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Project_Carmignac/brouillon/.ipynb_checkpoints/clus11mars-Copy1 (5)-checkpoint.ipynb

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{
"cell_type": "code",
"execution_count": 1,
"id": "2ef771e3-f905-4c82-97ab-7f879551824c",
"metadata": {},
"outputs": [],
"source": [
"# MEETING 11 MARS\n"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "f645c749-321e-46c4-ae5e-0ba2c1967f81",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Requirement already satisfied: networkx in /opt/python/lib/python3.13/site-packages (3.6.1)\n",
"Note: you may need to restart the kernel to use updated packages.\n"
]
}
],
"source": [
"pip install networkx"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "38108d9e-a00c-4026-afd8-706b7131566e",
"metadata": {},
"outputs": [],
"source": [
"import warnings\n",
"warnings.filterwarnings(\"ignore\")\n",
"\n",
"import numpy as np\n",
"import pandas as pd\n",
"import matplotlib.pyplot as plt\n",
"import seaborn as sns\n",
"\n",
"from sklearn.preprocessing import StandardScaler, RobustScaler\n",
"from sklearn.cluster import KMeans\n",
"from sklearn.mixture import GaussianMixture\n",
"from sklearn.metrics import silhouette_score, davies_bouldin_score, pairwise_distances\n",
"from sklearn.linear_model import LinearRegression\n",
"from sklearn.neighbors import kneighbors_graph\n",
"from sklearn.manifold import MDS\n",
"\n",
"import networkx as nx\n",
"\n",
"sns.set_style(\"whitegrid\")\n",
"pd.set_option(\"display.max_columns\", 200)\n",
"pd.set_option(\"display.max_rows\", 200)\n",
"\n",
"EPS = 1e-9\n",
"RANDOM_STATE = 42"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "558c8d6d-9a8d-4c82-9765-620f7ce8d116",
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"outputs": [
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" <td>PRIVATE CLIENT</td>\n",
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" <td>0.00</td>\n",
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" <td>358385.55</td>\n",
" <td>0.00</td>\n",
" <td>358385.55</td>\n",
" </tr>\n",
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],
"text/plain": [
" Unnamed: 0 Agreement - Code Company - Id \\\n",
"0 0 003 166 \n",
"1 1 003 166 \n",
"2 2 003 166 \n",
"3 3 003 166 \n",
"4 4 003 166 \n",
"... ... ... ... \n",
"2574456 2574456 PRIVATE CLIENT PRIVATE CLIENT \n",
"2574457 2574457 PRIVATE CLIENT PRIVATE CLIENT \n",
"2574458 2574458 PRIVATE CLIENT PRIVATE CLIENT \n",
"2574459 2574459 PRIVATE CLIENT PRIVATE CLIENT \n",
"2574460 2574460 PRIVATE CLIENT PRIVATE CLIENT \n",
"\n",
" Company - Ultimate Parent Id Registrar Account - ID \\\n",
"0 166 200127202 \n",
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"2 166 406533 \n",
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"4 166 406533 \n",
"... ... ... \n",
"2574456 PRIVATE CLIENT PRIVATE CLIENT \n",
"2574457 PRIVATE CLIENT PRIVATE CLIENT \n",
"2574458 PRIVATE CLIENT PRIVATE CLIENT \n",
"2574459 PRIVATE CLIENT PRIVATE CLIENT \n",
"2574460 PRIVATE CLIENT PRIVATE CLIENT \n",
"\n",
" Registrar Account - Region RegistrarAccount - Country \\\n",
"0 FRANCE FRANCE \n",
"1 FRANCE FRANCE \n",
"2 FRANCE FRANCE \n",
"3 FRANCE FRANCE \n",
"4 FRANCE FRANCE \n",
"... ... ... \n",
"2574456 LUXEMBOURG LUXEMBOURG \n",
"2574457 LUXEMBOURG LUXEMBOURG \n",
"2574458 LUXEMBOURG LUXEMBOURG \n",
"2574459 LUXEMBOURG LUXEMBOURG \n",
"2574460 LUXEMBOURG LUXEMBOURG \n",
"\n",
" Product - Asset Type Product - Strategy Product - Legal Status \\\n",
"0 EQUITY INVESTISSEMENT SICAV \n",
"1 DIVERSIFIED PATRIMOINE FCP \n",
"2 EQUITY INVESTISSEMENT FCP \n",
"3 EQUITY INVESTISSEMENT FCP \n",
"4 EQUITY INVESTISSEMENT FCP \n",
"... ... ... ... \n",
"2574456 FIXED INCOME SÉCURITÉ FCP \n",
"2574457 FIXED INCOME SÉCURITÉ FCP \n",
"2574458 FIXED INCOME SÉCURITÉ FCP \n",
"2574459 FIXED INCOME SÉCURITÉ FCP \n",
"2574460 FIXED INCOME SÉCURITÉ SICAV \n",
"\n",
" Product - Is Dedie ? Product - Fund \\\n",
"0 NO CARMIGNAC PORTFOLIO INVESTISSEMENT \n",
"1 NO CARMIGNAC PATRIMOINE \n",
"2 NO CARMIGNAC INVESTISSEMENT \n",
"3 NO CARMIGNAC INVESTISSEMENT \n",
"4 NO CARMIGNAC INVESTISSEMENT \n",
"... ... ... \n",
"2574456 NO CARMIGNAC SÉCURITÉ \n",
"2574457 NO CARMIGNAC SÉCURITÉ \n",
"2574458 NO CARMIGNAC SÉCURITÉ \n",
"2574459 NO CARMIGNAC SÉCURITÉ \n",
"2574460 NO CARMIGNAC PORTFOLIO SÉCURITÉ \n",
"\n",
" Product - Shareclass Type Product - Shareclass Currency \\\n",
"0 F EUR \n",
"1 A EUR \n",
"2 A EUR \n",
"3 A EUR \n",
"4 A EUR \n",
"... ... ... \n",
"2574456 AW & AW-R EUR \n",
"2574457 AW & AW-R EUR \n",
"2574458 AW & AW-R EUR \n",
"2574459 AW & AW-R EUR \n",
"2574460 AW & AW-R EUR \n",
"\n",
" Product - Isin Centralisation Date Quantity - Subscription \\\n",
"0 LU0992625839 2020-11-05 1636.000 \n",
"1 FR0010135103 2015-03-09 144.690 \n",
"2 FR0010148981 2016-10-26 0.000 \n",
"3 FR0010148981 2018-10-18 0.000 \n",
"4 FR0010148981 2019-04-08 0.000 \n",
"... ... ... ... \n",
"2574456 FR0010149120 2015-06-12 0.000 \n",
"2574457 FR0010149120 2015-09-18 328.726 \n",
"2574458 FR0010149120 2015-09-25 4.443 \n",
"2574459 FR0010149120 2015-11-09 0.000 \n",
"2574460 LU1299306321 2016-01-11 3595.000 \n",
"\n",
" Quantity - Redemption Quantity - NetFlows Value Ccy - Subscription \\\n",
"0 0.000 1636.000 280983.00 \n",
"1 0.000 144.690 99985.13 \n",
"2 -8.321 -8.321 0.00 \n",
"3 -22.083 -22.083 0.00 \n",
"4 -465.992 -465.992 0.00 \n",
"... ... ... ... \n",
"2574456 -20.000 -20.000 0.00 \n",
"2574457 0.000 328.726 564028.07 \n",
"2574458 0.000 4.443 7603.66 \n",
"2574459 -440.000 -440.000 0.00 \n",
"2574460 0.000 3595.000 358385.55 \n",
"\n",
" Value Ccy - Redemption Value Ccy - NetFlows Value € - Subscription \\\n",
"0 0.00 280983.00 280983.00 \n",
"1 0.00 99985.13 99985.13 \n",
"2 -9384.76 -9384.76 0.00 \n",
"3 -25227.40 -25227.40 0.00 \n",
"4 -563775.76 -563775.76 0.00 \n",
"... ... ... ... \n",
"2574456 -34294.40 -34294.40 0.00 \n",
"2574457 0.00 564028.07 564028.07 \n",
"2574458 0.00 7603.66 7603.66 \n",
"2574459 -754696.80 -754696.80 0.00 \n",
"2574460 0.00 358385.55 358385.55 \n",
"\n",
" Value € - Redemption Value € - NetFlows \n",
"0 0.00 280983.00 \n",
"1 0.00 99985.13 \n",
"2 -9384.76 -9384.76 \n",
"3 -25227.40 -25227.40 \n",
"4 -563775.76 -563775.76 \n",
"... ... ... \n",
"2574456 -34294.40 -34294.40 \n",
"2574457 0.00 564028.07 \n",
"2574458 0.00 7603.66 \n",
"2574459 -754696.80 -754696.80 \n",
"2574460 0.00 358385.55 \n",
"\n",
"[2574461 rows x 25 columns]"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import pandas as pd \n",
"df_flows = pd.read_csv(\"flows.csv\")\n",
"df_flows"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "b1b88d12-7909-435b-b5a8-7814d5ad09af",
"metadata": {},
"outputs": [
{
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"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Unnamed: 0.1</th>\n",
" <th>Unnamed: 0</th>\n",
" <th>Agreement - Code</th>\n",
" <th>Company - Id</th>\n",
" <th>Company - Ultimate Parent Id</th>\n",
" <th>Registrar Account - ID</th>\n",
" <th>Registrar Account - Region</th>\n",
" <th>RegistrarAccount - Country</th>\n",
" <th>Product - Asset Type</th>\n",
" <th>Product - Strategy</th>\n",
" <th>Product - Legal Status</th>\n",
" <th>Product - Is Dedie ?</th>\n",
" <th>Product - Fund</th>\n",
" <th>Product - Shareclass Type</th>\n",
" <th>Product - Shareclass Currency</th>\n",
" <th>Product - Isin</th>\n",
" <th>Centralisation Date</th>\n",
" <th>Quantity - AUM</th>\n",
" <th>Value - AUM CCY</th>\n",
" <th>Value - AUM €</th>\n",
" <th>repair_flag</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>166.0</td>\n",
" <td>166.0</td>\n",
" <td>200000647</td>\n",
" <td>FRANCE</td>\n",
" <td>FRANCE</td>\n",
" <td>DIVERSIFIED</td>\n",
" <td>PATRIMOINE</td>\n",
" <td>FCP</td>\n",
" <td>NO</td>\n",
" <td>CARMIGNAC PATRIMOINE</td>\n",
" <td>A</td>\n",
" <td>EUR</td>\n",
" <td>FR0010135103</td>\n",
" <td>2015-03-31</td>\n",
" <td>35.368</td>\n",
" <td>24648.6666</td>\n",
" <td>24648.6666</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>3</td>\n",
" <td>166.0</td>\n",
" <td>166.0</td>\n",
" <td>200000647</td>\n",
" <td>FRANCE</td>\n",
" <td>FRANCE</td>\n",
" <td>DIVERSIFIED</td>\n",
" <td>PATRIMOINE</td>\n",
" <td>FCP</td>\n",
" <td>NO</td>\n",
" <td>CARMIGNAC PATRIMOINE</td>\n",
" <td>A</td>\n",
" <td>EUR</td>\n",
" <td>FR0010135103</td>\n",
" <td>2015-11-30</td>\n",
" <td>35.368</td>\n",
" <td>22413.0553</td>\n",
" <td>22413.0553</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>3</td>\n",
" <td>166.0</td>\n",
" <td>166.0</td>\n",
" <td>200000647</td>\n",
" <td>FRANCE</td>\n",
" <td>FRANCE</td>\n",
" <td>DIVERSIFIED</td>\n",
" <td>PATRIMOINE</td>\n",
" <td>FCP</td>\n",
" <td>NO</td>\n",
" <td>CARMIGNAC PATRIMOINE</td>\n",
" <td>A</td>\n",
" <td>EUR</td>\n",
" <td>FR0010135103</td>\n",
" <td>2015-12-31</td>\n",
" <td>35.368</td>\n",
" <td>22051.2406</td>\n",
" <td>22051.2406</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>3</td>\n",
" <td>3</td>\n",
" <td>3</td>\n",
" <td>166.0</td>\n",
" <td>166.0</td>\n",
" <td>200000647</td>\n",
" <td>FRANCE</td>\n",
" <td>FRANCE</td>\n",
" <td>DIVERSIFIED</td>\n",
" <td>PATRIMOINE</td>\n",
" <td>FCP</td>\n",
" <td>NO</td>\n",
" <td>CARMIGNAC PATRIMOINE</td>\n",
" <td>A</td>\n",
" <td>EUR</td>\n",
" <td>FR0010135103</td>\n",
" <td>2016-03-31</td>\n",
" <td>35.368</td>\n",
" <td>21626.1173</td>\n",
" <td>21626.1173</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>4</td>\n",
" <td>4</td>\n",
" <td>3</td>\n",
" <td>166.0</td>\n",
" <td>166.0</td>\n",
" <td>200000647</td>\n",
" <td>FRANCE</td>\n",
" <td>FRANCE</td>\n",
" <td>DIVERSIFIED</td>\n",
" <td>PATRIMOINE</td>\n",
" <td>FCP</td>\n",
" <td>NO</td>\n",
" <td>CARMIGNAC PATRIMOINE</td>\n",
" <td>A</td>\n",
" <td>EUR</td>\n",
" <td>FR0010135103</td>\n",
" <td>2016-11-30</td>\n",
" <td>35.368</td>\n",
" <td>22489.4502</td>\n",
" <td>22489.4502</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
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" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5516128</th>\n",
" <td>5516128</td>\n",
" <td>4880294</td>\n",
" <td>PRIVATE CLIENT</td>\n",
" <td>PRIVATE CLIENT</td>\n",
" <td>PRIVATE CLIENT</td>\n",
" <td>PRIVATE CLIENT</td>\n",
" <td>SWITZERLAND</td>\n",
" <td>SWITZERLAND</td>\n",
" <td>FIXED INCOME</td>\n",
" <td>SÉCURITÉ</td>\n",
" <td>SICAV</td>\n",
" <td>NO</td>\n",
" <td>CARMIGNAC PORTFOLIO SÉCURITÉ</td>\n",
" <td>AW &amp; AW-R</td>\n",
" <td>EUR</td>\n",
" <td>LU1299306321</td>\n",
" <td>2020-10-31</td>\n",
" <td>3099.000</td>\n",
" <td>318422.2500</td>\n",
" <td>318422.2500</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5516129</th>\n",
" <td>5516129</td>\n",
" <td>4880294</td>\n",
" <td>PRIVATE CLIENT</td>\n",
" <td>PRIVATE CLIENT</td>\n",
" <td>PRIVATE CLIENT</td>\n",
" <td>PRIVATE CLIENT</td>\n",
" <td>SWITZERLAND</td>\n",
" <td>SWITZERLAND</td>\n",
" <td>FIXED INCOME</td>\n",
" <td>SÉCURITÉ</td>\n",
" <td>SICAV</td>\n",
" <td>NO</td>\n",
" <td>CARMIGNAC PORTFOLIO SÉCURITÉ</td>\n",
" <td>AW &amp; AW-R</td>\n",
" <td>EUR</td>\n",
" <td>LU1299306321</td>\n",
" <td>2020-10-31</td>\n",
" <td>3099.000</td>\n",
" <td>318422.2500</td>\n",
" <td>318422.2500</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5516130</th>\n",
" <td>5516130</td>\n",
" <td>4880295</td>\n",
" <td>PRIVATE CLIENT</td>\n",
" <td>PRIVATE CLIENT</td>\n",
" <td>PRIVATE CLIENT</td>\n",
" <td>PRIVATE CLIENT</td>\n",
" <td>SWITZERLAND</td>\n",
" <td>SWITZERLAND</td>\n",
" <td>FIXED INCOME</td>\n",
" <td>SÉCURITÉ</td>\n",
" <td>SICAV</td>\n",
" <td>NO</td>\n",
" <td>CARMIGNAC PORTFOLIO SÉCURITÉ</td>\n",
" <td>AW &amp; AW-R</td>\n",
" <td>EUR</td>\n",
" <td>LU1299306321</td>\n",
" <td>2021-07-31</td>\n",
" <td>2835.000</td>\n",
" <td>297618.3000</td>\n",
" <td>297618.3000</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5516131</th>\n",
" <td>5516131</td>\n",
" <td>4880295</td>\n",
" <td>PRIVATE CLIENT</td>\n",
" <td>PRIVATE CLIENT</td>\n",
" <td>PRIVATE CLIENT</td>\n",
" <td>PRIVATE CLIENT</td>\n",
" <td>SWITZERLAND</td>\n",
" <td>SWITZERLAND</td>\n",
" <td>FIXED INCOME</td>\n",
" <td>SÉCURITÉ</td>\n",
" <td>SICAV</td>\n",
" <td>NO</td>\n",
" <td>CARMIGNAC PORTFOLIO SÉCURITÉ</td>\n",
" <td>AW &amp; AW-R</td>\n",
" <td>EUR</td>\n",
" <td>LU1299306321</td>\n",
" <td>2021-07-31</td>\n",
" <td>2835.000</td>\n",
" <td>297618.3000</td>\n",
" <td>297618.3000</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5516132</th>\n",
" <td>5516132</td>\n",
" <td>4880296</td>\n",
" <td>PRIVATE CLIENT</td>\n",
" <td>PRIVATE CLIENT</td>\n",
" <td>PRIVATE CLIENT</td>\n",
" <td>PRIVATE CLIENT</td>\n",
" <td>SWITZERLAND</td>\n",
" <td>SWITZERLAND</td>\n",
" <td>FIXED INCOME</td>\n",
" <td>SÉCURITÉ</td>\n",
" <td>SICAV</td>\n",
" <td>NO</td>\n",
" <td>CARMIGNAC PORTFOLIO SÉCURITÉ</td>\n",
" <td>FW &amp; FW-R</td>\n",
" <td>EUR</td>\n",
" <td>LU1792391911</td>\n",
" <td>2020-07-31</td>\n",
" <td>2916.394</td>\n",
" <td>287410.6287</td>\n",
" <td>287410.6287</td>\n",
" <td>False</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>5516133 rows × 21 columns</p>\n",
"</div>"
],
"text/plain": [
" Unnamed: 0.1 Unnamed: 0 Agreement - Code Company - Id \\\n",
"0 0 0 3 166.0 \n",
"1 1 1 3 166.0 \n",
"2 2 2 3 166.0 \n",
"3 3 3 3 166.0 \n",
"4 4 4 3 166.0 \n",
"... ... ... ... ... \n",
"5516128 5516128 4880294 PRIVATE CLIENT PRIVATE CLIENT \n",
"5516129 5516129 4880294 PRIVATE CLIENT PRIVATE CLIENT \n",
"5516130 5516130 4880295 PRIVATE CLIENT PRIVATE CLIENT \n",
"5516131 5516131 4880295 PRIVATE CLIENT PRIVATE CLIENT \n",
"5516132 5516132 4880296 PRIVATE CLIENT PRIVATE CLIENT \n",
"\n",
" Company - Ultimate Parent Id Registrar Account - ID \\\n",
"0 166.0 200000647 \n",
"1 166.0 200000647 \n",
"2 166.0 200000647 \n",
"3 166.0 200000647 \n",
"4 166.0 200000647 \n",
"... ... ... \n",
"5516128 PRIVATE CLIENT PRIVATE CLIENT \n",
"5516129 PRIVATE CLIENT PRIVATE CLIENT \n",
"5516130 PRIVATE CLIENT PRIVATE CLIENT \n",
"5516131 PRIVATE CLIENT PRIVATE CLIENT \n",
"5516132 PRIVATE CLIENT PRIVATE CLIENT \n",
"\n",
" Registrar Account - Region RegistrarAccount - Country \\\n",
"0 FRANCE FRANCE \n",
"1 FRANCE FRANCE \n",
"2 FRANCE FRANCE \n",
"3 FRANCE FRANCE \n",
"4 FRANCE FRANCE \n",
"... ... ... \n",
"5516128 SWITZERLAND SWITZERLAND \n",
"5516129 SWITZERLAND SWITZERLAND \n",
"5516130 SWITZERLAND SWITZERLAND \n",
"5516131 SWITZERLAND SWITZERLAND \n",
"5516132 SWITZERLAND SWITZERLAND \n",
"\n",
" Product - Asset Type Product - Strategy Product - Legal Status \\\n",
"0 DIVERSIFIED PATRIMOINE FCP \n",
"1 DIVERSIFIED PATRIMOINE FCP \n",
"2 DIVERSIFIED PATRIMOINE FCP \n",
"3 DIVERSIFIED PATRIMOINE FCP \n",
"4 DIVERSIFIED PATRIMOINE FCP \n",
"... ... ... ... \n",
"5516128 FIXED INCOME SÉCURITÉ SICAV \n",
"5516129 FIXED INCOME SÉCURITÉ SICAV \n",
"5516130 FIXED INCOME SÉCURITÉ SICAV \n",
"5516131 FIXED INCOME SÉCURITÉ SICAV \n",
"5516132 FIXED INCOME SÉCURITÉ SICAV \n",
"\n",
" Product - Is Dedie ? Product - Fund \\\n",
"0 NO CARMIGNAC PATRIMOINE \n",
"1 NO CARMIGNAC PATRIMOINE \n",
"2 NO CARMIGNAC PATRIMOINE \n",
"3 NO CARMIGNAC PATRIMOINE \n",
"4 NO CARMIGNAC PATRIMOINE \n",
"... ... ... \n",
"5516128 NO CARMIGNAC PORTFOLIO SÉCURITÉ \n",
"5516129 NO CARMIGNAC PORTFOLIO SÉCURITÉ \n",
"5516130 NO CARMIGNAC PORTFOLIO SÉCURITÉ \n",
"5516131 NO CARMIGNAC PORTFOLIO SÉCURITÉ \n",
"5516132 NO CARMIGNAC PORTFOLIO SÉCURITÉ \n",
"\n",
" Product - Shareclass Type Product - Shareclass Currency \\\n",
"0 A EUR \n",
"1 A EUR \n",
"2 A EUR \n",
"3 A EUR \n",
"4 A EUR \n",
"... ... ... \n",
"5516128 AW & AW-R EUR \n",
"5516129 AW & AW-R EUR \n",
"5516130 AW & AW-R EUR \n",
"5516131 AW & AW-R EUR \n",
"5516132 FW & FW-R EUR \n",
"\n",
" Product - Isin Centralisation Date Quantity - AUM Value - AUM CCY \\\n",
"0 FR0010135103 2015-03-31 35.368 24648.6666 \n",
"1 FR0010135103 2015-11-30 35.368 22413.0553 \n",
"2 FR0010135103 2015-12-31 35.368 22051.2406 \n",
"3 FR0010135103 2016-03-31 35.368 21626.1173 \n",
"4 FR0010135103 2016-11-30 35.368 22489.4502 \n",
"... ... ... ... ... \n",
"5516128 LU1299306321 2020-10-31 3099.000 318422.2500 \n",
"5516129 LU1299306321 2020-10-31 3099.000 318422.2500 \n",
"5516130 LU1299306321 2021-07-31 2835.000 297618.3000 \n",
"5516131 LU1299306321 2021-07-31 2835.000 297618.3000 \n",
"5516132 LU1792391911 2020-07-31 2916.394 287410.6287 \n",
"\n",
" Value - AUM € repair_flag \n",
"0 24648.6666 False \n",
"1 22413.0553 False \n",
"2 22051.2406 False \n",
"3 21626.1173 False \n",
"4 22489.4502 False \n",
"... ... ... \n",
"5516128 318422.2500 False \n",
"5516129 318422.2500 False \n",
"5516130 297618.3000 False \n",
"5516131 297618.3000 False \n",
"5516132 287410.6287 False \n",
"\n",
"[5516133 rows x 21 columns]"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_aum = pd.read_csv(\"AUM_repaired.csv\")\n",
"df_aum"
]
},
{
"cell_type": "code",
"execution_count": 26,
"id": "4c799ab2-b16e-4cbe-85ee-818002c758c4",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array(['CARMIGNAC PATRIMOINE', 'CARMIGNAC INVESTISSEMENT',\n",
" 'CARMIGNAC PORTFOLIO INVESTISSEMENT',\n",
" 'CARMIGNAC EURO-INVESTISSEMENT', 'CARMIGNAC INNOVATION',\n",
" 'CARMIGNAC ABSOLUTE RETURN EUROPE',\n",
" 'CARMIGNAC PORTFOLIO CLIMATE TRANSITION',\n",
" 'CARMIGNAC EURO-ENTREPRENEURS', 'CARMIGNAC EMERGENTS',\n",
" 'CARMIGNAC PORTFOLIO EMERGING PATRIMOINE',\n",
" 'CARMIGNAC PORTFOLIO GRANDE EUROPE',\n",
" 'CARMIGNAC INVESTISSEMENT LATITUDE', 'CARMIGNAC COURT TERME',\n",
" 'CARMIGNAC SÉCURITÉ', 'CARMIGNAC MULTI EXPERTISE',\n",
" 'CARMIGNAC PORTFOLIO INFOTECH', 'CARMIGNAC PROFIL RÉACTIF 100',\n",
" 'CARMIGNAC PORTFOLIO GLOBAL BOND', 'CARMIGNAC PROFIL RÉACTIF 75',\n",
" 'CARMIGNAC PORTFOLIO PATRIMOINE EUROPE', 'CARMIGNAC CREDIT 2027',\n",
" 'CARMIGNAC PORTFOLIO ASIA DISCOVERY',\n",
" 'CARMIGNAC PORTFOLIO TECH SOLUTIONS',\n",
" 'CARMIGNAC PORTFOLIO FLEXIBLE BOND', 'CARMIGNAC PORTFOLIO CREDIT',\n",
" 'CARMIGNAC PORTFOLIO MARKET NEUTRAL',\n",
" 'CARMIGNAC PORTFOLIO EM DEBT',\n",
" 'CARMIGNAC PORTFOLIO LONG-SHORT EUROPEAN EQUITIES',\n",
" 'CARMIGNAC PORTFOLIO LONG-SHORT GLOBAL EQUITIES',\n",
" 'CARMIGNAC PORTFOLIO PATRIMOINE',\n",
" 'CARMIGNAC PORTFOLIO EURO-ENTREPRENEURS',\n",
" 'CARMIGNAC PORTFOLIO GRANDCHILDREN',\n",
" 'CARMIGNAC PORTFOLIO INVESTISSEMENT LATITUDE',\n",
" 'CARMIGNAC PORTFOLIO EMERGENTS', 'CARMIGNAC PORTFOLIO SÉCURITÉ',\n",
" 'CARMIGNAC PORTFOLIO INFLATION SOLUTION', 'CARMIGNAC CREDIT 2029',\n",
" 'CARMIGNAC CREDIT 2031', 'CARMIGNAC PORTFOLIO CAPITAL CUBE',\n",
" 'CARMIGNAC PORTFOLIO CHINA NEW ECONOMY', 'CARMIGNAC CREDIT 2025',\n",
" 'CARMIGNAC EPARGNE ACTIONS MONDE ISR',\n",
" 'CARMIGNAC PORTFOLIO FAMILY GOVERNED',\n",
" 'CARMIGNAC S.A. SICAV - PART II UCI PRIVATE EVERGREEN',\n",
" 'CARMIGNAC PORTFOLIO HUMAN XPERIENCE',\n",
" 'CARMIGNAC CHINA NEW ECONOMY', 'CARMIGNAC PORTFOLIO CHINA',\n",
" 'CARMIGNAC ALTS ICAV CARMIGNAC CREDIT OPPORTUNITIES',\n",
" 'CARMIGNAC PORTFOLIO MERGER ARBITRAGE PLUS',\n",
" 'CARMIGNAC PORTFOLIO ABSOLUTE RETURN EUROPE',\n",
" 'CARMIGNAC PORTFOLIO FLEXIBLE ALLOCATION 2024',\n",
" 'FP CARMIGNAC EUROPEAN LEADERS',\n",
" 'FP CARMIGNAC GLOBAL EQUITY COMPOUNDERS',\n",
" 'CARMIGNAC PORTFOLIO MERGER ARBITRAGE',\n",
" 'SOLYS - CARMIGNAC EQUITY SELECTION',\n",
" 'FP CARMIGNAC EMERGING MARKETS', 'MAPFRE CARMIGNAC F.P.',\n",
" 'FP CARMIGNAC GLOBAL BOND',\n",
" 'CARMIGNAC ALTS ICAV EUROPEAN LONG SHORT',\n",
" 'CARMIGNAC PORTFOLIO ACTIVE RISK ALLOCATION',\n",
" 'FP CARMIGNAC EMERGING PATRIMOINE', 'FP CARMIGNAC PATRIMOINE',\n",
" 'FP CARMIGNAC EMERGING DISCOVERY',\n",
" 'CREDIT SUISSE CARMIGNAC EMERGING MARKETS MULTI-ASSET FUND',\n",
" 'FONDITALIA CARMIGNAC ACTIVE ALLOCATION',\n",
" 'CARMIGNAC GLOBAL ACTIVE',\n",
" 'LUX IM - CARMIGNAC EMERGING FLEXIBLE BOND',\n",
" 'CARMIGNAC PORTFOLIO EVOLUTION', 'UFF GRANDE EUROPE 0-100',\n",
" 'CFP 1', 'CARMIGNAC PORTFOLIO SUSTAINABLE BOND',\n",
" 'CARMIGNAC PORTFOLIO CROSS ASSET OPPORTUNITIES',\n",
" 'CARMIGNAC PORTFOLIO ALPHA THEMES',\n",
" 'CARMIGNAC PORTFOLIO GLOBAL MARKET NEUTRAL'], dtype=object)"
]
},
"execution_count": 26,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_aum['Product - Fund'].unique()"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "09068a72-80a4-4239-947a-a73f6de10a57",
"metadata": {},
"outputs": [
{
"data": {
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" <td>0.000000e+00</td>\n",
" <td>-1.092380e+02</td>\n",
" <td>-1.092380e+08</td>\n",
" <td>0</td>\n",
" <td>1.386294</td>\n",
" <td>49.280511</td>\n",
" <td>0.998921</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>100000042</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>-6.601150e+02</td>\n",
" <td>-660.115000</td>\n",
" <td>NaN</td>\n",
" <td>0.000000e+00</td>\n",
" <td>-6.601150e+02</td>\n",
" <td>-6.601150e+08</td>\n",
" <td>0</td>\n",
" <td>0.693147</td>\n",
" <td>0.000000</td>\n",
" <td>0.999640</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>100000065</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>-1.746460e+02</td>\n",
" <td>-174.646000</td>\n",
" <td>NaN</td>\n",
" <td>0.000000e+00</td>\n",
" <td>-1.746460e+02</td>\n",
" <td>-1.746460e+08</td>\n",
" <td>0</td>\n",
" <td>0.693147</td>\n",
" <td>0.000000</td>\n",
" <td>0.999640</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>100000069</td>\n",
" <td>65</td>\n",
" <td>73</td>\n",
" <td>-7.479755e+03</td>\n",
" <td>-102.462397</td>\n",
" <td>2168.971331</td>\n",
" <td>3.332040e+04</td>\n",
" <td>-4.080016e+04</td>\n",
" <td>-1.224480e+00</td>\n",
" <td>0</td>\n",
" <td>4.304065</td>\n",
" <td>2168.971331</td>\n",
" <td>0.976619</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>100000073</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>-1.334020e+02</td>\n",
" <td>-133.402000</td>\n",
" <td>NaN</td>\n",
" <td>0.000000e+00</td>\n",
" <td>-1.334020e+02</td>\n",
" <td>-1.334020e+08</td>\n",
" <td>0</td>\n",
" <td>0.693147</td>\n",
" <td>0.000000</td>\n",
" <td>0.999640</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6837</th>\n",
" <td>422905</td>\n",
" <td>208</td>\n",
" <td>212</td>\n",
" <td>-4.329218e+03</td>\n",
" <td>-20.420840</td>\n",
" <td>331.677297</td>\n",
" <td>9.699140e+03</td>\n",
" <td>-1.402836e+04</td>\n",
" <td>-1.446351e+00</td>\n",
" <td>0</td>\n",
" <td>5.361292</td>\n",
" <td>331.677297</td>\n",
" <td>0.925180</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6838</th>\n",
" <td>422906</td>\n",
" <td>1146</td>\n",
" <td>1556</td>\n",
" <td>4.455099e+03</td>\n",
" <td>2.863174</td>\n",
" <td>201.071555</td>\n",
" <td>6.078686e+04</td>\n",
" <td>-5.633177e+04</td>\n",
" <td>-9.267095e-01</td>\n",
" <td>0</td>\n",
" <td>7.350516</td>\n",
" <td>201.071555</td>\n",
" <td>0.587770</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6839</th>\n",
" <td>8307</td>\n",
" <td>204</td>\n",
" <td>221</td>\n",
" <td>2.168303e+04</td>\n",
" <td>98.113249</td>\n",
" <td>2217.940406</td>\n",
" <td>1.204399e+05</td>\n",
" <td>-9.875688e+04</td>\n",
" <td>-8.199681e-01</td>\n",
" <td>0</td>\n",
" <td>5.402677</td>\n",
" <td>2217.940406</td>\n",
" <td>0.926619</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6840</th>\n",
" <td>OFF DISTRIBUTION</td>\n",
" <td>2656</td>\n",
" <td>27679</td>\n",
" <td>1.319043e+08</td>\n",
" <td>4765.499704</td>\n",
" <td>391347.475503</td>\n",
" <td>3.388942e+08</td>\n",
" <td>-2.069900e+08</td>\n",
" <td>-6.107804e-01</td>\n",
" <td>0</td>\n",
" <td>10.228465</td>\n",
" <td>391347.475503</td>\n",
" <td>0.044604</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6841</th>\n",
" <td>PRIVATE CLIENT</td>\n",
" <td>2690</td>\n",
" <td>32363</td>\n",
" <td>-4.181221e+05</td>\n",
" <td>-12.919758</td>\n",
" <td>7572.830139</td>\n",
" <td>1.354277e+07</td>\n",
" <td>-1.396089e+07</td>\n",
" <td>-1.030874e+00</td>\n",
" <td>0</td>\n",
" <td>10.384802</td>\n",
" <td>7572.830139</td>\n",
" <td>0.032374</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>6842 rows × 13 columns</p>\n",
"</div>"
],
"text/plain": [
" Registrar Account - ID n_days n_transactions total_netflows \\\n",
"0 100000028 3 3 -1.092380e+02 \n",
"1 100000042 1 1 -6.601150e+02 \n",
"2 100000065 1 1 -1.746460e+02 \n",
"3 100000069 65 73 -7.479755e+03 \n",
"4 100000073 1 1 -1.334020e+02 \n",
"... ... ... ... ... \n",
"6837 422905 208 212 -4.329218e+03 \n",
"6838 422906 1146 1556 4.455099e+03 \n",
"6839 8307 204 221 2.168303e+04 \n",
"6840 OFF DISTRIBUTION 2656 27679 1.319043e+08 \n",
"6841 PRIVATE CLIENT 2690 32363 -4.181221e+05 \n",
"\n",
" mean_flow std_flow total_subscription total_redemption \\\n",
"0 -36.412667 49.280511 0.000000e+00 -1.092380e+02 \n",
"1 -660.115000 NaN 0.000000e+00 -6.601150e+02 \n",
"2 -174.646000 NaN 0.000000e+00 -1.746460e+02 \n",
"3 -102.462397 2168.971331 3.332040e+04 -4.080016e+04 \n",
"4 -133.402000 NaN 0.000000e+00 -1.334020e+02 \n",
"... ... ... ... ... \n",
"6837 -20.420840 331.677297 9.699140e+03 -1.402836e+04 \n",
"6838 2.863174 201.071555 6.078686e+04 -5.633177e+04 \n",
"6839 98.113249 2217.940406 1.204399e+05 -9.875688e+04 \n",
"6840 4765.499704 391347.475503 3.388942e+08 -2.069900e+08 \n",
"6841 -12.919758 7572.830139 1.354277e+07 -1.396089e+07 \n",
"\n",
" churn_ratio churn_flag activity_score flow_volatility inertia_ratio \n",
"0 -1.092380e+08 0 1.386294 49.280511 0.998921 \n",
"1 -6.601150e+08 0 0.693147 0.000000 0.999640 \n",
"2 -1.746460e+08 0 0.693147 0.000000 0.999640 \n",
"3 -1.224480e+00 0 4.304065 2168.971331 0.976619 \n",
"4 -1.334020e+08 0 0.693147 0.000000 0.999640 \n",
"... ... ... ... ... ... \n",
"6837 -1.446351e+00 0 5.361292 331.677297 0.925180 \n",
"6838 -9.267095e-01 0 7.350516 201.071555 0.587770 \n",
"6839 -8.199681e-01 0 5.402677 2217.940406 0.926619 \n",
"6840 -6.107804e-01 0 10.228465 391347.475503 0.044604 \n",
"6841 -1.030874e+00 0 10.384802 7572.830139 0.032374 \n",
"\n",
"[6842 rows x 13 columns]"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_client = pd.read_csv(\"client_behavior_clean.csv\")\n",
"df_client"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "791d8a11-fa46-400b-bbf1-55c8c055f524",
"metadata": {},
"outputs": [],
"source": [
"\n",
"#external data projet-bdc-data /carmignac /Data Modélisation /Nav\n",
"PATH_NAV = \"s3://projet-bdc-data/carmignac/Data Modélisation/Nav/NAV_Bench_data.csv\" #C’est la table de valorisation / performance du produit.\n",
"PATH_RATES = \"s3://projet-bdc-data/carmignac/Data Modélisation/market data/esterRates.csv\"\n",
"\n",
"# optional competitors\n",
"PATH_COMP_FLOWS = \"s3://projet-bdc-data/carmignac/Data Modélisation/competitors/daily_estimated_flows.csv\"\n",
"PATH_COMP_PERF = \"s3://projet-bdc-data/carmignac/Data Modélisation/competitors/weekly_perf_full.csv\"\n",
"PATH_PEERS = \"s3://projet-bdc-carmignac-g3/peers/CAD_peers.csv\""
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "415015e5-4cdc-4ea9-9c0e-701c58314873",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"flows: (2574461, 25)\n",
"aum: (5516133, 21)\n",
"nav: (623914, 5)\n",
"rates: (2826, 2)\n",
"comp_flows: (963003, 6)\n",
"comp_perf: (2370192, 5)\n",
"peers: (31, 12)\n"
]
}
],
"source": [
"df_nav = pd.read_csv(PATH_NAV, sep=\";\")\n",
"df_rates = pd.read_csv(PATH_RATES,sep=\";\")\n",
"\n",
"df_comp_flows = pd.read_csv(PATH_COMP_FLOWS,sep=\";\")\n",
"df_comp_perf = pd.read_csv(PATH_COMP_PERF,sep=\";\")\n",
"df_peers = pd.read_csv(PATH_PEERS,sep=\"|\")\n",
"\n",
"print(\"flows:\", df_flows.shape)\n",
"print(\"aum:\", df_aum.shape)\n",
"print(\"nav:\", df_nav.shape)\n",
"print(\"rates:\", df_rates.shape)\n",
"print(\"comp_flows:\", df_comp_flows.shape)\n",
"print(\"comp_perf:\", df_comp_perf.shape)\n",
"print(\"peers:\", df_peers.shape)\n",
"\n",
"\n",
"#dbe bel ekhr un dataset avec une ligne pour : un client - un produit Carmignac - un mois "
]
},
{
"cell_type": "code",
"execution_count": 9,
"id": "0a52ab96-1c47-4530-83d0-148e72f70d9b",
"metadata": {},
"outputs": [
{
"data": {
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" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Dat</th>\n",
" <th>Isin</th>\n",
" <th>Aum Eur</th>\n",
" <th>Price (TF PartPrice)</th>\n",
" <th>PriceBench</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>623909</th>\n",
" <td>10/16/2025</td>\n",
" <td>FR001400KIF0</td>\n",
" <td>108424691,27</td>\n",
" <td>148,73</td>\n",
" <td>462,49002312</td>\n",
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" <tr>\n",
" <th>623910</th>\n",
" <td>10/17/2025</td>\n",
" <td>FR001400KIF0</td>\n",
" <td>107947215,67</td>\n",
" <td>148,08</td>\n",
" <td>462,6446111</td>\n",
" </tr>\n",
" <tr>\n",
" <th>623911</th>\n",
" <td>10/20/2025</td>\n",
" <td>FR001400KIF0</td>\n",
" <td>109597120,24</td>\n",
" <td>150,34</td>\n",
" <td>468,73416853</td>\n",
" </tr>\n",
" <tr>\n",
" <th>623912</th>\n",
" <td>10/21/2025</td>\n",
" <td>FR001400KIF0</td>\n",
" <td>110216503,12</td>\n",
" <td>151,19</td>\n",
" <td>470,33788616</td>\n",
" </tr>\n",
" <tr>\n",
" <th>623913</th>\n",
" <td>10/22/2025</td>\n",
" <td>FR001400KIF0</td>\n",
" <td>109692584,22</td>\n",
" <td>150,47</td>\n",
" <td>468,13202429</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Dat Isin Aum Eur Price (TF PartPrice) \\\n",
"623909 10/16/2025 FR001400KIF0 108424691,27 148,73 \n",
"623910 10/17/2025 FR001400KIF0 107947215,67 148,08 \n",
"623911 10/20/2025 FR001400KIF0 109597120,24 150,34 \n",
"623912 10/21/2025 FR001400KIF0 110216503,12 151,19 \n",
"623913 10/22/2025 FR001400KIF0 109692584,22 150,47 \n",
"\n",
" PriceBench \n",
"623909 462,49002312 \n",
"623910 462,6446111 \n",
"623911 468,73416853 \n",
"623912 470,33788616 \n",
"623913 468,13202429 "
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_nav.tail()"
]
},
{
"cell_type": "code",
"execution_count": 10,
"id": "e15c0a1d-d636-48f5-83ab-ff43b49b0e44",
"metadata": {},
"outputs": [
{
"data": {
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" <th></th>\n",
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" <th>Yld to Maturity</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>2821</th>\n",
" <td>16/10/2025</td>\n",
" <td>1.928</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2822</th>\n",
" <td>17/10/2025</td>\n",
" <td>1.928</td>\n",
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" <tr>\n",
" <th>2823</th>\n",
" <td>20/10/2025</td>\n",
" <td>1.928</td>\n",
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" <tr>\n",
" <th>2824</th>\n",
" <td>21/10/2025</td>\n",
" <td>1.927</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2825</th>\n",
" <td>22/10/2025</td>\n",
" <td>1.928</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Date Yld to Maturity\n",
"2821 16/10/2025 1.928\n",
"2822 17/10/2025 1.928\n",
"2823 20/10/2025 1.928\n",
"2824 21/10/2025 1.927\n",
"2825 22/10/2025 1.928"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_rates.tail() #Table de marché macro, ici probablement un taux de référence."
]
},
{
"cell_type": "code",
"execution_count": 11,
"id": "ede9c196-1d10-48fc-b99f-7369a2fb3d9b",
"metadata": {},
"outputs": [
{
"data": {
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" <th>SecId_MS</th>\n",
" <th>ISIN</th>\n",
" <th>FundId</th>\n",
" <th>Date</th>\n",
" <th>Estimated Fund-level Net Flow (Daily)</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>962998</th>\n",
" <td>TM Lansdowne European Spec Sit I Acc GBP</td>\n",
" <td>F00000VH4K</td>\n",
" <td>GB00BTJRQ064</td>\n",
" <td>FS0000BQP3</td>\n",
" <td>2025-06-27</td>\n",
" <td>183110.412</td>\n",
" </tr>\n",
" <tr>\n",
" <th>962999</th>\n",
" <td>Waverton European Dividend Gr B GBP Inc</td>\n",
" <td>F000011TLC</td>\n",
" <td>IE00BF5KV626</td>\n",
" <td>FS0000E90E</td>\n",
" <td>2025-06-27</td>\n",
" <td>16425.603</td>\n",
" </tr>\n",
" <tr>\n",
" <th>963000</th>\n",
" <td>Premier Miton European Opports B Acc</td>\n",
" <td>F00000WMCF</td>\n",
" <td>GB00BZ2K2M84</td>\n",
" <td>FS0000C8WZ</td>\n",
" <td>2025-06-27</td>\n",
" <td>-612606.416</td>\n",
" </tr>\n",
" <tr>\n",
" <th>963001</th>\n",
" <td>Incrementum Active Commodity Fund R EUR</td>\n",
" <td>F00000SVVR</td>\n",
" <td>LI0226274319</td>\n",
" <td>FS0000AMCV</td>\n",
" <td>2025-06-27</td>\n",
" <td>-5123.607</td>\n",
" </tr>\n",
" <tr>\n",
" <th>963002</th>\n",
" <td>AXAWF Inflation Plus A Cap EUR</td>\n",
" <td>F00001CSX2</td>\n",
" <td>LU2257473269</td>\n",
" <td>FS0000H62L</td>\n",
" <td>2025-06-27</td>\n",
" <td>393490.630</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" shareClass_name SecId_MS ISIN \\\n",
"962998 TM Lansdowne European Spec Sit I Acc GBP F00000VH4K GB00BTJRQ064 \n",
"962999 Waverton European Dividend Gr B GBP Inc F000011TLC IE00BF5KV626 \n",
"963000 Premier Miton European Opports B Acc F00000WMCF GB00BZ2K2M84 \n",
"963001 Incrementum Active Commodity Fund R EUR F00000SVVR LI0226274319 \n",
"963002 AXAWF Inflation Plus A Cap EUR F00001CSX2 LU2257473269 \n",
"\n",
" FundId Date Estimated Fund-level Net Flow (Daily) \n",
"962998 FS0000BQP3 2025-06-27 183110.412 \n",
"962999 FS0000E90E 2025-06-27 16425.603 \n",
"963000 FS0000C8WZ 2025-06-27 -612606.416 \n",
"963001 FS0000AMCV 2025-06-27 -5123.607 \n",
"963002 FS0000H62L 2025-06-27 393490.630 "
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_comp_flows.tail()\n"
]
},
{
"cell_type": "code",
"execution_count": 12,
"id": "71407c1d-dda2-4ec6-9df4-499d4502e560",
"metadata": {},
"outputs": [
{
"data": {
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" <th></th>\n",
" <th>Date</th>\n",
" <th>perfPeriod</th>\n",
" <th>shareClass_name</th>\n",
" <th>return</th>\n",
" <th>percentile</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>2370187</th>\n",
" <td>2015-12-30</td>\n",
" <td>WeeklyRet</td>\n",
" <td>BNP Paribas Emerging Eq Cl Eur C</td>\n",
" <td>-1.623478</td>\n",
" <td>83.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2370188</th>\n",
" <td>2015-12-30</td>\n",
" <td>WeeklyRet</td>\n",
" <td>Capital Group EM Debt (LUX) B</td>\n",
" <td>-0.162338</td>\n",
" <td>88.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2370189</th>\n",
" <td>2015-12-30</td>\n",
" <td>WeeklyRet</td>\n",
" <td>BGF Global Allocation A2 EUR Hedged</td>\n",
" <td>0.387712</td>\n",
" <td>44.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2370190</th>\n",
" <td>2015-12-30</td>\n",
" <td>WeeklyRet</td>\n",
" <td>Exane Funds 2 Exane Pleiade B EUR Acc</td>\n",
" <td>0.082896</td>\n",
" <td>60.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2370191</th>\n",
" <td>2015-12-30</td>\n",
" <td>WeeklyRet</td>\n",
" <td>Invesco Euro Short Term Bond A EUR Acc</td>\n",
" <td>0.034302</td>\n",
" <td>35.0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Date perfPeriod shareClass_name \\\n",
"2370187 2015-12-30 WeeklyRet BNP Paribas Emerging Eq Cl Eur C \n",
"2370188 2015-12-30 WeeklyRet Capital Group EM Debt (LUX) B \n",
"2370189 2015-12-30 WeeklyRet BGF Global Allocation A2 EUR Hedged \n",
"2370190 2015-12-30 WeeklyRet Exane Funds 2 Exane Pleiade B EUR Acc \n",
"2370191 2015-12-30 WeeklyRet Invesco Euro Short Term Bond A EUR Acc \n",
"\n",
" return percentile \n",
"2370187 -1.623478 83.0 \n",
"2370188 -0.162338 88.0 \n",
"2370189 0.387712 44.0 \n",
"2370190 0.082896 60.0 \n",
"2370191 0.034302 35.0 "
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_comp_perf.tail() #Performance des concurrents, avec rang relatif.\n"
]
},
{
"cell_type": "code",
"execution_count": 13,
"id": "05b87839-dc25-4f0d-8bf8-f43ac8c932ee",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Name</th>\n",
" <th>ISIN</th>\n",
" <th>SecId_MS</th>\n",
" <th>FundId</th>\n",
" <th>Global Broad Category Group</th>\n",
" <th>Global Category</th>\n",
" <th>Morningstar Category</th>\n",
" <th>Index Fund</th>\n",
" <th>Enhanced Index</th>\n",
" <th>Inception Date</th>\n",
" <th>Inception Date of Fund's Oldest Share Class</th>\n",
" <th>Domicile</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>26</th>\n",
" <td>Vontobel mtx EM Ldrs ex Chn S USD Acc</td>\n",
" <td>LU2601939452</td>\n",
" <td>F00001G9PT</td>\n",
" <td>FS0000IAHL</td>\n",
" <td>Equity</td>\n",
" <td>Global Emerging Markets Equity</td>\n",
" <td>EAA Fund Global Emerging Markets ex-China Equity</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>2023-09-20</td>\n",
" <td>2023-09-20</td>\n",
" <td>Luxembourg</td>\n",
" </tr>\n",
" <tr>\n",
" <th>27</th>\n",
" <td>PineBridge Asia exJapan SmCap Eq A</td>\n",
" <td>IE00B12V2V27</td>\n",
" <td>FOGBR05LNR</td>\n",
" <td>FSGBR06C4W</td>\n",
" <td>Equity</td>\n",
" <td>Asia ex-Japan Equity</td>\n",
" <td>EAA Fund Asia ex-Japan Small/Mid-Cap Equity</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>2006-04-19</td>\n",
" <td>1994-09-01</td>\n",
" <td>Ireland</td>\n",
" </tr>\n",
" <tr>\n",
" <th>28</th>\n",
" <td>abrdn-Asian Smaller Companies A Acc USD</td>\n",
" <td>LU0231459107</td>\n",
" <td>F0GBR06X7H</td>\n",
" <td>FSGBR05GSY</td>\n",
" <td>Equity</td>\n",
" <td>Asia ex-Japan Equity</td>\n",
" <td>EAA Fund Asia ex-Japan Small/Mid-Cap Equity</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>2004-05-14</td>\n",
" <td>2004-05-14</td>\n",
" <td>Luxembourg</td>\n",
" </tr>\n",
" <tr>\n",
" <th>29</th>\n",
" <td>Allianz Asian Small Cap Equity A EUR</td>\n",
" <td>LU2420271673</td>\n",
" <td>F00001DBJJ</td>\n",
" <td>FS0000ASMB</td>\n",
" <td>Equity</td>\n",
" <td>Asia ex-Japan Equity</td>\n",
" <td>EAA Fund Asia ex-Japan Small/Mid-Cap Equity</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>2022-01-05</td>\n",
" <td>2014-05-13</td>\n",
" <td>Luxembourg</td>\n",
" </tr>\n",
" <tr>\n",
" <th>30</th>\n",
" <td>Allianz Asia Ex China Equity A (USD)</td>\n",
" <td>LU0348788117</td>\n",
" <td>F000000F7V</td>\n",
" <td>FSUSA08CND</td>\n",
" <td>Equity</td>\n",
" <td>Asia ex-Japan Equity</td>\n",
" <td>EAA Fund Asia ex-Japan Equity</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>2008-10-03</td>\n",
" <td>2008-10-03</td>\n",
" <td>Luxembourg</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Name ISIN SecId_MS \\\n",
"26 Vontobel mtx EM Ldrs ex Chn S USD Acc LU2601939452 F00001G9PT \n",
"27 PineBridge Asia exJapan SmCap Eq A IE00B12V2V27 FOGBR05LNR \n",
"28 abrdn-Asian Smaller Companies A Acc USD LU0231459107 F0GBR06X7H \n",
"29 Allianz Asian Small Cap Equity A EUR LU2420271673 F00001DBJJ \n",
"30 Allianz Asia Ex China Equity A (USD) LU0348788117 F000000F7V \n",
"\n",
" FundId Global Broad Category Group Global Category \\\n",
"26 FS0000IAHL Equity Global Emerging Markets Equity \n",
"27 FSGBR06C4W Equity Asia ex-Japan Equity \n",
"28 FSGBR05GSY Equity Asia ex-Japan Equity \n",
"29 FS0000ASMB Equity Asia ex-Japan Equity \n",
"30 FSUSA08CND Equity Asia ex-Japan Equity \n",
"\n",
" Morningstar Category Index Fund \\\n",
"26 EAA Fund Global Emerging Markets ex-China Equity No \n",
"27 EAA Fund Asia ex-Japan Small/Mid-Cap Equity No \n",
"28 EAA Fund Asia ex-Japan Small/Mid-Cap Equity No \n",
"29 EAA Fund Asia ex-Japan Small/Mid-Cap Equity No \n",
"30 EAA Fund Asia ex-Japan Equity No \n",
"\n",
" Enhanced Index Inception Date Inception Date of Fund's Oldest Share Class \\\n",
"26 No 2023-09-20 2023-09-20 \n",
"27 No 2006-04-19 1994-09-01 \n",
"28 No 2004-05-14 2004-05-14 \n",
"29 No 2022-01-05 2014-05-13 \n",
"30 No 2008-10-03 2008-10-03 \n",
"\n",
" Domicile \n",
"26 Luxembourg \n",
"27 Ireland \n",
"28 Luxembourg \n",
"29 Luxembourg \n",
"30 Luxembourg "
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_peers.tail() #Ça permet de mesurer la pression concurrentielle."
]
},
{
"cell_type": "code",
"execution_count": 14,
"id": "9128c36b-33fd-44b2-a622-d3059f482c02",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Index(['Unnamed: 0', 'Agreement - Code', 'Company - Id',\n",
" 'Company - Ultimate Parent Id', 'Registrar Account - ID',\n",
" 'Registrar Account - Region', 'RegistrarAccount - Country',\n",
" 'Product - Asset Type', 'Product - Strategy', 'Product - Legal Status',\n",
" 'Product - Is Dedie ?', 'Product - Fund', 'Product - Shareclass Type',\n",
" 'Product - Shareclass Currency', 'Product - Isin',\n",
" 'Centralisation Date', 'Quantity - Subscription',\n",
" 'Quantity - Redemption', 'Quantity - NetFlows',\n",
" 'Value Ccy - Subscription', 'Value Ccy - Redemption',\n",
" 'Value Ccy - NetFlows', 'Value € - Subscription',\n",
" 'Value € - Redemption', 'Value € - NetFlows'],\n",
" dtype='object')\n",
"Index(['Unnamed: 0.1', 'Unnamed: 0', 'Agreement - Code', 'Company - Id',\n",
" 'Company - Ultimate Parent Id', 'Registrar Account - ID',\n",
" 'Registrar Account - Region', 'RegistrarAccount - Country',\n",
" 'Product - Asset Type', 'Product - Strategy', 'Product - Legal Status',\n",
" 'Product - Is Dedie ?', 'Product - Fund', 'Product - Shareclass Type',\n",
" 'Product - Shareclass Currency', 'Product - Isin',\n",
" 'Centralisation Date', 'Quantity - AUM', 'Value - AUM CCY',\n",
" 'Value - AUM €', 'repair_flag'],\n",
" dtype='object')\n",
"Index(['Registrar Account - ID', 'n_days', 'n_transactions', 'total_netflows',\n",
" 'mean_flow', 'std_flow', 'total_subscription', 'total_redemption',\n",
" 'churn_ratio', 'churn_flag', 'activity_score', 'flow_volatility',\n",
" 'inertia_ratio'],\n",
" dtype='object')\n",
"Index(['Dat', 'Isin', 'Aum Eur', 'Price (TF PartPrice)', 'PriceBench'], dtype='object')\n",
"Index(['Date', 'Yld to Maturity'], dtype='object')\n",
"Index(['shareClass_name', 'SecId_MS', 'ISIN', 'FundId', 'Date',\n",
" 'Estimated Fund-level Net Flow (Daily)'],\n",
" dtype='object')\n",
"Index(['Date', 'perfPeriod', 'shareClass_name', 'return', 'percentile'], dtype='object')\n",
"Index(['Name', 'ISIN', 'SecId_MS', 'FundId', 'Global Broad Category Group',\n",
" 'Global Category', 'Morningstar Category', 'Index Fund',\n",
" 'Enhanced Index', 'Inception Date',\n",
" 'Inception Date of Fund's Oldest Share Class', 'Domicile'],\n",
" dtype='object')\n"
]
}
],
"source": [
"for d in [df_flows,df_aum,df_client,df_nav,df_rates,df_comp_flows,df_comp_perf,df_peers]:\n",
" print (d.columns)"
]
},
{
"cell_type": "code",
"execution_count": 15,
"id": "7e82983d-aaf3-40c1-9186-2cb13c6274d7",
"metadata": {},
"outputs": [],
"source": [
"ID_COL = \"Registrar Account - ID\"\n",
"ISIN_COL = \"Product - Isin\"\n",
"\n",
"FLOW_DATE_COL = \"Centralisation Date\"\n",
"AUM_DATE_COL = \"Centralisation Date\"\n",
"\n",
"FLOW_QTY_COL = \"Quantity - NetFlows\"\n",
"FLOW_SUB_COL = \"Quantity - Subscription\"\n",
"FLOW_RED_COL = \"Quantity - Redemption\"\n",
"\n",
"AUM_QTY_COL = \"Quantity - AUM\"\n",
"AUM_VAL_COL = \"Value - AUM €\"\n",
"\n",
"REGION_COL = \"Registrar Account - Region\"\n",
"COUNTRY_COL = \"RegistrarAccount - Country\"\n",
"\n",
"NAV_DATE_COL = \"Dat\"\n",
"NAV_ISIN_COL = \"Isin\"\n",
"NAV_PRICE_COL = \"Price (TF PartPrice)\"\n",
"NAV_BENCH_COL = \"PriceBench\"\n",
"\n",
"RATE_DATE_COL = \"Date\"\n",
"RATE_VAL_COL = \"Yld to Maturity\""
]
},
{
"cell_type": "code",
"execution_count": 16,
"id": "6dd153e1-7e6f-47b7-81b1-c77fa763a087",
"metadata": {},
"outputs": [],
"source": [
"for df, date_col in [\n",
" (df_flows, FLOW_DATE_COL),\n",
" (df_aum, AUM_DATE_COL),\n",
" (df_nav, NAV_DATE_COL),\n",
" (df_rates, RATE_DATE_COL),\n",
"]:\n",
" df[date_col] = pd.to_datetime(df[date_col], errors=\"coerce\")\n",
"\n",
"df_flows[\"month\"] = df_flows[FLOW_DATE_COL].dt.to_period(\"M\").dt.to_timestamp()\n",
"df_aum[\"month\"] = df_aum[AUM_DATE_COL].dt.to_period(\"M\").dt.to_timestamp()\n",
"df_nav[\"month\"] = df_nav[NAV_DATE_COL].dt.to_period(\"M\").dt.to_timestamp()\n",
"df_rates[\"month\"] = df_rates[RATE_DATE_COL].dt.to_period(\"M\").dt.to_timestamp()\n",
"\n",
"for col in [FLOW_QTY_COL, FLOW_SUB_COL, FLOW_RED_COL]:\n",
" df_flows[col] = pd.to_numeric(df_flows[col], errors=\"coerce\")\n",
"\n",
"for col in [AUM_QTY_COL, AUM_VAL_COL]:\n",
" df_aum[col] = pd.to_numeric(df_aum[col], errors=\"coerce\")\n",
"\n",
"for col in [NAV_PRICE_COL, NAV_BENCH_COL]:\n",
" df_nav[col] = pd.to_numeric(df_nav[col], errors=\"coerce\")\n",
"\n",
"df_rates[RATE_VAL_COL] = pd.to_numeric(df_rates[RATE_VAL_COL], errors=\"coerce\")\n",
"\n",
"for df, col in [(df_flows, ISIN_COL), (df_aum, ISIN_COL)]:\n",
" df[col] = df[col].astype(str).str.strip()\n",
"\n",
"df_nav[NAV_ISIN_COL] = df_nav[NAV_ISIN_COL].astype(str).str.strip()\n",
"\n",
"''' Pourquoi le mois ?\n",
"Parce que :\n",
"les flux journaliers sont trop bruités\n",
"le churn et les comportements de portefeuille se lisent mieux au mois #le churn signifie qu’un client quitte le fonds\n",
"la plupart des comportements d’allocation sont plus lisibles à cette fréquence'''"
]
},
{
"cell_type": "code",
"execution_count": 17,
"id": "ac43bb83-5800-4000-af9f-8cd4cae9e9d5",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(4906475, 18)\n"
]
},
{
"data": {
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"\n",
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"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Registrar Account - ID</th>\n",
" <th>Product - Isin</th>\n",
" <th>month</th>\n",
" <th>aum_qty</th>\n",
" <th>aum_val</th>\n",
" <th>region</th>\n",
" <th>country</th>\n",
" <th>net_flow_qty</th>\n",
" <th>gross_flow_qty</th>\n",
" <th>sub_qty</th>\n",
" <th>red_qty</th>\n",
" <th>n_tx</th>\n",
" <th>region_flow</th>\n",
" <th>country_flow</th>\n",
" <th>active_rel_month</th>\n",
" <th>holding_rel_month</th>\n",
" <th>flow_to_aum_rel</th>\n",
" <th>turnover_rel</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>8307</td>\n",
" <td>LU0099161993</td>\n",
" <td>2017-03-01</td>\n",
" <td>200.0</td>\n",
" <td>37836.00</td>\n",
" <td>SWITZERLAND</td>\n",
" <td>SWITZERLAND</td>\n",
" <td>-138.0</td>\n",
" <td>138.0</td>\n",
" <td>0.0</td>\n",
" <td>-138.0</td>\n",
" <td>1.0</td>\n",
" <td>SWITZERLAND</td>\n",
" <td>SWITZERLAND</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>-6.900000e-01</td>\n",
" <td>6.900000e-01</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>8307</td>\n",
" <td>LU0099161993</td>\n",
" <td>2020-08-01</td>\n",
" <td>300.0</td>\n",
" <td>75603.00</td>\n",
" <td>SWITZERLAND</td>\n",
" <td>SWITZERLAND</td>\n",
" <td>100.0</td>\n",
" <td>100.0</td>\n",
" <td>100.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>SWITZERLAND</td>\n",
" <td>SWITZERLAND</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>3.333333e-01</td>\n",
" <td>3.333333e-01</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>8307</td>\n",
" <td>LU0099161993</td>\n",
" <td>2024-05-01</td>\n",
" <td>0.0</td>\n",
" <td>0.00</td>\n",
" <td>SWITZERLAND</td>\n",
" <td>SWITZERLAND</td>\n",
" <td>-100.0</td>\n",
" <td>100.0</td>\n",
" <td>0.0</td>\n",
" <td>-100.0</td>\n",
" <td>1.0</td>\n",
" <td>SWITZERLAND</td>\n",
" <td>SWITZERLAND</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>-1.000000e+11</td>\n",
" <td>1.000000e+11</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>8307</td>\n",
" <td>LU0164455502</td>\n",
" <td>2015-07-01</td>\n",
" <td>786.0</td>\n",
" <td>219144.66</td>\n",
" <td>SWITZERLAND</td>\n",
" <td>SWITZERLAND</td>\n",
" <td>-35.0</td>\n",
" <td>35.0</td>\n",
" <td>0.0</td>\n",
" <td>-35.0</td>\n",
" <td>1.0</td>\n",
" <td>SWITZERLAND</td>\n",
" <td>SWITZERLAND</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>-4.452926e-02</td>\n",
" <td>4.452926e-02</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>8307</td>\n",
" <td>LU0164455502</td>\n",
" <td>2015-10-01</td>\n",
" <td>886.0</td>\n",
" <td>231538.38</td>\n",
" <td>SWITZERLAND</td>\n",
" <td>SWITZERLAND</td>\n",
" <td>100.0</td>\n",
" <td>100.0</td>\n",
" <td>100.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>SWITZERLAND</td>\n",
" <td>SWITZERLAND</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1.128668e-01</td>\n",
" <td>1.128668e-01</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Registrar Account - ID Product - Isin month aum_qty aum_val \\\n",
"0 8307 LU0099161993 2017-03-01 200.0 37836.00 \n",
"1 8307 LU0099161993 2020-08-01 300.0 75603.00 \n",
"2 8307 LU0099161993 2024-05-01 0.0 0.00 \n",
"3 8307 LU0164455502 2015-07-01 786.0 219144.66 \n",
"4 8307 LU0164455502 2015-10-01 886.0 231538.38 \n",
"\n",
" region country net_flow_qty gross_flow_qty sub_qty red_qty \\\n",
"0 SWITZERLAND SWITZERLAND -138.0 138.0 0.0 -138.0 \n",
"1 SWITZERLAND SWITZERLAND 100.0 100.0 100.0 0.0 \n",
"2 SWITZERLAND SWITZERLAND -100.0 100.0 0.0 -100.0 \n",
"3 SWITZERLAND SWITZERLAND -35.0 35.0 0.0 -35.0 \n",
"4 SWITZERLAND SWITZERLAND 100.0 100.0 100.0 0.0 \n",
"\n",
" n_tx region_flow country_flow active_rel_month holding_rel_month \\\n",
"0 1.0 SWITZERLAND SWITZERLAND 1 1 \n",
"1 1.0 SWITZERLAND SWITZERLAND 1 1 \n",
"2 1.0 SWITZERLAND SWITZERLAND 1 0 \n",
"3 1.0 SWITZERLAND SWITZERLAND 1 1 \n",
"4 1.0 SWITZERLAND SWITZERLAND 1 1 \n",
"\n",
" flow_to_aum_rel turnover_rel \n",
"0 -6.900000e-01 6.900000e-01 \n",
"1 3.333333e-01 3.333333e-01 \n",
"2 -1.000000e+11 1.000000e+11 \n",
"3 -4.452926e-02 4.452926e-02 \n",
"4 1.128668e-01 1.128668e-01 "
]
},
"execution_count": 17,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"'''On veut s’assurer que l’univers des produits Carmignac détenus par les clients est bien cohérent avec l’univers des NAV \n",
"utilisés pour calculer les performances.'''\n",
"\n",
"#pour merge flux et aum c est un full\n",
"'''\n",
"Si un mois existe :\n",
"dans les flux mais pas dans les encours → la clé est gardée\n",
"dans les encours mais pas dans les flux → la clé est gardée\n",
"dans les deux → une seule ligne de clé\n",
"👉 C’est donc une logique proche d’un full outer join, mais construite manuellement.\n",
"'''\n",
"df_flows_rel_m = (\n",
" df_flows\n",
" .dropna(subset=[ID_COL, ISIN_COL, \"month\"])\n",
" .assign(\n",
" gross_flow_qty=lambda x: x[FLOW_QTY_COL].abs(),\n",
" sub_qty=lambda x: x[FLOW_SUB_COL].fillna(0),\n",
" red_qty=lambda x: x[FLOW_RED_COL].fillna(0)\n",
" )\n",
" .groupby([ID_COL, ISIN_COL, \"month\"], as_index=False)\n",
" .agg(\n",
" net_flow_qty=(FLOW_QTY_COL, \"sum\"),\n",
" gross_flow_qty=(\"gross_flow_qty\", \"sum\"),\n",
" sub_qty=(\"sub_qty\", \"sum\"),\n",
" red_qty=(\"red_qty\", \"sum\"),\n",
" n_tx=(FLOW_QTY_COL, \"size\"),\n",
" region=(REGION_COL, \"last\"),\n",
" country=(COUNTRY_COL, \"last\")\n",
" )\n",
")\n",
"\n",
"df_aum_rel_m = (\n",
" df_aum\n",
" .dropna(subset=[ID_COL, ISIN_COL, \"month\"])\n",
" .groupby([ID_COL, ISIN_COL, \"month\"], as_index=False)\n",
" .agg(\n",
" aum_qty=(AUM_QTY_COL, \"sum\"),\n",
" aum_val=(AUM_VAL_COL, \"sum\"),\n",
" region=(REGION_COL, \"last\"),\n",
" country=(COUNTRY_COL, \"last\")\n",
" )\n",
")\n",
"\n",
"keys = pd.concat([\n",
" df_flows_rel_m[[ID_COL, ISIN_COL, \"month\"]],\n",
" df_aum_rel_m[[ID_COL, ISIN_COL, \"month\"]]\n",
"]).drop_duplicates()\n",
"\n",
"df_rel_m = (\n",
" keys\n",
" .merge(df_aum_rel_m, on=[ID_COL, ISIN_COL, \"month\"], how=\"left\", suffixes=(\"\", \"_aum\"))\n",
" .merge(df_flows_rel_m, on=[ID_COL, ISIN_COL, \"month\"], how=\"left\", suffixes=(\"\", \"_flow\"))\n",
")\n",
"\n",
"for c in [\"aum_qty\", \"aum_val\", \"net_flow_qty\", \"gross_flow_qty\", \"sub_qty\", \"red_qty\", \"n_tx\"]:\n",
" df_rel_m[c] = df_rel_m[c].fillna(0)\n",
"\n",
"df_rel_m[\"region\"] = df_rel_m[\"region\"].fillna(df_rel_m.get(\"region_flow\"))\n",
"df_rel_m[\"country\"] = df_rel_m[\"country\"].fillna(df_rel_m.get(\"country_flow\"))\n",
"\n",
"df_rel_m[\"active_rel_month\"] = (df_rel_m[\"gross_flow_qty\"] > 0).astype(int)\n",
"df_rel_m[\"holding_rel_month\"] = (df_rel_m[\"aum_qty\"] > 0).astype(int)\n",
"df_rel_m[\"flow_to_aum_rel\"] = df_rel_m[\"net_flow_qty\"] / (df_rel_m[\"aum_qty\"].abs() + EPS)\n",
"df_rel_m[\"turnover_rel\"] = df_rel_m[\"gross_flow_qty\"] / (df_rel_m[\"aum_qty\"].abs() + EPS)\n",
"\n",
"print(df_rel_m.shape)\n",
"df_rel_m.head()\n",
"\n",
"\n",
"'''\n",
"On agrège séparément :\n",
"les flows au niveau client-produit-mois\n",
"les AUM au niveau client-produit-mois\n",
"\n",
"Puis on les fusionne.\n",
"\n",
"Datasets créés : df_flows_rel_m\n",
"Table des transactions mensuelles au niveau : Registrar Account - ID × Product - Isin × month'''"
]
},
{
"cell_type": "code",
"execution_count": 18,
"id": "2597a326-88f7-493f-830b-31826112eefa",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Product - Isin</th>\n",
" <th>month</th>\n",
" <th>ret_fund_m</th>\n",
" <th>ret_bench_m</th>\n",
" <th>active_return_m</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>617756</th>\n",
" <td>FR0007486709</td>\n",
" <td>2012-11-01</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>613737</th>\n",
" <td>FR0010135103</td>\n",
" <td>2010-03-01</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>613810</th>\n",
" <td>FR0010135103</td>\n",
" <td>2010-06-01</td>\n",
" <td>0.070565</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>614192</th>\n",
" <td>FR0010135103</td>\n",
" <td>2011-12-01</td>\n",
" <td>-0.024482</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>614258</th>\n",
" <td>FR0010135103</td>\n",
" <td>2012-03-01</td>\n",
" <td>0.028958</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Product - Isin month ret_fund_m ret_bench_m active_return_m\n",
"617756 FR0007486709 2012-11-01 NaN NaN NaN\n",
"613737 FR0010135103 2010-03-01 NaN NaN NaN\n",
"613810 FR0010135103 2010-06-01 0.070565 NaN NaN\n",
"614192 FR0010135103 2011-12-01 -0.024482 NaN NaN\n",
"614258 FR0010135103 2012-03-01 0.028958 NaN NaN"
]
},
"execution_count": 18,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_nav_m = (\n",
" df_nav\n",
" .dropna(subset=[NAV_ISIN_COL, \"month\", NAV_PRICE_COL])\n",
" .sort_values([NAV_ISIN_COL, \"month\"])\n",
" .groupby([NAV_ISIN_COL, \"month\"], as_index=False)\n",
" .tail(1)\n",
" .copy()\n",
")\n",
"\n",
"df_nav_m[\"ret_fund_m\"] = df_nav_m.groupby(NAV_ISIN_COL)[NAV_PRICE_COL].pct_change()\n",
"df_nav_m[\"ret_bench_m\"] = df_nav_m.groupby(NAV_ISIN_COL)[NAV_BENCH_COL].pct_change()\n",
"df_nav_m[\"active_return_m\"] = df_nav_m[\"ret_fund_m\"] - df_nav_m[\"ret_bench_m\"]\n",
"\n",
"df_nav_m = df_nav_m.rename(columns={NAV_ISIN_COL: ISIN_COL})\n",
"df_nav_m = df_nav_m[[ISIN_COL, \"month\", \"ret_fund_m\", \"ret_bench_m\", \"active_return_m\"]]\n",
"\n",
"df_nav_m.head()\n",
"#on agrège au niveau mensuel en prenant la dernière observation disponible du mois. pour nav et rates "
]
},
{
"cell_type": "code",
"execution_count": 19,
"id": "73cb228a-4c21-4407-a3f5-526c4b88639f",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>month</th>\n",
" <th>Yld to Maturity</th>\n",
" <th>delta_rate_m</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>2014-12-01</td>\n",
" <td>0.144</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>22</th>\n",
" <td>2015-01-01</td>\n",
" <td>0.086</td>\n",
" <td>-0.058</td>\n",
" </tr>\n",
" <tr>\n",
" <th>43</th>\n",
" <td>2015-02-01</td>\n",
" <td>0.064</td>\n",
" <td>-0.022</td>\n",
" </tr>\n",
" <tr>\n",
" <th>65</th>\n",
" <td>2015-03-01</td>\n",
" <td>0.050</td>\n",
" <td>-0.014</td>\n",
" </tr>\n",
" <tr>\n",
" <th>86</th>\n",
" <td>2015-04-01</td>\n",
" <td>-0.027</td>\n",
" <td>-0.077</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" month Yld to Maturity delta_rate_m\n",
"0 2014-12-01 0.144 NaN\n",
"22 2015-01-01 0.086 -0.058\n",
"43 2015-02-01 0.064 -0.022\n",
"65 2015-03-01 0.050 -0.014\n",
"86 2015-04-01 -0.027 -0.077"
]
},
"execution_count": 19,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_rates_m = (\n",
" df_rates\n",
" .dropna(subset=[\"month\", RATE_VAL_COL])\n",
" .sort_values(RATE_DATE_COL)\n",
" .groupby(\"month\", as_index=False)\n",
" .tail(1)\n",
" .copy()\n",
")\n",
"\n",
"df_rates_m[\"delta_rate_m\"] = df_rates_m[RATE_VAL_COL].diff()\n",
"df_rates_m = df_rates_m[[\"month\", RATE_VAL_COL, \"delta_rate_m\"]]\n",
"\n",
"df_rates_m.head()"
]
},
{
"cell_type": "code",
"execution_count": 20,
"id": "1e1f0fa9-9c62-4a5e-8ee1-2605c76fb80a",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Registrar Account - ID</th>\n",
" <th>Product - Isin</th>\n",
" <th>month</th>\n",
" <th>aum_qty</th>\n",
" <th>aum_val</th>\n",
" <th>region</th>\n",
" <th>country</th>\n",
" <th>net_flow_qty</th>\n",
" <th>gross_flow_qty</th>\n",
" <th>sub_qty</th>\n",
" <th>red_qty</th>\n",
" <th>n_tx</th>\n",
" <th>region_flow</th>\n",
" <th>country_flow</th>\n",
" <th>active_rel_month</th>\n",
" <th>holding_rel_month</th>\n",
" <th>flow_to_aum_rel</th>\n",
" <th>turnover_rel</th>\n",
" <th>ret_fund_m</th>\n",
" <th>ret_bench_m</th>\n",
" <th>active_return_m</th>\n",
" <th>delta_rate_m</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>8307</td>\n",
" <td>LU0099161993</td>\n",
" <td>2017-03-01</td>\n",
" <td>200.0</td>\n",
" <td>37836.00</td>\n",
" <td>SWITZERLAND</td>\n",
" <td>SWITZERLAND</td>\n",
" <td>-138.0</td>\n",
" <td>138.0</td>\n",
" <td>0.0</td>\n",
" <td>-138.0</td>\n",
" <td>1.0</td>\n",
" <td>SWITZERLAND</td>\n",
" <td>SWITZERLAND</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>-6.900000e-01</td>\n",
" <td>6.900000e-01</td>\n",
" <td>0.175000</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>-0.010</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>8307</td>\n",
" <td>LU0099161993</td>\n",
" <td>2020-08-01</td>\n",
" <td>300.0</td>\n",
" <td>75603.00</td>\n",
" <td>SWITZERLAND</td>\n",
" <td>SWITZERLAND</td>\n",
" <td>100.0</td>\n",
" <td>100.0</td>\n",
" <td>100.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>SWITZERLAND</td>\n",
" <td>SWITZERLAND</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>3.333333e-01</td>\n",
" <td>3.333333e-01</td>\n",
" <td>0.000000</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>8307</td>\n",
" <td>LU0099161993</td>\n",
" <td>2024-05-01</td>\n",
" <td>0.0</td>\n",
" <td>0.00</td>\n",
" <td>SWITZERLAND</td>\n",
" <td>SWITZERLAND</td>\n",
" <td>-100.0</td>\n",
" <td>100.0</td>\n",
" <td>0.0</td>\n",
" <td>-100.0</td>\n",
" <td>1.0</td>\n",
" <td>SWITZERLAND</td>\n",
" <td>SWITZERLAND</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>-1.000000e+11</td>\n",
" <td>1.000000e+11</td>\n",
" <td>0.085809</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.004</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>8307</td>\n",
" <td>LU0164455502</td>\n",
" <td>2015-07-01</td>\n",
" <td>786.0</td>\n",
" <td>219144.66</td>\n",
" <td>SWITZERLAND</td>\n",
" <td>SWITZERLAND</td>\n",
" <td>-35.0</td>\n",
" <td>35.0</td>\n",
" <td>0.0</td>\n",
" <td>-35.0</td>\n",
" <td>1.0</td>\n",
" <td>SWITZERLAND</td>\n",
" <td>SWITZERLAND</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>-4.452926e-02</td>\n",
" <td>4.452926e-02</td>\n",
" <td>-0.054839</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>-0.042</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>8307</td>\n",
" <td>LU0164455502</td>\n",
" <td>2015-10-01</td>\n",
" <td>886.0</td>\n",
" <td>231538.38</td>\n",
" <td>SWITZERLAND</td>\n",
" <td>SWITZERLAND</td>\n",
" <td>100.0</td>\n",
" <td>100.0</td>\n",
" <td>100.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>SWITZERLAND</td>\n",
" <td>SWITZERLAND</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1.128668e-01</td>\n",
" <td>1.128668e-01</td>\n",
" <td>0.000000</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>-0.007</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Registrar Account - ID Product - Isin month aum_qty aum_val \\\n",
"0 8307 LU0099161993 2017-03-01 200.0 37836.00 \n",
"1 8307 LU0099161993 2020-08-01 300.0 75603.00 \n",
"2 8307 LU0099161993 2024-05-01 0.0 0.00 \n",
"3 8307 LU0164455502 2015-07-01 786.0 219144.66 \n",
"4 8307 LU0164455502 2015-10-01 886.0 231538.38 \n",
"\n",
" region country net_flow_qty gross_flow_qty sub_qty red_qty \\\n",
"0 SWITZERLAND SWITZERLAND -138.0 138.0 0.0 -138.0 \n",
"1 SWITZERLAND SWITZERLAND 100.0 100.0 100.0 0.0 \n",
"2 SWITZERLAND SWITZERLAND -100.0 100.0 0.0 -100.0 \n",
"3 SWITZERLAND SWITZERLAND -35.0 35.0 0.0 -35.0 \n",
"4 SWITZERLAND SWITZERLAND 100.0 100.0 100.0 0.0 \n",
"\n",
" n_tx region_flow country_flow active_rel_month holding_rel_month \\\n",
"0 1.0 SWITZERLAND SWITZERLAND 1 1 \n",
"1 1.0 SWITZERLAND SWITZERLAND 1 1 \n",
"2 1.0 SWITZERLAND SWITZERLAND 1 0 \n",
"3 1.0 SWITZERLAND SWITZERLAND 1 1 \n",
"4 1.0 SWITZERLAND SWITZERLAND 1 1 \n",
"\n",
" flow_to_aum_rel turnover_rel ret_fund_m ret_bench_m active_return_m \\\n",
"0 -6.900000e-01 6.900000e-01 0.175000 0.0 0.0 \n",
"1 3.333333e-01 3.333333e-01 0.000000 0.0 0.0 \n",
"2 -1.000000e+11 1.000000e+11 0.085809 0.0 0.0 \n",
"3 -4.452926e-02 4.452926e-02 -0.054839 0.0 0.0 \n",
"4 1.128668e-01 1.128668e-01 0.000000 0.0 0.0 \n",
"\n",
" delta_rate_m \n",
"0 -0.010 \n",
"1 0.000 \n",
"2 0.004 \n",
"3 -0.042 \n",
"4 -0.007 "
]
},
"execution_count": 20,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_rel_m = df_rel_m.merge(\n",
" df_nav_m,\n",
" on=[ISIN_COL, \"month\"],\n",
" how=\"left\"\n",
")\n",
"\n",
"df_rel_m = df_rel_m.merge(\n",
" df_rates_m[[\"month\", \"delta_rate_m\"]],\n",
" on=\"month\",\n",
" how=\"left\"\n",
")\n",
"\n",
"for c in [\"ret_fund_m\", \"ret_bench_m\", \"active_return_m\", \"delta_rate_m\"]:\n",
" df_rel_m[c] = df_rel_m[c].fillna(0)\n",
"\n",
"df_rel_m.head()\n",
"\n",
"'''\n",
"Créer un dataset complet contenant : fsionner ala left uaane hasab rel m shufyo bkhdu inter\n",
"client + produit + performance + environnement macro.'''"
]
},
{
"cell_type": "code",
"execution_count": 21,
"id": "41a4ac43-9035-46f3-8cba-5ba0d88ec98a",
"metadata": {},
"outputs": [],
"source": [
"def weighted_mean(x, w):\n",
" x = np.asarray(x, dtype=float)\n",
" w = np.asarray(w, dtype=float)\n",
" mask = np.isfinite(x) & np.isfinite(w) & (w >= 0)\n",
" if mask.sum() == 0 or w[mask].sum() == 0:\n",
" return np.nan\n",
" return np.average(x[mask], weights=w[mask])\n",
"\n",
"def hhi_from_weights(w):\n",
" w = np.asarray(w, dtype=float)\n",
" w = np.clip(w, 0, None)\n",
" s = w.sum()\n",
" if s <= 0:\n",
" return np.nan\n",
" p = w / s\n",
" return np.sum(p**2)\n",
"\n",
"def compute_trend(y):\n",
" y = np.asarray(y, dtype=float)\n",
" if len(y) < 4:\n",
" return np.nan\n",
" x = np.arange(len(y)).reshape(-1, 1)\n",
" mask = np.isfinite(y)\n",
" if mask.sum() < 4:\n",
" return np.nan\n",
" reg = LinearRegression().fit(x[mask], y[mask])\n",
" return reg.coef_[0]\n",
"\n",
"def compute_beta(y, x):\n",
" y = np.asarray(y, dtype=float)\n",
" x = np.asarray(x, dtype=float)\n",
" mask = np.isfinite(y) & np.isfinite(x)\n",
" if mask.sum() < 6:\n",
" return np.nan\n",
" reg = LinearRegression().fit(x[mask].reshape(-1, 1), y[mask])\n",
" return reg.coef_[0]"
]
},
{
"cell_type": "code",
"execution_count": 34,
"id": "79facbea-b77c-4cf9-95f7-526e1dedf9b0",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(1002344, 21)\n"
]
},
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
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" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Registrar Account - ID</th>\n",
" <th>month</th>\n",
" <th>aum_qty</th>\n",
" <th>aum_val</th>\n",
" <th>net_flow_qty</th>\n",
" <th>gross_flow_qty</th>\n",
" <th>sub_qty</th>\n",
" <th>red_qty</th>\n",
" <th>n_tx</th>\n",
" <th>n_isin_held</th>\n",
" <th>n_isin_active</th>\n",
" <th>delta_rate_m</th>\n",
" <th>region</th>\n",
" <th>country</th>\n",
" <th>active_month</th>\n",
" <th>flow_to_aum_m</th>\n",
" <th>turnover_m</th>\n",
" <th>sub_share_m</th>\n",
" <th>red_share_m</th>\n",
" <th>aum_peak_to_date</th>\n",
" <th>aum_drawdown</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>7905</td>\n",
" <td>2015-01-01</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
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" <td>0</td>\n",
" <td>-0.058</td>\n",
" <td>LUXEMBOURG</td>\n",
" <td>LUXEMBOURG</td>\n",
" <td>0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>7905</td>\n",
" <td>2015-02-01</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>-0.022</td>\n",
" <td>LUXEMBOURG</td>\n",
" <td>LUXEMBOURG</td>\n",
" <td>0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>7905</td>\n",
" <td>2015-03-01</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
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" <td>0</td>\n",
" <td>0</td>\n",
" <td>-0.014</td>\n",
" <td>LUXEMBOURG</td>\n",
" <td>LUXEMBOURG</td>\n",
" <td>0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>7905</td>\n",
" <td>2015-04-01</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
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" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>7905</td>\n",
" <td>2015-05-01</td>\n",
" <td>0.0</td>\n",
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" <td>LUXEMBOURG</td>\n",
" <td>LUXEMBOURG</td>\n",
" <td>0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
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" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Registrar Account - ID month aum_qty aum_val net_flow_qty \\\n",
"0 7905 2015-01-01 0.0 0.0 0.0 \n",
"1 7905 2015-02-01 0.0 0.0 0.0 \n",
"2 7905 2015-03-01 0.0 0.0 0.0 \n",
"3 7905 2015-04-01 0.0 0.0 0.0 \n",
"4 7905 2015-05-01 0.0 0.0 0.0 \n",
"\n",
" gross_flow_qty sub_qty red_qty n_tx n_isin_held n_isin_active \\\n",
"0 0.0 0.0 0.0 0.0 0 0 \n",
"1 0.0 0.0 0.0 0.0 0 0 \n",
"2 0.0 0.0 0.0 0.0 0 0 \n",
"3 0.0 0.0 0.0 0.0 0 0 \n",
"4 0.0 0.0 0.0 0.0 0 0 \n",
"\n",
" delta_rate_m region country active_month flow_to_aum_m \\\n",
"0 -0.058 LUXEMBOURG LUXEMBOURG 0 0.0 \n",
"1 -0.022 LUXEMBOURG LUXEMBOURG 0 0.0 \n",
"2 -0.014 LUXEMBOURG LUXEMBOURG 0 0.0 \n",
"3 -0.077 LUXEMBOURG LUXEMBOURG 0 0.0 \n",
"4 -0.053 LUXEMBOURG LUXEMBOURG 0 0.0 \n",
"\n",
" turnover_m sub_share_m red_share_m aum_peak_to_date aum_drawdown \n",
"0 0.0 0.0 0.0 0.0 1.0 \n",
"1 0.0 0.0 0.0 0.0 1.0 \n",
"2 0.0 0.0 0.0 0.0 1.0 \n",
"3 0.0 0.0 0.0 0.0 1.0 \n",
"4 0.0 0.0 0.0 0.0 1.0 "
]
},
"execution_count": 34,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# =========================\n",
"# ULTRA LIGHT VERSION\n",
"# =========================\n",
"\n",
"tmp = df_rel_m.copy()\n",
"tmp[\"isin_held_flag\"] = (tmp[\"aum_qty\"] > 0).astype(int)\n",
"tmp[\"isin_active_flag\"] = (tmp[\"gross_flow_qty\"] > 0).astype(int)\n",
"\n",
"df_month = (\n",
" tmp.groupby([ID_COL, \"month\"], as_index=False)\n",
" .agg(\n",
" aum_qty=(\"aum_qty\", \"sum\"),\n",
" aum_val=(\"aum_val\", \"sum\"),\n",
" net_flow_qty=(\"net_flow_qty\", \"sum\"),\n",
" gross_flow_qty=(\"gross_flow_qty\", \"sum\"),\n",
" sub_qty=(\"sub_qty\", \"sum\"),\n",
" red_qty=(\"red_qty\", \"sum\"),\n",
" n_tx=(\"n_tx\", \"sum\"),\n",
" n_isin_held=(\"isin_held_flag\", \"sum\"),\n",
" n_isin_active=(\"isin_active_flag\", \"sum\"),\n",
" delta_rate_m=(\"delta_rate_m\", \"first\"),\n",
" region=(\"region\", \"first\"),\n",
" country=(\"country\", \"first\"),\n",
" )\n",
" .sort_values([ID_COL, \"month\"])\n",
" .reset_index(drop=True)\n",
")\n",
"\n",
"df_month[\"active_month\"] = (df_month[\"gross_flow_qty\"] > 0).astype(int)\n",
"df_month[\"flow_to_aum_m\"] = df_month[\"net_flow_qty\"] / (df_month[\"aum_qty\"].abs() + EPS)\n",
"df_month[\"turnover_m\"] = df_month[\"gross_flow_qty\"] / (df_month[\"aum_qty\"].abs() + EPS)\n",
"df_month[\"sub_share_m\"] = df_month[\"sub_qty\"] / (df_month[\"gross_flow_qty\"] + EPS)\n",
"df_month[\"red_share_m\"] = df_month[\"red_qty\"] / (df_month[\"gross_flow_qty\"] + EPS)\n",
"\n",
"df_month[\"aum_peak_to_date\"] = df_month.groupby(ID_COL)[\"aum_qty\"].cummax()\n",
"df_month[\"aum_drawdown\"] = 1 - (df_month[\"aum_qty\"] / (df_month[\"aum_peak_to_date\"] + EPS))\n",
"\n",
"print(df_month.shape)\n",
"df_month.head()"
]
},
{
"cell_type": "code",
"execution_count": 35,
"id": "1a2d234f-42f8-4cd1-b50e-7fc3f8c829d2",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
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" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Registrar Account - ID</th>\n",
" <th>Product - Isin</th>\n",
" <th>rel_n_months</th>\n",
" <th>rel_active_months</th>\n",
" <th>rel_holding_months</th>\n",
" <th>rel_aum_mean</th>\n",
" <th>rel_turnover_mean</th>\n",
" <th>rel_turnover_vol</th>\n",
" <th>rel_flow_to_aum_vol</th>\n",
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" <tr>\n",
" <th>2</th>\n",
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" <tr>\n",
" <th>3</th>\n",
" <td>7905</td>\n",
" <td>FR0010148999</td>\n",
" <td>80</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>7905</td>\n",
" <td>FR0010149096</td>\n",
" <td>80</td>\n",
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" <td>0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Registrar Account - ID Product - Isin rel_n_months rel_active_months \\\n",
"0 7905 FR0010135103 80 0 \n",
"1 7905 FR0010147603 80 0 \n",
"2 7905 FR0010148981 80 0 \n",
"3 7905 FR0010148999 80 0 \n",
"4 7905 FR0010149096 80 0 \n",
"\n",
" rel_holding_months rel_aum_mean rel_turnover_mean rel_turnover_vol \\\n",
"0 0 0.0 0.0 0.0 \n",
"1 0 0.0 0.0 0.0 \n",
"2 0 0.0 0.0 0.0 \n",
"3 0 0.0 0.0 0.0 \n",
"4 0 0.0 0.0 0.0 \n",
"\n",
" rel_flow_to_aum_vol rel_n_tx rel_full_exit_count rel_entry_count \n",
"0 0.0 0.0 0 0 \n",
"1 0.0 0.0 0 0 \n",
"2 0.0 0.0 0 0 \n",
"3 0.0 0.0 0 0 \n",
"4 0.0 0.0 0 0 "
]
},
"execution_count": 35,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"tmp = df_rel_m.sort_values([ID_COL, ISIN_COL, \"month\"]).copy()\n",
"tmp[\"prev_aum\"] = tmp.groupby([ID_COL, ISIN_COL])[\"aum_qty\"].shift(1)\n",
"tmp[\"full_exit_event\"] = ((tmp[\"prev_aum\"] > 0) & (tmp[\"aum_qty\"] <= 0)).astype(int)\n",
"tmp[\"entry_event\"] = ((tmp[\"prev_aum\"].fillna(0) <= 0) & (tmp[\"aum_qty\"] > 0)).astype(int)\n",
"\n",
"df_rel_feat = (\n",
" tmp.groupby([ID_COL, ISIN_COL], as_index=False)\n",
" .agg(\n",
" rel_n_months=(\"month\", \"nunique\"),\n",
" rel_active_months=(\"active_rel_month\", \"sum\"),\n",
" rel_holding_months=(\"holding_rel_month\", \"sum\"),\n",
" rel_aum_mean=(\"aum_qty\", \"mean\"),\n",
" rel_turnover_mean=(\"turnover_rel\", \"mean\"),\n",
" rel_turnover_vol=(\"turnover_rel\", \"std\"),\n",
" rel_flow_to_aum_vol=(\"flow_to_aum_rel\", \"std\"),\n",
" rel_n_tx=(\"n_tx\", \"sum\"),\n",
" rel_full_exit_count=(\"full_exit_event\", \"sum\"),\n",
" rel_entry_count=(\"entry_event\", \"sum\")\n",
" )\n",
")\n",
"\n",
"df_rel_feat.head()"
]
},
{
"cell_type": "code",
"execution_count": 38,
"id": "9b61ce6a-1f54-423a-8a54-9897fe1bce20",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(17238, 34)\n"
]
},
{
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" <th>n_months</th>\n",
" <th>n_active_months</th>\n",
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" <th>aum_qty_mean</th>\n",
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" <th>avg_n_isin_held</th>\n",
" <th>max_n_isin_held</th>\n",
" <th>sub_share_mean</th>\n",
" <th>red_share_mean</th>\n",
" <th>delta_rate_mean</th>\n",
" <th>aum_drawdown_last</th>\n",
" <th>aum_drawdown_max</th>\n",
" <th>region</th>\n",
" <th>country</th>\n",
" <th>n_isin_total</th>\n",
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" <td>0</td>\n",
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" <td>0.00000</td>\n",
" <td>-0.008925</td>\n",
" <td>1.0</td>\n",
" <td>1.0</td>\n",
" <td>LUXEMBOURG</td>\n",
" <td>LUXEMBOURG</td>\n",
" <td>12</td>\n",
" <td>0.000000e+00</td>\n",
" <td>0.000000e+00</td>\n",
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" </tr>\n",
" <tr>\n",
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" <td>-0.008925</td>\n",
" <td>1.0</td>\n",
" <td>1.0</td>\n",
" <td>LUXEMBOURG</td>\n",
" <td>LUXEMBOURG</td>\n",
" <td>5</td>\n",
" <td>0.000000e+00</td>\n",
" <td>0.000000e+00</td>\n",
" <td>0.000000e+00</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0.000000</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>7962</td>\n",
" <td>80</td>\n",
" <td>0</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.0</td>\n",
" <td>0.000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.0</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0</td>\n",
" <td>0.000000</td>\n",
" <td>0.00000</td>\n",
" <td>-0.008925</td>\n",
" <td>1.0</td>\n",
" <td>1.0</td>\n",
" <td>LUXEMBOURG</td>\n",
" <td>LUXEMBOURG</td>\n",
" <td>4</td>\n",
" <td>0.000000e+00</td>\n",
" <td>0.000000e+00</td>\n",
" <td>0.000000e+00</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0.000000</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>8307</td>\n",
" <td>130</td>\n",
" <td>77</td>\n",
" <td>0.592308</td>\n",
" <td>21568.649523</td>\n",
" <td>20698.97</td>\n",
" <td>58533.06</td>\n",
" <td>0.0</td>\n",
" <td>27252.124</td>\n",
" <td>177077.31</td>\n",
" <td>1362.133154</td>\n",
" <td>161.0</td>\n",
" <td>3508.455222</td>\n",
" <td>0.056109</td>\n",
" <td>0.136358</td>\n",
" <td>0.011473</td>\n",
" <td>0.134336</td>\n",
" <td>10.276923</td>\n",
" <td>15</td>\n",
" <td>0.260828</td>\n",
" <td>-0.36224</td>\n",
" <td>0.013723</td>\n",
" <td>1.0</td>\n",
" <td>1.0</td>\n",
" <td>SWITZERLAND</td>\n",
" <td>SWITZERLAND</td>\n",
" <td>29</td>\n",
" <td>5.479069e+09</td>\n",
" <td>3.702940e+10</td>\n",
" <td>3.702940e+10</td>\n",
" <td>36</td>\n",
" <td>36</td>\n",
" <td>46.068966</td>\n",
" <td>120</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>8354</td>\n",
" <td>64</td>\n",
" <td>0</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.0</td>\n",
" <td>0.000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.0</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0</td>\n",
" <td>0.000000</td>\n",
" <td>0.00000</td>\n",
" <td>-0.010063</td>\n",
" <td>1.0</td>\n",
" <td>1.0</td>\n",
" <td>SWITZERLAND</td>\n",
" <td>SWITZERLAND</td>\n",
" <td>1</td>\n",
" <td>0.000000e+00</td>\n",
" <td>0.000000e+00</td>\n",
" <td>0.000000e+00</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0.000000</td>\n",
" <td>0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Registrar Account - ID n_months n_active_months flow_freq aum_qty_mean \\\n",
"0 7905 80 0 0.000000 0.000000 \n",
"1 7912 80 0 0.000000 0.000000 \n",
"2 7962 80 0 0.000000 0.000000 \n",
"3 8307 130 77 0.592308 21568.649523 \n",
"4 8354 64 0 0.000000 0.000000 \n",
"\n",
" aum_qty_median aum_qty_max aum_qty_last net_flow_qty_sum \\\n",
"0 0.00 0.00 0.0 0.000 \n",
"1 0.00 0.00 0.0 0.000 \n",
"2 0.00 0.00 0.0 0.000 \n",
"3 20698.97 58533.06 0.0 27252.124 \n",
"4 0.00 0.00 0.0 0.000 \n",
"\n",
" gross_flow_qty_sum gross_flow_qty_mean n_tx_total net_flow_vol \\\n",
"0 0.00 0.000000 0.0 0.000000 \n",
"1 0.00 0.000000 0.0 0.000000 \n",
"2 0.00 0.000000 0.0 0.000000 \n",
"3 177077.31 1362.133154 161.0 3508.455222 \n",
"4 0.00 0.000000 0.0 0.000000 \n",
"\n",
" turnover_mean turnover_vol flow_to_aum_mean flow_to_aum_vol \\\n",
"0 0.000000 0.000000 0.000000 0.000000 \n",
"1 0.000000 0.000000 0.000000 0.000000 \n",
"2 0.000000 0.000000 0.000000 0.000000 \n",
"3 0.056109 0.136358 0.011473 0.134336 \n",
"4 0.000000 0.000000 0.000000 0.000000 \n",
"\n",
" avg_n_isin_held max_n_isin_held sub_share_mean red_share_mean \\\n",
"0 0.000000 0 0.000000 0.00000 \n",
"1 0.000000 0 0.000000 0.00000 \n",
"2 0.000000 0 0.000000 0.00000 \n",
"3 10.276923 15 0.260828 -0.36224 \n",
"4 0.000000 0 0.000000 0.00000 \n",
"\n",
" delta_rate_mean aum_drawdown_last aum_drawdown_max region \\\n",
"0 -0.008925 1.0 1.0 LUXEMBOURG \n",
"1 -0.008925 1.0 1.0 LUXEMBOURG \n",
"2 -0.008925 1.0 1.0 LUXEMBOURG \n",
"3 0.013723 1.0 1.0 SWITZERLAND \n",
"4 -0.010063 1.0 1.0 SWITZERLAND \n",
"\n",
" country n_isin_total rel_turnover_mean_avg rel_turnover_vol_avg \\\n",
"0 LUXEMBOURG 12 0.000000e+00 0.000000e+00 \n",
"1 LUXEMBOURG 5 0.000000e+00 0.000000e+00 \n",
"2 LUXEMBOURG 4 0.000000e+00 0.000000e+00 \n",
"3 SWITZERLAND 29 5.479069e+09 3.702940e+10 \n",
"4 SWITZERLAND 1 0.000000e+00 0.000000e+00 \n",
"\n",
" rel_flow_to_aum_vol_avg full_exit_count entry_count \\\n",
"0 0.000000e+00 0 0 \n",
"1 0.000000e+00 0 0 \n",
"2 0.000000e+00 0 0 \n",
"3 3.702940e+10 36 36 \n",
"4 0.000000e+00 0 0 \n",
"\n",
" avg_holding_months_per_isin max_holding_months_per_isin \n",
"0 0.000000 0 \n",
"1 0.000000 0 \n",
"2 0.000000 0 \n",
"3 46.068966 120 \n",
"4 0.000000 0 "
]
},
"execution_count": 38,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_rel_client = (\n",
" df_rel_feat\n",
" .groupby(ID_COL, as_index=False)\n",
" .agg(\n",
" n_isin_total=(ISIN_COL, \"nunique\"),\n",
" rel_turnover_mean_avg=(\"rel_turnover_mean\", \"mean\"),\n",
" rel_turnover_vol_avg=(\"rel_turnover_vol\", \"mean\"),\n",
" rel_flow_to_aum_vol_avg=(\"rel_flow_to_aum_vol\", \"mean\"),\n",
" full_exit_count=(\"rel_full_exit_count\", \"sum\"),\n",
" entry_count=(\"rel_entry_count\", \"sum\"),\n",
" avg_holding_months_per_isin=(\"rel_holding_months\", \"mean\"),\n",
" max_holding_months_per_isin=(\"rel_holding_months\", \"max\")\n",
" )\n",
")\n",
"\n",
"df_client = (\n",
" df_month\n",
" .groupby(ID_COL, as_index=False)\n",
" .agg(\n",
" n_months=(\"month\", \"nunique\"),\n",
" n_active_months=(\"active_month\", \"sum\"),\n",
" flow_freq=(\"active_month\", \"mean\"),\n",
"\n",
" aum_qty_mean=(\"aum_qty\", \"mean\"),\n",
" aum_qty_median=(\"aum_qty\", \"median\"),\n",
" aum_qty_max=(\"aum_qty\", \"max\"),\n",
" aum_qty_last=(\"aum_qty\", \"last\"),\n",
"\n",
" net_flow_qty_sum=(\"net_flow_qty\", \"sum\"),\n",
" gross_flow_qty_sum=(\"gross_flow_qty\", \"sum\"),\n",
" gross_flow_qty_mean=(\"gross_flow_qty\", \"mean\"),\n",
" n_tx_total=(\"n_tx\", \"sum\"),\n",
"\n",
" net_flow_vol=(\"net_flow_qty\", \"std\"),\n",
" turnover_mean=(\"turnover_m\", \"mean\"),\n",
" turnover_vol=(\"turnover_m\", \"std\"),\n",
" flow_to_aum_mean=(\"flow_to_aum_m\", \"mean\"),\n",
" flow_to_aum_vol=(\"flow_to_aum_m\", \"std\"),\n",
"\n",
" avg_n_isin_held=(\"n_isin_held\", \"mean\"),\n",
" max_n_isin_held=(\"n_isin_held\", \"max\"),\n",
"\n",
" sub_share_mean=(\"sub_share_m\", \"mean\"),\n",
" red_share_mean=(\"red_share_m\", \"mean\"),\n",
"\n",
" delta_rate_mean=(\"delta_rate_m\", \"mean\"),\n",
" aum_drawdown_last=(\"aum_drawdown\", \"last\"),\n",
" aum_drawdown_max=(\"aum_drawdown\", \"max\"),\n",
"\n",
" region=(\"region\", \"last\"),\n",
" country=(\"country\", \"last\")\n",
" )\n",
")\n",
"\n",
"df_client = df_client.merge(df_rel_client, on=ID_COL, how=\"left\")\n",
"\n",
"print(df_client.shape)\n",
"df_client.head()"
]
},
{
"cell_type": "code",
"execution_count": 56,
"id": "48d1b22b-f0ca-448e-a330-6b7d64fe48b3",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Index(['Registrar Account - ID', 'n_months', 'n_active_months', 'flow_freq',\n",
" 'aum_qty_mean', 'aum_qty_median', 'aum_qty_max', 'aum_qty_last',\n",
" 'net_flow_qty_sum', 'gross_flow_qty_sum', 'gross_flow_qty_mean',\n",
" 'n_tx_total', 'net_flow_vol', 'turnover_mean', 'turnover_vol',\n",
" 'flow_to_aum_mean', 'flow_to_aum_vol', 'avg_n_isin_held',\n",
" 'max_n_isin_held', 'sub_share_mean', 'red_share_mean',\n",
" 'delta_rate_mean', 'aum_drawdown_last', 'aum_drawdown_max', 'region',\n",
" 'country', 'n_isin_total', 'rel_turnover_mean_avg',\n",
" 'rel_turnover_vol_avg', 'rel_flow_to_aum_vol_avg', 'full_exit_count',\n",
" 'entry_count', 'avg_holding_months_per_isin',\n",
" 'max_holding_months_per_isin', 'flow_trend_12m_x', 'aum_trend_12m_x',\n",
" 'drawdown_trend_12m_x', 'beta_rate_x', 'flow_trend_12m_y',\n",
" 'aum_trend_12m_y', 'drawdown_trend_12m_y', 'beta_rate_y'],\n",
" dtype='object')"
]
},
"execution_count": 56,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_client.columns"
]
},
{
"cell_type": "code",
"execution_count": 57,
"id": "a9b34e2b-503f-41a1-9629-670ceb7615ba",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(17238, 42)"
]
},
"execution_count": 57,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_client.shape"
]
},
{
"cell_type": "code",
"execution_count": 40,
"id": "934507d6-8aaf-43e2-8a2d-d6cfea0b6af1",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(17238, 38)\n"
]
},
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Registrar Account - ID</th>\n",
" <th>n_months</th>\n",
" <th>n_active_months</th>\n",
" <th>flow_freq</th>\n",
" <th>aum_qty_mean</th>\n",
" <th>aum_qty_median</th>\n",
" <th>aum_qty_max</th>\n",
" <th>aum_qty_last</th>\n",
" <th>net_flow_qty_sum</th>\n",
" <th>gross_flow_qty_sum</th>\n",
" <th>gross_flow_qty_mean</th>\n",
" <th>n_tx_total</th>\n",
" <th>net_flow_vol</th>\n",
" <th>turnover_mean</th>\n",
" <th>turnover_vol</th>\n",
" <th>flow_to_aum_mean</th>\n",
" <th>flow_to_aum_vol</th>\n",
" <th>avg_n_isin_held</th>\n",
" <th>max_n_isin_held</th>\n",
" <th>sub_share_mean</th>\n",
" <th>red_share_mean</th>\n",
" <th>delta_rate_mean</th>\n",
" <th>aum_drawdown_last</th>\n",
" <th>aum_drawdown_max</th>\n",
" <th>region</th>\n",
" <th>country</th>\n",
" <th>n_isin_total</th>\n",
" <th>rel_turnover_mean_avg</th>\n",
" <th>rel_turnover_vol_avg</th>\n",
" <th>rel_flow_to_aum_vol_avg</th>\n",
" <th>full_exit_count</th>\n",
" <th>entry_count</th>\n",
" <th>avg_holding_months_per_isin</th>\n",
" <th>max_holding_months_per_isin</th>\n",
" <th>flow_trend_12m</th>\n",
" <th>aum_trend_12m</th>\n",
" <th>drawdown_trend_12m</th>\n",
" <th>beta_rate</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>7905</td>\n",
" <td>80</td>\n",
" <td>0</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.0</td>\n",
" <td>0.000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.0</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0</td>\n",
" <td>0.000000</td>\n",
" <td>0.00000</td>\n",
" <td>-0.008925</td>\n",
" <td>1.0</td>\n",
" <td>1.0</td>\n",
" <td>LUXEMBOURG</td>\n",
" <td>LUXEMBOURG</td>\n",
" <td>12</td>\n",
" <td>0.000000e+00</td>\n",
" <td>0.000000e+00</td>\n",
" <td>0.000000e+00</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0.000000</td>\n",
" <td>0</td>\n",
" <td>0.00000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00000</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>7912</td>\n",
" <td>80</td>\n",
" <td>0</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.0</td>\n",
" <td>0.000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.0</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0</td>\n",
" <td>0.000000</td>\n",
" <td>0.00000</td>\n",
" <td>-0.008925</td>\n",
" <td>1.0</td>\n",
" <td>1.0</td>\n",
" <td>LUXEMBOURG</td>\n",
" <td>LUXEMBOURG</td>\n",
" <td>5</td>\n",
" <td>0.000000e+00</td>\n",
" <td>0.000000e+00</td>\n",
" <td>0.000000e+00</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0.000000</td>\n",
" <td>0</td>\n",
" <td>0.00000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00000</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>7962</td>\n",
" <td>80</td>\n",
" <td>0</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.0</td>\n",
" <td>0.000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.0</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0</td>\n",
" <td>0.000000</td>\n",
" <td>0.00000</td>\n",
" <td>-0.008925</td>\n",
" <td>1.0</td>\n",
" <td>1.0</td>\n",
" <td>LUXEMBOURG</td>\n",
" <td>LUXEMBOURG</td>\n",
" <td>4</td>\n",
" <td>0.000000e+00</td>\n",
" <td>0.000000e+00</td>\n",
" <td>0.000000e+00</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0.000000</td>\n",
" <td>0</td>\n",
" <td>0.00000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00000</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>8307</td>\n",
" <td>130</td>\n",
" <td>77</td>\n",
" <td>0.592308</td>\n",
" <td>21568.649523</td>\n",
" <td>20698.97</td>\n",
" <td>58533.06</td>\n",
" <td>0.0</td>\n",
" <td>27252.124</td>\n",
" <td>177077.31</td>\n",
" <td>1362.133154</td>\n",
" <td>161.0</td>\n",
" <td>3508.455222</td>\n",
" <td>0.056109</td>\n",
" <td>0.136358</td>\n",
" <td>0.011473</td>\n",
" <td>0.134336</td>\n",
" <td>10.276923</td>\n",
" <td>15</td>\n",
" <td>0.260828</td>\n",
" <td>-0.36224</td>\n",
" <td>0.013723</td>\n",
" <td>1.0</td>\n",
" <td>1.0</td>\n",
" <td>SWITZERLAND</td>\n",
" <td>SWITZERLAND</td>\n",
" <td>29</td>\n",
" <td>5.479069e+09</td>\n",
" <td>3.702940e+10</td>\n",
" <td>3.702940e+10</td>\n",
" <td>36</td>\n",
" <td>36</td>\n",
" <td>46.068966</td>\n",
" <td>120</td>\n",
" <td>-0.01049</td>\n",
" <td>-1142.587413</td>\n",
" <td>0.01952</td>\n",
" <td>-0.131741</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>8354</td>\n",
" <td>64</td>\n",
" <td>0</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.0</td>\n",
" <td>0.000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.0</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0</td>\n",
" <td>0.000000</td>\n",
" <td>0.00000</td>\n",
" <td>-0.010063</td>\n",
" <td>1.0</td>\n",
" <td>1.0</td>\n",
" <td>SWITZERLAND</td>\n",
" <td>SWITZERLAND</td>\n",
" <td>1</td>\n",
" <td>0.000000e+00</td>\n",
" <td>0.000000e+00</td>\n",
" <td>0.000000e+00</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
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" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Registrar Account - ID n_months n_active_months flow_freq aum_qty_mean \\\n",
"0 7905 80 0 0.000000 0.000000 \n",
"1 7912 80 0 0.000000 0.000000 \n",
"2 7962 80 0 0.000000 0.000000 \n",
"3 8307 130 77 0.592308 21568.649523 \n",
"4 8354 64 0 0.000000 0.000000 \n",
"\n",
" aum_qty_median aum_qty_max aum_qty_last net_flow_qty_sum \\\n",
"0 0.00 0.00 0.0 0.000 \n",
"1 0.00 0.00 0.0 0.000 \n",
"2 0.00 0.00 0.0 0.000 \n",
"3 20698.97 58533.06 0.0 27252.124 \n",
"4 0.00 0.00 0.0 0.000 \n",
"\n",
" gross_flow_qty_sum gross_flow_qty_mean n_tx_total net_flow_vol \\\n",
"0 0.00 0.000000 0.0 0.000000 \n",
"1 0.00 0.000000 0.0 0.000000 \n",
"2 0.00 0.000000 0.0 0.000000 \n",
"3 177077.31 1362.133154 161.0 3508.455222 \n",
"4 0.00 0.000000 0.0 0.000000 \n",
"\n",
" turnover_mean turnover_vol flow_to_aum_mean flow_to_aum_vol \\\n",
"0 0.000000 0.000000 0.000000 0.000000 \n",
"1 0.000000 0.000000 0.000000 0.000000 \n",
"2 0.000000 0.000000 0.000000 0.000000 \n",
"3 0.056109 0.136358 0.011473 0.134336 \n",
"4 0.000000 0.000000 0.000000 0.000000 \n",
"\n",
" avg_n_isin_held max_n_isin_held sub_share_mean red_share_mean \\\n",
"0 0.000000 0 0.000000 0.00000 \n",
"1 0.000000 0 0.000000 0.00000 \n",
"2 0.000000 0 0.000000 0.00000 \n",
"3 10.276923 15 0.260828 -0.36224 \n",
"4 0.000000 0 0.000000 0.00000 \n",
"\n",
" delta_rate_mean aum_drawdown_last aum_drawdown_max region \\\n",
"0 -0.008925 1.0 1.0 LUXEMBOURG \n",
"1 -0.008925 1.0 1.0 LUXEMBOURG \n",
"2 -0.008925 1.0 1.0 LUXEMBOURG \n",
"3 0.013723 1.0 1.0 SWITZERLAND \n",
"4 -0.010063 1.0 1.0 SWITZERLAND \n",
"\n",
" country n_isin_total rel_turnover_mean_avg rel_turnover_vol_avg \\\n",
"0 LUXEMBOURG 12 0.000000e+00 0.000000e+00 \n",
"1 LUXEMBOURG 5 0.000000e+00 0.000000e+00 \n",
"2 LUXEMBOURG 4 0.000000e+00 0.000000e+00 \n",
"3 SWITZERLAND 29 5.479069e+09 3.702940e+10 \n",
"4 SWITZERLAND 1 0.000000e+00 0.000000e+00 \n",
"\n",
" rel_flow_to_aum_vol_avg full_exit_count entry_count \\\n",
"0 0.000000e+00 0 0 \n",
"1 0.000000e+00 0 0 \n",
"2 0.000000e+00 0 0 \n",
"3 3.702940e+10 36 36 \n",
"4 0.000000e+00 0 0 \n",
"\n",
" avg_holding_months_per_isin max_holding_months_per_isin flow_trend_12m \\\n",
"0 0.000000 0 0.00000 \n",
"1 0.000000 0 0.00000 \n",
"2 0.000000 0 0.00000 \n",
"3 46.068966 120 -0.01049 \n",
"4 0.000000 0 0.00000 \n",
"\n",
" aum_trend_12m drawdown_trend_12m beta_rate \n",
"0 0.000000 0.00000 0.000000 \n",
"1 0.000000 0.00000 0.000000 \n",
"2 0.000000 0.00000 0.000000 \n",
"3 -1142.587413 0.01952 -0.131741 \n",
"4 0.000000 0.00000 0.000000 "
]
},
"execution_count": 40,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"def compute_trend(y):\n",
" y = np.asarray(y, dtype=float)\n",
" if len(y) < 4:\n",
" return np.nan\n",
" x = np.arange(len(y)).reshape(-1, 1)\n",
" mask = np.isfinite(y)\n",
" if mask.sum() < 4:\n",
" return np.nan\n",
" reg = LinearRegression().fit(x[mask], y[mask])\n",
" return reg.coef_[0]\n",
"\n",
"def compute_beta(y, x):\n",
" y = np.asarray(y, dtype=float)\n",
" x = np.asarray(x, dtype=float)\n",
" mask = np.isfinite(y) & np.isfinite(x)\n",
" if mask.sum() < 6:\n",
" return np.nan\n",
" reg = LinearRegression().fit(x[mask].reshape(-1, 1), y[mask])\n",
" return reg.coef_[0]\n",
"\n",
"rows = []\n",
"\n",
"for acc, g in df_month.groupby(ID_COL):\n",
" g = g.sort_values(\"month\")\n",
"\n",
" flow = g[\"flow_to_aum_m\"].values\n",
" aum = g[\"aum_qty\"].values\n",
" delta_rate = g[\"delta_rate_m\"].values\n",
" drawdown = g[\"aum_drawdown\"].values\n",
"\n",
" rows.append({\n",
" ID_COL: acc,\n",
" \"flow_trend_12m\": compute_trend(flow[-12:]),\n",
" \"aum_trend_12m\": compute_trend(aum[-12:]),\n",
" \"drawdown_trend_12m\": compute_trend(drawdown[-12:]),\n",
" \"beta_rate\": compute_beta(flow, delta_rate),\n",
" })\n",
"\n",
"df_beta = pd.DataFrame(rows)\n",
"\n",
"df_client = df_client.merge(df_beta, on=ID_COL, how=\"left\")\n",
"\n",
"print(df_client.shape)\n",
"df_client.head()"
]
},
{
"cell_type": "code",
"execution_count": 58,
"id": "4a35e933-5417-4141-a1f0-ab4edb567d51",
"metadata": {},
"outputs": [
{
"data": {
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"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Registrar Account - ID</th>\n",
" <th>n_months</th>\n",
" <th>n_active_months</th>\n",
" <th>flow_freq</th>\n",
" <th>aum_qty_mean</th>\n",
" <th>aum_qty_median</th>\n",
" <th>aum_qty_max</th>\n",
" <th>aum_qty_last</th>\n",
" <th>net_flow_qty_sum</th>\n",
" <th>gross_flow_qty_sum</th>\n",
" <th>gross_flow_qty_mean</th>\n",
" <th>n_tx_total</th>\n",
" <th>net_flow_vol</th>\n",
" <th>turnover_mean</th>\n",
" <th>turnover_vol</th>\n",
" <th>flow_to_aum_mean</th>\n",
" <th>flow_to_aum_vol</th>\n",
" <th>avg_n_isin_held</th>\n",
" <th>max_n_isin_held</th>\n",
" <th>sub_share_mean</th>\n",
" <th>red_share_mean</th>\n",
" <th>delta_rate_mean</th>\n",
" <th>aum_drawdown_last</th>\n",
" <th>aum_drawdown_max</th>\n",
" <th>region</th>\n",
" <th>country</th>\n",
" <th>n_isin_total</th>\n",
" <th>rel_turnover_mean_avg</th>\n",
" <th>rel_turnover_vol_avg</th>\n",
" <th>rel_flow_to_aum_vol_avg</th>\n",
" <th>full_exit_count</th>\n",
" <th>entry_count</th>\n",
" <th>avg_holding_months_per_isin</th>\n",
" <th>max_holding_months_per_isin</th>\n",
" <th>flow_trend_12m_x</th>\n",
" <th>aum_trend_12m_x</th>\n",
" <th>drawdown_trend_12m_x</th>\n",
" <th>beta_rate_x</th>\n",
" <th>flow_trend_12m_y</th>\n",
" <th>aum_trend_12m_y</th>\n",
" <th>drawdown_trend_12m_y</th>\n",
" <th>beta_rate_y</th>\n",
" <th>gross_flow_to_aum</th>\n",
" <th>avg_ticket</th>\n",
" <th>flow_direction_balance</th>\n",
" <th>redemption_bias</th>\n",
" <th>activity_intensity</th>\n",
" <th>exit_rate_per_isin</th>\n",
" <th>entry_rate_per_isin</th>\n",
" <th>aum_final_to_peak</th>\n",
" <th>log_aum_qty_mean</th>\n",
" <th>log_gross_flow_qty_sum</th>\n",
" <th>log_n_tx_total</th>\n",
" <th>log_avg_ticket</th>\n",
" <th>country_grp</th>\n",
" <th>region_grp</th>\n",
" <th>cluster_k2</th>\n",
" <th>cluster_k5</th>\n",
" <th>cluster_k10</th>\n",
" <th>churn_hard</th>\n",
" <th>churn_soft</th>\n",
" <th>churn_warning</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>17217</th>\n",
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" <td>3.305042e+04</td>\n",
" <td>0.000000e+00</td>\n",
" <td>5.372034e+05</td>\n",
" <td>0.0</td>\n",
" <td>5.050329e+05</td>\n",
" <td>8.986925e+05</td>\n",
" <td>1.576653e+04</td>\n",
" <td>68.0</td>\n",
" <td>4.698930e+04</td>\n",
" <td>9.464842e+12</td>\n",
" <td>3.058166e+13</td>\n",
" <td>2.993416e+12</td>\n",
" <td>3.181011e+13</td>\n",
" <td>0.140351</td>\n",
" <td>1</td>\n",
" <td>0.219723</td>\n",
" <td>-0.241074</td>\n",
" <td>-0.009035</td>\n",
" <td>1.0</td>\n",
" <td>1.0</td>\n",
" <td>ITALY</td>\n",
" <td>ITALY</td>\n",
" <td>3</td>\n",
" <td>4.860324e+12</td>\n",
" <td>1.237445e+13</td>\n",
" <td>1.319247e+13</td>\n",
" <td>3</td>\n",
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" <td>0.000000e+00</td>\n",
" <td>0.000000</td>\n",
" <td>6.383533e+13</td>\n",
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" <td>0.000000e+00</td>\n",
" <td>0.000000</td>\n",
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" <td>-0.460796</td>\n",
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" <td>10.405820</td>\n",
" <td>13.708697</td>\n",
" <td>4.234107</td>\n",
" <td>9.489264</td>\n",
" <td>ITALY</td>\n",
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" <td>0</td>\n",
" <td>0</td>\n",
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" <td>1.0</td>\n",
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" <td>1.705393</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>ITALY</td>\n",
" <td>ITALY</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
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" <tr>\n",
" <th>17221</th>\n",
" <td>422864</td>\n",
" <td>44</td>\n",
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" <td>1.797300e+01</td>\n",
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" <td>0.000000e+00</td>\n",
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" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>1.000000</td>\n",
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" <td>0.0</td>\n",
" <td>0.597162</td>\n",
" <td>0.000000</td>\n",
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" <td>UNITED KINGDOM</td>\n",
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" <td>0</td>\n",
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" <td>1</td>\n",
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" <tr>\n",
" <th>17224</th>\n",
" <td>422876</td>\n",
" <td>77</td>\n",
" <td>0</td>\n",
" <td>0.000000</td>\n",
" <td>3.192987e-01</td>\n",
" <td>0.000000e+00</td>\n",
" <td>1.229300e+01</td>\n",
" <td>0.0</td>\n",
" <td>0.000000e+00</td>\n",
" <td>0.000000e+00</td>\n",
" <td>0.000000e+00</td>\n",
" <td>0.0</td>\n",
" <td>0.000000e+00</td>\n",
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" <td>0.000000e+00</td>\n",
" <td>0.000000e+00</td>\n",
" <td>0.000000e+00</td>\n",
" <td>0.025974</td>\n",
" <td>1</td>\n",
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" <td>-0.009182</td>\n",
" <td>1.0</td>\n",
" <td>1.0</td>\n",
" <td>ITALY</td>\n",
" <td>ITALY</td>\n",
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" <td>0.000000e+00</td>\n",
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" <td>0.200000</td>\n",
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" <td>0.000000e+00</td>\n",
" <td>0.000000</td>\n",
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" <td>0.000000e+00</td>\n",
" <td>0.000000e+00</td>\n",
" <td>0.000000</td>\n",
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" <td>0.000000</td>\n",
" <td>0.100000</td>\n",
" <td>0.100000</td>\n",
" <td>0.0</td>\n",
" <td>0.277100</td>\n",
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" <td>0.000000</td>\n",
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" <td>ITALY</td>\n",
" <td>ITALY</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
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" <td>48</td>\n",
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" <td>0.000000e+00</td>\n",
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" <td>0.000000e+00</td>\n",
" <td>0.000000e+00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000e+00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>0.0</td>\n",
" <td>3.603322</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>UNITED KINGDOM</td>\n",
" <td>UNITED KINGDOM</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>17230</th>\n",
" <td>422906</td>\n",
" <td>114</td>\n",
" <td>112</td>\n",
" <td>0.982456</td>\n",
" <td>1.319870e+03</td>\n",
" <td>8.466950e+01</td>\n",
" <td>1.216348e+04</td>\n",
" <td>0.0</td>\n",
" <td>2.488392e+03</td>\n",
" <td>2.901017e+04</td>\n",
" <td>2.544751e+02</td>\n",
" <td>367.0</td>\n",
" <td>3.655121e+02</td>\n",
" <td>7.934937e+10</td>\n",
" <td>1.987296e+11</td>\n",
" <td>-1.053788e+10</td>\n",
" <td>1.906889e+11</td>\n",
" <td>0.929825</td>\n",
" <td>3</td>\n",
" <td>0.373516</td>\n",
" <td>-0.622580</td>\n",
" <td>0.014351</td>\n",
" <td>1.0</td>\n",
" <td>1.0</td>\n",
" <td>SPAIN</td>\n",
" <td>SPAIN</td>\n",
" <td>4</td>\n",
" <td>6.943214e+10</td>\n",
" <td>1.266133e+11</td>\n",
" <td>1.355015e+11</td>\n",
" <td>39</td>\n",
" <td>39</td>\n",
" <td>26.500000</td>\n",
" <td>35</td>\n",
" <td>4.573969e+09</td>\n",
" <td>0.000000e+00</td>\n",
" <td>0.000000</td>\n",
" <td>2.857298e+09</td>\n",
" <td>4.573969e+09</td>\n",
" <td>0.000000e+00</td>\n",
" <td>0.000000</td>\n",
" <td>2.857298e+09</td>\n",
" <td>21.979558</td>\n",
" <td>79.046774</td>\n",
" <td>0.085777</td>\n",
" <td>-0.996097</td>\n",
" <td>3.219298</td>\n",
" <td>9.750000</td>\n",
" <td>9.750000</td>\n",
" <td>0.0</td>\n",
" <td>7.186046</td>\n",
" <td>10.275436</td>\n",
" <td>5.908083</td>\n",
" <td>4.382611</td>\n",
" <td>SPAIN</td>\n",
" <td>SPAIN</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>17232</th>\n",
" <td>67100</td>\n",
" <td>79</td>\n",
" <td>0</td>\n",
" <td>0.000000</td>\n",
" <td>5.677362e+03</td>\n",
" <td>1.621000e+03</td>\n",
" <td>3.962330e+04</td>\n",
" <td>0.0</td>\n",
" <td>0.000000e+00</td>\n",
" <td>0.000000e+00</td>\n",
" <td>0.000000e+00</td>\n",
" <td>0.0</td>\n",
" <td>0.000000e+00</td>\n",
" <td>0.000000e+00</td>\n",
" <td>0.000000e+00</td>\n",
" <td>0.000000e+00</td>\n",
" <td>0.000000e+00</td>\n",
" <td>2.291139</td>\n",
" <td>6</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>-0.009089</td>\n",
" <td>1.0</td>\n",
" <td>1.0</td>\n",
" <td>GERMANY</td>\n",
" <td>GERMANY</td>\n",
" <td>21</td>\n",
" <td>0.000000e+00</td>\n",
" <td>0.000000e+00</td>\n",
" <td>0.000000e+00</td>\n",
" <td>14</td>\n",
" <td>20</td>\n",
" <td>8.619048</td>\n",
" <td>21</td>\n",
" <td>0.000000e+00</td>\n",
" <td>-3.006628e+02</td>\n",
" <td>0.007588</td>\n",
" <td>0.000000e+00</td>\n",
" <td>0.000000e+00</td>\n",
" <td>-3.006628e+02</td>\n",
" <td>0.007588</td>\n",
" <td>0.000000e+00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.666667</td>\n",
" <td>0.952381</td>\n",
" <td>0.0</td>\n",
" <td>8.644418</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>GERMANY</td>\n",
" <td>GERMANY</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>17233</th>\n",
" <td>67104</td>\n",
" <td>63</td>\n",
" <td>0</td>\n",
" <td>0.000000</td>\n",
" <td>1.238095e+02</td>\n",
" <td>0.000000e+00</td>\n",
" <td>7.800000e+03</td>\n",
" <td>0.0</td>\n",
" <td>0.000000e+00</td>\n",
" <td>0.000000e+00</td>\n",
" <td>0.000000e+00</td>\n",
" <td>0.0</td>\n",
" <td>0.000000e+00</td>\n",
" <td>0.000000e+00</td>\n",
" <td>0.000000e+00</td>\n",
" <td>0.000000e+00</td>\n",
" <td>0.000000e+00</td>\n",
" <td>0.015873</td>\n",
" <td>1</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>-0.007476</td>\n",
" <td>1.0</td>\n",
" <td>1.0</td>\n",
" <td>GERMANY</td>\n",
" <td>GERMANY</td>\n",
" <td>11</td>\n",
" <td>0.000000e+00</td>\n",
" <td>0.000000e+00</td>\n",
" <td>0.000000e+00</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>0.090909</td>\n",
" <td>1</td>\n",
" <td>0.000000e+00</td>\n",
" <td>0.000000e+00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000e+00</td>\n",
" <td>0.000000e+00</td>\n",
" <td>0.000000e+00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000e+00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.090909</td>\n",
" <td>0.090909</td>\n",
" <td>0.0</td>\n",
" <td>4.826789</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>GERMANY</td>\n",
" <td>GERMANY</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>17236</th>\n",
" <td>OFF DISTRIBUTION</td>\n",
" <td>131</td>\n",
" <td>131</td>\n",
" <td>1.000000</td>\n",
" <td>1.309559e+08</td>\n",
" <td>1.483052e+08</td>\n",
" <td>2.226195e+08</td>\n",
" <td>0.0</td>\n",
" <td>1.319043e+08</td>\n",
" <td>5.035368e+08</td>\n",
" <td>3.843793e+06</td>\n",
" <td>27679.0</td>\n",
" <td>1.190158e+07</td>\n",
" <td>1.652515e+12</td>\n",
" <td>1.891390e+13</td>\n",
" <td>-1.557131e+11</td>\n",
" <td>1.782218e+12</td>\n",
" <td>164.839695</td>\n",
" <td>219</td>\n",
" <td>0.905278</td>\n",
" <td>-0.842048</td>\n",
" <td>0.013618</td>\n",
" <td>1.0</td>\n",
" <td>1.0</td>\n",
" <td>FRANCE</td>\n",
" <td>FRANCE</td>\n",
" <td>469</td>\n",
" <td>1.179033e+12</td>\n",
" <td>9.883021e+12</td>\n",
" <td>9.883389e+12</td>\n",
" <td>378</td>\n",
" <td>585</td>\n",
" <td>46.042644</td>\n",
" <td>130</td>\n",
" <td>-7.845547e+11</td>\n",
" <td>-2.180184e+06</td>\n",
" <td>0.009793</td>\n",
" <td>9.599623e+10</td>\n",
" <td>-7.845547e+11</td>\n",
" <td>-2.180184e+06</td>\n",
" <td>0.009793</td>\n",
" <td>9.599623e+10</td>\n",
" <td>3.845088</td>\n",
" <td>18192.017337</td>\n",
" <td>0.261956</td>\n",
" <td>-1.747326</td>\n",
" <td>211.290076</td>\n",
" <td>0.805970</td>\n",
" <td>1.247335</td>\n",
" <td>0.0</td>\n",
" <td>18.690371</td>\n",
" <td>20.037167</td>\n",
" <td>10.228465</td>\n",
" <td>9.808793</td>\n",
" <td>FRANCE</td>\n",
" <td>FRANCE</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>17237</th>\n",
" <td>PRIVATE CLIENT</td>\n",
" <td>131</td>\n",
" <td>131</td>\n",
" <td>1.000000</td>\n",
" <td>3.826133e+06</td>\n",
" <td>3.068170e+06</td>\n",
" <td>8.415289e+06</td>\n",
" <td>0.0</td>\n",
" <td>-4.181221e+05</td>\n",
" <td>2.603098e+07</td>\n",
" <td>1.987098e+05</td>\n",
" <td>32363.0</td>\n",
" <td>1.715429e+05</td>\n",
" <td>4.894466e+09</td>\n",
" <td>5.601972e+10</td>\n",
" <td>-4.894466e+09</td>\n",
" <td>5.601972e+10</td>\n",
" <td>37.954198</td>\n",
" <td>54</td>\n",
" <td>0.520087</td>\n",
" <td>-0.577005</td>\n",
" <td>0.013618</td>\n",
" <td>1.0</td>\n",
" <td>1.0</td>\n",
" <td>FRANCE</td>\n",
" <td>FRANCE</td>\n",
" <td>89</td>\n",
" <td>1.739254e+10</td>\n",
" <td>1.168016e+11</td>\n",
" <td>1.034362e+11</td>\n",
" <td>55</td>\n",
" <td>111</td>\n",
" <td>55.865169</td>\n",
" <td>130</td>\n",
" <td>-2.466058e+10</td>\n",
" <td>-6.690212e+04</td>\n",
" <td>0.007950</td>\n",
" <td>3.017409e+09</td>\n",
" <td>-2.466058e+10</td>\n",
" <td>-6.690212e+04</td>\n",
" <td>0.007950</td>\n",
" <td>3.017409e+09</td>\n",
" <td>6.803470</td>\n",
" <td>804.343896</td>\n",
" <td>-0.016062</td>\n",
" <td>-1.097093</td>\n",
" <td>247.045802</td>\n",
" <td>0.617978</td>\n",
" <td>1.247191</td>\n",
" <td>0.0</td>\n",
" <td>15.157366</td>\n",
" <td>17.074798</td>\n",
" <td>10.384802</td>\n",
" <td>6.691269</td>\n",
" <td>FRANCE</td>\n",
" <td>FRANCE</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Registrar Account - ID n_months n_active_months flow_freq \\\n",
"17217 422835 57 26 0.456140 \n",
"17220 422860 62 0 0.000000 \n",
"17221 422864 44 0 0.000000 \n",
"17224 422876 77 0 0.000000 \n",
"17227 422891 48 0 0.000000 \n",
"17230 422906 114 112 0.982456 \n",
"17232 67100 79 0 0.000000 \n",
"17233 67104 63 0 0.000000 \n",
"17236 OFF DISTRIBUTION 131 131 1.000000 \n",
"17237 PRIVATE CLIENT 131 131 1.000000 \n",
"\n",
" aum_qty_mean aum_qty_median aum_qty_max aum_qty_last \\\n",
"17217 3.305042e+04 0.000000e+00 5.372034e+05 0.0 \n",
"17220 4.503548e+00 0.000000e+00 2.792200e+01 0.0 \n",
"17221 8.169545e-01 0.000000e+00 1.797300e+01 0.0 \n",
"17224 3.192987e-01 0.000000e+00 1.229300e+01 0.0 \n",
"17227 3.572000e+01 5.358000e+01 5.358000e+01 0.0 \n",
"17230 1.319870e+03 8.466950e+01 1.216348e+04 0.0 \n",
"17232 5.677362e+03 1.621000e+03 3.962330e+04 0.0 \n",
"17233 1.238095e+02 0.000000e+00 7.800000e+03 0.0 \n",
"17236 1.309559e+08 1.483052e+08 2.226195e+08 0.0 \n",
"17237 3.826133e+06 3.068170e+06 8.415289e+06 0.0 \n",
"\n",
" net_flow_qty_sum gross_flow_qty_sum gross_flow_qty_mean n_tx_total \\\n",
"17217 5.050329e+05 8.986925e+05 1.576653e+04 68.0 \n",
"17220 0.000000e+00 0.000000e+00 0.000000e+00 0.0 \n",
"17221 0.000000e+00 0.000000e+00 0.000000e+00 0.0 \n",
"17224 0.000000e+00 0.000000e+00 0.000000e+00 0.0 \n",
"17227 0.000000e+00 0.000000e+00 0.000000e+00 0.0 \n",
"17230 2.488392e+03 2.901017e+04 2.544751e+02 367.0 \n",
"17232 0.000000e+00 0.000000e+00 0.000000e+00 0.0 \n",
"17233 0.000000e+00 0.000000e+00 0.000000e+00 0.0 \n",
"17236 1.319043e+08 5.035368e+08 3.843793e+06 27679.0 \n",
"17237 -4.181221e+05 2.603098e+07 1.987098e+05 32363.0 \n",
"\n",
" net_flow_vol turnover_mean turnover_vol flow_to_aum_mean \\\n",
"17217 4.698930e+04 9.464842e+12 3.058166e+13 2.993416e+12 \n",
"17220 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 \n",
"17221 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 \n",
"17224 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 \n",
"17227 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 \n",
"17230 3.655121e+02 7.934937e+10 1.987296e+11 -1.053788e+10 \n",
"17232 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 \n",
"17233 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 \n",
"17236 1.190158e+07 1.652515e+12 1.891390e+13 -1.557131e+11 \n",
"17237 1.715429e+05 4.894466e+09 5.601972e+10 -4.894466e+09 \n",
"\n",
" flow_to_aum_vol avg_n_isin_held max_n_isin_held sub_share_mean \\\n",
"17217 3.181011e+13 0.140351 1 0.219723 \n",
"17220 0.000000e+00 0.161290 1 0.000000 \n",
"17221 0.000000e+00 0.045455 1 0.000000 \n",
"17224 0.000000e+00 0.025974 1 0.000000 \n",
"17227 0.000000e+00 0.666667 1 0.000000 \n",
"17230 1.906889e+11 0.929825 3 0.373516 \n",
"17232 0.000000e+00 2.291139 6 0.000000 \n",
"17233 0.000000e+00 0.015873 1 0.000000 \n",
"17236 1.782218e+12 164.839695 219 0.905278 \n",
"17237 5.601972e+10 37.954198 54 0.520087 \n",
"\n",
" red_share_mean delta_rate_mean aum_drawdown_last aum_drawdown_max \\\n",
"17217 -0.241074 -0.009035 1.0 1.0 \n",
"17220 0.000000 -0.007677 1.0 1.0 \n",
"17221 0.000000 -0.009023 1.0 1.0 \n",
"17224 0.000000 -0.009182 1.0 1.0 \n",
"17227 0.000000 -0.007917 1.0 1.0 \n",
"17230 -0.622580 0.014351 1.0 1.0 \n",
"17232 0.000000 -0.009089 1.0 1.0 \n",
"17233 0.000000 -0.007476 1.0 1.0 \n",
"17236 -0.842048 0.013618 1.0 1.0 \n",
"17237 -0.577005 0.013618 1.0 1.0 \n",
"\n",
" region country n_isin_total rel_turnover_mean_avg \\\n",
"17217 ITALY ITALY 3 4.860324e+12 \n",
"17220 ITALY ITALY 3 0.000000e+00 \n",
"17221 UNITED KINGDOM UNITED KINGDOM 1 0.000000e+00 \n",
"17224 ITALY ITALY 10 0.000000e+00 \n",
"17227 UNITED KINGDOM UNITED KINGDOM 1 0.000000e+00 \n",
"17230 SPAIN SPAIN 4 6.943214e+10 \n",
"17232 GERMANY GERMANY 21 0.000000e+00 \n",
"17233 GERMANY GERMANY 11 0.000000e+00 \n",
"17236 FRANCE FRANCE 469 1.179033e+12 \n",
"17237 FRANCE FRANCE 89 1.739254e+10 \n",
"\n",
" rel_turnover_vol_avg rel_flow_to_aum_vol_avg full_exit_count \\\n",
"17217 1.237445e+13 1.319247e+13 3 \n",
"17220 0.000000e+00 0.000000e+00 1 \n",
"17221 0.000000e+00 0.000000e+00 1 \n",
"17224 0.000000e+00 0.000000e+00 1 \n",
"17227 0.000000e+00 0.000000e+00 1 \n",
"17230 1.266133e+11 1.355015e+11 39 \n",
"17232 0.000000e+00 0.000000e+00 14 \n",
"17233 0.000000e+00 0.000000e+00 1 \n",
"17236 9.883021e+12 9.883389e+12 378 \n",
"17237 1.168016e+11 1.034362e+11 55 \n",
"\n",
" entry_count avg_holding_months_per_isin max_holding_months_per_isin \\\n",
"17217 3 2.666667 8 \n",
"17220 1 3.333333 10 \n",
"17221 1 2.000000 2 \n",
"17224 1 0.200000 2 \n",
"17227 1 32.000000 32 \n",
"17230 39 26.500000 35 \n",
"17232 20 8.619048 21 \n",
"17233 1 0.090909 1 \n",
"17236 585 46.042644 130 \n",
"17237 111 55.865169 130 \n",
"\n",
" flow_trend_12m_x aum_trend_12m_x drawdown_trend_12m_x beta_rate_x \\\n",
"17217 0.000000e+00 0.000000e+00 0.000000 6.383533e+13 \n",
"17220 0.000000e+00 0.000000e+00 0.000000 0.000000e+00 \n",
"17221 0.000000e+00 0.000000e+00 0.000000 0.000000e+00 \n",
"17224 0.000000e+00 0.000000e+00 0.000000 0.000000e+00 \n",
"17227 0.000000e+00 0.000000e+00 0.000000 0.000000e+00 \n",
"17230 4.573969e+09 0.000000e+00 0.000000 2.857298e+09 \n",
"17232 0.000000e+00 -3.006628e+02 0.007588 0.000000e+00 \n",
"17233 0.000000e+00 0.000000e+00 0.000000 0.000000e+00 \n",
"17236 -7.845547e+11 -2.180184e+06 0.009793 9.599623e+10 \n",
"17237 -2.466058e+10 -6.690212e+04 0.007950 3.017409e+09 \n",
"\n",
" flow_trend_12m_y aum_trend_12m_y drawdown_trend_12m_y beta_rate_y \\\n",
"17217 0.000000e+00 0.000000e+00 0.000000 6.383533e+13 \n",
"17220 0.000000e+00 0.000000e+00 0.000000 0.000000e+00 \n",
"17221 0.000000e+00 0.000000e+00 0.000000 0.000000e+00 \n",
"17224 0.000000e+00 0.000000e+00 0.000000 0.000000e+00 \n",
"17227 0.000000e+00 0.000000e+00 0.000000 0.000000e+00 \n",
"17230 4.573969e+09 0.000000e+00 0.000000 2.857298e+09 \n",
"17232 0.000000e+00 -3.006628e+02 0.007588 0.000000e+00 \n",
"17233 0.000000e+00 0.000000e+00 0.000000 0.000000e+00 \n",
"17236 -7.845547e+11 -2.180184e+06 0.009793 9.599623e+10 \n",
"17237 -2.466058e+10 -6.690212e+04 0.007950 3.017409e+09 \n",
"\n",
" gross_flow_to_aum avg_ticket flow_direction_balance \\\n",
"17217 27.191559 13216.065779 0.561964 \n",
"17220 0.000000 0.000000 0.000000 \n",
"17221 0.000000 0.000000 0.000000 \n",
"17224 0.000000 0.000000 0.000000 \n",
"17227 0.000000 0.000000 0.000000 \n",
"17230 21.979558 79.046774 0.085777 \n",
"17232 0.000000 0.000000 0.000000 \n",
"17233 0.000000 0.000000 0.000000 \n",
"17236 3.845088 18192.017337 0.261956 \n",
"17237 6.803470 804.343896 -0.016062 \n",
"\n",
" redemption_bias activity_intensity exit_rate_per_isin \\\n",
"17217 -0.460796 1.192982 1.000000 \n",
"17220 0.000000 0.000000 0.333333 \n",
"17221 0.000000 0.000000 1.000000 \n",
"17224 0.000000 0.000000 0.100000 \n",
"17227 0.000000 0.000000 1.000000 \n",
"17230 -0.996097 3.219298 9.750000 \n",
"17232 0.000000 0.000000 0.666667 \n",
"17233 0.000000 0.000000 0.090909 \n",
"17236 -1.747326 211.290076 0.805970 \n",
"17237 -1.097093 247.045802 0.617978 \n",
"\n",
" entry_rate_per_isin aum_final_to_peak log_aum_qty_mean \\\n",
"17217 1.000000 0.0 10.405820 \n",
"17220 0.333333 0.0 1.705393 \n",
"17221 1.000000 0.0 0.597162 \n",
"17224 0.100000 0.0 0.277100 \n",
"17227 1.000000 0.0 3.603322 \n",
"17230 9.750000 0.0 7.186046 \n",
"17232 0.952381 0.0 8.644418 \n",
"17233 0.090909 0.0 4.826789 \n",
"17236 1.247335 0.0 18.690371 \n",
"17237 1.247191 0.0 15.157366 \n",
"\n",
" log_gross_flow_qty_sum log_n_tx_total log_avg_ticket country_grp \\\n",
"17217 13.708697 4.234107 9.489264 ITALY \n",
"17220 0.000000 0.000000 0.000000 ITALY \n",
"17221 0.000000 0.000000 0.000000 UNITED KINGDOM \n",
"17224 0.000000 0.000000 0.000000 ITALY \n",
"17227 0.000000 0.000000 0.000000 UNITED KINGDOM \n",
"17230 10.275436 5.908083 4.382611 SPAIN \n",
"17232 0.000000 0.000000 0.000000 GERMANY \n",
"17233 0.000000 0.000000 0.000000 GERMANY \n",
"17236 20.037167 10.228465 9.808793 FRANCE \n",
"17237 17.074798 10.384802 6.691269 FRANCE \n",
"\n",
" region_grp cluster_k2 cluster_k5 cluster_k10 churn_hard \\\n",
"17217 ITALY 0 0 0 1 \n",
"17220 ITALY 0 0 0 1 \n",
"17221 UNITED KINGDOM 0 0 0 1 \n",
"17224 ITALY 0 0 0 1 \n",
"17227 UNITED KINGDOM 0 0 0 1 \n",
"17230 SPAIN 0 0 0 1 \n",
"17232 GERMANY 0 0 0 1 \n",
"17233 GERMANY 0 0 0 1 \n",
"17236 FRANCE 0 0 0 1 \n",
"17237 FRANCE 0 0 0 1 \n",
"\n",
" churn_soft churn_warning \n",
"17217 1 0 \n",
"17220 1 0 \n",
"17221 1 0 \n",
"17224 1 0 \n",
"17227 1 0 \n",
"17230 1 0 \n",
"17232 1 0 \n",
"17233 1 0 \n",
"17236 1 0 \n",
"17237 1 1 "
]
},
"execution_count": 58,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"dfc.tail(10)"
]
},
{
"cell_type": "code",
"execution_count": 59,
"id": "e5f589f2-6433-4917-8358-864c425eda22",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(9672, 62)"
]
},
"execution_count": 59,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"dfc.shape"
]
},
{
"cell_type": "code",
"execution_count": 42,
"id": "3f78e685-b3e7-4c02-81d2-fc480d524814",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Nb clients = 9672\n",
"Nb features = 34\n",
"['log_aum_qty_mean', 'flow_freq', 'gross_flow_to_aum', 'turnover_vol', 'flow_to_aum_vol', 'activity_intensity', 'log_n_tx_total', 'avg_n_isin_held', 'n_isin_total', 'avg_holding_months_per_isin', 'exit_rate_per_isin', 'flow_direction_balance', 'redemption_bias', 'aum_drawdown_last', 'country_grp_FRANCE', 'country_grp_GERMANY', 'country_grp_ITALY', 'country_grp_LATAM', 'country_grp_LUXEMBOURG', 'country_grp_Other', 'country_grp_SPAIN', 'country_grp_SWITZERLAND', 'country_grp_UNITED KINGDOM', 'country_grp_UNITED STATES', 'region_grp_FRANCE', 'region_grp_GERMANY', 'region_grp_ITALY', 'region_grp_LATAM', 'region_grp_LUXEMBOURG', 'region_grp_NORDICS', 'region_grp_Other', 'region_grp_SPAIN', 'region_grp_SWITZERLAND', 'region_grp_UNITED KINGDOM']\n"
]
}
],
"source": [
"dfc = df_client.copy()\n",
"\n",
"dfc[\"gross_flow_to_aum\"] = dfc[\"gross_flow_qty_sum\"] / (dfc[\"aum_qty_mean\"].abs() + EPS)\n",
"dfc[\"avg_ticket\"] = dfc[\"gross_flow_qty_sum\"] / (dfc[\"n_tx_total\"] + EPS)\n",
"dfc[\"flow_direction_balance\"] = dfc[\"net_flow_qty_sum\"] / (dfc[\"gross_flow_qty_sum\"] + EPS)\n",
"dfc[\"redemption_bias\"] = dfc[\"red_share_mean\"] - dfc[\"sub_share_mean\"]\n",
"dfc[\"activity_intensity\"] = dfc[\"n_tx_total\"] / (dfc[\"n_months\"] + EPS)\n",
"dfc[\"exit_rate_per_isin\"] = dfc[\"full_exit_count\"] / (dfc[\"n_isin_total\"] + EPS)\n",
"dfc[\"entry_rate_per_isin\"] = dfc[\"entry_count\"] / (dfc[\"n_isin_total\"] + EPS)\n",
"dfc[\"aum_final_to_peak\"] = dfc[\"aum_qty_last\"] / (dfc[\"aum_qty_max\"] + EPS)\n",
"\n",
"for col in [\"aum_qty_mean\", \"gross_flow_qty_sum\", \"n_tx_total\", \"avg_ticket\"]:\n",
" dfc[f\"log_{col}\"] = np.log1p(dfc[col].clip(lower=0))\n",
"\n",
"dfc = dfc[(dfc[\"n_months\"] >= 6) & (dfc[\"aum_qty_mean\"] > 0)].copy()\n",
"\n",
"top_countries = dfc[\"country\"].fillna(\"Unknown\").value_counts().head(10).index\n",
"top_regions = dfc[\"region\"].fillna(\"Unknown\").value_counts().head(10).index\n",
"\n",
"dfc[\"country_grp\"] = np.where(dfc[\"country\"].isin(top_countries), dfc[\"country\"], \"Other\")\n",
"dfc[\"region_grp\"] = np.where(dfc[\"region\"].isin(top_regions), dfc[\"region\"], \"Other\")\n",
"\n",
"base_features = [\n",
" \"log_aum_qty_mean\",\n",
" \"flow_freq\",\n",
" \"gross_flow_to_aum\",\n",
" \"turnover_vol\",\n",
" \"flow_to_aum_vol\",\n",
" \"activity_intensity\",\n",
" \"log_n_tx_total\",\n",
" \"avg_n_isin_held\",\n",
" \"n_isin_total\",\n",
" \"avg_holding_months_per_isin\",\n",
" \"exit_rate_per_isin\",\n",
" \"flow_direction_balance\",\n",
" \"redemption_bias\",\n",
" \"aum_drawdown_last\",\n",
"]\n",
"\n",
"base_features = [c for c in base_features if c in dfc.columns]\n",
"\n",
"X_num = dfc[base_features].replace([np.inf, -np.inf], np.nan).fillna(dfc[base_features].median())\n",
"X_cat = pd.get_dummies(dfc[[\"country_grp\", \"region_grp\"]].fillna(\"Unknown\"), drop_first=True)\n",
"\n",
"X = pd.concat([X_num.reset_index(drop=True), X_cat.reset_index(drop=True)], axis=1)\n",
"\n",
"scaler = RobustScaler()\n",
"X_scaled = scaler.fit_transform(X)\n",
"\n",
"print(\"Nb clients =\", X.shape[0])\n",
"print(\"Nb features =\", X.shape[1])\n",
"print(X.columns.tolist())"
]
},
{
"cell_type": "code",
"execution_count": 43,
"id": "7e865952-a1d2-4f39-bbbc-306c8ed7857c",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>k</th>\n",
" <th>inertia</th>\n",
" <th>silhouette</th>\n",
" <th>davies_bouldin</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>1</td>\n",
" <td>4.524688e+27</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>2</td>\n",
" <td>5.112657e+26</td>\n",
" <td>0.999551</td>\n",
" <td>0.261256</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>3</td>\n",
" <td>2.261593e+26</td>\n",
" <td>0.999410</td>\n",
" <td>0.421666</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>4</td>\n",
" <td>1.116793e+26</td>\n",
" <td>0.999431</td>\n",
" <td>0.246382</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>5</td>\n",
" <td>4.183981e+25</td>\n",
" <td>0.999076</td>\n",
" <td>0.293760</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>6</td>\n",
" <td>2.520739e+25</td>\n",
" <td>0.999066</td>\n",
" <td>0.338481</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>7</td>\n",
" <td>1.141968e+25</td>\n",
" <td>0.998961</td>\n",
" <td>0.257299</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>8</td>\n",
" <td>5.503246e+24</td>\n",
" <td>0.998936</td>\n",
" <td>0.138057</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>9</td>\n",
" <td>1.305121e+24</td>\n",
" <td>0.998803</td>\n",
" <td>0.064578</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>10</td>\n",
" <td>5.648568e+23</td>\n",
" <td>0.998696</td>\n",
" <td>0.126169</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" k inertia silhouette davies_bouldin\n",
"0 1 4.524688e+27 NaN NaN\n",
"1 2 5.112657e+26 0.999551 0.261256\n",
"2 3 2.261593e+26 0.999410 0.421666\n",
"3 4 1.116793e+26 0.999431 0.246382\n",
"4 5 4.183981e+25 0.999076 0.293760\n",
"5 6 2.520739e+25 0.999066 0.338481\n",
"6 7 1.141968e+25 0.998961 0.257299\n",
"7 8 5.503246e+24 0.998936 0.138057\n",
"8 9 1.305121e+24 0.998803 0.064578\n",
"9 10 5.648568e+23 0.998696 0.126169"
]
},
"execution_count": 43,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"rows = []\n",
"\n",
"for k in range(1, 11):\n",
" km = KMeans(n_clusters=k, n_init=50, random_state=42)\n",
" labels = km.fit_predict(X_scaled)\n",
"\n",
" row = {\n",
" \"k\": k,\n",
" \"inertia\": km.inertia_\n",
" }\n",
"\n",
" if k >= 2:\n",
" row[\"silhouette\"] = silhouette_score(X_scaled, labels)\n",
" row[\"davies_bouldin\"] = davies_bouldin_score(X_scaled, labels)\n",
" else:\n",
" row[\"silhouette\"] = np.nan\n",
" row[\"davies_bouldin\"] = np.nan\n",
"\n",
" rows.append(row)\n",
"\n",
"df_kdiag = pd.DataFrame(rows)\n",
"df_kdiag"
]
},
{
"cell_type": "code",
"execution_count": 44,
"id": "04c350be-d871-4de8-93d6-3775d78e4725",
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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T/v37sdvtXHfddcW+XlhDMzg4mGHDhjFt2jS+//57OnToQK9evbj55pvx8fFxxJqXl+d4r4eHh+M1i8VCdHR0kb579uzJddddx7vvvuvY1vrw4cOOpM0/hYaGOl6/3KRGYmIigYGB1KhRo8jxC12nsxW3qtTPz6/EW3GJVDWGJTUyMzMJCwvj9ttvL/HSsH/auHEjYWFhjBw5ktq1a/O///2PMWPG4OPj45j59eGHHxb5y+vUqVPccsst58zaFimt1q1bExkZeUnvbdSoUZGfQ0JCMJvNjhoPiYmJmM3mIhl2gMDAQHx9fUlMTHQc69ChgyMJsm7dOiIiIoiMjKRmzZqsW7eO2rVrs3379iIzJc/njz/+oHbt2pc8LoD69esX+bmwKGnhl+j+/fsBLrgvZFpaGn5+fo6fGzZseMnxQMG+3pMnT2b+/PkcO3asSA2SSy0eJiIiUhW5ubnRunVrWrduTePGjXn22WdZunRpqe7Fzy6ceTnON+Hi7OLchT8PHz6c7t27F/ues++bSsNms9G1a1fHdg9nK9ySSkREpDo6evQoaWlpju/aJ598ko0bNzJixAhatGiBl5cXNpuN+++/v8i/t93c3Ojduze//PILL7/8MsnJyWzYsIF//etfFzyfzWbDZDLx2WefFXvf4eXl5fj12LFjue222/jtt99YtWoVr732GpMnT2bu3LnUrVuXxx57rMhk59tuu40333zzvOeuW7cuTZo0ITY2tsTXp1BJ72vKy/nu0f75mYg4E8OSGj179rzgjPDc3Fz++9//8sMPP5CWlsYVV1zBU0895dgb95/76gHce++9rFq1ip9//tmR1KhZs2aRNosXL8bDw0NJDalUzvfFd7GVFQDt27dn7ty5JCQksG7dOtq3b4/JZKJdu3asX7+eoKAgbDZbkWWS57N8+XK6d+9eovOez/lWqxR+iRb+/5lnnqFFixbFtv3nDQoULAm9HK+++qqjZkfbtm3x8fHBZDI5anSIiIhURxEREUBBjYyy1qBBA3bs2HHO6su9e/cCf0+CKJz8cPYkg39O4ICCmZhQsIXV2TMqz3ah+5jzvRYSEkJmZuZF+xYREamOvvvuO6Bgy8fTp08TExPDY489VmRSROEExrPdcMMNLFiwgJiYGPbs2YPdbueGG2644PlCQkKw2+00bNiwRCsVwsLCCAsL4+GHH2bDhg3ceeedfP3114wePZoxY8YUWakQFBR00f6sViuZmZmOn+vXr8++ffvOaXep9zXFadCgAatXryYjI6PIao3izisiBS6vkk05euWVV9i4cSP//e9/WbRoEX369OH+++8/71+UUPAXx9mJjH+aN28e/fr1O+ehqUhFOnDgwDk/22w2x4qEBg0aYLPZzml34sQJUlNTadCggeNYYV2NVatWERcX50heREVFsW7dOtavX4+XlxetWrW6YEypqals3LjxsraeKonChxLe3t5ER0cX+19JipaWJvFSWDdj7Nix9OnTh65du9K+fXut0hARkWph9erVxSbxC1d6Fm6dUJZ69OhBUlISS5YscRzLz89n5syZeHl5ERUVBRTc81gslnNmQ3799ddFfg4ICKBjx47MmTOn2CTMP7eHKtxPu7jveU9Pz2K3YLjhhhvYuHEjK1asOOe11NRU8vPzLzRcERERpxUTE8PEiRNp2LAhN99883lXA3zxxRfFHo+OjqZmzZosWbKEH3/8kdatWzueC5zPddddh8Vi4aOPPjrnHsZutzu2sk5PTz/nO7p58+aYzWZyc3OBgkkc/3zecLEtN/ft28e+ffsIDw93HOvZsydbtmxh48aNjmOZmZnMnTuXBg0aOPosXMnyz/saq9XK3LlzL3hOKLh3ys/PL3IPZLVa+fLLLy/6XpHqqlIWCj98+DDz58/nf//7n2Nv3BEjRrBixQrmz59f7FK1JUuWEBcXxyuvvFJsn1u2bGHnzp28/vrr5Rq7yMXMmjWLbt26OX4u/JLq0aMHUPCF+e677/LFF18U+f08bdo0x+uFgoODqVOnDtOnTyc/P5927doBBdtS/fvf/2bp0qW0adPGsefk+axcuRKgSFzlISIigpCQED7//HNuvPHGc/aLTElJwd/f/6L9FD6wSE1NdcyGOJ/ibrpmzpyJ1WotReQiIiJV02uvvUZWVhbXXnstoaGh5OXlsWHDBn788UcaNGhA//79y/ycgwYNYs6cOYwdO5b4+HgaNGjATz/9xIYNG3juuecchT19fHzo06cPX375JSaTieDgYP74449z9ucGePnll7nrrru46aabGDhwIMHBwZw4cYJNmzZx9OhRFi1aBECLFi2wWCx89tlnpKWl4ebmRufOnQkICKBVq1Z8/fXXTJw4kUaNGuHv70+XLl0YMWIEv//+Ow899BC33XYbrVq1Iisri507d/LTTz/x22+/lej+REREpCpbvnw5e/fuxWq1cuLECdasWcOqVauoX78+kyZNwt3dHXd3d6KiopgyZQp5eXnUqVOHVatWObbTPpurqyvXXnstixcvJisr64JbURcKCQnhySef5J133iExMZHevXtTo0YNDh06xK+//srAgQMZMWIEq1ev5pVXXqFPnz40btwYq9XKd999h8Vi4frrr7/oefLz8x2rUOx2O4cOHWL27NnYbDYeeeQRR7sHHniAxYsXM3LkSO655x78/PxYuHAhhw4d4sMPP3SsSr3iiito27Yt7777LqdPn8bPz48lS5aUaHJEr169aNeunWPMzZo14+eff9ZkTJELqJRJjZ07d2K1Ws/ZJio3N7fYlRirV6/mueee47XXXuOKK64ots9vv/2W5s2b07p16/IIWaqZwi/7s7Vr1+6isw4OHTrEQw89RPfu3dm0aROLFi3ixhtvdMwECA8P57bbbmPOnDmkpqYSFRVFXFwcCxYsoHfv3nTu3LlIfx06dGDx4sU0b97cUYuiZcuWeHl5sX//fm666aaLjmfZsmW0a9fOUTCrvJjNZl577TVGjhzJjTfeSP/+/alTpw7Hjh1jzZo1eHt788knn1y0n8KVJ6+99hrdunXDYrHQr1+/YtteddVVfPfdd3h7e9OsWTM2bdrEn3/+ecFVXSIiIs7imWeeYenSpSxbtow5c+aQl5dH/fr1ueuuuxg1atRFJwdcCg8PD2bOnMnbb7/NggULSE9Pp0mTJkyYMOGcJMoLL7xAfn4+s2fPxs3NjT59+vDMM8+cUw+sWbNmzJs3j48++ogFCxZw6tQp/P39admyZZEHD4GBgYwfP57Jkyfz/PPPY7VamTFjBgEBATzyyCMcPnyYKVOmkJGRQceOHenSpQuenp7MnDmTyZMns3TpUhYuXIi3tzeNGzfmscceK/f7IxERkcrggw8+AAoSETVr1qR58+Y899xz9O/f3zEhAeCdd97h1Vdf5auvvsJut9O1a1c+++yz89a96tu3L9988w0mk+miW08VeuCBB2jcuDHTp0/n448/BgrqXXTt2pVevXoBBdtOdevWjf/9738cO3YMT09PwsLC+Oyzz2jbtu1Fz5Gbm8szzzzj+Nnb25vIyEj+85//OAqbA9SuXZvZs2fzn//8hy+//JKcnBzCwsL45JNPuOqqq4r0+fbbb/PSSy/x6aef4uvryx133EGnTp0YNmzYBWMxm81MmjSJN954g0WLFmEymejVqxdjx47l1ltvLdE1E6luTPZKsKl8WFgYH3/8Mb179wYKVl089dRT/PDDD+fMsvby8iIwMNDx89q1a3nwwQcZO3YsgwYNKrb/zMxMunfvzuOPP869995bfgMRpzd//nyeffbZ877+z3+sh4WF8eijj/LYY48BBYXrP/roI5YsWcL777/PypUrcXFx4aabbuKZZ54pUjsiPz+fTz75hAULFnDs2DFq167NzTffzKOPPoqbm1uRc86aNYtXXnmFO++8k3HjxjmODxs2jD///JPp06cX+UI+W+FNyPDhw89bIPNsYWFh3H333bz00ksArFmzhqFDh/L+++8XSUYeOnSIa6655pyHGH/99RcTJ05k7dq1ZGZmEhgYSOvWrRk0aJAj1sLrFRMTc87sSKvVyoQJE1i8eDEnT57EbrezY8eOYq97amoqEyZM4H//+x85OTm0a9eO559/nvvvv5+OHTtesEiYiIiIiIiIiIiIVC6VMqmxb98++vTpw6xZsy5Y4HjNmjU89NBDPPXUU9x9993nbTd//nxefvllli9fTq1atco8fpGqbMuWLQwYMIDFixdfdH9JERERERERERERESMZtv1URkYGBw8edPx86NAh/vrrL/z8/GjSpIlj9vrYsWNp0aIFJ0+eJCYmhrCwMK666ipWr17NQw89xNChQ7nuuutISkoC/l4m90/ffvstvXv3VkJD5Dz+9a9/KaEhIiIiIiIiIiIilZ5hKzUKt6s522233cabb75JXl4ekyZNYuHChRw/fpyaNWvStm1bHnvsMcLCwhg7diwLFiw45/0dO3Zk5syZjp/37t3LDTfcwOeff07Xrl3LdUwiIiIiIiIiIiIiIlJ+KsX2UyIiIiIiIiIiIiIiIhdjNjoAERERERERERERERGRklBSQ0REREREREREREREqoQKLxRus9nIz8/HbDZjMpkq+vQiIiJOwW63Y7PZcHFxwWzWHIXi6J5DRETk8umeo2R03yEiInL5SnrfUeFJjfz8fOLi4ir6tCIiIk4pMjISNzc3o8OolHTPISIiUnZ0z3Fhuu8QEREpOxe776jwpEZhhiUyMhKLxVLRp690rFYrcXFxuh4G0LU3hq67cXTtjVMe176wT82YPL/yuOdw1j9HzjgujalqcMYxgXOOS2OqGnTPYRzdd5SMxlR1OOO4NKaqwRnHBM45LiPvOyo8qVG4DNNisTjNB1gWdD2Mo2tvDF134+jaG6c8rr22Nzi/8rzncNY/R844Lo2panDGMYFzjktjqhp0z1HxdN9ROhpT1eGM49KYqgZnHBM457iMuO/QVAsREREREREREREREakSlNQQEREREREREREREZEqQUkNERERERERERERERGpEpTUEBERERERERERERGRKkFJDRERERERERERERERqRKU1BARERERERERERERkSrBxegALpfVZmftvhSOp2UT5ONBxyb+WMwmo8MSERERJ2O12Vm9N5nYg1lk+ybTuWmg7jlERERESkj3UiIiUlaqdFJj6dYjjP9+G0dOZzuO1fPz4OWbWtInop6BkYmIiIgzOeeeY02s7jlERERESkj3UiIiUpaq7PZTS7ceYdSXG4okNACOns5m1JcbWLr1iEGRiYiIiDPRPYeIiIjIpdO9lIiIlLUqmdSw2uyM/34b9mJeKzw2/vttWG3FtRAREREpGd1ziIiIiFw63UuJiEh5qJJJjbX7Us7J8P+THThyOpu1+1IqLigRERFxOrrnEBEREbl0upcSEZHyUCWTGsfTzv+FeCntRERERIqjew4RERGRS6d7KRERKQ9VMqkR5ONRpu1EREREiqN7DhEREZFLp3spEREpD1UyqdGxiT/1/Dwwned1E1DPz4OOTfwrMiwRERFxMhe75wBwdzETXs+nwmISERERqSr0/EZERMpDlUxqWMwmXr6pJcA5X4yFP798U0ss5gs9ghARERG5sAvdcxTKybcxePJqEk9lVVxgIiIiIlVA4b3UhcqA6/mNiIiUVpVMagD0iajHpCHtqOtXdIliXT8PJg1pR5+IegZFJiIiIs7kfPcc9fw8ePaGcAJ93NlxLI1bP15F3KHTBkUpIiIiUjn1iahHeF3vYl978/ZIPb8REZFSczE6gMvRJ6Ie17asy4KNiTz1zWbcLGaWPX01bi5VNlcjIiIilVDhPcfqPUnEbt1JVERzOjcNxGI2cWOb+gyfFsuOY2kMnBzD+4Pbcl2rukaHLCIiIlIp7DyWxvaj6ZiA9wa2Yd+B/fywN5/dSRmczMwzOjwREamCqvzTf4vZxG1XNsDLzUKu1cbBlAyjQxIREREnZDGb6BwaQPcQTzqHBji2SWhQ05NvR3Wh+xW1ycqz8uCX65m6ch92+4U2WhARERGpHr74cz8A17Wqw41t6tE9xJP7uzUBYE5sgu6ZRESk1Kp8UgMKHjK0qOcLQPzhVIOjERERkerGx8OVz++L4q5OIdjt8OoP23h5UTz5VpvRoYmIiIgY5nRmHvM3JAJwb3Rjx/G+kXWp4WZh34kM1uxLMSg6ERGpqpwiqQHQqr6SGiIiImIcV4uZ12+N4Lm+4ZhMMCPmACNnrCM9J9/o0EREREQMMXddAll5VsLq+NAlNMBxvIa7Cze3rQ8UrNYQEREpDadJakTU9wNga6IKdIqIiIgxTCYTD/RoysS72uHuYuZ/O5IY8EkMR05nGR2aiIiISIWy2uzMWL0fgPu6NsZkMhV5fVBUCABL4o5wWrU1RESkFJwmqdHyHys1tB+jiIiIGOmGyHrMfqAztb3d+OtIKrd+vEoTL0RERKRa+X37cRJSsvDzdOXWtg3Oeb1NQz/C6/qQk29j4aZEAyIUEZGqymmSGs3r+OBqMXE6K4/EU5oNKSIiIsa6MqQWCx7uyhVB3hxLzWHg5Bh+++uY0WGJiIiIVIjCAuGDo4LxdLOc87rJZGJwVDAAX689qAmqIiJSYk6T1HBzMdO8jg8AWxNVV0NERESMF+zvxbejounaLIDMXCsjZ6xz/ANfRERELs2sWbPo1asXkZGRDBgwgC1btpTofYsXLyYsLIyHH364yHG73c77779Pt27daN26Nffddx/79+8vh8irj13H0li5+wRmEwzp3Oi87W69sgFuLma2H00jTqtaRUSkhJwmqQF/FwvfdlhfhCIiIlI5+Hm6Mn1YRwZ1CMZmh5cXxTP++3isNs1GFBERKa0lS5YwYcIEHnnkERYsWEB4eDgjRowgOTn5gu87dOgQ//73v+nQocM5r3322WfMnDmTcePGMXfuXDw9PRkxYgQ5OTnlNQynN/3MJI7eLeoQ7O913nY1vdzoG1EXgK/XqmC4iIiUjJMlNQqKhccf1koNERERqTxcLWbevD2Sp68PA2Daqv08OHM9mbn5BkcmIiJStUybNo2BAwdy++2306xZM8aPH4+Hhwfz5s0773usVitPPfUUjz32GMHBwUVes9vtzJgxg1GjRtG7d2/Cw8N56623OH78OL/++mt5D8cpnc7KY/6GghoZ93VtfNH2hQXDF21KJCNH90YiInJxTpXUiGhQsFJjq1ZqiIiISCVjMpl45OpmfHjnlbi5mPn1r2MMnBzDsdRso0MTERGpEnJzc4mPjyc6OtpxzGw2Ex0dzcaNG8/7vo8//piAgAAGDBhwzmuHDh0iKSmpSJ8+Pj60adPmgn3K+X2zLoGsPCthdXzoEhpw0fadQ/1pHOBFRq6VxXFHKiBCERGp6lyMDqAshdf1xWSCY6k5nEjPoba3u9EhiYiIiBRxU5v61K/pwcgZ69mamMptH69i6n1RtKjna3RoIiIildrJkyexWq0EBBR9UB4QEMDevXuLfc+6dev49ttvWbhwYbGvJyUlOfo4u88TJ06UOkar1Vrq91ysr7Lss7xZbXZmxOwH4J7OIdhstqKvn2dMA9o35D8/72T22oPcfmX9Com1rFTFz6kknHFcGlPV4IxjAuccV3mMqaR9OVVSo4a7C01q12BvUgbxh1Pp2TzQ6JBEREREztG+kT8LHo5m2PRY9iZlMOCTGD6660quCgsyOjQRERGnkZ6ezjPPPMOrr76Kv79/hZwzLi6uSvRZXmIPZ3MwJYsariaamJPYtKn4WidnjynM3YrZBBsOnmLRslhC/FwrItwyVZU+p9JwxnFpTFWDM44JnHNcRozJqZIaABH1/diblMHWxNNKaoiIiEil1SigBgtGdeXBL9exem8KI75Yx/ibWzGkcyOjQxMREamUatWqhcViOacoeHJyMrVr1z6nfUJCAomJiYwaNcpxrHDlQMuWLVm6dCmBgYGOPoKC/p5ckJycTHh4eKljjIyMxGKxlPp9xbFarcTFxZVpn+XtvxtjAbizUyM6dzj3+l1oTNfs3sAvfx0nLsObm3u2qJB4y0JV/JxKwhnHpTFVDc44JnDOcZXHmAr7vBinS2q0qu/Los2H2aZi4SIiIlLJ+Xm5MmN4J8bO38L8DYm8sHArB5IzePaGFpjNJqPDExERqVTc3Nxo1aoVMTEx9O7dGyhIUsTExDBkyJBz2oeGhvL9998XOfbee++RkZHB888/T926dXF1dSUwMJCYmBhatCh4kJ6ens7mzZu58847Sx2jxWIp84dV5dFnedh1LI2Vu5MxmeDe6CYXjLm4Md3ZKYRf/jrOgo2HGXNDC9xdKv+Y/6mqfE6l5Yzj0piqBmccEzjnuIwYkxMmNfwAiFexcBEREakC3FzMvDOgDY0DavDuLzv5bMU+ElKy+O+gtni6OdfNroiIyOUaNmwYY8aMISIigtatW/PFF1+QlZVF//79AXjmmWeoU6cO//d//4e7uzvNmzcv8n5f34IaVv88PnToUCZNmkSjRo1o2LAh77//PkFBQY7EiZTMF2dqafRuUYdgf69Sv79n8yDq+npwNDWbX7Yd48bWVau2hoiIVBwnTGoU3KDsT84kNTsPX4+qtw+jiIiIVC8mk4nHr7mCEH8vnvl2C0vjj3Lk0xg+u7cDQT4eRocnIiJSafTt25eUlBQ++OADkpKSaNGiBVOmTHFsP3XkyBHMZnOp+hw5ciRZWVm89NJLpKam0r59e6ZMmYK7u3t5DMEpnc7KY/6GRACGRTe+pD4sZhMDOzTkg993M3ttgpIaIiJyXk6X1KhVw40GNT1JPJXFX4dT6RQaYHRIIiIiIiVy65UNqF/TkwdmrmPzodPc9vGfTBsWRfM6PkaHJiIiUmkMGTKk2O2mAGbOnHnB97755pvnHDOZTDzxxBM88cQTZRJfdfTNugQyc600r+NNl6aX/hxmQIdgPvzfblbuPkFCSuYlrfgQERHnV7rpC1VEyzOrNeJVV0NERESqmI5N/FnwcFcaB3iReCqL2yf+ycpdJ4wOS0RERKRYVpudGTEHALg3ujEm06XXBQv296Jbs4JVN3PXJZRJfCIi4nycMqlRuAXVVtXVEBERkSqoSe0aLHi4K1GNa5GWk89909YyJ/ag0WGJiIiInOOPHcc5mJKJr4cLt13Z4LL7GxwVAhQkNfKttsvuT0REnI9TJjUizhQL36aVGiIiIlJF1arhxpf3d+KWtvXJt9kZMy+Ofy/djs1mNzo0EREREYfpf+4HYFBUMF5ul7/Lee+WQfjXcONYag7LdiZddn8iIuJ8nDKp0apBwUqNXcfTyc6zGhyNiIiIyKVxd7Hw3qC2PH7NFQBM+mMPj329Ufc3IiIiUinsPp7Gil0nMJlgaJfGZdKnu4uF/mdWfMyO1RZUIiJyLqdMatT19cC/hhtWm50dR9OMDkdERETkkplMJv51bXPeGdAGV4uJxXFHuPOz1ZxIzzE6NBEREanmvvizoJbGNeF1yrSo9+COwQD8vv04x1Ozy6xfERFxDpeV1Pj0008JCwvj9ddfL6t4yoTJZHLU1VCxcBEREXEGt7dvyIzhnfD1cGHjwVPcNnEVu4+nGx2WiIiIVFOp2XnM23AIgGFdG5dp382CfOjQqBZWm51v1h8q075FRKTqu+SkxpYtW5g9ezZhYWFlGU+ZaXWmrka8ioWLiIiIk+jSNID5D3clxN+LhJQs+k9cRcyeZKPDEhERkWrom3WHyMy1ckWQN9FNA8q8/0FRBas15q5LUE0xEREp4pKSGhkZGTz99NO89tpr+Pn5lXVMZaJwpcZWrdQQERERJ9IsyJsFD0fTLqQmqdn5DP18Dd9qBqOIiIhUIJvNzoyY/QDcG90Yk8lU5ufo17oePu4uHEjOZPU+TeIQEZG/uVzKm1555RV69uxJdHQ0kyZNuqQTW63lW+CyRV1vALYfSSUnNw8XS+UsH1J4Hcr7esi5dO2NoetuHF1745THtdfnWL0FeLvz1cjO/N83m1m85QhPfbOZg8kZjL62ebk8VBARERH5pz92HudAciY+Hi7cdqaod1nzcnPh5rb1mbXmIHNiE4huWrtcziMiIlVPqZMaixcvZtu2bXz77beXdeK4uLjLev/F2Ox2PF1MZOXbWLJyPSF+ruV6vstV3tdDzk/X3hi67sbRtTeOrr2UJQ9XCx8OvpJG/l5M/GMPH/y+mwMpmfz79tZ4uFqMDk9ERESc2PQzBcIHdQimhvslzZctkcFRIcxac5Aftx5lfGYuNb3cyu1cIiJSdZTqm+fIkSO8/vrrfP7557i7u1/WiSMjI7FYyvcf3K1i17DuwEmsvvVp27Z8Zg5cLqvVSlxcXIVcDylK194Yuu7G0bU3Tnlc+8I+pXozm0080yecxgE1eG5BHN9tOszhU1lMvqcD/jX0j34REREpe7uPp7N8ZxImEwzt0rhczxXRwJeW9XzZdiSVBRsTGda1SbmeT0REqoZSJTXi4+NJTk6mf//+jmNWq5XY2FhmzZpFXFxciR/WWCyWcn+oFtHAj3UHTvLX0XTuqOQP8CriekjxdO2NoetuHF174+jaS3kZGBVM/ZqejPpyPbH7T9J/4iqmDetIk9o1jA7NEFabndV7k4k9mEW2bzKdmwZiMWtbLhERkbJQWEvjmvAgQgK8yvVcJpOJwR2Deem7eGavTeC+cqrfISIiVUupkhqdO3fm+++/L3Ls2WefJTQ0lJEjR1a6BzWFxcLjD582OBIRERGR8tXtitrMeziaYdNi2Z+cyW0TV/HpPR3o2MTf6NAq1NKtRxj//TaOnM4uOLAmlnp+Hrx8U0v6RNQzNjgREZEqLjU7j3nrDwFwX3TFrJq4pW0DXl/8FzuOpbEp4RRXhtSqkPOKiEjlVarq2d7e3jRv3rzIf15eXtSsWZPmzZuXV4yXrFV9PwDiD6dit9sNjkZERESkfDWv48PCR7rSJrgmpzLzGDJlDQs3JhodVoVZuvUIo77c8HdC44yjp7MZ9eUGlm49YlBkIiIizuHbdYfIyLXSLMibrs0CKuScfp6u9IssmJgwJzahQs4pIiKVW6mSGlXNFXW8cbOYScvOJyEly+hwRERERMpdoI87s0d2pk+ruuRabTw5ZxPv/7rL6Sd4WG12xn+/jeJGWXhs/PfbsNqc+zqIiIiUF5vN7th66t4K3gZqUFQwAIs2HyY9J7/CzisiIpXTZSc1Zs6cyfPPP18WsZQ5V4uZsLo+gLagEhERkerD083CxLvb8WCPUAD+++tO/u+bzeTm2wyOrOzY7XZSMnLZnHCKH7Yc5oUFcees0CjSHjhyOpu1+1IqLkgREREnsmxnEvuTM/HxcKH/lQ0q9Nwdm/gTWrsGmblWFm85XKHnFhGRyqdUNTWqolb1fYlLPM3Ww6e5IVL7KIuIiEj1YDabeLZvC0ICvHjpu3jmb0jk8KksPhnSnppebkaHVyKZuQWrbRNSMkk4mVnw65OZBT+nZJKRay11n8fTzp/4EBERkfOb/ud+AAZ2CKaGe8U+TjKZTAyKCmbCj9v5em0Cg6JCKvT8IiJSuTh/UqOBH8QmEH841ehQRERERCrc3Z0a0bCWF4/M2sDqvSn0n/Qn0+6LolFADaNDI89q4/CprKLJipNZjqRFckbuRfsI8nEn2N8LT1cLK3efKEF7j7IIXUREpFrZk5TOsp1JmEwwtEsjQ2Lo364h//lpB5sSTrH9aCrhdX0NiUNERIzn/EmN+gVfckpqiIiISHXVs3kg347qwvBpsexNyuC2iX/y2dD2tG/kj9VmZ/XeZGIPZpHtm0znpoFYzGWzR7bNZicpPafoSot//PrI6SwuVuLC18OFYH8vQvy9CPb3IriWJw39vQiu5UXDWp54uFqAgpoa3f79O0dPZxdbV8ME1PXzoGMT/zIZm4iISHUy48wqjV5hQYZNjAj0cad3izosjT/KnNgEXr6plSFxiIiI8Zw+qdGiri9mEySl5XA8NZsgX83OExERkeonvK4vCx/pyogv1hGXeJo7P1vDPZ0bsSTuyN+1KNbEUs/Pg5dvakmfiJJt23k6K8+xsuLsLaIOncwi5yJ1PNxdzDSs5fl34qKWF8H+njSsVZDE8PN0LVEcFrOJl29qyagvN2CCYhMbL9/UsswSNiIiItVFWnYe364/BMB9XRsbGsvgjsEsjT/Kgo2JjOkT7pjcICIi1YvTJzU83Sw0DfRm1/F04g+nKqkhIiIi1VaQrwdzHuzME7M38cu2Y0xdue+cNkdPZzPqyw1MGtKOPhH1yM6zcuicehZ//zo1O/+C5zSboJ6fJ8H+nv9IWhQkLoJreVHb2x1zGSUa+kTUY9KQdoz/fts5RcNfvTWixIkaERER+du36w+RkWulaWANujWrbWgs3a8IpL6fB4dPZ/NT/FFuaVuxBctFRKRycPqkBhRsQVWQ1DjN1eFBRocjIiIiYhgvNxc+vqsdbV/5mcxiCm0XrnB47OuN1PTcSlL6xeta1PZ2c6ysCDmTrAg+k8CoV9MDV4u5jEdxfn0i6nFty7qs3pNE7Nad/JpgZ+vhVHYfT6+wGERERJyFzWZnRswBAO6LbozJZOyKR4vZxIAOwbz/2y7mxCYoqSEiUk1Vk6SGHws3HWZroupqiIiInG3WrFlMnTqVpKQkwsPDefHFF2ndunWxbfPy8pg8eTILFy7k2LFjNGnShKeeeooePXo42qSnp/P+++/z66+/kpycTMuWLXnuueeK9HnixAnefvttVq5cSVpaGh06dODFF1+kcePG55zTbrczcuRIVqxYwccff0zv3r3L/BpUN+sPnCw2ofFPeVa7I6Hh7e5y1hZRnmdWWxTUtfByq1y3lBazic6hAXiketKuZUOGTlvH7NiDPH7NFfjXcDM6PBERkSpj2a4k9p3IwMfdhf7tGhodDgADo4L54Pdd/LknmQPJGYbV+BAREeNUrn+BlpNWDc4UCz9y2uBIREREKpclS5YwYcIExo8fT5s2bfjiiy8YMWIES5cuJSAg4Jz27733HosWLeK1114jNDSUFStW8OijjzJ79mxatmwJwAsvvMCuXbt46623CAoKYtGiRQwbNowlS5ZQp04d7HY7jzzyCC4uLkycOBFvb2+mT5/OsGHDWLx4MV5eXkXO+cUXXxg+K9DZHE/Lvngj4P+ua86QTo2o6eVaZT+D6KYBtKrvS/zhVGbE7OfJ3s2NDklERKTK+OJMgfABHYKp4V45HiE1qOlJjysCWbYziTmxCTzTJ9zokEREpIJV3F4ABmpVzw+AhJQsTmflGRyNiIhI5TFt2jQGDhzI7bffTrNmzRg/fjweHh7Mmzev2PbfffcdDz30ED179iQ4OJi77rqLnj178vnnnwOQnZ3Nzz//zNNPP01UVBSNGjXiscceo1GjRnz11VcA7N+/n02bNjFu3Dhat25NaGgo48aNIzs7m8WLFxc5319//cXnn3/OG2+8Ub4XopoJ8ilZjbEOjfypVcOtyiY0AEwmEw/1bAoUPJjJzL1wDRAREREpsDcpnT92JGEywdAujYwOp4jBUcEAfLP+EPlWm8HRiIhIRascafZy5uflSsNanhw6mcW2w6l0aXruzFMREZHqJjc3l/j4eB588EHHMbPZTHR0NBs3biz2PXl5ebi5Fd2+x93dnQ0bNgCQn5+P1WrF3d39vG1yc3Mdx/55Xjc3N9avX8+AAQMAyMrK4v/+7/946aWXCAwMvORxWq0X3mbpUvoqyz6N0D7Ej7q+7hxLzXHU0PgnE1DXz4P2IX5Vdqz//KyuaxFIiL8nB1OymLP2YKV7MFNSzvL775+ccUzgnOPSmKqG8hiTM10fKZ3CWhpXhwXRuHbl2uLpmhZ1CKjhRlJaDv/bkcS1LesYHZKIiFSgapHUAIio78ehk1nEHz6tpIaIiAhw8uRJrFbrOdtMBQQEsHfv3mLf061bN6ZPn05UVBQhISHExMTwyy+/OB54eHt7c+WVVzJx4kRCQ0OpXbs2P/zwA5s2bSIkJASA0NBQ6tevzzvvvMMrr7yCp6cn06dP5+jRoyQlJTnONWHCBK688srLrqERFxd3We+vqD4r2j2tPPlPTE6xr9mBIS09iNuyuWKDKgeFn9X1jVz5LCWLSb/vpJV7ChZz1V194gy//87mjGMC5xyXxlQ1OOOYpGKlZefx7fpDQEGB8MrGzcXMHe0bMnn5XubEHlRSQ0Skmqk2SY1W9X1ZGn+U+MMqFi4iInKpnn/+eV544QVuuOEGTCYTwcHB9O/fv8h2VW+99RbPPfccPXr0wGKx0LJlS/r160d8fDwArq6ufPjhhzz//PN07NgRi8VCly5d6NGjB3Z7wbqB3377jdWrV7NgwYLLjjkyMhKLxXLZ/UDBbNW4uLgy7dMobdtCkyZHeeWHvzia+ndyo56fBy/2C+f6VnWNC64MnP1ZhbeyMm/nMo5n5JJoqcPNbeobHWKpOdPvv0LOOCZwznFpTFVDeYypsE+pXuatP0R6Tj5NA2vQ/YraRodTrIFRwUxevpfftx/n6Ols6vqVbHtNERGp+qpPUqOwWPhhFQsXEREBqFWrFhaLheTk5CLHk5OTqV27+H+8+vv7M3HiRHJycjh16hRBQUG8/fbbBAcHO9qEhITw5ZdfkpmZSXp6OkFBQTz55JNF2kRERPDdd9+RlpZGXl4e/v7+DBgwgIiICABWr17NwYMHiYqKKnL+xx57jA4dOjBz5swSj9NisZT5w6ry6NMIfVs34PqI+qzek0Ts1p1ERTSnc9PAKr2K4WyFn1UNi4Vh0Y1555edfLpiP7de2bDK1gpxlt9//+SMYwLnHJfGVDU445j+adasWUydOpWkpCTCw8N58cUXad26dbFtf/75Zz755BMOHjxIfn4+jRo1YtiwYdx6662ONmPHjj1nIkW3bt2YOnVqeQ6j0rLZ7I6tp+6Nblxpvy+bBnrTsbE/a/en8O36BB7tdYXRIYmISAWpNkmNiPoFxcJ3H08nK9eKp5vz3uCJiIiUhJubG61atSImJsaxxZPNZiMmJoYhQ4Zc8L3u7u7UqVOHvLw8fv75Z2644YZz2nh5eeHl5cXp06dZuXIlTz/99DltfHx8gILi4Vu3buWJJ54A4IEHHnDU1ih000038eyzz3L11Vdf0nileBazic6hAXiketI2NMCpEhpnu6dLIyYt28NfR1JZvusEPZtfeq0WERExxpIlS5gwYQLjx4+nTZs2fPHFF4wYMYKlS5ees6UmgJ+fH6NGjSI0NBRXV1f+97//8dxzzxEQEED37t0d7bp3786ECRMcP59dQ6w6Wb4rib0nMvBxd6F/u4ZGh3NBg6KCWbs/hTnrEnj4qmaYnfg+RkRE/lZtkhpBvh7U9nbnRHoO24+mcmVILaNDEhERMdywYcMYM2YMERERtG7dmi+++IKsrCz69+8PwDPPPEOdOnX4v//7PwA2b97MsWPHaNGiBceOHePDDz/EZrNx//33O/pcsWIFdrudJk2acPDgQd566y1CQ0MdfQL8+OOP+Pv7U79+fXbs2MEbb7xB79696datGwCBgYHFFgevX79+kRUfIqVR08uNwVEhfL5qH5/8sUdJDRGRKmjatGkMHDiQ22+/HYDx48fzxx9/MG/ePB544IFz2nfq1KnIz/feey8LFy5k/fr1RZIabm5uxd57VEdf/LkfgDs6NMTbvXI/NuobWY9x38eTkJLFn3uS6VZJt8oSEZGyVbm/ncpYq/q+LNuZRPxhJTVEREQA+vbtS0pKCh988AFJSUm0aNGCKVOmOLafOnLkCGaz2dE+JyeH9957j4SEBLy8vOjZsydvvfUWvr6+jjZpaWm8++67HD16lJo1a3LdddcxevRoXF1dHW2SkpJ48803SU5OJjAwkFtuuYWHH3644gYu1daI7k2YEbOfmL3JbE44RZvgmkaHJCIiJZSbm0t8fDwPPvig45jZbCY6OpqNGzde9P12u53Vq1ezb98+nnrqqSKvrV27li5duuDr60vnzp158sknqVWr+j032Hcig//tSMJkgnu7NDY6nIvydLNwa9sGzFx9gNmxB5XUEBGpJqpVUiOiQWFSQ3U1RERECg0ZMuS8202dXbuiY8eOLFmy5IL99e3bl759+16wzdChQxk6dGip4tyxY0ep2osUp0FNT25uU5/5GxOZvHwPE+9ub3RIIiJSQidPnsRqtZ6zzVRAQAB79+497/vS0tLo0aMHubm5mM1mXn75Zbp27ep4vXv37lx77bU0bNiQhIQE3n33XUaOHMmcOXNKXZvEarWWblAl6Kss+7yY6av2AXBV80CCa3mU+bnLY0wD2hckNX6OP0pSahb+NSp26zAjPqeK4Izj0piqBmccEzjnuMpjTCXtq1olNVqdqasRfzjV4EhERERExCgP9mzK/I2J/Lj1KPtPZNC4dg2jQxIRkXJUo0YNFi5cSGZmJjExMbz55psEBwc7tqbq16+fo21YWBhhYWH07t3bsXqjNOLi4so09vLqszhZeTbmxiYB0DUon02bNpXbucp6TE1rubDnZD4TF8dyY3Njvtcr6nOqaM44Lo2panDGMYFzjsuIMVWzpEbB1hjbj6aRZ7XhajFf5B0iIiIi4mzC6vrQKzyI37cf59MVe3njtkijQxIRkRKoVasWFouF5OTkIseTk5MdW2cWx2w206hRIwBatGjBnj17+PTTT8+pt1EoODiYWrVqceDAgVInNSIjI0u9uuN8rFYrcXFxZdrnhcyIOUBW/nFCa9fg3us6lkvR7fIa0305B3lx0TZWHrHz/IA2mEwVVzC8oj+niuKM49KYqgZnHBM457jKY0yFfV5MtUpqBNfywsfdhbScfHYfT6dFPd+Lv0lEREREnM6DPUL5fftxvl1/iCd7X0GQj4fRIYmIyEW4ubnRqlUrYmJi6N27NwA2m42YmJjzbqVZHJvNRm5u7nlfP3r0KKdOnbqkwuEWi6XMH1aVR59ns9nszFxzEIB7oxvj6lq+j4vKeky3tGvI6z9uZ9fxdDYnptG+UcXXQ6mIz8kIzjgujalqcMYxgXOOy4gxVaulCmaziZZnVmtoCyoRERGR6qtjE3+uDKlJbr6NL/7cb3Q4IiJSQsOGDWPu3LksWLCAPXv2MG7cOLKysujfvz8AzzzzDO+8846j/eTJk1m1ahUJCQns2bOHzz//nEWLFnHzzTcDkJGRwb///W82bdrEoUOHiImJ4eGHH6ZRo0Z0797dkDEaYcXuE+xNysDb3YXb2zc0OpxS8/VwpV9kfQDmxB40OBoRESlv1WqlBhTU1VizL4X4w6e5owp+UYuIiIjI5TOZTDzUsykPzlzPzJgDjLqqGd7u1e7WWESkyunbty8pKSl88MEHJCUl0aJFC6ZMmeLYfurIkSOYzX/P38zMzGT8+PEcPXoUDw8PQkND+c9//kPfvn2BgtmlO3fuZOHChaSlpREUFETXrl154okncHOr2ILTRipM8N/RvmGV/T68s2Mw8zYc4vvNR3jxxpb4eLgaHZJUIlabndV7k4k9mEW2bzKdmwZiKYct1kSkYlTNb6rLUFhXIz5RKzVEREREqrNrW9QhNLAGe5My+HrNQUb2CDU6JBERKYEhQ4acd7upmTNnFvl59OjRjB49+rx9eXh4MHXq1DKNr6rZdyKD37cfBwq2nqqq2jeqRdPAGuxJyuD7zUe4q1OI0SFJJbF06xHGf7+NI6ezCw6siaWenwcv39SSPhH1jA1ORC5Jtdp+CiCigR8A246kYrPZDY5GRERERIxiNpt48EwiY+rKfeTm2wyOSEREpOLNiNkPwNVhgTSpXcPYYC6DyWRicFRBIkNbUEmhpVuPMOrLDX8nNM44ejqbUV9uYOnWIwZFJiKXo9olNZoG1sDdxUx6Tj4HUzKNDkdEREREDHTrlQ0I8nHnaGo2321KNDocERGRCpWek8+36w4BVXuVRqH+7RrgajGx+dBptqmWarVntdkZ//02ipvSXHhs/PfbsGrSs0iVU+2SGi4WM+F1fQDYevi0wdGIiIiIiJHcXSwM79YEgMnL92olr4iIVCvzNxwiLSef0No16HFFoNHhXLYAb3eua1kX0GoNgbX7Us5ZofFPduDI6WzW7kupuKBEpExUu6QGQKszW1DFK2svIiIiUu3d1SkEH3cXdh9Pd+wpLiIi4uxsNrujQPjQLo0wO0nR5EFRwQAs2JhIdp7V4GjESMdSz5/Q+KfjaSVrJyKVR/VMahQWC1dSQ0RERKTa8/Vw5e7OjQD4ZNkeg6MRERGpGCt3n2BPUgbe7i7c3r6h0eGUmW7NatOgpiep2fks3XrU6HDEIHuT0vnkj5Ld1wX5eJRzNCJS1qppUuPMSo3E09jt2mJAREREpLob3rUxbhYz6w6cZN1+bUEgIiLOb/qZVRp3tG+Ij4erscGUIbPZ5FitMVtbUFU7+VYbnyzbww3vr2D7sTQutv6onp8HHZv4V0hsIlJ2qmVSI7yuDxazieSMXI6l5hgdjoiIiIgYLMjXg/7tGgDwybK9BkcjIiJSvvafyOB/Owq2XBzapZHB0ZS9O9o3xGyC1XtT2Hciw+hwpIL8dSSV2yb+yZs/bicn30b3K2rz6q0RmOC8yY22wTWxOMnWayLVSbVMani4WmgW6A1AvIqFi4iIiAgwskcoJhP8+tcxdh1LMzocERGRcjMj5gB2O1wVFkjomecjzqR+TU96Ni8ofD4nNsHgaKS85eRbeffnHdz04UriEk/j6+HCW3e0Zsbwjgzp3IhJQ9pR16/oFlM1PQtWJ/249SjzNxwyImwRuQzVMqkBf9fV2JqouhoiIiIiAk0DvbmuZR0AJi/Xag0REXFOGTn5fLOu4EH/vdGNjQ2mHA3uGALAt+sPkWe1GRyNlJeNB09y4wcr+eD33eTb7FzXsg6//qsnAzsEYzIVrMDoE1GPlWN6MWtEFE928mPWiCjWv3gtD1/VFICx8+KI1fajIlVK9U1qNDhTV0MrNURERETkjId6Fvzj9rtNiRw5nWVwNCIiImVv/oZDpOXk06R2DXpeEWh0OOWmV3gQtb3dOZGew+/bjxsdjpSxrFwrr/6wjf6T/mTX8XRqe7vx8V3tmHxPe4J8zy38bTGb6BwaQPcQTzqHBmAxm3jqujBuiKhLrtXGAzPWcSBZW5WJVBXVN6lxZqVG/GGt1BARERGRAleG1KJjE3/yrHY+X7nP6HBERETKlN1udxQIH9qlEWYnriXgajFzR/uGAMxeq4LhzuTPPSe4/r3lTF25D7sdbruyAb+M7km/1vUcqzNKwmw28e7AtkQ28ONkZh7Dp8dyOiuvHCMXkbJSbZMaLc8kNRJPZXEyI9fgaERERESkshh1ZrXGV2sOcjpT/7AVERHnsXL3CfYkZVDDzeJ44O/MBkUFA7BsZxKHT2kFZlWXmp3Hs/PjuOuzNRxMyaSenwfT7oviv4PaUquG2yX16elmYcq9Hajn58GepAwe/WqDtisTqQKqbVLD18OVRgFeAGw7otUaIiIiIlLgqrBAwur4kJFr5cs1B4wOR0REpMxMX7UfgAEdgvHxcDU2mArQpHYNOof6Y7MX1NaQquu3v45x3bvL+frMqpu7O4Xw8+geXB0edNl91/H1YMq9HfBys7Bi1wnGLYrHbrdfdr8iUn6qbVID/rkFlepqiIiIiEgBk8nEgz1DAZi2aj/ZeVaDIxIREbl8B5Iz+H1HQW2JoV0aGRxNxRkcVVAwfE5sAjabHlRXNSkZuTwxeyMjvljH0dRsGgV48fXIzrx+W2SZJuZa1ffj/cFXYjLBrDUHmXYmASgilVM1T2oUFAvfmqiVGiIiIiLyt5va1KdBTU9OpOcwb4NmdoqISNU3I+YAdjv0bB5IaKC30eFUmD4RdfH1cCHxVBYrd58wOhwpIbvdzqLNh+n97jK+23QYswke6BHK0id60KVpQLmc89qWdXjuhhYAvLZ4G79vP1Yu5xGRy1fNkxpaqSEiIiIi53K1mBnRrQkAny3fi1UzO0VEpArLyMln7roEAO6LbmxsMBXMw9VC/3YF9UPmxCYYHI2UxLHUbEbOWM/jX28kJSOXsDo+zH+4K8/1bYGnm6Vcz31/9ybc2TEYmx0e+2ojf2nLepFKqZonNQpWauw9kUFmbr7B0YiIiIhIZTIoKhg/T1f2J2fyU/xRo8MRERG5ZPM3JpKWnU/jAC96Ng80OpwKV1gw/OdtR0lOzzE4Gjkfu93OnNiD9H53Gb/+dQxXi4knrrmC7x/rRtvgmhUSg8lk4pVbIohuGkBGrpUR02M5npZdIecWkZKr1kmNQB93gnzcsdtR5lVEREREiqjh7sK9Z/Yc/2TZHhWMFBGRKslut/PFn/sBuDe6MWazydiADNCini9tGvqRZ7Uzf0Oi0eFIMRJSMhkydQ1j5sWRlp1Pm4Z+fP9YN0Zf2xw3l4p9fOlqMTPp7vaE1q7B4dMFq0ZUY02kcqnWSQ2AiAYFqzXiDyupISIiIiJF3RvdGHcXM1sOnSZmb7LR4YiIiJTaqt3J7D6eTg03C3e0b2h0OIYZdKZg+OzYg5qoUIlYbXY+X7mP6/67nFW7k3F3MfNc33DmjYomvK6vYXH5ebny+X1R1PRyZXPCKf5v7mYVmhepRKp9UsNRV0PFwkVERETkLAHe7gzsULBlxSfL9hocjYiISOlN/3MfAHe0b4iPh6vB0Rjn5rb18XKzsCcpg/UHThodjgC7j6cx4JM/eeWHbWTlWenYxJ+lT/bggR5NcbEY/8iyce0afDKkPa4WE4vjjvDfX3caHZKInGH83xAGK0xqbFWxcBEREREpxsjuoZhNsHxnEvG6ZxQRkSrkYHImv20/DsDQalYg/Gze7i7c2LoeAF+vVcFwI+VZbXz0+y76vr+SDQdP4e3uwmu3RjB7ZGea1K5hdHhFdA4N4I3bIgH48PfdLNh4yOCIRASU1HAUC995LI3cfJvB0YiIiIhIZRMS4EW/1vUB+HS5VmuIiEjVMSNmP3Y79GgeSNNAb6PDMVzhFlSL4w6Tmp1ncDTV09bE09zy0Sre/nknuVYbV4cF8vPoHgzp3KjS1nsZ0CGYUVc1BWDMt3HE7k8xOCIRqfZJjYa1PPHzdCXPamfX8TSjwxERERGRSujBHqEA/LDlCAkpmQZHIyIicnEZOfnMWVewIuG+6EYGR1M5tAupyRVB3mTn2Vi06bDR4VQr2XlW3lq6nVs+XsW2I6nU9HLlv4Pa8Pl9UdSv6Wl0eBf19HVh9GlVl1yrjQdnrudgsu4HRYxU7ZMaJpOJlvVUV0NEREREzi+igR/dr6iN1WZnygqt1hARkcpvwcZE0rLzaRTgxVXNg4wOp1IwmUwM7liwWmNOrLagqijr9qfQ94MVTPxjD1abnX6t6/Hrv3py25UNMZkq5+qMs5nNJt4d1IbIBn6kZOQy/ItYTmdptY+IUap9UgMgosGZpIb2SBYRERGR83ioZ8G2A3PWJZCSkWtwNCIiIudnt9v54s/9ANzbpXGl3dbHCLdd2QA3i5m4xNNsTdRzoPKUkZPPuEXxDJgcw96kDAJ93Jl8T3s+vqsdtb3djQ6v1LzcXJhybwfq+nqw+3g6j361gXyrtrIXMYKSGvxdVyP+sFZqiIiIiEjxopsGENHAl+w8m+NBkYiISGX0555kdh1Px8vNwh0dGhodTqXiX8ON61rVAbRaozyt2JXEdf9dzvQ/C+q6DGjfkF9H9+T6VnWNDu2y1PH1YMq9HfB0tbBi1wnGfR+P3W43OiyRakdJDaBV/YKVGtuOpGK16S8iERERETmXyWRyrNb4ImY/mbn5BkckIiJSvGmr9gNwR/uG+Hq4GhtMJXTnmS2oFm5KJCvXanA0zuV0Zh5Pf7OZe6auJfFUFg1qejJzREf+M6ANfl7O8XsxooEf7w9ui8kEX64+6PjzJiIVp1RJja+++oqbbrqJdu3a0a5dOwYNGsSyZcvKK7YKExrojYermcxcK/uTM4wOR0REREQqqRsi6hHi78WpzDzmananiIhUQgkpmfy2/RgAQ7s0NjaYSqpLaADB/p6kZefz49YjRofjNH6KP0rv/y7jm/WHMJngvujG/Dy6B92vCDQ6tDJ3Xau6PHtDOACvLd7G72f+zIlIxShVUqNu3bo89dRTzJ8/n3nz5tG5c2ceeeQRdu3aVV7xVQiL2USLwmLh2oJKRERERM7DYjYxskcoAJ+t2Eee9lEWEalws2bNolevXkRGRjJgwAC2bNly3rY///wz/fv3p0OHDrRt25ZbbrmFhQsXFmljt9t5//336datG61bt+a+++5j//795TuIcjQjpmC7n+5X1KZZkLfR4VRKZrOJQR2CAZi9VpMULldSWg6PzNrAgzPXk5SWQ2hgDb55sAvjbm5FDXcXo8MrNyO7hzKoQzA2Ozz21Ub+OqJniiIVpVRJjV69etGzZ08aN25MkyZNGD16NF5eXmzatKmcwqs4hVtQqVi4iIiIiFzIgPYNqe3tRuKpLBZv0exOEZGKtGTJEiZMmMAjjzzCggULCA8PZ8SIESQnJxfb3s/Pj1GjRjFnzhwWLVpE//79ee6551ixYoWjzWeffcbMmTMZN24cc+fOxdPTkxEjRpCTk1NRwyozmbn5jjoRw7o2NjaYSu6O9sGYTbB2fwp7ktKNDqdKstvtLNh4iGv/u4zFcUewmE08fFVTljzenQ6N/Y0Or9yZTCZevTWCLqEBZORauf+LdRxPyzY6LJFq4ZLTpVarlaVLl5KZmcmVV155Se+vTFrU9QFg66HTFRpb4bkq2/WoDnTtjaHrbhxde+OUx7XX5yhiHA9XC/dFN+btn3fyybI93NK2PiaTyeiwRESqhWnTpjFw4EBuv/12AMaPH88ff/zBvHnzeOCBB85p36lTpyI/33vvvSxcuJD169fTvXt37HY7M2bMYNSoUfTu3RuAt956i+joaH799Vf69etX/oMqQws2JpKanU+jAC+uah5kdDiVWl0/D3qFB/HrX8eZG5vAs31bGB1SlXL4VBbPLYjjjx1JALSs58tbd7QmooGfwZFVLDcXM5OGtKP/xD/ZeyKDB2asZ/YDnfFwtRgdmohTK3VSY8eOHQwePJicnBy8vLz4+OOPadasWalPHBcXV+r3lCfX9DwAtiSksHHjxgr/h2llux7Via69MXTdjaNrbxxdexHnMaRzIyb+sYftR9NYtjOJq8L04EhEpLzl5uYSHx/Pgw8+6DhmNpuJjo5m48aNF32/3W5n9erV7Nu3j6eeegqAQ4cOkZSURHR0tKOdj48Pbdq0YePGjaVOapTHJJaS9mm325l+pmDxPZ1CsNttVLZ5MJVtotWA9g359a/jfLP+EE9e0ww3l1JtaAJUvjGVlfONy2azMzs2gX//tIP0HCtuFhOP9WrGyO5NcLWYK/V1KK/Pysfdwqf3tOP2T1azKeEU/5q7ifcHtsFsLv9ni874+88ZxwTOOS4jJ3CWOqnRpEkTFi5cSFpaGj/99BNjxozhyy+/LHViIzIyEoul8mQtW+TbeO73X0jLtVOnSTj1a3pWyHmtVitxcXGV7npUB7r2xtB1N46uvXHK49oX9ikixqjp5cadHUOYunIfnyzbo6SGiEgFOHnyJFarlYCAgCLHAwIC2Lt373nfl5aWRo8ePcjNzcVsNvPyyy/TtWtXAJKSkhx9nN3niRMnSh1jedyflbTPuOM57DqejofFRHO3FDZtOlXmsZSVynIfW8tmp5aHmZSMXKYuXUuXhh6X3FdlGVNZsNrt/JWUy8lsG1uPr6NFoBsWk4nDafl8sv408UkFE4PDAlx5uIMfDX3TiI87f22byqa8Pqt/dfTm1eUnWRJ3FK/8NO6M8CmX8xTHmX7/FXLGMYFzjsuIMZU6qeHm5kajRo0AiIiIIC4ujhkzZvDKK6+Uqh+LxVKpHqp5WSw0C/Jm+9E0/jqaTnBAxRbTqmzXozrRtTeGrrtxdO2No2sv4lxGdGvCF3/uZ/XeFDYlnKJtcE2jQxIRkWLUqFGDhQsXkpmZSUxMDG+++SbBwcHnbE1VFspjEktJ+5z05QYA7ugQTNeolmUSQ1mrjBOtBp/YyaRle1l7woVRN7Yt9fsr45gux0/xR3nlh784mvp3TZm6vu50CQ1gydYUcvJteLpa+L/rrmBo50ZYKmA1Qlkp78+qLeARcIgx87fy7V8ZREc05Za29cv8PP/kbL//wDnHBM45LiMncF5yTY1CNpuN3Nzcy+2mUoho4Mf2o2nEH07lulZ1jQ5HRERERCqx+jU9ubltfeZvSGTysj1MGtLe6JBERJxarVq1sFgs5xQFT05Opnbt2ud9n9lsdkzObNGiBXv27OHTTz+lU6dOBAYGOvoICvp71V1ycjLh4eGljrE8JrGUpM+ElEx+334cgPu6Nq70D8wq02SfwR1DmLRsLyt2n+BoWi4NLnHnjso0pku1dOsRHvlqE/azjh9NzWHBpsMAdG0WwJv9WxPs71XxAZaR8vysBnVsxN7kTCYv28vY+VsJCahRIUXTneH339mccUzgnOMyYkyl2izwnXfeITY2lkOHDrFjxw7eeecd1q5dy0033VRe8VWoVvV9AYg/nGpwJCIiIiJSFTzUsykAS+OPsjcp3eBoREScm5ubG61atSImJsZxzGazERMTw5VXXlnifv45ObNhw4YEBgYW6TM9PZ3NmzeXqk+jzVx9AJsdul9Rm2ZBFbfljTNoFFCD6KYB2O0wNzbB6HAMY7XZGf/9tnMSGv/k5+nKF8M6VumERkUYc30417eqQ67VxgMz13MwOdPokEScTqmSGsnJyYwZM4Y+ffpw3333ERcXx9SpUx17UVZ1rer7ARB/+LTBkYiIiIhIVdC8jg/XhAdht8NnK/YZHY6IiNMbNmwYc+fOZcGCBezZs4dx48aRlZVF//79AXjmmWd45513HO0nT57MqlWrSEhIYM+ePXz++ecsWrSIm2++GQCTycTQoUOZNGkSv/32Gzt27OCZZ54hKCiI3r17GzLG0srMzWf22oMA3Bfd2NhgqqhBUcEAfLMuAavtQo/1ndfafSkcOZ19wTans/KI3X+ygiKqusxmE/8d1JaIBr6kZOQy4otYUrPzjA5LxKmUavupN954o7ziqBRanlmpceR0NikZufjXcDM4IhERERGp7B7s2ZTfth9n3oZDjL72CoJ8Lr3IqIiIXFjfvn1JSUnhgw8+ICkpiRYtWjBlyhTH9lNHjhzBbP57/mZmZibjx4/n6NGjeHh4EBoayn/+8x/69u3raDNy5EiysrJ46aWXSE1NpX379kyZMgV3d/cKH9+lWLjxMKnZ+YT4e3FVWNDF3yDnuL5VXWp6uXL4dDYrdiVVy+t4PO3CCY3StqvuvNxcmDI0ils+Xsmu4+k8MmsD0+6LwsVSqvnlInIel11Tw5l4u7vQpHYN9p3IIP7wabpfEWh0SCIiIiJSyUU1rkW7kJpsOHiK6av280yf0u/BLiIiJTdkyBCGDBlS7GszZ84s8vPo0aMZPXr0BfszmUw88cQTPPHEE2UWY0Wx2+188ed+AIZ2qVpFmysTD1cLt13ZgGmr9jMnNqFaJjVsJVyhoskbJVfXz4Op90Yx4JMYVuw6wbjv43n1lghMJv05FblcSg+epXC1xtZE1dUQERERkYszmUyO2hozVx8gTdsLiIhIBYnZm8yOY2l4uloY0CHY6HCqtMFRIQD8su0YSWk5BkdTcex2OzNj9vPs/LgLtjMB9fw86Nik/IteO5OIBn68N7gtJhN8ufog088kIUXk8iipcZYI1dUQERERkVLq3aIOTQNrkJadz9dn9jUXEREpb9NX7Qfg9vYN8PN0NTaYKi6srg9XhtQk32Zn/oZDRodTIY6ezubeabG8+F082fk2wup4AwUJjH8q/Pnlm1pqNdAluL5VXcaeWcn76g/b+N/24wZHJFL1KalxllZnVmpsO6yVGiIiIiJSMmaziQd7FKzWmLpyH7n5NoMjEhERZ5eQksmvfx0D4N4ujY0NxkkMPlMwfE5sAna7cxcMX7T5MNe/t5zlO5NwdzHz8k0t+fGJHnwypB11/YpuMVXXz4NJQ9rRJ6KeQdFWfQ/0CGVgh4bY7PDY1xvZflTPHUUuh5IaZylMauw9kUF6Tr7B0YiIiIhIVXHLlfWp4+vOsdQcFm5KNDocERFxcl+uPoDNDt2a1eaKOj5Gh+MUbmxdnxpuFvaeyGDtvhSjwykXpzJzeezrjTz+9UZOZ+UR2cCPxY93Y1jXJpjNJvpE1GPlmF7MGhHFk538mDUiipVjeimhcZlMJhOv3RpJ51B/0nPyGTF9XbXa5kykrCmpcZYAb3fqnclI/3VEWVMRERERKRl3FwvDuzYBYPKyPSUuuCkiIlJaWblWZscmAHBfdGNjg3EiNdxduKlNfaBgtYazWbYzievfW873mw9jMZt44pormP9wNM2CiibFLGYTnUMD6B7iSefQAG05VUbcXMx8MqQ9TWrXIPFUFiNnrCM7z2p0WCJVkpIaxShcrRGfqLoaIiIiIlJyd3UKwcfdhT1JGfym/ZJFRKScLNyUyOmsPIL9Pbk6PMjocJzK4I4FBcMXxx3hdFaewdGUjczcfF5cuJV7P1/LsdQcQmvXYN6oaEZf2xxXix4NVqSaXm5MvbcDfp6ubEo4xVPfbHb6rc5EyoP+5ipGyzPFwreqroaIiIiIlIKPhyt3d24EwCfL9hgcjYiIOCO73c4Xf+4HCmppaBZ92WrT0I/wuj7k5NtY5ATbSW44eJJ+H6xk5uoDQMHKnsWPd6dtcE1jA6vGQgO9mTSkHS5mEz9sOcJ/f91ldEgiVY6SGsWIKFypoaSGiIiIiJTS8K6NcbOYWX/gJLH7nXM/bhERMc7qvSlsP5qGp6uFAR2CjQ7H6ZhMJgadKRj+9dqqWzA8N9/GOz/v4I5Jf7LvRAZ1fT2YOaIj425uhaebxejwqr3oprV5/bYIAD74bRcLN1b9BJpIRVJSoxitGhSs1Nh1LI2cfO1tJyIizm3WrFn06tWLyMhIBgwYwJYtW87bNi8vj48++ojevXsTGRnJzTffzPLly4u0SU9P5/XXX+fqq6+mdevWDB48+Jw+T5w4wdixY+nWrRtt2rRhxIgR7N+/3/H6qVOnePXVV7n++utp3bo1V111Fa+99hppaWllOnaR8hDk68Ht7RsABbU1REREytL0P/cB0L9dA/w8XQ2OxjnddmUD3FzMbDuSytbEqjfhddexNPpPWsWHv+/GZodb29bnpyd70P2KQKNDk38YFBXCgz1CAXjm2y2sP6DJMCIlpaRGMer7eVDTy5V8m52dR9ONDkdERKTcLFmyhAkTJvDII4+wYMECwsPDGTFiBMnJycW2f++995gzZw4vvvgiS5YsYfDgwTz66KNs27bN0eaFF17gzz//5K233uL777+na9euDBs2jGPHjgEFWyY88sgjJCQkMHHiRBYsWECDBg0YNmwYmZmZABw/fpzjx48zZswYfvjhByZMmMCKFSt4/vnny/+iiJSBkd1DMZng17+Os/OYknEiIlI2Dp3M5JdtBfdUKhBefmp6uXFDRF0AZsceNDiakrPZ7ExZsZd+H65ka2IqNb1c+fiudrw3+Er8vJQAq4zG9AnnupZ1yLXaeGDGehJSMo0OSaRKUFKjGCaTiYgzdTXiD6tYuIiIOK9p06YxcOBAbr/9dpo1a8b48ePx8PBg3rx5xbb/7rvveOihh+jZsyfBwcHcdddd9OzZk88//xyA7Oxsfv75Z55++mmioqJo1KgRjz32GI0aNeKrr74CYP/+/WzatIlx48bRunVrQkNDGTduHNnZ2SxevBiA5s2b8+GHH9KrVy9CQkLo0qULTz75JL///jv5+fkVc3FELkNooDfXtyx4GDJ52V6DoxEREWcxc/UBbHbo2iyAK+r4GB2OUyvcgmrRpsNk5lb++89DJzO5a8pqXlv8F7n5Nq4KC+TnJ3vQr3U9o0OTCzCbTbw3uC2t6vuSnJHL8OmxpGY7R4F6kfKkpMZ5tFJdDRERcXK5ubnEx8cTHR3tOGY2m4mOjmbjxo3FvicvLw83N7cix9zd3dmwYQMA+fn5WK1W3N3dz9smNzfXceyf53Vzc2P9+vXnjTc9PR1vb29cXFxKMUoR4zzYs2A7ge82JXLkdJbB0YiISFWXlWtlTmwCAPdFNzE4GufXuUkAjQK8SMvJZ/GWI0aHc152u51v1x/ihvdWsHpvCp6uFl6/LYJp90UR5OthdHhSAl5uLky9N4o6vu7sOp7OI7M2kG+1GR2WSKWmpwLn0fJMUmOrVmqIiIiTOnnyJFarlYCAgCLHAwIC2Lu3+Jnl3bp1Y/r06URFRRESEkJMTAy//PILVmtBDSpvb2+uvPJKJk6cSGhoKLVr1+aHH35g06ZNhISEABAaGkr9+vV55513eOWVV/D09GT69OkcPXqUpKSkYs+bkpLCxIkTGTRoUKnHWRhbWSjsqyz7rAyccVyVYUytG/jSqUkt1uw7yZTle3mub/hl9VcZxlTWnHFM4Jzj0piqhvIYkzNdn6ruu02JnMrMo2EtT3qFBxkdjtMzmwsKhr+1dAdzYhMqZVH25PQcnlsQx0/xBVuStW9Ui3cGtKFx7RoGRyalVdfPgylDoxg4OYYVu07wyg/beOWWCKPDEqm0lNQ4j4gzxcK3H0nDarNjMZsMjkhERMR4zz//PC+88AI33HADJpOJ4OBg+vfvX2S7qrfeeovnnnuOHj16YLFYaNmyJf369SM+Ph4AV1dXPvzwQ55//nk6duyIxWKhS5cu9OjRA7vdfs4509PTefDBB2natCmPPvpoqWOOi4u79AFXYJ+VgTOOy+gx9W4Aa/bBrDUH6FE7E2+3y18obfSYyoMzjgmcc1waU9XgjGOq7ux2O9P/3A/AvV0a6xlFBbmjXUPe+Xkn6w6cZPfxNJoFVZ4tv37Zdoxn52/hRHourhYTT/ZuzkM9m+r3RhUW2dCP/w5qy0NfrmdGzAFCa9fgvq5alSVSHCU1zqNJQA283Cxk5lrZdyK9Un1xiYiIlIVatWphsVjOKQqenJxM7dq1i32Pv78/EydOJCcnh1OnThEUFMTbb79NcPDfM9dCQkL48ssvyczMJD09naCgIJ588skibSIiIvjuu+9IS0sjLy8Pf39/BgwYQERE0dlI6enp3H///dSoUYOPP/4YV9fSFziMjIzEYrGU+n3FsVqtxMXFlWmflYEzjquyjKlNGzvf7lrFjmPpbMn04+GOTS+5r8oyprLkjGMC5xyXxlQ1lMeYCvsUY63Zl8L2o2l4uloYWAlXDDirIF8PeoUH8cu2Y8xem8ALN7Y0OiTSsvN49YdtzF13CICwOj68O6gNrc7UhpWqrU9EXcb0CeffS7fzyg/baFS7BleHaWWWyNmU1DgPs9lEi3q+rD9wkq2JqUpqiIiI03Fzc6NVq1bExMTQu3dvAGw2GzExMQwZMuSC73V3d6dOnTrk5eXx888/c8MNN5zTxsvLCy8vL06fPs3KlSt5+umnz2nj41Pw/bp//362bt3KE0884XgtPT2dESNG4ObmxqRJk86p01FSFoulzB9WlUeflYEzjqsyjOmhq5oyes5mvog5wMgeTfFwvbx4KsOYypozjgmcc1waU9XgjGOq7qav2g/Abe0a4OdV+kkecukGRwXzy7ZjzN+YyNN9wnB3Me7P1pq9yfzfN5s5dDILkwlGdg/lX9c2v+x7C6lcHuoZyt6kdL5Zf4jHvtrIvFHRhNXVc0mRf1Kh8AuIcBQLV10NERFxTsOGDWPu3LksWLCAPXv2MG7cOLKysujfvz8AzzzzDO+8846j/ebNm/n5559JSEhg3bp13H///dhsNu6//35HmxUrVrB8+XISEhJYtWoVQ4cOJTQ01NEnwI8//siaNWtISEjg119/Zfjw4fTu3Ztu3boBBQmN4cOHk5mZyeuvv056ejpJSUkkJSVpb2+pcm5sXZ8GNT05kZ7Lt+sPGR2OiIhUMYdPZfHztqMA3Bfd2NhgqqGezQOp6+tBSkYuv247bkgM2XlW3ljyF4M/W82hk1k0rOXJ7JGdea5vCyU0nJDJZOL12yLp1MSf9Jx8hk+PJSktx+iwRCoVrdS4gMKle/GHUw2OREREpHz07duXlJQUPvjgA5KSkmjRogVTpkxxbD915MgRzOa/50Dk5OTw3nvvkZCQgJeXFz179uStt97C19fX0SYtLY13332Xo0ePUrNmTa677jpGjx5dZOuopKQk3nzzTZKTkwkMDOSWW27h4YcfdrweHx/P5s2bAbj22muLxPzbb7/RsGHDcrkeIuXB1WLm/u5NGP/9Nj5bsZc7O4Zov2sRESmxL9ccxGaH6KYBNK+j2doVzcViZkCHhnz4+25mxx6kX+t6FXr++MOn+deczew4lgbAoA7BvHBjC3w8tGLHmbm5mPlkSHv6T/qTfScyeGDmOr4e2VlJLJEzlNS4gJZnVmpsTTyN3W7HZNI/PkVExPkMGTLkvNtNzZw5s8jPHTt2ZMmSJRfsr2/fvvTt2/eCbYYOHcrQoUPP+3qnTp3YsWPHBfsQqUoGRQXz/m+7OJCcydKtRyv8gYiIiFQtVpud1XuTidmfyZebkwCt0jDSwA7BfPj7blbuPkFCSibB/l7lfk6rzc4ny/bw3q87ybPaqe3txoT+rbm2ZZ1yP7dUDrVquDH13g7cNvFPNh48xdPfbuGDwW31fFIEbT91Qc3r+OBqMZGanc+hk1lGhyMiIiIiVZSXmwtDuzQG4JNle7Db7cYGJCIildbSrUfo9u/fuXtqLB/FppKRa8VignyrvjuMEuzvRfcramO3wzfrEsr9fPtPZDBwcgz/+WkHeVY717Wsw09P9lBCoxoKDfRm0pB2uJhNfL/5MO/9usvokEQqBSU1LsDNxexY2qktqERERETkctzbpREermbiEk8TsyfZ6HBERKQSWrr1CKO+3MCR09lFjlvt8MhXG1i69YhBkcmgqGAA5q47hNVWPgkmu93OrDUH6PvBCtYfOIm3uwtvD2jD5HvaE+DtXi7nlMovumltXrs1AoD3f9vFd5sSDY5IxHhKalxEKxULFxEREZEyEODtzsAOBQ9EJi3bY3A0IiJS2VhtdsZ/v40LPS4f//22cnugLhd2bcs61PJy5WhqNst2ln3B8OOp2QybHsvzC7aSmWulc6g/S5/szh3tG2q7IWFwxxAe6BEKwNPfbGH9gRSDIxIxlpIaFxHRQMXCRURERKRsjOweisVsYsWuE2xN1KQZERH529p9Kees0PgnO3DkdDZr9+lhphHcXSz0b9cQgNlry3YLqsVbjnDde8v5Y0cSbi5mXujXgq/u70zDWuVfu0OqjjF9wrm2ZR1yrTYemLGe/ScyWL03mRUHs1i9N1kJT6lWlNS4CK3UEBEREZGyEuzvRb/IgiLhny7fa3A0IiJSmRxPO39C41LaSdkbfGYLqt+2Hy+Tz+F0Zh5Pzt7II19t4FRmHq3q+/LDY924v3soZrNWZ0hRFrOJ9wa1pWU9X5Izcun97jLunhrLe2tOc/fUWLr9+3dtUSfVhpIaFxFe1xeTCY6l5pCUlmN0OCIiIiJSxRVuHfDDlsMkpGQaHI2IiFQWQT4eZdpOyt4VdXxo36gWVpudeesvr67Bil1JXP/echZuOozZBI/1asaCh7s6aruKFKeGuwv3dAkBIP+slRlHT2cz6kvV3pHqQUmNi6jh7kKT2jUArdYQERERkcsX0cCP7lfUxmaHz1ZotYaIiBTo2MSfen4enG9+vgmo5+dBxyb+FRmWnKWwYPic2IPY7aXf7icr18rL323lnqlrOZqaTZPaNfh2VDT/d10Ybi56TCcXZrXZ+eC33cW+Vvi7UbV3pDrQ35YlEFFfdTVEREREpOyM6tkUgLnrEkhO12pgEREp2Frm5ZtaApyT2Cj8+eWbWmLRtkSGurF1PbzdXdifnMnqvaWrb7Ip4RT9PljBFzEHABjapRGLH+9Gu5Ba5RGqOCHV3hEpoKRGCaiuhoiIiIiUpS5NA4hs4Ed2ns3xYENERKRPRD0mDWlHXb+iW0zV9fNg0pB29ImoZ1BkUsjLzYWb29YHClZrlESe1ca7v+zk9kl/svdEBnV83ZkxvCOv3BKBl5tLeYYrTka1d0QKKKlRAq20UkNEREREypDJZOKhM6s1ZsTsJzM33+CIRESqjlmzZtGrVy8iIyMZMGAAW7ZsOW/buXPnctdddxEVFUVUVBT33XffOe3Hjh1LWFhYkf9GjBhR3sM4rz4R9Vg5phezRkTxZCc/Zo2IYuWYXkpoVCKFBcOXbD3K6ay8C7bdfTyN/hP/5IPfdmG12bm5TX1+frInPZoHVkSo4mRUe0ekgJIaJVC4UuNAciap2Rf+shIRERERKYk+EXVpFODFqcw85sQmGB2OiEiVsGTJEiZMmMAjjzzCggULCA8PZ8SIESQnJxfbfs2aNfTr148ZM2Ywe/Zs6tWrx/Dhwzl27FiRdt27d2flypWO/959992KGM55WcwmOocG0D3Ek86hAdpyqpKJbOBHi3q+5ObbWLjpcLFtbDY7n6/cR78PVhKXeBo/T1c+vPNKPrjzSvy8XCs4YnEWqr0jUkBJjRKoVcONBjU9Adim1RoiIiIiUgYsZhMju4cCMGXFPvKsNoMjEhGp/KZNm8bAgQO5/fbbadasGePHj8fDw4N58+YV2/6dd97h7rvvpkWLFjRt2pTXXnsNm81GTExMkXZubm4EBgY6/vPz86uI4UgVZTKZuLNjwWqNaav2seJAJqv3JjuKMyeeymLI1DW88sM2cvJt9GgeyM+je3BTm/pGhi1O4EK1d6CgpoZq70h1oI37SqhlfV8ST2URfziVzqEBRocjIiIiIk7gjvYNee/XnSSeyuKHLYe57cqGRockIlJp5ebmEh8fz4MPPug4ZjabiY6OZuPGjSXqIysri/z8/HOSFmvXrqVLly74+vrSuXNnnnzySWrVKn3xZqvVWur3XKyvsuzTaM40Jk/XgnnCCSezeW9tNqyNpa6vO9e3qsO8DYdJz8nH09XCszeEcVfHYEwmU5UatzN9VoWcZUzXtgji47va8soPf3E0NafIa2YTNA7wqtJjdJbP6WzOOK7yGFNJ+1JSo4Qi6vvxy7ZjKhYuIiIiImXGw9XCsK5N+M9PO5i8bC+3tm2AyaSZdSIixTl58iRWq5WAgKITDQMCAti7d2+J+nj77bcJCgoiOjracax79+5ce+21NGzYkISEBN59911GjhzJnDlzsFgspYoxLi6uVO2N6tNoVX1Mqw9l85+YU+ccP5qawxcxBcXDm/u78lhHP+q7p7B5c0oFR1h2qvpnVRxnGFMd4IPravJXUi4ns23U8jDzw84MYo/kMmb2Wl7sXqvK31M6w+dUHGcclxFjUlKjhArrasQnavspERERESk7Qzo1YuL/drP9aBp/7Ezi6rAgo0MSEXFKn376KUuWLGHGjBm4u7s7jvfr18/x68JC4b1793as3iiNyMjIUidCzsdqtRIXF1emfRrNGcZktdl55Kc/LtjGx92FhY9fhbtr1RwjOMdndTZnHFPbf4ype/sc+ry/gs3Hcjnp2YBe4VXzntIZPydwznGVx5gK+7wYJTVKqFWDgqTG7qR0svOseFThLyYRERERqTz8vFy5s2MIU1bu45M/9iipISJyHrVq1cJisZxTFDw5OZnatWtf8L1Tp07l008/Zdq0aYSHh1+wbXBwMLVq1eLAgQOlTmpYLJYyf1hVHn0arSqPae3+5HO2/DlbWk4+mxPT6NK06m9fXpU/q/Nx1jGFBvkwvFsonyzbwxs/7qBnWB3cXKpuOWVn/JzAOcdlxJiq7u/sClbX14OAGm5YbXZ2HE0zOhwRERERcSIjujfBxWxizb4UNh48aXQ4IiKVkpubG61atSpS5Luw6PeVV1553vd99tlnTJw4kSlTphAZGXnR8xw9epRTp04RGBhYJnGLczmell2m7UTK0qO9mlHb2519JzL44s/9RocjUm6U1Cghk8lEy8ItqA5rCyoRERERKTv1/Dy5pW0DACYvK9m+8CIi1dGwYcOYO3cuCxYsYM+ePYwbN46srCz69+8PwDPPPMM777zjaP/pp5/y/vvv88Ybb9CgQQOSkpJISkoiIyMDgIyMDP7973+zadMmDh06RExMDA8//DCNGjWie/fuhoxRKrcgH48ybSdSlrzdXXjm+jAAPvhtFyfSL7yqSKSq0vZTpdCqvh8rdp1gq4qFi4iIiEgZe6hnKPM2HOKnbUfZk5RO00Bvo0MSEal0+vbtS0pKCh988AFJSUm0aNGCKVOmOLafOnLkCGbz3/M3Z8+eTV5eHo8//niRfh599FEee+wxLBYLO3fuZOHChaSlpREUFETXrl154okncHNzq9CxSdXQsYk/9fw8OHo6G3sxr5uAun4edGziX9GhiQBwR/uGzFx9gLjE07zz8w4m9G9tdEgiZU5JjVKIaKCVGiIiIiJSPq6o40PvFkH8+tdxpqzYq3+Aioicx5AhQxgyZEixr82cObPIz7///vsF+/Lw8GDq1KllFps4P4vZxMs3tWTUlxswQZHEhunM/1++qSUWs6mYd4uUP7PZxEs3tWTAJzHMjk1gSOdGtKrvZ3RYImVK20+VQuFfANuPpJJvtRkcjYiIiIg4mwd7NgVg3vpEjqdqL24REZHKqE9EPSYNaUddv6JbTNX182DSkHb0iahnUGQiBaIa+3NTm/rY7fDK99uw24tbVyRSdSmpUQqN/L3wdnchJ9/GnqQMo8MREREREScT1dif9o1qkWu18fmq/UaHIyIiIufRJ6IeK8f0YtaIKJ7s5MesEVGsHNNLCQ2pNMbeEI6Hq5k1+1L4cetRo8MRKVNKapSC2WyiZb3CLahUV0NEREREyt6DPUIBmLX6AGnZeQZHIyIiIudjMZvoHBpA9xBPOocGaMspqVQa1PTkwR4Fq4DfWPIX2XlWgyMSKTtKapRSy/qqqyEiIiIi5ad3izo0C/ImLSefr9YcNDocEREREamiHurZlHp+Hhw6mcWUFXuNDkekzCipUUqtziQ1tiZqpYaIiIiIlD2z2cQDZ1ZrfL5qHzn5mlUnIiIiIqXn6WZh7A3hAEz8Yw9HT6tmmzgHJTVKKaJBQbHwbUdSVWRHRERERMrFLW3rU8fXnWOpOXy38bDR4YiIiIhIFXVzm/q0b1SLzFwrby3dbnQ4ImVCSY1SahbkjZuLmbTsfBJSsowOR0RERESckLuLhRHdmgDwyfI92GyaTCMiIiIipWcymXjpxpYAzN+YyMaDJw2OSOTyKalRSq4WM2F1fADYqmLhIiIiIlJO7uwYgo+HC3uTMvj1r2NGhyMiIiIiVVSb4Jrc0b4hAOO/36YJM1LlKalxCSIaFBYLV1JDRERERMqHj4crQzo3AuCTZXu09amIiIiIXLJnrg+jhpuFTQmn+G5zotHhiFwWJTUuQcv6BXU14g+nGhyJiIiIiDizYV0b4+ZiZsPBU3wRc4AVB7NYvTcZq2bXiYiIiEgpBPl68PDVzQB488ftZOTkGxyRyKUrVVJj8uTJ3H777Vx55ZV06dKFhx9+mL1795ZXbJVWq/oFKzW2JiqpISIiIiLlJ8jHg46N/QF4dfF23ltzmrunxtLt37+zdOsRg6MTERERkapkRLcmBPt7ciw1h0+W7TE6HJFLVqqkxtq1a7n77ruZO3cu06ZNIz8/nxEjRpCZmVle8VVKLer6YjbBifQcjqdmGx2OiIiIiDippVuPsHL3iXOOHz2dzagvNyixISIiIiIl5uFq4fm+LQD4dPleDp2sXs90xXmUKqkxdepU+vfvzxVXXEF4eDhvvvkmhw8fJj4+vrziq5Q83Sw0DfQGtAWViIiIiJQPq83O+O+3Ffta4eZT47/fVmW3orLa7Kzem6wttUREREQq0PWt6tIlNICcfBsTftxudDgil8Tlct6clpYGgJ+fX5kEU5W0qu/LruPpbE08zdXhQUaHIyIiIiJOZu2+FI6cPv+qYDtw5HQ27V/9hQBvN3w9XfH1cMXHw8Xxa19PF3w8XPF1HHM5c7zgdQ9XMyaTqeIGdcbSrUcY//22v8e3JpZ6fh68fFNL+kTUq/B4RERERKoLk8nESze1pN8HK1i85QhDOyfTKTTA6LBESuWSkxo2m4033niDdu3a0bx581K/32q1XuqpK4WW9XxYuAm2Hj59WWMpfG9Vvx5Vka69MXTdjaNrb5zyuPb6HEWc3/G0km1zeiorj1NZeZd0DhezyZHs8DmTBHEkRhzJj8LXzmrn6Yq3mwtmc+mSIku3HmHUlxs4e11G4ZZak4a0U2JDREREpBy1qOfLnR1DmLXmIK/8sI1Fj3bDUsp7OhEjXXJSY/z48ezatYuvvvrqkt4fFxd3qaeuFNwycwDYuP8EmzZtuuz+qvr1qMp07Y2h624cXXvj6NqLSGkE+XiUqN0bt0XQpLY3qdl5pGblkZadf+bX+aRl5zl+nZr9z9fysNkh32YnJSOXlIzcS4rRZAJv978TIH8nQ878/x+rRnw8XPB2d+H5hVvPSWhAwcoTEwVbal3bsq7+YS0iIiJSjv51bXMWbT5M/OFUvlmXwOCOIUaHJFJil5TUeOWVV/jjjz/48ssvqVu37iWdODIyEovFcknvrQyaZOUxbtlvHM+w0iSsFX6erpfUj9VqJS4urspfj6pI194Yuu7G0bU3Tnlc+8I+RcR5dWziTz0/D46ezi42CWAC6vp5MCgqpNQJALvdTmau9cLJj6wzxxy/PtPuTJvcfBt2O6Rl55OWnU/iqazLHnPhllpr96XQpam2QRAREREpLwHe7jxxzRW8tvgv3v55B31b18PX49Keb4pUtFIlNex2O6+++iq//PILM2fOJDg4+JJPbLFYqvRDNX9vC8H+niSkZLH9WDrRTWtfVn9V/XpUZbr2xtB1N46uvXF07UWkNCxmEy/f1JJRX27ABEUSG4UpjJdvanlJKxpMJhM13F2o4e5CvUssj5edZy2y8uOiK0Sy8jh0MpOjqTkX7bukW2+JiIiIyKUb2qUxX609yN6kDD76fTfP9W1hdEgiJVKqpMb48eP54YcfmDhxIjVq1CApKQkAHx8fPDxKtjzembSq50dCShbbDqdedlJDRERERORsfSLqMWlIu6JFtSlYoWF0UW0PVwserhYCfdxL/J6YPcnc+dnqi7YLqOF2OaGJiIiISAm4uZh5sV9Lhk2PZdqqfdzZMYQmtWsYHZbIRZUqqfH1118DcM899xQ5PmHCBPr37192UVURrer7sjT+KFsTTxsdioiIiIg4qT4R9bi2ZV1W70kidutOoiKa07lpYJWsOXGxLbUKvfrDNt66ow1tgmtWVGgiIiIi1dLV4UFcFRbIHzuSeH3xX0y5t4PRIYlcVKmSGjt27CivOKqkiAYFa/XjD6caHImIiIiIODOL2UTn0AA8Uj1pGxpQJRMacPEttexADTcLO46lc9vEVQzr2oT/u645Xm6XVApQRERERErghX4tWblrOb/+dYwVu5LofkWg0SGJXJDZ6ACqslb1fQHYk5ROVq7V4GhERERERCq/wi216voV3b62rp8Hnwxpx/JnrubWtvWx2WHqyn1c99/lLNuZZFC0IiIiIs6vWZA3Q7s0BuCV77eRb7UZG5DIRSipcRmCfD2o7e2OzQ5/HdVqDRERERGRkugTUY+VY3oxa0QUT3byY9aIKFaO6UWfiHoEeLvz3uArmT4sigY1PTl0Mot7P1/L6DmbSMnINTp0EREREaf0xDVXUMvLlV3H05m15qDR4YhckJIal6lwtYa2oBIRERERKbnCLbW6h3jSuZgtta4KC+Ln0T0Y3rUJZhMs2JhI73eXsXBjInb7hSpyiIiIiEhp+Xm58q/rwgD47687OZWpySRSeSmpcZkiGhQkNbYdVrFwEREREZGyVMPdhZduasn8h7sSXteHlIxcnpyziXunxZKQkml0eCIiIiJO5c6oYMLr+nAqM4/3ft1ldDgi56WkxmVqVb+gWPjWRK3UEBEREREpD22Da/L9Y914+vow3FzMLN+ZxHX/Xc6UFXux2rRqQ0RERKQsuFjMvHRjSwBmrj7AzmNpBkckUjwlNS5T4fZTO46mkaciOiIiIiIi5cLVYuaRq5ux9InudGriT1aeldcW/0X/iavYpq1gRURERMpEdLPaXN+qDlabnVd/2KZtP6VSUlLjMoX4e+Hj4UKu1cbu4+lGhyMiIiIi4tRCA735emRnJvSPxMfDhc2HTnPzRyt5a+l2svOsRocnIhVg1qxZ9OrVi8jISAYMGMCWLVvO23bu3LncddddREVFERUVxX333XdOe7vdzvvvv0+3bt1o3bo19913H/v37y/nUYiIVF7P922Jm8XMil0n+O2v40aHI3IOJTUuk8lkomW9gtUaWxNVV0NEREREpLyZzSbu7BjCb//qyQ0Rdcm32Zn4xx5ueH8FMXuSjQ5PRMrRkiVLmDBhAo888ggLFiwgPDycESNGkJxc/J/9NWvW0K9fP2bMmMHs2bOpV68ew4cP59ixY442n332GTNnzmTcuHHMnTsXT09PRowYQU5OTkUNS0SkUgkJ8GJ4tyYAvLZ4G7n52p1GKhclNcpAYV2NeC17FxERERGpMEG+Hkwa0p7J97Snjq87+05kcOdnqxk7bwunM/OMDk9EysG0adMYOHAgt99+O82aNWP8+PF4eHgwb968Ytu/88473H333bRo0YKmTZvy2muvYbPZiImJAQpWacyYMYNRo0bRu3dvwsPDeeuttzh+/Di//vprRQ5NRKRSebRXMwJ93NmfnMn0P/cZHY5IEUpqlIGIBgUrNbSXr4iIiIhIxbu+VV1++VdP7u4UAsDs2AR6/3cZS+KOaB9oESeSm5tLfHw80dHRjmNms5no6Gg2btxYoj6ysrLIz8/Hz69gcuKhQ4dISkoq0qePjw9t2rQpcZ8iIs7I292Fp68PA+DD33ZzIl2r16TycDE6AGfw90qN09hsdsxmk8ERiYiIiIhUL74errx+WyS3tG3A2Plb2JuUwcOzNnBtyzq8eksEdf08jA5RRC7TyZMnsVqtBAQEFDkeEBDA3r17S9TH22+/TVBQkCOJkZSU5Ojj7D5PnDhR6hit1rKr7VPYV1n2aTSNqepwxnFpTKV3W5t6zPhzP1sPp/Kfpdt547aIcjnPPznj5wTOOa7yGFNJ+1JSoww0DayBu4uZjFwrB1IyaVK7htEhiYiIiIhUSx2b+LPk8e5M/N9uJi3bwy/bjhGzJ5kxN4Rzd8cQTUASqcY+/fRTlixZwowZM3B3dy+Xc8TFxVWJPo2mMVUdzjgujal07gxz4fnDMHfdITrUzCK0lmu5neufnPFzAucclxFjUlKjDLhYzITX82VzwiniD59WUkNERERExEAerhb+dV0Y/VrXZ8y8LWxKOMWLC7fy3cZE3rw9kmZBPkaHKCKXoFatWlgslnOKgicnJ1O7du0Lvnfq1Kl8+umnTJs2jfDwcMfxwMBARx9BQUFF+vxnu5KKjIzEYrGU+n3FsVqtxMXFlWmfRtOYqg5nHJfGdGnaAquTN/P9liN8s9vGV/e3wWQqv0kizvg5gXOOqzzGVNjnxSipUUZa1S9IamxNTOXG1vWNDkdERKTEZs2axdSpU0lKSiI8PJwXX3yR1q1bF9s2Ly+PyZMns3DhQo4dO0aTJk146qmn6NGjh6NNeno677//Pr/++ivJycm0bNmS5557rkifJ06c4O2332blypWkpaXRoUMHXnzxRRo3buxok5OTw5tvvsmSJUvIzc2lW7duvPzyyxd9aCEiUiisrg/zRkUzM2Y///lpB+sOnKTv+yt55Or/b+/Ow6Kq3jiAf+8MDMMu+yKigMqOiomKW5qWYZpKmhZZRKmllWZp60+xRbM0l9xyT7SyTAtDK7PFBQVFlM0lUXFhc1D2dWZ+fyAYgQrKcGeG7+d5eIQ75555z9W6L/Pec05HvPSgB2QG3GKQSJfIZDL4+voiNjYWgwcPBoDaTb/DwsJue96aNWuwatUqrFu3Dv7+/nVec3FxgZ2dHWJjY+Ht7Q2gOpc5ceIExo8f3+QYpVJps39YpYk+xcYx6Q59HBfH1HRvh3jjt7RsxF24jl9SczEswElj71VDH/+eAP0clxhjYhbfTHydqzcLT7maL3IkREREjRcTE4N58+ZhypQp2LFjB7y8vBAREVHvCcgaixcvxrfffov3338fMTExGDduHKZOnYrU1NTaNu+99x4OHTqEBQsWIDo6Gn369EF4eDiys7MBAGq1GlOmTMGlS5ewYsUK7NixA23btkV4eDhKSkpq+/n444/xxx9/YPHixdi8eTNycnIwdepUzV4QItI7UomA5/q44dfXB2Cgpx0qlCp8vvcMHlu2H8cuXhc7PCJqovDwcGzbtg07duzAuXPnMGfOHJSWlmL06NEAgJkzZ2LhwoW17b/88kssWbIEH3/8Mdq2bYvc3Fzk5uaiuLgYACAIAiZMmICVK1fi999/x+nTpzFz5kzY29vXFk6IiFo75zbGmNTfAwDwcUwayir1Z18I0k0sajQTv5ubhadeLYBarRY5GiIiosbZsGEDxo4di9DQUHTs2BGRkZGQy+XYvn17g+1//PFHTJ48GQMGDEC7du3w1FNPYcCAAVi/fj0AoKysDL/++ivefPNN9OjRA+3bt8crr7yC9u3bY+vWrQCACxcuIDExEXPmzEFAQADc3d0xZ84clJWV4eeffwYAFBYWYvv27XjrrbfQu3dv+Pn54eOPP8bx48eRmJjYIteGiPRL2zbGWP9cDywd3w02pjKcyS7CE6sOYfaPySgqrxI7PCJqpJCQEMyaNQtLly7F448/jrS0NKxdu7Z2JmdmZmbt5t8A8M0336CyshKvvvoq+vbtW/tVk7sAwIsvvoiwsDD873//wxNPPIGSkhKsXbtWY/tuEBHposkDPOBkKceVG6VYuz9d7HColePyU83E09EcUokARXEFsgrK4GRpLHZIREREd1RRUYGUlBRMmjSp9phEIkFwcDCOHz/e4DmVlZWQyWR1jhkZGSEhIQEAUFVVBaVSWe9DgH+3qaioqD327/eVyWQ4duwYxowZg+TkZFRWViI4OLi2jYeHB5ydnZGYmIiuXbve+8CJqNUSBAEjujijX0dbfBSThu+PXcam2Iv4NTUbH470w0PeDmKHSESNEBYWdtvlpjZv3lzn53379t21P0EQ8Nprr+G1115rlviIiPSRsUyKtx71wmvfJGL5H+fwRPd2cLSUix0WtVIsajQTuaEUHe3McDq7EClXCljUICIirXf9+nUolUrY2NjUOW5jY4P09IafvOnbty82btyIHj16wNXVFbGxsfjtt9+gVFZPPzYzM0O3bt2wYsUKuLu7w9bWFrt27UJiYiJcXV0BAO7u7nB2dsbChQsxd+5cGBsbY+PGjcjKyqp9svLatWswNDSEhYVFvdj+/fRlY9TE1hxq+mrOPrWBPo6LY9INYo3JQi7FJ6P9MCLAEe/9mIKMvFJEbDqKYf6O+N9j3rA1u7+ns/l3pRs4pqb1SURENKKLMzbHXsTRi9exYM8pLHqyq9ghUSvFokYz8m1rUV3UuFqAwT58youIiPTPu+++i/feew+PPvooBEFAu3btMHr06DrLVS1YsADvvPMO+vfvD6lUCh8fHwwbNgwpKSkAAENDQyxbtgzvvvsugoKCIJVK0bt3b/Tv318jSzgmJSXpRJ/aQB/HxTHpBrHGZApg/gALfJsqQfTpYvyclIW/TmXj2S7mGNjBGIIg3Ff//LvSDRwTERFR4wiCgP8N98GILw7ih+NXENa7PQJdrcQOi1ohFjWaka+zJX5IuMLNwomISCdYWVlBKpXW2xRcoVDUrkv9X9bW1lixYgXKy8tx48YN2Nvb47PPPkO7du1q27i6uiIqKgolJSUoKiqCvb09pk2bVqeNn58ffvzxRxQWFqKyshLW1tYYM2YM/Pz8AAC2traorKxEQUFBndkaCoUCdnZ2TRqnv78/pFJpk865HaVSiaSkpGbtUxvo47g4Jt2gLWPq+QDw/JV8vLMjBSmZBVh+tAAJeQb4aKQf2tuYNLk/bRlXc+KYdIMmxlTTJxEREQAEuLTBmO4u+O7YZcyNTsUPLwVDIrm/B0GImopFjWbk61z9oUvK1QKRIyEiIro7mUwGX19fxMbGYvDgwQAAlUqF2NjY265TXcPIyAgODg6orKzEr7/+ikcffbReGxMTE5iYmCA/Px8HDhzAm2++Wa+Nubk5gOrNw5OTk2vXsvbz84OhoSFiY2PxyCOPAADS09Nx9erVJu+nIZVKm/3DKk30qQ30cVwck27QhjF1cbXGj1P7YN2B81j02xnEpufh0aUHMH1IZ7zQ1w0GUkmT+9SGcTU3jkk36OOYiIhIe7w51BMxSZlIvHQDP564glHdXMQOiVoZFjWakc/NosaVG6W4XlwBK1PZXc4gIiISV3h4OGbNmgU/Pz8EBARg06ZNKC0txejRowEAM2fOhIODA2bMmAEAOHHiBLKzs+Ht7Y3s7GwsW7YMKpUKL7zwQm2f+/fvh1qthpubGzIyMrBgwQK4u7vX9gkAu3fvhrW1NZydnXH69Gl8/PHHGDx4MPr27QugutgRGhqK+fPnw9LSEmZmZvjwww/RrVs3bhJORBpjIJVg0gAPDPVzxDs7knDwHwXm7z6F6BNX8UloAPzaWoodIhEREZHo7M3lmDKoIxbsOY35u0/hYR9HmBrxY2ZqOfzX1ows5IZob2OCi4oSpGYWoE/HhpfuICIi0hYhISHIy8vD0qVLkZubC29vb6xdu7Z2+anMzExIJLeeTi4vL8fixYtx6dIlmJiYYMCAAViwYEGdJaIKCwuxaNEiZGVloU2bNnj44Ycxffp0GBoa1rbJzc3F/Pnza5eTevzxx/Hyyy/Xie2dd96BRCLBq6++ioqKCvTt2xezZ8/W8BUhIgLa25giKqInvj92GR/+nIaUqwUY8cUBvNDPHdMHd4axjE/AExERUev2fB83fBN3CRl5JVj11znMeNhT7JCoFWFRo5n5OlvgoqIEyVfyWdQgIiKdEBYWdtvlpjZv3lzn56CgIMTExNyxv5CQEISEhNyxzYQJEzBhwoQ7tjEyMsLs2bNZyCAiUQiCgDEPtMODnvaYuysV0Seu4su/07E7ORMfj/JHv05N29+HiIiISJ/IDaV4J8Qbk6OOYfXf6Rj7QDu0s276XmRE96LpC8PSHfk6V09J574aRERERES6z87cCMvGd8P65x6As6Ucl/JK8cy6OMzYdgLXiyvEDo+IiIhINI/4OiDYwwYVVSrM331K7HCoFWFRo5nd2iw8X+RIiIiIiIiouQzycsCvrw/Ac8EdIAjA9oTLGLzoL/yYeAVqtbq2nVKlxuF0BfZnlOJwugJKlfoOvRIRERHpLkEQ8L/hPpAIwM9JmTicrhA7JGolWNRoZjUzNdKvFaO4vErkaIiIiIiIqLmYGRlgzghfbH8pGJ0dzKAorsBr3yTi+Y3xuHKjFHuSM9H3k314el08Fh/Jx9Pr4tH3k33Yk5wpduhEREREGuHlaIHxQa4AgLnRqXygg1oEixrNzM7cCA4WRlCrgVNZXIKKiIiIiEjfBLpaYdcr/fD6kM6QSSX443QuBn72JyZHJSAzv6xO26z8MrwUlcDCBhEREemt14d0hoXcAKmZBfju6CWxw6FWgEUNDeC+GkRERERE+k1mIMGrD3VCzGv98ED7NqioUjXYruZZxUg+uUhERER6ysbMCK8N7gwA+PSX0ygoqxQ5ItJ3LGpoQM2+GslXuK8GEREREZE+62hvhteHeN6xjRpAZn4Z/jqT2zJBEREREbWwCb3bw8POFIriCnyx7x+xwyE9ZyB2APqIMzWIiIiIiFqP3KLyRrV7fmM87M2N4G5nCnc7M7jbmsLDzgzudqZwsTKBVCJoOFIiIiIizTCUSvDeYz4I3xCPDQfPY3yQK9xsTcUOi/QUixoaUDNT40x2ISqqVJAZcEIMEREREZG+sjeXN7ptTmE5cgrLcTg9r85xmVSCDrYmcLc1u1X0sDOFh60ZLE0MmztkIiIiomY30NMeAz3t8MfpXHz0cyrWPttD7JBIT7GooQEuVsawNDZEfmklzmQXwq+tpdghERERERGRhgS5WcPJUo6s/DI0tGuGAMDRUo6fX+2Hi4pipOcWI/1aUfWfucU4ryhGRZUKZ7KLcCa7qN75Nqay6kLHzYJHzeyOdtYmMJTyASoiIiLSHu895oP9Z//G3rQc/H0mF/0724kdEukhFjU0QBAE+Dpb4NA5BVKvFrCoQURERESkx6QSAbOH++ClqAQIQJ3CRs2CUrOH+8DaVAZrUxm6uVrVOV+pUuPqjVKcyy3CudxipOcW1RY+sgvKoSiugKK4AvEXrtc5z0AiwNWmenaHh51pnWWtrE1lEITmWc5KqVLjcLoC8RmlKLNQoJeHHZfKIiIiogZ52JlhQu8OWH/wPD7YlYrdr/WDAR/CoGbGooaG1BQ1Uq7mA2gndjhERERERKRBQ/2csDIsEJHRqcjML6s97mgpx+zhPhjq53Tbc6USAe2sTdDO2gQP/mfP8aLyKpy/WeCoKXicyy3G+WtFKKtU1c722JtW9zxLY8ObhQ6z2lkeHnamcLUxgZGBtNHj2pOcWXdMR+Lh1IgxERERUev12kOdsDPxCs7mFGHLkQw8G9xB7JBIz7CooSE1m4Unc7NwIiIiIqJWYaifE4b4OOLwuVzEJ59BD7/O9z2rwczIAP4ulvB3qTv7W6VSI7Og7NasjtwipF+rLnBcuVGK/NJKJGTcQELGjTrnSQSgnbUJ3G3/tW/HzT/tzIzqzO7Yk5yJl6IS6i2plZVfhpeiErAyLJCFDSIiIqrH0sQQrw/pjPd2JmPRb2cwooszrExlYodFeoRFDQ2p2Sw8LbMASpWa07OJiIiIiFoBqURAL3cbyAuM0dXdRmO/B0gkAtq2MUbbNsbo16nuWtWlFUqcv1aMc/9axqqm8FFcocRFRQkuKkrwx+ncOueZGxnULmHVwcYEGw5daHCPEDWql9WKjE7FEB9H/q5DRERE9YwPckXU4Ys4lVWIxXvPIPJxP7FDIj3CooaGuNuZQW4oQUmFEhcUxfCwMxM7JCIiIiIiagWMZVL4OFvA5+aDVjXUajVyCstrix3/Lnpcvl6KwvIqnLicjxOX8+/6HmoAmflliDufh94eNhoaCREREekqqUTA/4b74Kk1RxB1JANP92qPzg7mYodFeoJFDQ2RSgR4O1ngeMYNJF/JZ1GDiIiIiIhEJQgCHCzkcLCQI9jDts5rZZXVMzhqlrH683ROvY3JG5JTWHbXNkRERNQ6BXvYYqivI/akZOGDXan46vmgOktdEt0rbj2vQTVLUKVyXw0iIiIiItJickMpPB3N8ai/E6YM7IjXh3je/SQA9uZyDUdGREREuuydEG/IpBLsP3sNv6fliB0O6QkWNTTI7+Zm4SksahARERERkQ4JcrOGk6Ucd3qW0sZUhiA36xaLiYiIiHSPq40JIvq5AQA+/DkV5VVKkSMifcCihgb53ixqJF/Nh1rd0BZ7RERERERE2kcqETB7uA8A3Lawcb2kAptjL/B3HSIiIrqjKQM7ws7cCBcUJdh06ILY4ZAeYFFDgzo7msFAIuBGSSWu5nOtWSIiIiIi0h1D/ZywMiwQjpZ1l5hytJQjyM0aKjUwJzoVs7af5FOXREREdFtmRgaY+Uj10pZLf/8HuYXlIkdEuo5FDQ0yMpCik4M5ACDlSr7I0RARERERETXNUD8nHJg1CFsiemBaT0tsieiBg7MG4duJvfBuiDckArDt6GWM+/Iwcgr4IBcRERE1LDTQBQEuligqr8LCX0+LHQ7pOBY1NKxms/Bk7qtBREREREQ6SCoR0MvdBv1cjdHL3QZSiQBBEPBif3dsCA+ChdwAxzNuYPgXB5B46YbY4RIREZEWkvxractvj15CMh8Ap/vAooaG1RQ1Uq/yP1QiIiIiItIvAzrb4aepfdHR3gzZBeUYuzoW249dFjss0nNbtmzBoEGD4O/vjzFjxuDkyZO3bXv27Fm88sorGDRoEDw9PbFx48Z6bZYtWwZPT886X0OHDtXgCIiIWqfu7a0xoosz1GpgbnQq9+Wie8aihob5ta3eLDyFMzWIiIiIiEgPdbA1xY6XgzHY2wEVVSrM+O4EPtyViiqlSuzQSA/FxMRg3rx5mDJlCnbs2AEvLy9ERERAoVA02L60tBQuLi6YMWMG7Ozsbttvp06dcODAgdqvrVu3amoIRESt2luPekFuKEHchTzEJGWJHQ7pKBY1NMzbyQKCAGTml0FRxE1wiIiIiIhI/5jLDfHlM93x6qCOAIC1B84jfGM8bpRUiBwZ6ZsNGzZg7NixCA0NRceOHREZGQm5XI7t27c32D4gIACzZs3CsGHDIJPJbtuvVCqFnZ1d7Ze1tbWmhkBE1Ko5tzHG5AEeAICPY9JQVqkUOSLSRSxqaJiZkQE62JgC4GwNIiIiIiLSXxKJgNcf9sSKpwNhbCjF/rPXMOKLgziTXSh2aKQnKioqkJKSguDg4NpjEokEwcHBOH78+H31ffHiRfTt2xcPPfQQZsyYgatXr95vuEREdBuT+nvA2VKOKzdKsebvdLHDIR1kIHYArYGvswXOXytGytUC9O98++muREREREREui7E3wlutqZ48aujyMgrwajlB7Hoya54xNdR7NBIx12/fh1KpRI2NjZ1jtvY2CA9/d4/FAsICMC8efPg5uaG3NxcLF++HE8//TSio6NhZmbWpL6UyuZ74rimr+bsU2wck+7Qx3FxTNpDJgVmDvXEtG9PYMWf5zC6mzMcLeUAdHdMd6OP49LEmBrbV5OLGvHx8Vi3bh2Sk5Nrb/aDBw9ucoCtia+zJXadzEQyNwsnIiIiIqJWwNvJAj9N7YspWxIQm67ApM3HMH1wZ7wyqCMkEkHs8IjqGDBgQO33Xl5e6NKlCwYOHIjdu3djzJgxTeorKSmpucPTSJ9i45h0hz6Oi2PSDi5qNbxsDHFKUYm3vzmM13q2qfO6Lo6pMfRxXGKMqclFjZKSEnh6eiI0NBRTp07VREx6x9fZAgCQyuWniIiIiIiolbA2leGriCB89HMaNh66gM/3nkFaZgEWju0CUyMuGkBNZ2VlBalUWm9TcIVCAVtb22Z7HwsLC3To0AEZGRlNPtff3x9SqbRZ4lAqlUhKSmrWPsXGMekOfRwXx6R95tvnY9TKWPydUYZXH+2Abq5tdH5Mt6OP49LEmGr6vJsmZ5IDBgyo8xQD3V1NUeP8tWIUllXCXG4ockRERERERESaZyiVYM4IX3g7meO9ncnYk5KFCyuL8eUzD8DVxkTs8EjHyGQy+Pr6IjY2tnbFCJVKhdjYWISFhTXb+xQXF+PSpUuws2v68tFSqbTZP6zSRJ9i45h0hz6Oi2PSHl1drfFEoAu+O3YZH8Scwo6XglEzDF0d093o47jEGBM3Cm8BNmZGcLq5LlxaJjfJIyIiIiKi1uXJHq74ZmJv2Jkb4VRWIUYsP4CD/1wTOyzSQeHh4di2bRt27NiBc+fOYc6cOSgtLcXo0aMBADNnzsTChQtr21dUVCAtLQ1paWmoqKhAdnY20tLScPHixdo2n3zyCeLi4nD58mUkJCRg6tSpkEgkeOyxx1p8fERErc2bQz1hKpPixKUb2Jl4RexwSEeINudXnzZFaQwfJ3Nk5pch6fJ1dHe1rD2uj5vE6Apee3HwuouH1148Ym6eRUREpC26t7dC9NS+mLT5KE5czseE9XF4b5g3ngvuAEHgPhvUOCEhIcjLy8PSpUuRm5sLb29vrF27tnb5qczMTEgkt57fzMnJwciRI2t/Xr9+PdavX4+goCBs3rwZAJCVlYXXX38dN27cgLW1Nbp3745t27bB2tq6RcdGRNQa2ZvLMXVQJ3yy5xTmxaTBUi5FUkYpyiwU6OVhByn34qIGiFbU0MdNUe7ERlICADiQchFdTW7Ue721XQ9twmsvDl538fDai4fXnoiIWjtHSzm+ndQb7/yQhB+OX0FkdCpSrxbgw1F+MDLQr6UYSHPCwsJuu9xUTaGihouLC06fPn3H/j7//PNmi42IiJru+b4dsP5AOnKLKhDxVUL1wSPxcLKUY/ZwHwz1cxI3QNI6ohU19GlTlMbIlWVjW+pxZJYZoGvXrrXH9XGTGF3Bay8OXnfx8NqLR8zNs4iIiLSN3FCKhWO7wMfZAh/HpOG7Y5dxNqcIq5/pDgcLudjhERERUQv741QOcosq6h3Pyi/DS1EJWBkWyMIG1SFaUUMfN0W5E/92VgCAf3KKUKVGvaeQWtv10Ca89uLgdRcPr714eO2JiIiqCYKAF/q5w9PRHFO3HkfipRsYvuwAVj/THd1crcQOj4iIiFqIUqVGZHRqg6+pAQgAIqNTMcTHkUtRUa0mbxReXFxcu8kWAFy+fBlpaWm4evVqswenT5wt5bAyMUSVSo0zWUVih0NERERERCS6fp3s8OOUPuhkb4acwnI8ufowvj92WeywiIiIqIXEnc9DZn7ZbV9XA8jML0Pc+byWC4q0XpOLGsnJyRg5cmTtRlvz5s3DyJEjsXTp0uaOTa8IggBf5+oNwpOv5oscDRERERERkXboYGuKHVP6YIiPAyqUKrzx3QnMjU5FlVIldmhERESkYTmFty9o3Es7ah2avPxUz54977rJFjXM19kCB/65hhQWNYiIiIiIiGqZGRlgdVh3LP79LJb+fhbrD57H6ewCfDE+EFamMrHDIyIiIg2xN2/cflqNbUetQ5NnatC9821bPVMj5WqByJEQERERERFpF4lEwOtDOmNVWCBMZFIc/EeBx5cfxOmsQrFDIyIiIg0JcrOGk6Ucd9otw87MCEFu1i0WE2k/FjVakK+zBQAgLbMASpVa5GiIiIiIiIi0z1A/J/zwcjDaWRsjI68Eo1YcxJ7kLLHDIiIiIg2QSgTMHu4DALctbJRVKZGRV9JyQZHWY1GjBbnZmMJEJkVZpQrpudwsnIiIiIiIqCFejhb4aUpfBHvYoKRCiclRx7B47xmo+HAYERGR3hnq54SVYYFwtKy7xJSDhRFc2hijsKwKYWuPIDO/VKQI6b+UKjUOpyuwP6MUh9MVLf4Af5P31KB7J5EI8HGywNGL15FytQCdHMzFDomIiIiIiEgrWZnK8NXzQfgoJg0bDl7A4r1nkZZZgIVju8LMiL/KEhER6ZOhfk4Y4uOIw+dyEZ98Bj38OqOXhx3yiiswdnUszl8rxjPr4rBtUm9Yc78tUe1JzkRkdCoy829u3n4kHk6Wcswe7oOhfk4tEgNnarSwmiWouFk4ERERERHRnRlIJZg93BcLngiATCrBLynZGL3iIC4qisUOjYiIiJqZVCKgl7sN+rkao5e7DaQSAXbmRoh6oSecLOX4J6cIz66PQ2FZpdihtlp7kjPxUlTCrYLGTVn5ZXgpKgF7kjNbJA4WNVqYr3P1ZuHJV7hZOBERERERUWOMfaAdvpnUC/bmRjiTXYQRXxzEgbPXxA6LiIiIWkDbNsbYHNET1qYyJF3JR8SmoyirVIodVqujVKkRGZ2KhhaaqjkWGZ3aIktRsajRwnzb3pqpoVZzPVgiIiIiIqLGCHS1QvQrfdGlXRvkl1ZiwvojWHfgPH+vIiIiagU62pvhq+eDYG5kgLjzeXh5SwIqlSqxw2pV4s7n1Zuh8W9qAJn5ZYg7n6fxWFjUaGGd7M1hKBVQUFaFy9e5uQ0REREREVFjOVjI8e3EXggNdIFKDXywKxVvfHeST2sSERG1An5tLbHuuR4wMpBg36kcvPHdCahaeIPq1iyn8PYFjXtpdz9Y1GhhMgMJOt/cIJz7ahARERERETWN3FCKz8YE4P3HfCARgO0Jl/Hkl4eRXaD5X6CJiIhIXEFu1lgV1h0GEgE/Jl7F/35K5qzNFmJvLm/WdveDRQ0R3NosnPtqEBERERERNZUgCIjo64avnu8JS2NDnLh0A8OXHUBCxnWxQyMiIiING+hlj8+f7ApBAKIOZ+CzX0+LHZLeU6vVd82zBABOlnIEuVlrPB4WNUTg17Z6s3AWNYiIiIiIiO5d3062+GlqH3R2MENOYTnGrT6MbUcviR0WERERadjwLs74aKQ/AGD5H+ew+q9zIkekv1Q3Nwj/9JdbxSPhP21qfp493AdSyX9fbX4saoigZqZG8hUuP0VERERERHQ/2tuY4oeX++ARXwdUKFWY+f1JREanoIqbhxIREem1p3q6YtZQLwDAvN2n8HVchsgR6Z+ySiVe+fo4Nh66AAB4b5g3VoUFwtGy7hJTjpZyrAwLxFA/pxaJy6BF3oXq8HK0gCAAOYXluFZULnY4REREREREOs3MyAArn+6OpfvOYvHes9hw8AJOZxVi+VOBsDKViR0eERERachLD3qgoKwSK/88h3d2JMFcboDHApzFDksvFJRVYuJXR3E4PQ+GUgELx3bFiC7V13aIjyMOn8tFfPIZ9PDrjF4edi0yQ6MGZ2qIwNTIAO62pgC4BBUREREREVFzkEgETBvcGavCusNEJsWhcwqMWH4Ap7L4OxcREZE+m/mIJ57u6Qq1Gpj+bSL+PJ0jdkg6L7ugDGNXxeJweh7MjAywMTyotqABAFKJgF7uNujnaoxe7jYtWtAAWNQQja8z99UgIiIiIiJqbkP9HLHj5T5wtTbBpbxSjF5xCHuSM8UOi4iIiDREEATMfdwPw7s4o1KpxuSoY4i/kCd2WDrrn5xCjF5xCKeyCmFnboRvJ/VCn462YodVB4saIqnZVyM1k0UNIiIiIiKi5uTpaI6fpvZB3462KKlQYnJUAhb9dgYqlRoAoFSpcThdgf0ZpTicroDy5nEiIiLSTVKJgEVju2Cgpx3KKlV4fkM89zO+B8cuXscTq2Jx5UYp3G1N8cNLwbUP52sTFjVE4te2ZqZGociREBERERER6Z82JjJsDO+B5/u4AQCW/n4Wk6KOYcfxK+j7yT48vS4ei4/k4+l18ej7yT7O5iAiItJxhlIJVjzdHUFu1igsr8Kz6+NwLrdI7LB0xm+p2Xh67WHcKKlE13Zt8P1LwWhnbSJ2WA1iUUMkNTM1MvJKUFypEjkaIiJqzbZs2YJBgwbB398fY8aMwcmTJ2/btrKyEl988QUGDx4Mf39/jBgxAn///XedNkVFRfjoo48wcOBABAQEYNy4cfX6LC4uxty5c9G/f38EBAQgJCQEX3/9dZ02ubm5ePPNN9GnTx907doVo0aNwi+//NJ8AyciIr1nIJXgf8N98OkTAZBJJfgtNRvTv01EZn5ZnXZZ+WV4KSqBhQ0iIiIdZyyTYt2zD8CvrQUUxRV4Zu0RXLlRKnZYWu/ruAxM2nwUZZUqDPS0w9YXe8LaVCZ2WLfFooZI2pjI0LaNMQDgwo0qkaMhIqLWKiYmBvPmzcOUKVOwY8cOeHl5ISIiAgqFosH2ixcvxrfffov3338fMTExGDduHKZOnYrU1NTaNu+99x4OHTqEBQsWIDo6Gn369EF4eDiys7Nr28yfPx/79+/Hp59+ipiYGDz77LP44IMP8Pvvv9e2mTVrFs6fP4+VK1ciOjoaQ4YMwbRp0+q8FxERUWOMeaAdtr7YE7fbw7Jm8anI6FQuRUVERKTjzOWG2BQeBA87U1zNL8Mza4/gWlG52GFpJbVajSV7z+LtH5KgUgNjH3DBmgkPwERmIHZod8Sihoh8nMwBAL+nl3AdVyIiEsWGDRswduxYhIaGomPHjoiMjIRcLsf27dsbbP/jjz9i8uTJGDBgANq1a4ennnoKAwYMwPr16wEAZWVl+PXXX/Hmm2+iR48eaN++PV555RW0b98eW7dure3n+PHjGDlyJHr27AkXFxc8+eST8PLyqjOj4/jx4wgLC0NAQADatWuHl19+GRYWFkhJSdHsRSEiIr1UqVTjTr9yqQFk5pch7jw3FiUiItJ1NmZG2BzRE23bGCP9WjEmrItDfmml2GFplSqlCu/sSMbne88AAF4Z1BGfhAbAQKr9JQPtLrnosT3JmYhNr06W/8oow1/r4uFkKcfs4T4Y6uckcnRERNQaVFRUICUlBZMmTao9JpFIEBwcjOPHjzd4TmVlJWSyulNQjYyMkJCQAACoqqqCUqmEkZHRbdsAQLdu3bBv3z488cQTsLe3x5EjR3D+/Hm8/fbbddrs3r0bDz74ICwsLLB7926Ul5cjKCioSeNUKpVNat+YvpqzT22gj+PimHSDPo4J0M9x6cOYsvJLGt1OqWyj2WA0RBN/T7r8d05ERK2bcxtjRL3QE2NWHUJqZgFe2BSPr57vCWOZVOzQRFdWqcQrXx/Hb6nZEARg7uN+eKZXe7HDajQWNUSwJzkTL0Ul4L8PCdWs47oyLJCFDSIi0rjr169DqVTCxsamznEbGxukp6c3eE7fvn2xceNG9OjRA66uroiNjcVvv/1W+4GHmZkZunXrhhUrVsDd3R22trbYtWsXEhMT4erqWtvP+++/j/fffx/9+/eHgYEBBEHAhx9+iB49etS2Wbx4MaZPn46ePXvCwMAAcrkcX3zxBdq3b1qilZSU1KT2YvWpDfRxXByTbtDHMQH6OS5dHlNBTuOWnSjIvoTExBwNR6NZuvz3RERE1JzcbE3x1fM98eSXsYi/cB2To45hzYQHIDPQ/tkImnKjpAIRm47i2MXrkBlIsHRcV537LJpFjRamVKkRGZ1ar6ABVE93FlC9jusQH0dIb7fgKxERkUjeffddvPfee3j00UchCALatWuH0aNH11muasGCBXjnnXfQv39/SKVS+Pj4YNiwYXWWjdq8eTMSExOxcuVKODs74+jRo4iMjIS9vT2Cg4MBAEuWLEFBQQE2btwIKysr7N27F9OmTcOWLVvg6enZ6Jj9/f0hlTbPkzhKpRJJSUnN2qc20MdxcUy6QR/HBOjnuPRhTP4qNVYe/xPZBeUN/j4GAAYSAT6endG1vVWLxtZcNPH3VNMnERGRrvJxtsDG8B4IWxuHv87kYvq2RCwd161VfvZ65UYpnl0fh39yimAhN8DaZ3sgyM1a7LCajEWNFhZ3Pg+Z+WW3ff3f67j29rC5bTsiIqL7ZWVlBalUWm9TcIVCAVtb2wbPsba2xooVK1BeXo4bN27A3t4en332Gdq1a1fbxtXVFVFRUSgpKUFRURHs7e0xbdq02jZlZWX4/PPP8cUXX+DBBx8EAHh5eSEtLQ3r1q1DcHAwMjIyEBUVhV27dqFTp061bY4ePYotW7Zg7ty5jR6nVCpt9g/gNNGnNtDHcXFMukEfxwTo57h0eUxSKTBnhC9eikqAADRY2KhSqfHkmiN4vo8bXn+4s9Zvknk7uvz31BhbtmzBunXrkJubCy8vL7z//vsICAhosO3Zs2exdOlSpKSk4MqVK3j77bfx3HPP3VefRESke7q3t8aqZ7rjhU3x+PlkJizkBvh4lD8EofUUNk5lFeDZ9XHILiiHk6Ucm54PQmcHc7HDuietd56NSHIKb1/Q+LdNhy7goqJYw9EQEVFrJpPJ4Ovri9jY2NpjKpUKsbGx6Nat2x3PNTIygoODA6qqqvDrr7/ioYceqtfGxMQE9vb2yM/Px4EDB2rbVFVVobKysl7yKJVKoVZXf8RUWloKoHqPj9u1ISIiaqqhfk5YGRYIR0t5neNOlnJ89kQARndrC5UaWHvgPIYu3o9D/1wTKVK6nZiYGMybNw9TpkzBjh074OXlhYiIiHoPadQoLS2Fi4sLZsyYATs7u2bpk4iIdNOAznZYMq4bJALwddwlzN99qtX8fnk4XYExq2KRXVCOTvZm2P5SsM4WNADO1Ghx9ubyuzcCsCclC3tSstCjgxVGB7ogxN8JlsaGGo6OiIham/DwcMyaNQt+fn4ICAjApk2bUFpaitGjRwMAZs6cCQcHB8yYMQMAcOLECWRnZ8Pb2xvZ2dlYtmwZVCoVXnjhhdo+9+/fD7VaDTc3N2RkZGDBggVwd3ev7dPMzAxBQUH49NNPIZfL4ezsjPj4eOzcuRNvvfUWAMDd3R3t27fH//73P8yaNQtt2rTB3r17cfDgQaxevbqFrxIREemToX5OGOLjiMPnchGffAY9/Dqjl4cdpBIBTzzQDsO7OuPdH5KQkVeCp9YewZMPtMM7w7z5+5iW2LBhA8aOHYvQ0FAAQGRkJP78809s374dEydOrNc+ICCgdsbFwoULm6VPIiLSXSH+Tpg32h+ztidh9d/psDA2xJSBHcUOS6NikjIx7ZtEVChVCOpgjTUTHoCliW7nNSxqtLAgN2s4WcqRlV/W4HRnAYCliSH8nC1w6JwC8ReuI/7Cdcz+KQUP+zggNNAF/TrZwkDKSTZERHT/QkJCkJeXh6VLlyI3Nxfe3t5Yu3Zt7fJTmZmZdWZLlJeXY/Hixbh06RJMTEwwYMAALFiwABYWFrVtCgsLsWjRImRlZaFNmzZ4+OGHMX36dBga3kqaFi1ahEWLFuGNN95Afn4+nJ2dMX36dIwfPx4AYGhoiC+//BILFy7E5MmTUVJSAldXV8yfPx8DBgxooatDRET6SioR0MvdBvICY3R1t6mzpvZAT3v8+voAfLrnFDbFXsS3Ry/hj9M5+GCkHx7xdRQxaqqoqEBKSgomTZpUe0wikSA4OBjHjx/Xij6VSuU9xXGnvpqzT7FxTLpDH8fFMemGlhjTE4FtkV9SgY93n8anv5yGuZEUT/d01dj7AeL9XX0VexFzf06DWg087OOAz8cGQG4oaZY4NDGmxvbFokYLk0oEzB7u0+A6rjVp9PzR/hjq54Ss/DL8mHgF2xMu40x2EXadzMSuk5mwNTPCyK7OGB3oAh9niwbehYiIqPHCwsIQFhbW4GubN2+u83NQUBBiYmLu2F9ISAhCQkLu2MbOzg7z5s27Y5sOHTpg2bJld2xDRESkCWZGBoh83A+PdXHGrO0nkZ5bjEmbjyHE3xFzRvg2egY+Na/r169DqVTCxqbu/pM2NjZIT0/Xij41sam6Pm7UzjHpDn0cF8ekGzQ9pu5mwBPepvg+rRizf0pFXvYV9HM11uh7Ai33d6VWq7E1uQg/nKre3uARD2NE+ACnUvTjPsWihghq1nGNjE6ts2m4o6Ucs4f7YKifU+3PkwZ4YGJ/d6RcLcD2hMv4KfEqrhWVY+2B81h74Dy8nSwQGtgWI7o6M7EmIiIiIiJqRj06WCPm1X5Ytu8sVv2VjpikLBz8R4H3H/NBaGDbVrW5KDWOv79/s23SrlQqkZSU1Kx9io1j0h36OC6OSTe05Ji6dFHDeFcaNh/OwBfxBfDt7IFBXvYaea+WHFelUoV3diTXFjReH9wJLz/o3ux5iybGVNPn3bCoIZI7reP6X4IgwK+tJfzaWuKdEG/8dToX2xMu4/e0HKRlFuDDnwswb/cp9O9ki9GBLhji4wC5oX78j4yIiIiIiEhMckMp3nzECyH+Tpi1/SSSrxTgje9O4MfEK/h4lD/aWZuIHWKrYWVlBalUWm8Db4VCUbt0pth9SqXSZv+wShN9io1j0h36OC6OSTe01JgiR/ihqFyJHcevYOrXidj0fBB6udvc/cR7pOlxFZdXYcrW4/jzdC6kEgHzRvljbI92Gns/QJx/f9yYQUQ167j2czVGr/+s43o7hlIJBvs4YGVYd8S9+xA+HOmHbq5toFSp8cfpXLzy9XH0+Ggv3v7hJI5eyINa3dDOHURERERERNQUvs6W2PlyH7z1qBeMDCTYf/YaHln8NzYcPA+lir93tQSZTAZfX1/ExsbWHlOpVIiNjUW3bt20pk8iItIdEomABU8EYLC3PcqrVHhh01GcvHxD7LDuiaKoHE+tOYw/T+dCbijBmgndNV7QEAuLGjqsjYkMYb3aY8fLfbBvxgBMHdgRbdsYo7CsCl/HXcITq2Lx4Gd/YvHeM8hQlIgdLhERERERkU4zkEoweYAHdr/WD0Fu1iipUCIyOhVjVh3C2exCscNrFcLDw7Ft2zbs2LED586dw5w5c1BaWorRo0cDAGbOnImFCxfWtq+oqEBaWhrS0tJQUVGB7OxspKWl4eLFi43uk4iI9JuhVIIvngpEb3cbFJVX4dn1cfgnR7fu6xmKEoSuPIQTl/NhZWKIr1/shUFeDmKHpTFcfkpPuNuZ4Y1HPPH6kM44cj4P2xMuY3dSJi4qSrB471ks3nsWQR2sMTqwLUICnGAhNxQ7ZCIiIiIiIp3kbmeGb17sha1xGZi/+xQSMm5g2NIDeGVQR0wa4AGZAZ8f1JSQkBDk5eVh6dKlyM3Nhbe3N9auXVu7VFRmZiYkklvXPycnByNHjqz9ef369Vi/fj2CgoKwefPmRvVJRET6T24oxZpnH8DTaw7jxOV8hK2Nw3eTe+vEMpPJV/Lx3IY4XCuqgIuVMTY9HwQPOzOxw9IoFjX0jEQioLeHDXp72GDu4774JSULPyRcwYF/riHuQh7iLuRh9k8peNjXEaGBbdG3oy0MpEy4iYiIiIiImkIiERDWqz0e8rbHuzuSse9UDhb+dgY/J2Xik9AAdGnXRuwQ9VZYWBjCwsIafK2mUFHDxcUFp0+fvq8+iYiodTAzMsDG8CCMXR2LszlFeGbdEWyb3Bv25nKxQ7ut/WdzMXnzMRRXKOHtZIFN4T1gb6G98TYXfpqtx0xkBhjVzQWbI3ri0FuDMGuoFzram6G8SoXoE1fx3IZ49J6/Dx/9nIpTWQVih0tERERERKRznCyNse7ZB7BkXFdYm8pwKqsQo1YcxMcxaSitUIodHhERETWBlakMmyN6wsXKGBcUJZiwLg75JZVih9WgncevIHxDPIorlAj2sMG2Sb1aRUEDYFGj1XCyNMZLD3rgt+n98dPUPnguuAOsTAyRW1iONfvPY+ji/QhZsh/rDpxHbmG52OESERERERHpDEEQ8HjXtvhten883tUZKjXw5d/pGLrkbxw6d03s8IiIiKgJHC3l2PJCT9iZG+FUViHCN8ahpKJK7LDqWPN3OqZ9m4gqlRrDuzhjQ3gPmLei7QZY1GhlBEFAgEsbzBnhiyPvDMaXz3THUF9HGEoFpGYW4INdqeg173c8vzEeP5/MRFllw08WKVVqxJ5T4MfEK4g9p4BSpW7hkRAREREREWkXGzMjLBnXDeufewBOlnJcVJTgqTVH8PYPJ5Ffqp1PeRIREVF97W1MERXRE5bGhkjIuIFJm4+hvEr8GZgqlRof7ErFRzFpAICIvm5Y8mRXGBlIRY6sZXFPjVZMZiDBw76OeNjXEdeLK7Dr5FVsT7iCxEs3sO9UDvadyoGF3ACPdXFGaGBbBLpaQRAE7EnORGR0KjLzy2r7crKUY/ZwHwz1cxJxREREREREROIb5OWAX6db45M9pxB1OANfx13CvlM5+HCkP4b4OIgdHhERETWCp6M5Nob3wNNrj2D/2WuY9k0ilo3vJtr+xOVVSrzx3UlEn7gKAHg3xBsv9ncXJRaxcaYGAaheL+6Z3h2wc0of7H19AKYM9ICzpRwFZVXYeiQDoStjMfCzP/HK1gRMjkqoU9AAgKz8MrwUlYA9yZkijYCIiIiIiEh7mMsN8eFIf3w7sRfcbE2RXVCOF786iqlbE3CtiEv+EhER6YJurlZYM+EByKQS7E7Owts/JEElwoo1hWWVCN8Qj+gTV2EgEbD4ya6ttqABsKhBDehob4Y3H/HCgVmDsPWFnhgd2BYmMikuKEoQfbLhokXNf8qR0alcioqIiIiIiOimnu422P1aP7z0oAekEgG7TmZi8KK/8EPCZajV/N2JiIhI2/XpaIul47tBIgDfHbuMj2LSWvQenlNQhidXH8ahcwqYyqTYEN4DI7u1bbH310YsatBtSSQCgjvaYtHYroh/dzBeftDjju3VADLzyxB1+CKuF1e0TJBERERERERaTm4oxayhXvhxSh/4OFngRkklXt92As9tiMfl6yVih0dERER3MdTPEQue6AIAWHfgPJbt+6dF3vdcbhFGrzyE1MwC2JrJ8O2k3ujXya5F3lubcU8NahRTIwN4Opo3qu3sn1Iw+6cUtDExhJutKdxsTeFua4oON793szWFiYz/9IiIiIiIqHXxa2uJH6f2wZd/p2PJ72fx15lcPPz535g11AvP9GoPiUQQO0QiIiK6jSe6u6CwrBKR0alY9NsZWMgN8FwfN4293/GM63h+Yzyul1Sig40Jvnq+J1xtTDT2frqEnyxTo9mbyxvVzsbUEIriStwoqcTxjBs4nnGjXhtHC3l1gcOuuuBRU+xoZ20CQ5E22yEiIiIiItI0Q6kEUwZ2xFA/R7y1/STiL1zH7J9SEH3iKuaHBqCjvZnYIRIREdFthPdxQ35pJRbvPYs50akwlxsitLtLs7/PvlPZeHlLAsoqVejiYol1z/WArZlRs7+PrmJRgxotyM0aTpZyZOWXoaFV4wQAjpZyHJg1CBVVKlxQFOP8tfpfecUVyCooQ1ZBGWLTFXX6kEoEtLMyvlnkMKtT9HC0kDfbk0tKlRqH0xWIzyhFmYUCvTzsIOVTUURERERE1EI87Mzw7cTe2HLkIubvPoWjF68jZMl+vDa4Eyb2d+fDXkRERFrqtYc6Ib+0EhsOXsDM7SdhLjfAw76Ozdb/tvhLeHtHEpQqNQZ0tsOKpwNhasSP8f+NV4MaTSoRMHu4D16KSoAA1Cls1JQDZg/3gVQiwFgmhbeTBbydLOr1c6Okok6RI/1aMc7nVn9fWqnEBUUJLihK8Mfp3DrnyQ0l6GBjCnc7U3SwubmslV118cPKxBCC0LiixJ7kTERGpyIzv6z6wJF4OFnKMXu4D4b6OTX9whAREREREd0DiUTAM707YJC3A97dkYQ/T+fi019OY9fJTCwIDYC/i6XYIRIREdF/CIKA94f5oLCsCt8fu4ypW49jY3gPBHe0va9+1Wo1vtj3Dxb+dgYAEBrogvmh/nzQoQEsalCTDPVzwsqwwLpFAVTP0GhsUaCNiQzdXGXo5mpV57harUZOYTnSc2sKHkW1RY8MRQnKKlU4lVWIU1mF9fq0NDas3bujZlmrmsLHvyuZe5Iz8VJUQr2ZJln5ZXgpKgErwwJZ2CAiIiIiohbVto0xNjzXAzsTr2BudCrSMgvw+PIDeLG/O6YP7gy5oVTsEImIiOhfJBIB80f7o7CsEr+kZOOFr45i64u90LVdm3vqT6lSY/ZPyYg6nAEAePlBD7z5iGejH+JubVjUoCYb6ueEIT6OiDufh5zCMtibyxHkZn3fyzcJggAHCzkcLOTo7WFT57UqpQqXr5femtlxrQgXrpXg/LViXLlRivzSSiReuoHESzfq9etgYQQ3W1O0tzFFTFJmg0tnqVE92yQyOhVDfBy5FBUREREREbUoQRAwqpsL+nWyQ2R0KqJPXMXqv9LxS3IW5ocGoJe7zd07ISIiohZjIJVg6fhuiNh4FAf+uYbnNsTh24m94elo3qR+yiqVmPZNIvakZEEQgDnDffFscAfNBK0nWNSgeyKVCPUKD5pkIJWgg60pOtiaYuB/XiutUOJiXvUSVukN7N+RXVCO7IJyHE7Pu+N7qAFk5pdh9k/J6NHBGrZmRje/ZLAykTXbfh73S6lSN3tBiYiIiIiItIOtmRGWje+GEV2c8d7OJFxQlGDcl4fxVE9XvPWoFyzkhmKHSERERDcZGUix+pnuCFt3BMczbuCZdUfw/eRguNqYNOr8/JJKvPjVUcRdyINMKsHicV0R4s9VZO6GRQ3SecYyKbwcLeDleOf9O35JzsIvqdl37S/qcEbtVK8aUokAa1MZ7MyMYGteXeiwqyl6mMv+VQAxgrWpTGNFhnr7gQA6tR8IN2gnIiIiImqcIT4O6Olujfm7T2HrkQxsPZKBfWk5+HCkHwb7OIgdHhEREd1kamSADc/1wJOrD+N0diGeXncY308OhoOF/I7nXb1Riuc2xOFMdhHM5Qb48pkHWvQhcl3GogbptX/v3+FkadyookZvDxtADVwrKkduUTlulFRCqVIjt7AcuYXlQOadz5cIgLXpzcKH+a3ZHjVFj9pj5jJYm8hg0MjNfnR9PxB92KCds2SIiIiIqCVZyA3x8Sh/DA9wxts/nMQFRQle+OooRnRxxuzhPrAxMxI7RCIiIkL1Z5CbI4IwZnUsLipK8My6I/h2Ym9YmcoabH8muxDPro9DZn4ZHCyMsOn5oAYf2KaG3VNRY8uWLVi3bh1yc3Ph5eWF999/HwEBAc0dG1GzCnKzhpOlHFn5ZQ3uqyGgesPzqIiedT6orlSqoCiqqC1yXCssx7WanwvLca2o5qsC10sqoLpZELlWVN7gpuZ13lMArE1kdWZ83JoNcqsYYm0qw5yfUnV2PxBdL8gAnCUjJl0vJunytSciIiLt0NvDBrtf64/Fe89gzf50/HTiKvafzcXs4b54vKszBEFgzkFERCQye4vqzxWfWHUIZ7KL8NzGeGx5oSeMDaV17tFSiRQTNx9FQVkVOtqbYdPzQWjbxljs8HVKk4saMTExmDdvHiIjI9GlSxds2rQJERER2LNnD2xsOD2GtJdUImD2cB+8FJUAAajzAXtNqj97uE+9xN9QKoGjpRyOlneeMgZUb2ieV1xRXfwoqrhV9Ci8VfioKXgoiiugVgOK4gooiitw+u6TSG6rZj+Ql7ccg6u1CQylEhhIJZBJBRhIJTCUSmAoFaqPSwTIDCQwkNw6Vt1eqNOutn3N9xIJDA1uni+VNHqPEaVKjcho3S3IALpflNHlWTK6XkzS5WtPRERE2sVYJsXbId4YFuCEmd+fxKmsQkz7NhE/Jl7BEB8HLNv3D3MOIiIikbWzNkFURE+MXR2LE5duIHTFQeSXViKroLy6wZH42rbd21th3bMPoI1Jw7M56PaaXNTYsGEDxo4di9DQUABAZGQk/vzzT2zfvh0TJ05s9gCJmtNQPyesDAus9yGpYzMl/AZSCewt5LC/y5p5wM0CSEkFrhVW/Gu2x83CR2H1rJDcm7NCFEXlDRYF/uuXlPuojDSRREBtQcTwZvFE9q/iSE3xpLSiqs61/q/agkzUMbS1MoGhVIBUUt2fgUSo7k8igVQi3Hyt+j0MbrYxlAg3X5PcPE+Awc02dc+7extBqF9U0fWijC4XZHQ5dkD34yciIiLtFODSBtGv9MXqv85h6e//4I/TufjjdG69dsw5iIiIxNHJwRwbw4MwdnUsTmcX3bbds73bs6Bxj5pU1KioqEBKSgomTZpUe0wikSA4OBjHjx9v9uCINGGonxOG+Dji8LlcxCefQQ+/zqJMzTaQSmBvLoe9+d0LIAf/uYan1x65a7uRXZ1hbyFHpVKFSqUKVUo1Km7+WX2s+s8qlQqVVWpUqu7S7ub3FUpVvfdSqYHyKhXKq+q/di8as9+JpkkE3Cqm3CyCKFVq3CitvO05NUWZRxb/BUtjGSQCIAgCJAIgEQRIBAFC7fe4+fO/v8dd20gkTeuzJgYAWHfg/G0LMgAw8/uTuHKjFFJBgEQiQED1uYIACKjpq/p74V9ja/AYauICgFtxCED1GP7VXsCtcQt1zq3+U61W490dyXeM/b2dyXCyNL7rf7v/rVUJEO74+r2c898ulCo13tt5+/i1vRhGRERE2s1QKsHUQZ0wxMcBjy07gEpl/ayDOQcREZF4/NpawtTIAOVVFQ2+LgCYt/sUhgU48x59D5pU1Lh+/TqUSmW9ZaZsbGyQnp7epDdWKpVNaq+vaq4Dr0fL69G+DeQFxvBv3wZQq6DNfwU92reBo4URsgsanrFRsx/IglB/jfyPUK1WQ6lSo0r136LHzWKISo3Kquo/K/5VUKlUqpBytQCLf//nru8xsoszHCyNUKVSo0qpRtXNfqtU1e9dqVRVx6CsLsZUH6uJq/r9qs+9dV7Nz/9t29AvfUB1oaaiSoWGbzd39k9OMYDiezhTXAVlVfhgV5rYYdyTa0UVeHz5QbHDuCc1xbDD53LRy/3elm7kfYOIiIjyiitvm9sCt3KOuPN56O3B5aKJiIhaStz5POQV3/4TJt6j7889bRTeHJKSksR6a63E6yEeXbn2z/ga49PY8gZfUwMI85Ej6eSJlg2qAQao+z+WYCs1NhtLoCi9/YwOG2MJnuqkhFQovUvvws0vyX3FqFaroQKgVAFKtfrmn7hZ9ABU6lt/nr5WiVUJBXftc7yvKdpZGkKlBtRqQA117fcqAFADKqirX7t57Nb3DR1X/6dN9TH1zWMqNf71fcPHrxRWIinn9rNManS2NoStifTmebf6qvn1WKWu+VN98/qhTptbf6obOAf/Okd913NrjpVUqpBffvdF18wMBRgZVBfy6rVW3/7HprRtuL36jq9XKNUob0TNIT75DOQF3AyMiIiI7k1O4e2Xeb2XdkRERNQ8eI/WrCYVNaysrCCVSqFQKOocVygUsLW1bdIb+/v7QyqVNukcfaRUKpGUlMTrIQJdu/ZduwJublmYuyvt1uZCqN40+f1hXnjE11G84O7iA1kWpmxNBNDwBu0fjApAdy2NX6lSY+c/f951lkzkuL5aN13wcLoCT6+Lv2u72aO63vNsAU1pbOyrJzygdbEDjY+/h19ndL2PmRq6UpQlIiIizWjMUrZNaUdERETNg/dozWpSUUMmk8HX1xexsbEYPHgwAEClUiE2NhZhYWFNemOpVKoTHyS3FF4P8ejStQ8JaItH/JwRdz4POYVlsDeXI8jNWus+TP+vkIC2WCmRaGyDdk2SSoE5I3zxUlQCBDRclJk93AcyQ9Emvt1WLw87OFnKkZVfdseCjBh7ytyNLscO6H78REREpBuC3KwblXMEuVm3dGhEREStGu/RmtXkNVzCw8Oxbds27NixA+fOncOcOXNQWlqK0aNHayI+IvoPqURAbw8bPN61LXp72OjMB6JD/ZxwYNYgbInogWk9LbElogcOzBqk1QWNGkP9nLAyLBCOlnWr546WcqwMC9TaMUglAmYP9wFQfyPrfxdktPHfkC7HDuh+/ERERKQbmHMQERFpJ96jNavJjxaHhIQgLy8PS5cuRW5uLry9vbF27domLz9FRK2PVCKgl7sN5AXG6OquOwUZoLqwMcTHUedmydQUZHRxlowuxw7ofvxERESkG5hzEBERaSfeozXnntZLCQsLa/JyU0REuq5mloyuqSnIHD6Xi/jkM+jh11lnlj3S1WJSDV2+9kRERKQ7mHMQERFpJ96jNUP7FoEnIqJmp8uzZHS1mFRDl689ERER6Q7mHERERNqJ9+jm1+Q9NYiIiIiIiIiIxLJlyxYMGjQI/v7+GDNmDE6ePHnH9rt378bQoUPh7++P4cOH46+//qrz+ltvvQVPT886XxEREZocAhEREd0HFjWIiIiIiIiISCfExMRg3rx5mDJlCnbs2AEvLy9ERERAoVA02D4hIQEzZszAE088gZ07d+Khhx7ClClTcObMmTrt+vXrhwMHDtR+LVq0qCWGQ0RERPeARQ0iIiIiIiIi0gkbNmzA2LFjERoaio4dOyIyMhJyuRzbt29vsP1XX32Ffv364YUXXoCHhwemTZsGHx8fREVF1Wknk8lgZ2dX+2VpadkSwyEiIqJ7wD01iIiIiIiIiEjrVVRUICUlBZMmTao9JpFIEBwcjOPHjzd4TmJiIp577rk6x/r27Yu9e/fWORYXF4fevXvDwsICvXr1wrRp02BlZdXkGJVKZZPPuVtfzdmn2Dgm3aGP4+KYdIM+jgnQz3FpYkyN7YtFDSIiIiIiIiLSetevX4dSqYSNjU2d4zY2NkhPT2/wnGvXrsHW1rZe+2vXrtX+3K9fPwwZMgQuLi64dOkSFi1ahBdffBHffvstpFJpk2JMSkpqUnux+hQbx6Q79HFcHJNu0McxAfo5LjHGxKIGEREREREREbVaw4YNq/2+ZqPwwYMH187eaAp/f/8mF0JuR6lUIikpqVn7FBvHpDv0cVwck27QxzEB+jkuTYypps+7afGihlqtBqBfU23uhz5OPdIVvPbi4HUXD6+9eDQ5JbPmvkr1aSLn0Nf/jvRxXByTbtDHMQH6OS6OSTfoe85hZWUFqVRab1NwhUJRbzZGDVtb2zqzMu7WHgDatWsHKysrXLx4sdFFDW24PkRERPribvdVQd3Cd96Kigq9nGZDREQkBn9/f8hkMrHD0ErMOYiIiJqPtuQcY8aMQUBAAN5//30AgEqlwoMPPoiwsDBMnDixXvtp06ahrKwMq1atqj02btw4dO7cGXPnzm3wPbKysvDggw9i+fLleOihhxoVF/MOIiKi5nO3vKPFZ2oYGBjA398fEokEgiC09NsTERHpBbVaDZVKBQMDriR5O8w5iIiI7p+25Rzh4eGYNWsW/Pz8EBAQgE2bNqG0tBSjR48GAMycORMODg6YMWMGAGDChAl45plnsH79egwYMAAxMTFITk6uLWgUFxfjiy++wCOPPAJbW1tcunQJn376Kdq3b49+/fo1Oi7mHURERPevsXlHi2clEolEK57uICIiIv3GnIOIiEj/hISEIC8vD0uXLkVubi68vb2xdu3a2uWkMjMzIZFIatsHBgbis88+w+LFi7Fo0SJ06NABy5cvR+fOnQEAUqkUZ86cwc6dO1FYWAh7e3v06dMHr732WpPyCOYdRERELafFl58iIiIiIiIiIiIiIiK6F5K7NyEiIiIiIiIiIiIiIhIfixpERERERERERERERKQTWNQgIiIiIiIiIiIiIiKdwKIGERERERERERERERHpBBY1iIiIiIiIiIiIiIhIJ7CoQUREREREREREREREOoFFDZGsXr0aoaGh6NatG3r37o2XX34Z6enpYofV6nz55Zfw9PTERx99JHYorUJ2djbeeOMN9OzZEwEBARg+fDiSkpLEDkvvKZVKLF68GIMGDUJAQAAGDx6M5cuXQ61Wix2aXomPj8fkyZPRt29feHp6Yu/evXVeV6vVWLJkCfr27YuAgAA899xzuHDhgjjBUpPp431769atGD58OAIDAxEYGIgnn3wSf/31l9hhNSt9uc8vW7YMnp6edb6GDh0qdlj3Td/ygkGDBtX7e/L09ERkZKTYod0zfc0hioqK8NFHH2HgwIEICAjAuHHjcPLkSbHDahLmHfqNeYdu0oe8gzmH7mDeoTt0Pe/Q1pzDQOPvQA2Ki4vD008/DX9/fyiVSixatAgRERH4+eefYWJiInZ4rcLJkyfxzTffwNPTU+xQWoX8/HyMHz8ePXv2xJo1a2BlZYWLFy/C0tJS7ND03po1a/D111/jk08+QceOHZGcnIy3334b5ubmmDBhgtjh6Y2SkhJ4enoiNDQUU6dOrff6mjVrsHnzZsyfPx8uLi5YsmQJIiIiEBMTAyMjIxEipqbQx/u2o6Mj3njjDbRv3x5qtRo7d+7ElClTsGPHDnTq1Ens8O6bvt3nO3XqhA0bNtT+LJVKRYzm/uljXvD9999DqVTW/nz27FmEh4fr9IdB+ppDvPfeezh79iwWLFgAe3t7/PTTTwgPD0dMTAwcHBzEDq9RmHfoN+Ydukef8g7mHLqBeYfu0PW8Q2tzDjVpBYVCoe7cubM6Li5O7FBahaKiIvXDDz+sPnjwoDosLEz94Ycfih2S3vv000/V48ePFzuMVmnixInqt99+u86xqVOnqmfMmCFSRPqvc+fO6t9++632Z5VKpe7Tp4967dq1tccKCgrUfn5+6l27dokRIt0nfb1v9+jRQ71t2zaxw7hv+nafX7p0qXrEiBFih9GsWkNe8OGHH6oHDx6sVqlUYodyz/QxhygtLVV7e3ur//jjjzrHR40apV60aJE4Qd0n5h36j3mHdtOnvIM5h+5i3qGd9C3v0Kacg8tPaYnCwkIA0PlKsa6YO3cuBgwYgODgYLFDaTX27dsHPz8/vPrqq+jduzdGjhyJbdu2iR1Wq9CtWzccPnwY58+fBwCcOnUKx44dQ//+/UWOrPW4fPkycnNz6/w/x9zcHF26dMHx48dFjIzulb7dt5VKJX7++WeUlJSgW7duYodz3/TxPn/x4kX07dsXDz30EGbMmIGrV6+KHdJ90fe8oKKiAj/99BNCQ0MhCILY4dwzfcwhqqqqoFQq6z05aGRkhISEBJGial7MO/QP8w7tpm95B3MO3cO8Q3vpe94hZs7B5ae0gEqlwscff4zAwEB07txZ7HD03s8//4zU1FR8//33YofSqly6dAlff/01wsPDMXnyZCQlJeHDDz+EoaEhRo0aJXZ4em3ixIkoKirCo48+CqlUCqVSienTp2PEiBFih9Zq5ObmAgBsbGzqHLexscG1a9fECInugz7dt0+fPo1x48ahvLwcJiYmWL58OTp27Ch2WPdFH+/zAQEBmDdvHtzc3JCbm4vly5fj6aefRnR0NMzMzMQO757oe16wd+9eFBYW6vxY9DGHMDMzQ7du3bBixQq4u7vD1tYWu3btQmJiIlxdXcUOr1kw79AvzDu0m77lHcw5dBPzDu2l73mHmDkHixpaIDIyEmfPnsXWrVvFDkXvZWZm4qOPPsL69eu5lmwLU6vV8PPzw+uvvw4A8PHxwdmzZ/HNN9/o/I1X2+3evRvR0dFYuHAhOnbsiLS0NMybNw/29va89kT3QJ/u225ubti5cycKCwvxyy+/YNasWYiKitLZDxj09T4/YMCA2u+9vLzQpUsXDBw4ELt378aYMWNEjOze6XtesH37dvTv318n1km+E33NIRYsWIB33nkH/fv3h1QqhY+PD4YNG4aUlBSxQyOqh3mH9tLHvIM5h25i3qHdmHdoBosaIps7dy7+/PNPREVFwdHRUexw9F5KSgoUCgVGjx5de0ypVCI+Ph5btmxBUlKSzm+Cpa3s7Ozg4eFR55i7uzt++eUXkSJqPRYsWICJEydi2LBhAABPT09cvXoVq1ev1unEQJfY2dkBABQKBezt7WuPKxQKeHl5iRUW3QN9u2/LZDK0b98eAODn54ekpCR89dVXmDt3rsiR3ZvWcp+3sLBAhw4dkJGRIXYo90yf84IrV67g0KFDWLZsmdih3Dd9zSFcXV0RFRWFkpISFBUVwd7eHtOmTUO7du3EDq1ZMO/QH8w7tFtryDuYc2g/5h3aT5/zDjFzDhY1RKJWq/HBBx/gt99+w+bNm/XiH7Iu6NWrF6Kjo+sce/vtt+Hu7o4XX3xR5xMObRYYGFi7LmKNCxcuoG3btiJF1HqUlZXVW1dTKpVCrVaLFFHr4+LiAjs7O8TGxsLb2xsAUFRUhBMnTmD8+PEiR0eN0Vru2yqVChUVFWKHcc9ay32+uLgYly5dqv0lQhfpc17www8/wMbGBg8++KDYodw3fc8hTExMYGJigvz8fBw4cABvvvmm2CE1C+Yduo95h25oDXkHcw7tx7xDd+hj3iFmzsGihkgiIyOxa9curFixAqamprVrkJmbm0Mul4scnf4yMzOrtw6piYkJ2rRpo/Prk2q7Z599FuPHj8eqVavw6KOP4uTJk9i2bZvOPpWjSwYOHIhVq1bB2dm5dgrnhg0bEBoaKnZoeqW4uLjOE0yXL19GWloaLC0t4ezsjAkTJmDlypVo3749XFxcsGTJEtjb22Pw4MEiRk2NpY/37YULF6J///5wcnJCcXExdu3ahbi4OKxbt07s0O6Zvt7nP/nkEwwcOBDOzs7IycnBsmXLIJFI8Nhjj4kd2j3T17xApVLhhx9+wMiRI2FgoPu/aulrDrF//36o1Wq4ubkhIyMDCxYsgLu7e52nrbUd8w79xrxDN+hj3sGcQ7cw79ANup53aGvOIaj1pdylYzw9PRs8Pm/ePJ35R60vnnnmGXh5eeHdd98VOxS998cff2DRokW4cOECXFxcEB4ejrFjx4odlt4rKirCkiVLsHfv3topgcOGDcOUKVMgk8nEDk9vHDlyBBMmTKh3fNSoUZg/fz7UajWWLl2Kbdu2oaCgAN27d8fs2bPh5uYmQrTUVPp4337nnXdw+PBh5OTkwNzcHJ6ennjxxRfRp08fsUNrVvpwn58+fTri4+Nx48YNWFtbo3v37pg+fbrOby6oj3nBgQMHEBERgT179ujF/9/1NYeIiYnBokWLkJWVhTZt2uDhhx/G9OnTYW5uLnZojca8Q78x79Bdup53MOfQLcw7dIOu5x3amnOwqEFERERERERERERERDpBInYAREREREREREREREREjcGiBhERERERERERERER6QQWNYiIiIiIiIiIiIiISCewqEFERERERERERERERDqBRQ0iIiIiIiIiIiIiItIJLGoQEREREREREREREZFOYFGDiIiIiIiIiIiIiIh0AosaRERERERERERERESkE1jUICIiIiIiIiIiIiIincCiBhHVeuutt/Dyyy/XObZnzx74+/tj/fr1IkVFRERE+oY5BxEREbUU5h1E+sdA7ACISHt99913iIyMRGRkJEJDQ8UOh4iIiPQUcw4iIiJqKcw7iHQfixpE1KA1a9Zg2bJl+PzzzzFkyBCxwyEiIiI9xZyDiIiIWgrzDiL9wKIGEdXz6aefYuvWrVi9ejV69+4tdjhERESkp5hzEBERUUth3kGkP1jUIKI6/v77b/z+++/YuHEjb/JERESkMcw5iIiIqKUw7yDSL9wonIjq8PT0RNu2bbFs2TIUFxeLHQ4RERHpKeYcRERE1FKYdxDpFxY1iKgOBwcHbN68GdnZ2XjhhRdQVFQkdkhERESkh5hzEBERUUth3kGkX1jUIKJ62rZti6ioKFy7do03eyIiItIY5hxERETUUph3EOkPFjWIqEFOTk7YvHkz8vLyeLMnIiIijWHOQURERC2FeQeRfmBRg4huy9HREZs3b8b169cRERHBmz0RERFpBHMOIiIiainMO4h0n6BWq9ViB0FERERERERERERERHQ3nKlBREREREREREREREQ6gUUNIiIiIiIiIiIiIiLSCSxqEBERERERERERERGRTmBRg4iIiIiIiIiIiIiIdAKLGkREREREREREREREpBNY1CAiIiIiIiIiIiIiIp3AogYREREREREREREREekEFjWIiIiIiIiIiIiIiEgnsKhBREREREREREREREQ6gUUNIiIiIiIiIiIiIiLSCSxqEBERERERERERERGRTmBRg4iIiIiIiIiIiIiIdML/AUKYPLR+glRdAAAAAElFTkSuQmCC",
"text/plain": [
"<Figure size 1600x400 with 3 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"fig, axes = plt.subplots(1, 3, figsize=(16, 4))\n",
"\n",
"axes[0].plot(df_kdiag[\"k\"], df_kdiag[\"inertia\"], marker=\"o\")\n",
"axes[0].set_title(\"Elbow / Inertia\")\n",
"axes[0].set_xlabel(\"K\")\n",
"\n",
"axes[1].plot(df_kdiag[\"k\"], df_kdiag[\"silhouette\"], marker=\"o\")\n",
"axes[1].set_title(\"Silhouette\")\n",
"axes[1].set_xlabel(\"K\")\n",
"\n",
"axes[2].plot(df_kdiag[\"k\"], df_kdiag[\"davies_bouldin\"], marker=\"o\")\n",
"axes[2].set_title(\"Davies-Bouldin\")\n",
"axes[2].set_xlabel(\"K\")\n",
"\n",
"plt.tight_layout()\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 45,
"id": "a7249fea-2ad6-4d32-af6f-c9a234cbfe1c",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"K=2 | silhouette=0.9996 | davies_bouldin=0.2613\n",
"K=5 | silhouette=0.9991 | davies_bouldin=0.2938\n",
"K=10 | silhouette=0.9987 | davies_bouldin=0.1262\n"
]
}
],
"source": [
"RESULTS = {}\n",
"\n",
"for k in [2, 5, 10]:\n",
" km = KMeans(n_clusters=k, n_init=50, random_state=42)\n",
" labels = km.fit_predict(X_scaled)\n",
" dfc[f\"cluster_k{k}\"] = labels\n",
"\n",
" RESULTS[k] = {\n",
" \"model\": km,\n",
" \"labels\": labels,\n",
" \"silhouette\": silhouette_score(X_scaled, labels),\n",
" \"davies_bouldin\": davies_bouldin_score(X_scaled, labels)\n",
" }\n",
"\n",
"for k in [2, 5, 10]:\n",
" print(f\"K={k} | silhouette={RESULTS[k]['silhouette']:.4f} | davies_bouldin={RESULTS[k]['davies_bouldin']:.4f}\")"
]
},
{
"cell_type": "code",
"execution_count": 46,
"id": "0dd70e26-f831-4140-8815-eebb40c9aba7",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"===== K=2 =====\n"
]
},
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>n_clients</th>\n",
" <th>aum_qty_mean_med</th>\n",
" <th>gross_flow_to_aum_med</th>\n",
" <th>flow_freq_med</th>\n",
" <th>n_tx_total_med</th>\n",
" <th>avg_n_isin_held_med</th>\n",
" <th>n_isin_total_med</th>\n",
" <th>avg_holding_months_per_isin_med</th>\n",
" <th>exit_rate_per_isin_med</th>\n",
" <th>flow_direction_balance_med</th>\n",
" <th>redemption_bias_med</th>\n",
" <th>aum_drawdown_last_med</th>\n",
" <th>aum_final_to_peak_med</th>\n",
" </tr>\n",
" <tr>\n",
" <th>cluster_k2</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>9667</td>\n",
" <td>200.644385</td>\n",
" <td>2.560684</td>\n",
" <td>0.058824</td>\n",
" <td>2.0</td>\n",
" <td>0.666667</td>\n",
" <td>2.0</td>\n",
" <td>13.34</td>\n",
" <td>1.0</td>\n",
" <td>0.000000</td>\n",
" <td>-5.714286e-02</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>5</td>\n",
" <td>75884.615385</td>\n",
" <td>4.651799</td>\n",
" <td>0.094595</td>\n",
" <td>11.0</td>\n",
" <td>0.750000</td>\n",
" <td>1.0</td>\n",
" <td>36.00</td>\n",
" <td>1.0</td>\n",
" <td>0.208726</td>\n",
" <td>-4.423077e+12</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" n_clients aum_qty_mean_med gross_flow_to_aum_med flow_freq_med \\\n",
"cluster_k2 \n",
"0 9667 200.644385 2.560684 0.058824 \n",
"1 5 75884.615385 4.651799 0.094595 \n",
"\n",
" n_tx_total_med avg_n_isin_held_med n_isin_total_med \\\n",
"cluster_k2 \n",
"0 2.0 0.666667 2.0 \n",
"1 11.0 0.750000 1.0 \n",
"\n",
" avg_holding_months_per_isin_med exit_rate_per_isin_med \\\n",
"cluster_k2 \n",
"0 13.34 1.0 \n",
"1 36.00 1.0 \n",
"\n",
" flow_direction_balance_med redemption_bias_med \\\n",
"cluster_k2 \n",
"0 0.000000 -5.714286e-02 \n",
"1 0.208726 -4.423077e+12 \n",
"\n",
" aum_drawdown_last_med aum_final_to_peak_med \n",
"cluster_k2 \n",
"0 1.0 0.0 \n",
"1 1.0 0.0 "
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"===== K=5 =====\n"
]
},
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
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" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>n_clients</th>\n",
" <th>aum_qty_mean_med</th>\n",
" <th>gross_flow_to_aum_med</th>\n",
" <th>flow_freq_med</th>\n",
" <th>n_tx_total_med</th>\n",
" <th>avg_n_isin_held_med</th>\n",
" <th>n_isin_total_med</th>\n",
" <th>avg_holding_months_per_isin_med</th>\n",
" <th>exit_rate_per_isin_med</th>\n",
" <th>flow_direction_balance_med</th>\n",
" <th>redemption_bias_med</th>\n",
" <th>aum_drawdown_last_med</th>\n",
" <th>aum_final_to_peak_med</th>\n",
" </tr>\n",
" <tr>\n",
" <th>cluster_k5</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>9665</td>\n",
" <td>200.507246</td>\n",
" <td>2.560684</td>\n",
" <td>0.058824</td>\n",
" <td>2.0</td>\n",
" <td>0.666667</td>\n",
" <td>2.0</td>\n",
" <td>13.333333</td>\n",
" <td>1.000</td>\n",
" <td>0.000000</td>\n",
" <td>-5.714286e-02</td>\n",
" <td>1.000000</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>2</td>\n",
" <td>94722.757683</td>\n",
" <td>5.784578</td>\n",
" <td>0.070374</td>\n",
" <td>10.0</td>\n",
" <td>0.586798</td>\n",
" <td>2.0</td>\n",
" <td>35.166667</td>\n",
" <td>1.500</td>\n",
" <td>0.001110</td>\n",
" <td>-4.646436e+12</td>\n",
" <td>1.000000</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>2</td>\n",
" <td>101582.362250</td>\n",
" <td>4.932272</td>\n",
" <td>0.082639</td>\n",
" <td>7.5</td>\n",
" <td>0.875000</td>\n",
" <td>2.0</td>\n",
" <td>28.000000</td>\n",
" <td>0.500</td>\n",
" <td>0.682622</td>\n",
" <td>-2.943535e+12</td>\n",
" <td>0.500000</td>\n",
" <td>0.500000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>2</td>\n",
" <td>152151.798643</td>\n",
" <td>3.417115</td>\n",
" <td>0.052149</td>\n",
" <td>7.5</td>\n",
" <td>1.381561</td>\n",
" <td>3.0</td>\n",
" <td>45.750000</td>\n",
" <td>0.875</td>\n",
" <td>0.199417</td>\n",
" <td>-9.113122e+11</td>\n",
" <td>0.940068</td>\n",
" <td>0.059932</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>1</td>\n",
" <td>75884.615385</td>\n",
" <td>4.651799</td>\n",
" <td>0.282051</td>\n",
" <td>15.0</td>\n",
" <td>1.000000</td>\n",
" <td>1.0</td>\n",
" <td>39.000000</td>\n",
" <td>0.000</td>\n",
" <td>0.201133</td>\n",
" <td>-6.666667e+12</td>\n",
" <td>0.685315</td>\n",
" <td>0.314685</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" n_clients aum_qty_mean_med gross_flow_to_aum_med flow_freq_med \\\n",
"cluster_k5 \n",
"0 9665 200.507246 2.560684 0.058824 \n",
"1 2 94722.757683 5.784578 0.070374 \n",
"2 2 101582.362250 4.932272 0.082639 \n",
"4 2 152151.798643 3.417115 0.052149 \n",
"3 1 75884.615385 4.651799 0.282051 \n",
"\n",
" n_tx_total_med avg_n_isin_held_med n_isin_total_med \\\n",
"cluster_k5 \n",
"0 2.0 0.666667 2.0 \n",
"1 10.0 0.586798 2.0 \n",
"2 7.5 0.875000 2.0 \n",
"4 7.5 1.381561 3.0 \n",
"3 15.0 1.000000 1.0 \n",
"\n",
" avg_holding_months_per_isin_med exit_rate_per_isin_med \\\n",
"cluster_k5 \n",
"0 13.333333 1.000 \n",
"1 35.166667 1.500 \n",
"2 28.000000 0.500 \n",
"4 45.750000 0.875 \n",
"3 39.000000 0.000 \n",
"\n",
" flow_direction_balance_med redemption_bias_med \\\n",
"cluster_k5 \n",
"0 0.000000 -5.714286e-02 \n",
"1 0.001110 -4.646436e+12 \n",
"2 0.682622 -2.943535e+12 \n",
"4 0.199417 -9.113122e+11 \n",
"3 0.201133 -6.666667e+12 \n",
"\n",
" aum_drawdown_last_med aum_final_to_peak_med \n",
"cluster_k5 \n",
"0 1.000000 0.000000 \n",
"1 1.000000 0.000000 \n",
"2 0.500000 0.500000 \n",
"4 0.940068 0.059932 \n",
"3 0.685315 0.314685 "
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"===== K=10 =====\n"
]
},
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
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" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>n_clients</th>\n",
" <th>aum_qty_mean_med</th>\n",
" <th>gross_flow_to_aum_med</th>\n",
" <th>flow_freq_med</th>\n",
" <th>n_tx_total_med</th>\n",
" <th>avg_n_isin_held_med</th>\n",
" <th>n_isin_total_med</th>\n",
" <th>avg_holding_months_per_isin_med</th>\n",
" <th>exit_rate_per_isin_med</th>\n",
" <th>flow_direction_balance_med</th>\n",
" <th>redemption_bias_med</th>\n",
" <th>aum_drawdown_last_med</th>\n",
" <th>aum_final_to_peak_med</th>\n",
" </tr>\n",
" <tr>\n",
" <th>cluster_k10</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>9655</td>\n",
" <td>199.700364</td>\n",
" <td>2.561100</td>\n",
" <td>0.058824</td>\n",
" <td>2.0</td>\n",
" <td>0.666667</td>\n",
" <td>2.0</td>\n",
" <td>13.333333</td>\n",
" <td>1.000000</td>\n",
" <td>0.000000</td>\n",
" <td>-5.714286e-02</td>\n",
" <td>1.000000e+00</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>7</td>\n",
" <td>11281.487500</td>\n",
" <td>1.646680</td>\n",
" <td>0.056000</td>\n",
" <td>7.0</td>\n",
" <td>0.637500</td>\n",
" <td>3.0</td>\n",
" <td>43.222222</td>\n",
" <td>1.000000</td>\n",
" <td>-0.260413</td>\n",
" <td>-2.225352e+11</td>\n",
" <td>1.000000e+00</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>3</td>\n",
" <td>16597.286822</td>\n",
" <td>2.348949</td>\n",
" <td>0.015504</td>\n",
" <td>3.0</td>\n",
" <td>0.751938</td>\n",
" <td>1.0</td>\n",
" <td>46.000000</td>\n",
" <td>0.333333</td>\n",
" <td>-0.547161</td>\n",
" <td>-6.525000e+10</td>\n",
" <td>1.000000e+00</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>1</td>\n",
" <td>40190.138462</td>\n",
" <td>7.329808</td>\n",
" <td>0.046154</td>\n",
" <td>11.0</td>\n",
" <td>0.538462</td>\n",
" <td>3.0</td>\n",
" <td>23.333333</td>\n",
" <td>1.000000</td>\n",
" <td>-0.206507</td>\n",
" <td>-4.423077e+12</td>\n",
" <td>1.000000e+00</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>1</td>\n",
" <td>75884.615385</td>\n",
" <td>4.651799</td>\n",
" <td>0.282051</td>\n",
" <td>15.0</td>\n",
" <td>1.000000</td>\n",
" <td>1.0</td>\n",
" <td>39.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.201133</td>\n",
" <td>-6.666667e+12</td>\n",
" <td>6.853147e-01</td>\n",
" <td>0.314685</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>1</td>\n",
" <td>20646.937000</td>\n",
" <td>1.000000</td>\n",
" <td>0.027778</td>\n",
" <td>2.0</td>\n",
" <td>1.000000</td>\n",
" <td>1.0</td>\n",
" <td>36.000000</td>\n",
" <td>0.000000</td>\n",
" <td>1.000000</td>\n",
" <td>-3.347944e+12</td>\n",
" <td>4.840572e-14</td>\n",
" <td>1.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>1</td>\n",
" <td>182517.787500</td>\n",
" <td>8.864544</td>\n",
" <td>0.137500</td>\n",
" <td>13.0</td>\n",
" <td>0.750000</td>\n",
" <td>3.0</td>\n",
" <td>20.000000</td>\n",
" <td>1.000000</td>\n",
" <td>0.365245</td>\n",
" <td>-2.539125e+12</td>\n",
" <td>1.000000e+00</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>1</td>\n",
" <td>152961.538462</td>\n",
" <td>4.491325</td>\n",
" <td>0.030769</td>\n",
" <td>6.0</td>\n",
" <td>1.630769</td>\n",
" <td>4.0</td>\n",
" <td>53.000000</td>\n",
" <td>0.750000</td>\n",
" <td>-0.149927</td>\n",
" <td>-6.461538e+11</td>\n",
" <td>8.801370e-01</td>\n",
" <td>0.119863</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>1</td>\n",
" <td>151342.058824</td>\n",
" <td>2.342905</td>\n",
" <td>0.073529</td>\n",
" <td>9.0</td>\n",
" <td>1.132353</td>\n",
" <td>2.0</td>\n",
" <td>38.500000</td>\n",
" <td>1.000000</td>\n",
" <td>0.548762</td>\n",
" <td>-1.176471e+12</td>\n",
" <td>1.000000e+00</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>1</td>\n",
" <td>149255.376905</td>\n",
" <td>4.239348</td>\n",
" <td>0.094595</td>\n",
" <td>9.0</td>\n",
" <td>0.635135</td>\n",
" <td>1.0</td>\n",
" <td>47.000000</td>\n",
" <td>2.000000</td>\n",
" <td>0.208726</td>\n",
" <td>-4.869795e+12</td>\n",
" <td>1.000000e+00</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" n_clients aum_qty_mean_med gross_flow_to_aum_med \\\n",
"cluster_k10 \n",
"0 9655 199.700364 2.561100 \n",
"6 7 11281.487500 1.646680 \n",
"9 3 16597.286822 2.348949 \n",
"1 1 40190.138462 7.329808 \n",
"2 1 75884.615385 4.651799 \n",
"3 1 20646.937000 1.000000 \n",
"5 1 182517.787500 8.864544 \n",
"4 1 152961.538462 4.491325 \n",
"7 1 151342.058824 2.342905 \n",
"8 1 149255.376905 4.239348 \n",
"\n",
" flow_freq_med n_tx_total_med avg_n_isin_held_med \\\n",
"cluster_k10 \n",
"0 0.058824 2.0 0.666667 \n",
"6 0.056000 7.0 0.637500 \n",
"9 0.015504 3.0 0.751938 \n",
"1 0.046154 11.0 0.538462 \n",
"2 0.282051 15.0 1.000000 \n",
"3 0.027778 2.0 1.000000 \n",
"5 0.137500 13.0 0.750000 \n",
"4 0.030769 6.0 1.630769 \n",
"7 0.073529 9.0 1.132353 \n",
"8 0.094595 9.0 0.635135 \n",
"\n",
" n_isin_total_med avg_holding_months_per_isin_med \\\n",
"cluster_k10 \n",
"0 2.0 13.333333 \n",
"6 3.0 43.222222 \n",
"9 1.0 46.000000 \n",
"1 3.0 23.333333 \n",
"2 1.0 39.000000 \n",
"3 1.0 36.000000 \n",
"5 3.0 20.000000 \n",
"4 4.0 53.000000 \n",
"7 2.0 38.500000 \n",
"8 1.0 47.000000 \n",
"\n",
" exit_rate_per_isin_med flow_direction_balance_med \\\n",
"cluster_k10 \n",
"0 1.000000 0.000000 \n",
"6 1.000000 -0.260413 \n",
"9 0.333333 -0.547161 \n",
"1 1.000000 -0.206507 \n",
"2 0.000000 0.201133 \n",
"3 0.000000 1.000000 \n",
"5 1.000000 0.365245 \n",
"4 0.750000 -0.149927 \n",
"7 1.000000 0.548762 \n",
"8 2.000000 0.208726 \n",
"\n",
" redemption_bias_med aum_drawdown_last_med aum_final_to_peak_med \n",
"cluster_k10 \n",
"0 -5.714286e-02 1.000000e+00 0.000000 \n",
"6 -2.225352e+11 1.000000e+00 0.000000 \n",
"9 -6.525000e+10 1.000000e+00 0.000000 \n",
"1 -4.423077e+12 1.000000e+00 0.000000 \n",
"2 -6.666667e+12 6.853147e-01 0.314685 \n",
"3 -3.347944e+12 4.840572e-14 1.000000 \n",
"5 -2.539125e+12 1.000000e+00 0.000000 \n",
"4 -6.461538e+11 8.801370e-01 0.119863 \n",
"7 -1.176471e+12 1.000000e+00 0.000000 \n",
"8 -4.869795e+12 1.000000e+00 0.000000 "
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"profile_vars = [\n",
" \"aum_qty_mean\",\n",
" \"gross_flow_to_aum\",\n",
" \"flow_freq\",\n",
" \"n_tx_total\",\n",
" \"avg_n_isin_held\",\n",
" \"n_isin_total\",\n",
" \"avg_holding_months_per_isin\",\n",
" \"exit_rate_per_isin\",\n",
" \"flow_direction_balance\",\n",
" \"redemption_bias\",\n",
" \"aum_drawdown_last\",\n",
" \"aum_final_to_peak\",\n",
"]\n",
"\n",
"profile_vars = [c for c in profile_vars if c in dfc.columns]\n",
"\n",
"for k in [2, 5, 10]:\n",
" print(f\"\\n===== K={k} =====\")\n",
" prof = (\n",
" dfc.groupby(f\"cluster_k{k}\")\n",
" .agg(\n",
" n_clients=(ID_COL, \"count\"),\n",
" **{f\"{c}_med\": (c, \"median\") for c in profile_vars}\n",
" )\n",
" .sort_values(\"n_clients\", ascending=False)\n",
" )\n",
" display(prof)"
]
},
{
"cell_type": "code",
"execution_count": 47,
"id": "9368ab73-fd28-4b0e-9a8a-17b860cbf6d4",
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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FCxEYGIisrCxMmjQJ9evXR926dbF//34EBQUhIiICL168gImJCRo2bIiJEycW+ncfNGgQDh8+jJCQEKSnp6NatWoYNmwYJkyY8NH7TZ8+Hfr6+ti7dy+ioqLQvHlzbN26FV9//fUnG/HnpnLlyti4cSOWLFmC1atXw8jICL169YKLiws8PDwUzv36669x/fp1/PTTTwgNDYWlpSVcXV0BAMuXL8e8efMQHBwMIyMjfPnll2jZsiVGjhyZrxxaWlpYv349QkNDcfDgQfz6668wMDCAlZUVhg0bhtq1awMAXF1d8ejRI+zfv18+C9DZ2Rmenp7yHWNLAwsLC2zduhVDhgzBmDFj8OOPP6JevXqFeqyEhAQAQFxcHOLi4pSOR0ZGssBGREREpYZEltulWiIiIqJSLi0tDU5OTpgyZQrGjx8vOg4RERERlWF57+tOREREVEq8fftWaSwsLAwA2PieiIiIiITjElEiIiIq9Y4ePYqIiAh89tlnKF++PC5cuIAjR46gbdu2paYHGRERERGVXSywERERUalna2sLbW1tbNmyBW/evIGpqSnc3d0xZcoU0dGIiIiIiNiDjYiIiIiIiIiIqCjYg42IiIiIiIiIiKgIWGAjIiIiIiIiIiIqAhbYiIiIiIiIiIiIikAjNznQsx8lOgIREVGxGnXxd9ERNNoPzV1FRyCiUoyvwcVvVUaC6AhERWKgry86QqkwTlIrz2MbZfdKLEdJ0MgCGxERERERERERiaWnJREdocSwwEZERERERERERCrHAhsREREREREREVERsMBGRERERERERERUBNplp77GAhsREREREREREamerqTsVNhYYCMiIiIiIiIiIpXjElEiIiIiIiIiIqIiYIGNiIiIiIiIiIioCFhgIyIiIiIiIiIiKgIW2IiIiIiIiIiIiIqABTYiIiIiIiIiIqIiYIGNiIiIiIiIiIioCMpSgU1LdAAiIiIiIiIiItI8elqSPG8FtX37dri6uqJJkyYYMGAALl++nOe5P/30E2xtbRVuTZo0Kcqv8kmcwUZERERERERERCqnraOaeV1Hjx5FYGAg/Pz80KxZM4SFhcHDwwPHjx+HqalprvcxNDTE8ePH5d9LJMU7m44z2IiIiIiIiIiISOW09bTzvBVESEgIBg4ciP79+6Nu3brw8/ODvr4+9u/fn+d9JBIJqlSpIr+ZmZkV9df5KBbYiIiIiIiIiIhI5STakjxv+ZWZmYmrV6+idevW8jEtLS20bt0acXFxed4vPT0dn3/+Odq3b4/x48fj1q1bRfpdPoVLRImIiIiIiIiISOW0dfOeqZaZmYnMzEyFMT09Pejp6SmMpaamQiqVKi0FNTU1xZ07d3J97Nq1a2PRokWwtbXFq1ev8MMPP+Crr77Czz//jGrVqhXyt/k4FtiIiIiIiIiIiEjltPXyXji5adMmBAUFKYxNmjQJnp6eRf659vb2sLe3V/i+W7du2LVrF6ZMmVLkx88NC2xERERERERERKRyH+u19s0332DkyJEKY/+dvQYAlStXhra2NlJSUhTGU1JS8t1XTVdXFw0aNEBiYmK+zi8M9mAjIiIiIiIiIiKV09bVzvOmp6cHQ0NDhVtuBTY9PT00atQIUVFR8rGcnBxERUUpzFL7GKlUips3b6JKlSoq+93+izPYiIiIiIiIiIhI5bQ+skS0IEaOHAlvb280btwYTZs2RVhYGDIyMtCvXz8AwMyZM1G1alVMmzYNABAUFITmzZujZs2aSEtLw9atW/H48WMMGDBAJXlywwIbERERERERERGp3MeWiBZEt27d8Pz5c6xduxbJyclo0KABtmzZIl8impSUBC2t/yvmpaWlYe7cuUhOToaxsTEaNWqEXbt2oW7duirJkxuJTCaTFdujC6JnP0p0BCIiomI16uLvoiNotB+au4qOQESlGF+Di9+qjATREYiKxEBfX3SEUuF8z055Hmtx+NcSTFL8OIONiIiIiIiIiIhU7mO7iGoaFtiIiIiIiIiIiEjlVLVEVB2wwEZERERERERERCqnrcsCGxERERERERERUaFJtCWiI5QYFtiIiIiIiIiIiEjltHTLTtmp7PymRERERERERERUYrT1yk7Zqez8pkREREREREREVGI4g60EPX/+HPv378fFixfx7NkzAICZmRns7e3Rr18/mJiYCE5IREREREREREQFpaWnKzpCidES+cMvX76Mrl27Ytu2bahYsSIcHR3h6OiIihUrYtu2bfjiiy8QHx8vMiIRERERERERERWCtq5OnjdNI/Q38vf3R9euXeHn5weJRHFnCZlMhvnz58Pf3x+7d+8WlJCIiIiIiIiIiAqjLC0RFTqDLSEhAcOHD1cqrgGARCLB8OHDcf36dQHJiIiIiIiIiIioKLT0dPK8aZoCF9gSEhLw/fffY/v27Xj+/LnCsdevX8PHxyffj2VmZvbRJaDx8fEwMzMraEQiIiIiIiIiIhKMS0TzcPr0aYwbNw61atXCmzdvsHbtWqxZswatWrUCALx9+xYHDhxAYGBgvh7Pw8MDc+fOxZUrV+Di4iIvpj179gxRUVHYu3cvZs6cWcBfiYiIiIiIiIiIRNPEmWp5KdBvGhQUBA8PD3h5eUEmk2HLli0YP3481qxZg88++6zAP3zIkCGoXLkyQkNDsXPnTkilUgCAtrY2GjVqhMDAQHTr1q3Aj0tERERERERERGJp65adXUQLVGC7desWli5dCuB9j7QxY8agWrVq+Pbbb7Fy5Uo0adKkwAG6deuGbt26ISsrC6mpqQCAypUrQ7cM/UcgIiIiIiIiItI0nMGWBz09PaSlpSmM9ezZE1paWvDy8oK3t3ehg+jq6sLc3LzQ9yciIiIiIiIiotJDoiV0b80SVaACW4MGDRAdHY3GjRsrjHfv3h0ymQyzZs1SaTgiIiIiIiIiIlJPnMGWh8GDByM2NjbXYz169IBMJsOePXtUEoyIiIiIiIiIiNSXlk7Zaf9VoAJbp06d0KlTpzyP9+zZE927dy9yKCIiIiIiIiIiUnNlqMBWqMWwW7ZsyXVcKpVi+vTpRQpERERERERERETqT6Krl+dN0xRqMezWrVthbGyMAQMGyMekUim8vLxw69YtlYUjIiIiIiIiIiL1JClDM9gKVWDbtGkTPDw8ULFiRXTt2hXZ2dmYMmUK7ty5g/DwcFVnJCIiIiIiIiIidaOjeTPV8lKoAlvTpk2xdu1aTJw4Ebq6uti3bx8SExMRHh4OMzMzVWckIiIiIiIiIiI1wxls+eDi4oKlS5di8uTJqFOnDrZt2wYTExNVZiMiIiIiIiIiIjWlib3W8pLvAtukSZNyHa9cuTKMjIwwb948+VhQUFDRkxERERERERERkfriDDZlFStWzHW8Xbt2KgtDRERERERERESagUtEcxEYGFjgBz9//jyaNGkCPb2yMyWQiIiIiIiIiIgAiZa26AglRqs4H3zMmDF4+vRpcf4IIiIiIiIiIiIqjXR0875pmEJvcpAfMpmsOB+eiIiIiIiIiIhKKW5yQEREREREREREVATswUZERERERERERFQU2iywERERERERERERFZpMq+yUnYr1N5VIJMX58EREREREREREVFpxF9G8yWQyPH78GO/evcvXuUREREREREREVPbItHXyvGmaQhXYOnfujKSkpE+eGxcXB2tr60IFIyIiIiIiIiIiNaalk/dNwxT4N9LS0kLNmjXx4sWLYohDRERERERERESaoCz1YCvwDDYAmDZtGpYuXYqbN2+qOg8REREREREREWkCLe28bxqmUKVEb29vZGRkoHfv3tDV1YW+vr7C8ZiYGJWEIyIiIiIiIiIi9VSWZrAV6jf19fVVdQ4iIiIiIiIiItIkWoVaOKmWClVg69u3r6pzEBERERERERGRJlHhDLbt27dj69atSE5Ohp2dHebOnYumTZvmef6xY8ewZs0aPHr0CLVq1cL06dPRvn17leX5r0KXEhMTE7Fq1SpMnToVKSkpAIC//voLt27dUlk4IiIiIiIiIiJSTzItnTxvBXH06FEEBgZi4sSJiIiIgJ2dHTw8POT1qP+6cOECpk2bhi+//BIHDhyAm5sbJk6cWKx7CRSqwBYTE4OePXvi8uXL+OWXX5Ceng4AuHHjBtatW6fSgEREREREREREpIZUtMlBSEgIBg4ciP79+6Nu3brw8/ODvr4+9u/fn+v54eHhaNeuHUaPHg0bGxtMmTIFDRs2xI8//qiK3ypXhSqwrVixAlOmTEFISAh0dXXl461atcLFixdVlY2IiIiIiIiIiNTUx2awZWZm4vXr1wq3zMxMpcfIzMzE1atX0bp1a/mYlpYWWrdujbi4uFx/7sWLF+Hi4qIw1rZt22KtWRVqMezNmzexfPlypXETExOkpqYWOVRRjbr4u+gIRERExeqH5q6iI2g0vpcgoo/ha3DxWyU6ABGphOwjM9U2bdqEoKAghbFJkybB09NTYSw1NRVSqRSmpqYK46amprhz506uj/3s2TOYmZkpnf/s2bOCxC+QQhXYKlasiOTkZFhbWyuMX79+HVWrVlVJMCIiIiIiIiIiUl/SHFmex7755huMHDlSYUxPT6+4IxWbQi0R7d69O5YvX47k5GRIJBLk5OTg/PnzWLJkCfr06aPiiEREREREREREpG6ksrxvenp6MDQ0VLjlVmCrXLkytLW1lTY0SElJUZql9oGZmZnSbLWPna8KhSqweXl5oU6dOujQoQPS09PRvXt3DB06FPb29hg/fryqMxIRERERERERkZqR5sjyvOWXnp4eGjVqhKioKPlYTk4OoqKiYG9vn+t9mjdvjrNnzyqM/f3332jevHmhfo/8KNQSUT09Pfj7+8u3OH3z5g0aNmyIWrVqqTgeERERERERERGpI2n+62gfNXLkSHh7e6Nx48Zo2rQpwsLCkJGRgX79+gEAZs6ciapVq2LatGkAAHd3dwwbNgw//PAD2rdvj6NHj+LKlSv47rvvVBMoF4UqsAUFBcHDwwMWFhawsLCQj799+xZbtmzBpEmTVBaQiIiIiIiIiIjUj1Smmgpbt27d8Pz5c6xduxbJyclo0KABtmzZIl/ymZSUBC2t/1uk6eDggOXLl2P16tVYuXIlatWqhfXr16N+/foqyZMbiUxW8N+2QYMGOH36tNIODqmpqWjdujWuX7+usoCFMU5SS+jPJyIiKm7cwa54cRdRIvoYvgYXv5dR34uOQFQkBvr6oiOUCkkv3uR5zKJShRJMUvwKNYNNJpNBIpEojSckJMDY2LjIoYiIiIiIiIiISL0VpNeauitQgc3JyQkSiQQSiQRdunRRKLJJpVKkp6fjq6++UnlIIiIiIiIiIiJSL6rqwaYOClRg8/X1hUwmg6+vLzw9PVGxYkX5MV1dXVhaWua5gwMREREREREREZUdqurBpg4KVGDr27cvAMDKygoODg7Q0SnUClMiIiIiIiIiItJw0hzRCUqO1qdPUVahQgXcvn1b/v1vv/2GCRMmYOXKlcjMzFRZOCIiIiIiIiIiUk9SmSzPm6YpVIFt3rx5uHfvHgDgwYMH8PLygoGBAY4fP45ly5apMh8REREREREREakhaU7eN01TqALbvXv30KBBAwDAsWPH4OzsjBUrViAwMBC//PKLSgMSEREREREREZH6KUsz2ArVRE0mkyEn5325MSoqCh06dAAAWFhYIDU1VWXhiIiIiIiIiIhIPWWVoW1ECzWDrXHjxtiwYQMOHDiA2NhYeYHt4cOHMDMzU2U+IiIiIiIiIiJSQ1k5OXneNE2hCmy+vr64du0aFi5ciHHjxqFmzZoAgBMnTsDe3l6lAYmIiIiIiIiISP1kSWV53jRNoZaI2tnZ4fDhw0rjM2fOhJZWoWp2RERERERERESkQTRxplpeClVgy0u5cuVU+XBERERERERERKSmcjRvolqeCj2DTSKR5Hn8+vXrhQ5ERERERERERETqL0vKGWwfFRQUpPB9dnY2rl+/joiICHh6eqokGBERERERERERqa+sMjSFrVAFto4dOyqNde3aFXXr1sXRo0cxYMCAIgcjIiIiIiIiIiL1VZZmsKl0R4LmzZvj7NmzqnxIIiIiIiIiIiJSQ1k5sjxvmkZlmxy8ffsW4eHhMDc3V9VDEhERERERERGRmsqSal4hLS+FKrA5OTkpbHIgk8nw5s0b6OvrY9myZSoLR0RERERERERE6ikrp+wsES1Ugc3Hx0ehwCaRSGBiYoJmzZrB2NhYZeGIiIiIiIiIiEg9cQbbJ/Tr10/VOYiIiIiIiIiISINwBlsuEhIS8v2gdnZ2hQpDRERERERERESaITObBTYlffr0gUQigUz28el9EokE169fL3IwIiIiIiIiIiJSXyyw5SIyMrI4cxARERERERERkQaR5rAHmxJLS0v515s2bYKpqSm+/PJLhXP27duH58+fY+zYsapLSEREREREREREaqcszWDTKsyddu/ejTp16iiN16tXD7t27SpyKCIiIiIiIiIiUm+Z2Tl53jRNoQpsycnJqFKlitK4iYkJkpOTixzqg6SkJPj4+Kjs8YiIiIiIiIiIqGS8y87J86ZpClVgs7CwwIULF5TGz58/D3Nz8yKH+uDly5c4cOCAyh6PiIiIiIiIiIhKRlmawZbvHmz/NmDAACxatAjZ2dlo1aoVACAqKgrLli3DqFGj8v04n9o44cGDB4WJR0REREREREREgmVKNa+QlpdCFdhGjx6NFy9ewM/PD1lZWQCAcuXKYfTo0fjmm2/y/TgTJ06ERCKBTJb3rhISiaQwEYmIiIiIiIiISCBNnKmWl0IV2CQSCWbMmIEJEybg9u3b0NfXR61ataCnp1egx6lSpQrmz5+Pjh075nr8+vXr6NevX2EiEhERERERERGRQJnZUtERSkyhCmwfVKhQAU2bNi30/Rs1aoSrV6/mWWD71Ow2IiIiIiIiIiIqnTRxM4O8FKnAVlSjR49Genp6nsdr1KiB8PDwEkxERERERERERESqwCWiJcTR0fGjx8uXLw9nZ+cSSkNERERERERERKrCAhsREREREREREVERZLPARkREREREREREVHg5OWWnrz4LbEREREREREREpHI5Us5gIyIiIiIiIiIiKjRpNmewERERERERERERFZq0DM1g0xIdgIiIiIiIiIiINE9Odk6et+Ly4sULTJs2DQ4ODnB0dISvry/evHnz0fsMGzYMtra2Crd58+YV6OdyBhsREREREREREamciBls06dPR3JyMkJCQpCVlQVfX1/MmzcPK1as+Oj9Bg4ciMmTJ8u/NzAwKNDPZYGNiIiIiIiIiIhUTlqMM9Vyc/v2bZw6dQr79u1DkyZNAABz5szB2LFjMXPmTFStWjXP++rr66NKlSqF/tlcIkpERERERERERCqXI5XleSsOcXFxMDIykhfXAKB169bQ0tLC5cuXP3rfw4cPo2XLlujRowdWrFiBjIyMAv1szmAjIiIiIiIiIiKV+9gMtszMTGRmZiqM6enpQU9Pr9A/79mzZzAxMVEY09HRgbGxMZKTk/O8X48ePVC9enWYm5vjxo0bWL58Oe7evYugoKB8/2wW2IiIiIiIiIiISOU+1oNt06ZNSgWsSZMmwdPTU+nc5cuXY/PmzR/9WUePHi1cSACDBg2Sf21ra4sqVapgxIgRSExMRI0aNfL1GCywERERERERERGRyn1st9BvvvkGI0eOVBjLa/baqFGj0Ldv34/+LGtra5iZmeH58+cK49nZ2Xj58mWB+qs1a9YMAHD//n0W2IiIiIiIiIiISJycnLx7rRVkOaiJiYnS0s/c2NvbIy0tDVeuXEHjxo0BAGfPnkVOTg6aNm2av9AArl+/DgAFKspxkwMiIiIiIiIiIlK5HGlOnrfiYGNjg3bt2mHu3Lm4fPkyzp8/j4ULF6J79+7yHUSfPn2Krl27yjc9SExMxPr163HlyhU8fPgQkZGR8Pb2hpOTE+zs7PL9szmDjYiIiIiIiIiIVE6anV3iP3P58uVYuHAhhg8fDi0tLXTu3Blz5syRH8/KysLdu3flu4Tq6uoiKioK4eHhSE9Ph4WFBTp37owJEyYU6OeywEZERERERERERCqXk5356ZNUrFKlSlixYkWex62srHDjxg359xYWFvjxxx+L/HNZYCMiIiIiIiIiIpXLySr5ApsoLLAREREREREREZHKiZjBJgoLbEREREREREREpHIssBERERERERERERWBlAU2IiIiIiIiIiKiwmMPNiIiIiIiIiIioiLgElEiIiIiIiIiIqIiyMnOEh2hxLDARkREREREREREKscZbEREREREREREREWQkyMVHaHEsMBGREREREREREQqx00OiIiIiIiIiIiIiqAsLRGVyGQymegQRERERERERERE6kpLdAAiIiIiIiIiIiJ1xgIbERERERERERFREbDARkREREREREREVAQssBERERERERERERUBC2xERERERERERERFwAIbERERERERERFREbDARkREREREREREVAQssBERERERERERERUBC2xERERERERERERFwAIbERERERERERFREbDARkREREREREREVAQssBEREREREREVgLu7O9LS0pTGX79+DXd3dwGJNMuRI0fyPLZkyZISTEKUfzqiA5RVUqkUP/30E86ePYuUlBTk5OQoHA8PDxeUTHNcvnwZ0dHReP78udLz6+PjIyiVZoiMjMz3uW5ubsWYRLMkJCTk+1w7O7tiTKKZ+vTpA4lEkq9zIyIiijmNZnr9+nW+zzU0NCzGJJrv2bNnWLJkCaKiovD8+XPIZDKF49evXxeUTHOkp6cjODg4z/dqBfm3kHJ34MAB7Nq1Cw8fPsTu3bthaWmJ0NBQWFlZoWPHjqLjaRypVIqbN2+ievXqMDY2Fh1H7cXExCArK0tp/N27dzh//ryARJplwYIFqFixItq3b68wvmjRIhw9ehTe3t6CkhHljQU2QQICAhAREYH27dujXr16+f7QR/mzceNGrF69GrVr14aZmZnCMT7XRTdx4kRIJBKlD3T/HZNIJPyQVwAfCkD/fV4/+HCMz2vh/PvD2rt377Bjxw7UrVsXzZs3BwBcunQJt27dwtdffy0oofpzdHT85Gss/4ZVY9asWUhKSsKECRNgbm4uOo5GmjNnDmJiYtC7d29UqVKF7x9UbMeOHVi7di2GDx+OjRs3yguYRkZGCAsLY4FNBQICAlC/fn0MGDAAUqkUQ4cORVxcHAwMDLBx40a0bNlSdES19O8Lov/88w+Sk5Pl3+fk5ODUqVOoWrWqiGgaZfny5Zg2bRo2btwIR0dHAMDChQvxyy+/ICwsTHA6otxJZHl9kqNi1bJlSyxdulSpIk+q0bp1a0yfPh39+vUTHUUj/f3331i+fDm8vLxgb28PAIiLi8Pq1asxdepUtGnTRnBC9fTo0aN8n2tpaVmMSTTf7NmzUaVKFUyZMkVhfO3atUhKSkJgYKCYYGouJiYm3+c6OzsXYxLNZ29vjx07dqBBgwaio2gsR0dHbNq0CS1atBAdRSN169YNU6dORceOHWFvb49Dhw7B2toaN2/exLBhwxAdHS06otr77LPPsH79ejRp0gS//fYb/Pz8EB4ejoMHD+Ls2bPYtWuX6Ihqyc7OTl5wz+2jtL6+PubMmYMvv/yypKNpnMOHD2PhwoX44YcfsG/fPkRGRiI8PBy1a9cWHY0oV5zBJoiuri5q1KghOobG0tLSgoODg+gYGmvRokVYsGCB/GoSALRr1w4GBgaYO3cujh07JjCd+mLRrOQcP34c+/fvVxrv1asX+vfvzwJbIbFoVnIsLCzynO1KqmFkZIRKlSqJjqGxHj58mGuBWE9PDxkZGQISaZ7U1FRUqVIFAPDXX3+ha9euqF27Nvr37892NEUQGRkJmUyGjh07Yu/evTAxMZEf09XVhampKbS1tQUm1Bw9e/ZEWloaBg8eDBMTE/z444+oWbOm6FhEeWKBTZBRo0YhPDwc8+bN45KDYjB8+HBs374ds2fPFh1FIyUmJsLIyEhp3NDQsECzsOjT/vnnHzx+/Fipxwd72xWNvr4+Lly4gFq1aimMX7hwAeXKlRMTSkNlZGTk+jfMPoJF4+vrixUrVsDPzw9WVlai42ikb7/9FmvWrMGSJUtgYGAgOo7GsbKywvXr15UuLp06dQo2NjaCUmkWMzMz/PPPP6hSpQpOnTqFBQsWAADevn3LAlARfPibLUjvXMqfvC5wmpiYoGHDhtixY4d8jD21qTRigU2Q8+fPIzo6GidPnkS9evWgo6P4nyIoKEhQMs3g4eGBsWPHomPHjqhbty6fXxVr0qQJFi9ejKVLl8p73D179gzLli1D06ZNBafTDA8ePMDEiRNx8+ZNhb5sHwry7F9VNMOHD8eCBQtw7do1NGnSBMD7jVH279+PCRMmCE6nGZ4/fw4fHx+cPHky1+P8Gy4aLy8vZGRkoFOnTtDX14eurq7C8YIs16XchYSEIDExEa1bt4aVlZXSewluhlI0I0eOxHfffYfMzEwA71+Djxw5guDgYPj7+wtOpxn69euHKVOmyHsItm7dGsD7nqN16tQRnE79RUREoHLlyujQoQMAYOnSpdizZw/q1q2LFStWcGVCIVy7di3X8Ro1auD169fy45ygQqUVC2yCGBkZoVOnTqJjaCx/f39ER0ejZcuWqFSpEl+EVWzRokWYNGkSOnToAAsLCwBAUlISatWqhfXr1wtOpxkCAgJgZWWF0NBQuLm5Yd++fUhNTcWSJUu4a5IKjB07FlZWVggPD8ehQ4cAAHXq1MGiRYvQrVs3wek0Q0BAANLS0rBnzx64u7sjKCgIz549w4YNGzBr1izR8dSer6+v6Agaj032i9eAAQNQrlw5rF69GhkZGZg2bRrMzc3h6+uL7t27i46nETw9PVGvXj08efIEXbt2hZ6eHgBAW1sbY8aMEZxO/W3cuFE+KzAuLg7bt2+Hr68v/vjjDwQGBvKCfiFs27ZNdASiIuEmB6SR7O3tsWrVKvkVJVI9mUyGM2fO4M6dOwAAGxsbtG7dmsVMFWnZsiXCwsJgZ2eHFi1aYO/evahTpw6ioqKwZMkSHDhwQHREoo9q27Ytvv/+ezRt2hQODg7Yv38/ateujcjISGzZsgU7d+4UHZGISomMjAykp6fD1NRUdBSifGvWrBmOHTuG6tWrY9myZUhOTsbSpUtx69YtDBs2DGfPnhUdkYhKGGewkUaqVKkSrK2tRcfQaBKJBG3btoWTkxP09PRYWFOxnJwcVKhQAQBQuXJl/O9//0OdOnVgaWmJu3fvCk5H9Gnp6enyxs/GxsZ4/vw5ateujfr16+e5BIQ+7vXr1zA0NJR//TEfziMqrR48eACpVIpatWrBwMBA3ufu3r170NHRYW9BFUlPT0dsbGyuvTDd3d0FpdIM5cuXx4sXL1C9enWcOXMGI0aMAACUK1cO7969ExtOQ8THx+PYsWNISkpS+vvlDEEqjVhgE+j48eN5vmCwr0fRTJo0CevWrUNgYCAbExeDnJwcbNiwAbt27UJKSgpOnDgBa2trrF69GpaWlhgwYIDoiGqvXr16uHHjBqytrdGsWTNs2bIFurq62LNnD4vHheTk5JTvQjD7VxVd7dq1cffuXVhZWcHW1ha7d++GlZUVdu3aJd/VjgrGyckJp0+fhqmpKRwdHXP9e5bJZJBIJOxxV0jOzs44fvw4TExMPvmawdeJovHx8UH//v2VNpu5dOkS9u3bx6ViKnDt2jWMHTsWGRkZyMjIgLGxMVJTU2FgYAATExMW2IqodevWmDNnDho0aIB79+6hffv2AIBbt26x/5oK/Pzzz/D29kbbtm1x+vRptG3bFnfv3kVKSgpbLVGpxQKbIOHh4Vi1ahX69euHyMhI9OvXDw8ePEB8fDyGDBkiOp7a27ZtGxsTF6Pvv/8eBw4cwIwZMzB37lz5eP369REWFsYCmwqMHz8eGRkZAIDJkyfjm2++wZAhQ1CpUiWsWrVKcDr1xJ5VJcvd3R3JyckA3l/0GD16NA4fPgxdXV0sXrxYcDr1FBYWBmNjYwDv30eQ6vn4+Mhn//n4+HB2djG6du0aAgIClMabN2+OhQsXCkikeQIDA/H555/Dz88PLVq0wJ49e6Cjo4MZM2awuKYC8+fPx+rVq5GUlIS1a9eicuXKAICrV6+yj6AKbNy4ET4+PhgyZAjs7e0xe/ZsWFlZYd68ebxQR6UWe7AJ0rVrV0yaNAk9evSAvb09Dh06BGtra6xZswYvX77EvHnzREdUa5+aMjxp0qQSSqKZOnXqhO+++w4uLi4Kf7+3b9/GV199hdjYWNERNdKLFy9gbGzMD3ykljIyMnDnzh1YWFjIl44SUdnVokULbNu2DQ0bNlQYv3LlCoYNG4a4uDhByTSHo6Mj9uzZgzp16sDR0RG7d++GjY0NLl26BG9vbxw/flx0RKI8NW/eHEeOHIGVlRVatmyJ8PBw2Nra4vbt2xg+fDhOnz4tOiKREi3RAcqqpKQk2NvbAwD09fXx5s0bAEDv3r3x888/i4ymESZNmvTRGxXN06dPUaNGDaVxmUyG7OxsAYk0j4+Pj1KPpUqVKiEjIwM+Pj6CUmmWxMRErFq1ClOnTkVKSgoA4K+//sKtW7cEJ9MMQUFB8lmYAGBgYIBGjRqhfPny7JuiAidPnsS5c+fk32/fvh29e/fGtGnT8PLlS4HJNMfVq1dx48YN+fe//fYbJkyYgJUrVyIzM1NgMs3g5OSETZs2QSqVysekUimCg4PRokULgck0h46ODrS03n/cMzU1xePHjwG879H45MkTkdE0SkZGBm7fvo2EhASFGxWNkZGR/DOyubm5/P1ZWlqawvsLotKEBTZBzMzM5G+ALSwscPHiRQDAw4cPwUmFVNrVrVtX4YPdB8ePH0eDBg0EJNI8Bw4cyLVB7tu3b3Hw4EEBiTRLTEwMevbsicuXL+OXX35Beno6AODGjRtYt26d4HSaYf369fLn9d8yMjKwfv16AYk0y7Jly+QfPG7cuIHAwEC0b98eDx8+5BJcFZk3bx7u3bsH4H1Dfi8vLxgYGOD48eNYtmyZ2HAaYPr06Th79iy6du0KHx8f+Pj4oGvXroiNjcXMmTNFx9MIDRs2RHx8PID3Bc21a9fi0KFDWLRoEerVqyc4nfp7/vw5xo4dCwcHB/To0QN9+/ZVuFHRODk54e+//wbwfvVXQEAA5syZg2nTpsHFxUVwOqLcsQebIK1atcLvv/+Ohg0bon///ggMDMSJEydw5coVNm1UATs7u48uo2Pz56KZMGECZs2ahadPn0Imk+GXX37B3bt3ceDAAWzatEl0PLX2+vVryGQyyGQyvHnzBuXKlZMfk0qlOHnyJJfXqcCKFSswZcoUjBw5Uj6bGHj/2vzjjz8KTKY5PjTb/6+EhAR5HzEqvIcPH8LGxgYA8Msvv8DV1RVTp07F1atXMXbsWMHpNMO9e/fkF42OHTsGZ2dnrFixAufPn8fUqVMxe/ZswQnVW926dXHo0CFs374dCQkJ0NfXR+/evTF06FBUqlRJdDyN4OXlJS/Ee3l5YebMmViwYAFq1aqFRYsWCU6n/gICAvDq1Svs2bMH7u7uCAoKwrNnz7BhwwbMmjVLdDy1N3fuXPnF5vHjx0NXVxcXLlxA586dMX78eMHpiHLHApsgCxcuRE5ODgDIG5fHxcXB1dUVgwYNEpxO/f13+VF2djauX7+OiIgIeHp6CkqlOTp27IiNGzdi/fr1MDAwwNq1a9GwYUNs3LgRbdq0ER1PrX3YGVAikaBLly5KxyUSCf+GVeDmzZtYvny50riJiQlSU1MFJNIcH3Ze/PA3/O8im1QqRXp6Or766iuBCTWDrq4u3r59CwD4+++/0adPHwCAsbGx0vJyKhyZTCZ/rxYVFYUOHToAeL/ygK8TqlG1alVMnTpVdAyN1aRJE/nXpqam2Lp1q8A0mic6Ohrff/89mjRpAolEgurVq6NNmzYwNDTEpk2b5K8ZVDj/LrRraWnx4hGpBRbYBNHS0pL3RACA7t27c7cZFerYsaPSWNeuXVG3bl0cPXqUu1wWQXZ2NjZu3Igvv/wSISEhouNonPDwcMhkMgwfPhzr1q1TmOmjq6uL6tWro2rVqgITaoaKFSsiOTkZ1tbWCuPXr1/n81tEvr6+kMlk8PX1haenJypWrCg/pqurC0tLS4VZg1Q4Dg4OCAwMhIODA+Lj47F69WoA72ddVatWTWw4DdG4cWNs2LABLi4uiI2NxYIFCwC8nz1oZmYmNpyGSEtLw+XLl5GSkqLUIuVD0ZiotEpPT5evKjA2Nsbz589Ru3Zt1K9fH9euXROcTjMkJiZi//79ePDgAWbPng1TU1P89ddfqF69Opc5U6nEAptA586dw65du/DgwQOsXbsWVatWxYEDB2BlZQVHR0fR8TRS8+bNuUNrEeno6GDr1q1841tMnJ2dAQCRkZGwsLBQKMST6nTv3h3Lly/HmjVrIJFIkJOTg/Pnz2PJkiX82y6iD31nrKysYG9vD11dXcGJNNO8efPg5+eHEydOYP78+fLC8MmTJ9GuXTvB6TSDr68vZsyYgd9++w3jxo1DzZo1AQAnTpxgkVgFfv/9d0yfPh3p6ekwNDRUmO0qkUj4WlxIffv2RWhoKIyNjdGnT5+PtkyJiIgowWSap3bt2rh79y6srKxga2uL3bt3w8rKCrt27UKVKlVEx1N7MTExGDNmDBwcHBAbGwsvLy+Ymprixo0b2L9/P9auXSs6IpESFtgEOXHiBGbOnImePXvi2rVr8t2oXr9+jU2bNrHAVgzevn2L8PBwmJubi46i9lq1aoXY2FhYWVmJjqKxLC0tkZaWhn379uH27dsAgHr16qF///4KM4KocLy8vPDdd9+hQ4cOkEql6N69O6RSKXr06MG+Hiri7OwMqVSKEydOKPwNu7q6QltbW3A69Ve9evVce176+voKSKOZ7OzscPjwYaXxmTNn8uKHCixZsgT9+/fH1KlTYWBgIDqOxnBzc4Oenh6A3Fd0kOq4u7sjOTkZADBp0iSMHj0ahw8fhq6uLjebUQH2yyV1JJFxy0oh+vTpgxEjRqBPnz6wt7fHoUOHYG1tjWvXrmHMmDE4c+aM6Ihq7UMPoA8+NIzX19fHsmXL4ObmJjCd+tu5cyfWr1+Pnj17olGjRkpvjPn8Fl18fDxGjx6NcuXKoWnTpvKxt2/f4ocffkCjRo0EJ9QMjx8/xq1bt/DmzRs0bNgQtWrVEh1JY9y/fx9jx47F06dPUbt2bQDA3bt3Ua1aNQQHB6NGjRqCE6qf169fw9DQUP71x3w4j6i0at68OQ4fPqy0VJ9IXWVkZODOnTuwsLDghlQq8O/PyP/++uHDh/jiiy/kO+QSlSacwSbI3bt3c52lVrFiRaSlpQlIpFn+ewVfIpHAxMQEzZo14+51KuDn5wcAufZgk0gk3KVVBQIDA+Hq6oqFCxdCR+f9S3V2djbmzJmDRYsWYfv27YITaobq1aujevXqomNoJH9/f1hbW2P37t3yRsWpqamYMWMG/P39ERwcLDagGnJycsLp06dhamoq3xDlvz7s3srX4cJxdnbG8ePHYWJionSx7r9iYmJKMJnmadu2LeLj41lgKwHx8fHymcR169ZF48aNBSfSTAYGBrwAqkLsl0vqiAU2QczMzJCYmKi0xO78+fN8o6ECH3oAUfFISEgQHUHjXblyRaG4Brzvfzd69Gj0799fYDLNIJVK8dNPP+Hs2bNISUmR7xT4QXh4uKBkmiM2NlahuAYAlStXxvTp0zF48GBxwdRYWFiY/CIR/0aLh4+Pj3z2H5fbFq/27dtj2bJluH37NurXr6/w7x3A2fCq8OTJE0ydOhUXLlyAkZERgPcbS9jb22PVqlXcEKUQAgMD832uj49PMSbRfOyXS+qIBTZBBg4ciICAACxatAgSiQRPnz5FXFwclixZggkTJoiOpzEyMjLw+PFjZGVlKYzb2dkJSqS+/n1V38fHB7Nnz+YSpGJkaGiIpKQk2NjYKIwnJSWhQoUKglJpjoCAAERERKB9+/aoV6/eR2epUOHo6enhzZs3SuNv3rzhxgeF9GETlP9+/TELFizA5MmTuVwpn/59gS6/F+uCg4Px1VdfyQsYlD9z584FAKxfv17pGGdhqsbs2bORnZ2No0ePok6dOgCAO3fuwNfXF7Nnz8bWrVsFJ1Q/+d0dlO8rio79ckkdsQebIDKZDBs3bkRwcDAyMjIAvP8wMmrUKEyZMkVsOA3w/PlzzJo1C6dOncr1ON+0Fdy/ex80aNAAZ86c4Qe2YuTv749ff/0V3t7e8sauFy5cwNKlS9G5c2fMnj1bcEL11rJlSyxduhTt27cXHUVjzZw5E9euXUNAQIC8j+ClS5cwd+5cNGrUiA2gS4iDgwMOHjzI2fHFiM8xlVZNmzbFrl270LBhQ4XxK1euYMiQIbh06ZKgZGXLkydPYG5uzs1RCon9ckmdcAabIBKJBOPHj4eHhwcSExORnp4OGxsbzkxRkYCAALx69Qp79uyBu7s7goKC8OzZM2zYsAGzZs0SHU8tNW/eHBMmTEDjxo0hk8ng7++PcuXK5XpuQabPU+5mzpwp/1+pVArg/RLRwYMHY/r06SKjaQRdXV022S9mc+bMgbe3NwYNGiRf+iWVSuHq6sqldyWI11GLH59jKq0sLCyQnZ2tNJ6TkwNzc3MBicqmbt26sQhfBNWrV4eFhQUAzgyk0o8FNsH09PRQt25d0TE0TnR0NL7//ns0adIEEokE1atXR5s2bWBoaIhNmzahQ4cOoiOqnWXLliE0NBSJiYmQSCR49eoV3r17JzqWxtLT08OcOXMwbdo0JCYmAgBq1KihtGMrFc6oUaMQHh6OefPm8c1aMTEyMsKGDRtw//59eXNtGxsb1KxZU3AyIiot0tPTERsbm2s7D3d3d0GpNMeMGTOwcOFCzJs3D02aNAHwfsODgIAAeHt7C05XdrAIX3h79+5FWFgY7t27BwCoVasWhg8fjgEDBogNRpQHFtgEeffuHbZt24bo6GikpKQovfBGREQISqYZ0tPT5csXjY2N8fz5c9SuXRv169fPd+8EUmRmZiafOeXq6oqlS5eicuXKglNprn/3ubO1tZWPp6enY+HChZwlWAiTJk1S+P7s2bM4efIk6tWrp9RcOygoqCSjaaSgoCB4eHigZs2aCkW1t2/fYsuWLUr/PYiobLl27RrGjh2LjIwMZGRkwNjYGKmpqTAwMICJiQkLbIX0391v09PTMXDgQGhrawN4P5NYW1sbvr6+6Nixo6iYRJ+0Zs0ahIaGYujQoWjevDkA4OLFi1i0aBEeP36Mb7/9VmxAolywwCaIr68vzpw5gy5duqBp06acQaFitWvXxt27d2FlZQVbW1vs3r0bVlZW2LVrF6pUqSI6ntr7/fff83Vez549ERwcLJ/WTfl34MABTJ8+XWkjibdv3+LgwYMssBVCxYoVFb7v1KmToCRlw/r16zF48GClWZcZGRlYv349C2xEZVxgYCA+//xz+Pn5oUWLFtizZw90dHQwY8YMFteKgEvwSVPs3LkTCxcuRI8ePeRjbm5usLW1xcKFC1lgo1KJBTZB/vzzTwQHB6NFixaio2gkd3d3JCcnA3g/a2X06NE4fPgwdHV12Vi7BD18+DDX3h+Ut9evX0Mmk0Emk+HNmzcKfe6kUilOnjzJzSUKqTBFyfPnz6NJkybQ09MrhkSaTSaT5XrxKCEhAcbGxgISEVFpcv36dfj5+UFLSwva2trIzMyEtbU1ZsyYAW9vb3Tu3Fl0RLWU391v/4074VJplJ2djcaNGyuNN2rUSN6fmKi0YYFNkKpVq3JDg2LUu3dv+deNGzfGH3/8gTt37sDCwoLFCSrVHB0dIZFIIJFI0KVLF6XjEokEnp6eApKVTWPGjGFj4gL6sDzpw9/wv4tsUqkU6enp+OqrrwQmVH/Z2dnYuHEjvvzyS1SrVu2j5/bq1YvvNwohOzsbR44cQdu2bWFmZvbRcx0dHfPc9IfypqOjI99V0dTUFI8fP4aNjQ0MDQ3x5MkTwenKlo0bN+KLL75gga2YcKVS4fTu3Rs7d+6Ej4+PwviePXvQs2dPQamIPo4FNkG8vb2xfPly+Pn5wdLSUnQcjWdgYIBGjRopjTs4OPDDM5Uq4eHhkMlkGD58ONatW6cw00dXVxfVq1dH1apVBSYsW9iYuOB8fX0hk8ng6+sLT09PhaW5urq6sLS0hL29vcCE6k9HRwdbt25Fnz59Pnmun59f8QfSQDo6Opg/fz6OHj36yXM3b95cAok0T8OGDREfH49atWrByckJa9euRWpqKg4ePIh69eqJjlem8N+64sXnt/D27duHM2fOoFmzZgCAy5cv4/Hjx+jTp4/CyoT/FuGIRGGBTZAmTZrg3bt36NixI/T19aGrq6twPCYmRlCysoX/4FFp4+zsDACIjIxE9erVP3nVc8GCBZg8eTJnZlKp8WF5kpWVFRwcHJQ2kPgvLk0qnFatWiE2NhZWVlaio2ispk2b4vr167wQWky8vLzw5s0b+dczZ87EggULUKtWLSxatEhwOiLVOXr0KMzNzUXHUDs3b95Ew4YNAQCJiYkAgEqVKqFSpUq4efOm/DzOEKTShAU2QaZOnYr//e9/8PLygpmZGV8YiEhBfj/QHTp0CB4eHiywUanzoVj8KVyaVDifffYZVqxYgZs3b6JRo0ZKm0m4ubkJSqY5Bg8ejMWLF+PJkye5Psd2dnaCkmmGJk2ayL82NTXF1q1bBaYhKrj09HQEBwfj7NmzSElJQU5OjsLxyMhIAOBmX4W0bdu2fJ335MkT5OTkyJecE4nEApsgcXFx2L17N9+cEVGRcBYmqTv+DRfOh6WfISEhSsckEgmuX79e0pE0ztSpUwEA/v7+8jGJRCLfwIPPMVHZNmfOHMTExKB3796oUqUKJ0wI0q1bN7b8oVKDBTZB6tSpg7dv34qOQVQo7969y1dD5++++w6mpqYlkIioePDNMpVWCQkJoiNovA+zT0h1+vTpk+/X1YiIiGJOQ1Q0J0+exKZNm9CiRQvRUco0Xqij0oQFNkGmTZuGxYsXw8vLC/Xr11fqwWZoaCgoWdnCD8+F4+joiKZNm8LJyQktW7aEvb099PX1lc7jDj+k7vimjdRBfi96UMGw95rqdezYUXQEygV3wi0cIyMjVKpUSXQMIipFWGATZPTo0QCAESNGKIxz2UHJ4ofnwgkJCcG5c+cQExODsLAwZGdno3HjxnB2doazszPatGkjOiKRSsTFxYmOQJQrqVSKjRs3YteuXUhJScGJEydgbW2N1atXw9LSEgMGDBAdUSMcOHAAu3btwsOHD7F7925YWloiNDQUVlZWLBYVwqRJk0RHKHNycnJw//59pKSkKL3vdXJyAsCdcAvr22+/xZo1a7BkyRKlHo1EVDaxwCZIeHi46Ahlxoc3E7nNVtu8eTOqVq1a0pHUnqOjIxwdHTFu3DhkZ2cjPj4eu3fvxpYtWxAcHMwCMZV6z549w5IlSxAVFYXnz58rfejg3zCVdhs2bMCBAwcwY8YMzJ07Vz5ev359hIWFscCmAjt27MDatWsxfPhwbNy4Ud7A3MjICGFhYSywUal38eJFTJs2DY8fP1b6d44X9IsuJCQEiYmJaN26NaysrJR2zeYyZ6KyhwU2QfK7u9qCBQswefJk7hBYCAcOHMDWrVtx7949AECtWrXg4eGBPn36yM9xdHQUE04D3L17FzExMYiJiUF0dDSysrLQoUOHfP9tk2r06tULFSpUEB1D7cyaNQtJSUmYMGECzM3NRccp07g0qXAOHjyIhQsXwsXFBfPnz5eP29ra4s6dOwKTaY4ff/wR/v7+6NixI4KDg+XjjRs3xpIlSwQm0wxSqRShoaE4duwYkpKSkJWVpXA8JiZGUDLNMX/+fDRu3BjBwcFswl8MWGQvHfh3TaUJC2yl3KFDh+Dh4cECWwGFhIRgzZo1GDJkCKZMmQIAOH/+PBYsWIAXL14oLc2lgmnXrh3evXsnXxI6ZswY2Nra8h84FVq3bh0mTpyotOX4q1evMH/+fKxcuRLA/+0kSAVz/vx57NixAw0aNBAdRaNxaVLxefr0KWrUqKE0LpPJkJ2dLSCR5nn48GGurxF6enrIyMgQkEizBAUFYe/evRg1ahRWr16NcePG4dGjR/jtt98wceJE0fE0wv3797F27VrUrFlTdBSNxCXPpQNb/lBpwgJbKccXjMLZtm0bFixYoDBbzc3NDfXq1cO6detYYCsiExMT3LlzB8+ePZPfatasyf4TKrRv3z6cOXMGy5Ytk287Hh0dDW9vb5iZmQlOp/4sLCz4+lrMuDSpeNWtWxfnzp1TasR//PhxFo5VxMrKCtevX1d6jk+dOgUbGxtBqTTH4cOH4e/vjw4dOmDdunXo0aMHatSoAVtbW1y6dEl0PI3QtGlT3L9/nwW2YrJmzRr5Zl+ciV28njx5AgCoVq2a0rGjR49yNQKVGiywkUZKTk6Gvb290ri9vT2Sk5MFJNIsBw8eRFpaGmJjYxEbG4uVK1fi9u3baNCgAVq2bAkvLy/REdXeoUOHMG/ePPTu3RuzZs3C3bt3sW3bNnh4eMDT01N0PLXn6+uLFStWwM/PD1ZWVqLjaCQuTSpeEyZMwKxZs/D06VPIZDL88ssvuHv3Lg4cOIBNmzaJjqcRRo4cie+++w6ZmZkAgMuXL+PIkSMIDg6Gv7+/4HTq79mzZ6hfvz4AoEKFCnj16hUA4PPPP8eaNWtERtMYw4YNw5IlS+TP9X97hNnZ2QlKphkuXryI0NBQZGdno0mTJnByckLLli3h4OAAfX190fHUXk5ODr7//nuEhIQgPT0dwPvXipEjR2L8+PHyVR4WFhYiYxIpkMh4Cb9Us7e3x6FDh+QzWCh/evTogR49emDcuHEK499//z2OHTuGw4cPC0qmeVJTUxETE4PIyEj8/PPPyMnJ4cwUFVq5ciWCg4Oho6ODzZs3w8XFRXQkjeDk5ISMjAxIpVLo6+tDV1dX4Th7/xRd8+bNcfDgQc6cKEbnzp3D+vXrkZCQgPT0dDRs2BATJ05E27ZtRUfTGIcOHUJQUBASExMBAObm5vD09OQmEirQpUsXLF26FM2aNcPgwYPx+eefY+zYsTh69CgWLlyIqKgo0RHVXm4FNIlEAplMxpnEKpKdnY1Lly7h3LlziImJQVxcHDIzM9GkSRPs3LlTdDy1tmLFCuzbtw+enp5wcHAA8L7FR1BQEAYOHMgL+lQqcQYbaSRPT094eXkhNjZW/oJ84cIFnD17FqtXrxYbTgP88ssv8s0Nbt++DWNjY7Ro0QLe3t7c5ECFtm3bhvDwcHTv3h1Xr16Fv78/VqxYwSvOKuDr6ys6gsbj0qTi5+joiJCQENExNFqvXr3Qq1cvZGRkID09HaampqIjaYxOnTohKioKzZo1w7BhwzBjxgzs27cPjx8/ZisPFYmMjBQdQePp6OigRYsWMDExgbGxMSpUqIDIyEhuNqMCERER8Pf3h5ubm3zMzs4OVatWhZ+fHwtsVCpxBlspxxlshXflyhWEhobK/4GrU6cORo0ahYYNGwpOpv5cXFzg5OQEZ2dnODk5wdbWVnQkjePh4YErV67Az88PXbt2xdu3bxEYGIiIiAh4enpizJgxoiMSfdSvv/6K1atXw8PDg0uTilF8fDxu374N4H1ftsaNGwtOpHlSUlJw9+5dAO/fS3DjqeIRFxeHixcvombNmnB1dRUdh+iTdu/ejZiYGMTExCAzMxOOjo5wdnZGy5YtufmXCjRp0gSHDh1C7dq1Fcbv3LmDPn364PLly4KSEeWNBbZSbv78+fj222/5Zo6ojBk5ciQWL16MqlWrKoz/+eefmDNnDk6fPi0omfp6/fo1DA0N5V9/zIfzqPC4NKl4PXnyBFOnTsWFCxdgZGQEAEhLS4O9vT1WrVqVayNoKpjXr1/Dz89P3v4AALS1tfHFF19g/vz5qFixouCERJ+WmJiIsLAwhUK8u7t7rrsQU8HY2dnBxMQEI0eOxNdff40KFSqIjqRRBgwYgGbNmmHOnDkK4wsXLkR8fDz27NkjKBlR3lhgE8TV1RX9+vVDv379UL16ddFxNE6DBg1w+vRppaUcqampaN26NT/YqYBUKsVvv/2m8IbNzc0N2tragpNpvufPn7PoXgj/fl2ws7PL9coyiz+q8+jRo48e/+/OjFQwHh4eePXqFRYvXow6deoAeH9V39fXFxUqVMDWrVsFJ1R/U6ZMwfXr1zFnzhz5xklxcXEICAhAgwYNsGrVKsEJ1d+dO3fw448/yt9L2NjYYOjQofK/aSqaU6dOYfz48WjQoIFCy5SEhARs3LgRbdq0EZxQvf3222+IjY1FTEwMbt++jYYNG8LZ2RnOzs5o0aIFDAwMREdUazExMfjmm29gYWGB5s2bA3i/sURSUhI2b94MR0dHsQGJcsECmyChoaGIiIjArVu30LJlS3z55Zfo1KkT9PT0REfTCHZ2djhz5oxSge3p06fo1KkTpxQX0f379zF27Fg8ffpUPm377t27qFatGoKDg3lVVAXc3Nywb98+VK5cWWE8LS0Nffv2ZV+VQoiJiYGDgwN0dHQ+uYkBewlSade0aVPs2rVLqe3BlStXMGTIEFy6dElQMs3RvHlzbNmyRelD3Llz5zB69GhcvHhRTDANceLECUydOhWNGzeWf3i+dOkS4uPjsXLlSnTp0kVsQA3Qp08ftG3bFtOnT1cYX758Oc6cOYOIiAhByTTPq1evcO7cORw/fhw///wzJBIJ4uPjRcdSe0+fPsWOHTsUWv58/fXXSis8iEoLbnIgyIgRIzBixAhcvXoVERERWLhwIfz8/NCjRw/0798fjRo1Eh1RLYWHhwN4vwxp7969KF++vPxYTk4OYmNjeVVUBfz9/WFtbY3du3ejUqVKAN7PDpwxYwb8/f0RHBwsNqAGePTokXxJ0r9lZmbi6dOnAhKpv38XzVhAKx6RkZH47LPPoKur+8ki8L+bFlPBWVhYIDs7W2k8JycH5ubmAhJpnkqVKuW6DNTQ0FC+LJcKb9myZRg7diy+/fZbhfG1a9di2bJlLLCpwO3bt3Pd3Kt///4ICwsr+UAaKDU1VT6LLTo6Gv/88w+MjIw4u0oFHj9+DAsLi1w3M3j8+DFXgVGpxAKbYI0aNUKjRo3g7e2NHTt2YPny5di5cyfq16+PYcOGoX///myQWQChoaEA3i/z2rVrF7S0tOTHdHV1YWVlBT8/P0HpNEdsbKxCcQ0AKleujOnTp2Pw4MHigmmAfxclTp06pfDhLicnB1FRUVxapwInT55E+fLl5W+At2/fjj179qBu3bqYN28ejI2NBSdUTxMnTpTPHp44cWKe53EZbtHNmDEDCxcuxLx589CkSRMA7zc8CAgIgLe3t+B0mmH8+PFYvHgxli5diipVqgAAkpOTsWzZMkyYMEFwOvWXnJyMPn36KI336tWLS5xVxMTEBNevX0etWrUUxq9fv84dcVWgZ8+euH37NoyMjODk5ISBAwfCycmJm/ioiJubW54tf9zc3Pg+gkolFtgEy8rKwq+//oqffvoJf//9N5o1a4Yvv/wST548wapVqxAVFYUVK1aIjqk2fv/9dwDAsGHDEBQUxA/JxURPTw9v3rxRGn/z5g10dXUFJNIcH4oSEokEs2bNUjimo6MDS0tLpXEquGXLlsmXzNy4cQOBgYEYNWoUoqOjsXjxYgQGBgpOqJ4SEhJy/ZpUz8fHBxkZGRg4cKC896VUKoW2tjZ8fX3h6+srP/dTS6Ipdzt37sT9+/fx+eefw8LCAgCQlJQEXV1dPH/+HLt375afy6V2Befs7Ixz586hZs2aCuPnz5/n7B8VGTBgAObNm4cHDx4o9GDbvHkzRowYITacBvjqq6/g5OSE+vXri46ikT70xf2v9PR0lCtXTkAiok9jgU2Qq1ev4qeffsKRI0egpaWFPn36wMfHBzY2NvJzOnXqhC+//FJgSvW1bdu2fJ3n4OCAgwcPwtraupgTaZYOHTpg3rx5CAgIQNOmTQG875uyYMECuLq6Ck6n3j4UJVxdXbFv3z5uZlBMHj58KH+9/eWXX+Dq6oqpU6fi6tWrGDt2rOB0mistLY1L61Tk3wU0Kh4dO3YUHUHj/HuWtqurK5YvX46rV6+iWbNmAN6/lzh+/Dg8PT1FRdQoEydOhKGhIX744QesXLkSAGBubo5JkybB3d1dcDr1N2TIEPnXH9qac+VR0X24yCmRSLB69WqFzSKkUikuX77MWYJUanGTA0EaNGiA1q1b48svv0THjh1znfWTnp6OhQsXciZFMbK3t8ehQ4dYYCugtLQ0eHt7448//oCOzvs6vVQqhaurKxYvXpxrzxoqHj179kRwcLB8dgXlj7OzM3bs2IG6deti8ODB6NOnDwYNGoSHDx+ie/fubBCvAsHBwbCyskK3bt0AAJMnT8Yvv/yCKlWqYPPmzXxzXEKCg4Px1VdfsbBZjI4cOQJXV1eFvq+Uu/z+/57LyFXv9evXAN73ECTVOXDgALZu3Yp79+4BAGrVqgUPD49clz9T/gwbNgzA+5Y0zZs3V/icrKenB0tLS4waNUpp6TNRacACmyCPHj1iH6VSgAW2orl37558Vx8bGxulZR5U/Pg3XDjjxo1DVlYWHBwcsGHDBkRGRqJq1ao4ffo0Fi5ciBMnToiOqPY+zE5xcHDAmTNnMGXKFKxatQrHjh1DUlISfvjhB9ERywTO1C5+fI6JyqaQkBCsWbMGQ4YMkS/BPX/+PHbs2IEpU6ZwGW4R+fj4YPbs2Z8sCj958gTm5uYKvbeJROESUUHc3d2xb98+VK5cWWE8LS0Nffv2/eTua0SlQa1atXj1iNTSvHnz4OfnhxMnTmD+/Pny7d5PnjyJdu3aCU6nGZ49eyafWfnHH3/giy++QNu2bWFpaYmBAwcKTld28Dpq8eNzXLw4U7tg+vbti9DQUBgbG6NPnz4fXbLI3oFFs23bNixYsEBhtpqbmxvq1auHdevWscBWRPldxdWtWzde5KBSgwU2QR49eoScnByl8czMTDx9+lRAIqKPK8hSZR8fn2JMQlR01atXx6ZNm5TG2ddKdYyMjJCUlAQLCwucOnUKU6ZMAfC+GCGVSsWGIyK18fDhQ2RnZ4uOoTbc3Nygp6cn/5o9wYpPcnIy7O3tlcbt7e2RnJwsIFHZxIscVJqwwFbC/j0z7dSpUwq9qnJychAVFcWloyWIbzry76effkK9evWgra0NiUSS5z9mfE6ptHr9+rV8mcGHXjR5YY+aouvcuTOmT5+OmjVr4sWLF/jss88AANevX+dyciKiYjJp0iT519wsonjVrFkTx44dw7hx4xTGjx49yhUeRGUUC2wlbOLEiQDeFyFmzZqlcExHRweWlpZK41R8eMUj/169eoV169bB1NQUbm5uuS5xJirNnJyccPr0aZiamsLR0THXYvCHLeHZXLvofHx8YGlpiaSkJMyYMQMVKlQA8P6K/9dffy04HRGR5svr/Rpb0qiGp6cnvLy8EBsbK+/BduHCBZw9exarV68WG46IhGCBrYQlJCQAeN/8ed++fTAxMRGcSDM9ePAgX+vwN2/eLO+9RB9nbGyMhw8fwtTUFI8ePWJxktROWFgYjI2NAQDh4eGC02g+XV1deHh4KI2zJw0RUclgS5ri1aVLF+zZswehoaHyYmWdOnWwd+9eNGzYUHA6IhKBBTZBfv/9d9ERNFqnTp1QrVo1ODk5wdnZGc7OzrkuSXJ0dBSQTj117twZQ4YMgbm5OSQSCfr375/nbj28IqoaUVFRiIqKQkpKitIb5A898b777juYmpqKiKd2nJ2dc/36YxYsWIDJkyfzYkg+RUZG4rPPPoOuru4nXwfc3NxKKFXZ5ujoiHLlyomOodEsLS2ho8O31FR6sCVNyWncuDGWL18uOkaZxvY0VJpIZJyGIkRBZk+4u7sXYxLN9PTpU0RHRyM2NhYxMTG4f/8+zM3N4eTkhFatWmHAgAGiI6qlkydPIjExEf7+/pg8ebJ8ydd/DR8+vISTaZ6goCCsX78ejRs3RpUqVZTePKxfv15QsrLFwcGBO1MVgJ2dHc6cOQNTU1PY2dnleR6X4Rbd1atXoaOjA1tbWwDAb7/9hp9++gl169bFpEmT5E3OqWjS0tJw4sQJJCYmwsPDA5UqVcLVq1dhZmbGGfAlxN7eHocOHeLrcAF8eP3NrWfuv1vSfP755yLiqbVP9XD9N/ZzLRl8jaDShAU2QVxdXZGamoqMjAwYGRkBeP8mzsDAQKFPgkQi4WwgFbh37x42btyIw4cPIycnhx/sisjHxwezZ8/mG4di1LZtW0yfPl1h63cqeXzTRqVV//79MXbsWHTp0gUPHjxA9+7d0alTJ8THx6N9+/aYPXu26IhqLyEhASNHjkTFihXx6NEjHD9+HNbW1li1ahWSkpKwdOlS0RHLhMOHD8PNzQ3ly5cXHUXtsCWN6tnZ2eV7xhQ/b5SMpKQkmJubQ1tbW3QUIi4RFcXLyws7duxAQEAA6tSpAwC4c+cO5s6di0GDBqFXr16CE6q3jIwMnD9/HjExMYiJicG1a9dQp04dDBkyJN9LwyhvH5YnUvHJysqSN8wl0mQ9e/ZEcHAwLCwsREdRK/fu3UODBg0AAMeOHYOTkxNWrFiB8+fPY+rUqSywqcDixYvRt29fzJw5E/b29vLx9u3bY/r06QKTaY78tELo2bOniGgagS1pVO/fq5AePXqEFStWoG/fvmjevDkA4OLFi4iIiMC0adMEJVRv/94F91OCgoIAgO8fqFRhgU2QNWvWYO3atfLiGvC+KaaPjw8mT57MAlsROTk5wcjICD179sSYMWPg6Ogob25OpA6+/PJLHD58WL7zMJGmevjwIbKzs0XHUDsymUxekIiKikKHDh0AvP+gkZqaKjCZ5oiPj8d3332nNF61alUkJycLSKRZPtUKgVQjKioKoaGhuH37NgDAxsYGw4cPR+vWrQUnU0//vlA/fPhwzJo1Cz169JCPubm5oX79+tizZw/69u0rIqJa+3e/QCJ1xAKbIMnJybl+oMjJyUFKSoqARJrls88+w/nz5/Hzzz/j2bNnePbsGZydnVG7dm3R0Yjy5d27d9izZw+ioqJga2ur1EDbx8dHUDIiKg0aN26MDRs2wMXFBbGxsViwYAGA9wVLMzMzseE0hJ6eXq79lu7du8cldyqwa9cuBAYGshVCMdq+fTsWLVqELl26yHs6X7p0CWPHjoWPjw+GDBkiOKF6u3jxIvz8/JTGGzdujDlz5ghIpP64SobUHQtsgri4uGD+/Pnw9/dHo0aNAABXrlzBggUL4OLiIjid+vv+++8BvO+fEhsbizNnzmDNmjXQ1taGs7MzVqxYITgh0cfduHFD3qT45s2bCsd4lZ+IfH19MWPGDPz2228YN26cfKfsEydOKCxnpMJzdXXF+vXrsXr1avnY48ePsXz5cnTu3FlcMA3BVgjFb9OmTfDx8cHQoUMVxh0cHLBx40YW2IqoWrVq2LNnD2bOnKkwvnfvXlSrVk1QKiISiZscCPL8+XN4e3vj1KlT8pkpUqkUbdu2xeLFi2Fqaio4oWaQyWS4du0aoqOjER0djdOnT8vHiIg+Zf78+fj22285W6UYcSMJ1Xr37h20tLSgq6srOorae/XqFSZPnowrV67gzZs3MDc3x7Nnz9C8eXMEBwez6X4RLVu2DOXLl2crhGJkb2+PAwcOyAvwH9y7dw99+/ZFXFycoGSa4a+//oKnpydq1qyJpk2bAgAuX76M+/fvY926dWjfvr3ghOrv+PHjOHbsGJKSkpCVlaVwLCIiQlAqorxxBpsgJiYm2Lx5M+7duyfviVCnTh0uYVSRkJAQREdH48KFC3jz5g1sbW3h5OSEgQMHwtHRUXQ8IioF0tLScPnyZaSkpOC/15o+LFnKbekHUWmSmZmJ58+fKzWIr169uqBEmqNixYoICQnB+fPnkZCQgPT0dDRq1Ii9q1SErRCKn6urK3799VeMHj1aYTwyMlLet5EKr3379jhx4gR27tyJO3fuAHj/nH/11VdsvK8C4eHhWLVqFfr164fIyEj069cPDx48QHx8PGdfUqnFGWylnIODAw4ePMgr+wXUv39/ODs7w9nZGY6OjmyYSUQKfv/9d0yfPh3p6ekwNDRUWHYrkUgQExMjMF3ZwhlshXP37l3Mnj1baQaKTCaDRCLB9evXBSXTHAcOHEC3bt2gp6enMJ6ZmYmjR4+yd1gRDRs2LM9jEolEYbdGKpzvv/8eP/zwAxwcHOS7XF66dAkXLlzAyJEjYWhoKD/3Q482otKia9eumDRpEnr06KHwXmHNmjV4+fIl5s2bJzoikRIW2Eo5fvAgIlK9Ll264LPPPsPUqVNhYGAgOo7GioqKQlRUFFJSUpRmWH1oZHz48GG4ublxuV0BffXVV9DR0cGYMWNgbm6u1JvxQw9HKrwGDRrg9OnTSm07UlNT0bp1axYxqdRzdXXN13kSiQSRkZHFnEbzDBkyRH5B38HBAeXKlRMdSaM0a9YMR48ehaWlJVxcXBASEgI7Ozvcu3cPgwYNQnR0tOiIREq4RJQ0VlpaGvbt2ydfglu3bl18+eWXnM1GRHj69Cnc3d1ZXCtGQUFBWL9+PRo3bowqVarkuTlHz549SziZZkhISMD+/fthY2MjOorG+jAb8L+ePn3K9xIq9uTJEwBgY3gV+/3330VH0Ght2rTBuXPnEBoaiuzsbDRu3Fih4Mb3GEVjZmaGly9fwtLSEhYWFrh48SLs7Ozw8OFDpdYeRKUFC2ykkeLj4zF69GiUK1dO3nQ0NDQUGzduxA8//CDfuZWIyqa2bdsiPj6es4OL0a5duxAYGMhldMXExsYGqampomNopD59+kAikUAikWD48OEKvcGkUikePnyIdu3aCUyoGXJycvD9998jJCQE6enpAIAKFSpg5MiRGD9+PLS0tAQnJPq4CRMmAACys7MRHx+P2NhYREdHY+vWrZBIJIiPjxecUL21atUKv//+Oxo2bIj+/fsjMDAQJ06cwJUrV9CpUyfR8YhyxQIbaaTAwEC4urpi4cKF8jfG2dnZmDNnDhYtWoTt27cLTkhEIrVv3x7Lli3D7du3Ub9+faXm2m5uboKSaY6srCw4ODiIjqFRXr9+Lf96+vTpWL58Oby8vFC/fn2lXUP/3VuJCqZjx44AgOvXr6Nt27aoUKGC/Jiuri4sLS3RuXNnUfE0xqpVq7Bv3z5MmzZN/lpx/vx5BAUFITMzE15eXoITqj+ZTIbjx48jOjo6181QgoKCBCXTLA8ePMDNmzeRkJCAGzduoEKFCtxUTQUWLlwo/5sdMmQIKlWqhLi4OLi6umLQoEGC0xHljj3YSjluclA4TZs2RUREhNLSmX/++Qf9+/fHpUuXBCUjotLgY/2p2CBeNZYtW4by5ctj4sSJoqNoDDs7O4Uli7ktYeQmB6oTERGBbt26sa9SMWnbti38/PyULmj89ttv8PPzw6lTpwQl0xz+/v7YvXs3WrZsCTMzM6XXiw+9MKlwpk2bhpiYGGRmZsLJyQlOTk5o2bIlbG1t82yLQESajTPYSjnWPwvH0NAQSUlJSgW2pKQkhSvRRFQ2JSQkiI6g8d69e4c9e/YgKioKtra2SrMEfXx8BCVTX9xVsWT17dtXdASN9vLlS9SpU0dpvE6dOnj58qWARJrn0KFDCAoKQvv27UVH0Ug///wzKleujAEDBqBVq1Zo0aIF+66pWFpaGi5fvoyUlBSlz8VsQUGlEQtspdzmzZtRtWpV0THUTrdu3TB79mx4e3vD3t4eAHDhwgUsXboU3bt3F5yOiNRFz549ERwcDAsLC9FR1M6NGzfkMwVv3rypcIxX9gvH2dlZ/vXjx49hYWGR6wy2pKSkko6mkaRSKUJDQ3Hs2DEkJSUhKytL4XhMTIygZJrBzs4O27dvx5w5cxTGt2/fzl1wVcTQ0BBWVlaiY2is6OhonDt3DjExMVi5ciVu376NBg0ayDc6aNu2reiIau3333/H9OnTkZ6eDkNDQ4V/7yQSCQtsVCpxiagg7IlQvDIzM7F06VLs2rULUqkUAKCjo4PBgwdj+vTp0NPTE5yQiNSBvb09Dh06xGX6VOo0aNAAp0+fhqmpqcJ4amoqWrduzSWiKrBmzRrs3bsXo0aNwurVqzFu3Dg8evQIv/32GyZOnAh3d3fREdVaTEwMvvnmG1hYWKB58+YAgIsXLyIpKQmbN29mDysViIiIwKlTp7Bo0SLo6+uLjqPx7t+/jw0bNuDw4cPIycnh63ARdenSBZ999hmmTp3KmYGkNjiDTZCAgICP9kSgotHT08OcOXMwbdo0JCYmAgBq1KjBF2ciItIIufVfA4D09HT2DFORw4cPw9/fHx06dMC6devQo0cP1KhRA7a2tuzlqgLOzs44fvw4duzYgTt37gAAOnXqhK+//pqrN1Tkiy++wJEjR+Di4gIrKyulpfoRERGCkmmG1NRUxMbGIiYmBtHR0bh9+zYqVqyIzz//HE5OTqLjqb2nT5/C3d2dn99IrbDAJgh7IpQMAwMD2Nraio5BRESkEh+akkskEqxevVrhg4dUKsXly5e5vE5Fnj17hvr16wMAKlSogFevXgEAPv/8c6xZs0ZkNI1RtWpV7hZajLy9vXH16lX06tWLF/SLQevWrVG5cmU4Ojpi4MCBcHZ25ucOFWrbti3i4+O5ioDUCgtsgrAngupNmjQp3+dyCS4REamja9euAXg/g+3mzZvQ1dWVH9PT04OdnR1GjRolKp5GqVq1KpKTk1G9enVYW1vjzJkzaNSoEeLj49lqopASEhJQv359aGlpfXKzGRaKi+6vv/7Cli1buNy2mBw6dAj16tUTHUNjtW/fHsuWLcPt27dRv359pRmY/92BmKg0YIFNEE9PT6xfv549EVSoYsWKoiMQEREVq23btgF4vwvr7NmzYWhoKDiR5urUqROioqLQrFkzDBs2DDNmzMC+ffvw+PFjjBgxQnQ8tdSnTx+cOXMGpqam6NOnDyQSidLOgMD7GZrsX1V01apV42tEMWJxrXjNnTsXALB+/XqlY3yNoNKKmxwI8vbtW0ycOBEXLlxgTwQViYyMRLt27XhVmYhUhpscENEHFy9eRFxcHGrWrAlXV1fRcdTSo0ePUL16dUgkEjx69Oij51paWpZQKs31559/Ytu2bfDz8+PKmWJy/PjxPHca5uc5orKHBTZBvv32W0RHR6NLly659kQoyHJHeq9BgwY4c+YMTExM8txdjYioIA4fPgw3NzeUL19edBQiBenp6QgODsbZs2eRkpKitBt5ZGSkoGSaISsrC/PmzcOECRNYYC8msbGxsLe3V7rInJ2djbi4ODaJVwEnJydkZGRAKpVCX19fYUk58H4nVyq88PBwrFq1Cv369cPu3bvRr18/PHjwAPHx8RgyZAj7CxKVQVwiKgh7IqieiYkJLl68CFdX1zx3VyMiAt6/Kc6NRCJBuXLlUKNGDTg5OaFnz54lnIwof+bMmYOYmBj07t0bVapU4b95Kqarq4tffvkFEyZMEB1FY7m7u+d6MfTVq1dwd3fn8i8V8PX1FR1Bo+3YsQMLFy5Ejx498NNPP2HMmDGwtrbGmjVr8PLlS9Hx1FJ4eDgGDRqEcuXK5fle7QN3d/cSSkWUfyywCcKeCKr31VdfYcKECZBIJJBIJGjTpk2e5/JNG1HZFhoaitTUVGRkZMDY2BgA8PLlSxgYGKB8+fJISUmBtbU1wsPDYWFhITgtkbKTJ09i06ZNaNGihegoGqtjx46IjIxkv7ViktfF0BcvXijsjkuF17dvX9ERNFpSUhLs7e0BAPr6+njz5g0AoHfv3hg0aBDmzZsnMp5aCg0NRc+ePVGuXDmEhobmeZ5EImGBjUolFtgEmTVrFpYtW8aeCCrk6emJbt26ITExEePHj0dgYCA3PiCiXE2dOhW7d+9GQEAAatSoAQC4f/8+5s2bh0GDBsHBwQFeXl4IDAzE2rVrBaclUmZkZIRKlSqJjqHRatasifXr1+PChQto1KiRUtGHH+4K50MbFIlEglmzZin0zpVKpbhx44a8aEFFl5iYiP379+PBgweYPXs2TE1N8ddff6F69eps0l9EZmZmePnyJSwtLWFhYYGLFy/Czs4ODx8+zHXzDvq0gwcPyj+//f7774LTEBUce7AJwp4IxSsoKAgeHh6fvAJ6/vx5NGnShBsjEJUxHTt2xLp169CgQQOF8WvXrsHT0xORkZG4cOECJk+ejNOnTwtKSZS3gwcPIjIyEkuWLOFsn2LysY0MJBIJ+9wVko+PD4D3DeC/+OIL6Ovry4/p6urC0tISAwYMgImJiaiIGiMmJgZjxoyBg4MDYmNjcezYMVhbWyM4OBhXrlzhBaQimj17NiwsLDBp0iRs374dS5cuhYODA65cuYJOnTph0aJFoiOqnX/30XZ3d0dQUBCMjIxExyLKN85gE4Q9EYpXfjeJGDNmDA4ePMgGxkRlTHJyMrKzs5XGs7Oz8ezZMwCAubm5fLkHUWkTEhKCxMREtG7dmruRFxPOnigegYGBAN7vEpqfi6FUeCtWrMCUKVMwcuRIhVmBrVq1wo8//igwmWZYuHChfIOZIUOGoFKlSoiLi4OrqysGDRokOJ16Kl++PF68eAFTU1PExMTk+l6NqDRjgU0Q9kQoHTiBk6hsatmyJebPnw9/f380bNgQwPvZawsWLECrVq0AADdv3uQSfiq1OnbsKDoC/X8ODg68WFcIvXv3xtOnT1GrVi2F8Xv37kFHR4evvypw8+ZNLF++XGncxMQEqampAhJpjuzsbGzcuBFffvklqlWrBgDo3r07unfvLjiZemvdujXc3d1Rp04dAMDEiROVVnp98KlNEIhEYIFNkMePH3/0ePXq1UsoCRFR2RMQEICZM2eiX79+8pk/UqkULi4uCAgIAPD+Kqq3t7fImER5yu9MbSp+vFhXOD4+Pujfv79Sge3SpUvYt28ftm3bJiaYBqlYsSKSk5OVir/Xr19H1apVBaXSDDo6Oti6dSv69OkjOopGWbZsGSIiIpCYmIjY2FjUq1dPYRk5UWnHApsgrq6uue6c9AF3uSQiKj5VqlRBSEgIbt++jXv37gEAateuLb9iCkA+k42oNLty5Qpu374NAKhXr558RiZRaXft2jX5BY1/a968ORYuXCggkebp3r07li9fjjVr1kAikSAnJwfnz5/HkiVLWBhSgVatWiE2NpazLVVIX18fgwcPBvD+37fp06ezBxupFRbYBDlw4IDC91lZWbh+/TpCQkLg5eUlJhQRURlx7tw5ODo6wsbGBjY2NqLjEBVYSkoKvLy8EBMTI//wkZaWhpYtW2LVqlVsEE+lnkQiybXP5atXryCVSgUk0jxeXl747rvv0KFDB0ilUnTv3h1SqRQ9evTA+PHjRcdTe5999hlWrFiBmzdv5rrTsJubm6BkmiG/s1i5TJ9KE+4iWsr8+eef2Lp1K6fFlxC+IBOVTY0bN4a5uTl69OiBXr16oW7duqIjERXIlClT8ODBAyxdulReJP7nn3/g7e2NmjVrYuXKlYITlh329vY4dOgQ30sU0Lhx41CuXDmsXLkS2traAN4v1ffy8kJ6ejq2bNkiOKHmePz4MW7duoU3b96gYcOGSstyqXDs7OzyPCaRSLgiqYTwNZhKE85gK2Vq166N+Ph40THKDNaXicqmkydP4ujRozhy5AiCg4Nha2uLnj17okePHvJmxUSl2alTpxASEqIwA7Nu3bqYP38+Ro0aJTBZ2fOxlh+Ut+nTp2PIkCHo2rUrHB0dAbyfXfz69WuEhYUJTqdZqlevzv7OxSAhIUF0BCIqZVhgE+T169cK38tkMvzvf/9DUFAQatasKSiV5nj79i1kMpl8qvajR4/w66+/om7dumjbtq38vLi4OFERiUggExMTDB06FEOHDsWDBw9w5MgRHDhwACtXroSjoyN3pqJSLycnJ9ed1XR0dJCTkyMgUdnFi3WFU7duXRw6dAjbt29HQkIC9PX10bt3bwwdOhSVKlUSHU9tBQYG5vtcHx+fYkxCRFT2sMAmiKOjo9IVT5lMBgsLCy7rUIEJEyagU6dOGDx4MNLS0jBw4EDo6OggNTUVs2bNwtdffy06IhGVEtbW1hg7dizs7OywZs0axMbGio5E9EmtWrVCQEAAVqxYId8N8OnTpwgMDISLi4vgdJohKCgIHh4eSn2V3r59iy1btsh3ct28eTN3ZCykqlWrYurUqaJjaJRr164pfS+VSlG7dm0AwL1796ClpYVGjRqJiKf2CnIBzt3dvRiTEFFpxB5sgsTExCh8r6WlhcqVK6NmzZrQ0WHds6hatmyJH3/8EfXq1cPevXuxbds2HDhwACdOnMDatWtx7Ngx0RGJqBQ4f/48Dh8+jBMnTuDdu3dwc3NDz5498dlnn4mORvRRSUlJGD9+PP755x/5suYnT56gXr162LBhA5c6q0CDBg1w+vRpmJqaKoynpqaidevW7K+kAufOncOuXbvw8OFDrFmzBlWrVsWBAwdgZWUlXzZKhRcSEoLo6GgsWbIExsbGAICXL1/Cx8cHjo6OXE5eCK6urgrfp6amIiMjQ2GzGQMDA5iYmCAyMlJExDKHPbWpNGElRxBnZ2cA7xsSP378GFlZWXj58iXu3bsHgLvOFNXbt29RoUIFAMDp06fRuXNnaGlpoXnz5nj8+LHgdEQk2ooVK/Dzzz/jf//7H9q0aYPZs2fDzc1NaaYKUWllYWGBiIgI/P3337hz5w4AwMbGBq1btxacTHPIZLJc+6slJCTIixVUeCdOnMDMmTPRs2dPXL16FZmZmQDet1HZtGkTC2wq8MMPP+CHH35Q+Hs1NjbGlClTMGrUKBbYCuH333+Xf3348GHs2LEDAQEBqFOnDgDgzp07mDt3LgYNGiQqYpnD+UJUmrDAJsiDBw8wadIk3LhxAxKJRP7C8OGNHK+KFk2NGjXw22+/oVOnTjh9+jRGjBgBAEhJSYGhoaHYcEQkXGxsLDw8PPDFF1/AxMREdByiQpFIJGjTpg3atGkjOopGcXJygkQigUQiQZcuXRSKbFKpFOnp6fjqq68EJtQMGzZsgJ+fH/r06YOff/5ZPu7g4IANGzYITKY5Xr9+jefPnyuNP3/+HG/evBGQSLOsWbMGa9eulRfXAKBOnTrw8fHB5MmT0atXL4Hpyg4u06fShAU2QQICAmBpaYmQkBC4ublh7969ePHiBZYsWQJvb2/R8dTexIkTMX36dHkvGnt7ewDAmTNn0KBBA8HpiEi0Xbt2AXg/i/jKlSvIyspSOM5ZxKQOLl++jOjoaDx//lxpYwM2Ly88X19fyGQy+Pr6wtPTExUrVpQf09XVhaWlpfx9BRXe3bt3c52lVrFiRaSlpQlIpHk6deoEHx8fzJo1C02bNgUAXLp0CUuXLkXnzp0Fp1N/ycnJyM7OVhrPyclBSkqKgESaRSaT4fjx43n+OxcUFAQAnO1KpQoLbILExcUhLCwMJiYm0NLSgpaWFhwdHTF16lT4+/vjwIEDoiOqta5du6JFixZITk6GnZ2dfNzFxQUdO3YUmIyISgPOIiZ1t3HjRqxevRq1a9eGmZmZwrHcljVS/vXt2xcAYGVlBXt7+1x3a6WiMzMzQ2JiIqysrBTGz58/z15KKuLn54clS5Zg2rRp8kKQtrY2vvzyS8ycOVNwOvXn4uKC+fPnw9/fX75pxJUrV7BgwQJuNqMCAQEB2L17N1q2bAkzMzP+20ZqgQU2QXJycuQ9wipXroz//e9/qFOnDiwtLXH37l3B6TRDlSpVUKVKFQDvp8ifPXsWtWvXho2NjeBkRCTaf2cR79u3D6mpqZxFTGojPDwcixYtQr9+/URH0SivX7+Wt5Jo2LAh3r17h3fv3uV6LltOFM3AgQMREBCARYsWQSKR4OnTp4iLi8OSJUswYcIE0fE0goGBARYsWICZM2ciMTERwPs2KuXLlxecTDMsWrQI3t7e6N+/v3yTOqlUirZt2yIgIEBwOvV36NAhBAUFoX379qKjEOUbC2yC1KtXDzdu3IC1tTWaNWuGLVu2QFdXF3v27OFVOxX49ttv4eTkhKFDh+Lt27fo378/Hj16BJlMhpUrV6JLly6iIxKRQP+dRSyRSDiLmNSKlpYWHBwcRMfQOE5OTvKdQx0dHXOdMfFh8wPOdC2asWPHIicnByNGjEBGRgaGDh0KPT09jBo1CsOGDRMdT6MkJycjOTkZTk5O0NfXz3MDDyoYExMTbN68Gffu3cPt27cBvO/BVrt2bcHJNIOhoaHSDFei0o4FNkHGjx+PjIwMAMDkyZPxzTffYMiQIahUqRJWrVolOJ36O3fuHMaPHw8A+PXXXyGTyRAbG4uIiAhs2LCBBTaiMo6ziEndDR8+HNu3b8fs2bNFR9EoYWFh8h0Xw8LCWIQoJlKpFBcuXMCQIUPg4eGBxMREpKenw8bGRv7aTEWXmpqKKVOmIDo6GhKJBL/88gusra3h6+sLY2NjzJo1S3REjVCrVi3UqlUrz+MODg44ePAgJ1EUkKenJ9avX49FixZBX19fdByifGGBTZB27drJv65ZsyaOHz+OFy9ewNjYmG/mVODVq1fyN8inTp1C586dYWBggA4dOmDZsmWC0xGRaJxFTOrOw8MDY8eORceOHVG3bl358qQPPjR/poJxdnaWf92yZUuBSTSbtrY2Ro0ahaNHj8LIyAh169YVHUkjBQYGQkdHB3/++Se++OIL+Xi3bt2wePFiFthKyIc+r1QwX3zxBY4cOQIXFxdYWVkp/TsXEREhKBlR3lhgK0UqVaokOoLGsLCwQFxcHIyNjXHq1CmsXLkSAJCWlgY9PT3B6YhINM4iJnXn7++P6OhotGzZEpUqVeLFuWKwbt06TJw4EVpaWgrjr169wvz58+XvLahw6tWrh4cPH/KiRjE6c+YMtm7dimrVqimM16pVC48fPxaUiih/vL29cfXqVfTq1YubHJDaYIGNNJK7uztmzJiB8uXLo3r16vKr0LGxsahfv77gdEQkGmcRk7qLiIjAunXr0KFDB9FRNNa+fftw5swZLFu2TF4Eio6Ohre3t9LOrVRwU6ZMwZIlS/Dtt9+iUaNGSo33uYlE0aWnp+e6tO7Fixe84Eyl3l9//YUtW7bA0dFRdBSifGOBjTTSkCFD0LRpUzx58gStW7eWX322trbGlClTxIYjolKJs4hJnVSqVIkzf4rZoUOHMG/ePPTu3RuzZs3C3bt3sW3bNnh4eMDT01N0PLU3duxYAO9nFP/7wgY3kVAdR0dHHDhwQOG9b05ODrZs2cIl0FTqVatWjYV2UjsSGReFk4b78CfOWSlERKQp9u/fj1OnTiEwMBAGBgai42i0lStXIjg4GDo6Oti8eTNcXFxER9IIMTExHz3+7354VDi3bt3C8OHD0bBhQ5w9exaurq74559/8PLlS+zcuRM1atQQHbFM4CYHhfPnn39i27Zt8PPz426ipDZYYCONdeDAAWzduhX37t0D8L7fhIeHB/r06SM0FxERUVH16dMHiYmJkMlkbP5cjLZt24YVK1bAzc0NV69ehba2NlasWAE7OzvR0Yg+KisrC6NHj8a0adNw5swZJCQkID09HQ0bNsSQIUNgbm4uOmKZYW9vj0OHDrHAVkBOTk7IyMiAVCqFvr4+dHV1FY5/qkhPJAKXiJJGCgkJwZo1azBkyBD5tPjz589jwYIFePHiBUaMGCE0HxERUVF07NhRdASN5+HhgStXrmDx4sXo2rUr3r59i8DAQAwcOBCenp4YM2aM6IhqJyEhAfXr14eWlhYSEhI+ei6LmEWjq6uLGzduwMjICOPHjxcdp0zbvHkzqlatKjqG2vH19RUdgajAOIONNJKrqysmT56sNFvtQ1Po33//XUwwIiKiEnTkyBG4uroqNZCnTxs5ciQWL16s9MH4zz//xJw5c3D69GlBydSXnZ0dzpw5A1NTU9jZ2UEikSC3jyLswaYaixYtgp6eHqZPny46ikaSyWQ4fvw4oqOj8fz5c+Tk5CgcDwoKEpSMiEThDDbSSMnJybC3t1cat7e3R3JysoBEREREJW/evHlo1qwZC2yFEBISgnPnzmHZsmV48OAB1q5di6pVq+LFixdYvXq16HhqKTIyEiYmJvKvqXhJpVLs3LkTf//9Nxo3bqzUr9HHx0dQMs0QEBCA3bt3o2XLljAzM2O/ZxV7/PjxR49Xr169hJIQ5R8LbKSRatasiWPHjmHcuHEK40ePHkWtWrXEhCIiIiphXKhQeCdOnMDMmTPRs2dPXLt2DZmZmQCA169fY9OmTXB0dBScUP1YWlrm+jUVj5s3b6Jhw4YAgLt37yocYzGo6A4dOoSgoCC0b99edBSN5Orq+tG/U85ypdKIBTbSSJ6envDy8kJsbCwcHBwAABcuXMDZs2d51ZmIiIg+acOGDfDz80OfPn3w888/y8cdHBywYcMGgcnUV0Fmrbm5uRVjkrJh27ZtoiNoNENDQ+5uWYwOHDig8H1WVhauX7+OkJAQeHl5iQlF9AkssJFG6tKlC/bu3YuQkBD5m7k6depg79698it5RERERHm5e/durrPUKlasiLS0NAGJ1N/EiRMVvv9vD7Z/z1bh7BQq7Tw9PbF+/XosWrQI+vr6ouNonNw2OmnSpAnMzc2xdetWdO7cWUAqoo9jgY00TlZWFubNm4cJEyZg+fLlouMQERGRGjIzM0NiYqLSDJXz58/D2tpaUCr19u+dQ//++28sX74cXl5e8r65cXFxWL16NaZOnSoqIlG+ffHFFzhy5AhcXFxgZWUFHR3Fj9YRERGCkmm22rVrIz4+XnQMolyxwEYaR1dXF7/88gsmTJggOgoRERGpqYEDByIgIACLFi2CRCLB06dPERcXhyVLlvA9hgosWrQICxYsUJgl2K5dOxgYGGDu3Lk4duyYwHREn+bt7Y2rV6+iV69e3OSgGLx+/Vrhe5lMhv/9738ICgpCzZo1BaUi+jgW2EgjdezYEZGRkRgxYoToKERERMJYWloqzaqg/Bk7dixycnIwYsQIZGRkYOjQodDT08OoUaMwbNgw0fHUXmJiIoyMjJTGDQ0N8ejRIwGJiArmr7/+wpYtW7jhSTFxdHRUKlrKZDJYWFhg5cqVglIRfZxExu2lSAN9//33CAkJgYuLCxo1aqS0Lbm7u7ugZERERKROMjMzkZiYiPT0dNjY2KBChQqiI2mEIUOGoFy5cli6dCnMzMwAAM+ePYO3tzfevXuHH3/8UXBCoo/r2rUrVq9enWuvMCq6mJgYhe+1tLRQuXJl1KxZkxeOqNRigY00kqura57HJBJJgXaxIiIiKm2cnJxyXY4kkUigp6eHmjVrom/fvujfv7+AdESfdv/+fUyaNAl3796FhYUFACApKQm1atXC+vXruQSMSr0///wT27Ztg5+fH3cTLUb//PMPHj9+jKysLIVx7jRMpRELbERERERqJjQ0FBs2bMBnn32Gpk2bAgAuX76MU6dOYcSIEXj48CEOHjyIuXPnYuDAgYLTEuVOJpPhzJkzuHPnDgDAxsYGrVu3Zi8rUgtOTk7IyMiAVCqFvr4+dHV1FY7/dwYWFcyDBw8wadIk3LhxQ2HH4Q+vD9xpmEojFthIIwUGBuY6LpFIUK5cOdSoUQNubm6oVKlSyQYjIiJSAU9PT7Ru3RqDBw9WGN+1axfOnDmDdevWYdu2bdizZw8OHz4sKCVR0fXs2RPBwcHyWW5EpcWndgnt27dvCSXRTOPGjYOWlhb8/f3h5uaGvXv34sWLF1iyZAm8vb3Z+45KJRbYSCMNGzYM165dQ05ODmrXrg0AuHv3LrS1tVGnTh3cvXsXEokEO3bsQN26dQWnJSIiKhh7e3scOHBAaRnd/fv30adPH8TFxSExMRG9evXCxYsXxYQkUgF7e3scOnQI1tbWoqMQUQlq2bIlwsLCYGdnhxYtWmDv3r2oU6cOoqKisGTJEhw4cEB0RCIlWqIDEBUHNzc3tG7dGqdOncJPP/2En376CSdPnkTr1q3RvXt3nDx5Eo6OjnnOdCMiIirNjI2N8ccffyiN//HHHzA2NgYApKensyE/EVExefz48UdvVDQ5OTnyf8MqV66M//3vfwDe74599+5dkdGI8sTtN0gjbd26FSEhITA0NJSPVaxYEZ6enhg1ahSGDx+OiRMnYtSoUQJTEhERFc6ECROwYMECnD17Vt6DLT4+HidPnsSCBQsAAH///TecnJwEpiQi0lyurq4f7RfIHmFFU69ePdy4cQPW1tZo1qwZtmzZAl1dXezZs4czWqnUYoGNNNLr16+RkpKitPzz+fPneP36NQDAyMhIaTcaIiIidTBw4EDY2Nhg+/bt+PXXXwEAtWvXxrZt2+Dg4AAAvIhERFSM/rtEMSsrC9evX0dISAi8vLzEhNIg48ePR0ZGBgBg8uTJ+OabbzBkyBBUqlQJq1atEpyOKHcssJFGcnV1ha+vL2bNmoUmTZoAeH9lf8mSJejYsSOA97ut1apVS2BKIiKiwmvRogVatGghOgYRUZlkZ2enNNakSROYm5tj69at6Ny5s4BUmqNdu3byr2vWrInjx4/jxYsXMDY25k7DVGqxwEYa6bvvvkNgYCC8vLwglUoBANra2ujbty98fHwAAHXq1EFAQIDImERERIWWk5OD+/fvIyUlBf/ds4pLQ4mIxKhduzbi4+NFx9BIlSpVEh2B6KO4iyhptDdv3uDBgwcAAGtrazZ7JiIijXDx4kVMmzYNjx8/ViquSSQS9v6hUu/du3coV67cJ887fPgw3NzcUL58+RJIRZR/H9rOfCCTyfC///0PQUFBuHPnDg4ePCgoGRGJwgIbERERkZrp3bs3atWqhcmTJ6NKlSpKy2UqVqwoKBlR/jRp0gRNmzaFk5MTWrZsCXt7e+jr64uORZRvdnZ2Sq+9MpkMFhYWWLlyJezt7QUlIyJRWGAjIiIiUjPNmzfHwYMHUbNmTdFRiArl3LlzOHfuHGJiYhD3/9q719gsy/sP4N+nwLRDLRTDNKwQO5G6EE8r1UYkhkdZlogxM8aoGYlxGy5xJNZtxBgVHQZdNomH8EJNjHhIPMTMIEs0Vo14okzB6dAaaWE4laNAgkQp7V64kT9/NgWr3N48n0/SpM/V+8X3xf2m3+e6rt/y5enr68vEiRPT1taWtra2nH766UVHhC/U1dW1x+e6urqMHDky48aNy9ChbmKCWqRgAwAomRkzZuTnP/95pkyZUnQUGLS+vr68+eabefjhh7No0aL09/c75kxpvPfee/nggw+yc+fOPdar1WpBiYCiqNYBAErmZz/7WW655ZZs3Lgxxx133F67Jf7bdDv4tunt7U1XV1e6urqydOnS7Ny5M2eeeWba2tqKjgZfau3atbniiivS3d2dSqWy+z7M/xwbVRJD7bGDDQCgZP5bgfaff/AMOaAMzjjjjHz66ae7j4S2tbVlwoQJe91pBd9Wl19+eerq6jJ37txUq9U8+uij2bJlS2655ZbMnj07ra2tRUcEDjA72AAASqazs7PoCDAojY2N6enpycaNG3f/jBs3LvX19UVHg32yfPny3HfffWlsbExdXV3q6urS2tqajo6OzJ07N3/+85+LjggcYAo2AICSGTNmTNERYFCeeOKJbNu2LcuWLcuyZcty6623ZtWqVTn++ONz6qmn5sorryw6Inyh/v7+DB8+PEkycuTIrF+/Ps3NzRkzZkx6e3sLTgcUQcEGAFACnZ2dmTJlSoYNG/alO9hcrk0ZHHHEEalWqznllFNy8sknp7OzM4sXL84bb7yhYONbb/z48enu7k5TU1NOPPHE3HPPPRk2bFgeeeSRNDU1FR0PKIA72AAASqClpSUvvfRSRo0a9YVDDNzBRhk8/fTTu4cbrFq1Kg0NDfnRj360+z42gzr4tluyZEl27NiRadOmZc2aNZk5c2ZWr16dESNGZP78+Wlvby86InCAKdgAAIADqr29PZMmTUpbW1smTZqUCRMmFB0JBm3Lli1paGgwrANqlIINAAAAAAbBHWwAACWwcOHCfX52xowZ32AS+Hrs2rUrzzzzTFatWpUkOfbYY1OtVjNkyJCCkwHA/rODDQCgBKZOnbrH548//jg7duzIEUcckSTZtm1b6uvr09jY+KVDEKBoa9asyS9/+cusW7cuxxxzTJKkt7c3Rx11VO66666MHTu24IQAsH8UbAAAJbNo0aI89NBDuemmm9Lc3Jwk6enpybXXXpsLL7ww5557bsEJ4Yv94he/yMDAQP74xz9mxIgRST4vjX/729+mrq4ud911V7EBAWA/KdgAAErmrLPOyu23354f/vCHe6y/9dZbmTVrVp599tmCksG+Oemkk/Lwww/vNdzgnXfeyUUXXZTly5cXlAwAvpq6ogMAALB/NmzYkL6+vr3W+/v7s2nTpgISwf75zne+k+3bt++1vn379gwbNqyARAAwOAo2AICSaW9vz/XXX5+///3vu9feeuutzJkzJ+3t7QUmg31z5pln5rrrrssbb7yRgYGBDAwMZMWKFZkzZ85e9w0CQBk4IgoAUDKbN2/O7Nmzs2TJkgwd+vlQ+F27dmXy5Mm5+eabM2rUqIITwhfbtm1bZs+eneeee26Pd3jq1Km5+eabc/jhhxecEAD2j4INAKCkent709PTkyRpbm7ePY0RymL16tW73+Ef/OAHGTduXMGJAOCrUbABAAAAwCAMLToAAABfbt68efv87NVXX/0NJoGvxjsMwMFMwQYAUAIrV67cp+cqlco3nAS+mscffzzjx4/PkCFDUqlU8r8O0niHASgjR0QBAIBvXEtLS1566aWMGjUq1Wo1jz32WEaOHFl0LAD4WtQVHQAAgK/uo48+ykcffVR0DPhSDQ0Nef/995Mk//znP//nDjYAKCNHRAEASqa/vz8LFizIvffem08++SRJMnz48Fx66aX51a9+lbo636Hy7TNt2rRccsklGT16dCqVSs4///z/+a52dnYe4HQAMDgKNgCAkpk/f34ee+yxXHXVVTnllFOSJK+99lruvPPOfPbZZ7nyyisLTgh7+/3vf5+zzz47//jHPzJ37txccMEFGT58eNGxAOBr4Q42AICSmTx5cm644YZUq9U91p955pnccMMNWbJkSUHJYN9cffXVueaaa3LYYYcVHQUAvhZ2sAEAlMzWrVvT3Ny813pzc3O2bt1aQCLYP/PmzSs6AgB8rVzQAQBQMi0tLXnwwQf3Wn/wwQfT0tJSQCIAgNrmiCgAQMl0dXVl5syZOfroo3PSSSclSVasWJEPP/wwd999d1pbW4sNCABQYxRsAAAltG7dujz00EPp6elJ8vnx0Isvvjjf+973Ck4GAFB7FGwAAAAAMAiGHAAAlNC2bdvyt7/9LZs2bcr//770vPPOKyYUAECNsoMNAKBknn322fzmN7/JJ598ksMOOyyVSmX33yqVSrq6ugpMBwBQexRsAAAl8+Mf/zhTpkxJR0dH6uvri44DAFDz6ooOAADA/lm3bl1mzJihXAMA+JZQsAEAlMzkyZPz5ptvFh0DAIB/c0QUAKAEOjs7d/++efPmLFiwID/96U9z3HHHZejQPedWVavVAx0PAKCmKdgAAEqgpaVln56rVCp5++23v+E0AAD8Xwo2AAAAABgEd7ABABykpk+fng8//LDoGAAABz0FGwDAQer9999PX19f0TEAAA56CjYAAAAAGAQFGwAAAAAMgoINAAAAAAZBwQYAAAAAg6BgAwAAAIBBULABABykbrzxxowaNaroGAAAB73KwMDAQNEhAADYdwsXLvyv65VKJYccckjGjh2bSZMmZciQIQc4GQBAbVKwAQCUzNSpU/Pxxx9nx44daWhoSJJs3bo19fX1+e53v5tNmzalqakpCxcuzNFHH11wWgCAg58jogAAJdPR0ZGJEyfm6aefztKlS7N06dI89dRTOeGEE3LNNdfk+eefz5FHHpl58+YVHRUAoCbYwQYAUDJnnXVW7rjjjhx//PF7rK9cuTK//vWv09nZmddffz2zZs3Kiy++WFBKAIDaYQcbAEDJbNiwIX19fXut9/X1ZePGjUmS0aNHZ/v27Qc6GgBATVKwAQCUzKmnnprrr78+K1eu3L22cuXKzJkzJ6eddlqS5N133833v//9oiICANQUR0QBAEpmw4YN+d3vfpdXXnklQ4cOTZLs2rUr7e3t+cMf/pAjjzwyr776avr6+jJ58uSC0wIAHPwUbAAAJbVq1aqsXr06SXLMMcekubm52EAAADVKwQYAUDJ//etf09raWnQMAAD+TcEGAFAyEydOzOjRo3POOefk3HPPzbHHHlt0JACAmqZgAwAomc2bN+cvf/lLnnzyyaxYsSITJkzI9OnTc8455+Soo44qOh4AQM1RsAEAlNjatWvz5JNPZvHixenp6Ulra2sWLlxYdCwAgJqiYAMAKLldu3blhRdeyG233Zbu7u68/fbbRUcCAKgpQ4sOAADAV/Paa69l0aJFeeqpp/Lpp5+mWq2mo6Oj6FgAADXHDjYAgJL505/+lMWLF2f9+vU5/fTTM3369FSr1dTX1xcdDQCgJtnBBgBQMsuWLctll12Wn/zkJ2lsbCw6DgBAzbODDQCgpN5777188MEH2blz5x7r1Wq1oEQAALXJDjYAgJJZu3ZtrrjiinR3d6dSqeQ/35dWKpUkMeQAAOAAqys6AAAA++emm27KmDFj8vLLL+fQQw/N4sWL88ADD2TixIm5//77i44HAFBzFGwAACWzfPnyzJo1K42Njamrq0ulUklra2s6Ojoyd+7couMBANQcBRsAQMn09/dn+PDhSZKRI0dm/fr1SZIxY8akt7e3yGgAADXJHWwAACUzfvz4dHd3p6mpKSeeeGLuueeeDBs2LI888kiampqKjgcAUHNMEQUAKJklS5Zkx44dmTZtWtasWZOZM2dm9erVGTFiRObPn5/29vaiIwIA1BQFGwDAQWDLli1paGjYPUkUAIADR8EGAAAAAINgyAEAAAAADIKCDQAAAAAGQcEGAAAAAIOgYAMAAACAQVCwAQAAAMAgKNgAAAAAYBAUbAAAAAAwCAo2AAAAABiEfwFOE0qWAyKnpwAAAABJRU5ErkJggg==",
"text/plain": [
"<Figure size 1400x400 with 2 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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",
"text/plain": [
"<Figure size 1400x400 with 2 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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"text/plain": [
"<Figure size 1400x400 with 2 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"def robust_zscore_col(s):\n",
" med = np.nanmedian(s)\n",
" mad = np.nanmedian(np.abs(s - med))\n",
" if mad == 0 or np.isnan(mad):\n",
" return np.zeros(len(s))\n",
" return (s - med) / (1.4826 * mad)\n",
"\n",
"for k in [2, 5, 10]:\n",
" prof = dfc.groupby(f\"cluster_k{k}\")[profile_vars].median()\n",
" prof_z = prof.copy()\n",
"\n",
" for c in prof.columns:\n",
" prof_z[c] = robust_zscore_col(prof[c].values)\n",
"\n",
" plt.figure(figsize=(14, 4))\n",
" sns.heatmap(prof_z, cmap=\"RdBu_r\", center=0)\n",
" plt.title(f\"Cluster signatures — K={k}\")\n",
" plt.tight_layout()\n",
" plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 48,
"id": "6e7fa1e2-cec7-41be-943f-d06b25e1e175",
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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",
"text/plain": [
"<Figure size 800x700 with 2 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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",
"text/plain": [
"<Figure size 800x700 with 2 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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",
"text/plain": [
"<Figure size 800x700 with 2 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"def plot_distance_matrix_sorted(X_scaled, labels, max_points=400, title=\"Distance matrix\"):\n",
" n = X_scaled.shape[0]\n",
" idx = np.arange(n)\n",
"\n",
" if n > max_points:\n",
" rng = np.random.default_rng(42)\n",
" idx = rng.choice(idx, size=max_points, replace=False)\n",
"\n",
" X_sub = X_scaled[idx]\n",
" labels_sub = np.asarray(labels)[idx]\n",
"\n",
" order = np.lexsort((np.arange(len(labels_sub)), labels_sub))\n",
" X_sub = X_sub[order]\n",
"\n",
" D = pairwise_distances(X_sub)\n",
"\n",
" plt.figure(figsize=(8, 7))\n",
" sns.heatmap(D, cmap=\"viridis\")\n",
" plt.title(title)\n",
" plt.tight_layout()\n",
" plt.show()\n",
"\n",
"for k in [2, 5, 10]:\n",
" plot_distance_matrix_sorted(\n",
" X_scaled,\n",
" dfc[f\"cluster_k{k}\"].values,\n",
" title=f\"Distance matrix triée — K={k}\"\n",
" )"
]
},
{
"cell_type": "code",
"execution_count": 51,
"id": "3c052322-6566-4567-b084-81148fa65538",
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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",
"text/plain": [
"<Figure size 800x400 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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",
"text/plain": [
"<Figure size 800x400 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"def intra_inter_distances(X_scaled, labels):\n",
" D = pairwise_distances(X_scaled)\n",
" labels = np.asarray(labels)\n",
"\n",
" intra, inter = [], []\n",
" n = len(labels)\n",
"\n",
" for i in range(n):\n",
" for j in range(i+1, n):\n",
" if labels[i] == labels[j]:\n",
" intra.append(D[i, j])\n",
" else:\n",
" inter.append(D[i, j])\n",
"\n",
" return np.array(intra), np.array(inter)\n",
"\n",
"for k in [2, 5]:\n",
" intra, inter = intra_inter_distances(X_scaled, dfc[f\"cluster_k{k}\"].values)\n",
"\n",
" plt.figure(figsize=(8, 4))\n",
" sns.kdeplot(intra, label=\"Intra-cluster\", fill=True)\n",
" sns.kdeplot(inter, label=\"Inter-cluster\", fill=True)\n",
" plt.title(f\"Distances dans l'espace original — K={k}\")\n",
" plt.xlabel(\"Distance\")\n",
" plt.legend()\n",
" plt.tight_layout()\n",
" plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 52,
"id": "b4a85ccf-34e1-4788-adfc-35da95ba774f",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"churn_hard 0.813586\n",
"churn_soft 0.847705\n",
"churn_warning 0.404880\n",
"dtype: float64"
]
},
"execution_count": 52,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"dfc[\"churn_hard\"] = (dfc[\"aum_final_to_peak\"] < 0.10).astype(int)\n",
"\n",
"dfc[\"churn_soft\"] = (\n",
" (dfc[\"aum_final_to_peak\"] < 0.40) &\n",
" (dfc[\"aum_drawdown_last\"] > 0.40)\n",
").astype(int)\n",
"\n",
"dfc[\"churn_warning\"] = (\n",
" (dfc[\"flow_direction_balance\"] < 0) &\n",
" (dfc[\"aum_drawdown_last\"] > 0.20)\n",
").astype(int)\n",
"\n",
"dfc[[\"churn_hard\", \"churn_soft\", \"churn_warning\"]].mean()"
]
},
{
"cell_type": "code",
"execution_count": 53,
"id": "e990eea6-b569-4fe9-9196-42ec4ccc0e17",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"===== CHURN PAR CLUSTER K=2 =====\n"
]
},
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
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" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>n_clients</th>\n",
" <th>churn_hard_rate</th>\n",
" <th>churn_soft_rate</th>\n",
" <th>churn_warning_rate</th>\n",
" </tr>\n",
" <tr>\n",
" <th>cluster_k2</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>9667</td>\n",
" <td>0.813696</td>\n",
" <td>0.847729</td>\n",
" <td>0.404986</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>5</td>\n",
" <td>0.600000</td>\n",
" <td>0.800000</td>\n",
" <td>0.200000</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" n_clients churn_hard_rate churn_soft_rate churn_warning_rate\n",
"cluster_k2 \n",
"0 9667 0.813696 0.847729 0.404986\n",
"1 5 0.600000 0.800000 0.200000"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"===== CHURN PAR CLUSTER K=5 =====\n"
]
},
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
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" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>n_clients</th>\n",
" <th>churn_hard_rate</th>\n",
" <th>churn_soft_rate</th>\n",
" <th>churn_warning_rate</th>\n",
" </tr>\n",
" <tr>\n",
" <th>cluster_k5</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>9665</td>\n",
" <td>0.813761</td>\n",
" <td>0.847698</td>\n",
" <td>0.404966</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>2</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>0.500000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>2</td>\n",
" <td>0.500000</td>\n",
" <td>0.500000</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>2</td>\n",
" <td>0.500000</td>\n",
" <td>1.000000</td>\n",
" <td>0.500000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>1</td>\n",
" <td>0.000000</td>\n",
" <td>1.000000</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" n_clients churn_hard_rate churn_soft_rate churn_warning_rate\n",
"cluster_k5 \n",
"0 9665 0.813761 0.847698 0.404966\n",
"1 2 1.000000 1.000000 0.500000\n",
"2 2 0.500000 0.500000 0.000000\n",
"4 2 0.500000 1.000000 0.500000\n",
"3 1 0.000000 1.000000 0.000000"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"for k in [2, 5]:\n",
" out = (\n",
" dfc.groupby(f\"cluster_k{k}\")\n",
" .agg(\n",
" n_clients=(ID_COL, \"count\"),\n",
" churn_hard_rate=(\"churn_hard\", \"mean\"),\n",
" churn_soft_rate=(\"churn_soft\", \"mean\"),\n",
" churn_warning_rate=(\"churn_warning\", \"mean\")\n",
" )\n",
" .sort_values(\"n_clients\", ascending=False)\n",
" )\n",
" print(f\"\\n===== CHURN PAR CLUSTER K={k} =====\")\n",
" display(out)"
]
},
{
"cell_type": "code",
"execution_count": 54,
"id": "4ad61286-89b0-473f-811d-f3affee994f0",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"===== NIVEAU 2 / EXPLICATION — K=2 =====\n"
]
},
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
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" }\n",
"\n",
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" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>n_clients</th>\n",
" <th>delta_rate_mean_med</th>\n",
" </tr>\n",
" <tr>\n",
" <th>cluster_k2</th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>9667</td>\n",
" <td>-0.004346</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>5</td>\n",
" <td>0.022432</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" n_clients delta_rate_mean_med\n",
"cluster_k2 \n",
"0 9667 -0.004346\n",
"1 5 0.022432"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"===== NIVEAU 2 / EXPLICATION — K=5 =====\n"
]
},
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
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" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>n_clients</th>\n",
" <th>delta_rate_mean_med</th>\n",
" </tr>\n",
" <tr>\n",
" <th>cluster_k5</th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>9665</td>\n",
" <td>-0.004350</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>2</td>\n",
" <td>0.018078</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>2</td>\n",
" <td>0.013204</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>2</td>\n",
" <td>0.024369</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>1</td>\n",
" <td>0.051667</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" n_clients delta_rate_mean_med\n",
"cluster_k5 \n",
"0 9665 -0.004350\n",
"1 2 0.018078\n",
"2 2 0.013204\n",
"4 2 0.024369\n",
"3 1 0.051667"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"#niv 2 explciation eocno \n",
"explain_vars = [\n",
" \"beta_rate\",\n",
" \"delta_rate_mean\",\n",
" \"aum_trend_12m\",\n",
" \"flow_trend_12m\",\n",
" \"drawdown_trend_12m\",\n",
"]\n",
"\n",
"explain_vars = [c for c in explain_vars if c in dfc.columns]\n",
"\n",
"for k in [2, 5]:\n",
" print(f\"\\n===== NIVEAU 2 / EXPLICATION — K={k} =====\")\n",
" out = (\n",
" dfc.groupby(f\"cluster_k{k}\")\n",
" .agg(\n",
" n_clients=(ID_COL, \"count\"),\n",
" **{f\"{c}_med\": (c, \"median\") for c in explain_vars}\n",
" )\n",
" .sort_values(\"n_clients\", ascending=False)\n",
" )\n",
" display(out)"
]
},
{
"cell_type": "code",
"execution_count": 55,
"id": "0ae8a553-1ec2-4789-8b6c-c70909495b18",
"metadata": {},
"outputs": [
{
"data": {
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ADCNYAAAAADCMYAEAAADAMIIFAAAAAMMIFgAAAAAMI1gAAAAAMIxgAdzgvL29zS4BAACAYAEUmWxHiXdps9nUvHlz2Wy2Eu/bjOMFAAClVzmzCwBuGlabFD9MOr3f7EqKn19TqV+M2VUAAIBShGABFKXT+6WT/zW7CgAAgBLHUCgAAAAAhhEsAAAAABhGsAAAAABgGMECAAAAgGEECwAAAACGESwAAAAAGEawAAAAAGAYwQIAAACAYQQLAAAAAIaZHixWrFihLl26KCgoSP3799euXbuu2X7p0qW69957FRwcrE6dOum1117TxYsXS6hauMORnWN2CQAAACgh5czsfMOGDYqKilJkZKRatmypuLg4RUREaOPGjfL19c3Vft26dZozZ45ee+01hYSE6Oeff9aUKVNksVg0depUE44A12KzWvT0h4n66dQ5s0spdp3t/nru3mZmlwEAAGAaU4NFbGysBgwYoH79+kmSIiMjtXnzZsXHx2vEiBG52icmJqp169bq1auXJKlOnTrq2bOn/vvf/5Zo3Si4n06d0+4TZ80uo9g19q9odgkAAACmMm0oVGZmpnbv3q3Q0ND/K8ZqVWhoqBITE/PcJiQkRLt373YOlzp69Ki++eYbderUqURqBgAAAJA3065YnDlzRg6HI9eQJ19fXyUnJ+e5Ta9evXTmzBk9+uijysnJ0aVLlzRw4EA9+eSTbvfvcDgKVTcKzmazmV0Cihm/RwCQW3Z2try9vZWdnW12KYBh7vytN3UolLu2bdumBQsWaPr06QoODtaRI0c0c+ZM/e1vf9OYMWPc2ldSUlIxVQlJ8vb2VvPmzc0uA8Vs3759ysjIMLsMAMiTh4eHApsHyFrOo0T7tdlspv0NzL6Upf/9uEdZWVmm9I+yzbRg4ePjI5vNptTUVJflqamp8vPzy3ObuXPn6oEHHlD//v0lSXa7Xenp6XrppZc0atQoWa0FH9kVFBTEJ+qAQXa73ewSAOCarDabFD9MOr3f7FKKn19TWfvFqEWLFmZXgpuIw+Eo8AfypgULT09PtWjRQgkJCeratauky5cOExISFB4enuc2Fy5cyBUeroSDnBz3bm1qs9kIFoBB/A4BuCGc3i+dLDs3euHcDLOYOhRqyJAhmjx5sgIDAxUcHKy4uDhlZGSob9++kqRJkyapZs2amjBhgiTp7rvvVmxsrJo3b+4cCjV37lzdfffd/BIBAAAAJjI1WISFhSktLU3R0dFKSUlRQECAYmJinEOhTp486XKFYtSoUbJYLHr77bf166+/qnr16rr77rs1fvx4sw4BAAAAgErB5O3w8PB8hz4tX77c5XW5cuU0duxYjR07tiRKAwAAAFBApj3HAgAAAMDNg2ABAAAAwDCCBQAAAADDCBYAAAAADCNYAAAAADCMYAEAAADAMIIFAAAAAMMIFgAAAAAMI1gAAAAAMIxgAQAAAMAwggUAAAAAwwgWAAAAAAwjWAAAAAAwjGABAAAAwDCCBQAAAADDCBYAAAAADCNYAAAAADCMYAEAAADAMIIFAAAAAMMIFgAAAAAMI1gAAAAAMIxgAQAAAMAwggUAAAAAwwgWAAAAAAwrFcFixYoV6tKli4KCgtS/f3/t2rUr37aPPfaY7HZ7rq8RI0aUYMUAAAAA/qyc2QVs2LBBUVFRioyMVMuWLRUXF6eIiAht3LhRvr6+udrPmzdPWVlZzte//fabevfurfvuu68kywYAAADwJ6ZfsYiNjdWAAQPUr18/NWnSRJGRkfLy8lJ8fHye7atVqyZ/f3/n13fffScvLy+CBQAAAGAiU69YZGZmavfu3Ro5cqRzmdVqVWhoqBITEwu0j/j4ePXo0UMVKlRwq2+Hw+FWe7jPZrOZXQKKGb9HAEq7svi3iHMzipI7P0+mBoszZ87I4XDkGvLk6+ur5OTk626/a9cu7d+/XzNnznS776SkJLe3QcF5e3urefPmZpeBYrZv3z5lZGSYXQYA5Kms/i3i3AyzmD7HwohPPvlETZs2VXBwsNvbBgUFlclPMYCiZLfbzS4BAHAVzs0oSg6Ho8AfyJsaLHx8fGSz2ZSamuqyPDU1VX5+ftfcNj09XevXr9dTTz1VqL5tNhvBAjCI3yEAKH04N8Mspk7e9vT0VIsWLZSQkOBclp2drYSEBIWEhFxz240bNyozM1MPPPBAcZcJAAAA4DpMHwo1ZMgQTZ48WYGBgQoODlZcXJwyMjLUt29fSdKkSZNUs2ZNTZgwwWW7Tz75RF27dpWPj48ZZQMAAAD4E9ODRVhYmNLS0hQdHa2UlBQFBAQoJibGORTq5MmTslpdL6wkJydrx44dWrJkiRklAwAAALiK6cFCksLDwxUeHp7nuuXLl+da1qhRI+3bt6+4ywIAAABQQKY/IA8AAADAjY9gAQAAAMAwggUAAAAAwwgWAAAAAAwjWAAAAAAwjGABAAAAwDCCBQAAAADDCBYAAAAADCNYAAAAADCMYAEAAADAMIIFAAAAAMMIFgAAAAAMI1gAAAAAMIxgAQAAAMAwggUAAAAAwwgWAAAAAAwjWAAAAAAwjGABAAAAwDCCBQAAAADDCBYAAAAADCNYAAAAADCMYAEAAADAMIIFAAAAAMMIFgAAAAAMMz1YrFixQl26dFFQUJD69++vXbt2XbP92bNnFRkZqY4dOyowMFD33nuvvvnmmxKqFgAAAEBeypnZ+YYNGxQVFaXIyEi1bNlScXFxioiI0MaNG+Xr65urfWZmpoYMGSJfX1/NnTtXNWvW1IkTJ1SlShUTqgcAAABwhanBIjY2VgMGDFC/fv0kSZGRkdq8ebPi4+M1YsSIXO3j4+P1+++/68MPP5SHh4ckqU6dOiVaMwAAAIDcTAsWmZmZ2r17t0aOHOlcZrVaFRoaqsTExDy3+eqrr9SqVSu9/PLL2rRpk6pXr66ePXtq+PDhstlsbvXvcDgM1Y/rc/f/CW48/B4BKO3K4t8izs0oSu78PJkWLM6cOSOHw5FryJOvr6+Sk5Pz3Obo0aPaunWrevXqpYULF+rIkSOKjIzUpUuXNHbsWLf6T0pKKnTtuD5vb281b97c7DJQzPbt26eMjAyzywBQQB4eHgpo3kIe5crem+2yhHMzzGLqUCh35eTkyNfXV6+88opsNpsCAwP166+/avHixW4Hi6CgoDL5KQZQlOx2u9klAHCTzWbT0x8m6qdT58wupdh1tvvruXubmV1GiePcjKLkcDgK/IG8acHCx8dHNptNqampLstTU1Pl5+eX5zb+/v4qV66cSyBo1KiRUlJSlJmZKU9PzwL3b7PZCBaAQfwOATemn06d0+4TZ80uo9g19q9odgmm4NwMs5h2u1lPT0+1aNFCCQkJzmXZ2dlKSEhQSEhIntu0bt1aR44cUXZ2tnPZzz//LH9/f7dCBQAAAICiZepzLIYMGaJVq1ZpzZo1OnjwoGbMmKGMjAz17dtXkjRp0iTNmTPH2f6RRx7Rb7/9ppkzZ+rQoUPavHmzFixYoEGDBpl1CAAAAABk8hyLsLAwpaWlKTo6WikpKQoICFBMTIxzKNTJkydltf5f9qlVq5YWL16sqKgoPfDAA6pZs6Yef/xxDR8+3KxDAAAAAKBSMHk7PDxc4eHhea5bvnx5rmUhISFatWpVcZcFAAAAwA2mDoUCAAAAcHMgWAAAAAAwjGABAAAAwDCCBQAAAADDCBYAAAAADCNYAAAAADCMYAEAAADAMIIFAAAAAMMIFgAAAAAMI1gAAAAAMIxgAQAAAMAwggUAAAAAwwgWAAAAAAwjWAAAAAAwjGABAAAAwDCCBQAAAADDCBYAAAAADCNYAAAAADCMYAEAAADAMIIFAAAAAMMIFgAAAAAMI1gAAAAAMIxgAQAAAMCwUhEsVqxYoS5duigoKEj9+/fXrl278m27evVq2e12l6+goKASrBYAAADA1cqZXcCGDRsUFRWlyMhItWzZUnFxcYqIiNDGjRvl6+ub5zaVKlXSxo0bna8tFktJlQsAAAAgD6ZfsYiNjdWAAQPUr18/NWnSRJGRkfLy8lJ8fHy+21gsFvn7+zu//Pz8SrBiAAAAAFczNVhkZmZq9+7dCg0NdS6zWq0KDQ1VYmJivtulp6fr7rvvVqdOnTRq1CgdOHCgJMoFAAAAkA9Th0KdOXNGDocj15AnX19fJScn57lNw4YN9dprr8lut+uPP/7QkiVLNHDgQK1fv1633HJLgft2OByGasf12Ww2s0tAMeP3CLjxcG6++XFuRlFy5+fJ9DkW7goJCVFISIjL67CwMH344Yd65plnCryfpKSkYqgOV3h7e6t58+Zml4Fitm/fPmVkZJhdBoAC4txcNnBuhllMDRY+Pj6y2WxKTU11WZ6amlrgeRMeHh4KCAjQkSNH3Oo7KCiIT20Ag+x2u9klAACuwrkZRcnhcBT4A3lTg4Wnp6datGihhIQEde3aVZKUnZ2thIQEhYeHF2gfDodD+/fvV6dOndzq22azESwAg/gdAoDSh3MzzGL6UKghQ4Zo8uTJCgwMVHBwsOLi4pSRkaG+fftKkiZNmqSaNWtqwoQJkqR33nlHrVq1Uv369XX27FktXrxYJ06cUP/+/c08DAAAAKBMMz1YhIWFKS0tTdHR0UpJSVFAQIBiYmKcQ6FOnjwpq/X/bl519uxZTZs2TSkpKapatapatGihDz/8UE2aNDHrEAAAAIAyz/RgIUnh4eH5Dn1avny5y+vnn39ezz//fEmUBQAAAKCADD3H4vDhw9qyZYsuXLggScrJySmSogAAAADcWAp1xeLMmTMaP368tm7dKovFos8//1x169bV888/r6pVq2rKlClFXScAAACAUqxQVyyioqJks9m0efNmeXl5OZeHhYVpy5YtRVYcAAAAgBtDoa5YfPfdd1q8eHGuJ103aNBAJ06cKJLCAAAAANw4CnXFIj093eVKxRW//fabPD09DRcFAAAA4MZSqGDRpk0brV271mVZdna2YmJi1K5du6KoCwAAAMANpFBDoZ577jkNHjxY//vf/5SVlaXZs2frp59+0u+//66VK1cWdY0AAAAASrlCBYumTZvqs88+0/vvv6+KFSsqPT1d3bp106BBg1SjRo2irhEAAABAKVeoYHHixAnVqlVLo0aNynPdrbfeargwAAAAADeOQs2xuOeee5SWlpZr+ZkzZ3TPPfcYLgoAAADAjaVQwSInJ0cWiyXX8vT0dJUvX95wUQAAAABuLG4NhYqKipIkWSwWvf322/L29nauczgc2rVrl5o1a1a0FQIAAAAo9dwKFj/++KOky1cs9u/fLw8PD+c6T09PNWvWTEOHDi3aCgEAAACUem4Fi+XLl0uSpk6dqhdeeEGVKlUqlqIAAAAA3FgKdVeoK0OiAAAAAEAqZLCQpKSkJP3zn//UyZMnlZWV5bLunXfeMVwYAAAAgBtHoe4KtX79ej3yyCNKTk7WF198oUuXLunAgQPaunWrKleuXNQ1AgAAACjlChUs5s+fr6lTp2r+/Pny8PDQCy+8oI0bN+r+++9XrVq1irpGAAAAAKVcoYLF0aNH1alTJ0mX7waVnp4ui8WiwYMHa9WqVUVaIAAAAIDSr1DBokqVKjp//rwkqUaNGjpw4IAk6ezZs8rIyCi66gAAAADcEAo1efuOO+7Q999/L7vdrvvuu08zZ87U1q1b9f3336t9+/ZFXSMAAACAUq5QwWLatGm6ePGiJGnUqFHy8PDQf/7zH3Xv3p0H5AEAAABlUKGGQlWrVk01a9a8vAOrVSNGjNDcuXNVo0YN9enTp0gLBAAAAFD6uXXFIjMzU/PmzdN3330nT09PDRs2TF27dlV8fLzefvtt2Ww2PfHEE8VVKwAAAIBSyq0rFnPnztXKlStVp04dHTt2TE8//bSmTZumpUuXasqUKdq0aZNGjBjhdhErVqxQly5dFBQUpP79+2vXrl0F2m79+vWy2+0aPXq0230CAAAAKDpuBYuNGzfq9ddfV3R0tJYsWSKHw6FLly7p008/VY8ePWSz2dwuYMOGDYqKitKYMWO0Zs0aNWvWTBEREUpNTb3mdseOHdPrr7+uNm3auN0nAAAAgKLlVrD49ddfFRgYKElq2rSpPD09NXjwYFkslkIXEBsbqwEDBqhfv35q0qSJIiMj5eXlpfj4+Hy3cTgcmjhxosaNG6e6desWum8AAAAARcOtYOFwOOTh4eF8bbPZVKFChUJ3npmZqd27dys0NPT/CrJaFRoaqsTExHy3+9vf/iZfX1/179+/0H0DMKBSDTmyHWZXUaLK2vECAOAutyZv5+TkaMqUKfL09JR0ORjMmDFD3t7eLu3eeeedAu3vzJkzcjgc8vX1dVnu6+ur5OTkPLf597//rU8++URr1651p/RcHA7eJBS3wgyNww3Cq5psVpumfDtFyb/n/bt6M2lUtZFm3TWL8wZuCpybb36cq1CU3Pl5citYXH0r2QceeMCdzQ07d+6cJk2apFdeeUXVq1c3tK+kpKQiqgp58fb2VvPmzc0uA8Us+fdk7UnbY3YZJWbfvn3KyMgwuwyg0Dg3lw2cq2AWt4JFVFRUkXbu4+Mjm82Wa6J2amqq/Pz8crU/evSojh8/rlGjRjmXZWdnS5KaN2+ujRs3ql69egXqOygoiE9tALjFbrebXQIAXBfnKhQlh8NR4A/kC/Xk7aLi6empFi1aKCEhQV27dpV0OSgkJCQoPDw8V/tGjRpp3bp1LsvefvttnT9/Xi+88IJuueWWAvdts9kIFgDcwjkDwI2AcxXMYmqwkKQhQ4Zo8uTJCgwMVHBwsOLi4pSRkaG+fftKkiZNmqSaNWtqwoQJKl++vJo2beqyfZUqVSQp13IAAAAAJcf0YBEWFqa0tDRFR0crJSVFAQEBiomJcQ6FOnnypKxWt25eBQAAAKCEmR4sJCk8PDzPoU+StHz58mtuO2vWrOIoCQAAAIAbuBQAAAAAwDCCBQAAAADDCBYAAAAADCNYAAAAADCMYAEAAADAMIIFAAAAAMMIFgAAAAAMI1gAAAAAMIxgAQAAAMAwggUAAAAAwwgWAAAAAAwjWAAAAAAwjGABAAAAwDCCBQAAAADDCBYAAAAADCNYAAAAADCMYAEAAADAMIIFAAAAAMMIFgAAAAAMI1gAAAAAMIxgAQAAAMAwggUAAAAAwwgWAAAAAAwjWAAAAAAwrFQEixUrVqhLly4KCgpS//79tWvXrnzbfv755+rbt6/atGmjVq1aqXfv3lq7dm3JFQsAAAAgl3JmF7BhwwZFRUUpMjJSLVu2VFxcnCIiIrRx40b5+vrmal+1alWNGjVKjRo1koeHh77++ms9//zz8vX11Z133mnCEQAAAAAw/YpFbGysBgwYoH79+qlJkyaKjIyUl5eX4uPj82zfrl07devWTY0bN1a9evX0xBNPyG63a8eOHSVcOQAAAIArTL1ikZmZqd27d2vkyJHOZVarVaGhoUpMTLzu9jk5Odq6dasOHTqkiRMnutW3w+Fwu164x2azmV0CUKQ4b+BmwLn55se5CkXJnZ8nU4PFmTNn5HA4cg158vX1VXJycr7b/fHHH7rrrruUmZkpq9Wq6dOn6y9/+YtbfSclJRWqZhSMt7e3mjdvbnYZQJHat2+fMjIyzC4DKDTOzWUD5yqYxfQ5FoVRsWJFrV27Vunp6UpISNCsWbNUt25dtWvXrsD7CAoK4lMbAG6x2+1mlwAA18W5CkXJ4XAU+AN5U4OFj4+PbDabUlNTXZanpqbKz88v3+2sVqvq168vSQoICNDBgwe1cOFCt4KFzWYjWABwC+cMADcCzlUwi6mTtz09PdWiRQslJCQ4l2VnZyshIUEhISEF3k92drYyMzOLo0QAAAAABWD6UKghQ4Zo8uTJCgwMVHBwsOLi4pSRkaG+fftKkiZNmqSaNWtqwoQJkqQFCxYoMDBQ9erVU2Zmpr755ht9+umnmjFjholHAQAAAJRtpgeLsLAwpaWlKTo6WikpKQoICFBMTIxzKNTJkydltf7fhZX09HRFRkbql19+kZeXlxo1aqTZs2crLCzMrEMAAAAAyjzTg4UkhYeHKzw8PM91y5cvd3k9fvx4jR8/viTKAgAAAFBApj8gDwAAAMCNj2ABAAAAwDCCBQAAAADDCBYAAAAADCNYAAAAADCMYAEAAADAMIIFAAAAAMMIFgAAAAAMI1gAAAAAMIxgAQAAAMAwggUAAAAAwwgWAAAAAAwjWAAAAAAwjGABAAAAwDCCBQAAAADDCBYAAAAADCNYAAAAADCMYAEAAADAMIIFAAAAAMMIFgAAAAAMI1gAAAAAMIxgAQAAAMCwcmYXAAA3u+zsbGVmZppdBm4iHh4estlsZpcBAC4IFgBQjDIzM3Xo0CFlZ2ebXQpuMtWqVdMtt9wii8VidikAIKmUBIsVK1Zo8eLFSklJUbNmzTRt2jQFBwfn2XbVqlVau3atDhw4IElq0aKFnn322XzbA4BZcnJydPLkSdlsNtWtW1dWK6NPYVxOTo7S09N16tQpSVKtWrVMrggALjM9WGzYsEFRUVGKjIxUy5YtFRcXp4iICG3cuFG+vr652m/btk09evRQ69at5enpqZiYGA0dOlTr169XzZo1TTgCAMjbpUuXlJ6erltvvVUVKlQwuxzcRLy9vSVJp06dUo0aNRgWBaBUMP3js9jYWA0YMED9+vVTkyZNFBkZKS8vL8XHx+fZfs6cORo0aJACAgLUuHFjvfrqq8rOzlZCQkIJVw4A1+ZwOCRJnp6eJleCm9GVsJqVlWVyJQBwmalXLDIzM7V7926NHDnSucxqtSo0NFSJiYkF2kdGRoYuXbqkqlWrutX3lT/4KD58goabjbvnDYfDoZycHEly/hcoSjk5OXI4HG79bHJuvvnxHgdFyZ2fJ1ODxZkzZ+RwOHINefL19VVycnKB9vHmm2+qRo0aCg0NdavvpKQkt9rDPd7e3mrevLnZZQBFat++fcrIyHBrm3LlyikjI4PJ2yhyFy9eVFZWlvbu3VvgbTg33+Qq1ZAj21GmwuMlxyX9uPtHrtyVEqbPsTBi4cKF2rBhg5YtW6by5cu7tW1QUFCZ+sUDYJzdbner/YULF3T48GF5e3vLy8urmKoqWceOHVPXrl21Zs0aBQQEmF2Oi9WrVysqKkrbt28vkf7mzZunTZs2ae3atSXS39WsVqs8PDzUpEmTm+bnCwZ5VZPNatOUb6co+feCfUB7I2tUtZFm3TVLLVq0MLuUm5rD4SjwB/KmBgsfHx/ZbDalpqa6LE9NTZWfn981t128eLEWLlyo2NhYNWvWzO2+bTYbwQKAW9w9Z9hsNlksFufXFY7sHNmsJXeL0KLs78pxXH1MpcGfa7sZ+8urf4vFwt8z5JL8e7L2pO0xu4wSw89/6WFqsPD09FSLFi2UkJCgrl27SpJzInZ4eHi+2y1atEjz58/X4sWLFRQUVFLlAkCRsFktevrDRP106lyx99WkRiXNHRhS7P0YlZmZWWonuWdlZcnDw8PsMgCg1DN9KNSQIUM0efJkBQYGKjg4WHFxccrIyFDfvn0lSZMmTVLNmjU1YcIESZeHP0VHR2vOnDmqXbu2UlJSJF2+O0bFihVNOw4AcMdPp85p94mzZpeRr+zsbC1evFirVq3SyZMn5efnp4cffli9evWSJB09elSvvfaadu3apfr16ysyMlIhIZcDzLx58/Tll1/q73//u3N/S5cu1bJly/TVV19JkqZMmaKzZ88qKChIK1askKenp5YtW6Z77rlH8+bN0/Lly/Pcd0Fs2bJFr732mn755Re1bt1aUVFRqlGjhiRp165deuutt/Tjjz/q0qVLCggI0NSpU12GUtjtdk2fPl3ffvuttm7dqoiICI0bN04LFy7U0qVLlZGRofvvv1/Vq1c3/H0GgJuJ6bebDQsL0+TJkxUdHa3evXtrz549iomJcQ6FOnnypDM8SNKHH36orKwsPfXUU+rYsaPza8mSJWYdAgDcdObMmaNFixZp9OjR2rBhg958802XIapvvfWWIiIitHbtWjVo0EATJkzQpUuX3OojISFBhw4dUmxsrBYsWFAk+75w4YKWLFmiN954Q++//75Onjyp119/3bn+/PnzevDBB/XBBx9o1apVql+/vkaMGKFz51yvHr3zzjvq1q2b1q1bp379+mnDhg2aN2+exo8fr/j4ePn7++uDDz5w63gB4GZn+hULSQoPD8936NPy5ctdXl/5tAsAUDzOnTunZcuW6aWXXlKfPn0kSfXq1VObNm107NgxSdLQoUPVuXNnSdJTTz2lHj166PDhw2rcuHGB+6lQoYJeffVV5xCooth3VlaWIiMjVa9ePUnSoEGD9O677zrXd+jQwaX9K6+8ojZt2mj79u26++67nct79uypfv36OV8/++yzeuihh9S/f39J0vjx45WQkKCLFy8W+HgB4GZn+hULAEDpkpycrMzMTLVv3z7fNn++Q5a/v78kKS0tza1+mjZtmue8CiP79vb2doYKSapRo4bLDUJOnz6tF198Ud27d9ftt9+u22+/Xenp6Tpx4oTLfgIDA11eHzx4UC1btnRZ1qpVqwLVBABlRam4YgEAKD0KcvvuP09mvnJXpCvP6rBYLLkeCJjXUCZvb2+393095cq5/lm7upbJkyfrt99+0wsvvKBbb71Vnp6eevjhh3PdA//KU60BAAXHFQsAgIsGDRrIy8tLW7duLdT21atX1+nTp13e0O/ZUzpuffmf//xHjz32mDp16qTbbrtNnp6eOnPmzHW3a9y4sf773/+6LLv6NQCUdQQLAICL8uXLa/jw4Zo9e7bWrl2rI0eOaOfOnfr4448LtH27du2UlpamRYsW6ciRI1qxYoW2bNlSzFUXTIMGDfTpp5/q4MGD+u9//6uJEycW6OFyjz/+uOLj4xUfH69Dhw4pOjpaBw4cKIGKAeDGwVAoADBBkxqVSnU/o0ePls1mU3R0tE6dOiV/f38NHDiwQNs2btxY06dP14IFC/Tee++pe/fuGjp0qFatWlWoWorSzJkzNW3aNPXp00e1atXS+PHj9cYbb1x3u7CwMB05ckSzZ8/WxYsXde+99+qRRx7Rv/71rxKoGgBuDJacqwfC3uQcDod27typVq1a8aTGEtAjekupvld/UXmgZS1FP9JaWnCXdLIMDI8I6i/1i9GAdQPKxNNdA6oHaFUv998UX7hwQYcOHVLDhg1dPhW/kZ+8jdIjv5+vguDcfJPi3Ixi4M57Z4ZCAUAJK+k3+YQKAEBJYCgUAOCGMGzYMO3YsSPPdSNHjtSTTz5ZwhUBAP6MYAEAuCHMnDlTFy5cyHNd1apVS7gaAMDVCBYAgBtCzZo1zS4BAHANzLEAAAAAYBjBAgAAAIBhBAsAAAAAhhEsAAAAABhGsAAAAABgGMECAFBgx44dk91u1549N99TfXNycjRt2jS1bdv2pj1GAChOBAsAKGnZjpu7vxvUt99+qzVr1mj+/Pn617/+pdtuu012u11ffvml2aUBwA2B51gAQEmz2qT4YdLp/cXfl19TqV9M8fdjUGZmpjw9PU2t4ejRo/L391fr1q1NrQMAblQECwAww+n90sn/ml1FvrKzs7V48WKtWrVKJ0+elJ+fnx5++GH16tVL0uU34a+99pp27dql+vXrKzIyUiEhIZKkefPm6csvv9Tf//535/6WLl2qZcuW6auvvpIkTZkyRWfPnlVQUJBWrFghT09PLVu2TPfcc4/mzZun5cuX57nvazl+/LheeeUV7dixQ1lZWapdu7YmTZqkTp06SZJ++OEHvfHGG9q7d6+qVaumBx98UM8884zKlSunKVOmaM2aNZIku92u2rVrO/c7ZswYSVLt2rWd9QMAciNYAABymTNnjj7++GNNnTpVt99+u06dOqVDhw4517/11luaPHmy6tevr7feeksTJkzQ559/rnLlCv5nJSEhQZUqVVJsbKzL8sLu++WXX1ZWVpbef/99VahQQT/99JMqVKggSfr11181YsQI9enTR6+//roOHTqkF198UeXLl9e4ceP0wgsvqG7dulq1apU++eQT2Ww2SVKHDh0UFRWlO++807kMAJA3ggUAwMW5c+e0bNkyvfTSS+rTp48kqV69emrTpo2OHTsmSRo6dKg6d+4sSXrqqafUo0cPHT58WI0bNy5wPxUqVNCrr77qHAJldN8nTpzQvffeK7vdLkmqW7euc90HH3ygW265RS+99JIsFosaN26sX3/9VW+++abGjBmjypUrq2LFirLZbPL393fZb5UqVXItAwDkxuRtAICL5ORkZWZmqn379vm2ufLmXZLzTXdaWppb/TRt2jTPeRWF3ffjjz+u9957TwMHDlR0dLT27t3rXHfw4EGFhITIYrE4l91+++1KT0/XL7/84lbdAIC8ESwAAC7Kly9/3TYeHh7Of195s56dne18nZOT49L+0qVLufbh7e3t9r6vpX///vryyy/Vu3dv7d+/Xw899JCWL19+3e0AAEXD9GCxYsUKdenSRUFBQerfv7927dqVb9sDBw5o3Lhx6tKli+x2u5YuXVpyhQJAGdGgQQN5eXlp69athdq+evXqOn36tEu4KKlnQtSqVUuPPPKI3nnnHQ0ZMkSrVq2SJDVu3FiJiYkuNe3YsUMVK1bULbfcku/+PDw85HBwu14AKAhTg8WGDRsUFRWlMWPGaM2aNWrWrJkiIiKUmpqaZ/uMjAzVqVNHEyZMYLwrABST8uXLa/jw4Zo9e7bWrl2rI0eOaOfOnfr4448LtH27du2UlpamRYsW6ciRI1qxYoW2bNlSzFVLM2fO1JYtW3T06FHt3r1b27Ztc87LePTRR/XLL7/olVde0cGDB/Xll19q3rx5GjJkiKzW/P8U1q5dWwkJCUpJSdHvv/9e7McAADcyUydvx8bGasCAAerXr58kKTIyUps3b1Z8fLxGjBiRq31wcLCCg4MlXb5jCQDcsPyalup+Ro8eLZvNpujoaJ06dUr+/v4aOHBggbZt3Lixpk+frgULFui9995T9+7dNXToUOfVg+KSnZ2tl19+Wb/88osqVaqkO++8U1OnTpUk1axZUwsXLtQbb7yhVatWqVq1anrooYc0atSoa+5z8uTJmjVrlj7++GPVrFmT280CwDWYFiwyMzO1e/dujRw50rnMarUqNDRUiYmJZpUFAMUv21GyD63Ldlx+KJ8brFarRo0alecb73379rm8rlKlSq5ljzzyiB555BGXZU8++aTz37Nmzcq13zp16hRo3/mZNm3aNde3bdtWn3zySb7rBw8erMGDB7ss69Kli7p06VKg/gGgrDMtWJw5c0YOh0O+vr4uy319fZWcnFzs/TNmtvhxz3fcbNw9bzgcDuXk5Di/nCxW6arJzcWqpPtDibjyc+VwONz62eTcjJsN7+mKlzvf3zL7HIukpCSzS7ipeXt7q3nz5maXARSpffv2KSMjw61typUrp4yMjALd1QjXNnbs2HyvaA8dOlQRERElXJG5Ll68qKysLJfb6l4P52bcjApzbkbxMC1Y+Pj4yGaz5ZqonZqaKj8/v2LvPygoiE9tALjlz89XKIgLFy7o8OHD8vb2lpeXVzFVVXZERUXpwoULea6rWrWq8ynbZYXVapWHh4eaNGnCzxfKNHfPzXCPw+Eo8AfypgULT09PtWjRQgkJCerataukyxPvEhISFB4eXuz922w2ggUAt7h7zrDZbLJYLM4vGHOt28KWRVd+rvh7hrKOn//Sw9ShUEOGDNHkyZMVGBio4OBgxcXFKSMjQ3379pUkTZo0STVr1tSECRMkXZ7wffDgQee/f/31V+3Zs0cVKlRQ/fr1TTsOAAAAoKwzNViEhYUpLS1N0dHRSklJUUBAgGJiYpxDoU6ePOlyf/FTp07pwQcfdL5esmSJlixZorZt2/J0VQAAAMBEpk/eDg8Pz3fo09VhIa9bEQIAAAAwn6lP3gYAAABwcyBYAAAAADCMYAEAKLBjx47Jbrdrz549ZpdSavE9AlBWmT7HAgDKGke2QzZryd0esaT7K+tq1aqlf/3rX/Lx8TG7FAAoUQQLAChhNqtNU76douTfk4u9r0ZVG2nWXbOKvR+jMjMz5enpaXYZ1+RwOGSxWFzuVpgXm80mf3//EqoKAEoPhkIBgAmSf0/WnrQ9xf5V2PCSnZ2tRYsWqVu3bgoMDFTnzp313nvvOdcfPXpUjz32mFq2bKkHHnhAiYmJznXz5s1T7969Xfa3dOlSdenSxfl6ypQpGj16tN577z117NhR9913n3MI0eeff57vvvOTk5Oj9u3ba+PGjc5lvXv3VseOHZ2v//3vfyswMFAZGRmSpNjYWPXq1UutWrVSp06dNGPGDJ0/f97ZfvXq1WrTpo02bdqksLAwBQUF6cSJE+rSpYvmz5+vqVOnKiQkRJ07d9ZHH33k3O7qoVDbtm2T3W5XQkKC+vbtq5YtW2rgwIFKTnb9f/Puu++qQ4cOCgkJ0QsvvKA333wz1/cRAEozggUAIJc5c+Zo0aJFGj16tDZs2KA333zT+YwhSXrrrbcUERGhtWvXqkGDBpowYYIuXbrkVh8JCQk6dOiQYmNjtWDBAkP7tlgsuuOOO/TDDz9Ikn7//XcdPHhQFy5ccD5Ydfv27QoKCpK3t7dzmxdeeEH/+Mc/NGvWLG3dulWzZ8922e+FCxe0aNEivfrqq/rHP/4hX19fSZdDSWBgoNauXatHH31UM2bMyBUUrvbWW29pypQpio+Pl81m0/PPP+9c9+mnn2r+/PmaOHGiVq9erVq1amnlypUF/E4CQOlAsAAAuDh37pyWLVum5557Tn369FG9evXUpk0b9e/f39lm6NCh6ty5sxo2bKinnnpKx48f1+HDh93qp0KFCnr11Vd122236bbbbjO877Zt2zqDxfbt29W8eXOXZT/88IPatm3rbD948GC1b99ederUUYcOHfTMM8/on//8p8s+s7KyNGPGDLVu3VqNGjVyhpK77rpLgwYNUv369TV8+HD5+Pho27Zt16xv/Pjxatu2rZo0aaIRI0YoMTFRFy9elCS9//77euihh9SvXz81bNhQY8eOVdOmTQvwXQSA0oNgAQBwkZycrMzMTLVv3z7fNna73fnvK/MJ0tLS3OqnadOmec6rKOy+77jjDv30009KS0vT9u3b1bZtW2ewyMrKUmJiokuw+P777/XEE0/ozjvvVEhIiCZNmqTffvvNOVRKkjw8PFzqyatGi8UiPz8/paamXrO+vI7ryjaHDh1ScHCwS/urXwNAaUewAAC4KF++/HXbeHh4OP9tsVgkXZ6XceV1Tk6OS/u8hjJd+fTfnX1fi91uV9WqVfXDDz84g0W7du20fft2JSUl6dKlSwoJCZF0eR7EyJEjZbfbNW/ePK1evVovvfSSpMtXKa7w8vJy1vBn5cq53vskr2O+1jbuHBcA3CgIFgAAFw0aNJCXl5e2bt1aqO2rV6+u06dPu7zRLolnOlgsFudk6wMHDuj222+X3W5XZmamPvroIwUGBqpChQqSpN27dysnJ0dTpkxRq1at1LBhQ506darYa8xPw4YNlZSU5LLs6tcAUNoRLAAALsqXL6/hw4dr9uzZWrt2rY4cOaKdO3fq448/LtD27dq1U1pamhYtWqQjR45oxYoV2rJlSzFXfVnbtm21fv16BQQEqGLFirJarWrTpo3WrVunO+64w9mufv36ysrK0vLly3X06FGtXbtWH374YYnUmJfw8HB98sknWrNmjX7++We9++672rdvX55XSwCgtOI5FgBggkZVG5XqfkaPHi2bzabo6GidOnVK/v7+GjhwYIG2bdy4saZPn64FCxbovffeU/fu3TV06FCtWrWqULW4o23btnI4HC5zKdq2batNmza5LGvWrJmmTp2qRYsW6a9//avatGmjZ599VpMnTy72GvPywAMP6OjRo3r99dd18eJF3X///erTpw9XLQDcUCw51xsUepNxOBzauXOnWrVqJZuNJ9EWtx7RW7T7xFmzyyh2D7SspehHWksL7pJO/tfscopfUH+pX4wGrBugPWnFP8TFbAHVA7Sql/tvii9cuKBDhw6pYcOG8vLyci7nydsoiCFDhsjPzy/XLXCvyO/nqyA4N9+kODejGLjz3pkrFgBQwkr6TT6hovTLyMjQhx9+qI4dO8pqtWr9+vX6/vvvFRsba3ZpAFBgBAsAwA1h2LBh2rFjR57rRo4cqSeffLKEKyo6FotF33zzjebPn6+LFy+qYcOGmjdvnkJDQ80uDQAKjGABALghzJw5UxcuXMhzXdWqVUu4mqLl5eWlpUuXml0GABhCsAAA3BBq1qxpdgkAgGvgdrMAAAAADCNYAEAxK2M330MJ4ecKQGlDsACAYnLltnyZmZkmV4KbUXp6uiTJw8PD5EoA4DLmWABAMSlXrpwqVKiglJQUeXh4yGrlsxwYl5OTo/T0dJ06dUrVqlXjmUwASg2CBQAUE4vFolq1aunQoUM6fPiw2eXgJlOtWjXdcsstZpcBAE4ECwAoRp6enrrtttsYDoUi5eHhwZUKAKVOqQgWK1as0OLFi5WSkqJmzZpp2rRpCg4Ozrf9P//5T82dO1fHjx9XgwYNNHHiRHXq1KkEKwaAgrNarfLy8jK7DAAAipXpA343bNigqKgojRkzRmvWrFGzZs0UERGh1NTUPNv/5z//0YQJE/TQQw9p7dq1uueeezRmzBjt37+/hCsHAAAAcIXpwSI2NlYDBgxQv3791KRJE0VGRsrLy0vx8fF5tl+2bJnuvPNODRs2TI0bN9Yzzzyj5s2b6/333y/hygEAAABcYWqwyMzM1O7duxUaGupcZrVaFRoaqsTExDy32blzpzp06OCyrGPHjtq5c2dxlgoAAADgGkydY3HmzBk5HA75+vq6LPf19VVycnKe25w+fVp+fn652p8+fbpAfV55oFBmZiYT34qZzWZTwC0VVb4MfJsb+HrL4XBI/i0ka3mzyyl+1RpJDoeaVm0qT4un2dUUuwZVGsjhcFz+fwzc4Dg338Q4N6MYXPn+FuShnKVi8nZJys7OliT9+OOPJldSNjzSWFLjCmaXUQLSL181qz9Mqm92LSVk5071qdhHqmh2ISWDq6K4mXBuvolxbkYxufIe+lpMDRY+Pj6y2Wy5JmqnpqbmuipxhZ+fX66rE9dqf7Vy5copKChIVqtVFoulcIUDAAAAZUBOTo6ys7NVrtz1Y4OpwcLT01MtWrRQQkKCunbtKulyGkpISFB4eHie27Rq1Upbt27V4MGDncu+//57tWrVqkB9Wq1WeXre/JcHAQAAgJJk+l2hhgwZolWrVmnNmjU6ePCgZsyYoYyMDPXt21eSNGnSJM2ZM8fZ/vHHH9eWLVu0ZMkSHTx4UPPmzdP//ve/fIMIAAAAgOJn+hyLsLAwpaWlKTo6WikpKQoICFBMTIxzaNPJkydltf5f/mndurXefPNNvf322/rrX/+qBg0a6G9/+5uaNm1q1iEAAAAAZZ4lpyBTvAEAAADgGkwfCgUAAADgxkewAAAAAGAYwQIAAACAYQQLAAAAAIYRLAAAAAAYZvrtZgEUXFpamuLj47Vz507nE+j9/PwUEhKivn37qnr16iZXCAAAyiquWAA3iF27dum+++7T8uXLVblyZbVp00Zt2rRR5cqVtXz5ct1///1KSkoyu0wAwFVOnjypqVOnml0GUOx4jgVwgxgwYICaNWumyMhIWSwWl3U5OTmaPn269u3bp48++sikCgEAedm7d6/69OmjPXv2mF0KUKwYCgXcIPbu3auoqKhcoUKSLBaLnnjiCfXp08eEygCgbNu0adM11x89erSEKgHMRbAAbhB+fn5KSkpS48aN81yflJQkPz+/Eq4KADBmzBhZLBZdaxBIXh8KATcbggVwg4iIiNC0adP0v//9Tx06dHCGiNOnTyshIUEff/yxJk2aZHKVAFD2+Pv7a/r06eratWue6/fs2aO+ffuWcFVAySNYADeIQYMGycfHR0uXLtXKlSvlcDgkSTabTS1atFBUVJTCwsJMrhIAyp4WLVpo9+7d+QaL613NAG4WTN4GbkBZWVk6c+aMJMnHx0ceHh4mVwQAZde///1vpaen66677spzfXp6uv73v/+pbdu2JVwZULIIFgAAAAAM4zkWAAAAAAwjWAAAAAAwjGABAAAAwDCCBQAAAADDCBYAgHwdO3ZMdrtde/bsMbsUt02ZMkWjR482uwwAKDMIFgCAErN69Wq1adPG7DIkSZ9//rmGDBmi9u3bq3Xr1nr44Ye1ZcsWs8sCgBsWwQIAcMNxOBzKzs42tI/t27crNDRUCxcu1OrVq9WuXTuNGjVKP/74YxFVCQBlC8ECAKDs7GwtWrRI3bp1U2BgoDp37qz33nsvV7u8rjh8+eWXstvtztd79+7VY489ppCQELVu3Vp9+/ZVUlKStm3bpqlTp+qPP/6Q3W6X3W7XvHnzJEmZmZl6/fXXdeedd6pVq1bq37+/tm3blqvfTZs2KSwsTEFBQTpx4oRbx7hr1y61b99eCxculCS98MILGj58uIKDg9WgQQM9++yzql+/vr766iu39gsAuKyc2QUAAMw3Z84cffzxx5o6dapuv/12nTp1SocOHSrUviZOnKiAgADNmDFDNptNe/bskYeHh0JCQvT8888rOjpaGzdulCRVqFBBkvTyyy/rp59+0ltvvaUaNWroiy++0LBhw7Ru3To1aNBAknThwgUtWrRIr776qqpVqyZfX98C15SQkKBx48bpueee08MPP5xnm+zsbJ0/f17VqlUr1HEDQFlHsACAMu7cuXNatmyZXnrpJfXp00eSVK9ePbVp00bHjh1ze38nTpxQRESEGjduLEnOYCBJlStXlsVikb+/v0v71atX6+uvv1bNmjUlSREREdqyZYtWr16tZ599VpKUlZWlGTNmqFmzZm7V88UXX2jSpEmaOXOmwsLC8m23ePFipaen6/7773dr/wCAywgWAFDGJScnKzMzU+3bty+S/Q0ZMkQvvvii/v73vys0NFT33Xef6tWrl2/7/fv3y+Fw6L777nNZnpmZ6XL1wMPDw2XIVUHs2rVLmzdvVnR0tLp27Zpvu3Xr1ulvf/ub3n33XbeuhAAA/g/BAgDKuPLlyxe4rdVqVU5OjsuyrKwsl9fjxo1Tz5499c033+jbb79VdHS03nrrLXXr1i3Pfaanp8tmsyk+Pl42m81l3ZWhUpLk5eUli8VS4FolqW7duqpWrZo++eQTderUSR4eHrnarF+/Xi+++KLmzp2r0NBQt/YPAPg/TN4GgDKuQYMG8vLy0tatW6/b1sfHR+fPn1d6erpz2d69e3O1a9iwoQYPHqwlS5aoe/fuio+Pl3T5qoPD4XBpGxAQIIfDobS0NNWvX9/l689DpgrDx8dHcXFxOnLkiJ555plcIegf//iHpk6dqjlz5qhz586G+gKAso5gAQBlXPny5TV8+HDNnj1ba9eu1ZEjR7Rz5059/PHHudq2bNlS3t7e+utf/6ojR45o3bp1Wr16tXP9hQsX9PLLL2vbtm06fvy4duzYoaSkJOd8i9q1ays9PV0JCQlKS0tTRkaGGjZsqF69emnSpEn6/PPPdfToUe3atUsLFizQ5s2bDR+fr6+v4uLilJycrAkTJujSpUuSLg9/mjx5siZPnqyWLVsqJSVFKSkp+uOPPwz3CQBlEcECAKDRo0dryJAhio6OVlhYmMaPH6+0tLRc7apVq6bZs2fr22+/Va9evbR+/XqNGzfOud5qteq3337T5MmTde+99+qZZ57RXXfdpaeeekqS1Lp1aw0cOFDPPPOMOnTooJiYGElSVFSUHnzwQc2aNUv333+/Ro8eraSkJNWqVatIjs/f319xcXHat2+fJk6cKIfDoVWrVunSpUt6+eWX1bFjR+fXzJkzi6RPAChrLDlXD5YFAAAAADdxxQIAAACAYdwVCgBwQwoJCcl33aJFi3I9IRwAULwYCgUAuCEdPnw433U1a9aUl5dXCVYDACBYAAAAADCMORYAAAAADCNYAAAAADCMYAEAAADAMIIFAAAAAMMIFgAAAAAMI1gAAAAAMIxgAQAAAMAwggUAAAAAw/4fg+6BoJf3DpcAAAAASUVORK5CYII=",
"text/plain": [
"<Figure size 800x400 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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",
"text/plain": [
"<Figure size 800x400 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"for k in [2, 5]:\n",
" tmp = (\n",
" dfc.groupby(f\"cluster_k{k}\")\n",
" .agg(\n",
" churn_hard=(\"churn_hard\", \"mean\"),\n",
" churn_soft=(\"churn_soft\", \"mean\"),\n",
" churn_warning=(\"churn_warning\", \"mean\")\n",
" )\n",
" )\n",
"\n",
" tmp.plot(kind=\"bar\", figsize=(8, 4))\n",
" plt.title(f\"Churn par cluster — K={k}\")\n",
" plt.ylabel(\"Rate\")\n",
" plt.tight_layout()\n",
" plt.show()"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "5fc62ca1-a145-4669-8798-33d72352d4d5",
"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": "3.13.12"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
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