xarray_02_subset_slicing_plot_CMIP6.ipynb

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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Subset and Plot CMIP Data Using Xarray\n",
    "\n",
    "In this notebook we demonstrate how to use Xarray to access, subset and plot the CMIP data:\n",
    "\n",
    "* Subsetting\n",
    "* Xarray.plot( )\n",
    "\n",
    "This example uses Coupled Model Intercomparison Project (CMIP6) collections. For more information, please visit [data catalogue]( https://geonetwork.nci.org.au/geonetwork/srv/eng/catalog.search#/metadata/f5194_5909_8003_9216) and [terms of use]( https://pcmdi.llnl.gov/CMIP6/TermsOfUse/TermsOfUse6-1.html).\n",
    "\n",
    "---------"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Authors: NCI Virtual Research Environment Team\n",
    "- Keywords: CMIP, Xarray, slicing, subset, plot\n",
    "- Create Date: 2020-Aug\n",
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Import the xarray and netCDF modules"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import xarray as xr\n",
    "import netCDF4 as nc\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Local path on /g/data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "path_CMIP6 = '/g/data/oi10/replicas/CMIP6/ScenarioMIP/CNRM-CERFACS/CNRM-CM6-1/ssp126/r1i1p1f2/Amon/tas/gr/v20190219/tas_Amon_CNRM-CM6-1_ssp126_r1i1p1f2_gr_201501-210012.nc'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
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       "</style><div class='xr-wrap'><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-9c385f01-431b-463b-ad7e-d6c30df7403a' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-9c385f01-431b-463b-ad7e-d6c30df7403a' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span>axis_nbounds</span>: 2</li><li><span class='xr-has-index'>lat</span>: 128</li><li><span class='xr-has-index'>lon</span>: 256</li><li><span class='xr-has-index'>time</span>: 1032</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-38b1be3b-39b1-4a89-a93a-e7115ce11949' class='xr-section-summary-in' type='checkbox'  checked><label for='section-38b1be3b-39b1-4a89-a93a-e7115ce11949' 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'>float64</div><div class='xr-var-preview xr-preview'>-88.93 -87.54 ... 87.54 88.93</div><input id='attrs-74ecc89c-1236-4eac-b125-59c400f66621' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-74ecc89c-1236-4eac-b125-59c400f66621' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-99aeae15-3fc8-45ad-8a55-d96120bdd946' class='xr-var-data-in' type='checkbox'><label for='data-99aeae15-3fc8-45ad-8a55-d96120bdd946' 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>axis :</span></dt><dd>Y</dd><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>long_name :</span></dt><dd>Latitude</dd><dt><span>units :</span></dt><dd>degrees_north</dd></dl></div><pre class='xr-var-data'>array([-88.927735, -87.538705, -86.141472, -84.742386, -83.342596, -81.942466,\n",
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       "        62.333803,  63.73453 ,  65.135251,  66.535966,  67.936673,  69.337372,\n",
       "        70.738059,  72.138732,  73.539389,  74.940023,  76.340629,  77.741196,\n",
       "        79.14171 ,  80.542146,  81.942466,  83.342596,  84.742386,  86.141472,\n",
       "        87.538705,  88.927735])</pre></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'>float64</div><div class='xr-var-preview xr-preview'>0.0 1.406 2.812 ... 357.2 358.6</div><input id='attrs-11f844fd-a9b5-4e3a-80c5-c4ec8a8a53df' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-11f844fd-a9b5-4e3a-80c5-c4ec8a8a53df' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0b62493c-afa6-4e71-ab2f-cc97bf4d56d7' class='xr-var-data-in' type='checkbox'><label for='data-0b62493c-afa6-4e71-ab2f-cc97bf4d56d7' 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>axis :</span></dt><dd>X</dd><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>long_name :</span></dt><dd>Longitude</dd><dt><span>units :</span></dt><dd>degrees_east</dd></dl></div><pre class='xr-var-data'>array([  0.     ,   1.40625,   2.8125 , ..., 355.78125, 357.1875 , 358.59375])</pre></li><li class='xr-var-item'><div class='xr-var-name'><span>height</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-6a68e9de-d39a-46b6-bbf6-67740d55a7ed' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-6a68e9de-d39a-46b6-bbf6-67740d55a7ed' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-33b43372-7df6-491d-94e9-8671a07ae441' class='xr-var-data-in' type='checkbox'><label for='data-33b43372-7df6-491d-94e9-8671a07ae441' 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>name :</span></dt><dd>height</dd><dt><span>standard_name :</span></dt><dd>height</dd><dt><span>long_name :</span></dt><dd>height</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>axis :</span></dt><dd>Z</dd><dt><span>positive :</span></dt><dd>up</dd></dl></div><pre class='xr-var-data'>array(2.)</pre></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'>2015-01-16T12:00:00 ... 2100-12-16T12:00:00</div><input id='attrs-31f389d6-622f-4889-b3d5-d640a78e2201' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-31f389d6-622f-4889-b3d5-d640a78e2201' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ed376209-e424-4824-b329-00f61f00524c' class='xr-var-data-in' type='checkbox'><label for='data-ed376209-e424-4824-b329-00f61f00524c' 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>axis :</span></dt><dd>T</dd><dt><span>standard_name :</span></dt><dd>time</dd><dt><span>long_name :</span></dt><dd>Time axis</dd><dt><span>time_origin :</span></dt><dd>1850-01-01 00:00:00</dd><dt><span>bounds :</span></dt><dd>time_bounds</dd></dl></div><pre class='xr-var-data'>array([&#x27;2015-01-16T12:00:00.000000000&#x27;, &#x27;2015-02-15T00:00:00.000000000&#x27;,\n",
       "       &#x27;2015-03-16T12:00:00.000000000&#x27;, ..., &#x27;2100-10-16T12:00:00.000000000&#x27;,\n",
       "       &#x27;2100-11-16T00:00:00.000000000&#x27;, &#x27;2100-12-16T12:00:00.000000000&#x27;],\n",
       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></li></ul></div></li><li class='xr-section-item'><input id='section-a15bfb79-b587-4eec-a5c4-609c5c7d7e9c' class='xr-section-summary-in' type='checkbox'  checked><label for='section-a15bfb79-b587-4eec-a5c4-609c5c7d7e9c' class='xr-section-summary' >Data variables: <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>time_bounds</span></div><div class='xr-var-dims'>(time, axis_nbounds)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-c7d5f972-ea2f-4fdf-bdcc-276e72a85761' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-c7d5f972-ea2f-4fdf-bdcc-276e72a85761' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3baccff5-26d4-49b2-9d5c-bc338f427839' class='xr-var-data-in' type='checkbox'><label for='data-3baccff5-26d4-49b2-9d5c-bc338f427839' 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><pre class='xr-var-data'>array([[&#x27;2015-01-01T00:00:00.000000000&#x27;, &#x27;2015-02-01T00:00:00.000000000&#x27;],\n",
       "       [&#x27;2015-02-01T00:00:00.000000000&#x27;, &#x27;2015-03-01T00:00:00.000000000&#x27;],\n",
       "       [&#x27;2015-03-01T00:00:00.000000000&#x27;, &#x27;2015-04-01T00:00:00.000000000&#x27;],\n",
       "       ...,\n",
       "       [&#x27;2100-10-01T00:00:00.000000000&#x27;, &#x27;2100-11-01T00:00:00.000000000&#x27;],\n",
       "       [&#x27;2100-11-01T00:00:00.000000000&#x27;, &#x27;2100-12-01T00:00:00.000000000&#x27;],\n",
       "       [&#x27;2100-12-01T00:00:00.000000000&#x27;, &#x27;2101-01-01T00:00:00.000000000&#x27;]],\n",
       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></li><li class='xr-var-item'><div class='xr-var-name'><span>tas</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-e4f97ad3-ffbb-496b-b8af-a139eba13ee4' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e4f97ad3-ffbb-496b-b8af-a139eba13ee4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9d6e0652-3bbd-4d02-b684-2d1162981ca8' class='xr-var-data-in' type='checkbox'><label for='data-9d6e0652-3bbd-4d02-b684-2d1162981ca8' 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>online_operation :</span></dt><dd>average</dd><dt><span>cell_methods :</span></dt><dd>area: time: mean</dd><dt><span>interval_operation :</span></dt><dd>900 s</dd><dt><span>interval_write :</span></dt><dd>1 month</dd><dt><span>standard_name :</span></dt><dd>air_temperature</dd><dt><span>description :</span></dt><dd>Near-Surface Air Temperature</dd><dt><span>long_name :</span></dt><dd>Near-Surface Air Temperature</dd><dt><span>history :</span></dt><dd>none</dd><dt><span>units :</span></dt><dd>K</dd><dt><span>cell_measures :</span></dt><dd>area: areacella</dd></dl></div><pre class='xr-var-data'>[33816576 values with dtype=float32]</pre></li></ul></div></li><li class='xr-section-item'><input id='section-f60eef64-3c79-442f-840c-3a5ff5bb9f91' class='xr-section-summary-in' type='checkbox'  ><label for='section-f60eef64-3c79-442f-840c-3a5ff5bb9f91' class='xr-section-summary' >Attributes: <span>(52)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>Conventions :</span></dt><dd>CF-1.7 CMIP-6.2</dd><dt><span>creation_date :</span></dt><dd>2018-11-15T10:36:39Z</dd><dt><span>description :</span></dt><dd>Future scenario with low radiative forcing by the end of century. Following approximately RCP2.6 global forcing pathway but with new forcing based on SSP1. Concentration-driven. As a tier 2 option, this simulation should be extended to year 2300</dd><dt><span>title :</span></dt><dd>CNRM-CM6-1 model output prepared for CMIP6 / ScenarioMIP ssp126</dd><dt><span>activity_id :</span></dt><dd>ScenarioMIP</dd><dt><span>contact :</span></dt><dd>contact.cmip@meteo.fr</dd><dt><span>data_specs_version :</span></dt><dd>01.00.21</dd><dt><span>dr2xml_version :</span></dt><dd>1.16</dd><dt><span>experiment_id :</span></dt><dd>ssp126</dd><dt><span>experiment :</span></dt><dd>update of RCP2.6 based on SSP1</dd><dt><span>external_variables :</span></dt><dd>areacella</dd><dt><span>forcing_index :</span></dt><dd>2</dd><dt><span>frequency :</span></dt><dd>mon</dd><dt><span>further_info_url :</span></dt><dd>https://furtherinfo.es-doc.org/CMIP6.CNRM-CERFACS.CNRM-CM6-1.ssp126.none.r1i1p1f2</dd><dt><span>grid :</span></dt><dd>data regridded to a T127 gaussian grid (128x256 latlon) from a native atmosphere T127l reduced gaussian grid</dd><dt><span>grid_label :</span></dt><dd>gr</dd><dt><span>nominal_resolution :</span></dt><dd>250 km</dd><dt><span>initialization_index :</span></dt><dd>1</dd><dt><span>institution_id :</span></dt><dd>CNRM-CERFACS</dd><dt><span>institution :</span></dt><dd>CNRM (Centre National de Recherches Meteorologiques, Toulouse 31057, France), CERFACS (Centre Europeen de Recherche et de Formation Avancee en Calcul Scientifique, Toulouse 31057, France)</dd><dt><span>license :</span></dt><dd>CMIP6 model data produced by CNRM-CERFACS is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License (https://creativecommons.org/licenses). Consult https://pcmdi.llnl.gov/CMIP6/TermsOfUse for terms of use governing CMIP6 output, including citation requirements and proper acknowledgment. Further information about this data, including some limitations, can be found via the further_info_url (recorded as a global attribute in this file) and at http://www.umr-cnrm.fr/cmip6/. The data producers and data providers make no warranty, either express or implied, including, but not limited to, warranties of merchantability and fitness for a particular purpose. All liabilities arising from the supply of the information (including any liability arising in negligence) are excluded to the fullest extent permitted by law.</dd><dt><span>mip_era :</span></dt><dd>CMIP6</dd><dt><span>parent_experiment_id :</span></dt><dd>historical</dd><dt><span>parent_mip_era :</span></dt><dd>CMIP6</dd><dt><span>parent_activity_id :</span></dt><dd>CMIP</dd><dt><span>parent_source_id :</span></dt><dd>CNRM-CM6-1</dd><dt><span>parent_time_units :</span></dt><dd>days since 1850-01-01 00:00:00</dd><dt><span>parent_variant_label :</span></dt><dd>r1i1p1f2</dd><dt><span>branch_method :</span></dt><dd>standard</dd><dt><span>branch_time_in_parent :</span></dt><dd>60265.0</dd><dt><span>branch_time_in_child :</span></dt><dd>60265.0</dd><dt><span>physics_index :</span></dt><dd>1</dd><dt><span>product :</span></dt><dd>model-output</dd><dt><span>realization_index :</span></dt><dd>1</dd><dt><span>realm :</span></dt><dd>atmos</dd><dt><span>references :</span></dt><dd>http://www.umr-cnrm.fr/cmip6/references</dd><dt><span>source :</span></dt><dd>CNRM-CM6-1 (2017):  aerosol: prescribed monthly fields computed by TACTIC_v2 scheme atmos: Arpege 6.3 (T127; Gaussian Reduced with 24572 grid points in total distributed over 128 latitude circles (with 256 grid points per latitude circle between 30degN and 30degS reducing to 20 grid points per latitude circle at 88.9degN and 88.9degS); 91 levels; top level 78.4 km) atmosChem: OZL_v2 land: Surfex 8.0c ocean: Nemo 3.6 (eORCA1, tripolar primarily 1deg; 362 x 294 longitude/latitude; 75 levels; top grid cell 0-1 m) seaIce: Gelato 6.1</dd><dt><span>source_id :</span></dt><dd>CNRM-CM6-1</dd><dt><span>source_type :</span></dt><dd>AOGCM</dd><dt><span>sub_experiment_id :</span></dt><dd>none</dd><dt><span>sub_experiment :</span></dt><dd>none</dd><dt><span>table_id :</span></dt><dd>Amon</dd><dt><span>variable_id :</span></dt><dd>tas</dd><dt><span>variant_label :</span></dt><dd>r1i1p1f2</dd><dt><span>EXPID :</span></dt><dd>CNRM-CM6-1_ssp126_r1i1p1f2</dd><dt><span>CMIP6_CV_version :</span></dt><dd>cv=6.2.3.0-7-g2019642</dd><dt><span>dr2xml_md5sum :</span></dt><dd>c2dce418e78ca835be1e2ff817c2c403</dd><dt><span>xios_commit :</span></dt><dd>1442-shuffle</dd><dt><span>nemo_gelato_commit :</span></dt><dd>49095b3accd5d4c_6524fe19b00467a</dd><dt><span>arpege_minor_version :</span></dt><dd>6.3.2</dd><dt><span>history :</span></dt><dd>none</dd><dt><span>tracking_id :</span></dt><dd>hdl:21.14100/05131cf0-ec69-4bfc-92f8-e1d40aae26f5</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:      (axis_nbounds: 2, lat: 128, lon: 256, time: 1032)\n",
       "Coordinates:\n",
       "  * lat          (lat) float64 -88.93 -87.54 -86.14 -84.74 ... 86.14 87.54 88.93\n",
       "  * lon          (lon) float64 0.0 1.406 2.812 4.219 ... 354.4 355.8 357.2 358.6\n",
       "    height       float64 ...\n",
       "  * time         (time) datetime64[ns] 2015-01-16T12:00:00 ... 2100-12-16T12:00:00\n",
       "Dimensions without coordinates: axis_nbounds\n",
       "Data variables:\n",
       "    time_bounds  (time, axis_nbounds) datetime64[ns] ...\n",
       "    tas          (time, lat, lon) float32 ...\n",
       "Attributes:\n",
       "    Conventions:            CF-1.7 CMIP-6.2\n",
       "    creation_date:          2018-11-15T10:36:39Z\n",
       "    description:            Future scenario with low radiative forcing by the...\n",
       "    title:                  CNRM-CM6-1 model output prepared for CMIP6 / Scen...\n",
       "    activity_id:            ScenarioMIP\n",
       "    contact:                contact.cmip@meteo.fr\n",
       "    data_specs_version:     01.00.21\n",
       "    dr2xml_version:         1.16\n",
       "    experiment_id:          ssp126\n",
       "    experiment:             update of RCP2.6 based on SSP1\n",
       "    external_variables:     areacella\n",
       "    forcing_index:          2\n",
       "    frequency:              mon\n",
       "    further_info_url:       https://furtherinfo.es-doc.org/CMIP6.CNRM-CERFACS...\n",
       "    grid:                   data regridded to a T127 gaussian grid (128x256 l...\n",
       "    grid_label:             gr\n",
       "    nominal_resolution:     250 km\n",
       "    initialization_index:   1\n",
       "    institution_id:         CNRM-CERFACS\n",
       "    institution:            CNRM (Centre National de Recherches Meteorologiqu...\n",
       "    license:                CMIP6 model data produced by CNRM-CERFACS is lice...\n",
       "    mip_era:                CMIP6\n",
       "    parent_experiment_id:   historical\n",
       "    parent_mip_era:         CMIP6\n",
       "    parent_activity_id:     CMIP\n",
       "    parent_source_id:       CNRM-CM6-1\n",
       "    parent_time_units:      days since 1850-01-01 00:00:00\n",
       "    parent_variant_label:   r1i1p1f2\n",
       "    branch_method:          standard\n",
       "    branch_time_in_parent:  60265.0\n",
       "    branch_time_in_child:   60265.0\n",
       "    physics_index:          1\n",
       "    product:                model-output\n",
       "    realization_index:      1\n",
       "    realm:                  atmos\n",
       "    references:             http://www.umr-cnrm.fr/cmip6/references\n",
       "    source:                 CNRM-CM6-1 (2017):  aerosol: prescribed monthly f...\n",
       "    source_id:              CNRM-CM6-1\n",
       "    source_type:            AOGCM\n",
       "    sub_experiment_id:      none\n",
       "    sub_experiment:         none\n",
       "    table_id:               Amon\n",
       "    variable_id:            tas\n",
       "    variant_label:          r1i1p1f2\n",
       "    EXPID:                  CNRM-CM6-1_ssp126_r1i1p1f2\n",
       "    CMIP6_CV_version:       cv=6.2.3.0-7-g2019642\n",
       "    dr2xml_md5sum:          c2dce418e78ca835be1e2ff817c2c403\n",
       "    xios_commit:            1442-shuffle\n",
       "    nemo_gelato_commit:     49095b3accd5d4c_6524fe19b00467a\n",
       "    arpege_minor_version:   6.3.2\n",
       "    history:                none\n",
       "    tracking_id:            hdl:21.14100/05131cf0-ec69-4bfc-92f8-e1d40aae26f5"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "f1 = xr.open_dataset(path_CMIP6) \n",
    "f1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Subsetting\n",
    "\n",
    "There are multiple ways to select subsets of the data using xarray. \n",
    "\n",
    "#### 1. Using the regular numpy method of indexing:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1032, 128, 256)\n",
      "(9, 128)\n"
     ]
    }
   ],
   "source": [
    "tas = f1.tas\n",
    "tas_subset = tas[1:10,:,0]\n",
    "print(tas.shape)\n",
    "print(tas_subset.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "But in the above it is not as simple to know *where* the values selected have come from. xarray.sel() permits label based indexing.\n",
    "\n",
    "#### 2. Using .sel( ) for label based indexing\n",
    "\n",
    "In the case below we find the values of tas at latitude of -12.46 and longitude at 130.85 degrees - approximately Darwin. This is done using the `.sel()` method which can be performed on a Dataset or DataArray. This method permits selection based on coordinates rather than indices. \n",
    "\n",
    "However, it is unlikely there is a specific lat/lon coordinate value at those exact locations. For example, look at the available values of longitude:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style><div class='xr-wrap'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'lon'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>lon</span>: 256</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-dd7f2fba-811f-4487-aeda-1671f3925c68' class='xr-array-in' type='checkbox' ><label for='section-dd7f2fba-811f-4487-aeda-1671f3925c68' 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>0.0 1.406 2.812 4.219 5.625 7.031 ... 353.0 354.4 355.8 357.2 358.6</span></div><pre class='xr-array-data'>array([  0.     ,   1.40625,   2.8125 , ..., 355.78125, 357.1875 , 358.59375])</pre></div></li><li class='xr-section-item'><input id='section-5d80af8e-3238-4183-ba85-4ac41a699041' class='xr-section-summary-in' type='checkbox'  checked><label for='section-5d80af8e-3238-4183-ba85-4ac41a699041' 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'>lon</span></div><div class='xr-var-dims'>(lon)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0 1.406 2.812 ... 357.2 358.6</div><input id='attrs-274be8d7-72ed-4f39-a668-79a37a264913' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-274be8d7-72ed-4f39-a668-79a37a264913' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a3390d90-0503-4487-9b4c-39280a3f874c' class='xr-var-data-in' type='checkbox'><label for='data-a3390d90-0503-4487-9b4c-39280a3f874c' 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>axis :</span></dt><dd>X</dd><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>long_name :</span></dt><dd>Longitude</dd><dt><span>units :</span></dt><dd>degrees_east</dd></dl></div><pre class='xr-var-data'>array([  0.     ,   1.40625,   2.8125 , ..., 355.78125, 357.1875 , 358.59375])</pre></li><li class='xr-var-item'><div class='xr-var-name'><span>height</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>2.0</div><input id='attrs-4246c572-b4b8-4388-a5b3-b70985e81ab6' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4246c572-b4b8-4388-a5b3-b70985e81ab6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-26f6d5fc-a7eb-4739-ba22-0ee1a38479c6' class='xr-var-data-in' type='checkbox'><label for='data-26f6d5fc-a7eb-4739-ba22-0ee1a38479c6' 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>name :</span></dt><dd>height</dd><dt><span>standard_name :</span></dt><dd>height</dd><dt><span>long_name :</span></dt><dd>height</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>axis :</span></dt><dd>Z</dd><dt><span>positive :</span></dt><dd>up</dd></dl></div><pre class='xr-var-data'>array(2.)</pre></li></ul></div></li><li class='xr-section-item'><input id='section-d2c89562-f486-4958-ab23-e600f2067322' class='xr-section-summary-in' type='checkbox'  checked><label for='section-d2c89562-f486-4958-ab23-e600f2067322' class='xr-section-summary' >Attributes: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>axis :</span></dt><dd>X</dd><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>long_name :</span></dt><dd>Longitude</dd><dt><span>units :</span></dt><dd>degrees_east</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.DataArray 'lon' (lon: 256)>\n",
       "array([  0.     ,   1.40625,   2.8125 , ..., 355.78125, 357.1875 , 358.59375])\n",
       "Coordinates:\n",
       "  * lon      (lon) float64 0.0 1.406 2.812 4.219 ... 354.4 355.8 357.2 358.6\n",
       "    height   float64 2.0\n",
       "Attributes:\n",
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       "    long_name:      Longitude\n",
       "    units:          degrees_east"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "f1.lon"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There is no longitude value at 130.85 deg E, thus if we wanted to find the tas over Darwin, we need to use interpolation. This is done by adding an argument to the `.sel()` command and in this case we are going to use the nearest neighbour method."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
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       "</style><div class='xr-wrap'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'tas'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 1032</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-4f9798c8-a73c-4943-a2ba-07e62c1c0584' class='xr-array-in' type='checkbox' ><label for='section-4f9798c8-a73c-4943-a2ba-07e62c1c0584' 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>301.50644 300.10107 301.72717 ... 303.33353 303.90588 302.9052</span></div><pre class='xr-array-data'>array([301.50644, 300.10107, 301.72717, ..., 303.33353, 303.90588, 302.9052 ],\n",
       "      dtype=float32)</pre></div></li><li class='xr-section-item'><input id='section-012c58eb-73bd-4226-8094-cc1ec0d25905' class='xr-section-summary-in' type='checkbox'  checked><label for='section-012c58eb-73bd-4226-8094-cc1ec0d25905' 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>lat</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-11.91</div><input id='attrs-78a4bcae-02a9-4d1b-8d67-e2c7b7474c45' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-78a4bcae-02a9-4d1b-8d67-e2c7b7474c45' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5121e4f4-de6f-4a9e-ac0d-69ee4cf218ad' class='xr-var-data-in' type='checkbox'><label for='data-5121e4f4-de6f-4a9e-ac0d-69ee4cf218ad' 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>axis :</span></dt><dd>Y</dd><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>long_name :</span></dt><dd>Latitude</dd><dt><span>units :</span></dt><dd>degrees_north</dd></dl></div><pre class='xr-var-data'>array(-11.90652333)</pre></li><li class='xr-var-item'><div class='xr-var-name'><span>lon</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>130.8</div><input id='attrs-cc48fff0-3d59-4fff-9f4e-f560d2403b4e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-cc48fff0-3d59-4fff-9f4e-f560d2403b4e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b3f23bbc-1358-40ae-94c0-ca7908f13342' class='xr-var-data-in' type='checkbox'><label for='data-b3f23bbc-1358-40ae-94c0-ca7908f13342' 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>axis :</span></dt><dd>X</dd><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>long_name :</span></dt><dd>Longitude</dd><dt><span>units :</span></dt><dd>degrees_east</dd></dl></div><pre class='xr-var-data'>array(130.78125)</pre></li><li class='xr-var-item'><div class='xr-var-name'><span>height</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>2.0</div><input id='attrs-9994bf52-b57f-424f-8be3-5df43112c3f7' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9994bf52-b57f-424f-8be3-5df43112c3f7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a0fc4d3c-5cf0-4328-b4b5-77987275171d' class='xr-var-data-in' type='checkbox'><label for='data-a0fc4d3c-5cf0-4328-b4b5-77987275171d' 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>name :</span></dt><dd>height</dd><dt><span>standard_name :</span></dt><dd>height</dd><dt><span>long_name :</span></dt><dd>height</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>axis :</span></dt><dd>Z</dd><dt><span>positive :</span></dt><dd>up</dd></dl></div><pre class='xr-var-data'>array(2.)</pre></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'>2015-01-16T12:00:00 ... 2100-12-16T12:00:00</div><input id='attrs-04761fd3-c0cd-4de5-a32e-970d7a858c5a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-04761fd3-c0cd-4de5-a32e-970d7a858c5a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7251a090-9973-4be7-a9d7-5cfe4a4bdd57' class='xr-var-data-in' type='checkbox'><label for='data-7251a090-9973-4be7-a9d7-5cfe4a4bdd57' 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>axis :</span></dt><dd>T</dd><dt><span>standard_name :</span></dt><dd>time</dd><dt><span>long_name :</span></dt><dd>Time axis</dd><dt><span>time_origin :</span></dt><dd>1850-01-01 00:00:00</dd><dt><span>bounds :</span></dt><dd>time_bounds</dd></dl></div><pre class='xr-var-data'>array([&#x27;2015-01-16T12:00:00.000000000&#x27;, &#x27;2015-02-15T00:00:00.000000000&#x27;,\n",
       "       &#x27;2015-03-16T12:00:00.000000000&#x27;, ..., &#x27;2100-10-16T12:00:00.000000000&#x27;,\n",
       "       &#x27;2100-11-16T00:00:00.000000000&#x27;, &#x27;2100-12-16T12:00:00.000000000&#x27;],\n",
       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></li></ul></div></li><li class='xr-section-item'><input id='section-caeaf902-0614-4434-b29b-3f27a66a9ea6' class='xr-section-summary-in' type='checkbox'  ><label for='section-caeaf902-0614-4434-b29b-3f27a66a9ea6' 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>online_operation :</span></dt><dd>average</dd><dt><span>cell_methods :</span></dt><dd>area: time: mean</dd><dt><span>interval_operation :</span></dt><dd>900 s</dd><dt><span>interval_write :</span></dt><dd>1 month</dd><dt><span>standard_name :</span></dt><dd>air_temperature</dd><dt><span>description :</span></dt><dd>Near-Surface Air Temperature</dd><dt><span>long_name :</span></dt><dd>Near-Surface Air Temperature</dd><dt><span>history :</span></dt><dd>none</dd><dt><span>units :</span></dt><dd>K</dd><dt><span>cell_measures :</span></dt><dd>area: areacella</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.DataArray 'tas' (time: 1032)>\n",
       "array([301.50644, 300.10107, 301.72717, ..., 303.33353, 303.90588, 302.9052 ],\n",
       "      dtype=float32)\n",
       "Coordinates:\n",
       "    lat      float64 -11.91\n",
       "    lon      float64 130.8\n",
       "    height   float64 2.0\n",
       "  * time     (time) datetime64[ns] 2015-01-16T12:00:00 ... 2100-12-16T12:00:00\n",
       "Attributes:\n",
       "    online_operation:    average\n",
       "    cell_methods:        area: time: mean\n",
       "    interval_operation:  900 s\n",
       "    interval_write:      1 month\n",
       "    standard_name:       air_temperature\n",
       "    description:         Near-Surface Air Temperature\n",
       "    long_name:           Near-Surface Air Temperature\n",
       "    history:             none\n",
       "    units:               K\n",
       "    cell_measures:       area: areacella"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tas.sel(lon=130.85,lat=-12.46,method='nearest')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Subsetting in Time \n",
    "Notice that the time variable has also been automatically decoded by xarray to represent dates rather than floats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
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       "</style><div class='xr-wrap'><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>: 1032</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-d57f27fb-46ad-4267-bcaa-c1002ed6f2db' class='xr-array-in' type='checkbox' ><label for='section-d57f27fb-46ad-4267-bcaa-c1002ed6f2db' 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>2015-01-16T12:00:00 2015-02-15 ... 2100-11-16 2100-12-16T12:00:00</span></div><pre class='xr-array-data'>array([&#x27;2015-01-16T12:00:00.000000000&#x27;, &#x27;2015-02-15T00:00:00.000000000&#x27;,\n",
       "       &#x27;2015-03-16T12:00:00.000000000&#x27;, ..., &#x27;2100-10-16T12:00:00.000000000&#x27;,\n",
       "       &#x27;2100-11-16T00:00:00.000000000&#x27;, &#x27;2100-12-16T12:00:00.000000000&#x27;],\n",
       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-section-item'><input id='section-6926a782-9b39-48c0-aa05-3fe9e893a488' class='xr-section-summary-in' type='checkbox'  checked><label for='section-6926a782-9b39-48c0-aa05-3fe9e893a488' 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>height</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>2.0</div><input id='attrs-dc1eef4d-f277-49c5-b8e9-3e515c166c10' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-dc1eef4d-f277-49c5-b8e9-3e515c166c10' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0e2616a0-2402-4028-b041-55717989bb6a' class='xr-var-data-in' type='checkbox'><label for='data-0e2616a0-2402-4028-b041-55717989bb6a' 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>name :</span></dt><dd>height</dd><dt><span>standard_name :</span></dt><dd>height</dd><dt><span>long_name :</span></dt><dd>height</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>axis :</span></dt><dd>Z</dd><dt><span>positive :</span></dt><dd>up</dd></dl></div><pre class='xr-var-data'>array(2.)</pre></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'>2015-01-16T12:00:00 ... 2100-12-16T12:00:00</div><input id='attrs-982649fa-c728-4ed7-8ecd-2c60aa946718' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-982649fa-c728-4ed7-8ecd-2c60aa946718' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9f855182-2720-404d-a129-505961e4110d' class='xr-var-data-in' type='checkbox'><label for='data-9f855182-2720-404d-a129-505961e4110d' 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>axis :</span></dt><dd>T</dd><dt><span>standard_name :</span></dt><dd>time</dd><dt><span>long_name :</span></dt><dd>Time axis</dd><dt><span>time_origin :</span></dt><dd>1850-01-01 00:00:00</dd><dt><span>bounds :</span></dt><dd>time_bounds</dd></dl></div><pre class='xr-var-data'>array([&#x27;2015-01-16T12:00:00.000000000&#x27;, &#x27;2015-02-15T00:00:00.000000000&#x27;,\n",
       "       &#x27;2015-03-16T12:00:00.000000000&#x27;, ..., &#x27;2100-10-16T12:00:00.000000000&#x27;,\n",
       "       &#x27;2100-11-16T00:00:00.000000000&#x27;, &#x27;2100-12-16T12:00:00.000000000&#x27;],\n",
       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></li></ul></div></li><li class='xr-section-item'><input id='section-2ba6c108-8f37-4ce1-9bbc-bb1a301b2d2f' class='xr-section-summary-in' type='checkbox'  checked><label for='section-2ba6c108-8f37-4ce1-9bbc-bb1a301b2d2f' class='xr-section-summary' >Attributes: <span>(5)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>axis :</span></dt><dd>T</dd><dt><span>standard_name :</span></dt><dd>time</dd><dt><span>long_name :</span></dt><dd>Time axis</dd><dt><span>time_origin :</span></dt><dd>1850-01-01 00:00:00</dd><dt><span>bounds :</span></dt><dd>time_bounds</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.DataArray 'time' (time: 1032)>\n",
       "array(['2015-01-16T12:00:00.000000000', '2015-02-15T00:00:00.000000000',\n",
       "       '2015-03-16T12:00:00.000000000', ..., '2100-10-16T12:00:00.000000000',\n",
       "       '2100-11-16T00:00:00.000000000', '2100-12-16T12:00:00.000000000'],\n",
       "      dtype='datetime64[ns]')\n",
       "Coordinates:\n",
       "    height   float64 2.0\n",
       "  * time     (time) datetime64[ns] 2015-01-16T12:00:00 ... 2100-12-16T12:00:00\n",
       "Attributes:\n",
       "    axis:           T\n",
       "    standard_name:  time\n",
       "    long_name:      Time axis\n",
       "    time_origin:    1850-01-01 00:00:00\n",
       "    bounds:         time_bounds"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "f1.time"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Compared to the original numpy data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
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       "</style><div class='xr-wrap'><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>: 1032</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-9c597ef6-2d61-4a8b-b7d3-5f6c7b2a2b6d' class='xr-array-in' type='checkbox' ><label for='section-9c597ef6-2d61-4a8b-b7d3-5f6c7b2a2b6d' 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>6.028e+04 6.031e+04 6.034e+04 ... 9.16e+04 9.163e+04 9.166e+04</span></div><pre class='xr-array-data'>array([60280.5, 60310. , 60339.5, ..., 91599.5, 91630. , 91660.5])</pre></div></li><li class='xr-section-item'><input id='section-3a92efac-4f99-4965-862c-9eebc04fc958' class='xr-section-summary-in' type='checkbox'  checked><label for='section-3a92efac-4f99-4965-862c-9eebc04fc958' 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>height</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-1b9b0d5f-c9bc-46b7-9763-99bfd7ba92b8' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1b9b0d5f-c9bc-46b7-9763-99bfd7ba92b8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-25d36add-a758-458a-a2f8-63823d050b97' class='xr-var-data-in' type='checkbox'><label for='data-25d36add-a758-458a-a2f8-63823d050b97' 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>name :</span></dt><dd>height</dd><dt><span>standard_name :</span></dt><dd>height</dd><dt><span>long_name :</span></dt><dd>height</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>axis :</span></dt><dd>Z</dd><dt><span>positive :</span></dt><dd>up</dd></dl></div><pre class='xr-var-data'>array(2.)</pre></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'>float64</div><div class='xr-var-preview xr-preview'>6.028e+04 6.031e+04 ... 9.166e+04</div><input id='attrs-b693976f-b3d6-4fd7-b7ca-19c51e9b883e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b693976f-b3d6-4fd7-b7ca-19c51e9b883e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c4d5eb41-231e-4ab2-8c97-273717d235a0' class='xr-var-data-in' type='checkbox'><label for='data-c4d5eb41-231e-4ab2-8c97-273717d235a0' 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>axis :</span></dt><dd>T</dd><dt><span>standard_name :</span></dt><dd>time</dd><dt><span>long_name :</span></dt><dd>Time axis</dd><dt><span>calendar :</span></dt><dd>gregorian</dd><dt><span>units :</span></dt><dd>days since 1850-01-01 00:00:00</dd><dt><span>time_origin :</span></dt><dd>1850-01-01 00:00:00</dd><dt><span>bounds :</span></dt><dd>time_bounds</dd></dl></div><pre class='xr-var-data'>array([60280.5, 60310. , 60339.5, ..., 91599.5, 91630. , 91660.5])</pre></li></ul></div></li><li class='xr-section-item'><input id='section-22e6963d-74eb-44c8-bdfb-08cc2f8221ac' class='xr-section-summary-in' type='checkbox'  checked><label for='section-22e6963d-74eb-44c8-bdfb-08cc2f8221ac' 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>axis :</span></dt><dd>T</dd><dt><span>standard_name :</span></dt><dd>time</dd><dt><span>long_name :</span></dt><dd>Time axis</dd><dt><span>calendar :</span></dt><dd>gregorian</dd><dt><span>units :</span></dt><dd>days since 1850-01-01 00:00:00</dd><dt><span>time_origin :</span></dt><dd>1850-01-01 00:00:00</dd><dt><span>bounds :</span></dt><dd>time_bounds</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.DataArray 'time' (time: 1032)>\n",
       "array([60280.5, 60310. , 60339.5, ..., 91599.5, 91630. , 91660.5])\n",
       "Coordinates:\n",
       "    height   float64 ...\n",
       "  * time     (time) float64 6.028e+04 6.031e+04 ... 9.163e+04 9.166e+04\n",
       "Attributes:\n",
       "    axis:           T\n",
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       "    calendar:       gregorian\n",
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       "    time_origin:    1850-01-01 00:00:00\n",
       "    bounds:         time_bounds"
      ]
     },
     "execution_count": 8,
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   "source": [
    "ft = xr.open_dataset(path_CMIP6,decode_times=False)\n",
    "ft.time"
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  },
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   "source": [
    "This decoding is very helpful in quickly selecting data over specific time periods."
   ]
  },
  {
   "cell_type": "markdown",
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   "source": [
    "### Exercise\n",
    "\n",
    "Using the `.sel` method shown above. Find the tasmax values during the year 2020. What are the dimensions of tasmax in this case?\n",
    "\n",
    "<a href=\"#ans1\" data-toggle=\"collapse\">Answer</a>\n",
    "<div class=\"collapse\" id=\"ans1\">\n",
    "<pre><code>\n",
    "tas.sel(time='2020')\n",
    "</code></pre>\n",
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    "### Selecting data within a range of values\n",
    "\n",
    "In the above examples we found the tasmax values at a nearest lat/lon location, and a specific time. To select data over a range of values you can use the `slice()` function under the `sel()` command.\n",
    "\n",
    "In the below case we are getting the subset of tasmax data from years 2000 to 2005 and between latitudes 20S to 20N."
   ]
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   "execution_count": 9,
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       ".xr-var-list > li:nth-child(odd) > label,\n",
       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-odd);\n",
       "}\n",
       "\n",
       ".xr-var-name {\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-var-dims {\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-var-dtype {\n",
       "  grid-column: 3;\n",
       "  text-align: right;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-preview {\n",
       "  grid-column: 4;\n",
       "}\n",
       "\n",
       ".xr-var-name,\n",
       ".xr-var-dims,\n",
       ".xr-var-dtype,\n",
       ".xr-preview,\n",
       ".xr-attrs dt {\n",
       "  white-space: nowrap;\n",
       "  overflow: hidden;\n",
       "  text-overflow: ellipsis;\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-var-name:hover,\n",
       ".xr-var-dims:hover,\n",
       ".xr-var-dtype:hover,\n",
       ".xr-attrs dt:hover {\n",
       "  overflow: visible;\n",
       "  width: auto;\n",
       "  z-index: 1;\n",
       "}\n",
       "\n",
       ".xr-var-attrs,\n",
       ".xr-var-data {\n",
       "  display: none;\n",
       "  background-color: var(--xr-background-color) !important;\n",
       "  padding-bottom: 5px !important;\n",
       "}\n",
       "\n",
       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
       ".xr-var-data-in:checked ~ .xr-var-data {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       ".xr-var-data > table {\n",
       "  float: right;\n",
       "}\n",
       "\n",
       ".xr-var-name span,\n",
       ".xr-var-data,\n",
       ".xr-attrs {\n",
       "  padding-left: 25px !important;\n",
       "}\n",
       "\n",
       ".xr-attrs,\n",
       ".xr-var-attrs,\n",
       ".xr-var-data {\n",
       "  grid-column: 1 / -1;\n",
       "}\n",
       "\n",
       "dl.xr-attrs {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  display: grid;\n",
       "  grid-template-columns: 125px auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt, dd {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  float: left;\n",
       "  padding-right: 10px;\n",
       "  width: auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt {\n",
       "  font-weight: normal;\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-attrs dt:hover span {\n",
       "  display: inline-block;\n",
       "  background: var(--xr-background-color);\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-attrs dd {\n",
       "  grid-column: 2;\n",
       "  white-space: pre-wrap;\n",
       "  word-break: break-all;\n",
       "}\n",
       "\n",
       ".xr-icon-database,\n",
       ".xr-icon-file-text2 {\n",
       "  display: inline-block;\n",
       "  vertical-align: middle;\n",
       "  width: 1em;\n",
       "  height: 1.5em !important;\n",
       "  stroke-width: 0;\n",
       "  stroke: currentColor;\n",
       "  fill: currentColor;\n",
       "}\n",
       "</style><div class='xr-wrap'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'tas'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 60</li><li><span class='xr-has-index'>lat</span>: 28</li><li><span class='xr-has-index'>lon</span>: 256</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-847f0857-c6c8-4978-b4f8-e8766fde7c07' class='xr-array-in' type='checkbox' ><label for='section-847f0857-c6c8-4978-b4f8-e8766fde7c07' 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><pre class='xr-array-data'>[430080 values with dtype=float32]</pre></div></li><li class='xr-section-item'><input id='section-77d787ab-63e3-490d-b934-ed6c2f04d844' class='xr-section-summary-in' type='checkbox'  checked><label for='section-77d787ab-63e3-490d-b934-ed6c2f04d844' 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'>float64</div><div class='xr-var-preview xr-preview'>-18.91 -17.51 ... 17.51 18.91</div><input id='attrs-53072169-83f8-4588-89dd-40cb94f734e5' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-53072169-83f8-4588-89dd-40cb94f734e5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a31cf7f6-81b7-40cc-9417-afc12677d538' class='xr-var-data-in' type='checkbox'><label for='data-a31cf7f6-81b7-40cc-9417-afc12677d538' 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>axis :</span></dt><dd>Y</dd><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>long_name :</span></dt><dd>Latitude</dd><dt><span>units :</span></dt><dd>degrees_north</dd></dl></div><pre class='xr-var-data'>array([-18.910357, -17.509591, -16.108824, -14.708057, -13.30729 , -11.906523,\n",
       "       -10.505756,  -9.104989,  -7.704221,  -6.303454,  -4.902687,  -3.501919,\n",
       "        -2.101151,  -0.700384,   0.700384,   2.101151,   3.501919,   4.902687,\n",
       "         6.303454,   7.704221,   9.104989,  10.505756,  11.906523,  13.30729 ,\n",
       "        14.708057,  16.108824,  17.509591,  18.910357])</pre></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'>float64</div><div class='xr-var-preview xr-preview'>0.0 1.406 2.812 ... 357.2 358.6</div><input id='attrs-5bf78565-891c-46dc-b001-4abf049dba52' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5bf78565-891c-46dc-b001-4abf049dba52' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f371388a-2204-4e8c-8966-550138ceac54' class='xr-var-data-in' type='checkbox'><label for='data-f371388a-2204-4e8c-8966-550138ceac54' 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>axis :</span></dt><dd>X</dd><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>long_name :</span></dt><dd>Longitude</dd><dt><span>units :</span></dt><dd>degrees_east</dd></dl></div><pre class='xr-var-data'>array([  0.     ,   1.40625,   2.8125 , ..., 355.78125, 357.1875 , 358.59375])</pre></li><li class='xr-var-item'><div class='xr-var-name'><span>height</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>2.0</div><input id='attrs-905cc1e7-2f06-4591-947f-60013f3527f0' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-905cc1e7-2f06-4591-947f-60013f3527f0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9652c8ac-0e99-4459-af11-1f6eed35f96f' class='xr-var-data-in' type='checkbox'><label for='data-9652c8ac-0e99-4459-af11-1f6eed35f96f' 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>name :</span></dt><dd>height</dd><dt><span>standard_name :</span></dt><dd>height</dd><dt><span>long_name :</span></dt><dd>height</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>axis :</span></dt><dd>Z</dd><dt><span>positive :</span></dt><dd>up</dd></dl></div><pre class='xr-var-data'>array(2.)</pre></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'>2016-01-16T12:00:00 ... 2020-12-16T12:00:00</div><input id='attrs-78da37f4-a130-4e89-b30d-98065b8cf609' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-78da37f4-a130-4e89-b30d-98065b8cf609' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c6a4be00-b1ba-4d0d-886d-c0cb966a529b' class='xr-var-data-in' type='checkbox'><label for='data-c6a4be00-b1ba-4d0d-886d-c0cb966a529b' 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>axis :</span></dt><dd>T</dd><dt><span>standard_name :</span></dt><dd>time</dd><dt><span>long_name :</span></dt><dd>Time axis</dd><dt><span>time_origin :</span></dt><dd>1850-01-01 00:00:00</dd><dt><span>bounds :</span></dt><dd>time_bounds</dd></dl></div><pre class='xr-var-data'>array([&#x27;2016-01-16T12:00:00.000000000&#x27;, &#x27;2016-02-15T12:00:00.000000000&#x27;,\n",
       "       &#x27;2016-03-16T12:00:00.000000000&#x27;, &#x27;2016-04-16T00:00:00.000000000&#x27;,\n",
       "       &#x27;2016-05-16T12:00:00.000000000&#x27;, &#x27;2016-06-16T00:00:00.000000000&#x27;,\n",
       "       &#x27;2016-07-16T12:00:00.000000000&#x27;, &#x27;2016-08-16T12:00:00.000000000&#x27;,\n",
       "       &#x27;2016-09-16T00:00:00.000000000&#x27;, &#x27;2016-10-16T12:00:00.000000000&#x27;,\n",
       "       &#x27;2016-11-16T00:00:00.000000000&#x27;, &#x27;2016-12-16T12:00:00.000000000&#x27;,\n",
       "       &#x27;2017-01-16T12:00:00.000000000&#x27;, &#x27;2017-02-15T00:00:00.000000000&#x27;,\n",
       "       &#x27;2017-03-16T12:00:00.000000000&#x27;, &#x27;2017-04-16T00:00:00.000000000&#x27;,\n",
       "       &#x27;2017-05-16T12:00:00.000000000&#x27;, &#x27;2017-06-16T00:00:00.000000000&#x27;,\n",
       "       &#x27;2017-07-16T12:00:00.000000000&#x27;, &#x27;2017-08-16T12:00:00.000000000&#x27;,\n",
       "       &#x27;2017-09-16T00:00:00.000000000&#x27;, &#x27;2017-10-16T12:00:00.000000000&#x27;,\n",
       "       &#x27;2017-11-16T00:00:00.000000000&#x27;, &#x27;2017-12-16T12:00:00.000000000&#x27;,\n",
       "       &#x27;2018-01-16T12:00:00.000000000&#x27;, &#x27;2018-02-15T00:00:00.000000000&#x27;,\n",
       "       &#x27;2018-03-16T12:00:00.000000000&#x27;, &#x27;2018-04-16T00:00:00.000000000&#x27;,\n",
       "       &#x27;2018-05-16T12:00:00.000000000&#x27;, &#x27;2018-06-16T00:00:00.000000000&#x27;,\n",
       "       &#x27;2018-07-16T12:00:00.000000000&#x27;, &#x27;2018-08-16T12:00:00.000000000&#x27;,\n",
       "       &#x27;2018-09-16T00:00:00.000000000&#x27;, &#x27;2018-10-16T12:00:00.000000000&#x27;,\n",
       "       &#x27;2018-11-16T00:00:00.000000000&#x27;, &#x27;2018-12-16T12:00:00.000000000&#x27;,\n",
       "       &#x27;2019-01-16T12:00:00.000000000&#x27;, &#x27;2019-02-15T00:00:00.000000000&#x27;,\n",
       "       &#x27;2019-03-16T12:00:00.000000000&#x27;, &#x27;2019-04-16T00:00:00.000000000&#x27;,\n",
       "       &#x27;2019-05-16T12:00:00.000000000&#x27;, &#x27;2019-06-16T00:00:00.000000000&#x27;,\n",
       "       &#x27;2019-07-16T12:00:00.000000000&#x27;, &#x27;2019-08-16T12:00:00.000000000&#x27;,\n",
       "       &#x27;2019-09-16T00:00:00.000000000&#x27;, &#x27;2019-10-16T12:00:00.000000000&#x27;,\n",
       "       &#x27;2019-11-16T00:00:00.000000000&#x27;, &#x27;2019-12-16T12:00:00.000000000&#x27;,\n",
       "       &#x27;2020-01-16T12:00:00.000000000&#x27;, &#x27;2020-02-15T12:00:00.000000000&#x27;,\n",
       "       &#x27;2020-03-16T12:00:00.000000000&#x27;, &#x27;2020-04-16T00:00:00.000000000&#x27;,\n",
       "       &#x27;2020-05-16T12:00:00.000000000&#x27;, &#x27;2020-06-16T00:00:00.000000000&#x27;,\n",
       "       &#x27;2020-07-16T12:00:00.000000000&#x27;, &#x27;2020-08-16T12:00:00.000000000&#x27;,\n",
       "       &#x27;2020-09-16T00:00:00.000000000&#x27;, &#x27;2020-10-16T12:00:00.000000000&#x27;,\n",
       "       &#x27;2020-11-16T00:00:00.000000000&#x27;, &#x27;2020-12-16T12:00:00.000000000&#x27;],\n",
       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></li></ul></div></li><li class='xr-section-item'><input id='section-48719342-df84-4e16-a170-bf2a748550b7' class='xr-section-summary-in' type='checkbox'  ><label for='section-48719342-df84-4e16-a170-bf2a748550b7' 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>online_operation :</span></dt><dd>average</dd><dt><span>cell_methods :</span></dt><dd>area: time: mean</dd><dt><span>interval_operation :</span></dt><dd>900 s</dd><dt><span>interval_write :</span></dt><dd>1 month</dd><dt><span>standard_name :</span></dt><dd>air_temperature</dd><dt><span>description :</span></dt><dd>Near-Surface Air Temperature</dd><dt><span>long_name :</span></dt><dd>Near-Surface Air Temperature</dd><dt><span>history :</span></dt><dd>none</dd><dt><span>units :</span></dt><dd>K</dd><dt><span>cell_measures :</span></dt><dd>area: areacella</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.DataArray 'tas' (time: 60, lat: 28, lon: 256)>\n",
       "[430080 values with dtype=float32]\n",
       "Coordinates:\n",
       "  * lat      (lat) float64 -18.91 -17.51 -16.11 -14.71 ... 16.11 17.51 18.91\n",
       "  * lon      (lon) float64 0.0 1.406 2.812 4.219 ... 354.4 355.8 357.2 358.6\n",
       "    height   float64 2.0\n",
       "  * time     (time) datetime64[ns] 2016-01-16T12:00:00 ... 2020-12-16T12:00:00\n",
       "Attributes:\n",
       "    online_operation:    average\n",
       "    cell_methods:        area: time: mean\n",
       "    interval_operation:  900 s\n",
       "    interval_write:      1 month\n",
       "    standard_name:       air_temperature\n",
       "    description:         Near-Surface Air Temperature\n",
       "    long_name:           Near-Surface Air Temperature\n",
       "    history:             none\n",
       "    units:               K\n",
       "    cell_measures:       area: areacella"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "subset = tas.sel(time=slice('2016','2020'),lat=slice(-20,20))\n",
    "subset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Exercise\n",
    "Find the tasmax values over Australia, between latitudes -45 to -10 deg N and longitudes of 110 to 155 deg E and during the years from 2015 to 2020."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Xarray .plot()\n",
    "\n",
    "Plotting with xarray is very simple. Xarray plots will automatically define the axes values and labels based on the information contained within the data array."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.QuadMesh at 0x7fc0408666d0>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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eVNXXAo9V1btTmbuBu9ZdLyJfIiJvFJE3Ltu9h6rbEyZMmJA85U/3ebTgpnIoKS/0CxlyjPxbEfm8016vqi8HXg5w7o4n6/4TZuw9zvVy6tAMVGd9BZrLRk74Bczv64h1UtKtUCzdzPWcQSnzLnUpKkPdR+4rFJT3inLZjqVycfgNY2r9KJU8vnYTSqrcF8WlGyhGrYSY+y7F79yngi1ynSbltZgyVdf0bQXxtI6UTsa3E3VNIY6OUaI4xQ3n1MtR6r+s6hjP7JPuZ51+ZxNXosLAua504lhxe/mcqI7uRdo1nLRzxqVkqt87yIruWWN6kbLh8v7LZ7rr+nOyNYetuZ2oK+LObFzHum5XDumGiACy6KBN3FAMQ59UR33SxQJNfc/cUN8HAO9Nme89Oq/HXFJCd2Hez5161+tsJGj/HqhneJfT+YPH1Gvv5eohhCmy6wg3W+T1PODtqvp+ABF5NfA3gfeKyONV9W4ReTzwvpvZyQkTJkxYhXJ2EWRuFdzsDeUvgY8VkW3gAHgu8EZgD/h84GXp+xdOqijUcOkpjsPHJMpEAG+/3ULQSlheFJoHoFFl73EV1QJca2W6rYHzKP8fQaZ2BNZZGhkFXfxeNd1K3y5RoBIHa5+R1U9JlZf6i3Vly8u86TckKqEWq19Bm7HpZ29CW+qGCge/XvfirCsSQXziKEbU7oqeaRUb9BZHikVdoezX3VweFtNb9H3J472qq9Kj43ak3ZLyjsWxExaKY7makxaZteMkhUmxgBRcSuIOrd1CPyBi5ruqZrVV+eGe57OBm648nD9n1l2Zozk4hMUSEQfn5v3Yd4+7eMS8XDrFHRh3YVZ/Bce06AaLscNFr6vRxXKoIJ3XGIfzUdEucSzikGSG7FI/+ru8sNu3F3a3+vGL2SRbpP8tQfv5VCc9J7a4WBMLpuTyE89G0q/AckopNcJN3VBU9bdF5GeBN2PGgL+LibDOAa8SkS/CNp2/e2JdFSwvQKzz6gKaNpToIczTxrEtLG63x9UtQUJS1DUM1wl02+WKnr4iSJfZ6mGRcl2x0Af6BcVlPejKfwngwnBO1sWXKxTYa73EV4uvbmRlm3ldaukXnHqvVKbmduh9J7IYLCswyzpPVETDaDHvlaF+5V5GG/KGjaYQA60/PxgI5E16GIdisysvypu5FvpaZfASL65bFaNJUiSvMzseCo2Jk/LYsYiKW+b6xxrfwWgk9htf3lyElb6sKLhHPh/ZjHZ7qxdv6c68FweFrUH+medKovbPxapIr4we4Q9CPz7+vj3rqyr6wINDPXn8XESllL2lm8risnyuC0gKLdD3wQld7qcMoiwzQtG+bH6GD253w/sNLM8wbVI8UV56MkRkDvxHYIatyT+rqt8qIrezwX1CRF4MfBEQgK9U1ddcd0fOADebQ0FVvxX41pXDC4xbmTBhwoSHJRTOSoeyAP5nVb0iIjXwGyLyK8CnY+4TLxORF2HuE98oIh+GuVc8A3gC8Gsi8nTVmx/6+KZvKGcGNS7BtYkargexhc5DL0mIO9ACsnSja3tUmkQzg/iHWFDpiaMhSCF+KriWNKXVvhAUXAv+IFXjoT4AIsQsnvEQZhxRupcKe9eLO4ZujrzIC1OS6iD2Yq5Mrdn5JP5LinnpdKC2ewWm66UZ4ZwftZNFfOX/dcRZX3+kNwLIjnrOCVoPF8VSRFVwY329uQ3lKIUvg8FEnocRNwWDoricXwWyMUa+fsTRFdcEhUrGIsfKG6VNwb0U3ErJubjy9U4UuFbrKf0cWVcrQTpFRRCng4lQnhgRixMGxDrPkcLcXmV32CUlfDRlOamdNommSq6groybCYHu4k5qh95IJXMosRo4gtCIied6Rbd9+6XCboULSn2pIzzhAtWlQ9xBizzmDrjvAbMSCTW6WFBCnCBVkkklDkpDQJp0rAvEC9t2PyLEyuEXMRmXyMg8ueRMluc9mp7ZUHAoboNj5NVCEcIZiLxUVYEr6W+dPooZLD0nHX8Fg/vEC4FXquoCeLuI/CnwMcAbrrsz14lJADhhwoQJ14iocqoPcGd2cUifLynrEREvIr+HGSD9qqr+NpvdJ54IvLO4/F3p2E3HrcWhBAb5ctKfaKNGlIn2SnoRhSYpCteQ2aMjunI0AipGhCZuRWPmWhK3ooNuxnWDfkZC4kaAmLZylzkRHShaiQP1jEIoKdncn6AjSssvB2WkusShpPoc2lP4wZv+Q5wpDVwX0Why6NxmbFxPqas3alQYuJLch55bcULMT1KW6wftqRXJ1qJi95YpyCz7HimBV/RAI27FDb/LsqtK5IKfYPVnprrVHeWwzERaEqdpv12nKAMVrJoo6pC4iaBjzk2HxtT3B0b6nLKjbmGT5K8cWrlDGyydVRYRuHLEWW3muTneVYoA7JZdH7/OLQvOJHNOhcOibjWDAj8rrhOHo97jFwEVIc4EWSqxdsaJyaAv1EqoFkqshjFXtbkPjY2bejFOZRnRi1tkXbg73IIQzZAgH8t9i2ocSeaY8vG7bgegu22bbsujTgjzzFamr+wsKgPH221J/0waR7Uyzy1nAkVYbvIdOIp7VPWjN9Zl4qpnishF4OdE5MOPqWudnO0kreZDgltnQ3HQnlNikzaOmcl9XJ2VgHkzKa4pnCp6nWYxLaPNps+8Z84PkiKhq2LtKckaB6QVwlyp9oTQQLM/VFOy3mCm+mCbSSSx48nyq1/U88vr8yZl7flkoVYaAvQLJ6k/qsQVb3dpLTS46xR1gmDWMS5tSr6IIFDef399XrDSYLa7NVoJ3UyItdg9ZBFZa9e5pSlorU27vNu2x295vrIFobIFf53X+xEc470uxVzmzTlbmA3+N0OZjN6DujbCIIsNxwYYK2OQqwuFSFHBdZHR5piby6IuHSu7s6e7phhZ0kWIAXHSW1j1CnDv7XeOW+UgzivcnomSerHarO7P917uqr3fSPa2FwkmxmoqXBsI2zVuGQjzFI8riyE3iDnzc5mJCtdCmDtcG+jOz6kuHaLntpCDpbVbVdB15nOSfGR0uTTfmt1zfb1dsj7rtlP0i6j4wwh+3B/1aV5F6ObFpiIQ5jb/YQaa+rf6Dl4rFIhnLORR1QdE5PXAC9jsPvEu4MnFZU/CIozcdEwirwkTJky4RoTk3HjS5ziIyGMSZ4KIbGH+eX8I/CLmNgFj94lfBD5LRGYi8oHA04DfuQG3d9W4ZTgUrZRwoUMqk5W4KhqFnDgTlzkUNyhSVYXQCRoF7ZXtzojdLMZKynmrN10IieJTJCbxlyZT2kKEZeUgFg7Hva9D7/NAJnUSV2Liq94/ZeQ7Yt+ZSh48gBO7Xz63milj6cVUmQNxOf6SmkLXFMKJg1k1hS0UsIMRQuKcMkF/aQki1IlydIXC38oVfdOhnvqSUd6ZI+q2/Ij6DLUQG1O8xrq8p2Ia+v/DzedDLt9XNpbonVcKcVshUgP6KAduadyUCzYXY9GbEGbJvDoxAZLEjzmygOtcihycx5VRvLjMwWV0j9kdD/sy2Nws2oGjCBG8QyWJjlqL7hsbe42zn0Yv/uorE7T2JsYKitRqUQ+y/4jr5VcAVA9YLLAsruoubhG2KmLjLLpEIV5STzFfVn65a2xgrCRxiIJrA76qcHUFi5bsO6PZe95749zKqMYpUnhshFALiDsS5y63H+vMkSSueceOt+etTLejgxi8XnnGrxGqQjg+c9xp8XjgFSLiMSL/Var6yyLyBta4T6jqW0XkVcAfYO4WX/ZwsPCCW2hDmTBhwoSHGvEE7uM0UNW3AM9ac/xeNrhPqOpLgZded+NnjFtnQxHw865XwDunpnwXEFFcIchuW09YeuJhhTtwSCf4NusZBgoaTRSQV2IDsY4W/dUrOB2J8SUI0trHLaV3dpRQULEhEaZh4EZcO6a6NVHmEjXJiE05Kjr0LVYyiqlVKqtzGddq4hTUlP2ZSo6J6g4mu3adffcKX5GU72WgQFfL5IiuuZzpGRRpIy5GO78aPbbHmDpUL73ytzoY4j5p7XBeiI0jNIIuxlxOhm+zfqaos5LeyEBL+X82BkjH7HxZl91rHru+vtLCPPXBdCVKtaf41hThEtTk+bUjeovOkHOEuGXAL4JFBm6TzmJdPhsKXUmkLyeqxp34xG3MCtfvpB/KYxebCinMhqWLaO3NLHwmvZ5KginfY2W6r1gJvlWkVfwiWn9DxB90ydhDkMq4hawvyUYgeaxjPZjBt9se3yrqKmYPgjv0cIhxIt6bzqSq+gjHujOn2zEFR6wdsfGjyMCxLjnFYg69vRPqTT8Sa9OdhJk5NIe5oo2iWZ/qz4hDgTMxG76VcOtsKE7xdUTcsHmIaPoMxQ4Pa2Ln0AcbXAR/MH4g1JsVi2QxVrTNotqDnAw8K3nDdtbUgusEfyC2QYRBue46BmurYpPJfiauV+YyiIOSsjFbqGTRgUOP+mhQbICpL2UffTsO2a9eCEV4lphFXWCWTEn5Pnj+543G+jfaTJwgnS2mbpGtjEB8NMVwXjTTgmcLs4z6YyIR2zBj7UzBnMKWq1ifqk6PiPNK5A23F4OlBd42FduUoofQOMvZ1IykXyaSCUr0ySqvtgW2HINSKS8pNLtExbVpM1MzcEDBH3RU2fdHhj72hh2VG4UJAfokWBKjlfUp53vt0XmVxjEpvxtLJBdr19dZbuqi5sfSZzAVRdqAV0Ur3xMkAFmuFWsTJ7XbZq3V7Xj8wiNBqfY6RKG+3CKhwi2FdifNUaV0tYklu21TzMca6j0QB9WhESlmFedQ5xDnoCk2ROeIO3MOnnyO5a71eXnOoZU9A2VUCRO3meWkRcBIz3q/wSXjjsoMdNQrbEVwSQyOrQtnAUVo9dZZQs8C02hMmDBhwjUinEHolVsJt8yGIoD3sedKAHxvKqy0nafrPN1ejSwd/mCg0tQpI0JDMuE3kLGrpqio4PeTyWfiOlww5eygUB++M8xsdKU+Lb4ZRDGxphdjad+nXM8gAhqZwvfKdTsZ1K0ohhmCOxYKd8CehijgtDCjHvxd+phJmYvKdXgGn4bUL/XS11964gN92gBcVtjGod+RXlTZcyWS0jgXHJlR4ozQm1cjZjKdfWI6xXdQHXSgiQNpHIsLrk9DEMVuqE0x/33pq1AMUnVonIZLHvSxMl+VUTIpMcMO18ahT5UbB8cURrG7XBugG8pnzlCWnd2LM+oeB24/XZPNhrto0QhK0+kyUGLOw67G2Tgc3U5lXMWOT2a1xYNQcugqaFVbgMjWODJ/GAuTdiE+pkocoHEMsTEOxS0Vf6hFINSILFtLD1xXLB+3S6yF5fmKWJupb+5HaIb0E2ErKd5n2r8/lkoCkBzZwt5jauNKpFLERbOdSeJpcZlD4UxwVp7ytxJumQ1lwoQJEx5qxLOx8rplcMtsKCKK94lyS6SrcxFVQVXoOk97WCGtgyBoDdEp2kSjaLJJYYrRJUuL256jC1vK30GHIarIMjm/dTLiLnrqWsfZ2rTQn7hQcDLpOqP4C8Xvikdv9KasPY7LzgmyYqVJSZrk+skU2bUR6VMXp2uWJSVrlJf0VGWhzM6xtzT305wiAeQwOcl5SY51KUmZY4jp1YWR3iA2PpmiGvXuWu2pT3UyTuWadSTZK3o2xMTKRgcjXZGDMHN0245Qmx6qWmjSA5mcPcwHDs0vV3QxuZvFXAJ0c6PaY5EMzC+MepcuG0/kdmy++phlMrCZOc2Am6VndemNo8mm10vThbj9pXEmVblwxWG+8pg71+uNTFGkI1WTzjxhXtHtVISZ64061unhss6oT30goI0zRXdTmR4uq0CSri9zJ2GeI3ULrhVccMbZLBxuYXq1cGGLsF3zwNNmhDkc3JXma65oeofVJ27DGbfRz8HSIYeO6orDL8At7Qa6naSAP9fiq0DVmAWMiPaMrnNrHHavA5NS/ihumQ1lwoQJEx5KKDLpUFZwy2woIuATBVL5SEzxtpZtRdc5ur2m5z5wavLYxqzCxA06B3NMdEYZBUErc14kYibBQcZ6jzBQcmuj6RTPW6k/iT7LcmV0royVlcOEnJh2N58rdTBV6rOmECs+WYrVxgq5djBFzlZVdrFR2/R6DhnFvXIB3CKmREsQZt6sivx8qMJlTivI1WsAACAASURBVEpSTK8wMjHuZf3JWooqUciJMyHpYfr4WcmKyMJrZL2VDnGc4lHnwzB3dFuOxXmh3THn0sytuZZRhGYEWqGvwC9tXn0ZFLcYf7fMcb5szHUO3ok5DBZzsBoFGQoz52S63DV2zjVusLJTem5G/bwfL7ccHBEzJ9JHFI4FBwmmM+nnwxFnxp0sz/shhE+rpv9Kca/KEDVglosSoN6PvSm6CnSV9AnoYgPdVrLuarDQRw7a84o7FJa7wuwBa6O6bw/aluVtcw7vqFjcBovbIGxFdB7tnevzGdl76bJ5fn4pZhbPq70gtAfedChecbOAqyPzOoz0qCKMrD7PEqpMVl4ruOmjkUIO/Ajw4djr+IXAH7EhscxmKHVlL1wXHAeLhvagQi/VSEgbQVLcaaMwC/hZQJyiQQiHOYaSs5Q1fUTE3NH8WVnds79A2RMp1viRctnKB2dexS7H4Eoc/aA0P1rfMGDHDEGKW9XXIYN3fK4n1JJMdMuNMS1YnTUQZmMRSL/JqSl6w9z1fhf9RpqUzJA2qeQzEZNPSM7zrV4G5XypOA86+DI0Zu6r3pS00ZvS2BY/qPYV30ofql+9cHi7p92xutpt6M4NJqxxpilQo20mbimWXK0gAkTBLZIoMs1LNi8epRJIZsXq6L3qQQiN4oLgF8MiPZq/TK80gq8En3xn8hoXavPxIMdUa8z/KJR7fVWYCacoAGZ84CwjZ35EVM3nJG1k3W5NqG0T6IOA6tDHUA/3mc3MzSjEnoV226dgo9r7e7Q52v1s8PdQD1ppvzHF3ciVJ3mWF4R633H4ARep71+wuM2zuCAcPD7aBjSL4E2Bnk17+3tJynTntBdhVU16FXcHtwBXbCJ23bChZLgz3lBAzsSx8VbCw0EA+P3Av1fVDwU+Engblkjmdar6NOB16f+ECRMmPGxgKj13qs+jBTeVQxGR88CzgS8AUNUlsBSRTYllNteFOf5ZPUJozRPeL005rD5FuZVEFmbJihTJjACtYhIXqIWnjyDLxN1UiRQXNa/4gsLtqfisjF8T1TpzDpkbydf0NE5WkmYF/Zqw7qVYTYpzI+W5DKK40BRObKXiNVPmycR4lISo4MxMZDaUUye9GM11igajELW2ce65ltQPvNDNzLHQuJMxx5XH0C8Hg4eDOz3dlolDcl+qPRNF+QUsd9N8eG9cSAWHj1GbOzAOtBjYXvHcSd8Hc7YbuA9VoLYx7DmSNB+9SJNMuZt4R0JSWFf00RHqy/btukEMlz3ITYwHvgHX+RTJIM9DGnIdHFlLB1P1Yor6JOazTyQndxtFaY5C2PKEuT0Q7bY7kiIgG4GM5sqlMV5G/EFMXv/CwZ3eOJxzg/izN4fO3EgFeE1iyTT2ldLd1tFdFMLMU+17mgcbFheh2zbHYPFq3IlfcUJOHIdLx8fOygNHsirayv/zWpA5CFe+OGeIR5JSXkRmKSnXDcPNHo0PAt4P/LiI/K6I/IiI7LA5scwIIvIlOWFNd2l/XZEJEyZMuCFQTpdc6yzyzl8PRORjROT3gT9J/z9SRP6PG9HWzdahVMBHAV+hqr8tIt/PVYi3VPXlwMsBdp72+D400rzu2HcRaQLhHEYFuYF6wymSTYy94lzoqduYIg/H4Kzs0hFdtDwnQXqlunrsWOZKKPQXm/qbKV4Z6ijjho2cG3OZNdR8T7X2J+jNlbO8XjJlXepvZKjXbhZzXCvLZFl6omNclxMqJYV2SAr9zP1E6Slmi30mR2jBWFmeiuWu0M0ZyJhMKae+S6SPBdWdU8LcKHDp7IYWM9OHxO1gpt6mcjDu0pUjUr7AimQvzCrpP1qLmusWybQ1OW52RVRot0LH5YjRYa6EnYhW0ewDvOKbXpnCfuvQKzV+z1FfEqqU/rmPFN1CyIYdQYakajmpVxw438zl9Mp971IE5Hx/3hw8o+lNYg5x4o0T6+YFvZjGN+vxkuqnT/SmKWlYNxeLS1ekanadcdzZmKGMT9dGmy9LroW9Z1lnpCYZQJTFkwOHwZ4XFCgMYuxDz5UAI51J/3iK4p3iksNi5kgcA9dSLt5OBm651J+c1QKvPGKU8j8AfCrw8wCq+l9F5BNuREM3ezTeBbwrpbsE+FlsQ9mUWGYjSmsOgPPnDgnb5oPinBKCS97y9ga5ZEHiXCRGR7baV3XEmPl4SS8ECDJ4z+dERXH9g7lqcTSywsqLfyFCKZW+o02iEDuR6yzqW6usz5uGHyrLYpWRCCeCuH4fGy3ypYgHLM5Vnx2y3wTMw92iAxTeytmiKW2OoREWF4TleTGLnhnm0ZzatOB+OogUffI9cMCBQ1rBHwrtrm0kzAOuin3yMye6JuvmWEGrKsPAejH/hgjdti2eBPvO/UAwH6MofRbQmPxFtFHY6qiaOFIWZzRNh263xCgsW8/hwsPSQWu+E64dIir4Bf24ujYv6vSBQ2NdzFUEieb/4pM/jSjg3Th3fZH4yycv/FglKlnywyRIFlMlkZx6ywyqklIy5OyO2MZWAbFLATSXSmwGUVt3Lm3Uil2Tfbb6TSGNaT3IfMvAreIYebXneQU771J8vryR5PNeYh8NI5f1xbw7UYiONjoO26o32inLXB9OznXyMIFT1b8os2Vipkdn39CNqPS0UNX3AO8UkQ9Jh56LxfjflFhmwoQJEx4WUMxT/jSfm4x3isjHAJpy13818Mc3oqGbzaEAfAXw0yLSAH8O/H1sozuSWOZ4jKmOxgeCWAKcqNJ70mcJlXORyhulGyJ0y5SkqHVDVS5Ra04HBWaWGTkZcRpllOCRpzX0yZf6npZ+HV3BDRTisz4J09FbG/msWAOD2Gh1NCwX+LgvIz+XLOZSqPZTfwqOJKcirg7HnXDLmEQy2nvVqxdikF6Z2zWOwzsch7dBuwvdudhHHLBKBpEImTrNHNvS0Tzg+3GItUITh8RpmYJ1elTMmGSFmcPLydVUGeJbqSBFwNvedDud05kQEwdaikydtz6sE8lorleUqorUdUC3oG0ruoUnxAZthdiZmE39wKFkTsh1xsHECigiGmSzXglCdaAjLjKLIM2se+AQR74zIXGRKlApkowreumnx4Jpi8AiD4gM3vSazdx1ZFhhIlBwVXrOGhv/XvTqi4HNIq2CMylTTYgbi7c2mQSX3Mnq8XxtnseoQogmnejFaf7siPNHCIfyjzCx1wcA7wV+LR07c9z0DUVVfw/46DWn1iaWmTBhwoSHA2zDuuncx7FIWSA/S1U/66Fo76ZvKGcHwbvYK9xUzeko6kBpVj5QV6GnZOycedhrNVAtGhOVGk3uLEGGOFs+aco9yUNbBr1Dx4jit8o46iSYdbhxoFDJ9a88n6t6k2Nuv+eYMvWbY1Zl5X6vpC+U7z0lG+zbdUp1oEXSLuPEstmvXafozBHrwbHRtQOnghO6bcfhbc681Bv6cUSGunvFdjYzFZOla/DQOtzS7qObpwH1g7w9z7GIrjJwiQrWI0N2GlpSoyMGIMntxRlX0lPCBXfUX4NRx66gqHM/VAU/a2l9pGsC3WEFexVu4ZAmjQn0ym5zIBz0JqTjmZOVLumvelNukDop9zUr9LXPvQNjXZtEhU4S9S99zpH+GUncUR81ekVPl40z8mBKB/WVxPkGQcURZxGpitw9pGgUI90Ja7kTKcZu1Tx4lVuJKvh+3I9yKkEFJ0rtAlpwo2fpF3IWdYnIk4GfBB6H8eQvV9XvF5GPBH4IOIc5eH+uql5K17wY+CLszf1KVX3NurpVNYjIZ2Acyg3HLbOhqEKI48m1hFT2YMfoeuVtwLzpYwocaX4I6cV2KQZIzpio9tekBWqe9OVLll4uzfEQ03Pd+5iUIT7AFOF5U0kvby++SD4q/YKb6hhH+WMQOaRy0RdiLQXSYpI3kWo/LUQ5MVGNLdapjliDboPu068gsVSuR/ufDQgGS6QccFKH8PJp7XettemXdswtwbWWhClkkVf29yk3TAG8Qh3ptlxv1SXbHa4ebyT9IlNsMHB0wd+IVZGiAD7g63Ed6zai0gci96fEYIlkRI73kVgLbR04AKJWiB+SeOUkY9KlzJ9pQ3FL8HnjT89iVqZLyDnbscW5y9MndDkE/er9UWSwlKSwT2HiJREVoRZLvNUTHjoEiXTj0dBk/VXpEGmhdckyMmdGzMRCIebKc9enKlgDi+4jDLRNElmhvbjLu2ibefGSiCiHXd2vB7O6o6kCbQo70MWz2VAswdYah7OrRwd8naq+WUR2gTeJyK9iEUT+iar+fyLyhcDXA98sIh8GfBbwDOAJwK+JyNOPySv/n5IF7SuBvb7/lnr4THHLbCgTJkyY8FDClPLXr0NJvnbZ7+6yiLwNeCLwIcB/TMV+FXgN8M3AC4FXJifFt4vInwIfA7xhQxN/K31/1Er3n33dnV/BLbWhlBTjMni6MHAlGVkEFqLrqUzzVCjIuMSliFNiMBFYz6bHIhmXJJ1x4U9AFlNkhadboRR75WaqsxSPJZFVphR7xXsftI+eSh15K1dwxAO9FJVE4xRy/vr2HHTzdE0SjWVOIlYWor2vJ9iN9v4jhV8EIrhW8V56kZfPyaZUqA6lH6TeaMELmnwcYj2m6jMpLS6iM1Pis9PhmsFUeETZrs5/kVBtE0puomeKVpS767CalKk36U59Oc5zu5ZIRKidMqs6tmYtD1TbhIMKWWQHlSRO7QRXCe7QOOHs75E5XY3JPqDkXBOnYSa/DLG43HA+O2llTjrHIuuTuTly3M2+3kFUmzi/Yojysxfzs0fBUedLSs6kF2vqkTksDRtWxzybC2eOpORO+jQVhZm4K79zsFiJHIa6jwx8lo6GV+Epf6eIvLH4//LkRzeCiDwVeBbw28B/Az4Ns3L9u8CTU7EnAr9VXPaudGwtVPXjT9vJ68UttaFMmDBhwkOF7Cl/StyjquuMj3qIyDng/wG+WlUvJTHXD4jIt2CuFMtcdG13Ntf7knXHVfW7T9Xzq8Ats6FkCiVER1ChC44uGPWQk2xlXWLPMBQObyUno4XJaR/jK2JmnHU0ObI3pb24pJ/osHDfhby71I0AfSTbPtprVlO4khocuJEyWVem/syclCPKe0lOZ/6Qkckv0Hvk1wfac0AShW4LWCaOKamOwizpWBLHVe0zxJSSHCG4pFDN8TE7OyKCVINORQruBFLbKWkZyZlQUvSC0eSIIheWFuPJaaEfKTiQksotnoF1KPUrJWWc68w4TajzdedKzqekpvN/z1DvzHWE88KluI1mr/fW2RjMI6Ex7s0tHP5QkhNmajsZf5QOkllhb/G8GPR6SbyfzcOPmJuTrxtWqFgzWpr657fgkhX6C2IFmqMbbKW0EFVKw+sS+91zKvSOikA/fz2nuIbDK7kTn3QmOU1FNh8uHZrz7+162f/voqOpOiTpUPQME23FM3LlE5Ea20x+WlVfDaCqfwg8P51/OvApqfi7GLgVgCcB7z6m+lK3Mk/1vPVMOr6CW2ZDAVO0L4MnRteLuxTQaGKuVaVttuTKm0aPbClWeML31kTOHnbNFl2puHMMocazMjOFa+kzL6ZrewV8FltI2ihKyCCO6tspRGdSWPG4JVQLC6BY70diJRzcYd7PvoV6T60PWaHvTAxV7TMoenN49+yVnR7BWIEPVo+JUpR2Z/DBKTNOxjqJxvrdwzYj14kZkYXxhiJd2ph94cuR5iMHC+wHWE4WS5VzZd/l0aPXrm4e668bR2AoEZMVUa5/ZGmEjurxK4vYVt3BxX2WOzbxOaMoyehDayVKRKukpE9EgPkJpU2/tQ+JqHEpWKVgOUr6YKG5h36wuBs9f+uGpyBkIKcBSGHqCwsudZhGXkxcZzeryXdn2CRyGPoyFFDeTFatuPJcVD72/iZ5I1kVb41EXBvmau47Gh96Q5NlOBNFuhkCnYH4TMyF/UeBt6nqPy+O36Wq7xMRB3wTZvEFxq38jIj8c0wp/zTgdzb3U79npb3vIYVhOWscu6GIyGlMzS6p6jedUX8mTJgw4REBRejWhRW/enwc8PeA3xeR30vHXgI8TUS+LP1/NfDjAKr6VhF5FRZVpAO+7BgLr3WYAR98Fh1fxUkcyguBbzmhzIuw3fOmIqpj0VW9d2z2AQB67kRzYDonvS+DOPOgz74qsXNGJXsdqDZJoi8tuBsvaHSmKK2FEJO4IWd3jGbjn5XaQz30CZxKCrHkVkqFe/YrGZkeZ1PRXCZxGGFmYrfmihIaodtJSlcRXDfEdQKLI2VmzXrU52Ak0rJ7ZX8ICJnziVtmw8yxSC+OKc1MkSHXeKwzJ5PGUNJ4ZQVuDnCpmJHEEUV4lokNZsN5dMs4TzCmVFc5m1GY9A3lTxPuvEyQaOateqQPq9xPbqepOryLyNyiUB60NZfdnPaggii4KhKDQzuHLp35jqgSwELlL6QXTcbWuNTMpQB9cjEYjC9ibRxGrJJH/nIQkZViydVb78WsMnBHcSu9P9mUu3WWxTIkpXednWpSvT2Xohs5k1IclsVZlTfOpHaBqhB1lWO8bq5K7iWq0EXHQWcDctjWR8pfK87CU15Vf4MjT3uP799wzUuBl56mfhH5XYZZ9cDjgX96ld08FU7aUP53VX3FcQVE5LYz7M+ECRMmPCKgnK3F2A3E3yl+d8B7blRelGM3FFX9FydVcJoyDwWy2a9zpiZbthWLgxq9UuMOkglmpeg84OaBpkmUVU5QVOhN8rEcVtv5ONKz9NyPGvcTO0fsnFHbuZ5EnWkUYjIN9fsWPTeHAVfoKcRY6F/6OEiJKwkrs5Q5gRwa3S9SHTUc3iGERmiuRPxSBsfJ7IkdldDQh9o372ztuY6seO/l757e7DdWktK9DvJ5CdZGb/FQeNTjoN2yOF6xKeor9Zi9UUTi0grqs/eE16SkLc17B6bvqFPhGhPefJyi/Cple1oO52h0YzNNXevgmCnvFRPX7MGdF6QDajs27/pnrVtWhDjomBQsUVwDsXHpORDcUozj7PJ8Jo4kz2GVjS3UkmBVitY2b67NypB0r9nkXYZnJDtOqlhdWiuSU/Um3YirA6S0wE7T3OXQ9AV3ss7zPXOX3sU+EnDWmVQSqb1xJ+V85VD1qzqTdbqUykUaH2hcjjZ8Vkr5h3/olYRvVtUvKA+IyE+sHjsLnEopLyKPAb4YeGp5jap+4Vl3aMKECRMeKXiE5JT/iPJPiu/1N25EQ6e18voF4D9hUSpvSBz964WImQyrQhc87UFFXHj87hJ/e8T5yLwxc6vFsiZGoaoCOCtf2lJmK6OqCWzNlmYhgvT158ROy7ZC1SMOfBOJwagyl6IYo2byGZ2RiiGC1BaRt9ejBNMhxDrJm7M8O1H8FtokUfyZis8xwDJ1X3A3Eo0aDYkTqhZkpQSxEbqZfaskOXo7ROvN5st+OUQbdm2K5VWJOURupTwdalyHJofJ3A+jeu13uwPLCxC2tDddNvPigqrc9D4KIw5jRPkXFkKryZas/FGLoUzdbuJWMk5jNlyitBxc5VBKKjlTxavy/6wXqF2krgJtZwbGMTjC0sPCIa3ruVVB0HlEdwLaCnSCVhbdWbrBCmzUx3zPXeqvT5yOV2IyRzZHSh1fINpb5GllnA0ei8E21G7z4VNcrhVOvoSTo7lNyiRZ2ZoLGOlNKon9OK2O4SZk3UrWbYFZe532+tNAFdqzUcrfEIjIN2I67l0RuS8fxlaOH70RbZ52Q9lW1WNzut982ENiSrzENu8s2d1aoAqLrmJWBXZnh+zVM+69tMPhQWPhyJ3iU3DIqhkUvU3dsVV3VCncdankCyk22OF+YxvXvOvDmm/Nl4goB4uGbuGHhb9R87TP5siDjhncYIopXtEUp8kdOlOqVgpR8AeCz3G5she1pkW94OSXu9J73KtIyseu5qvS6qD4l0I0kkLyuzAYJLjWNoNuy/xWwpatN9XBsHmV3tlALxY7vCN5uzcROsEfuHE4/TomfwWSH0oRRNAlBbcMAQQpFqBNwQPzPOVFarRobRBjrVtghg1oXQKvo3UAG0VeUWWkUF7XnnfRzN4PK2JwQz2d4BcFsRMgRk9sUuC3Ss3LXUCS8t11MhAmjDdx9aCzmEyRxTKVKlZfnoeQsmQqllisjrBl0Qp60ZjPImYjoEolexlWPo9fHhuXNo1ybryz4JuVDD4mjQ/9RrJJ8Q5jDiGXsTGOJo7Kz/GKCOwscJWOjTcD3wv8b5gCvs+Ee5UWYVeF024ovywin6yq/++N6siECRMmPNLwcBZ5qapiSvivF5ELmKnwPGduVNXfPOs2T/JDucxAQ79ERBZAm/6rqp4/i04kmd4bgb9S1U8VkduBf4PpbN4BfKaq3n9cHV30hOC4uH3A7fP9nnKoXGQZPffu77C3aAgq3LVzhdu29rn78nkuX95iuXA4P0SsdVWkaTq2m5atekmVwuLnOmunuEo5PztERHnwLy7i7q2IDbR1RO5SLuwc0GwfcMXNyARm7E2Rx4pQ+2sRxVwdjOO5XJl5aOEMWB1YqHDXDtwJQHVoJr2LC5ZmN5t5+kNzdnRdjlBrycRc0HHaYSd9BFuwVLC902QSZ+U6oVD6VumQWGywxe1G0Uqwb/OajkbNJjZIPWgyiLC84uneVziTMiXsJjPTdWITe57oo9CSriuV4uNn7yinUnKi/Q2vlDkO6xTGlawX2XSJrWuDZ7msLL5XELRWCzNcKzFaOl1/4HCL5OQYHGGmaB2RWs1ZdWGsSvDaG1wMjZLGPzkdRhPZEMTSLidTbklvu1Y2H1pH/FZH1YTjucGC8xjG9uh4ZG4kcx397yT+y+OUORR3ggI9K8Xdyvh20XPfYov7DrY5XNb98wLrjSquBcojw8orhXD5Oize1+9j+pPfAp5z1m0da6Kgqruqej59O1XdKv6fyWaS8FXA24r/LwJep6pPA15Hwa5NmDBhwsMFj5AUwF+DJTF8RwoU+ddJ0Y3PGqe18nqdqj73pGPXAhF5EhZb5qXA16bDL2TYPV8BvB44VofjRLnrwmXumO+PFHKHXUXjAk/cfZAHmjkPHm7xvr1z3Lm9x5PPP8C7XeSBB3d6KrTKupTkUKUqXFnOeGBvi+Wyomk6drcWNFVnlFDwg0MjigRheaXhsOnYmZkOp051Hhw2hC6TinkA7HcMDnGRqgksO5dS75pJ8Nb7Yev9AXXCclfotk2f0Z6zKrpt8EuhumIcSdavqBsU6GgKyaSJI8nmu1kmHgpCvCDCwxyW51M/Y1K4y0D9KqYTac9De3swClgxubsDXwczmPBK3DHquH+9cts5n0eRbCkPzSoXkqnjVTl8yYWso4DL52Tds3PSsUyJrnI6m8plfc9qfUeOFVyEc2qJqTTpMBYV0go6S6mT9+m5SOkEVykxxwBTMa6iw5wQwXKapL5oSq9spuxYrpIyPa/SJ4vDqYVTqSL1rKOug70PPjKvuiN6JZEhL8lqbK1191+OYf7vkg6lcoO+Mp8fjWse56Rsj+V4lnPi6E2NnSghCl2yv3fuaP+uCfqw16FkHKrqgYggIk3ytP/QG9HQSSKvOWZdfmdyYMyjdx6LIXMW+BfANwC7xbHHphwBqOrdInLXSZVULvD47csASQkaiOpYRk+njvP1gsdtX+b2+QGXljMWXcV81nH71j77hw2zxixAnnLhfpbRc//BNpcO5hwuakLn0GCe8QcHFQf3bCMLZwmOAjR7SSbpIZ5T/FbHufmC2geWXdWLVXzhz6KxCEApUDWBrvUsL81wlz3VvlDtQ30ZqgNl/7Gegzuh2wGSEt6n2KOz+4ZFrNtOVl+F0l2CSU9iBaExb+oc1j6HLy8tzCTYRgKwuAjLiyYS0UptsUkLj1taVsUwV/RCh/gIaWPMQR1ns5a2rQgLjzSRZjakqFTGVlWrytw8Nscp2bMyt7ymXKTg6CZwHDaKtOTo+auxNlq9pheJJYOInWbBctuzB+Ytj6Cd4JZm9ZWbCnPtE2sRBEsEJ2bU4bGNQLFdPy+cOdZWfkyyMUgZlDOk314Rb4TN9taC7aal9sG81l2kcWHjfVfu6GayyYt9+D1Wug9zNvikwPExs6K6I6Ixp0LjArO6wzvbUBZdle7/bDYUZRBZPsxxt4hcBH4JeE2y+HrvjWjoJA7lHwJfjW0eb2LYUC4B//J6GxeRTwXep6pvEpHnXMP1XwJ8CcDssbsnlJ4wYcKEs8MjRYeiqp+Wfn6ziDwXuAD8uxvR1kme8t8vIv8n8BJV/c4b0P7HAZ8mIp+MhVU+LyI/BbxXRB6fuJPHA+/b0L+XAy8HuO1D79LGdb3JYKZuGtf1lhg5kNu5eknnHXPfsV0tWZyvOFjWqYxju2pZNgv2lzXd0luI8STyYeks7LpLFLuCW3j8Auo9oTrwLJgRLuxxcX7ATr1kq7Zhvo9tDhbmMu59xLlIjI4YhboOhM7jLnsI5vm8c7dy7p1Luh3P/l0V7XntfUVCBV0SeeEUv+eo96DeSyHnk5jLL5TZpYjrlOU5x/K8M6/pajAr7RXyWSGLebdDEpkFiNsKW11B5Sph4dF9j84j9VZr/jdRzJTURZraqNm2rXBNYGt7yXaz7Oej9OEouY8SJ4myNolSVo9lZAXxKo7zvC7Lr6Omj0OmnEdUeRLHAji187fPD9iuWt7nzvH+w/Nwxfybcjh4OXTgIDaKW4BLaYJxmHFDHfCVWnKyFLeuj/iQ285znH9j3IpGiHgQ89Tfmi/ZmS05Pztku2p7cdQqN1HeV75Pv+E+N3EYq+XXKeGNAzk61rlun+SvI27IYRyK71J8P9+Luhp/dlazD/cNJRk8vVlVPxJAVV93I9s7kV9LNsuffCMaV9UXq+qTVPWpWI7k/6Cqn4eFZ/78VOzzMcfKCRMmTHjYIPuhnOZz0/po6/cfiMjGjI5nidP6obxWRD4DeHWybb7ReBnwKhH5IuAvsfSXx6JK1EhQCyl92FtYGNXZuI7KhT7poZm22AAAIABJREFUllFeZqb42K0r3Oe2gMFc8eLskP22YblVoUBI8bpky5SU5SC0FyvaBxrc0lFdEeoHPPfcZ+zDTrNgmZSBi2XNcq9GO4dUSrO9pKoCIqmPsxb3lCVdcITLu/il0u56EGH7fYpfCN12ynXhGLzZl0JzGbbujVT7RuEtLnj27xK6xwpXomfrHmguK9W+yeDDDIvflaPIml63dzrsnQ8dVAeCX3raXSFuRdy8wzcB3wS6ukaXjtA5tuZLZlWX0t1GDruKBy9tE5aeZmfJ7nzBvGp7yrJUdI/NTTcrs0ccCes5lLKc3cLKI7vp/S6Ol7oO0w2syOg3UOKrba7qBIJK/12iElMgn58fsrfbsK9zU+xXsc/nE3w0M+zoicGcULVS/KyjmoXeJBdS1IgiTp0quOSMmIcjxhQxG/BNoGosPfG52YLbZgecbw4tlpYLPfeQ78evjOmq2W4eu4xs6VRG512tYxPCMZzgKveYj80k4psDKhe4b7HNQVez01g8xLu29vgvp2r5ZDyc/VAK3Am8TUTeAOzlg6r66Wfd0Gk3lK/FlPNBRA44Yz8UrLLXY9ZcqOq9wFVZkLXR85Z7nkBQs/4I6QGuXOTC/IA753vMfUslsX8II0JEaHzHnfN9gN7Ddu5bPujCvcTz9/V27V3aoO493OaB/S1CdFQ+MG9a4s6C/YOG7t1bFhb87jn3vG/O+70OoehTfvQYBQ48S2rYgRiE/fu2qB6oklJc2L4X9h9jib7dEporyoW32w7S7jjbUJKPiF9G/KFlkuy2nXnOd0p1YH4sfgFE6Gag3jalkMOn5Lzy1aCcz9ZhkDYbsdAwcTtQnWup6zB4QDeBgIWp2U6hbZatp+08V67MCQcVft6xu33ITrMYKW7XLQbrxCnrfp9UPs/ltWLThrQq4oGx2OZIPYzLp+wBVDIssgDe2z1FhHbXc6kOqJq14bKt2HMz4mEFQYiVEs9Z2CBpAlVjm0mVxKgAlcaeeCqRwwbFtJGoRKRS6jpQV4F51bFTLznfHLJbHVIXoi7fj/nmMCi+OJfzrQ/v29UpsPOmmxepvp5SES653aEfdTLIqcX8yXaqlL3xlBvYqaEPf5FXwsseqoZOtaGo6qTxnjBhwoQCiulcH+5Q1dcl94ynqeqvJ+vdGxKE7NQpgEXk04Bnp7+vV9VfvhEdulYsQ8XeosGJmkKxGbzlz9VL5r6llkgUpY3eRGFJZBJVmHujrqMKsRSnZIrSG6X2wHKbw7ZGRJnVgwls41uci1y6yxHuayyoolfkzgWzZCqrQFMFFsuKpTT4WTAT0fsbZg86Zg9YoeVFePCZLVsXD+g6T/eeLbb/yqHOU1+JzO/tcF0kzFLQyZnQ7iaTyJjidHkxS9HURa0gVJait91l4EQqU/SC+TbkZEiZCMxilbgT8EncN2tavEtKbBepzxl3EqLjsK0I0eFd5LaLe8zv7NiulzQp0F9J0WZx5LHiKY6ama47fty110JFblI2j7mVoxzQOuWxX+FSsplrVEebPb2xpFsz1/VttMFzfnYIwD31DnsHMw4vz2xSsp/IvKOqAt4pdTVwjpKSU5UYiaDiYGpd+UjtIlv1kvPNgovNAeerQyoXqMUMKzyFuGsD57cq+nNJOj4cO6oMDytcy5hrHXM1WVToZAj7X149fk6szCI0vP3y7ewtZ/01Z7UJPAJieQG9p/yXY9ZdHwx8APCDwPPOuq1TjayIvAzzZv+D9PmqdGzChAkTHrXIgS9P+hwHEXmyiPy6iLxNRN4qIl+Vjj9TRH5LRH5PRN4oIh9TXPNiEflTEfkjEfmkE7r5lcDHYu4eqOofA4+9rhvfgNNyKJ8MPFPVbBxF5BXA7/IwComiCnUVhsQ8yQlrv6u50pqp7ty3NC70SsaI0CbdSCY8K4k0buA8VuXjmSKxcPmCiMlsKx9pqsDsjsDeuYa9B+e4+xvipYZ4R6LMBJNrC0kLJcx3lhxEx2LLsbxDaO73tLuRrYsH3LFr+rP9nUPuqy+ak5tzdFtWh+syZ5HNhJUwS+l4g+IXoHMhNIP5r7qkMxELP9+dD+YxHQWCeUtLpI98rPMIlTm79WPilFnVUfsAc9iqWg66mr1Fw7zu2J0d9uNfRtldjc10ksPh1ehEVhXded6CCrUbfp8Gq9zIap9XOY6T+j42jY091R1Ue7PhrBeo/ZLzO4c8bn6Jv9y/LZm2L9iuWu5ttrnXR5bLCp/mw/s4PPcr3F7p6BmLiL85TXZ2DJ1505vcPttntz5k27dUYtyJl3F8rlXuZHUMSv1Gf52sV14HdbgVruVouTDSxWSuZfW9DCrURfCyVj33LM/xV1cu8L7L55K+M8fyOtKVa8YZKeU74OtU9c0isgu8SUR+FYsW/O2q+ivJteJ7geeIyIdhVrHPwHwEf01Enn5MFOFDVV3moJDJlPiG4Gp4v4vF7wtn3ZEJEyZMeCRBk1L+es2GVfVuVX1z+n0Zi2v4RIzMzYZPF4B3p98vBF6pqgtVfTvwp8DHsBn/WUS+AYs0/AlY4N0borI4LYfyT4HfFZFfx2jrZwMvvhEdulZ4p2zV5oQ1r5K1UXJk7KLj/sMtItvs1EsuNgc9p2LlKg67YSgaH9iulmwlvUqmbLvoWUZvVKobUr5mykdE2WkWXJgfsL99wLv9ReS+hnZhdddzc7K6/dw+B7Mly7bi9nP7HM7t97LzLNw2NJHdrQUz31G5yGO3r3BhfsDbZ48l/FnN7H6o95RQJ6uXbYvPpYI5v3kAGSLHOuNGYg7J4iHMlLAd8ReX+DrQHlbEgwoCxBwhGMCp5cEQLO9FomprH9iqjOOb+46575j5ju2qLRIZZcumyCY9yKpj2zqc1lqrKWI09dGmJf9f7xy3qY3MiaxzTFyljkurp9U6+mtSmYCjpiXgcEivo4u4/jkLKuxUCx4zv8Llds45v2TfzWiDZ1Z3zOohnlblIk01cNQ5ZW6J1UVtNQ7Xxdkhj51f4mJ9MOJKhqRgccSVHRfs0EvoTYPLMV13TTnfg5MiaQyK8pqzi2V901FCvBIb48zNHHQ1f/7gHdx/ZZuowtZsybmZWXu14awIdMuLdErcKSJvLP6/PDlmj2sUeSrwLOC3sSglrxGR78OI/7+Zij0Rixac8a50bBO+AYso8oeY6uI1wA+ftuNXg9Naef1rEXk9FvZYgG9U1ffciA5dKzLrDkcVsarSK+IPpeI+3baNx6cAj3FYbKIKy+Dp4pxLzPs6OnUctDX7bdP7EiB6hH0O6qiI/LUL93B+dsifb91BLMw3l4cV+3XN/8/eu0fLlt11vZ/fXLWqatd+nN3n9OlzujvddhISEpEAISgQQeUhcK8ELiiCwhVFkSHKQwEVHNc7QBz4IIjcCxpBDMrDICqJF688JHADGEiQEEJCmjxId9Kn+7zP3rt2Va215u/+MR9rrlWratc+Z+/uc0h9x9hjV63HnHPNtWrN3/P7Ozsac8sMqawhN5bSWPo9MA8cMOiX3LcxZpiVcTwXR3vc/5FjHn/gfq5/cIfhUz36N1yb/T2lN1HH1ZUL9Ig14qsh2AGYqTfTDaDqK9VOBcOKzDtyxSiSu9BjM6jIcp99bJQsc6HWggtOCKGlbvFwodhbYtntJ+aOBZnTy/I5oBn+uWghSe/vQqd7sskGeoEVsChEtmtx6Bp/G3MmIg3tVg1zSYVicGaoUjMyLBf6twA419+nQhiXOXuzATcmrspZIGoMpIw9b+pNC1OBE6hKa+hnFcOsiHNW2gyLMMpm7OaHjMyMTGw0HaULiRujdG5vI0+ODbAL2gkLTXjFh4XE+DFYhEya5rGucPMw10b9tfkyptsbEwZ5SW6qBjPDSeEYVPhXVPUVyw4QkS3gJ4CvU9VbIvIPgK9X1Z8QkS/CVVn8DLozqRZelKpWIvL9wC/44x4P7ouTxnFMXga4AlwHXiwin3rE8WusscYav2+hnIzJC0BEctxi8sOq+h/95r8AhM8/Tm3WehJ4JDn9edTmsK62Pxt4D46m6vuB94jIn1z9SlfHqvT1/wj4s8A7qAvNKvCLpzGo20Emlo28mAsJdNJKcHg6KWVWZa7wFtmchBmO7fuww7D/oOhTVBl5VlEWOYrS71UxRBZoSIq7+ZgLg1vMKsdcDE4ymuU9jHEBASJKYQ33jw4QGXJY5Dx05ib3Dw9chrbYRvb/mcEhFx+8RfWg8M4bF3jv+1ygxuDpnMEVlzlvKqed9KaOdv7woyb0+hXVezadSUxcjXeGFSazLsnSCmKUbFCh1mVnZz1vrsosw35BWRmKMiPLLDuDCWcHY3pi2chmDe2unbTYdmI3nLYtc1Lbeb0MgZfJhS53ZGY3ZKUVtZNEG8lo3tP5Y4+WctvnVWrITUGlpiF5u/aqxOzjTDcjM+PR4TXGts/F/i0GpuSZ6TY3JhsUZcZ9G+PIwrA7mPggiIrNbMbAM0OALz7n2w5hwKXNMGI5rPpOSzEzRtk0mvnS0GY3D25GU9NdvM7E1FTPj0PUQLzGUSGYBdtDP+l5JBpL2ywW7kFaNx6xTG2fg7LP2eE4Fscr1RyLg20l6Mk4+MV5y38AeKeqvjrZ9SHgj+ESvj8NeNxvfz3wIyLyapxT/kXAry7p4p8Bn+GjuxCRF+PorF5656NvYlUfyucDH6mq05MewBprrLHGvYoTivJ6JfBlwNtF5Df8tm8G/grw3SLSAyZ4ZnVfz+R1uBSOEvjqI+rEPxMWE3/+u0Xk8kkMvI1VF5T34kyjd+2CYkTZyafO52AzJlWP0ppoSy68g17E0bJUlUl4mpo2+dKa6HwPUs12PuWR3euc7+/zmzcf5urE+WEqa1CV6OAP4bGHVZ9z+QGfcv49vG98PwCTKqdvSsZVn9IaMrFMypzSGgaZC8O9f3jAdm8Sr+ugHNAzFRtZwcCULgHNzPiMC7/DzXMfAOBWOeR9++f40M0zHBY9Ny5j2RrO+Nj7LtM3Je87e45LN3eYTXsMBqVLsCx6qBVvU06TOKsYaBB8JjMyNvKSC5t7XBjuRQm4i58qaA3tfU4DsfSk+ewHCXeZbyITS6WmU1uIDtzogBe6kugWYc4Oz7xW0vbntH0IQcLO0IWfG8EIod3UMS0SrzH4WkbZNFIEPTa8Qi4VV7Y2ubS3zazq0U/8hqPelIEpOd/fZ5TVP1WrJs5R4X0LhXF+moEpOaxcWH0uVbwHsVCWdM9j2zFuqOa0F6sS5zFqMIlmkvpZUq0FiD6vLj9LPKeVKFrYHjfLDfbKIaXNGHneOKtCj5pyKYSy3ylcpP6dLyiq+iYWM8x9/IJzvh1XlHAV/JaIvB54HW7Yfwb4VZ+sjqq+/ngjXoxVF5Qx8Bsi8nMki4qqfs1JDeROYVDO5IccVjkzmzEte0y9OSDzjsrUpKCtLO1gKgsLiqqwkRdxmxHlsOpzpjfm43c/wDv3L3JlsolBY1RNsJfObMZeOeBqscnDgxu8fOf3XL/+B3el3ObSdIcLQ9MwL4QFY1r1onMYaPApGSyFZgwp2PIvjYEpOX92H3ufUGjGfjXg0HvlbxUbGJSPOftBCptxqzfkzOiQssowgeo8cVamFREBNvKC3FSM8hkPbOxzf38/Rr+F7OnwkgpO5RQh+qZdwTAgvHByqk4zUvpizxe83BZt70LXotR80Xdwda1SmKv1clz2OTioK6TxUnRFNEP/VRznfb0DCs2wanjh8BnYhf3ZCzgscnYHh5Rq2C/6bPWm7PQmjLIpQyk7o6wqxC0wCIXtMZIZZ3qHDEwRF5R0fhpU+4mJaf766+MywuKP7zNxqCeLTtss1gicaC0uy8gow3zetH0OygHgcs7KhnlVY/mKO+F4a0Ko7N2fKY8rXngTCAmQe7jExj+DW2Ce9QXl9SfZ6RprrLHG7wechIZy2lDVL3u2+lo1bPi1y/aLyE+o6heezJBuH4V3dgYnnKqTICprKL103O+VLjTSaxUHRT8WbAqovAMvZHr3TRnLzI6rARfym0xHburGpeMPm1S96Eivs8I1SoQABU7KvL+3xwP5rejETv9fKbcZ+ETW/WoQ2wiylhEll4pCs1piVKeZjLIZWLivN+aBfsXE5gxMycXBLUZmxoc2d9nxztvr041GqHVhs1hOV0QZ+tyGndxpQecG+9zXG3N5ts1WNuVM75CJpyRexu2UIs1raCNLpPZVQofT4+8EiyTf1HyTmuIWwWCW7m8wC3tzTzNsQKPG0i5pm2EZmoKJ5mRi+djND3Dr7JBfv/wI1yYjdgYTDoo+V2STs/0DhlIyMi3rdKItuPEIhfTiNedS0ZeSXKpoHsulbJoy/bl5ku2fZv43t9faTNBY3DiC6SvRVMJzQdNZHx31yWV0mRqNWKY2Z2qdZt8TpScVpWZUWv/+2txmdwrVe2NBEZFHcVxej5G8859L+vqj8IITameNNdZY457BvUAOibMu/RDwM6wa8nibOKkF5QTZcW4PIkouFnwZ4MoaRJSNfhl9JqU653dpDUWVRa3EJlJx6rDrGRsT93JT0ZOK/WrA/bmzZVcYnp46Zn+XRe8kpL6p2PQOUquGwrsTK4Spzdmrhtyf7zEU74uQ2gn6YH6DieZcKbbZyAq2mFJo5iTHRFoeV30GnnNsYEq2zISRmbFfDRmYgpGZ8Tvji+zmhzyvf40nZ2c5P9xjq8oZlwPObxxwbbIRr3cjdxnuPXHXvNlzEu5Ob4IR5UxvTO6vf6s3IZeKCTlDUzhtaYnPoZb6V8uET530R2kI+cI9NdptHBUA0Gy/mGurE0flibXeO20ndCYVxmuzWZJYGPxDuVRxrjMsf+LMuzis+rz7xnkOyzz6BLeyKQNTLOXgClpFCF8O7ddh05a+VFFbyuLYa19VKDHn7lEyf5o403F+oUpNDPtN/SvBt9J4KScfwxy1tbVay6rnb1z1sSrkYr3WomxIUbM5Jz6UkyyK9ayUG7xzzFrhyKeGk1pQbgsi8ghu5byIWzlf4+vYn8XxzTwGvB/4IlW9vrQt/9+qi+wKMeiTqhez4h1Nvft/+XCTw6JPv1ey4etmu/PdMeFFa9XRY0yrXqz4tl8NOT+4xfP617jlI0o28iI6wnfzQwamZDub+Lh/19ZAKoZSMvHlEHP/I0sdmiMzZUjBfjXEVi6LOSCYDYam4P68iC+bsXVROud7ezyUX6cvFbmUXCm2GZqCgV+4dnoTMtzCG2jSR71ZjNHfzQ8xYv1LyS1Wo2wanbW5VLxo42kACu3FfblWC001AYsyzUP0Vhs5VSOya1GEV5iXRe2EtpZ9X9Ruu/0w9q7+wgtyEcKLM40oDPkm9THNjPJcnHDU9zUIcqkYiqNt2c0O+JTddzOpcq5MRlEgKTSLpquwQHRF4g2lYEZG5nNuZj6wop88rxlOOALi/nqsaTCBnVt83KJduf7Cj7PtfA+XKsm8tRbmrnvTlfcSntep5oyrPP5uB17AzESp/DEnpVUoEguV3eX4HhH5ezjKlTSo6jdPuqOTWlBu9w4tYtn8cuDnVPU7ROTv4FiN//bJDHWNNdZY42RwbygovBj4y8Dn0ExMP3G2k2MvKCJyH/BIa3W7rZe9qj4FPOU/74lIYNn8POCP+8Nei8sUXdqHEaVnKqa2xzAr2OpNvUTowgi3etOGlHzf0HB9Apv5LGbTugus2wPom4oNryU80N+j0Iy9aohVwyP9a1wpt3lmts3AlN7E5bKOG7XEQ8EkL+Wd7+0xkMI5ML20myfS5FCmvHT4QTJRLpfbXC63yVC2vMZjVRpmm7PZAbmUbJtDMrEU2iOn4vmDZ7hWbTHVnMI6s9n5/h4W4epsy2lHYnlwsM9AXHhwLlXsB4hhpMF8Ek1cqrVJymtASNu0spj2PEU77Lfh6E1MPkedH/prazbL0J0Fn4akmhh8EPuKPGXzprplCFJ1qs20+ali/gf13OeeSt71X5LjNMRcKvqmpJ+5kOub0yHvvHWRoSl44eDpyDzg2vFaQ/JcDhNNJHXAH9gBQ1P4e92jUqEfMtpb0n2qZTVDoG3dXyR39Cct0FScg/7oV7RViaHooa1RNiU3JZQwpeeeRZTc2Pg8xefEnNAycI845YEvAh57NhLTVy2w9UYR2fGmqLcBP+jT/gFQ1Z++04G0WDYv+MUmLDoPLDjnK33hmbccXr9rcy7XWGON36/QFf+eW/wmLhfl1LGqhnLGs1/+ZeAHVfXvi8iJ2d86WDZXOs/TP78G4MGPuk9HWREzkl0iYk6phnHpM4FNxYH//ODGTR7cuOnKhNoe+0WTWdiIxkSxnd4hRpSBKdiSiU8MM4xkykdtPMnEPp8rsy02faLhVHM2ZEalhqnNo+Q29LZw5zOp/SGVSpSgMrxDFMtQCrZNzjW2nKPVbxtmBZfLbcbWaV/3925xrref8EJZCjLO9fbpe2f+md4hFcK2mXCt3KJnKs71DzBiOZfvkaHsVUPOZIdsmzpTP/djDpLtxOYNiTna0jENyT70C6s72Zv8UPOZ1/W+eRu6G2utxRh8waljBrW0qekzqrlMejp9KEf7YVI+qvZLJpP0c52tn2om4bqDj+yh3nU+afc9fHB6H1PN+b2Ds9wqBjx+8AAP5jc429sHnMYwMlM+VNwHwPnerbnrzKX0Y4TdbMxQXJjy49MLZCjP61+lL1WjcFlAX6qaej8JA059JJVKfS+kDs13nGZeo1xRw4vzFLQU8PekBB9EEoIXglUg+BG72rkT3CMayjngXSLyZpo+lOcsbLgnIg/iVKdvOckBLGDZfFpEHlTVp3y/z5xkn2usscYaJ4F7JMprVYqWO8aqC8q34iIEfklVf01EXkDNfHnbWMKy+XocdfN3+P8/eVRbmVi2sgkDyagwlJqx1XO+jxAuWNjMM+QW7PjQ16ntee3ASUd75QCjzh9jfAgiwHY2YdtMONvbZ6/a4Fq5yVBmnMv2eenGh/iV4oUAbGSFD190yYcWwYTorKzkbG9/zh+QidKn8P4WG6XRvlSczfa5kY1iRJhFyKXkfG+PTZ+8NpRZlLpcKKihsBlDUzAyU8bWJWMe2AHb5jBeDwN4utjBquFsb4+tbOIjhOqCTf1EQgZ4sjzLVjbhfO9Wg5HWSDEnYeYdn9thvqm0mFN27uvSMpZpHmm0UdYSIJdJp4s0nzBnEbFtu7IGFP0LrcS/Rv8xwa/2p4UIqjC3QTsJeMngQ7yof4kninNsZRMuTXe4PN3mA9NzUYPdq4b8gcEVRmZKKJ9rYnvJnIfrwtEU9aXkIwdPYTEMZcZMe07y98m14XoapZf9HLYLaqURZsZrFkFLcX3PMzPPzc2CqLBCe0677o05aw68n7Gmmilsj5SRODfN5+x2oQp6D0R5qerPicjzgBep6s+LyJB5arQTwaqZ8j+O4+MP398LnERm/CKWze8AXiciXwF8AMc5sxQCbGUTyJzze78c+vwI5Uz/MBISDkzJRuYcjrlUkbI7YGp7hHj7gSnZyqbkxjmqd7Mxu8aZA54uzzDRPoVOOd+7xcXBLa4XI+fI9IWLcqk429vnbM/Vhm+bcMLLIiCE5ob/uZQMpYiLydnMmTCGpgCm8Uc382HIwXTgfrAmtjPMXF7CE8W5uAiN7cAtKsC1cpOb1YgH8+tJ+/WYwgsomGCGUjCUggLn6LdiouNzlRdsmmWdhvBa2iaNxY74ZfXh2+G4KdJw167qg12LSpcZrLPflmmv+5imqXB+7DV1fi5lNHG1z8k93b3VjKGUPNa/zGP9yxSbGe+aPkQuVVwIb1YbvH38CI8MrnI222di82hqTcOhh1IkvGwaF5UKw0x78Vi3jJrGmKF5T46q7OjOs4mZtg6dD/sWHZ/+jioME+25YBwpYqg+uPdAoRljBlwrN2M792cn52+9FzQUEflLuEz5M8ALgUeB78UV6zpRrFoP5cXA9+Gc5X9IRF4GvEpV/8GddH4Ey+an30nba6yxxhqnjntgQQG+Blec680Q6esvnEZHq5q8/hXwjfg6xKr6myLyI8AdLSgnCYONzuRCM/K8YmpzRtksUteHsFgXMun4joa+4FEtmdWS9iibMTIztrIJD+fXGcmUoRTsZoc8UZzjQ5P72BxNGckUg3KjGEV6+a1swoXezYa0HyS21MFa04U7LaAvLuEwQ11oqJlxMb/BpWKXQnvMfNb8UIqGiWK+wFHY5hy8lZroTN/JJvSlctffd2bBm9UGFuNDj5vO7JQR+EJ+k6G4c4Y+UGBRJvoiab1xbORusrRNZun+ufudlvjt0AZiUl27vVaI61xBp9b4VkEj01/99yNeNG1TXFdCqNNW0gTFWpsIJpxMXFJiFsLTpeJFg0tUatjxv4dzvX1+Zf9FPDXb5aYZEQpqXchvRo11jh8OaWjSwUTlji3j5665n7vWNIQYooPemZrLuo0jNBo3T3UycIY3EWcVIzOL2n0d6OI0sD07ZGRm3J/vNeb3ziH3ilN+oqqzEOwkIqdi7oLVSwCPVLVdEexkDJFrrLHGGvcq7o2w4V8SkW8ChiLyJ3AsJP/lNDpaVUO5IiIvxE+NiPxpfELi3QIjytAUteRjqyh1kTV5pEZmGrWTIKFFKVzOOOZeM6vpJLzVeDc7jKGIU5tzvRxRqVBIxoX8JtvbE85kY8719uM4KpWGVJxK7MHHESQ1IEqdqYawa8Y8bi/ydLnDyMyc5Jo1NYKQyJdKsaG9cH27mfPlDCkYZs6Rv2MmZAPl8emFqMFYTHTWVpiGnyMEAGQofS9TtKlM6uJLISHy6KTGRfxKcwWauqDdFC5Guv0ybc2lEeq6ApZJuHH+j9GeO35x+eO5+i3e32CRKNVXaigQJtqP/q5A8bNrxnzG9jsAR6FyqTzDnt2IXHLtexy453IqCrLoPwlznM5zqq20mYTrgmdNv0e8VrWNxMdmjZNuv1zgBwPnVQ7a+nZ2GH8DE5tTJK+2XCryrPYpraJVrYR7J7Hwl7lEAAAgAElEQVTxm3DVHt8FfC0uwOpfnkZHqy4oX43L93iJiHwQeB/wpacxoNtFDxcRFR6Wmcm4Vm4BJGqwM/uc6+0zlBlDH9GUoQx9Ma1a9a8J8oZSsGMmbHqyufeXu9ysNjgoB1yrtsitMx9dzG8ysbkzQVBnswen6kyzTjNGUN3TSoHhhzSxPb8YjCk0Yzcbs2mmcy+14IhOF6zQXuQAEzcHfamYqXMPxzwT1GdEl560L7zgmv1smmlcwGbaoy9l58szjeBJx7Eoe31ZjfZAKrgQ/jedLoIRXc3K/CLjxtzOAk8rTd45lgUuHLUAZY2Xal3wqiDzXFw9HzTh5mAoRRSiDuzAszFUDMXyWH6FG3bE2A7cCxnHL5c6xnMqxuqiAsOiEsYe2BgA0PpZrfNM5heW8Fw2AguEOf6v+nqrxv90e5MjzrJppmzLhEIyLpc7HNgBu9lBNH29oP8MmSgjccLjRFehFF0RJ7CgLOE0/PfAR/rDdoEbqvqx/py/C3wFLoLoa1T1v3W0+29U9ct9eeDv83+nilWjvN4LfIaIbAJGVfdOd1hrrLHGGvcATkbW6OQ0VNU/Gw4Qke/EVV1ERP4g8MXARwEPAT8rIi/uqCv/shMZ3TGwapTXBeAfAg+p6uf4C/okVf2BUx3dMZCJZcdMSAs0WTVcrbaiShxCYEcyZcdMYuZ6hbCJczRvm8PoGN8xLuvWhVS6gj9X7Yg9u8G46jO1vRiyu1dtxbE8OTuPVcOj/StsmmkS81+bCFLTVLuGeWqes17reEH+jCv9K0VidkrMCImWY6ga7QUNCQBvTtsxhdNS1LBrxnzE4NKcuQxq7SI4bA0aZcY0kzuMO86/dxJXHaGgdT/dddnn7m3Y32IcrkNOu01uwSnb7MPQp1yQad9sFyA3y5mOm+NcHC4c0F/iekzvadAW2uG0KVLNrZ+YTOM1SJ274o6vneo7ZsKNasTlcoeLvZveoe2CMTbNjPcX53iiOMdHD5/gUrlLpYZNM40aUMwpEZJtJuaLuPkKWkzTtNWYs0SzPqpIWZeWGsKsg6nLeC0t9yWUh/4ZTe/hroyX9nMsnMCCsoTT8Lch5ut9EfBp/pTPA37Mc3O9T0R+FxfF9Sutpkci8nEsiKRV1V+/89E3sarJ698AP0idJf9unGPnrllQ1lhjjTWeVSjHMXndLyJvSb6/xlNHNdDiNAz4FOBpVQ3J5A8D/yPZ/6Tf1sbDwHfSvaAo9QJ1Ylh1QblfVV/n7XaoaimyhP71OYBADJ+sEIZScDbbjzbW4HQcSpEk6FXkokw8N1fY3/eOvnCMa89JN79d7rBXDdnKpuz3poxtnyvVNmPbp0I4mx1wNjugIkh0VUPyb7P3dmkmwdYebNOFuqStM95/EhzdkU9LtVOCbpTSxfF7OUnNXTN+zgqy2rchdS2SFJFbioo+FTMy9uwQDNG52+6zawyLHKIhVW6ZwzRrPXLt7ymcpjSPnMr7d45mLw6awSIG5TT7ut5/+z+LoFmmyX7tflP/SarBpPuDRB7aqZJrCPdq27hQ+MenFzFieSDbo0LYNVNysZzLDshEvRazzQv6z3AuO4g+lKteI7e4rPihzKKvJDJ2x2c9BKU0rzf6ZGKxrqPnriucPVz7bubYtm8Q/EOl9zcqWVIfZnhCmfJwrMTGK6r6imUHtDkNk11fAvxoemjXUDq2/a6qnviisQyrLigHInKOOsrrE/H2vLsJ4eE50D4TzRlKwcO96xTa45lqOzoShzLjsfwquSgG9/IJ9OGbxmWgD6WMC06o/z0U8c5spz6X1tWIf17/WmMMIcM55GkEdC0iQDQ1wHwWcsBMM875TPkDT6mxyJHdfomDN3toHcET2mycJ3XmengZ9f2CGv6HsU0050a1yTaHDLOiYWLJfB7NomzwrpdCSmzZRurIXjUbP8xgZ45KshAFE13b3NagBWmcmwoA9bFdRcC6C3GtYDprH5Oc37XIpIuJQaN5cpEZ1V2v4ZwZs73xe43CX89UWzw+u8jD+XV+e/IQGcrHbbyf3eyQy958HIQicGShE5tD5guCeWEnBmJgGvc4naf420ii9FY1LYbrjvk3HpsyY0822IvmryqamcOznx1VXfM4sCcT5bWA0xAR6QFfAHx8cviTwCPJ9+cBHzqRgdwhVl1Q/iaOX+uFIvJLwHngT5/aqNZYY4017gGcRADgEk5DcPQo71LVJ5Ntrwd+xJcQeQh4EdDOE4TnoCjhkQuKiBhgCPwxXAibAL+jqsXSE59lGJRdr6EMteSZaosJOZlar9Yru1ntjJtoj4nWxY0CRflIivg9qOg5Sl+EAuXR/Brnevs8Pr3IZm/KmWzMI/lVgCgtBck/NU+E/QFBgg8SdMpvFT47qbneHySx1MTUbrc+r54Zd72GbTPBWsOl8gy72UEMO63HZKPJJGgqFhfYEEIzQ387ZkKRZTGPp7v/VLK2TYK/cGySKZ9KqJU2z21/vp1cgkVO/67tx8kjaQcbdBIbrtJOh6a06PxlhcMajnlS01DdTqGGAqeJF7giWrm4+//UbJdCM56YnOVjNp/ghfkN3uOZGi6XO42+QwiuKwwnYGhoICGXJZjCUu0mBpMckbsTzHpt53r4DTkTtvXpAZaH5Ebj/LylJR5VdG1lnFzSYienoar+FC6aKzV3oarvEJHX4Zz2JfDVHRFeJ1Kn6rg4ckFRVSsi36mqnwS841kY0xprrLHGPQA5kTyUZZyGqvrlC7Z/O88iLf2qWNXk9dMi8oXAf1S9e/k1ndTlsubPZQfOaYwruZuGZOZSeclq2jgXIMOwKWWUFAv/wFg0SvkjZlzN9hluFJzt7TP05XzrZEXpdKwGBEm0Iut0xKfHdSEkJgZ0ScSpD2OsfS6VuzySX2XbTDmwA2aacbF3sxF6HOYmoy4A9ky1zcTm9KVyGp9nDzA4x22Yp3YocFvSDs7bRf6P6EBWQ9tnEuZnGRZpH8dB29exyKa/SMLt8pccRxpui5irajbBd9J20Kf3JGi7rjhcnWx7YPvx2LHtxxLTH5zex8X+LV4yeIpChYnmbJtDblVD9uwwctRNfXnkwmRk9KgCnbsSWakjN1nrHnb5F9vXHxJxg68rT7Tzfisoo8KAQp+Kh3o3mNieD7Ap6ZsqPiP5MbTPI3HXvg0dPG/Xd6jqNz4b/R3Hh7IJlCIywVdzVtWdUxvZGmusscbdjrt8QVHVSkQ+XkTk2VAGVs2Uf1bqEd8pQqSKVSfNxFK2PbhUnmGieaRWObADJuaQCkORhFhO6LkILSnJUSaaMSPDWuGWHXq24QmP5tc40D6ZTwQLtVesiot6oVvqqtQ0wjiD5LWKT6ArSTC02d2Ha3soJQbL+2fneax/mUfyq+zZYSNhLiRFhn5CJNnlcifWgUkjoqpWUG69vW4zai+tRM022trHsoiurm3H0U7afoV07hqJhGpIubKaY5jvLzA6N46JjLxHj68rgdEmtUmW9Q3Ufor0GN9/EfwPybN2oDVfFxAjIPfsBvvVgJ3ehM/Z+U36VLy/3KUvJRPN2ckmjO0gjul5/avRj7ZXbTRYmyfkMUk4jOuoRM32byakAbTR1t6C37PQLFopNs3MXW/wiZ702185sSivU8b/BH5SRH4cOAgb02iyABH55yu0d0tV/17XjlUz5V/esfkm8HuqeiqswyLy2cB346Izv19Vv+Ooc4LZqk2nPvMLSKCND9izwxhSGF50u5EC3zC2ORPNo+OvLxXX7Baugl3Btpl4NdxyYPsMpZwLxW2Or6YHbywCx1TBl5lRurLQDZZH82tcKs9wqdzl4d71+BJIzSMhd8GFBI/imKHO8Qn/Y38+CCFLMq3TRaPrezrWZeajjHn+r0W5LIsc2ovar8KCEb7HQAga/6FJz99+tuqj5vsuWJ0lvIJGH40ABdoL3zyNPBAp5msnfNMkVGgG3rEdzEgFrgjVSGYUZLxt/CgAHzF8mkINl+1WzJKf2R65L/sQ+M36vr2J5nEO48KW8L21afHnyxu0BYeOxVxsNBMH012K2rQWBCPXrisFcUKO+BZOiObttHEWuEozkVGBuQUFl4X/fxzR3t8Bbn9BwVX3ejnwdv/9o4G3AedE5KtOOprA2/3+b+AzcTHXvyYir1fV3z7JftZYY4017gj3wIKiqn/xGId/l6q+dtkBInLfon2rLijvB75CVd/hG/yDuIJb34Zb5U46PO0P47I83+v7+zHcyrlwQVGabKXpZ1eqtoxOvEyUvtcmZpqxmx2y5008hbrw3In2uFptOTXfHEbTwOPTC0xtzkuHH+RF/cv0k8TBwidPrcqouyjMNN3f5Rhun9fpOE7MTQUZORXne7e4VO5yw46iCStFkKhd6VT3aKS16zOUCVk06TnWARcW2vcMyqn24DSX4EytFdnUAd9OekuPCZTpi9A2r7VDkNtYZGoJGsGifel/y7xk3EWx7q5hnql4WdhzQc2RFRzMy1iWqyWsue35LHxhtq5jQvj4m8cv5GqxySdv/y672QGXyjNOCxcbw4Mdk+84PhNhu7WuBEMoAhec9kXUaI8KqqhTUVOkZrCaJj8csyQZ1pu5qjk97cMHIvJNqvqPReR76Fj6VPVrOk77D0va+1xVfYOq/rNFx6y6oLwkLCZ+IL8tIh+nqu8NVcBOGA8DTyTfnwT+SPsgEflKHM8/Fx8+tSJka6yxxhqduMtNXu/0/9+y9Kgmfk5EPktV359u9HXpvwV4w7KTV11QfkdEvg/4Mf/9zwLvFpEBTrA6aazEVePJ1V4D8NKXDdQuiAl3jvM6RDiUsAUnld+oNmpaBsYcaM6eHfJ0eYanizPc39vjsf5lts3EJ3zdRy4V57O96E+JnFjelr/Iab7q9qOSvdJ9bWm0i08raCnnsj2uVtsMZRYl1ppLyTLRPpko53qO5iX4nYZSsKfDaE9Pw5KH4gqbubkcMdF+9NME/1Sd7KbkCZdSuGeBDiU4jrPEiRzms+1HaYcoN+agQ0tp82G5c7s1k2VBEl37Uq0kxXwxryTcuxEibue2he9dZZbjeUm55ibDdO23aCOEudd0OsKNaov3jc/ziTvv4aMHH+JdswfYNFM2Y5i5u3+bMvXPSa0duUJxvq6OzJjQ92NwxcwmNo/jbFCuJNfXde2wGr1QG1Fz9YeEYJmQvLzoPXFbuIsLbKnqG/z/hglLRIbA5y447euBnxGR/yWQUXoOxz+HS25filUXlC8H/hrwdbiX/ZuAb8AtJn9ixTaOgxPhqqlj16uoimeiZKoMZeZzKiSSJoZzcnEFi25UI/aqIWeyMRWGG3bE2eyA7c1J7CP3prP5yoC1k7ifvCzT/VnHdujIfD+C42hRpnxKJx+ie4ZSUqmL4to2kzg2g/VBBb0Gp9dEczaNW4DfPzvvcgvyqwzNlEJ7sf2wYO1mYw6s40/as0MO7IBz2QFDUyaROAk9vL8HQIzEiT9+recKaAYvJO+TRaSSXfM7z9HVynJvOZa7TWfd0WeuneUia8MsGGjdqc02odBZ+qJtF2VLxxBe0It44gLaAROBQHVih/HcQk1kfQhF1lzgSkWfMjrYm+1aDuyAoSkotIfBksthPUfiq5aqiwy0GEiCY5aZ9BZFfaVzuehanxUsL0Z6V8H7pf8kjmjys4D/D/jx9nGq+lMiMgX+q4h8PvCXgU8APlVVrx/Vz6phw4ci8r3Af1HV32nt3l+ljWPi14AXicjzgQ/i6Af+3Cn0s8Yaa6xx27jLTV6IyKfi3p3/K47v65XA81V1YVEYVf05Efly4I3ALwOfrqqTRcenWDVs+FXAPwH6wPNF5GOBb1XVV61y/nHh6fH/Oq72cQb869SH03kO83kPKffVbnbALbsRNRXjLz1KXV6IvVptujrrPiN8O5swNEUsnwswNAV71QY37MjTftPZ76Ks8UXb4r4OqXiVUOG0TwgSXBqoIORUPNC7xS27wZ4dspuNyamSsOlxQ9p3UqJrf9scspuNGyajQjOqKB2r17wsHyzv43enFxhXAx7s3+B87xY75pCz2TgWNis0a5giQmZzaqJI/zcYc1vaxyJJ12kk6VzVbbQp7rto7Y+qed/oq22uWsBCHMKhjbTvndOMQ1EvN97lknw77LsLaaZ8uq3QXmRGmNicc/0xfam45FmFQ25L4Rm2+zKvpRg0OuDdPJkkdN6dn4kPLffXUGiPiYory2CKaII+KnN+VbQ19BjooM33w4ngLl5QRORJ4AO40r/f6It3vW/ZYiIie7irEmAAfDrwjCewPDKZfVWT19/HRV69Edfqb/hCMKcGT4z2U6fZxxprrLHGHeEuXlBwdPifj/N5VyLykxwx4jtNYl91QSlV9eYpRXSdGNrJbalkO5IZE+lj1cSQ2MKHN060H52KFsdbVKnhQn7T2Zk158AOyMQyMlNuViPOZGMeyPZcvwmLbpMpeHWswvkUnNppmeNl6PLrFLgktsqYGP4LTdt+ZAamGeZ6sXeTHTPhQPtemrcxCz/FtkzY7Y05l+3HMrNPF2d4mjNUGB7KnSn2YnbTJUtaJ9GOtY+lJA9lctXPo7Lwmo+aj2WaTE497xZDV8GuRfVSGsfEYIFam0nrkXQWP1vg6+j7WjptzaWNkHHezorvDJ1OAz68ppJK8RPtUWiP+3ouVHhoikaYd2g3k/mk0onmPou+748Tx9mW+qQ0TQqtQ/cPbAYWjJl31reZurvmrM3zNhd6fgx26duB6N1t8lLVrxWRr8P5ub8EZ2XaEZEvAn5KVefcFSLy66ralci+0jGrLii/JSJ/DshE5EXA1+Bsa3cVwoPSjvIA5xjfNWMu2TOMbZ+RgZu2z7ZOMGKjOpxL5bPqK0ZmysQ6UjyLYa/aYGgKplrwSH6VoZQcaE6ooR5esiECaeEDfYys3bSAUBeVCSxxTsp8pnlwjG7LhM1sRoWwp0Nyqhj905670MfQ02CEio6Vmrls7TTjflNmbPZmMcLuRrXJ70we5GbpsvDfZx7gI4dPcT67xZ4dMvKBEmHBr/zClt7X9NrnimItmJ+ubfOklotyh5r/Yd4p33bELyvatSiaL3yve+imKEkz+rvqzzeuuWshS1/QnuVgJDN2swN++dZHcKXY4o/vvCseM9F+vNYgeE20X0dKijMbu/E7M1ZYTGyyuPX97yosNuniMdEcaw0jM50zp3XloaTPREpbZBbcw1PFXU694vm7/jvw330Rr8/GLS7fC9zfccpLReQ3lzQpwJlFO1ddUP4GLgZ5iuPm/2+4pMY11lhjjQ9b3M0aShu+htUbgDeIyMaCw16yQlMLc0VXjfIa4xaUb1nl+OcGcmR4bShMdWAHjHwo7I1qxMiHwMbjTEEmzrR1oxrxSH6NTAqulZvkUnExv8ludsjM550YUZ8V7fuOD9ntmb+AlVT3RZpJqmXYVv5CGlwQGMwKssjpFHIGggQ6Cg7TVs5L5D/TlnklOTbwfoU2Rr0ZD21dj+N71/RBDuyAi5kjsEzNbKmms8p8pNrLMk1lGVYuqqWtY+e0OTfvabCDa7/1O+xgQGiHNbc1lWXO+WWU+125SW6e3XN7PttjM5vy5Pg+bmyOvLbgwr8n1pGqzjSLprCglXTl39ioDYcCWv73Qa25pM9IYQc+k79saCjhutva1yIevJDj01UCOx3rKiWkV8Y9tKCkUNXDBdt/707aXbqgiMgbWDJlpxXltcYaa6xx1+Mu96E8FzhKQ/mn/v8XABeBf+e/fwmO3+uugcsxMs3lL/E/xBK/xmf6Wud4LzRj7Hm8oJY8r5aOZfVC7yZ7dsjVaoux960YbKS8H5oy8n+ldvnUn3IczEn7C3wGy6SsdF+dwbyYMyvS/LuT5zDR3LO71r6pEB66acqYpNnORA4SeSqZG1H6XmN+yeApPlCc5Zlqm3PZ/kJusoYmtMw53kocXaSptLWZZX6ZLiziVmsgZGknQQ2dbS3I9k81la4M/7A9leJTdt65csot7SVNRg3+q4/ZfIK3Xn2Et48f4TN33u5LGPS8pqHRD5L62tp8ZWliaOrYr/1ibv9IKoamaPhZ3G9rnqp/kY8oXlt7Wzq/0nwmXLsn6Gc5gQVFRB4Bfgj3jrXAa1T1u/2+vwH8dVyp3/9HVb/Jb/+7wFfgLvdrVPW/rdDPpqoeHHXcnWDpgqKqv+AH8m2q+qnJrjeIyC+e5sDWWGONNe56nIyGUgJ/S1V/XUS2gbeKyM8AF3CkuC9T1amIPACRnPeLgY8CHgJ+VkRe3FVX3h//ycD3A1vAoyLyMcBfVdW/diKjT7CqU/68iLwgYf99PnD+pAdzEmhH4LQjvgrNuFmNGsW2jGi07wbNJYQIh23b2cTRjvgkrht25CKjKBvScFfdijlb/gJqleAz6KppAnUxpHb7i2hAoLZnd/E9he+LpPWATZnFEGxItD26OahcMScfjusTEzNcPweaM/HS6q45JO8/E6/bUjPWNsarTe1zETVNVxRQuK42Ukqaxnwc8w2xSlgxzBcNS7nI0n7bRdTqsOb59kNtEWLbXlsJiYUtjaULlloTemH/Gc4Nx/zPaw/z8s33cy7b45bdiPQq2+aQvhATHd24baMGUIbF+KTg4D8BMD56L5eSHXPILbtRs1b7xOHA/VbPWT0/IcF08XVIpyZzJ3WHVsFJmLxU9SngKf95T0TeiSPI/Su48r1Tv+8Zf8rnAT/mt79PRH4Xlyf4Kwu6+C4c3crrfTtv8xn0J45Vdb+vB94oIm8UkTcCPw987WkM6HahKsw0IxQNsrh8k8YDKspE+4yrPvvVkLHtN1TuELIaOKgshqlfeHazMbmUMQQ2OPgjTXbrQU8XNveqtHMmmZhRn+Qr9H3mclprPvyFdtu1523jqMUvjkDSmDq7wb3E2iHXRrTxF6veSU0k2aceax9Xu3tTCnKxbErBUErnbA/zIOq2m5KhKZloz9X79i+LEIIcnPLhHqbXADSuIaXCj9fT2n+aoaQpK0Ljs7+X4S88X+nif5RzuNucY6hwhatSav30LyDkqTTIMP18hEVgphlDKRj7PJLPOv8Obk2G/KdnXs7Vaptr5Ra5VGybw1hsLk+uq8I0qjSGkOFcSjJxZQsMzTBhg3KzGlFoqPleRMLRME/pXzru9rW537pbWAvPeBaDTPxfOPZUngdd8Q/uF5G3JH9f2dWcTxj/OODNwIuBTxGRN4vIL4jIJ/jDutjYH146TNUnWptOhdV/1Siv/9fnn4SQsneFVXONNdZY48MSx3PKX1HVVyw7QES2cNntX6eqt0SkB9wHfCKOoPF1IvICVmRjT/CEN3upiPRxeYTvXHL8beOoKK+Xq+qvA/gF5G3Ljnmu0XBMxhDU2il5YPtcKzfBb7NqGPrw4aBSj8w0hhEXmjHTHtvmkEqFsWdVtZr7wkHKDhOsSsIo7MJwL5c7PNS77sfVNH0tzOru2H7cWvNxW0uaqzmfkoQwrbWoOUd4EgYaNJNlzLDgSzBLoLS3FCrMxJmqcrGMxHJZB7Ftx7PUa/TfZfZqa2NtHOX4ThPemkmeHSbAjtDjReasRc78zhrotMoaLHDad9H1L8JRRcGa19bUEMJvJUj5uVRMtMdLBh/iI+67wjuvXuDflp+IQfno3Q/xstETnMv2nAaQ3Ju+wAzndA+O+EJ79I2jrs+lOaZCe43SyEYsfSk753le6+8o2awmlj5eOE94k6j/ftRzfCycUJSXTzr8CeCHk1rvTwL/0Scn/qqIWFwy4nHZ2L8KV079YX/uTwNffTIjb+IoDeUHReSP070iBvwATkVbY4011vjwwslEeQnuPfpOVX11sus/4+rAv1FEXowj572C84X8iIi8GueUfxGOSbh7iKpXgD9/5yM9GkctKGeAt7J8Qbl8csO5fSgSa3lk2KipBKdgHxfyeLMasdWb+IREG2kggk8E4xMb1Z33UHadTZlxy9eFCJpLhYmcU6ljti8uDPNSeYaz2X5kbYUm71SXA3wVjq5VWYrbTtw5zieCBNukWmlL7KZDpw/JnIUaJtqLfpOZ5mxSMjJKjnBTvRQvbtzbJmOqMw7CY+d9XoECBJraxCKNo+2LCv6qLk0lZdl14d69+Lk9Z2FeGljiyD3KEX8S1DtQ37uGVE7rOjvKDae+jYL5wIxwrRPbZyS1Bftz738bk+oVvPvKeWazHu+7dpbHH3iAV53/Dad1C415zBCs104yUSYaQspdiP6BDhjbgfeNuTEOpIhajTlCO1l0/WEO0usM89AubJbO1UlVBBROLA/llcCXAW8Xkd/w274Z+NfAvxaR38Ipgn/BayvvEJHX4Uqil8BXL4rwAhCR1wJfq6o3/Pf7gO9U1b90IqNPcFTY8GMn3eFpIeShuHyJmmxuplmkRC80Y2RmDEyBVSGXyv+V0ZkYfli5lDyQ7cUqjXlWsmvG7OmQvWqDka9mB+6llktFpb34Anb11pfnoSyLPjoOKWFA+4fXdUxakCmYAtPggHbeTl0IKck+9vv27JAD7ZOZA/CL60QzKuvMW0acqS33DuuRDBmZCZeKYWwrl3LOxNNeEJbNR/29y4Hd5ABLo+KOMiXGYzpeGO2IMpjP7wnbFmFRhN6dEBeGwlxdcFUfnZm2sFmMcAx5VRPN43U9ll/miy/8Km/aeDG/deMiT904wxO3dnnL8Pm8dHPAC/tPM/Ev7Bl5dKoPxQVaVCJe8HI09RPrTMTbpoilH4og/KU5LCuauNoL6fwxXXNzwqYucD6UE2hSVd/EYqH9Sxec8+3At6/YxcvCYuLPvS4ip2JVOr3wlzXWWGON3+9YPcrruYTxWgkAInKW1VNGjoVTaXQViMg/wdU1ngHvAf5iopIdOwvUmbyCaUlciKzXVFAwtseBHWDEm7ekzs4dyizmlwylcLT2UrJrDrlUbfP2ySMMpeAjB0+xLROyzLJtJrF8bZD2btgRB3bAtXKL7cxln3dlZc+V/EiQoQQAACAASURBVF0ggVc+1LGNRcWelmkm6faUHt2qUKFk4mjH8Q7SVDPpykHJUEZmhrE2hmvn0ZQiFAjW/5C2DRhvFgHifep35BYs0k7S+ejUEJYEJixipw1oayypmaxTk9F6exrYsDzXo1l+t+6ryT/VzbjQzTxcX+c811fK+ltoD4uvS6/WO9ZTSvnaFFapK3V9sXeTLzr7Zv7ozn38h+EreNeVB/i1y4/yu/v380fPvYeXbXwA8Jp4Eh4/lBIMPFMOmFR5zN2y1l37xOZcrbbY9MEvuZSNYJr2dXY902EuY+Gs1j1qmMCS6zvRDPmA536xWAXfCfyyiPwH//3PsLp2cyw8lxrKzwB/SFVfBrwb+LswlwX62cD3+nrIa6yxxhp3FUJNlKP+nkuo6g8Bfxp4GngG+AJV/ben0deqJYAFFyXwAlX9VhF5FLioqgsjC46Cqv508vV/4C4Yjp8F6tqjdhQ6acwCGX3qrO1zvX3GdoBFIttwRtBSnIbian04JtYPVmd4YnaOcTVgKjmXqx1emD9DXxxRZzu0NsPy5OwcY9vnofx6dDZ3FW5K0U4q7NJMlklusETK13nJNpM6ZDhqK+o4tyo15KacOydP/CszXJb3OTNlIgUH6jOepaSPJRelUPGOeMtAMoaSYTDR9h6u0yzRTtL/6TWm2eNWV5A1GhJrR6Y98210aZEBRev4rvPn2+voY4Us7jrMuM6IT++9SXwjy3wo9TicZF+Qe4e6ocD9DorKleRF4ZYO6UvJC/qX+eT73sN7b54F4MZkg3cfXIgaSk5IcAz1hJwfxfkti8j7tec1H5cUWcYw/cD+0MXV1eWEd8cu0ARbmknXZ9feCdYwuTc0FFT1HSJyGRgCiMijqvqBk+5nVZPX9+Ke5k8DvhXYw8VMf8Kyk46BvwT8e//5YdwCE7AwC9Rnm34lwLmH+o5GJfzAGg9R5p2CEx7Kr/N0eYYb1Yhtc8hmVrBjJpHILvdO6xt2gydm57hZjbg/36Pvs+RnmjUcqn0fv79tZjzUu8XjUjIyniY+zTvw6KJoWYZVHO31seEHuLgwk9sfZkXiec41KwzFk13GjGe3QGyLewAmqtHEY3D5JrnMGqYaA/GcXAxTdZFAAzHkKOeymp/O5frUEV5uLAvyPiQUeso7KV8WoZ1/kC5CKV1HV5XAiCUO96XnLUEqJFjNSKs8LivF0A5gSB3b0fGcTGEuZU0prxKfk2gKU+NNT4bKOhPRdnbITHvcqDZ4KL/OzmDCpMzJjKuwOfFChHPGF9EseqB9Zr5WfAhwmWjO0BQUlTtvavOY3zWRPFLW11QyCaVPS+BYhXq++ax3UdackGHmhJzypw0ReRXO7PUQTkP5A7jExo866b5Wndk/oqpfDUzARQngYqKXQkR+VkR+q+Pv85JjvgUXIvTDYVNHU52/VFV9jaq+QlVfsX027zpkjTXWWOP0cG845b8Nl23/blV9PvDpwC+dRkeraiiF92MogIich6NFBVX9jGX7ReQvAH8K+HQfXw3HzwKNSLNmbZRwXG5IQQ8sTjruwe/N7o+Z766srb9QNdywG+zZIed7tyKHV1DV05ySggzUOZf7WG5pHyPKQ73rnhcsi4WJYJ6aPqBNbLcwI3gB2ppJTcy4WO3PGk5ip0Fk4hzsBst25qTGC6bHlIpKUynY5RpYkqADkWjmmgB9LEOBHBOzpS2WbZORefPiRIU9G8ZXm/mCtrNorkJgQfv6l2XMB/MetIt11a0EB24XTXxqZkvvUdO0EsJzjy8BB42k0KwR1t31zGRYF1TCvKYSzF7pOMK9zqUkwzBJzJ3hmpy24q5pornj+DLOXGXEMuoVPLO3zbBfsN2bxoCKoJ1kWPqiXLWOiSJPfyti2c0OuFZuOm1FCs5l++x6TbVL+27f3/TetrWOVef8NJzyz7V/ZEUUqnpVRIyIGFX9eRH5R6fR0aoz/M+B/wQ8ICLfDrwJ+Id30rGIfDbwt4FX+YqQAa8HvlhEBp7VeGkW6BprrLHGc4Z7Q0O54XnCfhH4YRH5bkLi2AljVXLIHxaRt+JUJQE+X1XvlFzs/wIGwM84nz//Q1W/yjuPVs4Crcfo/rcdt4F7aGgKDuyATTP1PpOK7WwSkxNTSc+Vw3VTc7ncZjub8Eh+1ZXI9fb7ykupmSgVlolmfKjcdb6arFldM0h+GcxJv+kxgYq8LWEucrgHdCV3NfZ32JRjuKpnSrZYMlUqDH2p2PZNbpkh2Ak3KaLvJfhIrIaw4hCO6xMbceHHLhxCyCWLGmOOYejFukItQ6kYSsVEMw48422qnXRlSQcpNfgO2tfUhfR6U0r/IP2mzL0B7Yzyuq3W94ZzfT75blXEEGRJ+ljxZRR9C2JAQ9ndZL+nlK8wPgm3GexgpIplfQ3qnn9PpDCxOTv9CZmxqArn+3tsG/eMu9+JY4wemYq8qpLyvo5V4sAOXKCLKOezvYYGH/rsnI+WltJ+ztuaSUqbXyXHt+/FibEN3x2LxUKIyMAHN30ezl3x9bjgqjM4X/iJ4yhyyLPJ12eAH033qeq12+1YVT9iyb7jZIGuscYaazzrEJZzUt0F+BXg5cC/UNUv89tee5odHqWhvBW3BgvwKHDdf94FPgA8/zQHdzuIEq23PxvURaRYF1F0y24wzPY429vnYnYzhjyGpLwbnhZiphl7doNr5RYAB9nAs6J66cf3NwtRSgaGMmM3c9a7WIQokWCOYocNnxf5CFwbRyc12tb5iyQ1d2wdeop1Ut5QCwo/8EOdYlEyXAJkLj46TJWpn+tCXSIjwFAqMkoGoowki+zGjrVJo9bixiwcaE4flxjanqc06ifVIMP1zFohw1HzmGNONomEnu7LvD+iqfE0zlshqqjBE2Uzf21HS8FdVCCV96DUGkf3sY1kxGSOjL+e9Jiw3/jw8NBPiOxyFEH172bi/SeF9rDWcLnc5tZsSC+zbA2mPNi/EcPsJ5oz9D4vqy7qq9AeV6tNrlWujHahGRfym7yw/7SfL2Hi6w3NJZXG8tG6kJernqvFCY1ZMg/o8Utxr4q7PMqr7/3UnywiX9DembAanxiO4vJ6PoCI/Avg9ar6U/775wBLHe7PPlqOulAB0X93jkdXP77ypI0BefJUTDTnarnFlXKbm+WGK8KFcL63Rz8rmWmv8VKr/EM7qZxbf9sceoru2hHcZTo5ivup/SNbyenYcmimP0TbejE3+k3NPBj27JAb/sU46sihqbx5y30WCjXROZ+LZWTcYpJjML7t0jv2Le4PIKeuLw9u7kMxqrAgt/m9wlwsCzjovMaWKTTNng6LTJsDKvBftdHkn5o3N7bvwyKE/I8URqznnaqFgPQ5WvTc9KVsLD6uOqKf27iI1AtruAarrnRDqJ4YjqvUcKA5u9mYQjOe2t9hWvR4/u41zmS1yzOnYmQKTxSa4apyOpPxxOZMNGdkZmyaKQZlrIPWnDWrP8Y5C5n8rcVk0SLSFiLav4F04VoUjn1buItNXjja+j+PUwA+t7VPgWd3QUnwCar6VXEkqv9VRL7tpAezxhprrHFP4S5eUFT1TSLyy8CT3o1w6lh1QbkiIn8P+He4KfxS4Oqpjeq2oA21HWrHHRBLpu5VQ26YkVPpcZxdAHt24P9v+HOdRlPaLLY3015M6ApmlG5puN6ftVT6Rr1777xNtwXtpy2FN9pfIPm2TVvt5LuuIlPhO+DKHVMBfa5VIwB2zR4Zwkybv5xwdpGE+rqxCUUwkkJ0ewcUWA5sMNfAyHg+L+vLtLbCgrtCooNJr329RxXaSo9pByV0MtEuMpBrtzTcHlfAMu6wEFobnwGtx+UkdtvQVkI/81T0pnF/UwTmh0aJAu8MrzPcK1CnJVo1VGIY2z672Zj9ckhRZpzf2udjdp7k0fxa/N0caB+rNedaKMHtSkMo1tZzm2onRVLCOGgmtvU7aCNoV11YtH2ZyeyOcRfQqhwFVbUi8qd4lnzSqxoWvwQ4jwsd/s/AA37bGmusscaHL+6NsOGfFpEv9BRap4pVw4avAV97ymO5Q8xrJykybLTN79kNxxbs7fd9qihh51JSSEZhe1wrNumbkrO9A4amYKK5k6w8fYXReam2IS37MsGZP85pLB33VOZDItufU8qMuK+jJocbwzz/VbMtg1WJdBmFZpFFuFLDhJxLnAHgbDZmM+FqQpUCl8TorkmZeGm3j0vwNLiQ4EwEFApKqqDBqJJFW7+4WikCNzA8U+5wrrcfGZ/baGsnbU0sTdTrYuh1hZyaPggb/QsdnGeJBN2ANB39oe/2PVoWhDEfPu6oV+K48AzJ4Rki8NOFo6URAp1K6F3BBLb1TAXtPfN1gmygCVI3F1PvU5nYnA9Nz7CzMeGV59/LJ20+ztlsHHnsApNz+37FAlxiXfKw1pp3+A3ViZguEKEg6wyCiA76JfJv2l47nHyZ//BOcZc75QP+JrAJlCIywb0sVVV3TrqjVckhf56OdVZVP+2kB3S7UJiPjlLBepOCJYsFtQLV/K4ZU9iswUPVl4qbmjG1bmpGWRHzSqyPWElfYO2Xd/1yz2vq/EBNLq6qYHhBQO2ErcfcfEG5sbcjnZpZ5DGiLLnuOKYO01l8EWr9uTYXuYXmwJsAL5XbXOztxXMnsU8T+w7RVoUYZmT0tWIqFRNvFskWyEUzdc79sTVcKneYas5etUGW2Ua+SZdJyTYc9vNZ9Y0ACEJugrsHbS6vcAwwt9ikUVOxvXauUzKeRYv5/FgWv9ycUz2LUYrtxcVdc3OBSc91/XQvyO66mrkawfCaLgpBSLhWbZKJ8uLdZ/jDW+/lYm+PifaY+N+Hi5I0keNuoj0CN9j53q2YUV94R31F/Rsq0uc+zltz3C4nJhzT/fYOJsQgHIT7tmhxX1YK4Li4201eAKq6/Wz1taoP5RuSz0PgCzmlTMs11lhjjXsCJ2TOEpFHgB8CLuLSSV+jqt8tIv8n8Feoy6x/cxJpu3LNKBH51M7hq/7inY++iVVNXm9tbfolEfmFkx7MnUATtbtL+gtSXx4lpl40gV2uNqNEPrZ99qwrUfu84XWe17/G2Wyfmc+SD9LVIo0gaAuhP0iKUqltaC2Z2E6zWYpU8m04q1uSu7vuljN4kYof50nIgrM9jtVlzIfLu1ZtMZTCsyrnMTwY6jyQglprC8cOpYhznRbpateoL9RwtdrkRjViIAUjM/Wan5d6F5iU0jkJ+zvnz/eZmvtSE1aqyXRqL5JoKl3tU2tqof267fpepdva9PfN8ZoYApupc2yH5yZoK+DmPDw7aRhzVyhyOhY3xoRlWpvjz7BxfFZNNFVdGOzxSO8ae7bPpfIM28bpqn2pfMhwjkW4ZTfiPQsM3rOgnWjNGTavUS43R6VaWxtp0MciTafrvpwITkZDKYG/paq/LiLbwFtF5Gf8vu9S1X+aHtyqGfUQ8LMi8uIljCLfmHwe4sqBvBXHHn+iWNXklWbMG+DjcavpGmusscaHJYSTMXmp6lPAU/7znoi8kwUlOzyOVTNKVRs5KF4j+sd3PvJ5rGrySjPmS+B9OHXrrkKR8AK1y8cG1lTndDTkWnFgB5GfK0g2uVRsmwmj/oxtM2E7c4mKQTuZaS9KnN2svlIzEePDaQMHVihkFf0nGoMFFmHO4ZyEFMdr859ty7YfkNqM2zU7iuSz055cyGcQcvdkyOVqu8FkG3wZY9tnYvNWYqRzwp7Jxs6+7kskh7oZOTaZP2GiOdeqLSyG3WxMJpaJzaO9vUua7tLWOucuaiX12Nz52ZymBMxJmxW1g7/L7L5oPO0xpX6sRWWg6/MMJnlmg58r1VYg+Hqyho8hxaJnalEoeRU1rV4cB8DQFGxlM585n7FnhxTaY8drKEMpuWUHkf8uJEg6zaVHoUT/Spu7K9Wy0+vvQqq1ue/Ni25qne02lydH3hFWX1DuF5G3JN9fo6qvaR8kIo8BHwe8GXgl8NdF5H8H3oLTYq5zjJpRC/Ak8IeOcfzKWHVBeamqTtINIjI4hfHcNkImfPpDdep/cGKGuuaGnJKJzbnGVnTU562M8LQ298S6BScsJl0v9IAKwVLXiG/8rBOzkvse+tK5TN+uAlJzC0e6oCQO4cZLY0H0WGfehYcRpZCev35P7hdyBeaCAQLxZf2SdPTnvVg4aeCr9hXSjGTKxHJgB5480BfPsjkT7UfzVjsIIr2mRbk67bnsuv72IgMLFhoFElOYic9TPdddZsjYZse9ssn39nGuT7eox+fXf89U516s7nqPXhzrfhZn9qfjLDRjYAoGpmicfzYbs2sO2fYVTwt1ZKJDirgYTUOuVmKuTX87i+aljfTZIjVFRsGsno9FZsSua1toCj4uFMSuvKJcUdVXLDvAMwL/BPB1qnpLRL4PV8tE/f/vxBUj7JJGFg5ERL4n2W+AjwXeturAj4NVF5RfxpGMpfiVjm1rrLHGGh82OKkoLxHJcYvJDweOLVV9Otn/r4D/4r8et2ZUqhmVwI+q6rNfYEtELuJUqQ0R+TjqlXEHGJ3GgG4XqtLgIwoI8pVzagoFIZu7F530weFJcmxwNC/STFIJKzU1OUe/afTdRtsckZL2NY7r2Nhl2kql5XlzXDs3ohZu2mSIEQpTL7nvVwM/Rm2Y6pYhE8t+NYya38AUTDVnIAWbZhq1wXE14HK5zURzzmYHTGweHbguzLQOskg1Qnddi6XMnKaW0paG03mNptA4Py3tTUzDkd8OR3btz0ve9THdGqXra0koqy8JbP3YwrObJYwFbU13GRazLjTnNM2tyaVibPtcmW2ym4/jtea+3ECYg4nm7NkNZj6s2ahGLSX8dpwJU5bex2WmWjcHdfh9uP5Mj774lYNVbgcnE+UlwA8A71TVVyfbH/T+FYD/Dfgt//n1wI+IyKtxTvmlNaNU9bW+vRxn6vrgnY+6G0dpKJ8FfDluBXx1sn0P+OZTGtMaa6yxxj2BE9JQXgl8GfB2EfkNv+2bgS8RkY/FLVvvB/4qwKo1ozyp7/f448/grEoVcFZEvkFVf7R9zp3iKLbh1wKvFZEvVNWfOOnOAUTkG4B/ApxX1St+28ox1nGs3oeSSuippGlE69BL8TxcXlMJnFttBMk4+AqOkiwDVfcihLEtkvDnNBcWlAXWWjJOx5r20+bAstqSjjuk9UUwMq+ZLNRuaIbYGlFyqdg3Qz/P9Xlj22dc9dnKpmybCTeqUeSSKkJwAHVIa1uaXeh4RsipGn2l9y+cFdqvNAmggKaFWmtHfndfi/0Q6fYona/g6wowPosdup3s7QCLNtoSfmdJ5VYod8rzNrU518tNemI50xtzoH3PbFwzJ4SSEHvVRnTyN0oReD9Y4UPAlyWjHhVcAcxpyVFb6ZiLo5JMTwQnE+X1Jrr9Ij+15JxVakZ9SkLq+xdxNeU/31ue/itJfauTwlEmry9V1X8HPCYif7O9P1XPbgc+fO0zcbVVwrbjxlivscYaazz7uPvJIWfJ588EfhxAVS+dFq3XUSavTf9/q2PfSUzldwHfBPxksu1YMdYBFuHQ9hvbpkmIYmRcpZaaM7HkUnUWZEoT57r8Eq5P932VIkzp8enMLTt3UTJgu/+Ao7SR9na4PbqDLo0lINUKwnFT6TG1PT/XGn0oBuVM79Db6gdRkg1zv0gzC0gl4RBuDDC1PUZmxpneYRxD23bv2sta/pRAhZMl0YFm7vlIsSxaqR2F59qrn6muMOMGM3UrkqtLw10k1TfGyHwhqzjGBT41g2Vqe+yXfbbzCdtmUkfjeVZicP9DFGQIw4c66q9Qx4tXa5lphF73c7xofI1xdvyGgp/vqLDhk4Jw13N53fBMwx/EmdW+AkBEesDGaXR4lMnrX/qPP9uOChCRV95JxyLyKuCDqvq21mp5WzHWVoVp1aTkXoQ03DA3du6lbjEU1sy9LEI/7n/yw/dU3Yv6Omo84bjQ1iKs5lhd7LiuTS1Hh0+m7SxzwscwVn9M2/QQ9h+S+x+8NhakgXFVMEMfXaG36ZjS49KxFZpRJtTvA1O60Ns02751va5SYXPOw2KSbm8vPCnmHO0dDuC2cBJepu066V33Ll0Ql9G3H4X5cbUc3v7eBPRM5X5TtsfAlJ7Ty3I223d15v2Ccq3c4lq1ybgaxPsz9KHGNWdXfW9SoWdZqPBxkf5+2s+I+3xaJq+7WkX5q8A/xyWhf52q/v/tnX+YLUlZ3z/f7jMz9969u+CyrBJAFwhqSEQEJAr4A+VBxBiiYoIPKiEYREVA5YniEoKij0SUJ2qicQNG8AFNeBYNASICARTlp+u6rKwIgj4gCBJk2bv3zp1zut/8UVXd1XWqzzlz75k5s3Pr+zwzp091ddVb1X36/Vlv/a0v/3rgtQfR4aphw7/EfIhwrmwASW8kv6L+WpzT6VG5yzJl2bsm6SnAUwBOf96RCjorKCi4BHCUTV5m9hfAozPlrweW+qUvBMt8KF8JPBS4a+JDuYI0WU4GZpbdJljSl+D2ow/ayT2AGyQ9hH3EWPuVptcB3PV+V9m5TNgw5NXmGOmWrn1K8EXmpvnV58vCabv6C7SQRc7uRWgSaSwnzUMmPLW7ZsEcZUjqxppIfrm5SLWYmNbT9R4n6z0qjJnV2Xs10EYMZq1z2G9VLdPWSdGtiSsmu77N84ALS86FVHfz4s1q6WZjedNXPPZeawjImbdCH2OaSSylLzJLhnYWId6LfVm93H3ox29uThB77YSzsx3O2Alun+1w5fbt3PfkJzhV7XHWm5dvbU5x6+ykvwcVk6rpzFvhfs7aus8g0f220szMF6aZxJpcoD0XMg7L53DfWFNyyMOEpBvM7MDWDy7TULZx/pMJEKdA/izwuAvt1Mzei9ukCwBJfwU82Mw+JWlfMdYFBQUFm8IR96HkcDAOJY9lPpS3Am+V9Otm9tcHSUjU50ox1ilaE+eare44YMyBucymmpPY20SKzPUxKM+IL8u0mFR7GdOQcu2uOr60jVUlt0Vhqimd8TiqaD+R1NF+Jrom9QXFGpbbTjbWVCrONjW3z3aYWcXJeto5d897J3C8vfJw47M+JDcNQw20VT4sOX1fBM0Fhv6xsZDgMc0khLiv6ufKacaLMNysS3MpY8aumfm5aHE+yd1mwqRqmVSN0whtwm1txd/PXLzOmeYE55sJ59otpkFzjDWEFcezUnBB4lcKZbHmWallMuZHOYB36R2QoRyI7yRgVR/KWUkvxIXyngiF69pgy8yuSb6vEmM9QGtid0WGsujhHTu36MFMf7y58kH9pUzFsp+rYJUxrgPZnFdxefjxk46lTe5JxZlmGJ0XEO8HPtiNz+D22TZnpm4V/5U7Z7l8stvV/fvG+dPGHOlxUECI/uteuNHLKU6X3m3E5ccVM5s4Bfxw1fzQ8R5MP+EYhi/c9GUbn49NYRdybxc9Q6lpd1I10MJe6xjKCWacrKecrneZWs2t09NdBoWp1ZxrtthrQ9LUmr1MX6kpL8cgV/ntjbcrz1D6vwOHcdSd8kiqgdcH94OZPecg+1s19OHlwJ/j/B4/gVu1+e4DoqmgoKDgDgHZan+bgrfunPUr5Q8cq2oodzGzl0h6RmQGe+tBErZftIjd2SSr1qZmlguV4ldTyxeYFFZ4snLaSarpdGaZEQk8nYNV6F4YGryi6S41u8X1ujasGmgtrZxUm7YZzC8wNNnMgJlV7IUtaOuGk/Ue55qtQWhqnbQ1pCletzCvObl8VO2cWSymMV0fM2baAubMW85Rr4G2kX8+0+wHi5/bXBBJh5Hibq0T/X2atTUzas7Otpi1FdtbIfeay2xwrtnunOszq9lrJ+w146+SmO6x394qJr2sGTppc/C7GQSGzP+O1oKjraAE7OLSurwBuD0UmtnT193Rqgwl5Dn8uKRvwkVd3WPdxBQUFBTcUSCOdthwhNdywL6TgFUZyk95lelHcOtPrgCeeWBUXQDMYLeZLJXGLWOfTjWYC0HcxjIaclJkTrqsMJSxB6/qqM+Vh/FrSZu58kGd1GcCVDbfZhqqGxy+oY3gsI+lx2ULKSsZp+o9TtWubnDMx9rQjKFmM6ShD2OuchpKsvhyoO0kGzzFq73jUOA0PDankaRBFIsc1asc5zTE/Bz245m11dx9qjD2mprb9lzY9cwqZm1NWwdfUcV5ryHO2pq9ZsIspj8EUWTonPcJjftQ4u829oz7ebRMe5X630/4La0VZkfehwJ9tuHDwKp7yoc8/LcCjwCQdKQYSmtid7Y1Vzb28l4XQvtxGNqiH4JkjIWsWcoovAN6XQ7GwbgXrC2JX8INQ+aTMpj4RdQx6ORlW5FPGzJmIhsNZsBobX4TrP4FNl82bLvtIsRcktBhapuU0eRMY3E910+c/FFZBtIyXHOSYyrLGEbOjJkGnMyZ9pLIrk7oseG5Cf0umpOq9QEuE3ZnW9RVy6x1DPJ841LQn2u22Wv7hJ1hHLM22k7BRsaRfFoyvvR5yDGSsTqOsbi2YsGpwqirA2Ao3DGivCTdF/gZ4H4Mg6ruve6+LiYfwVyyyIKCgoJLCUfdKe/x34FfwbkgHwG8DPiNg+hoVZNXDgcbl7pPmIm92dLF+x1Sc8iFQBpqJmMmh6FWHCSn+fOhLJWsc5LVsgSBKS3LzCFVFKLbZkxv4TNe0RxrL2NO+vRzQNNgs6j5sN258QxMZUPtInbOxnQP2vOaSaqRdFqg5c1vsUaSW4ker7FozZmI4u85TSSld1n4bK4smKtydQbzlekD9fVnkVw5aytmVnFutoVkbFUNp7fOU8s402x3DvgcvZWsM33FfzDURFIT1TItxMz9NnLWpZgOM3VaStymZKjtn6m1aSoGrL4F8CZx0szeJEl+PeHzJP0B8B/W3dHFMJQ7xEwWFBQUHBjuGG/BXUkV8AFJT8NlH756yTUXhGW5vG4jP2XigNIfXygMsdesrqHEyG0NsJLfwobSTrDfroSMFNVF1vrPEDzDQgAAIABJREFUtC0zjUpXQaoOY2mSMYzlSgqSYFw/9ZmkmkvsawljaNKxMK9tpJ+xzyX2t4zRud9FqqkEH/qMQ5hzfabaz/Bc3mge+0d6n0lGMxlxSufGs4qTPXaAL9Jacxprrp3wyG1VDRO11FXrFi1Oe+0r9Y2k/cZ/Fs2HWfqZ1zBSBA0lHOcQ/5aatpqrJ63PFzlodw1N+n2hXoZLpNsC15nZL0TnL3YTwmfitmx/OvB8nNnriRdP+TyWpV65fNH5goKCgksa64nymgE/YmY3SLoc+GNJbzCz961jE0Ize7e/zszsSesgeAwXY/I6UjCD6T58KLHEkkr9kq2U/VQCEp/CsqiyMSkpls5sgS/GrMrQO+/LicewzFcUpL8gbcaaUCyB9nZwX5+RhXTxeFngm/EhndlIsdiPwTwtKVYNnV4Wgn0xGGgkI36TlK40yik+nwsjzyHM79gzqyX+tNBX0OgqGdt105XvziZs1+4Jm7W9Xyi+NrQXrAQ5n0n4bFp1kVhBUwnXWGaO3Lns0KLz49FhhlO81r7lq60nysvMPg583B/fJukW3B5Q72MNmxD6rPEvwSX6/XxJXwp8r5l9/8VTP8QxYihiljF5xS9fRWVm/XtvbDvMZU48s/lzQ9PP/DWrhCzPh0WGTw1+dOpe9P1xbxqIfpgLunRmq3F6Az2LnO5jQQGQvCjnxmVZx/6cw52h833VvdRzjHyOVhs3p636Pdd+jmHkEjTG9ebue/T8mSnLkFdCIlg0yfOTji3MR7gPe1YP1vlkTXmReStuayAoBVNYxEzaNq1DVzfMXW6UuXD8sXNYvo2LhQCteR2KpGuALwPeucZNCP8T8A3AqwF8e1+9RrI7HBuGUlBQUHDoWF1DuUrSe6Lv1/n9nDpIOg1cj/N5zHAbEV7UJoTdSbOPJExp7QobHDOGYu285NVJK06c8IXqpD5fMO8gJ5IkM5Jurx0s0jhWMzW5duLjfJtj5ZIN2hoLvQ1IJfxF5r9cO6kjPjffi8wvHZ2Jecv8JLQ5U1iisaS05cxJMWKpekybiOkfXJuRunN9VYlGEc6v4jQPUnoukKLT4BK60jleGg6bDK2Kqjc2DPhoTd0ix3gc4bgL5kjyjYWxhO+pAz6YvLrnJHKg57QSM80FsOQ0aosGt9pvcz3Yh4byKTN78Gg70haOmbzczF61rk0IPT4i6aGASdrGOedvWZXw/eCANlouKCgoOOawffwtgBzHeAlwi5m9CMDM3mtmV5vZNX57j48CD/T7wr8aeLykHUn3YvkmhE8FfgBnFvso8AD/fe04NhqKAW071DyCBKMqkVjiOsxLMrGPIvVNdJLliF8hbSfn8O/P569LJaycpBXTFiPVNHL+hrEFlDHN+3FWj4V7Zuu2QxlGss5XEDSTyuYlZshrXnFo8DJfRycVx3UYStPpdWlZHOoaj7vLA6Z2LsQ1DuUe0sMcmuTZrTQfmp2GdcfBFIswN6cLNBaAaVPPaS0hs/K48zyeT+ac70ELSb9bcn0fQq/R30luAaP7Lhb6VNYGQ+tZ2Pgw4LtwGYFv9GU/bmavy/a6z00IfajxE9ZB6DJslKFI+kHgabhJea2Z/Ttfvp8Y6wFiZtCtsG2jB0zJA5Z5ccaPn0UvktBu6rwee5mmEVMpfcvGsMjxuMw0Ffpdluo+1/4iZjI21pTW1Mk63qcwzxwr/EZTnWkyrIMZ0t8mL8/0l7TKpk0xzfPO42E76bn0/jimGGibzzW2aOqz95H+2c0GAETHiyK88m0Pr82ZIAekt8O5jKO0ujqZex8zi7QsXJOatXJtWVoeCYXp96FZ7ODNXQMCL6oJextLMo9cyCaEkp67uEl7/qo0roqNMRRJj8CFv93fzM5LutqX7yvGuqCgoGAjsPWEDR8gbs+UXYYT1u+CW+S4VmxSQ/k+4AU+lhoz+6Qv31eMdYeMoJANg43rpdpKVz6UfnqzV6+dmI07ngOcBNi3s6oZaZFTPjaHLdJyQpZV8GsBMppK6lBetOp9GY1jJqHc9QMzhV+n4tKLD0128Sr9IE3EGQVG20yk1dRZnJbHdKcSdbguZ/pyfaWOyNXWMIVrcwEfTTDZ+D5S7TI1i42hD1Lp25+7t4n5KqdNx+arpq26UN8x5DSUeAQ5s5b7TOhP+4i/x5qKaV5zSesfFI5w+noz+/lw7BdMPgN4EvBbwM+PXXcx2KRT/guBr5L0TklvlfTlvvzuwEeieqMx1pKeIuk9kt7T3pZjxgUFBQUHiDU45Q8Skq6U9FPATTgF4oFm9qORAL9WHKiGIumNuPw0Ka71fX8O8BXAlwP/U9K92UeMtY/jvg5g5z53N9fnuEQ9dy58jV0qS3wbiwSSnKQXLzrsNBbISk9pWGncVydljtAYJMh4VXuTTGXnU7G81BtLsmOSatonzNve+5BQ5yAd0jDv6wEnlae50WIaFwUS5JDOX0pn3gHfn8tpKvF18TUm5wtaxTEeI/WJhXswl60gmcM+zHp+XFnYfKboVDvqqnqtZiyLb9tWc9pb+DSS+VmiSc75R7K099pLLp+XAp2pptJVONi3+boXNq4Tkl4IfCvuHfklZnbmoPs8UIZiZo8cOyfp+4BXmVt88C5JLXAV+4+xjtqcfwHlzrmCMZojRyv5KLCubnJuNLIktJnUmzPNoMH5sbEsM3eNoYkirOJV2OkLamrziuvAVMbQiR2udQ7b+cR8dRI+NOYMi+dl/pynY0lgQoxFDuFFQQO5oIhcT/FtiTMvLEMsHGR6H5hKc+ONTXNjSJn/XFqgzIBG09dEq9nD8cChDrSNL280cOQjF2WpNIQM8gxtocA2/D5gMCmzzPWxbuZiQHN0GQpuh93zwHOAa6OFje61ZnbFujvcpMnrd4CvA5D0hcA28Cn2H2NdUFBQcOgQhmy1v03AzCozO2lml5vZFdHf5QfBTGCzTvlfA35N0s3AHvBEr63sK8Y6IJV1YhU5i8Q5P2gr1jTIq/EDLSFISkF6S0xVnfkrMXdJNqe1hLbTMuhNUYtyi+Ucwm3bp1YPbXR+Ua+NLNLuUgdraGuVdQTgbqJrOOlnwdhjdOHMlQ2c9imWhS6nYaVZR/E+YPRa5iJNNkYfzm5DM2Om9bmw4QXt58yjKZqRpG45U9JYyG9n2mp7La6ZVu7Zbyqs8abLVo5cGapwi1z8NryqM5rS3A94dKjZMS+0+h2k2esIm7w2gY0xFDPbA75z5NzSGOuCgoKCjaMwlAGOz0p5G0qIktG28fdh/aGdNWksFgozTsS44kD6NTmJTEblbcZpiGWV2JO7o0QriUOUUyf2ogWPoU4ubXtoZ9ZUgza2Jk3XbtOKth2myK+6TAMjGklXDuZ9KHN51RZIkAqSbDf2dGwCQdPmNZzcHMRzkdI6hgtaDBcrZAvHGB6UvPaZW+2ey2adw/zi2fnzi6/P18+tbm8bYW1FO4uc8XsVagWNemXAwnS4e0cFVhtMWqy1XlsV41pKglX9hnPaWqKpLgu3XhnGfpJDXhI4NgyloKCg4LBxlKO8NoFjxVCsSfYKCRGFYqCthDJ3MG+TH2gBsXRjQ2mwmrRdW1XVdlKXgOm0xlrR3rYVESg40VBtN0y2mzl/QC6tC8xH36TXheitoHk4X868xhYkS5vWsFc56crg3OWzzrZtjbOFE2kWWydmLlLHS9DNtO7mQ5Wbw3bm7Ojs1j76RVAbbYWzmaepMjrCbF4yFQSxVZX1kUICVf2cXyhy0UIX+l7I+3zS717z8JpYfF2c0mfMp7Jo0WZcljs3ds0qiOckaCfNtHbPyJ7be0it85c4aV1z/gp1vhZDJrAK8/eU4EuJNJVFucbGxu1O9mXpGJZpcBcOg7aoKDGOD0Mx0U7ryASRvjWiQ0VnlTdBzDUf/6hr93KtJ60zBTSimdVRyKRgt4JGbH+67h92g3ZS024Z0x33I7It/7LdMqiNatJQTVqq2tjZmnXmMzOYNTWzpqKZVczOT/qX820T1EK9K2iFWqga/zv1TMOqfg7UuL9q5s41J2usgmYbqJzD3sJeZRVMJ1vOJNUINaLedX3QuvMmqH0/1Sy8ZHry2i3X9+wyw2poT7oYC6vc9c7LOzSXhHvWyQGeoYSXkCIG3o0rMiWGOcvev5GXev7GJy/JHEOcv8j30/cdVr4baQLTIS3h2j64IjHb2nxS0n7dT5UNWljVTBrQRA538AJNLKCIzrFuFWhP3VR0YSbpvJjn2m1/nzuz6KTtmO0gDX33wyFpa57mgBCAkv78XbvrsnVFdBQNZYDjw1AKCgoKDhtFQRng+DAUA5t66cQ7coM5xpXRay8WeQJTQTCYV6peMzBTZx4CuvDgdlbRnJtQna2Z3F4xOUsnucubhKo9qKe+6SC5V6KtBRW0FbQ70OzA9LTRXFbTnp5x8vR5wEna5/cmTKc1s7NbcPuEelds78ppGEA19X16GmVeCwmfjRuXVf7PC92Bxq3bnUbizou2prO5uKhiJ6GqcZoP0bVzAnpUHuhpJ+5vcrtodsAm7rFrJ77f2rCJ/wzmEN9WCDk1XFbioLKYvMklmJFiAVRG6+/hYEGdRZL3Iq0jxaIFeJkFdaH9oVN9qKUY/twCc1bOfAOLNi/rjweLWI1uBX4IO89pLTDMe2bmFjI2s5pmr3bO9z1RTateAzCclhpMk8MhufIwT4Zf1erMf2rxpi5nBlOlQQ59U/Q7HUQ/LIDXdrOnUg14DSg+lCGOD0MpKCgoOGwUhjLA8WEoFWin6SUhp6T0Ek+4761cnU6Am08LYa3zhbSG2xCqBd3mpqo+5xcCelV3e9f5DepzTlOogkZgUM0MGUzO9e23W056mp3wC79Ogs7D7JSvUBv1Vsve3oSpzDlBb91CjTjxKdf35GykAfixqaH3nTSg1joNoRtXGHPkSwnfLdbgEq+yzDCJNCNL5dNOKF12GtqpXFvyGls1g3qvr9Z6P0277R3W4UKvbTU7QXszmtPOzm51cj9DYESa2sM/A3HG3cFv3/pyWPBeWPV9oV4TGFzeOcv78QXfgNGHNMcLJONrB10k/pFcipgmOh/CveP8aSE9jjt252of6BDKgx8ikGyG86HNRH3WhQgH7dgE7ZYhyfkDwfvFYjUlmqTQZqRN0qjPyRU/Y+Gexk3F1yma7yrqG5s3RVV9wMuFpC3KwszFshd0OD4MZSomH9uhPt+/4GX0D1b0kgNot11Zu8XcizKHetd9Ts66z+DQDi9LzdwLtppBNTVvHkoYlXqGYrX7PtsBm8DslNFc1sJ268xsswqbVXC+ZuvW2jndz0d9A5Pd0LDvs42ZinkaIxNCNXxBdY73jpHg37z9j6T/7fWr6wMjC3SEXeu6gJtK3pQl2tr98Nsp3uzW09D6ALh22o8jntt24ubGKtFu1YN5m17u6k6vmrror8nMmccYkN8JFisLkilfWuLMTq+LDFwZe+B4X6kzW5m+AtNpW5dZrZn5qL6mf1FDz1zruu3Kc1kZAsMJjGQ6Cw+EQ121qGqxiYs+m2212LZc6oNZZJpL+mcSm636aYjjDvqhDa+12OQVLNghSrBjIL1ZdCwqrPVjqW+dOIGnWm7ZvCAUDWWAsqd8QUFBwYXCOVmX/y2ApHtKerOkWyT9maRn+PLnS7pJ0o2Sfk/SP4iuebakD0p6v6RvOOBRroxjo6HUe3DZ3/Qmla2zzgFaNYZCMikvoTTexBKc1DBvtpG5c20yQ51ZqzXnDIfugXEaQn8c+gpSebstF5oLzE66z3bHheuqxantexXtniOqOlszOSvUwMQnnp7sOlqrKUzO9w9q7Y9d2K7TVqqZPx80iIkPBggIdRNH6kDom1knSeciQcO8mm+33RJqfJp8uf3h24k3kQjnWE9MbtXUa22RSc7kxhI7UtttV37+SlfY3mWPyXZLVbu/bljTuiNwmQA5NIPNn1dqMp2r4D9i6TcTkjvM3NDbfFJHcRpOrIpubHU0RgH1pKWZVWhitI1zlLdNBTM3kc00E0xQ4cJ06T/TkPqAGbULxd+toYWdT/drT/p1H72m2a2pmanXZuO2w79gfkunw+f7CmXaDubMtrsPQQsN65FChgozHK1A9ZkJk5moz4kt/7sJ2v1aYXS/rYvEDPgRM7vBb4T1x5LeALzQzP49gKSnA88FnnqUd7UtGkpBQUHBBcGcfXWVv0WtmH3czG7wx7cBtwB3N7PPRtUuoxdrul1tzezDQNjVduM4NhqKZnDiM5Gdv7EutLXe85JO68q34gsTASP4PTp/ApHkbJEjOpJMggYSXxN8JWp7CU6NUwWsFpNzTkvZOgM6Ae1UTM5MurpVQ+f/6fw2weGP0z5y2lU1bSO/TmLLnhr1tKd/zratSLJMJK+ubtXXsYm6cVWh3SBgTuIV/ta1PQht3epPm38S2wndIsypD1SYXgHNqdZpLVfvOum8bjuJvWkq2mZ+H5ZYa+lyrkX7tcyttl/ka8nY33Or9c2AkHU6aCTZNjX0oTT9d7UaRr/K0FbLFJedIUjpVd06DaGp+uy/UV6t6nwvL1Z7dIsKc0EUzXbv1A791ruixmu9XssPPseguTenei2nC0cPPpUQyh6PxfqJ6wIigmZiuAW+4L7LmOy4jqsQTm5OAw2Zjc37f6rPTKiAyZmKrdtcEzufxvsUI2vCurFmH4qka4AvA97pv/808N3ArcAjfLW7A++ILhvd1fawcWwYChXMdtSZedqtPhJldqrqfkROXffmoaY/rnfDk+8+6mlGqrDoRWvWR1nRMx2r3Fuz3oXmRI1m1qnb09M122eMdmLMTojtPWcqCg7/2AQXr6uIfwwhYsw56fuH2UVQecYZMRO11vkwq1lMcN9PO+k5pjQ8H+LsO0ZihvDMYWYD605gosGcqNYxeGfSMvcyikxubk2OZ25TMIl65s2BBpNzsHtXf89aoat3sUYuk0AVHNTqTElpFE/MUNomvIVbt/gnTFQMn/okmwo+E2EVnOHtrOpTkZyvqPZczNLkvPrnrom6a5ln5gFVVB5euHX8fPW0dOYx64LdOiHKVUiGaf0LPo60shrqvSAI9c77dsdd2JwA8452l+AxFqaiuZn6xKC1y6pASBKxzOwYZXEIcyqAVsyaSPw7G0xuLtoM3LwGM3c19YwTV9aZ4pre1OyuX0zPyjD2E+V1laT3RN+v8zvO9nRJp4HrgWcG7cTMrsVtjvVs4GnAfyAr3qxzdc2F4/gwlIKCgoLDxuoayqfM7MFjJyVt4ZjJy83sVZkqrwBei2MoF7yr7UFjYwxF0gOA/wqcwDmlvt/M3uXPPRt4Mi6s/ulm9vpl7RlBFQ9SWxzmKvzCau+k9+aB2OyzVXVlAFZ5CSjWSjLhqJmBdYfVXoiv9ae8RdQqmOxU3XEXSlz118cSaixRVXu9aamKtKiqAU2Hpr0UNuch9YfxjyKYQzpJOdDWr0Npt/pAgzjkOtZQwn3w+QC7uoPQ5fiwMSo/z/VUXeqnoL2B2LvdiZztTgsnnNqmxIw2GG9mQykJFDu3M6G9TdMPqp0NF+90Ibo4s5Lrh+6ZUvRctBO/+huoIs1H7YjkHpE70Fwi05HzTffmVKI2u2ai9UmDz0gDbnZ6CX52RdMnatyKzISz6OYuCoEOAQax5rLVHw80grZ/RnJtVmGdl5/brdt6La/ai0yqGfezZn0oe8gp1/cVkbs2z7GtxeQltzfvS4BbzOxFUfl9zewD/us/B/7cH78aeIWkF+Gc8kdmV9tNaig/C/yEmf0fSY/x37/2KEcwFBQUFHQw1pVt+GHAdwHvlXSjL/tx4MmSvggnyv418FQAM7ugXW0PA5tkKAaEfY3vRK+ydREMwIclhQiGty9sTd6x24TGnT1c5jLc9nmlhLadhNPsBCflUMoIUg44v0SQdDSzyIcy9EkodtaHMOJuJbqXUvdaL+nL+WwqBmG8sfQ+EN4iKWhYHsqi87MRiWkg/arzZchsoHnFPg7wWkcIe56oW93eSYrmtZBanU9ktqPB9Z20nS4ui+34lfoQ7Qr2oh2vz9+ldRJ0IzjVoLqlmQ4X4cXz0TVfj8yF9f6HwXWpQhM7zdtErI3fIwLzkr1NFGkI6jLxNjgtJudHCXUHPo/oOJbGB9kPLLoPIftzuCZ6hlPMLnPPeOX9VcEf0W5HWbhb9eHcqTZxMiHeZ34O13V+kEYoCgyINZXmVNRGpGWaH0TIyze93Gkp4Hw53W9za/gsVVNQ5ULLqxldkEc6LykdF401aChm9jby9o7XLbjmSO5qu0mG8kzg9ZJ+DmdgeagvXzmCQdJTgKcAbJ3+nIOjtKCgoCCHslJ+gANlKJLeCHxe5tS1wNcDP2Rm10v6lzgb4iPZRwSDj5K4DuDU1fe0Ln9W6uvoCOoFQVWRLXuiQThukJSrGTTbwaZrXlDvtY3uXAtVLLJGCxzDtbljgNoNZIXR+q4jh0FqC04lr3bSaxcw9KEM/CaJb6OdqPeHeDTbVR+JUw01GYs0D7evSqyhDOm0RLFII9tCShVw2Zfby3rxXJfNRlOfhAzQQ6L7wzTXV5qyI5vlNyqP/S7WKtw4pyXEj2yDP+f9cYP7VQ0jrgYkmc8z15dUU5/ra9KH7gbfWr3LQNupmmFTw8HOL9ANc17twSyEtU+dwyvkS7PasO120M5czrQoG3WXaTrU2av6VCoVtFtJW+GZMeD88GF2PkFXYXbK3F4/QFMHH2FUd68fUxemHIX6dyl5FvhfLghmWHMkLE1HBgfKUMzskWPnJL0MeIb/+krgxf74giMYwstsTqWN3x2WFJl/uYa4+h11D2wINaz8yvTwkq2mhqK3pDXRU4tnJhHj6J2xCWMZk24SxhHGNlctF9mc+FEHIbDYkBFIibMyhAb3zn+byK90d9/biXfOh3DiujeDhfNzIsGC7yHfWmA0W7c5s0Wz7U0dt01oThrNSYO9bWe2PtlQ7USMpnK7TSp9k8Ymq30kcgp7zw/NYsHTnlZO7lMqwESOaqkdBAqYD4fuUId8bN7EuOPCqauZm3Pb7kNjzYfkhswNC8dTJc9FS3cPBEzOqnPQMxVWWc/4dyu69PQxkum0iQ3GCgzCi1PzVzXrzYHgwparvcHV/SZwg46G45gTHpPfes5EvD6nPOtaKX9ssMmV8h8DvsYffx0QohleDTxe0o6ke3GEIhgKCgoKBlhDLq/jhE36UP4t8AuSJsAu3hdyMREMnfN8P043Jaag1oYSnbn2YjNOvBArNGJT6x3itWArNWmEi416rx06v42VHroQ2jw3hLmsxpoTFZqtytE1KEvETA1NX8M2++Nq6vKSdWYTf3eCMz6YFKyadxCPKgqRg77ehS31+c4mZ52dp9mB6eVGtTshPLrN5S12cv7xUN0OzFqL9lsfo6dLMx9vyBZpY875PFRJ5rXjPp+Y1ea0kBB6HFKtg1u46SX2oA1U59WZegLqc/NSe24VeKqRVFO/0dmWux/hnjQnGGSyBreVwmDRbkcrSWHk6N4T7Ilqt5qbg/pcQm8Dms6XZU29ubJM8ER6GwcLSUNZGk59sTAre8on2BhD8ZENDxo5dyQjGAoKCgoGuIS0j1VwbFbKq4HtM+M3N14oOCgfM/pFTXVOer+JldVidrL3KNZ7zvfCTn9NNRK+q9Zot4edpnm34ro5urIaSYrgnPQLDKum9+u0E2ETDfZrCXnIcn1alZm6KLVFwNY5BikuaICp7y+y2fcDTAsYLIQMmWLBh4Pu9SGkoay9tWagNvh2mxNzw6A5YXP3u90ybCeRMmUDH83gVORLUEhH0oa8XdZL8ibwWaMHkdK7VecjiVHtOY0kIaPLSxWj22p6GXxzjX8uqybSJk+684MNz/yWzE4j7IfRpwmOmh7pv9tyOhnHXBizMlqEmAvxHbSRuSVzPpZAQ/ocW78V9/pQnPIpjg1DiU0mOStGtwf62A8hpJ0feT5GGURjI6at+Rd/en7wNUN7an5Sa97Ru7qDOd8OaG/4clVElNVDx3o6Z82WYzxpSvB2K58mvN6bjzIa0rd0CHA2vWhJezkeW8ttrDaAgIrZydQc57ilTZiLTFsF1V7+WVOya2WKsXPdDpwJ4k3bUjQ72WLXz+78PeleuGOPV8a0NKRx5MTYb27RNXjTXO63HK0tGZTbIkFugbn1QmEUp3yC48NQCgoKCg4bS1LTX2o4VgwlSJJxSPAcImlzIB0Fh3Vixgl7t49Jga6dJVLKivHvVWOLtRo0t5I9W6vJS7P7QuJ0jRFv7DWQ0M7N12XEyT/X3TqkxxXasAWBB12dKKfaonZW6m+Z9rVSG1o4P/WeZTXAVFt0WYtXGNcCDLIZL6q3guS+0tbuK9QZrHPK0B82y1s3DLrcfwUOx4qhFBQUFBwazIqGkuDYMBQZTHbXLC3ss7m5hVYrX2fRoqslIqKvN2YrXob9SmpGCDu9wLldUYILo15V8o/RaW2rSM4W+aJG5tppkvnGQtDAStI1zOVz2w9C+LoaWzolnXN6aTcblqgPs/t1+0wyKBrKELJjEvYm6e+A24FPbZqWBFdRaFoFR5EmOJp0FZpWwyKavsDM7noxjUv6Xd/HKviUmT36Yvq7I+DYMBQASe9ZtInNJlBoWg1HkSY4mnQVmlbDUaTpuGOTqVcKCgoKCo4RCkMpKCgoKFgLjhtDuW7TBGRQaFoNR5EmOJp0FZpWw1Gk6VjjWPlQCgoKCgo2h+OmoRQUFBQUbAiFoRQUFBQUrAXHgqFIerSk90v6oKQf2yAdfyXpvZJulPQeX3alpDdI+oD//JxDoOPXJH1S0s1R2Sgdkp7t5+79kr7hEGl6nqS/8fN1o6THHDJN95T0Zkm3SPozSc/w5RubqwU0bWyuJJ2Q9C5Jf+pp+glfvsl5GqNpo8/UJQ8zu0P/4bJz/SVwb2Ab+FPgfhui5a+Aq5KynwV+zB//GPAfD4GOrwYeCNy8jA7gfn7OdoBB5RPmAAAIBUlEQVR7+bmsD4mm5wHPytQ9LJruBjzQH18O/IXve2NztYCmjc0Vbs35aX+8BbwT+IoNz9MYTRt9pi71v+OgoTwE+KCZfcjM9oDfAh67YZpiPBZ4qT9+KfAvDrpDM/t94NMr0vFY4LfM7LyZfRj4IG5OD4OmMRwWTR83sxv88W3ALcDd2eBcLaBpDIdBk5lZ2J1my/8Zm52nMZrGcCjP1KWO48BQ7g58JPr+URb/AA8SBvyepD+W9BRf9rlm9nFwLwvg6g3RNkbHpufvaZJu8iaxYDI5dJokXQN8GU7SPRJzldAEG5wrSbWkG4FPAm8ws43P0whNcESeqUsRx4Gh5FLAbSoW+mFm9kDgG4EfkPTVG6JjP9jk/P0KcB/gAcDHgZ/fBE2STgPXA880s88uqpopOxC6MjRtdK7MrDGzBwD3AB4i6Z8sqL5Jmo7EM3Wp4jgwlI8C94y+3wP42CYIMbOP+c9PAr+NU6k/IeluAP7zk5ugbQEdG5s/M/uEfym0wH+jN0EcGk2StnAv7peb2at88UbnKkfTUZgrT8dngLcAj+aIPFMxTUdlni5VHAeG8m7gvpLuJWkbeDzw6sMmQtJlki4Px8CjgJs9LU/01Z4I/K/Dps1jjI5XA4+XtCPpXsB9gXcdBkHhZeTxLbj5OjSaJAl4CXCLmb0oOrWxuRqjaZNzJemuku7sj08CjwT+nM3OU5amTT9Tlzw2HRWwjj/gMbhomL8Ert0QDffGRZH8KfBngQ7gLsCbgA/4zysPgZbfxKn7U5xk9uRFdADX+rl7P/CNh0jTbwDvBW7C/eDvdsg0PRxn9rgJuNH/PWaTc7WApo3NFXB/4E983zcDz132bG+Qpo0+U5f6X0m9UlBQUFCwFhwHk1dBQUFBwRFAYSgFBQUFBWtBYSgFBQUFBWtBYSgFBQUFBWtBYSgFBQUFBWtBYSgFBQUFBWtBYSiXACSdWV7rotp/saT7+eMfv4Drr1GU1n7F+ud8Hqfc+edJetZ+6bgjQtKdJX1/9P0+Pm37gd7zgoIcCkMpuGiY2feY2fv8130zlAvEX5rL43RgkFQfZPtrwp2BjqGY2YHPS0HBGApDuUQh6QskvclnZX2TpM/35b8u6Rcl/ZGkD0l6nC+vJP2y38zoNZJeF517i6QHS3oBcNJLyC9PNQ9Jz5L0PH/8IL850tuBH4jq1JJeKOndnrbvXXE81/qNk94IfFFUfh9Jv+szQP+BpC+Oyt/h+/nJINFL+lq5Da5egVtxjaTvlNvM6UZJvxoYjaRHSXq7pBskvdIndETSCyS9z9P/cwtovquk6z0N75b0MF/+ED//f+I/v8iX/+OIjpsk3Rd4ARC0kheuMlcFBQeGTS/VL38H/wecyZT9b+CJ/vjfAL/jj38deCVO2Lgfbq8ZgMcBr/Plnwf8PfA4f+4twIPTvoBrGG6o9Szgef74JuBr/PELQz3gKcBz/PEO8B7gXgntabsPwr38TwFX4Pa6eJY/9ybgvv74nwL/1x+/BvgOf/zUQDfwtcDtoU/gH/m52vLffxn4buAq4PeBy3z5jwLPBa7EpfYIWSjuvOC+vAJ4uD/+fFz+LvwYJv74kcD1/viXgCf4423gZDoXi+55+St/B/03oeBSxVcC3+qPfwO3+17A75jL1vo+SZ/ryx4OvNKX/62kN19ox5LuhHvRvjXq/xv98aOA+wftB7gTLpHfhxc0+VXAb5vZWd/+q/3naeChwCtdzkXAMSlw4w8bQr0CiDWJd5nbhAng63EM692+jZO4rLpfgWO4f+jLt4G3A58FdoEXS3otjnGN4ZHA/SLarpBLMHon4KVeAzHc5lH49q+VdA/gVWb2gejagoKNozCUgoA4qdv56FjJ534wY2hWPRG1NZZETsAPmtnr99lXrr0K+Izt36dwe0LPS83s2XEFSd+M29TpO9KLJT0Ex4geDzwN+LqRfirgK83sXHL9LwFvNrNvkdtk6y0AZvYKSe8Evgl4vaTvAT60z7EVFBwYig/l0sUf4V54AE8A3rak/tuAb/O+lM/FmYZymMrt5wHwCeBqSXeRtAP8M+j2r7hV0sOj/gNeD3xfaEPSF8ptB7AIvw98i6STXsL/Zt/PZ4EPS/p235Ykfam/5h3At/njx6cNRngT8DhJV/s2rpT0Bf76h0n6h778lKf1NHAnM3sd8EzcRk9j+D0cw8G3EereCfgbf/yvo/P3Bj5kZr+Iy6R7f+A23N7zBQUbR2EolwZOSfpo9PfDwNOBJ0m6Cfgu4BlL2rgel3b+ZuBXcdvS3pqpdx1wk6SXm9kU+Elf9zW4PTQCngT8F++UjyX0FwPvA27wDv1fZYkmbW4P9v+BS/V+PfAH0eknAE+WFLYVeKwvfybww5LeBdxtZCyYi157Dm5r55uAN+BSov8d7mX/m778HcAX417ur/FlbwV+aAHpTwce7B3s78P5csCZH39G0h8CcaTZvwJulguX/mLgZWb2/3Bmt5uLU75g0yjp6wtWhqTTZnZG0l1wmxM9zMz+dgN0XAO8xswWbUO7rI1TwDkzM0mPxznoH7vsujsKJJ0xs9ObpqPg0kLxoRTsB6+R2yVvG3j+JpiJRwPcSdKNF+AfCXgQ8J/lvNqfwUW63eEh6T44Le0Tm6al4NJD0VAKCg4Ykq4Fvj0pfqWZ/fQm6CkoOCgUhlJQUFBQsBYUp3xBQUFBwVpQGEpBQUFBwVpQGEpBQUFBwVpQGEpBQUFBwVrw/wEA9iPkxUthogAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "tas.sel(time='2015-01-16',method='nearest').plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7fc0407c87d0>]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "tas.sel(lon=130.85,lat=-12.46,method='nearest').plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Exercise\n",
    "\n",
    "Try plotting the colormap of tasmax in the region over Australia. So that is between latitudes -45 to -10 deg N of and longitudes of 110 to 155 deg E. Pick the date for the year and month you were born and plot for that."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Summary\n",
    "\n",
    "This example shows how to use xarray to do subsetting and plotting.\n",
    "\n",
    "## Reference \n",
    "\n",
    "http://xarray.pydata.org/en/stable/"
   ]
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "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": "3.7.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}