python作业/HW11ENSO处理绘图.ipynb

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    "import numpy as np\n",
    "import xarray as xr\n",
    "import matplotlib.pyplot as plt\n",
    "import cartopy.feature as cfeature\n",
    "import cartopy.crs as ccrs\n",
    "from cartopy.mpl.ticker import LongitudeFormatter, LatitudeFormatter\n",
    "\n",
    "plt.rcParams['font.sans-serif'] = ['SimHei']  ###防止无法显示中文并设置黑体\n",
    "plt.rcParams['axes.unicode_minus'] = False  ###用来正常显示负号"
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
       "Dimensions:    (lat: 89, lon: 180, time: 2016, nbnds: 2)\n",
       "Coordinates:\n",
       "  * lat        (lat) float32 88.0 86.0 84.0 82.0 ... -82.0 -84.0 -86.0 -88.0\n",
       "  * lon        (lon) float32 0.0 2.0 4.0 6.0 8.0 ... 352.0 354.0 356.0 358.0\n",
       "  * time       (time) datetime64[ns] 1854-01-01 1854-02-01 ... 2021-12-01\n",
       "Dimensions without coordinates: nbnds\n",
       "Data variables:\n",
       "    time_bnds  (time, nbnds) float64 9.969e+36 9.969e+36 ... 9.969e+36 9.969e+36\n",
       "    sst        (time, lat, lon) float32 ...\n",
       "Attributes: (12/37)\n",
       "    climatology:               Climatology is based on 1971-2000 SST, Xue, Y....\n",
       "    description:               In situ data: ICOADS2.5 before 2007 and NCEP i...\n",
       "    keywords_vocabulary:       NASA Global Change Master Directory (GCMD) Sci...\n",
       "    keywords:                  Earth Science &gt; Oceans &gt; Ocean Temperature &gt; S...\n",
       "    instrument:                Conventional thermometers\n",
       "    source_comment:            SSTs were observed by conventional thermometer...\n",
       "    ...                        ...\n",
       "    creator_url_original:      https://www.ncei.noaa.gov\n",
       "    license:                   No constraints on data access or use\n",
       "    comment:                   SSTs were observed by conventional thermometer...\n",
       "    summary:                   ERSST.v5 is developed based on v4 after revisi...\n",
       "    dataset_title:             NOAA Extended Reconstructed SST V5\n",
       "    data_modified:             2022-01-07</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-3b596f9b-4282-468d-8d82-cd9bfcdc677c' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-3b596f9b-4282-468d-8d82-cd9bfcdc677c' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>lat</span>: 89</li><li><span class='xr-has-index'>lon</span>: 180</li><li><span class='xr-has-index'>time</span>: 2016</li><li><span>nbnds</span>: 2</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-05f689c0-2896-4244-af0a-4fe0fb5d3de0' class='xr-section-summary-in' type='checkbox'  checked><label for='section-05f689c0-2896-4244-af0a-4fe0fb5d3de0' class='xr-section-summary' >Coordinates: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lat</span></div><div class='xr-var-dims'>(lat)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>88.0 86.0 84.0 ... -86.0 -88.0</div><input id='attrs-5b75c0d1-8e28-4dc5-829e-6dd437d536d2' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5b75c0d1-8e28-4dc5-829e-6dd437d536d2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e5d10659-e09f-4d25-ae55-39e7a080f159' class='xr-var-data-in' type='checkbox'><label for='data-e5d10659-e09f-4d25-ae55-39e7a080f159' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degrees_north</dd><dt><span>long_name :</span></dt><dd>Latitude</dd><dt><span>actual_range :</span></dt><dd>[ 88. -88.]</dd><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>axis :</span></dt><dd>Y</dd><dt><span>coordinate_defines :</span></dt><dd>center</dd></dl></div><div class='xr-var-data'><pre>array([ 88.,  86.,  84.,  82.,  80.,  78.,  76.,  74.,  72.,  70.,  68.,  66.,\n",
       "        64.,  62.,  60.,  58.,  56.,  54.,  52.,  50.,  48.,  46.,  44.,  42.,\n",
       "        40.,  38.,  36.,  34.,  32.,  30.,  28.,  26.,  24.,  22.,  20.,  18.,\n",
       "        16.,  14.,  12.,  10.,   8.,   6.,   4.,   2.,   0.,  -2.,  -4.,  -6.,\n",
       "        -8., -10., -12., -14., -16., -18., -20., -22., -24., -26., -28., -30.,\n",
       "       -32., -34., -36., -38., -40., -42., -44., -46., -48., -50., -52., -54.,\n",
       "       -56., -58., -60., -62., -64., -66., -68., -70., -72., -74., -76., -78.,\n",
       "       -80., -82., -84., -86., -88.], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lon</span></div><div class='xr-var-dims'>(lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.0 2.0 4.0 ... 354.0 356.0 358.0</div><input id='attrs-a833fb67-5ab6-4ef7-a8d0-3261a84d376e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a833fb67-5ab6-4ef7-a8d0-3261a84d376e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e4baaff4-e912-4d81-802e-5070385e3f36' class='xr-var-data-in' type='checkbox'><label for='data-e4baaff4-e912-4d81-802e-5070385e3f36' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degrees_east</dd><dt><span>long_name :</span></dt><dd>Longitude</dd><dt><span>actual_range :</span></dt><dd>[  0. 358.]</dd><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>axis :</span></dt><dd>X</dd><dt><span>coordinate_defines :</span></dt><dd>center</dd></dl></div><div class='xr-var-data'><pre>array([  0.,   2.,   4.,   6.,   8.,  10.,  12.,  14.,  16.,  18.,  20.,  22.,\n",
       "        24.,  26.,  28.,  30.,  32.,  34.,  36.,  38.,  40.,  42.,  44.,  46.,\n",
       "        48.,  50.,  52.,  54.,  56.,  58.,  60.,  62.,  64.,  66.,  68.,  70.,\n",
       "        72.,  74.,  76.,  78.,  80.,  82.,  84.,  86.,  88.,  90.,  92.,  94.,\n",
       "        96.,  98., 100., 102., 104., 106., 108., 110., 112., 114., 116., 118.,\n",
       "       120., 122., 124., 126., 128., 130., 132., 134., 136., 138., 140., 142.,\n",
       "       144., 146., 148., 150., 152., 154., 156., 158., 160., 162., 164., 166.,\n",
       "       168., 170., 172., 174., 176., 178., 180., 182., 184., 186., 188., 190.,\n",
       "       192., 194., 196., 198., 200., 202., 204., 206., 208., 210., 212., 214.,\n",
       "       216., 218., 220., 222., 224., 226., 228., 230., 232., 234., 236., 238.,\n",
       "       240., 242., 244., 246., 248., 250., 252., 254., 256., 258., 260., 262.,\n",
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       "       288., 290., 292., 294., 296., 298., 300., 302., 304., 306., 308., 310.,\n",
       "       312., 314., 316., 318., 320., 322., 324., 326., 328., 330., 332., 334.,\n",
       "       336., 338., 340., 342., 344., 346., 348., 350., 352., 354., 356., 358.],\n",
       "      dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>1854-01-01 ... 2021-12-01</div><input id='attrs-5487debd-ac83-492f-a1ce-41ecaab3de0d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5487debd-ac83-492f-a1ce-41ecaab3de0d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-86f93567-39e2-438a-839e-0b8e5e3d6c58' class='xr-var-data-in' type='checkbox'><label for='data-86f93567-39e2-438a-839e-0b8e5e3d6c58' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Time</dd><dt><span>delta_t :</span></dt><dd>0000-01-00 00:00:00</dd><dt><span>avg_period :</span></dt><dd>0000-01-00 00:00:00</dd><dt><span>prev_avg_period :</span></dt><dd>0000-00-07 00:00:00</dd><dt><span>standard_name :</span></dt><dd>time</dd><dt><span>axis :</span></dt><dd>T</dd><dt><span>actual_range :</span></dt><dd>[19723. 81053.]</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;1854-01-01T00:00:00.000000000&#x27;, &#x27;1854-02-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;1854-03-01T00:00:00.000000000&#x27;, ..., &#x27;2021-10-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2021-11-01T00:00:00.000000000&#x27;, &#x27;2021-12-01T00:00:00.000000000&#x27;],\n",
       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-cb332349-2576-4d13-ae7d-421e68c05295' class='xr-section-summary-in' type='checkbox'  checked><label for='section-cb332349-2576-4d13-ae7d-421e68c05295' 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_bnds</span></div><div class='xr-var-dims'>(time, nbnds)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-7da6eed2-0bc8-4828-89d0-f5cf6ebe17a3' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-7da6eed2-0bc8-4828-89d0-f5cf6ebe17a3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1b0d361b-66d5-4a89-bf53-c263786e6289' class='xr-var-data-in' type='checkbox'><label for='data-1b0d361b-66d5-4a89-bf53-c263786e6289' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Time Boundaries</dd></dl></div><div class='xr-var-data'><pre>array([[9.96921e+36, 9.96921e+36],\n",
       "       [9.96921e+36, 9.96921e+36],\n",
       "       [9.96921e+36, 9.96921e+36],\n",
       "       ...,\n",
       "       [9.96921e+36, 9.96921e+36],\n",
       "       [9.96921e+36, 9.96921e+36],\n",
       "       [9.96921e+36, 9.96921e+36]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>sst</span></div><div class='xr-var-dims'>(time, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-90d8b72d-3f08-44de-9e4a-283df848a3a7' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-90d8b72d-3f08-44de-9e4a-283df848a3a7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-daeb1a3b-967b-4879-aac2-9c68bc9c4cf2' class='xr-var-data-in' type='checkbox'><label for='data-daeb1a3b-967b-4879-aac2-9c68bc9c4cf2' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Monthly Means of Sea Surface Temperature</dd><dt><span>units :</span></dt><dd>degC</dd><dt><span>var_desc :</span></dt><dd>Sea Surface Temperature</dd><dt><span>level_desc :</span></dt><dd>Surface</dd><dt><span>statistic :</span></dt><dd>Mean</dd><dt><span>dataset :</span></dt><dd>NOAA Extended Reconstructed SST V5</dd><dt><span>parent_stat :</span></dt><dd>Individual Values</dd><dt><span>actual_range :</span></dt><dd>[-1.8     42.32636]</dd><dt><span>valid_range :</span></dt><dd>[-1.8 45. ]</dd></dl></div><div class='xr-var-data'><pre>[32296320 values with dtype=float32]</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-d07fd2cf-fa74-43c8-88d2-e12f1d475d9b' class='xr-section-summary-in' type='checkbox'  ><label for='section-d07fd2cf-fa74-43c8-88d2-e12f1d475d9b' class='xr-section-summary' >Attributes: <span>(37)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>climatology :</span></dt><dd>Climatology is based on 1971-2000 SST, Xue, Y., T. M. Smith, and R. W. Reynolds, 2003: Interdecadal changes of 30-yr SST normals during 1871.2000. Journal of Climate, 16, 1601-1612.</dd><dt><span>description :</span></dt><dd>In situ data: ICOADS2.5 before 2007 and NCEP in situ data from 2008 to present. Ice data: HadISST ice before 2010 and NCEP ice after 2010.</dd><dt><span>keywords_vocabulary :</span></dt><dd>NASA Global Change Master Directory (GCMD) Science Keywords</dd><dt><span>keywords :</span></dt><dd>Earth Science &gt; Oceans &gt; Ocean Temperature &gt; Sea Surface Temperature &gt;</dd><dt><span>instrument :</span></dt><dd>Conventional thermometers</dd><dt><span>source_comment :</span></dt><dd>SSTs were observed by conventional thermometers in Buckets (insulated or un-insulated canvas and wooded buckets) or Engine Room Intaker</dd><dt><span>geospatial_lon_min :</span></dt><dd>-1.0</dd><dt><span>geospatial_lon_max :</span></dt><dd>359.0</dd><dt><span>geospatial_laty_max :</span></dt><dd>89.0</dd><dt><span>geospatial_laty_min :</span></dt><dd>-89.0</dd><dt><span>geospatial_lat_max :</span></dt><dd>89.0</dd><dt><span>geospatial_lat_min :</span></dt><dd>-89.0</dd><dt><span>geospatial_lat_units :</span></dt><dd>degrees_north</dd><dt><span>geospatial_lon_units :</span></dt><dd>degrees_east</dd><dt><span>cdm_data_type :</span></dt><dd>Grid</dd><dt><span>project :</span></dt><dd>NOAA Extended Reconstructed Sea Surface Temperature (ERSST)</dd><dt><span>original_publisher_url :</span></dt><dd>http://www.ncdc.noaa.gov</dd><dt><span>References :</span></dt><dd>https://www.ncdc.noaa.gov/data-access/marineocean-data/extended-reconstructed-sea-surface-temperature-ersst-v5 at NCEI and http://www.esrl.noaa.gov/psd/data/gridded/data.noaa.ersst.v5.html</dd><dt><span>source :</span></dt><dd>In situ data: ICOADS R3.0 before 2015, NCEP in situ GTS from 2016 to present, and Argo SST from 1999 to present. Ice data: HadISST2 ice before 2015, and NCEP ice after 2015</dd><dt><span>title :</span></dt><dd>NOAA ERSSTv5 (in situ only)</dd><dt><span>history :</span></dt><dd>created 07/2017 by PSD data using NCEI&#x27;s ERSST V5 NetCDF values</dd><dt><span>institution :</span></dt><dd>This version written at NOAA/ESRL PSD: obtained from NOAA/NESDIS/National Centers for Environmental Information and time aggregated. Original Full Source: NOAA/NESDIS/NCEI/CCOG</dd><dt><span>citation :</span></dt><dd>Huang et al, 2017: Extended Reconstructed Sea Surface Temperatures Version 5 (ERSSTv5): Upgrades, Validations, and Intercomparisons. Journal of Climate, https://doi.org/10.1175/JCLI-D-16-0836.1</dd><dt><span>platform :</span></dt><dd>Ship and Buoy SSTs from ICOADS R3.0 and NCEP GTS</dd><dt><span>standard_name_vocabulary :</span></dt><dd>CF Standard Name Table (v40, 25 January 2017)</dd><dt><span>processing_level :</span></dt><dd>NOAA Level 4</dd><dt><span>Conventions :</span></dt><dd>CF-1.6, ACDD-1.3</dd><dt><span>metadata_link :</span></dt><dd>:metadata_link = https://doi.org/10.7289/V5T72FNM (original format)</dd><dt><span>creator_name :</span></dt><dd>Boyin Huang (original)</dd><dt><span>date_created :</span></dt><dd>2017-06-30T12:18:00Z (original)</dd><dt><span>product_version :</span></dt><dd>Version 5</dd><dt><span>creator_url_original :</span></dt><dd>https://www.ncei.noaa.gov</dd><dt><span>license :</span></dt><dd>No constraints on data access or use</dd><dt><span>comment :</span></dt><dd>SSTs were observed by conventional thermometers in Buckets (insulated or un-insulated canvas and wooded buckets), Engine Room Intakers, or floats and drifters</dd><dt><span>summary :</span></dt><dd>ERSST.v5 is developed based on v4 after revisions of 8 parameters using updated data sets and advanced knowledge of ERSST analysis</dd><dt><span>dataset_title :</span></dt><dd>NOAA Extended Reconstructed SST V5</dd><dt><span>data_modified :</span></dt><dd>2022-01-07</dd></dl></div></li></ul></div></div>"
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       "<xarray.Dataset>\n",
       "Dimensions:    (lat: 89, lon: 180, time: 2016, nbnds: 2)\n",
       "Coordinates:\n",
       "  * lat        (lat) float32 88.0 86.0 84.0 82.0 ... -82.0 -84.0 -86.0 -88.0\n",
       "  * lon        (lon) float32 0.0 2.0 4.0 6.0 8.0 ... 352.0 354.0 356.0 358.0\n",
       "  * time       (time) datetime64[ns] 1854-01-01 1854-02-01 ... 2021-12-01\n",
       "Dimensions without coordinates: nbnds\n",
       "Data variables:\n",
       "    time_bnds  (time, nbnds) float64 ...\n",
       "    sst        (time, lat, lon) float32 ...\n",
       "Attributes: (12/37)\n",
       "    climatology:               Climatology is based on 1971-2000 SST, Xue, Y....\n",
       "    description:               In situ data: ICOADS2.5 before 2007 and NCEP i...\n",
       "    keywords_vocabulary:       NASA Global Change Master Directory (GCMD) Sci...\n",
       "    keywords:                  Earth Science > Oceans > Ocean Temperature > S...\n",
       "    instrument:                Conventional thermometers\n",
       "    source_comment:            SSTs were observed by conventional thermometer...\n",
       "    ...                        ...\n",
       "    creator_url_original:      https://www.ncei.noaa.gov\n",
       "    license:                   No constraints on data access or use\n",
       "    comment:                   SSTs were observed by conventional thermometer...\n",
       "    summary:                   ERSST.v5 is developed based on v4 after revisi...\n",
       "    dataset_title:             NOAA Extended Reconstructed SST V5\n",
       "    data_modified:             2022-01-07"
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;sst&#x27; (time: 3, lat: 89, lon: 180)&gt;\n",
       "array([[[-1.8, -1.8, ..., -1.8, -1.8],\n",
       "        [-1.8, -1.8, ..., -1.8, -1.8],\n",
       "        ...,\n",
       "        [ nan,  nan, ...,  nan,  nan],\n",
       "        [ nan,  nan, ...,  nan,  nan]],\n",
       "\n",
       "       [[-1.8, -1.8, ..., -1.8, -1.8],\n",
       "        [-1.8, -1.8, ..., -1.8, -1.8],\n",
       "        ...,\n",
       "        [ nan,  nan, ...,  nan,  nan],\n",
       "        [ nan,  nan, ...,  nan,  nan]],\n",
       "\n",
       "       [[-1.8, -1.8, ..., -1.8, -1.8],\n",
       "        [-1.8, -1.8, ..., -1.8, -1.8],\n",
       "        ...,\n",
       "        [ nan,  nan, ...,  nan,  nan],\n",
       "        [ nan,  nan, ...,  nan,  nan]]], dtype=float32)\n",
       "Coordinates:\n",
       "  * lat      (lat) float32 88.0 86.0 84.0 82.0 80.0 ... -82.0 -84.0 -86.0 -88.0\n",
       "  * lon      (lon) float32 0.0 2.0 4.0 6.0 8.0 ... 350.0 352.0 354.0 356.0 358.0\n",
       "  * time     (time) datetime64[ns] 2019-12-01 2020-01-01 2020-02-01\n",
       "Attributes:\n",
       "    long_name:     Monthly Means of Sea Surface Temperature\n",
       "    units:         degC\n",
       "    var_desc:      Sea Surface Temperature\n",
       "    level_desc:    Surface\n",
       "    statistic:     Mean\n",
       "    dataset:       NOAA Extended Reconstructed SST V5\n",
       "    parent_stat:   Individual Values\n",
       "    actual_range:  [-1.8     42.32636]\n",
       "    valid_range:   [-1.8 45. ]</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'sst'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 3</li><li><span class='xr-has-index'>lat</span>: 89</li><li><span class='xr-has-index'>lon</span>: 180</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-848c44e4-deb5-4c4a-b1d5-4f3b34956f57' class='xr-array-in' type='checkbox' checked><label for='section-848c44e4-deb5-4c4a-b1d5-4f3b34956f57' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>-1.8 -1.8 -1.8 -1.8 -1.8 -1.8 -1.8 ... nan nan nan nan nan nan nan</span></div><div class='xr-array-data'><pre>array([[[-1.8, -1.8, ..., -1.8, -1.8],\n",
       "        [-1.8, -1.8, ..., -1.8, -1.8],\n",
       "        ...,\n",
       "        [ nan,  nan, ...,  nan,  nan],\n",
       "        [ nan,  nan, ...,  nan,  nan]],\n",
       "\n",
       "       [[-1.8, -1.8, ..., -1.8, -1.8],\n",
       "        [-1.8, -1.8, ..., -1.8, -1.8],\n",
       "        ...,\n",
       "        [ nan,  nan, ...,  nan,  nan],\n",
       "        [ nan,  nan, ...,  nan,  nan]],\n",
       "\n",
       "       [[-1.8, -1.8, ..., -1.8, -1.8],\n",
       "        [-1.8, -1.8, ..., -1.8, -1.8],\n",
       "        ...,\n",
       "        [ nan,  nan, ...,  nan,  nan],\n",
       "        [ nan,  nan, ...,  nan,  nan]]], dtype=float32)</pre></div></div></li><li class='xr-section-item'><input id='section-df9f6de5-7905-4de7-be3d-d305f5f86108' class='xr-section-summary-in' type='checkbox'  checked><label for='section-df9f6de5-7905-4de7-be3d-d305f5f86108' class='xr-section-summary' >Coordinates: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lat</span></div><div class='xr-var-dims'>(lat)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>88.0 86.0 84.0 ... -86.0 -88.0</div><input id='attrs-ab6766c1-f86b-49bb-b12c-66892fe878df' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ab6766c1-f86b-49bb-b12c-66892fe878df' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c36d8c9c-b947-4d26-95bb-7ea929133328' class='xr-var-data-in' type='checkbox'><label for='data-c36d8c9c-b947-4d26-95bb-7ea929133328' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degrees_north</dd><dt><span>long_name :</span></dt><dd>Latitude</dd><dt><span>actual_range :</span></dt><dd>[ 88. -88.]</dd><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>axis :</span></dt><dd>Y</dd><dt><span>coordinate_defines :</span></dt><dd>center</dd></dl></div><div class='xr-var-data'><pre>array([ 88.,  86.,  84.,  82.,  80.,  78.,  76.,  74.,  72.,  70.,  68.,  66.,\n",
       "        64.,  62.,  60.,  58.,  56.,  54.,  52.,  50.,  48.,  46.,  44.,  42.,\n",
       "        40.,  38.,  36.,  34.,  32.,  30.,  28.,  26.,  24.,  22.,  20.,  18.,\n",
       "        16.,  14.,  12.,  10.,   8.,   6.,   4.,   2.,   0.,  -2.,  -4.,  -6.,\n",
       "        -8., -10., -12., -14., -16., -18., -20., -22., -24., -26., -28., -30.,\n",
       "       -32., -34., -36., -38., -40., -42., -44., -46., -48., -50., -52., -54.,\n",
       "       -56., -58., -60., -62., -64., -66., -68., -70., -72., -74., -76., -78.,\n",
       "       -80., -82., -84., -86., -88.], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lon</span></div><div class='xr-var-dims'>(lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.0 2.0 4.0 ... 354.0 356.0 358.0</div><input id='attrs-15b022b9-f7e0-4e96-8e74-b623e03480b2' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-15b022b9-f7e0-4e96-8e74-b623e03480b2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-662dbf73-046e-4fa1-8ae9-f7f1e9f0c529' class='xr-var-data-in' type='checkbox'><label for='data-662dbf73-046e-4fa1-8ae9-f7f1e9f0c529' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degrees_east</dd><dt><span>long_name :</span></dt><dd>Longitude</dd><dt><span>actual_range :</span></dt><dd>[  0. 358.]</dd><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>axis :</span></dt><dd>X</dd><dt><span>coordinate_defines :</span></dt><dd>center</dd></dl></div><div class='xr-var-data'><pre>array([  0.,   2.,   4.,   6.,   8.,  10.,  12.,  14.,  16.,  18.,  20.,  22.,\n",
       "        24.,  26.,  28.,  30.,  32.,  34.,  36.,  38.,  40.,  42.,  44.,  46.,\n",
       "        48.,  50.,  52.,  54.,  56.,  58.,  60.,  62.,  64.,  66.,  68.,  70.,\n",
       "        72.,  74.,  76.,  78.,  80.,  82.,  84.,  86.,  88.,  90.,  92.,  94.,\n",
       "        96.,  98., 100., 102., 104., 106., 108., 110., 112., 114., 116., 118.,\n",
       "       120., 122., 124., 126., 128., 130., 132., 134., 136., 138., 140., 142.,\n",
       "       144., 146., 148., 150., 152., 154., 156., 158., 160., 162., 164., 166.,\n",
       "       168., 170., 172., 174., 176., 178., 180., 182., 184., 186., 188., 190.,\n",
       "       192., 194., 196., 198., 200., 202., 204., 206., 208., 210., 212., 214.,\n",
       "       216., 218., 220., 222., 224., 226., 228., 230., 232., 234., 236., 238.,\n",
       "       240., 242., 244., 246., 248., 250., 252., 254., 256., 258., 260., 262.,\n",
       "       264., 266., 268., 270., 272., 274., 276., 278., 280., 282., 284., 286.,\n",
       "       288., 290., 292., 294., 296., 298., 300., 302., 304., 306., 308., 310.,\n",
       "       312., 314., 316., 318., 320., 322., 324., 326., 328., 330., 332., 334.,\n",
       "       336., 338., 340., 342., 344., 346., 348., 350., 352., 354., 356., 358.],\n",
       "      dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2019-12-01 2020-01-01 2020-02-01</div><input id='attrs-bed2aa9a-589d-47de-bd80-c5df25dd1e5b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-bed2aa9a-589d-47de-bd80-c5df25dd1e5b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-48e829fd-985f-4e3a-b59b-f779aeee0cda' class='xr-var-data-in' type='checkbox'><label for='data-48e829fd-985f-4e3a-b59b-f779aeee0cda' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Time</dd><dt><span>delta_t :</span></dt><dd>0000-01-00 00:00:00</dd><dt><span>avg_period :</span></dt><dd>0000-01-00 00:00:00</dd><dt><span>prev_avg_period :</span></dt><dd>0000-00-07 00:00:00</dd><dt><span>standard_name :</span></dt><dd>time</dd><dt><span>axis :</span></dt><dd>T</dd><dt><span>actual_range :</span></dt><dd>[19723. 81053.]</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;2019-12-01T00:00:00.000000000&#x27;, &#x27;2020-01-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2020-02-01T00:00:00.000000000&#x27;], dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-f6f540ca-32a7-44ca-a797-dc2670d5ad6f' class='xr-section-summary-in' type='checkbox'  checked><label for='section-f6f540ca-32a7-44ca-a797-dc2670d5ad6f' class='xr-section-summary' >Attributes: <span>(9)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Monthly Means of Sea Surface Temperature</dd><dt><span>units :</span></dt><dd>degC</dd><dt><span>var_desc :</span></dt><dd>Sea Surface Temperature</dd><dt><span>level_desc :</span></dt><dd>Surface</dd><dt><span>statistic :</span></dt><dd>Mean</dd><dt><span>dataset :</span></dt><dd>NOAA Extended Reconstructed SST V5</dd><dt><span>parent_stat :</span></dt><dd>Individual Values</dd><dt><span>actual_range :</span></dt><dd>[-1.8     42.32636]</dd><dt><span>valid_range :</span></dt><dd>[-1.8 45. ]</dd></dl></div></li></ul></div></div>"
      ],
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       "<xarray.DataArray 'sst' (time: 3, lat: 89, lon: 180)>\n",
       "array([[[-1.8, -1.8, ..., -1.8, -1.8],\n",
       "        [-1.8, -1.8, ..., -1.8, -1.8],\n",
       "        ...,\n",
       "        [ nan,  nan, ...,  nan,  nan],\n",
       "        [ nan,  nan, ...,  nan,  nan]],\n",
       "\n",
       "       [[-1.8, -1.8, ..., -1.8, -1.8],\n",
       "        [-1.8, -1.8, ..., -1.8, -1.8],\n",
       "        ...,\n",
       "        [ nan,  nan, ...,  nan,  nan],\n",
       "        [ nan,  nan, ...,  nan,  nan]],\n",
       "\n",
       "       [[-1.8, -1.8, ..., -1.8, -1.8],\n",
       "        [-1.8, -1.8, ..., -1.8, -1.8],\n",
       "        ...,\n",
       "        [ nan,  nan, ...,  nan,  nan],\n",
       "        [ nan,  nan, ...,  nan,  nan]]], dtype=float32)\n",
       "Coordinates:\n",
       "  * lat      (lat) float32 88.0 86.0 84.0 82.0 80.0 ... -82.0 -84.0 -86.0 -88.0\n",
       "  * lon      (lon) float32 0.0 2.0 4.0 6.0 8.0 ... 350.0 352.0 354.0 356.0 358.0\n",
       "  * time     (time) datetime64[ns] 2019-12-01 2020-01-01 2020-02-01\n",
       "Attributes:\n",
       "    long_name:     Monthly Means of Sea Surface Temperature\n",
       "    units:         degC\n",
       "    var_desc:      Sea Surface Temperature\n",
       "    level_desc:    Surface\n",
       "    statistic:     Mean\n",
       "    dataset:       NOAA Extended Reconstructed SST V5\n",
       "    parent_stat:   Individual Values\n",
       "    actual_range:  [-1.8     42.32636]\n",
       "    valid_range:   [-1.8 45. ]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Twinter=ds['sst'].loc[ds.time.dt.month.isin([12,1,2])].loc['2019-03-01':'2020-03-01']\n",
    "Twinter"
   ]
  },
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       "  --xr-font-color3: rgba(255, 255, 255, 0.38);\n",
       "  --xr-border-color: #1F1F1F;\n",
       "  --xr-disabled-color: #515151;\n",
       "  --xr-background-color: #111111;\n",
       "  --xr-background-color-row-even: #111111;\n",
       "  --xr-background-color-row-odd: #313131;\n",
       "}\n",
       "\n",
       ".xr-wrap {\n",
       "  display: block !important;\n",
       "  min-width: 300px;\n",
       "  max-width: 700px;\n",
       "}\n",
       "\n",
       ".xr-text-repr-fallback {\n",
       "  /* fallback to plain text repr when CSS is not injected (untrusted notebook) */\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-header {\n",
       "  padding-top: 6px;\n",
       "  padding-bottom: 6px;\n",
       "  margin-bottom: 4px;\n",
       "  border-bottom: solid 1px var(--xr-border-color);\n",
       "}\n",
       "\n",
       ".xr-header > div,\n",
       ".xr-header > ul {\n",
       "  display: inline;\n",
       "  margin-top: 0;\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-obj-type,\n",
       ".xr-array-name {\n",
       "  margin-left: 2px;\n",
       "  margin-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-obj-type {\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-sections {\n",
       "  padding-left: 0 !important;\n",
       "  display: grid;\n",
       "  grid-template-columns: 150px auto auto 1fr 20px 20px;\n",
       "}\n",
       "\n",
       ".xr-section-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-section-item input {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-section-item input + label {\n",
       "  color: var(--xr-disabled-color);\n",
       "}\n",
       "\n",
       ".xr-section-item input:enabled + label {\n",
       "  cursor: pointer;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-section-item input:enabled + label:hover {\n",
       "  color: var(--xr-font-color0);\n",
       "}\n",
       "\n",
       ".xr-section-summary {\n",
       "  grid-column: 1;\n",
       "  color: var(--xr-font-color2);\n",
       "  font-weight: 500;\n",
       "}\n",
       "\n",
       ".xr-section-summary > span {\n",
       "  display: inline-block;\n",
       "  padding-left: 0.5em;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label {\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in + label:before {\n",
       "  display: inline-block;\n",
       "  content: '►';\n",
       "  font-size: 11px;\n",
       "  width: 15px;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label:before {\n",
       "  color: var(--xr-disabled-color);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label:before {\n",
       "  content: '▼';\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label > span {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-section-summary,\n",
       ".xr-section-inline-details {\n",
       "  padding-top: 4px;\n",
       "  padding-bottom: 4px;\n",
       "}\n",
       "\n",
       ".xr-section-inline-details {\n",
       "  grid-column: 2 / -1;\n",
       "}\n",
       "\n",
       ".xr-section-details {\n",
       "  display: none;\n",
       "  grid-column: 1 / -1;\n",
       "  margin-bottom: 5px;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-array-wrap {\n",
       "  grid-column: 1 / -1;\n",
       "  display: grid;\n",
       "  grid-template-columns: 20px auto;\n",
       "}\n",
       "\n",
       ".xr-array-wrap > label {\n",
       "  grid-column: 1;\n",
       "  vertical-align: top;\n",
       "}\n",
       "\n",
       ".xr-preview {\n",
       "  color: var(--xr-font-color3);\n",
       "}\n",
       "\n",
       ".xr-array-preview,\n",
       ".xr-array-data {\n",
       "  padding: 0 5px !important;\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-array-data,\n",
       ".xr-array-in:checked ~ .xr-array-preview {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-array-in:checked ~ .xr-array-data,\n",
       ".xr-array-preview {\n",
       "  display: inline-block;\n",
       "}\n",
       "\n",
       ".xr-dim-list {\n",
       "  display: inline-block !important;\n",
       "  list-style: none;\n",
       "  padding: 0 !important;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list li {\n",
       "  display: inline-block;\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list:before {\n",
       "  content: '(';\n",
       "}\n",
       "\n",
       ".xr-dim-list:after {\n",
       "  content: ')';\n",
       "}\n",
       "\n",
       ".xr-dim-list li:not(:last-child):after {\n",
       "  content: ',';\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-has-index {\n",
       "  font-weight: bold;\n",
       "}\n",
       "\n",
       ".xr-var-list,\n",
       ".xr-var-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-var-item > div,\n",
       ".xr-var-item label,\n",
       ".xr-var-item > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-even);\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-var-item > .xr-var-name:hover span {\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-var-list > li:nth-child(odd) > div,\n",
       ".xr-var-list > li:nth-child(odd) > label,\n",
       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-odd);\n",
       "}\n",
       "\n",
       ".xr-var-name {\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-var-dims {\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-var-dtype {\n",
       "  grid-column: 3;\n",
       "  text-align: right;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-preview {\n",
       "  grid-column: 4;\n",
       "}\n",
       "\n",
       ".xr-var-name,\n",
       ".xr-var-dims,\n",
       ".xr-var-dtype,\n",
       ".xr-preview,\n",
       ".xr-attrs dt {\n",
       "  white-space: nowrap;\n",
       "  overflow: hidden;\n",
       "  text-overflow: ellipsis;\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-var-name:hover,\n",
       ".xr-var-dims:hover,\n",
       ".xr-var-dtype:hover,\n",
       ".xr-attrs dt:hover {\n",
       "  overflow: visible;\n",
       "  width: auto;\n",
       "  z-index: 1;\n",
       "}\n",
       "\n",
       ".xr-var-attrs,\n",
       ".xr-var-data {\n",
       "  display: none;\n",
       "  background-color: var(--xr-background-color) !important;\n",
       "  padding-bottom: 5px !important;\n",
       "}\n",
       "\n",
       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
       ".xr-var-data-in:checked ~ .xr-var-data {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       ".xr-var-data > table {\n",
       "  float: right;\n",
       "}\n",
       "\n",
       ".xr-var-name span,\n",
       ".xr-var-data,\n",
       ".xr-attrs {\n",
       "  padding-left: 25px !important;\n",
       "}\n",
       "\n",
       ".xr-attrs,\n",
       ".xr-var-attrs,\n",
       ".xr-var-data {\n",
       "  grid-column: 1 / -1;\n",
       "}\n",
       "\n",
       "dl.xr-attrs {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  display: grid;\n",
       "  grid-template-columns: 125px auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt,\n",
       ".xr-attrs dd {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  float: left;\n",
       "  padding-right: 10px;\n",
       "  width: auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt {\n",
       "  font-weight: normal;\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-attrs dt:hover span {\n",
       "  display: inline-block;\n",
       "  background: var(--xr-background-color);\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-attrs dd {\n",
       "  grid-column: 2;\n",
       "  white-space: pre-wrap;\n",
       "  word-break: break-all;\n",
       "}\n",
       "\n",
       ".xr-icon-database,\n",
       ".xr-icon-file-text2 {\n",
       "  display: inline-block;\n",
       "  vertical-align: middle;\n",
       "  width: 1em;\n",
       "  height: 1.5em !important;\n",
       "  stroke-width: 0;\n",
       "  stroke: currentColor;\n",
       "  fill: currentColor;\n",
       "}\n",
       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;sst&#x27; (lat: 89, lon: 180)&gt;\n",
       "array([[-1.7999998, -1.7999998, -1.7999998, ..., -1.7999998, -1.7999998,\n",
       "        -1.7999998],\n",
       "       [-1.7999998, -1.7999998, -1.7999998, ..., -1.7999998, -1.7999998,\n",
       "        -1.7999998],\n",
       "       [-1.7999998, -1.7999998, -1.7999998, ..., -1.7999998, -1.7999998,\n",
       "        -1.7999998],\n",
       "       ...,\n",
       "       [       nan,        nan,        nan, ...,        nan,        nan,\n",
       "               nan],\n",
       "       [       nan,        nan,        nan, ...,        nan,        nan,\n",
       "               nan],\n",
       "       [       nan,        nan,        nan, ...,        nan,        nan,\n",
       "               nan]], dtype=float32)\n",
       "Coordinates:\n",
       "  * lat      (lat) float32 88.0 86.0 84.0 82.0 80.0 ... -82.0 -84.0 -86.0 -88.0\n",
       "  * lon      (lon) float32 0.0 2.0 4.0 6.0 8.0 ... 350.0 352.0 354.0 356.0 358.0</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'sst'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>lat</span>: 89</li><li><span class='xr-has-index'>lon</span>: 180</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-3780bfdf-c185-43e4-b41d-f0c5ba420710' class='xr-array-in' type='checkbox' checked><label for='section-3780bfdf-c185-43e4-b41d-f0c5ba420710' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>-1.8 -1.8 -1.8 -1.8 -1.8 -1.8 -1.8 ... nan nan nan nan nan nan nan</span></div><div class='xr-array-data'><pre>array([[-1.7999998, -1.7999998, -1.7999998, ..., -1.7999998, -1.7999998,\n",
       "        -1.7999998],\n",
       "       [-1.7999998, -1.7999998, -1.7999998, ..., -1.7999998, -1.7999998,\n",
       "        -1.7999998],\n",
       "       [-1.7999998, -1.7999998, -1.7999998, ..., -1.7999998, -1.7999998,\n",
       "        -1.7999998],\n",
       "       ...,\n",
       "       [       nan,        nan,        nan, ...,        nan,        nan,\n",
       "               nan],\n",
       "       [       nan,        nan,        nan, ...,        nan,        nan,\n",
       "               nan],\n",
       "       [       nan,        nan,        nan, ...,        nan,        nan,\n",
       "               nan]], dtype=float32)</pre></div></div></li><li class='xr-section-item'><input id='section-2ef4f286-b5b2-4a37-82ed-fefc003e128b' class='xr-section-summary-in' type='checkbox'  checked><label for='section-2ef4f286-b5b2-4a37-82ed-fefc003e128b' 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'>lat</span></div><div class='xr-var-dims'>(lat)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>88.0 86.0 84.0 ... -86.0 -88.0</div><input id='attrs-10f43af2-64f1-4e81-95b1-2a389c1637c5' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-10f43af2-64f1-4e81-95b1-2a389c1637c5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-61e572b7-a78d-4e24-8c3c-f94da8dcf8bf' class='xr-var-data-in' type='checkbox'><label for='data-61e572b7-a78d-4e24-8c3c-f94da8dcf8bf' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degrees_north</dd><dt><span>long_name :</span></dt><dd>Latitude</dd><dt><span>actual_range :</span></dt><dd>[ 88. -88.]</dd><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>axis :</span></dt><dd>Y</dd><dt><span>coordinate_defines :</span></dt><dd>center</dd></dl></div><div class='xr-var-data'><pre>array([ 88.,  86.,  84.,  82.,  80.,  78.,  76.,  74.,  72.,  70.,  68.,  66.,\n",
       "        64.,  62.,  60.,  58.,  56.,  54.,  52.,  50.,  48.,  46.,  44.,  42.,\n",
       "        40.,  38.,  36.,  34.,  32.,  30.,  28.,  26.,  24.,  22.,  20.,  18.,\n",
       "        16.,  14.,  12.,  10.,   8.,   6.,   4.,   2.,   0.,  -2.,  -4.,  -6.,\n",
       "        -8., -10., -12., -14., -16., -18., -20., -22., -24., -26., -28., -30.,\n",
       "       -32., -34., -36., -38., -40., -42., -44., -46., -48., -50., -52., -54.,\n",
       "       -56., -58., -60., -62., -64., -66., -68., -70., -72., -74., -76., -78.,\n",
       "       -80., -82., -84., -86., -88.], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lon</span></div><div class='xr-var-dims'>(lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.0 2.0 4.0 ... 354.0 356.0 358.0</div><input id='attrs-ef88393c-bd04-4e23-9252-264f67f58117' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ef88393c-bd04-4e23-9252-264f67f58117' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-431d8317-4032-43fd-84b6-ec9cb93659b4' class='xr-var-data-in' type='checkbox'><label for='data-431d8317-4032-43fd-84b6-ec9cb93659b4' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degrees_east</dd><dt><span>long_name :</span></dt><dd>Longitude</dd><dt><span>actual_range :</span></dt><dd>[  0. 358.]</dd><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>axis :</span></dt><dd>X</dd><dt><span>coordinate_defines :</span></dt><dd>center</dd></dl></div><div class='xr-var-data'><pre>array([  0.,   2.,   4.,   6.,   8.,  10.,  12.,  14.,  16.,  18.,  20.,  22.,\n",
       "        24.,  26.,  28.,  30.,  32.,  34.,  36.,  38.,  40.,  42.,  44.,  46.,\n",
       "        48.,  50.,  52.,  54.,  56.,  58.,  60.,  62.,  64.,  66.,  68.,  70.,\n",
       "        72.,  74.,  76.,  78.,  80.,  82.,  84.,  86.,  88.,  90.,  92.,  94.,\n",
       "        96.,  98., 100., 102., 104., 106., 108., 110., 112., 114., 116., 118.,\n",
       "       120., 122., 124., 126., 128., 130., 132., 134., 136., 138., 140., 142.,\n",
       "       144., 146., 148., 150., 152., 154., 156., 158., 160., 162., 164., 166.,\n",
       "       168., 170., 172., 174., 176., 178., 180., 182., 184., 186., 188., 190.,\n",
       "       192., 194., 196., 198., 200., 202., 204., 206., 208., 210., 212., 214.,\n",
       "       216., 218., 220., 222., 224., 226., 228., 230., 232., 234., 236., 238.,\n",
       "       240., 242., 244., 246., 248., 250., 252., 254., 256., 258., 260., 262.,\n",
       "       264., 266., 268., 270., 272., 274., 276., 278., 280., 282., 284., 286.,\n",
       "       288., 290., 292., 294., 296., 298., 300., 302., 304., 306., 308., 310.,\n",
       "       312., 314., 316., 318., 320., 322., 324., 326., 328., 330., 332., 334.,\n",
       "       336., 338., 340., 342., 344., 346., 348., 350., 352., 354., 356., 358.],\n",
       "      dtype=float32)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-f0befd64-4592-4b96-a73d-75afec73f6e6' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-f0befd64-4592-4b96-a73d-75afec73f6e6' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.DataArray 'sst' (lat: 89, lon: 180)>\n",
       "array([[-1.7999998, -1.7999998, -1.7999998, ..., -1.7999998, -1.7999998,\n",
       "        -1.7999998],\n",
       "       [-1.7999998, -1.7999998, -1.7999998, ..., -1.7999998, -1.7999998,\n",
       "        -1.7999998],\n",
       "       [-1.7999998, -1.7999998, -1.7999998, ..., -1.7999998, -1.7999998,\n",
       "        -1.7999998],\n",
       "       ...,\n",
       "       [       nan,        nan,        nan, ...,        nan,        nan,\n",
       "               nan],\n",
       "       [       nan,        nan,        nan, ...,        nan,        nan,\n",
       "               nan],\n",
       "       [       nan,        nan,        nan, ...,        nan,        nan,\n",
       "               nan]], dtype=float32)\n",
       "Coordinates:\n",
       "  * lat      (lat) float32 88.0 86.0 84.0 82.0 80.0 ... -82.0 -84.0 -86.0 -88.0\n",
       "  * lon      (lon) float32 0.0 2.0 4.0 6.0 8.0 ... 350.0 352.0 354.0 356.0 358.0"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Twinterave=Twinter.mean(dim='time')\n",
    "Twinterave"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "lat=Twinterave['lat']\n",
    "lon=Twinterave['lon']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 648x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig=plt.figure(figsize=(9,6))\n",
    "ax=fig.subplots(1,1,subplot_kw={'projection':ccrs.PlateCarree(central_longitude=180)})\n",
    "# 添加海岸线和陆地\n",
    "ax.add_feature(cfeature.COASTLINE.with_scale('50m'))\n",
    "ax.add_feature(cfeature.LAND,facecolor='grey')\n",
    "# 添加坐标轴\n",
    "ax.set_xticks(np.arange(-180, 180 + 30, 30), crs=ccrs.PlateCarree())\n",
    "ax.set_yticks(np.arange(-90, 90 + 30, 30), crs=ccrs.PlateCarree())\n",
    "## 经纬度格式，把0经度设置不加E和W\n",
    "lon_formatter = LongitudeFormatter(zero_direction_label=False)\n",
    "lat_formatter = LatitudeFormatter(auto_hide=False)\n",
    "ax.xaxis.set_major_formatter(lon_formatter)\n",
    "ax.yaxis.set_major_formatter(lat_formatter)\n",
    "# 设置刻度大小\n",
    "ax.tick_params(axis='y',labelsize=10)\n",
    "ax.set_xlabel('经度')\n",
    "ax.set_ylabel('纬度')\n",
    "ax.set_title('2020冬季平均')\n",
    "\n",
    "# 绘图\n",
    "colorbar = ax.contourf(lon,lat,Twinterave,cmap='bwr',transform=ccrs.PlateCarree())\n",
    "plt.colorbar(colorbar, extendrect='True', pad=0.03, fraction=0.04, shrink=0.7)\n",
    "\n",
    "# 保存图片\n",
    "plt.savefig('../../picture/pythonhome/11/qu1.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
       "Dimensions:    (lat: 89, lon: 180, time: 2016, nbnds: 2)\n",
       "Coordinates:\n",
       "  * lat        (lat) float32 88.0 86.0 84.0 82.0 ... -82.0 -84.0 -86.0 -88.0\n",
       "  * lon        (lon) float32 0.0 2.0 4.0 6.0 8.0 ... 352.0 354.0 356.0 358.0\n",
       "  * time       (time) datetime64[ns] 1854-01-01 1854-02-01 ... 2021-12-01\n",
       "Dimensions without coordinates: nbnds\n",
       "Data variables:\n",
       "    time_bnds  (time, nbnds) float64 9.969e+36 9.969e+36 ... 9.969e+36 9.969e+36\n",
       "    sst        (time, lat, lon) float32 ...\n",
       "Attributes: (12/37)\n",
       "    climatology:               Climatology is based on 1971-2000 SST, Xue, Y....\n",
       "    description:               In situ data: ICOADS2.5 before 2007 and NCEP i...\n",
       "    keywords_vocabulary:       NASA Global Change Master Directory (GCMD) Sci...\n",
       "    keywords:                  Earth Science &gt; Oceans &gt; Ocean Temperature &gt; S...\n",
       "    instrument:                Conventional thermometers\n",
       "    source_comment:            SSTs were observed by conventional thermometer...\n",
       "    ...                        ...\n",
       "    creator_url_original:      https://www.ncei.noaa.gov\n",
       "    license:                   No constraints on data access or use\n",
       "    comment:                   SSTs were observed by conventional thermometer...\n",
       "    summary:                   ERSST.v5 is developed based on v4 after revisi...\n",
       "    dataset_title:             NOAA Extended Reconstructed SST V5\n",
       "    data_modified:             2022-01-07</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-940ed251-63b9-49c4-a35d-2e3a32116a9d' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-940ed251-63b9-49c4-a35d-2e3a32116a9d' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>lat</span>: 89</li><li><span class='xr-has-index'>lon</span>: 180</li><li><span class='xr-has-index'>time</span>: 2016</li><li><span>nbnds</span>: 2</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-26998510-0f5c-4ffd-8ca1-af50866a04c1' class='xr-section-summary-in' type='checkbox'  checked><label for='section-26998510-0f5c-4ffd-8ca1-af50866a04c1' class='xr-section-summary' >Coordinates: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lat</span></div><div class='xr-var-dims'>(lat)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>88.0 86.0 84.0 ... -86.0 -88.0</div><input id='attrs-f2211d90-8213-439a-a5a2-d04b4ddb1956' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f2211d90-8213-439a-a5a2-d04b4ddb1956' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0d9ee8b8-fb26-4bc7-b780-c8c5a249ff15' class='xr-var-data-in' type='checkbox'><label for='data-0d9ee8b8-fb26-4bc7-b780-c8c5a249ff15' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degrees_north</dd><dt><span>long_name :</span></dt><dd>Latitude</dd><dt><span>actual_range :</span></dt><dd>[ 88. -88.]</dd><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>axis :</span></dt><dd>Y</dd><dt><span>coordinate_defines :</span></dt><dd>center</dd></dl></div><div class='xr-var-data'><pre>array([ 88.,  86.,  84.,  82.,  80.,  78.,  76.,  74.,  72.,  70.,  68.,  66.,\n",
       "        64.,  62.,  60.,  58.,  56.,  54.,  52.,  50.,  48.,  46.,  44.,  42.,\n",
       "        40.,  38.,  36.,  34.,  32.,  30.,  28.,  26.,  24.,  22.,  20.,  18.,\n",
       "        16.,  14.,  12.,  10.,   8.,   6.,   4.,   2.,   0.,  -2.,  -4.,  -6.,\n",
       "        -8., -10., -12., -14., -16., -18., -20., -22., -24., -26., -28., -30.,\n",
       "       -32., -34., -36., -38., -40., -42., -44., -46., -48., -50., -52., -54.,\n",
       "       -56., -58., -60., -62., -64., -66., -68., -70., -72., -74., -76., -78.,\n",
       "       -80., -82., -84., -86., -88.], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lon</span></div><div class='xr-var-dims'>(lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.0 2.0 4.0 ... 354.0 356.0 358.0</div><input id='attrs-53ba2c3c-09f1-412b-bd1a-2802dc12810e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-53ba2c3c-09f1-412b-bd1a-2802dc12810e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f3cdb25f-e7b7-4150-ac8b-d618dd7e6b6e' class='xr-var-data-in' type='checkbox'><label for='data-f3cdb25f-e7b7-4150-ac8b-d618dd7e6b6e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degrees_east</dd><dt><span>long_name :</span></dt><dd>Longitude</dd><dt><span>actual_range :</span></dt><dd>[  0. 358.]</dd><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>axis :</span></dt><dd>X</dd><dt><span>coordinate_defines :</span></dt><dd>center</dd></dl></div><div class='xr-var-data'><pre>array([  0.,   2.,   4.,   6.,   8.,  10.,  12.,  14.,  16.,  18.,  20.,  22.,\n",
       "        24.,  26.,  28.,  30.,  32.,  34.,  36.,  38.,  40.,  42.,  44.,  46.,\n",
       "        48.,  50.,  52.,  54.,  56.,  58.,  60.,  62.,  64.,  66.,  68.,  70.,\n",
       "        72.,  74.,  76.,  78.,  80.,  82.,  84.,  86.,  88.,  90.,  92.,  94.,\n",
       "        96.,  98., 100., 102., 104., 106., 108., 110., 112., 114., 116., 118.,\n",
       "       120., 122., 124., 126., 128., 130., 132., 134., 136., 138., 140., 142.,\n",
       "       144., 146., 148., 150., 152., 154., 156., 158., 160., 162., 164., 166.,\n",
       "       168., 170., 172., 174., 176., 178., 180., 182., 184., 186., 188., 190.,\n",
       "       192., 194., 196., 198., 200., 202., 204., 206., 208., 210., 212., 214.,\n",
       "       216., 218., 220., 222., 224., 226., 228., 230., 232., 234., 236., 238.,\n",
       "       240., 242., 244., 246., 248., 250., 252., 254., 256., 258., 260., 262.,\n",
       "       264., 266., 268., 270., 272., 274., 276., 278., 280., 282., 284., 286.,\n",
       "       288., 290., 292., 294., 296., 298., 300., 302., 304., 306., 308., 310.,\n",
       "       312., 314., 316., 318., 320., 322., 324., 326., 328., 330., 332., 334.,\n",
       "       336., 338., 340., 342., 344., 346., 348., 350., 352., 354., 356., 358.],\n",
       "      dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>1854-01-01 ... 2021-12-01</div><input id='attrs-78426929-e40f-4202-9ff0-96ac81c3baf5' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-78426929-e40f-4202-9ff0-96ac81c3baf5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3f96509c-a464-4af0-807b-21b97f37f414' class='xr-var-data-in' type='checkbox'><label for='data-3f96509c-a464-4af0-807b-21b97f37f414' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Time</dd><dt><span>delta_t :</span></dt><dd>0000-01-00 00:00:00</dd><dt><span>avg_period :</span></dt><dd>0000-01-00 00:00:00</dd><dt><span>prev_avg_period :</span></dt><dd>0000-00-07 00:00:00</dd><dt><span>standard_name :</span></dt><dd>time</dd><dt><span>axis :</span></dt><dd>T</dd><dt><span>actual_range :</span></dt><dd>[19723. 81053.]</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;1854-01-01T00:00:00.000000000&#x27;, &#x27;1854-02-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;1854-03-01T00:00:00.000000000&#x27;, ..., &#x27;2021-10-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2021-11-01T00:00:00.000000000&#x27;, &#x27;2021-12-01T00:00:00.000000000&#x27;],\n",
       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-d6e1e26f-6259-4e54-b32c-4fbc47bc8ce9' class='xr-section-summary-in' type='checkbox'  checked><label for='section-d6e1e26f-6259-4e54-b32c-4fbc47bc8ce9' 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_bnds</span></div><div class='xr-var-dims'>(time, nbnds)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>9.969e+36 9.969e+36 ... 9.969e+36</div><input id='attrs-73ad854e-b772-4982-865c-c68cb815b078' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-73ad854e-b772-4982-865c-c68cb815b078' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f0d781a0-8062-45b9-9e47-e433a2c80d31' class='xr-var-data-in' type='checkbox'><label for='data-f0d781a0-8062-45b9-9e47-e433a2c80d31' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Time Boundaries</dd></dl></div><div class='xr-var-data'><pre>array([[9.96921e+36, 9.96921e+36],\n",
       "       [9.96921e+36, 9.96921e+36],\n",
       "       [9.96921e+36, 9.96921e+36],\n",
       "       ...,\n",
       "       [9.96921e+36, 9.96921e+36],\n",
       "       [9.96921e+36, 9.96921e+36],\n",
       "       [9.96921e+36, 9.96921e+36]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>sst</span></div><div class='xr-var-dims'>(time, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-3dee199a-6a46-4a42-9f5e-97e2f190ecde' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3dee199a-6a46-4a42-9f5e-97e2f190ecde' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8d068f12-3c67-4f64-a77a-bc0407fe6b9b' class='xr-var-data-in' type='checkbox'><label for='data-8d068f12-3c67-4f64-a77a-bc0407fe6b9b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Monthly Means of Sea Surface Temperature</dd><dt><span>units :</span></dt><dd>degC</dd><dt><span>var_desc :</span></dt><dd>Sea Surface Temperature</dd><dt><span>level_desc :</span></dt><dd>Surface</dd><dt><span>statistic :</span></dt><dd>Mean</dd><dt><span>dataset :</span></dt><dd>NOAA Extended Reconstructed SST V5</dd><dt><span>parent_stat :</span></dt><dd>Individual Values</dd><dt><span>actual_range :</span></dt><dd>[-1.8     42.32636]</dd><dt><span>valid_range :</span></dt><dd>[-1.8 45. ]</dd></dl></div><div class='xr-var-data'><pre>[32296320 values with dtype=float32]</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-9fcf1911-ae66-400c-b5c0-248c1d77c71c' class='xr-section-summary-in' type='checkbox'  ><label for='section-9fcf1911-ae66-400c-b5c0-248c1d77c71c' class='xr-section-summary' >Attributes: <span>(37)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>climatology :</span></dt><dd>Climatology is based on 1971-2000 SST, Xue, Y., T. M. Smith, and R. W. Reynolds, 2003: Interdecadal changes of 30-yr SST normals during 1871.2000. Journal of Climate, 16, 1601-1612.</dd><dt><span>description :</span></dt><dd>In situ data: ICOADS2.5 before 2007 and NCEP in situ data from 2008 to present. Ice data: HadISST ice before 2010 and NCEP ice after 2010.</dd><dt><span>keywords_vocabulary :</span></dt><dd>NASA Global Change Master Directory (GCMD) Science Keywords</dd><dt><span>keywords :</span></dt><dd>Earth Science &gt; Oceans &gt; Ocean Temperature &gt; Sea Surface Temperature &gt;</dd><dt><span>instrument :</span></dt><dd>Conventional thermometers</dd><dt><span>source_comment :</span></dt><dd>SSTs were observed by conventional thermometers in Buckets (insulated or un-insulated canvas and wooded buckets) or Engine Room Intaker</dd><dt><span>geospatial_lon_min :</span></dt><dd>-1.0</dd><dt><span>geospatial_lon_max :</span></dt><dd>359.0</dd><dt><span>geospatial_laty_max :</span></dt><dd>89.0</dd><dt><span>geospatial_laty_min :</span></dt><dd>-89.0</dd><dt><span>geospatial_lat_max :</span></dt><dd>89.0</dd><dt><span>geospatial_lat_min :</span></dt><dd>-89.0</dd><dt><span>geospatial_lat_units :</span></dt><dd>degrees_north</dd><dt><span>geospatial_lon_units :</span></dt><dd>degrees_east</dd><dt><span>cdm_data_type :</span></dt><dd>Grid</dd><dt><span>project :</span></dt><dd>NOAA Extended Reconstructed Sea Surface Temperature (ERSST)</dd><dt><span>original_publisher_url :</span></dt><dd>http://www.ncdc.noaa.gov</dd><dt><span>References :</span></dt><dd>https://www.ncdc.noaa.gov/data-access/marineocean-data/extended-reconstructed-sea-surface-temperature-ersst-v5 at NCEI and http://www.esrl.noaa.gov/psd/data/gridded/data.noaa.ersst.v5.html</dd><dt><span>source :</span></dt><dd>In situ data: ICOADS R3.0 before 2015, NCEP in situ GTS from 2016 to present, and Argo SST from 1999 to present. Ice data: HadISST2 ice before 2015, and NCEP ice after 2015</dd><dt><span>title :</span></dt><dd>NOAA ERSSTv5 (in situ only)</dd><dt><span>history :</span></dt><dd>created 07/2017 by PSD data using NCEI&#x27;s ERSST V5 NetCDF values</dd><dt><span>institution :</span></dt><dd>This version written at NOAA/ESRL PSD: obtained from NOAA/NESDIS/National Centers for Environmental Information and time aggregated. Original Full Source: NOAA/NESDIS/NCEI/CCOG</dd><dt><span>citation :</span></dt><dd>Huang et al, 2017: Extended Reconstructed Sea Surface Temperatures Version 5 (ERSSTv5): Upgrades, Validations, and Intercomparisons. Journal of Climate, https://doi.org/10.1175/JCLI-D-16-0836.1</dd><dt><span>platform :</span></dt><dd>Ship and Buoy SSTs from ICOADS R3.0 and NCEP GTS</dd><dt><span>standard_name_vocabulary :</span></dt><dd>CF Standard Name Table (v40, 25 January 2017)</dd><dt><span>processing_level :</span></dt><dd>NOAA Level 4</dd><dt><span>Conventions :</span></dt><dd>CF-1.6, ACDD-1.3</dd><dt><span>metadata_link :</span></dt><dd>:metadata_link = https://doi.org/10.7289/V5T72FNM (original format)</dd><dt><span>creator_name :</span></dt><dd>Boyin Huang (original)</dd><dt><span>date_created :</span></dt><dd>2017-06-30T12:18:00Z (original)</dd><dt><span>product_version :</span></dt><dd>Version 5</dd><dt><span>creator_url_original :</span></dt><dd>https://www.ncei.noaa.gov</dd><dt><span>license :</span></dt><dd>No constraints on data access or use</dd><dt><span>comment :</span></dt><dd>SSTs were observed by conventional thermometers in Buckets (insulated or un-insulated canvas and wooded buckets), Engine Room Intakers, or floats and drifters</dd><dt><span>summary :</span></dt><dd>ERSST.v5 is developed based on v4 after revisions of 8 parameters using updated data sets and advanced knowledge of ERSST analysis</dd><dt><span>dataset_title :</span></dt><dd>NOAA Extended Reconstructed SST V5</dd><dt><span>data_modified :</span></dt><dd>2022-01-07</dd></dl></div></li></ul></div></div>"
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       "Coordinates:\n",
       "  * lat        (lat) float32 88.0 86.0 84.0 82.0 ... -82.0 -84.0 -86.0 -88.0\n",
       "  * lon        (lon) float32 0.0 2.0 4.0 6.0 8.0 ... 352.0 354.0 356.0 358.0\n",
       "  * time       (time) datetime64[ns] 1854-01-01 1854-02-01 ... 2021-12-01\n",
       "Dimensions without coordinates: nbnds\n",
       "Data variables:\n",
       "    time_bnds  (time, nbnds) float64 9.969e+36 9.969e+36 ... 9.969e+36 9.969e+36\n",
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       "Attributes: (12/37)\n",
       "    climatology:               Climatology is based on 1971-2000 SST, Xue, Y....\n",
       "    description:               In situ data: ICOADS2.5 before 2007 and NCEP i...\n",
       "    keywords_vocabulary:       NASA Global Change Master Directory (GCMD) Sci...\n",
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       "    instrument:                Conventional thermometers\n",
       "    source_comment:            SSTs were observed by conventional thermometer...\n",
       "    ...                        ...\n",
       "    creator_url_original:      https://www.ncei.noaa.gov\n",
       "    license:                   No constraints on data access or use\n",
       "    comment:                   SSTs were observed by conventional thermometer...\n",
       "    summary:                   ERSST.v5 is developed based on v4 after revisi...\n",
       "    dataset_title:             NOAA Extended Reconstructed SST V5\n",
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;sst&#x27; (time: 2016)&gt;\n",
       "array([25.897709, 26.05581 , 26.908752, ..., 25.77267 , 25.762094,\n",
       "       25.544031], dtype=float32)\n",
       "Coordinates:\n",
       "  * time     (time) datetime64[ns] 1854-01-01 1854-02-01 ... 2021-12-01</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'sst'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 2016</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-41b3e2ed-de71-42c3-8c84-bc6e583707e4' class='xr-array-in' type='checkbox' checked><label for='section-41b3e2ed-de71-42c3-8c84-bc6e583707e4' 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>25.9 26.06 26.91 27.63 27.9 27.23 ... 26.35 26.15 25.77 25.76 25.54</span></div><div class='xr-array-data'><pre>array([25.897709, 26.05581 , 26.908752, ..., 25.77267 , 25.762094,\n",
       "       25.544031], dtype=float32)</pre></div></div></li><li class='xr-section-item'><input id='section-39b31e4d-dca2-455b-bc70-e5cfb3bdf9ed' class='xr-section-summary-in' type='checkbox'  checked><label for='section-39b31e4d-dca2-455b-bc70-e5cfb3bdf9ed' class='xr-section-summary' >Coordinates: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>1854-01-01 ... 2021-12-01</div><input id='attrs-5da0a806-858d-4b05-a33d-26ae331248ff' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5da0a806-858d-4b05-a33d-26ae331248ff' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6dfe8769-536d-42f3-b308-85e80b52fd0a' class='xr-var-data-in' type='checkbox'><label for='data-6dfe8769-536d-42f3-b308-85e80b52fd0a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Time</dd><dt><span>delta_t :</span></dt><dd>0000-01-00 00:00:00</dd><dt><span>avg_period :</span></dt><dd>0000-01-00 00:00:00</dd><dt><span>prev_avg_period :</span></dt><dd>0000-00-07 00:00:00</dd><dt><span>standard_name :</span></dt><dd>time</dd><dt><span>axis :</span></dt><dd>T</dd><dt><span>actual_range :</span></dt><dd>[19723. 81053.]</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;1854-01-01T00:00:00.000000000&#x27;, &#x27;1854-02-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;1854-03-01T00:00:00.000000000&#x27;, ..., &#x27;2021-10-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2021-11-01T00:00:00.000000000&#x27;, &#x27;2021-12-01T00:00:00.000000000&#x27;],\n",
       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-cb801537-da0f-4f98-9523-f8eaf45f868d' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-cb801537-da0f-4f98-9523-f8eaf45f868d' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.DataArray 'sst' (time: 2016)>\n",
       "array([25.897709, 26.05581 , 26.908752, ..., 25.77267 , 25.762094,\n",
       "       25.544031], dtype=float32)\n",
       "Coordinates:\n",
       "  * time     (time) datetime64[ns] 1854-01-01 1854-02-01 ... 2021-12-01"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 计算ENSO指数：计算5°s-5°N，170°W-120°W内的区域平均\n",
    "ENSOall=ds['sst'].loc[:,5:-5,190:240].mean(dim=['lat','lon'],skipna=True)\n",
    "ENSOall[:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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1IiJi2KA79HmREek5qCpq/xUL0epozzFhpiMKInMIr/ri5c7fg1STmPTaiYqJ9iKq4+71W7HqrJ/iklsfGnZTIrrEZMvOM4PI9l13Xtt/xQIAwPKFY7NaENl5wSgWzxsBMPty5kRBZA7hiD2XOH8PUmY2dXmeOzMY516/Bg9vnRx2M+Y0LrjxAQDAt6+8a8gtiegWNOT4IBgRrYFTPnohXvjZSypdv3B8BI87cAX2XDbPydkyW2Cm1Nc8bj+Yfd4smmYBREFkTuHE/Xd2/h5kZzV1zRZj1bWbJvCaL1+BN3z1D8NuypzGO3/4JwDASBKnql5istXBd668eyDj8WX/e1n+ezDGqhqrH9qKS29bV+n6VruDkURhtJlgYmr2xRExjEiiLOM8Hc+hYSCO7jkErjYchvvuMDYcz//MJVh11k9r3WP02rc/uKXkyohBoNlt8pAIEZ+64Gb847evwc+vu7/vdW0ji/t4c+bZiLTaGs2GwlgzmZWMiHneRCk0ckFkiA3qAlEQmUPoaI1GMqwJPR0Zw1DNXLa62s6IIqc4Y6yVGYGRRpyqeok716VJ0Le3BssA7LRwtNJ1P/vjfV1HYa07ZludDpqNBGPNZFYmjDPsh1Iqn7dmGyPSf8uhiBkDrYGGUmibgTqEvjpbrLmTWbqz2FExEhmRnsLE9hiEOy1F1fXxDV9LVaKrP3RGpevpvFJ3DW51NJqJApoNbJ+FqhmdyU6JAnQWMk7PMnkqbjPmENodlxEZqLFqVtlskdRnm03Ljo5mZER6CqMumTcALxaKfo0nqlKpO8e02hrNJMH+Kxfi9oe2YPNEq/ymGQRrI6KQzFJGJI7uOQSumhmosWr2Lw8zP1NhVEizbDzvsBgZmkpxx4TZ+TcH8F6fd8yeAIDdloz3bYGkbrd1q5hqdzDSUNhj6Ti0BjZsm+px6/oLx1g1+56zyTsRiILInILWAJ13hmH/MFskdUP1Dqq97//J9fjBVfcMpK7ZCBMfIaI3MN16EBsDpVIhJFGqkqqzG/Utjd5ad8waptiwQ9smZxsjkv6rlJq1XjPRRmQOYaiMiDYL++DqnA46A27v//z2dgDAXz5qj8FUOMswiPgTvcCm7VOYamvstKCaUeawMYidc0enagOlqi2Q3QQVo0amdR/JzIsLsj42zFQY3UAT1Yw9NqzWdIfIiMwhdLQeWmTT2aaaMc2MNiIzA7Ok2+CxH7oAR7/v3GE3oxTmdQ7CeLyjNVQW46LKcOrGYHQ6qpmOTrMCG2F36+TsEkSs+y6ijUjEzEe7k9J3BsPoqrNlgBiBaZY0d4fHbPG22rh9dtH6g9gYaG0NKauM/21dCCI00VvdOUZrDaVUHn5+29Ts+oausWpmI1LwXS++9UEnI/JMQBRE5hC01qDOB4Pc7edeM7NkQbGqmdnR3h0d8Tv0B4N4rykTi8o2It0xIt3biGid2rE08kW8dvVDhY0jgtIQ7zfdvwkv/tzv8a/nXD+g1lVDtBGZQ+CqmWHYiLRnyXpiBvcsae4OCbogzRL5ddZhEItuXRuR7V0EFXNUMzXv1bDtA2afOtY0N6kQ0Gz1Q2mk6Ps3bB9E0yojMiJzCGZCGCauunP9UOuvivYAvWZm28Q3KPzXhbfkv2cLkzbb0BpAllbXRqSCIELYjaqqo0nHRqReX+loEwYsu7/ifdffu9Gpd1jIVTNJeSBGk8Rz2fyZZUwdBZE5BDMhGAwjjshPrr1vcJVWwC0PbBLjBpj5eRDr39RsoYkGDPpdomqmt9ADVD3qjIlNlEIVuWc7sV+outBPODYidduXxt8wi3iVV3L3+q142n/8Bu/7yfBVHJ0ajIhhwGZa7qYoiMwhdDrD85qZiTqOTduncPonfo1/+NbV3rlcNTOAiXpqtimlB4RxEn48EiL9wUBUM52aqplWN4KIvadVU7A3jEgd1cxDm1Nm4eq7Hq5VVz9Ac83kjEhgwAyZEA8iCiJzCB0NFuJ9cLP7TIz0Z0I5n3fDA965QUZWrTtxzhW4gkh8R73EoNx3/7xmE665++FcNVPXRmTj9mpRTmkckbrqJg03YVyVN0KjmQ4bmrTFzO+zTXCPgsgcwjBVMzNxISnKtDlI6rpq6vGJVhu//FPv0rZPtNp494/+hPVbJntWZi+x25Lx/PdssxGZLe3t9wbhSf/2a9y3YXuqmkmqLZDUSPmhin2TjqG6Lsk6mxcVqqtmaDTTYYOqZqrGEZlp03EUROYQjD9//vcA6zaTw37LFwyw1mJsKoj50B6gjUhVYecjP78Jr/3Klbj0tod6Uu9PrrkPX7x4NT7yixt7Ul6vsWjcOvXNknU9x9QAjEB7gUEFGEyS1D22bhyRqkIyjYvRjftuQl1fK8yMpo7GDKBEKDujcmPV2TVgoiAyh9DuaDfXzIA6q9Y6N8icCQMXSNv0jP/8LQBZbzrICLBVJ417H94GwOqnpwtDYc9U1RD9BDNRtVeE2WKAPDBBRCmoynFErBBXlS3cSvLD1LV7SW1EVO45U6WrmffW6CEj8o3L7sSnL7ql/EIGI/NSG5HQM8zUQI1REJlD8OKIDKjeFpl9WjNka0sXipHEHwbDCPZWhmYWja5XLpeU0p2JoN9gtrk4v+oLl+Os717bVXCufuP/fn8nrrrzYQCDE0RUpjao5L5L48dUbN+WyfouvwZpHBHLJlSyEelYl9le4azv/REf+flNte9zI6umx0LvYKYyJVEQmUMYVhwRuuMept89ndSoZb7kyjbIHXjVmkzK9l4xGOYRZ6gc4ghoM3UCDeGy1evwjcvvwpu/cdXA6/7WFXdhzcZwwKq3ff+P+e9BvdekhrHqBBFEqo7DLROWEelGNYOaAc3aZPEfNmxAs9QNGUjfwZV3rMcnz/uzc+1MzfUVBZE5givvWIfzbljjuIoOam6n+vJBBFAKgU5Q1FC1KaiLBqqaqViXUWv1qm1GFz4D5lIRVFc/TA/ni256AO/84XVd3fvbmx/scWuK8eDmCfzTd67Fq754eaXrB6maSZSqVB+1EanaPpoxtw7rSj1OulHNzARBRMo109HAc86+GJ8872bn2lw1M8PiKURBZI7gHT/4EwDgtge3kKOD6YxThAUZtD0C3d3Q+YnSv2Mjfor5mbgBNwJTrwwhLSMy/MlUAn3MYapmXvGFy/HlS+7o6t5H77tTj1tTDMM4VrUjGhTz19EaUNXGFVWbVrn+Nzevxf0bt9m6aggiufcLFFHNlN9v5rFkiDZvrXYHk62Ok2smEVgd+jsyIhFDRUP40oOa280OZayZVDY+6xXowHMYEaKaGR/xX84gBuy7fngdDn/XL2rYiPSYETETWE9K6z3oU84E1Uw3wtAey+b1oSVh5EaUFRfIQbkZd1I5pNLWh/bvsr6+faqNl/7vZfjdLdaTjN4z2erg+f99CS5fvU68v1tGxDDLoQClazdNYP+3nYMr71hfXliXeMInfoUD3/Ez5r7rzxHO7wHGR6qDKIjMEVhraiIpD6hus0sbH2kMPL4CpWnp4Lv27g357/Gmz4gMYuH70iV3YPNEq3JdzcwyrlceGaaUGUqIOO9lJgSf7cbQutftvvKO9fj5deFYMjTvSBUM0ng8UarSpON895KxsXbThHeM3nPnuq247PZ1+OfvXBuoK/23SuZaCvPeQmzixbc+iHZH4wu/u728MIZLbq3mnn/HQ1sBUGGKhnj32woA7RnqzRUFkTkCI4jQgTMoqdhI5M1E9Vz4KdsxtQKMyE33b8p/zxsdjiBiUNdYtd1j1cxM0HNzHPIvP8c/f9cuHjOBEZnowtC6V9/K4DlnX4zXf/XKgvoyRqTiNx3UxsCEUK/yHek1Ze17QBBE3Hty3UthXY7ra2kLy1NAGNXNiERFl+BFn7u01vWWEbHfnbaL2gXmjAiA829Yg0+c6xqzDgtREJkjMFTtMJYcGvynl+vJvQ9vw/5vOwffufLu4DV0B0B3SjsvtNknpYV4kDvwqpR/I+OBe8WI5JNwT0rrHTZsncK2qbbTV2aC+243Hl+DZnLqqmYG6R1WkRBx4h2VtY+qWA0kFrTsbdA5oI6wFLrSGOVLhvC9hiNMCSHeqV2eEdI6HY0zv3QF/uN815h1WIiCyByBGQ9OiPcBKWfMQEkZkd7V+eDmdDf0P7+5LXgN9dLRjvGj3z6KXu7A122ZxCu/cBke2uzv3oDqjIi029kR8Z4f/8k7NhMCmnUniPROErmC2DnQAF5OfTUjfg5SUEqUqtR3O9oyCWWEjSSUS6qdEOtHDT3zSyp0NfPeLrppLT7761u980YYanbBiNQFjawqhXh3vRa1d34mIAoicwRSxL1B9UUzmTQa1SIrVoVJiibpiQ1Cxqrm14n77yyqd3o5UL948WpceNNafOVS2fOiqmBBXfN6iZk1JQFbhEV2Jhj7dyWI9LDdz/3MJfnvUJ6k3JujqmpmwAtSle/Y6WgriJTcMCV8E26sCoTtoGgMjjpeM7RdHzjHT5FgvsMgGJH8GRLr+UMF95bACs+E8UQRBZE5ggYJdGMweBuRpKernnmWot3yVEgQMcJRIsc26KnXTK4CkSelqt+hakKrqjDFzDSXviaztFQVI3L2G5IaoAy9thExCPWBVl3VzAC/vVLVbMTaWlf2EJsSKJ2WJIhAodXuODlpAKqerBniveQi0y4pWGKvQRkRiTUVVTMzYDxRREFkjkDaTQ9aNZMouc6b7t+EVhccsZnji2KTUBsR+uyOuki4vZfzM7XMl1C1KtVjRqQzjd1RPxcwvog2A8LioNGNbU6/4uaEXocZRzNREEkUKq3ybcqIlFxPwwHkm60AI/LKL16OQ975c+d+c6XjNVPawupG8t0Yq9YFzQScz/NkOqWqGZvMc/jjiSIKInMENPDOR597JIDBMCJT7Q5+9sfU3bCZJF6df16zCU/+5K/xHxd0kewpK6woWqtjIyI8cCNJxN1NL70J8gimgfNVJwXJBXsY+Oqld2D/t50TtHmZLnxBJJkRVHI3gnuvJvwzWaTUUFumaqpmBsqIoJrQqzUwWlEQocJhHnmYxgsigspvhCi3xm6sSsI4t43FF9U1Gp4OaGRVJbCmDiNiNh8zwB2eIgoicwR0PCyZNzKwen93y4P4zwtTIaOR+Lkmbn1gMwDgxvs21i67impBCuaT3msZEUno6OWOwUYwLT5fBhs1cfptArp/xo/9Mk3MtXG7bDDZa0j9pltcdef6rtg3oLv33qs4Heff+EClthjBeyZ6zSSqmrF6u0NVM8XXUtXMiMCIlMXNMO2hAc2q9LUyAS5PijcAlzQalE1iviX11Uww/qboiyCilFqilPqZUuqXSqnvK6V2UUqdo5S6Qin13/2oM6IYvUxXXQfrtthQ0w0hjsiGbVMAgKXz6wtHlhEpsBEJqGZyG5GGEgelJLR0izIj08qCiGDnMx3Y9tQrb2umZ+9Xj+LvuxEQFuviuns24Fmfvrjr2AndvPd+MQ5BG5Gsv8+0OCJAFkekggzY1jo38ixnREjOqIxFkQzUw9los7bl/6uomqn42kJ2YRSTrQ6e8slfVytQgHmniVJ5ILuQLWBZ/JNhoV+MyEsAfEJr/SQA9wN4AYCvaa2PBbBIKXVsn+qNCICqZlQNCnK6oAmspDgiZlGbJ+R7KcP1GYtS9BzOpETjC2T/NgO7bXptr3a101XNSFETp4Ouv7827RjMZJZ+I/lcu6Px9cvurMRy3L8hzUhLg9nVwUyypQkVaxbmqpFVe9m+H1x1T9CtWGUrfZXatLY2IqH2ff2yO3HDfRsdT6Y84J+z8CI7JpeTswkJMVet4mJc8t6o7UkZbn9wC27ssk8CrgtyQ/kCnPR7Jqg6KfoiiGitP621Pjf7cwWARQAOV0otBbAXgLv6UW9EGJQitO7ycm/8w53rseqsn+K+DdvE83VArdTNREGl8aTGLoTivg3b8Nbv/bH0OtdGxB43A3KkkYgGhXSgTneyNu95umPfTJQ/uvqeaZaUoltBwjxPvyYzXmyRaub/LrsTb/3eH/HFi1eXlmsEym4TlXXzvvoVQj20EA4roNk1dz2Mv/3m1XjH98NZipOK3k/UWDU09t76vT/iqf/+G+f9WnWOYBfmsKE+S2oiv7JLw20sEGzS/2y5ZaCscTeguWYkg3aX3c2OzTBJpK82IkqpEwAsA/BVAPsA+BsANwDwMhAppV6bqW6uWLt2bT+bNSfRcBiR9N/QnPC/v70dAHDZ7XKiqDqggkgj8ZmYRDhWBbev3VJ+EQriiGQ/x5qJGB+C3ifpWOsgnwunqZox3+3eDduxfpqTV1qvoWmrXX/56nW4be1m8jyDZETkujZmqr2HCt7HYz90Ac767rU2wm+XaspunnfQE75xVx+0sapZ7Nzs3i5SN+wqZaVzRRW3bdovjNs3Ha7S3U7kVWNIruq574b6475vPQdnfumKWobNG7dPOX8fueeSyvfStqRJ79Jjoey75tHnjNeMUmonAJ8C8CoA7wLweq31ewHcCOCV/Hqt9We11sdqrY9dsWJFv5o1Z0FjM5RJ/pszI8TF467dxpqN22tPXFOCex0tQQrAUwVFC49bvy3X2Rlk/44GBBEnMuE0XTDN3aHBX/XZ6WW92MnWLeJ5n7kEp338V+R5pt0EEUuZMXWjoYK2BTb/Trgx9zy8Dd+4/K5pezJ087z9mvDDNiLFxqp8Ye+VIGJUqyHVDJAyelWDhTWS1IulrJ/T04YRcZMl+vfTY9SQvI5XWpFq5oIbH7DtqiAQ8ivqdpmc6SPP4G7AaNmGzZwDgohSahTAtwG8VWt9B1JW5AilVAPA8Zh5wRznFMoMqDZlEvqCsWZONT6wcTuO/8D5+HjmMVEVdCJpCKqZbsOWV2Up3EnHGZFQKhVEJoSy6EAtCiFfBWUGYlWDXtE29cJQtNs1qN+T2QErFzp/N1SYETF9qkq8DpuZtktBpIsX1i9hLRxHpJj14ff1+htumQgHfUuS6mxDotJvW6baomOqKfQF6fno3GFOhzLXhlA2/eRySHlRXgbfut/ExkpRJHAlKU9ghemUMxMMV/vFiJwJ4GgAb1dKXQTgUgCfBbABwE4Avt6neiMCMB3wOUfvmR8LdcAJEgTojf/3B+z3tnOwNosZceFN9dRmdMA2hUGSRwutqf2oqnunNiJuQLN0khhrpIyIv1O0v6+7t75rMYWZCEIt7kbzE0o/XgdWQKp7n3t/r8E/bZGNiBQxOIQqYbdP/OD5+L/f31mpXVXQr0k+9LwmeFVI2OoXI2LKmSzozArV3LDTpHcKo40EUy3/+k5gt5/blZA6zDenpUjqWgW7QavyRiSmRjKYrjJM+Tup+0nMRiYhQdlcA1V7rRbG7gyQQ/pmrHq21nqZ1vqU7L8Pa60P01ov1Fo/UWu9uR/1VsHPr7sPP7iqN8Z+swkawKqd5+Njzzuysptau6Nxzh/vh9bdB9OiHb6RqYcoPWtdW8vLnWx18F8X3oKJVrtyxMpwrhkNpRRGm2mb+ARKr917p3mV6gq3wS8z1MYi0Am4F4yIzv/tbibq1wTG+9hIIw1o9k/fuQbPJ/lWAMKIVJBkyxKgaa1x74bteNv3ZSPoboSKfglrZennyxiRtzz5IByy2+KeJb0zfTgk4xn32Cpvo6NT1cxoM8Fk22dY2loe01Jk1TztPRVOBO+5RNlAIt2qZrYSD0Hk9ZYW5V1Tt59Z1YwS52lJKAl51QwLzWE3YNB4/Vf/AAD4y0ftMeSWDBjaGIBVN8qig62OwEDRciIfCvVmjali8/Cli1fjo7+4CY1EYf5oNXdfJ44Iy76rACuItDoYazbItfa+I/dYWqmuEMpc5qoKIs5r64EkokvaVYZBMSJJppr51hV3e9fm0TQrLKjmm4YYkdLx0MXjdjrA+i2TuPruh3HqQSvrFxBAqK1G7dAI5DhxjTt7FyjOjN8itW+iqkkinWzjM9ooNyR3bUR8RkQaW1LuFah6Y0qar6hhviQAhcCvqftN2oIg4rK/9H35Y34mONDEyKpzBBo676SW1i/ugXSw0aieD2zcjlVn/RS/ublcTSNZtbOG5eWWwcQc2TLREsM1S6BMB8++q5QNJc0nvNCuqxuUqUCqGp72euG3AlL9yRLon0cIL7XZCOeaMTv/KnY2Ze67pYaRXYZ4f+1XrsArv3B5bnvVLajwzV/Hp86/GQe87Rz86s/pmCzzDEqDX5XbYFRFlUiiClVVaB00GwojTSUaiksLK2Ajq9K+Yp6PliKxZ8k0vGZGGoaVowKQua5KWcV/l8EIVqnBranfZ0EA+x5CTPGwEAWROYJOx0r8VQcc7axU93jZ6tSt9+uXybr0UBliBuA8JkX5YKBux+dev6b0egCYIHQp14sqpTB/LCUFN0+41v693DGUGau+/POXVSqH2dpOG6aMKmVJC1bf4oiwBkmB8Og5oCIjoosZkbI+2J2NCHBb5mou7e6rYrSR4MBdFpG2uI35+Ll/RqujcwE95DXjZmrtzgCX4ltX3IV1Wybz/lFku1SREMFkO40jUoURoc1vCIJIO+sYdzy0NT8mGbMqEPVzRc8eA7PBkoxC21rj8tXr8LLPXxYMusfr65oRSRQJhyALGtJcFAWRiIFBQ+e0aVUK0vHSyAcpsHXCREMt1+zJXjOkXdrUVd4ey+NUHzguI0Lr1VAAVu28AEAa3ZBC0jN3C9OE6Q53JzbANMsC6hmdSgtCvwwxebFVvGYqMSLGfqJr1UwJYyKc72idLw7T6UcdrXP2rkpbQmM810RkXinTYbXufGgr/uk71+KNX/tDPl5C9ZqkcpWE3nYHIw2F0WYjN5yncFSsZCRIQdAkUzLHRiT7mST1IhfTayS3YdPXOlrjzV+/Cr/+81rcv3F7aVm0TVUhue9KLrv0eFTNzDBcecd6/Oiae4fdjL4jZQDYsZJ76ICnmSlN2PYqdhodgRGh9dZZDMsCsUmYmCpWzey2ZBwA8MAmN5NsiP7tBrledpojvtcTRh0bEcldun+uqW7BScGCaTOuVi83ZKxK6z3/hjU4/RO/cnaxZf1AOt3ROleTVDWwDrVtpGnbHWrKfssXlLTRvoMqcTrK2gQA927YVmoIDFRXzUy1Oykj0kxEL5y2sLACoTgiZYyKYURsiPf6qplwjhutXTs0CdO3ETFeMzagmcOCCK66UTUzw/Ccsy/G33z9qmE3o+9IF96MEak44KRJqqN1LojMqyCItB0K06cN8/DnlQQRK8gcs8+y0usBlxHh0QYVFBZnwbNMhM683c5Ani4jUn3BLwLd/fWCjTAlVGJESmKt9BK01FSFEN5JG1q8Sq4ZqpaQQPvqv/70BtzywGbctMbmACkjXfj7aGYqJSMsVYl9c9FND4ixejraLnhSXQYmeWTofVlGRE07meBItsBOtTr5u6NyiNdHVbVFfqqt0UySzLVe8JoJLKJScDtJpejEETFNI8aqVRhX+hxj2XugVZk20HD1ocCI/J3Udggg7z4U4v3vvnk1Vp31U9l9t0eeU9PBnPOaKYPWGnev34a9dpo/7Kb0FEYVAVBmobjDSzpPrct3lhSyjQhtl6mrtCjnnqqD1WVE3DKUAhaNNaGUL4i4qpnqbZNgiurWTZaXk5Y1feTxTSq8S+n7dLTGA5u2Y/P2FvZbsdC/oNt2kfaYjKKhSVzaAZahTG0BADstGMVtD25xAnSVqmbY36PNJFPNpH9XEURe8YXLAaT98T3PPDwtN7dtsYJIqClGYAy11ZSlUC1gWBHMu59sa3FO4E2oGnZ+KlfNJGKkVufZKCOSCKoZod9IQQ5VQbsl0DYYZthRzWQDpt3RpYyIF0ekpmDges2YMtxn/H4WskIyUI+MyAyCycz5xYtX4+SPXIjr7tkw5Bb1FlQ1U81nRk6WlEY9rB5TRLIRgTCP1FLNQAt6Vfn+b1xuDWp5ICTjObBwtIlNBcaq02Ufug0cxiHZ1kwH19z9MIBqE5/kaaA18Oh/PR+nffxX028MK9fAhN4OqRB6mZFYssEJBYaS73f/NvFPzHiZZMG5vn3FXfj5dfeLZX3pkju8ckeJaiYY0Kxl2bfLbl+HT573Z+e8eQYTubTOInTXuq3OYmpunWp3HM+N/Dy7v7LXTMYijDRkr5kgI5InvaObCP9+KREmDQZWBWL8EnJsO9kAGUbk8tXrcNc6azRry3L/luabf/7Otfjulb77OuDGEZHaIm78yLEoiAwZNHT3Yz54PoC0swCulfWOAA2yI6loayHtHLSutwuVGBGHGRAk9BBsmm5/sIZ2dvdtsAZibq4ZyxCNjzacicO0u07UziLUcZMtgqTSmg4uX70eQP3vaDCICcwYOYaEANOEOsJimdrCPeaPgWC57Jukgoi1EeGMyFu+cy1e/9UrS9tr2uCqZuRrKSPy/P++BJ8872a3jYa5SBQaqrqx6p0PbcXJH7kQ/3mBLc+0a6rdERkR3j+qJr2bmGqjmTEidbxmmoJRcGkckeynghwMLATJ2JPK6luyjQ01Mn7vT67HyR+50CvLtxHx6/vmFXfhH759jdgWGkyuLI6IzIiIxQ4Uc1YQ2T7Vxvt/esOwmzEwdLQmjEg10Z8bd+blwO/sVcqwNiL2vPlZZYKiyfr4JBcyBHwEyVviMQpZefNGGtg+5eqiO1qLIem7gZkoprNub5lo4U801Pw027TdcWsuv36Q7rs8gmxR+njJC6DreqU6hAUnBH77WDNJBfcaNiJyu9J/qSASTM9gvittt7Bwq4wNrCqIXHVXKrTe/IANik0ZEfNodGaRVDNlAvTFtz6ILZNt3Lp2C0abjVLbJFpHUzAalfqtxKgkyra9yiuRstvSdhk7uvT7l5Xl/t1t0Ejl5MuRBQ0rNFUXsAeBOSuIbJkIZ4ncEaG1kOUxMClIdhtUvVDHe4UO+kRgGPIdbXlRJP6J9uJGhEJ8J0ph+cIxr15a3vhI4kRFNNdK1vDdgKq1usWrvng5fnuLDeI23amD2sR0G9J6MMaqmXdH4BuUxaGhz1YmgEuTcx0K21fNKIcRmWx1MNXuYMO2eoHN6jEi/vuYcNQp2aKFYrfoUBuMYWZaVvrvVFuLXjMSI1I2lH50derBeMXqdRXjiNjfI5JqRjJWFdx300W8htdMB1ixaAxPPXxX3Lp2Cy659SGnLSb4Ykfr0vLoPJyqy9j5kgJorhmJ1ZGFJnJ/FESGh6IskTsiNJBLEBUDq5baiFSj9O3vnBGh7coFnPKyqADkqWYCjMhUp4PRhs9saG3jO8wbaeQ7GNpuo3Oe7o4hZ0SQ2iK99stX1BaEf3/7Oufv6c4drpqqHNLOstc7Ka01Oh3t9CulUgG2TlwEijpCZJlqptxY1T0/mjEipt+2Oqn3wlHv+WXlNgHERoSEbefv3tRhvEyk3XnaxhTGnqB6niN7X37MUYHYRJlhlMcRMZuGo/ZcmmbGluKIBBZZSZUqPV/bSYRp2IR6cYo0UsbUsDAv+tylTl1GENEoH1+0iWnwPveOkKG2AbURkVgdR8UuHOtXhOQ6mLOCCI+kuaNDa53bdlQ1VhXTR2sa7r2KIGIHfZ70ji4qpSVY0OyYfFGYCjAirba1Wud0pWVEfNWM1tpGTOzRoq+1xsd/eRN+ef0a/PTa+6ZV5nRtRMpSpXOINiI9dvt7xRcux35vO8fpH8YTQAcWlzJBVvp2oacNqSJtXcXt53UZrxmDttb4SRff3ZQx2gwzIrlBbG4jYtMXbBOiCxtj1cqCSE4d2GP0zlw1ExBU7PxTXJ/ZHHzmpcdgtKFEdZaUtA6grtwljIiwsCdErVGJEdHpPSEV5/ZcNVNeGFdf8/E4IbgwU0i5ZmgRtF3mfeoa/XoQmLOCyLYpWRCZCR+lH6CqmTIK0gxIiRGhTEKVOYyOeSmOSJ1Fnk4U/L4QI0Ld57ixp3kPY4Ig0u5oMS5BN1i3ZRJAunB3mzyQY9qMSMDgr8r19r7eDhaTJ8VRpxivmYDev0ztxdmVIkhluBN6CSPCzhuvGQPqSlrLuDb7d/6ojbbwfyy9Qu5K27ILjVGjULWjtRHJBJGK7TBXUfWWGzTLCji8LgDYNtWppJpptTtoJgpL5o1UNFa1vyXVjKSylQOauXGKymBs7mj7XNVMZqzaKS+QfoJGorzLuSE9hxtHxG8L/W1SANDv0Kt8Q9PBnBVEepX+eraALrxVA/d0hAmf3lFlISqLrJrvaEtLIvdAe3WHhAUTpTG9htZrBbNRwU2wrXWWrXh6KogHNm7HlXesz9tdJ6ZEEaY7dbhCZnlpA801Q35PTHUy1Yw8sZo+HPr+ZcKFc63wSegYKTVWZX+PNhKXySlRGXBccOOarN702gVjNoDgj1k0aB7IqqMtu+C6q+r8+kSpyqyWjcjqHwNI9t3A+a2TrSzEe/Fztzo6V4mGIqu6tmv2t2Fcy96zE9AsO23GpVIIdxAA512/Buu3TGbeg66ATOc6q5rRpfMsfSdNIchcHUZEEqbEhJXkWJVggP3GHBZE5M5Rx5d8NsFhRCpcC8g7B2p81dHpBHPlHet4Ed59gJxrhtdZBMrk8OtDC3sryIjYbz3SSLz7tU7bW+Q6WgXrtk7mvw2dC8CzSamLXtmtpO0qL0saL/2ytqftmWx3kCiFW9faXECOao/0Rbms7urNjwnhsUPgpz3VDNXLB8p62hG75r9f9cUr0nKzNojZqzPwaLEhlVK+8CqFRlKd7aNMCj8GuIuhdH7LRDuLI1Jcz1S7g5HsOUcbDbQ72mujxNQCJAtuiWomFOI9/Tfcxs0TLbz6y1fgFV+8PLOVC39Ta6wafFTSBvu7kSReP2oxJo0LKpQRAXwvM1E9SQWRyIgMD6FJZaaqZn567X3BgDZVYCR4fqwIksdAp6Mdvfw/fedaPOfsS7AmkNBJDPEOfyKp8tpd2lcejBwtwoi4uwQr2Iw0Eu/+dkdnVujTsyrnthjmG5QZoJWhSpM2bZ/CI9/7S1x864PeOaddFTZE9Ds+9fB0sewbI8LKLVpkTRv4eL7n4W247PZ1NY1VBUGkhi6dt2E0U82YfubunuUyJGGD2nWEwMd2yNuHqiLqqGZagqBBb7Uh3mUjkt2WjENVYUTaLiMC+BFJJRshwG50KAMkzQuSjUke2qDAxdjUe909G/JNRUgooi77dfrNGBNeeXtf/aUrMjsqKmzZXDPmGcoNdovPDxpzVhCZCS5LdfDG//tDMKBNFXQIJULjcZTew35r7Rry3XR/movj4a2ySyLXf6b3kfN1cs2Qej1j1SJGpCEwItoGNBsR3ATbOrWFSWq4OEpw2qV9XX4/ceP9m/Dw1il8/Jd/9s7V8QYB3MnKCHb9ct/l82KjYJENJe47+cMX4Pn/fUkt1sYt1z9W132XMyLccFUsQ2xXJgQUSCL+JoNuGPw2Jgky1Uy199Nu+14x0mInbRYee8DO+N9XHJuqOUvqaXU6uSdKSBAJvdO8X5LLQ0zeL/50Pz590S2OqgpI55jQZ6b5Y4yNSMiImtZVPs/a36kgItcLAOff+EBWrj1vNhXm3aeMCC1fErDJ/VEQGR5mwLsfKDSoSqaaW6pj9JV1dg13ki7LoyCFeO/WYpuqZviOMjQJtNo6p2z5hGwm1dGmb52vdRr/oWrq8hDoIO9okhJ+AB3QMFCSDlgy+CwC3WVKgsh0EwNS8KlbCYus/Q2vLenf7r9VIHnNYDo2Is1EXDB4XaE28HoVgLc8+SAAwFF7LiluiyB80PITpdCskWvG0v8BY1XCtOT1Zv8+6dBdsXLROFQF992ptsZI1m+NuzK3E2kH+m5VRqSjgdd95Up85Oc3EVUVsucLC0vc1sazEREersrbpddw4ZXXa7Bpu3W24GyUAnd39+ukdUQbkSGibHezw9mKaEo/5odEGHbYMfoi6hjKiBhBJGRQRd+z5FpWZ/miRrZciPpDZhBq8OVLVuPY95+HSWqsqjU+fdEt2Lh9KgvxblUzXBBJVTPK0wPXBc/0ad7B9/7QvZoNsO9w62QrKAQYml/O1yFb+4dArzE5T5xFtpeCCCvKV82Qa/OAZnJZ01fNFJ8vut+EeM/bUkVwEw7TXfsbTz0AKxeN4ZDdFlduSyjKZlIj+65VzdB22d+yjYhpN/J/q2x+Glw1w8amFCkWkLPvlgXis0KeXcTLGJG0XKOakculx/gzc5s6rprhxUh9eDNJBtjqaOe7pMIUFdaEdtFN5gzYlc9dQaTk5c8yzU0pqH1C3mcDz5iHcBd02h1tO7bWNtJiOLOk/d0UqGXznqu8b3O31mm5eyydhycftgsA4H9+e7tz7QfOuQEPZW6zRhC58Ma1+MjPb8K7f/SnzGYG+Xm+WHd0OlFzj4264HktzK7t3g2yTU1VaGhsm2zj0Hf+Ah/+xY3iNVISMAMztyeqmiBCn0NkRHo4YLgrdoP1GzGgWWA8u67Avo0ShWS3EbJHkOty/za7W9P6Knp5qW3miBm/TSH+Bx9ZHbZh4KUlKss1U/G7SYKGJIhK5817TwrYhk5H46zvXour7lxvjVVDNiJa/iY5A1iyyEqqPZcRkVvJGS2l3H73hzseLqzL4DlnX8KuoYJIo5L92xR5J2bTZMBZXOlpJEPjYWLuCiLDf/cDhYZrkFUE64tuj5nBT71m0lgFqUvhlYyRMJDcdyX3y0ogrmkdrfG4A5fjM391DMZHEhyw0k1DT+0KjDrE7Kw2bW85RoTNhhJ3XSZk8nTWWMqImMmrF9AauHVtmvfje3+4R7zG1CUFezN0L491EYJsI0LL692A4uwU76+yLUdAuCCHqSArX+svcCE2QYIXWZXHEaGCSKi9gjDEjVUbDSuI3PHQFv8G1lYtHK8bWfWjv7gJgCvwSKo5yX0399YrsLdav3US37j8Lqx+aKs1Vm2kc0uRsarklVcm8IkeJQ5rIzaRqVnTd0iP/c9vb/Pu0VqXzre0Pkk1Iybuc9RPHUcQ4QKS9Ilp/5tqd/Dhn9+I5559cWE7+4k5LIgUD8AdTTWTGmeynWVACJB2cKZja00MTMk9Hz/3z7gwM6SiKHPfzYWaCgIJZ0RMfohDdlvsDVZq2MeZGPoMQLpgeKoZncYRaSZydMeqcJgW7RsVGrz8hH2cPB5l0ACek00coZ26eSdS+825ERbrIgQ6cUmMSHuaXkAUvL2cSHMXEiMwyGXVMUqXbCnqRKCVGBFNhM8qXjPSOKDCA5AK2a2Oxjl/vA+P/+hF4rgL5Rqh6pKqbN8j3yuHpKd3inFE4La7yBC0SfLoNEsYEdmWR/ZOooLCC47dyzuvCUOUtjHs2dPmNiKJ+24XjY9kZYFcVx4ugfbd0RJjVYPJlj32p3s2Ou89TS5I21AsjLU7GmdfdCuuuGN9T2296mDuCiJzjBJxGBFzrOQViF4z0OKEDQDX30eyw2agg0iyTZEs+0Owg02DhqyX0plTOp+rpDqpJJKXl+Z38HcRiVIYC0R3rIoWs8XgHiAG80abtZgXrXWehyPkCmyqlqLOWkGkWpwU+n5HhRw8oaSD3YA/D1fNSLt9rTU2bJ3Cu3/0J8deqc44d204hGNljAg7z99tJUZEOMzZBuN2a+yibn5gk7fadTTylyMxSImSx40E6hHXFuYE2ka3DW67lfKjhtp2UUHXMpWAz+hROdWxeRHdpO3vpphzKmsbTBurMiK+seqisTTy7eJ5I7Z8lLOgrmrG9/ATBRHyEjZNtJycPArlQnOIzdy4vV5Cxl5hTgkidUOLX3b7Oty8ZlMfWzQ4UFVELhAE3oF5NxLF2dFhPS+15DbQwkTRtddM7u3j2rxI6cwd1Qwz0jX6c6Xca92IjB00EhVMvFUVRghIo2yGY0EU6aYl0Cspg3Al2dWY5ylKhc4NKkOQbUTs+V7K9dun2s578mNk+CtJR2t89Jc34osXr8YPrrqHn64ESfDmwaSKwL8fz24bWiDLxoMVHjJGJFFot60gOj7S8O4JxT+xwoHKM7127+LsC1YhoSetM1wXPWzYETMuuZATUs2YelwbEX/sSu/bMKgK4fmN23spZmNjGJwlRBDpdMpV4RQ2L1bKeL7rh9fJNiIFLC0XpqTxHVJfDWt/PscEEfu7CmX7/P++BE/8t1/3sUUDhLZGczR5nHipCZstUuA2hofW7jvdJEjT0nuWd7Tlj5Dfoy1jAciJohzVTIMzIsjd7+i1dEBOtW2umR9dcy8+df7N1RtIYCaM3HAxMCkV0dYS6LWmjt/e/CCec/bF+N/McDd36xPubzmCSHl9jmpGSCJYZTG7f8N2PBAIfEexZbLl5FXhr4zWRIVms1BItk38PgmSCqOOdxE/rZh9UYhNcO+TxosRHtK/G0mCttY58yOp9EILEWUAJJuKMoRsD/LFUhJ6SNTS8ObHnjDjzrTvnoe3Ba/lr0up8CJrfhW5HfNvRsEFoITZYph5ZvE4Y0Tk4mxZATu6K+9Yjy9dcofTB820NlWwOUpYBt+yocnt2IaBuSWI0N9DeuHDAmUALCPivoO71m3FRKttDQCdAW3uoXYd7i6wLGW3XYT9QVI19bb5lxp+NoR4CEU7aq11uqOBvZ+3td1JIzyufmgrgNQGphuYLM8Lx5rZLkq+jut1y+EKTQBw57q0rcaIlS5g37r8Lrzrh9fl93RyQcTXid/ywCZ87fd3OMfKjFWrrGWP+eD5ePQHzi+9butkG/NG7S7fC2gmsAlcKLbt6m63b56XGygW359e8IFnHYGr3/lEz109ZLtQZhBrDtn+npZlkqEZg3HnHi17zVB2JRdEar0jeYEz30QUepStM7z5sTD9y9z35m9c7VwbYjyUMgakchyRfF4TvjNli4NeM6wsHlnV9FOHESkY8/QaAHjfXx6O/VcszI6RZ2z7/UPKwWOgUG9s9sv7rQ7mlCAyE174dNGtq5UZOCFMtjo4+SMX4m+/cXXecUM7S1umdozuJLqQLhpJLgCRMvLIqtWewfxLn6chxEOgi5fv/mkEM+Vc6wZw6xTm9qiK9VunoBSwdP4IAN9gGABOPWhFlpm0+reVLjU7J77TVVD4p+9eiy9dYoULlxFxC3vqv/8Gb//+dc6xliOI+Cq2XkYq3jbZxnwiiPCIolqYZB2Bl1wbUoHcvGYTLr3tIadcKVBWnZw85nSzobB0/mje3y27Ii8uZRskGoQMyBiRjsuI8F7lDAfnfWVlJSQTdIHm8WGSKyl9Br8selwD+OHV92DVWT/FlizWBV3kqwRyM/03ZE/VCbxHIP324Tgi/neYZBFji1ibFveAg/vuzG1UEKHMKwXPBA4Af3X83mKsJTlxX7gvpgKfX34IPBnoMDCnBBH345ZfMxPRrQcHjWdgoNl5APjl9WvyQVJFp00hGUXSMZTHJ6GCiBEuStpP69Ww1ChgvQgolCCI2AylGjTEu+SuPEUishrUjUB434Zt+M4Vd2HJvJGgCuQfn3Qg/uflx9VXzQjHqHAB+EaOFK6NiHtOmuTo9zeqgE5wwp8etk62MI/YPfiLrL+Ip7ZLxf0vv0drPPHffo0XfvbSYLmS8FD1+3CVH1VrGoTo8I4GFo1btZRpLy23kakfzHcaafjT+B/v2ZCzce7u2DIA5rYiIfKR7z3X+fu7f7gbD22eENptn9GkFLgvi5WTVFjk6acz445vIAwcFSr5raA8A1KJEdHCd6B5Wsq80ExZvC5j1H7gLouc66TxJwnTqRegOSY/A2+3hFTgk+uS4KrbhrMAzilBpA4jMohcIN2gW0GEDgjJWLVsBxiakDX8QU3hqmb8e7T3o+AZSP2SsarWGv95wc24b8M2ZxIzOytzpN1hxqpmwWC6ZT4RFtGhEl742Utx74btGG0k+W6QT0pJklHk0/AXP2afZXmbAeI9wPTfADwhM/XskF9+KEOnpJrp5fy1dbLtGGDyosvifdBXGbbF8CEtDu5CVlyAb2/glkFvp33JHU/A/isW4s1PeERep2mCtYlK0Op0nPYUGURKqp/URiSLOFxTiHznj/5kG5vBqEO0tgK7sWOgXjPmmfw22t+GiQzl1nFiYLB5Wqny7LvuhiNrI7k/9Da4jY9S7jHznfdYNs+9TmBBucG1VV/lh+RrWbslcDuXqv02/V14ad8wpwQRitAOznSI6aZp7xck1qEK6BRpBwYVCIp3g5Ibo4Y7YMQohs6ORWqYv5CEkMcygc6t1gFrrHrLA5vxsV/+GW/46h9cQSTP+murpIGGJF35VJZ869SDVuTHjE6+KsyOcNP2Vj45+DEx0n/tBFTt+2oN7LxgFACwVzbxmfcvuSkaGDseN46IXIerS7fP3pTiiPRQEtk62c69BwB/rNK/uLqOQ4oZEUIZI1I2SZvbqU0E4OZpMnC9cWgZ6aJEo4TygGZmx2tuKwuUJ729NLJqeqSuIHL3+m1ZvfaYtSHTOUthVDOWbTDXSG20B+uoZpzUEpk8H4qsaup1mKmWK7wXsTZ848aTBhrhYOGYK0RLj8FtOPJNFWFt7Xm/QUWefNyItmxoVko90GfMKUFE2vGErpkJYW8ldM+IaGciS4/Z89Lu1k0VL0zobPKXGRGpLf75Km9bkx/0eZLMWNUMqK2TLddYlYV+TlUzVjCyunIygWXJt557zF75sVA+nRAWZxT7tql27tsfSuAmqa2KoEHiiHTcRVNieAy2TabPUGQjYiDZAAA0joh8rdjeGoLKtsm24wnCH0MOaEYFXkWuhfhbgqSKDBmVSjBn+e62RS29M4RUM6ZfGmGv1bZ2WI77fYW5TCqfsivdeM0AwDV3PeyXm28SrACxmbnzF3nrSU0I2bTRhZMvyNyA1GE5875ir/dsRJh9hdtGty8kCWNEsvlywViTXCf3O16WZWT8eaAs9QCHgqrlLVZHaOkX5pQgUkc1M1ONWSfbKSV7+4NyaOcQRNWMc97vuC1hEJuy0uu0M2gldVZbazz7UXvgqn95oqwSgr+QhECFFrqLaLCdiYISVTNmV6/hvg+JEUlVM4mzIG6f6uCy29fhW5ffVdpWAI7nRyh+CN9F12FEDB1uFjozEY6wBYYKPzkjktXTLFDNuG6Qxdl36+ZhKcJku+NEw+ULgyM0k7bKC5x/NNQUeqnJBVQWKtu9P72A724nBbdiRzXjtC1VOY6QYF7cfdczRqwhBFK7IW7DUhf0LpoCYnwk7R+bJ9p5e9N/w+11juUbDLlelxFxA3kV2YhILrftDn+31RgRoxqWDF+pIJJ6L0nPS36j+B1J36copQJ/xrLvW8cgu1+YU4IIfcV1Ps5MQqut8aWLV+PUj12Eq+5cX/k+mm1W0llK78aJoeAwItm/2h9QXrlaY95oA8sWjEJSzli6tMIzZBdrrbNET+lxns7cTOYGfOfXySYH8x4kr5mpdgcjDeWoCNqdDp7/35fgn757bXljGRKVLvghNYOvLCvGpbc9lKuKTJHWa8ZmG+bIY8SY+CaNJBxunNxO329TsBEpI+rqjqYiD6+OThP+3fnQVrn/kFtr7QyF9zXFwnoX3+/+bQTA3H6CtCAUOj4NgEVUM+Q6quKgO+2OdkfWglHXnVcao0rJ/b4OJEak07ECjm8IatvLIb3aKsaqfPOjwAVot40NFnPI9Gs7J4YjDXNmSbENUCtXzVBBpCIjwtR5ZfZX7RIbkTpu51Tb30sVax3MKUEkZHwpHQsNzjUbt2PVWT/F5avXief7jal2B3+482EAwB1ZjIsq0NrfYTgTFO3X2fFW6H2Q3U9Zt+WZIdPiJaGmfADwRcdMeFI682JBxGVE8p0heQetbNIaLVAR1IFSafm8jGcfvYfThqrzwHt+fH3+OzdAzf79rwtvwXevvFv0mumwb1uUayZEcY8IO+m6DONd67aWWv6HoDVw5pcux+M+emFebjhip3buK26jf6wdEBgCLQNgBRCrmjFttFdOBY1VU/WdVc1YRiQhi1UoTggAzB9zvW5cewRTlhID+dWBZMysYYWiFut/RdmP6SOY/hWyEaELJ1WXUq8T2y43pkiaeI+UlZ03c2N6v/w+3Ckw3Qi5yePS365qRov9xlPHMXVe2dgqYkS4EW15pnkt/h4k5pQgop0OKOx+KkSYM7EHvkxiMgwSk+2Op4uuAmq9LXuv+MKBY33uTGbZdZrr5qV6qfusf94KFxUEEbObZxR4M8u9QZkeyVi1TRpudjTpefOMGk/999/gL//rd2i1OxhpJI4Lb13a0mGbMzqdlnHqQSuw57L5zj3dUKNUwALSPvIP375GTkRGhEjAqGaKywVkRqSMPqag5zdPtHDyRy7E2773x4I7FPnldpyO1rj41oecNoTVS/Z3HWNVgzo7S3M7FRgAO6/Q211BhO20wY1V3fJ4W3i7F3qCiC+M0TqqeE1xfOvyuxzB3fQ1N0eKYUSyOrN/t0y08a8/vR7biUMAbYNZyEOeQCHVDOB72vAYGanqxe/XdaO/GtWwFEeJMlJhRoS2SxPWyP8mUnOKVTPKEaA5yzGPpQSIId4HjRqCCN+srdm4Hbc/uMUZyP3CDfdtxLotk+K5aXnNmAnBHBOEi/R3+kcrqJqxC1/pLrPjW/TzHWB6XdkT2DaatlBj1dRGwBYsRVZ1B681EKPJsm64byOuvuvh3E6hkVDvjfI2hqBg3hdtI1lsp9GhygQJyXjTvIvRAmNVrTWuWL0O19+7kUVWrUYfu2XZ34ZO/9l19wevD9nU8LLMOA31xTpxQKSdYx39ubnUvG+e+8RlRALtynbHjrEqEyhN8rhQwshRFlcklFulkUjjwqJosfun717rqnOFZ7T3uyrh/zj/ZnzuN7fjW1dYWyv6bs1CGVTNkGsnmY0Iv4Oql4/fb6dcRZqfb7tziaplI8IZEZvOgV4nFScJn2k7fGZU6neFxqoFjMjBuy7CO59xqFtWjQ1FvzCnBJGyIDFFKb+P/8D5OPVjF+WLXd2F46HNE5UzGz7133+DZ3zqt+K5qXancKcSgtaCG51z3p+sSo1VK6hmOlp7cTzchvltCcHUy40wTRZRKigkDiPi3mfK4saq9Jtvn+pgz2XzHKPJMtVeqL2ApdPpbXTHRxP61QWPX+EdV/6xNgmGVSTIPPczl+Bp//Eb53n3Xb7AKctcWwR3gUo/lAm4JYG2mY81dxx3vGP0cqld4R2vf0zq9yHwuYGnpZcCaaX1ukI+N1b1jWDdxnS0Lgw3IC1qiZK9xSjKvqn0/alnmLdhyP5dk+UbCsWK2XdF2r+CqpkiRoTd0+po7L5kHJe89TS89DH7ZAam7nmAeCQh7DXjCQ/EWHWUjCPaBs0fjtxvy/WNVctiexR9G+5W7GZAV56Q7wYmDBbbV8wpQUSS4Cno5BBiHrplRI55/3k47v3nVb6eJ3oyoN4BdbI6TrY6ZKdkFj25s5saQoGBdH5PYBtK0NFWKJDaa+6uIlxxV00nG2nHVXu42XfZgpD9V+S7DwC7L53n7MqkXX1VJImvykqExbZOBl6DMrWEcq7NzhHVTOjd0z7R6mgsHm9i9YfOwNL5afySOpb5jvBQgdWjLI4vYLntAjIKXHh3YvbVCu6ZBu2AC7tTntbYOtkKqmZsvRauIALcdP+m/HdqrEoZEbc8w67R+2mMG/4cshqnPNdMESMCcDWMHVvmqb1FPjuxPgsbz0OhA8DLT9gHzz1mTwC+TdtVd67HX3/tSsZc00XWn2PamV3Ibkvm5YuwJEBV8ZqRbET+5rQDALheUI4gosttRMqNVaV+Hf42ip2nvxPhHRWp+QaFOSWIlDEiNFwwpfQkw7I6QoCBkd7veGhL7XDhtl3yzq9K3YYyFG01yDRpHj2omsl+dnS5TrGtS1QzRLjYPtWuZARsI4imfxtBpO0MOFsp14UbgYAPfr7LGG0mQUakbkwRhcxrhlSROMKSqaNWsQD8ydRAdF3VGg9tnsBd67YhUfC8CEL380izJoW8vVZuk1RWJUGkgBERF0DKiCj5m9WJMJm3lenP79uwDeuZ2vR/f3s7Dn3nL3B/nlnY3d1K9VPVzP/9/k48+ZO/xsW3PJjbC5igdFPtjmd0zGNdcAGp6N2bX5QRCe2uyxgROoVdc/fDeV2mnXycGuHSPDtVYZgmHrfvTnkSPy7IvebLV+KcP96PBzZNOMd3WzIOADh+v529vtJmqmGemXYqt2Oxm6XQY3txRJTC3z/pIKz+0BnOdfS7pwIy8NgDdsYTDl4plkWDK+aeRZ3w9wWK000o5bfVoJH4PpPdpDHoNeaUIFKmd6MflwolfOIBurcRuffhbXj8Ry/Ch39+Y6CNJYPf9GxUUw/dv2E7vvb7OzDZaovpwm29Ql1B1YwVHhxKXBJwBNWMG8XV/vv271+H533mEty1TvYGMgPSzOHcWJW2ke6mJF241rY9NgCYW99oI3EWX1cQKRckNXs3mh2jbVQCS1UVwYmT7UjNtce8/zx89w93o5kkmc48VK4ruFN7Gb6zpL83bJvC/m87B1+99I78GK0iFCrfWTAc+5nwDs4xBBWeIxTBVIKsi3eZhhM+eAGO+1eX2fzxNfcCAO7JIo5yAVeqn25ubrx/IwDgpjWb8mczdhLbp9qecbrZtdsNgdtuvsmif1E1T6jfG5QJIlQYf3DzpFeZ7zXjtkFiZB0mjM0nW/NIre7xE/bbGas/dAb2WDrPO8e99nh/N4wXva0KYyZ5Axo0EoW3PPmg/J40r5Vi3wFYu2kiVbWTZ5JtRPw6ylQzITabjqUT99+5UGgZJJrll+w4oB2s3FjVnqf61/xol5LI2kya//3t8s6/jA5tdaxRphQPhOMVX7gMN2a0rxFEcoHAYSbob+21RYojApRL03TAlnnNXLb6Ia9M51rWlnxiFhgRKkBY91z2DILXDMVIM3Ey8Na1EaEwE6AruPmTbjfTQP4+WH8wz0OD33FBiE9Ezv3s+1N2SKlwBMvb1m4GAHzx4tX4q8fsk9br6OXlla+hFFqC7pN3G4kRqWIjUvZupWZJuWbKxqip32NyaLlkrjHj8q5123KVock+vHWynbMjduFL+xI1GnfazAQ9N6CZbZv5niHVTLkgEsotlZVrFnnPI8SUT9oIIyCF69uaRQU2YdkNnLEu2Ii4husyM+7MUeyxb7hvY2bjZY9xpoVCKYU3nnoALr3toSy9gz/O2h2N4z90Ps44cjcsmz9i1cTZdFMmHJQaqwbUpvRd7LpkPLevK6prEJizjEhZamUqUUphnqsIAfdt2IYLb3zAOcY7PkcZbd1u61r02ZqcLgZRzWRSd8B91zxu6B1QRqSsE1MbEXs/+Q2dH9u4Ld3xhKzlc0NL9g7NYCpTzdhYB2723RBFPdJI0HDcd/22FIFeoZTRF9tjcnrw0mI9BFUrQh+nRwwjEtSJC3FVDPzojX57aFbYKqoZhwWhJzyVk/1txmy6MPugi6yk9itzQa4jyJjz3PZIKn+SvANjtPn5390Ok2vGHNs21Sa2J3axdIQx1m6fQfKfwY0jIguGpYKIYCCbiiHueLPiU3YNG8eAKyBJCBn6Am4EY+l2V+BXnkBA6zXMJcVT//03ePInf+3MkVPtMCNiQA3UucrHPMNPr70vtwsy9wDlgggXhqm9DQ9zH5oXgWwTJzBTg0YURAgcY9WOzI7UUYs841O/xSu/eLlzLI/fEFhsp0rMltvaCiJV2kA7rDFWlRgR9xHTPxyGyFHNZPd0EOzw6Xl3l1PEiGitSdpyeTSYw5KNSEfb+rSWd0ltcp7eb+lQLogo10akpi6VXmMW/NDuJJ8gupgI2oH+IGcetceMvriajUjHe6d0PZCs/KW0AUA4X5KjqnKEkvDCmoftD6gI6Tv4zc0PZtfK56XXUCXXTH7UbFJypo5dJyxEgG8roYCcEdk22c7r5d4ndhy67ZpfEFmVelLZyKriYzlz4N+e/gjvfJgRQVaua3+RsC7OvYXSdtmPF5qfuGpv18xGhN9vj5HfrFxPfYQC420WM6SIvQGIHVW26aHlUmN3rSXWyBV6ODir+Ku3nOL8HYoB1GDjqqHqZertF/oiiCilliilfqaU+qVS6vtKqTcrpS7K/rtaKfXf/ai3DM4gIb/POHI3AIwRoR9S6AhVNDNGb+oE0DF+64FeXMqIUH1fhTbQ8rixaqgD8gUfkFUzPiPi1m3eN3fDk56wo+WdknNfTo2baIhGtZL+SycoWqPEeFA3w1wQYfWNeDYi/rNVhVnw6XuU4oh0Q42GI6P6x+j81UzSXXFQNeOMF1d4TgUR2VODPuOajdvx8+vuLzUUN2Xa3/b4ikVjznXOQiLkcZHOh9AOzAn5MVLwVy+9UywjF6azv0M2IhQ0ff2yzAtp3+ULcvdds8vfNtW29hO5rZXKbA/8NgLFkVVpksVEcGt37qOCpnCNKIh07POHIqtyZjNtV3YNKYvu8t2F2a135SIriEj5afg4cwVRk/TOCkuhHsOZlDKHhUaichsQrppxcg0RdlbaJJZFAQeQe7Klz6CC45E3OfHUOIWP1Df0ixF5CYBPaK2fBOB+ALdrrU/RWp8C4DcAPtenegtB3zF9+SuziY7qVttB19X6X4oOWFMWX5zXbprA6gfLvWl44K7bH9ziqF84qOScCyIFOV8AqsOl78Btg7mnKPZKHgE1sRNoeh+dgHR+b8iNNr+W1W8jo2aCCN1lkPuMjp222+w803Ky9guqGSf5WgH7UwaVMyL2mKSKuHv9Nqw666f44dX3VC7bBi5zIS0eDiOTqHxibnc0Vp31U/z3r24V7293Oo7wrBSClC79/aLPXYrXf/VKp5+YhYSr4Pj7eM7Re+L5x+6JV5y4CsetWpafkyJjVrFzye+nv0uEyzJ7kLQ89xq+u7V1BTY62RhdNN7MF/L5I6kwsVViRJLiher5x+7p/O16aNgyGiXjjc4d0hVlnmNchco3QGK7yDtbMNbE351+oHMe8IXLBY5qxp/bOPPo5PzxWNuw8TZntMoY6ZFGFmhRm9ge9H533uTvqB3oKwaFxqpJOCq2qJrZUW1EtNaf1lqfm/25AsADAKCU2gPALlrrK/g9SqnXKqWuUEpdsXbt2n40K7iQmAU6yIh0XBoNKN7tcJjU62kdaVnNhnv/iR86H6d87CLHW0eC6dhA2mlP/dhFOP4D5wevnxIYEQON1Kvmvg3bGC2d/hGipc37+OM9G5x4Jxu2TXmUI0DelaSaIdfmgzAgi5k2mGeiqhnA3WVICz7Nh0Hry3chrL6RhipgROQ2UkhGcuE4IukfN9yXelD88Op7yysoaUtZAqtmonKVkTHI/vfzb87PUwG61dYCI0Lb4AuXWgN3r0v7BzX4NpMkF8b5+/j484/CR557FBqJwvOP3Ut8rjyhnKb3QrzWto+cF3aDTz5sF9LW6m72HSYQeu675LcUs0hl1yjKiEy2HE+X9LqMEclK5M/44kfvnce34BWbZ6ReM1Xcd+mO++i9lzrtppCYLyvwm42IXz5VGVGMNH1hiatmxkuyXPOYNB3h+7sh3uX3wZ+tbA1oJEkWol97KlA+T3I1sbsJ9PtgcRwRJlxQFjZx+yF339+hBBEDpdQJAJZprS/NDr0RwNnStVrrz2qtj9VaH7tixYq+tCe0+zG2E1OOXYhsI2J+1ZBDnIFjJnfeic3iGpr47CJNO4rciHZHi3r43EYk35loPOaD5+OED14g7k4chsgZGOlvHs3xjoe24swvWZsYvpPLy2dtNdeayaC6jUjGiGT/UldOOpmYBdTYoMwbSUCz7yJ/H259KSMiG1xW0aVyr5giRsTqz+tPBB0iYDnHhTZyGxEjIBkGkApermDnG6uGsjPTZzT3GNdLwAoPnBFpMMaFQoo5ARQzckBokaW7Rf+bHrb7ErH8MvAAXkXGqnR8uuHe00XLCMHbptq+IaeywjTgP6NSCosDag1rbE9yMFVQzT1yr6X571efvB8A2Q3bmWPZ+zDNF1Uz2b/ejp0JL4BgrDrS8K6ndjKOjQizieCMiHm3Ejqs39Hue9ZTD/auH0kUWp1OvukJCaKpsar7jkI2HrT+EBLGVtKydiFqrPRabiMSLLav6JsgopTaCcCnALwq+zsBcCqAi/pVZxlCO6FRktdBC2wAjz9RF1T6NYPQLJY8RolkmAS4KbvLmvCiz12KR7z9Z95xiRGRfps2hkK8Fw0CYxBo2grQnVxWl7BDq+Iaa47aIETp32ZCNW59XDwzKgWTv2fBWNOxVM9VRuzNNhLOiMjvowoMJRyKI2IaU1dHe/ohK22fYvfKqhn7u5ExIh2tMdFOhUrKevDJsJgRob9NH7f3uKygL/SYMg04xU5j4Eh9lbaBLjy3rd0CDmfiFewgqLfPRAVGJBfcyxgRZzGl79ZKGmbRUkph/kgDWyfb3iKdsFVNGo+uxxLw11+7EqvO+inZSBFGJDCeTbnvOOMQHLOPVY3lGzf2bl706L0c26tQHBHbLkGIZNfYQH9UiNPOXOYkccuuH2kkOHLPJVkZfENAxnHbrVcBQUnE8ZrpdJxyX//4/b3rG4nK1pS0ZDoeXTWyJu/IZ0TMd6DPXKgaVizEe/a8R++9FO/9y8OdS4viAQ0S/TJWHQXwbQBv1VrfkR0+GcDv9bDMcsEXO3vcqmbkEO9SKOs6jIhkI6K1xtm/uhWPet+5jo1H0KPAdFBd7jVzWSBGyRiPrCosHvRwKI5IFb15WmbWdmMjIjTYSZYlTDpuee5OituIcNWLgWcsq9OLvAmSVdtQitmI0HaLTXRAqy2NI1JenIixkYbNfMoeQOpKdPg1EpXrxM3ESIOW8bg6ro1IgbEqEYyM+7OkmikaQx5F36CMiLso5fUK/fLzv7vdO+ZsSBzh0tRlK5+qEcrfMIg8k2peL/k9GWJEYCn/8dEGtk+1vTmH0/wSde96LGmc88f7neNOZNWS8WZyCxmYb+oLQApkGHsbEc5u8My49BoDyW6s1e5g8bg1yJ036jMiJmowwL2x3O/gxxEJ55pxjX7l+Yyi2VBp3CetvQXfYRs7vo2ItDGjtjChWDzps8iMyAuP29vLzMzdmXc0Y9UzARwN4O2Zp8wLADwZwK/7VF8l0HdMB+8IkfC5D7z3O+9A1ZcOiRHpaI0v/G41AGDTdktbhxKBmcGUtkXe+ZehMI4IG2RAOMBbSFji4O67pAZ7Tf4+KHUbKNDsPNtsghPiIdBn4ztvY/Brs6Sa9+HCxFr40LOPyO/j7S6CM08pYyNCy/evrSOmJwqO+x1/b9ICw1VDpg2mj1LBi/bbqSwbMa2brkOSa7Mm5W0ljEhINRMKSgYw1Qw5LtoZlIyMUGBDs0jTuqr0dTteXIGhMLIqe7dpuw1Nnx6fP5oxItrt72YxlTYM9hktJLbKjawqd7p8kWbfyXxTrppJGNvQyj1SkD8fbUNba1x864N4/0+uJ948LngQtLRenYeBB7hqxvxrNxG0P5jYHga514zzDBDB31OZ+24zSXKbPi4A0bHV6kjpJkDOp3/MH7VCRFG3VGDGqoyZomgwI9phMSJ9iayqtT4bvi3IN/tRVx24QYDscWqsmhuABWxEON1YBdS6nOp1t2e7RPrxJ6bkHiZlyqyb72a0kQ7YnABw+pw/oUt2Ifx3EXL33XxC8JHv5iuoZqzKyFXNmAknlFqdT6TG9ZEzInwQGuHv8D2WeOcrCSJsAoRmXitKeb/rTAQmiVe7o/Gli1fj7Itudc6LAc2YTYSp1yRNo8IBTaQ22eo4rATP8CktdvS5qGomZ188Gwp49xnsv2IheQj7MyQsF0EH2mp+0+c05R+622JcnxkSh9ASFjVWs9huaotF847My1Qz1MDU/BtidWw59Dd5RmJvUmojklXMv1NDGG9iu9hGJBe2zfl2By/+3O8BAE86bFfnGQ3sXOW+O9pPxwUbEap6cgR+MFsPT30cdmfnAl+5sapx3003PUfssQRX3flw/gz0ebj6msexUgoYG7H9spgRkT1hpPWCMzU7VByRmQpnshRtRFwp1WBSOF5HBJgUVDMdLbudhgKamcmg1akXWZWC24gEI+oZ5iFgI1J1wm8HBoDWwBHv/gVe9vnLHFe+kEBA7wN8RoTaz6TXud4pI5IgAniDn1fLKWV3sRWbyO63vw2d7ghIgmqmzqdNFPI4IO/60Z+88/SbPftRe3jlb9reytu4PROWqTfXtinLzm2f6jj9J1HuZBgKaGYekfbx3GC7iBFhA2z3pfPwzdc+xrtO8nopk89dtsB/43SRM4vtPBYkTCrPqpzcfpPXFXDfNJ5yKtuNm7vmMdVMvqCbdptNTYn3iiOUZP+mbJg7bgze+cPrcOFND+RzJGeubGZgd65Syh3Hdv6wwkHaHsOI2HvNZo0Lb9ZGxB7jggj9Nqb/NhKyAXJsRCpEVg0IcZwdK2NEqPuuUsA7zjgUb8q8mbixsrcpooxIFsWVMj+F7rtKjqwqehQplnunuiayp5hTggidgugkalzEqOsspbbe9PWr8t9FNFcIE5KxqtZkJy/bplCY+tqdjhiZsAp4QLNQpNB8xxJgQarWay7Lk96RHdGm7S38+s9rHcHMMAhhRiT996HM6DSnM817FNysgVTXTr9Xu6OdCZPv1Ay4jjkUDnyy1cG7f/Sn3BjWgE6AJlBSaLG1dirVv6kJMx2i1h1hO/v29NgDmybyNhp2jk7wVJ2yfaqdC+xpe4siq2Z9nDwO/TYhRoQ+ujS+zILjqDim0S8BrppJ/6UqKDN2X37iKuyy2A2sxsFVM/wZqErIoeYpIwLX84MyIjQ+SSqHpCdE1YwjNEu7Yz8rtcGXL7kDr/zC5UEPwYYwbwHWDoqyvunxrBzWNtoXNxv1NBdEEld4AVK1Fl1Ux4mAbISkVPVkfttrjbBkwIVHIKzW8gWvMkYkyY1VE6Uw2kxw1J5LAfhqT8uimQ2nu/lNlMv8FHpzKffdclsdijQmTf0x1GtUEkSUUnsGji/taWv6jJDkZ1QWUwJzAciMRpVcMwauq5Yt13QMN8eNLJJaOxZZWKoCG+LdX/ClySp0PuTZw+FPRP47ozYzpYwIExXM+8tzybTJAkiumzfScGqmk6VpGeDTklxv6xqr2T9+cu29+OLFq/Hhn7kZlWmdxjslpH6wnjv10FDhAEz0+5mdIr/WtGFCsBHZxgQRRzWThFVVUmh3ySaiyEZE4hyldxQymC2CpKqg5TYF1cwReyzBp19ytFieeQxfNRMWtKi6No8OywTkeSMNbCM2Iu6u3fYl6bl3W2rdNKV+S1UXoXmE29y87IR9sHLRGInbIwlAlo3kzKW1TUvR1tYF1tjGeaqZgI0Idaun38v0X8dGhDGPjr0bt2NR4ZDnkiqqCM3MfbdDaC7TbPruJlvEA0eYA1MbEteDr8hrL2SsKjWXR2GdsaqZzO32V0qpv1FKPV0ptTI7vi+An/e7gb1EaJdgdotUCg2pSMoCHN21biuO/8B5Tip7lxGxbZF2FqGAZubaiVYn7yxFekIJRsfoU6eysSoFHbwmbXkZcko5CU/I+XewYzXokRJUnUiBmcjP8ZGGMxkZ1YzdYXq3ALA7dsmFsN0Bzr1+Df68ZhMJM85KUO7vTocLouS0aUNgHgjNeUlSLQ6EESL4RGPaMJEzInZK2E48Xba3OhhxVDOKeVXZMvPDROCbYhOvaTtF6N0Y2HckC+NV51BHkBFUGFSVlwcgTJSz+EntygPtkcUwVO9Eyx/zxiPO3DVvtJmFeGcLOlx2TWJEzjhiN3zqRY/Knst/RqPWAwoEOHItALz3mYfjsrefTgR/+wwHrFyY24hwRsQK9O6/2ybbudGpEUT4Z0+Ebz7V7nhzioERuKkNjDvOXCYv74uOmtYXpE29UtsMzjxpX7z5CTYnT7NhGSJzqXXPJQwhidIqGqtmeW0og0jf/TOO2t1ph4JyI4Oz/uM+g7uRqah17zlKjVW11h2l1IMAbgKwD4APKaWWA9gJwGv63L6eQgrtC9g08C5jYv8448jd8NNr7wNgJ42QfvC7f7gbazZO4NtX3JUfkxgRDdsxHBuUEkFnsjUN1QxLehfc0Qr7ctoskyW3DHbSYwu+sCOl8QdCC2toEfUEBe0+w7yRRrpLyJ9Fo5kIO9eAoCMloupojdd8OQ0QbLxqijZI1nVRe8dCdVCMNRPssXQennbEbvjUBbc4ZYR2MfSw+fb8Sm6sShkRrpqhC7RvrKrxvT/cjcP3WGJtdcj/p9jES+u2ZdjfRe+SPkOVpHTe/SX9XmJEeEwZCZ4HRkHSOyrkmfvaHTjuuyMNlY53JhCYBd+8f8lGRCmFUw9e6dVLFyXTznAkY1sWhcSknHbwyjzGkVUZcXfm7D1k9zywacLZYAH+pkViI1MbEeDrr3kMrrprvXO9EbgbicoXbtfI2lerAu430xr42u/vwPt/cgOueucTnXqdtrG2/svTDxXbMtnqeHZDtCjJWJU7SCgwRiQ7/6/POhzPO8ZGHTbP4Nj/BVQzSvmqqhnpNaOU+hmA65H2nbsB7ApgDMBaAA0Am/vdwF4ilMRJ9lWXd9dGUAjpB3nKeV4WrddI71OOvjiwqGT/TrY6uOimNAR+3T6TB4XKml42AbnH6MCoxsSEdMTONblqBmgagaLERsSA7zxDmVTn5YyIzq8zUUVpu3yvGbf8sN4dznVSG8wESF9dURwRLlxoDZx+6C5OIjBTZ5mXEZAGcePH0jak/0o2IlQluY2pZpRy+8E9D2/DJ8+7GcfuswwvPWEfp92ADTZHy/VtROw1cs4Q/zt0ZbtEfkt2UtRg17AczYZynl+CGU/msXyvIFsX9UjK7aQ6Wf/IbhvJ3D95/zKLhxk7obg+Ur+meYmUSXpXogrlU53kvrt1soV5Iw1H/cj7ZW7/kL3TBzdP5OemmEBg7/GfodXWaCiFE/bfGSfsv7NzfYOMWRPvhBpZ8/DnXCg2XjPv+MF10JoxV2157IRgbDq2Tra8uYYyIq22n4CzwwQJymAB9pvvsmjcc0LgbKV1GvDbyD2dZioj8hcAHgdgNwDnArgOwLO11puVUnsD+AyAp/W3ib1DKH6A6bz0g7SEHRxQbh8hqQkcY1Ti2cEDcQFhlZCluGWDzFa74+zkJPAOG7SsFyYm93mq9VbuvpuXT3/ngoiGGaZ1bURM8VLIaABYON5EM1EwpqRauwxMmVpEmgwl33sunHbYwtrRRblmituQ0rv+TJKnGg/cY7Bo3Agi9thrH7cfYUT8yKpcdWeMutO2u/EHDEt224NbRMNn2m/z8cQep4wRkRg1h22sqKmUGAKKEUEF00wSlAwv8m2zfsn6Pe2f1EbEHOcUeqNBbAzAbETgsokSpPg4U8Q4M+/Xznf25wROBEkq5clWBwtGm/nYAnzXWNuGTn4PP8bHkMQUThapZhKrfjZ5m8zGzRx37N2MAMTerbmkJfXbDGU2IvNyQaTtsa+uzUsH45mKSlKbT3U0aMwfoNxpQkpYau5/zL6p8Paco/fEtXc/LMbSGTTKbETeAeBZAG5DGgfk3wBcpJR6HYCPAzhb1Q1mMUSEdlJ5FkpGh+W/HUGieLZrSoJIgB0x9UrGsELrvWvplXLeB7csnn23jjFqN5Sdt0ALPYVmxLVMTWiH7/5t9cHy8+yxdB5++MbHopG4u1muiy8zFJXdd/3ByxcqR+hIjADkl+vWERbCEgU8au9lznGlwgwSFdwWjLqMyJuf8Ai89akHWxuRPLIqYURYn+IUt2sQJ3uGmXcjxfvgjxqKsUKfNX0uC2fC7YIRkSZhnpASsFFoi8AZQP4MtH1StOXcWDU7PpKoNLYRE1BsJmd3wecw1btj2zAAdtEOMYlcsDIw95my/uKo3fGPTz4IJmifuc13jXUFGFc4lRdW8yf/tJxtMjBRcROlsPpBP7y/UjIjQqcoWpfjZt3igojYhBzjmU3ehKOaSc/xjaqRffNv1nHHllLMRqTEAFVKemfasPfO87H6Q2fghP139m29ZqggchdS9cvRABYD+BOANQBGkdqLnDPMkO11EdoJWaMtcj4olBRLosbYj5ZPO7Dp+JqUUYVtMMXxRGR5uUIoar6g86R3zo5XrDVcVhWYW4rcNKnlv0Qlh+4DfJsXbni434oFOCpL1uXk3uhUC2hmIE0OVVQzrsxqDNfoYuvXQXH7g1vwwZ/dkHtIKAUcs88yPHrVTvk1qddM+ftavijNnkqT2yml8r5vVAW0HdxeaZQFNOO7tvQpiUE2iEpRYAW5MFc2k+QCY2ChoFl/iyDt+ikkwc7k5SlCKPWAVK4jiJiFW7v9stkIqGayZ8iZlMDmSGLZqGrZLH6hDYn55cX2cKI8Ax957pFYuWg8pflJGX6Id/fZ6aaMh8fP6xLUcWkb5G9BDVQlhljBfd6JXCVkJwNalcRm27KqMSKm3rR4f8Mx1SIh3rMrW2xNSBLlPDPva0672CFuq0PBvWZq+j/0DGWCyHakNn6nI7ULeRuAAwDsAeB7AP6+r63rMUKqGWkBpIMklClTgsSItJg+EHCpdjehXEA1k/3rCBykKROSIMJGb5Mbq9aQhEO7ru++4YTgPSH3XfodqPGu5KpHwWVeI1zwycosgHSQ0vwh3GsmVL5BHgo70D+sm6MLXw2jnW8iTSK0Ca/8wmX471/dhnse3oY0K2t6/fwxEkkyUYVMwKLxJs77+8dh5wVpDAyebTSPI9Jqe/fyvj7C4og4u7ZAFlxJpWiuDakG0/L9Z7GMiLtbtL+1d14CPSuxATyrNJB5mZTsgKn9hbmHQrLVSH938mPUWLWZpJE5rddMen2ummEL/isfuwoXn3UaabMZF/5GJ1FyZFX6bqirL4XEQJrnpu67HiMClwF2Vd4BG5HckcCtK8yI2Dnh4887yjuvlNvXrNeM+wz5+QLVTJnx8riTAdgVxlxBo0MElfRfHgdEgTMi8vtK62BMHBvzzrVJdwbfvUaZILIfgCmkWXQPAvAeAA8h7a8fAXB85t47KxBiPMxuU8qTADB6LuuMITrcSK1copXKzesP2JNQmLZtCuSikRiRkHTLs2Om5bvXePYkQfo3PBhbuaEfS7ZHy9X+ohTMvssO5zSssLMDYVjSa7lqRpMFo1gAks4/tMUa2pnjRTYixk0upJqR8v9sy+02kszLKj1OJyTufkehkRooH7Bykaf64RPjdmHx5X3RU80EWEPKTOSqmZZ/bVGOC1E1k5dvj7kCoX9eBJ0HhEVY2mzQSKQhcBsPf0EI3GdsPTqW+QJs0rT81SrbFsko9JDdFmP3pfOcNgO+xwlgbER8gUISDj1GhDG5PPS8+a4m8CAVoGgbJNVMiBHxBJGgjYhlo55wyErvPI86yo1VebHOJpTNsc0SQWQByQ1jHitn35l6yPOqYfNhQhis9H5TrsRyuH+HbHXMMSdH13DkkFJB5INIhZDPA3gLgCUAPqq1fqvWugPgn7J/ZwVC+Uw6WnsTemiSpZFAA5UAcDuDZKiX7sj98kPMgzm8ZsN2csxeW4URMbATur94GDiptQvKKpqcjdDl6dyF90wXLV7Tsz79O3z10ju8yWgkF3DcwatzxsNeS9vQ6aA71Qw5/44fXGcfR8sDnX5KpYz+nAon8H5Li2wjySzbzWTluNEW2IhowHxtvgjwxVLqP54gwoxVQ6yfVc1YsWpKsCGR1FsGUq/iAbHSev2xWTaZco8EA3P41INX4NUn7etkKk2UcvrTZKuDv/vm1bhtrXUc9ONmcEFEnipzN9xcNWMYEZM0ze1fKmurVc3I/S9nPp0Nlm0jTXq3faqNG+7bKKpNuQoiFw48di0t/56Ht7EndBd5U67EOvPphLKk9FxZHJFmI8kNQN3yZBbMzgWuqkISoMvaYLDHMisUcpaMB8zkwhofW1RwBMIMEoSjbfsh/SuVnJdm0CgURLTWU1rrh5C67q7VWt+otf4+OX9bvxvYS3ScQcYlzrDBZtthRzre/RTSpODqQv37Jqq472b10Z04vVK0ESFlccGCtpWXJV0fZIAU8JM3nYRH7b00WL7NgunX5ZAY5l+tsWHbVO7ed9WdD6fudF7d7mLKFxU67m5baw3XDAWu2CJdxogURVmk5dg22GsaSRpkyDVgJYxI1pb7N1pB0/Q1mrodcBmR1GsmKBV7i6JvUJn+axgRZwJkzzhaYKwq0btaI/+o7niSBBG3rkLVTIC5lMqVIC22gGWjRpIE73j6odh54Wh+LlHA8oU2xPvvb38I37/qHifHD2fyvKzPgWaZ99juwGHqcjdZHnRLKUcFEsp/JdmIUEE0z9GkNf7h29fgqf/+G2zYNmXfR86esXKzf72EccFNiWmAe56qnUKCSEK+OZ1TBXtiANaQdoTYVLz2cfs5bZf6R+5BB/a+hLnf1lUsiCwn/YcKOoCgmmGMDFcDU1UaEBY+aRm2/OJrpXxNg0ahIKKU2kMpdQiAEwB8TSl1llLq8+S/rwymmb2CPxkC6UD0k//IE7JkaEch0WA802LaFOs+Sl35gu67rPysiByS14xjkEv6oMTEcEFj/mh1RuTwPZbg8N2XeOemyI4+rdcfCJfdvi7/ndt4aOD4D5yHY99/nnOtH+fDtCFrY24jYLxM5InCWPZz3XXIRqRMdUPL/cZld2LVWT/NJll7brSRYLLdYXFE/N9f+N3q/BnM89iFxjAi9L6waia9xy3flMW/icn6PCkYVhtwGxFnDAkqEvMcgJw+oZ3txL9z5d2CIOJ/u1yQJZeKApB3p4uQrRjfHXOvpvGRBl590r6YP9rApiw3Ch0n3M3W86IKsZ1kTtGkXqPStOoD275UBWLfI+D2i/Q65amcqddMkqjc6+qK1ek4NM+Vtsd9H7Rc6XlD67IJjMfLmWx38u9nd/gB9oVtLEKqGXPc/Lv6Q2fgbU87xGm7Z9vCxqEjiNDxwDZ7ZTYiXIXqPo87XmxRmaDC1hwqOJpjvO3S86TXuv2HwvOwGRIjUhZH5GAAj9dav1MpdSKA0wC8H8CjATwDwIv73L6egr5jHnkuUa7lvrTTAqiNiFwHT3xFj9HfGnZgTdDgRqFtk/bb4qhmBB2/Y5BLGmQGu2Qhb8CzjYb026HF/sKbHsiFDB4IKtTXKWVvvDgc4YDdxwe3+03lQWrKpxN+mWqG7lL2XDYPd693qWebVRT4YJZvZvP2llPeaDMVRFzhkH4TH1N8oREWyJDRnmkPr4u78vE4ItfcvSG/hzMi07ERcb1m0t8TrQ7+9ac34CuX3uGoQdL2+c8j2dE4Y9rqhArhMiL+GODxZejvJFOTmc3DWNPPiCoxBPsuXxC0fbKMSMpgmduMDVQe/TPftaeeHTygWciuhlabe/1lPa6hUjsUwyS4tkLutQZc8KftkmDql+KJdHJBJCBM5WWwTUig35t3Fgo+x1UzvKz03frzNeCrZkoFEcp4MmHNN/QNMyKGsaf9iRtGh54HsGtKiBGheW+G5QRbKIhorc9XSr0q+9P00PsBrAcwobW+p5+N6zVCjIcxBgrZXNz8gNUDU9XMpu1TaLU1li0YJWX5kr3rNeOrdhzVTEjfb54hoE6RGJGgdCsMBk47VlbNMP93g1d+4fL8t919y83h7XVVaP55A77b99+NXGHqEmm3WPlkV2Lc29GyECXZfXS0dtoz0kigtesV5bJUfls9RiTfadOdltzmtF3+BMi9ZrhLJYVvrOru8II2IkIfdeOI2Drvy2yetjAjbGlRkxgRitxGqEuvGQ4qqFLhxLW58Mdu/p3INx1tJEFWkSa9SxL75DxwGP1mqftutWeQFlYqWLW1Da5Iw/qb5nLhII+Q2tFO/wv1xaP2XCKeb7WtF1mIEaFCPh0iUqyX9HhSeJ7P82md7h+SKov/BsoFEXdMhxmR9Fr3nhYbW2nSOzjH/MbDqSNvN0vsx9vosorBx+krylQzDQBblVIjABYOpkn9QxVjVUmvSkGNVU/84AV41PvOdc7bTZm8yJvfaVjf9BjdhUgCBW27lEMAsLum0DM6Q0IQRLZMuIwKZ0RCgdyoAZ3ULsDXpYYWivzdBZ6B3zU/20VbRoTUoXVQ8OnotDAryPiMCgXVU/MJZP5ogxhJWnVQamRorzNeSDS0t+s149fLjRHz+BSU8i2YDCU7GMnTIW2X4DXDvuMoS3rHhXkDRzWT/XYDeFk1UFkMDIpQTIm83vw7yOd5m9K2Co3N63PrBbIds7bCFKXrc7aJLToAchWI2G4ytjVR2eaqGc9GxAjFbv8Ix4mwf3Nj5YZK3bDNGN0yKahmPOHAPq8q6cOH7LbYsm+sr062bR4dLmzZ9pu2aKcdIUbEPEfIfkOhmBFJVHiDV1c1Y8qj/5qqONvIvXa42lMptz4zj4jvgR1qs37J2zcTVDNlXjPnA1gF4KcAtiIN8f5pAC8EoJRSF/a1dT2Gs7t2JlG70yn7DmZH0dFadKWVLPcd1YxgYyLFWODIGZEAq2MGieRFAMDpnEo4v3XSfZZxxoiE3os0FrmdSx52OVDWykVj2XH/3YUWuC+/6tHYI3NVtIu/Wy9t2uuIwVqnE8i+W2IjkjMp7JxpV0fbyXaKXPusR+2RG3rSeB1FuWYoPF28w4iU7crcurzYLkYYluKIsImX55px49/4k1nqveTueAG3jxoXZf4Y0lOVqdDy9AniWQp54tWsHVR9YZAuVDrvaxMkKR3P6cGZhGCWZCJw0p2/STJoVTNZuzKWo80EEbErBBgcyq61O3aRo8yU+XZ8jJu+tGWyHVT/GlAWjasRXddYV9g2cIRPckoyvgdoiPeAIKKEXT/75qEUAnWNVQHfZkVSI5t2mfoBd30yNiJFLu0Ujuo2UU7sGKl9M54R0VqforV+IoDLAXwYwB+01qdrrZ+Y/XfqQFrZI4Sy77aznWwVYdAM5HA0S+38C8i+6EYfDDA3tpJcM6EgZFK0Qnpe6rB0MHBqvKqxql3MbQ1c2qepuSWYBU7yenCjpaa/3/eXh+NxB67Ij3vGqtqncvn3TtmL7Bmy46FBSA3mpGuooGDKnGqlxqqvfdx++LcXPDJnEyYcRsSWUSUeC9cjp5b0wdtEGxHOrlhGxO93nAUbaYQZEXqtqVfDvq9QGoNtmSogFD9CfK7A8W4YEbq2EG0dALrwu+3qaPc+W5bLTLg2AqqUEeG2S+Z+HmNDqWruu+kxZtTeEsqCztkXOg/kjEiApeAoYrHo8xjQeYKHWs/vJwHN6KkFY7JVQUglY9voG6tyQ/7QRoh7ypVtAkx99FrJw48ez5lLR3jMkt5JgkiJcOKocYUPx8fxjLQRUUp9GUALwJORJr57mlLKhO5rAfiJ1vpH/W1iD0EZEabi4O67IeT63MCl5nhot2i9buw9zm4xyIhor1zJ/XEqYGTrUqj+YNgy6e6IuSASapdkLFmmS+UlmcnDqrUsuFfD/isW4KWP2ce53+72aR2u18wJ++2M//3t7Thu1TJce/cGxwqf6qEl0MmQD9R2RztttPrz1DXRNMEwItRDilPCIXAWw7zP1z1+/7yMw3ZfjD/du9G5j24izb9THZfStXFEqkRWpZNaeJKWhEcp1wwQ9qgQGWdzLPCdLCNSPI5DwfM0XFUDj9xr2tDRWhTMW2znyY2RqzAidME19XPXVqMeypmYnHr3kW6wfIGRtlFru7unKtqyyKpSXRy64Dx3YZWuoR469BRXHRvwUAEcCmFD0bQ+t8081snCsSY2Z8JamdAD+MxjvmliXYELvQ4j0u4gUUpUBcnCn/1Nk31KrfW834ZEiZSpZt6b/XcngPcA+CiAm7LflwD4bF9b12OE1BoH7rIoN2Iq+w5l7rs2vLE9JoUypjYETlK8UNAjs0hr/xg9Hgq17VLO2bVUGGI0PFfNhD1KfLbDW8Byjtos+O75UbatL1ooivzmrfumG6ESAE4/dBdc++4n4dhVO6WTOFl0LCNS/Ixa6B9tYjeiyLWTrfT7mr9lGxFbTtHmitPpVJ9sfq9YNIZ9dp7v3JeyQq7AYXPNuG3ggubCsaYnUM4fdQN88TDUBlKgshAjsnH7VPZsbAGCD8lrhsKJX1IAelprjYlWG/c+vM1jRJoN+55pG7QG2gIlwnN6OIJmEvY8o3ZnlMlrMq8Z+t07muSaCQgM5h5p06NYWaYuqqI175GXGmREhGN0XikStoMh3gkbSQWGBSFBJOvYoS6QMh4FjAhTzTg5w9rpezJqoSqMiNmoNfJ50myamBo5n4vMRoaNrQAjEnLJNWiWMiJh9fcgUaaauUVrvRrAR7TWdwD4HwCbs98/AnBq/5vYO3DVzCG7LcY173oSDt19cSYZplcVIfd6CdKs8M63AoJI7hVRJZeNcFhyPXRDA5Pbyf1WJUTodFY2d38LScpctwkIjEjJzmFshAsi9rfrjVFM/3KKkRtnLR4fSQeeZtl3lb1HAp0MJUYkVwmQskywJs5iOMbIzo7ZbasrcJqdtnLKUrCpwdsd7U3itL9zyper1Li31pJ5I4Ig4ubOkHa0lNrW2rZ9MuAZtmZjGrTOY9YKKOeQoEHjyBTBod61xlu+fS1O/NAFmGx1nGqNAC25WZth6qgkO0bIc79Tep/yFh/ebvM7jyPiec3YctvatxGRFiUFbo/mMgsmkqgZ75spIwK33xmEFmBJXUL7UJG3V6st12W/OWdEAqqZEruNRPm5mbjBLT3L1Y+prYapqzy7SS485jYifrn0eJ6uwlPNKHHuE8U/2oeZOlVqnxsVeWYyIgb3KaXeBOD/AUiUUsdrrR/SWt/Qx7b1HNxYVSGdcAGjSy03VpUYEcluIxS2vUWMXXNGRMhCySHtAqV6Q/pNuoCaRY96Sphr/+dlx+KVj12FXRePu3WFFmlh4HvxJ5j7Li/JC8VM6qILWJgRMYKCfQcaEMdoI6MitbbnJUHGLR95+R4j0rELgvG+Aqgg4u6OHQHWkQ7FqgEUW9jnics6WtwR5/ck7jMWWekDaY4aXi+lw41AZ+DsqoiKxFwyyZg63lRu61K00ytTcZTpuflCc94NawBk6drJh5Bsm3LVmxRAkBmNcsarLI4IkAnebKHjidm4K7hlzAThTbndzND89nnS82NNwUakY8vgZUrgLGraNls5nSvG2JgP2ojkLJgLngvLwHyzUHyd9Dv4x+xv11bQEbazUOyJIGiGwFUudq5xr7Peh9l5Fok4UXaK+PsnHii2nZcFuOoj6Z0k7H0My0akVBBRSn0YwMcB7A1gFMA+AD6plPpQn9vWc3DKjQq0JlS2E4Jb+MiURjWYEFz46O6HLqYOI5ILLRXiiGghZDTpQFPtDi6+9UHnGUO5PEx/pO0295168Eq86xmHefrPULu4DhTwXZBXZkJNfgkrqogRoaqMEG2Y7/adRT6gM88aLDEm4fItIyIJZNTuJzdWbRv1kLuYOYG/aLvYJEHZlZCBKZRyvGG859WUsWJlsXr5wjraTAoZkSruu/R9ThIblFZbewHMOMQ4IrlAKN8jqS8l8DFC7XpotYbml3bykiqB7+qdEP5EReInlHSfIWdEjGqGqVO4TVWb2aZQpAHYXEaEq5o6VBChqhlzDXffDUjNTz9yN4+RcBgR0sf5OygL8d7puJuQkYAQYFRBe5I8L7ztRXFEFNg6wdhqmvOliiCSs2NsHgh5+FkbEp8RMf2Jt5eDHqOsjSS0pJqA8jm23yiLrAoAJ2mtH8sPKqUu7kN7+gq+E3J3BumApNeMNBIv0JMRKlz3qg6AdJI2h127D3ciSK8juSJKctGYtjeUQjvgevi5X9+G1Q9txcdI6usy/3AntgOxcwD8hTGca0ZgREjH3m/FAu88bwtnROh5hxEhC7vUBh5zpOjabVPtXACyC1x4FBYxZjSGi6lTCkLFUUSIGMZOw34bXhZnRHghVJjhbEJ+XMpcDJv5lWLR+Ej+2/ea8fsazzBqr+1g0VjTCSdetEvNj5nnCnwn0+/q7Oqoum+y5d5nFjsp3osUYtvU32ALxmgjQUPZPjHWSJiqSl6URkgcEdp/uE1VYbhv+IbxbjwOt19vFY1V3TJpWxaN2yVkfKSBd5xxCN794+vzY/Q56cLtCyKusJ23n2wCnAU24C52ykEr8ZTb1jn5ZdzyBLVI4n5fZ52gwnS744y5Io+1vGy2eaBqXrddrqDC51ul7Fcrc98PeSpJgpNvIzJDGREAW5RSr1NKHaqU2kcpdZhS6o0AtpTeOcPAA2U5kqXyqXc+4AGb2jpkUJl7rziJ7PxJh9oWTAnnpbbzjkTb8HCWrOoPd6637QqwIwbUU8I8gunDfl1iswilaDHVki/msSwMOCPiUqP0fWhxQadeLeZ+mjyMwsZLaOdGZ2VeM4ARVP04IoAb9p8GpzL3pfBb46jL2OkOEWp4UjNzF9VXm10TL98LHc0WrZCNSEMpjxGhLAZvr2NvpP1+7bKCGiPNJN+Fm+elEDebgUnalpH+GzKzsu2zv43rPpClYyfXWdWMy2wA7obBlMdVM/NGGthv+QL82wsemcZryO7xGBHWXr7jpknRAN8OwrqPy4sSt41zVBFw+/UWyVjVE0Tsgff8xWHuuQIPOXrfWIAR4d/d/K2pURdcDy6KVcsX4DMvPSZnYTkkN2qfEbF/+8yF8gTFIvBkdqFNDx+Pnh0LaairSvLrpEx/k0VD9q5VPNdM4EH6jCqCyEsBPArA15AGOPsygMMA/FUf29UXuAaA7sA1bmxOxtQCI0vXoJJOSv4kLOWaSV31UHit03aEJyAAWJAZb20nbrjGLZm31zy2o/Zg9L/n8x+MrJr+e9y+O9lnINfyQc7bAhR7zUy13UFSpBPljEiRncFku5OrGuwCX8SIqKBqxghe1O6H0+UiI0J+c7qbCsp5unVWiIL77LwKyoiA9YM84iqpj4IGQgKAg3ZZ5Jz3jA2FySw0wU220gWfGjdWS3on7ybt+RRcgOKg37lDFubJVtvpXzYIlT1GbYB423mW02YjwQX/eArOOHI3xygwZN+QP0dW3whRzTgumQFGJBRngjMiko2IuWazGNCMsxT2N1+MizxJHEakEVDNsF5s+jz/5lWEAAkKfl/j8V5C8aYAty9UM1bN/mXsWshYNTQekySgrixRzoyQNobmQ2n9GjRK36TWeg2Ad2qtH6W1PgDA3wH4fnZ8VsGnw+xv48ZGQXWabzx1f1aWzIjwRFTpb99GpKPtfZL3AYfWvmDkCkPGsswea3csi0J3AabzuoxI+NnT+8Vm5QvGkw/bFWccuRv2WDqv3FiQnS+6mu+0i1zYqAxHGQUKOn+N54yIuSfcDqXSdyhdYwQvKqhYutydgCgc1YzAiFjbBZcRMUgS5Qoi7AIpvkkoeVhZQq+/ecIjnL8lwYm2HfBDxBsY74MiRkRCmQrNtFkKV0+voX3ZYUSYseo5f7wfAPDApgmvDdQuiDMiklFgs6FyVqhUEDH3ZIvIVMvty3wRN0KvuCQpd7zR1BLpeVclTVUznCWl9xhwZqJYELG/uQ1aKPonNRSnp6pENZXAjazTY0QQAWNE2MbQZDQGgNFm9TZw1Ywv4Ki8fEASVGy9RfNGeq397TAikmommR1xRKCU+iCAL2a/zwLwHgDPU0p9vr9N6z18/3GXEeE7XjrgTjpghXNvWDWT/supaPubxhHR3nmpI5jrPEbE6UBZm8lwpcmspJgibmwHiLtBgxAjQq8yqg76mp1df2DB589cyIhIbTCLrCPZy9fSb24YkTKvGcAaM0tClqHcqa6de6dIE4b0bgyozQc3VqWUueM14xnf2smbe+2Y+kILB/3+P3jjY3HGkbs55/lm0GUI0n9DHmDGhdQRRNilsj5bFqY4pAR+tgzGiGg7cU+2O+J34vcDrhu/FT5dN1vedrP4SCpfCs5K+owIEwI9NaDbXvq6OCNiBBXTryXVTJFwwVmBovfnGJuGwgN499tvTr9614yI8iOreoyIs8HzmQmDKoxIfp9y/zXjcB7bDFkvNr/dIUGz6JgTRyTQP4JpQQaIKsaqJ2utT1JK7QbglQCO1FpPzLY8M4C/8+budTzXDO3rfF50dlUCtRViOcyi1Sb2KFKcEafd2rQnvAuVYhR0OnZ35qhmzHnOIJB7q9qIuPpmf8KQwHckfGERmR6UG6C2mCAijVw6GPNJIL8n3PLcmFliREhGZe0tSn69to0+S2XQJoJUyBhRwe6S2lp7gip16ZVUdPQ4R1mG3yJGxDxXqM+kbpAKDbKjNH2imSgvq6utM/wMHW3f04QQrj4vgy00NHGbURkVwar2snFMBJFcYBTWpyaxEeE2UX4b03/NNzAMkgFfhIuMVY1tk8EUE7a47ZPjAWhsTwrayiMw82tDc0rI2NSPWZL+qzWcCbxbo8p0jnKPuYJIeJNp25deX0cYyj1ezKYnq2PBWAPbptrWWNW477L5PFH2+xbNG7aN/rPJ7rvusVDS1X6jypvcrJR6LoCzAfwbgLZS6nFIXXlnFfhCw7M58uyqDmNSYJ/h6Mez7xhKljRFdlKyGkdgRLJ/uQROL5USX6UuyuHOx+l0FejABmWUsrWzscduW2ttmqWEToC/uOvAhBPKqMt3Gbw+Cvpc+UTCFukT9tsZP3ij6yhmJijZWNVfeL38H0K7qT6ePxd9Vp7VlSYjc7xmGCiDZO0r2DsKMiLFgZD4IclrxuCE/XZ2/jbMA40lYZ7R2I1I82FINWPGhXkHn//d7c750w9ZacuA7z23NlO9pAHN7INJHl/2vozZ1NTWK8xMNBKVT/KhhG0GnEVrt5nrKmdECgQRvvC22u6GI2WI7JibFFz6i+LTUE8q2oanHbErgDSiMX8uoDg7LoWNI+KOvSLWqwgSI3L3+m3B+ovUOCGDWQmhuD0mWrH3zT1hW0GykQoJn7aNZe677t9l9lX9QhVB5BUAjgFwrtb6MwAOBvDXAF7Tx3b1BXwNcXWlqRBBr1m3ZTL/7TEijt61g/OuX4NWFsQK4EHK6I7EMiKSYavkvmuuK/KakfJNtNlOKn+WJPW2mGK7nyJGBEjdDjl4ngauvnjyYbs453m7AeC4VTs5f7vv1mU5itzVeI6TqvpTvkg/9Yhd8ci9lnp1GKO+xeMukUgTIXqeLgIjctrBK3H8vjvhmY/cIz/mTYBE1WKNEd1rlGLGquyBHbsg5lnEmRKKtzz5IDTY2ODgdRVl8Bwf8XfwiVKOYGvuKVL954JYYEcbopUXM7fj0M56ii3S//Xio72ybNyVTPjs+MJp2c5zv+ULxXYamCtpv6ZFhtQacuwVlwGa6nQ890+qkpZyAhWpWxaNy6T6gtEmLnnraY5XjcOIBD60z4iYzYvbr4rsgIpQpnpLVVX2bz8vjUUtRoTNA1YQcVlZvvmg9VpGxG0vB1V50iBzRZtSg5DHY79R5U0+CcANADYppV4G4GgA5wA4tp8N6wd8SpcsokitpeklrtTNhQD7+7wbHsCrv3wF/vPCW/LF0HFdFNxzNaGSi1JNA5QRKRBEtD9ptDtWiOGRUpuNxGmXZyMi9PAR0sFXLhoDAIyPuhI3ZVC/8MrjcPZLjvHK4YP74F0XOYnswkHZZJdc09SNmQuz1iaLqX9tIjAiVkDKyhPqoP72nBmyNiL2WCiCKZC+u2++7gTsu9zuuPkksfrBLXZ3xHLN2DYxY1XW5jYxlOETYL7YCS/pDY/fv5QR4bcVxSLgUTSN0CQZqxbtvrnNjEFpWG+mZuJ9KtRurnYw9wN2TEssmfQI1K5j6fwR/wLhfnMHj3m0dL5MRodc2/2AZlTKNM9gztPYQm47JHBBhApDuy2Z5yzWIhvJ4KkfyeaFvmfOxFQFfUfc7gmw64AB3xgqZds0UsIQu/Uq51/Th+fldmoQz9P7TdNp+6SxSecnyr6FIqtSDIsRqWIjcir5rQHsB+BxAG5F6so7a8CtFzgjkqoVZInQhII3oIPiwc0ptXvHQ1vzY60AyyExJVJ2Xto2UxUfvEW6THOso4G9dpqHb772BOdcM1HOroJHGpVcl6mR3ZtPfwRe/Oi9nYXD7L5Ms8aaibjQ+cyUCp7ndh/SwNtl8TjmjTQc74aO9o03AXcw8kydZTYipi38O1ADZFM8ZzFc5kjYubK/t0y288ReuSGyoam1/ZtSvbxYKpyYf7knREhYc+2jyndS9En4a+SMiKlXFESyvyVLoxCjVpbLiH5zbjNB590Ws4aW7Bi4V0Nb+4a20ruhc8P19230zkt1OFFzmSArQRprPJIoF1hzhigXROi1YeNbg7GAmkkaScp5t3KZfGzQAGBaA685eV8cs88yR91WB3Qc7rPTfP+8x4hwWw1bQiiMvIRc9cKYSTMGiiIwm+PmEK1XagEdVzwtQ6hdBjPWRkRr/Uqt9SsBfAXATgA6AJ4N4MDCG2cg+ATpWo+HjRGbicIBKxfisQdYXbdk/NkiNK0jcAQ8TswCJjEitHuYSZlP6LTP2Dwb9lhHp4n1nnLYrth9qRvyuJm48QV4ZE5pkFFJW2t/0lBwLfBD8QekHST3ZODPld8nDKaRRoKdFthdos7b518rMSKeN4YkKCgbCjkkiNAcKiY0NE92xX/b8qVjZiHy+4T52xo0+oIIVRWFPE5CDESzJP5A0RTMhWIpB4lS7nusogbgnj+2rdUZESjfzZu2gZZUtIOcIqoZLsBKak0q9B+fxdzZb7lsg+LZCzBGZLclgWBdYlm+UODGTwrbPhUZwRp4777gWncTEDJWZX9nl6UsZ5qc7ymH71bMnBWgzGA2DQBnIdmIdFM1Zzx4gkTFzktu9rl3FDOu5Rh1VDNkHEuCKlfNzFRBRCn1aqXUtQDejjT77l8BuAyAz2vNcBRRqNle07nmf16Wap/MB9xnZztxSNEdUxuR9HeVIGXmGsl9inaQnGFgE7qb9M693/xOXXj9z+zpmXW5nz41zpImLjOIzZnQeC0yAONlO8aqCNsQ8Mlfo9yQiyc1K1PNWEbEvYLmH+J2Hfl3dAQRqQb32E4LRh1qPnQfnbiKPFm47tlGXJVfaNlkF1oIlJJUM/KET/ugbWo2EoUho7xrU5Tl/ODsjjNuHENx11hVWit5CH8amNDW4d9Hn/X1j98f5/394/DlMx9d2F5K07u2TQmWL/TVMyGPsqL2ca8Zijw+ScHKG1KxSOW5qpn6jEgnwIjWgbMRCUjYRUyzUsBTD0+XvgVjxUbHUr3csJ6rbOim1r2fqi9ZgxmoKtSxEamkmgmzwv1EFdXMRwFcjVRoeQuANtKnX4k0wuqshZf+Wbu7h70y6s4syrTjSnE5Uv2rYUTKJUvT16rkmgH8naU4ebBFPA1q5pfFqVHOIEjSM93FSIKYWYSo6sA5L1h9m/voo9DTrrGqhpJ8I+FOclY95D8DfRc2J4i7CwkJMO1cEPGFONu+7BhTzbgxa6Ty3b+XLxzF/Ru2Ayhw31X2O7WEGBg0h5snbOXtstefuP/OeNvTDgHgCqKhrK4h+MaqEiOi2Dcr330HVTMlixN3Z5QCEALpwuuoDwRJhH9f2UbEbw/dpTYbCQ5YuQj3PrzNu462ly5KfBGRhK+QfFtmw9LRsiqlCiPC22HsarZN+sakTpj6oPsuuydvY/qeS2TOUtBpT1S9Ac7LkDJf/8vTD8WbTjuglp2KZyOSfROuHs49+DxjVavybJTMJbSvza+pmpnJNiJXa61PBQCl1IVa69PM7762rA8oymeRq0rJJbyfUmbBdaFKCzrvhjU4eNc0FHaRQMFBQ6JPCWoc06Z5BVlqDTi70u5oOdKj5wrs7qglRoQWc+Cui7zzufsuiiewolwPQNhYVWt5lwpIE3PIa4buylz33VyAEn3zERREcg8KwirxsNu0RHlhlwRDuyNOr5GeJ/134/aWaKxahfI1WL5wDIfvscQ7XmUn5ba9CiPiCsNccJMQEmTLbES4EFgUFZmWVOT9MuUYq5J7Ag8gjacgGyWo0zxBpKBt3jGPEXG/rdbyXGLeUx0WwtjSbdw+JbTF/g5lz/Uy/ZJ3IKmD64LO4RIro7hqRmBEGonCzgtlO50QJJds2p6QMSutV2JGpffheM00i71meH+d7NIterqoIojoCr9nBQptRKAc+wYA2HNZyoiYAUk7rhtUxuLG+zcBqGf047AB2i/f1F+FEXGoZhPXQGI3CsLF83vSyTvt9K973H5YOn8UJ+6/XHwWhxFh50I7Wn+HRuly9z2EUpDziTlgTuKqZph+lkcdddtIGRH3Akc1wyaSnBFhLpNe+exvx96ELQhOPiRHv+i3i6tguFGow4I5bEDxriv0HWgdBpJBo2J12OzP4XJNW7mgUxbh0p1sXePNSeau6sTSERYqayPiR0im5/37hLJYwETT1XMD5+xfKcCb1Da5vxcLn8arRowYzDysqsC4Sm/c1vLOuYxIQBBhh3NVRgXGrAokRtSpH27/KlMjV4XnFaONIJIdd/dEYr25YFgyNjn7VtT2orl3kKgiiOyqlHox0ndEf+9SfNvMA3/H9BsYoy56yfhIA+975mE4KospQTtuWXz+6VJctEjzc7xZLojQhdtMstJuTAoX79LS9o+xZhr9L1HAWzPqXoJSaWM1/Zufh7T7cnchjsEt9ZoRyuRlp9cZ993iHUDuvmvqCghQpvyQ14yZNBxGxBgd5zsde30VY1UqSPF8F/k9cCd3Xq60QHJ2JcR8lGbtLFj7+felO7TRZpKltVfO7rTATjgHV4sYlNuIuO+I3k+jsLYYeyixDjbypc7bHUoLUQZavhljtL1W8PI3E5LwJQu4qlDwN+el9afINimEg3ZdhH12no+znnqw0D77O6Sa8QUR877dv7tFmbEqJ5DKcjBVhZ/0jpfHNgzcRkRRuxJ73AREo6DejdRVvAqLNpNDvH8TwCOE39/qS4v6iEJdrpKzq770hFX5b7o487DJfl1+/QvHmk40zTLozOvBLCi+10wxIzLFPDcoJFdgVzBji9JU+SSQG6vm70a+Xhpkblvsb57HJ2wkyQQrXa6aMYutxxYE7jPt5nFEHI8lsot166O70PIJgdLxvCz69pzw1KxMqpqxFLd7dYjmpc8ov/JwX+BjiDJ5h+62GFff9XBmrOoL9uaItEMPqWbKvGZc1sd1L6beLO2OdpgG2Vg1/dcY9WnWnjrrFP12YyNJLohIiRJDHmhVjkk2WbS90rwHWEa1zto/PtLAr95yqnjOUYsGXlQooFmbpUzoFs5GJDAO6avgm4CuBRG2ITHv26pm0uOUBaOgWZTpO1o45i/hlIEsi6zKH2dItqrlgojW+j2DaMggwN+xuzMwFGX4fipB0+smK369lYvHsHltCwtGG9giGHN57dWuhF5FNSPluAllA6XwaGnSQ42EXTYhqaxNQUbE7CZLjPtcatQ5UWkiMsaq0qVFz1UUR6SIEaE2IlzHaz0g3LLK0NE+A+AzTIqVy7+p/wx+4kf592gJpVtsI+L+PSa4EyrlfosqAc1Cqr1SRsQRttz+t3XSFUQUmRGLWAfXRiSgKhPa7pTlsI5+4K+Q2gwIs3ZSe33B3xU+uTBl0Gua3mFcQ4xI4B7PC61L0O8jefyYOcyAespNtfU0BJGsfLO5YGy1tSFxN0X2ftl9V4rRQ8dunXhAaWbq4Ugi1UPD7QAomoQpDQoAiwRJ01HNUD1zBQOf8ZEEO2UREUOREb32mn+zH777rn9PR2iXNHi4S2+HLdxU0rYTYwkjkignOVVoUvGYKYS9ZmhAId5GXga9zrBJXhvJIWNYx3faoURSpt08e6o5XmisWkL5+zY7ll4xBswitVrAiHSYcEk9fzhTQtsKuJN0Fd2y33aLUSHktOe+y9olrYEhtqSMEXFVM67XDBVE0m9GrxXawIRDrtYIsYbiGCTXrtlog/GJnlbs/rJFhba3yM4hUUU2IuUBzerAMYYP2Ih4jEjiCva9dN8V26DkgGZmzJf1tRD8xHpZe/L5Ac6/3NmhQSLkuoK13x6aVJELGhyO3U7i5+EZFPoiiCilliilfqaU+qVS6vtKqdHs+KeVUs/oR51VwJkL11jV6nsP3nURfv1Pp3r3S1RyWm4VQaSBZVnQLR6lNYR8QtemjHBkVQPagSdzy2xpEhTqIsdWkOiNdvEobq/ZTVivGfkGz2tGAXssHRfPO5FV4XsP0DLgXCmDTkS5IMIFpMACZN4t/36tfFGSWAyz47HXS7tBPsFpUtbWiXSxNFES6Wend/FXw9VtCr4djMOICPYz/Bp7LDzB+aoTW5aNJMkZEfkZKHz1klw/B40uqZTbv7hqxmFJCxZ7Ixx2OnK8Fg7TRi4USZA8rbpVzXDBi5erVLmNSI/kEKcfjYQCmrFvaf7qVVvKgqpxmxozts0msFtGJLQh4DFjzFUS62c2hwvGGvjwc47AUw7bVazLZUSKBWvnmzSSGW2s2g1eAuATWutzlVJnA3iKUmo9gF211j/uU52l4MwFn3TM8rXPzvNzoYGiEYijMdkKq1l2WTyGNRsnMN5s5IxIVUHEyiHZYKhgrEp3P7lqpoLbFl+0jPDxgmP3wuWr1wEosgpIYWjeoNdMXhc/rvD6x++PA1YuxD986xpHiHCDT4UnInq8k7Ey0rXOROQxG3K7zTHzbndiwaSsjQhNVOfqtMv043yh0eR7bMnsikzeCOoeTd+VF9BMa9CeRlkdyQ6BNoGKcmUh6XlsDt4v6c6TMiJFXjPSdKjYtXn5JV4z+6+wSeaaicJWMg9s44JIYUn+jpVn7A6pHMzc8T8vtym6Qs2WWDTeZaoyItw4lz6DOc8Nbg1yr7seSSIhY+gq91Rx764CagMktSFlK/y+PJ4J0NNVzZjf1msmYde5z2vbpfD3TzwIKxeN4+lH7o5GovCC4/YW66Ib1jL2pCjGziDRF0ZEa/1prfW52Z8rAKwH8DkAq5VSz5TuUUq9Vil1hVLqirVr1/ajWZhgAoMT0Ax2QIZcCOnE6dpihD/ezguy5HAjCZYuSJeFxfPqyX9mXBQZdRrQjmSy61aZtKTsrbd94Gn48HOP9Cy+Q1DKtbMJXS9Z8Tez0M1JIu9IAOM1Uz4R2KR31SZrqw4IMzlU177bknHssXQeXn7CPgC4+y6cY1J9MiPiC0XmXmPgPE9IwuZQ6rx/eDv8YldOOmkVZZ5Oy3LHTrBNcJlEapcjBcgrZkTca/N2lxmrkt9JElbNlNWfns8Ykbb95pVUM0LbQ/Ykso0IZ0SKhUNyoZhAjZZD7boopIzeBv/78mPxr886XKoxCNpkOpe+75nhuJjc22vaXjOO+lF+h/R1GWHMqBe7Vs3wdx5iRMzzckZEKcwbbeA1j9uvtL+PNigDWHyty4gor95Boa82IkqpEwAsQ5qX5noAHwHwaKXUm/i1WuvPaq2P1Vofu2LFir60hzMiUmAfSolzSAGYgGJXXeM+1eronBGh6hNqi7GALTQaGlfesR7rtk567eVtkI4Vue+KjAjfdRk6Wdg9S6DCHOC/x9BC4iyW4CwIEUR0eMdKhUfjBSBdKw1ivgsRGRHlGqv+7qzT8LQjdnPucwSofDfp1pHeL3wPwUYkV81ki+U8IR4HfZW8VJ4+3rERIc+Vt4G8m4c2Tzr3cdBDNEt1+v3ca6nAYTKWJko548lLeifMhyFDvrKJmceOMeMJALZNul5sRXFM0rLSf/Ms2iwtRGihMm2k4zPUbiX0Gd+bRLpP6tvFcXuKvGakjN4GTzhkF7zk+H3E9ocQiqy6x7J50uXOPT0zVqU2IsxjJf3tGmya72XY6N6oZmhkVddgPuS+W6des+Gtch9Vhe2QxqpKqZ0AfArAqwA8CsBntdb3A/gqgFP7VW8RPNUM+6NTsJMGwozIRIGx6mW3p2qNu9dvy9U9myda+WJEE1gtZiqbz/92NZ5z9sV4yed+nzaR73ilyUPQf0s7cE8Q6cgLMEA6a8lYMNR/uCubhYQfJYM0Uc67rZJ9F2BqBa2DAqWUH8JcVmSn4AQUMpMGW1yocSi3EQntBkPHqGHulmyx5F5TXGjz+kfHfbdK+c/IJyIDamBalVmybQ+rZigjItuIlAi7yu8/pYyI8+6TXMgZH0nqMyJG9UYYEcdrJtAW7iKe1mWvpdlkbeoBcj8bwnIflestZESgEPIW7NXiL6EshYCBz4hMr15XEEl/H7vPTqQtLhth3oFRd/RGNUPCtWcn+Pj3cs3U+AYmqNyKRWNieo9Quc0k2bEYkcw49dsA3qq1vgPALQD2y04fC+COftRbBp8Rsb8VTEwAHVxwwzYiYUFkb5JqelnGiGza3so7y24kK+6icVdl8+Gf3wgAuH/j9ryNFGWCyF3r0lwWuy72s3V67ErAywSQXVAlKOWGyQ+NHd99121XKBdIKDYI4L4bjXSnKj2P5HdvrisUoZQicRVU3lbA7o6p8MC9Zlz9eLlgCNJ+U69Z0OnrcxkRLsz4k1mR5xhtw7uecagtV4qnQX7vt8Img9TwBQVq+DpCYrfQuovYKF6vJ+jUEUSIULT70nmOjUgVmKoMC5raiNjzwRDvDSOgyuW+8rH7kjr8PsPVODJLJRyDHxuCJ/ZLmWBhLmlPP7dLCG7k3iJBhDEi06yXvscFY0187dXH43PEbkcpxsgaTznC5HVVb+B5uRFzmBGpV98v/vZx+OnfnFTaXq6aCSSK7zv6xYicCeBoAG9XSl2E1EbkVKXUrwH8NYCP9aneQnDmwjPUS1ew4MejlLoUr0PCu//C6j93yiizzROtXOjYnTAiEvXugLVL6jR0Eb9vQyaICGnDvWy1BWqPyqqZXNI3k4Zi5027i1UzTlh3ZiNSZSLIVWzCOVEQIffx9tBruJ7avEPrNRNOetcVI8IWPS+gGRMseLt9L5DiyKq0T1AXc5ERIde+iiyi3HjTPNvXXn08/v2Fj8yFsES55Zp7Vi5Obap2FrLLpu1W3u69zPCR9kP6jMsXjHnJ2UoFIbYwVo3yevCui9M6A8917Kpl+e/cXoDMzl4CyYAaxj/m0+2Kn9d+BmEgfcZ+sCEA0CjxyrLnjAAXDs5YBzzVwmMPWM6cB9z+ZTYBRjXTLV8QMgrnQqc5N92IrgftuggrF41XYAvdsTGTI6vWhtb6bABns8Nf6kdddTDZ6mDeiA2lTEekgoJGJ2hbAIRDvBcJIstJciTDiGyZaOV60d2WWEaER+zk4O2SaDR6zAheoTTszn2dMNtgBm85bZ0itKCbP/04InSQqjzYT9oue10RW0UrM8aq0rULREbEbZcYRySx7tDW7sPUZ+63Oxceg4HOB5VUZUSoKcqC6jAiip8rct/1v2nIeFK0RyC/qbugZG8w0kjw2APShfbWc/+clyAJIi85fh+8+Ph98PTM/kaqVyq/EAEhcKcFo2IES4rPv+JYh9XkgqZPoctNeOOpB+Ax++2MR++7k3ieesRJmlDfa8YvQ+q3Sskp5SlCNiJFc+F0McIEgiDy9539OU3ByFUJCdUp911YG5H0pXVrQyEJH+lv97ykwkvPd/fcdTJTjzQStHX1yN+9RL/cd2cknnTYLth96Tz823npZMilVK2L85lQo7sWoSO4amblojE8sCkNUETVLaJqJmMrEuW753Lwdol0qiAgyVkX3b87oZUbtjOXuUnmu5cCIzdbF73P/e1mECbvtoCtMlg6fyTNH6LliVlinXh8CqmK1Q9uzb1XJOocMKxSkrXbfQfKGfASI8K8ZjrWeyuPOSA07LDdF9vn8FQz7sOkAedyScR7hjL7BopQOHhpd+1Gs7UTLa3O3JMohTOOlIUQc57v2nYhqsdF401s2u5OpgtGpeB8wLIF5W70px3sptTi74IvTKEx0khUUAiRruV18W+z17L5uOrOh51jwYW1wN7AqOu0TgVKGhMpTbbXJ0aECQSfetGjRDWZuYzbZ3ULugmQNn5UWAfsPG+ChHXLGITiefD5wZwy9ZjcTGUCRQh1vGYaia+6HRTmVGTVJxyyC1550qr8b8+ACMUGkW6uGXucCyL77Gx3ULSzGwrwMfvvnAso80YbWL5wFB941hHYi+y8JHhxIkq8Zh7MPB+kTlzFa8bACDJlunhuWFaU0ts97v6mz+Bn3w0gt0JXWdI7Wb8donalCZuC5ggy7a3CYkjBqaTFyleVFahmiCyxbMEonnDwSqddBpKRYlGUzXqCiP3NI6TyyYx+b3Ntq+OGy67ivpte4HvU7E7srKRnoOo48+7njTREwb+sfl68zzZMf+HmNH362y33A88+Ap960aNK65a+OWda0nlPC1mlCyzYpwluDP2Mo3bH84/dy7sutxHpUUyTlSRQozSf8fdlPq/pK90G/CrygEqPpf9yxm1smvFL6iSEHGZk1TnFiHD4HgW6WDUT0EXziK1L5lk9sOnsJz9iOZJE4cJ/PAW7LB7Du3/0JwDpAnfFO54IANgyebtcb9ZBvIVG6DNSR5I2ad7OruC5zTOUdWqz25gqSZblL/juIOXCh0ERW2WuaiQmeWGFRY21oEq+E3ref4f2HLcnKQvm5Aki8HdHXtTJ7M+xPH+Lv1unRxSsXZE5HjJWpZB2jvRSep66bxtQd3UjiLQ7Hae93H03BPqdDI4j9hWS0E1Zydw4UClRZVmqfiwR9rqNM0Ehxe3hxS4ca+IZR+2OW9duxifPuzltm1SWwCDxKJ+GxRptJk4OrFYfjVXLchnxc4YZnW5zqL2cmGtGuUxzzog0p8eIhIRKUxUPYkcFoE1oTcNItqxddF5KdqyAZjMZdBHkC7SNIyJ/9FBY4u2MUqSDd3ykgWve+aQ8ouK+yxdg/mgTR++dTp67LLYS+qqdZUbExBfhrZJoNCncfCVGpMBaWgpPLcEsSCZwHL86X1hLvGaojQhdxGhSuRCaSQJoN8ppFZjATlK7Ocxr8NVbJNcMsydxGYTwjoiWZZ51ipXFTeYO3GURAD/CL48j4jwjm/jS56n+vkJxUTpae32J2uWYOCLpImfvq5AlIWuvL4hQY0OJFVjgMCLmg4So+WpCqEF/GBH/WOjb/O3pB5L7ZObKZ8bc851O6jHGF+Z2p94YqoOyFAK0fYCUzbo7UOEzlPTO8drLjVUzQaTLdbrhCH/+eWfDoHwBqK7XjK2rhBEhp5uJwpDkkLnHiNBJyYkOqWwK+9C3C00GlLYH3I8/f7QhCjYvOG4vHLLbYhy119L8mBQ5My2jiY3bfalYogklw9myhFtAibGqqimITGW7F3a5+duPI+LCsRGhjIgOCwnUL99ku6nPiLjtDCFsI2JFBD/XDNl5SDkuVBrynHrgIJ+Ew6H6Aat64ImyeMjyRFjEHUakxgujV7o2Im4d73vmYThgpQ2xPpq7sbq7bRvVtqxef7Kk8VWkZxiVMtuiu0WtXBCe/sItealVaatsI1LMiNDsu5whaus+uu823DaEwI03p2uy4gTXE0O8u/2L55rp9n1wbx2/XjjnjTBvvkm3Am4t1UwjqmYGhpFGgiccvBLn3/iAR1sj87YIfbrK+REK3O7ocSqEAGFj1Tx3ACtK2kWaBGkUYjRRdmyq0wnufkJBdzjGGoYRMQ2Tr/djWZBBmrg7Ejeyavh9mhggRqovElokpJRstWut14wgGGaF8Fwz9NJQP+K6aZ9dqTcZ8fclRVZ13309BsnA9ZpJDR9P2G9nfP21j/HuMwtBq6Od+uyzlzESvmpvnIybkAFiXr+JY5KoQDbSwupL+1S3RoVuGwxb5R8rvs8/ViZ8Jspm3+VZpaW0D70CFQiKup051ysbETr2QnmBKHjG7W5r5+/cIO/1VDiEnT9Mf+62X1UJuWDQTKJqZqAw797zmkGxsWpV6rrbwSvprAHZ4BGooZrpGSNS3F3MoDGqKl4t9wLJjyv3GpcRseeK2CqHESmLqCbARJik7QxeG2CIaFRZbtfhes3I71F7z5rew12BrbGqu3PWAH74xsfikZmAK6nA+KtxmZo6goj9TZ/H2IiEusoI0bW77rt+uRIk40sqfPAM1eYegzykNsop8lD9RejWqJCCBn3Ly61QrOh2rlShi7KxEdHa75etdni8TRe0fxS9U8/maporFu3jXPBK63P/nk6EU7dcmQHiNiLmt7URmV4gtTr9dfel8zDZ7uD2B7d0Vdd0MOcYESCw4wBJehf4dlWl0oZSmD/a8MJHlyEkiFh3WF94SI/7u/ml80fw8NYpAAErbTZhttpF8VPSf8sWKmsjYlQz7vVh1QwdhExHSwwO7t2w3bEfkdtqB3GouRf94ynezlmpcrdj20a//wCZrj2r2+q0078pDVxlsepoaTfoXmPqp+04aq+leOtTD8YLPnupoJqhdjD+M/A+8cM3PjbYh2mf8t13w7Y8RjXTYqqZqvEZFAT3b/IpeRh8gLFRRDCUGKA67o4SejGJz8/cjfm3K0NIsCoKaKaUVafxMVElG3G3cBmRcC25J17uvju9FlFhS2ImvfhKJssy5Dm4KhwbERKpWnSXJVWMTlM1U8bi03YduEuqQl23ZRL7Ll8QuqUvmJOCiEStp+67xrZA/nhVB0GigN/982m1w0eHVDMhI8o8lbRSaLEOvXSeFURERoTJPK1OpzAGAhD2GjIYzVUzsrGqQVFkVb6D43YPP7j6XnzyhY/yyjSP7+ieAy1YJQwyMyEXtdu2Mf1Xik5rJixuI2LixgBho2delgE31ONzlz3usjBp/XB+F7rvsv7NVYcU9MqRBhdEwmMod99td8QAgaUjTJi86TOUCSKmzpCwVKX+Iph0DNPBfGOc7ny7KoKILFgVffM0Um36zbjNRIt5NvUSPI5ICKb+H159b0/qdVQzkiDK/q4asC4EiXmkHjJjzMCf12Hm5G5VM6HNLW8fYOPtlG32+oG5qZoRdoOp7rmY/peO7ylkjkyUwrIFo058gyoYE2hlgLhcsvpzWk8YHUtIiG7Ra4Yd4ws+hRk4VRmR7QFjVYNCX3zlsiB1/fap+qjO2HVUMyX3mfP8HRrbFMAXHugCWbZLWTp/xCnLwHPfZe3Jr6P9mruos74ksQVVQNsyxoxVdYGRo3Xfde0PKrtOQ7J5sPeIAevIOzDvvtPRYkr4qmxYsH09WLetIKJsX6swU0t1c4YR8NUiWps4Ij4j0jdj1YqCCMe0bUSc+UES3Ny/zbuTVChVIGmJFfl3fmZoTplHqb926zVTFiSTCoTGkYMLX4PAnBRETF90d8w2oFmoq0md8HnH+EF4ut1FSDpLIGwxHoo2qBSwmMROEAMdcdVMgY1IVffdkTyOiPH5l6/3qXV3t0CFoqqDwlzVrddCyohUNZhUzr8GRtcOWNZHao5krU+vNQskXXCLXRwzRoT97Zfvuyh3a6xKwVmIdoFqhgY0c1QzFec+paxHwemHrMQLj9vLKUe2EbG/TR/W2n3eFVkqhtK06RUEpTr41VtOwU/edFJ6b3bzfOrZV6HeN5yyP5bNH8lVOk57VHFkVSPYae0b+hYlwpwuQkngyjB91Uy1sW1g7LP2W5GqLU48YOeu6pWeN8lU+ACwddIPmAjY+azbsVnGiLiCSNoWyc6w35iTgog18iPHlM1REursEqPOM+bycutg2YJRvOOMQ/C6x+3nHLeZJ92CjbTOVSrNRJXm3/AZkU6pbUwZPWikd0Pt8cvN32WRHt0Q7+617yZZYSkMm0EH1reuuKuwvbwNIeaJI+TOTI1Vi+IehFRgvFz66LQuniWY1+B8J/KTCiLmeCJMelVAn8tMYAZFgbBGHPddgREprdf2n2cfvSc+9JwjncVjr2V+LB7uGWDqo200i/90GY+6C/c+Oy/A4XssAQC8/IRVAFxWJ1+0Cr7NPz/lYFz1zicFAs/5qhn3vLUR8dx3+xjQrHtBpHf1SuBnzRg8ZLdFuPis0/DSx+xTqz6zPtBNRf64Kv3+gBsdmNqgmbvKmI0QQiy7bYt94lAYgEFgbgoiZhKm9GD2b2Goc+EEn4SB6VnOv/rk/fKEeAahhd30bT4BJUp2TSxqY6sgeJF5T2U2IqbMqcD2Nt+5FwQ0U1DMWNW99pmP3KOwDXQxvXv9tsJredv4Ah8CZy4MjFoC8HPNOG0MvEdLw9vFmrYvdD23HXEWeFoGfJdvWq7EJoRAm+MZ+BW4fVLWzA1oVlUtRr2bfByx5xL/HvKbCnluDpxq3gmhYfXXp+wfbFNVvPPph+LG9z1FtJ/olgm4a/3WXFVqELKN87xmOhrTe6Iwmo4gUv2+6TI0dRIkuocVdl86r3b9+y5PmZSN2yzjQb0gH3/gCnz1zOPx2pPt5pOyYNx7pi5KVTNUrZmxM9FGZECQ3GFTRqTYfVcaMGXBaboBv92GTJcZkSP2cCffRKnyrIs1VDNm0ijbMZs6Q4ITb7cBt2OgCQV9/XY9AasqHPuJkmvz/iNEVjXgLrcUocmQBz+jz06/J9/ghpgnwM+RU+SiLBl6huDGRVC49K1PwGtO3heAr3ahCKtmKgoiqO7qm98jsD4dpnaw3gllZdl7aF8z7246Yz9JlPcNbJ/orszr7tmY/7YeQ275nY7sNcNZo17CFbaqVzLdubU8X1ZoM9ZdfR957pH4f6ce4CQ85F53Jz1iuRPTxBxXsBuJbgWR8g2p/W3qCG0k+4k5KYjkH9rRlaY7gyLjyMXz/Gyd0iD6/e3retPADNbmwoWx0Kdh4oHMLaxkdy/GEQlcmzMiJaPRdPpWQHAyfxUtpIlSucsc4BsmhgZW7jVDzr//Lw8vbC9vW/VcM1lbOCMiMDmyaibAiJjzJEOtgeya6S5SOr/WXkwj7To2IkJ5dQSRBgtIteuScey0IO2HrU4n+J0M6zLVchmRqjbJSlW35bH32OvMu9Xa/X5mES5rB62R2huYSbzXNhWmtF4ESjNCYJMZdBuvGfMMKxaZ79i/OCJVA5pxTNdGxPTL3UnOGQpaOk0d0G2o+50WjOIfn3xQLZaLbkjy5Hc1xmYdSGrLyIgMCNZYiBxLsp2WDkvdKxeN4+d/ezL+8Uk0v4N/3fKFY/7BaaBVQFsr5YatB1LBoUzP100MhbLdhJnkjUQdupq3jDNTRTYipawMmeAeWeB+ylEv14wRzNwrSWBVL44IhaTLT9sAp1z67EVMEJ8kqbxI3yWd2CTUEUTod7Bp69O/2+2wasYYVG5vtQPuu2VvX9VmRCioIbC0GyyLLEkXD+qGnQsi9ZtUqb5uBRzKlhrBiQpQ6aYFgAZ2XjiGr555PD73sjQvVrtdntupWzScNlSvY7oMjVIKX3zlcfjBGx8bKN8XLtP7plcvxUhJlFbzjFToHu+SESkDFXCtjV9kRAYCpdx/AeTum0XGqgBw8K6LHb2bUsDlbz/duebTLzl6eu1jf5vJUfKqSZRy0pybY+u3TgIAXnL83mId0g4rKDhk/bI0+26+gBYbq/o2Iu6kVGQjEvo2NMR72bUS0kHvtjOEYEAzGkckwAoB8L5X3obsC1D1Aa8TEAQ5VgW9lk4qRv2Y1uWjjo2IG6vE7uAAo3aRX6IxxJxiUTvbRQ0jcNVL0vlqgqqnmmnY42X1SxjJGZHC22ujyYS8uvj+X5+Y/5aCYxmWzKhhTnrE8jzJZquvAc26FUSm36JTDlqJlYtlRmTMidJLjYanXW2OZu4qXs6ImP44HUbk315wFP71WTI7TOf03H6rKANqnzAnA5rx0NjZwSz5U/ng46oEQ2UaTJcRCY218ZEGXvTovXDBjQ9gzcaJrH4/WV6igPVZMLMnHrqLWJZophB8cH+Rl8s0hojy7tb87UVWdd6nq06YYtJ5mWqmUTOCqW1bjcRrhrngqhnCiEyxXDNAytBcfdfDBe615l+fEXG8ZphmgpdH67R5f9LrQlF6ATdnSxnEYFCGESmwEaF91VXN1PCa6YSfQYxUSWDVXu73M94FZe7itM3bSRCqBaNNnH7ISrzixH1LnqAeGnkcie5WQmp78ODmdHPyi+vuz4+ZJG/pvJfNi4SR65f77qhgE1EF/VIVGdD+6QrmvavYMGmhZ7HzgJ3/urURAYBnPWrP4DnJfmoYjMicFEQSYQJXQJY+vtwgsl+D07ZFLn98pIEPPvtI3LZ2M077+K/ytvAFsZEoTGYLUGj3LapmAu2py4gYQYJfbprJd52KXUQX4FaHW/wH2sjaUHStBJp1s0w9kHvNFLjvSjYi3379CYXUf5H7btGz5OcEIWOSRWwsshEJqYwkSIxaFUZkJEDJV022Rb2qulmbHbdR8rg5I1LSDnfiTjDVzvIqJQr/8/Lj6jeoBDQkfa9AhS3DMKVMcHrMfsewS/90Qfta2Xz77mccinf/+HoA/Z97KQvCme9eIWdEglfYjbJx/91vRX9CrtPxMH+0iRcet5eTLXtQmJOCSK6acY6pyowI9xjoNcKMSDp4lxMGJlH+gkhTf4cWlzrGb1UFEW6syl+ktNM37bXXuBMll86D2XeFOCJ1ggDRRboMoYBmJkIlYJ+BU5+FDGt2aa6aKXHftefcv11jVXfRKVpn68QRETM6E0Yk1FzzHM87Zk9HELBqsbJNgBytsipCKgEzTsoYESfqZZdCbx1w+5teI1GWyeMeOp0Ce7npgu7wy56Nzw/9BI3hQhmRXlZrWKqwasb++/HnHYVr7t6AlYtkVdJ0wd13P/ScI/tSTxnmpCBiwCMMmp1BqZ6a/u7DwAgVaaT1xeMjOGiXRbhpzSYkSonUvKHgQ66icgr0aoxH2flQZFU7wYUZEW4jUldfSeus52lgGZGy70/7TTOxuXEoI9LNYsmDV9FAVLLXjHufFq6d5F4zBYbEdeh/URAh37+IVbr9g0+DUgqX3vZQfqyqakaBBj+bXh924og0qzEiVHjq1uCyDvJcI31agel4M48g5UXpNajKqE4QuX61xyDEiPSy3tGSdAKUsV+5eBxPPLQ/QoipYyZgThqrGrjMhrUeLw1q1KWhVVUEGRGBKlTwFym64IQYEZsDJcnVN6EnMctrZffdgJePx5iwtpg2OF4zFfWV5qo1JOlYXWPVqosh1W/TBaKjtWdJWrcNADX6JTYi1J6ixDU7pPJwPIOEZoUivkowzz1/lPZJ2+6iuFHcuBXwo+0W3cujw9ZBI7AAjjYaldrhCqHduaDWgaHxeznP0LYq0HQEriBszvcb5TZZpD39ZkRG7Tcd7ZPXjOk34XhV/WXBnLaUBKkcFOa0IEL7gZngKhmrBn73G+7AsBOUZKtgJtRQ/hpqh7AyU/WEBpuN2FncPu6Hzi9PhAWWX8gHZ90ETBu2TZH6qt/n7LSFF/HVM4/Pf9OwyW7mWd+jpZYgkv1LM8Tm5ZCX/+zM+Oy4VTs5dZSxMIaGD6HOpGTauPdONqS6aWKroyvt4Kneu10xvD5Q7PlThjLVTJmtCq3TTdzWn5nAvMdeFP/Kx64CACwimaCVUp6reb9Vzxx18vf0uz10PDvft4czfZmNSCII6v1CaH0YNGZGK4YEbqxqNrTlVGF/J6BQp28y//+0fr8N7Q5KbUTMwkazbobqzTUWJY9qqOpQJNjcRqQgxDtvQmVBJLtsC0keVctrhizS0l0nPWJ5/rsok66fGbZyE0qMVW1BJz1iOVZ/6AysWr4gb7tUDocq+IvWWwXm2pAXTJVJdPnCMfz2n0/FwrEmURmVsZEo9Pyp2u60jfZ4ZUGEMiIDUc1kfaIH5T9i5SIAvls4f5+hXEX9Qh3VTL+bQwWRkS49e6rWESrTCp/9f/mlIe8HhJnRigFj2YJRAGzSUakgUpRrJr9Uyb97hkCZUursJPG9ZmgW2KCNiLKLXdVdV9kikbt/dWRGJK/TC/FuwdfCqYpR/kyJWyb8nA5VoFCstqCghnZcneEZvNboH1xH7wQ0KyjH2oj4njrSdbQuilrGqlkB8wKxFqoKNXsum4/xkaRGiPdqged42gODkI3IWEVjVfpYI40BqGYMjd+DCsaE6LGuOzSyf/snYL3+8fvj4F0XOcdKjVXJl+7FeygC9eqiv3urmjGFyYX220CZYrQ5gEoqYE4KIrtlwWwe2GTtCUxn17qKwVx/d0KhEiUDr0Qpr8Nun2qXe81kh7Uup9GrRtQ2NN/WydSl0dupZ3Xyyb5o4qvq1mnsO/ZfYV3P6kZs5LryEBxGhKvFmNxUx+7CvGBJIChqEz8T+pzcFoejDiNidtFUEOl2AXPsPkqvrZYlmac9MAhlfR2raKxKv2e3GWTroFli2FgHNoy960nFXc25DUkvcdZTD8bP//ZxzrGynT+35esn6Pd1GZHeVWyC34WexQoi/RcSjG3UsDEnBRFDJ086wZ5oQKvqA6MvhEigfkknreB32K2T7dyIMLTLNfd0tN1lhOo147E0/HWisGC0kbMSvteMv9NPj9vfvAnUsNX41BfhX55+KGl3vcWwKJQ+hcOIMNUMf7Y6bTCXFnmkSLBZjd2//QuL668z2W6fSoXNcWqsSs7XFgIr2n0oFCfuc6/04RiYCl4zZcaq1FNGYih7jXuyDNK9zDWzm5ByHvBtlPj5fqEsWBe35esn6HgeoTZ5PaxjpISBbggCYb8wMkMYkTnpvmut+8kxVA/xTc93m5WxsPzAcSdxUt4WJS5S33zdCbjgxjXB/CG0LDP2QvUa47ZN26cCV1jMH2tiS8aI8AIbAUGkiGGa6nQwPpLgK2cej0N2Wxys15Q4nbDMZYzIkXsuwbV3bwjqkQGf7RmpYQAaymGTngvf59uIyOfc49ObgLZl33j+iGwjUkf1TENZVxGWpHH6kecciX1XLMB9G7Z75yjcfk8EkTygWXH99J5+7ZgpHtqSRkPtRULU+aMN/OeLH5UbOQMyizVILxWggiDSZwaaghpvjtZwMa6DZolNXtnGsJcYLbFXGRTmpCBiJhMdMNqqZazKOtOhBYvldNFwJg37L91Jm0X+gJULCyPkORNyiYS+OBNENm5ryRcQLBxrYu2mCbE8KT4Gv04KEtZQypk8i+Dsymsaq7ZKBJGvnHk87l6/1TnmqWYqZgsOtSF0T1Gf5F4z9NrPk2ifZaqZOjDqN8dYlawndfOHVI+sCtFG5PnH7QUA+NE192ZlyveHAmnZgGbFkog0borq6xUmSITcbqEU8PQjd3eOSULrIHbiFKULrhJ/9gVNZ5PRH4HMumQHzvfQU6oMeY6k/ldViDmpmjETZofpSouCPVHQ87yz/ORNJ027faEOGEo0ZibEOsaG7u5VeccoTj90JQDglINWlJY7P+BFQesy7/moPVODwtCuPT9W4bkk19U6dLZS5YzYknkjOGx31wiyybb+fEEdqUGZFbnt1bMRsUdOPXilvS7AlHSDo7LMxqeR8h2vmRp9cdtUG3dnKogyg+j0O5WzJ6FyxgKhxfOAZiXyEB1j/doxS6A5g7qF1EZpEzAywOeqgkEEWDOg39dhvHpYh2UhihmRueS+OycZEetGao/RCa7s+7veB+7FvbDqrtL/KCOSu93VqLsojTvHYbsvweoPnVG7XKm4hDAPH37ukdhp/qgTwbCuoaaB8RjplsZVUEQ1U/k2wVjVFW676Q/iOyiYL8y53KY4cG0vJ7Zj9lmGG977FIcRoWOhjhC4LlM/pGUUX0vTF4gLa0ldNAaMa6xazWjPsRHp046Z4p+fcjA+/PMbc5uc6UAejz6rMwjblzoo2vj1GqOO/RcZSL1kRBLfaJjCdKuBeM1kz/iS4/fpf2UFmJOCiBQwCqChjst3ZQb96CxVgufQfCeNbgSRrAOONJSoG+4WSckkRqO+jjQSLx239AxVnksa03XsdxLVXXwKz1iVNKSiI0gOq5rxG17MiDBhOHBtkUqxG0hZn/PfXQ6MKmxkkWGreeWhb08FDrrOLF84Wql9VMAahFHnnstSw9JeMCJSv5ZsRAZtrFoGd74dHCMy2qc4MWbOCLmKl0Ve7SWSROGG9z5lWtl9e9KOodY+JFidurt7rZofxKEz+6Bdq9L/zCVKFXtbhNAk6cXNfb3IZ9EoWewaiSr0TOCqDqDagPyHJx0IAFhJ3DbrqWZUHk6+zgSw2xJXkKpq6yDBLgRy+0LwbHEClxZ5J/UC0u6613CZy6J3Ip8bDzAiRqhaMm/Eu4eCjpFTD5LVUr2EMb7ePtkbGxHvmHBeKZXbR/RzLQzFeuFwBJE+r1hUuO6Xasa829BcYZ5xUDLgvNFG3+OzlGFOMiLmnTt5OIDKxqqDmHDLQO0JzKRbx0bE2C40k6QnroF5uyowIu0CWxzpGaqoMZ/1qD3xrCz0+bMetQe+f9U9NVUz9PtXvg37LnfTc1eNhyG2oYARKXK+CUWw9cvob2el763bukrZSKhpZd8NJTJTUPjJm07CziXMCO2fj97XGlD3ax5YkaVfuJ/kUOoWZbZHTtTYJMFUu91Xz40fvPGxQfUEBf9O/cT8Ubsk9ssrKk+FERBEBhlHZKZgjgoiRiJ1jxUtkEFkF//d6Qdisj39XQtQrdObtSpR6IrRoIzIn+7dCAC48o71NVvqo2wBojYi0nOKqpmaA/Ijzz0S73z6ofWkfOUHdqqCbgOwBZoAQBa8ir1m0n91iY1TqH+MNhMctMsi8VwdODR/t6qZCmxk0Tgtq5XaiDSY0Hx4hR06v8f+7s+iYfLxUKPgbiE1kfY1+smaDQVM9debIn2X9WoY5MZvREip0ZNyS9IJmI3IHJJD5rYg0q1qRrLifvPpj+htI0tgdgY0smqdBdsMsmaierLbMghN1PR8q2AhoV4mzezauovaSCPJw/hXhUJ1Y2UKLiBMJ95Drpop2blyVLURCX2bm973lNr2LBIG4d1QlkG4DGNCBmug+kITsrPp1wK5eHwE1777SVg4Ov2pWmqik0iTXGHYgCEz9gDYOx/g6ux4D/XwRZiAZiFXcWusOgNe/oAwJ21EzIRMbRXqqGZcG5Heo0qZVp9L3L1qec2kn74XdiEUbgpxeUEtCqNNvRJMuPZBGFKVeWOE0Mv3V2isWuQ1o/jfcptC3yYUFK8uHNVMl5+srBUK1Ai4fpvdNtL765c1KCPKxeMjvfk+QhmuC7I9bmNZDH8xpC0YLCPS34BmYUZk5rz7QWFOCiJmQDo571T1fBfdxkuoikrGqsRGpCgiZwhGNdPr7ItlBpFJUhxKfYQUYMKHV3WtnA4SZfvD8ASRjKUSDEIK2+QJIvJldWyIukEvGJEqqpnpMCIUjmF1F2UNMr5FLyB9/9GAzYzNyD18DOs903HYS/sqw0JNteX1Rsr5s6Njbgoi2QfmO/PSXB0ZprePKofEJOzHjCLNMyiFrtx3zUTTa0bE9ZqRz1sdv38FZQPGsjZST4d+oVuavZcToympbkAzHlk1mDNomgtvGWiZ3fercjaySvbd8P0yzd9dWfLvmQqpD1G2kZ620T+H/2DDes8OW9TDKagsiNhMeveDwpwURIwl+kEkHXUdffEW4krXD/pMKvIwZkhHvWakQERlMPf3epcsRX9166XxOvz76S7E7BwGwoiUtDt4Xw9fX7H7bvi+qk0os9+ZLgZiIwKaa6YLdQr5Pd14Gc6GZBasGZJw6NiIOF4zZqfT92aVwlXNDK5B/VLNjJVsrGaSfc6gMCeNVQ/edTG++4YTHT92R2decj/N+9CXnSX7+zuvP8ELLW6uUcq679YZLGZX2UiUo5aYLsoYkSRRhYaRZgKk8U3KBm4vsNeyebjhvtR7qM43Ldr5/+otp9RqQ24jIjSgqB4jRGkUf8Res19eOwbgNeOo/oourNCfu42r8nenH4hH7b1UDAY2k1EmiNDTM2kxHBYjQrPv9lI1M1bCiORpMmZBn+oV5qQgAqQhqinqRIWkKp1+TEC8yGOFhG9W+OjO77zVtoLIgtEmNk2UJ7SrgipeM0Wggoh5y7c/uKUnbSvCXjvNz39Px32Xoq7nzsZtU8H76uSaCcHN3tz7ftuLOCKlDN001UshA9M678N4yN26drNY1kyF1EZXECGMiAloNgMokWEJfNRerZe2gGUbKxPLZPhvfnCYk6oZCXU8YXggtGFg7eaJ/PdYbvxUPQz0fisW4JDdFuO9zzwc88d6p/ooU3GU2Sk0iZHc3evSTLd3PLTVv7DHMBmGgXq7wCLBat5IvfdqUr7vysLel7XJvOcyW+t+q2bcBaO7MsoE1co0feAUXVin+z7cttS/f9CQ3u1YQ7YRMeh3JNMqGJpqpk/eemWq5qvvetj5dy6gL4yIUmoJgG8AaADYAuAFAG4BcFt2yZu01n/sR93dwlk0Szo7NXbuz8AoL/OarJP+6d6NeRjoyXYHuywew2sft3/p/eMjDfzszScDMBL4RPENFWFMPEKvxX3NYRVEohTu25DGN/nsS4/pSduKsHDcDoVajEjBClTXI+krZx6P71x5FxaN+2HGi+wh8oBmJeW7DEDvQe17ut1BNktWvuku+EoB73z6oZg/2pi2KmlY8S26hRifJrBxuO6eVE150/2b+t+wEvRCwO0GvfYoNBgtEXCMinguoV/y7ksAfEJr/SQA9wM4C8DXtdanZP/NKCEEqLe7aRHmod/eB1Vgdt4TUx38/m2n48yT9q11/8eed2S9CgtgJrbQI7jqAR+5nUSisD2zxdl75/nClb0FZS/qvH/zOL2YIB+97074yHOPEnehRcWbxbsoPkt6XX9n8XGaUK7LgVHGiCQljFqOglfxqpP2xQsfvbfznrtpr6POnflyiNivXINd//ym7b1R2U4HKvhHf9Gv8VIWF+mTL3hkX+qdyegLI6K1/jT5cwWAuwA8XSl1KoA/Anid1nr4PZzCUc0Ud0CaI2AQxqpFeMuTD8rdWydrqGYoDt51cVf3STATemiHWMY2mPtHGtaotWyX3AuEkqGVwXnerMGvOHEV/j5LwtcNpP5X1CTzesri4PRbNTMeCJ9eB2WTf5ldRz0hcno7bdfle+ZLItI3KXuf/WIF6oAO/0G8ZzOUpXg+vUAZI/KEQ3YBMJiwBTMFfX1SpdQJAJYBOBfA6VrrRwMYAfA04drXKqWuUEpdsXbt2n42S0SdOBLUFqM/2Xerl/nGUw/AmFHNdJkqvJfeFI0SRqRsR2vupxPgSJ8mBIrxETmwUxnyqLbklvGRhmNzUhfS5yjqZzkjUsNGpB9by/EuWSWKUkaEzFhSHWV2Mk6sk2mqVkKGrzMVZd5Y0qsfxNgrw6AFPhoaoR8osxFpJAr/9eKjce7fPb4v9c9E9M1rRim1E4BPAXgOgPu11sYI4QoAXmIWrfVnAXwWAI499tgeOZNWRx1XvhYxEunHZr1u9zeSc5VMlmJ9PRxvuWomUGaZ54bKGREqiAyaEal+X86IpMHHAUx/8pbsFYq+USPAiDz3mD3ZdUr83StQyrlrRqTk3VVWzVTAtBmRGnPGTIDUr5oOSzYzGRE6TQxCBWaq6FddZYwIAJxx5G79qXyGoi+9TCk1CuDbAN6qtb4DwFeUUkcppRoA/hLANf2odzpwvWaKe+BJj1he+drptqUKxpoNvPkJj8C3X39iV/X10kfeXZh9JCXqAbMO0cHaL4qU4lF7Lct/19kdNwTBa7qLfN3bG4KNyOoPnYGPPe8odp0tuB/677GRAduIdKGacXbX1EZkmsaq/Uj10GuUJVMUvWZmgITl8HgDZET6VdcgcmfNNvSLETkTwNEA3q6UejuACwF8BWmf+pHW+rw+1ds13CRgxdc+Zr+d898zZf75uyd2b5PQy8km30EFiix7X2ZCp6zCyABsRGjsjjrf1Lw6JwbDNDuFNAEWMiLZyTLVDG1XP4Q7yip1O4mXes1M1+U2IDBON0rrTJkHiiAJS2XRZavm3+onesmCVYE0pnuJfhuNz0b0y1j1bABns8Pv6UddvUKZW2mV+/rRlhAufesTsCELgDVd9CNYT6jE0sir2Xm6IPXLnz+EbpLe0VfYnCadTWtfOn8ED2+dKmTeTMbidsmiQXXT/TAApjk0umWFyhkR+7ubGlzhoYeqmVkQfkp6t7SrSu9g+GLI4G1xFo41MdGa7JtwORtcvQeNyBFl6H530wfVTIUyd10y7uTKmSkwtGNoAktKVpLcWJWqZga8g+gmsmrSQ7WHmLOlAiNSZiNEIzr2wwiRTrDdymJlgkgvE/dNNzfOdAWZQUPql2VsQ7d2Z71Etwkpu8VOGTu6bapdcmVErxAFEYMuJ7i+WJXPgkktBLPrDkV5dRkRaWJM/x2lqplBG8zVUc1k//bCUNOg7gJp6muX6GZonI/psjZl6HbnWibEubvJadp1BNQ0lcsKtmtmQvomIfXUp19yNIByL6RBYNCMyIsevTcAYNn8eika6mDf5QvwptMO6Fv5sw1zNtcMhzu9Ve/s/VgkZ/6UFkbOiAQmsDKNgJloRnpA83eLOtWZBd0xBJ2uasZZILNjBdebustsRCgj0m+WqdsFo5QRKXHfpThmn2U49aAVzjF6y7TjqjgLZBf3DxhlcUTo76XzU/fzmWAjMmjvpFc+dhVecNxeWDDWv+Xxwn88pW9lz0ZEQSRDtwZR/TD6mw27qxDKEjpVtdIfptvgeI0cMYYR66WxKi2rLEAcQASRMkaEPFe/32/XNiIlfb8qW6Sh8d03+F5kIVuw6atmZu6YHWsmmGh1RGGJzl+OkGbUfX1uWxW4mdH7/56VUn0VQiJ8xLedoVtj1dGZ4Gc/g1AlWI+B9JZtQDOFn7zpJFx2+7peNq8S6izSo3m6dGofMU1BhBoQlgSIo/WV7V6pINJvlqnbhbnMcLosX05ooXrJ8Xvja7+/s1K5VeHalc1cQeRH/+8k/PrPa0uTUEp9uEy4HQSo+ns2ME8R9REFkQzupFL9vqiacVFmM+MyIv615tBII8HheyzB4Xss6Wn7eg0T74Q+Sh1GRYKUIbZonTP9tcxrZnyA3kfdLBi/essp5eWWqFN0YA//vmcejnc+49CC1AOVmujASXo3g/cjB+26KGjYTt+nkzsn+2MGaGacmEKDFviO3nsp/nDnwwOtcy4iCiIZunXF64sgMgRJ5K8eszdOOmB5+YUlKJsoys6biW9GRHSsANNOunMcnW5kVcE4r6hEc025jcj0BKQ66IZx2WfnBaXXJBXHKT+XJApjSfj5px9HZHZuHxruxOcdnwFyiCOIDPo1f+f1J86KqLmzHVEQyeCm9K5+X1/cIIfAibz/L4/oSTmlSe2osaFw3iTum22CCNUpN6YZo6Nu2vOxZoKnHLYr/uox+xReN8gkWv2KNFrVfTfEjPQSs819V4LLiAiqmRlAiVD196Dt52ZDxNwdAVEQEVAvsmY/jFV7XuTAUBo5tWQhMW6/o83Z8RL2WDoPAPCC4/bCxbc+hAtufGDaBsySW2lRP1NK4TMvPaa03PES+51eopdpAyjKxttAhXiBuZptCEVWNb9ngBxSKTdLxOxGFEQyuPPIcCeV2TmlpShbKBwdv/CkJqHgMBiRr7/mMVi/dbLWPUvmj+C2DzwNSgFPPWI37HLBzXjs/tNTcUkLQi/6xHRtV+qgXwtzHffdfmPQbqX9QIhhmlGMSBREdnhEQSTDjKJZh13/NFA2IZftlKeGqJo5Yf+dyy8SYISrPZbOwweffeS02yEtCL3oE4NVzVS/9ry/f3xlAbDM2HmQUM7v2TloGwFjVTP8hi+GAGONwQnQEcNBFEQyOJPKkCe42UrzAlVsRGTjOANjIzKIjLszFb10BaYYJCNSp90HrFxY+VqXURsuZtTmpUuEIqvmBtAzwX13lqhpI7pH5LwyBIzHh4LZnJ2xrOlldPZUK5345nJ8FimkdS923DuCaiYp6T/7r0w9b06cpnqsCgYderwfCO0LjIdVawYIIoO0bYoYDiIjksFJrDTkNXA2W2qXMiIlAakWz0u75IpFY71s1qyCtNOebevc0nkjfSnXDWjmv5SDd12My99+OpYv7F+eEKn+2fZ9DELRZQcZc6YMs3k+jKiGKIhk6DaOSD8wmxmRsldXRtm/9DH7YP5oA889Zq8eNmp2QRLmetUjzvmbkzFvtP87zGUL+iMIVEnFMCghtttozDMVjiAyQPYsImLmiL0zCFXmlJMfsRzPO2bPvtQ/6CRvvUQZI7J10qbWlibvZiPBC47be1a/g+lCWuB6tc4duvti7Lu8PHBYt3j1SfsC6J+xcVmI90FiB5A9HPQyOnCvsXg87pl3ZMSvm6GuNf5Xzjy+b21pDls3NA2UyQ/3b9ie/97B5vGeQep+w2bpquIdTz8U73j6oX0rn8o3wxZWZ8s3qQrRW2uG4Lx/eDzuWrdt2M2I6BOiIJLBNTwbXjsAd7KdbShjRFqdTv57R9tR9gozadc/07B0vlX5DFtg39H670w2uF25aBwrF40PuxkRfUIURDLQIdivqJBVMd0Q4cNE2ZujRvg72o6yV5DsIGbwGjFQUPuPYbt4z+SFuxvwxzlmn2V46uG7DqcxEXMKURDJoGqqZvqJ2WysOux3tyNA+vzxtaaggkgv1AfPOXpP7L60u532jvZJuGD13TecOKSWRMw1REEkw0zSj9L6j9576fAa0gXKXh2NShAXVxmyMBdfFgCsWEgYkR6M048//6iu793R+u8s3v9EzHLMXh1Aj0HH4LAHJBVEvvm6E4bYkvoo9fmfAbkrZjroAjeV5d4Zm0FxHYaJXZdY9mLYG4Ydj/3b0Z4nYrYgzm4GNLDPDGJEhpFzZTooe3ORESkHpchtNuLZ1Q/6heWEEdnxBIHhYjarhCNmN+LsloGOwWEboc3mCaFscYiESDkShxHJBJFZJpBGzD4MItBdRISEOLtlcEK8zxDVzLCp525QbiNiJZHoNSODCsKtTDUTGZGIfiPmdIkYFuLslmEmJbDKBZFZSD2XvTsSRiSqZgJwbESyFzbbVHQRsw9jI7GPRQwHsedlcI1VZ0Z8gkEk7uo16ry7KIfIoExRno04MiIRfUY0iI4YFmLPyzCT3HfHRxr412cdPus8ZoByliOaiJSDdr+j91kKADh898XDaUzEnEE0/o0YFmIckQwzyUYEAF5y/D7DbkJXKBVEiLVqnPhkUEH4mY/cA//y9EOx25J5Q2xRxI6MVTvPx+qHtg67GRFzGFEQyeDYiMwESWSWIqpmpg8nyi8QhRCGFz16b/z+toeG3YwdBt96/QmYmOqUXxgR0SdEQSQDnfyHbSMym1H27qj7bnzN5YiskY8PPvuIYTdhh0JMJhcxbEQbkQwzKbLqbEa5jUi0EqmD2BcjIiJ2dERBJAPN5BkZke5RtnA+8dBd8t9xt1+O+IoiIiJ2dETVTAZqIBhtRLpHmXDx9088CK84cV8sHItdrwpi0LeZjXc/49BhNyEiYtYjrgYZaFj12RhIbKagjE1qJMpJ5R5RjNgVZzZe8dh9h92EiIhZj6iaydBI7KuIhEj3iK+ut4jqq4iIiB0dURDJ0IyqmZ4g2tf0FrErRkRE7OiIgkgGx0YkLqZdI7663iLaiEREROzoiIJIBocRiXN/14jJ2XqLKNhFRETs6IirRoboNdMbxORsvUUURCIiInZ0xFUjQ7NBjVXj7N8toiDSW0TVTERExI6OuGpkiO67vcFoVM30FLErRkRE7OiIq0YGqpqJk3/3GGnEl9dLRHYuIiJiR0dfBBGl1BKl1M+UUr9USn1fKTWaHd9FKXVVP+qcLmiI90a0EekaMe5FbxFfZ0RExI6OfjEiLwHwCa31kwDcD+Ap2fGPAZiROc2b0X03YgYi9sSIiIgdHX0J8a61/jT5cwWAB5RSpwHYglQwmXGIkVUjZiIiwxQREbGjo6+5ZpRSJwBYBuAPAH4B4FkAfhC49rUAXgsAe++9dz+bJaLp2IjEyX86OO3glTjx/7d3dzFylXUcx79/2vISraWltUQEpKQEk5pWXDEEkFWrISbEqDFIFF+gIRgUE69aX9Kg3miUG0LRgiQkYqKYcEGUCJg0YIKJVeuF1rcLCDE0viAgxiK7+/diztjp7Fl2Wnr2Oefs95NsOvPM2e7z6+z577/PPHvm/DNKT6MnsvQEJKlRjTUiEbEOuA34ILAT2JOZzy70Qz4z9wJ7Aaamppa8+rov5MS5+xNvLT2F3pidKz0DSWpWU5tVTwbuA3Zl5pPAduCmiNgHbIuIu5r4uq/EShsRtdDMnJ2IpH5rarPq9cBFwBeq5uP2zJzOzGngQGbuaOjrHjdXRNRGs3O+NCOp35rarHoHcMcCj0038TVfqZVeiEstcuqqkzj80hwzNiKSes6fvhVfmlGbXHr+egBmZ21EJPWbjUjFl2bUJsPvR1dEJPWdjUjFFRG1yfBKv+4RkdR3NiIVV0TUJhedsxaA151+auGZSFKzGr2gWZd4ETO1yfWXnccVF2xg88bVpaciSY1yRURqoYiwCZG0LNiISJKkYmxEJElSMTYikiSpGBsRSZJUjI2IJEkqxkZEkiQVYyMiSZKKsRGRJEnFeGXVETe8fRNPP3e49DQkSVo2bERGfP69byw9BUmSlhVfmpEkScXYiEiSpGJsRCRJUjE2IpIkqRgbEUmSVIyNiCRJKsZGRJIkFWMjIkmSirERkSRJxdiISJKkYmxEJElSMTYikiSpGBsRSZJUTGRm6TnMExF/A54sPY8FrAf+XnoSDelrtr7mgv5mM1f39DWbuU6cczNzw/hgKxuRNouI/Zk5VXoeTehrtr7mgv5mM1f39DWbuZrnSzOSJKkYGxFJklSMjcix21t6Ag3qa7a+5oL+ZjNX9/Q1m7ka5h4RSZJUjCsikiSpGBsRSZJUjI3ImIjYGBGPVbfXRsSPI2J/RHx77Lg9EXHVyP3vRMTjEfHFpZ7zpBbLtlDetmcby3VeRPwoIh6LiG+OHDMvQ9dzRcSaiHgwIh6KiPsj4uRqvNW5YLLnbOS4X4/cb3W2Y8jV9fpR9/3YqfpRd/5MWifammlokmxtqh82IiMiYi1wD/Cqauha4N7qd61XR8RUddzlwJmZ+UB1/wPAisy8BNgUEZuXfvYvb8Js88banq0m19eAr2Tm5cDrI2K6LkMfcgEfAW7NzPcAh4Ar254LJs429A3gtOrzWp1t0lw9qR912bpWP8bPnw8zQZ1oeaahRbPVHFOsftiIHG0WuBp4vrr/D2BLRJwOnA08FRGrgDuBJyLifdVx08APqtsPAZct1YSPwaLZFhibpt3ZxnNdAPyquv1XYA31GerG2mTRXJm5JzMfrsY2VOPTtDsXTPacERHvBP7NoEhC+7MtmqtH9aPuOetU/ag5fz7KZHWibqxVJsnWpvphIzIiM5/PzOdGhn4GnAvcDBwEngE+BvwO+DpwcUR8hsH/Ev5Sfc4zwMYlm/SEJsxWN9bqbDW5fgjsrpa9rwR+Sn2GPuQCICIuAdZm5s9peS6YLFu1TPwlYOfIca3ONuFz1pf6UZetc/UDjpw/DBqnSepE6zMNLZLtqGNK1g8bkZe3G7gxM78M/B74JPBmYG9mHgK+C7wDeIFq+Rh4Nd34d63LVjfWqWyZ+VXgQWAHcE9mvkB9hj7kIiLWAbcB11WHdioXLJhtJ7AnM58dObRT2RbI1Yv6sUC2ztWPsfNn0jrR6kxDE2RrTf1o5T9gi6wF3hQRK4C3AQn8GdhUPT7F4M35fsmRJaytwBNLO83jUpetbqyL2Q4A5wC3VvfrMnQ+V7VqcB+wKzOHbxLZxVww/znbDtwUEfuAbRFxF93MdoCjc/WlfsD8bJ2qHzXnz6R1orWZhibJ1qr6kZl+jH0A+6o/LwZ+y6BLfJhBh7i6evIeBR4HzgJeA/yGwQl5kMHr98VzHEe2urFOZBvmqm7fAlw7cn9ehp7k+hTwT2Bf9XF1V3Itlq3uuK5kW+Q560X9WCBbp+pHzfnz8UnqRJszHWO21tQPr6x6glS7yt8NPJqDZdfe6EO2ugx9yFWnr7mgv9n6mgu6lW3SOtGlTEOTzrlENhsRSZJUjHtEJElSMTYikiSpGBsRSZJUjI2IpKIi4oyIuKa6vSoiovScJC0dN6tKWnIR8Vngxcz8VkScAvwRuArYxeBqjnPVoW8B3pBHX9VTUo+sLD0BSf0UEVcA3wP+BFyYmWeOPDwDvFRd/God8DngUGZeM/Z37AP+uzQzllSCjYikpswA92fmpyPiFxFxHbClGt/KYNVjBrghMy+NiJ9UjcnQldWfLttKPWYjIqkps8D7I2IL8NrMvDsizs7MpyLiRuAw8H0G70kCsDIz3wWDlZDMnHG7iNR/blaV1JRZBisi08DTEXEa8EBErFng+Asj4pGIeITBiomkZcAVEUlNGf2PTmTmfyLidgZv9lbnYGZuh//vDZG0DNiISGrKSo68NHMWQGbeCRARm2uO31athgBsjQjrk7QMeKJLasoKjmxW3T322HDzx0nD25m5fvwvqN6qfG58XFJ/2IhIasp+4A8AmXnLcDAiPgTcDOxg8Fbxp9R9ckTcy2ADq7++K/WYFzSTtKSqTatzmfniIsetzsx/LdG0JBViIyJJkorx13clSVIxNiKSJKkYGxFJklSMjYgkSSrmf4K1JlRvX98sAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 648x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig=plt.figure(figsize=(9,6))\n",
    "ax=fig.subplots(1,1)\n",
    "ax.plot(ds['time'],ENSOall)\n",
    "ax.set_xlabel('时间')\n",
    "ax.set_ylabel('ENSO指数')\n",
    "ax.set_title('ENSO指数的时间序列')\n",
    "\n",
    "# 保存图片\n",
    "plt.savefig('../../picture/pythonhome/11/qu2.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
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       "\n",
       ".xr-section-summary-in:checked + label > span {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-section-summary,\n",
       ".xr-section-inline-details {\n",
       "  padding-top: 4px;\n",
       "  padding-bottom: 4px;\n",
       "}\n",
       "\n",
       ".xr-section-inline-details {\n",
       "  grid-column: 2 / -1;\n",
       "}\n",
       "\n",
       ".xr-section-details {\n",
       "  display: none;\n",
       "  grid-column: 1 / -1;\n",
       "  margin-bottom: 5px;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-array-wrap {\n",
       "  grid-column: 1 / -1;\n",
       "  display: grid;\n",
       "  grid-template-columns: 20px auto;\n",
       "}\n",
       "\n",
       ".xr-array-wrap > label {\n",
       "  grid-column: 1;\n",
       "  vertical-align: top;\n",
       "}\n",
       "\n",
       ".xr-preview {\n",
       "  color: var(--xr-font-color3);\n",
       "}\n",
       "\n",
       ".xr-array-preview,\n",
       ".xr-array-data {\n",
       "  padding: 0 5px !important;\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-array-data,\n",
       ".xr-array-in:checked ~ .xr-array-preview {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-array-in:checked ~ .xr-array-data,\n",
       ".xr-array-preview {\n",
       "  display: inline-block;\n",
       "}\n",
       "\n",
       ".xr-dim-list {\n",
       "  display: inline-block !important;\n",
       "  list-style: none;\n",
       "  padding: 0 !important;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list li {\n",
       "  display: inline-block;\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list:before {\n",
       "  content: '(';\n",
       "}\n",
       "\n",
       ".xr-dim-list:after {\n",
       "  content: ')';\n",
       "}\n",
       "\n",
       ".xr-dim-list li:not(:last-child):after {\n",
       "  content: ',';\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-has-index {\n",
       "  font-weight: bold;\n",
       "}\n",
       "\n",
       ".xr-var-list,\n",
       ".xr-var-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-var-item > div,\n",
       ".xr-var-item label,\n",
       ".xr-var-item > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-even);\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-var-item > .xr-var-name:hover span {\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-var-list > li:nth-child(odd) > div,\n",
       ".xr-var-list > li:nth-child(odd) > label,\n",
       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-odd);\n",
       "}\n",
       "\n",
       ".xr-var-name {\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-var-dims {\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-var-dtype {\n",
       "  grid-column: 3;\n",
       "  text-align: right;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-preview {\n",
       "  grid-column: 4;\n",
       "}\n",
       "\n",
       ".xr-var-name,\n",
       ".xr-var-dims,\n",
       ".xr-var-dtype,\n",
       ".xr-preview,\n",
       ".xr-attrs dt {\n",
       "  white-space: nowrap;\n",
       "  overflow: hidden;\n",
       "  text-overflow: ellipsis;\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-var-name:hover,\n",
       ".xr-var-dims:hover,\n",
       ".xr-var-dtype:hover,\n",
       ".xr-attrs dt:hover {\n",
       "  overflow: visible;\n",
       "  width: auto;\n",
       "  z-index: 1;\n",
       "}\n",
       "\n",
       ".xr-var-attrs,\n",
       ".xr-var-data {\n",
       "  display: none;\n",
       "  background-color: var(--xr-background-color) !important;\n",
       "  padding-bottom: 5px !important;\n",
       "}\n",
       "\n",
       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
       ".xr-var-data-in:checked ~ .xr-var-data {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       ".xr-var-data > table {\n",
       "  float: right;\n",
       "}\n",
       "\n",
       ".xr-var-name span,\n",
       ".xr-var-data,\n",
       ".xr-attrs {\n",
       "  padding-left: 25px !important;\n",
       "}\n",
       "\n",
       ".xr-attrs,\n",
       ".xr-var-attrs,\n",
       ".xr-var-data {\n",
       "  grid-column: 1 / -1;\n",
       "}\n",
       "\n",
       "dl.xr-attrs {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  display: grid;\n",
       "  grid-template-columns: 125px auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt,\n",
       ".xr-attrs dd {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  float: left;\n",
       "  padding-right: 10px;\n",
       "  width: auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt {\n",
       "  font-weight: normal;\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-attrs dt:hover span {\n",
       "  display: inline-block;\n",
       "  background: var(--xr-background-color);\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-attrs dd {\n",
       "  grid-column: 2;\n",
       "  white-space: pre-wrap;\n",
       "  word-break: break-all;\n",
       "}\n",
       "\n",
       ".xr-icon-database,\n",
       ".xr-icon-file-text2 {\n",
       "  display: inline-block;\n",
       "  vertical-align: middle;\n",
       "  width: 1em;\n",
       "  height: 1.5em !important;\n",
       "  stroke-width: 0;\n",
       "  stroke: currentColor;\n",
       "  fill: currentColor;\n",
       "}\n",
       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;sst&#x27; (season: 4)&gt;\n",
       "array([26.281464, 26.811226, 27.24905 , 26.232914], dtype=float32)\n",
       "Coordinates:\n",
       "  * season   (season) object &#x27;DJF&#x27; &#x27;JJA&#x27; &#x27;MAM&#x27; &#x27;SON&#x27;</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'sst'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>season</span>: 4</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-e0c61b85-2fa1-4223-983e-dd15de3de0bf' class='xr-array-in' type='checkbox' checked><label for='section-e0c61b85-2fa1-4223-983e-dd15de3de0bf' 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>26.28 26.81 27.25 26.23</span></div><div class='xr-array-data'><pre>array([26.281464, 26.811226, 27.24905 , 26.232914], dtype=float32)</pre></div></div></li><li class='xr-section-item'><input id='section-bc8c6808-e7c1-4c1a-ae46-6ec5520dcf38' class='xr-section-summary-in' type='checkbox'  checked><label for='section-bc8c6808-e7c1-4c1a-ae46-6ec5520dcf38' class='xr-section-summary' >Coordinates: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>season</span></div><div class='xr-var-dims'>(season)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>&#x27;DJF&#x27; &#x27;JJA&#x27; &#x27;MAM&#x27; &#x27;SON&#x27;</div><input id='attrs-0b973346-9e13-40bc-9654-110add8eff82' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-0b973346-9e13-40bc-9654-110add8eff82' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a91ea2ce-0319-49e1-80d5-9b6d7f437559' class='xr-var-data-in' type='checkbox'><label for='data-a91ea2ce-0319-49e1-80d5-9b6d7f437559' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;DJF&#x27;, &#x27;JJA&#x27;, &#x27;MAM&#x27;, &#x27;SON&#x27;], dtype=object)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-f7dee520-ba75-44d2-9e37-09939c005431' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-f7dee520-ba75-44d2-9e37-09939c005431' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.DataArray 'sst' (season: 4)>\n",
       "array([26.281464, 26.811226, 27.24905 , 26.232914], dtype=float32)\n",
       "Coordinates:\n",
       "  * season   (season) object 'DJF' 'JJA' 'MAM' 'SON'"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 分别计算四个季节的ENSO指数\n",
    "ENSOseason=ds['sst'].groupby(ds.time.dt.season).mean(dim='time').loc[:,5:-5,190:240].mean(dim=['lat','lon'],skipna=True)\n",
    "ENSOseason"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 648x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig=plt.figure(figsize=(9,6))\n",
    "ax=fig.subplots(1,1)\n",
    "ax.bar(ENSOseason.season,ENSOseason)\n",
    "ax.set_xlabel('季节')\n",
    "ax.set_ylabel('ENSO指数')\n",
    "ax.set_title('四个季节的ENSO指数')\n",
    "for i,j in zip(ENSOseason.season,ENSOseason):\n",
    "    ax.text(i,j.data,j.data,ha='center')\n",
    "# 保存图片\n",
    "plt.savefig('../../picture/pythonhome/11/qu3.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;sst&#x27; (lat: 89, lon: 180)&gt;\n",
       "array([[-1.7998481, -1.7998575, -1.7998646, ..., -1.7998819, -1.7998582,\n",
       "        -1.7998413],\n",
       "       [-1.799857 , -1.7998397, -1.7998296, ..., -1.7999817, -1.7999095,\n",
       "        -1.7998827],\n",
       "       [-1.8000275, -1.7999699, -1.799932 , ..., -1.7999892, -1.8000056,\n",
       "        -1.8000206],\n",
       "       ...,\n",
       "       [       nan,        nan,        nan, ...,        nan,        nan,\n",
       "               nan],\n",
       "       [       nan,        nan,        nan, ...,        nan,        nan,\n",
       "               nan],\n",
       "       [       nan,        nan,        nan, ...,        nan,        nan,\n",
       "               nan]], dtype=float32)\n",
       "Coordinates:\n",
       "  * lat      (lat) float32 88.0 86.0 84.0 82.0 80.0 ... -82.0 -84.0 -86.0 -88.0\n",
       "  * lon      (lon) float32 0.0 2.0 4.0 6.0 8.0 ... 350.0 352.0 354.0 356.0 358.0</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'sst'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>lat</span>: 89</li><li><span class='xr-has-index'>lon</span>: 180</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-6650b915-ac1b-4286-b1e9-36ced4778983' class='xr-array-in' type='checkbox' checked><label for='section-6650b915-ac1b-4286-b1e9-36ced4778983' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>-1.8 -1.8 -1.8 -1.8 -1.8 -1.8 -1.8 ... nan nan nan nan nan nan nan</span></div><div class='xr-array-data'><pre>array([[-1.7998481, -1.7998575, -1.7998646, ..., -1.7998819, -1.7998582,\n",
       "        -1.7998413],\n",
       "       [-1.799857 , -1.7998397, -1.7998296, ..., -1.7999817, -1.7999095,\n",
       "        -1.7998827],\n",
       "       [-1.8000275, -1.7999699, -1.799932 , ..., -1.7999892, -1.8000056,\n",
       "        -1.8000206],\n",
       "       ...,\n",
       "       [       nan,        nan,        nan, ...,        nan,        nan,\n",
       "               nan],\n",
       "       [       nan,        nan,        nan, ...,        nan,        nan,\n",
       "               nan],\n",
       "       [       nan,        nan,        nan, ...,        nan,        nan,\n",
       "               nan]], dtype=float32)</pre></div></div></li><li class='xr-section-item'><input id='section-cff90a3e-22f7-450c-8824-1eaa94955b21' class='xr-section-summary-in' type='checkbox'  checked><label for='section-cff90a3e-22f7-450c-8824-1eaa94955b21' 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'>lat</span></div><div class='xr-var-dims'>(lat)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>88.0 86.0 84.0 ... -86.0 -88.0</div><input id='attrs-209ce5fd-51cd-4bfd-a96e-cbef8da80092' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-209ce5fd-51cd-4bfd-a96e-cbef8da80092' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7c4904aa-0e58-4a5e-8b53-8213ed78d919' class='xr-var-data-in' type='checkbox'><label for='data-7c4904aa-0e58-4a5e-8b53-8213ed78d919' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degrees_north</dd><dt><span>long_name :</span></dt><dd>Latitude</dd><dt><span>actual_range :</span></dt><dd>[ 88. -88.]</dd><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>axis :</span></dt><dd>Y</dd><dt><span>coordinate_defines :</span></dt><dd>center</dd></dl></div><div class='xr-var-data'><pre>array([ 88.,  86.,  84.,  82.,  80.,  78.,  76.,  74.,  72.,  70.,  68.,  66.,\n",
       "        64.,  62.,  60.,  58.,  56.,  54.,  52.,  50.,  48.,  46.,  44.,  42.,\n",
       "        40.,  38.,  36.,  34.,  32.,  30.,  28.,  26.,  24.,  22.,  20.,  18.,\n",
       "        16.,  14.,  12.,  10.,   8.,   6.,   4.,   2.,   0.,  -2.,  -4.,  -6.,\n",
       "        -8., -10., -12., -14., -16., -18., -20., -22., -24., -26., -28., -30.,\n",
       "       -32., -34., -36., -38., -40., -42., -44., -46., -48., -50., -52., -54.,\n",
       "       -56., -58., -60., -62., -64., -66., -68., -70., -72., -74., -76., -78.,\n",
       "       -80., -82., -84., -86., -88.], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lon</span></div><div class='xr-var-dims'>(lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.0 2.0 4.0 ... 354.0 356.0 358.0</div><input id='attrs-7e05ac4d-9d36-46bd-bda7-af8552d89381' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-7e05ac4d-9d36-46bd-bda7-af8552d89381' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5eb34484-2063-4c93-9d92-6ce24de8e49a' class='xr-var-data-in' type='checkbox'><label for='data-5eb34484-2063-4c93-9d92-6ce24de8e49a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degrees_east</dd><dt><span>long_name :</span></dt><dd>Longitude</dd><dt><span>actual_range :</span></dt><dd>[  0. 358.]</dd><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>axis :</span></dt><dd>X</dd><dt><span>coordinate_defines :</span></dt><dd>center</dd></dl></div><div class='xr-var-data'><pre>array([  0.,   2.,   4.,   6.,   8.,  10.,  12.,  14.,  16.,  18.,  20.,  22.,\n",
       "        24.,  26.,  28.,  30.,  32.,  34.,  36.,  38.,  40.,  42.,  44.,  46.,\n",
       "        48.,  50.,  52.,  54.,  56.,  58.,  60.,  62.,  64.,  66.,  68.,  70.,\n",
       "        72.,  74.,  76.,  78.,  80.,  82.,  84.,  86.,  88.,  90.,  92.,  94.,\n",
       "        96.,  98., 100., 102., 104., 106., 108., 110., 112., 114., 116., 118.,\n",
       "       120., 122., 124., 126., 128., 130., 132., 134., 136., 138., 140., 142.,\n",
       "       144., 146., 148., 150., 152., 154., 156., 158., 160., 162., 164., 166.,\n",
       "       168., 170., 172., 174., 176., 178., 180., 182., 184., 186., 188., 190.,\n",
       "       192., 194., 196., 198., 200., 202., 204., 206., 208., 210., 212., 214.,\n",
       "       216., 218., 220., 222., 224., 226., 228., 230., 232., 234., 236., 238.,\n",
       "       240., 242., 244., 246., 248., 250., 252., 254., 256., 258., 260., 262.,\n",
       "       264., 266., 268., 270., 272., 274., 276., 278., 280., 282., 284., 286.,\n",
       "       288., 290., 292., 294., 296., 298., 300., 302., 304., 306., 308., 310.,\n",
       "       312., 314., 316., 318., 320., 322., 324., 326., 328., 330., 332., 334.,\n",
       "       336., 338., 340., 342., 344., 346., 348., 350., 352., 354., 356., 358.],\n",
       "      dtype=float32)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-6c3e9d14-adb8-4a1e-b4ad-78cfdb6dfdb3' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-6c3e9d14-adb8-4a1e-b4ad-78cfdb6dfdb3' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.DataArray 'sst' (lat: 89, lon: 180)>\n",
       "array([[-1.7998481, -1.7998575, -1.7998646, ..., -1.7998819, -1.7998582,\n",
       "        -1.7998413],\n",
       "       [-1.799857 , -1.7998397, -1.7998296, ..., -1.7999817, -1.7999095,\n",
       "        -1.7998827],\n",
       "       [-1.8000275, -1.7999699, -1.799932 , ..., -1.7999892, -1.8000056,\n",
       "        -1.8000206],\n",
       "       ...,\n",
       "       [       nan,        nan,        nan, ...,        nan,        nan,\n",
       "               nan],\n",
       "       [       nan,        nan,        nan, ...,        nan,        nan,\n",
       "               nan],\n",
       "       [       nan,        nan,        nan, ...,        nan,        nan,\n",
       "               nan]], dtype=float32)\n",
       "Coordinates:\n",
       "  * lat      (lat) float32 88.0 86.0 84.0 82.0 80.0 ... -82.0 -84.0 -86.0 -88.0\n",
       "  * lon      (lon) float32 0.0 2.0 4.0 6.0 8.0 ... 350.0 352.0 354.0 356.0 358.0"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 以sst>=|0.5K|为标准在过去的几十年中，El Nino和La Nina年分别是哪一年\n",
    "sstave=ds['sst'].mean(dim='time')\n",
    "sstave"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "sst=ds['sst']\n",
    "# 距平场\n",
    "sstjuping=sst-sstave\n",
    "# 距平场的区域平均\n",
    "sstjuping=sstjuping.loc[:,5:-5,190:240].mean(dim=['lat','lon'],skipna=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
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    "pycharm": {
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       ".xr-wrap {\n",
       "  display: block !important;\n",
       "  min-width: 300px;\n",
       "  max-width: 700px;\n",
       "}\n",
       "\n",
       ".xr-text-repr-fallback {\n",
       "  /* fallback to plain text repr when CSS is not injected (untrusted notebook) */\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-header {\n",
       "  padding-top: 6px;\n",
       "  padding-bottom: 6px;\n",
       "  margin-bottom: 4px;\n",
       "  border-bottom: solid 1px var(--xr-border-color);\n",
       "}\n",
       "\n",
       ".xr-header > div,\n",
       ".xr-header > ul {\n",
       "  display: inline;\n",
       "  margin-top: 0;\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-obj-type,\n",
       ".xr-array-name {\n",
       "  margin-left: 2px;\n",
       "  margin-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-obj-type {\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-sections {\n",
       "  padding-left: 0 !important;\n",
       "  display: grid;\n",
       "  grid-template-columns: 150px auto auto 1fr 20px 20px;\n",
       "}\n",
       "\n",
       ".xr-section-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-section-item input {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-section-item input + label {\n",
       "  color: var(--xr-disabled-color);\n",
       "}\n",
       "\n",
       ".xr-section-item input:enabled + label {\n",
       "  cursor: pointer;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-section-item input:enabled + label:hover {\n",
       "  color: var(--xr-font-color0);\n",
       "}\n",
       "\n",
       ".xr-section-summary {\n",
       "  grid-column: 1;\n",
       "  color: var(--xr-font-color2);\n",
       "  font-weight: 500;\n",
       "}\n",
       "\n",
       ".xr-section-summary > span {\n",
       "  display: inline-block;\n",
       "  padding-left: 0.5em;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label {\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in + label:before {\n",
       "  display: inline-block;\n",
       "  content: '►';\n",
       "  font-size: 11px;\n",
       "  width: 15px;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label:before {\n",
       "  color: var(--xr-disabled-color);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label:before {\n",
       "  content: '▼';\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label > span {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-section-summary,\n",
       ".xr-section-inline-details {\n",
       "  padding-top: 4px;\n",
       "  padding-bottom: 4px;\n",
       "}\n",
       "\n",
       ".xr-section-inline-details {\n",
       "  grid-column: 2 / -1;\n",
       "}\n",
       "\n",
       ".xr-section-details {\n",
       "  display: none;\n",
       "  grid-column: 1 / -1;\n",
       "  margin-bottom: 5px;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-array-wrap {\n",
       "  grid-column: 1 / -1;\n",
       "  display: grid;\n",
       "  grid-template-columns: 20px auto;\n",
       "}\n",
       "\n",
       ".xr-array-wrap > label {\n",
       "  grid-column: 1;\n",
       "  vertical-align: top;\n",
       "}\n",
       "\n",
       ".xr-preview {\n",
       "  color: var(--xr-font-color3);\n",
       "}\n",
       "\n",
       ".xr-array-preview,\n",
       ".xr-array-data {\n",
       "  padding: 0 5px !important;\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-array-data,\n",
       ".xr-array-in:checked ~ .xr-array-preview {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-array-in:checked ~ .xr-array-data,\n",
       ".xr-array-preview {\n",
       "  display: inline-block;\n",
       "}\n",
       "\n",
       ".xr-dim-list {\n",
       "  display: inline-block !important;\n",
       "  list-style: none;\n",
       "  padding: 0 !important;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list li {\n",
       "  display: inline-block;\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list:before {\n",
       "  content: '(';\n",
       "}\n",
       "\n",
       ".xr-dim-list:after {\n",
       "  content: ')';\n",
       "}\n",
       "\n",
       ".xr-dim-list li:not(:last-child):after {\n",
       "  content: ',';\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-has-index {\n",
       "  font-weight: bold;\n",
       "}\n",
       "\n",
       ".xr-var-list,\n",
       ".xr-var-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-var-item > div,\n",
       ".xr-var-item label,\n",
       ".xr-var-item > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-even);\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-var-item > .xr-var-name:hover span {\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-var-list > li:nth-child(odd) > div,\n",
       ".xr-var-list > li:nth-child(odd) > label,\n",
       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-odd);\n",
       "}\n",
       "\n",
       ".xr-var-name {\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-var-dims {\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-var-dtype {\n",
       "  grid-column: 3;\n",
       "  text-align: right;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-preview {\n",
       "  grid-column: 4;\n",
       "}\n",
       "\n",
       ".xr-var-name,\n",
       ".xr-var-dims,\n",
       ".xr-var-dtype,\n",
       ".xr-preview,\n",
       ".xr-attrs dt {\n",
       "  white-space: nowrap;\n",
       "  overflow: hidden;\n",
       "  text-overflow: ellipsis;\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-var-name:hover,\n",
       ".xr-var-dims:hover,\n",
       ".xr-var-dtype:hover,\n",
       ".xr-attrs dt:hover {\n",
       "  overflow: visible;\n",
       "  width: auto;\n",
       "  z-index: 1;\n",
       "}\n",
       "\n",
       ".xr-var-attrs,\n",
       ".xr-var-data {\n",
       "  display: none;\n",
       "  background-color: var(--xr-background-color) !important;\n",
       "  padding-bottom: 5px !important;\n",
       "}\n",
       "\n",
       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
       ".xr-var-data-in:checked ~ .xr-var-data {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       ".xr-var-data > table {\n",
       "  float: right;\n",
       "}\n",
       "\n",
       ".xr-var-name span,\n",
       ".xr-var-data,\n",
       ".xr-attrs {\n",
       "  padding-left: 25px !important;\n",
       "}\n",
       "\n",
       ".xr-attrs,\n",
       ".xr-var-attrs,\n",
       ".xr-var-data {\n",
       "  grid-column: 1 / -1;\n",
       "}\n",
       "\n",
       "dl.xr-attrs {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  display: grid;\n",
       "  grid-template-columns: 125px auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt,\n",
       ".xr-attrs dd {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  float: left;\n",
       "  padding-right: 10px;\n",
       "  width: auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt {\n",
       "  font-weight: normal;\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-attrs dt:hover span {\n",
       "  display: inline-block;\n",
       "  background: var(--xr-background-color);\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-attrs dd {\n",
       "  grid-column: 2;\n",
       "  white-space: pre-wrap;\n",
       "  word-break: break-all;\n",
       "}\n",
       "\n",
       ".xr-icon-database,\n",
       ".xr-icon-file-text2 {\n",
       "  display: inline-block;\n",
       "  vertical-align: middle;\n",
       "  width: 1em;\n",
       "  height: 1.5em !important;\n",
       "  stroke-width: 0;\n",
       "  stroke: currentColor;\n",
       "  fill: currentColor;\n",
       "}\n",
       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;sst&#x27; (year: 168)&gt;\n",
       "array([-0.27901283,  0.53887945, -0.79377913, -0.49743035,  0.01381396,\n",
       "        0.0156523 , -0.6605402 , -0.8556995 , -0.95602196, -0.56900066,\n",
       "       -0.35242435, -0.28149888, -0.3221899 , -0.39931533, -0.39802742,\n",
       "       -0.83826923, -1.2486324 , -0.11339114, -0.78456354, -0.56990427,\n",
       "       -1.071779  , -0.5927753 , -0.26286563,  1.6162647 ,  0.47199443,\n",
       "       -0.7494994 , -0.21297091,  0.27370268, -0.22139291,  0.06209752,\n",
       "        0.33742368,  0.50778013, -0.8580026 , -0.66962093,  1.1704594 ,\n",
       "       -0.06664982, -0.9321185 ,  0.27976942, -0.790312  , -1.1027724 ,\n",
       "       -0.9189873 ,  0.07730261,  0.968323  ,  0.15831277, -0.6120045 ,\n",
       "        0.22558771,  0.8393369 , -0.11646404,  1.2146426 , -0.21488364,\n",
       "       -0.28697327,  1.2190522 , -0.22441082, -0.30146205, -0.5932198 ,\n",
       "       -1.0453684 , -1.185916  ,  0.08344454,  0.22287995,  0.12389604,\n",
       "        1.0010883 ,  0.18555258, -1.0712985 , -1.3870167 ,  0.39811984,\n",
       "        0.26008293,  0.19583876, -0.2877738 , -0.6633001 ,  0.137851  ,\n",
       "       -0.61011195,  0.01340577,  0.2309341 , -0.21348888, -0.09869134,\n",
       "       -0.1239958 ,  0.79089445,  0.19254015, -0.15764934, -1.0652403 ,\n",
       "       -0.4786745 , -0.241648  , -0.26159406, -0.1477725 , -1.1184852 ,\n",
       "       -0.17742525,  1.0936302 ,  1.3195462 , -0.5813868 , -0.607824  ,\n",
       "       -0.02982099, -0.51498747, -0.5090077 , -0.16348676, -0.30373248,\n",
       "       -0.6234893 , -0.91125995,  0.38370433,  0.1016582 ,  0.675997  ,\n",
       "       -0.4642035 , -1.0084409 , -0.55972975,  0.96554905,  0.83548594,\n",
       "        0.15238537,  0.07394686, -0.05435823, -0.25894699,  0.6282403 ,\n",
       "       -0.4022585 ,  0.8829775 ,  0.46587762, -0.20255332,  0.22182052,\n",
       "        0.8555936 , -0.20786224, -0.8018796 ,  1.1038779 , -0.47148395,\n",
       "       -0.7369695 , -0.87838954,  0.10646022,  0.6879594 ,  0.06750116,\n",
       "        0.41535214,  0.4360534 , -0.01856835,  1.265782  ,  0.74676734,\n",
       "       -0.2291367 , -0.31765553,  0.49698195,  1.5277086 , -0.5609185 ,\n",
       "       -0.3592378 ,  0.5681549 ,  1.0546018 ,  1.0403054 ,  0.7367615 ,\n",
       "        0.8940851 ,  0.2435536 , -0.07897937,  1.5507613 ,  0.32457155,\n",
       "       -0.84567815, -0.4649423 ,  0.10109713,  1.0731276 ,  0.64620894,\n",
       "        0.85692674,  0.4534689 ,  0.52572626, -0.17803447, -0.34580478,\n",
       "        0.7258925 , -0.04623574, -0.4212134 ,  0.29608858,  0.12100772,\n",
       "        0.5414799 ,  1.8993014 ,  0.78147155,  0.23904468,  0.46577224,\n",
       "        0.9303055 ,  0.08714997, -0.2817711 ], dtype=float32)\n",
       "Coordinates:\n",
       "  * year     (year) int64 1854 1855 1856 1857 1858 ... 2017 2018 2019 2020 2021</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'sst'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>year</span>: 168</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-22e65dc5-c016-4b15-ac52-3cef9baafbc5' class='xr-array-in' type='checkbox' checked><label for='section-22e65dc5-c016-4b15-ac52-3cef9baafbc5' 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>-0.279 0.5389 -0.7938 -0.4974 ... 0.4658 0.9303 0.08715 -0.2818</span></div><div class='xr-array-data'><pre>array([-0.27901283,  0.53887945, -0.79377913, -0.49743035,  0.01381396,\n",
       "        0.0156523 , -0.6605402 , -0.8556995 , -0.95602196, -0.56900066,\n",
       "       -0.35242435, -0.28149888, -0.3221899 , -0.39931533, -0.39802742,\n",
       "       -0.83826923, -1.2486324 , -0.11339114, -0.78456354, -0.56990427,\n",
       "       -1.071779  , -0.5927753 , -0.26286563,  1.6162647 ,  0.47199443,\n",
       "       -0.7494994 , -0.21297091,  0.27370268, -0.22139291,  0.06209752,\n",
       "        0.33742368,  0.50778013, -0.8580026 , -0.66962093,  1.1704594 ,\n",
       "       -0.06664982, -0.9321185 ,  0.27976942, -0.790312  , -1.1027724 ,\n",
       "       -0.9189873 ,  0.07730261,  0.968323  ,  0.15831277, -0.6120045 ,\n",
       "        0.22558771,  0.8393369 , -0.11646404,  1.2146426 , -0.21488364,\n",
       "       -0.28697327,  1.2190522 , -0.22441082, -0.30146205, -0.5932198 ,\n",
       "       -1.0453684 , -1.185916  ,  0.08344454,  0.22287995,  0.12389604,\n",
       "        1.0010883 ,  0.18555258, -1.0712985 , -1.3870167 ,  0.39811984,\n",
       "        0.26008293,  0.19583876, -0.2877738 , -0.6633001 ,  0.137851  ,\n",
       "       -0.61011195,  0.01340577,  0.2309341 , -0.21348888, -0.09869134,\n",
       "       -0.1239958 ,  0.79089445,  0.19254015, -0.15764934, -1.0652403 ,\n",
       "       -0.4786745 , -0.241648  , -0.26159406, -0.1477725 , -1.1184852 ,\n",
       "       -0.17742525,  1.0936302 ,  1.3195462 , -0.5813868 , -0.607824  ,\n",
       "       -0.02982099, -0.51498747, -0.5090077 , -0.16348676, -0.30373248,\n",
       "       -0.6234893 , -0.91125995,  0.38370433,  0.1016582 ,  0.675997  ,\n",
       "       -0.4642035 , -1.0084409 , -0.55972975,  0.96554905,  0.83548594,\n",
       "        0.15238537,  0.07394686, -0.05435823, -0.25894699,  0.6282403 ,\n",
       "       -0.4022585 ,  0.8829775 ,  0.46587762, -0.20255332,  0.22182052,\n",
       "        0.8555936 , -0.20786224, -0.8018796 ,  1.1038779 , -0.47148395,\n",
       "       -0.7369695 , -0.87838954,  0.10646022,  0.6879594 ,  0.06750116,\n",
       "        0.41535214,  0.4360534 , -0.01856835,  1.265782  ,  0.74676734,\n",
       "       -0.2291367 , -0.31765553,  0.49698195,  1.5277086 , -0.5609185 ,\n",
       "       -0.3592378 ,  0.5681549 ,  1.0546018 ,  1.0403054 ,  0.7367615 ,\n",
       "        0.8940851 ,  0.2435536 , -0.07897937,  1.5507613 ,  0.32457155,\n",
       "       -0.84567815, -0.4649423 ,  0.10109713,  1.0731276 ,  0.64620894,\n",
       "        0.85692674,  0.4534689 ,  0.52572626, -0.17803447, -0.34580478,\n",
       "        0.7258925 , -0.04623574, -0.4212134 ,  0.29608858,  0.12100772,\n",
       "        0.5414799 ,  1.8993014 ,  0.78147155,  0.23904468,  0.46577224,\n",
       "        0.9303055 ,  0.08714997, -0.2817711 ], dtype=float32)</pre></div></div></li><li class='xr-section-item'><input id='section-5eaed352-bdfa-46df-905d-d997d935c904' class='xr-section-summary-in' type='checkbox'  checked><label for='section-5eaed352-bdfa-46df-905d-d997d935c904' class='xr-section-summary' >Coordinates: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>year</span></div><div class='xr-var-dims'>(year)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>1854 1855 1856 ... 2019 2020 2021</div><input id='attrs-5a800bc1-a16d-477a-bac2-fd8e84229d68' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-5a800bc1-a16d-477a-bac2-fd8e84229d68' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-cb23bcad-f129-4370-88e5-bd54edb2056f' class='xr-var-data-in' type='checkbox'><label for='data-cb23bcad-f129-4370-88e5-bd54edb2056f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([1854, 1855, 1856, 1857, 1858, 1859, 1860, 1861, 1862, 1863, 1864, 1865,\n",
       "       1866, 1867, 1868, 1869, 1870, 1871, 1872, 1873, 1874, 1875, 1876, 1877,\n",
       "       1878, 1879, 1880, 1881, 1882, 1883, 1884, 1885, 1886, 1887, 1888, 1889,\n",
       "       1890, 1891, 1892, 1893, 1894, 1895, 1896, 1897, 1898, 1899, 1900, 1901,\n",
       "       1902, 1903, 1904, 1905, 1906, 1907, 1908, 1909, 1910, 1911, 1912, 1913,\n",
       "       1914, 1915, 1916, 1917, 1918, 1919, 1920, 1921, 1922, 1923, 1924, 1925,\n",
       "       1926, 1927, 1928, 1929, 1930, 1931, 1932, 1933, 1934, 1935, 1936, 1937,\n",
       "       1938, 1939, 1940, 1941, 1942, 1943, 1944, 1945, 1946, 1947, 1948, 1949,\n",
       "       1950, 1951, 1952, 1953, 1954, 1955, 1956, 1957, 1958, 1959, 1960, 1961,\n",
       "       1962, 1963, 1964, 1965, 1966, 1967, 1968, 1969, 1970, 1971, 1972, 1973,\n",
       "       1974, 1975, 1976, 1977, 1978, 1979, 1980, 1981, 1982, 1983, 1984, 1985,\n",
       "       1986, 1987, 1988, 1989, 1990, 1991, 1992, 1993, 1994, 1995, 1996, 1997,\n",
       "       1998, 1999, 2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009,\n",
       "       2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020, 2021],\n",
       "      dtype=int64)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-07ec28a6-cb2a-47f2-99da-a22f034dece9' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-07ec28a6-cb2a-47f2-99da-a22f034dece9' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.DataArray 'sst' (year: 168)>\n",
       "array([-0.27901283,  0.53887945, -0.79377913, -0.49743035,  0.01381396,\n",
       "        0.0156523 , -0.6605402 , -0.8556995 , -0.95602196, -0.56900066,\n",
       "       -0.35242435, -0.28149888, -0.3221899 , -0.39931533, -0.39802742,\n",
       "       -0.83826923, -1.2486324 , -0.11339114, -0.78456354, -0.56990427,\n",
       "       -1.071779  , -0.5927753 , -0.26286563,  1.6162647 ,  0.47199443,\n",
       "       -0.7494994 , -0.21297091,  0.27370268, -0.22139291,  0.06209752,\n",
       "        0.33742368,  0.50778013, -0.8580026 , -0.66962093,  1.1704594 ,\n",
       "       -0.06664982, -0.9321185 ,  0.27976942, -0.790312  , -1.1027724 ,\n",
       "       -0.9189873 ,  0.07730261,  0.968323  ,  0.15831277, -0.6120045 ,\n",
       "        0.22558771,  0.8393369 , -0.11646404,  1.2146426 , -0.21488364,\n",
       "       -0.28697327,  1.2190522 , -0.22441082, -0.30146205, -0.5932198 ,\n",
       "       -1.0453684 , -1.185916  ,  0.08344454,  0.22287995,  0.12389604,\n",
       "        1.0010883 ,  0.18555258, -1.0712985 , -1.3870167 ,  0.39811984,\n",
       "        0.26008293,  0.19583876, -0.2877738 , -0.6633001 ,  0.137851  ,\n",
       "       -0.61011195,  0.01340577,  0.2309341 , -0.21348888, -0.09869134,\n",
       "       -0.1239958 ,  0.79089445,  0.19254015, -0.15764934, -1.0652403 ,\n",
       "       -0.4786745 , -0.241648  , -0.26159406, -0.1477725 , -1.1184852 ,\n",
       "       -0.17742525,  1.0936302 ,  1.3195462 , -0.5813868 , -0.607824  ,\n",
       "       -0.02982099, -0.51498747, -0.5090077 , -0.16348676, -0.30373248,\n",
       "       -0.6234893 , -0.91125995,  0.38370433,  0.1016582 ,  0.675997  ,\n",
       "       -0.4642035 , -1.0084409 , -0.55972975,  0.96554905,  0.83548594,\n",
       "        0.15238537,  0.07394686, -0.05435823, -0.25894699,  0.6282403 ,\n",
       "       -0.4022585 ,  0.8829775 ,  0.46587762, -0.20255332,  0.22182052,\n",
       "        0.8555936 , -0.20786224, -0.8018796 ,  1.1038779 , -0.47148395,\n",
       "       -0.7369695 , -0.87838954,  0.10646022,  0.6879594 ,  0.06750116,\n",
       "        0.41535214,  0.4360534 , -0.01856835,  1.265782  ,  0.74676734,\n",
       "       -0.2291367 , -0.31765553,  0.49698195,  1.5277086 , -0.5609185 ,\n",
       "       -0.3592378 ,  0.5681549 ,  1.0546018 ,  1.0403054 ,  0.7367615 ,\n",
       "        0.8940851 ,  0.2435536 , -0.07897937,  1.5507613 ,  0.32457155,\n",
       "       -0.84567815, -0.4649423 ,  0.10109713,  1.0731276 ,  0.64620894,\n",
       "        0.85692674,  0.4534689 ,  0.52572626, -0.17803447, -0.34580478,\n",
       "        0.7258925 , -0.04623574, -0.4212134 ,  0.29608858,  0.12100772,\n",
       "        0.5414799 ,  1.8993014 ,  0.78147155,  0.23904468,  0.46577224,\n",
       "        0.9303055 ,  0.08714997, -0.2817711 ], dtype=float32)\n",
       "Coordinates:\n",
       "  * year     (year) int64 1854 1855 1856 1857 1858 ... 2017 2018 2019 2020 2021"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 求年平均\n",
    "sstjupingyear=sstjuping.groupby(ds.time.dt.year).mean(dim='time')\n",
    "sstjupingyear"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "ElNino=[]\n",
    "LaNina=[]\n",
    "# 挑选年份\n",
    "for i in sstjupingyear.year:\n",
    "    if sstjupingyear.loc[i] >=0.5:\n",
    "        ElNino.append(int(i.data))\n",
    "    if sstjupingyear.loc[i] <=-0.5:\n",
    "        LaNina.append(int(i.data))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1855,\n",
       " 1877,\n",
       " 1885,\n",
       " 1888,\n",
       " 1896,\n",
       " 1900,\n",
       " 1902,\n",
       " 1905,\n",
       " 1914,\n",
       " 1930,\n",
       " 1940,\n",
       " 1941,\n",
       " 1953,\n",
       " 1957,\n",
       " 1958,\n",
       " 1963,\n",
       " 1965,\n",
       " 1969,\n",
       " 1972,\n",
       " 1977,\n",
       " 1982,\n",
       " 1983,\n",
       " 1987,\n",
       " 1990,\n",
       " 1991,\n",
       " 1992,\n",
       " 1993,\n",
       " 1994,\n",
       " 1997,\n",
       " 2002,\n",
       " 2003,\n",
       " 2004,\n",
       " 2006,\n",
       " 2009,\n",
       " 2014,\n",
       " 2015,\n",
       " 2016,\n",
       " 2019]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ElNino"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1856,\n",
       " 1860,\n",
       " 1861,\n",
       " 1862,\n",
       " 1863,\n",
       " 1869,\n",
       " 1870,\n",
       " 1872,\n",
       " 1873,\n",
       " 1874,\n",
       " 1875,\n",
       " 1879,\n",
       " 1886,\n",
       " 1887,\n",
       " 1890,\n",
       " 1892,\n",
       " 1893,\n",
       " 1894,\n",
       " 1898,\n",
       " 1908,\n",
       " 1909,\n",
       " 1910,\n",
       " 1916,\n",
       " 1917,\n",
       " 1922,\n",
       " 1924,\n",
       " 1933,\n",
       " 1938,\n",
       " 1942,\n",
       " 1943,\n",
       " 1945,\n",
       " 1946,\n",
       " 1949,\n",
       " 1950,\n",
       " 1955,\n",
       " 1956,\n",
       " 1971,\n",
       " 1974,\n",
       " 1975,\n",
       " 1988,\n",
       " 1999]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "LaNina"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "years=np.arange(1854,2022,1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "# 返回bool列表\n",
    "def boollist(years,yichang):\n",
    "    boolli=[]\n",
    "    for i in years:\n",
    "        if i in yichang:\n",
    "            boolli.append(True)\n",
    "            continue\n",
    "        boolli.append(False)\n",
    "    return boolli\n",
    "# 手动实现交集\n",
    "def jiao(list1,list2):\n",
    "    boolli=[]\n",
    "    for i in range(len(list1)):\n",
    "        if list1[i] != list2[i]:\n",
    "            boolli.append(False)\n",
    "            continue\n",
    "        boolli.append(True)\n",
    "    return boolli"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 648x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sel=jiao(boollist(years,ElNino),boollist(years,LaNina))\n",
    "fig=plt.figure(figsize=(9,6))\n",
    "ax=fig.subplots(1,1)\n",
    "ax.plot(years,sstjupingyear)\n",
    "ax.scatter(years[boollist(years,ElNino)],sstjupingyear[boollist(years,ElNino)],color='green',marker=\"*\",label='ElNino年')\n",
    "ax.scatter(years[boollist(years,LaNina)],sstjupingyear[boollist(years,LaNina)],color='orange',marker=\"^\",label='LaNina年')\n",
    "ax.axhline(y=0.5,color=\"red\",linestyle=\"--\")\n",
    "ax.axhline(y=-0.5,color=\"red\",linestyle=\"--\")\n",
    "ax.set_xlim(1854,2022)\n",
    "ax.set_xlabel('时间')\n",
    "ax.set_ylabel('sst距平')\n",
    "ax.set_title('第五问')\n",
    "plt.legend()\n",
    "# 保存图片\n",
    "plt.savefig('../../picture/pythonhome/11/qu4.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
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     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": []
  }
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