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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;sst&#x27; (time: 360, lat: 11, lon: 180)&gt;\n",
       "[712800 values with dtype=float32]\n",
       "Coordinates:\n",
       "  * lat      (lat) float32 10.0 8.0 6.0 4.0 2.0 0.0 -2.0 -4.0 -6.0 -8.0 -10.0\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] 1981-01-01 1981-02-01 ... 2010-12-01\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. ]</pre><div class='xr-wrap' hidden><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>: 360</li><li><span class='xr-has-index'>lat</span>: 11</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-2befce9f-ae96-4c64-8a0c-0f58b58073b0' class='xr-array-in' type='checkbox' checked><label for='section-2befce9f-ae96-4c64-8a0c-0f58b58073b0' 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>[712800 values with dtype=float32]</pre></div></div></li><li class='xr-section-item'><input id='section-4c571acc-a1b3-4b56-8b41-1edeff6ebdfd' class='xr-section-summary-in' type='checkbox'  checked><label for='section-4c571acc-a1b3-4b56-8b41-1edeff6ebdfd' 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'>10.0 8.0 6.0 ... -6.0 -8.0 -10.0</div><input id='attrs-57fb432c-c97c-4952-bbfb-0a054d06d520' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-57fb432c-c97c-4952-bbfb-0a054d06d520' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5e5839e3-3aac-49fd-8a3a-138d8e9aba14' class='xr-var-data-in' type='checkbox'><label for='data-5e5839e3-3aac-49fd-8a3a-138d8e9aba14' 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([ 10.,   8.,   6.,   4.,   2.,   0.,  -2.,  -4.,  -6.,  -8., -10.],\n",
       "      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-87bb2ae4-50e0-490d-9dea-21ef309e6b2b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-87bb2ae4-50e0-490d-9dea-21ef309e6b2b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-437fb1f3-a30c-48bd-82fe-94b90becac16' class='xr-var-data-in' type='checkbox'><label for='data-437fb1f3-a30c-48bd-82fe-94b90becac16' 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'>1981-01-01 ... 2010-12-01</div><input id='attrs-2c9f31f1-0bc8-4391-a19e-7a1f23ce94f8' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-2c9f31f1-0bc8-4391-a19e-7a1f23ce94f8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d971f94b-f79e-447b-bcc0-34b47a5ff822' class='xr-var-data-in' type='checkbox'><label for='data-d971f94b-f79e-447b-bcc0-34b47a5ff822' 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. 80870.]</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;1981-01-01T00:00:00.000000000&#x27;, &#x27;1981-02-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;1981-03-01T00:00:00.000000000&#x27;, ..., &#x27;2010-10-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2010-11-01T00:00:00.000000000&#x27;, &#x27;2010-12-01T00:00:00.000000000&#x27;],\n",
       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-861a7dd9-86fe-4c90-bb78-6eaec522f0ac' class='xr-section-summary-in' type='checkbox'  checked><label for='section-861a7dd9-86fe-4c90-bb78-6eaec522f0ac' class='xr-section-summary' >Attributes: <span>(9)</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></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.DataArray 'sst' (time: 360, lat: 11, lon: 180)>\n",
       "[712800 values with dtype=float32]\n",
       "Coordinates:\n",
       "  * lat      (lat) float32 10.0 8.0 6.0 4.0 2.0 0.0 -2.0 -4.0 -6.0 -8.0 -10.0\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] 1981-01-01 1981-02-01 ... 2010-12-01\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. ]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x216 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import xarray as xr\n",
    "\n",
    "dset = xr.open_dataset(\"sst.mnmean.nc\")\n",
    "\n",
    "sst=dset.sst\n",
    "\n",
    "#seleccionamos tropicos\n",
    "\n",
    "sst_trop=sst.sel(lat=slice(10,-10),lon=slice(0,360),time=slice(\"1981-01-01\",\"2010-12-01\"))\n",
    "sst_trop.sel(time=\"2010-01-01\").plot(robust=True,figsize=(10,3))\n",
    "sst_trop"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "m_autocorr_pearson = np.zeros((sst_trop.shape[1],sst_trop.shape[2]),dtype=float)\n",
    "\n",
    "lagtime=3\n",
    "\n",
    "def autocrr(x, t=lagtime):\n",
    "    return np.corrcoef(np.array([x[:-t], x[t:]]))[0,1]\n",
    "\n",
    "for ni in range(0,sst_trop.shape[2]): #long\n",
    "    for nj in range(0,sst_trop.shape[1]): # loop lat\n",
    "        \n",
    "        sst_info=sst_trop.isel(lat=nj,lon=ni).values\n",
    "        \n",
    "        array_has_nan = np.isnan(np.sum(sst_info))\n",
    "        \n",
    "        if array_has_nan == True:\n",
    "            \n",
    "            m_autocorr_pearson[nj,ni]=np.nan\n",
    "            \n",
    "        else:\n",
    "            m_autocorr_pearson[nj,ni] = autocrr(sst_info)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.contour.QuadContourSet at 0x15cee4b0d00>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x216 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data_set2 = xr.Dataset( coords={'lon': ([ 'lon'], sst_trop.lon.values),\n",
    "                                             'lat': (['lat',], sst_trop.lat.values)})\n",
    "\n",
    "data_set2[\"corr_pearson\"] = ([\"lat\", \"lon\"], m_autocorr_pearson)\n",
    "\n",
    "corr=data_set2.corr_pearson\n",
    "corr.plot.contourf(robust=True,vmin=0.3,figsize=(10,3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# con geopotencial"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       ".xr-section-inline-details {\n",
       "  grid-column: 2 / -1;\n",
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       "\n",
       ".xr-section-details {\n",
       "  display: none;\n",
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       "  margin-bottom: 5px;\n",
       "}\n",
       "\n",
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       "  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",
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       "\n",
       ".xr-has-index {\n",
       "  font-weight: bold;\n",
       "}\n",
       "\n",
       ".xr-var-list,\n",
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       "\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",
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       "\n",
       ".xr-var-item > .xr-var-name:hover span {\n",
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       "\n",
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       ".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",
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       "\n",
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       "\n",
       ".xr-var-dtype {\n",
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       "\n",
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       "\n",
       ".xr-var-name:hover,\n",
       ".xr-var-dims:hover,\n",
       ".xr-var-dtype:hover,\n",
       ".xr-attrs dt:hover {\n",
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       "  width: auto;\n",
       "  z-index: 1;\n",
       "}\n",
       "\n",
       ".xr-var-attrs,\n",
       ".xr-var-data {\n",
       "  display: none;\n",
       "  background-color: var(--xr-background-color) !important;\n",
       "  padding-bottom: 5px !important;\n",
       "}\n",
       "\n",
       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
       ".xr-var-data-in:checked ~ .xr-var-data {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       ".xr-var-data > table {\n",
       "  float: right;\n",
       "}\n",
       "\n",
       ".xr-var-name span,\n",
       ".xr-var-data,\n",
       ".xr-attrs {\n",
       "  padding-left: 25px !important;\n",
       "}\n",
       "\n",
       ".xr-attrs,\n",
       ".xr-var-attrs,\n",
       ".xr-var-data {\n",
       "  grid-column: 1 / -1;\n",
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       "\n",
       "dl.xr-attrs {\n",
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       "\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",
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       "\n",
       ".xr-icon-database,\n",
       ".xr-icon-file-text2 {\n",
       "  display: inline-block;\n",
       "  vertical-align: middle;\n",
       "  width: 1em;\n",
       "  height: 1.5em !important;\n",
       "  stroke-width: 0;\n",
       "  stroke: currentColor;\n",
       "  fill: currentColor;\n",
       "}\n",
       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;hgt&#x27; (time: 360, lat: 9, lon: 144)&gt;\n",
       "[466560 values with dtype=float32]\n",
       "Coordinates:\n",
       "    level    float32 200.0\n",
       "  * lat      (lat) float32 10.0 7.5 5.0 2.5 0.0 -2.5 -5.0 -7.5 -10.0\n",
       "  * lon      (lon) float32 0.0 2.5 5.0 7.5 10.0 ... 350.0 352.5 355.0 357.5\n",
       "  * time     (time) datetime64[ns] 1981-01-01 1981-02-01 ... 2010-12-01\n",
       "Attributes:\n",
       "    long_name:     Monthly mean geopotential height\n",
       "    valid_range:   [ -700. 35000.]\n",
       "    units:         m\n",
       "    precision:     0\n",
       "    GRIB_id:       7\n",
       "    GRIB_name:     HGT\n",
       "    var_desc:      Geopotential height\n",
       "    level_desc:    Multiple levels\n",
       "    statistic:     Mean\n",
       "    parent_stat:   Other\n",
       "    dataset:       NCEP Reanalysis Derived Products\n",
       "    actual_range:  [ -354.45834 32321.098  ]</pre><div class='xr-wrap' hidden><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'hgt'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 360</li><li><span class='xr-has-index'>lat</span>: 9</li><li><span class='xr-has-index'>lon</span>: 144</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-129f66d4-8c72-4b64-98cd-13d877c2ccb3' class='xr-array-in' type='checkbox' checked><label for='section-129f66d4-8c72-4b64-98cd-13d877c2ccb3' 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>[466560 values with dtype=float32]</pre></div></div></li><li class='xr-section-item'><input id='section-792160be-5740-42b4-8e73-cd651aea05c9' class='xr-section-summary-in' type='checkbox'  checked><label for='section-792160be-5740-42b4-8e73-cd651aea05c9' 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>level</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>200.0</div><input id='attrs-071e0c0d-68da-4248-8bf2-ebfbf0f03bab' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-071e0c0d-68da-4248-8bf2-ebfbf0f03bab' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1b8dd361-81ee-4d38-adfe-f006cf8cf460' class='xr-var-data-in' type='checkbox'><label for='data-1b8dd361-81ee-4d38-adfe-f006cf8cf460' 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>millibar</dd><dt><span>long_name :</span></dt><dd>Level</dd><dt><span>positive :</span></dt><dd>down</dd><dt><span>GRIB_id :</span></dt><dd>100</dd><dt><span>GRIB_name :</span></dt><dd>hPa</dd><dt><span>actual_range :</span></dt><dd>[1000.   10.]</dd><dt><span>axis :</span></dt><dd>Z</dd></dl></div><div class='xr-var-data'><pre>array(200., dtype=float32)</pre></div></li><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'>10.0 7.5 5.0 ... -5.0 -7.5 -10.0</div><input id='attrs-0a4bbf57-7d18-4716-adce-3eedce6b4420' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0a4bbf57-7d18-4716-adce-3eedce6b4420' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-27aca8b7-63e1-41f9-b070-cf8fff1c1498' class='xr-var-data-in' type='checkbox'><label for='data-27aca8b7-63e1-41f9-b070-cf8fff1c1498' 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>actual_range :</span></dt><dd>[ 90. -90.]</dd><dt><span>long_name :</span></dt><dd>Latitude</dd><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>axis :</span></dt><dd>Y</dd></dl></div><div class='xr-var-data'><pre>array([ 10. ,   7.5,   5. ,   2.5,   0. ,  -2.5,  -5. ,  -7.5, -10. ],\n",
       "      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 2.5 5.0 ... 352.5 355.0 357.5</div><input id='attrs-66fac75a-6c63-4e70-9969-d1f1a5643e73' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-66fac75a-6c63-4e70-9969-d1f1a5643e73' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0f38ea13-e15b-4088-9eab-cf31d9ce5fb2' class='xr-var-data-in' type='checkbox'><label for='data-0f38ea13-e15b-4088-9eab-cf31d9ce5fb2' 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.  357.5]</dd><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>axis :</span></dt><dd>X</dd></dl></div><div class='xr-var-data'><pre>array([  0. ,   2.5,   5. ,   7.5,  10. ,  12.5,  15. ,  17.5,  20. ,  22.5,\n",
       "        25. ,  27.5,  30. ,  32.5,  35. ,  37.5,  40. ,  42.5,  45. ,  47.5,\n",
       "        50. ,  52.5,  55. ,  57.5,  60. ,  62.5,  65. ,  67.5,  70. ,  72.5,\n",
       "        75. ,  77.5,  80. ,  82.5,  85. ,  87.5,  90. ,  92.5,  95. ,  97.5,\n",
       "       100. , 102.5, 105. , 107.5, 110. , 112.5, 115. , 117.5, 120. , 122.5,\n",
       "       125. , 127.5, 130. , 132.5, 135. , 137.5, 140. , 142.5, 145. , 147.5,\n",
       "       150. , 152.5, 155. , 157.5, 160. , 162.5, 165. , 167.5, 170. , 172.5,\n",
       "       175. , 177.5, 180. , 182.5, 185. , 187.5, 190. , 192.5, 195. , 197.5,\n",
       "       200. , 202.5, 205. , 207.5, 210. , 212.5, 215. , 217.5, 220. , 222.5,\n",
       "       225. , 227.5, 230. , 232.5, 235. , 237.5, 240. , 242.5, 245. , 247.5,\n",
       "       250. , 252.5, 255. , 257.5, 260. , 262.5, 265. , 267.5, 270. , 272.5,\n",
       "       275. , 277.5, 280. , 282.5, 285. , 287.5, 290. , 292.5, 295. , 297.5,\n",
       "       300. , 302.5, 305. , 307.5, 310. , 312.5, 315. , 317.5, 320. , 322.5,\n",
       "       325. , 327.5, 330. , 332.5, 335. , 337.5, 340. , 342.5, 345. , 347.5,\n",
       "       350. , 352.5, 355. , 357.5], 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'>1981-01-01 ... 2010-12-01</div><input id='attrs-08754a05-e7e0-4e03-b9dd-0e6b98afb9d8' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-08754a05-e7e0-4e03-b9dd-0e6b98afb9d8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-08d52510-4338-4229-8fcb-5bf45a26287d' class='xr-var-data-in' type='checkbox'><label for='data-08d52510-4338-4229-8fcb-5bf45a26287d' 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>0000-00-01 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>[1297320. 1941600.]</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;1981-01-01T00:00:00.000000000&#x27;, &#x27;1981-02-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;1981-03-01T00:00:00.000000000&#x27;, ..., &#x27;2010-10-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2010-11-01T00:00:00.000000000&#x27;, &#x27;2010-12-01T00:00:00.000000000&#x27;],\n",
       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-669f9fb3-43f6-4f1a-9a8f-07d57036ca51' class='xr-section-summary-in' type='checkbox'  ><label for='section-669f9fb3-43f6-4f1a-9a8f-07d57036ca51' class='xr-section-summary' >Attributes: <span>(12)</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 mean geopotential height</dd><dt><span>valid_range :</span></dt><dd>[ -700. 35000.]</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>precision :</span></dt><dd>0</dd><dt><span>GRIB_id :</span></dt><dd>7</dd><dt><span>GRIB_name :</span></dt><dd>HGT</dd><dt><span>var_desc :</span></dt><dd>Geopotential height</dd><dt><span>level_desc :</span></dt><dd>Multiple levels</dd><dt><span>statistic :</span></dt><dd>Mean</dd><dt><span>parent_stat :</span></dt><dd>Other</dd><dt><span>dataset :</span></dt><dd>NCEP Reanalysis Derived Products</dd><dt><span>actual_range :</span></dt><dd>[ -354.45834 32321.098  ]</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.DataArray 'hgt' (time: 360, lat: 9, lon: 144)>\n",
       "[466560 values with dtype=float32]\n",
       "Coordinates:\n",
       "    level    float32 200.0\n",
       "  * lat      (lat) float32 10.0 7.5 5.0 2.5 0.0 -2.5 -5.0 -7.5 -10.0\n",
       "  * lon      (lon) float32 0.0 2.5 5.0 7.5 10.0 ... 350.0 352.5 355.0 357.5\n",
       "  * time     (time) datetime64[ns] 1981-01-01 1981-02-01 ... 2010-12-01\n",
       "Attributes:\n",
       "    long_name:     Monthly mean geopotential height\n",
       "    valid_range:   [ -700. 35000.]\n",
       "    units:         m\n",
       "    precision:     0\n",
       "    GRIB_id:       7\n",
       "    GRIB_name:     HGT\n",
       "    var_desc:      Geopotential height\n",
       "    level_desc:    Multiple levels\n",
       "    statistic:     Mean\n",
       "    parent_stat:   Other\n",
       "    dataset:       NCEP Reanalysis Derived Products\n",
       "    actual_range:  [ -354.45834 32321.098  ]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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ZDA8OtFa8nqwTyQ5N061rFDthvFnkt6pRbHdpfe1keGF9fRGujU6Gl9SKNACLaus7hreqT/7hx7Jk/WW1onwLVZRpgM47utOkhBNqJfMSpuuMRbEPxmgm8U02xpJaWWd7kXcjih/HutrYZHhpM9m3KsJL6usmw1skx+KOgdaZsO8cLKaPWThQbPf2JP6ewQWT4dVDxTFeP5KcB4uS8OJiX67foqjb4JrWeg6sLY5TUqUW6W+mmbwupewakP7eGsl1Znxh63abw0m64WI/N4eKcCRhRopjXxsswoNDRbheL86DgSQ8NNh6Yaslx7XsmbFG8tsbHSv27ZrFRaXWLizC6f4fW1TsnNHFrR0Wi7Z85GR4+M/3FeW4o7jr1Ly3iG+OFr+3ljoMFXlrcXGuaOnSYt0tW6cmGt+iOHfWLUnKu7A4gI3hot7jSTi5tMyKRun1oftDfyVjtE8uSftN4Jslyy4B9ugQvw54SdeC5CSNkE0XsTswuWcj4jVVt1GVbyOamZlZJWNR6/hpTNVTMXd9CdgO+DvgArLxXyt7kZEbW2ZmZlbJKPWOn7Ierznu4RHxXmB1RJwKPAd4dC8y6pvbiGZmZtZbYyVjc+ZpY2vipv09kvYA/gLs1IuMpmxsSXpRhW2si4izZ6g8ZmZmNkc1ShpbMT9vIy6XtCXwXrInGRcD/9qLjLr1bH2O7JHKqfbiUwE3tszMzPrcWMlDN415OCopIv4nD14APKyXeXVrbP2g26h8SafNYHnMzMxsjhoreZR8PjW2JL0iIk4rm9y0F5OaTtnYiohXdNtAlTRmZmY2/5U1qmJ+jdmamIuj0wSmPXm9T+UB8pKeTDZwbHKdiPhiD8pkZmZmc9BYyQSLZWO55qKI+Gwe/FFE/CJdJmm/XuRZqbEl6UvALsBlFLPyB+DGlpmZ2WZitA9uIyY+BexdIW6jVe3Z2gfYreQdQ2ZmZrYZ6IeeLUlPAp4MbNs2bmsplDwBsJGqNrauJJtl9dZeFMLMzMzmvrKZ4jfuRV2b3BDZNA8DtI7buo/sBdYzrts8W98lu124BPi9pF8Bky8di4jn96JQZmZmNveU9Ww151HPVkRcAFwg6QsRceOmyLNbz9bHN0UhzMzMbO7rh6kfEsOSlnP/h/8OmOmMuk39cAGApOMj4l3pMknHk00EZmZmZpuB0sbWPOrZSnwdOAn4H4qH/3qi6pitZwLvaot7doc4MzMz61N9Ms/WhPGI+MymyKjbmK03AEcBu0i6PFm0BPhF57XMzMysH5X3bM3LxtZ3JR0FnEnrePS7Zjqjbj1bXwZ+AHwEODqJX9mLwpiZmdnc1Wdjtg7P//+OJC7owXsSu43ZulfSSuDRm2rEvpmZmc1NZU8dxjzs2YqInTdVXl3HbEVEU9LvJO0YETdtikJNRwzA+q3zcHLMlUz+oWT4m6YxPWvLOZbk0ZJfGp+uWzJNmsomJ5li0pIoybvKuV5lDGP71LVl+60lWVqOJI+WMiUrtByX8SJcb7/vv77zDk3L0UzO4uZQkXlzsEgU9SLcSMMDxcqj9dadriRdLQnXBzuPo6wlhVrXGJwMj9THJsPD9aKyC2pF/IL6aMu2hmtFuiX1dZPhLeprJsPbDtw3GV6WxI+o2O5gsqPrpOGkbm0/hnRZveQ1YY3kODVajlmx/8u2M6g03LbhJLuFKo5NPf1hJcGRKOo0lJyog7qvJH68YxhgsNZ9fGwtKaCSeqyuDU+Gx5Pzf7RW/PAbybnZGGnd7sCaolLJ6dKi7HfVEp9cZ5qDSXioCI8vaD2mzZHkdzKc/AaGinBtqNg3Q8PFfhsZLgq7aKg4h9N9OVgvwgO11t9Yuj+byYEdbRQVGWsW4aFkW7VkW6uTYzHWck1M/4lrPdmaA8UOGl+0ZVHe7YpplwbWFPWrjbaeL5N5DBblGx8p8htflFxblrb+AzC6pDhoY4uK+JZjlhQ9nW0hTTMb+qlnS9JC4G3AjhFxpKRHAI+MiO/NdF5VB8g/CLgqn2dr9USk59kyMzPbfKSN31RzHvZsAZ8HfkM2mzzACrInFGetsfX+mc7YzMzM5pc+m/phl4j4B0mHAUTEWkk9aTVWamxFxAWSHgg8Po/6VUTc1osCmZmZ2dzU7K+pH0YlLSAfwCBpF5KnEmdSpaaopJcCvwJeArwUuFhST94fZGZmZnPTWLPW8TNPp344DjgH2EHS6cCP6dH8oVX7/Y4FHh8Rh0fEq4AnAO/tRYHMzMxsbhqLesdPlQHykk6RdJukK5O4j0m6WtLlks6UtKxtnR0lrZL09iTuMElX5OucI2mbPH5Y0lclXSfpYkk7TVWeiPgh8CLg1cAZwD4RcX71vVFd1cZWre224Z0bsO60Sboh36GXSbqk1/mZmZlZufFmveOn4gD5LwAHtcWdB+wREXsC1wLHtC0/gWy+TwAkDQD/BTw9X+dy4E354iOAuyPi4fl6x09VGEk/jog7I+L7EfG9iLhD0o+rVGRDVR0gf46kc8lafgD/AJzdiwJ18PSIuGMT5WVmZmYlmiVjs6qM2YqIC9t7m/LepQkXAZNDlCS9ALieZBYEsjk8BCySdCewFLguX3Yw2a1BgG8AJ0pSROuERpJGgIXANpK2pJgXZCnw4K4VmYaqA+TfIenFwH55oZZHxJm9KJCZmZnNTWVTPzSaM3Kz6zXAVwEkLSIbP/VMYPIWYkSM5a8SvIKsEfZH4I354ocAN+fpxiXdC2wNtHfYvA54K1nD6tIk/j7gv2eiIu2q9mwREd8EvtmLQkyVLfBDZbMHfjYilqcLJR0JHAkwsGzLDqubmZnZTBkvmeIh7/E6IB1bRdYxs7zjCm0kHQuMA6fnUe8HToiIVelsDJIGgTcAjyXr9foU2a3HD9E+c23mfjMzR8R/Af8l6c0R8akq5dtYlRpbkl5Edu/zARRdeBERS3tYNoD9IuIWSQ8AzpN0dURcOLEwP4jLAUa232Eac8KbmZlZVWVjs/IbdT+JiCM3dJuSDgeeCxyY3PLbFzhE0keBZUBT0jrg4iy/+L983a9RvLt5BbADsCIf27UFMNV7nD8r6S3AU/PvPyXr2Cl5j8P0Ve3Z+ijwvIj4w0wXYCoRcUv+/9sknUn2FOSFU69lZmZmvTBeOoP89G4jSjqI7Hbh0yJi8r1jEfGUJM1xwKqIOFHSg4HdJG0bEbeT3WacaJucRfZy6V+Sjf36Sft4rTafBgbz/wO8EvgM8E/TqswUqja2/rqpG1r5/dpaRKzMw88CPrApy2BmZmaFLrcRpyTpDGB/soHpK4D3kd0CHCa7ewVwUUS8vmwb+d2u9wMXShoDbiSbugHgZOBLkq4j69E6tEuRHh8Rj0m+/0TS77pWZBqqNrYukfRV4Nsks6tGxLd6UajcA4Ez850/AHw5Is7pYX5mZmY2hfGSgfBVpn6IiMM6RJ9cYb3j2r6fBJzUId06ssnXq2pI2iW5JfkwoPsb6aehamNrKbCGrHdpQgA9a2xFxPXAY7omNDMzs02ifMzWvJxB/h3A+ZKuJxuL/lDgH3uRUdWpH6bMXNIxEfGRmSmSmZmZzUVltxEb8/DdiBHxY0mPAB5J1ti6OiJ68m7EylM/dPESwI0tMzOzPlZ6G7E5/xpb+eSmRwF/S3a37meSTspvR86omWpszb+9bGZmZhukbPLSeXob8YvASrK5ugAOA77Eho37qmSmGluzPsdVCBrDWTGUlqZlqFsyMdr4NDJJzqWW8yo598p2REv6JKy0fFPsxXT9SJ68LXvaVs3O4TSPdD+Vpgdodg6rrLxJmlqyn6PZOay0PlMNTWwv18T6SR5qJMc42VbUkx1YKwqe7r8YaN2ZzcFmsixZJ6n3unrxE7pHCybDY42iUoP1oiAj9aKwiweL3uqh2khL3sPJjruvXixbOVCE18VgkXdjUZFHrZgiZjDZOSMq4hfVirwH28aD1u93AtxfI9lxafp6coKNtByYIk26l2ttT2UPllyva8lateQHVFNJfHritbzpozDVo+pp/crSpU9fpf/QrE7+MRov25Vq3Wb6m24kM/y0HIqSfZMWr5lc0ZuDabjYz83h1n0eyTKGkuM0VJwXg8PF/lw4MjoZ3mJk7WR48WARP5Sc8wPpsW87t2rJRWS0URR+DYN0ko4XGqgX2xoYKMLjw0XejYXJsVjcus9HxztfxBuDRd6DI8l5PlrERzLJZnO4SDOW5De6uEgztrj14I0VP1caxWWj5fiV/XuTXotmQ/nreualR7Y9jXh+r55GnKmXSc/LJq2ZmZlV12jWOn4qvoh6rvmtpCdOfJG0L/CLXmQ0Uz1bX5+h7ZiZmdkcVXYbcbqTms6yfYFXSbop/74j8AdJV5C9JWfPmcqo6ut6Pkr23qG1wDlkUzK8NSJOIyvRv81UgczMzGxuKp36YROXY4Yc1C2BpMsrbOf2iDhwqgRVe7aeFRHvlPRCsncPvQQ4Hzit4vpmZmY2zzX6aJ6tiLhR0mOAiVcD/Swi2sds1YG/n2IzIntN0JSqNrYmRgb+PXBGRNyVvoXbzMzM+l/5bcT51yaQ9M/AaykmaD9N0vKI+FSS7HURcWOX7RzVLa+qja3vSrqa7DbiUZK2BWZ8HgozMzObu8rm05rydc9z1xHAvhGxGkDS8WQvsZ5sbEXEz7ttpEqaSiPaIuJo4EnAPhExRvbqnoOrrGtmZmb9IUKln3lItE4Q1aBkdgVJz5X0W0l3SbpP0kpJ91XNqOoA+YXAG8lG6h8JPJhsevvvVc3IzMzM5rdGf/VsfR64WNKZ+fcXUP5i7P8EXgRcEbHhta16G/HzwG+AJ+ffV5BN9+DGlpmZ2Wai2UczyEfEJyT9lOx1PQL+MSJ+W5L8ZuDK6TS0oHpja5eI+AdJh+UFXCuPkDczM9uslE/9MG+bBAuBlRHxeUnbSto5Iv7UId07gbMlXQBMvoIjIj5RJZOqja1RSQvIp9KQtEuamZmZmW0GSt9Jt0lLMSMkvQ/Yh2xY1OfJZl44DdivQ/IPA6uAEWBoQ/Oq2th6H9lkpjtIOj0vyKs3NDMzMzObv8qeRpyPUz8ALwQeC1wKEBG3SFpSknariHjWdDOq1NiKiPMkXQo8key+5j9HxB3TzdTMzMzmnygZs8X8bGyNRkRImrhrt2iKtD+S9KyI+OF0Mqo09UM+PuvZwOMi4nvAQklPmE6GZmZmNj9FlH/moa9J+iywTNJrgR8BnytJ+0bgHElrezb1A/BpoAkcAHwAWAl8E3h81YzMzMxsfouS24jzccxWRHxc0jOB+8jGbf1rRJxXkrbs9mIlVRtb+0bE3pJ+m2d6t6QNHiBmZmZm81dZY2s+Tv0A2TApoGMDC0DSdhHxl6m2USVNpduIwJikOsXTiNuS9XSZmZnZ5iLU+TMPTdwKbPvcLOlMSQ/Lk51dYVNd01Tt2fokcCbwAEkfBg4B3lNx3U1Gzdb/A1DWCk+amSppNrafP6rQTdpM9mjU0/yKlZWUqTbeuRztebdsq8J2Uy31i87xSl9Y0LY/Wupd5TeVbjddt2z/Jdu8329WJeGyNKmyvNNM0oEGjdYNScVJEsnOSv+yS1/KOtpIDsxo0fE7UC/WHR/o/PfNSH285ftordjW+mbnn+lgctCa9WK7Y1HMyjJSGytWSLIeivTEa9twskvqSb0b0f1vs1pywNPwYBIeaQm3bnMwKeSgin1QSwpZT9eJZsf0g8lJOJLsp4W10cnwkvralrxHkx/WuhicDK9tFsdybaOIXzRYhNe1hIv8GkPJOZScX837/RaSdMlpVHZtSqWnc3MwiR8sMomBKS5eJb+TdCbF9Fim58RArQgP1ZP9XB9N0hTx9baLaC3JcCi5GNZKLrZjzXrH+JbUJdeM+52+ybI0u1ojOYfHk+trEo7BYuXx4SI8tigJL07jW7MeT743RtKdniQqO/az3a7po6kfgE8AtwBfJtuzhwLbAdcApwD7A4/pMjZLZLchp9S1saXsX5w/kU3odWC+4RdExB+6rWtmZmZ9pHTM1my3AqfloIjYN/m+XNJFEfEBSe8GiIjOLfwN1PVP1YhoAv8REVdHxH9HxIluaJmZmW1+otn5U6VnS9Ipkm6TdGUS9zFJV0u6PL99t6xtnR0lrZL09iRuSNJySdfm6744jx+W9FVJ10m6WNJOXYrUlPRSSbX889K0qt1rVF3VMVs/lPRiv6LHzMxs86WmOn4qNk2+ABzUFncesEdE7AlcCxzTtvwE4AdtcccCt0XErsBuwAV5/BHA3RHx8Hy947uU5+XAK4HbgL/m4Vfkb8x5U6UaVVR1zNbbgEXAuKR1ZLcSIyKWzmRhzMzMbA7biDFbEXFhe29T2yShF5GNCQdA0guA64HVbZt6DfCofP0mMDHJ+sHAcXn4G8CJklT28uiIuB54Xklxfz51bTZMpZ6tiFgSEbWIGIqIpfl3N7TMzMw2J011/szMmK3XkPdi5bO5vwt4f5oguc34QUmXSvq6pAfmcQ8BbgaIiHHgXmDrmSiYpC9ViStTdQb5vTt8dpFUtWdsg0k6SNI1+b3Xo3uVj5mZmVXULPlkfUcHSLok+RxZdbOSjgXGgdPzqPcDJ0TEqrakA8D2wC8iYm/gl8DHJzbTYdMzNfZq97by1oHHVV15Q2aQ3xu4Iv/+aOB3wNaSXj/ddwWVySvx38AzgRXAryWdFRG/n8l8zMzMbANM3YP1k4io3MCaIOlw4LnAgcktv32BQyR9FFhGNph9HVnbYA3ZdFQAXycbqwVZe2EHYEXeGbQFcNeGlqetbMcA7wYWJFNACBgFllfdTtXG1g3AERFxVZ75bsA7gA8C3wJmtLEFPAG4Lr+fiqSvkN2LdWPLzMxslpTN/VZlHsqO60kHkd0ufFpErJmIj4inJGmOA1ZFxIn59++SzYH1E7IpqSbaBmcBh5P1dh1C1vgrLZmkYeDFwE4k7aGI+EAS/gjwEUkfiYj2wfuVVW1sPWqioZVn/ntJj42I63v0gOLkfdfcCrJWbou8i/JIgIEtt+xFOczMzCxX2qiqNvXDGWSNpG0krQDeR/b04TBwXt6euCgiXt9lU+8CviTpP4HbgX/M40/O468j69E6tMt2vkM2rus3wPqpEkbEMZIeAjyU1obZhV3yAKo3tq6R9BngK/n3fwCuzVuFY+WrTVul+64RsZy8G294hx3m5/y1ZmZm88VGTGoaEYd1iD65wnrHtX2/EXhqh3TrgJd0LUhh+4hon4qiI0n/TtZ4+z0w8VqEAGa0sfVq4CjgrWQNoZ8DbydraD294jY2xMR91wnbk02pb2ZmZrOlv17X87+SHh0RV3RPyguBR0bElD1gZSo1tiJiraRPA9+LiGvaFrc/KTATfg08QtLOwJ/JWpMv60E+ZmZmVtFMj9maZX8LvFrSn8huI07MIbpnh7TXA4N0ud1YplJjS9LzgY8BQ8DOkvYCPhARz59Opt1ExLikNwHnAnXglHTMmJmZmW16pS9Hn5+NrWd3SyDpU2S1WwNcJunHJA2uiHhLlYyq3kZ8H9kTgj/NN35ZhXcObZSIOBs4u5d5mJmZ2QYoHbO1aYsxE/KxX0h6ADBSkuyS/P+/IXvacVqqNrbGI+JevxrRzMxs81V2u3A+tg7yu3b/ATyY7P2IDwX+QDKBaUScOhN5VW1sXSnpZUBd0iOAtwD/OxMFMDMzs/mhz8ZsfRB4IvCjiHispKcDnZ6YRNIV3L//7l6ynq8PRcSdU2VU6XU9wJvJWnrrgTOA+8ieTDQzM7PNxdSv65lvxvJGUk1SLSLOB/YqSfsD4PvAy/PPd4GfAX8BvtAto6pPI64Bjs0/c5KiaHGrkXRopieAOoejVhLf1i+afm9p3Sd5pNtqDkcSX4SVTGgbo8VGa+PlHbFRT7dVVvbOlU3nz1WjY5IpteyHNL+SfdCiZJ+X7ue25n9LXUvSNetJmoFkP6XxJfuPwaIS6boASo9ZEq4NFOvUa2m4805oNIsMRxvFT27NFH8KDpUcnPXNZP3m0GR4pFZMdzeWVHwwigPeTCreSHbgCOOteScnSS0p4yJ1n1KvNX2xnYXJEISR5JWqw22vVx1UUfZayd+CzeTEayYn3voo6rEmqffqZH+k+2x1c7hluyubCybDqxrF8I1V48U6axpFeN14UfbRRnqy0Tk8DVXe7Vt6ntc7JO4kuV7GeLHPm+PFfm4MFPFjyQ9uLKl3s6Sw9eScGGi5ALWeL7VIfkuDnXfcaJL3aopjEek4oqQ+teT6OtD23PzInUUeC/9anNvDd64r1l/d+cGzxtLi/GiMLOiYpuXa1XYsWr5v6P23sgHqm8jGTGo6B90jaTFZo+l0SbdB2wWxsF9E7Jd8v0LSLyJiP0mv6JbRlI2tfEr80l3Yq6cRzczMbA7qr8bWwcBasjt1Lyd7l+IHStIulrRvRFwMIOkJwOJ8WVkDbVK3nq2JN2m/CNgOOC3/fhjZ+xLNzMxsM9FPY7YiYrWkhwKPiIhTJS0km26qk38CTsl7wkQ2nOqfJC0CPtItrykbWxFxAYCkD0ZEOjX+dyVVmqLezMzM+kM/zbMl6bVk71feCtiF7L3MJ5G93LpFRPwaeLSkLQBFxD3J4q91y6vq04jbSnpYRFyfF3BnYNuK65qZmVk/mIeNqim8kWwO0YsBIuKP+ZxbkyS9IiJOk/S2tnjydT5RJaOqja1/AX4q6fr8+05krUEzMzPbTPTTbURgfUSMTjScJA1w/+bkovz/SzYmo6pPI56Tz6/1qDzq6um+jNHMzMzmp366jQhcIOndwAJJzwSOIpvSYVJEfDb///s3JqMp59mStHeS4fqI+F3+Wd8pjZmZmfWxsnm2ZnlKimk6GrgduAJ4HdkrAt/TKaGkXSX9WNKV+fc9JXVM20m3nq3PS9qfqWcCORl4bNUMzczMbH7qp9f1REQT+Fz+6eZzwDuAiZ6uyyV9GfhQlby6Nba2IHv54lT78fYqGZmZmdn81k+3ESU9l+yVPQ8law8JiIhY2iH5woj4Vds7orvOrzWh29QPO1XdkJmZmfW5PmpsAf9JNo/oFRHRrQZ3SNqFvKaSDgFurZpR1acRzczMbDNXdptrPt5GBG4GrqzQ0IJsmojlwKMk/Rn4E9D1NT0T3NgyMzOzSvrpNiLwTuBsSRcAkw/+dZo7K59n9Bn5jPG1iFi5IRm5sWVmZmbV9Fdj68PAKmAEkjebdyBpGHgx2TyjA8mkpmXvUmxRqbGlbKsvBx4WER+QtCOwXUT8qsr6ZmZmNv+VTmo6P6d+2CoinlUx7XeAe8keGtzgeUar9mx9mqw9ewDZG7FXAt8EHr+hGZqZmdn81E9TPwA/kvSsiPhhhbTbR8RB081oyklNE/tGxBuBdQARcTddutzMzMysv6jZ+VPlNqKkUyTdNjExaB73MUlXS7pc0pmSlrWts6OkVZLe3mF7Z7Vta1jSVyVdJ+liSTt1KdIbgXMkrZV0n6SVku4rSfu/kh7dvZadVW1sjUmqUzzyuC3zdb5YMzMzm56y2eOrjdn6AtDeO3QesEdE7AlcCxzTtvwE4AftG5L0IrLxVqkjgLsj4uH5esdPVZiIWBIRtYhYEBFL8+8tc2xJukLS5cDfApdKuiZvGE7EV1L1NuIngTOBB0j6MHAIJVPaz5ZQ9gHKm5BJ/2fUkk7P9CRJowdaz56oJ1+SpmbardocTPIYSsIDxQpqJpnUi8JGI91QW6ds+rWkHzetU9SSvJNyq1GkqY0V8VVb3S1FKvtxpfuwNv3w/ZbVO8e37PP0mJUdy+HiWNSGiznp6oOtfz8o2YdKKjs8WKyzcKjYiSMDyQ5NNJOCDNYaxbpJ+pF669x4Q8n3oVoRHkwGRgyqUSFcrDuSHPARdQ4DLGxJ10zCxT6ol9w0SA/foIoDNpKEhzWYpN/wM28sivqtiaKs9zaL+HuaRR5/aRTXztvHi/Bfx7Zo2e6to8X3v6wr0t2xbtFk+J61CybDq9YNT4bXry3ya6wrLqtaW9S7vq7YZ/W1rfuvnowAqXU+jUp/C0lVSU/6ZnphS64N7UeuZVvJhI1RLxaMJ7+F0YEift1Akfm6Rno+F+FiL7Wev+3qJReUdFsDtWJrjWaxQ8bHkkqsKcKD9xb1WXxL6/aXXXlv8eXq6yeDzfXrinBJWQcetN1keLj2wKJMQyNFeLiexLeuX0u+t/xblO6D6Hw9n9bFegZtzIuoI+LC9t6mtlt4F5G1L7JtSi8ArgdWt+QlLQbeBhwJfC1ZdDBwXB7+BnCiJFWc2qHMczdi3UlVX0R9uqTfAAeS/VZfEBF/mIkCmJmZ2fygsnbLzDyN+BrgqwD5FAvvAp4JtN9C/CDwH8CatviHkM2dRUSMS7oX2Bq4Y7oFiogbp7tuasrGlqStkq+3AWekyyLirpkohJmZmc19XXq2DmgbW7U8IpZX2q50LNnrb07Po94PnBARq9JX5EjaC3h4RPxLhzFZnbrc58SkFN16tn5DVlABOwJ35+FlwE3Azr0snJmZmc0dXSY1/UlEHLnB25QOJ7tdd2Byy29f4BBJHyVrczQlrQMawOMk3UDWhnmApJ9GxP7ACmAHYIWkAbL3O5d2Ckn6OPD5iLhqQ8u8obq9G3HnvEAnAWdFxNn592cDz+h14czMzGwOKesnmmb/kaSDyG4XPi0iJm8LRsRTkjTHAasi4sQ86jN5/E7A9/KGFsBZwOHAL8nGfv2ky3itq4HlecPs88AZEXHvFOmnrepQu8dPNLQAIuIHwNN6USAzMzObm8qmfqgyQF7SGWQNoUdKWiHpCOBEYAlwnqTL8s6d6ToZ2FrSdWQD6I+eKnFE/E9E7Ae8imxm+MslfVnS0zeiDB1VfRrxDknvAU4ja7++ArhzpgszIW/Fvha4PY96d9rYMzMzs01PzbIB8t1bWxFxWIfokyusd1xJ/A3AHsn3dcBLuhYkkU9r9aj8cwfwO+Btkl4XEYduyLamUrWxdRjwPrLpHwAuzON66YSI+HiP8zAzM7OKNmbqh7lG0ieA5wM/Bv4teQXh8ZKumcm8qk79cBfwzzOZsZmZmc0vXQbIzzdXAu9Jx4olnjCTGVV9EfX5dNiVEXHATBamzZskvQq4BPh/+SuCzMzMbJb0Q8+WpL3z4GXAo9KpJQAi4tKZHihf9TZiOm/GCPBisvkwpk3Sj4DtOiw6luxJgw+SNfAmJi97TYdtHEk2gywDy7bcmOKYmZlZF+VjtjZtOTbSf0yxLIAZ70iqehvxN21Rv5B0wcZkHBGVpo6Q9DngeyXbWA4sBxjeYYf5dajNzMzmmX7o2YqIGX/asJuqtxHTmeRrwOPo3Cs1IyQ9KCJuzb++kOy+qpmZmc2m0tf1zKPWVkLSk8mmfZhsD0XEF2c6n6q3EdOZ5MeBP5G9XbtXPppPyR/ADcDrepiXmZmZVdAPPVsTJH0J2IVs7NbEm+wDmLXG1t/k81dMkjRclnhjRcQre7VtMzMzmx41+mLM1oR9gN26zDI/I6rOIP+/HeJ+OZMFMTMzszkupvjMP1fSwyFRqSl7tiRtBzwEWCDpsRRv1F4KLOxx2czMzGwOKXsaUfNozJak75I1D5cAv5f0K2D9xPKIeP5M59ntNuLfAa8Gtgc+kcSvBN4904UxMzOzuatPJjXd5G+nmbKxFRGnAqdKenFEfHMTlWl6BM2RiaNd4agnrfB0YF8kN1ZjuO2sGiw5yyKZEK1ebKw20CjikyTN8SKTSNI3G60Tq1XSTNZJ/7KIzmlqyf32qBfxMZqkabu5XBsrwi37qtk5PpXumqgnRUrOvBjoHN++TtSSsresn8QPpmmKAipJUx8ujsvIgtHJ8KLhIgwwWC/SKangcL2YYm7p0OQfQyweKMK1JH0tuToN18aT9EV+C2pteavIu96yfnEwFibrLKmtLepRW98xvKRWDLtcpHQ7yXkKLEqO2bCKk2FExcEYoAjXkzS1yiMTNsz6KMq7Kop6354U/fbGksnwbUn4lrFiDr4/ry/Ct67boiWPW9YsnQzfuWZRkd/qYnjq+JrBYoXVxUlYX1vstJEkPJDMS10vDgUDxeHKlyXnSzKDYfr7aSZZN4aKBY2RJD4ZSdsYSX7f6XWt7RC1/uZqSXz6uyq21WwWaRrJtWW0UZwTo8lGF1Ecr4G2c61ecuGoJXmMJxeBgeS3kF7ummNF+vqaIrzgjiLNsj/c15JH83e/75h3FeO3/qUoU7Mo04KBBxfbHywOTHOgdadHcixbrpEDyTGrp9fqNM3stmrKxmyVNsLmoIi4AEDS8RHxrnSZpOOBjZraqpNutxFfERGnATtJelv78oj4RIfVzMzMrA+V3y6cX11buWcC72qLe3aHuI3W7TbixJ93izssm5d71szMzKapD2aQl/QG4CjgYZIuTxYtofMDgRut223Ez+bBH0XEL9JlkvbrRYHMzMxsbuqHAfLAl4EfAB8Bjk7iV0bEXb3IsOoAi09VjDMzM7M+pUZ0/DC/xmzdGxE3RMRhwApgjKxvbrGkHXuRZ7cxW08Cngxs2zZmaykkI2TNzMys//XRkC1JbwKOA/5K0VwMYM+ZzqvbmK0hsvFaA2T3MifcBxwy04UxMzOzuUvNzl1YinnUtVV4K/DIiLiz1xl1G7N1AXCBpC9ExI29LoyZmZnNXX32up6bgXs3RUZV3424RtLHgN2ByclDIuKAnpTKzMzM5p6ygfDza4D8hOuBn0r6Pq0zyM/4tFZVB8ifDlwN7Ay8H7gB+PVMF8bMzMzmsEZ0/szLthY3AeeRDZlaknxmXNWera0j4mRJ/5zcWpzxGVbNzMxs7iods1U2/9YcFhHvB5C0JPsaq3qVV9XG1sS7Mm6V9BzgFrL3JZqZmdnmoqSxNR9vI0raA/gSsFX+/Q7gVRFx1UznVbWx9SFJWwD/j2x+raVko/jNzMxsc9EfL6KesBx4W0ScDyBpf+BzZFNezahKY7Yi4nv5JGBXRsTTI+JxwC4zXRgzMzObu9Rsdv5U6NmSdIqk2yRdmcR9TNLVki6XdKakZW3r7ChplaS3598XSvp+vs5Vkv49STss6auSrpN0saSduhRp0URDCyAifkrxmsIZVXWAfCf3ezG1mZmZ9bFGs/On2jxbXwAOaos7D9gjIvYErgWOaVt+AtmrdVIfj4hHAY8F9pP07Dz+CODuiHh4vt7xXcpzvaT3Stop/7wH+FOVimyojWlsacZKYWZmZnNfRMmnyqpxIXBXW9wPI2I8/3oRyXhwSS8gm57hqiT9moneqIgYBS5N1jkYODUPfwM4UNJUbZXXANsC3wLOzMP/2L0mG67qmK1O5ucdWjMzM5ueRkkP1sw8jfga4KsAkhYB7wKeCby9U+L8luPzgP/Kox5CNlEpETEu6V5ga+COTutHxN3AW2ai4N10ezfiSjo3qgQs6EmJzMzMbG5qNjrHZ7cRD5gYW5VbHhHLq2xW0rHAONm8npDN6XlCRKzq1DklaQA4A/hkRFw/Ed2pZB3WPWuqskTE86uUeUN0e11PTyb36ol6EIvznsh0dyvZzyWdiemRqA8WrfbhkdGWdEODnU+ydFxgWYfl+tFiV4+uH5wMp1vUULGh2kDb+aHOfzVEM8kwOsdHo7hb3BgrwhpN0tSL+BhtrUSU1KmW/IGjkt9fus+bydnWHErD0TE+K1d0DJPun+GiILWBoiBK0tcHijQjQ2OT4aUL1k2Gtxxe25L3SH18MjyUhBfUi/WXDCTrD6yZDC+sry/S1Io0C2tF/NJ6ET+i1nOtTC05yEPJTh/UeBJO6prEL0rSjyQn6mDbSVtLDtpgMtJgIHn3/KA2plO8MBbjLd/XJ9/Hksee1kRR9rsaRd63NRZPhm8Z33IyvGJ0q8nwjWu3ngzfvGbZZPjWlUtb8l65svj7sXFf8RutryrqPbwq2Tcri3WHktl5BlcVx2hwdTMJF3UYWN1a79q64pzSeLFODBZ5NxYWZRpbVOyDscVFmtGlRfnGFhbh9HfVaPuNNUaKcNSTBcmf0+lvaWgw+V0kv7eB5ILQcp7WivQLa63n+XCtdT9Mlr1WFKSWXPtWDhSFHap3vujUk+vXyF3JRerqngzFYfyvt02GBxctLPJesE2RZmSwZZ3GUHJskp9SI9n/LdfL5BoXw5XGRvVOWc9W9g/hTyLiyA3dpKTDgecCB0ZM/ou6L3CIpI8Cy4CmpHURcWK+fDnwx4j4z2RTK4AdgBV5Y2wL2m5b5p5E1gN2BnAxm2BY1MxcMc3MzKz/lb6uZ3qbk3QQ2e3Cp0XE5F+sEfGUJM1xwKqJhpakD5E1pP6pbXNnAYcDvwQOIWv8dSrZdmS3Jw8DXgZ8HzijF/NrTdiYAfJmZma2OWk0On8qPI0o6QyyhtAjJa2QdARwItkrcs6TdJmkk7psY3vgWGA34NJ8nYlG18nA1pKuI5sx4ehO24iIRkScExGHA08EriN7R+Kbq+yC6XDPlpmZmVVTNoN8hQHyEXFYh+iTK6x3XBJeQcltv4hYB7yka0HI5uQCnkPWu7UT8EmypxJ7wo0tMzMzqyQaZWOXZ3ks2QaQdCqwB9n8Xe+PiCu7rLLR3NgyMzOzasp6sObXZFCvBFYDuwJvSZ52FNkLqZeWrThdszZmS9JL8qn2m5L2aVt2TD7d/jWS/m62ymhmZmaJsjFbZbcX56CIqEXEkvyzNPks6UVDC2a3Z+tK4EXAZ9NISbsBhwK7Aw8GfiRp14gom1zAzMzMNoHS24jzqLE1G2atZysi/hAR13RYdDDwlYhYHxF/IntK4AmbtnRmZmZ2P2Wv67EpzcUxWw8hez/ShBV5nJmZmc2mkp6t+XQbcTb0tLEl6Udkk4e1OzYivlO2Woe4js1mSUcCRwLUt142nSKamZlZRWW3Ed27NbWeNrYi4hnTWG1iuv0J2wO3lGx/OdmU/QzvvL2PtJmZWe/E2Pg6BjR4vwXrs8nfV91vgQFzcwb5s4BDJQ1L2hl4BPCrWS6TmZnZ5u6Um7jufpGjsZ67uB3g3E1eonliNqd+eKGkFWQvhPy+pHMB8ncTfQ34PXAO8EY/iWhmZjbrTrmNFYzHWEvkTfyRHXk4EW0LbNKsDZCPiDOBM0uWfRj48KYtkZmZmZWJiPV/o725iet4GH8DZL1ad/AXVnHP0CwXb06bi7cRzczMbA66mt+OpL1b7tWqxo0tMzMzqyQi1m/Pw7iJ6yZ7tX7PJe7V6sKNLTMzM6tsonfren7vXq2K3NgyMzOzyiZ6t+50r1Zlij6ZiEzS7cCNwDbAHbNcnNngem9eXO/Ni+u9edmQej80IrbtZWE6kVQHFkSE59aqoG8aWxMkXRIR+8x2OTY113vz4npvXlzvzcvmWu9+5tuIZmZmZj3kxpaZmZlZD/VjY2v5bBdglrjemxfXe/Piem9eNtd6962+G7NlZmZmNpf0Y8+WmZmZ2ZzRN40tSQdJukbSdZKOnu3y9JKkGyRdIekySZfkcVtJOk/SH/P/bznb5dxYkk6RdJukK5O40npKOiY//tdI+rvZKfXGK6n3cZL+nB/zyyT9fbKsX+q9g6TzJf1B0lWS/jmP7+tjPkW9+/qYSxqR9CtJv8vr/f48vt+Pd1m9+/p4b/YiYt5/gDrwf8DDgCHgd8Bus12uHtb3BmCbtriPAkfn4aOB42e7nDNQz6cCewNXdqsnsFt+3IeBnfPzoT7bdZjBeh8HvL1D2n6q94OAvfPwEuDavH59fcynqHdfH3NAwOI8PAhcDDxxMzjeZfXu6+O9uX/6pWfrCcB1EXF9RIwCXwEOnuUybWoHA6fm4VOBF8xeUWZGRFwI3NUWXVbPg4GvRMT6iPgTcB3ZeTHvlNS7TD/V+9aIuDQPrwT+ADyEPj/mU9S7TL/UO6KYEHMw/wT9f7zL6l2mL+q9ueuXxtZDgJuT7yuY+mI13wXwQ0m/kXRkHvfAiLgVsos38IBZK11vldVzczgH3iTp8vw248Stlb6st6SdgMeS/dW/2RzztnpDnx9zSXVJlwG3AedFxGZxvEvqDX1+vDdn/dLYUoe4fn7Mcr+I2Bt4NvBGSU+d7QLNAf1+DnwG2AXYC7gV+I88vu/qLWkx8E3grRFx31RJO8TN27p3qHffH/OIaETEXsD2wBMk7TFF8n6vd98f781ZvzS2VgA7JN+3B26ZpbL0XETckv//NuBMsi7lv0p6EED+/9tmr4Q9VVbPvj4HIuKv+QW6CXyO4jZCX9Vb0iBZg+P0iPhWHt33x7xTvTeXYw4QEfcAPwUOYjM43hPSem9Ox3tz1C+NrV8Dj5C0s6Qh4FDgrFkuU09IWiRpyUQYeBZwJVl9D8+THQ58Z3ZK2HNl9TwLOFTSsKSdgUcAv5qF8vXExD8+uReSHXPoo3pLEnAy8IeI+ESyqK+PeVm9+/2YS9pW0rI8vAB4BnA1/X+8O9a734/35m5gtgswEyJiXNKbgHPJnkw8JSKumuVi9coDgTOz6zMDwJcj4hxJvwa+JukI4CbgJbNYxhkh6Qxgf2AbSSuA9wH/Tod6RsRVkr4G/B4YB94YEY1ZKfhGKqn3/pL2Irt9cAPwOuivegP7Aa8ErsjHswC8m/4/5mX1PqzPj/mDgFMl1cn+8P9aRHxP0i/p7+NdVu8v9fnx3qx5BnkzMzOzHuqX24hmZmZmc5IbW2ZmZmY95MaWmZmZWQ+5sWVmZmbWQ25smZmZmfWQG1tmZmZmPeTGltkMkbSqe6qN2v7Zkpbln6Omsf7+kr63genvlXR2yfIvSDpkQ8sxH+X74snJ93+RdJOkE2ezXGY2P7ixZTZPRMTf56/3WAZscGNrmn4WEX/fywwkzYfJlfcHJhtbEXEC8K+zVhozm1fc2DLrIUl7SbpI0uWSzpS0ZR7/U0nHS/qVpGslPSWPXyjpa3n6r0q6WNI++bIbJG1DNqP6LpIuk/Sx9h4rSSdKenUePkjS1ZJ+DrwoSbNI0imSfi3pt5IOrlAX5dv+vaTvAw9Ilj1O0gWSfiPp3OTddo/P6/LLvKxX5vGvlvR1Sd8FflhWHkn1fL1f59t5XR7/IEkX5vvgyon9V1LuZ+X5X5rnuTiP/9d8u1dKWq78tQyS3pLX8XJJX5G0E/B64F/y/ErzMjPrxI0ts976IvCuiNgTuILs1TsTBiLiCcBbk/ijgLvz9B8EHtdhm0cD/xcRe0XEO8oyljRC9kLb5wFPAbZLFh8L/CQiHg88HfiYsndtTuWFwCOBRwOvJe/pUfYS5U8Bh0TE44BTgA/n63weeH1EPAlof8XIk4DDI+KAKcpzBHBvHv944LXK3g/3MuDciNgLeAxwWck+2AZ4D/CMiNgbuAR4W774xIh4fETsASwAnpvHHw08Nj8Gr4+IG4CTgBPyff6zLvvJzKzFfOi+N5uXJG0BLIuIC/KoU4GvJ0m+lf//N8BOefhvgf8CiIgrJV2+EUV4FPCniPhjXp7TgCPzZc8Cni/p7fn3EWBH4A9TbO+pwBn5e9lukfSTPP6RwB7AeXnnUB24VdnLdpdExP/m6b5M0aABOC8i7upSnmcBeyZjw7YgexHvr4FT8obetyPispIyPxHYDfhFXrYh4Jf5sqdLeiewENgKuAr4LnA5cLqkbwPfnmJ/mJlV4saW2exZn/+/QfFb1DS2M05rL/VIEi57+amAF0fENRuYV6ftCbgq770qIvNbplNY3a08+a29N0fEuffLVHoq8BzgS5I+FhFfLCnbeRFxWNu6I8CngX0i4mZJx1Hst+eQNSyfD7xX0u5d6mFmNiXfRjTrkYi4F7g7GePzSuCCKVYB+DnwUgBJu5Hdsmu3EliSfL8R2E3ScN6bdmAefzWws6Rd8u9pg+Nc4M3JOKXHVqjShcCh+TiqB5Hd7gO4BthW0pPybQ1K2j0i7gZWSnpinu7QKbZdVp5zgTfkPVhI2jUf3/VQ4LaI+BxwMrB3yXYvAvaT9PB8/YWSdqVoWN2Rj+E6JF9eA3aIiPOBd5I9jLCY++9zM7PK3LNlNnMWSlqRfP8EcDhwkqSFwPXAP3bZxqeBU/Pbh78lu6V1b5ogIu6U9It8sPkPIuIdkr6Wp/1jvh4RsU7SkcD3Jd1B1pDbI9/MB4H/BC7PGzg30HqLr5MzgQPIxp5dS95wjIjR/DbfJ/PG3kC+7avIxlx9TtJq4KftdUmUled/yG6xXprH3w68gOzpwHdIGgNWAa/qtNGIuF3ZwwJnSBrOo98TEddK+lxelxvIbktCdgv0tLweIhundU8+kP8b+cD9N3vclpltCEWU3WUws01NUh0YzBtKuwA/BnaNiNFZKMv+wNsjolsjbKptLI6IVXn4aOBBEfHPM1PC2ZU34vaJiDfNdlnMbG5zz5bZ3LIQOD+/bSbgDbPR0MqNAntIOnsj5tp6jqRjyK41NwKvnqnCzSZJ/0I2HcQ3Z7ssZjb3uWfLzPqCpIuB4bboV0bEFbNRHjOzCW5smZmZmfWQn0Y0MzMz6yE3tszMzMx6yI0tMzMzsx5yY8vMzMysh9zYMjMzM+uh/w9IPNmbF56MxAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 720x216 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "dset2 = xr.open_dataset(\"hgt.mon.mean.nc\")\n",
    "\n",
    "hgt=dset2.hgt\n",
    "\n",
    "#seleccionamos tropicos\n",
    "\n",
    "hgt_trop=hgt.sel(lat=slice(10,-10),lon=slice(0,360),time=slice(\"1981-01-01\",\"2010-12-01\"),level=200)\n",
    "hgt_trop.sel(time=\"2010-01-01\").plot(robust=True,figsize=(10,3))\n",
    "hgt_trop"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "m_autocorr_pearson = np.zeros((hgt_trop.shape[1],hgt_trop.shape[2]),dtype=float)\n",
    "\n",
    "lagtime=3\n",
    "\n",
    "def autocrr(x, t=lagtime):\n",
    "    return np.corrcoef(np.array([x[:-t], x[t:]]))[0,1]\n",
    "\n",
    "for ni in range(0,hgt_trop.shape[2]): #long\n",
    "    for nj in range(0,hgt_trop.shape[1]): # loop lat\n",
    "        \n",
    "        hgt_info=hgt_trop.isel(lat=nj,lon=ni).values\n",
    "        \n",
    "        array_has_nan = np.isnan(np.sum(hgt_info))\n",
    "        \n",
    "        if array_has_nan == True:\n",
    "            \n",
    "            m_autocorr_pearson[nj,ni]=np.nan\n",
    "            \n",
    "        else:\n",
    "            m_autocorr_pearson[nj,ni] = autocrr(hgt_info)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.contour.QuadContourSet at 0x15cedc1f940>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x216 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data_set3 = xr.Dataset( coords={'lon': ([ 'lon'], hgt_trop.lon.values),\n",
    "                                             'lat': (['lat',], hgt_trop.lat.values)})\n",
    "\n",
    "data_set3[\"corr_pearson\"] = ([\"lat\", \"lon\"], m_autocorr_pearson)\n",
    "\n",
    "corr2=data_set3.corr_pearson\n",
    "corr2.plot.contourf(robust=True,vmin=0.3,figsize=(10,3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.8.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}