data/notebooks/Lab/Satelligence_data_ingestion.ipynb
{
"cells": [
{
"cell_type": "markdown",
"id": "ccb978b5",
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"source": [
"# 1. Exploring and ingesting satelligence data\n",
"\n",
"Methodology based on following document: https://docs.google.com/document/d/1NHhOUOcJBOme_wv344cIMPIpdOX6_tBDCzIZeMY6h6I/edit\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "bd134de0",
"metadata": {},
"outputs": [],
"source": [
"!pip install h3 --quiet\n",
"!pip install h3pandas h3ronpy mercantile --q"
]
},
{
"cell_type": "code",
"execution_count": 9,
"id": "d11645f9",
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"scrolled": true
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"source": [
"#from psycopg2.pool import ThreadedConnectionPool\n",
"import geopandas as gpd\n",
"import pandas as pd\n",
"\n",
"import folium\n",
"import matplotlib.pyplot as plt\n",
"\n",
"import h3\n",
"import h3pandas\n",
"from h3ronpy import raster\n",
"import rasterio as rio\n",
"from rasterstats import gen_zonal_stats, gen_point_query, zonal_stats\n",
"\n",
"\n",
"\n",
"\n",
"import mercantile as mt\n",
"from shapely.geometry import shape, Polygon, LineString\n",
"#import numpy as np\n",
"\n",
"#import statistics"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "1396a83a",
"metadata": {},
"outputs": [],
"source": [
"def convert_raster_h3(raster_path, vector_path, column='deforestat', resolution=6):\n",
" with rio.open(raster_path) as src:\n",
" gdf = raster.raster_to_geodataframe(src.read(1), src.transform, h3_resolution=resolution, nodata_value=src.profile['nodata'], compacted=False)\n",
"\n",
" gdf.plot('value')\n",
" gdf['h3index'] = gdf['h3index'].apply(hex)\n",
"\n",
" gdf_vector = gpd.read_file(vector_path)\n",
" clean_gdf = gdf_vector[['gfw_fid',column,'geometry']]\n",
" \n",
" _sum_calculated = []\n",
" for i, row in clean_gdf.iterrows():\n",
" filtered_gdf = clean_gdf[i:i+1]\n",
" #convert to h3\n",
" h3_gdf = filtered_gdf.h3.polyfill_resample(resolution)\n",
" #h3_gdf = h3_gdf.reset_index().rename(columns={'h3_polyfill':'h3index'})\n",
" h3index_list = [f'0x{h3index}' for h3index in h3_gdf.index]\n",
" #filter gdf by list and get value\n",
" _sum = gdf[gdf['h3index'].isin(h3index_list)]['value'].sum()\n",
" _sum_calculated.append(_sum)\n",
" \n",
" clean_gdf['estimate'] = _sum_calculated\n",
" return clean_gdf\n",
"\n",
"\n",
"def buffer_stats(raster_path, vector_path, buffer=50000, stat_='sum'):\n",
" \"\"\"\n",
" inputs:\n",
" -------------\n",
" raster_path: raster path for retriving the statisticts in EPSG:4326\n",
" vector_path: path to point file in EPSG:4326\n",
" buffer: distance in metres for coputing the buffer\n",
" stats: stadistics to compute\n",
" \n",
" output\n",
" -------\n",
" array with statistics\"\"\"\n",
" \n",
" #open vector file\n",
" gdf = gpd.read_file(vector_path)\n",
" #check projection\n",
" #if gdf.crs != True:\n",
" # print(gdf.crs)\n",
" # #project\n",
" # print('Dataset missing projection. Please assign one!')\n",
" if gdf.crs and gdf.crs == 'EPSG:4326':\n",
" #reproject\n",
" gdf_3857 = gdf.to_crs('EPSG:3857')\n",
" ## TODO:add other validations\n",
" \n",
"\n",
" #get buffer\n",
" gdf_3857_buffer = gdf_3857.buffer(buffer)\n",
" #reproject back to epsg4326\n",
" gdf_4326_buffer = gdf_3857_buffer.to_crs('EPSG:4326')\n",
" #get statistics\n",
" vizz_stats = []\n",
" for geom in gdf_4326_buffer:\n",
" stats = zonal_stats(geom,\n",
" raster_path,\n",
" stats=stat_,\n",
" all_touched = True\n",
" )\n",
" stat_sum = stats[0]['sum']\n",
" vizz_stats.append(stat_sum)\n",
" #add stats in dataframe\n",
" gdf['estimated']=vizz_stats\n",
" return gdf\n",
" "
]
},
{
"cell_type": "markdown",
"id": "f70f560e",
"metadata": {},
"source": [
"## RASTER: Replicate satelligence analysis\n",
"\n",
"Replicate satelligence analysis in raster format to double check outputs of calculations.\n",
"\n",
"- Use case - Aceh, Sumatra, Indonesia.\n",
"- 3 indicators:\n",
" - Deforestation\n",
" - Carbon loss \n",
" - Biodiversity loss\n",
" \n",
"### 1. Deforestation: \n",
"### 1.1 All deforestation in Aceh:\n",
"\n",
"Procedure \n",
"\n",
" 1. Retrieve all deforestation pixels within a 50 km radius for all mills within the UML. 2. Calculate deforestation extent as sum of those pixels times the pixel’s area for each mill. \n",
" \n",
" 3. The indicator is then the total deforestation within the sourcing area of a mill \n",
" \n",
" 4. Commodity scenarios: analyze e.g. mean and percentage deforestation for given commodity as a whole (in country or world) compared to the same values for company X (i.e. for mills company X is sourcing from:\n",
" \n",
" a. Mean deforestation of all mills (all mills within a country, world) \n",
" b. % of deforestation within current supply chain (all mills of company X) compared to all deforestation within all mills \n",
"\n",
"#### Generate tiles for area of interest:\n",
"\n",
"We perform the calculation in bunches in order to be more efficient. To do this, we generate tiles in zoom level 12 that will be used to clip the geometry and calculate the deforestation in ecah part."
]
},
{
"cell_type": "markdown",
"id": "63c80204",
"metadata": {},
"source": [
"### 1.2 Deforestation in mill locations:"
]
},
{
"cell_type": "code",
"execution_count": 81,
"id": "c478f87b",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
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"94.. 94.. 94.. 94.. 94.. 94.. 94.. 94.. 94.. 94.. 94.. 94.. 94.. 94.. 94.. 94.. 94.. 94.. 94.. 94.. 94.. 94.. 94.. 94.. 94.. 94.. 94.. 94.. 94.. 94.. 94.. 94.. 94.. 94.. 94.. 94.. 94.. 94.. 94.. 94.. 94.. 94.. 94.. 94.. 94.. 94.. 94.. 94.. 94.. 94.. 94.. 94.. 94.. 94.. 95.. 95.. 95.. 95.. 95.. 95.. 95.. 95.. 95.. 95.. 95.. 95.. 95.. 95.. 95.. 95.. 95.. 95.. 95.. 95.. 95.. 95.. 95.. 95.. 95.. 95.. 95.. 95.. 95.. 95.. 95.. 95.. 95.. 95.. 95.. 95.. 95.. 95.. 95.. 95.. 95.. 95.. 95.. 95.. 95.. 95.. 95.. 95.. 95.. 95.. 95.. 95.. 95.. 95.. 95.. 95.. 95.. 95.. 95.. 95.. 95.. 95.. 95.. 95.. 95.. 95.. 95.. 95.. 95.. 95.. 95.. 95.. 95.. 95.. 95.. 95.. 95.. 95.. 95.. 95.. 95.. 95.. 95.. 95.. 95.. 95.. 95.. 95.. 95.. 95.. 95.. 95.. 95.. 95.. 95.. 95.. 95.. 95.. 95.. 95.. 95.. 95.. 95.. 95.. 95.. 95.. 95.. 95.. 95.. 95.. 95.. 95.. 95.. 95.. 95.. 95.. 95.. 95.. 95.. 95.. 95.. 95.. 95.. 95.. 95.. 95.. 95.. 95.. 95.. 95.. 95.. 95.. 95.. 95.. 95.. 95.. 95.. 95.. 95.. 95.. 95.. 95.. 96.. 96.. 96.. 96.. 96.. 96.. 96.. 96.. 96.. 96.. 96.. 96.. 96.. 96.. 96.. 96.. 96.. 96.. 96.. 96.. 96.. 96.. 96.. 96.. 96.. 96.. 96.. 96.. 96.. 96.. 96.. 96.. 96.. 96.. 96.. 96.. 96.. 96.. 96.. 96.. 96.. 96.. 96.. 96.. 96.. 96.. 96.. 96.. 96.. 96.. 96.. 96.. 96.. 96.. 96.. 96.. 96.. 96.. 96.. 96.. 96.. 96.. 96.. 96.. 96.. 96.. 96.. 96.. 96.. 96.. 96.. 96.. 96.. 96.. 96.. 96.. 96.. 96.. 96.. 96.. 96.. 96.. 96.. 96.. 96.. 96.. 96.. 96.. 96.. 96.. 96.. 96.. 96.. 96.. 96.. 96.. 96.. 96.. 96.. 96.. 96.. 96.. 96.. 96.. 96.. 96.. 96.. 96.. 96.. 96.. 96.. 96.. 96.. 96.. 96.. 96.. 96.. 96.. 96.. 96.. 96.. 96.. 96.. 96.. 96.. 96.. 96.. 96.. 96.. 96.. 96.. 96.. 96.. 96.. 96.. 96.. 96.. 96.. 96.. 96.. 96.. 97.. 97.. 97.. 97.. 97.. 97.. 97.. 97.. 97.. 97.. 97.. 97.. 97.. 97.. 97.. 97.. 97.. 97.. 97.. 97.. 97.. 97.. 97.. 97.. 97.. 97.. 97.. 97.. 97.. 97.. 97.. 97.. 97.. 97.. 97.. 97.. 97.. 97.. 97.. 97.. 97.. 97.. 97.. 97.. 97.. 97.. 97.. 97.. 97.. 97.. 97.. 97.. 97.. 97.. 97.. 97.. 97.. 97.. 97.. 97.. 97.. 97.. 97.. 97.. 97.. 97.. 97.. 97.. 97.. 97.. 97.. 97.. 97.. 97.. 97.. 97.. 97.. 97.. 97.. 97.. 97.. 97.. 97.. 97.. 97.. 97.. 97.. 97.. 97.. 97.. 97.. 97.. 97.. 97.. 97.. 97.. 97.. 97.. 97.. 97.. 97.. 97.. 97.. 97.. 97.. 97.. 97.. 97.. 97.. 97.. 97.. 97.. 97.. 97.. 97.. 97.. 97.. 97.. 97.. 97.. 97.. 97.. 97.. 97.. 97.. 97.. 97.. 97.. 97.. 97.. 97.. 97.. 97.. 97.. 97.. 97.. 97.. 97.. 97.. 97.. 97.. 97.. 98.. 98.. 98.. 98.. 98.. 98.. 98.. 98.. 98.. 98.. 98.. 98.. 98.. 98.. 98.. 98.. 98.. 98.. 98.. 98.. 98.. 98.. 98.. 98.. 98.. 98.. 98.. 98.. 98.. 98.. 98.. 98.. 98.. 98.. 98.. 98.. 98.. 98.. 98.. 98.. 98.. 98.. 98.. 98.. 98.. 98.. 98.. 98.. 98.. 98.. 98.. 98.. 98.. 98.. 98.. 98.. 98.. 98.. 98.. 98.. 98.. 98.. 98.. 98.. 98.. 98.. 98.. 98.. 98.. 98.. 98.. 98.. 98.. 98.. 98.. 98.. 98.. 98.. 98.. 98.. 98.. 98.. 98.. 98.. 98.. 98.. 98.. 98.. 98.. 98.. 98.. 98.. 98.. 98.. 98.. 98.. 98.. 98.. 98.. 98.. 98.. 98.. 98.. 98.. 98.. 98.. 98.. 98.. 98.. 98.. 98.. 98.. 98.. 98.. 98.. 98.. 98.. 98.. 98.. 98.. 98.. 98.. 98.. 98.. 98.. 98.. 98.. 98.. 98.. 98.. 98.. 98.. 98.. 98.. 98.. 98.. 98.. 98.. 98.. 98.. 98.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 100 - Done\r\n"
]
}
],
"source": [
"!gdal_calc.py --calc \"A*6.69019042035408517*6.69019042035408517* 0.0001\" --format GTiff --type Float32 --NoDataValue 0.0 -A \"../../datasets/processed/Satelligence_data/Deforestation_IDN_2021-01-01-2022-01-01 (1).tif\" --A_band 1 --outfile \"../../datasets/processed/Satelligence_data/Deforestation_IDN_2021-01-01-2022-01-01_area_ha.tif\"\n"
]
},
{
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"id": "c78aba8c",
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{
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" <th></th>\n",
" <th>fid</th>\n",
" <th>gfw_fid</th>\n",
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" <th>group_name</th>\n",
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" <td>LAE TANGGA</td>\n",
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" <td>PERKEBUNAN LEMBAH BHAKTI</td>\n",
" <td>PERKEBUNAN LEMBAH BHAKTI 1</td>\n",
" <td>Not RSPO Certified</td>\n",
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" <td>2021-07-14</td>\n",
" <td>2.314164</td>\n",
" <td>...</td>\n",
" <td>Aceh Singkil</td>\n",
" <td>1-Fully Verified</td>\n",
" <td>None</td>\n",
" <td>0</td>\n",
" <td>b2d76be0-1ace-7a54-8e5e-74bb01111787</td>\n",
" <td>444.9278149230584</td>\n",
" <td>295.5592552826714</td>\n",
" <td>74432.2796810086</td>\n",
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" <td>SOCFIN INDONESIA</td>\n",
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" <td>2021-07-14</td>\n",
" <td>2.391111</td>\n",
" <td>...</td>\n",
" <td>Aceh Singkil</td>\n",
" <td>1-Fully Verified</td>\n",
" <td>None</td>\n",
" <td>0</td>\n",
" <td>aff58ced-c18c-a5d8-d084-eb5a6a74416b</td>\n",
" <td>552.3038111406324</td>\n",
" <td>400.8360709155834</td>\n",
" <td>95543.40760696075</td>\n",
" <td>POINT (97.95667 2.39111)</td>\n",
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" <td>NAFASINDO</td>\n",
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" <td>621.152405</td>\n",
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" <td>5.0</td>\n",
" <td>1516</td>\n",
" <td>PO1000008193</td>\n",
" <td>TENERA LESTARI</td>\n",
" <td>ENSEM LESTARI</td>\n",
" <td>ENSEM LESTARI</td>\n",
" <td>Not RSPO Certified</td>\n",
" <td>None</td>\n",
" <td>2021-07-14</td>\n",
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" <td>Aceh Singkil</td>\n",
" <td>1-Fully Verified</td>\n",
" <td>None</td>\n",
" <td>0</td>\n",
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"</div>"
],
"text/plain": [
" fid gfw_fid uml_id group_name parent_com \\\n",
"0 1.0 706 PO1000004155 IBRIS PALM DELIMA MAKMUR \n",
"1 2.0 717 PO1000004167 ASTRA AGRO LESTARI PERKEBUNAN LEMBAH BHAKTI \n",
"2 3.0 264 PO1000001775 SOCFIN SOCFIN INDONESIA \n",
"3 4.0 738 PO1000004197 UNKNOWN NAFASINDO \n",
"4 5.0 1516 PO1000008193 TENERA LESTARI ENSEM LESTARI \n",
"\n",
" mill_name rspo_statu rspo_type \\\n",
"0 DELIMA MAKMUR Not RSPO Certified None \n",
"1 PERKEBUNAN LEMBAH BHAKTI 1 Not RSPO Certified None \n",
"2 LAE BUTAR RSPO Certified RSPO Certified, IP \n",
"3 NAFASINDO Not RSPO Certified None \n",
"4 ENSEM LESTARI Not RSPO Certified None \n",
"\n",
" date latitude ... district confidence alternativ \\\n",
"0 2021-07-14 2.24508 ... Aceh Singkil 1-Fully Verified LAE TANGGA \n",
"1 2021-07-14 2.314164 ... Aceh Singkil 1-Fully Verified None \n",
"2 2021-07-14 2.391111 ... Aceh Singkil 1-Fully Verified None \n",
"3 2021-07-14 2.43594 ... Aceh Singkil 1-Fully Verified None \n",
"4 2021-07-14 2.456777 ... Aceh Singkil 1-Fully Verified None \n",
"\n",
" gfw_area__ gfw_geosto deforestat \\\n",
"0 0 256170af-2d4f-4a5b-a417-b981205849be 349.5068535490499 \n",
"1 0 b2d76be0-1ace-7a54-8e5e-74bb01111787 444.9278149230584 \n",
"2 0 aff58ced-c18c-a5d8-d084-eb5a6a74416b 552.3038111406324 \n",
"3 0 3cee289b-cdd6-a33d-e0f6-15ea8e7423fa 621.1470874150178 \n",
"4 0 128b643f-9046-0f4e-c71e-59ca37fb221f 600.0120538952435 \n",
"\n",
" biodiversi carbon geometry estimated \n",
"0 200.138293908663 57812.37323319753 POINT (98.02851 2.24508) 349.507996 \n",
"1 295.5592552826714 74432.2796810086 POINT (97.99572 2.31416) 444.931091 \n",
"2 400.8360709155834 95543.40760696075 POINT (97.95667 2.39111) 552.307617 \n",
"3 469.4242228971635 109557.3760590317 POINT (97.91529 2.43594) 621.152405 \n",
"4 448.9605690952982 104970.0756673934 POINT (98.06502 2.45678) 600.016846 \n",
"\n",
"[5 rows x 27 columns]"
]
},
"execution_count": 56,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"gdf = buffer_stats(\n",
" \"../../datasets/processed/Satelligence_data/test_rasters_2/Deforestation_2021_2022_area_ha.tif\",\n",
" '../../datasets/processed/palm_oil_mills/satelligence_mills_4326_point.shp',\n",
" buffer=50000,\n",
" stat_='sum')\n",
"gdf.head()"
]
},
{
"cell_type": "markdown",
"id": "59d0c309",
"metadata": {},
"source": [
"## H3: Replicate pipe for ingesting satelligence data into the ddbb\n",
"\n",
"### 1- Ingest summing the pixels with deforestation in the resampling - AREA\n",
"\n",
" Ingest by summing the pixel values * area (masked values) in the resampling.\n"
]
},
{
"cell_type": "code",
"execution_count": 82,
"id": "fddf1acd",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Creating output file that is 40P x 48L.\n",
"Processing ../../datasets/processed/Satelligence_data/Deforestation_IDN_2021-01-01-2022-01-01_area_ha.tif [1/1] : 0Using internal nodata values (e.g. 0) for image ../../datasets/processed/Satelligence_data/Deforestation_IDN_2021-01-01-2022-01-01_area_ha.tif.\n",
"...10...20...30...40...50...60...70...80...90...100 - done.\n"
]
}
],
"source": [
"!gdalwarp -s_srs EPSG:4326 -t_srs EPSG:4326 -dstnodata 0.0 -tr 0.0833333333333286 0.0833333333333286 -r sum -multi -of GTiff \"../../datasets/processed/Satelligence_data/Deforestation_IDN_2021-01-01-2022-01-01_area_ha.tif\" \"../../datasets/processed/Satelligence_data/Deforestation_IDN_2021-01-01-2022-01-01_areaSum_ha.tif\""
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "5e0c78b1",
"metadata": {},
"outputs": [
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" <td>109557.3760590317</td>\n",
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"text/plain": [
" gfw_fid deforestat biodiversi carbon \\\n",
"0 706 349.5068535490499 200.138293908663 57812.37323319753 \n",
"1 717 444.9278149230584 295.5592552826714 74432.2796810086 \n",
"2 264 552.3038111406324 400.8360709155834 95543.40760696075 \n",
"3 738 621.1470874150178 469.4242228971635 109557.3760590317 \n",
"4 1516 600.0120538952435 448.9605690952982 104970.0756673934 \n",
"\n",
" geometry \n",
"0 POLYGON ((98.47767 2.24508, 98.47705 2.22159, ... \n",
"1 POLYGON ((98.44488 2.31416, 98.44426 2.29068, ... \n",
"2 POLYGON ((98.40582 2.39111, 98.40521 2.36762, ... \n",
"3 POLYGON ((98.36445 2.43594, 98.36383 2.41245, ... \n",
"4 POLYGON ((98.51418 2.45678, 98.51356 2.43329, ... "
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"gdf_sat = gpd.read_file('../../datasets/processed/Satelligence_data/test_rasters_2/satelligence_mills_4326_50kmbuffer.shp')\n",
"\n",
"clean_sat_mills = gdf_sat[['gfw_fid','deforestat','biodiversi','carbon','geometry']]\n",
"clean_sat_mills.head()"
]
},
{
"cell_type": "markdown",
"id": "c11f8eb7",
"metadata": {},
"source": [
"### Calculate deforestation with res6:"
]
},
{
"cell_type": "code",
"execution_count": 18,
"id": "9e232f54",
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/opt/conda/lib/python3.8/site-packages/geopandas/geodataframe.py:1351: SettingWithCopyWarning: \n",
"A value is trying to be set on a copy of a slice from a DataFrame.\n",
"Try using .loc[row_indexer,col_indexer] = value instead\n",
"\n",
"See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
" super().__setitem__(key, value)\n"
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" <th>2</th>\n",
" <td>264</td>\n",
" <td>552.3038111406324</td>\n",
" <td>POLYGON ((98.40582 2.39111, 98.40521 2.36762, ...</td>\n",
" <td>1122.990967</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>738</td>\n",
" <td>621.1470874150178</td>\n",
" <td>POLYGON ((98.36445 2.43594, 98.36383 2.41245, ...</td>\n",
" <td>1416.131714</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>1516</td>\n",
" <td>600.0120538952435</td>\n",
" <td>POLYGON ((98.51418 2.45678, 98.51356 2.43329, ...</td>\n",
" <td>1104.849243</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" gfw_fid deforestat \\\n",
"0 706 349.5068535490499 \n",
"1 717 444.9278149230584 \n",
"2 264 552.3038111406324 \n",
"3 738 621.1470874150178 \n",
"4 1516 600.0120538952435 \n",
"\n",
" geometry estimate \n",
"0 POLYGON ((98.47767 2.24508, 98.47705 2.22159, ... 669.573975 \n",
"1 POLYGON ((98.44488 2.31416, 98.44426 2.29068, ... 860.595398 \n",
"2 POLYGON ((98.40582 2.39111, 98.40521 2.36762, ... 1122.990967 \n",
"3 POLYGON ((98.36445 2.43594, 98.36383 2.41245, ... 1416.131714 \n",
"4 POLYGON ((98.51418 2.45678, 98.51356 2.43329, ... 1104.849243 "
]
},
"execution_count": 18,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"gdf = convert_raster_h3(\"../../datasets/processed/Satelligence_data/Deforestation_IDN_2021-01-01-2022-01-01_areaSum_ha.tif\", \n",
" '../../datasets/processed/Satelligence_data/test_rasters_2/satelligence_mills_4326_50kmbuffer.shp',\n",
" column='deforestat',\n",
" resolution=6)\n",
"gdf.head()"
]
},
{
"cell_type": "code",
"execution_count": 19,
"id": "cba4fe07",
"metadata": {},
"outputs": [],
"source": [
"gdf.to_csv(\"../../datasets/processed/Satelligence_data/Deforestation_area_res6_h3_stats.csv\")"
]
},
{
"cell_type": "markdown",
"id": "b47fddb5",
"metadata": {},
"source": [
"### Calculate deforestation with res5:"
]
},
{
"cell_type": "code",
"execution_count": 20,
"id": "9a8d09ff",
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/opt/conda/lib/python3.8/site-packages/geopandas/geodataframe.py:1351: SettingWithCopyWarning: \n",
"A value is trying to be set on a copy of a slice from a DataFrame.\n",
"Try using .loc[row_indexer,col_indexer] = value instead\n",
"\n",
"See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
" super().__setitem__(key, value)\n"
]
},
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>gfw_fid</th>\n",
" <th>deforestat</th>\n",
" <th>geometry</th>\n",
" <th>estimate</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>706</td>\n",
" <td>349.5068535490499</td>\n",
" <td>POLYGON ((98.47767 2.24508, 98.47705 2.22159, ...</td>\n",
" <td>69.865265</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>717</td>\n",
" <td>444.9278149230584</td>\n",
" <td>POLYGON ((98.44488 2.31416, 98.44426 2.29068, ...</td>\n",
" <td>122.547684</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>264</td>\n",
" <td>552.3038111406324</td>\n",
" <td>POLYGON ((98.40582 2.39111, 98.40521 2.36762, ...</td>\n",
" <td>140.941498</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>738</td>\n",
" <td>621.1470874150178</td>\n",
" <td>POLYGON ((98.36445 2.43594, 98.36383 2.41245, ...</td>\n",
" <td>237.884247</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>1516</td>\n",
" <td>600.0120538952435</td>\n",
" <td>POLYGON ((98.51418 2.45678, 98.51356 2.43329, ...</td>\n",
" <td>147.027191</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" gfw_fid deforestat \\\n",
"0 706 349.5068535490499 \n",
"1 717 444.9278149230584 \n",
"2 264 552.3038111406324 \n",
"3 738 621.1470874150178 \n",
"4 1516 600.0120538952435 \n",
"\n",
" geometry estimate \n",
"0 POLYGON ((98.47767 2.24508, 98.47705 2.22159, ... 69.865265 \n",
"1 POLYGON ((98.44488 2.31416, 98.44426 2.29068, ... 122.547684 \n",
"2 POLYGON ((98.40582 2.39111, 98.40521 2.36762, ... 140.941498 \n",
"3 POLYGON ((98.36445 2.43594, 98.36383 2.41245, ... 237.884247 \n",
"4 POLYGON ((98.51418 2.45678, 98.51356 2.43329, ... 147.027191 "
]
},
"execution_count": 20,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"gdf = convert_raster_h3(\"../../datasets/processed/Satelligence_data/Deforestation_IDN_2021-01-01-2022-01-01_areaSum_ha.tif\", \n",
" '../../datasets/processed/Satelligence_data/test_rasters_2/satelligence_mills_4326_50kmbuffer.shp',\n",
" column='deforestat',\n",
" resolution=5)\n",
"gdf.head()"
]
},
{
"cell_type": "code",
"execution_count": 21,
"id": "8132c406",
"metadata": {},
"outputs": [],
"source": [
"gdf.to_csv(\"../../datasets/processed/Satelligence_data/Deforestation_area_res5_h3_stats.csv\")"
]
},
{
"cell_type": "markdown",
"id": "61d47b0a",
"metadata": {},
"source": [
"### 2- Ingest summing the pixels with deforestation in the resampling - COUNT"
]
},
{
"cell_type": "code",
"execution_count": 188,
"id": "ed8001ef",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
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"
]
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{
"name": "stdout",
"output_type": "stream",
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]
}
],
"source": [
"!gdal_calc.py --calc \"A*1\" --format GTiff --type Float32 --NoDataValue 0.0 -A \"../../datasets/processed/Satelligence_data/Deforestation_IDN_2021-01-01-2022-01-01 (1).tif\" --A_band 1 --outfile \"../../datasets/processed/Satelligence_data/Deforestation_IDN_2021-01-01-2022-01-01_mask.tif\"\n"
]
},
{
"cell_type": "code",
"execution_count": 189,
"id": "b128d1de",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Creating output file that is 40P x 48L.\n",
"Processing ../../datasets/processed/Satelligence_data/Deforestation_IDN_2021-01-01-2022-01-01_mask.tif [1/1] : 0Using internal nodata values (e.g. 0) for image ../../datasets/processed/Satelligence_data/Deforestation_IDN_2021-01-01-2022-01-01_mask.tif.\n",
"...10...20...30...40...50...60...70...80...90...100 - done.\n"
]
}
],
"source": [
"!gdalwarp -s_srs EPSG:4326 -t_srs EPSG:4326 -dstnodata 0.0 -tr 0.0833333333333286 0.0833333333333286 -r sum -multi -of GTiff \"../../datasets/processed/Satelligence_data/Deforestation_IDN_2021-01-01-2022-01-01_mask.tif\" \"../../datasets/processed/Satelligence_data/Deforestation_IDN_2021-01-01-2022-01-01_Sum_count.tif\""
]
},
{
"cell_type": "markdown",
"id": "be988e5d",
"metadata": {},
"source": [
"### Calculate deforestation with res6:"
]
},
{
"cell_type": "code",
"execution_count": 96,
"id": "15567316",
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/opt/conda/lib/python3.8/site-packages/geopandas/geodataframe.py:1351: SettingWithCopyWarning: \n",
"A value is trying to be set on a copy of a slice from a DataFrame.\n",
"Try using .loc[row_indexer,col_indexer] = value instead\n",
"\n",
"See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
" super().__setitem__(key, value)\n"
]
},
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
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"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>gfw_fid</th>\n",
" <th>deforestat</th>\n",
" <th>geometry</th>\n",
" <th>estimate</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>706</td>\n",
" <td>349.5068535490499</td>\n",
" <td>POLYGON ((98.47767 2.24508, 98.47705 2.22159, ...</td>\n",
" <td>669.573916</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>717</td>\n",
" <td>444.9278149230584</td>\n",
" <td>POLYGON ((98.44488 2.31416, 98.44426 2.29068, ...</td>\n",
" <td>860.595433</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>264</td>\n",
" <td>552.3038111406324</td>\n",
" <td>POLYGON ((98.40582 2.39111, 98.40521 2.36762, ...</td>\n",
" <td>1122.990978</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>738</td>\n",
" <td>621.1470874150178</td>\n",
" <td>POLYGON ((98.36445 2.43594, 98.36383 2.41245, ...</td>\n",
" <td>1416.131728</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>1516</td>\n",
" <td>600.0120538952435</td>\n",
" <td>POLYGON ((98.51418 2.45678, 98.51356 2.43329, ...</td>\n",
" <td>1104.849319</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" gfw_fid deforestat \\\n",
"0 706 349.5068535490499 \n",
"1 717 444.9278149230584 \n",
"2 264 552.3038111406324 \n",
"3 738 621.1470874150178 \n",
"4 1516 600.0120538952435 \n",
"\n",
" geometry estimate \n",
"0 POLYGON ((98.47767 2.24508, 98.47705 2.22159, ... 669.573916 \n",
"1 POLYGON ((98.44488 2.31416, 98.44426 2.29068, ... 860.595433 \n",
"2 POLYGON ((98.40582 2.39111, 98.40521 2.36762, ... 1122.990978 \n",
"3 POLYGON ((98.36445 2.43594, 98.36383 2.41245, ... 1416.131728 \n",
"4 POLYGON ((98.51418 2.45678, 98.51356 2.43329, ... 1104.849319 "
]
},
"execution_count": 96,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"gdf = convert_raster_h3(\"../../datasets/processed/Satelligence_data/Deforestation_IDN_2021-01-01-2022-01-01_Sum_count.tif\", \n",
" '../../datasets/processed/Satelligence_data/test_rasters_2/satelligence_mills_4326_50kmbuffer.shp',\n",
" column='deforestat',\n",
" resolution=6)\n",
"_estimated = [el*6.69019042035408517*6.69019042035408517*0.0001 for el in gdf['estimate']]\n",
"gdf['estimate']=_estimated\n",
"gdf.head()"
]
},
{
"cell_type": "markdown",
"id": "aadcc308",
"metadata": {},
"source": [
"### 3- Ingest summing the pixels with deforestation in the resampling - DENSITY"
]
},
{
"cell_type": "code",
"execution_count": 202,
"id": "825e38c4",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"0.. 100 - Done\r\n"
]
}
],
"source": [
"!gdal_calc.py --calc \"A/1929012.345678793\" --format GTiff --type Float32 --NoDataValue 0.0 -A \"../../datasets/processed/Satelligence_data/Deforestation_IDN_2021-01-01-2022-01-01_Sum_count.tif\" --A_band 1 --outfile \"../../datasets/processed/Satelligence_data/Deforestation_IDN_2021-01-01-2022-01-01_Density.tif\"\n"
]
},
{
"cell_type": "markdown",
"id": "70e9a708",
"metadata": {},
"source": [
"### Calculate deforestation with res6:"
]
},
{
"cell_type": "code",
"execution_count": 22,
"id": "684188a1",
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/opt/conda/lib/python3.8/site-packages/geopandas/geodataframe.py:1351: SettingWithCopyWarning: \n",
"A value is trying to be set on a copy of a slice from a DataFrame.\n",
"Try using .loc[row_indexer,col_indexer] = value instead\n",
"\n",
"See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
" super().__setitem__(key, value)\n"
]
},
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>gfw_fid</th>\n",
" <th>deforestat</th>\n",
" <th>geometry</th>\n",
" <th>estimate</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>706</td>\n",
" <td>349.5068535490499</td>\n",
" <td>POLYGON ((98.47767 2.24508, 98.47705 2.22159, ...</td>\n",
" <td>280.183496</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>717</td>\n",
" <td>444.9278149230584</td>\n",
" <td>POLYGON ((98.44488 2.31416, 98.44426 2.29068, ...</td>\n",
" <td>360.116453</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>264</td>\n",
" <td>552.3038111406324</td>\n",
" <td>POLYGON ((98.40582 2.39111, 98.40521 2.36762, ...</td>\n",
" <td>469.915963</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>738</td>\n",
" <td>621.1470874150178</td>\n",
" <td>POLYGON ((98.36445 2.43594, 98.36383 2.41245, ...</td>\n",
" <td>592.580812</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>1516</td>\n",
" <td>600.0120538952435</td>\n",
" <td>POLYGON ((98.51418 2.45678, 98.51356 2.43329, ...</td>\n",
" <td>462.324545</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" gfw_fid deforestat \\\n",
"0 706 349.5068535490499 \n",
"1 717 444.9278149230584 \n",
"2 264 552.3038111406324 \n",
"3 738 621.1470874150178 \n",
"4 1516 600.0120538952435 \n",
"\n",
" geometry estimate \n",
"0 POLYGON ((98.47767 2.24508, 98.47705 2.22159, ... 280.183496 \n",
"1 POLYGON ((98.44488 2.31416, 98.44426 2.29068, ... 360.116453 \n",
"2 POLYGON ((98.40582 2.39111, 98.40521 2.36762, ... 469.915963 \n",
"3 POLYGON ((98.36445 2.43594, 98.36383 2.41245, ... 592.580812 \n",
"4 POLYGON ((98.51418 2.45678, 98.51356 2.43329, ... 462.324545 "
]
},
"execution_count": 22,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"gdf = convert_raster_h3(\"../../datasets/processed/Satelligence_data/Deforestation_IDN_2021-01-01-2022-01-01_Density.tif\", \n",
" '../../datasets/processed/Satelligence_data/test_rasters_2/satelligence_mills_4326_50kmbuffer.shp',\n",
" column='deforestat',\n",
" resolution=6)\n",
"_estimated = [el*3612.9 for el in gdf['estimate']]\n",
"gdf['estimate']=_estimated\n",
"gdf.head()"
]
},
{
"cell_type": "code",
"execution_count": 23,
"id": "231be373",
"metadata": {},
"outputs": [],
"source": [
"gdf.to_csv(\"../../datasets/processed/Satelligence_data/Deforestation_density_res6_h3_stats.csv\")"
]
},
{
"cell_type": "markdown",
"id": "d8f549cd",
"metadata": {},
"source": [
"### Calculate deforestation with res5:"
]
},
{
"cell_type": "code",
"execution_count": 100,
"id": "b41a5add",
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/opt/conda/lib/python3.8/site-packages/geopandas/geodataframe.py:1351: SettingWithCopyWarning: \n",
"A value is trying to be set on a copy of a slice from a DataFrame.\n",
"Try using .loc[row_indexer,col_indexer] = value instead\n",
"\n",
"See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
" super().__setitem__(key, value)\n"
]
},
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>gfw_fid</th>\n",
" <th>deforestat</th>\n",
" <th>geometry</th>\n",
" <th>estimate</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>706</td>\n",
" <td>349.5068535490499</td>\n",
" <td>POLYGON ((98.47767 2.24508, 98.47705 2.22159, ...</td>\n",
" <td>204.646269</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>717</td>\n",
" <td>444.9278149230584</td>\n",
" <td>POLYGON ((98.44488 2.31416, 98.44426 2.29068, ...</td>\n",
" <td>358.961277</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>264</td>\n",
" <td>552.3038111406324</td>\n",
" <td>POLYGON ((98.40582 2.39111, 98.40521 2.36762, ...</td>\n",
" <td>412.839641</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>738</td>\n",
" <td>621.1470874150178</td>\n",
" <td>POLYGON ((98.36445 2.43594, 98.36383 2.41245, ...</td>\n",
" <td>696.800144</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>1516</td>\n",
" <td>600.0120538952435</td>\n",
" <td>POLYGON ((98.51418 2.45678, 98.51356 2.43329, ...</td>\n",
" <td>430.665555</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" gfw_fid deforestat \\\n",
"0 706 349.5068535490499 \n",
"1 717 444.9278149230584 \n",
"2 264 552.3038111406324 \n",
"3 738 621.1470874150178 \n",
"4 1516 600.0120538952435 \n",
"\n",
" geometry estimate \n",
"0 POLYGON ((98.47767 2.24508, 98.47705 2.22159, ... 204.646269 \n",
"1 POLYGON ((98.44488 2.31416, 98.44426 2.29068, ... 358.961277 \n",
"2 POLYGON ((98.40582 2.39111, 98.40521 2.36762, ... 412.839641 \n",
"3 POLYGON ((98.36445 2.43594, 98.36383 2.41245, ... 696.800144 \n",
"4 POLYGON ((98.51418 2.45678, 98.51356 2.43329, ... 430.665555 "
]
},
"execution_count": 100,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"gdf = convert_raster_h3(\"../../datasets/processed/Satelligence_data/Deforestation_IDN_2021-01-01-2022-01-01_Density.tif\", \n",
" '../../datasets/processed/Satelligence_data/test_rasters_2/satelligence_mills_4326_50kmbuffer.shp',\n",
" column='deforestat',\n",
" resolution=5)\n",
"_estimated = [el*25290.33 for el in gdf['estimate']]\n",
"gdf['estimate']=_estimated\n",
"gdf.head()"
]
},
{
"cell_type": "markdown",
"id": "10578786",
"metadata": {},
"source": [
"### 4- Ingest summing the pixels with deforestation in the resampling - centroid"
]
},
{
"cell_type": "code",
"execution_count": 269,
"id": "2092653d",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>h3index</th>\n",
" <th>value</th>\n",
" <th>geometry</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>0x866552937ffffff</td>\n",
" <td>6390.222168</td>\n",
" <td>POINT (96.43162 5.12213)</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>0x86655664fffffff</td>\n",
" <td>6390.222168</td>\n",
" <td>POINT (96.46763 5.17446)</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>0x86655666fffffff</td>\n",
" <td>6390.222168</td>\n",
" <td>POINT (96.49527 5.11735)</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>0x86657269fffffff</td>\n",
" <td>44.000000</td>\n",
" <td>POINT (97.22921 3.47041)</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>0x866509497ffffff</td>\n",
" <td>155.000000</td>\n",
" <td>POINT (98.04976 4.55916)</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" h3index value geometry\n",
"0 0x866552937ffffff 6390.222168 POINT (96.43162 5.12213)\n",
"1 0x86655664fffffff 6390.222168 POINT (96.46763 5.17446)\n",
"2 0x86655666fffffff 6390.222168 POINT (96.49527 5.11735)\n",
"3 0x86657269fffffff 44.000000 POINT (97.22921 3.47041)\n",
"4 0x866509497ffffff 155.000000 POINT (98.04976 4.55916)"
]
},
"execution_count": 269,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"centroids_gdf = gpd.read_file('../../datasets/processed/Satelligence_data/Deforestation_IDN_2021-01-01-2022-01-01_Sum_count_h3/centroids.shp')\n",
"centroids_gdf.head()"
]
},
{
"cell_type": "code",
"execution_count": 270,
"id": "086e6ba9",
"metadata": {},
"outputs": [],
"source": [
"src_raster = \"../../datasets/processed/Satelligence_data/Deforestation_IDN_2021-01-01-2022-01-01_Density.tif\""
]
},
{
"cell_type": "code",
"execution_count": 271,
"id": "d023ae79",
"metadata": {},
"outputs": [],
"source": [
"gpq_stats_h3_centroid = gen_point_query(\n",
" centroids_gdf,\n",
" src_raster,\n",
" interpolate = 'bilinear',\n",
" property_name = 'bilinear_stat',\n",
" geojson_out=True,\n",
" )"
]
},
{
"cell_type": "code",
"execution_count": 274,
"id": "a325e4f7",
"metadata": {},
"outputs": [],
"source": [
"h3_point_stats = []\n",
"for gen in gpq_stats_h3_centroid:\n",
" h3index= gen['properties']['h3index']\n",
" value = gen['properties']['bilinear_stat']\n",
" h3_point_stats.append({\n",
" 'h3index':h3index,\n",
" 'value':value\n",
" })"
]
},
{
"cell_type": "code",
"execution_count": 275,
"id": "a6a92829",
"metadata": {},
"outputs": [
{
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" h3index value\n",
"0 0x86655666fffffff 0.002016\n",
"1 0x86657269fffffff 0.000023\n",
"2 0x866509497ffffff 0.000080\n",
"3 0x866556c47ffffff 0.002455\n",
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"source": [
"gdf_pointStats = gpd.GeoDataFrame(h3_point_stats)\n",
"gdf_pointStats.head()"
]
},
{
"cell_type": "code",
"execution_count": 276,
"id": "1fa9d48a",
"metadata": {},
"outputs": [],
"source": [
"#get values for each mill\n",
"def_sum_calculated = []\n",
"for i, row in clean_sat_mills.iterrows():\n",
" filtered_gdf = clean_sat_mills[i:i+1]\n",
" #convert to h3\n",
" h3_gdf = filtered_gdf.h3.polyfill_resample(6)\n",
" h3index_list = [f'0x{h3index}' for h3index in h3_gdf.index]\n",
" #filter gdf by list and get value\n",
" #3617 is the area of the hexagon for res6 in \n",
" def_sum = gdf_pointStats[gdf_pointStats['h3index'].isin(h3index_list)]['value'].sum()*3612.9\n",
" def_sum_calculated.append(def_sum)"
]
},
{
"cell_type": "code",
"execution_count": 277,
"id": "9a3d21b9",
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/opt/conda/lib/python3.8/site-packages/geopandas/geodataframe.py:1351: SettingWithCopyWarning: \n",
"A value is trying to be set on a copy of a slice from a DataFrame.\n",
"Try using .loc[row_indexer,col_indexer] = value instead\n",
"\n",
"See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
" super().__setitem__(key, value)\n"
]
},
{
"data": {
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"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>gfw_fid</th>\n",
" <th>deforestat</th>\n",
" <th>geometry</th>\n",
" <th>vizz_h3_def</th>\n",
" <th>vizz_h3_def_res5</th>\n",
" <th>vizz_h3_def_sum_res6</th>\n",
" <th>vizz_h3_def_density_res6</th>\n",
" <th>vizz_h3_def_density_res5</th>\n",
" <th>vizz_h3_def_density_res7</th>\n",
" <th>vizz_pointStat_res6</th>\n",
" <th>vizz_pointStat_res5</th>\n",
" <th>vizz_pointStat_res7</th>\n",
" <th>vizz_pointStat_bilinear_res6</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>706</td>\n",
" <td>349.5068535490499</td>\n",
" <td>MULTIPOLYGON (((98.14902 2.13469, 98.14809 2.1...</td>\n",
" <td>560.544861</td>\n",
" <td>63.809914</td>\n",
" <td>560.544857</td>\n",
" <td>234.560211</td>\n",
" <td>186.909221</td>\n",
" <td>231.401121</td>\n",
" <td>199.776774</td>\n",
" <td>236.914138</td>\n",
" <td>227.961338</td>\n",
" <td>233.588704</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>717</td>\n",
" <td>444.9278149230584</td>\n",
" <td>MULTIPOLYGON (((98.14902 2.13469, 98.14809 2.1...</td>\n",
" <td>745.314514</td>\n",
" <td>115.216705</td>\n",
" <td>745.314570</td>\n",
" <td>311.877149</td>\n",
" <td>337.487756</td>\n",
" <td>304.880902</td>\n",
" <td>274.914993</td>\n",
" <td>305.928887</td>\n",
" <td>301.532865</td>\n",
" <td>309.002917</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>264</td>\n",
" <td>552.3038111406324</td>\n",
" <td>MULTIPOLYGON (((98.14902 2.13469, 98.14809 2.1...</td>\n",
" <td>978.444763</td>\n",
" <td>115.216705</td>\n",
" <td>978.444883</td>\n",
" <td>409.430680</td>\n",
" <td>337.487756</td>\n",
" <td>389.348195</td>\n",
" <td>347.807809</td>\n",
" <td>305.928887</td>\n",
" <td>379.853230</td>\n",
" <td>391.183398</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>738</td>\n",
" <td>621.1470874150178</td>\n",
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" <td>1270.436890</td>\n",
" <td>223.985703</td>\n",
" <td>1270.436874</td>\n",
" <td>531.614798</td>\n",
" <td>656.089080</td>\n",
" <td>474.391836</td>\n",
" <td>440.995694</td>\n",
" <td>462.876846</td>\n",
" <td>475.096984</td>\n",
" <td>492.348037</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>1516</td>\n",
" <td>600.0120538952435</td>\n",
" <td>MULTIPOLYGON (((98.14902 2.13469, 98.14809 2.1...</td>\n",
" <td>963.031921</td>\n",
" <td>140.251221</td>\n",
" <td>963.031963</td>\n",
" <td>402.981160</td>\n",
" <td>410.817765</td>\n",
" <td>404.388462</td>\n",
" <td>365.531112</td>\n",
" <td>431.031216</td>\n",
" <td>403.059286</td>\n",
" <td>408.697513</td>\n",
" </tr>\n",
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],
"text/plain": [
" gfw_fid deforestat \\\n",
"0 706 349.5068535490499 \n",
"1 717 444.9278149230584 \n",
"2 264 552.3038111406324 \n",
"3 738 621.1470874150178 \n",
"4 1516 600.0120538952435 \n",
"\n",
" geometry vizz_h3_def \\\n",
"0 MULTIPOLYGON (((98.14902 2.13469, 98.14809 2.1... 560.544861 \n",
"1 MULTIPOLYGON (((98.14902 2.13469, 98.14809 2.1... 745.314514 \n",
"2 MULTIPOLYGON (((98.14902 2.13469, 98.14809 2.1... 978.444763 \n",
"3 MULTIPOLYGON (((98.14902 2.13469, 98.14809 2.1... 1270.436890 \n",
"4 MULTIPOLYGON (((98.14902 2.13469, 98.14809 2.1... 963.031921 \n",
"\n",
" vizz_h3_def_res5 vizz_h3_def_sum_res6 vizz_h3_def_density_res6 \\\n",
"0 63.809914 560.544857 234.560211 \n",
"1 115.216705 745.314570 311.877149 \n",
"2 115.216705 978.444883 409.430680 \n",
"3 223.985703 1270.436874 531.614798 \n",
"4 140.251221 963.031963 402.981160 \n",
"\n",
" vizz_h3_def_density_res5 vizz_h3_def_density_res7 vizz_pointStat_res6 \\\n",
"0 186.909221 231.401121 199.776774 \n",
"1 337.487756 304.880902 274.914993 \n",
"2 337.487756 389.348195 347.807809 \n",
"3 656.089080 474.391836 440.995694 \n",
"4 410.817765 404.388462 365.531112 \n",
"\n",
" vizz_pointStat_res5 vizz_pointStat_res7 vizz_pointStat_bilinear_res6 \n",
"0 236.914138 227.961338 233.588704 \n",
"1 305.928887 301.532865 309.002917 \n",
"2 305.928887 379.853230 391.183398 \n",
"3 462.876846 475.096984 492.348037 \n",
"4 431.031216 403.059286 408.697513 "
]
},
"execution_count": 277,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"clean_sat_mills['vizz_pointStat_bilinear_res6'] = def_sum_calculated\n",
"clean_sat_mills.head()"
]
},
{
"cell_type": "code",
"execution_count": 278,
"id": "c418d9ee",
"metadata": {},
"outputs": [],
"source": [
"clean_sat_mills.to_csv('../../datasets/processed/Satelligence_data/Deforestation_comparative_sat_vizz_centroid.csv')"
]
},
{
"cell_type": "markdown",
"id": "c75cb083",
"metadata": {},
"source": [
"## Carbon indicator: \n",
"\n",
"Procedure:\n",
"\n",
" 1. Multiply carbon stock to Indicator 1 for aboveground biomass loss. \n",
" 2. Use the same methodology as in indicator 1 to determine per mill carbon loss and compare with other mills inside/outside supply chain of company X. \n",
"\n",
"\n",
"### RASTER: Replicate satelligence analysis"
]
},
{
"cell_type": "code",
"execution_count": 52,
"id": "be361369",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Creating output file that is 55000P x 66667L.\n",
"Processing ../../datasets/processed/Satelligence_data/carbon_analysis/AboveGroundBiomass_GLO_2001-01-01-2002-01-01.tif [1/1] : 0...10...20...30...40...50...60...70...80...90...100 - done.\n"
]
}
],
"source": [
"#download carbon layer to same resolution as deforestation area file\n",
"!gdalwarp -s_srs EPSG:4326 -t_srs EPSG:4326 -dstnodata 0.0 -tr 6e-05 6e-05 -r near -te 94.99998 2.1 98.29998 6.10002 -te_srs EPSG:4326 -multi -of GTiff '../../datasets/processed/Satelligence_data/carbon_analysis/AboveGroundBiomass_GLO_2001-01-01-2002-01-01.tif' '../../datasets/processed/Satelligence_data/carbon_analysis/AboveGroundBiomass_GLO_2001-01-01-2002-01-01_downsample.tif'"
]
},
{
"cell_type": "code",
"execution_count": 53,
"id": "bbc848d5",
"metadata": {},
"outputs": [
{
"name": "stdout",
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99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 100 - Done\r\n"
]
}
],
"source": [
"#multiply the two rasters using gdal calc\n",
"!gdal_calc.py --calc \"A*B\" --format GTiff --type Float32 -A '../../datasets/processed/Satelligence_data/carbon_analysis/AboveGroundBiomass_GLO_2001-01-01-2002-01-01_downsample.tif' --A_band 1 -B '../../datasets/processed/Satelligence_data/test_rasters_2/Deforestation_2021_2022_area_ha.tif' --outfile '../../datasets/processed/Satelligence_data/carbon_analysis/carbon_loss_T_downsample.tif'"
]
},
{
"cell_type": "code",
"execution_count": 54,
"id": "973c2265",
"metadata": {},
"outputs": [
{
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" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>5 rows × 27 columns</p>\n",
"</div>"
],
"text/plain": [
" fid gfw_fid uml_id group_name parent_com \\\n",
"0 1.0 706 PO1000004155 IBRIS PALM DELIMA MAKMUR \n",
"1 2.0 717 PO1000004167 ASTRA AGRO LESTARI PERKEBUNAN LEMBAH BHAKTI \n",
"2 3.0 264 PO1000001775 SOCFIN SOCFIN INDONESIA \n",
"3 4.0 738 PO1000004197 UNKNOWN NAFASINDO \n",
"4 5.0 1516 PO1000008193 TENERA LESTARI ENSEM LESTARI \n",
"\n",
" mill_name rspo_statu rspo_type \\\n",
"0 DELIMA MAKMUR Not RSPO Certified None \n",
"1 PERKEBUNAN LEMBAH BHAKTI 1 Not RSPO Certified None \n",
"2 LAE BUTAR RSPO Certified RSPO Certified, IP \n",
"3 NAFASINDO Not RSPO Certified None \n",
"4 ENSEM LESTARI Not RSPO Certified None \n",
"\n",
" date latitude ... district confidence alternativ \\\n",
"0 2021-07-14 2.24508 ... Aceh Singkil 1-Fully Verified LAE TANGGA \n",
"1 2021-07-14 2.314164 ... Aceh Singkil 1-Fully Verified None \n",
"2 2021-07-14 2.391111 ... Aceh Singkil 1-Fully Verified None \n",
"3 2021-07-14 2.43594 ... Aceh Singkil 1-Fully Verified None \n",
"4 2021-07-14 2.456777 ... Aceh Singkil 1-Fully Verified None \n",
"\n",
" gfw_area__ gfw_geosto deforestat \\\n",
"0 0 256170af-2d4f-4a5b-a417-b981205849be 349.5068535490499 \n",
"1 0 b2d76be0-1ace-7a54-8e5e-74bb01111787 444.9278149230584 \n",
"2 0 aff58ced-c18c-a5d8-d084-eb5a6a74416b 552.3038111406324 \n",
"3 0 3cee289b-cdd6-a33d-e0f6-15ea8e7423fa 621.1470874150178 \n",
"4 0 128b643f-9046-0f4e-c71e-59ca37fb221f 600.0120538952435 \n",
"\n",
" biodiversi carbon geometry \\\n",
"0 200.138293908663 57812.37323319753 POINT (98.02851 2.24508) \n",
"1 295.5592552826714 74432.2796810086 POINT (97.99572 2.31416) \n",
"2 400.8360709155834 95543.40760696075 POINT (97.95667 2.39111) \n",
"3 469.4242228971635 109557.3760590317 POINT (97.91529 2.43594) \n",
"4 448.9605690952982 104970.0756673934 POINT (98.06502 2.45678) \n",
"\n",
" estimated \n",
"0 57812.324219 \n",
"1 74432.179688 \n",
"2 95543.171875 \n",
"3 109557.250000 \n",
"4 104969.828125 \n",
"\n",
"[5 rows x 27 columns]"
]
},
"execution_count": 54,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"gdf = buffer_stats(\n",
" '../../datasets/processed/Satelligence_data/carbon_analysis/carbon_loss_T_downsample.tif',\n",
" '../../datasets/processed/palm_oil_mills/satelligence_mills_4326_point.shp',\n",
" buffer=50000,\n",
" stat_='sum')\n",
"gdf.head()"
]
},
{
"cell_type": "code",
"execution_count": 55,
"id": "8e7725e7",
"metadata": {},
"outputs": [],
"source": [
"#export to csv\n",
"gdf.to_csv('../../datasets/processed/Satelligence_data/carbon_analysis/carbon_loss_T_stats_v2.csv')"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "93e8195e",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Creating output file that is 40P x 48L.\n",
"Processing ../../datasets/processed/Satelligence_data/carbon_analysis/carbon_loss_T_downsample.tif [1/1] : 0Using internal nodata values (e.g. 3.40282e+38) for image ../../datasets/processed/Satelligence_data/carbon_analysis/carbon_loss_T_downsample.tif.\n",
"...10...20...30...40...50...60...70...80...90...100 - done.\n"
]
}
],
"source": [
"## upsample the carbon_loss_T for ingesting\n",
"!gdalwarp -s_srs EPSG:4326 -t_srs EPSG:4326 -dstnodata 0.0 -tr 0.0833333333333286 0.0833333333333286 -r sum -multi -of GTiff '../../datasets/processed/Satelligence_data/carbon_analysis/carbon_loss_T_downsample.tif' '../../datasets/processed/Satelligence_data/carbon_analysis/carbon_loss_T_sum_upsample.tif'"
]
},
{
"cell_type": "markdown",
"id": "42a8ec1d",
"metadata": {},
"source": [
"### Ingesting the data precalculated:"
]
},
{
"cell_type": "code",
"execution_count": 31,
"id": "c78a8a5f",
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/opt/conda/lib/python3.8/site-packages/geopandas/geodataframe.py:1351: SettingWithCopyWarning: \n",
"A value is trying to be set on a copy of a slice from a DataFrame.\n",
"Try using .loc[row_indexer,col_indexer] = value instead\n",
"\n",
"See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
" super().__setitem__(key, value)\n"
]
},
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>gfw_fid</th>\n",
" <th>carbon</th>\n",
" <th>geometry</th>\n",
" <th>estimate</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>706</td>\n",
" <td>57812.37323319753</td>\n",
" <td>POLYGON ((98.47767 2.24508, 98.47705 2.22159, ...</td>\n",
" <td>111082.382812</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>717</td>\n",
" <td>74432.2796810086</td>\n",
" <td>POLYGON ((98.44488 2.31416, 98.44426 2.29068, ...</td>\n",
" <td>146149.562500</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>264</td>\n",
" <td>95543.40760696075</td>\n",
" <td>POLYGON ((98.40582 2.39111, 98.40521 2.36762, ...</td>\n",
" <td>197099.781250</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>738</td>\n",
" <td>109557.3760590317</td>\n",
" <td>POLYGON ((98.36445 2.43594, 98.36383 2.41245, ...</td>\n",
" <td>253342.375000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>1516</td>\n",
" <td>104970.0756673934</td>\n",
" <td>POLYGON ((98.51418 2.45678, 98.51356 2.43329, ...</td>\n",
" <td>193674.187500</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" gfw_fid carbon \\\n",
"0 706 57812.37323319753 \n",
"1 717 74432.2796810086 \n",
"2 264 95543.40760696075 \n",
"3 738 109557.3760590317 \n",
"4 1516 104970.0756673934 \n",
"\n",
" geometry estimate \n",
"0 POLYGON ((98.47767 2.24508, 98.47705 2.22159, ... 111082.382812 \n",
"1 POLYGON ((98.44488 2.31416, 98.44426 2.29068, ... 146149.562500 \n",
"2 POLYGON ((98.40582 2.39111, 98.40521 2.36762, ... 197099.781250 \n",
"3 POLYGON ((98.36445 2.43594, 98.36383 2.41245, ... 253342.375000 \n",
"4 POLYGON ((98.51418 2.45678, 98.51356 2.43329, ... 193674.187500 "
]
},
"execution_count": 31,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"gdf_res6 = convert_raster_h3('../../datasets/processed/Satelligence_data/carbon_analysis/carbon_loss_T_sum_upsample.tif', \n",
" '../../datasets/processed/Satelligence_data/test_rasters_2/satelligence_mills_4326_50kmbuffer.shp',\n",
" column='carbon',\n",
" resolution=6)\n",
"gdf_res6.head()"
]
},
{
"cell_type": "code",
"execution_count": 32,
"id": "cac7cf24",
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/opt/conda/lib/python3.8/site-packages/geopandas/geodataframe.py:1351: SettingWithCopyWarning: \n",
"A value is trying to be set on a copy of a slice from a DataFrame.\n",
"Try using .loc[row_indexer,col_indexer] = value instead\n",
"\n",
"See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
" super().__setitem__(key, value)\n"
]
},
{
"data": {
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"<table border=\"1\" class=\"dataframe\">\n",
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" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>gfw_fid</th>\n",
" <th>carbon</th>\n",
" <th>geometry</th>\n",
" <th>estimate</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>706</td>\n",
" <td>57812.37323319753</td>\n",
" <td>POLYGON ((98.47767 2.24508, 98.47705 2.22159, ...</td>\n",
" <td>10773.863281</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>717</td>\n",
" <td>74432.2796810086</td>\n",
" <td>POLYGON ((98.44488 2.31416, 98.44426 2.29068, ...</td>\n",
" <td>20534.408203</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>264</td>\n",
" <td>95543.40760696075</td>\n",
" <td>POLYGON ((98.40582 2.39111, 98.40521 2.36762, ...</td>\n",
" <td>23541.343750</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>738</td>\n",
" <td>109557.3760590317</td>\n",
" <td>POLYGON ((98.36445 2.43594, 98.36383 2.41245, ...</td>\n",
" <td>41870.093750</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>1516</td>\n",
" <td>104970.0756673934</td>\n",
" <td>POLYGON ((98.51418 2.45678, 98.51356 2.43329, ...</td>\n",
" <td>24832.167969</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" gfw_fid carbon \\\n",
"0 706 57812.37323319753 \n",
"1 717 74432.2796810086 \n",
"2 264 95543.40760696075 \n",
"3 738 109557.3760590317 \n",
"4 1516 104970.0756673934 \n",
"\n",
" geometry estimate \n",
"0 POLYGON ((98.47767 2.24508, 98.47705 2.22159, ... 10773.863281 \n",
"1 POLYGON ((98.44488 2.31416, 98.44426 2.29068, ... 20534.408203 \n",
"2 POLYGON ((98.40582 2.39111, 98.40521 2.36762, ... 23541.343750 \n",
"3 POLYGON ((98.36445 2.43594, 98.36383 2.41245, ... 41870.093750 \n",
"4 POLYGON ((98.51418 2.45678, 98.51356 2.43329, ... 24832.167969 "
]
},
"execution_count": 32,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"gdf_res5 = convert_raster_h3('../../datasets/processed/Satelligence_data/carbon_analysis/carbon_loss_T_sum_upsample.tif', \n",
" '../../datasets/processed/Satelligence_data/test_rasters_2/satelligence_mills_4326_50kmbuffer.shp',\n",
" column='carbon',\n",
" resolution=5)\n",
"\n",
"gdf_res5.head()"
]
},
{
"cell_type": "markdown",
"id": "b277df4e",
"metadata": {},
"source": [
"### Ingesting raw data and calculate it on ddbb:\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 12,
"id": "9dc8d593",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Creating output file that is 120P x 120L.\r\n",
"Processing ../../datasets/processed/Satelligence_data/carbon_analysis/AboveGroundBiomass_GLO_2001-01-01-2002-01-01.tif [1/1] : 0...10...20...30...40...50...60...70...80...90...100 - done.\r\n"
]
}
],
"source": [
"\n",
"## upsample the carbon_loss_T for ingesting\n",
"!gdalwarp -s_srs EPSG:4326 -t_srs EPSG:4326 -dstnodata 0.0 -tr 0.0833333333333286 0.0833333333333286 -r sum -multi -of GTiff '../../datasets/processed/Satelligence_data/carbon_analysis/AboveGroundBiomass_GLO_2001-01-01-2002-01-01.tif' '../../datasets/processed/Satelligence_data/carbon_analysis/AboveGroundBiomass_GLO_2001-01-01-2002-01-01_upsample.tif'"
]
},
{
"cell_type": "code",
"execution_count": 11,
"id": "0ddc8dbd",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
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"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>h3index</th>\n",
" <th>value</th>\n",
" <th>geometry</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>0x86655475fffffff</td>\n",
" <td>0.000236</td>\n",
" <td>POLYGON ((96.34271 3.95695, 96.34547 3.99329, ...</td>\n",
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" <tr>\n",
" <th>1</th>\n",
" <td>0x8665506e7ffffff</td>\n",
" <td>0.000112</td>\n",
" <td>POLYGON ((95.49100 4.95352, 95.49369 4.98998, ...</td>\n",
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" <tr>\n",
" <th>2</th>\n",
" <td>0x8665540c7ffffff</td>\n",
" <td>0.000358</td>\n",
" <td>POLYGON ((96.48875 3.78162, 96.49153 3.81794, ...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>0x866556a47ffffff</td>\n",
" <td>0.000346</td>\n",
" <td>POLYGON ((96.50300 4.38386, 96.50579 4.42026, ...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>0x866556a6fffffff</td>\n",
" <td>0.000346</td>\n",
" <td>POLYGON ((96.46707 4.33165, 96.46985 4.36804, ...</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" h3index value \\\n",
"0 0x86655475fffffff 0.000236 \n",
"1 0x8665506e7ffffff 0.000112 \n",
"2 0x8665540c7ffffff 0.000358 \n",
"3 0x866556a47ffffff 0.000346 \n",
"4 0x866556a6fffffff 0.000346 \n",
"\n",
" geometry \n",
"0 POLYGON ((96.34271 3.95695, 96.34547 3.99329, ... \n",
"1 POLYGON ((95.49100 4.95352, 95.49369 4.98998, ... \n",
"2 POLYGON ((96.48875 3.78162, 96.49153 3.81794, ... \n",
"3 POLYGON ((96.50300 4.38386, 96.50579 4.42026, ... \n",
"4 POLYGON ((96.46707 4.33165, 96.46985 4.36804, ... "
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"raster_path=\"../../datasets/processed/Satelligence_data/Deforestation_IDN_2021-01-01-2022-01-01_Density.tif\"\n",
"with rio.open(raster_path) as src:\n",
" gdf_densres6 = raster.raster_to_geodataframe(src.read(1), src.transform, h3_resolution=6, nodata_value=src.profile['nodata'], compacted=False)\n",
" gdf_densres6.plot('value')\n",
" gdf_densres6['h3index'] = gdf_densres6['h3index'].apply(hex)\n",
"gdf_densres6.head()"
]
},
{
"cell_type": "code",
"execution_count": 14,
"id": "9cc8f936",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>h3index</th>\n",
" <th>value</th>\n",
" <th>geometry</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>0x86652b68fffffff</td>\n",
" <td>289</td>\n",
" <td>POLYGON ((99.18197 2.86583, 99.18503 2.90196, ...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>0x86652b6d7ffffff</td>\n",
" <td>289</td>\n",
" <td>POLYGON ((99.11896 2.87070, 99.12201 2.90683, ...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>0x86652b6f7ffffff</td>\n",
" <td>289</td>\n",
" <td>POLYGON ((99.14588 2.81408, 99.14893 2.85020, ...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>0x866553d87ffffff</td>\n",
" <td>289</td>\n",
" <td>POLYGON ((95.55106 5.33424, 95.55375 5.37075, ...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>0x866553d97ffffff</td>\n",
" <td>289</td>\n",
" <td>POLYGON ((95.58695 5.38666, 95.58965 5.42317, ...</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" h3index value geometry\n",
"0 0x86652b68fffffff 289 POLYGON ((99.18197 2.86583, 99.18503 2.90196, ...\n",
"1 0x86652b6d7ffffff 289 POLYGON ((99.11896 2.87070, 99.12201 2.90683, ...\n",
"2 0x86652b6f7ffffff 289 POLYGON ((99.14588 2.81408, 99.14893 2.85020, ...\n",
"3 0x866553d87ffffff 289 POLYGON ((95.55106 5.33424, 95.55375 5.37075, ...\n",
"4 0x866553d97ffffff 289 POLYGON ((95.58695 5.38666, 95.58965 5.42317, ..."
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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l/GCI07TFM5nmyKo4jDZLF01Xqbcckjjj4p0rCqfO8Yv7fO1//bP4mV/7Jev3R9bq/7ET/cdqfdhC/1vf9ff5vr/yL5BkGaMq1NVkzfJmRaNbpz1skmU5k2figRw/GNEdtciynNANkWQB7EiSVJ3mAhEN3JDVZINmqrQHLYo85/rphOX1iv3nx/QPehR5gbf2oSypd+ooqiyKSwJZkgj9GHfpCWS4WxN3y3evWU03HL98QHfcJolTtnMXRVdp9erIikJZip0bSdyng22IVbew6yb+JuDZF8/J0oyHn76P03KI/BBv5YsrStNGkqpikWXKImc7d0milGa/gWZoLK5XnL91SWfUYv/5Maqm4q19iiyn1q6hagp5XqAosvhdeBGr2xW6qdPsN8iSjKvHAjQ6eeWI1qBBGmeEXohm6OimRl49mKqukMXVx6/FhmA3LbxlwORsRnPQYHjYQ1bFNYkSJEUm8mOyNMVpOsiyxOxiycU7l/QPe4zuDUV3s9hCCc1eA0VTqp9ZQpYktkuP1e2aWtuh0a0TBTGX71whyTInrxxi1UzSJBNdm6aSphlplGDYpugCVx5Pv3CGqsnce/UE0xGAZ5oV1IZtdAqKskQqS5Rqs5lMA1Rdob/XQtJUZoXE6dc02WvVaWgmuVqQqCWSCfJNSvF2TLgnMw502GRcnk5J/vEPcfjiPq1+kzzJSJMUpZpwhF6EoilYjinwnTcuCLYh9z95RGvcRTMNfusf+3V0hs0PrJuPXKH/mW/9S5y+fkGe5SxvVrjrgM6wSa3lEHgBF29dk6UZJ584otGpEUcJ/tpHM3ScloUkyZQV4Abgb0P8jY9dF0UTBwlnb17grX3uvXpMe9AkDhNW0zW6qdMeNlEUhaIoKnxAJgljAjfCdAysmkkcJDz94ilxkHL8yoF48LyI5c0as2bS3Wu99zWqAo38kCRKMR0TwzLw1h5PXztDURVOXj7ErJm4Kxd36dPs1Wl06yBJFHmB6HpL/E1InuXU2g6yIjO7mHP96JbOuM34/kB0PbMNZQHtQRPN1CiLkpISWZKIggRv5aGbGrV2jSiIOXvjnDhIufeJQ5r9BnEY461DDFvHaVii2CQJRZXJ0ozZxYI4SOjtdzFsneV0w+TphPaoxd6D0a7lhhLN0MiSjKIs0Q0NWZGYXS6YPJvRHrXoH/Yo85z59RJZUejvd9BMTQCFJciy2KC9dYBVM3CaDsE24PHnnqEZGvc/eYxdt4ijhDhMMC0DWZHI0hxVV9E0FW/jc/bmJaqmsv/cCN3UWc82xEFCd7+H5YjDhLIUVzU/ZHm7RlYU2vtdSlnl9mrB4pUDDsctzJMaUb0kVAoUp6QoJVxiuo9lmpFOJOe8Ji2Jr2I++65Lu2GRJhnBfI1u6eiGRpbmSLKEYWrEUcrZm5f4a5/jVw7Fs+RH+G5MvV3jt/+Zb2D/4egD6+YjV+h/58/8Q/7Gd/5dkiSlMxCz8e18y9PXzrDqFkcv7WPXLIJtgLcJsBs2tZaNJAtQo5QQrbEb7IAm0xEn59PXTimLkuNXDnEaNqEbsrxZYzctca/TFPI037XYSZSQJhmmLebyd+BfnuUcvXSAVTNwVz7L6xX1Xp3+fgdJkcmTTLT6ikKapGRphmmbqLqCu3C5ePcGTdfYf26Eoimsp1u8lUd3r02r36QsS7I0Q0ICWWw0UGI6JiBAuptnU3r7bQaHffK8YHG1AEmif9DFtHXyvCTPc5QKgAzcCN3UsGoWkR/y5AunpHHOvU8eUW/VCLwQd+nhNG3q7RqSxG5CkecFm9mGNMlp9eu7sdnlu1cMjvrsPRwiSzKBF0FZvU4JykKATJIksZ5udi13u5poTE5nSLLM3oMhVs0UOEqSo2gKaZwS+RGGbWDaBt4m4PEPPUXRVB586hjLMQnckDhMsBtWhVwXKKqMrCpEbshtNeobHveRFYXl9RJ/HTA86YuNVJZF0VFS5DneOhAgXtshT3Mu3r1mO3e5/9nncX7688SWxqKdoxoKckMhbMZISokVWGRhzvYfznh6kvKg12WADe/MKf7NObpSUm+9b9NUFLI04+bJLd42Yu/BCKdu4q09Vrcb0bnutWkPW3z9f/draPV/AlD3H6v1YQv9d/zsP4AkSe+dWE8mtAYN9u6PUDSZzWxL5Mc0+w1qbQcJiTzLKREnQOTH5FmOVTN3QNPlOzeousL+c3vohspm7rKarGkNmnT32kiyTJqkAv3UFYpUoPS6qaFoCv424PrRLZIiM7o3QNdVlrdrlrdrumNB4JFkiaTCBjRTJc8KyrxAq0gj7tLl9tkMs2YwOOwhSTA9X+CuPMb3h7QHTYq8JIliJEVG1QQqjQS6oVGWMLucs7he0dtr0xq2SKKUm6cTFEVm/7kxpm2QpRlpmqOqorOJoxTd0NAtjdCLOP3iOWkiOiKrZuCtfFYzl/agIR4oCYqsEFcfSvxNQJ4V1FoOsiJxezrj6tENo3t99u6PKMsSd+UD0OjUkFUxlZAV0VG5C4/VdEOt5dDo1cWE4q0rJFni+JUD7LpFGqXVKaxRlgVpkqHrGpqp4W98nn3xHFmROX7pAMM22C62hF5Ea9DErluUJTugLwkTVpM1JdAZtaAsuT2dsZm77D8c0t3rUGTie8iKjKypRG6ILAuiVZZknFez/ONXjug82COtOWzjlPxBE/XIIlVLchMUSyKVMm6eeWzCiIdSnbpl4eYxy1ufo3e29E0dwpAiSXd4xep2RehGtMdtDMtgs/KYrxMaas7wZCC6p6Kk9tWv8K3f/IvQtC9N/Hr/+sgV+t/+rn/A93z7/8b8asHguEdv3KEoCmZXK/I0o7ffpd62yYuSLEkBCUUVJ6dUgmbpSJLEZr5lejbHqpv0D7ogScwv5mwWLoPjPr29DmVR7IA7w9IpyhKKElVXkWUZd+0xv1igmhr9/S5QMjmbs5qsGd8f0jvoUhYFoR8LRlbNpChKyrJEVRUkSWa7cller3CaNs1BdR9+94bADTl++ZBWv0GW5QRuKHgDtkGeVai6qlIUOfPrFdvZlt5+B6flELgCMVYNlXuvHGHWDJIoJY0SdEswBrNUIOyqphK4IVePbqCE8f0huqmxnKxZ3W7oH3ZFQQBZmonfpyKLGW8JZs2AEiZnc26f3TI6GdA77FJkBYvbNaoi0xm30HSNonxvNh9sgwpnsKk1Bcr95PPPBAHoM/dFyx3EJGGC6RgoqkKWFaia/N5rfvcGSZIYPxiiairLyRpv7TM47NHo1igroFWSBZLvrnxkWYCfRV5w+eiG5c2Kk1eO6O6JWX7oReiOjWGqpFGCBGiGSpbmXL57zWa25eC5PRq9GpGfsJptkX7hV2B/0gFNodAKClMmL2C6DliaHsdBm1qh4V8HnAZbug2bE7uJvYhRr32K2RKpLPFXW5JQkGE0Q2N5veT8nRsGr9xj76CFnKckYSqen/0e+qtH/L5v+oU/+Qo9SzP+s8FvYHSvT6PTIAwibp5MkBWZg+fGWHWLLMmIwxhVU8QdMM2RJEkQYoqS9XzLarKm3qnT7AsyzfWjW/xt8F5hpQK4k1UZq2YDojjlCqzyVz7r+Qa7btPo1kiTjMt3BKHj+JUDOsM2WZrhrX0UVaHednY/gyRJIImxmb/yseom9XaN0I959sUziqzg5NUjGt0aSRDjrQN0S8dpOkBZnUwKRV6wvF0R+THtYQvLMdjMXU5fP8eqW9x79RDDMgi9kCTOsGqm4AwUBYosI2sKkRcxu1xQliX9g241m1+wmmwY3xvQ3e9ACXEQi41F1yiyHCircWLB5Fx0Ef2DLq1Bc9dya4bK8GSAbumi8yhKJEVMPOIgwaoZGJaBu/Z5+vlTZFXmwSePsWoW3iYgiRJqTRvdNihycUWQFZnIj5hfLcVr3heveXo+Y7Nw2X8wpjVsUpYFWZyJCYYsEfsxkiJhOqZgJb59yXqy5eilfTrjNmmcChDV0sS1RNMokVAoSOOU6fkMbxUwOOrhNG28tc/0bEZj1KH91c9RPBjg9jOSfolqp7hpSCLFWGEbI9ZZrAKeLlfcn9ocHHRQZYk0SJFyaJ8GpBe3FGmGrkrIsszsasHlO9e0h9UoUVMJtiFFnuM0BGMvf/GYn/LVL/IrfvlP/VD19ZEqdID/+dv+V/7JX/uXXL57Ta3lcPD8HrqlE4cxsZ9g2HpFECmQZAVFkciLgvVkQ+hFNHt17IaNv/F5+toZkiRz/9VDnLYg03jrYMdvFiOvQgB4VIQcN8RuWNh1i9CLePbFc5Io5f4nj6i360RhzHa2xazGYrIsURZ3v0dxkoV+jNO0dlTWp6+dAyX3P3mC3bDwNz7eOqDWcqpN4o4YBEVe4K580iSl3q6hmxqLmxVnb1zQ7Dc4fEGwqry1T5ZkOC0bwzYpi4ISQSqJg4TVdIOiSjR7TYqi4PbplOXtmoPnx/T2uxR5TrANUVQZs2aJ6UJZVptFyfJ2xWqypjtqU+/WCd2Qs7cusWomhy/sYzgGaZySV8BXkeUkcYZhaWiGhrcOOH39HEVTOHxxH8PU2S5cIr9quRsWFFCUosjTJGMz2yLJEo1uffeaV5MNhy/s0d3vkGc5kR8hqwqGqZGlAqjUDI08y7l694btwq2493XiIGJxvcZuCAxGUWSBrCsKJRKr6znBNqQ9bGHaOuvZlst3rmkOmhw+P0Zr1olbDpOvbuH0UrZaRqkndEyJotS5CTzemK3phV2ekztYoYwXJ+RSSSvV0eIS69EadbVFKguWV3OuHt9QbzsMj8UYdz3fAhKtYQtNUyjyXDAOB22CZo0/9Ad/+a74//fWR6rQp+czft3zv5XuQZfxvQGyIhNsfdJE7HS6qYuikkR7mGcF7tIjSzIanRqqqbGZbnn2+jl23eLopYOq2Hy8tY/Tsql36gLFzis2DhBsQ9Ikxa7b6JaOt/J59sUzJFni5JUjrJqJt/bZzLfU2g6dYQtJksjyHAqQFYENpHGKVbfEOGex5fTNK3RT4/CFPQxLZzN3cZce7WGLRq+OBGRZLl6GJKioRV7gNGxkWWZ6teDq3RvaoyZ790dIsgC1sjSnPWphOSZFnld3YpkkzvDXPoouU2vWyFLRiaynW45fPtiBYO7CRTM1AUjBDuEuy5L1bIu38kUXUTdxF4KcZDom9z95jFFtummcYdh314SiuiYoeOuAy0fXaLrC6F5Fvrle4bshw6MetXadsizEuE4ROIK38VEUGbthk2c5l+9cs5psOHppn+5ehzRJ8VYBuikENmU1dlNUmSIrKsLQluHJAKdu4a19Jqczap0a4/sDFEUhz3LxZssSwSYkSTJqDQu1wltOXz+nNWxy/NIhqqYQRQlSq43ZMJn8h03KVyJMTaZpJBTShEV8zbPZQ0ZWnzJSWa0SzBtoNWx0TUHdgD2NMVYx7lunzC9XGLpEZ9yiKEpm53OyNGPv/hCrYZPn1YSm3ySdbNguAzRd4Vd80y/gZ/6yDz7VP1KFDvCdv/W7OXvjku3SpSxKGt26IEEUglEmSYKZ5W8DoMRpOEiKxPJ6yeW7N7T6Tcb3h8iqzGqyIvIT2sMm9U4dKMlT8YbLikwcxOR5IYA7RWE933L17jWGbbD/cISsKaxvN2yXXgWANQGJLBH0VVVXSKKUosgxLBNZkdnMt1w9usFp2Dvxy+JqRbANGRx1afQaUJakcYYkIUgoUQplieEYSFQ87PMZvb0O3f0ORV4wu1iQFwXj4z5mzaLIM7JE8ASKoiDyI1RN3d3Xz9+6wlv5HL28T7PbIApiMWFomLRHLRRZ3o2VkMBb+cRhQq3toJs6m9mGp6+dUe/UOX75AN3Q8N2QLE5xGjaqoVEU4n6sVFTR+cUcRVPo7XUAIRjxNwH7D0e0hoK7EIcJiqoiq4IjLysyhmWQpxUINnc5eGG8G3sub1c4DYdmpfy6ewbKEhY3KyIvoj1qYtoGm9mWi3eud52Pqqs7ZZ9uaMRRQpEV6LaOqqqiU3rzgvagyd7DEaqm4C59yrKg0W+iHI3IDZX1Zw3KexBKG8aNN7AUE0vpcbOt8fkbmXKmcqQ1qBc6mVKS6yWNSyinEcHlFvXpOfWmRRpnXL17TRIlHL9yTK1do5Ak4raDWpaUbojXa2NdzjE1CcvS+L1/9Zs+VH19pAr9nR94wh/+lf8PVF2jt99BM1TyvCDPCpTqxInDBFlWMB2doiiYns2ZXS0YHPbojNvkacb0YkEapYzuDcQvMy9I40RwrDW1ut+BqmtIMmymArirdRy6FUg3OZsLQs79Ae1hiyIviPwYWZbQTCFCKAFNV0GS2My2zC8XO0ZYmhVMnt0S+QmHP/VlaqM6aZGTPZshy6AaKkVWgFSiG2Ijm18sWE4Ekt8cNIiDmJvHE5Dh4IU9nLpNmmSkUYqiK8hIpGmGqqniQQ5jzt++JvRCDl7Yo95wcDce88sljV6D/kFH8P0rnr2iKERBXHUzlgC8btecv3VFvetw8NweqiY2wDzJaA1aGJZOXhRIUPEDYrbzLYqm0uzXyZKc68c3uEuPo5cOaA+b5GlOWG1EuqWTZxWuYmjVmGnCZr5l7+GIeruG7wbMzhc0OjW6+x0x9qxOZAkJbxMQBxH1Th1N11hOVpy9cUlr0OTopX1UTVCci0JMXygl8jxH01VkRWY1WXP9ZEKtZTM8HiBJsJpsKPKC7riN2W9RqirpXoeiZXDxfILbXmAoKl85CNGUgCfuhu9/NuaFbpsaDqxk8lUBNYVcK1DeiGnOchxZJlpsOPsHP4i/DXnw6RNa+z0yWRObXr9O9rBLHmdImoTpxchvT3ny/e/gLV2+8/u/jYefvveB9fWRKvQkSvjvv+5PErgBWZKTJSmqIUgfWZqh6YKnniaC9uivfQZHPWpth9CLuXjnCkVROHpxD7vlVLNYAdwZpl5xi0HRhJJtPdmwnm1o9ho0unWSKOHy3RuSKOHoxQMavTpZnBL6EYqqYNrG7rUqqkJZlmwWLpvZlkanTr3jEAUxF29fkyUZx68c0jwYkGoqi4GDLsk0Hl9TxploPRVBZV1NVmyXPp1RS/CwNwGnb1yiGSr3XhVXhyRMxKjM1FFVmSzL0TQVRVOIw4SbJxOSOGV8TwBk6+mG2cWC9qjFqKL1JlGF6OoKWSJUeIZtCBnu7YrrJxOhiT/pIwHz6yV5ktM/7GLVbco8J88F6JanGf4mRNVVMSKLU87evMBfBxx/4pD2oEUSJXgrH8M2cBoWJUBZVgh7zvxigbcR76FVs9hWwp5mr87ewzGKKpNEAuhTdKVS+mVYjomqKcyvV1y8fUWr3xCglirjrnzKStt/J0CSFRlJlnFXHrPzGVbdojtuk2cFk7MZeZrv6Kl5llPoOmqnhTus4aUpwQsa5kglckKe+hOSMuE/2KujZG3cWMLzQZdU7JmGr2eoskItUlFfWzH5nh8gNGxODlo0OzaRH+NvAuyDAcawSa6rbO9ryLZKFhecT1bUvucxo47OV/6sl/i6/9uv+lD19ZEqdIB/8Te/n7/+f/9bGJUJwB3PWVEU4ihhfrkgiVL6h100U8ddbLl4+xq7Ie7khqUT+TGhH2E6JpZtUFQUVEWRBfljviV0BXBn1S38dQXcyRL3P3lMreUQ+THexse0DZymzd2F/o6l5i49Ajek3qlj1Sz8tcezL56DBA8+dYzdcIT8M0hw9gcYL+xRyKC8eY5UFJRFwXq6IfRj2oMmhq2znrk8e+0Up+Vw7xNHGJa+I4U4DTGSuZOHKopEHKUCoS4KunsdZFlmfr1kcjpleDLYFWzoRUApQLe8pCgLNK3qRKZbpudz6t3aTlRzezolSzMOHu5hVw9/mqSoqhjzRIGQ5or7esLp6+cEbrhjK4ZexHq2wW7YtPoNQWaq7tVFLjbYMIzoDttohsZqshYFO2xy+OI+qio6jaIoMS2dfCcx1ZFlidV0zdWjWxrdOoOjHpIksbpdkxcFvb3ODxuVSrJM6IdsZlsMyxBsyjjl8p0rkijl5BNH1NsOWZqTximqrkKzSYZMftyiuOcw76S85c4IWy5fdXaE0zfxRz7rKKfRLND9ptBFTGSoq8RFyuSNFdY/mXJ41MVq2/hRxnbmkn9Vh56qIZcSqSU0B1mRc64FeEnCvahGM1TYns/Y/N0f4q9cfte/Y6LxpdZHrtC/+3f/ZR5/7lnFTgNZfg9FliWJ1qCJLEtMLxfcPJnQ3WtXc1YFfx0QRQn19zmqgFSxvArcpU+eZdTbNVRdYzVdc/r6BXbD4vilAwHErX2CTUCtU6PeEmMzgUgj7rJrnzTJqLccNENjM9/y7Iti5HX4/B6GY7Jdbgm3IY1eg3qviWToZJYBcQJrd8dDd1pOdTItOX/zkvaozf7DEaqusp1vydK8wijEz1IWd1LbnO1sC7KQhyLB9ZMJs4sFe8+NGBz2KcsCb+UjK3IlqhF3clkR3PHNYst6uhVEloo7fv7WJZRw/MohtZZDGifEYYpeqfXSJENRxVgzDoU0N/JjDl/cw67ZuGuhIGz06/T2u8iyJBh+kowE+NuANM6otcXPPbtYcP72Fb39zk6x5m8CyrIUHH9Zfk+vIEtsFx7Ts1l1PeqQphnTsxlFXrL3cIhdt8Q1LRHXmTROBT9BV7Ebd1OUM5Io48GnT2h0aiSR6Pp0S9uNCtWjMVrNYV2DL1oe80HCg4M6/ZVDfJGxCSKkT2v0mjpGlpPODbRIQtoWnOo+YZryYGvT8CA4WzLxItqdGsNejfDQJDIkkiYgwyaMiIqclmmiSwqLjc/2ny/Y/4HH/Oe/45fwi37Tz/lQ9fWRK/S/+Ae+hzf/9bsgQRIkuBtfjMMaNnmac/30lvnlkv3nxvT22xQ5rKcriqKkM2yJoihK8iJHkmQoSwI3RJIkrLqFJEvMLxdcP76lNWgyqtD91WRN5MV0xi3hNFOUO07ynZqtLMGqCaup5e2a6ydCNzy6N0RRZRY3a2JfSC/rnZqQgxaCZ05ZEvkRJWDXBJV1ejFj8mxGZ9xicNynLGA1WZOnGZ29Dk7dIi9yMWeWJLKsIHQDFFXGqouT9urRDcsbMTbr7rWF4cZ0g2ZoO3FIWckhJUmMEP1NgFU3cRoOwdbnyWvnSBI8+PQ9nIYgskR+jOEYO2rpnXotjRJuT2ekccrguIdh6qyqa0J33GZw3BfXhDgDhEBEAJbimqDIErOrJVePbsTHnwyQZNjOXAAh1KlMQu6ss7xNwHq6wXRMWj0x4rx455oyLzl6aZ96R3Ad0jjdbQpJmKDqKoapE/oRT187I43S91F+I/yNj9O0BbuuAEkGRVXJbIdJkHNzolP/aW0MS2UVRUyXHi89tWjv14nGEqWRUKQaxlWJt40o3Ax7aFM2ZW5in/Plik99vqT1U/ZQNBlfLkiaErqssqmnFHGBrWmoyExWLjevLXl+obF/0Eb1I+z1ht/3kxGMA/jzv/+v8oXve504SrFsE93WiIOE0zfOif2Egxf2xIw0TJicz9ANjcGR8JDLsoIsSStvtnInrxQOIzmTiwXL6yW9/S6dYYs0y5idzcmSjNH9IbWWU9EjU4Emq5JAx2UJo9KjL25WzC4WQvc+apMXOdPTGWmSMbo3oNGpk+U5aSQoj5qmkMQZsiShWTplXjC9XLC8XtHdaws1XmXAQMUCs+vCjkqcoMLDMw4TVE0V8tBISBm3C5fDF/Zp9sSJPL1YYNUM+oc9VE0wzaRStMxREBN5MXbdqowxXJ584RRVV7n/6jFmTegBIi/EaTvVwy+eD1mRK5eaFXle0Bm1UBSZ6eWcyemc4UmP8b0hIBH5EQC6pVcbbolhqEiSxPx6ye2zKa1hk/6BYNfNLhdIksTwuCfQ97uNTZZJoliM1RydWsMmcEOh9EsyHn7FfWothySMicLkfYKRDEURXndRIOi2cZhw+OI+Vt3EW3qs51tavQbNQVNcxSoOepEXrGZb8oN9QQduaDxr5Lyxt2KvW+Og1sT0ZOKogELC9mS2eoY6z2llGoUtsbh0eafc0tq3+fStAYbCZKRQSiVmQyPTISsKNFNBQWa+8TmbrGhbJg/kFqZbkr21pHx0Trtl83V/8Ffywlc9/MC6+cgV+u//T/8IsR+jaCr+xuPq0S1FUQhzCEvHXXpMzwW6Pb43FMhtnJLEGaqhoMhCxKFoCpqukSQp0/M5/toXxgytGqEfcvnODZIicfTiPk5DoNlxEKPqAsHOsxxJFrLMoihZXK/YzDZiBl4pjC7fvaHIC05eOcRu2eRxSujHqJqGYb33NTRdJcsyFlcrtguX7l5bAIhuBSCqCscvH2DVTdK4csDRFQFapbkwnNDFeOjq0Q2hG7H//Bj7bmb8bEqt4zC+N0TWFDH+ywuUqn1NkxSrJlB1d+Fy+uYFhmVw8NxIAHezLYEb0hm2xGZXkWdAoNXe0gVJptZykCi5fjbl9umUgxf2GB71KArBiZdlCadlA+8JWihhPduwuF7R7DVoDhokYcLVo1sURebghT0s571NWtWF+45op3UM2yDYBjz6oadIksy9V4+otUTRB9tQnMg1i7wokGVph+VMz2ckUcbwuIema+I1XC3pH3bp7nUED6KyihLXikoZ2KohNSyu+k3ONi6jfoPWVzSJxyVBkUCkYMsaZQTaukSXZdRMYnm55vO9Ld2ezbHZAiQ2XsD4H3kUXz1ENmXieomCRKmVrIOI661HXdYY2XVIS7ZPPcoSHmxUnIsJhiLx6//wr+bg+b0PrJuPVKGXZclf+oN/nX/7vZ9jerFAN1V6+z1kWZwGy+tV5ag5FAKWICZLhN+arL5nqqCoCmmcML9ckkQp3b0OuqWxXbicvnGJXTe59+oxhm0Q+RGRH2M6BoZpvIcNKAp5mrG8XRP6EZ1hC7Nm4q18nnzhFNM2BCJet4i8iNCPsBwT3dZ3MllZEej48mZN5EV0Ri0Mx2C78Dh9/Zxa0+b4lUMM2yD2I+KdflqIYhRFKLGSONl1DYOjXuXIIq4OnVGL/YdjZFUhDiLKHHTrznAjRzM0ZEXBXXpcP7nFtPVdez2/WhK6EYPj/o47niV31FIIPeG5Zlcc/qvHNyyuluw9N6ZXXRM28y2KqtDqNwWBpQBZuDbhLl28lWAAOi0bf+tz/obY2E5ePcKum0IhGGfopgCc0jRH1zVUXSXYBpy/dUmRlzsnIX/t4a0Dmr06tU5tZ2ghSZBlAuDM00w4rqoKs8s5s/MFw5MBo8pRNYlTgN09Pi9KLFsDSWZyNuPm6YT+z/+pNH7KCG9fYeYEKLlC0zSQC1mAmaWClEss3IDVmYu5Z9BvCzut6Y2LvZEZ9us0QomgKZOZJaUusVViFoWPoat0ZJskLThbr0iLnFeKPp1Uw3p3S/nogkbL4f/6p76BWsv5UuXyw9ZHqtBDP+I3f+Z3Yjo6jW6DLM24fjJhM9ty9OI+7VFrx6SiZGeDfCdsuDN9XM+3KLJwdb3TQF++e0P/oLsD7ryVTxyl1NvvAXclErIMRVayXWyFaqtdQzPEfPnirUvq7RoHL4zRTQNv4xEHCU7LqVxFy53ENc8y3IVHlubUuzV0XWVxs+b0jQvawyYHz++hGirBOiSNE5yKqEIhnlpZEffM9WxLmRe0hk0kWRb4wpNbhicDxvcGwtzRjSiKErth7Xz2ZEVGliS8jc/8conh6HSGLYq85PrpLbGfsPdwSGvQJM9ykjBBVhRUXRXXDkUShhNZwc3TyY4f36qILJOzKZZjMTjqoeoKeVaI6pYhcquNr2Zi1Szclcfjzz3DsHXuvSokpnduslbNFBr0TIBusioRezHXz6YUac7wvlALriYbNvMt/f2ueA6APM0qlR0Em4CiKHdGHbdnousY3R8yvjegLEuCbYCsCKMHIcApdoj2/Eq4DHX32nRfOSE56PH2cYqsy3QHtrDgksTPKCGxjWPWy4iaptMrDZIo5x1WaFu43+vQTnXyrCRWcqSaQppmeFkKfXBMnaBIeHc1I96WvDQY0C9sCj/HMxP6b+XYn79EzUu++c/+ZjqjD85I+UgVOsB3/67/iS/80zd59sUz8jTn3qtH1DviTr6ZbdFNTfiQq8rOqFCS5B3CqmqKAO7ygpsnt8yvlozuDegd9HYjrTzPqxPaqkwOxNfI85zQjZAkCbtuCsXb5YKrx7d0x+1K1yzMHdI4pT1qCaS3KCnzYldkgSuskJy6DZXJ49WjG+GmctIHSWI9XZNnBa1BUzx4d8w/RSZPc9yVh6yIdrksSm6e3jI5m7P/3IjBUV9w4pfCBLHRrdxwqhHW+51yDdug2a2ThAnn7wiU/N4nDmn0GoJn4EaopopZqeZkCZSqfZ6cz3GXHsPjPnbTItgEXD26xWnZ7D8cCY55mpPnOaqqksQpaZxiVI6227nLs9cv0E2V45ePMGxBL46DmHqnhlW3KHJBnpFkmTRKmV8voRS+arIsM72Ys7xZCbXgfpeSUmxKsoyiCYPLOw18URTcPpsyOZ2Jjz/sUuSFcPxRZRrd+o52q6gKJSXr6Zbl7ZpGt06rXycKEm6fTXF/zVcxfKGNYWkkakGm5mCXxHmGm8RYkk4Dg2CT8lY0p6DkxXGblmxRzIF5huKoFBpEWo4qy5iqhlfEvLme4esxLzeHtAyLMEsI4hRb1mjFOvKqxLpw+VS7xa/7Pb/0Q9XXR6rQz9++4hs/+y0YtsbBc3uYjtBLC455jfagiawqwtM7zytjh4wkSsR4xDR2pBd/7bP3cESrL+ifk/M5mqYwPBpg1oydyYGkiMLY0TGdCrg7nTG/XtI/6NEdC7Xa7HJBnhWM7vWx6zZFLgwnZVkGSSIJY2T57msU3J5OmV8uGRz16e69x9qjLBkeD6oAiYIsEwCSYN+F4o7vGOSp4H0vJ2uOXtzfWTwtbpbohghKuFOslUVZWTZFBNsA0zGxG8Kb/fHnnlJkJfc/dUy9XSPyI2HaUTOxKnEJkvCiy7Kc5fVKGDeOO8KLfL7l/O0rWv0mBy/uoenqjj6sG/rOKEM3hH7fXXpcvHONYenCdUZTdlMNgU/UKAsxG99tbEsXSa42trLk5smE2eWcwxf3GRwKmy/fDVAVVfx+sxyqAI+iLJlW79fgqEtr2CaNUmaXcxGssd8VJ3JWVHQIqfJ593EaFnbDJnADnr1+gdGsU/8dPx2z4xAXOQkFmiqTmTm+nmIoKqas4sUJby2n1F+TufdCH6upE6UpfpFSx6Duq0RGjqzLaJJMWOY8WSxx9YT7Jw4NW2cbpyzdmIZm0jZs5ExGW0hYWwkWPuk/esyf/Ge//98Jh/hS6yNV6AB/5Nd9J9PzGavJBm/l093r0Bo0do4vgiX1nqurUTmZhn7I9aNb0iQTyHXVMt48ucVuWOw9HGNYBlmSksQpmqZVDC9hfazqKmmaMn02w135DE/61FsOgRdx+e41iqZw9OI+Vs0iS1LiKEHTNYENJOJrKLpCkRXcns5wly6DQ8Hai/yIq3dv0EydgxfGWI75HpVVE+OgLMlEMoqpkcYpF+9cVzzxMY2OcICZnM4wa6Ywv6i80Yu8QNFkkjAljcRpatj6Tj2W5wX3KyzBW/t4K596x6HREVZVd6O3oixwFx5plFLv1tEMlcXNksu3b2gNGxy+cICqygSuEN5YdasiIOWCu67I+Gt/NwnpHXahhNmFUIiNHwxpdBvV5pjvlGSRHyMrMpYjOoqLd65Y3W44eGEszB+TDG/lidSargOleOiFfXPJ8mbNerahPWpVYzNhEmLWTAHgmhppKpB8pfLRD/0YyxG/J3fl8ey1c2RV5uGn72F3GqyObGavtrEMDVWWSYsCqVmiWDJ+nPB4saD0JD6RdbF1jYWa4EYRrZpFzTKQshIllCiaEmmWs3riEscZtU85mC3Y4HGz9elbNQZGHRmZPAfFU7AmJdtJiPZ9j/mN3/yL+em/6Cs/VH19pAr9B/7+F/j2X/8nSKKU/UpqmOclcRAhyRK6qVetdommC7vkYBMyvZyjairdvTaKorC4XTJ5NqO71xHqJVUh9GOyJMOqTA6EWaK4E4rdf0ESp/QrL7TNfMv5W1fYTZuTytkk8iPiKMW0DRRdgby6C6sKWZIyu1wSeqGwc3JM3KXLxdvXWHWT45cP0W2dJIxJIiHnlBXRnag7KmvM7bMZcRgzujfEtHRBC306pdGts/9giKJrJJGwoFZ1wZTLkgzd0tB0DW/tc/H2NbIis/dwhG5qrKcb3KVHZ9yi1RdGg3l+p5q7K94cu2GjyDKzywUX71zRO+iy92CEJElCZJSXNPo1NE3QiSVJ8N1DL2R1+57xZpaKQIbADTl8YY92pd9PKkGLpgshjyxLGJbwmhcquw0Hz49p9VvEYczieoVZM8T7qiqCfls9K+7SxV8H1Ls17LrNdrXl/I0rdFvn/qtHGLYQ92RpLmTNmRhX6mYlo135PHv9XEw8XjrAqMwik1Ih/wX3kB90yBBXGVlTCMqEs2JNWmbcSzsYkspmFlD+0JZ+r0azX6NUIC8LsrpC2IKZFBKTMYpstFLmtnC5Gt4wMhsM1R5yqRAXGQAqMvGixHonp6brmJuI9PMX/NG/97s/VH19pAq9KAp+7y/+NsoS0ujO6ljHqpk7lpusSJWwwWe78DAsjUa3IaibzyYsb9aMHwwZHPUoi5JgG5LnBU7TFrrpokCW5ArsSoUeuCxp9RpIqsziSqjgevsd9u4PUXS1YnSlwijhfVLZuxCD9WxDlma0+y00XZyEF++Ik/Dg+X1UTRHe9D9C+SXL8g50W9ysKYqczqiNoiksrpbcPJ3S3e9w8HAkJLt+TJmJKQOyRFH5w4t4qoDJ6RTNUOkdiEnF7HLBduEyOhkIAKssSOPqwdJUkiihhB2Hf3Y+5/Z0Sm+/S/+gI8aKNyuojCveP+dWFJk4TPDWPrqpC+PNMObsjUv8bcCDT50Ih9k4xdsEmJYhrkx5NQZTlcrDfoa3dBndG+A0bfzK663ethmeDNA0VaTjFGIKEbqhMLZo2him2JCffuEU526CYRki1qgS6ihKtalrgvATbAIu371GlhXGDwaohsZ27uJvApGEs9fDf6FDMqhR2DKxnHFbhiRWRrduowDXrsvqLOTTVzWGrZow7yhzkr5MfQYLK2dzX8JRNZRcYn6+5Wy5ZjBucPgpE0kGdyKDBKajkCs5OSXWVsdayXjPXFb/0/fT7NT4xv/2V/If/idf8YH19ZEqdIB/+72f409/y18Sd6e6ENzfFTmIwg29CLtuYdVNIj/m7K1Lwm3I8SuHtPpN0iRlPduiagqtXgPFUIVx5J3JQfXwqaqC03IoioKbJxMmZzP2HgwF2FWUbOZbirygPWzuCDNlJc3M0hxvI3zb622HshR6+qvHt4xO+kIVJUs79L7Zb1SbRFG9jirpZOGhKJK4t5Ylt6dTbp9NOXhuzOCoD5R464BSkqm3HRRZoijyHS4QuiGryXpn3VzkBdePb9kuXI5e2qc9Ehlvd0ClXinv7tJSyrJkfjlnebuhO27R7DdFZ/F0iqqrjO8L48YsfS/hJYtTQeAxNOyaSegLHCBLCu69ekijUyf0I7yVj1UzqXVqUJbCGEMWY9DFzYpwG9Ld7+zCM64f39Ls1tl/fg9FV0hDwajTqlSXNBVGnaqmsppuRMdVt9h/flzFWblkSU6zWxeMvlxYcgl/95jZ5Rwkie5+B0USTi/eymd0r0+z3wRFplB1yrZF+KDHSk4pZTBbBqVWcr7asH7icThocoKNEpZEYQKaTPqigV8v6Xw+QXF0wr7K9XrD7c2WXrdG/0GNtF2wyQMkGTpuHVmSKeQSWRfRWttpzMVqw2ih8/AqZtys8dv/6H/5k/OO/od/1Xfgb0OgfK9VkyB0I7I0w65bVevlcfbmJQDHLx1gNQQqPL9e4TQtkTGmqmR5TpHlyJXzZhwkGKZWtXYJF+9c7xhmrUGDJEzFVUBVdqmteVGQJ5kAAvOC0I/QNFW4rRal8HafbBjdH+6EIfOrJbIs0zuoRBaVMEOW5Z13maIKs4Usybh8V0QZ7T83ruS2InhPVkSApGoZAplPRcsb+hH+OhDKsOo0PX/rCn/tc/Lq0U495m+qAIiGILJIlOLnKEphp73waA+b1WkacP7mFYalc/yKsKBOKydczdCQSkiqcAPN0IT99pvXJHEiCD81gQNs5y71rgBP7xhnyAIPcBceaZJR6zjous7ydsX5W1e0R9XIcScxLbAdk1KCPL3jA0i4C4/LxzdYNZPhUR9JkVhPNoIvsd/GqYwpRMek7DZ9RZGod+oURcntUzGy3XtuVLntlAJI1VX0Rg3fVglfGaA2NXIZTr0Nt9MtR90WJ4UDUU44DdFKiXrdBEXCG0rEhyq1s4LwnS1vD2Jq+zY92yYvS27dDfmoZFyvY2kKeSAhZzJSIeHnCbdsqG0s9iIbaZMS/t3H5HnBH/1f/hv6e60PrJuPXKF/z7f/Tb7/ez8vCCeyTBwlUILhGFU2lUhrsWoWw+M+iioQ3fV0Q3dPpLWIOCaRMKrqqhgBZTmqoaLrmmDGvXtTYQHjKoDA4/Z0hlU32Xs4wjB1kjgjTzNUTaEsJbJUcKl1UyONM66f3OKuPPbuD6l16oSuSD7RTYE265ZOnmZkVfgBJYL1pinivh5lXLx9KUC358aV/VHM9HyOWTPoH/QqTX4JiopU5iRBTBxE6JaB6ZiE7p11c8q9Tx7TaIvTdDt3seomzV5dFFsp+PJFWbKdC0unRreGaZtsFi6nr19g1YyKSKQTejFZIsA99Q7TUGUUVSUOIq6fTCjyQvjGmRqr6Yb17ZrOWEQmIwmAUYLdNCDPcuy6jaxITC8WXL17TWevI0w+ZElITItKYqqoFGUVOiFL+OuA2cUc3Tbpjls7L4IoiNl/ONr5DggKrExZQuAGSLKMXTfJ04KLajO9c67J0qzqdFTsmkmel8imQfKJAeFhnbPMZer5DNs1hrpDuc4IHrnoqsKw5qBWmxeyTKnCs5ME/m1I3dbRjy2W7YLz+Rotk9h/oYFtaWQU5GWBUiikYck2jNDrJXVsEr/gyWyB/ijnK5YKX/WV9/k1v+X/jKL+JDOHBPgj/9V3spysd5xlUWQly8mG1c2qOinu5JQT/G3I3v0R7VGToiiJfeGBLsQtUBRCty0rCp4bMr+YI0nQO+ihqgqr6YbJqeBfj+8LMk0SJALEsbTKYVQ8cIqmkoQJkzMBmA1PBpiV/fDVY2FkcFgloCZRKqJ9DFW0+ncxP7oqwg8f3wjl16fuiwTT+Zabty+otR32H4wEfTVJKbIcRdcokUl84Xen6epOs15ScvyiAJPclcdmtqXZa9AeitM0z98jsgSbQJymTRtVF06wp188o9kXHmmqoQmbpSih3nbQKoPIO/vtJPyRc26hIpxfLhjfH9I/7AFCjETl5XYX4GCYGhISsytB+OntdyscRbjJykBn3NnFaN3ZN0deyHr+nsQ0jYUTTRImHL+0T6MniFVxlOxAzTRMkVWR1Z7dRTFXzjWdYYs4TFjPNpiOtfNML6uElrKEuR8zvzfCfLGG3TDJlgmX0w3drcxB20FtmqR3EVtlSbZN8YOY7L5FO1WJtjFfdOeERyovDPs0dJ2YnKTMkbqAXBLGOXqmYikaAQlvuRN4s+RTVp9WzaLYRNSfuHzbX/yGD1VfH71C/3XfyWoikjLyLGN5uxEGfoOGIG24EedvXlDkJccvH9LoCafXYCty1O2GvdOMy7KCJImQxvVsi6arNAcNyrxk8mzK/HrJ6P6A4ZEgsQRbkXJp160fYVogEYcxq9sNZVnSHrZQVIX51ZKbJ7f0Drq7TWIXCNkQJ5egwwpyR+TFzK7mlEVJd78rHF2mW6Yzj3ZN3IcVVSb2Y6G/tgzEFaZA1TRUTYBu149vUTRFdDSawvJ2zWa2rfLn2mKmHyfvgY5RKvTSFVV4frnk+onQc4/vD3fqvSzN6dx50ZXljsqbJBleRc4RGfIl149vBZHl4Ujw3fNSRGIpElZNCGLKskDVNUBoBeZXS9rDFu1hkzROmJzNkSSZ8b0BVt3ckW9E0mtK6EVohoZdN4mChNMvnhEFCfc/eUyzJ0hUoRdhVJz44g7o01TyVMRGuUuP8f0B9Y4wuJxVLkC9/a4gXRWFGNnJJe7Sx9+EWD/n01h1i3Uc83lzQ9dVefXSx+w0yZKSVJOQRg0iHaIixy5lFEdjKSfc/JtbJODwlQHKiY0nJyRuhmXoaKZMZGfImoSmKoRxyuP5El+Ledjs0M1t8mcZ0XVIfxbx1T/teX7VN/4Epan+WK0P5TATp/zpb/4LXLxzzWa+Jc9yGl0hW9wstpx+8RyzZnD88iFWzSJwA/x1IACftrPTL7PLOYsI3RDDETLXOEi5eOeS7cIV5vyjptB2L1wkRabVraPoagWYUSmoEtylh6qp1DsOeV6xr85m7D8Y0T/uQQmbpYsMgjJbgVyUYt4bBbH4HrJEq9egLEsm53Om50v2f8anGR51KTcu3vWcMs+EOaQq72a/d9lxi+slqq7RGQsq8PR8znq2FSywvTZlURKFMZIkoRs6eZ5Xfmk6SBLL2xWTsxmtXkO4quY588slWZozujfArps7vvvd9w/dEFlTRGub5Jy/K+bcRy/tV7HI2e730+jVkav8cqmKxFrPtmzmWxrd+o6sc/7WJXIVRSUsvEX3o+iquN6ECaqhYlj6zok3DmLuvXpMvVMTMU1VNt2diy6IDTXPc2YXYszZ229jOtaO59/s1Rk/GFV2YikFoFbvTxwm2DUTzdRZhymnGESfbfKK1qSWySTrgPzdC6xxD+oWqaWTD03KuoqbJjy5XRL1Sl4NGpiOzszM8JMEZ2DSKHWRaW+CZEAoZVx5W4I04aDRwNJ0tkHE6jrkZGIyaNUwFhGvHvb5L37rT0I9urf2+X2/5L+rQDfhtz57PwX13gBVVdguXAI3pNVviIzpUsghQYgbAi8iTzLMmolh6iLI8PVz8qzg+OUDai3BGFvcrLBqJt1xR6jUsmJnzpBEKVEQY5g6piNmskIa6nH4wlh4nCcZq9sNqqbQGQu3lPdTatMkJajslpyGRZpmXD+6FbbLL+zRHbcpVI3tNkLVFBotCznLKFKR0CLrOqEXsp1t0C2demWPdfnONW7lMd8eCeDO34iOxmlau/RSWVOgKFnPNixvNjR7NZq9BnGUcvXohrIs31Pvpe/pucXPn6FW7W+aZJy/dYm79Dh8sZLF+iJR1nIMuvtdIWipJhuSJOGtfUI3oNaqCTfZlceTz5+iaArPfeYBtmO+l5nm6KKDSzLUyuQi9mPRoscpRy/uC17CymM93dDqN2kNmsKxJhOeAWUJm8WWNEhp9OvCueb2fc41L+yhasIwoygKDFOvSFcFuqWhqgqb+ZazNy9xXnzAwfNj8nsNVmWGfhFRn84wFIlC05EbNnK7xqauchr6FFLJaNBA1RVWm5DyzTXWKx1qA4fcKCmzEkmVie2CKT5xljHQHLRCZj0LuF669BsOo3EDy5MwLlK0my21Uub/8gf/M7qjn2Qhi0mU8Kd++1/g6tE1V09u2cxchkc9WpXB4B0FdXDYo9ayd9RLSpBVkddVFAWGpaOoCpu5y82TW1RNZXR/uJu7Lm9FjnpvvyvGRe9jmGVpLggoho5mqrvxXRIKLXyt5RBsQ6bnc4H8nvTRdI08E6DbHXAVhwmaoWJYBkll2hhsAg5e2KfRqxH7MbOrBbVhv6KyymRJShmL9JA4TIgzMGomtmMQeSGP/+27xL442e6iljdLF8PWaXbrO3fUu2nMduHirn2anbrwk98GPPnCKZouAgqtukUaJUSBkISKnyMXBpiGRhKlXD26JvRj9u5Xqakrj9vTOc1ujcFxX5yOldmkrIrQiDROsWomuqmxWbg8/twznIbN0cv7WLaJvw2JgxinZYuc8LxKTVUk4kAEKhR5IezCKl7C8nZDb78jnGsU4QN/B/SFbkSR5Vh1G6USMV28c01vr83ewxGKquC7IWVeYjetHSipqgqyLLNZuNw8nWA6QsuvWAZrw2b6Qp1Bu0Y3kFEe3VD6EbJlCpXc9ZLF1xzQPOgg5zC/2TKPIh4mJp1xg8KQSQqR6iLJ4CcJoVNgtzUKU+Z25XK22XCYOxx3OkiahK+kSHnJ8ImE/niFVMA3/J5fxL2X9j+wvn7cCl2SpN8G/EYE1PNF4OvKsox+tI//MIV++sYFv+cX/rcEXsje/SF2wyLYhlw9vkXTVaHBblhkyXtqK90QhApJEiQQSRKm+PPLJXbdpLPXRiqFm8tmITaO7n53V0xFlaNeFmWVtyZy1EM35PbJhKIoxf3bUNnMhL9aa9BkdCIQ/yROyTMx/inzysTSFCaWd7FCWZoxfjjCcgy8dcDkrCqUox6aI9Jn8iRDUSTSJBWe6ZaO0WsT5RKnr50R3Uw5eXkPuy7MIzezDU5LeMzLivA3RwbB4/ZJwgS7aYsZ9UzYXemWJkwmHINgK4gndsPEtIWaC0lCVRTiOGFxtSIOY/oHXTRDZz1d70wj9h6Mdm4zRVEKDXmWkSd5ZaUsnGMv3r7CdAQVVdPFxhsHCa1Bc5fUKkA3iSzOqyTYgmZfnNazyvZ6fH/I8LiPhFRdTQTHPUtz8f5ZmvDLu+v+Ri0Gx73qWXAFIarfEOBiJT6SqqTW+fUKw9Ro9RuUJUzOZ0RezMFP/wTGS0NySuSLFYqiUm5DQhQkWcYqU7xjh0dNmenK4xNJnd64iRwX+IVwdNUsjTQvIC9QTRUUmUtvy9v1DcO6w5HdRI0l3DCmkKFeN1AKCedxRm0WMd5r85t/9y9EUeQPU48/9oUuSdI+8M+Bl8uyDCVJ+mvA3ynL8s/9aJ/zocG4r/tO5pdLVpMN149vqHdqIq3FFPa4kR9j2LqYTb8v0rfIBcHFXXrUOzVxHwxirh/fEGyrnLNhQ1gPuxHIkpCWSggvNlURMb3bkMXNCk1XaQ9bSIg3f3G9YnDUF4aL1fWA4i7htLqPa0pFzoiYns0pKcQJoSksb9bMLxd0xx2GJ73q/i9AMs1QKUvEhqFrwv8uzLh1RaLqeNxAlUqWT69YTzZ09tp0Ru2KIy8ilAQqHpOlOaZjouoq6+mGszcvseomhy/soRvihE2ChGavgdUwKxcZYTOVZzmbuUueFzQrRdz0YsHt0wm9w14VhiBSU8uixHIEo64oSjRNRVJktkuXybMphm3sjBvnV0tCL2J40qPerlHkZUWhFeMpfxsigQhnKEuuntwwPZ1z8OIew0qpF7ghsiKJj8nFBqHoKgCr21WV1Nqgs9cWPIbLJSCSVA3b3FGnZUkijhK2Sw/NUKm3a6RRwuW7Irbr5JVDWidD0mEbt6WjllB7NCUJxYjUbDhkWc7F59/lrZ97wkm/zX5hot8mbP0Q1dKp1U0yXSIzZDQkcg1mS5+bzRbtMw79mjD3mPs+SigzqDkYhkqqFmgx1M9zync3rKZbvuMvfQOtbu3D1OSPW6H/a+BTwBb4G8AfK8vy7/9on/NhC/3y0TW/4eXfxujOxVQS7h9pnFJrOxiWUQFuVA+KyFtLo5R6pybGXSuPZ6+dIStK5UjiEAXRLke90alVyZbCJ0wCfDfEX/mYjonTskmjlOsntyJ074U9unsiEshb+8iyjNNydtJQWbrzOI9YTTeomkrzDnQ7mzG/WjJ+MGBwKIwPgm1ISYldMysAqUDRqo3GjZjM1hgHI/qVGcN8EbJ44zHDk/4uHCEOE0Akre4yyE1xsq0ma66fTqi3HAbHfZBgebMmDmL6h71K+lqQ5wUSIjU12ASUQK1pVwy9GbfPpozvCzoxZYm78gCJereGqio7We2dLHZxvcJ0DFqD5s5LIPYjDl/cFzFLWUESC3svuUo+FQEOglB08e41i6slBy/sCdJQkotpiaEKIwlF3mEASLCdu6ymWxqdWmW1nYhgRlni8MU9nIYt0mVjMdrM8yroQtcqTXzE6evnhF4sCrzKlvO9COMzL1Ae1ymSDPuNG3TDpJAVbt+9ZjXbMmhp1F88IOo1WV1vsE2ddqeGZGqUlKQNBamEzSZkmkc4TYtmzSQqM941tkhyyb16G0fRycuCVC3AhDTKCa9jmtOCX/IffZJf+gs+82Fr8setdf9vgD8MhMDfL8vyv/gSH/P1wNcDHB0dfeXZ2dkHft1v+pm/T5BcspzV7UoE0g9bu9C/otKfF0VBuA0pigKn6aAoMsvbFZfv3uxokbopMsC8lU+9W6PRcUASc3EokSQhyBCJniamYxC6EU+/KLLDj185oFn5062nAhBrDZpiLJOLrDNJlogD4dVtWDp2w65OiBs2c3cX9JelAp1WlEoXLYuH9o4z77sh2/kWs9VA+rnPk+gqwd98B28dsPfKAZ0yJfVDwq2PLCtY9bvTWNyNJUliPdsyv1rQaNdoj1o7M80szth7bky97VDkhfDSU2XRVYQJUhUZnOcF149uWN1uGN4b0Bu3SdOM5fUKVVfp7LXRdLUC3QQVOPIivLWPVoGFceUmGwfJDkvIkqwalSnoplEFOLyXmXZd4THj+0MRrexFTC8XWI4pjC0q3IPq9+1vAsJtiFXRpIONz7PXL4Rd96vHOE2HOIpJoxTN1FAUeedgexd0cfrGJZEfc/TSHvWO+J6bhYdTN6l/5jmoO2SWgupFyE9uhQd9lNN/8RAzj/EmiyoEosb41WP0VoMSSLsN0GTWZkG0jDCaBlrPYGkmvLOdkzVLXuj1qes6WV5QLgsUSwSEenKCXipYikYeZKz/5ZL/7bt+04etxx+XE70N/C/Afw6sge8B/npZln/pR/ucD3ui/+A//AJ/5L/6E5iOwfCkh66LMVGWZMiKApJI/1QUGd02oCyZXSyYXS5o9psMDjrVCbYh8AJ6B12a3bpojdNMjM1UmTRMybIM0zFQVRV37XH+1hWSBEcVAcVb+axu19TatZ2CKsvuUk7E6C0OxCZhWMJt9Nlr51XkzoEIZQyECsuqmXT32uI+Xd1NBZU1JHTFdcRp1oiSnNNHN1z8kuf57Fyma5hEyLizLdp0Sq0twiHvRDEAm4WLtxKOpkLSGnHx9hVZknHvk8c7tD4KEnRDRa2sqmRZpNBmqfBy38xEflm9UyPyY25PJ1iOxeikL8gnWb6bVadJRuQLhp7lmARuyNPXTknjjAefOhZzaz8SjrM1A6fhcPe8qaqgI08v5vgbofZzGhbu2hdxVi2b/Ydio87eLzGNYpJQvGe6JRRojz/3DM3QOH7lEKfS3wvnGqOarZdVBLVMHKVcP74h9GL2nxsJyu7KY3GzptWr09nvCLfdURc6DaS8xL1eEb35jOawjdltsglSLh5PMNdzoUi8kwtLEvqgS1y3icoSpW+h2RqbNOaHygWU8Nz9Pk5Nx5dj4jTHUFRqvkakCm8FTZeI0pynswX6v4z4jt/1y/jMK4cftiZ/XAr9lwM/ryzL31D9+dcCP60sy2/80T7nwxb6N372W2j0ajtTiTTJ0A11R6lUNQVVF26f0/M527lL/7BLo1MnjmJun03J05z958bUWmLunUQxsqzsHmoQkUiSJOEuXW6fzQTieiDGRIvrFev5VmSf7bUrLXwMJaiGRh4LYodualXkbSDsh8uS/YdjzMpldXEj0P3uXhulioISijOFOExJ48ojztQItgHPvnguzCa/5tM4gzrRjZhBm6Q0x22ULKs2CYGsu+uAOIixmzZWxYx78vlTZEXm4adPcBoOoR8ResLPzqyJ1FUk8eCnacH0YkbkRvQOOlh1MXO+evdmF4ihGRpJmFSRRkJ1l8YiMUYzxOs+ff2Coih2fHd/4++wkl2Q412AQyECHKIwpj1ooZkCS7h4+5pmr87RiwdopkocJGRZvmvr8zRDMwUrcLt0OXvjcucRYFg67sondIWXvtOw3lM7ygLgXFyvSONMTDgMlfVkU1lHdSoqtXC6LcoStdskarfIVz5m4KPJgoNw+oVTGr06hy/uo+kKkR8J6XPNQnYsCtNGdQzo19mUOacXC2aflDn6RB1HMtiuMoIsod7ScWSDMgN9KyPZEklZcL5cE4QJ+80GrdKgHqr80d/ytR+2Jn9cCv2nAn8W+CpE6/7ngB8oy/KP/2if82EL/W/88b/D9/3Vf0lZ2QOJdI+7O6zgsM+vFpXpYxvDNnFXLmevX2A6JscvH2DXLZL4jlmlYlaCEAkE460UwN16tt2lieRpxs0zYRhx8Pwe7ZHIW4uDGAmpClEodu6msqLgb0Rqp6or9A56KIrE4nrFarKhd9ChX8lFkwrdv2tV81yg+4qq4q99Lh/doCgy4/tDNFNjMxcxT609MU4iy8QIqyxRZPl9eWlCeruZCzKRYescvXS4s2wKvZB6u1YlzbC73xZFwWqyJU0SWv0mmv5eWkpzIGbOmq4S+jF5mu3eBwEWCjpx4IZcP7pBkiRG9wdibj3dsK02yFYlaMkyEeAAIsChyArshlUxCxecv3VFd6/DwfNjFE0h3IRkVU64rNzhAAqqIuNvBStQ1VUGxz0UVWF5IwIs+wddGt26GLnmRaXyK9guPMqypN6uCcruxZzrx7eMTgaCFSjLhEGMVJbotk6e5OTDHkbDQaFkeb7g8vvfxrZV9h4M0XQNdyUSfJv9VuV3l6M06yimgS9LXM890r5N57hJ0VOZZxHbIKI5tug4DqiC96FmEmVQciuJ0Ie+6WDJKqttyOxsQ7fm8G2/9ufzytHow9Tkj9sd/Q8gWvcM+BzwG8uyjH+0j/8whZ6lGd/6c/+Q0Fgr0u4OK0miTd7OXeGR1hN33MX1gst3b2kNGhw8P0YzdPxtdco1rB+WlSbJEmVesl16hG5IrV0T6R1uyOmbF5W5/7HwV4sqSq2mVNZGVLN6kTgiAgXWGI5Bq9fc3TO3c5f958Z09zuURSnQfYlKT19Q5qAaijBtXAdML+Zopia6Bir9+Fy0z9299k4EIzoJMa8W0UTCtGIz24hWt+kwOukjK0LgE7pC/lnv1Heg2127729CKIWRpKIo3J7PuH50W5lMiAffXYv8srtElTvDS1mWiLyI+dUSSZbo7gnxyuxizma2ZXRvQHfcFnfVKK0mImIEWRRCRy/JMtPzOTdPbqsJhEic3c5diqKgOWhiGBpFkQOCwht4EaubNYqm0B42d6xAd+kxfjikPWhRVgYckixVuElCSYllC++/m2cTbp9OGd7rMT4eAOBtQyQknKa1i42SVRnFtHCjnNsnN9iOSbdjQVGwvFmRpRnd/a4Ib6w2MKnKENh4KfGrR7S7NVAkpguPKyOme69Br+OQm5DmBbIkoSTg5gl5VGAMNFRVYuL5PFutGKc1nnPaHDVbfPPX/gxsQ/8w9fjRIczkec7/+Nv+vGBtFUJLnMQpoStOZrtuV+mbtyyuV4xOBsKyCNjMRG54q9/EqlVc7VKc4ncGFFmaYTdtNF3klz997QzN0Lj36jF2TdwztysPp25Rr5D5sqhenATBNiBwQ+yaJTYJP+bsjXMiP6lQW2G4uF35aLpKo+0Iu6aKEipLEu4mYDvbik2iWydNMi4fXxOsQw5f3KM9bAl02IsqkMx8H29cRUJiNduyuFriNC064zZFljM5mxMHMaMHQ5rdhgiiqCSt0h3oJoFRbX6TszmT0yn9w67oGkpYTdcAtIetKr+soKzy3+MwZbtwURWZerdemUZMWE+3HDw/plfFOwdeiKIo4n6c5cINyNAokVhcL5iez+mMWuJ15wXzywVliQhadEyRE16+Z/jpLj1UXaHWqpEmKZePrnEXPscvH1SS4ILoDrOxhFllCTvt/+3pjOn5jMFJn/64Q54XrCYbFFWmPWqhamoV4ECVCiNwGcM2aA1aZFnO7dNb0igV18F2jSLPhWutIQBMb7kBoN5yKJoNboKM5fmK8acO6O63SCyJJMkJ9lWkpkJCQRnkmIYGusSt6/HEXdLrONzXWliuTLJK0FSF7/gtX4tj/iQrdG/t8x3f8F0EW6GgSpMM09Lfy/p65wZ/43PwvEhrScKE2eUcWVFE0kclbMjTYueGGgWCw2NV7ef8esn1YyHoGN0bomoy65lLsAloDRo0ug2QIM8y7u54gReRxSlW3RQ2z2uP0y9eUJalCG9oOgTbgO3Cxa6bNAdNgarf8e4pCb2YYBti1Y2dF/zZG5fEYcK9V49odEUOmLf20QxNcPcRvHFFEZvFdinUaU7LodkVPIHLd65J4pSTV0TAYZZmREGyo69mqTjlNF0jzwtmF3MW10u6+106oxZpLAIuZEVmdDzYmWPmlXY+TzMC7y41VYwdz9++wl24HL18IBJvKl83zdCqAAdBeL+T5q4mG9bTKrW2J6S4148nSAocPL+HXbPIKqsnTVN2YzBN1zAdgzhMOHvjAnflce/VY9qDJkmcinGpqVFr1hC2FpUCrYD59UKYaey1Be03iJmcztBMVRhzVlOGIs8F98FP8O/iv5o2UZBw/vY1aZRy/LKg/WZpThzGIsVWVUmrRBlNl8nilIvHt2wHQ/aOOvRbNYokwX12TfEVD3Acg6AvE3ZkVEVCTiVmvs95vKVRMxj16pQSLIMAdS0xLixeGPb5bb/sZ+zCLf/31keq0N2Vx7f/+j+Jtwl2wX7+JuDm2RTKUkQvOSbbpcvtUzHa2Hsg7rVZkpPESdX2iwdUluVdAun8asnyZkVr0BQnQVowvVoQezGj+33qnTqUpbBakqhCIFLKokSzNBRFwV24XD66QTc0xg9ESsx27rJZuLQGDUGwkcRd9k72GAeJMG2scsz8tfAqE5vEkXAgrTYJp2GJmCDg/e+Ntw4IvZBay8F0RHTSky+cIknw4FP3qLVs4jAh2IYiiMIxduCXoohx4uxysTPbdCoV4MXbwmTi8MV9YVhZ8d3vRDkiMUZFtwziIObszUtCN+TopX1q7RqxH7GcbrDr1nsMvbzYcXDdlScCK6uIJ28d8PSLp+iGXlFwTdJIuNWI68hdyo6KZqjEYcqz189IgoSjF/epdWqVo84Wp2nSHrZ3GI6wcS5YTTf4m4BWr4HdsPE3PudvXaFbGscvCzONLE7JM9EhZZWLsGZou8nJk8+fkkQpDz5zn0a7RhyIkI/3q+QkCfH5aV753q/Zuz+i9WCf1HJYLz205ZJWpwavHJEbCv6RSlaTWPsRm5sAq6XTsSwSueAq2JI4BYdWk4ZsIMUl92ttvvU//Vkfqr4+UoW+nm34rt/+F9guXLyNz/JmjW5qdEYtoRO+WjC7WNA/7DI8HqCoApjKkwzDNoTZYlEgK9Iuzmh+ucTfBnTHbZyWTeRFnL99JaSEL74X0BcHCYoqyBt5KmyBVU0BSWIz2zC/XGI1THp7XcqyrArHY3gyoD1sVZtEtTFUI5f8jnevyGwXHlePbtBMjdH9IbomKKHbhUt71NqJNPI0r1RvEqEnQDerbqLrepXceobpmBy9tI9pG3gbcZ2ot2ycpkj0KCp5bZEJWmkcprR6DfQ708s3r3Ca1g64i/2QNMmr36GwXxaRViqRF3H1+JYoFAYPds1iu/JZXi9pdOv0D7vic7JctPlVBxQHCU7DEkabsy1PXjvDckxOPnGI5Qj7qTiIsGq2aLmzAlmVURWFqLKyisOY8f0hhi0mCvPLBc1Bk8H+e98T2EmMoyCm3qqhmSKV5+kXTrGqVB7TNoijhCzO0G0dWZKF54GmCDMSL+L0jQuSKOHkE0c7+rW38nBaNvV2jR+ukhPdkbf26e13cJoOvh8xcdo4jsFeTUONIrIwojwcUjYtLh/CKk1wDI1WblDeJEy+OGF7qHNv3MEcGcRaQRHnaLmClsl8yy/9Gk4GP8kCHE7fuOB/+E3fhbv0sBoWtaZDHMZcP74VxgHP79Hba1dZXz5lWVJrOjvTxzuDhDROWU+FzLXZawgn1NmW09fPseomJ584xnYMwiAWembbwHLei1m+MyDYzDZsFy71Tp16p0YcxiL7zIs4eumQVr9OnhcEnvAbNyvLYgBVFySWTcW7t2qm2LAkifnFHG8dMDzu0x41d6CbJCHGcElKnotscFmRWU433Dy+warbO1fb9VTo9DvjNo1OTSSXZHlFxCnw1wFZVog0G01hcb3i/M0L2sMWB8+PUTUV3w3J4gynaaOZ2m70dpeAOruYk6c5vcNKO397R+NtMzjp7z6uLIQr7x3v/86VZjXbcPbGBU7D5uB5geRvVx5JkFDv1SpvfHE63r1vi5s1eZLRGYv78+JGSGt7+x3G94ci6CEWAKWiia4re5+X3PJ2zdmbF9h1i6OXD9Ar9WISpdRbNpqp7zTvYoIRcf10QpZkjO+Ndg7Aq8ma9lBgCbKEsDWrJLibxZbYi2j2m+imxnbhcfbmBfVOnaOf/RlMyyBbuqKrjGNy0yBGZvvz9tAclW2WcP79E0okPqO30fcdAjVn2U6xcw3Flim2OVam8F/+rK/kp7908oH19ZEqdIDv+Po/JU7LdcDpG+cUWcHxK4ei0IJ4ZyDRHjbfCy8o2WVxuyth+mg1TCRJYno+5/rxDb39LqN7AzRdxV15xGFCreVgNywoEQmeqmg9t0shvmh0amImvvJ59sVzAO5/6pha5dHmb0IM2xBRSFRGLpIEiPn8duFiN8VpkMQiWDDyBCW0NWiSpxmhH6Io6q4lpCxRDUF93cy2wiihadEddyiLgun5nMiPRXJrr0FZiDzwu00uDmLh7OoYFZIvxkndcZvhiTCs3My25GlOe9SsNAPcSfhJk4zNbIsENCr3lenZnOn5jNH9IcPjHkgCfaf6PmVekheF8JWTJLYLl5snE+yGiGxCgtWtCHDoH3VFkONduk11MntLjxJ2+vLJ+YybJxP2HgwZngyQJYnQj6EsdgBlXhlHyrLMarbhuppADCvB0Xq6IYkSOqO2SNQpS6iCLtI4ZXW7Is9LOiNx7VhcLZldLhge9+gf9ndyZana/EIvIstybMdEMcQmdP7GBY1OnaOXD9BMjSjOKEsJq1mjLHJSN0CzdBjVuf5Un0fTOf645FNRF8fS8cqEpZlQ03Rqhk6ZQWGXWJ5Co9T5Q7/656Kr6gfWzUeu0F/7Z2/ye3/Rt2E4Onv3ReyPu/JZ365p9Op09tqoimCoFXmxC1CIwxRVU8QdLBF+bqvbNaN7/QrhLVlNNlX2uHjjy/eJKwqEyUKRFVgNC+3OafTNS6yaycHzVXJMlS/uNC3q7ZqwmiqKnd+4t/YJvZhay96RR05fv6DIC8G7rzYsYYGs4zQrxtj7wKvNwmU7c6l1HBqdCnR7dEsSxvR/y8+h5yUoj+eEW1+gzaZGmlbRzIawYppdzsXpu9fZmU1OL2YC4T7u4VT56kVaIGvCsDLYhiiqjFO3yTLhzb64FtnrvQMRb+St/F1U1N0I9M7JZ7v0WFYa//ZQINaT0ylxmLD3YESzWxcU3CRFlgVHPg5iSkkSQY55wfXTW2bnC8b3B/Qry25v5SPJwm1XVmSKEpRqjLZduMwuF6JjGrcpCxEaEQcJo3t9Gh3RdRVV2mqei24HRHZfWcDt6ZTJ2ZS9hyPh3itB6EVIsoxhChCzyHJ0U0dSZFa3K87fvqbZrQvLaFXFXfvkaUa9WxdOu6WEJJVVumvK1fmCydc+R++lDrVMJZhEePOQdsPEeOhQ6AjthQqZUuA+CwmCmD/6db+YF/cHH1g3H7lC/8av+hbsmmA23dnxDo57tActJBnSqGKXGSplXuwcQhVdJfZjrp/cErjCR+4uJeX2dIqiqIwfDLDrFllakKXiYRPBjClIEqYtxhjz6yU3Tyc0e3WGxwOQYDMV0cK9vQ71jiMcQzLRTUiKOOGyJMdumKi69j4Si8Hhi3uYjkmwEbx7p+2ITUKSKMs7ZB68tUfoRtTaAnTz1j5PvyCYbs/9jE+ivjJi27UIvIjevz7D1BXKPKekFN1NVjC/XuIuPZFc0q4RehFXj26QFYmjlw4q6+aMLM5RdWF6mUaJuKtaOmksTCnW080uez1NMlaTDbqp0R61dmIeEDbFwTbEXXoYtr6j/V68c1Uh1ocVYp0R+jGarqEZ6m7mfUciun02ZXmzYnRvQGvYJItzljdLVF2lO26jGtp7YiZZJtyGbOZbdEun2RPch8tHN8RBXHnJNSnyXNhSq8rO6UdW5N3o7+rxLbOLBfvPjxgcCDssb+0hKTK1Vg1ZgqIUOW2SBOvplsnZFKfh0D/sUFYThSxJ6e53dlcREEBoGmUsJxtk26L22eeQLZ35yufcjhi2GoyadSQkEiUn6ksUBmzTGF9O6MU2nznc45v+T//Rh6qvj1yh/6u//QN8+9f9CbI05+CFPZpdsSOHboisyJi2SUlZMdSEMCPyhXNqURT0D7rolcHE1aMb6m2Hg+f30S1hpJBEaUVdVXYWzqqmUuQ586sV68lagGP9BmmScftsQpbmjO8PqXdq71kDqwqqKu82ifcrx26eTrHqJqOTAbIqs55uCTYBnb0WjW4DCYSGHjG7Dbz3WVlXWd5nb1yg27qIgXJMgjDF77bRnu/TWGxRbjdQEWGyLGcz2xL5Ma1+A9Mx2C49nr52tnN2tSpHlyRMMUwdWZcp0rsoKZU0TLh6fIO/Cdl7OKTWqhFsA2GwURd89zuxkfDSU4iDuIqLNjAck3Ab8vQuHPOTIuctDsVY0bR1rCqvTpJllCrmenaxYDN3GRx2qbUdAjeqTCBM9h4MKyddYYklKyJUMXBDDEsEewSeUKDFQczDT9+n0RMGkqEX7pD0PCveGzFW5KbV7Zrx/eFOsba8WaFb+g+brYuCFVFTy8kau27R7NZIEhEWkiU5ew9H1XMhCDtKperz1j6SrAjdfVkwOV+wiCTGn31If78NqkxUFHiHOrqusG7lpEqOYahoKMzdgHfdBT/4Db8ZR/9JNkcH+GX9X8/hi2Osmk0URASbEN3ScVr2bq4sy+JCGXkRm7mLpqvUe3UoSmbnC25OJyIP+0Qg88KwMa9acuFpLvLLhbhifrUk9mLaY5GO6m98zt++QpZlTl45FLLVOK1OJOEak2cZSBKaplIirgWLK4FEtwbiNJmeL4j8iNG9wS5cIY1S8aBr73PEsY335KWPb6k1bWGbpSlik3BDup+4T2PcpdAU8umKvKYgb2OCmyVZlFJrOai6MIo8ff2cRrfO0UsH6IZG4IbEYYLTtHfBBrIiCd/zOGF6sRDXgsM+hiU2yZsnE5r9OvsPRshVjniRl8J+Os1IYhFNpeoq/loEFJalwFOcunCz8ZY+tU6N2t1svSqcIi9Y3a4JvYj2sIHhmLgLj4u3r3BaDkcv7QsLq1i48aq6KLw0SndecsE24skXnlHkJfc+cbjbJPy1j1U3cVrv4yFUqTDTixne0qd30BWzci/i5tkU0zHEplLZZolrlELkhQTbEN02doKZu07l5JVDGl3RqSSRuDZKqkIaiU7RqCYJV4+umV0u2X9uzPDlY0rbxi9AsnSMtom3r5EYJVJTobBg4QdcTLfYDY3f/h/8dH7pyy9/qPr6yBX63/yT38vf/e5/TOSHNDp14QkHP0wWGnoRkRdhOgZ23SIKE67evma7dN/TMmc526VHWVTuIrvWT9zt8iRjs3Ap8oJGt7ZL/zh9/Vygpy/tY1gGkRcRBRGmbWLWhYX0HUpc5MKPzVt5NLp1aq0aoSc847M04+jlAxod8TDEXoyiK7sTBsrKIRVWE4FmNzr1Hcd+ei7umeMHQ3FVUDSyLEfpd0nHNq6poAY5taWPfDljfrXg6tENnVGb4b2+sEiaueRZRrPfEADWnbNrxTNYz7YUeUmzJ+KEZ5cLbp7c0j/ssfdgVLW7EUVWYFYmE3ku5s+qouCtfa6f3CLJwslVM1XWMxd34dLd69DsNZAkdg5AkiThrXySOKPWstENjcXtiou3rmh0hVhENzXCO7GIY+7AOlUTvIrADbh694Y8L9h/boxZ8frXM5dmv0570ASknU11kZdsFlsiL6LZa4hgxaVQKloNi5OXD6srS0KRi4iru7x4zdTQLWPnnS/UgEc0uw3BW3BDTLtSyVU4jWaa5HnO1btXrCZrxlUcVpoVbAsNebuhfdBD6TXJujVKWydtKSyIeNx0sTSVoV0TBKkk5u/86l/7oerrI1Xo/sbnN3/lt9AZt3CaInEjq2alsiKTBDFZVmDawt8scIW3eZ7mHL64j9Oyif2I+fUK3dTp73eEfVBRiNGTIpNnBaEXIssKdt2o7IPm3D6d0N1vi3ZbEdG/cRjT6DWEO2p5pxoTANR24Vb+53URLbzwePKFUwxL516VXhr7Md42wHLM6msAiDtfUZRsphu2S7FJiLSVhMt3rsjzguOXDqi3a2SZmPGrhoZm6GSdOv4nBmi2guyXrJ+tWX3PP6Mzaol45yxncbUU3noV6FZUP78ADnOCbYRcBRsUZcmkMpkY3RtUlk3grn1AGFGomkpeFO9zpI2YXy6QFZnenrCXnl8u2C49hsc9OuMOUJLEwtdNUUVOW16UWJawnF5cLrh8dEur3xAce03BX/mkaUazU0et7L3kCnSL/JjZ5YKyLOmOO2hV97Kabujvdejud4SIJhWMRkkWcVV31tuqrrK8WXH6xjnNboOjV0S3E/kxaRW+KVUusqqm7qy7T1+/IM9yjiqxVLCtcJaW8//h7k+jbVv3uyz06XXvo67HrOcqd3mK5CSQBgQDoYFUNoIXBIkXpL5AkEIFFMPFCESRqIBUGiQUYrxyaQ1tGi8iQVGSAAnJObtae69q1qOuR6+L++H/jrH2QcPe2kij7cxv55xVnDXneHt/3//7+z0P1VYZcnW3bshQdjpYsZptaPVrVOoegdo1lCoe/UcH2AoxrTkuNCtsbJ1BU8OqeZj3bUIr48V8TropuNdq8Nt++jfwcx49/MT19Zla6EVR8J//nr/C869IMkk3dIEPxpJW213frOcb7p6PsGyDg3s9TNtgNd0wuZpS69bon3UwbIE+pkm2P3MlUYJhmdiu6mA/HzIfLlXopb5HEOW5gAlL1RJFkYtpRXW//bUqhVQ9NYFdqC1niePHgmtazzeEm/DVtrV49YYpcinWRH5EpVnGK7us5mtefuUK3dR58IV7lGueMMvXcn3nlhwKNDA1/J92wuqtEnf5mui9mG61zOnfH5C+nHPzVNFVXlN0lTTbk1011aLbDaOyJOXm2YD5ndxMtI/apEnKYrhEN3VaBw1FtX11Vo22EcvJCtMxqberpLEq80zXnL5xLIlD1fjTdR2ntNsKg+WaCis1Z/BiSLPfoHvapshhNpiRZzmd47a6Osv3YIs4TFSZCXWdKIWWyc2Uo4cHdBX9Zlf+sV2LOEoo8hzLtcWuezvj6skN9W6do0cHmJbBerohTTNqrfL+ZaChSYNuIwPcIpc0pu3I3GQ5XtM8qAvGS9PESKtpFMBqsiLcxsp+47CcKpNuxeHe22fYnkUSZ8qCa5JhEPohdsnBfnDIpmTxA6cr1knEa+cdunqZkm7x7/68n0O7XPrE9fWZWuijyzH/wa/+Tyko9j1kTZNvfp7LW3QxXFKul+TMm+YML0csRsI2bytwQBhE5GmB48nWOMtyTDV026mJg23AwT2ZwvubgNunw1cAyqoAG4VKou9DGpqu4XrOPhk3upxQ71bFUFLIFjzyY2mONcrkFORJrkpO8obJ0gyv6mHZJvPhksv3rylVPU7eOMJxbdaLLeFWrufKtdJ+F6CZJrmh8dE/d8DsOKHllShjE78zZ/PHfgCjSHn4hXt4VZc4ECCG7dlqKypDR8sSB/ztsxHrmRBddqz1wYsRbsWlf95VeuqMLM33dJZgHWB5Nl7ZJQ4iXr53zXax5f7nz2h066RxynqxEbNqraSy53K3D9JBn95KP7/RrRFHCcMXYwoEvlmqlyjSjFR58vIsw1+HwomremRpxvWHQr/5+HVfsA7QdMWSU+Ufy7IoNJgP5wxfTqi1q7SPm1BozIdzkjilc9KiVPHUg0x52sKE+WhBAdS7dXRNY3wzYXI94+B+j+5JW8kxE4pCUxFnuW1xqw6mLYjpF1+5oNaucfbGEU7JIQoikijDKdvygIgzzJKDZeoEQcxHtRphmFL/RQeUex5hmrJKImqOy3f9/J/Pca32ievrM7XQtyufP/Fbv5vlZKlInTpFlrOYLIm2MdVWBa8q1dLLD66Jg4Szt05o9uokSUqwCtF0gQwahsQUdUNYbNE2ZDZYkOc5rcMmpmWwHK+4/vCOarvCyWOZzEdhTBLIZN5SU2YUiSXPcsZXMxYTZR5t14mjmLvnI/JMJrCVhrSb4kiGMlLTVP44T4kUbufcvRhSa1Xo3eth6Dqz4Zw4SGgdNKQaS7GnwFBIpTJLc9Jf9jbZsc2t4/P8+ZyG5/HT/84KLy7YzlaEgURPdxhldDANkQ2Or6ZEQUznuIVbdtTNxIBKs7xXSSVRQpYog2n2j6GvNxEv370k3EZC0GlVCTYhy/Fy3+vXdZ0sz9HRKDRYTzds5gKhKNWUaef9G3RDk0JQzVN5cxm0GbqQYExDx3IlSnytJJanbx7T6jVI4kSGsI5Fo1NF0/U9TRY01tM1s7u5lH8+dnuSpTnHj/qU6zIl3x3n8izHXwWgF0LCyXNun40YXY45eXxA77yHBoTbCDRwSjJnybN8j6qaDeShXWkKHWenzUqihHKjLLJOlcnXdX2f+IyDmP4//3Xob3a4Pk+ZbH3qjsshJbr1Cr/3538Tddf9xPX1mVroAH/7r/5d/tZf+V/J0hx/5ZPlym1umiwnS16+c4XjOZy+eSyDuG3IarrB9Wxq6oNWFK/gkVEgphXLMak0KhSFVBeHL8f0ztocPpCh03bpk8YJ5XoZW4Ukdv7yNBHNb7SJaB02cCsum/mWF1+5wCk5+w9sHCZE20hwz64IFjVdJvM71/jsbk6zV5dKa5Ixuhir67ueSozlpHG2H/hFfgw7CIdpMJkGDC4nbP71r+W428BZJVjf+xFJklBzDLyKQDZ2mexUDR3TNKPermFaAmu4enJD67DJyeNDTFsMpplyr78agBnKbhpw8+GAJEk5fnQgacHZltndnFq3SudIWOtZKjl9Tdf3XIByrSSx0umGl1+5xLQNHnzxHK/iEfuRSDJKjggVMjkimSpOe/t0wGa+4ejRIZVmmWAdMr6cUK57dM86ImLMs/1NzHYZsFlsKNdKqkYs+fU8zdV1X1nKT2GEaVvoptR3peKqjjMfDRhfTzh944S2Ispu5lsMU6ei4BW5QolJxFmot6W6R+ekg6ZSgHGk7K5VgW3uOv1pnDK9m6lkYgPLMpkFKZNKjeJndegd1NEcjWKT06uU+Y5f+fMw9H9GuOf/O1+fdqH/zb/4d/gfvvtvKTqouMcmt3OGL0fU2lV6Z4JPXo5XbBc+tU51r+fNsvxj960R4TaWNpdnE4cxl09uWE82nLx+RLNfl2ue0QpN02j06jiupZxjqqKZShy2yAsqzQqmqTMbLLh8/3o/JXZcG3/tEwYJpaqL7dmqB6/tr3XmgwWbhU+jX6NcK+GvA67U9d352yeU6yXSOCPyI5loO+Z+sduOPHSmNzPGN1M6D46pfuPrRA2bl/qWPMp48wcHlGslsigWc4kaGPqrADT2PrrBizG3zwb0zjscPeijobFZ+uRZtldJ5SqWKvmEgMnNHIqCznEL3TSY3s2Z3szoHLfonSlsdZRI79wyiUO5EnMrrkg0xitevnu1Txc6JWnwhb6IHL2KglyqB1sap4yvp0S+7Dx27vS750Ma3TqHD/v7uU2R5ximlGDiIMEt2ziey3a55aN/9BxdN7j3udN9ujDcypnYK6lOgnoIp0nK3XMFqLwvgMo4iJgNJCS0s/DszuQ7E81itMQtOdS7InocvhwT+THHjw/2mYv9riEv2C6kn1Gul9A1jdHNlMHzEd1f8o30HrTJ6za+kRLWwbJM6qbDH/3WXyi7uk/4+swt9D/3b/4lLt+/IU1SxldCXGkfNam3ayRJuo839u91qXfk7BKpQcx+65ll2I6NaZv4qy1XT25JopTT149VRTNgfDXFLTv0Tjv7UEaa5ooRL31o3ZA3ZFHIAOju+YjOcYv+WUcY5tM1aZxS61TxKh4UxSs9cZazmm2Io5haq7oP8bz8yiVO2eH+587wKi7hNiLYRrglG7fsfmwuIUeP2e2c1XRN67Ap2OpNyPXFmOSbv0jvi12atyvMD4Yk4auhWxLK0M32nD0gYnI95eB+n/aRAB9mgzm6rtPsN3E8ZTBV+OY4jFlNN+imTk1RagYvx4yvJhw/PqJ7JgKMYBOiado+p1/kOZZtoRk6y8mK26eDvc1G14VSG24j2odNKs2yst3uHqoZq9maNE2pNasYlslUsQNahw2OHx2K6TaIBcX1cZacY2HaJuv5lsv3rkEBPr2qVHr9pU+5WaLSqAiCX1WIdx777dJXyTaZql9/eEe5Xtrrs9N9SEjY+dtVIM75qvLSf3BDuI249/aJ6jBIIs8wDUzbIApi+T6p+c7gxZDBizH98y4H97sUhx2CSpmwZmL2HJK6Rlrk/NyHD/nWn/XPGPf8f/Xr07/Rv5/v/aN/gziIaR82ccoO26XPxbtXmJbJ+dsnVBpl0lgAC7qh7VnvuXqraArVNL6aoFs63eP2XmgwupzQ6Nc5OJdpfRIJj82yTNCk1GFahih/1WR+NlxwcK+3nwVMb2bkRUH/tINX8RTAIN/VsPHXAUUB5ZqHputMbmZcfXBN86DB0cMDTNtks9gSbiJq7YpCTUGxq0CmOcvxgihIqHequCVXapdfuaBU9Xjw9a/hnHXwtZx85GMtlorsmu0TYGmSMng+VFdeXWptIbsOLyZYjsnBfVE+Z0lOmqb7Wm+4DjBtQ3RNUSIDsL1TXDLzm6WPYRhUmiV2GihdnY838w2TmxluxaXVb5DnGaOLKaEfcfigR60tRZw0Sfdvx2ATkueCtwIYXowYPB/TPW1xeL8vyuuV4K1KtdJe2LGLtm5XPnfPh2iGpn6u8rNezzbykujW0TT2Xfk8z1lP5eaj1qniuDbz8ZLL926otsqcv3mqwjMxWZJhOiZ5Wkhv3bVkF7cJeP5jF0RBzMMv3lO7gBh/5cuuoeKSp/LQNi3Bgw8ux0yupvTOOjQPmxRZwXKyRC+VaDw6RG+ViWomWadE4RgkUcZ3/ZZ/4ScfYQbgX//ZfwBdlx/g5G7OzRMJU5yobXK4DfE3AW5J7qYBSTIZOgUam8WG1WSDW3KodarkWcbw5Vgpkvv0z6R9FfkRWapUwrszqWlgWDpRkDJ8OcLf+Bzc61OqCmbq+kOBTpy8fiQLPE1lcGQaCkcsV4K2K9dSoytVLDlo0j5uUqAxu52RpRmto5ZMp/N8jyQWppuom8v1EoZpML2bcfneNY1uXaqerkmwEftnuVnF7TXJpnPVXjNIk0SZUYI922xHdnWrLudvHGOXbNJIbhUEMsH+6tHxbHlLvX/NduFz8uYxjXZNaaMFsdQ6bGAocGOhLC/+OmA1XeOUXWrNsmQCPpTs+T5FlqZEQYJhCBAkUdVcW+0IRpdjhhcT0WYdNSkKYRQANHt1VaWVH/mOpz+7m6MbOs1+Q3ZeV0IGPnzQp3XQUPTgZO/nC7chWZbjVVwMQ2dyO+Pi3StaB035/joyr0jjDK/qflU/X3aIAVcfXJPGGadviCLLX/usptJbr3eqgAY7JkBeMLubs5ysaPTq8sANZDBqmDrHX3oNxzZI/YgiTaBVYdtvMHDEaPtbf+k38s994cEnrpvP3EL/vv/if+av/uG/zuRmJnVBNeBYzTbEYUKtU6FUEdPqjmpaFLBdbAn8kHKtJJP5lc/lExEb3vvcGc1+nTTJ2MzF8y3gQ5Nc8cl0BRScD+V6pdmry+BqsOTqyQ2Nbk3UUJ5NtA2JFebKUOgjw9AxTVOOFwry2DlpS0U1jBk8HwFw+PCASqNEluRqcelomkxhgb3iaXw1ZfBiROuwQU95xxbjJVmSUe/VJeySF6o9JsCK5WStVNNVJSecc/XBDbVujTOVOgs3KiBSkdhtmuZYtoFuSnb95qMBkR9x/PgQryLFmtHVhGqzTO+su0+OySxEiiLhJsStiMPNX/k8+/JLiqzgwRdEcxwHMZuVj+s5uFVXKsGqey83GWPmQ3VE69aIg5jR1QTTMunf66obBOG77yqmm/kW0zIo18skScrd0zsW4zVnb8jOI89zwm20f/CmSQZ5geVaaJrO5GbK7bOB8uj10HdHsTSj0a6qh48MBzVdI9pGjFQ/v3vWwXKE1jsbzFVYqYVuaK+MsshnNtyGr8hAK6HdmJbJ+edOqRx0yC2bZOtjFDnZxsffhFjNGsU33KPVKPMHfse/8KnW12dqoed5zi9t/qscPjqg1ioThwmT6ykA/fMuXsWTqXQixk8UlTTPC0nLWZaET965wjANeeKqjPJitMQtO7T6DRmSqR67oWtEofDHDEunUhcv1uhiwuDFiN55m8N7fTTDwF/5pLGAGnZixkJ1lVO1pY/8iOZhE08VS64+uMV2Te69farspYkyrVoKRaTqpa61j77OBgtah03hsSUpk6speZ5zcK+HV/MoslffgwJpj2kalGqeJP0uxtw+HdA/63LwoIeu62wWW7I0o9IsY6sqq6aBYRiEQcj0ek6W53SOWhi2wfxuwfBiTPOgIZJJNWjL1Tk8jRKSNMNxbZV33/Ly3SuAPXJ7uw7YLnzKNY9Ko7yLu6OpK635UIaUzX4dr+rJPOWDW9ySw8kbR3hljyxJ9+70PC+ItiGmQnjHYcLl+zespivO3z59xa9bCJyz3CgDBUUukAqKgsVowehqRr1dpXnYIM8KJjdT8lSFpOriditySRImcSqE2kKiwpqmqQrwjN6ZkI4EmiE4btMyCbdilHUrrsKNrXjxlStM2+DhF+/hVVziMCHJIfnm16hebcimW7S17IjCqscTT6P4vif82e//dzh51P/E9fWZWugA//V/+Df423/173L3fEilUeb40aFKWCUkoaB/dAXbR9NwXAs0jflwwehijFcV2IGhWmPLyYpmX9JMuiFa5CLP94KIKIiwHRun7OxVSvPRkuPXjmgfNsiTjPl4BRSKjiq5ZnJ1XaJUS0VRUGmWMUxZJBfvX9HoNfaCyGATEgcRXtVTW/tctbgMsjRlcj1js/TpHDUp1T38Vcj1kxtM21T1Uk95xHYOc122voaEeLI05+7FkNndnO5pW1xxWc78bg4awsD3nL1OSVd0mM18CxpUWxUoCkaXUwYvhnvuuaZJ/DTPc9yKHJXyNMdUu4DtfMvNRwM0U+Po/isu/Wa2odGrCSILyNSDBVDxYhlS7n79y69c4FYcHnzhHk7JIfQj0jjFcW05FsUplmlgOhZRGHH53g3+yufszROqzTLhVqbkpapLU4EkUNN8gOVsw3qyotIoU2mWiYOY26dDsizn9A3RYWdpRpbIAocCf61yGVWZwwyeDxleTKSgogSSwVYBOEoOWVaQZxmWY2JYJov5lqt3rnBci+PXjnA9i83SF9Ntw0P72nPi0xaV2xB74RNd3nL7bMR2seX4iw/5Gb/k6/mXf+cv+FTr6zO10Cc3U371o9+umHBdTMsg3EbEsTSlLNuSmqNKy2Vpxny4ZDlZU+tUqbcqZGnO4OUIf+Vz8LBPq9eg0CAJEvIix3YVpD/PsB0L0zQJtiHXT24Jg2gfH/U3IcOXr9JiUkbJ1LDO3E/mDcNQBpSCwUv5IHRP2/ROJUW1mm1IE9lOeyVbWKXFq4fEarImiRNqbYW8Gq24eO+Kcr3EvbdP9371MIj3bbEszeSoYJnEcbJnnPfOOvsP/d3LEZZlcvhQdM1Zmn0sICKQDdMycKsuWZJz/eSG2WDJ0aM+3ZMORZ6zXmzRkIeAruqXO2DEdh0wuZlhWobk3YHx9YzVdE3/XpfWgfx3u/OxYRqyeNOMkrp6mw3mvHz3mnq7quYPlsI+xZQbZbnu3IVMVEX17sWIJEzon3dwS0L/GV5OaPRq9E7b6sEppJ6dGjncRrhlV2qtK5/nXxYH4IMv3qPaFF1VFAgI07QMRewBx3NIk4ybpwLgOHp4IAPJLGe72KBpOtVOFUPBR3ZhmPVsw/BqivPwTK4gbYPFNiL4B+/T7NfFgZfnRG8eYNTqaPOQ1csp6/ee0elW5d+19Bm8GPH/Hf15NO0n4fXan/5df4EXX7mUD1lRUGlURCu8u2vVddIsYzlaEfohjW4dt6Qm8+9fk6UZDz5/LuzxJMVfhRiGjlt12LWadNPA0DQ2q4DJ9QRN0+mctDAtU7Z2l1Nq7SqHD/vYtkUUxWSxWErQIIkUVNARssvgxYjFaEn/vEejJxnw6e0MNE1UR2U5T+cK3p8XBdulr1TB3v7MePXkhtZBk2MVYtkut8RhQll5zgu1C9AN0SVPbqdE24j2kdw3r2cbLj+4plTzOH9LHhJJlAjZ1TGFlR4n+1uFOEq4/uBWsuoKMhFHCdO7ObZjvZIq5rtsgeSzV9MNtmvJIokzbp/e7fPu7UNh+gVb+b5brkWeiLLYdm3QCmZ3C+6eD6m2KnL1pklFN1V4q1JFudM10NBIE2EApkm6l1zOBgsGL8e0jxoc3u8raq+CkqjblERVaS3VP3j+5Qs0XeP+584o10qEfoS/DvCq3h6JhQaWJTcQw4sxi/GK/lmXartCEsr3xnJMOsetPc1HQkLgr0MWwyWWbdA8aJIbJmNsNkXBwWZJo1WR4WuagaaheS6rkkexjmVmlKZM333B5QfXVNtlfuN3/iv83F/9sz7V+vpMLfSnP/qCf/eXfReOa+2pIrv7WU1RTbfLACgo1SXmOr0TGGC1Uebk9WPcilRLN/MNbkXhnjSUEEIcyf46YDXZ4HgW1VaVLMsYXUyY3s7p3+uqIIhGuInIMvGNS/JL1FCmYRCHiewcNj6H9/p4VZfNMuDu2QDbszl97RC3LFir3UR7VyzRdB23JPfAu/vp3lmHzvGOWrIgS3PaRw3ckkdeZPujQpZmrKZrigI5KhivQjzNgyanrx9h2rtzorSyDFN2ILqlY5pyr3v3bEiwDTm836dUc9ksfYYvJ5RqLof3dwjtdD90S8JEUmye1DIjP+Lle1cEq4Dzt09o9BukUaJQ3RalqseOprjTYC1GIp6otioyHFXgiSzJ6J93Ke90zkkq8ecCNsvt3p2uoZqGL0b0zjuiVFIsuSIXlhwaciVmGximwWa+5eqDGwzT5PjxAXbJYTPfSNiqXaHaquyhoDt09PRuLqCQwwZe1WO72HKjMgEnrx0JXiqVCK1hSrx4u5S7da/iEkcS213PN9z7wn2aRy2KMNqLJkxbPj+a40oorOwwnwdcvntF/f4Bx6dNzDCgrMO/81//7k+1vj5TCz2OEr7r1/1ptmrolapvZPEx1a9TcsnzjPHFmNHVlPZhS5DDukylg41QVqqtqtxyZBkglhR/ExD50V6RHAXx/hrp9M1jWn0xf6wXMpmvtsQDLoMrbQ8LnA/m5FkhgEpVTrl6cku9W1VncpsoiIlD2W4bpqF45TqWZRHHKaOXY9bz9d4ZFgYRo4sJhmnQv9+Te9gsI0sEEpHnBeE2QtM00QEVyFHh5ViOOvd6+2ZfnmZUW5V9K+vjtpbZYEGSpLQPmnuaze2zAfVuneNHcsefhIl0sx15QCRxInl3dfX08itXpEkqZ9tmhXATshiv8KoujZ6UQUSnpfju0w2r2YaqCq2Efsj1h3dkScbZW8dSx00ykijGNEx0UyMOpLG4I+sOXowYXU325ZIiz1nPNqAUXeYe9W2ot2vA6GKCaRm0j+XzMRvMWc+2dE/bNHo1NFRsVxOQyWa2JYkSKs0ylmOxGC55+d4V1WaZ87dPcVybOEok/69SlEmYYNomjmsR+jHPv3yxD8/Ue3XSKMFfh0qA4VLkiHXHstBNg/ky5L3XmtTrJVrnVXRbZzldUf/e92n26vymP/qtPP7ST8LrtR/47/4h/5//8G9geTZaUexB+6ZyeA0vRqynG3rnXWGyRwnjyylpknJwv6cSV8W+l2yoPHOq7sxN22Az97l475Isy7n31ilerUS4CZgPd/fEzf08oFB02CRKWC98TMugUi+R5wXDC+lx9+91OLgn3rLNakOWyD24ZVv7euou3il33KFUMiuSGb/58FYJEk9EpKCkAqZj7sWSu0hwqo4K88GC7llHySgypnczQI4Krtrm56o/n6UZ6+kKTZe8NhrKfzakd9bm6OGh6nzLHXOpIgTdPM0xbHNP6bn9aEBe5Bw+OMBxrT0MstGr75uD2X5QqbFdhURBSKni4ZZcNgvp7JuWyb3PnVFplIlDlQz0bOkYKOyTqbBPg+dD5qMl/XNJQiax3G6YtknnpI3tmEJ3LSRsFGwjVpMVhm1Iqi/LuX0xYjPbcPToQO7WkZfKzg0X+QKdcMo2pmlIwOnJLbV2Rd2tyzA1iRJKNQ/TFPSYYRpSa92GXH9wS+hHnLx2JP6AbcRytMSrejR6uw6G3P9TwGq6Yno7o/7mQ7w3eqxOS1ynG9Is56hd5eR/vaGimfzGf/9bhaT7CV+fuYX+X/z+/4oPf+TZPnK4a/pMb+dCcD1s4ng2q9mWqw+usV17f3UlablIzqCuRRZLUsy0hLG+mm0YvBjheDa9sw6mZTAfr5jfLaj36nRPWvuzXpHnGJYuGfQwxnFtycwreOJ8uOTo0SHtA+mxL8YqM99vyBBpBzLUdNI03UsEa60qhiqWXD+5pd6tc/KauMD9raifnJIrZ+M0k3mC2hEML8ZsFlv6511KdY9A8dVsxxLaStkhSzKSRJJuFAXBVraLbllCKTcf3TG+nnL8WKqeRV7ImxGotasCjMxkB7ODXo5v5xiGdNR1XWSQs8FCDR07Ur4JYsn32waJerA6nly9raYrLt693t8guGWH7con3ISU65J3l28W6qpS4BmbhWCfdrmI22dD3LLD8aMD6dSnqex4lG89UG9Or+KSRAlXH9ywnm1EHXUgVNp4G6Op78duaLdTaM8Hc26fqdnBuaL0TFekifgB9ux/NSsS9v2UOIwlSu3KLGB0NaXeqdA76+0j1UWOevj5CnflUamXCKOMu5slaZrR/5avp9x0SUnQf/iaRsnld//Z37i34f6Tvj5zC/2//EN/jXf+9ydoGkRBxHq+xbRMqs2yIsPOuHs6oHXY5OjhgapPihnEKbt4JUee8IrkUuQFy6loiMv1ErV2VaGPJ/sJcedI6CRRGFNkhWzL8kJy1LaF6ZiEW/GcBduA49eO5IekIqW2Z3Fwr7efzGf7TrWCWpq6cMUz2QWI3LBD/14HTU1psySj1qqoDHeBpst9cxanzO6kDdXsN5QwYM3VBzdUGmVO3zxWpR01dLOV2kgtdsn/pyInnG05etin3qkRhzHT2zmWbdI6aSmDqRhf5YgirT/DNKm2ymRJxt2LEbPBQh4Sxy31IBEssquuHXf37LqusZ5tuH56h+PZHNzvYlqisNqufJq9OtV2FSjIpV8imuPJWq7e2lVsz2Y5XnL5/i3lhhowujZxnJLG8m/VNJFfmLYpgSA/4uU7V/irgHufEwJtHCSspiscz6HarkocNhenHRosx2umtzPK9bIASJT8MY0Tevf6goXexZzVrGg52WHIquhKl333fEjnuMXRwwPFBBSfvWXJbjQKYtE6eTb+yufpj76EAh79zM9TPm7h92tEfkL55QgzkWPf7/7PfrNi3f+Tvz5zC/17/sD38uX/9X3i3TelJG/Rqye3rMYrjh7JFUeRw2q2JkvzPdVDMRrUDzJnPd8SbkOqzYqQVJUiOY1Szt8+pd6pkaXZV8EL2BFm1V11sA4YXU0pKCQz75gsRysGL0fUOzV52DivWluWbQLa3gNnq0717dOBqIXvdWn0GlKouJuiawad46ZM1dVkfjd02y625EVBtSFm1/HNlOsnt3SOWzKZV7gjwSV5klpTKT3dNEjCmMHLMeE2onfWoVTz2C7EMV6ulzh82MfxbNIk28c8szQj3EgoZRfsuHr/huVkpd6MTVIVSjFMQ6yzGvsdmKZprBdbJldT3IpD66BJXuRMrkSNdahAmTtMmPYx7FOeiTtd13WmCvu0u4WwHGsfRBHegMApdq56sehIqu9IqaP8tc/0bk6lUaZ93MTQDbKsQOrrYvtZz31KVZeysgJdf3hLnhacvnlMtVkmy3LSKN2f/cNNSMFuTlIweDlSx7cuh/d7QuLxI4nZlhzQEMqRY2GZBpvlhpfvXFMUBWdvnsj0P80JMg39S+fUlyH6YkseJ7z2hRN+3R/+VZ9qff1E+tEbwHcDn5Plxa8riuIHfrxf/2kX+r/3K76L7ULc5P4q4PrDW+Iw4eS1Q/lh+BHj6wmmbdE9aastabG/X0eTu9M8y4QVZhp7HZPlWnImr+x67Gvciiu+dU3OTgXyoAi2IeupwA2qzQp5gQwAlcnj4J76oW7F3rHPzCuajW4KYXXwYsx2tRWKSrVEsA64fT7E8WyOHvZxy65qzskdN0VB5EeyvSzJQE768yMO7onUAGA1XZNlOY1ObU/j2VU94zBhMZLtYKtXx7BM5sMFNx/d0ejV95nuKJDShu2YFGikcYrpmNjqTHr5wTXBOvzHQikLSRgeNvdoY5BrMH8TsJpu8CoulWaZJEy4eiLNrtM3jhWuKycMIkxDsuNJvENA2WgajK+lJdjsN8SdrswvWZJS79Yl9KPmHpqmicbpZibUmOMWpi0/7/HllEa/wcG9rqClk2zfIw/9iGgb4VZdHM9muwp48eUL8rzg4RdEspGEqTDhbfl+pOkrQUaWZgwuxowuxvTu9eget0S8MVujoVFvVyTeu79bF4/e4PmQoijo3+vuxSTLyZpmr0bjuEPerqLPt+gIvTZarvljf/sPftr1+BO20P8i8HeLovhuTdNsoFQUxeLH+/Wflhn3l7/jv+GH/+aP7cMYzUO5y12Ml4wvp1SbZZEUupKWS6NUPGcK8aPrOo4rnfDJ7YzBi1c99h0+ebPY0ujVJbWlvbKf6qooEfgxrie10SiIuflQ7pp3hNk8FW43iELolVxApttJGDMbzMmyXLbbtqmUQQOqrQonr0taLgpi0jjFckwppEQJuikT3DROuXk+YDFaCn+8WyOJJEev67qYZRV9VHYBaug29zEMnXJ91wQbc/tsl3STh5O/9smzQoodpnozGq8GS4PnIxlu3uvhlBxW07U6d1bpn3f2TTfZ+aiF40v33y05+OuAF+9cEgcJD75wTr1TJQpjglWI5VqUKs6+rSfho4L5cMH4ekqjW6Nx0CDPMmY3c7I0p3PaoVL3yJVxRdN0sjRlNdtCnu9d9qPLKcMLuYU4fCB361Jrlahuloq4w3IsLMdiM9/u79bFbCs66+3Sp1STt3yWvWLJ5WnG+GbGfLigc9ySoo7qz+uGQe+sjePZEo3Oi71XfjZcoCvmAVrB9GbOfLigd9al83GopSombZdbom3Er/6Dv5yf8S3f8GnX4z/9ha5pWh34UeBB8Sn/kE+z0KMg4rf91N+H49qUm2XSJGX0csL0bsbhgwN6px0ZEG0jsiTDLdl7uIOmkmJZmjG+mbCebGkd1Kl1qiRRKs4yP5KtY7tGQSHgQtjfhWaJaHcsxxTk0btXxFHC6RtHwlbzIzG8ejadoyaGZSqQoTjXklj85royfaDA/XfPh5KZV5VLfx2QJSmlagnTNsiVSXSXmR5ejgk3Eb3T9r5Tff3hLaVaiePHh8KVT7K9DbTIpUIp4EeXPE25+eiOye2co4cHdI5b+/MvQP2r8NcyWAqDmMVwQQ60eg05d97NGL4Y0zntcHhfpIpRKINK07bIkowsTSVh6Ag66eU7V6RpyoPPnePVBPu1nm0o1Txq7eoekKnpmmqnrVjP1gqXXSb0I24/uqMAub6rlUlT9W81hLQS7uYCCiR592LE6GLC4YMevVNxvW1XAvEs18tyg5BnGKapaEJbrj+6w7JMEVO4lroCXNPsN6i3pYGWF1JqyfOCxXCBvwqodWqUaxJRvvrwBtuVzISnuHa7+UymFFe6oe8LSHfPhkzv5hw97O8n6XGYACh7TUYaJdhKGJkmKf/B3/z2f3bJOE3Tvgb4z4D3gC8CPwz8jqIotv/Yr/tNwG8CODs7+7qLi4t/4p+b5zl/4ff/V3zl777Hi3evSMKUszePaXRr0jxTabJKqyxXV/vtqkEaJ8wHS+Iopt6tiydtISACeWKfUm1WSJKUaBthmLo0lD5uP0Vns1gzeDnGMA16Z10sx2Q1XUvQo12ld9rex1DTJMe05Dwd+a8c2zvO2WK04ujhDvZQsJysgYJ6t4at7rh3QY00fmWAbXTrGJbOfLTk5sMB9U6Vk9flmicOYpJY2VaUgMIwdUz1vw2eD1lO1xwq11nkS+vKdm26px21zc+UWlkSduv5FtPUKTfKEuJ5LnfWhw/7HJx3KVBn06LYF2fyVM6duq6zXW25fTpAN3QO7vWxXZPlZM1iLD2DZr8hV29ZjiYmSjaLDdE2otKs7L3xz798gWZoPPrifcr1EnEYEwYxtmtjK7zUTuecJRm3z4fMFMW2ddAUiu1oiWmaNFTGYZeo3OXSZ7dzdEOjfdAENMZ3UzZTiQ83DxqANAFfsQVC4iimXCtjOyaLyYoXX7mkVPU4f1vKO3GYkoSx7MxMCcMYpr6/d799OmB2N+f48RHtYwF/+EtfHgJ1of/kaY5hmeimtOhuPrzDtAx+71/67Xzdz/3ip1mTPyEL/euBHwR+RlEUP6Rp2h8HVkVRfPuP93s+zRv95ukdv/Xrfy+WY3H8+GBfe5yPlnvtsGkL0md3b7ozqBqGDNN0Q2d6O+Py/WsRMbxxjFOWtNwOuF+qlig0qRLu7jU3iy3z4QKv4qlhUc7wcsJ8sKB/3qF32kUzhEGXZzm2u6tOyj2/ZZsiQ/zwjmAdcPz4UL2hQibXM2zHoqtoNrvfJxNc6aDvnvzAnmbTPW1xcL+PYepsVzJ0K9dEcZyrN8dONzy8GBP5Eb1zqXVu5htJc9U8Th8f7dHLWSIsuCzP5eFkSwc9iVIun1yzGkuUtXnQkCz+dI1hGNS7Vemgq2qwpuv4Kxl2mZZJ67CxT66tJmt6513aR4JFTsOEQpMGWexHQk2tuHKUGq/2+qnzN45FTLkJiP0Yr+Luzamarh7oacr4YsJqtqZ72qHaqBBsA0aXExzPpn+vJ9t0daSRHELMerbFdEwq9ZIARVRs+ejxAd2jNjkFsR+h6Tq2igfLz1lKNYvxSoSbVY+T149wPUGIRaGotHZmFl3XMCyZPdw9G7KarsXUo3aW88ECSznsTLUjRJNA12blM7meyU3IYYPT14/5XX/uN3/aNfkTstAPgB8siuKe+s8/E/h9RVH8oh/v93zaYdwf+/V/WgVC5qymG1oHDVpHrT2kMUvlLZqnOVGYYCk5YJbmDC5GjC8ncvY5abHzkyehkFpK9bKcDXOhwQiJZkOwiSjVvP2Q7vrDWzYLn7O3TmgfCCAw2ASARqkq9pCvuuf3Q0aXE9I0p3ci0ojldMXwxZhqq8LhgwPpRKtrIdO2KCiIgwTTFM56EqfcPR/uRYPtwyZZVrAcLykKUftaavbwcfXvcryiUBVKwxQP+vWHt7SPWhw/lqRb5MekSYrjOqCzX+yWI9dR109u8dcBJ7trwyBmcj3Frbh0j1pYJYdc06FcAt8nWvkyqLRNKs0SaZRy+0yCLedvntBSuKrIj1+BJVSH3VK7gOVkxfVHd3gVd6+zXk3WBNuIeremePgylBLjSsZ8uCDwQ5r9Jq5ns55tuXpyg1d1OX/rFNuzyKKURP378lwWr2Gb+yTkPtv/xjGtw8YeQmqYQtUBKNTDe/f5uX02xK24HKii1XKyJvQjGv06lZoIMnaLNVVugGAT0D5q4VWEYXf7fESlXuLwQV/x8tWuSuVE9v36RoU8zRlcDFnPtnzPkz++Lwh9wpr8CRvG/V3gNxRF8UTTtD8IlIui+Dd/vF//aRb6j/zPX+E7f9V/Ii2y149odOsUBYS+QBkcx1IY5AzDMrEskyhMuHt+h78K6Z93qLYE6TO6nlDkhbDba6JIThVqaV+9zDLcsjTC1rMtL98RB/r5Wycqqhmxmq6xXUuinTtFsiak09CPWI6XmJZBrV2noGBypQZCJ20OH/T2QIc0yeS6RUEiTMvAsHa00zv8VcDB/d5+uj14OVL3z/09Z3139ZZnOf46QNc1SjUpgAxfjITOct4R9a+hsV1uydOccqMiswAlZTRMaQWOryYkSUr3RLb0q+ma4csx1VZZOujqHJ4XYHgOabtBtAlwcrDjgHC15eK9a7ZLX/LuvQZJlLBd+vsPra5rEsM1DTRECT25nlKqebQOmhR5sU8L9u91qbVER5Rl2e5zprr0KeV6RcFAFly8e0mjWxeNk2cRbmPSOMEpOxiqR25aUmuN/XiPqT55Xa7NkjBmNlrilSW2Kzs7Ke4UwGa+YXYnNwyNXl3EEVdjIj/h4H53n5HPU9ltFHnBer4liVKqLYGazMcLbp7cUWmWxYPniq02TVIsR36mu4CXtxv8fnTHcrzi9I1jfs63/kx+xe/5lk+7Hn/CFvrXINdrNvAc+LVFUcx/vF//ad/ov/8X/RGKolBT2gDDNimrQkPOKy92uImY3s3I84L2YVMFSTZcPbnBrTicvSHnpyRK9hI807ZIooQC9j32xWjJ4MUQp+R87M2yYTldUWtXaR00JK2V5moyLymwcBNgew5exSGJUq4+vGU5WnHy2iHt45Yyau483GXJzKvq5O7PmN7OSOOEzlFbiCVTeXtUm2VOHh9ifdXNgvkqHGIaWJ5NmqRKcbzi8EFP7qzTjPloAWi0DhvqIfFqFpCECYuxcPNr7SqajrKnjGgftzh+dCAKpSglT3O5eitUn/zz93CygvhqxvO/9w5JGHP2xjHleplgEzJXxtHWQWO/69mdjzfzLavZilLVo9qsEocx109uZdj5+rHCfuVqqGhgWYYEmAqUzlq0TzdPBzT7dQ7u9zAtk+1iQ5rmyuUmW/bdlVYcJgxfjgj9SEg1ZY/NYsvkakKlWaF31sa0VNRZIaP9TchmvsUt2aLJCiUJGflSYW6oK8JE/f80LHmQF0WOU3ZF+nA9kdlKt8bxa0fYjpSM0lheLPq++yDzhjiIufzgVnrorx/S6NYVXz/jj//vf/jTrsfPVmDmR7//Hf6T3/KfUW2URbCoK4e42sJF/qu0XKVVFljCleS2OydtCbDY0jGPgwS3ZH8sbSY0mFyRRmaDJbVWhUa3TpbnTK4mrOdb+ve6NPtNdO3VVNS0xeSRJBmOZ+3zz5fvXRP6MSdvHFJrVvZ32JZj0ezXZbqdFeR72EPKerZG16HSVMSSK2lktY9ae2XQTjToluUK7NUuQHrZt0+H+Ctf0FT10l4j5JRkF/DxlN7uOsxfSQfdq8hV1c3zO8aXEoftnQqya7v0JaTz+jl6mpHHKXqWgmfh95vcTbc479/SrlmYWsF6umZ8PaPRr9E77aojVgqFprLnYnL1VJFoo0CfSZTw4Av3qLXlexZuQxlmKn4coCqyOdPbuVy99eq0D5sUhdhrszSjfdiQf09R7B/EqfK551lGvVvDMKV+PHgxotGvc/RAlXeUusmwTNIoIQxUO8+zJVz17hWRH8sVYbsqkpBNgGVb+whtUUgSsAAmNzMGz3e6qQ66KenANBLRpePZsiPUdQxDJw4S7l6MCLeBEJTU9d74ekqlVuZb/rVfwC/+zT/vU62vz9xC/wPf8h+ID1sVWqBAM2SIE4e7hesQhZJiWo3XHL92ROtA4IDr2YYsk0y5W1J9YWToVmSFIIf9SMwhCu979eSWOIy59/YZ9U5V0mF+jK7vEL05eS49Zc2QIdTtsyFFkXN4v7+fGo+uplSaZTnL2eJ3E+6YQZZnhCqLLSGZlJunA6a3M7kCO5GK6mbhq0x8RYo16qigGwZRGDG7nZMmGa2Dj8Vhn9xS79Q4fu0Qx7GI1NtAwI8FSaTItmrreP3kluVkxfFrh1IXjXOW46WCLKqHU6OBRoHuSCV1+mMfoZu6ILZ3qqKrKf3zLr2zrnqIiZHGsOXBlMap5N0tod6+eOcSTdNkF9AoEaxDNir3vYt5FoVIGYtc7tYXoxX1bo1aW3YBgxcj8rzg+NEBpXpJIZ8lbFQouCaaRlmlHEeXE26fDenvaq2GAkTmOV5Z0m07NbNpmWxXWy7euSbPC87ePKZcE1Hi7oqwqo4WGhIVllrxktlgIS+NXk2d06fkWbFPJO7u1uWGJWUxWZNEibAJVU5kl/k4etjHdmzuf/GcX//POhn3f/XrU2uT/9T/yP/y3/w9dF2RQmOBCdiOaHO3y4CrJzdkSSZNoXqJcCtPQduVtJytut4CepB7UH/lUxRQqrqC+RktefnuJW7J5fTNI8q1shBmVx/3sQskwlB8On/pMxss9m9rDY3JzZTJzYzuaUd67KY8qfM0FZpNXuxz2ZZjEgeSFvNXAUeP+tTaNZIoZnI7x7QtOkdNZS3ZHRU04liQTxpQbVZkoV1PGTwf0jqSzL9pmUS+gB/dsqO45SI4MC2DMIy4ezog2EQcPuhTVruA0cUYt+LQP+tieTZ5qhJkpkGimQSZhpHEeJbQYm6fDVhMAw7+xZ9OzwRtvMLfBOjlMk4SykMxE6WTsOo2XD8dYpgah/cP9teV69lGwjG70FKurt40jfV8TbCW6LJbcdguA16+c4lu6Nz//Nkr4UUYYdmWBGNCeTDvjip3L0aMLif07/XonbblJTDfoCH1Y8PUXxlXdJ1gI338PMvpnXdxXIvlbMNytKTWrapCj7E/jlAUbBY+m8WWcr2kQBYhNx8NyLOck9ePhCST5eLwM16ZaIs8x6t6aLrG9GbKzUcDmopdbzkmSZiSpRnleonf/p/+enpn3U9cN5+5hf4d/9J3qdxzsW8zoXrW0x266ESQQYvRkvHVhFq7zsH9DpZrq8m24J7QEK6arkliCfYLpN6p0b/XwzR1VvMNwSqQ0MZOu7M712rgbwK2Sx+v4lKqlkjihOuP7liOloo62iIvZECmoVOqOqBJh9xUufNwGzK8mBBHMf3TrmiNFhsFe/DkKe5K7jxNsv1bLdyGGKaEQ7I05+75kNHlmKNHh3RP2oAMgfK8EDOovePRaXv80uR2RhaltI9aWI7JYizmk1q7yvFrh9i2RawCQ6ZtUmQ5UZTg9uQ6MCrg4gc+YHU35t7n79M4aJKUPWamQfBmmZZuYacFmR+i6xrlD2b44yWT6ymWa9Hq1wGN0ZVCXp135B5bRVg1TUM3dcJNSJqkeBVPpSFfRZdFeOER+iGRMvDYri1HIsVOT9Oc4cVYXYl2qXUqpFHK5G6heulNHMdWVBi5uYiCmOVwAbqmxIoF09sF07s5nZNdO08jiVOKHExTnPGhuv5zPIfNYsPLd64oioL7nz+n2iqTRF8dod39O22VnBtejLh9NqR70ubgXhfd1PFXAVmSUW6Uhb3g2vzW/+TXcnCv94nr5jO30P/kt303N88GoinOC1aLDf4ywKs4VBqVV33s4YLDBwd76ES4Dfedc12Xaw5T9YXTNGNyNWUxeoUUThVdNQ5TeucCfyiKgiyWKKJmSDlhN0CxHEslvy5JooR7nzuj1qoQRwmr6RrLtqh1KhimqYI8AqoItxGL4RLN0Gh0ami6zmwwZ/hyTONgd140iAO13fZsQJpzhmnu73RvPrpjNVtz/OiQWrsqWKPbGbqpyy5GzSF2rLQkSljPt+i6LrsTNSS6fTqgd9bl8KFERKOtcNy8kg0qWmraJoYpO4SrJzfEn3+d45MWJb0gvhgxvVtifv6M0pe6aFVBTWWaGlRuI4wfWeBcC9wznq64fT5krSLE7UN19RbEaLomHfREUM6Wp67exisZqpYd9ZazRHixjai2ypTrypyKph7KGdPbOZuFT/uoIaz1VcDtswG2a3Hy2tG+wrv7XKSpwoyZkr/YPUQn11OOHh3QO+tKg9KPyYt8D+DMMpmYm5bBerbh5btXGIbB2ZtHeKrL4G9CShVXGV4l1CVR44LJzYTp7UKCRAcN0UYP55AXtA6bUs7KxUXbv9fl2/7Er5OX1id8faYWehzG/Inf9t1Mbmb4q4AkTijXBK28G+JEQcyDz59R69TIkoz1fIOua+JGU+4wYG8xnd8tiMOEZl/Sctulz9WTOwxT5/SNYyr1kiTbwhhDFS2yNCXPhHGmGzJQuflogGkZ9B/0cD2bzcJnPlxSaZRoHzb3fPcd8ikOxdphORblWkkZPUfSBd95vTXw1fCrXPPkw5AW6KaGYcjUefhyTBRE9M66uBWX7WLD7dOhKIMeHuCULNIkV7sYoayEfrzvoGdpxu3TAdPbOQcP+vtdwGbuU6icuGGb+1mCbmhyj34piOn2UQunVmKqFbzztTUef1Rw3KpiOBabOiweWVipQRbnbPOE9iTDqnhEm4Th3x8Q/c2/L1dv3TppkrJd+liWSan+saagZQBSXhldTPCqLt3jlqiP7xb4m4DuaYdaU11pqbz7jjITR6nMNByT5XjFi3evqNRLnL91grtDK0eJKs5IBdewjP111/WTWxbjFSevHdA6bP0fxYpqd6Yb2l7IefdsiGEZCmJqspqv2S586p0q9XYN1JXbzoO3HK9YzTbU21U1zZfCE8Dxoz5erUSR5qSZgD93D8Pv/L7fL7qvT/j6TC30YBPwR37VH8ffBNiOhabL9df1h3e4ZZejB32R9K0CFsMlXtXdK5Cl8CBnriSSGqWmydWWZshb9PK9a+odETG4JYcwiAi3EbZn4ancNJqGbhqKAiI9ZUnlNSmA2e2MxXhF96QtVBVdtnW5IuGkcUocJTiuhe3ahH7ExftXBKuQk9ePpBqbpMzHK0zT2A9j8l0cVtNI4pjFcEWRF9R7dXRTE6DiswGtg6ZM5tW1TBqn2J5cFabKvLLDQd08HbCabaSD3q2ShCmzuzmGbdI5aCrzifLFGRpxKEk4Dah1qgCMr+dMJhu6Jy2cX/slrMLCuBDllFnWmb2pkwY5pdwiOTLYRBG3X57T/OGAs7M25SQmfP+CxWhJqeaJWnl/9SapxO1yy3KyolQtUW2Jbuvm2ZDYjzl6fEijUyUvClKFt9YNg8APIRfLrK5rTG5mSoFd5VSBPCRNmKhOgbnvFJiW4uY9H7KebTh40KPeUjaa4QLLtekcShNuZ6aVc7x87nT1c9sVp9bTDb2zNs2D5j6tibqu264CoiCiVPX21NqL96/RdU1RdmTesEONiRQjBuBn/0s/nV/ybT8Jcc/+OuDP/Rt/idHVhNndjPlwRa1ZoXlQJy+EHrqermkftZQWSP9YWs4gzSRzbprGK9aYQjD3zzqqFKOzmW/2cAOv4lG8imCBmtxvlz7lhmwT4yDm+sNb/HXI6esy4c/znGAbowNO2d4bOkxLiDD+OuD26YAkTjh6eCDRzpXP5GqKV3XpnXUlLZfmFGkmrPokk5SWugIrMkn7DV9OOLjXFS6crolOOssp10qq/fWxOKzqoO/isF5VFM+D50O8qifiwF0HXWGj8zwn2IQYpolXkS3u9dM7pjdzYZg/Psa/X+Hyp9SggFZs0XgJWpRj+wlFxWZlZfzo2QbQOK5Uqc3A+P4xiyc31DSpkOq6Rpamr+6styHRJsSteMIL2PhcvntDEiWKF1AlTcQya5jyBs7UnbdUa2F2J8CHWqdKX13vrWYbkkjkl6Wqp9RRil4TpUxuZwTbkO5xG6cknYi750NKVU8AkkqsmCvpZhKlbJdbLNuiVPdIIkkxLkZL+TwcSW4iDlM0rcB2VDAmzXBKNoYpqb/nX36J4zmcvXksTTnV+nNKDm5Jfiaapr26VryZ86f+4b+PZVufuL4+Uwv99tmAf+9f+i78VUC9WxOE0Cbg5qM71UkX3XGRF3tvuK3IJlmaif1EaXTvng/ZLrYcPOhTa1WFLXczhQIxclQ9BT+QtBiaWkC7YZBrsZnLkCXPC+5/7lR6ykHCZrnFciwq9dLer6Xrr4SBk9sZui7oJcM0XlUw+6L/2fHvdtPpLBM9k2lbOK5FHCbcPL1jMVpy/PiQZl9AFcvJCk2DRq+B7ai3MQWGUhfPhxKXrXdrmKbBfDBXAZPGHiGdKLih5ciHJ4mFZ245NkkYc/nBDev5ltPXj2QWECcsJxuMxyfEv+oIs2KRFxnutYEVQXEdMF9HmGhkP6NK7sBwvmHz3OewXuXh2sS9WZOEKYXvY+gayTYgDmPZ9ajZx/MvX1AA528dU1WpxM1ii1v2hPBSyLnVNCVvPx/Mmd0tqLarNHo1qZBez0iTlP69HuWap5JrufLaFaynG7Iso9qsYFgG88GCqye3NDri9rPV917gj3LnHYcJlmNguw6RH3Px3iVbFY9uHTRU2WqLaZpSDS6QIawlZtvldMXd0yGmY+5Ze5vFFn8dUmmWqdRLqDG+CDbznPndgvlwQaNX51t++y/kF/6Gn/OJ6+sztdAB/tS/9l9w/eEt89GCl+9e45Rs6QpXPeIwZrsKsGxD1Q9RFVEdzYBwG4leJy/oHLb2i/Xqw1u8ssvpm8eCYE5S0jARHpshgAhAhkGawWK85PrDW9yyy+HD/v5Mvp6tKdfL1Hs1DF0ny2RbJy7xkPV8g+3ZVOpl0lR82dObGYeqlvhxc6hbdqRDnmSYlogDQj/i9qlQUvr3e1TqZYJ1wPBivM9aW7u3Wpar1l4qcVhDk7JOIRPd4csJ3bM2h/dFyRRsQrI0p1R10U056kgHXaK8gxcjgs0O/yy7j8GLIeV6haMvvYZ2UuHumz0yJ8fKNdI7g4CISlpQ1h18Cp6kK8bbLV/8Wo3O5TEsNOwfW6LFKSWALCeLE4wohLKFfyvcPEPXOXwgKK7VfMNqsqbWrdLs1kHT9y6zAljPt2wXG8qtGuWaQ7gOufrgmiKHszePqTTL5GlOHCcYhrGXMcLOa4cErJ4OaB21OHrQx7B0/E1AHud4dU+RaBT8UYknbj78WB+gKQCUxWiFW3Jo9GoKNCFOOU0T7/z0Zobl2rQO5ApxNpizmft0Tpo0eo19a01sMpKrDzfyAPAqHpZj8of+u3/rU62vz9xCf/6VC377N/xbtA6b9E47GJbBer7BX4dUm2XhtKv8NMUOYBiwWfiYtkmlIRTYye2Uu2fDfdrMsi1CPyQJpe9rO6YQRzWUbLFgNlgwu5tTbZZVkaBgejtnuwxonzRpdut7w8oOuhAFsUp+OdieTbiJuHjvCl+RWRq92t79bRomlVYFw9D3d+S7D+L0bkGeZWrmIBjmwfOhxCgfHWA65scMqBZQEAUJpm3sOWp3TwdMbmccPzrciwZXkzVFUdDo1T/WQZdpdaJkDVma7Wcd89GSu2fy9548PsK0DVLNJNISgl/4mPg8JnAT3JFH/SQmtUNebEbcrHK+1Ktw4HoUesI68hn9wDFHSxNcHXMckFUNjE1M9u6Qy0cdms83tG0dLcuZXk9YrkK6h3WaChmdZblcnxUF/mxNHMSU+02cTpPN0ufFDz8jnc149IVzVWtNCP0Yp2QLHDSRn6+lhpTjqynDywntwwad4zYgc5g8y6n36rieRGgBNesRwnAUxULtLdmsZps9AKWvYrhZlu3tqcFWdiK2I5/FJM4YvJBZwNGjA0n2UZDGkuU3VBcijhK8iotlW6zna57/2AWWY/FH/9b/m9e//uEnrpvP3EL/N775D0q1MI6Z3MxJ45TOaYtas0oBZArjrBtiP41D+cG6JVfVRG9ZTdbyTVUAw818S5bmVNuVV4JBXT7s4idb4K8DGr2ayO39iJund0RBzMnr0ofPs0zFYTUcz1L3qgWWbWJYBttVwNUHNxKWeO0Ir+rirwLmgwVe1RMhhRruvKpPJmzmGwzTkAdUUTC8nDC6GNM96wioQt0vZ2mmdgH6Pg5r2iZRGHP3dMh2uZU4rCrFTK6mmLbYSHchkizPMHTpsG8XW3TTUFtccZLfPR9xcL/Hwf2eIJc2gn92yw6aYbD9mhPsg4Lgazxyd81dOCLMUzpmn7ptY5pLJtGCpmNhxkf4yyo6GZlRYMU6SR4yXmRURhb9eYF5sWbw3o1ojk9bdI7bxB2P5XEFw0+ojWPyQvr+tqljUrC6W3B5s8KyDE4PyjimxmYwxV8HVNp1ShWXQtPA0GXXlaRMr0fMB0sFIqmRhCnjqwkFBQf3u3jl0t6nh6K9rGebV9ReU0Qht0/v5MXxsK+OQRJssZQsMwpibNfC8cQdd/n+NdtlwNmbss3Ps4xgE6LvZkixJEAtW4AYi7H4ASzH5PT1Y17/KY/4f33Xr/lnB574v/P1qbPuf+cdvv1f+PexXGtfmMgS8ZDvvNrpLi3nCvHUXwVcfnBDGiecvnFCWQ06dlunzlFz7+DeubzyTHBQRVZQqsmTdDFZ8fKdKyzHlARWVa5Bgo3ksEsVl4JCJs6mAZr02MeXUwzboHPc2gd55gM5Y3VPRfiYJqlsl02BJkR+JFbQkmiTbj4aMB8uOHp8QPuoRZEVbOYbCgrBMFsKoqBrewPs5Ga2R2DbrhBWb54OJAjz6ADbUwGijwVwIlXwkX56xs1Ht8zvFvsYbpblIl5EnGuW6v/rpoHZbrItm/zoTzOons44r7hU7IJFsiXniqvlI77YcXEMGC8cZhsPr5ySaxmWs6JqauRxmZuxy9PlFH+Y8FqzQZ8KaVqwjWMqUx2v7kIG7jjGLnS0HFargMHdAqtmc9CqYG0T1pdTNjcjWr0alZqLatCglT0K3WB5NcKfbwQe6llspiuxthg6Z2+fUN7Ra+JE/Tw1wm2Apil6TVEwfCkPQCEEHcj14yYkz8Et2wpFJhqoncfv8v1rgk3I2RvHoo0OJdPguBbVVhlNV71+9bJZzzYML8ZYjiX+OMtgNd0QbiP+3I/+MdyS84nr5jO30H/N428TQINjEXwsBeWU7b0bS9N1dE1jtdgwu55hOhbto6YMoEZLxtdTau0aB/c6+7vSTE1Qi6IQ64uu45QdKETuePt0IGm5+z1shUXy1yHlhke1UQE0JKIhZ0V/6bOebbBLDvVWhSTJGL4csRhLk6x73JaWnR9R5AVOSSbzUlG0sCwBVVx+cE3kxwpUUSLyE6Z3U2zXpnPc3nvndgO/JEolyqlrVBoVdB1GV1Pung1Uqae/D7ukaaoCRLpa7LILiMOIu2cj1nPBP9daFaJAEnSWZdI5bQkBJ1NXb7pGnGYERom7X9Ki3Kxgt0JWzph2+X0qRoee20UrDAxtyzRxmG/bbH2LVE+omBrtakKYBVwFA9552uEXvplyUNWI84Tles0q6qFHXcytQbI2sfwMIhN/FhA83+BZJrV2mdjVuA63bP2Yk1KJ40WKFiWkUSKMAFMn1GXHVSpSDAqWgzkvfuQ5tmtx/3Nipo2DiCgUeo2legm6qe/pNYOXY8bXU3pnbdnm5wVLxb+vdypYtq3gIbs2YsTw5ZgkSuiedXDLDv5SRJTleonOSRtLYcPkM6zhrwJWkzWmY1Lv1MiznMn1jPV8Tf9ej2/65T+NX/F7vuUn5xv9//c9389f/+P/PWmSUm9XcSseFIr9Lcgw1vPNPpJaaZT3TLj5cCHutFPhpYdBRJHm2CV732oyTF2FYnJGlyPmw5XALQ4a5LmcyeMwpnPcpNqsUiBb7f2V0DokCmLKNe+rWk5xkHD21gnNngRDNisfQ9ep1Etomi7MMksgluE6ZHSlctWnHYmlTteMLydUmxUO7vfk6i3ZCQpUgEJ1l52SS5amr4Iw93t0TztIHFYFYZRWKN8ZUFWIZ3I9JQ4T2sctvLKIGW+fDSnVXI4fH30M/yxc+Ez9vYYlZRz/uMIH90t0vvkf0rV7nJTlvn2TBJTNCJ06ceFws66gFSZFYbD2U7TyCzwzwioOydIKqb6gbF/QslwM7ZAcm1Vk4icWVSvmauywfGFilXXauUMyTbkcLolHIffP2rSqJbI8JxtuqVxvsSydxNDIPJmVaJrGOAi4fj6l4cccVgysPGW72BD5MeVaSbr8mTLpKH31+GrKcrqmcyTwxyiImN7M0QyN/mkXpyy3PIWCRiSx1H6LrKDRraEZxr4p1zwQAaRpy3EpTyUclMQJwTrA8Rzcsnjerz+8ZTlZc/6mRKqLoqDaqvJv/5e/41Otr8/UQs/SjF//9u/cDz52vumdrzpYq7Nq1d1fy0haLtmnr7IkY7vcgqbtfeWFwjbtzsWT2xnhNqR92KJUcfE3ITdP79AKOHnjiEqjsj+T67q+j6jmRYHjCVpoPdtw+f4NaHD2xgle1SVYBywna7yKS71T28MeiqKQ9pkfsRyvMCxdAJWFTIAn11Paxy0O7/dUQSMReYRrqzhrimWrt3GUcP3hHZvZhuNHB3v45WywQNc1OidtkQCqe31RQSXCqyug2q7IBPhuzs1Hd7SOmtJ9t19RaW3196ZJKt13xyIOY66e3LBe+LR+88+h9nMM0paPad6RFzYdu0rbTdmmOmluUqCzTTNugwhTy2naFapWTMqQ+9aPgHZCxTnC1gumsUmQmehYXGzLtNw5w+Eh63mLrRbwJLsj12PuG4e08wpRlLHxExzdoL2yqb+/Jjd0jJKNhs5isWU4WpJ8oU6/UcH0M7IfHhEFCc0koFyyKLJsj8XK84LFaEmwCcTOWxYg5+UH1ziew+kbR3gVjyxLSWPZGZEXUubRdfHkfVzRtW/K6YRhQpFmig6rrjMdKTiFm4iL9xW4480TGv06aZyyXfuYpkm1XeHXfsev/MnpXvuLf+B7efcHP5QPt2VIkERNKG3XAk3IobdPB1iWydGjA3nTrH0WoyVuyaF11MJS01B5E+skScJmKlK+cqOMaerMhkuu3r+mXC9x9uYxbsUj9iMFJLT2LTJNZZVRjPHR5QSnZNM7bqObMkRZTqSN1T5qCt89ETSxYcqbNNxEWJ4tbrUo5uajAbPBgtPXj2gfNaEo2K4CiqKg3CirGKTCVRliYR1ejknClO5pew+/vHs2olzzOHx0gKOCHq+CMIUMfwydUsUlzyXPPbyY0L/XVWx60QTlaUapVpawRpahGQamulUYvBgTByKBML7+AYsHFldfuKVs2py4TaoO6HqMqaUYukaaZwRZgKEZmFqFMCsYhBM864Ke0+PtqsWBtcDTlpyYa6KixiArM0hK6JrFMG7xfOLwd37EgH7IvXqLimXh5yHDacpRP6OuVdEyAwLQfR3mJulHPsH1lpJnYbxWJzILbicrwijhcVGiW5jgR6RxijacQpERrALSJKNUdTFti/lwwcW7V3g1j3tvnyoElRwNLMeS7kScSlNOzUBEADnl4H6PzrFAJ7dLn6IoBDpi7Ui/xh6PLbKJkMOHBxKJ3Uoqzy27wpNTx6x/9Tt+Bfc/f/6J6+YztdAX4yXf8cu/a1+tzFIZnO0qhfPBgvlgsdfuFupKbDle0zlu0jxo7j1leZ7vfyhxINYUp2wLj+ulwPe7p226px3pIS+3REFMpV7GVS6wQt2RF4UAFlbTNaV6mXq7IljmizHruURMWwcN0DTiIFEucDknSifbEa3TJuTle1dE21gQ0q0KSRAzH68ECHjQUAjpVx36JIhZTtdQyFBOU5rkwfMhjX6d44cHmI5FEsYkcYbtWipGm6g0mUUSpdw+25liejQPhJIyHy7RdGj2mzieRZ4WoEncM9kFcLKcRr+OYerMh0tung9pfek17N/1CKujExUpuh7Rr8ZkBaRFTMPO8AyDJE8ZRnds0g2Odg8t7mMQ888f/13KRoanVTm1A/QiZVF4hIVNmIX86cv7XH5wTL9SxbRh5iwZLkMO6i59p07JSym0lLyALDNZxzHLqUF9ZVM1bcJZzNWLCatqzmudLi3XQ18lcBfibTPMNCG9mZAnCZZtiGn1bs7Vh3dUW2WOHvSxXJvtMiCJ1DGt5OytuqZyvQ0vx+qz16LRE+7+7G6Obui0jlq4Sqyh6aJ+SqKE6e2cNM1oH4gWfD1dM7wYU+/URDaxi3NnBbV2hd/xZ34TlcZPMiVTFET8yd/255kO5vuscJpmLMdLwm1MrVPBq3gE21B1qyWS2uw3KXIpAQAq9bUDCghCKg5i7p5J9vvgvvjH0kQQ0Xma0T5pSVquUFokTY4Lm8WWOEwo10s4nsgBd4SU+58/U4bPjGDjYxgGXtWFQqg4himBDX8dMngxoshzSca5Jtu5LySRZpneWQfbtkgziV3KjkCAlLopf2aRFdy9GDK6EHXwTgLor30JwtS8feZf0+VtHEcSh/VXAQf3upRqHv46ZHgxwnYdDh/05YiU5jJZ13XyLGOreOTlquLRXY65ezbk4F6Xg/t99KM2q8M6GQXBNxmY5YKSu8U2wTM0TG3DIr1lkaR07D73KxkUW0bxGltz+IZ6RNeMcEhoGzE5OsO0YJst0TWLi/gxPzw55YduDJbMOCk36OQd9NigmGREpwmuqWHYAVGWU7c1orDCbJXy3nyCdW3weqdDreKyIWaTxNTXFvXCovoihNkaa+lTZBmz2wnjywmVZpnOUQtUvyL0YzqHTcr1EvnHCEd5njMfLPHXPs1+E6/i4K8Cbp7eYTkWJ68d4pZdcuVP1025w9/Mt2i6RrnmoaExupkyeDbc47tMyyQOE+WuE5x4Afye7/k2YdV/wtdnaqGPLsf86d/5PXt3VbAOQPnADUNnMRE0sOVanL95QrleVi0xQSRVGmXQUE2jVybW8eWULE3pHLdxSjbr+Zbbp3c4ZZfjxweUyh5pmgmJxdT3FdUC9qql5XjF5fvXWK7F6etHKpsdspmLgqjarsrbX9USNU0jWIcsxktMy1RkFpnwT2/mtI9b9M+7AngII7Ks2GOnkviVNDBWzLLleMXx40MavTq5sreiISYYNXSjQOiwccJCoZYaXSnNLMcrrj+8pdqucPr6sURto0QFcIR4mqi0oKO2pNdPb5kP5BahfdSSGOlsTW6bVL7pC2gti9ljDcvNMGoZWAGTeIlphJyXPTxDY54ETKIJ39BcYRrnVEx4bF9h6xE2OvesgKzYcpWWyLQKSZ7xNyYO82TL87ufyWG1Qp4XrJYFWqHRnDmUXltRmClVN6fkZCT5hh8cpWgFVKIzvMQhGKWMC59GyaXpurgbA83PKU0ynHHI5qNbFs9vKVdd2VnFKcOXIyI/5ujRgaotSytQ03U0RAqRpbLNtxxL4CXvXOFWXe5/7gy3LBbXNEoxXRNdk2PbrluepRnDiwmDF0N6510OzrsynN2G5FmOW3HVVWyGaZo8+tJ9fsN3fuunWl+frYV+NeFPftt3K4GfKazsTBJNo8sJ9U6F/nlPusDzLdvFlkqzvMcb7coLolaKWc02WJYhTnDExDp4PqJ10ODw4QG2K/6xJJQKo+nI1YquaXv4/2y0kCd+o0L7pLnvS28XPvVeXZFmhFoqbgI5gwXrAKdkq+iuNMkWwwUnrx/ROWpTUOBvQshzvIqHbmgylTV1dMsg9mNunw32dplSrUSwCbh7MaRU8ejf66k8dkaW5PsPiL8K1JvjFUrp7vmIzklLyK6WQbCNSKNEsdVFz7wz3SRRzO3zkfQE7nepNCvEfsz4eoplW/TO2tieQ9prkBCx+efOSPOMYTXAbkb0KgaOlbBMZuTaFVWjyYHXxTMgKyIa5or77paSEWAXGV/rZgSFxUdJRBB/xGPzDt94gy/ffA1/7ctfZKoF2KZO0yqhVcHJE87Oplh2QZyvmcUrDE1HL3ps4jIvFgnDZcSBXadj1tBTgyzKKY3A9jWiZQQfjCmbJnbkE0zXvHz34tUOrV0lTTNJHaoj5C4cJbc3GrPbOZcf3FBtVTl61Mf1HPxNSBzEeP8HxrscQYcvxUTbPWnRPJBO/nKyggLq/bpoxLJ8b9GNA+EN/Odf/o9kPvQJX5+phQ7wV/7QX+P9H/yIJEoYXU3YLLZ0j9vy1I0Sxtcz4iCmf96Rp+5XxQl1Ij+W7rEnCaV9QWS45FDpiWRYst3jemzXZgcC03URJc5HS9azNfVOTYUeYgbPR0RBvJ92U0ASpXvHdhwmpIlMWC3b3DvI0ijl9M1j6u0qcZiwnKywbEukCErGpykueBgIqCLPC5r9usRShyvung9o9Op7e2sSpTIVt000UHYQYdynScbg+ZDJzYyD+939dc16uqYooNaqYnvWviqqGzLZn97OiKOE1kFzH/e8/vCWcq0ku4CSkGfTWPL5uaYx+OYzNCPGOqwTdRIGxZjQuOZLbQebI7LCYJuF1KyAumVx5G2p6j62VvC2u+GeuaDILvmhMOGx0wG9xCrbcL3e8B+//H/Q2vaxI5siLMjsHOco5LhzQ5Bv8XSdmuUwi20utivm8YZjr4undVn5Hqut1GBbL2yyTUqS55Q0EzcuiH/sjhd/60egKDh/64RqQ2yqwTrAdu39Ma6gwDBNUEDKwcsR1WaF7qnkJNbTDVEQUe/WKdcUF55CrlTTjOndnM18S0sdA0I/YnQxRjd0Du/3cCue4LeUkCNNMpbTFXma86v+7X+Rn/0rv/FTra/P3EL/29/7v/FX//BfBwr5QLoWS+UDtxyL87dENZsmKUmUoukalsqtSxtMJqP+Wnxl4TbiVLG8wyBiPlhimAbtwyZO6ZUQD4WPWs82JPErkMFOxGdaJvc+d0qlWSGNhQZqWua+DguoxpLOZrll8GyIpmkSQfUctosN09s51VaF9lFrf/W2D8LEqVhkbePV2fhizOhiQu+svTe2BJuILElxK97eEmpahlIBCahiPd/Qv9elqqi046sJpmmIxaUsCcFMxXDzLGM921AUUGnsTDcigWgd7pTF5v4B6pYc8barAI7lWoQVh2edMh/+9BVfPLLolj0yUqbhBkPTOSp51CwoyOnYG+pWjKetMLnlLXNBzS6joVHkU+bJJZvimP9h+i0Mog5hVBAMXNzAINFyVl7E150P+KlHd1jahmX8ku+bHnNWOkCnT5RrbNOI8aZGHtSxZzrOlQZlGbqt/IjL8YLGTc55Ae58w2Y4Zz1fU22UxdIDQupRc5rVdM18tKLSKIlYMckYX06Iw5j+/Z4AMShkN7gb3i62pFFCuV7G9ixWsw2X711j2Sb3vnBGqVIiS4QqbJgGGkJJ0jQNtyJBruVkzZ/54T/6qdbXZ26h/7u/7I8RbASgN76acqPyxYcP+iK630YEm3C/LS4KsbAapsD3tgtf1UQ12sctLFtIqcOLsRQR7vXE7JKkCvJvAqr2CnhlF03XmNzOuf3oTokN+zhKDxVsQtyKS6VW2vfXNaU73q58ZsMljmPS6DWBnMntnPlgQfu4JSUdVZbI8hzLssiylDhMsB3rY2fyAfPRkpPXDlVGOmc12wiIolsTuuv+bSxDt+nNjDROaR42cTyb9WzD9ZNbyg1hxLtlZ69kMgyJ70aBKIi8kkOW59w9HzC6mAjZ9VzIrtvFljTJqDbKmI40tAxdQzMMUUFdTQlXPt2zDqXTDovHVf7R2yHlxOGsVqfWT3GsiDQF28rx7ICue03f3pDSwtZiavoHPNL/ESlnWPY5CSb/aNVmmdhcxafUk4Dbly2o6kS6R90b0Wn/EDkBFfM+rlGmIGSZbHm5PaFs1ln6FdZbB+9Oh9QkWMeMFxtsDHq2R/lqw/ZHr1lO17S6VdrdKmQZaRCyVyQtBV/lVT28iuQkrj+6IwklHFVrV8kzme3ohi7XkWEiWY+Sg2GKbOKlIt6cvnEsxBtVhLJdS+wyscx1xOCSfxX++9d/57fyTb/8p33iuvnMLfT/6S//L3zPt38vi7Hogpv9BkWes5puSOKEeqdOqeaKrmdH5EQGJdvlFrfkUmkIHmr4csLkdkr/nkypDTVky9Ic27P2BRHD2plYcyY3MyY3U1r9xv7KbDFeEW0jGv2atOdUswoN8YKvfYJ1iFtxKFdLhIHUTVfTjbonbwno0Q8pctEVF7DXSxmmuSeIRn7E4cM+lXp5T7d1PEfOxo79j2mSc9bzDVCoOKzO5HrK7bMBzQNFFXUt4iAiDtN92CdN5J7dtC3SOOHu5Zj5cM7BvR6NfoMsTpkN5qBB67CNp8AaGoAOaZyynKxJk1QJIQ0WkxV3RoVq08P4zecqZJMTewmltUmua4Rmgm5pvHa0xnK2xNkzXvd+jJ9ZNbDMHnlR4OcbFplLWa+g6yZ/a/oG4dblZx5/RFoE/NDaxNV9akYb3fAIUp9psqRuWiyTNzB0iyiDme+xCMr4ccrqpsAxdZq5S74suBsuCV9ueON6Q0eJFZMgpkgTzGaVeDwj2UhyzbIFVf3sx16iaRrnb59Qa1aJo0R1IEy8iqsY72JFhYL5YMntswGleonD+z1M22S78EUs2SrvgSca7Hvo09uZKt80qHdr1Ls1fv9f/Z0/OSOw/8r93yKQvIpLsAoYXIywLIuDBz355uwmoZquKqpCDnXLLrZrScHl/WuCTcTZm8c0ew3SNGO7EhNrubGjsrwyeqRxxvhmSrAOaB00KNdF3zt8OSbPMg4f9Kk2ZfqbJikaKENHTJKkr+CR8y3Pv3JBkRWcv326P9uvZ1ssxxQ+m6FR5HJHrukCj5zezimKnOaBuOCX4xWDlyPqnRqH93tCt01SiaVacl4MtzGGpeOWhAs3eDFmdDWhd9ahf9aR8+N8S56JxcRyX93pGsrCOr6csF35wh6vegJUfD7Eti2OXjtQ10TZPidf5Lka9unqGhHGVxOuPryld9bh6PERZqfBtlFm/IaLW7bIDgtYaViJge5pBHrKy9Wc4mDEabnFeQVer7ygZT6lYhRUrTZ9w6ahB0zTlL/w3s/iYlXmZ7/x/Rx6KxyjjqFprJIF/2Bj8PeefCPntRq9aki9PqdsBiS5yyYxeLooQ+7gGhZBmvJkOme2jvni9JBuqYx5u8F8Z44Wx5Q8m8K2yD0XI4nRV5KbuH7vCt3QVTBL3O/r2YZyvUStXZNOeS4qp902f3o7o1Qv7yEps7s5wTakcywQUg0EeGJokBcsJivCdUilVcGruITbkLvnI6DgT/y9P8Lhg/4nrpvP3EL/G3/6f+S//VP/I4OXYxrdGkcPD7A9aw/4My0TUwkC8kJ8bOg7gOMdhmlw+KCHW3EJNxHzwQKnZH/VuXg3HU/TlNVE+si1thBcV9O18N7LLudvn1CulojjRNhytqm2/TkFxf5aaj3fcPt0gGGZHD7oCfBisWU5WlFplAV7ZUribJeRjsJYifVMKo0SeS7GmdHlhO5pex+HDbcRWZrhlBwVS80wTV29MRPunouxs3+vu1f5zAZzNE2OLm5JEnFFUUjHO5Vpb5pm1NsVlQZbcv3khmqzwukbRzglR32/UyzHQNcleKQZmtpVZNw9HzG5ntI979A9bgljb7Yh13Rq9/rkXzokrtlEHdA1g3SbcW2sCIuUXqVKo5MS2Gs08xkVw+Fr6i6vlZY0zSUPzAADjR/aNvjL73+eJHP4xuM7HnRuSZJL4vyaefE5hv7XcL1q43gLjktz0Eq4Rso4cgAbPy4x9uHD5YxVkHPf6dPQPZJpzmId0pzq3LvLMRIBeWi2KZbYxZbxjz5FJ6f1sbDQcryiedigfdCQrIOazYjhJsBf+XhVob9GgRSHwm3E8eMD8bcVBWmU7OPY4TYkiVO8skS6V/MNL75yiabDg8/f42t+9tv8mu/4FXLU+oSvz9RC3658fln31+275BoQbCLSOMGreq+c4poub8ZCPUHv5ngVcX6BTEfnoyWtg+a+OpqmUhAxTf1VUcM09lre0dWYu+cjmv26GC9tC3+9JfKT/Rltt9XSdA3QWM/WzO6kb97oSF9+fDNjM9vQOWnRPmzu3e15JnihJEmIgwTLsXFKtvDoPrpjNV5JRfWwRZ7lbFc+RSYdenNHL9G1vRFldDlV5ZsWXtlls9xy89GAUtXj6PEBbsmROGyc7ZOFMuzRcSuOhDYuR9w8HdA5bgtpxZb0XhwllCqu0GxSsaAYlkEaJYyupqzGK7pnnT3uenw1RdM1+uedve4pt2yyrzll3YCZlqJFULcdNEdnuN1yMZnj/BSNb36sU7YKGuaMn1L5EFu3ODUSLEK+b/Mak6TFOoFvqP5PjJKAinlMzDGzpMR/9+yYo/aKhlWi54VoRU5SWBSFzjzReLbcMvEd+m4NW3OYzjKGNwFNw+OoXsUNdMofBdiLBCMvCKKE9WKLo9JwWZxw9+VnzAZzqfEeC1o8jhIoZPgahcJEcMsutiMq5edfviBLMs7fOqHeqwtleBuhGzqO5whQIxP0mWGIWfbiPclonL0u5/jQj8hTca/9pNy6/4Vv/6/4sb/zLsvJGl3XaPYbSr5Q7COI5LLAg01ItVXZg/1vnw7YrgKOHx/QORLFURhEkBfYnv0x/Y6k5dIoYfBizGK8pHfaljBKVjAfL8mSjGavLgzx4lVNVIwfW8JtSKnqUap6BH7E7UdiRD1780Rt2XKpqAKuJ/HJPM0wdly7bcDVkztidV1XbpSI/Jjp3QzbdegcN1X7rFC7AJnMr2cbhWmWOOxiOOfmoyH1TpWT146wPIskiIjjDNux9g8a3RDvt4Q2xkxvRVCwI54sRtLAqvdq6uGn3la6gB9md3OiIKbVbwhQcS7KYsuzuPfWKV7ZeRU6sgwx1nbamGkBZ1U298pcL1c815acaFV6b5XRLZ1wkZDlGYfHMb/s4Y9RMX2OzBhD04nyiBfhlKzYcGQ1MY06UR6zSSeAwzB9i1HaJy6gaW2x9YKrqESRTynQMPQWQ7/Gk2XB+4OAnlvhvOjirW2KVUZKjhMVlD/0SVYh+UmNchCTvRxz8aMvWA5nnL5xROe4RZ7JgxKQh34BWSqVY9002M63XL5/Q5alnL15SqnmEfmiQ3ZKNrVWDbRi34JEUz30l2Ockk33tC2++MlaZCKdKt/4S7+Bf/n3/dJPtb4+Uwv96skNv+sbv51Kq0LvtC3Uz1Sg+9Ilh1B5yt2Sg24ZrCZrLt67RtPh/K1Tqo0ycZSwVWipcsND1yWG+HHL5uRqShhEdI5blCoe/ibg7vkIw5TzWKnq7fvjmppuR35ImuR4VRfTNFnPNjz/yksMw5Brv0aZOIrZLHxs26LSLCuut4j1dF0j2ARMbuYURUHnqIVpm6wmK0ZXU6qtCgf3uliO+nerW4E8z+VtbOiUyh55lnF3MWJ8MaF72qF/TwaNwsJPKdU9SctlcnbUTY0szhheTdjMN3RP2pQV92x8KW/jg/s9ShWPTOmZdV1klNvlVqmsPHRDZzZccPnuFY1unZPXj3BK9seyCzamKVx8XdexOk1yw2Q8WPH+50rU3qjRc0oQFkzSLeFJRjeq4rkWqZdyUpvzpaNrXnMuSLMRJbb42gm2bmEVY6LkBaZeITXeQNM81plOQY6laUSFgZ9HFIVBWLSYpCVebNdM4imD9edomz2y2CKYFmCnNEYlyCGLM2qTAtsyCYKY8X//ZYJ1yFHPo9oQAvB6usG0DeqdGroCeOiaYMy2q4DR5RgNjd5pB9M1WU83LIZLqp0qncOWqhlnoMw/25XParrB8WyqrYpM2q+nrGcbJfhsoGsaZ28d85v/w1/zqdbXZ2qhF0XBf/Qb/wxT9fbI0xxLlTREpKdh2hL0mA/mDC7GVBpluqdtLMtks/RZTzeUGyWa/Vc+86IQIV4UxKxnG3Rdp9qS6fl8tOD6yR21dnWfU47DmMiPsR1TiDYKX2U5pkzhRyLEc0sO/XsiVNwstqxnG7mL7dWUmUP93SoIs5lthGvXrIiW5+WYye2M7mlrf08e+7HgiVwbTWM/CLMd0SbdPt1pkvs0enWyLGcxXEgwRGUD8uwVizxLUxbDFXGU0OjUsDyL9WzN1QcCvzx/62R/9SZ9/R02OkHTUZLJguHFhOFL6VjvVFabhfjAK82K7LoK6QjohhB1JtdTNsuA7te+RuWgycopeGbHRKcmZ04Nr2OSxRpROQOv4Kw9plO/5W33ipLpkRUGQTZhEA74qZ7PRP86EspYrGloC0IqeFpBSd/iaBoxHlGeEaaX/L1tlXX+Fkl6zF1QYRbJC8KKmtiBjbYwMGKwEg3tJuJ2tSFJMs4DKM/WrAdTxlcTyjWP3nkHSw1wC7VYQz9iPd2gmxrVlnTyp7czJtczeucdukoblqrzv2HKNj/yIyxXWoyhH3LzdMB6uubsjWNaR4I+2xl8S1WP3/If/6scPTz4xPX1mVroAB/80If8yW/78/vp+P6MqFJDs7sZ6/l2b9hMkoTJ1YxgE9I969Ds1vZDqyLLVdE/lW+wY+GUbLIk4+6ZJMe6Z1365529gSNLUkq1EpZrqdCEtn+7Lccr5qMFlboEK/JM2NubxZbeeYemEgbu4JG6KXfNUSBoY6fsEAUx10/uWI6XnL5xLE7zXLxtRV5Ih96Qh4Ru6upMnjC6GBFuY7qnbSn2qDis64nX3S27FJkw7jVDp8jUnwmUqx6aoTO5nXL9wS21bo3T1wRvHPkRURDhll0s1xI8smKLZ2kmjP3BgvZhk+ZBgzzNmCiQZee4RalaIs+z/QMty3JW0zVxkFDvVLFcm/V8w8t3LqkcdDj73Cn51/WJSh6pn5EemmQ1Dd+K+frXLii7GjVzy7H+o0yTMbp2wOfKHiU9J8x8vn9zjw/mr/NLD9+jYgaYGrwMDzlzbrGzZ/jFllQ74+9vvo4XQYlhsAYcqmYbLS0RxDqEOubSRB/mzLY+3jCj1ixj+wnBDzzn7uWEuqtxeK+LZRskSbrXT+dZvg9LeRWPNJZm4ORmytHjQ/pfpXISfkGe5aRJiuPaGJZJsAm4ePea7crn/ufO9oqw7dIXfmC9DFqBZVv8+u/8Vu69ffqJ6+YndKFrmmYA/xC4KYriF/+Tfu2nXej/+e/9K7x890q2OgX7t9JysiZLU6oqLbdZ+HvD5vlbJ1RbFbI0lxCIpksrK5FWluWIEjfcyhN0u/Q5enhAtVUlTaQAAkj0s+woqoyci4UHvibyY0rNEqWyLLLrD2+Jw4SzN0+od6vkWSFYYQ2ckoD/sjzHdiwMU8dfB3KGSzKOHx9QaZSJthGz4QLbtWkfNT9WUZXJfBwlLMfK2NKpops689GS248GVFsVTl8/wvJs0kgm5KaCDCaRADNs9SEbXY4ZvhzTPm7TP2urm4ItSZRQbVfwyq6af4ieOUtSpoMF/mJL87BJWbXe7p4NQNc4e+0Ir1aSsMjHTKHBNiLPC8oVF83QmNzOuHj3mma/LnacWoUgyZh9/oByycM2DfwqZFUN3TXATuD0Kcfee6CfUDdrWHrEQ/vHeGyN+Cj9Gu6yI96d9nm7csNhaY2thXzfrMdblRe0zDIGJro2R0ve43sGv4Aj9wDHcIiznEVgkCYueaqx9lOazw1qsQNZznC4ZPlDdxzkCQfdErqG4LiiWJhtGvsCkGkrEMcHNywnK07fOFbzHck16JpOtVXe+9Z0U8fQ5ftz+3RAHCUcPTigVHMJNgHz4Qq37MrtjGVQpAJbaR82+e1/6jdIRPuT1+JP6EL/3cDXA7V/Wgv9+/78/8zf/qv/m+rlZoRbNR0vOWqqPd1/0I8fH+B6LsE2fFUiqXnqXMwepxxsQibXM/I8o3MsZ//1fMPgxQiv4nD0UO6M0zQjT1I0Q0US/YA8L/DKLoZpsFTwSNMxVRRXzrmb5RbHcyjX//G/W1zq4+spmgbt4za2bbKYrphez6i1q3Q/VlHN4kw53DKCdYhh6XgluRUYXMhi7Z60OHgggsRgE5JGiVRUFR5rR8RJYrknX45XdE87VNsV1YeekefQP2vjVUUC+FUus/mrDoBlWywnK168c0m55nH21omUdIKEOIyxXGHfJbHMMUTxpLDKF2Ma/bq84XSNzWxDFKfUD5vY5x2wbbJulbxkkhY510bAgohOtYx7bBC7W+rNd7hfWtB3e9x3Yl5GDaaJyzSosoxcPHtKyYrwDJd1XOI2WLNKr/lF3QHd0hGbrMEwtJjHBrruMA+rTNc2WmpiYqHfweSDNbPLJa8HFq1uVYCf4wXabEO5ZGLqSA5d12TxB9J5CDYBB/f6lOruPgvheDatkxaOaiHmuwd2EDMfLijygka/hmGaLCdrRhcjGt06vfMutiOfgSIr9vbWaBvxHf/t76PZq3+atfgTs9A1TTsB/iLwh4Hf/U/vjf6XefqPXsh03DL3dpEdE65z0qLZr0Ohs5wsiPyYerdOtVUGJK++uwILtiHb+RbTsSTwkmVMrmcMLsa0j5p7bG+k7ugdVyXHohTNNET7owIPg5djyo0S/dMOpm2xmq3xVz61dpVqq7Iny+4GLsEmZDXbYLsWtWaZNC0YX02Y3c3pnrbp3euKYSWIybJcrg4pSKNUCQBFtXv3XFpPkhKsU2QFy6m85Ru9+r6corGrqKbMBwviIKbRq+OUXbYLmZDbrs3pG8eUq96efqobQs5JwhgocEoumibe7qsP76h1FFHWtQnWgTDsK55k5vNX9J08zZnezZmPFjR6DRpdkWCOr6ekaUr/rPsxCypgmRTdFsMjjzjP8VoOpmMwsgLe8YZ4psnPfWxwWktI84Qwzzh0QjZZmVFoARp1CzJgEs74h9OMx3WLntOkaSfcc59hEJHQJsEjzg0mYZ1R2GDh6zwbxGg/kHFY8mh4HkxCVh9OCR/UODZtSmFKEQl4Uldv8MXliGTtU296WMqmevN0QKVR4ujRIY7nSMBIgUjzLMdf+WiGRqkibcLh5YThixGt4xYnjw7kaKl86JZjCrxU8fq//ud/Lb/y93zLp12PP2EL/a8B3wlUgX/j/2yha5r2m4DfBHB2dvZ1FxcXn/jn/tl//S9y+f71V8EMozCmcyT3xcE2ZPByDBQcPTygXC9TZDlpqhpsyniSRgmu52B5ttL/3rKebTh+LHKDIpeJcp4XVOplKcZkMh03DE2ulAZLVpMVjV5dyf8yJjczIj+ie9qh3q0BEgmFQlk9YqkrlhzsskO4Dbl+cst6vhVtsIJQ+quAgoJyrbT/UMgW75VF1V8H9M9lgQTbgOGLMbZnc3Cvh1tSKqpM/OB5AdvFhqIoKNVKGGpCfvXBLdVWmdPXjnBKLnEYEfoxtmdhOzKv0HRtr4baDZTqvZqAGDRYjlfEQUzzoEGpVtrDOqXzoXhr65B6t4ZbcfGXPlcf3kpk9C3hBmRpRhzFmIaJbmrEcQ71CuE3naEZBvPhhtk/GFCrebQ/10Y7tcjeHNMqT/F0F9u0aFkrynqCqesURcY6nfHhNqdm1hmsHrMKLYa+zy9+9CO0nTp1w4CiIMEgKmz8VOfLS4NniypNuwSbCotlxsVkQf+pycNGnVLJpQhSik2Ks4ohzQiDmMKQToC22TJ9/4qrd15Q78msw3LlQZBGqRB+1DBTV4SfLE65fTFkfDnh8EGf7qlYe/y1T54VlKqe8rsLidZU3MDhxZi/9NF/+mnX4z/9ha5p2i8GfmFRFL9V07SfxY+z0D/+9X/ljf7BP3jGdrFBNwwqjRIamnxon9xS71Q5fizT8VQl1kzbkg5wIsC/3Vl1u/S5+eiOLM05fnSAV3OJtrKNshyL1mEDx5UAQ1Hk+4HfarYmCROqrQpOyWaz2HL1wS1FUXDv7TOqzbKEboIITdNwPEfwVZncEpimwWax4eqDW/K84Oi1A8pVjyhMWAwWOCWH5kFdRXE/diYPYxYjeVvXVIV1NVpy/XRArV3h5PERtidc+52rTdc1ojAWfLXnkBc548vJnkJ6cK8r97wLnziMRfejzuS7imqR5syGC1aTjcrzl/cPmyzJOH50SLkhb+NUPRgkzCSaJ6/qyh3weMWLr1ziVVzuvX2KV3WJ/IQoiL6KwVdQ4NSrRIcVLo5cgh+YEn1znUebMoWrcesGREVOs+tw/sjHsFNsPcXUoOcuSPMxWb5mnp3RNC1yfG78GVmu0bcPuV+LcYhwdR8Ng7AwGcY6aVGwTrpssgqTYM3/9BxqpsPjeofK1iOeFJjXKY5pYOWgjQPMtMDRdfI4YzhcMbqZ0enV6FYs9CBgM16Qxqm8LFz5eWq6tu+xD18IArx/3t0P3Raj5atbEuW11/RiDy8dX8/wNwH/5p//bXzdz/vip12TPyEL/TuB/yeQAi5QA/56URT/yo/3ez7tQv+3fsEfEgmgYp3dPRswvVvQP+/QOWljGAb+yicO5WzqVhzJjqsueVGIhnd2N8d0TJq9OoZpshgvmNzMqbdr9M6FG58mqdzRGzoastUXoo0j29e7BRfvXVFrVTh+Tagy4TbEXwe4JQev6u0577oaSK3nG6Y3M3RDp3si8MjVZMXsbkGtU6N70tqfp0XooJMm+d6iWqq45NmrpF73dMdqNwi2IUmYirrHERa5oesYtkmWpIyupixGS9pHDeUjz5gNFuRZTmeHysohL2SyLn7xrTwAGmVpvS18XnzlJZZjcf/tM8qN0p67Z5gmpmuShgkFO1inxuxuxu2zEZVGSVKFitAbbkIx0irhIbzq+y/HKxajJdVv+iJ8qcm2oTMar3Cfp5R/apNaxSEzcqxagFUUGO2YbRbQL01oWDaaZjONI662N9zz1rS9B9RNG1MLqesTXENjEHXwjC0lI6JsGCwzjw+3KT+6CKmZVfzNG4CFH4WENxY13aY1MyEt0DNwIyj8hPnLGauLCa1ulXq/LsPKdy7INmvaRw28svdKe61rosUeLAi2EY1eXZGGVY/Asjh63Mcti+V1N1fJ84z1fEuapFQaZSxVOPquv/Mdn3ZN/sRer/3TfKNnacb3fPv38t4PfsjN0wHhNuT44YGoauOU+UBNxw8bexuqJNagKAQmEWxCvIpDqVYiDlMGL0Rve6SgE7pqsKVpjlNy0HVIogzD1FRQJWV0PWN6PaXZr9M+Et3vcrIm9CMa3foeWbWDOIIonzfzDU7JodooiwTgcsxitKJ/3qF7Kld4cRCT5wW2I3qm3bR8X1FVkIyjRwdy9ZblrGZr8rwQzr26Jy80DcOQQs50MJd2Xa++V0HdfnSHYRmcvHYk/f0sI40ki6DrkinI8wK3ZEsQZrDg8v1ryvUyp28of7yqBLtlZ4801jQwDGU0HS6Z3EypNis0DxtoBcyGC8KtBJGqzco+VYgGWiFM/mAbUW2WBau8Crj+8I7wmz5P96cdUK25hG5BlKZ4W42iqZNqEfp9H1s3KdsZUTHl0h/Sd3Ws4gE1W6NkzsgIOHQzDK2EXmSM4hpVK6NiBHT0DwjyKeP0Po7RZZtafP9dnShL6ThVvEmDYqFTvs0xUg3SnPl4gx8kdDMoGzrhbMPgakpRwPF5C8+CfBuQ/v+5+9OobfP0Lgs9rnm65/GZ36HGrh4ySSBETQwYAQGjCLKAjSiYvUBjQEBMIshCUZcLEbeMbkAJ6l5btygQw7RQAhKRENKku6preMdnvufpmsf94bzu+63WRbr8wMLqd/WnTlWnnqfu67r///P8/Y6j5u1RVfibkDLPcdseuqGznm54+d4Vjmfz8PMX0nTLBCWu1YrtvQTSqo0si9sll1+5pjPq8Dt++Pv45p/3+U/yHH56HvTtYsdv/sd+J1DV38Qa68WWyfMZTtPh9PUj7IYtgoE0R9MUlPoXVeQltmvVdcCAF+9ekac5F585pT1sk2cZ/ipAVVUatTa3OjjYBAe9uF4SbEL6Jz3clksWp0wv5+R5wdGj4eGDW2RFHbGUU0CWZNiefRA6XH1wS7SLufjMq7VLuAsBagusfJuqqnqYKezpNeMHgwM2ano5x3JMRhfDmkIqU1mlhmT461DaaW0XzdRZTddcvneD13YPLPI0lpmBYRn1KaZAUcEwJV67vBe/eHvYZHQ+RNNV1rMtaZTRGjTlpVZJdFNRhce3m+/w1wHNbgO37ZBGKbdPJ2Rpztmbx0L+KSuyNAf2IsGUIpemn24abGZbnv30C+yGy9kv+BY8XSGdLPHbLtrIRR86KIUOQYz6eSibDtPuhrt0xeN2wZHdQkHhPsrYpjk/axTjag4NLcHPTKLKoqHF3EQZDX3Km25CTp+gLFklE6apjcIjkuyIJFdIdwreT9iUekU+TUirioZpYmcl4eWKl0+m6FHEozeP8BomaZwJXZgSrSpJo1SuJI4pp5zbFZfvX9Pqtzh940h8bIHIP2zPOmiZocIwDMqqYn4t24r2sMX4fMDpG8d8/x/9XnmJfO3n8NMTmCmKgj/8/f8FL969ZvpyxuxaiKej8wGaqZNECVmcCT7Z1EVvo4oHTVFEO3v/fIphG4wuhpiOSbgJWE02uC2X/nH3QKPZ20zzrBCjZlnS7HgYlina5A9u0S2dh++c43Vcsv3xVVOFmZaI0MGss867lc/1BzcoisLJG8d4TZvIT1hNNzieLWuV+psQRVzqaZTKfQ1oD5qHb4DbZxMaXY+zN04OQse8Xr3tv40VVTmk1uY3Yo7tjNocPRbDZ7gJSWIpZ9gNh6qoy0C6Inn+yZr1dEt72KTdb5JlObNLsbgcPRrR7DXqbkANR1Qg2ITkaY7dsLEci91yx7MvvUTX9UMtN0/FaKrr8nsq8rrpZ+ioqiI5gCd3OA1HGHamTrCLCNYBrbMxrVEbqKjyErUsKauK7Srg+nNjqs+Cd9yisFNm5YqWu6BrDGkbTY7cDQ0zoKklbDKXVZYzMudM4yOyqkFUhGjal4lyjUfeKbbaJC0rbkKTWdCm8YFDeaMSDBWavoqRKaxXATcfzujNI05PO1i6RJjjIMW2dWzboEozqjRDM1WqSmF5u2B6OaPZa8oJUlXZrnySIKY9aB26E/BK8rm/xrT6TVqDJkVesrxbkWc5f+hv/4fCRPwafz5VD/qTLz7nd/z831NbTE9o1z/0duWjAM2uV78J94k1haIshbk+l+hrs+uJw+p2xXaxo3/aZXjar40lOUVRHvrocZig6Qq2K/el2bU42LpHHambWgaRHxMHMU7DlTJDKQMBTZO37G7lM79eYjoG/eMeiiagivX9hu5Rm95x95AyE7GCTpaJkkc3dJymoIGnV3Pun88OFVXd1ImDhDSRZpRu6ocmmmZo5FnJ/HrOarKhu1dKFSXL+xV5VtCvGWVVWR0+WFQV22VdBupKGSjYRbx897KekJ/Xg8aCNJIijG5qZEkNR3SkJLOebbl+/wbTMQ+x4ciPa02WhbfnkFccFNGbuqftNV26Rx0qKhY3K8JtxPC8T3vYQnVs2XwAiqqxm29IkxzbVHFbLn5V8VOfbzP7TMw3Pmhw1DBk3qDGNIyMgZWjKRG73ETHoGHmJPmGm+iKH3v6eX7xWzlN0yQpEqIiATyoGkwDD/Pv2uSmTqWprFYByxc7RmuFsWNixBm7hU80W9NqmjQ7nhxvikJyCHnOdrFjt4nxPEPAJ/VqMQkTRg+HtHpNFIWDqruqIFgHREGC15bPVuhH3Hx4R5HlnL99xs/6Bd/Ir/md//zX3zc6wO/9Vf8J69mWcBMyv1nhNGwpuNQprz3rrCor/E1Anma4LRfLlorg1QeiO754+5TuuH2oZ1ZUOK5NWVWUeYluaLLDTDImL2asZ1uG53264w5lKfnxIi/pjNp4LXkLH35dCvirgHAbYjcsGp1X8Eh/HYgL7Uz820mcQinkkbIoJV1VK3nSKOP26f1hhtCt1bq7lU9ZVLQHLUzboCoBparhgTnL2yWRn9Adt3GaDtEu5u7ZfQ1IOBYWeR27lPLFx5DCnl0DJwVxZHs2F585q7cCqcARHfNwJwcOFNLNbMv0cobTsBmc9VF1jc10Q7iNaI/bdAZNFJT6SCq/KH8dEGxDvKYjRpIo5eajO6JAbKPtYbveHadfpb8qiuIgQNytA15+cM/0134Dr50NsUcGwSABb0XfKVHKJraR4GgZjl4RFSZBvsXSnmBpFn2jxyZp8e7M4qT/gobWxlRb5JWBnxlEqUm0sFkucsLbhEFg0HRtlF3G+tmG4umUcVdkmpSlXN3SDNXUCbYxqS+fA9u15er2peckUcrDzwlVtigqsiQ9rDGTKCXPCmxXIrH+yufp33tRn4zODlxC0zb5vT/yA5/o+frUPeiX71/zr33rD9A/7TK+GB5sK/smF5pCGmaggmULfnc93XD5/g2WY3H25hFO7TjfrXws26TZb0jF9EB2Ed774nZFGqX0TzpYrkRb759N0Aydk9fGuPW0OE+LOh6q1v34HLthYVqyenv5nggdHn7uohZD5ES7GrvcdpEgz/7oLTOF++dT4jDh6OEQpymVxsnLGZZjMn4wxHQtqq+COIrmucgLGl0PXdcPP7fTsLh4RyyhWZwRRzGmaWI6BlmWQ1lPyIHVZM3t0wmNjnco5OwWu3pA1qDZleBRVdaGEWC39Nksdngtl0bXpUgL7l/OiPz4UK6BOk+ggKYK8jpLc2zXxLRNgk3I07/3Aip48Nkz2oMWaVKvR3XtsD0pyxLT1EFV2C533H40wbB0sdwOmmxck8tvaNN0Tbpvq1itjKKEhpWhKSUla57uKmzN5O1WwWvePQ3lGf/bpoelnKIGR2SKRu6kUnmuNCZhxf0zD8+w6M81yuuEp9sN5kcBDx706doG1TaSok+cCIU3iCkA0zLRNfkdPXvvGsXzePjmEQ3HIPVDgpWPYSg4Tbk+lVWFbuhiA17uuPnoXhqTrx0Jl86P2a583KbDd/zyb+NX/LZ/5hM9X5+6B/23fdfvFqZ6Jd6wqhALiVr3olVdPVg+l3drlrcSJe0eSVttPd/hr3w6w7a0yOrEmtzJJT++WwWomkqz5YKmsLqXHX2z1+Di7dPacJnWCSX9oFdSKg5g/s18y/VHd5i2ycnjMaZr1sKGNV7LpTPqHPxnitSPicKUbS1eaA/kn2013XL/fCJO8zeOMWydPC1ExmdIWy4NE7mTuzZUFbPrObdPp3RGLY4ejzFNnXAbEkdiCd27vYEDdGI92bCaiM6qM2yTFwWLmyVxkDK+GNAaNKgqhIJTW0nCXSThn4aN7QpG6cV7VxRpwYPPnsteOM9Jw1czgyKX46xhSsZ/s9hx9cEtqqZy9uaxFHJ2MbuVj+3ZtIdN1FpJvZ9dBGtZjxqOQW/cpQKWt0t2y4D+RZ/Gd3+ebGCxe7sCs8CwC0x7S1zGDMyKihZRntI3f4qYlG9o2DSMHrvA4i//2DvoBbjdEvN8Q+GlJFmTfOuxmpbs/ocJQV/j4kGPVssmyQuK+5jGMqepqRDEVHmJDii6gj/fcfvBDZqhc/RggHncJygq/I/ucNSCTr8pP538clEAfxOyuF2hG3pNH9JYzzZs5zs6wxbd4y6aqvLZb3+LX/WDv+wTPV+fugf9b/3I3+GP/NY/he1adAYtNPOrix5FnrOcbA/mS7dpH/huSZRy/NqYzlC+YbLamW2YOlmWk8UiyjPrIsjdc8EhjR+OGJ7LPX6/o/faDpYrd/J9g03umluWdxvclk13JHfN9f2a7VLY3b2j9gEzVJXSJMvTnHAbYVgGTsM64IKnl3OGFwOOH41QNY04iOW04FmHe/hB85zlzK7mLO7WDE56dI6ER7a6W5OlOf2T3mFffbCEVpXcLf2YRtetW28J1x9cU+Ql528La74oS/m2qqu4aZJRFCWWY6LrGpvljsv3rlFURQw5LeGT71Z+DeP0ZCJfVYeCi78KmF4tMC2d/mlfpvnTDZv5lt64Q/e4K4m+GnOlqCqRHxFsIhzPkkx9Ileb3cLn9M1jSeo5NnmjRWEqLP+pNomZk9kZvUaJawDallnygqwq+Nl9j6Fp0tGW5FVAVZr89b/5j5KubSqlpPuFLamnMo9zPlxuUVB5870xjmqwVjKWSUTDMjmpHKxdhRoXKGVF5kH1ZM0iLdB1jfaggZIX7J5OWWoavZ7HSAUlyyiC6LCxiOsVrGnruE1HGAGXM9ZTAaHuCTZZmlFV4LVcvu8P/Qb6x92v+dx86h707//2H8JpSomkyAWCoGkaRVFJblgBp+WgqSqb+Y6X770qmexd6XGN7dk7y6pauLhXF18/uSPxE8aPZEiSpRmLuyWqIo0hy7PrlVI9xKJit/QJ/ZhGx8VtuMRhxO3TCbEv1FYhtXBYs5imSVEIPMKwdAzLJI0Srmrl8cnrR3RGLcpCBmRVBZ1h89C336/98kzECnF9J3db8o14+2yCqiqcvn6E2/Eo61KMoop9NfJj8qzAaTjopsa2LuSousrDz13UwoKU0I+xLAOn6Rwag5qpA7Bb+Ny/mGDaJqOLwUEKuF34tAcN2qP2wfpay8mItpFcmRyTRk/umpOXc7bzLcevjetYrXJ4Ce+7DFkdH92vKJ9/+SVplNfO+dbBQqM5Fl63SdlvMf3ODlobymbFLk156a9otgO+oWfxWiPC0RfE+ZqmptM1WiyzFu/fnpDMHdQbhfLnzHnqF2iKQttokYYmd8uEeRwydjxO3AZ6rqJOQU0qrBAyP2PaKelNoKVKvPXK91ltI85bTY4aDlqcU2xCyqJApyLzQ7IgxrQtTEdwXZd/7zm7lS+x6Fqw8XENWFWKS/37/rNf//XZR//v/5M/z4//ub8jdzXbrO/I+cEMmmeSN59dzml9TNHkb0OiTYTbdml2G/vnsz42K8RhwvJ+TVVV9MZiLd2tdtw9nbzirHm2kF0SmW4LZTahKkpMz8IwNbYLn5fvXgHw8LPnNLrycgm3IZqp47VcFGoyqK6haSqRHzN5MSNLM0YXQxzPkv/ucobt2XInr3l4e1JrWYjgsSxK6ebrOpvZhpdfucH2LB68c4bbcsjinCSK0U1DIBmpUGp1y0BRFdaTNTdP7nFaDse1xinYBASbsN5SNFDq4aaiSCFnuwpYT9e4DYdmv0FVyHVhtwoYPxjSPRICSnbI+AuuOo0yLNfCdk0iP+by/WvCbcyDdwSvVeSy6VBAjLX1VUEzdXRdI9gEXH1wR5EXnL5xjNdyiIKE1WSF23TpjtqomkZFRdVx4ajL9MLi2ckO3YbTjoFtlWyzDZp2xz8+3NEzuhiqAlXGJGlSlhovli1Wf1Ql+0UW7pFBamfchisuNxHD/JSTRgtdL0nVFIUSE4N0p1HdV5i6jpepqC8Trq6WRLOAo7dHDNoeZVoSRwlqXNBQJCZcxSmapqBVFdH9kqsP7wk3PqeP+rTaHlmW468CNE2l2W8eiEKKqnDy+Ijf9Ad+3defkinPcn7gF/7eWsagvkIy65IBnlzOiDYRvZOeDIXqhz5PcgZnPaHGoBy86HuOe7gT/rbbdMSDdTVj9nLO4OzVKiuJawebVTvYchnA7bltm9mWu2cTbM/i6OFILC6b8BVVZtg6ACqgfrkEMdv5DkVTaPcFiLG63zCp64nHr40PD+fhTq7Wd3JdsuuU8pDdPZvSHu7v5AbhLiQOE9yWg1MDLvdrv6qqWM+2LO9WeB2P3qhNWVUs7lZE24jBeZ92v1mz4SX8oyqS7ouDBLthS+8+kN59tIu/CpIhvXulfjkV5FmJaelotX768r0bsizj4jOnspGIUrbzHaZjyp28Jv8oqoqqKIR+zOJmgaKodI87grye11XeUUsQTXvffVnXOAuIC5XVzx+hvdmh0FLu9TnLcsPPPYZzr0HfjFCrkI6xQ1FsoOBIn/Cjf+U72Oz6ZGHG5KfvmP/aKV/oNWjpXbaJTlgkbCObtmkILbgsUVIDbSPZ/RfLDcb7CQ+6HZqORUTOehNhoTE2LAxFRYlzSlNDKyvyRcD8fk0+WTEYt7EMFX+2YnazxDY1IdjU7rWqNsRkaUawCfmP/5ffjdNwvubz9al60Iui4I/8lj/F7ZO7+t6mkMV5HWipaPYkVijhlFsMy+D87RMaHY8iL8mS7LDCyJL8AO8zTDG8XH14w27pc/7W6QG/u//WbPYaYjOtu9n7MMN6tqnxVB6tvvC95jdLwk3E8KJPZ9g+oJM4UGVSokCINo5nkyU59y8mLG6WjB+NGNdx2CiIydPi1VUl2+/J5UUzu5yzvF/RP+nRPWofHPFZktM/6X6sSSa/K6qK7f5O3vEkYx3G3H50J5CMt09pDVpU9Z0cVcEwdNI0o0gFKa0Z2qtkYZbz6LMXNGo+fbAJ0Q1dJvOqIt1pXUNTwd9EzK7mVFSML4aHY/7ybkWr32Rw1quR12WNnFJI4oxg5ddsP6kRTy/nzK8XHD0cMarJP1ksMwPDMimLnCzOsQYdTNsk1Cp+/Fs1gnnO6etdxh0HtxEROzs+17+jpZu4ak5JSUdLeWSt+evPzvkzP3qE+e4O9+cd0RiYZGrCfR5g2iqjroOmykC4KsGZG4RGwu08oYrhpHBwKoOpEXG78xlvLU56LSxdp0oLSEvcdUGWF2ziBHMd4+kKWlWyWsbcvpjSaVqcPBxg1O22fOujFjllWRL5Caqq8PN+zT/OL/2N/9Qner4+VQ/6buXz+3/DHyEKE4q0IA4TDFOX+GdRMLt6ZSE5eU060kn4StHkNOxD4X9PIg23EffPppRVxejBQAD5fsLsei53z3pHL7l5+ZapCgEwpElGs+1huibhNuTq/ZtXQ6xBk7KsSKNX324SipGXi24YxL7kuINtxNkbR7QHTfK8lG95BVqjdg1xlKz0HoG1vFsR1jIJ97Ann6B87E5e1Tt5Ra3pJWEiL42GKcCIxY6X716jKPDocxcyv0gzUUxbgkGiqg53QUWVAdrdswmqpnH8aIhhy/pwPdvS7Lh0j7qomvoxQqzMArbzHYZlvDrm3yzkpfZgyPDilYaqLOROnqcZaZxh2AambZLFKVcf3LGdbzl585hhfWcNdzFUFW7LFrZ8LsMvTVeJQ9nHp5rF8eMR+ueHTL6pzaTw6b0eMrA9HjV9hvYOtSqx9ZKyjPi8+ZP89r/xz9Ewu4yfQPY05O++EeMNLc5bbQxDo7QzKq3AvdOIrZLsg4LKVGjYJrmncGXvuP9ww7DZ4EG/gxtqlGFBVhSYikZlgXITYWYKtia22unNmsWXr+kNGpxc9NFUhThMhUJEiVYWZFEiWyUqyqxgdrvgj/3U76u1YT/zn0/Vg+6vA/7j3/BH2NUQRV3XSKKUu+ciKTh6OBIJXlWxne9I44zOqHX4ZtvHS6Ei2kVsFwGGpdXwPoXV/YrZ9YLOsMXRo5GUWLKcLEnRTANVEd0yigAY9pHNq/dfQRvcpk0SpoTbEMM2JAVWVVDVDTZVsFGTl1OoYHAmEoVgGzJ5OcVrunInd8xDg02gFQX+OqQqqwMxZj3Z8OK9K7y2y8VnTg90lySSF6Bpm9LDr6pDZVUipvdYnsnJ46OvupO7LZfWQEwhZS10oJYPrCZrLMekM2xRlnLMX083DM/6h2lwkeWUpazs0jgjjhJM28R2ZR15/eEtm9mWs7dOxVpbVcRBQlVV2J4NCgf2mmHqRH7MzYd3BNvwkJFPY8Fn6YZGe9SSznwpnXtFldjw4nZFlhUMjrsYtsFuEzJVbeyhh/sLH2KcVpSDmIvOjLaZYSgJprJAVRQszeG95TF/+6XF9G8kjDyXx402xZnCrlNgPFVoBjnxIwXnxyX74KQVVZJxO9vwt76t4LTb5rTdRlUVwjhFWZW0cxNDrQfILRWrUDFfxqzfnbJ67yXD4x69sx6oKv5O6r1NV1a3ZZKCqqCrKnmSMvngis1sQ++4yz/7fb+IX/jrf97XfL4+VQ/68n7FH/2tfwp/HZKECfPbFVVZ0jvq1Ln1kLvnEwzT4PjxGK/l1mk5QemqqlIXBzJszxIUcZRy91QabMevHzE87aNoKkmwVzlZteBBKp+6oVNVQkuZXi5oD5qHBtt24RNsAzrDtnDVVeXVt5siBZfdMkA3VJpdwRItbpdMLxf0jtocPRp/tfLY0FAU6aGrqlIXV0pmV0tun97RO+5y9HCEbumEm4gkinGbroQv6lqkpslcYDuXY7LXcugcdaCExf2KYB3QP+sLuBJ50Pb/vAdhpWfiNBxJrT2RrcD5W6f0jrtARRwmUFXSd6+1VIZp1DHdmMuv3BD5MedvndDqN8jSnM1ii2HotPqtesBUbwRUmZvMrubkWcHwTE5U/spncrnAaVqcPD6qk4QVZVnrhJMcfyOlJK8td9b5zZLbWkBx+vYZ6rjN/OcOSN8MsAwVT0+xzICunXJqJ+hKwjS654+9/xonTosHt2MUFALXJyzAbVl0Li2cFzF6UaHZBkmWs/ryHesoZThuo39jm+Vjg0UcgKLQd1y80kALKiqlQlUEBbZ+usH4ySW9QZOGoZCuAxbzgEpXGY/bOB2XKi8pqxJMnULT8F9M8K/vaXca2JYYiX7vj/zgJ3q+PlUP+tUHN/zB7/uTtT9cx22JPnhxs+Tu+ZTuqM3J62MsxxK6apyiG7rcc7Ki3pkbqLpYVa8+uCFLc87fOq1Xbxnb5Q5FVemO2uimdqiaKrVCeHG3JtpFUkCondnTyxlplHH8aCxe9FqRuz/2pnFGEiZYzquXy+2T+8PLZXDaF0SwHwmooeEIEy8vUHXtACmYXS0El3XaE5RzXrCebkjTnMHx//lOXtV38vhje/IkSLj56I4kTjl784ROfQLaI4T34Z+85rCLkSbi2Zdekic5Dz57LnTdJKuppPLS2r/U9uu7OJDsQpEXDM/72K5FsA2ZXS9xmvUmwTRqqIecHtJaQKGqyoF5v7hdcvv0nv5JT9zvZr3Hzwt0Uz/gjzVDE3BFXnL77J7ZpRSehucDVF16AXlVkfwL76B/U0ahlqSxybCZMvCWVMolQb5F4ZQwO2EVWFwtSkoqjloWZmFT+SrWXMVIwXmZsFiHBFHCwNRpKBBHOS+0EsXU6HxTD/3IpFArt1FR9wAAcc9JREFUqlh+vtwsCKcZRZxx/qLCCyuC6ZbLdy8xLYOzb35Ew7UokozM0FENDVVRiFKZQTimgV6V7C7nPPlrP4VhGfy+v/rv8MY3P/6az9en6kEH+MPf/ye5eXpPXH9gtwuf48cj+ic9AMJtSJbmXxVoqVDQa8GDvwqY3ywwbIPeUQdd19mufBY3C5q9JsOz/ld/W9Tx0mATySqr7dQOti2X79+iagoPP3tRD/xy4vCVWqcsygPrS9MF43v1/i1xGHP21imtrkcaZ2znO1RdoTvqCFK5qA5rP4FDrIgD2ZPvCyK3TyS7fvqG4LLKsiBPX93lkzAhywpcT5p8u9rbVVUVDz93cXhYIz9GN7TDnbwsS3TdQNVEC3z79B4qOH58hOWZBOuQ1WSN27Lpn4g95MCnr6PDm/kWVVVFGKgqLO9XB83vyWsnBzhlkWa1eEO01KLAksDQ3bMJ8+sFowc1bltVCf2YIi9wmg66plGUJVqtg8pq4s1mvmN4MZD6b1awnK5RUITgO+wQvz4ke6ckPNaJMphnWx4eX+KqPQzFJshT7uIlGip2cYGleJRKQZ5XEJhoS5Vwm2BfJbQ1i6qnc9lLebJY8tZfjHhw3kU/a+P3NEKjoGqruDsFXy/I2hVerNN9PyD60oyrJ1Ochs35a+ICzEqBS6odB6PtUKQ5alZnPID13Of6xQzbszjrN3jzrTHf+7v+Wb4ulUw//ud+gj/4r/5x8qJg/GAkOKIgYXG3QtM0hue9mpcmH1pVUagQ6ES4jcRd3nZJk5zJS0kdHT0c1rQXKbEUeY5hmKBCFqWiK3JMqhLmd0vunk5o9jyOHsneOfIjwl2M41nSzwbZ0Wvi0Q62EbPrBaqm0jvuYJoivp9eLcTJ/mAo7LA6ACR7a3m5COPNqeOwG16+d02jI/AHtyHpsCRKMQxN7uRZDooiuKy6mnv94S2Wa8nD6kquXIyfDu1Bu25L1egoINjFrKdrdFOXFKGqsLxZHib8w/MBui4Pa1nJw5YnOXGUYpi6PKxpwd3ze+bXK44eDxk/GB3yCmUJtmOg2hZ5mqHXAMmsTrod5i2jFmVWsJlvqYDuqHPgoFN3C/I0Z367IvZj+scdadz5IffPZpimznHNKCiLkspzURyLomny9GeZ5HbFo0aCN6zwjTUfbafYussDd4yKxy5WiPICpTLRE41wq6DFKi46haFwV225Mu5p3/X4/MKhPylJ5j6BUlJ8YYRXqsRuhb2Rk11Zlcx2AdHfvMbTFIanXQxDJ9hGxGGKN2ji9jyKhkmpgKbKZ3ez8llfrbFNncGwhVpV7K7mhEHMH/vLvwPrHzbu+f/Kn0/6oP/W7/x3UHWp5a0mayYvZqIPfm0sD0smGl9Vf3XfKwuhxeiGjr8JuPzKNWmUiuRu2KbMa5mBUtFouqi6JtogXUXT6njpzVIwTEddWoMmZVWxnghltnfUodkXs0tZlHUQp6bMbkIMS6+/8WUzIF6znjjZDZ0sru0rlqCX0jirTwVmvU1YMnkxFTzyw6H8HGufNMrw2u7hCqNUHJRAm/mW5b3k6rtj2ZMv79b4q4DBmRz91dqhTr2qjMOE2E8wHeMApbh7OmEz33H8WHr/1EjjshCQR1mXegxTQzd1kUJ+eMduFXDy2li01EXObuGjqArtzz3GKArKJEXptFE3W9JtwPRyRhLEDM8HtX44ZvJyimEanLw2PpzO8qJAVep24j4w1HaFSTfd8uK9KxodTwwzrkWe5aSxWHb3Q0JUDefBGGyb+XLH336jxPkmuGj1aRgKcZWgWTvGHuyCDmGmo85MnJ2ONS+ZBiEvSx/zNZOjYxtDrwgvdzT/XEqr4eB1XbKWuPxURaFKxQW4u93iNW06HYcqK5jNtiRhSudxn55lyn5cV6lMDaWq8IOEIEpxDA2v6ZLlOdPphvhqw/FJm+/8pz7PL//13/GJnq9P3YP+Y//tj/OHf8t/yXaxk/zvWe/QhsqzHMu1MEz5dlSo0C2JjG6XPndPJxi2ztGDEVZdMllNNjgNi+5RR8oyRVUz3hSyREB9eZrT6jexXItwG3Lz5A5FUTh945hmT/RJaZKhKEr9zZTWKTAT07aIw5ir92/w1yHnb53QO+oIZXYXUZUlbks+qEVeouoKui4Dudn1nO18R++4S3vYoshylvcb8iwXC0pLEMHVvvVZR3HjIMFpieAxCRJuntyRhCmnbxwLErqCNE4BDkqpfe3RsHSiXcyLd69IQnHItwbCQfPXAaqm0Og06jKM/J72waP7FzPSOGV4MRBf3TaULr5rcvRgiDXoULZb5C0btagoghj/p59RpBnNjlvrh9dcf3BLs9fkvHa3ycwgRzc1of9GKUotwqiA+dWcmycTOqMWx4/HmLZJ7MfEUYrTtLH3gEVAq9XY86nP2m6QfvcRveM27nFI1pty+RMDel2L/hha3YBIUWkZKS/ueuxuc/KPQqyRQ8uxSNWKp401+a7ks/Mmg0ynSlK0mw2KYVJ1G2yzjDRIaagqjm0SRym31yvSbUTv2y4YODaFqVKEGXpcoqsqcZ6TpDmOY2IaGmGccfV8RpgVPGo69DtSi/ZMjR/8I//SJ3q+PnUP+j83+Jc4fjymUVNdtvOdfFMMWnUkVj71qiaCh+18x3bp47YcWr2G4JHuVmzmW/onPQYnvbogUkggZS+G8JNXIEhVZXm35PL9Wzk2v3WC07TJ4pw4iNFNXeKxdTLLMHQUTSXYBFx/dEeVl5y8cSR10yBhPd3UM4LuoYcO1Nn1gtVkTZZktAYtbMfE34bcfHiLZuhcvH2K13bJ9ycXZY+wrlFM9cllu/K5fPeasix5+LlzWl25k/vbEMM0cJuyzqqKUoY+qiq2lWcTyrzg6NEYu2ESbiMWt2vcps3gtC/knprUKjHXjM1sRwW0+w0UTWM9WXP75J7OqM3pG8eYlkFWQdVuo5kG2cAjLksUFTxVo1qHLP7eM27+3nP6J12OHg0xDFmvZWn2SgeVyWReN+WluLhdMrte0Dvq0D/uHTTZaZzSGbdxGw6gHMJVZVGymW3YrXxa/RaNrkts6Tz9woDOP33J2BqQPD0jj3TKMqf1mQ2mndO2JnzxdkBVNWnFJvkXC+Y/MSE1FB4+GMA3eai7CmYZ9pevsXYxRVGRnp9gNyxoGNwMSp7cTSkqeP14wLCyUDYFYZKj6wq2ZaAGOWpUoGuq2HvWIZdPpkSewflZH2/gkqUZ+fsztDKn023wa37rL+Stb3zwNZ+bT92D/j/+wR/lR//4X2Vxu6LZ9RhdDF8Nzwr5IEBFsIko0hy7KVijYBtw9f4NaZwfoBNVVRGHKVUpTSyQmK1m6JimTJ+nV3NmV3P6J12GZwNUTXRFaZQKwbTtyj9YRd3PFib78m6FYRlyRNbkm2p+s6Q77jA67x++Scuikj15XhD5MYqq4DQcFBUWtyuuP7il0fU4f/tUBI9RShwmmB/zxFUIuEJBYT3fcvd0UvPdh5i2RbgN2Cx2NDoenUFL/v/VQMZ9xHQz3aBqKp1RC0VRWU5WzK4WssJ7MESrgyxFPRyqilLKQbqK44lldfJiyuTl/EDBUWteX57m2G0Xs9+jUFTKozaqa5BXJXfLHYulz6jTZFRVqLMdqydXFHlBe9jG8axXhFhNCLGLuzXB2qc76uB1XNIw5f7llDIvheW/R0/ncsxHgXATkqaZfMPbFv4m4PmXr9C/8BkevDHC+naT6PMFizsLY2limQrWoxU5BT2nxLFKPtjoPNlOqX68z+NBl35pot2kbNSE4rtKWnMX+2/P0G5y9KqkOhviJxV3L+Zc/tIGF502rmEQhSnhfYJnGLQbDt4aqJ121q4k3sXM5jvUXcpg1MRwTHZZznK2paEoDLsNjDzD0BR+3Q99D+evj7/mc/OpetB3K59fefq90gY76aHpmqxaaueVeMVyFJWDj2o1XXPz0T2GZYhnvOWSRAn+ur47dxtoqnpYSe2P7LPrBeE2ZHDWx2u5pEnK7HpJmeWMHoxo1Meng31FVYh2UU1FtXEaYl2dvJiymmw5ejRkeC4psL19xbB1uZclOZr+MT/55Zzp5YzBaZ/huSCsdyufNKq5602ZkMMrhPV2vmU13eC2HAkNlRXL+zW7xY7BWZ/eUQdUhTIrhIyrS3Q02kUYttzJiyzn9umE5d2Kk9ePhBKjSkioKAosx2IvidRNXdZ+Sc7dM8khjB+O6NXzgM18CxW0+03Mev+v6QY0HArX5j4vCLKC7nEbzzZIg5T1//YUgHFDxXVrYlBeiuMOCNdBvVFxMS1DYA5ffolpmTx455xG15U7eZSh6RqaKfv1sq7U7sGWL9+7xrAMLj5zivfaBbGr8/IXpVgtExsHdapT7TQMRaGyYWH7LN0bTE3j2O1i3DbYXJUoP72j5Vh4xx7htxUUakX/b5RoKwgXPndNHVVTOEInemDyYpAyiwMGrsdF2kR1FFKnxJqBnkicO30ZoKoKXcNESXJWd1vudgH9QZPjURuzqijClHKzo9G0+dd/36+m1Wt8zefrU/WgA/yJH/yv+cr//hHBJkRRFCGcGrWsTlNEApjnrKfCSm/2PLrjDiiwme/wlz7tYYvOuI2+74VD3fOVPS7wqhG22HL1lRvshi3H5pboitJYRIWGJWqkPC+wbBPd0Ai2IS/evSKLMy7eOaW9v+NupEbrdRoY+zu5qqIZ8rBPL2fs1gGjsz6NruTHF7dLirxk9GCI13Kg4hAA2iOr0ijFaVo4DVfSZB/dEQcJ528d0x13Dnfyqqrq6X5JnmaYtoluGnWo5ZpgE3L+9qva527lo6gqrbpDsPeiK3VkdfpyRujHjM4HNQUnZno5RzM0jh6OD1qmw/ajrNhtQkpFo3HURx+2WVUlL66W6G2b10cdGopKuQlJdiHqdouqq+RxRlnJw6qqqoBAPryTNdNbJziuRVxHnU3HwGt5h6CSqguTf7PYcv9sit2wpQRjauyWAb4f0/78I+JfP0JpKihKCYmKcukQbBNWQYThaDTf0sDN2VYr1mFI++qUU8fGClXSVCHtVOTDCvdv5SSbFG+XoZ15+B2d1UcblpuQ4UmL1uea5F2VIijItRLNUcGHJClorhQapUEZF8y+Mmc+2/BAMxi8LqejNEqFZqyoqH6AoSl8/+/71fTG/xDda/9X/3ySB/3lV675gV/w7+E0HfonAlXcww2VeoIs0deURreB7VmiaHo2JQkTTl4/Ei8bdd+5KNHrb9E0zmpZo9hCJi9n3D+b0D/pSVbaNIjDmCRIsF3rQGmpKtAMFRDyyf2LGaqmHFZmwTZmebeq77i9Q82wqhRUVfzmm8WWMi9pdhsS2VxJDt325OXitpx6m1Bn13VNihx5gemaGIbOdrnjxbvXUFVCXO14ZGmOv/IxLAOv49ZbgUraf6pK7MfcPZ+QZ7lonBo20S5ifr3E8izxyFk6ZVkdOvB5PRepqopmT7DY69mGmw/u6rXfKVY97Zb6cL39iFKpn3YaoKgsJxuu1xnNPOL09SOMXpMkyYlNG+20hVdUVC/vUcoKvQYsLicbZpdzGh1XYre6ynaxEybdsEWr1xBvW510rpAX4Xaxw2nYNHsNyrxkdj3HXweMH4zonvbRmw0Wn7fxv+CheSVpnhJ+oONVBo5lEJcZl5s104drvvHYopGMSXcmSZVjrip6FzG7tcHaqxi9Z9DYQHUXcHmz5P1v1HnD7XJW2hibnI2WkTsaPHDI25AbJUaiYccqxS7n9nqNPw15bDXpdTzKUqbvqV7RdWzsAsqsQMsLHo07fO8P/JKvvz16kRf8/n/lj8okPCvqdJSk16IgRlXlQVXqO/HLd6+wXZuzt45pdhtkaUbsJ6i6guM5VEBZlOi6Jm/MWFhtu2VwcKmXZclquqHIShnwNOVbdU9p2UP59zCFZr9BlQtldnm/YnDaFyiDXnu0swLN1KhKSIIYVVXlm6/GMt8+mYhW6vVjLNeUlVeQCJDRsw4QB+3jCOsXM0zbOBhm/HXIdrGl0W7QGbdfIasA6jDNZrY9DDGlHrtm8nImhNtH4wN1p6hnFlWdS9dUFcuzoILJ5ZT7Z1N6x6+ElHGQCMfetdBrnzq1T70sCua3wgrojDoMznooqsZ2G5OGKe1veB3rrENpayh5iZ5WVHnO7suXbK4mNNoOjYZNXqcEoyDmeN9vqD8f1NeSyI9JQmGk296eoHpL7Cecv3UioM16W6J5HtvvPsE/N0itHMc3cEODrZHxwWRGVGW89kaL456O7USstwXbaYtOZtH0MqwHW5LMIrltEe1yXiw3jP67Ca1/7ALr2CMpC9bbiMFXQpoXHZS2iT9WKR3IO5AmBXfRljjMuMhaNEyTKM2Zznf0Lyv6Z21sx4C0lJ+vAKKc4nbL7/vT/8rXJwX2i3/ty/wXP/T/wfJsFIX6OKnUHPeC6c2S5c3Hq48awSYUkELLkdLGAaQg++4kSlneryiKgu5I6o3BNmLyfIKma5y+cYTbdCnKsl7b7UGQMWmc4TREzhD5EVcf3hHUa7T+cZeqEo92UZbYniVRyKxANTQMQwZc08s5y/s1/dMevXEHRZUHOE8KmoPG4ci+Rw5VlcAGt/MdbtOm2W9Q5tIK2y19xhfDAyOvyHIpqGg6WZqRBLG00zyHPM24fSb12OPXjhjVuKwkTOUq4ppoqipDrfqaUqQ59y9nLO9XDM/6knevKjbzHXle0B425SVaZ+1lgFawvpdpd3vYkrhxLPyALM05fu2IVq9J6dpkjoHSblB5FvFKfr/NosTeizf+55+sxRtnsnIsClm3qQqWbZKnxSGNqBs6wSasK7UFD945o9VrkCYZ/ibCMFSaww6KbTP77jHZqSQC09uU9Zd2pEXByVEHxzEJ8pTn4x3d05yzho2SWZQ+aF6B6RSEC4WXTyGxC3pdB0tViScxxV9YYbZsTk7bNBOFTFUoypzkyCBqakzaEcq6wj030A2VVRzx/MWGVmbylt2jlxjkRYWyydBrWkqyTVAXEb/4V3wr3/093/yJnq9P3YP+J37gv+bpT7+gKgBVhlFZmrK82xDtIjrjNl7bJY1SZlciHBg96EsCDHnrVxU1qy0j3MbohlqX9yvmN0tuPro7rIakeSW+b93U5cNeJ9hEzqCyXUr/vSpLTt88odF2JTe/2KGbOu1++7C/3dNisyRjfrMi2kX0T4XntmfbUcHxa2PcGiO9h1woikKwiUgiAUrYjk3kh1x+cEsappy9fSrDsLIkCTNEc2wd0M6GaaBbGrEv8WF/HXL6xhHtYYsyL9guAxSg1d937+u0nCZH9tn1kmAbMjjt4bUdEj9lcjlDURSOH4+FQV9WtQxCgI7BKiDPCryWU3vnA168e4mmazx454xmt0GeF2IX1eRlkhsGedPB9GzUbhN/EXDzxUvypy94UK8X0zgl3EQYln7gxFdlKScdVSHYhNw9naBoCkcPRpiuQbCOWN2v8doO/dOeKKFVFVSV7XeeszxSmVYB/fc0up6Nqqos/JC79Q79CzYngxZOC9BSKqOgiE3iHMIqwry1aZgmlQ53uc+zZMm3/BWL05MudllSTtakeYGhayiORerHpEcuxrBF1Fd57vg8UWcM0iaPWh28yiCJctK4oBmpNCIVdZtjBBm6qqDkJf4q5D/+09/79UeYAfhzf/gv8jf/x5+QD1/NCVMUcJouulFPVd+9QjM0Hn3+gcDy86p+64PlmORZSZHl6JaGYYrT7PrJPdvphtHDIYOTLoqq1obRjGbXxfJqEGT9LVVVFf4qYHazwHYtekfdw51xebeiNWgyOO1j1OSTstzbWHN2K3mgvLYnIZHZlsv3rvBaLud13XQfEpEPvwRoBJ9loGka2+WOl+9do6oq52+f4LXEcBpsQnRTo9Vt1ESbGpShqyR+zOTlXAI3532RAmxDZpcLbM+q1U5mbaopa4pPwW7pUxQljY6Hbups5jsu37vCbTo8eOccu2GRZfLPK9lzvR7+UQ/QFFbTLZdfucbZD9AadZ13F2E5Jk7LgXreIv13DT/KuWk2MS2dUddDn68Int+xmW1p95t0xu1XpzMAlZq0K6WnfeV2cbdicbuif9pj/HBcO+mkKKLpGlkFd9/1EN3SMXoGxjJn/cGW+dMVo3GL4Ws9yr7G1isJuhnNTomql/i+ilLquIlBkVRc7TZcGSuOvSavbXs4W4U4SnH/12sctcLQVEpFhpmGppAOWtwrFX/vzZi+4zLquyimwiaLydYlbcOig4USVWipQmNeoAUF/s2a5bN7HNfkX/63fgk/97v/b+Be+6R/PumD/p//m3+aJz/1/FDj1E2DMi+YXM2ZvpA75uhC7qqRH9fQCYtG261lJNUrGIMfM71akCUZw7O+TI6jlMXNEkWB4flAAJJVVRdN5OjkrwPiXXxIn6VJrb+di2ihfyppvTRJKYoK09Apq5I0ztEM9aAHnryYMb2c0zvucPRg+Mr8EiY49d2yrCqUmrsmR/Ydk5dzLNdkeNZHMzV2C5/tbEuj16B/0q1RTBVVVaJqGmmYsK1bYc1eAxS5k989mxwgHYKsysizQkAGilw5FEXB9iyqCmZXc26fSgLt5PEY0zGJg4Q4SnBcC8u16yyDJATLUtzos8s5XlsGaHvEc7gNaQ/bNd5LBmcgKL9gE+LvUkzHpNm2KXKxzmwXO4bnffrHvfpFn1N9vP8eSL7AbthkScbd03uWkw0nr40Zng8PfXVZFZoHzbNu6ZRvjwmGDh84IcZfWnN60mXQ8Sh1hZWeszlVaDRtaCvQTFEraeklkcL9y4BFEXJWNmk9tCAt0P/CjsjT6HVdesucKssodZXSKFFWMYvtjk23Tce1sU5ddk2Ybn2WjxOO3SZdzUWNFZRdhbMEpawoXwQkNxsc18SzdLotm3/zD/x9BcVf9edT96D/oe//k1x/dIumCXRi8nJK7Cf0T3o0emIpXd5Jsqx/3KWxB0GWJWXFwU22W/qoukaj46GqsJpsuH06odn1DhqhPHv1raoZ4nYr8hLLka61vw54/qVLyrzk4p2zWkWcS/BFAbfloCjCm9drM2Yap0wvF2zmW4anfSlulOI6K9JcSK5tT+7kZQl1CMdfycDPbTo0uzJRn17O2S19jh6N6B11awKN6JFUXXbIWSJXDsuxyLOMu6cTFreruhU2PPQBsizH8SzBVNVdAd2o5x5Xcxa3K7rHHfpHghbeLrakSU5n2KpFFghcQxVE83bhs1vu8Doeja4nR//LBZEfcfRIBmiKIrbXqsZAR6EM5SzPOtharz+8ZbcKePDOmcwDyoo4SoHqAMzMs6Luv2uCBPvghmAd1tqulrQPtxEoipxKdGHSqap6yBNMbjeEu4j+d30eb9QhjzJWN2s0Q6PxZp/sDYtCh1wvsAONoFUwywLCLGVcNvAUgyBPeWovUFWVn/13G3iOrPmUncSjS03hfgRxmWOOLQxHw08S7j5cU5QVj4YdnDdsMqMkTcTa6gY6yk1GEeZ0diW2ohFEKTcf3BP4Cf/v//5f4/Si/zWfm0/dg/6f//Y/zYc/+YTdKgCg0fHQDA1/5XP1wS2GoXNeQwdFcSQ88r1eKUtkf2zappQ2nksd8vjx+NDK2jvNLNfCqkkvChI1VZRXO1nd0iXD7UoJYzPbYjesmiKr1RPy/Y4+YzXZkMU5nXELyzHxtxH3TyeousLZG6IvLku5TyuK8jHzi8RAhQbj8/IrN/WOXnbeZV4RBjEo4NQo6rIQyqxh6kRBzO2TCf5aiiadYesgj6yqis6wjenUd/K9wzsXZFWwDukdd3HbEt+dXs0p85Lj18Z4ba92eMu3uMwQQrIkx/lYIvHlu1cUecmDd85pD5sURUkaZodgU5EX5LkYWHRTcgiX792QxikX+wFanOOvfXRDpzXqyLs7l5fwPtRz/3xKEqccPRzhHfWJs4rlZIty3mXYdrC2AVUUy1G/btyt5xuKrKA1aGJYBrtNxM2TO+yGw/nnH+COehS6it/XiV5rglKRrDLiRonjmhiVxnoT8nSyQHNUXn88pKWaaPcFaV7gLSqcEvIgQwlT4s+0yIY6izzmo9mCvFPyBUY0WiahleFrCefHO4Jtn8LXUXcKbqjiLkriZczl9YKsrHjN8/jZ3/46/4/v/Sc+0fP1qXrQy7Lkh37xf0AapYcHcHo5EzDBcU8abI5JGqUSnrCNgyOsKmV/rOpajSi6JQlTTl4/kilwlrOdbQ8ffLuOXlY1maYqS7ZLn93Sx613slUJs7sFu/lOcvP1gEeGdcWBPR/uYuHIe5IsW9ytuHr/hvagxembJ9heDcr4eN00l286wxQs83ax4/rD23oLcCxsuyDBX/oYjkl70KzXaOXHOukp8+uFSBVPu1iuRbAOmVzOsByLk8ejg9opz4vDGs5fBZRlidd0MSydzWLHiy9fYjmCkfbarhhY6gSacN72MwQTTZPj+eV716i6ysXbInVIwgR/42O6Fo32vs5boWgaqiosgcmLORUwejDAqlVNy7sVbsthcNbHaDfBtOS0s91K/322BaDVbx1EEHfPp/Q+85jjkxbV4yFp16EIE7wv3VDWcE6ocBs2APPbJTcf3tEetDh58/iAv8oKBbPpoNs6m9c6VB1X7tddg/s44Jm/ol/anA5bUgHOUvJphtk1aCom9rRAKRSsKEePC5ZJwld6KQx0RqMGpqORFgHe0TXvdEM24TmGXTHbOZQrm2xnEwUZ8Rc3qLuc0aCFYaiEq5jgcsV/9T/9G5/o+foH8qArinIO/DAwRq5f/3lVVf/pz/T3fFLc85/4wf+GJ3/3GXfPJofQQ3PQgFKSb3mW0+o36mOzcuCRoyCZ7skGRVXojNroes1ufz7Fa7kcPR4ddtV5VqKqgArhNiZPM5ymg2mJQOD6w1vCbcjDz57TGXeggjSq12i1rjhPczRDq++/BXfPJyzv1gxOuwzPBFklA7+URkcIMAeijSZ7tN3KZ3G7wnalYadqKuvpls18Q2fQEoyVrlIUFZQViqaQxVndNFPxOi5Ugo26q02wp68fC3QxSckSEUhIFDerB5ZyJ5/fLrn96I5G1+PkjWMs2yQO4kPM162RVRIa0lAQos3k5RzLsxie9g4zhN3Cpzlo0Bm1D5FjkHWh8PuEAyjudFjeL5ldLRieSQ5BM3TyLKPUTcxRjyLOiG6mqEp56KhPXs6ZvJgyejDi+J0HGLVjLy4yqp/1EDPO0L88gSzFQE4i0mWQYozQaFTJxccZbtvBbTiyLdFVNNuBbpvV0ufJmYk9thm0PSpFYRPEbJWEoenRbFtUhphwzB1Y24pdGLP2E4ymjnPkUKoVsyhA+8Jzzhoeb7fB00uyAiapxzb1WG9Ubqcq7lanl1gYQcVqumP77pJu1+VX/6pv4xf+4m/6pM/kP5AH/Rg4rqrq7yqK0gR+Evieqqre+/v9PZ/kQV/PNvz2n/97KDLZ1xqWTrCOmLyYivjw8Ri344p8sNbPKqpKHMbCI3etuqMsEsD51ZzBWa/2cBtkcUaaZJiWcYil7vHQiqqynW+5fP8G3dA5e+P4YP/01wGaodPqSX2zqL9V97n26eWc0I8ZnPbqHXLK7GZJVVYMzweiSoIDUEGpLaOiY5YHKkky7p9N2C19Tl4/ZnAsd/I0ySiLsva6F2SxvFws1yJPcm6f3rG4WzN+OJCsfQ2nzNIcr+UIl74m2mg1LWZxK1Pq9rBF77gDiCl1T7lpdsXDVlUy7a7qKO5uJaedRrdBkeVMPj5DOO4eYsb8HxBbhmXKqSbNuPnonvVkzfFrR6LB2sMqCskhgELVaqOlERqQJhl3T25Z3q05ejw6tNiCbUI5HtFMdhiGTvzZE+ybLXrd+ps+uWY739E96tAaNMhTUUoXacHgrPdVQFGljhtvVwGx18LTKty2S6oo3K1ibj7vMXyjQ7fpomWQqAWFCbquEGYJXOe4uoFXmCR5zvVszW4d4/2SBsctFxNoFFtopgyaObtcZZlqxJsWSuGQBRX3Vzvu1z6fWXmcNpuYBbRtix/6Xd/zSZ/Jf/BHd0VR/izwB6uq+it/v7/mkzzoWZrxB7/vT3L79J75zZL7ZxPa4xanrx1jO/JBSaK0Fh9Kj7kqX+27o13E5Qe3RNuI87dOaQ+blEVFsA0o85LGwa9egxVriMN6umFxt8ZrOXSPOiiKIoGVxY5Wr0n3uIOmaZSl7Oj3xZjtYkdVlrT6TVRdVmLX799iOSYX75zjNmWinKWSm9d1jSROKbISyzXlvroOePrTL6GEi3fOxAmfFaKf0rTDyWVPT9UNjThMuHs6IdxGHD0a0qzVR8v7NYqq0quRVBXV4b5a5iXr6aaehreE3BOm3D2fUaQ5p28cHbr3MkPYq50SsjTDcsTAEm4jLj+4IfZjmSGMO9J0C1NQwHYk/75nvumG9jHaayS01/pn3K18VEWh2W/WL7KyHowaZHnB9Okd/iZkdDGg0XaJg4TZ9RLd0iV/fzamCCJK30c1DFBE0xVvdjR7jQPH7vrDW1RVlQxE14OiIjvMSeQKlCUZlmdj2QbBNuJqEpA3Wzx4fYT1do/NhYmv5agKOKpOYlekjRTT0DBUhXBXMP1rK+Iw4eFpn77j4o9zFoMYY2nQ1CzMoxi7EZLlNk0nJ8wKvjRN8L8Ep+0mPc9DjSvKrwTo65Rux+X/+a/+fF5/4/8GSiZFUR4Cfx34XFVV2//D/+17ge8FuLi4+JaXL1/+jP9bT37qOb/rn/kP2S59jh+PDxCFvfiw0fEOGXRFQcIeikqwDZheL1BVhcFxD8s1CDYRi9sVpmMyPO8fgiX7THdVyLE5DmK8jovbcEiijPvn9wTbiOPHI3rHPZS6k10VwoqXsEqKXn+rFkU9R6hppEePx1i2CCOSMMF0zcOHv6yqgyF1u9iJotnUOXpYgzK2EavZFrfp0B236xVWvdtXFZI4ZXW/psgLuuOODJdWPnfPJjieLVop1z4M/PbRyahmsblNB92o7+Rfeolu6jz63AVexyPPcpIwQdO0g4u+zAt0S6you5XP1fu3lGUpsM36tLNb+ZiWIS+7r5ohyLR/ejkjzwoGpz1szybYBMyuFzieLXt9x6ixYBWqqpDnBbvFjiIvaHYbaKZ+eIE6DZuztyRTUBQ5maKjGQZKHJKEKWVZ1S02jfV0w4t3L7Fdm4vPiv89S3LiKEE3dJkBFSVVUWJYgsrerQIuv3IFiiIdhF6LNM0Jooz0O97APrIpXZWiKik6CkqjIigTJtWU6NrhtNnCbWrEeUb+v8YUZwa9sYNlq+BmnPRXTJM2uwRibUZcpFjlkObcI96U3G636Cg8rtp0I4WmZfEbf/13Mhi2Pslz+A/uQVcUpQH8GPB7q6r6Mz/TX/tJp+7/3r/w+9mu/EP5QtVVxg+GImc47LuhUhTCTUTsR5i2idd2KfKcxe2K2fWS3lGb40dj9I/tjzVdk2ZWDc63PQtNU6UO+aWXVFXFo88/oNVriCfMj2TP3Pz4t6p64KndPZuymW8ZXQzojsSksplvybOc9qAluXn20VaFqiolpz7bYnkW7dqasrhbi2X0qCNsu7p1JwM/nTyr3W66Lr8HKhbXQk/tHXdrzJZJFiekiaz69n34qqrd6JXci2+fTPA6HsePRiI09GPCTYjTtA88vGrPqFcgWIfMrhdoukb/tCvQi6XP+l4UzPsYclVUEsVVxcDiLwUt1axpr8vJmrunE7rjzgELlmcFRSpZe4CklmE4NVlmdr3g5sPbQ4rRcqQbkEQplmMdOHz7vb6CUvvf73FbLsePRhimLl6+XYzXcmQdKz8lSs2K36185tdLDNOQn1HX2C52rGdbWoMW/bM+nHSp8or00YDCUZifFMxMH0WDQctASwxm65RrZUnXNfmc6WJrJkkCvqJheCVNz+dyLZmLnqejKhqTecr1T4VUr1W80RjR31oo6xI2OS3P5t/+jb+Ahmt9kmfxH8yDriiKAfwI8Jeqqvr9X+uv/6QP+tOffsH3/ewfoDNuc/LakeyH04ysFi3uIYNFXmI6BoahE+4irj64JdxGnLx5zOCoA8g3WZ4XuA27zsrL3lo3pCizmm6YvpzhNB2G531MyyDYhGyXAXbDEiS08epbVal39Kv7NXla0Bm3sR2T0I+4ezZB0zRO6k58WVYUeX74MIXbiCRKcZuOfHsHMTcf3bNb7njwzjm9en+dRAmUFYZjUhVV3Q2XhF+W5tw+u2d5u2L8cMTgtIuiqATbkCLNcduuDNpq6ISmywtjPdvWHvkG3XGXqpIiT7SN6R516AxboPBVVtR4F+NvAkzLwOt6lFnB5HrO6m7N+MGQ0cUARZWYsRhYpJabRh+jvab5QU09ejBkfDFE1QUVXaQ5dsOWF1mRC1PflOP77HrBvH5ZD056h61EluQ0+g28pvsxWIUMZNezLavpmkZbPHiUFYv7NeE2pH/ao1Pv9aWEgwxvtzHhNsR0DBot0XpNr2esp1tG5wMGZxIAytOcogSj4RA/PsI3IXjTwG7opM2c22zFdbDhpOFx5vZxDPC8DQU5amGymnWJiwKnndK1S3Q959ku49JfYJVNztQRlmYQRTmZX9CbGDTRabo2/9a//PNpOP+QHnRFImR/ClhWVfWbP8nf80kf9O/7th/EadgoQBTI/dBpSo66qrPkmq4dutp3zycoiiLE2IaEMNazDbqh0z3uYlqG7J33YY/apxZsQpodD6/rUWQli5sF/jpgeDaQwoimkmcllHJvzHOJ46q6gtsQltvyfs3VB7c0ew0u3j7FdiUqmkQJhiG5eZkjVIKE1iQ3f/mVGwDO3z6l2fVIo1cMda9b89rqbrimSwll8nJGsA0Z11AM6bKvAOrUn6wZqVt3ZVmxW+6I/YRG18NuiEf+7sk9SfTVzPcsyUEB3ZCQ0p4vZzq1s27Pw3v7mN5R79AmrMoKt2kfVFISGpJ47N2z2q7zaERn2KLISzaLHZQV7WEby32111c1OS0tarhlZ9ym1fFIkoz59YIszTh6OJITx6GvL92A3SogCRPcpoPdsElDkVDEYcLpGyd0Ry0qBfKk/vdQc+Mlb2Ec0n83H96zXWw5fetE1M5A7MevLDNAqerw5gnpax5BA+ZfWfHB44DzBw7HDRdNK9mkCXmh0FRtOo0CTcvZrRooqFRmzr0fEuhTGlqDntamKDTWYUq8rhhFLh3dwogUyCsen/T5Lb/iHyIcUlGUfxT4G8CXgD268AerqvrRv9/f80kf9L/1Iz/JH/r+P4luaPRPeoJhrqoDyZSqkuPvcoft2TIhLiuW9yuWd2s6oxajiyGmZZDnOWVeCjm1kjVPWVRYnlXXPX0u378hSzIevHN+OH4nUQKAZZs1BTUXUYSlk6U5kxczZldzRhdDObpqKuE2Io1T3LYrc4RSIJS6Juir7bI+HtoGg1M57vqrgNVEjsC9427NUJdJ8H6CvZ1vKaqKdq+Jbmj1vv0OyzN58JlznIZ9OAIr2l6uIPgs0zUxdLmTP//yS3RD58Fnzmj2GmRJVqOiNNymfUB16aaBpqmHIVae5py8cUyj7ZHGKZvFDl3X6IzaUk0ty8OpJUty5tcL4jBhcNrHblhEu4j7F1MMy+T40QjHkxnCHgsmtNeQPM1xWw6mY7Jb+rz8yhWarvHwsxc0Oy55UZLFKYoi3YA0ysg/xtDbrX2e1UPNB589o9VvkmcFkR+halrNl6soygpdE3/bvo0YBzFnb54cqs7bpY+mqbQHLYxui0qRa4wKZEXFfBlx/7iF/o81aY50Ej1jFu2w7Iy+0cXGoshVdCvFtgviSON+lREqMaO2QtOwifKCSbxluVW5KPsMcg8thSopMQJZozqmyX/0G3/JJ30mPz2BGYB/6e1/nf5pTwZdeSFFirqxFAcyVLJcC8MyiP2Ymyf3+KuA09cFP6XUkgE52uvomkaWyj3OtAyqqmI12Ug6yrM5eTzGbtgS9lgHmLZJo+sdZIJKvUZLwpTJ5ZRoJ8jiRscjTTIWt0soK/pnPbyWS1Vy8FtTcfCeOQ1bgihpweRqxna+Y/RgwOCkLxLCLK+786IvigMZGpmuHOGnV3NuPrpleDbg+PEYwzYk2hpnmI6BaZnkeSlkXFPuvKvpRgZ1DYejR/LyC7cR/jrAaTq0B3sDyys3euQLQ19RFDrjNqYlCuP59YpG1+XooWiliqKs5ZBC/PHXIQBe2xWG3v2aqw9uaO1pr551KPJohn5IE74qxgjHTmg/FmdvndYDUtFeW67xKofwMTHkbuVz+0TqxkePRrIZ2MVs5huchqCw98f7/QspCRPmN1Jb7h11sByL3Spgejml0RYOv1HHbyvHQe+0yaOE7fM7siikedTHbLvs/JTLpODml7f4wjsWbdsgyRWKqiD0HRwnI9ciVluDk3bGsJmzihO+stkw2Zo8Vk7pGB5VAfmmQEsV3FRDW5XESsEv+44v8It+7juf6Pn61D3o/8N/9qP82H/342IG9Sy0+u6nVAq6qYEi1s+bj+5AgeNHcqRL4gx/5aOqKu3hXigv/3K1+ui9mqzZTLe0Bk1afXGjracb/HVId9SmM2qh1cm3Cmptc8Zu5QtxpdNAN8XicvXBLZZn8+DtE5ymKy+letKtaqqEa7L6W9XSCbcRL9+7Ig4SHnz21ekh2klG2/FslJpIo+u67Pnrgd92vj1cKagqtssdeSqxTqfeQlBP5stSKDyb+e7AlysLccmF65DeaVc68YpooalLQEmUEG4jUWG15eeZXy64v5xx9HDI+OGozvJnlHUfnEr23K8Y9RX3LyZMLxd0jzscPxwd/GxxmOK2HCzLOMw8tBoqsppsmF7OaHQ8KcYYGrulT7gNafYa8u+KPQxkD4MMWE02GLakBlVVBn7r+w3d447QfnTJHpSllH/2milVU/HaLlUFi7vFV8E19tetPM1lQ6Kp5M02ShhhGirkOYubBTezkEbD5uzNI+LvPME/1qkulmiYWKpOkoJlp5S5QxXoLMsYtfke68RjaBxhRG2iEHZRSmuj0TAszFjBnBcYeUXZ0KlMlf/wX/0lmObXmU01iRK+/9v/bZrdhlgm6yO7ABIF9St5c1vIKYrCerZhOdnQ7jcZnvcPKKf9t01ZlPhrnzwt5IPmWDI8e3pPHCacvXlCdyRMrv19WgZLokTWTV3WTXnB9GrB7ZM7+qd9aXfVa7Q4TIR04si3KiCMckVhu/S5/egeTVc5ejQ+zBE2ix2WbdIZtQ7/zCDT7v39O40yuscd3IZNFMRMXsxQVZWjxyPpshfl4aGp6gl5EiV4LRfLtUTb/NEdkR8LGfeoKx62KDvw5QTkIAM/0xJv3OX71+yWAadvHNOvwRPBbq+sqhn19QxBNhAZ06sFm9mW/kmX9qhNVVSsZxuKLP8YuUeBqpSOeFWyme3YrXwanQbNnidcvas5kR9z9GAoxZj6/r+31cZB/IrIswd0Pp+ymm4ErnHWR9GU+oVUotvivM+SHF2X1WGe5Ny9nDJ7OWP8cMT4YoCqaURBRJ7mOA1buARZgaKpmN02la4zXwRMf/oprV6DwbiFZmhsHZ3L7xzTe7Og1dUoSjnmu0ZOUapcLRSWX04w0Dj9FgXDVpntMu7vS1qmychu0lhpkFaYyxI7LCnnIbNtRIXCb/+BX8znPnf2NZ+vT9WDXpYlf/x3/Fc8/9Jl/UFSQFHEt5bUdzjbJA5jbp/eE6xCjh+PDzHRbE9ftXRAqaULcjREUdhMN1y+f4NhG5y/dUKjJcfvyI/RdPUgTBDumtzjkjjl/tn0oBFqDZoijPg/EFf4WDilKmWNtpltMG2TzqiNoojSeHW/pjNu13w5jaIQkIMMpEp2632X3RVN02TD5fvXuC2XB++cyZ08LQSUYQhlJU1TqkIeXK0GZTz/ksAfLj5zVg/vxI2uGRqN9p4vJ8x3TZOw0d2zujTyaCTwhzBlNZGGV/+4K2GjqqrR11BkJavJmihI6I3bWHsDy7MplVId0oV7NPP+6BxtJblne5YUYzYBl+/fkKcFDz5zKo2/oiSNpBW2vyoU+SuLa+THXH3lBn8TcvGZU7qjDqWqEecV5W530FztnepqzeG7fy7/LscPh2LkyUvW8y1VUdIdtbE8G8qSSqHu65esJ0LPafYatLoeBQqLXGXxdp/RsIXxmofSz8g6GaigKRXoW+6DEks1cYMm+UZnst2yu4kYtRsc1ZKMKswxllJQUjcZQZLjXEuW4vzxiN/0Q1+Hd/Tl/Yr/4Nf8vwAO3xSKoqDXgYbtfMfVB7eUVcnF29Jgy9OMYBuhqmptRBE2uKrJ3booSlb3axY3S7yux7B2fe9WAf46oNn1aA1ah8KI3FW/OpzSGbWxHJNgI4Ml3dA4fjzGbTlQQl7UH2Ig3MUkUYLTsLE9iyRMuX16z27pc/zaiOHZEEWBJBZ4pWHL3CBPMrR64FfkJZOXUyYvBAk9ejDAMA2iXUgSZbgNB9MzD7l5TddQKlgvtswuF5iOITLJumq7me9o9Bp0Ry0J4RT7HLqsCzfTDQCtYQtd19jMt9w9m9Doepy+diTzkqygKIoayiGDzaqqxAyrqaxmG158uYZVfOYMtyVwjTRK0HQd05Ejcbl/IWkqm+WWq/fvUBVFgjA1WSbYhBimTqPTAJVDYUlRauDlswlpmnHy6AinIYDQ1WSL6Vq0vuuzGFWFsghQN4FIKOKE1WRNGud0Ri0sxyLaRtw+n2BYouB2mzJALYpSmsMo+NuQ9LAStQh3ITcf3pOlGQ9+zmexvv0xqacRmSWlBtUQEisjUTO6/RxTNfCjiifvxSzCkMfNHidmAzMA7XkAaYVTyQslL0vMKENXFIqyYjrdMr3b8MN/5bfT6X+d4Z4jP+I//U1/nM1sw37tUpYV29mWxf0Kt+nQGXbQDBV/5bOabmm03ZoY+zFFr6pSFjnrOr/dGrRwGzZxKP32JEwYP5I6p6qoZFkmR0O9lgkGMZqh1/dfyYZf74Mbrx9heQI+SOMMw3w1jZc4romqqfgbqW9maS6T4zqmGmwCVE3D63ivXi6qgl7ffyeXc/zljsF5n3avSZ7lrGc7iqyge9T+6oy2otQGWZ/dysdp2jS7TXHAXS9qkMNAWnf1EbgsygOvPQriWpooFprpyxn3z6eMH4wYPxRxRhKmB3S0amgU9WBTUFQl85sFd8+mtActxg9Hh3lEHES4TfeAywLZeYPEi2fXCwzLYHhWh3AWPpv5lla3Qfe4e/C+708PSZgK8FJTaPdbaIbOZh0xeTmj2bI5+cbXUYdNgrMmVVxiFCXGu1cECx9SUTep6sdElu2a9tOQF1KWZmha3dRLMspSXkiqrrFb7Hjx3jWKAhefOafZccmKkihOSf7JdzD6UhIqi4qqo6KNcqJmwAt/wTIoefv6hJ7uUCQluyCmMS/pFyoGFVUqajENhTxKWUw3bDYx7XGLf/pX/Gx+6a/+tk/0fH2qHvTbp/f8sd/2wwdM0W7lk8WZ8NM8sarev5zhrwOOHg5rLpg8nEVeyrAOiIIYBUX28QqsZ1su37tGM4Rj1ug0KPKcJExRVVXisVVJkcl6Sa8LL3fP71nebRid9w+yg2ATkSYpXtvFdqUbXlEJnwzYrXbMrhaYlkH/pIdh6exWIevJGrftMDzpo31Me6SpQk/ZLrbkWUGr36x76QG3TyeomsLZm/JtV5YVRW1T1TSVKIilG14rjeJdxMv3bwi3IY8+d0FnJBHiOJQQjuXubTVFTe/RSeOMmyf3bOdyNekdd6RVt677Ab1Gva0o68GmRp6LI249WQtDf9iiLGSoloQxvZMuzW7jsM+Xp1UCKv7Kx3Itml1PyifXczbTLeMHw7oGrMrcoH4h5VlOHCQYpiYvpKxkcjljcrXg+Fs/S/ebz1B1iGyVWFMwGxaKoVDtCtwXa8yyhLsls+s598/lhXT0aFQf/yPiIMFtOEIaqk9Ie8nnbukzeTGTF9J5/wAV3c23uG2X3mmf+FvOiUcOYsVR2ZkJ94UPD7a0lS56aREvMrZfjqhODN70G3ilBlmJEmSYfk6V52xn4rlv9TwsxyCJMmbXS/703/ydn+j5+lQ96JOXM/7Qb/6T+DV0wrRl7bJd7Hj53hWKKhnkRrdBWUgPXFEUnKaDWt8ZVV0V+moNVphezWl0PUZnokkKdzHBOqgHekKMLcoSTVEO0+fZ5Zw0ThmcvsJPzW8WKIrC8KyH23SpqCjzercPB22z5QnWqshKptdzVvdrRg8GDE/7aLp8cIu81h6VFXEgXfZ9KGNxu+Tq/Rua3QbnnxFNUxa/0jQZjkmZyUtCN8QUslsKlENRFE7eOMJrOaRRKrYaU6fZa9ZHdvGhqZpw2GdXC5IwYXDWw2tKaWR6vUA3NUbnfbG77PsBikJZlewWPkmY0Ow2sDyL2I+5/uiOIss5f/uUVq95qPBWSGNOTgUSwrEck9iPuPzwlnATcfbmEf2TPlVZEocJVQX2x15IhqnXfnRp6q1nW45fO6b3+ASl1WCT5YQnDcxjWyKxSklpK+iVgnmfsbn18X/kJ2j3mnR6Mi/YzLbEYULvqCslF6r9f1BA8N6LHYZj0up6VJXC8m7JZr6jf9I9WIQqRSU+axF/fkzg5ARBQnZSMRyCppfME593rxMuPmhy/KCL0dExJgXqvJCtSlulug3RtimNWMFAYbvyefnBHVQVv/MP/Yv8I9/x9id6vj5VDzrAD//u/5YP/s4TykJimss7EQD2jrtohqCddwsfp2HTGbcxasMIinS8i1TWaME2ojNq4bVdsiRncbsk9hOG5/0acyS1USG1SFfbX4VyrG450gufbbj+4Ban5XDx1ilOU7RGWSrkk0MbLRdGmW6KqfTlV64JtxEP3jmVyGlZEYcxVQmOJ1z6ffZerwWQs6s5s+sFg9M+/eOuHP/XAUmU0uy4ommq+/eaplIh3zir+zWGpdc1UXkpriYbWoMG/ZPe/zmEk2Qfo/eIQnoz23D9wR1ex+X8rRNszyHP80OGYd/wKutTga5rcif/0iWmbXLxzlktlMiIgxTN0LBdieKWhbzUNF0l2IRcf3hHnuZiqu3KWnS39NEN9XAk319nVFWm59OrOdEuYnwxrF+80mLTdI3Rd30zVtcjdSEY6lSWSm6ULJOYoEh5410FTzVItiG3f+F/J8tyzt44ptlvQiVDwv1EPwkTkjjFrslDcZRw/3TKZr7j5I0jBsc9mejXWmnTtUFVWX1hTPCPeBiObGtuZ1su4y1HPZfzZgcz1QhIqBYKjVyjGevEVknWVjBRMdcV+RdXzL58R3XR4vi4i9W28Xou/+5v/sWf6Pn61D3of/6P/mX+wp/4q8RBTKPbqFcoGdPLGeEmYngxoH/ceRUyyWsJfVkR7UKqEtymLXer5Y4X716h15bSVr/GHMXJIWFV5hVpmskazTLI6+Tb9GrO8KzH8EIGYXEgDHLbNbHqI6SivBIt+OuAmyf3qIrC0eMxdsMiDVPW8y1mLWPUTV3IKZWCqivkiQgPoiCiN+7itR3iKGVxvSTLMsYPhjQ6DRRkSAQSagl3MdEuwnItvHrodf9ixnq64fjRkOHZAEVXyeK8bqDVQomobt3V9J7Jyyl3zyYMzwcc1ffrpC6N2K6F4UiXHQUMXSQPq+mGyYspTsORYIkpXQN/HeC2HJo9abFVRR0aUhQiP2JxswRVoX8sBp7tYsf8doXXcRnXSUY5PUhIKc9y/KVPVckLSTd01vMNV+/fCk33rVPspk3Z8MjyEh6N2T02CcnJjArH1CV9OPXx//wt1d95l8efOT3gp+MgRtM1LMeiLApZxdW12nAXc/n+DXGQcPbmca20Lgg2AYqqiM+vHkoqqgqPRqzfaPLB2Ge9CBk3mvSaLqVascxC2sMlDa2FGrYwqoKmlbALPYpSYb2Jqf7aBlc3GRVSupppFYsiY2za/MC/8Yt4/Gj4NZ+bT92D/u/9yt+Pv/JRNI3tfMfl+zdQVTVbrElVlERhDAi9VAHyNJcjuymVx/nNgsnLmaScHg1lJRfERLsYyzXx2t7hG33faEtDsbiE24jhgwGtngy11tPNoRa6lx8eLC5QT7W3mI600VBgfb9iNdnQHrYZnPUwTYO8KKhqf1yZF/jbqM6KO+imxnbu8+LdSyz3Fc6pyAqhwmhqPSQSE4zhGPKAbSNevHdF7Mc8/Ow57WGbsiiIdoJRcpqurO2K4hD7TFMh2m7mQlztjtoUZcV2LjOCzrCFU+OXDq27Gga5Xexw60RdUQMsws3HQji1NkvwUSppGONvY0zLqGO2JdOrJbOrGcPzAcePRgfW395wo1QqSZJKa9CVzcLsesHtk3va4zanrx0deIBxkGA58uLN3jhn95qN4umgwiIOebFd4+UGZ5027VWC/r+8ZHc3l2tbr1lTg8U4q2gSiZ1eziiLSiK8rkkUJMxvpFY7OOsdePh72nChqqzbHZIkp/xlLgxtgrBk8aUQ/R9Zcep1GTgGRQFr32LY8mnYGbdBRZjnlFkDK2igfFAR/28LtpuQk16TzoMuw5MOv+k3/BO4/zDba/9X/3zSB/3P/IEf4S//8I8xu17gNh2GZ310S47E28UWpyFprz00AkVBUyVFtrhb4a99uqO2DNyKkvV0TeQn9I7ah5DN4aivCiFmM98BIjbQDB1/5XPz0R22a3H65jFuU3BDeSpEGlXXiHYhabzfBUt98u7ZlN1ix9HjMcOznuCbYhHn6bZ+GLypmioZ/rJ89SEetjh+PD7kBKKdhHDclkNV7HFOKigKwSrk/vmECjh6KBXeaBezmm6xPVO66vXAj/oInMUZy8mGNErpjttYrkXkR9w/n6Kq6sdadyVFIbDMfesuDhO8tvvKVvOB5MMffObsMPBLYxFKmHU9NksLDOtVCOf6wxs2c5+T18YMTqU0Ij35ErflSKYgkzyBVjPq5tcLFrdLesfdmg0g15UkSmn3m7jtV/os9WRIOm5wM1aYpCGeYdJzHRQFFkGIP40Y2jYnmYG5DFCfTYWmWweUdktJVTa6HigK68maycs5rX6T45q9V9SaMFWTYV28i6gqcD73mHxs88HnSz7K7zAUjc+lZ3hjjdLK6HXmFKVOnDRou1sgQ1VMosxjsau4/KsRQZzxsy5VurUpNw5SLFvnd/+nv+Yws/iZ/nzqHvTf8Pl/A6dh4TQc8iRjdrMg3Mby7TNuH1zjZQGqLjHOYB0K7LDloBsGu7Wk0cpKIAmtvqCR8jSnqqoadliQRgmGZRySb/eXwo7vHXXkX65tEYcpWZJiWrokyfKKihLdkAc32IRcfuWGLMs4f+uEVq8lx851gKJAsy6jHHb79bF0drNgM9vRO+rQGrSoqpL1dEsSpfTGLRqdppw66t1+RUW0FfaaYRsHle5qsmF+s6QzbjOqrxlFnou8oE4UBtsIALfpSFR0uuHyvWvcps2Ddy5wmzZZlpPFGZq+N8jK70pad6J1fv7lS1RVrTMMEjb6eAgHVaUqBCWtaRqRn3D37J44iDl6NBbMVpJJCEfX6I47WG5N/KnEypPnJav7FdEupj1s4bZlsDh9OafIC44ejeRhrJBsv1KhqBpRJPSh7DtfQ3mjTVLk3Cy2bKKEs0GLI7uBkUDlF5RxhnO3hvsN2cLHqK8zeV4weTHj/sWU0YOhTOcNnTRMSZMU27EOLyGl1oQpyO/z9sWU+b/zLZw97OCWFsm6ZFvEWGOFN49DLCMnKxXy1Cb3TbTOgmU+5YvPj3hj3efkoxLlLmS3DlEck27T5uGbY/6V3/YLvz5NLf/Nv/9n+Jv/4//+VVXOzrB1aJVVVXWAM2ZpjqprWPX9bnm34ubJPc2ux/EjuSfHYUoSxuiGftjp7gMYmqa9Sr4td4zr2GVVUsdmc5q9xuEoW1agaVJW2a19lrcrVF1lcCrR293KZ3GzotH1GJ716jt5JVP9vcVlviNLcxrdBpZnEm4kjltWFadvHNPqNg4/m/jcRdCQxOkB55TGKTcf3bGebjl+/UhED/UwKc8FUyVMdbGd6qZBWZTMrubcv5jSO+rWLwW5XydhKuy6hk1Zj58VTT0gtaZXM0xTVkxa7TtbTzY0amW1XpNw4NUpaT3dUJUV7WELw9TZrHzuntzjtBzOXj/G8uRuvP8WryqFaBdS5AVOTcLZrXY8/9IlhmXWeidZyaVxhqapB1FknuWitG55bE97/MQwIs4yXhv36XoOeVURZilqptAuTXngowzvi9eYcU4SiyprPdtwtJc6VhX+0ifPS9r9BpZrHU6QqqpQlPJi3kw3eC0X45/7JoquwyqKmH5hxefaJZbeJi5MWs2AvuWTVybBruQrP9pG+841XcdFUzW29yl3f6ViuIWLRgPT1KnSgqOLHt/3O/7pT/R8faoe9M18y69761/HazsMz4eHLLm/9rFsg1a/Jd/oRXVYE+Vpzuxmib/06YzawomrI6qxn9AeNmn2XrW0oDpofleTNUUh91LTluTT9HKBrmuMHw1ljbY/yu7bXbuYcBdiN2wJW2Q5s5dzFnciTRhdDOpAimStVUOT8kcs907LFST0arIWhdEBj+SSpXndWhNwg1BmysO3arCNuPrgmjhIuHj7jGZfQjj+SoZErX7jUOZBVdBq4cP8Zslu4QsosS+8ttV0QxZn9I47NGsJRlkP/ACCbYC/CrE9Uxj6ecHsZsl6umF43peEoa4eQjiaoVGkOZGfoJvyz18WFfPrBTdP7hic9Tl+JLjuLE4lbGTp6KZBnmSgKBi2gYIkJG8+usdtOhy/foTtWiRhLC02xzwUUkQModUDyojbZ1OUR+fo3/MAq+sQpRnrVYTa0xk4Dqamo8QVWlJh+DnWjz1j8XJGEsYi1mg4REHC9OUMVVc5ejDEaTnixytKVEWlQpxokR/hNV2clkMap9zfLNm+8zqNX+vw4LHOyAsIsoq8VOhaGX1jybPQJX63y2Z1RDWKWAym3IZLXLo8LEeYW4dyUpDmGe2ZMPt/4/d+F4/Ovg4FDn/4t/wXfPR3n7G8WxNuIwanPUEIawp5LvvjvUXUXwXkRUGj7dXfTjE3T+6ggpPXj8TNVck+tipLNEOve9gxmqEdao+L2yXXH93RHrY4ff0Yx7PIkpw0TdF1Hd3SyeJMkm+2MMmCXcTV+zdEu4jzt0W0sAcylKXwxDVdpyhqE4wuLbz5zZL5zYJWvynfxIZGsAqI/Bi37RyCJvuBn6IohPXUWlEUgWnYBtulz+JG5hjjC6lVlodkoIActkufPMkFiumYhNvwsG8/f/uEZrcphY80r9tkUsdN46wO4dSZ8vdvDjaV/lGXCoiD+nTlWkD1Cl5pSkrwvkZfDy8GQonZyx/SHK/jyvqtnpVoumwj1tMN85sVjY5L76iDoqps5lv8TUBn2H5FiakJOgInidnOd6iqSudYuPurlc/NwzF8Y4PRiZwoiqKQDY0q+fVkntD7S89xFJHlrOdynXGajvDimk69oUklMWfsV6mlnB4sWfXuRRRnv/Tn0Hk4Ju0VbP/RHa93ZxiKQ1mBo4WcWVtcDZ4uO/zZL/a4MrZ868OSI6dBURpcLnTiaxvHNrAtHee6wgtVfu33/Gy+8Pbp13xuPlUP+pMvPuf7v/2HpIxRr8OqqiKN67aVZdSDn1QGWrZAKSSiekOj7XH25gl20yZLUpJIjnh2LVA8kEl1nThOuH86OUyf+8ddFE2VAk2c47YdbFcisAoKiqagIB/W+xdTFFVhdDHAciziIGY12WA6Rr0+Mg5c8/1Dt7xfEe4iOsMOXtshjTNmV3PSKGP0cECr35Tjdl6iVNWhqRXuZGotBJmS2dWSycvpq5WYaZCl9dTa0A5KKtlKSJlndb/iZf0hfvD2mfx+4owoiDBNE7shEVgqccCpmkKwFosrZclJXU6RoVWAbmi0+k3BcxUFiqKiaQppLLOHYBMyPOtLcCfOmN3MURSV0VlfviHLSib6SlUnIAPCXUSj9eob8u75lHgXcfzaEd26FJRl+SGqnNV3ct0ycDyzBoJMmd+sGF30Of6WtykfdAiVgvWFhe7pqChkRYEeK/SWKupsy/p//hJ3Tyc0eg1OXhvLwDFISAJx+u1BGaCgGZK391c+t0/vATh6PMZtOKQoBIrF/fd16TZtXu9t8MyIgRGQY6IR4PKUL/pd2kaP8PqE2cqmeHyDQYyjDMjWAzIDosxAmRt0c5Pf9cu/m6b5dTh1//d/1R9gu9gRh3U/2jZodjwUVaGs11OapsmR+WrBZir9405dNfXrb8dmryFgwv2RXVGEIR4kLO9XZGlG76iD7UkGfnm7AgVRBrdc4bCXMn3eN+j8VSBiwK4nPrX7NfObJb2jDqPzAbptUGR5/dDJ3jncRVRFWd87tTrld42iKjz87DnNeo2XxVmdIZdSy16DbFgCmLj68IbNbMfZ68f0T3soVAS7mHL/v21qlLnsoHVDrjeL2yWz6wXtQfOQ5tqtfMJdRLPriQDxIMFQQZF+/PJug6qrdMcdNF0+2JPLBa1+g/H5AMM2avBEeXiR+auAsqjwuq7AFZc+V+/fYNpyv3ZbjvxcSYaqq6iaRhIllPVMQTeE+PPi3SvKvOTBZ89oDwRBFYfiupMXL+R5LeA0ZO9/8+Se3VJ0VN2x9PyDXYxycoR73IGBTdhRSPvSYjMmGbuXPuH/93+lNWjRHbUA4dLFQSxyyK4n67eDIEQh3EWsJxs0Q6U9bKFrGqvZlmXNyD/65rcJvrVDMtbovjXH1QvOnSmaMkcjpa3bLPIetzuVv/BfDtE1hW/6lQl9z0CpCvxCJc4MtmGD9VwlKyr+k5/3i3jY6X7N5+ZT96A/f/eSf+u7/136x9263inFj6qS/nOeCU89z0qa9ZE02kmjqaoqEfzVoIKiKOrKqWTXD8y3pgsIdOLmozvclsP526c4DVseuqRAVUE39ZqhVmC5JoapE4cJNx/csV35nL5xxOC0h4Loc8tc/jqZHheodfNuj7q6fTqh0XYZPxxhu9K6inYRpmvRrI0rZf1trqryUrp/NiGOM44fjfBaTg2n3KDpKr3jLpYrp5qyRO7kuawUw11Eq9+UZGCcSdw1SBg9GtZrRiiymr22B0/sIixbet55XjB7OWd2s2B43ufo0bgGc+aipDa1w+DtsC6sKubXS64+uKE77sjO2zFJokR23q6JXWO3y3Kvo4Ltwuf2iXT2j+uwURykbBdbbMeiNWwdVNbqzxBVjoOY+e0SXdcYXgxxjwaUTYfCtQkuTMKeyiqJMd+Nabk27Sd3ZHdr7l5MyOKc49fG8ruBugNfSfCoVoDptoHr1XOZyzmL2yX90y7Hj47QLZ2k5bD41iM0R6Xx+R25EfOwsaRrgKXlWNUlu+wSXXV5f/0dmEqDprfBVVdUisksGZGUcL/2yGOPz3RH/MDP+s5P9Hx96h70f/uX/Ae1Eriq78Wy4ikrcaBLCcWs6SArbj+6w2k6nNX77jyr2d2ahunIhyrPilck1STj/kVNJr0YMjzvoelyF03DBLthYzdsoYXWeXIU6pLJlCIvOH4kFdUsydgudmi6RmvYkqvFASAhpJrV/ZrNfEer79WBlr1IIaJ33KU1aKIq6lfJIeIgZbfYig2220DTFNbTrYSAOp48DN6rve5+Qh5uI8qykFSbZbJb+bx894qyLAVj3X91ekARJl6ei9nEtHShwYQCg9wu/BpW0ZHZwy6iLEvcpns4PVCvmIqsYH61YDlZ0xm16R93pP+/2JEECe1hi0ZbTkkfB4kEm5DVdINu6vTGHVAVNtM1q/sN7VGL4elAhnxFcdh5y/DRBxS8joumaaxna64/uKPRbXD25jHO3pSbFahNF/V0wM3nHHJNwXQ1PF8j/+k5d//136AqaznkoCmnhzhFRRgG5cdMroapE0cJ1x/csplvOXvzhN5RB5Bv+qIosEct8m97ROrqmO/E9Fsxnf4GjSu2+ZJTs8TSTqgUi2WaEZcxedVhbGvkpc489Ugzg0XU4H6pMwkC/uL3/MvY+tcZYQbgb/7Zv80P/+7/DsPS6vgnhx20bmik9W59fb+hO+7QO2qjahq7tU/kJzRaLo2eWz88VS0TkPjn7HJOHNQljhp2uJqsqUroH3dxWk5tYC4PybdwF7GZbdFNjdagdciTz64WNHsNxg8GmJZ5OD3sAZa7dSBrtLb7yuf20S1FVkqsctCUaXxtg93/bFmSoRsGtmuSZTmTl+JYH18MGD0YSuMskoGZ6J2FhIICRo0cWk83XH14i+3ZnL5+VF9PEoJ1gOWYhyn7vtKrqtKEu38+JUszRhcj3KZ06fc77/5pT+Kih523QlHkrCdbQj+W00PLIYkzJi+m5EnO0eOx7PsVcd2B/H2xnxCHMaZr4XoWSSz369VkLTqqusUmWO8aW1VKh3/vny9LcbHdP5/WuQfBQCWRRHhN28R2TYoSVM8h/YYzso7OTSvmcrPh9Itwsd7h1Qk7fyXY51avgaIpVIWsGDVVIQoTJi+mpHHG6KKP13RJ4pTl/QpN0+iddOWkUlaonTbVsMX6zQbLMET5uZd852iGqTWAHD9bAlt09QRT6xGXJhqZXMUKBz8reRkozJYt/sU3vo1f9vrnPtHz9al70P/Nf/L3iBVVk7YVdQwzzzJW0y15mtPqN7E9k8iPmV0tyNNC2HE9j72vfM9Cy5KUYBOBqtJo2YdJ7s1Hd7hNh7M3T3CaDmVeCAWlVjAnYUwS1dl21yKJRNu7vF9z/HjI8Hwo7rUkk2ZWrYVKY9EvGZaBogig8aqm2ly8dYpbD6j2azSnWdNJ8xJd19Bq5ZJAL31OXjsSB3gpzbGiLGj3WzW/vZTZw8fZ5vcrvE6DzqiJUqmsZmuCdSirxz0Tr8glb6+qxGHySo3Ua6BoKtuZ2EobXY+Tx0f1qk+++bU6FRb6Mh9wGzaaobFZ7Hj57hUo8NoXHtHougfOu6KqWLZBnssJZO86j3bxgaN3/vYJnVFHXOebEKoKr+2hGXUByNDR6yvY9HrOerKhf9ylO+5AVbFZbsmSgvagWUM6hcKrmgZl02E97LBcB2x/js3jewtrHuP/2JdZzdZ0Rx0pTenqwcy7TxNu67x9s+uhm/Jz3j2dYLnW4RRZFvK7UVUVxbGJWy3uvqGBeqpj2jqlvWHtXvLNvQ8w1cf0zCZ5qRKVJaaWUVUWOimbXGGVtIizFqGvcbPI+Iv//K87fOn8TH8+dQ/6/+/3/3n+1v/0kwelcFkUxGF64H1RwXKy4vIr13j1lN1ru5R5QRKlQnu1zY8NtGQfmaUFkxdTJpdzhmd9xg8GGFZ9fwwlM227sruuKik4qKpgnK8/vCMOayRwr0lZFOxWAQoVzV4Tw9Br8aIEXMq8YD3bML9d0eh49MbtumTjH3jy7WELVVcPpRFVkWn58n5FUZT0Rm1MxyTYhtw/n2I6Fsc1c07kEHuqTcVuXZNQ2rK2isOY+2dTwl3EyWtjWVUpClkqoETdkFVfEgoTby+mnLyYcvd8wvjBkKNHRximRhImpEmO7Zq18TRHVZXaripAxpuP7sRW+0iuFJEfEW4jbNeqa6CvTg9KjZKevpyTZxlHD0bCnPdjlvdrTNtgcNKTdeH+9KAJNGM12RD5MZ1hC7dpy877ciY+98fjQ2Lu4wCScBdKn/3bP4tyZrEaw/V6h/VjAefX9wwGrZppIKeH/UwlCRO0+jMnNh3Z7beHTU5f//+3d+ZBdmV3ff+ce999+/5675Y0ErN4DDauscdbxYY4GLOkGCo4JCkKnDLghMIYMgmUHSpFKqkKGAIOSbEaKJwEskIIlZCCOJWY/GMHh3jsGXsWadRSb6/77dvd7z3543fek+yMRmJGI/VE71ulavXr99Q/dd/fu+f8zneR1JhFFHY2g5PPildALk96vkb0wBpdJ+Apu8/ma9q8YyXDVtEiTFNOAoh1hkomRyGTMItsMpaFrSxOZlme6Ifs7llsFCv80CNv47H7H75p37yqGj1JEn78W/4RGFsmWUJft2Tf79JvD2mu1Wma6Bx34uFOfArlHOVGGUtBkuhF2mnghxzvdnAnLmtnVig3yyRRwrAzJgnnxoXiFTfXI4tJgsugPULZFo212iLnrHc4oFDOs3ZmReibZrC0cGDtTc3Uv0ShXCB0Q072urgTj43za3JUZDzbdarFMiuKZdjjZCiUhYHVPRhweEkurJ0HtsgVHLO0nx8R2hIznEI2J0qtcV+EMQrFmYe3qc6VWlMflKJULSx+zuI0axN6IQeX2ox7EzYvbBgTiZTJYEoSS4BkvphD62uzhzRJGXVGDE/GFOtF6itVNLJlcIcujY0atbWaCcyYC0AUoRcw7k2xbItKs2wcX4YcX+5QbZXZun/zWlRTLBJZrTXexAeNSXaRIMXdJ6+aib7QceM4IfQlBFGaLiGOZaWVcTJ4bsj+xQ5+Ns9WU9SEaRRfUzxW84s3aWVbZJwMSRRzstejdziguVETb8K5fNho8guVgvn/iSWUAiZuwNHZDdztDKvNCmrTYuwETBonvGFtTN2pkbEyWMTYSrwQEh0zikZ86somzWyRIkWKaYFfec+331J/vaoaPU1TfunxT7D/zCFzBlvoRww7YyI/orZaER82L6S73ycKYlbPtqi2yijm9sVyBwiNzllZinK9JEGB/Sn7c7HKA5si4jDOMvMlsD8N8WeicsuX8sRBTGe/K8do2022jDtJFAj1MuPYC5pqquchf2IJffnJq9i2zZmHt6m1ZNjjuT6WUuSKeTDf286KyWMUiHlh72jA2tkVVrYaWLYtIZNBJO6uJUM0gQUHetSbcHKlSzYvNFUnb6KluhMK5bwckzk2pJoUkwbrhfSOBiRRQn29vkhKPbnaxXZsNu5bk7P7RC+2CAqYGI18ae764wYcXGrjjT22H9igYaKlEsOYc3Kiugv8ECfnkDOpuO3dE453T9i6sMHaudXFdikORdtvG52+mEM6oFO6RwOOLp1QrhdNMKVwGNyxkezWiihzJGaZLAB37IpZZcZhdatGvpInmPgMO2OcrOj4nZxQlefU3ziM6B0N8afCty9VCoRBTO+wRxQmrJ1dodIoXYvCRraX/tRnOpzh5B2Kb34Ync3Q707Z0wH1tRL1r61RWw/Ib4xw7IiMpVA6BTUFnZKz8xxPmjw/snm238W2bf79N7yP+2srN+2vV1WjTwZTfvZ7fxF/FpIaxxHLthbqnUFbHFHzpRznHj5DsVY0bqEhli0553GUEJlklWwhSxLGHO91aF/u0NpsLNJOAy8i8MS1JVeUO0CqRdueyUjU794zB+KC8tCWqLRSORdP44RirbjYWigTnTSPYD6+0qVQzkvT5Ry8qcekPzXKuzKWJUF7lrKMuYK4vXgzn9ZWw5BTokXk0spOk2JFiCZzr3plIqmmxi2n0iiRGEvqcW/C6naTFUNTTeJkIcmNwhh/GkgSSDkvdNz2kKtPH1BtlWVmUc4TBbEZDBqHlzBCxylZk1Y66U95/skrpEnKhdedo2L87fyph7JsCpU8CrUw9sg4Fr4xypwOZiaqqbZYPaRJSqVZEWmqlvXV3GN/1BkzOB5RbpSEgahl9TAbudTXa4tV0pzCK2EfIgCyMzaVRgnLshh2x19mJ5Uz9txpLLOH+e83jsUa3MlmmAxm7D0tuotzD2/L1s3YR6O1sCbDhMgPyRayZHMOkbI5niQMOhNWvvYsza9qEa5mGJUTIhXTfN2U1WJIGKdEkUPeVlhZzSQacuR3ULpA6j3AG1fO8Xde//X//+3R3YnHP/kbv8KwMxbRiQlUPNnr0jsc0No0Nj6OjTv28KaeXOR1UXKlWhRbc7eQ9m6H2chl7UyLastw4HsT4iim2iyLyAWM8FrECu7Yo3vQA61objXIFRxJLznskc3nWNlpkS84pJqFSUKaSM7ZbDijWCtQqpVIYzGVmA1dVraaNDYbi4yx+VFREidM+jNQStxezKBw7+kDitUiO6/ZEqFJnBIGIlCxMqKdT6KEbD6LY6it+88eMhu5nH1YMstBThp0mpIrZOXuEydYxgAxjROOr3Y5udKhtdlk7dzKwnQh9OWYsVAqmO0TWJaNUhIt1b7cMauHFbIFSX8ZnowpVvM01msSnKDFYMM27jSD4yFRENEwySje1Of4ygmO47B+3qTlGkeaecrNdDjDm/mUaiUKZeOoe7GNO/XYfmBzcbwVB5HYVmUs4iAhDELh2xfkmO34Sofjq11WdlpsXTCKtCAi8iOcvLNYTSmlcPKiSBuejNh/9ggn77DzoPwewiDCm/hksjbFmph0znn+lm0RzEL2nzvEmwZsv/vNlNerhEWbjopJ7JR6OQ/rWdKNgPrqDN8tMOkXmOgh6eoBawVNjlXApjON2N/L8d+/6/3YJv76xfCqavTuYZ9ffvwTeBOPKIzot0fEQUR1pSIBel5I96AvR0A7Kwtm13xoo4xV0rgnySrlRgknJ5bHx5dFqLB9/yaFagHSuWuLWDO50wB/4uMUHJmkxgndowGdqz0a61U2z6/j5LPG803uAMqyTExUSr6UxXEcZmOXq08fEHgh514rem00BH6INs6iOtVEYWTy3BzSOKW9e0xnr0djo87aOXFcCd0A3wvJ5h052080pCkZQzSZDV32nztCay2EGnNkOBnMxJppxdg7m0x40e1HdPYlULK12aTcKJFEMf32kCRKaJoVBaiFS4xGMxu6TIczcoUclUaZNBW1YL89oLXVFGVbxl6sHqyM7HfdsY+ylLiwKmWCKQ8o1Yqce3iHvPGpjwKRyNqOGFaitTShYfNd+vwulhKJbHWlShLFeK5k5M3jqZM4kRVIxib0A9rPS7DDxoU1mut1ccwdzkiihFKjRMGQd5RSMhhNNaPOiO7BkFI1T2OzLhLd/pRxf0q1WTa6C3GoVWagE3ghw+MhcZxQX62RP7uBZ2U5Phlz8p46GxeKbD6fJdtNCXMa96xNVNb0rQB3mnDfg1MquQxZx6XtjdibpRTSTb5j61He99oX7N3/B6+qRm/vnvDPfvDXGA+mWBmLQiEHSBrL1S/t4+Qd7vvqM5TrEnG7eBfOihlk5EdCjMlnSeOUk70uh5fatDYkQzxfzBEam+aMI1z5OEqIo5RsXgZcvhuw/9wh0/5MwiE2GyjLwp25JFFKvmhMCCOJRLbN2fWkN+Hg4hEZJ8PmhXUKxl56ZvZslWZ54TM/9yiPgpDOfp9Jf0Jrq0mlVUEnCaOuiVwyRJMvd7VRzMYuw5MhGcemviY01WFnTO9wQLVVYe1My/DtU6HFZiySKGE6mJIkKaV6iWxOZLUHz7axbIszD21SbkgufLI4ZlQEbkQYRELCMXlv+88eMeqOOfPQFivbLfGpn/uoFXIohTSuY5PNSob58dUux7sntLaarJ9bIZN1Fi6shXJeBEY6RWuFnVEoZTHpTWjvnmA7NuvnVoWHPvEZdkcUSnnq63XjiZcuKM7zPDzfDWhtNimYXL3ufg9tKTbOriwGaDqRlRzIttGf+uRLstePwoiTqz0mgykb51bl+M1Qr9M4xTb5Ad7Ex7IUhbKYXPSPh+w/e0RtZ538Bx/FaRaIMgnaDWh9AaxihlnGrAYrGTI5m6ET8aVsh/OvfY5mZot6rsbMz+FQ4efe8t5b6q9XVaMD/Obf+9dc/NxlouuGYPV1YUk5JqHDm3rkS3lKVbnzpFqOtuyM8KfblztM+lMxglyrorR4uoVBRKlekunz3JnElggdsRHqkUQJqzstGdqYd2rLtmhuNBaGhxquEWP6U0bdCblSTqbPWqKIxt0ptdUqrc26sMeu80NLooRxb0ISJ5RN/JQ7cjl8/hilYPv+TcqNkvC6I1GWWcbeOfRCcsWsDLWC2MQRDRcS2Xk+WhIL2w3NQtsux5Oa3tFAnGabFbbvX5fTAT/EmwY4uYwM4eI5TdVGWbZRvh0Q+hFnHjKT7igWZxbbotosi+7dkI3mR2Kd/R7T/nUSWWPPFQUxzY26rB6U2FWJwQZ4Y28RWVVplgGpeXA8orFeY/XMihxpJglxnModdn7+jqZYlZSbYUe2QYVynjOv2V7w7aMwwlJK1Ix+JHr2QpZM1hFi0zOHuBOPM68R9ptONb4XgLLJNyoQxcR+gDJeCHEQcrLf43i3Q3O9ztp9aziFHON6Fm+lRqaVI7OdpfJMSpS3ySiwNEySmMP9PpOyZnujQXUlw8TRtJszbG3TKhb50Te9k9evbty0b151jf7J3/pj/s1Hf4/QC6mt1siXc0S+vEsHfsjqzopM2ZX4k80tj8IgYtQZkybpwhvdnXicXOmglGL9vOSKi9H+/KjITEpHLk7OWSwBh+0R7SsnxkZoXXzfk0SyuCxrMWENg2hhJeXNfA4vHeOO5Ox6roYL/Yg0EdtiDYvBYa4gnvC99oD9Zw4p10tsP7ApZphBROiGZLIZGRTGqTn/llXHbOKy/4xkqu08uEG1VV0MknSqKdWLOCbuaR4OERsFXW+/T9W8ASnLYjKY4k18qs0y5aYwEfV8ZmGEHL2jIbYt6apO1mHSn9A9GFCsFlg7t0I25yyIJspSpHHKpD8ljmLK9ZK8kU0kBw4NOw9tGImsJo5iQEn8lRcSuOGX23NdOqbfHrDz0DarOy1zTHfNeReliIMYZSvJ6zMa+KPnj2XodkGGbpKR55Mr5CiU8wuadSaTkW3QyOPw4pFQnL9qnVK1SBjIyY2dsamt1bGaZYKHakSrKVmdkl4MmX3mkNHVDo3VKlXjpTc6GRFGmvo7XifxVzolmXlYQYQq5PHimPHJjGizTK1Zxsrb9FTIwXhC9CaLnUqVgpWllMnykbd+HduV6k375hVrdKXUNwE/D9jAr2mtf+rFnn+rjf4PvvNnmY1c5gGKe18yrLKHdyjXS8JCCkyIgRkqhV5AxnEWTdE97MnyqVUR2Wo5TxzGBH5EJmMZXzPD0spJYEPgRRxcOmLYHrF5YV1yz81SPg7FwWS+RwdDN1US27v39AE61XKna4jtsTv2Fkd7lmWZRE8TExUldA/69NtD6qtVIbTYohLzZwGVZtkcFanFGT1K4U89Ovs9QCi72UIed+LSPxyQK2TFiHJBU5WlfpKkjDpjvKlHbUWMHwMvpH3lhMiPF44qMBe5yLGm74UEM19koEbs093v09nr0txqyMwiJ6YRURjj5DImkjgEIF/KLSSyV790QK4oee7FaoEwiAhmAbYh62jDRZjbQntTscyOvIjtBzepNsvEUYI7dplz3J2cY4I0ZfgaR/JGNmyPqK5VJThTa0bdCf7sOgOShVoPQBxqhycjLEtRXavhmJnA8ZUu5WZZtgx5hzjr4H31JtO32cSFFFdPmQ5synaR1Ysh2T/cZ/+J50nihK37DcW5VZPhqxtgBxGx1gQRZApZCutNYtuiHQTsD6fUm0W2zzRRKxmiVDNsJtTyBX7yW7+ReqlwK/14+xtdKWUDzwLvBvaBPwH+mtb6izd6za02+n/++Cf5nY/9J3qHfRE2bDXJ5MRpRiyOs5TqZdm3monnnOp6vNtl1B2xsrOyOHKZDWcEfkSpVjB3bLVQwinLwp16HO92CLyQzftWKVaLxKGc3SsU1bWqRDPN9eUmJmo2nNE/GpItSCKqnbGZDqb020PKjTIrWw2j177maiPxSmMCV2KiCiWxuursdYnDmPWzq1RWKsLvj5OFg4rvBbhjFyfrUKzKkWK/PaC926GxXmPrwrVBYRxKaqgy9Fatr3mxL2iqwPnXnaXSmCfWzA0rRX4qiTUZHMOrP3iuzeDE8NA3GwvJZhKZY6icY4gmamEi0W+P6Ox1KNVLrOy0ZCUymjEbupQaJeGUWwqdaNH6KyXqs4MBaZqaNzI5UTi5Is6ta2db5Aryu9DG+z1NUsb9Kf7Mp9IoS9b9LODo8gmhH4o9V6tiFGmxCIcyFqEbip49mxE3HHM02dmTyOft+zdEBBUlRF6A3SyTvGaN7iMpqa2oBgor1LT9lPijX8DO2GyvFijXikLecQOUgkK5cE0cUyrg5HNEdob2MGDQndJ4cJ2VDdFQeFOfME2x1woEX5OlnivyE9/8LqqF/K305CvS6G8D/r7W+j3m848AaK1/8kavudVG//7XP75gY3kzn+5Bn9APWd1uUW7MBQfmDmCLfbE3cklTTXVFctGnQ3lHRmvW71ulVBNq5DVfM0XoBnjGLlhWCumCzlmsFBbL6DSZW0mxUIjF8+DFfJbpaMbhxWNGnZHECpmVgGRsJwuhie8GZDIWra0maaI5vnrC5c9fxc7a3PfaM5Tq13PDlZzxRonZP+ZYPdNi1Blz6YldBu0RmxfWxLllPigMUwrl3CKDTinIOBla2032nj7gyheFf7Bxfs0k1ni4I5dcMSeNYCkxxTOusflinstPXpXghHOr12iqx0OUZdHaalAo5b7M+linqcRLdSeU6iXKdZP+crXLbCzMxPp6zWjYr2WzR0FIFIijbqFSEG/9K10Onjukvlpj+8FNMziVjLRMJmOOUH0s22ZluwVac3y1w8X/c1kMLF+7Q6VZJokTAldsvAqlLKnZLmRzWTYurDE8HnHp81foHw7YvLBGc6slbjhjlyRKKFTzZA1fAssiW8izet8aB1f7XPnss2QLWVa3mziOzWw8Y9KdiJ30Wg3bmjsFyXav0ixx+QtXGfcmtLZb1LZWCeOU/tAl2GzRqhWEG5KzGJ1XrF6o8Q+//t232pM3bPSba99ujG1g77rP94G3vMA3/wDwAYCzZ8/e0j/88c//3OJuFvpixdw7HIgNkR9Jyunx0OzdhhQqBf7Kjz7Go+95g4hZFARuwKg7YdAe4k58IhON3Dvsc3TpmHF/Qn21ytu/7VHe8R1vlZRQ84466owZdccmsEHcXE+udDjZ6xF6IevnMjzwyHm+9QPvlsmt1nhTn3FvwmzkErohnuszaI9oXz5h1BkLl92yKNdLfPdP/GVAaKiimprhuwGRHzHqjukc9Ont9wm8QPafxhPu8Y//zcXrAjfEHbuL5WzvcEBnX3zzUq1JkoThyZj9Zw54/FfldWkq3PYokKSZ4cmI7n7PCIUi4ihhNnI5ev6Yo+eP+fC/+NDiGG1u5TwbufSPhvSPBnizgNhQdzt7PY4utXEnHj/48+/nwuvPmYz6lCQRrkD/SH6HoS/88P5hn8NLx4x7EzKOzdsfezNvf+zRxemC7wZMB1PGvelCkTbqjGnvdhgcDRaKxtWdFu99XKKFtdYEXsh0OMOfBYRewGQwo3vQFz2+F6CAKIiZjT0+9Avf92Wv86a+/J8mcq10DwZM+lOSWCi5o+6Uw90uH/rY9yxeF/oRoUnrGXUndPd74ubrhyRRgjvxON494eBimx/+xe83pwtaPAVTOXbtH4/oT0NmU59QpwRRQseb8VQ8+jO25Qvj5dzR3wt8k9b6+8zn3w28RWv9wRu95lbv6EssscSfHS92R7853ebGOADOXPf5jnlsiSWWOGV4OY3+J8ADSqnzSqks8FeB3789ZS2xxBK3Ey95j661jpVSHwT+EDle+w2t9VO3rbIllljituHlDOPQWv8B8Ae3qZYllljiFcLLWbovscQSrxIsG32JJe4BLBt9iSXuASwbfYkl7gEsG32JJe4B3FGZqlKqA1y5haeuAN1XuJyXitNcG5zu+pa1vTTcam3ntNarL/SFO9rotwql1GdvROW72zjNtcHprm9Z20vD7ahtuXRfYol7AMtGX2KJewCntdF/9W4X8CI4zbXB6a5vWdtLw8uu7VTu0ZdYYonbi9N6R19iiSVuI5aNvsQS9wBORaMrpX5YKfWkUuoppdSPmMeaSqn/qpR6znxsnKLafkYp9bRS6vNKqf+glKqfltqu+9rfVkpppdTN0/nuYG1KqR8yP7unlFI/fVpqU0q9QSn1aaXU55RSn1VKvfkO1vMbSqkTpdST1z32gte/EvxTpdRFc/09ckvfZO55drf+AF8DPAkUEdnsJ4H7gZ8GPmye82Hgo6eotm8EMuY5Hz1NtZmvnUF8Aq4AK6elNuDPm7/nzPPWTlFtfwR8s3nOtwD/4w7W9E7gEeDJ6x57wevf1PZfkHTvtwKfuZXvcRru6A8jxbpa6xj4FPCXgMeAT5jnfAL49tNSm9b6j8znAJ9GbLRORW3max8Dfgy4W5PWG9X2A8BPaa0DAK31ySmqTQPzlIQacHinCtJa/zHQ/4qHb3T9Pwb8cy34NFBXSm3e7HuchkZ/EniHUqqllCoi71hngHWt9ZF5ThtYP0W1XY/3I++wdxovWJtS6jHgQGv9xF2o6UVrAx40j39GKfUppdSjp6i2HwF+Rim1B/xj4CN3obbrcaPr/4Xcl7dv9o+9LIeZ2wGt9ZeUUh9Flk4z4HNA8hXP0UqpO353ulltSqkfB2Lgt05JbTng7yJbi7uGF/m5ZYAmsuR8FPi3SqkL2qxJ73JtPwD8La317yilvhP4deAb7lRdL4bbcf2fhjs6Wutf11q/UWv9TmCAJMAcz5ck5uPdWObdqDaUUn8d+IvAd93JC/UmtT0FnAeeUErtIluKP1VK3Tyh75Wv7Vnk7vO7Ztn5v4AUEWychtreB/yuecq/A+7YMO4GuNH1/5Lcl09Foyul1szHs8h+6bcRR9n3mae8D/iPp6U2kzn3Y8C3aa3du1HXDWr7hNZ6TWt9n9b6PqSxHtFat09Bbb8N/B4ykEMp9SCQ5S4oxm5Q2yHwdeYp7wKeu9N1fQVudP3/PvA9Zvr+VmB03RL/xrhTk8WbTB3/J/BF4AngL5jHWsB/Q37gnwSap6i2i8g+6XPmzy+fltq+4uu73IWp+4v83LLAv0T2yX8KvOsU1fbngP9tHvsM8MY7WM+/Ao6ACHlz/t4bXf/ItP0XgEvAF4A33cr3WFJgl1jiHsCpWLovscQSryyWjb7EEvcAlo2+xBL3AJaNvsQS9wCWjb7EEvcAlo2+xBL3AJaNvsQS9wD+L/3BucraPSurAAAAAElFTkSuQmCC\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"raster_path='../../datasets/processed/Satelligence_data/carbon_analysis/AboveGroundBiomass_GLO_2001-01-01-2002-01-01_upsample.tif'\n",
"with rio.open(raster_path) as src:\n",
" gdf_carbres6 = raster.raster_to_geodataframe(src.read(1), src.transform, h3_resolution=6, compacted=False)\n",
" gdf_carbres6.plot('value')\n",
" gdf_carbres6['h3index'] = gdf_carbres6['h3index'].apply(hex)\n",
"gdf_carbres6.head()"
]
},
{
"cell_type": "code",
"execution_count": 18,
"id": "62b7cf44",
"metadata": {},
"outputs": [
{
"data": {
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"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>h3index</th>\n",
" <th>value_x</th>\n",
" <th>geometry_x</th>\n",
" <th>value_y</th>\n",
" <th>geometry_y</th>\n",
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" <td>POLYGON ((96.34271 3.95695, 96.34547 3.99329, ...</td>\n",
" <td>119</td>\n",
" <td>POLYGON ((96.34271 3.95695, 96.34547 3.99329, ...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>0x8665506e7ffffff</td>\n",
" <td>0.000112</td>\n",
" <td>POLYGON ((95.49100 4.95352, 95.49369 4.98998, ...</td>\n",
" <td>1469</td>\n",
" <td>POLYGON ((95.49100 4.95352, 95.49369 4.98998, ...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>0x8665540c7ffffff</td>\n",
" <td>0.000358</td>\n",
" <td>POLYGON ((96.48875 3.78162, 96.49153 3.81794, ...</td>\n",
" <td>455</td>\n",
" <td>POLYGON ((96.48875 3.78162, 96.49153 3.81794, ...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>0x866556a47ffffff</td>\n",
" <td>0.000346</td>\n",
" <td>POLYGON ((96.50300 4.38386, 96.50579 4.42026, ...</td>\n",
" <td>1705</td>\n",
" <td>POLYGON ((96.50300 4.38386, 96.50579 4.42026, ...</td>\n",
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" <th>4</th>\n",
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" <td>1705</td>\n",
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],
"text/plain": [
" h3index value_x \\\n",
"0 0x86655475fffffff 0.000236 \n",
"1 0x8665506e7ffffff 0.000112 \n",
"2 0x8665540c7ffffff 0.000358 \n",
"3 0x866556a47ffffff 0.000346 \n",
"4 0x866556a6fffffff 0.000346 \n",
"\n",
" geometry_x value_y \\\n",
"0 POLYGON ((96.34271 3.95695, 96.34547 3.99329, ... 119 \n",
"1 POLYGON ((95.49100 4.95352, 95.49369 4.98998, ... 1469 \n",
"2 POLYGON ((96.48875 3.78162, 96.49153 3.81794, ... 455 \n",
"3 POLYGON ((96.50300 4.38386, 96.50579 4.42026, ... 1705 \n",
"4 POLYGON ((96.46707 4.33165, 96.46985 4.36804, ... 1705 \n",
"\n",
" geometry_y \n",
"0 POLYGON ((96.34271 3.95695, 96.34547 3.99329, ... \n",
"1 POLYGON ((95.49100 4.95352, 95.49369 4.98998, ... \n",
"2 POLYGON ((96.48875 3.78162, 96.49153 3.81794, ... \n",
"3 POLYGON ((96.50300 4.38386, 96.50579 4.42026, ... \n",
"4 POLYGON ((96.46707 4.33165, 96.46985 4.36804, ... "
]
},
"execution_count": 18,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"merge = gdf_densres6.merge(\n",
" gdf_carbres6,\n",
" on='h3index',\n",
" how='inner'\n",
")\n",
"merge.head()"
]
},
{
"cell_type": "code",
"execution_count": 26,
"id": "cee8ca34",
"metadata": {},
"outputs": [
{
"data": {
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" </thead>\n",
" <tbody>\n",
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" <td>2130.313171</td>\n",
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"</table>\n",
"</div>"
],
"text/plain": [
" h3index value\n",
"0 0x86655475fffffff 101.632527\n",
"1 0x8665506e7ffffff 594.287329\n",
"2 0x8665540c7ffffff 588.005546\n",
"3 0x866556a47ffffff 2130.313171\n",
"4 0x866556a6fffffff 2130.313171"
]
},
"execution_count": 26,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"h3index_array = []\n",
"value_array = []\n",
"for i,row in merge.iterrows():\n",
" density = row['value_x']\n",
" carbon = row['value_y']\n",
" h3index=row['h3index']\n",
" value = density*carbon*3612.9\n",
" value_array.append(value)\n",
" h3index_array.append(h3index)\n",
"\n",
"gdf_estimate = gpd.GeoDataFrame()\n",
"gdf_estimate['h3index']=h3index_array\n",
"gdf_estimate['value']=value_array\n",
"gdf_estimate.head()"
]
},
{
"cell_type": "code",
"execution_count": 28,
"id": "c896de97",
"metadata": {},
"outputs": [],
"source": [
"carb_sum_calculated = []\n",
"for i, row in clean_sat_mills.iterrows():\n",
" filtered_gdf = clean_sat_mills[i:i+1]\n",
" #convert to h3\n",
" h3_gdf = filtered_gdf.h3.polyfill_resample(6)\n",
" h3index_list = [f'0x{h3index}' for h3index in h3_gdf.index]\n",
" #filter gdf by list and get value\n",
" #3617 is the area of the hexagon for res6 in \n",
" carb_sum = gdf_estimate[gdf_estimate['h3index'].isin(h3index_list)]['value'].sum()\n",
" carb_sum_calculated.append(carb_sum)"
]
},
{
"cell_type": "code",
"execution_count": 30,
"id": "8885b7e1",
"metadata": {},
"outputs": [
{
"data": {
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" <th>biodiversi</th>\n",
" <th>carbon</th>\n",
" <th>geometry</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>706</td>\n",
" <td>349.5068535490499</td>\n",
" <td>200.138293908663</td>\n",
" <td>57812.37323319753</td>\n",
" <td>POLYGON ((98.47767 2.24508, 98.47705 2.22159, ...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>717</td>\n",
" <td>444.9278149230584</td>\n",
" <td>295.5592552826714</td>\n",
" <td>74432.2796810086</td>\n",
" <td>POLYGON ((98.44488 2.31416, 98.44426 2.29068, ...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>264</td>\n",
" <td>552.3038111406324</td>\n",
" <td>400.8360709155834</td>\n",
" <td>95543.40760696075</td>\n",
" <td>POLYGON ((98.40582 2.39111, 98.40521 2.36762, ...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>738</td>\n",
" <td>621.1470874150178</td>\n",
" <td>469.4242228971635</td>\n",
" <td>109557.3760590317</td>\n",
" <td>POLYGON ((98.36445 2.43594, 98.36383 2.41245, ...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>1516</td>\n",
" <td>600.0120538952435</td>\n",
" <td>448.9605690952982</td>\n",
" <td>104970.0756673934</td>\n",
" <td>POLYGON ((98.51418 2.45678, 98.51356 2.43329, ...</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
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"text/plain": [
" gfw_fid deforestat biodiversi carbon \\\n",
"0 706 349.5068535490499 200.138293908663 57812.37323319753 \n",
"1 717 444.9278149230584 295.5592552826714 74432.2796810086 \n",
"2 264 552.3038111406324 400.8360709155834 95543.40760696075 \n",
"3 738 621.1470874150178 469.4242228971635 109557.3760590317 \n",
"4 1516 600.0120538952435 448.9605690952982 104970.0756673934 \n",
"\n",
" geometry \n",
"0 POLYGON ((98.47767 2.24508, 98.47705 2.22159, ... \n",
"1 POLYGON ((98.44488 2.31416, 98.44426 2.29068, ... \n",
"2 POLYGON ((98.40582 2.39111, 98.40521 2.36762, ... \n",
"3 POLYGON ((98.36445 2.43594, 98.36383 2.41245, ... \n",
"4 POLYGON ((98.51418 2.45678, 98.51356 2.43329, ... "
]
},
"execution_count": 30,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"clean_sat_mills.head()"
]
},
{
"cell_type": "code",
"execution_count": 34,
"id": "caa2b6d2",
"metadata": {},
"outputs": [
{
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" <td>197099.781250</td>\n",
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"text/plain": [
" gfw_fid carbon \\\n",
"0 706 57812.37323319753 \n",
"1 717 74432.2796810086 \n",
"2 264 95543.40760696075 \n",
"3 738 109557.3760590317 \n",
"4 1516 104970.0756673934 \n",
"\n",
" geometry estimate \n",
"0 POLYGON ((98.47767 2.24508, 98.47705 2.22159, ... 111082.382812 \n",
"1 POLYGON ((98.44488 2.31416, 98.44426 2.29068, ... 146149.562500 \n",
"2 POLYGON ((98.40582 2.39111, 98.40521 2.36762, ... 197099.781250 \n",
"3 POLYGON ((98.36445 2.43594, 98.36383 2.41245, ... 253342.375000 \n",
"4 POLYGON ((98.51418 2.45678, 98.51356 2.43329, ... 193674.187500 "
]
},
"execution_count": 34,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"gdf_res6.head()"
]
},
{
"cell_type": "code",
"execution_count": 36,
"id": "b0b336ce",
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/opt/conda/lib/python3.8/site-packages/geopandas/geodataframe.py:1351: SettingWithCopyWarning: \n",
"A value is trying to be set on a copy of a slice from a DataFrame.\n",
"Try using .loc[row_indexer,col_indexer] = value instead\n",
"\n",
"See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
" super().__setitem__(key, value)\n"
]
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" <th>carbon</th>\n",
" <th>geometry</th>\n",
" <th>ddbb_calc</th>\n",
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" <th>ddbb_calc_res6</th>\n",
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" <td>255198.295925</td>\n",
" <td>111082.382812</td>\n",
" <td>10773.863281</td>\n",
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" <td>444.9278149230584</td>\n",
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" <td>20534.408203</td>\n",
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" <td>552.3038111406324</td>\n",
" <td>400.8360709155834</td>\n",
" <td>95543.40760696075</td>\n",
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" <td>507528.896615</td>\n",
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"text/plain": [
" gfw_fid deforestat biodiversi carbon \\\n",
"0 706 349.5068535490499 200.138293908663 57812.37323319753 \n",
"1 717 444.9278149230584 295.5592552826714 74432.2796810086 \n",
"2 264 552.3038111406324 400.8360709155834 95543.40760696075 \n",
"3 738 621.1470874150178 469.4242228971635 109557.3760590317 \n",
"4 1516 600.0120538952435 448.9605690952982 104970.0756673934 \n",
"\n",
" geometry ddbb_calc \\\n",
"0 POLYGON ((98.47767 2.24508, 98.47705 2.22159, ... 255198.295925 \n",
"1 POLYGON ((98.44488 2.31416, 98.44426 2.29068, ... 359015.846009 \n",
"2 POLYGON ((98.40582 2.39111, 98.40521 2.36762, ... 507528.896615 \n",
"3 POLYGON ((98.36445 2.43594, 98.36383 2.41245, ... 684702.356393 \n",
"4 POLYGON ((98.51418 2.45678, 98.51356 2.43329, ... 498207.265702 \n",
"\n",
" res6_precalc res5_precalc ddbb_calc_res6 \n",
"0 111082.382812 10773.863281 255198.295925 \n",
"1 146149.562500 20534.408203 359015.846009 \n",
"2 197099.781250 23541.343750 507528.896615 \n",
"3 253342.375000 41870.093750 684702.356393 \n",
"4 193674.187500 24832.167969 498207.265702 "
]
},
"execution_count": 36,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"clean_sat_mills['ddbb_calc_res6']=carb_sum_calculated\n",
"clean_sat_mills['res6_precalc']=list(gdf_res6['estimate'])\n",
"clean_sat_mills['res5_precalc']=list(gdf_res5['estimate'])\n",
"clean_sat_mills.head()"
]
},
{
"cell_type": "code",
"execution_count": 37,
"id": "648baf6c",
"metadata": {},
"outputs": [],
"source": [
"clean_sat_mills.to_csv('../../datasets/processed/Satelligence_data/carbon_analysis/carbon_loss_stats.csv')"
]
},
{
"cell_type": "markdown",
"id": "d6238617",
"metadata": {},
"source": [
"## Biodiversity indicator:\n",
"\n",
"Procedure \n",
"\n",
" 1. Identify pixels in biodiversity layer with “high density of species”(threshold tbd) \n",
" 2. Pair Indicator 1 with “high species density” and determine the area (in ha) of high biodiversity area lost \n",
" 3. Additionally provide the area (ha) of (critically) endangered species loss 4. Use the same methodology as in indicator 1 to determine per mill biodiversity loss and compare with other mills inside/outside supply chain of company X.\n",
" \n",
"SPECIES_RICHNESS_THRESHOLD = 264\n"
]
},
{
"cell_type": "code",
"execution_count": 43,
"id": "7ba3f037",
"metadata": {
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"0.. 0.. 0.. 0.. 0.. 0.. 0.. 0.. 0.. 1.. 1.. 1.. 1.. 1.. 1.. 1.. 1.. 1.. 2.. 2.. 2.. 2.. 2.. 2.. 2.. 2.. 3.. 3.. 3.. 3.. 3.. 3.. 3.. 3.. 3.. 4.. 4.. 4.. 4.. 4.. 4.. 4.. 4.. 5.. 5.. 5.. 5.. 5.. 5.. 5.. 5.. 5.. 6.. 6.. 6.. 6.. 6.. 6.. 6.. 6.. 7.. 7.. 7.. 7.. 7.. 7.. 7.. 7.. 7.. 8.. 8.. 8.. 8.. 8.. 8.. 8.. 8.. 9.. 9.. 9.. 9.. 9.. 9.. 9.. 9.. 9.. 10.. 10.. 10.. 10.. 10.. 10.. 10.. 10.. 11.. 11.. 11.. 11.. 11.. 11.. 11.. 11.. 11.. 12.. 12.. 12.. 12.. 12.. 12.. 12.. 12.. 13.. 13.. 13.. 13.. 13.. 13.. 13.. 13.. 13.. 14.. 14.. 14.. 14.. 14.. 14.. 14.. 14.. 15.. 15.. 15.. 15.. 15.. 15.. 15.. 15.. 15.. 16.. 16.. 16.. 16.. 16.. 16.. 16.. 16.. 17.. 17.. 17.. 17.. 17.. 17.. 17.. 17.. 17.. 18.. 18.. 18.. 18.. 18.. 18.. 18.. 18.. 19.. 19.. 19.. 19.. 19.. 19.. 19.. 19.. 19.. 20.. 20.. 20.. 20.. 20.. 20.. 20.. 20.. 21.. 21.. 21.. 21.. 21.. 21.. 21.. 21.. 21.. 22.. 22.. 22.. 22.. 22.. 22.. 22.. 22.. 23.. 23.. 23.. 23.. 23.. 23.. 23.. 23.. 23.. 24.. 24.. 24.. 24.. 24.. 24.. 24.. 24.. 25.. 25.. 25.. 25.. 25.. 25.. 25.. 25.. 25.. 26.. 26.. 26.. 26.. 26.. 26.. 26.. 26.. 26.. 27.. 27.. 27.. 27.. 27.. 27.. 27.. 27.. 28.. 28.. 28.. 28.. 28.. 28.. 28.. 28.. 28.. 29.. 29.. 29.. 29.. 29.. 29.. 29.. 29.. 30.. 30.. 30.. 30.. 30.. 30.. 30.. 30.. 30.. 31.. 31.. 31.. 31.. 31.. 31.. 31.. 31.. 32.. 32.. 32.. 32.. 32.. 32.. 32.. 32.. 32.. 33.. 33.. 33.. 33.. 33.. 33.. 33.. 33.. 34.. 34.. 34.. 34.. 34.. 34.. 34.. 34.. 34.. 35.. 35.. 35.. 35.. 35.. 35.. 35.. 35.. 36.. 36.. 36.. 36.. 36.. 36.. 36.. 36.. 36.. 37.. 37.. 37.. 37.. 37.. 37.. 37.. 37.. 38.. 38.. 38.. 38.. 38.. 38.. 38.. 38.. 38.. 39.. 39.. 39.. 39.. 39.. 39.. 39.. 39.. 40.. 40.. 40.. 40.. 40.. 40.. 40.. 40.. 40.. 41.. 41.. 41.. 41.. 41.. 41.. 41.. 41.. 42.. 42.. 42.. 42.. 42.. 42.. 42.. 42.. 42.. 43.. 43.. 43.. 43.. 43.. 43.. 43.. 43.. 44.. 44.. 44.. 44.. 44.. 44.. 44.. 44.. 44.. 45.. 45.. 45.. 45.. 45.. 45.. 45.. 45.. 46.. 46.. 46.. 46.. 46.. 46.. 46.. 46.. 46.. 47.. 47.. 47.. 47.. 47.. 47.. 47.. 47.. 48.. 48.. 48.. 48.. 48.. 48.. 48.. 48.. 48.. 49.. 49.. 49.. 49.. 49.. 49.. 49.. 49.. 50.. 50.. 50.. 50.. 50.. 50.. 50.. 50.. 50.. 51.. 51.. 51.. 51.. 51.. 51.. 51.. 51.. 51.. 52.. 52.. 52.. 52.. 52.. 52.. 52.. 52.. 53.. 53.. 53.. 53.. 53.. 53.. 53.. 53.. 53.. 54.. 54.. 54.. 54.. 54.. 54.. 54.. 54.. 55.. 55.. 55.. 55.. 55.. 55.. 55.. 55.. 55.. 56.. 56.. 56.. 56.. 56.. 56.. 56.. 56.. 57.. 57.. 57.. 57.. 57.. 57.. 57.. 57.. 57.. 58.. 58.. 58.. 58.. 58.. 58.. 58.. 58.. 59.. 59.. 59.. 59.. 59.. 59.. 59.. 59.. 59.. 60.. 60.. 60.. 60.. 60.. 60.. 60.. 60.. 61.. 61.. 61.. 61.. 61.. 61.. 61.. 61.. 61.. 62.. 62.. 62.. 62.. 62.. 62.. 62.. 62.. 63.. 63.. 63.. 63.. 63.. 63.. 63.. 63.. 63.. 64.. 64.. 64.. 64.. 64.. 64.. 64.. 64.. 65.. 65.. 65.. 65.. 65.. 65.. 65.. 65.. 65.. 66.. 66.. 66.. 66.. 66.. 66.. 66.. 66.. 67.. 67.. 67.. 67.. 67.. 67.. 67.. 67.. 67.. 68.. 68.. 68.. 68.. 68.. 68.. 68.. 68.. 69.. 69.. 69.. 69.. 69.. 69.. 69.. 69.. 69.. 70.. 70.. 70.. 70.. 70.. 70.. 70.. 70.. 71.. 71.. 71.. 71.. 71.. 71.. 71.. 71.. 71.. 72.. 72.. 72.. 72.. 72.. 72.. 72.. 72.. 73.. 73.. 73.. 73.. 73.. 73.. 73.. 73.. 73.. 74.. 74.. 74.. 74.. 74.. 74.. 74.. 74.. 75.. 75.. 75.. 75.. 75.. 75.. 75.. 75.. 75.. 76.. 76.. 76.. 76.. 76.. 76.. 76.. 76.. 76.. 77.. 77.. 77.. 77.. 77.. 77.. 77.. 77.. 78.. 78.. 78.. 78.. 78.. 78.. 78.. 78.. 78.. 79.. 79.. 79.. 79.. 79.. 79.. 79.. 79.. 80.. 80.. 80.. 80.. 80.. 80.. 80.. 80.. 80.. 81.. 81.. 81.. 81.. 81.. 81.. 81.. 81.. 82.. 82.. 82.. 82.. 82.. 82.. 82.. 82.. 82.. 83.. 83.. 83.. 83.. 83.. 83.. 83.. 83.. 84.. 84.. 84.. 84.. 84.. 84.. 84.. 84.. 84.. 85.. 85.. 85.. 85.. 85.. 85.. 85.. 85.. 86.. 86.. 86.. 86.. 86.. 86.. 86.. 86.. 86.. 87.. 87.. 87.. 87.. 87.. 87.. 87.. 87.. 88.. 88.. 88.. 88.. 88.. 88.. 88.. 88.. 88.. 89.. 89.. 89.. 89.. 89.. 89.. 89.. 89.. 90.. 90.. 90.. 90.. 90.. 90.. 90.. 90.. 90.. 91.. 91.. 91.. 91.. 91.. 91.. 91.. 91.. 92.. 92.. 92.. 92.. 92.. 92.. 92.. 92.. 92.. 93.. 93.. 93.. 93.. 93.. 93.. 93.. 93.. 94.. 94.. 94.. 94.. 94.. 94.. 94.. 94.. 94.. 95.. 95.. 95.. 95.. 95.. 95.. 95.. 95.. 96.. 96.. 96.. 96.. 96.. 96.. 96.. 96.. 96.. 97.. 97.. 97.. 97.. 97.. 97.. 97.. 97.. 98.. 98.. 98.. 98.. 98.. 98.. 98.. 98.. 98.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 100 - Done\n"
]
}
],
"source": [
"#reclasify raster:\n",
"!gdal_calc.py --calc \"(A>=264)*A\" --format GTiff --type Float32 --NoDataValue 0.0 -A '../../datasets/processed/Satelligence_data/biodiversity_analysis/SpeciesRichness_IDN_2021-01-01-2022-01-01.tif' --A_band 1 --outfile '../../datasets/processed/Satelligence_data/biodiversity_analysis/SpeciesRichness_high_density.tif'"
]
},
{
"cell_type": "code",
"execution_count": 44,
"id": "865e5b27",
"metadata": {
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
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]
}
],
"source": [
"# generate mask of high density of species\n",
"!gdal_calc.py --calc \"(A>=264)\" --format GTiff --type Float32 --NoDataValue 0.0 -A '../../datasets/processed/Satelligence_data/biodiversity_analysis/SpeciesRichness_IDN_2021-01-01-2022-01-01.tif' --A_band 1 --outfile '../../datasets/processed/Satelligence_data/biodiversity_analysis/SpeciesRichness_high_density_mask.tif'"
]
},
{
"cell_type": "code",
"execution_count": 48,
"id": "3e1d5811",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Creating output file that is 55000P x 66667L.\n",
"Processing ../../datasets/processed/Satelligence_data/biodiversity_analysis/SpeciesRichness_high_density_mask.tif [1/1] : 0Using internal nodata values (e.g. 0) for image ../../datasets/processed/Satelligence_data/biodiversity_analysis/SpeciesRichness_high_density_mask.tif.\n",
"...10...20...30...40...50...60...70...80...90.."
]
}
],
"source": [
"#downsample\n",
"!gdalwarp -s_srs EPSG:4326 -t_srs EPSG:4326 -dstnodata 0.0 -tr 6e-05 6e-05 -r near -te 94.99998 2.1 98.29998 6.10002 -te_srs EPSG:4326 -multi -of GTiff '../../datasets/processed/Satelligence_data/biodiversity_analysis/SpeciesRichness_high_density_mask.tif' '../../datasets/processed/Satelligence_data/biodiversity_analysis/SpeciesRichness_high_density_mask_downsample_v2.tif'"
]
},
{
"cell_type": "code",
"execution_count": 49,
"id": "7fb0d433",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
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2.. 3.. 3.. 3.. 3.. 3.. 3.. 3.. 3.. 3.. 3.. 3.. 3.. 3.. 3.. 3.. 3.. 3.. 3.. 3.. 3.. 3.. 3.. 3.. 3.. 3.. 3.. 3.. 3.. 3.. 3.. 3.. 3.. 3.. 3.. 3.. 3.. 3.. 3.. 3.. 3.. 3.. 3.. 3.. 3.. 3.. 3.. 3.. 3.. 3.. 3.. 3.. 3.. 3.. 3.. 3.. 3.. 3.. 3.. 3.. 3.. 3.. 3.. 3.. 3.. 3.. 3.. 3.. 3.. 3.. 3.. 3.. 3.. 3.. 3.. 3.. 3.. 3.. 3.. 3.. 3.. 3.. 3.. 3.. 3.. 3.. 3.. 3.. 3.. 3.. 3.. 3.. 3.. 3.. 3.. 3.. 3.. 3.. 3.. 3.. 3.. 3.. 3.. 3.. 3.. 3.. 3.. 3.. 3.. 3.. 3.. 3.. 3.. 3.. 3.. 3.. 3.. 3.. 3.. 3.. 3.. 3.. 3.. 3.. 3.. 3.. 3.. 3.. 3.. 3.. 3.. 3.. 3.. 3.. 3.. 3.. 3.. 3.. 3.. 3.. 3.. 3.. 3.. 3.. 3.. 3.. 3.. 3.. 3.. 3.. 3.. 3.. 3.. 3.. 3.. 3.. 3.. 3.. 3.. 3.. 3.. 3.. 3.. 3.. 3.. 3.. 3.. 3.. 3.. 3.. 3.. 3.. 3.. 3.. 3.. 3.. 3.. 3.. 3.. 3.. 3.. 3.. 3.. 3.. 3.. 3.. 3.. 3.. 3.. 3.. 3.. 3.. 3.. 3.. 3.. 3.. 3.. 3.. 3.. 3.. 3.. 3.. 3.. 3.. 3.. 3.. 3.. 3.. 3.. 3.. 3.. 3.. 3.. 3.. 3.. 3.. 3.. 3.. 3.. 3.. 3.. 3.. 3.. 3.. 3.. 3.. 3.. 3.. 3.. 3.. 3.. 3.. 3.. 3.. 3.. 3.. 3.. 3.. 3.. 3.. 3.. 3.. 3.. 3.. 3.. 3.. 3.. 3.. 3.. 3.. 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98.. 98.. 98.. 98.. 98.. 98.. 98.. 98.. 98.. 98.. 98.. 98.. 98.. 98.. 98.. 98.. 98.. 98.. 98.. 98.. 98.. 98.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 99.. 100 - Done\n"
]
}
],
"source": [
"#multiply deforested area time high density of species mask\n",
"!gdal_calc.py --calc \"A*B\" --format GTiff --type Float32 -A '../../datasets/processed/Satelligence_data/biodiversity_analysis/SpeciesRichness_high_density_mask_downsample_v2.tif' --A_band 1 -B '../../datasets/processed/Satelligence_data/test_rasters_2/Deforestation_2021_2022_area_ha.tif' --outfile '../../datasets/processed/Satelligence_data/biodiversity_analysis/biodiversity_loss_ha.tif'"
]
},
{
"cell_type": "code",
"execution_count": 50,
"id": "4266815a",
"metadata": {},
"outputs": [
{
"data": {
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"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>fid</th>\n",
" <th>gfw_fid</th>\n",
" <th>uml_id</th>\n",
" <th>group_name</th>\n",
" <th>parent_com</th>\n",
" <th>mill_name</th>\n",
" <th>rspo_statu</th>\n",
" <th>rspo_type</th>\n",
" <th>date</th>\n",
" <th>latitude</th>\n",
" <th>...</th>\n",
" <th>district</th>\n",
" <th>confidence</th>\n",
" <th>alternativ</th>\n",
" <th>gfw_area__</th>\n",
" <th>gfw_geosto</th>\n",
" <th>deforestat</th>\n",
" <th>biodiversi</th>\n",
" <th>carbon</th>\n",
" <th>geometry</th>\n",
" <th>estimated</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>1.0</td>\n",
" <td>706</td>\n",
" <td>PO1000004155</td>\n",
" <td>IBRIS PALM</td>\n",
" <td>DELIMA MAKMUR</td>\n",
" <td>DELIMA MAKMUR</td>\n",
" <td>Not RSPO Certified</td>\n",
" <td>None</td>\n",
" <td>2021-07-14</td>\n",
" <td>2.24508</td>\n",
" <td>...</td>\n",
" <td>Aceh Singkil</td>\n",
" <td>1-Fully Verified</td>\n",
" <td>LAE TANGGA</td>\n",
" <td>0</td>\n",
" <td>256170af-2d4f-4a5b-a417-b981205849be</td>\n",
" <td>349.5068535490499</td>\n",
" <td>200.138293908663</td>\n",
" <td>57812.37323319753</td>\n",
" <td>POINT (98.02851 2.24508)</td>\n",
" <td>200.138779</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>2.0</td>\n",
" <td>717</td>\n",
" <td>PO1000004167</td>\n",
" <td>ASTRA AGRO LESTARI</td>\n",
" <td>PERKEBUNAN LEMBAH BHAKTI</td>\n",
" <td>PERKEBUNAN LEMBAH BHAKTI 1</td>\n",
" <td>Not RSPO Certified</td>\n",
" <td>None</td>\n",
" <td>2021-07-14</td>\n",
" <td>2.314164</td>\n",
" <td>...</td>\n",
" <td>Aceh Singkil</td>\n",
" <td>1-Fully Verified</td>\n",
" <td>None</td>\n",
" <td>0</td>\n",
" <td>b2d76be0-1ace-7a54-8e5e-74bb01111787</td>\n",
" <td>444.9278149230584</td>\n",
" <td>295.5592552826714</td>\n",
" <td>74432.2796810086</td>\n",
" <td>POINT (97.99572 2.31416)</td>\n",
" <td>295.561371</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>3.0</td>\n",
" <td>264</td>\n",
" <td>PO1000001775</td>\n",
" <td>SOCFIN</td>\n",
" <td>SOCFIN INDONESIA</td>\n",
" <td>LAE BUTAR</td>\n",
" <td>RSPO Certified</td>\n",
" <td>RSPO Certified, IP</td>\n",
" <td>2021-07-14</td>\n",
" <td>2.391111</td>\n",
" <td>...</td>\n",
" <td>Aceh Singkil</td>\n",
" <td>1-Fully Verified</td>\n",
" <td>None</td>\n",
" <td>0</td>\n",
" <td>aff58ced-c18c-a5d8-d084-eb5a6a74416b</td>\n",
" <td>552.3038111406324</td>\n",
" <td>400.8360709155834</td>\n",
" <td>95543.40760696075</td>\n",
" <td>POINT (97.95667 2.39111)</td>\n",
" <td>400.839111</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>4.0</td>\n",
" <td>738</td>\n",
" <td>PO1000004197</td>\n",
" <td>UNKNOWN</td>\n",
" <td>NAFASINDO</td>\n",
" <td>NAFASINDO</td>\n",
" <td>Not RSPO Certified</td>\n",
" <td>None</td>\n",
" <td>2021-07-14</td>\n",
" <td>2.43594</td>\n",
" <td>...</td>\n",
" <td>Aceh Singkil</td>\n",
" <td>1-Fully Verified</td>\n",
" <td>None</td>\n",
" <td>0</td>\n",
" <td>3cee289b-cdd6-a33d-e0f6-15ea8e7423fa</td>\n",
" <td>621.1470874150178</td>\n",
" <td>469.4242228971635</td>\n",
" <td>109557.3760590317</td>\n",
" <td>POINT (97.91529 2.43594)</td>\n",
" <td>469.428223</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>5.0</td>\n",
" <td>1516</td>\n",
" <td>PO1000008193</td>\n",
" <td>TENERA LESTARI</td>\n",
" <td>ENSEM LESTARI</td>\n",
" <td>ENSEM LESTARI</td>\n",
" <td>Not RSPO Certified</td>\n",
" <td>None</td>\n",
" <td>2021-07-14</td>\n",
" <td>2.456777</td>\n",
" <td>...</td>\n",
" <td>Aceh Singkil</td>\n",
" <td>1-Fully Verified</td>\n",
" <td>None</td>\n",
" <td>0</td>\n",
" <td>128b643f-9046-0f4e-c71e-59ca37fb221f</td>\n",
" <td>600.0120538952435</td>\n",
" <td>448.9605690952982</td>\n",
" <td>104970.0756673934</td>\n",
" <td>POINT (98.06502 2.45678)</td>\n",
" <td>448.964172</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>5 rows × 27 columns</p>\n",
"</div>"
],
"text/plain": [
" fid gfw_fid uml_id group_name parent_com \\\n",
"0 1.0 706 PO1000004155 IBRIS PALM DELIMA MAKMUR \n",
"1 2.0 717 PO1000004167 ASTRA AGRO LESTARI PERKEBUNAN LEMBAH BHAKTI \n",
"2 3.0 264 PO1000001775 SOCFIN SOCFIN INDONESIA \n",
"3 4.0 738 PO1000004197 UNKNOWN NAFASINDO \n",
"4 5.0 1516 PO1000008193 TENERA LESTARI ENSEM LESTARI \n",
"\n",
" mill_name rspo_statu rspo_type \\\n",
"0 DELIMA MAKMUR Not RSPO Certified None \n",
"1 PERKEBUNAN LEMBAH BHAKTI 1 Not RSPO Certified None \n",
"2 LAE BUTAR RSPO Certified RSPO Certified, IP \n",
"3 NAFASINDO Not RSPO Certified None \n",
"4 ENSEM LESTARI Not RSPO Certified None \n",
"\n",
" date latitude ... district confidence alternativ \\\n",
"0 2021-07-14 2.24508 ... Aceh Singkil 1-Fully Verified LAE TANGGA \n",
"1 2021-07-14 2.314164 ... Aceh Singkil 1-Fully Verified None \n",
"2 2021-07-14 2.391111 ... Aceh Singkil 1-Fully Verified None \n",
"3 2021-07-14 2.43594 ... Aceh Singkil 1-Fully Verified None \n",
"4 2021-07-14 2.456777 ... Aceh Singkil 1-Fully Verified None \n",
"\n",
" gfw_area__ gfw_geosto deforestat \\\n",
"0 0 256170af-2d4f-4a5b-a417-b981205849be 349.5068535490499 \n",
"1 0 b2d76be0-1ace-7a54-8e5e-74bb01111787 444.9278149230584 \n",
"2 0 aff58ced-c18c-a5d8-d084-eb5a6a74416b 552.3038111406324 \n",
"3 0 3cee289b-cdd6-a33d-e0f6-15ea8e7423fa 621.1470874150178 \n",
"4 0 128b643f-9046-0f4e-c71e-59ca37fb221f 600.0120538952435 \n",
"\n",
" biodiversi carbon geometry estimated \n",
"0 200.138293908663 57812.37323319753 POINT (98.02851 2.24508) 200.138779 \n",
"1 295.5592552826714 74432.2796810086 POINT (97.99572 2.31416) 295.561371 \n",
"2 400.8360709155834 95543.40760696075 POINT (97.95667 2.39111) 400.839111 \n",
"3 469.4242228971635 109557.3760590317 POINT (97.91529 2.43594) 469.428223 \n",
"4 448.9605690952982 104970.0756673934 POINT (98.06502 2.45678) 448.964172 \n",
"\n",
"[5 rows x 27 columns]"
]
},
"execution_count": 50,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"gdf = buffer_stats(\n",
" '../../datasets/processed/Satelligence_data/biodiversity_analysis/biodiversity_loss_ha.tif',\n",
" '../../datasets/processed/palm_oil_mills/satelligence_mills_4326_point.shp',\n",
" buffer=50000,\n",
" stat_='sum')\n",
"gdf.head()"
]
},
{
"cell_type": "code",
"execution_count": 51,
"id": "e9b385e9",
"metadata": {},
"outputs": [],
"source": [
"#export to csv\n",
"gdf.to_csv('../../datasets/processed/Satelligence_data/biodiversity_analysis/biodiversity_loss_ha.csv')"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "b58fa0f9",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Creating output file that is 40P x 48L.\n",
"Processing ../../datasets/processed/Satelligence_data/biodiversity_analysis/biodiversity_loss_ha.tif [1/1] : 0Using internal nodata values (e.g. 3.40282e+38) for image ../../datasets/processed/Satelligence_data/biodiversity_analysis/biodiversity_loss_ha.tif.\n",
"...10...20...30...40...50...60...70...80...90...100 - done.\n"
]
}
],
"source": [
"#upsample biodiversity raster\n",
"## upsample the carbon_loss_T for ingesting\n",
"!gdalwarp -s_srs EPSG:4326 -t_srs EPSG:4326 -dstnodata 0.0 -tr 0.0833333333333286 0.0833333333333286 -r sum -multi -of GTiff '../../datasets/processed/Satelligence_data/biodiversity_analysis/biodiversity_loss_ha.tif' '../../datasets/processed/Satelligence_data/biodiversity_analysis/biodiversity_loss_ha_upsample.tif'"
]
},
{
"cell_type": "markdown",
"id": "3cb0bfb7",
"metadata": {},
"source": [
"### Resolution 6:"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "1e74089b",
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/opt/conda/lib/python3.8/site-packages/geopandas/geodataframe.py:1351: SettingWithCopyWarning: \n",
"A value is trying to be set on a copy of a slice from a DataFrame.\n",
"Try using .loc[row_indexer,col_indexer] = value instead\n",
"\n",
"See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
" super().__setitem__(key, value)\n"
]
},
{
"data": {
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"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
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" <th></th>\n",
" <th>gfw_fid</th>\n",
" <th>biodiversi</th>\n",
" <th>geometry</th>\n",
" <th>estimate</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>706</td>\n",
" <td>200.138293908663</td>\n",
" <td>POLYGON ((98.47767 2.24508, 98.47705 2.22159, ...</td>\n",
" <td>379.696045</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>717</td>\n",
" <td>295.5592552826714</td>\n",
" <td>POLYGON ((98.44488 2.31416, 98.44426 2.29068, ...</td>\n",
" <td>569.285217</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>264</td>\n",
" <td>400.8360709155834</td>\n",
" <td>POLYGON ((98.40582 2.39111, 98.40521 2.36762, ...</td>\n",
" <td>828.127014</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>738</td>\n",
" <td>469.4242228971635</td>\n",
" <td>POLYGON ((98.36445 2.43594, 98.36383 2.41245, ...</td>\n",
" <td>1120.113037</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>1516</td>\n",
" <td>448.9605690952982</td>\n",
" <td>POLYGON ((98.51418 2.45678, 98.51356 2.43329, ...</td>\n",
" <td>814.255249</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" gfw_fid biodiversi \\\n",
"0 706 200.138293908663 \n",
"1 717 295.5592552826714 \n",
"2 264 400.8360709155834 \n",
"3 738 469.4242228971635 \n",
"4 1516 448.9605690952982 \n",
"\n",
" geometry estimate \n",
"0 POLYGON ((98.47767 2.24508, 98.47705 2.22159, ... 379.696045 \n",
"1 POLYGON ((98.44488 2.31416, 98.44426 2.29068, ... 569.285217 \n",
"2 POLYGON ((98.40582 2.39111, 98.40521 2.36762, ... 828.127014 \n",
"3 POLYGON ((98.36445 2.43594, 98.36383 2.41245, ... 1120.113037 \n",
"4 POLYGON ((98.51418 2.45678, 98.51356 2.43329, ... 814.255249 "
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"gdf_res6 = convert_raster_h3('../../datasets/processed/Satelligence_data/biodiversity_analysis/biodiversity_loss_ha_upsample.tif', \n",
" '../../datasets/processed/Satelligence_data/test_rasters_2/satelligence_mills_4326_50kmbuffer.shp',\n",
" column='biodiversi',\n",
" resolution=6)\n",
"\n",
"gdf_res6.head()"
]
},
{
"cell_type": "markdown",
"id": "33dfa03c",
"metadata": {},
"source": [
"### Resolution 5:\n"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "eb84aa35",
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/opt/conda/lib/python3.8/site-packages/geopandas/geodataframe.py:1351: SettingWithCopyWarning: \n",
"A value is trying to be set on a copy of a slice from a DataFrame.\n",
"Try using .loc[row_indexer,col_indexer] = value instead\n",
"\n",
"See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
" super().__setitem__(key, value)\n"
]
},
{
"data": {
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" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>gfw_fid</th>\n",
" <th>biodiversi</th>\n",
" <th>geometry</th>\n",
" <th>estimate</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>706</td>\n",
" <td>200.138293908663</td>\n",
" <td>POLYGON ((98.47767 2.24508, 98.47705 2.22159, ...</td>\n",
" <td>43.937572</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>717</td>\n",
" <td>295.5592552826714</td>\n",
" <td>POLYGON ((98.44488 2.31416, 98.44426 2.29068, ...</td>\n",
" <td>95.903854</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>264</td>\n",
" <td>400.8360709155834</td>\n",
" <td>POLYGON ((98.40582 2.39111, 98.40521 2.36762, ...</td>\n",
" <td>112.520752</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>738</td>\n",
" <td>469.4242228971635</td>\n",
" <td>POLYGON ((98.36445 2.43594, 98.36383 2.41245, ...</td>\n",
" <td>209.463501</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>1516</td>\n",
" <td>448.9605690952982</td>\n",
" <td>POLYGON ((98.51418 2.45678, 98.51356 2.43329, ...</td>\n",
" <td>121.099487</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" gfw_fid biodiversi \\\n",
"0 706 200.138293908663 \n",
"1 717 295.5592552826714 \n",
"2 264 400.8360709155834 \n",
"3 738 469.4242228971635 \n",
"4 1516 448.9605690952982 \n",
"\n",
" geometry estimate \n",
"0 POLYGON ((98.47767 2.24508, 98.47705 2.22159, ... 43.937572 \n",
"1 POLYGON ((98.44488 2.31416, 98.44426 2.29068, ... 95.903854 \n",
"2 POLYGON ((98.40582 2.39111, 98.40521 2.36762, ... 112.520752 \n",
"3 POLYGON ((98.36445 2.43594, 98.36383 2.41245, ... 209.463501 \n",
"4 POLYGON ((98.51418 2.45678, 98.51356 2.43329, ... 121.099487 "
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"gdf_res5 = convert_raster_h3('../../datasets/processed/Satelligence_data/biodiversity_analysis/biodiversity_loss_ha_upsample.tif', \n",
" '../../datasets/processed/Satelligence_data/test_rasters_2/satelligence_mills_4326_50kmbuffer.shp',\n",
" column='biodiversi',\n",
" resolution=5)\n",
"\n",
"gdf_res5.head()"
]
},
{
"cell_type": "markdown",
"id": "9e7cf635",
"metadata": {},
"source": [
"### Ingesting raw data and calculate it on ddbb:"
]
},
{
"cell_type": "code",
"execution_count": 9,
"id": "74d0d715",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Creating output file that is 43P x 46L.\r\n",
"Processing ../../datasets/processed/Satelligence_data/biodiversity_analysis/SpeciesRichness_high_density_mask.tif [1/1] : 0Using internal nodata values (e.g. 0) for image ../../datasets/processed/Satelligence_data/biodiversity_analysis/SpeciesRichness_high_density_mask.tif.\r\n",
"...10...20...30...40...50...60...70...80...90...100 - done.\r\n"
]
}
],
"source": [
"## upsample the high density species as mask for ingesting - max in resampling to get 1 values\n",
"!gdalwarp -s_srs EPSG:4326 -t_srs EPSG:4326 -dstnodata 0.0 -tr 0.0833333333333286 0.0833333333333286 -r max -multi -of GTiff '../../datasets/processed/Satelligence_data/biodiversity_analysis/SpeciesRichness_high_density_mask.tif' '../../datasets/processed/Satelligence_data/biodiversity_analysis/SpeciesRichness_high_density_mask_upsample.tif'"
]
},
{
"cell_type": "code",
"execution_count": 10,
"id": "77bffbec",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
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"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>h3index</th>\n",
" <th>value</th>\n",
" <th>geometry</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>0x866553c47ffffff</td>\n",
" <td>0.000060</td>\n",
" <td>POLYGON ((95.22458 5.24807, 95.22723 5.28457, ...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>0x866553c57ffffff</td>\n",
" <td>0.000060</td>\n",
" <td>POLYGON ((95.26041 5.30049, 95.26307 5.33699, ...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>0x86650d267ffffff</td>\n",
" <td>0.000381</td>\n",
" <td>POLYGON ((97.99217 3.06624, 97.99511 3.10243, ...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>0x86650d34fffffff</td>\n",
" <td>0.000381</td>\n",
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" <th>4</th>\n",
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" <td>0.000381</td>\n",
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"</table>\n",
"</div>"
],
"text/plain": [
" h3index value \\\n",
"0 0x866553c47ffffff 0.000060 \n",
"1 0x866553c57ffffff 0.000060 \n",
"2 0x86650d267ffffff 0.000381 \n",
"3 0x86650d34fffffff 0.000381 \n",
"4 0x866572c97ffffff 0.000381 \n",
"\n",
" geometry \n",
"0 POLYGON ((95.22458 5.24807, 95.22723 5.28457, ... \n",
"1 POLYGON ((95.26041 5.30049, 95.26307 5.33699, ... \n",
"2 POLYGON ((97.99217 3.06624, 97.99511 3.10243, ... \n",
"3 POLYGON ((98.01934 3.00955, 98.02228 3.04574, ... \n",
"4 POLYGON ((97.95618 3.01436, 97.95911 3.05055, ... "
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"raster_path=\"../../datasets/processed/Satelligence_data/Deforestation_IDN_2021-01-01-2022-01-01_Density.tif\"\n",
"with rio.open(raster_path) as src:\n",
" gdf_densres6 = raster.raster_to_geodataframe(src.read(1), src.transform, h3_resolution=6, nodata_value=src.profile['nodata'], compacted=False)\n",
" gdf_densres6.plot('value')\n",
" gdf_densres6['h3index'] = gdf_densres6['h3index'].apply(hex)\n",
"gdf_densres6.head()"
]
},
{
"cell_type": "code",
"execution_count": 11,
"id": "31add180",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>h3index</th>\n",
" <th>value</th>\n",
" <th>geometry</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>0x866508247ffffff</td>\n",
" <td>1.0</td>\n",
" <td>POLYGON ((98.27966 4.24863, 98.28264 4.28497, ...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>0x86650824fffffff</td>\n",
" <td>1.0</td>\n",
" <td>POLYGON ((98.21628 4.25352, 98.21926 4.28987, ...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>0x866508257ffffff</td>\n",
" <td>1.0</td>\n",
" <td>POLYGON ((98.31582 4.30070, 98.31881 4.33705, ...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>0x86650825fffffff</td>\n",
" <td>1.0</td>\n",
" <td>POLYGON ((98.25244 4.30560, 98.25542 4.34196, ...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>0x866508267ffffff</td>\n",
" <td>1.0</td>\n",
" <td>POLYGON ((98.30687 4.19166, 98.30985 4.22800, ...</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" h3index value geometry\n",
"0 0x866508247ffffff 1.0 POLYGON ((98.27966 4.24863, 98.28264 4.28497, ...\n",
"1 0x86650824fffffff 1.0 POLYGON ((98.21628 4.25352, 98.21926 4.28987, ...\n",
"2 0x866508257ffffff 1.0 POLYGON ((98.31582 4.30070, 98.31881 4.33705, ...\n",
"3 0x86650825fffffff 1.0 POLYGON ((98.25244 4.30560, 98.25542 4.34196, ...\n",
"4 0x866508267ffffff 1.0 POLYGON ((98.30687 4.19166, 98.30985 4.22800, ..."
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"raster_path='../../datasets/processed/Satelligence_data/biodiversity_analysis/SpeciesRichness_high_density_mask_upsample.tif'\n",
"with rio.open(raster_path) as src:\n",
" gdf_speciesres6 = raster.raster_to_geodataframe(src.read(1), src.transform, h3_resolution=6, nodata_value=src.profile['nodata'], compacted=False)\n",
" gdf_speciesres6.plot('value')\n",
" gdf_speciesres6['h3index'] = gdf_speciesres6['h3index'].apply(hex)\n",
"gdf_speciesres6.head()"
]
},
{
"cell_type": "code",
"execution_count": 12,
"id": "cacde972",
"metadata": {},
"outputs": [
{
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"2 0x866572c97ffffff 0.000381 \n",
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"4 0x866572567ffffff 0.002318 \n",
"\n",
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"4 POLYGON ((97.66751 2.98168, 97.67041 3.01786, ... "
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"merge = gdf_densres6.merge(\n",
" gdf_speciesres6,\n",
" on='h3index',\n",
" how='inner'\n",
")\n",
"merge.head()"
]
},
{
"cell_type": "code",
"execution_count": 13,
"id": "7fd0e975",
"metadata": {},
"outputs": [
{
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"text/plain": [
" h3index value\n",
"0 0x86650d267ffffff 1.378058\n",
"1 0x86650d34fffffff 1.378058\n",
"2 0x866572c97ffffff 1.378058\n",
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"metadata": {},
"output_type": "execute_result"
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],
"source": [
"h3index_array = []\n",
"value_array = []\n",
"for i,row in merge.iterrows():\n",
" density = row['value_x']\n",
" h3index=row['h3index']\n",
" value = density*3612.9\n",
" value_array.append(value)\n",
" h3index_array.append(h3index)\n",
"\n",
"gdf_estimate = gpd.GeoDataFrame()\n",
"gdf_estimate['h3index']=h3index_array\n",
"gdf_estimate['value']=value_array\n",
"gdf_estimate.head()"
]
},
{
"cell_type": "code",
"execution_count": 14,
"id": "1151d49a",
"metadata": {},
"outputs": [
{
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"text/plain": [
" gfw_fid deforestat biodiversi carbon \\\n",
"0 706 349.5068535490499 200.138293908663 57812.37323319753 \n",
"1 717 444.9278149230584 295.5592552826714 74432.2796810086 \n",
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"4 1516 600.0120538952435 448.9605690952982 104970.0756673934 \n",
"\n",
" geometry \n",
"0 POLYGON ((98.47767 2.24508, 98.47705 2.22159, ... \n",
"1 POLYGON ((98.44488 2.31416, 98.44426 2.29068, ... \n",
"2 POLYGON ((98.40582 2.39111, 98.40521 2.36762, ... \n",
"3 POLYGON ((98.36445 2.43594, 98.36383 2.41245, ... \n",
"4 POLYGON ((98.51418 2.45678, 98.51356 2.43329, ... "
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"clean_sat_mills.head()"
]
},
{
"cell_type": "code",
"execution_count": 15,
"id": "571ef928",
"metadata": {},
"outputs": [],
"source": [
"bio_sum_calculated = []\n",
"for i, row in clean_sat_mills.iterrows():\n",
" filtered_gdf = clean_sat_mills[i:i+1]\n",
" #convert to h3\n",
" h3_gdf = filtered_gdf.h3.polyfill_resample(6)\n",
" h3index_list = [f'0x{h3index}' for h3index in h3_gdf.index]\n",
" #filter gdf by list and get value\n",
" #3617 is the area of the hexagon for res6 in \n",
" bio_sum = gdf_estimate[gdf_estimate['h3index'].isin(h3index_list)]['value'].sum()\n",
" bio_sum_calculated.append(bio_sum)"
]
},
{
"cell_type": "code",
"execution_count": 16,
"id": "15637aff",
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/opt/conda/lib/python3.8/site-packages/geopandas/geodataframe.py:1351: SettingWithCopyWarning: \n",
"A value is trying to be set on a copy of a slice from a DataFrame.\n",
"Try using .loc[row_indexer,col_indexer] = value instead\n",
"\n",
"See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
" super().__setitem__(key, value)\n"
]
},
{
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" </tr>\n",
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" <td>545.373341</td>\n",
" <td>1120.113037</td>\n",
" <td>209.463501</td>\n",
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" <tr>\n",
" <th>4</th>\n",
" <td>1516</td>\n",
" <td>600.0120538952435</td>\n",
" <td>448.9605690952982</td>\n",
" <td>104970.0756673934</td>\n",
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" <td>419.375901</td>\n",
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],
"text/plain": [
" gfw_fid deforestat biodiversi carbon \\\n",
"0 706 349.5068535490499 200.138293908663 57812.37323319753 \n",
"1 717 444.9278149230584 295.5592552826714 74432.2796810086 \n",
"2 264 552.3038111406324 400.8360709155834 95543.40760696075 \n",
"3 738 621.1470874150178 469.4242228971635 109557.3760590317 \n",
"4 1516 600.0120538952435 448.9605690952982 104970.0756673934 \n",
"\n",
" geometry ddbb_calc_res6 \\\n",
"0 POLYGON ((98.47767 2.24508, 98.47705 2.22159, ... 237.234755 \n",
"1 POLYGON ((98.44488 2.31416, 98.44426 2.29068, ... 317.167758 \n",
"2 POLYGON ((98.40582 2.39111, 98.40521 2.36762, ... 426.967292 \n",
"3 POLYGON ((98.36445 2.43594, 98.36383 2.41245, ... 545.373341 \n",
"4 POLYGON ((98.51418 2.45678, 98.51356 2.43329, ... 419.375901 \n",
"\n",
" res6_precalc res5_precalc \n",
"0 379.696045 43.937572 \n",
"1 569.285217 95.903854 \n",
"2 828.127014 112.520752 \n",
"3 1120.113037 209.463501 \n",
"4 814.255249 121.099487 "
]
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"\n",
"clean_sat_mills['ddbb_calc_res6']=bio_sum_calculated\n",
"clean_sat_mills['res6_precalc']=list(gdf_res6['estimate'])\n",
"clean_sat_mills['res5_precalc']=list(gdf_res5['estimate'])\n",
"clean_sat_mills.head()"
]
},
{
"cell_type": "code",
"execution_count": 17,
"id": "4229f65a",
"metadata": {},
"outputs": [],
"source": [
"clean_sat_mills.to_csv('../../datasets/processed/Satelligence_data/biodiversity_analysis/biodiversity_stats_h3.csv')"
]
},
{
"cell_type": "markdown",
"id": "950feaf5",
"metadata": {},
"source": [
"## Check satelligence deforestation using distinct:\n",
"\n",
"1. Translate raster to h3 using density and res 6\n",
"2. Use distinct query to compute deforestation\n",
"\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "4ddf0dcf",
"metadata": {},
"outputs": [],
"source": [
"def convert_raster_h3(raster_path, vector_path, column='deforestat', resolution=6):\n",
" with rio.open(raster_path) as src:\n",
" gdf = raster.raster_to_geodataframe(src.read(1), src.transform, h3_resolution=resolution, nodata_value=src.profile['nodata'], compacted=False)\n",
"\n",
" gdf.plot('value')\n",
" gdf['h3index'] = gdf['h3index'].apply(hex)\n",
"\n",
" gdf_vector = gpd.read_file(vector_path)\n",
" clean_gdf = gdf_vector[['gfw_fid',column,'geometry']]\n",
" \n",
" _sum_calculated = []\n",
" for i, row in clean_gdf.iterrows():\n",
" filtered_gdf = clean_gdf[i:i+1]\n",
" #convert to h3\n",
" h3_gdf = filtered_gdf.h3.polyfill_resample(resolution)\n",
" #h3_gdf = h3_gdf.reset_index().rename(columns={'h3_polyfill':'h3index'})\n",
" h3index_list = [f'0x{h3index}' for h3index in h3_gdf.index]\n",
" #filter gdf by list and get value\n",
" _sum = gdf[gdf['h3index'].isin(h3index_list)]['value'].sum()\n",
" _sum_calculated.append(_sum)\n",
" \n",
" clean_gdf['estimate'] = _sum_calculated\n",
" return clean_gdf"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "e2eb8543",
"metadata": {},
"outputs": [],
"source": [
"gdf = convert_raster_h3(\"../../datasets/processed/Satelligence_data/Deforestation_IDN_2021-01-01-2022-01-01_Density.tif\", \n",
" '../../datasets/processed/Satelligence_data/test_rasters_2/satelligence_mills_4326_50kmbuffer.shp',\n",
" column='deforestat',\n",
" resolution=6)\n",
"_estimated = [el*3612.9 for el in gdf['estimate']]\n",
"gdf['estimate']=_estimated\n",
"gdf.head()"
]
},
{
"cell_type": "code",
"execution_count": 26,
"id": "6adc6526",
"metadata": {},
"outputs": [],
"source": [
"#raster to translate\n",
"#sat_def_raster = \"../../datasets/processed/Satelligence_data/Deforestation_IDN_2021-01-01-2022-01-01_Density.tif\"\n",
"sat_def_raster = \"../../datasets/processed/Satelligence_data/Deforestation_IDN_2021-01-01-2022-01-01_areaSum_ha.tif\"\n",
"\n",
"mills_locations = '../../datasets/processed/Satelligence_data/test_rasters_2/satelligence_mills_4326_50kmbuffer.shp'\n",
"resolution = 6"
]
},
{
"cell_type": "code",
"execution_count": 27,
"id": "06f553b2",
"metadata": {},
"outputs": [
{
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" <tr>\n",
" <th>0</th>\n",
" <td>0x86657374fffffff</td>\n",
" <td>11.959510</td>\n",
" <td>POLYGON ((95.99883 2.77929, 96.00155 2.81546, ...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>0x866550417ffffff</td>\n",
" <td>5.858907</td>\n",
" <td>POLYGON ((96.06363 4.91120, 96.06637 4.94766, ...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>0x866550437ffffff</td>\n",
" <td>5.858907</td>\n",
" <td>POLYGON ((96.09131 4.85415, 96.09406 4.89060, ...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>0x866550407ffffff</td>\n",
" <td>0.026855</td>\n",
" <td>POLYGON ((96.02770 4.85888, 96.03045 4.89533, ...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>0x86655040fffffff</td>\n",
" <td>0.026855</td>\n",
" <td>POLYGON ((95.96409 4.86360, 95.96683 4.90006, ...</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" h3index value \\\n",
"0 0x86657374fffffff 11.959510 \n",
"1 0x866550417ffffff 5.858907 \n",
"2 0x866550437ffffff 5.858907 \n",
"3 0x866550407ffffff 0.026855 \n",
"4 0x86655040fffffff 0.026855 \n",
"\n",
" geometry \n",
"0 POLYGON ((95.99883 2.77929, 96.00155 2.81546, ... \n",
"1 POLYGON ((96.06363 4.91120, 96.06637 4.94766, ... \n",
"2 POLYGON ((96.09131 4.85415, 96.09406 4.89060, ... \n",
"3 POLYGON ((96.02770 4.85888, 96.03045 4.89533, ... \n",
"4 POLYGON ((95.96409 4.86360, 95.96683 4.90006, ... "
]
},
"execution_count": 27,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"#translate raster to h3\n",
"with rio.open(sat_def_raster) as src:\n",
" gdf = raster.raster_to_geodataframe(src.read(1), src.transform, h3_resolution=resolution, nodata_value=src.profile['nodata'], compacted=False)\n",
"\n",
" gdf.plot('value')\n",
" gdf['h3index'] = gdf['h3index'].apply(hex)\n",
"gdf.head()"
]
},
{
"cell_type": "code",
"execution_count": 28,
"id": "020bf83b",
"metadata": {},
"outputs": [
{
"data": {
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" <th></th>\n",
" <th>gfw_fid</th>\n",
" <th>deforestat</th>\n",
" <th>geometry</th>\n",
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" <tbody>\n",
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" <td>444.9278149230584</td>\n",
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"text/plain": [
" gfw_fid deforestat \\\n",
"0 706 349.5068535490499 \n",
"1 717 444.9278149230584 \n",
"2 264 552.3038111406324 \n",
"3 738 621.1470874150178 \n",
"4 1516 600.0120538952435 \n",
"\n",
" geometry \n",
"0 POLYGON ((98.47767 2.24508, 98.47705 2.22159, ... \n",
"1 POLYGON ((98.44488 2.31416, 98.44426 2.29068, ... \n",
"2 POLYGON ((98.40582 2.39111, 98.40521 2.36762, ... \n",
"3 POLYGON ((98.36445 2.43594, 98.36383 2.41245, ... \n",
"4 POLYGON ((98.51418 2.45678, 98.51356 2.43329, ... "
]
},
"execution_count": 28,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"gdf_vector = gpd.read_file(mills_locations)\n",
"#gdf_vector\n",
"clean_gdf = gdf_vector[['gfw_fid','deforestat','geometry']]\n",
"clean_gdf.head()"
]
},
{
"cell_type": "code",
"execution_count": 31,
"id": "3d90f751",
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/opt/conda/lib/python3.8/site-packages/geopandas/geodataframe.py:1351: SettingWithCopyWarning: \n",
"A value is trying to be set on a copy of a slice from a DataFrame.\n",
"Try using .loc[row_indexer,col_indexer] = value instead\n",
"\n",
"See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
" super().__setitem__(key, value)\n"
]
},
{
"data": {
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" text-align: right;\n",
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"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>gfw_fid</th>\n",
" <th>deforestat</th>\n",
" <th>geometry</th>\n",
" <th>estimate</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>706</td>\n",
" <td>349.5068535490499</td>\n",
" <td>POLYGON ((98.47767 2.24508, 98.47705 2.22159, ...</td>\n",
" <td>365.641333</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>717</td>\n",
" <td>444.9278149230584</td>\n",
" <td>POLYGON ((98.44488 2.31416, 98.44426 2.29068, ...</td>\n",
" <td>449.477260</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>264</td>\n",
" <td>552.3038111406324</td>\n",
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" <td>641.739516</td>\n",
" </tr>\n",
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" <th>3</th>\n",
" <td>738</td>\n",
" <td>621.1470874150178</td>\n",
" <td>POLYGON ((98.36445 2.43594, 98.36383 2.41245, ...</td>\n",
" <td>737.965135</td>\n",
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" <td>1516</td>\n",
" <td>600.0120538952435</td>\n",
" <td>POLYGON ((98.51418 2.45678, 98.51356 2.43329, ...</td>\n",
" <td>609.284527</td>\n",
" </tr>\n",
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"text/plain": [
" gfw_fid deforestat \\\n",
"0 706 349.5068535490499 \n",
"1 717 444.9278149230584 \n",
"2 264 552.3038111406324 \n",
"3 738 621.1470874150178 \n",
"4 1516 600.0120538952435 \n",
"\n",
" geometry estimate \n",
"0 POLYGON ((98.47767 2.24508, 98.47705 2.22159, ... 365.641333 \n",
"1 POLYGON ((98.44488 2.31416, 98.44426 2.29068, ... 449.477260 \n",
"2 POLYGON ((98.40582 2.39111, 98.40521 2.36762, ... 641.739516 \n",
"3 POLYGON ((98.36445 2.43594, 98.36383 2.41245, ... 737.965135 \n",
"4 POLYGON ((98.51418 2.45678, 98.51356 2.43329, ... 609.284527 "
]
},
"execution_count": 31,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"_sum_calculated = []\n",
"for i, row in clean_gdf.iterrows():\n",
" filtered_gdf = clean_gdf[i:i+1]\n",
" #convert to h3\n",
" h3_gdf = filtered_gdf.h3.polyfill_resample(resolution)\n",
" #h3_gdf = h3_gdf.reset_index().rename(columns={'h3_polyfill':'h3index'})\n",
" h3index_list = [f'0x{h3index}' for h3index in h3_gdf.index]\n",
" #filter gdf by list and get value\n",
" _sum = sum((gdf[gdf['h3index'].isin(h3index_list)]['value']).unique())\n",
" _sum_calculated.append(_sum)\n",
"\n",
"clean_gdf['estimate'] = _sum_calculated\n",
"_estimated = [el for el in clean_gdf['estimate']]\n",
"clean_gdf['estimate']=_estimated\n",
"clean_gdf.head()"
]
},
{
"cell_type": "code",
"execution_count": 32,
"id": "2b6e1116",
"metadata": {},
"outputs": [],
"source": [
"clean_gdf.to_csv('../../datasets/processed/Satelligence_data/deforestation_stats_areasum_unique_h3.csv')"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "acbdd949",
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
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