05, -3.4034252e-05],\n",
       "        ...,\n",
       "        [           nan,            nan,            nan, ...,\n",
       "                    nan,            nan,            nan],\n",
       "        [           nan,            nan,            nan, ...,\n",
       "                    nan,            nan,            nan],\n",
       "        [           nan,            nan,            nan, ...,\n",
       "                    nan,            nan,            nan]],\n",
       "\n",
       "       [[-5.3429604e-04, -5.1703054e-04, -4.9649674e-04, ...,\n",
       "         -4.0187439e-04, -4.8724809e-04, -5.4604810e-04],\n",
       "        [-5.0445396e-04, -5.6445599e-04, -6.0094398e-04, ...,\n",
       "         -1.4868379e-04, -3.3017001e-04, -4.2128563e-04],\n",
       "        [-1.4215708e-05, -1.7046928e-04, -2.7028719e-04, ...,\n",
       "         -1.1832515e-04, -7.5548887e-05, -3.4034252e-05],\n",
       "...\n",
       "        [           nan,            nan,            nan, ...,\n",
       "                    nan,            nan,            nan],\n",
       "        [           nan,            nan,            nan, ...,\n",
       "                    nan,            nan,            nan],\n",
       "        [           nan,            nan,            nan, ...,\n",
       "                    nan,            nan,            nan]],\n",
       "\n",
       "       [[-5.3429604e-04, -5.1703054e-04, -4.9649674e-04, ...,\n",
       "         -4.0187439e-04, -4.8724809e-04, -5.4604810e-04],\n",
       "        [-5.0445396e-04, -5.6445599e-04, -6.0094398e-04, ...,\n",
       "         -1.4868379e-04, -3.3017001e-04, -4.2128563e-04],\n",
       "        [-1.4215708e-05, -1.7046928e-04, -2.7028719e-04, ...,\n",
       "         -1.1832515e-04, -7.5548887e-05, -3.4034252e-05],\n",
       "        ...,\n",
       "        [           nan,            nan,            nan, ...,\n",
       "                    nan,            nan,            nan],\n",
       "        [           nan,            nan,            nan, ...,\n",
       "                    nan,            nan,            nan],\n",
       "        [           nan,            nan,            nan, ...,\n",
       "                    nan,            nan,            nan]]], dtype=float32)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-8b09b6ba-90db-4f8c-9a4f-6bf756deb785' class='xr-section-summary-in' type='checkbox'  ><label for='section-8b09b6ba-90db-4f8c-9a4f-6bf756deb785' class='xr-section-summary' >Indexes: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>lat</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-55879e0f-5bc9-41c9-a753-011748d25e16' class='xr-index-data-in' type='checkbox'/><label for='index-55879e0f-5bc9-41c9-a753-011748d25e16' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([ 88.0,  86.0,  84.0,  82.0,  80.0,  78.0,  76.0,  74.0,  72.0,  70.0,\n",
       "        68.0,  66.0,  64.0,  62.0,  60.0,  58.0,  56.0,  54.0,  52.0,  50.0,\n",
       "        48.0,  46.0,  44.0,  42.0,  40.0,  38.0,  36.0,  34.0,  32.0,  30.0,\n",
       "        28.0,  26.0,  24.0,  22.0,  20.0,  18.0,  16.0,  14.0,  12.0,  10.0,\n",
       "         8.0,   6.0,   4.0,   2.0,   0.0,  -2.0,  -4.0,  -6.0,  -8.0, -10.0,\n",
       "       -12.0, -14.0, -16.0, -18.0, -20.0, -22.0, -24.0, -26.0, -28.0, -30.0,\n",
       "       -32.0, -34.0, -36.0, -38.0, -40.0, -42.0, -44.0, -46.0, -48.0, -50.0,\n",
       "       -52.0, -54.0, -56.0, -58.0, -60.0, -62.0, -64.0, -66.0, -68.0, -70.0,\n",
       "       -72.0, -74.0, -76.0, -78.0, -80.0, -82.0, -84.0, -86.0, -88.0],\n",
       "      dtype=&#x27;float32&#x27;, name=&#x27;lat&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>lon</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-36b8c98b-3cbb-49dd-835f-7dee9863b060' class='xr-index-data-in' type='checkbox'/><label for='index-36b8c98b-3cbb-49dd-835f-7dee9863b060' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([  0.0,   2.0,   4.0,   6.0,   8.0,  10.0,  12.0,  14.0,  16.0,  18.0,\n",
       "       ...\n",
       "       340.0, 342.0, 344.0, 346.0, 348.0, 350.0, 352.0, 354.0, 356.0, 358.0],\n",
       "      dtype=&#x27;float32&#x27;, name=&#x27;lon&#x27;, length=180))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-4102d463-ecad-4729-b9f9-b8eea88014c7' class='xr-index-data-in' type='checkbox'/><label for='index-4102d463-ecad-4729-b9f9-b8eea88014c7' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(DatetimeIndex([&#x27;1960-12-31&#x27;, &#x27;1965-12-31&#x27;, &#x27;1970-12-31&#x27;, &#x27;1975-12-31&#x27;,\n",
       "               &#x27;1980-12-31&#x27;, &#x27;1985-12-31&#x27;, &#x27;1990-12-31&#x27;, &#x27;1995-12-31&#x27;,\n",
       "               &#x27;2000-12-31&#x27;, &#x27;2005-12-31&#x27;, &#x27;2010-12-31&#x27;, &#x27;2015-12-31&#x27;,\n",
       "               &#x27;2020-12-31&#x27;, &#x27;2025-12-31&#x27;],\n",
       "              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, freq=&#x27;5A-DEC&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-cac119f4-00fc-46b5-9546-cee3204ef442' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-cac119f4-00fc-46b5-9546-cee3204ef442' 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:  (time: 14, lat: 89, lon: 180)\n",
       "Coordinates:\n",
       "  * lat      (lat) float32 88.0 86.0 84.0 82.0 80.0 ... -82.0 -84.0 -86.0 -88.0\n",
       "  * lon      (lon) float32 0.0 2.0 4.0 6.0 8.0 ... 350.0 352.0 354.0 356.0 358.0\n",
       "  * time     (time) datetime64[ns] 1960-12-31 1965-12-31 ... 2025-12-31\n",
       "Data variables:\n",
       "    sst      (time, lat, lon) float32 -0.0005343 -0.000517 ... nan nan"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds_anom_resample = ds_anom.resample(time='5Y').mean(dim='time')\n",
    "ds_anom_resample"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x78264bc2f550>]"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ds_anom.sst.sel(lon=300, lat=50).plot()\n",
    "ds_anom_resample.sst.sel(lon=300, lat=50).plot(marker='o')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.QuadMesh at 0x78264bbc32d0>"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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hGIbJcXiKKn14gJOAUKdSBKQPTqAD5ZmK62pIP0M+NVbQyP+jlRVSr+Llo5GIutcelPXLeiknka7toVikbw2VCRXLfDPafHLtFtvLReU/6mLPL1ev+DxejyxPep2OfW3BD+WZcmh95OdgX++5ddLdAECoU3f5qbtn2bZEYPjRzVq/b+ABKZetl54k9oZSw0KeRPVOnUpozEkJ6yFdEWlZhOwPgObFY+iL6PrQr5nYrirHvlV+raGHp9KctKl//yl7f7omh7Q3VVKfJq+vQkNDBgA1z94BAIhU2ddOZMcOe5OC+LVe84Odg8wny/oOmwIACEstihc3FA4CAHQJ2cfo98ufsFd4eQdt32zve/OLdv2kX9LyQ5HeLdbB9qKifG2k1yFtHQD4u/UCEPfZ8ffbz37vvU/CeKOfvw4g/jujvHLC8XPuD5c7tqEcT3UVQ+0Yftqo1oW6VKAlaFj/tf2+4Ru1bNMiW3vTsNPWzXTdyz4exX16AHD+ZpJ/mcqvR3pLWYZ+S6O7nNcSkzvwAIdhGIZhchyfZWXsY9PefHB4gMMwDMMwOY7lt2D5MhugWDzAYRiGYRgml/D5LfgyHOC0tzs4/Jg4wzAMwzBtDr6Dk4BAj17K4IlEkQ4TLBLaBZxGf0T4iLMz2n9Ttq+d/zcAQGjQvgAAEVmq1nXsZIseyYCt4Oe/BwB0QPPg27U1/qVT2xcX5yLZSniYinC6/l0plJXXSFQa/+liZiUulslWs2FQl4zQ6F9mtH3hpEvS3qb2pXsBADWyrf6uPdU636ARAIDralY7tqHEmV7HI/rFWwCAwIhjG913bMOXju/+PQ8BAFh6skmJFbHjIzM8JTpOYjqpi5UBOIz+VHJYKeKObLDbWDhgpL28ILvCYj3ZqJngd/N/nwYArHvjCwDAwOPibdrtiAPtspQUVf5++7vaImlH4l/qu2Rw6vO+HxDcuctzedbx+1x/Z9LGal/ZNnmAwzAMwzA5juWzYGVoZGOBp6gYhmEYhmHyGr6DwzAMwzA5js9vwZfhHRxfO7uDwwOcBPjLKkDTtMqgrMFtxEXzvspkL4V5/7o3HpEV2/PV6ehtlk2aoD4HCu3TV36AbezV7ZI7HGULdx+Tcr3ZhszGmLZHTNN0iF22CR4ZBZI2IiY1OGQECAA1m/4HAOh6wZyWCLNVIG1bOiTTIvmHjEu9os12EtBo9Y/2+1bbJNDStDJ0XgJl0pivo520kwz/vIjttJO5RrdssrfdzU6Wq3QsAFBkmwxSwlBaE1v1LgDAl+Zv0ScbtgEA9lhiG54GR8rfPakV8lW4E/aSjqjb1GkAgO6n2HGLbZtdZU1dmUpiqydxtRqZ4JBlA9U7k5fLEpYvcw2OJdqXBoenqBiGYRiGaXPwHRyGYRiGyXF4iip9eIDDMAzDMDmO5eenqNKFBzgJsIIF8BUGHcscnh5SP0Pz26l4etB8dGzHNgBA4fEXpR3X8Gfnp70Nw2QDSroZq41rDiiRZZR8cMgzRF4f+jVTX237hXx59vEAgK9f+RYA8FKlncTyPrG2eQJvg6y9aor6HCq23axCJfZ7uJP0GSrvDSCe1BSI62YilevsBYFKAIDYssH+6uG3Q1oefzc7+S55HSFc6CpLCT+tQFhuLH2R1nysyiTz2iH2Dtg+Wg1H2ZqmrQ12vwrJP/BBzdHXB1tXYkVlkmFKWhq047NkTDoNP6xzfFdlZB0AYEmNjUigxbGidlJiEXQnQGZyAx7gMAzDMEyOY9/ByVBkjFjjhdoQPMBhGIZhmByHNTjpw09RMQzDMEyOY1l2NvGMXmkm25w9ezb2339/FBcXo3v37pg0aRJWrlzZTC3MPnwHJwHC8ilvG+V1oxFMIT8PEVu92P7gt+sJ731g5gEyjEH9+0/ZH6RGgnI+AUC0ehsAIDzu9Ebr+eEOOwdTx8GDAACBHraWg/RmPpn7BwAg90HaDtqPunZkrh8ACBbZ2+314PP2u1x+fKMRMcT2v10LAOg0cDe1LCCPK3mk+AqL7BWkg6rRfFrk+TC9t0hfFVn6oloW22X7yFgFRbJ+ef5Lu7oDk7oXITUswi//tHj4tkRkzqzAbns5luv5paqHSN+biFN7QzcwYpqfi5p08YUc7/5wRzvuWFwjQ/oc0us4fG8Ap/eNuY6+CyM3Vab5oXKYhQsX4vzzz8f++++PSCSCa665BhMmTMAXX3yBoqKi1g6vUXiAwzAMwzA5js/vgy9DDY5PpLf9K6+84vj+4IMPonv37li6dCkOPfTQjGJpCfJq6LlhwwacccYZ6Nq1Kzp06ID99tsPS5fGM2YLITBz5kxUVFSgsLAQ48aNw4oVK1oxYoZhGIbJHHpMPNNXJmzfbjuXd+nSJRtNanbyZoCzdetWjBkzBsFgEC+//DK++OILzJ07F506dVJlbr31Vtx+++24++67sWTJEpSXl+PII49EdXV16wXOMAzDMDlEVVWV41VXV9foNkIIXHrppTj44IMxdOjQFogyc/JmiuqWW25B79698eCDD6pl/fr1U5+FEJg3bx6uueYaTJ48GQDw8MMPo0ePHnj00Ufxu9/9Lq39WbEIRMQe7VIOKhGpT7ZJQmJKl2CPJ62gPUfcsOR5WSA+10u+IaFDTmnSvpj8RnnNSK8kIlAxAEA83w4Q9xOJfvEWgLhGQsgiuqdJTPrUbL33ansb2adj9baXR7hzXK/TodzWWIi6GrvsD7ZHitWhxN6vrsGQfTcm+y3pcywqo10zsXq7nm8vOc3+3mDve9DdT5iHwUX0u09l20LyXdMBUa4l4fTgIR+TQM/Bjdaf65A+JdzT1t6IiJYXj/ROujYKUMdexLScUZovkY6ZmwmI58yj36QoHdciux/EqreqstQnLNKwBGy9ofKQ0bQtwi9//zavtb9Tn949rk30S58bIbU20Zh8pzq0OOUqVZYgQa3umROS/cYv11HuLIrT8tLgGO9WzO63iEad781MVoz+hL197969HctnzJiBmTNnJt32ggsuwPLly7Fo0aKMYmhJ8uYOzvPPP4+RI0fixBNPRPfu3TF8+HD85S9/UevXrFmDyspKTJgQT0YZDocxduxYvPfeewnrraurc41mGYZhGCaXIA1Opi8AWL9+PbZv365e06dPT7rvCy+8EM8//zzefPNN9OrVqyWamxXyZoDz7bff4t5778XgwYPx6quv4txzz8VFF12ERx6x/5dRWWk7cvbo0cOxXY8ePdQ6L2bPno3S0lL1Mke2DMMwDNOWKCkpcbzC4bBnOSEELrjgAjz99NN444030L9//xaONDPyZooqFoth5MiRuOmmmwAAw4cPx4oVK3DvvffirLPOUuXM5/yFEEmf/Z8+fTouvfRS9b2qqooHOQzDMExukYUpKoj0tj///PPx6KOP4rnnnkNxcbG6WVBaWorCQneqjlwjbwY4PXv2xJAhQxzL9tprLzz1lO39UV5eDsC+k9OzZ09VZvPmza67OjrhcNhz9Coi9RA19hx2bJc9baX7SdS88Gf7g6HLKZx0iauu6JZN9gepH6DcMD45l+0fMi5hfEzbhXKTkT8SAFid7X4cKO4EABAy71Nk83r7u9YHI2s+t5dJPUbBhF8n3FfNV8sd3/3S2ylUbHtZBDrEf6ws6aOi/E+knkL5QWm+IqS1IP0H6TUoV5vSxwAo3n0gAKBDT1uvEdlplyVvl1DXuLYn2Ht3e/NO3ewFIRkf6TW0epV+QjXA1mKQIqOh8pt42wxvk3zR51Cuu+j8vwEAfAVxDxJ1zOU7+eGQZkrsik+7R7b+kPI+w4ed5bmctDmkJQSAiNy3v3N3e13YzosFqbcRQY87BKR3Cdp9JxbS2hS1z169fG8gGYzU2ST7TytJbvyqB8TLqs1Ir0O6GlNvo3+WmhvKPaU0XpT7KtK4QDcb+CwLPl+GTsZpGv3de++9AIBx48Y5lj/44IOYOnVqRrG0BHkzRTVmzBiXg+KqVavQt29fAED//v1RXl6OBQsWqPX19fVYuHAhDjrooBaNlWEYhmHyHSGE5ysfBjdAHt3BueSSS3DQQQfhpptuwkknnYQPP/wQDzzwAB544AEA9oh+2rRpuOmmmzB48GAMHjwYN910Ezp06IDTTjutlaNnGIZhmKZj+X2ZJ9uM5c09jayQNwOc/fffH8888wymT5+OG264Af3798e8efNw+ulx6/krr7wSNTU1OO+887B161aMGjUK8+fPR3FxcZKaGYZhGCa3yUqyzVj7SrZpCdM8oJ1TVVWF0tJSbHnrCRQJqcGp2gIAEHVxDwnTE4c8SJQmQZufNn0qSIMTGnNS9hvA5DzRNR/bH2RfiYU6qnX+nXZfi27ZaK/baZtUCg//ErVMagIKj7/Isb5W6jWAuJYHhjaG+qTuoWKF5TLKI0WaDvndkZuN+r2miQHiOhDdr4X0QzEZi349AXFNGqDlOyqwrxUhNTgiYGs5dD8gtU/DB8eVS8gLGXewe7/Gy+YJda/ZXmF07GM74xqcWO0uAEDH06/LeD+7/nOr+ky6LX9nWzPlk/of1ZcK4n08FrbLihC923qdWDCuA6uVf4h31NvnsF6a3ZDnjf53nnxuyDsn4CP/G3t9OBC/a+GTehnKSYWGWsd3laMKiGtvqA/XSsNY+dvvH3o4gPjfjO3bt6OkJN6HswXV/85xh6FjMLN7EjsaIjjkv280W6y5Rvu6X8UwDMMwTLsgb6aoGIZhGKa9whqc9OEBDsMwDMPkOD4/sqDByVIweUL7Gs4xDMMwDNMu4Ds4CQgMPwoN/50HAIjW2qKySE3c0MkfsoWWOzbYxlm9rv8LGCYlSFxcUCrf42I/q8EWgZJA1PLLBJoexnm+jp3sMrroF/GEnST4tOuzyyqTMhIMk8i4MG6yRgJhta1MqKmWG4JiudIuK8W/gspo/m5WBzuGACUrjDmN0xzmfdIgDv4EiTQ9kyJKEziql5briR49kj8CQP2P36vPobL8ybXjRfiIswEANc/fCQCIaPn1Ss6+IWv76XDila5lZAKobhRIQblfM4e0pLGlEhmrvqP1q5j3rQbyudMN60hcHPI734OyiNUQF7NbEfk5SuZ9TnGxQ2Qsk2j6++0HIJ7wlMT9HldBs2L5LFgZGv1lun2+wQMchmEYhslxfL54sswm1xFtX5M27au1DMMwDMO0C/gODsMwDMPkOFYWkm1mnKwzz+ABTgK2P/L/UNBgz9PG5HuPK+9ylevUkkExbQJ/733sd/m9fmulWifCMhGrTKaoErVKTH0MENfTRD6db3+nZJkocpV11UN6CM04Txj6FNKyeDqCKqM/Q7fjoXUhbYwg/QTtM4UEgC5dja6VIN0P7cs0+NO+q1yLaoE0PNTK1P9kmyyGulQ0GlcuQ8aPtfde3ez7Wn2BbVpa3MdObNyxt/3u72onPrZ07VhRZ+fGpMHx6GCkp6HkmiQhsTyM/tzamxr7PaJrcGS/IS0XJduk/qQZU9Iy0rSR9oaME8nocNcutwlnc5CVx8Qz3D7faF+tZRiGYRimXcB3cBiGYRgmx7F8Ps87uOnW0Z7gAQ7DMAzD5Dg+fxaeompnU1Q8wElA6Vl/bBfJyJjWx1/1P/U5WmQnmfRJbxsrbCcgVD41mj+I8rAp6OAs6/W/NKmBIH8ZkUA744D2ZZEXDWU61PxqPJJeJoLKWiD/G1Mro4kwDG8bwrI8/FF80tMkjZhUWeFzLtCo3/6jo75wcadG681FOv/+5oy2f77nUABAMGyf96hMgFnQOZ6gtUOZ3feqv98GACgfZZcpJb8lrZ+RP5NFiS+lRsav6XRCUhtmWfb5D7n6QfyEkS6HkmyqesnbRkuMrJJsmpou8p2i5XAmirV3JNugefq0KFnQ4KCdDXDaV2sZhmEYhmkX8B0chmEYhslxLF8WnqJiDQ7DMAzDMLkEi4zThwc4DNNK1M7/GwDAX9pVLfN3t+f3RZ2tS1A6G/numP8PGDqdgDMnlQPDl0ZIbYTyotG9YnSPGR3Ki6VrXEzPnET71fahtrfIgyRibhX3pSHvHLVPD68bw9MEVoNz37pmyDgOLl8cx3b0PffM0Xb++0b1efs3GxzrQiW2JqvsorlZ2dfxmz4HENfi1ETt4xz7qcZVtlDqcqrW2P5N/qB93jpq/dYn+7sv3BEAYAXshGUq/xjih95H58Jnnne3aY4VMXKbeem4yPcmlkAHpkP9ivRwdM2Qrigsr81WkuQwjcMDHIZhGIbJcWyjv8xSfFr+9jUa4wEOwzAMw+Q47GScPu2rtQzDMAzDtAv4Dk6W2fGvGwAAHU+/Liv1kCis6NRrAQD17z/lKhsa/ctG63tl4HC7Ppmv5cVvtgIA+nWI6zYurV6VQcRMqtQ8eweAuK6GctwAgNi22f4g9TRKnyP1AEL3wSFdAvmHJNPDKO1JAj2Jtm1CvxrlixOPQddNNFavitPURvjlfnTtD8lyzLxSVK2+XNVH+oqoc39RLQbyASK9krFto23IEej3AIDKOEa6nIbqXQCArTIH1c7KLapsr+v/0uR9bqixT8qOiH2sCrXEjf0D9jGqrbI9aIJF9vHdseEHAM5+WyI9ncKyb1sh6ePk1/4c0fkw85V5nQujP1nJziXpwJQGi/q0R+402UdExPldad1kvjgr2jL9w+fzwZehSDjT7fMNHuAwDMMwTI7DU1TpwwMchmEYhslxeICTPu2rtQzDMAzDtAv4Dk4KNCx53r1QzikLmeckNOYkAIC/qBgAUPP8napo4fEXpb1P0leQ90LtKw8AAHwe+XDq3voXACA87vSE9XUbUmZvL+fNT+5uzx+PWfRO2rExTaP2pXvtDz7no54iGn90MzD8aABA7NuP7HWmJ4u+XSJtiOn1oS+jXDxSryAChgZBK5soV5TDt0bmgVJaHEMrofx29DJG+5XfSP2ueLUNNc5lRi4i6G0jfRLlDjI0OLo/kEXtjNpxxj15dK2QjD0HtTfJCPTsDwAoOtX+Hah+ZCYAoLhPD1XmhzsuAQA07LR1XxXX3uuo4419D1Cfd/xgH/svpK7mx3r7uHaUepvdCuPntrjC9rQpKJGeNjH7fNVt2wEA8IXi56DDtp8AAMGd1faCoi4AnP2KzrbyKfIbHk/JfJDM/qvpzqgPKq8nlV/Kqc0BAIvyYdG2yncqnoMLAKyA28epObCsLBj95VmfzhQe4DAMwzBMjsNTVOnTvlrLMAzDMEy7gO/gMAzDMEyOw3dw0ocHOCngk74NulbC1CfUv/uEXbZTdwBOPQytI0ivkw4FR/8WQFxv40DqgOpee9De9xFnu4qM+O8CAMDaq6YAAIp72RqHr3//f6rM4HufTDsuxok616T/0PQmlINHNDi1Irr9evTz1+1lhbaWy+XP4dATOC9f0jBYpCdoqIuXrXPmDbKCthbBR/oUXeNgGRoU5bMj9+3z0OtYhqZFxiaChVq89j4jUmChdBayuqB2rOjq8pF2qEHm5pIaIt07yEQdX5/blt4ytRxqheYPZOoUkvmq5BCmBs9faPvLiOgOtazbJXd4bvvuwYcAAHyh+DkISY+sPtL3przAPqfd5PJue5epsp372zqacCe730YbpDZL/mYGO8R1K/5w2LlzpZnRzpfqe2Y+KY+yiaA+qC2yqB6jj6tTrmu9lP7Nbm9g2ATP3YSrqgCc23g8GeLz++DLcICS6fb5RvtqLcMwDMMw7YK8HeDMnj0blmVh2rRpapkQAjNnzkRFRQUKCwsxbtw4rFixovWCZBiGYZgsYPksWD5fhq8ETuZtlLwc4CxZsgQPPPAA9t13X8fyW2+9FbfffjvuvvtuLFmyBOXl5TjyyCNRXV3dSpEyDMMwTOaQBifTV3si71q7Y8cOnH766fjLX/6Czp07q+VCCMybNw/XXHMNJk+ejKFDh+Lhhx/Grl278Oijj7ZixAzDMAzDtDQpiYwnT56cdsX33XcfunfvnvZ2jXH++efj2GOPxRFHHIEbb7xRLV+zZg0qKysxYUJcCBYOhzF27Fi89957+N3vfpfWfqLfLUe0o0wCJwWfyiQNWvI4MuSLSRGdFL9Flr2iyloB74SEDR88K+uKC+YocaaeTE8nmZlfKhT3ts9JtLa+kZIMUS8TYJLQFVo/IDO9QMUe9neZQJPOuRXURK1SRCsa5LGXxnSBEceqItGv3nHsI250RiZ52v9JSCApY1AC3FpbVCpqdqqiMUOUS+JnFa9mXkbLEJDJQKn/kjDTp/1smMtkWYq3wRfv+5GoLeCMGp59AXnbPKDVqwzNSOhJ4k8PwS8dT12sDSD+IIBuLEjx+j0M/lSFUthMiRnzQ2PsosOJVyZc9+XZxwMAhDwZpX1L7O+x+MmJSmO/bvX2AfCHZOLf7vbvYUn/nvF9ldsCel/QPq4xKTKm94AuMu62GwCtzymRsdswTwnRhYchZSKorBLduw0v48aXch2ZWHrE4Bt4gGtZa8BPUaVPSgOcZ599FieddBIKCwsbLwzg0UcfxY4dO7I+wHnsscfw8ccfY8mSJa51lZWVAIAePXo4lvfo0QPfffddwjrr6upQVxd/2qSqqipL0TIMwzBMdmAn4/RJ+THxO++8M+UBy5NPZv9x4/Xr1+Piiy/G/PnzUVBQkLCcZTlFVEII1zKd2bNn4/rrr89anAzDMAyTbSy/Hz7zLmUT6mhPpDSce/PNN9GlS5eUK3355Zex2267NTkoL5YuXYrNmzdjxIgRCAQCCAQCWLhwIe68804EAgF154bu5BCbN2923dXRmT59OrZv365e69evz2rcDMMwDMO0PCndwRk7dmxalR588MFNCiYZhx9+OD777DPHsrPPPht77rknrrrqKgwYMADl5eVYsGABhg8fDgCor6/HwoULccsttySsNxwOI2waTwG28ZehL9Bv73nN1TqIeGhcpNbGM3mnhIzimmIGmAqF3eRAlTQYwQTGZ0ycZCIM45avr9gWvltBmXTQQ69CegJ//58BAKLffRovU2DrGwyZCiD/5+VVnxknaVJU8kk9XEoYGDA0PbpOxUcmfbINUoujNEQeiSnVNlJ7Ewva20Qi8WNH2huSeSR9YlVpb4wEiqb5IKC0THSMVJ9WfTx+faukjTJOz2SbysRTanvk94YfbG1TsFufJIHnB133thNzkiYjUGAfI18o3r9ENOZ4p3XBjrbxqZ741yqwl6nzYhjx6RovXwdb70N9kcwALWg6MbpWorJfmedJN7z0GYaXhnGgSqwJKJ2PJfuM+h03+5m2L0p86xswEq0Ja3DSp0lOxrFYDKtXr8bmzZsRMxx9Dz300KwEZlJcXIyhQ4c6lhUVFaFr165q+bRp03DTTTdh8ODBGDx4MG666SZ06NABp512WrPExDAMwzAtAQ9w0iftAc7ixYtx2mmn4bvvvoMQzv9nWpaFaDQFC+1m4sorr0RNTQ3OO+88bN26FaNGjcL8+fNRXFzcajExDMMwDNPypD3AOffcczFy5Ei8+OKL6NmzZ1IBb3Pz1ltvOb5bloWZM2di5syZrRIPwzAMwzQH5EacaR3tibQHOF9//TWefPJJDBo0qDniyRn8ffaBv6Qk5fKx1YudC/SORHoH0uXU2XPNIuah7ZBz2JGlLwKIz10rbxK96O5jUo6P6HDy9LS3ae+EOpcDAOp//B6AM2GjMJP+Fdp9JkY+K/64DwxpV4LlAwEAkU1f28vDHePbN5boMdljngGplSiSniZeT0wofx4PjRDtkrQRpFNRuhWPnwszGajPuU+/JrShNaYGxy//k2Tp14PZbtL/CJ8jfgAA6TuMtikNna7BIW8fapPH8VS6jKjhjZKvhjgedB57uOO7l1eXkL9XlNiUkpiqJ3G03zjl+xRw6p+UT5jeF40+YkXIX0rXdpHmxrlP1V/1fmvqfZQPjtRQRdy+VXGvHEOnpsdm6L6iaz72bFu0Op7MtDnhKar0Sbu1o0aNwurVq5sjFoZhGIZhmKyQ0h2c5cuXq88XXnghLrvsMlRWVmKfffZB0HgKx8wPxTAMwzBMZlg+K/M7OO0s2WZKA5z99tsPlmU5RMW/+tWv1Gda19oiY4ZhGIZpi7AGJ31SGuCsWbOmueNoO9CcvjbStnzST4IWyLls+Mj/ITHkEaG8Pnzty4kylxAhOzcZNF8Nyk8V2fClvcDv1Ag4fWvsM92wea0sK7Uumv5Bec4Y2hClHfDKq+Nz6hN89COm6X9UXiXl7SS3SUVXQ1BuH02LojxxSGshl/tkmVCSXE/qgiC/Eq1tKkcQbWIcVzTErxpLZpCh9sdIc2N489jLnF4+lof/ico5pzxT5PmW/8GLrv0kXp88p/6+w9ztzEEaKr8BAFi77eFcYfjDAIBPamNErZ3TTCjtoPs/sSq3mamH8viD6tIeqvri9Zq5pwRkfVSHvm/DM4nOl3keAW9vKEDTDiXpr/4EPjh+Tu+Ts6Q0wOnbt6/6/Pbbb+Oggw5CIODcNBKJ4L333nOUZRiGYRgmcyyfXw0kM6mjPZH2/arx48fjp59+ci3fvn07xo8fn5WgGIZhGIbR8Pmz82pHpP2YeKLklVu2bEFRUVFWgmIYhmEYRsPnc9qPNLWOdkTKA5zJkycDsAXFU6dOdeRvikajWL58OQ466KDsR5gn+AYd6PhO+Ut0aD7aP/xox/KGD55NeT/+PQ9JPzgmK4iA3ed1DQrpMtT8vzD0VjqkJ1A6LakRCcQ9jlQOJ2kWQ/+VCNC+HXl1pGbBqnfUT9FZPncOHpVnyWfobHTtgaFLUVoGo42e25NeRfmVePygyhhc+dz0GGifdMwChg+U7tvic+aMSyl3FhGR2hPND0X5slC+IjrmdLz1/wXTMf/mQ8f31s5bpFP/08b4F9mPBGR/Iu1N1KljARD3pQk49S/weJBE+dyYfjgepPQcjzrWcMRJ15Xl5SFmarnoWvTKyaZ0alIHp/qr+w5HvuirGDcpD+dKS0tRWloKIQSKi4vV99LSUpSXl+O3v/0t/vnPfzZnrAzDMAzTLrH8/qy80uHtt9/Gcccdh4qKCliWhWeffbZ5GtdMpHwH58EHH4QQAkII3HXXXZzfiWEYhmFaimxoaNLcfufOnRg2bBjOPvts/PKXv8xs361AWhocIQQeffRRXHPNNTzAYRiGYZg2zDHHHINjjjmmtcNoMmkNcHw+HwYPHowtW7Zg8ODBzRVTmyCdOfjgqEnNF4gBeXj4++3XYvtsK3h50cQ1DIaexOfWCCgtiNQpeM37k6QnZoh4fOS34vDpIA2O4emigouXVV4wHnGZZdUi0yOG2qq3n+pXuYNkLJa334ijPle+qfjPUdyvh+qVe6IDpJeldzN3lqrXo210vkjroWubpMaEtDfxXExaGROpCQlIfV1s1bv2rpuQL86L6Hef2jGYfkBwH3s6VsHu/ZzLgbg/lzoHhi7MoVey67UChm7LS/8C2jwF1QP1d9NPx+vugulxY+q2tHXxfhqVX91xxnNlkUcSfTf6m15fruDzZeEOjt3OKsO7JxwOO3S1bYW0JdW33norrrjiCnz++efNEQ/DMAzDMAbkZJzpCwB69+7t0NHOnj27lVvXPKT9mPgZZ5yBXbt2YdiwYQiFQigsLHSs9/LIYRiGYRgmN1i/fj1KSkrU97Z49wZowgBn3rx5zRAGwzAMwzAJsbIgMrbs7UtKShwDnLZK2gOcKVOmNEccDMMwDMMkohWeosp30h7gALax37PPPosvv/wSlmVhyJAhOP744+FP8xl7JnUcCf4kmQiFo+s/s+vovU+T62hv+HZtBWCYwtXvsj+YJnCmOFbHNAXUxJY+KaoMG2JNKyb3qe9bimBVMki5zjQ6c1aUQHbnVdaohxIxego9SVRsGrzpQk8vIacWk9OQz+8dL2mNPbaHIRj1iiEuCqfjScfQfVzJIE6Ji+m7VlYJkOtq7Ore+pcdSududgEyAATgG3gA0kUZhvqNZJD6caHjR2WkELl++49yxx7CWZfAWx5vPTGpYVpJSYM9z3/CBrhNAePXSIJznCxOz34aNYrIOKkf6H/UzSS4Rp8M7LaXRyPaLzt27MDq1avV9zVr1uCTTz5Bly5d0KdPn1aMLDXSHuCsXr0aP//5z7FhwwbsscceEEJg1apV6N27N1588UUMHDiwOeJkGIZhmHaLLhLOpI50+Oijjxw5Ji+99FIA9kzOQw89lFEsLUHaA5yLLroIAwcOxOLFi9GlSxcAdh6qM844AxdddBFefPHFrAfJMAzDMO2aVpiiGjduHITwTDyTF6Q9wFm4cKFjcAMAXbt2xc0334wxY7Lj+cAwDMMwjEYWfXDaC2kPcMLhMKqrq13Ld+zYgVAo5LEFkw1Ib0PamUzriWz40n7fuDK+MoGZFul0sq0DyhfIZM0n/yfjOD6GJkDpSKLOBJiAlmSQtA0k0xFujYhLg6ASX2r7TpBc0FO/YBimJYpfR8UVNfbjocGwSAdCqwzTOXs7MoxzlvE0PDSTdZo6De04KC0TmeCZyTw1jYbSLTXUOb7r2qa4rka+K+2N1OY0aGVrdzreVbLJsJ3w01cUf1Il8v0Ku0ivvZE2RlJXB3TcyEDSb/wO6/2LPhu6J2XY6Gn0J9dFkxhLJjKZNPVWXmVT0NWk84fdpb3RDS+NfikSadKYNkHaZ/cXv/gFfvvb3+KDDz5QuakWL16Mc889F8cff3xzxMgwDMMw7ZrWSLaZ76Q9wLnzzjsxcOBAjB49GgUFBSgoKMCYMWMwaNAg/OlPf2qOGBmGYRimfePzZefVjkh7iqpTp0547rnn8PXXX+Orr76CEAJDhgzBoEGDmiM+hmEYhmGYtGmSDw4ADB48mBNutgL6/Hpk09cAgEDP9M9DKn4PpBlQuh9dTyE1AaRP8fcdlnYMuU50zccA4loOIXUbIom3h2XomByeKUaiP6Uz0I+rqZUhnU0iX5BEy/T9eJU1vHgcfjUJnppQeg0vXxXTn4Xa5PPSP3hrbxz+OLSdqSchdF2F0tqQTsnp12NFauPbkZdPg+1bE6vZ6VgOuDU3VC/54Xj64EhdjtKyGPodnXSuW1Mz4uUZ5EpMStoTda69Kk5di5U4uMQ+Sy5ti/7d7Hte9ZmaG7M+r32bXkFpkBf+N2z0lzZpD3Ci0SgeeughvP7669i8eTNiRsbWN954I2vBMQzDMAwDWD4/rAwHKJlun2+kPcC5+OKL8dBDD+HYY4/F0KFDYXk5tTIMwzAMw7QiaQ9wHnvsMTzxxBP4+c9/3hzxMAzDMAxjYmVBJNzOHotPe4ATCoVYUNyKBCr2UJ8b/remRfYZ98rw0n80jxaH6jP3k6nvTk2trY3wRaQPitRiWHU7VRlf3Q57Gelo6ux8UyonkRek05A/QOpxzEBcQ+KTGhzSUVmBesd3T9LRRnjlKWoMpdNIwa00kb5CR7ZbJPFBUe31GWX9WtnGcmZF4ufCMvVK5HUTdeaQAuLnm7QznvmlvDRWWrxAg2sZ+d5YoQLHdx3yMHLl4koGHSM6NirHme5X4/SlUfF7aV0MzZXpFaSOmb4uatSXTi4qD/+lpHoyYzvh1V5zWzotKsdV4lxkcT8h+71JnkStBE9RpU/aw7nLLrsMf/rTn/LavplhGIZhmLZN2gOcRYsW4V//+hcGDhyI4447DpMnT3a8movZs2dj//33R3FxMbp3745JkyZh5cqVjjJCCMycORMVFRUoLCzEuHHjsGLFimaLiWEYhmFaBErVkNGrfU1Rpd3aTp064YQTTsDYsWNRVlaG0tJSx6u5WLhwIc4//3wsXrwYCxYsQCQSwYQJE7BzZ3xq4dZbb8Xtt9+Ou+++G0uWLEF5eTmOPPJIz9QSDMMwDJM3sNFf2liimeaa3n33XYwcORLhcLg5qscPP/yA7t27Y+HChTj00EMhhEBFRQWmTZuGq666CgBQV1eHHj164JZbbsHvfve7lOqtqqpCaWkptm/fjpKSksY3aEUaflgHIK5hCJX1ykq95H9DpOOVQn4dTdXixL79yK7e1JN4zP/7Bh6Qdv111dvsekinUW/ra0ibAQA+qccR9VKvQV4pHtoMpXcw7BKULiIU12JYQdLeSL+OgH1tCPLv0LZLKUeOqeUwNTJedRjHUddcKMy2pBKbywfHo6yRGyiu0/GIN5HmQupYnPnAjBxhSoNDPjh18bLSI0f510QT6G2AuK4qYvjf6Hod0mUZWhxfh2L7vbAoXjZkfyY/pFT0H+paNDRNep8Jdu8HAKjfttm7Eg8Njspppr7LNmnH1dTguHKeeZGoD3ppcJKgtDeGpkvpjJL1QXOx3ldiTs0ZrfMNGNloTIlo7r8ZVP9P7z+Hko5FjW+QrK4dO9Fl9MS8+PuWDZptOHfMMcdgw4YNzVU9tm/fDgAqq/maNWtQWVmJCRMmqDLhcBhjx47Fe++9l7Ceuro6VFVVOV4MwzAMw+Q3zTbAaU4RshACl156KQ4++GAMHToUAFBZWQkA6NGjh6Nsjx491DovZs+e7Zhi6927d7PFzTAMwzBNImP9TRackPOMvJyQu+CCC7B8+XL8+9//dq0zjQeFEEnNCKdPn47t27er1/r167MeL8MwDMNkBA9w0qbJuahaiwsvvBDPP/883n77bfTqFdeclJeXA7Dv5PTs2VMt37x5s+uujk44HG42nVBzE+zWBwBQ/+P3jvemanEiG750fHdpb/S7cuY8upprT//OXfSrd7RqKN+RU6+RcL9pYjWQ5qbO8d1XH9fgiFrbB4e0N6S9CI6aBABo+OBZLRzS4Mh30mKQrkTXs8gyIibLkA5Gi08I+5K0LLmdIQp0aA/oWJAuI1E+KLtiY53USpAnjSMPELxJontIqPvxyLPl0t54YebMMjQjej+Le7l4+7PovjMUjVVoa1iS6kHofJFXDuWiqo/ntlIaK9kW0tyo5QHtt8X4j5a63mQMXpqcVHQ6dN0n8n9JSfOivK48kHGr45golxSQksdPwjL68THbYvriJN2B4bPjcQ2m5MXD5D15cwdHCIELLrgATz/9NN544w3079/fsb5///4oLy/HggUL1LL6+nosXLgQBx10UEuHyzAMwzBZw/L5svJqTzTbHZxs56g6//zz8eijj+K5555DcXGx0tWUlpaisLAQlmVh2rRpuOmmm1Sm85tuugkdOnTAaaedltVYGIZhGKZFsbIwxWTxFFVWyLbI+N577wUAjBs3zrH8wQcfxNSpUwEAV155JWpqanDeeedh69atGDVqFObPn4/i4uKsxsIwDMMwTG6Ttg9OTU0NhBDo0KEDAOC7777DM888gyFDhjge0c5X8skHh6A5eJpXJm1OMqLrPwPg7SeRUHuTgv+FQivr7/8z7xg+f929UOWTMbQsqlotBvInkT4dgWGp9z/KdUX+H6S7AeLam1gt+d8Y7Q7EPUhIK0RaHKUdMrVE2nYuPxzdBydRvicvXY3pS2N6paTgK5NWXqCk/jeW93J9l01I9JcwPg89mMunxUsrksjjxwvySiGvoKg7b5XpjaS0N/KcOjQjqeRKShADkU4uNvLJSnpuTQ8hh7+QcfzSOXYmSaZGUuoXybydSMuWwu9W3NtHnktZ1r/nIY3HkICW8sHZuux1lBR3zKyu6h3oPPzwvPr7lglp/+JMnDgRjzzyCABg27ZtGDVqFObOnYuJEyequywMwzAMw2QRy5edVzsi7dZ+/PHHOOQQe7T75JNPokePHvjuu+/wyCOP4M4778x6gAzDMAzDMOmStgZn165dStMyf/58TJ48GT6fDwceeCC+++67rAfIMAzDMO0dYfmaNM1r1tGeSLu1gwYNwrPPPov169fj1VdfVbqbzZs3t4s5PYZhGIZpcXiKKm3SvoNz3XXX4bTTTsMll1yCww47DKNHjwZg380ZPnx41gNkGsc09qMEfcocDYiLIamD+5vwuGAqF0caxlmeiQ4psR8l2TMfi9REnWr7JogfKbmmMnGri5u3xSgRYwMlIJQxSXGwZ32a8BjQxcYex9k0/PO543eZqHkJfUkgKk0BhV9ezpRs0ktknIagMyXzvgTbeq5Kx1QtoZFkzCOG5G1zJvxMEKeXgSK907USJXG4t6FgY6iElknyeyaC2hBd+4kzJg1/732cCzySw8YrTHAukonDrcbPX6JkmJ6mgKmcA1olxcGeD0UoE0inqNhLoC6kwad6QKFBE4znOpaV/NpLtY52RNoDnP/7v//DwQcfjE2bNmHYsHjG6MMPPxwnnHBCVoNjGIZhGIZpCk26X1VeXo7i4mIsWLAANTX2/4T3339/7LnnnlkNjmEYhmEY2I/aZ+PVjki7tVu2bMHhhx+O3XffHT//+c+xadMmAMBvfvMbXHbZZVkPkGEYhmHaOyQyzvTVnkh7iuqSSy5BMBjEunXrsNdee6nlJ598Mi655BLMnTs3qwEy6ROfe/bwcKTEeV76ikw0Eua+NWKrFwMAfIMONFYk3p8y9PMl0dkYCS4jy16xYwjaehj/0MMT109mbTQXL9/tZU7tTVJ7dNPQj8z8SEOk64xkG+isWKptKWhEaLGuJzA+WHS+ad+Jo9YqTKxfMTUMKiGjdi7cOookppCmjibResCtFUjB6M9MGOmZmDEdPRHtg8zh/HI/XsZxZv/0uPbUeTe1IcqgTtOXGfWRxksECrz3h7h5J2lxrATJRx0xyeMR6DkYABDZuFLbafp/DIVxjlPSXaWi26LEoUj8O+DaF32PajobeZ3HahKYeDJtirQHOPPnz8err77qyOQNAIMHD+bHxBmGYRimOcjGU1B8Byc5O3fuVGkadH788UeEw+GsBMUwDMMwjAYPcNIm7dYeeuihKlUDAFiWhVgshjlz5mD8+PFZDY5hGIZhGKYppH0HZ86cORg3bhw++ugj1NfX48orr8SKFSvw008/4d13322OGJk0cflheBDZ8GXilWayOkLXHqSSpDHRvj+db+wviV+H8oxxl1G6F1pneNFEv3pHfaZkerFV7zrrVV43Hu2ghJ/+JAk0E+Dp0UPbN8WDyOM4q3NAvjW0giyPtN0k9RxJUL+5TqlVtG0Taiy8/FQS78FjnyQssrzrc0hn/I44BYzj29T/tcp9KO2MmcwTgPBLbySfkfDTy+OJtjG0QulcQ55+MPRZ9jXywYKXBskgKLU3RKBij5RjSfobYvbJJGWSnp9E2iuv3yJzG+oPumYswXlpWPI8ACC4//HJIm5d+A5O2qTd2iFDhmD58uXYf//9ceSRR2Lnzp2YPHkyli1bhoEDBzZHjAzDMAzTrhGWlYWnqNjor1HKy8txww03ZDsWhmEYhmGYrNCk+1XvvPMOzjjjDBx00EHYsGEDAOAf//gHFi1alNXgGIZhGIYB56JqAmnfwXnqqadw5pln4vTTT8fHH3+Mujo7t0d1dTVuuukmvPTSS1kPkskeat48maYjhZwuXt4djaFrYhrDS3OTqEwqmhjS3pj+Igrd4ZO8bMx1HvuhOfvI0hfljtLXJOkxJWxLU/xE9FhSyCMU387pn5LMy8Sl7TF0H0mNxZJ50njl3gK8nVhjhkbI3HeSbdTuvPo25fqSub1UTP54TjJa59LeePnAJNKtKZ1ZCucoib8M+f7E/ZAS538K9Nq78X01QmC3uA+awz9HJ53znyRflaf/TWP10rbJ/soZxzz6+evqczIfrVaBc1GlTdrDuRtvvBH33Xcf/vKXvyAYjIs6DzroIHz88cdZDY5hGIZhGPAdnCaQdmtXrlyJQw891LW8pKQE27Zty0ZMDMMwDMMwGZH2AKdnz55YvXq1a/miRYswYMCArATFMAzDMEwczkWVPmlrcH73u9/h4osvxt///ndYloWNGzfi/fffx+WXX47rrruuOWJsFaLfLUe0UymAeP4XPbdNsEf/VokrU1y5czxy+sTzv1BZqRGIaDldiERamRR0MV64tDemLiFZNlxZ1j9sgv11lduXyZLbK/0OTbN61Bsc/UsAQP37T8lt3W0i/4yE2hm9XjMXlSoT35aOsZWonV77kbl2XL44ulaEzh3lqSJ9QpS8XeL9IGEOIy/dltqXzJWk4pR6EH1zun5MHUAivY1OsvNO59T8/5pX7quY2cdT0HpRW6TexqcfH6m5iV9XxrFLkl/KRZJYRH0KedFg6yFV35HnxJnjq3n+wCXyz2mo/Kbx/Zo+S15Qm9QxjNfX6B9t3beJziXl9qqvdXxXxwxA7NuP7D0NGJm8/pbC8mWeDZwHOMm58sorsX37dowfPx61tbU49NBDEQ6Hcfnll+OCCy5ojhgZhmEYhmHSIq0BTjQaxaJFi3DZZZfhmmuuwRdffIFYLIYhQ4agY8eOzRUjwzAMw7Rv2Mk4bdIa4Pj9fhx11FH48ssv0aVLF4wcmSO37hiGYRimLcMDnLRJe4pqn332wbfffov+/fNTg5IOwmsOX9Kwea39QZYJduvTQlE1jejaT+wPybxtpJZDaQUituYgnq8pdc8bB03R4yTSK3jkdgqMOBYAEFn2iqOo01/G51qmx6bP/wekt03DB8/KbY34tXlwKxByLXPEmYq3icdxVVokc99e9antnRopKxD/Tu22TI8f0ldJLYK9KPXz7CsssuszNS0+mTPJH/+JoX0qLY7PqQdyHMMk116jqGOUgn9PkPQ1mnaG9FnkfyN1NVaD1Lg4NDjympEaJ9FQn36cKfj0mLF5QseTcp6FC+13LUeb1cJ/4ILldvoe9XsJePg12W963irT50edL3/68et1xfO1yT5I+sok25MWJ16JM7ZY9Y60Y2JahrR7y6xZs3D55ZfjhRdewKZNm1BVVeV4MQzDMAyTZdgHJ23SvoNz9NFHAwCOP/54WNrTEEIIWJaFaBr/+2MYhmEYpnEo2WamdbQn0h7gvPnmm80RB8MwDMMw7ZRHHnkEJ598MsLhsGN5fX09HnvsMZx11llp15n2AGfs2LFp74RhGIZhmAxo4yLjs88+G0cffTS6d+/uWF5dXY2zzz67ZQY4y5cv91xuWRYKCgrQp08f1wgsHxGBYDxxYA53imTo4jhXcj753WHeR6LiulrHOhFJQzhJaOLYZCJdF2SGR2JKU1Sp1yvfyWyPEl+qhHm6gZ5Rj3/IOLvsF2/Z3z0S6wVHTQKgJdIkdNFm0BYZK+OwmFNs6yU6tfymcDjJtG4y8zdzOtgQpjr2bZgMqnMgz3FMExnDy9ARiCch1dpPe1SCVrnK81a6KWwX8gwmK2uSyrVoClE1AbHrOpACeodwmETFtCziLCOk2Nhe5xTiq2slibjc7Ivq+vC4LtT1qa4HeR49pABkWmmFbOGs6mdavb7dD0wYV3MS7N5Pfa7/8XsAujGl7AcOlbEhMm9K0k4ykPRap5lg6sud/UAea/o7YMbiYajZrLTxZJskczH5/vvvUVpa2qQ60x7g7Lfffp5BEMFgECeffDLuv/9+FBQUNCmoTLnnnnswZ84cbNq0CXvvvTfmzZuHQw45pFViYRiGYZiMaaU7OM3993T48OGwLAuWZeHwww9HIBAflkSjUaxZs0Zpf9Ml7QHOM888g6uuugpXXHEFDjjgAAghsGTJEsydOxczZsxAJBLB1VdfjWuvvRa33XZbk4LKhMcffxzTpk3DPffcgzFjxuD+++/HMcccgy+++AJ9+uT2o9wMwzAMkyu0xN/TSZMmAQA++eQTHHXUUQ7T4FAohH79+uGXv/xlk+pOe4Aza9Ys/OlPf8JRRx2llu27777o1asX/vjHP+LDDz9EUVERLrvsslYZ4Nx+++349a9/jd/85jcAgHnz5uHVV1/Fvffei9mzZ7d4PAzDMAyTKdlIlpnu9i3x93TGjBkAgH79+uGUU07JqsQl7QHOZ599hr59+7qW9+3bF5999hkAexpr06ZNmUeXJvX19Vi6dCmuvvpqx/IJEybgvffeS68yfyg+t+o1JZdKgsBWIrZ6scdC55y9aeYH6DoC+S51GUnNy0xDOqV70XQcpuaENAHJDAANzYEXZtJKU4sT+XS+Vp+zHjIFDAxPfOtTGQcaGgnS3QBxvYMruSTpODS9iktP4dX+NMwUSWOh9Bik+yGjOg8NkithqtIKeWixyByQzpeX7oOMDiluQ2ejG+ipBJ+k/xLGd/0UmXqUplxnpqmlV5xGWQdmYs6o2/CSjj1pb1zn2KM+hZH41dEfTM1NQ+P1i3p7ezpSgs5NoIU0Immi+gMtsNyJf5ukxUlSJuFvOiWbTabFisltfM7ffqFd481KC09RZfXvaQocdthh+OGHH9CrVy8AwIcffohHH30UQ4YMwW9/+9sm1Zn20dpzzz1x8803o74+/kevoaEBN998M/bcc08AwIYNG9CjR48mBZQJP/74I6LRqGvfPXr0QGVlpec2dXV1bFbIMAzDtBvMv3l1dXWuMk35e5oJp512mrKhqaysxBFHHIEPP/wQf/jDH3DDDTc0qc60Bzh//vOf8cILL6BXr1444ogjcOSRR6JXr1544YUXcO+99wIAvv32W5x33nlNCigbmCLoROpsAJg9ezZKS0vVq3fv3i0RIsMwDMOkjG30l/kLAHr37u34u5dsuimdv6eZ8Pnnn+OAAw4AADzxxBPYZ5998N577+HRRx/FQw891KQ6056iOuigg7B27Vr885//xKpVqyCEwP/93//htNNOQ3FxMQDgzDPPbFIwmVJWVga/3+8aXW7evDnhHaXp06fj0ksvVd+rqqp4kMMwDMPkFELEZ9IyqQMA1q9fj5KSErXcS/fSlL+nmdDQ0KDieO2113D88bbUYM8992yy5CXtAQ4AdOzYEeeee26TdtichEIhjBgxAgsWLMAJJ5ygli9YsAATJ0703CYcDnueXOHzp+QZ4PLVaEViq951fHcllnQUbkJKDV17YNTtOlK6/sPQbqQ09k/g92F5lfF5a1oCwyaoz6TPSQtDG0GaF11XE/fCkMtIOxILOusAYAXsfuYbMBIAEP3qnfRj0lGJQp2aGdqnw9umMR8VXYtlevl4aHqahErIKbUN6ruhxYCHts1y6rYc632G9kLVm8a1qddH2yuNjIyPzrGexJX0ZeQ9Q/F5aMhS8XZKiLmNx7VoyetA+VhJ7Q354+QKobJeju8NP6xrfKM0fHBcfUf/LVKJXhMkx9V/801Nj8/UUMm6WsoHJ4uUlJQ4BjheNOXvaSbsvffeuO+++3DsscdiwYIF+H//7/8BADZu3IiuXbs2qc4mKZb+8Y9/4OCDD0ZFRQW+++47AMAdd9yB5557rklBZJNLL70Uf/3rX/H3v/8dX375JS655BKsW7cuJwdkDMMwDJMKMSGy8kqHlvx7esstt+D+++/HuHHjcOqpp2LYsGEAgOeff15NXaVL2ndw7r33Xlx33XWYNm0abrzxRpVcs3Pnzpg3b16zjOzS4eSTT8aWLVtwww03YNOmTRg6dCheeuklzye/GIZhGCYfEDBcmZtYRzq05N/TUaNG4fvvv0d9fT06d+6M7777Ds888wz69u2LSy65pEl1pj3Aueuuu/CXv/wFkyZNws0336yWjxw5EpdffnmTgsg25513XquKnBmGYRimLdBSf08nTpyIyZMn49xzz8W2bdtwwAEHIBQK4ccff0QsFsPvf//7tOtMe4CzZs0aDB8+3LU8HA5j586daQeQ09DtPC+dDXk2yDlbmkcOdmt5t2TS3pi+JVaSnE+eI3mlZZHbUe4h2kbzw0mouTH0Ks4yKcyIJsm1Yy5X+zC0FvXvPgEACI05SS0jb5z6959ylpXfvfQlVir+Fmp+3shLQ9VpfUfIz3QU/Hsmtjs39VTJEOrYJ9ZpuI5rIj0ItH6jNChJtCwJPI28vG0scxsDXTthGXngVH9VUgmtYpHck8pZr1roLAMtfto3+Z+Y3zXNhWV4pZBPjUW+OHp/VW1wapxUDI7zZWh5ZD1WzLhGAXfuMNJOSQ2WX+ZUy1VS+c2MbPjSuSAVPxfqkymVpX4W/5OofvfIr4fOfyvpLmPCfmVaR67y8ccf44477gAAPPnkkygvL8eyZcvw1FNP4brrrmvSACdtDU7//v3xySefuJa//PLLGDJkSNoBMAzDMAyTHCFEVl65yq5du9ST2PPnz8fkyZPh8/lw4IEHKq1vuqR9B+eKK67A+eefj9raWggh8OGHH+Lf//43Zs+ejb/+9a9NCoJhGIZhmMS09Ts4gwYNwrPPPosTTjgBr776qtLdbN68udEnvhKR9gDn7LPPRiQSwZVXXoldu3bhtNNOw2677YY//elPOOWUU5oUBMMwDMMw7ZfrrrsOp512Gi655BIcfvjhGD16NAD7bo6XLCYVLJHBPSsS/3Tv3r2pVeQcVVVVKC0txY+rlqG0Y5G90GvuVc7LC0MrEOzRv6VCVSTyv0mqwfHQASiNTcTISUV6Aj1fkaEfUPmZSLejazKUJsjb70PXHqhcO7TvBFocIIHOpxFCh9iDcNLpqLr0fD1mvKQrkn4iKv8UAEhvG/LBUfl/fIn/70Bz+sHygY3Gq/KKJcuv0yBt1uk8SR+UlHxwkvkhmboaj/b7Coucy+g4GNeF/dmZy0fFr3RM2jFrLNebwwfHwxvH0Y7UtV+A5s+j3u2fSE8NBmlwKOcWeQZ5liVfIUPD4aGHUteakUNMXSua7sZ1Lk0dnNZ+XZeWj7i0OED8t9j0raH+pvULt0dOAj8ceHicJchjVlVdjbLdh2P79u1NvtOQDPqbtOb7TSjOsP7qqir079Wz2WLNlMrKSmzatAnDhg2DT56bDz/8ECUlJSoVVDo0yeiPKCsry2RzhmEYhmFSoK1PUQFAeXk5ysvLHcua6oEDpDjAGT58eMq5Jz7++OMmB8MwDMMwDJMNUhrgTJo0SX2ura3FPffcgyFDhqg5ssWLF2PFihXsPcMwDMMwzUA2noLK5aeomoOUBjgzZsxQn3/zm9/goosuUnki9DLr16/PbnStjZxrVfPpuh7EIk8Mym2S0Wxfs6DP9SfT47gwfFUc+hSF4RFD8/6kwdG2UblwPOvRvD2g5dGh3EikI0knr5AXsv3KB8fUA2nHR8VOWhzSMqjlHtof0zOJ3pPM/0c2fW1X23OwWhZd+4mjjH/QgfbyNe47o6QRoGOufrpkP9X3prRN1M6IU+PhOL6Gt01ciySPh65BMo+FqYPQdFKeupwky531GneQvY5rOn3cpcXQ4ok5yyhPH4pB+yNhGVoh5YcjfWwc2RHpfJnaDvoe1TRu1NdiTv8b0uYILXeYlem1ke/QcaWvZh/UiJ/LxvVa6hJWH0jTY5zbVHx2skAMygYqozraE2mfmf/85z8466yzXMvPOOMMPPXUUx5bMAzDMAzDtCxpD3AKCwuxaNEi1/JFixahoKDAYwuGYRiGYTJBiOy82hNpz6tMmzYNv//977F06VIceKB9+3zx4sX4+9//juuuuy7rATIMwzBMe6c9PEWVbdIe4Fx99dUYMGAA/vSnP+HRRx8FAOy111546KGHcNJJ+e2x4ECfV40ZWhwgPkFLGoaA/Z18GgK77dXsIRK+3ccAAKJfveNcoWtbTL8TL/+TBDly4jtK7DtjGXoNpbsBlJ5A5XYy9UoOfyG5fYT8VKTHiekDkizOJLG79CT0Xcs7RZ4+Kl7p7eLpzWJqTUx9SQrz87q3h5lfK7r+M2d8mt5CCKfWgPQgpLcSmleK0nAYvkJCdulUfIDU8qCmryI9jnlsZDuEh7eNKqu0N26/EhcemiYXdGxMLY7XNsnq8RvnUF3rbg2NMGOnNplaLI/6LKNePceVuZ1l6AH1c9vYdaDnkIssewUAEBh+dNJtco2kuaga80xyJERLXy+j9FXmd6o3ye8i07o0SRl70kknta3BDMMwDMPkMPwUVfrk3qM/DMMwDMM44Keo0iel+3VdunTBjz/+mHKlffr0aXL2T4ZhGIZhnAhkQWTc2o1oYVK6g7Nt2za8/PLLKC0tTanSLVu2IJokhxDDMAzDMExzkvIU1ZQpU5ozjtzD53cL0nSxmjTlchlHtZDpkyceSfsIV9JKr2SbZkLGdES8Zh1aYk4lVo2ZyfDcx8w0rVP2bmQgqCcZTBAv7S9Z/Ka42Es4KwKUQNJI5phEtJpK8tWGH9bZZZMJZ60E6xzufU6xqjKX85NIOi6KF/U19jLYglNLJslU6/VEqmpf3kaHDtPIBOcyqbjaJcCVZUUK/Y3KaMZ/LoM3M7bGlpnL6bMpEKbF+kkg4TG120jQmQy32DjSeFlK6un3Ns30wheKi4yFNM6Mfv66Xc3Qw1OupzVQ4uIUzq26DmiBSEG8TngIyBtD+I2Ey81MTAjEMtTQZLp9vpHSmYm1d6dMhmEYhmlFBDKfYmpfw5smGP0xDMMwDMPkOvwUFcMwDMPkOGz0lz48wEmCmls1Et8BgBX10CwACPTau7nDSoyhOXHobuQ0Ixl8NXzwrF3G0/DPIwGjiWGmpq4br23JZM7UuyjzvSQ3EqmMuR8AVoxMAOudZQwtTqZQPxBJdA/B8oEA4vqaYLc+iesLGilNNO1FsItdDyXiTB6Y1ByoZIPOpLC6JsUKhu0PiY51gkSoOuo8+YOudab+hbQipFMwKrLfkmhOXFqeFDQ9SpUTNXRxyfqXV71p6DCobqXLSUvLIfuv2q92XE0dEF1Xvog7RnNfZptiWr2kz0p27HOJTLSN6ph5VKu0UuaxSqIDa21Dv2ykWmhnAxyeomIYhmEYps3Bd3AYhmEYJseJQSCW4S2YTLfPN5p0B+ebb77Btddei1NPPRWbN28GALzyyitYsWJFVoNjGIZhGCYLJn+cTbxxFi5ciGOOOQZjxozB22+/jVmzZqF79+5Yvnw5/vrXv+LJJ59sjjhbHOELQKgki24/BUGahhzwv6EEeibJEuqRH4Zjmcsrx/DV8ZqDVsukLxB5xkDzqyGNjNR50L7Vd00jobZP5Nujxyvrdfn2kOZHn083km0qnU7Mo03qnMrzbnjb6MfB1Nok094QoU7dAQB1O7bLeJNchsm0HKafTDLNFMUeCHvWa+mHIZGmI1myUTMWWaHybQESa2G8zq3U7liJri/9WlSfnX1GbRuztIVGGygmr6SgjWlbvMok0OSkhFf9dBxI05REv5TQe0hvm9TgCEraSclcZZz+vsNSj7clSMVXqTGS/VU3vZeSJeY0tF6qaDqaLaZFSbvXXH311bjxxhuxYMEChEJxYeL48ePx/vvvZzU4hmEYhmHiT1Fl+mpPpH0H57PPPsOjjz7qWt6tWzds2bIlK0ExDMMwDBMnG1NM7W2KKu07OJ06dcKmTZtcy5ctW4bddtstK0ExDMMwDBOHRMaZvtoTad/BOe2003DVVVfhP//5DyzLQiwWw7vvvovLL78cZ511VnPE2Dr4AoD0K1Hz1dpctuXKp9PyRD6d71yQQu6outcebLSM8MhTZX/XdDumHoe++9w6FVVvXa1jnRUMusqa3jWmrkbXNCTUDFH9fo8YaD/mNjqGPkMY2ptUdDapEO6YOHltoOdgAEBk40p7gUd/s0ztDemKUumTyfKsJdC7pJKLKx6ncNfrtUyvQl9O58XUQ1FVmr+OlUif46kZEs5w4aG3MfOKJcF1rF26HY/j2tg2ju1lLKTFoU3049HIcXW0n9pEnk6k5ZHvUdLkAPD33idxXLlMIl2Yvq6xbR3LjL5n6rdaM/8gk5S0z8ysWbPQp08f7LbbbtixYweGDBmCQw89FAcddBCuvfba5oiRYRiGYdo1/BRV+qQ9wAkGg/jXv/6FVatW4YknnsA///lPfPXVV/jHP/4Bv8f/mLPB2rVr8etf/xr9+/dHYWEhBg4ciBkzZqC+vt5Rbt26dTjuuONQVFSEsrIyXHTRRa4yDMMwDJNvUDbxTF/tiSYb/Q0cOBADBw7MZiwJ+eqrrxCLxXD//fdj0KBB+Pzzz3HOOedg586duO222wAA0WgUxx57LLp164ZFixZhy5YtmDJlCoQQuOuuu1okToZhGIZhcoOUBjiXXnppyhXefvvtTQ4mEUcffTSOPjru6TJgwACsXLkS9957rxrgzJ8/H1988QXWr1+PiooKAMDcuXMxdepUzJo1CyUlJU0PgOattTl5Yc7vyvf6nzYCAEJdKpq+vyREv3gro+1d+pokuh1heng4yhq5uHzOvDqO/FIJcriIWvd6My9VsnhpnSuXFeVV0pfLZZZfeub4oo4yVpLjQHmmWoNAxR4ANG1ELLGmJbX8RwnQdQSJ6knBByauC/LImWRi5k7T2maZWi5T/6P/T1T5FXlrhVwaHQBCBBzxCg8fnLjexdi3pskQMp+U695/CufCpePz0ooYsas4PXJRKc8hUwdluX2A4vXJ+MmLKUdyVEW+l6ax5jlMoncRCTROlsexSstXxzL6oFreshqcaEylWcuojvZESgOcZcuWOb4vXboU0WgUe+xh//iuWrUKfr8fI0aMyH6ECdi+fTu6dOmivr///vsYOnSoGtwAwFFHHYW6ujosXboU48eP96ynrq4OdXV16ntVVVXzBc0wDMMwTSAbU0w8ReXBm2++qT7ffvvtKC4uxsMPP4zOnTsDALZu3Yqzzz4bhxxySPNEafDNN9/grrvuwty5c9WyyspK9OjRw1Guc+fOCIVCqKysTFjX7Nmzcf311zdbrAzDMAzDtDxp31ubO3cuZs+erQY3gD2QuPHGGx0DjlSYOXMmLMtK+vroo48c22zcuBFHH300TjzxRPzmN79xrLOMx/kAQAjhuZyYPn06tm/frl7r169Pqw0MwzAM09zEhEA0wxffwWmEqqoq/O9//8Pee+/tWL5582ZUV1enVdcFF1yAU045JWmZfv36qc8bN27E+PHjMXr0aDzwwAOOcuXl5fjggw8cy7Zu3YqGhgbXnR2dcDiMcDjsXqHN23rO19LccLJ59OYgBa+bVLYXkYb069P1L43kihKJ8g55lIVHLirXfhJ580DTEZCeJijbFiqI10sf/IWyrDM3lSOcASMTx95KqBxE8NBeJOp7Xn3R1Ckk0w8kysXjVa8rL5bRz/RlifqO3r8CtkeS6g+BkGMTT42XqZUztDlAXMOitDd0GPQfflpn5sOS2zr1OpbzXcUURGMoj6ckx1XtizRuHrmorGiDY53rvOvXW6Lr0iPPWmtiNUjZgOmvJfHKh2YZ35P9JrvKqoo9+jadd+kdFJeBtawXmp1qIdMpqiwFkyekPcA54YQTcPbZZ2Pu3Lk48MADAQCLFy/GFVdcgcmTJ6dVV1lZGcrKylIqu2HDBowfPx4jRozAgw8+CJ/R4UePHo1Zs2Zh06ZN6NmzJwBbeBwOh1tUG8QwDMMwTOuT9gDnvvvuw+WXX44zzjgDDQ32/xwCgQB+/etfY86cOVkPELDv3IwbNw59+vTBbbfdhh9++EGtKy8vBwBMmDABQ4YMwZlnnok5c+bgp59+wuWXX45zzjknsyeoGIZhGKaV4aeo0iftAU6HDh1wzz33YM6cOfjmm28ghMCgQYNQVFTUHPEBsO/ErF69GqtXr0avXr0c64S8Zef3+/Hiiy/ivPPOw5gxY1BYWIjTTjtNPUbOMAzDMPkKP0WVPk02+isqKsK+++6bzVgSMnXqVEydOrXRcn369MELL7zQ/AExDMMwTAtCQuFM62hPpD3AGT9+fNKnkt54442MAsoZRCwu/qNFHsI2V/LGHKXujUcaL5RIcJxMeGgIRIV6T3wvVBj3SfXvlt/neKd19B5rcBuR+YJ2N/aF5HviaDVxsSFaNIWvOUag196uZbFvPrQ/uESlycwbnQaNLpNEwH2+TWGrV1JMcz/1touj47gm6Csu0bleRoXgTNQqvITpPue7JYXKDiGpIdqF38vEU4qLjeStworI6rT6pPDUTMiq6vL4zVC/Kz5VSO43caJHEhtH6ID44qLrIJ0fujTM/qBXm0gQayYfbQGo/4oa++EU0RDvm1bYfkBAnUO/U7StmzcmNGL0Sj5riOvN80aCbec6Z79XYmOKJUfMERk3aQ9w9ttvP8f3hoYGfPLJJ/j8888xZcqUbMXFMAzDMIwkhsyfgmpnEpz0Bzh33HGH5/KZM2dix44dGQfEMAzDMIyTaEwgmuEIJ9Pt842s3Y8844wz8Pe//z1b1TEMwzAMwzSZJouMTd5//30UFBQ0XjBPsGIRV+I5y2s+nWhm06fIp/NTLkvahvp3n3CvNJJWioZ6tco02XNt00RMHU08Tg9jM1rW4Nw2Wm8v0DU4Sq8j1wViTsNGXxJth9JVBOxtvQz/ch3RUOdckEICVVOn4nUOlJ5MaUSM8+Zh3md+V9obraxwlZX9IlLvjE2rxzLiFLFaJIL0GpY0BRTK+DHkKoOA7CsqIaXWryhpp2kcSO96M1Tscp3fMIVz/GY0otTT2k/7FqTBkWGSSDSi/09c7jMoE8iamhBdB5TQFLIFiX0r3enJoLDOPqex+vi5tegznUOfqbPy0GAZZpB0bmINRmJgvV5jW0cfJV1OUPYVWke7IS1VJP4b2pyILDxFJVhknBzTzE8IgU2bNuGjjz7CH//4x6wFxjAMwzCMTVTYr0zraE+kPcApKSlxPEXl8/mwxx574IYbbsCECROyGhzDMAzDMExTSHuA89BDDzVDGAzDMAzDJIKN/tIn7QHOgAEDsGTJEnTt2tWxfNu2bfjZz36Gb7/9NmvBtSZWpB6+up0APHwWvMobHiGR71eodV4eJmlDWolkSSzNsrHEfiVKe6OVEU3YB+knTC2HrrdRGpwk3jiq2vqIDNfpp0PL9TqsqB0n+d9QGUvN23vMjdO8uopb+m1kmsS0FRA1O1MvTMdEfU+sOaJjbCpGRJKEpy7tjdLMePjgqPqMsroPSjDo3j4Rqi9KTYf0zCEtjpfPjvIw8buTjoqYPFaW3Lc/4irjqs9IyKi28Qf0Qo53YSQFFQ5vF2/tDaFLcOrkvIM/aPdlX/0uu1qv5J3mbxn1A/k9XFScsI1ZI2Jrx2I7bf+baPU2O7ZarT8bWjkX2nLlSyTPN+lq4n1R74NGvUpnE3TV65PJeinxq6/Q6dYvSJsTbRkNDj9FlT5pP0W1du1aRD1M0erq6rBhw4asBMUwDMMwDJMJKd/Bef7559XnV199FaWlpep7NBrF66+/jn79+mU1OIZhGIZheIqqKaQ8wJk0aRIAwLIsl2NxMBhEv379MHfu3KwGxzAMwzBM7j9FNWvWLLz44ov45JNPEAqFsG3btubbWYqkPMCJyXns/v37Y8mSJSgrK2u2oHIBK1ILkD7DQ4Mj0vDBIT1OU7Q4DR88a+86EHStU/PGNOdMEgYfzTPrOXiMuWzTg0SvNwVPGNJGkC+NV44oVTbqrb2h5eRn46zfqb2JeuWgShibO2+Vn9qUQHuTktYjxxCmV4jSnCRpC/l+pOCVo7Q4StPkzL/mWU8KOquEaHVRX3Z5M6WgDxNevifGPkhXQR45Viju4WWZGpmI3SZfkmucyloB2b8oR5Vf82ZROa3Ig8WdB8sFaUSMP0x+TSBFq8gbJ6TOHzXI7cVDcTXIjUma4XSSyh7Rr95Rn0k7Ft2+xd73jm328p1Vqkws4jyH9DuQDNLiWYm8c4CE2h71W6p56cRkn/AZuiSfoVuyIkn6WxbJ9Ts49fX1OPHEEzF69Gj87W9/a7b9pEPaIuM1a9Y0RxwMwzAMw+Qp119/PYDcetI6pQHOnXfeid/+9rcoKCjAnXfembTsRRddlJXAGIZhGIaxicUEYhk+BUXbV1VVOZaHw2GEw811/671SGmAc8cdd+D0009HQUFBwmSbgK3P4QEOwzAMw2SXWBY0ODQ+6t27t2P5jBkzMHPmzMwqz0FSGuDo01LtZYpKWD41Ny780pNDnyuned5oJF4egEWeCEL3a2l8/tikYcnzjv2Y+7U/m/PI8gPNM+u5WUhzkyjHCzRdDfmJJNPIGLoapc+g/STQ3XitozxTybb3ecRgJdBjKN8dn+7xI/P00ALDg0XXjkSWvQIACAw/OmEbVOyfv27XSxoO2VfU+df0D76BBzRaXzrEpGbBCjtzwKWUVyuZz0gjHiT6cTd/b6kPkg7G0b9U/Qn8dDy0ErSNW4vjd39W+at8jv3ENL8gOlZKNxFx5kOy2+DUY5j9zDN/l4+Spxn9SdfrKO8Z+TsSM/x19JxRlNMqIOOV/covj0NU0+D4pK7GJ4+WJXM8KX8W7XdLyBxc5LNDRjvNlaMo+sVbdv3a8SXNjXqv3goAaKjepcpEap151uiaVpo/j98X+o3wBQPOd48ce/SbRvWSfoe2AQB/UUfnNnSNkx9ODuT1airr169HSUmJ+p7o7s3MmTPV1FMilixZgpEjR2Y1vmyRtgbnhhtuwOWXX44OHTo4ltfU1GDOnDm47rrrshYcwzAMwzDZFRmXlJQ4BjiJuOCCC3DKKackLZPL9jBpD3Cuv/56nHvuua4Bzq5du3D99dfzAIdhGIZhskxUCJejdVPqSIeysrK8fmI67QGOEMKRbJP49NNP0aVLl6wExTAMwzBM/rBu3Tr89NNPWLduHaLRKD755BMAwKBBg9CxY8fkGzcTKQ9wOnfuDMuyYFkWdt99d8cgJxqNYseOHTj33HObJchWwfIpXY3S3gTjWgdaJgJSpxKRc8xyPtan5SehOXHSawgj1YVLXwDN04S0Mn4PT4fG8rT4tP2QjkDlXpE+HR75qpJpb1R1QWfXiSGScH1j9el+NUo/Q3PuRk4qHdJG+ENBR/20nObV7WUexw9aTq4k/ipKD2XWpddHeg00JIw3tupdx3ff7mMS7jMRNc/GRf66d4szFvd2FDP1vaSaFrWNL+E6s17ESIMl+5dcLzSLELUsHa8cU4ujlmt1NBKn/t8x0oK4tD3a+adrT3lPGRonx/Wr8l/Jdoel9ihEWp+4lkR55ai45TmRehvL4Vcjj6e/Rr7LWCjflN/DF6uhRr7L36IkWirak99vay+iLjVVZijtDXneyHxTQFx7Qz44DVW29qZhZ018e/mbQL8D/pCzU3vmupPXP+lz/LTc8TsgfxuMeL18dvyk0zL1eoYPTrIcZdkkm09RNQfXXXcdHn74YfV9+PDhAIA333wT48aNa7b9JiPlAc68efMghMCvfvUrXH/99Y5UDaFQCP369cPo0aObJUiGYRiGac9EkQUn46xE4s1DDz2UUx44QBoDHErP0L9/fxx00EEIBt3/g2AYhmEYhskF0tbgjB07Vn2uqalBg2GLnooym2EYhmGY1Mn1VA25SNoDnF27duHKK6/EE088gS1btrjWR6PNeROsBbF8ytNEhOwnxkSwUK2OyZl0n5CaBtPTwguapzd8O3S/GpVHJZG+IhkxQ4tQp8VCeYWoXjWvrJ0v+dkXpFi8dSt6WeUvY3hQeHmxmPmeqIyu1yE9DtUXrXXnyjLxFzj9SrziVv4nZk4vaodH/qL695+yqzHOhd4OtS+Z4yqWrP8HQ46v6fjteKFyRbnOpcfdVaXHMH2VqE8myVtGfdrL74P0aeQDJb2HXJoZeOVKM3yc9LJm3PTuoVczMc+/nm/NfDxC6WkatH5GXkx0PdDyJNeg2gf1J/Lecei1DD2dvO591Cd1vxr6HJDXQ1Rq/sjjJqj5lpC2p97WsPjqdzjbofth0R84eb58cttOHYsSti0dXNobyjdVFf9bEd1ZDQCo22q/R3bZmqGGnXGvHML0tKHfhUCHAldZU9tlavQc9YXoeDq38Wt+MJb0u1F6KvodoHND5zGFPpkNWuMpqnwnbXXUFVdcgTfeeAP33HMPwuEw/vrXv+L6669HRUUFHnnkkeaIkWEYhmHaNbGYQDTDV3OKjHORtO/g/Pe//8UjjzyCcePG4Ve/+hUOOeQQDBo0CH379sW//vUvnH766c0RJ8MwDMMwTMqkfQfnp59+Qv/+/QHYepuffvoJAHDwwQfj7bffzm50DMMwDMNkfPeGXu2JtAc4AwYMwNq1awEAQ4YMwRNPPAHAvrPTqVOnbMbGMAzDMAx4gNMU0p6iOvvss/Hpp59i7NixmD59Oo499ljcddddiEQiuP3225sjxlYhFuqAWLij+gwAdRFdkGZ3lJCfRJpOca0zMacU4oZleguVkI9ExnFhmzAFbCapiLjJbFBPimjUp1qiCWaVCVzMbX4GQJma2btwGvCZ4mjdSM5VL0GmW/64YNAKOM21rA7OJJ6WhyGbEm+GC51lPAz5EpnXOYzvqCyJPw1hsqWJeL1MGh1owkfR4BRMkzC14YNn1bLgqEkA4gJnZUxXK0WruiBdiVONGJQhWZL/v5hmdh4mc74BzgR6sdWLXWWFmUBS9j1lWpcsOawSAZM5oibeprgihvjb4/zFRcXO9nouN00GaZ/mfrR1SkgvY7I0sTidU8vwiVPXx67qeDwkUqVrhMqqePX2yw8yq6ZIJvQ2kj5aDdJckB580MX9wmlWFz8ymYmMI5/Ot6uvtU37lLhYvkd27FBl6cEBeidxcUR7oIB+u+gBAhIHBwplslBNHEzJepWJn7wmSUhMddjL5LEPu0XKcmP1Uf2WkdhYvqvkyzIRqgikn0yZaRnSvoNzySWX4KKLLgIAjB8/Hl999RX+/e9/4+OPP8bFF1+c9QBN6urqsN9++8GyLGUFTaxbtw7HHXccioqKUFZWhosuugj19Y0/hcMwDMMwuUw0lo27OK3dipYlY4/pPn36YPLkyejSpQt+9atfZSOmpFx55ZWoqKhwLY9Gozj22GOxc+dOLFq0CI899hieeuopXHbZZc0eE8MwDMM0JzxFlT5ZS6Lx008/OfJQNAcvv/wy5s+fj9tuu821bv78+fjiiy/wz3/+E8OHD8cRRxyBuXPn4i9/+QuqqqqaNS6GYRiGYXKLtDU4rcX//vc/nHPOOXj22WfRoUMH1/r3338fQ4cOddzdOeqoo1BXV4elS5di/Pjxae1PBAsRDdr7qW2w7+tFtNGv32fPjZMzpJ/0BKSR0Oe9KXFeTM7hG/P9Qtd4UBJPv6zHzNyu6xTM+Xil7XHPCVMtriRzugka6RPkMmWCZhifAc4EmXbYlPDSw2TPMDZ0JXzUy8pDIaJSV0PJGz0SB7rM+0hX4qEDIh2NlwGhXa9HssWgs95kKG2PeQz1MjIeZSrocVxJj6P0KbStaTbmiD1BkkkvDZLf0CIlSRgY+/Yjs0J3WZfOhZJ6yvOnlTVN9uIaNHluvJJwJkoYmURfZPYrx37N+jyMHkkb5dJ4kfbG0Qel+Sed2zpKdCnb4ri2SStmnEuP46q0eCmcJ9KCWNTnSA/o8TsAv9GXvco0gmnmBwAxqTWKbt0MAKirrAQA1GzZbq/3SGZJujrS3sTqtd/FkFP35jO0broGx9TckF6HTPt0vY3VwXbaN/VrnklX5fnxKcM/+1zHZL9QWhx/y/wZzcYdmPZ2BycvBjhCCEydOhXnnnsuRo4cqZ7i0qmsrESPHj0cyzp37oxQKIRKebF5UVdXh7q6eMZfvtvDMAzD5BqxLAxw2pvRX8vkeU/AzJkzYVlW0tdHH32Eu+66C1VVVZg+fXrS+izzbgfswZHXcmL27NkoLS1Vr969e2fcLoZhGIZhWpeU7+BMnjw56fpt27alvfMLLrgAp5xyStIy/fr1w4033ojFixcjrOUJAYCRI0fi9NNPx8MPP4zy8nJ88MEHjvVbt25FQ0OD686OzvTp03HppZeq71VVVTzIYRiGYXKKqMjCFFU7y0WV8gCntLS00fVnnXVWWjsvKytDWVlZo+XuvPNO3Hjjjer7xo0bcdRRR+Hxxx/HqFGjAACjR4/GrFmzsGnTJvTs2ROALTwOh8MYMWJEwrrD4bBr4AQAIhBGfdStvUmI4QciQvHEnCJK2ht7Koy0My4PEWhz73TXifQ7NM+r6xQM/wvlHeM1r066AdP/xJHoU3quJEoKqjfX1EskS8xplknm5WPum/DSYhj7VD4lcs7dkSTT9LIx/Wu89CpmEkQvDxJDE6GOPekttHUqGSC1X+l2NC8iOocyhvA4O/WJ7pXjIqH/izvRY6LzJDy0HabGSxiJNQGtX5rrfPI4RLQjQAk5/SS0MjxzUji+rlj0MrR9soS3dF0ZP/R6Wy1zWSKtGxA/dwmS4/qCWr8LOJPCKp2dqd/T20DXv9km/fqTbRJB6ctCvz20XKvXpRuRx7GuehsAIFzcCYmIrv3EEW+sPp4ck7Q3u9atBwBUrbVlAbVSg6MTLLLj9BeQp42hyQMQkN418bLy2Pkpqav7HKsydJyldsbXoViVUZ42piaPrnntfCmNlPQpi8njSudNHe+Ah4dSM8AanPRJeYDz4IMPNmccSenTp4/je8eOtgHfwIED0atXLwDAhAkTMGTIEJx55pmYM2cOfvrpJ1x++eU455xzUFJS0uIxMwzDMEy24AFO+rSqBieb+P1+vPjiiygoKMCYMWNw0kknYdKkSZ6PlDMMwzAM07bJi6eoTPr16wfhMZfYp08fvPDCC60QEcMwDMM0H5GYgD/DOzApyS3aEHk5wGlpvJ7CMvPIxOTcto80FPo8vellQXWQFkfTzKjtKO8VaQ2iNGfeSO4jj/rtACnnjvTKIC+eiEcqCzNvE9w5j8xbf2ruOll8Mfdce6J9q3rNGLzKqvlzp76I5tv1ZYn24+VBQlokpddIphGhdX7SWbjLWmZuIMrBBG0O39AaNSx53t62wPZkSnbshKFtSppfS7aR8k1F13/mrs/87qVtUcfK1OC49TounyYjZ5ajflOD4nP+VLk8oBLFZ8aQqH/q9RnnO37e7HcrqmncDN2bRfZVpN/Q92d62QRIDxNyLtfboo6nEbffrUESQenTQjZh9Puie9/I36moT77LthZ3iGsGEyGCthbFV7/Tva7BmV8qWmvrDSO1bj2gL2gvIw2OT3re6LnzSHsT7mTrZ/yFsv/L3xmHH5ThPea6/vXfrQJnXikzN51+rMifTP0eUO4peRxUH/e1zJ9RnqJKnzYzRcUwDMMwDEPwHRyGYRiGyXHY6C99eIDDMAzDMDlOVIiMfWzYB4dRBHxOpY2uxQn57c+UI8mSuWw8PWiMeXSay1VaBN2Dw9Qn0Hw/zbk75ogN/x5LOPen1UseLJRzh/LtOPIsxUhz4O1TY4U8PGioHlO346V1iEmtQArpb8xcMZ5lVG4fqR8IOOfnrXA8Z5mp4dAqsd81vQrpH5TOg86FlUz/5Jzt9foZsShHEM3tU7xabjJzO5cfkJcPEJWlJnrl7fI5dRqkI4ht+touEPDIcUUxJfOVoX2Yfi1e/SAq22mcC+GlMzHrM/L9CIe+zEMblTBeQyPk5XVD10EivyqfppmLUs4x2V8jAce2XtCxV31SeSd56GrIM8jQ8elaEdL/xTeVmhH5e6X/r78+an+ua4g61hW70/spdtXYfjeBDp1lA+y2+bRcVJSvKVRiv5N2hnJGxbTcUfEytt2HX+aQIh8bACjsZu/LV2y/m7nYfJq+TnlOGTnklE7H76Hfc/VXtxeRy6cobMdrepW1VC4qJn34zDAMwzBMjsMi4/ThAQ7DMAzD5Dg8wEkffoqKYRiGYZg2B9/BSYAlYghIyY0/QJoMbfQrP1tRQ3uTbO5dztXSu/LT0HMRkfdG1PCn8cpJY2hllBdHQ13iWIz5aodOg7xsEmgadJ8V0umQn05S7Y2Bua1nmVQ0ONL3Rmlv6F0ud+huSPdiehFFDc0TAAsJzqFH/huVV8rMg+QRtyrip5xZHt4jMr8PaW8or5a3pilBTi+Xj1FcewPDB8eV+8yLRPoarZ6EOZP8Hn41pu4lyTXjyvFm6qMAwDJEXWZ9lke8xjo9bpc/lanFiml6LdJV0bWnQqC2ab8ZfqcGT+lrPDROKp6A0ZcpXk1/R+Zt9J9zkg42RNy59EiDUyvf6XsyqurtflZa2IkaZ+9HKxOUfZk0M+FyOzdVVOa4In8cQMsZZWpnNF2Nv3M3uz7yrZHXgVVA3jaJ/3TRsYqZ3lfJoPxSybyYVA414bm+ueE7OOnDAxyGYRiGyXGiIoZoOoL6BHW0J3iAwzAMwzA5DvvgpA9rcBiGYRiGaXPwHRyGYRiGyXGiMQEfa3DSggc4CbAi9fDVVTde0DDmi4uNtY6kkjY65z9JoJxMZGkZyTB14ahLDEyiPRIo6+I3qoeEvVRWF6I2YsDn3LczwaVbvJzk5iCZDhpJJ4G4kFklijSFiB7CQarHV1Ril5EGf0IX5tL58Tm/xyvxuPATCW+95sGV4NByxO25DxLKSsM3xzmQYk21zDQp80igKlTbjG08BJPCFBeTiZlnIk1jGW2rJ28ks7MEok+9XmEcT5/Z/7VzQNeRpwkgACE0UbBaaJ5Tj/NEAuIEYmN9n6peZcQn1+s/m0EpGKaHA+R1pq5t/WEBdfycSRzjhpKaaNxvHHujfzVo3ZX+aJFemP6GkWutLiSukcLjmgb7vTaS+Lfn1ZW2ULhfZ7uNfrnv4kJp+Kf1AytsG/v5OnYBAAR2q7HbWLvDftdMAenhAiEF9b6OnRzfAcBXbC9DoX1NxwpLncF59ddk4vJEUJ+ksl7CffP8UP+I1Ml3j4TFzUAkBlgZZxPPUjB5Ak9RMQzDMAzT5uA7OAzDMAyT4/AUVfrwAIdhGIZhchwe4KQPD3ASIWLxuVdpBudIpGlqNoSpwYm51tEcvFVvz08rrYzDtMxIlEnzvl66CtIpGIZ5lEhTN+bT57ftagwjOcQTcTZmIKeXUUZ/hjGfMg3Ut6NtVGJSuZ+QFgMlL6VtvOozUG2hetQx1M6Rof/IKEGeqY+AR2JGrzjpMCQxIFP1mCu8jOOIRNto50slEUykvfHSDJlGZwEjSaQenmxcTMZHsUQ1/QedAkEmmTI7KB1Ovy9+jlQSW+O4Kr2Kdjop2aRlGgfSfpIlwPUgnvA24iyr3j2EDIYuS8WrHSvX8aMEqKQU0PokGVAK00BQbhsIxA9A1DD6I2O/hphbg6OM/qQYY1eD81q/d/Fa9bl7kd1nSK+zS5k22vGGAnFjvpBMROmTCTljpE+p32W/N9Sosv6a7XaZHdsc+yZzPwCIdehkt1eaIsY6lskDINuiJyg2z7uZsNgDpZ+JOs+xp+GjXOarrbK/B8l8k/985jp8hhiGYRgmx+E7OOnDAxyGYRiGyXHY6C99+CkqhmEYhmHaHHwHJwFW/U5YMmelp1Ym0XZURtPF0Pyz8n+os/Uwwkv3kGDeWNfKxMtKTYvU2iidjekzo5VVmpYCj2SbhSFnWaN+RzykiTESXHr51BCCvHMMTY4jBnM/ZtJNPTEp+b+YCSTJZ0TX2RjJCs1z6aUrcc3tqxVuzxTR2DZaGTN5n75vl17EjNeR8DWWdJ+ONtHxM7UGybRIRoJLpWnR9qMSG5raG0MXAgB+shyRYhxfkvye5GEk4PS/icScx1vHT32brIikF43w0riZ/idRTcdmHk/SaSTTKam4SV9DfkNaQlUzoSPF6+WZY6C8h+Q2+v/k6SNpm5QPjvzgsOQyrjAq+8TyjQCADsH48f7lPhUAgKXrtwEAaqP28aDzGInF66ojKYvUYvl9thdVsMjW6YSidfG2yGPjozbJ4xwLxY+VCNnbxQpsH5wa2NeKX+48oHUe5adEui3VuCTHk/RQCDtXaNeD6jf0OyP346utdsTt27Uj4X6ySTQmMvbB4SkqhmEYhmFyCiEERIYDFOH1kEIbhgc4DMMwDJPjxGIiYw0Na3AYhmEYhmHyHL6DkwBfbTV8YTmbS7oYjzl4YWg7VE4a7cgqfQb5dUjNCGlmlP+MZyBGjhQjJ48en8s7BnE9jNLjmG3R9S8+b32DWS8Qz6MEmssOGPoCR3zSyyPqzNPl6U+ifE7cvhQAoEsyVL4i8xz4Db2NF8l8UBJpWQztDACVi0iVVR8S50Gi3DWqbYF4Th9hFTjXkU6HfFX0Y0aaAy/vJcDh05HMn8fE5elj6lZ0ZNmALBOhdFuybCjJbv1JRDh0Jz2W4Ja615ZUlnQm6php5yvRcXCUMfdh+t7odSTQ5cX9jNx6rUR6KIdnjlkf9XVZb9AX32/EsmOP61JIe+P8rreqOBSQ9fjku8xxpf0P/721W+yyYaknkqvqY3T9xsua8VLeKoqpYyiudSko6mpvIzU36nrQ2x+yNTy1Pns78u0hK54C7feVrh7SSlp18bxXACCCms7G+P2j3ySlk9TzrBn1unR6pl9SMyOEyHiKiaeoGIZhGIbJKUQsCxocnqJiGIZhGIbJb/gODsMwDMPkOCwyTh8e4CRAhDqoPChqmT73bsyjxwt55AUKOrUSaq5Y6lcoLwoAV14p1/70XTXIelSeJqnXkL41ll/bxlhGeZvIbwIABJU38+r4nd4egOb/ovQphq+IQ5tAnhO0sVODY0WcebIcMXh5j1A1Mb+jjFsz4qGZMnUQXvUa3isuPZCmu1F+GqSjoHNpHhfEfTRiYaeniUN7kCAuyi9k+TSNgJHDyTJziDn8emSZVObgTT+WBN5B+roYTG8bqYfR2u+jHqB8Zui4Ss2MFlrU8HRJph0IGbnBfEL6N/koP1b8mFIMwtQTCS0HEfk/Ue44U+PkmWeOtHKJc5IpXU5MLUjYpvhGzmOvVDVaCKGg08uFjl3AZ79HdM8cKiM/1EelRiZMGkKPEMhXSO496i07cpS15HGmvumQW1EfN32BonEtC/1GhuX1FQrb2/ga7N8Kq0HPC+jMQaW0PVKLQ7n/7C+0L2NbM5egHpeZB40gvY6Hbqc50NMjZlJHe4KnqBiGYRiGaXPk1QDnxRdfxKhRo1BYWIiysjJMnjzZsX7dunU47rjjUFRUhLKyMlx00UWor0/sZskwDMMw+QA9RZXpqz2RN1NUTz31FM455xzcdNNNOOywwyCEwGeffabWR6NRHHvssejWrRsWLVqELVu2YMqUKRBC4K677mrFyBmGYRgmM1iDkz55McCJRCK4+OKLMWfOHPz6179Wy/fYYw/1ef78+fjiiy+wfv16VFTYOVTmzp2LqVOnYtasWSgpKUlrn7FgQdzvJKmWI4HeQ5/sJD2JcOYeskjjk8wPRqLmfzV9jBUucqxTOhAP3xLL0LLEaP5bnz+m8sYoXx0HvQ6Vayd5jidn7E4tg4iRL1BcQ0B+FBYMrxizLnhog0xdkKZnEYEE8+SpTEobeiAvfYWvfqczBhWwpqkx5vmFdPCwdK2M1P+YPjAN0cQ/TL4Y6R3sesh7xKFpiSXXtOi5ffykDRNOzYzSf2iaoUhcWKX9q+e8ie9HaYXkjeO6iLtMYyRJX6X0NZap8fHYSumV6JrRdEdKE5YoB5mXD07M+3p1ViE1c4k8iaIevwN+47qic6GdA7+W5wmIW0UF5fUhtPxSdEqjql/Zx4a8bXR9jcptlWBb/agWBu04Q/Ich2TOqKDMB2XpXl+mpomuGel9A8ClkVP5pki3pmuoEv32Kn1UwL3O2FZ4/QYb3mYJz3GAZwlylbyYovr444+xYcMG+Hw+DB8+HD179sQxxxyDFStWqDLvv/8+hg4dqgY3AHDUUUehrq4OS5cuTVh3XV0dqqqqHC+GYRiGySXIByfTV3Owdu1a/PrXv0b//v1RWFiIgQMHYsaMGa0uEcmLAc63334LAJg5cyauvfZavPDCC+jcuTPGjh2Ln376CQBQWVmJHj16OLbr3LkzQqEQKisrE9Y9e/ZslJaWqlfv3r2bryEMwzAM0xSyMbhppgHOV199hVgshvvvvx8rVqzAHXfcgfvuuw9/+MMfmmV/qdKqA5yZM2fCsqykr48++ggxeWvwmmuuwS9/+UuMGDECDz74ICzLwn/+8x9Vn+VhJS+E8FxOTJ8+Hdu3b1ev9evXZ7+hDMMwDJMBMSGy8moOjj76aDz44IOYMGECBgwYgOOPPx6XX345nn766WbZX6q0qgbnggsuwCmnnJK0TL9+/VBdXQ0AGDJkiFoeDocxYMAArFu3DgBQXl6ODz74wLHt1q1b0dDQ4LqzoxMOhxEOh90rLJ8794iuQTHnfc35eX2ulxbRMpobjyXRq0iSaVBUfiqXP0MQiYjnOGo8X5NLX6MNFBNpb+KeIVqemoQ+EoZmBkAs3NEuK3UQlKfGFX8jywCkdr7UjrU5eNJCqHxdhh+GIx+U9PKp3+Wsnubvde2PMZfvlSnIR/P6pNOSmgOfx/+8aOAeNHI6Ke2Mdr6oRwhZNkKhpPJ7Z/gAxTT1BekxpORCaYVI0+F3/OdC6jzkPmsjlDMpcRDmf05IkqK3uSHmjIESwdG+fZo/kGXqMwydkV2okf/3aedf9U/Dg8gydEuAh2eUqs/wL9JjMLUhXseKctxR35HXttqL+TuGuM6K3n1SfNOghUYeRnRcSUNFx9Xr/41xDyE7/nqpM4tZ8euATp1Zj9405acjyLfI2w/Isc44b6LQI6+Uyh/l/O2M2zcl0eQZOkYh+5lX/qpcx5RiJPw7mAHbt29Hly5dslpnurTqAKesrAxlZWWNlhsxYgTC4TBWrlyJgw8+GADQ0NCAtWvXom/fvgCA0aNHY9asWdi0aRN69uwJwBYeh8NhjBgxovkawTAMwzDNjBBZyEUlB4ymFGPGjBmYOXNmRnXrfPPNN7jrrrswd+7crNXZFPLiKaqSkhKce+65mDFjBnr37o2+fftizpw5AIATTzwRADBhwgQMGTIEZ555JubMmYOffvoJl19+Oc4555y0n6BiGIZhmFwim8k2169f7/i7mOjuzcyZM3H99dcnrXPJkiUYOXKk+r5x40YcffTROPHEE/Gb3/wmo3gzJS8GOAAwZ84cBAIBnHnmmaipqcGoUaPwxhtvoHPnzgAAv9+PF198Eeeddx7GjBmDwsJCnHbaabjttttaOXKGYRiGyR1KSkpS+o9/qjISYuPGjRg/fjxGjx6NBx54INMwMyZvBjjBYBC33XZb0gFLnz598MILL7RgVAzDMAzT/MRigJWx0V965VOVkQDAhg0bMH78ePUQkC9JHsGWIm8GOC2NCITjSRVJmKYbsiUR4NoFPDpiIvM+vQi9JxAXC32bRII4D1FhorIOIbXZFjPJpCMgZ6JEZWiXLDFjwng1Uy/DgE2JdNV3d6JL8osjUSQlW7Q8zLso4aVLvKknvJSiQcsUECc5p177AuBMpEqJOQ2Bt24yhxiZNTbIsraANOS3t032FAQJfkngq5elT36Z4JHEuzQnX6PpXH2yTNjv7NNxE7/4eayPOs3gyChuV4P7XAeM5KBmU/RLiHocCVLjCR8h2xYvS8kk6QxQNSSs9mtmiyTaJjGsugZ1hzuVBNYpIE50jgHtHCZLEmsae1LfMYw67e2NaziR6SA0Y0KVSDLgqNeRzJaSgcrryidjCpKBnma2J/xGIllKEkxt1WKIhZymo9Qh1OMOui8fXYPUB+l6c/y2Gc31SnRqrjN/r7x+i5LUA8DztzPhQwy0PslDHdkkG6kWmitVw8aNGzFu3Dj06dMHt912G3744Qe1rry8vFn2mQo8wGEYhmEYpsnMnz8fq1evxurVq9GrVy/HutbMf9X695AYhmEYhkmKiGXn1RxMnTo1J5N78h0chmEYhslxYjGRBQ0OJ9tkYOslYsFCANqcscO8z6k5STZPG59b9jvqEfB7b6DV6yqrj4iNuWeXDkjXDBlme+Gi4oS7rt5V4/iezNjLTAoZj06bnycvNZ8936+SQcr1QcSPa9z8ztDB1NnJLH07t8T3HbbbUNCtDwCgrspO2xEucZtL1e2slsE49RVBua3O9p12+8OUY1SWFQX2/nRjrwby1DPq6Nih0FUvsWmb3ZaOIWdyTB2f1O6QRor0NVHPHyhnIk0y0tOrpX0oTQtt6XFu6X9ddUaCT5Lk6CFYhkubzLmIDkH39eCxKCHUI2iTZG7kMY/26sstR6+k9jqvW6chn9SjSH2KSo6q9Dqa0R+tI2NGU1+jmwJKLZepAxORBlcMVoA6X8AZZxJtm2kOKaSGMBb2uNap/UYZPYml0inJvrhVSFNAy46tOKxpm+j699L/meEaxoS6Ti1eIXU2QyuVTEOjEiAnMTF1GZImScxpHPO4SaqhuWkpDU4WHxNvL/AUFcMwDMMwbQ6+g8MwDMMwOQ7fwUkfHuAwDMMwTI4TEyKeZy6DOtoTPMBJhvJecdtY01x1JObUP5iJ6eyVsrpEfg0e+3QlrfTyWzHqiUrfig6Fca+YplCcRD+SCNKtmBoPAAiTREhqWcIdOmUQXWK8tDdqHWmOkmiPiNIio/2FvbwLAmjKke7ZqSiFUs6aRa3tZeKl1zExdVFAvJ/uVAku4ahPt7yhvhtIsCufaVKCePJL0soEYs7EmuY+HPF6LLOa8DtsXoNe9dMxsczjqGsvTD2GMDQWWnJM0uW4fKo8tCIWaXekBieZXidm6EhM3y1P7xXTeyeZFsXAv+MHV7zUzi2Fto9JSB6zwiBpU5r4x9L8/VPH2cPjBs5VSZNiJvKk0stGnetcyY11JzzDg8vlq5PCcWVaFx7gMAzDMEyOw1NU6cMDHIZhGIbJcbKZTby9wPfYGIZhGIZpc/AdnAQUdChK6HECxOf5Vd4bY05fHygXdnBqLnbV2HoKL2uPwoLM9DOthUu34kn62h7Gpin9orYm7mfkD5DnjlMb4/U/OvLaSUHuo8qQboc28fudXkf6OsoZZgoenVqZ1ParYy7yJfFvMnH4vxj/7xOm9sKv5YFyaW+cget6EOWflEiv44WhU3HlWdK2T+jtkiSHFmRMscJSAEC4Y6laVbvL9msq7+CtGdP7V1bw0LSoY6ZswJyaJ3shtTPqXmfid/7Jc3UzPScd5Y6TyyjnHW1DPabBapk/oyImMjbq4ykqhmEYhmFyilxOtpmr8BQVwzAMwzBtDr6DwzAMwzA5Dj9FlT48wElCU/xgUiFTnxqGSYWCwsb7745d5F8U16CY2phkeh2Ctg64jEu8bhJ7a3x0HYy5KzMmn4ewxpWKTRiiiRQRRkWqHi+NiCpkrCOvF7g9tOIbZ9dPxaVPIU2KrsFJoPuptWytS72Wh85vJcmV11ok8xDzmdqjqPO7Xo2RX8rL66wggfbIJCZziTU3sZhoXJyWSh3tCB7gMAzDMEyOI2JRCM1ksql1tCdYg8MwDMMwTJuD7+AwDMMwTI7Dd3DShwc4DNMO2LZjFwCgQPrTkKeHl1eM39CgxJUYKeTBopL0wcuTJIHmRM9t5Pa0SbSn5IuaAsVhanHM73ZZ+U75paL1srDd7lhBqWubVEhFP5VNQi26tyyg5/hKkLcvmbYpVFrWXJE1GyIWy8IAJ4lHUBuEp6gYhmEYhmlz8B0chmEYhslxRDQKEc3wDk6G2+cbPMBhGIZhmBxHiCxocET7GuDwFBXDMAzDMG0OvoPDMO0AEheTUJb+Z0O6WV3g6xIGK3M1KUhO9r9IKutLbBKXcHsvIz3aZxKTwZiUJLOBZstQU2snC9aV6cnOT0tBYuN8FBCnAj9FlT48wGEYhmGYHIcHOOnDU1QMwzAMw7Q5+A4OwzAMw+Q4fAcnfXiAwzDtgJY2jmPaLoUFrHVqDdjoL33yZopq1apVmDhxIsrKylBSUoIxY8bgzTffdJRZt24djjvuOBQVFaGsrAwXXXQR6uvrWylihmEYhskOsVg0K6/2RN4McI499lhEIhG88cYbWLp0Kfbbbz/84he/QGVlJQAgGo3i2GOPxc6dO7Fo0SI89thjeOqpp3DZZZe1cuQMwzAMw7Q0eTFF9eOPP2L16tX4+9//jn333RcAcPPNN+Oee+7BihUrUF5ejvnz5+OLL77A+vXrUVFRAQCYO3cupk6dilmzZqGkpKQ1m8AwDMMwTYY1OOmTF3dwunbtir322guPPPIIdu7ciUgkgvvvvx89evTAiBEjAADvv/8+hg4dqgY3AHDUUUehrq4OS5cuba3QGYZhGCZjaICT6as9kRd3cCzLwoIFCzBx4kQUFxfD5/OhR48eeOWVV9CpUycAQGVlJXr06OHYrnPnzgiFQmoay4u6ujrU1dWp71VVVc3SBoZhGIZhWo5WvYMzc+ZMWJaV9PXRRx9BCIHzzjsP3bt3xzvvvIMPP/wQEydOxC9+8Qts2rRJ1WdpzpqEEMJzOTF79myUlpaqV+/evZulrQzDMAzTZGSyzUxe4GSbLccFF1yAU045JWmZfv364Y033sALL7yArVu3Ki3NPffcgwULFuDhhx/G1VdfjfLycnzwwQeObbdu3YqGhgbXnR2d6dOn49JLL1Xfq6qqeJDDMAzD5BRCRAFOtpkWrTrAKSsrQ1lZ43lDdu3aBQDw+Zw3nHw+H2Lyuf7Ro0dj1qxZ2LRpE3r27AkAmD9/PsLhsNLpeBEOhxEOh5vaBIZhGIZhcpC8EBmPHj0anTt3xpQpU/Dpp59i1apVuOKKK7BmzRoce+yxAIAJEyZgyJAhOPPMM7Fs2TK8/vrruPzyy3HOOefwE1QMwzBMXkNGf5m92Ogv5ygrK8Mrr7yCHTt24LDDDsPIkSOxaNEiPPfccxg2bBgAwO/348UXX0RBQQHGjBmDk046CZMmTcJtt93WytEzDMMwTGbwU1TpkxdPUQHAyJEj8eqrryYt06dPH7zwwgstFBHDMAzDMLlK3gxwGIZhGKa9ImIxIMMppvY2RcUDHIZhGIbJcUQsC09R8RQVwzAMwzC5BA9w0icvRMYMwzAMwzDpwHdwGIZhGCbHicWisPgOTlrwAIdhGIZhchwRjQFWhgOcaPsSGfMUFcMwDMMwbQ6+g8MwDMMwOQ7nokofHuAwDMMwTI4jYtHMp6jamQaHp6gYhmEYhmlz8B0chmEYhslx+A5O+vAAh2EYhmFyHB7gpA8PcAyEEACAqqqqVo6EYRiGyXXobwX97Wg2og3IeA/RhmxEkjfwAMeguroaANC7d+9WjoRhGIbJF6qrq1FaWpr1ekOhEMrLy1H5xRNZqa+8vByhUCgrdeU6lmj2YWd+EYvFsHLlSgwZMgTr169HSUlJa4eUVaqqqtC7d29uW57BbctP2nLbgLbdvlTbJoRAdXU1Kioq4PM1z3M7tbW1qK+vz0pdoVAIBQUFWakr1+E7OAY+nw+77bYbAKCkpKTNXbQEty0/4bblJ225bUDbbl8qbWuOOzc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      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "(ds_anom_resample.sel(time='2015-01-01', method='nearest') -\n",
    " ds_anom_resample.sel(time='1965-01-01', method='nearest')).sst.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Rolling\n",
    "\n",
    "Rolling is also similar to pandas.\n",
    "It does not change the length of the arrays.\n",
    "Instead, it allows a moving window to be applied to the data at each point."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
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       "}\n",
       "\n",
       ".xr-array-wrap {\n",
       "  grid-column: 1 / -1;\n",
       "  display: grid;\n",
       "  grid-template-columns: 20px auto;\n",
       "}\n",
       "\n",
       ".xr-array-wrap > label {\n",
       "  grid-column: 1;\n",
       "  vertical-align: top;\n",
       "}\n",
       "\n",
       ".xr-preview {\n",
       "  color: var(--xr-font-color3);\n",
       "}\n",
       "\n",
       ".xr-array-preview,\n",
       ".xr-array-data {\n",
       "  padding: 0 5px !important;\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-array-data,\n",
       ".xr-array-in:checked ~ .xr-array-preview {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-array-in:checked ~ .xr-array-data,\n",
       ".xr-array-preview {\n",
       "  display: inline-block;\n",
       "}\n",
       "\n",
       ".xr-dim-list {\n",
       "  display: inline-block !important;\n",
       "  list-style: none;\n",
       "  padding: 0 !important;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list li {\n",
       "  display: inline-block;\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list:before {\n",
       "  content: '(';\n",
       "}\n",
       "\n",
       ".xr-dim-list:after {\n",
       "  content: ')';\n",
       "}\n",
       "\n",
       ".xr-dim-list li:not(:last-child):after {\n",
       "  content: ',';\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-has-index {\n",
       "  font-weight: bold;\n",
       "}\n",
       "\n",
       ".xr-var-list,\n",
       ".xr-var-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-var-item > div,\n",
       ".xr-var-item label,\n",
       ".xr-var-item > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-even);\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-var-item > .xr-var-name:hover span {\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-var-list > li:nth-child(odd) > div,\n",
       ".xr-var-list > li:nth-child(odd) > label,\n",
       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-odd);\n",
       "}\n",
       "\n",
       ".xr-var-name {\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-var-dims {\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-var-dtype {\n",
       "  grid-column: 3;\n",
       "  text-align: right;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-preview {\n",
       "  grid-column: 4;\n",
       "}\n",
       "\n",
       ".xr-index-preview {\n",
       "  grid-column: 2 / 5;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-name,\n",
       ".xr-var-dims,\n",
       ".xr-var-dtype,\n",
       ".xr-preview,\n",
       ".xr-attrs dt {\n",
       "  white-space: nowrap;\n",
       "  overflow: hidden;\n",
       "  text-overflow: ellipsis;\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-var-name:hover,\n",
       ".xr-var-dims:hover,\n",
       ".xr-var-dtype:hover,\n",
       ".xr-attrs dt:hover {\n",
       "  overflow: visible;\n",
       "  width: auto;\n",
       "  z-index: 1;\n",
       "}\n",
       "\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-data {\n",
       "  display: none;\n",
       "  background-color: var(--xr-background-color) !important;\n",
       "  padding-bottom: 5px !important;\n",
       "}\n",
       "\n",
       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
       ".xr-var-data-in:checked ~ .xr-var-data,\n",
       ".xr-index-data-in:checked ~ .xr-index-data {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       ".xr-var-data > table {\n",
       "  float: right;\n",
       "}\n",
       "\n",
       ".xr-var-name span,\n",
       ".xr-var-data,\n",
       ".xr-index-name div,\n",
       ".xr-index-data,\n",
       ".xr-attrs {\n",
       "  padding-left: 25px !important;\n",
       "}\n",
       "\n",
       ".xr-attrs,\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-data {\n",
       "  grid-column: 1 / -1;\n",
       "}\n",
       "\n",
       "dl.xr-attrs {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  display: grid;\n",
       "  grid-template-columns: 125px auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt,\n",
       ".xr-attrs dd {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  float: left;\n",
       "  padding-right: 10px;\n",
       "  width: auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt {\n",
       "  font-weight: normal;\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-attrs dt:hover span {\n",
       "  display: inline-block;\n",
       "  background: var(--xr-background-color);\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-attrs dd {\n",
       "  grid-column: 2;\n",
       "  white-space: pre-wrap;\n",
       "  word-break: break-all;\n",
       "}\n",
       "\n",
       ".xr-icon-database,\n",
       ".xr-icon-file-text2,\n",
       ".xr-no-icon {\n",
       "  display: inline-block;\n",
       "  vertical-align: middle;\n",
       "  width: 1em;\n",
       "  height: 1.5em !important;\n",
       "  stroke-width: 0;\n",
       "  stroke: currentColor;\n",
       "  fill: currentColor;\n",
       "}\n",
       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
       "Dimensions:  (lat: 89, lon: 180, time: 756)\n",
       "Coordinates:\n",
       "  * lat      (lat) float32 88.0 86.0 84.0 82.0 80.0 ... -82.0 -84.0 -86.0 -88.0\n",
       "  * lon      (lon) float32 0.0 2.0 4.0 6.0 8.0 ... 350.0 352.0 354.0 356.0 358.0\n",
       "  * time     (time) datetime64[ns] 1960-01-01 1960-02-01 ... 2022-12-01\n",
       "    month    (time) int64 1 2 3 4 5 6 7 8 9 10 11 ... 2 3 4 5 6 7 8 9 10 11 12\n",
       "Data variables:\n",
       "    sst      (time, lat, lon) float32 nan nan nan nan nan ... nan nan nan nan</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-57ceb149-2fc9-44c2-a5f4-873e463473b4' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-57ceb149-2fc9-44c2-a5f4-873e463473b4' 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'>lat</span>: 89</li><li><span class='xr-has-index'>lon</span>: 180</li><li><span class='xr-has-index'>time</span>: 756</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-c1372a8c-c1e5-4dc2-a8e0-88876d08fe27' class='xr-section-summary-in' type='checkbox'  checked><label for='section-c1372a8c-c1e5-4dc2-a8e0-88876d08fe27' 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'>lat</span></div><div class='xr-var-dims'>(lat)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>88.0 86.0 84.0 ... -86.0 -88.0</div><input id='attrs-f39e3e4a-c49e-47d8-a588-b2d717c80409' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f39e3e4a-c49e-47d8-a588-b2d717c80409' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-079085ae-c82d-4aed-b170-5cc9d236302b' class='xr-var-data-in' type='checkbox'><label for='data-079085ae-c82d-4aed-b170-5cc9d236302b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degrees_north</dd><dt><span>long_name :</span></dt><dd>Latitude</dd><dt><span>actual_range :</span></dt><dd>[ 88. -88.]</dd><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>axis :</span></dt><dd>Y</dd><dt><span>coordinate_defines :</span></dt><dd>center</dd></dl></div><div class='xr-var-data'><pre>array([ 88.,  86.,  84.,  82.,  80.,  78.,  76.,  74.,  72.,  70.,  68.,  66.,\n",
       "        64.,  62.,  60.,  58.,  56.,  54.,  52.,  50.,  48.,  46.,  44.,  42.,\n",
       "        40.,  38.,  36.,  34.,  32.,  30.,  28.,  26.,  24.,  22.,  20.,  18.,\n",
       "        16.,  14.,  12.,  10.,   8.,   6.,   4.,   2.,   0.,  -2.,  -4.,  -6.,\n",
       "        -8., -10., -12., -14., -16., -18., -20., -22., -24., -26., -28., -30.,\n",
       "       -32., -34., -36., -38., -40., -42., -44., -46., -48., -50., -52., -54.,\n",
       "       -56., -58., -60., -62., -64., -66., -68., -70., -72., -74., -76., -78.,\n",
       "       -80., -82., -84., -86., -88.], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lon</span></div><div class='xr-var-dims'>(lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.0 2.0 4.0 ... 354.0 356.0 358.0</div><input id='attrs-a5e02982-bd6c-4232-bf30-af6ca1525ad2' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a5e02982-bd6c-4232-bf30-af6ca1525ad2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-85e53dd2-0e9d-45a2-8ef2-807ce3fd9671' class='xr-var-data-in' type='checkbox'><label for='data-85e53dd2-0e9d-45a2-8ef2-807ce3fd9671' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degrees_east</dd><dt><span>long_name :</span></dt><dd>Longitude</dd><dt><span>actual_range :</span></dt><dd>[  0. 358.]</dd><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>axis :</span></dt><dd>X</dd><dt><span>coordinate_defines :</span></dt><dd>center</dd></dl></div><div class='xr-var-data'><pre>array([  0.,   2.,   4.,   6.,   8.,  10.,  12.,  14.,  16.,  18.,  20.,  22.,\n",
       "        24.,  26.,  28.,  30.,  32.,  34.,  36.,  38.,  40.,  42.,  44.,  46.,\n",
       "        48.,  50.,  52.,  54.,  56.,  58.,  60.,  62.,  64.,  66.,  68.,  70.,\n",
       "        72.,  74.,  76.,  78.,  80.,  82.,  84.,  86.,  88.,  90.,  92.,  94.,\n",
       "        96.,  98., 100., 102., 104., 106., 108., 110., 112., 114., 116., 118.,\n",
       "       120., 122., 124., 126., 128., 130., 132., 134., 136., 138., 140., 142.,\n",
       "       144., 146., 148., 150., 152., 154., 156., 158., 160., 162., 164., 166.,\n",
       "       168., 170., 172., 174., 176., 178., 180., 182., 184., 186., 188., 190.,\n",
       "       192., 194., 196., 198., 200., 202., 204., 206., 208., 210., 212., 214.,\n",
       "       216., 218., 220., 222., 224., 226., 228., 230., 232., 234., 236., 238.,\n",
       "       240., 242., 244., 246., 248., 250., 252., 254., 256., 258., 260., 262.,\n",
       "       264., 266., 268., 270., 272., 274., 276., 278., 280., 282., 284., 286.,\n",
       "       288., 290., 292., 294., 296., 298., 300., 302., 304., 306., 308., 310.,\n",
       "       312., 314., 316., 318., 320., 322., 324., 326., 328., 330., 332., 334.,\n",
       "       336., 338., 340., 342., 344., 346., 348., 350., 352., 354., 356., 358.],\n",
       "      dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>1960-01-01 ... 2022-12-01</div><input id='attrs-92697efd-aa4f-4c8f-9c9b-1893cd81eae9' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-92697efd-aa4f-4c8f-9c9b-1893cd81eae9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1aff22d8-6616-4fe1-95ba-181956bade56' class='xr-var-data-in' type='checkbox'><label for='data-1aff22d8-6616-4fe1-95ba-181956bade56' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Time</dd><dt><span>delta_t :</span></dt><dd>0000-01-00 00:00:00</dd><dt><span>avg_period :</span></dt><dd>0000-01-00 00:00:00</dd><dt><span>prev_avg_period :</span></dt><dd>0000-00-07 00:00:00</dd><dt><span>standard_name :</span></dt><dd>time</dd><dt><span>axis :</span></dt><dd>T</dd><dt><span>actual_range :</span></dt><dd>[19723. 81722.]</dd><dt><span>_ChunkSizes :</span></dt><dd>1</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;1960-01-01T00:00:00.000000000&#x27;, &#x27;1960-02-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;1960-03-01T00:00:00.000000000&#x27;, ..., &#x27;2022-10-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2022-11-01T00:00:00.000000000&#x27;, &#x27;2022-12-01T00:00:00.000000000&#x27;],\n",
       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>month</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>1 2 3 4 5 6 7 ... 6 7 8 9 10 11 12</div><input id='attrs-cde17193-4d1c-4d85-b695-5662bfb7fd8a' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-cde17193-4d1c-4d85-b695-5662bfb7fd8a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a547f6a0-be65-4ffc-81b9-3562a181ddd5' class='xr-var-data-in' type='checkbox'><label for='data-a547f6a0-be65-4ffc-81b9-3562a181ddd5' 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,  5,  6,  7,  8,  9, 10, 11, 12,  1,  2,  3,  4,  5,\n",
       "        6,  7,  8,  9, 10, 11, 12,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10,\n",
       "       11, 12,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12,  1,  2,  3,\n",
       "        4,  5,  6,  7,  8,  9, 10, 11, 12,  1,  2,  3,  4,  5,  6,  7,  8,\n",
       "        9, 10, 11, 12,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12,  1,\n",
       "        2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12,  1,  2,  3,  4,  5,  6,\n",
       "        7,  8,  9, 10, 11, 12,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11,\n",
       "       12,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12,  1,  2,  3,  4,\n",
       "        5,  6,  7,  8,  9, 10, 11, 12,  1,  2,  3,  4,  5,  6,  7,  8,  9,\n",
       "       10, 11, 12,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12,  1,  2,\n",
       "        3,  4,  5,  6,  7,  8,  9, 10, 11, 12,  1,  2,  3,  4,  5,  6,  7,\n",
       "        8,  9, 10, 11, 12,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12,\n",
       "        1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12,  1,  2,  3,  4,  5,\n",
       "        6,  7,  8,  9, 10, 11, 12,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10,\n",
       "       11, 12,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12,  1,  2,  3,\n",
       "        4,  5,  6,  7,  8,  9, 10, 11, 12,  1,  2,  3,  4,  5,  6,  7,  8,\n",
       "        9, 10, 11, 12,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12,  1,\n",
       "        2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12,  1,  2,  3,  4,  5,  6,\n",
       "        7,  8,  9, 10, 11, 12,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11,\n",
       "       12,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12,  1,  2,  3,  4,\n",
       "...\n",
       "        6,  7,  8,  9, 10, 11, 12,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10,\n",
       "       11, 12,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12,  1,  2,  3,\n",
       "        4,  5,  6,  7,  8,  9, 10, 11, 12,  1,  2,  3,  4,  5,  6,  7,  8,\n",
       "        9, 10, 11, 12,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12,  1,\n",
       "        2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12,  1,  2,  3,  4,  5,  6,\n",
       "        7,  8,  9, 10, 11, 12,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11,\n",
       "       12,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12,  1,  2,  3,  4,\n",
       "        5,  6,  7,  8,  9, 10, 11, 12,  1,  2,  3,  4,  5,  6,  7,  8,  9,\n",
       "       10, 11, 12,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12,  1,  2,\n",
       "        3,  4,  5,  6,  7,  8,  9, 10, 11, 12,  1,  2,  3,  4,  5,  6,  7,\n",
       "        8,  9, 10, 11, 12,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12,\n",
       "        1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12,  1,  2,  3,  4,  5,\n",
       "        6,  7,  8,  9, 10, 11, 12,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10,\n",
       "       11, 12,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12,  1,  2,  3,\n",
       "        4,  5,  6,  7,  8,  9, 10, 11, 12,  1,  2,  3,  4,  5,  6,  7,  8,\n",
       "        9, 10, 11, 12,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12,  1,\n",
       "        2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12,  1,  2,  3,  4,  5,  6,\n",
       "        7,  8,  9, 10, 11, 12,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11,\n",
       "       12,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12,  1,  2,  3,  4,\n",
       "        5,  6,  7,  8,  9, 10, 11, 12])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-f6a34a78-346a-4957-a4af-5b21e5a779f4' class='xr-section-summary-in' type='checkbox'  checked><label for='section-f6a34a78-346a-4957-a4af-5b21e5a779f4' class='xr-section-summary' >Data variables: <span>(1)</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'>(time, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>nan nan nan nan ... nan nan nan nan</div><input id='attrs-05e7db8a-f1e8-416c-aefe-d3792c9f696e' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-05e7db8a-f1e8-416c-aefe-d3792c9f696e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-319a82f9-0a83-40e3-b372-f47fd6becb97' class='xr-var-data-in' type='checkbox'><label for='data-319a82f9-0a83-40e3-b372-f47fd6becb97' 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([[[nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        ...,\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan]],\n",
       "\n",
       "       [[nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        ...,\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan]],\n",
       "\n",
       "       [[nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        ...,\n",
       "...\n",
       "        ...,\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan]],\n",
       "\n",
       "       [[nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        ...,\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan]],\n",
       "\n",
       "       [[nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        ...,\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan]]], dtype=float32)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-8aec047d-ce7c-4ec8-96d1-47b729dd32bd' class='xr-section-summary-in' type='checkbox'  ><label for='section-8aec047d-ce7c-4ec8-96d1-47b729dd32bd' class='xr-section-summary' >Indexes: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>lat</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-80f4c855-f3dc-432c-83c4-58dbf5e325a2' class='xr-index-data-in' type='checkbox'/><label for='index-80f4c855-f3dc-432c-83c4-58dbf5e325a2' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([ 88.0,  86.0,  84.0,  82.0,  80.0,  78.0,  76.0,  74.0,  72.0,  70.0,\n",
       "        68.0,  66.0,  64.0,  62.0,  60.0,  58.0,  56.0,  54.0,  52.0,  50.0,\n",
       "        48.0,  46.0,  44.0,  42.0,  40.0,  38.0,  36.0,  34.0,  32.0,  30.0,\n",
       "        28.0,  26.0,  24.0,  22.0,  20.0,  18.0,  16.0,  14.0,  12.0,  10.0,\n",
       "         8.0,   6.0,   4.0,   2.0,   0.0,  -2.0,  -4.0,  -6.0,  -8.0, -10.0,\n",
       "       -12.0, -14.0, -16.0, -18.0, -20.0, -22.0, -24.0, -26.0, -28.0, -30.0,\n",
       "       -32.0, -34.0, -36.0, -38.0, -40.0, -42.0, -44.0, -46.0, -48.0, -50.0,\n",
       "       -52.0, -54.0, -56.0, -58.0, -60.0, -62.0, -64.0, -66.0, -68.0, -70.0,\n",
       "       -72.0, -74.0, -76.0, -78.0, -80.0, -82.0, -84.0, -86.0, -88.0],\n",
       "      dtype=&#x27;float32&#x27;, name=&#x27;lat&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>lon</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-3c2ac6c6-96b7-46a9-88e9-a649158f46f8' class='xr-index-data-in' type='checkbox'/><label for='index-3c2ac6c6-96b7-46a9-88e9-a649158f46f8' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([  0.0,   2.0,   4.0,   6.0,   8.0,  10.0,  12.0,  14.0,  16.0,  18.0,\n",
       "       ...\n",
       "       340.0, 342.0, 344.0, 346.0, 348.0, 350.0, 352.0, 354.0, 356.0, 358.0],\n",
       "      dtype=&#x27;float32&#x27;, name=&#x27;lon&#x27;, length=180))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-67086440-ad3f-468b-baf0-ace18360c5ba' class='xr-index-data-in' type='checkbox'/><label for='index-67086440-ad3f-468b-baf0-ace18360c5ba' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(DatetimeIndex([&#x27;1960-01-01&#x27;, &#x27;1960-02-01&#x27;, &#x27;1960-03-01&#x27;, &#x27;1960-04-01&#x27;,\n",
       "               &#x27;1960-05-01&#x27;, &#x27;1960-06-01&#x27;, &#x27;1960-07-01&#x27;, &#x27;1960-08-01&#x27;,\n",
       "               &#x27;1960-09-01&#x27;, &#x27;1960-10-01&#x27;,\n",
       "               ...\n",
       "               &#x27;2022-03-01&#x27;, &#x27;2022-04-01&#x27;, &#x27;2022-05-01&#x27;, &#x27;2022-06-01&#x27;,\n",
       "               &#x27;2022-07-01&#x27;, &#x27;2022-08-01&#x27;, &#x27;2022-09-01&#x27;, &#x27;2022-10-01&#x27;,\n",
       "               &#x27;2022-11-01&#x27;, &#x27;2022-12-01&#x27;],\n",
       "              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, length=756, freq=None))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-086d0838-f985-4c6a-b81a-5b68fc428314' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-086d0838-f985-4c6a-b81a-5b68fc428314' 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:  (lat: 89, lon: 180, time: 756)\n",
       "Coordinates:\n",
       "  * lat      (lat) float32 88.0 86.0 84.0 82.0 80.0 ... -82.0 -84.0 -86.0 -88.0\n",
       "  * lon      (lon) float32 0.0 2.0 4.0 6.0 8.0 ... 350.0 352.0 354.0 356.0 358.0\n",
       "  * time     (time) datetime64[ns] 1960-01-01 1960-02-01 ... 2022-12-01\n",
       "    month    (time) int64 1 2 3 4 5 6 7 8 9 10 11 ... 2 3 4 5 6 7 8 9 10 11 12\n",
       "Data variables:\n",
       "    sst      (time, lat, lon) float32 nan nan nan nan nan ... nan nan nan nan"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds_anom_rolling = ds_anom.rolling(time=12, center=True).mean()\n",
    "ds_anom_rolling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x78264bcaf510>"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plt.figure(figsize=(20,6))\n",
    "ds_anom.sst.sel(lon=300, lat=50).plot(label='monthly anom')\n",
    "ds_anom_resample.sst.sel(lon=300, lat=50).plot(marker='o', label='5 year resample')\n",
    "ds_anom_rolling.sst.sel(lon=300, lat=50).plot(label='12 month rolling mean', color='k')\n",
    "plt.legend()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Coarsen\n",
    "\n",
    "`coarsen` is a simple way to reduce the size of your data along one or more axes.\n",
    "It is very similar to `resample` when operating on time dimensions; the key difference is that `coarsen` only operates on fixed blocks of data, irrespective of the coordinate values, while `resample` actually looks at the coordinates to figure out, e.g. what month a particular data point is in. \n",
    "\n",
    "For regularly-spaced monthly data beginning in January, the following should be equivalent to annual resampling.\n",
    "However, results would different for irregularly-spaced data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div><svg style=\"position: absolute; width: 0; height: 0; overflow: hidden\">\n",
       "<defs>\n",
       "<symbol id=\"icon-database\" viewBox=\"0 0 32 32\">\n",
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
       "Dimensions:  (time: 63, lat: 89, lon: 180)\n",
       "Coordinates:\n",
       "  * lat      (lat) float32 88.0 86.0 84.0 82.0 80.0 ... -82.0 -84.0 -86.0 -88.0\n",
       "  * lon      (lon) float32 0.0 2.0 4.0 6.0 8.0 ... 350.0 352.0 354.0 356.0 358.0\n",
       "  * time     (time) datetime64[ns] 1960-06-16T08:00:00 ... 2022-06-16T12:00:00\n",
       "Data variables:\n",
       "    sst      (time, lat, lon) float32 -1.8 -1.8 -1.8 -1.8 ... nan nan nan nan\n",
       "Attributes: (12/39)\n",
       "    climatology:                     Climatology is based on 1971-2000 SST, X...\n",
       "    description:                     In situ data: ICOADS2.5 before 2007 and ...\n",
       "    keywords_vocabulary:             NASA Global Change Master Directory (GCM...\n",
       "    keywords:                        Earth Science &gt; Oceans &gt; Ocean Temperatu...\n",
       "    instrument:                      Conventional thermometers\n",
       "    source_comment:                  SSTs were observed by conventional therm...\n",
       "    ...                              ...\n",
       "    comment:                         SSTs were observed by conventional therm...\n",
       "    summary:                         ERSST.v5 is developed based on v4 after ...\n",
       "    dataset_title:                   NOAA Extended Reconstructed SST V5\n",
       "    _NCProperties:                   version=2,netcdf=4.6.3,hdf5=1.10.5\n",
       "    data_modified:                   2023-11-03\n",
       "    DODS_EXTRA.Unlimited_Dimension:  time</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-7f16ca0c-e9c5-482c-9e8a-44cc55ff73fa' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-7f16ca0c-e9c5-482c-9e8a-44cc55ff73fa' 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'>time</span>: 63</li><li><span class='xr-has-index'>lat</span>: 89</li><li><span class='xr-has-index'>lon</span>: 180</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-ea139c3c-a03b-4d3d-96ae-3c8be856fc7f' class='xr-section-summary-in' type='checkbox'  checked><label for='section-ea139c3c-a03b-4d3d-96ae-3c8be856fc7f' class='xr-section-summary' >Coordinates: <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 class='xr-has-index'>lat</span></div><div class='xr-var-dims'>(lat)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>88.0 86.0 84.0 ... -86.0 -88.0</div><input id='attrs-b610b510-e024-44fb-bd11-479bd0f81f25' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b610b510-e024-44fb-bd11-479bd0f81f25' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-68361655-ef92-4658-b62a-f5b32b6e5a84' class='xr-var-data-in' type='checkbox'><label for='data-68361655-ef92-4658-b62a-f5b32b6e5a84' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degrees_north</dd><dt><span>long_name :</span></dt><dd>Latitude</dd><dt><span>actual_range :</span></dt><dd>[ 88. -88.]</dd><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>axis :</span></dt><dd>Y</dd><dt><span>coordinate_defines :</span></dt><dd>center</dd></dl></div><div class='xr-var-data'><pre>array([ 88.,  86.,  84.,  82.,  80.,  78.,  76.,  74.,  72.,  70.,  68.,  66.,\n",
       "        64.,  62.,  60.,  58.,  56.,  54.,  52.,  50.,  48.,  46.,  44.,  42.,\n",
       "        40.,  38.,  36.,  34.,  32.,  30.,  28.,  26.,  24.,  22.,  20.,  18.,\n",
       "        16.,  14.,  12.,  10.,   8.,   6.,   4.,   2.,   0.,  -2.,  -4.,  -6.,\n",
       "        -8., -10., -12., -14., -16., -18., -20., -22., -24., -26., -28., -30.,\n",
       "       -32., -34., -36., -38., -40., -42., -44., -46., -48., -50., -52., -54.,\n",
       "       -56., -58., -60., -62., -64., -66., -68., -70., -72., -74., -76., -78.,\n",
       "       -80., -82., -84., -86., -88.], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lon</span></div><div class='xr-var-dims'>(lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.0 2.0 4.0 ... 354.0 356.0 358.0</div><input id='attrs-1ee73ceb-7650-4649-bd24-41975ae0446c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1ee73ceb-7650-4649-bd24-41975ae0446c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-075abe79-2d24-422f-8982-91fc66453ad3' class='xr-var-data-in' type='checkbox'><label for='data-075abe79-2d24-422f-8982-91fc66453ad3' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degrees_east</dd><dt><span>long_name :</span></dt><dd>Longitude</dd><dt><span>actual_range :</span></dt><dd>[  0. 358.]</dd><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>axis :</span></dt><dd>X</dd><dt><span>coordinate_defines :</span></dt><dd>center</dd></dl></div><div class='xr-var-data'><pre>array([  0.,   2.,   4.,   6.,   8.,  10.,  12.,  14.,  16.,  18.,  20.,  22.,\n",
       "        24.,  26.,  28.,  30.,  32.,  34.,  36.,  38.,  40.,  42.,  44.,  46.,\n",
       "        48.,  50.,  52.,  54.,  56.,  58.,  60.,  62.,  64.,  66.,  68.,  70.,\n",
       "        72.,  74.,  76.,  78.,  80.,  82.,  84.,  86.,  88.,  90.,  92.,  94.,\n",
       "        96.,  98., 100., 102., 104., 106., 108., 110., 112., 114., 116., 118.,\n",
       "       120., 122., 124., 126., 128., 130., 132., 134., 136., 138., 140., 142.,\n",
       "       144., 146., 148., 150., 152., 154., 156., 158., 160., 162., 164., 166.,\n",
       "       168., 170., 172., 174., 176., 178., 180., 182., 184., 186., 188., 190.,\n",
       "       192., 194., 196., 198., 200., 202., 204., 206., 208., 210., 212., 214.,\n",
       "       216., 218., 220., 222., 224., 226., 228., 230., 232., 234., 236., 238.,\n",
       "       240., 242., 244., 246., 248., 250., 252., 254., 256., 258., 260., 262.,\n",
       "       264., 266., 268., 270., 272., 274., 276., 278., 280., 282., 284., 286.,\n",
       "       288., 290., 292., 294., 296., 298., 300., 302., 304., 306., 308., 310.,\n",
       "       312., 314., 316., 318., 320., 322., 324., 326., 328., 330., 332., 334.,\n",
       "       336., 338., 340., 342., 344., 346., 348., 350., 352., 354., 356., 358.],\n",
       "      dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>1960-06-16T08:00:00 ... 2022-06-...</div><input id='attrs-295045d5-cfc0-483b-b6dc-104b4999ff5e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-295045d5-cfc0-483b-b6dc-104b4999ff5e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a69988e0-0f70-48bd-a2d3-5ff04765470a' class='xr-var-data-in' type='checkbox'><label for='data-a69988e0-0f70-48bd-a2d3-5ff04765470a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Time</dd><dt><span>delta_t :</span></dt><dd>0000-01-00 00:00:00</dd><dt><span>avg_period :</span></dt><dd>0000-01-00 00:00:00</dd><dt><span>prev_avg_period :</span></dt><dd>0000-00-07 00:00:00</dd><dt><span>standard_name :</span></dt><dd>time</dd><dt><span>axis :</span></dt><dd>T</dd><dt><span>actual_range :</span></dt><dd>[19723. 81722.]</dd><dt><span>_ChunkSizes :</span></dt><dd>1</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;1960-06-16T08:00:00.000000000&#x27;, &#x27;1961-06-16T12:00:00.000000000&#x27;,\n",
       "       &#x27;1962-06-16T12:00:00.000000000&#x27;, &#x27;1963-06-16T12:00:00.000000000&#x27;,\n",
       "       &#x27;1964-06-16T08:00:00.000000000&#x27;, &#x27;1965-06-16T12:00:00.000000000&#x27;,\n",
       "       &#x27;1966-06-16T12:00:00.000000000&#x27;, &#x27;1967-06-16T12:00:00.000000000&#x27;,\n",
       "       &#x27;1968-06-16T08:00:00.000000000&#x27;, &#x27;1969-06-16T12:00:00.000000000&#x27;,\n",
       "       &#x27;1970-06-16T12:00:00.000000000&#x27;, &#x27;1971-06-16T12:00:00.000000000&#x27;,\n",
       "       &#x27;1972-06-16T08:00:00.000000000&#x27;, &#x27;1973-06-16T12:00:00.000000000&#x27;,\n",
       "       &#x27;1974-06-16T12:00:00.000000000&#x27;, &#x27;1975-06-16T12:00:00.000000000&#x27;,\n",
       "       &#x27;1976-06-16T08:00:00.000000000&#x27;, &#x27;1977-06-16T12:00:00.000000000&#x27;,\n",
       "       &#x27;1978-06-16T12:00:00.000000000&#x27;, &#x27;1979-06-16T12:00:00.000000000&#x27;,\n",
       "       &#x27;1980-06-16T08:00:00.000000000&#x27;, &#x27;1981-06-16T12:00:00.000000000&#x27;,\n",
       "       &#x27;1982-06-16T12:00:00.000000000&#x27;, &#x27;1983-06-16T12:00:00.000000000&#x27;,\n",
       "       &#x27;1984-06-16T08:00:00.000000000&#x27;, &#x27;1985-06-16T12:00:00.000000000&#x27;,\n",
       "       &#x27;1986-06-16T12:00:00.000000000&#x27;, &#x27;1987-06-16T12:00:00.000000000&#x27;,\n",
       "       &#x27;1988-06-16T08:00:00.000000000&#x27;, &#x27;1989-06-16T12:00:00.000000000&#x27;,\n",
       "       &#x27;1990-06-16T12:00:00.000000000&#x27;, &#x27;1991-06-16T12:00:00.000000000&#x27;,\n",
       "       &#x27;1992-06-16T08:00:00.000000000&#x27;, &#x27;1993-06-16T12:00:00.000000000&#x27;,\n",
       "       &#x27;1994-06-16T12:00:00.000000000&#x27;, &#x27;1995-06-16T12:00:00.000000000&#x27;,\n",
       "       &#x27;1996-06-16T08:00:00.000000000&#x27;, &#x27;1997-06-16T12:00:00.000000000&#x27;,\n",
       "       &#x27;1998-06-16T12:00:00.000000000&#x27;, &#x27;1999-06-16T12:00:00.000000000&#x27;,\n",
       "       &#x27;2000-06-16T08:00:00.000000000&#x27;, &#x27;2001-06-16T12:00:00.000000000&#x27;,\n",
       "       &#x27;2002-06-16T12:00:00.000000000&#x27;, &#x27;2003-06-16T12:00:00.000000000&#x27;,\n",
       "       &#x27;2004-06-16T08:00:00.000000000&#x27;, &#x27;2005-06-16T12:00:00.000000000&#x27;,\n",
       "       &#x27;2006-06-16T12:00:00.000000000&#x27;, &#x27;2007-06-16T12:00:00.000000000&#x27;,\n",
       "       &#x27;2008-06-16T08:00:00.000000000&#x27;, &#x27;2009-06-16T12:00:00.000000000&#x27;,\n",
       "       &#x27;2010-06-16T12:00:00.000000000&#x27;, &#x27;2011-06-16T12:00:00.000000000&#x27;,\n",
       "       &#x27;2012-06-16T08:00:00.000000000&#x27;, &#x27;2013-06-16T12:00:00.000000000&#x27;,\n",
       "       &#x27;2014-06-16T12:00:00.000000000&#x27;, &#x27;2015-06-16T12:00:00.000000000&#x27;,\n",
       "       &#x27;2016-06-16T08:00:00.000000000&#x27;, &#x27;2017-06-16T12:00:00.000000000&#x27;,\n",
       "       &#x27;2018-06-16T12:00:00.000000000&#x27;, &#x27;2019-06-16T12:00:00.000000000&#x27;,\n",
       "       &#x27;2020-06-16T08:00:00.000000000&#x27;, &#x27;2021-06-16T12:00:00.000000000&#x27;,\n",
       "       &#x27;2022-06-16T12:00:00.000000000&#x27;], dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-fc1ed85e-0d57-4be7-af47-3abf72b4348e' class='xr-section-summary-in' type='checkbox'  checked><label for='section-fc1ed85e-0d57-4be7-af47-3abf72b4348e' class='xr-section-summary' >Data variables: <span>(1)</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'>(time, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>-1.8 -1.8 -1.8 -1.8 ... nan nan nan</div><input id='attrs-e87d1118-7c00-40b9-bc9f-9ceb9f7377fd' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e87d1118-7c00-40b9-bc9f-9ceb9f7377fd' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-373f9fef-1382-4e00-a37b-b6a435838a96' class='xr-var-data-in' type='checkbox'><label for='data-373f9fef-1382-4e00-a37b-b6a435838a96' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Monthly Means of Sea Surface Temperature</dd><dt><span>units :</span></dt><dd>degC</dd><dt><span>var_desc :</span></dt><dd>Sea Surface Temperature</dd><dt><span>level_desc :</span></dt><dd>Surface</dd><dt><span>statistic :</span></dt><dd>Mean</dd><dt><span>dataset :</span></dt><dd>NOAA Extended Reconstructed SST V5</dd><dt><span>parent_stat :</span></dt><dd>Individual Values</dd><dt><span>actual_range :</span></dt><dd>[-1.8     42.32636]</dd><dt><span>valid_range :</span></dt><dd>[-1.8 45. ]</dd><dt><span>_ChunkSizes :</span></dt><dd>[  1  89 180]</dd></dl></div><div class='xr-var-data'><pre>array([[[-1.7999998, -1.7999998, -1.7999998, ..., -1.7999998,\n",
       "         -1.7999998, -1.7999998],\n",
       "        [-1.7999998, -1.7999998, -1.7999998, ..., -1.7999998,\n",
       "         -1.7999998, -1.7999998],\n",
       "        [-1.7999998, -1.7999998, -1.7999998, ..., -1.7999998,\n",
       "         -1.7999998, -1.7999998],\n",
       "        ...,\n",
       "        [       nan,        nan,        nan, ...,        nan,\n",
       "                nan,        nan],\n",
       "        [       nan,        nan,        nan, ...,        nan,\n",
       "                nan,        nan],\n",
       "        [       nan,        nan,        nan, ...,        nan,\n",
       "                nan,        nan]],\n",
       "\n",
       "       [[-1.7999998, -1.7999998, -1.7999998, ..., -1.7999998,\n",
       "         -1.7999998, -1.7999998],\n",
       "        [-1.7999998, -1.7999998, -1.7999998, ..., -1.7999998,\n",
       "         -1.7999998, -1.7999998],\n",
       "        [-1.7999998, -1.7999998, -1.7999998, ..., -1.7999998,\n",
       "         -1.7999998, -1.7999998],\n",
       "...\n",
       "        [       nan,        nan,        nan, ...,        nan,\n",
       "                nan,        nan],\n",
       "        [       nan,        nan,        nan, ...,        nan,\n",
       "                nan,        nan],\n",
       "        [       nan,        nan,        nan, ...,        nan,\n",
       "                nan,        nan]],\n",
       "\n",
       "       [[-1.7999998, -1.7999998, -1.7999998, ..., -1.7999998,\n",
       "         -1.7999998, -1.7999998],\n",
       "        [-1.7999998, -1.7999998, -1.7999998, ..., -1.7999998,\n",
       "         -1.7999998, -1.7999998],\n",
       "        [-1.7999998, -1.7999998, -1.7999998, ..., -1.7999998,\n",
       "         -1.7999998, -1.7999998],\n",
       "        ...,\n",
       "        [       nan,        nan,        nan, ...,        nan,\n",
       "                nan,        nan],\n",
       "        [       nan,        nan,        nan, ...,        nan,\n",
       "                nan,        nan],\n",
       "        [       nan,        nan,        nan, ...,        nan,\n",
       "                nan,        nan]]], dtype=float32)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-6c948ff6-3099-4de3-8272-eae8a078f3ff' class='xr-section-summary-in' type='checkbox'  ><label for='section-6c948ff6-3099-4de3-8272-eae8a078f3ff' class='xr-section-summary' >Indexes: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>lat</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-f5e12fb6-1179-4288-921f-62b8c96760d5' class='xr-index-data-in' type='checkbox'/><label for='index-f5e12fb6-1179-4288-921f-62b8c96760d5' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([ 88.0,  86.0,  84.0,  82.0,  80.0,  78.0,  76.0,  74.0,  72.0,  70.0,\n",
       "        68.0,  66.0,  64.0,  62.0,  60.0,  58.0,  56.0,  54.0,  52.0,  50.0,\n",
       "        48.0,  46.0,  44.0,  42.0,  40.0,  38.0,  36.0,  34.0,  32.0,  30.0,\n",
       "        28.0,  26.0,  24.0,  22.0,  20.0,  18.0,  16.0,  14.0,  12.0,  10.0,\n",
       "         8.0,   6.0,   4.0,   2.0,   0.0,  -2.0,  -4.0,  -6.0,  -8.0, -10.0,\n",
       "       -12.0, -14.0, -16.0, -18.0, -20.0, -22.0, -24.0, -26.0, -28.0, -30.0,\n",
       "       -32.0, -34.0, -36.0, -38.0, -40.0, -42.0, -44.0, -46.0, -48.0, -50.0,\n",
       "       -52.0, -54.0, -56.0, -58.0, -60.0, -62.0, -64.0, -66.0, -68.0, -70.0,\n",
       "       -72.0, -74.0, -76.0, -78.0, -80.0, -82.0, -84.0, -86.0, -88.0],\n",
       "      dtype=&#x27;float32&#x27;, name=&#x27;lat&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>lon</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-65484343-b871-4307-92ad-f0ae51b787b8' class='xr-index-data-in' type='checkbox'/><label for='index-65484343-b871-4307-92ad-f0ae51b787b8' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([  0.0,   2.0,   4.0,   6.0,   8.0,  10.0,  12.0,  14.0,  16.0,  18.0,\n",
       "       ...\n",
       "       340.0, 342.0, 344.0, 346.0, 348.0, 350.0, 352.0, 354.0, 356.0, 358.0],\n",
       "      dtype=&#x27;float32&#x27;, name=&#x27;lon&#x27;, length=180))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-32e9eabc-8bca-42fd-82db-93daa16e7857' class='xr-index-data-in' type='checkbox'/><label for='index-32e9eabc-8bca-42fd-82db-93daa16e7857' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(DatetimeIndex([&#x27;1960-06-16 08:00:00&#x27;, &#x27;1961-06-16 12:00:00&#x27;,\n",
       "               &#x27;1962-06-16 12:00:00&#x27;, &#x27;1963-06-16 12:00:00&#x27;,\n",
       "               &#x27;1964-06-16 08:00:00&#x27;, &#x27;1965-06-16 12:00:00&#x27;,\n",
       "               &#x27;1966-06-16 12:00:00&#x27;, &#x27;1967-06-16 12:00:00&#x27;,\n",
       "               &#x27;1968-06-16 08:00:00&#x27;, &#x27;1969-06-16 12:00:00&#x27;,\n",
       "               &#x27;1970-06-16 12:00:00&#x27;, &#x27;1971-06-16 12:00:00&#x27;,\n",
       "               &#x27;1972-06-16 08:00:00&#x27;, &#x27;1973-06-16 12:00:00&#x27;,\n",
       "               &#x27;1974-06-16 12:00:00&#x27;, &#x27;1975-06-16 12:00:00&#x27;,\n",
       "               &#x27;1976-06-16 08:00:00&#x27;, &#x27;1977-06-16 12:00:00&#x27;,\n",
       "               &#x27;1978-06-16 12:00:00&#x27;, &#x27;1979-06-16 12:00:00&#x27;,\n",
       "               &#x27;1980-06-16 08:00:00&#x27;, &#x27;1981-06-16 12:00:00&#x27;,\n",
       "               &#x27;1982-06-16 12:00:00&#x27;, &#x27;1983-06-16 12:00:00&#x27;,\n",
       "               &#x27;1984-06-16 08:00:00&#x27;, &#x27;1985-06-16 12:00:00&#x27;,\n",
       "               &#x27;1986-06-16 12:00:00&#x27;, &#x27;1987-06-16 12:00:00&#x27;,\n",
       "               &#x27;1988-06-16 08:00:00&#x27;, &#x27;1989-06-16 12:00:00&#x27;,\n",
       "               &#x27;1990-06-16 12:00:00&#x27;, &#x27;1991-06-16 12:00:00&#x27;,\n",
       "               &#x27;1992-06-16 08:00:00&#x27;, &#x27;1993-06-16 12:00:00&#x27;,\n",
       "               &#x27;1994-06-16 12:00:00&#x27;, &#x27;1995-06-16 12:00:00&#x27;,\n",
       "               &#x27;1996-06-16 08:00:00&#x27;, &#x27;1997-06-16 12:00:00&#x27;,\n",
       "               &#x27;1998-06-16 12:00:00&#x27;, &#x27;1999-06-16 12:00:00&#x27;,\n",
       "               &#x27;2000-06-16 08:00:00&#x27;, &#x27;2001-06-16 12:00:00&#x27;,\n",
       "               &#x27;2002-06-16 12:00:00&#x27;, &#x27;2003-06-16 12:00:00&#x27;,\n",
       "               &#x27;2004-06-16 08:00:00&#x27;, &#x27;2005-06-16 12:00:00&#x27;,\n",
       "               &#x27;2006-06-16 12:00:00&#x27;, &#x27;2007-06-16 12:00:00&#x27;,\n",
       "               &#x27;2008-06-16 08:00:00&#x27;, &#x27;2009-06-16 12:00:00&#x27;,\n",
       "               &#x27;2010-06-16 12:00:00&#x27;, &#x27;2011-06-16 12:00:00&#x27;,\n",
       "               &#x27;2012-06-16 08:00:00&#x27;, &#x27;2013-06-16 12:00:00&#x27;,\n",
       "               &#x27;2014-06-16 12:00:00&#x27;, &#x27;2015-06-16 12:00:00&#x27;,\n",
       "               &#x27;2016-06-16 08:00:00&#x27;, &#x27;2017-06-16 12:00:00&#x27;,\n",
       "               &#x27;2018-06-16 12:00:00&#x27;, &#x27;2019-06-16 12:00:00&#x27;,\n",
       "               &#x27;2020-06-16 08:00:00&#x27;, &#x27;2021-06-16 12:00:00&#x27;,\n",
       "               &#x27;2022-06-16 12:00:00&#x27;],\n",
       "              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, freq=None))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-c4879695-8509-4e16-b80a-24e6ce2b2593' class='xr-section-summary-in' type='checkbox'  ><label for='section-c4879695-8509-4e16-b80a-24e6ce2b2593' class='xr-section-summary' >Attributes: <span>(39)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>climatology :</span></dt><dd>Climatology is based on 1971-2000 SST, Xue, Y., T. M. Smith, and R. W. Reynolds, 2003: Interdecadal changes of 30-yr SST normals during 1871.2000. Journal of Climate, 16, 1601-1612.</dd><dt><span>description :</span></dt><dd>In situ data: ICOADS2.5 before 2007 and NCEP in situ data from 2008 to present. Ice data: HadISST ice before 2010 and NCEP ice after 2010.</dd><dt><span>keywords_vocabulary :</span></dt><dd>NASA Global Change Master Directory (GCMD) Science Keywords</dd><dt><span>keywords :</span></dt><dd>Earth Science &gt; Oceans &gt; Ocean Temperature &gt; Sea Surface Temperature &gt;</dd><dt><span>instrument :</span></dt><dd>Conventional thermometers</dd><dt><span>source_comment :</span></dt><dd>SSTs were observed by conventional thermometers in Buckets (insulated or un-insulated canvas and wooded buckets) or Engine Room Intaker</dd><dt><span>geospatial_lon_min :</span></dt><dd>-1.0</dd><dt><span>geospatial_lon_max :</span></dt><dd>359.0</dd><dt><span>geospatial_laty_max :</span></dt><dd>89.0</dd><dt><span>geospatial_laty_min :</span></dt><dd>-89.0</dd><dt><span>geospatial_lat_max :</span></dt><dd>89.0</dd><dt><span>geospatial_lat_min :</span></dt><dd>-89.0</dd><dt><span>geospatial_lat_units :</span></dt><dd>degrees_north</dd><dt><span>geospatial_lon_units :</span></dt><dd>degrees_east</dd><dt><span>cdm_data_type :</span></dt><dd>Grid</dd><dt><span>project :</span></dt><dd>NOAA Extended Reconstructed Sea Surface Temperature (ERSST)</dd><dt><span>original_publisher_url :</span></dt><dd>http://www.ncdc.noaa.gov</dd><dt><span>References :</span></dt><dd>https://www.ncdc.noaa.gov/data-access/marineocean-data/extended-reconstructed-sea-surface-temperature-ersst-v5 at NCEI and http://www.esrl.noaa.gov/psd/data/gridded/data.noaa.ersst.v5.html</dd><dt><span>source :</span></dt><dd>In situ data: ICOADS R3.0 before 2015, NCEP in situ GTS from 2016 to present, and Argo SST from 1999 to present. Ice data: HadISST2 ice before 2015, and NCEP ice after 2015</dd><dt><span>title :</span></dt><dd>NOAA ERSSTv5 (in situ only)</dd><dt><span>history :</span></dt><dd>created 07/2017 by PSD data using NCEI&#x27;s ERSST V5 NetCDF values</dd><dt><span>institution :</span></dt><dd>This version written at NOAA/ESRL PSD: obtained from NOAA/NESDIS/National Centers for Environmental Information and time aggregated. Original Full Source: NOAA/NESDIS/NCEI/CCOG</dd><dt><span>citation :</span></dt><dd>Huang et al, 2017: Extended Reconstructed Sea Surface Temperatures Version 5 (ERSSTv5): Upgrades, Validations, and Intercomparisons. Journal of Climate, https://doi.org/10.1175/JCLI-D-16-0836.1</dd><dt><span>platform :</span></dt><dd>Ship and Buoy SSTs from ICOADS R3.0 and NCEP GTS</dd><dt><span>standard_name_vocabulary :</span></dt><dd>CF Standard Name Table (v40, 25 January 2017)</dd><dt><span>processing_level :</span></dt><dd>NOAA Level 4</dd><dt><span>Conventions :</span></dt><dd>CF-1.6, ACDD-1.3</dd><dt><span>metadata_link :</span></dt><dd>:metadata_link = https://doi.org/10.7289/V5T72FNM (original format)</dd><dt><span>creator_name :</span></dt><dd>Boyin Huang (original)</dd><dt><span>date_created :</span></dt><dd>2017-06-30T12:18:00Z (original)</dd><dt><span>product_version :</span></dt><dd>Version 5</dd><dt><span>creator_url_original :</span></dt><dd>https://www.ncei.noaa.gov</dd><dt><span>license :</span></dt><dd>No constraints on data access or use</dd><dt><span>comment :</span></dt><dd>SSTs were observed by conventional thermometers in Buckets (insulated or un-insulated canvas and wooded buckets), Engine Room Intakers, or floats and drifters</dd><dt><span>summary :</span></dt><dd>ERSST.v5 is developed based on v4 after revisions of 8 parameters using updated data sets and advanced knowledge of ERSST analysis</dd><dt><span>dataset_title :</span></dt><dd>NOAA Extended Reconstructed SST V5</dd><dt><span>_NCProperties :</span></dt><dd>version=2,netcdf=4.6.3,hdf5=1.10.5</dd><dt><span>data_modified :</span></dt><dd>2023-11-03</dd><dt><span>DODS_EXTRA.Unlimited_Dimension :</span></dt><dd>time</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:  (time: 63, lat: 89, lon: 180)\n",
       "Coordinates:\n",
       "  * lat      (lat) float32 88.0 86.0 84.0 82.0 80.0 ... -82.0 -84.0 -86.0 -88.0\n",
       "  * lon      (lon) float32 0.0 2.0 4.0 6.0 8.0 ... 350.0 352.0 354.0 356.0 358.0\n",
       "  * time     (time) datetime64[ns] 1960-06-16T08:00:00 ... 2022-06-16T12:00:00\n",
       "Data variables:\n",
       "    sst      (time, lat, lon) float32 -1.8 -1.8 -1.8 -1.8 ... nan nan nan nan\n",
       "Attributes: (12/39)\n",
       "    climatology:                     Climatology is based on 1971-2000 SST, X...\n",
       "    description:                     In situ data: ICOADS2.5 before 2007 and ...\n",
       "    keywords_vocabulary:             NASA Global Change Master Directory (GCM...\n",
       "    keywords:                        Earth Science > Oceans > Ocean Temperatu...\n",
       "    instrument:                      Conventional thermometers\n",
       "    source_comment:                  SSTs were observed by conventional therm...\n",
       "    ...                              ...\n",
       "    comment:                         SSTs were observed by conventional therm...\n",
       "    summary:                         ERSST.v5 is developed based on v4 after ...\n",
       "    dataset_title:                   NOAA Extended Reconstructed SST V5\n",
       "    _NCProperties:                   version=2,netcdf=4.6.3,hdf5=1.10.5\n",
       "    data_modified:                   2023-11-03\n",
       "    DODS_EXTRA.Unlimited_Dimension:  time"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds.coarsen(time=12,boundary = 'exact').mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Coarsen also works on spatial coordinates (or any coordiantes)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.QuadMesh at 0x78264b9d15d0>"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ds_coarse = ds.coarsen(lon=4, lat=4, boundary='pad').mean()\n",
    "ds_coarse.sst.isel(time=0).plot(vmin=2, vmax=30, figsize=(12, 5), edgecolor='k')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## An Advanced Example\n",
    "\n",
    "In this example we will show a realistic workflow with Xarray.\n",
    "We will\n",
    "- Load a \"basin mask\" dataset\n",
    "- Interpolate the basins to our SST dataset coordinates\n",
    "- Group the SST by basin\n",
    "- Convert to Pandas Dataframe and plot mean SST by basin"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
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       "}\n",
       "\n",
       ".xr-dim-list {\n",
       "  display: inline-block !important;\n",
       "  list-style: none;\n",
       "  padding: 0 !important;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list li {\n",
       "  display: inline-block;\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list:before {\n",
       "  content: '(';\n",
       "}\n",
       "\n",
       ".xr-dim-list:after {\n",
       "  content: ')';\n",
       "}\n",
       "\n",
       ".xr-dim-list li:not(:last-child):after {\n",
       "  content: ',';\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-has-index {\n",
       "  font-weight: bold;\n",
       "}\n",
       "\n",
       ".xr-var-list,\n",
       ".xr-var-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-var-item > div,\n",
       ".xr-var-item label,\n",
       ".xr-var-item > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-even);\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-var-item > .xr-var-name:hover span {\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-var-list > li:nth-child(odd) > div,\n",
       ".xr-var-list > li:nth-child(odd) > label,\n",
       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-odd);\n",
       "}\n",
       "\n",
       ".xr-var-name {\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-var-dims {\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-var-dtype {\n",
       "  grid-column: 3;\n",
       "  text-align: right;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-preview {\n",
       "  grid-column: 4;\n",
       "}\n",
       "\n",
       ".xr-index-preview {\n",
       "  grid-column: 2 / 5;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-name,\n",
       ".xr-var-dims,\n",
       ".xr-var-dtype,\n",
       ".xr-preview,\n",
       ".xr-attrs dt {\n",
       "  white-space: nowrap;\n",
       "  overflow: hidden;\n",
       "  text-overflow: ellipsis;\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-var-name:hover,\n",
       ".xr-var-dims:hover,\n",
       ".xr-var-dtype:hover,\n",
       ".xr-attrs dt:hover {\n",
       "  overflow: visible;\n",
       "  width: auto;\n",
       "  z-index: 1;\n",
       "}\n",
       "\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-data {\n",
       "  display: none;\n",
       "  background-color: var(--xr-background-color) !important;\n",
       "  padding-bottom: 5px !important;\n",
       "}\n",
       "\n",
       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
       ".xr-var-data-in:checked ~ .xr-var-data,\n",
       ".xr-index-data-in:checked ~ .xr-index-data {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       ".xr-var-data > table {\n",
       "  float: right;\n",
       "}\n",
       "\n",
       ".xr-var-name span,\n",
       ".xr-var-data,\n",
       ".xr-index-name div,\n",
       ".xr-index-data,\n",
       ".xr-attrs {\n",
       "  padding-left: 25px !important;\n",
       "}\n",
       "\n",
       ".xr-attrs,\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-data {\n",
       "  grid-column: 1 / -1;\n",
       "}\n",
       "\n",
       "dl.xr-attrs {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  display: grid;\n",
       "  grid-template-columns: 125px auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt,\n",
       ".xr-attrs dd {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  float: left;\n",
       "  padding-right: 10px;\n",
       "  width: auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt {\n",
       "  font-weight: normal;\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-attrs dt:hover span {\n",
       "  display: inline-block;\n",
       "  background: var(--xr-background-color);\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-attrs dd {\n",
       "  grid-column: 2;\n",
       "  white-space: pre-wrap;\n",
       "  word-break: break-all;\n",
       "}\n",
       "\n",
       ".xr-icon-database,\n",
       ".xr-icon-file-text2,\n",
       ".xr-no-icon {\n",
       "  display: inline-block;\n",
       "  vertical-align: middle;\n",
       "  width: 1em;\n",
       "  height: 1.5em !important;\n",
       "  stroke-width: 0;\n",
       "  stroke: currentColor;\n",
       "  fill: currentColor;\n",
       "}\n",
       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
       "Dimensions:  (Y: 180, Z: 33, X: 360)\n",
       "Coordinates:\n",
       "  * Y        (Y) float32 -89.5 -88.5 -87.5 -86.5 -85.5 ... 86.5 87.5 88.5 89.5\n",
       "  * Z        (Z) float32 0.0 10.0 20.0 30.0 50.0 ... 4e+03 4.5e+03 5e+03 5.5e+03\n",
       "  * X        (X) float32 0.5 1.5 2.5 3.5 4.5 ... 355.5 356.5 357.5 358.5 359.5\n",
       "Data variables:\n",
       "    basin    (Z, Y, X) float32 ...\n",
       "Attributes:\n",
       "    Conventions:  IRIDL</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-b2ad2adc-a3a2-42bc-8542-06b9e0dca66c' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-b2ad2adc-a3a2-42bc-8542-06b9e0dca66c' 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'>Y</span>: 180</li><li><span class='xr-has-index'>Z</span>: 33</li><li><span class='xr-has-index'>X</span>: 360</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-eb4cf0bb-4039-49e0-a808-fe8ce20e480a' class='xr-section-summary-in' type='checkbox'  checked><label for='section-eb4cf0bb-4039-49e0-a808-fe8ce20e480a' class='xr-section-summary' >Coordinates: <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 class='xr-has-index'>Y</span></div><div class='xr-var-dims'>(Y)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>-89.5 -88.5 -87.5 ... 88.5 89.5</div><input id='attrs-f5938616-96af-4e28-9a1c-573b1cc8e287' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f5938616-96af-4e28-9a1c-573b1cc8e287' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-bc472637-2eb7-42dc-9225-bf897726ba42' class='xr-var-data-in' type='checkbox'><label for='data-bc472637-2eb7-42dc-9225-bf897726ba42' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>pointwidth :</span></dt><dd>1.0</dd><dt><span>gridtype :</span></dt><dd>0</dd><dt><span>units :</span></dt><dd>degree_north</dd></dl></div><div class='xr-var-data'><pre>array([-89.5, -88.5, -87.5, -86.5, -85.5, -84.5, -83.5, -82.5, -81.5, -80.5,\n",
       "       -79.5, -78.5, -77.5, -76.5, -75.5, -74.5, -73.5, -72.5, -71.5, -70.5,\n",
       "       -69.5, -68.5, -67.5, -66.5, -65.5, -64.5, -63.5, -62.5, -61.5, -60.5,\n",
       "       -59.5, -58.5, -57.5, -56.5, -55.5, -54.5, -53.5, -52.5, -51.5, -50.5,\n",
       "       -49.5, -48.5, -47.5, -46.5, -45.5, -44.5, -43.5, -42.5, -41.5, -40.5,\n",
       "       -39.5, -38.5, -37.5, -36.5, -35.5, -34.5, -33.5, -32.5, -31.5, -30.5,\n",
       "       -29.5, -28.5, -27.5, -26.5, -25.5, -24.5, -23.5, -22.5, -21.5, -20.5,\n",
       "       -19.5, -18.5, -17.5, -16.5, -15.5, -14.5, -13.5, -12.5, -11.5, -10.5,\n",
       "        -9.5,  -8.5,  -7.5,  -6.5,  -5.5,  -4.5,  -3.5,  -2.5,  -1.5,  -0.5,\n",
       "         0.5,   1.5,   2.5,   3.5,   4.5,   5.5,   6.5,   7.5,   8.5,   9.5,\n",
       "        10.5,  11.5,  12.5,  13.5,  14.5,  15.5,  16.5,  17.5,  18.5,  19.5,\n",
       "        20.5,  21.5,  22.5,  23.5,  24.5,  25.5,  26.5,  27.5,  28.5,  29.5,\n",
       "        30.5,  31.5,  32.5,  33.5,  34.5,  35.5,  36.5,  37.5,  38.5,  39.5,\n",
       "        40.5,  41.5,  42.5,  43.5,  44.5,  45.5,  46.5,  47.5,  48.5,  49.5,\n",
       "        50.5,  51.5,  52.5,  53.5,  54.5,  55.5,  56.5,  57.5,  58.5,  59.5,\n",
       "        60.5,  61.5,  62.5,  63.5,  64.5,  65.5,  66.5,  67.5,  68.5,  69.5,\n",
       "        70.5,  71.5,  72.5,  73.5,  74.5,  75.5,  76.5,  77.5,  78.5,  79.5,\n",
       "        80.5,  81.5,  82.5,  83.5,  84.5,  85.5,  86.5,  87.5,  88.5,  89.5],\n",
       "      dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>Z</span></div><div class='xr-var-dims'>(Z)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.0 10.0 20.0 ... 5e+03 5.5e+03</div><input id='attrs-f59526d6-23c6-479c-9840-a4e5eb294f02' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f59526d6-23c6-479c-9840-a4e5eb294f02' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9f1a59e0-a8ee-446e-ab33-df22de271470' class='xr-var-data-in' type='checkbox'><label for='data-9f1a59e0-a8ee-446e-ab33-df22de271470' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>gridtype :</span></dt><dd>0</dd><dt><span>units :</span></dt><dd>m</dd></dl></div><div class='xr-var-data'><pre>array([   0.,   10.,   20.,   30.,   50.,   75.,  100.,  125.,  150.,  200.,\n",
       "        250.,  300.,  400.,  500.,  600.,  700.,  800.,  900., 1000., 1100.,\n",
       "       1200., 1300., 1400., 1500., 1750., 2000., 2500., 3000., 3500., 4000.,\n",
       "       4500., 5000., 5500.], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>X</span></div><div class='xr-var-dims'>(X)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.5 1.5 2.5 ... 357.5 358.5 359.5</div><input id='attrs-e01690e3-14e8-4975-956e-200917b4a8b9' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e01690e3-14e8-4975-956e-200917b4a8b9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-09723732-3864-4192-8089-fdf908941890' class='xr-var-data-in' type='checkbox'><label for='data-09723732-3864-4192-8089-fdf908941890' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>pointwidth :</span></dt><dd>1.0</dd><dt><span>gridtype :</span></dt><dd>1</dd><dt><span>units :</span></dt><dd>degree_east</dd></dl></div><div class='xr-var-data'><pre>array([  0.5,   1.5,   2.5, ..., 357.5, 358.5, 359.5], dtype=float32)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-e0a90195-4ab6-490a-984d-5b15817abca9' class='xr-section-summary-in' type='checkbox'  checked><label for='section-e0a90195-4ab6-490a-984d-5b15817abca9' class='xr-section-summary' >Data variables: <span>(1)</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>basin</span></div><div class='xr-var-dims'>(Z, Y, X)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-d395dfbf-4e2a-460c-a849-1533e84c3cd0' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d395dfbf-4e2a-460c-a849-1533e84c3cd0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1bda6195-cd4a-427e-939d-7b5bef511167' class='xr-var-data-in' type='checkbox'><label for='data-1bda6195-cd4a-427e-939d-7b5bef511167' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>basin code</dd><dt><span>units :</span></dt><dd>ids</dd><dt><span>scale_max :</span></dt><dd>58</dd><dt><span>CLIST :</span></dt><dd>Atlantic Ocean\n",
       "Pacific Ocean \n",
       "Indian Ocean\n",
       "Mediterranean Sea\n",
       "Baltic Sea\n",
       "Black Sea\n",
       "Red Sea\n",
       "Persian Gulf\n",
       "Hudson Bay\n",
       "Southern Ocean\n",
       "Arctic Ocean\n",
       "Sea of Japan\n",
       "Kara Sea\n",
       "Sulu Sea\n",
       "Baffin Bay\n",
       "East Mediterranean\n",
       "West Mediterranean\n",
       "Sea of Okhotsk\n",
       "Banda Sea\n",
       "Caribbean Sea\n",
       "Andaman Basin\n",
       "North Caribbean\n",
       "Gulf of Mexico\n",
       "Beaufort Sea\n",
       "South China Sea\n",
       "Barents Sea\n",
       "Celebes Sea\n",
       "Aleutian Basin\n",
       "Fiji Basin\n",
       "North American Basin\n",
       "West European Basin\n",
       "Southeast Indian Basin\n",
       "Coral Sea\n",
       "East Indian Basin\n",
       "Central Indian Basin\n",
       "Southwest Atlantic Basin\n",
       "Southeast Atlantic Basin\n",
       "Southeast Pacific Basin\n",
       "Guatemala Basin\n",
       "East Caroline Basin\n",
       "Marianas Basin\n",
       "Philippine Sea\n",
       "Arabian Sea\n",
       "Chile Basin\n",
       "Somali Basin\n",
       "Mascarene Basin\n",
       "Crozet Basin\n",
       "Guinea Basin\n",
       "Brazil Basin\n",
       "Argentine Basin\n",
       "Tasman Sea\n",
       "Atlantic Indian Basin\n",
       "Caspian Sea\n",
       "Sulu Sea II\n",
       "Venezuela Basin\n",
       "Bay of Bengal\n",
       "Java Sea\n",
       "East Indian Atlantic Basin</dd><dt><span>valid_min :</span></dt><dd>1</dd><dt><span>valid_max :</span></dt><dd>58</dd><dt><span>scale_min :</span></dt><dd>1</dd></dl></div><div class='xr-var-data'><pre>[2138400 values with dtype=float32]</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-7bd2aa23-0a2b-48a8-ad39-bcfa0c5e47d0' class='xr-section-summary-in' type='checkbox'  ><label for='section-7bd2aa23-0a2b-48a8-ad39-bcfa0c5e47d0' class='xr-section-summary' >Indexes: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>Y</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-177da057-df4f-4118-9ca7-3f29a91de8a6' class='xr-index-data-in' type='checkbox'/><label for='index-177da057-df4f-4118-9ca7-3f29a91de8a6' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([-89.5, -88.5, -87.5, -86.5, -85.5, -84.5, -83.5, -82.5, -81.5, -80.5,\n",
       "       ...\n",
       "        80.5,  81.5,  82.5,  83.5,  84.5,  85.5,  86.5,  87.5,  88.5,  89.5],\n",
       "      dtype=&#x27;float32&#x27;, name=&#x27;Y&#x27;, length=180))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>Z</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-fd6a298f-32a5-4550-a902-3175509ac537' class='xr-index-data-in' type='checkbox'/><label for='index-fd6a298f-32a5-4550-a902-3175509ac537' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([   0.0,   10.0,   20.0,   30.0,   50.0,   75.0,  100.0,  125.0,  150.0,\n",
       "        200.0,  250.0,  300.0,  400.0,  500.0,  600.0,  700.0,  800.0,  900.0,\n",
       "       1000.0, 1100.0, 1200.0, 1300.0, 1400.0, 1500.0, 1750.0, 2000.0, 2500.0,\n",
       "       3000.0, 3500.0, 4000.0, 4500.0, 5000.0, 5500.0],\n",
       "      dtype=&#x27;float32&#x27;, name=&#x27;Z&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>X</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-12c53934-cde4-4bde-8685-2a5e5eecc554' class='xr-index-data-in' type='checkbox'/><label for='index-12c53934-cde4-4bde-8685-2a5e5eecc554' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([  0.5,   1.5,   2.5,   3.5,   4.5,   5.5,   6.5,   7.5,   8.5,   9.5,\n",
       "       ...\n",
       "       350.5, 351.5, 352.5, 353.5, 354.5, 355.5, 356.5, 357.5, 358.5, 359.5],\n",
       "      dtype=&#x27;float32&#x27;, name=&#x27;X&#x27;, length=360))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-18ffb861-0af7-4b88-8c6d-195bcf3bc7d1' class='xr-section-summary-in' type='checkbox'  checked><label for='section-18ffb861-0af7-4b88-8c6d-195bcf3bc7d1' class='xr-section-summary' >Attributes: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>Conventions :</span></dt><dd>IRIDL</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:  (Y: 180, Z: 33, X: 360)\n",
       "Coordinates:\n",
       "  * Y        (Y) float32 -89.5 -88.5 -87.5 -86.5 -85.5 ... 86.5 87.5 88.5 89.5\n",
       "  * Z        (Z) float32 0.0 10.0 20.0 30.0 50.0 ... 4e+03 4.5e+03 5e+03 5.5e+03\n",
       "  * X        (X) float32 0.5 1.5 2.5 3.5 4.5 ... 355.5 356.5 357.5 358.5 359.5\n",
       "Data variables:\n",
       "    basin    (Z, Y, X) float32 ...\n",
       "Attributes:\n",
       "    Conventions:  IRIDL"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "basin = xr.open_dataset('http://iridl.ldeo.columbia.edu/SOURCES/.NOAA/.NODC/.WOA09/.Masks/.basin/dods')\n",
    "basin"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
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       "\n",
       ".xr-attrs dt,\n",
       ".xr-attrs dd {\n",
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       "\n",
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
       "Dimensions:  (lat: 180, Z: 33, lon: 360)\n",
       "Coordinates:\n",
       "  * lat      (lat) float32 -89.5 -88.5 -87.5 -86.5 -85.5 ... 86.5 87.5 88.5 89.5\n",
       "  * Z        (Z) float32 0.0 10.0 20.0 30.0 50.0 ... 4e+03 4.5e+03 5e+03 5.5e+03\n",
       "  * lon      (lon) float32 0.5 1.5 2.5 3.5 4.5 ... 355.5 356.5 357.5 358.5 359.5\n",
       "Data variables:\n",
       "    basin    (Z, lat, lon) float32 ...\n",
       "Attributes:\n",
       "    Conventions:  IRIDL</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-bb0b8f41-29c7-4086-b884-007dab6e2d50' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-bb0b8f41-29c7-4086-b884-007dab6e2d50' 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'>lat</span>: 180</li><li><span class='xr-has-index'>Z</span>: 33</li><li><span class='xr-has-index'>lon</span>: 360</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-5ce43938-d5b2-436a-8837-c8b804795f8b' class='xr-section-summary-in' type='checkbox'  checked><label for='section-5ce43938-d5b2-436a-8837-c8b804795f8b' class='xr-section-summary' >Coordinates: <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 class='xr-has-index'>lat</span></div><div class='xr-var-dims'>(lat)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>-89.5 -88.5 -87.5 ... 88.5 89.5</div><input id='attrs-29d4df34-d198-4dce-b9fe-04fbc8e1a79f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-29d4df34-d198-4dce-b9fe-04fbc8e1a79f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b0d78dde-0893-4e9d-ba3e-6038c22a82b2' class='xr-var-data-in' type='checkbox'><label for='data-b0d78dde-0893-4e9d-ba3e-6038c22a82b2' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>pointwidth :</span></dt><dd>1.0</dd><dt><span>gridtype :</span></dt><dd>0</dd><dt><span>units :</span></dt><dd>degree_north</dd></dl></div><div class='xr-var-data'><pre>array([-89.5, -88.5, -87.5, -86.5, -85.5, -84.5, -83.5, -82.5, -81.5, -80.5,\n",
       "       -79.5, -78.5, -77.5, -76.5, -75.5, -74.5, -73.5, -72.5, -71.5, -70.5,\n",
       "       -69.5, -68.5, -67.5, -66.5, -65.5, -64.5, -63.5, -62.5, -61.5, -60.5,\n",
       "       -59.5, -58.5, -57.5, -56.5, -55.5, -54.5, -53.5, -52.5, -51.5, -50.5,\n",
       "       -49.5, -48.5, -47.5, -46.5, -45.5, -44.5, -43.5, -42.5, -41.5, -40.5,\n",
       "       -39.5, -38.5, -37.5, -36.5, -35.5, -34.5, -33.5, -32.5, -31.5, -30.5,\n",
       "       -29.5, -28.5, -27.5, -26.5, -25.5, -24.5, -23.5, -22.5, -21.5, -20.5,\n",
       "       -19.5, -18.5, -17.5, -16.5, -15.5, -14.5, -13.5, -12.5, -11.5, -10.5,\n",
       "        -9.5,  -8.5,  -7.5,  -6.5,  -5.5,  -4.5,  -3.5,  -2.5,  -1.5,  -0.5,\n",
       "         0.5,   1.5,   2.5,   3.5,   4.5,   5.5,   6.5,   7.5,   8.5,   9.5,\n",
       "        10.5,  11.5,  12.5,  13.5,  14.5,  15.5,  16.5,  17.5,  18.5,  19.5,\n",
       "        20.5,  21.5,  22.5,  23.5,  24.5,  25.5,  26.5,  27.5,  28.5,  29.5,\n",
       "        30.5,  31.5,  32.5,  33.5,  34.5,  35.5,  36.5,  37.5,  38.5,  39.5,\n",
       "        40.5,  41.5,  42.5,  43.5,  44.5,  45.5,  46.5,  47.5,  48.5,  49.5,\n",
       "        50.5,  51.5,  52.5,  53.5,  54.5,  55.5,  56.5,  57.5,  58.5,  59.5,\n",
       "        60.5,  61.5,  62.5,  63.5,  64.5,  65.5,  66.5,  67.5,  68.5,  69.5,\n",
       "        70.5,  71.5,  72.5,  73.5,  74.5,  75.5,  76.5,  77.5,  78.5,  79.5,\n",
       "        80.5,  81.5,  82.5,  83.5,  84.5,  85.5,  86.5,  87.5,  88.5,  89.5],\n",
       "      dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>Z</span></div><div class='xr-var-dims'>(Z)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.0 10.0 20.0 ... 5e+03 5.5e+03</div><input id='attrs-4cab0dcc-43f4-49f6-af61-f35251cd1a87' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4cab0dcc-43f4-49f6-af61-f35251cd1a87' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5f6a2806-b4a0-450b-a37a-ab047f92d913' class='xr-var-data-in' type='checkbox'><label for='data-5f6a2806-b4a0-450b-a37a-ab047f92d913' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>gridtype :</span></dt><dd>0</dd><dt><span>units :</span></dt><dd>m</dd></dl></div><div class='xr-var-data'><pre>array([   0.,   10.,   20.,   30.,   50.,   75.,  100.,  125.,  150.,  200.,\n",
       "        250.,  300.,  400.,  500.,  600.,  700.,  800.,  900., 1000., 1100.,\n",
       "       1200., 1300., 1400., 1500., 1750., 2000., 2500., 3000., 3500., 4000.,\n",
       "       4500., 5000., 5500.], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lon</span></div><div class='xr-var-dims'>(lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.5 1.5 2.5 ... 357.5 358.5 359.5</div><input id='attrs-a602b38b-4bd3-40d2-836c-0af7b13d9171' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a602b38b-4bd3-40d2-836c-0af7b13d9171' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0c1fae2c-c833-4210-9220-20d6ab4c10c0' class='xr-var-data-in' type='checkbox'><label for='data-0c1fae2c-c833-4210-9220-20d6ab4c10c0' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>pointwidth :</span></dt><dd>1.0</dd><dt><span>gridtype :</span></dt><dd>1</dd><dt><span>units :</span></dt><dd>degree_east</dd></dl></div><div class='xr-var-data'><pre>array([  0.5,   1.5,   2.5, ..., 357.5, 358.5, 359.5], dtype=float32)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-f85ab91c-c668-40be-b820-11d6ba845a70' class='xr-section-summary-in' type='checkbox'  checked><label for='section-f85ab91c-c668-40be-b820-11d6ba845a70' class='xr-section-summary' >Data variables: <span>(1)</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>basin</span></div><div class='xr-var-dims'>(Z, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-b624dcf5-e489-4bee-a834-1ffde10b966b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b624dcf5-e489-4bee-a834-1ffde10b966b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-482a950f-e32b-4d38-a951-38f8ec42a60e' class='xr-var-data-in' type='checkbox'><label for='data-482a950f-e32b-4d38-a951-38f8ec42a60e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>basin code</dd><dt><span>units :</span></dt><dd>ids</dd><dt><span>scale_max :</span></dt><dd>58</dd><dt><span>CLIST :</span></dt><dd>Atlantic Ocean\n",
       "Pacific Ocean \n",
       "Indian Ocean\n",
       "Mediterranean Sea\n",
       "Baltic Sea\n",
       "Black Sea\n",
       "Red Sea\n",
       "Persian Gulf\n",
       "Hudson Bay\n",
       "Southern Ocean\n",
       "Arctic Ocean\n",
       "Sea of Japan\n",
       "Kara Sea\n",
       "Sulu Sea\n",
       "Baffin Bay\n",
       "East Mediterranean\n",
       "West Mediterranean\n",
       "Sea of Okhotsk\n",
       "Banda Sea\n",
       "Caribbean Sea\n",
       "Andaman Basin\n",
       "North Caribbean\n",
       "Gulf of Mexico\n",
       "Beaufort Sea\n",
       "South China Sea\n",
       "Barents Sea\n",
       "Celebes Sea\n",
       "Aleutian Basin\n",
       "Fiji Basin\n",
       "North American Basin\n",
       "West European Basin\n",
       "Southeast Indian Basin\n",
       "Coral Sea\n",
       "East Indian Basin\n",
       "Central Indian Basin\n",
       "Southwest Atlantic Basin\n",
       "Southeast Atlantic Basin\n",
       "Southeast Pacific Basin\n",
       "Guatemala Basin\n",
       "East Caroline Basin\n",
       "Marianas Basin\n",
       "Philippine Sea\n",
       "Arabian Sea\n",
       "Chile Basin\n",
       "Somali Basin\n",
       "Mascarene Basin\n",
       "Crozet Basin\n",
       "Guinea Basin\n",
       "Brazil Basin\n",
       "Argentine Basin\n",
       "Tasman Sea\n",
       "Atlantic Indian Basin\n",
       "Caspian Sea\n",
       "Sulu Sea II\n",
       "Venezuela Basin\n",
       "Bay of Bengal\n",
       "Java Sea\n",
       "East Indian Atlantic Basin</dd><dt><span>valid_min :</span></dt><dd>1</dd><dt><span>valid_max :</span></dt><dd>58</dd><dt><span>scale_min :</span></dt><dd>1</dd></dl></div><div class='xr-var-data'><pre>[2138400 values with dtype=float32]</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-066db090-e86c-4b2a-9a61-9f76c6a97a1a' class='xr-section-summary-in' type='checkbox'  ><label for='section-066db090-e86c-4b2a-9a61-9f76c6a97a1a' class='xr-section-summary' >Indexes: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>lat</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-eb486828-635f-4fac-95d6-c9b0ac056f17' class='xr-index-data-in' type='checkbox'/><label for='index-eb486828-635f-4fac-95d6-c9b0ac056f17' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([-89.5, -88.5, -87.5, -86.5, -85.5, -84.5, -83.5, -82.5, -81.5, -80.5,\n",
       "       ...\n",
       "        80.5,  81.5,  82.5,  83.5,  84.5,  85.5,  86.5,  87.5,  88.5,  89.5],\n",
       "      dtype=&#x27;float32&#x27;, name=&#x27;lat&#x27;, length=180))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>Z</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-5aa4caf7-1d68-4618-b518-5217e0dd1f42' class='xr-index-data-in' type='checkbox'/><label for='index-5aa4caf7-1d68-4618-b518-5217e0dd1f42' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([   0.0,   10.0,   20.0,   30.0,   50.0,   75.0,  100.0,  125.0,  150.0,\n",
       "        200.0,  250.0,  300.0,  400.0,  500.0,  600.0,  700.0,  800.0,  900.0,\n",
       "       1000.0, 1100.0, 1200.0, 1300.0, 1400.0, 1500.0, 1750.0, 2000.0, 2500.0,\n",
       "       3000.0, 3500.0, 4000.0, 4500.0, 5000.0, 5500.0],\n",
       "      dtype=&#x27;float32&#x27;, name=&#x27;Z&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>lon</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-aa579a8e-6b5d-4327-ba82-2a0109f436b0' class='xr-index-data-in' type='checkbox'/><label for='index-aa579a8e-6b5d-4327-ba82-2a0109f436b0' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([  0.5,   1.5,   2.5,   3.5,   4.5,   5.5,   6.5,   7.5,   8.5,   9.5,\n",
       "       ...\n",
       "       350.5, 351.5, 352.5, 353.5, 354.5, 355.5, 356.5, 357.5, 358.5, 359.5],\n",
       "      dtype=&#x27;float32&#x27;, name=&#x27;lon&#x27;, length=360))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-115a39e6-18de-4f6d-b5b5-85250b01e8e9' class='xr-section-summary-in' type='checkbox'  checked><label for='section-115a39e6-18de-4f6d-b5b5-85250b01e8e9' class='xr-section-summary' >Attributes: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>Conventions :</span></dt><dd>IRIDL</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:  (lat: 180, Z: 33, lon: 360)\n",
       "Coordinates:\n",
       "  * lat      (lat) float32 -89.5 -88.5 -87.5 -86.5 -85.5 ... 86.5 87.5 88.5 89.5\n",
       "  * Z        (Z) float32 0.0 10.0 20.0 30.0 50.0 ... 4e+03 4.5e+03 5e+03 5.5e+03\n",
       "  * lon      (lon) float32 0.5 1.5 2.5 3.5 4.5 ... 355.5 356.5 357.5 358.5 359.5\n",
       "Data variables:\n",
       "    basin    (Z, lat, lon) float32 ...\n",
       "Attributes:\n",
       "    Conventions:  IRIDL"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "basin = basin.rename({'X': 'lon', 'Y': 'lat'})\n",
    "basin"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
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       "\n",
       ".xr-var-preview {\n",
       "  grid-column: 4;\n",
       "}\n",
       "\n",
       ".xr-index-preview {\n",
       "  grid-column: 2 / 5;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-name,\n",
       ".xr-var-dims,\n",
       ".xr-var-dtype,\n",
       ".xr-preview,\n",
       ".xr-attrs dt {\n",
       "  white-space: nowrap;\n",
       "  overflow: hidden;\n",
       "  text-overflow: ellipsis;\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-var-name:hover,\n",
       ".xr-var-dims:hover,\n",
       ".xr-var-dtype:hover,\n",
       ".xr-attrs dt:hover {\n",
       "  overflow: visible;\n",
       "  width: auto;\n",
       "  z-index: 1;\n",
       "}\n",
       "\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-data {\n",
       "  display: none;\n",
       "  background-color: var(--xr-background-color) !important;\n",
       "  padding-bottom: 5px !important;\n",
       "}\n",
       "\n",
       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
       ".xr-var-data-in:checked ~ .xr-var-data,\n",
       ".xr-index-data-in:checked ~ .xr-index-data {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       ".xr-var-data > table {\n",
       "  float: right;\n",
       "}\n",
       "\n",
       ".xr-var-name span,\n",
       ".xr-var-data,\n",
       ".xr-index-name div,\n",
       ".xr-index-data,\n",
       ".xr-attrs {\n",
       "  padding-left: 25px !important;\n",
       "}\n",
       "\n",
       ".xr-attrs,\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-data {\n",
       "  grid-column: 1 / -1;\n",
       "}\n",
       "\n",
       "dl.xr-attrs {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  display: grid;\n",
       "  grid-template-columns: 125px auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt,\n",
       ".xr-attrs dd {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  float: left;\n",
       "  padding-right: 10px;\n",
       "  width: auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt {\n",
       "  font-weight: normal;\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-attrs dt:hover span {\n",
       "  display: inline-block;\n",
       "  background: var(--xr-background-color);\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-attrs dd {\n",
       "  grid-column: 2;\n",
       "  white-space: pre-wrap;\n",
       "  word-break: break-all;\n",
       "}\n",
       "\n",
       ".xr-icon-database,\n",
       ".xr-icon-file-text2,\n",
       ".xr-no-icon {\n",
       "  display: inline-block;\n",
       "  vertical-align: middle;\n",
       "  width: 1em;\n",
       "  height: 1.5em !important;\n",
       "  stroke-width: 0;\n",
       "  stroke: currentColor;\n",
       "  fill: currentColor;\n",
       "}\n",
       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;basin&#x27; (lat: 180, lon: 360)&gt;\n",
       "[64800 values with dtype=float32]\n",
       "Coordinates:\n",
       "  * lat      (lat) float32 -89.5 -88.5 -87.5 -86.5 -85.5 ... 86.5 87.5 88.5 89.5\n",
       "    Z        float32 0.0\n",
       "  * lon      (lon) float32 0.5 1.5 2.5 3.5 4.5 ... 355.5 356.5 357.5 358.5 359.5\n",
       "Attributes:\n",
       "    long_name:  basin code\n",
       "    units:      ids\n",
       "    scale_max:  58\n",
       "    CLIST:      Atlantic Ocean\\nPacific Ocean \\nIndian Ocean\\nMediterranean S...\n",
       "    valid_min:  1\n",
       "    valid_max:  58\n",
       "    scale_min:  1</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'>'basin'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>lat</span>: 180</li><li><span class='xr-has-index'>lon</span>: 360</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-26446dba-f07d-488e-9ced-f129babfc38a' class='xr-array-in' type='checkbox' checked><label for='section-26446dba-f07d-488e-9ced-f129babfc38a' 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>...</span></div><div class='xr-array-data'><pre>[64800 values with dtype=float32]</pre></div></div></li><li class='xr-section-item'><input id='section-fb9b9971-a63f-4724-a8af-b4913df10df2' class='xr-section-summary-in' type='checkbox'  checked><label for='section-fb9b9971-a63f-4724-a8af-b4913df10df2' class='xr-section-summary' >Coordinates: <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 class='xr-has-index'>lat</span></div><div class='xr-var-dims'>(lat)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>-89.5 -88.5 -87.5 ... 88.5 89.5</div><input id='attrs-15fe7243-dacd-4f63-b466-28ee86572d4e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-15fe7243-dacd-4f63-b466-28ee86572d4e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5bc66b06-c4e5-4525-a011-bfaf87eb9769' class='xr-var-data-in' type='checkbox'><label for='data-5bc66b06-c4e5-4525-a011-bfaf87eb9769' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>pointwidth :</span></dt><dd>1.0</dd><dt><span>gridtype :</span></dt><dd>0</dd><dt><span>units :</span></dt><dd>degree_north</dd></dl></div><div class='xr-var-data'><pre>array([-89.5, -88.5, -87.5, -86.5, -85.5, -84.5, -83.5, -82.5, -81.5, -80.5,\n",
       "       -79.5, -78.5, -77.5, -76.5, -75.5, -74.5, -73.5, -72.5, -71.5, -70.5,\n",
       "       -69.5, -68.5, -67.5, -66.5, -65.5, -64.5, -63.5, -62.5, -61.5, -60.5,\n",
       "       -59.5, -58.5, -57.5, -56.5, -55.5, -54.5, -53.5, -52.5, -51.5, -50.5,\n",
       "       -49.5, -48.5, -47.5, -46.5, -45.5, -44.5, -43.5, -42.5, -41.5, -40.5,\n",
       "       -39.5, -38.5, -37.5, -36.5, -35.5, -34.5, -33.5, -32.5, -31.5, -30.5,\n",
       "       -29.5, -28.5, -27.5, -26.5, -25.5, -24.5, -23.5, -22.5, -21.5, -20.5,\n",
       "       -19.5, -18.5, -17.5, -16.5, -15.5, -14.5, -13.5, -12.5, -11.5, -10.5,\n",
       "        -9.5,  -8.5,  -7.5,  -6.5,  -5.5,  -4.5,  -3.5,  -2.5,  -1.5,  -0.5,\n",
       "         0.5,   1.5,   2.5,   3.5,   4.5,   5.5,   6.5,   7.5,   8.5,   9.5,\n",
       "        10.5,  11.5,  12.5,  13.5,  14.5,  15.5,  16.5,  17.5,  18.5,  19.5,\n",
       "        20.5,  21.5,  22.5,  23.5,  24.5,  25.5,  26.5,  27.5,  28.5,  29.5,\n",
       "        30.5,  31.5,  32.5,  33.5,  34.5,  35.5,  36.5,  37.5,  38.5,  39.5,\n",
       "        40.5,  41.5,  42.5,  43.5,  44.5,  45.5,  46.5,  47.5,  48.5,  49.5,\n",
       "        50.5,  51.5,  52.5,  53.5,  54.5,  55.5,  56.5,  57.5,  58.5,  59.5,\n",
       "        60.5,  61.5,  62.5,  63.5,  64.5,  65.5,  66.5,  67.5,  68.5,  69.5,\n",
       "        70.5,  71.5,  72.5,  73.5,  74.5,  75.5,  76.5,  77.5,  78.5,  79.5,\n",
       "        80.5,  81.5,  82.5,  83.5,  84.5,  85.5,  86.5,  87.5,  88.5,  89.5],\n",
       "      dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>Z</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.0</div><input id='attrs-52d4e5da-146d-486e-b4da-9c85bca515b3' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-52d4e5da-146d-486e-b4da-9c85bca515b3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-fffacff5-c26e-4a16-8e7a-ff0f70889237' class='xr-var-data-in' type='checkbox'><label for='data-fffacff5-c26e-4a16-8e7a-ff0f70889237' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>gridtype :</span></dt><dd>0</dd><dt><span>units :</span></dt><dd>m</dd></dl></div><div class='xr-var-data'><pre>array(0., dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lon</span></div><div class='xr-var-dims'>(lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.5 1.5 2.5 ... 357.5 358.5 359.5</div><input id='attrs-4d3258e0-bd60-4667-a963-3926423d107e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4d3258e0-bd60-4667-a963-3926423d107e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-53c9298f-dee6-456e-8d78-fd41916c1385' class='xr-var-data-in' type='checkbox'><label for='data-53c9298f-dee6-456e-8d78-fd41916c1385' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>pointwidth :</span></dt><dd>1.0</dd><dt><span>gridtype :</span></dt><dd>1</dd><dt><span>units :</span></dt><dd>degree_east</dd></dl></div><div class='xr-var-data'><pre>array([  0.5,   1.5,   2.5, ..., 357.5, 358.5, 359.5], dtype=float32)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-965168e2-de85-4de7-9f99-e9ec93946943' class='xr-section-summary-in' type='checkbox'  ><label for='section-965168e2-de85-4de7-9f99-e9ec93946943' class='xr-section-summary' >Indexes: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>lat</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-2ea3e4b0-e4b4-4d8e-b5e3-01663eeffdb7' class='xr-index-data-in' type='checkbox'/><label for='index-2ea3e4b0-e4b4-4d8e-b5e3-01663eeffdb7' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([-89.5, -88.5, -87.5, -86.5, -85.5, -84.5, -83.5, -82.5, -81.5, -80.5,\n",
       "       ...\n",
       "        80.5,  81.5,  82.5,  83.5,  84.5,  85.5,  86.5,  87.5,  88.5,  89.5],\n",
       "      dtype=&#x27;float32&#x27;, name=&#x27;lat&#x27;, length=180))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>lon</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-3b0cb4c4-5ee7-416e-b070-968ce78efaec' class='xr-index-data-in' type='checkbox'/><label for='index-3b0cb4c4-5ee7-416e-b070-968ce78efaec' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([  0.5,   1.5,   2.5,   3.5,   4.5,   5.5,   6.5,   7.5,   8.5,   9.5,\n",
       "       ...\n",
       "       350.5, 351.5, 352.5, 353.5, 354.5, 355.5, 356.5, 357.5, 358.5, 359.5],\n",
       "      dtype=&#x27;float32&#x27;, name=&#x27;lon&#x27;, length=360))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-c9bd761e-8985-4d6e-9bb9-5314c45a4f88' class='xr-section-summary-in' type='checkbox'  checked><label for='section-c9bd761e-8985-4d6e-9bb9-5314c45a4f88' class='xr-section-summary' >Attributes: <span>(7)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>basin code</dd><dt><span>units :</span></dt><dd>ids</dd><dt><span>scale_max :</span></dt><dd>58</dd><dt><span>CLIST :</span></dt><dd>Atlantic Ocean\n",
       "Pacific Ocean \n",
       "Indian Ocean\n",
       "Mediterranean Sea\n",
       "Baltic Sea\n",
       "Black Sea\n",
       "Red Sea\n",
       "Persian Gulf\n",
       "Hudson Bay\n",
       "Southern Ocean\n",
       "Arctic Ocean\n",
       "Sea of Japan\n",
       "Kara Sea\n",
       "Sulu Sea\n",
       "Baffin Bay\n",
       "East Mediterranean\n",
       "West Mediterranean\n",
       "Sea of Okhotsk\n",
       "Banda Sea\n",
       "Caribbean Sea\n",
       "Andaman Basin\n",
       "North Caribbean\n",
       "Gulf of Mexico\n",
       "Beaufort Sea\n",
       "South China Sea\n",
       "Barents Sea\n",
       "Celebes Sea\n",
       "Aleutian Basin\n",
       "Fiji Basin\n",
       "North American Basin\n",
       "West European Basin\n",
       "Southeast Indian Basin\n",
       "Coral Sea\n",
       "East Indian Basin\n",
       "Central Indian Basin\n",
       "Southwest Atlantic Basin\n",
       "Southeast Atlantic Basin\n",
       "Southeast Pacific Basin\n",
       "Guatemala Basin\n",
       "East Caroline Basin\n",
       "Marianas Basin\n",
       "Philippine Sea\n",
       "Arabian Sea\n",
       "Chile Basin\n",
       "Somali Basin\n",
       "Mascarene Basin\n",
       "Crozet Basin\n",
       "Guinea Basin\n",
       "Brazil Basin\n",
       "Argentine Basin\n",
       "Tasman Sea\n",
       "Atlantic Indian Basin\n",
       "Caspian Sea\n",
       "Sulu Sea II\n",
       "Venezuela Basin\n",
       "Bay of Bengal\n",
       "Java Sea\n",
       "East Indian Atlantic Basin</dd><dt><span>valid_min :</span></dt><dd>1</dd><dt><span>valid_max :</span></dt><dd>58</dd><dt><span>scale_min :</span></dt><dd>1</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.DataArray 'basin' (lat: 180, lon: 360)>\n",
       "[64800 values with dtype=float32]\n",
       "Coordinates:\n",
       "  * lat      (lat) float32 -89.5 -88.5 -87.5 -86.5 -85.5 ... 86.5 87.5 88.5 89.5\n",
       "    Z        float32 0.0\n",
       "  * lon      (lon) float32 0.5 1.5 2.5 3.5 4.5 ... 355.5 356.5 357.5 358.5 359.5\n",
       "Attributes:\n",
       "    long_name:  basin code\n",
       "    units:      ids\n",
       "    scale_max:  58\n",
       "    CLIST:      Atlantic Ocean\\nPacific Ocean \\nIndian Ocean\\nMediterranean S...\n",
       "    valid_min:  1\n",
       "    valid_max:  58\n",
       "    scale_min:  1"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "basin_surf = basin.basin[0]\n",
    "basin_surf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.QuadMesh at 0x78264ba04610>"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "basin_surf.plot(vmax=10)\n",
    "#basin_surf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.QuadMesh at 0x78264afdfe50>"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "basin_surf_interp = basin_surf.interp_like(ds.sst, method='nearest')\n",
    "basin_surf_interp.plot(vmax=10)\n",
    "#basin_surf_interp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<__array_function__ internals>:200: RuntimeWarning: invalid value encountered in cast\n"
     ]
    },
    {
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       "  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",
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       "\n",
       ".xr-var-name {\n",
       "  grid-column: 1;\n",
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       "\n",
       ".xr-var-dims {\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-var-dtype {\n",
       "  grid-column: 3;\n",
       "  text-align: right;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-preview {\n",
       "  grid-column: 4;\n",
       "}\n",
       "\n",
       ".xr-index-preview {\n",
       "  grid-column: 2 / 5;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
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       ".xr-var-dims,\n",
       ".xr-var-dtype,\n",
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       "\n",
       ".xr-var-name:hover,\n",
       ".xr-var-dims:hover,\n",
       ".xr-var-dtype:hover,\n",
       ".xr-attrs dt:hover {\n",
       "  overflow: visible;\n",
       "  width: auto;\n",
       "  z-index: 1;\n",
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       "\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-data {\n",
       "  display: none;\n",
       "  background-color: var(--xr-background-color) !important;\n",
       "  padding-bottom: 5px !important;\n",
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       "\n",
       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
       ".xr-var-data-in:checked ~ .xr-var-data,\n",
       ".xr-index-data-in:checked ~ .xr-index-data {\n",
       "  display: block;\n",
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       "\n",
       ".xr-var-data > table {\n",
       "  float: right;\n",
       "}\n",
       "\n",
       ".xr-var-name span,\n",
       ".xr-var-data,\n",
       ".xr-index-name div,\n",
       ".xr-index-data,\n",
       ".xr-attrs {\n",
       "  padding-left: 25px !important;\n",
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       "\n",
       ".xr-attrs,\n",
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       "\n",
       ".xr-attrs dt,\n",
       ".xr-attrs dd {\n",
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       "\n",
       ".xr-attrs dt {\n",
       "  font-weight: normal;\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-attrs dt:hover span {\n",
       "  display: inline-block;\n",
       "  background: var(--xr-background-color);\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-attrs dd {\n",
       "  grid-column: 2;\n",
       "  white-space: pre-wrap;\n",
       "  word-break: break-all;\n",
       "}\n",
       "\n",
       ".xr-icon-database,\n",
       ".xr-icon-file-text2,\n",
       ".xr-no-icon {\n",
       "  display: inline-block;\n",
       "  vertical-align: middle;\n",
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       "  height: 1.5em !important;\n",
       "  stroke-width: 0;\n",
       "  stroke: currentColor;\n",
       "  fill: currentColor;\n",
       "}\n",
       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;sst&#x27; (time: 756, basin: 14)&gt;\n",
       "array([[-1.8       , -1.8       , 23.455315  , ..., -1.8       ,\n",
       "         3.3971915 , 24.182198  ],\n",
       "       [-1.8       , -1.8       , 23.722523  , ..., -1.8       ,\n",
       "         0.03573781, 24.59657   ],\n",
       "       [-1.8       , -1.8       , 24.601315  , ..., -1.8       ,\n",
       "        -0.26487017, 26.234186  ],\n",
       "       ...,\n",
       "       [ 0.89445347,  4.685296  , 29.049557  , ...,  8.882076  ,\n",
       "        16.515127  , 29.450462  ],\n",
       "       [-0.31460398,  1.8985674 , 27.785666  , ...,  3.4794273 ,\n",
       "        11.925127  , 27.901617  ],\n",
       "       [-1.8       , -0.24241269, 26.120224  , ...,  1.3552847 ,\n",
       "         7.9607453 , 25.901285  ]], dtype=float32)\n",
       "Coordinates:\n",
       "  * time     (time) datetime64[ns] 1960-01-01 1960-02-01 ... 2022-12-01\n",
       "  * basin    (basin) float32 1.0 2.0 3.0 4.0 5.0 ... 10.0 11.0 12.0 53.0 56.0\n",
       "Attributes:\n",
       "    long_name:     Monthly Means of Sea Surface Temperature\n",
       "    units:         degC\n",
       "    var_desc:      Sea Surface Temperature\n",
       "    level_desc:    Surface\n",
       "    statistic:     Mean\n",
       "    dataset:       NOAA Extended Reconstructed SST V5\n",
       "    parent_stat:   Individual Values\n",
       "    actual_range:  [-1.8     42.32636]\n",
       "    valid_range:   [-1.8 45. ]\n",
       "    _ChunkSizes:   [  1  89 180]</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'>time</span>: 756</li><li><span class='xr-has-index'>basin</span>: 14</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-5bbc34ad-6703-47b3-8ccc-12015491cd27' class='xr-array-in' type='checkbox' checked><label for='section-5bbc34ad-6703-47b3-8ccc-12015491cd27' 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>-1.8 -1.8 23.46 13.2 1.804 7.341 ... -1.8 4.073 -1.8 1.355 7.961 25.9</span></div><div class='xr-array-data'><pre>array([[-1.8       , -1.8       , 23.455315  , ..., -1.8       ,\n",
       "         3.3971915 , 24.182198  ],\n",
       "       [-1.8       , -1.8       , 23.722523  , ..., -1.8       ,\n",
       "         0.03573781, 24.59657   ],\n",
       "       [-1.8       , -1.8       , 24.601315  , ..., -1.8       ,\n",
       "        -0.26487017, 26.234186  ],\n",
       "       ...,\n",
       "       [ 0.89445347,  4.685296  , 29.049557  , ...,  8.882076  ,\n",
       "        16.515127  , 29.450462  ],\n",
       "       [-0.31460398,  1.8985674 , 27.785666  , ...,  3.4794273 ,\n",
       "        11.925127  , 27.901617  ],\n",
       "       [-1.8       , -0.24241269, 26.120224  , ...,  1.3552847 ,\n",
       "         7.9607453 , 25.901285  ]], dtype=float32)</pre></div></div></li><li class='xr-section-item'><input id='section-cb11bc1e-f4c8-4fc5-ae27-592b7cc05af6' class='xr-section-summary-in' type='checkbox'  checked><label for='section-cb11bc1e-f4c8-4fc5-ae27-592b7cc05af6' class='xr-section-summary' >Coordinates: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>1960-01-01 ... 2022-12-01</div><input id='attrs-218966af-ce30-4c70-b023-678bc6a93567' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-218966af-ce30-4c70-b023-678bc6a93567' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f56dd762-7db2-4179-8281-c334c3e8e43e' class='xr-var-data-in' type='checkbox'><label for='data-f56dd762-7db2-4179-8281-c334c3e8e43e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Time</dd><dt><span>delta_t :</span></dt><dd>0000-01-00 00:00:00</dd><dt><span>avg_period :</span></dt><dd>0000-01-00 00:00:00</dd><dt><span>prev_avg_period :</span></dt><dd>0000-00-07 00:00:00</dd><dt><span>standard_name :</span></dt><dd>time</dd><dt><span>axis :</span></dt><dd>T</dd><dt><span>actual_range :</span></dt><dd>[19723. 81722.]</dd><dt><span>_ChunkSizes :</span></dt><dd>1</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;1960-01-01T00:00:00.000000000&#x27;, &#x27;1960-02-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;1960-03-01T00:00:00.000000000&#x27;, ..., &#x27;2022-10-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2022-11-01T00:00:00.000000000&#x27;, &#x27;2022-12-01T00:00:00.000000000&#x27;],\n",
       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>basin</span></div><div class='xr-var-dims'>(basin)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>1.0 2.0 3.0 4.0 ... 12.0 53.0 56.0</div><input id='attrs-eded6fd9-d1a5-418e-b899-f8e102835405' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-eded6fd9-d1a5-418e-b899-f8e102835405' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-89e7c561-bdb6-4f04-be82-982d93b9cc31' class='xr-var-data-in' type='checkbox'><label for='data-89e7c561-bdb6-4f04-be82-982d93b9cc31' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>basin code</dd><dt><span>units :</span></dt><dd>ids</dd><dt><span>scale_max :</span></dt><dd>58</dd><dt><span>CLIST :</span></dt><dd>Atlantic Ocean\n",
       "Pacific Ocean \n",
       "Indian Ocean\n",
       "Mediterranean Sea\n",
       "Baltic Sea\n",
       "Black Sea\n",
       "Red Sea\n",
       "Persian Gulf\n",
       "Hudson Bay\n",
       "Southern Ocean\n",
       "Arctic Ocean\n",
       "Sea of Japan\n",
       "Kara Sea\n",
       "Sulu Sea\n",
       "Baffin Bay\n",
       "East Mediterranean\n",
       "West Mediterranean\n",
       "Sea of Okhotsk\n",
       "Banda Sea\n",
       "Caribbean Sea\n",
       "Andaman Basin\n",
       "North Caribbean\n",
       "Gulf of Mexico\n",
       "Beaufort Sea\n",
       "South China Sea\n",
       "Barents Sea\n",
       "Celebes Sea\n",
       "Aleutian Basin\n",
       "Fiji Basin\n",
       "North American Basin\n",
       "West European Basin\n",
       "Southeast Indian Basin\n",
       "Coral Sea\n",
       "East Indian Basin\n",
       "Central Indian Basin\n",
       "Southwest Atlantic Basin\n",
       "Southeast Atlantic Basin\n",
       "Southeast Pacific Basin\n",
       "Guatemala Basin\n",
       "East Caroline Basin\n",
       "Marianas Basin\n",
       "Philippine Sea\n",
       "Arabian Sea\n",
       "Chile Basin\n",
       "Somali Basin\n",
       "Mascarene Basin\n",
       "Crozet Basin\n",
       "Guinea Basin\n",
       "Brazil Basin\n",
       "Argentine Basin\n",
       "Tasman Sea\n",
       "Atlantic Indian Basin\n",
       "Caspian Sea\n",
       "Sulu Sea II\n",
       "Venezuela Basin\n",
       "Bay of Bengal\n",
       "Java Sea\n",
       "East Indian Atlantic Basin</dd><dt><span>valid_min :</span></dt><dd>1</dd><dt><span>valid_max :</span></dt><dd>58</dd><dt><span>scale_min :</span></dt><dd>1</dd></dl></div><div class='xr-var-data'><pre>array([ 1.,  2.,  3.,  4.,  5.,  6.,  7.,  8.,  9., 10., 11., 12., 53., 56.],\n",
       "      dtype=float32)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-36cf655a-1c4d-474c-a32c-5fd1a2e75234' class='xr-section-summary-in' type='checkbox'  ><label for='section-36cf655a-1c4d-474c-a32c-5fd1a2e75234' class='xr-section-summary' >Indexes: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-f1068832-2a2b-4daa-84b9-21847f9fb594' class='xr-index-data-in' type='checkbox'/><label for='index-f1068832-2a2b-4daa-84b9-21847f9fb594' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(DatetimeIndex([&#x27;1960-01-01&#x27;, &#x27;1960-02-01&#x27;, &#x27;1960-03-01&#x27;, &#x27;1960-04-01&#x27;,\n",
       "               &#x27;1960-05-01&#x27;, &#x27;1960-06-01&#x27;, &#x27;1960-07-01&#x27;, &#x27;1960-08-01&#x27;,\n",
       "               &#x27;1960-09-01&#x27;, &#x27;1960-10-01&#x27;,\n",
       "               ...\n",
       "               &#x27;2022-03-01&#x27;, &#x27;2022-04-01&#x27;, &#x27;2022-05-01&#x27;, &#x27;2022-06-01&#x27;,\n",
       "               &#x27;2022-07-01&#x27;, &#x27;2022-08-01&#x27;, &#x27;2022-09-01&#x27;, &#x27;2022-10-01&#x27;,\n",
       "               &#x27;2022-11-01&#x27;, &#x27;2022-12-01&#x27;],\n",
       "              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, length=756, freq=None))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>basin</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-930e307c-15e8-4f64-b474-57a4edd76b37' class='xr-index-data-in' type='checkbox'/><label for='index-930e307c-15e8-4f64-b474-57a4edd76b37' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 10.0, 11.0, 12.0, 53.0,\n",
       "       56.0],\n",
       "      dtype=&#x27;float32&#x27;, name=&#x27;basin&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-2ba5ce99-13bf-4ad5-8be1-5501e0e2a292' class='xr-section-summary-in' type='checkbox'  ><label for='section-2ba5ce99-13bf-4ad5-8be1-5501e0e2a292' class='xr-section-summary' >Attributes: <span>(10)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Monthly Means of Sea Surface Temperature</dd><dt><span>units :</span></dt><dd>degC</dd><dt><span>var_desc :</span></dt><dd>Sea Surface Temperature</dd><dt><span>level_desc :</span></dt><dd>Surface</dd><dt><span>statistic :</span></dt><dd>Mean</dd><dt><span>dataset :</span></dt><dd>NOAA Extended Reconstructed SST V5</dd><dt><span>parent_stat :</span></dt><dd>Individual Values</dd><dt><span>actual_range :</span></dt><dd>[-1.8     42.32636]</dd><dt><span>valid_range :</span></dt><dd>[-1.8 45. ]</dd><dt><span>_ChunkSizes :</span></dt><dd>[  1  89 180]</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.DataArray 'sst' (time: 756, basin: 14)>\n",
       "array([[-1.8       , -1.8       , 23.455315  , ..., -1.8       ,\n",
       "         3.3971915 , 24.182198  ],\n",
       "       [-1.8       , -1.8       , 23.722523  , ..., -1.8       ,\n",
       "         0.03573781, 24.59657   ],\n",
       "       [-1.8       , -1.8       , 24.601315  , ..., -1.8       ,\n",
       "        -0.26487017, 26.234186  ],\n",
       "       ...,\n",
       "       [ 0.89445347,  4.685296  , 29.049557  , ...,  8.882076  ,\n",
       "        16.515127  , 29.450462  ],\n",
       "       [-0.31460398,  1.8985674 , 27.785666  , ...,  3.4794273 ,\n",
       "        11.925127  , 27.901617  ],\n",
       "       [-1.8       , -0.24241269, 26.120224  , ...,  1.3552847 ,\n",
       "         7.9607453 , 25.901285  ]], dtype=float32)\n",
       "Coordinates:\n",
       "  * time     (time) datetime64[ns] 1960-01-01 1960-02-01 ... 2022-12-01\n",
       "  * basin    (basin) float32 1.0 2.0 3.0 4.0 5.0 ... 10.0 11.0 12.0 53.0 56.0\n",
       "Attributes:\n",
       "    long_name:     Monthly Means of Sea Surface Temperature\n",
       "    units:         degC\n",
       "    var_desc:      Sea Surface Temperature\n",
       "    level_desc:    Surface\n",
       "    statistic:     Mean\n",
       "    dataset:       NOAA Extended Reconstructed SST V5\n",
       "    parent_stat:   Individual Values\n",
       "    actual_range:  [-1.8     42.32636]\n",
       "    valid_range:   [-1.8 45. ]\n",
       "    _ChunkSizes:   [  1  89 180]"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds.sst.groupby(basin_surf_interp).first()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<__array_function__ internals>:200: RuntimeWarning: invalid value encountered in cast\n"
     ]
    },
    {
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       "  display: inline-block;\n",
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       "\n",
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       "\n",
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       "\n",
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       "}\n",
       "\n",
       ".xr-attrs dt,\n",
       ".xr-attrs dd {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  float: left;\n",
       "  padding-right: 10px;\n",
       "  width: auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt {\n",
       "  font-weight: normal;\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-attrs dt:hover span {\n",
       "  display: inline-block;\n",
       "  background: var(--xr-background-color);\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-attrs dd {\n",
       "  grid-column: 2;\n",
       "  white-space: pre-wrap;\n",
       "  word-break: break-all;\n",
       "}\n",
       "\n",
       ".xr-icon-database,\n",
       ".xr-icon-file-text2,\n",
       ".xr-no-icon {\n",
       "  display: inline-block;\n",
       "  vertical-align: middle;\n",
       "  width: 1em;\n",
       "  height: 1.5em !important;\n",
       "  stroke-width: 0;\n",
       "  stroke: currentColor;\n",
       "  fill: currentColor;\n",
       "}\n",
       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;sst&#x27; (time: 756, basin: 14)&gt;\n",
       "array([[18.585499 , 20.75755  , 21.572077 , ...,  6.238062 ,  6.889794 ,\n",
       "        26.499819 ],\n",
       "       [18.705072 , 20.816761 , 21.902283 , ...,  4.8877654,  5.44638  ,\n",
       "        26.57709  ],\n",
       "       [18.845848 , 20.865032 , 22.031416 , ...,  4.686406 ,  5.5322194,\n",
       "        27.90856  ],\n",
       "       ...,\n",
       "       [20.133    , 21.700815 , 20.31083  , ..., 17.463427 , 18.683998 ,\n",
       "        29.5153   ],\n",
       "       [19.80138  , 21.430943 , 20.964071 , ..., 13.358289 , 14.617571 ,\n",
       "        28.847633 ],\n",
       "       [19.636013 , 21.297836 , 21.741606 , ..., 10.26373  , 11.0815325,\n",
       "        27.899845 ]], dtype=float32)\n",
       "Coordinates:\n",
       "  * time     (time) datetime64[ns] 1960-01-01 1960-02-01 ... 2022-12-01\n",
       "    Z        float32 0.0\n",
       "  * basin    (basin) float32 1.0 2.0 3.0 4.0 5.0 ... 10.0 11.0 12.0 53.0 56.0\n",
       "Attributes:\n",
       "    long_name:     Monthly Means of Sea Surface Temperature\n",
       "    units:         degC\n",
       "    var_desc:      Sea Surface Temperature\n",
       "    level_desc:    Surface\n",
       "    statistic:     Mean\n",
       "    dataset:       NOAA Extended Reconstructed SST V5\n",
       "    parent_stat:   Individual Values\n",
       "    actual_range:  [-1.8     42.32636]\n",
       "    valid_range:   [-1.8 45. ]\n",
       "    _ChunkSizes:   [  1  89 180]</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'>time</span>: 756</li><li><span class='xr-has-index'>basin</span>: 14</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-783eba80-673f-416f-a0dd-6d7fa93ed01e' class='xr-array-in' type='checkbox' checked><label for='section-783eba80-673f-416f-a0dd-6d7fa93ed01e' 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>18.59 20.76 21.57 15.46 2.553 8.513 ... 1.691 -0.95 10.26 11.08 27.9</span></div><div class='xr-array-data'><pre>array([[18.585499 , 20.75755  , 21.572077 , ...,  6.238062 ,  6.889794 ,\n",
       "        26.499819 ],\n",
       "       [18.705072 , 20.816761 , 21.902283 , ...,  4.8877654,  5.44638  ,\n",
       "        26.57709  ],\n",
       "       [18.845848 , 20.865032 , 22.031416 , ...,  4.686406 ,  5.5322194,\n",
       "        27.90856  ],\n",
       "       ...,\n",
       "       [20.133    , 21.700815 , 20.31083  , ..., 17.463427 , 18.683998 ,\n",
       "        29.5153   ],\n",
       "       [19.80138  , 21.430943 , 20.964071 , ..., 13.358289 , 14.617571 ,\n",
       "        28.847633 ],\n",
       "       [19.636013 , 21.297836 , 21.741606 , ..., 10.26373  , 11.0815325,\n",
       "        27.899845 ]], dtype=float32)</pre></div></div></li><li class='xr-section-item'><input id='section-a769844b-1166-4320-a477-048e98230c7e' class='xr-section-summary-in' type='checkbox'  checked><label for='section-a769844b-1166-4320-a477-048e98230c7e' class='xr-section-summary' >Coordinates: <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 class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>1960-01-01 ... 2022-12-01</div><input id='attrs-9c810d44-5624-434e-a723-71b91ba541f8' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9c810d44-5624-434e-a723-71b91ba541f8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3c8c2de4-01a2-40d4-b3c8-a88bc50d933e' class='xr-var-data-in' type='checkbox'><label for='data-3c8c2de4-01a2-40d4-b3c8-a88bc50d933e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Time</dd><dt><span>delta_t :</span></dt><dd>0000-01-00 00:00:00</dd><dt><span>avg_period :</span></dt><dd>0000-01-00 00:00:00</dd><dt><span>prev_avg_period :</span></dt><dd>0000-00-07 00:00:00</dd><dt><span>standard_name :</span></dt><dd>time</dd><dt><span>axis :</span></dt><dd>T</dd><dt><span>actual_range :</span></dt><dd>[19723. 81722.]</dd><dt><span>_ChunkSizes :</span></dt><dd>1</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;1960-01-01T00:00:00.000000000&#x27;, &#x27;1960-02-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;1960-03-01T00:00:00.000000000&#x27;, ..., &#x27;2022-10-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2022-11-01T00:00:00.000000000&#x27;, &#x27;2022-12-01T00:00:00.000000000&#x27;],\n",
       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>Z</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.0</div><input id='attrs-0a634d3b-0fa2-4f3f-b439-642dfd2f8573' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0a634d3b-0fa2-4f3f-b439-642dfd2f8573' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-19e6f57d-dc09-4bd5-9d92-b174360346f2' class='xr-var-data-in' type='checkbox'><label for='data-19e6f57d-dc09-4bd5-9d92-b174360346f2' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>gridtype :</span></dt><dd>0</dd><dt><span>units :</span></dt><dd>m</dd></dl></div><div class='xr-var-data'><pre>array(0., dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>basin</span></div><div class='xr-var-dims'>(basin)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>1.0 2.0 3.0 4.0 ... 12.0 53.0 56.0</div><input id='attrs-51d4948a-d969-428b-bbfe-c79b6d8da045' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-51d4948a-d969-428b-bbfe-c79b6d8da045' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d07a551c-b016-4352-a01a-5c9faa66e5a5' class='xr-var-data-in' type='checkbox'><label for='data-d07a551c-b016-4352-a01a-5c9faa66e5a5' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>basin code</dd><dt><span>units :</span></dt><dd>ids</dd><dt><span>scale_max :</span></dt><dd>58</dd><dt><span>CLIST :</span></dt><dd>Atlantic Ocean\n",
       "Pacific Ocean \n",
       "Indian Ocean\n",
       "Mediterranean Sea\n",
       "Baltic Sea\n",
       "Black Sea\n",
       "Red Sea\n",
       "Persian Gulf\n",
       "Hudson Bay\n",
       "Southern Ocean\n",
       "Arctic Ocean\n",
       "Sea of Japan\n",
       "Kara Sea\n",
       "Sulu Sea\n",
       "Baffin Bay\n",
       "East Mediterranean\n",
       "West Mediterranean\n",
       "Sea of Okhotsk\n",
       "Banda Sea\n",
       "Caribbean Sea\n",
       "Andaman Basin\n",
       "North Caribbean\n",
       "Gulf of Mexico\n",
       "Beaufort Sea\n",
       "South China Sea\n",
       "Barents Sea\n",
       "Celebes Sea\n",
       "Aleutian Basin\n",
       "Fiji Basin\n",
       "North American Basin\n",
       "West European Basin\n",
       "Southeast Indian Basin\n",
       "Coral Sea\n",
       "East Indian Basin\n",
       "Central Indian Basin\n",
       "Southwest Atlantic Basin\n",
       "Southeast Atlantic Basin\n",
       "Southeast Pacific Basin\n",
       "Guatemala Basin\n",
       "East Caroline Basin\n",
       "Marianas Basin\n",
       "Philippine Sea\n",
       "Arabian Sea\n",
       "Chile Basin\n",
       "Somali Basin\n",
       "Mascarene Basin\n",
       "Crozet Basin\n",
       "Guinea Basin\n",
       "Brazil Basin\n",
       "Argentine Basin\n",
       "Tasman Sea\n",
       "Atlantic Indian Basin\n",
       "Caspian Sea\n",
       "Sulu Sea II\n",
       "Venezuela Basin\n",
       "Bay of Bengal\n",
       "Java Sea\n",
       "East Indian Atlantic Basin</dd><dt><span>valid_min :</span></dt><dd>1</dd><dt><span>valid_max :</span></dt><dd>58</dd><dt><span>scale_min :</span></dt><dd>1</dd></dl></div><div class='xr-var-data'><pre>array([ 1.,  2.,  3.,  4.,  5.,  6.,  7.,  8.,  9., 10., 11., 12., 53., 56.],\n",
       "      dtype=float32)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-a1f4a8fb-8b0e-4b56-bbd2-41eb08e6b66d' class='xr-section-summary-in' type='checkbox'  ><label for='section-a1f4a8fb-8b0e-4b56-bbd2-41eb08e6b66d' class='xr-section-summary' >Indexes: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-c51137eb-3cbd-43dd-b97a-ce4f64add5e4' class='xr-index-data-in' type='checkbox'/><label for='index-c51137eb-3cbd-43dd-b97a-ce4f64add5e4' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(DatetimeIndex([&#x27;1960-01-01&#x27;, &#x27;1960-02-01&#x27;, &#x27;1960-03-01&#x27;, &#x27;1960-04-01&#x27;,\n",
       "               &#x27;1960-05-01&#x27;, &#x27;1960-06-01&#x27;, &#x27;1960-07-01&#x27;, &#x27;1960-08-01&#x27;,\n",
       "               &#x27;1960-09-01&#x27;, &#x27;1960-10-01&#x27;,\n",
       "               ...\n",
       "               &#x27;2022-03-01&#x27;, &#x27;2022-04-01&#x27;, &#x27;2022-05-01&#x27;, &#x27;2022-06-01&#x27;,\n",
       "               &#x27;2022-07-01&#x27;, &#x27;2022-08-01&#x27;, &#x27;2022-09-01&#x27;, &#x27;2022-10-01&#x27;,\n",
       "               &#x27;2022-11-01&#x27;, &#x27;2022-12-01&#x27;],\n",
       "              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, length=756, freq=None))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>basin</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-48097d2e-b15d-4bbc-8cc6-394c186a221e' class='xr-index-data-in' type='checkbox'/><label for='index-48097d2e-b15d-4bbc-8cc6-394c186a221e' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 10.0, 11.0, 12.0, 53.0,\n",
       "       56.0],\n",
       "      dtype=&#x27;float32&#x27;, name=&#x27;basin&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-e7e9d6c4-ae5c-46b9-a613-5c48a5fa4eda' class='xr-section-summary-in' type='checkbox'  ><label for='section-e7e9d6c4-ae5c-46b9-a613-5c48a5fa4eda' class='xr-section-summary' >Attributes: <span>(10)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Monthly Means of Sea Surface Temperature</dd><dt><span>units :</span></dt><dd>degC</dd><dt><span>var_desc :</span></dt><dd>Sea Surface Temperature</dd><dt><span>level_desc :</span></dt><dd>Surface</dd><dt><span>statistic :</span></dt><dd>Mean</dd><dt><span>dataset :</span></dt><dd>NOAA Extended Reconstructed SST V5</dd><dt><span>parent_stat :</span></dt><dd>Individual Values</dd><dt><span>actual_range :</span></dt><dd>[-1.8     42.32636]</dd><dt><span>valid_range :</span></dt><dd>[-1.8 45. ]</dd><dt><span>_ChunkSizes :</span></dt><dd>[  1  89 180]</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.DataArray 'sst' (time: 756, basin: 14)>\n",
       "array([[18.585499 , 20.75755  , 21.572077 , ...,  6.238062 ,  6.889794 ,\n",
       "        26.499819 ],\n",
       "       [18.705072 , 20.816761 , 21.902283 , ...,  4.8877654,  5.44638  ,\n",
       "        26.57709  ],\n",
       "       [18.845848 , 20.865032 , 22.031416 , ...,  4.686406 ,  5.5322194,\n",
       "        27.90856  ],\n",
       "       ...,\n",
       "       [20.133    , 21.700815 , 20.31083  , ..., 17.463427 , 18.683998 ,\n",
       "        29.5153   ],\n",
       "       [19.80138  , 21.430943 , 20.964071 , ..., 13.358289 , 14.617571 ,\n",
       "        28.847633 ],\n",
       "       [19.636013 , 21.297836 , 21.741606 , ..., 10.26373  , 11.0815325,\n",
       "        27.899845 ]], dtype=float32)\n",
       "Coordinates:\n",
       "  * time     (time) datetime64[ns] 1960-01-01 1960-02-01 ... 2022-12-01\n",
       "    Z        float32 0.0\n",
       "  * basin    (basin) float32 1.0 2.0 3.0 4.0 5.0 ... 10.0 11.0 12.0 53.0 56.0\n",
       "Attributes:\n",
       "    long_name:     Monthly Means of Sea Surface Temperature\n",
       "    units:         degC\n",
       "    var_desc:      Sea Surface Temperature\n",
       "    level_desc:    Surface\n",
       "    statistic:     Mean\n",
       "    dataset:       NOAA Extended Reconstructed SST V5\n",
       "    parent_stat:   Individual Values\n",
       "    actual_range:  [-1.8     42.32636]\n",
       "    valid_range:   [-1.8 45. ]\n",
       "    _ChunkSizes:   [  1  89 180]"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "basin_mean_sst = ds.sst.groupby(basin_surf_interp).mean()\n",
    "basin_mean_sst"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "        vertical-align: middle;\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Z</th>\n",
       "      <th>sst</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>basin</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1.0</th>\n",
       "      <td>0.0</td>\n",
       "      <td>19.317692</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2.0</th>\n",
       "      <td>0.0</td>\n",
       "      <td>21.204735</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3.0</th>\n",
       "      <td>0.0</td>\n",
       "      <td>21.147755</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4.0</th>\n",
       "      <td>0.0</td>\n",
       "      <td>19.902565</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5.0</th>\n",
       "      <td>0.0</td>\n",
       "      <td>8.199746</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6.0</th>\n",
       "      <td>0.0</td>\n",
       "      <td>15.138650</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7.0</th>\n",
       "      <td>0.0</td>\n",
       "      <td>28.522148</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8.0</th>\n",
       "      <td>0.0</td>\n",
       "      <td>26.654783</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9.0</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.345633</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10.0</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.550839</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11.0</th>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.799598</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12.0</th>\n",
       "      <td>0.0</td>\n",
       "      <td>12.162644</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53.0</th>\n",
       "      <td>0.0</td>\n",
       "      <td>14.433341</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56.0</th>\n",
       "      <td>0.0</td>\n",
       "      <td>28.495367</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Z        sst\n",
       "basin                \n",
       "1.0    0.0  19.317692\n",
       "2.0    0.0  21.204735\n",
       "3.0    0.0  21.147755\n",
       "4.0    0.0  19.902565\n",
       "5.0    0.0   8.199746\n",
       "6.0    0.0  15.138650\n",
       "7.0    0.0  28.522148\n",
       "8.0    0.0  26.654783\n",
       "9.0    0.0   0.345633\n",
       "10.0   0.0   1.550839\n",
       "11.0   0.0  -0.799598\n",
       "12.0   0.0  12.162644\n",
       "53.0   0.0  14.433341\n",
       "56.0   0.0  28.495367"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = basin_mean_sst.mean('time').to_dataframe()\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1                 Atlantic Ocean\n",
       "2                 Pacific Ocean \n",
       "3                   Indian Ocean\n",
       "4              Mediterranean Sea\n",
       "5                     Baltic Sea\n",
       "6                      Black Sea\n",
       "7                        Red Sea\n",
       "8                   Persian Gulf\n",
       "9                     Hudson Bay\n",
       "10                Southern Ocean\n",
       "11                  Arctic Ocean\n",
       "12                  Sea of Japan\n",
       "13                      Kara Sea\n",
       "14                      Sulu Sea\n",
       "15                    Baffin Bay\n",
       "16            East Mediterranean\n",
       "17            West Mediterranean\n",
       "18                Sea of Okhotsk\n",
       "19                     Banda Sea\n",
       "20                 Caribbean Sea\n",
       "21                 Andaman Basin\n",
       "22               North Caribbean\n",
       "23                Gulf of Mexico\n",
       "24                  Beaufort Sea\n",
       "25               South China Sea\n",
       "26                   Barents Sea\n",
       "27                   Celebes Sea\n",
       "28                Aleutian Basin\n",
       "29                    Fiji Basin\n",
       "30          North American Basin\n",
       "31           West European Basin\n",
       "32        Southeast Indian Basin\n",
       "33                     Coral Sea\n",
       "34             East Indian Basin\n",
       "35          Central Indian Basin\n",
       "36      Southwest Atlantic Basin\n",
       "37      Southeast Atlantic Basin\n",
       "38       Southeast Pacific Basin\n",
       "39               Guatemala Basin\n",
       "40           East Caroline Basin\n",
       "41                Marianas Basin\n",
       "42                Philippine Sea\n",
       "43                   Arabian Sea\n",
       "44                   Chile Basin\n",
       "45                  Somali Basin\n",
       "46               Mascarene Basin\n",
       "47                  Crozet Basin\n",
       "48                  Guinea Basin\n",
       "49                  Brazil Basin\n",
       "50               Argentine Basin\n",
       "51                    Tasman Sea\n",
       "52         Atlantic Indian Basin\n",
       "53                   Caspian Sea\n",
       "54                   Sulu Sea II\n",
       "55               Venezuela Basin\n",
       "56                 Bay of Bengal\n",
       "57                      Java Sea\n",
       "58    East Indian Atlantic Basin\n",
       "dtype: object"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "basin_names = basin_surf.attrs['CLIST'].split('\\n')\n",
    "basin_df = pd.Series(basin_names, index=np.arange(1, len(basin_names)+1))\n",
    "basin_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "df = df.join(basin_df.rename('basin_name'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='basin_name'>"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.plot.bar(y='sst', x='basin_name')"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.11.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}