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sharkinsspatial/noaa_sst_virtualizarr_hdf_reader.ipynb

Created July 2, 2024 21:16
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noaa_sst_virtualizarr_hdf_reader.ipynb
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"import pandas as pd\n",
"import xarray as xr\n",
"from virtualizarr import open_virtual_dataset\n",
"import fsspec"
]
},
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"DatetimeIndex(['2022-09-29', '2022-09-30', '2022-10-01'], dtype='datetime64[ns]', freq='D')"
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"start_date = \"2022-09-29\"\n",
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"dates"
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"url_pattern = (\n",
" \"s3://noaa-cdr-sea-surface-temp-optimum-interpolation-pds/data\"\n",
" \"/v2.1/avhrr/{day:%Y%m}/oisst-avhrr-v02r01.{day:%Y%m%d}.nc\"\n",
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"['s3://noaa-cdr-sea-surface-temp-optimum-interpolation-pds/data/v2.1/avhrr/202209/oisst-avhrr-v02r01.20220929.nc',\n",
" 's3://noaa-cdr-sea-surface-temp-optimum-interpolation-pds/data/v2.1/avhrr/202209/oisst-avhrr-v02r01.20220930.nc',\n",
" 's3://noaa-cdr-sea-surface-temp-optimum-interpolation-pds/data/v2.1/avhrr/202210/oisst-avhrr-v02r01.20221001.nc']"
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"metadata": {},
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"urls = [url_pattern.format(day=date) for date in dates]\n",
"urls"
]
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{
"cell_type": "code",
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"id": "764cabfb-79c9-4ec4-aa2b-5da22ae073f1",
"metadata": {},
"outputs": [],
"source": [
"vds_list = [open_virtual_dataset(url, indexes={}) for url in urls]"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "e3acc40d-160e-426f-b13d-2a0bbd2235f6",
"metadata": {},
"outputs": [
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"</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt; Size: 100MB\n",
"Dimensions: (time: 3, zlev: 1, lat: 720, lon: 1440)\n",
"Coordinates:\n",
" lat (lat) float32 3kB ManifestArray&lt;shape=(720,), dtype=float32, chu...\n",
" lon (lon) float32 6kB ManifestArray&lt;shape=(1440,), dtype=float32, ch...\n",
" time (time) float32 12B ManifestArray&lt;shape=(3,), dtype=float32, chun...\n",
" zlev (zlev) float32 4B ManifestArray&lt;shape=(1,), dtype=float32, chunk...\n",
"Data variables:\n",
" anom (time, zlev, lat, lon) float64 25MB ManifestArray&lt;shape=(3, 1, 7...\n",
" err (time, zlev, lat, lon) float64 25MB ManifestArray&lt;shape=(3, 1, 7...\n",
" ice (time, zlev, lat, lon) float64 25MB ManifestArray&lt;shape=(3, 1, 7...\n",
" sst (time, zlev, lat, lon) float64 25MB ManifestArray&lt;shape=(3, 1, 7...\n",
"Attributes: (12/37)\n",
" Conventions: CF-1.6, ACDD-1.3\n",
" title: NOAA/NCEI 1/4 Degree Daily Optimum Interpolat...\n",
" references: Reynolds, et al.(2007) Daily High-Resolution-...\n",
" source: ICOADS, NCEP_GTS, GSFC_ICE, NCEP_ICE, Pathfin...\n",
" id: oisst-avhrr-v02r01.20220929.nc\n",
" naming_authority: gov.noaa.ncei\n",
" ... ...\n",
" time_coverage_start: 2022-09-29T00:00:00Z\n",
" time_coverage_end: 2022-09-29T23:59:59Z\n",
" metadata_link: https://doi.org/10.25921/RE9P-PT57\n",
" ncei_template_version: NCEI_NetCDF_Grid_Template_v2.0\n",
" comment: Data was converted from NetCDF-3 to NetCDF-4 ...\n",
" sensor: Thermometer, AVHRR</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-b096c056-db1a-43fe-b3f3-6680e06a95a0' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-b096c056-db1a-43fe-b3f3-6680e06a95a0' class='xr-section-summary' title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span>time</span>: 3</li><li><span>zlev</span>: 1</li><li><span>lat</span>: 720</li><li><span>lon</span>: 1440</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-57cb3878-08ad-4836-b3f6-64da5189bd32' class='xr-section-summary-in' type='checkbox' checked><label for='section-57cb3878-08ad-4836-b3f6-64da5189bd32' class='xr-section-summary' >Coordinates: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>lat</span></div><div class='xr-var-dims'>(lat)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>ManifestArray&lt;shape=(720,), dtyp...</div><input id='attrs-903042a3-734a-43f2-ae65-d5fc149fc048' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-903042a3-734a-43f2-ae65-d5fc149fc048' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-06472418-27ac-404f-bdb0-f2474286ef4d' class='xr-var-data-in' type='checkbox'><label for='data-06472418-27ac-404f-bdb0-f2474286ef4d' 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>Latitude</dd><dt><span>units :</span></dt><dd>degrees_north</dd><dt><span>grids :</span></dt><dd>Uniform grid from -89.875 to 89.875 by 0.25</dd></dl></div><div class='xr-var-data'><pre>ManifestArray&lt;shape=(720,), dtype=float32, chunks=(720,)&gt;</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lon</span></div><div class='xr-var-dims'>(lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>ManifestArray&lt;shape=(1440,), dty...</div><input id='attrs-bfb0ae3a-ebfd-446a-93b1-d2330efd5cdf' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-bfb0ae3a-ebfd-446a-93b1-d2330efd5cdf' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-71291ff9-d8e8-499f-b11a-55365e69bb19' class='xr-var-data-in' type='checkbox'><label for='data-71291ff9-d8e8-499f-b11a-55365e69bb19' 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>Longitude</dd><dt><span>units :</span></dt><dd>degrees_east</dd><dt><span>grids :</span></dt><dd>Uniform grid from 0.125 to 359.875 by 0.25</dd></dl></div><div class='xr-var-data'><pre>ManifestArray&lt;shape=(1440,), dtype=float32, chunks=(1440,)&gt;</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>ManifestArray&lt;shape=(3,), dtype=...</div><input id='attrs-639f35f8-08f4-4f01-bff4-30ca41bc5b65' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-639f35f8-08f4-4f01-bff4-30ca41bc5b65' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7eaf213a-b46a-4ae0-8c77-3f8c1892f2b7' class='xr-var-data-in' type='checkbox'><label for='data-7eaf213a-b46a-4ae0-8c77-3f8c1892f2b7' 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>Center time of the day</dd><dt><span>units :</span></dt><dd>days since 1978-01-01 12:00:00</dd></dl></div><div class='xr-var-data'><pre>ManifestArray&lt;shape=(3,), dtype=float32, chunks=(1,)&gt;</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>zlev</span></div><div class='xr-var-dims'>(zlev)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>ManifestArray&lt;shape=(1,), dtype=...</div><input id='attrs-7af185f5-e28d-4d89-83c9-3d18c608e02d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-7af185f5-e28d-4d89-83c9-3d18c608e02d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3ffb4ce2-b01e-4940-9178-c4955f0b3015' class='xr-var-data-in' type='checkbox'><label for='data-3ffb4ce2-b01e-4940-9178-c4955f0b3015' 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>Sea surface height</dd><dt><span>units :</span></dt><dd>meters</dd><dt><span>positive :</span></dt><dd>down</dd><dt><span>actual_range :</span></dt><dd>0, 0</dd></dl></div><div class='xr-var-data'><pre>ManifestArray&lt;shape=(1,), dtype=float32, chunks=(1,)&gt;</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-70e5621c-e356-46ad-a60b-2871c7f38656' class='xr-section-summary-in' type='checkbox' checked><label for='section-70e5621c-e356-46ad-a60b-2871c7f38656' class='xr-section-summary' >Data variables: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>anom</span></div><div class='xr-var-dims'>(time, zlev, lat, lon)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>ManifestArray&lt;shape=(3, 1, 720, ...</div><input id='attrs-9207cd1d-71cf-4539-828d-ae496472140c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9207cd1d-71cf-4539-828d-ae496472140c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-49c1fc05-0618-4a9d-b0c7-7f35cf2e8b0f' class='xr-var-data-in' type='checkbox'><label for='data-49c1fc05-0618-4a9d-b0c7-7f35cf2e8b0f' 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>Daily sea surface temperature anomalies</dd><dt><span>units :</span></dt><dd>Celsius</dd><dt><span>valid_min :</span></dt><dd>-1200</dd><dt><span>valid_max :</span></dt><dd>1200</dd></dl></div><div class='xr-var-data'><pre>ManifestArray&lt;shape=(3, 1, 720, 1440), dtype=float64, chunks=(1, 1, 720, 1440)&gt;</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>err</span></div><div class='xr-var-dims'>(time, zlev, lat, lon)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>ManifestArray&lt;shape=(3, 1, 720, ...</div><input id='attrs-cc6156c4-a5f1-450f-83a0-476560a1c1e6' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-cc6156c4-a5f1-450f-83a0-476560a1c1e6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0f13d0e8-0db0-41a8-b621-1f625d7c5c52' class='xr-var-data-in' type='checkbox'><label for='data-0f13d0e8-0db0-41a8-b621-1f625d7c5c52' 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>Estimated error standard deviation of analysed_sst</dd><dt><span>units :</span></dt><dd>Celsius</dd><dt><span>valid_min :</span></dt><dd>0</dd><dt><span>valid_max :</span></dt><dd>1000</dd></dl></div><div class='xr-var-data'><pre>ManifestArray&lt;shape=(3, 1, 720, 1440), dtype=float64, chunks=(1, 1, 720, 1440)&gt;</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ice</span></div><div class='xr-var-dims'>(time, zlev, lat, lon)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>ManifestArray&lt;shape=(3, 1, 720, ...</div><input id='attrs-af48babb-e6af-4bf8-9458-8850e520dc01' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-af48babb-e6af-4bf8-9458-8850e520dc01' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a4f82860-6b2a-4903-8f77-537afba4bd4b' class='xr-var-data-in' type='checkbox'><label for='data-a4f82860-6b2a-4903-8f77-537afba4bd4b' 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>Sea ice concentration</dd><dt><span>units :</span></dt><dd>%</dd><dt><span>valid_min :</span></dt><dd>0</dd><dt><span>valid_max :</span></dt><dd>100</dd></dl></div><div class='xr-var-data'><pre>ManifestArray&lt;shape=(3, 1, 720, 1440), dtype=float64, chunks=(1, 1, 720, 1440)&gt;</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>sst</span></div><div class='xr-var-dims'>(time, zlev, lat, lon)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>ManifestArray&lt;shape=(3, 1, 720, ...</div><input id='attrs-71a167ef-0a4b-41b9-9d32-0f1341ba6bb7' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-71a167ef-0a4b-41b9-9d32-0f1341ba6bb7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-481cd4c5-f741-4e08-8825-b3c5666c2732' class='xr-var-data-in' type='checkbox'><label for='data-481cd4c5-f741-4e08-8825-b3c5666c2732' 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>Daily sea surface temperature</dd><dt><span>units :</span></dt><dd>Celsius</dd><dt><span>valid_min :</span></dt><dd>-300</dd><dt><span>valid_max :</span></dt><dd>4500</dd></dl></div><div class='xr-var-data'><pre>ManifestArray&lt;shape=(3, 1, 720, 1440), dtype=float64, chunks=(1, 1, 720, 1440)&gt;</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-44516f0a-b7cb-4d29-b03c-eb2cf026e737' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-44516f0a-b7cb-4d29-b03c-eb2cf026e737' class='xr-section-summary' title='Expand/collapse section'>Indexes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'></ul></div></li><li class='xr-section-item'><input id='section-ed05800d-7e47-4d74-a16a-40839b07f71d' class='xr-section-summary-in' type='checkbox' ><label for='section-ed05800d-7e47-4d74-a16a-40839b07f71d' 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>Conventions :</span></dt><dd>CF-1.6, ACDD-1.3</dd><dt><span>title :</span></dt><dd>NOAA/NCEI 1/4 Degree Daily Optimum Interpolation Sea Surface Temperature (OISST) Analysis, Version 2.1 - Final</dd><dt><span>references :</span></dt><dd>Reynolds, et al.(2007) Daily High-Resolution-Blended Analyses for Sea Surface Temperature (available at https://doi.org/10.1175/2007JCLI1824.1). Banzon, et al.(2016) A long-term record of blended satellite and in situ sea-surface temperature for climate monitoring, modeling and environmental studies (available at https://doi.org/10.5194/essd-8-165-2016). Huang et al. (2020) Improvements of the Daily Optimum Interpolation Sea Surface Temperature (DOISST) Version v02r01, submitted.Climatology is based on 1971-2000 OI.v2 SST. Satellite data: Pathfinder AVHRR SST, Navy AVHRR SST, and NOAA ACSPO SST. Ice data: NCEP Ice and GSFC Ice.</dd><dt><span>source :</span></dt><dd>ICOADS, NCEP_GTS, GSFC_ICE, NCEP_ICE, Pathfinder_AVHRR, Navy_AVHRR, NOAA_ACSP</dd><dt><span>id :</span></dt><dd>oisst-avhrr-v02r01.20220929.nc</dd><dt><span>naming_authority :</span></dt><dd>gov.noaa.ncei</dd><dt><span>summary :</span></dt><dd>NOAAs 1/4-degree Daily Optimum Interpolation Sea Surface Temperature (OISST) (sometimes referred to as Reynolds SST, which however also refers to earlier products at different resolution), currently available as version v02r01, is created by interpolating and extrapolating SST observations from different sources, resulting in a smoothed complete field. The sources of data are satellite (AVHRR) and in situ platforms (i.e., ships and buoys), and the specific datasets employed may change over time. At the marginal ice zone, sea ice concentrations are used to generate proxy SSTs. A preliminary version of this file is produced in near-real time (1-day latency), and then replaced with a final version after 2 weeks. Note that this is the AVHRR-ONLY DOISST, available from Oct 1981, but there is a companion DOISST product that includes microwave satellite data, available from June 2002</dd><dt><span>cdm_data_type :</span></dt><dd>Grid</dd><dt><span>history :</span></dt><dd>Final file created using preliminary as first guess, and 3 days of AVHRR data. Preliminary uses only 1 day of AVHRR data.</dd><dt><span>date_modified :</span></dt><dd>2022-10-14T09:14:00Z</dd><dt><span>date_created :</span></dt><dd>2022-10-14T09:14:00Z</dd><dt><span>product_version :</span></dt><dd>Version v02r01</dd><dt><span>processing_level :</span></dt><dd>NOAA Level 4</dd><dt><span>institution :</span></dt><dd>NOAA/National Centers for Environmental Information</dd><dt><span>creator_url :</span></dt><dd>https://www.ncei.noaa.gov/</dd><dt><span>creator_email :</span></dt><dd>[email protected]</dd><dt><span>keywords :</span></dt><dd>Earth Science &gt; Oceans &gt; Ocean Temperature &gt; Sea Surface Temperature</dd><dt><span>keywords_vocabulary :</span></dt><dd>Global Change Master Directory (GCMD) Earth Science Keywords</dd><dt><span>platform :</span></dt><dd>Ships, buoys, Argo floats, MetOp-A, MetOp-B</dd><dt><span>platform_vocabulary :</span></dt><dd>Global Change Master Directory (GCMD) Platform Keywords</dd><dt><span>instrument :</span></dt><dd>Earth Remote Sensing Instruments &gt; Passive Remote Sensing &gt; Spectrometers/Radiometers &gt; Imaging Spectrometers/Radiometers &gt; AVHRR &gt; Advanced Very High Resolution Radiometer</dd><dt><span>instrument_vocabulary :</span></dt><dd>Global Change Master Directory (GCMD) Instrument Keywords</dd><dt><span>standard_name_vocabulary :</span></dt><dd>CF Standard Name Table (v40, 25 January 2017)</dd><dt><span>geospatial_lat_min :</span></dt><dd>-90.0</dd><dt><span>geospatial_lat_max :</span></dt><dd>90.0</dd><dt><span>geospatial_lon_min :</span></dt><dd>0.0</dd><dt><span>geospatial_lon_max :</span></dt><dd>360.0</dd><dt><span>geospatial_lat_units :</span></dt><dd>degrees_north</dd><dt><span>geospatial_lat_resolution :</span></dt><dd>0.25</dd><dt><span>geospatial_lon_units :</span></dt><dd>degrees_east</dd><dt><span>geospatial_lon_resolution :</span></dt><dd>0.25</dd><dt><span>time_coverage_start :</span></dt><dd>2022-09-29T00:00:00Z</dd><dt><span>time_coverage_end :</span></dt><dd>2022-09-29T23:59:59Z</dd><dt><span>metadata_link :</span></dt><dd>https://doi.org/10.25921/RE9P-PT57</dd><dt><span>ncei_template_version :</span></dt><dd>NCEI_NetCDF_Grid_Template_v2.0</dd><dt><span>comment :</span></dt><dd>Data was converted from NetCDF-3 to NetCDF-4 format with metadata updates in November 2017.</dd><dt><span>sensor :</span></dt><dd>Thermometer, AVHRR</dd></dl></div></li></ul></div></div>"
],
"text/plain": [
"<xarray.Dataset> Size: 100MB\n",
"Dimensions: (time: 3, zlev: 1, lat: 720, lon: 1440)\n",
"Coordinates:\n",
" lat (lat) float32 3kB ManifestArray<shape=(720,), dtype=float32, chu...\n",
" lon (lon) float32 6kB ManifestArray<shape=(1440,), dtype=float32, ch...\n",
" time (time) float32 12B ManifestArray<shape=(3,), dtype=float32, chun...\n",
" zlev (zlev) float32 4B ManifestArray<shape=(1,), dtype=float32, chunk...\n",
"Data variables:\n",
" anom (time, zlev, lat, lon) float64 25MB ManifestArray<shape=(3, 1, 7...\n",
" err (time, zlev, lat, lon) float64 25MB ManifestArray<shape=(3, 1, 7...\n",
" ice (time, zlev, lat, lon) float64 25MB ManifestArray<shape=(3, 1, 7...\n",
" sst (time, zlev, lat, lon) float64 25MB ManifestArray<shape=(3, 1, 7...\n",
"Attributes: (12/37)\n",
" Conventions: CF-1.6, ACDD-1.3\n",
" title: NOAA/NCEI 1/4 Degree Daily Optimum Interpolat...\n",
" references: Reynolds, et al.(2007) Daily High-Resolution-...\n",
" source: ICOADS, NCEP_GTS, GSFC_ICE, NCEP_ICE, Pathfin...\n",
" id: oisst-avhrr-v02r01.20220929.nc\n",
" naming_authority: gov.noaa.ncei\n",
" ... ...\n",
" time_coverage_start: 2022-09-29T00:00:00Z\n",
" time_coverage_end: 2022-09-29T23:59:59Z\n",
" metadata_link: https://doi.org/10.25921/RE9P-PT57\n",
" ncei_template_version: NCEI_NetCDF_Grid_Template_v2.0\n",
" comment: Data was converted from NetCDF-3 to NetCDF-4 ...\n",
" sensor: Thermometer, AVHRR"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"vsd_concat = xr.concat(vds_list, dim=\"time\", coords=\"minimal\", compat=\"override\")\n",
"vsd_concat"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "8ac04f0f-ff80-48ea-bdfb-858c2cf82631",
"metadata": {},
"outputs": [],
"source": [
"vsd_concat.virtualize.to_kerchunk(\"sst_kerchunk.json\", format=\"json\")"
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "b0e7d869-cb01-444c-9b5e-ccec56653568",
"metadata": {},
"outputs": [
{
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" --xr-disabled-color: #515151;\n",
" --xr-background-color: #111111;\n",
" --xr-background-color-row-even: #111111;\n",
" --xr-background-color-row-odd: #313131;\n",
"}\n",
"\n",
".xr-wrap {\n",
" display: block !important;\n",
" min-width: 300px;\n",
" max-width: 700px;\n",
"}\n",
"\n",
".xr-text-repr-fallback {\n",
" /* fallback to plain text repr when CSS is not injected (untrusted notebook) */\n",
" display: none;\n",
"}\n",
"\n",
".xr-header {\n",
" padding-top: 6px;\n",
" padding-bottom: 6px;\n",
" margin-bottom: 4px;\n",
" border-bottom: solid 1px var(--xr-border-color);\n",
"}\n",
"\n",
".xr-header > div,\n",
".xr-header > ul {\n",
" display: inline;\n",
" margin-top: 0;\n",
" margin-bottom: 0;\n",
"}\n",
"\n",
".xr-obj-type,\n",
".xr-array-name {\n",
" margin-left: 2px;\n",
" margin-right: 10px;\n",
"}\n",
"\n",
".xr-obj-type {\n",
" color: var(--xr-font-color2);\n",
"}\n",
"\n",
".xr-sections {\n",
" padding-left: 0 !important;\n",
" display: grid;\n",
" grid-template-columns: 150px auto auto 1fr 20px 20px;\n",
"}\n",
"\n",
".xr-section-item {\n",
" display: contents;\n",
"}\n",
"\n",
".xr-section-item input {\n",
" display: none;\n",
"}\n",
"\n",
".xr-section-item input + label {\n",
" color: var(--xr-disabled-color);\n",
"}\n",
"\n",
".xr-section-item input:enabled + label {\n",
" cursor: pointer;\n",
" color: var(--xr-font-color2);\n",
"}\n",
"\n",
".xr-section-item input:enabled + label:hover {\n",
" color: var(--xr-font-color0);\n",
"}\n",
"\n",
".xr-section-summary {\n",
" grid-column: 1;\n",
" color: var(--xr-font-color2);\n",
" font-weight: 500;\n",
"}\n",
"\n",
".xr-section-summary > span {\n",
" display: inline-block;\n",
" padding-left: 0.5em;\n",
"}\n",
"\n",
".xr-section-summary-in:disabled + label {\n",
" color: var(--xr-font-color2);\n",
"}\n",
"\n",
".xr-section-summary-in + label:before {\n",
" display: inline-block;\n",
" content: '►';\n",
" font-size: 11px;\n",
" width: 15px;\n",
" text-align: center;\n",
"}\n",
"\n",
".xr-section-summary-in:disabled + label:before {\n",
" color: var(--xr-disabled-color);\n",
"}\n",
"\n",
".xr-section-summary-in:checked + label:before {\n",
" content: '▼';\n",
"}\n",
"\n",
".xr-section-summary-in:checked + label > span {\n",
" display: none;\n",
"}\n",
"\n",
".xr-section-summary,\n",
".xr-section-inline-details {\n",
" padding-top: 4px;\n",
" padding-bottom: 4px;\n",
"}\n",
"\n",
".xr-section-inline-details {\n",
" grid-column: 2 / -1;\n",
"}\n",
"\n",
".xr-section-details {\n",
" display: none;\n",
" grid-column: 1 / -1;\n",
" margin-bottom: 5px;\n",
"}\n",
"\n",
".xr-section-summary-in:checked ~ .xr-section-details {\n",
" display: contents;\n",
"}\n",
"\n",
".xr-array-wrap {\n",
" grid-column: 1 / -1;\n",
" display: grid;\n",
" grid-template-columns: 20px auto;\n",
"}\n",
"\n",
".xr-array-wrap > label {\n",
" grid-column: 1;\n",
" vertical-align: top;\n",
"}\n",
"\n",
".xr-preview {\n",
" color: var(--xr-font-color3);\n",
"}\n",
"\n",
".xr-array-preview,\n",
".xr-array-data {\n",
" padding: 0 5px !important;\n",
" grid-column: 2;\n",
"}\n",
"\n",
".xr-array-data,\n",
".xr-array-in:checked ~ .xr-array-preview {\n",
" display: none;\n",
"}\n",
"\n",
".xr-array-in:checked ~ .xr-array-data,\n",
".xr-array-preview {\n",
" display: inline-block;\n",
"}\n",
"\n",
".xr-dim-list {\n",
" display: inline-block !important;\n",
" list-style: none;\n",
" padding: 0 !important;\n",
" margin: 0;\n",
"}\n",
"\n",
".xr-dim-list li {\n",
" display: inline-block;\n",
" padding: 0;\n",
" margin: 0;\n",
"}\n",
"\n",
".xr-dim-list:before {\n",
" content: '(';\n",
"}\n",
"\n",
".xr-dim-list:after {\n",
" content: ')';\n",
"}\n",
"\n",
".xr-dim-list li:not(:last-child):after {\n",
" content: ',';\n",
" padding-right: 5px;\n",
"}\n",
"\n",
".xr-has-index {\n",
" font-weight: bold;\n",
"}\n",
"\n",
".xr-var-list,\n",
".xr-var-item {\n",
" display: contents;\n",
"}\n",
"\n",
".xr-var-item > div,\n",
".xr-var-item label,\n",
".xr-var-item > .xr-var-name span {\n",
" background-color: var(--xr-background-color-row-even);\n",
" margin-bottom: 0;\n",
"}\n",
"\n",
".xr-var-item > .xr-var-name:hover span {\n",
" padding-right: 5px;\n",
"}\n",
"\n",
".xr-var-list > li:nth-child(odd) > div,\n",
".xr-var-list > li:nth-child(odd) > label,\n",
".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
" background-color: var(--xr-background-color-row-odd);\n",
"}\n",
"\n",
".xr-var-name {\n",
" grid-column: 1;\n",
"}\n",
"\n",
".xr-var-dims {\n",
" grid-column: 2;\n",
"}\n",
"\n",
".xr-var-dtype {\n",
" grid-column: 3;\n",
" text-align: right;\n",
" color: var(--xr-font-color2);\n",
"}\n",
"\n",
".xr-var-preview {\n",
" grid-column: 4;\n",
"}\n",
"\n",
".xr-index-preview {\n",
" grid-column: 2 / 5;\n",
" color: var(--xr-font-color2);\n",
"}\n",
"\n",
".xr-var-name,\n",
".xr-var-dims,\n",
".xr-var-dtype,\n",
".xr-preview,\n",
".xr-attrs dt {\n",
" white-space: nowrap;\n",
" overflow: hidden;\n",
" text-overflow: ellipsis;\n",
" padding-right: 10px;\n",
"}\n",
"\n",
".xr-var-name:hover,\n",
".xr-var-dims:hover,\n",
".xr-var-dtype:hover,\n",
".xr-attrs dt:hover {\n",
" overflow: visible;\n",
" width: auto;\n",
" z-index: 1;\n",
"}\n",
"\n",
".xr-var-attrs,\n",
".xr-var-data,\n",
".xr-index-data {\n",
" display: none;\n",
" background-color: var(--xr-background-color) !important;\n",
" padding-bottom: 5px !important;\n",
"}\n",
"\n",
".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
".xr-var-data-in:checked ~ .xr-var-data,\n",
".xr-index-data-in:checked ~ .xr-index-data {\n",
" display: block;\n",
"}\n",
"\n",
".xr-var-data > table {\n",
" float: right;\n",
"}\n",
"\n",
".xr-var-name span,\n",
".xr-var-data,\n",
".xr-index-name div,\n",
".xr-index-data,\n",
".xr-attrs {\n",
" padding-left: 25px !important;\n",
"}\n",
"\n",
".xr-attrs,\n",
".xr-var-attrs,\n",
".xr-var-data,\n",
".xr-index-data {\n",
" grid-column: 1 / -1;\n",
"}\n",
"\n",
"dl.xr-attrs {\n",
" padding: 0;\n",
" margin: 0;\n",
" display: grid;\n",
" grid-template-columns: 125px auto;\n",
"}\n",
"\n",
".xr-attrs dt,\n",
".xr-attrs dd {\n",
" padding: 0;\n",
" margin: 0;\n",
" float: left;\n",
" padding-right: 10px;\n",
" width: auto;\n",
"}\n",
"\n",
".xr-attrs dt {\n",
" font-weight: normal;\n",
" grid-column: 1;\n",
"}\n",
"\n",
".xr-attrs dt:hover span {\n",
" display: inline-block;\n",
" background: var(--xr-background-color);\n",
" padding-right: 10px;\n",
"}\n",
"\n",
".xr-attrs dd {\n",
" grid-column: 2;\n",
" white-space: pre-wrap;\n",
" word-break: break-all;\n",
"}\n",
"\n",
".xr-icon-database,\n",
".xr-icon-file-text2,\n",
".xr-no-icon {\n",
" display: inline-block;\n",
" vertical-align: middle;\n",
" width: 1em;\n",
" height: 1.5em !important;\n",
" stroke-width: 0;\n",
" stroke: currentColor;\n",
" fill: currentColor;\n",
"}\n",
"</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt; Size: 100MB\n",
"Dimensions: (time: 3, zlev: 1, lat: 720, lon: 1440)\n",
"Coordinates:\n",
" * lat (lat) float32 3kB -89.88 -89.62 -89.38 -89.12 ... 89.38 89.62 89.88\n",
" * lon (lon) float32 6kB 0.125 0.375 0.625 0.875 ... 359.4 359.6 359.9\n",
" * time (time) datetime64[ns] 24B 2022-09-29T12:00:51.924828160 ... 2022...\n",
" * zlev (zlev) float32 4B 0.0\n",
"Data variables:\n",
" anom (time, zlev, lat, lon) float64 25MB ...\n",
" err (time, zlev, lat, lon) float64 25MB ...\n",
" ice (time, zlev, lat, lon) float64 25MB ...\n",
" sst (time, zlev, lat, lon) float64 25MB ...\n",
"Attributes: (12/37)\n",
" Conventions: CF-1.6, ACDD-1.3\n",
" title: NOAA/NCEI 1/4 Degree Daily Optimum Interpolat...\n",
" references: Reynolds, et al.(2007) Daily High-Resolution-...\n",
" source: ICOADS, NCEP_GTS, GSFC_ICE, NCEP_ICE, Pathfin...\n",
" id: oisst-avhrr-v02r01.20220929.nc\n",
" naming_authority: gov.noaa.ncei\n",
" ... ...\n",
" time_coverage_start: 2022-09-29T00:00:00Z\n",
" time_coverage_end: 2022-09-29T23:59:59Z\n",
" metadata_link: https://doi.org/10.25921/RE9P-PT57\n",
" ncei_template_version: NCEI_NetCDF_Grid_Template_v2.0\n",
" comment: Data was converted from NetCDF-3 to NetCDF-4 ...\n",
" sensor: Thermometer, AVHRR</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-476a851d-73e2-4e10-95ff-43040bac342b' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-476a851d-73e2-4e10-95ff-43040bac342b' class='xr-section-summary' title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 3</li><li><span class='xr-has-index'>zlev</span>: 1</li><li><span class='xr-has-index'>lat</span>: 720</li><li><span class='xr-has-index'>lon</span>: 1440</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-598b50dd-3ef3-41a8-8778-7a4beddb3b0e' class='xr-section-summary-in' type='checkbox' checked><label for='section-598b50dd-3ef3-41a8-8778-7a4beddb3b0e' class='xr-section-summary' >Coordinates: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lat</span></div><div class='xr-var-dims'>(lat)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>-89.88 -89.62 ... 89.62 89.88</div><input id='attrs-515c591f-5e7d-4795-9722-18c8bf8d915d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-515c591f-5e7d-4795-9722-18c8bf8d915d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-931b0959-470f-4b43-a91d-75e034f2201c' class='xr-var-data-in' type='checkbox'><label for='data-931b0959-470f-4b43-a91d-75e034f2201c' 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>Latitude</dd><dt><span>units :</span></dt><dd>degrees_north</dd><dt><span>grids :</span></dt><dd>Uniform grid from -89.875 to 89.875 by 0.25</dd></dl></div><div class='xr-var-data'><pre>array([-89.875, -89.625, -89.375, ..., 89.375, 89.625, 89.875],\n",
" dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lon</span></div><div class='xr-var-dims'>(lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.125 0.375 0.625 ... 359.6 359.9</div><input id='attrs-2c9353bb-959a-43e5-99b3-74baa5fc84e3' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-2c9353bb-959a-43e5-99b3-74baa5fc84e3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d3b88ece-3cf0-4adc-ba6f-f4c1783f2cf2' class='xr-var-data-in' type='checkbox'><label for='data-d3b88ece-3cf0-4adc-ba6f-f4c1783f2cf2' 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>Longitude</dd><dt><span>units :</span></dt><dd>degrees_east</dd><dt><span>grids :</span></dt><dd>Uniform grid from 0.125 to 359.875 by 0.25</dd></dl></div><div class='xr-var-data'><pre>array([1.25000e-01, 3.75000e-01, 6.25000e-01, ..., 3.59375e+02, 3.59625e+02,\n",
" 3.59875e+02], 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'>2022-09-29T12:00:51.924828160 .....</div><input id='attrs-0292e375-7cd4-465d-8843-a1840463079f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0292e375-7cd4-465d-8843-a1840463079f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a96fce01-fca2-4c7d-afcf-d702827dad73' class='xr-var-data-in' type='checkbox'><label for='data-a96fce01-fca2-4c7d-afcf-d702827dad73' 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>Center time of the day</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;2022-09-29T12:00:51.924828160&#x27;, &#x27;2022-09-30T12:01:41.026562048&#x27;,\n",
" &#x27;2022-10-01T12:00:12.689342464&#x27;], dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>zlev</span></div><div class='xr-var-dims'>(zlev)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.0</div><input id='attrs-dec529ce-124e-4af8-b5ca-4b75b651ac7c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-dec529ce-124e-4af8-b5ca-4b75b651ac7c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c32f68fc-880d-45cd-9b88-9bac385f142c' class='xr-var-data-in' type='checkbox'><label for='data-c32f68fc-880d-45cd-9b88-9bac385f142c' 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>Sea surface height</dd><dt><span>units :</span></dt><dd>meters</dd><dt><span>positive :</span></dt><dd>down</dd><dt><span>actual_range :</span></dt><dd>0, 0</dd></dl></div><div class='xr-var-data'><pre>array([0.], dtype=float32)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-ef6b328b-96cf-45ba-970a-d4ed06035918' class='xr-section-summary-in' type='checkbox' checked><label for='section-ef6b328b-96cf-45ba-970a-d4ed06035918' class='xr-section-summary' >Data variables: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>anom</span></div><div class='xr-var-dims'>(time, zlev, lat, lon)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-c2d6fb04-2e55-4d23-8edd-502d1cc1febf' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c2d6fb04-2e55-4d23-8edd-502d1cc1febf' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a0dde986-b310-47ea-abdc-f7970c44ea47' class='xr-var-data-in' type='checkbox'><label for='data-a0dde986-b310-47ea-abdc-f7970c44ea47' 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>Daily sea surface temperature anomalies</dd><dt><span>units :</span></dt><dd>Celsius</dd><dt><span>valid_min :</span></dt><dd>-1200</dd><dt><span>valid_max :</span></dt><dd>1200</dd></dl></div><div class='xr-var-data'><pre>[3110400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>err</span></div><div class='xr-var-dims'>(time, zlev, lat, lon)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-bb1c5fb0-d8ec-40d4-8d6c-e2703b612db9' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-bb1c5fb0-d8ec-40d4-8d6c-e2703b612db9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8efe296e-9f0e-42a2-b34f-e1c8750932b8' class='xr-var-data-in' type='checkbox'><label for='data-8efe296e-9f0e-42a2-b34f-e1c8750932b8' 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>Estimated error standard deviation of analysed_sst</dd><dt><span>units :</span></dt><dd>Celsius</dd><dt><span>valid_min :</span></dt><dd>0</dd><dt><span>valid_max :</span></dt><dd>1000</dd></dl></div><div class='xr-var-data'><pre>[3110400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ice</span></div><div class='xr-var-dims'>(time, zlev, lat, lon)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-941df53d-8183-4817-89ea-6d9e71010d72' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-941df53d-8183-4817-89ea-6d9e71010d72' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d943c4ce-b2f9-4944-8bb9-09b47243e331' class='xr-var-data-in' type='checkbox'><label for='data-d943c4ce-b2f9-4944-8bb9-09b47243e331' 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>Sea ice concentration</dd><dt><span>units :</span></dt><dd>%</dd><dt><span>valid_min :</span></dt><dd>0</dd><dt><span>valid_max :</span></dt><dd>100</dd></dl></div><div class='xr-var-data'><pre>[3110400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>sst</span></div><div class='xr-var-dims'>(time, zlev, lat, lon)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-63791e59-8cb4-4003-a9b2-fe151186c4f8' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-63791e59-8cb4-4003-a9b2-fe151186c4f8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e5203742-4776-442b-909a-bb498446292f' class='xr-var-data-in' type='checkbox'><label for='data-e5203742-4776-442b-909a-bb498446292f' 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>Daily sea surface temperature</dd><dt><span>units :</span></dt><dd>Celsius</dd><dt><span>valid_min :</span></dt><dd>-300</dd><dt><span>valid_max :</span></dt><dd>4500</dd></dl></div><div class='xr-var-data'><pre>[3110400 values with dtype=float64]</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-2d167629-9ac3-48db-acdb-c7c58336d627' class='xr-section-summary-in' type='checkbox' ><label for='section-2d167629-9ac3-48db-acdb-c7c58336d627' class='xr-section-summary' >Indexes: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>lat</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-15fa06f4-cbf3-4097-af56-492755637c4c' class='xr-index-data-in' type='checkbox'/><label for='index-15fa06f4-cbf3-4097-af56-492755637c4c' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([-89.875, -89.625, -89.375, -89.125, -88.875, -88.625, -88.375, -88.125,\n",
" -87.875, -87.625,\n",
" ...\n",
" 87.625, 87.875, 88.125, 88.375, 88.625, 88.875, 89.125, 89.375,\n",
" 89.625, 89.875],\n",
" dtype=&#x27;float32&#x27;, name=&#x27;lat&#x27;, length=720))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>lon</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-47a1ac72-627a-4bdd-ae90-c5a9598ba894' class='xr-index-data-in' type='checkbox'/><label for='index-47a1ac72-627a-4bdd-ae90-c5a9598ba894' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([ 0.125, 0.375, 0.625, 0.875, 1.125, 1.375, 1.625, 1.875,\n",
" 2.125, 2.375,\n",
" ...\n",
" 357.625, 357.875, 358.125, 358.375, 358.625, 358.875, 359.125, 359.375,\n",
" 359.625, 359.875],\n",
" dtype=&#x27;float32&#x27;, name=&#x27;lon&#x27;, length=1440))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-c0c5d86d-3f00-4e4a-8e31-796dfa676173' class='xr-index-data-in' type='checkbox'/><label for='index-c0c5d86d-3f00-4e4a-8e31-796dfa676173' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(DatetimeIndex([&#x27;2022-09-29 12:00:51.924828160&#x27;,\n",
" &#x27;2022-09-30 12:01:41.026562048&#x27;,\n",
" &#x27;2022-10-01 12:00:12.689342464&#x27;],\n",
" dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, freq=None))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>zlev</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-afadf277-89a8-400d-b58e-c035f34c3ecf' class='xr-index-data-in' type='checkbox'/><label for='index-afadf277-89a8-400d-b58e-c035f34c3ecf' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([0.0], dtype=&#x27;float32&#x27;, name=&#x27;zlev&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-5717073d-8e23-40b4-b71c-52cb336bfe42' class='xr-section-summary-in' type='checkbox' ><label for='section-5717073d-8e23-40b4-b71c-52cb336bfe42' 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>Conventions :</span></dt><dd>CF-1.6, ACDD-1.3</dd><dt><span>title :</span></dt><dd>NOAA/NCEI 1/4 Degree Daily Optimum Interpolation Sea Surface Temperature (OISST) Analysis, Version 2.1 - Final</dd><dt><span>references :</span></dt><dd>Reynolds, et al.(2007) Daily High-Resolution-Blended Analyses for Sea Surface Temperature (available at https://doi.org/10.1175/2007JCLI1824.1). Banzon, et al.(2016) A long-term record of blended satellite and in situ sea-surface temperature for climate monitoring, modeling and environmental studies (available at https://doi.org/10.5194/essd-8-165-2016). Huang et al. (2020) Improvements of the Daily Optimum Interpolation Sea Surface Temperature (DOISST) Version v02r01, submitted.Climatology is based on 1971-2000 OI.v2 SST. Satellite data: Pathfinder AVHRR SST, Navy AVHRR SST, and NOAA ACSPO SST. Ice data: NCEP Ice and GSFC Ice.</dd><dt><span>source :</span></dt><dd>ICOADS, NCEP_GTS, GSFC_ICE, NCEP_ICE, Pathfinder_AVHRR, Navy_AVHRR, NOAA_ACSP</dd><dt><span>id :</span></dt><dd>oisst-avhrr-v02r01.20220929.nc</dd><dt><span>naming_authority :</span></dt><dd>gov.noaa.ncei</dd><dt><span>summary :</span></dt><dd>NOAAs 1/4-degree Daily Optimum Interpolation Sea Surface Temperature (OISST) (sometimes referred to as Reynolds SST, which however also refers to earlier products at different resolution), currently available as version v02r01, is created by interpolating and extrapolating SST observations from different sources, resulting in a smoothed complete field. The sources of data are satellite (AVHRR) and in situ platforms (i.e., ships and buoys), and the specific datasets employed may change over time. At the marginal ice zone, sea ice concentrations are used to generate proxy SSTs. A preliminary version of this file is produced in near-real time (1-day latency), and then replaced with a final version after 2 weeks. Note that this is the AVHRR-ONLY DOISST, available from Oct 1981, but there is a companion DOISST product that includes microwave satellite data, available from June 2002</dd><dt><span>cdm_data_type :</span></dt><dd>Grid</dd><dt><span>history :</span></dt><dd>Final file created using preliminary as first guess, and 3 days of AVHRR data. Preliminary uses only 1 day of AVHRR data.</dd><dt><span>date_modified :</span></dt><dd>2022-10-14T09:14:00Z</dd><dt><span>date_created :</span></dt><dd>2022-10-14T09:14:00Z</dd><dt><span>product_version :</span></dt><dd>Version v02r01</dd><dt><span>processing_level :</span></dt><dd>NOAA Level 4</dd><dt><span>institution :</span></dt><dd>NOAA/National Centers for Environmental Information</dd><dt><span>creator_url :</span></dt><dd>https://www.ncei.noaa.gov/</dd><dt><span>creator_email :</span></dt><dd>[email protected]</dd><dt><span>keywords :</span></dt><dd>Earth Science &gt; Oceans &gt; Ocean Temperature &gt; Sea Surface Temperature</dd><dt><span>keywords_vocabulary :</span></dt><dd>Global Change Master Directory (GCMD) Earth Science Keywords</dd><dt><span>platform :</span></dt><dd>Ships, buoys, Argo floats, MetOp-A, MetOp-B</dd><dt><span>platform_vocabulary :</span></dt><dd>Global Change Master Directory (GCMD) Platform Keywords</dd><dt><span>instrument :</span></dt><dd>Earth Remote Sensing Instruments &gt; Passive Remote Sensing &gt; Spectrometers/Radiometers &gt; Imaging Spectrometers/Radiometers &gt; AVHRR &gt; Advanced Very High Resolution Radiometer</dd><dt><span>instrument_vocabulary :</span></dt><dd>Global Change Master Directory (GCMD) Instrument Keywords</dd><dt><span>standard_name_vocabulary :</span></dt><dd>CF Standard Name Table (v40, 25 January 2017)</dd><dt><span>geospatial_lat_min :</span></dt><dd>-90.0</dd><dt><span>geospatial_lat_max :</span></dt><dd>90.0</dd><dt><span>geospatial_lon_min :</span></dt><dd>0.0</dd><dt><span>geospatial_lon_max :</span></dt><dd>360.0</dd><dt><span>geospatial_lat_units :</span></dt><dd>degrees_north</dd><dt><span>geospatial_lat_resolution :</span></dt><dd>0.25</dd><dt><span>geospatial_lon_units :</span></dt><dd>degrees_east</dd><dt><span>geospatial_lon_resolution :</span></dt><dd>0.25</dd><dt><span>time_coverage_start :</span></dt><dd>2022-09-29T00:00:00Z</dd><dt><span>time_coverage_end :</span></dt><dd>2022-09-29T23:59:59Z</dd><dt><span>metadata_link :</span></dt><dd>https://doi.org/10.25921/RE9P-PT57</dd><dt><span>ncei_template_version :</span></dt><dd>NCEI_NetCDF_Grid_Template_v2.0</dd><dt><span>comment :</span></dt><dd>Data was converted from NetCDF-3 to NetCDF-4 format with metadata updates in November 2017.</dd><dt><span>sensor :</span></dt><dd>Thermometer, AVHRR</dd></dl></div></li></ul></div></div>"
],
"text/plain": [
"<xarray.Dataset> Size: 100MB\n",
"Dimensions: (time: 3, zlev: 1, lat: 720, lon: 1440)\n",
"Coordinates:\n",
" * lat (lat) float32 3kB -89.88 -89.62 -89.38 -89.12 ... 89.38 89.62 89.88\n",
" * lon (lon) float32 6kB 0.125 0.375 0.625 0.875 ... 359.4 359.6 359.9\n",
" * time (time) datetime64[ns] 24B 2022-09-29T12:00:51.924828160 ... 2022...\n",
" * zlev (zlev) float32 4B 0.0\n",
"Data variables:\n",
" anom (time, zlev, lat, lon) float64 25MB ...\n",
" err (time, zlev, lat, lon) float64 25MB ...\n",
" ice (time, zlev, lat, lon) float64 25MB ...\n",
" sst (time, zlev, lat, lon) float64 25MB ...\n",
"Attributes: (12/37)\n",
" Conventions: CF-1.6, ACDD-1.3\n",
" title: NOAA/NCEI 1/4 Degree Daily Optimum Interpolat...\n",
" references: Reynolds, et al.(2007) Daily High-Resolution-...\n",
" source: ICOADS, NCEP_GTS, GSFC_ICE, NCEP_ICE, Pathfin...\n",
" id: oisst-avhrr-v02r01.20220929.nc\n",
" naming_authority: gov.noaa.ncei\n",
" ... ...\n",
" time_coverage_start: 2022-09-29T00:00:00Z\n",
" time_coverage_end: 2022-09-29T23:59:59Z\n",
" metadata_link: https://doi.org/10.25921/RE9P-PT57\n",
" ncei_template_version: NCEI_NetCDF_Grid_Template_v2.0\n",
" comment: Data was converted from NetCDF-3 to NetCDF-4 ...\n",
" sensor: Thermometer, AVHRR"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"ds_from_virtual = xr.open_dataset(\"sst_kerchunk.json\", engine=\"kerchunk\")\n",
"ds_from_virtual"
]
},
{
"cell_type": "code",
"execution_count": 9,
"id": "f5c90dcb-b9e0-4fcc-b763-b7d03c3ce630",
"metadata": {},
"outputs": [],
"source": [
"fs = fsspec.filesystem(\"s3\", anon=True)\n",
"ds_list = [xr.open_dataset(fs.open(url), engine=\"h5netcdf\") for url in urls]"
]
},
{
"cell_type": "code",
"execution_count": 10,
"id": "615a09f5-2b05-47dc-bffc-b3d846d3beca",
"metadata": {},
"outputs": [
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".xr-dim-list {\n",
" display: inline-block !important;\n",
" list-style: none;\n",
" padding: 0 !important;\n",
" margin: 0;\n",
"}\n",
"\n",
".xr-dim-list li {\n",
" display: inline-block;\n",
" padding: 0;\n",
" margin: 0;\n",
"}\n",
"\n",
".xr-dim-list:before {\n",
" content: '(';\n",
"}\n",
"\n",
".xr-dim-list:after {\n",
" content: ')';\n",
"}\n",
"\n",
".xr-dim-list li:not(:last-child):after {\n",
" content: ',';\n",
" padding-right: 5px;\n",
"}\n",
"\n",
".xr-has-index {\n",
" font-weight: bold;\n",
"}\n",
"\n",
".xr-var-list,\n",
".xr-var-item {\n",
" display: contents;\n",
"}\n",
"\n",
".xr-var-item > div,\n",
".xr-var-item label,\n",
".xr-var-item > .xr-var-name span {\n",
" background-color: var(--xr-background-color-row-even);\n",
" margin-bottom: 0;\n",
"}\n",
"\n",
".xr-var-item > .xr-var-name:hover span {\n",
" padding-right: 5px;\n",
"}\n",
"\n",
".xr-var-list > li:nth-child(odd) > div,\n",
".xr-var-list > li:nth-child(odd) > label,\n",
".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
" background-color: var(--xr-background-color-row-odd);\n",
"}\n",
"\n",
".xr-var-name {\n",
" grid-column: 1;\n",
"}\n",
"\n",
".xr-var-dims {\n",
" grid-column: 2;\n",
"}\n",
"\n",
".xr-var-dtype {\n",
" grid-column: 3;\n",
" text-align: right;\n",
" color: var(--xr-font-color2);\n",
"}\n",
"\n",
".xr-var-preview {\n",
" grid-column: 4;\n",
"}\n",
"\n",
".xr-index-preview {\n",
" grid-column: 2 / 5;\n",
" color: var(--xr-font-color2);\n",
"}\n",
"\n",
".xr-var-name,\n",
".xr-var-dims,\n",
".xr-var-dtype,\n",
".xr-preview,\n",
".xr-attrs dt {\n",
" white-space: nowrap;\n",
" overflow: hidden;\n",
" text-overflow: ellipsis;\n",
" padding-right: 10px;\n",
"}\n",
"\n",
".xr-var-name:hover,\n",
".xr-var-dims:hover,\n",
".xr-var-dtype:hover,\n",
".xr-attrs dt:hover {\n",
" overflow: visible;\n",
" width: auto;\n",
" z-index: 1;\n",
"}\n",
"\n",
".xr-var-attrs,\n",
".xr-var-data,\n",
".xr-index-data {\n",
" display: none;\n",
" background-color: var(--xr-background-color) !important;\n",
" padding-bottom: 5px !important;\n",
"}\n",
"\n",
".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
".xr-var-data-in:checked ~ .xr-var-data,\n",
".xr-index-data-in:checked ~ .xr-index-data {\n",
" display: block;\n",
"}\n",
"\n",
".xr-var-data > table {\n",
" float: right;\n",
"}\n",
"\n",
".xr-var-name span,\n",
".xr-var-data,\n",
".xr-index-name div,\n",
".xr-index-data,\n",
".xr-attrs {\n",
" padding-left: 25px !important;\n",
"}\n",
"\n",
".xr-attrs,\n",
".xr-var-attrs,\n",
".xr-var-data,\n",
".xr-index-data {\n",
" grid-column: 1 / -1;\n",
"}\n",
"\n",
"dl.xr-attrs {\n",
" padding: 0;\n",
" margin: 0;\n",
" display: grid;\n",
" grid-template-columns: 125px auto;\n",
"}\n",
"\n",
".xr-attrs dt,\n",
".xr-attrs dd {\n",
" padding: 0;\n",
" margin: 0;\n",
" float: left;\n",
" padding-right: 10px;\n",
" width: auto;\n",
"}\n",
"\n",
".xr-attrs dt {\n",
" font-weight: normal;\n",
" grid-column: 1;\n",
"}\n",
"\n",
".xr-attrs dt:hover span {\n",
" display: inline-block;\n",
" background: var(--xr-background-color);\n",
" padding-right: 10px;\n",
"}\n",
"\n",
".xr-attrs dd {\n",
" grid-column: 2;\n",
" white-space: pre-wrap;\n",
" word-break: break-all;\n",
"}\n",
"\n",
".xr-icon-database,\n",
".xr-icon-file-text2,\n",
".xr-no-icon {\n",
" display: inline-block;\n",
" vertical-align: middle;\n",
" width: 1em;\n",
" height: 1.5em !important;\n",
" stroke-width: 0;\n",
" stroke: currentColor;\n",
" fill: currentColor;\n",
"}\n",
"</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt; Size: 50MB\n",
"Dimensions: (time: 3, zlev: 1, lat: 720, lon: 1440)\n",
"Coordinates:\n",
" * time (time) datetime64[ns] 24B 2022-09-29T12:00:51.924828160 ... 2022...\n",
" * zlev (zlev) float32 4B 0.0\n",
" * lat (lat) float32 3kB -89.88 -89.62 -89.38 -89.12 ... 89.38 89.62 89.88\n",
" * lon (lon) float32 6kB 0.125 0.375 0.625 0.875 ... 359.4 359.6 359.9\n",
"Data variables:\n",
" sst (time, zlev, lat, lon) float32 12MB nan nan nan ... -1.8 -1.8 -1.8\n",
" anom (time, zlev, lat, lon) float32 12MB nan nan nan nan ... 0.0 0.0 0.0\n",
" err (time, zlev, lat, lon) float32 12MB nan nan nan nan ... 0.3 0.3 0.3\n",
" ice (time, zlev, lat, lon) float32 12MB nan nan nan nan ... 0.9 0.9 0.9\n",
"Attributes: (12/37)\n",
" Conventions: CF-1.6, ACDD-1.3\n",
" title: NOAA/NCEI 1/4 Degree Daily Optimum Interpolat...\n",
" references: Reynolds, et al.(2007) Daily High-Resolution-...\n",
" source: ICOADS, NCEP_GTS, GSFC_ICE, NCEP_ICE, Pathfin...\n",
" id: oisst-avhrr-v02r01.20220929.nc\n",
" naming_authority: gov.noaa.ncei\n",
" ... ...\n",
" time_coverage_start: 2022-09-29T00:00:00Z\n",
" time_coverage_end: 2022-09-29T23:59:59Z\n",
" metadata_link: https://doi.org/10.25921/RE9P-PT57\n",
" ncei_template_version: NCEI_NetCDF_Grid_Template_v2.0\n",
" comment: Data was converted from NetCDF-3 to NetCDF-4 ...\n",
" sensor: Thermometer, AVHRR</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-19c305dd-6ba2-4d37-b2a8-85f28fa73002' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-19c305dd-6ba2-4d37-b2a8-85f28fa73002' class='xr-section-summary' title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 3</li><li><span class='xr-has-index'>zlev</span>: 1</li><li><span class='xr-has-index'>lat</span>: 720</li><li><span class='xr-has-index'>lon</span>: 1440</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-8e4a078f-53ea-43e5-9c9d-05ceb0b90e04' class='xr-section-summary-in' type='checkbox' checked><label for='section-8e4a078f-53ea-43e5-9c9d-05ceb0b90e04' class='xr-section-summary' >Coordinates: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>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'>2022-09-29T12:00:51.924828160 .....</div><input id='attrs-c1216a80-a6a6-41c6-a2b4-e8e1f88f66c2' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c1216a80-a6a6-41c6-a2b4-e8e1f88f66c2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c171a2aa-eec5-40b2-971a-405bff13c5c8' class='xr-var-data-in' type='checkbox'><label for='data-c171a2aa-eec5-40b2-971a-405bff13c5c8' 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>Center time of the day</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;2022-09-29T12:00:51.924828160&#x27;, &#x27;2022-09-30T12:01:41.026562048&#x27;,\n",
" &#x27;2022-10-01T12:00:12.689342464&#x27;], dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>zlev</span></div><div class='xr-var-dims'>(zlev)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.0</div><input id='attrs-2b3be932-918a-41fa-a12f-cba36d95f5e4' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-2b3be932-918a-41fa-a12f-cba36d95f5e4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-bb676ec3-2341-4423-b5fa-cb0d23545cde' class='xr-var-data-in' type='checkbox'><label for='data-bb676ec3-2341-4423-b5fa-cb0d23545cde' 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>Sea surface height</dd><dt><span>units :</span></dt><dd>meters</dd><dt><span>positive :</span></dt><dd>down</dd><dt><span>actual_range :</span></dt><dd>0, 0</dd></dl></div><div class='xr-var-data'><pre>array([0.], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lat</span></div><div class='xr-var-dims'>(lat)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>-89.88 -89.62 ... 89.62 89.88</div><input id='attrs-cfc8e131-eef4-4b37-ba0f-1dc4e235972e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-cfc8e131-eef4-4b37-ba0f-1dc4e235972e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1143458d-9058-48ed-a139-7ef22a882f71' class='xr-var-data-in' type='checkbox'><label for='data-1143458d-9058-48ed-a139-7ef22a882f71' 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>Latitude</dd><dt><span>units :</span></dt><dd>degrees_north</dd><dt><span>grids :</span></dt><dd>Uniform grid from -89.875 to 89.875 by 0.25</dd></dl></div><div class='xr-var-data'><pre>array([-89.875, -89.625, -89.375, ..., 89.375, 89.625, 89.875],\n",
" dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lon</span></div><div class='xr-var-dims'>(lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.125 0.375 0.625 ... 359.6 359.9</div><input id='attrs-440e3957-2410-4d57-b43d-62a8d69deeff' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-440e3957-2410-4d57-b43d-62a8d69deeff' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5650e4bc-5a73-4ed5-8782-fdd18a7a7e73' class='xr-var-data-in' type='checkbox'><label for='data-5650e4bc-5a73-4ed5-8782-fdd18a7a7e73' 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>Longitude</dd><dt><span>units :</span></dt><dd>degrees_east</dd><dt><span>grids :</span></dt><dd>Uniform grid from 0.125 to 359.875 by 0.25</dd></dl></div><div class='xr-var-data'><pre>array([1.25000e-01, 3.75000e-01, 6.25000e-01, ..., 3.59375e+02, 3.59625e+02,\n",
" 3.59875e+02], dtype=float32)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-46808a68-ccfa-4d6e-857f-03fa03c3a534' class='xr-section-summary-in' type='checkbox' checked><label for='section-46808a68-ccfa-4d6e-857f-03fa03c3a534' class='xr-section-summary' >Data variables: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>sst</span></div><div class='xr-var-dims'>(time, zlev, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>nan nan nan nan ... -1.8 -1.8 -1.8</div><input id='attrs-1cbe9407-1306-4830-8107-49a53cbe8e15' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1cbe9407-1306-4830-8107-49a53cbe8e15' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b12c376d-cd69-46f5-aefa-49a594a4feb5' class='xr-var-data-in' type='checkbox'><label for='data-b12c376d-cd69-46f5-aefa-49a594a4feb5' 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>Daily sea surface temperature</dd><dt><span>units :</span></dt><dd>Celsius</dd><dt><span>valid_min :</span></dt><dd>-300</dd><dt><span>valid_max :</span></dt><dd>4500</dd></dl></div><div class='xr-var-data'><pre>array([[[[ nan, nan, nan, ..., nan,\n",
" nan, nan],\n",
" [ nan, nan, nan, ..., nan,\n",
" nan, nan],\n",
" [ nan, nan, nan, ..., nan,\n",
" nan, nan],\n",
" ...,\n",
" [-1.66 , -1.6999999, -1.7199999, ..., -1.7199999,\n",
" -1.6999999, -1.66 ],\n",
" [-1.66 , -1.73 , -1.77 , ..., -1.77 ,\n",
" -1.74 , -1.66 ],\n",
" [-1.8 , -1.8 , -1.8 , ..., -1.8 ,\n",
" -1.8 , -1.8 ]]],\n",
"\n",
"\n",
" [[[ nan, nan, nan, ..., nan,\n",
" nan, nan],\n",
" [ nan, nan, nan, ..., nan,\n",
" nan, nan],\n",
" [ nan, nan, nan, ..., nan,\n",
"...\n",
" -1.6999999, -1.66 ],\n",
" [-1.66 , -1.73 , -1.77 , ..., -1.77 ,\n",
" -1.74 , -1.66 ],\n",
" [-1.8 , -1.8 , -1.8 , ..., -1.8 ,\n",
" -1.8 , -1.8 ]]],\n",
"\n",
"\n",
" [[[ nan, nan, nan, ..., nan,\n",
" nan, nan],\n",
" [ nan, nan, nan, ..., nan,\n",
" nan, nan],\n",
" [ nan, nan, nan, ..., nan,\n",
" nan, nan],\n",
" ...,\n",
" [-1.65 , -1.6899999, -1.7099999, ..., -1.7099999,\n",
" -1.6899999, -1.65 ],\n",
" [-1.66 , -1.73 , -1.76 , ..., -1.76 ,\n",
" -1.73 , -1.66 ],\n",
" [-1.8 , -1.8 , -1.8 , ..., -1.8 ,\n",
" -1.8 , -1.8 ]]]], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>anom</span></div><div class='xr-var-dims'>(time, zlev, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>nan nan nan nan ... 0.0 0.0 0.0</div><input id='attrs-b8b0e323-187f-460b-b86a-c6a6f9adb1ba' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b8b0e323-187f-460b-b86a-c6a6f9adb1ba' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f7bee720-f504-43dd-bf20-7e5be82ecb91' class='xr-var-data-in' type='checkbox'><label for='data-f7bee720-f504-43dd-bf20-7e5be82ecb91' 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>Daily sea surface temperature anomalies</dd><dt><span>units :</span></dt><dd>Celsius</dd><dt><span>valid_min :</span></dt><dd>-1200</dd><dt><span>valid_max :</span></dt><dd>1200</dd></dl></div><div class='xr-var-data'><pre>array([[[[ nan, nan, nan, ..., nan,\n",
" nan, nan],\n",
" [ nan, nan, nan, ..., nan,\n",
" nan, nan],\n",
" [ nan, nan, nan, ..., nan,\n",
" nan, nan],\n",
" ...,\n",
" [0.14 , 0.09999999, 0.08 , ..., 0.08 ,\n",
" 0.09999999, 0.14 ],\n",
" [0.14 , 0.07 , 0.03 , ..., 0.03 ,\n",
" 0.06 , 0.14 ],\n",
" [0. , 0. , 0. , ..., 0. ,\n",
" 0. , 0. ]]],\n",
"\n",
"\n",
" [[[ nan, nan, nan, ..., nan,\n",
" nan, nan],\n",
" [ nan, nan, nan, ..., nan,\n",
" nan, nan],\n",
" [ nan, nan, nan, ..., nan,\n",
"...\n",
" 0.09999999, 0.14 ],\n",
" [0.14 , 0.07 , 0.03 , ..., 0.03 ,\n",
" 0.06 , 0.14 ],\n",
" [0. , 0. , 0. , ..., 0. ,\n",
" 0. , 0. ]]],\n",
"\n",
"\n",
" [[[ nan, nan, nan, ..., nan,\n",
" nan, nan],\n",
" [ nan, nan, nan, ..., nan,\n",
" nan, nan],\n",
" [ nan, nan, nan, ..., nan,\n",
" nan, nan],\n",
" ...,\n",
" [0.14999999, 0.11 , 0.09 , ..., 0.09 ,\n",
" 0.11 , 0.14999999],\n",
" [0.14 , 0.07 , 0.04 , ..., 0.04 ,\n",
" 0.07 , 0.14 ],\n",
" [0. , 0. , 0. , ..., 0. ,\n",
" 0. , 0. ]]]], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>err</span></div><div class='xr-var-dims'>(time, zlev, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>nan nan nan nan ... 0.3 0.3 0.3 0.3</div><input id='attrs-a09634d1-69fe-4e1c-a117-b33df09a8743' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a09634d1-69fe-4e1c-a117-b33df09a8743' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-52dc3ca6-ea10-4f50-94d6-393bdf83618a' class='xr-var-data-in' type='checkbox'><label for='data-52dc3ca6-ea10-4f50-94d6-393bdf83618a' 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>Estimated error standard deviation of analysed_sst</dd><dt><span>units :</span></dt><dd>Celsius</dd><dt><span>valid_min :</span></dt><dd>0</dd><dt><span>valid_max :</span></dt><dd>1000</dd></dl></div><div class='xr-var-data'><pre>array([[[[ nan, nan, nan, ..., nan,\n",
" nan, nan],\n",
" [ nan, nan, nan, ..., nan,\n",
" nan, nan],\n",
" [ nan, nan, nan, ..., nan,\n",
" nan, nan],\n",
" ...,\n",
" [0.29999998, 0.29999998, 0.29999998, ..., 0.29999998,\n",
" 0.29999998, 0.29999998],\n",
" [0.29999998, 0.29999998, 0.29999998, ..., 0.29999998,\n",
" 0.29999998, 0.29999998],\n",
" [0.29999998, 0.29999998, 0.29999998, ..., 0.29999998,\n",
" 0.29999998, 0.29999998]]],\n",
"\n",
"\n",
" [[[ nan, nan, nan, ..., nan,\n",
" nan, nan],\n",
" [ nan, nan, nan, ..., nan,\n",
" nan, nan],\n",
" [ nan, nan, nan, ..., nan,\n",
"...\n",
" 0.29999998, 0.29999998],\n",
" [0.29999998, 0.29999998, 0.29999998, ..., 0.29999998,\n",
" 0.29999998, 0.29999998],\n",
" [0.29999998, 0.29999998, 0.29999998, ..., 0.29999998,\n",
" 0.29999998, 0.29999998]]],\n",
"\n",
"\n",
" [[[ nan, nan, nan, ..., nan,\n",
" nan, nan],\n",
" [ nan, nan, nan, ..., nan,\n",
" nan, nan],\n",
" [ nan, nan, nan, ..., nan,\n",
" nan, nan],\n",
" ...,\n",
" [0.29999998, 0.29999998, 0.29999998, ..., 0.29999998,\n",
" 0.29999998, 0.29999998],\n",
" [0.29999998, 0.29999998, 0.29999998, ..., 0.29999998,\n",
" 0.29999998, 0.29999998],\n",
" [0.29999998, 0.29999998, 0.29999998, ..., 0.29999998,\n",
" 0.29999998, 0.29999998]]]], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ice</span></div><div class='xr-var-dims'>(time, zlev, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>nan nan nan nan ... 0.9 0.9 0.9 0.9</div><input id='attrs-e12ea3e6-f7cb-447f-81be-26c98135bacb' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e12ea3e6-f7cb-447f-81be-26c98135bacb' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-868aebb0-ec05-4b51-893b-b1d5c7ce1b96' class='xr-var-data-in' type='checkbox'><label for='data-868aebb0-ec05-4b51-893b-b1d5c7ce1b96' 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>Sea ice concentration</dd><dt><span>units :</span></dt><dd>%</dd><dt><span>valid_min :</span></dt><dd>0</dd><dt><span>valid_max :</span></dt><dd>100</dd></dl></div><div class='xr-var-data'><pre>array([[[[ nan, nan, nan, ..., nan,\n",
" nan, nan],\n",
" [ nan, nan, nan, ..., nan,\n",
" nan, nan],\n",
" [ nan, nan, nan, ..., nan,\n",
" nan, nan],\n",
" ...,\n",
" [0.93 , 0.93 , 0.93 , ..., 0.93 ,\n",
" 0.93 , 0.93 ],\n",
" [0.90999997, 0.90999997, 0.90999997, ..., 0.90999997,\n",
" 0.90999997, 0.90999997],\n",
" [0.9 , 0.9 , 0.9 , ..., 0.9 ,\n",
" 0.9 , 0.9 ]]],\n",
"\n",
"\n",
" [[[ nan, nan, nan, ..., nan,\n",
" nan, nan],\n",
" [ nan, nan, nan, ..., nan,\n",
" nan, nan],\n",
" [ nan, nan, nan, ..., nan,\n",
"...\n",
" 0.93 , 0.93 ],\n",
" [0.9 , 0.9 , 0.90999997, ..., 0.90999997,\n",
" 0.90999997, 0.90999997],\n",
" [0.89 , 0.89 , 0.9 , ..., 0.9 ,\n",
" 0.9 , 0.9 ]]],\n",
"\n",
"\n",
" [[[ nan, nan, nan, ..., nan,\n",
" nan, nan],\n",
" [ nan, nan, nan, ..., nan,\n",
" nan, nan],\n",
" [ nan, nan, nan, ..., nan,\n",
" nan, nan],\n",
" ...,\n",
" [0.91999996, 0.91999996, 0.91999996, ..., 0.91999996,\n",
" 0.91999996, 0.91999996],\n",
" [0.9 , 0.9 , 0.90999997, ..., 0.90999997,\n",
" 0.90999997, 0.90999997],\n",
" [0.89 , 0.89 , 0.9 , ..., 0.9 ,\n",
" 0.9 , 0.9 ]]]], dtype=float32)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-7eb9f1e2-dcf9-4c12-a9fe-508013d5ad99' class='xr-section-summary-in' type='checkbox' ><label for='section-7eb9f1e2-dcf9-4c12-a9fe-508013d5ad99' class='xr-section-summary' >Indexes: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-36453bbf-c9ef-4d93-adab-6c869c1b9a4e' class='xr-index-data-in' type='checkbox'/><label for='index-36453bbf-c9ef-4d93-adab-6c869c1b9a4e' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(DatetimeIndex([&#x27;2022-09-29 12:00:51.924828160&#x27;,\n",
" &#x27;2022-09-30 12:01:41.026562048&#x27;,\n",
" &#x27;2022-10-01 12:00:12.689342464&#x27;],\n",
" dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, freq=None))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>zlev</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-94f79643-1701-4fc2-9338-c42b0a408ceb' class='xr-index-data-in' type='checkbox'/><label for='index-94f79643-1701-4fc2-9338-c42b0a408ceb' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([0.0], dtype=&#x27;float32&#x27;, name=&#x27;zlev&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>lat</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-62e72746-5339-4ac4-a15f-a8feb1c73c4f' class='xr-index-data-in' type='checkbox'/><label for='index-62e72746-5339-4ac4-a15f-a8feb1c73c4f' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([-89.875, -89.625, -89.375, -89.125, -88.875, -88.625, -88.375, -88.125,\n",
" -87.875, -87.625,\n",
" ...\n",
" 87.625, 87.875, 88.125, 88.375, 88.625, 88.875, 89.125, 89.375,\n",
" 89.625, 89.875],\n",
" dtype=&#x27;float32&#x27;, name=&#x27;lat&#x27;, length=720))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>lon</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-b1608303-03cb-4d46-a0f0-b590dd9a9ff7' class='xr-index-data-in' type='checkbox'/><label for='index-b1608303-03cb-4d46-a0f0-b590dd9a9ff7' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([ 0.125, 0.375, 0.625, 0.875, 1.125, 1.375, 1.625, 1.875,\n",
" 2.125, 2.375,\n",
" ...\n",
" 357.625, 357.875, 358.125, 358.375, 358.625, 358.875, 359.125, 359.375,\n",
" 359.625, 359.875],\n",
" dtype=&#x27;float32&#x27;, name=&#x27;lon&#x27;, length=1440))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-8d8b2a7a-b8c2-42a5-b7af-a68d25066783' class='xr-section-summary-in' type='checkbox' ><label for='section-8d8b2a7a-b8c2-42a5-b7af-a68d25066783' 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>Conventions :</span></dt><dd>CF-1.6, ACDD-1.3</dd><dt><span>title :</span></dt><dd>NOAA/NCEI 1/4 Degree Daily Optimum Interpolation Sea Surface Temperature (OISST) Analysis, Version 2.1 - Final</dd><dt><span>references :</span></dt><dd>Reynolds, et al.(2007) Daily High-Resolution-Blended Analyses for Sea Surface Temperature (available at https://doi.org/10.1175/2007JCLI1824.1). Banzon, et al.(2016) A long-term record of blended satellite and in situ sea-surface temperature for climate monitoring, modeling and environmental studies (available at https://doi.org/10.5194/essd-8-165-2016). Huang et al. (2020) Improvements of the Daily Optimum Interpolation Sea Surface Temperature (DOISST) Version v02r01, submitted.Climatology is based on 1971-2000 OI.v2 SST. Satellite data: Pathfinder AVHRR SST, Navy AVHRR SST, and NOAA ACSPO SST. Ice data: NCEP Ice and GSFC Ice.</dd><dt><span>source :</span></dt><dd>ICOADS, NCEP_GTS, GSFC_ICE, NCEP_ICE, Pathfinder_AVHRR, Navy_AVHRR, NOAA_ACSP</dd><dt><span>id :</span></dt><dd>oisst-avhrr-v02r01.20220929.nc</dd><dt><span>naming_authority :</span></dt><dd>gov.noaa.ncei</dd><dt><span>summary :</span></dt><dd>NOAAs 1/4-degree Daily Optimum Interpolation Sea Surface Temperature (OISST) (sometimes referred to as Reynolds SST, which however also refers to earlier products at different resolution), currently available as version v02r01, is created by interpolating and extrapolating SST observations from different sources, resulting in a smoothed complete field. The sources of data are satellite (AVHRR) and in situ platforms (i.e., ships and buoys), and the specific datasets employed may change over time. At the marginal ice zone, sea ice concentrations are used to generate proxy SSTs. A preliminary version of this file is produced in near-real time (1-day latency), and then replaced with a final version after 2 weeks. Note that this is the AVHRR-ONLY DOISST, available from Oct 1981, but there is a companion DOISST product that includes microwave satellite data, available from June 2002</dd><dt><span>cdm_data_type :</span></dt><dd>Grid</dd><dt><span>history :</span></dt><dd>Final file created using preliminary as first guess, and 3 days of AVHRR data. Preliminary uses only 1 day of AVHRR data.</dd><dt><span>date_modified :</span></dt><dd>2022-10-14T09:14:00Z</dd><dt><span>date_created :</span></dt><dd>2022-10-14T09:14:00Z</dd><dt><span>product_version :</span></dt><dd>Version v02r01</dd><dt><span>processing_level :</span></dt><dd>NOAA Level 4</dd><dt><span>institution :</span></dt><dd>NOAA/National Centers for Environmental Information</dd><dt><span>creator_url :</span></dt><dd>https://www.ncei.noaa.gov/</dd><dt><span>creator_email :</span></dt><dd>[email protected]</dd><dt><span>keywords :</span></dt><dd>Earth Science &gt; Oceans &gt; Ocean Temperature &gt; Sea Surface Temperature</dd><dt><span>keywords_vocabulary :</span></dt><dd>Global Change Master Directory (GCMD) Earth Science Keywords</dd><dt><span>platform :</span></dt><dd>Ships, buoys, Argo floats, MetOp-A, MetOp-B</dd><dt><span>platform_vocabulary :</span></dt><dd>Global Change Master Directory (GCMD) Platform Keywords</dd><dt><span>instrument :</span></dt><dd>Earth Remote Sensing Instruments &gt; Passive Remote Sensing &gt; Spectrometers/Radiometers &gt; Imaging Spectrometers/Radiometers &gt; AVHRR &gt; Advanced Very High Resolution Radiometer</dd><dt><span>instrument_vocabulary :</span></dt><dd>Global Change Master Directory (GCMD) Instrument Keywords</dd><dt><span>standard_name_vocabulary :</span></dt><dd>CF Standard Name Table (v40, 25 January 2017)</dd><dt><span>geospatial_lat_min :</span></dt><dd>-90.0</dd><dt><span>geospatial_lat_max :</span></dt><dd>90.0</dd><dt><span>geospatial_lon_min :</span></dt><dd>0.0</dd><dt><span>geospatial_lon_max :</span></dt><dd>360.0</dd><dt><span>geospatial_lat_units :</span></dt><dd>degrees_north</dd><dt><span>geospatial_lat_resolution :</span></dt><dd>0.25</dd><dt><span>geospatial_lon_units :</span></dt><dd>degrees_east</dd><dt><span>geospatial_lon_resolution :</span></dt><dd>0.25</dd><dt><span>time_coverage_start :</span></dt><dd>2022-09-29T00:00:00Z</dd><dt><span>time_coverage_end :</span></dt><dd>2022-09-29T23:59:59Z</dd><dt><span>metadata_link :</span></dt><dd>https://doi.org/10.25921/RE9P-PT57</dd><dt><span>ncei_template_version :</span></dt><dd>NCEI_NetCDF_Grid_Template_v2.0</dd><dt><span>comment :</span></dt><dd>Data was converted from NetCDF-3 to NetCDF-4 format with metadata updates in November 2017.</dd><dt><span>sensor :</span></dt><dd>Thermometer, AVHRR</dd></dl></div></li></ul></div></div>"
],
"text/plain": [
"<xarray.Dataset> Size: 50MB\n",
"Dimensions: (time: 3, zlev: 1, lat: 720, lon: 1440)\n",
"Coordinates:\n",
" * time (time) datetime64[ns] 24B 2022-09-29T12:00:51.924828160 ... 2022...\n",
" * zlev (zlev) float32 4B 0.0\n",
" * lat (lat) float32 3kB -89.88 -89.62 -89.38 -89.12 ... 89.38 89.62 89.88\n",
" * lon (lon) float32 6kB 0.125 0.375 0.625 0.875 ... 359.4 359.6 359.9\n",
"Data variables:\n",
" sst (time, zlev, lat, lon) float32 12MB nan nan nan ... -1.8 -1.8 -1.8\n",
" anom (time, zlev, lat, lon) float32 12MB nan nan nan nan ... 0.0 0.0 0.0\n",
" err (time, zlev, lat, lon) float32 12MB nan nan nan nan ... 0.3 0.3 0.3\n",
" ice (time, zlev, lat, lon) float32 12MB nan nan nan nan ... 0.9 0.9 0.9\n",
"Attributes: (12/37)\n",
" Conventions: CF-1.6, ACDD-1.3\n",
" title: NOAA/NCEI 1/4 Degree Daily Optimum Interpolat...\n",
" references: Reynolds, et al.(2007) Daily High-Resolution-...\n",
" source: ICOADS, NCEP_GTS, GSFC_ICE, NCEP_ICE, Pathfin...\n",
" id: oisst-avhrr-v02r01.20220929.nc\n",
" naming_authority: gov.noaa.ncei\n",
" ... ...\n",
" time_coverage_start: 2022-09-29T00:00:00Z\n",
" time_coverage_end: 2022-09-29T23:59:59Z\n",
" metadata_link: https://doi.org/10.25921/RE9P-PT57\n",
" ncei_template_version: NCEI_NetCDF_Grid_Template_v2.0\n",
" comment: Data was converted from NetCDF-3 to NetCDF-4 ...\n",
" sensor: Thermometer, AVHRR"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"ds = xr.concat(ds_list, dim=\"time\")\n",
"ds"
]
},
{
"cell_type": "code",
"execution_count": 13,
"id": "d0b01613-e994-4271-af72-a050286bd385",
"metadata": {},
"outputs": [],
"source": [
"xr.testing.assert_allclose(ds, ds_from_virtual)"
]
},
{
"cell_type": "code",
"execution_count": 14,
"id": "dc552227-d89d-40bb-b4d3-1e341d192f38",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.collections.QuadMesh at 0x15a4c3f20>"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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WADCpxf/85z8VxyGE1MFNnz690/3Zc889u3T/PeaYYzpcz1NPPSUAiDvuuKPiM53i/eSTTzrdnxTrFtzepWRrLw466KBYakE/RR588MGx+fT0Dz74ANOmTcMjjzyCMAxx9NFHmxAzIMPue+65JxYtWtThdsePH4/nn3++S/u45ZZbdmk+jYcffhinnXYavvGNb5jonI2OUgG1Pnv//ffxla98BRMnTsQf/vCHDrf/hz/8AZdffjnOOeccHHrooWa6EKIi/eW60aXWlf068MADMWXKFPz4xz/GmDFj8PnPfx7PPvssLrjgAjiOE/su7e8FkCnGatuodb4opTjttNPws5/9DD/72c9wyimnoKWlBWeeeSba29vNPP2Fl156CYcffji+8IUvYPbs2RWfJ49Xozvfd615k9OrbSuZWtl7772x9957Y9ddd8XVV1+NG2+8seZ+dAcnn3wybrrpJhx55JH47W9/i7Fjx+LOO+/EX/7yFwC1vxMhBE444QQ8+eSTuOeeezBx4sTY8Rx//PH41re+hS9/+csdbr+xsRGHHnoo2tvbMXfuXEycOBGvvfYafvazn+GrX/0qHnzwwdh1rVEqlXDYYYdh6dKlWLhwIYYMGRL7/NRTT8W9996Ln//85/jc5z6H5cuX45prrsE+++yDRYsWxVKyXYGOoJRKJTz00ENm+f333x877bQTLr30UhPZAXp2X+gq3njjDXz1q1/Fbrvthj/+8Y8dru/vf/879t57b4wfPz52nR144IH40Y9+hCeeeALbbLMNtt12W0yfPh1z5swxx/HGG2/gueeew69//etO9+l3v/sdWltbO51v1KhRXTjCvj1/KQYf1hsSNXLkyNj/nud1OF2HZT/99FMAwOc///mq6+1scPU8DzvssEOX9tFxnC7NB8hy/MMOOwz7778/5s6dW/Hj3WCDDSrKsAEYHVDyuAFg6dKl2HvvveG6Lh577LGq82jMmTMHp5xyCk4++WRcc801sc9uvfXWihC7UKnKru6X53l4+OGHcdRRR2HGjBkApH7riiuuwM9+9jNstNFGZtmknmjOnDkVWozOztfFF1+MtrY2XHbZZbj44osBSIJ93HHH4Q9/+ENse32JxYsXY//998fmm2+Ohx56qGo6K4kRI0aAENKl87rBBhsAkDqwMWPGVMzb0XfeEXbeeWdsscUWePbZZ3u0fDVsvfXWuPfee3HKKadg2rRpAICJEyfiuuuuw+mnn171OxFC4MQTT8Sf//xn3HrrrTFyDwA33HAD3nvvPdx1113GIkWniUqlEpqamtDQ0ADHcXDVVVfh5ZdfxtKlSzFu3DgAwJe+9CVstdVW2GeffTB37lwcc8wxsfWXy2V87Wtfw7/+9S/8/e9/xy677BL7fP78+bjllltw99134xvf+IaZPmPGDGyyySaYNWsW5syZ063zpL/TrbbaKkbACCH48pe/jNmzZ2PFihUYPXo0NthgA5RKJbS3t6Ouri62ntWrV1foz7qDTz75BAcccAAmTJiAv/3tb+ZeWguffvopHnjggap6QABYuXKlea/1a2+++Sa22morzJkzB9lsFkcccUSn+zVlypQuSSU6u5fbv50kVq9eDUKI0balWH+w3pConkI/nfz1r3/t9hMiICM7kydP7tK8ixYt6pLH1SOPPIKZM2dizz33xD333FP1ZrXtttvi1VdfrZiup+lBSWPp0qXYa6+9IITA448/jgkTJtTc/pw5c3DiiSfimGOOwW9/+9sKQnLIIYfUjL51Z7+mTJmCZ555Bh9//DFWr16NzTbbDM3NzfjhD3+IPfbYw8yX3FbyfHflfLmui+uvvx6XXnoplixZglGjRmHcuHH48pe/jMmTJ3d4PnoLixcvxn777YdJkyZhwYIFFeLjWtC+RLXOaz6fx6abbgog0sK8+uqr2Gabbcx8YRjizTff7NKgVAtCiF6P2B144IFYunQp3nnnHYRhiC222AJ33XUXAMSuAb39E088EXPmzMEtt9yC7373uxXre+2119Dc3IzNN9+84rP/9//+H/7f//t/WLx4MXbYYQe8/PLL2GijjQyB0tAPVEn9ULlcxsyZM7Fo0SL83//9H/bdd9+Kbbz88suxdWgMHz4cU6ZM6ZEmabPNNqsgRBqaPOjvxf7+bYK3fPlyrFy5suK+0FW0tLTgoIMOAuccDz30UJeu3VGjRmG77bbD5ZdfXvXz8ePHm/dHHHEEzj77bPzpT3/C5Zdfjttvvx0zZ87EiBEjOt3OvvvuiyeeeKLT+Y455hj86U9/qvn5Zptthnw+X/N3NmXKlKpeaSnWcQxYInEN8Itf/EIAEP/9738rPquliUrqHRYtWiQAiLvvvjs2fc6cOQKAeP75583yruuKq666qkf7Wi6XxfPPP9+lV0tLS6fre+SRR0QulxP77befKBaLNef7zW9+IwCIZ5991kwLgkBMnTpV7LLLLrF5ly5dKjbZZBMxceJE8e6773a4/Tlz5ghKqTj66KNjGrOuojv7VQ1nnXWWqK+v71B7Y6Or56saXnzxReE4jrjhhhuqft6bmqjFixeLkSNHiu22206sXLmyW/sphBDnnXee8Dwvpv9paWkRG264ofjWt75lpmmLgwMOOCC2/B133CEAiIcffrjb2xZC6nIopeLMM8+sOU93NVHVUC6XxS677CJ22GGH2HTOuTjhhBMEIaRCO2PjjTfeEIsWLYq99LF/73vfE4sWLTIapuOOO064rltxrS1YsEAAiF0XpVJJHHjggcLzPPH3v/+95vZvvfVWAUDceeedsekrV64UDQ0NYubMmVWX68zi4IgjjhCZTCamq+Kcix122CFmP7Jq1SqRy+XE9773vdjys2fP7rHFQblcFvvss48YNmyYePXVV2vOl9z/E088UYwfP16sXr26S9v51re+JcaNG2f0ko888kiXlnvzzTe7dP/timXO4YcfLkaPHh27Vy9dulR4nid+/OMfd2l/UqxbGJQkShOgU045RTz99NMxAtLbJEoIKRp0XVeccsop4t577xWPP/64+Mtf/iLOOecccfHFF/fdgSbw5JNPinw+LzbZZBOxcOHCmNHhM888E/MuKZVKYurUqWLixIli7ty54tFHHxVf+9rXKkwtP/30U7HpppuKbDYr/vznP1es88MPPzTz3nXXXYJSKj73uc+Jp556qmLezsS23dkvIYS46qqrxK233ioWLVok7rzzTnHYYYcJSqmYO3dur58vbTo4f/588fDDD4tLLrlE1NXViYMPPliEYVh1/Z2RqEKhIO6++25x9913i3POOUcAELNmzRJ33313zI/pzTffFBtssIEYOXKkeOCBByr2M2niV03svWLFCjFu3Dix7bbbinvvvVc89NBDYo899hANDQ3ijTfeiM17++23G2H2okWLxM033yyGDx9e1Wyz2ra22247cfXVV4sHHnhAPProo+Lyyy8Xw4cPF+PHj68Q1r7++uvmHEyfPl1suOGG5n97wH788ceF4zgVJqg/+MEPxF//+lexaNEiccstt4jtt99ebLDBBhUGmj/4wQ8EAHH88cdXnD/bz6kaan2PL7zwgvA8T2y99dbi1ltvFQsXLhS/+MUvxOjRo8WYMWNiPllf+cpXBABx4YUXVmzfPs7W1lYxadIkMWLECHHttdeKhQsXirlz54oddthBOI5TQbIfeughcffdd4s//vGPRtSuz1+hUDDzvfPOO2L48OFiyy23FHfccYd48MEHxde+9jVBCKm4x2mzzQsuuEA8/vjj4pprrhHZbLbCbFOfl84E19///vcFAHHFFVdUHLs2qhWikkR98sknYtKkSWKrrbYSv/nNb8Rjjz0mHnzwQfHrX/9aHHzwwbF7jxDygQiAmDBhgpgwYUKPHuLWFG+88YYYMmSI2GOPPcRDDz0k/va3v4lp06ZVNdus9tvZZ599hOM4sWmXXHKJcBwndv+r9XtIsfZhUJIoIYQ4//zzxfjx4wWlNPaE3xckSghZLbb33nuLoUOHimw2KyZNmiS+8Y1vVBjx9SW0yWGtV/IGvHz5cnH00UeLkSNHilwuJ77whS+IRx99NDaPPg+1XvZNT7tF13p11fy0K/slhLy5bLbZZiKbzYrhw4eLAw44QPzzn//sk/P11FNPiV122cV8v9OmTRPXXnttVRNMjc5IVEeVmXZERl9ztV5z5syJrRdA7BrXeOedd8TMmTPF0KFDRV1dndh3333Fiy++WHXf5s2bJ7bbbjvheZ4YO3asOOOMMyqqyGpt69vf/raYMmWKqK+vF5lMRkyaNEl873vfq1qZ1NF3YF9b+jpMRloOPfRQMW7cOJHJZMTYsWPFscceGzMU1Zg0aVKXznU1dPQ9vvTSS+JrX/uamDBhgshms2LTTTcVJ554YkXFX0ffX/L8LVu2TPzgBz8QU6ZMEblcTowfP14cfPDBVY0yOzqu5O/t1VdfFQcffLBoaGgwv6sHHnig6jHfeOONYostthCe54mNN95Y/PSnP6241l999VUBQPzkJz/p8Px1VP1mE7Bq3+9nn30mzjjjDDF58mSRyWTEyJEjxfTp08WFF15YYQLMGBMTJ040ZHWg8MILL4h9991X1NXViaFDh4qZM2fGyKJGte9enysb+jdi349q/R5SrH0gQvTQnChFihQpUqyz+M1vfoPzzjsP7777bkURQooUKSQGndlmihQpUqToeyxatAhnnHFGSqBSpOgAaSQqRYoUKVKkSJGiB0gjUSlSpEiRIkWKFD1ASqJSpEiRIkWKFCl6gJREpUiRIkWKFClS9AApiUqRIkWKFClSpOgB0rYvCXDO8cknn6ChoSFtJpkiRYoUKTqEEAKtra0YP358nzUqL5VK8H2/V9bleV7anqYXkZKoBD755JNY1/cUKVKkSJGiM3z44Yd90mOzVCphg/wQtIP1yvrGjh2LJUuWpESql5CSqAQaGhoAAP9543/mfYoUKdYclAA8NVRJsY6htbUV2229RZ+NF77vox0MR2MjeGuowPHBcdvyj+H7fkqiegkpiUpAp/AaGhowdOjQAd6bFClSpEgxGNDX8g8PFB5Zw3Rh+hDT60hJVIoUKVKkSLGWwyEEzhoSNQckJVK9jJREpUiRIkUXQdUYlqYlU/Q3KAGcNQx2USAlUb2M1OIgRYoUKbqIlDylSJHCRhqJSpEiRYpuICVSKQYCvZbOS9GrSElUihQpUqRIsZbD6YV0ntM7u5LCQkqiUqRIkSJFhxgsWjC9n4QQCCHW+v1NMfiRkqgUKVKkGESgVjSiP0gCHUQZIHM+xLrHntJ03tqJlESlSJEixSBCf0dX9PZsMlXLOLUzwpVGhnqONJ23diKtzkuRIkWKFN1CSoZSpJBII1EpUqRIkaJTpMRpYJGm89ZOpJGoFClSpEjRK+CiNtnqCQkb2VDXrRclg0vD1R0QyAF7TV7dPTU33XQTtttuOwwdOhRDhw7Frrvuiocffth8LoTArFmzMH78eOTzeey11154/fXX1+g4BxvSSNRahuQNgItUZ5AixZpifW5+PLKhDqtb2817G6tb2yumJaGX7Sr0uU7et5KC+M622xMMHyLX2d19HgwYiEjUhAkTcOWVV2LKlCkAgFtvvRWHHnooFi9ejKlTp+Lqq6/G9ddfjz/96U/YYostcNlll2H//ffHW2+91WcNmdc2ECHWwTKGNUBLSwuGDRuGJR8t61YD4mo3DvumbZfeApLBdwV6/mrkqqvr6e/BoyPSlxSprq8DW4q+QzXCtD6QqI7IUm+jFkmp9tsnhGBYfR4A0NQml9Nkpz/Q14SqpaUFkyeMQ3Nzc580rddj0kW5TZFbwwbEJcFxWem9NdrXkSNH4pprrsHxxx+P8ePH48wzz8SPf/xjAEC5XMaYMWNw1VVX4ZRTTlmjfR0sGDTpvDAMcdFFF2Hy5MnI5/PYdNNNcemll4Jzbubpr9CiDhnbLwBwKAEh0YsSOS3jEHgORdalyFIgS4EcFci6FC4l5uU51Lyyrnx5DkGWcGQI4FL5JEIQJ2Wdvartb1dfPTkfer8cSirOSXLauhp6X1fR3WujP9CVa3agCFRfnatqqSx7el9gdWu7edXah+FDKl+aQAEw0wBJqDSp6ot97Qw6/TdYoKvz1vQFSGJmv8rlcqfbZ4zhzjvvRKFQwK677oolS5Zg+fLlmDFjhpknm81izz33xNNPP91Xp2Gtw6BJ51111VX47W9/i1tvvRVTp07FCy+8gOOOOw7Dhg3DD3/4QwDos9BireiSjjy5lIASAioYAIAIRex4AMJDgIeAJntU8lbCQgCAQyjgWF8DoRDUBQgFaFSQKgAQUBAAhBIIARCrk2RHYwQT8X2PkT61LBfAsPp8xU2tqzcZTYgkwSOwo86MC7MNoDJ6ZkcJakXc+tsbZ6DRV8e7Juutlp7pzjp6Y9vVIky15l8brpPe2oeBPh6blFDSO5GkvoxGJYlkLWJp78PangKUJGhN03kSEydOjE3/6U9/ilmzZlVd5tVXX8Wuu+6KUqmEIUOG4N5778U222xjiNKYMWNi848ZMwZLly5do/0cTBg0JOqZZ57BoYceioMPPhgAsMkmm+COO+7ACy+8AEAOzDfccAMuvPBCHHbYYQBk/nbMmDGYN29et0OLyaiKhqOm6ykOJaAsABgDCUtyIgtBmCJQgsuXDsMSCkGo/F9FlQQLJbnS0zUEh6AuiOAggkuipD4niR9UtaweUxMdSsCF3HchBBx1YJpANdRFT4qaGGrH3+FD6tBcKJqnyeZCMXaONGnS+0SEAAQD4XLfeYzqVSdqel3VYE+vdtNd2298NmppRWpBfwddmb8WCe3sM3vf7Hlrfd7d5TrbbmfoynXRlWU7IyADTVJqodrgv7pVRnDs30Py/95AU1u7uV77Oj3Yl+ho3/Uxrm/48MMPY+m8bDZbc94tt9wSL7/8MpqamnDPPffgmGOOwRNPPGE+JwliJ4SomLYuY9CQqC9+8Yv47W9/i//973/YYost8Morr+Bf//oXbrjhBgDoNLS4JvlZTTxcTZg4B4SQJCkoRYRJkyYNQiEcTxKfxHS1Ykk4wABOogiUIliCuhDUAXh8OWIzJkXQkpesIAQOCLgQGKJIUrFUAkAM4dLXeXuxBC4EQi7MDae1vQgQgrb2IjKUoFgqgQiBDJXrpNaPRL8l+pzoYxUclFATOQMEmgtFQw5s2JGs2HEgipKtzehMB6Y/r6UZqYXONHRJolXthpYkCPYytbRD3SU+3Zm/p4NWT/ZLozOton4/GAZU/RvVxEk/lKxuba94AOoukg84g5k8dYYkgVrbv//eNNvU1XZdged5Rli+00474fnnn8eNN95odFDLly/HuHHjzPwrVqyoiE6tyxg0JOrHP/4xmpubsdVWW8FxHDDGcPnll+OII44AIL9IoPuhxXK5HMsHt7S0mPf6ZqQHJhll4SAskIRJR5yYL/83C0ZRJ1AHmdGbxLYZfPYBMhtuHJvmr/wovrx6CQGAOjHiJAgx+yIn8AqiRiCJGAVR5AnmGMzvUJMpyBThkLo82tqLZhogo0wUQhJHQkEIQEFM1EkkCYDgJiwWRc8cGTkzsa8oxWgjGdUSAuCdCOdtMW1n6Asxe0ekSBOf5DyONaGr+9IVolXrsyQBq5ZO7ej/dQ0dDZbJz9bmgdXclyzhtkZvaY0GQgjeH0jeM+yHnK5+3x09GPUF1hafKCEEyuUyJk+ejLFjx+LRRx/FjjvuCADwfR9PPPEErrrqqjXezmDBoCFRf/nLX/DnP/8Z8+bNw9SpU/Hyyy/jzDPPxPjx43HMMceY+bobWpw9ezYuueSSiukmMkKI0iMBhPkgfgEkKEvipFJ17oSpZrnwk7fktI22BgD4qz+B37QCAOANHw0AFQSq3NoEksnJ/VVRKAAgPAQltELwROyoQjJdaA5cTqOCy3Xqee3PzUpkJKvUziouCGIL99W2if6n2r7Y4AyEqFQk0XouRxFDqZUConMt32uyCkMCKQHa2oughKAun0MS9pM5UDvSYE9bk4GxM/Eytb4wO3Uql40IKFekkouI2NjXKk2kbHUEsDNiCUSXDBfq/NZQzfX2ANCV6tPuktm+IDGdpTZrXUNrE8HSxMlOkWuy0x3SoyNY6wNqkadq07qb2l4XccEFF+DAAw/ExIkT0draijvvvBOPP/445s+fD0IIzjzzTFxxxRXYfPPNsfnmm+OKK65AXV0dvvOd7wz0rvcbBg2JOvfcc/GTn/wE3/72twEA2267LZYuXYrZs2fjmGOOwdixYwF0P7R4/vnn4+yzzzb/t7S0YOLEicj7LcgGDoSTkQSFM9BSC0hYBpgvheFUisDDT96CO35LADB/w2Vvwx23ObyR4wEAfuNy+M0rJaGhDsCZ2aYmH8LWROk0nR1tAlQKsBppYvH/CTHLVpAbe4AjxKybcBbl5mLrim+rcn2J/5MQPHr+4QDRejDFD11NNIQw54XYaUqVEgRkSjKfqyRSWqtlV/50hO4KlbsCTbrNeyjxfiz9ygBQGcFLpNjs5YDoY/2VuGqqPhfVeIqeV0fxzHEiiqp2x16jJ/P21DVlTfRPvY010YB1BjtF1lkUVX/emZhbk/XW9mJM49gRuqoHWteiUBpJctyZhs7+nBAChwAZESIMC323k4ntr2k6r7vl+J9++imOOuooLFu2DMOGDcN2222H+fPnY//99wcAnHfeeSgWi/j+97+PxsZG7LLLLliwYMF64xEFDCIS1d7eDkrjl4DjOMbioKehxWw2W1VUR/wCSJHKAR+QREDpnkgiilNBaAC44zaP/e+NkCTPb1wOaL5jL2dpoeT2pF4KCSJkdFNKbA7ARJrionRhiZWs6RZ5k2TMiVKBtSJ2eh/sbenpdlqx6rJq+0ISNKKWo4SCEmpSk4SHFesmFnEkqlJRUBelYiRu1+nEDCWoy8dLqTVsMXx3KgM1bIJgz1utIlESUeuaYZXnxj5OaVkR7RNNfAcmMmdPS4raqoAqsmVmhUy9dnfwtyNjPSFHHRGxdf2JvlaqWUd+hg+J2xFoUqOn2ct29L11hzhVg62DW1cJU7XvQR9z8tx29mClf/cuuMpM9E9xy0Ck82655ZYOPyeEYNasWTUr+9YHDBoSdcghh+Dyyy/HxhtvjKlTp2Lx4sW4/vrrcfzxxwNA74cWqWMiRiZlZmuPOFNEJQQIRfjJWxDURWbsZlVX5zdKzZYhU00rEqSHyxSh9b/5a5OkJKlKkhoFnUJT/1R8Hi0jECnXqyA2AMaJjY2K6FRsHdG5E1RecoSo49DHJISZZtav12kfd4JsEfVslVcEqq29GEtldaRH0jckW38lUCvNWP3mo60ttGVFRZTPnqY2RAQHUVWagjrxH6GoEaHU58HMx2Op1IriBcBYYgiVFu1KGnBNYGt0qk0HOhbIdzbP2oSuEMCOoqFcVA7smnRpF/EkmdLrq2bAa1fQdgVNbe2yWAaomSZfl5AsmOgo5V8rTauXdwjgEoCWCwALZfFQivUWg4ZE/fKXv8T/+3//D9///vexYsUKjB8/HqeccgouvvhiM0+vhhaT0SUdKRJc2hloYhBwgDoQrgdQB/7Kj+CNmhBb1G9cXhGx8oaPRrlltVy1Tc70i/PI9sDeDVvADlgi8YTeSXCpZ7KtEyqiSAniY32uyY75yCZliWmVKccE8bO2SZgPY/MAAGA1CZhtBaHXY+u7hJmWEM9DBsCG1edNdWGktbJToep/IckchfzMdWlFBSPhrOLcyzQvj6cgk/sfm2DvNzMaMflZIqJnE0XCK6ZF66z8TJ83HaUTQtpdsB7koLpCaDorZ44VZ3ThSbraPNX2oxpJ7oqdQ7X1aPSkKjF5WjtL13UUobLfJ6NVNimz91NHoboSkbL1TwQqmsI5Su3yd5itXzfTMHYVI9D591xLK+VQApdAyjr0/aAzOUMvoTer81L0HtK2Lwloi/3P3vsvhtXnZSVegqCQ0I8NnMLJAJSCZ+oAJwPhuJEWavUncUJha570NA0eynUrfygk0pcVyyanW0SMsDCK/mhDT3s+bUWgiZb+a68zQYQqBOrW+ong8QpFex1JwpY8D1VIlzx+JyJSNtFMkAmbLNiWDhrFUkmSoC5ot2JpUDNdVBIo+xzUiBZWnIvk//Zy1vsY+Yq9r5VvTBAoNa0jErUmdgHR7lQXkdteYxr2wB0LoCX+18tXQ3daHHX32LpLpPrDpNGOniSPy46SdiWVZ+uf9HIuJZJE6Xscocg2DO/dg1gL0ZPKRX0tZ6iKPPPQVGm3NDViw0236fO2L78cPgV5smY0qCgYTm96p8/2dX3EoIlE9TdIWAZhbkQ2ag2CgEnt0bAZcDPgmToEn30gZxORYaaBNVgLry4xXUY2jLammq7JImBy0EwIyJOf08TAzsMYyQKrjDJVIwKGSBBqKg0BwG9eGd9HmzRVTSFW7mdVAiF4NMhq/RQiHZXZL1XpxoSoOqDkczmU2guA4MgOGRb7rNzWbG1PRBE8PSlJEDXxBCLyCUjFpianSVQjVjYptCdXizbZ+2cTKWv5WFGCBdst3tYn9YRAJUlTUiemU6X6aTkpkq9GmABUCOntddvb7arQvbe0VtWIZjWDy1rRMI2u2HAk59HrTEa1kua3XcXwIdH6TecAAII66voGIDjKrU0AIRW/k8GM1vZit3qNJmFaeBECh5VlFMrKGFQ8YPUR1haLgxRxpCSqBghnleaZQERyLKKgxdIAIMIAlLdB6FYuhIJQtzK6QIhMAaplTXRL8LhhZWwZa9DVESBNtKxoia2FIoRDwIpqsTC27zGNlaDRfitoDVc1aOuGmqgV+bHJhhLKGxJAncoonSJQQgAgxFTnaQLUlRt+rq6+6nS9bLm1CUbMbt0Uid5Xi1iZlBzn8TSnIdW8kmBZx+6NGAu/cTkEdZEdOjKq2kyeo45QLV2qoGshdeSpmtFmT5AkTRpJw0qbFGnyVOvWXW1PKImOIYnOLBTWxIyzo+icXfVpp4aaC8WqYmzb7bsWOmpNotff2l40ZyKj2Omw+jyaC0Wzz0nCWSsyqKODsuuC5fWmfoOEBWoBGWUvlkoQAqjL51AqFkECVaBBHTAnO2h0VPaDVXOh2OXrX7fFooTA4QEQBiBhKXoANb6B/UOiUqydSElULSSIjHnaCIP4k4dNSDiTGhfhRpEm14sGZ8BojQR15SDMSnHypKv/WFSZJ0XuSpStl49FbES0HyIx/BAKQoKqJK6qCBowqUgbfvPKjkmRfT46AucRqUgQKOFkpEM7UGEuWs3WoFeflm0CZZMhBUOWKIXQB8AtAsUTx53oleiNHA+/cbkhpTY55V49ckoYr3VycqMkulHr7ZvTogTo6ru0I3a2gHxNfYySg7IehGv5Wsm/RG27cuPVok7V9rUzD6au+FF1Fx2ZjtopIE2k7KrPJOzPmwvFiogSJZEYvFUZ3NZy5teRRHt/9HIOQazqsiPTTYfKSAaFgKz8ldOTUXIiOPzmlaAqCuuXmkATURdCXfhlD8LNrlNRKyC6xl2i7vuqpRcJfYAFsftdVRlDH8FBL2iiUvFOryMlUTVAzKBq32D0XSehY2EBhBEYy6c54uVk1YbgkizoefWgZ9J2UbsYEkb6Kx0tEknSZEe/rP+NXinR7FgO/K4Rr9tmnvYxJF3VNUyqLpmCq5bi1NMtEtKp2FqfT0u/A0TVdv0Bv2lFFE20o2BOdM7NYSS1YwlUVNSp+WPauARy9rFa89iDU6wIwY5GAgBoLNTjUIKwG+yplthbCGFIih6Ao0rGKMqkkWw87ZDIYiHZKkhXQibTLMQQsMppduSluym+rqArRr16W01t7bHPdGSoQbn+D6nL10y5acF4c6EYI2L2/A4lFalp3U1AoytaKDuCFhGo+D1GHVj0IGE/jCUfKFQ0nPAQKJUBL0Sp3a0Z6V2bUCqq76i+rkIXZTeTdwlMpbTWPWmtakWFdK37YB+A9kI6L2mhkmLNkZKoWghlU2EAVXUrpqLOPJlRM7iKMAAvtIAOGQ7hNFhVZlYEiMt2MST04+J1W4htUnhCEiwNQmTajbrxKrpkdIpzSQSS2iM9OOn0X5XDj6XqrPx/DHpzdpqxM9JUrdGyqr7r7xuxtp0AUHl8FBH5BaqL/O1JCcJY1YKChfFKzcQNOHmuyq1N8e0hijYRa6BLLhcRl+rHnSQGdtSnGiEhxIpgcB5znu/onqxbDGmLBbkuFWUkugdjpNeKH4Oo6DloRNaJVF9P7RGSbZ00jPcXjUf1hDoAW6AvhDBESE8eYlXLVSOJhJCKRt56fiBKf7YXS5EkUsj+ndojLZfP1zSeTaK9WILnUNCkIW90EHECZSNJGuxphIL4RTihDz8owhs2qtN9GUjk8nkE7UWZIrW+R0oAz4nIE2EBwHxzP0g+CJrzpCNQ/RSJSrF2IiVRNUBEVI5fEzwEOLOiUBIiUH5P6odGNNFhPkhQjp7kRHy5ClBU7IMkZLos3vZt4pF2x6T3rEiBikpF2400SZmEMajftCJGipI5f0MiGZdkrgaBqqj2U9urAGeAQ7s8KKwpgs8+iCoWq6Uf9X4ntVmxaGJyWgjB4kasVQmk9bk3fHQkbI+5mlvkKslSlDmqAOR5I1Hjaq68oESilUxn0H0M9QBvdlcP4Ho37OwvZ8rjC5XnKQHCWaxfIxG6MbUiYhCRO7uejRA4iFenCSHAqhCu2La6GJmqZrlgEx1ZtRairkaqStsJJFN6OiLVlf3quPWMIpnqjOTzebQXSyqKLUxD8FKxaKowdTGK3c+SCAGno1L8JIFKknv93qHR9W3pgQDI6zAoodyyGtmhI2sf1ACivSgtUOzLWGvEskTe68290XqgIvY9OpH21Oi06reX0CsWB2kgqteRkqgaiJXW13o64wwiDORfxmKeQXAzUmReao3m12k/JKI/ehmaKF/lXEW4nMTALaLmvsyv+SPWRAcABKcgzCJsSq+VNIoz0RmbECSfxACABfGUY7UUly1+1pEUnXYUvN+fXEvFoqy6zORi1YWAsqIA4I2aYN5XQJEVvd9ajG4iQwmYafo7MCSDA46LcmuTKSkvF1qjwQlIECmaWB7GWkMoIqLJk9lVFbGp5a8UO6waWiBi/lepVlUdGSPYyYGlg9+KHYklUKlsQkGpa6JLtj+pjlTp/Ug2bk4eW2ceVLX8p+zSf4cSKSgO5QNSqb1QNUKqU2k6BaejTtXa9titd/T8SfKVTM3pqJQt3rbftxdL0K3E66wHj1KxCCKESREXSyXk6hvMA0q50BrdbwhRLwdCxHWNybSxlCVQeVlW+Y6rFsOsRajL51AslUBVFS9gWTyE5ep9RSk1v92qKVCNZIS+j9Ar1XlpOq/XkZKoGjA/Fov4xD6nND6dc4gwchwnISA4hygWAEpBHCciSVWiT4Kx+Dx6OtRNmcIM1pI8AUSJi+2qvKp5ep320f9TByKTM33+NPzG5VHkKXmjsAZ3IlS4m0S97TIjJ6iWNuqpN+lLlUgpev38xKp1KkB1HYktprffG1JZJcqS9NTRthYVqJIOESwEIX4kJLee/E0qEID84iVRNaJ7zmpaGnQEoy1CZZVd0gXbvtVyIQVQlBAIEBDqmFY9SU1NxTa5VQ6uCwiURk8eg3wgcNR0HdkyVaeQf6iVOpSXoqhJEmuhWurOJlCuIlA0LJvfaGcp5lKxiFBEJEhHohwrXQlUGmLaEQEmKh3HE4G/GNqLpap+aNUIn47s6r/Z+oYoTWy3KLIKTSoIlP5LKEDk/YdogbWuplXRqLUN5ZbVZj8pAEFd03XLJZDN5KtE880DaDLiZL2MZja1WlyvkZKoWmA+RJAgPVbESHAWRaCsz4j6TH9uer5B3RjtaFCCnElypMiU2g44U6kbDiAAcTNqgEmYXOqUCbV64WloQbl+bxMcBR19IUE5aq2SXA+iG4ndgDmz4cYArJY2qz8BBJel/Cs/AlzPrKvqk1w/IDngdBUdWTwkQcIyhJuNu7jHZrB8u9T5TaY/ym3N8ad9Oy2rUnZQ0Sf5ufxDSfSEDUTkoKPGwEnjRjuCoue0dUnMDLJR+TdhgblWqlWtxkxflX1GhS2EeiowA7PSyFHqRr0VrWMFIXBA4FIAILGWPdX0VfrYq6XupGA+EsVTFkgfIKDDdh6lYhGEh8glHL5tItSmCFOr0uE01OVjEScd4RtSV5nCHqIE6pr8a7sBrdFKDtvFUgn5GoSv3NYs70/UqaykM9epihDbKa1qJJ1QwPXkd8mk4WRUuVyuuv2BgK6EzQ4dqR5UHEXSOQgoHJJ4+FT61IroGwAjv1Dv9XT5O6/SyaCPkKbz1k6kJKoGZJpO3USTkaOK/y0dDGfx/9X8hDpynVY0ysxLpT5ErzeKcCkxuUqVyUiVqhrjYRQt0Los6IGQRduxyJbUATlSkJ68QXLpXKybaZKkdin59KV7B9LKGwgJ/bjgXfBYKbS/+hMEn31gyNe6AtMXMEGgTLrT0lOJKgQKqG3bUG5ZDQIf1PHABUlEZSTsUL1uaqzF0B2l8AgqK+u0y3kt3Y7Zrr4O9fctuBlcY1E16sqHCEcPZJXpyVg6kFDA8QAnU7FtyiO7Bz2AUeqCEwcOiYiGPiZ5LNWF9jqFR3QhBo+sRWq5d/vNK0GBTtPRmrjrir34duWJ5UKYzzoi+oZIqghXxffJAgDV9YTJa8qkkG1vNPvcG62T9du2I5/JQoZaDw0DCG/EWPkwZ9nCADDXCA3LUeSNh1EEPXlftPz37AcD46lla6b6GGk6b+1ESqJqQAQ+RFB5AwcQL3NPpPE0gRJW9IpQR0aibKIUBmYeAkWc9DrtSjxNoNQ6EAaRVkovZ++39ZmaEHMa19OSjZJj5qGCQ/DQaIDMOsJIByXTMg7cjbaOrSf8+A0QABk93XErojnVfKjWCVipWEMQtOkmoYa8aoPNboMzSaRU2qvawKW3K4M0NNIaIW4xYMNUgCkTWdvEtUtNi233e0AdM5F7wXxz/QjHU/vsy8FJi+I1sQpVX8VMLkZIdaNm+X8ibaiF0QAc1WPQsT9XERjqeAgRF9FrLyBjnstZ1ARccDkIc17RC9OkfbqBJEFKnldKCNqLJaN7qkqshBTkg5CK6JVO03WnbYuet6IZekIKEPurrmECyIgUpfKhKXEp+is/gvDqBlRoblpvNa2Q2kFCwLlAhidJUMKkGIhb0FjXNmGhjDgHMuUrAh+8vdC/B5ZirUJKomqB83i6jVvRHZWqAwBQx6TwgDh5MoSKqs9svZNaH4lNq/I0p8lVQk/VkTBdqH2PyJRN1hwQhGBLX4Hw6uCqyrzMhhtLTQ8PgaAEonU41dI0AOimO1U9be5GW4N9+Kr5vzvpsMEOcw6ByoE3ge6K6vVgVG5ZHRmRJiI9gNLJaSE4dc31VesJlCi9U0z7wUNACPXkK6NmSa1NudAqHax1AYIlnpdEW1RcNzSobH2iDWRlhaHcRy361+X8GrqFDSGuIYTUpapFk/XgoUm+jsAKeQ27hMJx4mSMqKgWdzIy+kqISeeBhSBc2VLo34L1EGGbp/YE9m+YqfOtq8iSyOXzUSWeiEiWSyKfsZ72vdPnO2ZrkqzOA2IRbJPCTWinguXvyumEgBSbgQEgUfoBRf/GvOGj5bkTijSHQaJSOVEgoVOUducC/RAZFMGLhei+HAZA2D/pPErIGvs8pT5RvY/uKVPXV6jUm1CtWYT1ioVyq5AgwRhEIKNHUJV8NoHK7DLTzGu/72x/Kl5Jgmal+ATn8j1T22dqGebHVluRXuNWSXOssq/jy6bD/m/rOPo6RZkdOtKkFbJDR8IbNgresFHINgyXg6ioLnQlQphXBZLCdw3O5IDCApRbm1AutMpXW7O8lkI/lgqySUYs+mk70wPxBxRbY2Lp6wDIVj9W+T4lxKSwmRBgQonLnYyMViWuu0i8TgzhpCyQETe7qtUQAwItfEdCu5Y8V2uKIXV5cJVi1Kk5OzpFCamZ3iMk+jzXi6a03vDRlcdXRRdZ8bnRsZGKe0PNYou+REdRwlrHou+jRrIQGsNlwpTYnIcQvkVyrWxCf4A4pFdeKXoXaSSqFiy7AqGtDAAI7UhOHYjQl0+Tyao7HRFiCYKjI0PUgbfr182mahGp4N/3WfsTJ0ZEZRq1UJ1QZoToNmIeVpRG6QrIpyu25CVzExSOG7mmu5mKKj17kA2Wv1uRErTBlrwEZ/Lnan6+LqNfiFQHn5XbmiEcT6byrKo2jWRLHW1bEDORlaKleEojtEiHZRJrCBO1vNIS+hkZrZLXnjaKBaEmEloNHXmG6Qo1JgQYKJxMXcyYUkanlMdaJOCKR0/0sQoOIiLSRUz7oawcJK1iDaNtV+QvZthKXQgnAwjRpaiQFqE3tbWDgxg9V61ogY5G6fSlnf7rDF1N9ZniEBV9A4lSslXF5ir9itDyWaJOzQ4IfQ1/5Ufyjf5udMTMyYAmCwUs/Z6dxjVESrXeIsw38gtRLlmL9x+BAgDqENA17K6dRqJ6HymJ6ghuxpApU2Wn9E+mOCoIQBzr6UaFeEXgRxUxex0JAPCfuSdGnjpCnEDxSDOlNVZBfH7z47ZF6PaPvIq9guABgKiihqjPiZcDnfw5sKWvxCNQ1uBDi801952EwXpLoNYKCAESFOE4GXAtzLYIg6CO9AxqbQJBlAYqlkrgwpEBBVPtqRbVrS+UwWdMK6fWndSPmCiGnk9fw47XIXnqCjoiD23tRTiZnBwwFAkUbtaIgIWTAQFkCo8z5OrqTRPonE6bahNUdf4EdZWvleXjJXivpKt1CxhbbO5QUt181tbnkIhMapg0J4QkBtbvvdzWDMKCLqWSvRFjlZbIBXE8VQEcSq0cEtFm8/1KbZk7Wj5cBZ99IPWX/Uiokml0O0VpW3JoMbm8rkvRg4Dd89JKlyP0IcolS64RzdffZCrF2oWURNWAYAGEX5KeOFT6N5nqOjuNpy0INALfRK0Ipcjuc7T5qKsECqhM7QX/vs8SrXMAQVWDS+J68odtkR+S8eBssxf4u89FvlehH59He1Sp4+PvvQCniu6Jv/eCnF9wsPdfhrPJDl0+phT9g65EHPzG5Ug+k3YU+Sm3NkmiFJQizRBgnvRjvRv1oMpYbCASSvsUq9zsA0RpsJwsb+chwF3Qwqqa0dMksbArSZOFEOXWJrlOFiJY8T4A9JgolFtWg3t1sdJzO42nyZ2OPmptWlt7UTqIichiNWnYWS6EAKwSfB09W/lRp5o9QKb39PYJoaqCzY8qJwlVnlFhjKyFy96GO25zEL8gtWNd3F5vwnhhudmYPi6mnQNklFLr3VhlgRAAgLognvptBIHJCBg5R39VJjo0XjTUE5Aq6fwUa4SURNWACBUZojw2DUD8aQWQhMRKm+UOOBnlf8ypMM5cE3RMqlSkTEWqDBI/OBGU5b4GiuRlMiD5hkgDokLXdMoXwN5/GWzpK3AmbR9bhxaU8/deADogUvy9F8Cz9XA32rrqQJRiYKEjKMbssxOYsviW1VJIqQcj6iDWRE9dQwCknQYnkWkhIRXVnH0NO/XJVr7X7eWrXbc2SfWbVkTC7A7IgjZ9JMyHyOSNWD87dCRKxaJ0zjb3EIvM8hDZEWMrROwOJcjncrF+e5GhrNrP+gYpsrZILOGhdOXvArGxq/ZkRMpK6VnRRk2SbdF9uOxtM89AECjCQ3lf4wxEBPFmysSKMllpbGkBox4GwiAWuRKcxz3+9L2Ws7hOqg9B6JprmkjFo1OKNUVKompB3dC0zxP0j8jWPan/desXE8kBkN3vuD7dvcwuM+E/c4/5X9hmm5TC3X4GAIC9+SREuYhw8XyTanSnHwz238fhbLMX2Ievwpm4bcX6hZevcDS3oclU+NHrFZ85m+wA9v7LZsBMCdTai+6WoGeHjkS5VT2181D1VYyq4eRgm4lSTmEZKLVWjWr2N/oixWy3D/JGTTBC6mS5fFbp5ILPPpA9E1tWw28qQFAX1CIfglBZgRmWzENSNcKjo4a2B1VSiO6v/CjqD2mRHW2s21l1oTd8tIxYJn3lEqk8QZTPHYsfR7InZ19Cd1uAseegpsKOhH7UK5PQCmd1Ow0tHE+eezu6ZAp49MOzjNYTNwMRompGIMX6g5REdQRdRZT8kVgib/0UQqyoVH9Bpwf9p+4CoHRanII60ZOsKLSoiBpF5vNfNdOdbfaSf6sQKAAdEqjYfBOmVp0u3BoeWykGPYy/UPNK46ZPrLSdSZ+owUlksmBLXpLVcWsBmeor2NEXAiAzdjOZ7hPCFGLoogNNXs3gDxgCkOzr2BHa2otwKKneeSTR8FqnILsTGTJC8+aVkcZN9Tw0xIzSyIALqC5A72X4Kz9S/SPdysyAqQJVOj4WgOrIFHUil31ExTKmS4Ne3tr/ZIN5WLoo0ovZhs5AHQK6hpEomkaieh0piaqBqu1ZEhVyCC3hpg7v9uOPSsPb/XBDpMA5nGn7gv33cbDGFRBBAJqvjxGovgZb+grcRBowxboHb9goNbiGEJbfjiDSu4kWmyOTTaXL4e88CzrlCwO8532DamL5zrRSnQnTg0+XIDNmcs3PdUuYakJ7b+R4+I3LI5NTHhqi4TevVP+7IH57pxWl3rBRUbUbINOSbi4y3lQ2EgCkmLyDyt01BXv/ZTiEQHh1icwAN62FNGEEADgZpd3yAU5NdSMQkSfjs8b8qB2MLiawESvMSYjM+xiErrkmqqrFSYo1QhqH7ADStVwKsHUqL24ZEKXvtJg8d8DJA7W7aj98hC8+CGebveBuuBEyEzbruv9UL4C99lileDPFOgtv2CiQoARv5Hh4oyZImwwWwGlvBIotEEEZJCxJPZ7yKWNvPjnQuz1o0BGB0qhVEGA3zxbUlaQnUye1TjqlJXiXLTm8EWPNy6TLCJXr1f0xFckKP36jS+vsLoLPPpAVdUERYJatgiZw2gIm9KXNhV8AbW8EALmfmizxELRcAAnK8q9flMsE5ahjhD4e3SNVmR7rdl39GYVKsfYijUR1BbyK9ilRoQfXQ+6gU/tsF4Ln74cIgwqDTiBK53m7fj1mjUC32L3P9qcWnGn79vs2Uwws7GiLN3I8gk+XyH/07wbyKVpfj+y/jxtNXoreQbnQCsASkwORwzh1Yv3tBFRkqpPUXkfeUsLx4kJtDUM8et/Fu1xoBXJDlQGsb3y5BJXEXbgytUfbPgNp/hS80AL3819F+MoCUPswhAAJS6BTvoDwlQUgjhNV3yk9n4nXxNKhvHokqp+kC2k6b+1ESqI6g9XrLkagAGMVAOqAdOOH5D95J7wvfbtbu5H5/FcRvvggRBhIoqR7skHaGmhi1Z9RpxTrF9iHr0IQWlMHp5EZMxn8vVUAZFrcnfal2OcpeeoDsAAgFKX2AnLDRslqQB0lJ6r3II+LqGNWFVVAwlJtiwfArJ+wIBJv62V74ZDKhdY4KbQ0S0JZLMjqOyGjTSo6xhs/BSsV4O1+OADA3X5GTdLubj8D7LXHIhNjBICbjTdl7izi1E8+Ub3hOJ5W5/U+UhLVBWgCZfekM9MYA3G9bqXxRLmI0oJbQPP1EIwZM85Ol+MypKx79WmfEn2zSJGiL+FM3BbhR69LE1ZI5/EkoTK907L1IISCFBrBXnssjVD2MYjfDm/UBJTbmuE3r5SG83AsAbiQVYJDhsFvWgESlDrVLXkjxiL47IOYjUO5rdn4gAntAK5E28asV9scfPR6p4S7FvzmlaB+O/yyjLCR0AfhDE57I3jzSpBMxjzQCsbA2prkfF7OSCv8p+4y3SE6Iu7OtH3BXnsMcD1l2WE52iuCRBwnik7pdi+pyWYKpCSqNhJiQaOFoo55LxgDcRzkv3Jal1dbmn8zAPljJ14OXjciR8IvgXg5uNMPBgCUH58rbyYpUvQDjJ0FjVc2xUAdCK8OwsmAAqDMBy+09N9OrocIPl1i4gvaJLTcslr6d4mI1Gh0p/ovs+HG8Fd/IlNpnMnyfyCq1FMmnDqqZXRS1K1+fXQBWstF21ZGE+0oU2uTMge20ohVqqi783BJvFzU3geIEyiqInlGF6v+BoHqDNFPwnKHgDhrKCxHPxmDrkdISVQtaBM1u12KnQ9X1Xv5mWd1e73Ey8EZMbrbTzLe7oej/PhclObfDFo/tMsRrBQpeorwk7dAgpLxxQG3XJ+V2SocJzJsdbMAJMHSAyrN10ufMv0QEvpSlOtmjJ9ZijWAbS+hIP28mlTUKDLb1PAbl4OEfpdE5d7I8dK7yhZwa40VlwSKhCVlOeAZHRZEz4YX4Xiy+s/Lg/hFoNgiuygEAbhfksRFwyYwnJsoFKgjI1GcdyqdYP99XHo+lQrywTibhygrE9OMp677yCeKGHG51PvFKrn7EKkmau1ESqJqQIQBkCHRj9Ju86IiUHC92ivoCIqY6YhSd5ASpxT9Ce0Xxpa8pMTJLNKKaINXTgGiXPD9IoRfiByfAYhMHiQbyP6OlMrWREC/PcGviwg/el02CAZAEo2cg+XvAk7GGHz6zSuBoATiehGxgtRKdbWbQHboSGWLoGwsqCvXaREoGQ3ixuRSUBfhx29026WellrgtK6QHmNhAFEsmAbAcgZa0WzdNGLXn1soPz636n3TLsIRqpMDyWRASu1RFwiddVDtXgBLUG4KjPopEkUIyBo2ICY8JVG9jZRE1YKwKu8SPyJQB/mv/6hbqyve/wv5REOdWD+9FCkGA4jgQKCEyFaqw3yuDRgFBxiBs8kO4O88K32IeBjp+RwHUL0cU/QM7MNXZe86TWYdz/Sr0738wAJTJUkgvx9XaaYIl5V5RHAIUNPexe4fWG5ZXelmr6v7VPUd1TYD+rog1LwnPATPNUBk4i7qncFvXA4alsGbPov5MBkNUlLkbRXYAIpM6YdcwDSCLy+8zVRRE8cxpEv4pShtl6s3kVJwDkFl6o6EGVkZrYuH9PyZDEQ51UWt70hJVGdQBIorZ3LqZlD3zfO6tYri338t31BnwH2kUqToKWLGg0n9iJXqIZCRK7g5Oc0vyocQSlOBeS9AuDmZRlOERVfFhcveNt+Ru9HWCD95K/pOeIhg+bugyrHbuI5zDyIDE9XSyA4dafoqmtQgdUFoKKNRQVkSKJ1KtEk0IKOUvgvheN2KRhG/CLL6I7BSQa0noXWybWUS0Sj7c5NuM8bIgfkrkhY1GqEvxeN21wnqyPntdSstVKwNWD+AOhR0DTVRVKTWkL2NlER1AME5eLEAHsgbU8PRs7q9juL9v4jrqlKk6GcEz9/fI8d63aolVq0ExNtiJKdZJfRaq6MHNHfHA3rhaNZvaHE/YbZVgRr0RRlwHDgTt5Vic/3dUBfgIZz2RtN2R1dYuuO3NBWV2t0c1EF2yDATiSq3NQOEgDDVK5Ez6ewdBnGjSyBmCUNQgMND8Gx9l47NX/0JnGIzmLZQUP1KTQIqEXWy59FWMzHiZO1Psm2LPQ+hVNnVBBHZUiSNuEAyjSfCIKrU688GxL1hcSDSdF5vY1DR0o8//hjf/e53scEGG6Curg477LADXnzxRfO5EAKzZs3C+PHjkc/nsddee+H11ysb5HYFIgzlkwnnIA7tEYECENdS9dOPLcX6ifCVBfK1eL6Z5j9zD7gyYuwq2NJXJIGyCRGh8ad3HX2w++VRVw7YFskSpTaZUkndndcYbOkrICwE9Ysxcb+swhPqO+EIP34jqqKDakeTIB/OpO3hqNZMmbGbSVIUlEDLbSB+AeVCK8ptzSi1FyI/Jr9daqDU9054KE0vdXpXcNlPVAvZOYcotYEEZdlXsBMQFgJtq4zOqLuCbambUvsS+lFbLt3n1HrZ17LZ3zCIXvb5sjpTmOUUcRVhIN34U6y3GDQkqrGxEbvvvjsymQwefvhh/Pe//8V1112H4cOHm3muvvpqXH/99fjVr36F559/HmPHjsX++++P1tbuDSIAIgH5GvYrskO+3bkplObfbOwQUqToDOErC8z7WMSHs3g1U2fr+eh16ejMQ2ngyAIgLEuhsB6AVPTBGNBakSqhmg8DSkfVj8LbdRn8vRfUGxaP/ADxaJAQylMpNGQn/PgNVO9QHMEdv6UhCMTqCap7Ica2ndx+sh1WItVmiFYXILWnXbxe7J6m1r7EXcUjUmc+qzZ/tc8AJKNattVNrWX6CjoStaavFL2LQUOirrrqKkycOBFz5szBzjvvjE022QT77rsvNttMGsYJIXDDDTfgwgsvxGGHHYZp06bh1ltvRXt7O+bNm9ft7ZlqD+0L1VOEgem/153BTP9Qy4/P7fm2U6wXYK89BpqXKRP7Gguevx/E9brsjs+WvKT6iBUggrIcgKyn844GGwNlXWAiVYAcfEI/RvR6gvX5oULoaJDWHgFGjxR7cZluk55GXKbgQqvHXAcgYUk16WUgQbskXpxJR3LBZSNfx5MRR02uLIIlwiAW/TGWFsVWEL/YYT89f+VHAAti0XriONH1rB5k7QfRmj1NgcqIk03wql27HU3X6wyt/eNMWiIEfuUyfQStiVrTV4rexaA5o/fffz922mknfPOb38To0aOx44474ve//735fMmSJVi+fDlmzIh8Z7LZLPbcc088/fTT3d9grOKj5yRKcCbz7JzLEu8uL8ejkHSKFDXAXnsMJFcP4UkSJcolBM/fj+Df91Xts2iWSzQBZktekgOoShPp6Ktuwo3QjxpyWwOUIVgaQkQ+QbAGPa056SFKC26JFWWUF96G0oJbery+wQT+7nORXYTqh2e3WBG6wbDulWc+kNEnwoIuRYLcCVPhjt9SkjEWgpTb5CsswRs2CiKTg8hkIZQXmE7bmmvBatYuyiWIIIAoF+X7xuWg5QKCT5cg+HRJJaGiVF5/6n3yM6huEUCcPCH0Y+QtltIzQnC/kiRVe2+lnE32wb7GbU2UL49P70+K9ReDRlj+3nvv4aabbsLZZ5+NCy64AM899xzOOOMMZLNZHH300Vi+XLrcjhkzJrbcmDFjsHTp0prrLZfLKJejnHZLi+WurEpr14RE1R9xEQp3XAZpFhei6eYLAADDT74CANB62yzQjPJ7cT2QfD0Ipch/5TSUHroJ2f2O6/G2U6zbYK89JttcuLJ5Kh0yHKLYCt68CuC8gkDx/z0lzTAFB3EzYEtegjP5c+D/e0pGXK2BwqRirHJwkvHkg4DWg1jTbe8habroAtwDyebhKEPN4N/3wX/mHtMouzt9HpMp9ew+R6P00E2m8pVQitxBp6J438+7b4C7tsPWpdmTbddwQmQqzzQZRqRLUzYT3WkKLJRDOAAroqj+Oq60WAgjwh1LFtqRe6tFCm/8FI7g4Nl68NwwBJ8uQWbMZLmO0ActtSI0hIZGuihWm/zEW3JVisiFisglk5nSabwG+eFMVelpkbl9PL5ZpyF7/ZWu7o10XCos73UMGhLFOcdOO+2EK66Q5GPHHXfE66+/jptuuglHHx35LhESv0iEEBXTbMyePRuXXHJJlQ3GS2Rb5lyMocdd2qN9F5xDMPkijtRYtcy5GNRz4dQPkQNVuQSSzcVayOQOOrVH20uxboG9+aQkEdQBnfIFM12miDNmABVuFiTHgcbPYik89t/HY02yheNJzYzgct2u7FwPy3DQgDqmOkqnLmLRJT2QaANG5V6uB2BCKcJXFsgnd/Wb8p+6q8vHXl54m1xPVvoNlRbcYgo+AJiqLMEZivf9HIIxtN9zrVmmOy2Z1lpwBlBpUyCd4UkkJNfQ/1s2EwIAHPVdsm5EtDURU7YH3qgJcp0sVFFGyGgU84GAV3pg6zReIoouwgBs9XIQLwe6gbRq0L5WtNwKUWy1iFCVCrvE+wrSz1lEcACzfaL1S1akSVjXrnlQYNGDg1mXRhhUt1RI7l8fghICuoZmm7SDsTBFzzBoSNS4ceOwzTbbxKZtvfXWuOeeewAAY8eOBQAsX74c48aNM/OsWLGiIjpl4/zzz8fZZ59t/m9pacHEiRPBwwA8dAzx0TYHSXSFXLGS/jFTODkP1M1AcIb6Iy7qcLkU6w/CFx8E3AxINm8aXGtzVuLljQ4FgGy1AhV5cjwlJi5BlAoIm1dVaKBE4MtoEVeDhhWREOVirPDBbuyqiZcIdUqkytO3ldaDmzPtXwSRDSZEaAmFOTMpkC4PPGo+XiyY9F1S/2LWnUiriGIB7Xdf3W1ft7UNuv2U7O/mViFQlW1fKkC6odwQqrUPdUx6F5B99IJPl0AgA+G4INQ1TvUAZMRHGVUCgXH9FtZ3RQCIUjvIZx/Kop2MtFQQ5aKlN4rSdqbxb0LvBDWt4lqw/k9qWavSB0rjDwWAZV9gb6dSM2UeLvqJRKVYOzFoNFG777473nrrrdi0//3vf5g0aRIAYPLkyRg7diweffRR87nv+3jiiSew22671VxvNpvF0KFDYy8AEFxEkaMaYrymmy/oVHS+6lfnxv4nlErhbYoUCuHi+aDDNwRtGAlk6wE3C5IbApHJSyFvJitf2XqEy96G8PIQXh6sfgMINwPiF8BWLQdTabzkus3g4JciPYd6b9IaVSpIReDL+cql2HpJRrVtoTQWtSJhSQrTddTDzaiBP/r92ML38sLbUP7HHJT/Mcf8XxWKQOhKWZPa06XnupLWFiJrcIb2u6/u5BtYu+FM2xfONnvJ70tV3dlicgPbswuQ5EQRWkEo6GY7d2l77kZbS5F5uRWZsZvFPjNVf0CsF18tiNCPaeGM15JfAm9vBW9tAm9eBVFqjxNuwEqdRdEmE32yo5E2qhAae74KwqO1VoYASg2r/H34EKEPVi6DB1F16hoVGq0B9Fi0pq8UvYtBc0bPOussPPvss7jiiivwzjvvYN68ebj55ptx2mkyXE8IwZlnnokrrrgC9957L1577TUce+yxqKurw3e+851ub0+n3zScnGf0TADQfMtFnV6Qq351LqjjgGbcSKjLOYKmJoTtJbTN7Vl6MMW6A/bmk6DDRkG4WaMnguBAWJbePYLHNCGCShdo4UgiQ9ubwBo/g/BL8HY/PBaFCl9ZoLQl1kCkTQlrgXNVYefFK6Os1AcgW15EZCry0BF+EaRcMKkf4eZA64eC5utBc/UguTrQfL1s7KqjEJyjNP/mCh81Ta5INie3l81H29LEys1EuhdmpWzsgTP0Bz2RAqAKTcpR2k69YkRKkybqSl2a0lIRwSObhC7A3WhruBOmVk4ft3lciO1YHkpJQbgdgUq4hNtkRpMVO5qoCxiSXk9Vq+j054GMfulXEoJrv6jalXp6WU2aeBDGxoEhR14cm7c/oRsQr+krRe9i0KTzPv/5z+Pee+/F+eefj0svvRSTJ0/GDTfcgCOPjBpLnnfeeSgWi/j+97+PxsZG7LLLLliwYAEaGhq6vb3Y04v6ETk5D823XFQxvfmWizDshMtqrosHYUwYO+yEy9B08wXgfteFninWPbD/Pg7Uj5C6PV1BpfUoupqNOiCZrNTBqAomEpQhnAxIWAZr/Ay8WKjaYNXdfgbYa4+B+yWZRmHyKdse0GwNiYksaaKlBkdz27WjPFYECIyBZPOmSk/4DIRQGL8pzmSzbsZAuHaV1hYiUutVi0Dpwgqto9IRNAAQpYI6Dp1S9CPtioIeOInSTA1m0bls4Cyb4hodm06dJogUCFG98QCoaCYA8HeejenqeoLM2M2klkmRZFIuyP1SHk+x1FeVqI39uQiDuP2LEnWLMIicxBOC8crquyhFnNSfAuqBmHMZxVSa1GSKMdq3+H1fr8c2W9ZEqnDHZf1KpHrFsTxtQNzrGDSRKAD4yle+gldffRWlUglvvPEGTjrppNjnhBDMmjULy5YtQ6lUwhNPPIFp06b1eHuCc3A/jEWcdCUd90MMP/kKEId2Wr3H/dBoqvQPUK8nxfoJaU0wRDpAhyVDoGw/JiMED8oggYzwoHUVeOtqoHUlRLkdvNDSob4oerIPjLhblEvmhZj/kxXNMdNYZeTKJmA6BZMoExeBjKQJv2gdE1dRB0XkADPIEkqRm3ECABjtU4xAcQ5eaDH6IL0fJv1InUh/U61KS0Uqivf/opvf1NoDd/rB4K1NcLb6kkyDtTVB96wz0Sddqacjh5qUO57xmuoVcCavVzdr+u5JPZ22xgjM9WTSwtpIMwzk9RgEkc8Ts6JQfiluXQDE/coU5Lpra+wE41FESd2D9SuZkuNhYKJO+n6uyRcgta/VQDqK6qZYL5CO5DUgWCWB0u+5H2LEqVea+QTjaLr5AmNboEHV0xVnDBQARxR56mmlX4rBj/CVBSDDx0BQKiNDquqKBMUooqKjQG5GDkAlq7SaM/BSuxTFhoEhH9Xgbj8DwfP3q1RbSVVNBWbdMa2TC5P+M/YF2XykTYFK5yWrlCiNExfXk0/7xdZokNOfwYpEcG4q/jRh0hEobTRLMpnqBErNY6rQ9DFUbCuKTCAI4VDabXuFtQl6v7UrffjKAtB6SZIiLZTVY46ziOQE5V5rv0MEhxAUIADPNYCGZRmBpA5AZQTMGFGaiGcizWbviyZLsTRsoB4mos9rgVvkytyn1YOrTYoAQFDrmqdUEqhEVoA41OxLraIi83nYP9Go3tA0ET6o4iaDAukZrQH7h8dKPljJN08xHc0PACuu/aERlOsfIwuCNfKbSrFuwH/mHpCho5SPknUz5yHgZGR0ystFT+9+yZgYIgxkJEfrPkrtHRKo8MUHETx/P0QYgPtSHG6iT6ZHWGJg41wSn0SEKdJHWYaEjmOKJKSxoopwFQvghVYlSJek0Ija7aiUImbZfaRFiS0sJ9mcrMwKAvBiQS5XLSKm9rlW2sj+zQkmoyC8tbHmORtscLefAVEqSAJu9HS6HY8wTuXuRlsD5UIvRqJCGfGCtDvgdcNBcvWmmECSXyuSaUc27Zdtkmm7getiCP07sCJVUv/kx6OoiB5o9b1aFyEwPwBnDJwx+T6hc7IjVvplR6X0A7WN1ttmyWX7UWQuM+xrqonq3jZnz56Nz3/+82hoaMDo0aMxc+bMigKv3uxZOxiRkqgasFm//gFyX/6weKKUesSpV1ZM40GIsFRGWEqbU6aQ8J+8EzRXL4W+QVm201CDnCi3y79OBiI/FLR+qEmFmOoho//RKZGOn851iTmAeDTAdny2qpF4qaCiYNQiR75Jm9hRqHgqxXIv16TJRBZ0uq36rSa7z9FRhR63bBXKJUme2luk9kkdg0kllhMDa+K4K6YxDuq54GEAtmo52v8yu7Ova9DAmbYv6Ba7A2VFpuwKScFB/SLYa49BFAtyvl6Au9HWJnIKISAydeD5YSBDR4HWN4DWDTXeXtG1ESccMWKNSOsUbzHEY9FM852HgbKhCeIi9cSDqi0QN0RJpem4HyJsL8UiVvbLnqah03oNR88y0ox1+eH4iSeewGmnnYZnn30Wjz76KMIwxIwZM1AoFMw8vdqzdhAiTed1AP3UQjMuqOOYtJx9Mxh+8hVovuUiCM6x8hfnmOn6pq3FkYRSUC893esrSgtuAXEzcHc8IGrVokwvI0F2SQqymY7Y2L5MLCJSQLz0uwOYdQeVbuTgychORNA6NBakjurNxioMFc16lHg30ilJMTRBBlxVEgIJWwPbsqBUiH5nVkWeSevYflVWmtBEMHhiMLT0jYyV4WSl0Hpd8JHScLb6Uux/XY0nSm0AEDdR7QW4G20NAAg/eUvqsdyc1GdlsqBuFiSTAWttAm9tMsuYijsgii5q/R9ngJ1uikWyKj3KkkSngqRZejvb6FgvExSK5n8tvbDF6PrBeIMfXANAVlvr9wMBQgnIGpptdnf5+fPnx/6fM2cORo8ejRdffBF77LFHRc9aALj11lsxZswYzJs3D6eccsoa7e9gQBqJqoGwvRg9oah0nI4u2U8ejTf9BADg5rIYdcZ1cDIZUM9Fpj6PTH0eG/zgGri5LBwv02EFX4p1G8TLAZyh/I85sqKueRVQaARrXAHWuCJKfZXbwZpXga1aDl4qgLc1gbe3yHScBmdRVKgGwhcflA7RzasglJ4o5vAMS7OiI0+UQoQ+uOUjBc6UvUDObNtEx2zoQU79NREwN6N0MrIKz51+MAApFjfNtRPamBiBQqLli+upikXP7A9gla9b6RebQOm/ehoPAxONGsxi8w4Rqig4Z9Jratq+fbIZd/yWUfNiwQHqyurRfIMkztoXT/UCtSssTWRRN2pXovJY0YCGJvicGQ2UTsUl02+A1K6GpTJYyY+uiyAEZ8zcx42uSYvadUowkJEzmzRt8INrsPIX5+Czn0cVnoRS0Ez/iMsp7YUGxOq31NLSEnvZrc86QnNzMwBg5MiRAPqgZ+0gREqiasD+QbKSD7+lHX5LO4KCDP/qnPiIU6+EYNyIyp2ch+zwBow49UpDmoaffEWF6DzF+oXsXkdK8TSVLSbc6QdLglNqj3RDjIE1r1J6oiJEoSUiK1ozktATlebfXLEt9tpjclAqyD6Q9oAlOI+RKVtnEvNWMmm5RAWV0UsxEy2TEQVbzMyRFBBr083g+fvNPAaWbibWzNjsSwfpkqQjtlUtW8sY0da4VI2krSMw5LEfGuSSoAwSlEBCX/qEhdagrIoaRKj0bbpiz7wCQ8yNri5pl6DJVlLDhyjSxPwAzA/MfTsoFE23iJjOicdTdDaR4oyBqfThqDOui22n8aafSCF6EBrN62DtOjFx4kQMGzbMvGbP7jy9LYTA2WefjS9+8Yum6r2jnrX6s3UdaX6pBrQgPCz6sXRcThEkGzZBSqvuUnQIyxPJ2/1wlP8xJ9KOKMG37cZMIBtTJ32U7PUlIRgDb22M0iU1eodFlWxOZTsXyOiZCAOQMKho4Fp9XyiqCbxNM1nlAm1IoO06zSsHNTm9ekoxJjKu4ulmD5R2RZM9b923zpcNwHtLbL2WQZSLAKTDfJ9XJBYaJVF2s0BYlteXlwfN16uG2MxEWwFA99ZLtoUx+277RFmwC3tsQsSZNMgkDgWHjA7YFXb2upJV14BNOCuvGbNtFl1rdoRqyLd+Apzc9xq7XvGJUst/+OGHpjsHIKNHneEHP/gB/vOf/+Bf//pX5Xq72bN2XUJKojpAWIx63uU2GDqg+fAU6waSppi81I68Ku8Pnr8/FjWI3LhZTJgdE4UnwD58Vc6j7QBs8mRHXajuSSYHBdowPFY5B6gUJNSAZi2nB8DoIFiFvorm662KwsBUWMWqqSx9jG3uWVPrFdODabIUpWH036QeyoY9SDbfchHcuhwEYyje/wvkv3pG9e0OUrjbz5B2Gjr12YfQqUL+zrMR8QnLILl6kHw9oCOuPE5+dSpO++bpSKJtlglE31u16uhawm6acSs8ofQDcbVla723l+V+iDHn/bLDc9FX6BWLA7W83eKsKzj99NNx//3345///CcmTJhgpve0Z+26hDSdVwOjTrsylg5ICVSKvgZRwmmSzYPWN4Dk602puDEntN2ZExon/u5zQPMKKeK2CJDtwWM3b7WF5sIvmYbHWiMltC2CNj60l7NJjBVNoA3DQYdtoETggRQWFwuqJN2PUoq2QWdiPw2hSpTGV6YT/YooVDKioMvazSsIYvqX+iMukpVeKvW5LqG88Dbw9laIYqHf0pax1LFKP9Mhww1x4UFoLGM0WQqLkW7JNiYGIosZY13gh+al5w8KxQqjTEA2fueMgzMe67nILA1VrDKPx19JcL/SpHNdhxACP/jBD/C3v/0NCxcuxOTJk2Of97Rn7bqENBJVA5/M/iFynINSCifX909yKdZPxETTgDSqVPohXmiVXkl2JV6sQs2JR5cEBy8WQFwPpH4oRPMqNZnHqti0fQHxclIwbiqkPJk6VGkWuJmoeawWn6u0nJ5GtRVCGIDk6uT7YgG8vUURng4G70QFYCzFCFSNPNnRjGRUIql1Meu0Ujn6c+bL42qZczGo50Iwjra5l8Z6ow12ZPc5Gv6Td4IVC53P3FuI2Q3ISCTJeDIixTlIuMoQmKTxY7Uoky7m0d9hUCjBybhwch5EEEhCpvRtxKGgPKq2Y3o7tnGmJm5WSi+ZNuSMY9z5vzb/r7j2hyYKFJYGTkMXa769BuvoDk477TTMmzcP//d//4eGhgajcxo2bBjy+XysZ+3mm2+OzTffHFdccUWPe9YORqQkqgaEUDdkSrHRT3830LuTYh2FrXUSqhLOpO5CH4I7FQTK9AGjFMLWOVl+SqYiSv1fDboBMNGpPUpV1R+NPk84lMfK5O0bMmcgGU+KglUEK3aciTRKrf2q1ivNnt+k7yxSlGy9ZEehktOrkSzbSHFdI1KCMaMv6ndw6UhPHFlNaa5ZVl2vJveXx1J6dhrP/K8uQR1pIg6N6ZyqefkBEYGyU7/29jnjoB2kywTjGH/RTT0/H2sIXWG3puvoDm66SR7vXnvtFZs+Z84cHHvssQB6t2ftYERKomqA+yFIxkVmaN1A70qKdRhhSzNa5lwsCxI4B3F16T6PV6xprxzbvFK12ACkVw9vbVJ2Ak7MZDNGwvQ0VSWoyRZR6TfBuSJy2qLAkySrrIhRNi9brQQBRKk9MtUMAiOA58VCvI2HSflZqUQLFULyisrBROrP0jxVTbswZnx/5HrimhjBOai69QWFIpxMBm59TkYyVBp0XUF2ryNRmn8zsh042/cGygtvQ3afoyGKBWVBkZHmreUSaL5eRj29HIjrgWZ8cITGhw9IkGwl4A4KJVDl7cSCUL5XJICVfIRFv4KEccbBlZaVOjQWcbJhXzd63Xp5wTiWzT7NkCwn45oI1MeXnAIehHByXv8Tql7QRKGbywvReUmJ7lk7a9asHu7U4EZKomqAc45Nrrp1oHcjxTqOeEk+M/3shIrscDUoxQTiipAQ5e4d/Ps+0GEbmP8FZ9ImoVRICNU9tR25rPBLIJkMBGCqpAgAwagy05SEylgchIEZGCucpQEwtc/xqjsrqmRrnBTMOiwndINEq5ekF1RFqbodubAqqToSmIclH87wDHgQws3IdiWaEKwryB1wcp+u33/qLvAWmTpmzatU+q5O+pNxDpGJejQS5aMHQHV0iAiPJlHEofBb2o09gZPzTNWdYBxM9SDlQQjH8cwyNsKSH49MqenViDcr+RCJhvD2tRQq93e9vomX3dLDM5ViXURKompg0qVpCi9F/8AMAJyBuPWSTKiGw1rsLUpR3zNCqTSdBJSmiSL8ZImJAIhySQqKg4TnkmpsbAwQtXg7DMCDgqyiotxEsYQSgnO/ZKXpLJKjSA1RUTFDiFwPSM6r5k9aEmhUGHhqZ2lLU2WTpqq2BlYap5Y+iutp1jJBoQiHeTL63NqO4TPPQoquoTT/ZogwQH7mWca0lNY1mDSijqCKclFWaJZLhhzZhqhQeiPd846VfGmIyTk458gOHxJL8dlkmnpuRW87HoQgrHoEyu4iofVSdhrRaKWsBxw9TUs7Pjj/ODBlqdBa7h+dlN7fNV3HYMftt9+O3/72t1iyZAmeeeYZTJo0CTfccAMmT56MQw89tN/3JyVRKVIMIOwnXm/3wxG+skBOD4LqJAQAqBSEE+qA1jfICrj2VunrZPtJGW2VdndWESZFnIhnRX84k1VcAAhX7VpCHzywok1VCBQQRbZi+2kRplhJeZWUXkw4bi0vODPpHj2g2dVU+q/d47LqOU6IipPQ0ZCQc7h1uSi9mgLhiw8CkFFA3tpkri9DqF3P+FGBMxk5NQUMHoAAXEVYTW9FiwgThyJoaYdgnqzcU98vpRRuLqscx8sIC65MtyrbAq2B0t5QMWF4oroPQRTNSsK+HuxrLTaP2k/qUKy49ocy/ee54JyD+/3XWH4ghOVrG2666SZcfPHFOPPMM3H55ZeDqd/88OHDccMNN6QkKkWK9RFxTVCk/zHpPASROFhFoKiXA8nXy0q4xhUyyqRay8TsAiwrAqK9nFSDYOGXgHbHRKWI3pdiAUKbgur1qHYrMSIV+mZ/Iu8nBvB4ixmRJFj2sWsCZS1vp+FsshQtkyhl96MGthXRJxZFn5LRChtaF6O9izQab/oJwlIZG57185rLrqsInr8/ZvRK64cis8/R0iXfSs3Wff1HAIC8FcEr3HEZiKe+02IBtL4h9t0zPwDNuCCUg3kugvYSwkLRENrs8AZDjpycNII09gbq+nCURsh2oLdTeEm7Az091lw+iGumbOF60jbB8VxkRzSgtKoZYT8RpxRx/PKXv8Tvf/97zJw5E1deGZle77TTTvjRj340IPuUkqgUKQYIbXMvhWAcw064DMW//xqiWIA7YTOZbnMc00ssVmkHGQUg9UNB3AzCT5ZEDV4VoWHNq6LBQrWp4EGIzBCVpjNZPvUmm5ORgmreTKqnGcnGrQKSMOaeQEzbRJzENM7jVX06SpZI88UIlJWCiUUOqqTu7M+rmTWa/5NO2H4IeC6gBtv2u68GyeYw4tQr0TJn7a3Wa71tFpxsFnXfOr9X1+s/cw+8Xb8emxb8+z6UHroprmOrAVby4VoGqsZsFTDGmo6XAfMDOEGIclMrSo2FSOAdBOCFEJxZJCqpZQpCuE48guWoIS05rwhCOGq7yerAapWAyYgN90PwXBTR1GRv05/PQ0tLC/Cruzo4m70DSf7WrE8fcQa3O/+SJUuw4447VkzPZrMoFPrRysNCSqJSpBggaGPBljkXI7PBKNCG4QCk2JoXWuRAFfoyipSrl0SnVACpkwRKOB54qd2sT1h+QDwITRoD0IJaTVQkcaFabxX4JhIFzuItXixhOC8XDQkyN3NdhceYTBUCUWqOUpmWpGq61shY/fGqkafYXytlmIwu2G08bBjSlRAR1+qjF50fDq4Gz7CtDS6A0oJb4G00qWK5tQHtf5mNzNiJIG6mVx3Xw1cWVBCo0vyb5TVYN9R4eYn2FhUprcTQ4y5F29xLQaj0H9P6PEIdc03yIAR1HLg5D5m6PHyvHYIJUE9eW5qolFY1I7fBMFBVpWeD+6ERqgOSPIUFmV7UZI16sjiCoUo7F+UFaC8PtU5jyeBYIvNiOeYh1Z/oTcfywYrJkyfj5ZdfxqRJ8d/kww8/jG222WZA9iklUSlSDAB0A2vAsgHgsr8YW7UMvNQO6uWM8SXJ1YNkc+DtrfDGbyLnbV0RI05GR2QLdhVoxq3wRJKDhGPaggi7Gk7rWFT0SDqOxxsZx5sOs3i6z1gmsEiobtsXJFJ2dnrFRsXnlu7JFhlrjx/blVrPU+1vchtA1GuN+SH81gIE5xj2zfMq5l8b0P6X2dLR3svJFjt+CYU7LuuVhrju9jNi/2vBOHEzMoVcVlFH10Pd10+tuo6mmy8AzbhSO1Rsl6k7lRLWaeOgUIyl17yhdSiubEUmk5U9S5mAk8uAOhRBS6Gq6XEQBCAlLQRX15kWiVukWZvWJsXjgnGwpF+UIlJ6miZj3A9NWjDFwODcc8/FaaedhlKpBCEEnnvuOdxxxx2YPXs2/vCHPwzIPqUkKkWKAYST89Bw9CyUHroJrHkV0LwKYXtJkYGSrD7KSCNNzhmcERvKqEyxgODjd2WESJEbTZBs8XXQ0m6e1HXkSw8kTs6LPKFCX3o96f85g/BZlG7TLuaA2l4gPZUsA0xb8yIHSqvFC1CRkrOrrLR7dTVtCwsCOMjEBkZbZK4dqzkQq7qL/a0SyUqmfKC3TaV5Y0jLaL1tFhqOntXFb7N/0H731dKRvlhAuGwJMhOmSJLdRx5X+a+egcIdlwHUAaUOeMsqiCAAbRgu09DloiHMrORj6HGXYvjJV6BlzsXy+yv5CAslODkPbl1eeZQp5/CSD0c5xnsN9SiubJVtWXwmU3mAcR7njCNTHx2jNtsEpF6JwvIWgyI9alknl1VEqjLqqUEpNdcEVy7/lFJwROL1oFDCstmnAUC/R6QopbGoWU/XMZhx3HHHIQxDnHfeeWhvb8d3vvMdbLTRRrjxxhvx7W9/e0D2KSVRgxQfnH8cqOdiwiW/79ZyH19yCqjnIlOfw6gzruujvUvREdrmXmpEq9r0kRdaAQB+UxucnIfMiBEIW5qVx00IovrP0WEbwP/fYtA66QYsOENYUMJfFW0yYlsmBzWHeXC8SFdlky2ahyRCQSDtBKiq4NPEzCJoOmIQkZGoXJ1m3NhTfrWIktzf6qm7ZLSJJpZlCEzJuh4c7WWTiPtvdUyg9HQTkVCl9m59Dm6+8+72/YnmWy6SEZ7QR/0RF6H1tlmSfOtCgT6C7Hf3KTIbRNcKb22S0bBsXqag1XfSMudiBIUisiMaTFQ0LJVl30J1XsNSGYAkP1wResFlNDEoyGvKzcnrOSwGoA4FY4EhXHZaz8moaSzuDRWWZDQrhG/0gTpaxf3AsjlwDLngfiAd1vVvCJDR1iDaFitVmnz2B9b3dF4Yhpg7dy4OOeQQnHTSSVi5ciU45xg9evSA7ldKotZivHfWd7Dpz+eZ/5f86CgpuPRDZIbWY+NuEqgPLzpB3nzaq6c1UvQPdDVRUCiCBSGGAghaWpAZUh/pO/TTuqoWIw6Fk80h/GSJSb/pyI6uTtM3/rBUVhEsNzao6L9M61NKFE62JJsN63YyKqJg+ptZDtG6xFwPPmGhhLBUhqNawWiSkyRMdgRKkzvqOPHUHOdwkDEmmcmrUxO/pIGmPkc6jWfvs54/JhpOkKcojRhEx+BlQK1trk0YdsJlsf+TUbKmmy/A8JOv6PXtBoWiacL+2c/PwoZn/VyK2gGZ4lNRSN2H0M1nY9FCIPLkMtek9V2WVjcjKJQQlEJQh4BQAsEEAA7BBZz6jEm1slKgolIRweWJbcn1ChCHGH0cINN6+ruOkeqEJ5QhWaxyX/XyH/30pG4/xK4J1ncS5bouTj31VLzxxhsAgFGjRg3wHkmkJGotxTs/OByCcbx18mFw8x42u/FO8CBAUCipdEPQ+UoSCLTg0stUbfaZon9QWiUjTG0ffwbBOVb96lzkx2yoKtiUbmnVSqkZ4Rz5DUfAGTEaJFcP9ukH8qmfOjKl19IuI0oZ1xAznSKxYYiVGuT0zTRsL4J6kdcNR2DSfswP4Oaz8npjDI6KMGnSZrdWsdNwSSJjf65JErPSeob8sbKZpvdTQ+ueagnPAVRE9zp6UKimkUpW9rn5wdN4fNns00AdCrcuh6abLzC/dW9ofQXx6gk2+ME1+PTq0wFIwvLp1acjO7xB6oXKNBYBNSLsQsl83/p6cDIZhMUygkLJzFf8rBHlxoJJzRFKzHZEIMB8hqBQhpvPqHURUCtWqVN2knRFIA6RUTtb76QeQgEY8XhEtC1HfPv6tFJgWrQ+0M2I11fssssuWLx4cYWwfCCRkqhewuKZM0Acgh3ueaRX1heWfFOevtnNfwMAbHbjnQCAN477aixC1RnePHGmKe/V4ky/ZWDKQdd3FO64TBGRENTLoLSqGS3vL4ebzyIsyhSH4Fa0iFJkJkyRN/j2FoA68BubQNuLxtU5LPkQKqVnR69oxoWb8yAcqeXwW9uRqcvBzXvyCVuVjtsCbQQ6XRYNNo5FurU4XShjShMhslJyxKEmqqSXSZaV6/Ulm74SKgcnfWOyyVOSVNUiQPagbaJfVdOICRsGtX1KZcSNZgbH7XHFtT8EAIRFH2PO+yVW/uIc85kmU2uCppsvQFgqY8x5v6zYptHJWVE7bjRMLKbPc3IeWBCgtKol5kpeXClT2dKnS0DwiAxxJiCYQLnFR1gKkRuRl9t1SCw6xHwG6lCZGvQck77T/fZMxNGPR8b0X47ooZQHQSROV9cP86WPmE4jEkZBnPIan9vugJBeMNskgzcSBQDf//73cc455+Cjjz7C9OnTUV9fH/t8u+226/d9Ghx3iUGAHe9bgFcOPxCvHXkwps19cI3W9eaJM+VNJ+OClQRePeIgbHvHQ3j1iIMAAGGxe1GksBggLAZwPAfUc9OegAOE4v2/QNDajnJjG4qrmqUexGeYfO3t+OSyU5EdPgSOl0G5qRXMD5HfYBhy48YAlIIt/wB+Y5NJxxGVbjAkwTIG1INGaVWz0Xfo9ASlFNRz4dhl3dayeuDTRA9QepggBLWE3NpLR4ttATVQei4oXHAapQR1ZExHGiinsahSaJepW4SOs4iw6QcKeSzWgG2RI61rSWqyKgTkiAhUNdsDe9AdDERKD+y6JcmoM67Dimt/aEjxmqBt7qXwhjXEUoQrf3EOvKH18IY1yFRe4FcQJztSqL93b2g9CstWwW8pGC0dC0IILqNNbs4FZwLUIWA+g2ByOguUZi0g8Oo9ee1wAUIJHM8xESg9nfkMjqcKBBg3MSstTtdkiyKunQOqp7v09S5M8QMDcTiE17/XxvqezgOAb33rWwCAM86I7DwIIRBCgBBiHMz7E2v/HWItxTP77IldFz5h/l88cwbcvBt7iuoJ3jxxJsotZXj1GTmIMA4n4+C1Iw9WT2XyqeuZffaEYAK7PfHPDtf39qnfQFjSUQSBrefcuUb7l6JnKN73cwSNjWj94FNpLNhURHZo1gww4y+6Cctmn4ZMfQ7tK5qQHT4E2ZHDAOqg8J+XTGRI9wkzxIVL7ZMW2wrGIUqqIXDJlykLJsWzug8ZdSSRYqplhY4YsZJvSJFJ71ktNjSh0PqooFCKWRMYUTnzgZIvn9zVAKQFwTwIIZRWSxMiu2Iw2aIl1nDYiirZnkEVbToSaTxddRUfLJ0YkbJ9pWxx/GAgUdXSllJUzQ2x6i7a5l4K6mZAKDVGnjr6RD35/YXtRbgAWFmmYUtNrQjVNZGpz0V9Cq399FvbUW5qM/ckJ+OAUALmczDfh+NRMCb1TEEpRLm5DG+IB+LIFF+xUV73jkel8JwJsx197xVcRq84QggmIDwHCCINHE/sk/7L1UOAhq7SlNeEgGDMeFgBUVVoiv7DkiVLBnoXKrD23yHWQjy95x7Ij8jhuQP2wc7zFwKQkaiXv/5lEIdi8cwZ2PG+BT1at2AcVN0wuGpFQT0H8OXNgTGYcHW5pfNw8uY3/RUvf/3LEEwgLIYmqpWif8EKrSitakZQKKJ9lUylOhkZidLQJdNtPz5GEotsDmHTahRXNQOIdBhhoQQWhGCqwsmtlymOUPnu2A1ZaSYT6ZQohaPSVE4mAxYECBRx4oyb9QKV+iAKGNNB7RStyZStD6F25Z/qc0YcGkvJhX7JaJ5YEILSeF88AGbw0gSOBXHvK454OTtxovJ0VIlOaUivIItAschTyBBCRTSr+RKtrRh/0U3m/SeXnQoehBWi50+vPh1+SzvCko/J195ec10tcy4G9VyUVzfDVZYCOrJleyaFpTJ4EKK4qllq85TRJbHOHbW+e7uQIFPvmQdCwWX0qe3TArLDsqAqwgTIe56TiSIwQqXrdPSJBXKdTsYB40wK0rkwpIszDijpEvUiawVNphwnbuDJfEn4ZVFGZAzqZFyLtJO0Om+AsDZpoTRSEtUF/PfoQ8AZx7S5D+LFQ/bH8EnDwAIGvxCPle9wzyN48ZD94Q3J4LUjDwYLOLa/6+FubUswoZ7M9FOyfNJiAQf35ZOQm6NgAYdf8PH0nnt0Go1iPrfeD27b/8GItrmXotzYitKqlhhRCArVSbCTceE11IEXC+BBiOzwIeBBaHRNukKouKoNhBJk1I3fjgxp2ETDzWVlKblKCerIgNZZlJuUNkVV4OnBxnGyxoBQl6bLSAdL9DYLEADI1MmBl5V8cM7hwDWDrNwnHnsPlaqzm89yWP+r/edBYErS7RL16Fi5IUmaOCaNFO1zYmtqYmaLfiAH05wXb6y8FuOTy041RGr8RTfhw4tOiH1uKnMRvyaSaLr5Ajg5z0SUaMZF622zYmlPJ5NBWCqjsHw1BONo+WCliZ67uUjIPWSjDREWfbh5D8xo9QLkNhgGwRiCQgntK4vmoZEFHO0ri8gO9cC5iMkWzDxq16UmSoArMkUojwiUqeyLoMmU1kwB0UOqSYErDZW+nr2GOhCHxgiqjbdOPqzmeewLUIfG9IM9Xcdgxm233dbh50cffXQ/7UmElER1gteOPFhqiUDx2pEHw81LbUe5uYywGFak9QCYMLPO5XcHLGDyqUo9YXEmwIuhSeUBUZRAeqewTomU/QTPfPSKbitFbXx8ySnI1Ocw+kc3om3upWhftgrtnzWBlcqWwJWBOSQWFdTeX2HJR13OM6abWj+i/aB4EKDcKKNZggsEhTJYKTDl3Ny6PmSExYml3col39gf6AID6mVMesKkBxU50945garwswXiehm7R19c8M2s1KCq6stkzDp1Ow4HUTWe9uYxFaienN/W2SRTWPr4bNdqvX17X8z7pOmnpSUDAL9VttMZdUbv9qTrCyR1YgAw8bJbzPslPzoKYaGIzW/6a6fr0pWXQaEIN5c1pF+Tbx6E8IMQ7Sua0PbxKhQbSwjaAlCPwgfgFwJkh2bhMe383o5yUyuGTh6HoFAyBCUsFLHi1U9RbpEpPMeLk9VSYynSRwU89jlnAq7nmHuiUH9NBSDVpEx932pRFjC1TgpALhcUyiZqpaOPuoLPzXmY+NPoPCaxpSr4SdF/+OEPfxj7PwgCtLe3w/M81NXVDQiJGty0tB8gQ8PUiBa5z1Bu8cFVhCj5xCOFtGowcghe/vqXu7ytV484yCrvFeYmYRMozkQssgTIp7Ind9290/VzLsz+vX3qN7q8Xym6B016mm6+AEFLe0xjZCOZVmVBiLDkw28poH35arR+8Cn81oKc1tqunJzlDT4oheZa0dFK+T7eLw6Q5IH7gankC0s+gvaSLEFXEa6YEFhHgJwozcZKZWOcyKyokV2tF4ncA+sVqmonFiNgcn4W7V/FPlv6KBV1MtMTLz1dryv2UtvpKPpSDfq42+Ze2q3lBgLJVCgA46r9wfnHdevYeRCl6bRZqy4i4EGIsOjLlGChiLAYyqo4j6prRZj9MdHJIEC5qc1Mp5SClcpoW9aCcosfSRcUYdL/29DRc56411IrrSYMoYo0dfb8MZuDxH1bv7dThnbhw9oCQonp59fzV+X5HUxobGyMvdra2vDWW2/hi1/8Iu64444B2adBS6Jmz54NQgjOPPNMM00IgVmzZmH8+PHI5/PYa6+98Prrr6/RdjiTxClUIsf2lUX4Bd9oody8i3/vv7eZf6eHHoPfFqDcUjbVI4tnzqi1+optCS5QbpFRLv1igaxQ0SQIkE98JuXH4mXBSez00GMAAKp+QHKdKZHqC7x54kwUlq9CaVULiqua0fbxZyg3tZooDAA4GWpu8P89+hC8eeJMAPLpvHnJCpSailj52of49KX30bJkGcpNbWYdQaGEcoscwPR3Tigxg1hQ8BEWg9igyko+Sqta0b6iGcUVjSiuaELbRyvR8sEKlBoLYKUArFRWhIjBbylK48NCGUyRrFJjAUGhjKBQRlgMoKvuuM8UuQstEiiJY1j0pbC8CsnSA795+ZJoySiA3aCYG3G7DV10AcAQtYi0hTEyWUEqq0SybEQVjYOjV1pYKMUiT4DU1737w28jLJWx6c/ndSkKBchzqb8/fd35LQUUPl6Jpnc+RvvyVSitakbjktVgPoNX7yE3NIuwGJrvIyyGhjQXV7aifWURze9+jNKqZnDO0fpRI5qWNpttEofCyVA4GQdOxlHriq5tQN3vAgbOBVjAELT58NsCoynUD5fMVxV6QfQdy/snM0RNzl+tYpPHqlLXNuhI6Zq+1jVsvvnmuPLKKyuiVP2FQXlGn3/+edx8880VnhBXX301rr/+evzqV7/C888/j7Fjx2L//fdHa2trj7cVtPloWtqM9pVFtK8qqrJrVbI7xEN+RA7EIXh6zz3MMtMfeNQIJruD7e962ESgAPnj3+mhx7Dz/IXY5dFF2Hn+whiR0gLLsBRWPKUlsdNDjyEzJBLKujkvNYvrA+ibetBeRGlVC0qrmmXVm/rOgkIJhRXtpiggKIUot5Tx1smHRdEk9RTtDcmoKBFTKbAQfmvJpECYz1BuLiMoSAfnoBSi2FgCC6RglzhUEplAbiNQxJsF8qEgLIVGhMv8EGExQFAK4Rd8lFvK8AuSJOnUtE1c9LGygMnlCmUzb1hSvc+0VYKV4uOMq+ay8ciSJj7G6FOlDjURA6ToV79iJInJ5fS88XRiJYGq1qBZQ2qupAC/WppsbcTGs+dUTFvyo6MgGO+Wn9zbp34DYdE30Undm67l/WVofv9TFFcVUGpqB2ccpcYS2la0G3E4cQi4L2UObt6FN7Qefms7mpe2oNxSRuPbn2H5Sx9hxSsfymvU16aq8TSejvgDkQ6KKMsDrZESTBgyxXxmpfV4LJIVWR8I88CpdVM6emuIuM9MlFZef8GgKixY3+E4Dj755JMB2fag00S1tbXhyCOPxO9//3tcdlnkxCuEwA033IALL7wQhx0mBX+33norxowZg3nz5uGUU07p1nae3HNf1EHm6v22AI6n+ob5DI7n4Ok994Cbd42fCYCYNmmnhx7Di4fsDwBVQ9S1oIXoLxy0b1URuI6A/Xv/vdUNRN4E9nrhmS6tn6vyXznYCLxy+IHdFr+n6BjSYbkENxf1+CJMeyr5ZjDQ18vimTMQFMpwPAdOhqJclEQmO9RT1ZkZtH200pAg4sjrUl8ffkHqocotvhTcMo7AZwhLzWA+hzckY/YrOzQL4pFYWF8wAb9Ugt8WNxkkDkXgyDYcYTEEdTJmENIPCXp/9MCnI7cA4CgximlLQ6MGwhwwzYIBWSWl0yh6utZ06fml3kqnXlQqU6UX9bo1CUqSNO1DZWtnahEpXcEov681N6scCHRUfZfEa0cejNyI+qh/XcZVAn2KoKUdrR81YvXbjeBMID8iB6AVy5Y0AQCCgo+hE4bK94o8t3zUimLj/wAoHVIpul4EEyr9R2KRIRuEEkAV2FCHmodW6lEZTdeSByW10O91pJ35zJC05D3Uvk5NwYIyPRCBgJOR1+xAVeB1hLQ6D7j//vtj/wshsGzZMvzqV7/C7rt3LmnpC3SJRGlS0h389re/7ZPGgKeddhoOPvhg7LfffjEStWTJEixfvhwzZkSps2w2iz333BNPP/10t0mUYAKM6fAwh2BEphuYgOM5hiw9uevusZJbW2g+/YFHAcCQqSSe3nMPEEem/HaevxAvHLQvHM/BjvctMCm4Wtjl0UXdOh55TByUEvhF+bS/zW0PdHsd6yt0gcHWc+6vOc+7P/y2qf7RwmzuMwSlEE6GIiyF8AtShMt9bq4hN++aa0jrkLRLMwAUVrTKVIUSymY8YpycAfn0znzZDsPJOCi3+GogYcplWRYraB2f40Tl4wDAODMRKgBwHFkpRVTpuXAI/IKvNCiSULn5RF8+Rwp9NUGUEVU7ihPExMHVnJdtvZQtkNdEyB4QKajcl5KvzBUdY1WQXB9nHESRJ+08zZXmyslEJqCOIqxaRE0zLjLKPmJdxZsnzoSbz0hDVMbBc1nZm7M+D+6HaH7/U7R+0mb8mUrqr88FhrgUq1cV4bcF8IZk5L2SyevUbwvM9UQdAtTrqE50nVUzOnU8B34hQCbnqgeCuHZJkyWurl3Ho/ALgfwMAGECzPKgimtWOUJfVES/bGJFqNwn18usdanc1LEcmDlzZux/Qgg23HBD7LPPPrjuuusGZJ+6RKLuu+8+HH744cjnu3ZDmTdvHtra2nqdRN1555146aWX8Pzzz1d8tnz5cgDAmDFjYtPHjBmDpUuX1lxnuVxGuRyVmre0tAAAktV1hArlpyMQFqOnJy0iZwE3g8xTX/wSdv/Xk/jnLruBUIL8iBxe/vqXYy1hHt9pVzgZCs9KsXVGnNYUrZ+0mQhG0p4hRW1o/6+OKhq1DYZ+2iYOkfqhUqgGF3mzj0hGfABpXykjHtmhHvyCJBylxpJlShgJaHXFpmBcFjmwqHopVOlBA0dqVPSAYhuvalIiNXLcpE2CtgB+wZfrHJEz29URL72cTg9rZ2kNNx9FfWTkwTHb0wQGiKwU5H5HbY7k+eMxkminxgklynGaRgOyalZL7IiWgtZA6TSgjmoRa1/0/nI/kCallsfVuowoYqcq3FoL5vzxIITfFiDQJEUIUELk9+uHyFCKDAVYyFFsKoMSnX7jEMwyQlXRUZlejqdHNQETXEifJ0ZkJDPnmmtKP2BE3lBy/zKqCppSAqauQW2+SR0KQTk4oqhq8puMC8/lPgqtmVLpvbUJxHFi/Sp7uo7BjGrdBwYaXU7n/eIXv+gyKfrrX7smYuwOPvzwQ/zwhz/EggULkMvlas5HSDw8rO3ga2H27Nm45JJLKqazgIH7UdWGcCwtSOLpKFCDCweNDSacCTiUwC8E5slJQ1ai6Kac/XNhCy4HUSfjIGjz8eIh+5toWYrqePnrXwbzOdy8/K7sCkpNql45/EAzv0xTcIAB7auK1o1f6ToUybG/87Ao9UnEISg1llBqLIE4JOZoriuKtP4EkFGBsBiCKmKsCbJ9fXIWRcH0togV7QpKoYkuZJVAOCgEZp1BW2AiAvagxgIOtEWDDHFoJNr1mbQCgYz8EBWpMsdhOUrb+6KJEwvkYAroykP79xYRxiAIDbk0x2v9/mLpugxiBM1E0RJeU9q1PVOXB3EoiquaMbzKdbEuodxSNsQf0Olo+ZDFAvld5kbkzEOijpByIZDJOoDP4XgqrZYg1BQcnEX3X/3w5uZk818WMJRbuPk9UM+RDyAqemuuFUWMYvdXLgCVTiZcACz6nXDoRsXSY8+N+YDpnonRfukCxpjvVBcJ9MKd9uh8phS9gksvvRQ/+tGPUFdXF5teLBZxzTXX4OKLL+73ferSVbJo0SKMHDmyyyt9+OGHsdFGG/V4p6rhxRdfxIoVKzB9+nS4rgvXdfHEE0/gF7/4BVzXNREoHZHSWLFiRUV0ysb555+P5uZm8/rwww8BRD8m22ZAQz89L9xuZyMEZz43T/Qs4Fi04y7Y64VnlCmmjEA89cUvmXUkB4ZaKb/ehH0D0tUuKWrjhYP2NU/O1CF45fADLdF//IkoLEoyUlTRo6AQoNRYQuHTAvw23xKAVxYA2NP0evVAFavSVCQsaAuMTxmgIyjcPInbZCYo+Ci3+ErMK1OLcl3cei/TfX6bbyI+Mp0SRb+qQV/rdrGD7nsm94UbgqQrTzUp4io9FwnXtUhY/tZYwE3BRFzfJMxvTrCoebFZH+OmejDaDyFtGdTxA4javNhCeWs7Yalc4ZK+ruDVIw7Cf48+BG8c91WTNi4qQu4XZOQpUKlnwQTcnIvsUA+Zepmya2vz4ZDImkDrRW0iTR0CFvKK612fc1nUwI3ezlfb1NcDtwpz9H1KPthqXyiRWKe2V4j/1Q+vtkUMtwhj5XLcEPW1rcdoWp0HXHLJJWhra6uY3t7eXjUY0h/o0hndc8894bpd16B/8YtfRDab7fFOVcO+++6LV199FS+//LJ57bTTTjjyyCPx8ssvY9NNN8XYsWPx6KNRZMX3fTzxxBPYbbfdaq43m81i6NChsRcAiIDFiFTy7z+mfl4+6TgEjie1GCxgRji89+J/S5KlBpOwFGL3fz1ptisJltai8H6JCBmxZcCMjUKK6njugH0MSdAiWCCKhNgRkO3vehgsYGhf2Y5yc9kQFh0VMmRDaYaq6RrsiBMAQ5g0mdDvDeEItOGlFMAGpUh0rcXduqxbVtCFhsyXW2SaUUfAkr5j9n4QFVlLDjp21ML28eFKt6KJmlmfqq4Ki9priqtjihMn7cyvB1PbTFHDJmf2g4AmaTp6YSwgVKomWl5U6HGSTs467cFK/qDwiuoOwqLUQ2pSyXyG5qXNaFragrZP2ox9iiSk8nopNcmq0qaV7SgyDo8SOG7t4YM4FEwIsFBHUHnsfqN/G9FDKDPWBvqlCb4m4PpaNo2J1b0zKEX2CjbsdDDnEfFOfm7/TU7rCvZ5oeOOEb2FlETVziy98sor3Qr09CZ6VJ3HOcc777yDFStWVOQo99ijb0KbDQ0NmDZtWmxafX09NthgAzP9zDPPxBVXXIHNN98cm2++Oa644grU1dXhO9/5Tvc36FBQQUA9J1Zaq4mTNnrTRnaEUkAJz3Vs2Ah1IVMjj++0K/Z64Rn8c5fdsMe/n45Fhv69/949Eot3B1rTpcXO3blRrG/Yef5CPHfAPibyASuNJphApj7+kKArg9y8qyrZqKXliHQnYSmUT+6qWsxsw496guloif1dATLtqyM9+nPH1nAoshcU/JjwHAAYmLRFABAUAikMp0qgrh4A7Io9m6TpfdcpFZ3e4yyKCjBHCtFNQ1jPAXhUuWq0SlwoLUt0PjVx0scERBWtenl7ANbpPJ3adBLu1dRzjEBYE6roPVdC80i0zFgA4TnG1kC7ueu+e9opfl2B7uupCx1KjSU0FgI47QHqG0vIDvPg5mSxQ7mljGJTGW2hEt8TwKMEnmqtYqfE5HUh3+vvi4nI4FdDV+XZ91WzvBpPZGGBrKATjEVaJVAAkQ6OsaiAQq8j2o7UzZltZFQVH2ShUFITpcF8jvZiO9476ztdsohI03l9jxEjRoAQAkIItthiixiRYoyhra0N3/ve9wZk37pNop599ll85zvfwdKlSyFEfBAmhIB10xm4N3HeeeehWCzi+9//PhobG7HLLrtgwYIFaGho6Pa6vLoM8nkPmZyLlo9aYk8oKlZgSBJhFI5Hsd/rkeD9H1M/b97bgx0A7PHvp/HIFp9DJidPv6yq6vsnBP10JzhXEYGURHUEXTE5/YFH8cJB+wKQA3ig9Eu2PcRODz2GRTvuIgmK+l512kATMfkUzQFQ7L343wAk+Sorc9agJO0EgjapG9GVTkFbYES9uRE5gEXuzlpQDWiBOKsgUBrS90het5p8CCY1Qdo4kXFFZlSfxgpxt4OYZw+AmFbFjlDZg2M1PStRyxmiCvngQanUF+oIoI7YCiZMFZ4e9HR0K/aEndBFEav0Pb79iAzCR0SiPGUJEYRw4KK4qhn+tT/E6B/dWOUqGZzQ8oGgLUDzioIhRiXG0aKKHDxKwISAQwjyDoFDov+TkAS20lWcCUlKHA+x65Kb5aIqu2pREkOqoymKSCFGjvV168CJEX89HnHwWNSyYj/N9UuxtsoctOv4mq5jMOKGG26AEALHH388LrnkEgwbNsx85nkeNtlkE+y6664Dsm/dJlHf+973sNNOO+HBBx/EuHHjOhRt9zUef/zx2P+EEMyaNQuzZs1a43W7eUc2wvS5qTbSYD4HYwIHvLsY8zfbEYKFoE4W/5j6eQjOsf8bL1YKVtWNZOF2O2Of/zwnS87tSEEP+ux1F1/+30t4ZIvPRc0802bEnULf2HXl5OKZM8yTdSbn4rkD9onc63MugkKATH1GphAskbfWfwiHYv83XjTrL7f48As+Vn9agENgBijHlQ7kYTEEVw8rAecgLWXp+2SE2gIiYMYnSQ9U0hspIkF2ew2pBxJmXhnd4kC9ai1hlZZrfx9NRLSpIgs4nAw11XbJ2icdpdDrEpRH5en6ureMEk3DYJ8BngMKjiCIIlsxMsc4GEO8WIMzI07mTIAo2weqGtXa+6SPK7I6EKa/GgCwUlTdKJh0ZK8bPbyLV8zggE6Lta9qR5EJSZJcCidghjxpwpSzoo8AQAkBF7Jaz0F0XekKPrMNISNXXAhApXbN56GwqvlILIJPHIJaWSf7oUE/dCSjl/a8dl88m8BRdHy/1fN+cP5xKDe1VnV8/8fUz0s5Rz8VBq3PPlHHHHMMAGDy5MnYbbfdkMlkBniPInSbRL399tv461//iilTpvTF/qw1yNRlQB2KXf/1RIfzHfDuYtw/bhqoEkUSh+LRrafH5onKuCWx+sfUz5tqFkKJSf/8c5fd4LcF2O/153Hv2KkAgK8tj9rWzNtwG/hc4NhVb/T4uL78v5d6vOz6CK1V06193JwrEwo+Q6beMwQKAL70zFNYuN3OKLf48OozJlVm44B3F8f+b13WhmLBh8+F1JmoccAmPRoOkWJd5nPVcd6aJxEBts0E9SBlUiwBg+Ck4slcV0TZaWpNoIzI10qdCC6roAQXplJO+zcBiPlCaQsF+0lfMGLMX4myQNDGilDVgXYEOJaq4SIWnQIAYVVUac8iXTEb67Fm9DW2hUmlvxRxpOUC4RzBOpbSk4LyMlavKpprTp9rjxIAUdRJn3fiEGS0kL/MwIQAQklJNHzt31Tl2ZonMhfV9ompyk77gcDxqIk+AZDsDAB1hEnzATLVR2gloSfmd6L9wBxDwG3PKTWTIe4tH6yCV+8hKIWxqPP7Pz4GpVXNqBuVl/eBcHD3oxtM2HPPPc37YrGIIIhb9WhNc3+i2yRql112wTvvvLPOk6id//5wl74QTXbCYggWclDCAbhGbK5vBGYAYNETs+1FZetC5m+2IxxCkG3w8Pfx26LEuLk5rQmBStFzaD2Qhk6NJf2/4pEOYptyxyJQGmExNHoTOTbIwYuq6jkWVj4xy/QVAXVc0+/ONhC0eyrGyrgDZlKAzIoAaZ2f9JPyZG8/vwopc4i5XnU0SOuRAEW4vLhBon3uBBPwfd987uZdM3DaqTVe5MapWi+rbSOoQ8Ec29vIKn8POGATI5VGlBGpuLC4liM190OrnD6E47mg6qm38aafYMSpV1YsMxjBGZeVo1wg71CZdlPnxsvQ2HWXTLkJxkFJFK1CyE1USUNHocz2RDyKpUGqRI+4zyBUqldGSWHSgdQhUdTVoksmhWen9jKWv5QXrT+mFWTxfTHpXcgosa6SzSm/tDdPnCn1WpYez6H9I4uQUeI1TecNbsLX3t6O8847D3fddRdWrVpV8flAyIm6RKL+85//mPenn346zjnnHCxfvhzbbrttRVgt2c9uXcfXlr+Ov4zeBk7I5Q0FugFnvP0EEA1WQFQpp1MaOizteBQsIKgbmZdPYD6DU+Lq6TDFQOCpL34JmSHRdW4/2doECojStkzPF0TTq6EQMv1gLQcydQ1xIVBu9U00IKlF0Zo2O1pDPaeC/Ojrygi3E9s3FaYZx1T9JfV5upkrh7oeM9G1bVct6qiP4LJ9ho4qaP8eAIYY2du3yZCOeIXF0KRrGGdRuxkuq8Z0yyUtxueJIxNMxNLwxqqECxClUbQjTrJprewfCERRNDfvQTfT9VsLVb/DwYjiyiJWtvnIO9SQHX0d1tJKSm2nTisJZBSF6SzCZF+7tQhULBWofkN6m9prSpN//ZkhUpY2UI+hxFFRKU3ALd/MapV8ckejBw99TSULG1o+aoFXnzH7vNsT/5QGzZZGp6+wPmuiNM4991wsWrQIv/nNb3D00Ufj17/+NT7++GP87ne/w5VXDswDTpdI1A477ABCSExIfvzxx5v3+rOBFpYPFBxCTKRIDoICXlkgk3B50E9RtmB2v9dlem3RjrtEdzFEVV6AJGp/HTO1aog8Rd/in7tIe4wMMlI/lNODPoNI3Ix1GlcwAoAb8XgSfx+/rSEBRSZiRIkJRbJFPC2SFPMmCZTUAQnT/kXropgfT0/Zfjlm8LF0JfoBwOiKGIy2SDt+Z5Cx9iNBXow5JoMDRXAUcZJRK16RGtTCcVswrkX4ZpBM9DyTWi5q7UcUYUhG36rBjtLp7YTFwETadBUi90OVggzRvqIJK9YBgfmiHXfBZ8vbTLRI33b0+xLjFfca+wEQUD5QFvHRhQ8ArOs5WqdDZBQq4Dra5cQeKPV0R0WqiPVgCSBWvakjqCbar4XmShso1yvdx3W6WRNxWxtVC+Y35UdVq/o6t9P3tu9fiv7BAw88gNtuuw177bUXjj/+eHzpS1/ClClTMGnSJMydOxdHHnlkv+9Tl0jUkiVL+no/Bi3mbbiNeW/feHwOUDWg6bJ0LZiUaQZZzvvo1tNBKDUaKV0KLM3m5JP+/eOmxW52KfoPe/z7aTy56+4m6qHBfGbanvxzl92gm57q79EmUH8fvy2+8smrmL/ZjtKUk3Gw1rKJPOlBRhMpDZs42dP1YGUPBraVAnWIbG2inuKBiEAZsbmy4zCO0kwTMg4W6Mo/ywKBq0hERrZwkYMoYtsnTtxLjVCu/Kn8SJNkO5CrdHYUPeNx7Yueag2c+txXGxA1QSOMWK1novSM0XJRSQ71+hwvbhIZL/gI4arQBw8Gf6ukhzfZHsznWO1zDFMRR58LZV1AY9eZhhaQ270SuSJQEdmP0nRJwq/vi9y61nVUCZAFE8n0X1QFXdkhggMxEToYixoa62uI6WbHsgLVUa2HOGTvyGrXReyYtZ2HygBUa8ll+/71Bwh1QOgatn1Zw+UHGqtXr8bkyZMBSP3T6tWrAUhvylNPPXVA9qlLsb1JkyaZ19KlS7HRRhvFpk2aNAkbbbRRhz3q1lUUWZTGS5Ic85SmftC6BD1MeDTpadqoU6goFWdC6axI1bLiFP0DPVhrL6hAm2AWQzyzz55gPjPaCUIp9vnPcwCA+8fFfc3a2nw1YAj4XJjopUby6d8e0OQgQ2JpEf3S0AMbcWhFRake+EzlXjKVokxhtYu4Xp/+q6NRNsnQg6rWVSW3J32IfLPNqEouMtTUFYya3Olp2qwz5npuXNCFSr+xKE1orR+AcbbWx2cPmtqgUUe7tKljssWMTAlJ8hkWivL8BCHePvUbGKxgPkdLwOBRGImAtjcAEEvtaZJPSTwKpe9L+jq209C1ouVMiNi9kguBgPOY3lPPx9Rn2qjTfgHRw0MyElsNyfS2Xi5eVMFjL1l4EV0v9sPTgII6vfMaxNh0003x/vvvAwC22WYb3HXXXQBkhGr48OEDsk/dTpDuvffehv3ZaG5uxt57790rOzWYcMLqNwGg6hMcEN3AdUSKq4hFUIrceTXBCsrMpGPCYhgNZoakpaGogYD9xBkRC2763JUaS+ppV3qFzd9sR/xj6ufx1WWvAZDpESCKOlW73ycHL/3ernbS3z9xSEzIa66PMBJNUyV6lf/bVhssdt0ZrVBip3QEKlk2DsAQn6SbuCZAQcFXBEjEXctZ5GSuHxJsTZWeRw5iSfF4tM96Pvu7iO935C2lodcTawWjHK+1w7rtfcVZZFEiW8qohs9NbeBMtmlaPHNG5Re5FuPRraejEDIUGTdknAlJojIqChVFRaPlbA+nam1wkkTKhr6GZdq6+vVtR2Tty7Ar9zu75YuZlqiIta9/7WiffF/tf0DKKnZ7on8cyTsFpb3zGsQ47rjj8MorrwCQLdt+85vfIJvN4qyzzsK55547IPvU7eq8Wrbrq1atQn19fa/s1LoGLgQog7kzaWM6AKaqqiJkbQ0YKXlaO6B1GED0NLv34n8bjZPGAe8uxiNbfA4A8JfRMt07b8NtYkRIDmKiSylaW2vSFdipkHhxQ2XFno3kNVgtrBBpqSLSow9CD17GXLHCO4obkgTEvXoE63ifkr5revsMlSk4+xiSImJbN6XFw1TtSa1ndMG5MeLkfgDq0JrbXFvx6NbTUW72Y/on+281cbhOMVcDTaSek4UP9nXek9NkX352ZZ/+X/tTadS6rmtd612B7jqQYu3BWWedZd7vvffeePPNN/HCCy9gs802w/bbbz8g+9RlEnXYYYcBkCLyY489NtYbjzGG//znPx32qFuXcdTKNzBvw21MSDw52Jkff7W7iR85TFPlycJ8ZqIKOmQu3/fhQaToFLJ6LZ5CsJ3pmc/x8Cbb48D3X0HQFsT0cvpJG4i0T8kBLfn9elQWLATcjhwIUCYqBhYb2hVdI4r2xBuvJgejCi8mJgXi9vERR0DalkfkJkm+9PYNqeLS1T9p+RFLndE4EdP6Fl3OnjwW7douGEOoptkRE9nSJrJLMMdsWVVUG2Bt7RUPrN6SGcs3yqHI1GcQFAaPRspvC9ASsBrRouj+oomTvhdF7a0s0qzgEAKPRmk/qa2KX9tA9e3VilzF5wOQEJybtTK78XH8e0xWoOnCgSQiHRVix6ajr3Wj8qZFztoA4jgg1az/u7mOwYogCDBjxgz87ne/wxZbbAEA2HjjjbHxxhsP6H51mURpm3UhBBoaGpDP581nnufhC1/4Ak466aTe38NBgu989l/z/t6xU5XmBXCIMDelgz+MrCIenCitIJjPYzcHqsYsfYPR4e7kNlL0P3QzVtmAWDYhDotSGAvl3aS/41IiApJ8So/rSCqFuFL/JP+XRpwWyVKkyn4yt8GsEvEkDAHsQEAs51NtPBBFS/W0JBmziQcQT7VF26wdEQIiDyub8NkCYqDSZZoroqYHTVvcbvyDFJkCIuNQ7bBuW1U4TuTd5ni215eq0mMc8OONbt28i1ePOAjb3vFQtVO91uDhTbZHsypY0UQnKmIgFVEpc11a1wJQ3ZXchhao2++ZqFYgQYwmsFqkq9rDYjLSJa/VCMnIYKV7eeL3aPmbVXu4dTLOWkWgAPSOpmkQa6IymQxee+21Ae2SUg1dJlFz5syBEAJCCPzyl7/sUT+69QXakkAPlh6tjBjowVabdSaho09J8XGKgcFuT/wTT+66uxWFkq1PgoJvnpgdkTQc1N9/NFDJ6fGqPD0NiAY4PQDZg040v0wFauNCPehlFJkIuGywqweWjkq67dSfhk1k9P9Jd3S7PF2bWtqNlrV7v5xHpe+qplYsvRaLPHrs7RN729Z6TINvajcsVgO+Wm8y/mCsEiDPkV6/LhrQBNBRvlS6mkt+xq3uA6Hs47eW+7c9OHE7tJWloWveiYvHAVQQqVqIVSwmCh70X30N29uoHnGS26wFmzDVyCqbfXKc6tdVzMIjAR3tl+8ro1Yk48Z84VKsPTj66KNxyy23DJgnVDV0SxMlhMC8efNw4YUXpiSqE0Ql67V1BUC8rctfx0yFvgl9a4WMOt0+ausObyQp+g92mxBZeRYgKMubsT1Y/HXM1FgEEQDsdFzys2iQIbHBp3rlkyRQAIGTGMz0bd8hkTGh7l/HLFdpu1CBCQFHEKNxoYlIlf48RnZC3qmGKUr9WdVULCqwsBHXbKkzERMyxwc6jvjAaXsF6TkFk5Yi2mcqCV1xaQiaMuHUlYPG/JbqcxnEXOGJQ2N6MAC4vmELnN36v6rnZaAgmBR1ayRNe/X1ZU/Xnk6A9X2GHI5LzTIacX+8jklPZ6jV3Nj+PNrHyAsNoPHIp4pO2ibGyWpUkiDdNpnKDsuiYdyQnh1EX4LSXohEDW5hue/7+MMf/oBHH30UO+20U4UO+/rrr+/3feoWiaKUYvPNN8eqVauw+eab99U+rRPQ5Oj+cdO6LAj+xqcygvWNTyNiddTKtM3L2gZtVRG0BYaIAHJA+cvobSwCFA0scdJUOdjY6Ts5jzbhjJaxIclYZWsNPfgZHZVq9Gq320g+6euolk3EqqFWoYNOQceq53QxBRCPlqmkXNRKpIZWkEXRK7tBsdqiiZ7pSJqeomchVLYEIVXGHDt6ZKclmdZ/+ZEHEUPkbZVBlNp0Msrl3GdY8qOjMPna2+FRgl8N2xI/aH6r+gkcAPCEpjLuZRelk6ullvV3FHBpQ+AlhOQ2wa+8PqP57HUmU4b2tOT0WrA1W0AUDbVhEynb2bwaiFUoQBwKSgky9blO96O/kTqWA6+99ho+9zlZtPO//8UfWAYqzdft6ryrr74a5557Lm666SZMmzat8wXWc3x12Wu4f9w0U+7eGWwClWLtQUxAHih/I5GMKKFqBEpPT5ppJlGZ3rNF6DoFYqdC4mkRm8zZ/wMy0tDRtm3naXt9ep9sLYydetT7Riw7AU2abCf22HbC6DNSoZ1K7BciIhXNx0GUYaKZr4oeRruumyiVLSR2Oq7cinRVMr1DAeNLlXRKDwtF839/+rndPmprANG1Uq2vZlH5MNmCb30tRNdYNL9HI4Kiv3NN+ossTuq1VUKy6jSZwusoPRdd1xH0+uLkq/L61QTa1krHrgPwTkmDrYHT101QChGW/I4WSzFAWLRo0UDvQgW6TaK++93vor29Hdtvvz08z4sJzAFU9ZBa39FVAtWX+NMGW+PjYogL298e6F0ZlNjv9ecBRK1dgPhAEUWe4jf+pObJjkJFKd8oXeeQaF22xiTaHjHT7J56kXYq7v4cDWg05nqf1FwlBz0btQhR7Nh9hpztRyUql5WIb0enD2sRGhNpsohRpVaLmkGfOJFztTTKFNCidh6rWIycq+1tU9XkNZ4u1CnKSEdjdyFgVhWfQ4BbRm5l/OP6EkmycvuorTEs4yCTdXDg+6+YeeRffX1URoOKTBjDTW2uKZgwTuLJ61y3uUpe00Dcz0xvP/nVViNvnR9r/PrjQsBxqRWpjJhUrECB235fwtLOxQm4aSWTocqotUZ/vYEE6QVhebXQ7CDEO++8g3fffRd77LEH8vl8Teul/kC3SdQNN9zQB7uRoi/xpw22xmqfocA4ZuWnYFbxnYHepUGJR7eebly0mc+tAQqxQaVWNMI2ztTLJb14knYW1dJ4mqjp5atFoOL9HOMFCnECVTnodZSa0X9lRCP+WWBZEcRTmcIycEycG86RoTSeBqzSJzCWvhMCUCkcLXRP+vnYLUAs6qROQFSJB8RTejqyoQdhJ1ExaNZjDdpBwcdrRx6MHzS/hdtHbd3rxSB/2mBr897nAic3Rga/NiHxuUBzwDDMWjaqvIsiRD6HZccSEXw9LYokxrdhE/vaabzq3mfJdGFnBCpe0Vc5f7VrRKOaZq5aRMpopnRLrgxFdlgWmZyLrefc3/EODgTW8+o8QPpRHn744Vi0aBEIIXj77bex6aab4sQTT8Tw4cNx3XXX9fs+dZtEHXPMMX2xHyn6EMeuegOz8lMAdE1zkKI2tNu1JgzVhOJ2qXdEeKoPOslolJ6nWgWUHUGo9OPpeFm9T3pwSorWZRSiY51MkqzFjyMapO0S+rgovpoAmRjLBo1kCb09YBJH6rySiHRTUYWf1koxHmmkgChVx3VfQUYAB8b6QNBKsbHdPoQk0nnl5jKyw7J47oB9cNTKN0yarTdw84itYv97lBiiVu27lyLyaF+/teK/uGn4VuZzILoW/CrBFk2SpK0GgY5u2ssno5lJRCS9OllKagFrIZkiBISpQK2o2ETcZiOJzsTmjufAG+LBq88g9//Z+/JwSYoq+5MRmfnq9esVGpp9aegGmkVRREHZnA8QEEVG0WllRBEFQZCmYUCRfZNVFEWZUREVZQRxUAcExR8KMiDQCIqAbNJCN0vT9PL6vVdVmfn7I/JG3IiMzKp6+5O+31dfVeUSGblFnDj33BszukvLeaPZ7373O1xyySV48MEHsWTJEtx888045JBD9Posy3D22WfjmmuuwfLly/H2t78dX//617H99v6o86HaiSeeiCiK8Pzzz2O77cx79uEPfxgnnnjimICoQanMkiTBTTfdhPPOOw/nn38+br75ZiRJcY6itTY8Rlmvh2I90kw2utYGZ+RK4gBKTWdhgILNSkFvp76LZXI2ijoewwLYHZfLEtF/yrljs0s+wBY4ZRXBlss6FX+bb5N2wQZM/LfvvN1n0JxzVtiXG58/jf930xuUpXQwU8eYvFFmXRFR8GltzHyBxdQRNG2MjHLReRBo4DIU+/Y623ruOb/XRVYTgBWNp+pT1BnV0+J9om35s1YmDKdjuua69sx+JnVHmXEXt89Kbqs2K8WG8wEUkMrSVOckIyCmWCiJsBYi7omx7X/9rPpAY2QkLB/qpxPr7e3Fm970Jlx11VXe9RdffDEuv/xyXHXVVfjjH/+IDTbYAPvuuy9WrVo1HKdcsNtvvx1f/vKXsckmm1jL58yZM2Zz93bMRD311FM48MAD8cILL2CbbbZBlmV48sknsemmm+KXv/wlttpqq5Go5xvaKN3B1dO3xTGvD05rcfLqJ/GFLnVvvlTbCuf2Pz1s9Xsj2G1b7WzNxWZARFFcTt/kYikbsfusfeG5D9jYrI8rUi9qnfzl+XL4+Fx/ZXWuAotkPp0MXSdfR2tEzqpT526/gpg4T5DpT20QWFnN7XWmg6FoQJWLiKLAEp2ksbFaCY/JjdjoN7oouufXzNhWu94GYwR0TKoUtZwDFeWaM8t9z5qPdbJZngzcReyeBzGGPNCgDCzxY5Ytc5nNMmDFXX90LLrvVdMXAWXZ9/P77wCJQAp0TY3x1p/fUVreuLAxcOcdcMABOOCAA7zrsizDV77yFXzxi1/UM5p873vfw6xZs3D99dfjM5/5zNDq6rHe3l5MmjSpsPzVV1+1ZlEZTeuYiTr++OOx1VZbYfHixXjooYewaNEiPP/889hyyy1x/PHHj0Qd1xqULqK7otFox2rMZXO83HKYavbGsKSeoN5ItNjW504hc/UcPrcJraOOyZcSwc9qFT++47sj+iqgUyzTMELEcnHAWAUIXYZL1cc+Dj++zWC5Zal1aUYi53KGyv5vLjgffRP74ANXRX1UWlgHqOeg2ddESkk7E5pUOcGut90JwB8l16ldNW0bdr0N2PCxiEWABMulaDOURaaSWCJab394ufaydpujwUYsGgBng/2yjOmuuUlbhQ9UxWrmgUkzix3zuDPKEzWkj3oXVq5caX0GBgY6rs6zzz6LpUuXYr/9zETcXV1d2GuvvfCHP/xh2E6b25577onrrrtO/w+CAGma4pJLLsE+++wzIsdsZR2DqLvuugsXX3wx1llnHb1s3XXXxUUXXYS77rprWCu31mwbIobCGX1PWaP9E0MbSJFuaq3ZdsuGO6CvnlhgArABDnevuI2/D0SQ+QAUN7dz48f1lWULiX1usyKAovKpHlS+AU5ZYVturmjdt02ZiN1d7/tfJlYma3eS2UAGmjkiFoqSbvosTTMLTKVpppcJESBNM9R763r/e961h3Vu9TTDlVO3aaturrnuOsDWnfEq+5ghfk94gIHPPWjcffazRf+5u9jsU7zmLuChOvNzso9Zfd84yHPLJSu6eAcXVRf1jA2LMVa26aabYtq0afpz4YUXdlzG0qVLAQCzZs2yls+aNUuvG2675JJL8K1vfQsHHHAA6vU6TjnlFOywww743e9+hy9/+csjcsxW1rE7r6ury+vvXL16NeK4ZMKutTZkW91MhyWZX48UWNk0k3oujGZDBjQ1yRBR2j+xuayA22mZiDX7GlLDz9kCH+Pillllvrn23P24kNwHYHwslXs+rvk6Tx/4M5GB1bmxfHoetyx7++LzyaeJ0ekNaFmVu4dN/8LnxxMQQA6QADuTOZCzGvn6pJ4grad6vzRJ8dud345mfxPHvP44rpy6Tem5t2scRPclRXBpRNe0rPw5Krv/5hk1z4kr6vaB3LJLXHXpXZdjGejnz1oZaPPlCeM5xYIKgGz2T9G9/iTMufrG0u3Giw3nBMSLFy/G1KlT9fKhuMLc1AIjmW5g3rx5eOSRR3D11VdDSone3l4ceuihOPbYY7HhhhuOyDFbWccg6r3vfS8+/elP49vf/jZ23XVXAMB9992Ho48+Gu973/uGvYJrTdlxK57A1dO3bauTbWWXNp7BieGWrNEKKhu+N7rxzoVEuy6gInMZAhfE+KPefOyArRuh7cs6FNeMziXTgMquI5/qI7CYC9+58NQN/kljy/bPKn4X82j5TAaBBU4tXVRJPIvSMwU6kznljHKjslxLk1TPpUdAKmkk1pyJBNyEFBqEAcgzmBsR+lAAlGGXuBi8eB854KXtYgGsbmZaQ+m6UKl8bm5qDVOH4nJ+bHdZ1fm0qoMboFBmbnoDwGig7FxQ5RF75NqNe2JM23xG5fHGjQkx9Glb8v2nTp1qgajB2AYbbABAMVIcwLz88ssFdmo4bYMNNsDZZ589YuV3ah3fka9+9avYaqutsNtuu6FWq6FWq+Gd73wntt56a1x55ZUjUce15tjlU+YOet+GR3fTl6S4YEAJzS+ZPBeXT1Ef1xVxy4YqQ/0vN91p0MefaKamcbHddtz9xvUkaj0tNxMPl2mT7Cg813VmdyZV7hP3P9dZcdBH3z73ots5+zpNX9Sd6870leke0/9djEh0zQUL3MilR8Jx3nlyITGPtCuzLMk0gBI0d149sQILrHo1iJHK9P6mzsD5kzqfIsu+rjYLWrY9T0/BjYMhkx/KlFVPkSfhtEG9D3y1ukdu/V1rx43Hz8FXDtdE8d88CSt9qwjL1Hb5pSlErKLxosnRhGChxqNtueWW2GCDDXDHHUaQX6/Xcdddd2H33XcfseMuX74cl156KY488kh86lOfwmWXXTamSb47ZqKmT5+O//mf/8Hf/vY3PP7448iyDPPmzcPWW6/V04y0Uec9FLcbReXxEeY3s+cAKESdZBm6pdDHuHr6toiFadQo7HookYITzZIMmP/KYzpnD3VEZZF3BKDUb2KPip1P1UjfLU9t4zIcpqPhIIi7DqvcZD6XTBVTwJkPmymrBnjlrp2s8L+1Tsa/DbEPFJnnW+9Ga5VNSkv/AZW40U1/QLnCkK9L6okdRp9vv2DVk7hk8lzIAINKcuu669woPR+7Q2CLso/zVAsnr34SV07dxnNf/dd0OFhvbu3c206sSmDuA8p0zwOhMp3LWCLuqZotcpzZGETnrV69Gk89ZZ7bZ599Fg8//DDWWWcdbLbZZvj85z+PCy64AHPmzMGcOXNwwQUXYNKkSZg/f/7Q6llid911F97//vdj6tSp2GWXXQAoYuecc87BLbfcgr322mtEjltlHYMoMrpoa2307ISVT+CSyXNR5vZo1xZGs3VjXEXJU6PWlwDkFiLrrkhs989i16+n8nOZ65BZEZJlc3sRK6CAVDHKiYzrQ6pYljIWokyP4tuOgJULmGg+NL92yXa38aAE+5yGBuxVvcr3H6qw3AVQej/hA1s0RYi6YJSi09XWJPVET0wMqGScNDlxUk9x8wbb60nIk6xzyt9lLMk4qLJdpAZYu+8wicIvnzK3AJqpTDsAItPJV32sIv9fNoigcqvP0d6+ajt3G5eN8rn4AJ553l4eiABhd4iwNugucNQtEBLBEEFUp/s/8MADVtTbggULAKik29deey1OOeUU9PX14bOf/axOtnn77bdjypQpQ6pnmR177LE47LDDtCYKAJIkwWc/+1kce+yx+POfR3+KtY6foCRJcO211+I3v/kNXn75ZaSpPUq78847h61ya81v1CAuWPVk6409dmnjGRwdbIGvp89Zy1MAyBtdAgtuZmxXh/FGMJ9rq3p7xc6Y706OVRTUtsfQtKdpK3Pf+YC5yzKVHcNdXlUPDsQ6Md8+1HFWaZzKzN2nLO8QZ6Fovj0AFoAirVWQTwfUcNpEVWwwLFMuubo6d65GfsxOnruyiLp29y2Cq87KaBV8QNu0KtcFu1UAW0bSSri61oq29957I6to64MgwFlnnYWzzjprVOrz9NNP46abbtLvIQBIKbFgwQIr9cFoWscg6oQTTsC1116Lgw46CDvssMOYTfr3RjYucB6skQuPGzXw50+ag1hkuaAXqKf2JLmAYlqGGik4no1YKMCf98cXGWXWAYq5U0wUbQ903rlV62AMe1XmVjP1KRoXzJe55HwMWRnz0K6Vddhl5biMH6AAVFUHqd17wjBJheSLuXuHi8PVutSK+MuSDEkjQZDro8iFl+T0HmXAzpJA5YzKmcPvz9wOJ69+Ehf1zFGs5CCi7937XxXtSNvzAAAfi1cGerl7ukrT1MnydqwKVBfdzTYrx803XRC/7xSxSc+GkAF2/NH/Dr7io23BMAjLg4ntQXjLW96Cv/71r9hmG1uv+9e//hVvfvObx6ROHYOoH//4x/jv//5vHHjggSNRn7XWwniEzkhNJvzFNX/DlVO3wbSIGqmsdJ6tK6dugxNW/nMBKT7hKzXaV03bho2yAYpoc0feriDYJ6T1dRjVqQDsHdwROdc2tQJpfD3X2rhGwKpV5+jqtVzdTrvWznE4gGrXstRMFpwlGUQsIWSgdU2iWzWBZr694tQxOmljmgESaPY31UTUDuPU6G+qXFSpDUR8Yu7BWplbl99z9VwGWrNIgSh2Pi/7GipheftgphPjz8hgWC7atx3jzyG5+SjtAQ82EBNQjjAW7rzxZscffzxOOOEEPPXUU3jHO94BAPi///s/fP3rX8dFF12ERx55RG+7006jEwDVMYiK43itiHwM7Ytr/qbFqu1m7h2MrROLliNZ0k4MxbU4nuzq6dtqNyYBJB7dVhT0tgc0yNzrGItiaoEynZVvmypNSasOq2oORTcCr+r8ipqZ4jQlvN5lYnLfuqr6t5tgM00yyMhoY4IkRQphrxdBwQ2k1qVAwnVSGYAEzXyKF594OUtoklyV2f7adbfDtEhgRUPBs6EOfHy5k9S3eU5JO0nv5YJVT+LyKXML15ieXwJd3NpPpTE4cOUCv07K8eU+E0EA4Ukn4j4naZIhEGtdeBPV/u3f/g0AcMopp3jXBUGg81SN1ny+HYOok046CVdeeSWuuuqqta68MbaRHEvRbPQ0oiZxsiswTTJYAGokovYu6pmDU3v/Nqxlcrt8ylydbJRnZuaC3HIReXnSQZ8Z0OB31ahv3qkEDJgUGSj1TUuKomIX5BW1bcX6+c7H576sirrzbVvGJvjciVVaMMoPVTaFC2BH33Gwo9w7yFkJMxFxo9GEjIQWkpMWKmmkuowsSZEk0G48Yq+yJEVGCTtz9kPdYzt6syYC9A9Bg2Nn7la5s5S73Qac9DxzoGwDKbs8Mt+zMFggVR7xV1zuBi/4yne39bny6Lq7x9CJOJECEBA1Ba52/tntLc9tXBlN+zLUMiawPfvss2NdhYJ1fEXvvvtu/PCHP8RWW22Fgw8+GIceeqj1GSm78MIL8ba3vQ1TpkzB+uuvj0MOOQRPPGG7kbIsw1lnnYWNNtoI3d3d2HvvvfGXv/xlxOo0Vnby6pFnfW7eYHsAtkvK18DVUzuMuorhaNdo+plzurfG+ZPmdKQhamXnOFPbaL0KTCQTzwMF+PVAXDuSZO7ULW4+pXI2iZsv3YE74q7O1VSsMwdF9ijduByr61lYZB27EyvrkAm80u/BRvq5iRWtiYkZoHKnBmn2NdHorSs3XSNB0lAj2CzNkNYTNHobaPYp9kmDplw3RdqoZl9TRejlbiRXS0bnJaAmAB+M8fvTLYs5yGgbGQTaxX75lLl5RK8Z7JQxl+obVr3b1fJ1qvPzbd+u67jMUufd4G5fEv9nicoRRdP/TCijZJtD/Uxg23zzzdv+jJYNKk/UBz7wgZGoS6XdddddOPbYY/G2t70NzWYTX/ziF7HffvvhscceQ09PDwDg4osvxuWXX45rr70Wc+fOxXnnnYd9990XTzzxxIiFXI6lDTWsvJVRmLMK0+fHVd+csRku+0LXVpCBATuD0VGU2TndW6ORZdqlcsnkuQxAwQEWJuzbP9J2/xu2qB1mygUvPvBQJQim47idte96caanDKhxF6HLXLWjafEJgG3mpBUTBVCGddtNVTQRBJCxsJIqujmfyLg7R8hAgaQ6INkMVTrDeF3pZtR2KdI8/xNpn5K60ljZzIbpvNOBDDIUQLMIEnyu2yrjjIp73WIR4NPLbbbXTcBLbBTte8nkuTh59ZMaSPHtOcguskRm8OS+92XvRCcaKF9kn69Mn7kDBN+xitF66v66ub8mgg3ntC8T2V544QXcc8893uwAxx9//KjXp2MQ9d3vfret7e655x7ssssuQ5qTh9ttt91WqMf666+PBx98EHvuuSeyLMNXvvIVfPGLX9SM2Pe+9z3MmjUL119/PT7zmc8MSz3GkzWyDOd0b40zhllcfuOs7a0RKInKDagqggA16/zQonToGOq4NvN1/qQ5+OKawbv0VJLBYudcFmHHzW3oOYBx80NxK4t6M6kPWneqfh0RL6dovuVVLFdVWZ1YFZCqejZMnYruR8Dej0flqc7QBkzuZLTUaXCgpRinADKSGkzJSFqgjAOoZl9TCdKhUhjwiK+kmWoWMhYAmqZRJ4aSzsEX6Vhm5LKrvl7KfPmffMfgbvEFq560ZiQYzLvrAz9lQKic/WrvoHww43sffOWn+plX37GUCGSAvuX9bR1zrY0/++53v4ujjz4acRxj3XXXtSRFQRCMCYgaMW7vgAMOwAsvvDBSxWPFihUAgHXWWQeA8pUuXboU++23n96mq6sLe+21F/7whz+UljMwMICVK1dan4lgSZbBqDqGz3419y2o5W4Rrp8BiuCDW9mUE52aABBZo9+gAKgGY0mmcl8pHUmgMzcXXWPcbVauGXIFrIaFcuc38wOVVgDIdc0ZHZV/f9fl6Ku7fQ7+lVVsQBWgc4/t24Z/u8v48e1z959DmmReYbfP3Cg6Xa9Ggnpvg7nvFPvU7GtqAMXnwtPHzuvXGEjQl6RaO+e6gt26xyJAtxRtCcuPW/GE5x2w2UR9Hi3cbGU6ohNWPlH5HPqeqbJjtLKq/VXd+G9+XN+AwH9c950l97y+N40EaT3BwKo61rzap2cgmDBGGcuH+pnAdsYZZ+CMM87AihUr8Nxzz+HZZ5/Vn2eeeWZM6jRiIKoqQddwlL1gwQK8613vwg47qPncli5dCgCFiQ9nzZql1/nswgsvxLRp0/Rn0003HbF6j5T9Rzx72MrqmqqYQx+D4O/gh6Zj4XZW99aO+8DOpHxRz+Ay5HPXhQwCnLz6ybwDsYET4G/oeaNs9E+mPF52mdapXabHFa1Tp1zWMdsArrz+fN1wu4FbuVzs7/L9qF71NMMHX/qLd12aZQUXjZlw1mahyAgQ1XsbBUDU7GsqTVQecVfvbaDZ39QMFKVCoO2SeoIkrwNnmeg9iD1Z0GkbBaKCjua+NGWbZWVuQfs55QyYKeuSyXOtT5IpMHXCyidw3Ion2H5BoVz3N69j1Xrfdu4x/OdTDqCKZRvXMT9/F0j19jVRTzOsXl0vuETHva0FUVizZg0+8pGPQIwjbdf4qUkHdtxxx+GRRx7Bj370o8I6N2KQwh3L7LTTTsOKFSv0Z/HixcNe35GwNP8M9w1s9NYLGZd95jIuQ2WKzuneGoKV42tgyzqoTqysYW7FDFEnSY2ya25H14n5gZdbv2Kd3O19YK2q02l13OI6f2RVlfuN17cKSNoRWgFunLV9eUVy4xPO2pPN8og6s02zr6mF4YACUPVGokXGFlByGCglIk+RNI0OihsxnC5TyZ9Z7k5uZ0BQlsi2Kp2Iy8S41uo55UBqJK3qObVBYGCt9z/j/kAQ91okGbC6mSLJMqxoJPj2OhOMiVprOPLII/GTn/xkrKth2cSZOCi3z33uc7jlllvwu9/9DptssolevsEGGwBQjNSGG26ol7/88ssFdopbV1fXsOm2RtOoQ0sxfMzCnTvtivrqRuU2bti9WTZ0IFUW+kyWZEYgO1SjiCX3OK4rTjXCGbiGySyz6+ar82DM567gYvfW+/s6T9tFUgWg2tXH+LbxL7MX+tiOMpetry6U4oCnOgCQC8PtLNVJPXFE56lOUVBvJEgyYq0EGgPKrScSw2TxzOW0fV6SVUfAdudxFtWIxG02sJ1n+bgVT+Dq6dtqoODTQ1WZC8b5u0pgz9Ub8mO2Iwyv+l8djMDTfdh1NdfKnexamSsfoHMxTK5fc8izsg/HuzqaFgihM/APpYyJbBdeeCHe+9734rbbbsOOO+6IKLInkL788stHvU4TBkRlWYbPfe5zuPnmm/H//t//w5Zbbmmt33LLLbHBBhvgjjvuwM477wwAqNfruOuuu/DlL395LKo8onZu/9M4q3tr3VAsjGbj0sbQfMJJI9EjbcAetdrgSf02y4dm50+aU+hEudZIWXFk365RjhwSxrsdj6s3stepbx4yzvcbbvONsu3f5bq0qo6hKIJv173YejlnkdoROLcCyza48h/fl2g2zTKgWZyMlsARAARJ8fo2BhKIINUdLO2fJZnOP5QlWQGQ8DL6EvM7yaBE5o6516xNSReAclaqSqfku3a2oFw9T7GATuDrDoyOef1xK4UJrz//bQOg9oImqsweSBRdwcb8zzXdBxUYUwRynUZLjgsLhsEdF0xsd94FF1yAX/3qV3raF1dYPhY2YiBquE/o2GOPxfXXX4//+Z//wZQpU7TOadq0aeju7kYQBPj85z+PCy64AHPmzMGcOXNwwQUXYNKkSZg/f/6w1mW82Fl9T2k91FDBzK+3fxv6Xh+wpjLhnaJplA2oKdPYXDNj2471Bq3qT8kwh852tT5eVXi5bzTfqpxWVh3dZx+3vfLKBOLtAyfftW43vUGr7ds5llu2HelnzoMDJsrRBEAPBlxAxd17ZKozZfcgTZEOZBaoojQGXH9GdSnq6gzY9bF7fP9LJs9FPc0qI0+rkteevPpJHZ0H+Jkh/lwVBwGZ3o4/r3Str5q2jfXfZ77nttX2ts6yeFxuZRnVlfGBnTkv950iMLXWJrZdfvnl+M53voMjjjhirKuibcIIy6+++mqsWLECe++9NzbccEP9ueGGG/Q2p5xyCj7/+c/js5/9LHbZZRe88MILuP322/8pc0RxowbmWLHFoMvoX97vzK1V/Hb1FD7qvpMR6CWT5+KinjmsI/XlSrLdI9yOlzYbWWULVj0J0tsQIHN1LNxaAaB2WYR22SpX0+QTu/Nvvr29X6tjFH+bug7ejecXtNsL/fopn1vRf06+a0JCc+7CI8BTTzOt73PrwkXo7nVMMqAvSfOPYqPsyZqhy3e1OrzO9j2yXXn8fZKBYmMHYwSK3IEPt3bd7RzIVAH6MqsemLjlFctuR5vYznIX0LrnY4I1yus7Li0I1ATCQ/pMMB+mY11dXXjnO9851tWwrGMmqq+vD1mWYdKkSQCAv//977j55psxb948K73AqlWrhq+WaA+UBUGAs846C2edddawHnuiWKfupZs32N4aWbsNXSwC9CXuKNAeAfqs3caJN9q8E6BlZGW/p0WdjQHc47mui7LzcYW8fu0GXzZ8DVWxMyv+5vVv59hlOpky6+R82nPltT9VjntO7rPKQU0E4QFeAD2zvomLZVB067hg1u10XfbJFT+7jJN63sxvcqOZOqg6DnZ6o3ZST/isU5ebzQbawK3o0vODMttlb5dRZVUsKNcsksver58y+yRZNcM3Lo2A0FDLmMB2wgkn4Gtf+xq++tWvjnVVtHUMot7//vfj0EMPxdFHH43XX38db3/72xFFEV599VVcfvnlOOaYY0ainmutxKgR4W64duyWDXdAX5KhWxb1DKpcM92LLaLm/+3Gu5POtkrYzaOY+HLe6F7UMwdTQ4mrpm1TqhdxjbRRvOzyyB6zjCZy9Yl43Y6j3O3gN38H2JpVcuswWDdnJ264To7RqSCZ7+e/Hvy55KCKXa8WWahJ30S/fWDMVx/OQvlYMZ8V3Xt2mTbIMEwsvRdccF41wbdPP+aKrDvRzrUyFxgRk8ZBkguUXBF+Wb34MXzvob8O9r1xB2E2gzixWZi1Btx///2488478Ytf/ALbb799QVj+05/+dNTr1DGIeuihh3DFFVcAAG688UbMmjULixYtwk033YQzzjhjLYgaZXMbj3bNNyr3raMRtPnvjrg7b5hUfhp7FN86Zwx9q+zo5JIbDCVfDhIC738SlRcBZbV1MtJvDbY6cSG2LqPdTtRl64DyTrhdZmkwZgNL46LxAZQqc+dXU/sU3WA+cOUCqDKdmu02tVkQXo4LJjgg4VMSxSIoZano2eTGByju+bhWBajKmCLfuVSZewyf1s8sL4In9xqbY7YebBA7xQNhhgIix9KyQCAbIpM01P3H2qZPnz6ic/QOxjoGUWvWrNEao9tvvx2HHnoohBB4xzvegb///e/DXsG1Vm1XNJ/FieGWANpvGK6cug3W65LgkSvUmHNxripTAQd3ZMgbo6EAGR97U9zW54IaXCu4YNWTbYll/cJqoN3pWqiMVtu2Spw4nOxPq/I6EQ+7ZQ5VX+ICkqJrzN7WMAytrxEvuyryz75XRRdzcZvy8/aDu3LtndEt2SwVnwapXTt59ZPePFSDBQ+c2bKX2wMhvj0/13ZceZxRqnbd8WO7gx4/A1dW/wlna915bU87N5rW8RXdeuut8bOf/QyLFy/Gr371K62DevnllzF16tRhr+Baa21XNJ8F0Loj+1JtK5zTvbUWzQKm0abGmgCU8ETqkPlC1N3GebimVPCJYvnxBtMp8Hw+vDN2j+tGKLlWJYQt297et+hO5MdvZ5lZN/gOwifob+XmKpbhlld9PP5dBUxcVscVbhe3b30tIyH0xy/E5p2v/x6Zdf5zLCvX9+y6zzYFPBhmSoGsTjP2lzFuVD/32GXn0apsV5Rftr97z8rue7uuUle7RuWT4J8DKsCeaWBCWhAMz2eCW7PZxK9//Wt861vf0trrF198EatXrx6T+nQMos444wwsXLgQW2yxBXbddVfstttuABQrRfmZ1tr4tVgEmBZJLcD0NaKuq2+wo9fBmNuhjIS5Hclg6urbp9iB+FwQQ3PLtVo2HNfMZkUG9yy0BjZFANWuW3qwLkGygJ1EO25U9c33Gfyx/QMQe1ksTEoPvt9ggfJwRKJ1euxqF5v7XXxPygYX/vJcMFYefTdRXXlrTdnf//537Ljjjnj/+9+PY489Fq+88goA4OKLL8bChQvHpE4dg6gPfvCDeP755/HAAw/gV7/6lV7+L//yL1ortdZG376aPFvqGjqne2uc1b01AKBbCmw2Jcb663QDMKPyOJLancdtMA1O+3qbKn2Sf1lRuzK4FpGPUF0mrRhppFbSlBhl0YJAO6Po9rctr7v94curj8e3LaaUsFmScpdK0Q1ml2uWl6VsyLydpM1oZIV92rVWLjfKYJ62EDf7z8lfptquuk6+583cRxrUqBXdUs2zR8cnhuryKXO9mqcqK2MGy1gjH4PlW87Ld8tx31MfQOaskW9/lxFsXX+XqTTpUf4pTIjh+UxgO+GEE7DLLrtg+fLl6O7u1ss/8IEP4De/+c2Y1GlQyTY32GADrF69GnfccQf23HNPdHd3421ve9uYZQxda8rchvILXVvp5byBDLtDRLVQN9LRZBXhkKyq62kzUtYYVR3P5wbz1cW3bycsh09f5G5fFcXkmltW+cSxxqVHDTu5V8qEt+3oZobKpgzFfFGPnaxvx/xRVabMdran5b5O0HU1+8TLZeAhzZ9vrkVqx/h9J6t63g04cp/bYnSeD5z6XKRJBvQlQ0mHUAQ3ZfejEyBNdexU6F8G5Ozr42ct3f35dXSfBffZm4hs1FphOXD33XfjnnvuQRzH1vLNN98cL7zwwpjUqeMrumzZMvzLv/wL5s6diwMPPBBLliwBAHzqU5/CSSedNOwVXGvt29fT57TInACUrfvJUyFEKvV/rRYikAGEDJDWk8qIPdq/DDRV/XatyiVV1ajzbWVgdCM0kWinnT1PulmlCUqyTKdR6HRkO5wAqtMOSe3j/121T9X2Zft0cu3L9ETt6W94/cqvB18XRxKRMwL3dax8vyrXbGfXpQj8jd7JPHfmv80ScsaR3Hz0vF4yuT1WqpU7z3ahVZ9Xu2xU2fatdGS+7coBdvF/1TPOWb+1NjEtTVMkSVJY/o9//GPMkmp3zESdeOKJiKIIzz//PLbbbju9/MMf/jBOPPFEXHbZZcNawbXWmcUiwJdqBkC51pukGFg5gK6patLlLMnymevV5K1JPsu5LxdP+8JP1Vh9f+Z2OPzVvxbW04i1ii0oM99o/cjXHte/28kbZTqyAGUpC1ppYBasejJP1dCJTsjPUHV2/uWj/eK16XwOM34/qtiRwTAYVcaZBZtFMMf1ARseieXuS7+FVBN1RxBW5KkNpAw7ZDNFZQyl/e3+NsvUwnqaeXRO9jnSMs6k8Dne6HntlnzS3qwwkbE/Mk+dC5XtvssctPHrWMZE2iyszRj5j+2/Lrw8tyz7uK2nYSrWy3Wb8vdkAgKptdF52HffffGVr3wF11xzDQCVYHv16tU488wzceCBB5bu95a3vKWj4wRBgFtuuQUbb7xxy207BlG33347fvWrX2GTTTaxls+ZM2dtioNxYqbx4FOaqEbo3P6n8f2Z22HDqV0QsUSaZNas9n15y0UTqHYi8CQbrAuovLzOwEAnCTh9Db9vmZm0tT0Qqcpp5/jl9aoa9Zd1XHxdKxdZK5dNu3Xl+3A3TNVxjflcdX63k7ve1KsIpAg01KTQ8+QFMoBI3ClfXEAU6DLt87ZdSmYfDrbLr6erg/KlyiBwby9TEwTzdxowmbmBQEfaFo9pH4MDKXc7mpj4hJVPWEllbRYnqLwP7QwS3GekNfPMV9ppE/j1Lnet22CP35+rp287sbKWrwVRuOKKK7DPPvtg3rx56O/vx/z58/G3v/0NM2fOxI9+9KPS/R5++GGcdNJJmDx5cstjZFmGiy66CAMDA23VqWMQ1dvbq6d84fbqq6+iq6ur0+LW2jDbl+vP5JMSF1umCwaeBgDMiENk+ZxiMpZI6ooe5Sypj43qxFptb3f27WkVfA1uPVWgyTdJaZm5HVU77IkMoIGZmfPPDwD8dfeNxn3HCazf7bpP7JF2+QTKVfUxy9u/724HWU8NAG/nuOWaJ3v7MuDoE59zN5l2VycZhAyQJGo+PSNc5qBI/fd1yIahaZ36w6eF4kyPW2+qh+3eo7LpfhiGrJ6q/c11Fh3pAd3zobxSdEzKzu/eGwJuNDsCudzKNF10nuZa29fUfv/9DKgLlPzALbDKpfMrnrMNjGUwAYHUG9w22mgjPPzww/jxj3+MBx98EGma4sgjj8RHP/pRS2jus5NPPhnrr79+W8fpxKPWMSzdc889cd111+n/QRAgTVNccskl2GeffTotbq2NsMkgwKWNZ/Dl+jN62aSZ3UjqKdJ6ojuZLPEzTq42wY5+qe5kkwy4Yf15nuW+zrBYb96pqGXw/j5uxRNWIyyDAFdP9+ep+v7M7QrLeMfldoyq7GI5p/b+zWr0fYxdu+5PXge/CLk8vJ0zHFXllpXZav9OrF2gXaU3Kt+n6IazWQm7fGv7eqrc1PVUT/fiM65LUv9djZKtSeKuN3ed2d+91v5njZfHQX5xP7cO9vpOrmuSZdoNeGrv3yyXoH1Nise3yzHf9qcIcOk9Mbmc+PtTVlZxnb1NkXktslHFZ11dx8pLNL5syJMPDwOTNcb2u9/9DlEU4ROf+ASuuuoqfOMb38CnPvUpRFGE3/3ud6X7Pfvss1hvvfXaPs5jjz2GzTffvK1tO2aiLrnkEuy999544IEHUK/Xccopp+Avf/kLXnvtNdxzzz2dFrfWRsA4YCqzpJ5o8BRIgTRTNFTRjVLdQFllOiPBMk1JeYNfro9y2RUakVYxWHyUSaCqvHz/efFEm+TuGKwwnJ/HSFiR3Wh3v86P5bIGPi1KmTuHfw8WwHF3T1FHY54l0vipOhVZMF5fc242+8KXczbKPIMGYNjll5fLzQZtgXXt/Pq3TNfL9yyd2vs3XNQzx3t8+t+KteLrL58yF90ywKeXP45LJs/VUb1V5msv/IM0dR3V+fjYWnNPy9oN14Xtc/HZ+/C2I8C319lW6yrHs2VBMAzRecM0Yhoj22effbBkyZICo7RixQrss88+XtE5gLYBEdmmm27a9rYd35F58+bhkUcewdve9jbsu+++6O3txaGHHopFixZhq6226rS4tTYGVu9t6Fw5WZIiS1JvZF57DIGfkaJlbu6qy6fMLe04i2yDrwOB1Ui6xzTbGrqeWyduP5d1Izu192+4fMpcnfXcN+L2/R+Me22w1q4b0N5ncHXxgTCfnqmVu8Xevz0tHheHu3oZykzvfqhsH7vBmSVutquuuI0PLLngiK/3aarKz9E+jp9dLD7rZQCqU1uw6kk9GHHZKpudzpz/xe0MC5VZmcXp3vj28/3m5+VaFVgrMt4VJ77Wxp1lWeZNpbRs2TL09PS0VcZtt92Gu+++W///+te/jje/+c2YP38+li9f3nGdBp0n6pxzzhnMrmttHBixUGmSIUsyyFhAxgJxmmq9Qxnz4+ogyqysk5BBgONWPOEVanNhsG/fts6tdPRZnVixzJ3oY+IorPzU3r/h/ElzPAxEWSSRr3EffEPeDhBp17gwW9nwJCmselZcbUuZS7NV+f4yTMddBrp9deF1qGJFSeztW8e/q8yUX3xHDCujfvvAHn8X6XgUVEFsFJV38uonLcH4UIy0gVXBFi6g9Q+yivvw/YYKcIoMqD8aWAYBrl13OxyxrBhJPK7sDSwsp0mHgyDAEUccYemvkyTBI488gt13372tsk4++WR8+ctfBgA8+uijOOmkk7BgwQLceeedWLBgQcfz8w3qiv7+97/Hxz72Mey+++46wdX3v/99C92ttfFpd2z3Vh2dRN8UvURziQF85GbT8j5WoV27ato2LAuzWa46pc5HyrxzpmziLoNxzOuP6xE671DdTs4dpbpiU9qeOiJ3RD4UwFGmERoJt187bAEtt1m0wR2vnHUsr585fnvlF4XG/DzhsE8+hpO+XbaoeuoVm5Uq1+/56+3miCo/P/7t6glpucohpdxinJEiAHXVtG20i64TAXorq9Lr+cx3D/j+dK/UtjYI48C4Hb0Xf4b+KRinIHjDzp03bdo0TJs2DVmWYcqUKfr/tGnTsMEGG+DTn/40fvCDH7RV1rPPPot585RW96abbsJ73/teXHDBBfjGN76BW2+9teO6dcxE3XTTTTj88MPx0Y9+FA899JAOA1y1ahUuuOAC/O///m/HlVhrY2N6suEESAHIWACNpDB69DEqnbjEfrnpTjho8SOYHAqrwzIdAm9IfZS9n9HwjWbd/YwWKrAaX8600bbcrp6+rfcaUH4ofgy+np831bGqAS+7li6Q8m8zcp2DT99Ttq5dczu0duo/mHP0u4Bctqd4jLJrbQdAZAXg4Hs+y5i34nUsMl/us2MzoqauHLyZZ9XopSij+VXTttHLyjLzD8ZOWPmE1gxWs3wm71bVdva94myuy8y1Z+67VebSnDAA6w3MRBE7tMUWW2DhwoVtu+58Fscx1qxZAwD49a9/jX//938HAKyzzjpYuXJlx+V1fEXPO+88fPOb38R//ud/IooivXz33XfHQw891HEF1tr4MWKkOAvl05yYbapHg9Rp9NUT3LLhDlYDfszrjzsdAe1jmCJXOF4mEuVuBb7c56Lj9effxf3LQaQviqkdq9Jw0f9WnUx5favYnfauBS13gW6Z28X9lG1jyi4/t9G0ojvH/u+yVj6AXM5m+ctT3+5yvr8Nhly3Fn8f3PtF0Xomag+4dt3tcO2627Gy1fbt5k9r145b8YQXmHGWzq6b/yHwab1sVrHIRJVdV18ZVaaucYDr1ytGEq+18WVnnnnmkAAUALzrXe/CggULcO655+L+++/HQQcdBAB48sknC/kv27GOQdQTTzyBPffcs7B86tSpeP311zuuwFobPbtv332QJpk1iz0JyrNcHwXYo+qyTrLM3IaNRKNA1fx0Rfeee3y+rXssMnJTcBGxXacq1ooYgfJzW7BK5dOpmmqjyGJQXQd/XLXex64Ur0/5carLL6vLcOiiOqlHGTNYtq3v4zO/KNnviqvav4wRrHpHOtF6+XR4hsHzM2QcZNE7Rud27brb6WtJoGq4Lcn87zZ309tA0NwrFwCaqMPyY7XbFrXraiwLJhhvRnPnDfXzRrerrroKYRjixhtvxNVXX62zkt966614z3ve03F5HbvzNtxwQzz11FPYYostrOV33303Zs+e3XEF1troWbOvaWVrLjM+8utUvMxHvbQ/j4pyzaXYixoQ213iEyknWabTDxTrYI9meVm8DuXCWBOCrRIQAqf2KrDGhbv+a9MOaLCP59/GL1Ivi9LqVAfSqqMpO071Pvb5d1KnKtduWZ3scosJMV3XMX2rBJLVbqYy892DwZTjmk+31wkAs+uUDasLz2dJlllpD2QQsGlu7AhdEs1zho0DP6Nhg7OPzx1aDrg7AePqHNS21683D/Nfeay9nUbbAgGIN6Y7bzhts802wy9+8YvC8iuuuGJQ5XUMoj7zmc/ghBNOwHe+8x0EQYAXX3wR9957LxYuXIgzzjhjUJUYr3bzBtvrl3rcvlgdWKO/mac0KPbuKetkgKLGYzDGARExUrw8LiZXHZufLeDfpjyqZ5GNunLqNl72yleWrwxyeZCInEbIfYkCghf1zNF6E9q/3c50ODrZTq3ovirvWdoDOZ0ft9Wz5LIqgI9lywrbAkUWRHXavFMHXC1NLAJEQkWlinoKpCkIfPlAa5VOql1rhyUrc2l1dgy+jz0n4EjYcSuewLfX2RbTIqEBU7dUnbUbnGAPiHwBJXzeQhvwKnMBVuf1LQP0tPzGWdvjgy/9pfOC19qEsOeff75y/WabbdZReR2DqFNOOUUnturv78eee+6Jrq4uLFy4EMcdd1ynxY1bu23uOxDB1uVMdGv01vXvtKJH4w0xByvtNMKmE7KvG88Jc+2621mj12pWofy/ewo09cUJK5/AJZPnOo2v2ccVFbv1d7flolgepecf+Zs6dgKYqhipKt3WcHeMZexZJ8CJu2NVmVmhXFcDx/flLh07z1gxGMGnqYtFpgGvOUbG1isAFXar5k/GAOqAZPfYdZH6mK7BXnsfo9IuWHLdff5nj9dvcCxbp+Ymq/z+zO3ygQfgtiWmfv5323X/cS0VPRM+kXg7eivfffUB1xvWn4cPvzzOBs5vYGG5z/r7+1Gr1Treb4sttvDmmiIrS9hZZh1d0SRJcNddd+Gkk07Cq6++ivvvvx//93//h1deeQXnnntuRwce75Y6o8OJLjq85117qGkv8laEpnlJPY23zQ61V36ZVoS+7dBl/8ibN5gu20AdG30+vfxxazugGLrt1/iUn0OSZVYZJCBPMuiJmTsB1O246uzj+8FFp8cbbuDfDqiiexeLAN3S/nbZBg6WuGaGa2IAvyDZB7roOxICIv8un4Il3y8WljZQxgJRl0QcSURdErVaiFotRBxJw1oF/noM9loWAUN7ZfI6cIDaykXtJr4dSTv81b9qlx43DozKhObudS57RnzvlX1NO38PBvPe3LFDe/mJhmxrp31BmqY499xzsfHGG2Py5Ml45hk1O8eXvvQlfPvb326rjEWLFuGhhx7Sn/vuuw/f/OY3MXfuXPzkJz/puE4dXVEpJfbff3+sWLECkyZNwi677IJdd921rZmRJ5p1SqOPd0saCbKS2d71Nkwb0h7r1N7ywVzLdvahCD9iuHgyQQOAysuxNTvtacVc66QzbZdha7c+7n7tgLVWrrxWdSxbZvb35VIqYwiKQuOi67V8X76dm3HfZajKLEtSi5UVMkAg7WZRBIGaYzJQHx+j1on52L4yt7V9LU20pI+F89VpLNzH/NhFtxzY/yrmqBwElbFQ5c9l8dlox3zzft6x3Vvxq7lv0f9v3eJNlaz+WhteO++883Dttdfi4osvRhzHevmOO+6I//qv/2qrjDe96U3WZ5dddsFRRx2FSy+9FF/96lc7rlPHsHTHHXfU6O+f2XwN+0S2gZUD+mWnEbifPbKTbLqalCrXkn+5D1SV7+emVeBmmCqzjJijziIIbc0X1cFlsi6ZPLfAotll+MPf3e2qRsScXWsXBNnsSvl2rcsqsiFl6/l/fznFd6UqWs1lndzjuGXz5W59eb0DqQCPYo7szrsK0Jl3Q1jfVCYvu3je/mti19Vd5ossNOu52Nq9di6A4mX4jLtAR9OOWPZX9CVZfr9NPX3spGtV51T1rNLvsu2AoiaqasBy46ztAQB37rQr7tjurXri9lu3eBNu22rn8hMYCVvLROG6667DNddcg49+9KOQUurlO+20Ex5/fGjzH86dOxd//OMfO96vY03U+eefj4ULF+Lcc8/FW9/61kLOhqlTp3ZcifFovpEtH5kkGSaM2LxqtCSCIJ/Zvij8bic1gC/SrZ3taNuyqDi13iyj6WhIX8EnF45F4HVVqO2DQrn8WATYqnI/+c6/KuSdn1unHVc7EX1Vx/XVwd3HvV9FNxCs7VztiM1YukDFJ8qmfU1nbjpRtZw6zLJnjbb1XRtiinznX6UJKotUDWuqWWz2N5HVm3p5IAO9jwxUhv9ABhBJBum8K3TsSAjLZe6bozJl+3CdU/E/rOW8zDTj7nL/+Q7WBTkU+/RyNWMATTBcT+3nxWjQ7Oc0ybIcbJW74ymy0s9g2s92VXSo773g9ouNdtTPF02XpSZvr2b2h9vWTkAMvPDCC9h6660Ly9M0RaPRaKsMN6FmlmVYsmQJzjrrLMyZM6fjOnUMoiiPwvve9z5LnEUTA3YqypqoRqBq3IkPPVZFN6es4XXzKlVZdaRV+/vwBrSMsSKw42qcaJ4wEpKX1aWqrlWghdfLV46r9/ABq2IHUV6eXe/2xca83FbbuMcoY3/Iqty7vKMyWjZ/PRTQNb/L3VnFCDl+zdy6++olZIAUKhO/2b8IJgHFOAmk+reMleBcyABCBkjqCZKmWi8SG/DQtmmSIa0nSOr2RN4E7iSLDKRO2LJmprcnQMTr6oJx3+DGHNd2yZdFG46mKdBt2CdeJ9/z4orOy9lWoAxY8+ej3XMvawv6kxQyf3ZlU22QJX7gvtZG1rbffnv8/ve/x+abb24t/8lPfoKdd26PGZw+fXpBWJ5lGTbddFP8+Mc/7rhOHYOo3/72tx0fZCJammVAYHeME1En9ctNd/Iud0euPHqOC1ZpPVmV+6jKpTeU9sYHoDjLBCgm6ZLJc3WEnl03WPu6oMY3lxjlhKJtBiN7KLsmra6Fy0TwZZ0dH/BNbmtHuPmP7bJNvrKL7iTf8Y0ZF5W5l34gUATyLivDt3PZHctt3bQBiQwI3HBhuRrdy1hoFgoARCwhY4ksydBIU/u5CQVELBEIBcIyKSDj4oCFNFZ0rCzJICU0i5Empv6a7WDjUF/gBz8fAmqcVXOfd379xsJ4O2KAjb1NGYPJ9/MZZ6pcLR4B+2KUp39733J+Di4YR1Pdu2y0BPtro/Nw5pln4vDDD8cLL7yANE3x05/+FE888QSuu+46b+4nn7kYRgiB9dZbD1tvvTXCsGNI1DmI2muvvTo+yES1VrliJhqoypLManBt7ZPdWftHh/51fH1Z2HWx0eyc8SIww3VGPMlmO0kFOTiocuFRdnJeBxdMDTXFgA+c+XQaVcewI/qK21exN1UAyldXOp67TztuWs4mmQSXxeO4wKedY1UZF2LHQoGOsDtU80QCOoO/kAFkJK19hVRsU5YIoM5ZW/Ud1UKIWCKtJ3lZAQJ2gQgURbXQAlFpkilXYZJa2xHgypLUfCcZ4maqz4EDRqo3AM28lV13QEXMjYWdsPIJPYfltEiUMlC+59YFgmpZZj3H5A7m+9H9lgExoFVgtHj8VtvQ/Ugzf4TziFgQDH0C4Qnuzjv44INxww034IILLkAQBDjjjDPwlre8BT//+c+x7777tlXGcGOYjkHUI4884l0eBAFqtRo222wzdHV1Dbli483KOpHxbD4WygAocyIu42Dcenx56468lfvLtw8/jlsfMu4m4jomt7M+YeUTuHzKXFwyeS5OXv2ktzN3XYNV5u5fBR5p+3bLMsvtsl32yecKLPvvluvbf7g8ELYrsL1Cy8LTCyLhUADN1Hu93DroyDnmJnO1TpyBIrZJa1uQKjZJ2u6ZIK9nWAuR1FMEMrMYIhlLhN0hwlqIpgwsMMVNxDIHWwJCCuUerCcQMkCjvwnUEy1ij3siiFjqhLiN/ibSviZkKCABBf5yoJc0nGPVE3Vz6zbLPF6MD9bswZbd1pAR0OZmgJPLWAaFfe30FuWsLmdU+TrfANqnZxtVW8tEAQD2339/7L///h3tc8stt+CAAw5AFEW45ZZbKrd93/ve11HZHYOoN7/5zZWJqqIowoc//GF861vfGlQirOGwb3zjG7jkkkuwZMkSbL/99vjKV76CPfbYY1jK9ulFxqP9ctOdNPME2B2LX3NRxUKYxs5nfIoHU4bd0LXb0bouN85++dxFMggsNmrBqidx+ZS5eplvlnmebbzKOEhxAYsBA5l2Jbr7KmsPrLnX1wVmrmbJFXa7AmTTMdjiW34fylyFVeCxClBzZoAnxqR1xASlWuNj15dMsSs2kPIxUASKdCRdkujnPC2Ax8DSOwUygADAtcHE/ig9lECaM0JhziSR7kmGyj0XCFVOWAsVwxSZDipLMwQiQDw51uCIrLG6jq5pXWisbigglZ9zbYZpLwdWDCBpJAqs1UKE3SHinggylqivrqPR29AC56SRwJU42y6usW+o+MCqzD3Mf7vbksuYv3d8IOVuV3ymAf68uesiQVGZQQGE+wIXOKAec3A1wjaS/Wmn9sc//hFpmuLtb3+7tfy+++6DlBK77LKLd79DDjkES5cuxfrrr49DDjmktPzB6Lo7hqU333wz5syZg2uuuQYPP/wwFi1ahGuuuQbbbLMNrr/+enz729/GnXfeidNPP73ToofFbrjhBnz+85/HF7/4RSxatAh77LEHDjjggJap3n3WimEYr6LyW7d4k9UQlEUhcTeQ27D49EPufu5/19XlumzoOFVsSJmbhxrNMq0WN5/GaTBG5chAAS9ef8V0mWfDdQ2610KVU2zgXV0RLeNW7GCKI2c/gLK3KZZVTGrqO26xfva64n52QsVuKTSAAuxOxwVQZEIGkKEoJLnU5Yf5lC2xhIxF/pGFzk4GgU6gyZkcblmaWikOAhEgzdEVCcxlJNmxcnZJkpYqZ6XYp2tqF7qmdaFragwRy7wMARkJ1GbU0D2jhskbTcb0zadi+uZTMWXDyZi6yVRM3ST/van6vc6cGZi57TpYf95MTNloMmQkEE+OEfVE+lgyUscnYbxw7ulQXM7DZfQOULoQ+7k3zxRnkeyP79ml3yhsV/6s2e8hHYvSV2iATSCdnrN8nX4e8mStbtqLkbSxmIB4OPvT4bBjjz0WixcvLix/4YUXcOyxx5bul6Yp1l9/ff277DOYwLggyzrjfXfddVece+65BTrtV7/6Fb70pS/h/vvvx89+9jOcdNJJePrppzuu0FDt7W9/O97ylrfg6quv1su22247HHLIIbjwwgtb7r9y5UpMmzYNP1p/W0wSxQaXLMkyfGDp+JxfiVgowD+y8oVDJ1lmicvrabVmySc6toWjfpeKvczWOLlCY9qP6udbTqkPaL47bldO3cY6Dm1fpYXipvJEZV7mSs3PZ9IjEBPlAspWwvAy8OpuU6U3q+pcXPbMXVZVB37Nu6Ww9vWxW2WaNN9IvZEa7oRSBejt899l7jnT2QlLF0T7EGNE20oCPw6ACmSApJ4iS5VLTwOx/DxIMExlpkmmt+XMkFX3fN9A5h1vDqCoTOHkn1KgTSBLU0Q9NQRCIG0odkrWuhD11ND38nKEPd1o9vah9+VVSOopkoZyC2ZJhnpvA1mS6uVpPUF/f1M/M+Ohnbp8ylwNWCiTPZnLWPJ3iNbx6E++zmVJKbs8tzRv2/h2dCzuDua5wYxerZgvLNPPpdpmdaOJw158DCtWrBiRFD/UJ720dOmQy1+5ciVmbbBB23Udan863DZ58mQ88sgjmD17trX82WefxU477YRVq1YNqtzXX38d06dPH9S+HTNRjz76aCG8EAA233xzPProowCUy2/JkiWDqtBQrF6v48EHH8R+++1nLd9vv/3whz/8wbvPwMAAVq5caX3Iqmja8TC685nLQrVjbmMElDMM7noCQfay1gCK71d+DB9gMKwEp/HJbVdmnQ4WuXvuop45uKhnDi6ZPFdHAPJzpEzp5axMkVkqY+R8QLOKES07L5dlrEpuWHXvuQulrDwf6whAj+T99VM7uwDK2l8SIDEjfmIFeOemWSRhgIvFDNUUW0PHSXNxNwANroRThyw121CZMhYGbEXG/SPY9UnTTAMofl4GkNmON1VnARmFuaBcQta6IKIQYU1lZO6aPgVhLUY0tQddU7sgZIC4J0I3MVrrT1Is1qZT0DW1C7UZNdRqIWQoUKt1Hm003HZRzxwsWPUkGyzxeQ3NM+abHsjVN/meVb6dC6Dc7eh4kRDaLcufJ/Os5dGXznNH25BN1Gzlbp83MDBQ2GYw/elIW1dXF1566aXC8iVLlrQdWfflL38ZN9xwg/7/oQ99COussw423nhj/OlPf+q4Th2DqG233RYXXXQR6nUzmW2j0cBFF12EbbdVERgvvPACZs2a1XFlhmqvvvoqkiQpHHvWrFlYunSpd58LL7wQ06ZN059NN90UQJEOJnqXf8aj+V7q8kSE/H+xrFaAhlsZKGrHXNdQe6LvwR2rXfODPv8J+tyHZSDJLdMu36c/Gpz5RfDtb98KePH93H35+1EF6MmN4po7DQv/uJYlmY54I5aI3C0uQOLb8brxjhOwARQHUoEUBpTl6Q8AIGmkuh5ZkhVE36k+rvokjUQzSWmjaQGrtNFAmicNbPbXkdabul5hLUY8dZJy5dVCDZimbDINUzeZpl2BXVO70D1zEuLJkZ5keSyNmFzSLZY95/4BR3XWctctR9FyboJTvi1nn/gzFTi/yY0rI5XKQoN07c4T3mdypEwl2xz6BwA23XRTq9/zsUqD6U9H2vbdd1+cdtppWLFihV72+uuv4wtf+ELb0Xnf+ta3dD9/xx134Ne//jVuu+02HHDAATj55JM7rlPHb9jXv/51vO9978Mmm2yCnXbaCUEQ4JFHHkGSJDpPwzPPPIPPfvazHVdmuMyXSKtMDH/aaadhwYIF+v/KlSv1BSZz87iMV7t1izcVlnFxuWuu2634H2hHGG1v7zdXoF3GwtiC2GpNlltfnsXcHNd2gXVyC2lf7grolkEhszufsw+wQZW7jrsv6X+VDTfjORigWybu76QsXxg4H4gUAx9EwU3H/xezRauUmYJJrKnTo/0z5kLM0hQpBKRmF4rMVqG+uVtOOHmfXHZJQKDR38wBnHn/SG5BnW+W5sBLpnndYaU3EABSKYA0hRBqmax1Iezu1+cn4xAiChFIiShntAZW9qHR28CqJatRX91eFufRsk8vV+/nt9fZFvW0OOk04JcDcGs10PC5vd3UEIANkK1t6ZlwWC2VxFVY2zT7Ri9reZapz1DLAIDFixdb7ryqiPpO+tORtssuuwx77rknNt98c51c8+GHH8asWbPw/e9/v60ylixZovv4X/ziFzjssMOw3377YYsttigI1tuxjkHU7rvvjueeew4/+MEP8OSTTyLLMnzwgx/E/PnzMWXKFADA4Ycf3nFFhsNmzpwJKWUBJb/88sulzFhXV5f3AaLoG268QR7NEUgn5hv5V7MBfjahDOy4DVinIICLwjmQcI/nJsfzgYli+gL7P6U8MFotALCj+Xym9E7qt6ufumbGtvk6oylbsOpJPc+ee71onRuy3Q4AoeMMFkjZ17h1Oa0iqEydqi3NskLm7TLz6fUK5TmskGsW02RVNkGW+pmuLMkg42LmabXO7hjTHNC5AIqDJ2KhAhlAwTOBBAkAaV00VddUl5kmKYIkLw9GMxVIpZMiUJXmv5u9fbpz5wBKRiFEHOp9w+46+pb3o9lnpq0ZT3bkawpMfX/mdvnzZu6B/Z7YkcNVz3A7iWkLUXZtAijfNjKSqi9IJ94sHVOnTm2piRpMfzrStvHGG+ORRx7BD3/4Q/zpT39Cd3c3PvGJT+Df/u3fEEVRW2XMmDEDixcvxqabborbbrsN5513HgAFDgcjLB8U1zt58mQcffTRg9l1RC2OY7z1rW/FHXfcgQ984AN6+R133IH3v//9HZUlcq0DB0w0WhWFgOKxt19uupMBeS1G/e5cW4DqNIvAxS6DGruq3EX2/uVi6FbHcc3OneTWqarsQKc64CChHUsy6JxTgAJXNHI2wDPTInMAOHn133BRz5yCGJ2L0AGUTlND25axP0OxstG93YHZ36RhaRfI8SSE7RzbZ/xZ9YnLfcyqAT4mWSUA602l3FAZA2WByCBl9TxoWUKwSMEfPvFpxsor25cn0FS5qQACUtIZqKU5A2UxY3XFJqkoQooaFBpoBVKqsutNCCEgohAySSFjaaVWGI92+Kt/xfXrzSvo8ADzzLQDjtrZBrCZ+bKBcDsAKmBACo3Ryb00HIk9O9l/OPvT4bSenh58+tOfHvT+hx56KObPn485c+Zg2bJlOOCAAwAoRss3L18rGxSI+v73v49vfetbeOaZZ3Dvvfdi8803xxVXXIHZs2eP6cUFgAULFuDwww/HLrvsgt122w3XXHMNnn/++c5BnxQQkdQZiO2GW700v5r7Fuz/5EPDVPOhGXUQZS8JsQP0m4vJfW49bsVcPT63X9E6yUjugpsySr/TJII8V5SrvaoyYq44E0VRi0XQZ+pE4IjYJ64D4WX5cku5ZZGZyZfbc+/53IV+QGwf08/2+YGUbx9z//wsoluXYi4w9kzBTp5paVycwaIFlJDqJJq0BFDvb+oBYsrFZ3eCPsBGQEoKO9q1VRBHmpq66KlemBvRlMHOwspanmhGSuWwsiPI9DZJiqTRZMBK5IL68Z9ckb9XdsqNzqaCoX2rWFA3mpMLyqusTDslZICwa3R0ZxmGnvGr0/2HrT8dZnvsscfw/PPPW9psoL1EmVdccQW22GILLF68GBdffDEmT54MQLn5BiND6vjuX3311TjjjDPw+c9/Huedd56mv2bMmIGvfOUrYw6iPvzhD2PZsmU455xzsGTJEuywww743//9X29EYZXJKNdT+ISv+fd4cen5tFBkvEEpa1jszqx81nu1De3Dl/mpeFpX1km7jFLxWEUXWLHhtP/TpMQ+O27FEy2j+EyZRlRNzNKCVU9arj5eR2KnaJkLvnzh/+2yUsYdqe6PC1xsnVJ70+4Uz7lqlN8ZI6VYuvJnqgxM8eOgmVpJDvmx+YDA7fy4609YDJPwpvsAbIF5K8vSDEmaaBccNwI4HOgACkhZ28EAtyzNtPsvSxKV+TxJ4UKfLFEsFHf30XIASBpNZHlbTOBKTW8jx9Vgz2d8Oprr15uH+a88hhtnbV+pySwDUFXGI/HoGrVipdz97f+Kmxwv/cBI2HD1p8NlzzzzDD7wgQ/g0UcfRRAEoAxNpNFqxx0XRREWLlxYWP75z39+UHXqOE/UvHnzcMEFF+CQQw7BlClT8Kc//QmzZ8/Gn//8Z+y999549dVXB1WR8WKUk+Nn2+yMKXFoRf2QUXI+enn2/euDY1VdAMBtW+2sc8a4QMlyjTguPOO6MZ2Ymz+K1nNAUxZd45bnMgxluqZWNH2VyDwWQcEN6YKoK6duU9rocpBC+xHQ6kuKOaXILUh2wsonrOzox614ApdMngsZdJb0s4yVcgFJN7kRKsTy9mjeXVfOTrlguJ2UCvxYZffJuD+rXS6+sjl7StYq0INcNm6Hyc2ar07YYmHbhe/rOP2dbiCCPCN6OftDKREoso/yWHG9lYhClo8q1ixU6jBRbr1krQtZoiL+AikwsLwXr/99BXa/63el9RmvdsP686yBCVkZXuHPlE9EDsCSZWRJql2dFsvU5rPAn6fV9QYOfvTBEc8T9fyLw5MnarON2s8TNd7s4IMPhpQS//mf/4nZs2fj/vvvx7Jly3DSSSfh0ksvLc2k3mqqF24jPu3Ls88+q1Xx3Lq6utDb29tpcePWdJ6XJCtE+dDM7ePFuJ6jjMoOZKDdIG7n3C5LYTRO7W3P/7enx6kGVFWhzlX7EtDh23PjLBEBLromXJjugiIZqIhA+gZQmGamlYidjMAaTXrs1lHNoZohyfw5t6g+6j9d6yLQKmP/NANklVt08VaLzsuYTv/23NplFtoBUGRpkkFKu8MjQOUDQT7hOqVFMNsXAVnh+FJpm6gu0qNLIgF7kGubBADkm2VpirSeIJApsrj9JjptNCCiCEHu/ot6Ju4cph9+2bBRQ2FRAdcVZzN5rVikqvVlQHukLMsydMh5eMuYyHbvvffizjvvxHrrraf0f0LgXe96Fy688EIcf/zxWLRokXe/qqleuI3KtC9bbrklHn744cLyW2+9FfPmzeu0uHFrPG/MeLbbtjKAVo+uhxh+Wu3OG74GoywL+VDLIUBD5qYZKDvmVdMMgCpmdFffxFZV6aJonY8BG0lrVzA/vMf06daGVpfhfMaqBOOFbdPUSoPArSx7un9bkzeK/vNvt15ldcxyoEUslFuG7z9lQBd5tN5EZKHIOtU/dmKuNqr9/cSY9QtpNjyfiWxJkmgN08yZM/Hiiy8CUMm+n3iifLBaNdXLUKd96fhpOPnkk3HsscfihhtuQJZluP/++3H++efjC1/4wqASVY1X43Q/ZSzm/yn5GmADmfFmaVaeJ6rMWgmwyzKQk4TM70Yqzn+l9iu+1Xwbvp/7AZALUu2EfEmWWUBKZUv2dfbkXuLHKlae5snjbBZtXyzT1JkYv6unb1sAdmVGUX2+LPIpiikXzLZFV2tZSgXaxv1UWav0DC7YLP7OvL+B9oT+ZIONTiIwosshHZQgbVLqZaL4Mp7p3LceKLISLpDS+6UZ0pRAktlGueUVaOIfDvD4cvccASCsdSGsdaG2zrSqSzLu7cMvP1b5rJW9r2TFbOQlrlBHqkFeiKp2859ZBzWebYcddsAjjzwCQE1Jc/HFF+Oee+7BOeecU5gKph3r7+8fcp06dud94hOfQLPZxCmnnII1a9Zg/vz52HjjjXHllVfiIx/5yJArNF7MnnIiz+nC/qfjNP+KBn+J6XB4WG9VVB4tU2YABncXmX0CcMGwATfVifLcYxEAKNPxcEDXSh/Bz4sbn9dObdNam8PPSzFKChj5hOm2eJ6WFl2fBKTchKBVxq+lgGHVeMQe39Z1u7nLyo9D51Jdlypdm09H555DVV3V8W0wXLaPG6pelkPKjtRDvq9KC6D/lwAoa58cwAR50ktI20VHqQuqktt665dm5MVDs9+0KTRNjJUgNDF1KNSP3JRCGBdhB67A8WoffEnN+3fjrO0B2GCdX+YyvV3ZfHhcC+eam7ncZR9tDdXgGK3B2gQnkoZsp59+upYNnXfeeXjve9+LPfbYA+uuu641lUuVJUmCCy64AN/85jfx0ksv4cknn8Ts2bPxpS99CVtssQWOPPLIjuo0KF7yqKOOwt///ne8/PLLWLp0KRYvXtzxgSeKBVIgqoVW4k3BGu+qnCKjZXqyU+flr5qeppxlou/AAjZcS8SZqGr2qfoY5ccpbl/GZPnrrj7XzLAZIJetqdKEyUDlg6KJd1tZUQcGa86vTtpZYqNiEeCMvqdwRt9TOCv/NvXPvMJ1l5Vyl1Vtb8ouZ6nKggvKzGbGikxZVb389XPSHTgXtgzAlLEQLoCiQJJ25kTzJQF1p5Lhy3yi8CzJ1PQvuftPTSKcatYrrZuIOyu1gsftmCXKJUG/AeDF845peR4TwQhMcasarHXqriPPAp+k2jufnlOmkGLUsne/Ud15jzzyiH6u999/fxx66KEAgNmzZ+Oxxx7Dq6++ipdffhnvfve72yrv/PPPx7XXXouLL74YcRzr5TvuuCP+67/+q+P6DQkBzJw5E+uvv/5Qihi3FgQ0d5KAyGd3F/l8WQSoUtZgjrXp0acDpNoxF7z4gUyRIfAJyHmZvCx3HrZYFI/pq5N7zLL6l1mSZeiWJuUAdcpuJ21/ysstc1Gp7e05uqiMIkvV2k7t/RtkoJgnYp8oKzrV8aKeOR3pj6quYRmQcpcV9wOrU/Ha8u3K6uFGh/L19TTLp9lx3Ggt2M4q7RJ/T8rAUhWQcsvmrsLUATvWvHguEMp/J41UZxbPEiUq15/8f1JPiq5Cj4YrrTcst+VGp1/tPYc3gnHgTO0jpTWoYqHc/ejbZDMP9Afwg+O1Nny2884766j/2bNnY9myZdb6ddZZpyMge9111+Gaa67BRz/6UStx7k477YTHH2/fS0DWFt+78847t13Jhx4av/lIOjUespzm+UDoxZGxQFKn7MEB7tjurWOe6gDIX3akBYBH7kiZKTccYELPeYqAdhNv+lxnthusyMyQC9CsrwYXVctdF6PRSBk3F+mTyH22YNWTOtqOn0NZHidaZzMpGSjqrVX9fMxdkql5w2jaiyqj+gIEoPh9Ud8nrzbbVCXL5OdT5mZzt6Vl/HjkDq3K+cXr2E7EHl0vfm3LjB/XneallSvNTazYjguvnezVkFKzSeTSo/rQ/Hj8uD5TkxabBl3nuUozNBrNPOdVBhEJLwvlY9qyJMWSC4/Fhqd9vfI8J7K1Ix3gLrlABhbbBJSkq3CAFAdQah+hAfLa6LyRtenTp+PZZ5/F+uuvj+eee06zUoO1F154wZuZPE1TNBqdzzXZFoji4YH9/f34xje+gXnz5mG33XYDAPzf//0f/vKXv4zppMPDbUIK/SGjXC4AFCMlhR5Bui/maFlST7R4khoLF0iRqYbY7kC5CJrM1UXpYzkdn2tleiibbQqs367OhrM2ZQCOl2lPw1LUCcXCBi1luZvcSDrq0KvYMD/oyAruTm6dtLeUb4q7Hn3btFNuEQwVy6sCMe3sr9abe1oGrPj+Zrsie8XLNHWwk26imZVOZMzNlysKgJeR4r+lsPVQtF4xQ0X3jtpXJeMkXZNKnmkme/FPW5MhQaKnmuLHyZIMiCWAxJrGxQV4qg2Q+nzp8/KlJ2D9hVd6r8tENt/7R0aRjSonlLmfpVO9EEhqg1Wy+gQZIMhGyZ0HDDmxzvhJzNO+/eu//iv22msvbLjhhgiCALvssovFIHF75plnWpa3/fbb4/e//30hYehPfvITb/qmVtYWiDrzzDP170996lM4/vjjce655xa2Wbx4cccVGK8WyAAiZiNXFvmol4sUWSKQ1H0ljI5lTktidBhlI6zAmjZDBAFioaLcAL+Am4yDslXtigAA23hJREFUIPO/zM1TnrfJ1UQV19tMD2e3XPBndEcEAGygRQzTDevPgwwCr7aCwEoZk1N0S1WdW4BPL39c54/i50H1b4eFUsdS58TZP16Wb/t2spW3Gr1XJcWsAkZ836q6+JixVgDdZg9twM8nPHYBFU33Mljxb1nKA9VJJxDdxSbUzI9HZZis5IAa+FDuKD4XninbCKANq5UiSYCwZsBgIQlwkgJsDlaVb6qJsNvoPiay+dqmqneCggpcBtK9bmUAqqC3Y2x1IANIKVV7mq11542kXXPNNTj00EPx1FNP4fjjj8dRRx2FKVOmDLq8M888E4cffjheeOEFpGmKn/70p3jiiSdw3XXX4Re/+EXH5XUcvvGTn/wEDzzwQGH5xz72Meyyyy74zne+03ElxqMpYaLQGYZFLURa99PoQo4dRVolpPXlnyG3Ho/eA+zM30V3lM1EFDvZYmZt18rYLbdh9KVP8IEp48aj7RWAMhF/NltFy2my024p8L4lfwZg0gZcPX3b0nOoAg/uOVwzY1v4dGWtjLKhk2j81N6/WZnMy9g5AiF0b6rCvqtG71SeW3arMsoAUJVb2HcOVXXk19h9FglIAeaZdgMqeJQesTVDiaoiYOayvdZEwwWWygApn9g8SzKgMCFxsRydpLPNoJY0SfHqV0/CzOMva+/kJrgVwbTJTu5eT3dyeXc5GfdC6GV5H4Hm6PA7WaY+Qy1jItp73vMeAMADDzyAE044YUgg6uCDD8YNN9yACy64AEEQ4IwzzsBb3vIW/PznP8e+++7bcXkdg6ju7m7cfffdmDNnjrX87rvvRq1W67gC49UIQJGpkGFRcBfISOjImd/v9k7sce89o13VUnPn1yITMkCSmIaGRvE2Q1QELWQuluQAimuLfO436vB966jzLGNZ3LpwoEJASgb+edpcNqmeZrhx1vaW5mlyGGB1EzhuhWGKrp6+LY55/QnrP5VHiTd5BKALrNxRciwCXLvudvq4n15ujkXXro8xhVXAtHiN2gdSvK5833at0+1bH78IpGibVmDOd43cjhTg8+mZyvNOtd20CdzcpJluJmw7qtd0wj4GyjcgMjmLnKARISxdjtrfJIIUcWjE7vUmstpEdOS0bwUWKsv0JNY+0MR/0wTQhamBhHLJcgBlTyMzugzUcETXTcToPLJms4kf/OAHWLhwIXbYYYchlbX//vtj//33H5Z6dQyiPv/5z+OYY47Bgw8+iHe84x0AlCbqO9/5Ds4444xhqdS4MGpMhRutEWihOUBgSyCQGBMAVeXjp3Wuu1E19nb+E+EVlPtH9TIw7iX1H3D1Q3ktmEYor4sGa7brjcrxCdJdpoMLtumbyiUgoiK6imUq16UdhcfPOxaGSaLjubmhZBDguBVPFHRUfjF90T1J1wbIcpCm3H/d0lxDmofv1N6/salgzN5GA2YW+twaZe7ZMravE+sEjLUCdrSNKs8PmFqV41vnE5tnSYqkbgTHKYpaqlYuQLsD9gMgF0C1I0CmyYqF5TqivEYB+28mO9buKiEgI7tJD6QSoneSuX28WjHoxSwnK0vpkrGHtYzB41MC6WvtuPDc34EMgPGZMvCfzsIwxOabbz6orOIAsHz5cvzgBz/Axz/+8cK8gStWrMB1113nXdfKOobSp556Kq677josWrQIxx9/vJ6v5tprr8Wpp57aaXHj1kQQFAAUCc1JLyXYqHOsMtge8NyfvMt5VnXfy0+/A6lYqNTTAXHww/e13WjqP7nM/GX43T9lKRJoe98lLd8nKKwnUOWWV659Kv4npoivo2g/AmK+/Vzg4gMtdG2umbEtS/lA+anMDpQ3qsp4+SesfKJS99QZ49R66NpKz9SJVQGkdoEY1ZknmzVZwY3GKM2zg/vSDrRrPB9UmSi5IER2XUX5Mi9wcpgP7sbjzBOJysvY5zJt10SxmzfY3vrvPme+nHh6dokSaQOZe/9c1x1nofS9ahMUD6dRdN5QPxPZTj/9dJx22ml47bXXOt73qquuwu9+9zsvSJo2bRp+//vf42tf+1rH5Q6KjzzssMNwzz334LXXXsNrr72Ge+65B4cddthgihq3xvMuyVjonFHU2EW1kAnM1cjl3nfvNSZ1pclMyYoJ4dSLT+fBk3P6wB91VNQwuY2FcICKD/RU9UV8Wxd8+YCOm2epCmTRvgRGbPdiGStjA0L6nWTKVXfciicKx+Lz8RkwZSe6rBKfc3BJ9aS6UpLP78/czrlm5aCT/7fr5q1CR1aVFLOV+fVx7W9btb6cZasupx03XTvmuvvJ2h1QWR01G7AJzTiZdzUQgc5vZNhvUQBS9E3tAc9fteLbpw/2VMfcfJG/vvvMgY69XOggAw4oC21b4b/Q5WVJVojYzisymFPq2NJh+kxk++pXv4rf//732GijjbDNNtvgLW95i/WpsptuuglHH3106frPfOYzuPHGGzuu08SfF2CEjMCTnvIlyRxxqrDcegC8wvPRMp2zyjNjPGCYqSylEGszYiVNFGADKK4P0RoSKs/DqNpsgT29DJnP/cRzR7kuRSOcdlMHFOfL48Zde75tuNvJZpIMu0Tbu+48yjWlOnG7TDofXmfXfILrsojEa9dVQGrBqidxzYxt0ZeUa81cO2HlE3q+v2Id7P0Ha7Y7011nzs13f9otl5fV7v78mQbaz+XTKbvQSpDs0824zBRnoOh3O0DMdUu58wNacwXWm2j2DbQsczzaLRvuUBhIkUkPAwUUBeMW4PWkhuD7mDJEAZAVZBAl+eVGwjIMg7B8WGoydsbTLXVqTz/9dEHLzW3OnDl4+umnOy63LRC1zjrr4Mknn8TMmTPbKnSzzTbz5mGYSOaOAgUosq34wpHAc6wyl2eJcscFifokWqeRtTFHlACQFnVPOYDkYtksSY3OKhema12JjogzkXO8o+YaK30ML9AoZ2/s/+Z3WW4hkz+Kh977yqbjFgGUC7D4Mp/+h+eXqqdALNpLTOkzXo/vz9xOu/jqqV8T5V4HF0C5gKeMxalKyNmqvmWgrhOWya6nvdy95r7j8YAJwE44O1hTDJB//7YAj66DH0C5mbHdzNlA7paUatgm9fYSWZIgcPLmcDaKT7L8z2DSub9k7n0m9onfH2KiZCSL27NEmkWGirGEItDaNQCQ4djkCHwjGk+31KlJKfHiiy9is802865/8cUXIQYxjVtbIOr111/HrbfeimnTprVV6LJlywYt/hovRsnaIKVmnAKpwJRJaqkauySf32qsQBSNuJOmATk6EkkqkCRjNS+UCrJOLVq3rN4mX41J4knLffsUO79MjxRdzZUIFNMXQbFoqWaxiikPTCJGvswYBxQ8SpD2tTOye0/Va67w3I4YM+CQ50ZasOpJXDl1G4vRioXRZrllyiDAEcv+iuvXm+cc22YG3WtCIM1n7QjLq/fx3Vs/s9Yuo1XlwvMxa+X1KK83N3feOv82xZB1MtOxioL7p6xMLkLO2DNniZA9xypE9PkAVJohiFn5SYpM2g8Ad+2l9aZexiP1JprdsqE/CqtMQO6b9ooPIil5appkkOw6tzOFC92XLElt8DtK4JS0q0Mt441qO++8M372s5/pgDjXbr755pFLtgkAH//4xzsufCKbeQEVUApjlcWOZlqn6QMoYzkAnUBvLC1NMjXPX56xnNyPSR0IayGQZGpUzTKaCxlAxNLK8gvY0SpuBJOMpRLn5mBSIrDypXChJyX45B0QMV3UMCX1BEIzWn7Gin6TcRcfpS0AYOmLFCNlT23jMzdaj7skefRfVV0Axf70JSlkYObrA6Cj7/i2rcwFQBRx6JZh+o1ix1KViqDKFWi2KYKrdvI/tSMCb1WGvU1xn3auo08b02qbwnrGQtkTDFd0uG24eSw3nqcOvjQJps5FlxSNonmyThHRtFVq+UTLF+W6ZV2rypNHRswTWQpbG1rFQPlE5K54f7SCijIM3R030SGUENUTPleRN8cddxw+8pGPYJNNNsExxxyjs54nSYJvfOMbuOKKK3D99dd3XKe2QNRQ56qZiGaE5aohAoCwO1LZhzX4UICFmKhdb7tzzOrra2z43GIEpjRTBYGs3kSWpAgp67IMkCac4i4bqRvXg5Rce2ESCRKNnjLQZI6RaQBF5RJQQzPzJv6k361MAR13mWGz/PsQYCq6JE2qBBdM+IFeX5J6o+kooo80Tq7Nf+UxnbeKl+cCKV/2cn7uZrviMreu5lzMtmVasbL927F2AVArFspd105oe5lxpsp199BzK5xn2GagizmFXBPOM67KT1uyHWpfA6CE5O+V2TZL3MzbwgJQ9B0IASQTM82BYahtJpTnAbPZJ6OBasf96kbiuW5X/Z9p1WhZUs/LmniXdcLazTffbP1vNBpYtGgRvve97+Hss8+u3Pdf//Vfccopp+D444/HF7/4RcyePRtBEODpp5/G6tWrcfLJJ+ODH/xgx3VaKywvMSGho/H43FmkjwLUu6PD/seQhXI1IACBQNX4Z0mGRjPFAYvuw5077cq2CRBIqUfalnBe2CM5wCQg5B0QF6lrQOVzizi6Kg6ytNsxSUoTf7oaK/VtOlO1zM5Jxa9JNWNiR+753JJqW7U9aa2I7eJuxrK5+QA1hx+dh8tq+ayd9XQO/v/l25gyytkqH6jirFcZiOuU2WrH5VjFqFUBqFYsgcs8EGMhYAAK7zj9Lr9iNmv3GKSbtBJuppnFkPhC7X31FORSisN8nV8P5aZnUUElAi9d/DnMOqXzUO7Rtl9stKP1n4vI0ywrBUq03KcFLeQMoyl5nGvlgiefVk3ms+kE6egwUW/0ZJsA8P73v7+w7IMf/CC233573HDDDTjyyCMr9z///PPx/ve/Hz/84Q/x1FNPIcsy7Lnnnpg/fz523XXXyn3LbC2IKjGrAWO6CCEFktSmDIUUYwqiXJ2G24CmWeZ1Z7XKyqxF5Kn5zTVXbsdCuil77i9/tNBgrKqzVVXwJfzMXa9p6466rHwfc8MjBeGZSHm4rCxisWqbzo/h/121rgqbjNS18JkPQHWiTfR1tkIGcB9VX9LOVtbu9mVAj7Jlm/LsqV6IicrIhRGx5WxbV1De7nQxE9GqrjkHqb62qCAm9wAogA8mc7CbjtL1zIZh2pYJDqLK7O1vfzuOOuqo0vWPPPIIdthhBwghsOuuu7YETH/5y1+wzTbbIAxbQ6R/3rdpiCYjCRmr5HUyDm3aXAQIayHC7jDP7xKgNmPsp7zhjUCWpBawo4l33VBfk5DOjN7cSCQZSZO407OeylA6J6WvKkYW2dNTcKM8V7oepWJqf1JPwdaVmXHR2Z9YBIiE0KwNLTf7Ffcno/p1Szs5Zpkd+drjXnejKc8PPkwdUKinWz93H7Nf4NnGDzrdsn3L3U+Zla2rirZsBQrL1vFw9MLcZ1Y+pep71SqPFJ+Cxa9XKuYpUnXyu4halU3sB+WI4tuqb/WeuykO/Povgainu/L8xpvxZ8U3N6KbLwuAxSK6SUsB5TnwidBdC5zrzYMAzGewZ7bWhsP6+vrwta99DZtssknpNjvvvDOWLVvWdpm77bYbnn/++ba2XctElRiJMmUcak1Us7+uOnmodVEuiE7TDHFP1KLEkbODFj9SoL7JNSdkgIMW/7mwjx5NeUbh3HTjwkTotJ/P9UfLuBCdd0oKmNo0vMtg0eF87iFO51v19oiehbNfFUPCBep0/GJeKXMMGSih9wkr1fx5ylXnLRqAykq+TiwtFyCxZ2T1NCuAMbNNpl2ISZahngZsfWsdk1tmlRbKZt785RavY2uxeSfs1FCYLJc1LU7b5C/bYlnZQKGMwWglHq9Ka5C26VcpTnrr/pcMTLXIzJ0PgGQtbuvY49GsYBUPCOLMIoEnEwxgZAuAXw/lglT3GXCF6GmSthVEMByWIkM6RCppqPuPtc2YMcMSlmdZhlWrVmHSpEn4wQ9+ULpflmX40pe+hEmTJrV1nHq93nqj3NaCqBLjUyvQd1iLIaIQA6+vhszDhsNaiEAKbPfdW8ayutqqOhAy3rDYfn57CgxuMp98mdI8mIbILl9GUgO4xDqG0OX4TGurWHQFdewEflzXjQuKAhlAZjZYCmQAGasoSje/DM9x5QI2Fe3Hj8PdfeoYBKAAxTKVJbZU5dqMELlX68zVcvirfwUA3LD+PLaPAXcGRAF2MlNT71hkSLLAA3ooQlFFK9I5lte3mHrA5N0qRgZyzRpfzstz61N23JE0CrZoZ148wLBAVsQb60jV9uVRXanez7BPgVQDkDJmrKxT9rnlVI4oFZnH6xgIWxcVCJXqQEg5YfNFuXm/ykCjO0CjbQFoTSsBKFdrxnN3kbk5A/lyKSVGK01UNgzuvIme4eCKK66wQJQQAuuttx7e/va3Y8aMGaX77bnnnnjiiSdK17u22267obu7PcZ2UCDq6aefxne/+108/fTTuPLKK7H++uvjtttuw6abbortt9++dQETwHh6fzXakwhqqrFKe5oQUYgUzTHXQwHArVu8CQCs2enJ3vP0otL9qjoSX2h4IFmOKDZaLwjNnd5ZMOqcQBZgOgVKF0F5byhtRBWz40sOqupggBItUwBQIB0wAI2DKzdVgm+aGcNEqXXHvF58ITmocu3I1x7H9evNw+RQAFCMkwJqxZMkAENgyQAnG4hRKgVXM0XAk+eRov0oGShfpszVWUFvx8tW2xXdgkZwXxS5u6yXzzoRvA/GeGRdJ/toNkLKgpvPdc/ZEX9Cs9b+sh1WxMkVxcvX+7ShZfLppmh5Wm+qdXVgxbdPx7Qjz2tZ3libyzyX3T+e14ubGwhAOf98EXkAvBPOB1JARqY/4AECgRQIJjoymUB2xBFHDGq///f//t+w1oNbx5qou+66CzvuuCPuu+8+/PSnP8Xq1asBKOHWULKJjjuTJmM3H+0BQJhrClSEjNTuvrE0Xy6VssmJfY2EvT5wNCRFXYDWA8RKO0ZuOqAYckwUO2UJ5g0e1xbQ+kAGWqsUsUaRN6RcO8XPgZa75yViWZgLUAEpdQyTV4qzOrQs0Mky6XswNv+Vx1BPTQRhlYaLzJds011n/pvffOJls8yeCseNlONaMxu0+bVXfHueFsLWbAVWOW79q9f7z3kwbBUHUFU5mdo1F0Bx87FSgK2B4owsB1Bu0sdCNBkP1EjN/HiUhoa3RVma5okllVYqaTT1Ps2+gXE/lx5pLPm7TuZmJfeZBkD5II5HQpr15nrTwIraI6CYAoNvO9pG0XlD/Uxku+2223D33Xfr/1//+tfx5je/GfPnz8fy5cvHpE4dg6hTTz0V5513Hu644w7EsfGt77PPPrj33nuHtXJjaYEwIIobz5kl4hAyCtE1Y8poV88yd4Sc1NNSAEVGDYjVgEtRAB8cMPn25yJYHyNnCdnZMdzydD6pSCKeHCGeFFkdTSEiLT+WDIXVoPI6uOenhe+sM9X1YSCCknXyDp4L2o987fHKa1tlH3zpL/pcRKCylfvMByrUdbBBnqmru29Q+l8t8zFgfkG6OW7relaBJw7U7PXlYHA4rSp7OTefMFy5f/w6HG7uBLW+UPlWplms/FkuY5e4+QTlJgjETg6ZNBpI+utY8/JyLLvq5Jb1GSvjbQ9vb+y2yh8QU2jHBC/DEfiXuPHI1FQxWcE16xP7j6SRO2+on4lsJ598MlauXAkAePTRR7FgwQIceOCBeOaZZ7BgwYIxqVPHFMqjjz7qzeq53nrrdaR+nwhGQApAYU6dsNYFAGigD9GksYvMu22rndVoiXVE/SXi0jt32jV3a9gJ+SwdVR7pw7OZByJAlpoRGSUXdU1F8QVo9jeR9mfaTafLIRcGlGswSFIkdcetJwMAAjLKUO9tAEgtNsqUZYM4wDCDxrWX5VnVDWsW0HnlAngZC+t8hAYoYAk/jatv/iuPec+9E/vA0r/gxlnbo9YCLSgNFPI6mOUyAMt+bj5UR76dW38faHHNB6Rsd6Yv5YOvjCI48iXzrLoMvrp2ktagXHfE8wIFhW3KwI5PS9MO82TnbAqs3/4Bis0CC+e/2ZYBLU/kHqDcTzJvy9J602an+ut4+dITsP7CK73nO1b2q7lvAcDOI+Gu+OK19N1DvhwwbKSrg+JGeetMW5QzfRCQrlZKA6jRQSZrheXAs88+i3nzlGb0pptuwsEHH4wLLrgADz30EA488MAxqVPHEHr69OlYsmRJYfmiRYuw8cYbD0ulxoOZjt3pwIVy6/FGK+oZfRB121Y749Yt3uQFNJTOwGcKWLQbGRR4Ra6+DkaP5pi7T0YSYXeeCiKnyfmIPqyFiCdHVh4uYpPC7lA3eDIWuhwqi+pH6RRoChoN1vJpaXiEjSq/6OqTsbTcodx9YNxi7bnfOrFGicD3gy/9pQAeXMbJTVnQLV03WnEbH7vGyyfjx+ZT6dC6cvaL/w6c//YyWu4e02axyq93eRqBInDhDIabF6oVgNJuHY8fxNcht3LzcDcR3z8Q5l3TYmgKbmFBLtbHCX7JEuW+c5kqvtyeB1C9d83+OpZceGxlvcfCitfXZqB825vfRgjui8Lj28pIeMTk9rVea+PD4jjGmjVrAAC//vWvsd9++wEA1llnHc1QjbZ1zETNnz8f//Ef/4Gf/OQnCIIAaZrinnvuwcKFC/Hv//7vI1HHMbMsNVmLteaAAShKdkeZg0fb0hwQ8SkQWhmnosty3XDWCchHaxJIGvZokO/vNkyBEJCxA2CELew0UYINddwkg4y5LiTQU9JEtVDPwE7nrhvKSEUcJXXDLslIogET8lyWdV3VExB9dmOdApBpa9ZmsFYFdAGbrZEBcjYu9WqjSD+mJkI10Ynd0s+m0Xp3eh3OHNH2H35ZMW83b2AHjBh2yl9v2oZPQs3Nl27CjZ4koyz2ZfOo+TvUdtxm7QEeoPiOtGs+wbha7mj2PPXlCX45cDIDjup8bHzi4ZR9c/deQsvrTYw30ykn4LjeK1hFwAZQ1eX758Tz1QEw2c2lHJtAorXRecC73vUuLFiwAO985ztx//3344YbbgAAPPnkk5V5okbSOobY559/PjbbbDNsvPHGWL16NebNm4c999wTu+++O04/fWSEis899xyOPPJIbLnlluju7sZWW22FM888s5DL4fnnn8fBBx+Mnp4ezJw5E8cff3xH+R64uS+mboyYmJMS1432nFS3bbUzknpiMUrUuaRJhuvXm+fdrx0ARcv4yJj2NaO6QE2Jw0Z5PhqdhxWrZe41zTTTZO+bR9VFElEtRNfULkQ9kWatSPsQ90R5UlSBqCdG3KPWRz0xY76EBlVRLURYCyEjs46uGRexknH9TiQEPrC0GvgMpxGI4ZMOkwDeKwiPhcXgENNks09FFsmerLkonL9h/Xm4cdb2en9+XJeZ48chEX8kBBP6GzDFt5FhDjRYoIA7lZFvaqN2zAX2HIT4thmMuc+1EEHp5MI+JrRQTwbwO80uTgk3fYyUYWyLLHuWpnjxvGM6OtZIWkHMzdhnwFwbepery6pKq9J+HVxGi9yihfT2I2Rplg3LZyLbVVddhTAMceONN+Lqq6/W3q9bb70V73nPe8akTh1TKFEU4Yc//CHOOeccLFq0CGmaYuedd8acOXNGon4AgMcffxxpmuJb3/oWtt56a/z5z3/GUUcdhd7eXlx66aUA1EzMBx10ENZbbz3cfffdWLZsGT7+8Y8jyzJ87WuDnyeK2CiaWqGgVYhCNPvreP2aL2D6py8Y4pm2tl9uupMWTxOTQyP0NMsgEnTsdrLm83Ln96KUBsxc4MVDu3X+qViBFlrX7C+OdGldWAuZC8OUHXYr9kmHF0sgnqxSIASxAkRJQzVgUS1EIgMk9cSwTQSg2CTRgRSIJ0sNQkn7RYwXsXtKMyUY0Bvd0WeSZeiWIgdRGfqTFN2xhMzZoqSZWgwNoET2cTPV+yugAzaJMpVuu/E460XpFCiKEOBMmF0/AkhkKcvn5QPVxACKnNC0XDX5PoJ5p928QG55gD33qwtW2nVbm338uqUyK0uZkKZZIVTePY6P5WrVqVssVBvgSk//wnRVSf5+u0AqSKoTdY628Slr+L3miTQBnx6tfOBWjHTsfCqftTa2ttlmm+EXv/hFYfkVV1wxBrVRNmg/1FZbbYWtttpqOOtSau95z3sslDl79mw88cQTuPrqqzWIuv322/HYY49h8eLF2GijjQAAl112GY444gicf/75mDp1akfHLI5WTSfqzk3nTrcwkiZkUHksd/LeKiubed79r8GaNJOols1GT41z3BNpYJLUE+3yAwABYQGTQAZIGinSeqKPJ6XMO0G1TORsFQErwK9ToTKzJFX6KemfH5CAlQAgJH8NkrxjFqSxRyAD7P/kQ75LOGL2gaV/wS833Qk9ef1XDzSRNFNEXSpfkWZvElU/xaZJc77NFDIUkADQSDQoEkGgXTgEjmh0Gkf5/Wymhag6AAWQQ8vI3HV6uTQJJvWBHSOXDT3ZLgii8/Ldz6KrrDz9AADNZPr28ZUHmGfN986UvQtWskvGApeVY68XFthRdTDyAj7Viy8ViVsHtyxzrjlYkQJpY/y49PhAiEwKE1QClLtsjdi8uN4HpFKgoIkiMTkvr8x813UkbDhIr3GEk/9prC0Q1Uno4OWXXz7oynRiK1aswDrrrKP/33vvvdhhhx00gAKA/fffHwMDA3jwwQexzz77eMsZGBjAwMCA/s/FaWYkZyJguPEXPGk0hnZCbdgvNtoRMhZWJ8LZKMCfL4qMj7xUVvFqVonKNll/A60HkLoR9wCUqJiAtJh6gLEVSQ4OZYCkkUDC5Jwy0X2mM4p7ItR7G7pT1WUmgQWstJBdVVifa9IwoJe7VrIkRZAISAGd+HMs7aDFj+C2rXZG3C0wLZZYvboOSakgcuF8kt8fcz0V+CPgkiUpZFOxTcT2xFmgQVUgA6CpQBndo3qufSPQ5YruAQPeTMSmzVgK9pyZfW1WgD9XFDVJwJXOjVy9BMJ4hCUAvUztk5lksIO0qg6zXV1UWToDzkC5iRvtcHxhAjTyZTZYKrKibvQwNwUITBku6FOMbTiuQFQgA6/WxCdJ4O0AYDN7Q2Gb7IzvxpU3VgzWcLjjJro7bzxaWyBq0aJF1v8HH3wQSZJgm23UNBdPPvkkpJR461vfOvw19NjTTz+Nr33ta7jsssv0sqVLl2LWrFnWdjNmzEAcx1i6dGlpWRdeeCHOPvvs4ooSyE7Ccm6+fFIjYbrDZ+G+fPQOqM7NN0HrnTv5Z63O0gxw2uSAARN3OW9AeCcIoACcaI6wLGdJAOSh1cXMz6RVavQ3EQgl3rSYLNbpht0h6r0NDdaa/c3CPH1hLUSWZl62KqqFaPQ39T2jUS+vf5YEePcj93uv2Wjae55ehDu2eyu6psbIkhSNgQRRl9KBNftSfV04qwdAdYpJBkBAhhlQTxRLiUC7/UQQ5Mymud9pnQAUEHUp4XKzr5lrxsxzzp85NdCwrzNt68urpJdJnrHbrAuEQLO/iUBmFpMjKYBDBgy0GbaAGERuZUykW6fC8jytB31zSxNbXOxnPNpjMarqQOX480IlEHHkLLOnfeH7mmfdMF06OIZJE8aD/WGvPa3/ZS47MldETqyb286YZ8wG89z16rtfaZIV0huQTdQpdNba8FlbqsXf/va3+nPwwQdj7733xj/+8Q889NBDeOihh7B48WLss88+OOiggzo6+FlnnYUgCCo/DzzwgLXPiy++iPe85z340Ic+hE996lPWusAjOs2yzLuc7LTTTsOKFSv0Z/HixWoFYyeAcuqYGqIsSfH6NV9o+9wHY+00yABKBdA+QSvgp6NNuoJyvQZnk4RUUyPw1ATuJKu8TLchi3sidE2NEeXgh5cvY5k3dIEW7cY9kdf1CBgmLMqF524DG3aHWmQe5ACOrq2MJfZZdN+4AFBkSpOXoWuqyk2W5lquQJrUDxQJyfMOUQZ4GUud5JMyMMtQIJocwY14IsARCaHL5UYpJch1SPvrrNB59nralrYPWN1IFOzNWC84aJLWM8b3o/PlEaC+/3pqInYsn/mAimY0WJCFm0yzysqOFYhAv3MuC9WqTrqMHMQRA5UW5AflLj7SVnHN1HgK5adBgWCzILh15OkL3Pebvt1AEb7ev85/v4TTXpl6ji6ASrMsnw5q8J+1TNTwW8dDj8suuwy33367NdnfjBkzcN5552G//fbDSSed1HZZxx13HD7ykY9UbrPFFlvo3y+++CL22Wcf7Lbbbrjmmmus7TbYYAPcd9991rLly5ej0WgUGCpuXV1d6OrqqqwDTfsCqMZKIAdPqRnFqRD7kXXppUmGMPZ3BJ2MuKsi88w2pjFKGe9AAEhro1DUgwhp8q6kqU19K5qe6U8c14eIi9oNJTxPrUicLilQ761bo03TUasGVInNU9RX19W+ucicwEWzr4kszYw+Sgq88+7fl16TsTLKh5UlKeJJEZJ6ovRj+blk0mhlAFs7A6jrcttWOwNQDXGYu8ZkJJEgKbpeczcfzXOoNVe5m5A0TUnd1E+xQ9Cgy3KxMBDkpprgcynaz7Wqm7vc1cr43IZqGUvH4WigqvRQ5hpmFhtVWM/c3S6T61qBgUN5xBhZweUmi5F6xIrzpTz9it4uZ/AoJQvgF6aPVubtKiMWKhABpMhd/PXic6DTpTgMG9BaoE/G2x5fUIIB78V1tN9ompq2ZajuvGGqzFrT1jGIWrlyJV566aXCRMMvv/wyVq1a1VFZM2fOxMyZM9va9oUXXsA+++yDt771rfjud79b0ADstttuOP/887FkyRJsuOGGAJTYvKura0huRg2e6g1Nn6dpiiAfxaks2eYyjmSU3kGLH8Gv5r7Fenldl0k7ZkW8wWaijGbK5HKS0oAMQDE9ZJytSlPbNQOohipj25QZjYp9LkEhAyRJPvJjbpSoFmp3oSpDdXgEltw6cnAnIzX5MLECMpbY/a7ftXP5Rt3e/cj9+P1u70QCJdhv5ACQ7gIxM9qlCWGBFEBpvCbHNaxe3q/20fdSApHKAUZMEo+wDPJrw5O6Ugcc1gwo0mkiBB27aW2vtVNp7gKRBhDzAYAJPgBcdCIcMML34a2BiKWVU4yLkXWdPG4y9z3yuYLN/n5mtpi0sVpQrjrl1AuSOJvkkxC4bFOnzIilBZQCYvBxRsNmYXeonrV6CUOuZ0+w2yC9vnCf/ffJ/LaninG38bWtnNFX96wkomKtjYj98Y9/xE9+8hM8//zzhRRGP/3pT0e9Ph1D6Q984AP4xCc+gRtvvBH/+Mc/8I9//AM33ngjjjzySBx66KEjUUe8+OKL2HvvvbHpppvi0ksvxSuvvIKlS5daWqf99tsP8+bNw+GHH45FixbhN7/5DRYuXIijjjqq48g8slYjDd9objRGJypsvzjPnZAB3vvio6X7cSrcl9upDOgIUXSthCx9gbVdJK2JPnlnLSPjlnO1CPQd1ULUpnYVhOiAAmmctQi7VQ6p2oya1k8pF5RKrwCYhpjXNWkkitmKJYRU4GO8AihuUS3UrkgOdsiEC0CZe4ue1Wnr9wCwOwnusqPs8OSeC2shuqbGuZtOWAwQueB0OZHRZfF8PrQsYPUQ7Hmi7el5Ni7CosuuEJQA6O3d8/JF4fFr47qHVH2DwketN4OOVgMCl9koHtsfsUfHEFIY/RfsNkXEygXrE5Lbwv2iO8/9LaJQf2QUjQtNlGKbi+fGB25cOO5z2/Fngd83t1ydlJPt77o33dxQtA230WLwKDpvqJ+JbD/+8Y/xzne+E4899hhuvvlmNBoNPPbYY7jzzjsxbdq0MalTx2/NN7/5TSxcuBAf+9jH0Mgj0sIwxJFHHolLLrlk2CsIKEbpqaeewlNPPVXISprRHF5S4pe//CU++9nP4p3vfCe6u7sxf/58nQKhU+O0dyClzhPl5l6hZamz30gZ76yABEmujaGO6LatdsZ7nrYDAX739t2t/zzDOKUuAGzhpTsKdOcBI0FykmReYWYK3mAHmilJEtVQGuZJIEtM+Dp1pKKeIM1dbcRQWVFZLPle2B3mSQSFBhgilrl4PIOIGeuW2u4/nzZiPNoe996DP+y1J0IZaqF3mmT63HXHHUskvXXNRgGGUcoS5RYMk0yJ8aXN7AEmGpK7x1wARevpnpJLjrvwZCQVWNVaHZoOKFPzkEUCCXgElGGNAPP8BDrNBKzzVMdngCVnSy0hurTTJBSE1I57iMzH0Joy2ht48HVVAyub0XCWefbj4EmLxZm7zl0HtNfJu27BsbCH/3V/1HvrSPOgAWLAabDDpQMuaK66L1QWYIJhfJMN+wTrgM3KW9uPcFvv2troPOCCCy7AFVdcgWOPPRZTpkzBlVdeiS233BKf+cxntAdqtK1jEDVp0iR84xvfwCWXXIKnn34aWZZh6623Rk9Pz0jUDwBwxBFH4Igjjmi5XVkirsGa3bC1TrYoPNMwjJQFMsgT5DXNiEqYzqi8jkVRN/n/eQMlUGzUveJbhw3jxwGgXUqqA0tMJwgaBeZ11mH6rPMXfCZ747LhTAevFxfpCnZepIPKktQCUcSKTRSzOgahUjFQpKGOUMr1QtyF5QIVGQskDXL32ccgV5hAWohKIq0VgRhfHidVT3WsIPFnB3c1PPxbnZvaxg1d58CcP0O0DwdYvB4ukCpoohjb0E7OH3NdHSbQ0c7w8+rU5d66DsV3siySr8pESfqW0bL73/NurXUUObtK0Y/07mrg46mjj4EiK9veB6Ds/ar1pWNlJA4fahkT2Z5++mkdwNbV1YXe3l4EQYATTzwR7373u/2R9iNsg+Zve3p6sNNOOw1nXSaEEZWuO+4ozBvm0Ylw4R2fcDvVvHMsszL/ftm3LwcUnV9CTFEkEU+O0OxrFvJXAchF+MVRorucAJVgnY9i3UQ+Z5VAWBM6Q7liP1KVxiDJtIA9S1KI7lgzCzKW6Jrapd0BAyvrSBqJ7nDGg5i2Xdvtzrtw3777aJYl7okhYoE4fyCSepJr3SKtHeGiXGKARB6V2OwzuiXKLq+uU2itAwxDFXaHCESABsvTRaJ3upL6WDE0y8jBs8s6+dIIEJAiRktF7qWW0FvAZpDIhRuIFAmUCyhjYL4VkHHnl3MZWe9z3MLV41onLsHiMqkCWxzWypdwk0/o7UsGmiWpZiLdMkbThAh0Yss0f+aorqR5dJ/FQhmtrjkTkPNo64IGroSl9BlPhjpayTbXmppomLTXG2+8Mf785z9jxx13xOuvv64nJh5t6xhE7bPPPpUpA+68884hVWi8WJa6M56bkGJrFBjlWoYo7HgUOBjjIAOwowMD6b+dlAMprIVo9jcLeW58nQEtB2wXphYIJ6SVEt7oJdMwF3VbnBWx3Q6KTUkaKts4AR/qUIUUurMt69zM9cj0qJPKEZKApi0E5XqL8W4yEvo+6vsmocXxWZIhiO2oMcoYL2ByeakUEcQopXquQVtDEhS0V1FPDtgaKZBHCHJ3Gm0rIwHkAJim4uFG9zGsmWc2qSd6OZ2XYlwDa5m5GOa8uRGLatc90c+pG3FX1Qn7Q9vtnGKtAFFZNKzryivLfG4fW0kHZIvt3DQGvGw/QBvdd+BPhx2ANHe/NvubQCM1gJ+531Xd7Gg5cq3xYAfazmccGKUQDntXbDv0/zajl0fLUgw9um6iw7099tgDd9xxB3bccUccdthhOOGEE3DnnXfijjvuwL/8y7+MSZ06BlFvfvObrf+NRgMPP/ww/vznP+PjH//4cNVr7K0EELkvWpakSOrNytDh4bLfvX13M6eUI5j2h4kb41oXXkNqnKiR5aHbZZEtWc4s8JEbj5RyXXa03gt22Hlw9xExI1QPAn/ec4slZF11GAS+fJFVHDxpYJhmhU54PFuaZuia1qUTjBrGz4ApgHRoElit9uOAggcHZElmmJ78eotYIILJ9wUAQWIAVpqkivWphZ65FjPtHqOOrqyDDhx3qgaBjovGzVdmhZ8n3G1p67g4m8rPjwMxfr0IfLnPWZXLyDXB6qLrKOzOX5VTbC+syW1bMEO+9W6UX1VbFEgBGVGeMLXPaGUsf/TfDszrECCrJ/Y9yM+JD2z4c8H1l+T60/vmyX0LaVMcxtvo7YpSBLf99A4u3VQTowSkkjRDMkQUNdT9x9quuuoq9PerCOPTTjsNURTh7rvvxqGHHoovfelLY1KnjkFU2UR/Z511FlavXj3kCo0b49Qv5WPxhCATY9UEEPV0j1h17nnXHpb4GlCNSNwTMdeUiqa6c6dddbLIBw78l7yuJPjlDbWtNWhHH8TnnIOUuVvAjq4SzrGSRmqNAqmxj2phgRVS4ctFts3K1cJyxNDv7hkSAysHcqF5hGZfQzemxDzIyITqpylNMjyxbNfb7sSDB++rrnMsdMdhOhjzbMhIIolVJ6VDx8EafZm7ACERTY4gchYQAES3mTMRsjjVBSVVbaxu6MhIQLnSAOi5Dok9pPsk83QKXIfE6wOYrPYaCMIcnwTG9BzqZYl/uhef8NgCV9Jo7jSIhChsz+vpY0Fb6Wxca8d95kt94Aa08GUFJkXaAC0QttxAxKGdO2qUwIByxTe1G17Xz3P/ZKRmLmhqV5/RK2mmWrJgmzTPuO9k8OfPr+SDCM1sMykCcy9Tm8blG4UEqRNIEjDRjU/1JoTAKaecglNOOWUMazSIFAdl9rGPfQzf+c53hqu4cW8UIgzYI5ORGM3d8649kDRYgkWWETruiXX2bQDomtqFsDvE73d7J+59915o9Dc1qwPACuvnc65RQ2RGcaZzMNmo3UaajfAoii4SOjScjkURhTIyujHXxeFmHlb1CXVjRWVR52ui81TjG0/t1nqdQAiE3RGiybHl3ks9HRed7+OfOmQot2jUjVxtlM2dRug8jQRg0iIIYZgk0j4RSKH1MpY5KFfryRUqI2Hl3tJ5tygnV2TCzHmYPmA6OGKGwu4QUT45NYEWAkD6WNLUjafXoKz4lO/LDV+nyExeF/pPxkFVwQ2Yby9iYb0X/Jx9rm/fXHntiJxbGQGoqqAW/T7lqQoIKPEPAAtAcWBGmk7eno2k/e2YD+prk9STgt6JJ+mlZ5NmQiD9HaXXCPhzmC+z06aY+1JgAOldyNsdnrKDtzNk+hpL+zoCI+t94Jbl0XlD+WQjKCw///zzsfvuu2PSpEmYPn26d5vnn38eBx98MHp6ejBz5kwcf/zxhVxPrezpp5/G6aefjn/7t3/Dyy+/DAC47bbb8Je/+GfqGGkbtrt/7733olarDVdxY252Y2Q3Ym7mXxGHkHGIpNEYERClxIuZ0Z8Amsmh9bzTUtOYKKCU1stH6DofEOV3iolxC9gyaU3f4YtasULbrY6WOmyp10W1UOt6aBnPDaMbNQeghrUYUY+dP0pIgainC1FPzVpG+0VsOhcAuQhdsRzU2HIWZaIYNfYuO8Q7F9Kr0TUnXYy1fT59TpyDGrrPFE3JPxycEMACyG1WDq45KAEUe6CBf7cB9+Qi1s8MqwcBKQPCA/Yd6Gee9iFwyYEigXACWvx8OLDi26trWkwAy/fjDFSVGJnYq7Js125gSpGB8l1nP0Pl/udaKF7uaE9bQpYlSiyepplug8jC7hBxPl2TyO8hDcTc+0TvAX8WbK2are2jZ4LnkaP7S3ns3Lay1BXtYfZG2pJseD4jZfV6HR/60IdwzDHH+OufJDjooIPQ29uLu+++Gz/+8Y9x0003dTTLyV133YUdd9wR9913H376059q79cjjzyCM888c1jOo1PruAdxE2pmWYYlS5bggQceGDOf5EgZjQJ9FLreJqd6UwBBvYk0aqL/f69G7UD/gzRYo+SQPDJJHd9OR8A1TVwDQ24Q+k8dKzfTAAlvBw0YkbprCig5rjlHt0XuOSmlAkkUZVgTaPbX9XkIYp2kWpKwSKNACiT1BgQI3IVaz6Gzj+fH56CBKHoS2ANAPFk11n3L+7Htf/2s/OKPMwtkYE2Pw++jBrSSOhe1nPLtGODt5OWSAbtGJvu53tbJE0ZGU8EkDZvloeeS2CWKskrTDFGc148dw0qXoAFaPhCAsNJZmel/TK4w7qLjbIQ1/ony5zh3a/q0NZQKAwDg0ZGR+5RfC3Ix8TpwK5s6hiK8qBzuNmrXqDMvm+NTSNGSKaFZF5L+zhiBwRhFs2VJqsFxmmRoJk3DWIoAgfSlU3HdjqnOBeYuI1acojONJjRVg8woNBGMQqhIRbgpIwIt0Of3yqrTKOeKGs9G6QWuvfZa7/rbb78djz32GBYvXoyNNtoIgJpG7ogjjsD555/fVlLsU089Feeddx4WLFiAKVOm6OX77LMPrrzyyqGfxCCsYxA1depUKzpPCIFtttkG55xzDvbbb79hrdxYGtcJCCEKE3yS8ZcuQRNB/wCy5vDOoaf9844uiEfZcYGlO58crbd0AZzRyUPUKWkiB0z8eAAghdIcEaND0VGBtKPe0iSFgHK7uaJVYplkHBYaLb4/RSBxVlDmUZCkF6H9k3pTN3LU6GVpplk5rXfJR51JI0FYC72RYxPB7GkvmNsJ5n7yzh2AHt0DAOpJPh+enXk8BV3XxOwnA/CEqIBJtsmnhHGj5zh4FrBZMAWyAEAxoM2+ph4oFBK1SqNLUc+cCaIouOpYh+qCq8DpcBVzl+fZ8uRFo2GHnjqHdDGO+47qyM+5zNz1nEVuB0AJR7/EwZO7zD0fqxw2OBxNt1SWplp3x+9DWAv1oCZNzFRWnKGWwpnLMKKUJ2x6H+aSpHYq0aBZAXuX5ZZxiKTeZHOhmoGeGnwqK4ueHC0gNZzJNleuXGktb2cO2aHavffeix122EEDKADYf//9MTAwgAcffBD77LNPyzIeffRRXH/99YXl6623HpYtWzas9W3XOgZRZSjzn83IjUeNlpubxTXq1Jv9dSSrXh+ROvFJUXWepLyDoGWqLlwUqVgoNxyYMxE8YoVrlShSjoMQGp3JKETSaCJIAoS1WIu8levH1IF0TTxvDRn9Tuom0ixgQIiAkdvJiCjUoIwAVZamkLVI78vTUyjgFKLRO6D/0/lQqoC/fuJ92O67twzl9oyavfmmX+EPe+2Jrqmm0dPBBbnLN0+wr++tui95pxEp9oQi53iUnIACnxm7F6p82PNF5scMa2Eu2E+RCZNRnEflETPGI6eAnHHM1/P5+lA3AI7OjadRQGKyoxOTARBbK+CyRzY454ELJjs+HLeczm0FNjellHDd2eQ+dYXi1uCA6WuKDK95tgvAqEXnXAZ+zHW3l/vySdHvQAjIWlx5vOGwYl3V9YuiItNjTe9CkZOwU1oIKdDob1ppTLS7V09RVA48dDSgA6Q481TMFzY27NNwRudtuumm1vIzzzwTZ5111pDKbmVLly7FrFmzrGUzZsxAHMfWFG5VNn36dCxZsgRbbrmltXzRokXYeOONh62unVjHT8Ps2bO9iO/111/H7Nmzh6VS48UCaTNQbsPjmyldSIFmb/+I1YlEj/Q7TVKIWHVmSnwpdGQUbUM6AJUbSOQhw3YOJyvzszCi3LA7gqxFuQA1sABRWIvVaC3XhIXdkV4ua5Fxt3lcCrzzcCNzODByGyxqzMK8wTduGFUHkbNVgTDXKRDM9SWMlmEiJ8lT+o/c5akjkexkpVw0S9oTehZkJHMAJKwOvjDdjww06DIBBlKDNTo+1SXszuf3mxxrMbByP0pLB0V15YDGlBlodpO7Ggmkl00cTNotAsl8Wcjc10bTZLLmu9ouV+On6yjNXH5c6OzWh9fdvW+AzXS7H3UvUqSNZiHjO9+HyvG58HQ0niiCJTIe/RcIiZ5/O71wrOE27r7Uzy9jy/W9F0UAzE27a2tRrn0UVjtiT9vF52UMSq+pjEN9zdycWjJv4zTAiiIoqcfESY/CbfHixVixYoX+nHbaad7tzjrrLARBUPl54IEH2j6uL8dklmWVuSe5zZ8/H//xH/+BpUuXIggCpGmKe+65BwsXLsS///u/t12P4bSOmajnnnsOSVKctXpgYAAvvPDCsFRqPFqhM8+ZEKKG+SgqqTew8rtnYOonzhnyce951x76+Bw8cVOpAoy7ROSOCM0GxUYgnq4csEXBjCK3Rny5Ow5IEdbiHNSYhilNUoTUsOSaDBmFuhPmNLjO7gsjoCUTcZg3SEKPBM35Kuo9Seq6wdcjRzZqdvUkgRSQ0mgZzLGkBp4p08WQm2gimRE+B9o9SUaJNTU7KQEZ2Ykp6V67gufMmeSZ51eiY0Q9MVKW3wegCalTK2u4kALk2CZtFADjznF0Vjy/F2dCVV1sDZjryiOgBaiRYZKY7fQ5pMYFqfOaMQF+s6+py8w86UTounGtGR23GOnnD+bwsRjW4EwI9kymOogl8EzPUiZs9rmd1PWNCtvLri5ASGCUXFJJo6ldzV79liSNV2bdb+2yQ9FlqQcR7BxsqYBpoxM0dXtktRmOa4+Oy5k7rY8aI+A0nO68qVOntqVBOu644/CRj3ykcpstttiirWNvsMEGuO+++6xly5cvR6PRKDBUZXb++efjiCOOwMYbb4wsyzBv3jwkSYL58+fj9NNHfhDgs7ZB1C23GFfHr371K2vG5CRJ8Jvf/KbtizkRjLRQAJCmqZUhmGtyEBUbNkCBrDU/uRiTPjS0HBYGlNhJB8ktEMY0uld10jmDYoG0rvQAKYtS42JOavypweIdhZ78kzXeNBdaVsLOiTiEzPVPmdXBdiGQEmk+YbUl1owVeyVrXRBRiPqqNXobdb6KzUrzhk2w2e3Dnu5cu5O7MUWCFE0tlA1rKsUB7cvHnxxQZDJA0kjwt2M+iDlX3zjoezWapnU5ItDMS7Pf6M7I3WuDAtMh0P3xRTOl+XyMVDYACJaTJ+qpoYF+legyv5fEBMhapO5tYqLxVO4v4xImoMZzQJHo33YD2m7krJFnOWfZ7AEDFgNyIzJQY+nAYKJBgSR37ZlIUc4qcfc378QtvRVnhNnkuEU2z+6I3eWuiSi0hM6tOm6bQSuCJ802OUyXYpUTBACCyJlEcQTsuf8wyZhdBi9LM4huExGZysCrU6OJqGkZDc6IJUrr+VyiUYgU+XPM2muJ0LomKsNffgxRFOmbVBCRVRd3zsLRsOGIrut0/5kzZ2LmzJlDO2huu+22G84//3wsWbJETxZ8++23o6urC29961vbKiOKIvzwhz/Eueeei4ceeghpmmLnnXfGnDlzhqWOg7G2QdQhhxwCQNFxbmbyKIqwxRZb4LLLLhvWyo2lCdbo0EvoGn95NNiBmbeKEr8N1u5/z7strZM7HUKapKhN7dKuiGZfE0gT1cHGEmk9ZdtmCJI0B1zqnMx5BEBigAUfDfJzpNGdYS9Sw8RFISiS0U3zQI0XAR7eCJFo33WVUqMIKIBl9FC57ilJtEsvaTQV4I1C1SSy/oBSUIgoQtpoIGUCdECJTxu9dV3ORDHKo0NpG0QUImACbzdqEzBMiDWKZ0JcGYfIolTPSehqT9IcQAH2qF9dY7+gWTKZDYGDNDHScVWfRLvqhMw06KKgBAIUlKSR5z0DzICC9lcb54wMlZEa9iuQASTUXHzE2qRJql2bbj4xivIiPZVh6mzwwplgfs25lQmT00ZTv0O+9Rz80HWj/xbodBglfk9cEboVoVY46vDa86d9wqqDm5EcsJ8pDiQtF2Saag2mekZCBKwdMm5RicCjw5S1GCKOzLVIjXQgmtStdJ7SPh43fn/oXRotEDWcTNRI2PPPP4/XXnsNzz//PJIkwcMPPwwA2HrrrTF58mTst99+mDdvHg4//HBccskleO2117Bw4UIcddRRbbFi3GbPno3Zs2cjSRI8+uijWL58OWbMmDECZ9Xa2gZRpA3acsst8cc//nHY0Ol4NZ+Wx+0kaO48/iIl9aaeRy8dYpRelphIFNIKZEmmR87N/iZLOsnEs6lJbxD3RIgnR6ivbujoNBLpkvHOh2uNZC0qNPrUaNF+BIr4nIJp3gAGuStCMWSp3lZGIbI8szVNqMq1ZzIKIWtdupHSdao3WOeR6E5A5o+xug+GOdOjz/x+yCjEQH2VcilotxG5KIO2oqPGi+mkhLk4Po5CHY1kGnUuJIYFJtQyYjfNtRJRCNE7YEXZUXAAJWYEKBKPPRtSIBXFMHF9j9i+5D41dVPMJ4GxQAY6MkqdBRMAe4CRyWqv9qektEGiErZSGbYuCggSw3JJKa3fBKayNLOivCijPoEx141M9bEjZP2pC+ia2vvSOpmDS5H/zufA80xArO9HC0uTRAcE0HFH65kXcYjG66t0qg2t55Qm6hawr1VYi62oW3L5c+AUSIlMJPp6WceMImtZIETOigtkIm+TEsM00WAPgB7wZUlSYN6FEAAb/Il0bITm483OOOMMfO9739P/d955ZwDAb3/7W+y9996QUuKXv/wlPvvZz+Kd73wnuru7MX/+fFx66aVtH+Pzn/88dtxxRxx55JFIkgR77bUX/vCHP2DSpEn4xS9+gb333nu4T6uldayJevbZZ0eiHuPOgqC9F8MdCab5sqTRgBhC3pX73/Puirni7Dw+xq2g8vUAqsOIeyJEPZF2k1TpflTCwljnXUJ/3WrQyEUmogiZUA0TCb8t1i5vqJr9A3m5ZuJmsO2oc6WIrzS/VuSCo2/ShHAglCUpRN4YpmzkGEihR5KS5aGickQcQtZiNPvrCESgj+FmJx7v9ti/H6zOPWdGdAg3E9PSdyGySBjA5E73oRitCFGPivpz81AJDrZiEyZOE93KfD3XzvFvXQepNEU6wpMAEHt/uHZOsueQAgcAkz9M5EEFOl+bNM+MrmeSIuyOczDUNHVnbEcmjdsnkIDIkSR15ipnVWqxVTIKkcCU55Zr6fScjOA0CCkzFxjoSOE41AM1cmm55RbKEgJCSqRJotzywkwqrp6JobHmVfbMifMRT+1BIw+40VO0IAfQFMHraBopCzs9Xzq7urBBqcsO+aIa3czvgVR8YSYlRBxZIE4iBFidfAy1iEOg3ixNfTMSlqaZlaNssGWMlF177bUto/c322wz/OIXvxj0MW688UZ87GMfAwD8/Oc/xzPPPIPHH38c1113Hb74xS/innvuGXTZg7W2QNRXv/pVfPrTn0atVsNXv/rVym2PP/74YanYeDA3skVT0bW4NDO5kKpTSetNNNA36GNTskyeiZxGcTQiU8dz8uRAsRTdM2qWFoTYK8CIh6NI6nQB1DFqgWUtBumYeKcroxDNJNGUOGegyHWWpSlCAM3+Aasjc0OsSetE30mSIuzpVvsQc0GuuYbqdMOeGlIWimxFGDkdlcwF8fp4UYiu6VOQ9NcRyKbuRIhpmCjW6G9CyADNvibCbjWJMN1DApoAAz9s35RdLy7kJZdSliRatC/S1OmsiqBIM3+kBURTj+a5FZgWB+hpdqS/jiwtCnpTNIEGVNb6/P2j4+iEq2jm7nRhXD1WlJbUA4Ck3tQiYp4tvNlnD3xkDljIBHWadeNmI62gNU1ITGyxKo/eA0tPaV2fIpPCry/dUwJQZS48Og/XKC+UgGHQLcAxgmCga/oU9L+2Qs1nKYXJS5fnYuI54egc1LdKTZKmKZJ8UEZueVrvujldc4GTawTgXBDHnz9uHDQRkBo9d97QNVETfP5hvPrqq9hggw0AAP/7v/+Lww47DHPnzsWRRx7ZEpuMlLUFoq644gp89KMfRa1WK52AGFB6qX8qEEUMRuQmhRQQCC0gRaNg3qhlSYoV3z4d0448r6PjPnjwvlZyTHK7ANDRTwB0jh5eL8rmTC4PGjVHPV2alibXBIX78tEddTYcjGQi0eJWGj0LISBqscVEID//LN83BIEx5upjkS8yCi03D1YqUTl14rxxomVhLUbKlvNOJUtSvZ2OnmRCdOo0w55uBHmjrKMKRTrkKXteueJE/Xu9E8vfk6GajNT0JWteXYOU7mOSKfaEOiUHuAbSCOxdRoQAsdGQGFcSWUFrU6IVCRg7lHlAWFpX0VFUP66NErESpXNNkqx1sTopkB3kAxXjCsvrSx1fwsXUBoRQB0jH5C4tlarDgGkOrmhbOqe0ThFmNgPNn1dd/8gGeoC/M3f3dV3cnMHl26ZJijAKrftrlethAQvHEyMXbbb49CMR9tRQz99tnmeOzNUhqToZYCt129HQQFPfl3y7ZlK3ABO546z7zsokZpqOxaOCOdDVA8PEfnZc1/haGx2bNWsWHnvsMWy44Ya47bbb8I1vfAMAsGbNGh0ENdrWFojiLrw3ijuPzB2VWL/T8kYRUB25r2FrZc1+k3ySmAbK70NzvVHuHxrVASTWzHPzsFwnvDNI8k4srMWapubbFs49LXYM3A1AjJLFVuX/CUBRHUQUaoDFy6BRclpv5iwRA0n0m3UgpGNIGnaHTFE6AJA4I3JfJ0YieYosSwcpYXv50hMK140A1UiAKRlLfS3SJEMYmwg0nTSwRIMjpdDAJG001PUXOQhIeCi9DaQoihJwp8ZwQvtzdy8BkiQht6659m4dUlZfup8EgLT+Rwhk+fMkhADy/TnYoAGBjEOEPd0QQrl3taYLsMAOiY/5wIC0eIEc0PWiuvDnhru+iQFT5x/quqjrFlrXUHXQiY5O5WBAuzKFDRDouun3jUAFux9lDBSgWCjXXDZspKLz4qmTCoJxIAd/3UZ/KaJIs85po4FASuVud0BgWm9YkXKA82xFIZMSCH3f3eMXgiDYwFfvy4AS5cMjFpwfO8hGi4ka38Ly0bBPfOITOOyww7DhhhsiCALsu+++AID77rsP22677ZjUqWNN1DnnnIOFCxdi0qRJ1vK+vj5ccsklOOOMM4atcuPJfI2Uz/cOmAY0GQSAIhaKT9WRiVTnedLTIlgJ6bhmhCYODosNBWuARRzmwllbf0LmpnUoy97Oyy7774IZa5RHnVPDuPgk64h4OgQAVsdD++hOynP8jLEybv0oL1Xi5JNq11zwpOs7whE7momRRgtH+iL1v5x9sDruvNcKpPC6c7hIn4MV9bz5XE907upZSZLUcoW5yVM5IObHDBjrWXb+vjXcZSdyBoFHfpL7BVBaGNrHJxzOIr+L19YlRTYDJg3YIvcTv44GKCVFkMjW+96lAqMk/NfAZ1XPonpmR24Eb0ALmyfRed9k/s7r54sxiT4rPEMO2HSlBrx99JXBWc0yI4AGYFAD4+GwJMuQDBEEDXX/sbazzjoLO+ywAxYvXowPfehDeqoaKSVOPfXUMalTxyDq7LPPxtFHH10AUWvWrMHZZ5/9TwOifA0PaWxMZ2FYFqCYHyZtNPWoqF1r9jd1dvEsyRD1mFEX7xx5pmnT2CSQkUTU02U1KtYovxbrRkyPBJmLzb0GEko4S52Sz3wslk7CySb51HVwcq4QCyXz3E7kkiNWSrscpYCMzdQuFLUH2FNl+ISgvC4yCpH0D1jupkCIlg0pt5cu/hwAk9iQp7jg5/bqV0/CzOOHL/XHU8cdpgETTftCOiFy95gRvWFSeF4u7UphwnuJ0HKX0Hb8Oc8sEb9xlWiBsxBWLi4ZhQhYGoqkf6DA6gRSIuzpVsfM9xeIDOPErqWII6tj1FZv5nPcGbeunkFAsCAGYaY2EXGo9UrclSNZ2cZ1JK368HQeSkPWlW+fIuqpaSDNgabWBtabEDUzXQ9nqrI0rQRGZQMTzZp42BUZR+x8bE2iOW4yYkAqrHUhyTVMxPgmTptAkXF0RlkOLq17nLPExIi6oJ4/FyHLq6EBVoRiEEp+/zUrTu04AW2nnjofWnestXMiCjuf9mOtDck++MEPFpa5aZdG0zoGUWUp2v/0pz9hnXXWGZZKjQdzG2rSOiVoag1Elams2M2OdDYP/+v+AFRagtoM1bEU0yqYLOQiCjUQavT2W2DGBgc2/c1dM7QNicitlAXUASUqHBgUXmyNvG0gpxvsNC2wTvp4ns6A6xMkaVNqsYooYuCGgBSJYwElYPfpE6ys6fxeRopFCGupjr7pJDrvpYs/p11mxOj5cvTQ+Sy/+lTMOOYiAEo3NRQXH7lJRRwCPXQ+SmzLO3fA1i0JJ1t14Z7kACut5x2eM7rXyU7z5QnUsyAAneBQMUtpobMmViCLbHBEnRsH+jIv2xexRcwj1ddiG+k5ykGkHij01LyDnqTehExSdR45aNMgFOSy67LOgX7z90frA3NXD9UrnjIJSX/dRB9K+/3g193twPl5GfBrsm67OZH4PeXlSo8Llt5nEYUIQtV2ZM3BRxFX2atfPQlRTzd6ly6DyXqvnqOwO0ZY69IpGzhjFKQs+pNftzy4RIN2wHstAuG/LsR6BjmYc9tWPf0R1cN1JSZ2ZCag2q0oHh0tzniPznujWtsgasaMGXqunLlz51pAKkkSrF69GkcfffSIVHKszU2q5y7jxtkoEYeFaB+fPX/aJ9C7dBnqvQ3UZtTQNbXLUMe54Nk0CiaqJeqp5SO9JgJRtxIeUgfvajl8YdbUiDWSxBbC5vvJWqwjtagBBgzbxM+XykzrTcgoQppHQ4XdZrRO2+g6CKE7LL4uzMuXgM0aSbsDVZ23aSApaksBBb82KJ4yCVmeOLLZX0fSP1BJ0z9/2ic0eNHXIWfOfCJVfR/SVAO516/5Qkdsl8/4caj8LEnU9c5ZFhe4KkAbFdywVBZ13JIBCLez1524c8+1TocnbiRAJex7FEqTQT6QwoqepLKyNEUU1RRAy8EOgQnhuEp9wIQDyCxluhs6D2ncP2FPDTJJ0ezt1/XlgvxMGt0Sn/CaWBBK26GXs3eGvwsuYOUTaPO6yyi0WGEO3ty680GSe/3ca0EsFRelB2Gs6x4ICYwAExX1dCOtN9G7xMy1SnWUta5CdBtFiZZF2rmpVBApMOam8KBzl4yBTDzXm0cNtzINcp02SEYh0BhaPsB2LcEwZCwflpqsNW5tt+hf+cpXkGUZPvnJT+Lss8+2pn2J4xhbbLEFdttttxGp5FiZHnV49AhAMUKJ70cC7vqqNXjp4s9h1ilfAwC8cPZnjJsqZ0F6l7yG/uX9iGqhFX1nKG6ngxIUwm00LSQoDoSwmBECSVwb4usgiYngx+DrgVyInU+KKnPRJ5+ShbuNCHAESXFUx/9z4SavFyXjtFivmj8CSUaRAmxJAiElEhJM53lx3CgxXV5edhyFGMiZQ5899x8f1/sToI2nTLKExZbGI4qQoOE95rrHXeI9Rjv29An+OazSRlNF5IEnCSRdkScDNru/Qgpk0gQD+JgT2tYKFHCiUwEU3xMqI7UBHdfbeRmDnCkQUag7wKS/Xkgtot3onuNKGReuvwWgmMvHB2hc17WUsXHzMPE3Z6+s5zcHe1QPuqYaQLLyCgyhZIEe0pk8W4oCyBWSzQLAp2lygJvL7rAC0P3eYzFYe3bh4djy0u9by5ZddTLiqT1Y8/Jydn1VxnsXELvX0NVMUf05a0dax0AINdUUT0ORa6ysqLpUPefqdIWlKUs8IIybfa1FJfO81t541jaIIp/jlltuid133x3RKMy1NNZGLxpnH9z1lsskf8HSJEV91Rolru0fQH3lGrx86QnWvHNJo4mB5avQ6B1QAGpyDBkJBDJQYetCKCZGmtwuPOxaRKGlC+BpGHgnSKM3ARSmV7HOMw4Rpv6Gnc6NC4Wl0yjz0SQ16npkKY2OTLtbmKuCj6w5o1HoLMJIL9d1Z8flcMEkWRRI6makyBtufk9r607FmkYDT5/wEWx15Y8BqM6B1lE+K9IjuRMgUwRUmguNJQwLBwDLrz61VFPWidluUZp7joXbszB97hLzgSJuKTFr+TPgslkuMyLiPL+YKLoFOXOjnj3GcAlhJrTOj8k7pjRfriK28v1TgYSVZbEt7PkhVoI6OEp4yc1lDAMhEPXU1ACEgSyXwSRG0XKt5ddXJ/905oykTr6ZD5p4MlEZh7pTBwwgdBlaAAXNjQ2CpXVvfAwWP2/XAimt2QsGY1te+n28cPZnIOJQDS6kRNitpmrqe3m5Aq212GQY52J/lqcJYCkh8v8JdxWzbTJpi8Gjnpp2y3PtWyCEfk5lbLO4ACzXXPHa+CMeNQij52CUxNpro/PGp3Xcqu+11176d19fHxoOldnpHDjj1fQ0EznjQo2yD0wBdoPX7KsjrTd0pE2zv45g1RpEeaLIZv8A1ry8As1+1UDUZtQQdnOdRdFVpjsnDXJYJnAgz+BdnPLEGt23OGcXGGjtAV2LPCeT5Y5wOlGRTxgMmKgXqwPPG6Bmo99qiMx5svnaohCBkEb4KiRECCBUYljNYsE/wjYh7xEavX26ziRIz2SqG+mop4Z4Sg/qq3rx9Akf0TqysBZDSIF43an6/NCrkqiSi7LZN8AYGnP9KNN2miRIkwSoY1B5w8pMZWUPUW80LSG1dl3FtjDc1Y8UpvSRAhue/Z/4x5lH6ee84B6RLC0EA9IcAAXSuFdpehjXzc2fLVWvZv5sGWbNAn95PaizJP0R1U0DNI+7VAuCczZDh/xTDimW74wGKPSu03XjqTfIjcsBvzv40MeTAgHr3Pn5cLCTJimksKc/oW15MIt9v5z0ElykXuKiUu+rhJo9IN8/jL3bdmL0fBATHE2dhL6Xl6Oet3ukyUv6B7z6PHU+uYuMdUv0jNiuafu5JpMIdRumA1ASI4fQ95akCU6CVCC/hgwM8/tBzyUFD+jrPzBK7ry10XnYe++98clPfhIf+tCH0N3dPdbVATAIELVmzRqccsop+O///m8sW7assD4ZwekDRtN0+gAHyFTuQ6PA3n6IOEKzt0+/6PWVvRB5DpP+ZauQpRm6pnZZI1leRrFsNT1Bs69uRRDRPgAsFwVF1Ln103lafC4MaRJlUofJGy/twpPScav4R7Ku/sewU1KXBSjglKJh/Qegha/U6GtzJnYOpNAsVdps2EwI01lQbhqKUOT6rqS/DhGFiKf0IGk0EU9R0ae6MWWuzgjdKuqyu0t1/EzXwa+vcemo48845iKs+PbpWPndM5ClaUdg6pkT57PzZakHohCifyCfwDbS0XJq/sEYSX+9skOlcrjJWqzz4fC0DYgI2Ka6M+PgIBCpYX/i0OqgQuaKC4RyzXL3FonGw1oXknrDej6DRBiWKbbfFUA9902Yd4cDokAIDYC47g5s+zRJCh0zPTdW0lZdTi6K5vOy5Z0/Hzjw/fW15S61hgEJdAzOAJI7WuaggwA5YBgofh34++bmdzPlMv3TMOqgAqmuebN/IAfXNfa+qyhGyjQumWuOwDrPDM+fR31Ny4JUXJclY+aorBDQbn1BzFukcslxNz2Vx13cHJTVV/YijEJ9P0bb0jRD8gYXlr/1rW/FKaecgs997nM47LDDcOSRR+Id73jHmNap4yfh5JNPxp133olvfOMb6Orqwn/913/h7LPPxkYbbYTrrrtuJOo4JibiMO8kcpcJ61C46dFimqLZX0ezv54njRwwIdR5w1dftSaP2EkQ1kgYboSRVQDKPaYvV1OZcTpcn1dU7Iz0fGCOu4I6a7fBklGkQ6gDqSh0DbSi0Lu/ZpTiGoIwVv/DCCKMcrFrDhDimkoAKIQRwEqJrFlH2myYqKV8X1Upqf4z1ybdy3hqD7rXnYau6VMK10e7SuMQ8dRJiKdMUqkWYttlKTyNv76GQhTPl133QKooPYrYHIyeIuDsAZh+xOk4AGgwZdUpDr3Pl7p0ii1acuGxkHnQgguCeaqJIGdNiI2i8ukZCNlzoLV57HdY61LPD2N6w5pK1SCkRNTTbZijvNx4ikncSMcOa12Ip/ZodlG7k4XQz2fU042QhMwwLld9HrHS1LmJMqOebrVvd5eqG8thRmCGjkPbxlN6EPV0q/KILWYdOn8vZKQYrXhKD8JJNcgoUhPkcr0Vu7fEqrqDFs4Gu8xd2TJ244ekhyLb8LSvKylDo6nqJwTiKT2QsWGtRRRphpe3M26wCT0fGlTl50fXju4v3WshpdUWWWXmzw9tw9tAuld0LF429wSENFiKzfOumdQhBoqstc7ssssuwwsvvIDrrrsOr7zyCvbcc0/MmzcPl156KV566aUxqVPHT8DPf/5zXHfddZpW22OPPbD11ltj8803xw9/+EN89KMfHYl6jrrJri79cgVOvifflBb1lWuUT14INPsH9GSbfJukfwCN3gHISFoT7ALQ80dRR0mjNpp1nPQWPjF7mc7Hx47wMF4BR8ydmNxLritBnzu5QNw0CU4nzhtAndCPj3ypQafvMFIJH9PEGikXk2kwBiUvM2vW1e80AcIIQSoha7Y+JcqZJWJmaA0xLlFPTWt0autOQ6O3T+eRsY4pBbL+1DSoUWiF0fNrxwXVgRA6Vw1QHpRQZjz9AD8G/Zayy6QliJQ+R7FTBrjwyXIFe164Sw0A1l94JQDgxfOO0ccgJk86x3eZAa0NE4nK5J0zmgD0b+7W0lN65Doz3clF9uSzWWKm9MnSVHeI1JHFU3vQ7Buw6kHAnu6jnhKoL7HczAJKpxc0mpZ+ju+TCQNIKeqMX39+bgC0O5CzKzz4IZAs5UMU6gjUQAjLneSzdlgQl7kJhDAReSM0XYkC393oWkcFHWVpiu71ZmDS+jPQt2yFGqzogaWTUy9JIRztnpRFJk+fX87S0bZk1m/2nPmYRgp+SfrrEEmi3fMyifUxCMBnSYp4So8+L8AMMt2UMSNlyTAwUUPdfzyYlBLvf//78f73vx+vvPIKvvWtb+FLX/oSvvCFL+DAAw/E8ccfj3e/+92jVp+OQdRrr72GLbfcEoDSP7322msAgHe961045phjqnadkKZeZhOVpFiLBsKaEk4SYFI6jQay3NVFol5Z68qp7CaSelOzUDwhZNitRqMDr69CICWinhoGXs9BVE8NUU8N/ctWot7fi6inpqemoPrwb7dz8jJbwoxsaV93/7LfbnlVI19+vPLry+a7anq0BRx4pYnqCNIEGQNbaaMJ2ZUzWJFaL9MuJAMD5nxZORS9B0DP1RZPUe6ZpL+ONGoqt1Le4FPjyc8tZPdAdnUpjVbeafIkfa543mU72rWwFqPOOnjK46UAhe0G4bmOKMpR5mwRuUd4B6PPzef+ZU1EmR7QZ4aFkSy9gERAzCW//vWmxR4SO5Uin1RZ5K7EPBKUBjc87YDMNUoaJCeJml6mq8voX8IIqJmBgTsYIqbHOo/ITkcgowjhpBqa6Eezb8Biqmkbfh480ScAPYExuTT5tSXgCEDnuvIFUfB7VcgCL2zgbrvvzMAlkMOf1qC27jSEkycj6Koh7V0JEYWorTsVgRSYvPF6yNIUvUuWodnbr2YKYEDUfbZ4u6YZV+f5LDBy0rwTfJm1jceFrYILuhFO6kbabOh7wN9lmWfGTgYGzITtdG/bALXDYWtBlG33338/vvvd7+JHP/oR1l9/fRxxxBFYsmQJDj74YBxzzDG49NJLR6UeHd/92bNn47nnngMAzJs3D//93/8NQDFU06dPH866jalZ7qAoRDiphmhyD8Kemh5JUtbj+qo1CkClqWaQtGssCtHo7Ud9ZR+S/gbCmhkBN/vrSJMUkzdeD1M2mwVAvdzN/rpmpMJajK7pU/QcVNGkbq13mbTBOpi04TpeYOOCJL2OGnNnH5dx4qCJXHdl2qcqAKXWO/sJ3nkwfRM1+I7byiyXCLpq6kPuPiqHQBXbTkQhZFeXcROGkcVmAOSWDC2GIE0SlUOoFiPqqSGaOkmPSEVehnbLMBckCeHpOpS6znJXwcrvnlFYt/qH51j/V373DJ1fKuoxQkoOXl23h2RuNXJdWC4ijwvCfWYAaHfTRqdfbeYndLR0tttKWjohvpy7fTmI58+OvidhhEA4bizGkNK9IYaAd3R0zuSCAYBwUrfaP3f30v3jzyXVRUTm2qlraurBtTCyFqtj8OSzznlZrBpzo/N74b5npBfUdWTgWzNKjEXhmep1Wbmb3AegAinVQCM07s/hsFXXnYVw8mSIninIGnU1W0MOlhq9fehftgKN3j50rzsNtXWnFqKJ6f7TsqTRsDVnHoDvXj+jzbNF/Pz66vfBeTd1gEQuB6B3Wz2LQjHl7N2WtVivX+vSGz17+eWXcdlll2GHHXbAHnvsgVdeeQU//vGP8dxzz+Hss8/GNddcg//5n//BN7/5zVGrU8d3/xOf+AT+9Kc/Ya+99sJpp52Ggw46CF/72tfQbDZx+eWXj0QdLRsYGMDb3/52/OlPf8KiRYvw5je/Wa97/vnnceyxx+LOO+9Ed3c35s+fj0svvRRx3Hn0idVRxjW9XIQRQjZqklGogZOIIj1nVtpoIpUCTTGApL+BQAbFDmq6AkSk05E5u8VNZ/idVLOARdf0yaitOy1vMAUGXl9tJQ3kozFyVzXyDif1jPgI8NFvX6PVLgth7cdFrIUynZEkT5uRM06WcTdf/uQGAGTaVSxfSO2+CKIYWUPN8h509yCeUrcTYmqXqnLzEBvRNX0Ka5TNscPJk/Pf+fE0u5ADOKGiCTORWNcySepWow+ozgcApvz7WYVrt/qH52Dg9VVapE4dZrPfLkczK1bWdzaadtgMcluW6fvIyK336ldP0tuqvGl58sxcw5SlqZ7vDPVieT4Gxd1GxpFZl7tmLUYljJVrl56JMIIMjSg4S1IglpBdXQbcMhPU0glp1qeJ1dkqBixnN/MABV7PMHf5AoDI2wRKfUECeRFGSGEzqtY9iuzIPx+I0RGpBHhzQT8iMxWSBkm5ZXQu/H2ja8UAlDbuNh+irfnJxZDdkxBEEYK4BlnrQX3Za6iv6vWeY/f6M7D6hVes9480SmnO2qtTkMhyN5yMIyT1hiWoV+fkuX7tsOUEiIS0olBB1zVNzHUTKt8aDxoQcU3rNWVtdKLEknToTNIgmvBxZZtssgm22morfPKTn8QRRxyB9dZbr7DNrrvuire97W2jVqeOQdSJJ56of++zzz54/PHH8cADD2CrrbbCm970pmGtnM9OOeUUbLTRRvjTn/5kLU+SBAcddBDWW2893H333Vi2bBk+/vGPI8syfO1rX+v4OOGUKUoflHfCEBJZX696abonobFyJQDVIKx55XXtvmvm4e+BFGj0Kg2UmjhYXWrqACdtsK5OPEfbRz01NHN3hAswZD6XmXYndXepzitNFI3e3YWVzy4BYEZ0vDERcYhYTtJidzJq4EmjpUfkLFGllwJnYK3M1WeZyy4BpqG3GnwCIgLUXLiNcMHtF5JYNXet5ICJ7l0QRqzzjRGtOxPZQL++hxoo5TR9OKmmj2uidqQ+jnWsJDECeFoX14BcB0Ydf5YmupPOHHCYJakGU4EUWP3Dc9T552CdJkqmevJoTosxZYCNwBN3O5i6Exspde4j+p+UZF9WoCm2OiQeKUesEGmQqpg41zSjwq63Tw8XRBGyBsyzFMWQAERUZ4A5ApJEv7No1pE1GubepakZFKW2psYsVxN80zYijJCxb1U/wwhRGSIKFQMKaCBFwCYTJiWHvt4hZ1Jt3WAAE86v9UwAROy4tbiWML9urtuOErCa9Rx8Db5XXfOTizWQCKRUA58whujuQTOfJ5GuDQUK8BxwZJRfKp4+Gc01/ajnoDicXkPQL7Qe1QVQAMzzxlhAd7BnaSjpGuiDq/xyad6mBNSW5AM6DlmC7h5gIE9tEMbI+noVcBylnIlr3XnAb37zG+yxxx6V20ydOhW//e1vR6lGgwBRrm222WbYbLPNsHjxYnzyk5/Ed77zneGol9duvfVW3H777bjppptw6623Wutuv/12PPbYY1i8eDE22mgjAErJf8QRR+D888/vOH+VmDQFopu5fmh+qaZqkMNJNaXF6TbRXAo49aPZ24ewFqOR9COpJ4hr3RbjE/XUdKg9YHQX1HEGUkXfhT01k3wuMiHrPLquuaZPuRYm1QoaD55fRgGlLtRXrrGSEqZ5eHHU060bKz5VCTc+otY6n9Rmtbwdp5CqoXdddPRNn2bdauDKXA1ZoziVDmk8lIA71mxVIETuyotV+WmiQRVddxGFCKdO0/XJGnXWMZEQPEGAyO6MoDo7zXqliQaFugxAi911p+10Yu511ZcoF0wnjSaiWOWmafT2a0aKz7NWNSLXYd1AARi7Llp3suTXr/mCLovPJafOw3ZjAYZV4EyL61YuuA1lrJmdIE0NAxBFyt1Ly4QEBANIgHLtRpEFDgLhuIgJ0EiJDAwApykCKPCkQUCaKBAexcgG+o17OZWqw8zvrd5eXVQEkoGgMFI6CetZtgG1rlteNpp1A4hyYCJcLRNy4MWBU769Zt5o0MeOocGodY3ye+LTIbawNTdcmLu36D7lAxUhIbp7AKGelXqd8rJ1IV53HSBNkfStUcfVjF6I7nWnIZ42BRASaX21OVdiUfsHvJpNuh5ZlFoCdP4+0bXX94IxTFR3SGndL64Xo/sNIS2PhDq40Pd7rY2OtQJQY2HD5sx97bXX8L3vfW/EQNRLL72Eo446Cj/72c8wadKkwvp7770XO+ywgwZQALD//vtjYGAADz74IPbZZx9vuQMDAxgYMFE9K3N2ApnR2ATNOrK8AdcdYVxT7gT2QsZTezAZwMDrq/VL3OjttwSUYS3G5I3XQyAEautO06N2KWN0zZis8zF1rzvNgKGcRo56alrsTCJagLkzYBqnME/syZMEko6CABRtHzth/+SisPQqHoDkgjUyN3qu1NwGvaCd8jNVgZSKXeACWbYNjSZd9x/vvIK4BhH16muJkCdcJNARQegy4+JoPk2s/fznKHTnzS0AG/163Axps6H1O2V5iRou20jpDIQ9fx9tD0B1Fo52iZdBoGn6py/Qy9xUCbr8lAnlGWAnTQpPXgkAKfxRZ0EOGpCLv61ODNDARzOK7LnJ8vuhwTIApEaInAEWyApggHrQJZEN9CsQoFko5taJIvWcURmhAgtBGCFrNhBERtOnksBG+X6qY6ZnJNNRZBJZQz2fBBY54CG3M22LfJ1mkoR5jhTA5GAgKjK6QIHh1deP/+7UwsjUO7QBVBDXkDXriKZMQiNn5UUcIu1X4ElEIZprlFYqnjIJXTOmIJo+XQOsREsjKNGuk2A0fw4pgCBLE8iuLivIQF8Ty73JWfR8WUi56iIjH3CvH8yzR9qorNlQwLlrdJM9vlGZqJ133tmaq7fKHnrooRGuTdEmhCIuyzIcccQROProo7HLLrtoYTu3pUuXYtasWdayGTNmII5jLF26tLTsCy+8EGeffXZheRDVVOMQxkBcA+r9uvEEoBvutHeV3iecpASvXdOnoH/ZirwDiYzYvN7ElM1mKdZIqkZHRw0B6JquIsGoUxt4fZUlJo56utG/bKXq1Lp70FzTj7Te1I1VIFU0VdeMyZg0ayYaq3vV3FV5RFMgBbrXnaaW0XlKgWjqJAwsX2VR4RSO7QIp/dvRXFmC2jBWM8NbjbYouhVcc5dRg+Zqo0gfw8WxQpoO0GNZvd+h8anRV8yGl/UihgmK8Sh0OtR5u/WnjpSdh+4UhUQ20MdYKWF3FoxNIdCndRgARE+31o1ETKND26kiFAhMhRHLF0bw+bfLZHGXWu+PzjPPA4uq42X5AhJIM5LW+01HJiRk/lwUtTxGc6KBSBgZ9xddl/y+62vFdVJO5xcIAl2pen/Z8UrvG927OAc/VH5+LKv8EMjSHJQxQGGVx9lKXb6RB/DnOogiDUbc8+HPOjcNZHyDFc/7pYCtzdJ1H9h5RLUFcsMoD/KIISZN0WBFrrshsFQlYxZRqAdmzTX9GFi+Cl3TJ6N7/Rmq7UtTpPV+JUTPn7euGcV8boFU0ZZBdw8CsRKNVWv0s8W1f5rVZUwh1ZtAN10P69qUrMsoeCQfjCFNFDiP8zYBfR1fw8HYGzXZ5iGHHDLWVai0MQVRZ511lhfAcPvjH/+IP/zhD1i5ciVOO+20ym19aDXLskoUe9ppp2HBggX6/8qVK7HpppuqxjyMVeeJ/AVMFJiyOnUh0DV9MuKpPRA9U1V4/TQltq2vWoMpm60PAFj9wquYsuks5Z6pq2kRMhZqD0CLR9NGE/WVvTrXFIXYW9Eok6YCy5ahvqpXueSiENPnbKYalCnTASERpksQT5mELE0VM9VoIuypIZ46SbmFohDd689AtPFWAJ5G38vLdbQTZypamdUYkTbF7UhybYa2wYyCWcNtUejuCJvKd9Zphor+13oQcKZLKLdjxjQ1rqvI18jqOgEKyDFAlzUbNrhirBXXupALatKHTlHuEuu0pQVwzfQ1XXrkbuUQoxxJTCPCXW/u6J6zS1M/cQ5WfPt0a33U021FwnkF47moOhM0RY+wkqDSdQy6aup6NOvqeagzxrerCAbUdbXvNYEKzWDSOvYMECAJ2LuaNRuGcdL3PDWdoXvs/DwswMOPAQbKQpsJ0oxRGCFrOmXycxECWcpYjzDKmTTnuaWyu7qL7JJrHCTSNWIg3WLt2rQ1N12KSf+6UP2hZzZn5XS0bHcP0l7F5IseA4Jkl0o3MrB8lc4HVltXuc8bq3v1ICHMI2HTehOiNglZvd9KXRFIxQQFYQTRMxWiX7lAAykhMUkl4m0YgX/QVcvd6obJswNd7LbCah9YYEvA1+UsNpoNzaAGkePmW2vDameeeeZYV6HSxhREHXfccfjIR/wz05NtscUWOO+88/B///d/6Oqyp23YZZdd8NGPfhTf+973sMEGG+C+++6z1i9fvhyNRqPAUHHr6uoqlEumNRDkR08S3cgBuZutuweTtthC0dhrVppyN90Sab2JrpnrIOiq6Y6IJndV32sgu9XLD/AGtqnp6vrKNTp6j0ZYsqbEm5QAMUtSyCkxwo22VI1ZXEPa34uBxc8CALrXm6Hqt1KxWF3Tp+hcO9EGm0JOmY502rpIX3hFu2KaaxSLEPV0e914ZREw2n1F2g4tJPWAsnaBVIttvCJ04bh0QuiOiLQwEBJZvd/SOpDLiJZlsAGS1hPRKLf5/9v78ji7ijLtp+qcu/VNd+ikCZ1AEhIwIIJRo2AIDPsmS5DRUQchGR3mcxhQRlRAHBPnJ8Kwj7h8OJ8fi5+/AWYAHdlBFkEEJCKbELbExJAYDCFperv3nvN+f1S9darOPbe70+mk+3bq+f066T5rVZ1zqp563+d9K6XPslwlbPZvSLy0AF3dN+m0Wz6VTBa6blzqtLVZ4FiH2wfIGcJru90AixxZ+hMATqJI1ssJKc0yNOnwfo5SjVGrC1qwdVz2GoewLWs2EWH3p90OTLC4zfu61eDYiBSniIB6lgkZMQRKW+SMO01GKf1KzlibSVvNkuco1fMLlauP+rodzZt5nvmiqyuyLGGGBOVdq1Pa7SYy6mjcmOZ9TohbneWNdXiasAu7jlwG67ppMj8UiCBA7x3fTzSFgBGSm2enn7sIc6htWJdkAC+VIWpV1Hr6TGoIJ4nqhALkxMnJs+jZjNrGt5QFva1NWYq73lHlsPSOKlVB4q4zEos4SiyaQdIudd9hRvSveq8S167qx1Jk1baExxFkqV5esi0Q0Qi485p87byxiCGTqFNOOWXA/e+8884W37yjowMdHR2DHvfd734X3/52ss7Ym2++iWOOOQY333wzDjjgAADA/PnzcdFFF2Ht2rWYOnUqACU2LxQKmDdv3haXLdHayHq3UroT1ANDzO4qva8waaIx0+fa2pR7I5CIqlWT8LFofP+NLT8sIgZgcheRGUxD1KKKcvsZ03oOMi6qtAhSGguZypHC+ZU2qXDwUlkNfg3EkayNsju9wUKIMwnTEJAMxhmde8r9kbUt7aIR1nayz0HSqVIUJe4i/t8eqC3Uu9xkto6LYZNEi1zZEU1pS1XPzRc7JMoGu/RiWBqnfAhU1DaKYmO1cixeKS2TEXpb7dz2d//qtGP3f367Lk2ACCTs6P2G0XcpKwiDokgRGU2AHDedlOYdpAYWlsz3wzpOpP5OyqmJVMqayL9zJKdbh0BZqfg52UQn/VxTKQ0MSamlrsfnZ5Uj47gsi1GagLpWzsh9Rwe715aAyVmcEGJuG8EaKUC1o36OMtBrZPK6llGM0LaIMukvWNIJDY64M7dPEcO0IN6QqSAARRmRwEgRUmjLZGqbsprmEzF5o3ZzvuvtkzdgR9VETZo0Ca+88go6OjrQ3t4+oGeJk39vTwyZRE2cOHHQ/aeffvpWFygLM2bMcP6eoPP07LHHHthtt90AAEcffTT22WcfnHbaabjsssvw9ttv4ytf+QrOOOOMLY7MA5SVwIS7ypQvPdC6FrYgFcvJ9moVkKrzkhMnJ5aPXA6iVoEMc4j6NiDSkVVRXwWte+4O6EggUSgiCHMIit2IqjW1hlvrTojeWo+gmEe5czJy7e2I31mP3ISy6QQK02cpy0FJZ9eOIuRmzEFYrUJqM3sweSpk2yQAgNywzghq2c0RRzEkWKcjtV6qDVSroNrVY5bECK3oK45si3u7U66KJAydwR1TcozuzNi6Y1sW1I0c7ZCxDljn1s0uUwQ3TbzYemDcW4WSKyrX7gI+32hwLIuTQzjTpEnfg8K8mpVbFjG2FjiaGO2OYL0Y9afKa2vNpAQFsZNFnLNni0iFXvMSNDLMqTUGI1uvlpSVrU9ZpFhZOVXkl23ZsjOuG/ddBvl3xP4AqLfbjVYEHBea42KznqUrJLfSS6RvyETI1C1KdCts5YLlerPD/i1rqSHPvJ/1L7YFWrquP5HxjpkyhVYmc229pEpfsj9N3Cx3Ell6P7IDEAz5lHXnEWDc0+Z7sQlnA8tv310/RHEwbRQ/J+7PwhxEoaQCNFhQXukz75hsaVVJT9vaQBWl3VRRyQWTqDJg17/uP5kcxd1dKuBl5ykQxTKoZ7ORVZjJT7VbvX+W6N48kzB5h0haLmR2ATNZ69N5rCz9XR3BzuWMiz9NSs37U9nyCMfhYEclUVdddRVaW5U35uqrrx7dwmRgyCTquuuu25bl2GoEQYA777wTZ555JhYsWOAk2xwWhK3z0R1orZpYWWSgdEna305RpD74aLNKg1AoJQO+FdGHWLnTerT+qFrpQ23zJgTlCWZWJ9smo1SrmqSPolhGYdcZxmQuCkVQ92aIXdogNqyDnLCT0iCkI1km7KTODwLIcqvqrIQEhDSEjzt1kcujuFMralY4sXI79hkXpGlrdkvGsUo8GeYBdDecWQtNWti95VhfUsdye4l80US/xL2qs4MmJpnIIGcuwbE1Icl22dKabG9wXdbGCE2C7Gsb64cetO1zzCDJbWxm6a5+Skj1btWVGQmhtS2C7n4tqpUxZDlMQuABk6/IFnc7wmy47jhAaV/s9rKjNO3MzE6yQnbjAQkRsOuviQxVK8nEhNshzBD1GxKQkBogI7u9qUOKvMOO6Msn5Nu6Vp31iUlLkJCF9PfO3x7iyETtAbBcWdkWWEN+cglJyoRdJ37XbDG9LVyvVerE5o7bkOvab4mebUueDMwzHpRAAcbaRFEEEUdmAsY/AED9fepdiANEmzYY63n/+rdUuo7WsuovYpVbzZAnGUAUSopo1yqAlChM2xXB5E7VXHGkXbxavB5HkOU2s42tg4nQO2lLEQIIy8kkiJ8D72eSXasmqSvSzyJf1OlRAp1eIvUebq2Vz2NALFq0KPP3sYKmiM5LY/fddwdl+HZnzJiBO+64Y0TvZWtVEOa0Lzxn8tqwnon1Uvxx25YVqkF/6EonlGttQQuAqFJF1FdB34ZNKJd1FuwwrwjR5E4E/f1q7a9KH8JdpkOEecR93RBhDsG0Wbp8OeOuIx1NR3GsCJSQAMWAkCAhgdjyL4R5Zf0yf+dQmjVbkSo9SHT/aS2qm3vAS6KosOQyZLEFQvYZgXPctdG0hZNLRbsAZOtOqqy1qiJEPLPWs1d2cZnZOpePl/+IIqBWcQehdEfG/2e5/ACT4Txrnx29A8CNIrRcQWbGa1k0+HjAIlT2ddkiFdoWkSQ5oiFG7CKxEvf1/uwqV8ityZlyv+rFZPVaXxToKE/LkgIZqND7Bp08W8jYldd31w8tV4Yi2tLoR+KkTtK14DiLP2ckVDUkQ1sD6vRAdvQUW30sq6/JR8TXtO+Rof/hdlX3kOY4YR9vLDZ6AWunXXTGebZopPR9plxA8m7kcmqQ5WvwdsDJf5akz8h2e5sIMstdhlwZ6O9zLGeEvGMlMeWxyxeqxKNpy6w9ieh/8EYUDh/cgyBsq4++tproWIlL9bXjns3oX/8XdctyG8Q776hVEyaU9futCRQ/e/7+CzrKr7XdeAJIE6YYajIpy62gvp5Ei6Ujpp3oW0soTrF+/2yrpj5P/WiS3dfj5J+rI+ZhPpE9xIH1e9zY5TfCqMWEYCstSbUmtEQ1Qm9vL6qp5MDD8TptLZqSRG0vmOgc3TkIXnet0pcS0UbKOlTTM+2WNn2+RNA+BbW31ih3V05ZmZQvfj0AtaBl16o/I+7rQdCqLEpCKldgToueRbnNWoE9AgtiqVZN1m7T7gMAELkCKEyJ5YlAMoSIawDpQb3cBlGrKPN5HCFon2IsLlHXOwj/8rYRLXO6g8LMPSFKZUR/XgWgG0H7zqj+eXUyaPGMX8/wZLkNwcTJoGoF1NcDUVU5t4SUilzVqiqiR89GRb5Yl+hT5HKq47ZyOHHUjS5cNoGyyQ7n/MlyaViECLA60PTgnQW2fNjX5UHTjmLSs3j7nlxGx8IkA2UNsgZsZ20uHgA4XQKgEkGmSEwiqk6RoBRsLRRxNu+0u4otbZpcAYnA12QE53vaz8FYp/KuvifLaqjBzzqxAFmi9CyLlSFFcZ0l0IkSDVmonSqDNUAmxJaJtztgqu8tB6pE2e9GinAykTFuRSAhcbVqQopkoL+bxDJJ/ZqAGytW8rsqo2Upt8hV3bubTjDKJw2gg8wE50nTZTDaSqkmV8QWL+t55tpaIMttKEzdFXHXO8atZrvMKfU9imJLQlqhJlpxd5ciY8UWY82n3m6T2FOUyioxaq2irscT0TgGOO+WPibJ+ZS0kyyVQZyMtrfbFatb33CSQyxJ5dLQqrgNsKO682x0d3fjvPPOwy233IINGzbU7Y+GqcfdGngS1QAiX3BM3oD6EEWuAOrvQayjRaStKQJU5wgkWY9rVdUBWEnagomTQX3dZqXzULvHAhnoHEtVk2dJFEqQxXLSSeplFRAk2ZOBlCZISIAIEMLp2EUcO9YpThUg+To6G7szgAdqKZCwWEBp9nsQTpsFxBHijeshqlUE7VMQbViX3BuWiVyq3FDU34e4r1stm2OseipLtWifotqut1snsrOi3WpVEHhwkQ2JwKDgTtMaaOuXw0iRDftve/DO+kjZyoFEj2MGvYxM7eZYaxu7/My5ceSQLkNm2Dph63vs8tqzcbZ82dYIU3/pEKi+u35ocv7Yz8gMdrYL1r5WmHetHXYZ0gRJWwMoZdFJ5y4y5KhWddywafAztdc4S67jasCE1FFypi6WJc0MkLn669m5otjqwFYJ/TxMFKZpk5zRLQkZOIk3+bok2VrikiNVroz2s91IlkXSHOe0oXs9m+gl2dqjemI/GJjQsaVQJi5b+/2QE3ZCceouasIYaVLTPkX1gVqHZM4HXLeksXjGTn8iwrxJeBuzxVaTOcgAKJYR92wGqlW19E5Lq2Ndst3ITjRuLg/KlSDCguoH7UmB/R5YxDzdR4hgC8iox1bha1/7Gh566CH84Ac/wOmnn47vf//7WLNmDa699lpccsklo1ImT6IaQLa0QZTyIJ33RBSKQKGsonSCPCQ0USqUTB4RdltFG9ebDzXu7UbQuhNE+86orVtlOqHc9DlKN/DuOyjvqqJRqL/XuLiov88SOCdpA0SxBaLUarRNkoXkQU6RoxRISAjSA0MtVhaqOFLbRDLbg9QajFoV0aYNSgyqO9hcuYTizD0Q7rYnhJSI+3shimUE+aIyt7dPQbxxvbpuqaxM9ZoMUaUP0Ya1ZhYPALLYoganUtnNNSMDY5WCtEPTrUGBBz6efaatUWkYd4HuAKOkA3YIUZo8NYATNm+XiztpAAhKzqDHZK2OMADJQKR1F8b1i2TAi/oqCFuUPsxZ0oPLZC+DkqGbAiwXVRyBorh+wWMZqLxZOUv7YrlqRKEIslMQWGQocfEkpKsuatK2SrGlLsMqpI7XA72MzGA6kMvEBH40QprMWeviGUuy5R6tu75efsa4ZPNBRqCBO/kgaV2T9XSmPJb1K7WPf1c6wuR9Mb/bJM+qmwl8SVnTIAPt6qq34uUP/jQqj940UMsBACq/vsXJs+Tkz2ICwa4t3Vb5Pd+PaONbqiylsnL5W25agK1keceC6XwfsQQqkSZQOacOJh9dSbsFaxXIYhlx3KUmZ4WSErrnrLx1IQwRM9bRQFkKKVcAgpxaR6/rHTWZi2NQraKsWvaENWdFEsYRRHnQJhwR7KjJNm384he/wI033ohDDz0Un/vc53DwwQdjzz33xMyZM/HTn/4Up5566nYvkydRDUBhASi0JGHy+VIS5hwEEMUJEPmae5IMQTKEbG3XWaljBPmiSTwXtE9Rg1EUqYiWUhmodSLu7VbWHKncYOrDVS5B0xHogax+/SZdJrYwQREntkIJi1hRkHTqiVlfrx+Wk6Awh7inC9TXrUTjJZVLKr/bbISdM7Qrow9U6VPJ7iZOVmbtfBGRtrQFOt9LtHE94r6eZMkcQFnVWloRVywtVhzpzNaBE+bsaGHyRaMHA1RHKMutiMPuRKzen1hy3Jlt2kKirRtpopEWjOr72G4edY1si5gZ5FIuEidSLCVaZX0URztxZnzq2WwtJ9JglpsiIE5EYRwlZCw126datY5A9d31Q52nJ6PdbDLEi7JWkvIDcCLJzEBuC8atsioiGZjf05YSdZyEbYmor3u9izKdBT3znLRV0exLRN8EuETKPl5a7yRn5de/Z5bLOtaU0SJN9rNNW81Muewy1lmcbJLF5bQiii0tpyqX5aYNYlR+fcugk4bKr29J1cXSq0mZTMTstABAspKDtijK1vakflbErvOdW23qkK00+bLakQXlxP1jrZokSJYBiF2HoV6/0rSlVBYoiiFI6cooV4Aot0OW2iCiiuoHq1Wg3A7KFSB73lH1KpSAYmsyaa0N3IYjhYhoq/M8NXueqLfffhuzZik9cFtbm0lpcNBBB+Ef/3HLs++PBDyJagDKFdRHpTsJ4twUbL4WEkKGQK0/mZGHAGQIFFuTDjKO1GwtqppM4rBJRJiHbM2bpRNEmIPQ6Q4ojiAn7JQIJMOc444zonEZamtT6FqjONFloPfrOpCQybEiEQzLUtloBuLuLhSLLRCFEoL2ncFRdUxWgsmdiEsT1WAQWGH6OqOy6CsDfT06mk0TnTAHMblTD/I60zZfU1tp7A5QWciSnC8S5cTaFapFXjkUPwZUBvhc3qRxiDe/Xae5AJ+TAlsTyHo2yqWUPSg7GirpkgFnjb4gNkTMTpPhuNvi2M2zw2SqqlwkMoQh3/zOZGbrtkhQXULLQqBWnc8aNMM8ZLGclNsSvjckHlwXqcTnygVddZI52sekz0sLd+uvrQMNzMCXfYy5B1BvhTH3T4nMs+plExR7wM6y/LHlLaVLMpZTKziAnzOlyma7Zx3YAn7Ada9ntMGAFjrAEUDbmjCSEfIL/qbhuQBQffJnyR8pa6BZy09I84xFoeQIvam/N0nHoNvbec+5f6pWQZUuJ8u5aYtUnSmKHN2eyfOmiY/UViv+jmW+CNm6k9FtOYtPU6wnnJrE1Sp6iZdQfTvFVoioBsqrZJpxy07K5ZcrgXJFcKAO5bZPigMPYPbs2Vi5ciVmzpyJffbZB7fccgv2339//OIXv8BOO+00KmXyJKoRZKg+ppwiK8Ky8ijCEoAQQMQ1PdMvgGRoBNCGpHAOniCnLEGAIl9xTXVKnHIgX1LnV7p1h1S0Vn23iJFdDgB2xJ3Z1gg8C+H0DbocBmEBIsghKJaVbiuVY0ZICdG6EyBDxIXEhk35kiKbuv6i0gvZ0mbcbWxpka07QRRaFEnU22zSAq0tSzQX1iCpdRAUyURfpjURVKsaF1HQuhMwYTJEXEPc06Xcd8YVMoD+Q7tK7fJQ2r3AA22qY+coooZWKnsdvZROikPGIWWixSiqNRXBiyyzIBecAFCaKDfXguEK1G1SZ0ce9fzXpWj55NcAJFYodb7lGtJtolzWrvVT3cd1tcpyCXH35nrrE1zCmfV30q6WyzZFfNJkIU1K6q5nfk8E1gBcwbqNtLURcNxMTtmZNITWuSkXbGKVSgU98HvNpCKV38pYxKJkceJMYbhTdj2J0640G3bmdGQl/xwINkm0XXi8XfeFJJNAF7utOGLO5K5DSjPI96hWTIQvu87q69j42427N0O0dYByJSAsgigG0Kffy1ZQqQ2id7P6Nri9+XpCuppFnjQHOd2f1VS/GtdUv9fSXnesCoHd9vDCcuDv/u7v8Oyzz+KQQw7BBRdcgOOPPx7XXHMNarUarrzyylEpkydRDUBCJNFsQDJjoVj9sCUgyAOhcoXxxyRQARFbevSHxuQnzAEUJCZgoc613W7QLjIICUQZ+YP07E8ZlxLLGG9TG5gwiWTGFcrkg9dpD/h8oetFXAdRgwiQ1DmqgvJlUL6kttnkTYaggvUqBUrvFJZmJdYzfT8SihTIiR1Kh6B1FGwJoYqlBbOWsaCKNUjJIMmJE+T04tB55QIttUHU+nQEZADkcooc9XQlgw1nSAYSd2CsZrRqiY+e+kHWGowM+UvNzp2QfyDTveccy9YWvcyEyMHorUxuGvveVpRbXeZq82pIs1xIsv5afY6s3p9dpUhn+xRIne/MHAMo8XSt4hBA6rfCyO3/ZaDctJzPy7Y+aYG8Y6mxiUb6epl1qt/nroFmW5QyXH0Z2asbbXcsT3yPMA8hbW1dA+IWRY7+DYCjdVLXzznbneAGUx+XoDkL/trnpNrYWDatVARGu2eL6aVE/oCTM9vEaYswX6fLMsTPdv1a7s+4t9udiNSqyfqU2kLFAScmkjSngmWEnkiIHMxzdb8v93tiDWq8aYNyv4VlVT/u+gpFZTWSIZArATJJrmmsaEGQ9O3cl1qkyPSrdruYMSDUfeYQCOkIwJMo4J//+Z/N74cddhhefvllPP3009hjjz0wd+7cUSmTJ1EDwCE2/JHFamZi9svAmIANUdJWLP5bUAxEUDOeQOdB4mtENUMwAAC5EiSQkB0WjGvCQEEeEEK56KRUIexa/0QyVBaZrFkbE5kGsybTkchApUMI8qaDERQDtVARQEt7ZQiilZiU6y70uSRcMiL7uoB8EXFeC6W1qFeUypDFCYje+pMWoesTmCiFljne6vQoyCtBdEkaSx/nnhLFFgi0GPE9VfogQ+XuYxcoVXoRb9pghKSsORP5oooE6u02BA6AFq0WEcexGVyM4HcQJOswugNx2gIkAOXSA0zOMYojRaqK5UTgHOZdosUDbS3RppC2IJqFa41+LKeySuuErK71xopkS79LxrLiWiljOzcau+ukBOnxtZ4sJBYmW/SeJkx1BCrTSqEnNWkyxe6i9L2BeguQk3sqI3S9rlwZxM22ODHh1u1Bppz6OjaxTLv40vd1rFgBeLFqR2sGKxeVtS+xRsaOVar62/9B7iMnYSCEHzxWFfWFX2ZYuJKJEfeTCYEPINomQRRbVPoUJt+1itL7hXkEE0uWZSsw1lDWUslSPmkPq/78vlK1H1Qoq++4rzvp20QMGeko4yCHOFcAZKis5TqRatyyk+6TLWuU/b+ppABEYFYfAAAR19Tk1XYFps/z2G6YMWNG3Yom2xueRDWAIEqsTozYWY7dIROKwPQl+6z/iS1SWngOwLjiKAhdEpIrqA/VIjSgWFmRgkB3FLoDAwBZSyxe2v0ooorqINIaKLtsQKKp4ogpKJGwub9zH+3e5E7TqqMhSppQUpyD4OPSRJTTK0Q1M6AbMhLkELTvrKNjtCsrdEWoolhOCFCQ18TOSuUQRRAFvSAot1VcgywHQLkNFOQR5wpK6xADIldIrIqVPuVO0Po0RVSq7kLCWzDrHCyizMAa8FlcKwCIcqsK5y6WgZ7NyVpslvie4LaPGZR5wAVANU1W9PkcoCB08EIdLBGxQJIfCRxODiDu61ZpPvjZ8UDJhFDnFeOEkJyglq2NTHxs4mRcRsNBlm4o5S7jNkr2B4mlh/+299W1S1BPrqxjFUlJsqM792XhtfU3ANf97hBr1zXL26DvkQbZCztbFipzvmTSlu0eHQjKKpr6Bvh8JlKxWsFAlsrKYh3mIKMaZPdGFXELqDxx/C5ZwQMqci5nUsWInHaPxzqHU7+KLhS5PEjLHqSQiIO8+r4nTFaaVW0dolwhmRAGeaNfFaVWZTXPtQBRBaLWrSa3YbE+KMfWngKqvLovFDrnHtcfA6zlNpLY0S1RcRzj+uuvx2233YaVK1dCCIFZs2bhE5/4BE477bQB19TblvAkqhEy0gU4FhBjuYl11Fvg7rNPYzJhE6/09Zhc2URNSpc0WbM+Jll1uijbpcjkyS5vqj5q5qgyXhsiliZI3EnqaxsSk7qm6Vg4dDiqJm7CQJGzxL1J6rqlMiR39kSgwgS1BEP3ZjOoUrVfkR1OIqrdAcYyxu2hNWEkVKJRCnR5IpGEYnPVa/3mOau8PUFiheI8TFqrpJaaiJPUCGk9zlDzV8kkMadjddFWLeMusdZY47QDyBdNCgEnGSBfW0cXqhQLlrsslwN6uxMipNd/s9eGrCsjkzE7bQOg3jfdBoEe3Ew5GwzKJhTduKlUzq6YM9UDTt0Hbr/EkjIUOC4vIKV7auBGHOTazjprjc6XsZOlnLcbV6EdGBAndcpaQLvRYsNprRXn4LKvwYRKCfRd8hfOO37AembWyb6eDU06RK6gtJIyVO61IIRonaxSofR1g/JFZUnSBIpqlWSyJANQ3JdkYa8qd7wotADlRIdEMlQToQJMf2pbyNXEqgDkSbn1ZQgIASIJCgvKqsRSDXbpm36jvn8EkFg6LW1sMoncfgN3RDGi4ebKs67RjCAinHTSSbjrrrswd+5c7LfffiAivPTSS1i8eDFuu+02/OxnPxuVsnkS1QCkXWciqiTRb4BDHBLSYM3KzAwl+biI9AfIJII1Rfzha3MzgMSaZd0rIVIiuRbFylDGZUtZxczvQc7RR1mFSo4BINgVZtXPWJyEUNfh84Q14+WknoDrQglCpYUAVKdqkSlTFilBrR2QuQKo2q+29W42EYAmIk2vp8VESGnV4jpLF/HMNEBCWCk2IfgGMgTJfmWxAyDKbQjKbUrcX+lVUWw6F4zJAm9rqDLgEAE7X1CKHCGOEvegbYHS75BsbVciW73PEDfbhZYR+eaEtDt1DcwgqNYvVJYFW+ybjjIz/+tBOj2Qs0VFlso6wWwlIQd2ElOrPVRwQXINY5VKtWcjt159vWR9eww2wAxGwjJcdmbx6NT2zGvwd5MrWFqo5BqDadicMurfbQI7UFmd7anIT/v4LRny4zeeVv2UXZ40gaIYEIGRGZjvLoogI5XFnPq6tWg+n6xcEOolsrSOLq70KYI1eVfI/m5Eb69T71exFXGhFQhCiGqfmvwEuURSEdcA7lOiiimP0SwB+rnEoHyL6tdlCCEkYrSYPs6x4MGagMUxEKSinoVI+s/tiB05T9T111+PX/3qV/jlL3+Jww47zNn34IMP4uSTT8aNN96I008/fbuXzZOoRjBahmR5DQCO7kktoULJDCadudaQLa3XiLRIPcwDtUB1CEIq83NdpFxOmZ2zZjoyMGTKFoOrewoAmkixdSy9kIq9jUmRsRAlJmzbIpWAryXNdXJTdgcAVN5+05i9TRvG2tUZVRR5yVsDdlRJSF8hBAUhqGsjEMc6b1FOJTu13Ry1ftOBiWq/qb8ggnGXCpe0OqZ3RqEMaGMUNPkiISDyAHVvBvXrpX44L5dp43oSxWHknKaCeruNToo1VOa59Vs5sWTgRidKNZs3bceuDgDgCD3bGqb1ZA4s8bBpMycZZ6CiJKVFchqBrRwmkkqXpVbVazjmzVpm1N9rliyKe7oS64LtBooTiwgnUGT3pQoEkNlE0LRH4loxmdq5jqm/GyJtzcmAs6TKQO3DE4707F5YzxGAgJUB2yZgtm4qq4xCqujetPWDf6cYkHCPta3DgYSQ7CZWm+xkrkOCHYWZLp99jJDGWsN9GuUKENV+iJaJQN+7ZlUE8+73J8tnBRMnI540HVSaiLgVEK1TTE8joioQVZ0gHyF1XxBVQGERFBYgAbOslU2y4iDn9gN8jSCnI/kA02cCbrqYQDrtTmEeiGqJxyBtvfLYJvjP//xPfP3rX68jUABw+OGH4/zzz8dPf/pTT6LGEowoOpBqJgJogbcenM3shEWtISjWWh/HOhKawZ1CJAN7TqrIOyGtmZTeF+QR51p0mK00eiUAZrZHUrv24ig5ju/Ngkhbf5XlTuTrwbZoadJmkyrneCZWidWq+tYqtS3MO+eRSBLakVA6BduNptqyz1i7BCnyBKlzsUgJKZQ7j1NIiFqfunaxFejvVlayMO8ONDIAoti14qVmkhTkgWJCRM3zCvOQelkeHhxIholrK64pa06xRUXxQROTUjlxB7buZCwsyRphtgtIGl1SzGkUrASgKhljQtaYoKXTLwxonWAipSOajLsnLNcnh2yEtDuKXVB2wsUwD5JWpKJFUtJpJdLWMrWmoVpIl/r7kuVYMmDWadNWsEaL95r24f3pdmr0+1CIRXpVAH43KGUVTUfl2sEhPLFJu/XT59nb6yqaMbkZ7HchsyN9BwKXMX18o3LFcZLShSdihTJiISEKZch+ZXWiXAEU5CH7ulQfUSgjDgsq9xJ031tsU4kw+991rPMQUpEqIdT2OFZ9aZhHDEBWutX5YUHV19TBtRADum9mi7aAmQAysTJWc8CVIvC5gOmztweimCB3UE3Uc889h0svvbTh/uOOOw7f/e53t2OJEngS1QhCk6c0mZASJNlyIV0SpEWMhiCw0NH6GClQA74ATLoAEkoMbkzL+ZLaZ1unbGuRJXI0HS9fhwXiXH6KDaEw9bL/t5DovCw3U3o/39/ejoxOxGo/E40YVSBq/YkGQZMf1leJWhVUmKDaWGhi2lKE6O82C4QSacugXmBZ1VNnG2cCaXeCQijyYDRcSeoKttaZPDCs0cmppXwMaZFWGHRYVKJ5KZOZcqGkLA9831wJKOgZdFxzrU1AQjbCglq+RgtvDcJckpBVEy7T7lpLYgTctYqlO5Kue8ykUUiiAgdadDYz5D/jeJHLJ2uWmfskC/XyIrI2YZL5oskazfmMSOfwMuvv8Vp5aSLFx6SITmbqgsHcVun0BYDjamtocbEtPLallicsZLmXbA0kH5+lRRmMnFjftrMt45w6vSTvto+3tE21Z+9DOPfo+jJpxK8/VU/0uB7pcpo+ktR35mzTfVpYRBTkFfEJ8ohzJTVRSl8rrqlvjC1KMoTSbOb096q+AVHrVxaoIKeSYQpFpIhiE6xDQWis/6r+0vTLifcgVKSJJzGhu9i3aXYgIcNaZ6XOj0BBb8N2HEnUYkBsJQmqNackCm+//TZ22WWXhvt32WUXbNy4cTuWKIEnUY2Q7rzsDoRNvdrywrNR+xylWWINlP7grARvBKioEH0s+OOPdVQdR5VwJ6QtWgBcqxLfkzvTIKwvs0Zdh2V1Ks42a1/mOXztLGSUycC6npqBtgJSqhlqXEusYnwe6x0Ca7AWEpQruhGH3GZCuUgpVwIkTGiyENKQCNIkkWReReixpZBCY6anXFENCPxsuc4U62Sq6m9ZbkusjEDinuQM8RG09UeLkbVQXeSUZSkulJULwhJog2K1PE6tapbDAeotL4lGy9omA/VFx/X6qHqXkftsMhNg2pYlfry1qtKx6PvHPV3JMkZSpTYQ5TaVpJMJH6AIVBAAhZIiKnZiRh0hR1o3xqSRqhXXosaupSBVNtviZVueBrC4ZIm4GxxY93cSuWqdlaWTSRMp+1I2yUgfB5cUOX+nymN/n3XfarosgEmKORCBiv74bNIe7AobyPqVJTlIa5KEAAWFums4hCaqJVZ/AKKqrc4WUeJ2IB10k8gqUvpHIVX/YVug9DOo47NpLZ1dbm7zgPvXZOKn2keaCZ3HtkMURQjDxnQlCALUarWG+7clPIlqhCwCxWikG9IDOQAQDZI9nI8nSkTmQqooOel2Wk4+kiyTv9W5NOr0zUdvi3DTnYpNkDJnzgN00jbS7oxUmQTruDQJjQuA7N1k3Ue7SOKaIqJcXhaxG4tYA11LquwUhMqylBKLUrodpQQhBAICcnEdqTO5ZUxb6hlyEELUqm5bx3F9G8og0SdpCyXJfqQhwpxymaUsWLBIiVncluvj3Md697IE2EOEWRvN1ojwPo7gY8tXmpSklvdIFrCV2iIGUGyRIF0nsz4k57Tic2ERSaPvUtcyRDIr+q4R0u/OABqprUWaEAGo/8aGSIYaRpCZA9zvTYXkx3Xi8OiFXyLY94gBCp1hBctCBoFyiJG5luXmlaEiSybHWcpCZFvUsvozAILd9AOWLaONKcvSWX8dU46hPJftmOJgR3XnEREWL16MQiGbsPb31/ej2wueRDWC9RGRNagDSLbbM9I0WRFI1tvj6wXSsppYsyrkzTWFfV3+iHPF+rLZ5bBSDwDITBJKcS0hTgDqBKnW9cwxQaojHEp7WeVzXAyBzspe61ehxno9KkWmCkBfF0AEyhUQ58tAEEL2bNSzyyJEX5c6njMQ8/Wl1Q7ajK/E59YzsgXnOiLP6UxtC6PQs9wgBxH0m/Zk/YXgCKAgZ7Qdqq3YjWFHQMaOFkpIqZagqPW7UZxaUM26JYQFs44hVatJZJO1XA6vH0hB4OTaMtFtetFVACpTdEZUYaNM3gASEsIDMD/fWsUsQ4P+vrpElcpFlwrZt/ILmaSPQc6Mq4iqoBogy0WTUNYsp8RlYeJk53TSZFTkQ/VeVbbAGrUFUXrqxtakyVw7gzQ3+E7s1Q/S13SOG2DQdkhFo++xEengegkJQNU9fu0JyD0/WneJYOZcxG88XU9iUvcMdv8AotXPqz9ii/ikysyRxA6xlyHAaViEBEJlzWVLDwCALc5cFat/cY5Lt0mjdkq3DWu4LKt/Vp3t6Ow6/RtQb4HcRtiRSdSiRYsGPWY0ROWAJ1ENQWHeDccXgZmNm1B6hk1kkMzETLZguyPgb9smAgBUXD6AWp+2muh72647vleW1SltHaO4nkxJDNwJ21Y1Ox1DurPKOqfBfjsyLs6XVSoFHhCrvYqUVBMxvlr/Kq/TPpT1cjM6izvFimDZEWi1mnMP5YqLIaKqcd2xJcW0NXfiWZ0v/y+SjpKXhDGdpbFA5ZOcWTJMBglj+QlVVJSQSuRqkUcAej0uadx3ZpmKXAEyrikxOad7sDVI2r1FNZh8TjKnSFXc2w1UNIlhEqbdiHVIRYnV/a7/Vq7IfpglXDgRaaEIGbea5JkUK60feM03k2vLTsWQPZsXOsM0BSqhIiq9ELLmLqqcJn1xZAiyCGJApoiiDNQisnEN0AENJmWBbXlKE670e5Fl4bXroa2kdd8bUqQiw7rh3K8Rss4bqoVIR9/CdjnJAEB1wGvUpWrh+lkTt+iPzyKYOTchUrblUv/YNhr+JkVU04E2eeM643xO+kJ1kZY2iUnez1R/x/2W3UZCQlAt0W1akggRVxLrtJCulquRdanRM/TYprjuuutGuwgN4d+EBlAfecFoApJtRb1NaCsCL29i5YWyOtB03qWspVBIplxLUSImNx9rXINZcsYa7LPTEFjXbvSx83XSZvF0OgCHWEi3TANdO30fJiRhAbEWiVJYgKj2QfZ3g/JlxKWJhgTJ/neTKBshERfKimCx2J/F9bZbi2LVRqESrtrpFhz3aFhUgzWT3/TM1QqTZssDZ0fnjMm29szk8xIWudPWLCqoXDdUmKBSWYAJupW4NF9SUYmtHaoNwqK2HsawI9xEvqQXcS6pfDu93UqDVCippIRhwXGjmcVerfQGmeHp9v9afC6sCDx1rlr42UmXkC9qXVig0xSklqBJ3c8QqPRsn4/jgZI1Lvzd8L2ZSNpWLtsqYN1TlbWgnlmQM+lHHOuYDevdrnsnMiYS/G4ItmgK4brdrfci/Z6k75v5jQ72rXF/k9Zl8U8KTh/D6U8aIFr5e/c6HLSRdewfn3WtUHafYrvl7PoGoU58qYXhQc6yklng5zkUwpjunyyyQ7mimUSZZxzkHZ1Vcg1hfmfNlVM3qy2T9hxitONWgjOWb+3PtsDKlSvx+c9/HrNmzUKpVMIee+yBJUuWoFKpOMetWrUKJ554IsrlMjo6OvDFL36x7phmg7dEDQTrQxFxDRSGzsfJxzBICAhCZqfbsLO07mVcV7KmrRuBSZYpUsep+2XMmoxuSEcMCZHoIlLmbrJnZVYnSSJZmDhdx8y/G20z+1T57IU7icXz+RZFKvJl5YbjXDA6HwuXD1q8bf4WUu1PC0cZQVJHQXG9qT5rNmkG8foBQ60lmNdi98Q6WOeicbRB0mRYdtwADqksIubOma0FbD0AkgSdtSpEPtTvRwjq71WReZw00wpfNykMLBdenbCche7ONv47mThAwnEF8fprxk0ZW+veWaJwkw6B72/lTWKSYpYXQvJuiLgGVJP30bg4hdR5j1IRc0DyDEptST1sXY5Npk0aCes5xVF9PqAGhEcFFsTG+kjWLrNEU6p8NrIE4/b/jc7LRBaRyjrGLpfdrlEV8RtPQ87+sHNK2k3nlDdLi2S3pf1dSXd4MWQFUGWyXMaOdV2lD7f6TtW3mvfA9MFWOhaKtHs/dPpsQH1jHP3sBuvkTcLdzHa0+3Eup7FWctkH6ddHEGM52ebLL7+MOI5x7bXXYs8998QLL7yAM844A93d3bj88ssBKHH48ccfj5133hmPPfYYNmzYgEWLFoGIcM0112yTcm0PeBLVCPrjiSF0/srQWJsElOXJcYUTEBNBimTWL6wDbEsVbzfX4+O0qZqEUOHx6kKqE0kXz+5MHMG1Hpwti5JARVUnq6O2CJkZuNKzTqujNxonW+ORNRDY99C/O8nuCIAQysVX609mu0ycWAcW1RKXjWkja3kZHtCCxHrg5O4RrmvAWbLGqr/tgrAF2yb1AZNcnsXHkclZZTr2NNEDwNnaBRM+fjassxCiLtRbRQgWlEurVE4i/IRUgvNIQJbb1JPmqENbe5XLgapVXQxriRonR5LMXJi3LiLPtipQnIi97YSU2nLFOi37WkLn96IgSEidHkyNC4fLEiWWLKOh0YOdfc1E+1ZR3wnFysJZmKC39wGVXsdygJx6r0UAtz7chvyupCdH1qBPZhkcrYGScL8NAJR2o9vgY4F68bV9z0YYioA5Ix9S3T3sb8gqa/zaE4AMIWd/OCEiEiYu3l45wdwLAxPAzDrGSmNI+SRrfjo9g6mp832513EIuH0/m8TZ+3QqFKNro2Q5KhJuDjDnPch4nvZCDR7Asccei2OPPdb8PXv2bCxfvhw//OEPDYm677778Ic//AGrV6/GtGnTAABXXHEFFi9ejIsuughtbW2jUvathSdRjSC0i470RM5mMkIgisn8aZP7iAhSAIIAKYQrTRDWtaBmBVIIx8rkmJztWSufzPosa9kW16TvuhBJCDVg24Xg8/TadoPNpBxXWKqNGrVdpktBCLV4p66zIFIzxCyxN+C4Nc0xdicZ5EFhSssAGBE78eAorfbi+6QsIA6Bk/a+xCqUaZlI6USyrFv11ipl1SFO5slJSvk6NSDOtUCWoTI82xbEQGn1KAgh7dQasAagXAGQPYoc9Ce5rpL76+OsqDcRBGZtPqedBtF/CF7TT8rMpJKcCNaxbloLRtdFnnL5SH9fYc5JIcKaKZOAlkgtzM1aOyInMSKnsaCwmIiHw6LKWaafNyeAbUigrN+dKFd7cGWiL6K6CU/6Go7mZ6Bvb6ijdKNos9jqO3SfYb4VTfrY8hu/8mtABsYqFe72PtTWvORKC6zrD0gALULq5mRS74cAVGQeE2R25RKp14BThtgTHKdPSz2ntPVP95XOslwRzMTURN5pC53jmrWRui+ApP8aBQIVxbTVeaK2p7B806ZNmDRpkvn7N7/5Dfbdd19DoADgmGOOQX9/P5YtW5aZjbwZ4ElUA1QJqMVkCJIUQMAh7wREBJAemNPvJQkgkAIRkXLvQfGvAGqbjVgTGyEAdnHY1hTHBUdpHUfjL9m2cpGtK7BnytJaPgZwLEV1x5p7Wsc36kgzCZTbEdn1orCQWN7YHB/k6hdX1haJ+uhDbYXj7O36fkJCPQynjJYFxCaimsCJqKaKJnW0n85qbN9XEYLYrMGVbjNHBxTVDAFM3KeklgLibPjpQSFQmquoUFbrhUVVNXMXUue+CZN3xKqXISY1KI2UkCofVXdXcpw14Dq5pzgRJ793/DzSSxnZYItcruAOmkHeeU+4HbKvEbrWQR64ZAhRqxjdjEmEmi/pNtUDYpgzyW5FTa+HGMcmkS0HSJCQgCZSoFi5fZj0SqmWELKfQV05ZTKJCKRpJ9slaZ4D0t9B8n6pCFmRbEvDnnUNZu7IEulnJSvlY+NYkSnOAafbPX7tCcg5C+pOCXd9r3Lr1dUvVWSyEmymM3tbRIqC0JBaUatAVHqAPECylFgUKaxbDLgwYSL6393kTBB5QgZAW8V5EXNdx1TeLifBLmAFUKSsaBmThiwvgr2PZMqau41ARKCtJEE8Zm3evNnZXigUGqYPGA5ef/11XHPNNbjiiivMtnXr1tUlzGxvb0c+n8e6detG7N7bG4PYj3dcVCNCNSL14pIiU9WYzA9va/ROs4ivpn8ifV5kETNAkS3zjaZmPQbceTh6kBQJsGdpGdcwHYFlBnfW16sjREJHS+WS/bYVK/1jn5cBpUsibd2LHZ0SWQOaLQImnqkCZn2suqU3ADOjtNsr+3dLQ5K2TNltI1P1MkLxMEmkqYkehQWVfZnLBmj3kO7gg7wqe77FRPMZwmNFJ9ltqawtZf3TYiIBmSzI3k0qQallpaQgEbtSvoSYBe2lNnex4fRzyeUhCiVXwEuxqosW9We+kzJwrQjcfkEOVGxV984VEquFZLIrzDYKkghYDtpg0T9kqJKq6ohG075aX0ZBTh2rl0cyBBNIMlaHBdWGlkXCiPtlqNyoLHDWRNB5V0w0pkxNAlLWxzohulDaKWnV246sNBca4LsZrqkjpcmr22cE+QSz/JGQiF9/CtGK39VdLpi+n1tedqFHNfWT0gwJtrzGtcSdromUqCUpDagwQQdblJI+gN83e/ImJPq73nELlbqn0UelXdH6fSP7uwoLiZXURNkOMFHg27BHQss4opjM/7XtZN2JYxqRHwCYPn06Jk6caH4uvvjizHsuXboUQogBf55++mnnnDfffBPHHnssPvnJT+Lv//7vnX0ia4JNlLm9WeAtUYMgJmWF2hIzaMNDtauPiBBDQNreAmtW5VigssAdE8/GbX2DkErHxbdM664ANXPSFii2qvB102LjdI4UY/Ux2ywily5fo3roGb2Ia8kM0tSdIKJ+y80jQEjWAawTvZvrJySTjPvC6pDT4nvAdLAm47EMnZB6Z1Zq9DOWFc5kd9dLR0Q1VTarbShXAKHgWGLqiK5NgAGYhIQUA0EeQvQluiAZAv3d6rpCJlnX9X2Jl8ywBg5RbAV6u5PbcQqCrPXlbKukVXcnVD3MGQJFyENUexMLlkjNyuPYuGgQhkm9dD3VOweL4OpnCD3Q5VSkIvRCxaDYjbbSLjzZt1nts13DOiebsSBS0r5ue4egXAuETM5NEquSqYP9DgjUDNlLHmyckGwbluvStaJqy3aQyu9mu+G2MEFqHRqdb00oOKBBRFVEK36HYNaHEL/xtFUnnsjkkuAKwO0HNDKlAVrjplyvOsLZfseBpAxpyx6g+ie9LRGHqz6UtacU5FHTn3WOrPVL7QmgkMZqlKVbM7o2Q4YT7WtEVNevR3qC3Yy5l1avXu1okBpZoc466yx8+tOfHvBau+++u/n9zTffxGGHHYb58+fjRz/6kXNcZ2cnnnzySWfbxo0bUa1WB1zSZazDk6ghYCS/EeMehOoEopgQ2iNUulOyhL3p9AacXsEMcdYAlvYE2OZvxyRtzOOJJSTdwbjaLOn+j9glJWxREgKgJAKLZKAO04JykdWmHIHDy73o+qkyJ4Ninahdk6Q4yDmkLF2/pF4ZLkr9v7OMRJroWKvIu20Ay12jXTXc+Jwd3V7sNDWw1Q2uKWLKC7OiptYejFraQYUyENUQ9G50iZQM3FxlhiDqdiyUkoi3OAb19ybLqHAbAxCSF5AuJK4ua9kip114Js/b4xqEJf63LZ+UKyTBAPxs+XJG9+S6OHlwM80TFq1BMVTvfxEQ/e8q03pUUWWANcGwYVscY9bMCZX0FlB/1/oBUVOZ6Pk5shuMiZLlWlXnJbni7ECFbBF5YpEzRCxyM3jbyTzt76DOKgZk7nei0ZB6z+xca3wv5CCiqlo3z36+thWbIyspdr5DdsEn71wS7GJyt1X7EmsQ4J6b8YyQ7nNsaMIeaV1pTMoiFEgBo3fSRC2GUPpBWJPVBve0tWqx1sHGmkARufIO/n17cSj2imztNQCgra1tSELujo4OdHR0DOnaa9aswWGHHYZ58+bhuuuug0z1c/Pnz8dFF12EtWvXYurUqQCU2LxQKGDevHlbWJOxA0+iRgkxAYExF6VmWhmRM7bf3bFaDQCL22TstK/tWrKMft5209h/o55I2CkE2PpFQiCWOaX70p0dl4vXpTMdLiPIuRouvkdWtKImWSKqqs5ZX9duo6yIGxPlpxcmNnW0Bnsmf/UDkDUIcUdvPT8eMOrWJBQykcFwpmT7nk59deABhErBatchLKhMzjKEoB7tWqmqyDS7DfV5oq8fotqrnh9H4gmp0gUEUAJfzruUjrzj9yFkQXClPi0CEqJPoZVTTUiIIF9PKFjoLexITcsCY7tSHXKrk92aKNQEEREQFhEIgTgIIfq7TWZ5YWuidJAFaWKr7hFby49o64jp/AtAUFXaHRtBXi9aW0sIv90eTPo08XYCH/h9sqJJne38d2DVnS+c+j3Lylsn5gbq3uOkHhaJtYiUWbBbhsm6lTbscliu7bg0Ue2Lo4S8awE5u/AM4Sa9iHCQQzpSsK78lsXKlgEIAUNyIl2gKCYr4k71RXFMgAgSLmiSdIpk0mX3Z1a7RhkECth+xMkGxSOgidpGBX/zzTdx6KGHYsaMGbj88svx1ltvmX2dnZ0AgKOPPhr77LMPTjvtNFx22WV4++238ZWvfAVnnHFG00bmAZ5EjSqkJgOJiZlM58GmagZzgpj0bCu1zb2uJYTU0g7u9hL3HpLZlj6OLVkC7sDO7rGBQrezZnakOzhCIsrn8gopVKoIdheyjkjAdSGaSmkXjMkxlUvaLMiBI/4IWYwR9dcTUjWCTVrt9tYdd129bBcek0A7uztbrCQcosDnQAImmaTlJrNdeYl1TeUJM6kUpEwGbIpNhFqiW0vqroTUeVChbJasIQCIepUlSkiQtjCJfDEhwzoFgOtq0ftrgYlkM22hxcp2hn87DUZmcEIcA7yYqGXhM+4+5xkp0hTbz1sPfoJixBD6/ScgKCDQlh2Ka6p9oqq5DulM+CJEKmAhNBbDxC2aN1YUydeyXLlCt4uxSJnypl120tlHlBBosz1NcFIE0snCbQptnWO/o9Y7W0dErPPq7sukLZAQUvUWTqSw/U2yMF6o9BwAQPkWREFB7bIswpziwim6SL79xE2vrNO2DKBR5LC9ja1PtoWmLyIIESJv6SVYxyqEQIF5K0FNIFirydtEoAOAmtNVNxq477778Nprr+G1117Dbrvt5uzjZxMEAe68806ceeaZWLBgAUqlEv72b//WpEBoVngSNYqwP1ABLTK39c4pSxKTJduEDSCxHKWun7ZAsX9fedpYA6PzW+l8WJy/aSDUdWxWxxwbM1EyQ1Smb0uTZdVBJW8EhNBLtVAMkSH0TN/TXE4TPK4XXxfsvhxoxi5iV1TqWIOgjE4807YsTib6TFiJTO0kfwCc/Epp2NezbmfqqMmV0NFDFBYVEUIyyAg90Mct7cbNaV9bQFl44mIrSEjISh+ot1utp6cFtejvcTVgxmLjWgFN/XIS0GkpIAOVhqHaA7Mmo2kTy+IVW+1qX5ddYdxWgBH6OsJtbYXi50raBS4gIWVgol0NGZUh4kJOTRwoUpGNnIE+LeLX9TIWKWtQt5MwUr6s2iGqGB0WCR38oMuZdqPZz8oQESGzKX7KGlXXVvb+VO4jpy5pFzTZz0W6++3fU4SO6qyrJcSFCVp3xv6swCTJTX+bstoHEVUQFduSa1hyAkBNEmVYgB35Zu5vkUCSAWoxIWftM5POOEoszyIhUrGeNEYxzITTNAkRYigrVhwTQLo/imLnOrHptzBmYAvDt+Ya2wKLFy/G4sWLBz1uxowZuOOOO7ZJGUYLnkSNIsz7HOvohDhJ1qkSd7rWG0IyWPDf2ddVe5gYAXA6K9twxddhIgXAdHY8y+d8V2nBJYNncizAtOtHHAVk1VcKnSICKgVEyIOMzs6uyqu1FfbAx4NTg4qnZ41JXipZZx1xkDHbVdvZVGgl7tRaLRFV65cHYdhWnLRAu5E1LzWoORbJXDEJoxUiKX5YBIVIXFcc/WRZVES1T7kAWydD6vXp4ryyHsi8tbA1W58Ak5W7PsNrrHIyIa8sNUEOFIWAiB1rg+MS06TDkBhetzDLvcTWALY2anBOtihFxKvW8zbvKJIcbjEkEBQgw6Ly5EXVZHFqLlu6fmmCEtdUlKAOyweQrPnGbR5awSBsTea6ICFVIovUUCLcNm5mm3xbBMz8zW5KbmvrHvY5joXMRgPSZiyaUdVKfGotF2O3v1DkPpI5RETIIU54fK0PotoHFNtckoRkIudsI8vCLmyrvDBRzkIKSO2SSyaXUk8CgXS6mZhgouYImhBBESR+bzJT1Ojgn7rtYwDp13O41/AYWTQYPcYm7rzzThxwwAEolUro6OjAKaec4uxv1nV5uBOIiEWMyaARkZsmIdIzJ5uU8DXsfRFx9EhipODOitM0RCkiFvG9hVAdmC4TX4dkgFgEqJG7jhN3dlUrfUO6Y8vSE3CZawRj2jfh6kgGU/MjA+N+NO00hI6OtNbFDM72QAY9IGT92CkggGTdRLhWI3uQc9I22ATL1vII6QqSeVvqbyaltZh0+LcW7ttrjckgCfXndQR1WgXEMUSlG7Laq5aXmdCBuKUdgmLISq8Tmq9CzHOuBoYTXXI9OQ1BoWyWsjGpF7LWORMSJjVErqjInZ2fiQ9L9ez24GkntWXw+2QLbSNKUopElEw2IlJkqxIR4iCXpKPIan9NTkSa0HKaisKEJFrQ7AvdKFTdDrzALrE7i91+pq0LqUXGRV272O+neU46rYZJl2GnKNHvlfPu2VYq+5rpRdT5PQ7zzjXZZS0qPaBiq/opTFCuYv0tBFYEJKAsd3FLe/I3Jc+V+yNLVZVY1K1+hfsYPqpmpYiJuA+z3oGsdDPcv/Dv5l2xUs1kEaWxRp48xjaaxhJ166234owzzsB3vvMdHH744SAiPP/882Z/s6/LY0zQYN+8a9ExIAJZM6V0LkmkZvCxSATsfBnWBqQ7C95OcbayiDs1ATXzj6xOyb52lHHdRhBCGPIXiABBXHUE6lni+MR6llizjHfPuW9yoqt5qLdQuYVKBhcmC3V6KdaIkHATlmZZN+zrZblp+HcnnDKGFDIdR6iIJAWQICtCUd8nyKsILx40g1ARKhZks5A+yopgFOpYLnMAd7kVrnfa/SKh6m9bS6y16hzRcJoo2roXJhfadcfvmf0uSTMZSFkciVCN4VgQ6r4LAMpjIxGEBQgZmgSPQGIlqotyY0tPOhrUdnfp8tdpeIQEcnmVWJViN/VEmNeaLX0vSix29jXTrnU7Ua5yN2sCwlZce8aUBS63DFRiy7TeyspQzpMa5FSS0jjfkgTdxRFiERhLdRzkEplBkDORuNqznrjvRWJlctKuoN66bhMfVSXVPybEeXDCw+lk0sLwZsRIRud5jByagkTVajV86UtfwmWXXYbPf/7zZvtee+1lfh8P6/LEhMadX/q4jN9tHz5/LERk8qdkIWtAighGG5CQrXR4b4YpvEEZ+ZzM/dwhAogEoTSAHIstaXyqTJFD/t12TbKLj12jnEXe6LHSs3H+P8PFlyZdRuOSziQPZA6ABkaPRvXbkBAtEdcQ6MGpRoAMCmZggrbeGRKg1wRz8lTZA2EQQlR6lfWoUE6E14C2FuUdklSXDZ3X6ON2CbQ7K6oB7Oq1F/oN8q6FRrCWzPbHSJNIlK1PiKnOysrIsjQ0+jvr/WP3Ti0m5INANbnQaThYV0bkEosUsXWIIP/NkZYUJyTEublOJBlb0X+RlcvIgq3RU1bHMLkPJ7esc0drKydFVtuKRAyepZUCkudpWaFEpTfJ1SQDUGECIu0ONbo0Aki73VibKS0LsZEFaMJUV0e2SCHZn9YgNRrst1TozZPTZidQwNjWRO3IaAoS9bvf/Q5r1qyBlBIf/OAHsW7dOnzgAx/A5Zdfjve9730Ahr8uT39/P/r7k4ijdDr8ZgJ3GPbfaWTN5jNFlEZXlcz6GJHpp0VdZzdUUtUITlQYJdowdm1xJ8oZbrncTPg44tGcB73mVFpkz4pSJJY1joZMLxwtrFY1FgJ7INN5htTJ2ZGKygqhRfCW6NwJwbePT7u4rDrVtxlHTgY6kSol6R9kX3JckDMZu1WqgZSonNccFDJxvQkJ1PqSXFFBmFrWRV+fLXYsfg5yKtQ9ynCnC2G0ZZHkkPdE11SnbRviOzYQ0udERBAsWJc5SBNpmUpgqwpQT6obWRTtBYCFJSbnXGH83KnmHOMEBtjXAiwBvgQiS1tmp4QAFEm2IYNE5M/1sCxumvaAgkJyHZ1nTEXeFU1OJ1uXZr+BWe+jaRZLW6kmYSn3uyZgtstNFXPgCdqWwvMGj22JpiBRb7zxBgCVgv7KK6/E7rvvjiuuuAKHHHIIXnnlFUyaNGnY6/JcfPHF+Na3vrVNy789MViHsaUkZ8DrjbBpOCagPwakkJrgkKVtccONbZO+s00IR3wPJITLIY4phyW7JgNrsxQiWSSaUyxwhw+phLDMdbIGVdtqpV1Vdjg1h3nXkTJhkbaE6yV10htCFcIHQYRI5pJxWC9sDBkiLrYlRIZD/DnKzE56yPoluHoxZaEomuvVhcprC5Za/7DipkDgtjAWHItoSmncQaaN9D9Z1oiRHghZF8N3DqVAqC15rMuzc5g56y02EKYbLZWEacO6JLUsJg/z9VYrrbFSFbasooDzTFQOp3xyH/u4NJgwUawsVUFO3RsARTVD8IybMd9irke5kgkuqbf2pi3SiQuWyX7NvKrZpCidmsXd13DXDouxnCdqR0b9lHk7Yqjr8sS6Q7nwwgvx13/91yYjqhAC//Vf/2WuN5x1eS644AJs2rTJ/KxevXrkK+oxZNiC4kSAnm2OZ2HoQG6cRn3GUPQFduSiKRclgnylrxrEQmGTEUJdtFKW27DuXC5z6ncmgqRn9ET1ZTLr7gGuBYy0FYMX1s24n6PjMYLwdLSVtl5YwugkkkwkIvsgl4j7NaG0B2cemNOC8W2F9LsTcbuZIAQrX5dDZCyLUVab8T4nqi1FcPg5BG5eLKPBYtfoQMjU8mX0c/ysWbxuZ4NPlZmYQIYF9cOu2dShTvAI36bumMZu2boiEm2XZ9700CRqa348Ox15jKolaqjr8nR1qVXo99lnH7O9UChg9uzZWLVqFYDhr8sz0qtXe2w9jPYKpAnC4FoIR2Ml6y1PmdcX9SSbZ9P8u3IFZpPwmAiB0CkaMmBbnHhGHxirmLKzGd1I2lKhl8lJz9b5L6NTs+rJrhbhLMrIOZvc6EDSonNQDGK3kN7niMGtcPq0nondcjEEpK5DnCuBgwIEkRKoCyvtAPFsePDOfHv19+wGr5JAoN2LmU/cFnWbbZYlTVv5bIudeZ/YjZZK2aEIjiaeoeUmTWdv1+eYxKBpCxdbkdLba5UkHUKYd9yuTrZ+nd7AzsPELnRXC5nRdqnGsnPY2celkflNeyLVEDFRpsZsS6/hMbIYVRI11HV55s2bh0KhgOXLl+Oggw4CAFSrVaxcuRIzZ84EMH7X5dmREetRd0sH06zOudEAALg6snRkIeCSsTShYouQ3TkZnQglxIZtLkyMOAlpyOkkYs7sri00cfYgNJQ1u+xzAyEAnZrC1DVfVscJCVHrhwhSYmXWpHEIPRdcSBj/pf67qskcEzeCSoBpC1jJuvZYnQjzuwbJIfvc4IFjXXMW404tgZO5tAqf66RN4HfItvBJy4WYcuHZBJuz3mcQdyPct+4rZAhUehKXHV9XhnW6rzjMJUtKpQhUI0gB/X7ryyA7yi65zRh9ATw8homm0ES1tbXhC1/4ApYsWYLp06dj5syZuOyyywAAn/zkJwGM33V5dnRsD3HpltyDCReTqThlLVKJRF3xre3WSM8EmfAl7qwhWtyGcAyRSnHhWAqEANjaIgDkSuDkoenoRAoLiIVOp8CpE7hOnJhRewhqVsHYJbclZR5LUJoeGPE1CZmQUCY6jLTLLmt5Fef/JPpRxLW6ZKzKzZeH0YNTbNyoIqoa96lNuoReVzK5nraA8vV4AWsrUs9ZCLjWDxHXIHUCWc5HJ0Vj0s7kyW0392+TMqWJ34WxBMqYXA3nGh4ji6YgUQBw2WWXIQxDnHbaaejt7cUBBxyABx98EO3t7QDG77o8HmML7L5Id0Y2qeIIwaFcqxGytHx8z4E0fgOlrFD3dAlfTICUeQiZ19sEgriaLPgM7ZZkNxCQCMLJHWBHOqpqNMDuW0UiJHIm91QSRZbXa8MJ2xoUpyxQDDtvk+1ShSZSdlABwSFJivSQsQZSah26GAIykHDSMvD9OGWDECaHlFnnkZSaLhZF5T0Li5AUmbK5LuN6BCJx2RkXbYPBeTy8E2MFXlg+NtE0JCqXy+Hyyy8fkBSNx3V5PMYebNcaY6DIPz52KG4Ne+Y+2MCUdU4jZO1nUuCGmKulh4Iw1OvvkcUI1SDruurGzyDp6OoAkACE1opFxHmSACKhovgsAiREnBAlG8aVJ5xIuYEW83a0U4DRL5Fwl1uSTIxkkCzQbJMph7BpchYlSUEFJSsgVElCQrky63R4lKQPsSNfs1zf6tbCOXc8vBseHo3QNCTKw2Mswxko9CBkkyzOmpw+Nr1Ol9wCwXWjDN6ZZdoCxATEkVuHtCZrrK4vNpKw3ZRcZ6FJBmvABBJiY6LgNJkyRMhOiprO/yVkIjy3IwOhAw4aZMIXcWQIUxwWEkMXByboBKZAosMjIREHYfLsYvfdiQiIM4It0oEX3B587k4TWvB2V49pH3OsJ1AjijgGxFY2aNyAu3sMH55EeXgME4P1Zw5ZapA1eagpGRpdPx0ZNZzrDHaPrL93lMGxUT3tpWlAynonocgNADf/lxHqBwDVp5Gw00twVKbhIrydE87GKsJTsK6Jk85SBCCxYMV6IWYmO4EVsWq7d9NIW0HTx/H1dprQYra93dVTd40d5f3YnvDLvoxNeBLl4bEdsK0GlR3FKjQWwKkQsta3BAihlAi0iLsWKz9ZKAO1JA+TI74Wp7eA66ID6j2CvN9ObBlDWYUEEyTt6kvWsSRUbX9brI5Lu36H4grm96u1pYR33u0xBCpNnlTmCv8SeuxY8CTKw2MbY1JrS92AM5Lw5Gn7YSCiUInICMzYtaXcgdI8pCQpQrKGI/vI2JVrSBNTrozbpb3HNV0u2/IUSGGiPzmFQyCF41oeCLYVit+xRgTKY9sja9nE4VzDY2ThSZSHh4fHCIPJVnppals3xOkwAJgkqXaepnS+JXPdFCLt5nH2xQmZc1ywDRh3emkkFpLH2oVon/bOu55AjQbimEZAE+VnXCMNT6J2UExqbXH+Hi8zyy2p13htA4+xDRNgkI7iZFecZVFKo1Hai6Ho7extWVq6tA6KyxCT+634cXh04FMcjE14ErWDIU0c0tvHCpGwXWD8e/r/9LFZdWtElAY6tlEbZJUjffxg7ZtVFo8dB+koTpvM1AYY3+zoTdtqxJo4WxfHv2dl6W8UiJCVXwyAIyDv6ukddPklD48dDZ5E7SCwB/xGAz0fN5qDu122rN/T/2cdO9TrD+eYrPsPROK29D6eWO1YSFuGGlmKGuUkG+i4rL+HgnT0HQBs6u51yuex/eEtUWMTnkSNcwyHbAx0zEgO8ltKOsYyRqouo01iPUYHg6W6GA4Z2hrYUXhMoHx4/OjCL0A8NuFJ1DjDtiYmja4/1IF/PBGnbQW7jTyh8gCyLUBZqS3SZIvdewMZIBoRNNu66nM/eXhkw5OoJsFgFopmICfNUMaxhkaEylusPLZUTD7QdQbKNTaxXPIReWMA3p03NuFJVBPA1uFkDZyjTU6GMpiPdhnHAxq5Zj2Z8thaDJb/yeeHGn0QjQCJ8u68EUfGsuMeYwnDFVCPBUxqbTE/HtsOvp09tgbpSFj+fyjRrh4eOzq8JWoMYrCOaqiRdqOJsVqu8Q7v5vPYUvj3pTlAMW11skzvzht5eBI1DAyUr2ggDJR/aLjlGE3Y9bE1FaNdrh0dXpjusTUYbsqOHQmjMVnxCxCPTXgSNQgGS564JR3NeOuUuD626HS81bHZsbXRlB7jF5NaW/DOuz1GTO6JU2N4/aFHI3gS1QCTWlvQ5juUQeGjdpoT3u3nMZAVfaC8VOP53clajWCsyCd8dN7YhBeWewwbaQKVznLsMbbhxehjD9vzedj3ypoMNZogjVcCBdQTpUa/89/b8xuKtSZqa388RhbeEuUxbOw0ocXJbOzRfBjPA2KzYntp2tJrUzKBGMy6PJ4tUWMZFEegONrqa3iMLLwlymOr4AmUh8fwkbVA9jvvqp/BiMrWrNWYJmrsvrPvOdC3Pd4smD5NiMdw4S1RHh47KLw1IRvbU0RsExghBCaWS1t0/lCsVmw5GoqFaagYL+9OM5Emb4kam/AkysNjB8J4Gfy2JdKJJ9N/p49rhMHWrGOMlDU3q3wDRRE3E4HYFrCji5vBok5xPAIkKh6h0ngwPIny8NhB4AnUliHdXnY6gCyktUI7TRiadmhbRX3t6CTJRpaOiy1zzUCgPMYuPIny8Bjn8ORJYSBSMVJEZriJKhtZu8YamvVdyip3s5EniiJQtJWWqK0836MenkR5eIxjNOugN1IYKikZSfIyUFi8x9hAMz4XohHQRJEnUSMNT6I8PDzGBZpxYGwGjBci7t8Pj20BT6I8PDyaCum1K/3gODTY0XmNXFmDRfANVSw/WhjP74KPzhub8CTKw8OjqcCWkfFiIdle4OS4MQ2/7cYigRrPxMmGJ1FjE55EeXiMYzRbdumsATGr/H5B2OGBLVDjod12FPLkMbbhSZSHxzhHsxCpRoPiQIOlH0gHRiMCur2Wlhlp7MjP21uixiaaZtmXV155BQsXLkRHRwfa2tqwYMECPPTQQ84xq1atwoknnohyuYyOjg588YtfRKVSGaUSe3iMHTTD4MPrt9k/HlsHezkT23o3Gm2cLsuWLLPil2RJkm1u3Y9PtjnSaBpL1PHHH485c+bgwQcfRKlUwtVXX40TTjgBr7/+Ojo7OxFFEY4//njsvPPOeOyxx7BhwwYsWrQIRIRrrrlmtIvv4THq2NEHIY+x+Q6MxTKNRcRxBGylJSn2lqgRR1NYov7yl7/gtddew/nnn4/3v//9eM973oNLLrkEPT09ePHFFwEA9913H/7whz/g//2//4cPfvCDOPLII3HFFVfgP/7jP7B58+ZRroGHh4eHh4fHeENTkKjJkyfjve99L2688UZ0d3ejVqvh2muvxS677IJ58+YBAH7zm99g3333xbRp08x5xxxzDPr7+7Fs2bKG1+7v78fmzZudHw8PDw8Pj7GErXflbb2maiCcdNJJmDFjBorFIqZOnYrTTjsNb775pnPMeJTcNAWJEkLg/vvvxzPPPIPW1lYUi0VcddVVuOeee7DTTjsBANatW4dddtnFOa+9vR35fB7r1q1reO2LL74YEydOND/Tp0/fllXx8PDw8PDYYox1EnXYYYfhlltuwfLly3Hrrbfi9ddfxyc+8QmznyU33d3deOyxx3DTTTfh1ltvxbnnnrvNyrQ9MKokaunSpRBCDPjz9NNPg4hw5plnYsqUKXj00Ufx1FNPYeHChTjhhBOwdu1acz0hRN09iChzO+OCCy7Apk2bzM/q1au3SV09PDw8PDzGK/75n/8ZH/3oRzFz5kwceOCBOP/88/HEE0+gWq0CGL+Sm1EVlp911ln49Kc/PeAxu+++Ox588EHccccd2LhxI9ra2gAAP/jBD3D//ffjhhtuwPnnn4/Ozk48+eSTzrkbN25EtVqts1DZKBQKKBQKW18ZDw8PDw+PbYUoAsmttCTpBYjTpGWkx8G3334bP/3pT3HggQcil8sBGFxyc9hhh43Y/bcnRpVEdXR0oKOjY9DjenpUGK6UruFMSolYh2zOnz8fF110EdauXYupU6cCUMy3UCgY3ZSHh4eHh0czgmjro/N4AeK0bGXJkiVYunTpVl0bAM477zx873vfQ09PDz760Y/ijjvuMPuGK7kZ62gKTdT8+fPR3t6ORYsW4dlnn8Urr7yCr371q1ixYgWOP/54AMDRRx+NffbZB6eddhqeeeYZ/PKXv8RXvvIVnHHGGcZ65eHh4eHhsaNj9erVjozlggsuyDxuqJIbxle/+lU888wzuO+++xAEAU4//XQQJWsFDUdyM9bRFHmiOjo6cM899+DCCy/E4Ycfjmq1ive97334+c9/jrlz5wIAgiDAnXfeiTPPPBMLFixAqVTC3/7t3+Lyyy8f5dJ7eHh4eHhsHSiOt94SpT03bW1tQzIuDFVyw2Dv0pw5c/De974X06dPxxNPPIH58+cPW3Iz1tEUJAoAPvzhD+Pee+8d8JgZM2Y45kMPDw8PD4/xABqBZJtbGp03VMlN5r20Baq/vx/A+JXcNA2J8vDw8PDw8Bh7eOqpp/DUU0/hoIMOQnt7O9544w1885vfxB577IH58+cDcCU3l112Gd5+++1xIblpCk2Uh4eHh4fHjgy1dt7W/2wLlEol3HbbbTjiiCOw11574XOf+xz23XdfPPLIIybqjyU3xWIRCxYswN/8zd/g5JNPbnrJjbdEeXh4eHh4jHGMhjtvqNhvv/3w4IMPDnrceJTceBLl4eHh4eExxjGWSdSODO/O8/Dw8PDw8PAYBrwlysPDw8PDY4wjjiMIb4kac/AkysPDw8PDY4yDohgQW0miom0jLN+R4d15Hh4eHh4eHh7DgLdEeXh4eHh4jHGM5Np5HiMHT6I8PDw8PDzGOCiOtt6d5zVRIw7vzvPw8PDw8PDwGAa8JcrDw8PDw2OMw1uixiY8ifLw8PDw8Bjj8CRqbMKTqBR45enNmzePckk8PDw8PMY6eKzgsWObIapiq+8QVUeiJB4WPIlKYcOGDQCA6dOnj3JJPDw8PDyaBV1dXZg4ceKIXzefz6OzsxPr/nDLiFyvs7MT+Xx+RK7lAQja5vS5ufDOO++gvb0dq1at2iYfxGhi8+bNmD59OlavXo22trbRLs6IwtetOeHr1pwYz3UDtqx+RISuri5MmzYNUm6bWK2+vj5UKpURuVY+n0exWByRa3l4S1Qd+COYOHHiuOwcAKCtrc3XrQnh69ac8HVrXgy1ftt6wl0sFj3xGaPwKQ48PDw8PDw8PIYBT6I8PDw8PDw8PIYBT6JSKBQKWLJkCQqFwmgXZcTh69ac8HVrTvi6NS/Ge/08Rg5eWO7h4eHh4eHhMQx4S5SHh4eHh4eHxzDgSZSHh4eHh4eHxzDgSZSHh4eHh4eHxzDgSZSHh4eHh4eHxzDgSZSFH/zgB5g1axaKxSLmzZuHRx99dLSLtMVYunQphBDOT2dnp9lPRFi6dCmmTZuGUqmEQw89FC+++OIolrgxfvWrX+HEE0/EtGnTIITAz372M2f/UOrS39+Ps88+Gx0dHSiXyzjppJPwpz/9aTvWIhuD1W3x4sV1z/GjH/2oc8xYrdvFF1+Mj3zkI2htbcWUKVNw8sknY/ny5c4xzfrshlK3Zn12P/zhD/H+97/fJJicP38+7r77brO/WZ8ZMHjdmvWZeYw+PInSuPnmm3HOOefgwgsvxDPPPIODDz4Yxx13HFatWjXaRdtivO9978PatWvNz/PPP2/2XXrppbjyyivxve99D7/97W/R2dmJo446Cl1dXaNY4mx0d3dj7ty5+N73vpe5fyh1Oeecc3D77bfjpptuwmOPPYZ3330XJ5xwAqJodFczH6xuAHDsscc6z/Guu+5y9o/Vuj3yyCP4p3/6JzzxxBO4//77UavVcPTRR6O7u9sc06zPbih1A5rz2e2222645JJL8PTTT+Ppp5/G4YcfjoULFxqi1KzPDBi8bkBzPjOPMQDyICKi/fffn77whS842/bee286//zzR6lEw8OSJUto7ty5mfviOKbOzk665JJLzLa+vj6aOHEi/e///b+3UwmHBwB0++23m7+HUpd33nmHcrkc3XTTTeaYNWvWkJSS7rnnnu1W9sGQrhsR0aJFi2jhwoUNz2mWuhERrV+/ngDQI488QkTj69ml60Y0vp5de3s7/Z//83/G1TNjcN2Ixtcz89i+8JYoAJVKBcuWLcPRRx/tbD/66KPx+OOPj1Kpho9XX30V06ZNw6xZs/DpT38ab7zxBgBgxYoVWLdunVPPQqGAQw45pOnqOZS6LFu2DNVq1Tlm2rRp2HfffZuivg8//DCmTJmCOXPm4IwzzsD69evNvmaq26ZNmwAAkyZNAjC+nl26boxmf3ZRFOGmm25Cd3c35s+fP66eWbpujGZ/Zh6jA78AMYC//OUviKIIu+yyi7N9l112wbp160apVMPDAQccgBtvvBFz5szBn//8Z3z729/GgQceiBdffNHUJauef/zjH0ejuMPGUOqybt065PN5tLe31x0z1p/rcccdh09+8pOYOXMmVqxYgX/5l3/B4YcfjmXLlqFQKDRN3YgIX/7yl3HQQQdh3333BTB+nl1W3YDmfnbPP/885s+fj76+PkyYMAG333479tlnH0MUmvmZNaob0NzPzGN04UmUBSGE8zcR1W0b6zjuuOPM7/vttx/mz5+PPfbYAzfccIMRSo6HejKGU5dmqO+nPvUp8/u+++6LD3/4w5g5cybuvPNOnHLKKQ3PG2t1O+uss/Dcc8/hscceq9vX7M+uUd2a+dnttdde+P3vf4933nkHt956KxYtWoRHHnnE7G/mZ9aobvvss09TPzOP0YV35wHo6OhAEAR1M4r169fXzbyaDeVyGfvttx9effVVE6U3Huo5lLp0dnaiUqlg48aNDY9pFkydOhUzZ87Eq6++CqA56nb22Wfjf/7nf/DQQw9ht912M9vHw7NrVLcsNNOzy+fz2HPPPfHhD38YF198MebOnYt///d/HxfPrFHdstBMz8xjdOFJFNTHNW/ePNx///3O9vvvvx8HHnjgKJVqZNDf34+XXnoJU6dOxaxZs9DZ2enUs1Kp4JFHHmm6eg6lLvPmzUMul3OOWbt2LV544YWmq++GDRuwevVqTJ06FcDYrhsR4ayzzsJtt92GBx98ELNmzXL2N/OzG6xuWWimZ5cGEaG/v7+pn1kjcN2y0MzPzGM7Y7tL2ccobrrpJsrlcvTjH/+Y/vCHP9A555xD5XKZVq5cOdpF2yKce+659PDDD9Mbb7xBTzzxBJ1wwgnU2tpq6nHJJZfQxIkT6bbbbqPnn3+ePvOZz9DUqVNp8+bNo1zyenR1ddEzzzxDzzzzDAGgK6+8kp555hn64x//SERDq8sXvvAF2m233eiBBx6g3/3ud3T44YfT3LlzqVarjVa1iGjgunV1ddG5555Ljz/+OK1YsYIeeughmj9/Pu26665NUbd//Md/pIkTJ9LDDz9Ma9euNT89PT3mmGZ9doPVrZmf3QUXXEC/+tWvaMWKFfTcc8/R17/+dZJS0n333UdEzfvMiAauWzM/M4/RhydRFr7//e/TzJkzKZ/P04c+9CEnbLlZ8KlPfYqmTp1KuVyOpk2bRqeccgq9+OKLZn8cx7RkyRLq7OykQqFAf/VXf0XPP//8KJa4MR566CECUPezaNEiIhpaXXp7e+mss86iSZMmUalUohNOOIFWrVo1CrVxMVDdenp66Oijj6add96ZcrkczZgxgxYtWlRX7rFat6x6AaDrrrvOHNOsz26wujXzs/vc5z5n+r+dd96ZjjjiCEOgiJr3mRENXLdmfmYeow9BRLT97F4eHh4eHh4eHuMDXhPl4eHh4eHh4TEMeBLl4eHh4eHh4TEMeBLl4eHh4eHh4TEMeBLl4eHh4eHh4TEMeBLl4eHh4eHh4TEMeBLl4eHh4eHh4TEMeBLl4eHh4eHh4TEMeBLl4THCWLlyJYQQ+P3vf79Nri+EwM9+9rNhn//www9DCAEhBE4++eQBjz300ENxzjnnDPteHgODn8NOO+002kXx8PAYBjyJ8hhXWLx48aDEYFtj+vTpWLt2Lfbdd18ACWl55513RrVcaSxfvhzXX3/9aBdjh0Cj93Lt2rW4+uqrt3t5PDw8RgaeRHl4jDCCIEBnZyfCMBztogyIKVOmjAkLSLVaHe0ijBo6OzsxceLE0S6Gh4fHMOFJlMcOhUceeQT7778/CoUCpk6divPPPx+1Ws3sP/TQQ/HFL34RX/va1zBp0iR0dnZi6dKlzjVefvllHHTQQSgWi9hnn33wwAMPOC422523cuVKHHbYYQCA9vZ2CCGwePFiAMDuu+9eZ4X4wAc+4Nzv1VdfxV/91V+Ze9mryDPWrFmDT33qU2hvb8fkyZOxcOFCrFy5covbpru7G6effjomTJiAqVOn4oorrqg7plKp4Gtf+xp23XVXlMtlHHDAAXj44YedY/7jP/4D06dPR0tLCz7+8Y/jyiuvdMja0qVL8YEPfAD/9//+X8yePRuFQgFEhE2bNuEf/uEfMGXKFLS1teHwww/Hs88+61z7F7/4BebNm4disYjZs2fjW9/6lvP8li5dihkzZqBQKGDatGn44he/OKS6D1avDRs24DOf+Qx22203tLS0YL/99sN//ud/Otf47//+b+y3334olUqYPHkyjjzySHR3d2Pp0qW44YYb8POf/9y479Jt5uHh0ZwY21NlD48RxJo1a/Cxj30Mixcvxo033oiXX34ZZ5xxBorFokNcbrjhBnz5y1/Gk08+id/85jdYvHgxFixYgKOOOgpxHOPkk0/GjBkz8OSTT6Krqwvnnntuw3tOnz4dt956K/76r/8ay5cvR1tbG0ql0pDKG8cxTjnlFHR0dOCJJ57A5s2b6/RJPT09OOyww3DwwQfjV7/6FcIwxLe//W0ce+yxeO6555DP54fcPl/96lfx0EMP4fbbb0dnZye+/vWvY9myZfjABz5gjvm7v/s7rFy5EjfddBOmTZuG22+/Hcceeyyef/55vOc978Gvf/1rfOELX8C//du/4aSTTsIDDzyAf/mXf6m712uvvYZbbrkFt956K4IgAAAcf/zxmDRpEu666y5MnDgR1157LY444gi88sormDRpEu6991589rOfxXe/+10cfPDBeP311/EP//APAIAlS5bgv//7v3HVVVfhpptuwvve9z6sW7eujoQ1wmD16uvrw7x583Deeeehra0Nd955J0477TTMnj0bBxxwANauXYvPfOYzuPTSS/Hxj38cXV1dePTRR0FE+MpXvoKXXnoJmzdvxnXXXQcAmDRp0pCfi4eHxxjG6K5/7OExsli0aBEtXLgwc9/Xv/512muvvSiOY7Pt+9//Pk2YMIGiKCIiokMOOYQOOugg57yPfOQjdN555xER0d13301hGNLatWvN/vvvv58A0O23305ERCtWrCAA9MwzzxAR0UMPPUQAaOPGjc51Z86cSVdddZWzbe7cubRkyRIiIrr33nspCAJavXq12X/33Xc79/rxj39cV6f+/n4qlUp07733ZrZDVnm6uroon8/TTTfdZLZt2LCBSqUSfelLXyIiotdee42EELRmzRrnekcccQRdcMEFRET0qU99io4//nhn/6mnnkoTJ040fy9ZsoRyuRytX7/ebPvlL39JbW1t1NfX55y7xx570LXXXktERAcffDB95zvfcfb/5Cc/oalTpxIR0RVXXEFz5syhSqWSWe9GGEq9svCxj32Mzj33XCIiWrZsGQGglStXZh470Ht53XXXOe3j4eHRPPCWKI8dBi+99BLmz58PIYTZtmDBArz77rv405/+hBkzZgAA3v/+9zvnTZ06FevXrwegxNjTp09HZ2en2b///vtvs/LOmDEDu+22m9k2f/5855hly5bhtddeQ2trq7O9r68Pr7/++pDv9frrr6NSqTjXnzRpEvbaay/z9+9+9zsQEebMmeOc29/fj8mTJwNQ7fPxj3/c2b///vvjjjvucLbNnDkTO++8s1OPd99911yH0dvba+qxbNky/Pa3v8VFF11k9kdRhL6+PvT09OCTn/wkrr76asyePRvHHnssPvaxj+HEE08cVJs2lHpFUYRLLrkEN998M9asWYP+/n709/ejXC4DAObOnYsjjjgC++23H4455hgcffTR+MQnPoH29vYB7+3h4dHc8CTKY4cBETkEircBcLbncjnnGCEE4jhueI3hQkpp7s+wRdbpfelyAsrlN2/ePPz0pz+tO9YmKYMh615pxHGMIAiwbNky44JjTJgwwVynURvbYPJhX3vq1KmZWiHWU8VxjG9961s45ZRT6o4pFouYPn06li9fjvvvvx8PPPAAzjzzTFx22WV45JFH6p7pltbriiuuwFVXXYWrr74a++23H8rlMs455xxUKhUAKpjg/vvvx+OPP4777rsP11xzDS688EI8+eSTmDVrVsN7e3h4NDc8ifLYYbDPPvvg1ltvdQb6xx9/HK2trdh1112HdI29994bq1atwp///GfssssuAIDf/va3A57DuqQoipztO++8M9auXWv+3rx5M1asWOGUd9WqVXjzzTcxbdo0AMBvfvMb5xof+tCHcPPNNxsx9nCx5557IpfL4YknnjAWuY0bN+KVV17BIYccAgD44Ac/iCiKsH79ehx88MGZ19l7773x1FNPOduefvrpQe//oQ99COvWrUMYhth9990bHrN8+XLsueeeDa9TKpVw0kkn4aSTTsI//dM/Ye+998bzzz+PD33oQw3PGUq9Hn30USxcuBCf/exnASji9eqrr+K9732vOUYIgQULFmDBggX45je/iZkzZ+L222/Hl7/8ZeTz+brn7+Hh0fzw0Xke4w6bNm3C73//e+dn1apVOPPMM7F69WqcffbZePnll/Hzn/8cS5YswZe//GVIObRP4aijjsIee+yBRYsW4bnnnsOvf/1rXHjhhQDqrUSMmTNnQgiBO+64A2+99RbeffddAMDhhx+On/zkJ3j00UfxwgsvYNGiRY4l5Mgjj8Ree+2F008/Hc8++yweffRRcy/Gqaeeio6ODixcuBCPPvooVqxYgUceeQRf+tKX8Kc//WnIbTZhwgR8/vOfx1e/+lX88pe/xAsvvIDFixc77TJnzhyceuqpOP3003HbbbdhxYoV+O1vf4t/+7d/w1133QUAOPvss3HXXXfhyiuvxKuvvoprr70Wd99996DWuyOPPBLz58/HySefjHvvvRcrV67E448/jm984xuGhH3zm9/EjTfeiKVLl+LFF1/ESy+9hJtvvhnf+MY3AADXX389fvzjH+OFF17AG2+8gZ/85CcolUqYOXPmgPceSr323HNPY2l66aWX8L/+1//CunXrzDWefPJJfOc738HTTz+NVatW4bbbbsNbb71lSNbuu++O5557DsuXL8df/vKXHTqtg4fHuMIoabE8PLYJFi1aRADqfhYtWkRERA8//DB95CMfoXw+T52dnXTeeedRtVo15x9yyCFGSM1YuHChOZ+I6KWXXqIFCxZQPp+nvffem37xi18QALrnnnuIqF5YTkT0r//6r9TZ2UlCCHOtTZs20d/8zd9QW1sbTZ8+na6//npHWE5EtHz5cjrooIMon8/TnDlz6J577nGE5UREa9eupdNPP506OjqoUCjQ7Nmz6YwzzqBNmzZltlEjoXtXVxd99rOfpZaWFtpll13o0ksvrWuPSqVC3/zmN2n33XenXC5HnZ2d9PGPf5yee+45c8yPfvQj2nXXXalUKtHJJ59M3/72t6mzs9PsX7JkCc2dO7euXJs3b6azzz6bpk2bRrlcjqZPn06nnnoqrVq1yhxzzz330IEHHkilUona2tpo//33px/96EdERHT77bfTAQccQG1tbVQul+mjH/0oPfDAA5ltkMZg9dqwYQMtXLiQJkyYQFOmTKFvfOMbdPrppxux+B/+8Ac65phjaOedd6ZCoUBz5syha665xlx//fr1dNRRR9GECRMIAD300ENmnxeWe3g0LwTREMQQHh4eDfHrX/8aBx10EF577TXsscceo12cQfHwww/jsMMOw8aNG7dLss0zzjgDL7/8Mh599NFtfq9mxPXXX49zzjlnzGW09/DwGBxeE+XhsYW4/fbbMWHCBLznPe/Ba6+9hi996UtYsGBBUxAoG7vtthtOPPHEuqSRW4vLL78cRx11FMrlMu6++27ccMMN+MEPfjCi9xgvmDBhAmq1GorF4mgXxcPDYxjwJMrDYwvR1dWFr33ta1i9ejU6Ojpw5JFHZmb3Hqs44IAD8OqrrwJIos9GEk899RQuvfRSdHV1Yfbs2fjud7+Lv//7vx/x+wwVjz76KI477riG+1mjNhrgRarTUYEeHh7NAe/O8/DwGNfo7e3FmjVrGu4fKNrPw8PDYyB4EuXh4eHh4eHhMQz4FAceHh4eHh4eHsOAJ1EeHh4eHh4eHsOAJ1EeHh4eHh4eHsOAJ1EeHh4eHh4eHsOAJ1EeHh4eHh4eHsOAJ1EeHh4eHh4eHsOAJ1EeHh4eHh4eHsOAJ1EeHh4eHh4eHsPA/weoqG9DVavF5QAAAABJRU5ErkJggg==",
"text/plain": [
"<Figure size 640x480 with 2 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"ds_from_virtual.isel(time=0).sst.plot()"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "5b79b4f9-c5d4-44f1-8621-468d778b95ad",
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.12.4"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
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