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{
"cells": [
{
"cell_type": "markdown",
"id": "13c6141d",
"metadata": {},
"source": [
"# Behavioral Clustering of Carmignac Investors\n",
"\n",
"This notebook implements two complementary clustering analyses:\n",
"\n",
"| | Scope | Approach |\n",
"|---|---|---|\n",
"| **Part 1** | All active accounts (~7,000) | Global behavioral clustering |\n",
"| **Part 2** | Top ~400 accounts (AUM > €5M) | High-conviction clustering with performance reactivity features |\n",
"\n",
"Both analyses share the same preprocessing pipeline (RobustScaler, MAD winsorization) and visualization conventions (robust z-score heatmaps).\n",
"\n",
"---\n",
"**Structure:**\n",
"1. Imports & Configuration\n",
"2. Data Loading\n",
"3. Monthly Panel Construction\n",
"4. Feature Engineering\n",
"5. **Part 1** — Global Clustering (all accounts)\n",
" - 5a. Feature selection & preprocessing\n",
" - 5b. K-selection & clustering\n",
" - 5c. Cluster profiles (behavioral + allocation)\n",
" - 5d. Asset-type sub-clustering & cross-analysis\n",
"6. **Part 2** — Top 400 Accounts Clustering\n",
" - 6a. Account selection & feature engineering\n",
" - 6b. K-selection & clustering\n",
" - 6c. Cluster profiles & churn analysis\n",
"7. Cross-Analysis: Global vs Top 400\n"
]
},
{
"cell_type": "markdown",
"id": "28e588fe",
"metadata": {},
"source": [
"---\n",
"## 1. Imports & Configuration\n"
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "3bc1ffe0",
"metadata": {},
"outputs": [],
"source": [
"import os\n",
"import re\n",
"import s3fs\n",
"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 RobustScaler\n",
"from sklearn.cluster import KMeans\n",
"from sklearn.metrics import silhouette_score, davies_bouldin_score\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": 2,
"id": "69d2dc25",
"metadata": {},
"outputs": [],
"source": [
"# Column names\n",
"ID_COL = \"Registrar Account - ID\"\n",
"ISIN_COL = \"Product - Isin\"\n",
"FUND_COL = \"Product - Fund\"\n",
"ASSET_COL = \"Product - Asset Type\"\n",
"FLOW_DATE_COL = \"Centralisation Date\"\n",
"AUM_DATE_COL = \"Centralisation Date\"\n",
"FLOW_QTY_COL = \"Quantity - NetFlows\"\n",
"FLOW_SUB_COL = \"Quantity - Subscription\"\n",
"FLOW_RED_COL = \"Quantity - Redemption\"\n",
"AUM_QTY_COL = \"Quantity - AUM\"\n",
"AUM_VAL_COL = \"Value - AUM €\"\n",
"REGION_COL = \"Registrar Account - Region\"\n",
"COUNTRY_COL = \"RegistrarAccount - Country\"\n",
"NAV_DATE_COL = \"Dat\"\n",
"NAV_ISIN_COL = \"Isin\"\n",
"NAV_PRICE_COL = \"Price (TF PartPrice)\"\n",
"NAV_BENCH_COL = \"PriceBench\"\n",
"RATE_DATE_COL = \"Date\"\n",
"RATE_VAL_COL = \"Yld to Maturity\""
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "bf5b7a0a",
"metadata": {},
"outputs": [],
"source": [
"# SHARED UTILITIES\n",
"def robust_zscore(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",
"def plot_heatmap(dfc, profile_vars, cluster_col, title, figsize=(16, 4)):\n",
" \"\"\"Cluster signature heatmap using robust z-scores, capped at ±3 for readability.\"\"\"\n",
" dfc_viz = dfc[profile_vars + [cluster_col]].copy()\n",
" for col in profile_vars:\n",
" vals = pd.to_numeric(dfc_viz[col], errors=\"coerce\").to_numpy(dtype=float)\n",
" lo = np.nanpercentile(vals, 2)\n",
" hi = np.nanpercentile(vals, 98)\n",
" dfc_viz[col] = np.clip(vals, lo, hi)\n",
" prof = dfc_viz.groupby(cluster_col)[profile_vars].median()\n",
" prof_z = prof.apply(lambda col: robust_zscore(col.values), axis=0)\n",
" prof_z = prof_z.clip(-3, 3) # cap for readability\n",
" plt.figure(figsize=figsize)\n",
" sns.heatmap(prof_z, cmap=\"RdBu_r\", center=0, annot=True, fmt=\".2f\",\n",
" xticklabels=profile_vars,\n",
" yticklabels=[f\"Cluster {i}\" for i in range(len(prof))])\n",
" plt.title(title)\n",
" plt.xticks(rotation=45, ha=\"right\")\n",
" plt.tight_layout()\n",
" plt.show()\n",
" return prof\n",
"\n",
"def winsorize_mad(series, n_sigma=3):\n",
" \"\"\"Winsorize using MAD n-sigma rule. Falls back to p95 clip when MAD~0.\"\"\"\n",
" vals = pd.to_numeric(series, errors=\"coerce\").to_numpy(dtype=float)\n",
" med = np.nanmedian(vals)\n",
" mad = np.nanmedian(np.abs(vals - med)) * 1.4826\n",
" if mad > 0:\n",
" return np.clip(vals, med - n_sigma * mad, med + n_sigma * mad)\n",
" else:\n",
" return np.clip(vals, 0, np.nanpercentile(vals, 95))\n",
"\n",
"def add_months_since_last_tx(dfc, df_month, id_col, suffix=\"\"):\n",
" \"\"\"Adds months_since_last_tx[suffix] to dfc.\"\"\"\n",
" col_name = f\"months_since_last_tx{suffix}\"\n",
" reference_date = df_month[\"month\"].max()\n",
" last_active = (\n",
" df_month[df_month[\"active_month\"] == 1]\n",
" .groupby(id_col)[\"month\"]\n",
" .max()\n",
" .reset_index(name=\"last_active_month\")\n",
" )\n",
" last_active[col_name] = (\n",
" (reference_date.to_period(\"M\") -\n",
" last_active[\"last_active_month\"].dt.to_period(\"M\"))\n",
" .apply(lambda x: x.n)\n",
" )\n",
" dfc = dfc.merge(last_active[[id_col, col_name]], on=id_col, how=\"left\")\n",
" max_months = dfc[col_name].max()\n",
" dfc[col_name] = dfc[col_name].fillna(max_months + 1)\n",
" return dfc\n",
"\n",
"\n",
"def add_months_since_last_tx_by_group(df, id_cols, active_col=\"active_month\", month_col=\"month\", suffix=\"\"):\n",
" col_name = f\"months_since_last_tx{suffix}\"\n",
" reference_date = df[month_col].max()\n",
"\n",
" last_active = (\n",
" df[df[active_col] == 1]\n",
" .groupby(id_cols)[month_col]\n",
" .max()\n",
" .reset_index(name=\"last_active_month\")\n",
" )\n",
"\n",
" last_active[col_name] = (\n",
" (reference_date.to_period(\"M\") - last_active[\"last_active_month\"].dt.to_period(\"M\"))\n",
" .apply(lambda x: x.n)\n",
" )\n",
"\n",
" return last_active[[*id_cols, col_name]]"
]
},
{
"cell_type": "markdown",
"id": "312153e6",
"metadata": {},
"source": [
"---\n",
"## 2. Data Loading\n",
"\n",
"Three data sources are used:\n",
"- **AUM** (repaired): monthly share quantities per account and ISIN\n",
"- **Flows**: daily net transactions, aggregated to monthly\n",
"- **NAV / Rates**: fund performance and interest rate data for enrichment\n"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "001d9af2-07db-4813-937e-693103823359",
"metadata": {
"scrolled": true
},
"outputs": [
{
"data": {
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" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>reg_orig</th>\n",
" <th>reg_used</th>\n",
" <th>Agreement - Code</th>\n",
" <th>Company - Id</th>\n",
" <th>Company - Ultimate Parent Id</th>\n",
" <th>Registrar Account - Region</th>\n",
" <th>RegistrarAccount - Country</th>\n",
" <th>Product - Asset Type</th>\n",
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" <th>Product - Shareclass Currency</th>\n",
" <th>Product - Isin</th>\n",
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" <th>Quantity - AUM</th>\n",
" <th>Value - AUM CCY</th>\n",
" <th>Value - AUM €</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>18872</td>\n",
" <td>18872</td>\n",
" <td>L104</td>\n",
" <td>2257.0</td>\n",
" <td>33675.0</td>\n",
" <td>Switzerland</td>\n",
" <td>Switzerland</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>2015-01-31</td>\n",
" <td>49094.915</td>\n",
" <td>3.242523e+07</td>\n",
" <td>3.242523e+07</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>18872</td>\n",
" <td>18872</td>\n",
" <td>L104</td>\n",
" <td>2257.0</td>\n",
" <td>33675.0</td>\n",
" <td>Switzerland</td>\n",
" <td>Switzerland</td>\n",
" <td>Equity</td>\n",
" <td>Investissement Latitude</td>\n",
" <td>FCP</td>\n",
" <td>NO</td>\n",
" <td>Carmignac Investissement Latitude</td>\n",
" <td>A</td>\n",
" <td>EUR</td>\n",
" <td>FR0010147603</td>\n",
" <td>2015-01-31</td>\n",
" <td>1717.000</td>\n",
" <td>4.767422e+05</td>\n",
" <td>4.767422e+05</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>18872</td>\n",
" <td>18872</td>\n",
" <td>L104</td>\n",
" <td>2257.0</td>\n",
" <td>33675.0</td>\n",
" <td>Switzerland</td>\n",
" <td>Switzerland</td>\n",
" <td>Equity</td>\n",
" <td>Investissement</td>\n",
" <td>FCP</td>\n",
" <td>NO</td>\n",
" <td>Carmignac Investissement</td>\n",
" <td>A</td>\n",
" <td>EUR</td>\n",
" <td>FR0010148981</td>\n",
" <td>2015-01-31</td>\n",
" <td>8254.870</td>\n",
" <td>9.862671e+06</td>\n",
" <td>9.862671e+06</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>18872</td>\n",
" <td>18872</td>\n",
" <td>L104</td>\n",
" <td>2257.0</td>\n",
" <td>33675.0</td>\n",
" <td>Switzerland</td>\n",
" <td>Switzerland</td>\n",
" <td>Equity</td>\n",
" <td>Euro-Entrepreneurs</td>\n",
" <td>FCP</td>\n",
" <td>NO</td>\n",
" <td>Carmignac Euro-Entrepreneurs</td>\n",
" <td>A</td>\n",
" <td>EUR</td>\n",
" <td>FR0010149112</td>\n",
" <td>2015-01-31</td>\n",
" <td>278.923</td>\n",
" <td>7.664525e+04</td>\n",
" <td>7.664525e+04</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>18872</td>\n",
" <td>18872</td>\n",
" <td>L104</td>\n",
" <td>2257.0</td>\n",
" <td>33675.0</td>\n",
" <td>Switzerland</td>\n",
" <td>Switzerland</td>\n",
" <td>Fixed Income</td>\n",
" <td>Sécurité</td>\n",
" <td>FCP</td>\n",
" <td>NO</td>\n",
" <td>Carmignac Sécurité</td>\n",
" <td>AW &amp; AW-R</td>\n",
" <td>EUR</td>\n",
" <td>FR0010149120</td>\n",
" <td>2015-01-31</td>\n",
" <td>1807.267</td>\n",
" <td>3.078318e+06</td>\n",
" <td>3.078318e+06</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",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1088393</th>\n",
" <td>Private Client</td>\n",
" <td>Private Client</td>\n",
" <td>Private Client</td>\n",
" <td>Private Client</td>\n",
" <td>Private Client</td>\n",
" <td>France</td>\n",
" <td>France</td>\n",
" <td>Diversified</td>\n",
" <td>Inflation Solution</td>\n",
" <td>SICAV</td>\n",
" <td>NO</td>\n",
" <td>Carmignac Portfolio Inflation Solution</td>\n",
" <td>F</td>\n",
" <td>EUR</td>\n",
" <td>LU2715954330</td>\n",
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" <td>9.533293e+06</td>\n",
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" <td>Private Client</td>\n",
" <td>Private Client</td>\n",
" <td>Private Client</td>\n",
" <td>Private Client</td>\n",
" <td>Private Client</td>\n",
" <td>France</td>\n",
" <td>France</td>\n",
" <td>Diversified</td>\n",
" <td>Inflation Solution</td>\n",
" <td>SICAV</td>\n",
" <td>NO</td>\n",
" <td>Carmignac Portfolio Inflation Solution</td>\n",
" <td>A</td>\n",
" <td>EUR</td>\n",
" <td>LU2715954504</td>\n",
" <td>2025-10-31</td>\n",
" <td>6853.363</td>\n",
" <td>7.978685e+05</td>\n",
" <td>7.978685e+05</td>\n",
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" <tr>\n",
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" <td>Private Client</td>\n",
" <td>Private Client</td>\n",
" <td>Private Client</td>\n",
" <td>Private Client</td>\n",
" <td>Private Client</td>\n",
" <td>France</td>\n",
" <td>France</td>\n",
" <td>Private Assets</td>\n",
" <td>Evergreen</td>\n",
" <td>SICAV</td>\n",
" <td>NO</td>\n",
" <td>Carmignac S.A. SICAV - PART II UCI Private Eve...</td>\n",
" <td>A</td>\n",
" <td>EUR</td>\n",
" <td>LU2799473124</td>\n",
" <td>2025-10-31</td>\n",
" <td>4212.234</td>\n",
" <td>5.263608e+05</td>\n",
" <td>5.263608e+05</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1088396</th>\n",
" <td>Private Client</td>\n",
" <td>Private Client</td>\n",
" <td>Private Client</td>\n",
" <td>Private Client</td>\n",
" <td>Private Client</td>\n",
" <td>France</td>\n",
" <td>France</td>\n",
" <td>Equity</td>\n",
" <td>Tech Solutions</td>\n",
" <td>SICAV</td>\n",
" <td>NO</td>\n",
" <td>Carmignac Portfolio Tech Solutions</td>\n",
" <td>A</td>\n",
" <td>EUR</td>\n",
" <td>LU2809794220</td>\n",
" <td>2025-10-31</td>\n",
" <td>31469.523</td>\n",
" <td>4.438147e+06</td>\n",
" <td>4.438147e+06</td>\n",
" </tr>\n",
" <tr>\n",
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" <td>Private Client</td>\n",
" <td>Private Client</td>\n",
" <td>Private Client</td>\n",
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" <td>France</td>\n",
" <td>France</td>\n",
" <td>Equity</td>\n",
" <td>Tech Solutions</td>\n",
" <td>SICAV</td>\n",
" <td>NO</td>\n",
" <td>Carmignac Portfolio Tech Solutions</td>\n",
" <td>F</td>\n",
" <td>EUR</td>\n",
" <td>LU2809794576</td>\n",
" <td>2025-10-31</td>\n",
" <td>554.301</td>\n",
" <td>7.871629e+04</td>\n",
" <td>7.871629e+04</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>1088398 rows × 19 columns</p>\n",
"</div>"
],
"text/plain": [
" reg_orig reg_used Agreement - Code Company - Id \\\n",
"0 18872 18872 L104 2257.0 \n",
"1 18872 18872 L104 2257.0 \n",
"2 18872 18872 L104 2257.0 \n",
"3 18872 18872 L104 2257.0 \n",
"4 18872 18872 L104 2257.0 \n",
"... ... ... ... ... \n",
"1088393 Private Client Private Client Private Client Private Client \n",
"1088394 Private Client Private Client Private Client Private Client \n",
"1088395 Private Client Private Client Private Client Private Client \n",
"1088396 Private Client Private Client Private Client Private Client \n",
"1088397 Private Client Private Client Private Client Private Client \n",
"\n",
" Company - Ultimate Parent Id Registrar Account - Region \\\n",
"0 33675.0 Switzerland \n",
"1 33675.0 Switzerland \n",
"2 33675.0 Switzerland \n",
"3 33675.0 Switzerland \n",
"4 33675.0 Switzerland \n",
"... ... ... \n",
"1088393 Private Client France \n",
"1088394 Private Client France \n",
"1088395 Private Client France \n",
"1088396 Private Client France \n",
"1088397 Private Client France \n",
"\n",
" RegistrarAccount - Country Product - Asset Type \\\n",
"0 Switzerland Diversified \n",
"1 Switzerland Equity \n",
"2 Switzerland Equity \n",
"3 Switzerland Equity \n",
"4 Switzerland Fixed Income \n",
"... ... ... \n",
"1088393 France Diversified \n",
"1088394 France Diversified \n",
"1088395 France Private Assets \n",
"1088396 France Equity \n",
"1088397 France Equity \n",
"\n",
" Product - Strategy Product - Legal Status Product - Is Dedie ? \\\n",
"0 Patrimoine FCP NO \n",
"1 Investissement Latitude FCP NO \n",
"2 Investissement FCP NO \n",
"3 Euro-Entrepreneurs FCP NO \n",
"4 Sécurité FCP NO \n",
"... ... ... ... \n",
"1088393 Inflation Solution SICAV NO \n",
"1088394 Inflation Solution SICAV NO \n",
"1088395 Evergreen SICAV NO \n",
"1088396 Tech Solutions SICAV NO \n",
"1088397 Tech Solutions SICAV NO \n",
"\n",
" Product - Fund \\\n",
"0 Carmignac Patrimoine \n",
"1 Carmignac Investissement Latitude \n",
"2 Carmignac Investissement \n",
"3 Carmignac Euro-Entrepreneurs \n",
"4 Carmignac Sécurité \n",
"... ... \n",
"1088393 Carmignac Portfolio Inflation Solution \n",
"1088394 Carmignac Portfolio Inflation Solution \n",
"1088395 Carmignac S.A. SICAV - PART II UCI Private Eve... \n",
"1088396 Carmignac Portfolio Tech Solutions \n",
"1088397 Carmignac Portfolio Tech Solutions \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 AW & AW-R EUR \n",
"... ... ... \n",
"1088393 F EUR \n",
"1088394 A EUR \n",
"1088395 A EUR \n",
"1088396 A EUR \n",
"1088397 F EUR \n",
"\n",
" Product - Isin Centralisation Date Quantity - AUM Value - AUM CCY \\\n",
"0 FR0010135103 2015-01-31 49094.915 3.242523e+07 \n",
"1 FR0010147603 2015-01-31 1717.000 4.767422e+05 \n",
"2 FR0010148981 2015-01-31 8254.870 9.862671e+06 \n",
"3 FR0010149112 2015-01-31 278.923 7.664525e+04 \n",
"4 FR0010149120 2015-01-31 1807.267 3.078318e+06 \n",
"... ... ... ... ... \n",
"1088393 LU2715954330 2025-10-31 81065.419 9.533293e+06 \n",
"1088394 LU2715954504 2025-10-31 6853.363 7.978685e+05 \n",
"1088395 LU2799473124 2025-10-31 4212.234 5.263608e+05 \n",
"1088396 LU2809794220 2025-10-31 31469.523 4.438147e+06 \n",
"1088397 LU2809794576 2025-10-31 554.301 7.871629e+04 \n",
"\n",
" Value - AUM € \n",
"0 3.242523e+07 \n",
"1 4.767422e+05 \n",
"2 9.862671e+06 \n",
"3 7.664525e+04 \n",
"4 3.078318e+06 \n",
"... ... \n",
"1088393 9.533293e+06 \n",
"1088394 7.978685e+05 \n",
"1088395 5.263608e+05 \n",
"1088396 4.438147e+06 \n",
"1088397 7.871629e+04 \n",
"\n",
"[1088398 rows x 19 columns]"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_aum = pd.read_csv(\"s3://projet-bdc-carmignac-g3/paco/AUM_paths.csv\")\n",
"df_aum"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "3eea8321-6d49-46e1-a678-3b972c8ccd09",
"metadata": {},
"outputs": [],
"source": [
"df_aum = df_aum.rename(columns={\"reg_orig\": \"Registrar Account - ID\"})"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "027cf8e1-3e4c-416c-bc9e-cd299cdbd7c3",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Index(['Registrar Account - ID', 'reg_used', 'Agreement - Code',\n",
" 'Company - Id', 'Company - Ultimate Parent 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 €'],\n",
" dtype='object')"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_aum.columns"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "011958df",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"flows: (2514419, 25)\n",
"aum: (1076643, 20)\n",
"nav: (623914, 6)\n"
]
}
],
"source": [
"PATH_FLOWS = \"s3://projet-bdc-data/carmignac/Flows ENSAE V2 -20251105.csv\"\n",
"PATH_NAV = \"s3://projet-bdc-data/carmignac/Data Modélisation/Nav/NAV_Bench_data.csv\"\n",
"PATH_RATES = \"s3://projet-bdc-data/carmignac/Data Modélisation/market data/esterRates.csv\"\n",
"\n",
"df_flows = pd.read_csv(PATH_FLOWS, sep=\";\")\n",
"df_nav = pd.read_csv(PATH_NAV, sep=\";\")\n",
"df_rates = pd.read_csv(PATH_RATES, sep=\";\")\n",
"\n",
"\n",
"# Date parsing\n",
"for df, col in [\n",
" (df_flows, FLOW_DATE_COL), (df_aum, AUM_DATE_COL),\n",
" (df_nav, NAV_DATE_COL), (df_rates, RATE_DATE_COL)\n",
"]:\n",
" df[col] = pd.to_datetime(df[col], errors=\"coerce\")\n",
" df[\"month\"] = df[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",
"for col in [AUM_QTY_COL, AUM_VAL_COL]:\n",
" df_aum[col] = pd.to_numeric(df_aum[col], errors=\"coerce\")\n",
"for col in [NAV_PRICE_COL, NAV_BENCH_COL]:\n",
" df_nav[col] = pd.to_numeric(df_nav[col], errors=\"coerce\")\n",
"df_rates[RATE_VAL_COL] = pd.to_numeric(df_rates[RATE_VAL_COL], errors=\"coerce\")\n",
"\n",
"for df in [df_flows, df_aum]:\n",
" df[ISIN_COL] = df[ISIN_COL].astype(str).str.strip()\n",
"df_nav[NAV_ISIN_COL] = df_nav[NAV_ISIN_COL].astype(str).str.strip()\n",
"\n",
"# Remove technical accounts (not investable)\n",
"df_flows = df_flows[~df_flows[ID_COL].isin(\n",
" [\"Off Distribution\", \"Private Clients\", \"Private Client\"]\n",
")]\n",
"df_aum = df_aum[~df_aum[ID_COL].isin(\n",
" [\"Off Distribution\", \"Private Clients\", \"Private Client\"]\n",
")]\n",
"\n",
"print(\"flows:\", df_flows.shape)\n",
"print(\"aum: \", df_aum.shape)\n",
"print(\"nav: \", df_nav.shape)"
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "cadf5a78-53f3-4157-878f-edc615ef1964",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Index(['Agreement - Code', 'Company - Id', 'Company - Ultimate Parent Id',\n",
" 'Registrar Account - ID', 'Registrar Account - Region',\n",
" 'RegistrarAccount - Country', 'Product - Asset Type',\n",
" 'Product - Strategy', 'Product - Legal Status', 'Product - Is Dedie ?',\n",
" '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', 'month'],\n",
" dtype='object')"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_flows.columns"
]
},
{
"cell_type": "markdown",
"id": "acd89acb-a9c1-4488-a47e-db4ca76b9214",
"metadata": {},
"source": [
"## Etude fund et fund families sur aum et sur flows"
]
},
{
"cell_type": "markdown",
"id": "b568bbc3-55ad-4dcb-bdb0-94e9a154575d",
"metadata": {},
"source": [
"---\n",
"## 3. Part 0 — Define Family Funds et Faire clustering sur les top 15 funds\n"
]
},
{
"cell_type": "code",
"execution_count": 9,
"id": "70c8591f-29aa-4d2f-94bb-d3a280cbe5bb",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Nombre de couples ISIN/Fund : 348\n",
"Nombre de funds uniques : 62\n",
"Nombre de families uniques : 58\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>Product - Isin</th>\n",
" <th>Product - Fund</th>\n",
" <th>fund_family</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>FR0000999999</td>\n",
" <td>Carmignac Court Terme</td>\n",
" <td>Carmignac Court Terme</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>FR0010135103</td>\n",
" <td>Carmignac Patrimoine</td>\n",
" <td>Carmignac Patrimoine</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>FR0010147603</td>\n",
" <td>Carmignac Investissement Latitude</td>\n",
" <td>Carmignac Investissement Latitude</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>FR0010148981</td>\n",
" <td>Carmignac Investissement</td>\n",
" <td>Carmignac Investissement</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>FR0010148999</td>\n",
" <td>Carmignac Profil Réactif 75</td>\n",
" <td>Carmignac Profil Réactif &lt;NUM&gt;</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>FR0010149096</td>\n",
" <td>Carmignac Innovation</td>\n",
" <td>Carmignac Innovation</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>FR0010149112</td>\n",
" <td>Carmignac Euro-Entrepreneurs</td>\n",
" <td>Carmignac Euro-Entrepreneurs</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>FR0010149120</td>\n",
" <td>Carmignac Sécurité</td>\n",
" <td>Carmignac Sécurité</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>FR0010149161</td>\n",
" <td>Carmignac Court Terme</td>\n",
" <td>Carmignac Court Terme</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>FR0010149179</td>\n",
" <td>Carmignac Absolute Return Europe</td>\n",
" <td>Carmignac Absolute Return Europe</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>FR0010149203</td>\n",
" <td>Carmignac Multi Expertise</td>\n",
" <td>Carmignac Multi Expertise</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td>FR0010149211</td>\n",
" <td>Carmignac Profil Réactif 100</td>\n",
" <td>Carmignac Profil Réactif &lt;NUM&gt;</td>\n",
" </tr>\n",
" <tr>\n",
" <th>12</th>\n",
" <td>FR0010149278</td>\n",
" <td>Carmignac Euro-Investissement</td>\n",
" <td>Carmignac Euro-Investissement</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13</th>\n",
" <td>FR0010149302</td>\n",
" <td>Carmignac Emergents</td>\n",
" <td>Carmignac Emergents</td>\n",
" </tr>\n",
" <tr>\n",
" <th>14</th>\n",
" <td>FR0010306142</td>\n",
" <td>Carmignac Patrimoine</td>\n",
" <td>Carmignac Patrimoine</td>\n",
" </tr>\n",
" <tr>\n",
" <th>15</th>\n",
" <td>FR0010312660</td>\n",
" <td>Carmignac Investissement</td>\n",
" <td>Carmignac Investissement</td>\n",
" </tr>\n",
" <tr>\n",
" <th>16</th>\n",
" <td>FR0010956607</td>\n",
" <td>Carmignac Emergents</td>\n",
" <td>Carmignac Emergents</td>\n",
" </tr>\n",
" <tr>\n",
" <th>17</th>\n",
" <td>FR0010956615</td>\n",
" <td>Carmignac Investissement</td>\n",
" <td>Carmignac Investissement</td>\n",
" </tr>\n",
" <tr>\n",
" <th>18</th>\n",
" <td>FR0010956649</td>\n",
" <td>Carmignac Patrimoine</td>\n",
" <td>Carmignac Patrimoine</td>\n",
" </tr>\n",
" <tr>\n",
" <th>19</th>\n",
" <td>FR0011147446</td>\n",
" <td>Carmignac Emergents</td>\n",
" <td>Carmignac Emergents</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Product - Isin Product - Fund \\\n",
"0 FR0000999999 Carmignac Court Terme \n",
"1 FR0010135103 Carmignac Patrimoine \n",
"2 FR0010147603 Carmignac Investissement Latitude \n",
"3 FR0010148981 Carmignac Investissement \n",
"4 FR0010148999 Carmignac Profil Réactif 75 \n",
"5 FR0010149096 Carmignac Innovation \n",
"6 FR0010149112 Carmignac Euro-Entrepreneurs \n",
"7 FR0010149120 Carmignac Sécurité \n",
"8 FR0010149161 Carmignac Court Terme \n",
"9 FR0010149179 Carmignac Absolute Return Europe \n",
"10 FR0010149203 Carmignac Multi Expertise \n",
"11 FR0010149211 Carmignac Profil Réactif 100 \n",
"12 FR0010149278 Carmignac Euro-Investissement \n",
"13 FR0010149302 Carmignac Emergents \n",
"14 FR0010306142 Carmignac Patrimoine \n",
"15 FR0010312660 Carmignac Investissement \n",
"16 FR0010956607 Carmignac Emergents \n",
"17 FR0010956615 Carmignac Investissement \n",
"18 FR0010956649 Carmignac Patrimoine \n",
"19 FR0011147446 Carmignac Emergents \n",
"\n",
" fund_family \n",
"0 Carmignac Court Terme \n",
"1 Carmignac Patrimoine \n",
"2 Carmignac Investissement Latitude \n",
"3 Carmignac Investissement \n",
"4 Carmignac Profil Réactif <NUM> \n",
"5 Carmignac Innovation \n",
"6 Carmignac Euro-Entrepreneurs \n",
"7 Carmignac Sécurité \n",
"8 Carmignac Court Terme \n",
"9 Carmignac Absolute Return Europe \n",
"10 Carmignac Multi Expertise \n",
"11 Carmignac Profil Réactif <NUM> \n",
"12 Carmignac Euro-Investissement \n",
"13 Carmignac Emergents \n",
"14 Carmignac Patrimoine \n",
"15 Carmignac Investissement \n",
"16 Carmignac Emergents \n",
"17 Carmignac Investissement \n",
"18 Carmignac Patrimoine \n",
"19 Carmignac Emergents "
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import re\n",
"import numpy as np\n",
"import pandas as pd\n",
"\n",
"# =========================================================\n",
"# 1) Fonction conservative de construction de famille\n",
"# - même nom => même famille\n",
"# - différence purement numérique => même famille\n",
"# - sinon famille différente\n",
"# =========================================================\n",
"\n",
"def build_fund_family_numeric_only(fund_name):\n",
" if pd.isna(fund_name):\n",
" return np.nan\n",
" \n",
" s = str(fund_name).strip()\n",
" family = re.sub(r\"\\d+\", \"<NUM>\", s)\n",
" family = re.sub(r\"\\s+\", \" \", family).strip()\n",
" return family\n",
"\n",
"fund_family_table = (\n",
" df_aum[[ISIN_COL, FUND_COL]]\n",
" .dropna(subset=[ISIN_COL, FUND_COL])\n",
" .drop_duplicates()\n",
" .sort_values([ISIN_COL, FUND_COL])\n",
" .reset_index(drop=True)\n",
")\n",
"\n",
"fund_family_table[\"fund_family\"] = fund_family_table[FUND_COL].apply(build_fund_family_numeric_only)\n",
"\n",
"print(\"Nombre de couples ISIN/Fund :\", fund_family_table.shape[0])\n",
"print(\"Nombre de funds uniques :\", fund_family_table[FUND_COL].nunique())\n",
"print(\"Nombre de families uniques :\", fund_family_table[\"fund_family\"].nunique())\n",
"\n",
"fund_family_table.head(20)"
]
},
{
"cell_type": "markdown",
"id": "d34f5ecf",
"metadata": {},
"source": [
"---\n",
"## 3. Monthly Panel Construction\n",
"\n",
"A full outer join of AUM and flows at `(account, ISIN, month)` granularity, enriched with NAV returns and interest rate changes.\n"
]
},
{
"cell_type": "code",
"execution_count": 10,
"id": "25f3dce4",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"df_rel_m shape: (1365583, 20)\n"
]
}
],
"source": [
"df_flows_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_m = (\n",
" df_aum\n",
" .dropna(subset=[ID_COL, ISIN_COL, FUND_COL, \"month\"])\n",
" .groupby([ID_COL, ISIN_COL, FUND_COL, \"month\"], as_index=False)\n",
" .agg(\n",
" aum_qty=(AUM_QTY_COL, \"sum\"),\n",
" aum_val=(AUM_VAL_COL, \"sum\"),\n",
" asset_type=(ASSET_COL, \"last\"),\n",
" region=(REGION_COL, \"last\"),\n",
" country=(COUNTRY_COL, \"last\")\n",
" )\n",
")\n",
"\n",
"keys = pd.concat([\n",
" df_flows_m[[ID_COL, ISIN_COL, \"month\"]],\n",
" df_aum_m[[ID_COL, ISIN_COL, \"month\"]]\n",
"]).drop_duplicates()\n",
"\n",
"df_rel_m = (\n",
" keys\n",
" .merge(df_aum_m, on=[ID_COL, ISIN_COL, \"month\"], how=\"left\")\n",
" .merge(df_flows_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",
"\n",
"df_rel_m[\"flow_to_aum_rel\"] = np.where(\n",
" df_rel_m[\"aum_qty\"].abs() > 0,\n",
" df_rel_m[\"net_flow_qty\"] / df_rel_m[\"aum_qty\"].abs(),\n",
" np.nan\n",
")\n",
"\n",
"df_rel_m[\"turnover_rel\"] = np.where(\n",
" df_rel_m[\"aum_qty\"].abs() > 0,\n",
" df_rel_m[\"gross_flow_qty\"] / df_rel_m[\"aum_qty\"].abs(),\n",
" np.nan\n",
")\n",
"\n",
"print(\"df_rel_m shape:\", df_rel_m.shape)"
]
},
{
"cell_type": "code",
"execution_count": 11,
"id": "0e95eaaa-3c49-44b9-b049-fee5ecba66ef",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Panel with NAV/rates: (1365583, 22)\n"
]
}
],
"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",
")\n",
"\n",
"df_nav_m[\"ret_fund_m\"] = df_nav_m.groupby(NAV_ISIN_COL)[NAV_PRICE_COL].pct_change()\n",
"\n",
"df_nav_m = df_nav_m.rename(columns={NAV_ISIN_COL: ISIN_COL})[\n",
" [ISIN_COL, \"month\", \"ret_fund_m\"]\n",
"]\n",
"\n",
"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",
")\n",
"\n",
"df_rates_m[\"delta_rate_m\"] = df_rates_m[RATE_VAL_COL].diff()\n",
"df_rates_m = df_rates_m[[\"month\", \"delta_rate_m\"]]\n",
"\n",
"df_rel_m = df_rel_m.merge(df_nav_m, on=[ISIN_COL, \"month\"], how=\"left\")\n",
"df_rel_m = df_rel_m.merge(df_rates_m, on=\"month\", how=\"left\")\n",
"\n",
"for c in [\"ret_fund_m\", \"delta_rate_m\"]:\n",
" df_rel_m[c] = df_rel_m[c].fillna(0)\n",
"\n",
"print(\"Panel with NAV/rates:\", df_rel_m.shape)\n"
]
},
{
"cell_type": "code",
"execution_count": 12,
"id": "f4c2a3fd-2595-445b-95f5-bd544355e23d",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Missing fund_family: 416901\n"
]
},
{
"data": {
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"\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>Product - Fund</th>\n",
" <th>fund_family</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>LU0099161993</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>LU0164455502</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>12</th>\n",
" <td>LU0336083497</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>15</th>\n",
" <td>LU0336083810</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>19</th>\n",
" <td>LU0553415323</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21</th>\n",
" <td>LU0592698954</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>29</th>\n",
" <td>LU0592699093</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>30</th>\n",
" <td>LU0592699259</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>37</th>\n",
" <td>LU0705572823</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>40</th>\n",
" <td>LU0807689582</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>41</th>\n",
" <td>LU0807690838</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>42</th>\n",
" <td>LU0992624949</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>53</th>\n",
" <td>LU0992625086</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>57</th>\n",
" <td>LU0992626993</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>60</th>\n",
" <td>LU0992627611</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>67</th>\n",
" <td>LU0992629153</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>69</th>\n",
" <td>LU0992629401</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>71</th>\n",
" <td>LU0992631563</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>73</th>\n",
" <td>LU1163533422</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>79</th>\n",
" <td>LU1299306321</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Product - Isin Product - Fund fund_family\n",
"0 LU0099161993 NaN NaN\n",
"3 LU0164455502 NaN NaN\n",
"12 LU0336083497 NaN NaN\n",
"15 LU0336083810 NaN NaN\n",
"19 LU0553415323 NaN NaN\n",
"21 LU0592698954 NaN NaN\n",
"29 LU0592699093 NaN NaN\n",
"30 LU0592699259 NaN NaN\n",
"37 LU0705572823 NaN NaN\n",
"40 LU0807689582 NaN NaN\n",
"41 LU0807690838 NaN NaN\n",
"42 LU0992624949 NaN NaN\n",
"53 LU0992625086 NaN NaN\n",
"57 LU0992626993 NaN NaN\n",
"60 LU0992627611 NaN NaN\n",
"67 LU0992629153 NaN NaN\n",
"69 LU0992629401 NaN NaN\n",
"71 LU0992631563 NaN NaN\n",
"73 LU1163533422 NaN NaN\n",
"79 LU1299306321 NaN NaN"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"#merge famille\n",
"\n",
"df_rel_m = df_rel_m.merge(\n",
" fund_family_table[[ISIN_COL, FUND_COL, \"fund_family\"]],\n",
" on=[ISIN_COL, FUND_COL],\n",
" how=\"left\"\n",
")\n",
"\n",
"print(\"Missing fund_family:\", df_rel_m[\"fund_family\"].isna().sum())\n",
"df_rel_m[[ISIN_COL, FUND_COL, \"fund_family\"]].drop_duplicates().head(20)"
]
},
{
"cell_type": "code",
"execution_count": 13,
"id": "f4fb4a9f-b354-42d0-8699-bf6fc1e26bb9",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"df_family_month shape: (539285, 23)\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>fund_family</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>ret_fund_m</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>18872</td>\n",
" <td>Carmignac Absolute Return Europe</td>\n",
" <td>2015-01-01</td>\n",
" <td>100.000</td>\n",
" <td>30624.0000</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</td>\n",
" <td>0</td>\n",
" <td>0.004902</td>\n",
" <td>-0.058</td>\n",
" <td>Switzerland</td>\n",
" <td>Switzerland</td>\n",
" <td>0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>100.000</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>18872</td>\n",
" <td>Carmignac Absolute Return Europe</td>\n",
" <td>2015-02-01</td>\n",
" <td>100.000</td>\n",
" <td>31886.0000</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</td>\n",
" <td>0</td>\n",
" <td>0.026230</td>\n",
" <td>-0.022</td>\n",
" <td>Switzerland</td>\n",
" <td>Switzerland</td>\n",
" <td>0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>100.000</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>18872</td>\n",
" <td>Carmignac Absolute Return Europe</td>\n",
" <td>2015-03-01</td>\n",
" <td>100.000</td>\n",
" <td>32026.0000</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</td>\n",
" <td>0</td>\n",
" <td>0.024272</td>\n",
" <td>-0.014</td>\n",
" <td>Switzerland</td>\n",
" <td>Switzerland</td>\n",
" <td>0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>100.000</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>18872</td>\n",
" <td>Carmignac Absolute Return Europe</td>\n",
" <td>2015-04-01</td>\n",
" <td>100.000</td>\n",
" <td>32843.0000</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</td>\n",
" <td>0</td>\n",
" <td>0.000000</td>\n",
" <td>-0.077</td>\n",
" <td>Switzerland</td>\n",
" <td>Switzerland</td>\n",
" <td>0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>100.000</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>18872</td>\n",
" <td>Carmignac Absolute Return Europe</td>\n",
" <td>2015-05-01</td>\n",
" <td>100.000</td>\n",
" <td>33545.0000</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</td>\n",
" <td>0</td>\n",
" <td>0.000000</td>\n",
" <td>-0.053</td>\n",
" <td>Switzerland</td>\n",
" <td>Switzerland</td>\n",
" <td>0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>100.000</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>18872</td>\n",
" <td>Carmignac Absolute Return Europe</td>\n",
" <td>2015-06-01</td>\n",
" <td>100.000</td>\n",
" <td>32390.0000</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</td>\n",
" <td>0</td>\n",
" <td>0.000000</td>\n",
" <td>0.020</td>\n",
" <td>Switzerland</td>\n",
" <td>Switzerland</td>\n",
" <td>0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>100.000</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>18872</td>\n",
" <td>Carmignac Absolute Return Europe</td>\n",
" <td>2015-07-01</td>\n",
" <td>100.000</td>\n",
" <td>32163.0000</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</td>\n",
" <td>0</td>\n",
" <td>0.003115</td>\n",
" <td>-0.042</td>\n",
" <td>Switzerland</td>\n",
" <td>Switzerland</td>\n",
" <td>0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>100.000</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>18872</td>\n",
" <td>Carmignac Absolute Return Europe</td>\n",
" <td>2015-08-01</td>\n",
" <td>100.000</td>\n",
" <td>30573.0000</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</td>\n",
" <td>0</td>\n",
" <td>-0.004630</td>\n",
" <td>-0.008</td>\n",
" <td>Switzerland</td>\n",
" <td>Switzerland</td>\n",
" <td>0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>100.000</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>18872</td>\n",
" <td>Carmignac Absolute Return Europe</td>\n",
" <td>2015-09-01</td>\n",
" <td>100.000</td>\n",
" <td>29065.0000</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</td>\n",
" <td>0</td>\n",
" <td>0.000000</td>\n",
" <td>-0.012</td>\n",
" <td>Switzerland</td>\n",
" <td>Switzerland</td>\n",
" <td>0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>100.000</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>18872</td>\n",
" <td>Carmignac Absolute Return Europe</td>\n",
" <td>2015-10-01</td>\n",
" <td>443.206</td>\n",
" <td>127980.1646</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</td>\n",
" <td>0</td>\n",
" <td>-0.083823</td>\n",
" <td>-0.007</td>\n",
" <td>Switzerland</td>\n",
" <td>Switzerland</td>\n",
" <td>0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>443.206</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Registrar Account - ID fund_family month \\\n",
"0 18872 Carmignac Absolute Return Europe 2015-01-01 \n",
"1 18872 Carmignac Absolute Return Europe 2015-02-01 \n",
"2 18872 Carmignac Absolute Return Europe 2015-03-01 \n",
"3 18872 Carmignac Absolute Return Europe 2015-04-01 \n",
"4 18872 Carmignac Absolute Return Europe 2015-05-01 \n",
"5 18872 Carmignac Absolute Return Europe 2015-06-01 \n",
"6 18872 Carmignac Absolute Return Europe 2015-07-01 \n",
"7 18872 Carmignac Absolute Return Europe 2015-08-01 \n",
"8 18872 Carmignac Absolute Return Europe 2015-09-01 \n",
"9 18872 Carmignac Absolute Return Europe 2015-10-01 \n",
"\n",
" aum_qty aum_val net_flow_qty gross_flow_qty sub_qty red_qty n_tx \\\n",
"0 100.000 30624.0000 0.0 0.0 0.0 0.0 0.0 \n",
"1 100.000 31886.0000 0.0 0.0 0.0 0.0 0.0 \n",
"2 100.000 32026.0000 0.0 0.0 0.0 0.0 0.0 \n",
"3 100.000 32843.0000 0.0 0.0 0.0 0.0 0.0 \n",
"4 100.000 33545.0000 0.0 0.0 0.0 0.0 0.0 \n",
"5 100.000 32390.0000 0.0 0.0 0.0 0.0 0.0 \n",
"6 100.000 32163.0000 0.0 0.0 0.0 0.0 0.0 \n",
"7 100.000 30573.0000 0.0 0.0 0.0 0.0 0.0 \n",
"8 100.000 29065.0000 0.0 0.0 0.0 0.0 0.0 \n",
"9 443.206 127980.1646 0.0 0.0 0.0 0.0 0.0 \n",
"\n",
" n_isin_held n_isin_active ret_fund_m delta_rate_m region \\\n",
"0 1 0 0.004902 -0.058 Switzerland \n",
"1 1 0 0.026230 -0.022 Switzerland \n",
"2 1 0 0.024272 -0.014 Switzerland \n",
"3 1 0 0.000000 -0.077 Switzerland \n",
"4 1 0 0.000000 -0.053 Switzerland \n",
"5 1 0 0.000000 0.020 Switzerland \n",
"6 1 0 0.003115 -0.042 Switzerland \n",
"7 1 0 -0.004630 -0.008 Switzerland \n",
"8 1 0 0.000000 -0.012 Switzerland \n",
"9 1 0 -0.083823 -0.007 Switzerland \n",
"\n",
" country active_month flow_to_aum_m turnover_m sub_share_m \\\n",
"0 Switzerland 0 0.0 0.0 NaN \n",
"1 Switzerland 0 0.0 0.0 NaN \n",
"2 Switzerland 0 0.0 0.0 NaN \n",
"3 Switzerland 0 0.0 0.0 NaN \n",
"4 Switzerland 0 0.0 0.0 NaN \n",
"5 Switzerland 0 0.0 0.0 NaN \n",
"6 Switzerland 0 0.0 0.0 NaN \n",
"7 Switzerland 0 0.0 0.0 NaN \n",
"8 Switzerland 0 0.0 0.0 NaN \n",
"9 Switzerland 0 0.0 0.0 NaN \n",
"\n",
" red_share_m aum_peak_to_date aum_drawdown \n",
"0 NaN 100.000 0.0 \n",
"1 NaN 100.000 0.0 \n",
"2 NaN 100.000 0.0 \n",
"3 NaN 100.000 0.0 \n",
"4 NaN 100.000 0.0 \n",
"5 NaN 100.000 0.0 \n",
"6 NaN 100.000 0.0 \n",
"7 NaN 100.000 0.0 \n",
"8 NaN 100.000 0.0 \n",
"9 NaN 443.206 0.0 "
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"#construction du panel mensuel compte × famille × mois \n",
"tmp = df_rel_m.copy()\n",
"\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_family_month = (\n",
" tmp\n",
" .dropna(subset=[ID_COL, \"fund_family\", \"month\"])\n",
" .groupby([ID_COL, \"fund_family\", \"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",
" ret_fund_m=(\"ret_fund_m\", \"mean\"),\n",
" delta_rate_m=(\"delta_rate_m\", \"first\"),\n",
" region=(\"region\", \"first\"),\n",
" country=(\"country\", \"first\")\n",
" )\n",
" .sort_values([ID_COL, \"fund_family\", \"month\"])\n",
" .reset_index(drop=True)\n",
")\n",
"\n",
"df_family_month[\"active_month\"] = (df_family_month[\"gross_flow_qty\"] > 0).astype(int)\n",
"\n",
"df_family_month[\"flow_to_aum_m\"] = np.where(\n",
" df_family_month[\"aum_qty\"].abs() > 0,\n",
" df_family_month[\"net_flow_qty\"] / df_family_month[\"aum_qty\"].abs(),\n",
" np.nan\n",
")\n",
"\n",
"df_family_month[\"turnover_m\"] = np.where(\n",
" df_family_month[\"aum_qty\"].abs() > 0,\n",
" df_family_month[\"gross_flow_qty\"] / df_family_month[\"aum_qty\"].abs(),\n",
" np.nan\n",
")\n",
"\n",
"df_family_month[\"sub_share_m\"] = np.where(\n",
" df_family_month[\"gross_flow_qty\"] > 0,\n",
" df_family_month[\"sub_qty\"] / df_family_month[\"gross_flow_qty\"],\n",
" np.nan\n",
")\n",
"\n",
"df_family_month[\"red_share_m\"] = np.where(\n",
" df_family_month[\"gross_flow_qty\"] > 0,\n",
" df_family_month[\"red_qty\"] / df_family_month[\"gross_flow_qty\"],\n",
" np.nan\n",
")\n",
"\n",
"df_family_month[\"aum_peak_to_date\"] = df_family_month.groupby([ID_COL, \"fund_family\"])[\"aum_qty\"].cummax()\n",
"\n",
"df_family_month[\"aum_drawdown\"] = np.where(\n",
" df_family_month[\"aum_peak_to_date\"] > 0,\n",
" 1 - df_family_month[\"aum_qty\"] / df_family_month[\"aum_peak_to_date\"],\n",
" np.nan\n",
")\n",
"\n",
"print(\"df_family_month shape:\", df_family_month.shape)\n",
"df_family_month.head(10)"
]
},
{
"cell_type": "markdown",
"id": "9121da21",
"metadata": {},
"source": [
"---\n",
"## 4. Feature Engineering\n",
"\n",
"Features are built at three levels of granularity:\n",
"- **Account × month**: activity flags, turnover, drawdown\n",
"- **Account × ISIN**: entry/exit events, holding duration, performance reactivity\n",
"- **Account (static)**: aggregated behavioral summary used for clustering\n",
"\n",
"Asset type and fund composition shares are computed separately and used as **descriptive** post-clustering variables only.\n"
]
},
{
"cell_type": "code",
"execution_count": 14,
"id": "b7fdb2fe-26ce-4881-903d-01fd6e473ac4",
"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>fund_family</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>ret_fund_m</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",
" <th>total_client_aum_qty</th>\n",
" <th>total_client_aum_val</th>\n",
" <th>family_share_of_client_aum</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>18872</td>\n",
" <td>Carmignac Absolute Return Europe</td>\n",
" <td>2015-01-01</td>\n",
" <td>100.000</td>\n",
" <td>30624.0000</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</td>\n",
" <td>0</td>\n",
" <td>0.004902</td>\n",
" <td>-0.058</td>\n",
" <td>Switzerland</td>\n",
" <td>Switzerland</td>\n",
" <td>0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>100.000</td>\n",
" <td>0.0</td>\n",
" <td>179864.637</td>\n",
" <td>7.043266e+07</td>\n",
" <td>0.000435</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>18872</td>\n",
" <td>Carmignac Absolute Return Europe</td>\n",
" <td>2015-02-01</td>\n",
" <td>100.000</td>\n",
" <td>31886.0000</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</td>\n",
" <td>0</td>\n",
" <td>0.026230</td>\n",
" <td>-0.022</td>\n",
" <td>Switzerland</td>\n",
" <td>Switzerland</td>\n",
" <td>0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>100.000</td>\n",
" <td>0.0</td>\n",
" <td>186761.736</td>\n",
" <td>7.317400e+07</td>\n",
" <td>0.000436</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>18872</td>\n",
" <td>Carmignac Absolute Return Europe</td>\n",
" <td>2015-03-01</td>\n",
" <td>100.000</td>\n",
" <td>32026.0000</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</td>\n",
" <td>0</td>\n",
" <td>0.024272</td>\n",
" <td>-0.014</td>\n",
" <td>Switzerland</td>\n",
" <td>Switzerland</td>\n",
" <td>0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>100.000</td>\n",
" <td>0.0</td>\n",
" <td>190357.718</td>\n",
" <td>7.653007e+07</td>\n",
" <td>0.000418</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>18872</td>\n",
" <td>Carmignac Absolute Return Europe</td>\n",
" <td>2015-04-01</td>\n",
" <td>100.000</td>\n",
" <td>32843.0000</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</td>\n",
" <td>0</td>\n",
" <td>0.000000</td>\n",
" <td>-0.077</td>\n",
" <td>Switzerland</td>\n",
" <td>Switzerland</td>\n",
" <td>0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>100.000</td>\n",
" <td>0.0</td>\n",
" <td>191429.324</td>\n",
" <td>7.509285e+07</td>\n",
" <td>0.000437</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>18872</td>\n",
" <td>Carmignac Absolute Return Europe</td>\n",
" <td>2015-05-01</td>\n",
" <td>100.000</td>\n",
" <td>33545.0000</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</td>\n",
" <td>0</td>\n",
" <td>0.000000</td>\n",
" <td>-0.053</td>\n",
" <td>Switzerland</td>\n",
" <td>Switzerland</td>\n",
" <td>0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>100.000</td>\n",
" <td>0.0</td>\n",
" <td>189056.475</td>\n",
" <td>7.650176e+07</td>\n",
" <td>0.000438</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>18872</td>\n",
" <td>Carmignac Absolute Return Europe</td>\n",
" <td>2015-06-01</td>\n",
" <td>100.000</td>\n",
" <td>32390.0000</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</td>\n",
" <td>0</td>\n",
" <td>0.000000</td>\n",
" <td>0.020</td>\n",
" <td>Switzerland</td>\n",
" <td>Switzerland</td>\n",
" <td>0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>100.000</td>\n",
" <td>0.0</td>\n",
" <td>188737.275</td>\n",
" <td>7.291397e+07</td>\n",
" <td>0.000444</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>18872</td>\n",
" <td>Carmignac Absolute Return Europe</td>\n",
" <td>2015-07-01</td>\n",
" <td>100.000</td>\n",
" <td>32163.0000</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</td>\n",
" <td>0</td>\n",
" <td>0.003115</td>\n",
" <td>-0.042</td>\n",
" <td>Switzerland</td>\n",
" <td>Switzerland</td>\n",
" <td>0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>100.000</td>\n",
" <td>0.0</td>\n",
" <td>204372.466</td>\n",
" <td>7.523034e+07</td>\n",
" <td>0.000428</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>18872</td>\n",
" <td>Carmignac Absolute Return Europe</td>\n",
" <td>2015-08-01</td>\n",
" <td>100.000</td>\n",
" <td>30573.0000</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</td>\n",
" <td>0</td>\n",
" <td>-0.004630</td>\n",
" <td>-0.008</td>\n",
" <td>Switzerland</td>\n",
" <td>Switzerland</td>\n",
" <td>0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>100.000</td>\n",
" <td>0.0</td>\n",
" <td>204294.776</td>\n",
" <td>6.919099e+07</td>\n",
" <td>0.000442</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>18872</td>\n",
" <td>Carmignac Absolute Return Europe</td>\n",
" <td>2015-09-01</td>\n",
" <td>100.000</td>\n",
" <td>29065.0000</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</td>\n",
" <td>0</td>\n",
" <td>0.000000</td>\n",
" <td>-0.012</td>\n",
" <td>Switzerland</td>\n",
" <td>Switzerland</td>\n",
" <td>0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>100.000</td>\n",
" <td>0.0</td>\n",
" <td>200179.763</td>\n",
" <td>6.492010e+07</td>\n",
" <td>0.000448</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>18872</td>\n",
" <td>Carmignac Absolute Return Europe</td>\n",
" <td>2015-10-01</td>\n",
" <td>443.206</td>\n",
" <td>127980.1646</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</td>\n",
" <td>0</td>\n",
" <td>-0.083823</td>\n",
" <td>-0.007</td>\n",
" <td>Switzerland</td>\n",
" <td>Switzerland</td>\n",
" <td>0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>443.206</td>\n",
" <td>0.0</td>\n",
" <td>193229.149</td>\n",
" <td>6.541687e+07</td>\n",
" <td>0.001956</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Registrar Account - ID fund_family month \\\n",
"0 18872 Carmignac Absolute Return Europe 2015-01-01 \n",
"1 18872 Carmignac Absolute Return Europe 2015-02-01 \n",
"2 18872 Carmignac Absolute Return Europe 2015-03-01 \n",
"3 18872 Carmignac Absolute Return Europe 2015-04-01 \n",
"4 18872 Carmignac Absolute Return Europe 2015-05-01 \n",
"5 18872 Carmignac Absolute Return Europe 2015-06-01 \n",
"6 18872 Carmignac Absolute Return Europe 2015-07-01 \n",
"7 18872 Carmignac Absolute Return Europe 2015-08-01 \n",
"8 18872 Carmignac Absolute Return Europe 2015-09-01 \n",
"9 18872 Carmignac Absolute Return Europe 2015-10-01 \n",
"\n",
" aum_qty aum_val net_flow_qty gross_flow_qty sub_qty red_qty n_tx \\\n",
"0 100.000 30624.0000 0.0 0.0 0.0 0.0 0.0 \n",
"1 100.000 31886.0000 0.0 0.0 0.0 0.0 0.0 \n",
"2 100.000 32026.0000 0.0 0.0 0.0 0.0 0.0 \n",
"3 100.000 32843.0000 0.0 0.0 0.0 0.0 0.0 \n",
"4 100.000 33545.0000 0.0 0.0 0.0 0.0 0.0 \n",
"5 100.000 32390.0000 0.0 0.0 0.0 0.0 0.0 \n",
"6 100.000 32163.0000 0.0 0.0 0.0 0.0 0.0 \n",
"7 100.000 30573.0000 0.0 0.0 0.0 0.0 0.0 \n",
"8 100.000 29065.0000 0.0 0.0 0.0 0.0 0.0 \n",
"9 443.206 127980.1646 0.0 0.0 0.0 0.0 0.0 \n",
"\n",
" n_isin_held n_isin_active ret_fund_m delta_rate_m region \\\n",
"0 1 0 0.004902 -0.058 Switzerland \n",
"1 1 0 0.026230 -0.022 Switzerland \n",
"2 1 0 0.024272 -0.014 Switzerland \n",
"3 1 0 0.000000 -0.077 Switzerland \n",
"4 1 0 0.000000 -0.053 Switzerland \n",
"5 1 0 0.000000 0.020 Switzerland \n",
"6 1 0 0.003115 -0.042 Switzerland \n",
"7 1 0 -0.004630 -0.008 Switzerland \n",
"8 1 0 0.000000 -0.012 Switzerland \n",
"9 1 0 -0.083823 -0.007 Switzerland \n",
"\n",
" country active_month flow_to_aum_m turnover_m sub_share_m \\\n",
"0 Switzerland 0 0.0 0.0 NaN \n",
"1 Switzerland 0 0.0 0.0 NaN \n",
"2 Switzerland 0 0.0 0.0 NaN \n",
"3 Switzerland 0 0.0 0.0 NaN \n",
"4 Switzerland 0 0.0 0.0 NaN \n",
"5 Switzerland 0 0.0 0.0 NaN \n",
"6 Switzerland 0 0.0 0.0 NaN \n",
"7 Switzerland 0 0.0 0.0 NaN \n",
"8 Switzerland 0 0.0 0.0 NaN \n",
"9 Switzerland 0 0.0 0.0 NaN \n",
"\n",
" red_share_m aum_peak_to_date aum_drawdown total_client_aum_qty \\\n",
"0 NaN 100.000 0.0 179864.637 \n",
"1 NaN 100.000 0.0 186761.736 \n",
"2 NaN 100.000 0.0 190357.718 \n",
"3 NaN 100.000 0.0 191429.324 \n",
"4 NaN 100.000 0.0 189056.475 \n",
"5 NaN 100.000 0.0 188737.275 \n",
"6 NaN 100.000 0.0 204372.466 \n",
"7 NaN 100.000 0.0 204294.776 \n",
"8 NaN 100.000 0.0 200179.763 \n",
"9 NaN 443.206 0.0 193229.149 \n",
"\n",
" total_client_aum_val family_share_of_client_aum \n",
"0 7.043266e+07 0.000435 \n",
"1 7.317400e+07 0.000436 \n",
"2 7.653007e+07 0.000418 \n",
"3 7.509285e+07 0.000437 \n",
"4 7.650176e+07 0.000438 \n",
"5 7.291397e+07 0.000444 \n",
"6 7.523034e+07 0.000428 \n",
"7 6.919099e+07 0.000442 \n",
"8 6.492010e+07 0.000448 \n",
"9 6.541687e+07 0.001956 "
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"#part du fmaily fund dans ptf du client\n",
"\n",
"df_client_total_month = (\n",
" df_rel_m\n",
" .groupby([ID_COL, \"month\"], as_index=False)\n",
" .agg(\n",
" total_client_aum_qty=(\"aum_qty\", \"sum\"),\n",
" total_client_aum_val=(\"aum_val\", \"sum\")\n",
" )\n",
")\n",
"\n",
"df_family_month = df_family_month.merge(\n",
" df_client_total_month,\n",
" on=[ID_COL, \"month\"],\n",
" how=\"left\"\n",
")\n",
"\n",
"df_family_month[\"family_share_of_client_aum\"] = np.where(\n",
" df_family_month[\"total_client_aum_val\"].abs() > 0,\n",
" df_family_month[\"aum_val\"] / df_family_month[\"total_client_aum_val\"].abs(),\n",
" np.nan\n",
")\n",
"\n",
"df_family_month.head(10)"
]
},
{
"cell_type": "code",
"execution_count": 15,
"id": "d4a01bcc",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(539285, 44)\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>fund_family</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>ret_fund_m</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",
" <th>total_client_aum_qty</th>\n",
" <th>total_client_aum_val</th>\n",
" <th>family_share_of_client_aum</th>\n",
" <th>prev_aum</th>\n",
" <th>entry_event</th>\n",
" <th>full_exit_event</th>\n",
" <th>ret_fund_m_lag1</th>\n",
" <th>buy_on_perf</th>\n",
" <th>sell_on_perf</th>\n",
" <th>ret_fund_mean3_lag1</th>\n",
" <th>ret_fund_mean6_lag1</th>\n",
" <th>buy_on_perf_mean3</th>\n",
" <th>sell_on_perf_mean3</th>\n",
" <th>buy_on_perf_mean6</th>\n",
" <th>sell_on_perf_mean6</th>\n",
" <th>turnover_m_mean3</th>\n",
" <th>flow_to_aum_m_mean3</th>\n",
" <th>turnover_m_mean6</th>\n",
" <th>flow_to_aum_m_mean6</th>\n",
" <th>turnover_m_mean12</th>\n",
" <th>flow_to_aum_m_mean12</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>18872</td>\n",
" <td>Carmignac Absolute Return Europe</td>\n",
" <td>2015-01-01</td>\n",
" <td>100.000</td>\n",
" <td>30624.0000</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</td>\n",
" <td>0</td>\n",
" <td>0.004902</td>\n",
" <td>-0.058</td>\n",
" <td>Switzerland</td>\n",
" <td>Switzerland</td>\n",
" <td>0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>100.000</td>\n",
" <td>0.0</td>\n",
" <td>179864.637</td>\n",
" <td>7.043266e+07</td>\n",
" <td>0.000435</td>\n",
" <td>NaN</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>NaN</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>0</td>\n",
" <td>0</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>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>18872</td>\n",
" <td>Carmignac Absolute Return Europe</td>\n",
" <td>2015-02-01</td>\n",
" <td>100.000</td>\n",
" <td>31886.0000</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</td>\n",
" <td>0</td>\n",
" <td>0.026230</td>\n",
" <td>-0.022</td>\n",
" <td>Switzerland</td>\n",
" <td>Switzerland</td>\n",
" <td>0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>100.000</td>\n",
" <td>0.0</td>\n",
" <td>186761.736</td>\n",
" <td>7.317400e+07</td>\n",
" <td>0.000436</td>\n",
" <td>100.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0.004902</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0.004902</td>\n",
" <td>0.004902</td>\n",
" <td>0</td>\n",
" <td>0</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>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>18872</td>\n",
" <td>Carmignac Absolute Return Europe</td>\n",
" <td>2015-03-01</td>\n",
" <td>100.000</td>\n",
" <td>32026.0000</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</td>\n",
" <td>0</td>\n",
" <td>0.024272</td>\n",
" <td>-0.014</td>\n",
" <td>Switzerland</td>\n",
" <td>Switzerland</td>\n",
" <td>0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>100.000</td>\n",
" <td>0.0</td>\n",
" <td>190357.718</td>\n",
" <td>7.653007e+07</td>\n",
" <td>0.000418</td>\n",
" <td>100.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0.026230</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0.015566</td>\n",
" <td>0.015566</td>\n",
" <td>0</td>\n",
" <td>0</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>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>18872</td>\n",
" <td>Carmignac Absolute Return Europe</td>\n",
" <td>2015-04-01</td>\n",
" <td>100.000</td>\n",
" <td>32843.0000</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</td>\n",
" <td>0</td>\n",
" <td>0.000000</td>\n",
" <td>-0.077</td>\n",
" <td>Switzerland</td>\n",
" <td>Switzerland</td>\n",
" <td>0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>100.000</td>\n",
" <td>0.0</td>\n",
" <td>191429.324</td>\n",
" <td>7.509285e+07</td>\n",
" <td>0.000437</td>\n",
" <td>100.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0.024272</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0.018468</td>\n",
" <td>0.018468</td>\n",
" <td>0</td>\n",
" <td>0</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>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>18872</td>\n",
" <td>Carmignac Absolute Return Europe</td>\n",
" <td>2015-05-01</td>\n",
" <td>100.000</td>\n",
" <td>33545.0000</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</td>\n",
" <td>0</td>\n",
" <td>0.000000</td>\n",
" <td>-0.053</td>\n",
" <td>Switzerland</td>\n",
" <td>Switzerland</td>\n",
" <td>0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>100.000</td>\n",
" <td>0.0</td>\n",
" <td>189056.475</td>\n",
" <td>7.650176e+07</td>\n",
" <td>0.000438</td>\n",
" <td>100.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0.000000</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0.016834</td>\n",
" <td>0.013851</td>\n",
" <td>0</td>\n",
" <td>0</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>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>18872</td>\n",
" <td>Carmignac Absolute Return Europe</td>\n",
" <td>2015-06-01</td>\n",
" <td>100.000</td>\n",
" <td>32390.0000</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</td>\n",
" <td>0</td>\n",
" <td>0.000000</td>\n",
" <td>0.020</td>\n",
" <td>Switzerland</td>\n",
" <td>Switzerland</td>\n",
" <td>0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>100.000</td>\n",
" <td>0.0</td>\n",
" <td>188737.275</td>\n",
" <td>7.291397e+07</td>\n",
" <td>0.000444</td>\n",
" <td>100.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0.000000</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0.008091</td>\n",
" <td>0.011081</td>\n",
" <td>0</td>\n",
" <td>0</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>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>18872</td>\n",
" <td>Carmignac Absolute Return Europe</td>\n",
" <td>2015-07-01</td>\n",
" <td>100.000</td>\n",
" <td>32163.0000</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</td>\n",
" <td>0</td>\n",
" <td>0.003115</td>\n",
" <td>-0.042</td>\n",
" <td>Switzerland</td>\n",
" <td>Switzerland</td>\n",
" <td>0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>100.000</td>\n",
" <td>0.0</td>\n",
" <td>204372.466</td>\n",
" <td>7.523034e+07</td>\n",
" <td>0.000428</td>\n",
" <td>100.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0.000000</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0.000000</td>\n",
" <td>0.009234</td>\n",
" <td>0</td>\n",
" <td>0</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>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>18872</td>\n",
" <td>Carmignac Absolute Return Europe</td>\n",
" <td>2015-08-01</td>\n",
" <td>100.000</td>\n",
" <td>30573.0000</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</td>\n",
" <td>0</td>\n",
" <td>-0.004630</td>\n",
" <td>-0.008</td>\n",
" <td>Switzerland</td>\n",
" <td>Switzerland</td>\n",
" <td>0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>100.000</td>\n",
" <td>0.0</td>\n",
" <td>204294.776</td>\n",
" <td>6.919099e+07</td>\n",
" <td>0.000442</td>\n",
" <td>100.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0.003115</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0.001038</td>\n",
" <td>0.008936</td>\n",
" <td>0</td>\n",
" <td>0</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>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>18872</td>\n",
" <td>Carmignac Absolute Return Europe</td>\n",
" <td>2015-09-01</td>\n",
" <td>100.000</td>\n",
" <td>29065.0000</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</td>\n",
" <td>0</td>\n",
" <td>0.000000</td>\n",
" <td>-0.012</td>\n",
" <td>Switzerland</td>\n",
" <td>Switzerland</td>\n",
" <td>0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>100.000</td>\n",
" <td>0.0</td>\n",
" <td>200179.763</td>\n",
" <td>6.492010e+07</td>\n",
" <td>0.000448</td>\n",
" <td>100.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>-0.004630</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>-0.000505</td>\n",
" <td>0.003793</td>\n",
" <td>0</td>\n",
" <td>0</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>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>18872</td>\n",
" <td>Carmignac Absolute Return Europe</td>\n",
" <td>2015-10-01</td>\n",
" <td>443.206</td>\n",
" <td>127980.1646</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</td>\n",
" <td>0</td>\n",
" <td>-0.083823</td>\n",
" <td>-0.007</td>\n",
" <td>Switzerland</td>\n",
" <td>Switzerland</td>\n",
" <td>0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>443.206</td>\n",
" <td>0.0</td>\n",
" <td>193229.149</td>\n",
" <td>6.541687e+07</td>\n",
" <td>0.001956</td>\n",
" <td>100.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0.000000</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>-0.000505</td>\n",
" <td>-0.000252</td>\n",
" <td>0</td>\n",
" <td>0</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>0.0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Registrar Account - ID fund_family month \\\n",
"0 18872 Carmignac Absolute Return Europe 2015-01-01 \n",
"1 18872 Carmignac Absolute Return Europe 2015-02-01 \n",
"2 18872 Carmignac Absolute Return Europe 2015-03-01 \n",
"3 18872 Carmignac Absolute Return Europe 2015-04-01 \n",
"4 18872 Carmignac Absolute Return Europe 2015-05-01 \n",
"5 18872 Carmignac Absolute Return Europe 2015-06-01 \n",
"6 18872 Carmignac Absolute Return Europe 2015-07-01 \n",
"7 18872 Carmignac Absolute Return Europe 2015-08-01 \n",
"8 18872 Carmignac Absolute Return Europe 2015-09-01 \n",
"9 18872 Carmignac Absolute Return Europe 2015-10-01 \n",
"\n",
" aum_qty aum_val net_flow_qty gross_flow_qty sub_qty red_qty n_tx \\\n",
"0 100.000 30624.0000 0.0 0.0 0.0 0.0 0.0 \n",
"1 100.000 31886.0000 0.0 0.0 0.0 0.0 0.0 \n",
"2 100.000 32026.0000 0.0 0.0 0.0 0.0 0.0 \n",
"3 100.000 32843.0000 0.0 0.0 0.0 0.0 0.0 \n",
"4 100.000 33545.0000 0.0 0.0 0.0 0.0 0.0 \n",
"5 100.000 32390.0000 0.0 0.0 0.0 0.0 0.0 \n",
"6 100.000 32163.0000 0.0 0.0 0.0 0.0 0.0 \n",
"7 100.000 30573.0000 0.0 0.0 0.0 0.0 0.0 \n",
"8 100.000 29065.0000 0.0 0.0 0.0 0.0 0.0 \n",
"9 443.206 127980.1646 0.0 0.0 0.0 0.0 0.0 \n",
"\n",
" n_isin_held n_isin_active ret_fund_m delta_rate_m region \\\n",
"0 1 0 0.004902 -0.058 Switzerland \n",
"1 1 0 0.026230 -0.022 Switzerland \n",
"2 1 0 0.024272 -0.014 Switzerland \n",
"3 1 0 0.000000 -0.077 Switzerland \n",
"4 1 0 0.000000 -0.053 Switzerland \n",
"5 1 0 0.000000 0.020 Switzerland \n",
"6 1 0 0.003115 -0.042 Switzerland \n",
"7 1 0 -0.004630 -0.008 Switzerland \n",
"8 1 0 0.000000 -0.012 Switzerland \n",
"9 1 0 -0.083823 -0.007 Switzerland \n",
"\n",
" country active_month flow_to_aum_m turnover_m sub_share_m \\\n",
"0 Switzerland 0 0.0 0.0 NaN \n",
"1 Switzerland 0 0.0 0.0 NaN \n",
"2 Switzerland 0 0.0 0.0 NaN \n",
"3 Switzerland 0 0.0 0.0 NaN \n",
"4 Switzerland 0 0.0 0.0 NaN \n",
"5 Switzerland 0 0.0 0.0 NaN \n",
"6 Switzerland 0 0.0 0.0 NaN \n",
"7 Switzerland 0 0.0 0.0 NaN \n",
"8 Switzerland 0 0.0 0.0 NaN \n",
"9 Switzerland 0 0.0 0.0 NaN \n",
"\n",
" red_share_m aum_peak_to_date aum_drawdown total_client_aum_qty \\\n",
"0 NaN 100.000 0.0 179864.637 \n",
"1 NaN 100.000 0.0 186761.736 \n",
"2 NaN 100.000 0.0 190357.718 \n",
"3 NaN 100.000 0.0 191429.324 \n",
"4 NaN 100.000 0.0 189056.475 \n",
"5 NaN 100.000 0.0 188737.275 \n",
"6 NaN 100.000 0.0 204372.466 \n",
"7 NaN 100.000 0.0 204294.776 \n",
"8 NaN 100.000 0.0 200179.763 \n",
"9 NaN 443.206 0.0 193229.149 \n",
"\n",
" total_client_aum_val family_share_of_client_aum prev_aum entry_event \\\n",
"0 7.043266e+07 0.000435 NaN 1 \n",
"1 7.317400e+07 0.000436 100.0 0 \n",
"2 7.653007e+07 0.000418 100.0 0 \n",
"3 7.509285e+07 0.000437 100.0 0 \n",
"4 7.650176e+07 0.000438 100.0 0 \n",
"5 7.291397e+07 0.000444 100.0 0 \n",
"6 7.523034e+07 0.000428 100.0 0 \n",
"7 6.919099e+07 0.000442 100.0 0 \n",
"8 6.492010e+07 0.000448 100.0 0 \n",
"9 6.541687e+07 0.001956 100.0 0 \n",
"\n",
" full_exit_event ret_fund_m_lag1 buy_on_perf sell_on_perf \\\n",
"0 0 NaN 0 0 \n",
"1 0 0.004902 0 0 \n",
"2 0 0.026230 0 0 \n",
"3 0 0.024272 0 0 \n",
"4 0 0.000000 0 0 \n",
"5 0 0.000000 0 0 \n",
"6 0 0.000000 0 0 \n",
"7 0 0.003115 0 0 \n",
"8 0 -0.004630 0 0 \n",
"9 0 0.000000 0 0 \n",
"\n",
" ret_fund_mean3_lag1 ret_fund_mean6_lag1 buy_on_perf_mean3 \\\n",
"0 NaN NaN 0 \n",
"1 0.004902 0.004902 0 \n",
"2 0.015566 0.015566 0 \n",
"3 0.018468 0.018468 0 \n",
"4 0.016834 0.013851 0 \n",
"5 0.008091 0.011081 0 \n",
"6 0.000000 0.009234 0 \n",
"7 0.001038 0.008936 0 \n",
"8 -0.000505 0.003793 0 \n",
"9 -0.000505 -0.000252 0 \n",
"\n",
" sell_on_perf_mean3 buy_on_perf_mean6 sell_on_perf_mean6 \\\n",
"0 0 0 0 \n",
"1 0 0 0 \n",
"2 0 0 0 \n",
"3 0 0 0 \n",
"4 0 0 0 \n",
"5 0 0 0 \n",
"6 0 0 0 \n",
"7 0 0 0 \n",
"8 0 0 0 \n",
"9 0 0 0 \n",
"\n",
" turnover_m_mean3 flow_to_aum_m_mean3 turnover_m_mean6 \\\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 \n",
"5 0.0 0.0 0.0 \n",
"6 0.0 0.0 0.0 \n",
"7 0.0 0.0 0.0 \n",
"8 0.0 0.0 0.0 \n",
"9 0.0 0.0 0.0 \n",
"\n",
" flow_to_aum_m_mean6 turnover_m_mean12 flow_to_aum_m_mean12 \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 \n",
"5 0.0 0.0 0.0 \n",
"6 0.0 0.0 0.0 \n",
"7 0.0 0.0 0.0 \n",
"8 0.0 0.0 0.0 \n",
"9 0.0 0.0 0.0 "
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"#feature engineering temporel au niveau client × famille × mois\n",
"tmp = df_family_month.sort_values([ID_COL, \"fund_family\", \"month\"]).reset_index(drop=True)\n",
"\n",
"tmp[\"prev_aum\"] = tmp.groupby([ID_COL, \"fund_family\"])[\"aum_qty\"].shift(1)\n",
"\n",
"tmp[\"entry_event\"] = (\n",
" (tmp[\"prev_aum\"].fillna(0) <= 0) & (tmp[\"aum_qty\"] > 0)\n",
").astype(int)\n",
"\n",
"tmp[\"full_exit_event\"] = (\n",
" (tmp[\"prev_aum\"] > 0) & (tmp[\"aum_qty\"] <= 0)\n",
").astype(int)\n",
"\n",
"tmp[\"ret_fund_m_lag1\"] = tmp.groupby([ID_COL, \"fund_family\"])[\"ret_fund_m\"].shift(1)\n",
"\n",
"tmp[\"buy_on_perf\"] = (\n",
" (tmp[\"net_flow_qty\"] > 0) & (tmp[\"ret_fund_m_lag1\"] > 0)\n",
").astype(int)\n",
"\n",
"tmp[\"sell_on_perf\"] = (\n",
" (tmp[\"net_flow_qty\"] < 0) & (tmp[\"ret_fund_m_lag1\"] < 0)\n",
").astype(int)\n",
"\n",
"tmp[\"ret_fund_mean3_lag1\"] = (\n",
" tmp.groupby([ID_COL, \"fund_family\"])[\"ret_fund_m\"]\n",
" .transform(lambda s: s.shift(1).rolling(3, min_periods=1).mean())\n",
")\n",
"\n",
"tmp[\"ret_fund_mean6_lag1\"] = (\n",
" tmp.groupby([ID_COL, \"fund_family\"])[\"ret_fund_m\"]\n",
" .transform(lambda s: s.shift(1).rolling(6, min_periods=1).mean())\n",
")\n",
"\n",
"tmp[\"buy_on_perf_mean3\"] = (\n",
" (tmp[\"net_flow_qty\"] > 0) & (tmp[\"ret_fund_mean3_lag1\"] > 0)\n",
").astype(int)\n",
"\n",
"tmp[\"sell_on_perf_mean3\"] = (\n",
" (tmp[\"net_flow_qty\"] < 0) & (tmp[\"ret_fund_mean3_lag1\"] < 0)\n",
").astype(int)\n",
"\n",
"tmp[\"buy_on_perf_mean6\"] = (\n",
" (tmp[\"net_flow_qty\"] > 0) & (tmp[\"ret_fund_mean6_lag1\"] > 0)\n",
").astype(int)\n",
"\n",
"tmp[\"sell_on_perf_mean6\"] = (\n",
" (tmp[\"net_flow_qty\"] < 0) & (tmp[\"ret_fund_mean6_lag1\"] < 0)\n",
").astype(int)\n",
"\n",
"for w in [3, 6, 12]:\n",
" tmp[f\"turnover_m_mean{w}\"] = (\n",
" tmp.groupby([ID_COL, \"fund_family\"])[\"turnover_m\"]\n",
" .transform(lambda s: s.rolling(w, min_periods=1).mean())\n",
" )\n",
"\n",
" tmp[f\"flow_to_aum_m_mean{w}\"] = (\n",
" tmp.groupby([ID_COL, \"fund_family\"])[\"flow_to_aum_m\"]\n",
" .transform(lambda s: s.rolling(w, min_periods=1).mean())\n",
" )\n",
"\n",
"print(tmp.shape)\n",
"tmp.head(10)"
]
},
{
"cell_type": "code",
"execution_count": 16,
"id": "965cd530-9985-4d57-938c-cef531459c39",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"df_family_client_base shape: (7282, 49)\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>fund_family</th>\n",
" <th>n_months</th>\n",
" <th>n_active_months</th>\n",
" <th>flow_freq</th>\n",
" <th>family_aum_qty_mean</th>\n",
" <th>family_aum_qty_median</th>\n",
" <th>family_aum_qty_max</th>\n",
" <th>family_aum_qty_last</th>\n",
" <th>family_aum_val_mean</th>\n",
" <th>family_aum_val_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>sub_qty_sum</th>\n",
" <th>red_qty_sum</th>\n",
" <th>n_tx_total</th>\n",
" <th>avg_n_isin_held</th>\n",
" <th>max_n_isin_held</th>\n",
" <th>net_flow_qty_vol</th>\n",
" <th>flow_to_aum_mean</th>\n",
" <th>flow_to_aum_vol</th>\n",
" <th>turnover_mean</th>\n",
" <th>turnover_vol</th>\n",
" <th>sub_share_mean</th>\n",
" <th>red_share_mean</th>\n",
" <th>aum_drawdown_last</th>\n",
" <th>aum_drawdown_max</th>\n",
" <th>entry_count</th>\n",
" <th>full_exit_count</th>\n",
" <th>buy_on_perf_rate</th>\n",
" <th>sell_on_perf_rate</th>\n",
" <th>buy_on_perf_mean3_rate</th>\n",
" <th>sell_on_perf_mean3_rate</th>\n",
" <th>buy_on_perf_mean6_rate</th>\n",
" <th>sell_on_perf_mean6_rate</th>\n",
" <th>turnover_mean3_avg</th>\n",
" <th>turnover_mean6_avg</th>\n",
" <th>turnover_mean12_avg</th>\n",
" <th>flow_to_aum_mean3_avg</th>\n",
" <th>flow_to_aum_mean6_avg</th>\n",
" <th>flow_to_aum_mean12_avg</th>\n",
" <th>family_share_of_client_aum_mean</th>\n",
" <th>family_share_of_client_aum_max</th>\n",
" <th>ret_fund_m_mean</th>\n",
" <th>delta_rate_m_mean</th>\n",
" <th>region</th>\n",
" <th>country</th>\n",
" <th>months_since_last_tx_family</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>18872</td>\n",
" <td>Carmignac Absolute Return Europe</td>\n",
" <td>130</td>\n",
" <td>8</td>\n",
" <td>0.061538</td>\n",
" <td>886.504908</td>\n",
" <td>672.748</td>\n",
" <td>3012.748</td>\n",
" <td>0.000</td>\n",
" <td>3.479000e+05</td>\n",
" <td>0.000000e+00</td>\n",
" <td>-123.206</td>\n",
" <td>5403.206</td>\n",
" <td>41.563123</td>\n",
" <td>2640.000</td>\n",
" <td>-2763.206</td>\n",
" <td>9.0</td>\n",
" <td>0.830769</td>\n",
" <td>1</td>\n",
" <td>237.868298</td>\n",
" <td>-0.015569</td>\n",
" <td>0.211558</td>\n",
" <td>0.037561</td>\n",
" <td>0.208752</td>\n",
" <td>0.375000</td>\n",
" <td>-0.625000</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>0.007692</td>\n",
" <td>0.000000</td>\n",
" <td>0.015385</td>\n",
" <td>0.000000</td>\n",
" <td>0.015385</td>\n",
" <td>0.007692</td>\n",
" <td>0.036878</td>\n",
" <td>0.035899</td>\n",
" <td>0.034487</td>\n",
" <td>-0.015286</td>\n",
" <td>-0.014881</td>\n",
" <td>-0.014528</td>\n",
" <td>0.011541</td>\n",
" <td>0.050205</td>\n",
" <td>0.001311</td>\n",
" <td>0.013723</td>\n",
" <td>Switzerland</td>\n",
" <td>Switzerland</td>\n",
" <td>29.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>18872</td>\n",
" <td>Carmignac China New Economy</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0.000000</td>\n",
" <td>19.545000</td>\n",
" <td>19.545</td>\n",
" <td>19.545</td>\n",
" <td>19.545</td>\n",
" <td>1.209445e+03</td>\n",
" <td>1.209445e+03</td>\n",
" <td>0.000</td>\n",
" <td>0.000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000</td>\n",
" <td>0.000</td>\n",
" <td>0.0</td>\n",
" <td>1.000000</td>\n",
" <td>1</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>NaN</td>\n",
" <td>NaN</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>1</td>\n",
" <td>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>0.000000</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>0.000037</td>\n",
" <td>0.000037</td>\n",
" <td>0.000000</td>\n",
" <td>0.002000</td>\n",
" <td>Switzerland</td>\n",
" <td>Switzerland</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>18872</td>\n",
" <td>Carmignac Court Terme</td>\n",
" <td>47</td>\n",
" <td>1</td>\n",
" <td>0.021277</td>\n",
" <td>28.340426</td>\n",
" <td>0.000</td>\n",
" <td>666.000</td>\n",
" <td>0.000</td>\n",
" <td>1.061668e+05</td>\n",
" <td>0.000000e+00</td>\n",
" <td>-666.000</td>\n",
" <td>666.000</td>\n",
" <td>14.170213</td>\n",
" <td>0.000</td>\n",
" <td>-666.000</td>\n",
" <td>1.0</td>\n",
" <td>0.042553</td>\n",
" <td>1</td>\n",
" <td>97.146084</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>1.000000</td>\n",
" <td>1</td>\n",
" <td>1</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>0.000000</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>0.001850</td>\n",
" <td>0.044187</td>\n",
" <td>-0.000342</td>\n",
" <td>-0.004468</td>\n",
" <td>Switzerland</td>\n",
" <td>Switzerland</td>\n",
" <td>94.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>18872</td>\n",
" <td>Carmignac Credit &lt;NUM&gt;</td>\n",
" <td>23</td>\n",
" <td>6</td>\n",
" <td>0.260870</td>\n",
" <td>7901.739130</td>\n",
" <td>5455.000</td>\n",
" <td>14240.000</td>\n",
" <td>14240.000</td>\n",
" <td>9.231846e+05</td>\n",
" <td>1.711014e+06</td>\n",
" <td>14240.000</td>\n",
" <td>14240.000</td>\n",
" <td>619.130435</td>\n",
" <td>14240.000</td>\n",
" <td>0.000</td>\n",
" <td>6.0</td>\n",
" <td>3.043478</td>\n",
" <td>5</td>\n",
" <td>1747.389385</td>\n",
" <td>0.118826</td>\n",
" <td>0.278576</td>\n",
" <td>0.118826</td>\n",
" <td>0.278576</td>\n",
" <td>1.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>1</td>\n",
" <td>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>0.154477</td>\n",
" <td>0.182237</td>\n",
" <td>0.217451</td>\n",
" <td>0.154477</td>\n",
" <td>0.182237</td>\n",
" <td>0.217451</td>\n",
" <td>0.034637</td>\n",
" <td>0.061213</td>\n",
" <td>0.000000</td>\n",
" <td>-0.085826</td>\n",
" <td>Switzerland</td>\n",
" <td>Switzerland</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>18872</td>\n",
" <td>Carmignac Emergents</td>\n",
" <td>130</td>\n",
" <td>37</td>\n",
" <td>0.284615</td>\n",
" <td>2250.665769</td>\n",
" <td>606.000</td>\n",
" <td>8900.649</td>\n",
" <td>167.799</td>\n",
" <td>1.930753e+06</td>\n",
" <td>2.563734e+05</td>\n",
" <td>-7116.537</td>\n",
" <td>7708.537</td>\n",
" <td>59.296438</td>\n",
" <td>296.000</td>\n",
" <td>-7412.537</td>\n",
" <td>55.0</td>\n",
" <td>1.000000</td>\n",
" <td>1</td>\n",
" <td>219.774317</td>\n",
" <td>-0.025186</td>\n",
" <td>0.147716</td>\n",
" <td>0.034426</td>\n",
" <td>0.145825</td>\n",
" <td>0.179537</td>\n",
" <td>-0.820463</td>\n",
" <td>0.981148</td>\n",
" <td>0.985394</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0.000000</td>\n",
" <td>0.015385</td>\n",
" <td>0.007692</td>\n",
" <td>0.053846</td>\n",
" <td>0.023077</td>\n",
" <td>0.053846</td>\n",
" <td>0.034430</td>\n",
" <td>0.034449</td>\n",
" <td>0.034690</td>\n",
" <td>-0.025185</td>\n",
" <td>-0.025190</td>\n",
" <td>-0.025412</td>\n",
" <td>0.040489</td>\n",
" <td>0.103768</td>\n",
" <td>0.006332</td>\n",
" <td>0.013723</td>\n",
" <td>Switzerland</td>\n",
" <td>Switzerland</td>\n",
" <td>32.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>18872</td>\n",
" <td>Carmignac Euro-Entrepreneurs</td>\n",
" <td>80</td>\n",
" <td>9</td>\n",
" <td>0.112500</td>\n",
" <td>2321.171212</td>\n",
" <td>184.023</td>\n",
" <td>15725.000</td>\n",
" <td>0.000</td>\n",
" <td>8.452421e+05</td>\n",
" <td>0.000000e+00</td>\n",
" <td>-7093.880</td>\n",
" <td>7963.880</td>\n",
" <td>99.548500</td>\n",
" <td>435.000</td>\n",
" <td>-7528.880</td>\n",
" <td>10.0</td>\n",
" <td>0.625000</td>\n",
" <td>1</td>\n",
" <td>578.951646</td>\n",
" <td>-0.024076</td>\n",
" <td>0.148697</td>\n",
" <td>0.056100</td>\n",
" <td>0.139609</td>\n",
" <td>0.222222</td>\n",
" <td>-0.777778</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>0.012500</td>\n",
" <td>0.000000</td>\n",
" <td>0.012500</td>\n",
" <td>0.000000</td>\n",
" <td>0.012500</td>\n",
" <td>0.000000</td>\n",
" <td>0.051944</td>\n",
" <td>0.046856</td>\n",
" <td>0.042090</td>\n",
" <td>-0.022293</td>\n",
" <td>-0.020170</td>\n",
" <td>-0.017985</td>\n",
" <td>0.017608</td>\n",
" <td>0.113984</td>\n",
" <td>0.009313</td>\n",
" <td>-0.008925</td>\n",
" <td>Switzerland</td>\n",
" <td>Switzerland</td>\n",
" <td>63.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>18872</td>\n",
" <td>Carmignac Euro-Investissement</td>\n",
" <td>80</td>\n",
" <td>0</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000</td>\n",
" <td>0.000</td>\n",
" <td>0.000</td>\n",
" <td>0.000000e+00</td>\n",
" <td>0.000000e+00</td>\n",
" <td>0.000</td>\n",
" <td>0.000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000</td>\n",
" <td>0.000</td>\n",
" <td>0.0</td>\n",
" <td>0.000000</td>\n",
" <td>0</td>\n",
" <td>0.000000</td>\n",
" <td>NaN</td>\n",
" <td>0.000000</td>\n",
" <td>NaN</td>\n",
" <td>0.000000</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>0</td>\n",
" <td>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>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>-0.008925</td>\n",
" <td>Switzerland</td>\n",
" <td>Switzerland</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>18872</td>\n",
" <td>Carmignac Investissement</td>\n",
" <td>130</td>\n",
" <td>69</td>\n",
" <td>0.530769</td>\n",
" <td>5287.072846</td>\n",
" <td>3200.800</td>\n",
" <td>13396.330</td>\n",
" <td>1575.000</td>\n",
" <td>4.084883e+06</td>\n",
" <td>2.922383e+06</td>\n",
" <td>-8322.670</td>\n",
" <td>18663.578</td>\n",
" <td>143.565985</td>\n",
" <td>5170.454</td>\n",
" <td>-13493.124</td>\n",
" <td>159.0</td>\n",
" <td>3.146154</td>\n",
" <td>4</td>\n",
" <td>448.840939</td>\n",
" <td>-0.015065</td>\n",
" <td>0.102880</td>\n",
" <td>0.025599</td>\n",
" <td>0.104667</td>\n",
" <td>0.094726</td>\n",
" <td>-0.905274</td>\n",
" <td>0.882430</td>\n",
" <td>0.882430</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0.023077</td>\n",
" <td>0.076923</td>\n",
" <td>0.030769</td>\n",
" <td>0.130769</td>\n",
" <td>0.038462</td>\n",
" <td>0.153846</td>\n",
" <td>0.025715</td>\n",
" <td>0.025793</td>\n",
" <td>0.025706</td>\n",
" <td>-0.015182</td>\n",
" <td>-0.015068</td>\n",
" <td>-0.014639</td>\n",
" <td>0.111235</td>\n",
" <td>0.154495</td>\n",
" <td>0.005852</td>\n",
" <td>0.013723</td>\n",
" <td>Switzerland</td>\n",
" <td>Switzerland</td>\n",
" <td>2.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>18872</td>\n",
" <td>Carmignac Investissement Latitude</td>\n",
" <td>80</td>\n",
" <td>8</td>\n",
" <td>0.100000</td>\n",
" <td>891.837500</td>\n",
" <td>1222.000</td>\n",
" <td>1717.000</td>\n",
" <td>0.000</td>\n",
" <td>2.310840e+05</td>\n",
" <td>0.000000e+00</td>\n",
" <td>-2958.000</td>\n",
" <td>2958.000</td>\n",
" <td>36.975000</td>\n",
" <td>0.000</td>\n",
" <td>-2958.000</td>\n",
" <td>9.0</td>\n",
" <td>0.787500</td>\n",
" <td>1</td>\n",
" <td>184.846907</td>\n",
" <td>-0.036575</td>\n",
" <td>0.148040</td>\n",
" <td>0.036575</td>\n",
" <td>0.148040</td>\n",
" <td>0.000000</td>\n",
" <td>-1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>0.000000</td>\n",
" <td>0.012500</td>\n",
" <td>0.000000</td>\n",
" <td>0.025000</td>\n",
" <td>0.000000</td>\n",
" <td>0.037500</td>\n",
" <td>0.047173</td>\n",
" <td>0.053641</td>\n",
" <td>0.057770</td>\n",
" <td>-0.047173</td>\n",
" <td>-0.053641</td>\n",
" <td>-0.057770</td>\n",
" <td>0.004618</td>\n",
" <td>0.008707</td>\n",
" <td>0.002647</td>\n",
" <td>-0.008925</td>\n",
" <td>Switzerland</td>\n",
" <td>Switzerland</td>\n",
" <td>66.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>18872</td>\n",
" <td>Carmignac Multi Expertise</td>\n",
" <td>80</td>\n",
" <td>1</td>\n",
" <td>0.012500</td>\n",
" <td>243.500000</td>\n",
" <td>164.000</td>\n",
" <td>564.000</td>\n",
" <td>0.000</td>\n",
" <td>4.327213e+04</td>\n",
" <td>0.000000e+00</td>\n",
" <td>-400.000</td>\n",
" <td>400.000</td>\n",
" <td>5.000000</td>\n",
" <td>0.000</td>\n",
" <td>-400.000</td>\n",
" <td>1.0</td>\n",
" <td>0.875000</td>\n",
" <td>1</td>\n",
" <td>44.721360</td>\n",
" <td>-0.034843</td>\n",
" <td>0.291519</td>\n",
" <td>0.034843</td>\n",
" <td>0.291519</td>\n",
" <td>0.000000</td>\n",
" <td>-1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>1</td>\n",
" <td>1</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>0.012500</td>\n",
" <td>0.033875</td>\n",
" <td>0.032520</td>\n",
" <td>0.030488</td>\n",
" <td>-0.033875</td>\n",
" <td>-0.032520</td>\n",
" <td>-0.030488</td>\n",
" <td>0.000971</td>\n",
" <td>0.002025</td>\n",
" <td>0.003772</td>\n",
" <td>-0.008925</td>\n",
" <td>Switzerland</td>\n",
" <td>Switzerland</td>\n",
" <td>109.0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Registrar Account - ID fund_family n_months \\\n",
"0 18872 Carmignac Absolute Return Europe 130 \n",
"1 18872 Carmignac China New Economy 1 \n",
"2 18872 Carmignac Court Terme 47 \n",
"3 18872 Carmignac Credit <NUM> 23 \n",
"4 18872 Carmignac Emergents 130 \n",
"5 18872 Carmignac Euro-Entrepreneurs 80 \n",
"6 18872 Carmignac Euro-Investissement 80 \n",
"7 18872 Carmignac Investissement 130 \n",
"8 18872 Carmignac Investissement Latitude 80 \n",
"9 18872 Carmignac Multi Expertise 80 \n",
"\n",
" n_active_months flow_freq family_aum_qty_mean family_aum_qty_median \\\n",
"0 8 0.061538 886.504908 672.748 \n",
"1 0 0.000000 19.545000 19.545 \n",
"2 1 0.021277 28.340426 0.000 \n",
"3 6 0.260870 7901.739130 5455.000 \n",
"4 37 0.284615 2250.665769 606.000 \n",
"5 9 0.112500 2321.171212 184.023 \n",
"6 0 0.000000 0.000000 0.000 \n",
"7 69 0.530769 5287.072846 3200.800 \n",
"8 8 0.100000 891.837500 1222.000 \n",
"9 1 0.012500 243.500000 164.000 \n",
"\n",
" family_aum_qty_max family_aum_qty_last family_aum_val_mean \\\n",
"0 3012.748 0.000 3.479000e+05 \n",
"1 19.545 19.545 1.209445e+03 \n",
"2 666.000 0.000 1.061668e+05 \n",
"3 14240.000 14240.000 9.231846e+05 \n",
"4 8900.649 167.799 1.930753e+06 \n",
"5 15725.000 0.000 8.452421e+05 \n",
"6 0.000 0.000 0.000000e+00 \n",
"7 13396.330 1575.000 4.084883e+06 \n",
"8 1717.000 0.000 2.310840e+05 \n",
"9 564.000 0.000 4.327213e+04 \n",
"\n",
" family_aum_val_last net_flow_qty_sum gross_flow_qty_sum \\\n",
"0 0.000000e+00 -123.206 5403.206 \n",
"1 1.209445e+03 0.000 0.000 \n",
"2 0.000000e+00 -666.000 666.000 \n",
"3 1.711014e+06 14240.000 14240.000 \n",
"4 2.563734e+05 -7116.537 7708.537 \n",
"5 0.000000e+00 -7093.880 7963.880 \n",
"6 0.000000e+00 0.000 0.000 \n",
"7 2.922383e+06 -8322.670 18663.578 \n",
"8 0.000000e+00 -2958.000 2958.000 \n",
"9 0.000000e+00 -400.000 400.000 \n",
"\n",
" gross_flow_qty_mean sub_qty_sum red_qty_sum n_tx_total avg_n_isin_held \\\n",
"0 41.563123 2640.000 -2763.206 9.0 0.830769 \n",
"1 0.000000 0.000 0.000 0.0 1.000000 \n",
"2 14.170213 0.000 -666.000 1.0 0.042553 \n",
"3 619.130435 14240.000 0.000 6.0 3.043478 \n",
"4 59.296438 296.000 -7412.537 55.0 1.000000 \n",
"5 99.548500 435.000 -7528.880 10.0 0.625000 \n",
"6 0.000000 0.000 0.000 0.0 0.000000 \n",
"7 143.565985 5170.454 -13493.124 159.0 3.146154 \n",
"8 36.975000 0.000 -2958.000 9.0 0.787500 \n",
"9 5.000000 0.000 -400.000 1.0 0.875000 \n",
"\n",
" max_n_isin_held net_flow_qty_vol flow_to_aum_mean flow_to_aum_vol \\\n",
"0 1 237.868298 -0.015569 0.211558 \n",
"1 1 0.000000 0.000000 0.000000 \n",
"2 1 97.146084 0.000000 0.000000 \n",
"3 5 1747.389385 0.118826 0.278576 \n",
"4 1 219.774317 -0.025186 0.147716 \n",
"5 1 578.951646 -0.024076 0.148697 \n",
"6 0 0.000000 NaN 0.000000 \n",
"7 4 448.840939 -0.015065 0.102880 \n",
"8 1 184.846907 -0.036575 0.148040 \n",
"9 1 44.721360 -0.034843 0.291519 \n",
"\n",
" turnover_mean turnover_vol sub_share_mean red_share_mean \\\n",
"0 0.037561 0.208752 0.375000 -0.625000 \n",
"1 0.000000 0.000000 NaN NaN \n",
"2 0.000000 0.000000 0.000000 -1.000000 \n",
"3 0.118826 0.278576 1.000000 0.000000 \n",
"4 0.034426 0.145825 0.179537 -0.820463 \n",
"5 0.056100 0.139609 0.222222 -0.777778 \n",
"6 NaN 0.000000 NaN NaN \n",
"7 0.025599 0.104667 0.094726 -0.905274 \n",
"8 0.036575 0.148040 0.000000 -1.000000 \n",
"9 0.034843 0.291519 0.000000 -1.000000 \n",
"\n",
" aum_drawdown_last aum_drawdown_max entry_count full_exit_count \\\n",
"0 1.000000 1.000000 1 1 \n",
"1 0.000000 0.000000 1 0 \n",
"2 1.000000 1.000000 1 1 \n",
"3 0.000000 0.000000 1 0 \n",
"4 0.981148 0.985394 1 0 \n",
"5 1.000000 1.000000 2 2 \n",
"6 NaN NaN 0 0 \n",
"7 0.882430 0.882430 1 0 \n",
"8 1.000000 1.000000 1 1 \n",
"9 1.000000 1.000000 1 1 \n",
"\n",
" buy_on_perf_rate sell_on_perf_rate buy_on_perf_mean3_rate \\\n",
"0 0.007692 0.000000 0.015385 \n",
"1 0.000000 0.000000 0.000000 \n",
"2 0.000000 0.000000 0.000000 \n",
"3 0.000000 0.000000 0.000000 \n",
"4 0.000000 0.015385 0.007692 \n",
"5 0.012500 0.000000 0.012500 \n",
"6 0.000000 0.000000 0.000000 \n",
"7 0.023077 0.076923 0.030769 \n",
"8 0.000000 0.012500 0.000000 \n",
"9 0.000000 0.000000 0.000000 \n",
"\n",
" sell_on_perf_mean3_rate buy_on_perf_mean6_rate sell_on_perf_mean6_rate \\\n",
"0 0.000000 0.015385 0.007692 \n",
"1 0.000000 0.000000 0.000000 \n",
"2 0.000000 0.000000 0.000000 \n",
"3 0.000000 0.000000 0.000000 \n",
"4 0.053846 0.023077 0.053846 \n",
"5 0.000000 0.012500 0.000000 \n",
"6 0.000000 0.000000 0.000000 \n",
"7 0.130769 0.038462 0.153846 \n",
"8 0.025000 0.000000 0.037500 \n",
"9 0.000000 0.000000 0.012500 \n",
"\n",
" turnover_mean3_avg turnover_mean6_avg turnover_mean12_avg \\\n",
"0 0.036878 0.035899 0.034487 \n",
"1 0.000000 0.000000 0.000000 \n",
"2 0.000000 0.000000 0.000000 \n",
"3 0.154477 0.182237 0.217451 \n",
"4 0.034430 0.034449 0.034690 \n",
"5 0.051944 0.046856 0.042090 \n",
"6 NaN NaN NaN \n",
"7 0.025715 0.025793 0.025706 \n",
"8 0.047173 0.053641 0.057770 \n",
"9 0.033875 0.032520 0.030488 \n",
"\n",
" flow_to_aum_mean3_avg flow_to_aum_mean6_avg flow_to_aum_mean12_avg \\\n",
"0 -0.015286 -0.014881 -0.014528 \n",
"1 0.000000 0.000000 0.000000 \n",
"2 0.000000 0.000000 0.000000 \n",
"3 0.154477 0.182237 0.217451 \n",
"4 -0.025185 -0.025190 -0.025412 \n",
"5 -0.022293 -0.020170 -0.017985 \n",
"6 NaN NaN NaN \n",
"7 -0.015182 -0.015068 -0.014639 \n",
"8 -0.047173 -0.053641 -0.057770 \n",
"9 -0.033875 -0.032520 -0.030488 \n",
"\n",
" family_share_of_client_aum_mean family_share_of_client_aum_max \\\n",
"0 0.011541 0.050205 \n",
"1 0.000037 0.000037 \n",
"2 0.001850 0.044187 \n",
"3 0.034637 0.061213 \n",
"4 0.040489 0.103768 \n",
"5 0.017608 0.113984 \n",
"6 0.000000 0.000000 \n",
"7 0.111235 0.154495 \n",
"8 0.004618 0.008707 \n",
"9 0.000971 0.002025 \n",
"\n",
" ret_fund_m_mean delta_rate_m_mean region country \\\n",
"0 0.001311 0.013723 Switzerland Switzerland \n",
"1 0.000000 0.002000 Switzerland Switzerland \n",
"2 -0.000342 -0.004468 Switzerland Switzerland \n",
"3 0.000000 -0.085826 Switzerland Switzerland \n",
"4 0.006332 0.013723 Switzerland Switzerland \n",
"5 0.009313 -0.008925 Switzerland Switzerland \n",
"6 0.000000 -0.008925 Switzerland Switzerland \n",
"7 0.005852 0.013723 Switzerland Switzerland \n",
"8 0.002647 -0.008925 Switzerland Switzerland \n",
"9 0.003772 -0.008925 Switzerland Switzerland \n",
"\n",
" months_since_last_tx_family \n",
"0 29.0 \n",
"1 0.0 \n",
"2 94.0 \n",
"3 0.0 \n",
"4 32.0 \n",
"5 63.0 \n",
"6 0.0 \n",
"7 2.0 \n",
"8 66.0 \n",
"9 109.0 "
]
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"#table finale de features au niveau client × famille\n",
"months_since_last_tx = add_months_since_last_tx_by_group(\n",
" tmp,\n",
" id_cols=[ID_COL, \"fund_family\"],\n",
" active_col=\"active_month\",\n",
" month_col=\"month\",\n",
" suffix=\"_family\"\n",
")\n",
"\n",
"df_family_client_base = (\n",
" tmp\n",
" .groupby([ID_COL, \"fund_family\"], 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",
" family_aum_qty_mean=(\"aum_qty\", \"mean\"),\n",
" family_aum_qty_median=(\"aum_qty\", \"median\"),\n",
" family_aum_qty_max=(\"aum_qty\", \"max\"),\n",
" family_aum_qty_last=(\"aum_qty\", \"last\"),\n",
"\n",
" family_aum_val_mean=(\"aum_val\", \"mean\"),\n",
" family_aum_val_last=(\"aum_val\", \"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",
" sub_qty_sum=(\"sub_qty\", \"sum\"),\n",
" red_qty_sum=(\"red_qty\", \"sum\"),\n",
" n_tx_total=(\"n_tx\", \"sum\"),\n",
"\n",
" avg_n_isin_held=(\"n_isin_held\", \"mean\"),\n",
" max_n_isin_held=(\"n_isin_held\", \"max\"),\n",
"\n",
" net_flow_qty_vol=(\"net_flow_qty\", \"std\"),\n",
" flow_to_aum_mean=(\"flow_to_aum_m\", \"mean\"),\n",
" flow_to_aum_vol=(\"flow_to_aum_m\", \"std\"),\n",
" turnover_mean=(\"turnover_m\", \"mean\"),\n",
" turnover_vol=(\"turnover_m\", \"std\"),\n",
"\n",
" sub_share_mean=(\"sub_share_m\", \"mean\"),\n",
" red_share_mean=(\"red_share_m\", \"mean\"),\n",
"\n",
" aum_drawdown_last=(\"aum_drawdown\", \"last\"),\n",
" aum_drawdown_max=(\"aum_drawdown\", \"max\"),\n",
"\n",
" entry_count=(\"entry_event\", \"sum\"),\n",
" full_exit_count=(\"full_exit_event\", \"sum\"),\n",
"\n",
" buy_on_perf_rate=(\"buy_on_perf\", \"mean\"),\n",
" sell_on_perf_rate=(\"sell_on_perf\", \"mean\"),\n",
" buy_on_perf_mean3_rate=(\"buy_on_perf_mean3\", \"mean\"),\n",
" sell_on_perf_mean3_rate=(\"sell_on_perf_mean3\", \"mean\"),\n",
" buy_on_perf_mean6_rate=(\"buy_on_perf_mean6\", \"mean\"),\n",
" sell_on_perf_mean6_rate=(\"sell_on_perf_mean6\", \"mean\"),\n",
"\n",
" turnover_mean3_avg=(\"turnover_m_mean3\", \"mean\"),\n",
" turnover_mean6_avg=(\"turnover_m_mean6\", \"mean\"),\n",
" turnover_mean12_avg=(\"turnover_m_mean12\", \"mean\"),\n",
"\n",
" flow_to_aum_mean3_avg=(\"flow_to_aum_m_mean3\", \"mean\"),\n",
" flow_to_aum_mean6_avg=(\"flow_to_aum_m_mean6\", \"mean\"),\n",
" flow_to_aum_mean12_avg=(\"flow_to_aum_m_mean12\", \"mean\"),\n",
"\n",
" family_share_of_client_aum_mean=(\"family_share_of_client_aum\", \"mean\"),\n",
" family_share_of_client_aum_max=(\"family_share_of_client_aum\", \"max\"),\n",
"\n",
" ret_fund_m_mean=(\"ret_fund_m\", \"mean\"),\n",
" delta_rate_m_mean=(\"delta_rate_m\", \"mean\"),\n",
"\n",
" region=(\"region\", \"last\"),\n",
" country=(\"country\", \"last\"),\n",
" )\n",
" .merge(months_since_last_tx, on=[ID_COL, \"fund_family\"], how=\"left\")\n",
")\n",
"\n",
"for col in [\n",
" \"net_flow_qty_vol\",\n",
" \"flow_to_aum_vol\",\n",
" \"turnover_vol\",\n",
" \"months_since_last_tx_family\"\n",
"]:\n",
" if col in df_family_client_base.columns:\n",
" df_family_client_base[col] = df_family_client_base[col].fillna(0)\n",
"\n",
"print(\"df_family_client_base shape:\", df_family_client_base.shape)\n",
"df_family_client_base.head(10)"
]
},
{
"cell_type": "markdown",
"id": "ac6b1959",
"metadata": {},
"source": [
"---\n",
"## 5. Part 1 — Top 15 Fund Families\n",
"We restrict the clustering analysis to the 15 largest fund families, measured primarily by total final AUM and client coverage.\n",
"---"
]
},
{
"cell_type": "code",
"execution_count": 17,
"id": "9cabda05",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Top 15 families:\n",
"01 - Carmignac Patrimoine\n",
"02 - Carmignac Sécurité\n",
"03 - Carmignac Credit <NUM>\n",
"04 - Carmignac Investissement\n",
"05 - Carmignac Portfolio Sécurité\n",
"06 - Carmignac Portfolio Flexible Bond\n",
"07 - Carmignac Portfolio Credit\n",
"08 - Carmignac Emergents\n",
"09 - Carmignac Court Terme\n",
"10 - Carmignac Portfolio Long-Short European Equities\n",
"11 - Carmignac Portfolio Grande Europe\n",
"12 - Carmignac Portfolio Global Bond\n",
"13 - Carmignac Portfolio Emergents\n",
"14 - Carmignac Portfolio Patrimoine\n",
"15 - Carmignac Portfolio Emerging Patrimoine\n"
]
},
{
"data": {
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"<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>fund_family</th>\n",
" <th>n_clients</th>\n",
" <th>total_family_aum</th>\n",
" <th>avg_family_aum</th>\n",
" <th>total_gross_flow</th>\n",
" <th>total_tx</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>Carmignac Patrimoine</td>\n",
" <td>341</td>\n",
" <td>6.415987e+09</td>\n",
" <td>3.604052e+07</td>\n",
" <td>4.524176e+07</td>\n",
" <td>213046.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>Carmignac Sécurité</td>\n",
" <td>322</td>\n",
" <td>5.282111e+09</td>\n",
" <td>2.309570e+07</td>\n",
" <td>2.832674e+07</td>\n",
" <td>156020.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>Carmignac Credit &lt;NUM&gt;</td>\n",
" <td>194</td>\n",
" <td>4.003308e+09</td>\n",
" <td>8.805209e+06</td>\n",
" <td>4.940239e+07</td>\n",
" <td>55630.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>Carmignac Investissement</td>\n",
" <td>321</td>\n",
" <td>3.977901e+09</td>\n",
" <td>1.103948e+07</td>\n",
" <td>5.399207e+06</td>\n",
" <td>103479.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>Carmignac Portfolio Sécurité</td>\n",
" <td>253</td>\n",
" <td>2.590833e+09</td>\n",
" <td>7.606207e+06</td>\n",
" <td>1.139422e+08</td>\n",
" <td>63976.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>Carmignac Portfolio Flexible Bond</td>\n",
" <td>317</td>\n",
" <td>2.438397e+09</td>\n",
" <td>5.677381e+06</td>\n",
" <td>7.345324e+06</td>\n",
" <td>67493.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>Carmignac Portfolio Credit</td>\n",
" <td>266</td>\n",
" <td>2.238925e+09</td>\n",
" <td>4.120852e+06</td>\n",
" <td>2.850970e+07</td>\n",
" <td>43262.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>Carmignac Emergents</td>\n",
" <td>317</td>\n",
" <td>1.049202e+09</td>\n",
" <td>2.728114e+06</td>\n",
" <td>2.646927e+06</td>\n",
" <td>62600.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>Carmignac Court Terme</td>\n",
" <td>207</td>\n",
" <td>1.002651e+09</td>\n",
" <td>2.141392e+06</td>\n",
" <td>9.506942e+05</td>\n",
" <td>28295.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>Carmignac Portfolio Long-Short European Equities</td>\n",
" <td>236</td>\n",
" <td>6.875695e+08</td>\n",
" <td>2.336299e+06</td>\n",
" <td>1.743013e+07</td>\n",
" <td>23017.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>Carmignac Portfolio Grande Europe</td>\n",
" <td>299</td>\n",
" <td>5.477161e+08</td>\n",
" <td>1.730144e+06</td>\n",
" <td>1.320832e+07</td>\n",
" <td>40485.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td>Carmignac Portfolio Global Bond</td>\n",
" <td>280</td>\n",
" <td>5.166370e+08</td>\n",
" <td>2.343729e+06</td>\n",
" <td>9.028186e+06</td>\n",
" <td>48365.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>12</th>\n",
" <td>Carmignac Portfolio Emergents</td>\n",
" <td>193</td>\n",
" <td>4.760573e+08</td>\n",
" <td>1.521340e+06</td>\n",
" <td>6.876949e+06</td>\n",
" <td>15278.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13</th>\n",
" <td>Carmignac Portfolio Patrimoine</td>\n",
" <td>216</td>\n",
" <td>4.435119e+08</td>\n",
" <td>2.804551e+06</td>\n",
" <td>1.852971e+07</td>\n",
" <td>31854.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>14</th>\n",
" <td>Carmignac Portfolio Emerging Patrimoine</td>\n",
" <td>293</td>\n",
" <td>3.188049e+08</td>\n",
" <td>1.703089e+06</td>\n",
" <td>1.061354e+07</td>\n",
" <td>57784.0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" fund_family n_clients \\\n",
"0 Carmignac Patrimoine 341 \n",
"1 Carmignac Sécurité 322 \n",
"2 Carmignac Credit <NUM> 194 \n",
"3 Carmignac Investissement 321 \n",
"4 Carmignac Portfolio Sécurité 253 \n",
"5 Carmignac Portfolio Flexible Bond 317 \n",
"6 Carmignac Portfolio Credit 266 \n",
"7 Carmignac Emergents 317 \n",
"8 Carmignac Court Terme 207 \n",
"9 Carmignac Portfolio Long-Short European Equities 236 \n",
"10 Carmignac Portfolio Grande Europe 299 \n",
"11 Carmignac Portfolio Global Bond 280 \n",
"12 Carmignac Portfolio Emergents 193 \n",
"13 Carmignac Portfolio Patrimoine 216 \n",
"14 Carmignac Portfolio Emerging Patrimoine 293 \n",
"\n",
" total_family_aum avg_family_aum total_gross_flow total_tx \n",
"0 6.415987e+09 3.604052e+07 4.524176e+07 213046.0 \n",
"1 5.282111e+09 2.309570e+07 2.832674e+07 156020.0 \n",
"2 4.003308e+09 8.805209e+06 4.940239e+07 55630.0 \n",
"3 3.977901e+09 1.103948e+07 5.399207e+06 103479.0 \n",
"4 2.590833e+09 7.606207e+06 1.139422e+08 63976.0 \n",
"5 2.438397e+09 5.677381e+06 7.345324e+06 67493.0 \n",
"6 2.238925e+09 4.120852e+06 2.850970e+07 43262.0 \n",
"7 1.049202e+09 2.728114e+06 2.646927e+06 62600.0 \n",
"8 1.002651e+09 2.141392e+06 9.506942e+05 28295.0 \n",
"9 6.875695e+08 2.336299e+06 1.743013e+07 23017.0 \n",
"10 5.477161e+08 1.730144e+06 1.320832e+07 40485.0 \n",
"11 5.166370e+08 2.343729e+06 9.028186e+06 48365.0 \n",
"12 4.760573e+08 1.521340e+06 6.876949e+06 15278.0 \n",
"13 4.435119e+08 2.804551e+06 1.852971e+07 31854.0 \n",
"14 3.188049e+08 1.703089e+06 1.061354e+07 57784.0 "
]
},
"execution_count": 17,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# 28 - Table des top families\n",
"\n",
"top_family_table = (\n",
" df_family_client_base\n",
" .groupby(\"fund_family\", as_index=False)\n",
" .agg(\n",
" n_clients=(ID_COL, \"nunique\"),\n",
" total_family_aum=(\"family_aum_val_last\", \"sum\"),\n",
" avg_family_aum=(\"family_aum_val_mean\", \"mean\"),\n",
" total_gross_flow=(\"gross_flow_qty_sum\", \"sum\"),\n",
" total_tx=(\"n_tx_total\", \"sum\")\n",
" )\n",
" .sort_values([\"total_family_aum\", \"n_clients\"], ascending=[False, False])\n",
" .reset_index(drop=True)\n",
")\n",
"\n",
"top_15_families = top_family_table.head(15)[\"fund_family\"].tolist()\n",
"\n",
"print(\"Top 15 families:\")\n",
"for i, fam in enumerate(top_15_families, 1):\n",
" print(f\"{i:02d} - {fam}\")\n",
"\n",
"top_family_table.head(15)"
]
},
{
"cell_type": "code",
"execution_count": 18,
"id": "3be8b56b",
"metadata": {},
"outputs": [
{
"data": {
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",
"text/plain": [
"<Figure size 1200x600 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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",
"text/plain": [
"<Figure size 1200x600 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# 29 - Visualisation des top 15 families\n",
"\n",
"top15_plot = top_family_table.head(15).copy()\n",
"\n",
"plt.figure(figsize=(12, 6))\n",
"sns.barplot(data=top15_plot, y=\"fund_family\", x=\"total_family_aum\")\n",
"plt.title(\"Top 15 fund families by total AUM\")\n",
"plt.xlabel(\"Total family AUM\")\n",
"plt.ylabel(\"Fund family\")\n",
"plt.tight_layout()\n",
"plt.show()\n",
"\n",
"plt.figure(figsize=(12, 6))\n",
"sns.barplot(data=top15_plot, y=\"fund_family\", x=\"n_clients\")\n",
"plt.title(\"Top 15 fund families by number of clients\")\n",
"plt.xlabel(\"Number of clients\")\n",
"plt.ylabel(\"Fund family\")\n",
"plt.tight_layout()\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"id": "9b5257a3",
"metadata": {},
"source": [
"---\n",
"## 6. Part 2 — Additional Performance Features for Family Clustering\n",
"We enrich the `client × family` dataset with two types of performance-sensitive variables computed from the monthly family panel:\n",
"\n",
"1. **Correlation between flow intensity and past family performance**, using rolling average returns over 3 and 6 months.\n",
"2. **Buy-after-good-performance shares**, i.e. the proportion of buy months occurring after positive rolling average returns.\n",
"\n",
"These variables extend the original notebook by explicitly measuring momentum-like behavior at fund-family level.\n",
"---"
]
},
{
"cell_type": "code",
"execution_count": 19,
"id": "3ae47862",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Performance features merged. Shape: (7282, 53)\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>fund_family</th>\n",
" <th>corr_flow_ret_3m</th>\n",
" <th>corr_flow_ret_6m</th>\n",
" <th>buy_after_good_perf_share_3m</th>\n",
" <th>buy_after_good_perf_share_6m</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>18872</td>\n",
" <td>Carmignac Absolute Return Europe</td>\n",
" <td>0.051116</td>\n",
" <td>0.031925</td>\n",
" <td>0.666667</td>\n",
" <td>0.666667</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>18872</td>\n",
" <td>Carmignac China New Economy</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>18872</td>\n",
" <td>Carmignac Court Terme</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>18872</td>\n",
" <td>Carmignac Credit &lt;NUM&gt;</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>18872</td>\n",
" <td>Carmignac Emergents</td>\n",
" <td>-0.062404</td>\n",
" <td>0.018069</td>\n",
" <td>0.142857</td>\n",
" <td>0.428571</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Registrar Account - ID fund_family corr_flow_ret_3m \\\n",
"0 18872 Carmignac Absolute Return Europe 0.051116 \n",
"1 18872 Carmignac China New Economy 0.000000 \n",
"2 18872 Carmignac Court Terme 0.000000 \n",
"3 18872 Carmignac Credit <NUM> 0.000000 \n",
"4 18872 Carmignac Emergents -0.062404 \n",
"\n",
" corr_flow_ret_6m buy_after_good_perf_share_3m \\\n",
"0 0.031925 0.666667 \n",
"1 0.000000 0.000000 \n",
"2 0.000000 0.000000 \n",
"3 0.000000 0.000000 \n",
"4 0.018069 0.142857 \n",
"\n",
" buy_after_good_perf_share_6m \n",
"0 0.666667 \n",
"1 0.000000 \n",
"2 0.000000 \n",
"3 0.000000 \n",
"4 0.428571 "
]
},
"execution_count": 19,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# 30 - Additional performance features for family clustering\n",
"\n",
"def safe_corr(x, y, min_obs=6):\n",
" x = pd.Series(x, dtype=float)\n",
" y = pd.Series(y, dtype=float)\n",
" mask = x.notna() & y.notna()\n",
" x = x[mask]\n",
" y = y[mask]\n",
" if len(x) < min_obs or x.nunique() <= 1 or y.nunique() <= 1:\n",
" return np.nan\n",
" return x.corr(y)\n",
"\n",
"tmp_perf = tmp.copy()\n",
"\n",
"# Past family returns already available in tmp:\n",
"# ret_fund_mean3_lag1 and ret_fund_mean6_lag1\n",
"\n",
"tmp_perf[\"buy_flag\"] = (tmp_perf[\"net_flow_qty\"] > 0).astype(int)\n",
"tmp_perf[\"good_perf_3m\"] = (tmp_perf[\"ret_fund_mean3_lag1\"] > 0).astype(int)\n",
"tmp_perf[\"good_perf_6m\"] = (tmp_perf[\"ret_fund_mean6_lag1\"] > 0).astype(int)\n",
"\n",
"perf_rows = []\n",
"for (acc, fam), g in tmp_perf.groupby([ID_COL, \"fund_family\"]):\n",
" g = g.sort_values(\"month\")\n",
"\n",
" buy_months = g[\"buy_flag\"].sum()\n",
"\n",
" perf_rows.append({\n",
" ID_COL: acc,\n",
" \"fund_family\": fam,\n",
" \"corr_flow_ret_3m\": safe_corr(g[\"flow_to_aum_m\"], g[\"ret_fund_mean3_lag1\"], min_obs=6),\n",
" \"corr_flow_ret_6m\": safe_corr(g[\"flow_to_aum_m\"], g[\"ret_fund_mean6_lag1\"], min_obs=6),\n",
" \"buy_after_good_perf_share_3m\": (\n",
" ((g[\"buy_flag\"] == 1) & (g[\"good_perf_3m\"] == 1)).sum() / buy_months\n",
" if buy_months > 0 else np.nan\n",
" ),\n",
" \"buy_after_good_perf_share_6m\": (\n",
" ((g[\"buy_flag\"] == 1) & (g[\"good_perf_6m\"] == 1)).sum() / buy_months\n",
" if buy_months > 0 else np.nan\n",
" ),\n",
" })\n",
"\n",
"df_family_perf = pd.DataFrame(perf_rows)\n",
"\n",
"# Merge into final family-level table\n",
"df_family_client_base = df_family_client_base.merge(\n",
" df_family_perf,\n",
" on=[ID_COL, \"fund_family\"],\n",
" how=\"left\"\n",
")\n",
"\n",
"for col in [\"corr_flow_ret_3m\", \"corr_flow_ret_6m\",\n",
" \"buy_after_good_perf_share_3m\", \"buy_after_good_perf_share_6m\"]:\n",
" if col in df_family_client_base.columns:\n",
" df_family_client_base[col] = df_family_client_base[col].fillna(0)\n",
"\n",
"print(\"Performance features merged. Shape:\", df_family_client_base.shape)\n",
"df_family_client_base[\n",
" [ID_COL, \"fund_family\", \"corr_flow_ret_3m\", \"corr_flow_ret_6m\",\n",
" \"buy_after_good_perf_share_3m\", \"buy_after_good_perf_share_6m\"]\n",
"].head()"
]
},
{
"cell_type": "markdown",
"id": "6b781457",
"metadata": {},
"source": [
"---\n",
"## 7. Part 3 — Clustering Features\n",
"To stay close to the original global clustering notebook, we keep a compact set of core behavioral variables:\n",
"- activity frequency\n",
"- gross flow intensity relative to average AUM\n",
"- family concentration/importance\n",
"- exit behavior\n",
"- flow direction balance\n",
"- recency of activity\n",
"\n",
"We then add the new **performance-sensitive features** and the **rolling turnover averages**, which are central to the family-fund approach.\n",
"---"
]
},
{
"cell_type": "code",
"execution_count": 20,
"id": "2d120f23",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Number of clustering features: 15\n",
"['flow_freq', 'gross_flow_to_aum_family', 'avg_n_isin_held', 'exit_rate_per_isin_family', 'flow_direction_balance', 'log_family_aum_val_mean', 'months_since_last_tx_family', 'family_share_of_client_aum_mean', 'turnover_mean3_avg', 'turnover_mean6_avg', 'turnover_mean12_avg', 'corr_flow_ret_3m', 'corr_flow_ret_6m', 'buy_after_good_perf_share_3m', 'buy_after_good_perf_share_6m']\n"
]
}
],
"source": [
"# 31 - Final clustering features\n",
"\n",
"# Derived ratios closer to the initial notebook\n",
"df_family_client_base[\"log_family_aum_val_mean\"] = np.log1p(\n",
" df_family_client_base[\"family_aum_val_mean\"].clip(lower=0)\n",
")\n",
"\n",
"df_family_client_base[\"gross_flow_to_aum_family\"] = np.where(\n",
" df_family_client_base[\"family_aum_qty_mean\"].abs() > 1,\n",
" df_family_client_base[\"gross_flow_qty_sum\"] / df_family_client_base[\"family_aum_qty_mean\"].abs(),\n",
" np.nan\n",
")\n",
"\n",
"df_family_client_base[\"flow_direction_balance\"] = np.where(\n",
" df_family_client_base[\"gross_flow_qty_sum\"] > 0,\n",
" df_family_client_base[\"net_flow_qty_sum\"] / df_family_client_base[\"gross_flow_qty_sum\"],\n",
" np.nan\n",
")\n",
"\n",
"df_family_client_base[\"exit_rate_per_isin_family\"] = np.where(\n",
" df_family_client_base[\"avg_n_isin_held\"] > 0,\n",
" df_family_client_base[\"full_exit_count\"] / df_family_client_base[\"avg_n_isin_held\"],\n",
" np.nan\n",
")\n",
"\n",
"df_family_client_base[\"family_aum_final_to_peak\"] = np.where(\n",
" df_family_client_base[\"family_aum_qty_max\"] > 0,\n",
" df_family_client_base[\"family_aum_qty_last\"] / df_family_client_base[\"family_aum_qty_max\"],\n",
" np.nan\n",
")\n",
"\n",
"df_family_client_base[\"aum_drawdown_last\"] = df_family_client_base[\"aum_drawdown_last\"].clip(0, 1)\n",
"df_family_client_base[\"family_aum_final_to_peak\"] = df_family_client_base[\"family_aum_final_to_peak\"].clip(0, 1)\n",
"\n",
"# Compact but rich feature list\n",
"family_cluster_features = [\n",
" \"flow_freq\",\n",
" \"gross_flow_to_aum_family\",\n",
" \"avg_n_isin_held\",\n",
" \"exit_rate_per_isin_family\",\n",
" \"flow_direction_balance\",\n",
" \"log_family_aum_val_mean\",\n",
" \"months_since_last_tx_family\",\n",
" \"family_share_of_client_aum_mean\",\n",
" \"turnover_mean3_avg\",\n",
" \"turnover_mean6_avg\",\n",
" \"turnover_mean12_avg\",\n",
" \"corr_flow_ret_3m\",\n",
" \"corr_flow_ret_6m\",\n",
" \"buy_after_good_perf_share_3m\",\n",
" \"buy_after_good_perf_share_6m\",\n",
"]\n",
"\n",
"print(\"Number of clustering features:\", len(family_cluster_features))\n",
"print(family_cluster_features)"
]
},
{
"cell_type": "markdown",
"id": "e455f9cf",
"metadata": {},
"source": [
"---\n",
"## 8. Part 4 — Robust Clustering Utilities\n",
"Compared with the first family notebook version, the functions below:\n",
"- apply **stronger filtering of outliers**\n",
"- winsorize a broader set of variables\n",
"- reject values of `K` that create **tiny clusters**\n",
"- therefore avoid the pathological case “one giant cluster + one or two clients alone”\n",
"---"
]
},
{
"cell_type": "code",
"execution_count": 21,
"id": "8b43106b",
"metadata": {},
"outputs": [],
"source": [
"# 32 - Robust clustering helpers\n",
"\n",
"from sklearn.decomposition import PCA\n",
"from sklearn.impute import SimpleImputer\n",
"\n",
"def prepare_family_matrix(df_family, features, winsorize_cols=None):\n",
" X = df_family[features].copy()\n",
"\n",
" # Median imputation\n",
" imputer = SimpleImputer(strategy=\"median\")\n",
" X_imp = pd.DataFrame(imputer.fit_transform(X), columns=features, index=X.index)\n",
"\n",
" # Broad winsorization, inspired by the original notebook\n",
" if winsorize_cols is None:\n",
" winsorize_cols = [\n",
" \"gross_flow_to_aum_family\",\n",
" \"avg_n_isin_held\",\n",
" \"exit_rate_per_isin_family\",\n",
" \"months_since_last_tx_family\",\n",
" \"family_share_of_client_aum_mean\",\n",
" \"turnover_mean3_avg\",\n",
" \"turnover_mean6_avg\",\n",
" \"turnover_mean12_avg\",\n",
" ]\n",
"\n",
" for col in winsorize_cols:\n",
" if col in X_imp.columns:\n",
" X_imp[col] = winsorize_mad(X_imp[col], n_sigma=3)\n",
"\n",
" # Variables bounded by construction\n",
" for col in [\n",
" \"flow_freq\",\n",
" \"family_share_of_client_aum_mean\",\n",
" \"buy_after_good_perf_share_3m\",\n",
" \"buy_after_good_perf_share_6m\",\n",
" \"family_aum_final_to_peak\"\n",
" ]:\n",
" if col in X_imp.columns:\n",
" X_imp[col] = np.clip(X_imp[col], 0, 1)\n",
"\n",
" # Correlations should live in [-1, 1]\n",
" for col in [\"corr_flow_ret_3m\", \"corr_flow_ret_6m\", \"flow_direction_balance\"]:\n",
" if col in X_imp.columns:\n",
" X_imp[col] = np.clip(X_imp[col], -1, 1)\n",
"\n",
" # Same logic as in the original notebook:\n",
" # clip and log-transform gross flow intensity\n",
" if \"gross_flow_to_aum_family\" in X_imp.columns:\n",
" vals = X_imp[\"gross_flow_to_aum_family\"].to_numpy(dtype=float)\n",
" vals = np.clip(vals, 0, np.nanpercentile(vals, 90))\n",
" X_imp[\"gross_flow_to_aum_family\"] = np.log1p(vals)\n",
"\n",
" scaler = RobustScaler()\n",
" X_scaled = scaler.fit_transform(X_imp)\n",
"\n",
" return X_imp, X_scaled, imputer, scaler\n",
"\n",
"\n",
"def evaluate_k_range(X_scaled, k_min=2, k_max=6, min_cluster_size=10, min_cluster_share=0.02, random_state=42):\n",
" n = X_scaled.shape[0]\n",
" k_max = min(k_max, max(2, n - 1))\n",
"\n",
" rows = []\n",
" for k in range(k_min, k_max + 1):\n",
" if k >= n:\n",
" continue\n",
"\n",
" km = KMeans(n_clusters=k, random_state=random_state, n_init=20)\n",
" labels = km.fit_predict(X_scaled)\n",
"\n",
" cluster_sizes = pd.Series(labels).value_counts().sort_index()\n",
" min_size = int(cluster_sizes.min())\n",
" min_size_required = max(min_cluster_size, int(np.ceil(min_cluster_share * n)))\n",
"\n",
" valid = (min_size >= min_size_required)\n",
"\n",
" if valid:\n",
" sil = silhouette_score(X_scaled, labels)\n",
" dbi = davies_bouldin_score(X_scaled, labels)\n",
" else:\n",
" sil = np.nan\n",
" dbi = np.nan\n",
"\n",
" rows.append({\n",
" \"k\": k,\n",
" \"silhouette\": sil,\n",
" \"davies_bouldin\": dbi,\n",
" \"min_cluster_size\": min_size,\n",
" \"min_cluster_required\": min_size_required,\n",
" \"inertia\": km.inertia_,\n",
" \"valid_k\": valid\n",
" })\n",
"\n",
" return pd.DataFrame(rows)\n",
"\n",
"\n",
"def choose_best_k(metrics_df):\n",
" valid = metrics_df[metrics_df[\"valid_k\"]].dropna(subset=[\"silhouette\"]).copy()\n",
" if len(valid) == 0:\n",
" return None\n",
" return int(valid.sort_values([\"silhouette\", \"davies_bouldin\"], ascending=[False, True]).iloc[0][\"k\"])"
]
},
{
"cell_type": "code",
"execution_count": 22,
"id": "ceb97cfd",
"metadata": {},
"outputs": [],
"source": [
"# 33 - Family clustering pipeline\n",
"\n",
"def run_family_clustering(\n",
" df_base,\n",
" family_name,\n",
" features,\n",
" k_min=2,\n",
" k_max=6,\n",
" min_clients=40,\n",
" min_cluster_size=10,\n",
" min_cluster_share=0.02,\n",
" random_state=42,\n",
" make_plots=True\n",
"):\n",
" df_family = df_base[df_base[\"fund_family\"] == family_name].copy()\n",
"\n",
" # Quality filters\n",
" df_family = df_family[\n",
" (df_family[\"n_months\"] >= 6) &\n",
" (df_family[\"family_aum_val_mean\"] > 0)\n",
" ].copy()\n",
"\n",
" # Stronger quantile filtering, inspired by the initial notebook\n",
" for col in [\"family_aum_val_mean\", \"gross_flow_qty_sum\", \"n_tx_total\"]:\n",
" if col in df_family.columns and df_family[col].notna().sum() > 0:\n",
" cap = df_family[col].quantile(0.99)\n",
" df_family = df_family[df_family[col] <= cap].copy()\n",
"\n",
" n_clients = df_family[ID_COL].nunique()\n",
" if n_clients < min_clients:\n",
" print(f\"[SKIP] {family_name}: only {n_clients} usable clients.\")\n",
" return None\n",
"\n",
" X_imp, X_scaled, imputer, scaler = prepare_family_matrix(df_family, features)\n",
"\n",
" metrics_df = evaluate_k_range(\n",
" X_scaled,\n",
" k_min=k_min,\n",
" k_max=k_max,\n",
" min_cluster_size=min_cluster_size,\n",
" min_cluster_share=min_cluster_share,\n",
" random_state=random_state\n",
" )\n",
"\n",
" if len(metrics_df) == 0:\n",
" print(f\"[SKIP] {family_name}: no K evaluated.\")\n",
" return None\n",
"\n",
" best_k = choose_best_k(metrics_df)\n",
" if best_k is None:\n",
" print(f\"[SKIP] {family_name}: no valid K with cluster size constraints.\")\n",
" return None\n",
"\n",
" km = KMeans(n_clusters=best_k, random_state=random_state, n_init=30)\n",
" labels = km.fit_predict(X_scaled)\n",
"\n",
" df_family[\"cluster\"] = labels\n",
"\n",
" cluster_sizes = (\n",
" df_family[\"cluster\"]\n",
" .value_counts()\n",
" .sort_index()\n",
" .rename_axis(\"cluster\")\n",
" .reset_index(name=\"n_clients\")\n",
" )\n",
"\n",
" cluster_profile = (\n",
" df_family.groupby(\"cluster\")[features]\n",
" .median()\n",
" .sort_index()\n",
" )\n",
"\n",
" # Churn proxies at family level\n",
" df_family[\"churn_hard\"] = (df_family[\"family_aum_final_to_peak\"] < 0.10).astype(int)\n",
" df_family[\"churn_soft\"] = (\n",
" (df_family[\"family_aum_final_to_peak\"] < 0.40) &\n",
" (df_family[\"aum_drawdown_last\"] > 0.40)\n",
" ).astype(int)\n",
" df_family[\"churn_warning\"] = (\n",
" (df_family[\"flow_direction_balance\"] < 0) &\n",
" (df_family[\"aum_drawdown_last\"] > 0.20)\n",
" ).astype(int)\n",
"\n",
" churn_profile = (\n",
" df_family.groupby(\"cluster\")[[\"churn_hard\", \"churn_soft\", \"churn_warning\"]]\n",
" .mean()\n",
" .sort_index()\n",
" )\n",
"\n",
" # PCA for visualization\n",
" pca = PCA(n_components=2, random_state=random_state)\n",
" coords = pca.fit_transform(X_scaled)\n",
" df_family[\"pca1\"] = coords[:, 0]\n",
" df_family[\"pca2\"] = coords[:, 1]\n",
"\n",
" result = {\n",
" \"family\": family_name,\n",
" \"n_clients\": n_clients,\n",
" \"best_k\": best_k,\n",
" \"metrics_df\": metrics_df,\n",
" \"df_family\": df_family,\n",
" \"cluster_profile\": cluster_profile,\n",
" \"cluster_sizes\": cluster_sizes,\n",
" \"churn_profile\": churn_profile,\n",
" \"features\": features,\n",
" \"pca_explained_var\": pca.explained_variance_ratio_,\n",
" }\n",
"\n",
" if make_plots:\n",
" # Choice of K\n",
" fig, axes = plt.subplots(1, 3, figsize=(15, 4))\n",
" axes[0].plot(metrics_df[\"k\"], metrics_df[\"silhouette\"], marker=\"o\")\n",
" axes[0].set_title(f\"{family_name} - Silhouette\")\n",
" axes[0].set_xlabel(\"k\")\n",
"\n",
" axes[1].plot(metrics_df[\"k\"], metrics_df[\"davies_bouldin\"], marker=\"o\")\n",
" axes[1].set_title(f\"{family_name} - Davies-Bouldin\")\n",
" axes[1].set_xlabel(\"k\")\n",
"\n",
" axes[2].plot(metrics_df[\"k\"], metrics_df[\"inertia\"], marker=\"o\")\n",
" axes[2].set_title(f\"{family_name} - Inertia\")\n",
" axes[2].set_xlabel(\"k\")\n",
" plt.tight_layout()\n",
" plt.show()\n",
"\n",
" # Cluster signatures\n",
" plot_heatmap(\n",
" df_family,\n",
" profile_vars=features,\n",
" cluster_col=\"cluster\",\n",
" title=f\"{family_name} - Cluster signatures\",\n",
" figsize=(18, 5)\n",
" )\n",
"\n",
" # PCA projection\n",
" plt.figure(figsize=(7, 5))\n",
" sns.scatterplot(\n",
" data=df_family,\n",
" x=\"pca1\", y=\"pca2\",\n",
" hue=\"cluster\",\n",
" palette=\"tab10\",\n",
" alpha=0.8\n",
" )\n",
" plt.title(\n",
" f\"{family_name} - PCA projection \"\n",
" f\"(explained var: {result['pca_explained_var'][0]:.2f}, {result['pca_explained_var'][1]:.2f})\"\n",
" )\n",
" plt.tight_layout()\n",
" plt.show()\n",
"\n",
" # Cluster sizes\n",
" plt.figure(figsize=(6, 4))\n",
" sns.barplot(data=cluster_sizes, x=\"cluster\", y=\"n_clients\")\n",
" plt.title(f\"{family_name} - Cluster sizes\")\n",
" plt.tight_layout()\n",
" plt.show()\n",
"\n",
" # Churn by cluster\n",
" churn_long = churn_profile.reset_index().melt(\n",
" id_vars=\"cluster\",\n",
" value_vars=[\"churn_hard\", \"churn_soft\", \"churn_warning\"],\n",
" var_name=\"churn_type\",\n",
" value_name=\"rate\"\n",
" )\n",
"\n",
" plt.figure(figsize=(8, 4))\n",
" sns.barplot(data=churn_long, x=\"cluster\", y=\"rate\", hue=\"churn_type\")\n",
" plt.title(f\"{family_name} - Churn analysis by cluster\")\n",
" plt.ylabel(\"Rate\")\n",
" plt.tight_layout()\n",
" plt.show()\n",
"\n",
" print(f\"\\n=== {family_name} ===\")\n",
" print(f\"Clients after filtering: {n_clients}\")\n",
" print(f\"Chosen K: {best_k}\")\n",
" print(cluster_sizes)\n",
" print(\"\\nChurn profile:\")\n",
" print(churn_profile.round(3))\n",
"\n",
" return result"
]
},
{
"cell_type": "code",
"execution_count": 23,
"id": "b1161a03",
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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y/679++9McnKyqXXr1qbnnnsuz/kRkZvpmiKTril0TZGTW11T3Is4X331VZO3t7dpypQpN8UwfPjwm/4O/DO2f56jb3W8fPfddzcd81I0aDqXUiguLg64/a9h//bPedBiY2OJioqiRYsWnD9/ntjY2Gxtvby8aNOmzS37efTRR7ONTG3UqBEmk4mePXtma9eoUSMuX75MWlpajjFt2LCBJk2aUL9+ffO28uXL07Vr11u2r1OnDs2bNzc/dnFxoWbNmpw/f968zdbW1ryidnp6OtevX8fBwYGaNWty+PBhc7uffvqJChUq0K9fv5veJze/FJ4+fZqgoCCCgoLo3LkzixYt4r777uO9994Dsuc7NTWV69evU61aNZycnLLFcSe3+yxu5bHHHssWf9bn89hjj5m3GY1GfH19s+Vt/fr1GI1G+vfvn62/IUOGYDKZWL9+/R3f+4knnsj23s2bNyc9PZ2LFy8Cmb/QxsTE0KVLF6Kiosx/rKysaNy48U23X98NNzc3vvnmG3r37k1MTAzLli3jpZdeIigoiM8//zzfC4n27t072+PmzZtz48YN89/JP//8E4DBgwdnazdkyJBsz+fFDz/8QLNmzXBycsqWt1atWpGens6OHTvysyvmvsuVK0fr1q2z9d2wYUMcHBwK9DMRKc503s2k8+7NdN7Nvaw5RuPj44HcHzsdO3bE2tqatWvXmrcdP36ckydP0rlz5xzf78iRI5w9e5auXbty/fp18/4nJCQQFBTEjh07zNOiOTk5sW/fPq5evZqvfRs3bhyhoaGEhoby4YcfEhgYyBtvvMFPP/1kblMQn/mdbN68mdTUVPr165ft2PjntBB34uDgkG2uYFtbW/z8/LIdvyKSf7qmyKRripvpmuLO7kWcffr0uauY/nm8JCcnExUVRePGjQHyPS2c3DuazqUUcnR0BP7+EpIbu3bt4tNPP2Xv3r03zeEcGxtLuXLlzI9vdxtOlSpVsj3Oel3lypVv2p6RkUFsbCwVKlS4ZV8XL1685ZzVOd1+9e/3AHB2ds42d3NGRgYLFixgyZIlXLhwgfT0dPNz5cuXN/9/WFgYNWvWzPeqyp6enkycOBGDwYCtrS01atTA1dXV/HxSUhIzZ85k5cqVXL16NVvR9t8XOreT11ui8vL5/DNvFy9exMPDw3xsZcm6HS3rpJSX93ZycgIy5/MGOHv2LJDzF7l/v/c/paen3zTXmbOz820XRvHw8GD8+PG8/fbbnD17lo0bNzJ79mymTZuGh4cHjz/++B336d9y2sfo6GgcHR25ePEiVlZWNx3D7u7uODk55SqP/3bu3DmOHTtmnvP83241B1xe+o6Njc2x78jIyHz3LVKS6Lz7N513s9N5N/cSEhKAvwtHuT12XFxcaNmyJevWreOFF14AMqdysba2Nk9fditZ+//qq6/m2CY2NhZnZ2defvllxo4dy3333UfDhg1p164d3bp1o2rVqkDm3/2s+CGziOHi4mJ+3KhRo2wLiz7yyCN069aNd955h/vuuw9bW9sC+czv5NKlSwDUqFEj23YXFxecnZ1z1UelSpVuKkI5Oztz7Nixu45PRHRN8U+6pshO1xR3VtBxWltbU6lSpTzH8U83btzgs88+Y+3atTd9f87L8SKFQ0X0UsjR0REPD49cL+AQFhbGoEGDqFWrFmPHjqVy5crY2Njw559/Mm/ePPMonCy3W70765fh3G7P74jfWzEajXdsM2PGDD755BN69uzJ6NGjcXZ2xsrKivfee69AY3FwcKBVq1Y5Pj9hwgTznJtNmjShXLlyGAwGXnzxxTzFkdeV1PP6+RSkOx0DWf/94IMPcHd3v6nd7T7fy5cv88ADD2TbtmDBAgIDA+8Yl8FgoGbNmtSsWZP77ruPBx98kG+//TZfRfTcHud3M+/ZPy8WIfNisnXr1gwbNuyW7f/9RTkvMjIycHV1ZcqUKbd8/p8FApHSTOfdnOm8q/Nubp04cQJXV1fzF9i8HDtdunThtdde48iRI9SvX59169bRsmXL256nsvoYM2ZMtpGS/5Q1Or5z5840b96cn3/+mU2bNhESEsLs2bP59NNPadeuHXPnzs22uKmnpye//fZbju9tZWVFYGAgCxYs4Ny5c+b5fnMjp2uIf18f3Cu5+XsvIvmna4qc6ZpC1xSFHec/737IrxdeeIE9e/YwdOhQ6tevj4ODAxkZGQwbNqxAj1spGCqil1L3338/X375JXv27MHf3/+2bX/77TdSUlKYPn16tl/uLD1Vg6enJ+fOnbtpe35W1M7y448/EhgYaL4VK0tMTEy2X9GrVavGvn37SE1NvScLJ/74449069Yt28rXycnJN/0SWVQWmfD09GTLli3ExcVl+3X29OnT5ufvVtZoLldX19tetNyKu7s7oaGh2bbVq1cvXzE4OTkRHh6e59fmhqenJxkZGZw7dy7bojIRERHExMRky6Ozs7P5F/MsKSkpN8VWrVo1EhIS7piz2x1LOT1XrVo1tmzZQtOmTfN8kSdS2ui8e2s67+ZPaTnvZtmzZw9hYWHZFtrM7bED0KFDB8aNG2ee0uXs2bOMGDHitu+Ztf+Ojo652n8PDw/69u1L3759iYyMpHv37syYMcM8Kr1Zs2bmtnZ2dnfsL6vonTWCPbefedaI8X9fI2SNMr+drH9vzp49a95/yLxr7Z+jFkXEsnRNcWu6psif0nZNcTt3E+c/5fazjY6OZsuWLTz33HPZFuLNGhEvRY/mRC+lhg0bhoODA2+88QYRERE3PR8WFsb8+fOBv39t+/dtSCtWrCicYHMQHBzM3r17OXLkiHnbjRs3WLNmTb77NBqNN/3at27dupvmuHzwwQe5fv06ixcvvqmPgvi18Fa/xC5cuPCmUURlypQBLH+bT9u2bUlPT78pH/PmzcNgMNC2bdu7fo82bdrg6OjIzJkzSU1Nven5201LYmdnR6tWrbL9ud1tyfv27ct223WW/fv3c+PGjTuujJ5f7dq1AzD/3cuSddGQ9TxknuB37tyZrd3y5ctvOkYefvhh9uzZw4YNG256v5iYGPNchVnH0r+/dN/uuYcffpj09HS++OKLm16TlpZ2y75ESiudd29N5938KWnn3du5ePEiY8eOxcbGhqFDh5q35/bYgczbtYODg1m3bh3ff/89NjY2dOjQ4bbv6+vrS7Vq1Zg7d+4tp03I2v/09PSbjgdXV1c8PDxISUkBMs/Z/8zFPwvqt5KamsqmTZuwsbEx/6ie28/c0dGRChUq3HSNsGTJktu+J0CrVq2wsbFh0aJF2XL77+sSEbEsXVPcmq4p8qc0XVPcyzj/qUyZMrn6XHMaga/zbtGlkeilVLVq1ZgyZQovvvginTt35tFHH8Xb25uUlBT27NnDDz/8QI8ePQBo3bo1NjY2PP300/Tu3Zv4+Hi++uorXF1d79mI3NwYNmwY3377LYMHD6Zfv344ODjw1VdfUblyZW7cuJGvX3bvu+8+Pv/8c1577TX8/f05fvw4a9asyTYaB6Bbt26sXr2aSZMmsX//fpo1a0ZiYiJbtmyhT58+d/xilps4vvnmGxwdHalTpw579+5l8+bN2eZyA6hfvz5Go5HZs2cTGxuLra0tLVu2zDYnW2Fo3749gYGB/O9//+PixYv4+PiwadMmfv31VwYOHJjj3HZ54ejoyNtvv82YMWPo0aMHnTt3xsXFhUuXLvHnn3/StGlTxo0bVwB7A9988w1r1qyhQ4cO+Pr6YmNjw6lTp1ixYgV2dnY8/fTTBfI+/1avXj26d+/Ol19+SUxMDAEBARw4cIBVq1bRoUMHWrZsaW77+OOP89Zbb/Hcc8/RqlUrjh49ysaNG28afTd06FB+++03nn76abp3707Dhg1JTEzk+PHj/Pjjj/z666+4uLhgb29PnTp1WLduHTVq1KB8+fLUrVsXb29vGjZsCMDEiRMJDg7GaDTSpUsXWrRoQa9evZg5cyZHjhwx/1tx9uxZfvjhB15//XU6dep0T3IlUtzovHtrOu/mT0k772Y5fPgw33zzDSaTiZiYGA4cOMBPP/2EwWDggw8+yDbyLLfHTpbOnTvzyiuvsGTJEoKDg83zoObEysqKiRMn8tRTT/HII4/Qo0cPKlasyNWrV9m2bRuOjo7MmDGD+Ph42rVrx0MPPUS9evVwcHBg8+bNHDhwINsoxNtZv369ecRfVFQUa9as4ezZswwfPtw8KjAvn/njjz/OrFmzeP311/H19WXnzp2cOXPmjnG4uLgwZMgQZs6cyYgRI2jXrh2HDx9m/fr1Oc5rLCKFT9cUt6ZrivwpqdcUloyzYcOGrF27lkmTJuHn54eDgwPt27e/5fsFBAQwZ84cUlNTqVixIps2beLChQv3YvekAKiIXoo98MADfPvtt4SEhPDrr7+ydOlSbG1t8fHxYezYsTzxxBMA1KpVi2nTpvHxxx/z/vvv4+bmRp8+fXBxceG///2vxeKvXLkyCxYsYOLEicycORMXFxf69u1LmTJlmDhxYq5ulf23p59+msTERNasWcPatWtp0KABM2fOZOrUqdnaZZ3spk+fznfffcdPP/1E+fLladq0KT4+Pne9b6+//jpWVlasWbOG5ORkmjZtSmho6E3zWru7uzN+/HhmzpzJ66+/Tnp6OgsWLCj0E6+VlRXTp09n2rRprF27lpUrV+Lp6cmYMWMYMmRIgb1P165d8fDwYNasWYSEhJCSkkLFihVp3ry5+UKxIPTq1Qt7e3u2bt3Kb7/9RlxcHBUqVKB169aMGDGCBg0aFNh7/dvEiRPx8vJi1apV/PLLL7i5uTFixIhst3dB5sriFy5c4Ouvv2bDhg00a9aM0NBQBg0alK1dmTJlWLhwITNnzuSHH35g9erVODo6UqNGDZ577rlsCwlNnDiRCRMmMGnSJFJTUxk1ahTe3t48+OCD9O/fn++//55vv/0Wk8lEly5dAHjnnXfw9fVl2bJl/O9//8NoNOLp6cn//d//0bRp03uWJ5HiSOfdm+m8mz8l7byb5bvvvuO7777D2toaR0dHqlevzsCBA+ndu/dNi4Hl9tjJ0r59e+zt7YmPj6dz5865iicwMJAvv/ySL774gkWLFpGQkIC7uzuNGjWiV69eQOactX369GHTpk389NNPmEwmqlWrxltvvcWTTz6Zq/eZNm2a+f/t7OyoVasWb7/9Nr179zZvz8tn/uyzzxIVFcWPP/7IunXraNu2LXPmzMlxIfB/euGFF7C1tWXZsmVs27aNRo0aMXfu3DtOfyMihUvXFDfTNUX+lNRrivwqiDiffPJJjhw5wsqVK5k3bx6enp63LKIDTJ06lQkTJrBkyRJMJhOtW7dm9uzZtGnTpiB3SwqIwaSZ6qWEeffdd81zxGlxIxERkXtL510REREpCLqmEJGiTHOiS7GWlJSU7fH169f59ttvadasmU66IiIiBUznXRERESkIuqYQkeJG07lIsdarVy9atGhB7dq1iYiIYMWKFcTFxfHMM89YOjQREZESR+ddERERKQi6phCR4kbTuUix9tFHH/Hjjz9y5coVDAYDDRo0YNSoUbRq1crSoYmIiJQ4Ou+KiIhIQdA1hYgUNyqii4iIiIiIiIiIiIjkQHOii4iIiIiIiIiIiIjkQEV0EREREREREREREZEclIiFRTMyMkhLS8PKygqDwWDpcERERPLEZDKRkZGBtbU1Vlal7/dtncdFRKQ403lc53ERESm+cnsez1cRffHixYSEhBAeHk69evV48803adSo0S3b/vTTT8yYMYOwsDDS0tKoXr06gwcPplu3btmCnTZtGl999RUxMTE0bdqUt99+mxo1auQqnrS0NA4cOJCfXRERESky/Pz8sLW1tXQYhU7ncRERKQl0HhcRESm+7nQez3MRfe3atUyaNInx48fTuHFj5s+fz9ChQ/nhhx9wdXW9qb2zszMjR46kVq1a2NjY8Pvvv/Pf//4XV1dX2rRpA8Ds2bNZuHAhkydPxsvLi08++YShQ4eydu1a7Ozs7hhT1q8Efn5+GI3GvO7STdLT0zlw4ECB9VeaKHf5p9zln3KXf8pd/hR03rL6K42j10Dn8aJEucs/5S7/lLv8Ud7yT+fxgqXz+L2hPCgHWZSHTMpDJuXBcufxPBfRQ0NDeeKJJ+jZsycA48eP548//mDFihUMHz78pvaBgYHZHg8cOJDVq1eza9cu2rRpg8lkYsGCBYwcOZIOHToA8MEHH9CqVSt++eUXunTpcseYsm4ZMxqNBXoAFXR/pYlyl3/KXf4pd/mn3OVPQeettN4CrfN40aPc5Z9yl3/KXf4ob/mn83jB0Hn83lIelIMsykMm5SGT8lD45/E8/VSekpLCoUOHaNWq1d8dWFnRqlUr9uzZc8fXm0wmtmzZwpkzZwgICADgwoULhIeHZ+uzXLlyNG7cOFd9ioiIiIiIiIiIiIjcK3kaiX79+nXS09NvmrbF1dWV06dP5/i62NhY2rZtS0pKClZWVrz11lu0bt0agPDwcHMf/+4zIiIiL+GRnp6ep/Z36qeg+itNlLv8U+7yT7nLP+Uufwo6b8q/iIiIiIiISNGVr4VF86ps2bKsXr2ahIQEtmzZwuTJk6latepNU73crYJezESLo+Sfcpd/yl3+KXf5p9zlj/ImIiIiIiIiUvLlqYheoUIFjEYjkZGR2bZHRkbi5uaW4+usrKyoXr06APXr1+fUqVPMmjWLwMBA3N3dzX14eHhk67NevXp5CU8LmRQByl3+KXf5p9zln3KXP/dqIRMRERERERERKXryVES3tbWlYcOGbNmyxbwIaEZGBlu2bKFfv3657icjI4OUlBQAvLy8cHd3Z8uWLdSvXx+AuLg49u3bR58+ffISnhYyKUKUu/xT7vJPucs/5S5/lDcRERG5V2bOnMlPP/3E6dOnsbe3x9/fn5dffplatWqZ24SHh/PBBx+wefNm4uPjqVmzJk8//TQPPfSQuc2NGzeYMGECv//+O1ZWVjz44IO8/vrrlC1b1tzm6NGjvPPOOxw4cAAXFxf69evHU089Vaj7KyIiUpTlaWFRgMGDB7N8+XJWrVrFqVOnePvtt0lMTKRHjx4AjBkzhqlTp5rbz5w5k02bNnH+/HlOnTrF3Llz+fbbb/m///s/IHPl0wEDBjB9+nR+/fVXjh07xpgxY/Dw8DAX6kVERERERERKk+3bt9O3b1+WL19OaGgoaWlpDB06lISEBHObV199lTNnzjB9+nTWrFlDx44deeGFFzh8+LC5zcsvv8zJkycJDQ1lxowZ7Ny5k3Hjxpmfj4uLY+jQoVSpUoWVK1cyZswYPvvsM7788stC3V8REZGiLM9zonfu3JmoqCimTZtGeHg49evXZ86cOebpXC5fvoyV1d+1+YSEBMaPH8+VK1ewt7enVq1afPjhh3Tu3Nnc5qmnniIxMZFx48YRExNDs2bNmDNnDnZ2dgWwiyJFX3qGia2nI9kRlkiSUyQta7tjtDJYOiwRERERKYJ07Vg6hISEZHs8efJkgoKCOHToEAEBAQDs2bOHt956i0aNGgHwzDPPMH/+fA4dOkSDBg04deoUGzZs4Ouvv8bPzw+AN954g+HDhzNmzBgqVqzIt99+S2pqKu+99x62trbUrVuXI0eOEBoaSq9evQp1n3Vsi4hIUZWvhUX79euX4/QtCxcuzPb4xRdf5MUXX7xtfwaDgdGjRzN69Oj8hCNSrP1w8DLj1xzmcnRS5oZtO6jsbM9bXRvQybeyZYMTERERkSJF146lV2xsLADOzs7mbf7+/qxbt4777rsPJycn1q1bR3JyMi1atAAyi+xOTk7mAjpAq1atsLKyYv/+/XTs2JG9e/fSvHlzbG1tzW2Cg4OZPXs20dHR2d7vXtKxLSIiRVm+iugiUjB+OHiZkYt2Y/rX9ivRSYxctJvp/ZrqglFEREREAF07lmYZGRm89957NG3aFG9vb/P2jz/+mBdffJHAwECsra2xt7fns88+o3r16gBERETg4uKSrS9ra2ucnZ0JDw83t/Hy8srWJutO84iIiFwX0dPT0/O9fz8eusKzS/bmeGx//mQTHmpYKd/9F0dZ+bybvBZ3ykEm5SGT8pBJeSj4HOS2HxXRRSwkPcPE+DWHb7pQBDABBmD8msN0bFBJtzCKiIiIlHK6dizdxo8fz4kTJ1iyZEm27Z988gkxMTHMmzePChUq8Msvv/DCCy+wePFifHx8CjXGAwcO5Ot16SYTb34fnuOxDfDmqv24pVzGaCh9x3Z+81qSKAeZlIdMykMm5aHwc6AiuoiFbD8T9fetirdgAi5HJ7H9TBRBtV0LLzARERERKXJ07Vh6vfPOO/zxxx8sWrSISpX+Ho0dFhbGokWL+O6776hbty4A9erVY+fOnSxevJh33nkHNzc3oqKisvWXlpZGdHQ07u7uQOao84iIiGxtsh5njUjPDT8/P4xGY573b+vpSCITr962TWRiBqnO1WhWq/Qc2+np6Rw4cCDfeS0JlINMykMm5SGT8lDwOcjq705URBexkGuxOX8Jyk87ERERESm5dO1Y+phMJiZMmMDPP//MwoULqVq1arbnExMTAbCyssq23Wg0YjJljuH29/cnJiaGgwcP4uvrC8DWrVvJyMgwL0bapEkTPv74Y1JTU7GxsQFg8+bN1KxZM0/zoRuNxnwVMyLiU3PdrjQWjPKb15JEOcikPGRSHjIpD4WfA6s7NxGRe8GjnH2BthMRERGRkiv314529zgSKSzjx4/n22+/ZerUqZQtW5bw8HDCw8NJSsr8oaRWrVpUr16dcePGsX//fsLCwpg7dy6bNm2iQ4cOANSuXZs2bdrw5ptvsn//fnbt2sWECRPo0qULFStWBKBr167Y2Njw+uuvc+LECdauXcuCBQsYPHhwoeynvheJiEhxoJHoIhZyMjz2jm0qO9vToqbLHduJiIiISMnWoqYLlZ3tuRKddMu5o7PM+PMUNd0cqeSsgmNxt3TpUgD69++fbfukSZPo0aMHNjY2zJo1i6lTp/L000+TkJBAtWrVmDx5Mu3atTO3nzJlChMmTGDgwIFYWVnx4IMP8sYbb5ifL1euHCEhIbzzzjv06NGDChUq8Mwzz9CrV69C2c/cHNv6XiQiIpamIrpIIcvIMPHhT8eY/scp8zYD3PKCsbKzPRkmE0ZK3wI6IiIiIvI3o5WBt7o24OlFu296Luta0trKwJ/HI3jwf3/y9v81pLu/J4ZSuBBjSXHs2LE7tqlRowaffvrpbduUL1+eqVOn3rZNvXr1blq0tLBkHdsjF+3O8XtRh/oVtWCuiIhYlKZzESlEyWnpvLh8r7mA/mIHb6b3bXrTSKEKDjYYrWB32A2eX7qH1PQMS4QrIiIiIkVIJ9/KdKjvcdP2Ss72zOjXlHWj29DYy5mYpDT+s3wfwxfu0hzpUix08q3M9H43fy9ytMsc97dkexjrj4dbIjQRERFAI9FFCk10QirDF+5k25korK0MTO7ZiMeaeQHwYMNKbD0Vzo6Dxwnw9aZlbXf+OHaNkYt2s+7gFZ5fuodpffyxMep3LxEREZHSKi45jW2nowB4+UFv0qKvmq8ds0bprhjZipnrT/PxL8f5+fBVdpyN4p1HfenaqLJGpUuR1sm3Mh0bZP9eFFjLjVe+3s+qPRd5ZvFulo8IokEVJ0uHKiIipZAqciKF4ML1BHrO2My2M1E42lkzb3ALcwEdMm9hbFnLlTbVytCylitGKwMP1K/IzP7NsDVase7gFZ5bohHpIiIiIqXZ1zvPE5ucRi33soxoUzPbtWMWa6MVz95fhzXPBdOwihM3ElJ5fukenl2ym8i4ZAtGL3Jn//5eZG204v2ejWhZy4W45DSGzNvB5ehES4cpIiKlkIroIvfYwYvRdP9iMyevxVHJyZ6vng4iuK5brl57fz0PcyH9h0NXGLVkNylpKqSLiIiIlDbpGSZCN58FYHDrmljdYX7oepWcWP1sa0Y/UBdrKwNrD1zhwf+t54eDlwshWpGCY2ttxcx+zanj4ciVmCQGh+4gNinV0mGJiEgpoyK6yD30+9FrPDFzC+GxydSrVI5Vz7aifuW83X54fz0PZg5ohq21FT8euqpCuoiIiEgp9NvRa5yLTMDJ3pqeTT1z9RoboxUvdvRm9bOt8alYjsj4FJ5etJvRy/ZwIyHlHkcsUnCcHWwIHRSAm6MdR6/E8szi3bpLV0RECpWK6CL3yJJtYQxbsJOElHSC67ix/OkgKjuXyVdf9/t4MKt/ZiH9p8NXeVaFdBEREZFSZe7GMwD0CayGg23elrby9XTm2+da88x9tbEywDd7L9Hxf+v59cjVexGqyD1R1cWBuYOaU8bGyIYTEbyx6iAmk8nSYYmISCmhIrpIATOZTHz441H+u+oA6Rkmejb1Yu6gAJzsbe6q3/t8PJg9oDm21lb8fPgqzyxWIV1ERESkNDh0KZotpyMxWhkYGFQjX33YWRsZ06keK59pTW33soTHJjN0/k5e/mof0YmaGkOKh0Ze5fnsSX+sDPDlzvN8/vtJS4ckIiKlhIroIgUoJS2DF7/cy+e/nwJg9AN1mfJ4I2ytC+avWjtvd+YMaI6dtRW/HLnKM4t3kZyWXiB9i4iIiEjRFLrpLAAP+1aiSvn83dmYpUnV8nz/fBuGt62FwQBf77pAp4/X8+fx8AKIVOTee6B+Rcb/X0MApvx0nNV7Llo4IhERKQ1URBcpINGJqQycu53Vey9hbWXgg8ca8WJHbwyG2y/6lFdtvd2ZMzCrkH6NZxbtViFdREREpIQKj03m272XABgSXLNA+rS3MfLfzvX5akQQNVwduBydxMC523lt5X7iktMK5D1E7qX+QTUY3rYWAK98vY8tpyItHJGIiJR0KqKLFICLNxJ5bPpmtpyOxNHOmrmDAniiedV79n5t6roTMjAAO2srfj16jZEqpIuIiIiUSIu2niMlPQP/auVpWq1CgfbdvIYL60a3ZVCrGgAs3X6eh/63ns0nIwr0fUTuhbGd6tHFrzKp6SZGLNzJyWuxlg5JRERKMBXRRe7SwYvRdP98EyeuxVHRyY7lI4Jo6+1+z983uK6buZD+29FrPL1wF0mpKqSLiIiIlBRJqeks3nYOgCGtC2YU+r+VsTXy9v81ZOlTLfGqUIaLNxJ5cs42xn1zkIQUjUqXosvKysDUJxrTvHoFYpLSGDh3B9dikywdloiIlFAqoovchd+PXaPXzC1ci03Gp2I5Vj3TmgZVnArt/YPrujF3UAD2Nlb8fiycpxepkC4iIiJSUqzZd4mIuBSqONvzsG+le/peQbVd+fGFtvQNrAbAgi3n6PTxBrafibqn7ytyN+xtjMwe0JyabmW5eCORofN26scfERG5J1REF8mnZdvDGDZ/J/Ep6bSu48pXI4PueqGn/Ghdx425AzML6X8cC2eERqSLiIiIFHsmk4mQjWcAGNCqBtbGe//VraydNe9292Ph0BZUcbYnLCqBXrO2MOG7w7q+lCKrQllb5g0OwKWsLQcuRvPckj2kpWdYOiwRESlhVEQXySOTycTUn44xduUB0jNM9GjqSeigFjjZ21gsplZ1/h6R/udxFdJFSqsdO3bw9NNPExwcjI+PD7/88stt21+7do2XXnqJhx56iHr16vHuu+/e1GblypX4+Phk++Pn55etjclk4pNPPiE4OJhGjRoxaNAgzp49W5C7JiJS6mw5HcnRK7GUsTHSJ6Baob53m7ru/PBiW55o7oXJBCEbz9D5kw3sDrteqHGI5FZ117LMGdjcvGbU+DWHMZlMlg5LRERKEBXRRfIgJS2Dl5bv49PfTgLw/AN1mfp4Y2ytLf9XqVVtN0IHtaCMjZE/j4czXIV0kVInISEBHx8f3nrrrVy1T0lJoUKFCowcOZJ69erl2M7R0ZGNGzea//z+++/Znp89ezYLFy7k7bffZvny5ZQpU4ahQ4eSnJx8V/sjIlKazf1rFPpjzbxwdij8wRpO9jZ88FhjQgcFUNHJjtMR8Tw2fTOT1x3VNaYUSU2rVeCT3k0wGGDh1nPM3nDa0iGJiEgJYvnKn0gxEZ2YysC521m55yJGKwPv9/TjPx29MRgMlg7NLKi2K6GDAyhjY2T98XCeWrBTX3JESpF27drx4osv0rFjx1y19/Ly4o033qBbt26UK1cux3YGgwF3d3fzHzc3N/NzJpOJBQsWMHLkSDp06EC9evX44IMPuHbt2h1HwouIyK2diYjn16PXABjUuoZFY7m/ngc/vdCO7v6eZJhgxp+n6PrpRg5ciLZoXCK30sm3Mq93rg/Ae2uP8v3+yxaOSERESgoV0UVy4dKNRB6fsZktpyMpa2tk7qAAehXybbW51bLW34X0DSciVEgXkbuWkJDA/fffT7t27Rg5ciQnTpwwP3fhwgXCw8Np1aqVeVu5cuVo3Lgxe/bssUS4IiLF3vzNZzGZ4H4fd2q7O1o6HJwdbPhfrybM7N8MN0dbTlyLo9sXm/jop2OkpGnuaSlahgbXZFCrGgC8uHwvO89qcVwREbl71pYOQKSoO3QpmiHzdnA1JhmPcnaEDg6gYRVnS4d1Wy1ruTJvcACD5+1gw4kIhs3fyewBzSlja7R0aCJSzNSsWZP33nsPHx8fYmNjmTt3Lr179+b777+nUqVKhIeHA+Dq6prtda6urkREROTpvdLTC+YHv6x+Cqq/0kS5yz/lLv+Uu+xiElNZvvM8AINbVc8xL5bIW4d67jR9Ppi31xzm+wNXmPbbSX4+fJUPH/OjfmWnQovjbhV07nTsFi0Gg4E3H2nAxRuJ/Hz4Kk8t2MnKZ1pT062spUMTEZFiTEV0kdv483g4zyzaRXxKOt4VHQkd3ALP8mUsHVauBNZyZd7gFgwK3c7GkxEMW7CDOQMCVEgXkTzx9/fH398/2+POnTuzbNkyXnjhhQJ9rwMHDhTp/koT5S7/lLv8U+4yfXssnoSUdKo5WeMQe569ey/ctr0l8jakHtQrW55Zu6M5ciWWRz/fzOMNHOleryzWVkVnqsM70TFXchmtDEzr7U/vWVvYdyGaQaHbWTmyFa6OdpYOTUREiikV0UVy8OWOMP676iDpGSaCarkyo38znMsU/qJOd6NFTRdzIX3TyUiGzt9ByEAV0kUk/2xsbKhfvz5hYWEAuLu7AxAZGYmHh4e5XWRk5G0XK70VPz8/jMa7//cpPT2dAwcOFFh/pYlyl3/KXf4pd39LS8/g+Z/XAzDygXr4+3vl2NbSeWvSBJ64P5k3vznMT4evsuxQHAevW/HhY354V8x5nY2ioKBzl9WfFC1lbI3MGRhAj+mbOBeZwLAFO1n6VEvsbUr3vzMiIpI/KqKL/IvJZOJ/Px9n2m8nAeju78n7PRtha108lxBoUdOF+UNaMGjudjafUiFdRO5Oeno6x48fp127dkDm4qTu7u5s2bKF+vUzF/KKi4tj37599OnTJ099G43GAi0EFXR/pYlyl3/KXf4pd/Dj4WtcvJGES1lbujf1ylU+LJm3is4OzOzfjG/2XuKtbw9x8FIMj36+hRc7ejO8bS2MRXxUuo65ks+9nB2hg1rQc/pm9oTd4MUv9/L5k02xKuLHpoiIFD3Fsyooco+kpGXw0lf7zAX059rX4aMnGhfbAnqWgBqZhfSytkY2n4pkyLwdJKSkWTosESlg8fHxHDlyhCNHjgCZi34eOXKES5cuATB16lTGjBmT7TVZ7ePj44mKiuLIkSOcPHnS/Pxnn33Gxo0bOX/+PIcOHeKVV17h0qVLPP7440DmvKMDBgxg+vTp/Prrrxw7dowxY8bg4eFBhw4dCmnPRURKhrkbzwDQN7BasRktazAY6ObvyU8vtqV9PQ9S0jN4/4ejPDZjM6fC4ywdngh1PByZ1b8ZtkYr1h28wntrj1g6JBERKYY0El3kLzFJqYxctItNJyMxWhmY2M2XPi2qWTqsAtO8hgsLhrZg4NwdbDmdWUifOygAB1v9MyBSUhw8eJABAwaYH0+aNAmA7t27M3nyZMLDw7l8+XK213Tr1s38/4cOHeK7777D09OT3377DYCYmBjefPNNwsPDcXZ2pmHDhixbtow6deqYX/fUU0+RmJjIuHHjiImJoVmzZsyZMwc7O807KiKSW/vO32DnuevYGA30b1nd0uHkWUUne0IGNuerXReYsOYwe8Ju0PmTDbzykA9DWtfUyF+xqMBarnz4eCNGL9vLnI1nqOriwMBWNSwdloiIFCOqnokAl24kMjh0B8euxlLW1shnfZtyv4/HnV9YzDSrnjkifeDc7Ww9HcXg0B2EDlYhXaSkCAwM5NixYzk+P3ny5Ju23a49wH//+1/++9//3raNwWBg9OjRjB49OneBiojITeZuyhyF3rVRFTyc7C0cTf4YDAaeaF6V4DpuvLpiPxtORDDx+yP8dOgqHz7eiOquZS0dopRijzbx5ML1RD788Rjj1xyiSvkydGxQ0dJhiYhIMVG856gQKQCHL8XQ/YtNHLsai3s5O74cEVQiC+hZmlWvwPwhLXC0s2bbmSgGhe4gPllTu4iIiIhYypXoJL7fn3mn0JDgmhaO5u5VKV+GBUNa8F53P8raGtl+NopOH29gwZazZGSYLB2elGLP3FebPi2qkmGC55buZt/5G5YOSUREigkV0aVUW388nCdmbuFqTDJ1PRxZ9UwrfD2dLR3WPdesegUWDG1BOTtrtp/JHJGuQrqIiIiIZSzYcpa0DBMtarqUmGtRg8HAk4HV+OGFtrSs5UJiajrjvjlEv5BtnI9KsHR4UkoZDAYmPOpLO293klIzGDp/h45HERHJFRXRpdRavvM8Q+btIC45jZa1XPj66VZ4VXCwdFiFpmm1fxTSz6qQLiIiImIJiSnpLNkeBsCQ1sV/FPq/VXVxYMmwloz/v4aUsclc5L7Tx+tZuj0Mk0mj0qXwWRut+LxvUxpUdiIiLoVBoduJTki1dFgiIlLEqYgupY7JZOJ/Px9nzNf7Scsw8WiTKswf0gJnBxtLh1bo/KtVYOGwQMrZZxbSB4VuJ06FdBEREZFCs2rPRW4kpFLVpeTOz2xlZWBgqxqsG92G5tUrEJ+SzmsrDzAwdAeXoxMtHZ6UQo521oQODqCKsz2nwuN5auFOktPSLR2WiIgUYSqiS6mSkpbBy1/t55NfTwDw7P21+bhXE+ysjRaOzHKaVC3PoqGZhfQdZ68zaK4K6SIiIiKFwWQymRcUHdSqJkYrg4UjurdquJXlyxFBvNGlPnbWVqw/Hs6D/1vPVzvPa1S6FLqKTvaEDv57istXvtqvOftFRCRHKqJLqRGblMqQeTtYsfsCRisD73X345WH6mEwlOwvK7nR+B+F9J3nrjNw7nZik3RLo4iIiMi9tP5EBCevxeFoZ80Tzb0sHU6hMFoZGNamFt8/34YmVcsTm5TGK1/v56kFO7kWk2Tp8KSU8alUjhn9m2FtZeDbfZeY8tMxS4ckIiJFlIroUipcjk7k8Rlb2HgyAgdbI3MGNOfJwGqWDqtIaVy1PIuHBeJkb80uFdJFRERE7rm5GzNHoT/RvCrl7EvX1IJ1PBz5+ukgxnTywdZoxS9HrtHxf+v5Zu9FjUqXQtW6jhuTezYC4Is/TrFkW5iFIxIRkaJIRXQp8Y5cjqH755s5eiUW93J2LB8RxP31PCwdVpHUyKs8i4e1xMnemt1hN1RIFxEREblHTl6L5c/j4RgMMKhVDUuHYxHWRiueua8Oa54LxtfTiejEVEYv28vIRbuJiEu2dHhSijzWzIvRD9QF4M1vDvL7sWsWjkhERIoaFdGlRNtwIpzHZ2zhSkwSdTwcWTmyFb6ezpYOq0jz83JmyVMtcS5jw+6wGwyYu50YFdJFRERECtTcTWcB6Fi/ItVcHSwbjIX5VCrHqmda82IHb6ytDPxw6AoP/m89aw9ctnRoUoq80KEuPZt6kZ5hYtTi3Ry6FG3pkEREpAhREV1KrK92nmdw6A7iktMIrOnCiqdbUdWldH9ByS1fT2cWDwvEuYwNe8JuMCBEhXQRERGRgnI9PoWVuy8AMDS4poWjKRpsjFaM7lCXb0a1pl6lckTFp/DM4t08t3QP1+NTLB2elAIGg4FJPfxoVduV+JR0hszbwaUbiZYOS0REiggV0aXEMZlMfPLLCV75ej9pGSb+r3EVFgxtgbND6Zpn8m5lFdLLO9iw97wK6SIiIiIFZcn2MJJSM2hYxYkWNV0sHU6R0rCKM9+OCua59nUwWhlYs+8SHf+3np8PX7V0aIVu5syZ9OzZE39/f4KCgnjmmWc4ffr0Te327NnDgAEDaNKkCU2bNqVv374kJf29SOuNGzd46aWXaNq0Kc2bN+e///0v8fHx2fo4evQoTz75JH5+frRr147Zs2ff8/0rimytrZjerxneFR25GpPM4NAd+g4kIiKAiuhSwqSmZzDm6/3875fjAIy8rzYf92qCnbXRwpEVT/8upPcP2U50oi4iRURERPIrNT2DBVvOAjCkdU0MBoNlAyqCbK2teOlBH1aObEUdD0ci4pJ5asFO/rN8L9EJpedadPv27fTt25fly5cTGhpKWloaQ4cOJSEhwdxmz549DBs2jODgYL766iu+/vpr+vbti5XV31/1X375ZU6ePEloaCgzZsxg586djBs3zvx8XFwcQ4cOpUqVKqxcuZIxY8bw2Wef8eWXXxbq/hYVzmVsCB3cAo9ydhy7Gsszi3aTmp5h6bBERMTCVESXEiM2KZUh83bw1a4LWBlgYjdfXu1UDysrfTG5Gw2r/F1I33f+BgNCtqmQLiIiIpJPaw9c5mpMMu7l7HikcWVLh1OkNa5anu+eC2ZEu1pYGWDl7os8+PGfpWbRx5CQEHr06EHdunWpV68ekydP5tKlSxw6dMjcZtKkSfTv35/hw4dTt25datWqRefOnbG1tQXg1KlTbNiwgYkTJ9K4cWOaN2/OG2+8wffff8/Vq5mj+7/99ltSU1N57733qFu3Ll26dKF///6EhoZaZL+LAs/yZZg7KAAHWyMbT0bw2soDmEwmS4clIiIWZG3pAEQKwpXoJAaFbufolVjK2Bj57El/Hqhf0dJhlRgNqzizZFhL+s7Zyr4L0fQP2cbCIYGaIkdEREQkD0wmEyEbzwDQv2V13S2ZC/Y2Rl57uD4PNqjEy1/t40xEPINDd9CreVXeeKQ+5exLz/VobGwsAM7OzgBERkayb98+unbtSu/evQkLC6NWrVq88MILNG/eHMgcqe7k5ISfn5+5n1atWmFlZcX+/fvp2LEje/fupXnz5ubCO0BwcDCzZ88mOjra/H53kp6eXiD7mdVPQfWXX/UrOTKtdxNGLNrN17su4FXenufa1ym09y8qebAk5SCT8pBJecikPBR8DnLbj4roUuwdvRLD4NAdXI5Ows3RjrmDmtPIq7ylwypxGlRxYvFfhfT9F6LpF7KNRUNVSBcRERHJrd1h19l/IRpbayv6BlazdDjFSrPqFVj7fBs+/PEYoZvP8OXO82w8GcEHjzWidR03S4d3z2VkZPDee+/RtGlTvL29ATh//jwAn332GWPGjKF+/fqsXr2aQYMG8d1331GjRg0iIiJwcck+7761tTXOzs6Eh4cDEBERgZeXV7Y2bm5u5udyW0Q/cODAXe3jve4vPyoAw5qUY+buGD7+9STpMeHcV6NMocZQFPJgacpBJuUhk/KQSXko/ByoiC7F2qaTETy9cBexyWnUdi/LvMEtqOriYOmwSqwGVZxY8lRL+s7ZxoGLKqSLiIiI5EXWKPTuTTxxdbSzcDTFTxlbI+O6NuChhhV55ev9hEUl0HfONvq1rMZrD9enrF3J/Xo7fvx4Tpw4wZIlS8zbMjIy5+nu1asXPXv2BKBBgwZs2bKFFStW8NJLLxVqjH5+fhiNd393RXp6OgcOHCiw/u5WkyaA4zFmrj/D9F0xtPDzplVt13v+vkUtD5agHGRSHjIpD5mUh4LPQVZ/d1JyrzKkxFux6wKvrthPWoaJFjVcmDWgGeUdbO/8Qrkr9Ss7seSpQJ6cnVlI7xuylUVDA5V7ERERkdu4cD2BHw5eAWBwcA3LBlPMBdZyZd3oNkxed5SFW8+xaGsYfx4P58PHGtOy1r0vbha2d955hz/++INFixZRqVIl83Z3d3cAateuna197dq1uXTpEpA5ojwqKirb82lpaURHR5tf7+bmRkRERLY2WY+zRqTnhtFoLNCCTkH3dzde7VSfS9HJrNl3iWeW7GHFyFZ4VyxXKO9dlPJgKcpBJuUhk/KQSXko/BxoYVEpdkwmE9N+PcFLX+0jLcNE18ZVWDC0hYq4haheJSeWPtUS17K2HLwYQ98527iRkGLpsERERESKrPmbz5JhguA6btSr5GTpcIq9snbWTOjmy+JhgXiWL8P5qER6z9rK+DWHSEwpGfPEmkwm3nnnHX7++Wfmz59P1apVsz3v5eWFh4cHZ86cybb97NmzeHp6AuDv709MTAwHDx40P79161YyMjJo1KgRAE2aNGHnzp2kpqaa22zevJmaNWvmeiqXks7KysCHjzUioEYFYpPSGBy6g2sxSZYOS0RECpGK6FKspKZnMHbFAT76+TgAT7erzSe9mmBvU7p/fbMEn0rlWPJXIf3QpRienL2N6/EqpIuIiIj8W1xyGst2ZM5fPUSj0AtU6zpu/PBCG3oHZBaYQzedpfO0Dew6F3WHVxZ948eP59tvv2Xq1KmULVuW8PBwwsPDSUrKLN4aDAaGDh3KwoUL+eGHHzh37hwff/wxp0+f5rHHHgMyR6W3adOGN998k/3797Nr1y4mTJhAly5dqFixIgBdu3bFxsaG119/nRMnTrB27VoWLFjA4MGDLbbvRZG9jZHZA5pTy70sF28kMmT+DuKT0ywdloiIFBIV0aXYiE1KZej8nXy58zxWBpjwaEPGPlwPKyuDpUMrtXwqlWPp8Ja4Odpy+HLmiHQV0kVERESyW7HrArFJadRyK8t93h6WDqfEKWdvw+SejZg3OIBKTvaciYjn8RlbmLT2CEmpxXdU+tKlS4mNjaV///4EBweb/6xdu9bcZtCgQYwYMYJJkybx6KOPsnXrVubOnUu1an8vXDtlyhRq1arFwIEDGT58OE2bNuWdd94xP1+uXDlCQkK4cOECPXr0YPLkyTzzzDP06tWrUPe3OCjvYMu8QS3Md+SOWrKbtPQMS4clIiKFQHOiS7FwNSaJQaE7OHI5hjI2Rj7t40+HBhUtHZYA3hXLsfSplvSZvZXDl2N4cs42Fg8LxKWsptcRERERycgwEbopc7qNwa1raADIPXSfjwc/vtiWd9YcZsXuC8xcf5pfj15j6uONaVy1PADpGSa2no5kR1giSU6RtKztjrGIfibHjh3LVbvhw4czfPjwHJ8vX748U6dOvW0f9erVy7ZoqeSsmqsDIYMC6D1rC78fC2fct4d4t5svBkPRPI5ERKRgaCS6FHnHrsTS/fNNHLkcg5ujLcuGt1QBvYip+1ch3c3RjiOXY3hy9laiNCJdREREhN+OXuNsZAJO9tb0aOpl6XBKPOcyNkx9ojGzBzTHvZwdJ6/F0WP6Zqb8eIw1+y4S/P5v9A3ZwcfboukbsoPg93/jh4OXLR22FDNNqpZnWm9/DAZYsi2MGX+etnRIIiJyj6mILkXa5pMRPDZjM5eik6jlXpZVz7Q2jyKRoqVuxXIsGx6Im6MdR6/EqpAuIiIiAsz9axR6n8BqlLXTjcCFpWODivz0Qlv+r3EV0jNMfPb7SZ5bupfL0dkXg7wSncTIRbtVSJc8e7BhJcY90gCA9384yrf7Llk4IhERuZdURJcia9WeCwwM3U5sUhoBNSqw4ulWVHVxsHRYcht1PMqxbHhL3Mv9XUiPjEu2dFgiIiIiFnH4UgybT0VitDIwIKiGpcMpdSqUtWVaH38+75M5YvhWTH/9d/yaw6RnmG7dSCQHg1vXZEjrmgC8vHwf288U/wVtRUTk1vJVRF+8eDHt27fHz8+Pxx9/nP379+fYdvny5Tz55JMEBAQQEBDAoEGDbmofERHB2LFjCQ4OpnHjxgwdOpSzZ8/mJzQpAUwmE5/9doIXv9xHarqJLo0qs3BoIBU0x3axUMfDkaVP/bOQvo0IFdJFRESkFMqaC72TbyU8y5excDSll4ujHabb1MdNwOXoJBVAJV9e71KfhxpWJCU9g6cW7ORUeJylQxIRkXsgz0X0tWvXMmnSJJ599llWrVpFvXr1GDp0KJGRkbdsv23bNrp06cKCBQtYtmwZlStXZsiQIVy9ehXILJg+++yznD9/ni+++IJVq1bh6enJ4MGDSUhIuLu9k2InNT2D11YeYMpPxwEY0bYWn/b2x97GaOHIJC/qeDiybHhLPMrZcexq5oh0FdJFRESkNAmPTeabvZnTOwwNrmnhaEq3a7FJd26Uh3Yi/2S0MvBxL3+aVC1PdGIqg0K367uPiEgJlOciemhoKE888QQ9e/akTp06jB8/Hnt7e1asWHHL9lOnTqVv377Ur1+f2rVrM3HiRDIyMtiyZQsAZ8+eZe/evbz99ts0atSIWrVq8fbbb5OUlMT3339/d3snxUpcchrD5u9k2Y7zWBngnUcb8lrn+lhZaZXz4qi2uyNL/yqkH78ap0K6iIiIlCqLt50jJT2DJlXL07RaBUuHU6p5lLMv0HYi/1bG1sicgc2p5uLA+ahEhs7fSWJKuqXDEhGRApSnInpKSgqHDh2iVatWf3dgZUWrVq3Ys2dPrvpITEwkLS0NZ2dnc58AdnZ22fq0tbVl165deQlPirGrMUk8MWMLfx4Px97Gihn9mmneyBKgtnvmiPSKTpmF9D6zthIeq0K6iIiIlGzJaeks2noOgCEahW5xLWq6UNnZnpyG5hiAys72tKjpUphhSQnj5mjHvMEBlHewYd/5G4xetkfz7IuIlCB5Wh7++vXrpKen4+rqmm27q6srp0+fzlUfU6ZMwcPDw1yIr1WrFlWqVGHq1Km88847lClThnnz5nHlyhXCw8PzEh7p6QXzS29WPwXVX2mSn9wdvxrL0AW7uHQjCZeytszp35TGVcuXuvyX1OOuuksZFg9tQb+Q7Zy4ljkifdHQANwc7e784lwqqbkrDMpd/hR03pR/EZGS5du9l4iIS6Gysz0P+1aydDilntHKwFtdGzBy0W4M/L2YKGAurL/VtQFG3QErd6mWuyOzBzSn75xt/HT4KhO/P8xbXRtaOiwRESkAeSqi361Zs2axdu1aFixYYB55bmNjw6effsrrr79OixYtMBqNBAUF0bZtW0y3W/3lFg4cOFCg8RZ0f6VJbnN34FoyH2y+QUKqiSqORl5v44Qp8ix7bz3FfqlQUo+7N1qXY9wfqZy4FkfPz9bz9n0uVLAv2LnuS2ruCoNylz/Km4iI/JvJZGLuprMADAiqgY0xzzNoyj3Qybcy0/s1Zfyaw1yO/nvu80rO9rzVtQGdfCtbMDopSQJquDD18cY8t3QPoZvOUrWCg+5IEREpAfJURK9QoQJGo/GmRUQjIyNxc3O77WtDQkKYNWsWoaGh1KtXL9tzvr6+fPPNN8TGxpKamoqLiwuPP/44vr6+eQkPPz8/jMa7L8qlp6dz4MCBAuuvNMlL7r7Ze4l3Nx4gNd1E02rlmdW/KRUcbAsp0qKnpB93TYD69ePpG7KDC9FJTNqawKIhAXg43f3ckyU9d/eScpc/BZ23rP7u1o4dOwgJCeHgwYOEh4fz+eef06FDhxzbX7t2jffff5+DBw9y7tw5+vfvz+uvv56tzfLly1m9ejUnTpwAoGHDhvznP/+hUaNG5jZjx45l1apV2V4XHBxMSEjIXe+TiEhxs/V0FEcux1DGxkifFlUtHY78QyffynRsUImtp8LZcfA4Ab7etKztrhHoUuC6Nq7CxRuJTF53lAnfH6ZK+TJ00l0pIiLFWp6K6La2tjRs2JAtW7aYv5RnLRLar1+/HF83e/ZsZsyYQUhICH5+fjm2K1euHJC52OjBgwcZPXp0XsLDaDQWaBGooPsrTW6XO5PJxBd/nOLDH48B0NmvEh890QR7G+UaSvZxV9vDiS+Ht6T3rK2cCo+n79wdLHuqZYEU0qFk5+5eU+7yp6jlLSEhAR8fH3r27MmoUaPu2D4lJYUKFSowcuRI5s2bd8s227Zto0uXLjRt2hRbW1vmzJnDkCFD+P7776lYsaK5XZs2bZg0aZL5sa1t6f1RVERKt5CNZwDo2cyT8qV4gEhRZbQy0LKWK/YxZWhSy1UFdLlnRrStxfmoBBZvC2P0sj0sG94Sfy0yLCJSbOV5OpfBgwfz6quv4uvrS6NGjZg/fz6JiYn06NEDgDFjxlCxYkVeeuklIHMKl2nTpjF16lQ8PT3N85w7ODhQtmxZANatW4eLiwtVqlTh2LFjvPfee3To0IHg4OCC2k8pItLSM3jzm4Ms3X4egKfa1OS1h+tjpYvXUqO6a1mWDW9Jn1lbOR0eT+/ZWwu0kC5SmrVr14527drlur2XlxdvvPEGACtWrLhlm6lTp2Z7PHHiRH788Ue2bNlCt27dzNttbW1xd3fPe9AiIiXI2Yh4fj16FYDBrTV9g0hpZjAYGP9/DbkcncRvR68xbP5OVj7TiuquZS0dmoiI5EOei+idO3cmKiqKadOmER4eTv369ZkzZ455OpfLly9jZfX3vH/Lli0jNTWV559/Pls/o0aN4rnnngMgPDycyZMnExkZibu7O48++ijPPPPM3eyXFEHxyWk8u2Q3fxwLx2CAtx5pwCB9uSiVMgvpQfSZ/VchfdZWlg5vSUUV0kWKvMTERNLS0nB2ds62ffv27QQFBeHk5ETLli154YUXqFBBo61EpHSZt/ksJhPc7+NObXdHS4cjIhZmbbTi0z7+9Jq1hYMXYxgcuoMVI1tRoazuUhERKW7ytbBov379cpy+ZeHChdke//bbb3fsb8CAAQwYMCA/oUgxcS0miSHzd3DwYgx21lZM6+PPQw01J1xpVs3VgWV/Te1yOiKePiqkixQLU6ZMwcPDg1atWpm3tWnTho4dO+Ll5cX58+f56KOPeOqpp/jyyy/zNN1Nenp6gcSY1U9B9VeaKHf5p9zlX0nJXWxSKl/tzLzbclCr6vd8f0pK3iyhoHOnz0Bup6ydNXMHBtD9i82cjohn+MKdLBwaqOlMRUSKmXwV0UXy4sTVWAaF7uDijURcytoyZ2BzmmouOAGqumQvpPeetZWlT7WkkrMK6SJF0axZs1i7di0LFizAzs7OvL1Lly7m//fx8cHHx4cOHTqYR6fnVkEsrnov+ytNlLv8U+7yr7jn7tvj8cSnpFPVyZqysefZu/dCobxvcc+bJSl3Ulg8nOwJHRxAz+mb2XH2Oi9/tY9pvf01ramISDGiIroUmPQME1tPR7IjLJEkp0ha1nZn+5kohi/cSWxSGjXdyjJvcIDmgJNs/llIPxMRT+9ZW1g6vCWVnctYOjQR+YeQkBBmzZpFaGgo9erVu23bqlWrUqFCBc6dO5enIrqfn1+BLNSanp7OgQMHCqy/0kS5yz/lLv9KQu7S0jN4/uf1AIxs74O/f9V7/p4lIW+WUtC5y+pP5Ha8K5ZjZv9mDJy7ne/2X8azQhlee7i+pcMSEZFcUhFdCsQPBy8zfs1hLkcnZW7YtoPyZWyIS04jLcNE02rlmTMwABfN/Sa3kFVI7zN7K2cjE+g9ayvLVEgXKTJmz57NjBkzCAkJwc/P747tr1y5wo0bN/K80KjRaCzQQlBB91eaKHf5p9zlX3HO3U+Hr3HxRhIVHGzo0axqoe5Hcc6bpSl3Utha1Xbj/Z6N+M/yfcz88zRVKzjQr2V1S4clIiK5YHXnJiK398PBy4xctPvvAvpfbiSmkpZhwr9qeZY81VIFdLmtrEK6V4UynPurkH7pRqKlwxIpVuLj4zly5AhHjhwB4MKFCxw5coRLly4BMHXqVMaMGZPtNVnt4+PjiYqK4siRI5w8edL8/KxZs/jkk09477338PT0JDw8nPDwcOLj483v+f7777N3714uXLjAli1beOaZZ6hevTpt2rQppD0XEbGsuZvOANA3sLrmORaR2+rR1Iv/dPQGYNw3B/nt6FULRyQiIrmhkehyV9IzTIxfcxjTbdpciUnCxqjfa+TOvCr8PSL93D9GpFcprxHpIrlx8ODBbAt1T5o0CYDu3bszefJkwsPDuXz5crbXdOvWzfz/hw4d4rvvvsPT09O8MPiyZctITU3l+eefz/a6UaNG8dxzz2E0Gjl+/DirV68mNjYWDw8PWrduzejRo7G11Y+nIlLy7b9wgx1nr2NjNNA/SCNKReTOnmtfhwvXE1i+8wKjluzhy+FB+Hk5WzosERG5DRXR5a5sPxN10wj0f7scncT2M1EE1XYtpKikOMsspAfRe9YWwqIyC+lLh7fEU4V0kTsKDAzk2LFjOT4/efLkm7bdrj1gLqbnxN7enpCQkNwFKCJSAs3dmDkK/ZFGVajopMXRReTODAYD73b343J0EhtORDBk/g5WPdMKrwoOlg5NRERyoOHBcleuxd6+gJ7XdiIAnuXL8OXwIKq5OPxVSN/CRU3tIiIiIkXM1ZgkvtufeYfPkNY1LRyNiBQnNkYrvujblHqVyhEem8zg0B1EJ6ZaOiwREcmBiuiSb5duJPLN3ou5autRTqNyJG+qlC/DsuEtqe7qwPmoRHrP2sKF6wmWDktERETEbMGWs6RlmGhRw0VTMYhInpWztyF0cAAVnew4cS2OpxfuIiUtw9JhiYjILaiILnkWGZfMhO8Oc9+UP/jtaPht2xqAys72tKjpUjjBSYlycyF9K+ejVEgXERERy0tMSWfJtjAAhgTXsGwwIlJsVXYuw9xBAZS1NbLldCRjV+wnLT2Dracj2RCWyNbTkaRn3G4VMhERKQyaE11yLToxlTkbTjN34xniU9IBaFHDhbbebkz96ThAtgVGDX/9962uDTBaGRDJj8rOmYX0PrO2cvYfi41WddF8gSIiImI5q/Zc5HpCKl4VytCxQSVLhyMixVjDKs580a8ZQ+btYOWei/x0+CpxyWmZT27bQWVne97q2oBOvpUtG6iISCmmkehyRwkpaUz/4xRtP/idT387SXxKOn6ezswf0oIvR7RkVPu6TO/XlErO2adsqeRsz/R+TXWil7uWWUgPooarAxdvaES6iIiIWJbJZGLupswFRQe1qqEBIyJy19p5u9MnoCrA3wX0v1yJTmLkot38cPCyJUITERE0El1uIzktnWXbz/PZ7ycJj00GoI6HIy8/6M1DDSthMPz9ZaGTb2U6NqjE1lPh7Dh4nABfb1rWdtcXCikwlZztWTY8iD6zt3ImIt48Ir1K+TJsPR3JjrBEkpwiddyJiIjIPbfhRAQnr8VR1tbIE38VvURE7kZ6holfj1675XMmMu/0Hr/mMB0bVNL3HRERC1ARXW6Slp7Bqj0X+fiXE1y8kQhAVZcyvPCAN938PXM8YRutDLSs5Yp9TBma1HLViV0KXGYhPXNql9MR8Tz62SaMVgbC4zJ/5NGtjiIiIlIYQjZmjkJ/IqAqTvY2Fo5GREqC7WeiuBydlOPzJuBydBLbz0QRVNu18AITERFARXT5h4wME+sOXuGjn49xKjweAI9ydjz3QF16Na+KrbVm/xHLq+hkz9LhLfm/Tzdy9a87JP4p61ZHTSUkIiIi98LJa7H8eTwcgyFzKhcRkYJwLTbnAnp+2omISMFSEV0wmUz8cTycKT8e49ClGADKO9gwsl1tBgTVoIyt0cIRimTn5mhHTuvT61ZHERERuZdCN50FoEP9ilR3LWvZYESkxPAoZ3/nRnloJyIiBUtF9FJu+5koPvzxKDvOXgegrK2RYW1qMbRNTd2aKkXW9jNRXLvFKPQsutVRRERE7oXr8Sms2H0BgKHBNS0cjYiUJC1qulDZ2Z4r0Uk5DhjyKGdHi5ouhRqXiIhkUhG9lDpwIZopPx3jz+PhANhZWzEgqDoj76uDS1lbC0cncnu61VFEcpKeYdJiwyJyzyzdEUZSagYNKjsRqEKWiBQgo5WBt7o2YOSi3RjgloV0k8lEZHyyRqOLiFiAiuilzImrsXz083HWHbwCgLWVgV4BVXmufV0qOetELMWDbnUUkVv54eBlxq85/PeiXFpsWEQKUGp6Bgs2nwMyR6EbDPqBTkQKViffykzv1zT79QyZI9DTM0yEx6UwIGQ7y4a3pLyDBr+JiBQmFdFLifNRCXz8ywlW7blAhgkMBujWxJMXOtTVXI5S7NzpVkcDUMnZXrc6ipQiPxy8zMhFu2/6N0GLDYtIQVl74DJXYpJwc7Tjkcb690RE7o1OvpXp2KASW0+Fs+PgcQJ8vWlZ250L1xN4fMYWjl6JZWDoDhYPC8TRTiUdEZHCYmXpAOTeuhaTxJurD9J+6h+s2J1ZQH+oYUV+GN2W//VqogK6FEtZtzpCZsH8Vt7q2kBTOIiUEukZJsavOXzr257/+u/4NYdJz8hphlERkdszmUzM3XgGgP4tq2NnbbRwRFIazJw5k549e+Lv709QUBDPPPMMp0+fvmVbk8nEsGHD8PHx4Zdffsn23KVLlxg+fDiNGzcmKCiI999/n7S0tGxttm3bRvfu3fH19aVjx46sXLnynu2X3JnRykDLWq60qVaGlrVcMVoZqO5alkXDAqngYMO+8zcYNn8HSanplg5VRKTUUBG9hLoen8KktUdo++HvLNx6jtR0E23quvHNs62Z2b85PpXKWTpEkbuSdavjv6chsjVaacSpSCmz/UxUtlue/y1rseF1By4XXlAiUqLsDrvOvgvR2Fpb0bdlNUuHI6XE9u3b6du3L8uXLyc0NJS0tDSGDh1KQkLCTW3nz59/yymG0tPTGTFiBKmpqSxbtozJkyezatUqpk2bZm5z/vx5RowYQWBgIN988w0DBw7kjTfeYMOGDfd0/yTvvCuWY8GQzBHoW09H8czi3aSkZVg6LBGRUkH3/pQwcclphGw4w5wNp4lNzhxd0Kx6BV5+0Ieg2q4Wjk6kYP3zVsffdh1l7t5YUtIz8K6oH4lESpPcLiI8aukePvrlOG3quNG6jhtBtV0pZ29zj6MTkZJg7sazAHRrUgU3RzvLBiOlRkhISLbHkydPJigoiEOHDhEQEGDefuTIEebOncuKFSsIDg7O9pqNGzdy8uRJQkNDcXNzo379+owePZopU6YwatQobG1tWbZsGV5eXowdOxaA2rVrs2vXLubNm0ebNm3u/Y5Knvh5OTN3UAAD5m7jt6PXeHH5Xqb19tdduCIi95iK6CVEUmo6C7ecY/qfp4iKTwGgfmUnXnnIm/t9PLTwkZRYWbc62seU5XSiPb8fC+fLHed5rXN9S4cmIoUkt4sIG4DT4fGcDo9n/pZzGK0MNKlanuA6brSp60bjquWxMeomPRHJ7sL1BNYdzLyTZUhwTQtHI6VZbGwsAM7OzuZtiYmJvPTSS4wbNw53d/ebXrN37168vb1xc3MzbwsODubtt9/m5MmTNGjQgL179xIUFJTtdcHBwbz33nt5ii89vWCmFsnqp6D6K65ul4dm1ZyZ3tef4Qt38/3+yzjYWPFeN1+sSlghXcdCJuUhk/KQSXko+Bzkth8V0Yu51PQMlu88z7RfT3A1JhmAWm5l+c+D3nT2rVziTqIit9M7oCq/Hwvn610XeOlBH2ytVQwTKQ1yu9jw98+3YfuZKDaeDGfTyUjORMSz69x1dp27zie/nsDRzpqWtVwIruNGcF13aruX1Y/QIsKCLefIMEHrOq7Uq+Rk6XCklMrIyOC9996jadOmeHt7m7dPmjQJf39/OnTocMvXRUREZCugA+bH4eHht20TFxdHUlIS9va5+7H6wIEDud4fS/RXXOWUByfghRbOTN1yg692XSQh5jqDG5crkdcuOhYyKQ+ZlIdMykPh50BF9GIqPcPEt/su8r+fTxAWlTknXhVne17o4E2Ppp5YaySdlEL3ebtR0cmOqzHJ/Hz4Kl0aaV50kdIga7HhkYt2Y4BshfSsr5FvdW2AS1lbOvlWopNvJSBzdOnGExFsOBnB5pMRXE9I5Zcj1/jlyDUAKjvb0/qvUeqt67hpCgeRUig+OY2l28MAGNJao9DFcsaPH8+JEydYsmSJeduvv/7K1q1bWbVqlQUj+5ufnx9G490vupuens6BAwcKrL/iKjd5aNIEPDwv8srXB/j+RAK1vCrzQoe6hRvoPaRjIZPykEl5yKQ8FHwOsvq7ExXRixmTycRPh68y9adjHL8aB4Cboy3P3l+HJwOrYWddOv8CiQBYG614onlVPv3tJEu3h6mILlKKZC02PH7N4WyLjFZytuetrg1uudiwVwUHereoRu8W1cjIMHH4cgwbTkSw8WQ4O85e53J0El/vusDXuy4AUK9SOdrUzRyl3qKGC2Vsdc4VKelW7L5AbFIaNd3Kcr+Ph6XDkVLqnXfe4Y8//mDRokVUqlTJvH3r1q2EhYVlmx8d4LnnnqN58+YsXLgQNzc39u/fn+35iIgIAPP0L25ubuZt/2zj6OiY61HoAEajsUALOgXdX3F1pzw83rwaiakZjPvmEJ/+fopyZWwY3rZ2IUZ47+lYyKQ8ZFIeMikPhZ8DFdGLCZPJxMaTEUz58Rj7LkQD4GRvzYh2tRnUqgZl7fRRigA80bwqn/1+ko0nIzgXGU9117KWDklECsk/FxvecfA4Ab7etKztnquFtqysDPh6OuPr6czI+2qTlJrOjrNRmSPVT0Rw+HIMR6/EcvRKLLM3nMHWaEXzGhXMI9UbVnHWgl4iJUxGhonQTWcBGNy6hqZJlEJnMpmYMGECP//8MwsXLqRq1arZnh8+fDiPP/54tm1du3bltdde4/777wegSZMmzJgxg8jISFxdXQHYvHkzjo6O1KlTx9xm/fr12frZvHkzTZo0uUd7JgVtQFANYpPS+PDHY7y39iiOdjY8GVjN0mGJiJQoqrwWA7vORfHhj8fYejoKgDI2RoYE12B4m9o4O9hYODqRoqWqiwNt67rz5/Fwlu04z6ud6lk6JBEpRH8vNlyGJrVc813Ytrcx0qauO23quvMaEBmXzKZTkWw8Ec7GExFcik5i86lINp+K5MMfj1HewYZWtV0JruNOm7puVHVxKNgdE5FC9/uxa5yJiMfJ3pqeTb0sHY6UQuPHj+e7777jiy++oGzZsuY5zMuVK4e9vT3u7u63XEy0SpUq5oJ7cHAwderUYcyYMbzyyiuEh4fz8ccf07dvX2xtbQHo3bs3ixcv5oMPPqBnz55s3bqVdevWMXPmzMLbWblrz95fh7jkNKb/cYrXVx+grJ2RR5t4WjosEZESQ0X0IuzwpRim/nSMX49mzs1qa7Sib8tqPHNfHdzLaV5WkZz0aVGNP4+H89XOC/ynozc2WiNARO6Sq6Md/9e4Cv/XuAomk4kzEfFsPJk5Sn3rqUhuJKSy9sAV1h64AkB1V4fMUep13GhV200/eosUQyEbzwCZ1xW661MsYenSpQD0798/2/ZJkybRo0ePXPVhNBqZMWMGb7/9Nr169aJMmTJ0796d559/3tymatWqzJw5k0mTJrFgwQIqVarExIkTadOmTcHtjBSKMQ/5EJeUxsKt5/jP8n2UsTHyYMNKd36hiIjcka4Gi6DT4XF89PNxvtt/GcgcVfdYUy+e71AXz/JlLBydSNH3QH0P3MvZER6bzK9Hrt5yLmQRkfwyGAzUcneklrsjA4JqkJaewb4L0Wz8az71PWE3OBeZwLnIMJZsC8PKAH5e5QmukzlSvWn18lrDRKSIO3I5hs2nIjFaGRjQqoalw5FS6tixYwXyGk9PT2bPnn3b1wUGBrJ69eo8v58ULQaDgfH/15D45DRW7rnIqCV7mDsogOC6bpYOTUSk2FMRvQi5eCORab+c4OvdF0jPMAHQtXEVXuxQl1rujhaOTqT4sDFa8XgzL7744xRLtp9XEV1E7ilroxXNqlegWfUKjO5Ql7jkNLadjvxrkdIITl6LY9/5G+w7f4PPfz9FGRsjgbVcCK7jRnBdN3wqlsNg0FzLIkVJ6KbMUeidGlbSIBYRKVasrAx88Fgj4lPS+PHQVZ5asJNFw1rQrLqLpUMTESnWVEQvAsJjk/nij5Ms3hpGSnoGAA/U8+A/D3rTsIqzhaMTKZ56B1Tjiz9OseFEOOejEjQ/sYgUGkc7ax6oX5EH6lcE4HJ0IhtPRLDpZAQbT0YSEZfMH8fC+eNY5ty27uXsMgvqfxXVKzrZWzJ8kVIvIi6Z1XsvATAkuKaFoxERyTtroxXT+vgzbP5ONpyIYFDoDpYNb6n6gojIXVAR3YKiE1KZteEUczeeJTE1HYCWtVx45aF6NKtewcLRiRRv1VwdaFPXjQ0nIvhyx3lefsjH0iGJSClV2bkMjzevyuPNq2IymTh6JfavqV8i2HYmkvDYZFbtuciqPRcBqOvhSHBdN9rUdSOwpqvmYhYpZIu3hpGSlkHjquVpWq28pcMREckXO2sjM/s3Y+Dc7ew4e50BIdv5ckQQdTx0l7uISH7oW5kFJKSkEbrpLDP/PEVMUhoAjb2ceeWherSu46pbukUKSJ8W1dhwIoLlO8/zQoe6WGuBURGxMIPBQP3KTtSv7MRTbWuRnJbOrnPXzSPV91+M5sS1OE5ciyN001msrQw0rVaB4LqZo9QbeTrr3zKReyg5LZ2FW88BMDS4pq7LRaRYc7C1JmRQAE/O3srBizH0m7ONr54O0l26IiL5oCJ6IUpOS2fJtjA+//0kEXEpAHhXdOSlB314sEFFXaSLFLAO9Svi5mjLtdhkfjt6TSvTi0iRY2dtpFVtN1rVzlzw60ZCCptPZc6nvulkBGFRCWw/G8X2s1F89PNxytlbE1TLlTZ13Qiu604NVwddP4gUoDX7LhMRl0wlJ3se9tV1g4gUf072NiwYEkivmVs4cS2OfiHb+GpEEB6aPk5EJE9URC8EaekZrNx9kU9+PcHFG4kAVHNx4D8dvenauApGK335FbkXbK2t6NnMi5l/nmbp9jAV0UWkyCvvYEtnv8p09stcEDksMoENJ8PZeCKCzaciiU5M5afDV/np8FUAPMuXMc+l3rqOGy5lbS0ZvkixZjKZmLsxc0HRAa2qY6O7PkSkhHApa8uiYYE8PmML5yIT6BeyjS+HB1FB1w0iIrmmK8N7KCPDxJp9l3jwf+sZs2I/F28kUsnJnne7+/LrS+3o5u+pArrIPdY7oBoAfxwPN/+IJVJS7dixg6effprg4GB8fHz45Zdfbtv+2rVrvPTSSzz00EPUq1ePd99995bt1q1bR6dOnfDz86Nr1678+eef2Z43mUx88sknBAcH06hRIwYNGsTZs2cLardKtWquDvQNrM70fs3Y/WZHvnm2Na885EPLWi7YGA1cvJHIlzvP89zSPTSd8DNdpm1g0rojbDwRQdJf662ISO5sOxPF4csx2NtY8WSLapYOR0SkQFV0smfxsEAqOtlx/GocA0O3E5uUaumwRESKDRXR7wGTycRvR6/S5dONPLd0D6cj4qngYMMbXerzxyv30TdQI1tECktNt7K0qu2KyQRf7jhv6XBE7qmEhAR8fHx46623ctU+JSWFChUqMHLkSOrVq3fLNrt37+all17iscceY/Xq1TzwwAM8++yzHD9+3Nxm9uzZLFy4kLfffpvly5dTpkwZhg4dSnJycoHsl2QyWhloXLU8z95fh2XDg9j31oPMGxzAsOCa1KtUDoBDl2KY+edp+oVso/H4n+g3Zxsz/jzFwYvRZGSYbtt/eoaJracj2RCWyNbTkaTfob1ISRPy1yj0nk29KO+g0ZkiUvJUdXFg8bBAXMrasv9CNEPn7yQxRT+6i4jkhqZzKWBbTkXy4Y9H2R12A4BydtYMa1OLIcE1KGdvY9ngREqpPi2qsflUJMt3nOf59nW0KJ+UWO3ataNdu3a5bu/l5cUbb7wBwIoVK27ZZsGCBbRp04Zhw4YB8MILL7B582YWLVrEO++8g8lkYsGCBYwcOZIOHToA8MEHH9CqVSt++eUXunTpcpd7JTlxsLXmPh8P7vPxAOBabBKbT2bOp77xZDhXY5LZeDKCjScjAHAta0urOm4E13EluK47nuXLmPv64eBlxq85zOXopMwN23ZQ2dmet7o2oJNv5ULfN5HCdi4ynl+OZE6TNLh1TQtHIyJy79TxKMeCIS3oM3sr289E8fSiXcwe0Bxba31HEhG5HRXRC8i+8zeY8tMxNpzI/KJqZ23FoNY1eLptbc0zJmJhDzasiEtZW67EJPHHsXA6NKho6ZBEio29e/cyaNCgbNuCg4PNU8VcuHCB8PBwWrVqZX6+XLlyNG7cmD179qiIXog8ytnTzd+Tbv6emEwmTl6Lyyyin4hg6+lIIuNTWLPvEmv2XQKglltZguu6UcbWyKw/T/PvcedXopMYuWg30/s1VSFdSrzQTWcxmeA+H3fqeDhaOhwRkXvK19OZ0EEB9A/Zzp/Hwxm9bA+f9vHXYCMRkdtQEf0uHbsSy9SfjpkX+LK2MtCnRTVGta9DRa12LVIk2FkbeayZF7PWn2bZjjAV0UXyICIiAjc3t2zbXF1diYjI/NE4PDzcvC2nNrmVnl4wtxNn9VNQ/RVXtdwcqOVWjQEtq5GansG+89GZRfVTkey/EM3piHhOR8Tn+HoTYADGrzlMex93reNyBzru8s/SuYtNSuWrnZlTvg0Kql5sPkNL5604K+jc6TOQ4qh5DRdmDWjG0Hk7WXfwCmNXHuCDno2w0vleROSWVETPp3OR8Xz8ywlW772IyQRWBujm78kLD3hTzdXB0uGJyL/0CqjKrPWn+e3oNS5HJ1LZucydXyQiherAgQNFur/izhq4zw3uc7Mnvqkth66l8NvZRHZcynnuehNwOTqJpb9sx9fDrtBiLc503OWfpXK35ng88SnpeDlZ4xh3nr17L1gkjvzSMZd/yp2Udm3qujOtjz/PLtnN17su4GhnzVtdG2AwqJAuIvJvKqL/S9aiWjvCEklyiqRl7ewjr65EJ/Hpbyf4csd50v5acOth30r8p6M3dSuWs1TYInIHtd0dCazpwrYzUSzfcYHRHepaOiSRYsHNze2mEeWRkZHm0enu7u7mbR4eHtna5LRYaU78/PwwGo13GXHmiMADBw4UWH8lVWug0r5L7Fi+/45tnSpWpUnjKvc+qGJMx13+WTJ36RkmRv+yHoCR7X3w969aqO9/N3TM5V9B5y6rP5HiqJNvJaY83ogXv9zHvM1ncbSz5uWHfCwdlohIkaMi+j/cblGtFjVdmf7HSRZsOUdyWgYAbb3deflBbxp5lbdc0CKSa08GVmPbmSi+3BHGqPZ1NDWBSC40adKErVu3ZpsXffPmzTRp0gTIXJzU3d2dLVu2UL9+fQDi4uLYt28fffr0ydN7GY3GAi0EFXR/JVEl59zdPVfJ2UG5zCUdd/lnidz9fOQyF64nUsHBhp7NqhbLz07HXP4pdyKZuvt7EZeczpurD/LZ7ycpa2fNyPtqWzosEZEiRUX0v/xw8DIjF+2+5aJaTy/ajb21FUl/Fc8DalTg5Qd9CKzlenNHIlJkPdSwEuUdbLgUncT64+HcX8/jzi8SKUbi4+MJCwszP75w4QJHjhzB2dmZKlWqMHXqVK5evcoHH3xgbnPkyBHza6Oiojhy5Ag2NjbUqVMHgAEDBtC/f3/mzp1Lu3btWLt2LQcPHuSdd94BwGAwMGDAAKZPn0716tXx8vLik08+wcPDgw4dOhTi3kt+tKjpQmVne65EJ910DQSZc6JXcranRU2Xwg5NpFDM3XgWgL6B1bG3UTFVREqv/i2rE5+cxuR1R3n/h6M42hnpH1TD0mGJiBQZKqKTeRvn+DWHb/nlMWtbUloGDSqX45VO9bjP211zhIkUQ/Y2Rno29SJk4xmWbg9TEV1KnIMHDzJgwADz40mTJgHQvXt3Jk+eTHh4OJcvX872mm7dupn//9ChQ3z33Xd4enry22+/AdC0aVOmTJnCxx9/zEcffUSNGjX4/PPP8fb2Nr/uqaeeIjExkXHjxhETE0OzZs2YM2cOdnaaQ7uoM1oZeKtrA0Yu2o0Bbnkt9FbXBrpzR0qkAxei2X42CmsrA/2Dqls6HBERi3u6XW3iktL47PeTvPnNIcraWdOjqZelwxIRKRJURAe2n4n6ewqX23ijSwNa1XErhIhE5F7p06IqIRvP8OvRa1yNSaKik72lQxIpMIGBgRw7dizH5ydPnnzTttu1z/Lwww/z8MMP5/i8wWBg9OjRjB49OneBSpHSybcy0/s1zT6l3V+Gt61JJ9/KFopM5N6au+kMAI80qqzrARGRv7z0oDdxyWnM23yWV77ej4OtNZ18K1k6LBERi7OydABFwbXYOxfQAcLjku9xJCJyr9XxKEdAjQqkZ5j4aud5S4cjIlIkdPKtzMZX27N4aAAvBDrzaOPMwvn2s9cxmW41Pl2keLsak8SafZcAGBpcy8LRiIgUHQaDgXGPNOCxZl6kZ5h4fuke1h8Pt3RYIiIWpyI64FEudyNPcttORIq2Pi2qAbB0+3kyMlQcEhGBzKldWtZypU21Mvy3cz1sra3YE3aDneeuWzo0kQK3cMs50jJMBNSogJ+Xs6XDEREpUqysDEzu4Udnv0qkpGcwfOFOdpyNsnRYIiIWpSI6fy+qldNsnwagshbVEikxOvtVxsnemos3EtlwMsLS4YiIFDlujnb0bOoJwMw/T1k4GpGClZSazuJt5wAY0rqmhaMRESmarI1WfNzLn3be7iSlZjAkdAcHL0ZbOiwREYtREZ2/F9UCbiqkZz3WoloiJYe9jdG8QM6y7WEWjkZEpGga1qYWBgP8cuQaJ6/FWjockQKzas9Friek4lWhDA821Dy/IiI5sbW2Yka/ZrSo6UJschr9Q7Zx4qquCUSkdFIR/S9Zi2pVcs4+ZUslZ3um92uqRbVESpisKV1+Pnw11+siiIiUJrXdHelQvyIAs9efsXA0IgXDZDIxd2Pm8TyoVQ0NkhERuYMytkZCBjankZcz1xNS6ReyjbDIBEuHJSJS6FRE/4d/L6q1eGgAG19trwK6SAnkU6kcTauVJy3DxNe7Llg6HBGRIunpdpkLLq7ac5FrMfrBUYq/jScjOHEtjrK2Rp4IqGrpcEREioVy9jbMH9wC74qOXI1Jpm/IVq5E67pAREoXFdH/5Z+LarWs5arRKSIlWNZo9GVaYFRE5JaaVXehWfUKpKRnELr5rKXDEblrIX+NQn+8eVWc7G0sHI2ISPFRoawti4YGUt3VgfNRifQL2UZUfIqlwxIRKTQqootIqfVIoyqUs7cmLCqBzaciLR2OiEiRNLxt5mj0RVvPEZecZuFoRPLv5LU4/jgWjsEAg1vXsHQ4IiLFjoeTPYuGBlLZ2Z6T1+IYMHcbMUmplg5LRKRQ5KuIvnjxYtq3b4+fnx+PP/44+/fvz7Ht8uXLefLJJwkICCAgIIBBgwbd1D4+Pp533nmHtm3b0qhRIzp37szSpUvzE5qISK6VsTXS3d8TgKVaYFRE5JY61q9ILbeyxCalaTFmKdbmbc4chd6hfkWqu5a1cDQiIsVTVRcHFg0LxLWsLQcvxjAkdAcJKfqRXURKvjwX0deuXcukSZN49tlnWbVqFfXq1WPo0KFERt56FOe2bdvo0qULCxYsYNmyZVSuXJkhQ4Zw9epVc5vJkyezYcMGPvzwQ9auXcvAgQOZMGECv/76a/73TEQkF3oHZE7p8tPhK0TEJVs4GhGRosfKysBTf41Gn7vxDKnpGRaOSCTvbiSksGLXRQCGtK5p4WhERIq32u6OLBjagnL21uw8d50RC3eRnJZu6bBERO6pPBfRQ0NDeeKJJ+jZsyd16tRh/Pjx2Nvbs2LFilu2nzp1Kn379qV+/frUrl2biRMnkpGRwZYtW8xt9uzZQ7du3QgMDMTLy4tevXpRr169245wFxEpCA2qONG4anlS002s0AKjIiK31N3fEzdHOy5FJ/H9/suWDkckz5ZuP09iajr1KzvRspaLpcMRESn2GlZxZt7gFjjYGtlwIoLnl+4hTT+0i0gJZp2XxikpKRw6dIgRI0aYt1lZWdGqVSv27NmTqz4SExNJS0vD2dnZvM3f35/ffvuNxx57DA8PD7Zt28aZM2d47bXX8hIe6ekF88tnVj8F1V9potzln3KXf3ebu97Nvdh3/gZLtocxtHV1DIbSs6Cwjrv8Kei8Kf9S1NnbGBnUqjpTfjrOjD9P8WiTKqXq30op3lLTM5j/18K4Q4Nr6tgVESkgzapXYPaA5gwO3cGPh64y5uv9THm8MVZW+ndWREqePBXRr1+/Tnp6Oq6urtm2u7q6cvr06Vz1MWXKFDw8PGjVqpV525tvvsmbb75J27Ztsba2xmAwMHHiRAICAvISHgcOHMhT+8LurzRR7vJPucu//OauKhmUsTZwLjKBRT9vx8/DroAjK/p03OWP8ialSb+W1fnij1McvRLLhhMRtPV2t3RIIrmy7uAVrsQk4eZoS9fGlS0djohIidK6jhuf923K04t2sXLPRcraWfPOow31g6WIlDh5KqLfrVmzZrF27VoWLFiAnd3fRaqFCxeyd+9epk+fTpUqVdi5cyfjx4+/qdh+J35+fhiNxruOMz09nQMHDhRYf6WJcpd/yl3+FUTuul88xJLt59l53Y7+DzYp2ACLMB13+VPQecvqT6QoK+9gyxPNqzJv81lmrT+tIroUG3M3Zi4o2q9ldeysda4TESloHRtU5KMnGvPCl3tZuPUcjvbWvNqpnqXDEhEpUHkqoleoUAGj0XjTIqKRkZG4ubnd9rUhISHMmjWL0NBQ6tX7+x/TpKQk/ve///HZZ59x3333AVCvXj2OHDlCSEhInoroRqOxQItABd1faaLc5Z9yl393k7snA6uzZPt5fjp0jeikdFzK2hZwdEWbjrv8Ud6ktBkaXJOFW8+x8WQEBy9G4+vpfOcXiVjQrnPX2Xv+BrZGK/q1rG7pcERESqxHm3gSn5zOf1cdYPofp3C0s+bZ++tYOiwRkQKTp4VFbW1tadiwYbZFQbMWCfX398/xdbNnz+aLL75gzpw5+Pn5ZXsuLS2N1NTUm271MRqNmEymvIQnIpJvvp7O+Hk6k5KewcrdWmBURORWqro40MUvczqM2RtyN5WfiCXN3ZQ5Cv3RJlVwcyx907WJiBSmJwOr8Xrn+gB8+OMx83oUIiIlQZ6K6ACDBw9m+fLlrFq1ilOnTvH222+TmJhIjx49ABgzZgxTp041t581axaffPIJ7733Hp6enoSHhxMeHk58fDwAjo6OtGjRgg8//JBt27Zx/vx5Vq5cyerVq+nQoUMB7aaIyJ31aVENgCXbw/QjnohIDoa3rQXAd/svc+F6goWjEcnZxRuJ/HDwCgBDgmtaOBqRvJs5cyY9e/bE39+foKAgnnnmmWxrkd24cYMJEybw0EMP0ahRI+677z4mTpxIbGxstn4uXbrE8OHDady4MUFBQbz//vukpaVla7Nt2za6d++Or68vHTt2ZOXKlYWyj1LyPNW2Fs8/UBeAt749xNe7NEBJREqGPM+J3rlzZ6Kiopg2bRrh4eHUr1+fOXPmmKdzuXz5MlZWf9fmly1bRmpqKs8//3y2fkaNGsVzzz0HwEcffcRHH33Eyy+/THR0NFWqVOHFF1+kT58+d7NvIiJ58n9NqjDx+8OcDo9n+5koAmu53vlFIiKljK+nM63ruLLpZCQhG8/wVteGlg5J5JYWbD5LeoaJVrVdqV/ZydLhiOTZ9u3b6du3L35+fqSnp/PRRx8xdOhQvv/+exwcHLh27RrXrl3j1VdfpU6dOly8eJG3336ba9euMW3aNCBz3ZURI0bg5ubGsmXLzO1tbGz4z3/+A8D58+cZMWIEvXv3ZsqUKWzZsoU33ngDd3d32rRpY8kUSDH1Yoe6xCWlMXfTGcZ8vY+ytkYe9tPCziJSvOVrYdF+/frRr1+/Wz63cOHCbI9/++23O/bn7u7OpEmT8hOKiEiBcbSz5tEmVVi6/TxLt4epiC4ikoMRbWuz6WQkX+44zwsPeOPsYGPpkESyiU9OY+n2MACGtNYodCmeQkJCsj2ePHkyQUFBHDp0iICAALy9vfn000/Nz1erVo0XXniBV155hbS0NKytrdm4cSMnT54kNDQUNzc36tevz+jRo5kyZQqjRo3C1taWZcuW4eXlxdixYwGoXbs2u3btYt68eSqiS74YDAbefKQ+8clpfLnzPM8v28NsWyP3+XhYOjQRkXzL83QuIiIlWdaULmsPXuFGQoqFoxERKZra1HWjfmUnElLSWbTtnKXDEbnJit0XiElKo4arA+3rqWgjJUPWNC3Ozjkv6hwXF4ejoyPW1pnj5fbu3Yu3t7f5znGA4OBg4uLiOHnypLlNUFBQtn6Cg4PZu3dvAe+BlCYGg4H3evjRpVFlUtNNPL1oF9tOR1o6LBGRfMvXSHQRkZLKz9OZBpWdOHw5hpW7L2oOVRGRWzAYDAxvW5MXv9xH6KazDA2uib2N0dJhiQCQkWEidNNZAAa3romVlcGyAYkUgIyMDN577z2aNm2Kt7f3LdtERUXxxRdf0KtXL/O2iIiIbAV0wPw4PDz8tm3i4uJISkrC3t4+VzGmp6fnen9y009B9VdclZQ8TOnpR0JyGr8fC2fo/B0sHNKCRl45/xD0TyUlB3dLecikPGRSHgo+B7ntR0V0EZF/MBgM9AmsxpurD7J0exiDW9fAYNCXbxGRf3ukURU+/OEYl6KTWLXnovlOHhFL++P4Nc5ExFPO3prHmnlZOhyRAjF+/HhOnDjBkiVLbvl8XFwcI0aMoHbt2owaNaqQo8t04MCBIt1fcVUS8vBUQyuuRdlyKDyF/iFbmXCfC9Wccz8VXEnIQUFQHjIpD5mUh8LPgYroIiL/8miTKrz3/RFOXItj17nrNK/hYumQRESKHBujFUOCazLx+yPM3nCaXs2rasSvFAkhG88AmVO0lbXT1x0p/t555x3++OMPFi1aRKVKlW56Pi4ujmHDhlG2bFk+//xzbGz+Lk66ubmxf//+bO0jIiKAzLXJstpkbftnG0dHx1yPQgfw8/PDaLz7u5LS09M5cOBAgfVXXJW0PCzxTWPA3B3suxDNe5vjWDa8BTVcy972NSUtB/mlPGRSHjIpDwWfg6z+7kRXlSIi/+Jkb0PXxpVZvvMCS7aHqYguIpKD3i2q8cmvJzgdHs8vR67yYMObizsihenolRg2nYzEygADgqpbOhyRu2IymZgwYQI///wzCxcupGrVqje1iYuLY+jQodja2jJ9+nTs7OyyPd+kSRNmzJhBZGQkrq6uAGzevBlHR0fq1KljbrN+/fpsr9u8eTNNmjTJU7xGo7FACzoF3V9xVVLy4OxgZP6QFvSetZWjV2IZMHcnX48MorJzmTu+tqTk4G4pD5mUh0zKQ+HnQAuLiojcQta0BN/vv0x0QqqFoxERKZoc7azp1zKzUDlr/WkLRyMCoRvPAvCwb2W8KjhYNhiRuzR+/Hi+/fZbpk6dStmyZQkPDyc8PJykpCQgs4A+ZMgQEhISePfdd4mLizO3yZrfNTg4mDp16jBmzBiOHj3Khg0b+Pjjj+nbty+2trYA9O7dm/Pnz/PBBx9w6tQpFi9ezLp16xg0aJCldl1KqPIOtiwY2oIarg5cvJFIvznbiIhLtnRYIiK5oiK6iMgtNKlannqVypGclsHqvRctHY6ISJE1uFUNbI1W7Dx3nV3noiwdjpRiEXHJrPrrnD0kuIZlgxEpAEuXLiU2Npb+/fsTHBxs/rN27VoADh06xL59+zh+/DgdO3bM1uby5ctA5ii9GTNmYGVlRa9evXjllVfo1q0bzz//vPl9qlatysyZM9m8eTOPPvoooaGhTJw4kTZt2lhkv6Vk8yhnz6JhgVRxtudUeDwDQrYTnahBSyJS9Gk6FxGRWzAYDPRpUY23vj3E0u1hDAiqrgVGRURuwcPJnm7+VVi+8wIz/zzNrAGaAkssY8m2MFLSMmjs5UzTahUsHY7IXTt27Nhtnw8MDLxjGwBPT09mz559x75Wr16dl/BE8s2rggOLhgXyxMwtHL4cw+DQ7SwcGqh1LESkSNNIdBGRHHTz98TO2oqjV2LZc/6GpcMRESmyhretBcDPR65yOjzOwtFIaZScls7CrecAGBJcUz98i4gUcbXcHVk4NBAne2t2h91g+MKdJKWmWzosEZEcqYguIpID5zL/396dx0Vd7X8cfw3DpiIoiwoCKpqooeKKGmpuZXtq5W6WpXUzu3Xbb/dnlilZti+uWe5Z2mJpt8xMTUVcSHALBQUElcWFRQSG+f1BUlxFAcHvAO/n48Hj3pk5c/jMR+LMfDjfz3Hg9nY+ACwLjzc4GhER29WiQV36t26A1QpzN8UZHY7UQN/9nkxKxnkaujpxa1tvo8MREZFSaO3tymcPdqW2o5nfDqXx+LLd5FkKjA5LROSSVEQXEbmMESF+AKzek8TZHPXqExEpyfhezQFYuSuRlAwdEibXjtVq5ZPfCv94M6Z7UxzM+ogjIlJVdPCvz7z7O+Nob8dP+07w9Be/U1BgNTosEZGL6B2miMhldPSvT8uGLuTkFfBNZJLR4YhcVkREBI888gihoaEEBgaybt26Kz4nPDycQYMGERQUxIABA1i1alWxx/v27UtgYOBFX1OmTCkaM3r06Ise/7//+78Kf31i27o0rU+wXz1y8wv4bMsRo8ORGiQ8Lp29SWdxdrBjRFd/o8MREZEy6tHck49HdsTezsQ3kUm89E00+ZYCtsWmsSn+HNti07CosC4iBtOpDSIil2EymRjWxZ9XvtvH0vB4RoX4q8+q2Kzs7GwCAwMZMmQIEydOvOL4hIQEJkyYwLBhw3jzzTfZunUrL730El5eXvTs2ROAL7/8Eovlr/6UMTExPPDAAwwcOLDYXPfddx+TJk0qul2rVq0KelVSVZhMJib0CuDRJbtYtO0oj97YXAeEyTXxyebCXeiDO/pSv46jwdGIiEh59GvdkLeHBjNp+W6Whsfzze5jZOX++R40PAJvN2cm39GGgUFq2SUixtBOdBGRKxjcsTGO9nbsTz7LnsQzRocjUqLevXvz5JNPMmDAgFKNX758Ob6+vjz//PM0b96cUaNGcfPNN/Ppp58WjXF3d8fLy6vo65dffsHf35+uXbsWm8vZ2bnYOBcXl4p8aVJF3HR9I5p61ObMuTxW7EgwOhypAeLTsvlp/wkAHryhqbHBiIjIVbmjvQ+jQgqvKCoqoP/p+JkcHl28ix+ik40ITURERXQRkSupV9uR2/48pGzZdh0wKtVHZGQk3bt3L3ZfaGgokZGRlxyfm5vLt99+y5AhQy66ImP16tWEhIRw++23M3PmTM6dO1dZYYsNM9uZeKhnAADzNsWRr8PBpJIt2BKH1Qq9W3rRokFdo8MREZGrYCmwsm7/yUs+dqGZy5TV+9TaRUQMoWtsRURKYXhXf77afYxvf0/ipdvb4KIWBVINpKam4unpWew+T09PMjMzycnJwdnZudhj69atIyMjg0GDBhW7//bbb8fHx4cGDRpw8OBB3nzzTeLi4vjggw/KFM/f28ZcjQvzVNR8NUlF5G5QsDdv/fQHx06fY/Xvx7izvU9FhWfT9HNXfuXNXUZOPl/8ecXD2B7+NS73+pkrv4rOnf4NRCrG9rh0ks/klPi4FUg+k8P2uHS6N/e4doGJiKAiuohIqXRpWp/mXnU4nJLFt5FJjAjRwWVS86xcuZJevXrRsGHDYvcPHTq06P8HBgbi5eXF2LFjiY+Px9+/9P+tREVFVVislTFfTXK1uRvQ1JHP9+by3o/78Cs4UaPOktDPXfmVNXff/ZFF5nkLvnXN1M1MJDLyWCVFZtv0M1d+yp2IbTmZUXIBvTzjREQqkoroIiKlYDKZGN7Vn6nf72fZ9ngV0aVa8PT0JDU1tdh9qampuLi4XLQL/dixY2zZsoX333//ivO2b98egKNHj5apiN62bVvMZnOpx5fEYrEQFRVVYfPVJBWVO//rcvnmjw3Enc4n29WfG2rAbjH93JVfeXJnKbDyz3UbAXikbys6dPCrzBBtkn7myq+ic3dhPhG5Og3qOl95UBnGiYhUJBXRRURKaXBHX2b8cJCoY2eISjxDW183o0MSuSrBwcFs3Lix2H1btmwhODj4orGrVq3Cw8ODG2+88Yrz7t+/HwAvL68yxWM2myu0EFTR89UkV5s7L9daDO3sx2dbjzJv8xF6tWxQgdHZNv3clV9ZcvfT/uMknDpHvdoODOnkV6Nzrp+58lPuRGxL12bueLs5c/xMDiV1PTeZ0JkrImIIHSwqIlJK7nUcGRjUCIBlETpgVGxPVlYW+/fvLypiJyYmsn//fpKSkgCYOXMmzz77bNH4YcOGkZCQwIwZMzh8+DBLlixh7dq1jB07tti8BQUFrFq1irvvvht7++J/f4+Pj+fDDz8kOjqaxMREfv75Z5577jm6dOlCq1atKvcFi017qGcAdibY+EcK+5LOGh2OVDOfbI4DYGSIP7UcVQQVEakOzHYmJt/RBoCSGsFZrXD/gu3M2xSL1aoDRkXk2lERXUSkDIZ3LWxN8c3uY2Sdzzc4GpHioqOjufvuu7n77rsBmD59OnfffTfvvfceACkpKSQnJxeN9/PzY/bs2WzZsoW77rqLBQsWMHXqVHr27Fls3i1btpCUlMSQIUMu+p4ODg5s3bqVcePGccstt/D6669z0003MWvWrMp7oVIl+LnX5pa23gDM3RRrcDRSnUQlnmH7kXTs7UyM7tbU6HBERKQCDQzy5uNRHWnkVrxli7ebM+8NC2ZIR18KrDD1+/388/NIzuXqYF8RuTbUzkVEpAy6BbjTzLMOcalZfLcniaFd1BtdbEdISAgHDx4s8fGwsLBLPufrr7++7LyhoaElzuvt7c3ixYvLFKfUHBN6BfD9nmRW/57EMzcH4lOvltEhSTWw4LfCXei3tfO+qMgiIiJV38Agbwa0acS2wylERP9Bl6CWdGvuhdnOxB3tfWjb2JVXv9/PN5FJxJzIZPboTvi51zY6bBGp5rQTXUSkDAoPGC08vGzp9gSDoxERsW3tfOvRPcCD/AJrUfsNkatx8mwOq/cUtqgaF9rM4GhERKSymO1MdAvwoKd/LboFeGC2K2zwYjKZGHtDM5Y8FIJHHUf2JZ/lzg8289uhVIMjFpHqTkV0EZEyGtLRFwezid8TTrM36YzR4YiI2LTxvQMAWLY9njPn8gyORqq6RduOkmex0rlJfdr51jM6HBERMUi3AA9WPx5KO183TmXnMXp+uPqki0ilUhFdRKSMPFycuOn6wgNGl2s3uojIZd3Y0ovAhnXJyrWwJPyo0eFIFZaTZ2FJeOHB3tqFLiIiPvVqsWJCd/VJF5FrQkV0EZFyGPHnAaNf7z5Gdq4OGBURKYnJZOLhXoW70Rf8doTz+fpgK+Xz9e5jpGfl0rheLQa0aWh0OCIiYgOcHcy8eW87Xr6jDWY7E99EJjHk4y0kpGcbHZqIVDMqoouIlEP3AA+aeNQm43w+3+1JNjocERGbdmd7Hxq5OpOScZ5vdicZHY5UQVarlU/+PFB0bI+m2Jv1MUZERAqpT7qIXAt69ykiUg52diaGdSncjb58e7zB0YiI2DZHezseDG0KwJxNsRQUqF+plM1vh9L440QmdRzNDP3zgG8REZG/U590EalMKqKLiJTTPZ18sbczsSv+NAeOnzU6HBERmza8qz91new5dDKTXw6eNDocqWLmb44F4N7Ofrg6OxgcjYiI2Cr1SReRyqIiuohIOXnVdSrqyaoDRkVELq+uswMjQgqv4Jn9a6zB0UhVcjglk18OpmAyFbZyERERuRz1SReRyqAiuojIVRj+5wGjq3YlaneDiMgVPHBDMxzMJrYfSWd3/Cmjw5EqYsGfvdD7tWpIU886BkcjIiJVgfqki0hFUxFdROQqhLbwxLd+Lc7m5LMmSgeMiohcTiM3Z+4KbgzAnI3ajS5Xdjo7l5U7jwEU9dUXEREpLfVJF5GKoiK6iMhVsLMzFe1GXx6hA0ZFRK5kfK8AAH7Ye5wjqVkGRyO2bnlEAufyLLT2dqV7gIfR4YiISBWkPukiUhFURBcRuUr3dvLFbGci4sgpYk5kGB2OiIhNa9mwLn0CvbBaYe4m7UaXkuVZCvhsyxEAHryhKSaTydiARESkylKfdBG5Wiqii4hcpQauzvRr1QCAZTpgVETkisb3ag7AlzsTSc08b3A0Yqt+iD5O8pkcPF0cuaO9j9HhiIhIFac+6SJyNVREFxGpAMNDClu6rNyVSE6eLgsUEbmcbgHutPN143x+AQu3HjU6HLFRn/x5oOiobk1wdjAbHI2IiFQX6pMuIuWhIrqISAXodZ0XjevV4sy5PH6IPm50OCIiNs1kMjHhz93oi7YeUU9Suciu+FPsjj+No9mOkSFNjA5HRESqGfVJF5GyUhFdRKQCmO1MDO3iB8Cy7TpgVETkSgYGNcLfvTansvP4YqdaYUlxn2wu3IV+Z7APXnWdDI5GRESqI/VJF5GyUBFdRKSC3NfZDzsThMelczgl0+hwRERsmtnOxEM9mwGFB4zmWwoMjkhsxbHT51j751VdD97QzOBoRESkOlOfdBEpLRXRRUQqSCM3Z/r+ecDocu1GFxG5ons7+VG/tgMJ6ef4Ya9aYUmhhVuPYCmw0j3AgzY+rkaHIyIiNYD6pIvIlaiILiJSgYZ3LTxg9MudiZzPVz89EZHLqeVoZkz3pgDM2agPqgLZufksCy/8Q/SDodqFLiIi1476pIvI5aiILiJSgXq39MLbzZlT2Xn8d+8Jo8MREbF5Y7o3wcnejj2JZ9gWm250OGKwlTsTOZuTTxOP2vT78+ouERGRa0V90kWkJCqii4hUIHuzHfd1/vOA0XC1dBERuRIPFyfu7ewLwOyNhw2ORoxUUGBlwW9HAHigR1Ps7EzGBiRisNmzZzNkyBA6dOhA9+7d+cc//kFsbGyxMefPn2fKlCmEhITQoUMHHn/8cVJTi/dyTkpKYvz48bRv357u3bvz+uuvk5+fX2xMeHg4gwYNIigoiAEDBrBq1apKf30itkp90kXkUlREFxGpYPd1KTxgdGtsGnGpWUaHIyJi8x4KDcBkgg0HUzh4PMPocMQgv8akEpuaRV1ne+798w/SIjXZ9u3bGTlyJCtWrGDBggXk5+czbtw4srP/2hE7bdo0fvnlF9555x0WLVrEyZMnmThxYtHjFouFCRMmkJeXx/LlywkLC+Orr77ivffeKxqTkJDAhAkTCAkJ4ZtvvuH+++/npZdeYtOmTdf09YrYGvVJF5G/UxFdRKSCNa5Xi94tvQBYHqHd6CIiV9LUsw4Dr28EFPZGl5rpwi70YV38qONkb2wwIjZg/vz5DB48mOuuu45WrVoRFhZGUlISe/fuBSAjI4OVK1fy/PPP0717d4KCgpg2bRq7d+8mMjISgM2bN3Po0CHeeOMNWrduTe/evXniiSdYsmQJubm5ACxfvhxfX1+ef/55mjdvzqhRo7j55pv59NNPDXrlIrZDfdJF5AK9OxURqQTDu/rzy8EUvtyRyL8GBOJor79ZiohczvheAayNPs63vx/jmZsDaeTmbHRIcg3Fn8njt8Np2JkoOmxWRIrLyCi8UsfNzQ2A6Oho8vLy6NGjR9GY5s2b4+PjQ2RkJMHBwURGRtKyZUs8PT2LxoSGhvLyyy9z6NAh2rRpQ2RkJN27dy/2vUJDQ5k2bVqZ4rNYKqaoeGGeipqvqlIebCcHDnbw+uDrud6nLq+tOcA3kUnEnMjg45Ed8K1fu9K/v63kwWjKQyHloeJzUNp5VEQXEakEfVs1oEFdJ05mnOenfSe4rZ230SGJiNi0Dv716drMne1x6Sz4LY4Xbm1tdEhyDVgKrGyLTWP+7rMA3Hx9Q/zcK78gIVLVFBQUMG3aNDp27EjLli0BSE1NxcHBAVdX12JjPTw8SElJKRrz9wI6UHT7SmMyMzPJycnB2bl0f9SMiooq+wu7hvNVVcqD7eSgfS2Y3Ks+b249zb7kDG5/bxNPdatHu4ZO1+T720oejKY8FFIern0OVEQXEakE9mY7hnbx4/31h1i2PV5FdBGRUpjQK4DtceksCY/nsb4tcHV2MDokqUQ/RCczZfU+ks/kFN23Pe4UP0QnMzBI66bI302ZMoWYmBiWLl1qdCglatu2LWaz+arnsVgsREVFVdh8VZXyYJs5CAb6dDnHP5buJurYWV7ddIrnBwby4A1NMZkq50BsW8yDEZSHQspDxefgwnxXoiK6iEglua+zHx/8cojNh1KJT8vG30M760RELqdPYANaNHDh0MlMloXHM6F3c6NDkkryQ3Qyjy7exf8ezZaelcuji3fx8aiOKqSL/OmVV15hw4YNLF68mEaNGhXd7+npSV5eHmfPni22Gz0tLQ0vL6+iMXv27Ck2X2pqKkCxMRfu+/sYFxeXUu9CBzCbzRVa0Kno+aoq5cH2cuDn4cIXj/Tg319Fs3JXItPWHmRvcgZhg9tRy7Hy4rS1PBhFeSikPFz7HKhJr4hIJfFzr03P63TAqIhIadnZmRjfMwAoPGQyN7/A4IikMlgKrExZve+iAjpQdN+U1fuwFFxqhEjNYbVaeeWVV/jpp5/47LPP8PPzK/Z4UFAQDg4ObN26tei+2NhYkpKSCA4OBiA4OJg//viDtLS0ojFbtmzBxcWFFi1aFI3Ztm1bsbm3bNlSNIeIXMzZwcyb97bj5TvaYLYz8U1kEkM+3kJCerbRoYlIJVERXUSkEo3oWvhhZ8WORPIsKgaJiFzJXR18aFDXieNnc/j29ySjw5FKsD0uvVgLl/9lBZLP5LA9Lv3aBSVig6ZMmcK3337LzJkzqVOnDikpKaSkpJCTU/jfT926dRkyZAhhYWFs27aN6OhoXnzxRTp06FBUAA8NDaVFixY8++yzHDhwgE2bNvHOO+8wcuRIHB0dARg2bBgJCQnMmDGDw4cPs2TJEtauXcvYsWMNeuUiVYPJZGLsDc1Y8lAIHnUc2Zd8ljs/2Mxvh1Kv/GQRqXJURBcRqUT9WjfE08WJ1Mzz/Lz/hNHhiIjYPCd7Mw/c0AyAORsPY7VqN3J1czKj5AJ6ecaJVFfLli0jIyOD0aNHExoaWvS1Zs2aojEvvvgiN954I5MmTWLUqFF4enry/vvvFz1uNpuZNWsWdnZ2DB06lGeeeYa7776bSZMmFY3x8/Nj9uzZbNmyhbvuuosFCxYwdepUevbseU1fr0hV1S3Ag9WPh9LO141T2XmMnh/OvE2xeg8jUs2oJ7qISCVyMNtxX2dfPtpwmKXbE9TfVSpVREQE8+fPJzo6mpSUFD788EP69+9/2eeEh4cTFhZGTEwM3t7ePProowwePLjo8ffff58PPvig2HOaNWvGDz/8UHT7/PnzhIWFsWbNGnJzcwkNDWXy5Ml4enpW7AuUGmNEiD8frI/hjxOZbDiYQp9WDYwOSSpQg7ql67Fc2nEi1dXBgwevOMbJyYnJkyczefLkEsc0btyYuXPnXnaekJAQvv7667KGKCJ/8qlXixUTuhf1SZ/6/X6ijp2p9D7pInLtaCe6iEglG9bFH4BNMSnqkSeVKjs7m8DAwMt+kP67hIQEJkyYQEhICN988w33338/L730Eps2bSo27rrrrmPz5s1FX0uXLi32+LRp0/jll1945513WLRoESdPnmTixIkV9rqk5nGr5cDwroW/O2dvPGxwNFLRWnnXxWxnKvFxE+Dt5kzXZu7XLigREZGrpD7pItWbiugiIpXM36M2Pa/zxGqFFTsSjA5HqrHevXvz5JNPMmDAgFKNX758Ob6+vjz//PM0b96cUaNGcfPNN/Ppp58WG2c2m/Hy8ir6cnf/q7CVkZHBypUref755+nevTtBQUFMmzaN3bt3ExkZWYGvTmqaB0ObYW9nYltsOnsSTxsdjlQQS4GVf634vcRDQy+U1if/WYAQERGpStQnXaT6KlcRfcmSJfTt25e2bdty7733smfPnhLHrlixghEjRtClSxe6dOnC2LFjLxofGBh4ya958+aVJzwREZtzYTf65xEJ5OuAUbERkZGRdO/evdh9oaGhFxW/jx49SmhoKP369eNf//oXSUl/HfYYHR1NXl4ePXr0KLqvefPm+Pj4qIguV8WnXi3ubO8DwOyNsQZHIxVl5o8HWX/gJE72drxwSyu83Yq3bGnk5szHozqq/ZmIiFRp6pMuUv2UuSf6mjVrmD59OlOmTKF9+/Z89tlnjBs3jh9++AEPD4+LxoeHh3PbbbfRsWNHHB0dmTdvHg8++CDff/89DRs2BGDz5s3FnrNx40b+/e9/c/PNN5fzZYmI2JYBbRriUceRkxnnWX/gJDdd38jokERITU29qG+5p6cnmZmZ5OTk4OzsTLt27Zg+fTrNmjUr6rM+cuRIVq9ejYuLC6mpqTg4OODq6lpsHg8PD1JSUsoUj8ViuerX9Pd5Kmq+msTWcjcutCmrdh9jbVQycSkZ+LvXNjqkEtla7mzRd3uS+WhDYXuesMFB3Nnehwd6NCE8NpWd+w7RqU0LQgI8MduZlMdS0M9c+VV07vRvICKXoj7pItVLmYvoCxYs4L777mPIkCEATJkyhQ0bNrBy5UrGjx9/0fiZM2cWuz116lT++9//snXrVu6++24AvLy8io35+eefCQkJwc/Pr6zhiYjYJEd7O+7p7MvsX2NZtj1eRXSpMnr37l30/1u1akX79u3p06cPa9eu5d57763Q7xUVFWXT89UktpS74IaORJ7I5fWvd/BwR9crP8FgtpQ7WxJ3Oo8X16cBcFdgHfytJ4mMPAlAbaCnfy3IPEbUnmMGRlk16Weu/JQ7EalsF/qkt23syqvf7+ebyCRiTmQye3Qn/Gx4c4CIXKxMRfTc3Fz27t3LhAkTiu6zs7OjR48e7N69u1RznDt3jvz8fNzc3C75eGpqKr/++ithYWFlCQ3QDjZboNyVn3JXflUld/d2bMzsX2PZ8EcKCWmZ+NSrZXRIVSZ3tqa67GDz9PQkNbV4f8bU1FRcXFxwdna+5HNcXV1p2rQp8fHxRXPk5eVx9uzZYrvR09LSLvoj+ZW0bdsWs/nqd+VYLBaioqIqbL6axBZz91TdNMZ8EsGG+BxeHdoN9zqORod0SbaYO1uRlpXL4x9tIdcCva7z5I1RnYr1O1fuykd5K7+Kzt2F+URELuVCn/RW3q48tmRXUZ/0D0Z05IYWnleeQERsQpmK6KdOncJisVzUtsXDw4PY2NL1qnzzzTdp0KBBsd6pf/fVV19Rp04dbrrpprKEBmgHmy1R7spPuSu/qpC7tg0ciTqZy/vf72Do9XWNDqdIVcidLarqeQsODmbjxo3F7tuyZQvBwcElPicrK4uEhISiAnlQUBAODg5s3bq1qA1bbGwsSUlJl53nUsxmc4UWgip6vprElnLX8zovghq7En3sLEu3J/JE/+uMDumybCl3tiDPUsDEZZEknc6hmWcd3h/eEUeHS38EUe7KR3krP+VORK6lC33SH1m8kz2JZxg9P5wXb23NuNBmmEw6TFvE1pW5ncvVmDNnDmvWrGHhwoU4OTldcszKlSu54447Snz8crSDzXjKXfkpd+VXlXI3zi6Zf37+OxuPWXh1WDvszeU637nCVKXc2RJb3cGWlZVVtEMcIDExkf379+Pm5oaPjw8zZ87kxIkTzJgxA4Bhw4axZMkSZsyYwZAhQ9i2bRtr165l9uzZRXO8/vrr9OnTBx8fH06ePMn777+PnZ0dt99+OwB169ZlyJAhhIWF4ebmhouLC1OnTqVDhw5lLqKLXIrJZGJ8r+ZMWrabz7YeYULvAJwd9Puqqnhl9T62x6Xj4mTP3DGdcKvtYHRIIiIihlGfdJGqq0xF9Pr162M2m0lLSyt2f1pa2kUHk/2v+fPnM2fOHBYsWECrVq0uOWbHjh3ExcXxzjvvlCWsItrBZjuUu/JT7sqvKuTulrbeTFm9j+Nncth8OJ1+rRsaHRJQNXJni2wtb9HR0YwZM6bo9vTp0wEYNGgQYWFhpKSkkJycXPS4n58fs2fPZvr06SxcuJBGjRoxdepUevbsWTTm+PHjPPXUU5w+fRp3d3c6derEihUrcHd3Lxrz4osvYmdnx6RJk8jNzSU0NJTJkydfg1csNcWtQY2YUb8WiafO8cXOREZ3a2J0SFIKS8PjWbTtKCYTvDM0mBYNbOcKLBEREaOoT7pI1VSmIrqjoyPXX389W7dupX///gAUFBSwdetWRo0aVeLz5s6dy6xZs5g/fz5t27YtcdyXX37J9ddfX2KRXUSkqnOyN3NPJ1/mbopj2fZ4mymiS/UQEhLCwYMHS3z8UueNhISE8PXXX5f4nLfffvuK39fJyYnJkyercC6Vxt5sx7jQZkxZvY95m2IZ0dW/WE9tsT07jqQz+dtoAP41oCX922i9ExERuUB90kWqnjL3EXjggQdYsWIFX331FYcPH+bll1/m3LlzDB48GIBnn32WmTNnFo2fM2cO7777LtOmTaNx48akpKSQkpJCVlZWsXkzMzP54YcfuPfee6/yJYmI2LZhXf0BWH/gJMlnzhkcjZSVpcDKttg0NsWfY1tsGpYCq9EhidQIQ7v4Ua+2A0fTsvlx73Gjw5HLSDp9jkcW7yLPYuW2tt481qeF0SGJiIjYpAt90tv5unEqO4/R88OZtykWq1WfMURsTZl7ot96662kp6fz3nvvkZKSQuvWrZk3b15RO5fk5GTs7P6qzS9fvpy8vDwmTZpUbJ6JEyfy+OOPF93+/vvvsVqtRT1WRUSqq+ZeLoQ0cyc8Lp0vdiQyqZ9tH5Inf/khOpkpq/eRfCan8I7wCLzdnJl8RxsGBnkbG5xINVfb0Z7R3Zrw/vpDzNoYy8CgRjqEywbl5FmYsGgnqZnnadWoLm/c207/TiIiIpdxuT7pjvZ2bItNIyL+HDmuaXRr7qWr8UQMUq6DRUeNGlVi+5ZFixYVu71+/fpSzTl06FCGDh1annBERKqc4V39CY9L5/OIBB7r00JvhKqAH6KTeXTxLv53T8jxMzk8ungXH4/qqEK6SCUb070pszfG8nvCabbHpRMS4GF0SPI3VquV51fuIerYGerXdmDumM7UdizXxw0REZEa5VJ90nceOcV5SwEpGecLB2kDj4ihytzORURErt7AoEa41XLg2OlzbIxJMTocuQJLgZUpq/ddVEAHiu6bsnqfWruIVDKvuk4M6egLwJyNsQZHI/9r7qZYvo5Mwmxn4qOROhxNRESkLC70SV/yUAguTvYknj73VwH9Txc28PwQnWxQlCI1l4roIiIGcHYwFxWCloXHGxyNXMn2uPS/WrhcghVIPpPD9rj0axeUSA31cM9mmEzw84GTxJzIMDoc+dOGgycJW3sAgMl3tKF7c10lICIiUh5dmrpT29F8yce0gUfEOCqii4gYZHhXP6CwEHTybMkFWjHeyYzS/fuUdpyIlF+Alws3tWkIFO58FuPFpWbx+LLdFFhhaGc/RndrYnRIIiIiVdb2uHRO/s8O9L/TBh4RY6iILiJikOsa1qVL0/pYCqx8sTPR6HDkMhrUda7QcSJydcb3ag7A17uTOKE/QhoqIyePhxfuICMnn47+9Xjl7ut1kKiIiMhVKO3GnOhjZyo5EhH5OxXRRUQMNLyrPwDLtsdToMvxbFYjV2cud/arCfB2c6ZrM/drFpNITdapSX06N6lPrqWABb8dMTqcGqugwMqTn0dy6GQmjVydmTWqE072l778XEREREqntBtzXluzn3GfRrDlcCpWqz5LilQ2FdFFRAx0a1tvXJ3tSTx1js2HUo0ORy4h8VQ2o+aHU9LfOC7U1iff0Qbz5SrtIlKhxvcKAGBJ+FEyz+cbHE3N9Pa6P1i3/ySO9nbMHt2JBq66GkdERORqdW3mjrebM5f7ZOFkX1jO+/nASUbMDefW9zazcmciufkF1yZIkRpIRXQREQM5O5gZfOGA0e06YNTWJJ0+x/C52zh2+hzNPOvw+uC2eLsVLxI1cnPm41EdGRjkbVCUIjVT/9YNCfCqQ0ZOPsv1+/Oa+35PMu+vPwRA2OC2tPerZ2xAIiIi1YTZzsTkO9oAXFRIN/359e6wYNb/qzejuzWhloOZ/cln+dcXv3PD6+v5YH0M6Vm51zpskWpPRXQREYMN+/OA0Z/2nSDlMgfIyLV1/EwOI+ZuIyH9HE08arPs4W4M7erP5uf6smRcF/4Z4saScV3Y/FxfFdBFDGBnZ2J8z8Ld6J9sjiPPop1X18q+pLM8/cXvADzcs1nRH4NFRESkYgwM8ubjUR1pdJkNPAFeLrx6dxBbX+jLswMDaejqRErGed788Q+6T/+ZF7+K4tDJTINegUj1Y290ACIiNV2rRq509K/HrvjTfLkzkUdvbG50SDXeyYzCAvqRtGx869di6cPdit7Amu1MdAvwwPlsLYIDPNTCRcRAd3dozJs//kHSmRy+25PEoA4q5la29KxcHl64g3N5Fnpe58lzA1sZHZKIiEi1NDDImwFtGrHtcAoR0X/QJagl3Zp7XfT5o15tR/5xYwseCg1gTVQy8zbHEn3sLEvD41kaHk+fQC8e6hlAj+YeOvxb5CpoJ7qIiA24cMDo8ggdMGq01MzzjJgbTmxqFo3r1WLZw91oXK+W0WGJyCU4O5h54IamAMz+NVaHalWyPEsB/1iyk2OnC6/QeX94B+zN+jghIiJSWS5s4OnpX4tuV9jA42hvx90dGrN6Yiifj+/GgDYNMZngl4MpjJwXzi3vbuKLHQmcz7dcw1cgUn3oXa+IiA24rZ03dZ3sOZqWzdbYNKPDqbHSs3IZOTecQyczaeTqzNKHQ/Bzr210WCJyGaNCmlDb0cyB4xlsjNEBzZXpte/3sy02nTqOZuaO6Uy92o5GhyQiIiL/w2QyERLgwdwxnfnlXzdyf/fCvukHjmfwzJd7uCHsF977OYa0TLUSFSkLFdFFRGxAbUd77u7QGIClOiDPEKezcxk5L5yDJzJoUNeJZeO70cSjjtFhicgVuNV2YFiXwqt55mw8bHA01dfnEfF8uuUIAG8PDaZlw7rGBiQiIiJX1NSzDlPuCmLbC/14/pZWNHJ1JjXzPG/99Ac9wtbzwqo9xJzIMDpMkSpBRXQRERtxoaXLj3uPk6pdAdfUmew8Rs0PZ3/yWTxdnFj6cDeaeaqALlJVPBjaFLOdid8OpRF97IzR4VQ7O4+m89LX0QA8NaAlN13fyOCIREREpCzcajvwSO/mbHquD+8OC6adrxvn8wtYtj2BAW9v5P5PtrMpJkWt8UQuQ0V0EREb0cbHlfZ+9cizWFm5M9HocGqMszl5jPkknOhjZ/Go48jSh0No0cDF6LBEpAx869fm9nbeAMzeGGtwNNVL8plzTFi0izyLlVuCGjGxTwujQxIREZFycjDbcVdwY7557Aa+eKQ7N19f2Df91z9SGD1/OwPf2cSKiARy8tQ3XeR/qYguImJDRnT1A2B5RIJ2AVwDmefzGfvJdn5PPEO92g4sfihELQpEqqjxvQIAWBOVTEJ6tsHRVA85eRYmLNpJauZ5WjWqy5v3tsfuMgeaiYiISNVgMpno0tSd2aM7s+HpGxnboym1Hc0cPJHBsyv3EPr6et5Z94eukBb5GxXRRURsyO3tfKjjaCYuNYttselGh1OtZZ3P54EF29kVfxq3Wg4sHhdCa29Xo8MSkXK63seN0BaeWAqszN8cZ3Q4VZ7VauXFVVHs+fOPjHPHdKaOk73RYYmIiEgFa+JRh5fvvJ6tL/TjxVtb4ePmTGpmLu+si6FH2Hqe+3IPf6hvuoiK6CIitqSOkz13/XnA6DIdMFppsnPzefDTCCKOnKKusz2Lx4UQ1NjN6LBE5CpN6F24G/3ziAROZ+caHE3VNn9zHKt2H8NsZ+LDER3xc69tdEgiIiJSidxqOTC+V3N+fbYP7w3vQHtfN3LzC/h8RwI3vb2R0fPD+fUP9U2XmktFdBERGzPizwNGf4g+TnqWikAVLSfPwkOf7SA8Lh0XJ3sWPtiVtr4qoItUB6EtPGnj7cq5PAuLtx01Opwqa1NMCtPW7Afgpdtac0MLT4MjEhERkWvFwWzHne19+PqxG/jyke7cEtQIOxNsiknl/k+2c9PbG1m+PV5906XGURFdRMTGBDV2o21jN3ItBazapQNGK1JOnoWHF+5gy+E06jia+ezBLnTwr290WCJSQUwmU1Fv9E+3HNGHu3I4kprFxKW7KbDCvZ18GdujqdEhiYiIiAFMJhOdm7rz8ahO/PpMHx68oRl1HM3EnMzk+VVR9Ahbz1s//UFKhvqmS82gIrqIiA0a/udu9GXb43W5XAU5n2/h0cU72RSTSi0HMwse6EqnJu5GhyUiFey2dt5FvTxX7TpmdDhVSub5fB5euIMz5/Lo4F+PqYOCMJl0kKiIiEhN5+dem/+7ow1bX+zHv29tTeN6tUjPyuW9n2O4IWw9z3zxOweOnzU6TJFKpSK6iIgNujPYh9qOZg6nZBFx5JTR4VR5ufkFPLZkN78cTMHZwY75YzvTtZkK6CLVkYPZjnE9C3ejz9sUS0GB/hBZGgUFVp78PJKYk5k0dHVi9qhOONmbjQ5LpEaLiIjgkUceITQ0lMDAQNatW1fs8aysLF555RV69epFu3btuPXWW1m2bFmxMefPn2fKlCmEhITQoUMHHn/8cVJTU4uNSUpKYvz48bRv357u3bvz+uuvk5+fX+mvT0SqHldnBx7uFcCvz9zIByM6EOxXj1xLAV/sTGTgO5sYNS+cXw6e1PsvqZZURBcRsUEuTvbc2d4H0AGjVyvPUsCkZbtZt/8EjvZ2zBvThR7N1d9XpDob1sUPV2d7YlOz+Gn/CaPDqRLe+TmGn/adwNFsx6xRnWjg6mx0SCI1XnZ2NoGBgUyePPmSj4eFhbFp0ybeeOMN1qxZw/3338+rr77Kzz//XDRm2rRp/PLLL7zzzjssWrSIkydPMnHixKLHLRYLEyZMIC8vj+XLlxMWFsZXX33Fe++9V+mvT0SqLnuzHbe3K+ybvvLRHtzatrBv+uZDqTywIIKb3tnI0nD1TZfqRUV0EREbdaGly/dRyZzO1gGj5ZFvKeCfn0fyw97jOJrtmDO6E6HXqYAuUt3VcbJnVLcmAMz+9bDB0di+H6KTee/nGACmDW6rsyJEbETv3r158sknGTBgwCUf3717N3fffTchISH4+voydOhQWrVqxZ49ewDIyMhg5cqVPP/883Tv3p2goCCmTZvG7t27iYyMBGDz5s0cOnSIN954g9atW9O7d2+eeOIJlixZQm6u3n+KyJV1alKfj0YW9k0fF9oMFyd7Dp3M5MWvCvumz/zxICczcowOU+SqqYguImKj2vm60cbbldz8AvX1LQdLgZV/ffE73+9JxsFsYtbojtwY2MDosETkGhnboymOZjt2xZ9mx5F0o8OxWQeOn+WpFb8D8OANzbink6/BEYlIaXXo0IH169dz4sQJrFYr27ZtIy4ujtDQUACio6PJy8ujR48eRc9p3rw5Pj4+RUX0yMhIWrZsiafnX5sMQkNDyczM5NChQ9f09YhI1ebnXpv/3N6GrS/05aXb/uqb/v76Q4SG/cK/VvzOviT1TZeqy97oAERE5NJMJhPDQ/z5z9fRLI+I54EbmuqAt1KyFFh55svf+SYyCXs7Ex+O6EjfVg2NDktErqEGrs4M6tCYz3ckMHtjLJ2b6hyE/3UqK5eHF+4gO9fCDS08ePHWVkaHJCJl8J///If//Oc/9OrVC3t7e0wmE1OnTqVLly4ApKam4uDggKura7HneXh4kJKSUjTm7wV0oOj2hTGlZbFUTNuGC/NU1HxVlfKgHFxQ1fJQ28GOB3o0YXSIHz/tP8n8346wO/40K3clsnJXIj2ae/Bgjyb0bumFnV3pP99WtTxUFuWh4nNQ2nlURBcRsWF3Bfsw7fv9/HEik13xp+jUREWgKykosPLCqj2s2nUMs52J94d34KbrGxkdlogY4OFeAXy+I4F1+09wOCWT5l4uRodkM/ItBTy2dBcJ6efwd6/NB8M7Ym/WRaoiVcmiRYuIjIzk448/xsfHhx07djBlyhQaNGhQbPf5tRIVFWXT81VVyoNycEFVzIM38FKIM3+0cGf1H9lsO5bDlsNpbDmchk9dM7dfV4cbm9TCyb70xfSqmIfKoDxc+xyoiC4iYsNcnR24vZ03X+xMZGl4goroV2C1Wnnpm2hW7EjEzgTvDA3mlrbeRoclIgZp0cCF/q0bsm7/CeZtimX64HZGh2QzXluzny2H06jtaGbumM7Ur+NodEgiUgY5OTm8/fbbfPDBB9x4440AtGrViv379zN//nx69OiBp6cneXl5nD17tthu9LS0NLy8vIDCXecXeqhfkJqaClA0prTatm2L2Wy+ildVyGKxEBUVVWHzVVXKg3JwQXXIQzBwXz84duocn207yucRiSRl5DNn11lW7D/HiBA/Rof4X/Zg8+qQh4qgPFR8Di7MdyUqoouI2LjhIf58sTOR7/Yk8X+3t8GttoPRIdkkq9XK5G/3sjQ8HpMJZt7Xnjva+xgd1jUVERHB/PnziY6OJiUlhQ8//JD+/ftf9jnh4eGEhYURExODt7c3jz76KIMHDy56fPbs2fz444/Exsbi7OxMhw4dePrppwkICCgaM3r0aLZv315s3qFDh/LKK69U7AsUKYcJvQNYt/8EK3ce48kBLWlQt+QPZzXFih0JLPjtCABv3RdMYKO6xgYkImWWn59PXl7eRa3+zGYzVqsVgKCgIBwcHNi6dSs333wzALGxsSQlJREcHAxAcHAws2bNIi0tDQ8PDwC2bNmCi4sLLVq0KFNMZrO5Qgs6FT1fVaU8KAcXVIc8+Hu68J/br+fJAYGsiEhgwZY4EtLP8dGGWOZuiuOOdj48GNqMoMZuxZ5nKbAScTSNiPhz5LiepltzL8xlaAVTHVWHn4erda1zoCK6iIiN6+BXj1aN6nLgeAZfRx7j/h5NjQ7J5litVl79bj8Ltx7FZIIZQ9oxqEPNOxwvOzubwMBAhgwZwsSJE684PiEhgQkTJjBs2DDefPNNtm7dyksvvYSXlxc9e/YEYPv27YwcOZK2bdtisVh46623GDduHN9//z21a9cumuu+++5j0qRJRbdr1apV8S9QpBw6N6lPB/967I4/zWdbjvDMzTW77/fu+FO89FU0AE/0u46BQWp3JWKrsrKyiI+PL7qdmJjI/v37cXNzw8fHh65du/LGG2/g7OyMj48PERERfP311zz//PMA1K1blyFDhhAWFoabmxsuLi5MnTqVDh06FBXRQ0NDadGiBc8++yzPPPMMKSkpvPPOO4wcORJHR12hIiKVw8XJngdDm3F/j6b8tO848zbFsePoKVbtPsaq3cfoFuDOQ6EB9G3VgB/3HWfK6n0kn8kpfHJ4BN5uzky+ow0Dg3TVsVw7KqKLiNg4k8nE8K7+TP52L8u2xzOmexMdMPo3VquVsLUH+OS3OACmD2rLvZ39DI7KGL1796Z3796lHr98+XJ8fX2LPmw3b96cnTt38umnnxYV0efPn1/sOWFhYXTv3p29e/cWHVwG4OzsXObLvkWuBZPJxIRezXlk8U4Wb4vnHze2oI5TzXwLfOJsDhMW7STXUsBNbRryRL/rjA5JRC4jOjqaMWPGFN2ePn06AIMGDSIsLIy33nqLt956i6effpozZ87g4+PDk08+yfDhw4ue8+KLL2JnZ8ekSZPIzc0lNDSUyZMnFz1uNpuZNWsWL7/8MkOHDqVWrVoMGjSo2B/GRUQqi9nOxMAgbwYGeROZcJr5m+NYE5XMtth0tsWm06CuEyczzl/0vONncnh08S4+HtVRhXS5ZmrmJwgRkSrm7g6NmbZmPweOZxCZcJoO/vWNDskmWK1W3vzxILM3xgIw9e4ghnX1NziqqiMyMpLu3bsXuy80NJRp06aV+JyMjAwA3NyKX2K5evVqvv32W7y8vOjTpw//+Mc/tBtdbMaANg1p5lmHuNQsPo9I4MHQZkaHdM3l5FmYsGgnJzPO07KhC28NDcauhl8GLWLrQkJCOHjwYImPe3l5FRXWS+Lk5MTkyZOLFc7/V+PGjZk7d2654xQRqQjBfvV4f3gHnr+lFQu3HGFJ+NFLFtABrIAJmLJ6HwPaNKrxrV3k2lARXUSkCnCr5cBt7bxZtesYy7bHq4j+p3fWxfDhL4cBmHLn9Yzq1sTgiKqW1NRUPD09i93n6elJZmYmOTk5ODsX7x1dUFDAtGnT6NixIy1btiy6//bbb8fHx4cGDRpw8OBB3nzzTeLi4vjggw/KFI/FYin/i7nEPBU1X01SnXP34A1N+M83+5i3OZYRXX1xMNtV6Py2nDur1cqLq6KITDiNWy0HZo3sSC17k83Easu5s2XKW/lVdO70byAiUnEa16vFC7e2JiTAgwc/jShxnBVIPpPD9rh0ujf3uHYBSo2lIrqISBUxoqs/q3YdY/Xvybx0extcnWv2AaPv/xzDuz/HAPDSba3VK/4amDJlCjExMSxdurTY/UOHDi36/4GBgXh5eTF27Fji4+Px9y/9lQGlORG9LCp6vpqkOuauudmKq5MdSadzmPV9OD39K+dKCVvM3XcxWayKzMAOeKKLC6cS/uBUgtFRXcwWc1cVKG/lp9yJiNiujJy8Uo3bm3RGRXS5JlREFxGpIjo1qc91DVyIOZnJN5FJjK7Bu64/3nCYmT/9AcDzt7TioZ4BBkdUNXl6epKamlrsvtTUVFxcXC7ahf7KK6+wYcMGFi9eTKNGlz+IsH379gAcPXq0TEX0tm3bVsjp6haLhaioqAqbryap7rl78Mwh3vn5ED/GFzDxjvYVer6Erebut8NpLNyzA4AXbm3F/Tc0NTagS7DV3Nk65a38Kjp3F+YTEZGK06Cu85UHAVO/389P+04wrKsftwR54+ygNVEqh4roIiJVxIUDRl/5bh/LwuMZFeJfIw8Ynbcpltd/OADA0ze15JHezQ2OqOoKDg5m48aNxe7bsmULwcHBRbetViuvvvoqP/30E4sWLcLP78qHtu7fvx+gzAeNms3mCi0EVfR8NUl1zd39PZoxe2Mc+5Iz2BZ3mtDrPK/8pDKypdzFp2UzaXkklgIrgzs25qGeATa9bthS7qoS5a38lDsREdvVtZk73m7OHD+Tg7WEMU72duTmFxAel054XDr/981eBnVozH2d/Qhq7FbCs0TKp2KbQYqISKUa3LExjvZ27Es+S9SxM0aHc80t+C2Oqd8XFmif6HcdE/teZ3BEtiUrK4v9+/cXFbETExPZv38/SUlJAMycOZNnn322aPywYcNISEhgxowZHD58mCVLlrB27VrGjh1bNGbKlCl8++23zJw5kzp16pCSkkJKSgo5OTkAxMfH8+GHHxIdHU1iYiI///wzzz33HF26dKFVq1bX7sWLlEL9Oo4M7VL4h6DZGw8bHE3lyjqfz8MLd3A6O4/2vm5MG9TWpgvoIiIiIn9ntjMx+Y42QOEhon9n+vPr3WHB/PZ8X54a0JLG9WqRkZPPwq1Huf39zdz+/iYWbzvK2VK2hRG5Eu1EFxGpQurVduS2tt58tbvwgNF2vvWMDumaWbTtKFNW7wPgsT7N+Wd/FdD/V3R0NGPGjCm6PX36dAAGDRpEWFgYKSkpJCcnFz3u5+fH7NmzmT59OgsXLqRRo0ZMnTqVnj17Fo1ZtmwZAKNHjy72vaZPn87gwYNxcHBg69atLFy4kOzsbLy9vbnpppv4xz/+UZkvVaTcxoU2Y+HWI2yKSWVv0hmu96l+u5QKCqw8tSKSgycy8KrrxOzRnXVps4iIiFQ5A4O8+XhUR6as3kfymZyi+xu5OTP5jjYMDPIGYFK/65jYpwW/HU5leUQCP+49TvSxs7x0LJqp3+/j1rbeDOviT5em9bWpQMpNRXQRkSpmWBc/vtp9jG8ik/j3bW1wcar+v8qXbY/nP19HAzChdwBP3xSoNz+XEBISwsGDB0t8PCws7JLP+frrr0t8zuXmA/D29mbx4sWljlHEaH7utbm1rTff7Ulm7sZY3hnWweiQKtz76w/x370ncDTbMWtUJxq5la6nqIiIiIitGRjkzYA2jdh2OIWI6D/oEtSSbs29MNsV/zxoZ2ei53Ve9LzOi/SsXFbtSuTziARiTmayatcxVu06RoBnHYZ28WNwR1+86joZ9IqkqlI7FxGRKqZrM3cCvOqQnWvh28gko8OpdF/sSODFrwoP6xoX2oznB7ZSAV1ErsqEXoVnKazek8yx0+cMjqZi/Xfvcd5eV3jw8tRBQXRqUt/giERERESujtnORLcAD3r616JbgMdFBfT/5V7HkYd6BvDjk71Y+WgPhnb2o7ajmdjULKavPUD36T/zyKKd/HLwJJaCkjquixSnIrqISBVjMpkY0dUfKNyhXZ19tTuRZ1fuwWqFsT2a8tJtrVVAF5Gr1tbXjR7NPbAUWPlkc5zR4VSYP05k8NTnkUDh78z7Ol/5IGARERGR6spkMtGpSX1ev6cd2//dn7DBbQn2q0d+gZUf9h7ngQURhL6+nrd++oOE9GyjwxUbpyK6iEgVNLijL45mO6KOnSG6mh4w+u3vSfxrxe9YrTAyxJ/Jd7RRAV1EKsz4XgEALN8ez5nsqn/g1OnsXB5euIOsXAvdAzz4922tjQ5JRERExGa4ONkzrKs/Xz92Az/8sycP3NCUerUdSD6Tw3s/x9DrjV8YPT+c7/YkcT7fYnS4YoNURBcRqYLc6zgyMKgRUD13o3+/J5knP4+kwFrYA/7Vu4JUQBeRCtW7pRetGtUlK9fC4vCjRodzVfItBTy+bDdH07LxrV+LD0d2xMGst/kiIiIil9KqkSuT77iebS/0473hHbihhQdWK2yKSWXi0t10m/Yzr363j5gTGUaHKjZE765FRKqoYV0LL9P/JjKJrPP5BkdTcf679zhPLN+NpcDKkI6+TBvUFrsr9LwTESkrk8nEwz0Ld6N/uuVIld5xFLb2AJtiUqnlYGbumM6413E0OiQRERERm+fsYObO9j4seagbG5/pw8Q+LWjo6sSp7Dzmb45jwNsbGfzRb6yISKhWn7mlfFREFxGporoHeNDUozaZ5/P5bk/1OGD05/0nmLh0F/kFVu4O9mHGPe1UQBeRSnNHex+83ZxJyTjP17uPGR1Ouazcmci8P/u6v3Vfe1p7uxockYiIiEjV4+9Rm6dvDuS35/oy//7ODGjTELOdiV3xp3l25R66vraOF1btITLhNFarDiOtiVREFxGpokwmE8P/PGB06fYEg6O5ehsOnuTRxbvIs1i5vZ03b97b/oqnrouIXA1HezsevKEZAHM2xlJQULU+EEUmnOaFr6IAmNS3Bbe09TY4IhEREZGqzd5sR7/WDZk7pjNbn+/LcwNb0dSjNlm5FpZtT+DuD3/jlnc3seC3OE5n5xodrlxDKqKLiFRhQzr54mA28XvCafYlnTU6nHLbFJPC+EU7ybUUcEtQI94ZGoy9+vmKyDUwrKsfdZ3sOZySxc8HThodTqmdPJvDhEU7yM0voH/rhvyzf0ujQxIRERGpVhq4OvPojc355ekbWT6+G4M6NMbJ3o4DxzOYsnofXaf9zKRlu9lyKLXKbcaQslOFQkSkCvN0ceKm6wsPGF0eUTUPGN1yOJWHPissBA1o05D3hndQAV1Erpm6zg6M6FZ4Vc+cjYcNjqZ0zudbeGTxTk6cPU+LBi68PbS9Wl+JiIiIVBKTyUS3AA/eHhrM9hf788pd19Pa25Xc/AK+/T2JEfPC6f3mL3ywPobjZ3KMDlcqiaoUIiJV3PAuhcWfr3Yd41xu1ToYLzw2jXGf7uB8fgF9WzXggxEdcFABXUSusQdvaIaD2UTEkVPsij9ldDiXZbVa+b+v97Ir/jSuzvbMHdOZus4ORoclIiIiUiO41XZgTPemrJkUyuqJoYwM8aeukz0J6ed488c/6BH2M+M+jeDHvcfJsxQYHa5UIFUqRESquB7NPfB3r01GFTtgdMeRdB74NIJzeRZ6tfTio5EdcbI3Gx2WiNRADV2duTu4MQBzfo01OJrLW7j1KJ/vSMDOBO+P6EgzzzpGhyQiIiJS45hMJtr6uvHaoLaE/7sfb97bni5N61NghZ8PnGT8op30CFvP6z8c4EhqltHhSgVQEV1EpIqzszMxrKsfAMu2V42WLrviTzF2QQTZuRZCW3gyZ3QnnB1UQBcR44zvFQDAf/cdJ85GP+hsOZzKK9/tA+CFW1rTu6WXwRGJiIiISG1He+7p5MsXj/Rg3VO9mdArAI86jqRknOfjDYe58c0NDJuzla93HyMnr2pdPS5/URFdRKQauKeTL/Z2JnbFn+bg8Qyjw7msPYmnuX/+djLP59MtwJ25YzqrgC4ihruuYV36tmqA1QpzN9nebvSE9GweW7ILS4GVQR0a81DPZkaHJCIiIiL/o0UDF164tTVbX+jHrFEduTHQC5MJtsWm88/PI+n62jomfxPNvqSzRocqZaQiuohINdCgrjMD2jQEbHs3evSxM4yaF07G+Xy6NK3P/Pu7UMtRBXQRsQ0XdqN/uTOR1MzzBkfzl+zcfB5euINT2Xm083Vj+uC2mEw6SFRERETEVjna2zEwyJtPH+jK5uf68mT/ljSuV4uzOfl8tvUot763iTs/2MzibUc5m5NndLhSCiqii4hUE8O7Fh4wumpXok1eIrY/+Syj5odzNiefjv71WPBAV+o42RsdlohIkZBm7rT3q0dufgELtxwxOhyg8CDRp7/4nQPHM/B0cWK22l+JiIiIVCmN69Xiif7XsfHZPix8sCu3tfXGwWxiT+IZXvo6mq6vreNfK34n4kg6Vqu1xHksBVa2xaaxKf4c22LTsBSUPFYqnqoXIiLVRGgLT3zr1yLx1DnWRCUzuKOv0SEV+eNEBiPnhXM6O4/2fvX47MGuuKiALiI2xmQyMaFXAP9YsouF247yyI3Nqe1o7O+qD385xJqo4ziYTcwa1RFvt1qGxiMiIiIi5WO2M9GrpRe9WnqRlnmer3YfY3lEAodOZrJyVyIrdyUS4FWHYV38GNzRF08Xp6Ln/hCdzJTV+0g+k1N4R3gE3m7OTL6jDQODvA16RTWLdqKLiFQTdnYmhnWxvQNGD53MZMTccNKzcmnb2I2FD3alrrOD0WGJiFzSzdc3oolHbU5n57EiIsHQWH7ad4I3f/wDgFfuCqJzU3dD4xERERGRiuHh4sRDPQP46clerHy0O/d28qWWg5nYlCymrTlAt2k/8+jinWw4eJI1e5J5dPGuvwrofzp+JodHF+/ih+hkg15FzVKuIvqSJUvo27cvbdu25d5772XPnj0ljl2xYgUjRoygS5cudOnShbFjx15y/OHDh3nkkUfo1KkTwcHBDBkyhKSkpPKEJyJSY93b2Q+znYmII6eIOWH8AaOxKZmMmLuN1MzztPF2ZdG4rrjVUgFdRGyX2c7EQ6GFh3bO2xxHvqXAkDhiTmTw5OeRAIzp3qSoZZeIiIiIVB8mk4lOTdx54972bP93P6YPbkt7v3rkF1hZG32csQsimLhsF5dq3HLhvimr96m1yzVQ5iL6mjVrmD59Oo899hhfffUVrVq1Yty4caSlpV1yfHh4OLfddhsLFy5k+fLleHt78+CDD3LixImiMfHx8YwYMYKAgAAWLVrEt99+yz/+8Q+cnJwuOaeIiFxaQ1dn+rVqAMByg3dQHk3LYsTccE5mnKdVo7osfiiEerUdDY1JRKQ07unkh3sdRxJPnWNt9PFr/v3PZOfx8MIdZJ7PJ6SZO/+5vc01j0FERERErq26zg4M7+rPN4/dwNonejK2R1PqOJq5XH3cCiSfyWF7XPo1i7OmKnMRfcGCBdx3330MGTKEFi1aMGXKFJydnVm5cuUlx8+cOZORI0fSunVrmjdvztSpUykoKGDr1q1FY95++2169erFs88+S5s2bfD396dfv354eHiU/5WJiNRQw0MKdyuuNPCA0YT0bIbP2cbxszlc18CFxQ+F4F5HBXQRqRpqOZoZ070JAHM2xl72gKeKlm8pYOKyXRxJy6ZxvVp8NLIjDmZ1YBQRERGpSVp7u/Lyndcz5a7rSzX+ZEbOlQfJVSnTSUm5ubns3buXCRMmFN1nZ2dHjx492L17d6nmOHfuHPn5+bi5uQFQUFDAhg0beOihhxg3bhz79u3D19eXCRMm0L9//7KEh8VSMcWiC/NU1Hw1iXJXfspd+Sl3xd0Q4I5PPWeSTuewNiqJO9v7lDi2MnKXdPocw+dtJ+lMDgGedVj0YBfq17KvVv8+FZ236pQbkepiTPemzPr1MFHHzrA1No0ezT2vyfed8d+DbIpJxdnBjjljOuHhoiszRURERGqqxvVql2rcb4dS6dzUncb1dAh9ZSlTEf3UqVNYLJaLdoh7eHgQGxtbqjnefPNNGjRoQI8ePQBIS0sjOzubuXPn8s9//pOnn36aTZs2MXHiRBYuXEjXrl1LHV9UVFTpX4wB89Ukyl35KXflp9z9pWdjez4/DfN+2Y+/9eQVx1dU7tKyLfxnQzonsix4u5h5oVttjh3ez7EKmd326GdOpPpyr+PIvZ38WLTtKLN/jb0mRfSvdx9jzsbC99Rv3tue633cKv17ioiIiIjt6trMHW83Z46fyblkX/QLVuxI5IudifRo7sGQjr4MDGpEbccylX3lCq5pNufMmcOaNWtYuHBhUb/zgoLCw5r69evH2LFjAWjdujW7du1i+fLlZSqit23bFrPZfNVxWiwWoqKiKmy+mkS5Kz/lrvyUu4s1bJbDF/s2sDclD9fGLQjwcrnkuIrM3YmzOfxr3nZOZFnwd6/F0odC8HZzvqo5bVVF/8xdmE9EbMtDPZuxJPwov/6RwoHjZ2nVyLXSvteexNM8t3IPAI/1ac7t7Uq+ikhEREREagaznYnJd7Th0cW7MEGxQrrpz/8d3b0JB49nEB6Xzm+H0vjtUBr/+TqaW9p6c08nX7o2dcfOznSJ2aUsylREr1+/Pmaz+aJDRNPS0vD0vPzunPnz5zNnzhwWLFhAq1atis1pb29P8+bNi41v3rw5O3fuLEt4mM3mCi2gVfR8NYlyV37KXfkpd3/xda9D31YNWLf/JF/sSuLFW1tfdvzV5i4l4zyjP4ko6uG79OFu+NYv3WVnVZl+5kSqtyYedbglyJvvo5KZszGWt+4LrpTvczIjhwmLdnI+v4B+rRrwrwGBlfJ9RERERKTqGRjkzcejOjJl9T6Sz/zV+7yRmzOT72jDwCBvoPBsslW7jrFyVyLx6dl8uTORL3cm4lu/FoM7+jKkY2OaeNQx6mVUeWU6pcjR0ZHrr7++2KGgFw4J7dChQ4nPmzt3Lh999BHz5s2jbdu2F83Ztm1b4uLiit1/5MgRGjduXJbwRETkb4Z3LTxg9MudiZzPr7ye22mZ5xkxdxuHU7LwcXNm+fiaUUAXkZphfK8AAL6NTCL5zLkKn/98voVHF+8i+UwOzb3q8PawYO0UEhEREZFiBgZ5s/m5viwZ14V/hrixZFwXNj/Xt6iADuDnXpsn+l/Hr8/cyIoJ3Rna2Q8XJ3sST53jvZ9j6P3GBu6btZXPI+LJyMkz8NVUTWUqogM88MADrFixgq+++orDhw/z8ssvc+7cOQYPHgzAs88+y8yZM4vGz5kzh3fffZdp06bRuHFjUlJSSElJISsrq2jMuHHjWLt2LStWrODo0aMsXryYX375heHDh1fASxQRqZl6t/Sikasz6Vm5/Lj3RKV8j/SsXEbOCyfmZCYNXZ1Y+nA3/NxVQBeR6qO9Xz1CmrmTX2BlwW9HKnRuq9XK5G/2svPoKeo62zN3TGdcnR0q9HuIiIiISPVgtjPRLcCDnv616BbggbmEjRcmk4muzdx5/Z52RPy7P+8OC6bndZ6YTLD9SDrPrYyiy2vr+Ofy3WyKScFScLlu63JBmYvot956K8899xzvvfced911F/v372fevHlF7VySk5NJSUkpGr98+XLy8vKYNGkSoaGhRV+ffPJJ0ZgBAwbw8ssvM2/ePO644w6++OIL3nvvPTp37lwBL1FEpGayN9txXxc/AJZtj6/w+U9n5zJqXjgHjmfgVdeJZQ93o6mnLg0TkepnQu/C3ehLw+M5W4G7dhZvO8ryiARMJnh/eIcSz68QkZopIiKCRx55hNDQUAIDA1m3bt1FYw4fPswjjzxCp06dCA4OZsiQISQlJRU9fv78eaZMmUJISAgdOnTg8ccfJzU1tdgcSUlJjB8/nvbt29O9e3def/118vPzK/31iYhI5avlaOau4MYsGhfCluf78uzAQJp71SEnr4CvI5MYPX87oa+vZ8YPBzickml0uDatXAeLjho1ilGjRl3ysUWLFhW7vX79+lLNec8993DPPfeUJxwRESnB0C5+vL8+hi2H04hLzaJZBRW5z5zLY/T87exLPouniyPLHg5R8UdEqq0bWzbgugYuxJzMZGl4PI/0bn7lJ13Bttg0pqzeB8BzA1txY2CDq55TRKqX7OxsAgMDGTJkCBMnTrzo8fj4eEaMGMGQIUOYNGkSLi4uxMTE4OTkVDRm2rRp/Prrr7zzzjvUrVuXV199lYkTJ7J8+XKg8HDzCRMm4OnpyfLlyzl58iTPPfccDg4OPPXUU9fstYqISOXzdqvFP25swaO9mxOZcJqVuxL/bFmYw0cbDvPRhsME+9VjSCdf7mzng1ttXSH5d2XeiS4iIlVH43q1uLGlFwDLIypmN3pGTh73f7KdqGNncK/jyJKHutGiQd0KmVtExBbZ2ZmKeqMv+C2O3PyCq5ov8VQ2/1iyi/wCK3e292HCn3OLiPxd7969efLJJxkwYMAlH3/77bfp1asXzz77LG3atMHf359+/frh4eEBQEZGBitXruT555+ne/fuBAUFMW3aNHbv3k1kZCQAmzdv5tChQ7zxxhu0bt2a3r1788QTT7BkyRJyc3Ov1UsVEZFryGQy0cG/PlPvbsv2f/fnwxEd6duqAWY7E5EJp/nP19F0eW0djy3ZxfoDJ8i3XN173+pCRXQRkWruwgGjK3cmXnXhJ/N8PmMXRBCZcJp6tR1YPC6EwEYqoItI9XdXcGMaujpx4ux5vok8Vu55snPzGb9wJ+lZuQQ1duX1Ie0wmXSQqIiUTUFBARs2bKBp06aMGzeO7t27c++99xZr+RIdHU1eXh49evQouq958+b4+PgUFdEjIyNp2bJlUXtWgNDQUDIzMzl06NA1ez0iImIMZwczt7Xz5pOxXdj6Ql9euq01rRrVJddSwPdRyTz46Q66TV/Pa9/v48Dxs0aHa6hytXMREZGqo2+rBjSo68TJjPOs23+CW9t6X/lJl5Cdm8+DCyLYefQUrs72LB4XQhsf1wqOVq5GREQE8+fPJzo6mpSUFD788EP69+9/2eeEh4cTFhZGTEwM3t7ePProo0WHhV+wZMkS5s+fT0pKCq1ateI///kP7dq1K3r8/PnzhIWFsWbNGnJzcwkNDWXy5MnFPpCLVHWO9nY8cEMzwtYeYM7GWIZ09MWuhMOcSmK1Wnnmyz1FrbBmj+5MLUdzJUUsItVZWloa2dnZzJ07l3/+8588/fTTbNq0iYkTJ7Jw4UK6du1KamoqDg4OuLoWf7/m4eFRdI5ZamrqRev1hdt/P+usNCwWy1W8oovnqaj5qirlQTm4QHkopDwUqsw8eNR24IEeTRjb3Z99yRms2n2MbyOTSM08z9xNcczdFMf13q4M7ujDHe198KjjWOExlEZF56C086iILiJSzdmb7bivsx8f/HKIZdvjy1VEP5drYdynO9h+JJ26TvYsGhdCUGO3SohWrsaVeqf+r4SEBCZMmMCwYcN488032bp1Ky+99BJeXl707NkTgDVr1jB9+nSmTJlC+/bt+eyzzxg3bhw//PBD0eXiV+q3KlJdjAjx54P1h4g5mcmGP07St1XDMj3/ow2H+X5PMg5mEx+P6kTjerUqKVIRqe4KCgqvLuzXrx9jx44FoHXr1uzatYvly5fTtWvXax5TVFSUTc9XVSkPysEFykMh5aHQtcjDHY1hoLc7u5PPs+HoOXYmnWdv8ln2fn+WaWsO0NHbiT5Na9HR2wmHMm4uqQjX+mdBRXQRkRpgaBc/PtxwiE0xqcSnZePvUbvUz83JszB+0Q62xqbh4mTPZ+O60t6vXuUFK+XWu3dvevfuXerxy5cvx9fXl+effx4ovMR7586dfPrpp0VF9AULFnDfffcxZMgQAKZMmcKGDRtYuXIl48ePL+q3+uabb9K9e3egsKh+6623EhkZSXBwcMW+SBEDuTo7MCLEnzkbY5n9a2yZiujrD5zgzR8PAvDyndfTpal7ZYUpIjVA/fr1sbe3p3nz4gcdX1jLoXBHeV5eHmfPni22Gz0tLQ0vL6+iMXv27Ck2R2pqKkDRmNJq27YtZvPVX11jsViIioqqsPmqKuVBObhAeSikPBQyIg9dgPFAelYuq/cks2rXMaKTzhKRdJ6IpPPUr+3AHe29GdyhMUE+rpXeqrCic3BhvitREV1EpAbwc69Nz+u82PhHCssj4nl2YKtSPe98voUJi3ayKSaV2o5mPn2gCx3961dytHKtREZGFhW+LwgNDWXatGkA5ObmsnfvXiZMmFD0uJ2dHT169GD37t3Alfutqogu1c0DNzTlk81xhMel83vC6VL9UfHQyUyeWBaJ1QojQ/wZGdKk8gMVkWrN0dGRtm3bEhcXV+z+I0eO0LhxYwCCgoJwcHBg69at3HzzzQDExsaSlJRUtD4HBwcza9Ys0tLSiq4w27JlCy4uLrRo0aJMMZnN5got6FT0fFWV8qAcXKA8FFIeChmRBy/XWjwYGsCDoQH8cSKDlTsTWbX7GCkZ51m4NZ6FW+MJbFiXIZ0ac3dwYxq4OldqPNc6Byqii4jUECO6+rHxjxS+2JnIkwNa4mC+/NnSufkFPLp4F7/+kYKzgx2fjO1CZ+2crFZK6oOamZlJTk4OZ86cwWKxFH2ovsDDw4PY2NiiOa7Ub7W01EvVeMrdlTVwceSO9t58tTuJ2b8e5v3hwUDJuTt7Lo+HF0aQcT6fLk3r89KtrZTf/6Gfu/JR3srPqF6qZZWVlUV8fHzR7cTERPbv34+bmxs+Pj6MGzeOJ598ki5duhASEsKmTZv45ZdfWLhwIQB169ZlyJAhhIWF4ebmhouLC1OnTqVDhw5FRfTQ0FBatGjBs88+yzPPPENKSgrvvPMOI0eOxNHRmF63IiJi+1o2rMsLt7bmmZsD2XQolZU7E/lx3wkOnshg2poDhK09QK+WXtzTyZf+rRvi7FD1//ChIrqISA3Rr3VDPF2cSMk4z8/7TzIwqFGJY/MsBUxcuov1B07iZG/HJ/d3oVuAR4njRSqCeqnaDuXu8np65fEV8EP0cX7YvINGLn+9pf577ixWK9M3nyIuNRfPWnY82taefdF7LjGjgH7uykt5Kz9bz110dDRjxowpuj19+nQABg0aRFhYGAMGDODll19mzpw5TJ06lWbNmvHee+/RuXPnoue8+OKL2NnZMWnSpGKHf19gNpuZNWsWL7/8MkOHDqVWrVoMGjSISZMmXbsXKiIiVZa92Y4+gQ3oE9iAM+fy+G5PEit3JrIr/jQbDqaw4WAKrs723N7eh3s6+dLBr16lt3upLCqii4jUEA5mO+7t7MvHGw6zbHt8iUX0fEsBTyzfzY/7TuBob8fcMZ3p0cLzkmOlavP09Czqe3pBamoqLi4uODs7Y2dnh9lsJi0trdiYtLS0oh3spem3WlrqpWo85a50goGv43awMSaVrem1mRLa5pK5m/Hfg+w+fgJnBzvmP6ADmUuin7vyUd7Kz6heqmUVEhLCwYMHLzvmnnvu4Z577inxcScnJyZPnlyscP6/GjduzNy5c8sdp4iICIBbLQdGhjRhZEgTYlMyWbXrGKt2JZJ0Joel4fEsDY8nwLMOQzr5MqhDY3zq1TI65DJREV1EpAYZ1sWPjzccZmNMCgnp2fi4ORV7PN9SwJMrfmdN1HEczXbMHt2JXi3LVgiVqiM4OJiNGzcWu2/Lli1Fl3g7Ojpy/fXXs3XrVvr37w9AQUEBW7duZdSoUUDp+q2Wlnqp2g7l7soeubE5G2NS+XJXIk/dFIibc2G+LuTum8hjzN5Y2Kt4xj3tae+vdlhXop+78lHeyk+5ExERqRwBXi48fXMgTw1oydbYNFbuTGRt9HFiU7N4478HefPHg9zQ3JMhnRpz8/WNqO1o+yXqyzfEFRGRaqWJRx1CW3hitcKKHQnFHrMUWHnmyz2s/j0JB7OJj0Z2pE9gA4MilfLIyspi//797N+/H/ird2pSUhIAM2fO5Nlnny0aP2zYMBISEpgxYwaHDx9myZIlrF27lrFjxxaNeeCBB1ixYgVfffUVhw8f5uWXX+bcuXMMHjwYKN5vddu2bURHR/Piiy8W67cqUh11D/CgbWM3cvIKWLj1SLHHoo+d4dkvC9u2PNK7OXe29zEgQhERERERY9nZmbihhSdvDQ0m4qX+zLinHSHN3LFaYfOhVJ78/He6TF3HM1/8TnhsGgUFVqNDLpHtl/lFRKRCDe/qz+ZDqXweEU+XJvXYFX+Oc3XT+Coyia92H8NsZ+L94R3p36ah0aFKGV2pd2pKSgrJyclFj/v5+TF79mymT5/OwoULadSoEVOnTqVnz55FY2699VbS09N57733SElJoXXr1sybN6/YgaRX6rcqUh2ZTCbG9wrg8WW7+fS3ONo1dmVP/DlOOBzn1e/2cz6/gBsDvXjm5kCjQxURERERMZyLkz33dfbjvs5+JKRns3JXIqt2HSM+PZsvdibyxc5E/NxrMbiDL0M6+uLvUfuiOSwFVrbFphERf44c1zS6NffCbHdteqyriC4iUsMMaNMQFyd7TmbkMmbBjsI7wyMAsDPBe8M6XPbQUbFdV+qdGhYWdsnnfP3115edd9SoUUXtWy6lNP1WRaqjW4Ia4VHHkbSsXB78bGfhneGRADSs68S7wzpcszf1IiIiIiJVhZ97bf7ZvyVP9LuOiCOn+HJnAmuijpOQfo53f47h3Z9j6NrMnXs6+nJrO29cnOz5ITqZKav3kXwmp3CS8Ai83ZyZfEcbBgZ5V3rMKqKLiNQw6w+cIPN8/iUfK7CCWY2+RERKZd3+E6Rl5V7ysRMZ59l6OPWavKEXEREREamKTCYTXZu507WZO1PuDOK/e4+zclcimw+lsj0une1x6fzft9G0863H9rj0i55//EwOjy7excejOlb6+26VSkREahBLgZUpq/eV+LgJmLJ6HxYb7kMmImIL9PtURERERKTi1HI0c3eHxiwaF8Jvz/XlmZsDCfCqQ05ewSUL6AAX3mlfi/fdKqKLiNQg2+PS/7r06RKsQPKZnBIXKBERKaTfpyIiIiIilcOnXi0e69OCn5/qzat3XX/ZsdfqfbeK6CIiNcjJjJILPuUZJyJSU+n3qYiIiIhI5TKZTLjWcijV2Mp+360iuohIDdKgrnOFjhMRqan0+1REREREpPLZyvtuFdFFRGqQrs3c8XZzxlTC4ybA282Zrs3cr2VYIiJVjn6fioiIiIhUPlt5360iuohIDWK2MzH5jjYAFy1AF25PvqMNZruSlicREQH9PhURERERuRZs5X23iugiIjXMwCBvPh7VkUZuxS91auTmzMejOjIwyNugyEREqhb9PhURERERqXy28L7bvtK/g4iI2JyBQd4MaNOIbYdTiIj+gy5BLenW3Es7JkVEyki/T0VEREREKp/R77tVRBcRqaHMdia6BXjgfLYWwQEeKviIiJSTfp+KiIiIiFQ+I993q52LiIiIiIiIiIiIiEgJVEQXERERERERERERESmBiugiIiIiIiIiIiIiIiVQEV1EREREREREREREpAQqoouIiIiIiIiIiIiIlEBFdBERERERERERERGREtgbHUBFsFqtAFgslgqZ78I8FTVfTaLclZ9yV37KXfkpd+VT0Xm7MM+F9aym0TpuO5S78lPuyk+5Kx/lrfy0jlcsreOVQ3lQDi5QHgopD4WUB+PWcZO1Gqz0ubm5REVFGR2GiIjIVWnbti2Ojo5Gh3HNaR0XEZHqQOu4iIhI1XWldbxaFNELCgrIz8/Hzs4Ok8lkdDgiIiJlYrVaKSgowN7eHju7mtdpTeu4iIhUZVrHtY6LiEjVVdp1vFoU0UVEREREREREREREKkPN+zO5iIiIiIiIiIiIiEgpqYguIiIiIiIiIiIiIlICFdFFREREREREREREREqgIrqIiIiIiIiIiIiISAlURBcRERERERERERERKYGK6CIiIiIiIiIiIiIiJVARXURERERERERERESkBCqil2DOnDkEBgby2muvGR1KlfD+++8TGBhY7GvgwIFGh1UlnDhxgqeffpqQkBDatWvHHXfcQVRUlNFh2by+ffte9DMXGBjIlClTjA7N5lksFt555x369u1Lu3bt6N+/Px9++CFWq9Xo0KqEzMxMXnvtNfr06UO7du0YNmwYe/bsMTos+R9ax8tG63j5aR0vH63j5ad1/OpoHa8aauo6rvX4L1pftVaC1ry/q4nrV0REBI888gihoaEEBgaybt26Yo9brVbeffddQkNDadeuHWPHjuXIkSOVFo99pc1che3Zs4fly5cTGBhodChVynXXXceCBQuKbpvNZgOjqRrOnDnD8OHDCQkJYe7cudSvX5+jR4/i5uZmdGg278svv8RisRTdjomJ4YEHHqixbzLLYu7cuSxbtozXX3+dFi1aEB0dzQsvvEDdunUZM2aM0eHZvJdeeomYmBhmzJhBgwYN+Pbbb3nggQdYs2YNDRs2NDo8Qet4eWkdLzut4+Wndbz8tI5fHa3jtq+mr+Naj7W+XqC1Umve39XE9Ss7O5vAwECGDBnCxIkTL3p87ty5LFq0iLCwMHx9fXn33XcZN24ca9aswcnJqcLjURH9f2RlZfHMM88wdepUPv74Y6PDqVLMZjNeXl5Gh1GlzJ07l0aNGjF9+vSi+/z8/AyMqOpwd3cvdnvOnDn4+/vTtWtXgyKqOnbv3k2/fv248cYbAfD19eX777+v9n/Frgg5OTn8+OOPfPTRR3Tp0gWAxx9/nF9++YWlS5fy5JNPGhyhaB0vP63jZad1vPy0jpef1vHy0zpu+7SOaz0Gra8XaK3UmndBTV2/evfuTe/evS/5mNVqZeHChTz66KP0798fgBkzZtCjRw/WrVvHbbfdVuHxqJ3L/3jllVfo3bs3PXr0MDqUKufo0aOEhobSr18//vWvf5GUlGR0SDZv/fr1BAUFMWnSJLp3787dd9/NihUrjA6rysnNzeXbb79lyJAhmEwmo8OxeR06dGDbtm3ExcUBcODAAXbu3EmvXr0Mjsz25efnY7FYLvqrtpOTE7t27TIoKvk7rePlp3W87LSOVwyt42Wjdbz8tI7bPq3jWo9B6+ul1NS1UmteIa1fF0tMTCQlJaXYelG3bl3at2/P7t27K+V7aif633z//ffs27ePL7/80uhQqpx27doxffp0mjVrRkpKCh9++CEjR45k9erVuLi4GB2ezUpISGDZsmU88MADPPLII0RFRTF16lQcHBwYNGiQ0eFVGevWrSMjI0M5K6Xx48eTmZnJLbfcgtlsxmKx8OSTT3LnnXcaHZrNc3FxoUOHDnz00UcEBATg6enJd999R2RkJP7+/kaHV+NpHS8/rePlo3W8YmgdLxut4+Wnddy2aR3XenyB1teL1dS1UmteIa1fF0tJSQHAw8Oj2P0eHh6kpqZWyvdUEf1PycnJvPbaa3zyySeV0jenuvv75RWtWrWiffv29OnTh7Vr13LvvfcaGJlts1qtBAUF8dRTTwHQpk0bYmJiWL58eY1bHK/GypUr6dWrV7XtA1bR1q5dy+rVq5k5cyYtWrRg//79TJ8+nQYNGujnrhRmzJjBiy++SK9evTCbzbRp04bbbruNvXv3Gh1ajaZ1/OpoHS8freMVQ+t42Wgdvzpax22T1vFCWo8LaX29WE1dK7Xm/UXrl/FURP/T3r17SUtLY/DgwUX3WSwWIiIiWLJkCVFRUTXyQI/ycnV1pWnTpsTHxxsdik3z8vKiefPmxe4LCAjgv//9r0ERVT3Hjh1jy5YtvP/++0aHUmXMmDGD8ePHF/UICwwMJCkpidmzZ9e4NyLl4e/vz+LFi8nOziYzM5MGDRrwz3/+s0b2abQlWscrltbx0tE6fvW0jped1vGro3XcNmkdv7Sauh5rfS2uJq+VWvP+ovWruAtnR6SlpdGgQYOi+9PS0mjVqlWlfE8V0f/UrVs3Vq9eXey+F154gYCAAB5++OEauWBfjaysLBISEmr8gShX0rFjx6LeXhccOXKExo0bGxRR1bNq1So8PDyKDhqRK8vJybmoj57ZbMZqtRoUUdVUu3ZtateuzZkzZ9i8eTPPPPOM0SHVaFrHK5bW8dLROn71tI6XndbxiqF13LZoHb+0mroea30triavlVrzLqb1q5Cvry9eXl5s3bqV1q1bA5CZmcnvv//O8OHDK+V7qoj+JxcXF1q2bFnsvtq1a1OvXr2L7peLvf766/Tp0wcfHx9OnjzJ+++/j52dHbfffrvRodm0+++/n+HDhzNr1ixuueUW9uzZw4oVK3jllVeMDq1KKCgoYNWqVdx9993Y2+vXWWn16dOHWbNm4ePjU3RJ3IIFCxgyZIjRoVUJmzZtwmq10qxZM+Lj45kxYwYBAQHFdk7Jtad1/OpoHS8freNXR+t4+Wgdvzpax22T1vFCWo8LaX39S01fK7Xm/aUmrl9ZWVnFrsRJTExk//79uLm54ePjw5gxY/j4449p0qQJvr6+vPvuuzRo0ID+/ftXSjw1779AqRTHjx/nqaee4vTp07i7u9OpUydWrFiBu7u70aHZtHbt2vHBBx/w1ltv8eGHH+Lr68uLL75Y4w7JKK8tW7aQlJRUIxfQq/HSSy/x7rvvMmXKlKJLn4YOHcpjjz1mdGhVQkZGBm+99RbHjx+nXr163HTTTTz55JM4ODgYHZpIuWkdLx+t41dH63j5aB2/OlrHxZZpPS6k9fUvNX2t1Jr3l5q4fkVHRzNmzJii29OnTwdg0KBBhIWF8fDDD3Pu3Dn+7//+j7Nnz9KpUyfmzZtXaWdrmKw1+RoIEREREREREREREZHLsDM6ABERERERERERERERW6UiuoiIiIiIiIiIiIhICVREFxEREREREREREREpgYroIiIiIiIiIiIiIiIlUBFdRERERERERERERKQEKqKLiIiIiIiIiIiIiJRARXQRERERERERERERkRKoiC4iIiIiIiIiIiIiUgIV0UWkRKNHj+a1114zOgwREREpB63jIiIiVZfWcRHboiK6iIiIiIiIiIiIiEgJVEQXERERERERERERESmBiugiUmobNmygU6dOfPvtt0aHIiIiImWkdVxERKTq0jouYix7owMQkaph9erVTJ48mZkzZ9KnTx+jwxEREZEy0DouIiJSdWkdFzGeiugickVLlizh7bffZtasWXTt2tXocERERKQMtI6LiIhUXVrHRWyDiugicln//e9/SU9PZ+nSpbRr187ocERERKQMtI6LiIhUXVrHRWyHeqKLyGW1adOG+vXrs3LlSqxWq9HhiIiISBloHRcREam6tI6L2A4V0UXksvz8/Fi4cCE///wzr776qtHhiIiISBloHRcREam6tI6L2A4V0UXkipo1a8bChQv58ccfee2114wOR0RERMpA67iIiEjVpXVcxDaoiC4ipRIQEMBnn33G999/T1hYmNHhiIiISBloHRcREam6tI6LGM9kVVMlEREREREREREREZFL0k50EREREREREREREZESqIguIiIiIiIiIiIiIlICFdFFREREREREREREREqgIrqIiIiIiIiIiIiISAlURBcRERERERERERERKYGK6CIiIiIiIiIiIiIiJVARXURERERERERERESkBCqii4iIiIiIiIiIiIiUQEV0EREREREREREREZESqIguIiIiIiIiIiIiIlICFdFFREREREREREREREqgIrqIiIiIiIiIiIiISAn+H9SLIaHKWJ0lAAAAAElFTkSuQmCC",
"text/plain": [
"<Figure size 1500x400 with 3 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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",
"text/plain": [
"<Figure size 1800x500 with 2 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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",
"text/plain": [
"<Figure size 700x500 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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",
"text/plain": [
"<Figure size 600x400 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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8dnZ2Vs+ePRUbG6sjR45Ikpo0aaKSJUtq48aNlvWOHz+uY8eOZTjEK9XXX38tBweHdM/KZsfFDVLPyOzcuVNJSUlZ2vby5cvas2eP2rdvr+vXr1s+a5cuXVLTpk116tQpXbhwwWqbHj16WJ21AfIShkIBOSj1f7g3btzI9Da//PKL5s2bp4MHD6b5n/S1a9eshhXcazhK2bJlrR6nblemTJk0y1NSUnTt2jUVL1483X2dO3dOAQEBaZZnNAzp7jak25d6vXLliuVxSkqKwsLC9Omnn+rs2bMymUyW54oVK2b5+5kzZ1SpUiUVKvRg/1yVK1dO06ZNk4ODg5ydneXj42M1zOXmzZtauHCh1q1bpwsXLljNU8js+HDp3n2Rnqz0z53v27lz51SyZEnLsZUqdVhWajjISttFihSRJF29elXS7fHykjIc+nJ323cymUxp5msULVo0w2FzHh4eWQ4EpUuXTvOFsGjRojp27FiW9nOnUqVKZVjj3X2SGjJS36+MZPYYT3W/fpGk8+fPKzQ0VDt27LA6LqT//ZCRkWeffVZbtmzR/v37Vb9+ff3000+KiYmxCmnPPfectm7dqiFDhqhUqVJ67LHH1L59ez3xxBMZ7rd8+fIaMGCAli5dqo0bN6pevXpq2bKlnn766fsOg5KkkiVLppmA7OPjI+l///Y4OjqqU6dOWrVqlWVi/caNG+Xi4qJ27drdc/9nzpxRyZIl033Ps0ODBg3Utm1bzZ8/X8uWLVODBg3UunVrderU6b4XUDhz5ozMZrPee+89vffee+muExsbazVsjstdIy8jWAA5yMPDQyVLlsz0hNczZ86of//+qly5ssaNG6cyZcrIyclJ33//vZYtW6aUlBSr9e91BaiM7tGQ0XLzXRN/bZGZX9M+/PBDvffee+ratatefvllFS1aVI6OjpoxY0a21uLu7q4mTZpk+PzUqVO1bt06vfDCCwoICFDhwoXl4OCgMWPGZKmOrF6NK6v9k53udwyk/vett96St7d3mvXu1b9RUVFq1aqV1bKwsLAML7FZuXJl/fHHH4qKiko3kKYnM8dXRr9E3/0ZSnWv/suovfsdH1k9xu/XLyaTSQMGDLDMMalcubLc3d114cIFjRs3LsPXlqpp06by8vLSl19+qfr16+vLL7+Ut7e31efD09NTGzZs0I8//qgffvhBP/zwg9atW6dnn31Ws2bNynDf48aNU+fOnfXtt99q9+7dmjZtmhYuXKjw8HCVLl36nnVl1rPPPqslS5Zo+/bt6tixozZt2qTmzZtnKrw8iIyOoTsDYup6oaGhOnjwoL777jvt2rVLb7zxhpYuXarVq1ff84xcap8NHDgwwzOed/+AwxW5kJcRLIAc1qJFC61evVoHDhxQYGDgPdfdsWOHEhMTtWDBAqtfL7Nz6MmDKFeuXJqr00h6oKsypfrqq6/UsGFDy5CkVFevXrU6c1KhQgX99ttvSkpKypFLw3711Vd69tlnra6edOvWrTRnK/LK/SDKlSuniIgIXb9+3erMwYkTJyzP26p8+fKSbn/JvFcoS4+3t7eWLl1qtax69eoZrt+iRQtt2rRJX375pV588cWsF5uB1F/77+7HzJzRyS6ZPcYz6/jx4zp16pRmzZplmcwvKdPDlgwGgzp27Kj169frtdde0/bt29MdVuPs7KyWLVuqZcuWSklJ0eTJk7V69WoNHz5cFStWzHD/qVc7Gj58uH799Vf17t1bq1atSveqXneKjo5Oc9nU1LNmdx7Pvr6+qlmzpjZu3KjSpUvr/Pnzmjhx4n1fd4UKFfTjjz/q8uXLWTprcecZo9S/S7fPGqUnICBAAQEBGjNmjDZu3KjXXntNW7ZsUffu3TP89yP1s+bk5JTlzxqQFzHHAshhgwcPlru7uyZOnKiYmJg0z585c8ZyKcfU/8HfPRxn7dq1uVNsBpo2baqDBw/q6NGjlmWXL1+2Gu+cVQaDIc2vtlu3bk0znrhNmza6dOmSVq5cmWYf2XFmI71fo1esWJHmV8nUexpkZXhUTnjiiSdkMpnSvB/Lli2Tg4PDPYesZNbjjz8uDw8PLVy4MN0x4+ldmjaVi4uLmjRpYvXnzvkJd2vbtq18fX314Ycf6sCBA2mev379uubOnZvl15D6K+++ffssy0wmk8LDw7O8rweV2WM8s1LPaNy5T7PZrLCwsEzv45lnntGVK1f05ptvKj4+Ps38hEuXLqVpM/XqRXdfljrV9evX08zR8vX1laOjY4bb3Ck5OVmrV6+2PE5MTNTq1atVokQJ1apVK039u3fv1vLly1WsWLFMHe9t2rSR2WzW/Pnz0zx3r39D0juG4uPj01w6+8qVK2n2kzofLfX1p/77cffwOU9PTzVo0ECrV69WdHR0mhru9VkD8iLOWAA5rEKFCnr77bc1ZswYdejQwXLn7cTERB04cEDbtm2zTBp97LHH5OTkpJdeekm9evXSjRs3tGbNGnl6eurixYt2ew2DBw/Wl19+qQEDBqhv376Wy82WKVNGly9ffqBf85s3b673339f48ePV2BgoI4fP66NGzdafsFL9eyzz2rDhg0KCQnRoUOHVLduXSUkJCgiIkK9e/dW69atbXptzZs31xdffCEPDw9VrVpVBw8e1E8//ZTml80aNWrIYDBo0aJFunbtmpydndWoUSOr+Rq5oWXLlmrYsKHmzp2rc+fOyWg0avfu3fr222/1wgsvPNDld+/m4eGhyZMna+zYserSpYs6dOigEiVK6Pz58/r+++8VFBSkN998Mxteze1faufPn285ttq1a6egoCA5OTnpr7/+0qZNm1SkSJH7/up9t2rVqikgIEDvvPOOrly5oqJFi2rLli33vEhBdsvsMZ5ZlStXVoUKFTRr1ixduHBBHh4e+uqrr+471+NONWvWlK+vr7Zt26YqVaqk+eI+ceJEXblyRY0aNVKpUqV0/vx5ffLJJ6pRo4bV5ZXvtGfPHk2ZMkXt2rWTj4+PTCaTvvjiCxkMBrVt2/a+NZUsWVKLFi3SuXPn5OPjoy1btujo0aOaOnVqmrOUHTt21OzZs/XNN9+od+/emTqL2ahRIz3zzDNasWKFTp8+rccff1wpKSn65Zdf1LBhw3QvDCHd/ve4bNmymjBhgk6cOCGDwaC1a9eqePHiVmct1q9fr1WrVql169aqUKGCbty4ofDwcHl4eFiCj6urq6pWraqtW7fKx8dHxYoVU7Vq1eTr66tJkybpueeeU6dOndSjRw+VL19eMTExOnjwoP799199+eWX932NQF5BsAByQatWrfTll19qyZIl+vbbb7Vq1So5OzvLaDRq3Lhx6tGjh6TbXxxCQ0P17rvvatasWfLy8lLv3r1VokSJTN2fIKeUKVNGYWFhlnHTJUqUUJ8+feTm5qZp06Y90Jjfl156SQkJCdq4caO2bNmimjVrauHChZozZ47Veqlf5hcsWKBNmzbp66+/VrFixRQUFJTuzauyasKECXJ0dNTGjRt169YtBQUFaenSpRo8eLDVet7e3goODtbChQs1YcIEmUwmhYWF5XqwcHR01IIFCxQaGqotW7ZY7kMyduxYDRw4MNva6dSpk0qWLKmPPvpIS5YsUWJiokqVKqV69eplePWkB1WxYkVt2LBBy5Yt0zfffKNvv/1WKSkpqlixorp3757mCliZ9fbbb+vNN9/URx99pCJFiqhbt25q2LChBgwYkK31ZySzx3hmOTk56cMPP7R8Dl1cXPTkk0+qT58+WboPyDPPPKPZs2enu83TTz+t8PBwffrpp7p69aq8vb3Vvn17jRo1KsM5IEajUU2bNtV3332nCxcuyM3NTUajUYsWLUr3og93K1q0qGbOnKlp06YpPDxcXl5eevPNNy3/Lt7Jy8tLjz32mL7//vssveaQkBAZjUZ9/vnneuutt1S4cGH5+fndc3hqaugNDg7We++9J29vb73wwgsqUqSI5eZ30u3J24cPH9aWLVsUExOjwoULq3bt2nr77betQuS0adM0depUhYSEKCkpSSNHjpSvr6+qVq2qtWvXav78+Vq/fr0uX76sEiVKqGbNmhoxYkSmXyOQFziYs3OWJICHyvTp0y3zR7j8IZA/LF++XCEhIdqxY0eaK1HlByNGjNDx48ctN6ADkHcwxwJApty8edPq8aVLl/Tll1+qbt26hAognzCbzfr8889Vv379fBkqoqOjs3y2AkDuYSgUgEzp2bOnGjRooCpVqigmJkZr167V9evXNXz4cHuXBuA+4uPjtWPHDu3du1fHjx/XBx98YO+SsiQyMlK//vqrPv/88zQ31AOQdxAsAGRKs2bN9NVXXyk8PFwODg6qWbOmpk+frvr169u7NAD3ERcXp1dffVVFihTRSy+9lOZeI3ndvn37NH78eJUtW1YzZ85M9/4qAOyPORYAAAAAbMYcCwAAAAA2I1gAAAAAsNlDN8ciJSVFycnJcnR0fKCbegEAAAAPC7PZrJSUFBUqVCjD+9mkeuiCRXJysg4fPmzvMgAAAIB8w9/fX87Ozvdc56ELFqlJy9/fn2vvAwAAAPdgMpl0+PDh+56tkB7CYJE6/MlgMBAsAAAAgEzIzBQCJm8DAAAAsBnBAgAAAIDNCBYAAAAAbPbQzbEAAADIz0wmk5KSkuxdBgoIJyenbJt3TLAAAADIB8xms/79919dvnzZ3qWggClWrJhKly5t8z3eCBYAAAD5QGqoKFmypNzd3bnRL2xmNpsVHx+v6OhoSVKZMmVs2h/BAgAAII8zmUyWUOHp6WnvclCAuLm5SZKio6NVsmRJm4ZF2XXy9r59+/TSSy+padOmMhqN2r59+3232bt3rzp37iw/Pz89+eSTWrduXS5UCgAAYD+pcyrc3d3tXAkKotTjyta5O3YNFvHx8TIajZo0aVKm1o+MjNSLL76ohg0b6osvvtALL7ygiRMnateuXTlcKQAAgP0x/Ak5IbuOK7sOhWrWrJmaNWuW6fU/++wzPfrooxo3bpwkqUqVKvrll1+0bNkyPf744zlVJgAAAID7yFf3sTh48KAaN25staxp06Y6ePCgfQoCAABAhs6ePSuj0aijR4/auxTkgnw1eTsmJkZeXl5Wy7y8vHT9+nXdvHlTrq6umd6XyWTK7vIAAAByhMlkktlstvzJL1JrzYt1r1u3TiEhIdq3b5+9S7G71P4xmUxpviNn5TtzvgoW2enw4cP2LgEAkI85OTnJr1ZNORrs97/SFFOyfj/yBzdLe0gUKlRICQkJSklJsXcpmXbz5k3Lf+Pj47Ntv0lJSXJycrJpH4mJiZbLrT7sbt26paSkJP3555827SdfBQsvLy/FxMRYLYuJiZGHh0eWzlZIkr+/f7bdZRAA8HByNBgUs26ckmJO5HrbTl6V5dVlpmrVqpXrbSP33bx5U6dPn5abm1uWv/PkhpSUFH388ccKDw9XVFSUvLy81LNnT3Xs2FGSdPHiRc2dO1eHDh1SxYoVNXnyZAUGBkqS5s2bp2+//VYbNmyw7G/58uVavny5duzYIUkaN26crl27Jn9/f61cuVLOzs5avny5WrdurdDQUH3yySfp7jsje/fu1eTJkyVJQUFBkqQRI0bI0dFR27Zt08aNG63Wf/bZZ9WiRQu9/PLLllpq1KihlStXKjExUR07dtSECRPk7OxseT8WLVqk8PBwxcTEyMfHR8OGDVO7du1sfq9zgqOjo5ycnFS1atU0x5fJZMr0D/L5KlgEBATohx9+sFr2008/KSAgIMv7MhgMBAsAgM2SYk4o6V/7jR/n/2UPB4PBIAcHB8ufvOadd97RmjVrNH78eNWtW1fR0dE6efKkpdZ3331Xr7/+uipWrKi5c+fqtdde09dff61ChQpZ1rn7dd35Wh0cHBQRESEPDw8tXbrUav177TsjQUFBeuONNxQaGqpt27ZJun3J1WvXrun999/X4cOHVbt2bUnSH3/8oWPHjmn+/PmWmiIiIuTi4qIVK1bo3LlzGj9+vIoXL64xY8ZIkj766CN9+eWXCg4Olo+Pj/bt26exY8fK09NTDRo0yK63Pdukvi5bvx/bdfL2jRs3dPToUcuEnrNnz+ro0aM6f/68JGnOnDkaO3asZf1evXopMjJSb731lv755x+tXLlSW7duVf/+/e1RPgAAwEPv+vXrCgsL03//+1917txZFSpUUL169dS9e3fLOgMHDlTz5s1VqVIljR49WufOndPp06ez1I67u7umTZumatWqqVq1ajbt29nZWYULF5aDg4O8vb3l7e2tRx55RKVLl1bTpk2t7pO2bt061a9fX+XLl7fafsaMGapWrZqaN2+u0aNHKywsTCkpKUpMTNTChQs1Y8YMPf744ypfvry6dOmip59+WqtXr87Sa85v7HrG4vfff1e/fv0sj0NCQiRJnTt31syZM3Xx4kVFRUVZni9fvrwWLlyokJAQhYWFqXTp0po2bRqXmgUAALCTEydOKDExUY0aNcpwHaPRaPm7t7e3JCkuLk5VqlTJdDu+vr6WoUbZve879ejRQ2+88YbGjx8vBwcHbdy4UePHj0/TZuodqyUpMDBQ8fHxioqKUnx8vBISEjRw4ECrbZKSklSjRo0Hqim/sGuwaNiwoY4dO5bh8zNnzkx3mzvH4AEAAMB+XFxc7rvOnROtU4cwpU5Cd3BwSHPFqOTk5DT7uPOLfGb3/SBatGghZ2dnffPNN3JyclJycnKW5kakTgZfuHChSpUqZfVcesGoIMlXcywAAACQt/j4+MjV1VV79uyxGi6UWSVKlFBMTIzMZrMlGOTGfS+cnJzSvZRqoUKF9Oyzz2rdunVycnLSU089lWZC87Fjx6xudXDw4EG5u7urTJkyKlq0qJydnXX+/Pk8OZ8iJxEsAAAA8MBcXFw0ZMgQzZ49W05OTgoKClJcXJz++uuvNDc2Tk/Dhg01ZcoULVq0SO3atdOuXbu0a9cueXh45Gjd5cqVU3x8vCIiIixDm1LPinTv3l0dOnSQJK1atSrNtomJiZowYYKGDRumc+fOad68eerbt68cHR3l4eGhgQMHKiQkRGazWXXr1tW1a9f066+/ysPDQ507d87R12VPBAsAAADYZPjw4TIYDAoNDVV0dLS8vb3Vq1evTG1bpUoVTZo0SQsXLtSCBQvUpk0bDRw4UOHh4Tlac1BQkHr16qX//Oc/unz5skaOHKlRo0ZJun0WJjAwUFeuXFGdOnXSbNu4cWNVrFhRffr0sVxuNnVbSfrPf/6jEiVKaOHChTp79qwKFy6smjVr6qWXXsrR12RvDua8dhvEHGYymXTw4EEFBARwiT4AgM2iPuphl8vNOpWuoTJDc/aLF/KOmzdv6uTJk6pUqVKevI9FQWM2m9WmTRs999xzGjBggNVz48aN09WrV/XBBx/Yqbrsd6/jKyvfnTljAQAAAPx/cXFx2rx5s2JiYtSlSxd7l5OvECwAAABQ4AwePFi//PJLus+9+OKLGQ5Laty4sYoXL64pU6aoaNGiOVligUOwAAAAQIEzffp03bx5M93n7hUY7nUrBCn92yHgNoIFAAAACpy77yGBnOdo7wIAAAAA5H8ECwAAAAA2I1gAAAAAsBnBAgAAAIDNCBaAnZlTTHZt3/SQtw8AALIHV4UC7MzB0aCYdeOUFHMi19t2rdpUxVuOVvBXwTp16VSut+9T3EeT2k7K9XYBAPZ39uxZtWrVShs2bFCNGjXsXY6VdevWacaMGdq/f3+utDdv3jxt375dX3zxRa60l1MIFkAekBRzQkn/Hs31dgt5VpIknbp0SscvHs/19gEAtjGlpMjgmHsDUHK7PeQvBAsAAIB8yuDoqImf7tLJ6Cs53lalkkU17bnHc7wdWyUmJsrZ2dneZaQrKSlJTk5O9i4jxxAsAAAA8rGT0Vf057k4e5eRoZSUFC1ZskTh4eGKioqSl5eXevbsqU6dOkmSIiMjNWPGDB06dEgVK1ZUcHCwAgMDJaU/RGjZsmUKCwvTjh07JEnjxo3T1atX5e/vr5UrV8rZ2VlhYWFq1aqV5s2bpxUrVqS778zYtWuXZsyYoX///VdBQUEKCQlRyZIlJUmHDh3S3Llz9ccffyg5OVk1atTQ+PHjVatWLcv2RqNRkyZN0g8//KA9e/Zo0KBBGjVqlD766CMtW7ZMCQkJat++vUqUKGHz+5wXcC4LAAAAOWbOnDlatGiRhg8fri1btujtt9+Wl5eX5fm5c+dq0KBB2rBhg3x8fPTqq68qOTk5S21ERETo5MmTWrp0qRYuXJgt+75586Y+/vhjvfXWW/rkk08UFRWlWbNmWZ6/ceOGnn32WX366acKDw9XxYoVNXToUF2/ft1qP/Pnz9eTTz6pjRs3qmvXrtqyZYvmzZunMWPGaO3atfL29tann36apdebV3HGAgAAADni+vXrCgsL05tvvqnOnTtLkipUqKB69erp7NmzkqSBAweqefPmkqTRo0frqaee0unTp1WlSpVMt+Pu7q5p06ZZhkBlx76TkpIUHBysChUqSJL69OmjDz74wPJ848aNrdafOnWq6tWrp3379qlFixaW5R07dlTXrl0tj1955RV169ZN3bt3lySNGTNGERERunXrVqZfb17FGQsAAADkiBMnTigxMVGNGjXKcB2j0Wj5u7e3tyQpLi5rQ7t8fX3TnVdhy77d3NwsoUKSSpYsqdjYWMvjmJgYTZw4UW3atFHdunVVt25dxcfH6/z581b78fPzs3r8zz//qE6dOlbLAgICMlVTXscZCwAAAOQIFxeX+65z52RmBwcHSbfnZaQ+NpvNVuunN5TJzc0tS/s2m82WxxkpVMj6a/Ldtbz++uu6fPmyJkyYoLJly8rZ2Vk9e/ZUUlKS1Xbu7u5p9n33ayooCBYAAADIET4+PnJ1ddWePXtUvnz5LG9fokQJxcTEWAWBo0dtvzy7g4ODki6dlTk5/eFHydeiJXOKEi/+Y1mWdCVKkizLfv3lF018dbga13xUkvTvhShdunRJphuxaba783Glio/q0KFDlqFhkvTbb7/Z/JryAoIF7suUYpLB0fDQtg8AAB6Mi4uLhgwZotmzZ8vJyUlBQUGKi4vTX3/9lWaOQnoaNmyoKVOmaNGiRWrXrp127dqlXbt2ycPDw+bazMm3ZE66mf6TpiTJbLZ+3nT7TETqsgqPltbGbdtVs2pFXb8Rr3cWhsnVxVlmU1Ka7e583LdbJ02c8a78/PwUFBSkjRs36q+//nqg4JXXECxwXwZHA3dmBpAncbMu4Pb9JfJyO8OHD5fBYFBoaKiio6Pl7e2tXr16ZWrbKlWqaNKkSVq4cKEWLFigNm3aaODAgQoPD3+gWrJT8H9HaMqcBeo59DWVLumpUYP76J0Fy++7XbtWzXT+cqJmz56tW7duqW3bturdu7d+/PHHXKg6ZzmYC+ogrwyYTCYdPHhQAQEBMhj4FTyzBnw2wC53Zvb19tXSXktzvd3cFvVRD7vcedutVgd5d51F/yJfy62bg92tibGsRrQPstvn16l0DZUZav8vV8gdN2/e1MmTJ1WpUiW5urpalnPn7QeXePGfjM9Y5CAHJ1c5e2f+ile5IaPjS8rad2fOWAAA8jV73RzMx7tIrrcJ3C23v+QXlFCBnEGwAAAAwENl8ODB2r9/n5TOuJ3BfbpoSN9uuV9UAUCwAAAAwENl+vTpunb+eLpXhSpa2PaJ4Q8rggUAAAAeKqVKlVJxx+t2mWNRkDFQDgAAAIDNCBYAAAAAbEawAAAAAGAzggUAAAAAmxEsAAAAANiMYAEAAIBcd/bsWRmNRh09mvt3rs9pZrNZwW8vUNOn+6l2iy768++T9i4pVxAsAAAA8ilziqlAt5df/fjzAX3x1XeaP+MN7Vi7RFUrVVDtFl2048e99i4tR3EfCwAAgHzKwdGgmHXjlBRzIsfbcvKqLK8uM3O8HVslJibK2dnZrjWcPf+vvD2LKcCvul3ryG0ECwAA8iHHRzxlSjHJ4GiwS/v2bBvWkmJOKOnfvDucKCUlRUuWLFF4eLiioqLk5eWlnj17qlOnTpKkyMhIzZgxQ4cOHVLFihUVHByswMBASdK8efO0fft2ffHFF5b9LVu2TGFhYdqxY4ckady4cbp69ar8/f21cuVKOTs7KywsTK1atdK8efO0YsWKdPd9L+f/jdaM0MU6cPiokpKTVa5USb3yUj893qiuJGn/wSN6Z+FyHfvnlIoW9tDTbVto5KDnVMhg0MSZ8/TlV99Jkmq36KKypbwt+/3P/82SNEvlypWz1F+QECwAAMiHHF2LyOBoUPBXwTp16VSutu1T3EeT2k7K1TaRf82ZM0dr1qzR+PHjVbduXUVHR+vkyf/NOZg7d65ef/11VaxYUXPnztWrr76qr7/+WoUKZf5rakREhDw8PLR06VKr5Q+67xnvLVJScrKWvjdN7q4u+uf0Wbm5uUqSLlyM1fDx0/RM2xaaPn60Tp45p+C3F8jZ2UnD+/fS6yMHqnzZUvp80zda9eFbcnS8PfOgeecBmvr6SDVt0kiuJatk+rXlJwQLAADysVOXTun4xeP2LgNI1/Xr1xUWFqY333xTnTt3liRVqFBB9erV09mzZyVJAwcOVPPmzSVJo0eP1lNPPaXTp0+rSpXMf/l2d3fXtGnTLEOgbN13VHSMWj/RSL6VK0qSHi1b2vLc6i+2qbS3l954eYgcHBxUqcKjio6J07sfrdBL/XqosMcjcnd3k8HRUV4lilvtt7DHI/LyLCHnEiUy/dryEyZvAwAAIEecOHFCiYmJatSoUYbrGI1Gy9+9vW8PG4qLi8tSO76+vunOq3jQfT/XpYMWrfhc/UaO1/tLP9Pxf05Znjt55qzq1PKVg4ODZVmgX3XFJ9zUhYuxWaq7oCFYAAAAIEe4uLjcdx0nJyfL31O/rKekpFgem81mq/WTk5PT7MPNzS3L+76Xrk89qS2fLlDHNs3198nT6vXSWH26bvN9t3vYESwAAACQI3x8fOTq6qo9e/Y80PYlSpRQTEyMVbjIrftelC7ppR5Pt9XcKa/rhR5Pa+3m7ZKkShUe1W9HjlvVdOD3P/WIu5tKeXtmuL9ChQrJZLp/qMnPCBYAAADIES4uLhoyZIhmz56tDRs26MyZMzp48KDWrFmTqe0bNmyouLg4LVq0SGfOnNHKlSu1a9euHK5amjV/iXb/fEBnoy7oj+P/6OcDh1W5QjlJUs9n2unfizEKCV2sk2fO6rsff9aCZav1fPdOlona6SlX2lt7fz2kmNg4XblyJcdfgz0weRsAACAfc/KqnKfbGT58uAwGg0JDQxUdHS1vb2/16tUrU9tWqVJFkyZN0sKFC7VgwQK1adNGAwcOVHh4+APVklkpKSma8d4iXbgYq0cecdNj9QM1dsRASVIpb099EDJR7yxcrm6Dv1HRwh7q3KGVhj7f/Z77fHVYf739wTKt2/yCSpUuzeVmYR/mFJMcuFY4AAC4iznFlKs3rXuQ7ySOjo4aNmyYhg0blua5Y8eOWT0uUqRImmW9e/dW7969rZa99NJLlr/PnJn29T/66KOZ2ndGxo8ecs/n6wXU0qcL3srw+ee7ddLz3TpZLWvepL6aN6kvBydXOXtzuVnYSW7eVfNurlWbqnjL0bneLgAAuL/c/uGRHzpxL3YPFitXrtSSJUt08eJFVa9eXf/3f/+n2rVrZ7j+smXLtGrVKkVFRal48eJq27atXn311UxddSA/s9ddNQt5Vsr1NgEAAHLS4MGDtX//PsmcznN9umhI32451raDYyGZzWary9Xmtpxq367BYsuWLQoJCVFwcLDq1Kmj5cuXa9CgQdq2bZs8PdPOqt+4caPmzJmjGTNmKDAwUKdOndK4cePk4OCg8ePH2+EVAAAAIL+ZPn26rp0/LnPyrTTPFS3skbONOxrk4OCg81fO65Ypbfs5zcXgorJFy+bIvu0aLJYuXaoePXqoa9eukqTg4GDt3LlTa9eu1dChQ9Osf+DAAQUFBalTp9tj1h599FF17NhRv/32W67WDQAAgPyrVKlSKu54Xeakm3ar4Zbplm4l5X6wyEl2u9xsYmKijhw5oiZNmvyvGEdHNWnSRAcOHEh3m8DAQB05ckSHDh2SJEVGRur7779Xs2bNcqVmAAAAAOmz2xmLS5cuyWQypRny5OnpqRMn0p+k3KlTJ126dEnPPfeczGazkpOT1atXL6srA2SWyWR6oLrtwWBgolR+6q+son8Ldv8iZ/H5sS8+u7nHZDLJbDZb/sA29pzfkFfceRylHlcmkynN5zorn3O7T97Oir1792rhwoWaNGmSateurTNnzmj69Ol6//33NWLEiCzt6/DhwzlUZfZyc3NTzZo17V2G3R07dkwJCQn2LiPb0b+3FdT+Rc7i82N/fHZzV6FChZSQkKCUlIJ99+ac5ujoKDc3N3uXYXc3b960HEu3bt1SUlKS/vzzT5v2abdgUbx4cRkMBsXGxlotj42NlZeXV7rbvPfee3r66afVvfvtG5AYjUbFx8frzTff1LBhw+55t8O7+fv780tXPmI0Gu1dAnIQ/QvkT3x2c8/Nmzd1+vRpubm5ydXV1d7loAC48zhydHSUk5OTqlatmub4MplMmf5B3m7BwtnZWbVq1VJERIRat24t6fZdDiMiItS3b990t7l582aa8JAaDrJ6WtBgMBAs8hH6qmCjf4H8ic9u7jEYbl9JKPUPYKs7j6PU48rW78d2m7wtSQMGDFB4eLjWr1+vf/75R5MnT1ZCQoK6dOkiSRo7dqzmzJljWb9FixZatWqVNm/erMjISO3evVvvvfeeWrRowT9uAAAA+cjZs2dlNBp19Gju36crv7gQdUHPPPGMTvyV+zdJfhB2nWPRoUMHxcXFKTQ0VBcvXlSNGjW0ePFiy1CoqKgoqzMUw4YNk4ODg959911duHBBJUqUUIsWLTRmzBh7vQQAAAC7MaWYZMjFu2HndnsPO6+SXlq2fpmKFC1i71Iyxe6Tt/v27Zvh0KcVK1ZYPS5UqJBGjhypkSNH5kZpAAAAeZrB0aDgr4J16tKpHG/Lp7iPJrWdlOPt2CoxMVHOzs72LuOeTCZTpibhGwwGFfcsngsVZQ+7BwsAAAA8uFOXTun4xeP2LiNDKSkpWrJkicLDwxUVFSUvLy/17NnTcsPjyMhIzZgxQ4cOHVLFihUVHByswMBASdK8efO0fft2ffHFF5b9LVu2TGFhYdqxY4ckady4cbp69ar8/f21cuVKOTs7KywsTK1atdK8efO0YsWKdPedEbPZrOadB2jCmKFq0+z2/da6D35FsZcua8fajyVJvx4+qiGvTtKPX66Qm6uLwsK/1IZtO3Q26oKKFvZQs8b19MpL/eT+/68+9cW2HXpr/seaNn603lv8qU5HntOSNUs0dvhYtenURlHnorT7u93yKOyhHv16qO3TbSXdHgo1tOdQzV0yV5WrVdbhA4c18eWJmjJ3ipZ/uFyRpyJVqWoljR4/Wo9WeNTyGsKXh2vT2k26deuWmrZsqiJFi+jAzwf07sfvZkOPZsyucywAAABQsM2ZM0eLFi3S8OHDtWXLFr399ttWVwCdO3euBg0apA0bNsjHx0evvvqqkpOTs9RGRESETp48qaVLl2rhwoU27dvBwUFBtWtq/8EjkqSr167rxJmzunUrUSfPnJUk7f/tiPyMVeXm6nJ7G0cHjRs1SOuXvqdp40br5wO/650Pw6z2m3ArUUtXrVfwG69p06ZNKla8mCTpi9VfqKqxqt5d8q7aP9teH77zoc7+/3Yy8smiTzRwxEDNWTRHBoNB82bOszy38+udWrNijfq91E/vLH5H3qW8te2LbZl7I21EsAAAAECOuH79usLCwvTf//5XnTt3VoUKFVSvXj3LrQMkaeDAgWrevLkqVaqk0aNH69y5czp9+nSW2nF3d9e0adNUrVo1VatWzeZ91w+opX0Hf5ck/fLbH6petZLqBfhp3/8PG/sP/q66dWpZ1n++Wyc1CPRXudIl1TDIXyMH9dbXO3+y2mdycrIm/GeoAmvXUuXKlS2Xda3bqK46dO6gMo+WUdc+XVW4aGEdPnDvy7v2HdJXfgF+quBTQV37dNWfv/+pxFuJkqTN6zar9VOt1bpDa5UrX069+vdSxcoVM/Eu2o5gAQAAgBxx4sQJJSYmqlGjRhmuc+f9ULy9vSVJcXFxWWrH19c33XkVD7rvenVq6cTps4q7fEX7fzui+gF+qhdQS/sP/q6k5GQdPHJM9QP+Fyz2/PKbBr8ySa27D1ajDs9pwoxQXb56TQk3b1nWcXIqJN8qPmnaqljlf1/6HRwcVLxEcV25dOWe9fncsZ8SniUkSVcu397m3JlzqlajmtX6dz/OKQQLAAAA5AgXF5f7ruPk5GT5e+q9FVInNjs4OKS5V1l6Q5kyupP2vfZ9L9UqV1TRwh7a/9sR7f/tiOoF1FL9AD/t/+2Ijvz5t5KTTapTq7ok6dy/0Ro5foZ8q1TUnOD/6rOFb+uNl4dIkpLuqNXV2Tnde5AUMlhPeXZwcJA55d73Z7O6zcL/32VeuCM7wQIAAAA5wsfHR66urtqzZ88DbV+iRAnFxMRYhYvcuO/F7XkWNbRz9z79cypSQf415Fu5ohKTkrVm49eqZawid7fbQ5n+OPaPUsxmvTasv+rUNMqnfFldjM3aGZfsVK5COf39599Wy+5+nFMIFgAAAMgRLi4uGjJkiGbPnq0NGzbozJkzOnjwoNasWZOp7Rs2bKi4uDgtWrRIZ86c0cqVK7Vr164crvq2enX8tPXbXTJW9ZG7m5scHR1Vt3ZNbdn+g+rdMb+iQrnSSk5O1qfrtujs+X+18eudCv/yq1ypMT1PdXlK32z+Rju27tD5yPMKXx6uU/+cspzZyElcbhYAACAf8ynuk6fbGT58uAwGg0JDQxUdHS1vb2/16tUrU9tWqVJFkyZN0sKFC7VgwQK1adNGAwcOVHh4+APVkhX1AmrJlJKi+gF+Vsu+2/2zVbAwVq2k/w4foKWfrVfo4k8UVLumXh7SVxNCQnO8xvQ0b9NcF6IuaOkHS5WYmKimLZqqZbuW+uvPv3K8bQfz3QPXCjiTyaSDBw8qICDAenxaHhf1UQ8l/Zv7t7x3q9VB3l1nacBnA+xyjWxfb18t7bU0R9swpaTI4Gjfk3f0L/Dg+ry7SX+ey/1hB+0CfDS9zxMP5eeXz27uu3nzpk6ePKlKlSpZriYkcedtWyRe/EfmpJu53q6jW1E5FX9UJ+NO6lbSrftvkA3efOVNFS9RXGMmjpGLk4sqlahk9XxGx5eUte/OnLHAQ8/g6KiJn+7Syeh7X4EhJzQxltWI9kG53i4AoGDI7S/5BSVUFGS3bt7Sti+2KbBBoBwdHfXDtz/ot/2/Kfid4Bxvm2ABSDoZfcUuv3j6eBfJ9TYBAHjYDR48WPv375PSGbczuE8XDenbLfeLyi4O0v49+7VmxRolJiaqXPlyGjd1nALqBeR40wQLAAAAPFSmT5+ua+ePy5ycdihS0cIedqgo+7i4uGjq3Kl2aZtgAQAAgIdKqVKlVNzxul3mWBRkXG4WAAAAgM0IFgAAAPnEQ3YxT+SS7DquCBYAAAB5nJOTkyQpPj7ezpWgIEo9rlKPswfFHAsAAIA8zmAwqFixYoqOjpYkubu7y8EhF26lXIAlJpmk5JRcb9chMVmmmzdlSjQpxQ7tm8wm3bx5e26J2WxWfHy8oqOjVaxYMZvv8UawAAAAyAdKly4tSZZwAduYrl2U2ZSU6+06OF+TIS5BMTdilGxKzvX2CxkKKemS9esuVqyY5fiyad827wEAAAA5zsHBQWXKlFHJkiWVlJT7X4gLmujV7yk55kSut+ta7QmVaPOaPtr8kU5dOpXr7fsU91HIUyGWx05OTjafqUhFsAAAAMhHDAZDtn0RfJg53YqTbkTlfrtJ1+Tq6qrLSZcVnZD7Z5+KeRSTq6trjuybydsAAAAAbEawAAAAAGAzggUAAAAAmxEsAAAAANiMYAEAAADAZgQLAAAAADYjWAAAAACwGcECAAAAgM0IFgAAAMhVppQUe5eAHMCdtwEAAJCrDI6OmvjpLp2MvpLrbTcxltWI9kG53u7DgGABAACAXHcy+or+PBeX6+36eBfJ9TYfFgyFAoAcZE4xPdTtAwAeHpyxAIAc5OBoUMy6cUqKOZHrbTt5VZZXl5m53i4A4OFEsACAHJYUc0JJ/x7N9XYdH/GUKcUkg6Mh19tOZe/2AQC5h2ABAAWUo2sRGRwNCv4qWKcuncr19n2K+2hS20m53i4AwD4IFgBQwJ26dErHLx63dxkAssDeZ/vs3T7yJ4IFAABAHsPZRuRHBAsAAIA8iLONyG+43CwAAAAAmxEsAAAAANiMYAEAAADAZgQLAAAAADYjWAAAAACwGcECAAAAgM0IFgAAAABsRrAAAAAAYDOCBQAAAACb2T1YrFy5Ui1btpS/v7+6d++uQ4cO3XP9q1evKjg4WE2bNpWfn5/atm2r77//PpeqBQAAAJCeQvZsfMuWLQoJCVFwcLDq1Kmj5cuXa9CgQdq2bZs8PT3TrJ+YmKgBAwbI09NT7733nkqVKqXz58+rSJEidqgeAAAAQCq7BoulS5eqR48e6tq1qyQpODhYO3fu1Nq1azV06NA0669du1ZXrlzRZ599JicnJ0nSo48+mqs1AwAAAEjLbkOhEhMTdeTIETVp0uR/xTg6qkmTJjpw4EC62+zYsUMBAQGaMmWKmjRpoo4dO+rDDz+UyWTKrbIBAAAApMNuZywuXbokk8mUZsiTp6enTpw4ke42kZGR2rNnjzp16qSPPvpIZ86cUXBwsJKTkzVy5MgstZ+fwojBYLB3CXaXk/3F+2t/+enzmFUcX3x+C7KC/Nm1t7xwbPPZLdgy279ZOQ7sOhQqq8xmszw9PTV16lQZDAb5+fnpwoULWrJkSZaDxeHDh3Ooyuzl5uammjVr2rsMuzt27JgSEhKyfb+8v3lDTvWvvXF83cbnt+AqqJ9de8srxzaf3YItJ/rXbsGiePHiMhgMio2NtVoeGxsrLy+vdLfx9vZWoUKFrFJu5cqVdfHiRSUmJsrZ2TnT7fv7+5OW8xGj0WjvEpCD6N+Cjf4tuOjbgo3+Ldgy278mkynTP8jbLVg4OzurVq1aioiIUOvWrSVJKSkpioiIUN++fdPdJigoSJs2bVJKSoocHW9PDzl16pS8vb2zFCqk26fgCBb5B31VsNG/BRv9W3DRtwUb/Vuw5UT/2vU+FgMGDFB4eLjWr1+vf/75R5MnT1ZCQoK6dOkiSRo7dqzmzJljWb937966fPmypk+frpMnT2rnzp1auHCh+vTpY6+XAAAAAEB2nmPRoUMHxcXFKTQ0VBcvXlSNGjW0ePFiy1CoqKgoy5kJSSpTpoyWLFmikJAQPf300ypVqpT69eunIUOG2OslAAAAAFAemLzdt2/fDIc+rVixIs2ywMBAhYeH53RZAAAAALLArkOhAAAAABQMBAsAAAAANiNYAAAAALCZTcHi9OnT2rVrl27evCnp9g3sAAAAADx8Hmjy9qVLlzRmzBjt2bNHDg4O+vrrr1W+fHm98cYbKlq0qMaNG5fddQIAAADIwx7ojEVISIgMBoN27twpV1dXy/IOHTpo165d2VYcAACAPZhTTPYuAch3HuiMxe7du7VkyRKVLl3aarmPj4/Onz+fLYUBAADYi4OjQTHrxikp5kSut+1atamKtxyd6+0CtnqgYBEfH291piLV5cuX5ezsbHNRAAAA9pYUc0JJ/x7N9XYLeVbK9TaB7PBAQ6Hq1aunDRs2WC1LSUnR4sWL1bBhw+yoCwAAAEA+8kBnLP773/+qf//++v3335WUlKTZs2fr77//1pUrV7Rq1arsrhEAAABAHvdAwcLX11dfffWVPvnkEz3yyCOKj4/Xk08+qT59+qhkyZLZXSMAAACAPO6BgsX58+dVpkwZDRs2LN3nypYta3NhAAAAAPKPB5pj0apVK8XFxaVZfunSJbVq1crmogAAAADkLw8ULMxmsxwcHNIsj4+Pl4uLi81FAQAAAMhfsjQUKiQkRJLk4OCgd999V25ubpbnTCaTDh06pOrVq2dvhQAAAADyvCwFiz/++EPS7TMWx48fl5OTk+U5Z2dnVa9eXQMHDszeCgEAAADkeVkKFitWrJAkjR8/XhMmTJCHh0eOFAUAAAAgf3mgq0KlDokCAAAAAOkBg4UkHT58WFu3blVUVJSSkpKsnps/f77NhQEAAADIPx7oqlCbN29W7969deLECX3zzTdKTk7WX3/9pT179qhw4cLZXSMAAACAPO6BgsWHH36o8ePH68MPP5STk5MmTJigbdu2qX379ipTpkx21wgAAAAgj3ugYBEZGalmzZpJun01qPj4eDk4OKh///4KDw/P1gIBAAAA5H0PFCyKFCmiGzduSJJKliypv/76S5J09epVJSQkZF91AAAAAPKFB5q8Xb9+ff30008yGo1q166dpk+frj179uinn35So0aNsrtGAAAAAHncAwWL//u//9OtW7ckScOGDZOTk5N+/fVXtWnThhvkAQAAAA+hBxoKVaxYMZUqVer2DhwdNXToUL333nsqWbKkOnfunK0FAgAAAMj7snTGIjExUfPmzdPu3bvl7OyswYMHq3Xr1lq7dq3effddGQwGvfDCCzlVKwAAAIA8KkvB4r333tPq1avVpEkT/frrr3r55ZfVpUsXHTx4UOPGjVO7du1kMBhyqlYAAAAAeVSWgsW2bds0a9YstWrVSsePH9fTTz+t5ORkffnll3JwcMipGgEAAADkcVmaY3HhwgX5+flJknx9feXs7Kz+/fsTKgAAAICHXJaChclkkpOTk+WxwWCQu7t7thcFAAAAIH/J0lAos9mscePGydnZWdLtydyTJ0+Wm5ub1Xrz58/PvgoBAAAA5HlZChZ3X0r26aefztZiAAAAAORPWQoWISEhOVUHAAAAgHzsgW6QBwAAAAB3IlgAAAAAsBnBAgAAAIDNCBYAAAAAbEawAAAAAGAzggUAAAAAmxEsAAAAANiMYAEAAADAZgQLAAAAADYjWAAAAACwGcECAAAAgM0IFgAAAABsRrAAAAAAYLM8ESxWrlypli1byt/fX927d9ehQ4cytd3mzZtlNBo1fPjwHK4QAAAAwL3YPVhs2bJFISEhGjFihNavX6/q1atr0KBBio2Nved2Z8+e1axZs1SvXr1cqhQAAABARuweLJYuXaoePXqoa9euqlq1qoKDg+Xq6qq1a9dmuI3JZNJrr72mUaNGqXz58rlYLQAAAID02DVYJCYm6siRI2rSpIllmaOjo5o0aaIDBw5kuN37778vT09Pde/ePTfKBAAAAHAfhezZ+KVLl2QymeTp6Wm13NPTUydOnEh3m/379+vzzz/Xhg0bbGrbZDLZtH1uMhgM9i7B7nKyv3h/7S8/fR6ziuOLz29Bxme3YOOzW7Bltn+zchzYNVhk1fXr1zV27FhNnTpVJUqUsGlfhw8fzqaqcpabm5tq1qxp7zLs7tixY0pISMj2/fL+5g051b/2xvF1G5/fgovPbsHGZ7dgy4n+tWuwKF68uAwGQ5qJ2rGxsfLy8kqzfmRkpM6dO6dhw4ZZlqWkpEiSatasqW3btqlChQqZatvf35+0nI8YjUZ7l4AcRP8WbPRvwUXfFmz0b8GW2f41mUyZ/kHersHC2dlZtWrVUkREhFq3bi3pdlCIiIhQ375906xfuXJlbdy40WrZu+++qxs3bmjChAkqXbp0pts2GAwEi3yEvirY6N+Cjf4tuOjbgo3+Ldhyon/tPhRqwIABev311+Xn56fatWtr+fLlSkhIUJcuXSRJY8eOValSpfTqq6/KxcVFvr6+VtsXKVJEktIsBwAAAJB77B4sOnTooLi4OIWGhurixYuqUaOGFi9ebBkKFRUVJUdHu18VFwAAAMA92D1YSFLfvn3THfokSStWrLjntjNnzsyJkgAAAABkAacCAAAAANiMYAEAAADAZgQLAAAAADYjWAAAAACwGcECAAAAgM0IFgAAAABsRrAAAAAAYDOCBQAAAACbESwAAAAA2IxgAQAAAMBmBAsAAAAANiNYACjQTCkp9i4BAICHQiF7FwAAOcng6KiJn+7Syegrud52E2NZjWgflOvtAgBgDwQLAAXeyegr+vNcXK636+NdJNfbBADAXhgKBQAAAMBmBAsAAAAANiNYAAAAALAZwQIAAACAzQgWAAAAAGxGsAAAAABgM4IFAAAAAJsRLAAAAADYjGABAAAAwGYECwAAAAA2I1gAAAAAsBnBAgAA5DmmlBR7lwAgiwrZuwAAAIC7GRwdNfHTXToZfSXX225iLKsR7YNyvV0gvyNYAACAPOlk9BX9eS4u19v18S6S620CBQFDoQAAAADYjGABAAAAwGYECwAAAAA2I1gAAAAAsBnBAgAAAIDNCBYAAAAAbEawAAAAAGAzggUAAAAAmxEsAAAAANiMYAEAAADAZgQLAAAAADYjWAAAAACwGcEiE0wpKfYuAQAAAMjTCtm7gPzA4OioiZ/u0snoK7nedhNjWY1oH5Tr7QIAAABZQbDIpJPRV/Tnubhcb9fHu0iutwkAAABkFUOhAAAAANiMYAEAAADAZgQLAAAAADYjWAAAAACwWZ4IFitXrlTLli3l7++v7t2769ChQxmuGx4erueee07169dX/fr11b9//3uuDwAAACDn2T1YbNmyRSEhIRoxYoTWr1+v6tWra9CgQYqNjU13/b179+qpp55SWFiYPvvsM5UpU0YDBw7UhQsXcrlyAAAAAKnsHiyWLl2qHj16qGvXrqpataqCg4Pl6uqqtWvXprv+nDlz1KdPH9WoUUNVqlTRtGnTlJKSooiIiFyuHAAAAEAquwaLxMREHTlyRE2aNLEsc3R0VJMmTXTgwIFM7SMhIUHJyckqWrRoTpUJAAAA4D7seoO8S5cuyWQyydPT02q5p6enTpw4kal9vP322ypZsqRVOMkMk8mU6XUNBkOW9o3sl5X+yir61/7o34KN/i246NuCjf4t2DLbv1k5DvL1nbc/+ugjbdmyRWFhYXJxccnStocPH87Uem5ubqpZs+aDlIdsdOzYMSUkJGT7funfvIH+Ldjo34KLvi3Y6N+CLSf6167Bonjx4jIYDGkmasfGxsrLy+ue2y5ZskQfffSRli5dqurVq2e5bX9/f9JyPmI0Gu1dAnIQ/Vuw0b8FF31bsNG/BVtm+9dkMmX6B3m7BgtnZ2fVqlVLERERat26tSRZJmL37ds3w+0WLVqkDz/8UEuWLJG/v/8DtW0wGAgW+Qh9VbDRvwUb/Vtw0bcFG/1bsOVE/9p9KNSAAQP0+uuvy8/PT7Vr19by5cuVkJCgLl26SJLGjh2rUqVK6dVXX5V0e/hTaGio5syZo3LlyunixYuSJHd3dz3yyCN2ex0AAADAw8zuwaJDhw6Ki4tTaGioLl68qBo1amjx4sWWoVBRUVFydPzfxas+++wzJSUlafTo0Vb7GTlypEaNGpWrtQMAAAC4ze7BQpL69u2b4dCnFStWWD3esWNHbpQEAAAAIAvsfoM8AAAAAPkfwQIAAACAzQgWAAAAAGxGsAAAAABgM4IFAAAAAJsRLAAAAADYjGABAAAAwGYECwAAAAA2I1gAAAAAsBnBAgAAAIDNCBYAAAAAbEawAAAAAGAzggUAAAAAmxEsAAAAANiMYAEAAADAZgQLAAAAADYjWAAAAACwGcECAAAAgM0IFgAAAABsRrAAAAAAYDOCBQAAAACbESwAAAAA2IxgAQAAAMBmBAsAAAAANiNYAAAAALAZwQIAAACAzQgWAAAAAGxGsAAAAABgM4IFAAAAAJsRLAAAAADYjGABAAAAwGYECwAAAAA2I1gAAAAAsBnBAgAAAIDNCBYAAAAAbEawAAAAAGAzggUAAAAAmxEsAAAAANiMYAEAAADAZgQLAAAAADYjWAAAAACwGcECAAAAgM0IFgAAAABsRrAAAAAAYDOCBQAAAACbESwAAAAA2IxgAQAAAMBmeSJYrFy5Ui1btpS/v7+6d++uQ4cO3XP9rVu3ql27dvL391enTp30/fff51KlAAAAANJj92CxZcsWhYSEaMSIEVq/fr2qV6+uQYMGKTY2Nt31f/31V7366qvq1q2bNmzYoFatWmnEiBE6fvx4LlcOAAAAIJXdg8XSpUvVo0cPde3aVVWrVlVwcLBcXV21du3adNcPCwvT448/rsGDB6tKlSr6z3/+o5o1a+qTTz7J5coBAAAApLJrsEhMTNSRI0fUpEkTyzJHR0c1adJEBw4cSHebgwcPqnHjxlbLmjZtqoMHD+ZkqQAAAADuoZA9G7906ZJMJpM8PT2tlnt6eurEiRPpbhMTEyMvL68068fExGSqTbPZLOl2qDEYDJnaxmAwqFrponI2OGRq/exU3vMRmUwmGbx9leLonOvtOxavIJPJpKolqsrJ0SnX269YrKJMJpNMJlOOtUH/0r85hf6lf3OSPfuXvs1ZfHZzFv2btf5NXS/1O/S9OJgzs1YOuXDhgp544gl99tlnCgwMtCx/6623tG/fPq1ZsybNNn5+fpo5c6Y6duxoWbZy5Uq9//77+umnn+7bZmJiog4fPpw9LwAAAAB4CPj7+8vZ+d5BzK5nLIoXLy6DwZBmonZsbGyasxKpvLy80pyduNf6dytUqJD8/f3l6OgoB4fcT8kAAABAfmE2m5WSkqJChe4fG+waLJydnVWrVi1FRESodevWkqSUlBRFRESob9++6W4TEBCgPXv2qH///pZlP/30kwICAjLVpqOj433TFgAAAICssftVoQYMGKDw8HCtX79e//zzjyZPnqyEhAR16dJFkjR27FjNmTPHsn6/fv20a9cuffzxx/rnn380b948/f777xkGEQAAAAA5z65nLCSpQ4cOiouLU2hoqC5evKgaNWpo8eLFlqFNUVFRcnT8X/4JCgrS22+/rXfffVfvvPOOfHx89P7778vX19deLwEAAAB46Nl18jYAAACAgsHuQ6EAAAAA5H8ECwAAAAA2I1gAAAAAsBnBAgAAAIDNCBYF3MqVK9WyZUv5+/ure/fuOnTokL1LQjbYt2+fXnrpJTVt2lRGo1Hbt2+3d0nIJgsXLlTXrl0VGBioxo0ba/jw4Tpx4oS9y0I2+fTTT9WpUycFBQUpKChIPXv21Pfff2/vspBDPvroIxmNRk2fPt3epSAbzJs3T0aj0epPu3bt7F1WnkKwKMC2bNmikJAQjRgxQuvXr1f16tU1aNCgNHc6R/4THx8vo9GoSZMm2bsUZLOff/5Zffr0UXh4uJYuXark5GQNGjRI8fHx9i4N2aB06dJ67bXXtG7dOq1du1aNGjXSiBEj9Ndff9m7NGSzQ4cO6bPPPpPRaLR3KchG1apV048//mj58+mnn9q7pDzF7vexQM5ZunSpevTooa5du0qSgoODtXPnTq1du1ZDhw61c3WwRbNmzdSsWTN7l4EcsGTJEqvHM2fOVOPGjXXkyBHVr1/fTlUhu7Rs2dLq8ZgxY7Rq1SodPHhQ1apVs1NVyG43btzQf//7X02bNk0LFiywdznIRgaDQd7e3vYuI8/ijEUBlZiYqCNHjqhJkyaWZY6OjmrSpIkOHDhgx8oAZMW1a9ckSUWLFrVzJchuJpNJmzdvVnx8vAIDA+1dDrLRlClT1KxZM6v/B6NgOH36tJo2bapWrVrp1Vdf1fnz5+1dUp7CGYsC6tKlSzKZTPL09LRa7unpyXhtIJ9ISUnRjBkzFBQUJF9fX3uXg2xy7Ngx9erVS7du3ZK7u7vef/99Va1a1d5lIZts3rxZf/zxhz7//HN7l4JsVrt2bYWEhKhSpUq6ePGi3n//ffXp00cbN26Uh4eHvcvLEwgWAJBHBQcH66+//mIMbwFTqVIlbdiwQdeuXdNXX32l119/XZ988gnhogCIiorS9OnT9fHHH8vFxcXe5SCb3TkEuXr16qpTp45atGihrVu3qnv37nasLO8gWBRQxYsXl8FgSDNROzY2Vl5eXnaqCkBmTZkyRTt37tQnn3yi0qVL27scZCNnZ2dVrFhRkuTn56fDhw8rLCxMU6ZMsXNlsNWRI0cUGxurLl26WJaZTCbt27dPK1eu1OHDh2UwGOxYIbJTkSJF5OPjozNnzti7lDyDYFFAOTs7q1atWoqIiFDr1q0l3R5WERERob59+9q5OgAZMZvNmjp1qr755hutWLFC5cuXt3dJyGEpKSlKTEy0dxnIBo0aNdLGjRutlo0fP16VK1fWkCFDCBUFzI0bNxQZGclk7jsQLAqwAQMG6PXXX5efn59q166t5cuXKyEhweqXFORPN27csPqF5OzZszp69KiKFi2qsmXL2rEy2Co4OFibNm3SBx98oEceeUQXL16UJBUuXFiurq52rg62mjNnjp544gmVKVNGN27c0KZNm/Tzzz+nuRoY8icPD48086Hc3d1VrFgx5kkVALNmzVKLFi1UtmxZRUdHa968eXJ0dFTHjh3tXVqeQbAowDp06KC4uDiFhobq4sWLqlGjhhYvXsxQqALg999/V79+/SyPQ0JCJEmdO3fWzJkz7VUWssGqVaskSc8//7zV8pCQEH4UKABiY2P1+uuvKzo6WoULF5bRaNSSJUv02GOP2bs0APfx77//6pVXXtHly5dVokQJ1a1bV+Hh4SpRooS9S8szHMxms9neRQAAAADI37iPBQAAAACbESwAAAAA2IxgAQAAAMBmBAsAAAAANiNYAAAAALAZwQIAAACAzQgWAAAAAGxGsAAAAABgM4IFACDbnD17VkajUUePHrV3KQCAXEawAADkWevWrVO9evXsXQYAIBMIFgCAAs9kMiklJcXeZQBAgUawAABkWUpKihYtWqQnn3xSfn5+at68uRYsWJBmvfTOOGzfvl1Go9Hy+M8//9Tzzz+vwMBABQUFqUuXLjp8+LD27t2r8ePH69q1azIajTIajZo3b54kKTExUbNmzdLjjz+ugIAAde/eXXv37k3T7rfffqsOHTrI399f58+fz6F3AwAgSYXsXQAAIP+ZM2eO1qxZo/Hjx6tu3bqKjo7WyZMnH2hfr732mmrUqKHJkyfLYDDo6NGjcnJyUmBgoN544w2FhoZq27ZtkiR3d3dJ0pQpU/T3339r7ty5KlmypL755hsNHjxYGzdulI+PjyTp5s2bWrRokaZNm6ZixYrJ09MzW147ACB9BAsAQJZcv35dYWFhevPNN9W5c2dJUoUKFVSvXj2dPXs2y/s7f/68Bg0apCpVqkiSJRhIUuHCheXg4CBvb2+r9detW6fvvvtOpUqVkiQNGjRIu3bt0rp16/TKK69IkpKSkjR58mRVr179QV8qACALCBYAgCw5ceKEEhMT1ahRo2zZ34ABAzRx4kR98cUXatKkidq1a6cKFSpkuP7x48dlMpnUrl07q+WJiYkqVqyY5bGTk5PVkCsAQM4iWAAAssTFxSXT6zo6OspsNlstS0pKsno8atQodezYUd9//71++OEHhYaGau7cuXryySfT3Wd8fLwMBoPWrl0rg8Fg9VzqUClJcnV1lYODQ6ZrBQDYhmABAMgSHx8fubq6as+ePSpfvvw91y1evLhu3Lih+Ph4y5f+P//8M816lSpVUqVKldS/f3+98sorWrt2rZ588kk5OTnJZDJZrVujRg2ZTCbFxcVxKVoAyEO4KhQAIEtcXFw0ZMgQzZ49Wxs2bNCZM2d08OBBrVmzJs26derUkZubm9555x2dOXNGGzdu1Lp16yzP37x5U1OmTNHevXt17tw5/fLLLzp8+LBlvkW5cuUUHx+viIgIxcXFKSEhQZUqVVKnTp00duxYff3114qMjNShQ4e0cOFC7dy5M7feBgDAXThjAQDIsuHDh8tgMCg0NFTR0dHy9vZWr1690qxXrFgxzZ49W2+99ZbWrFmjxo0ba9SoUfq///s/SbeHSl2+fFmvv/66YmJiVLx4cbVp00ajR4+WJAUFBalXr176z3/+o8uXL2vkyJEaNWqUQkJCtGDBAs2cOVPR0dEqVqyYAgIC1Lx589x8GwAAd3Aw3z34FQAAAACyiKFQAAAAAGxGsAAAAABgM4IFAAAAAJsRLAAAAADYjGABAAAAwGYECwAAAAA2I1gAAAAAsBnBAgAAAIDNCBYAAAAAbEawAAAAAGAzggUAAAAAmxEsAAAAANjs/wH1ALkDZCuP7QAAAABJRU5ErkJggg==",
"text/plain": [
"<Figure size 800x400 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"=== Carmignac Patrimoine ===\n",
"Clients after filtering: 319\n",
"Chosen K: 6\n",
" cluster n_clients\n",
"0 0 62\n",
"1 1 16\n",
"2 2 158\n",
"3 3 20\n",
"4 4 41\n",
"5 5 22\n",
"\n",
"Churn profile:\n",
" churn_hard churn_soft churn_warning\n",
"cluster \n",
"0 0.242 0.823 0.903\n",
"1 0.750 0.938 0.938\n",
"2 0.342 0.671 0.665\n",
"3 0.900 1.000 0.850\n",
"4 0.293 0.561 0.659\n",
"5 0.682 0.909 0.818\n"
]
}
],
"source": [
"# 34 - Test on one family\n",
"\n",
"test_family = top_15_families[0]\n",
"result_test = run_family_clustering(\n",
" df_base=df_family_client_base,\n",
" family_name=test_family,\n",
" features=family_cluster_features,\n",
" k_min=4,\n",
" k_max=10,\n",
" min_clients=40,\n",
" min_cluster_size=10,\n",
" min_cluster_share=0.02,\n",
" random_state=RANDOM_STATE,\n",
" make_plots=True\n",
")"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "e339c5fb",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"==========================================================================================\n",
"Running clustering for family: Carmignac Patrimoine\n",
"==========================================================================================\n",
"\n",
"==========================================================================================\n",
"Running clustering for family: Carmignac Sécurité\n",
"==========================================================================================\n",
"\n",
"==========================================================================================\n",
"Running clustering for family: Carmignac Credit <NUM>\n",
"==========================================================================================\n",
"\n",
"==========================================================================================\n",
"Running clustering for family: Carmignac Investissement\n",
"==========================================================================================\n",
"\n",
"==========================================================================================\n",
"Running clustering for family: Carmignac Portfolio Sécurité\n",
"==========================================================================================\n",
"\n",
"==========================================================================================\n",
"Running clustering for family: Carmignac Portfolio Flexible Bond\n",
"==========================================================================================\n",
"\n",
"==========================================================================================\n",
"Running clustering for family: Carmignac Portfolio Credit\n",
"==========================================================================================\n",
"\n",
"==========================================================================================\n",
"Running clustering for family: Carmignac Emergents\n",
"==========================================================================================\n",
"\n",
"==========================================================================================\n",
"Running clustering for family: Carmignac Court Terme\n",
"==========================================================================================\n",
"\n",
"==========================================================================================\n",
"Running clustering for family: Carmignac Portfolio Long-Short European Equities\n",
"==========================================================================================\n",
"[SKIP] Carmignac Portfolio Long-Short European Equities: no valid K with cluster size constraints.\n",
"\n",
"==========================================================================================\n",
"Running clustering for family: Carmignac Portfolio Grande Europe\n",
"==========================================================================================\n"
]
}
],
"source": [
"# 35 - Run clustering on the top 15 fund families\n",
"\n",
"family_results = {}\n",
"family_summary_rows = []\n",
"\n",
"for fam in top_15_families:\n",
" print(\"\\n\" + \"=\" * 90)\n",
" print(f\"Running clustering for family: {fam}\")\n",
" print(\"=\" * 90)\n",
"\n",
" res = run_family_clustering(\n",
" df_base=df_family_client_base,\n",
" family_name=fam,\n",
" features=family_cluster_features,\n",
" k_min=4,\n",
" k_max=10,\n",
" min_clients=40,\n",
" min_cluster_size=10,\n",
" min_cluster_share=0.02,\n",
" random_state=RANDOM_STATE,\n",
" make_plots=False\n",
" )\n",
"\n",
" if res is not None:\n",
" family_results[fam] = res\n",
" best_row = res[\"metrics_df\"].loc[res[\"metrics_df\"][\"k\"] == res[\"best_k\"]].iloc[0]\n",
"\n",
" family_summary_rows.append({\n",
" \"fund_family\": fam,\n",
" \"n_clients\": res[\"n_clients\"],\n",
" \"best_k\": res[\"best_k\"],\n",
" \"silhouette\": best_row[\"silhouette\"],\n",
" \"davies_bouldin\": best_row[\"davies_bouldin\"],\n",
" \"min_cluster_size\": int(best_row[\"min_cluster_size\"]),\n",
" })\n",
"\n",
"family_summary = pd.DataFrame(family_summary_rows).sort_values(\n",
" [\"silhouette\", \"n_clients\"],\n",
" ascending=[False, False]\n",
").reset_index(drop=True)\n",
"\n",
"family_summary"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "f5c7e517",
"metadata": {},
"outputs": [],
"source": [
"# 36 - Visual and business review for one selected family\n",
"\n",
"selected_family = top_15_families[0]\n",
"if selected_family in family_results:\n",
" _ = run_family_clustering(\n",
" df_base=df_family_client_base,\n",
" family_name=selected_family,\n",
" features=family_cluster_features,\n",
" k_min=4,\n",
" k_max=10,\n",
" min_clients=40,\n",
" min_cluster_size=10,\n",
" min_cluster_share=0.02,\n",
" random_state=RANDOM_STATE,\n",
" make_plots=True\n",
" )"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "4ba643a5",
"metadata": {},
"outputs": [],
"source": [
"# 37 - Summary table of clustering results across families\n",
"\n",
"family_summary"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "67c30d96",
"metadata": {},
"outputs": [],
"source": [
"# # 38 - Optional exports\n",
"\n",
"# family_summary.to_csv(\"family_clustering_summary.csv\", index=False)\n",
"\n",
"# for fam, res in family_results.items():\n",
"# safe_name = re.sub(r\"[^A-Za-z0-9_]+\", \"_\", fam)\n",
"# res[\"df_family\"].to_csv(f\"clustered_family_{safe_name}.csv\", index=False)\n",
"# res[\"cluster_profile\"].to_csv(f\"cluster_profile_{safe_name}.csv\")\n",
"# res[\"churn_profile\"].to_csv(f\"churn_profile_{safe_name}.csv\")"
]
},
{
"cell_type": "markdown",
"id": "ccc09ae0",
"metadata": {},
"source": [
"### Notes for interpretation\n",
"For each family, the clustering combines:\n",
"- **activity** (`flow_freq`)\n",
"- **intensity** (`gross_flow_to_aum_family`, rolling turnover features)\n",
"- **allocation importance** (`family_share_of_client_aum_mean`)\n",
"- **recency and exit behavior** (`months_since_last_tx_family`, `exit_rate_per_isin_family`)\n",
"- **performance sensitivity** (`corr_flow_ret_3m`, `corr_flow_ret_6m`, buy-after-good-performance shares)\n",
"- **churn proxy** (analyzed after clustering)\n",
"\n",
"This is the family-level analogue of the original global clustering notebook, but adapted to the finer and more sparse `client × family` structure."
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "ca108af5-69fe-4ae6-bf6e-088c6e7b0fcb",
"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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