{ "cells": [ { "cell_type": "markdown", "id": "valid-miracle", "metadata": {}, "source": [ "# Global overview tables and statistics" ] }, { "cell_type": "markdown", "id": "b725109e-366d-4929-9842-894d0c1de43f", "metadata": {}, "source": [ "This is a bit messy, but there is a bunch of code producing maps and stats for the technical report." ] }, { "cell_type": "code", "execution_count": 1, "id": "durable-article", "metadata": {}, "outputs": [], "source": [ "import pandas as pd\n", "import geopandas as gpd\n", "import subprocess\n", "import matplotlib.pyplot as plt\n", "import matplotlib.patches as mpatches\n", "import seaborn as sns\n", "import numpy as np\n", "import sys, os\n", "import os\n", "import matplotlib.patches as mpatches\n", "from oggm import utils\n", "import csv\n", "import seaborn as sns\n", "import cartopy\n", "import cartopy.crs as ccrs\n", "from matplotlib.image import imread" ] }, { "cell_type": "code", "execution_count": 2, "id": "composed-detail", "metadata": {}, "outputs": [], "source": [ "# go down from rgi7_scripts/workflow\n", "data_dir = '../../../../rgi7_data/'\n", "\n", "final_dir = os.path.join(data_dir, 'rgi7_final')" ] }, { "cell_type": "code", "execution_count": 3, "id": "5dd742d4-683f-4828-b9da-d04c544de3e8", "metadata": {}, "outputs": [], "source": [ "user_guide_dir = '../../../../rgi_user_guide/'" ] }, { "cell_type": "code", "execution_count": 4, "id": "a0cd0cad-6b11-45a2-bc4f-b6150bff6ced", "metadata": {}, "outputs": [], "source": [ "overlap_dir = os.path.join(data_dir, 'rgi7_rgi6_links')" ] }, { "cell_type": "markdown", "id": "b59e3fb7-4db8-43d6-ad8b-e32f3e22a42e", "metadata": {}, "source": [ "## Read RGI6 and RGI7 attrs " ] }, { "cell_type": "code", "execution_count": 5, "id": "86efdd76-c228-4643-be36-19aa9ce778b7", "metadata": {}, "outputs": [], "source": [ "df_rgi7g = pd.read_csv(os.path.join(final_dir, 'global_files', 'attributes', 'RGI2000-v7.0-G-global-attributes.csv.zip'), \n", " index_col=0,\n", " compression='zip',\n", " dtype={'o1region': str})" ] }, { "cell_type": "code", "execution_count": 6, "id": "69edb4f6-eded-4002-91b3-d231df43b943", "metadata": {}, "outputs": [], "source": [ "df_rgi6g = pd.read_hdf(utils.file_downloader('https://cluster.klima.uni-bremen.de/~oggm/rgi/rgi62_stats.h5'))\n", "df_rgi6g = df_rgi6g.loc[df_rgi6g.Connect != 2]" ] }, { "cell_type": "code", "execution_count": 7, "id": "2e920c14-c39a-4402-a1ee-f1ec2d8d017f", "metadata": {}, "outputs": [], "source": [ "df_rgi7c = pd.read_csv(os.path.join(final_dir, 'global_files', 'attributes', 'RGI2000-v7.0-C-global-attributes.csv.zip'), \n", " index_col=0,\n", " compression='zip',\n", " dtype={'o1region': str})" ] }, { "cell_type": "code", "execution_count": 8, "id": "1c8f769b-a8c6-4171-9326-28f0685904c7", "metadata": {}, "outputs": [], "source": [ "np.testing.assert_allclose(df_rgi7g.area_km2.sum(), df_rgi7c.area_km2.sum())" ] }, { "cell_type": "code", "execution_count": 9, "id": "f6136b81-8023-45b6-b657-94d43604fa87", "metadata": {}, "outputs": [], "source": [ "df_reg_o1 = gpd.read_file('zip://' + final_dir + '/RGI2000-v7.0-regions.zip/RGI2000-v7.0-o1regions.shp')\n", "df_reg_o2 = gpd.read_file('zip://' + final_dir + '/RGI2000-v7.0-regions.zip/RGI2000-v7.0-o2regions.shp')" ] }, { "cell_type": "markdown", "id": "4897e484-ef16-4693-b4ec-4278d6cf9f0d", "metadata": {}, "source": [ "## RGI6 vs RGI7 table for `overview.md`" ] }, { "cell_type": "code", "execution_count": 10, "id": "0f935615-6d80-42c5-8e8f-e07abfc8fb7b", "metadata": {}, "outputs": [], "source": [ "df = df_rgi6g.groupby('O1Region')[['Area']].sum()\n", "df.columns = ['Area RGI6']" ] }, { "cell_type": "code", "execution_count": 11, "id": "2d72d066-8080-485c-a85e-279cfe345593", "metadata": { "tags": [] }, "outputs": [], "source": [ "df['Area RGI7'] = df_rgi7g.groupby('o1region')[['area_km2']].sum()" ] }, { "cell_type": "code", "execution_count": 12, "id": "2e63e99c-a4a4-4d8b-8079-f74ed9153faa", "metadata": {}, "outputs": [], "source": [ "df['Diff A (%)'] = (df['Area RGI7'] / df['Area RGI6'] - 1) * 100" ] }, { "cell_type": "code", "execution_count": 13, "id": "7bf8c766-a646-48dc-80d0-f457a7c4a1ae", "metadata": {}, "outputs": [], "source": [ "df['N RGI6'] = df_rgi6g.groupby('O1Region').count()['Area']\n", "df['N RGI7'] = df_rgi7g.groupby('o1region').count()['area_km2']\n", "\n", "df['Diff N (%)'] = (df['N RGI7'] / df['N RGI6'] - 1) * 100" ] }, { "cell_type": "code", "execution_count": 14, "id": "bab226ff-882d-4f63-85b3-a2958fd65c28", "metadata": {}, "outputs": [], "source": [ "df.loc['20'] = [0] * 6" ] }, { "cell_type": "code", "execution_count": 15, "id": "46c9e067-21c3-4c2a-bab9-8b5ea5b16468", "metadata": {}, "outputs": [], "source": [ "ss = df.sum()\n", "ss.name = 'Global'\n", "df = pd.concat([df, ss.to_frame().T])\n", "\n", "df['Diff A (%)'] = (df['Area RGI7'] / df['Area RGI6'] - 1) * 100\n", "df['Diff N (%)'] = (df['N RGI7'] / df['N RGI6'] - 1) * 100" ] }, { "cell_type": "code", "execution_count": 16, "id": "13f63546-158e-44db-aec0-abcb0d70be09", "metadata": {}, "outputs": [], "source": [ "df['Area RGI6'] = df['Area RGI6'].astype(float).round(0).astype(int) \n", "df['Area RGI7'] = df['Area RGI7'].astype(float).round(0).astype(int) \n", "df['N RGI6'] = df['N RGI6'].astype(int) \n", "df['N RGI7'] = df['N RGI7'].astype(int) \n", "\n", "df['Diff A (%)'] = df['Diff A (%)'].round(1).replace(-0, 0).replace(np.NaN, 0)\n", "df['Diff N (%)'] = df['Diff N (%)'].round(1).replace(-0, 0).replace(np.NaN, 0)" ] }, { "cell_type": "code", "execution_count": 17, "id": "eb8418c9-ffa1-4f4d-9418-00f437240de2", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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Area RGI6Area RGI7Diff A (%)N RGI6N RGI7Diff N (%)
0186725867080.027108275091.5
0214524145210.01885518730-0.7
031051111053700.24556521614.5
044088840538-0.974151100948.5
0589717904820.919306199943.6
0611060110600.05685680.0
0733959339590.0161516663.2
08294929480.034173410-0.2
0951592515950.0106910690.0
10241026439.65151715538.9
11209221241.5392740793.9
12130714077.61888227520.5
1349303503442.1544297561338.9
143356833075-1.5279883756234.2
1514734160498.9131191858741.7
1623411929-17.62939369525.7
172942927674-6.0159083063492.6
181162886-23.735373018-14.7
191328671334320.427522742-0.4
20000.0000.0
Global7057397067440.121554727453127.4
\n", "
" ], "text/plain": [ " Area RGI6 Area RGI7 Diff A (%) N RGI6 N RGI7 Diff N (%)\n", "01 86725 86708 0.0 27108 27509 1.5\n", "02 14524 14521 0.0 18855 18730 -0.7\n", "03 105111 105370 0.2 4556 5216 14.5\n", "04 40888 40538 -0.9 7415 11009 48.5\n", "05 89717 90482 0.9 19306 19994 3.6\n", "06 11060 11060 0.0 568 568 0.0\n", "07 33959 33959 0.0 1615 1666 3.2\n", "08 2949 2948 0.0 3417 3410 -0.2\n", "09 51592 51595 0.0 1069 1069 0.0\n", "10 2410 2643 9.6 5151 7155 38.9\n", "11 2092 2124 1.5 3927 4079 3.9\n", "12 1307 1407 7.6 1888 2275 20.5\n", "13 49303 50344 2.1 54429 75613 38.9\n", "14 33568 33075 -1.5 27988 37562 34.2\n", "15 14734 16049 8.9 13119 18587 41.7\n", "16 2341 1929 -17.6 2939 3695 25.7\n", "17 29429 27674 -6.0 15908 30634 92.6\n", "18 1162 886 -23.7 3537 3018 -14.7\n", "19 132867 133432 0.4 2752 2742 -0.4\n", "20 0 0 0.0 0 0 0.0\n", "Global 705739 706744 0.1 215547 274531 27.4" ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df" ] }, { "cell_type": "code", "execution_count": 18, "id": "d6360c7b-ca5f-4637-85e3-89dfa2503d59", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "N in RGI7: N=274531, A=706744 km²\n", "Same as RGI6: N=73797, A=410788 km²\n", "New in RGI7: N=200734, A=295955 km²\n", "New in % area: 0.41875848090612894\n", "New in % number: 0.7311888274912487\n" ] } ], "source": [ "print(f\"N in RGI7: N={len(df_rgi7g)}, A={int(df_rgi7g['area_km2'].sum())} km²\")\n", "\n", "s1 = df_rgi7g.loc[df_rgi7g.is_rgi6 == 1]\n", "print(f\"Same as RGI6: N={len(s1)}, A={int(s1['area_km2'].sum())} km²\")\n", "\n", "s2 = df_rgi7g.loc[df_rgi7g.is_rgi6 == 0]\n", "print(f\"New in RGI7: N={len(s2)}, A={int(s2['area_km2'].sum())} km²\")\n", "\n", "print(f\"New in % area: {s2['area_km2'].sum() / df_rgi7g['area_km2'].sum()}\")\n", "\n", "print(f\"New in % number: {len(s2) / len(df_rgi7g)}\")" ] }, { "cell_type": "code", "execution_count": 19, "id": "54ce2ccf-9e3e-488f-b9f3-02e36c107f86", "metadata": {}, "outputs": [], "source": [ "df.index.name = 'Region'" ] }, { "cell_type": "code", "execution_count": 20, "id": "a6f57487-edee-49d7-9d03-8132c43a81ce", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "| | Area
RGI 6.0 (km²) | Area
RGI 7.0 (km²) | Area
Diff. (%) | Count
RGI 6.0 | Count
RGI 7.0 | Count
Diff. (%) |\n", "|:-------------|------------------------:|------------------------:|--------------------:|-------------------:|-------------------:|---------------------:|\n", "| [](rgi01.md) | 86725 | 86708 | 0.0 | 27108 | 27509 | 1.5 |\n", "| [](rgi02.md) | 14524 | 14521 | 0.0 | 18855 | 18730 | -0.7 |\n", "| [](rgi03.md) | 105111 | 105370 | 0.2 | 4556 | 5216 | 14.5 |\n", "| [](rgi04.md) | 40888 | 40538 | -0.9 | 7415 | 11009 | 48.5 |\n", "| [](rgi05.md) | 89717 | 90482 | 0.9 | 19306 | 19994 | 3.6 |\n", "| [](rgi06.md) | 11060 | 11060 | 0.0 | 568 | 568 | 0.0 |\n", "| [](rgi07.md) | 33959 | 33959 | 0.0 | 1615 | 1666 | 3.2 |\n", "| [](rgi08.md) | 2949 | 2948 | 0.0 | 3417 | 3410 | -0.2 |\n", "| [](rgi09.md) | 51592 | 51595 | 0.0 | 1069 | 1069 | 0.0 |\n", "| [](rgi10.md) | 2410 | 2643 | 9.6 | 5151 | 7155 | 38.9 |\n", "| [](rgi11.md) | 2092 | 2124 | 1.5 | 3927 | 4079 | 3.9 |\n", "| [](rgi12.md) | 1307 | 1407 | 7.6 | 1888 | 2275 | 20.5 |\n", "| [](rgi13.md) | 49303 | 50344 | 2.1 | 54429 | 75613 | 38.9 |\n", "| [](rgi14.md) | 33568 | 33075 | -1.5 | 27988 | 37562 | 34.2 |\n", "| [](rgi15.md) | 14734 | 16049 | 8.9 | 13119 | 18587 | 41.7 |\n", "| [](rgi16.md) | 2341 | 1929 | -17.6 | 2939 | 3695 | 25.7 |\n", "| [](rgi17.md) | 29429 | 27674 | -6.0 | 15908 | 30634 | 92.6 |\n", "| [](rgi18.md) | 1162 | 886 | -23.7 | 3537 | 3018 | -14.7 |\n", "| [](rgi19.md) | 132867 | 133432 | 0.4 | 2752 | 2742 | -0.4 |\n", "| [](rgi20.md) | 0 | 0 | 0.0 | 0 | 0 | 0.0 |\n", "| **Global** | 705739 | 706744 | 0.1 | 215547 | 274531 | 27.4 |\n" ] } ], "source": [ "df_formd = df.copy()\n", "df_formd.index = [f'[](rgi{i}.md)' for i in df_formd.index[:-1]] + ['Global']\n", "df_formd.columns = ['Area
RGI 6.0 (km²)', 'Area
RGI 7.0 (km²)', 'Area
Diff. (%)', 'Count
RGI 6.0', 'Count
RGI 7.0', 'Count
Diff. (%)']\n", "print(df_formd.to_markdown(floatfmt=(\".0f\",\".0f\",\".0f\",\".1f\",\".0f\",\".0f\",\".1f\")).replace('Global ', '**Global**'))" ] }, { "cell_type": "code", "execution_count": 21, "id": "944c0cec-a550-4cd4-8008-5f1723a19e24", "metadata": {}, "outputs": [], "source": [ "df['Full name'] = list(df_reg_o1.full_name.unique()) + ['']\n", "df['Code'] = ['`' + r + '`' for r in df_reg_o1.long_code.unique()] + ['']" ] }, { "cell_type": "code", "execution_count": 22, "id": "158e2b39-b95a-4e81-ba23-16ede6b44c60", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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Area RGI6Area RGI7Diff A (%)N RGI6N RGI7Diff N (%)Full nameCode
Region
0186725867080.027108275091.5Alaska`01_alaska`
0214524145210.01885518730-0.7Western Canada and USA`02_western_canada_usa`
031051111053700.24556521614.5Arctic Canada North`03_arctic_canada_north`
044088840538-0.974151100948.5Arctic Canada South`04_arctic_canada_south`
0589717904820.919306199943.6Greenland Periphery`05_greenland_periphery`
0611060110600.05685680.0Iceland`06_iceland`
0733959339590.0161516663.2Svalbard and Jan Mayen`07_svalbard_jan_mayen`
08294929480.034173410-0.2Scandinavia`08_scandinavia`
0951592515950.0106910690.0Russian Arctic`09_russian_arctic`
10241026439.65151715538.9North Asia`10_north_asia`
11209221241.5392740793.9Central Europe`11_central_europe`
12130714077.61888227520.5Caucasus and Middle East`12_caucasus_middle_east`
1349303503442.1544297561338.9Central Asia`13_central_asia`
143356833075-1.5279883756234.2South Asia West`14_south_asia_west`
1514734160498.9131191858741.7South Asia East`15_south_asia_east`
1623411929-17.62939369525.7Low Latitudes`16_low_latitudes`
172942927674-6.0159083063492.6Southern Andes`17_southern_andes`
181162886-23.735373018-14.7New Zealand`18_new_zealand`
191328671334320.427522742-0.4Subantarctic and Antarctic Islands`19_subantarctic_antarctic_islands`
20000.0000.0Antarctic Mainland`20_antarctic_mainland`
Global7057397067440.121554727453127.4
\n", "
" ], "text/plain": [ " Area RGI6 Area RGI7 Diff A (%) N RGI6 N RGI7 Diff N (%) \\\n", "Region \n", "01 86725 86708 0.0 27108 27509 1.5 \n", "02 14524 14521 0.0 18855 18730 -0.7 \n", "03 105111 105370 0.2 4556 5216 14.5 \n", "04 40888 40538 -0.9 7415 11009 48.5 \n", "05 89717 90482 0.9 19306 19994 3.6 \n", "06 11060 11060 0.0 568 568 0.0 \n", "07 33959 33959 0.0 1615 1666 3.2 \n", "08 2949 2948 0.0 3417 3410 -0.2 \n", "09 51592 51595 0.0 1069 1069 0.0 \n", "10 2410 2643 9.6 5151 7155 38.9 \n", "11 2092 2124 1.5 3927 4079 3.9 \n", "12 1307 1407 7.6 1888 2275 20.5 \n", "13 49303 50344 2.1 54429 75613 38.9 \n", "14 33568 33075 -1.5 27988 37562 34.2 \n", "15 14734 16049 8.9 13119 18587 41.7 \n", "16 2341 1929 -17.6 2939 3695 25.7 \n", "17 29429 27674 -6.0 15908 30634 92.6 \n", "18 1162 886 -23.7 3537 3018 -14.7 \n", "19 132867 133432 0.4 2752 2742 -0.4 \n", "20 0 0 0.0 0 0 0.0 \n", "Global 705739 706744 0.1 215547 274531 27.4 \n", "\n", " Full name \\\n", "Region \n", "01 Alaska \n", "02 Western Canada and USA \n", "03 Arctic Canada North \n", "04 Arctic Canada South \n", "05 Greenland Periphery \n", "06 Iceland \n", "07 Svalbard and Jan Mayen \n", "08 Scandinavia \n", "09 Russian Arctic \n", "10 North Asia \n", "11 Central Europe \n", "12 Caucasus and Middle East \n", "13 Central Asia \n", "14 South Asia West \n", "15 South Asia East \n", "16 Low Latitudes \n", "17 Southern Andes \n", "18 New Zealand \n", "19 Subantarctic and Antarctic Islands \n", "20 Antarctic Mainland \n", "Global \n", "\n", " Code \n", "Region \n", "01 `01_alaska` \n", "02 `02_western_canada_usa` \n", "03 `03_arctic_canada_north` \n", "04 `04_arctic_canada_south` \n", "05 `05_greenland_periphery` \n", "06 `06_iceland` \n", "07 `07_svalbard_jan_mayen` \n", "08 `08_scandinavia` \n", "09 `09_russian_arctic` \n", "10 `10_north_asia` \n", "11 `11_central_europe` \n", "12 `12_caucasus_middle_east` \n", "13 `13_central_asia` \n", "14 `14_south_asia_west` \n", "15 `15_south_asia_east` \n", "16 `16_low_latitudes` \n", "17 `17_southern_andes` \n", "18 `18_new_zealand` \n", "19 `19_subantarctic_antarctic_islands` \n", "20 `20_antarctic_mainland` \n", "Global " ] }, "execution_count": 22, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df" ] }, { "cell_type": "code", "execution_count": 23, "id": "03c99796-c4fa-4f46-969b-e63683249446", "metadata": {}, "outputs": [], "source": [ "for_csv = df[['Full name', 'Area RGI6', 'Area RGI7', 'Diff A (%)', 'N RGI6', 'N RGI7', 'Diff N (%)']].copy()\n", "for_csv.columns = ['Full name', 'Area RGI 6.0', 'Area RGI 7.0', 'Diff. Area (%)', 'Count RGI 6.0', 'Count RGI 7.0', 'Diff. Count (%)']\n", "for_csv.reset_index().to_csv(user_guide_dir + '/docs/appendix/RGI2000-v7.0-G-comparison-rgi6.csv', index=False, quoting=csv.QUOTE_NONNUMERIC)" ] }, { "cell_type": "markdown", "id": "2e83efb6-ad5a-4e8c-9d87-babc795c1f50", "metadata": {}, "source": [ "## Add overlap info " ] }, { "cell_type": "code", "execution_count": 60, "id": "bafcac2e-ef38-4639-8557-a3ae56fb76ea", "metadata": {}, "outputs": [], "source": [ "dfoverlap = pd.read_csv(os.path.join(overlap_dir, 'RGI2000-v7.0-G-global-rgi6_links.csv'))" ] }, { "cell_type": "code", "execution_count": 61, "id": "cd726080-c21e-4196-99d5-07b3867ce80b", "metadata": {}, "outputs": [], "source": [ "dfover = df.copy()\n", "dfover['Area overlap'] = 0" ] }, { "cell_type": "code", "execution_count": 62, "id": "68befa7e-7a82-4e2d-9c37-298f039c964a", "metadata": {}, "outputs": [], "source": [ "dfover.loc[:'19', 'Area overlap'] = dfoverlap.groupby('rgi7_reg')['overlap_area_km2'].sum().values\n", "dfover.loc['Global', 'Area overlap'] = dfoverlap['overlap_area_km2'].sum()" ] }, { "cell_type": "code", "execution_count": 63, "id": "4e8b6293-6f94-4b39-be7c-1d6c6499db77", "metadata": {}, "outputs": [], "source": [ "dfover['Area removed from RGI6'] = dfover['Area RGI6'] - dfover['Area overlap']\n", "dfover['Area removed from RGI6 (%)'] = dfover['Area removed from RGI6'] / dfover['Area RGI6'] * 100\n", "dfover['Area added in RGI7'] = dfover['Area RGI7'] - dfover['Area overlap']\n", "dfover['Area added in RGI7 (%)'] = dfover['Area added in RGI7'] / dfover['Area RGI7'] * 100" ] }, { "cell_type": "code", "execution_count": 64, "id": "e0708328-3e1f-412b-8332-dda8b659925f", "metadata": {}, "outputs": [], "source": [ "for c in ['Area removed from RGI6 (%)', 'Area added in RGI7 (%)']:\n", " dfover[c] = dfover[c].round(1).replace(-0, 0).replace(np.NaN, 0)\n", "for c in ['Area removed from RGI6', 'Area added in RGI7', 'Area overlap']:\n", " dfover[c] = dfover[c].astype(float).round(0).astype(int)" ] }, { "cell_type": "code", "execution_count": 65, "id": "78bead5e-809f-4407-a4f8-74df8a26f7d1", "metadata": {}, "outputs": [], "source": [ "for_csv = dfover[dfover.columns[[6, 0, 1, 2, 8, 9, 11, 10, 12]]].copy()\n", "for_csv.columns = ['Full name', 'Area RGI 6.0', 'Area RGI 7.0', 'Diff. Area (%)',\n", " 'Area overlap', 'Area removed from RGI6', 'Area added in RGI7',\n", " 'Area removed from RGI 6.0 (%)', 'Area added in RGI 7.0 (%)'] \n", "for_csv.reset_index().to_csv(user_guide_dir + '/docs/appendix/RGI2000-v7.0-G-comparison-rgi6-area.csv', index=False, quoting=csv.QUOTE_NONNUMERIC)" ] }, { "cell_type": "code", "execution_count": 74, "id": "32ffb398-2fbf-432d-ad2f-9546ea777c24", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "| | Area
RGI 6.0 (km²) | Area
RGI 7.0 (km²) | Area
Diff. (%) | Area overlap (km²) | Area removed from RGI 6.0 (km²) | Area added in RGI 7.0 (km²) | Area removed from RGI 6.0 (%) | Area added in RGI 7.0 (%) |\n", "|:-------------|------------------------:|------------------------:|--------------------:|---------------------:|----------------------------------:|------------------------------:|--------------------------------:|----------------------------:|\n", "| [](rgi01.md) | 86725 | 86708 | 0.0 | 86627 | 98 | 81 | 0.1 | 0.1 |\n", "| [](rgi02.md) | 14524 | 14521 | 0.0 | 14521 | 3 | 0 | 0.0 | 0.0 |\n", "| [](rgi03.md) | 105111 | 105370 | 0.2 | 102891 | 2220 | 2479 | 2.1 | 2.4 |\n", "| [](rgi04.md) | 40888 | 40538 | -0.9 | 39927 | 961 | 611 | 2.4 | 1.5 |\n", "| [](rgi05.md) | 89717 | 90482 | 0.9 | 89546 | 171 | 936 | 0.2 | 1.0 |\n", "| [](rgi06.md) | 11060 | 11060 | 0.0 | 11060 | 0 | 0 | 0.0 | 0.0 |\n", "| [](rgi07.md) | 33959 | 33959 | 0.0 | 33938 | 21 | 21 | 0.1 | 0.1 |\n", "| [](rgi08.md) | 2949 | 2948 | 0.0 | 2899 | 50 | 49 | 1.7 | 1.7 |\n", "| [](rgi09.md) | 51592 | 51595 | 0.0 | 51591 | 1 | 4 | 0.0 | 0.0 |\n", "| [](rgi10.md) | 2410 | 2643 | 9.6 | 2114 | 296 | 529 | 12.3 | 20.0 |\n", "| [](rgi11.md) | 2092 | 2124 | 1.5 | 2081 | 11 | 43 | 0.5 | 2.0 |\n", "| [](rgi12.md) | 1307 | 1407 | 7.6 | 970 | 337 | 437 | 25.8 | 31.1 |\n", "| [](rgi13.md) | 49303 | 50344 | 2.1 | 46126 | 3177 | 4218 | 6.4 | 8.4 |\n", "| [](rgi14.md) | 33568 | 33075 | -1.5 | 30849 | 2719 | 2226 | 8.1 | 6.7 |\n", "| [](rgi15.md) | 14734 | 16049 | 8.9 | 13433 | 1301 | 2616 | 8.8 | 16.3 |\n", "| [](rgi16.md) | 2341 | 1929 | -17.6 | 1802 | 539 | 127 | 23.0 | 6.6 |\n", "| [](rgi17.md) | 29429 | 27674 | -6.0 | 25995 | 3434 | 1679 | 11.7 | 6.1 |\n", "| [](rgi18.md) | 1162 | 886 | -23.7 | 827 | 335 | 59 | 28.8 | 6.7 |\n", "| [](rgi19.md) | 132867 | 133432 | 0.4 | 128955 | 3912 | 4477 | 2.9 | 3.4 |\n", "| [](rgi20.md) | 0 | 0 | 0.0 | 0 | 0 | 0 | 0.0 | 0.0 |\n", "| **Global** | 705739 | 706744 | 0.1 | 686151 | 19588 | 20593 | 2.8 | 2.9 |\n" ] } ], "source": [ "df_formd = dfover[dfover.columns[[0, 1, 2, 8, 9, 11, 10, 12]]].copy()\n", "df_formd.index = [f'[](rgi{i}.md)' for i in df_formd.index[:-1]] + ['Global']\n", "df_formd.columns = ['Area
RGI 6.0 (km²)', 'Area
RGI 7.0 (km²)', 'Area
Diff. (%)', 'Area overlap (km²)', \n", " 'Area removed from RGI 6.0 (km²)', 'Area added in RGI 7.0 (km²)',\n", " 'Area removed from RGI 6.0 (%)', 'Area added in RGI 7.0 (%)']\n", "print(df_formd.to_markdown(floatfmt=(\".0f\",\".0f\",\".0f\",\".1f\",\".0f\",\".0f\",\".0f\",\".1f\",\".1f\")).replace('Global ', '**Global**'))" ] }, { "cell_type": "markdown", "id": "5a71b948-5ef9-4096-b05d-11ca44d1c98f", "metadata": {}, "source": [ "## Tables Appendix (no RGI6) " ] }, { "cell_type": "markdown", "id": "981ee942-9055-43a6-a41c-e03138cda298", "metadata": {}, "source": [ "### O1 " ] }, { "cell_type": "code", "execution_count": 23, "id": "9dd2e602-c945-4b00-88ad-10760d9b4245", "metadata": {}, "outputs": [], "source": [ "for_csv = df[['Full name', 'Code', 'N RGI7', 'Area RGI7']].copy()\n", "for_csv['Code'] = [s.replace(\"`\", \"\") for s in for_csv['Code']]" ] }, { "cell_type": "code", "execution_count": 24, "id": "4428b2bf-e473-4a27-bf47-6c0e2ac7badb", "metadata": {}, "outputs": [], "source": [ "for_csv.columns = [['full_name', 'long_code', 'count', 'area_km2']]\n", "for_csv.index.name = 'o1region'\n", "for_csv.reset_index().to_csv(user_guide_dir + '/docs/appendix/RGI2000-v7.0-G-o1region-summary.csv', index=False, quoting=csv.QUOTE_NONNUMERIC)" ] }, { "cell_type": "code", "execution_count": 25, "id": "c8428d21-f2cf-416b-8d6d-c34ace6f32ed", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "| Region | Full name | Code | Count | Area (km²) |\n", "|:---------|:-----------------------------------|:------------------------------------|--------:|-------------:|\n", "| 01 | Alaska | `01_alaska` | 27509 | 86708 |\n", "| 02 | Western Canada and USA | `02_western_canada_usa` | 18730 | 14521 |\n", "| 03 | Arctic Canada North | `03_arctic_canada_north` | 5216 | 105370 |\n", "| 04 | Arctic Canada South | `04_arctic_canada_south` | 11009 | 40538 |\n", "| 05 | Greenland Periphery | `05_greenland_periphery` | 19994 | 90482 |\n", "| 06 | Iceland | `06_iceland` | 568 | 11060 |\n", "| 07 | Svalbard and Jan Mayen | `07_svalbard_jan_mayen` | 1666 | 33959 |\n", "| 08 | Scandinavia | `08_scandinavia` | 3410 | 2948 |\n", "| 09 | Russian Arctic | `09_russian_arctic` | 1069 | 51595 |\n", "| 10 | North Asia | `10_north_asia` | 7155 | 2643 |\n", "| 11 | Central Europe | `11_central_europe` | 4079 | 2124 |\n", "| 12 | Caucasus and Middle East | `12_caucasus_middle_east` | 2275 | 1407 |\n", "| 13 | Central Asia | `13_central_asia` | 75613 | 50344 |\n", "| 14 | South Asia West | `14_south_asia_west` | 37562 | 33075 |\n", "| 15 | South Asia East | `15_south_asia_east` | 18587 | 16049 |\n", "| 16 | Low Latitudes | `16_low_latitudes` | 3695 | 1929 |\n", "| 17 | Southern Andes | `17_southern_andes` | 30634 | 27674 |\n", "| 18 | New Zealand | `18_new_zealand` | 3018 | 886 |\n", "| 19 | Subantarctic and Antarctic Islands | `19_subantarctic_antarctic_islands` | 2742 | 133432 |\n", "| 20 | Antarctic Mainland | `20_antarctic_mainland` | 0 | 0 |\n", "| Global | | | 274531 | 706744 |\n" ] } ], "source": [ "for_print = df[['Full name', 'Code', 'N RGI7', 'Area RGI7']]\n", "for_print.columns = ['Full name', 'Code', 'Count', 'Area (km²)']\n", "print(for_print.to_markdown().replace('Global ', '**Global**'))" ] }, { "cell_type": "markdown", "id": "dbe1cc80-20eb-42fc-9e99-5abeccb920fd", "metadata": {}, "source": [ "### O2 " ] }, { "cell_type": "code", "execution_count": 26, "id": "74e03dd8-a10b-4e71-af16-80e13b651140", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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o1regiono2regionfull_namelong_codegeometry
00101-01North Alaska01-01_north_alaskaPOLYGON ((-169.00000 64.00000, -169.00000 65.0...
10101-02Alaska Range (Wrangell/Kilbuck)01-02_alaska_range_wrangell_kilbuckPOLYGON ((-144.40436 61.48531, -144.48100 61.5...
20101-03Alaska Peninsula (Aleutians)01-03_alaska_peninsula_aleutiansPOLYGON ((-180.00000 57.00000, -179.00000 57.0...
30101-04West Chugach Mountains (Talkeetna)01-04_west_chugach_mountains_talkeetnaPOLYGON ((-151.32973 58.74602, -151.57953 58.8...
40101-05Saint Elias Mountains01-05_saint_elias_mountainsPOLYGON ((-144.25108 61.44018, -144.07510 61.3...
..................
861919-21Northeast Antarctic Peninsula 7I219-21_northeast_antarctic_peninsula_7i2POLYGON ((-62.41123 -69.50624, -62.41913 -69.5...
871919-22Southeast Antarctic Peninsula 7I319-22_southeast_antarctic_peninsula_7i3POLYGON ((-50.00000 -74.80000, -51.00000 -74.8...
881919-23Ronne-Filchner Ice Shelf 7J19-23_ronne_filchner_ice_shelf_7jPOLYGON ((-42.00000 -74.80000, -41.00000 -74.8...
891919-24West Queen Maud Land 7K19-24_west_queen_maud_land_7kPOLYGON ((-10.30000 -69.40000, -10.00000 -69.4...
902020-01Antarctic Mainland20-01_antarctic_mainlandPOLYGON ((131.99251 -66.18084, 132.10675 -66.1...
\n", "

90 rows × 5 columns

\n", "
" ], "text/plain": [ " o1region o2region full_name \\\n", "0 01 01-01 North Alaska \n", "1 01 01-02 Alaska Range (Wrangell/Kilbuck) \n", "2 01 01-03 Alaska Peninsula (Aleutians) \n", "3 01 01-04 West Chugach Mountains (Talkeetna) \n", "4 01 01-05 Saint Elias Mountains \n", ".. ... ... ... \n", "86 19 19-21 Northeast Antarctic Peninsula 7I2 \n", "87 19 19-22 Southeast Antarctic Peninsula 7I3 \n", "88 19 19-23 Ronne-Filchner Ice Shelf 7J \n", "89 19 19-24 West Queen Maud Land 7K \n", "90 20 20-01 Antarctic Mainland \n", "\n", " long_code \\\n", "0 01-01_north_alaska \n", "1 01-02_alaska_range_wrangell_kilbuck \n", "2 01-03_alaska_peninsula_aleutians \n", "3 01-04_west_chugach_mountains_talkeetna \n", "4 01-05_saint_elias_mountains \n", ".. ... \n", "86 19-21_northeast_antarctic_peninsula_7i2 \n", "87 19-22_southeast_antarctic_peninsula_7i3 \n", "88 19-23_ronne_filchner_ice_shelf_7j \n", "89 19-24_west_queen_maud_land_7k \n", "90 20-01_antarctic_mainland \n", "\n", " geometry \n", "0 POLYGON ((-169.00000 64.00000, -169.00000 65.0... \n", "1 POLYGON ((-144.40436 61.48531, -144.48100 61.5... \n", "2 POLYGON ((-180.00000 57.00000, -179.00000 57.0... \n", "3 POLYGON ((-151.32973 58.74602, -151.57953 58.8... \n", "4 POLYGON ((-144.25108 61.44018, -144.07510 61.3... \n", ".. ... \n", "86 POLYGON ((-62.41123 -69.50624, -62.41913 -69.5... \n", "87 POLYGON ((-50.00000 -74.80000, -51.00000 -74.8... \n", "88 POLYGON ((-42.00000 -74.80000, -41.00000 -74.8... \n", "89 POLYGON ((-10.30000 -69.40000, -10.00000 -69.4... \n", "90 POLYGON ((131.99251 -66.18084, 132.10675 -66.1... \n", "\n", "[90 rows x 5 columns]" ] }, "execution_count": 26, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df_reg_o2 = df_reg_o2.drop_duplicates('o2region')\n", "df_reg_o2" ] }, { "cell_type": "code", "execution_count": 27, "id": "21454baf-0b6c-4e7e-8a53-8fb66f989bb0", "metadata": {}, "outputs": [], "source": [ "dfsr = df_reg_o2[['o2region', 'full_name', 'long_code']].copy().set_index('o2region')\n", "\n", "dfsr.columns = ['Full name', 'Code']\n", "\n", "dfsr['Count'] = df_rgi7g.groupby('o2region')['cenlon'].count()\n", "dfsr['Area (km²)'] = df_rgi7g.groupby('o2region')['area_km2'].sum()\n", "\n", "ss = dfsr.sum()\n", "ss.name = 'Global'\n", "dfsr = pd.concat([dfsr, ss.to_frame().T])\n", "\n", "dfsr.loc['Global', 'Full name'] = ''\n", "dfsr.loc['Global', 'Code'] = ''\n", "\n", "dfsr['Area (km²)'] = dfsr['Area (km²)'].astype(float).round(0).fillna(0).astype(int) \n", "dfsr['Count'] = dfsr['Count'].fillna(0).astype(int) \n", "\n", "dfsr['Code'] = ['`' + r + '`' for r in dfsr.Code]\n", "dfsr.loc['Global', 'Code'] = ''" ] }, { "cell_type": "code", "execution_count": 28, "id": "0c231782-3efe-40cf-ab9c-af77ca557c5d", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "| | Full name | Code | Count | Area (km²) |\n", "|:-------|:-----------------------------------------|:-----------------------------------------------|--------:|-------------:|\n", "| 01-01 | North Alaska | `01-01_north_alaska` | 706 | 364 |\n", "| 01-02 | Alaska Range (Wrangell/Kilbuck) | `01-02_alaska_range_wrangell_kilbuck` | 5812 | 16284 |\n", "| 01-03 | Alaska Peninsula (Aleutians) | `01-03_alaska_peninsula_aleutians` | 872 | 1912 |\n", "| 01-04 | West Chugach Mountains (Talkeetna) | `01-04_west_chugach_mountains_talkeetna` | 4529 | 12005 |\n", "| 01-05 | Saint Elias Mountains | `01-05_saint_elias_mountains` | 5039 | 33178 |\n", "| 01-06 | North Coast Ranges | `01-06_north_coast_ranges` | 10551 | 22964 |\n", "| 02-01 | Mackenzie and Selwyn Mountains | `02-01_mackenzie_and_selwyn_mountains` | 1235 | 657 |\n", "| 02-02 | South Coast Ranges | `02-02_south_coast_ranges` | 7390 | 8806 |\n", "| 02-03 | North Rocky Mountains | `02-03_north_rocky_mountains` | 5063 | 4386 |\n", "| 02-04 | Cascade Range and Sierra Nevada | `02-04_cascade_range_and_sierra_nevada` | 3127 | 529 |\n", "| 02-05 | South Rocky Mountains | `02-05_south_rocky_mountains` | 1915 | 144 |\n", "| 03-01 | North Ellesmere Island | `03-01_north_ellesmere_island` | 2573 | 27690 |\n", "| 03-02 | Axel Heiberg and Meighen Is | `03-02_axel_heiberg_and_meighen_is` | 624 | 11852 |\n", "| 03-03 | North Central Ellesmere Island | `03-03_north_central_ellesmere_island` | 902 | 21336 |\n", "| 03-04 | South Central Ellesmere Island | `03-04_south_central_ellesmere_island` | 261 | 19322 |\n", "| 03-05 | South Ellesmere Island (Northwest Devon) | `03-05_south_ellesmere_island_northwest_devon` | 633 | 10042 |\n", "| 03-06 | Devon Island | `03-06_devon_island` | 216 | 15000 |\n", "| 03-07 | Melville Island | `03-07_melville_island` | 7 | 128 |\n", "| 04-01 | Bylot Island | `04-01_bylot_island` | 616 | 4883 |\n", "| 04-02 | West Baffin Island | `04-02_west_baffin_island` | 96 | 3767 |\n", "| 04-03 | North Baffin Island | `04-03_north_baffin_island` | 597 | 497 |\n", "| 04-04 | Northeast Baffin Island | `04-04_northeast_baffin_island` | 1953 | 8195 |\n", "| 04-05 | East Central Baffin Island | `04-05_east_central_baffin_island` | 1905 | 9431 |\n", "| 04-06 | South East Baffin Island | `04-06_south_east_baffin_island` | 3036 | 7603 |\n", "| 04-07 | Cumberland Sound | `04-07_cumberland_sound` | 2620 | 5907 |\n", "| 04-08 | Frobisher Bay | `04-08_frobisher_bay` | 83 | 235 |\n", "| 04-09 | Labrador | `04-09_labrador` | 103 | 20 |\n", "| 05-01 | Greenland Periphery | `05-01_greenland_periphery` | 19994 | 90482 |\n", "| 06-01 | Iceland | `06-01_iceland` | 568 | 11060 |\n", "| 07-01 | Svalbard | `07-01_svalbard` | 1583 | 33841 |\n", "| 07-02 | Jan Mayen | `07-02_jan_mayen` | 83 | 117 |\n", "| 08-01 | North Scandinavia | `08-01_north_scandinavia` | 1832 | 1430 |\n", "| 08-02 | Southwest Scandinavia | `08-02_southwest_scandinavia` | 1216 | 1216 |\n", "| 08-03 | Southeast Scandinavia | `08-03_southeast_scandinavia` | 362 | 302 |\n", "| 09-01 | Franz Josef Land | `09-01_franz_josef_land` | 412 | 12762 |\n", "| 09-02 | Novaya Zemlya | `09-02_novaya_zemlya` | 480 | 22132 |\n", "| 09-03 | Severnaya Zemlya | `09-03_severnaya_zemlya` | 177 | 16701 |\n", "| 10-01 | Ural Mountains | `10-01_ural_mountains` | 161 | 15 |\n", "| 10-02 | Central Siberia | `10-02_central_siberia` | 394 | 64 |\n", "| 10-03 | Cherskiy/Suntar Khayata Ranges | `10-03_cherskiy_suntar_khayata_ranges` | 427 | 271 |\n", "| 10-04 | Altay and Sayan | `10-04_altay_and_sayan` | 3033 | 1247 |\n", "| 10-05 | Northeast Russia | `10-05_northeast_russia` | 3091 | 1041 |\n", "| 10-06 | East Chukotka | `10-06_east_chukotka` | 44 | 4 |\n", "| 10-07 | Japan | `10-07_japan` | 5 | 0 |\n", "| 11-01 | Alps | `11-01_alps` | 4034 | 2120 |\n", "| 11-02 | Southeast Europe | `11-02_southeast_europe` | 45 | 4 |\n", "| 12-01 | Caucasus and Middle East | `12-01_caucasus_and_middle_east` | 2181 | 1381 |\n", "| 12-02 | Middle East | `12-02_middle_east` | 94 | 25 |\n", "| 13-01 | Hissar Alay | `13-01_hissar_alay` | 5340 | 1954 |\n", "| 13-02 | Pamir (Safed Khirs / West Tarim) | `13-02_pamir_safed_khirs_west_tarim` | 15744 | 10465 |\n", "| 13-03 | West Tien Shan | `13-03_west_tien_shan` | 14682 | 9299 |\n", "| 13-04 | East Tien Shan (Dzhungaria) | `13-04_east_tien_shan_dzhungaria` | 6255 | 3083 |\n", "| 13-05 | West Kun Lun | `13-05_west_kun_lun` | 7906 | 8086 |\n", "| 13-06 | East Kun Lun (Altyn Tagh) | `13-06_east_kun_lun_altyn_tagh` | 3854 | 3212 |\n", "| 13-07 | Qilian Shan | `13-07_qilian_shan` | 3256 | 1666 |\n", "| 13-08 | Inner Tibet | `13-08_inner_tibet` | 11185 | 8221 |\n", "| 13-09 | Southeast Tibet | `13-09_southeast_tibet` | 7391 | 4357 |\n", "| 14-01 | Hindu Kush | `14-01_hindu_kush` | 6857 | 3049 |\n", "| 14-02 | Karakoram | `14-02_karakoram` | 17559 | 21675 |\n", "| 14-03 | West Himalaya | `14-03_west_himalaya` | 13146 | 8350 |\n", "| 15-01 | Central Himalaya | `15-01_central_himalaya` | 6127 | 6494 |\n", "| 15-02 | East Himalaya | `15-02_east_himalaya` | 5503 | 5454 |\n", "| 15-03 | Hengduan Shan | `15-03_hengduan_shan` | 6957 | 4102 |\n", "| 16-01 | Low-latitude Andes | `16-01_low_latitude_andes` | 3647 | 1920 |\n", "| 16-02 | Mexico | `16-02_mexico` | 7 | 2 |\n", "| 16-03 | East Africa | `16-03_east_africa` | 36 | 4 |\n", "| 16-04 | New Guinea | `16-04_new_guinea` | 5 | 2 |\n", "| 17-01 | Patagonia | `17-01_patagonia` | 17454 | 24040 |\n", "| 17-02 | Central Andes | `17-02_central_andes` | 13180 | 3634 |\n", "| 18-01 | New Zealand | `18-01_new_zealand` | 3018 | 886 |\n", "| 19-01 | Subantarctic (Pacific) | `19-01_subantarctic_pacific` | 15 | 134 |\n", "| 19-02 | South Shetlands and South Orkney | `19-02_south_shetlands_and_south_orkney` | 401 | 3715 |\n", "| 19-03 | Subantarctic (Atlantic) | `19-03_subantarctic_atlantic` | 550 | 2475 |\n", "| 19-04 | Subantarctic (Indian) | `19-04_subantarctic_indian` | 489 | 796 |\n", "| 19-05 | Balleny Islands | `19-05_balleny_islands` | 50 | 694 |\n", "| 19-11 | East Queen Maud Land 7A | `19-11_east_queen_maud_land_7a` | 22 | 2681 |\n", "| 19-12 | Amery Ice Shelf 7B | `19-12_amery_ice_shelf_7b` | 1 | 305 |\n", "| 19-13 | Wilkes Land 7C | `19-13_wilkes_land_7c` | 7 | 2685 |\n", "| 19-14 | Victoria Land 7D | `19-14_victoria_land_7d` | 54 | 652 |\n", "| 19-15 | Ross Ice Shelf 7E | `19-15_ross_ice_shelf_7e` | 111 | 2619 |\n", "| 19-16 | Marie Byrd Land 7F | `19-16_marie_byrd_land_7f` | 63 | 17950 |\n", "| 19-17 | Pine Island Bay 7G | `19-17_pine_island_bay_7g` | 17 | 376 |\n", "| 19-18 | Bellingshausen Sea 7H1 | `19-18_bellingshausen_sea_7h1` | 20 | 14861 |\n", "| 19-19 | Alexander Island 7H2 | `19-19_alexander_island_7h2` | 119 | 61170 |\n", "| 19-20 | West Antarctic Peninsula 7I1 | `19-20_west_antarctic_peninsula_7i1` | 637 | 9287 |\n", "| 19-21 | Northeast Antarctic Peninsula 7I2 | `19-21_northeast_antarctic_peninsula_7i2` | 166 | 5377 |\n", "| 19-22 | Southeast Antarctic Peninsula 7I3 | `19-22_southeast_antarctic_peninsula_7i3` | 6 | 1780 |\n", "| 19-23 | Ronne-Filchner Ice Shelf 7J | `19-23_ronne_filchner_ice_shelf_7j` | 0 | 0 |\n", "| 19-24 | West Queen Maud Land 7K | `19-24_west_queen_maud_land_7k` | 14 | 5878 |\n", "| 20-01 | Antarctic Mainland | `20-01_antarctic_mainland` | 0 | 0 |\n", "| Global | | | 274531 | 706744 |\n" ] } ], "source": [ "print(dfsr.to_markdown())" ] }, { "cell_type": "code", "execution_count": 29, "id": "2160a584-3083-4615-9c5a-d973c25c1063", "metadata": {}, "outputs": [], "source": [ "dfsr['Code'] = [s.replace(\"`\", \"\") for s in dfsr['Code']]\n", "dfsr.columns = [['full_name', 'long_code', 'count', 'area_km2']]\n", "dfsr.index.name = 'o2region'\n", "dfsr.reset_index().to_csv(user_guide_dir + '/docs/appendix/RGI2000-v7.0-G-o2region-summary.csv', index=False, quoting=csv.QUOTE_NONNUMERIC)" ] }, { "cell_type": "markdown", "id": "892037d2-e4a4-4a49-9b07-b714f72662dc", "metadata": { "tags": [] }, "source": [ "## Global statistics (`06_dataset_summary.md`) " ] }, { "cell_type": "code", "execution_count": 30, "id": "baa4f9f8-bb2e-4068-b3be-03f8bcd0497b", "metadata": {}, "outputs": [], "source": [ "rgi6 = df_rgi6g.copy()\n", "rgi7 = df_rgi7g.copy()" ] }, { "cell_type": "markdown", "id": "ab975535-94e8-40ba-9e34-735a33a8f72d", "metadata": {}, "source": [ "### Target year " ] }, { "cell_type": "code", "execution_count": 31, "id": "81c9b80b-f9e4-4868-8093-413aa5e2a5fa", "metadata": {}, "outputs": [], "source": [ "rgi7['year'] = [int(y.split('-')[0]) for y in rgi7['src_date']]\n", "rgi6['year'] = [int(y[0:4]) for y in rgi6['BgnDate']]" ] }, { "cell_type": "code", "execution_count": 32, "id": "7785a4e0-343c-4471-a463-33242477fbe6", "metadata": {}, "outputs": [], "source": [ "dy6 = np.abs(rgi6['year'] - 2000)\n", "dy7 = np.abs(rgi7['year'] - 2000)" ] }, { "cell_type": "code", "execution_count": 33, "id": "22d941e1-6d1c-4bac-b281-53c03f081fe5", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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RGI 6.0 (%)RGI 7.0 (%)
Outline year
2000 ± 2 years48.958.0
2000 ± 2-5 years15.918.7
2000 ± 5-10 years27.019.5
2000 ± > 10 years8.23.8
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" ], "text/plain": [ " RGI 6.0 (%) RGI 7.0 (%)\n", "Outline year \n", "2000 ± 2 years 48.9 58.0\n", "2000 ± 2-5 years 15.9 18.7\n", "2000 ± 5-10 years 27.0 19.5\n", "2000 ± > 10 years 8.2 3.8" ] }, "execution_count": 33, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ydf = pd.DataFrame()\n", "ydf.loc['2000 ± 2 years', 'RGI6 (%)'] = (dy6 <= 2).sum() / len(rgi6) * 100\n", "ydf.loc['2000 ± 2 years', 'RGI7 (%)'] = (dy7 <= 2).sum() / len(rgi7) * 100\n", "ydf.loc['2000 ± 2-5 years', 'RGI6 (%)'] = ((dy6 <= 5) & (dy6 > 2)).sum() / len(rgi6) * 100\n", "ydf.loc['2000 ± 2-5 years', 'RGI7 (%)'] = ((dy7 <= 5) & (dy7 > 2)).sum() / len(rgi7) * 100\n", "ydf.loc['2000 ± 5-10 years', 'RGI6 (%)'] = ((dy6 <= 10) & (dy6 > 5)).sum() / len(rgi6) * 100\n", "ydf.loc['2000 ± 5-10 years', 'RGI7 (%)'] = ((dy7 <= 10) & (dy7 > 5)).sum() / len(rgi7) * 100\n", "ydf.loc['2000 ± > 10 years', 'RGI6 (%)'] = (dy6 > 10).sum() / len(rgi6) * 100\n", "ydf.loc['2000 ± > 10 years', 'RGI7 (%)'] = (dy7 > 10).sum() / len(rgi7) * 100\n", "ydf = ydf.round(1)\n", "ydf.index.name = 'Outline year'\n", "ydf.columns = ['RGI 6.0 (%)', 'RGI 7.0 (%)']\n", "ydf" ] }, { "cell_type": "code", "execution_count": 34, "id": "3811feaf-1d4f-4ef3-b97b-99cf98e24818", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "| Outline year | RGI 6.0 (%) | RGI 7.0 (%) |\n", "|:------------------|--------------:|--------------:|\n", "| 2000 ± 2 years | 48.9 | 58 |\n", "| 2000 ± 2-5 years | 15.9 | 18.7 |\n", "| 2000 ± 5-10 years | 27 | 19.5 |\n", "| 2000 ± > 10 years | 8.2 | 3.8 |\n" ] } ], "source": [ "print(ydf.to_markdown())" ] }, { "cell_type": "markdown", "id": "01a7179b-55ad-4993-a3f5-90ac766dc784", "metadata": {}, "source": [ "Same but with area (less good):" ] }, { "cell_type": "code", "execution_count": 35, "id": "56eaf5ac-aeaa-4cca-88ba-d89998114d56", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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RGI 6.0 (%)RGI 7.0 (%)
Outline year
2000 ± 2 years54.455.8
2000 ± 2-5 years11.415.4
2000 ± 5-10 years20.920.0
2000 ± > 10 years13.38.8
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" ], "text/plain": [ " RGI 6.0 (%) RGI 7.0 (%)\n", "Outline year \n", "2000 ± 2 years 54.4 55.8\n", "2000 ± 2-5 years 11.4 15.4\n", "2000 ± 5-10 years 20.9 20.0\n", "2000 ± > 10 years 13.3 8.8" ] }, "execution_count": 35, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ydf = pd.DataFrame()\n", "ydf.loc['2000 ± 2 years', 'RGI6 (%)'] = rgi6.loc[(dy6 <= 2)].Area.sum() / rgi6.Area.sum() * 100\n", "ydf.loc['2000 ± 2 years', 'RGI7 (%)'] = rgi7.loc[(dy7 <= 2)].area_km2.sum() / rgi7.area_km2.sum() * 100\n", "ydf.loc['2000 ± 2-5 years', 'RGI6 (%)'] = rgi6.loc[((dy6 <= 5) & (dy6 > 2))].Area.sum() / rgi6.Area.sum() * 100\n", "ydf.loc['2000 ± 2-5 years', 'RGI7 (%)'] = rgi7.loc[((dy7 <= 5) & (dy7 > 2))].area_km2.sum() / rgi7.area_km2.sum() * 100\n", "ydf.loc['2000 ± 5-10 years', 'RGI6 (%)'] = rgi6.loc[((dy6 <= 10) & (dy6 > 5))].Area.sum() / rgi6.Area.sum() * 100\n", "ydf.loc['2000 ± 5-10 years', 'RGI7 (%)'] = rgi7.loc[((dy7 <= 10) & (dy7 > 5))].area_km2.sum() / rgi7.area_km2.sum() * 100\n", "ydf.loc['2000 ± > 10 years', 'RGI6 (%)'] = rgi6.loc[(dy6 > 10)].Area.sum() / rgi6.Area.sum() * 100\n", "ydf.loc['2000 ± > 10 years', 'RGI7 (%)'] = rgi7.loc[(dy7 > 10)].area_km2.sum() / rgi7.area_km2.sum() * 100\n", "ydf = ydf.round(1)\n", "ydf.index.name = 'Outline year'\n", "ydf.columns = ['RGI 6.0 (%)', 'RGI 7.0 (%)']\n", "ydf" ] }, { "cell_type": "markdown", "id": "16897360-a0c1-4b61-85de-b167ebc2a99d", "metadata": {}, "source": [ "### More year statistics " ] }, { "cell_type": "code", "execution_count": 38, "id": "353b0cdf-15fe-48e1-85d8-05b4b82c425d", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(1943, 2021)" ] }, "execution_count": 38, "metadata": {}, "output_type": "execute_result" } ], "source": [ "rgi7['year'].min(), rgi7['year'].max()" ] }, { "cell_type": "code", "execution_count": 40, "id": "50fbfb6e-4b38-45f3-bfbc-9f9522cd8b35", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0.028608790992638355" ] }, "execution_count": 40, "metadata": {}, "output_type": "execute_result" } ], "source": [ "(rgi7['year'] < 1990).sum() / len(rgi7)" ] }, { "cell_type": "markdown", "id": "72f463fe-9959-45cc-9615-c82d3fbcafff", "metadata": { "tags": [] }, "source": [ "### Size classes " ] }, { "cell_type": "code", "execution_count": 74, "id": "4523f5fc-b250-48c7-b161-ca0200026b52", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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RGI 6.0 (N)RGI 6.0 (%)RGI 7.0 (N)RGI 7.0 (%)
Area
< 1 km²17057679.122962683.6
1-10 km²3805417.73808113.9
10-100 km²59552.858302.1
> 100 km²9620.49940.4
Total215547100.0274531100.0
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" ], "text/plain": [ " RGI 6.0 (N) RGI 6.0 (%) RGI 7.0 (N) RGI 7.0 (%)\n", "Area \n", "< 1 km² 170576 79.1 229626 83.6\n", "1-10 km² 38054 17.7 38081 13.9\n", "10-100 km² 5955 2.8 5830 2.1\n", "> 100 km² 962 0.4 994 0.4\n", "Total 215547 100.0 274531 100.0" ] }, "execution_count": 74, "metadata": {}, "output_type": "execute_result" } ], "source": [ "adf = pd.DataFrame()\n", "adf.loc['< 1 km²', 'RGI6 (N)'] = (rgi6['Area'] < 1).sum()\n", "adf.loc['< 1 km²', 'RGI6 (%)'] = (rgi6['Area'] < 1).sum() / len(rgi6) * 100\n", "adf.loc['< 1 km²', 'RGI7 (N)'] = (rgi7['area_km2'] < 1).sum()\n", "adf.loc['< 1 km²', 'RGI7 (%)'] = (rgi7['area_km2'] < 1).sum() / len(rgi7) * 100\n", "adf.loc['1-10 km²', 'RGI6 (N)'] = ((rgi6['Area'] >= 1) & (rgi6['Area'] < 10)).sum()\n", "adf.loc['1-10 km²', 'RGI6 (%)'] = ((rgi6['Area'] >= 1) & (rgi6['Area'] < 10)).sum() / len(rgi6) * 100\n", "adf.loc['1-10 km²', 'RGI7 (N)'] = ((rgi7['area_km2'] >= 1) & (rgi7['area_km2'] < 10)).sum()\n", "adf.loc['1-10 km²', 'RGI7 (%)'] = ((rgi7['area_km2'] >= 1) & (rgi7['area_km2'] < 10)).sum() / len(rgi7) * 100\n", "adf.loc['10-100 km²', 'RGI6 (N)'] = ((rgi6['Area'] >= 10) & (rgi6['Area'] < 100)).sum()\n", "adf.loc['10-100 km²', 'RGI6 (%)'] = ((rgi6['Area'] >= 10) & (rgi6['Area'] < 100)).sum() / len(rgi6) * 100\n", "adf.loc['10-100 km²', 'RGI7 (N)'] = ((rgi7['area_km2'] >= 10) & (rgi7['area_km2'] < 100)).sum()\n", "adf.loc['10-100 km²', 'RGI7 (%)'] = ((rgi7['area_km2'] >= 10) & (rgi7['area_km2'] < 100)).sum() / len(rgi7) * 100\n", "adf.loc['> 100 km²', 'RGI6 (N)'] = (rgi6['Area'] >= 100).sum()\n", "adf.loc['> 100 km²', 'RGI6 (%)'] = (rgi6['Area'] >= 100).sum() / len(rgi6) * 100\n", "adf.loc['> 100 km²', 'RGI7 (N)'] = (rgi7['area_km2'] >= 100).sum()\n", "adf.loc['> 100 km²', 'RGI7 (%)'] = (rgi7['area_km2'] >= 100).sum() / len(rgi7) * 100\n", "adf.loc['Total'] = adf.sum()\n", "adf[['RGI6 (N)', 'RGI7 (N)']] = adf[['RGI6 (N)', 'RGI7 (N)']].astype(int)\n", "adf[['RGI6 (%)', 'RGI7 (%)']] = adf[['RGI6 (%)', 'RGI7 (%)']].round(1)\n", "adf.index.name = 'Area'\n", "adf.columns = ['RGI 6.0 (N)', 'RGI 6.0 (%)', 'RGI 7.0 (N)', 'RGI 7.0 (%)']\n", "adf" ] }, { "cell_type": "code", "execution_count": 75, "id": "64ed8b26-17f7-40ec-bd90-40b0f069c19b", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "| Area | RGI 6.0 (N) | RGI 6.0 (%) | RGI 7.0 (N) | RGI 7.0 (%) |\n", "|:-----------|--------------:|--------------:|--------------:|--------------:|\n", "| < 1 km² | 170576 | 79.1 | 229626 | 83.6 |\n", "| 1-10 km² | 38054 | 17.7 | 38081 | 13.9 |\n", "| 10-100 km² | 5955 | 2.8 | 5830 | 2.1 |\n", "| > 100 km² | 962 | 0.4 | 994 | 0.4 |\n", "| Total | 215547 | 100 | 274531 | 100 |\n" ] } ], "source": [ "print(adf.to_markdown())" ] }, { "cell_type": "code", "execution_count": 78, "id": "ab9454ff-7796-4b1d-b9d1-53b0a8092e92", "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "with sns.plotting_context('talk'), sns.axes_style('ticks'):\n", "\n", " bins = np.logspace(-2, 3, 100)\n", "\n", " h7c, b7c = np.histogram(df_rgi7c['area_km2'], bins=bins)\n", " h7, b7 = np.histogram(rgi7['area_km2'], bins=bins)\n", " h6, b6 = np.histogram(rgi6['Area'], bins=bins)\n", "\n", " f, ax = plt.subplots(figsize=(10, 6))\n", " ax.plot(b6[:-1], h6, label='RGI 6.0');\n", " ax.plot(b7[:-1], h7, label='RGI 7.0');\n", " ax.plot(b7c[:-1], h7c, label='RGI 7.0 C');\n", " ax.set_xscale('log')\n", " ax.set_yscale('log')\n", " ax.set_ylim([7, 5e4])\n", " plt.legend()\n", " ax.set_title('Number of glaciers per size category (global)')\n", " ax.set_xlabel('Glacier area (km², logscale)');\n", " ax.set_ylabel('Glacier number (logscale)');" ] }, { "cell_type": "code", "execution_count": 81, "id": "8f393785-4bc8-455b-ac6e-e2654307d678", "metadata": {}, "outputs": [ { "data": { "image/png": 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+/Szfv0I8bSRpIIQoVv9NGiiKorz11luKu7u70rdvX8MPBEUpuaRBnz59DD8wMvrggw8My4wYMSJTbOkGDx6suLu7K9OmTcsyL/1HiLu7u/Lbb79lmZ+amqoMGTJEcXd3V5577rlM85KSkpSOHTvmuG66cePGKe7u7spXX32VaXr6j3R3d3dl4sSJOa7/OBcuXDBsZ+PGjdkuk55UaN26tSEpkq6wSQN/f3+lbt26iru7u7Jw4cJsl0m/YCxI0iA3KSkpygsvvKC4u7sr27ZtyzRPp9MZfhwPGjQo2/dPTnKKJywsTGncuLFSp04d5dixY9muq9PplJdeeklxd3fPclGX/tlyd3dXZs+eneP+e/furbi7uyt//fVXnmPOi/R9N2jQQLl582aW+aGhoUqrVq0Ud3d35fPPP88079q1a0qdOnWUxo0bK1evXs12+zExMUqnTp2yvWDM+B5Yu3ZtgY+hUaNGiru7u3L27Nl8rZffpIGPj4/StGlTxd3dXZk6dWqmeYX97D/ODz/8YLhw8/b2zte6ufn5558Vd3d35Y033sgyL69Jg/Tvkg8++CDHZdITay+++GKm6du2bVPc3d2VOnXqKF5eXlnWS0xMVHr16pXja7V06VLD+/fatWtZ1o+JiTF8dseMGZNlfvp2c7qgTTdhwgTF3d1dmTJlSrbzZ86cmevFbW7S/x7NnDkzx2UKe55y+9tbVOewIN8hjxMbG6u0a9dOcXd3z/a485I0CAsLM8SYU9JHiKeFFEIUQpS4KVOmoNFo8PHxYceOHSW+/zfeeAOtVptleocOHQyPR48enW1T0vRlrl27luP2nZ2deeWVV7JMV6vVjB8/HoCbN29m2sa///5LUFAQjo6O2a6brl+/fgAcPXo0x2UK03z/77//BqBSpUqG6tH/NXnyZAAiIiLw9PQs8L6ys3fvXvR6PRYWFgwfPjzbZSZMmFCk+0yn0Wjo2LEjAGfOnMk07+TJk/j5+QHw8ccfZ/v+ya+//vqLhIQEPDw8cuwaYGJiYuirnNNrrlarc+0vbWtrC6T1GS4OvXr1yrbPcfny5XnttdeAtKbuGf3+++8oikLnzp2z9HlPZ21tTbdu3QA4cuRItsvY2dkxcODAAseePgxmcZ0bgODgYMaNG0dcXBytWrXiiy++yDS/KD/72fnjjz8AePXVV3Pt4pJfzzzzDJBWvT81NTXf60dGRvLPP/8AuX9n9e3bF0jrSpLehQHSRrAAaNmyZbbdiczMzHjzzTdz3G56LYCePXvi7u6eZb61tTWjRo0C0l6jmJiYHOOrVKlSjvsZNGgQAP/88w/h4eGZ5iUlJfHnn38CFOh9nN7doFy5cjkuU9jzlJuiOocF+Q55HCsrK1q2bAnA2bNn87VuOnt7e0N3p/RzLcTTSmoaCCFKXM2aNXn55Zf57bff+Omnn+jVq9djCwsWpZz6J2fsg9+wYcNcl4mOjs5x+61atcqx72rLli0xMTEhJSWFy5cvGy6Y0i9So6KiMiUv/iu9v3NO1ZzNzc1z7dv6OJcvXwagdevWOfYNr1mzJk5OTgQFBXH58mW6du1a4P39l7e3NwAeHh7ZFsADcHV1xdnZmcDAwALt4/Tp0/z++++cP3+eoKAg4uPjsywTFBSU6fm5c+cAqFChQo7vjfxKf81v3LiRaz/19BEpcnrNXV1dKV++fI7rP/PMM5w7d47vv/+e27dv0717d5o1a4a1tXUhon+kTZs2uc5bsmQJkZGR+Pr6GgqipR/7v//+m+uxp782OR17w4YNC5XA6dKlC5s3b+ajjz7i7NmzdO3alYYNG2JhYVHgbWaUkJDAuHHjCAwMpFq1asyfPz9LvEX12c+Ov7+/4WKnS5cu+Q2f0NBQ1q9fj6enJ3fv3iUmJiZLgiAhIYGoqKh8F5w7f/48er0eSEvk5kVAQIDhO/jKlSsAhgvD7ORU0C85OdmQtM2tlkf6e1Ov1+Pt7Z3te71Zs2a5xty+fXtcXV25f/8+27dvZ8SIEYZ5u3btIjo6GktLy8cWMsxOehLC3t4+x2UKc55yU5TnsCDfIekOHjzI9u3buXTpEmFhYSQkJGTZRnq9hfxKr5MSFRWVJeEjxNNGkgZCCKOYNGmSYWSBjRs3MnTo0BLbd06FpjIWDMzpgip9mZyKmQE4OTnlOE+r1WJvb09oaChhYWGG6ek/7HU6Xaa7aTnJOLRhRhnvjBREeky5HQOktUQICgrKdAxFIf2HWcWKFXNdzsnJqUBJg++++y7TuNsajQY7OztD0io+Pt7wL6P0O9GFqV7/X+mveWJiYo6vZ0Y5LZNbwgDgzTff5OrVq+zatYvNmzezefNmVCoVtWvXpkOHDgwYMIDq1avn/wAeyu29kvF1DA8PN/zgTz/27M51dnI69oJWRk/3wQcfcO/ePU6ePMmqVatYtWoVGo2GunXr8swzzzBw4MDHfhZyotfref/99/H29sbOzo6lS5dme3FXVJ/97GTcXn7fu+fOnWPMmDGZEqSWlpZYWFigUqlITU0lIiICINsLtcfJeOc2L8f93/3k5bsip9cuKirKkPzI7fXN2IIgp4vGx33+VCoVAwYM4H//+x+bN2/OlDTYvHkzAC+88EKBhqhNL7qZW+KsMOcpN0V5DgvyHaLX6/nggw8ytVY0MTHJ9H0eExNDUlJSgd6f6dJHwklOTi7wNoR4EkjSQAhhFE5OTrz++uv8/PPPLF68OM/Do5UFObUyyE36HbeOHTtmuqjNr6Iapzuvx1CQYy2K7SoPRxrID09PT8O5HTx4MIMGDaJmzZqZztmPP/7I4sWLCxxXfqS/5q+99lqWJuv58bjX3NTUlB9//JFx48axd+9ezpw5w8WLF7l+/TrXr19nzZo1vP/++4wcObJA+y/M+z19GNaCKuz73dbWlrVr13L69GkOHjzI2bNnuXz5Mt7e3nh7e7NixQq+/vrrAt0F/vbbb9m3bx+mpqbMmzcvx8RMUX32Hyc/r1NKSgrvvfce0dHR1KtXjylTptC8efNMydT79+/TvXt3oGCfx/QLTnNzcy5cuJDv9dMV9jOZ2/p52XZekrSvvPIK8+bN4/bt23h5edGyZUtu3bplaGUyYMCAvAecgb29PSEhIYbRP3JTXN/Vj9t2XvZbkNh+//13duzYgUajYdy4cYYhQzO+Hh988AF//vlngd6f6dLPbW6tOYR4GkhNAyGE0YwdOxY7OzvCwsJYtWpVrsumD2MF5DqkWU59JktSbk0hk5OTiYyMBDLfoUpvcps+JJqxpMf0uLv46cdY2Du9/5W+vcf1Hy1I/9L0eg0dOnTgs88+w93dPctFZ053PCtUqABgqGtQFEr6Na9bty5vv/02a9aswcvLi9WrV9OyZUtSU1P59ttvuXr1aoG2m9v7PePrlPG9Ulre7+latGjBBx98wIYNGzh9+jSLFi3C3d2dxMREpk2bluc74ek2btxo+E777LPPcm1+XZznIv19CxiGpsuL8+fP4+/vj0ajYenSpXTu3DlL66vC1oFIjy0xMZF79+7le/28fFf8t5tROjs7O8NnP7fvuozzCvNd5+DgQI8ePYBHrQvSh1ls0KABHh4eBd4ukGvSoDDnKTdFeQ4L8h2S/n3ev39/3n77bapVq5YlgZPfz+1/JSUlGX5vFPXfOiHKGkkaCCGMxtbW1lDAbeXKlbn2GUwv5gY5/8C4c+dOrrUGSoqXl1eOdzZOnz5t6NqQ8Ydier/YoKAgTp8+XfxB5iA9ppMnTxrugP7XrVu3DD8yi6p/f7r0egyXL1/Osdm6r69vgbompL9vcioGpygKJ06cyHZe+usTGhrKpUuX8r3v3LZ54cKFfF3QFQUTExPatm3L0qVL0Wq1KIrCsWPHCrStkydP5jgv/Xza29tn6oucfuyHDx8mLi6uQPstLmZmZjz77LMsWLAASLtw+G9hzNwcPXqUL7/8EkjrGpJTQdF0xfnZr1y5sqF5+MGDB/O8Xvrny8HBIcem48ePH89x/fSLt9zu8DZt2tRwhzn9AjA/0j/Hp06dynGZnN6bWq3WUE8mp888YPhMqNXqQtWKgUcFEffs2UNISAjbtm0DClYAMV2tWrWAtO/EnBTmPOWmKM9hQb5DHvd9HhcXV6gWLJD5vNaoUaNQ2xKirJOkgRDCqIYNG0alSpWIi4tj0aJFOS5naWmJq6srkPajKztLliwplhjzKyAggK1bt2aZrtfrDTHWrFkzU9X4rl27Gu68zZo167F9MNNbKxS1Pn36AGkXMOl3wv5r3rx5QFrF7nbt2hXp/rt3745arSY+Pp61a9dmu0xu3Qdyk36nNKc76hs2bMjxx3fr1q0NP1hnz55dJP1b+/bti7m5OampqcycOTPXCvR6vb7ACbHcYtVqtYa7hQWthbF7925u376dZXp4eDibNm0CoHfv3pnmDRgwAJVKRXR0NN9++22u29fpdMWSWEhJSckxMQZpzebT5bUbxI0bN5g8eTIpKSl069aN999//7HrFPdnP31Eht9++81QFO9x0keVCA0NzfZu7YMHD1i3bl2O66d/1nJ7z5YvX55nn30WgBUrVnDnzp1cY/rvcffs2RNIS9Jml9RJTk5m5cqVOW7vueeeA9L+nmTXyiMuLs7QXaRz586Gc1JQLVq0wN3dnaSkJKZMmUJERESBCyBm3CaQayKzsOcpN0V1DgvyHfK47/NFixYV+nsjPeng6OgoSQPx1JOkgRDCqMzMzJg4cSLw+Dth6Re0W7Zs4ddffzUUBAsMDOSTTz5h586dRVb1vDBsbGz4/PPP2bx5s6FpY2BgIO+++67hjsqUKVMyrWNmZsbnn3+OSqXC29ubQYMGceTIkUwXfOlFI/v378/69euLJfZGjRoZfmR++eWX/PLLL4aLmJCQED799FPDEF6TJ082FIkqKi4uLvTv3x9IS06sWLHC8MMvIiKC2bNn88cff2RqeZJX6cMp/vvvvyxcuNDQkiE6OpolS5bw1Vdf5dhvVaPRMH36dFQqFWfOnGH48OGcPn3acNEZGxvLyZMnef/997l582ae4qlQoQLvvfceAIcOHWLEiBGcOXPGkDxQFIVbt26xatUqnn/++XzdKc6oS5cufP/995w/fz7T++nevXu8//77JCQkoFarDecnv8zMzBg1ahTHjh0z3Fm+ePEiI0aMICIiAisrqyx1C+rVq2eomL9x40befvttfHx8DOunpqZy9epVFi5cSPfu3fHx8SlQbLl58OABPXr0YNGiRVy5ciVTcdOrV68aLvgtLS2zHaruv8LDwxk7diyxsbE0aNCA7777Lk+JmOL+7I8cORI3NzeSk5MZPnw4mzdvJjY2FkhLyNy5c4cFCxawYsUKwzrNmzfH0tISRVF45513DBf0qampHDly5LGFa2vXrg2kfS5yGypv6tSp2NvbExsby+DBg/n9998zdTELDw9n7969TJw40fBZSffcc89Ru3ZtFEVh0qRJ7Nu3z/DZuX37NmPHjs21efqgQYOoUqUKOp2O0aNHc/jwYcPn+dq1a7z55pv4+flhamrKO++8k+vx5lV6qwIvLy+g4AUQ06WPeuDv75/jsRb2POWmqM5hQb5D0r+vfvvtNzZt2mT4vISEhDBr1ix+/vnnQtchSE8a5DbyhBBPCymEKIQwupdffpmVK1dme6cho9GjR/PPP/9w8+ZNZs6cyVdffYW1tTXR0dGYmpryzTff8P3335d4U+//Gjx4MKdPn2b69OnMnDkTS0vLTH1Ox48fbyggllG3bt349ttvmTFjBj4+PowaNQoTExOsra2Jj4/PdBGRfoeuOHz99ddERERw6tQpvvzyS2bPno2VlRXR0dGGH3QjR440NLctalOnTjUUCfv222/5/vvvDa+zoiiMHz+e06dP4+Xlla+kRb9+/di2bRunT59m3rx5zJ8/H1tbW2JiYtDr9TzzzDPUq1cvx5YMnTt3Zs6cOUyfPp0zZ84wZMgQtFot5ubmme6o5mfM82HDhpGcnMzcuXM5efIkgwcPxtTUFCsrK+Li4gzD7EHBC5mFhoaybNkyli1bZhhCLDEx0ZDQUqlUfPTRR9mOk54XU6dO5ccff2TEiBGGyvrpCRmtVsvcuXOzrdz/4YcfoigKa9asYc+ePezZswczMzMsLCyIjY3NdBFfXEXcfH19+emnn/jpp5/QaDTY2NhkOu+mpqbMnj07TxcfN27cMHz33Lt3L9vPeLqmTZsauj9A8X72ra2t+fnnnxk3bhw3b95k+vTpfPbZZ9jY2BAbG2u4gBw2bJhhHRsbGz788EM+//xzvLy86NWrF5aWlqSmppKUlES5cuWYPXs248ePz3af1apVo23bthw/fpwpU6bw6aefGs7hsGHDGD58OABVq1Zl1apVTJw4EX9/fz755BM+/fRTbG1t0el0mboo/bdVk1ar5aeffuKNN94gJCSEt956C61Wi5mZGTExMWi1WubNm8e4ceMAsnxXWFtbs3jxYkaNGsWDBw8YM2YMZmZmmJqaGpIqWq2W7777jrp16+brnOekX79+fP/994bjKmgBxHQ1a9akbt26XL16lQMHDmS7vcKep9wU1TksyHfIyJEj2bNnD7dv32bGjBl8/vnnWFtbExMTg6IoDBw4kOTk5Gxb/eWFXq/n8OHDAIVqDSLEk0KSBkIIo9NoNLz77ruGFgc5sbKyYv369SxevJh//vmHoKAgTExM6NmzJ2PGjMHDw4Pvv/++hKLOmampKatXr2bVqlXs2LEDX19fbGxs8PDwYMSIEXTu3DnHdV988UXatGnD+vXrOXLkCPfv3ycmJgZLS0tq1qxJ8+bN6datW7He+bCxsWH16tVs3bqV7du3c+3aNeLj43F0dKRZs2YMGTKkQON655WVlRWrV69m3bp1bN++nXv37qEoCi1btmTYsGF0796dF1980RBrXpmamrJy5UqWLVvGjh078Pf3R1EUGjVqRL9+/Rg4cCALFy7MdRv9+vWjRYsWrF27Fk9PTwICAtDpdLi6uuLu7k6PHj3yffE9atQounfvzvr16zl+/Dj+/v7ExMRgbW2Nq6srrVu3plu3bjRp0iRf2023cuVKTp48yZkzZwgMDDTcVaxWrRrNmzdnyJAhBS7EBmkXflu3bmXx4sUcOnSI4OBgypcvT9u2bZkwYUKO50Oj0TBt2jT69evHxo0bOXXqFEFBQcTGxmJra4ubmxvt27enW7duRXbRlpGTkxOLFy/m5MmTnD9/ngcPHhAWFoaJiQnVqlWjdevWDBs2DDc3t3xvOzY21nDRlJ3sCtcV52c//TX6/fff2bVrF9evXycuLo7y5cvj4uJCp06d6Nu3b6Z1Bg0aROXKlfn555+5fPkyqampODk50blzZ0aPHp0poZWdefPmsXDhQg4dOkRgYKAhofLfYrX169dn586d/PHHH+zbt4+rV68aEsFubm54eHjw7LPP0qlTpyz7qFmzJn/++SeLFi3iwIEDBAcHY2ZmRocOHRg7dmymC83svivc3d35+++/WbNmDfv27ePevXskJyfj6upKu3btePPNNw3d4oqCtbU17du3559//ilUAcSMBg4cyBdffMFff/2VYxKisOcpN0VxDgvyHWJra8vGjRtZuHAh+/btIzg4GI1GQ6tWrRg4cCB9+vRh6tSp+TqWjE6dOsWDBw9wcnKiS5cuBd6OEE8KlVKYcUiEEEKIEhYXF0fr1q3R6XT8+uuveWo6Lopeek2OtWvXFmsSSYiC8vT0ZOTIkWi1Ws6ePYupqalR40lOTqZjx45ERkYyc+bMQhVBTBcbG0vnzp2Ji4tj//79uLi45Hsbpe08lQYff/wxW7ZsYdKkSY+9oSHE00BqGuTDm2++SZ06dfjhhx+MHYoQQjy1Vq1ahU6nw97evshHbxBCPBkURWH58uUAtG3btlRcCO/YsYPIyEisra2LrMm7tbU1Y8aMyXS8+VEaz5OxBQYG8tdff+Hg4GDoSiPE006SBnm0Y8cOrl27ZuwwhBDiiRcbG8uUKVP4999/M9UK8Pf355tvvjH0BR82bFiRF2IUQpQdJ06c4Ouvv+bSpUuGwriKonD58mXGjRvH8ePHUalUjBo1ysiRwv379/npp58AeO211wpVAPG/hg8fTuXKlfn999+zHY62LJ2n0mDJkiXodDomTpxoGKVBiKed1DTIg+joaGbPns3HH3+cpXqvEEKIoqXX69m5c6eh6nr6j+uMw2f17NmTsWPHGiU+IUTpEBsby9q1aw3Ds9rZ2WVb5LNVq1ZGi3HQoEH4+fkRGhqKXq+nUqVKRf7dZWZmxpw5czh16hQBAQE4Oztnml8WzlNpodfrqVy5Mu+8806RdB8R4kkhSYM8+O6776hVqxbPP/+8JA2EEKKYWVpaMmPGDDw9Pblx4wbh4eEkJSVRoUIFPDw86NevHz179iy2ivpCiLKhcePGTJ48mePHj+Pn50d4eDiQVlivRYsWDBkyxOhdmIKCgggODsbe3p6WLVvywQcfFGjI2Mdp3bp1jrVFysJ5Ki3UarUkpIXIRpkqhBgSEsKxY8e4dOkSly9fxsfHh8TERBo0aMCWLVseu/6JEydYtWoVFy5cID4+nsqVK9OrVy/GjBmDpaVltuucPn2aESNGsH37dmrUqEGdOnUYN25cljHWhRBCCCGEEEKIJ02Zamnw999/M3v27AKtu27dOr7++msURaFSpUo4Oztz8+ZNFi9ezN69e1m/fn2WcZh1Oh2fffYZI0eOpEaNGkVwBEIIIYQQQgghRNlRppIG1tbWtGvXDg8PDzw8PLh79y5z58597HqXL19m1qxZAMycOZMBAwagUqkICgpi/PjxeHt7M336dObPn59pveXLl5OYmMj48eOL5XgAunTpQnh4OGZmZlSpUqXY9iOEEEIIIYQQQgD4+fmRlJSEg4MDBw8ezHXZMpU06N+/P/379zc8z0uXBIBFixah1+vp169fpqImTk5OzJ07l969e7N3716uXr1K3bp1AQgICGDJkiV89dVXJCcnk5ycbFgvOTmZ6OhorKys0Gg0hTqm8PBwEhMTSUxMJCoqqlDbEkIIIYQQQggh8iq9zkluylTSoCDi4uI4cuQIAAMGDMgy383NjTZt2nDs2DF2795tSBr4+vqSlJTEBx98kGWdlStXsnLlSrZt20a9evUKFZ+ZmRmJiYmYm5tTs2bNQm1LCCGEEEIIIYR4nFu3bpGYmJin4auf+KSBj48PycnJaLVaGjVqlO0yzZs359ixY1y4cMEwrV69eoahaTIaNmwYL774Iv3798fV1bXQ8VWpUoWoqChq1qyZ55YTQgghhBBCCCFEQb388st4e3vnqYv8E580uHPnDgCVK1fG1NQ022XSL/7TlwWwtbXNceiaypUr5zgPYOPGjWzevDlP8d26dStPywkhhBBCCCGEECXtiU8apNcJsLOzy3GZ9HlFVVMgJCQEb2/vItmWEEIIIYQQQghhLE980iApKQkgx1YGAFqtNtOyubl27dpjl6lQoQINGjTIU3zpfUmEEEIIIYQQQojS5olPGqQXdtDpdDkukz4yQl6KQOTFa6+9xmuvvZanZdP7kgghhBBCCCGEEKWN2tgBFLe8dD3ISxcGIYQQQgghhBDiafPEJw3c3NwACAgIyLG1wf379zMtK4QQQgghhBBCiKege0L9+vUxNTUlOTmZixcv0rx58yzLnDlzBoAmTZqUcHRCCCGEEEKIsk5RFBRFMXYY4imjUqlQqVTFvp8nPmlgZWVFhw4dOHjwIJs3b86SNLh79y4nTpwAoFevXsUSQ3x8fI7z9Hp9sexTCCGEEEIIUXwSEhKIiooiJiaGlJQUY4cjnlImJibY2NhgZ2eHhYVF8eyjWLZaykyYMIFDhw6xfft2mjVrxoABA1CpVAQHB/Puu++i1+vp1q0bdevWLZb9N23atFi2K4QQQgghhCh50dHR+Pv7GzsMIUhJSSEiIoKIiAhcXFywtbUt8n2UqaRBYGAg/fr1MzxPH/Xg2rVrtG7d2jB91KhRjB492vC8UaNGTJ06lTlz5jBjxgwWL15MuXLluHnzJsnJyVSvXp0vv/yyxI5DCCGEEEIIUTYlJCQYEgbW1taUK1cOc3Nz1OonvlycKGX0ej2JiYlEREQQGxuLv78/pqamRd7ioEwlDVJTU4mMjMwyPSUlJdP0xMTELMsMHz6cOnXqsHLlSi5evEhYWBiVK1emV69ejBkzBisrq2KL+9y5cznOGzx4MD4+PsW2byGEEEIIIUTRSR95zdramipVqpRIn3IhsqNWq7G2tsbKygo/Pz9iY2OJiop6upMGVapU4dq1awVev23btrRt27YII8obS0vLHOdJRlIIIYQQQoiyIyYmBoBy5cpJwkCUCiqVinLlyhEbG0tMTAyVKlUq0u3LFasQQgghhBBC5IGiKIaih+bm5kaORohH0t+PKSkpRT6ShyQNhBBCCCGEECIPMl6MSYthUZpkfD9K0kAIIYQQQgghhBAlokzVNCir4uPjc5yn1+tLMBIhhBBCCCGEECLvJGlQApo2bWrsEIQQQgghhBBCiHyT7glCCCGEEEIIIYTIlrQ0KAHnzp3Lcd7gwYPx8fEpwWiEEEIIIYQQQoi8kaRBCbC0tMxxnlRdFaJ46JOT0d2/D2oNKq0pKtOH/7TaR4/l8yeEEEIIUeyGDh3KqVOnMk3TaDTY2NhQu3ZtevXqxcCBAzE1NX3sti5evMj27dvx8vIiKCiI2NhYLC0tcXFxoWHDhnTr1o0OHTqg0WgyrXfy5EmGDRsGwLVr1wp0HBEREaxZs4YDBw7g5+eHoig4OjrSqFEjXnvtNVq2bJnvbcbFxbFs2TL27NlDQEAAlpaWNG7cmJEjR9K6desCxVnUJGkghHiipMbGEvHresJXrSI1MjL3hTUaTMqXx6xWLcxq10JbqxbmtWujrVULjbV1kcWkT05GZWIiSQohhBBCPNWcnZ1xdnYGICkpCT8/P7y8vPDy8uLPP/9k9erVOd5wjY2NZdq0aezZswdIu/latWpVXF1diYmJ4fbt2/j4+LB582bc3NxYvnw5rq6uRRb76dOnmThxIhEREWi1WqpXr45arebBgwfs2LGDihUr5jtpEB4ezuDBg7lz5w5arZZatWoRHh7OoUOHOHz4MNOnT2fIkCFFdgwFJUkDIcQTITUmhohffiFs9Rr0UVF5XCmVlOBgUoKDiTt2LNMsE2fntGRCzZqoLS1BrQa1Ku3CX5X5sZKSQmpUJKlRUeijokiNjCI16tE/JTERlVaLaeXKmFapgmkVF7RVqqQ9dkl7rrG3R6VSFcOZEUIIIYQoHV555RUmTZpkeK7X6/ntt9/47LPPuHDhAsuXL2fy5MlZ1ktISOD111/Hx8cHe3t7Jk2axIsvvoitra1hmaSkJE6cOMHatWs5evQogYGBRZY0uH37NqNHjyYxMZHJkyczfPjwTMmNW7duERcXl+/tfvLJJ9y5c4cGDRqwePFinJycUBSFzZs3M2PGDL7++muaNWtGvXr1iuQ4CkqSBkKIMi01KorwtesIX7sWfUxM5pkaDSq1GkWny/d2UwIDSQkMJO7IkSKJU0lOJvnuXZLv3s12vtraGvP69bFo0gSLJo2xaNwYk/Lli2TfQgghhBClkVqtZuDAgZw6dYodO3awe/fubJMGX3/9NT4+Pjg4OLBx40aqVauWZRkzMzM6d+5M586d2bt3b6aEQmHNmDGD+Ph43nvvPcaMGZNlfs2aNfO9zStXrnDgwAHUajU//PADTk5OAKhUKgYOHMiZM2fYvn07ixYtYv78+YU+hsKQpIEQoljok5NJjYxEHx396K77wzvw+pgY9ImJKImJ6JMSURKTDP+nTUtCZWqKScWKmFSsgGnFig8fP/qn6HSEr11LxLpf0MfGZtq32sqKcq+/jsPwNzApVw5FUUCnQ8n4LzkZfVIyuoAAkm7eIOnGDZJu3iT5xk308fElf75iY4k/dYr4DP39TKtWTUsiNG6MRZMmmNdxR5WHvn5CCCGEEGVJo0aN2LFjB/7+/lnm+fr6smXLFiDtznx2CYP/6tGjR5HF5u3tjZeXF+XLl2f48OFFtt30bhZt2rTJ9pgGDhzI9u3bOXz4MPHx8bnWyStukjQQQhSYkpJC8v37JF27RuK1ayRdvUbSzZukhIWhJCQU785VKlCUTJPUNjY4DB2Kw7ChaOztMyyqAq0WlVabZTNmNapj3aG94bmiKKQEBJB082ZaEuHe/bSWCno9iqIHvZLlMRoNGju7R//s0/+3R2Nnh9rWltTIKHT+fuj8/Ej280Pn54/OL+15TkkKna8vOl9fov/6K+04tFq0NWti7l4bM3d3wz+TihWla4MQQgghyqzExEQALCwssszbtWsXqamplC9fnl69epV0aOzbtw+Ajh07kpqayrp16zhx4gRxcXE4OzvTtWtXunXrlu/fYufPnwegefPm2c5v1KgRpqamJCUl4ePjk+NyJUGSBiUgPpe7lnq9vgQjESJ7iqIQvnoNERs3oDIxRWNjg9rWBo2NreF/ja0NahtblMQEEq9dJ+laWoJASUoyVtCGh2pbWxzeGIbD0KFoCtkUTaVSYerigqmLC9adOxc2SvR6hWO3wnAp70L1Ou5Z5iuKQmpkJMm3bpFw4QIJ5y+QcP48KSEhWZdNTibJx4ek/wzTqrazw7x2WiLB1MUFTXkHTMqXR+PggImDA5ry5VFnkzARQgghRPFKSE4lObVs/d7XatRYaDWPX7CIKIrCoUOHAKhfv36W+WfPngWgadOmmJiU/OXrpUuXALCzs+Oll17izp07meZv2bKFNm3asHDhQqzzUUj77sMuqzm1nDA1NaVy5crcu3ePO3fuSNLgSde0aVNjhyBEjhRFIfh//yN8xcpi2b5KqzXcfVfb2aGxtUNjY4PK0gK1mTkqczPU5uaozMxRm5sZ/tcnJhmKFKaEBKMLDiYlKDjtYjo1FQCNnR0OI4ZTbsgQNDY2+Y4tOCYRU7WaclbFc0GdlJLKhF/Osv9qMCoVvN21NpOfrY1a/SgTrVKpMClXDpMWLbBs0QJ42NohMJCE8+cNiYTEK1dyrM2gj4oi/vRp4k+fzjEWtbV1WjLBsQJat2qY1aiJtmYNzGrWxLRyZVSakvtxIIQQQjwNvvjLmzXH7qJXHr9saaJWwRvt3PjshQbFup/k5GTu3r3L8uXLOXv2LBqNhrFjx2ZZLigoCIAqVaoUazw5CXl4I2f9+vUAfPrpp7zwwgtotVoOHjzIF198wYkTJ/j000/58ccf87zdqIeFu+3s7HJcJn1edHR0AaMvGpI0EOIppuj1BH31FRHrNxR8I2o12urVMa/jjpl7nUd3ux820VebmxddwKTFnBoeTmpUFKZVqqA2M8v/NhSFRYduMfef6yiKQi+PSrzZoTrNXMsVWTP/pJRUxv9ylgNXgx/uE37af4PzvpH8OLBJrokKlUqVNtJC5crYPvcckFYjIvnWLZKuXyfx+nWSrl0n6fp1UoKD8xSPPjYWfWwsunv3SThzJvP+zMzQurlhVrMG2ho1sWzWFMu2baXLgxBCCFEIa4/fK3MJA0jrfbn2+L1iSRosWLCABQsWZJleo0YNPvzwQ9q0aZNlXvqoBNl1XYC0lgD9+/fPMr1nz57MmzevkBE/ajWu0+l47733GDp0qGFenz590Gq1TJw4kV27dvHWW29Ru3btPG036WFrXdNc6lVpH7YUTe++YSySNCgB586dy3He4MGD8flPU2MhSoKSkkLgp9OJ2rbNMM2sbl0qTJqIPi6O1OgY9DHRpEbHkBoTjT46Bn1s2ugEZrVrpyUI6tTBrFbNIk8M5EalVmPi6IiJo2OB1k9J1TN9+2U2nPI1TNt56QE7Lz2gcRU7RnaoTm8PZ7Qm6gLH+N+EQUaHr4fw/PyjLHm9OQ2r5JxZ/i+1Vot5vXqY16tHxrVSIyPTkgjXb5B06yapoaGkhIWTGhZGSnh41hElsqEkJaV1N7l2zTDNokVznD7+GIsGxXuXQQghhHhSDWtbrUy2NNCoVQxr+/higwXh7OyMs7MzkHb3/N69e+h0Ouzs7GiQw28OKysrIG3YxZzmN2vWzPA8MDCQwMDAIovZ7OENKlNTU15//fUs87t3707VqlXx9fXlyJEjeU4amJmZkZCQgC6XUb6Sk5MBMC/B39rZkaRBCcit0qVaXfALEyEKSklOxv/Dj4jZvdswzbxxI1yXLUOTSxOpsi42KYW3fj3L4etZ6wUAXPCLYvLG88yy9WFYWzcGtXLFIZ9dF7JLGPRskDaEzh7vtOZ1/pEJvLLkGDNfbMBrrfI2fnBOXSk09vZYtWqFVatW2a6nT04mNTyclLAwUsPD0QUGknzrNkm3b5N86xa6gIBs10s4fYa7/V/F7qWXqPDOZEwrVsxTnEIIIYRI89kLDfiwZ12paZDBK6+8wqRJkwzPQ0JCmDp1KkePHmXs2LFs2rTJcHc9nZOTE1euXMHPzy/bbdaoUYMNGx61mp0/f362rRkKKn3oRmdn5xyv62rUqIGvr2+OMea03YSEBEM3heykzyvK4SMLQpIGQjxl9ElJ+L89mdjDhw3TLFu2pMrixWisrYwYWfEKik5kxCovrgQ+6hPWzNWeEe2r8+vJe5y4HZ5h2SS+23ONeftv8HIzF0a0r4670+NrJmSXMOjVoBLzBzfFRK1i6b+3+Xb3VfQKJKfombrlEmfvRzCzrwfmppn/OKek6jl7P5IDV4M5eDWYa0FpLQa61avIxK61aVLVPk/HrdZqUVeqhGmlStnO18fHk3z3Lkm3bpN0+xYJZ88Rf/Jk2kxFIWrLFqJ378ZxzGgchg8v0VYlQgghRFlnodVggdQNykmFChX46aefeO6557hy5QqrV69mzJgxmZZp1qwZBw8e5OzZs6SkpJR4McQaNWpw5swZQ4uD7KTPS31Ydysv3NzcCAoK4t69e9nO1+l0BDy8uePm5pb3gIuB3OYW4imij4vDd+y4TAkDq44dqbps6ROdMLj6IJp+Cz0zJQx6e1Ri/eg2vNC4MhvHtOXvtzvwSrMqaDWPvhaTUvRsOOVLjx/+ZdCyE+y+HEhKDncLcksYmGrUqFQqxnWuyS9vtqZ8htYCm0/78criY/iGxxMel8zWc35M2nCO5l/tY8DS4yw5fMuQMADY5xNMv4WeDF1xklN3wsmrRF0qR2+Esu2cP77hj0Z0UVtaYl6/PnYvPE/FyZOptmY1VZcvR1urpmEZJT6ekB9/4tZzzxG9cyeKUsbaWQohhBCi1LK2tmby5MkALF26NMud9969e6PRaAgPD2fXrl0lHl961wc/P78cR767f/8+kNYqIq+aNGkCwJn/1JpKd/HiRXQ6HWZmZtSrVy8fERc9aWkgxFMiNToa37HjSMhQY8Omezcqf//9Ez0c39EboYz/5QwxSSmGaaM7Vufj3vUyjWLQoLId3w9ozEe96/Drifv8cuIeYXHJhvnHb4dx/HYYLvYWDGnjymstH3VdeFzCIKN2tRzZ8XYHJvx6lnP3IwHwDoim+w+HSUrRk9fr8SM3QjlyI5RW1R2Y1LUWHWo5ZipcqCgKt0Pj+Pd6CIevh3DidhiJukd/6Jq62vN8o8r0aehMJbvMrQesO3bAqu02In/7jZCf5pEamRZnSkAg/u++h8Uvv2L34ouYVHLC1MkJk0qV0NjbS+FEIYQQQhRI3759WbhwIf7+/qxZs4a3337bMK9q1aq8/PLL/Pbbb8yaNYtGjRrlOExhcejatauh/sDOnTt5/vnnM8339vbm6tWrALRt2zbP2+3ZsydLly7lxIkT3Lt3L8sxbdq0CYBOnToZ6joYiyQNhHjCKYpC4qVLPPj8CxKvXDFMt33hBSrPnoXKCOPdlpTfTvvy8ZZLpDysQKRWpfUvfKOdW47rVLQxZ0p3d8Y/U5M/LwSw2vNuphYK/pEJfLv7Gj/uu8GLjSszuLUrCw7czFPCIJ2znQWbxrTl67+vsOZ4WpO0jBf06Uw1KlpXL0+XuhXpUqcCd0LjmH/gJud9Iw3LnLoTztAVp2hS1Z4Jz9REr8C/N0L493oIfhHZFwwCOHc/knP3I/nq7yu0dHPghUbO9G7ojKN1WvM6lYkJ5QYNwrZPH0IXLSb811/hYaGehLNnSXg4ZnI6lVaLidPDJIKTE6ZVqmDZogWWLZqjzqHasRBCCCEEgImJCaNGjeKLL75g7dq1jBgxApsMw2lPmzaNy5cv4+Pjw4ABA5g0aRIvvvhilr7+Xl5eeHp6Fmls9vb2jBgxgiVLljBnzhyqV69uKNro7+/Pxx9/DECrVq1o2rRppnV3797Nt99+C8CBAwcyzWvQoAFdunTh4MGDTJkyhSVLllCxYkUURWHz5s38+eefqNVqxo8fX6THUxAqRdqZGtXLL7+Mt7c3DRo0YMuWLcYORzxBUmNiiPrrLyI3/0bSw+xnOvsBA6j0+WeontBCnKl6hZ/2XWfegZuGaeamauYPakb3+nlvNgZpSZcz9yJYfewuuy8/MCQgcvK4hMF/bTvnz8dbLpGgS+sDV9HGjC51KtKlbkU61HbE2ixzUkdRFDxvhjHvwI18dU8AsDDV4GijxTc8+2SCWgVta5anbxMXXmxcOVOdheS7dwn67n/E7t+fr31iaopl48ZYtm2DVdu2WDRsiCqXoYWEEEKI0kyv13Pt4WhDderUkaLmeTR06FBOnTrFxIkTMxVCzCg5OZmuXbsSEhLCpEmTmDhxYqb5sbGxTJs2jT179gBpBeVdXV2xs7MjJSUFf39/Ih+2jnRzc2PGjBm0b9/esP7JkycZNmwYgOE1zCudTseECRP4999/AahVqxampqZcv36d1NRUqlevzpo1a7J0T9iyZYshqZDdPsPDwxk0aBB3795Fq9VSq1YtIiIiCAwMRKVS8cknn2Qa4jE3+X1v5uc69Mm9xSjEU0hRFBIvXCBi829E79qFks3QNA5vDKPi1KlPbFPygMgEpmw6z8kMF9SO1lpWvNGSxnksHpiRSqWihZsDLdwcCIpO5NeT91l/8j6hsUlZls1vwgCgX1MXmlcrh+fNUDxc7KjvbJup20R28XSo7UiH2o6cvB3GgoM3OXIjNMfl61ayobN7BTq5V6CFWzm0GjU+gTHsuBjAjouB3M9Q30CvgOfNMDxvhjF7pw9DWldjaNtqONmao3Vzo+rCBcSdOEnUn3+i8/cnJSgIXVBQtu8zA52O+NOniT99mtD5C1BbWmLRsgVWbdpi07UL2hJsXiiEEEKI0kur1fLmm28yZ84c1q5dy/Dhw7G2tjbMt7a2Zt68eVy8eJFt27bh5eVFUFAQfn5+WFlZ4ezsTI8ePejevTvt27dHoym6ApSmpqYsXbqUzZs3s2XLFm7evElKSgo1atSgZ8+ejBgxIlOseeXg4MAff/zB8uXL2b17Nzdv3sTS0pJOnTrx5ptv0qZNmyI7hsKQlgYlID4+Psd5gwcPxsfHR1oaiEJJjYkh6s8/01oV5JA5tWjRHIfXX8emZ88nNmGw81IgH2+5RFTCo/Fua1awYvWIVlR1yHno0/xKTtGz63Iga47d5ezDugS9PSoxb1D+EgZF5bxvJAsO3OTgtWBszE3oUMvRkChwss15tANFUbjoF8WOiwH8fTGQgKjELMuYalQ836gyI9tXp2GVrMNxKoqCPjoaXVAQKQ//6QIfkHjlCvGnTqGPi8s5cLUa+4EDqDBpEiYODgU6diGEEKIkSUsDUVoVZ0sDSRqUgDp16jx2GUkaiIJQdDoiNmwkZOFC9NmM8aqxs8OuXz/sB7yKWc2a2WzhyRCXlMLMv66w6bRvpunPN3Lm65caYmdRfM3hrz2IISI+mdbVHYyejNGn127IpaVCbuuevR/B1nP+bDnrb+gukVFLt3KMbF+dHg0qofnPPpJT9EQn6ohO0BGdmIK1mYYa5cxJ8vYm7sQJ4o6fIOHcOZTk5CzbVdvY4DhhAg5DBqN6gotyCiGEKPskaSBKK+meIITIIvbffwma8w3Jt29nmWfZsiX2AwZg06M76lzGlH0SXPKL4u2N57gT+uiOtqVWw8y+HrzSzKXYL+TrVLJ5/EIlpCDJgozrpnfD+LBnXTZ43WfNsbsEZmh94HU3Aq+7EbjYW1DJzvxhgkBHdEJKjkmGqb3r0XxcExzHjUOfmEjC2bPEHT9B9J496B4OT6SPiSH4m2+I3LSJih99iPUzzxg9ASOEEEIIIdJIS4MSIN0TRFFKunWLoDnfEHfkSKbpaisr7AcMwP7V/pjVqGGk6EqOXq+w7Mhtvt97DV3qo6+xxlXs+Om1prg5GndomieBLlXPHu8HrDh6xzA8ZEH0bODEh73qUrPCo75+SnIy4evXE7pwEfqYmEzLW7Vrh9PHUzGrXbvA+xRCCCGKg7Q0EKWVtDQo4ywtc+5LLV80Iq9SIyMJWbCQiA0bIDXDXV2VCvv+/akw+W1MHB0LvH2/iHj2eAfRoZZjqbp7ni45RU9gVAL+EQn4RSaw7Zw/x26FGearVDDhmZq8083dKHUFnkSmGjXPN6rM840qc/Z+BKs877LzUiCpuYwgoVGrsNJqiE5MMUzb4x3EPp9gBrasyjvP1qairTkqrZbyw4dj9+KLhMyfT+SmzaBPG3Yy7tgxbvftR7nXBuIwciTaKlWK/ViFEEIIIUT2JGkgRCmkpKSQGhODPiqK1JgYEs6dI2Thoix1CyxbtcLp46mY16tXqP3p9QrDV3lxMzgWE7WKD3vVYVSHGoVq7v5fiqJwJTCaAz7BXAmMRq1SoTVRo9Wo0ZqoMX34v9ZEjZmJmtiklLQEQUQ8/pEJBMckkVO7KGc7c+YOaELbmuWLLF6RWTPXcjRzLcfHvevieTMUjVqFrbkptham2FqYGB5baTWoVCqO3Qrlm11XueCX9p5N1SusP3mfrWf9GdWxOmM61cDG3BQTBwecP/uMcoMGETxnDnHHjqftUK8nYv0GItZvwKx2Layf6YJ1ly5YNG6EqgirIQshhBBCiNxJ0kCIEpQaHY0uICDtn3+A4XFqWBip0dGGRIE+ly4tAKZVq1Lxww+w6datSPp+3w+P52ZwLAApeoVZO69y/FYY3w9ogoNVwQvTJepSOXYrlP0+wRy4Gpypf3xR6e1RidkvN8TeUgrolYTK9ha82qLqY5drV9ORbW+15+9LgXy35xr3wtLe0wm6VOYfuMmvJ+8zqmN1etR3omYFa8zd3am6YgWxBw8R/M03JN+7Z9hW0o2bJN24Sdjy5WjKlcO6Uyesu3TBqkN7NAUY3kgIIYQQQuSdJA2EKCaKohCzaxdRf+1A5++PLiAAfWxsobaptrLCcfw4yg0bhroIq8xfDsg68sLBayE899MR5g1qSqvqeR8O70FUIgeuBrPfJwjPW6Ek6vRFFqdKBRVtzHCxt6BKOUt6NqjEcw0rSdG8UkqlShuusUf9Smz0us9P+24QFpc2ekJ4XDLf7r7Gt7uvUaWcBc/UqUCXOhVp26EjNTq0J3z9eiI3/5al0GdqRARR27cTtX07mJpi1bIlts/1xqZ7dzR2WYeEFEIIIYQQhSNJAyGKgT4+ngdffEHU9j8LtyGVCrWtLRo7O6w7tMdx/HhMKlQomiAzuOT/KGlgbqo2XOg/iE7ktWXHebe7OxOeqZVjd4Ww2CR2XgrkzwsBeN2NyHE/KhU0qWpPx9oVsDDVkJyiJzk1Ne3/FD3JqXqSUvToUhW0GjUu5SyoUs6CKvYWuJSzwNnOAq2J1Csoa7Qmaoa1dePlZlVY9u9tfj5ym/jkR3U5/CIS+OXEfX45cR+tiZrW1R14pk5nuqzpT424UOIOHybm4CHiT5+GlEe1EtDpiDt2jLhjxwj8YibWHTpg26cPNl27oM6llowQQgghhMg7SRoIUcSSbt3Cb/Jkkm/eyna+yswMU2dnTCtXxtSlMqaVK2NSsWJacsDWDo2tDRpbW9S2tqitrFCVQLFMb/9ow+Mp3dzRqFXM2XWVFL2CXoH/7b3OyTvhzB3QhAo2aUM4xiTq2OsdxJ8XAjh6MzTH4nhWWg2d3CvwbD0nnqlTAUfrJ3sISJEzazMT3u3uzuttXFl3/B77H9a3yCg5Rc+RG6EcuRHKlzvAxd6C9rWa0P6dbrR20mJ5/jQxBw8Sd/hfUjPW+NDpiD14kNiDB1FZWGDT5Rls+/TBqmPHIm2VI4QQQgjxtJGkgRBFKOqvHQR+9hlKhpoEFs2b4zB0qCFBoHFwKFXN6RVFydQ9oaGLHe1qOdLCzYGJ68/iF5EAwJEboTw37wgTu9Ti5J0w9vsEk5SSfdcDVwdLnq1XkWfrOtGquoO0DhCZVLQx570edXivRx2CohM5fC2EQ9eDOXI9lJiklEzL+kcmsPm0H5tP+wHg7mRN+45v0H7oOzSJuU/K/n1E795NatijkTSUhASid+4ieucu1DY22L3UjwqTJqGxKX2jggghhBBClHaSNBCiCOiTkgiaNZvITZsyTS8/6k0qTJ6MytTUSJE9nl9EApHxOsPzBi5p/cKbVLXn77c78tHvF9nt/QCAkJgkPvvTO9vtVLI15/lGzvRt4oKHi22pSoyI0svJ1pwBLasyoGVVdKl6zt6L4ND1EA5eDebqg5gsy18PiuV6UCyrPO+iUato7vosY5eOoHXUXaJ37iRm7z/oYx6tp4+JIWLtOmJ27cZp+qfY9uhRkocnhBBCCFHmSdJAiEJKvn8fv3feIemKj2Ga2taWynNmY9O1qxEjyxvvDK0MXB0ssbN4lOCwszBl8evNWHfiHl/t8CE5NXPLAntLU3p7ONO3SWVauTkU6RCN4uljqlHTukZ5Wtcoz0e96hIcncixW2F43gzF82YoAf8ZfSNVr3Dqbjin7obTpKo97w1/h3YzZhDv6Un0jr+JOXgQJSGtpUxKSAj+b08m6tlnqTT9U0wrVTLGIQohhBBClDmSNBCiEKL/+YfAaZ9kurNp7uGBy48/oq3iYsTI8u5yhnoGHi62WearVCqGtXWjmWs5Pvj9Iv4R8XSpW5G+TSrToVYF6Xogik1FW3P6NXWhX1MXFEXhbli8IYFw/HZYphYy530jGbriFK2qO/Be98a0ntsVfXw84WvXEbpoEUpy2qgNsfv3c/vECSpMmUK5Qa+h0miMdXhCCCGEEGWCJA1KQHyG/u3/pdcX3XB0omTo4+KI3rOXqK1biffyyjSv3JAhVPzowzJVeC3jyAkeLjkPWefhYseuyR1LIiQhslCpVFR3tKK6oxWvt6mGXq9wzjeChQdvceBqsGG5U3fCGbjsBB1rO/Jud3eajhuLba+eBH72OfEnTwJpn+Ggr74i6q8/cZ75JeZ13I11WEIIIYQQpZ4kDUpA06ZNjR2CKCRFryf+9Gmitm4jes+eTIUOAdSWljh/9SW2zz1npAgLRlEULmdMGlSWce5F2aBWq2hezYGVwx04cy+Cuf9cw/Pmo2KI6SMwPFu3IpOerU2jVSuJ3rqNoG+/Rf9w1IXECxe588orlB85EscJ41GbmxvrcIQQQgghSi1JGgiRi2Q/f6K2byNq6zZ0fn7ZLmPu4UHl777FrHr1Eo6u8IKikwiLSzY8z62lgRClVfNq5fh1VBuO3wpj7j/X8LobYZi3/2ow+68GU7eSDQNaNOXF37eS9OP3RP/9d9oCKSmELVtGzL59VP5mDhYNGxrpKIQQQgghSidJGpSAc+fO5Thv8ODB+Pj45DhflCxFryfxig9xR48Qe+QoCWfOZLuc2sYG2z7PYf/SS5g3alRmRwrI2DXBxd4CB6uy061CiP9qW7M8m2u05d8boczde40Lfo/e31cfxDBzxxVma1R0bzaI15t3osLPP5ESEABA8u3b3H1tEOXHjKbC+PGoylAXIyGEEKK0Gzp0KKdOnco0TaPRYGNjQ+3atenVqxcDBw7ENA8jjl28eJHt27fj5eVFUFAQsbGxWFpa4uLiQsOGDenWrRsdOnRA85+6RSdPnmTYsGEAXLt2Lc+xT506la1bt+Zp2QMHDuDikr+6ZnFxcSxbtow9e/YQEBCApaUljRs3ZuTIkbRu3Tpf2youkjQoAZaWljnOU6uliJyxpYSGEufpSexRT+I8PUkND89+QZUKq/btsXupHzbPPvtENGXO2DWhQeWsRRCFKGtUKhWd3SvQqbYj+3yCWfbvrUwtD3SpCjsvPWAnGlw7v8dHgYepcfgvUBRITSVs8RJiDx2m8pw5UutACCGEKGLOzs44OzsDkJSUhJ+fH15eXnh5efHnn3+yevXqHK+dYmNjmTZtGnv27AHSrqOqVq2Kq6srMTEx3L59Gx8fHzZv3oybmxvLly/H1dW10DG7ubnRrFmzHOffvXuX8PDwTMeWV+Hh4QwePJg7d+6g1WqpVasW4eHhHDp0iMOHDzN9+nSGDBlS2EMoNEkaiKeSLjCQiA0biT16JNNQidnRurlh9/LL2L34whM3TFvG4Rala4J4kqhUKrrXd6J7fSduh8Ty2xk//jjjR3BMkmGZ+wkKb9l3wqO9CzO8/8AmIq2gYpKPD3f696fCpEmUf3OkjLAghBBCFJFXXnmFSZMmGZ7r9Xp+++03PvvsMy5cuMDy5cuZPHlylvUSEhJ4/fXX8fHxwd7enkmTJvHiiy9ia/vopldSUhInTpxg7dq1HD16lMDAwCJJGowbN45x48ZlO09RFLp37054eDh9+/bN9w3hTz75hDt37tCgQQMWL16Mk5MTiqKwefNmZsyYwddff02zZs2oV69eoY+jMOQ2t3jqJFz25s5LLxO2bFn2CQONBotmzXB8exJuv22mxq6dOI4Z/cQlDCBz94SGkjQQT6gaFaz5qFddjk3tyoo3WtCzgRMm6kddii471mRY+8n8WzfD6CA6HSFz53JvyOsk371b8kELIYQQTwG1Ws3AgQPp06cPALt37852ua+//hofHx8cHBzYvHkzr7/+eqaEAYCZmRmdO3dmxYoVzJ8/P8v84uDl5YWvry8AL730Ur7WvXLlCgcOHECtVvPDDz/g5OQEpN34GDhwIH379iU1NZVFixYVedz5JS0NxFMl/uxZfMeMRR8bm2m6SWVnrNt3wKpDB6zatkFTAl8yxhYck0hQ9KO7rg1cnvxjFk83E42aZ+s58Ww9J0Jjk9h61p8NXve5HRJHookZs+v2ZX/5ukzz/gOzyLRuSgnnz3O730tUeGcy9v1fRWNtZeSjEEIIIZ48jRo1YseOHfj7+2eZ5+vry5YtW4C0O/PVqlV77PZ69OhR5DFmJ73WQbNmzXBzc8vXuundLNq0aZPtMQ0cOJDt27dz+PBh4uPjc+3yXtykpYF4asQdP879N0c9ShiYmFDhnXeo8fcOau3fj/OXM7Ht2eOpSBgAeAdEGx5XtDGjok3Zr9EgRF45WpsxulMN9rzTibe61CS9lumpCnV4vd07XKrf1rCskphI8JxvuNG+PX6T3yH6n3/QJyXlsGUhhBBC5FdiYiIAFhYWWebt2rWL1NRUypcvT69evUo6tBzFx8cbWka8/PLL+V7//PnzADRv3jzb+Y0aNcLU1JSkpCSjF86XpIF4KsQcPIjv2HEoCQkAqLRaqsyfh+O4sZjVrFlmRz8ojMt+0jVBCFONmg961mX9qDZUsk1LnMVqLfnQ/RV+6DiSVFt7w7JKUhIxe/bgP+ltbrTvQMDH04g96omSkmKk6IUQQoiyT1EUDh06BED9+vWzzD979iwATZs2xcSk9DSU37NnD/Hx8VhYWNC7d+98r3/3YffHnFpOmJqaUrlyZQDu3LlT4DiLQuk560IUk+jdu/F//wN4+MNeZWFB1UULsWrb9jFrPtkuZyiC2ECSBuIp17ZmeXa/05Gpf1xit/cDAPaWr8/J9lWZGXGcuj4n0Uc9+szoY2OJ2rqVqK1b0ZQvj22vXpQbMgSzGtWNdQhCCCFKIX1CAopOZ+ww8kVlaoo6mzv+RS05OZm7d++yfPlyzp49i0ajYezYsVmWCwoKAqBKlSrFHlN+pHeZ6N69O9bW1vleP+rh7wo7u5x/h6fPi46OznGZkiBJA/FEi9y6jcBPPgG9HgC1tTVVly3FMpdhU54Wl/0fffl4yHCLQmBvqWXx683Y5OXLF39dIUGXSpSZDZMr9aB6necZogmk6U0vrLw8Da2WAFLDwoj49Vci//gDl7nfY9O1qxGPQgghRGnxYNYsIn751fA7tMxQqyn3+hAqTZtW5JtesGABCxYsyDK9Ro0afPjhh7Rp0ybLvLi4OCD7rgsAly5don///lmm9+zZk3nz5hUy4uylDxUJBeuaAGmjPUBai4KcaLVa4FH3DWORpIF4YoWvX0/QzC8NzzV2dlRdsQILjwZGjKp0iIhLxj/y0UVPwyrS0kAISKtY/ForV1pWd+DtDecMtT/uRCXzFeXBoRcW3brQP/keXfzPUenqOVQPWzEpiYn4TZyE07RpOLxu/DGVhRBCGFfEr+vLXsIAQK8n4tf1xZI0cHZ2xtnZGUi7e37v3j10Oh12dnY0aJD9b3Qrq7QixAkZEvb/nd8sww3BwMBAAgMDizjyzLZu3YqiKLi4uGSb6MgLMzMzEhIS0OXSEiU5ORkAc3Pj1h6TmgbiiRS2YkXmhIGjI67r1krC4KGMXRPKW2kNfbmFEGlqVrBmy4R2jOlUg/+WPEnQmLHOwp2RtQYyoMcMFjbtT6L24d0PvZ6gr74i6JtvUcriD0UhhBBFptyQwaAug5dbGk1a7MXglVdeYcOGDWzYsIG///6bgwcP0qFDB86dO8fYsWMNF8kZpQ9F6Ofnl+02a9SoYdjmhg0beOWVV4ol9nSKorBt2zYA+vbtW+DaaOlDQkZl6P74X+nzSmL4yNxISwPxxAlZtIjQefMNz02cnam2aiXafA6D8iTL2DWhgYvdU1kIUojHMTPRMO25egxu5crh6yEcvxXGiTthRMY/uiMQq7VkR7U2XLavxhcnVlAxIRKA8FWr0AUEUPmbOaiNfHdACCGEcVSaNo2KU6ZITYNcVKhQgZ9++onnnnuOK1eusHr1asaMGZNpmWbNmnHw4EHOnj1LSkqK0Yshnjp1Cj8/P1QqFS+99FKBt+Pm5kZQUBD37t3Ldr5OpyMgIMCwrDFJ0kA8UUIWLCQ0Qz8pU1dXqq1aiamLixGjKn0u+2ccOUHqGQiRGzdHK9wcrXijnRt6vYLPg+i0BMLtME7eDicmKYW7ds5M6TSJmSdWUDMq7Q98zJ493A8KosriRZiUK2fkoxBCCGEMagsLKKEL8LLK2tqayZMnM23aNJYuXcrAgQMzFQfs3bs3P/74I+Hh4ezatYsXXnjBiNGmdU0AaNGiBa6urgXeTpMmTTh58iRnzpzJdv7FixfR6XSYmZlRr169Au+nKJTB9jJlT3x8fI7/9NJ8tciEzF+QKWGgrV6dauvWScIgGxm7J3hUlnoGQuSVWq2iQWU7RnWswc9vtOTcjO5sf6s9XepUINzCjg86TMCrYh3D8gnnz3P3tddIzuEughBCCCHSmvm7uLgQGxvLmjVrMs2rWrWqodjgrFmzcrwzXxLi4+PZs2cPQKFaGUBaoUaAEydOZHtMmzZtAqBTp06Gug7GIkmDEtC0adMc//n4+Bg7vCdCyPwFhC5caHiurVGDamvXYOpU0YhRlU5RCTruhcUbnnvIcItCFJiJRk3jqvasHN6Sr1/yAEsrvmgzkl3VWhuW0d27z92BrxF/7pwRIxVCCCFKLxMTE0aNGgXA2rVriYmJyTR/2rRp1KtXj/DwcAYMGMAvv/yS7TCEXl5eeHp6Flucu3fvJj4+HktLS3r16pWn5bt27UrXbEZWatCgAV26dEGv1zNlyhSCg4OBtJoJmzZt4s8//0StVjN+/PgiP478ku4JokxTFIXQ+QsIXbTIME1bsybVVq/CpEIFI0ZWenlnaGVgZ2FKlXLSZE6IwlKpVAxpXY32NR2Zsvk881T9CbJ0YLjPLgBSIyO5P3wEFd+dQrlBg1A9HEJJCCGEEGn69+/PokWLCAkJYc2aNUycONEwz9LSkl9++YVp06axZ88evvzyS77++mtcXV2xs7MjJSUFf39/IiMjgbQaAAMHDizyGNO7JvTs2TNPd//j4+Px9/fPcf6sWbMYNGgQ3t7ePPvss9SqVYuIiAgCAwNRqVRMmzYtx1ElSpIkDUrAuVzuLg0ePFhaGxRQWsJgPqGLFhumaWvWpNqa1Zg4OhoxstLNO0MRRA8XWymCKEQRcnO04rexbVly+BY/atQEWZbj3bObMFVSUZKSCJo9h/BffqXi++9j06O7fP6EEEKIh7RaLW+++SZz5sxh7dq1DB8+HGtra8N8a2tr5s2bx8WLF9m2bRteXl4EBQXh5+eHlZUVzs7O9OjRg+7du9O+fXs0Gk2Rxufr64uXlxdQ+K4J6RwcHPjjjz9Yvnw5u3fv5ubNm1haWtKpUyfefPPNAg/nWNRUiqIoxg7iafbyyy/j7e1NgwYN2LJli7HDKTMURSFk3jzCFi8xTNPWqkm11ZIweJzJG8+x/XxaobaxnWrw8XPGLawixJPqsn8UUzadx9z7PNNPrcFGl3l8adPGTXCZNhWLxo2NFKEQQoj80uv1XLt2DYA6deqgLotDKoonUn7fm/m5DpV3uShzFEUh5KefJGFQQJcyjJwg9QyEKD4eLnb8NakDLft1Z8yzH/K3W1tSVY/+7OounOfuwNfwHjeJ5BzGnhZCCCGEMDZJGogyRUlNJWTuXMKWLDVMM6tdi2pr1kjCIA9ik1K4ExpneC5JAyGKl7mphhkv1Gfluz25M3QiE599j5NOmVv3qA/t41rP3ni+P4OEsAgjRSqEEEIIkT1JGogyI+nOHe69PpSw5T8bppnVro3r6tWYlC9vxMjKDp/AaNI7JFmbmVDNwdK4AQnxlGjmWo6FQ5qx8evBhH8ym9ndJnLT7tFwsCapKTjs+I3LXbtz7afF6BMTjRitEEIIIcQjkjQQpZ6i1xO+Zg13+r1EQoaikmkJg1WSMMiHS36PuiY0qGyLWi1F2IQoSZXszHm3Rx2W/ziO1EWr+K37SELNH7X4sU6KQ794Hte69SRi82aUlBQjRiuEEEIIIUkDUcol37vHvaHDCJo9ByUpyTDdtk8fqq1bKwmDfLocIPUMhCgNzEw09GtelRnzP8Du9214PTuQeBOzRwuEBvNgxmfcfv4FonfvRtHrjResEEIIIZ5qkjQQpZKi1xO+dh23+/Yj4cwZw3SNgwMu837C5fv/obG3N16AZdR/h1sUQhhfo1qVGLbwc/wX/sLWmp3QqR8NEZV89y7+70zh7qsDiD3qiQx4JIQQQoiSJkkDUeok+/pyf9gbBM2ahZKhX6/tc72p8fcObHv0MGJ0ZVdCcio3gmMMzxtKSwMhSpUXO3vgMeszxvT4mD2urUjlUfehRG9vfEeN4v7wESR4exsxSiGEEEI8bSRpIEqV6F27uP1iX+JPnzZM0zg44PLjj7jMnYtJuXJGjK5s83kQjf7hTUoLUw3VHa2NG5AQIosXG1dm+siuLGgxkPHPvs9R54aZ5sefPMndVwcQ9M236OPjjRSlEEIIIZ4mkjQQpUbCZW8CPvwIJSHBMM2mVy9q7PgL2149jRjZk8Hb/1E9g/qVbdFIEUQhSqU+jZxZMLgZgXaV+Lr1G0zu/DaXK9Z+tIBeT/iqVdx+4UVij3oaL1AhhBBCPBUkaSBKhdSYGPynTEHR6QBQ29nh8sNcqvz4AyYODkaO7slwKUPSQLomCFG69fKoxOLXm2OqUXG9nCsftB3Dl53Goq9cxbCMzt8f31Gj8P/wQ1LCw40YrRBCPD1Uqkc3XVJTU40YiRCZZXw/ZnyfFgVJGgijUxSFwE8+RefrmzZBrabKTz9h27u3cQN7wlzOUASxQWUpgihEade9vhPLhrZAa6IGlYpjDrUZ3GYSsf2HgImJYbnoP//i9nN9iNq+XQolCiFEMVOpVJiZpY12Ex0d/ZilhSg56e9HMzOzIk8amDx+ESGKV8QvvxKzd6/huePEt7Bq09qIET15klJSuR70qAiiDLcoRNnQpW5Flg9rwZi1p0lK0ROl1/CqvimvvVGf4Z6/oly9AkBqZCQBH00l6s+/qPTF52irVHnMloUQQhRUuXLlePDgAcHBwaSkpGBjY1MsF2pCPI6iKCQlJRETE0P4w1aH5YqhBpwkDYRRJVy6TNC33xqeW7Vri+PYsUaM6Ml0/UEsKQ+rIGpN1NSqKEUQhSgrOrtXYMUbLRm11otEnR6AjWFm/FFvBJ/V8qb5/k3wsBZMnKcnt59/gcrffiMjzQghRDGxs7MjMTGRyMhIwsPDDRdrQhibvb09dnZFf3NQuicIo0mNjsZ/yhR4WMfApEIFKn/3HSqN5jFrivzKWM+gnrMtphr56AtRlnSo7cjv49rRzNXeME2nqPjUxIPxz35AqEcLw3QlMRH/Ke8S9dcOI0QqhBBPPrVaTaVKlXBxccHW1haN/HYVRqTRaLC1tcXFxYVKlSqhVhf973xpaSCMIq2OwSfo/PzSJqjVVP7+f5iUL2/cwJ5QR2+GGB57SD0DIcokDxc7/hjfjl2XHzBn11Xuh6cNuXjXxJahNQfSz8GDUWf+QBMXA6mpBHz4IUpyEvavvGLkyIUQ4smjUqmwtbXF1jbtd5WiKFJXRpQ4lUpVIt1iJGkgjCJi3Tpi/tlneF7h7UlYtWplxIieXOfuR7Dz0gPD83Y1HY0YjRCiMFQqFc81dObZehX55cR95u2/QVSCDlQqtjl4cL6VA9+dWI51Qgw8LDKrJCdTbtAgY4cuhBBPtJK6eBPCGKSNcgmIj4/P8Z9erzd2eCUu4eJFgr77n+G5VYcOlB8zxogRPbkUReHLHVcMz+s729LLo5IRIxJCFAUzEw1vdqjOvx90YVSH6phq0n6o3rWrzLvtxhFu/qhF0YMvZhK2erWRIhVCCCFEWSctDUpA06ZNjR1CqZEaGYn/OxnqGDg5Ufnbb1AVQ98bAX9dDOTs/UjD8+nP10ejliy4EE8KO0tTPn2+PsPauvHNnqv8fTEQXxsnPugwntmeS6mYEAlA8JxvUJKScRwrCVohhBBC5I8kDUSRU/R69PEJ6ONi0cem/UuNjUUfE0vk77+jCwhIW1CjwWXu95g4OBg34CdUoi6Vb3ZdNTzvUd+JtjWlZoQQTyLX8pYsHNyM11qGMGXTBQKowAcdJjDHcwnO8WlVvUN++AElKQnHSROlCa0QQggh8kySBiXg3LlzOc4bPHgwPj4+JRhN8VAUheBvvyNqyxZSo6MhD4VgKkyejGXz5iUQXdmlKAprj99jw6n7tKlRnmnP1UNrkrdWGSuO3sE/Mm0YNlONimnP1SvOUIUQpUDH2hXYObkD722+wJEb8GHHCcz2XEqV2LRiqKGLFqFPSqTi++9L4kAIIYQQeSJJgxJgaWmZ47ziGBLDGOL+/ZfwVavyvLxV506UH/VmMUZU9sUmpfDh7xcMRQyvPoghIj6ZHwY0Qf2YLgbBMYksOnjT8PyNtm64OVoVa7xCiNKhoo05a0a0YvHhW8z95zofdhjPLM+luMUEARC+YiVKQgJOU6ei0mqNHK0QQgghSrsn44pVGJWiKIQsWpTtPJWpKRoHB0xdXTGrXw/Lli2xf20gLt99J3UMcnEjKIa+C45mGvUAYPv5AL762+exQ/p8v+c6ccmpAJSzNGXSs7WLLVYhROmjVqt4q0stNo1pg4VTRT7qMIFbdpUN8yPWb+DOwNdIvH7diFEKIYQQoiyQlgai0OI8j5F44aLhedWlSzBv2BC1tTVquYuVb39dCOCjPy4S//CiH8DG3ISYxBQAVnreoaKtGeM618x2fe+AKDaf8TU8n9LdHTsL0+INWghRKrVwc2Dn5I58+PtFpqrG8eXx5dSNSPt+SPLx4dbLr2A97i1cx49GpdEYOVohhBBClEZyq1cUiqIohC5caHhu1aED1p07Y+LgIAmDfEpO0fPFX95M2nAuU8JgYpdaHP2oKx4uj4ZQm7PrKr+d9s2yDUVR+GqHj6GkRK2K1gxu5VrssQshSi97Sy1Lhzbng/4tmdFpAn+7tTXMU6ekEL/gJ/b3fJnDB8+Qqn98PRohhBBCPF0kaSAKJf7ECRIyFHp0nDDBiNGUXQ+iEhm0/ASrPO8aptmam7DijRa837MOdhamrBreimrlH9XHmLrlEgeuBmXazj9Xgjh+O8zw/JM+9TDRyMdciKedSqXijXZubJzcBe+B4/i03WhCzR8lIl38rmM7cQSfjviCuXuu4hseb8RohRBCCFGayNWEKJTQhY9qGVi2bYNls6ZGjKZsOn4rjOfnH+HMvQjDtHrOtuyY1JFn6zkZplWwMWPtyFY4WpsBkKpXmPDrWcN6ySl6Zu18NBJHJ/cKdKlTsYSOQghRFjSobMeqEa2YP3c8F2Yu5kSNloZ55qk6hp3chNNXH/HSzK18tv0ySSmpuWxNCCGEEE8DSRqIAos7dYr406cNzyu89ZYRoymbTt0J5/UVJwmNTTZM69+8ClsntMO1fNZRN6qVt2L1iJZYm6WVI0nU6Rm52osbQTGsPX6Xu2Fpdwc1ahWf9pEhFoUQ2atsb8HEF5sxbMcaIj/8ggQLa8O8ZiHXWbzvf+hWLWfct38Zhm4VQgghxNNJkgaiwEIXLTY8tmzVCssWLYwYTdn0477rhj7EWo2a2S835Lv+jTA3zbkgmYeLHcuGNUf7sNtBVIKOYStPMW//DcMyg1pVxd3JpniDF0KUeRq1irYjB9Don11oOz1jmG6dksjQq3v5cO3HnHzhVU4uWEVqTIzxAhVCCCGE0UjSQBRI/JkzxJ84YXgutQzy79qDGI7delR/YNmw5gxq5YpKpXrsuu1qOvLja01IXzQwKpHoh6Mr2JibMKWbe7HELIR4Mpk4OlJj6SKcZ81CZW2daV69kFvYLvgWn3Yd8Hv3PWKPHEFJSTFSpEIIIYQoaZI0EAWSsZaBRfPmWLZuZcRoyqY1x+8aHtd3tqWze4V8rf9cQ2dm9vXIMn1S11qUf1j3QAgh8kqlUmH/8kvU2rWTCu+9S1LlzCOvaHTJxOzcie/oMdzo0oXg7+eij4szUrRCCCGEKCmSNBD5Fn/uHHHHjhmeV3hrQp7ujotHouJ1bD3rb3g+vL1bgc7h0DbVePvZ2obn1cpb8kY7t6IIUQjxlDKpUAHH0aNpvH83LFnNwfqdidJmrrGSGhJK2PLl3On/KolXrxopUiGEEEKUBBNjByDKntDFj2oZWDRpgmXbtrksLbKz+bQvCbq0quTlLE15sXHlAm9rSrfaVLQx47xvJG91qYWZSc71EIQQIq9UKhX1nmmNS5tmfLjhDJGHDtPt/mlaPfDBVEn7/kq+c4e7AwbiNO1j7AcOlASyEEII8QSSpIHIl4SLF4n794jhuaO0Msi3VL3C2hN3Dc8HtXLNtfDh46hUKl5vU43X21QrguiEECIzW3NTFg9vzfKaFZi9uyGWibG8cuMwr944iBoFJTmZB59/QdyJkzh/ORONjRRhFUIIIZ4k0j1B5EvGERPMGzXCqkMHI0ZTNh24GoxveNoQZhq1Si72hRClnkqlYkynmvw6qjVmDg6sbvAcn7YbTYTZo6KJMbt3c+X5vsRduGjESIUQQghR1CRpIPIswdub2EOHDM8dJ4yXVgYFsObYXcPjng2cqGxvYbxghBAiH9rUKM/OtzvwYuPKXHauw1td3uVchUd1VUyCArk9aDB/fvo/gqMTjRipEEIIIYqKdE8QeZaplUGDBlh37mzEaMqmG0ExHL0Zanj+Rls34wUjhBAFUNHWnHmDmhISU5/fzviy2KkC7U/9zes+e9CgYKJPpfbvK9h19DhXh7/DW32bU6Wc5eM3LIQQQohSqUhbGsTGxhIWFkZqampRblaUAok+PsTu3294LrUMCibjMIt1K9nQqrqD8YIRQohCqGBjxoRnanH4o2fp/c00/nh9GqHmdob5rR5coce8jxj8xe/M2ulDVLzOiNEKIYQQoqAK3NLAz8+Po0eP4uXlxblz5wgJCSElJcUw38bGhho1atCqVStatmxJ27ZtMTGRhg1lVfi6XwyPzerWxbpLFyNGUzZFJejYkmGYxREFHGZRCCFKE7VaRWf3CnT+9HUChj7LzXc/pIL3aQAqxUcw++ACPkkawyYvXyZ2qcXQttUKVfxVCCGEECUrX1fxer2effv2sWnTJo4fP46iKCiKku2y0dHRnD9/ngsXLrB8+XLKly/PK6+8wquvvkqVKlWKJHhRcvSxsYbHFSa+JRe7BfDbaV/ik9Na4dhbmtK3iYuRIxJCiKJVuZozzr+tIWT5z4T98AMADkkxfHt0EZ+2G83XO3WsPnaX93u607exC2q1/C0RQgghSrs8Jw327dvH999/z927dw2JAldXVxo1akT9+vUpV64cdnZ2mJubExkZSVRUFH5+fly8eJHLly8TGhrKsmXLWLFiBa+++iqTJk3CwUGaZpcVFT/8EBPH8pjVqYtNt27GDqfMSdUrrD1+z/D8tZaFG2ZRCCFKK5VaTcWxY9BWqEDgp5+CXo+NLoHZnkv5ovUILlKLKZsusPzfO3z8XF061q5g7JCFEEIIkYs8JQ2GDh3K6dOnURSFunXr8uKLL/L8889TsWLFPO1Er9dz/Phx/vzzT/bt28eGDRv466+/+Pbbb+natWuhDkCUDG0VFyrNmGHsMMqsQ9eCuR8eD4BaBa+3cTVyREIIUbzsX34JtZUV/u+/DzodlilJfHn8Z2a1HMpJ5wZcCYxm6IpTvNayKl/288BUIwM6CSGEEKVRnv5Ce3l50b59ezZt2sS2bdsYOXJknhMGAGq1mvbt2/PNN99w9OhR3n33XUxNTbly5UqBAxeiLFmdYZjFHvUrSSVxIcRTwbZnD6ouWoTK3BwArT6FGafW8IzvWcMyG718eWPlKSmUKIQQQpRSeUoabNq0iZ9//pnGjRsXeocWFhaMGTOG/fv307Nnz0JvT4jS7mZwLEduZBhmsZ2b8YIRQogSZt2xA64rV6C2tgZArej58OwGxkSeNyxz7FYYLy/25H5YvJGiFEIIIURO8pQ0KIpkwX9ZWlpSu3btIt+uEKXN2v8Ms9imhtTyEEI8XSybNaPa2jVoHtYyUikKLx36hc9iTxuWuRUSR79Fnpy5F26sMIUQQgiRDelAKEQxik7U8ccZP8PzN9rJMItCiKeTef36VPvlF0wqVTJMa7NvI8sf7MIqOQGA8LhkBi0/yfbz/jltRgghhBAlrMiSBoqiEB4eTkBAQFFt8okRHx+f4z+9Xm/s8EQx+v20H3EPh1m0szClnwyzKIR4ipnVqI7br79gWu1RMdgqJ/az8diPdHzgDUByip7JG88zb/+NHId1FkIIIUTJyfOQiznx9vZm8eLFHDt2jISEBFQqVaYCh1FRUXz//fcAfPrpp2i12sLussxp2rSpsUMQRhCVoMtUAPG1llWx0Mowi0KIp5upiwtuv/yC74S3SLx0CQCTyDCmnVjFCdcm/FS/L5HmNsz95zp3Q+OY/UpDzEzku1MIIYQwlkK1NNi2bRsDBw5k3759xMfHoyhKlrsCdnZ2+Pn58dtvv3Hs2LFCBStEWREQmcCAJcf/M8xiNSNHJYQQpYNJhQq4bVhPhffeRZXhZkKb++f5+eB3dLvvBYrClnP+DP35FLFJKUaMVgghhHi6FThpcOvWLaZPn05KSgpDhw7ljz/+oFy5ctku27dvXxRFYf/+/QUOtCw7d+5cjv/q1atn7PBEEbv6IJqXFx3jWlCMYdrwdtWp6iDDLAohRDqViQmOo0dTffs2LFo0N0y3SornvbOb+Or4cirGhXPqbjhvbzhHql66KgghhBDGUOCkwapVq9DpdAwZMoRPPvmEBg0aoNFk33ywTZs2AJw/f76guyvTLC0tc/ynVkstyifJsVuhvLr4OA+iEw3TJjxTk+nPS3JICCGyY1a9OtXWrqXSZzNQW1kZpjcPvs6SA//jhdtHOeATxKydPkaMUgghhHh6FfiK9cSJE6hUKkaPHv3YZZ2cnLCwsJAiieKJtv28P2+sPEXMw2a0ahV82c+DD3vVlREThBAiFyq1mnKDBlFjx19YdepomG6RmsyEi9v4xGst6w/6sOHUfSNGKYQQQjydClwIMTg4GAsLCyplGDopN2ZmZsTGxhZ0d0KUWoqisOzf28zeddUwzdxUzbzXmtKjQd4+H0IIIcDU2ZmqS5cSvWMHQV/PIjUyEoAOAZdwjQliVvIIqjk8T7tajsYNVAghhHiKFLilgVarRafT5Wk4pMTERGJiYrC2ti7o7oQolVL1Cl/8dSVTwqCcpSnrR7eRhIEQQhSASqXC7oUXqPH3DqzatzdMd40J5vuDP/HznNXcDpGbEEIIIURJKXBLAxcXF65fv87du3epXr16rssePnyY1NRUatWqVdDdCVGiFEVhxdE7nLkXgdZEjbmJBjNTNWYmasxNNZiZqDEz0XDyThj7fIIN61V1sGDNiFbUqCAJMiGEKAyT8uWpumwpIfPmE7Z0KQBWKYl8cHQFWycFMHL515SzsTBylEIIIcSTr8BJg44dO3Lt2jXWrl3LZ599luNyERERfPfdd6hUKjp37lzQ3QlRonZcDOSrv/NXdKtRFTtWvNGSCjZmxRSVEEI8XVQaDRWnvIO5RwN8P5yKOiFtGNvnzu/i6Kt+dP91CeblHYwcpRBCCPFkK3D3hOHDh2NpacnGjRtZsGBBlnoFiYmJ/PXXX7zyyiv4+flhb2/PoEGDCh2wECVh6zn/fC3fpU4FNo5pIwkDIYQoBrbdu1Prj9+IdapimFbr7iUuvvAyCT4yqoIQQghRnArc0sDR0ZHvv/+et99+m4ULF7J06VJDfYM+ffrg6+trqHmg1WqZO3eu1DQQZUJUvI4jN0IMz4e2qYadhSmJulSSUvQkpaSSqEv7PyVVoX0tR4a1rYaJRobPFEKI4mJWowZN/97KnqETqOHjBYBNeBC3Bg7CZfon2L/yCioZxlgIIYQocgVOGgB06dKFX3/9lZkzZ3L58mXD9Fu3bhke169fn88//5xGjRoVZldClJi9Vx6gS01LgDlaa/nshfqSEBBCiFLAxNqarptWsnTcZ3Q7thUNCprkJB5Mn0Hkb79TafqnWDRsaOwwhRBCiCdKoZIGAI0aNeL333/n6tWrnDlzhuDgYPR6PY6OjjRr1oyG8sdblDE7LwUaHvfyqCQJAyGEKEUstCYM+WkG06ZVZtShldjq0uocJF68yJ0BA7Hp9xLO77+LSfnyRo5UCCGEeDIUOmmQrm7dutStW7eoNieEUaR1TQg1PH+uobMRoxFCCJGdCjZmvP/JMEabOzL47FY6+18AQKUoxG7dwpVdu7Ee/xbV3xyGyqTIfuoIIYQQTyW5hSpEBnuuPCBF/6hrQuvqcqdKCCFKozqVbFj6bm9OD3ufqR3Gcce2kmGeaWI8ST98h2fX3pz7c7+h5pIQQggh8k+SBkJk8PfFR10Tens4o1GrjBiNEEKI3NRztuXnN1rw05xReH38EyubvESsiblhfvlgP8w/nMjmF4bh433HiJEKIYQQZVee2ux9/PHHRbIzlUrFrFmzimRbQhS1yPhkPG9K1wQhhChr3Byt+OylRkT3rsfW/f1JXrKADjeOG+Y3unmayMEDOPTepzwzrK8RIxVCCCHKnjwlDbZu3YpKpSpw8770dSVpIEqzvd5BGbommNGquoORIxJCCJEftuamvNGnGam9V/Dvn4dI/eE7XILSWhjYJ8XCrKnsOe5Jtx9mojE3f8zWhBBCCAF5TBr069cPlUqaaYsn244MoyY817CSdE0QQogySqNW0aVfF5QXO3Pqx+WYr1iINlUHgOvBvzjW8yKNlszDrp67kSMVQgghSr88JQ3mzJlT3HEIYVQRcckcy9A1oY90TRBCiDJPpVbT+t2x3GjXmntT3sMlIgAAx6B73H31Vezf+wDX4UPkxogQQgiRCymEKASwN8OoCRVszGjhJl0ThBDiSVG7TROa/72VU027GaZpU5KJ/+ZrvN8cR0pEhBGjE0IIIUo3SRoIAezIMGrCcx7SNUEIIZ405RxsGfzLT/w7fCpRWivDdM2xf7ny3AvEnThpxOiEEEKI0kuSBuKJk5SSysqjd9h6zg+9/vHFOyPikjl2K8zwvE+jysUZnhBCCCMx1agZO/UN7n23jHMVH9UzMI0I496IkQSuWmPE6IQQQojSKU81DXKTnJzMnj17OHPmDA8ePCAhISHHURZUKhVr1sgfZFG8ftx3g8WHbgFwMziWD3rWzXX5Pd4PSH2YXKhoY0aLauWKPUYhhBDGM7BnM47X+JlfZnzPwPM7MFVSUSl6Ir+Zg4+XN22//xILCzNjhymEEEKUCoVKGpw9e5YpU6YQHBxsGFIRMCQNMhYWyjhfiOKi1yv8ccbP8HzhwVvUd7ajT6OcCxv+nWnUBGfU0jVBCCGeeG1rV8BlwQw+/74+w3ctokJCFADOB/7iz+dvofnsa/q1d8dEI40yhRBCPN0K/JcwMDCQsWPHEhQUhLu7O6NGjUJRFCwsLBg/fjyvvvoqVapUQVEU7O3tGT9+PG+99VZRxi5EFhf8IgmOSco07f3fLnAlIDrb5cOzdE2QUROEEOJp4VrekvmfDebCxz9ws1xVw/RG/lewfH88r325lZ2XAnNsQSmEEEI8DQqcNFi1ahUxMTF06tSJbdu28f777wNgaWnJ5MmT+fLLL/nnn3+YMWMG0dHRXL16lYkTJxZZ4EJkZ++VoCzTEnSpjFl3mvC45CzzMnZNcLI1o7mrdE0QQoiniZWZCZMGtKX1n5vx9WhtmF49+gFTtn7DT/O38uICT/69HiLJAyGEEE+lAicNPD09UalUTJo0KdduB4MHD2bSpEkcOnSIzZs3F3R3QuTJXu8Hhsc96juR3tPALyKBievPkpKqz7T83xlGTejtIV0ThBDiaVWxgj3dN6/E9PU3DNMckmL45uhi7E79y7CVp3h38wV0//k7IoQQQjzpCpw0CAgIQK1W06BBg0zTdTpdlmWHDBmCSqViy5YtBd2dEI91MziWWyFxhufTn6/PR70eFUE8diuMr3f6GJ6HxSZx7Fao4fnz0jVBCCGeaiq1mlqfTsX5669BowHATJ/CJ17rGHhtP1vP+jF23RkSdalGjlQIIYQoOYWq7mNlZYVa/WgTFhYWxMXFZWm+Z2Njg42NDXfu3CnM7oTI1d4rj1oZ1He2paqDJWM61eDFxo+GUFzleZffHxZK3OMdRPqIjJVszWkmXROEEEIA9q+8jOvKlajt7AzThvvsYsLFrRzwCeKNlaeIScx6k0QIIYR4EhU4aVCxYkViYmJITn7UT7xSpUqkpqZy69atTMsmJCQQHR1NQkJCwSMVZZ6iKHjeDOXw9RD0+qLvF7rX+1E9g54NKgFpI3h880ojGlS2NcybtvUS530j+ftSgGFa74aVpGuCEEIIA6vWrXDbuAHTaq6GaS/cOcZLt/7l5J1whvx8MttaOUIIIcSTpsBJAzc3NwD8/B4Nb9e4cWMANm7cmGnZVatWoSgKLi4uBd2deALs8X7AkJ9P8sbKU/x1MeDxK+RDUHQi530jDc97NHAyPLbQalg6tDkOVloAklP0jFl7muMZRk2QrglCCCH+y6x6ddw2bsS8cSPDtFGXd9A60JuLflEMXHqcB1GJRoxQCCGEKH4FThp06tQJRVE4ePCgYVr//v1RFIVff/2V0aNH88MPPzB27Fjmz5+PSqWiT58+RRK0KJv2ZGgJcOBqcJFu+58MoyZUdbCgbiWbTPOrlLNk0ZBmmDxsTRAck5Spa0LTqtI1QQghRFYm5cpRddEiTB/e+FCj8OGZ9bhFBXAjOJZXlx7jfli8kaMUQgghik+BkwZdu3alZcuWBAY+qj7fokUL3nzzTRRF4ciRIyxbtozDhw+jKAotWrRgzJgxRRK0KJuuB8UYHt8PL9ofWBmHWuxRv1K2I3q0qVGeGS/UzzL9uYYyaoIQQoicmZQvT5XFi1BbWQFgmZLE5ydWUi4xGt/wBPovOca1BzGP2YoQQghRNpkUdEVnZ2fWrVuXZfoHH3xA+/bt+fvvv3nw4AHW1tZ07NiRfv36YWJS4N2JMi5Vr3AzONbw3LcIkwbRiTqOZxgFoUd9pxyXHdqmGt7+0Ww67WuY1ke6JgghhHgMc3d3XH6Yi++48aDX45QQyYyTq/mow3iCY2DgsuOsHtGKJlXtjR2qEEIIUaSK5Sq+Xbt2tGvXrjg2Lcqo++HxJKU8Gts6NDaZuKQUrMwK/xY8eDUYXWpaXwMHKy0t3BxyXFalUjGzXwMeRCdy+HoInd0r0FR+4AkhhMgD606dcJo6laBZswCoG3Gfd89tYk7zIUTG6xiy/ATfD2hMLw9JRgshhHhyyK1/USIydk1I5xsRT91KttksnT8ZuyZ0q1cRzWO6GpiZaFg9oiV+EQlUtreQrglCCCHyrNzQ10m6c5vIDWlFnzv7ncffpiLr6vQgLjmVcb+c5Y221fj4uXqYm2qMHK0QQghReAWuaZCcnMzVq1ezDK+YnVu3bnH16lV0OhnT+Gl1I5ukQVEUjkpKSeVQhqKKPepXytN6KpWKqg6Wj00wCCGEEBmpVCoqTZuGVbu2hmmDffbS48EFw/M1x+/xyuJj3A2NM0aIQgghRJEqcNJg586dvPTSS6xZs+axyy5ZsoSXXnqJPXv2FHR3ooy7HhSbZVpRFEM8diuMuORUACxMNXSo7VjobQohhBC5UZma4vLjj2hr1DBMm3J2E71Uj+rreAdE8/z8o/x5oWiHGBZCCCFKWoGTBnv37gWgb9++j102fShGSRqUXckpenZfDuRmcMGqQ2fbPaEIkgZ7Mwzj2Nm9gjQFFUIIUSI0trZUXbIYjb192oTkZKYcXsaHDhGgpNXZiU1K4e0N5/h4y0USdanGC1YIIYQohAInDW7cuAFA3bp1H7tsgwYNALh+/XpBd2cUZ8+eZeTIkXTo0AEPDw/at2/P2LFjuXDhwuNXfsLM3uXDuF/O8txPR/N9sZ+Squd2SNYmmoVtaaDXK/yTcajFBjmPmiCEEEIUNa2rK1XmzwNTUwD04eF0Wfk1W6+uoXXUHcNyG0750neBZ4ET70IIIYQxFThpEBwcjI2NDVYPxyzOjbW1Nba2tgQHBz922dIkOjqaGjVqMG3aNFauXMmnn35KVFQUQ4YM4eLFi8YOr0SlD5eYnKpnn0/QY5bO7G5YPMmp+izTC5s0OOcbQWhsEgAatYqudSsWantCCCFEflm2bInzzJmgelQjx/zaZT4/uJB5p1dQN/wuANeCYnhhvidbz/kZKVIhhBCiYAo8eoKpqSmJiYl5WlZRFBITEzExKVuDNTzzzDM888wzmaZ17NiRNm3asH37dho1amScwIzA3cmGIzfS+mpe8ovK17oZiyCaalSG4RF9IxLQ65UCj16QsWtCmxoO2FtqC7QdIYQQojDsX+qHWa2ahPw0j7ijRw3Ta/v58IOfDyed6rGuXk9u2VdhyqYLpKQqvNqiqhEjFkIIIfKuwC0NqlSpgk6n49y5c49d9uzZsyQnJ+Pi4lLQ3ZUalpaWaLXaMpcAKaxGVewMjy/4ReZr3YxFEFtXL294nJyiJzgmqUDxKIrCHu8Hhud5HTVBCCGEKA4WDRvi+vNyqv2yDssWLTLNax3kw4JDP/LJyTVUiQnmoz8usutSoJEiFUIIIfKnwEmDdu3aoSgK33//PSkpKTkul5KSwty5c1GpVLRv376guwMgJCSE7du389VXX/Haa6/RuHFj6tSpw8svv5yn9U+cOMHYsWNp06YNjRo1olevXvz444/Ex+feTF6v16PT6QgICGDmzJmoVCoGDBhQqGMpaxpVsTc8vh0aR0xi3ofPzFgEsZmrPQ5Wj1oEFLSLws3gWO5mGLKxe32pZyCEEML4LFu0wHXdWlxXrsC8ceYWiR0CLzH/0A+0DPDm7Y3nOHw9xEhRCiGEEHlX4KTBsGHDMDMz48yZM4wYMYIrV65kWcbb25vhw4dz5swZtFotw4YNK1Swf//9Nx9++CHr1q3j3Llzee4eAbBu3TqGDx/OoUOHMDMzo2bNmvj7+7N48WL69+9PZGRkjuuOHj0aDw8PunTpwj///MOyZcuoWbNmoY6lrHErb4mNeVrrCkWBy/7ReV43Y9KgtpMNVR0sDc8LmjTYm6EAYkMXOyrbWxRoO0IIIURRU6lUWLVrh9vGjVRZvAizDEWjzVN1TD+5mh43PRm77jSn74YbMVIhhBDi8Qrcxr5SpUp88cUXfPzxx5w+fZpXXnkFR0dHQxcEPz8/wsLCUBQFlUrFzJkzqVy5cqGCtba2pl27dnh4eODh4cHdu3eZO3fuY9e7fPkys2bNAmDmzJkMGDAAlUpFUFAQ48ePx9vbm+nTpzN//vxs158+fTrR0dEEBwfz+++/M3r0aJYuXUrLli0LdTxliUqlolEVOzxvhgFw0S+StjXLP2attC4Id0IfjZzg7mSDq4MlF3wjgYInDTJ3TZBWBkIIIUoflUqFTZcuWHfuTPSuXTyY8Rn6uDg0KEy8sAWn+HBGroL1Y9rh4WL3+A0KIYQQRlCojvn9+vWjXLlyzJw5E39/f0JCQggJydzUrmrVqkyfPp1OnToVKlCA/v37079/f8PzLVu25Gm9RYsWodfr6devHwMHDjRMd3JyYu7cufTu3Zu9e/dy9erVbIeQdHNzMzx+9tlnGTRoEF999RXbt28v+MGUQY2q2D9KGvjnrRji3bA4UvRphQ9N1CqqO1rh6vCoVUB+h28ECIhM4GKGYow9PaSegRBCiNJLpVZj16cPZrVq4TtmLClBaa3lXr1xiIrxkYxSKfz6VidqVrA2cqRCCCFEVoWu5te5c2f27t3LyZMnOXv2LKGhaRX2K1SoQLNmzWjdujVqdYF7QRRaXFwcR44cAci2DoGbmxtt2rTh2LFj7N69O9ukQUYqlQoPDw82b95cLPGWZo0y3AW5mMdiiNcePOqaUN3RCq2JGtdCdk/IOOSjW3lLaleUH1lCCCFKP/M6dXDbtBHfseNIunYNgM7+53H4J4pxpLD6ne64SHc7IYQQpUyRDAGg0Who164d7dq1K4rNFSkfHx+Sk5PRarU5DpHYvHlzjh07xoULFx67vZSUFM6ePUu1atWKOtRSr1FVe8Nj3/AEIuKSKWeV+zCHGYdbdHeyAchU06AgLQ0yDrXYo0ElVKqCDdn4f/buOzyKcm/j+Hc2vZOQBAihd0ILVSwgRQUFRQVEyrGCiigI9qMcXxt6VFREsSEgqAiKwFEUFARBmkDoAemQBEhISO/Zff8ILIkkEDZls8n9ua69rszMMzO/9cwJu3eeIiIiUtFcatemwVfziH58PGnr1wPQNv4IT/34NuMt2cx4ciBBPm52rlJEROSCKr9u4JEjRwAICQnBxcWlyDb169cv1Pa8SZMmERoaSuvWrfH39+fUqVMsXLiQyMhIpk2bVr6FV0Ihfu7U9HIlPi0byB+i0LN50CXPKbjcYrNa+T0CCvY0iE3JIiM7Dw9XpxLVkJSew8bD8dZtzWcgIiKOxsnbm3qffMzJ/7xE0rmhlvVS45i4+E2ez0nnrReGUcPz0qG8iIhIRSmX0CAvL49vvvmGP//8EycnJ3r27MmQIUPK41aXlZSUP/bdz6/4CYbOHzvf9rzw8HCWLl3K/PnzSUlJwc/Pj/bt2zNnzhy6du1a7PXmz59f4uELhw4dKlG7yuD8ZIi/78+ft2JXVOLlQ4PYi3sa1PHzwNlkWOc6iDqbTrNzxy5n45F463mB3q6E1/e/4vchIiJib4aLC3VeexWXuiGc+WA6AP5ZqYxd8jZPnYmjz4NDGNK5Hk4m9aYTERH7sjk0+P7773nhhRe48cYbef/99wsdmzhxIitWrADAYrGwcuVK1q9fz7vvvlu6am2QlZUFUGwvAwBXV9dCbc8bOXIkI0eOvOJ7xsXFsWfPnis+zxG0Da1hDQ12RF16MsTMnDyOxV8YftD8XE8DJ5NBqL8HR88dO55Q8tBgT8yFpR47NwjQhykREXFYhmEQ9OijuISEEP3vFzGZ83DPy2HCHzOZcyaaedcP4j+3tqFLwwB7lyoiItWYzaHBunXrABg4cGCh/Zs2bWL58uUAdOzYEXd3dzZs2MAvv/zCLbfcQt++fUtR7pVzc8sfF5iTk1Nsm+zs7EJtSysoKIiwsLAStT106BCZmZllct+K0D70Qo+NXZcJDQ7HpZF3rleAq5OJBjW9rMfqBXgWCg1Kam+B0KB1iG+JzxMREamsatx+O87BwRx59DGcMzMwYeG+vT/ze/IpRpwYyk0dG/Bs/5aaJFFEROzC5tAgMjISyA8GClq8eDGQv1LByy+/DOQveTht2jR++OGHCg8Niht6UFBJhjBciWHDhjFs2LAStb3jjjscqldC2wKhwankTGKTMwn2dS+y7YECQxMaB3nh4nRhFQ1bV1DYG3Phf8cwhQYiIlJFeF9zDc0XfsvRh8dijo4CoFdUBCGpZ3g5+1767D3Fwz2b8FCPJiWeB0hERKQs2LwW4tmzZ3F1dSUgoHCXuQ0bNmAYBqNGjbLuGzFiBAC7d++29XY2a9iwIQAxMTHF9jY4fvx4obZSvGAfd+r4XQgJdl6it8HfBVZO+Ofwg/o2rKBwNi2bmKQLvTLU00BERKoSt2bNaPLdAjy7dbPua5F4gvfXvE/92KO899sB+ryzmp93nbRjlSIiUt3YHBqkpaVd1J0/NjaWU6dOUbNmTZo1a2bd7+fnh7e3NwkJCbZXaqPWrVvj4uJCdnY2O3fuLLLN1q1bAejQoUMFVua42ta90NtgZ1Rise0KrpzQPNi70DFbehpEnrwwNMHf04XaxfRwEBERcVTO/v7U//wz/Iffbd0XmJnMf9d+RM+oCGKSMnnkq20s2hZlxypFRKQ6sTk08Pb2JiUlhYyMDOu+v/76C8hfdaAoZTVnwJXw8vLi2muvBShyRYOjR4+yceNGAPr161cuNaSnpxf7MpvN5XLP8tS+Xg3rzzujbetpUO8foYHFYrnsfQtOghgW4odhaBJEERGpegwXF2pPnkzt/0wGp/yhCG7mXJ7d8hX37P0Zw2Jm8pI9HI8v+fA+ERERW9kcGpzvSfDzzz9b9y1evBjDMOjSpUuhtikpKaSmphIYGGjr7Upl7NixGIbBkiVL+Pbbb61fUGNjY5k4cSJms5m+ffvSsmXLcrl/eHh4sa/zc0M4ksI9DZKK/MKfkZ1XqAdBi9r/GJ5Q80JokJljJi618MoVRdl7UpMgiohI9eF/993UnzkTpwJzLg37eyUvbJpDdnoGE76NIDfP8f74ICIijsXmiRAHDBjAX3/9xcsvv8yOHTs4c+YMa9euxdXVlf79+xdqGxERAZR+zoCTJ08yaNAg6/b5VQ/2799PtwLj/x588EFGjx5t3W7Xrh3PPvssb7zxBpMnT2bGjBn4+/tz8OBBsrOzadSoEa+88kqpaqtO2hWYDDEhLZvoxAxC/T0LtTkUl8r5LMHN2VRoOAKAr7sLNTxdSEzPn2fiREI6wT6XHm6wR5MgiohINeN1VTcaLlzAiUfGkn3oEABXn9rDxG3zedM0gg9/P8T4vs0ucxURERHb2RwaDB48mOXLl7N+/XoWLFiAxWLBMAwmTJhAUFBQoba//PJLkT0QrlReXh6JiYkX7c/NzS20v6glDO+9915atGjBF198wc6dO4mPjyckJIR+/foxZswYvLy8LjqnrJwPTYoyfPhwh+ttUMPTlfoBntaeBDujki4KDQoOTWgS5I2T6eKhBPUDPElMzw8Cjiek06lB8etQZ+bkcSguzbrduo5CAxERqR5c69en4bfziZn0JKlr1gDQM3oHJ70CmebkxLXNAunUwN/OVYqISFVlc2jg5OTE559/zo8//khERAS+vr706NGDTp06FWqXnZ1NXFwcnTt3pkePHqUqNjQ0lP3799t8fvfu3enevXuparCFp6dnscdMJptHiNhVu1A/a2iwIyqRm9vWKXS80CSItQpPgnhevQBP6+oLx+Mzimxz3v5TKeSZ87suuLuYaBxU9DVFRESqIidvb0Knf8CJhx4mbf16IH+oQrRXIE9868Gy8dfh7WbzxzoREZFilepfF5PJxK233sqtt95abBtXV1c+++yz0txGKqF2oX78uDN/yaddRSy7eKlJEM+7khUUCs5n0KK2b5E9F0RERKoyw8WFuu+/x9G77yb7YP5Qhce3f8e/Pf15aWkAbw9pb+cKRUSkKnLMP3OL3bULrWH9eVdUEmZz4ckQC4YGLUoQGpy4TGig+QxERETAyceHeh9/glPNmgC4WPJ4cfMcNqzexk/nwnwREZGypNBAbNKmrh/nVzxMycrlaPyF+QbSsnKJOnthuEHzsuhpUGC5Rc1nICIi1ZlraF3qfTgd49xS1j45Gby8cSavf7OemMRLD/cTERG5UjYPT3juueeuqL2bmxu+vr40bdqU7t27XzRZojgWbzdnmgR5czA2f+6CnVFJ1nkGzu8D8HBxItTfo8hrFAwNTiVnkpmTh7uL00Xt8swWIk9e6LmgngYiIlLdeXToQMibbxA94QkAQtLimfDHTJ6qF8SXD1+nYXwiIlJmbA4NfvjhBwyj5P8gnV9dAfInUbz99tt59tlny3XVgsoiPb34v6KbzY67vnK7un6FQoNB4XUB2F9gaELTYG9MxXxwqePnjpPJsE5wGHU2g6bBF09weDQ+jYycPABMBrSsrdBARETEt18/sp84Tty77wLQJv4IcYs+5rPWITx8fVM7VyciIlWFzaHBoEGDMAyDVatWkZSUhIeHB2FhYdSqVQuLxUJsbCx79uwhIyMDPz8/evXqRXJyMnv37uXUqVN89913HD16lNmzZ+PkdPFfl6uS8PBwe5dQLtqF+rEoIhqAnVGJ1v0HCoQGxQ1NAHB2MlG3hod1aMKJhPQiQ4OCQxMaBXrh4Vq1nxcREZGSqjlmNNnHjpG0aBEAvaIi+Pr9D9jd7GXa1PWzc3UiIlIV2DynwRtvvEF2djbJycmMGzeOdevWMW/ePN555x2mTp3KvHnz+PPPP3nsscdIScn/EvnRRx+xevVq3njjDZydndmyZQtLly4tszcjFattgckQ98Qkk5uX32uiJMstnleSeQ32FAgNwkL0AUhEROQ8wzCo89J/cO3cxbpveOQK5kyezu7oi1c3EhERuVI2hwYLFixg2bJlPPHEE4wbN67IYQaenp48+uijTJgwgSVLlrDoXAo+aNAgHnvsMSwWCz/++KPt1TuIiIiIYl+tWrWyd3k2Cwu5sPRhRk4eB+Pyw4KS9jQAqFeC0KDgcoutNZ+BiIhIIYarKw0//IC80PrWfff/OY9VD4znh7X77ViZiIhUBTaHBt999x0mk4kRI0Zctu2IESMwmUwsWLDAuu/OO+8EYN++fbaW4DA8PT2LfZlMjruAhbuLU6FQYGdUEsmZOcQkZVr3NStlTwOLxcJeLbcoIiJySU5+fjT/4jNyvC/8O3nD0U3UGH8f06YuIDvXcedQEhER+7L5G+vhw4fx9vYu0USGXl5eeHt7c/DgQeu+mjVr4uPjQ3Jy8iXOlMqufeiF4QI7oxI5UGBogperE3VrFL1ywnkFQ4MTRYQGcSlZnEnNtm5ruUUREZGiudavT4sF88lt2sK6r3b6Wfp8+hKzRz3ByVgNVxARkStnc2hgNptJSUkhKeny/wAlJSWRkpJy0UoBubm5eHp6FnOWOIK2BUKDXVFJhYYmNKvlc9kVNv7Z08BisRQ6XnA+g9q+7tT0dittySIiIlWWW+NGtPlhIW4PPkSekf8xz4SF6yJWsHvgIP5asd7OFYqIiKOxOTRo3rw5FouFTz755LJtP/30U8xmM82aNbPuO3v2LBkZGdSsWdPWEqQSaF9gMsTIkymFvuRfbhJEKBwapGfnEZ+WXei45jMQERG5MoaLC42fnEC9b74mMSjEuj806RTu40ez/NnXMefk2LFCERFxJDaHBkOGDMFisTBr1iwmT55MdHT0RW1iYmKYPHkyX3zxBYZhMHToUOuxzZs3Azj0RICSP9Ghq1P+Y5SdZ+bn3ScLHbscP08XfN0vrPz5z3kN9mg+AxEREZv4dWhPt19/Ivam2637nC1m6i+eyx/97iD5eJQdqxMREUfhfPkmRbvzzjtZt24dP//8MwsXLmThwoXUqVOH4OBgDMMgNjaWmJgYIH8yu379+lknPwRYsWIFPj4+9OjRo/TvQuzG1dlEqxBfdpxIBCg0/0CzEoQGAPVrerI7Or9HwYmEdDrW97ce21ug54LmMxAREbkyJnd3er7/Ojv+14fkl14kMO0sALWiDxIxdBQtFn5D7Xq17VyliIhUZjaHBgDvvPMOrVq14tNPPyU1NZWYmBhrUHCet7c3o0eP5sEHH7zo3OoiPb3opQSBi+Z5cETt6vpZQ4OCSjI8AfKHKJwPDY7HX/hvlZKZw9EC22EhfhedKyIiIpfXfmAfYru0Z+Wjz9FhzzoAghNP8deIB2gxbzbN6wfZuUIREamsShUamEwmxowZw7/+9S/WrVvHnj17SEhIwGKxULNmTcLCwrjmmmvw8Lj0DPpVXXh4uL1LKFftQi/+Mu/j7kxtX/cSnV+vmGUX9526MKmij5szof7V+zkSEREpjeDagQxZ8AmLJrxM21+/BaBp7GHW3z+OMx9N4+rmtexcoYiIVEalCg3Oc3d3p2/fvvTt27csLicOpl2ByRDPa16ClRPO++cKCucVHJrQqo4vJlPJriciIiJFc3YyMWTaf1j9UBK1//gFgG5RO/nfEy9w8v8mc2fnenauUEREKpsyCQ3k0iIiIoo9Nnz4cCIjIyuwmrLXNNgbDxcnMnLyrPtKOjQBCocGJwqEBgUnQdTKCSIiImXDMAyu/+gttv3rLJ7bNgEw8NA6Pp8yjahHxvB4n6YlDv5FRKTqK5PQIDs7m/Xr17N7927i4+MxDIOAgADatm1L9+7dcXV1LYvbOCxPT89ij5lMNi9gUWk4mQza1PXlr6NnrfuaBZdsEkQoHBqcTM4kKzcPN2cnLbcoIiJSTgxnZ8I/+5DIu0di+nsfAA/u+ZE3ZvvxZMJAptzRFldnx/+MIiIipVfq0ODbb7/l/fff5+zZs0Ue9/f3Z8KECYWWW5Sqp11ojUKhQYvaJQ8NQmp4YDLAbAGLBaLPZlAvwJO/T6Va22i5RRERkbJl8vKixazPOTjkLiwx+UtnT9o6nxfcfLg3KYMZIzvh5+Fi5ypFRMTeShUhv/XWW7z00kvWyQ+Dg4Np164d7dq1Izg4GIvFQkJCAv/5z394++23y6pmqYT+ORlisysYnuDiZCKkxoVJDo8npHMwNpXsPPO548YV9VwQERGRknGuWZPGX3yOqUb+csculjwmb5pNzLbdDPl4PUnpOXauUERE7M3m0GDz5s3MnDkTi8XCjTfeyE8//cSaNWv49ttv+fbbb1mzZg3Lli3jpptuwmKxMHPmTLZs2VKWtUsl0rlhAM7nJiqsF+BBkLfbFZ3/z3kN9hSYBLFZsI+6SIqIiJQT14YNqf/JDAz3/FWPvHIzeWXD5yQcjeKZ73disVjsXKGIiNiTzd/EvvrqKwAGDx7MtGnTaNKkyUVtGjduzPvvv8/gwYOxWCzMmzfP9kqlUqtbw4O3hrTj5ra1eXdohyueQOmfKygUXDlB8xmIiIiUL4/27ak79R04N9dSYGYSr63/lL0btjNv4zE7VyciIvZkc2gQERGByWTiiSeeuGzbCRMmYBgG27Zts/V24gBuDw/loxGd6Nww4IrPrfeP0KDgygmaz0BERKT8+fTuTe3JL1q366fEMn31u+x7exp7jyfYsTIREbEnm0ODs2fP4uPjQ82aNS/bNjAwEF9f32InSxQp2NPgWHx64ZUT6ig0EBERqQj+w4YR+Oij1m0Xcx4j9/xM1N13cXb7LjtWJiIi9mJzaODl5UVaWhpZWVmXbZuZmUlaWtollx6sytLT04t9mc1me5dXKRQMDf4+nUJKZq51u5V6GoiIiFSYoMfGEfrxDPJqBln31YuPImb4MGLfew9zdrYdqxMRkYpm85KLLVq0YPPmzXz33XeMGDHikm2///57cnNzadmypa23c2jh4eH2LqHSKxgamC2F9/u6a7knERGRiuRz/fW0+uUnfn383zTY8CsAJrOZ+I8/IeW33wh57TU82re3c5UiIlIRbO5pMHDgQCwWC2+++SYLFy4stt3ChQt54403MAyD2267zdbbSRVXw9MFH7eLMywNTRAREbEPJx8f+n7+HrNun8QpT3/r/uyDhzh693BOv/lfzBkZdqxQREQqgs09De644w6WLFnCX3/9xeTJk/noo4/o1q0btWrVAuDUqVNs2rSJ06dPY7FY6Nq1K7fffnuZFe5IIiIiij02fPhwIiMjK7CayskwDOoFeBaaywA0CaKIiIg9OTuZGP/MCG53qsXt25Zy6+E/MWEBs5mEWbNIWbWSkNdfx7NTJ3uXKiIi5cTm0MBkMjFjxgyef/55VqxYwcmTJ1myZEmhNufX9b3xxht5/fXXr3gZvqriUnM5mEw2d/aocuoXERpouUURERH7qlvDg1fu7sJDuSbW1W3PhIgFhKbGAZBz7DjHRo7Cf9RIgidMwFRN568SEanKbA4NALy9vZk2bRo7d+5k2bJl7N69m/j4eABq1qxJmzZtuPnmm2nXrl2ZFCtVW/2aF3/QCAvxs0MlIiIiUtBNYbW59+qGzF4Pj/aayMjI5Qw+/AeG2QwWC2e/nEvq76txfnYyx+u14HBcKkfj0wnwcmVMj8a4uzjZ+y2IiIiNShUanNeuXTsFA1Jq9QIKhwYBXq7U8nWzUzUiIiJS0LP9W7L5SAJ7TybzRZsBbG/UgWd3fofP6SgAck6cIOfR0axvfC2zWvcnyzn/3/B9p5L5cHjHatvjVETE0alvvFQa9f8RGoSF+OoDhoiISCXh7uLE9OHheLrm9xrY5hXKiK6P8m2z3uRx4d/r2w6vY8aqqbSNOwjAsl2n+OSPw3apWURESk+hgVQa/wwNtHKCiIhI5dI4yJvXbm9j3c5xcmF22M1M7PkYR31qWffXSY/nv39+zCM7FuGSl8t/f9nHugNn7FGyiIiUUomGJyxevLjMbjho0KAyu5ZULXVreGAYcG7+TE2CKCIiUgndHh7K2bQcVuw9RbCPO42DvGgcFE4938H4fj+X5FlfQF4eALceWY9fdjpvdh7OY99sY+m4ay8ajigiIpVbiUKDZ599tky6iRuGodBAiuXqbKJdXT92RCXh4mTQuWGAvUsSERGRItx/bSPuv7bRxQeenEhAv5s4+dxzZB04AEDP6O1EewUyt3U/HvlqK989fLUmRhQRcSAlCg1CQkLKuw4RAN4e0p65G49xXbMg6tbwsHc5IiIicoU82oTR8PvviHr4EdLWrwdg+N+/Ee0dxCo68e8fdvP2kHaat0hExEGUKDRYtWpVedchAkCzWj68fFubyzcUERGRSsvk6krd997l6N3DyT50CIAJ2xdw2tOf77dBh3p+jOre0L5FiohIiWgixAqQnp5e7MtsNtu7PBEREZEy5+TrS71PPsYpIH+4oYs5jxc3z6ZO6hn+73972XI0wc4ViohISZSop4GUTnh4uL1LEBEREalwrqGhhE6fzvF778WSnY1fdjr/t3EmE3s8xiNfbeOnx64l2Nfd3mWKiMglqKeBiIiIiJQbz47h1HntNet2vdQ4/r35SxKS0nnkq21k56rXpYhIZVai0GDmzJlkZmaW6Y137tzJmjVryvSalVVERESxr1atWtm7PBEREZFy5TdwAIHjxlm3O5w5yLgd37P1aALPLdpFSmaOHasTEZFLKVFo8NZbb9G3b19mz55NcnJyqW64ZcsWHnroIe666y527dpVqms5Ck9Pz2JfJpM6e4iIiEjVF/joWHwHDLBu9zu2mTsPrub7bVFc99/f+fD3g6Rm5dqxQhERKUqJvrE+9NBDpKam8uabb3Lttdfy+OOPs3z5cuLj4y97bk5ODjt37uS9996jb9++jBo1ijVr1tC2bVv69u1b6jcgIiIiIpWfYRjUee1VPArM9XT/nmVcHbOLxPQc3lq+n+veXMWM1YdIU3ggIlJpGBaLxVKShqdPn2bq1Kn8+OOP5OXlWdfWrVOnDi1atCAgIAA/Pz9cXV1JTk4mKSmJEydOsG/fPnJy8rucWSwW6tevz/jx47nlllvK7105kDvuuIM9e/YQFhbGokWL7F2OiIiISLnKTUjg6NC7yImKAiDbyYVXu4zir9qtrW0CvFx5qEdjRnVvgKer5u0WESlrV/I9tMShwXmnT59mwYIFfP/995w6derChc6FCAWdv7SzszM9e/bkrrvu4rrrriuybXWl0EBERESqm6yDBzk67G7MqakAmE1OvNd1OL/Wbl+oXaC3Kw/1aMJdXevh6+5ij1JFRKqkcg0NCvr777/566+/2LlzJ7GxsSQkJJCVlUWNGjUICAigadOmdO7cmY4dO+Lt7W3rbao0hQYiIiJSHaVv2cKJhx7GnJaWv8Mw2D74IV6mBRk5eYXauruY6N+mDkM6hXJV45qYTPoDlIhIaVzJ99BS9fdq3rw5zZs3Z8SIEaW5jIiIiIhUM56dO1N/zhxOPPggeYmJYLHQYeHHLB/3OPMaX8+XG46SmZO/HGNmjpkfIqL5ISKaUH8PBncK5c6OodQL8LTvmxARqQY0db+IiIiI2IVHmzAafDUP51q1rPvSpk/jwchlrHnqesb0aIy/Z+FhCVFnM3jvtwNc99/fGf7ZRhZHRJORnffPS4uISBlRaCAiIiIiduPWpAkNvvoKlwb1rfviP/sc89Q3ee6m5mx8vg8fjehIrxZB/HNUwvpD8Uz4djs93vqdbcfPVnDlIiLVg0IDEREREbEr19C6NJw3D7cWLaz7Eud/S8xTT+NqMXNz2zrMuq8rG57rw9P9WtA40KvQ+XEpWYz8fBPrD56p6NJFRKo8hQYiIiIiYnfOQUE0+HIOHh06WPclL1vGiXHjSNu8meyjRwlyymPs9U1ZOakn3z/SnWFd6uHqlP9xNj07j3tn/8Vve0/b6R2IiFRNWvhWRERERCoFJz8/6n8xk6jHHiftzz8BSFvzB2lr/rC2MXl54RwcTGBwMI8FBzPE2ZtPYwzWBbYiDQ8enreVqXd14Nb2IfZ6GyIiVYpCAxERERGpNEyenoTO+IiYJ58iZcWKi46b09LIPnKE7CNHAPAEJgDjTE5sC2rG2pD2/PvLNNLu6sbdXetfdL6IiFwZhQYVID09vdhjZrO5AisRERERqfxMrq7UnfoO8bNmkbLiV3JjY8k9cwbyil8lwdmcR9fT++h6eh85252I2NKMpf37ccsjw3Dy86vA6kVEqhaFBhUgPDzc3iWIiIiIOBTD2ZnA0aMJHD0aAEteHnkJCeTExuaHCLFx5MbGkhMdTerateTFx1vPdbHkBwjM3se+udPxubo7wU9Owr3ARIsiIlIyCg1EREREpNIznJxwDgrCOSgIwsIKHbPk5ZH+1xaSl/9C0i8rsJxNsB4z5eWStnYtx7Zto/HPy3AJDq7o0kVEHJrNoUHv3r0xmUzMnDmTBg0alGVNVU5ERESxx4YPH05kZGQFViMiIiJStRhOTnhd1Q2vq7pR+4UXiF6znmXT59Hm0Db8s1KB/LkQzsz4mDr/mWznakVEHIvNSy7GxcWRkJCgwKAEPD09i32ZTFr1UkRERKSsGE5OhPa+jsFzP+CN+99hTqt+1mNnFywg+8QJO1YnIuJ4bP7GGhwcjMViKctaRERERETKRICXK/Me6s6BXrdx2sMfACMvj8NvvWvnykREHIvNocHVV19NZmYme/fuLct6RERERETKhK+7C9Pv6cai9v2t+/JW/ELyHg0NFREpKZtDgzFjxuDh4cHLL79MRkZGWdYkIiIiIlIm6vh5MHDSAxz3yZ8A0YSFTS++bueqREQch82hgZOTEy+//DJ///03AwYMYPbs2ezYsYMTJ04QExNT7EtEREREpCLd0DaE44P+Zd0O3buF1YtW2rEiERHHYfPqCX369LH+nJGRwZtvvnnZcwzD0HAGEREREalwI5+6hz+Wf0f92KMAnHnvPaJ7daeuv6d9CxMRqeRs7mlgsViu+GU2m8uydhERERGREvFwdabxc09at8NiD/LBf78mN0+fT0VELsXmngYrV6pLl4iIiIg4jmb9+/DX5x3w3rMdgO6/f8u0ldcx8cYW9i1MRKQSszk0qFu3blnWISIiIiJS7sJefIZjw+4GoHliFAvnLWJDkzF0b1KzyPaH4lJZGXmarcfO0rqOH+N6N8XJZFRkySIidmVzaCAiIiIi4mg8O3TAo1dvMn5fBcCovb8w8ZuO/PRELwK8XMnJM7Pl6FlWRp5m5b5YjpxJs567fM9pXJwNxl7f1F7li4hUuDILDRISEoiOjiYzM5MuXbqU1WVFRERERMpUnYkTOLT6dwyLhfqpsbTZs56xX/kS7OPO6v2xJGfmFnvue78eoE/LWrSo7VOBFYuI2I/NEyGet3LlSm6//XauueYahg4dyj333FPoeFJSEg888AAPPPAA6enppb2diIiIiEipuDVrRo1bb7Vuj9i3gq0HYlm6I6bIwKB1HV8Cvd0AyM4zM3HBdnI0gaKIVBOlCg0+/fRTxo0bR2RkZKFVEgry8/PDw8OD9evXs2bNmlIVKyIiIiJSFgIfGwcu+Z1ua2UkcvPRDdZjrk4mejYP4pWBrVj7QFu+u86LD2qfITQlFoA9Mcl8+PtBu9QtIlLRbB6esGPHDt59912cnJx46qmnuO222xgwYADx8fEXtb311lv57bffWLVqFf379y9VwSIiIiIipeUaGor/kKGc/fprAIYfWEmHUF9ak0pwajzm7dHkREWRmpVFKlAD+NjZhae6P0RkzYZMX3WQvq1q0aaunz3fhohIubO5p8GXX34JwEMPPcQ999xDjRo1im17fo6DXbt22Xo7EREREZEyFfjIwxgeHgD4ZqZy1c9z8f35BzLX/kH2oUNYsrIKtXfKzeG5iG/wyMkk12xh4oLtZOXm2aN0EZEKY3NosHXrVgBGjBhx2bb+/v54enpy+vRpW28nIiIiIlKmnIOCCBg16rLtnGrUAFP+x+ag1Hge3rUEgL9Pp/LebwfKs0QREbuzeXhCfHw8Xl5eBAQElKi9i4sLaWlpl28oIiIiIlJBAh95mJyTJ8naF4lz7Tq41gvFJbQeLvVCcQ0NxSU0FCcfH+I+mM6ZDz8E4Mbjf7G5Viv+rNuOT9Yc4obWtehY39/O70REpHzYHBp4eHiQnp6O2WzGZLp0h4XU1FSSk5NLHDBUNZdaNcJs1sy7IiIiIvZi8vCg7lv/vWy7wIcfInXdWjJ37ARgws7viQxoQIKHH08u3MGyx6/D3cWpvMsVEalwNocGDRs2ZNeuXezfv59WrVpdsu3y5cuxWCy0bNnS1ts5tPDwcHuXICIiIiKlYLi4UPfNNzl8x51Y0tPxzkpjYsS3vNj9QQ7HpfHW8v28OKC1vcsUESlzNs9p0KtXLywWC59++ukl2x07dox33nkHwzDo06ePrbcTEREREbEr14YNqfXcs9btTrF/M/DwegC++PMImw5fvIqYiIijs7mnwahRo5g3bx6//PIL7u7uPPjgg4WOnzhxgp9++omZM2eSkpJCaGgogwcPLnXBjigiIqLYY8OHDycyMrICqxERERERW9UYPJjU1WtIXbkSgAf2/sT2oKYc963Nk9/t4JfxPfBys/kjtohIpWNzTwNvb28++ugjvL29Wbx4MQMGDCA+Pj9dDQ8P58Ybb+T9998nJSWFGjVqMH36dFxdXcuscEfi6elZ7Oty80GIiIiISOVhGAZ1XnkZp8BAAFzzcnh669e45OVyIiGDl5buwWKx2LlKEZGyU6pvrO3bt2fx4sXccMMNGIaBxWLBYrGQkZFh/WXZt29fFi5cWG3nMxARERGRqsU5IICQ11+zbjdJimHUvl8AWLg1iv/7314FByJSZZS671TdunWZNm0aSUlJbN++ndjYWPLy8ggMDKRjx47VdsUEEREREam6vHv0wH/4cM5+/TUAdx5Yw1/BLdkV1JTZ649isVh46dYwDMOwc6UiIqVTZgOu/Pz86NmzZ1ldTkRERESkUgt+6knSNm4k+/BhTFh4fscCHuzxBGmuHszZcAwL8H8KDkTEwWlAvYiIiIiIDUweHoT897/gnP93uBqpCby+6xt8s9IA+HLDMf6jOQ5ExMGVSU+DuLg4VqxYwe7du62TIdasWZM2bdpwww03EBwcXBa3ERERERGpVDzahBH02GPEvfsuAM1P7OWz+Km82WEo24Jb8OWGY1gs8PJt6nEgIo6pVKFBTk4OU6dOZe7cueTl5QFYk1TDMFi8eDFTpkxh5MiRTJw4sdquniAiIiIiVVfNBx8gY9dOUn/LX4bRNz2J19Z/xqImPZjduj9zNx7DgoWXb22DyaTgQEQci82hgdlsZuzYsaxbtw6LxYK7uzthYWHUqlULgNOnT7Nnzx4yMzOZM2cOBw4c4PPPP1fCKiIiIiJViuHkROi0aZydO5fYt9/BkpMDwB2H/qBD3AHe7DyCeRvBYoFXblNwICKOxebQ4JtvvmHt2rUYhsEjjzzCAw88gLe3d6E2aWlpfPHFF8yYMYP169fz9ddfM2LEiFIXLSIiIiJSmRgmEwH33IPnVVcR8+STZB04CEDj5JNMW/0eM9sM4CvLNYCCAxFxLDZPhPj9999jGAbjx49n/PjxFwUGAF5eXjz22GM8/vjjWCwWvv/++1IVKyIiIiJSmbm3aEHDhQvxHznSus/NnMvYnYv5v40z+WnNHv69eBdmsyZHFBHHYHNocOTIEUwmE6NGjbps21GjRuHk5MSRI0dsvZ2IiIiIiEMwubtT+4V/U+/TT3AKDLTu73p6Hx+vehvjy5m8NGs1eQoORMQB2BwauLq64u3tjZeX12Xbenl54e3trYkQRURERKTa8O7Rg8ZLFuN9/fXWfX7ZaYzat4Khb4/jl8H3kbJho5ZkFJFKzebQoFmzZqSkpHD27NnLtj179iwpKSk0b97c1tuJiIiIiDgc55o1CZ3xEbX/MxnD3d2638lipvHeTUTddx+HbxlAwryvyEtJsWOlIiJFszk0GD58OGazmY8++uiybT/66CPMZjPDhw+39XYiIiIiIg7JMAz8776bpit/o8bj40n0qVnoePbhw5x+9VUO9Lyek/95ieyjR+1TqIhIEWwODW6++WYefPBB5s2bx3PPPceJEycuanPixAmee+455s2bx+jRo+nfv3+pihURERERcVTONWtSZ+zDtP9jFQtuH89fwS0KHbekp5P47bccvv0O0jZssFOVIiKFlWjJxX/961/FHvP29mbx4sUsXryYOnXqEBwcjGEYnD59mpMnTwLg4+PDjh07uOeee5gzZ07ZVC4iIiIi4oA8PVx55uXRPDyvIzO2RnLz0Q3ceOwvfHPSAbBkZHDi4UcInT4d7+uutXO1IlLdlSg02Lx5c4kuFhMTQ0xMzEX7k5OT2bx5M4ah9WhFRERERNxdnPhkVCceNRnM9A5kbqt+XH9iG4/uWYJrTjaWrCyixo6l7rT38enVy97likg1VqLQYNy4ceVdh4iIiIhIteLm7MRHIzrx2DfbWL7nNCsadiPaO4jXNs3ELScLS04OUY+Pp+7Ud/C94QZ7lysi1ZRCAxERERERO3F1NjF9eEcmzN/OT7tOsiewMc92H82r6z/HKzcTcnKInvAEvP0WvpofTETswOaJEEVEREREpPRcnEy8P6wDt4fXBWBfQEOev2YMKS4e+Q3y8oie9CRJS5fasUoRqa4UGoiIiIiI2Jmzk4mpQ9sz7e5wgnzc+Nu/Ps9d8zBJrp75DcxmYp55lsTvF9m3UBGpdko0PKEkMjMzSU5OJjc395LtQkJCyuqWIiIiIiJVhmEY3No+hJ7Ng3hnxX7mboRnr3mEKX9+TI3sNLBYOPnvf2POySFg2F32LldEqolShQZpaWl8/vnnLFu2jOPHj1+2vWEY7N27tzS3FBERERGp0vw8XHj5tjbc2TGUfy/exTPGI0z58xMCslIAOP3SSyRmW2j8r2F2rlREqgObQ4P4+HhGjBjBsWPHsFgsJTqnpO1ERERERKq79vVqsOTRa5m7IZSX3F2ZvOpDAjOTADj79n/JHHAT7gH+dq5SRKo6m0ODd999l6NHj+Lh4cF9993HtddeS2BgIE5OTmVZn4iIiIhIteVkMrj3mkb0b1uH9xsHcvvHz+Odm4lndgZrp3zADW9NtneJIlLF2RwarF69GsMwmDJlCv369SvLmkREREREpIBavu68Pq4/X+/dQPiqhQAE/LyIMxMeJrBusJ2rE5GqzObVE1JSUnBxceGGG24oy3qqpPT09GJfZrPZ3uWJiIiIiIPoO3k8qedWVPDMzeL3l6fauSIRqeps7mlQu3ZtYmNjNRyhBMLDw+1dgoiIiIhUAcG1a7L1liF4/zAHgKbrlrF3z8O0Dmto38JEpMqyuadB3759yczMZOfOnWVZj4iIiIiIXEKv5x4lxd0bAPe8HDa89q4mHBeRcmNzaPDggw8SEhLCSy+9RHJyclnWVOVEREQU+2rVqpW9yxMRERERB+Lu64Nl2Cjrdqftq1ixdo8dKxKRqszm4Qn+/v7Mnj2bSZMmcfPNNzNs2DDatGmDl5fXJc/r0qWLrbd0WJ6ensUeM5lszm1EREREpJrqMn40Wxd+jXdaEm7mXA68O52e3T/E3UVDh0WkbNkcGgA4OTlRt25ddu7cyYcffnjZ9oZhsHfv3tLcUkRERESk2jN5eOD34IPkvf8OAD32r2P24s08PKS7nSsTkarG5j9zR0VFMWzYMH755RcALBbLZV9aKUBEREREpGw0vX8UGX4BALiY88iY9TkxiRl2rkpEqhqbexpMmzaN2NhY/P39efLJJ7n22msJDAzUagoiIiIiIhXA5OZG7UcfIen11wDoc2QT079Zy+uP3GjnykSkKrG5p8GGDRswDIOpU6dy5513UqtWLQUGIiIiIiIVqPawoWTXDAbAxZJH0OKv2Hwkwebr5cbFETftAw706Mn+zl1IXbuurEoVEQdlc2iQkpKCu7s7V111VVnWIyIiIiIiJWRydaXe+HHW7RuOb2H6vN/JM1/ZEowZu3YR/fTTHOjdhzMffURubCzm1FROvvAC5uzssi5bRByIzaFBSEgIFosFwzDKsh4REREREbkC/rcPwlwnBAAni5kuf/zAwi0nLnueJSeH5GXLODrsbo4OGUry0v9BTk6hNrmnT5P0w+LyKFtEHITNoUH//v3Jyspiw4YNZVmPiIiIiIhcAcPFhbqPP2bd7nViG/MW/sGppMyL2uYlJ5OxcydnPvmUg31vIHriJDK2by/UxrlWLYw2ba3b8Z9+iuUfYYKIVB82T4Q4evRofv31V1588UVmzZpFvXr1yrIuEREREREpIb+BAzg942PMx4/hhIXbtyzh35NOMjIEWuSeJfvoMbKPHiUvofj5DjzCw3G7624+yAll5drdfL5nD84WMznR0ST9+BM1bh9UcW9IRCoNm0ODX375hcGDBzN9+nRuvfVWbrzxRtq1a4eXl9clzxs0aJCttxQRERERkSIYzs7UfmwcMU89BcDVJ3dz9cndACRd6kQXF/xu7k+NkSNZlu3P68siSUiLAc8AVtXrxI3H/wIg/pNP8Lt1IIYmPhepdmwODZ599lnrfAYWi4WlS5eydOnSS55jGIZCAxERERGRcuB7c3/OfPwx2YcOXbKdycsL14YN8b7+evyH3cVhszsTftjN5qPHC7X7tnlv+hzfghMWso8eJWX5cnxvvrk834KIVEI2hwYhISFlWYeIiIiIiJSC4eREnVdeIWrsWPJSUon3C+Jv1wCivQOJ9g4i2juIum1b8MK/riPU35P07FzeWnmAmWuPkFtgtQUPFyf6t6nNogj4I7QDvaIiADgz42N8+vXDMNk8LZqIOCCbQ4NVq1aVZR0iIiIiIlJKnh3DabZhPeTlgZMTJ7dGseDHvaRk5gKw62Qu695by71XN+SHiGiiEzMKnX9TWC0mDwwjxM+dqMQM5if3sYYGWQcOkLpqFT59+1b4+xIR+1FMKCIiIiJShRiGgeHsjGEYDOlcj1+f6EnvlsHW46lZuUz//WChwCDU34OZ93Tmk1GdqVvDA8MwmDygNSf8arOuzoWVFM7M+BiLxYKIVB8KDUREREREqrDafu7MvKczU4e2x9e9cEdjFyeDR3s14dcnetKnVa1Cx9rU9eOuzvWY3+JCz4LMPXtIW7u2QuoWkcpBoYGIiIiISBVnGAZ3dAzlt4k96d+mNs4mg2ubBvLz+Ot46qaWeLgWvSrCpBtbcLpWAzbVamXdd+ajGeptIFKN2DynwXPPPXfF5xiGweuvv27rLUVEREREpBSCfd2ZMbITuXlmnJ0u//fDIB83xvVuyvyTfel2OhKAjO3bSd+0Ga+rupV3uSJSCdgcGvzwww8YhlFsynh+OcbzLBaLQgMRERERkUqgJIHBefdd05CvN7ViW2QzOsYdAODMjBkKDUSqCZtDg0GDBl0UDBSUkpLC7t27OXXqFDVq1KBXr1623kpEREREROzEzdmJ529uxYd/97WGBumbNpG+bRueHTvauToRKW82hwZvvPHGZdtYLBYWLVrESy+9hJeXFy+88IKttxMRERERETu5KawWs7t0ZndkI9rEHwEg7qMZNPj8M5uveSopk5eW7iErN49Xb29L3RoeZVWuiJShcp0I0TAM7rzzTiZNmsRXX33F8uXLy/N2IiIiIiJSDgzDYPLANnxbYCWF9HXryNi126brRSdmcNenG/hlzyl+3x/H2Hlbyckzl1W5IlKGKmT1hMGDB2MYBvPmzauI24mIiIiISBlrHeJLs1v6sr9GPeu+mPfeJzsqCktOTomvcyIhnbs+2cCx+HTrvh1RSXz4+8EyrVdEyobNwxOuhLe3N97e3uzbt68ibiciIiIiIuVg0k0tePLnm3hm3ecAZP+5jkN9bwCTCefgYFzq1MElJCT/VTcE14YN8ezaFcOU/7fKY/Fp3P3pRmKSMi+69gerDtKrRTDt69WoyLckIpdRIaFBYmIiycnJeHhonJKIiIiIiKMK9Hbj6lG3cWjnMpokx1w4YDaTe+oUuadOkRERUegc9/btqPfxxxzPdeHuzzZyOjnLeuypm1rwzebjRJ3NIM9s4Ylvt/PT49fh4epUUW9JRC6jQoYnvPPOOwA0atSoIm4nIiIiIiLl5J5rGvFVn/uJCGpGgpvPZdtn7tjJgRH/4sH3fy0UGLx8WxiP9mrK1KEdOL8o2+Ezabzxc2R5lS4iNrC5p8HixYsveTwrK4uTJ0/y22+/cejQIQzD4I477rD1diIiIiIiUgm4OTvx73EDeMivDtGJGbjk5RCUkUhw+lnaOKVzax0TdbOTyT5ymMwdO/NPOnyQZ2Lf4/lrHiLB04/Xb2/L3V3rA9C1UQBjrmvMJ38cBmDOhmP0bV2L65oF2estikgBNocGzz77LMb5SPASLBYLAIMGDWLEiBG23k5ERERERCqJNnX9WDmpJzPXHeGj3w8S4xREjHcQ24F52XBt00D+Pb4llk8+xJg/F4D6qbH8d91HpLz2HoPOBQbnTbyxOWv+jmPfqRQAnlq4k+UTeuDn6VLB70xE/snm0CAkJOTSF3Z2xtfXl5YtW3LLLbfQvXt3W28lIiIiIiKVjLuLE4/2asqQzqG8++vffPvXCcz5fy9k3cEz3DJtHR7O4dzW8jSj9q0AICQtHuf/Pk12q1m4NmhgvZabsxNTh3bgtg/XkZNn4VRyJi8u2c20u8Pt8dZEpACbQ4NVq1aVZR0iIiIiIuKAgn3cmXJHO/7VvSGv/rSXPw/GA2C2QFqOma9b3kiOsyv37/4RgNyYkxwbOYr6s2fh1qSJ9TqtQ3yZeEML3vwlf8W1pTtiuKF1LQa2v/QfK0WkfFXIRIgiIiIiIlK1tarjy7wHujHzns40DvKy7nc2GfT+z0RqvfCCdV9uXBzHRv2LzH8syT6mR2O6NPS3br+weDenilieUUQqjkIDEREREREpE4Zh0KdVLZZP6MFrt7fhjvC6fPVgN/q3rUPAyBHUefUVzi+VkJeQwLF77iVj1y7r+U4mg6lDO+B1bsnFpIwcnv5+p3WeNBGpeAoNRERERESkTLk4mRjRrQFT7+pAt8Y1rftrDB5MyH/fBKf8UMCclMTxe+8jftZszOnpANQL8GTywNbWc/74O455G49V7BsQESub5zQ4b9OmTfz000/s37+fxMREcnNzi21rGAa//fZbaW8pIiIiIiIOym/gQAxXN6InTYLcXMxpacS++Sbxn31Gzfvvo8awuxnauR6/7j3Nb5GxALy2LJKbwmoT7Otu5+pFqh+bQwOLxcLzzz/P4sWLrduXU5IlGkVEREREpGrzvelGDLcPiJ44Ccu5HgZ5CQnEvv0O8Z99TsB99/LabYPZdjyRhLRsMnPM/BARzUM9m1zmyiJS1mwODebOncsPP/wAQFhYGL179yY4OBhn51J3XhARERERkSrO5/rrabr8F+K/mMXZ+fOxZGQAkJeURNx772P6YhYvXXMzzxutSXX15PttUYzp0Vh/iBSpYDZ/w1+0aBGGYTBkyBBefvnlsqxJRERERESqAeegIGo98zQ1Rz9IwqzZnP3qK+vcBubkZJr9PJ85zm583eIGvrf0ZE9MMm3q+tm5apHqxeaJEI8ePQrApEmTyqoWERERERGphpwDAgieNJEmK38jcOwjmLy9rcc8c7N4cM+P9Du2iUXbou1YpUj1ZHNo4Obmhq+vL35+VTfpW758OePHj6d37960a9eOG2+8kVdffZWzZ8/auzQRERERkSrH2d+foMcfp+mqlQQ+/himAt81Htm5mO2rN5ObZ7ZjhSLVj82hQfPmzUlNTSUtLa0s66lUZs6cSV5eHo8//jiff/459913Hz///DPDhg0j49yYKxERERERKVtOvr4EjR1L46VLMfn7A+BqzuWR1TNZt0PLL4pUJJtDgxEjRpCXl8f3339flvVUKh9//DHTp09n0KBBdO3albvvvpu3336bo0ePsnz5cnuXJyIiIiJSpbnUCib0nbexkD/5YWjaGZJf/b8SrdwmImXD5tCgX79+jBgxgrffftu67GJVExAQcNG+tm3bAnD69OmKLkdEREREpNrxuvpqUob+y7rdbO8mTn45r8Tn5+aZmfXnEb7bGqWwQcQGNq+e8NxzzwHg4eHBc889x7Rp02jTpg1eXl7FnmMYBq+//rqttyQuLo7169eza9cudu/eTWRkJJmZmYSFhbFo0aLLnr9x40ZmzZrFjh07SE9PJyQkhH79+jFmzBg8PT1LVMPGjRsBaNJEa8SKiIiIiFSEDv+exNI1G2hz+m8Azr71Fv6dOuLRJuyy5764ZA/fbD5u3R7cKbTc6hSpimwODX744QcMw7CmdTExMcTExBTZ9ny70oYGP/30E1OmTLHp3Llz5/Laa69hsVioXbs2derU4eDBg8yYMYMVK1bw9ddfU6NGjUteIykpiSlTptC8eXN69eplUx0iIiIiInJl3NxcODD6KULemkhAVgqm3ByiJ0yg0aLvcfL1Lfa8nVGJzP/rQmDw1aZjCg1ErpDNocGgQYMwDKMsa7ksb29vrr76atq0aUObNm04evQoU6dOvex5u3fvtoYVL7/8MkOHDsUwDE6fPs0jjzzCnj17ePHFF/nggw+KvUZWVhbjxo0jOTmZTz75BCcnpzJ7XyIiIiIicmm39GzDC6tHMmXdxzhhIScqipP//jd1p00r8nuJ2WzhpaV7KDgiIeJ4Isfj06lfs2S9jEWkFKHBG2+8UZZ1lMjgwYMZPHiwdbskQxIAPvroI8xmM4MGDeKuu+6y7q9VqxZTp06lf//+rFixgn379tGyZcuLzs/JyeHxxx9n165dzJw5k6ZNm5b+zYiIiIiISIm1C/UjvVU75sb3497InwFI+fU3zn75JQH33HNR+8Xbo9l2PPGi/Uu2R/NYn2blXa5IlWHzRIiOIi0tjbVr1wIwdOjQi443bNiQq666CoBffvnlouN5eXlMmjSJ9evX8+GHH9KpU6fyLVhERERERC5iGAZ3dAxlQfNe/BXcwrr/9Ftvk7F9e6G2qVm5TPl5n3Xby/VCL+HF26M1IaLIFajyoUFkZCTZ2dm4urrSrl27ItucDwJ27NhRaL/ZbOaZZ55h5cqVvPvuu1xzzTXlXq+IiIiIiBRtUHhdMJl4u9Nwzrj75e/MzSVq4kSyDh7EkpcHwAerDhCXkgWAm7OJz+7pbL3Gobg09sQkV3jtIo7K5uEJjuLIkSMAhISE4OLiUmSb+vXrF2p73iuvvML//vc/7rnnHgIDA9leIMGsXbs2tWvXLvJ68+fPZ8GCBSWq79ChQyVqJyIiIiJS3dWt4cFVjWqy4TBM6TKSt/6cgclsJjfmJIcHDMRwc4P6DfDL8mGIdy2O+damV7+r6N4ogE4N/Nl67CyQP0ShTV0/O78bEcdQ5UODpKQkAPz8iv+lcP7Y+bbnrVmzBoA5c+YwZ86cQsfGjRvHY489VuT14uLi2LNnj801i4iIiIhI0e7oWJcNh+PZW7MRX7cbyMjtS6zHLFlZcOBvegHWtc42fsH+/3ryZPO2jA65hVRXT5buiOHZ/q1wMlXsxO4ijqjKhwZZWfndkorrZQDg6upaqO15q1atsumeQUFBhIVdfs1YyO9pkJmZadN9RERERESqm/5t6/Dikt1k5pj5qsG13Nw+hFqrfyLn+AkoZq4CS3o6NbZv4oFEg/c7DOZ0chabDsdzddPACq5exPFU+dDAzc0NyF8BoTjZ2dmF2pbWsGHDGDZsWIna3nHHHeqVICIiIiJSQt5uzvQLq83i7TFgGMyuezWfLX+cjJRUHnptEc7Hj9Iw5STtcxNonXWG3JgY67k3HtvED42v5bhvbRZvj1ZoIFICVX4ixOKGHhRUkiEMIiIiIiJSOdzRMdT68+/7YklIy2bOttP8QU1W1e/E7DYDaP7FZzRbtZLmW/7CtWFDAEwWC/fv+QmAn3efIjMnzx7liziUKh8aNDz3CyImJqbY3gbHjx8v1FZERERERCqva5oGEuyT30s412xh5rrDfLDygPX4yKsa0KqOLwBO3t4ETZpoPdbtdCTt4g6SkpnL6v2xFVu4iAOq8qFB69atcXFxITs7m507dxbZZuvWrQB06NChAisTERERERFbOJkMbg+va93+8PdDpGXn9xqo4enCxBuaF2rv07cvHh07Wrcf2PMjhsXM4ogYROTSbA4NWrZsSevWrTl27FhZ1lPmvLy8uPbaawGKXAbx6NGjbNy4EYB+/fqVSw3p6enFvsxmc7ncU0RERESkKru9Y90i9z95YwtqeLoW2mcYBrWefsq63Twxip5R21m1L5akjOLnPhORUkyE6O7ujrOzMw0aNCjLesrF2LFjWb16NUuWLKFjx44MHToUwzCIjY1l4sSJmM1m+vbtS8uWLcvl/uHh4eVyXRERERGR6qplbV9a1/Fl78lk675WdXy5u2v9Itt7dOiAz003kbJ8OQD37v2ZP0Pasnz3KYZ2qVchNYs4IptDg1q1anH69OmyrOWyTp48yaBBg6zb51c92L9/P926dbPuf/DBBxk9erR1u127djz77LO88cYbTJ48mRkzZuDv78/BgwfJzs6mUaNGvPLKKxX2PkREREREpPTu6FiXvT9dCA3+79YwnExGse2DJz5ByqpVkJNDrYyz3Hr4TxZvr63QQOQSbB6ecP3115OVlcXmzZvLsp5LysvLIzEx0fpKT08HIDc3t9D+zMzMi8699957mTVrFj169CAjI4ODBw8SEhLCww8/zPfff09AQEC51R0REVHsq1WrVuV2XxERERGRqmxwp1Aa1PQE4N6rG9K10aU/07s2aIB/gaXR7/p7Jbv2neBU0sXfH0Qkn2GxWCy2nJiQkMDAgQPx8/Nj9uzZBAcHl3Vt1cIdd9zBnj17CAsLY9GiRfYuR0RERETEoSRn5nA6KZOmwd4YRvG9DM7LPXuWQzfciDk1FYBFTXoQ/MwzjO7RuLxLFak0ruR7qM3DEw4dOsSECROYMmUKt9xyC7fddhsdO3YkICAAJyenYs/r0qWLrbcUEREREREpxNfdBV93lxK3d/b3p+ZDY4h7ZyoAAw//yVtrdyg0ECmGzaHBqFGjCiV5X331FV999dUlzzEMg71799p6SxERERERkVILGDWKuLlfQexpXCx5XL1qAQdH30DTYG97lyZS6dg8pwGAxWK5opeWFxQREREREXszubtTZ+IE6/b10dtZvWS13eoRqcxs7mmwb9++sqyjSjs/YWNRFKSIiIiIiFQ8v1tv5cCHn+F94jAAQV99gvmBmzGZSvV3VZEqx+bQQEouPDzc3iWIiIiIiEgBhslE7aefIvWxRwBofuogOxf+SIe7brVzZSKVi2I0ERERERGplurdcD0HG7a1bmdOn4YlL8+OFYlUPmXS08BsNrN7925iYmLIzMxk0KBBZXHZKiMiIqLYY8OHDycyMrICqxERERERkfPyRj9K3r8fwQkLfnHRJK76Hf8b+tq7LJFKo9Shwdy5c5kxYwZnz5617isYGiQlJTFixAhyc3OZP38+NWrUKO0tHY6np2exxzRmSkRERETEfnrdfDULPmrHtdE7ADj+6RcKDUQKKNU31pdffpnXX3+dhIQEvLy8Ci3BeJ6fnx9hYWEcO3aM1atXl+Z2IiIiIiIiZcrPw4WoXgOt2667Isjc/7cdKxKpXGwODdauXcvXX3+Np6cn06dPZ8uWLQQEBBTZdsCAAVgsFlatWmVzoSIiIiIiIuWh4y09OehX17p9Zu5cO1YjUrnYHBrMnz8fwzB4/PHH6dv30t13zq8eoLH7IiIiIiJS2fRpXZufmvWwbicvXUpugeHXItWZzaHBjh35Y37uvPPOy7b19vbG29ubuLg4W28nIiIiIiJSLrzdnHHuewOJrl4AGNnZJH733RVdI89s4de9p9kbk1weJYrYjc2hQWJiIj4+Pnh7e5fsRiYTeVq+REREREREKqH+HRuyrFF363bCV19jyc0t8fmvL4tk9JdbuO3Ddaw7cKY8ShSxC5tDA29vb1JTU8nJybls24SEBJKTk/H397f1dg4tPT292JfZbLZ3eSIiIiIi1V7vlsGsan4tuUb+V6S8U6dI+W1lic6NTclk3vojdDq9jzpnT/Lsop2kZZU8cBCpzGxecrFZs2Zs2bKFXbt20bFjx0u2Xbp0KQBhYWG23s6hnZ/TQUREREREKicPVyc6dmzO2oj29IqKACBh3lx8+9102XO/2nCMR/+azw0ntpCHwVudh/PW8lq8dGv1/P4jVYvNPQ1uuOEGLBYL06dPv+Rfy7dt28b777+PYRj079/f1tuJiIiIiIiUqwHt6rCk8bXW7YwtW8ncu/eS52Tm5HFy7lfccGILAE5YeGrrNxz8bglbjiaUa70iFcHm0GDYsGE0aNCADRs28OCDD/LHH39Yw4OEhAQ2bNjAiy++yL/+9S8yMjJo2bIlAwYMKLPCHUlERESxr1atWtm7PBERERERAXo2DyK6ThP2+dez7kuYO++S5/y66HdGbl1UaJ+Txcwzf33FvHfmkpmjed3Esdk8PMHV1ZVPPvmE+++/n/Xr17NhwwbrsWuuucb6s8VioX79+kyfPh2TyeaMwqF5enoWe6y6/jcREREREals3F2cuKF1LZYevI6WW78GIPnHHwl+chLONWte1D434SwBb/0HF3N+MJDh7Ye7YcFIScbZYub+3z7j2w9qcs/EkRX6PkTKUqm+sTZs2JAlS5Zw33334evri8ViKfTy8vLinnvuYeHChdStW7esahYRERERESkXA9rVYW3ddiS4+QBgyckhceHCi9pZzGb2PDaRgNT8IQh5hgnv19+k0ewvyPbIX7rRxZJH+OdvsGfJ8op7AyJlzOaeBuf5+PjwzDPP8Mwzz3Dw4EFiY2PJy8sjKCiIZs2a4eTkVBZ1ioiIiIiIlLvrmgXh6enOT426M2rfCgDOfv0NNR94AMPFxdruzMcf47p1o3V7TY8hPHpjTwDqf/4ZR+69H4+cTFzMeeQ8/xRJgT74XXN1xb4ZkTJQpn3jmzZtytVXX811111Hy5YtFRiIiIiIiIhDcXU2cVNYbZY17E6OKf/7TG5sLMkrVljbpP75J2c+mG7dXl+nDW0njrVu+3cKJ+f1qaQ7uwHgkpfDiUfGkv7XXxX0LkTKjgbUi4iIiIiIFDCgfQiJ7j6sqdvBuu/sl3MByDl5kpgnnwKLBYAYr5osufF+rmseVOga3Qb2Ys09z5LplN87wSk7i2NjHiJ927aKeRMiZUShgYiIiIiISAFXN6mJv6cLS5pcZ92XsWMH6du2ET3hCfLOngUgy+TMq13v4e4+YRiGcdF1Hhh3J+/1eogs07lR4RkZnBg9hoydOyvkfYiUhRLNadCnTx8AGjRowBdffFFo35UwDIPffvvtis8TERERERGpKC5OJvq1qcM36TnsCWhIWMJRAE489DDmlBRruw/b30FSSENuDy960nc/DxdGPHInL6dn8dLGL3Ax52FOSyN6whM0WbEcw7nUU8yJlLsSPaXR0dEAuLm5XbTvShSVvlUH6enpxR4zm80VWImIiIiIiJTEwHZ1+GbzcZY0udYaGhQMDH5p0JVfG3TlsW71cXcpfi63G8Nqs7Tv9bxiNvOfTbNwspjJiYkhffNmvK7WxIhS+ZUoNJgyZQqQv1LCP/fJ5YWHh9u7BBERERERuQLdGtck0NuN9XXacsbdj8DMJOuxQ34hzGh3Oy5OBqOuanDZa710axg3HDzDliMt6HY6EoCkZcsUGohDKFFocPvtt5don4iIiIiISFXgZDK4uW1tvtxwjP81vpr79v4MQIarB692vYdsJxfuaBdCsK/7Za8V6O3GfwaGsXh/uDU0SPxlBbUnT8bk6lqu70OktDSIpgJEREQUe2z48OFERkZWYDUiIiIiIlISA9qF8OWGYyxpfB0tz56gs3Mq/6nfj1NeNQG4/9pGJb7WbR1C+Lrz1WRuX4h7Xg5Gagpp6/7Ep3ev8ipfpEwoNKgAnp6exR4zmbSAhYiIiIhIZdS5gT+1fN04nQwvd7sXH3dnUjJzAejaKIA2df1KfC3DMLinT2s2/diantE7ADi9eKlCA6n0bP7GeuDAAcaNG8e777572bZvv/0248aN49ChQ7beTkREREREpEKZTAY3t61j3T4fGAA8cAW9DM67KawWu5t3tW5nrP4d8yUmTRepDGwODZYuXcrKlSsJDQ29bNvAwEBWrlzJ0qVLbb2diIiIiIhIhRvQLuSiffUDPOnbqtYVX8vZyUT4kJtJc86fB8E5O4szv64qdY0i5cnm0ODPP/8E4Jprrrls2xtuuAGLxWI9R0RERERExBF0rF+DujU8Cu2775qGOJlsW05+6NVN+Cu0rXX7wPxFparvn7IOHeLki5M5NnIUqWvWlOm1pXqyOTQ4efIkzs7O1KlT57Jt69Spg7OzM6dOnbL1diIiIiIiIhXOMAxuaXfhO4+PmzNDOtez+Xo+7i4433DThe0df5GdmHSJMwpLzcpl3sZj7IoqfE7Grl1EPfYYhwcMJHHhQtK3bOHE2EdJ+t+PNtcqAqUIDVJTU/Hw8MAwLp+wmUwmPD09SUoq+f8ZREREREREKoMR3erj6eoEwKO9m+LtVrr55Pv9awBJrl4AuJhz2fRlyXobZObkMfTjDbyweDdDPlnPgdPJpG3YwLH77uPokKGk/PobWCwXTsjLI+bpp0n8YXGp6pXqzean3d/fn7i4OM6ePYu/v/8l2549e5aUlBRq1qxp6+1ERERERETsokFNL9Y+3Yu41Cxa1PIp9fXqBfmxoV132m75DYCzP/4Ej9932fNe+ymSvSeTMSxmwo/tJmb4h+RGXzzZvGvTJuTFJ5B39ixYLJx8/nksOdn4Dx1a6tql+rG5p0HbtvnjcH744YfLtl20aBEWi4WwsDBbbyciIiIiImI3Nb3daFnbt0Q9rUuixd13WH9ufCKSbTsuvdLcz7tOMnfjMZqfPc6MVe8wefNsgv8RGLi3b0foh9NpvHQpDb6cg1NgYP4Bi4VTk/9DwldflUntUr3YHBrccsstWCwW3n//fdauXVtsuz/++INp06ZhGAYDBw609XYiIiIiIiJVRrv+PUnyzu+x7WQxs2n2d8W2PZGQztPf78Q/M5mXN3xOg5TThY57XX019WfPpuH8+fj06YNhMuHWrBkNvvwS5+Bga7vTr7xK/KzZ5fJ+pOqyeXhC//79+frrr9myZQsPPfQQPXv2pFevXoSEhGAYBtHR0axatYo//vgDs9lMly5dGDBgQFnWLiIiIiIi4pAMkwmjV1/430IAAjev4UTCY9QL8CzULjvXzLhvIkjNyOaVbfPxy04HwIzB+jpt+KFVX7787z14ebtddA+3xo1oMG8ux+69l9yYkwDEvvkmluxsAh8aU87vUKoKm0MDwzD44IMPGDt2LBEREaxevZrVq1df1M5isdCpUyemTZtWmjodWnp6erHHzGZzBVYiIiIiIiKVRZtRQzh+LjRoE3+Eb5dt4cmRPQq1eXvFfnacSOS2w3/SKfZv6/45XYewIKQrALP/PMqTN7Uo8h6u9evTcO5cjt1zLzlRUQDEvfsulpwcAh8dW2bDLaTqKtW0n/7+/sybN49FixaxaNEidu3aRW5ubv6FnZ1p164dgwcP5rbbbsPJyalMCnZE4eHh9i5BREREREQqGc+2bcgMroN7bH4vgDP/W0by4O74ursA8Pv+WD794zANk05y/56frOd59+5NwyEjYXl+iDBnw1Ee6tkYn3Pn/ZNL3bo0mDeX4/fcS/axY/n3mj4dS04OQRPGKziQSyrdWiGAk5MTQ4YMYciQIeTl5ZGYmAhAjRo1qnVQICIiIiIicimGYRB820CSP/sUgO7HtrLgrxM8eF1jTiVlMmnBDlzycnh661e4mvP/OOsUFEidV19hpKcPM1YfJiUrl5TMXL7adJyHezYp9l4utWtTf+6XHL/vfrIP5U+gGP/JJzj516DmvfeW+3sVx2XzRIhFcXJyombNmtSsWVOBQQERERHFvlq1amXv8kRERERExE4Cb70w71vzxCh++nkzWbl5jJ8fQUJaNvfvWUaj5FPWNiGvT8E5IABfdxdGdm9g3T9z3REyc/IueS+X4GAafDkHt+bNrfti336H9G3byvAdSVVTpqGBFM3T07PYl8mk/wlERERERKort2bNcGrazLrdcu9GRn6+iU1HEuh4ej+DDl9Yqc7/X6Pwvu5a6/b91zTCzTn/+0RcShbfbY267P2ca9ak/qwvcK5dO39Hbi7RE54gNz6+jN6RVDWlHp5w3pkzZzh9+jQZGRlYLJZi23Xp0qWsbikiIiIiIuLwAgYOIO7ddwG4PjqCb470xS87jUnb5lvbuDVrRvCkSYXOC/JxY2jneszdmD9PwSd/HGJYl3o4O136D5PONWtS992pHBv1L8jNJTc2luhJT1J/5ucY6jEu/1Dq0GDevHnMnTuX48ePX7atYRjs3bu3tLcUERERERGpMnxv7m8NDeqnxNIw+ST/ilxOQFYKAIarKyFvv43J7eJlFcf0aMzXm4+TZ7ZwIiGDn3ad5LYOdS97T8/wcGo9/TSnX38dgPSNG4mb9gHBT0wouzcmVUKp+sY/8cQTvPbaaxw7dgyLxXLZl5YXFBERERERKcy1Xj3c27ezbj+75Su6n9pj3Q5+8kncWzQv6lTqBXhya/sQ6/aM1Ycu2fP7vFNJmeQOGoJP/37WffGffELK77/b8hakCrM5NPjpp5/4+eef8fb2Ztq0aWzfvh2AwMBA9u7dy5o1a5gyZQoNGjTA39+f2bNns2/fvrKqW0REREREpMrwu/lm688NUk5bf/a69lr8R4285LkFV03YdyqF3/fHFts2JjGDx76J4KopK7n2v7+zdchYXBs3vnD8mWfJjrr83AhSfdgcGixatAjDMBg/fjw33ngj7u7uFy5qMlGrVi1uv/12Fi1aRJ06dRg7dizHzq0JKiIiIiIiIhf49OsPhlFon5O/P3Vefw3jH/v/qUVtH/q2CrZuf/T7oYvaZObkMW3lAXq/s5r/7YgBIDvXzBP/+5vIh5/D8PAAwJycTPTj4zFnZZX2LUkVYXNoEBkZCcCtt95aaP8/u8J4eXnx4osvkp6ezmeffWbr7URERERERKosl1rBeP5j0vg6r72KS3BwMWcU9sj1Ta0/bzl2ls1HEoD872fLdp2kzztrmPrr32TmFB4ybrbAYxuSOHHfBOu+zL17Of3a6za+E6lqbA4NkpOT8fT0xNfX17rP2dmZ9PT0i9qGh4fj4eHB+vXrbb2diIiIiIhIleY/fLj15xp3D8Ond+8Sn9upgT/dGgVYtz9afZB9p5IZ/tkmxn61jejEDOuxQG9XnuvfkkDv/IkVLRYYc7Im8X0HWtskLlhA4uLFpXg3UlXYvHpCjRo1SElJKbTP19eXs2fPkpycXChMOO/MmTO23k5ERERERKRK8+13E8aMjzCnpeN7c/8rPn9sr6ZsOrIZgNX74/jj7zjMBTqCO5sM7rumIY/1aYavuwt9WgUz7NNNnEnNwmKB+zyvYX7jv/E8vB+AUy/9H+6tWhc7CWNiejbJGbnUr+l55W9WHIbNPQ1q1apFdnY2CQkJ1n1NmuRPwLFp06ZCbffs2UNGRgYe58bJiIiIiIiIyMV8evXCb8AtGKYr/6rWo1kgYSEX/nhbMDC4vkUQy5/owb9vaY2vuwsATYN9mD/mKoJ88nsc5JiceaTpneR6+QBgyczk2MiRnHrtdbIOH87fZ7Gw8XA8477eRudXf6PHW7/z+drDtr5dcQA2hwbh4eFAfiBwXs+ePbFYLLz55pvs3LmTnJwcdu3axbPPPothGNZzREREREREpGwZhsEj1zcptK9RoBdf3NuZ2fd1pUmQ90XnNA32Zv6Yqwg+FxzEegbwUru7sJybfNGcksLZuXM5fPMtbBx0F08/9i4jPv6TH3eeJPdcKvHR6kNk5eaV87sTe7E5NOjduzcWi4WlS5da9919993UqlWLqKgo7rrrLtq1a8fQoUM5cOAATk5OPPLII2VStIiIiIiIiFzs5jZ1uPfqhrSs7cNz/VuyfEIPeresdclzmgR5802B4GBrrZZMa38nee6Fe4r77dvJ/b99xpfLX2VU5C8EpicCkJCWzW97i1/mURybzXMadO3alS+//LLQUoteXl7MmTOHZ599lu3bt1v3h4SEMHnyZNq3b1+qYh1VUZNDnmc2m4s9JiIiIiIiciVMJoOXbg274vOaBOX3OLj7s42cTs7il4ZXsaZuB3pFbeOWIxtonHzS2jYgK4Xh+39j2N8r2Vg7jI/b3sb8v45zS7s6ZflWpJKwOTRwdnama9euF+1v2LAh8+fP59SpU5w8eRIfHx+aNm1axBWqDw3LEBERERGRyq5xkDfzx3Tn7k83cio5kwwXd5Y1upplDbvTOuEot5/YSPcTO3DKywXAZLFw9cnduOdm84LXGE4kpFMvQJMiVjU2D0+4nNq1axMeHl7tAwMRERERERFH0SjQi/ljrqKO34Ue5R3q+/PAw4MY9b85tFy7hqBJE3EJDbUe7xj3NyHJsSzcGmWPkqWc2dzTQEouIiKi2GPDhw8nMjKyAqsREREREREpXsNAL5Y9fh2/RZ6mdYgvYSF+Fw4GBBA4ejQ1H3iAI7fdRtaBgwDccnQDC7fUZ3yfZjiZDDtVLuWh3HoayAWenp7Fvkw2LKUiIiIiIiJSnvy9XBnSuV7hwKAAw2Sixt13W7f7Ht9CQnwyfxyIq6gSpYKUqKdBnz59yuRmhmHw22+/lcm1RERERERExH78br2V2LffwZKejk9OBj2jt/Pt5vr0ahFs79KkDJUoNIiOji6TmxmGuqmIiIiIiIhUBU7e3vjdOpDE+d8CcMuR9UyK7EZcShZB55ZvFMdXotBgypQp5V2HiIiIiIiIOBj/u++2hgbNE6NoHH+MRduieKhnEztXJmWlRKHB7bffXt51iIiIiIiIiINxb9ECj44dydi2DcjvbfDtX60Y06PxZXuaWywWTr8+heSlSwm4714CH364IkqWK6RZ+ERERERERMRm/gUmROwZtZ3YmDj+Onr2suclLVrE2blzyUtKIu6998nYsaM8yxQbKTQQERERERERm/ncdCNOAQEAuJlzueH4X8z/6/glz8k5HcvpN94stO/0W29hsVjKrU6xTYmGJ5TE9u3bycnJKfZ4+/btcXV1LavbiYiIiIiISCVgcnWlxp13Ev/ZZwDcfGQDj+/syX8GhuHn4XJRe4vFwqlXXsacklJof8aWraT+/js+vXtXSN1SMlcUGsyZM4eff/6ZDh068OyzzxY6Nm7cOOLj44s9d8KECTz00EO2VSkiIiIiIiKVVo277iL+88/BYiE07QwtY/5m6Y42jLqqwUVtU5YvJ/W3ldZtp6BA8uLOABD79jt49+iB4Vxmf9+WUirx8ITU1FQ++OADdu3axZAhQ4psY7FYin199tlnZGZmllnhIiIiIiIiUjm4htbFu0cP63b+hIgXD1HIPXuWU6+8at327NqV+p/PBFP+V9Psw4dJ/O778i9YSqzEocHvv/9OamoqvXr1okmTopfPMAyDlStXXvS6/vrrSUtLY/ny5WVWuIiIiIiIiFQeNe4eZv35qpN7OHXwOLujkwq1iX3jDfLO9VA33N3ZPvQRJu/KIOeGm61t4qZPJy81rWKKlssqcWiwdu1aDMPgtttuu2S7unXrXvQaPnw4FouFP//8s9QFi4iIiIiISOXjfd11uNStC4ATFvod3cS3f52wHk9ds4akJUut29+3u4Xx6+JZsCWK8R5dMNzdAcg7c4aEWbMqtngpVolDg8jISAA6dep0xTfp2LEjAHv37r3ic0VERERERKTyM5ycqHHXXdbtfsc28b9tx8nIziMvNZWTL71kPbbPvx4z63Szbh/BizP97rBux8+aRU5sbIXULZdW4tDg9OnTuLq6EnBuKY1/utTSGN7e3nh7exMXF3flFYqIiIiIiIhDqHHnHeCSv2JCzcxk2hzZzs+7T7LnpdfJPXkKgBzDiffCh2I2Cn8d/aLeddalGy3p6ZyZ/mHFFi9FKnFokJ6ejoeHR7HHv/nmG3766adijzs7O5OWpnEpIiIiIiIiVZVzzZr43nSTdfuWI+v55rOluPz4g3Xf/BZ9OOZbh35htXnjjrbW/b9HpeFy/2jrduJ335F16FDFFC7FKnFo4OXlRWpqarHH69evT+PGjYs9npKSgpeX15VVJyIiIiIiIg7Ff/jd1p87nDnE4xu+tG4f8a1NVP+hLHn0Gj4e1Ym7utSjfoAnABYL/NzwKlwbnFum0Wwm9u13KrR2uViJQ4PAwEDy8vI4ZEPSc/DgQfLy8ggMDLzic0VERERERMRxeISH49aihXU7MDN/BQWzYVDzP//Hlw9dQ/t6NYD8Ffju7BhqbfvdjlMETZxo3U79/XfSNm+umMKlSCUODdq3bw/Ab7/9dsU3+fXXXwtdo7pJT08v9mU2m+1dnoiIiIiISJkxDAP/Assvnhd47z10u6XHRfvv6FjX+vPhuDQOtuyMR3i4dV/sW29j0fcmuylxaNC7d28sFguzZ8/mzJkzJb5BbGwsc+bMwTAMevfubVORji48PLzY1/lVKURERERERKoK3wEDMRUYnu7SoD5Bjz9eZNt6AZ5c1fjChPvfb4sm+KmnrNuZu3aR8ssv5VesXFKJQ4M+ffrQsGFDEhMTGT16NNHR0Zc9Jzo6mjFjxpCYmEijRo3o27dvqYoVERERERGRys/J24uAB+4HwHBzI+TVVzFdYmL9gkMU/rcjBlPbdvjccIN1X+zUdzFnZ5dfwVIs55I2NAyDN998k5EjR7Jv3z4GDhzIoEGD6NWrF61atcLPzw+ApKQkIiMjWbVqFUuWLCE9PR1XV1feeOONcnsTlV1ERESxx4YPH67eBiIiIiIiUuUEPvIIXl274hwYiGvDhpds279tHSYv2UNGTh7Jmbn8FnmaGyY+Qcrvv0NuLjlRUSTMnEngI49UTPFiVeLQAPLnJHj33Xd5+umnSU9P55tvvuGbb74ptr3FYsHDw4O33nqLdu3albpYR+Xp6VnsMZOpxJ09REREREREHIZhGHh27lyitt5uzvRvU5tFEfk92r/fGsWA+7riP3QoZ7/+GoC4D6bj3rYd3tdeU241y8Wu+Btr3759+e6777jxxhuB/GCgqBfAjTfeyPfff69hCSIiIiIiInJJgztdGKLwx4EzxCZnEvT4Y7iEnttvNhM9aRLZJ07YqcLq6Yp6GpzXuHFjpk2bRlxcHJs2beLgwYMkJiYCUKNGDZo2bUq3bt0ICgoqy1pFRERERESkirqqcU3q1vAgOjGDPLOFxdujGdOjCaHTP+DosLuxZGZiTkoi6tFxNJz/DaZL9OiWsmNTaHBeUFAQAwYMKKtaREREREREpJoymQxuD6/L9N8PAvD91mhGX9cY95YtqfPaq8RMehKArL//5uQLLxDyzjsYhmHPkqsFDagXERERERGRSuHOAkMU9p9OYU9MMgB+t9xCwP33W48lL/uZhC++qPD6qiOFBiIiIiIiIlIpNAr0olMDf+v2d1ujrD8HT3wCr6u7W7dj35lK6p9/Vmh91ZFCAxEREREREak07ux4obfBku3RZOeaATCcnQl55x1c6tbNP2g2Ez1REyOWN4UGIiIiIiIiUmnc0q4Obs75X1XPpufw+/5Y6zFnf39CP5yO4e4OkD8x4rjHMKen26XW6kChgYiIiIiIiFQafh4u3BhW27pdcIgCkD8x4quvWrez9u/n5AsvYrFYKqzG6kShgYiIiIiIiFQqd3asa/35932xxKdmFTruN+AWAu67z7qdvGwZCV/MqrD6qhOFBiIiIiIiIlKpXNcsiFq+bgDkmi0s3RFzUZvgSRPx7H6VdTv2nXdIXr6iwmqsLhQaiIiIiIiISKXiZDIYFH6ht8E/hyhA/sSIdadOLTQxYsyTT5K2fn1FlVktONu7ABEREREREZF/GtwxlE/WHAZgT0wy//e/PXi4OF3Uznvk01zzwQu4pqdgycnhxLjHaDDrCzzat6/okqskhQYiIiIiIiJS6TSr5UP7UD92RCUBMOvPo8W2bdH5Xqb8+QkeedlY0tM5PuYhGs6bi1uzZhVUbdWl4QkiIiIiIiJSKY3o1qBE7fYHNOCVbveSY+T3RDAnJXH8gQfJjrp4WINcGfU0EBERERERkUppSOdQcsxmdkcnXbLdsl2niAhuzn87D+e5LfMwWSzkxsZy/P4HaPjVPJyDgiqo4qpHoYGIiIiIiIhUSoZhlKi3wZ0dQxn++SbW1W3PtJxMJmxfCEDO8eMcHz2GBl/OwcnXt7zLrZI0PEFEREREREQcWueGAUwb1gHDgOUNuzEz7Bbrsax9+zjx8COYMzLsWKHjUmggIiIiIiIiDq9fmzq8NDAMgO+a9WJBs17WYxnbthE1fjyW7Oxiz09IyyYpPafc63Q0Cg1ERERERESkSrjn6oY83LMJALNa38zPDbpZj6X9sZbDA28l/vPPyT1zBoDMnDyWbI9m+Gcb6fjKr1z9xsrLzp9Q3WhOAxEREREREakynr6pBaeSMli8PYbpHe7EKyeDHjE7Acg+dozYt98h9t33iGrVmXmBHVjr1wSzkf/39LTsPH6IiKZNXT97voVKRaGBiIiIiIiIVBkmk8F/B7cnLjWLPw/G83an4WQ7udAnahuGxZLfKC+P0N2beJZN3O9RgxX1u/Brgy7EegZwPCHdvm+gktHwBBEREREREalSXJ1NfDyyE63q+JLj5Mw7ne7m3r7P8VWLG4jzKNyLIDgjkZH7f2XWiilM3jiLMyfj7FR15aTQQERERERERKocH3cXZt/Xhbo1PACI9QpgXqubuPfGfzP5qgfY1Tgci5OTtb0JC91P7aH7usVYzvdIEA1PEBERERERkaqplq87c+7vwl2fbCQ+LRsfd2cGdajLXV160KauH7lxcSQuXkzCgu/IO3EcgD5HNhF3OoHg2jXtXH3loNCgAqSnFz8mxmw2V2AlIiIiIiIi1UvTYB9WTurJgdhU2oT44eF6oXeBc1AQgaNHEzB8ONuuug6vnAw8c7M4+e13BI9/yI5VVx4KDSpAeHi4vUsQERERERGptmp4utKlYUCxx01eXmwJu46e21fk71i0AMu4BzEKDF+orjSngYiIiIiIiFR7R6+7mTwMAFxPx5C6Zo2dK6oc1NOgAkRERBR7bPjw4URGRlZgNSIiIiIiIvJPNRo3YENIG66N2QVAwpdz8end285V2Z96GlQAT0/PYl8mk/4nEBERERERsbd6AR4sbtLDup2+cSOZ+/fbsaLKQd9YRUREREREpNqrH+DJnoCG/F0j1LovYe5cO1ZUOSg0EBERERERkWqvfoAnGAZLGl9n3Ze89H/kJiTYsSr7U2ggIiIiIiIi1V5IDQ9MBqyt254ENx8ALNnZJH77rZ0rsy+FBiIiIiIiIlLtuTiZCKnhQY6TMz82utq6/+zX32DJzrZjZfal0EBERERERESEc0MUgGWNrsLsnL/YYG5cHMnLl9uzLLtSaCAiIiIiIiLChdAgyc2H4+EXVlJImPMlFovFXmXZlUIDEREREREREaDeudAAYF373tafM3fvJiNiux0qsj+FBiIiIiIiIiJc6GkAsM0lGM+uXa3bCV9+aY+S7E6hgYiIiIiIiAiFQ4MTCen4/2uUdTvl11/JiYmxR1l2pdBAREREREREhMKhQWpWLjldr8ElNDR/R14eZ7/+2k6V2Y9CAxERERERERGghqcLPm7O1u0TSVkEjBpp3T67YCHm9HR7lGY3Cg1EREREREREAMMwCk2GeCIhHb8778Tk5QWAOTmZpKVL7VWeXSg0EBERERERETmn4BCF4wnpOHl743fHHdZ9CV/OxWI226M0u1BoICIiIiIiInJO/ZqFexoABIwcAYYBQPbhw2Tt22eX2uxBoYGIiIiIiIjIOfX+0dMAwLVBAzzatbPuT9u0ucLrsheFBiIiIiIiIiLn/HN4wnme3bpZf07ftKlCa7InhQYiIiIiIiIi5xQMDWISM8jJy5+/wLNrV+v+9C1bsOTlVXht9qDQQEREREREROScujU8zk9fgNmSHxwAeHYMB+f85RjNqalk7o20V4kVSqGBiIiIiIiIyDmuziZC/Dys2+eHKJg8PfFo29a6P31z9ZjXQKGBiIiIiIiISAH1Ai4ODQA8u10YopC2uXrMa6DQQERERERERKSA4iZD9CowGWLGlq1YcnMrtC57UGggIiIiIiIiUkDB0OBEgdDAo0MHcHEBwJyWRubevRVdWoVTaCAiIiIiIiJSQL1iehqYPDzwaN/Oup1WDZZeVGggIiIiIiIiUkCh4Qnx6YWOeRVcenHzXxVWk70oNBAREREREREpoGBokJyZS1J6jnXbs+uFeQ3St27FkpNDVabQQERERERERKSAAC9XvFydrNvHC81r0B7j3LwGlvR0MnbvrvD6KpJCAxEREREREZECDMMofl4Dd/f8CRHPqepDFBQaiIiIiIiIiPxDcaEBgGeBpRfTq/hkiAoNRERERERERP6h/qVCg65drD+nR0Rgyc6usLoqmkIDERERERERkX8oGBqc+Edo4NG+PYabGwCWjIwqPa+BQgMRERERERGRf7hUTwOTm1vheQ2q8BAFhQYiIiIiIiIi/1BwToPoxAxy88yFjnt262r9OW3z5gqrq6IpNBARERERERH5h1B/D+vPeWYLJ5MyCx33KjAZYsa2CMxVdF4DhQYiIiIiIiIi/+Du4kRtX3fr9j+HKLi3bYvhnn/ckpVF5o4dFVpfRVFoICIiIiIiIlKES85r4OqKZ8dw63ZVHaKg0EBERERERESkCPUuERoAeHa9MK9B+qaqGRo427uAyuzUqVN89tln7N69m3379pGZmcnKlSsJDQ21d2kiIiIiIiJSzi7V0wDAs2uBeQ22b8eclYXp3FKMVYV6GlzCsWPH+Pnnn/H19aVz5872LkdEREREREQqUP2aFyZDPFFEaODRJgzDI7+NJTubjO1Vb14DhQaX0KVLF9avX89nn31Gv3797F2OiIiIiIiIVKDL9TQwXF3x7NjRup2+aVOF1FWRFBpcgsmk/zwiIiIiIiLVVcE5DRLTc0jKyLmojWeBpRfTq+BkiA41p0FcXBzr169n165d7N69m8jISDIzMwkLC2PRokWXPX/jxo3MmjWLHTt2kJ6eTkhICP369WPMmDF4enpe9nwRERERERGpPoK83XB3MZGZYwbyhyj41fUr1Maraxfizv2csWMH5sxMTO7uVBUO9af0n376iaeffpq5c+cSERFBZmZmic+dO3cu9957L6tXr8bNzY0mTZoQHR3NjBkzGDx4MImJieVXuIiIiIiIiDgcwzAKDVEoal4D97AwTOf+CG3JySEjIqLC6qsIDhUaeHt7c/XVVzNmzBimTZvGxIkTS3Te7t27ef311wF4+eWXWb16NT/88AO//fYbYWFhHDp0iBdffLE8SxcREREREREHdNl5DVxc8OjUybqdVsWGKDhUaDB48GBmzZrFpEmTuOmmmwgKCirReR999BFms5nbbruNu+66C8MwAKhVqxZTp07FZDKxYsUK9u3bV57li4iIiIiIiIOpd5nQAMCrW1frz+mbFBo4lLS0NNauXQvA0KFDLzresGFDrrrqKgB++eWXCq1NREREREREKrfL9TSAwpMhZuzahTm96HaOqMqHBpGRkWRnZ+Pq6kq7du2KbNPpXFeSHTuq3pqaIiIiIiIiYrvLzWkA4N6qFSYvr/yNnBzSq9C8Bg61eoItjhw5AkBISAguLi5Ftqlfv36htgWd732we/duAP744w8CAgIICAiga9euF7UXERERERGRqqNgaBB1NoM8swUnk1GojeHsjGvHTmSu/QOAEyvX0uqaayq0zvJS5UODpKQkAPz8/Iptc/7Y+bYFjR8/vtD2//3f/wHQtWtX5s6dW+T15s+fz4IFC0pU36FDh0rUTkRERERERCpeqP+F0CDXbOFkUkahfQCxKZl8lxfMgHPbhzdtp1UF1lieqnxokJWVBVBsLwMAV1fXQm0L2r9//xXfMy4ujj179lzxeSIiIiIiIlK5eLg6EezjRmxK/vfF4wnphUKDQ3Gp3PPFZtJ9WnG9szveuZmY27S3V7llrsqHBm5ubgDk5OQU2yY7O7tQ29IKCgoiLCysRG0PHTpEZmZmmdxXREREREREyl79AE9raHAiIR2a5O/feuwsD875i7PpOeDhx303Ps/oFl6Me7CfHastW1U+NLjU0IPzSjKE4UoMGzaMYcOGlajtHXfcoV4JIiIiIiIilVj9AE+2HDsLXFhBYcWeUzz2TQRZuWZruwm3d+LB6xrbpcbyUuVDg4YNGwIQExNDTk5OkcMUjh8/XqitiIiIiIiIyHn1Ci27mMG8jceYvGQ3Zkv+PlcnE28Pbc+t7UPsVGH5qfJLLrZu3RoXFxeys7PZuXNnkW22bt0KQIcOHSqwMhEREREREXEEBVdQ+HXvKV5YfCEw8HF3Zs79XatkYADVIDTw8vLi2muvBShyRYOjR4+yceNGAPr1K59xJ+np6cW+zGbz5S8gIiIiIiIidlO/5oXQIDPnwne42r7uLHy4O92b1LRHWRWiyg9PABg7diyrV69myZIldOzYkaFDh2IYBrGxsUycOBGz2Uzfvn1p2bJludw/PDy8XK4rIiIiIiIi5a/eP5ZYBGhey5vZ93UlpIaHHSqqOA4VGpw8eZJBgwZZt8+verB//366detm3f/ggw8yevRo63a7du149tlneeONN5g8eTIzZszA39+fgwcPkp2dTaNGjXjllVcq7H2IiIiIiIiI4wj2ccPX3ZnkzFwAujUK4NN/dcbP4+I586oahwoN8vLySExMvGh/bm5uof1FLWF477330qJFC7744gt27txJfHw8ISEh9OvXjzFjxuDl5VVudUdERBR7bPjw4URGRpbbvUVERERERKR0TCaD/wwM48PfD9KjeRDP3dwSN2cne5dVIRwqNAgNDWX//v02n9+9e3e6d+9ehhWVjKfnxV1ZzjOZqvy0EiIiIiIiIg7vzk6h3Nkp1N5lVDh9YxURERERERGRIik0EBEREREREZEiKTQQERERERERkSIpNBARERERERGRIjnURIiOKj09vdhjZrO5AisRERERERERKTmFBhUgPDzc3iWIiIiIiIiIXDENTxARERERERGRIqmnQQWIiIgo9tjw4cOJjIyswGpERERERERESkahQQXw9PQs9pjJpM4eIiIiIiIiUjnpG6uIiIiIiIiIFEmhgYiIiIiIiIgUSaGBiIiIiIiIiBRJoYGIiIiIiIiIFEmhgYiIiIiIiIgUSasnVID09PRij5nN5gqsRERERERERKTkFBpUgPDwcHuXICIiIiIiInLFNDxBRERERERERIqkngYVICIiothjw4cPJzIysgKrERERERERESkZhQYVwNPTs9hjJpM6e4iIiIiIiEjlpG+sIiIiIiIiIlIkhQYiIiIiIiIiUiQNT7CzqKgoAA4dOsQdd9xh52pERERERESkqjt06BBw4fvopSg0sLOsrCwAMjMz2bNnj52rERERERERkeri/PfRS1FoYGcBAQEkJCTg5uZGaGgoZrPZuppCq1atLjlRYknblqRdSdocOnSIzMxM3N3dadKkyZW90UrkSv4bO8J9S3tdW8/Xs1r+qtKzWhbXrGrPalV5TkHPallc40rPKatntTr9TgU9q2VxjfJ4VvXvf2H2ek7L6976rFo5n9WoqCiysrIICAi4bFvDYrFYKqAmKaH09HTCw8OB/KUaL7XyQknblqRdSdrccccd7Nmzh7CwMBYtWnRlb6wSuZL/xo5w39Je19bz9ayWv6r0rJbFNavas1pVnlPQs1oW17jSc8rqWa1Ov1NBz2pZXKM8nlX9+1+YvZ7T8rq3Pqs6/rOqiRBFREREREREpEgKDURERERERESkSAoNRERERERERKRICg1EREREREREpEgKDURERERERESkSFo9QUrM0Wb5lOpLz6o4Aj2n4ij0rIqj0LMqjsLRnlX1NBARERERERGRIik0EBEREREREZEiKTQQERERERERkSIpNBARERERERGRIjnbuwBxHEOHDiUuLo6goCB7lyJySXpWxRHoORVHoWdVHIWeVXEUjvasavUEERERERERESmShieIiIiIiIiISJE0PEEq3PLly1m2bBm7du3izJkz1K5dmx49evDoo4/i7+9v7/JErE6dOsVnn33G7t272bdvH5mZmaxcuZLQ0FB7lybV1MmTJ5kyZQp//vknFouF7t278/zzz1O3bl17lyZipd+d4ij0mVQcxbZt25g+fTp///03iYmJ+Pn50aZNG8aOHUv79u3L/f4aniAVbujQoQQHB9O3b19CQkI4dOgQ06dPx9vbm8WLF+Ph4WHvEkUA2LRpE0888QRhYWGYzWbWrVunD75iN+np6dx22224ubkxYcIEAN5//33S09NZunQpXl5e9i1Q5Bz97hRHoc+k4ihWr17NunXr6NixI4GBgcTHxzNnzhx2797N119/Tbt27cr1/uppIBXu448/JiAgwLrdtWtXGjZsyL333svy5csZNGiQ/YoTKaBLly6sX78egIULF7Ju3To7VyTV2YIFC4iKiuKXX36hQYMGALRo0YKbbrqJb7/9lvvvv9/OFYrk0+9OcRT6TCqO4vrrr+f6668vtO+6667jqquuYsmSJeUeGmhOA6lwBX85n9e2bVsATp8+XdHliBTLZNKvSKk8Vq1aRfv27a2BAUC9evXo2LEjq1atsmNlIoXpd6c4Cn0mFUfm6emJq6srzs7l3w9APQ2qmLi4ONavX8+uXbvYvXs3kZGRZGZmEhYWxqJFiy57/saNG5k1axY7duwgPT2dkJAQ+vXrx5gxY/D09Cy3ujdu3AhAkyZNyu0eUrk46rMqcl5FP8MHDx6kb9++F+1v2rQpv/zyS5m8J6ma9PtWHEVleFb1mVRKwl7PqtlsJi8vj7i4OD799FMMw2Do0KFl+daKpNCgivnpp5+YMmWKTefOnTuX1157DYvFQu3atalTpw4HDx5kxowZrFixgq+//poaNWqUbcFAUlISU6ZMoXnz5vTq1avMry+VkyM+qyIFVfQznJycjK+v70XX8vPzIzk52aY6pHrQ71txFPZ+VvWZVErKXs/q6NGjrUO+AgMD+fTTTysk4FJoUMV4e3tz9dVX06ZNG9q0acPRo0eZOnXqZc/bvXs3r7/+OgAvv/wyQ4cOxTAMTp8+6FAIkQAAISdJREFUzSOPPMKePXt48cUX+eCDDwqd98cffzB69OjLXr9r167MnTv3ov1ZWVmMGzeO5ORkPvnkE5ycnEr4TsXROdqzKvJPFf0MAxiGUebvQ6o+ezyrIraw57Oqz6RyJez1rL744oskJycTGxvLd999x+jRo/nkk0/o0qVLmb6/f1JoUMUMHjyYwYMHW7dL0j0G4KOPPsJsNjNo0CDuuusu6/5atWoxdepU+vfvz4oVK9i3bx8tW7a0Hu/cuTPLli277PWLmn02JyeHxx9/nF27djFz5kyaNm1aolqlanCkZ1WkKBX9DPv6+pKUlHTR9ZKSkorsgSByXkU/qyK2stezqs+kcqXs9aw2bNjQ+nOfPn24++67efXVV1myZIntb6YENFONkJaWxtq1awGKHBPTsGFDrrrqKoCLxs16enrSpEmTy75CQkIKnZeXl8ekSZNYv349H374IZ06dSqndydViT2eVZGyVJpnuGnTphw4cOCicw4dOqQPuFLmSvOsilSk0j6r+kwqFaWsf68ahkGbNm04cuRI2RZaBIUGQmRkJNnZ2bi6uha7XMf5X6A7duwo9f3MZjPPPPMMK1eu5N133+Waa64p9TWleqjoZ1WkrJXmGe7duzc7duzgxIkT1n1RUVFs27aN3r17l1/RUi3p9604itI8q/pMKhWprH+v5ubmsm3btkKrKpUXDU8QazoVEhKCi4tLkW3q169fqG1pvPLKK/zvf//jnnvuITAwkO3bt1uP1a5dm9q1a5f6HlI1VfSzCheS3t27dwP5cyMEBAQQEBBA165dy+QeUn2U5hkeOnQoX331FWPHjmX8+PEYhsH7779P7dq1C3VxFCkLpf19q9+dUlFK86zqM6lUpNI8q5MmTSI0NJTWrVvj7+/PqVOnWLhwIZGRkUybNq18C0ehgYB1jKyfn1+xbc4fK2o87ZVas2YNAHPmzGHOnDmFjo0bN47HHnus1PeQqqmin1WA8ePHF9r+v//7P0ATJoptSvMMe3p6MmfOHKZMmcLTTz+NxWKhe/fuPP/883h5eZVf0VItlfb3rX53SkUpzbOqz6RSkUrzrIaHh7N06VLmz59PSkoKfn5+tG/fnjlz5lRIEKvQQMjKygIoNvECcHV1LdS2NFatWlXqa0j1VNHPKsD+/fvL5DoiUPpnOCQkRDPVS4Uo7bOq351SUUrzrOozqVSk0jyrI0eOZOTIkeVX3GVoTgPBzc0NyJ85tjjZ2dmF2orYg55VcXR6hsVR6FkVR6FnVRyFIz+rCg2kRN25S9KdRqS86VkVR6dnWByFnlVxFHpWxVE48rOq0ECs633GxMQUm3wdP368UFsRe9CzKo5Oz7A4Cj2r4ij0rIqjcORnVaGB0Lp1a1xcXMjOzmbnzp1Fttm6dSsAHTp0qMDKRArTsyqOTs+wOAo9q+Io9KyKo3DkZ1WhgeDl5cW1114LwIIFCy46fvToUTZu3AhAv379KrQ2kYL0rIqj0zMsjkLPqjgKPaviKBz5WVVoIACMHTsWwzBYsmQJ3377LRaLBYDY2FgmTpyI2Wymb9++tGzZ0s6VSnWnZ1UcnZ5hcRR6VsVR6FkVR+Goz6phOV+pVAknT55k0KBB1u3s7GzS09NxdnbG29vbuv/BBx9k9OjRhc6dPXs2b7zxBhaLhTp16uDv78/BgwfJzs6mUaNGfP311wQEBFTUW5EqTs+qODo9w+Io9KyKo9CzKo6iuj2rzvYuQMpWXl4eiYmJF+3Pzc0ttD8zM/OiNvfeey8tWrTgiy++YOfOncTHxxMSEkK/fv0YM2YMXl5e5Vi5VDd6VsXR6RkWR6FnVRyFnlVxFNXtWVVPAxEREREREREpkuY0EBEREREREZEiKTQQERERERERkSIpNBARERERERGRIik0EBEREREREZEiKTQQERERERERkSIpNBARERERERGRIik0EBEREREREZEiKTQQERERERERkSIpNBARERERERGRIik0EBEREREREZEiKTQQEZFytWjRIlq0aEHv3r3tXQpQ+eqRS3v33Xdp0aIFn3322UXHWrRoQYsWLf6/vXsPi6paHzj+hQHkNopYeTtppg4H70mppYYWhHnvmAfN1DLSvF9JsF+mJ1PTwsAkMyuPKF4STUEzE1HUCsE7HkxDAUVFAZEBhAbYvz98Zh9GZpCL56Cd9/M8PA/M3mvvtfbsPcx+91rvIi4urhZq9mCQY1AzI0eOxM3NjeXLl9/X7e7cuRM3Nzfefffd+7pdIYSoDTa1XQEhhBAPttLSUqKjo9m3bx8nT54kKyuLvLw8HB0dadiwIe7u7vTs2ZMXXngBZ2fn2q6u+BO5du0aa9aswdXVlREjRtR2dSolOzuboKAgTp48yZUrV1AUhSeeeAJfX1+GDh2KtbU8r/lf8PLLLxMaGsqOHTsYPXo0bdu2re0qCSFEtcl/LiGEEBadPHmSvn37MmnSJLZu3UpycjJ6vR5nZ2cKCws5f/48O3bswN/fH09PT9asWVPbVb4nrVZLixYtePzxx2u7KuIeli1bRmFhIX5+fjg6OtZ2dSolJycHvV7PzJkziYyMZPPmzbRo0YK5c+eyZMmS2q6e+C+xtrZmwoQJKIrCxx9/XNvVEUKIGpGeBkIIIczau3cv06ZNw2Aw4OLiwujRo/H29qZVq1ZYWVkBkJWVRUJCAtu3bycmJoZdu3bxxhtv1G7F78Hb2xtvb+/aroa4h4yMDCIjI7G1tWXIkCG1XZ1Ke/LJJwkODjZ57eOPP2bfvn189913BAQE1FLNxH+bj48Prq6uxMXFcfr0adq3b1/bVRJCiGqRngZCCCHKSU5Oxt/fH4PBgJubGzt27GDChAm0bt1aDRgANGjQAB8fH7UbbqdOnWqv0uJPZfPmzZSUlODp6YmLi0ttV6dGsrKyMBgM1K9fv7arIv6LbGxsePnllwHYtGlTLddGCCGqT3oaCCGEKCc4OJiCggIcHR1ZsWIFDRs2vGeZ1q1bM2fOnCrtx2AwcOTIEWJiYjh27BjXr18nJycHrVZLmzZteOWVV+jXr59JoOJupaWl7N69m6ioKE6fPs3NmzdxdnamSZMmPPfccwwcOBCdTqeuv3XrVgIDA2natCn79u0zu82ioiI2bdrEnj17OH/+PPn5+dSrV4+OHTsybNgwnn/+ebPl3NzcAFi7di2tWrVi1apV7N+/n2vXrlFYWMhvv/1WqeNSWFjI4cOHiYmJ4fTp02RkZJCXl4eLiwsdOnTA19cXT09Ps2Xvbt+vv/7K2rVrOXXqFFlZWQwaNIjFixfXuK1XrlwhJiaGAwcOkJqaSkZGBlZWVjRu3Jju3bvz5ptv0qRJk0q1926KorBlyxYA+vfvX61tlJaWMn/+fDZu3Ii9vT2ffvopXl5eACxfvpzPP/+cLl26EBYWRnR0NGvXruXs2bMYDAZ0Oh1+fn7q+gDff/89GzduJDk5meLiYtq0acOkSZN49tlnK6xHSUkJAQEBGAwGpk6dWq221ERRUREbNmxg9+7dJCcnU1hYyCOPPMIzzzzDm2++ibu7u8WyBoOB9evXs23bNlJTU7Gzs8PNzY0RI0bQp08fRo4cyZEjR5g0aRKTJ082KVtYWMj69evZs2cPFy5coKCgAK1Wi6urK+3bt+eFF17Ax8fH7H6Tk5NZt24dcXFxXL16FYDHHnuMNm3a0KdPH7y9vU1yQ1y4cIF9+/Zx6NAhLl++zPXr17GxsaFZs2Z4enoyevRoXF1dq30M09LS+Oc//8nPP//MtWvXKC0tpUmTJvTo0eOe53n//v1Zv349O3fuJDAwECcnp2rXQwghaosEDYQQQpi4fv06e/bsAWDgwIH/0bH/x44dY8yYMerfdnZ22Nrakp2dzaFDhzh06BA//fQTy5YtM5tALjs7mylTphAfH6++ptVqycvL48yZM5w5c4YLFy4QGhpa6TqlpKQwbtw4UlJSALCyssLZ2ZnMzEyio6OJjo5m+PDhzJs3z+I20tLSmDFjBpmZmdSpUwcbm6r9u921axeBgYHq3/b29tjY2HDjxg21DmPGjGH27NkVbmft2rUsXLgQRVHQarVoNJr71tbZs2dz5MgR9W+tVkt+fj7JyckkJyezbds2Vq5cydNPP12ltgOcO3eOa9euAVSrfFFRETNnzuSnn36iXr16fPHFF3h4eJhdNyQkhBUrVmBtbY2TkxP5+fkcP36ciRMnMm/ePIYNG0ZgYCDbtm3DxsaGOnXqUFBQQEJCAm+99RahoaH06tXL7LYVReH//u//+Pnnn3nttdcYMGBAldtSExkZGfj5+XHu3DkAbG1tsbe358qVK2zfvp3IyEjmzJnDyJEjy5UtKChg7Nix6rWl0Wiws7MjPj6eI0eOMHbsWIv7zcvLY8SIEZw9exa4c15ptVr0ej03b94kOTmZ+Ph4s0GDVatWsWzZMkpLSwHU6yclJYWUlBR27dpFfHw8devWVcv4+fmRnp5ebl9JSUkkJSWxbds21qxZw5NPPlnlY7h582b+8Y9/YDAYgDufUdbW1ly4cIELFy6wdetWQkJC6N69u9ny7du3NzlnLAX7hBDiQSZBAyGEECbi4uJQFAXgPz4tob29Pf3792fgwIG0bduWBg0aYGVlRU5ODjt27CA4OJjdu3fj4eHBqFGjTMoWFxczceJEjh07hp2dHVOmTGHIkCG4urpSUlLC1atXOXz4sHozURm5ubmMGTOG9PR0unXrxuTJk+nQoQN2dnbo9XoiIiIIDg5mw4YNtGjRgtGjR5vdzsKFC2nYsCGffPIJXbt2xdramosXL1a6HnXr1sXX15d+/fqh0+nUbu3Xr19n8+bNrFy5km+++Yann36aF1980ew2MjMzWbx4Ma+88gpTpkyhcePGlJSUqMejpm1t3bo1PXv25MUXX6Rp06bY29tTXFzMv/71L0JCQjh48CDTp0/np59+wt7evtJtB0hISACgcePGPProo1Uqm5uby/jx40lISKBRo0Z8/fXXtGrVyuy6SUlJHD16lGnTpvH666+j1WrJyMhgzpw5HDp0iKVLl3Ljxg127drF/PnzGTRoEA4ODqSkpDBz5kwSExOZP38+zz//fLmglqIofPDBB2zdupVXXnmF999/v0rtqKmSkhImT57MuXPn0Gq1zJ07lz59+mBnZ8elS5f46KOPiImJYcGCBeoT+bIWL15MfHw81tbWzJgxg9deew0nJyeys7MJDQ1l1apVJjfuZRl7bbi4uPDhhx/Sq1cv7OzsKC0t5caNG/zyyy8cPXq0XLnw8HA+/fRT4M5nz5QpU9SeEDk5OZw5c4YtW7aUO9YdO3ZkzJgxPP/88zRq1Ag7Ozv++OMPjh49SlBQEKdOnWLWrFls3bq1Ssdw7969vP/++9ja2jJ27FiGDRum9iq4ePGi+vk0ZcoUIiMjzfY4sLW1pU2bNhw/fpz4+HgJGgghHk6KEEIIUUZQUJCi0+kUnU6nZGRk1Hh7ERERik6nU3r37l3lsj/88IOi0+kULy+vcss2b96s6HQ6xc3NTdm/f/99qc/ixYsVnU6njBo1SjEYDGbL79mzR9HpdErXrl3LrWM8bp07d1auXr1a6TpV1erVqxWdTqeMHj263DJj+3Q6nTJp0iSL26hpWytSXFysDBgwQNHpdMr3339f6XJG/v7+ik6nU8aNG1fhesZ2/vrrr4qiKMrVq1eV/v37KzqdTunbt6/F9yAkJEQtGxoaWm65Xq9XOnXqpK6zffv2cuukpqaqy+Pj402WGQwGZdasWYpOp1MWLVqklJaWVrbpVXb3MTDauXOnuiw2NrZcOYPBoAwdOlTR6XRK//79TZalp6crf/3rXxWdTqesWLHC7H5nz56tbj8kJMRkmZ+fn6LT6ZSVK1dWuh05OTnKU089peh0OmX69On37Zjl5eUpzz33nNn3SVEU5fXXXzfbhqKiIqVnz56KTqdTvvvuO4vbf+eddxSdTqcsWLDA4jrz589XdDqdMmLEiOo3RAghapEkQhRCCGEiJydH/d1SArrU1FS6d+9u9ufYsWP3rS7Gbt9paWlcv37dZFlERAQAnp6e9+XpnaIo6pPIMWPGWBxS4OXlhbOzMzdv3uTMmTNm1xk0aBCNGjWqcZ0sMR6XEydOUFJSYnE9S13I72dbzdFoNPTs2RPA7BPlezG+11VJHPj7778zbNgwzp07R+fOnQkPD7/ne1CnTh2zvUWcnZ3VpJ5NmjQxO6ygWbNmNG/eHMAkV0VhYSETJkwgMjKSGTNmMGHCBPR6Pbm5ueTm5la6PTW1a9cuAJ566in1vSjLxsaGiRMnAneGg5Rtw549eygtLcXBwcHibCgTJkywuG9jD4QbN25Uur4//vgj+fn52NraEhAQUGEek6pwcnLimWeeAajSZ1NsbCwZGRk88sgjFc7eMXjwYAAOHTpkcZ2yPYWEEOJhJMMThBBCVFlxcTGZmZlmlxnH/lZWXl4eGzduZP/+/SQnJ6PX681uIyMjg8cee0zdf2JiIgC9e/euYu3N+/3339WASUBAgNkcCkYFBQUApKen07Fjx3LLO3fuXOP6ZGZmEh4ezuHDh0lJSUGv15cLENy+fZtbt26ZTfJmb29P27ZtzW77frU1ISGBLVu2cOLECTIyMtR1y8rIyKiwneZkZ2cDloNWdzt+/DjffvstOTk5vPjiiwQFBVVqSESrVq1wdHQ0u6xBgwYAtGvXzuINbIMGDUhNTTUJBpw8eZIDBw4AEBQURFBQkEmZyibDrCnj9VFRosZu3bqh0WgoKSkhMTFRTeRpDBC1a9fO4vFp1qwZjRs3VhMVltWrVy+ioqJYt24d2dnZ9O3bl86dO1eYjPD48eMAtG3bVr3OqyImJobt27dz+vRpsrKyuH37drl1jHkyKsMY7Lp16xY9evSwuJ7xs+rKlSsW16lXrx7w7/NaCCEeNhI0EEIIYaLsjVpOTo7ZL/AtW7Y0ufm5fPmyxbH1Fbl48SJvvPGGyZd5BwcHtFqteiNrDE6UvQnIyclRv6xXN0P/3co+Bazsl/vCwkKzrxtvOKvr+PHjjB071uRm1NHREQcHB6ysrCgpKeHmzZsAZm+O4M77aCkYcD/aunTpUlavXq3+rdFoqFevHra2tsCdYIPxp6qKioqAO0nnKmPZsmXAndkrli9fXi7hoyUVZbI39r6ozDrFxcXqa127dv2vBQYqkpWVBVDhzCd16tShfv36ZGZmquvDv8+Je928N2zY0GzQYMCAAZw6dYp169axc+dOdu7cCUDz5s3p3r07Q4YMoV27diZljL0Sqno9l5aW4u/vT1RUlPqajY2Nybmo1+spKiqyeK2YY7xGDAaDxQBpWZY+CwA1gGU8r4UQ4mEjQQMhhBAmyiaNS0pKqtZTv8oKDAzk2rVrNG3alHfffZdu3bqZBC1KSkpo06YNgJqc8W73qxtz2af4hw8f5pFHHqn2tip6cn8vxcXFzJw5k9zcXNzd3Zk+fToeHh44Ozur66SlpeHt7Q1YPi4V3TjXtK2HDx9WAwavvfYaw4cPp2XLlib7/Oyzz/jiiy+qtF2j+vXrk5KSwq1btyq1/oABA4iKiuK3334jODiYGTNmVGu/f0aVvT7MrXevspbOPYD33nuP119/nd27d5OQkMCJEydITU0lNTWV8PBwRo0axXvvvVft+hpt2bKFqKgoNBoN77zzDoMGDeLxxx83uQb9/f3ZsWNHhfW9m3H2hp49e5oEx6rD2Kunsj1nhBDiQSM5DYQQQpjo2rWr+sV93759/7H9XL16Ve2SHBQURJ8+fcp9qbb0hM/FxUV9iliV2REqUjZLv3GKutpw4sQJ0tPT0Wg0fPnll3h6epoEDKBqY8XNqWlbjU+Oe/TowQcffIBOpysXpKjM01lLjGPAKxs0GDp0KB9++CFWVlZ8+eWXLFmypNr7/rMw9nYx1xPAqKioSL2hLTt0wPj7vcbg32t58+bNGTduHF999RVxcXFs2rQJLy8v4M4MC9HR0eq6xnOyqtez8Vx89dVXmTJlCs2bNy8XtKvOuWgMpN2PzwLjeVzR8AwhhHiQSdBACCGEiccee4yXXnoJgB07dnDp0qX/yH7K3swYexPc7eeffzb7uo2NDe3btwfujGW+H1q3bq3enBtvRGqD8bi4urpa7Fr+yy+/1GgfNW2rcTiJpfdNURR+/fXXatfP2NulKufe0KFD+eijj7C2tubrr79m0aJF1d7/n4Gx+39F70NcXJw6tMJ4PQFqLozExESLw0suXbpUYUDibtbW1nTq1ImQkBB1CELZ69uYByQxMbFKCQPvdS7m5+dz8uTJSm/v7vpkZGSoU4BW1+XLl4E7w7qEEOJhJEEDIYQQ5UydOhVHR0cKCgqYOHFitZLZ3YtWq1V/P3v2bLnleXl5FXZvf/XVVwE4cOCAmniuJmxsbNQs6d9///09bxTKzjJxPxmPS2ZmptknpNeuXSMsLKxG+6hpW40BB3PvG8CGDRtqFGx6+umn1e3/8ccflS43ZMgQFi1ahLW1NWvWrGHBggXVrsPDrm/fvsCd/BjmMvsXFxcTGhoKgE6nQ6fTqcu8vb2xtramoKCAtWvXmt1+RddmRe+ZRqNRewmV7RHQp08fnJ2dKS4uZtGiRZUeSnCvczE0NJT8/PxKbausF154Qe39sHDhwnvmQ6jo8+DUqVMA6iwOQgjxsJGggRBCiHJatmzJ0qVLsbW15bfffmPgwIGEhoZy/vx5ky/zeXl5xMbGVuvmrFWrVuoTxzlz5qjZ3uHOjc6oUaMq7J4+aNAgPDw8UBSFyZMns3r1ajWBW0lJCZcvX2bNmjUsXbq00nWaMGECzZo1o7i4mLfffptvv/3WJFGgXq8nNjaW2bNnM2LEiKo2uVI8PDxwdHREURSmTZvGxYsXgTttOnjwICNHjrwv+6lJW41T+MXGxrJixQr1aXRubi4rV65kwYIFNRq/7eHhgY2NDQaDgaSkpCqVHTx4MEuWLEGj0RAWFsb8+fOrNJb9v2358uW4ubnh5uamPpG+H3x8fNTZLqZNm0ZkZKSaPPTSpUtMnjxZHR40a9Ysk7JNmzZVg3IhISF8/fXX6o33zZs3WbRoEREREerUincbOnQoCxYsIC4uzqSnQkZGBh9++CGpqakAJlOlarVa/P39gTvTRU6cONHkvb916xb79+9n/Pjx5OXlqa8bz8XvvvuOTZs2qQGLGzdusHDhQlavXl2tc7FOnTrMmzcPKysrzpw5w/Dhwzl48KBJQOTSpUts3LiRV199lfDwcLPbyczMVGdW6NKlS5XrIYQQDwJJhCiEEMIsLy8v1q1bR0BAABcvXiQ4OJjg4GA0Gg1arZbi4mKTL+9OTk74+fmp89vfi5WVFXPnzmXSpEmcP3+eIUOG4ODgANyZEcDBwYEvvvjC4jzxNjY2fP7550yePJmEhASWLl3KJ598glar5fbt2+oNUlVmdXBxceGbb75h0qRJnD17lsWLF7N48WLq1q1LaWmpSXubN29e6e1WhVar5d1332XevHnEx8fTp08fHB0dKSkpoaioiPr167No0SLGjx9fo/3UpK2DBw9WeyiEhISwfPly6tati16vp7S0lF69euHu7l7tRIjOzs54enoSHR3Nvn37zE5rWZEBAwag0Wjw9/cnPDyckpIS5s+ff9+SZj4MNBoNy5cv56233uL8+fPMmjWLwMBAHBwc1Fk5rK2tCQwMNLl5NwoICCA5OZmjR4+yZMkSPv30U5ydncnNzUVRFMaPH09CQgLx8fHUqVPHpKxerycsLIywsDCsrKzUz4uyAYQ33nij3FSGw4YN49atW3z22WdER0cTHR2Nvb09Go3GpLeAMUkhwJgxY/jxxx+5cOECc+fOZd68eTg7O6PX61EUBV9fX/744w+2bdtW5WPo5eXFkiVLmDt3LklJSfj5+WFjY4OzszMFBQUmAQRLnzPGvA3u7u4yPEEI8dCSoIEQQgiLOnXqxK5du9i7dy8xMTGcOHGCrKws8vLycHR0pGXLlrRp04YePXrw0ksvWZzT3ZLevXuzbt06Vq5cybFjx7h9+zaPPvooL7/8Mm+//TZPPvlkheVdXV0JCwsjKiqKyMhIzpw5Q25uLnXr1qVRo0Z0796dQYMGValOjz/+OBEREURFRbF7924SExPJycnB2tqav/zlL7i7u9O7d2969+5dpe1WxfDhw2nSpAmrV68mMTGRkpISGjZsiKenJ2+//bYaEKmp6rbV1taWb775hlWrVhEVFUV6ejqKotChQwcGDx6Mr68vK1asqFHdfH19iY6OJjIykmnTplX5hr9v375oNBpmzpzJpk2bKC0tVZMlPkiMQ38aN25832cqadiwIREREWzYsIEffviB5ORkbt++TePGjenSpQtvvvkm7u7uZss6OTmxZs0awsLC2L59O6mpqSiKwjPPPMOoUaPw9vZm4MCBgOlQI7iT2PTQoUMkJCRw+fJlMjMzKS4upmnTpnTs2JG///3vPPvss2b3O27cOHr37k1YWBhxcXFkZGSgKAotWrSgbdu26jAGo7p167Jx40ZWrFjB3r17uX79OhqNhi5duuDr60u/fv0ICAio9jEcOHAg3bp1Izw8nIMHD5KWloZer1c//zw8PPDy8rI49CAyMhK4cz4LIcTDykp5kPvsCSGEEOJ/UmlpKT4+PqSlpbFu3bo/7Xhwb29v0tLSWLBgAUOHDq3t6lRafn4+Xbt2xWAwsH79ejUPhfi3y5cv4+XlhZOTEwcOHCg3C4oQQjwsJKeBEEIIIR441tbWTJ06FYBVq1bVcm3+M65evUpaWhpPPPEEf/vb32q7OlXy7bffYjAYcHFxMZl5QfzbV199haIojBs3TgIGQoiHmgQNhBBCCPFA6tevHx06dCA2NrZa0+Y96IzTIU6dOhWNRlPLtTGVl5fH9OnTiY2NVXMgAKSnp/Pxxx/z+eefAzBq1KhyOQ3EnYBQREQETZo0YfTo0bVdHSGEqBEZniCEEEKIB1ZSUhJ79+6lffv29OrVq7ar8z8jNzfXZEiIk5MTgElCQh8fH4KCgrCxkRRZd0tISOCXX36ha9euMmuCEOKhJ0EDIYQQQghhori4mE2bNnH48GHOnz9PdnY2RUVFuLi40K5dOwYPHoyPj88Dl1hSCCHE/SdBAyGEEEIIIYQQQpglOQ2EEEIIIYQQQghhlgQNhBBCCCGEEEIIYZYEDYQQQgghhBBCCGGWBA2EEEIIIYQQQghhlgQNhBBCCCGEEEIIYZYEDYQQQgghhBBCCGGWBA2EEEIIIYQQQghhlgQNhBBCCCGEEEIIYZYEDYQQQgghhBBCCGHW/wNHmx4y0ri04gAAAABJRU5ErkJggg==\n", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "with sns.plotting_context('talk'), sns.axes_style('ticks'):\n", "\n", " bins = np.logspace(-2, 3, 100)\n", "\n", " h7, b7 = np.histogram(rgi7['area_km2'], bins=bins)\n", " h6, b6 = np.histogram(rgi6['Area'], bins=bins)\n", "\n", " f, ax = plt.subplots(figsize=(12, 7))\n", " ax.plot(b6[:-1], h6, color='C0', label='RGI 6.0');\n", " ax.plot(b7[:-1], h7, color='C3', label='RGI 7.0');\n", " ax.set_xscale('log')\n", " ax.set_yscale('log')\n", " plt.legend()\n", " ax.set_title('Number of glaciers per size category (global)')\n", " ax.set_xlabel('Glacier area (km², logscale)');\n", " ax.set_ylabel('Glacier number (logscale)');\n", " plt.savefig(user_guide_dir + '/docs/img/global_stats/global_histogram.png', dpi=100, bbox_inches='tight')" ] }, { "cell_type": "markdown", "id": "5301c8ef-9e8b-49e5-8671-2ee16de8da2f", "metadata": {}, "source": [ "### Global attributes statistics " ] }, { "cell_type": "code", "execution_count": 96, "id": "027597ae-e0a1-454c-9fd2-517979333fba", "metadata": {}, "outputs": [], "source": [ "rgi7['rgi_id'] = rgi7.index\n", "rgi6['RGIId'] = rgi6.index" ] }, { "cell_type": "markdown", "id": "fa1de6f9-5cf5-4b02-94d3-fe5d3a70637c", "metadata": {}, "source": [ "#### Terminus " ] }, { "cell_type": "code", "execution_count": 97, "id": "14208a8d-a0c2-4c38-ae1d-9e3ada1828a2", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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Terminus typeRGI 7.0 (N)RGI 6.0 (N)RGI 7.0 (Area)RGI 6.0 (Area)
Value
0Land-terminating02120050397096
1Marine-terminating15613075159302197514
2Lake-terminating0298027172
3Shelf-terminating0169083958
9Not assigned27297005474420
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" ], "text/plain": [ " Terminus type RGI 7.0 (N) RGI 6.0 (N) RGI 7.0 (Area) \\\n", "Value \n", "0 Land-terminating 0 212005 0 \n", "1 Marine-terminating 1561 3075 159302 \n", "2 Lake-terminating 0 298 0 \n", "3 Shelf-terminating 0 169 0 \n", "9 Not assigned 272970 0 547442 \n", "\n", " RGI 6.0 (Area) \n", "Value \n", "0 397096 \n", "1 197514 \n", "2 27172 \n", "3 83958 \n", "9 0 " ] }, "execution_count": 97, "metadata": {}, "output_type": "execute_result" } ], "source": [ "rdf = pd.DataFrame(index=[0, 1, 2, 3, 9])\n", "rdf.index.name = 'Value'\n", "rdf['Terminus type'] = ['Land-terminating', 'Marine-terminating', 'Lake-terminating', 'Shelf-terminating', 'Not assigned']\n", "rdf['RGI7 (N)'] = rgi7.groupby('term_type').count()['rgi_id']\n", "rdf['RGI6 (N)'] = rgi6.groupby('TermType').count()['RGIId'].reset_index(drop=True)\n", "rdf['RGI7 (Area)'] = rgi7.groupby('term_type')['area_km2'].sum().round(0).astype(int)\n", "rdf['RGI6 (Area)'] = rgi6.groupby('TermType')['Area'].sum().round(0).reset_index(drop=True)\n", "rdf = rdf.replace(np.NaN, 0)\n", "rdf[['RGI7 (N)', 'RGI6 (N)']] = rdf[['RGI7 (N)', 'RGI6 (N)']].astype(int)\n", "rdf[['RGI7 (Area)', 'RGI6 (Area)']] = rdf[['RGI7 (Area)', 'RGI6 (Area)']].astype(int)\n", "rdf.columns = ['Terminus type', 'RGI 7.0 (N)', 'RGI 6.0 (N)', 'RGI 7.0 (Area)', 'RGI 6.0 (Area)']\n", "rdf" ] }, { "cell_type": "code", "execution_count": 98, "id": "6ff319f3-f933-4dec-90ff-55973bd8b886", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "| Value | Terminus type | RGI 7.0 (N) | RGI 6.0 (N) | RGI 7.0 (Area) | RGI 6.0 (Area) |\n", "|--------:|:-------------------|--------------:|--------------:|-----------------:|-----------------:|\n", "| 0 | Land-terminating | 0 | 212005 | 0 | 397096 |\n", "| 1 | Marine-terminating | 1561 | 3075 | 159302 | 197514 |\n", "| 2 | Lake-terminating | 0 | 298 | 0 | 27172 |\n", "| 3 | Shelf-terminating | 0 | 169 | 0 | 83958 |\n", "| 9 | Not assigned | 272970 | 0 | 547442 | 0 |\n" ] } ], "source": [ "print(rdf.to_markdown())" ] }, { "cell_type": "markdown", "id": "fe233104-0106-42d7-8342-09881de0f310", "metadata": {}, "source": [ "#### Surging " ] }, { "cell_type": "code", "execution_count": 99, "id": "5d4a115d-af49-4011-a129-fad610e090b0", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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Surge typeRGI 7.0 (N)RGI 6.0 (N)RGI 7.0 (Area)RGI 6.0 (Area)
Value
0No evidence27190142515566418127426
1Possible6305092333423622
2Probable8253833125419376
3Observed11754488573843066
9Not assigned01716920492249
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" ], "text/plain": [ " Surge type RGI 7.0 (N) RGI 6.0 (N) RGI 7.0 (Area) RGI 6.0 (Area)\n", "Value \n", "0 No evidence 271901 42515 566418 127426\n", "1 Possible 630 509 23334 23622\n", "2 Probable 825 383 31254 19376\n", "3 Observed 1175 448 85738 43066\n", "9 Not assigned 0 171692 0 492249" ] }, "execution_count": 99, "metadata": {}, "output_type": "execute_result" } ], "source": [ "rdf = pd.DataFrame(index=[0, 1, 2, 3, 9])\n", "rdf.index.name = 'Value'\n", "rdf['Surging'] = ['No evidence', 'Possible', 'Probable', 'Observed', 'Not assigned']\n", "rdf['RGI7 (N)'] = rgi7.groupby('surge_type').count()['rgi_id']\n", "rdf['RGI6 (N)'] = rgi6.groupby('Surging').count()['RGIId']\n", "rdf['RGI7 (Area)'] = rgi7.groupby('surge_type')['area_km2'].sum().round(0).astype(int)\n", "rdf['RGI6 (Area)'] = rgi6.groupby('Surging')['Area'].sum().round(0).astype(int)\n", "rdf = rdf.replace(np.NaN, 0)\n", "rdf[['RGI7 (N)', 'RGI6 (N)']] = rdf[['RGI7 (N)', 'RGI6 (N)']].astype(int)\n", "rdf[['RGI7 (Area)', 'RGI6 (Area)']] = rdf[['RGI7 (Area)', 'RGI6 (Area)']].astype(int)\n", "rdf.columns = ['Surge type', 'RGI 7.0 (N)', 'RGI 6.0 (N)', 'RGI 7.0 (Area)', 'RGI 6.0 (Area)']\n", "rdf" ] }, { "cell_type": "code", "execution_count": 100, "id": "acd3b2ec-7ccf-4a0b-9800-1af203beb0e7", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "| Value | Surge type | RGI 7.0 (N) | RGI 6.0 (N) | RGI 7.0 (Area) | RGI 6.0 (Area) |\n", "|--------:|:-------------|--------------:|--------------:|-----------------:|-----------------:|\n", "| 0 | No evidence | 271901 | 42515 | 566418 | 127426 |\n", "| 1 | Possible | 630 | 509 | 23334 | 23622 |\n", "| 2 | Probable | 825 | 383 | 31254 | 19376 |\n", "| 3 | Observed | 1175 | 448 | 85738 | 43066 |\n", "| 9 | Not assigned | 0 | 171692 | 0 | 492249 |\n" ] } ], "source": [ "print(rdf.to_markdown())" ] }, { "cell_type": "code", "execution_count": null, "id": "f8ac9bb4-545d-4774-ae1b-96e73ff42da7", "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.10.8" } }, "nbformat": 4, "nbformat_minor": 5 }