4409 lines
396 KiB
Plaintext
4409 lines
396 KiB
Plaintext
{
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"cells": [
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 1,
|
||
"id": "2e8cf88b-cecf-409f-9c2d-c3762b233f05",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"Requirement already satisfied: openpyxl in /opt/python/lib/python3.13/site-packages (3.1.5)\n",
|
||
"Requirement already satisfied: pycountry in /opt/python/lib/python3.13/site-packages (24.6.1)\n",
|
||
"Requirement already satisfied: et-xmlfile in /opt/python/lib/python3.13/site-packages (from openpyxl) (2.0.0)\n"
|
||
]
|
||
}
|
||
],
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||
"source": [
|
||
"!pip install openpyxl pycountry"
|
||
]
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||
},
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||
{
|
||
"cell_type": "code",
|
||
"execution_count": 2,
|
||
"id": "0c550103-c268-4c43-8a76-4b7633418091",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
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||
"output_type": "stream",
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||
"text": [
|
||
"Requirement already satisfied: kaleido in /opt/python/lib/python3.13/site-packages (1.2.0)\n",
|
||
"Requirement already satisfied: choreographer>=1.1.1 in /opt/python/lib/python3.13/site-packages (from kaleido) (1.2.1)\n",
|
||
"Requirement already satisfied: logistro>=1.0.8 in /opt/python/lib/python3.13/site-packages (from kaleido) (2.0.1)\n",
|
||
"Requirement already satisfied: orjson>=3.10.15 in /opt/python/lib/python3.13/site-packages (from kaleido) (3.11.5)\n",
|
||
"Requirement already satisfied: packaging in /opt/python/lib/python3.13/site-packages (from kaleido) (25.0)\n",
|
||
"Requirement already satisfied: pytest-timeout>=2.4.0 in /opt/python/lib/python3.13/site-packages (from kaleido) (2.4.0)\n",
|
||
"Requirement already satisfied: simplejson>=3.19.3 in /opt/python/lib/python3.13/site-packages (from choreographer>=1.1.1->kaleido) (3.20.2)\n",
|
||
"Requirement already satisfied: pytest>=7.0.0 in /opt/python/lib/python3.13/site-packages (from pytest-timeout>=2.4.0->kaleido) (9.0.2)\n",
|
||
"Requirement already satisfied: iniconfig>=1.0.1 in /opt/python/lib/python3.13/site-packages (from pytest>=7.0.0->pytest-timeout>=2.4.0->kaleido) (2.3.0)\n",
|
||
"Requirement already satisfied: pluggy<2,>=1.5 in /opt/python/lib/python3.13/site-packages (from pytest>=7.0.0->pytest-timeout>=2.4.0->kaleido) (1.6.0)\n",
|
||
"Requirement already satisfied: pygments>=2.7.2 in /opt/python/lib/python3.13/site-packages (from pytest>=7.0.0->pytest-timeout>=2.4.0->kaleido) (2.19.2)\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"!pip install --upgrade kaleido"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 3,
|
||
"id": "126c8a80-d9ad-4816-84f0-0c3d580f62c8",
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"import pandas as pd\n",
|
||
"import numpy as np\n",
|
||
"import matplotlib.pyplot as plt"
|
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]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 4,
|
||
"id": "ff2261fb-9516-4410-b42d-3acc8dc1a460",
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"import os\n",
|
||
"import s3fs\n",
|
||
"os.environ[\"AWS_ACCESS_KEY_ID\"] = 'NJ0M3U6LSD2MKTFHA9E9'\n",
|
||
"os.environ[\"AWS_SECRET_ACCESS_KEY\"] = 'qS4fOELSpa4DhuhpbByIqF2A6WPX7bEXGoNro8qA'\n",
|
||
"os.environ[\"AWS_SESSION_TOKEN\"] = 'eyJhbGciOiJIUzUxMiIsInR5cCI6IkpXVCJ9.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.B6ak9wmZSk4l0_9O1CV03rdBgkz9y-tV6OrKHDCLbNGBJuZXDPX4jhvLUCwxsq99cd5bKwioh3Lzq_FWQvp_tA'\n",
|
||
"os.environ[\"AWS_DEFAULT_REGION\"] = 'us-east-1'\n",
|
||
"fs = s3fs.S3FileSystem(\n",
|
||
" client_kwargs={'endpoint_url': 'https://'+'minio-simple.lab.groupe-genes.fr'},\n",
|
||
" key = os.environ[\"AWS_ACCESS_KEY_ID\"], \n",
|
||
" secret = os.environ[\"AWS_SECRET_ACCESS_KEY\"], \n",
|
||
" token = os.environ[\"AWS_SESSION_TOKEN\"])"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "3d36f3f0-bd40-4a83-96d1-b46d75f5a4c5",
|
||
"metadata": {},
|
||
"source": [
|
||
"# data exploration"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "eaf5c5a0-eb1c-4242-b893-7600e6def109",
|
||
"metadata": {},
|
||
"source": [
|
||
"Fonctions utiles"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 6,
|
||
"id": "37e008b1-32d4-44be-9d23-1b90a5a26f89",
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"# 2. BASIC INSPECTION\n",
|
||
"\n",
|
||
"def quick_info(df, name):\n",
|
||
" print(\"\\n\" + \"=\"*80)\n",
|
||
" print(f\"DATASET : {name}\")\n",
|
||
" print(\"=\"*80)\n",
|
||
" print(\"\\nShape :\", df.shape)\n",
|
||
" print(\"\\nColumns :\", df.columns.tolist())\n",
|
||
" print(\"\\nDtypes :\\n\", df.dtypes)\n",
|
||
" print(\"\\nMissing values (%) :\\n\", df.isna().mean().sort_values(ascending=False)*100)\n",
|
||
" print(\"\\nSample rows:\\n\", df.head(5))\n",
|
||
" print(\"\\nUnique values per column:\\n\", df.nunique().sort_values(ascending=False))"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 7,
|
||
"id": "e67a99ea-ddf4-4627-8f48-ec183c671acb",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stderr",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"/tmp/ipykernel_3144/2877144729.py:5: DtypeWarning: Columns (0,1,2,3) have mixed types. Specify dtype option on import or set low_memory=False.\n",
|
||
" flows = pd.read_csv(f, sep=\";\")\n",
|
||
"/tmp/ipykernel_3144/2877144729.py:12: DtypeWarning: Columns (0,1,2,3) have mixed types. Specify dtype option on import or set low_memory=False.\n",
|
||
" stocks = pd.read_csv(f, sep=\";\")\n"
|
||
]
|
||
},
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"\n",
|
||
"================================================================================\n",
|
||
"DATASET : STOCKS\n",
|
||
"================================================================================\n",
|
||
"\n",
|
||
"Shape : (4880297, 18)\n",
|
||
"\n",
|
||
"Columns : ['Agreement - Code', 'Company - Id', 'Company - Ultimate Parent Id', 'Registrar Account - ID', 'Registrar Account - Region', 'RegistrarAccount - Country', 'Product - Asset Type', 'Product - Strategy', 'Product - Legal Status', 'Product - Is Dedie ?', 'Product - Fund', 'Product - Shareclass Type', 'Product - Shareclass Currency', 'Product - Isin', 'Centralisation Date', 'Quantity - AUM', 'Value - AUM CCY', 'Value - AUM €']\n",
|
||
"\n",
|
||
"Dtypes :\n",
|
||
" Agreement - Code object\n",
|
||
"Company - Id object\n",
|
||
"Company - Ultimate Parent Id object\n",
|
||
"Registrar Account - ID object\n",
|
||
"Registrar Account - Region object\n",
|
||
"RegistrarAccount - Country object\n",
|
||
"Product - Asset Type object\n",
|
||
"Product - Strategy object\n",
|
||
"Product - Legal Status object\n",
|
||
"Product - Is Dedie ? object\n",
|
||
"Product - Fund object\n",
|
||
"Product - Shareclass Type object\n",
|
||
"Product - Shareclass Currency object\n",
|
||
"Product - Isin object\n",
|
||
"Centralisation Date object\n",
|
||
"Quantity - AUM float64\n",
|
||
"Value - AUM CCY float64\n",
|
||
"Value - AUM € float64\n",
|
||
"dtype: object\n",
|
||
"\n",
|
||
"Missing values (%) :\n",
|
||
" Product - Asset Type 6.471553\n",
|
||
"Company - Id 2.330801\n",
|
||
"Company - Ultimate Parent Id 2.330801\n",
|
||
"Product - Strategy 0.001537\n",
|
||
"Product - Shareclass Type 0.000717\n",
|
||
"Agreement - Code 0.000000\n",
|
||
"RegistrarAccount - Country 0.000000\n",
|
||
"Registrar Account - Region 0.000000\n",
|
||
"Product - Legal Status 0.000000\n",
|
||
"Registrar Account - ID 0.000000\n",
|
||
"Product - Is Dedie ? 0.000000\n",
|
||
"Product - Fund 0.000000\n",
|
||
"Product - Shareclass Currency 0.000000\n",
|
||
"Product - Isin 0.000000\n",
|
||
"Centralisation Date 0.000000\n",
|
||
"Quantity - AUM 0.000000\n",
|
||
"Value - AUM CCY 0.000000\n",
|
||
"Value - AUM € 0.000000\n",
|
||
"dtype: float64\n",
|
||
"\n",
|
||
"Sample rows:\n",
|
||
" Agreement - Code Company - Id Company - Ultimate Parent Id \\\n",
|
||
"0 3 166.0 166.0 \n",
|
||
"1 3 166.0 166.0 \n",
|
||
"2 3 166.0 166.0 \n",
|
||
"3 3 166.0 166.0 \n",
|
||
"4 3 166.0 166.0 \n",
|
||
"\n",
|
||
" Registrar Account - ID Registrar Account - Region \\\n",
|
||
"0 200000647 France \n",
|
||
"1 200000647 France \n",
|
||
"2 200000647 France \n",
|
||
"3 200000647 France \n",
|
||
"4 200000647 France \n",
|
||
"\n",
|
||
" RegistrarAccount - Country Product - Asset Type Product - Strategy \\\n",
|
||
"0 France Diversified Patrimoine \n",
|
||
"1 France Diversified Patrimoine \n",
|
||
"2 France Diversified Patrimoine \n",
|
||
"3 France Diversified Patrimoine \n",
|
||
"4 France Diversified Patrimoine \n",
|
||
"\n",
|
||
" Product - Legal Status Product - Is Dedie ? Product - Fund \\\n",
|
||
"0 FCP NO Carmignac Patrimoine \n",
|
||
"1 FCP NO Carmignac Patrimoine \n",
|
||
"2 FCP NO Carmignac Patrimoine \n",
|
||
"3 FCP NO Carmignac Patrimoine \n",
|
||
"4 FCP NO Carmignac Patrimoine \n",
|
||
"\n",
|
||
" Product - Shareclass Type Product - Shareclass Currency Product - Isin \\\n",
|
||
"0 A EUR FR0010135103 \n",
|
||
"1 A EUR FR0010135103 \n",
|
||
"2 A EUR FR0010135103 \n",
|
||
"3 A EUR FR0010135103 \n",
|
||
"4 A EUR FR0010135103 \n",
|
||
"\n",
|
||
" Centralisation Date Quantity - AUM Value - AUM CCY Value - AUM € \n",
|
||
"0 2015-03-31 35.368 24648.6666 24648.6666 \n",
|
||
"1 2015-11-30 35.368 22413.0553 22413.0553 \n",
|
||
"2 2015-12-31 35.368 22051.2406 22051.2406 \n",
|
||
"3 2016-03-31 35.368 21626.1173 21626.1173 \n",
|
||
"4 2016-11-30 35.368 22489.4502 22489.4502 \n",
|
||
"\n",
|
||
"Unique values per column:\n",
|
||
" Value - AUM € 1697923\n",
|
||
"Value - AUM CCY 1689620\n",
|
||
"Quantity - AUM 554404\n",
|
||
"Registrar Account - ID 15532\n",
|
||
"Agreement - Code 2521\n",
|
||
"Company - Id 1970\n",
|
||
"Company - Ultimate Parent Id 1392\n",
|
||
"Product - Isin 491\n",
|
||
"Centralisation Date 130\n",
|
||
"Product - Fund 74\n",
|
||
"Product - Strategy 52\n",
|
||
"RegistrarAccount - Country 39\n",
|
||
"Registrar Account - Region 15\n",
|
||
"Product - Shareclass Type 11\n",
|
||
"Product - Legal Status 6\n",
|
||
"Product - Shareclass Currency 6\n",
|
||
"Product - Asset Type 5\n",
|
||
"Product - Is Dedie ? 2\n",
|
||
"dtype: int64\n",
|
||
"\n",
|
||
"================================================================================\n",
|
||
"DATASET : FLOWS\n",
|
||
"================================================================================\n",
|
||
"\n",
|
||
"Shape : (2574461, 24)\n",
|
||
"\n",
|
||
"Columns : ['Agreement - Code', 'Company - Id', 'Company - Ultimate Parent Id', 'Registrar Account - ID', 'Registrar Account - Region', 'RegistrarAccount - Country', 'Product - Asset Type', 'Product - Strategy', 'Product - Legal Status', 'Product - Is Dedie ?', 'Product - Fund', 'Product - Shareclass Type', 'Product - Shareclass Currency', 'Product - Isin', 'Centralisation Date', 'Quantity - Subscription', 'Quantity - Redemption', 'Quantity - NetFlows', 'Value Ccy - Subscription', 'Value Ccy - Redemption', 'Value Ccy - NetFlows', 'Value € - Subscription', 'Value € - Redemption', 'Value € - NetFlows']\n",
|
||
"\n",
|
||
"Dtypes :\n",
|
||
" Agreement - Code object\n",
|
||
"Company - Id object\n",
|
||
"Company - Ultimate Parent Id object\n",
|
||
"Registrar Account - ID object\n",
|
||
"Registrar Account - Region object\n",
|
||
"RegistrarAccount - Country object\n",
|
||
"Product - Asset Type object\n",
|
||
"Product - Strategy object\n",
|
||
"Product - Legal Status object\n",
|
||
"Product - Is Dedie ? object\n",
|
||
"Product - Fund object\n",
|
||
"Product - Shareclass Type object\n",
|
||
"Product - Shareclass Currency object\n",
|
||
"Product - Isin object\n",
|
||
"Centralisation Date object\n",
|
||
"Quantity - Subscription float64\n",
|
||
"Quantity - Redemption float64\n",
|
||
"Quantity - NetFlows float64\n",
|
||
"Value Ccy - Subscription float64\n",
|
||
"Value Ccy - Redemption float64\n",
|
||
"Value Ccy - NetFlows float64\n",
|
||
"Value € - Subscription float64\n",
|
||
"Value € - Redemption float64\n",
|
||
"Value € - NetFlows float64\n",
|
||
"dtype: object\n",
|
||
"\n",
|
||
"Missing values (%) :\n",
|
||
" Product - Asset Type 0.079589\n",
|
||
"Company - Id 0.059818\n",
|
||
"Company - Ultimate Parent Id 0.059818\n",
|
||
"Product - Strategy 0.000233\n",
|
||
"Product - Shareclass Type 0.000078\n",
|
||
"Registrar Account - ID 0.000000\n",
|
||
"RegistrarAccount - Country 0.000000\n",
|
||
"Agreement - Code 0.000000\n",
|
||
"Registrar Account - Region 0.000000\n",
|
||
"Product - Legal Status 0.000000\n",
|
||
"Product - Is Dedie ? 0.000000\n",
|
||
"Product - Fund 0.000000\n",
|
||
"Product - Shareclass Currency 0.000000\n",
|
||
"Product - Isin 0.000000\n",
|
||
"Centralisation Date 0.000000\n",
|
||
"Quantity - Subscription 0.000000\n",
|
||
"Quantity - Redemption 0.000000\n",
|
||
"Quantity - NetFlows 0.000000\n",
|
||
"Value Ccy - Subscription 0.000000\n",
|
||
"Value Ccy - Redemption 0.000000\n",
|
||
"Value Ccy - NetFlows 0.000000\n",
|
||
"Value € - Subscription 0.000000\n",
|
||
"Value € - Redemption 0.000000\n",
|
||
"Value € - NetFlows 0.000000\n",
|
||
"dtype: float64\n",
|
||
"\n",
|
||
"Sample rows:\n",
|
||
" Agreement - Code Company - Id Company - Ultimate Parent Id \\\n",
|
||
"0 003 166 166 \n",
|
||
"1 003 166 166 \n",
|
||
"2 003 166 166 \n",
|
||
"3 003 166 166 \n",
|
||
"4 003 166 166 \n",
|
||
"\n",
|
||
" Registrar Account - ID Registrar Account - Region \\\n",
|
||
"0 200127202 France \n",
|
||
"1 406533 France \n",
|
||
"2 406533 France \n",
|
||
"3 406533 France \n",
|
||
"4 406533 France \n",
|
||
"\n",
|
||
" RegistrarAccount - Country Product - Asset Type Product - Strategy \\\n",
|
||
"0 France Equity Investissement \n",
|
||
"1 France Diversified Patrimoine \n",
|
||
"2 France Equity Investissement \n",
|
||
"3 France Equity Investissement \n",
|
||
"4 France Equity Investissement \n",
|
||
"\n",
|
||
" Product - Legal Status Product - Is Dedie ? ... Centralisation Date \\\n",
|
||
"0 SICAV NO ... 2020-11-05 \n",
|
||
"1 FCP NO ... 2015-03-09 \n",
|
||
"2 FCP NO ... 2016-10-26 \n",
|
||
"3 FCP NO ... 2018-10-18 \n",
|
||
"4 FCP NO ... 2019-04-08 \n",
|
||
"\n",
|
||
" Quantity - Subscription Quantity - Redemption Quantity - NetFlows \\\n",
|
||
"0 1636.00 0.000 1636.000 \n",
|
||
"1 144.69 0.000 144.690 \n",
|
||
"2 0.00 -8.321 -8.321 \n",
|
||
"3 0.00 -22.083 -22.083 \n",
|
||
"4 0.00 -465.992 -465.992 \n",
|
||
"\n",
|
||
" Value Ccy - Subscription Value Ccy - Redemption Value Ccy - NetFlows \\\n",
|
||
"0 280983.00 0.00 280983.00 \n",
|
||
"1 99985.13 0.00 99985.13 \n",
|
||
"2 0.00 -9384.76 -9384.76 \n",
|
||
"3 0.00 -25227.40 -25227.40 \n",
|
||
"4 0.00 -563775.76 -563775.76 \n",
|
||
"\n",
|
||
" Value € - Subscription Value € - Redemption Value € - NetFlows \n",
|
||
"0 280983.00 0.00 280983.00 \n",
|
||
"1 99985.13 0.00 99985.13 \n",
|
||
"2 0.00 -9384.76 -9384.76 \n",
|
||
"3 0.00 -25227.40 -25227.40 \n",
|
||
"4 0.00 -563775.76 -563775.76 \n",
|
||
"\n",
|
||
"[5 rows x 24 columns]\n",
|
||
"\n",
|
||
"Unique values per column:\n",
|
||
" Value € - NetFlows 2018916\n",
|
||
"Value Ccy - NetFlows 1972319\n",
|
||
"Value € - Redemption 1323531\n",
|
||
"Value Ccy - Redemption 1296468\n",
|
||
"Value € - Subscription 955890\n",
|
||
"Value Ccy - Subscription 926633\n",
|
||
"Quantity - NetFlows 667586\n",
|
||
"Quantity - Redemption 374378\n",
|
||
"Quantity - Subscription 359661\n",
|
||
"Registrar Account - ID 9805\n",
|
||
"Centralisation Date 2780\n",
|
||
"Company - Id 1929\n",
|
||
"Agreement - Code 1626\n",
|
||
"Company - Ultimate Parent Id 1283\n",
|
||
"Product - Isin 474\n",
|
||
"Product - Fund 70\n",
|
||
"Product - Strategy 49\n",
|
||
"RegistrarAccount - Country 34\n",
|
||
"Registrar Account - Region 15\n",
|
||
"Product - Shareclass Type 10\n",
|
||
"Product - Shareclass Currency 6\n",
|
||
"Product - Legal Status 6\n",
|
||
"Product - Asset Type 5\n",
|
||
"Product - Is Dedie ? 2\n",
|
||
"dtype: int64\n",
|
||
"\n",
|
||
"================================================================================\n",
|
||
"DATASET : NAV/PRICES\n",
|
||
"================================================================================\n",
|
||
"\n",
|
||
"Shape : (30333, 13)\n",
|
||
"\n",
|
||
"Columns : ['NavDate', 'LegalForm', 'Cod', 'PortfolioName', 'PTFCurrency', 'PortfolioAum_Eur', 'ShareClassIsin', 'ShareClassName', 'ShareClassCurrency', 'ShareClassPrice', 'NumberOfShares', 'ShareClassAumLocalCur', 'ShareClassAum_EUR']\n",
|
||
"\n",
|
||
"Dtypes :\n",
|
||
" 0\n",
|
||
"NavDate object\n",
|
||
"LegalForm object\n",
|
||
"Cod object\n",
|
||
"PortfolioName object\n",
|
||
"PTFCurrency object\n",
|
||
"PortfolioAum_Eur object\n",
|
||
"ShareClassIsin object\n",
|
||
"ShareClassName object\n",
|
||
"ShareClassCurrency object\n",
|
||
"ShareClassPrice object\n",
|
||
"NumberOfShares object\n",
|
||
"ShareClassAumLocalCur object\n",
|
||
"ShareClassAum_EUR object\n",
|
||
"dtype: object\n",
|
||
"\n",
|
||
"Missing values (%) :\n",
|
||
" 0\n",
|
||
"NavDate 0.0\n",
|
||
"LegalForm 0.0\n",
|
||
"Cod 0.0\n",
|
||
"PortfolioName 0.0\n",
|
||
"PTFCurrency 0.0\n",
|
||
"PortfolioAum_Eur 0.0\n",
|
||
"ShareClassIsin 0.0\n",
|
||
"ShareClassName 0.0\n",
|
||
"ShareClassCurrency 0.0\n",
|
||
"ShareClassPrice 0.0\n",
|
||
"NumberOfShares 0.0\n",
|
||
"ShareClassAumLocalCur 0.0\n",
|
||
"ShareClassAum_EUR 0.0\n",
|
||
"dtype: float64\n",
|
||
"\n",
|
||
"Sample rows:\n",
|
||
" 0 NavDate LegalForm Cod PortfolioName \\\n",
|
||
"0 31/12/2009 SICAV CC Carmignac Portfolio Climate Transition \n",
|
||
"1 31/12/2009 SICAV CFB Carmignac Portfolio Flexible Bond \n",
|
||
"2 31/12/2009 FCP CCT Carmignac Court Terme \n",
|
||
"3 31/12/2009 FCP CE Carmignac Emergents \n",
|
||
"4 31/12/2009 SICAV CAD Carmignac Portfolio Asia Discovery \n",
|
||
"\n",
|
||
"0 PTFCurrency PortfolioAum_Eur ShareClassIsin ShareClassName \\\n",
|
||
"0 EUR 941059600 LU0164455502 A EUR ACC \n",
|
||
"1 EUR 57063272.31 LU0336084032 A EUR ACC \n",
|
||
"2 EUR 788828666.5 FR0010149161 A EUR ACC \n",
|
||
"3 EUR 1508087050 FR0010149302 A EUR ACC \n",
|
||
"4 EUR 149490224.2 LU0336083810 A EUR ACC \n",
|
||
"\n",
|
||
"0 ShareClassCurrency ShareClassPrice NumberOfShares ShareClassAumLocalCur \\\n",
|
||
"0 EUR 287.21 3276555.83 941059600 \n",
|
||
"1 EUR 1016.833 56118.62745 57063272.31 \n",
|
||
"2 EUR 3687.84 213899.9161 788828666.5 \n",
|
||
"3 EUR 559.82 2693878.478 1508087050 \n",
|
||
"4 EUR 884.9 168934.5962 149490224.2 \n",
|
||
"\n",
|
||
"0 ShareClassAum_EUR \n",
|
||
"0 941059600 \n",
|
||
"1 57063272.31 \n",
|
||
"2 788828666.5 \n",
|
||
"3 1508087050 \n",
|
||
"4 149490224.2 \n",
|
||
"\n",
|
||
"Unique values per column:\n",
|
||
" 0\n",
|
||
"ShareClassAum_EUR 30211\n",
|
||
"ShareClassAumLocalCur 30032\n",
|
||
"NumberOfShares 28910\n",
|
||
"ShareClassPrice 14747\n",
|
||
"PortfolioAum_Eur 5505\n",
|
||
"ShareClassIsin 416\n",
|
||
"NavDate 210\n",
|
||
"ShareClassName 90\n",
|
||
"Cod 55\n",
|
||
"PortfolioName 55\n",
|
||
"LegalForm 6\n",
|
||
"ShareClassCurrency 6\n",
|
||
"PTFCurrency 2\n",
|
||
"dtype: int64\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"with fs.open(\n",
|
||
" \"projet-bdc-data/carmignac/Flows ENSAE V2 -20251105.csv\",\n",
|
||
" \"rb\"\n",
|
||
") as f:\n",
|
||
" flows = pd.read_csv(f, sep=\";\")\n",
|
||
"\n",
|
||
"with fs.open('projet-bdc-data/carmignac/Monthly AUM and NAV since 2010.xlsx', 'rb') as f:\n",
|
||
" nav_raw = pd.read_excel(f, header=None, engine=\"openpyxl\")\n",
|
||
"nav = nav_raw[0].str.split(\",\", expand=True)\n",
|
||
"nav.columns = nav.iloc[0]\n",
|
||
"with fs.open('projet-bdc-data/carmignac/AUM ENSAE V2 -20251105.csv', 'rb') as f:\n",
|
||
" stocks = pd.read_csv(f, sep=\";\")\n",
|
||
"\n",
|
||
"nav = nav[1:].reset_index(drop=True)\n",
|
||
"\n",
|
||
"quick_info(stocks, \"STOCKS\")\n",
|
||
"quick_info(flows, \"FLOWS\")\n",
|
||
"quick_info(nav, \"NAV/PRICES\")"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 8,
|
||
"id": "9bc92c9f-216c-475e-bfb8-edc1a4e839f6",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"Date conversion done.\n",
|
||
"NAV numeric conversion done.\n",
|
||
"String normalization done.\n",
|
||
"\n",
|
||
"ISIN missing in FLOWS but present in STOCKS : 17\n",
|
||
"\n",
|
||
"ISIN missing in STOCKS but present in FLOWS : 0\n",
|
||
"\n",
|
||
"ISIN missing in NAV but present in FLOWS : 67\n",
|
||
"\n",
|
||
"ISIN missing in NAV but present in STOCKS : 76\n",
|
||
"\n",
|
||
"Accounts in STOCKS but NEVER in FLOWS : 5777\n",
|
||
"\n",
|
||
"Accounts in FLOWS but NEVER in STOCKS : 118\n",
|
||
"\n",
|
||
"CLIENT BEHAVIOR (first 5 rows):\n",
|
||
" Registrar Account - ID n_days n_transactions total_netflows mean_flow \\\n",
|
||
"0 100000028 3 3 -109.238 -36.412667 \n",
|
||
"1 100000042 1 1 -660.115 -660.115000 \n",
|
||
"2 100000065 1 1 -174.646 -174.646000 \n",
|
||
"3 100000069 65 73 -7479.755 -102.462397 \n",
|
||
"4 100000073 1 1 -133.402 -133.402000 \n",
|
||
"\n",
|
||
" std_flow total_subscription total_redemption churn_ratio \n",
|
||
"0 49.280511 0.000 -109.238 -1.092380e+11 \n",
|
||
"1 NaN 0.000 -660.115 -6.601150e+11 \n",
|
||
"2 NaN 0.000 -174.646 -1.746460e+11 \n",
|
||
"3 2168.971331 33320.402 -40800.157 -1.224480e+00 \n",
|
||
"4 NaN 0.000 -133.402 -1.334020e+11 \n",
|
||
"\n",
|
||
"FUND BEHAVIOR (first 5 rows):\n",
|
||
" Product - Isin n_accounts n_days total_netflows vol_flows\n",
|
||
"0 FR0010135103 2690 2723 -2.571327e+07 2622.609244\n",
|
||
"1 FR0010147603 733 2719 -2.562187e+06 1206.248205\n",
|
||
"2 FR0010148981 1841 2722 -3.609440e+06 1051.069183\n",
|
||
"3 FR0010148999 454 2306 -7.130297e+05 1265.364138\n",
|
||
"4 FR0010149112 934 2000 -9.438901e+05 1834.961721\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"# 1. CLEAN DATES (formats différents)\n",
|
||
"\n",
|
||
"stocks[\"Centralisation Date\"] = pd.to_datetime(stocks[\"Centralisation Date\"], errors=\"coerce\")\n",
|
||
"flows[\"Centralisation Date\"] = pd.to_datetime(flows[\"Centralisation Date\"], errors=\"coerce\")\n",
|
||
"nav[\"NavDate\"] = pd.to_datetime(nav[\"NavDate\"], format=\"%d/%m/%Y\", errors=\"coerce\")\n",
|
||
"\n",
|
||
"print(\"Date conversion done.\")\n",
|
||
"\n",
|
||
"# 2. CLEAN NUMERIC COLUMNS FOR NAV FILE\n",
|
||
"\n",
|
||
"num_cols = [\"PortfolioAum_Eur\",\"ShareClassPrice\",\"NumberOfShares\",\n",
|
||
" \"ShareClassAumLocalCur\",\"ShareClassAum_EUR\"]\n",
|
||
"\n",
|
||
"for col in num_cols:\n",
|
||
" nav[col] = (\n",
|
||
" nav[col]\n",
|
||
" .astype(str)\n",
|
||
" .str.replace(\",\", \".\", regex=False)\n",
|
||
" .str.replace(\" \", \"\")\n",
|
||
" .astype(float)\n",
|
||
" )\n",
|
||
"\n",
|
||
"print(\"NAV numeric conversion done.\")\n",
|
||
"\n",
|
||
"# 3. STANDARDIZE STRINGS FOR JOIN KEYS\n",
|
||
"\n",
|
||
"def norm(df):\n",
|
||
" for col in df.columns:\n",
|
||
" if df[col].dtype == \"object\":\n",
|
||
" df[col] = df[col].astype(str).str.strip().str.upper()\n",
|
||
" return df\n",
|
||
"\n",
|
||
"stocks = norm(stocks)\n",
|
||
"flows = norm(flows)\n",
|
||
"nav = norm(nav)\n",
|
||
"\n",
|
||
"print(\"String normalization done.\")\n",
|
||
"\n",
|
||
"\n",
|
||
"# 4. ANALYSE RELATIONS ACROSS FILES\n",
|
||
"\n",
|
||
"# Unique sets\n",
|
||
"isin_stocks = set(stocks[\"Product - Isin\"].unique())\n",
|
||
"isin_flows = set(flows[\"Product - Isin\"].unique())\n",
|
||
"isin_nav = set(nav[\"ShareClassIsin\"].unique())\n",
|
||
"\n",
|
||
"print(\"\\nISIN missing in FLOWS but present in STOCKS :\", len(isin_stocks - isin_flows))\n",
|
||
"print(\"\\nISIN missing in STOCKS but present in FLOWS :\", len(isin_flows - isin_stocks))\n",
|
||
"print(\"\\nISIN missing in NAV but present in FLOWS :\", len(isin_flows - isin_nav))\n",
|
||
"print(\"\\nISIN missing in NAV but present in STOCKS :\", len(isin_stocks - isin_nav))\n",
|
||
"\n",
|
||
"\n",
|
||
"# 5. CLIENTS: STOCKS VS FLOWS\n",
|
||
"\n",
|
||
"acc_stocks = set(stocks[\"Registrar Account - ID\"].unique())\n",
|
||
"acc_flows = set(flows[\"Registrar Account - ID\"].unique())\n",
|
||
"\n",
|
||
"print(\"\\nAccounts in STOCKS but NEVER in FLOWS :\", len(acc_stocks - acc_flows))\n",
|
||
"print(\"\\nAccounts in FLOWS but NEVER in STOCKS :\", len(acc_flows - acc_stocks))\n",
|
||
"\n",
|
||
"\n",
|
||
"# 6. CLIENT ACTIVITY METRICS (DETAILED)\n",
|
||
"\n",
|
||
"client_behavior = flows.groupby(\"Registrar Account - ID\").agg(\n",
|
||
" n_days=(\"Centralisation Date\", lambda x: x.nunique()),\n",
|
||
" n_transactions=(\"Quantity - NetFlows\", \"count\"),\n",
|
||
" total_netflows=(\"Quantity - NetFlows\", \"sum\"),\n",
|
||
" mean_flow=(\"Quantity - NetFlows\", \"mean\"),\n",
|
||
" std_flow=(\"Quantity - NetFlows\", \"std\"),\n",
|
||
" total_subscription=(\"Quantity - Subscription\", \"sum\"),\n",
|
||
" total_redemption=(\"Quantity - Redemption\", \"sum\")\n",
|
||
").reset_index()\n",
|
||
"\n",
|
||
"# Add churn metric\n",
|
||
"client_behavior[\"churn_ratio\"] = (\n",
|
||
" client_behavior[\"total_redemption\"] /\n",
|
||
" (client_behavior[\"total_subscription\"] + 1e-9)\n",
|
||
")\n",
|
||
"\n",
|
||
"print(\"\\nCLIENT BEHAVIOR (first 5 rows):\\n\", client_behavior.head())\n",
|
||
"\n",
|
||
"\n",
|
||
"# 7. FUNDS ACTIVITY METRICS\n",
|
||
"\n",
|
||
"fund_behavior = flows.groupby(\"Product - Isin\").agg(\n",
|
||
" n_accounts=(\"Registrar Account - ID\", \"nunique\"),\n",
|
||
" n_days=(\"Centralisation Date\", lambda x: x.nunique()),\n",
|
||
" total_netflows=(\"Quantity - NetFlows\", \"sum\"),\n",
|
||
" vol_flows=(\"Quantity - NetFlows\", \"std\")\n",
|
||
").reset_index()\n",
|
||
"\n",
|
||
"print(\"\\nFUND BEHAVIOR (first 5 rows):\\n\", fund_behavior.head())\n",
|
||
"\n",
|
||
"\n",
|
||
"# 8. SAVE INTERMEDIATE\n",
|
||
"\n",
|
||
"client_behavior.to_csv(\"client_behavior.csv\", index=False)\n",
|
||
"fund_behavior.to_csv(\"fund_behavior.csv\", index=False)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 9,
|
||
"id": "afb51598-3a7b-41f2-8d25-5b4b8bfb1c8a",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"FULL usable ISIN : 407\n",
|
||
"Stocks only ISIN : 17\n",
|
||
"Flows only ISIN : 0\n",
|
||
"Missing NAV : 76\n",
|
||
"All ISIN groups saved into 4 separate files.\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"valid_full = isin_stocks & isin_flows & isin_nav\n",
|
||
"stocks_only = isin_stocks - isin_flows\n",
|
||
"flows_only = isin_flows - isin_stocks\n",
|
||
"missing_nav = (isin_stocks | isin_flows) - isin_nav\n",
|
||
"\n",
|
||
"print(\"FULL usable ISIN :\", len(valid_full))\n",
|
||
"print(\"Stocks only ISIN :\", len(stocks_only))\n",
|
||
"print(\"Flows only ISIN :\", len(flows_only))\n",
|
||
"print(\"Missing NAV :\", len(missing_nav))\n",
|
||
"\n",
|
||
"pd.DataFrame({\"isin\": list(valid_full)}).to_csv(\"isin_full.csv\", index=False)\n",
|
||
"pd.DataFrame({\"isin\": list(stocks_only)}).to_csv(\"isin_stocks_only.csv\", index=False)\n",
|
||
"pd.DataFrame({\"isin\": list(flows_only)}).to_csv(\"isin_flows_only.csv\", index=False)\n",
|
||
"pd.DataFrame({\"isin\": list(missing_nav)}).to_csv(\"isin_missing_nav.csv\", index=False)\n",
|
||
"\n",
|
||
"print(\"All ISIN groups saved into 4 separate files.\")"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 10,
|
||
"id": "61e0c71a-a1c6-4ed8-ba15-b7a9badc4d4a",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
" Registrar Account - ID n_days n_transactions total_netflows mean_flow \\\n",
|
||
"0 100000028 3 3 -109.238 -36.412667 \n",
|
||
"1 100000042 1 1 -660.115 -660.115000 \n",
|
||
"2 100000065 1 1 -174.646 -174.646000 \n",
|
||
"3 100000069 65 73 -7479.755 -102.462397 \n",
|
||
"4 100000073 1 1 -133.402 -133.402000 \n",
|
||
"\n",
|
||
" std_flow total_subscription total_redemption churn_ratio \\\n",
|
||
"0 49.280511 0.000 -109.238 -1.092380e+08 \n",
|
||
"1 NaN 0.000 -660.115 -6.601150e+08 \n",
|
||
"2 NaN 0.000 -174.646 -1.746460e+08 \n",
|
||
"3 2168.971331 33320.402 -40800.157 -1.224480e+00 \n",
|
||
"4 NaN 0.000 -133.402 -1.334020e+08 \n",
|
||
"\n",
|
||
" churn_flag activity_score flow_volatility inertia_ratio \n",
|
||
"0 0 1.386294 49.280511 0.998921 \n",
|
||
"1 0 0.693147 0.000000 0.999640 \n",
|
||
"2 0 0.693147 0.000000 0.999640 \n",
|
||
"3 0 4.304065 2168.971331 0.976619 \n",
|
||
"4 0 0.693147 0.000000 0.999640 \n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"eps = 1e-6\n",
|
||
"\n",
|
||
"client_behavior[\"churn_ratio\"] = (\n",
|
||
" client_behavior[\"total_redemption\"] /\n",
|
||
" (client_behavior[\"total_subscription\"] + eps)\n",
|
||
")\n",
|
||
"\n",
|
||
"client_behavior[\"churn_flag\"] = (\n",
|
||
" client_behavior[\"total_redemption\"] > client_behavior[\"total_subscription\"]\n",
|
||
").astype(int)\n",
|
||
"\n",
|
||
"client_behavior[\"activity_score\"] = np.log1p(client_behavior[\"n_transactions\"])\n",
|
||
"\n",
|
||
"client_behavior[\"flow_volatility\"] = client_behavior[\"std_flow\"].fillna(0)\n",
|
||
"\n",
|
||
"client_behavior[\"inertia_ratio\"] = (\n",
|
||
" 1 - client_behavior[\"n_days\"] / flows[\"Centralisation Date\"].nunique()\n",
|
||
")\n",
|
||
"\n",
|
||
"print(client_behavior.head())\n",
|
||
"\n",
|
||
"client_behavior.to_csv(\"client_behavior_clean.csv\", index=False)\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 11,
|
||
"id": "8ee7e911-eb73-4846-b545-661140411c1b",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stderr",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"/tmp/ipykernel_3144/1645623303.py:17: RuntimeWarning: invalid value encountered in scalar divide\n",
|
||
" .apply(lambda x: x[\"Value - AUM €\"].max() / x[\"Value - AUM €\"].sum()) \\\n"
|
||
]
|
||
},
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
" Registrar Account - ID n_isin_held n_funds_held n_asset_types \\\n",
|
||
"0 100000014 1 1 1 \n",
|
||
"1 100000016 2 2 2 \n",
|
||
"2 100000028 1 1 1 \n",
|
||
"3 100000038 3 3 2 \n",
|
||
"4 100000042 1 1 1 \n",
|
||
"\n",
|
||
" n_strategies total_aum median_aum concentration_ratio \n",
|
||
"0 1 0.0000 0.0 NaN \n",
|
||
"1 2 0.0000 0.0 NaN \n",
|
||
"2 1 126236.2184 0.0 1.0 \n",
|
||
"3 3 0.0000 0.0 NaN \n",
|
||
"4 1 446362.9015 0.0 1.0 \n",
|
||
" n_isin_held n_funds_held n_asset_types n_strategies total_aum \\\n",
|
||
"count 12501.000000 12501.000000 12501.000000 12501.000000 1.250100e+04 \n",
|
||
"mean 5.514759 4.408367 2.082473 4.109271 4.218474e+08 \n",
|
||
"std 10.434698 5.472756 1.254048 4.714800 5.618341e+09 \n",
|
||
"min 1.000000 1.000000 1.000000 1.000000 -2.586805e+08 \n",
|
||
"25% 1.000000 1.000000 1.000000 1.000000 0.000000e+00 \n",
|
||
"50% 2.000000 2.000000 2.000000 2.000000 2.587605e+05 \n",
|
||
"75% 6.000000 5.000000 3.000000 5.000000 8.817014e+06 \n",
|
||
"max 469.000000 67.000000 6.000000 48.000000 4.780234e+11 \n",
|
||
"\n",
|
||
" median_aum concentration_ratio \n",
|
||
"count 1.250100e+04 7708.000000 \n",
|
||
"mean 2.573991e+05 0.790503 \n",
|
||
"std 3.487976e+06 0.261535 \n",
|
||
"min -2.317333e+06 -2.591840 \n",
|
||
"25% 0.000000e+00 0.576503 \n",
|
||
"50% 0.000000e+00 0.972159 \n",
|
||
"75% 1.474502e+02 1.000000 \n",
|
||
"max 2.215373e+08 2.983529 \n"
|
||
]
|
||
},
|
||
{
|
||
"name": "stderr",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"/tmp/ipykernel_3144/1645623303.py:17: FutureWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.\n",
|
||
" .apply(lambda x: x[\"Value - AUM €\"].max() / x[\"Value - AUM €\"].sum()) \\\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"# Diversification per account\n",
|
||
"account_div = stocks.groupby(\"Registrar Account - ID\").agg(\n",
|
||
" n_isin_held=(\"Product - Isin\", \"nunique\"),\n",
|
||
" n_funds_held=(\"Product - Fund\", \"nunique\"),\n",
|
||
" n_asset_types=(\"Product - Asset Type\", \"nunique\"),\n",
|
||
" n_strategies=(\"Product - Strategy\", \"nunique\"),\n",
|
||
" total_aum=(\"Value - AUM €\", \"sum\"),\n",
|
||
" median_aum=(\"Value - AUM €\", \"median\")\n",
|
||
").reset_index()\n",
|
||
"\n",
|
||
"# Concentration ratio per account\n",
|
||
"aum_by_account_fund = stocks.groupby(\n",
|
||
" [\"Registrar Account - ID\", \"Product - Fund\"]\n",
|
||
")[\"Value - AUM €\"].sum().reset_index()\n",
|
||
"\n",
|
||
"concentration = aum_by_account_fund.groupby(\"Registrar Account - ID\") \\\n",
|
||
" .apply(lambda x: x[\"Value - AUM €\"].max() / x[\"Value - AUM €\"].sum()) \\\n",
|
||
" .reset_index(name=\"concentration_ratio\")\n",
|
||
"\n",
|
||
"# Merge diversification + concentration\n",
|
||
"account_static = account_div.merge(concentration, on=\"Registrar Account - ID\", how=\"left\")\n",
|
||
"\n",
|
||
"print(account_static.head())\n",
|
||
"print(account_static.describe())\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 12,
|
||
"id": "76f6fa0d-9d7a-4145-af1c-986d83947f91",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
" Registrar Account - ID country region\n",
|
||
"0 100000014 SWITZERLAND SWITZERLAND\n",
|
||
"1 100000016 UNITED KINGDOM UNITED KINGDOM\n",
|
||
"2 100000028 UNITED KINGDOM UNITED KINGDOM\n",
|
||
"3 100000038 SWITZERLAND SWITZERLAND\n",
|
||
"4 100000042 UNITED KINGDOM UNITED KINGDOM\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"# Geographic info per account\n",
|
||
"geo = stocks.groupby(\"Registrar Account - ID\").agg(\n",
|
||
" country=(\"RegistrarAccount - Country\", lambda x: x.mode()[0]),\n",
|
||
" region=(\"Registrar Account - Region\", lambda x: x.mode()[0])\n",
|
||
").reset_index()\n",
|
||
"\n",
|
||
"print(geo.head())\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 21,
|
||
"id": "b31584e4-b3c4-4fdd-9fd9-87372a7440ec",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
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"data": {
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"application/vnd.plotly.v1+json": {
|
||
"config": {
|
||
"plotlyServerURL": "https://plot.ly"
|
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},
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"data": [
|
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{
|
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"colorscale": [
|
||
[
|
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0,
|
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"rgb(255,247,236)"
|
||
],
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[
|
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1,
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"rgb(255,247,236)"
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]
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],
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"customdata": {
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"bdata": "AAAAAAAAJEAAAAAAAAAIQAAAAAAAABBAAAAAAAAAGEAAAAAAAAAYQAAAAAAAACJAAAAAAAAAFEAAAAAAAAAkQAAAAAAAAABAAAAAAAAAFEAAAAAAAAAAQAAAAAAAABRAAAAAAAAACEAAAAAAAADwPwAAAAAAAPA/AAAAAAAAAEAAAAAAAAAUQAAAAAAAABhA",
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"dtype": "f8",
|
||
"shape": "18, 1"
|
||
},
|
||
"geo": "geo",
|
||
"hovertemplate": "<b>%{hovertext}</b><br><br>account_bin=Very low (1–9)<br>iso3=%{location}<br>n_accounts=%{customdata[0]}<extra></extra>",
|
||
"hovertext": [
|
||
"CANADA",
|
||
"CZECH REPUBLIC",
|
||
"DENMARK",
|
||
"FINLAND",
|
||
"GREECE",
|
||
"HONG KONG",
|
||
"IRELAND",
|
||
"ISRAEL",
|
||
"JAPAN",
|
||
"LIECHTENSTEIN",
|
||
"LITHUANIA",
|
||
"MALTA",
|
||
"MAURITIUS",
|
||
"NORWAY",
|
||
"POLAND",
|
||
"SOUTH AFRICA",
|
||
"TAIWAN",
|
||
"UNITED ARAB EMIRATES"
|
||
],
|
||
"locations": [
|
||
"CAN",
|
||
"CZE",
|
||
"DNK",
|
||
"FIN",
|
||
"GRC",
|
||
"HKG",
|
||
"IRL",
|
||
"ISR",
|
||
"JPN",
|
||
"LIE",
|
||
"LTU",
|
||
"MLT",
|
||
"MUS",
|
||
"NOR",
|
||
"POL",
|
||
"ZAF",
|
||
"TWN",
|
||
"ARE"
|
||
],
|
||
"name": "Very low (1–9)",
|
||
"showlegend": true,
|
||
"showscale": false,
|
||
"type": "choropleth",
|
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"z": [
|
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|
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|
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|
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|
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1,
|
||
1,
|
||
1
|
||
]
|
||
},
|
||
{
|
||
"colorscale": [
|
||
[
|
||
0,
|
||
"rgb(254,232,200)"
|
||
],
|
||
[
|
||
1,
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"rgb(254,232,200)"
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]
|
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|
||
"dtype": "f8",
|
||
"shape": "17, 1"
|
||
},
|
||
"geo": "geo",
|
||
"hovertemplate": "<b>%{hovertext}</b><br><br>account_bin=Low (10–49)<br>iso3=%{location}<br>n_accounts=%{customdata[0]}<extra></extra>",
|
||
"hovertext": [
|
||
"AUSTRIA",
|
||
"MONACO",
|
||
"NETHERLANDS",
|
||
"PORTUGAL",
|
||
"SINGAPORE",
|
||
"SWEDEN",
|
||
"ARGENTINA",
|
||
"BRAZIL",
|
||
"CHILE",
|
||
"COLOMBIA",
|
||
"MEXICO",
|
||
"PERU",
|
||
"URUGUAY",
|
||
"PARAGUAY",
|
||
"BOLIVIA",
|
||
"ECUADOR",
|
||
"VENEZUELA"
|
||
],
|
||
"locations": [
|
||
"AUT",
|
||
"MCO",
|
||
"NLD",
|
||
"PRT",
|
||
"SGP",
|
||
"SWE",
|
||
"ARG",
|
||
"BRA",
|
||
"CHL",
|
||
"COL",
|
||
"MEX",
|
||
"PER",
|
||
"URY",
|
||
"PRY",
|
||
"BOL",
|
||
"ECU",
|
||
"VEN"
|
||
],
|
||
"name": "Low (10–49)",
|
||
"showlegend": true,
|
||
"showscale": false,
|
||
"type": "choropleth",
|
||
"z": [
|
||
1,
|
||
1,
|
||
1,
|
||
1,
|
||
1,
|
||
1,
|
||
1,
|
||
1,
|
||
1,
|
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1,
|
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|
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1,
|
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1,
|
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|
||
1,
|
||
1,
|
||
1
|
||
]
|
||
},
|
||
{
|
||
"colorscale": [
|
||
[
|
||
0,
|
||
"rgb(253,212,158)"
|
||
],
|
||
[
|
||
1,
|
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"rgb(253,212,158)"
|
||
]
|
||
],
|
||
"customdata": {
|
||
"bdata": "AAAAAABAZEAAAAAAAIBWQA==",
|
||
"dtype": "f8",
|
||
"shape": "2, 1"
|
||
},
|
||
"geo": "geo",
|
||
"hovertemplate": "<b>%{hovertext}</b><br><br>account_bin=Medium (50–199)<br>iso3=%{location}<br>n_accounts=%{customdata[0]}<extra></extra>",
|
||
"hovertext": [
|
||
"BELGIUM",
|
||
"GERMANY"
|
||
],
|
||
"locations": [
|
||
"BEL",
|
||
"DEU"
|
||
],
|
||
"name": "Medium (50–199)",
|
||
"showlegend": true,
|
||
"showscale": false,
|
||
"type": "choropleth",
|
||
"z": [
|
||
1,
|
||
1
|
||
]
|
||
},
|
||
{
|
||
"colorscale": [
|
||
[
|
||
0,
|
||
"rgb(253,187,132)"
|
||
],
|
||
[
|
||
1,
|
||
"rgb(253,187,132)"
|
||
]
|
||
],
|
||
"customdata": {
|
||
"bdata": "AAAAAABwfkAAAAAAALB0QAAAAAAAQHVAAAAAAABwdEAAAAAAAHCDQA==",
|
||
"dtype": "f8",
|
||
"shape": "5, 1"
|
||
},
|
||
"geo": "geo",
|
||
"hovertemplate": "<b>%{hovertext}</b><br><br>account_bin=High (200–999)<br>iso3=%{location}<br>n_accounts=%{customdata[0]}<extra></extra>",
|
||
"hovertext": [
|
||
"ITALY",
|
||
"LUXEMBOURG",
|
||
"SPAIN",
|
||
"SWITZERLAND",
|
||
"UNITED KINGDOM"
|
||
],
|
||
"locations": [
|
||
"ITA",
|
||
"LUX",
|
||
"ESP",
|
||
"CHE",
|
||
"GBR"
|
||
],
|
||
"name": "High (200–999)",
|
||
"showlegend": true,
|
||
"showscale": false,
|
||
"type": "choropleth",
|
||
"z": [
|
||
1,
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||
1,
|
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1,
|
||
1,
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||
1
|
||
]
|
||
},
|
||
{
|
||
"colorscale": [
|
||
[
|
||
0,
|
||
"rgb(252,141,89)"
|
||
],
|
||
[
|
||
1,
|
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"rgb(252,141,89)"
|
||
]
|
||
],
|
||
"customdata": {
|
||
"bdata": "AAAAAADgpEAAAAAAADiSQA==",
|
||
"dtype": "f8",
|
||
"shape": "2, 1"
|
||
},
|
||
"geo": "geo",
|
||
"hovertemplate": "<b>%{hovertext}</b><br><br>account_bin=Very high (≥ 1,000)<br>iso3=%{location}<br>n_accounts=%{customdata[0]}<extra></extra>",
|
||
"hovertext": [
|
||
"FRANCE",
|
||
"UNITED STATES"
|
||
],
|
||
"locations": [
|
||
"FRA",
|
||
"USA"
|
||
],
|
||
"name": "Very high (≥ 1,000)",
|
||
"showlegend": true,
|
||
"showscale": false,
|
||
"type": "choropleth",
|
||
"z": [
|
||
1,
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||
1
|
||
]
|
||
}
|
||
],
|
||
"layout": {
|
||
"geo": {
|
||
"center": {},
|
||
"coastlinecolor": "rgba(0,0,0,0.3)",
|
||
"domain": {
|
||
"x": [
|
||
0,
|
||
1
|
||
],
|
||
"y": [
|
||
0,
|
||
1
|
||
]
|
||
},
|
||
"projection": {
|
||
"type": "natural earth"
|
||
},
|
||
"showcoastlines": true,
|
||
"showframe": false
|
||
},
|
||
"legend": {
|
||
"title": {
|
||
"text": "Number of registrar accounts<br>with flow activity"
|
||
},
|
||
"tracegroupgap": 0
|
||
},
|
||
"margin": {
|
||
"b": 0,
|
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"l": 0,
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"r": 0,
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"t": 40
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},
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{
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"error_x": {
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"color": "#2a3f5f"
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},
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"error_y": {
|
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"color": "#2a3f5f"
|
||
},
|
||
"marker": {
|
||
"line": {
|
||
"color": "#E5ECF6",
|
||
"width": 0.5
|
||
},
|
||
"pattern": {
|
||
"fillmode": "overlay",
|
||
"size": 10,
|
||
"solidity": 0.2
|
||
}
|
||
},
|
||
"type": "bar"
|
||
}
|
||
],
|
||
"barpolar": [
|
||
{
|
||
"marker": {
|
||
"line": {
|
||
"color": "#E5ECF6",
|
||
"width": 0.5
|
||
},
|
||
"pattern": {
|
||
"fillmode": "overlay",
|
||
"size": 10,
|
||
"solidity": 0.2
|
||
}
|
||
},
|
||
"type": "barpolar"
|
||
}
|
||
],
|
||
"carpet": [
|
||
{
|
||
"aaxis": {
|
||
"endlinecolor": "#2a3f5f",
|
||
"gridcolor": "white",
|
||
"linecolor": "white",
|
||
"minorgridcolor": "white",
|
||
"startlinecolor": "#2a3f5f"
|
||
},
|
||
"baxis": {
|
||
"endlinecolor": "#2a3f5f",
|
||
"gridcolor": "white",
|
||
"linecolor": "white",
|
||
"minorgridcolor": "white",
|
||
"startlinecolor": "#2a3f5f"
|
||
},
|
||
"type": "carpet"
|
||
}
|
||
],
|
||
"choropleth": [
|
||
{
|
||
"colorbar": {
|
||
"outlinewidth": 0,
|
||
"ticks": ""
|
||
},
|
||
"type": "choropleth"
|
||
}
|
||
],
|
||
"contour": [
|
||
{
|
||
"colorbar": {
|
||
"outlinewidth": 0,
|
||
"ticks": ""
|
||
},
|
||
"colorscale": [
|
||
[
|
||
0,
|
||
"#0d0887"
|
||
],
|
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[
|
||
0.1111111111111111,
|
||
"#46039f"
|
||
],
|
||
[
|
||
0.2222222222222222,
|
||
"#7201a8"
|
||
],
|
||
[
|
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0.3333333333333333,
|
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"#9c179e"
|
||
],
|
||
[
|
||
0.4444444444444444,
|
||
"#bd3786"
|
||
],
|
||
[
|
||
0.5555555555555556,
|
||
"#d8576b"
|
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||
}
|
||
}
|
||
},
|
||
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"
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"# ============================================================\n",
|
||
"# GEOGRAPHIC COVERAGE — AUM MAP (ACCOUNTS WITH ≥1 FLOW ONLY)\n",
|
||
"# ============================================================\n",
|
||
"\n",
|
||
"import pandas as pd\n",
|
||
"import numpy as np\n",
|
||
"import plotly.express as px\n",
|
||
"import pycountry\n",
|
||
"\n",
|
||
"# ------------------------------------------------------------\n",
|
||
"# Helper: country name → ISO-3 code\n",
|
||
"# ------------------------------------------------------------\n",
|
||
"\n",
|
||
"def country_to_iso3(name):\n",
|
||
" try:\n",
|
||
" return pycountry.countries.lookup(name).alpha_3\n",
|
||
" except:\n",
|
||
" return None\n",
|
||
"\n",
|
||
"# Harmonize country labels\n",
|
||
"stocks[\"RegistrarAccount - Country\"] = stocks[\"RegistrarAccount - Country\"].replace({\n",
|
||
" \"US OFFSHORE\": \"UNITED STATES\"\n",
|
||
"})\n",
|
||
"\n",
|
||
"flows[\"RegistrarAccount - Country\"] = flows[\"RegistrarAccount - Country\"].replace({\n",
|
||
" \"US OFFSHORE\": \"UNITED STATES\"\n",
|
||
"})\n",
|
||
"\n",
|
||
"# ------------------------------------------------------------\n",
|
||
"# 0. Identify registrar accounts with at least one non-zero flow\n",
|
||
"# ------------------------------------------------------------\n",
|
||
"\n",
|
||
"active_accounts = (\n",
|
||
" flows.loc[flows[\"Value € - NetFlows\"] != 0, \"Registrar Account - ID\"]\n",
|
||
" .unique()\n",
|
||
")\n",
|
||
"\n",
|
||
"# Restrict STOCKS to accounts with flows\n",
|
||
"stocks_active = stocks[\n",
|
||
" stocks[\"Registrar Account - ID\"].isin(active_accounts)\n",
|
||
"].copy()\n",
|
||
"\n",
|
||
"# ------------------------------------------------------------\n",
|
||
"# 1. Aggregate number of ACTIVE accounts per country\n",
|
||
"# ------------------------------------------------------------\n",
|
||
"\n",
|
||
"country_accounts = (\n",
|
||
" stocks_active\n",
|
||
" .groupby(\"RegistrarAccount - Country\")[\"Registrar Account - ID\"]\n",
|
||
" .nunique()\n",
|
||
" .reset_index(name=\"n_accounts\")\n",
|
||
")\n",
|
||
"\n",
|
||
"# ------------------------------------------------------------\n",
|
||
"# 2. Handle LATAM explicitly (visual redistribution)\n",
|
||
"# ------------------------------------------------------------\n",
|
||
"\n",
|
||
"latam_countries = [\n",
|
||
" \"ARGENTINA\", \"BRAZIL\", \"CHILE\", \"COLOMBIA\", \"MEXICO\",\n",
|
||
" \"PERU\", \"URUGUAY\", \"PARAGUAY\", \"BOLIVIA\",\n",
|
||
" \"ECUADOR\", \"VENEZUELA\"\n",
|
||
"]\n",
|
||
"\n",
|
||
"latam_value = country_accounts.loc[\n",
|
||
" country_accounts[\"RegistrarAccount - Country\"] == \"LATAM\",\n",
|
||
" \"n_accounts\"\n",
|
||
"]\n",
|
||
"\n",
|
||
"if not latam_value.empty:\n",
|
||
" latam_share = latam_value.iloc[0] / len(latam_countries)\n",
|
||
"\n",
|
||
" latam_df = pd.DataFrame({\n",
|
||
" \"RegistrarAccount - Country\": latam_countries,\n",
|
||
" \"n_accounts\": latam_share\n",
|
||
" })\n",
|
||
"\n",
|
||
" country_accounts = pd.concat(\n",
|
||
" [\n",
|
||
" country_accounts[\n",
|
||
" ~country_accounts[\"RegistrarAccount - Country\"].isin(\n",
|
||
" [\"LATAM\", \"INTERNATIONAL\", \"UNKNOWN\", \"US OFFSHORE\"]\n",
|
||
" )\n",
|
||
" ],\n",
|
||
" latam_df\n",
|
||
" ],\n",
|
||
" ignore_index=True\n",
|
||
" )\n",
|
||
"\n",
|
||
"else:\n",
|
||
" country_accounts = country_accounts[\n",
|
||
" ~country_accounts[\"RegistrarAccount - Country\"].isin(\n",
|
||
" [\"INTERNATIONAL\", \"UNKNOWN\", \"US OFFSHORE\"]\n",
|
||
" )\n",
|
||
" ]\n",
|
||
"\n",
|
||
"# ------------------------------------------------------------\n",
|
||
"# 3. Convert country names to ISO-3\n",
|
||
"# ------------------------------------------------------------\n",
|
||
"\n",
|
||
"country_accounts[\"iso3\"] = country_accounts[\"RegistrarAccount - Country\"].apply(country_to_iso3)\n",
|
||
"\n",
|
||
"# Diagnostic: unmapped values\n",
|
||
"unmapped = country_accounts[country_accounts[\"iso3\"].isna()]\n",
|
||
"if not unmapped.empty:\n",
|
||
" print(\"Unmapped country labels:\")\n",
|
||
" print(unmapped[\"RegistrarAccount - Country\"].unique())\n",
|
||
"\n",
|
||
"country_accounts = country_accounts.dropna(subset=[\"iso3\"])\n",
|
||
"\n",
|
||
"# ------------------------------------------------------------\n",
|
||
"# 4. Discretize by order of magnitude (explicit labels)\n",
|
||
"# ------------------------------------------------------------\n",
|
||
"\n",
|
||
"bins = [0, 10, 50, 200, 1000, np.inf]\n",
|
||
"labels = [\n",
|
||
" \"Very low (1–9)\",\n",
|
||
" \"Low (10–49)\",\n",
|
||
" \"Medium (50–199)\",\n",
|
||
" \"High (200–999)\",\n",
|
||
" \"Very high (≥ 1,000)\"\n",
|
||
"]\n",
|
||
"\n",
|
||
"country_accounts[\"account_bin\"] = pd.cut(\n",
|
||
" country_accounts[\"n_accounts\"],\n",
|
||
" bins=bins,\n",
|
||
" labels=labels,\n",
|
||
" include_lowest=True\n",
|
||
")\n",
|
||
"\n",
|
||
"# ------------------------------------------------------------\n",
|
||
"# 5. World map — AUM coverage (active accounts only)\n",
|
||
"# ------------------------------------------------------------\n",
|
||
"\n",
|
||
"fig = px.choropleth(\n",
|
||
" country_accounts,\n",
|
||
" locations=\"iso3\",\n",
|
||
" color=\"account_bin\",\n",
|
||
" hover_name=\"RegistrarAccount - Country\",\n",
|
||
" hover_data={\"n_accounts\": True},\n",
|
||
" category_orders={\"account_bin\": labels},\n",
|
||
" color_discrete_sequence=px.colors.sequential.OrRd,\n",
|
||
" #title=\"Geographic distribution of registrar accounts with observed flows\"\n",
|
||
")\n",
|
||
"\n",
|
||
"fig.update_layout(\n",
|
||
" margin=dict(l=0, r=0, t=40, b=0),\n",
|
||
" legend_title_text=\"Number of registrar accounts<br>with flow activity\",\n",
|
||
" geo=dict(\n",
|
||
" showframe=False,\n",
|
||
" showcoastlines=True,\n",
|
||
" coastlinecolor=\"rgba(0,0,0,0.3)\",\n",
|
||
" projection_type=\"natural earth\"\n",
|
||
" )\n",
|
||
")\n",
|
||
"\n",
|
||
"\n",
|
||
"fig.show()\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 14,
|
||
"id": "18dd9229-825c-4bcf-b802-cecb1d51871e",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"Global raw totals (flows):\n",
|
||
" - sum netflows €: -18256951755.077896\n",
|
||
" - sum abs netflows €: 283031932185.3349\n",
|
||
" - n rows: 2574461\n",
|
||
" - n accounts: 6842\n",
|
||
"\n",
|
||
"Accounts with >1 country label in STOCKS (should be low): 136\n",
|
||
"\n",
|
||
"Top accounts with multi-country labels (examples):\n",
|
||
" Registrar Account - ID country n_countries_in_stocks\n",
|
||
"8724 406197 SWITZERLAND 16\n",
|
||
"6933 366024 SWEDEN 14\n",
|
||
"12030 420207 LUXEMBOURG 13\n",
|
||
"12499 OFF DISTRIBUTION LUXEMBOURG 13\n",
|
||
"3539 200127410 SWITZERLAND 12\n",
|
||
"5534 348454 SWITZERLAND 11\n",
|
||
"6352 365376 SWITZERLAND 11\n",
|
||
"5470 307388 SWITZERLAND 9\n",
|
||
"36 13463 SWITZERLAND 8\n",
|
||
"12400 422778 LUXEMBOURG 7\n",
|
||
"\n",
|
||
"Top 15 countries by total_abs_flows (robust):\n",
|
||
" country total_abs_flows total_signed_flows n_accounts_active \\\n",
|
||
"13 ITALY 4.263941e+10 -3.201953e+09 478 \n",
|
||
"26 SPAIN 4.099818e+10 1.575358e+09 330 \n",
|
||
"6 FRANCE 3.488683e+10 -4.092174e+09 2627 \n",
|
||
"17 LUXEMBOURG 3.100404e+10 -1.909475e+09 310 \n",
|
||
"7 GERMANY 1.502239e+10 -4.128081e+09 80 \n",
|
||
"1 BELGIUM 1.437804e+10 -3.546591e+09 145 \n",
|
||
"28 SWITZERLAND 1.141769e+10 -2.356821e+09 275 \n",
|
||
"31 UNITED KINGDOM 3.397122e+09 -1.094136e+08 605 \n",
|
||
"27 SWEDEN 1.038465e+09 8.745456e+07 37 \n",
|
||
"14 JAPAN 6.973177e+08 -2.703144e+08 2 \n",
|
||
"20 MONACO 6.387020e+08 -9.208744e+07 12 \n",
|
||
"21 NETHERLANDS 5.477981e+08 -2.581311e+08 32 \n",
|
||
"23 PORTUGAL 5.187647e+08 3.011212e+07 14 \n",
|
||
"0 AUSTRIA 3.782388e+08 2.282761e+07 9 \n",
|
||
"32 UNITED STATES 3.641362e+08 3.272825e+07 1161 \n",
|
||
"\n",
|
||
" n_flow_days share_multicountry_accounts avg_abs_flows_per_active_account \n",
|
||
"13 2758 0.022587 8.920378e+07 \n",
|
||
"26 2718 0.054746 1.242369e+08 \n",
|
||
"6 2724 0.034566 1.328010e+07 \n",
|
||
"17 2719 0.574681 1.000130e+08 \n",
|
||
"7 2715 0.156396 1.877799e+08 \n",
|
||
"1 2721 0.049440 9.915890e+07 \n",
|
||
"28 2720 0.605867 4.151887e+07 \n",
|
||
"31 2748 0.001728 5.615078e+06 \n",
|
||
"27 2658 0.585541 2.806663e+07 \n",
|
||
"14 1629 0.000000 3.486588e+08 \n",
|
||
"20 1863 0.596097 5.322517e+07 \n",
|
||
"21 2611 0.173396 1.711869e+07 \n",
|
||
"23 1433 0.000000 3.705462e+07 \n",
|
||
"0 2118 0.647902 4.202653e+07 \n",
|
||
"32 1116 0.000000 3.136401e+05 \n",
|
||
"\n",
|
||
"France vs UK diagnostics:\n",
|
||
" country total_abs_flows total_signed_flows n_accounts_active \\\n",
|
||
"6 FRANCE 3.488683e+10 -4.092174e+09 2627 \n",
|
||
"31 UNITED KINGDOM 3.397122e+09 -1.094136e+08 605 \n",
|
||
"\n",
|
||
" n_flow_days share_multicountry_accounts avg_abs_flows_per_active_account \n",
|
||
"6 2724 0.034566 1.328010e+07 \n",
|
||
"31 2748 0.001728 5.615078e+06 \n",
|
||
"\n",
|
||
"France: top 10 accounts driving flows:\n",
|
||
" country Registrar Account - ID acc_abs_flows\n",
|
||
"2797 FRANCE PRIVATE CLIENT 2.379152e+09\n",
|
||
"2695 FRANCE 418652 1.162739e+09\n",
|
||
"787 FRANCE 200127454 8.858410e+08\n",
|
||
"1265 FRANCE 365596 7.407851e+08\n",
|
||
"875 FRANCE 200127809 7.233502e+08\n",
|
||
"1540 FRANCE 405760 7.060290e+08\n",
|
||
"877 FRANCE 200127811 5.845121e+08\n",
|
||
"2648 FRANCE 417622 5.822746e+08\n",
|
||
"271 FRANCE 200002327 5.160009e+08\n",
|
||
"1593 FRANCE 406163 5.046222e+08\n",
|
||
"\n",
|
||
"UK: top 10 accounts driving flows:\n",
|
||
" country Registrar Account - ID acc_abs_flows\n",
|
||
"4971 UNITED KINGDOM 200000225 3.112362e+08\n",
|
||
"5127 UNITED KINGDOM 200009976 2.835532e+08\n",
|
||
"5150 UNITED KINGDOM 200048865 2.719247e+08\n",
|
||
"5454 UNITED KINGDOM 365486 1.840476e+08\n",
|
||
"5161 UNITED KINGDOM 200072503 1.233866e+08\n",
|
||
"5200 UNITED KINGDOM 200102162 7.662158e+07\n",
|
||
"5039 UNITED KINGDOM 200001522 6.594899e+07\n",
|
||
"5165 UNITED KINGDOM 200079169 5.913266e+07\n",
|
||
"5230 UNITED KINGDOM 200127603 5.872504e+07\n",
|
||
"5260 UNITED KINGDOM 200128363 5.599629e+07\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"import pandas as pd\n",
|
||
"import numpy as np\n",
|
||
"\n",
|
||
"# ============================================================\n",
|
||
"# 0) QUICK SANITY: global totals\n",
|
||
"# ============================================================\n",
|
||
"\n",
|
||
"print(\"Global raw totals (flows):\")\n",
|
||
"print(\" - sum netflows €:\", flows[\"Value € - NetFlows\"].sum())\n",
|
||
"print(\" - sum abs netflows €:\", flows[\"Value € - NetFlows\"].abs().sum())\n",
|
||
"print(\" - n rows:\", len(flows))\n",
|
||
"print(\" - n accounts:\", flows[\"Registrar Account - ID\"].nunique())\n",
|
||
"\n",
|
||
"# ============================================================\n",
|
||
"# 1) DEFINE ACTIVE ACCOUNTS (≥ 1 non-zero flow)\n",
|
||
"# ============================================================\n",
|
||
"\n",
|
||
"active_accounts = (\n",
|
||
" flows.loc[flows[\"Value € - NetFlows\"] != 0, \"Registrar Account - ID\"]\n",
|
||
" .unique()\n",
|
||
")\n",
|
||
"\n",
|
||
"flows_active = flows[flows[\"Registrar Account - ID\"].isin(active_accounts)].copy()\n",
|
||
"\n",
|
||
"# ============================================================\n",
|
||
"# 2) COUNTRY MAPPING FROM STOCKS + DIAGNOSTIC\n",
|
||
"# Check if accounts are multi-country in STOCKS (bad sign)\n",
|
||
"# ============================================================\n",
|
||
"\n",
|
||
"acc_country_counts = (\n",
|
||
" stocks.groupby(\"Registrar Account - ID\")[\"RegistrarAccount - Country\"]\n",
|
||
" .nunique()\n",
|
||
" .rename(\"n_countries_in_stocks\")\n",
|
||
" .reset_index()\n",
|
||
")\n",
|
||
"\n",
|
||
"print(\"\\nAccounts with >1 country label in STOCKS (should be low):\",\n",
|
||
" (acc_country_counts[\"n_countries_in_stocks\"] > 1).sum())\n",
|
||
"\n",
|
||
"# Use mode for mapping (as you did), but now we will measure if it's messy\n",
|
||
"account_country = (\n",
|
||
" stocks.groupby(\"Registrar Account - ID\")[\"RegistrarAccount - Country\"]\n",
|
||
" .agg(lambda x: x.mode().iloc[0])\n",
|
||
" .reset_index()\n",
|
||
" .rename(columns={\"RegistrarAccount - Country\": \"country\"})\n",
|
||
")\n",
|
||
"\n",
|
||
"# Merge diagnostics\n",
|
||
"account_country = account_country.merge(acc_country_counts, on=\"Registrar Account - ID\", how=\"left\")\n",
|
||
"\n",
|
||
"# Look at the worst offenders\n",
|
||
"print(\"\\nTop accounts with multi-country labels (examples):\")\n",
|
||
"print(account_country.sort_values(\"n_countries_in_stocks\", ascending=False).head(10))\n",
|
||
"\n",
|
||
"# ============================================================\n",
|
||
"# 3) CLEAN FLOW AGGREGATION TO AVOID DOUBLE COUNTING\n",
|
||
"# Key step: aggregate flows first, then take abs, then sum.\n",
|
||
"# Two options:\n",
|
||
"# - Option A: Account x Date (recommended for \"activity intensity\")\n",
|
||
"# - Option B: Account x ISIN x Date (keeps instrument dimension)\n",
|
||
"# ============================================================\n",
|
||
"\n",
|
||
"# Option A (recommended): aggregate per account-date\n",
|
||
"flows_acc_day = (\n",
|
||
" flows_active\n",
|
||
" .groupby([\"Registrar Account - ID\", \"Centralisation Date\"], as_index=False)[\"Value € - NetFlows\"]\n",
|
||
" .sum()\n",
|
||
")\n",
|
||
"\n",
|
||
"flows_acc_day[\"abs_flow\"] = flows_acc_day[\"Value € - NetFlows\"].abs()\n",
|
||
"\n",
|
||
"# Attach country\n",
|
||
"flows_acc_day = flows_acc_day.merge(\n",
|
||
" account_country[[\"Registrar Account - ID\", \"country\", \"n_countries_in_stocks\"]],\n",
|
||
" on=\"Registrar Account - ID\",\n",
|
||
" how=\"left\"\n",
|
||
")\n",
|
||
"\n",
|
||
"# ============================================================\n",
|
||
"# 4) COUNTRY TOTALS (robust)\n",
|
||
"# ============================================================\n",
|
||
"\n",
|
||
"country_flows = (\n",
|
||
" flows_acc_day\n",
|
||
" .groupby(\"country\", as_index=False)\n",
|
||
" .agg(\n",
|
||
" total_abs_flows=(\"abs_flow\", \"sum\"),\n",
|
||
" total_signed_flows=(\"Value € - NetFlows\", \"sum\"),\n",
|
||
" n_accounts_active=(\"Registrar Account - ID\", \"nunique\"),\n",
|
||
" n_flow_days=(\"Centralisation Date\", \"nunique\"),\n",
|
||
" share_multicountry_accounts=(\"n_countries_in_stocks\", lambda x: (x > 1).mean())\n",
|
||
" )\n",
|
||
")\n",
|
||
"\n",
|
||
"# Helpful ratios\n",
|
||
"country_flows[\"avg_abs_flows_per_active_account\"] = country_flows[\"total_abs_flows\"] / country_flows[\"n_accounts_active\"].replace(0, np.nan)\n",
|
||
"\n",
|
||
"# Sort\n",
|
||
"country_flows = country_flows.sort_values(\"total_abs_flows\", ascending=False)\n",
|
||
"\n",
|
||
"print(\"\\nTop 15 countries by total_abs_flows (robust):\")\n",
|
||
"print(country_flows.head(15))\n",
|
||
"\n",
|
||
"# ============================================================\n",
|
||
"# 5) FOCUS CHECK: France vs UK\n",
|
||
"# ============================================================\n",
|
||
"\n",
|
||
"focus = country_flows[country_flows[\"country\"].isin([\"FRANCE\", \"UNITED KINGDOM\", \"UK\", \"UNITED-KINGDOM\"])].copy()\n",
|
||
"print(\"\\nFrance vs UK diagnostics:\")\n",
|
||
"print(focus)\n",
|
||
"\n",
|
||
"# ============================================================\n",
|
||
"# 6) EXTRA CHECK: is France driven by a few accounts?\n",
|
||
"# ============================================================\n",
|
||
"\n",
|
||
"flows_acc = (\n",
|
||
" flows_acc_day\n",
|
||
" .groupby([\"country\", \"Registrar Account - ID\"], as_index=False)\n",
|
||
" .agg(acc_abs_flows=(\"abs_flow\", \"sum\"))\n",
|
||
")\n",
|
||
"\n",
|
||
"fr = flows_acc[flows_acc[\"country\"] == \"FRANCE\"].sort_values(\"acc_abs_flows\", ascending=False)\n",
|
||
"uk = flows_acc[flows_acc[\"country\"] == \"UNITED KINGDOM\"].sort_values(\"acc_abs_flows\", ascending=False)\n",
|
||
"\n",
|
||
"print(\"\\nFrance: top 10 accounts driving flows:\")\n",
|
||
"print(fr.head(10))\n",
|
||
"\n",
|
||
"print(\"\\nUK: top 10 accounts driving flows:\")\n",
|
||
"print(uk.head(10))\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 19,
|
||
"id": "99b7401c-ca9a-4571-96ed-f2db30ce8c70",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"application/vnd.plotly.v1+json": {
|
||
"config": {
|
||
"plotlyServerURL": "https://plot.ly"
|
||
},
|
||
"data": [
|
||
{
|
||
"colorscale": [
|
||
[
|
||
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|
||
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|
||
],
|
||
[
|
||
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|
||
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|
||
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|
||
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|
||
"customdata": {
|
||
"bdata": "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",
|
||
"dtype": "f8",
|
||
"shape": "29, 2"
|
||
},
|
||
"geo": "geo",
|
||
"hovertemplate": "<b>%{hovertext}</b><br><br>flow_bin=< €0.5bn<br>iso3=%{location}<br>total_abs_flows=%{customdata[0]:,.0f}<br>n_accounts_active=%{customdata[1]}<extra></extra>",
|
||
"hovertext": [
|
||
"AUSTRIA",
|
||
"CANADA",
|
||
"CZECH REPUBLIC",
|
||
"DENMARK",
|
||
"FINLAND",
|
||
"GREECE",
|
||
"HONG KONG",
|
||
"IRELAND",
|
||
"ISRAEL",
|
||
"LITHUANIA",
|
||
"MALTA",
|
||
"MAURITIUS",
|
||
"POLAND",
|
||
"SINGAPORE",
|
||
"SOUTH AFRICA",
|
||
"TAIWAN",
|
||
"UNITED ARAB EMIRATES",
|
||
"UNITED STATES",
|
||
"ARGENTINA",
|
||
"BRAZIL",
|
||
"CHILE",
|
||
"COLOMBIA",
|
||
"MEXICO",
|
||
"PERU",
|
||
"URUGUAY",
|
||
"PARAGUAY",
|
||
"BOLIVIA",
|
||
"ECUADOR",
|
||
"VENEZUELA"
|
||
],
|
||
"locations": [
|
||
"AUT",
|
||
"CAN",
|
||
"CZE",
|
||
"DNK",
|
||
"FIN",
|
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QfmjaqB52/vMfWnXqjebte4KUmW59h2H52i24etNHGDzXdquLPXv2YPTo0bCxsYGSkhJq1XKFjLQUs9IYQCQSSWQoz5GMjAw4OlWBs5MDfhnQB+NGj0Czxg1gamoKDQ2Nr961hIQEeN8LhOA4ISIeXj7+iIhJxgFm9VBmFifNikawsHYCcnMQHxOB6KhI5LzJRcWKOlBRU4O0jDRysjKhUaEC2rZuJVy3vJ2g6zh+3BiMHjUyb3ax4oTNIGZVHMV49O3zM1yqVIaxsbGgBKqrqwv3dcOGDQWe+vr6mDF9GlatYpbIgQNgYWGB6tWrwc2tNpNZFypM8dTS0hLOQoVvcKDrPKB/H/T9uTvquFaDorwcGjRqjPY/dUPbTt1QQbsStPXNoMXCpau3BCtVXHw8Dh09gbPnLsL3nh9omRst0fsG4vMmOQLfBQJkzXHw6CssYardephkwEiz4dMWbkFYeBRocEk0RAv2oTKipTxxEM+os+Ii/ReVR1R0vLD07WPtkEziN2HxHAAAEABJREFUMjpTWiyA2NJB+RTICkDWAHF53rOY9v1+ED8KRDt/1U7cuhuEFZv3foAX8W3eY5KQT22RxYd4Ur2Cgpj+2LkbIFqqQ0HclriOmI7KKBCtmG9h8ojr05kUhBHTVguy12gxBHTtCH8qez9Q+9QOhbx01Ca1TeXiOsQ3L6ZiWSmfaKgN4kG8KFCc8qisoEBteN6+D7eaTlBSVJCQEN+PWXe6tmsIBxtTCW3Pjk1wxy9Ych9LCliEKzkMBP5ffhCg5S4kLXkc69y5M9q2bYvWbIA5YMAAys4Xzp49i7CwMOTkZMPKwhR62mp4EfoUc2bPBnk8e/L0KU6dPIlhw4aBZsG3bN2KSZOnIC09DR5sYGhhbsosNpE4fuocdu87hJXrtqA7U1Cmz16EK563oK2lCfc6rmjCFBlqmGbRSQkyN/v/w0f59evVRq2a1WBuaoIBA/pj+/bt+KlLD9RwdYOFtR0ysgBv30CERsQhITUXrzOkIJKvANo3o6unTyzKfXj6+CEunjkKfd0KaNWyBapUqSIolN+yY+rq6mjWsB5ehj6DqooKatR2R3BwELS0dWFhZQ1NLW3UrOWGeg1aIDMrFz537uDGtUtQkJOGpakBXJxsmXLTHM2aNkSlSnoFdoUsWSJR6SipItGHfKk9KSmpAmUpC5kikUhY8lmzZk3QpAJZl5o2qAudCvJQVxKhAvuNS0l4hdAnDyElykXYi3BmYXuISkYWTPkxQfzrLFy+7i1Yz3zv3UNhExxlob9cBo5AWUOABqyRUXHwOrlJmB1fOG0QBo5n1p+HIcLAkdJ5rT95LT9tmtYR6tBsOi2BomVC4sFtUftZFB5/7jmFnWunCW293w4pJHnlp35Q2zQopgEzDez1dDWFuiQnWQF6j1z42XuFZozpDVcXO9BSKuJ349gGASdqj/hOHtFd0ha1S+2THCTTx8ICpjiNH9JVqEf9u+h5F2IcP8X3Y/K83xZZOEhmkp0wEsv9Pl1h94MSUzrcmPJBeIv7dPayt6AEkydV4hUVk8B+P5XgaGMmYDx+zgbQPURYUaD4TZ9AIi0wPAuNxMvIGNSqai8pF2NAyrZYuSSFSYyRhPBdhCxVZLEiy9W7LMmp7P4aSkT8biO8Y0VA4Pnz55KBTMeOHTFw4EAMHz5M2J9AM/G050FBQVGYtSZ2pASdOHECtASpbt26bPBZCQYGhoC0PGjjflRULPT0dIT3uVhbWSGdWRcqqKkiLjYGN65fF5wKHD58BMkZ2di87W/MmLccj0IiWN27uOnlg5jYOFRQV8e65fOxc+saTBk3HGqqKtS0JNCm7kkzFqDXwFEYOX4Gxk+ZC/qS6NS5C7p1645r1zwRHvkKuSIZGJlaoUr12uxsAUUlFaiqqsLYxFSQWbYML0mTdLYIkbTUVCTGxUBZSQFvmMJZgVk+ilDtq5CosWvfuWMbaKnJw/vGRfjf80Ggvx/27t6JkGdPhCVT/n5eaN+mGUaOGIbY+ET4Bz3Gk+eR8L57D8GPHn0VOb/nRhQUFIQlivb29nB0dET9um5o06qZENzZZENmVjbCnj5EYvQL5GYlQ1dLDapK8sjKzAS5Qqdlgd8zPrxvHIGSQoAGjz73H6F356aSWXMaBNtZmaCwgSi1TzPn00b1oqgQKE3LhJ4+jxDSRTlQnaLwIMVBR0tdYEl1qB0aXFPGk5CXIGWCBuCUpjMpYkQvHjDTzD6VUWjLFDM6iwflFC+JQEusqjpZgZQJMT9qlwbsJIc4r6AzDfypX1RGA3TCX4zjl/AlfsUJRbkfzE0qIfxVLFJS0wUlhsYyPTs2hvh60H2jqqwo/L6TwkNjMF0tDYkYhM/7+2wkhSxCigkpKIQDSwr/dC3pHlBTUQIpgQGXtgsKIfESCN470D1A94QYw7zFUnkTPM4R+FYI0Htf6B0wE8aPZ5aVoWjWrBloiQ5ZWTox5Ybk6tSpE8zNzYWBJ83g2tpYw9DIENuZZYTeI0M0IpEIO3Zsx6RJk0CDp82bN4PCqFGjsGXLVnRhSkaDhk2watUqYU+Dk5MTkl4nIzvnDVVHTEyMcNbT0wNjxeK5wt4Sev9NFSd7TJ80WrDgLFuzSfB8xgjy/S9asR4tOvRCanomU8ZGYPjI0fiTybd+/Xo0atRIoG3Xri2U5GWREPsKN65exD87t2L3X3/g2uWzuHrpnEDzvRyysrKY8qYE7YqG8Pa6g5s3veBz1xd0/fDRz9ctIOuHDbuXenTriooVK6KCqjqsbOygW7ES/HxuopFHPdD9YG1tBVoyVrWyA5KTYpliHAstTc2vK2wptkb7ZUJCQgTFoRSbKRZrWabo13WrhZ86toVH/XqoxxSguLgEaFY0QmqWNB48eYHDx08JbtQfP34Mek6pAfqhpTOFvHFK88AR+FERoEGoKhs40qy7GAMaINJsPSkP4ryPnWkm3cHj7TI3Oh8+7Ymi1MvL73N4WJgaCBOFZE1oUr86qF1qv3mPScLAW8z/YwNmUkYKGgCL633OmfotloNkodBl8Gw2nkj5HHaSOqXFV9JAnkhR7gfxvUJKIgVSJto1c8ejZy8F7Eleun/oPiJFhRQWwoHwIKtbnuaKHCXli5aqJSWnoqi86B4hWd5vROr9DJ7mCHwtBLy9vTFv3jxMnDABvXv1wt07t2FQSQdtWzRC3TquePr0GbNsqMDUzAz//vsvaA/N9OnTmVKzA81btGAzC2l4+eIlLl++nG/QTOv+abCzZMkSHDx4UHiJZvdu3dC/f3/s27cPnp7XhIHc/v/+g4xULkh5sbI0h6GhPmRlZdC/X182GL8BkZQ0kpKSmDISBTNjfdCGeikpERrUqw3Xai7Yvmsvtvz5D+YuXIFTZy/hxKnzuOPjB1q21r17d9SuXRu2trYf7IGgQRdZe8JevICsvAJ69B4I8p7WuFkb1G/UDLEx0Qh9HoKw0OdsIB3DZCi/G7BvXLuAsycOIeLFI/zUpTO0dXVBP1SE0de6z4rajrS0NMaOHonOP7WFnCibWXYuo0nDesyqpi9h0bxZUzRsUB/t27ZGj25doMv6IyksJ5EX7L4jZT42NhYUJ+Xg5KnTuOLphZCXsTh3+Ybw3qVv2R2y0Io9H9K9Qu9Qou+L8xcuQpspzFY29oIiau9YGbXcGiIxDbj/8Bn+238Ip89dBL3Ydh+L7/3vIA4fO4VLl6+y75OnwruYvlW/qE/itun5F8eLdebEHIFvhAAta5q2cEu+mfV2zdyKJU1J8KDZ/AA2s09OFWg5E+0del/ZKZZQX0BM/SdZ8oaPLQsrTjOlxbc4MohpaQWGfkUtkJJI1htSMkmZqVRRE2SxIssOWXuInhSdP1ZOBi2PIwuheKlZcZUdsuQs/3UYyJJD15nwLcwaRG1/LHAl52PI8PxSR4AsLnfv+jCzuZwwWLwfEIQTp8/j7z2H2MMThupVK2P4L31BMwPPngTjv717cPLkSdy9exenT51iioYzVJSVcebMGYwcMZxZfkYJLn2joqJBA4pLly6BFJnoVxFsxl0Dk8cMhrOdJYKDg4X9IN179EByagbkFJWFfRiREa8wekhfNrCti4iISNAyGHf3unjNaJJSMhARFcdkO4xjJ8/jwtWbuOnlC6+79+HjF4B9B4/DN/AxOv30E1auXAk3t49/+YtEInRo31YIFXW0EBx4FxkpsUiKDUfUiyfCWUXuDRSksxAfFYZXYY9x9eIpBD8IREx0VKlfl5JsoEateqiobwh5pQrMIiaFzp3aw71OrZJsokR5KSgoMKXGAM2aNkKHdq2g+RFLDVl/SrThUmZGyz6v37iFU2cu4PHzCGEP2HV2/966c1/YsMlub0jLyCIq8gUM9XRQpbJzKUv0lj1ZcKPpZbKhoXjInss5c+fh4OHjOHXuMt5OHJzBiVPnQO9SSnsjD009E5iaW76t/O4oLy8PcofuXLkaqtRwg6VtZeHFtm4ezeDeoDlq1vGAjr4ZwqKScMsnAHfu3H1X8+uccnJysGffAaZ8XcbBIydw/uJl7Nr1L+vnMVxnlk0v7zvw9/cHXSOiFUtVlqydYpn4uXwjoKutjtdsdpxm5MU9oUkn2vhtwawllEdLjcjaQ3FxIJpI9vu3cNogYT+KOL845y/hQTP0ZEGgQbS4TbGyQ8uZaOKQrBIk+/vLxcgqQEv0xANxcf2inqlNavt9esKLMKF+vV/2JelP8f2YPJ/TZlHuB2qPLDV0j5D1hjCmPBUlRfx94Axep6QJ+3Hytk/lpOyQckIKG12/vOVFiTvYmIKWKe7cdwZFwZjaIOzw3kfqvTRPcgS+GgJr1qzF5s2/o2p1V9jZ20NDQxMikQgOtlYY3K8nKjvZweuOL7PS5ID233Tv3IYpLXuxYf06NvhUh421JaaMH4EqjjaIj09ADLOA9O7dG2vXrkWVKpUFC0pGZiYePnoCl8r2uHT1BvYdOgoHewd07NCBKUonYGNFFhwjpvRow7V6VZiaGKF75/bYvnkVerBzyNMnoFkFWRkRcrLS4WhnhX69O6Nj26YY1Lcb2rVsDI0KaqB2aMlcN2Yxsra2RlE+tCeoU4e2aNWiKVxrVIO7W2141K8LN6YEODo6oLKzE+q610H9eu7o+lMH6Goo4urlc4hjM/BF4V8WaOidPc5VqiM5OYVdh2f448/t2PfffsmyorIg48dkoMHzx8rKQz5Za3zYhMAf2//GsxcxUNHQg4WtM8wsbJCZkQGp3DegHzl7axPUquaAenWqoUunduwedBdcf5dWH8mKQcrNXd97uOntyyYJgpjiFYno+HQ0a/0TXN0bwrVOfVSvVRc2DtVg41gVNWq5w8LCkimgRoKltCDZ6F4jZxF0fr+c3iFlZWWDyi7VEZecAe87d94nKbU0OYLQUFeHupYurOycoVPJFFVr1we9sPeNlAKiE9IEZyPnr9zAjZu3BTk2bd6Cbdt34u9/9sLzxm3cYAoqWbLyKkECIT9wBIqBAP2W0dKtvAPHW3eDEPToOfJu/H4RHoW8ipC4CZrJF8cvXr8rLBsTp4t6Li4Paoc25vfs2EQY7I6auSafFy1aoiYSiYTvMrIwGOhpI687YdrjQrKJl11RXBxoME4KDA3gxQNpao+WoYlpxOe8NJRHeBFu81ftpKQQiAdZq0ixEjI+41BUvu/L8xlNCWObotwP5iaVQPeJqrIiCGNqiyw656/6gPLI2kN5hB31n+IUCA9SBC1MDShZYCCl6X3FVEw49pfOwn6g95057Dl8Id89IG6H5BTXFZ+lxJEf98x7XtoI0NKx//77D+vWrRPcOM+aORPrWfwOG2jQUhQzMzO0aNECtLysb9++yJWSwZGTF7Hh9x24eMUT/oEP8ODhY2zdsYcpPG+gVkFdeOeK521f/HvgGBskpOC3RYvg6loLjRs3ZgMhAyxbthy0x2fw4MFwd3fH45BwJLDBzaRJUxDNlCFSSmjAEPQgWFgGR9adF+ERWL3jbP8AABAASURBVLhsPZat+R3Dxk7DS2bNsTAzhpZGBfZloMGsTToMKhFue99DQnwiLl+9iftBwYh4FYUxY8awstL9p43ZnTu0waMH9/CKWadKt7WS4x7+8gWOHtiH2KiXqFbVBTVrVBf2S5VcC5xTQQhERERg567dqF2vMaxt7aGgqIgnwYHwvn4JjRu4MeW6CeoyxdqJKdSGhoZQU1MriE2J5r1+/RoTJ09hlporeCOtDKcqNVmoAVt7J5hbWoGcbuRtUFFJCQUpLXlpihp//jwEXjevIezZI8gyqxW5NC9q3S+hE4lEaNzIAwY6qnjFLLUhTx8g9Okj+HpdQ0ZyDJu8UEJc1As2Q24BlyrOQlNtWrcEWTyNDA3w6MkT+DMr9/2AB2wiJ0Yo5weOwOciQJv0aWBPboUdPPqClqBtXT6J3X+mAkuaQR/YszVoYCmU/7YFpAwsnjEYPvcfgfIokLJCs/Qo4qc4PMRtUzt55SMele0tJfs0xOW0tIkUOCqn5U2RzOpEZRT2Hb0E8tRG5QWJOmNMbyFbjEdB/RIPtommduthwgCbcDqyY2E+TKgc7POxtljRJ/+LwrcgeT7J+CMEn7ofqBopiEb6uvncPH8sL+89QnjQvVbYUjNSmkgxvVmABzbCkd77RDyIF11PCsmp6ZL7leSjZXMFWZSojCs5hAIPpYrA1KlTEeB3F4qygLmxHmrVcIaliR5OHDuCGdOn4c9tW7Hr753YsmULaC/LtGnTMXfePHTq1AkNGzZC3Xr1MeiXXzBq9Bhs37EDq1evxswZM5hyYo/q1WsIVhlSlEaPHo0RI0YgNDRUUKhov09KSooweCOvZT179kTlypWhp1cJ5mamIAUn7MVL/PffPqSlpSMjKxvVarjC0toOzZs3R1DwU/gFBsPbNwBqGrpQUdNEWmYO4pNSEJ+cCRaFnLwSyJ01LY8jk3mpAsmY6+rqws7GCs/YIImUNJZVev+fyZkwF1f1u3cXxw//h+Ytm8OSDWJpj5KJiYm4mJ9LCYGLl67grl8g+v8yUthLlpWVhRtXL8DKzACdOraFElMeSqnpfGxTU1MFKyw5NaA9QJR2dHBAZHgYIiPDQVadfBVKIZGc/BqXzp/Gg/veSIqPRmJ8HA4dPcasIzdBzxApO2T1IvnIwkTPMk3MfEwUoiGvbmKaovSBrDlWVlZo1KA+WjRpCPfa1dCuTUvo6mjDy+s2zEwMheWCtHQvOTlZmKhxsLdD/Xpu6NurOwYN7It+fXqxiRbdj4nF8zkCHyBAS7pokEiDxbyFNLANuLRd8Fh1451L5LzlNCgVlxMtlREP4pU3n8ooUPnH2qIycfgUj/fLqa335csrW0HlpOiIl0pROclMfMUyUH0qJzrKozOliZYC9UccqJwC1Sc+VJ5Xnrz5VEaB6lKdgoKYnrASl4vbJ7nEeWI64icOefnmLc8rj7i++Ew8qW/URmF5xFvcTkH8xO0RPzGfwvLEvOhMvMV1CjqTbOLlcGSRKYiGeBAvccgrB9GT5Y5+20gmSucNUnkTPM4RKEkE6KV9Q4YMgaK8HMaMGIgBfbqjS8c2aNm0IZo1aYBZU0Zj6oThqFrFAampycJm//79+4E2QtMadao7duxYDBo0CE2aNIGFhQXOnz8PymvVqhUCA/xwx+sWli5dwiw82fD09AQNpsaOHYOM1CRUr2yL+74+uH37Ji5duiS8BHT8+PFwd3XBknlTcebIbhzf/xec7K2F9fDOzs7w8/PDvn178SriJYwNK0GEN3jCZlIPHTqI06dP43ViPGTYU3P16lWkp75GYkIs/t65A55XL2EIsxrRAKkkMSyIVyW9ikiMjUV6WlpBxd8sb9+ev7Fo7gz8/cdG/Pv3NsGRRDq7rppaGoIFp2WL5ijvS8C+GbjFaJgG3QEBAQgPf4mTxw7h/JkTeBh4D83Zc0fPUDFYSUj9/QOw7Y8d7Bk4I8krLBIeHo4HD4Px38GjuOHli+u3fXHHNwj3Ah7BzMoeNWrXh7WNPWjwXxifLy27dO4Url08BQum3GVkZuFFZAzqNmqJevUbQ0ahAo6cOINjp87h7v1g3PbxF5wvnDl/Ffv2H0ZQUBCioqJASg0916QMkSXq6ImzuP/gKU6dvYiTp8/h+InTwj4bem/Ps2fPPimyrKwsaNKFCJ2cnNC1Sxc24fIYD4Kf4Pzl69j21y48eVowH5FIRNV44AhwBDgC3w0C5Oab3FTvPnSh2H2iJXLi5YwFVWbDtYKyeR5H4PMRoJn8jZs2YeDAAejcrgUWzp6ECmpqAsPs7Gw8evIMM+YsEVwtDxw2HvsPnxDcMderUwtxsXFMiRmD7X9uQy9meSFvam3btsXAAQMEReXA/n3o1LYp+vXuint+/sjMfoOVK1cJZSePH8PsX2exAYQaXienoAUb1OXkZEOODSrs7e1x7txZ9O3egVlydDB51m+YtWAFRk6YhfCoeGGp3OPHj2BrYYi/t65Bt05tEPjgEUQiKfw2dyq2bViOg7u3Yv6siZg3cwL0dHWE8vTUNLyKimGDnCuIiIzAACYnzcQKnS2lQ4UKFdDAw40pZgUPhEqp2U+ypSV0FSqoIjomFrdu3GQWnL2oVd0ZI4cNgY211Sfrc4IvRyAnJwfrNmyEqZUDWrTuiHoNGjNlOAUWJvqoyKyAn9MCeTa7cs0TL15GwMv7LrOCnIAve/bEvP7e9Q97fjOE5MuXL9lkxU3s/ncPVJSVoMwsRvqGpnB2qQHHKtXgWLkarG0dYWpmDvJWKFQqxYONgzPMrR0hq6SFBk1aoU+/QdA3MIQNLY+zsoFrHQ/Udm8IB2cXVK5aE9Vc3ZgCVheNmrfFq/g03PUPxrUb3rh09TpOnjmPsxcuIzMrE7b2zqhZxwNW9i6oVtsDBqY2yIIiQl5GMaXnVLF6pKmpiRbNGwsvl42PiYSejhabWHmKm7e9ccfHF4FBDxASEiIsVSNLGClc9D1arEY4MUfg6yDAW+EIFBsBssBMHtEd5I2tOJ7YSMGhpY20vNLBxrTAdrmSUyAsPPNzEaClYt27d4e5gQ6O7tuBRg3cJawOHDmJHv1HYua8ZXj6PBRDBvbG4T3bcejfP7Bh5UK0a9MMnTu2xp+bVmLFb7PQoG5t2FiYoE/PLqhZvTLWr1+PX/r1hHvtmnC0txGUmbVr1wrL0WgZVEJSErMIpWL4oJ8xe9o4od3hg/syPnWEZSk0M7t2858YM2EmGjdpJoR58xcIFpiNG9cL3tfatGyKLKaInTp/BXJyspg5eTSqVnYS3o0jMGSH+UtWIzwyEi4sPyYuAWOGD8DG1b9h7IhBTJlTwc8//wzaJMxIS+X/9JmzePzsBaysbUFLcUqlkc9gOnjoGGFwaGhoCJeqVWHDBrN6enqfwYlX+VwEaECspqYOSytr6OhWhJyMHGysLAQr6OfypGVcjlVqYtCw0eg/ZBSqVHdD3OsMkLWIBtxkmTh24owwED94+CgWLVoKQ2MLeAnu1KXhfeMyblw5i7veN/Eo+OHnivFZ9SpV0mcWIzs2sVFJ+L4oDhNrGzs4MaXMpUYdVK5Whyk1DVDLvRGat+oAaWlpgZV42Z+aWgUYGBrBxs4ZFQ0tsHbdeqE874Hc0edN513eSkrM6bPnYetUHfaVXVHJ2AqKqjp4I62E+ORshITHMTwDcO6iJ06fv4wTzIJEmF+7fgNiV9t5efM4R4AjwBEoTwg0qOMiLJ98fylaYX0oSh2pwhh81bJy2JiPrx+OHDuFa9eufxXpSYHYxCwktHyitK0Fn9shY2Nj1KpVCyfOXoTf/SA263sKu/YcwMJl6+D/4Kmw9IwGTST/2QtXkZiYJGnKzsYKvbp1kigUA/p2x889OuGn9q2QmZUDU2ND2NlaCfSvXyfDyMhIiNOBBtbr2MBi8ZKl2LjtL/x36DhGjJuBwaOmwPOWD8ia07C+G7Q0tTBt+nS0b98e9erVg7q6OsaNG4u6THEiL2zEi2TLYrO14eERWLPpD8rKF1qzWddVi2YzRWos1i2fB3qnj6W5GRrUcxOWY6WnpQr7i4YOGQJacleUJSz5Gigk8eDhQ6bghDEKafjdvYm/tm3EK6ZwfauZXbIcMGFAA63Hj4NhamaBXv0Go5abBwz0KyIrK4uKf7hAS8a+RaeT2HOhVkETJ44cENyN6+nrQ0VdB3fZd1XeQXVRZaPr+zz0BSqoq0uqkPvsNzlZwoTCP//ug4mlAzS0K8L7jg9kFZTg0agJKmjqwNWtARxdasGBKUiKymqIjorAqaMHcfXiOQmv7zGiwb5jNLQq4prnDcl7eZ4+fYr9B48xJeWK8OLS+/f9QUrKmbMX4ONzF7Fx8dBlSumDgHvMwqUCLW0dpqTqghQnM3MLQVGrXK0mqtdyR83a9dm5Ppyq1gK5/t7173/MwuaDBw8egPb0FIQpPZ9f81mk+/9ZyPOCROF5HAGOAEfgqyEg9dVa+s4a8rx+G0+ePRdm/TPYQI7Wa5dkF319fbFixQqsWbMGEydOxPhx49h5ArQqKGL2rJkYM2a0YIEYPnw4aHkYtU2bZ2lmleJfIzxkA+6C2pk2bRpsbO2x7vcdiIhJQq60IurWbyhs0L916xb7MdcBLV97k5MDr7v3cIopROcvXQMNpgriJy0thZFD+mLpghmCskI0G7fuROfOnfH+wI2WfsyfvxCxiWmYOn0GVFSU0aZFI/To3AYjBvdDz67thReEikQiYiMMwpWUlLGPDUA2bPkLE6bPQ9efh+LmLW8YGxlgybzpAl3eQxVnB+jr6wlZiooKwll8mDtjAubMmAjaWCwlLUJkZAR+/XUW5syZg7///ltMVqxzXmJbGxvUd6uJlKRoGDEZ7G2t4XfnOh4E+ucl+yrxyPAXOLz/X6xfvQIrly6Atqocrl08DZFIBGtbO6Rl5QoD4a8izDdohJ552q9FS4ho0Hrq1FlcvnIFPnd9seOvv4U9LDTwpz1m8fHxX0VCt9quqFWjMhTkpOHrdRV3vK5DillzQiNisXnrn7jKJmTEg10aiH5KqEtXrkEkowwdnYr5SC2t7BCblA4Xl8p4GfoUxibm0K5kAvUK6lBWUkSAnxd8vG8JntGMTUxRrWYdQelxZZMJmhqqbHIjIR+/7ymhqKiIhk1bQbOiCW5638eZc+cRH5+AChoaMDazRfobeaTnysGlpjvMrJ0gJV9BwLheo5Zo1KQVsyDLFQkOsqA5OVdBo2ZtIK1QAfEpObgf9Ji1dxG3ve7g+o1bOH3mPP47cBinzlzAydPnmaJ1BGfPX8St2954/rz0lJCoqCj4+j3Azdt3i9QXTsQR4AhwBEoDAa7kfCaqDvbWaNGkAdq2agbymPOlG6r37NmDsWPHYsrkyWjevDkO/LcHNhaGMDeuiJZN6mHIgJ7Ys2MjOndojZWLf8XY4QMwuH8P2FqaCi/CbNKkibAfpF/fvpjMeJAHsqVLl2LHjh24ceMGtm5aiY/CAAAQAElEQVTdio0bN0pmFovbbVK6tm/fjj/+eGvZ8PT0xNo1q3Do0EGBFW3IFSLvDsOGDcPm33/H0KFD0atXL9StW1coadOmDVNMwJSLbIRHRuHPv/Zgy/bd2Lztb4ye9CtGT5wl0L1/kJOVzZe1dtk87NyxHX379sG2bdvyvXdFQUEBv/zyC66wAacbG1S1at4I1VychfoW5iaIiYli7b+1MNBAoXv3brBglhgFeXlkZGRi1ND+6NurC1Os+uGmlw9oMBgbG4/b3ndx1fMWHj1+CnIxvXbTdryvVKqpqqBGtSrozSxSvbp0wMJfp2DogF6wszKBj9dtLFiwgPU/V5Dlcw45TDEMfvQEPj73cJkNWBWUtRCXkAglZZXPYfdFdf7btxverE9packgS1rYywiYm5ng6IFdOHNsP5TlZaGkpITv5UOTCeRRi+4HWvq46fetIOvhzF/nIiYpjVnYQvA0JAxxr9OhrqWHWHZdcmVVERIej7/+2YPAwCABCqovRErpoM+sN8OGDkYPdl/XdHGEVHYyzI10UbWKM8LDwyElJYUHDx6yAe9hwcnH+xMFecVqUL8uoiKf43XS/y2uVE7WHOcq1RES+hLNGnvA1/s6zMytUNejCVq174JuvQbCwakKkUpChQrMGiSSgb6eDvbs+lOS/wWRMl1VXUMDVarVhJV9NWSJFFGjVl2oqKoK1hkDAyPhOiirqECXvD1a2UBGRgYVK1US8lGMD9XTq6QPE1Nz2DpUERQnGSUNqGrpw8K2MmrXa4LqZP1hoVbdxqzcGbLKmnj2IhoHDh+VfBcWpcmEhATh+yssLExQkkiZIcs3WYqoPt3bZFWuWLEi6rhWgZWFMWXzwBHgCHAEvgkCUt+k1e+gUXV1daiwHyhZWdkv6s25c+cwZMhgxLx6ie6dWqNvz444sm87yCLQqlkj0NKoBvXqsB8L03ztONjZwKWyI0ayAfm6FQtw9ui/OLznD/y7YwN6dWkDazN9VLa3QGxUOC6cO42crFToaqqgb9+3StCECRPwO1NC8jFliYiICNA+l5MnT+LIkSMYxyxIPXr0wJzZs7F37x4W9mLEiOHwvHIRndu3wtWr14SlWaNGjsBsRkPWp8GDf8FgpmQsXLgAvswiRT96jDXI29rmzZvQvHF99OzWAaOG9BesM7OmjMGY4QNB5wljhhBpvuDjez9fmhLKykqIT2Czo2zQcPr0KZBiQ4M1spjMmzsXO3fuRFBgIC5cvIILlz1BHpGuXr+Fjt0HMqUFILmID8nm4dEAXbt1x4iRo9CjZ2/s3ncEf+zcg6m/LsLSlRvRd/A4TJ+3FAuWrsXCZWsFZczcworxvYZ+Q8ayQUI2sfog1HatBns7a9R2rQ7yKpf4OgnXrl3DiRMnPqAtagYtV0vLlkKTVu2hoqyGl2GPkZSYgCsXz4AUoKLyKQm64aMmYOHS1ZgwZRbasIGtiaU9Xr6Kg7S0LDp1bIeOHdriS5+PkpDzS3nQAO7Ro0c4cvQYlq9cg82bt7LZ8suQlVNG196DUM+jMciy0bRFO1Sr6Y5zJw7C1+sKVOVzsWrxbNxjCoCctDRTgp7j/MXL2Lf/ELy9vYUlfiQbDQzp/CUhJiYGL1++zMeCBr802KzqUgVOjg6oWaM6unb5CdJMFn39SpBlg+oLV65j5eq1+HfvARw/eQYXL18Rlj49e/ZMmDgghYgUd797d/Ca3b/5GmAJbV19PH0Wku+7kJ5DkUgkLN1kJPn+a9auh4RUQFNTC0cP7ctX9r0maAKMrFmE+9foo7KyMvSY4qSlpQ0lFheJRJJmRSIRaOKByq1tHWDnVJ0pu0dw5aongh89Fpa7XfO8IaSv37gJstYHsu/Rx48fY9eu3fh1zjysWLlamNw5d+kGLnl64dK12zh68iwOHjmOA4eO4vCxk0IIZpNBYS9egpbqkctt+q6VCMIjpYwAZ88R4AgQAlJ04KFwBGg5SkksN7l06ZKwVGors6rQiy/Hjx+Hez5eWLVoFob/0gc1q1eBrbWlZElW4VL9v5T8jP8/BTg52AmexZo2qo9xI3/B9IkjMbhfT3Rq1xI1q1WGKDcHjerVgrqKPHr06C5YZ/7880/0ZMrMyhXL2Ay8FJ4+eoBnjx9i0M+dWR1nKCgqYMn8mYICNnJwH0waOxT169ZGo/p1kPo6XtjcnJgQiypMsVq/fB7WLp+L+rWrY93aNejevTsWL16M//77DzVruoLcth45fg7/HT6BMxeuoLKTPerUqs5mmp1gZKCPl+GROHH6AtZu+gP9h47HklWbMHD4RIyfMkdQLO7c9QMpIez3Gv0GDMSGDRsxdswYdOvWFbWrO+O3OZOQmpyAJ0+fwtTMHGs2/oEDh0/CwsyU4dyXDe6kcfPmTfzyyyCMGT1agK569eqgQApIU6aEHfhnK9Ytny9gFhMby6xrLTBw4EDQLHlWVhbMzc2hoCCPxKRk9B40Eo+ePMPHPqRgbd+1F6FhLyEvJwcdHZ2PkX4y38rSEpqq8kiICYeDrQWbre+K3Jxs1KztJgxeP8mgBAlEoreDp/i4WERGvMCzh/cFt929e3aD2EVuCTb3VVm9fv0aCUyJpkb37NuPtes2QF1bHz37DkZ8UiI8L59DwL3bSIiPRdUatRD2/BlTeuSwl1koBvXpijnTxgoWvDYtm8DI1AKtOnSFYxVXGJnZwc2jGRJTs5midElYSnT81Dlcu34TpFiQQkVtFifQHrejx8/ghrcfjh4/ma8qzbQnvWeFIQIaeDs52qNu7RqoWaMGRDKysHGoikpG1pBSqICwyEScPX8Fp89eQMWKerAw1kP0yyds4iD/skhLZoEIDn4CBztrNvFxHtcuncXh/f/gv3//wh2vG2zQ/IqayxfM2LPzU7ef0bBJy3z5PPH1EahQQV24HzUrGiM6MQ13/YOhrmsELT0TZgkyYFbJbCRliBAamQDHqnXQsUtvqKqp4wmzWFqa6UMG2dDU0hE81Lm6NUSd+k1Rm1mLatZpAA3GJ+ONHJ5HxMP/YQiOHD+Fy1euSpT7r99b3mKBCLzJBHJYoPPHQt7yApnwTI5A2UTgAyWnbIr57aTKzQVS2PN/7aYP/O7n/4EvqlS0RGTcuLG4ce0K/HzvID0lAQ42phg7vD/Gj/qFDZbz7+soKt/i0i1bvQmQksKiuVOZEtQAXTq2xuK506Aom4vkpDjMnj4WS+ZNQ/+fuzFl4GeMHtYfdrbW+LlHZ+z+cz0c7W0EBYy8NYnbbtnUg9ENwJTxw7Fy0Ww09HAXll3IMQuXe50a2Lp+Kfbt3ISaLvaQZj+ICXHRqF2zGrOm5MDE1AyPHodgwLAJ6NZ3GNp26YfBo6Zi3NQ52LJ9F06dvYSIyFfCErucN7lIz8rGqfPXcOTUJRYHhg0fCRcXF2hra8PB0RFycvLYvG0n5i1ahRNs4Dhp0iSEhIQgN/cN5s2aCP1KFdG2VVMGgTTu3LmDX6eMgaaGmmD1CQ4OFvbMvHwRxhSbFkL3aDNwGFO4KlbURZ06ddCmTRs4ODhg6tSpaNmyJRvIq7E25eDo6IzZC1dgxdrfWVvshhFq//9wzz8Qe/47wmb7LbF4yRJB5v+XFi8mx5Skpk0aQUaUiyfPQnD4yDG0aNeZ8bYrHqNiUEe9ihSsRKkpKXgYFIjnIU9x/54PvG9ew42r5xETGQJ7K2O0bdMSZmZmwkxxMdiXSdKzZ89hytSZ2LL1D2TlvEHrDt1w/tRBrF02F5EvQtG8fhWEvwzDX1vWYNzwfjj475+YPOYXONmZMWXaROgTzVxfvnYTjs5V2TMhKzznKqqq7P6Tgp1DZVR1rQtaPuRSww2aOkYIjYhjSv9l0P4eUlwKsvCsX79BsLhcunwV9O6aXPYFpampCXt7a1TUrgBaXvbixQvc9vLGqdNncfLsRRxiM+w0SXPH5y72s5n2YydO4yKz4IRHv8b1m16gssyMdEFJU6tQAZUq6cPS2gbVatWFubUTcqQUEZOQjqSkFKQlRcPvrpfQP/HBhfXDi008WJgaIDUlCXaOVVClmiuiIl8i7OlDRL2KEJPmO5PFIV8GT3wTBMjCpKmlzZ5dSzg4uUCTWdk0WCBLkAmbJDI2NoWtnYPgCMGK3RetmcJuaeMMWSVNSMsqQltXr0C5iY+RsYmwP9PesTJquTeCjoEFYpMy2PdkgVV45rdAIPM1kMUCnT8W8pZ/Cxm/YpvTftsCCl+xSd5UKSLAlZxCwM3OyUUU+3HPyhGBNs5KyanC8/qNQmoUXCQrK8sGROGwtbHEr1PHChvgaSmaoX6lgiuUQu76zduRkPgaMyaOysfdyFAf3Tu3x8jBfWFlYZ6vTJzQ1FAXRz/7TEvuhgzohaioaNy9FwD6EaUBsYKSshDX0NCAJbNSjBk7Fvr6BkwJegMlZj1q5OGGhbOnwMTYECEhz9lAURGVK1cGDZCOHDmC335bKOw38vW9i5/at0DPbh3g5x/EZp91seX3zejYrgVTtJbB0OD/WA8Z0BvrV8zDjl37oKCkips3bmD06NEwM9TFHxuXQ1VFRejn3Xv+SEnLFPZd6OrqCnnjxo1Dw4YNMXHCBLi5VkXrFo3h7e0l9OvaDS8cPXFOoMt7mDFpNHZv34Cfu3TA5o0b8dNPnTBo0EDQQDYvXVHj585fZH0MxvPnYUhJzYCBobGgbBW1flHpaAD9mFn0AnxvCc4NgoN8YWqoDXVlGTjbmaNh/dro1L416td1h+5nvoOlqLJ8bToTU1M4OzuyQb4d0pJf449NK+BR3RJdm1XBL13qol+XJjjw+wyMHdAW7RpVh7amGv7euhpd2cSBWFZaLjZ3xnhMGTeEWTZ2iLPznWk5GAV1pqhY2dgzq5A70tns920ff5w4fQ7kxID2AYkrNW7cCHHxiWywaI77QU+x779DgqMHJwd7KDAF+PotLwQ/CwcNQCsaWqBO3UaoWtMNF6/egIKqLqq51oNT1dpwqFwDFswK49G4FYwsHNCoaWvQ95S4HfFZmT0LRsYmcKzsAgcXV8GtsbWdk7hYONNGewfnanidnIa2rVsiKSEOZOFp0eYn0FKnRw8fftcOKAQQvpODSCQqUk/ovjA1s0CN2nVBy7aLVIkRqatrIFckw6xG6aCJK5bF/zkCRUKA3oni4NEX4rBt9wlJvejYBDTvMUlSRrSSwgIipMSI+YjPefkVUIVnlVMEuJLzkQuXkfXmgy9iTW1dpOfIsEHtHdBsaUJCgjDD/REWkmxanvTvnj04fuo8G7ich7fPPUnZ14j8uXMPnoWGCS+y/BrtFdbGzz06oUlDd9hYmuB1fDTMjPTg7uqCX/p0R/NG7li4YD5TGKJAisG+v3/HhNFDUb1qZcycNArH9/+Fvt3bIz46HNJvMuBka47qle2gKJOL8SMHoV2rZvDxDWBKkj5qVXcBuXfu0qE1dLS18olUq6YLlJWU0O/nrshgM9gZGWmYOGYo3GpVo1MTpwAAEABJREFUy0fXsL47G5ylgPYp5S1Yu24dsrIyEM+Uxrv3ArFq1Wp07NgRrq6uuOVdsDch9QpqqM34L5o7FTu3roWxoR7I0hQXFwcfH5+87AuNX7h0BTqVzNCr30D0HTQcnbr1FiwDhVb6jEKyItxkVhptNXn07NENrVo0RctmjWFmagI7Wxs221+pWIObzxDhm1XJycnBG2Yh0apohLtMgbXUlcLu1ePQqkE11KhsDQdrE4ls1Z2t0LF5bfgFBBaoaOpoaeLI3j/h7+uN5Dx7WghfCZP3IgaGRqhctSaqVHdDrowy9h8+zma+31oIDQ0NQV4Jnz0Jho6GIjzquwm1T585j8SUHJhaOsLW3gkV9SoJQVlZGZpaOqjl1oBNJmgKMpIyIy0tLdRTZM9BURVUqmfn4MgmGhTyfe/l5OTgVcRL6GhpCIqSjLSMpLxj159B1tsXYSFCe/zAESAEst9NINLvLKV54AgUhgApINMWbsHezbMRcGm7EFSUFBDwMASpaemYPH8zOrfxEPKJZtHaf4Sywni2a+Ym0Iv5DejesjByXlZOEZAqp3KXqtjJ6dkFmtSjol6xgXkUQl9G4SGbLb3seRun2Gzrx4ShzcqHDx8GeTU7f/48OnbqhLMXr2PV+q0fq1Li+bv3HcZdvwAsWzCzxHnnYyhigyZpeUBGCaA4Cv7QnqM+PbtgUN8eGNCnGwvd0b5Nc7hUcWSWATfB5ayFmTEqOzvkYyAtLSWkzc1MhDq9uncS6jVpWI9ZbzrCzMRIKB81tB/aNG+Afr27COnCDpUq6sLe2hRkZfKoW5tZYc5gw5YdOHbyHKbOXowlqzdh/vwFsGQWJuT5DB82DE2btQDNvK9YuVKwyJBVJjE+FlPHD89D+f+o1x1fISEnJwPayN2+dQvBFfSE8eOxZ/c/OFEEZwS0/Ck1NQUXz58UNoULDEvgQHzzsqH9KHe9rqF1y6awt7fPW1Sk+Pse596vRJYJ2uf2fn5ZSBMWpFSImHJRt34DPH0UABd700JFU1SUx6CuzTB83NQC6ciyaGhiARVVNUn508fBOHZoD3y8boKsZQkJ8UzhzpCUU0RKSgqV9A2hpV0RkZGRII9WWVlZ8KhXGySnvIo26IWb+w8ehUoFTdg5OqE4s+rUxueG4AdBOHrwX1w5fwqrVyxEzKsXMDDQx7kLl2FiYQ3pd0oUbXJv0rIdrJmV6nPb4vW+TwTYPILwO5vCfm+/zx4C4B37JALTftsCsqZ8zPpCVppTF29j6/JJoGX+YoZd2zUU0s9CI/E6JQ1tm9YRisyM9WCgp42bPoFC+nMOkVFxEstQ7dbDJAoTKVXNuk/E6q37BZlJbpIf/FNmEXg7ciyz4n1dwWh29SRTWs6dO19gw/SSRzkFJdRy94CNrQOqu7pDJKuICxcvCvS0r+Ovv/7CeDZwpZn92bN/xR2vWzh//hxoaVVQUBDS0tPgUtlJoC/tw6Gjp3HF8ybWLJ1bsk1JyQIyioCsKiCvDihovT3LqgDScoCc2tsgkipWuzIyMti0+jfBsxzt6SlW5XfEujraaN2iCWiA+C5LcrpxyxsvwiMk6eTkFDZLfhKHj59FrwGjcPTUBWaJuQdPr3to264DyKGBgoKChF4cId50fVu1aoVpU6fi3927mDWqChbOnswGmcpiMuFMe4omTp+PfYdOYenqzRA7KLC3tQYtxdPQUEdi0mthr49QoZADKQ/pmblo2baTMJteCGmRi15FhmPj2uV4HvIML8JCcef2NYQ9DUKbls1Am9OLzIgRkmXz/IVLOHv+EmjPCD1P9/z8EBMbh0vMAnWN3YvXb9zCkeOncfLMBWFPCatWpv7JtbKZpS2UVdSwZsmvGNq9EbNQVChURvqRjYiOg6Lch/cKVXSws4GuhhLu3LpGSSFYWttCp6IhYqMjkZvxGvGvQuF/9yZuX7+I+8zqExRwH/d8bsOHXQ9NNQVERcfiadgr3PBm9+fN25CVkRaWm9k7V4ere0M4OFcVlq0JzL/CgSw6FgwnKWlpODs6IyUlFQeOnIRjFVdoarLvgzwy0D6yPEke5QjkQyAxJQsJyZn58nji+0eArDOkJFiYGggWlQZ1XD7otNhKExgcgi6DZ4Po31cqomLj8To5VVKXlrnr6WriSchLSV5BkcOnPQV+xJNkyUsT/ioWO9dOE+Qa2LM1lm/aI1iMiCbpdQpese97sgBd2r8KPvcf4WMKGtHzUDACXyu3eKPQryXVN2gnJTUNe/7bjwpqqsjKzsVd71t48uhBPkmMTcxg61AFD4L8EejnAz+fmzA10EEtV1dh8/qcObNBg5O2zJKwffNK7Nq2lg3Yx2Py2GHo2LoJbMwNMW/GeMHZQD7GpZA4dfYyjjOFbeOq30qe+5ssIIdmnd8gn9Umh/1QCSEdyEwCclk5ivchBaJ4NYpOTVatCdPmY8uf/+DwsdMYMmYqpKWkBQbpGRlYs2at8F6h3xYuRO3atYX8vIdz585h3bp1uMiU2tOnT2P58uXIZDPrs6eOBVmC8tKK45437+BpyHMsWrwYKmoaWLF2C549D8PilRswf8la6BsYYsPGjTAxMRFX+ehZVVUVutrqDNY3H6UpSkHy69e4fuU8vG5eg9eNK5CVlYG8VCYUpDPRpEFdNG5Yvyhs8tGEh4fj73/2IC4pFRns+XkaGo5DR44h8OEz3GBKo66hJTQqGkGpgi5q0Ps62MA8LDIe/+7dj+jo6Hy8vlUiKSkJgcFPmUXCCLv/+h12xhXgaGNSqDj/HLmGZduOwycoHNFx8WjSphvuBzzIV0daWgo9OrfD2ZNHQZY4caFrbTc0aNoaEdGJsLG2RJtWzdG+TUtUr2IPa7NKwpneKRMRFYuMN7LMcuiAyi414dG4tfA9RHxIEZWSkqLoVw/2TlXg7tEEjVu0Q6Pm7dC4WWumHKp8dTl4g+UfgdSMHMQkZuBNbvnvC+9B4QiQQuDg0VcgIkVhQPeWQrygAyksvTs3hauLHbxObhKUjoVTB31Aql9RC8pKBU8yfUDMMogHtU1h7+bZ2LrrWD5FpaqTFZvcUmeUQK2q9ngZGQOazKIMNVVl9OzYhKJCm9S2kOCHMonAt/l1LGNQ0Ptbbt28hZbNmsKlijOqV7ZhAww7+JMntHQ2YGfypqamIijQDw/9vWBhpItaNSqjQb06sLe3E7xJ1apVCyL2Z2ttDnKtrCAvx2oBKaxeMpvlfPQkhA0e636wP0QgKuGD500vHD91FlvWLS1hznnYSbH+0XqD7FQgOw3ISITgoUWczkNastHP57ZqyRzBXfXZi5exZfs/kJOTx4jBfZkiOgE1qlXB2LFjsG/fvgIbIAcHPt63IC/9Bjc8r8D3jhfkZXLRqW0zKCoqFFiHMps2rAsNdXUcPXoUXbt2FfZJLFm5CRGvYjB23HjB6lecQWo99zp4/NAfL16ESvY9UDtFDbdvXMbdO55oWL8OGtarhb4/98TMaZNR1aUKnJ2cPttCpK2tDQP9iogIfYrkpES412sI+8qucK/fCNVq1IK6hgY0NDShW1FPsBCJRCI2YK8GTV1DhOexrhW1HyVNl5OTg5u3vUEvbKR4WkoS6lS3L7SZRDajd+bqXWzY9DuWLl+JHX/tBL1jas5vK9jMYnK+uiGhL6DJMFJSym/pI+ulvqEhU5azhet53z8QV5nF6/XrZCGtqanJlNpcGBoZ5+OnqKiYL10WEtLMqlMW5OAylC8EAvz9cO+uN6KZxZfcTdN7l8pXD7i0RUVg2+4TOHvZW1BWClNuxPzIkrNz3xncuhuEGi2GCEvIomMTxMWSM1leUlLfjtUkmUWM0BK4Bm4uePr8/6s88lbVZROLpNjkzePx8oMAV3LYtdJgA7CGDT1QoUIFNmBVhJmZGSpWrIg2rZvjxpWzCHn6GN43LsGkkhZaMEWIZt3V1NTyDQjV2UBWWkYGj5+Goveg0Rg0cjJ69BuB4WNn4M+/9+GK5y3kZOew1kr3P+jBI6zesA1rl88v3YZy2BeKWKGhc2526bb3hdyjY2IRH58IC1NjqKtroEmTJvBwd4Vb7RqwtbbA0AG9sHbpHDx/9hj0DqO8zdH7fRLj42BpZoxYxiM84hWSU1LgH/RIcHNdmJKixiyDm9csxuWL5/HLL79g9uzZ2LhpE1auXAlra+u8zRQpTsp2+MtwpoB7IeLliyLVISIauD96EIiI8DC0bdkcNHhWZZYhOTk5ZsmRJZIvCsSnkl4luLrWgKK8rMCTnpGCBr60r+R5yDP4eN2A9Jt0VK7s/EVtf2ll/4AAnGCTApCWB1lGTh47COncTFia6hfKOpspRuSJj5blEeGJkyexfu1qZq2dKPHQR/kUnB3t2GykPf7+cxMl8wVzC2tcvHRNcFEvJSUSFJ5bXr7wC3wCP/8HeJ0UjwcB/vnqFJjgmRyBcohA2POnCH8ZisvnjuPGpdPQ1tIsh73gIhcFAVJsmtSvLiwTI4XnU3XIkrN4xmBh7yxZXE79s0RiYRHX1dXSYN+3SuIkSDGiPTUWpgaSvC+JRMUkgJaofQkPXvfbISD17ZouOy3TAK0gaciTUacObaEkm4P42CjQCyRpSUtBtORBbceOHejesze2bt2GxYuXYPSYMejQsSNkZaSxbcMyKCv//0EsiMeX5sXFJ2DijPnY+9eHA6kv5V3e6tPem/8OHcO5i1fRtc9QzFu8GguWrUN49NtZoGtXrzCLWy3k5LzBNmbVGTt1LkaMn4mE+HjhxY60r0rc56ZNm6J9x58QGftasEpMmjwF7Tp0wqZNm6FvaIwJ0+Yh8EGwmFw4//nXvwh98XZNsLS0FJYumCEoxSlMOSICmsGnc3GDkhLdQzkwt7CFobFJkatnZmYCbAAtYtbG0rIC1KrlCiMjI7yMCIeP93U8fvSQDdCTGMY5wkb5R8EPQPtM7rEyFflc1KrujEafsTSuyJ3+BGFoaChoD9GzF9HQ0jUCvc9m17a1CH3oA2UVJUHZKIgFKTV+Qc8wd/VeNGvRUtj/tXTpUgTevytMLtjZWhVUDS2aNsALNqCjvU95CaSkpFCvUXNARgXPQsMhIyOFChVUmEx6qF6rLho0bQtbB8e8VXicI/DdINCsZTu0aN0RNd0aoqKhKTxveOHZs2cl2j/OrOwg0KCOi2DJIYkcPPriU8qOjpY6mjeoiYHjl0gcAFDdPYcvCGlyNKCqrIgjZ65TNvsOjQQtL6NlZpRBzgJqtx6Gi9fvUpJZDBOwZtt+IU4HKr/jFywsS6P0+2HXgbOo5mwtODl4v4ynyz4CUmVfxG8rIW08J89XZ86cxpVLF/HLoEE4c+bMB0JduHAB06dPE76caa/G4kW/YfXqVWwG1g8bVy2ErKzMB3VKOqN732FMwdlc0mzLHb/c3Fw2npfCrj0HcfTURfz99y6sWbseK1auwvDhw4VlanLyCli2aiP6Dx2HxNQsTJgwEfU9GiA2IQnSMvTNkCIAABAASURBVLLMIuEq9Hv4iBEYOnQIzM3NMWzYMMECZGxsjOrVqwvl9H6djKwcrN74h5Cmg+/9QJy/7Im5i9Zg49a/8CwklMlyCLm5b9gAVoZIvii41qyB10xZymX9LAojsuJcu3gWr+Oj8XOvHkWp8tk0pIT17d0TzRrWQyVtFYQ/D4bPrSvwZYqNYUV11HBxQNvWzeHk6CC8xPWzG/qCijRRERISgoW/LcHzsJe443UTKSmJOHpwLyz15DBvXDfMGNYRcgU8s+TFZ/Lindh59BYmTJmJhg0boX///pCVeoMJowZ/UqqqlR2El/G+v99PWloaFSvpo2qNOnCqUguWdpVRiaWJISlBdOaBI/A9IiASiYRu6TFLsJ1DZVSuXge0X+/8hct4332/QMgP3wUCZNWhPTHkIICUHbESUlDniHbhtEEQOx8g+uTUdEHxEFt79h29BMonmikjewhlBfFSVlKAr/9jgfZj9HmdEpBVaMaY3gWx+h7yvvs+cCWnCJeYXgB58OAhuNaugzpubmxg01BS6/bt21i+YjlOHDuCn7u1x9PHD1DXtQpWLpqFf/5Yh8njhrFBtZyEvrQiXX8egoWzp0CJPcCl1UaR+Irolnr7o5WfnuWR9zV5DUBGOX9RCab27D+Mtl36McvNGrjVqcMUzdVMwZSVtCArKys4D6AlaW51PdC5azckxMdi+bIloGu5ceMmzJ03X1BmqNIANoDV1tYR3mWTmJhIWYiJicGxY8eEOB3I6mNsZILRk34VlJsqTvaY/+tkZjNhXZVXwcz5y7D/8DF23zQSlkNRnS8J9na2iIt6WSQ30q9fJ+H61fPQ1dFAy+ZN2P2h9CVNf7Kuuro6tLS0QK6MrSwt0cCjLtq1aYk2LZvCwtzsmyk2YsGDgh5g7vyFCAx+jnGTZ6E5m0H+ZehoODi5QFFJGWERsWLSD86R0fEYOHkdRo2bgmXLV4L6umjRIvTr9RPGDB/4AX1BGX17dcafm5Zj1/bNgpUrL40ss+BQWllFheGkQ1EeOALlBoGEhHgE+t3FXR+vIn03FdYxOTkF5Moq4totH/jcvccmiLhHgsLwKs9lYicAZOEprB9UTkqROJDiI6Ynaw8tZROXEa24jPbc3Di2AeI8Uor+WDlZsCYVRC+WR1xGtFSH+BGv07uXShQoyqdyMW+i4aFsISBVtsQpm9KQNYckGzBgAEaNGiXMxkdFRWHGjBn4b98eNHSrjmULZ8LGygIjB/dFXTdXIv9qYeT4GejQpgWquTh/tTZRWEsKmgApNJACpOQAaQVASgagvTsZ8eycUljtzyqjJUQjx89EcgawYMECrN+wARMmTvooLwMDA7Ro0QJ7/t0t7K8hz3k0YKUZ/smTJ2LO7Nkgb2r//P0XmjZww8UL5zBp4gRMmDAOdB/4sx/zgwcPCvxNTU0xnd0Lw4aPxLOwKAwZPVXw4OZWqyr7wb+DeUxpOnTosFBPqPCFB9o3YmlhjscPAz7JScSUTlFuNlo0bwopKalP0pcWgUgkKi3WReL7+PFjHD95Fs9fRqNrz/6oUq0mVFRV89W1sLJBRExSvry8iYXr9+Of3f+CLHmUT3uOSGmmJY+ULmpYt/kv9Bk4HKpqapIq0lIiKMpLQ1VJVpLHIxyBsoYAWY8zMjIQGxuDxISEfOI9DLoPGVEmwsOeIuOdw558BEVIREdFwvPSaagrS8FAWxWV2OTMw+CHwnLXIlTnJJ+DgDT7jZaSBQo8s7L38z+nDV6HI/CNEPh2o55v1OGSaJZe8jn4l1/QtUNzLJk3DS6VHb+YLe0hiY2NKzafhUvXwMTYAN06tyt23S+vIAJE4luIxaXlATlVCB+Ky1cARCyfnBS8yQJy36CkP/RSxEUrNqDvkHEYOnwEBjDLi7OzM6SKMKAnRxPLl6/AzJm/Yvbs2SDlgaw5MtKyWLBwIW57eaFRA3e0at4Ii+dOxeY1izB17FAc3vMHpjALXVx0BLPirZB0ycbGBgMHDhQsQf0GDIJIVhmmZuZMMZqA8+fPS+hKIuJWpxakRdmIiAiXsKMBiCTxLpLCLDlOjl9+f75jV25P167fhImlPZxdqsPA0KjAfqiqqiElLQu0JO19glcxCXgDdi+/V9CmTRt43vTOl1tYYs5vq1C9tgds2b2Sly7nTa7AXVVRBvKy4mcqLwWPcwS+LQL0/eJ/z5tZa7yQGP0SoU8CcM/7GqJevRIEMzG1hIysHFxrVMfr12+t3kJBEQ60pPbqxdPISo1H507tYWtrCzs7O7i71UH3rl0gyyzwRWDDST4HAVlV9rvNJlwKPLOy9/M/pw1ehyPwjRDgv6afAfyNGzcEqw1Zbj6jer4qp89dws+DRmPUhJmYOGMB0tLShRdWrtqwDbv3HkZMIYrP9r/3IS4+ERNGD83H86smpOXffkHmtd68yQaymLWGrDbC+3RKT6JZ85dBRk4Rf/21E/b29sVuSFdXF+RdT1yxS5cuGD9hAkQiEfzv+6FRfTdxkXDWyuP5p12rpnjyOBhdunRGYGCgUE4H4mlpaYk+ffpg0qRJqFGjBho2bEhFJRrcatfC04f38CDQH8+fPRHOeRtITExAzKsw2Nna5M3+4eI0gNLU0hIcPxTWeRVm2dHQroRDZ25/QCZCLrJz3kBGhlkk85TS0kUVVZU8OQVHI19Fo8vPQ1GtdkPUr+cODVU56GkqQoUpNeIa9FJECgpy0uIsfuYIlBkEaEIpMyOVTfo0YcpHLTRr2hjudWoh9FkwXr2KRMiTIJBS4uzkiIeBvgUtWftoXy5fOI0ObVuierWqH6XhBRwBjgBHoLgIcCWnCIi9fPkSv//+O+bPn4+AgACcO3sWPbt1gJycHDPbxxWBw4ck+w8fRz9mfdj217+oWsUJc2dOFF4S+uRZCH5dsALhkTEIfByCIaOn4HnoC4FBSmoqaFkWJWhTPb3sk1wUT5+9iLK+QcgFstMgvPgzPRagQIpNZiJA1ptSlujfA8ehq2cgWEpKsilTU1P4+voKg2K6xh/jra2lgQ0rFgh7oVavXsXuhVh0795dqJu3Tvv27YtkWcpbpyhx8pLWqUN7OFgbQ7uCPFSYvnnz2iXBuhMWFgrvG1dQp1aNorD6rmloU7+TvR3OnDxUaD8VFBShra0LVWW5D+h0tTVgXEkD48eORFRUlFAeEBgk7N0KCAzGxOkLcO2ml5D/OjkZFy57Yuv23Rg6ZhqGjJmK+cvWoW79hjAyNUd0QhpTmHIhJQLUlGQhTRGhJpCSng1SdN4l+YkjUGYQiI+LgUElPZBFRywUuaF3q1UNL54FwaOum5Dt5X0H+kZmku+89PR0QeF5ERaCuLhY3GbfS9cvn4Gv9w2EPHuK8BfP4V67hvB7KjDgB47AD4cA73BpIcCVnCIgu3TpUvj6eOPhwweIj4+Hto42Dh49jZETZmHUxFlISUktApe3JL5+AYIr4+u3fBETE4sp44ZDVUUZG7fuxL/7j2HNph0IDQ1DrVq1MG/uXDaAn4i1v+/EwBET0ZXNBDdv3xMz5i6DvLwsFOTloayoiPSMzLfM2XHn7v+wfPUmFvu+///YuQfJqZkYM2ZMqXT0+vXrMDc1LhLvG7fuwMOjAZLZ4LZTp06oUqWKUC84OJhZc37G8mXLJMqpUFCCBykpKVSsWBFWVlZwda0JfT0tPAr0wYZVi1HF2R7Kysol2Fr5ZaWrq4O01DRkZGQU2Ik0NoGQnZ2FR8H+sLcs+LpPG/YTpg1ph/mzp+H48eO46+snXNf169ejTbv22Lz1bwxjSs2g4ZNw6vxVqGrooGu37mxyZCGWLF6CmjWr48zxQzh94jCiE9MFhSYmMQO0VK1AoXgmR+ArI5CdnY2QkGdISIgHKSd5FRpykU5WGpFIlE8qLWYlbdOqJTQ1NYT80LCXsLVzEpSWJ8EPEHTfB3duXUZuZjLOnjiEpg3rgl7NUN/dFRoqMrhz21P4DRQq8wNHgCPAEShBBKRKkNd3y2rVqlWo4VqbDVYWwN3dHSNHjoKFlS2MjE1Q2dkRYN/5n9p8fOHKdTRp0w07dh+EuYU1/O7fh5mZMdb//hdSMt5g7dr16NylG+M9UnBR3bFjR9CHlJ1lbJC8cuVqwaMXua9u2LgJHj8LR3JKKhp6uCEi8hV+W7oG46fOw+MnIWwgl4XZC5ZR9bIRpGQAGUVAWh4QsTi+7JOckoLrN+8I+1++jFPBtf/48082u56LCaMHF0zwXu4t77to1aoVTExM8NNPP0lKV65cgRkTR8LezgqXLl2S5JdmpEb1aujWpTNWrVwOJyen0myqXPFOSkqCnKyMoJS8L3hQoD+OHtqDwwf2wsZYC9bmBoCULAr6HD5zE7Rvp1mzZujVo6swWy0rK4t69ephCZsMGc6+GwYNGca+L9ygoa2PpNQseDIl+NzFK0jNFOGn7n3gXr8Rmw2HYLHJzH5TUDM8jyNQKgj43/OBj9cNIXjfvAbPyxdw8thB+Pl6w/uWJ04fOwSZnBTERIQg8N5tnDyyD/73fUHKj5GpFf7auRuHDh/9qGwPHgZD39gCIpFIoImNjUHtmi5o3KA+rK0sMWzIIMHDo0gkEiZg7Gxt0L/vz4JHRqECP3AEOAIcgRJEQKoEeX3XrPr16ycMYqmTZmZmsLS0xK1bN3Hr9h3MW7wWs+Ytxaz5S7FoxXpMnD6fyCThzt37oJdDtmQD4ZUrV4Jm+ps0aYLBQ4Zj8++/Cx7baEbe2dlZGJiKRG9/ICQMWISWBbCT8E/7O6ZMnYqlbFC1dvN2NmPvgMjoGNR3r4lRwwbAo15tPH76XKD9ZgeRNASlRkoOwoguOx2g/Tm52V8s0r6DxyErJ4e5zNL1sZn5z2mEfsgvXrqEhNhXGDusv2Ap+xSf62wA+ywkFMOHDUN0dLQwGNi0aRMGDRqIedPHw8rSHD+1a4nd/+xCRETEp9h9cTktzSImGhoadPpuQmRkpNCXq1ev4sKFC/iTKaKJ71x6CwUfHvLlaGtrw9jYCGlp+a2ud+/cQnpyHHt2auPy+RNo7OYMKFaESMMGIOU8D5dte87iTmAoqlavgff35pAsf+7YhTs+vqhkbIU69ZvCsUp1VHd1R9UabrB3rgEbW3uQcws1tQp5uPIoR+DrIHDl0hlBgXevVRV1a1eDR11XtGhaH62aNURVJxs0ql8bzZt6MItjDdRzr4PWLZtBv5IeggP94Mnq5mYmwcHBDubmpgVOFlAvEhISoaqqRlEhWNs6gBQfzxu3cPDIMeH+FwryHGiSIE+SRzkCHAGOQIkhwJWcz4Ty9evXaNOmreCquGev3gh/FQ0zCxvc9fVH44b1BK4+vvexc/cBrN38J1JS03Dz+nV4eXmBvMbQhnQHB4cPBktCxSIeSNFas2YtAoIeIfjRU2zftQ8Dhk3A05AwaGtr4baPTxE5lQIZeVIjpeZNJlNyclgDuSyUzH+/Xl3Qr2cnREdF4ubNmyWPT7x2AAAQAElEQVTDlHGhgeuDBw/YtWIKGUsX5d/B1gobV/6GhbMnY/y4cYJFR6uCEmZNGcOugabAQlNTnf3wq4DcGAsZ/FAsBA7s34txo0fg1KlT+HfHZnhdOMBmnw/h0sWLReZDAykbGxs8ZAO24OCHiIx4iXs+t1CrmhNaNm8qOK3QYc+Mp08wkPUaubH+ADnQeNfC/QchuOL9AKvXbsDw4SNASs27ImHAR97bNLQ0YcVmq/2Y4pSc/BriDymeSkpK4iQ/cwS+OgLx8XFMwTGFqakp1NXVQZ4l1dTUQO+z0tPTAzlLoaWtRkZG+WRr2aIZBvbvg+5dO6FeXXe41qwOZ2Yhpkm5fITvEgls4kFJWeVdCkhJSWZxEapUdkLXnzqyOP8vcwhkJADFCWWuA1wgjsDHEeBKzsexKbSkcePG6Nu3L+gHgpYFdejQAYcPH8aqJXOQzBSgLr2HYPWGP3DhiifkmdXhzZscdOjYUfC0VSjjYhaSIwJFRQWYGBsgJydHUJouXL6G56Fh0NXSKSa3kiTPLTozEbsNhffqFL1KzeoukJaWQeXKlYteqQiUQ4cMgVoFDezZd7gI1GCDBTVmITCAkaE+Rg8biNOH/0HnDq1gZFBJUl+ZDXBJGdr511+SPB4pGgJxcXGCsw96p9Gxg3sxb5AbMjKzmCVPAVcuncOSRQuKxohR2VpbgvYD6KjJITcrGW6uVYVlMmfPngVNPnTv0Rth4TEAvc+J0ef9D3j0HA0aNBCer4UL5mHX3/+/ljTgI4udtkYF1K7lCmdHG0SGv8hbncc5At8UAVo2pvyZL4pWUFD4pOxBDx7i793/wc65puCwRVyB3LVLy6vAz+8BLly6gsxMNuklLuTnMoFAbsJDFCeUCaFLS4h3fLftPoH+YxcjNa3oE57vqvJTGUOAjS7LmETlSBzaaP7ff/9h4MABuHr5Inp0bo+Z85chJjEN2jrasLe1hIWpMV69isKIESPRrVu3Eu8dzcY5sJm1V1HRyMrKEjaLutaoChVlZTYQLHhfQYkL8SUMRdKArCobWKYVi0tGRqaA69SpU9nM+jDMmTNHUDIJg2IxKoDY0NAQCUn/n4kvgKTArGoujgXmU2Z8QhK0tLUpykMxENDU1MTMWXNQr0Fj5GSnQ01JHj2a2GBS96ro5q6PiOcPQUpKUVnSsk8HB3tUc6kCL29vdO7cGWvXrsG2rVsFhVlNUxenLt35gJ2etgaCAh/g5MmT8GEW0rxLD8PCwhDw8BkcXerg4JET7LmTQ0oeS84HzHgGR+ArIpCWlobEmHCQJbO0mrWytICyigrIavl+G0Ym5nCsWhMy8qp4+TL8/WKe/sEQuHj9Lhw8+koCKRRiCKJjE9C8xyRJGdGKy94/v09LPGu3Hsa+i0NQlM+037ZI2qG6eeUoSn1OUz4QkCofYpYtKQ8dOoQxY8agS5cu8Lx6GUlJSZCSlkVUfDK2bNmKwYMHC3t2YhNew8jUAoeYhYcsP6XViweBgcKyA3J3bGdjgaMnzkBaSgrnL10trSZLhq+Ibr9cIDMRyM0pFk95eTn0/7krBvTujKXzpqNp/drw8b6FgQMGCNejOMy2bdsGshiI61y7ehWODjbiZImcya0wLVPMzs4uEX4/GpMlixfDQFsFUlIimOtrwMlCFy9i09CkZXvBjXNx8aAJimNHj7J7JREOtjbIzskWJgnGjp+E/advITY+v5J76cZ9+N67Bzc3N0yfMQtz5/3fgvTg4SNoMqupv99d5IqkceWaF3vuzfEyLLS4YnF6jkCJIxAUcA/137l3LnHm7xiGhIRAX9+APZ/0nf4uM8+JlgJXdqmGl9EJOHrsZJ4SHv2RECBFYtrCLdi7eTYCLm0XggqzMAY8DBGsJpPnb0bnNh5CPtEsWvvPJ5WWdQtHC/TE78axDXCwMf0kpGILzaX9q4S61NbWXcdQmFL1SaacoEwiUPA3UpkU9dsJ9fz5c6xbtw7+/v7MIjMCly6eh4lhRViYm8GpsguWLFmKxUuWCGUikUgQdOTIUUI+vRBSyCjFg45uRbx+nQIpkRSCH4eAlq317vETwsIiMGXWwlJs+QtZ074dCp/JpknDeqhetTKU2Jdk7VrVMGf6eEwaMxRDhgwB7d8oCtt58+bh2LGjePHihUC+e/duaGpWgFsJv18mOydH2JNDP/ZCQz/MoWQ6ampqAk/fEOy5ECwwzMjMxsnrj9nssIqQLu4hICAAigpy2PXHekyZMEJ4Zs6eO4fVq1cjk1lE4xPfKjkrtx1D77ErEZOUCQ+P+iDLqYuLi8R7FLUbGRmO8yf2IyIkEPe8ruL5kwDGdwPOn9yPTWsWIzY2msh44Ah8EwRkpaTy3a+lIYSlpSVT6kOE/WmF8VdRroDk1HScPnO2MDJe9h0iQJaXUxdvY+tyZqnJo4h0bddQUEyehUbidUoa2jatI/TezFgPBnrauOkTKKRL8qCkqICFUwdBR0tdYEtt2VmZ4OnzCCFNB5Klz+hFEmuPWAGifpC1afXW/SDLEVmB+NI2QqxsBqmyKVbZkMrX1xdLmfIyfdo0RL4Mw6xZMxEaGgrasyEtp4y169ajf//+MDU1/UBg2uj8QWYpZcyePRt16tRB107tWAu5qO1aDY083BH2Mhz13WuxvB/n38HeGsN/6YOA+74YMXw4bt26hcuXLwt7LvJuFhcjUq1aNezZs1d418w0dp0fBvlj5qRR4uISOy+YNQmZGamYNHlyifH83hnROzoCmZWS3llEXtXofTLHmGLzNDwe8nIyWD7SAxeP/ws/P99iQ0HKSmxMHJSVlDB28mwoKCji3JkzMDPUQVp6Jnbuv4B5q3cjKjEdK1atweq16zBt2vQC2wllkyDmJgYYNbQf5kwbi7nTx2LR7ImYPOYXDOzVEds2LENQgF+BdXlm+UdAQU4aUszCWOyefIUK3revw9nJBrRMs7Sbq1GtCh49LHxASg57HGwt4Van9gfiPHv27IM8nvF9IECWE7LSBAaHoMvg2YLiQMvF8vYuKjYer5NTJVmkiOjpauJJyEtJXkGREdNWC/xI4SCLUEE0n8pLYYp3+KtYmJv8fy/ti/AozB7fV7D0kLVo8brdIAVHzOtewGOc37cCXic3CVm7D10QzvxQthCQKlvifHtpduzYgZEjRwjKyzQ26L3ndw+WVlaoqG8ovJjzyJEjINfFo0aN+vbC5pEgIiICVzxvwtbWDvcDHmL0xFmoVtkJLZo2zEP1Y0TdalXH+JGDMG/meFy5eBbHjx7G1i2/o2fPnli3fr0EBFo69urVK8yYMQPTp01F/TrVMXvaOEl5SUfmTBsPbXVVdm/1A1kGr127VtJNfDf8aB8BLT1cMHcm5s+bA8OKGnCvWxfqKvIIfBqD0zefQFZWGoPbOuO3hQuK3W9aOiivqIhBIyYiLj4eGRlpcHJ2wrFTF+Dm5g49UzsYWjpDr5IBU26mCQ4HqJE7d+5g586doPfukBLmHxCI6JgY3Pa5j18XrMSBI6eITAgikQjGRgaCB3VuzREg+S4PstIiKMtLl6m+0b0ZxyyI5Omxkp4ebt/2wr7/DiKzFDf+GxkZQUNVAWHPn30UCwv2WyqSU8FdX98PaOh9b6vXrPkqrvY/aJxnlCoCpLD07twUri52IKUg4NJ2wZLyfqP6FbXYxJMC3s8vKE1WmFP/LBGUEOI3sGdrjJ+zAXkVkYLqFZS38vd9qOpkhQZ1XCTFZNkhCw9lONqY0Qn+D/9/b1N/qF8USBkTCPihzCEgVeYk+oYCjR07Fnv+/VfwjmZtbQ1KL126DLOZpWTkyJGCteQbivfRpmmpFbmk1tDSxvTp02FkZAh5eXkM6t/zo3V+hAIN9QqYOGYolsyfjs1rFqF186Zs0Pl/F6lLmJUuLOQJurRvjq3rl6JZ4/qlCou8vBwmjR2KCaMG42FQAJYvX46HDx+WapvljfmjR4+EJWNr2GAnPj4WjubamD2gHppUM0BszCsYGFvi3P14HL0RguTUTBjrVUC3pk4YPKh/sbtKSpRSbipqmqrBoKIOm+1Ww4SJk6CjpYE7t2/g9g1PjBkzBhMmTMDFixcxfvw4HDtyEOnJ8SxvHP7++29s27oFyioVULlabcgoqmH3vsPYf+SERJZJMxagXqMWcK/XSJLHI98fAoryMmWqUyKRCKpq6gh9EY79h46y2fDnkJaRAS1lTk1NRUJCAsiqIhaaJhViY2PFyc8+k6toHQ1l4futICbU/tPHwXj6LAzUZl6aOrVrofNPPwl74/Lm83j5R4AsOTv3ncGtu0Go0WKI4FygIGWErCkpzKryOT2mZW6qKkqIikkQ9vfQEjJaSpY31G49DO9be8iiFBkVhxljeuNjH/JMSArYx8p5ftlF4DtTcooGdHp6ukD4119/YebMmUKcDr/88gu2/fEH/ty+A1OmTBE2NFeq9H/zJdGUtUCD5Nm/zhKcHZCyIycnhzc52WjfpnlZE/Uj8ogAKbm3ZTKKgKzK23gJH+PiEhD85Clat24t4ezo6AhtLU1hX48k8ytE6Mf94aMnkJOVxaVLl75Ci+WnCU9PT8RFv4KWujL69OyKoOfx2Hw0CEc8n6Bvn36YNnM21qzbCDNrR/x+5J7QsTauRhjUwhKTxo0W0kU5kEX2TW4uEtNzMLWNHZqa5OLcicNYtWgOkh5cQ0NzRaS8TsT+/fuxYvky3PC8glFD+uDXqWMxqF9PzGbn7PTX6NejI9xrOEJHFbAy1kb/n7vg0uXr6PPLGIydOg92lV1R282jKCJxmnKKgLS0FGSYNaesLVmjJdP1G7VEzToNYGntBFLETp89h2ue13H4+BlcunYTPnfvCdYdaWlpbPtzO27evIUv/ZibmUBNWYZZOxM+YPUo+AFMjSsJ791RZJbU9wn09fVhbGz8fjZPl3MEyNqxeMZg2Fubgjb5kwVGR0s9X690tTRASoo4kxQjUj4sTA3EWUU+U3t/rJwssfKQpYfC+44JxAoOLUejOh9rgBQvUsA+Vs7zyy4CUmVXtJKX7MmTJ+jT52cMGNAf9N6NA2wAY2FpKWmIlrCUdaVGIuy7yPnz59kMd6zwvpbt27cL+0/InfSKtZvhedPrHVUZPpFSQ57V5NgoUUYJxfWyVtSe+d73F2Z3goODJVW8vb3gVqeGJP21ItVcnHHsvx2wMDdBQly0sFyu1NsuJw2Ql7uomBi0bNoQ7Vo3w1/bNmLFkrmooFUJdg6O8PS8hoHs+Q3w94eNsaakV1VtKuFl+Avcu/dW8ZEUvIvQ0kRfX1+MHjUSvbt3wc1T/2HXxuWY2tpWoGjooIcNfarit45WGNzAFN3rmGDRT7bYuX0rNq76DdMmjIAJs5AKxOygX0kP/Xp3hbOTPVo0bYCObVui20/t0KJJA3Tp2AaWNvbo98tYNGvZllHz/+8VAdqKQ3tysrLfgJaIlcV+kgJjYGQM9YomeANZZL+RgrGJGarVTksihwAAEABJREFUdEcu+/49fuocAgIC4eTojFu3fRDyPPSLu0Eu2qNfPkXI08f5eFlZ28I/6BHoecxXwBPfPQKk1DRvUBMDxy/JZ03Zc/iCkKalYarKijhy5rqABTkieBkZg1pV7YU0WWDIEiN2AEBnCkIhO1A9qk98WPKT/6TgEBEpQ4UpOERDvMmSQ8vtKM1D+UHgh1JyTp8+DRkZWWb1GIJhw4aBXDv37dOn/FytAiSl2bC0jHRMnz5DKG3atCnrowzS0zOQkZ4p5JXpQ1bKW/GkZN+e2ez620jJHvcdPIHIyEgsWbIId+/eFQYk6cyiV0FVtWQbKiI3srgtnD0Fndu3wqtXkSCXxkWs+l2T0XP5Kiomn/tzejs7LbHJysrCvr3/Iv11LAa0sEb7ulb5sLAx0sLKZb9h4phhwvUVF9KAatzY0Vi5Yin6dajNLGhSsNKRxeLOtnA2VheToYKSLFQV3t2HLFdeRgqGWmrIyXnDUkX/r+vmiufPHiMzqxw8f0Xv1ndL+SUdU1OWAyk6b3LZ/AwLX8KrtOuaMMWmllt9VKleG5bWdkJzlSrpw9WtAUIj4pCQEI9adT3w+Hk4XrwMF8q/5FDX3Q36uuoI9PcT2JASeMPzEpwdbNnvU7qQVxIHmrDcu3dvSbDiPEoZgQHdW2LhtEES5wMOHn1B3vYcbExBigZZe/YdvQTKJwcFU0b2EDyvFSQWWX6mLdwi0BI91aP6xKcg+rx5tFTO5/4jHD7tKalPPGiJG1mQiFa8tI7yi8Ob6vJQdhAot0pOfHy8BMXff/8d+/btk6Q/FhkyZAjonSj16tVDmzZtPkZWrvIrV64MWVk5GBkZCXKLRCK0bNkKFStWxJJVG/D4aYiQX3YPbABJlpw3WaUqYqe2zeHg4AAPj4YgzEQiEXKyc5CSklqq7X6K+aHjp6GmqgYFBYVPkf4Q5efOnWNYyMPexlror/cdX8z9bSUMDI1AHqJsbB2gqqaKWg4GH7jF7dLQCr9PbAIbPRnQnh6BATuQtVNXQwlV7c3gYG2KjLR01LH89ItZNVXkYaatiCUr1jIuRf//79AJmFvaoZK+QdErccpyh4CSvDTIipOSno2E5PKl0JJ1Jy/gNVxro5KRKZ48CoCdvTOueN4ETSzkpfmcuKmJEWiPzqvIcOF5tbNzYt+5KaCX/H4Ov4LqrFy5Eh07diyoiOd9GQKlUrtBHZd8y8hI8RE3pKOlDlrKFnDp7Tt0iFZcRooQLTcT54nTYlqqR/XF9IWdiY7oxXXFZ7FVh2QS59GZaKkO8aQzpcVyUB65o6Y6FOehbCFQrpSci5cuYcSIERgzZjRmz54tQfL48eNwdHSUpD8WkZIqV939WDfy5VetWhVbtmyBsrKyJL9JkybIYNad0cMGwNLcVJJfpiPZaaUqXuOG9QTPZioqKnj+/Dl+++03tG/TFA72NqXa7seYR8fE4satO7jl5YN+/fsL1reP0f4o+bSn7Pz5c8JStYzsLCxcth5Hz1yGbiVDYe/cpg3rcO3SWbSrYya47H0YGpsPGmtjbZCLae/HiRg4cKBQ9vLlSxzYtxcernbo1qYuxv26ARrKsrCqpCaUf+rgZqUh7BH6FF3e8vruroiJisCbN2/yZvP4d4KAHLPwKSvIQF1FDnFJGUhMyQK5Ny/v3bO2dUJy4mvcunEFVWvUwVXPGyXSpcrOjshKjUfo86fQ1NZhVqLIEuErZmJra8u/P8VgfM5Znlmz5TUASfhE+nPa4HU4At8IgdIZ9ZdSZ+idFqNGjWIzumr49ddfJa0cPnwYtJ9GkvGDRfT09CQ9pqU59F4RO2tLlCv30W+ygVwWUHprPtq2bgrPa9eE9x0lxcehRlVnfM1PVlY2Fq/YgH5DxmHI6ClYumojU06VQJ67vqYcZbUtQ0NDWFvb4OzFqzh/+SY8GjbGnDlzQRZYmqDo028AmrZshz9OBqLjtP+w9ejbZTDI8/nnjD9ep6Tjxo0b2LxuDcaNGIzEmAgcPnwWw6evQTKz3A1pYJ6nRuFRi4qq0JTLxt///lc4YZ5SHW0tKMjLIiY6Kk8uj34vCNCyK1JqYpiCk5n9/Siy5PmsIvstcavpgkdB92FsYY9zFy6XyGWrWaM6VBWl8SDAH9qabEBdIlw5k5JAQKRuA5G6dZ5QeLok2uQ8OAJfC4FypeTUrFmTDYKsMW/evBI1d38tsEu7nejoaIwfNxYnT55EUPATYTAdFvYSqamlayUpuX6JgJyskmP3HicLMxN0bt8CO7esxm9zp7CBqPx7FKWXvM6sNpNmLkCNWm5YsnQZNDU0MXJof2aBdAa5Jy+9lssX56FDh2LHjr+Y5WbWBy7bFRUVcfbMSbR3N0OjamaCB7bfj95Hn/nHMWjJaYxcdR7KjMbJzhZHt67B5dMnYKKpiElt7LC4iyOaOlREaloG7Aw+bcV5nZ6FXddDMWbnXfg8eQXvu/fx585/BetMekYGPvXp070Djuzf/SkyXl4OEcjKyUV6Zg4ys74fBYcuAy1hk1dUBi2drVrZHs+fPoKRmS3Onb9IxV8cjA0NkPo6hj3Xrl/MizPgCHAEOAJFQaBcKTlF6dCPTEMb69PS0tDYoxZsrC0QFR2Nxas3YdLMBeUDloxEJmfpDhxq1azK2iiZ/4tXrmP63KWYNmcJeg0che59h2HEuJkYPXk2RoyfidWb/sTEGfPx64IVOH7mMmbPmYeGDRti+PDhGD2sLzZs+UuIl4w03zeXiIgIjBw6CCoKMjjv8wIvXsvBqXZLaFnUxrRZ87Bg8SoMHDZe2D8T+eoVepvLYXUddTipiLDwYCD+ux2KgQ0soCwrjSOsfmFozfjvPgZs9cKdaCksWbYY//79J6aMHY5794OwZuMf7NrOwFXPm4WxgLdvAHR0dQul4YX5EOCJMoCAmaUtfP38YWBgADUVBWRmZgoWnWPHT36RdLR08+o1T8H5i5yc3Bfx4pU5AhwBjkBREeBKTlGRKgd0Tk5OqOPmji1/7kbDerUwqG9PJCYmCRs+T56+gMysrI/2IjomRnBScD/gAW57++LKtZs4c+4yjhw/g70HjgovOfx332EcPnZKyL96/Rbu3PVD4INghDwPQ3xCwkd5F70gF3HxJcGn6C1+DiV52zp87DQuXvPCT527YsDAXzBs2HBhBpRm+StU0GD5XWDBBgxNmrYQll0tWLAA5CVs0qRJaNOiibDfxNjIAPPnz4e/vz8uXLjAX4JXyMWoVKkSatetj+mzf8OyVRuZ8rES5DwkODAAL54Gg2ahX4WFYva4EaglnwAXbTloykuhh4kMRjiq4V5gOHqs90QKs2reD40vpCWgrrU2DPX1sWrpfBhUqgg5ORno6eli1ZI5wqbpX6eOw7//HcHE6fOwY9c+gVfwo6fCs0CDwvVbdiI2KQvtO/cSyviBI1BeECDnHlKyynj06DHq1KqJwPt3oKKqBufq7jh99sJnd0NKSgrNmjZB1y4/fTaPH7ci7zlHgCPwuQhIfW5FXq9sItCqVSvo6GjjMFNOpKSlQPuYFBSUsG3nv5i9YDlGTZiJAcMmoGf/Efip5yC06dwHTdt2x/hp8zB+yhxGswzL12zC73/+jX/2HcLRk+dw+eoNPA0JxbWbXjjOlKXd/x3Cpq07sXjFesyYs1jgOWbSr2jevifade2HLj8Pwc+DRgv7TiYwvktXb8S8xasE3jPmLsaUWQuFASINEilOebMXLhdo+g8Zh4NHv2zWsLSuDM1Grt7wB3r0G44LV2+B1uavWrUKS5csATl+mD1nLhQU5ZGRmoy//tqBli1bomnTpmjQoIEg0s6dO5Gbk4Xe3Tti/syJcKtVAzLsCdzx5x/4b98+0CZ5gZAfCkSgR49eMDIygoaGBoKCggSa1OQk3Dm4A2MG9sW5fduxrposOhsyUIXSt4e6utKYW1UVA8zlUVFFFs9eJGDUH17YeDb4LcF7x2bOlWCjJY1BQ8ZISq563kJGRiamThgJ9QqqaNqkARRVNJCYIY0ps5di3dZdOHrmGuYtXQe/+4Fo0KSFpC6PcATKEwIWVjbwDwoG7dGp7uKMR8EPEPUqAi/DI7B67frP7gpNRKipfXqp6Gc3wCtyBDgCPxYCReht/tFAESpwkrKNgI6ODnbu/BtZ2cCFS9fRvXNbJCYlQUlJCbo6WqjvXgs9urTH2JG/YNHcafhz00oc378Tf/2+Gof3/on9/2zFnr824e9t67B980psWbcE61cuxPSJI7Fu+Xz8vnaJUGfXH+uwd+dmHNi9DUfY4HIHq3/w323YtHoR5kwbjyEDegsWiyrO9tDV1oI6+3HT1dWBkaEBrCzM4GBnIwSKUx7RqDAZ69ZxxX8HjmPIqCmCoiVGOzubdehdgmbjIasCyCoD0nLvckv3lMAsYgOHT4Ifs3SR1UpLQw0tm7hj3ozxmDGJYbN2LV69eoXp02eiSrUa6NTpJ0ix2UuxVAcOHMClixewcPYUIcvczAQd2zbHr1PHoPtPbZCWno7ff/9dKOOHghEQlMoVyzFz3Ej8s3IBls2aiuioaLzOfIO/6lfAwsoKBVd8l9vQQBErXdWxqGYFdDOSwrWgV0hMLdj17+D6RmhgoYS+A4cJLqzJmjls7FRhn9uc39YgJVOW3aOT8FPXXujSexBT3H9D7wEj0H/oBLTp2B1a2jrvWuUnjkD5Q0BLpxLCwsJgYWGOxNhIxMZEoWWzRmjXplX56wyXmCPAEfhhEZD6YXv+nXeclq4FPQzG73/sQmJiAuLi4jFyyAB0at8KjTzcUZ3N0FlamEFbSxOysjIlgoaiggIq6VWEna0V6tSqjlbNG6FXt07o07MLaJP9sEF9MLh/Lwzo0x19e3URAsUpb9gvfQXFa8wINmCcMJzV6QxFBUV0ZVahQSMmYtzUOVi0fD2u3roPKUUtptzIs8AGtbKqgJRsceUvFn18QiIbvI6HunoF9OraDicP7kK7Vk3x739HcefuPYwcPwPt2rcHOcaoVKkSunfvDrKoiRshq8Off/6JYb/8jIKwVlVVZdcnTiBPZ8qOEOGHDxCIjo7GpfPnYJEagtm2b1At7SGGGKZhvL2CQCtNb2UUYoUftBSkmQUmFxVU5CErU/BXYEh0Cv65HoJ+fXrD2ckOa5mCv23DcrRv0xwV9Q3h0aSlpBE9PX1JXF5eAVVr1JKkeYQjUB4RUFBURHJKiiB6Q4+6iHj5QrDkmJqaCnn88P0gkPs6DBTw7kxxCh9Lfz895z35ERAo+Bf+R+j5d95HWvr025xpmDhmKLKZWad5Yw9IS5f9y00yOjnYobZrNfTu3gnrVyxgSlEPTJsyCSPHjEPd+h5QZD/A+S6fnBogo5Qvq6QS5y95shn7KXC0twXtc2pQz03AkdwEy0jL4q/d++FSrRpat2790SYXLlwAelfE8tW/Y9GKDSA30vOXrMGMuUuwcesOwTkBOYwgBgpMUaQzDx8ioKuri0nuVGgAABAASURBVLFjxuJmegXMChLheboMnDRlUUFO6kPiT+REpuciIiED4/7xQ7/fb+PUvXDsuxWKY3dfYu6Rh1h4NAhaWuqoX7c22rduLuGmpKgEHV09lIUPWbZKXg7OkSMAREeGw9zMTIBCRUUFlZmi//hpCMLDw4U8fvh+EMgNPQcKb96dKU7hY+nvp+f/78m037aAwv9zeOx7QaD4o4PvpeffeT/87t2DjZU5AgIfwsLcFEOZFaU8dlm7ogFq1W0EPQNTKCurAG8y34b3OpNTCk7ZJkybi03bdkJeXg4pqSk4d+kadu05JLSsX6kifpszCbbWVujUsaOQ9/7hzJkzQtasWb8yS09HzJs/nylpDWFj74TIqFj4Bz5AwIOnwovsfurQBmJFR6jEDwUiUL9JU2za/jd6j5+JO1nq+Dckp0C6wjL3Ps+CZ9wbTGhti4WdHNDMqSK2Xg7B0YAE7PKKRkB4CgYOGYa+P/f6gM2JMxfh6ubxQf7XyKAlmw+D/OF3zxtnjh9E8IOAL242IiIcfne9ER39CtFRr0COE76YKWdQ7hHIzExH3gmXKpWdIS8nj7v3H+Dxkyflvn+f1QFeqUQQuHj9Lhw8+krCtt0nBL6paenoP3axJJ9oiFYoLMaB+BWksBAv4kmheY9JiI4t+06OitFtTvoRBLiS8xFgynN2TEwMm4XWYkqBEqq6OGHJ/OmC9aHc9UlK5q1Ck5MBZCUD6XFA5msgKwXIzQbeZAHZaWzW8SWOHj9Vot27edsHSa+TQS9V7dCmOebPnIRmjTxw8sx5STvkCjU9IxNr1qzB+PHjsXTpUkybNk0oHz1qFLZt3SrEraysUKNGDVhYWMDNzQ3t27cXrFHTpk0HeV0j69Xde/5YsmSJQM8Pn0bA1tYWs+YtwI3YHISnFl3RORGWgWOhqdj6iyvcrHWgqSqPpk6VUM1EDbJpSWio+QYdKmZj46rVWL/hd8xesAznmXI79dfFGDnxV/QfMgYVKqijpD+hIU9w7dI5HD+6/wPWryIi4HXzKu7duQ5tdUUEB/pDm1mTzCysP6AtagY50fD1voHcjETUqVkZCVEvgMwk3L97C99yyaSIdaCCsiwoqCrJQkleGnKyUpCRFkFFUQZSRVySCP75IgSys9l3ax4OIpEIBvoVmeKjgj17D+DI0eN5SnmUI1A0BAQFZOEW7N08GwGXtgtBRUkBAQ9DkJKaDj1dTXid3CTkr1s4GtMYLZUVhfvFd8rTis17PyAnHovW/iNpt3MbD0yevxmkWH1AzDOKjUBZriBVloXjsn0eAhs3bkCXjm/3DNjZWH0ek7JQ6w1TZHKZiYaUGlJ0kPtWKspjgzMalIHl67CBn6aaAu49fIGsXDlARhGQkmW0IhY+79/Z0R4WZibQZz/sHdq2gKIi438/AB51a+djaGluAlIqg4Mf4q7PHWE5x4jhw/Aq6hWkZaTh5+eXj16cIIWoZs2aILfSW7ZsxbLlKyArSzKLKfj5Uwjo6OhgzuqNWBgsjbBkdq98qgIrvxyRDgP5XPRY54mR273RZeUVjN1+G69jX2NDHXUMtJZHVwtlbKqtigbauUhNeA2/h2Ho1ncYxkyaA52KlRiXkv2PehWJxLhItGzWAFLsnvdnlprTzFKTmJiIJ48eICM5Go3q10Gbls3g5OgIW/ZMG5tagJTsz5EkMSEBXtcvop5bTVSvVhXk8ap+PXdUZrP1FdRUmEUx9XPYlkgdUmzkZKWRmJKF5LQsZGaz55899lJMuaH4mzcsUSItcSaFIaCopPyBS3u6V0KeBKETszo7ONghowgvxSWF+cbN28JLdAtrj5d9/wiQ5eTUxdvYunwSHGxMJR3u2q6hkNbRUsfCqYOgxH5rqdDRxgz0fRQVW7jLf6Kl0KCOi6AcjRvchZL5wk2fQFRzthbaoYJaVe3xMjIGz0IjKSmEyKg4kIXHwaMvarceJiheYB9SzMjCNGrmGomViRQqVsT/ywECUuVARi5iMRCIi4tDRHg4GtZ3L0atb0X6ue0y5UVWGZDXYEEdkJZHvYZN2CCtMmQVVSHsz6F9OgqarAERC8X/37J9FwKCgjGobw9J5aSkZGFvDmU8Dw0T9tacOH0eP7VrAScHeygpK0FDQx3PnoUgJSUVzs7OQiD6woKamhrIBXVhNLysYAR0dHRhYmGJR0lFU3IW16wASzUZKDBFuauBFPY00sLvbupYUF0NcsxaAPZ5k5uLYy+y8fRNBYRGRaN5m07Q0a0IRSUlVlqy/7REMfz5IzRr0hi096F/vz7QqqDMlGolyMvLQ0VVHUnJKXj9+jXI21VgYCCSUjKgzfr9OZI8CPTDy5CH6NCuNag9MY/k5GQ8ffoU0TGx7B6m50Zc8nXPCnLSrH+ZQqPsMiA7J1dQdDKz3oCCUMAPpYoAKSay0tLC/Yc8H5FIhN69uuPUqZOwMDf/oDwPKTIzM/Hg4UP4+t4TpqakpKTyFvP4D4YAWUzIchIYHIIug2cLykJBS8rywhIVkyC8pkFXSyNv9mfFn4S8zFdPV1tdeH9gXgUq/FUsdq6dJihKA3u2xvJNeySWnlt3g9ChRV2hjCxMi9ftBilt+ZjyRJlEgH/zlMnL8vlCbdu2DZ3atfh8BmW+pgiQVQKkFZikuQAtX8tJB8iyw2bBWebbfzaIFSLSzLIjRD48/LHzX4wYPxMTps/HgGET0H/oOPQbMhYjJ8wUvlz/2rIa0uzHXlyzoYcbtrE6Q0ZPxdzFa3DL6w5UmGLz6EkIJo0ZCtfqLgBr19DICPPmzcOUKVPFVfm5FBHwu+8PK6a4FLUJaWYVqGugBFedt/eG3DvlRlw/IjUH5+OkMW3pGvTs3Q+3rl3E82ePxcUleo4IfwkDg0oSniKRCNIyMhDl5rCBYjpTrnQhr1gBh46exP4DR3Dq7CVUruqKz/nExcbgTWYqGjfyyFc9NjYWF65cx+PQV7C2r5Kv7GsnYhLTQcrN126Xt/d/BEKePGKTNjb/z8gTI0+RD5mi3L1bFzx+/PFn4vyFyzhw8AisrCxRp1bNPBx49EdEgKwzvTs3hauLnWQ52kJmtfkYFqQUkZLRpW0DifXlY7RFzbcwNSiUtKqTFciaRETvW3pIbgpURkqXqgobg1CChzKPgFSZl5ALWGQEvLy88ORxMBp5fMdWHGn5twoO7cfJSAAbtf0fH8lsIVN+aA/Pm0yAKR3/J/h/rM8voxHLzOD9ev6EDm2a4efundCvV1cM6NMdg/v3xJjhAyXE/x06hh279mPJyg3sR9sGTsxCo15BFUsXzIS+vj4iXkVj1sIVSEzJxLTpM7F582ZUqVJFUp9HShcBWxMDJGYUfV9OelY2QpMy2UweEJ2WjVNhafkElGdKDwVaSljXvbawPCfi5XOmdGTmo/vSRFJSIuJehcLB3i4fK9rkTYpIwD1f3GQKlpa6IsgxxeBf+qNX958Q5OeFoAA/JCUl5av3qcQdr+twq1MrH1l8fDxTng4jMvIVHJ2qQEtLO1/51068yWXzFrRE7Ws3zNuTIJCUGAtyhS/JyBOpXLky3Oo2RJ2qNvhj81rMnz8PUVFReSiAFy9e4Pnz55g8cRy7n7TylZVEgvMofwiQ0rJz3xmQRaRGiyHCsrCPWUKIdsS01cL+nAHdWwqdpbz+7zklcPDom29ZmUBYyOF9a04hpCBLj5qqcmEkvKycIMCVnHJyoYoi5vr16zB53LCikJZfGhlFJjuNhF6zc55/WWXkiF8YmpkMvMlioyVGQ2eBTASIpOF5yweLV/0uKDMTmfWlWlX2o12rhuAquK6bK9xr1xSWpL158wbXb3lj9oIVeJ32Bo5VqoG8pZHyQntvZk4eg6WrN6Nb9x4YMnQY1qxZiwnjx0NHRwf8UzIInDt3TsKIrockkSdCg/QX4eFw1GLKb578wqJdLJTxKCkLK+4nY85DGTwyckfzk9EITZcWqmkrSINcU69ZuQLq6hWgqqoqWOnu+/kI5SV1CHkajPr13CHDLDfv89TS0kK1KnZo26opHB3shftKUVERurq6aNu6BazN9BERGoyIF6HvV/1ourZ7A5y9eBl5XU/TUkkdXR1YWtl8tB4v+HEQiIyMgKG+HpsAYN+XH+l25y5dEPj4JWaP6QIHEzXMmDoRUydPwNChQ3Dp0iUYGhpiyOCBkM5jBf8IK579gyBAlpzFMwbD3toUezfPxql/lkisJnkhIGVGrODktfRQ/T9WThaWi4kdFtD5xrENRbL0vG/F+dRSOCpPep2SV7TyEOcyFoCAVAF5PKscIrBjxw40qu8GMxPjcih9EUUmBUfEbtnsdFaBKTrsKPyL2OA0Ow1RES8QHc1mFcmCIxQA/g+fIyaZ0dL+HHl1VKvpzhTB4ajnVusdRcEnWsJ2+sJ1tGrbHv369UO1atUEQtpnI2IyzJy3DH369oOrq6uwF0go5IcSQcDX1xeLZ0/Hrb1bMXpQP3T7qROG9+mBYQP6Yf369Zg5eTyWL/4NZDFbws5mSh9v9nXWG6x4kIk5vqk4GZomEKrLS0NFQQHRFYyx9o+/MGbceEGBHXb5FWg/DhH1M8yGashtjBk8gJKCxc7R2gx3vG6UiOtmYpqelo60tLcyUfr9YGpqio85FzAyMkKD+nUhjQw8f/bk/aoFplVUVGFsZgsfn7uSchqIdmjXBuSlMCGhaBt8JZV5pNwjkMSsiXk7ERsbDb2KOnmzPojfvHkTaekZLGSifdNa2LxwGDo3q4pfOtfD0iWLmYU89oM6PIMjQEvBmjeoiYHjl0g29RMqew5fENJiBcetphPyKjhE86WBlp/d8QsW2iFe5IjAQE8bZsZ6lPwg7DpwNp+jgg8IeEa5QUCq3EjKBf0oAjR7dtfHW3h55keJCisoL2UyHxnN5uYAuW9w5MRp5KQl5umNCFc8byAxJgw5KVHISY2FgogNKiXWnTykeaITp89H5y7dMGfOHJDr5zxF0NPTw2+LFmPpsuWoXbt23iIeLwEEHj16hH/XLkbDjIeYYiuFEYYpWFxZGiucgX4VX6PSwzNonPsMFuG3UDHwJByTH+FxQjp+u5eEoyFvFRnfGFKCgYTMHMzwf4M6fcaiWqd+eKBbFSO8MzHoRipmrViP5avXC1YU2ltAwc21Jo6Fvq1LXelvIQeDnHj88ccfEIlEMDMzRe3qzjAz1IXXzWtISWYWQyL8jPAgyB/W5obC/fQZ1SVVaGmblpo8rl44DdrfIyn4SIR1A1kFLAcji9ID/zt49vQxUlNTP1KbZ39PCJByfP3iaTx6GCg4tgh9HgKZNxkgS0xh/dy5YzuUFOWQkPj/mW4XBws8DouCh5sLNm5YX1h1XvYDIzCge0ssnDZI4nyAlpwlp6YL1hjydBb06DnIBTTli8O037YUCTHyeEZ1qP7h056gOOVRZQcbU0wZ2UPS7r6jl0CWJbIQUTkFcR2qFxkVhxljelM2D+UcAa7klPMLSEvn9TW8AAAQAElEQVRPdu36G+NGDirnPfmE+CKZ/xO8p6Tk5OTg1wXL2RelOaRlZOHNZmwePHuF2JRcmJsYwtTIANLS0pCWIha5dPhouHr9FuQUFAtVYLS1taHALAEfZcILPhsBKysrppzkSiwq5qoyMFSWgRy7eFW15dDOVBnuegpoY6IkhG7mivi1sjIsGd3lqCz4J77BnpBsTPV6jZleSchhtg43Nze0a9cOE6bNxIZd+/DH3oOSgdzDoEDMnzQac2dMw4y583EyXhGnX2ZI5P/FSg5ebCAozqBrb8Kspc0a1cWTh354wJQVcVlRz5mZmUhJioednV1RqxRKZ21tha6d2yMrJQ5hz58WSqujUxEZOSI8CwnJR0fPR0dm0dFVV4Cfz23QM0XfLfmIeOK7QYDcPwcH+qFz546obG+ByNBgqMrnooFHvUL7+OLFCyjIyyCLTSAcPH8Ho+f+gX3Hr+HEBS/4P42DtaMrZsycVSgPXvhjI9DgnatnWm5GgRQfQoQUEVp+Rnl5w8KpRRvbvM+XeFAe8aZAccqj8P5yOWqD8sWBlsaJFSCSL2+a5Nz3++wCl9tROzyULQSEYV/ZEolLUxwERCIRQkKew8hAvzjVyh+t1DslJ4cNQN9Tctb9vhMWllbQN7KEjr4pDAxNmGUnG9JZSWjeyB00gCtKh3Ny3uDR42cgRSnqvc20RanPaUoGASl5JawNTMXO4BQs8k9DSPKbQhkbqMigs7kSltWsgIlOyvitugoaVZJBCyMFRCUk5nu5pUgkgpTEQQVw9OhR1NWRhsY7bzm///UPAtSdsIspSmAfFVkpLHYUYVivLlg4dw42r13FciEouc2bNoJJJU3cuHZJUAqEggIOeZWFV5ERwhI0AyNz3LvnVwD126zHj5/A88YtXLnqiVu3vQX3zoUtbaNatWrVRGxUxCcdJFjZ2CM6IQ33/QOoWr5gYGAAGWkpeN+4gtvXC+9Xvoo8Ua4Q8PP1QreunYT9Zurq6sLeMAcH+0/2gaw86hpakJWTwd37j9G9V188jkzHqj8Ow8DAUJhM+CQTTlCmEBCpGgEsiFSN350LT5cp4bkwHIFPIMCVnE8AVNaLHzx4gFo1qwvLab5MVhEgKsO3Ayk25DGNAvJ/fhnYH25udfEmJxPISEAlTXnYmhtAXV0tP2EBqfSMDFy+egP7Dx3HmMm/4vip87h54wZ8790rgJpnfQ0EMnNz8duc4XipYwE7NrM81zcZoUV82SfJl/kmF0pMOdn5NBMzZswUFBLKLyi0bd8Bl2MASwdnSfHEmbMRVdEBGx5m4FpEOpQZr5VVpFA14S4unDuLq1evSmgtLCxga2mMx8GBkjyKpKYm49ypYzi4dyfWr1yAv/7YjHOnjzElaQm8b3uiYiV9vAiPwsPgYNA7asQKDDlYOHzsJCJikqGuYwgtPRPIq2ohkqWPnTyLyFevEBkZSU1IgliJoveb5ORkQ5yWEBQQMTQyxavYJCQkJuUrVVJSQnZWBtRUlVCzWmV4e93MV84T5R8BeueSsoI85OXlP6sz8xcuQk6uCFnsPtmwfh0mTpyEs2fPomfPnp/Fj1f6tgiIjBtDigWRcaN358LT31ba8tA6l7EsIVCGR7VlCaayK8uBAwfQyMOtZASkjf0lw6nkudC+G7LiFMBZUfYNzA3UYWmsCxBdATTvZ2VmZSHyVRTa/NQHi1asxx8796Bb955Yv2GDsAm9aZMm71fh6a+EgL29PQIfh2HaiK7w8g5EJzNFGDNrTVGaD8uSR6dz8TiZa45l6zbCxbXwfVMxTGlQU5THkyB/ZIu987GGJkz/FVp12+Nstj7m+GchLTsXTQyVsLS6MvZtWIZJQwfC0/Mao4RgmZGTVxSUC69bnti+ZTXWL18AudzXzNKjjppV7PEo6B6uXTwNXW0NyMrKIfxFKNLS05hSfRbrN20R3n+z78BhHDpyCk4utWBhZS28lFNDUwu6unowtbCCW/0mCI1IgF/QE5w4dQ63ve7g9NkLOHriNG7fvoMz5y/DqWrtIg9e7R2r4PzFqx/swWnVshmSEhOgqKgIMwMdeN9820+hs/xQ5hCIiY5EwH3fQq2JeYVWVVWFqqYugoKC8mYXK75+w2Z2nykgLi4OAwf0F+oqKysLZ37gCHAEOAJlBQGpsiIIlwMoLga0IbqSrgY86hY+kCsa31wgK6VopGWQSiRilqhiyHX+4jWMnTIHFXV14eTkhMWLFzNrkBv09PTAP98WgWfPnkFHq4IgRMSrGOjKvRHinzqkZ7/BisA0/Lt3LxYuWQpjY+NCq7x8+RI7N6/FLHsR5NKTQF6j8lbo0as35q9ch+4T52JakAz8YjJgoCyDhS5KSImJxOFNy+F3+wbIGigtLcMGmdk4d+IQmtV3xcTRv+Dileu4eesOrty4g169f0btOm5QqaCJ27duIORxEIyN9KGjqQ4NTW24unkwJaYpatVtUKjlydDIGI7OVWHjWBUySuows3aESw13yChroHqtepCTk0NxPrXcG+DQsVM4cfKUpJpIJEKulCy87vrjRWQ07JgyJCnkkTKFQHLyazwNDhD21fj73irUW19ewUOfPIStrW3erGLF6dlRV1PBsukD0K5pLfyxdUux6nNijgBHgCPwNRCQ+hqN8DZKB4HUtDToaGmWDvPvnOuz0DA0adIUf+/ahSVLlsDR0fE773H56B5typcR5cLFwQKJSSnM2pEJDXmpIgm/4EEuhk75FfTuF6rg6emJ3+bMxuQJ43Hi6BHKyhdo/0lFIxPcj8vGmzc5oOVe+QjeJezs7DBl3m9YFpyDgNdSmO+fgY5DxiJTWQeLFi6ArIwMwp8/wl9bN6JT22ao5uIMXz9/WFrZ4FVMLCZNmoQGDRoIrsg1NDRgbW0PK/sqkFfWgpVDNbjXb/SupaKfyMqip6cPcgstLS3NlPNKRa+ch5I2oCsxS5ZLlcqSXFKUlBTkQV7YqtZwg7KKiqSMR8oOAjk5Obh72xMtmzdl1j5mxWaiSUlJCYrO40cPcdfHS4izbMk/1bl87gSr0/iLljibmJigor4hHodEokOT6ngdG4o1a1ZJ2uERjgBHgCNQFhCQKgtCcBk+DwEdbW3ExMZ9XuUfrFYsw+mmlw9ue/vC644vomIS0L9//x8MhbLf3WPHjsLR2kgQdNK8LaiiIQ1LNVkhXdjh2PNUmLjUkXgsS0xMxNZVy5HyzA9auhXhUr1GwdVzAQWpXEgx64VYOSqIkKxCu/47hJm346CnIIL/rWtYtel3DBw1TpgRr16tCgLu34VHvTpC9ZbNGiE05CmqVavOFBA9IY8OkRERoJeAysrJQbeiHrM2mUBdXYOKvkkQiUSg5XOVKuVXkpo1bYScjNe4ddPzm8jFGy0cgYT4ePh634Jb7RqC9S45ORlJSa8Fpeah/x0YVayAWlUd8OThPXheOS8wo71aPreuo0mj+lAugaVlU6ZMw8XbQQiPjMGovq2REP0SXl5eQlv8wBHgCHAEygICXMkpC1fhM2SgDcr0g9LIw/0zav9YVdIzMvDLqMmIjY3H5j93sfAPZs+e82OBUE56u3fPHjRycxKkHdirFZKymRYipAo/mKjKIDgoQCAaNXQwAgICMGPRUtT6qb9gSXl/EC8QsoOhsQn2vniDdFruVb06yyn8v3OXLtCQfYO7d31BHvgaNmwoVCAlaMbMmZgxd6nEu1laWjouXryA58+fCzQpKSkgpwG+PrcQUsQXeAoVS/Hw8kUYRLlvPmhBJBKhSeNGSH0dDxocf0DAM74ZAg8C7+PZI39oVFAGeTsjQWRlZfE6MR6BvjeYlaYJzMzMoKWlhaaNGkBbQw2vk5IEy41T1RrCMspHjx9TtS8O6hpaeB4eLfAxrKiFJ0+eCHF+4Ah8cwS4ABwBhgBXchgI5fGfliXY2NjgFrNOlEf5v6bMN2/fgbSUNGrVdGED0Cxs2rT5azbP2yoiArTx38nRAYs27kcKUxB27j+HRpWK5gFKS0Fa2ARNTS1dtQZ16tSBhYUFWrduTVkfDQOHjUCP0dOwdvMfoGcqL2FWVhZ8fX0lWcHBwfDxuoXLEVkYOHo8dHXfLhESE4SHhyMuIQlTZy/C+KlzYGhQCcnJKRg9ahTu3bsHaWlpNtuehA4dOrLZ96L1S8y7tM6kwFhamBXInjytVXZyQFhYaIHlPPPbICArJ4eKOpqoWaMq6B4lKchTmqtrTXTs0J6S+QJZKBPi31r8FRQUQA4snoW+gt99/3x0xUnQs5rMrEcpqWlITctAVnY2njx/CT09veKw4bRlAIHcmEcoTigDInMROAJFRoArOR9CVW5yaB/JletebHCXUG5k/haCamloIDsnG0tWbgLNvMvIyHwLMXibn0CArsuIUWMYlRSUFRXgXt0WXjGZn7QkxKfnYNCVOPz573+sLkADPiFSxIOrq+sHlLGxsRg/ZAB2r1yIiUP6Y0iPTvht8lg8fRoCSzt7uLt/aEFNSEhgihIQwyyGSoqK8PMPYsqMHKhfCxcsEOILFy4UlK9X4aFM4c78oN2vnaFWoQIiIqM+2qycrAyymbL3UQJe8NURsLC0gZSCOk6cuYjL165Lng96vxHtsXpfoPS0VLx8+Rw+3rcRFBgA2pfjWLkqZJQ0cO8zFZ3jx48LXtVeRb1CJV0t5GTn4PnLaHh4eLzfPE+XcQRyffeiOKGMd6fExQt4GIJm3SeCziXOnDMsdQS4klPqEJdeAy4uLmxAFYuo6JjSa+Q74Hzi7EVhIKCmrils/v4OuvSNulD6zZL1IDsnBxmZWTh28ircdaSFZTYfazkh4w1m+mejbetWHyP5aD5ZMcSFT58+BS0/E6dpqY+zgwMSmUWptWo8llWWgaKCHKbNnoMp8xahoE/nzp2xZs06qKtXwC/9e6J2rerQ1tZCl65dMXvOHKYAScHIyAg1atSAs6MdQsrAkjUdHV3IK6vj5KmzePDg4Qfd0tHRQXLSWyvAB4U845shoFfJADVr14OMvAr8/Pzgdz8AL14l4NS5S7jmeRO3ve8I33lgHze3OmjawB2V7c0hnZOM0OfPWC6gzr4P07Ok8CykeJa6ZUsXIy42GvPmL4ChnjbsrYygoCCPrm3csWb1KoE3P3AEPobAxet34eDRVxK27T7xAem037agoHzKE9ftP3YxsyKmf1C3oIzo2AQ07zFJ0ibFKS8v7efyzsuDx8seAlzJKXvXpMgS0YycjpY2G0hxD2sfAy0tPR0PHj5GvXr1MXTo0I+R8fwygkBQUBCSU1LRedgiVNQ3wNl0HdxLALLfvN2bc+tVBo6HpuFQSCpOhqVhim8GVv6xC8MEC9D/O0HLeFJSUhASEgKvOz64fuMWHj1+goiICJy/cBk3bnnh1JnzoHfNnDh9DuExr3Hx8g2cPnsO8QkJAqOB4yZj6PR52BenhnVP3kBPSRbR0dGCsiIQFHCgJW9NmjTD5JkLoSgvh0qVT0X7KQAAEABJREFU9NGlSxfY2dnlo3Z0sMfV8yewce0KRL16JSkLefYYjx8Hg55tSSaLJCTEIzY2BklJCSwksZyS+zc2tYCFbWXEvc7ExYtXhD7ScqRXTK7QsBdITUpCYkJCyTX4PXL6Rn2ytrFHTHwqEpIz4OBUBS7V6yAlMxe3bt/BixcvJFKpqKgICnadOnXg632T3UOJQpmRiRnuBz2S7BsTMgs50HPl5eWF0LCXmDl9KqYMaSehrlfDAeTVTZLBIxyB9xAgRWLawi3Yu3k2Ai5tF4KKkoLESkLlpMQcPv2hwxNSjvYdvYRL+1cJ9fR0NTF/1c73Wig46f/wGTq38RDqUbsUnzx/s0RJ+hLeBbfIc8sKAlJlRRAuR/ERIA850TExSE1NK37lH6RGfHwi2rRsjNDQUGhqcmWwrF92Z2dn/LtnHw4cOIhl637HkCmzMd8nHjlMxxnllYbzUma4p1UVgTrVcF/TBQtWrmWzyAqSbj148ABHj58SXpB59fpthLyMgayiBpQq6CIyJhmBj0JhbOkAJTUdOFRxhaySJuyda8Dcwhp16jfCi/BXwvIyvPtUrlwZ67btQJtRv0LO1B6tWrV6V/LxU+vWrbF4yVJERMfjIZOnoCVEpAzVql0bWuoqiAwLhp/vHfjf90XUyxDoaynD6/pFQakht9ahIc8Q9iQQsZHP8eSBH549/dDi8nFpilaioqoKU3NLaFUyga//Q5w6ewF+gY+RkSsPa8cqqKCuXjRGnOqrI2DnVAWW1m+V6Ns3rkJfRw39fu4hKDXvC0MOa3R0dSAnJy8pqlK1Jnzv+UvShUXIwYGtjTW0FDKxe+1EYVmpmJ720b2KihEn+ZkjICAgPpDl5NTF29i6nFlUbEzF2ejariEc3qUHdG8pKCLtmrlJysWRs5e9BUVFR+vtd1GT+tXhc/8RiK+Y5mPnBnVcQLzx7mNuUgnhr2KRkpou5BSF9+a/j0gsQWRpEiqyA8VHzVwDsiyRgla79TCJ0gb++eYIcCXnm1+CLxNAQ1MD5MXpy7h8v7VXrP0dW/78B7169fp+O/kd9owGU9QtfX19LF6xGoNvp2Pw1DmY+dsyzJw5UwhTZ86Crr6hsCznAVMmrlz1FCwyVV3roZZ7IzhXrQVrWwfoVtQTgoWVNewcnAX3uTq6FQVlpqKeHhvwyVFToI3U6mqqUFVREdJ5Dw6Ojpgyc3berELjtCxtwYKF+H3LFsHhQEHEz5ny8iAoEM2bNYWpgQ4MdNXRtEkjWFpaomP7NkiKfoEgPy8oy+eiWdPG8KjnDjNTY8jLKxXErkTytHV04eBcDVVr1mXnqjA0NIKmlnaJ8OZMShcBslxKi97AlikhZLkpqDUFBQV0YvfW/bu3heeGaGjPmLyyKlOqYyn5QSBFmzK9vb0FF9EhIc8xvG9bysoXFJjlspqTFc6dO5cvnyc4Aqlp6SDLSWBwCLoMni0oC6QcFBUZqh8ZFZePXFdLQ7iHo2KKb2UmpUa/ohaUmRWpKLyTXqegsr2loIBdYpYkUq7I+iMWyOvuA4wf0lUoH9izNZZv2gPiKy7n52+HQBlWcr4dKOWp5Xbt2uP46QvlSeSvKmv3zu1hoF9J8oLIr9o4b6xEEIhl1rj2fYYhIvIVYpjlkqxypNTQ4Cs1NRVHjp1EVEIGKhpZwtbOsdA9PIUJVKFCBRgYWyCogL0phdUrrIysOD179kD//v0wiylnv/32G86fPy9UGThwIDZt3izEbW1t4GBvByUlJSFNB1fXGtDV0QLxyBJv/s/NhZKyMhXzwBHIh0DAvdvo0K41xBME+QrzJOhekpIS5XtOtHX14O8fmIfqbXTO7FmYNmUiWrduhS2b1mL/vztgbWGApNepbwneHdPSM7Bw3V68ycnC7du33uXyE0fgLQJKigro3bkpXF3s4HVyk6AMLJw66G1hMY5kgSkG+Qek4uVwpKQsnjEYJJeYqDDeaqrKqFXVXiAlS1JVpsyToiRksEMDNxeJNaowPoyU/39lBKS+cnu8uRJGoGnTpsLG0czMzBLmXP7ZnT1/GTv+2Y927TvAysqq/HdI3IMf6JyTk4MX4ZGoXdsdBqa2uOP3AE9Co/EyOgknz5zH+cvX4VjFFZbMSqOqqvbFyCirqCIpKemL+YgZkJvpli1bCUr2k6dP4O3thQ0bNgj7XsQ0Hzvv3XcAGW/kcMv7Hu77Bwhk5BAhKb7gGXeBgB9+SAQiI8KhoqRY6H4xMTBkzVFWlAdZfp49fYxbnhcR9SIEpFSLaei8bds2KEpnYcaw9jj4+3RMGdoJ9Wvao0+H+kjPzMLIXzfDN/AJLl73w8/jViAiJhH+j1+gRYuWVJ0HjoAEAbJq7Nx3BrfuBqFGiyGCE4CiLDOTMHgXefo84l0s/4k8n9VuPUywEDl49JWcaQkZtS2mpiVrAZe2Y/KI7ug9ciHyyvAx3uK6ec8WpgZ5kzxehhHgSk4ZvjhFEY1mtNmUHH5dsAKhYS+LUuWHoFmwdC2ue98HebZq06bND9Hn77GTT58+g24lI6FrtGzKqXI12NjZw87eCa5uDVGtphsU81g/BMLPPJDF5MnD+3CtWeMzORRcrU+fPli1ajW2bfsDv/++Bfv374eOjk7BxHlyW7dqjpyMRHT9qS2qulQRSjQ1NZllh+/BE8Ao4PCjZulV0oe6tj4OHD6GuPj4T8JgZ2eDO7euQU1JGm1aNkXzZo0le9vI6+D06VMR9jQQbRpWxeEzNzFu/p+YsugvXPUNw7Ktx5Celg4paWls3XcFh87fxZSpM9CpU2esXbsB5PXzkwJwgh8KAbKYkOXE3toUezfPxql/loAsIkUFgerr6WrmI4+KjWdDHxF0tdUFK8qNYxsECxEpMeLwx8rJ+aw1YgaONmZQVVECLXX7FG9xnbznJyF8rJUXj7Ic50pOWb46RZDN1tYWs36djf4Df8HiVZtw5PjZItT6fklo/82ICTPRtHkr/PrrrwW+z+T77f331TNalhb89Dmz0th8lY55e91AXbdaTInIKJX2aAadLDFFZU4vcaxf1x3aWlqSKv4BgdA3NJOkeYQjIEZA39AYNWp74NzFa4iLy79/QUwjPlfS00O3zu1hb2sj7E0jRxjisrS0NJDDjPDIaCzadBB+T6IRFR2HXmTBSY5DZFQM4pNTkZKagaXLVmDVmnWoXr06OnbqBA0NDTEbfi5bCHxzaXS01NG8QU0MHL8k38b8PYcv5Et/TNAm9auDvKuJrS+0XIyWjRHfj9UR59Mytbx7aI6cuY7X7B4mBYloisObrEZ3/ILRs2MTqspDGUeAKzll/AIVRTyaFbayssL69RsQ9Pg5du7eX5Rq3x3NrwtXQlZRFdOmzRDeRfLddfAH6VBGRgZOnzmPp6GvUKVa7a/Wazd3D9y6cw+nzpwVnBB8tYaL0dCz52HCDHoxqnDSHwgBaWZdIevmzdt3Ptnru7738N/+Q/nonj9/jsmTJ6B3h0bCDLiigiJSk5OwePLPOHP1HhJep2LuvPlY/cdxeDRoCCVmRc2rIOVjxhMcgfcQGNC9JRZOGyRxPuDg0RfJqemCJYZISRmhPHIhvWLzXtASNFIqqKxBHRfBu5pHpzFwYPXIEcGMMb2p6JOB9tOQ62qqR4GUpZ1rp0msSZ/iTY4HxA4T6DxlZA+JzJ9snBN8UwQ+reR8U/F448VFYPLkybjNBmrePvdw8swF/PHXv8IL4orLpzzRR0ZGYcqvv6FV67bo27cv9PX1y5P4362s9/zuIyEhoVj9Iw9nh4+fEd7bYv0FTgSK1WgeYqcqNWFq6YjLnrfgd9+fhfu47e0jODzIQ1ZoNCQkRHgvzzXPG/neVVJopSIUkmXrwL5/sefvrUWg5iQ/KgKKiorQ0jXEHZ+7KOxjYmyMuu75JxHIK2B8bBz+PXYJw3u3ROM6DlBUVsWCDfvgXNUVM2bNgbW1Dbb9uQPde3CPleCfYiNACoV4ORmdSfERM6E45YkDLUFzsDEVFyNv+ceWokmI80SIB/ES8y1oudzHeL9fl3hQH8TsF04dBAriNJUVRzZxPX4uHQS4klM6uH4zrrSeOjo6Br8tX4cDh0/i4aMn2PH3XixYsuabyVSaDQcEPcLiVZvRt99A1KxZszSb+oA3zygcAVrrHMsGTIVT5S+9dv2msM9G6Rt5EKOZcHIvTS9VzMyVR8YbeWYd1MDd+8HYQ44AmJUpv8T5U/f8AhASHgc1bQNo6pnghtc9BAQEgBwo0DtK8lMXL3Xr1i2QNzktrU/v5ykeZ079vSFgZGKKtEzA+47PR7umqanxwYQQWWWWr1oFN/f62LbvPOTlZWBuZga3eo3RvXt3mJubC/yIjlxPCwl+KNcIiLStINJh4b0z3kuL6cp1Z7nwPxwCXMn5zi65SCSCoaEB1i2bjy3rl2Jw/96YNmEU5ORk0eeX0Rg1cRYCAh8W2OujJ85g+LhpQjjBrEAFEpWhzGOnzuP37buxkv0o096kMiQaF4UhQEsoLSzeDopY8pP/8fHxyJWSA81Ef5L4KxDoGxjCwNAI9C4dB2cXuLo3EqyjH2s6ODgY0fGvYW1jB01NLaira7DBYUO8fJWIE6fOYf/Bo4iIiAS5vial52N8KL+gcuJPex5atPmJSMpy4LJ9YwSys7Nx46Ynu+cOYd/+A6DJr6KKVFFXD1ZW1pCXlYGCgjwS4uNAFnIVFRXwz/eHgKhKF4gqs/DeWeq9tJju+0OA9+h7RkDqe+7cj9o3OXkFiD3sjJs6G1v+2oOk5FT8uWkl6ru5YvbCFZg4YwEGDp+IQSMm4pcRk9B38Bjs3X8UHnXrCOHk6QvwvOlVZiF8EPwYx05dwOrVq8usjFyw4iFw4/YdODpXLV6lr0gtJycHPX1jYSna8+fPP1iKR7PbCgqKH0jkWNkF1WrVg5tHU3je8sbJsxex/9BxnDt/CeHh4YJ1RlyJlvhd87zJrLDHcfnyVbx+/Rrk0po2kj8PecYsQtm4fu2CmJyfOQIFIkBWlp+69IKNrSNOnTqN4loRySp+624gtu4+jeiY2ALb4JmfgwCvwxHgCHxNBLiS8zXR/kptGRoaIij4CcLCXsLOzg6LFi1Cteo1seH3HejUvhX2/b0ZP7VrgZ5dO4Beltmtc1v07v4T1iybh84dWgth0dxpIE9lX0nkYjdDS/EGDBhQ7Hq8QtlEgGaaU5KTQcvFyqaEb6UyNjWHYgUdhLyMFpainT57AaSAUKmenh4SEwr3alWnbiO41vGAu0cTGFva4+HTFzh38SouXr6Ci5euIlskDw1dI7jVbwIpeRVcu+mDqzfvwPO2L2Lj4tG6WUNERRb8rgjwD0cgDwLy8vJo2qINWrfrBD+/+3lKPh0li+HYsWOZRVKbWcpXf7oCp+AIcAQ4Al+CQCnV5UpOKQH7Ldnq6uoiis2+ZefkIDs7R/CAExERgYoVdSRiudaoigb16qBhfTcW3NHIwx0aGkPc4KcAABAASURBVOqScmVlJUwZPxzDxkyT5JVWZOv23Zg5bxl+/mUMRo6fIbzzZ9nqzYh5t5+DvG1lZmXla55eEEkKXL5MnijXCGhoaOSzapTVzlSsWAlWNg6oXLUmzKwdcdvHH0EPHuLateuwYjPnRZVbRUUVNnZOqM6sPJWMrGFgZotK+kbQ0NQUWFhY2QptuFSrhWo1agvhnn8QTM2thPKSPNBM//VrF5GYmFCSbDmvb4hAZmYmrl85DyU5ERwc7IstSbNmzbFx8++Ci+liV+YVOAIcAY5AGUCAKzll4CKUtAhSUlJ4+TICcfEJwsuyiL+xsTHu3gugaJGDnKwsRNL/v0UOHD0l7Ok5yM5FZeLl44sbt72xZ99heN3xxUNmYfJjAzVx/dkLlrNBoh969PoZEyZMRA3X2mjSrAWq1ayN6XOXYvvOPRgyeioWr9iUb115eEQkpFg/AYhZ8XM5RkAkEiE9I6PMW3Leh5gUFWeXGkhKfQNrpxpQ+cx9C2oVKhRY995db6SkpAjNNm7WGgFBwVBQVBTSJXUIefYMWzeuhpKCDB4F+uImXw5XUtB+Uz60ZA3SMlBWr4hDR09I7iPwD0eAI8AR+EEQ+P8I9gfp8I/QzY4dOyIrOxcmJoYwNtBFjx7doaWlhYTEpGJ139TECOpqqli0Yj1+W7YWfvcD0K1TG9A7GIqi6Pz+5y4sWbkRpy9cR1qOFPYeOoWde4/g4PFz2L3vEDZt+5vNgD8CbU4nq4yzszN+/vln4QWeDRo0wPARI5GYmoU/t++AnIIiVqz9XSI/bYiNioqSpHmkfCMQEBgEdS090BKb8tgT8maloKBQoqKTdSUyPBRPggMFvobGJqhkYAh1DS0h/elD0Sjk5OXg7OyAJg0boG3rFmjZrBHu3L5WtMqcqswiQJNAddzqw9zCCvKKqnj69GmZlZULxhHgCHAESgMBruSUBqplgOeSpUvRf8g4VK3sBFlmkdm9ezeePQvBk2fPiywd/Ugu+HUymwFMZbPsmfh16jjUqVUDc2ZMwIHDJ0DLIQpiRvsr6IWkPr4B2Lnzb8yePUfwzrOUyTR//nz8+utsPHwSBluHyqhbvz6ePw8tiA2cnZwwevRooWzy5Ml48OgZHj8NEdJ1atXE/fvFW2cuVOSHUkeArn9iYqLQTnZ2tnAWH7LeW3ZI+fQ+j5S0N8JgjNI8vEWAnj8H5+qICAtBfHwckpNfo5K+IYyYsvOWomSO+voG7FnOkTAjZc3GwhTPnj6W5PFI+UagQoUKH7iLLt89+kLpeXUJArlP7yL3qQ8LRTtLKvIIR6AcICBVDmTkIn4mAr/8MhgBD4LRvHEDODGFoWmzZrh07Waxuc2bOREzJo2WLH1TkJeHkpIioqJjP+C1dtN29PllDGITU7Fp82bQgOkDIpYxe/ZseHh4YODAgdiwYQPL+fS/hoa6ZMmao50VgoLeznB/uian+JoI0EZ8Ly9vvHwZjoMHjwoexMglMinFy1esFDyGkTxpaWm46+uHzFw5GJoW3dU01f1RgqGRMWQVlPDoQQCbbEgRnkF5eYUS7765jQMePAyW8LW0tEB42DNJmkfKNwJRr6Jw379435dPnjzBkMG/MAU7vnx3nktfKAJvDi3Fm0PLWCjauVBm5awwOjYBzXtMwsXrd8uZ5KUn7vfGWep76xDvD4TN22vXrsWJ48fQsV1LZGVlgN7NQd5ykl6n4a/d+4sNk6ysTL46brVr4M+/9+TLmzJrESrqG+EvZr0ZM2ZMvrKPJT6mBL1Pf/nyZSgpyMHKwkwoSmUDZAVFJSHOD2ULAVoa2bhxI2RkZkBbVwsXLl6Gv38AsxZk4qdOHYX34Bw5dhLnLnkiR0oRJlzBKfQC1m3QFGkpr3H/rhesmfLh632zUPrPKTQ0NEYgmxAhRZTqkxWJrLZ3bnsi7DlXdgiT8hoSExPwJjsVNapXK1YXZGVlUa1aNWhoaBSrHifmCJQGAqSIOHj0hThs231C0sy037ZI8qmcaCWFLEK0lE+h/9jFSE1LZ7n8/0dAgCs53+FVVlJSgre3F8YM7w8tDXW4166Jhw8fCD11r1tPcAAgJL7g8HP3nxD5KhrPX7wUuPyz5yAM2Kxzly5dhHRJH9TU1Ngs9v9vV309PdCG6ZJuh/MrOQTMzczQoH49tG7VApUrOwsb6y0tLXHpyjVYWDuhWk036BsYllyD3yknGmzqsckD1+qV0fmnjqjiZIvgB8WblS8KNG71m+L0uYsSUgP9Sqjh4ggF6Wz4+d6R5IsjZJ17+SJMnOTnMooALRE10NeHsnLxJoXIWc2gXwaX0V5xsX4kBEhJmbZwC/Zuno2AS9uFoKKkgICHISBrDGHhdXKTkE80i9b+I5RR/kVmpdl39BIu7V8llOvpamL+qp1UxMMPgMD/R40/QGd/pC6uWbMWoyf+ilNnL8LczATmpkY4deoUatSogVq16mDPgWNfBAfttSDrkImhgcDnlrcvGjZsKMRL4+Di4gJfv/sS5wkmJiZISPj/MgraB/LixYvSaJrz/EIE1NXVJRxiY2ORkpoJDa2S3TwvaaAsRkpAJnMrWzx+/HY/mqmpCWREmYiNiS4Bzv9nQdYbO8dq2H/oiGRZKL37hxRUJ1tz3Ll5FSEhT5GYkICXL19g5x+b8exRALMwef+fCY+VKQSSk5MR5H8PDva2xZaL6ha7Eq/AESgEAVJWHJg1hhSPQsjyFZESc+ribWxdPgkONqaSsq7tGgppHS11LJw6CEqKb5fx6mqrswlREaJi344Pzl72Ruc2HiA6qtykfnX43H8kUY4oj2hILgp5LT0kL6VHzVwjsRQVR3bizcO3RYArOd8W/1JrnTaanjx5Er4Bj/HP3sPo0qE1DhzYLwxeevbqhfuBwbhx+85nt0/uSemHc8joKXj8JASycrLQfPd+j89m+omKHh4N8Pfu/QJVaOhzyMvLC3E6iEQixMbG4b/9ByjJQxlE4PXr17jqeRvVa7mXQenKtkhkzRHJyiPxnUMH9zq18TQ4oMSFVlJWhoNzTVy+clXgLV6+ZmCgj2ZNPKChLI1XL55AOjsFvwzsw/IaQk1ZDlGvIgV6fihbCJCzigoq8sX6biavfrSs9N9//yn1zvAGfiwEBnRvKVhTxErFpxQGWlY2ef5mBAaHoMvg2YKiQUvTCkPN/+EzJCYlQ1dLQ1iWFhkVl4+c8mlSNComIV8+WYjIGkSZuw9doJMQbt0NQocWdQW51y0cjcXrdudTkAQifiizCHAlp8xempIRbMqUKbh15x68fe4JCo5IJAJZYcJfvkTtmsVbo/2+RKOHDkDzJh5Yt/lPREfHoLS9nQ0YMABBwY+Ffnje8Iajo5NEJPrSeh4ahoYNPCR5PFJ2EKCB06Ejx1HTzUOYZSs7kpUfSSxt7HHb20cicI1qlRHo7ytJl1REjk0eJKemIejBQxw5dgqpqakCazk5OdjZ2sKjvjuqVq2CR48eYcSIkVBXV0PwgwBER70S9v4JxPxQJhDQ0NBEVtabYslCFr127duhUaMmxarHiTkCRUWALC+kUOzcdwa1Ww+TLC3Dex+yzvTu3BSuLnYg+oBL2wWrzXtkQpKWrhGvEdNWY+G0QYKVRyhgB3OTSuz48X+y7lAptedW0wmet++DFCzKo7YpUJwUJFUVJYqWduD8SwgBruSUEJBlmY2WthabydOA2FUz/YjVdnPDynVbBYXhc2WXlpZC+9bNMX3SKOS8eYOmTZt+Lqsi1aMXLTo5V8H8Jatx7eZtDB8+XFJPJBKhbZtWrJ+akjweKTsIPH/+HCbmNmVHoHIoiYqKKuQU1dhz/NYNvL6+PjRUFfAqIqJEe0POQFQraOPmTS/WnjLIC15BDcyZMwd2NhaIiopCg7quSE+KRuC927h+5Rzu3rkJP19vPA95hoiIcNDencePHiLk2RPcvnkVT58+ht/d27hz6yo8L58TnFIU1AbP+zIEHj8Khq2tlYRJUFBQkSajevfuAzOzt05eJJV5hCNQggiQQvHHysk4v28Flm/aU6CHM1I0SBEia0qNFkNAntBo+VpBYtBSthvHNoD23pC1Ja+V6Onzon9HfkohKqhtnld2EeBKTtm9Np8n2Xu1IiMj8TDoAerWqQmPem64du0apKSkMGjgIMQkvMaGLTveq1H85OY/dmHYsP8rHMXnULQatEY8JjYWyamZWLt2naTS1WueOHzkqCTNI2UPgZiYWCirqpU9wcqZRCZmlrh3//9OBywtzBEe/navTkl2xdTcCnEJCZCVUxCUmPd5D+jfH5PGDkNo2EumsDyFjo4OXF1roHXLZujQtiXq1qqGmi4OUJHPhcybVMjkpsFARxVaqrJo1bQBKmkqCU4NGtZ3Q8P6dXCNKTr0LqD32+Hpz0cgISEeiQkx0NbSFJhcuHAB/+zaiS1bNiMsLEzI4weOwLdCgBQY2u/SqPM4jB/SFQ3quHwgCilCi2cMhr21KfZuno1T/yyR7K35gPhdho6WOqo6WYEUG6pPjgbeFQkn2qsjEolAe3eEjPcOVO+9LJ4sxwhwJaccX7yiiK6npwcDQwOs3fQn4uIT2IAoXFJt3ty5eBryEmnp6ZK84kZCQsOQ9DoVderUKW7VYtNfuXIFSoqKWLx4sTCoIgbk4SklNR1qahUoyUMZRCAjIwMvIqJQqZL+N5Xue2icloypaeiALGPUH9p7V0FFWbCUULqkAu0B6t57ADLS00DX7+XLlzhz5gwePnyIqVOnwqNuLdCzp6NnAHXtivmalZaWZs+jmmBVdXJ0QGVnJ7hUqQwLCwvY2NiAeFOcXI0rKyuDzs0b14fPrWtISUnJx4snPg8Bsr7du3MD5sb6EhfQx44dQ5MGblBSKFhx/byWeC2OQPERoH01ZJmhpWhkfSErzMe4kNLSvEFNDBy/JN+ytj2HLwhpWqa2cM3fkuqUvuh5F2KLTJP61UHe1cQWINoPREoQ8ZVUehchGqIluZTeOTJ4V8RP5RQBqXIqNxe7GAgsWbIUkJaHqbERnjx6gG3btgm1yaKzfMUKTJi2AGQlETKLeQgIeAgjY+Ni1vo88pYtW2LcuHH5KtOAqnHD+mjgUS9fPk+UDQTovrpw+Tpc3RqUDYG+AymsbR0QEPRIssTLvY4rctITEPUqvMR7Z+fghFmzfsWUyZNw/txp/PvP37A0M0Lv7p0QHRMPcwtrVHeth/2HjgnvQvpcC4G2tjZqu1bH4yBfBNy7I+wbLPHO/EAMnz15BAtzc1R1qSL0mvbEhYWFMou+q7DXgJRjoeDtgR85Al8FAfJW5uDRF6R4BFzaXqD1piBByGEB7bMROx8gHslscpOUIzNjPTx+9lJwSkD5REO0YssQncm7mkenMQJNZFQcZozpna8Z2sdDdYmGaKlOPgKeKLcIcCWn3F664gk+cuRIJKdlwtbaAv737+erXL1GDWzd8W++vKImbt3xBb1Poaj0n0NHG5+/CQGqAAAQAElEQVRDQ0MRFxcHsXepvHzI01veNI+XHQRCQp5DS6fwTZ9lR9ryI0n2mzf5hK3lWhORYU8RHxebL/9LE6qqahgzcQbIDfjiudPw69QxGPBzV4FtWHgEjEwtQBMNNet4ICVLGgHBIfC6c1coL+7B0tICdd1qITMrA+Jnmp794vIpaXqyLj17HFzSbEuN35PHD6GmJI2a1V0kbYSFhcHIyFBw+qFeQe2j+6wkFXiEI1AKCJCyElAM5SavCKR4UF1xIF5UThYX2tsjzqcz0VKZOBAt5VMgWqpDZWTNoSVwlC8OREtlFAZ0b4m89KRU7ft99ieXzFFdHsoGAlzJKRvX4atI0aBBAxw8egrjxo/P116/fv1wx9efzczG5ssvSiImNh6urq5FIS02DS3JOXX6LPb8dwg3vPxwy/s+O/vi6PGTuHfPr9j8eIWvj0Dgw0cwNDb5+g1/5y1qaenhvr9/vl66u9XG82eP8uWVRMLW3hE9+w/FhOkLMX7afKxctwW7/zsMv/uBsGeWHmqDFB0jdp2dq1RH5hsZ3LrtBfJ4SGXFCSoqKmhIewcvn8WDQH9cv3QG4eHf9v1XCfFx8PG+Lkyy0NK94vSntGnPnT6OhPh4kFOH+74+eBDgh9SkGNSsUT1f0/ResdTUNGFp8suISJT2xFS+xnmiTCMgsqgKkTkLRTyX6c5w4TgC7yFQLpQcet/Le3KX+WRZFJCUEVq6ZmRk9IF4PXr0wJ4DRz/ILywj+PFTYYaX9v0URvc5ZcGPHsPb9wEgqwRHB1uksB/u+IR4GJpYoEqNusjMlcep0+dw547P57Dndb4CAn5+96FvxD00lQbUZpZWyBEp4NYtLwl7UhDk5aSRlJQkySupiDF77noPGokW7bpB18gGkbFp6N1vcIHszcwtkcEUHe/PfDYVFRWgr6uFsGcPYeNYGfr6hkI7pDTFlPALUAXGHzmkpqTgzu0bSEl+DQuG95MgH9y+fhH07pmPVPnq2enpGfC5dQUGOmqo6mwNKzN9NG3cqEA5hgwZihHjZgp7qfhytQIh+iEzpdpNgFR7Fop4/iFB4p0utwhIlXXJ//77b/z7725BzEuXLglnfvh8BD6mkKSnp0NKVLzbQU1VBalpaZ8tTFxcHG7cuCHUpzXjQoQdSJar165Ds4IiKijJgAYbnTp2gJOdJRKiw+DLZlVzGZ2KRkVIK2ri3PlLoI3RLIv/lxEE4tnscsiLV8KejfdE4skSQsDQ2AzJmSI8efJUwlEKIvYci1AaHx2dirB3dEaDxs3RtVd/OLvU+GgzCgpKkJOV/Wj5pwoq6ulBR88IRqyPYtqXL8Jw4vAe5P2uEJeV5Dk66hXOnT6G4EAf1KtTDerK0jAxMkS7tm3gZG+DmOjokmyuUF4vwkLx+nUSUt+9q4iIyZr06EEAThzZD2MDXfTq2Q0WFuaCMxaawBKJCr7+VapUwbz589GxYydiwwNHgCPAEfjuEZAq6z3s2LEjduz4C9evX8f6deuwcuVK4SV0ZV3u8iZfmzZt8CI8EvRSz6LKrldRFy9fvERxFIwXL15g7ty56MksR/PmzsG5M6fQv39fbNiwQdIsvaejfdtWgjMBY2Z1ojXxampqcHJyhHud2mhY3w3yUplIiglHyutEmFg54sHTF/C+cxcxMTESPhR59eqV4B2K4jx8PQQSEhKgpq7x9Rr8QVsyNDTC87D/L+fS1lJHYmLCN0cjJvIFrK2tPlsOK0sLWJkbws/3joSHtIw0dHV0EfUqUpJX0pG42GhEvXyKDm2ao2XzpiCLBykHDva2QlMGBgYIf/FMiJf84f8cs7OzcfPaRcgiHTERz+F5+TT7bo6C1/UruMHi1uYG6NOrK5o3a/L/SkWI0buV2rZtWwRKTsIR4AhwBMo/AlJlvQtKSkqCiHXq1MHoMWMEBWfu3NnYu3cvaMZfKOSHL0aANqY+fRrCZgO1i8XLxtqSDaoSi1Tn6dOnWL5sKRq418Bvc6Zg5aJZmDl5FMaPHAx6YWBeJuRWltIGBvpo2aI5RKL/z06S21lyS1u3bh0kxEUjMuIl7OydIaVQAReu3AS9DyIrK0tQbg4cOIzw8AhixcNXRODJkyfwvHLhK7b4YzalypT/tPQskBc7QqBSpUqIKQUva8S7qOFBoB9cKttDUVGxqFUKpCNFp4aLA+7cvoZHwUGIDn+OVi2bIyK89N7xEhkRAXs7G6iqqhYoU4UKFWBpZozYUlw2R0rqqWP74V67uuB62612TdRxrYF7vt5wrVEZPbp3BV1ncsVdoJA8kyPwvSHA+8MR+EwEpD6z3jep5u7uLsz4V6lSFb6+vqAZ/28iyHfWKCmLS5cuxahhA4vds6pVHHHgwIEi1Tt06BBqVHUCvZjU2MhAqBMS+gJ//XsQCxYsFNLFOdBApEmjepAXZeD2zWtMQdNFzdp1Iaeig73/HUZ8QgJ++WUAzMxMi8OW05YAAo0bN0bXnzog0P9eCXDjLApDQFpGVuIti1wx0zN21/tWYVVKrczP5zbCQ5/gY8tii9uwlqYG3GtVh6m+Fpo3bQRdXV3kZKUL+0qKy6so9NnZmfkmVAqqU6WyM5489Me+3Ts/y7lCQTzz5kVHR6GeW22hr+J8KysryEuLcOHiFcm1FpfxM0eAI8AR4AgUjEC5UnLEXahatSpSU15j1qyZ4qyPnXl+ERBQUFAAWcwM9XWLQJ2fpGPbFkhMiEdwcHD+ggJS9II6Y6O3m4jFxf/sPYQuXbqKk8U+i0Qi3PPzB95k4c7NK/D19kRsVDg0NdSgyPp16cpVHDl2SqA5ffY8jp44hUePHpf6uv5id+Q7rKCuro6cnOzvsGdlq0tyCoqC1VIslba2Fqo4WuGW59fdw0jPN3Kz0b1b108qCmJZi3LW0NAAeQcj2pSUFMjKyAluqyldUiEnJwc+XjehraEKfX19fOrTtnVztGvdFFcunkFmZuanyItcTntvkuMiYWtrk6+O4GCFfdfJK6ng8hXPfGU8wRHgCHAEOAIFI1AulZzdu3ejQd3aSE9Lw7Nnpb8+umDovq/cn3/+GcvW/I64+IRid0xfTweBgYGF1jtx4gS7Xilwr/3/zcq07vzx0xC4uLgUWrewQlqy0b1bF7Ru0RTt27ZE+zYt0bJZQ7R4t55eTVUNaekZyHgjCyv7qqhS3R0vo5KwY+c/uOZ5vTDW33lZ6XcvPDycWdf4O3JKG2llNvBNSEzK14yBgQFq13SBv59PvvzSTJD3M5GUVIkrIHllVlFRYdaTnHxKXd7yz4mTguN94zJcqzmietUqRWIhxfpJypCyvDQePwoqUp2iEEWGv4SlhVk+0tjYWMQnZ8Ddoxmq1XQDRGAYkOsV8A9HgCPAEeAIFIJAuVRyunXvjute9zDr19kwM8v/g1BIX3lRIQg4OzujT9/+mL1wVbFnJrv91BanT5/G1q1b87Vw7do1bN68GatXrYLXrRuYN2O8pJwGFpu27YRIJAUaMEgKihkhT0O09l9GRkZSkxQfckDwP/bOAiCOpGnD7+Lu7u7uBAJxt8u5u7vff+76nbu7X9w9IVhwDe7u7vZPTw4Ogi2wwC5U7mZ2pqW6+plldqqru4atU8jNzYY09y03MTXjpzey93nYOThCW88IufklKCkt4+sxfdi0PfbJJ9BuxgTMzMxQXX2B74yFkYBxCWhoaSMnL3/Ug6+BgQEEfd1D63XGFSCCDPZ3cy7yDEyMZt+oZVPYGhrqRaD1BRFpyTFYtSKEM8h1LyQIuZeTk4M953EJO3mc81j2CVlr4mK9nEdJWvq/exkrXV1dDQVFFQgEAlRVVkBfT5c/ZnnT3di9kXnFpluf6hEBIkAEJIEA9/gnCWqO1HHF8uV46623wEb1RubQ2UwIeHt748WXXsJdDz2NqJh41NTWCyXO0EAf//fwXehsbcQTTzyBp5/+P1x+2WU4euQQZAW9sLM0xgv/99AIWf/3wps4cPgE7OzsRqRP5aSxsREHj5zArr0HERYeOfSQx368j50Mw29/7kBVdR2yc/NQVJjPP4iEnzmBVG50e6C3C5qaGkhNP88bdadOh2PvgaP4e+eeKUWLm4q+i62shoYGOlpHehgWG4O56C9bm+bk5oddu/cN/Q0MtmtjbYGqilGG5mC2yD4TYiOxYc1yDEYhE5ngMQTp6+mgpblxjJypJ6WnpcCO85wwhlOvDdhz969bb74BudmZ06k+qo6dvQOy8gpHrLuxtrZGZWkB0lOT+XcHeXq4j6o3lQQ2AHTqbBTCImJw/ORpCuAzFXhUlggQAYkiIJFGjkQRljBltbS08OKLL+HoqSi8+f5nSEnPFqoHZmYmuO+um7F+VTAu2bgKX3/6Dl5+5hHccsNVWLliKT8V7qEnXwTb3vn4G6zadCleeeUVPPnkk0LJH6sQi3QkzblpVNU1kJmVjZiYWL4Yi762ZeNaLAsJwsb1a3DNFdvR1VKHc5GnsTI0EJ7Otti6eT3WrgrFutUrwbxA7e1tUFJUgIaGOtgIOOifSAgIBFIikUNCJibAHtK9A5dh34HDIwrynu6+DojS8zG8gdrqKpw5cQg6mmrcoIHm8KxZO2aRxeqqK2Ysv7KiHArSfbC2spyRLBMTYzQ11M5IxvDKHt6BOHjs1NC7cZjH6LLtW+DmaInLtm8dXnRax8zrpqikAnfvAPQOyCCXGwSalqAFWYk6RQSIwEIiQE8gC+lqiqgvZmZmeOGFF/Duu+/j0PEwvPb2x6itE86rs5wzLHy9PaChrjakzSdffo+S8hpsu+ImfvNfugZS3H8mJiODEAxVEPJAIBBgy8Z1CPB2w803XAt/f7+hmszQsbO1AYtKZGFhgZCQYGzbtA7a2tpDUYsUFBQgJcVpwm1bNm/Apg1rOMNnBaSlpYfkLPYD9kBUM82XH7JF1LJy8osd4Zz1n03T1DUwQU5Ozog2lwYFoqo0n/N+NI9In8kJm9rZ2tqKzIwU7m9wLYKWBMxE3JTqysvLQ09HC/X1dVOqN7xwdVUZOpprsTR4yfDkaR2z+4WOljrq6v4zdFpbW8DWKE1LIFfJzd0XkZHR3NF//+vr6/93MoMjdt/r/TdYgr2jC/KLSmYgjaoSASKwKAhIaCfJyJHQCzdXajNPi49/IO584Cn8/tceREbHTblpfV09BIcsh52DE7/Z2DpwDwB9IjMmmEeHPeBNpBgzZpjHZqIyLE8gELAP2v4lkJ9fgDxu+/d0Sh/FxSVQ19KbUh0qPDMCWjp6YGHZL5bi6uyA4sLci5OnfN7U1Ij01CRkpMajIDsV/j4eYAMKUxY0wwp6utporK+flpTy0lLUV5dxhpn/tOqPVam1jXmClfisjvZ2JMREIuzUMX4qLJ84xZ2yigr0TawRfpGhM0UxYxZnaxg7Ozv4PGYwqmnoorCwkD+nHREgAkRgIREgI2chXc1Z6svatWuxY8cO9EAGh09G4N1PvkZO7siodnGJ5bit8QAAEABJREFUKfjp178RFjHy/Rx1dfWwNDfhHopicfLYIV7D+ro6KMjJgL3Tg0/4dzeTkc9/RczaBwsT+/eOXSgpWVyjntbWVggY5iGbCuDyyiro6OpOpQqVnSEBVRZNsKN71MO1LrsOfd0j1npMtamG+lrkZiTC2c4cG9etwppVy2HOeX2nKkcU5TU1Nbm+tE5ZVHVlBcrLCuHs6DjluhNVKC+vRFpKIqqqKhETdRounFHp4WKP2OizE1WbME+bGxxS0zIEC30vyntjUnIyWGTSwcZrqyvApikPntMnESACRGChECAjZ6FcyTnoBwsz/fLLL8PZxQOff/srHv2/V7Bn/xHc/dD/YcfeI5BRVENkbAp+/n0Xrw2b6vToM69h39EwDEjJIzAoFD09PYiKOA02ne3gkWMIj4jiRivPYffeg/hn5x7sP3gEpdxIKy9AjHbsIYPpbGpqKkZazb4qzAM23Vba2jqgpKQ83eqLoN7sdFGJM3Sqq6tHCVdWVgKbRjUqQ4gE9v1PTojBhnVrwUInsyoCwfx5PZnRJjXQg5aWZqaKUFvsuXDk56RhRUgg9PVF62FkRo2cVB/27/wdocGBnBFlDwsLc8jIzGzqqw5n6JhZO+Pg4WNC9VGYQq2tbVDV0OaLZmakwtvDGWpqavw57YgAESACC4kAGTkL6WrOUV/WrVuH995/Hy+89DLKa5pwy62344033sQ111yDp556Cs3tXXjvk2/wwWff4oprb8H9jz6L2+95BIpKSmDTytZt3Ia2HgHcvJZAXdcEGjpG8F2yDEHL1sLdJwhZ+aUoKimFOP1j0zra2tsnfR+QOOksSl3Yix7T0tOFFllZWQkFZVXuIU9G6DpUUDQErGwcsGPXXmTn5I4QyAwVWRnZEWmTnbB3WZWXlSIlKQ6eHm4im2I6WbvC5Htx+hTkZQtTFNkZ6XCwNsOmDeugoqIiVJ2pFPLz9cH69WvxxOOP8kagQCAAmxYmIy014+hlbDqusbktYuNE884jB3s7yMnJ8t1raW4CC+TAn4jTjnQhAkSACIiAABk5IoC4WEWw0b+7774bvr6+IxDce++9aOUMnfyC4jEfctlCXTMzC/6BSVtbB1rcJhAIeBksz9HZHcmpGWAPynyimOyClwSiu7tHTLSZWzWYkVdRUcmNnLdM2jDz4MUmJMPByW3SslRA9ATYwvLLrr4JaZl5OHnqDB86nbViamKE6soydjjpxt4/lZqcgNjI00B3C/y9XOBobztpvbkswKasqSjKoK62ZtJm+7kSzLhhkcq4w1n7n/2dDBduxjEfHpBgeN5UjvX0DVDb0Iy6uukHWxhsj3ni2prqEH7mJFTVNFBcXDyYRZ9EgAiIOQFSb2oEyMiZGi8qLSSBxx9/HB6ePpCRlQUbQRay2lAxGSkpsZtCoaGhgc6urqGHxiFlF8GBFHc9Vq9aCVVV1Ul7ywxVRXk5tLe3TVqWCswOAYFAgIAlIdAxssS+A0f4RthDflfXhQXnfMIYu+bmZj5qWWLsWXg42+DSSzbDx8cL4rpmw8vDHUX5k3tz2LTJhobGMXo8u0kmxkZoqqsSSSO6BiYoLBKNQbJu7Sq0NNZARkYeySnpOBcbN+0AIyLpHAkhAkSACMwCATJyZgGqeIqcW63YVI3bb78VupoqiOFGgxNiI1BeXgY2/WUyTWpqqqCpqQYlJaXJis5pvoyMDNgifPYQP6cNS2BjQUsCkJ2RKoGaLyyVdXR04eoVgIOHjvKGSn9fN1j45+G97O/v5w330pJClBVlo4Lbtm3eCH0RhSwe3paoj9k9QkNNBQ2TeDhUOOO8oUn49TvC6tn9byjm8cqzkPXZWRloamwcKsICr7C1iUMJQh4YGRmjtHTm7wdizTFvn7GJMXdPLuG+D13Yv/8Q9nLb73/+jW+/+2FUGHJWZz62yfjOh07UJhEgApJDgIwcyblWEqmpq7MjLtmyAStDgyDT34akuHBkZ51H3LmIoWhrF3eMhYY10NO5OJnOJYhATU0NNDS0JEPjBa4lG3DQMTJHQmISFOUV0NXVOdTjnMx0JESHcX+XEVCWHcDKZUuxetUK/v1RQ4XE/MBAXwd1dRNPWdPS0kYjZ+QIM8gyle7Gch6QydaqreCY5mRdWM9WVVmJmooCRJ49OS0Pt5S0NHf9uqai4rhlW5tb0ch5mTw9XKGhrgpHO2v09glgZGQIc3PzcevNZcbevfu5azvzKXpzqTO1RQSIgPgQICNHfK7FgtaEPWh5uLtjy8b1sDLRhbW5Ieet0Rzzh763twfMayIqIKJ+sBGVXgtdTk8fWwmx0HspGf0zMTEDZJS5B8ZaZKQmo6gwH+kp8bAw0cOWzeuxecNaODs7CdWZvr4+pKSkIjMzU6jys13IwMAArS1NkzbT3z8waZmpFigrK0fDJO/rcXBwgLamKs+evZ9GQ00V0oIB5OZkTdlg0dTRF8laRTaFeBlnfJlbWCOvsBi33nIT1q1bCzdnOySnpiM/P5/zGpViqjxEXf7SSy8B84aJWi7JIwJEYHEQkFoc3aReihMBMzMz7oHKGfq6GqgoLxulWn9Pj0jX4zz5xBO49557+AeKI0curE8Y1SgliJQAW//R1twgUpkkbHwCUoLx8wZzDIxMYGBiBQNdNWipyMDb3YkbseeMn8EC43yyyHrDs/r7+5GYnIa4+KThyfN2zL5r6ioK/Fqi8ZRob2+HooK8SAdPWFsmpiYwE8LroaGmgo6OdlYFLPjBxvVrYKqvjoKcFM7YyeHThdnV19WCRVsTpuxEZSoqKiCQkoKNpTEG+vuG7rf5nMFjxvXpXGwC4pMyUFRcMpGYWc8TCIT4Ys+6FtQAERhFgBIkhAAZORJyoRaimj5enigvyYMAA5CXvfBVZOsF+vq6RPJDHh8fj/vuvReNjQ2o5R4ODh06hO+++xZPPPG42Mw5X4jXlfVJS0sLbi722PnXz6ifZCoRK0/bzAjIy0ljsudBtr4hJTEWjo6OsLe359foTNZqeXk5jhw9ieMnTg0VZWHgL9u+Bddde9VQ2nwfeHm6o3iCAARlpSWwtpzcoJtqP4o5I0BTQ2PSauzvoa25GV2dHVBWVuI2ZVhZWWFFaAjQ3cR51gomlcEKaGnpoKFh5oMHTB8WvdLWxoYPqx0ZGYUMzjPHptb1coNMtjZWkJKWQmraedYsbUSACBABiSQgJZFaS7LSpPsIAmtWLkP8uXCoKctBRlqAOu6B2NBAf0SZqZz89NNPeOSRR3DPPXfjx++/Q19fD+rqG3D77Xfg5MmT0NDQgLycDD779BMkJiaOK/rAgQPj5lGGcATMOY/dzTdci/zcLO469AlXiUpNi0BHVx8GJpmNVVZaDENjC2SN8f4c1mhdXR2ys7MRn5CE8Mho/LNzL9Iy8uHg6o0+znvDygxuysrKg4di8amoqAhnB2vkZI39UC4tJTUr38F1a1cL9d6dfo7fgGAAzY11o95LExKyFFXlhSOCE4wFlQ3WNNRVwNLScqxsodNY0IO4uHg4O12YnigjI4MBgRTyiiqxd99BXHvNVZyHvQLxsdGoqKwCi7gntHAqSASIABEQIwJk5IjRxViMqigoKMDE2AAJqdno7RuArq4+KquqpoXipZdeQl9nK9atWooH7roJVpam0NbUhLGRIfbt2YVrr9iCLz98A688+xjefuX/8MH77+HMmTP48ccf8f77749o87fffgV7senjjz+G66+/HsePH8ffO3ahqWnyuf8jBC3yE/Yw7GxvhRNH9qOjo2NCGpQ5uwQsLK3hF7gEXb1SyMzMAlurlpubixOnTiEpORWJqZmobe4B5NSgqWuKwJBVcPHw5t9nhYHZ1U0U0k1MTFBbXT6mKFU1dTQ2NY+ZN91Etq7lfEam0MZTX28/523rh7y8/KgmdbQ0cPzwXuTmjg6H3dvby3l68lFdmsd7XUZVnmICM7jCwyM4o/i/i9rHtdHM3du6+/qwa/du/pprc/diJweboalsU2yGihMBIkAE5p2A1LxrQAosegJstLCyohzsx1dOTg4DUnJTnpLx+++/896gm2+4EmtWhMDBzgaJyel45skHOMOkGU6O9vD38RxizabcXH/15di14x9ID3RBSU4K99x9N77++mt8/PFHUOFGqleFBsDYUJfXq6ioGPUNTWDT6YaE0IFQBKytreHsaIeamkqhylOh2SEgEAh4wbb2Tqiqb8X+Q8dQXtsCSzsP9ErJw8nVCxaWVrynQVNLi3sgv1BeRlaWryfuOxba3cTIAFWVFaNULS8pHJU20wSBQAAjQwPeIJhMVmlZOeJiY2DCDbiMVTY4aAnuvP1mSPW2ISsjDczbwsrV1FQh7MQBaCrLYFno0qFrwvKmu0WfOwdtXV20trYOiTA3M4WroxUEAwKkpKRBRlYa2hpqaGluAzPmhgrSAREgAkRAgghISZCupOoCJeDn64Omxjrs3/0X30MnFw+cOH2WP754V1BQgCZuxPHi9MLCQliY/zfnvqSsDKoqyvjtz124ZPt25OQXorpmZCjSVcuD8MHbL+K6qy7Fnbdeh+efegAKsgOwtzLBF5zHZ9XypbjzluugpCiPxMQEaKopCbWO4WLd6Bzw9fGCPLqQkZ5MOMSAADN0/JYsg42tA9hULyMj03G1Kiku5Lyi//1tjVtQDDLYw3p9bdUITXKyMmBqrMMZ2TU4n5ExIm+mJxYWFpCSmvxnlHmTLczNeYNoPKOBDfAwY8dIVx0F+bm8ajWV5VgWEgwHB3v+fKa7Uu6+GB0di1tvvhGqqqpD4iwtLfhBHBk5BbS1tSIruwB6etpwdnFEGVdnqCAdEAGxIkDKEIGJCUx+d564PuUSgRkT0NLUwLqVIbj2qstx+th+/qHB2d0PBw4dHfKc5OTk4MOPPkFCWg4iYpIREXVuqN3TZ86go60Zl2xeN5T20usfwJ37gXZ2ssfZs2Ho6uzmHuYUhvLHOjDQ18P1nMGzZmXo0IipooICvv7kHbzxwuPIzc7Avn37xqpKaUIQ8PT0hLSgD2xajBDFqYiYEOjp7uQXyouJOhOqwRbUd3AP6cMLVVeVw0Bfnw9ZH5+UjubmluHZc3Lc0NAIEzML5OYVTrqYn60hVFfXQHREGBxsLcGm4YlCye7ubvzxx9/YunUzf4+9WKarixPnQeqCi7MzLr1kE1YsX4662jrs3ncEjWMMLF1cn86JABEgAuJGQErcFJorfagd8SEgEAi4UUM9bmRRBctDg5GUEMs/VOnom+DEybM4dOQ4apo6oW9kBlVFacjKSENeRRtffPUNmGfn919/wUvPPIrh/5594gGcjoiGtpYmtqxfiZefeRjMszO8zFSOVVVVUFpWjry8PKSlpU2lKpUdRsCIMyRraqqHpdChOBPo7OxEe0sjjIyMxFnNId2YN8TEWB9lpSV8WnTkGSzx84SOjg7qGhqxbOU6nDwdxufN5a6+oQGa2tpwcPVCDtaqlxwAABAASURBVGfoMINjvPalpaWQnBQHJztzmBgbjldsSulsPdyvv/0JLy9PONjbjVlXS0sLV166BX5+vjA2NkZCQiIysvOwcs0GREbF8NN2x6xIiUSACBABMSUgJaZ6kVqLlICBgQFqK0tw6tgBzqDIhJm1Hfdg4IPe7i64ONpizaqVcHGwRn5OFs6fT8eDDz6AR++/Y8jzMojNwtyUM27W4o+/92JFaBA3iqsxmDXtz/vvugXWZgZ4//33Jh2NnXYj819xVjXo6+uFgpxkrPGYVRASIrystBiODrYSou0FNT3c3dDV1oDcnEzo62jxD+xsvV9nZxc/XUxbzxjZ2TkXCs/RnnlmkuPPQUNDE2aWNsiaoH13N1ds37wODg4OItOO9d/F2ZHzzoSOK5OVSUxKRn5xFb7/6VdUN7YicOlKqKiqQkpahg9UMW5lyiACRIAIiCEBMnLE8KIsZpUEAgGuv+4aXHHpVlyyaT0fXvocNxrb1tYGHW1NHk17exsMDA2hr6ePF59+DHa2Vnz6xTsWEtfYSP/i5Gmfm5kaIT4lA3fe/wQqapqQnEIenanCZO9oaW2qBQs0MdW6VH5uCXR1daG1qQY21tZz27AIWgte4g9ddQUEBfrx0lhfVFTU+WNrW3ukZ+YKHRWNr8Tvpr9jBsY2znA5F34ahkYmyC8sHleYgoKCyCOasSiHvr4+47bJMg4fPY7ElEx09XRBS1sfHp5+YJ4xaWlpyMjKo7GxkRWjjQgQASIgMQTIyJGYS7V4FGU/qoqKivz0CCNDfSjKy6Czs537oZXlIbBoXYf27kCwnyu8PFz4tLF2gQG+OHriDI6fCsOufYdw9OTMpqkUl5RxOoEPc+3i5oH65g6UlFyYFjNW+5Q2NoHQkCBUlOSAeQnGLkGp4kCgoqwYNpbm4qDKtHRgBvVgRXY/aW1pGooUpmdgwk91Hcyf6ScL88zCSY8nh72LhhkaXp4uKMjLgYKiCqqrq+fMO9LX1zfU97F0ZNPZ8vPy4eXmgNBAHzjbWyA9NYUvyoKFyCkqIb+giD+nHREYRYASiICYEiAjR0wvDKkFfl3OmtWrsHH9Wj4stI629hCWG264Drv3Hxk6H+vA090ZD99/O/78Zy8OHz3FR1r76rtfh4qyB5Ohk0kOurt78feug1BUUoY8N9LKisvIyiL6XNyEDw+sHG0jCUhJSXHXdB0UpHqQkhg3MlOEZ+z6tre3o7m5Ce2cJ1CEoheFqJrKMtjaStZUtYkujIaGGtj3gZXp5rwVAoGAHYpkY9/prKysSe8F5mamaG2sgZ2jK+IS03Dw8PFZN3TY+p/9h45i95694/aVGWEyMlL8mh1dXV20tXdAR1cPjQ0NaKqrQgznTXd0GHstz7hCKYMIEAEiMM8E5tvImefuU/OSQID9ALNR0OG6slHawsJiNDQ2DU8edezv44WvP/0fvvz4bfzw5fuIS0rB2+9/ji+//QVPv/AmPv/mp1F1xkpgc+h7BqRx2z2PDGVbWNogaPk67OG8RFVVVWDvnWCjs+y4gXs4GCpIB2MScHV1gbuzDRJjI8bMn25iWWkJYqLOIuFcGLLSE1Gan4G8rGTk52ROV+Siq1dRUQ4TI4MF1W8jA30U5uWiID8P3Z2tsLS0FFn/mJFzybato9YGjtVAcFAAMtMS4eETCA/fID6wCvO0jFVWFGmJickws7SHqaUj9uzdN0okmwrMBgTs7Wwhyw3csAKGHKvSogJ+QEdFTQu2Vpb8eibQPyJABIiABBEgI0eCLhapOpJAe0cH1FRVRiZOcvbVR29BRVmRn4/v4+2GtPNZk9S4kB3PGUcenhfm919IwdCPvpGZNWISUnE6PAYp53O5LQ/h0fH4/Y+/UVQ03Skeg60s7E9DQ0MsXeKH2MjTM+poT08PzqcmIjLsONQUBVi7cim2bdmIjetWYdXK5Vi7eiXUlWVQVJA7o3YWS+WG2moYiyiyl7gwc3Z2grG+OjSVpTnPcCiYYTJbuiWnpIAt5B9LvqqqKuRkBGhpboaMjAycPfyxY8/+ccuPJUPYNDbgUl3XCH0DQ2hqaUNGXgUnToWhsrJySER+fj4yMjKxfPnyISPH2NgYIUHeSI4NRy33XQgNDYGKytTutUMN0AERIAJEYJ4IkJEzT+Cp2ZkRYFMwDLjRRrZ+Z6qS7rnjJtxzx404dSYKd912PYT5d9N1VyL+3Bk8/ejdqK+rHVHFxNQM3n5B8PINhJOrJ5zdPPnzwNA1KCitwf6DR0aUp5ORBNTU1LB+7UqwKTEXTytjD4oF3Mh7VkYakhNiEBcdjuTEOORzI/INDfX8VLT0lASkJ0bB1dEaWzauBRuRZmswRrYCuLq6oq2pfmjK0sX5dH6BQFNTI2SkemFgIKaenAtqTmvv5OQItgkEgmnVF6YSCzFfXtOCvdzf/anTYTh8+NgoA0ad+86zdTBMnoKCAoJCVmPX3gPsVGQbG2CJjomHs7sPL1NRSQmePgGQU9bkdDuKwUAC7O+ChZbmCw3bqaurY/slW3Dptk3Q0NAYlkOHRIAIEAHJIEBGjmRcJ9LyIgJycnL8KGh7e+dFOcKf1jU0QOqih52+vv4xBVRV1UBBXhayMlIIP31izDIXJzId7Ryc+ZHav3fuHfWgc3H5xXzOWK1bvQzZGUn8wmxmxOblZvOeGVX5flia6MLHwwmrVwTD280eehoKaKgsRmlBJm/cbFi/Fvr6+nw0qIk46upqoaa6il87UccZq2xqW1pyIjIzz09UbVHlFeZlwdvTY1H1WZSdtbKyQn93BzfQsRQmVk6wdHDHzj0HMGjUsLYMDPTQ0PDfYIlAIIC+kTnYe7hY/ky2gYEBpKSmIfV8DlQ1dcGMqEF5tbU1aKotx7KlSyCs4SIvL89Xn80pdXwDtJsxARJABIjASAJk5IzkQWcSQuDYsWMw0NODkpLCtDVmLwdtbmkdUf/YyTO4474nEBuXhPLKaoSFn8NnX/+IZ155m3vwc8UXH76FvJx0dLS3j6g30Ql7SGCjqddffz2ysoSbHjeRvIWaxx7GNqxdBX0tJWSlxUNfU4kPJe7m5gYLCwvo6uqCeWj0uOtuZ2eL4OBArFqxDFPxOLi7uUJ6oAMRZ46iqaYM0v3tsDDRQU1ZAbIyMhYqWqH7VV5aAkM9bbApVUJXooIjCChxHhM/Xw+kJsXxwVMYSzsnd5w8dXqonALnvWHv/hpK4A4UFBTR0trGHY39PwuawLwzY+f+l8rWDxaV18KL8y47OLoMZbAw2nnZ6VBVUYGNtdVQurAHiUlJNP1WWFhUjggQAbEg8K+RIxa6kBJEQGgCq1atQh83Ynny9PQXra9avhTnM3NGtLlu9XLUcx6eH379G48/8zI++uJbsIkt33zyDjauXQlpaSnOWyCLpqaGEfUmOzm45x8s8ffCN998M1nRRZ9vY2ODtatXgBkyswHDz8ebM562gS0A9/L0AAtJzqbMtTfX8F6e2WhTEmS2tDSjsbYMHu6ukqCuWOuoq6ODFaGBOH38ANh6MR0dXWhoGyIy6hyvtw6XL41ePlgJn8Dt2lpboa6myh2N/X95RQUSk1L59YRjlwDYFLSSsip4+/iPKsLW/8jLSGMpNzggJTX1n34fb2+YmpqOkksJRIAIEAFxJTD1O5249oT0WlQE9u/fD2NDPaxYFjTtfnu5uyAiOg6dXV0jZFy+fTPvIfr120/w7uvP4+7bbxzKT8/I4h4kmmFgaDyUNtlBZWU5WhqrsWXjGrQ0N01WnPLnmAAbHW/v7ESAvw8qyxfve4+y0xOxYnnoHNNfuM0pyMtj84a1iDp7ku+kpY09lDX08ec/u/mpq0uDlyAzLQGD/5SUlbl7y/j3Bxtra2zbuokbaJEerDL0yYwbNtUtLDwSto5uQ+nDD/JysuHs5DCjgAvTMY6G60DHRIAIEIG5JEBGzlzSprZEQoAZOHt278Ylm9fOSB4LJX359k1gDyPDBWlpaKCmtg5lFVUwNzMZnoWCwmL4+I9vWJ2LPIvTJ47yddgILls4v/uvXxHo6wFTYyNU19SOiGzEF6TdvBJgayi2btrAT4Xr6emeV13mq/Go8FNYFhI0pQfg+dJVEtpl0cs++/xLPiJZaLA/sv9d86Wrp48lIauwZ99Bfk2hvq4WCvJz+S5paGqhtr6RP57Krri4BOFRcSivaYaLZyDnaZYbs7qdgyMSktN5z9KYBaaQyIwqFnp6ClWoKBEgAkRgzgmQkTPnyKnBmRD47bffEBMdgZeeeQQmRoYzEYX/e+w+/PLbP0i/KIz06pUh0NPRwcuvvzdKvr2dDUqL80eksweYvNws/PL9l8hOjUFTdSE+fe91fPDWC3jvzeehoaYANg2OVVqzMhQvvvQSO6RNjAjIyMigk/PmyMqO/YAoRqqKVJXGxgZEhJ3A5vWrwdaOiFT4IhbG1okFBvijubkZhoaGUJAdQFZGOk9EWloalrYu+PLr73hDp7WhistL5dfvqGnqc/ejydeGMblsjQ0TeD4rG2zNj7Wtw4ggAyzv4k1TRw+FhYUXJ0/pvKGxEW+/+z6ycy4YZ1OqvDgKUy+JABEQEwJk5IjJhSA1JicwMDAA9gMrJSWAkaH+5BUuKsG8KHc98BTWX3IdNl92I5579X9obWtDRHTsiJK//LETre3teI8zUkZkcCe21pZoaWpATXUlykqL8eM3nyE67DBiwo6gu60B9915I+6+7To8/sDNeP+NZ/Hik/fjkXtv5Wpe+P/KSzejrroauzlP1IUU2osLgfr6eiipqImLOrOuR1FBHiqLczgDZxU3+i876+0ttgY8PT3AwjAzj0eAnw90NBQ5r002j0FPXx9rNm7HqTMRyM3JgZWpPhLORUBWXh7JKWl8mfF2zEt0/HQkjh4/hQMHDkBOXoX3GI1Xfni6to4uZ3i1Dk/ij4dHfuMT/t2VlpbhfEbmv2fgvUA7du7lvH5LYWoi/JTdIQF0QASIwCImMPddJyNn7plTi9Mk8OVXXyEnK5MbQSxAYdHU104Ul5ajprYWb7/2LL76+G38/v2nMNDXQ15+EWrr6pGRmYNX3voQsQnJ+Oz916GspDSmpm+/8n/46+cv8ct3n8Lf3RpPPXwXHr3/Njz75ANQVr5QR0Ndna+rpqbKfw7uNDXU8eKzjyIycvoBEwZl0adoCaSmZ8DSyka0QsVUWmF+LtSUZPg1OAoK049QKKbdExu12HTVAwcP42x4JCzMzbgBjqoh3Rj3K665EbpG5vj4k08R4OeBzpYaYKAPKSmpQ+UuPkg7nwl3Lz/YOnrA2NIJGtq6FxcZ91ybK1vb3InEpBS+DAsLXVhUjL0HjiArO5uP/tja2oq6ujr++MTpCLBw7nxhbtfQ0IDe/gH+XnnqzFkuhf4nAkSACIgvATJyxPfakGbDCLz66qsQ9HXilece5bbH8OrbH2L/wWP8D3BF5X8PDsOqjDo0MtDnRzzfevdTaGt+glrpAAAQAElEQVRr8vnvvv4crrnqEjzw+PP48LNvYGdjiQ/ffonPm2j38jOP4kPO07Ni2dKJio3Ka2/vwGtvf4ybbrp5VB4lzB0B5hUc3ho7Zw+dUwkNPry+JB03NNRBBt38InRJ0lsSdWUL9Vkwh9y8AvT29kJBTgYsitpgX2RkZBC0dBl0DYxx9NhxuDo7YdPGDdDQ0BgsMuqzra2DXz+mxg2k6OkbwMDAcFSZ8RIy05Mgix5+bSAzcGLjErBjz35Y27sgLikTKRkF2LFzP6Jik5GWXQxNdTV4uP8XyEBLSwsNNdVoa+/ExvVrx2uG0okAESACYkGAjByxuAwLTwn2A5qaOv5o5FR7zEYQfbzcwN7xYG1lgW8/exfJ6ZnYdtWtePv9z/Dwky/g6IkzE4r9+MvvOc+NLl74v4chJ3dh7YWKigrcXZzw23ef4IuP3sKVl26ZUMZgprT09P507n/sOd6b9NRTT+HHH3/kp38MyqTPuSOwe89e5OTkDDUoEAjQ0dXFvyR0KFHMDuRkpKChIgdpKcFwzaZ0XFVZgcaqEvj5ek+pHhWePgEWLvrqKy+FpqYm/P28kZebMUKYQCDA9TfdAR1Dc86jcpi/NxkbG40oM/xET1cbdTWcx2d4ohDHDQ31EAz0ct67ELAQ7bWcVzs9MxsrQ5ZA0NOKAG9nuNibw8ebM2r6e6Gtroi1a5aPkMyMMhU1ds+0R/Iwb1N5eTnv/RlRmE6IABEgAvNMYHpPavOsNDUv/gQKCgrw5dffIi8vD42NjTNSmBlLMtyDnbcH9+M7TNIzj9+Pgzt/xrtvvIDC4lIcPx2BmPjkYSVGHro42MHMxAS2NlYjM+bo7MU33sfAQD8CuAcdYAB7du/Gww8/hJiYGHpAmKNrMNjMJdu2wtbWdvAUbBG3AFJQUR05vRBi8k9LVQ466vJQkpeGurLstLRqb29HXWUxgoICp1WfKk2fgMK/UwLZGh0zI13kZmeOEmZqZgnmKcnNzcPefft5z8+oQlyCnJws2jrauKOp/d/V2Qn5fwd3WE32nW9rboK7uzu8vTxgaWkJR0dH6OhoY/3alVgWEgz2ImNWdnBj64vY90hOXh4ssMJg+vGTp7F77wEww+lsRBTiExLBgiMM5rMpb6y9wfO5+aRWiAARWOwEpBY7AOr/7BAwNjaGm5snjh4/ibi4hBk18s8//+DSLevGlSElJYXLtm2GnY0FXn79XRQUFY9Z9tqrtqOkrIzT6fSY+bOd+PiDd+K7z9/DK889jr1//YCdv3/DjawO4Kcfv8dbb7yORx99BCkpKbOtBskfg8Ae7qHSyU08vRucfQ8Fuf/ejSIQTM+Tk5IQjdCQoDF6T0lzQYCtz2lsbIQTZ0h0dzajtaVlRLPMOyzFDX4UFRdjWWgIH3ltRIF/T5wcHRAddmJcI+jfYqM+WLAUBQX5ofTWtnYoqaqhhdODrcM5efo09h84gt//3oXDR44PlRt+wPpgamoOZjANDAwMZa1cHgoHezuciYzlPFKWkJLXwNmoeMTExvFl8rlBr/fe/wg10/BA8QJoRwSIgOgILCJJZOQsoos9l11VVFTE2tXLcPutN2PVqhUzarqhrg5enq4Tyvjpt79wxSVb8C1nRDz/yv9QVFKGsf7Z2VgjJT1zrKxZTxseyKCxsQk//PwncvPy8eZL/4e3X30ajz94B37/9Sc88cQTCA8P50Maz7pS1ABPwNrKin/Y40/EeMceLFs7eqasYVZmOjzdnSErOz0v0JQbpAqjCGRmZuLPf3ahra0Nzg52KC7KG1FGRUUVhua2aGrt5v/2mSekkTOKRhTiTthUWxVVZYSfPsp5hv8zNLiscf+PjT6Lgd4OuLtduI82NTWhpLQK6zdtx4lTZ3HwyAlo61ugsaUDA329aOI8POy7xjwzzLAZFKykpARtLXWsX78OAoGAT2blWOTKP//+B0aGpnxEOX0DA3j6BKChpRMVFRVgf1/+/r7IyMwC+1dUVIQybsCJHdNGBIgAEZgtAmTkzBbZhSl3Sr0yNzcfdzRyKoL6BvpR39A4bpVf/tgBGytLqKmpQF9PB/97/Xk8+9LbfPnu7l7+kz0wsAMWQctAT5cdzsvW09OLex5+Gjfd+TD+3LkPb7/+HFRUlHldmF6vPv8kvNwccerEUbz11lt8Ou1mn4CVpQXqaypmv6FptCDNXDn/1mvr7ENXT/+/Z8J9VFdXQ06qD5YWFsJVoFKzQsDJyQmG+vpgXhP27pyO1uZR3hgdHT1YWNmgqLiEMzxO4djp8FHTfaWlpXHt1Vdx9zu1IUNjIoUbGxqgoiiHdWtWgdVlZdnUMYGMDF/f0y8Y5lb2yDmfhFXLArF180ZsWLcGBQWF+PnXP/Hp51/gnXf+B2YYsXr19Y1goaWZHLZ98ukX4KwtPHT/PejuaOSMmjK+X2yKWmN9LdiaJGYIJSclcZ6dWERGRSEtKx/ZBaU4fSYMLHQ7kyPs1tPTg3PnzoGt+xS2DpUjAkRgcRIgI2dxXneJ6XVxcTH6entx+mw0jp88O0rvs1ExSE7LxKfvvzaUxwwdYyMDXH3TPXj4qRewdus1eOjJF/Hplz+grKISTo52Q2Xn+kBWVgb333kzXn/pKfzyzcdwsrcdoYK0tBSuumwLHnvgTlRWVuC+++7Dfffeix9++IF/OBpRmE5ERoAtCldTVuCZT0/o7NVi3wkmnYXulRpm8LC0ybaCgjw0VBUiKNB/sqKUPwcENm1cB33O0Ons7ISujhZnwDSMatXC0goNzR0oKshFZ0cnyssvGN/l5eWcLXHBc8M85b093bwxwQQM97aw8+GbsooK6jnPMTMOBtOZYaGieiGCW0tzMwwMjfh39BgY6MOKGzDS0NDgP6+4bBtuu+VmTmc9vi3mybn6qsthbm7Gi2LGC+uTi4szV94KoSHByM9Kw9EDO3Hi8F6cT0/j+tjIB1Ng6w9tbDhPVVsP3Dx8Ye/oBn1TW8TEp+B0WDhi4xI4A+lCX3nh4+yYsZWUkoqL3+3TwBlzKVz6ONUomQgQgUVIgIycRXjRJanLZmZmePa55/H9z3+AGTSDuu89eAzrL7kWf/y1B++8+sxg8tDnay88gUc5Q+GpR+7FgR0/8WthcvIK0NDQCC+PC1M2hgrP8YGjgy2cHOzAwlhLc6OyYzWfnZsPtgBZWVEBLzz1IOdlqMQrL7+E5557Dtdccw3uuvMOPPnkkxSwYCx400wL8PdFVVkBxG2EuLO7D5UNnajmtsbWbqF7V1VRATl0YfmyUKHrUMHZJSAlJcUPVnz08Sdwc3VGXtbYESjdvfwQsmo9SooLwd6Lw7xxJ8MiERERNWToqKqqIic7E5nnU7ly+bziLHoeMzzYxidwu6qKMtjb2gxNVWQeluzcQjBjqqmhHtXl+chIS4KRqTUuNhLY2kpmUF199dVobGzkQ/ZzInH8xEnsO3AY+w8dQ0tr21CUSLbmprKyGjZ2TrDmtsuuvhH7DxziDaSTp87AxtED7p6+Qx4ldXUNuHn5w9DMDp29MkjPzAdbx1lVVcWaGXNjnqE7b7+N84CrjMgXCAToHxjAjp07+YA3IzLphAgQgUVJgIycRXnZJavT7EfN2MgQl1+ykVf82ImzOHz8FH797tMRHhw+898dMx58vNxhamLET5nLzslDGTciqqyk9G8J8f5Q5Iybu2+/CW+98n/Q1dXGI/ffjpefeQRXbV+PN19+Cp+8+wp3vAEvv/wijh8/Lt6dkSDt1q9ZiZiIk/xDmTip3d9/YQR/Kjq1t7VAS1NjKlWo7BwQUOE8K9dddy0//WvlshCkpyaO2aqJiRmcnZyxfu0qPvy0Huf5CQjw46eYsQrLQoJgpKMCM0MtzlApxfnUBHS01iEpLgphp45g8H08hw7sRm9PF6sytDEvSE1VJcrLytDf2w0lFVXo6OqhqroW/f39Q+UGD06fOYu8kmqwd5P9s2Mnjh0/jY6ubkhJSSMpNQNsTQ/vndLVxeaNa6CgpMQbRNraOnDx8OMMjz3QN7aClpb2oMgRn6qcwWZtyw3+uHnB2t4daZyxs2vPft67M6LgBCcaGhrwcHfDurVrcfDwEZzgjKoJilMWEZh3AqTA7BOQmv0mqAUiMDMC9913Lzo6O2Fna80LevfjL/Da809O6QGunvPgKHCGg5KyEi9D3Hd2NlbYvmXdCDXl5eXg7GgPMxNj3nDzdHfBh2+9iIP793EPAxeiGI2oQCfTIsDeIcIeGKdVWQSVFIZFUpuuODY1qa6mAtbWF/5mpiuH6s0OASNDQxhym5qaKlQVZXD+fNqohmqqq2FsZABmAMjKymLNqhX83/1gQWnOC8xCPtvY2HB5y+DhYo8l/r5YEboEvp6uSE9LxrmI05whIgUWunqwHgtcsDw0CJnpiZBTkIeevj4a62p57wozXFiAhOGeIFZvOWeMtTfXQU9Xiw8dHRKyBFs2rEFosD/3uRo9XZ18sARWlr1LJzvrPHo6mxEdfhKcVQbvwGXQ5AweCPFPkTOQHF084B0QClllTezeexB5+QVC1LxQRFlZGTffeAOKi4uRmpZ+IZH2RIAILEoCZORI5GVfPEq/9NJLcLCxhJRAgF//2AW2psbQQB+aGupCQTgTHoUnnn0VH3z6DTc638cZSlZC1ZOkQu+9+TyOHj6A77//XpLUFltd2Ui7p6sDMriHxPlQUkleesbNFhXmw5b7u5mxIBIw6wQC/P2gIi9ARUX5iLZkZWXQxRkPIxLHOVFUVATzeLNsdmxpaQlTQ21sXLcSD9x71yhjV09PDzra2nwYaxXOKDAx0kVRYQFq6xvxzfc/Dk2JY/LYxgyXzRvXIzgoEGwK28b166CgoMBPGWMeFLZOh32ysiYmJlgW5I+N69diM2cIdbTUIicjBTlZGWhvE/79PqxNPT0D+C5ZhrKqBkSfi2XiJ93YeqPdew5yvxlS/BTniaa+TSqMChABIiDRBKQkWntSfsETYPPHQ5cG4uXnHkNOXj5eeu09brTSW6h+//zbDuzedwQsXOsfP36O37//FC/+3yNC1ZW0Qs88dh/qairBjMIvvvgC33zzjaR1QTT6ikgKWxyuIIspR34SRfPSUgI2+D1tUWwqUnN9NRwd7KctgyrOLYEAPx/0dzWhpOg/j0V//8DQOpqpasO8PN5envyC/+FenEE5zFPT3NoGBc47LC8vDzMzUxTnZ3HGSBrk5WTHXJfGZA7Wn+yTraVkZWRlZRESHAR/H3dYGGsjLzMJMZFhYBHfWL4wm0AggI2dI6Tl1RETM7mhw6Yla+nqQ50bCLOzMgcz6IRph8oQASKw8AiQkbPwrumC6tGqVatwMiwKluZmeP3Fp3Dnrdfh9puvnbSPUTHxOH4qDO+/9SJuvO6KScsvhAKPPXAHVi8LgBI3KtzSWItPPvlkIXRr3vrg4uyIksLcOW+/p28AcjLTvzXX1lTD3Mx4zvWmBmdGwNfbC/3dGM3uYAAAEABJREFULSjmDJ2SokJUVVVCifPQzEzqf7WHH5WUlEBOQRkVleWcQS2AmqoqujpbwcL129rZj7kuZ3h9YY/7+/t5r5CWlhbMzc2xds0qLA8JQG5WMr9mR1g5rBybalxaVo7q6mp2ym/sPWORUdHIyMjAzp27cO5cDMqrG9HQ2IAlAX5YtXIF3z9WOD8/n33QRgSIwCIiMP1f0kUEibo6fwQMDAyQlZ3LR/BhWnh7urGPSbdTZyKxdfPaScsttAJL/H1ww9WX4eH7bsfhw4dF9rCy0DgJ0x85OTm0Njfi0L6dwhQXWRk2yi47EyOnugI5Odno7e0VmU4kaG4IBAb4o7WxGpWlufwne4jPyspCbFw8HzJZVNeUeVqszAzg5+nKTz9j3/Xbbr0V11x5Ba66/FLIc94dUfT4m+++w/HjJ0aIUldXx+oVy5CaGDsifbITTS1t2Dp7IjUzD2fDI3gjqbamFoWl1SitboaFnRuKq+rR09UGfy+XER4cFhTh0NGTFIxgMsiLM596vYAJkJGzgC/uQuiao6Mj7B3s8f0vf02pO83NLejq6p5SnYVW2M/XixvZPLfQujVn/WFrG7q7O+HgLJxhLSrF+vsHuNHv6UuTkpbmRrtr+ehd05dCNeeLwCrOANi6ZTOsLMygrmOE+tZeyKvoYN/BY4iNT+K+GwO8aizU+fGTp9HS0jLCoGXpfIFJdmw6o42N9YhSHh7uYO/CGZE4zZPm5maUlpZDVU1tlAT2t9XZ0TpC71GFxkjQ1taFo7MH5JR18PfOPfD19UZIoBdUFKRQWpgFTydbrFoeAhsbmxG12fqhFaHBaG7rRFr6+XmZhjpCITohAkRgTgiQkTMTzFR3Tgg8+eT/QUlFA//7+Cuh2tux9xB6+/tx9eXbhCq/UAu5O9sjMjJioXZvTvq1NCgQ3V0dc9LWYCOtnb1QUZSBvOz0bs9uHj7Q0dMHizI1KJM+JYsAW1fFpmbVVJZD38AIOrp6CF62Gl09A/jz753Ys+8gmIGjoqGPiJgk7Ni1H/9wD/3ffv8zfvj51yFDaC573dPTM6I5ZuQEBgTA3893RDo7Yd5KVxcnhJ05joaGepYk9FZeXsYZNJnwdHflI8KxQAeB/r7YsmkDZ9xY8wERxhLm6OgANSV53vg/dOgYqjkv0FjlKI0IEIGFQ2B6v6ILp//UEwkhcNttt6GuvglhEdGTarzvwBGsX71s0nILvYCxkRE3yts6L91cKI2yB6jmxqk9hM207wPcQH19SzcUpxlKWiAQQF5OASw61Ux1ofrzQ4B5OjasXwsXRxukJsYgIy0JrZzHRs/AGA4u3nD1CoS1gwfMzC3AjNrg5WtQWV0NNRUlGBvoDa1DEZX2DQ0NGL4W5mK5bDrYN9/+yHluSoey2N/O+nVrxtSFGUThEZE4n5qEooLx1721trYgMf4cV+a/gAwa6hpQUVWBE2e0DDUm5AELhR20JBCXXroVO3bsBDPECgoLhaxNxYgAEZA0AlKSpjDpu3gJPPLIo3j/468nBeDl4YbS8spJyy30Au0d7VOeDrLQmUy1f1JSUjDQ00FNzfhvYJ+qTGHK9/T2Q0pKIEzRUWW6u7sBQf+YD5egf/NNQOj22XfPyckRy5YGwNneEqUFGaivKkJ3Wy3OJ8eioqIUzU1NQ2GZ12/cDhlFVcip6OCX335HVlYW3xabzsYfcLu8vLwRkdOauPpc8oj/+zkvOPMkNTY28kZAWlo6vv/xZ5SUlY3wELFpcadOnQYzWLKysmFhac6v7xkhbIwTVu+vHbthaesCBXllzmDRGFWKrT0qKS7CgV1/wcPZDh0tNYiLPsuvw1FSVoaeoRViYuMx1X+MKauTnZMDI0MDfiCgrKwcb7797tC9kvWHlaGNCBABySdARo7kX8NF0wNdXV1cdfXV+PanPybs88a1y/m56xMWWgSZGZm5Qj10LAIUM+qitZUFqsrLZiRjOpW7OUNnOlPWmJEjJyfHT+WZTrtUR7wIsIX6LADLiuWh/Htq/Hx9sGnDGpjoqqGmPA+FOamIPHMM6akJnG3bh5bmJuTnFSIjtwR//bMTh4+fwo6du3Hk6HEc4Ladu/ejtq4OmVk5OHE6EvsPHUFCYiK3JeHFl1/Bp599gc+/+BI///o7du/dj+aWVtjb2SMtLQPDI5SxkNIBAf58mGt3dzewtUQCweSGeUEB88oIICfVg82b1kF2oAPpKQkjoDMDJzczBZdfto27hxmB9X3tqlBEnj7KB1PRNzBAbWMbioqKRtQT9sTI0BBFpWWIiIjimC7B9ku24iDHgfUvKTlVWDFUTqQESBgRED0BKdGLJIlEYPYIXHnlldi55yA6Ojox3j9rK0tcunU9Hnj8+fGKLIr04rIK+Pv7L4q+zmYnW1rbID9LoXwn0ru1oxe9fQMTFRkzjy0c7+zkvDlj5lLiQiHAXvi5NDgIq1Yux+WXbsXalSHYtmU9nO3MceutN8HcWBfLQoLRUNeAiqpadPYOoK66mvPwDaC3pwcFBflQVVEEC8KXmJiKlLTzuPnGG3HD9dfi9ttu5UMwq2nqoKm9F+0dXbC1sRr1UlE2rW6QJ3snzuDxRJ/W1tbYvnUj2Ho3T87rHhgYACszQ6Qkx3PeqXKUlhZDUUkJsjJyYANbg7Lk5eWxJNAPmecvGCHmFlZITU3n+nFhuhnzQA2WZZ/nz2fwBhE7vnhjL0594N67ERy8hM+ys7XhObKTuPgk3mPEjmkjAkRAsgksCCNHsi8BaT9VAldwhs7eg0cnrGZpboburi68/cEXQ+UefvIlNDc3D50PP2Cj3xFRMcgvmN7I4HBZ4nB8Li4RcnIK8PLyEgd1JFoHNqVHRUVtXvrQ1z/ayJlsrJxNydHSMUBu7vhrHealM9TorBJgRgBrgEUWMzYyhKeHOx9G+cbrr+KMig0I8vPESy88hxXLQnA2IhoDAwL4+Xhi9Ypg3HrLDbjp+mtgzt03mefo5Omz6OiVgRT6YaijgtWcAbWEM0aY/JluAoFgVAQ3a2sr2FmaoL+zCcoyvehrr8fGDasx2KfBNi0szNFUX4mmxkawkNLaBuZITEnno0g++dTTqKys4ouy6W4dHe1gfwt8wji7QSOtoqICe/YdQF1DI1TVlOlvZxxelEwEJI0AGTmSdsVIX6xdswZ/7tg7IQkLc1O8+sKT3I9hEy679nZce/O9aGxqgtoY4Ux37D6AG+94CP9wn6+8+cGEciUhs5Mz7l56/T288OKLkqCu2OuoqaGOlpZmsdFTXUWOG42fWB0NLW2UlZdPXIhyFwUBZigYGRlBR0eH7y8Lp6yqqgJ1bX0cPHxilCHAorbpm1iiuqoMa1cth4e7OzQ0NPi6s7mzsrSAr48XnJ2d4evrM250wPVr14BNzSsrLYG0rAwMjCyQkJCMK6+4DOEREbyKMjIy8Pb25o+F2enp6UFZRRXM+7OG84w5ODgIU43KEAEiIOYEyMgR8wtE6o0mYGBgAAcHR4SFR4/OHJaio62FF55+GE8//gA+ePslVPw7yjdYpK29Hfc+/Aw/N/v3Hz7Dow/cCXkF+cFsif189KlX8P7770us/uKmuLm5OZrrq7mR79FelfnQtbunD7LSE9+6tbV10C+tDLZofD50FP82F6+GbDrjujWrYGmqz3l6tDDozWBECgsLkZOdg67WOmxat2qUJ4WVme+N6b9p3UrICrqgKNWDuuoSQEoGLa3tWLN61bTUk5aWxpaN63DdNVehrq4On372JZgHd1rCqBIRIAJiQ2DiX0qxUZMUIQIjCTz88MN4/9OvRyaOccYWYHt5uEJXVwdamhq45e5HcNWNd+P2+57APQ89jUu2rMMj993B1zQxNkJPdw/YQls+QQJ3b7z7Ka69/nrY29tLoPbiq7KjvQ3y88Rj+ldv/8CknhxG0sLSGnkFRaOMs4vXLrCytC0+AkaGBli5/L9Q+2yK17ETJ3HDDdcidGkw2L1zMiqsDivDIpKxqcCtra3sdNpbW1sbvvvuR3R0dEwog3mjPNxc4erqgs2ccbJxwxrY2FiN8NR3cR7tCYWMkcmMHUdHR7Bpvn19/WOUWARJ1EUisIAIkJGzgC7mYuqKrq4ugoKC8csfO4XuNvPmPHD3rWBPiAG+nnjnteewavlSDP5jL6jrH+jH4A/3YLqkfJ4Oi0RXTz+WLFkiKSpLjJ59fb3c10YgHvoOAEoKMpPqUlNdBUsLsxF6s2hUySkXFm5PKoAKLCoCbIrX7bfeAmUlJaH7/fkXX+GDjz/jX0S6/8AhfPHlt0hNSx/TSGH318kEJyQlQ9/UCsXFxZMVHZFvZmoCUxOTEWmJiUmIiY0bkSbsSWCAL7S0NIUtTuWIABEQUwKiNHLEtIuk1kIl8Nhjj+GPf3ajvX3iUb/B/utx3hwPN2e8/sKTiE9KhZ6u9mAW/ykQCHDjtVfg9Xc+5s8lbbfv8Els3LhR0tSWCH1ZdCkW8UkclJWTkeK8M5NrIisrh27OMzm8pIyMLGpr64YnjXlcXV0N9k6VMTMpkQhwBLq7u2FgYAAtDU14e3mAhXk2NTNFfUsXdu49hLj4BBQUFiE9PR05ObmoqroQFICrOub/zIvT0toFE1NTNDW3jFlmKokBAf7Q1Rl5jxe2vpQUPRoJy4rKEQFxJkB/yeJ8dUi3SQncd9/9+PG3HZOWG17AytIc+no6fKCB4enseNnSQFx9+VZcf9sDuO3ex/HV97+w5Blus1u9qrqGe6BIgZS0NHx9fWe3sUUovaenB/lFJTA0NBKL3styRg5blzOZMgKBAMwzObycsbERVq9aMTxp1HFfXx9YpMHsvIJReZRABAYJyMnJIZAzJLp72d9HBRJTsuDuHQhbO0csX7UekFVFSUU9qhs6cODQMRw7cYozursHq4/6PBcTCy09Q6ira6KyqmZU/nQSLC0tp1ON6hABIrBACEgtkH5QNxYpgXXr1iElPROJyWlTIlBVVQsLs5HTGwYFeHu64d03nscbLz6J1tYOPPb0q9yI5NSmTwzKmovPz77+Ca/970M8++xzc9HcomqDjS4fPnYKAUHLxabfnT19I6agjadYf38fZKQnn9Y2Vn1mRMkrcg+ppaVjZYsujSRJLIH6+nokJJ/HqnVb4ebpwxk4flBT/y8Km5GxCWzsHGBr7whbB2f4+fqMu86HTWVrb++ElpY2X0YgkEZnZ6fEsiHFiQAREA8C0zZyamtrsXP3XrCHAFrIKh4Xc7Fq8fzzz3NemYNgP5TCMhAIBNwDoPS4xdnUNl1dHTxy/+24ZPMavPzmB7jprofw+LOvjltnvjLa2jvw5ptvgb3fYr50WKjtns/IhJmlPcRp+gp7dw739Z0UOdOZvStq0oLDCtTXNyArOxd2jq7Q5UbVC4vE17gfpjYdzjKBsQIB5Oblw9jUkjdKJmo+ITYSOurysLezHbcYe4boGxiAgoICX2aA2zOPIvexaP+njn6mIx0AABAASURBVBMBIjBzAtM2crS1taGtpcW5oQ+jp6dn5pqQBCIwTQJlZWWQk5eHQCD8wnA2jWfwB3WyZoMC/fDJu6/imccegIuTHZ5+8c3JqsxZPnsoleZG662srOaszcXUUGlZBTS5+5w49bm7p1+owAOysrIYmMLfBHuoPB0WjtKqBugbGEJFVQ3tbR3i1HXSZY4JsO9EfHw8fvjxF5SUlg21npObh5aOfhgaGw+ljXdQzw2IhkdETfiCzfz8fGjrGUFG5oLncWCgH6xtZvyMJ5fSiQARWJQEptTpaRs5AoEAoSHBYC/mkuceMKfUKhUmAiIk4OPjg5raOkRExQotVSAQ3iBiQpWVlWBrY4Ul/hfaYmnisKmoKCMlNQ3sgVYc9FlIOrS3t0NWTn7owUuc+lbb2MkZ9RNrxF5u2NDQILSHs7GxEQpKKnBxdecFs4dMTO3PhK9Hu4VB4EzYWfz59040c8bMmk2XIiO7EKWlpWDfqfj4JBibmE3a0ayMNAT4e2PNmtVQUFQctzz7rgkE/3nWdfUN8deO3VOehjxuAyLMYLqKUByJIgJEYBYJTNvIGdRJVVV18JA+icC8Efjkk0/x564DyM7NH1+Hf3N+/mMHrCzM4WBv82+K8B/mZqZoaWlBfkGR8JVmsSRb/CupIa9nEYtIRNfX10NJRV0kskQtpH8AnPEysVT2MCbFefkEgsktlaKiIvz+5z/Q4UbTB6Wyly6yqHIDA1xjg4n0uSgIsBDOxSWVWLZ6E+wdXfgXhmpq6+LwsRMIi4hGb18vhPGyVJYVg90zXV2cYTKB14dNwZSW/u9xxMLKDms3bkd3nzSysrLEhjnzOL33wUf8NH2xUYoUIQJEYFwC/91Vxi1CGURAMgh89NHH+O3v/fjl9x0TKrx18ybU1jegZRovrpOTlUVrWzvk5eQmbGMuM924Bwj24zuXbS6GtgqLSqBvOPl0HHFlUV9bDVMj/QnVYy9MZB6rrNwi2Du5wcT0wug8M5yjI85ATl4RbP3lhEIoc0ERyMzKxpHjp2BubTuiX0WF+bCycYRPQCiWr9kMDU3NEfljnbj7BOLoiTMTRlVj9QqLS2Fw0d+atLQ0enq6xcqTygaV5BSU+YEupjdtRIAIiDcBMnLE+/qQdlMk8OJLL6G6oRV3PvAUUtMzx6wtw/143v/AQ1BQEP6ld8MFrV+zEu9/+jV+/3s39h44OjxrXo6luRHQioqKeWl7oTbKvCC1DY3Q1NSSyC4yw6W+tgLOzs5j6s+8VPEJSdjHfX//2rkP9s4ecHByHSrLRunraiohLz0AXV3doXQ6WLgE2DVn95HsnFx4+wXDxtZ+RGe9fQP474hAIIBAIBiRN96JhoYmPH2DcPDoCaSfzxgqxtoaPKmpqeGMaSUwA2IwbfBTIBDMd9APDP9nYmICawtj9PT2gnm7hufN5Li8vBx1dXUzEUF1iQARGIMAGTljQKEkySbwyCOP4Kn/exrf/7oDz7/6LnIuet+HkoIMkhPO4Y+d+6fV0dtuvBry8gqIOhePv3buxVff/TItOaKqlF9YBAsLC1GJIzkcAWbkDLA5YdyxpP3PvDOJcZFYuWzpmKqz0LwHDh+DQF4NvktCsWb9Fn5NlzRn/A9WYA+cevr6WLIkYDCJPhcwgRMnT+JseDj27D8CGTkl6OqN9gBOd+0tCybgF7gM9S3dSExKAVv7tWv3bjQ1NYH9Kykth7auATsctXV2dYBNmxyVMY8JtjY22L17H2Ji4zHTqZzspbt//b0Du/fuF7t+ziNialoiCEiGkgvayJnpDUgyLiFpORYB9hK49957D9u2X4ZX3/4Iv/+zd0Sxn375A8uDfEekCXsiLy+H1154Ah/97xVsXLsKuQWFwlYVeTk2rainpxfGE8x3F3mji0AgM3KkpCTr9lhVWYHM86nITIvH1o1rx53mU8uNGBubWsLAwJA3bsa6nFWVldDX1aEHr7HgLLC0nNx89EKBn5ZoamYJN09fDDd4RdVdaxs79AzI4tiJM2CGgoqKCm/wVFTXwWCcF+0aGpmC6ScqHUQhx87ODjfdeB3YOqOZPGO0trZyRl8y1NTUcekl2/h1T6LQj2QQASLwHwHJ+hX/T+9Jj5g7PCs7h+bOTkpq7gvMZYteXl748cefkFdYhtff+ZhvOje/ELo62jAxNuTPZ7K76vKt6OjoxGHuh3smcqZbVyAQQEdbCz0Uxn26CMesx7wdCorKY+aJY2Lm+RT0dTXBztIIbDol88SMp2dicirsHJzHy+bTK8uK4Ohgxx/TbuESYNPTcotK4eTihpq6Ftg7uczq9DATMwv4BIYir6AYhYWFOHM2Ck6uXuMC1tDQRG1dw4w9JuM2MI0MgUAA5oExNDKcNqtTZ87i2Mkw+Hh7YR03UKavrzcNTagKESACkxFYsEYOG4XV0tLiXPCRkzGg/EVA4Nlnn4WDsyueeelthEfFor6hCYeOnpxxzwUCAT58+2V88/2vYF6VGQucogC27sjYxGTcEfkpiqPi/xJgRkJPd9e/Z3P2Ma2GEmMjYWNuCB8vT7A1A5MJ6e3pm3RNhZS0NP+ekslkUb5kE2BTwbo6u3nPzZbtV8yJ505eXh5e/iEoq26Bm3cg2Pl4FFtamqGqqjzp93W8+rOVzta8SUtJT0t8YmISOnulMABpGoSdFkGqRASEJ7BgjRyGQE9XBxvWr2WHtBEBbN9+KQyMTHDyTDg+fvcV/PHPHpFQYQa1irIS0jPmPtTp/iMnsXnzFpH0g4T8R0BZWRmqygpobm7+L1EMjzIz0mBpbgwzMzOhtVNUVEB7W9u45bu7u9Hf0wUtbpBo3EKUIfEE2JTME6fPwsNretN2ZwKAvdfLxs5+QgOHyWceamaIAexMfDZXV1e0t7dNGjVuLI1dXJxhoK2CAB830FrKsQhRGhEQHYEFbeSIDhNJWigEtm/fzj3gdUBLU4MftZyuYcLekxMeFcMbNm3t7airb4C768RTgGaDIXsgnQ8P0mz0RdxkLgn0R2ZagripNaRP9vlUWBjrwcnRYShNmANtLY0J3/NRWlIMc3MTYURRGQkmcPDwUTi6eo8Z1UxcuiUvr4D6ugax8yoy4ys2PoEzdNqnjIoZeJ4e7jAyMpxyXapABEYRoIQJCZCRMyEeylxoBNgCfVU1VZRXVOHzD97AV9/9ioKiEqG7+cKr/8P1tz2AJ559Dd/++Du++fEPfPz5d9BQVxNahigLmnA/lGx+uChlkqwLBNjDiJWFKUpKii4kiMm+rbUVsVFnYWttAitL8ylrZaCvxz041oxbr7a6HBbmwnuGxhVEGWJFgHlumEJ1dXU4Gx4Fe2dvqKtrsCSx3VRVVWHn7Inf//pHrEIss3sD+vvF2kAU24tKihGBOSRARs4cwhazphatOk7OLkhNz0BWdi6aWlpgaW4qFItX3voAurpa+N8bL+CfX7/C91+8j/fffIGTlYWu7h6hZIiyUGZOLrJzC7F161ZRiiVZwwhYWpijuqJkWMr8H6YmxXFeQ3sYGxlNSxkWlEVaWnrMug31ddBQVeK9nGMWmCBx586d+PDDDycoQVnzQaCxsRFl5eU4fioMf/2zC3FJ6VDXNYIKZ0DMhz5TbVNZRQWhKzfiVFgkiktKp1p9WuUHw1tPVHn58mXT+juZSCblEQEiIFoCZOSIlidJkwAC7DV2tXX1fMQeDTXhPDDsx7WsvAr33XkL9HW1R/Ty+aceQl9v74i0uTg5GxEHJ+e5nyI3F32b3zb+a52NJGuoKqOxoeG/xHk+6u3rhpmpcIb5WKqei0uErb3jWFmorCyHtZXw71zq6vovOMOBAwf4dTwzCas7plKUOGUCJSUlyMnJQXZ2DqLjU5CVW8J5brwQvHwdXD18oaOjN2WZ81mBGeUBwStw4uQZDHqkZlOfffsP4+SpsyOaYCGfhycoKioOP6VjIkAExJAAGTlieFFIpdklYGhoiNbWdjjY20JTQx0fffbtpA3KSMuOu8jU3s4aiooKYKF5JxUkogJp6ZkoKi3HLbfcIiKJJGY8AsZGBqivqx4ve87Tza3skZSUPK12z8XEwMJq/DU8DbVVYH8fwghnb2n//a8dQ+sSmBfn+uuvF7tIWML0ZSGVYeGJ8worUVHXhtrmbrhxRo2Ds5toplbNIygW4MXI1BzMMzXbarg4O6C7TwpnwsL5pjIyMvDHnzv4Y9oRASIgOQTIyJGca0WaiohASEgI8gourLN44elHkJqRhaam/6Jotba2jWqppLx8XCOHFTbiDKcSzuhgx3OxhUfHwtvbZy6aWvRtWFlZoa66XGzeRaSsrIL2jv88KMJeoAbOG9Xa0QsTs7HX8dTW1oAZdDIyMkKJ1NfXh6mxIfj1CVwN9nJH7oP+n2MCbPohazI7Jxd7OQ+EkZkd7JxcYGNrDwtLK5a1YDZdfSMUFc/+9FEPD3d0dTRxho402H3d0dERWzavx549I18qvWDAUkdmTIAEiCcBMnLE87qQVrNIgIUFrq6pHWpBX1cHYREx/Pm7H32F5155B8+/9i4OHD6BP3fuw7pt1+KTL76DhoYaX2asnby8HA6fCMOHn3+Dx55+GeyFo2OVE1VaS0vbnEzbEJW+ki7Hw90F+XnZs9qN7u4uHNj7D44d3ouU5AQUFeSP2Z6mphYaOKN8qtN20tMzYGgy/lS0yvISodenMcXYFKKVK5YPGTksjba5J3Dy1Bn89c9OZOWVwsM3CKpq49+n5l470bbY39fHv1uG3cNFK3m0tM0b1wEDXQgLP4e0tHTo6elh9epVowtSChEgAmJLgIyceb80pMBcE2APh8zzMtiutZU5jp08gzfe/Rg1tbV46L7b4O/tjl//3Invf/odP3z5Pp55/AF09/QOVhn1+ewTD2L18mBoqKrB29MNL73xPioqq0aVm05Ca1sbH6K6sKgEbW3tePbld6CupYvLLrtsOuKozjQImJuZoaezleM/2ss3DXGjqiTERuLNF5+ErqoUVGW7kZsWgz3//ITKivJRZQUCARQUlaY8bae9sxNysnKj5LEEFhK3u6MFBgYG7JQ2CSHApgyWlFdDS8cA3r7+ENYLJyHdG6Um6198YhIioi4MSo0qIMIENj1u9YplsLc1h62tDS9ZHN/ZwytGOyJABMYkQEbOmFgocSETKC0thbHxfw9z115xKcA9OJaUVsDFyR7mpibYuG4Vfvv+U/z09UdgIXfZ+p3rrrwU9z36DMb6xzw5l2xejxuvuwJXX74NdtaWOHz01FhFp5TG3uPzxLOv438ffY0Hn3gB26+5DTJyCrj55punJEciC4uZ0r29PbPyEPnjN5+hovA8Pnv/VWzbtA7bt2zAfXfcAFtLMyQnxo5Joa+/f8q6MI9lfUP9mPKqKitgYmw0Zh4lii8BNlXNzMISTq6e4qukCDXT0zfAxq1XoLO7d8pG/nTV8PbyhLy8/HSrUz0iQATmkQAZOfMIn5rIiAwYAAAQAElEQVSeHwK1nLdGTUV5qHE5ORl89M7LnJ0jgLzcyB8zPV2doXIhQX7YsGYVrr3lPmRm5WKil3B6ebrwa31++2sXpvOPrQs6ePgU3vngS9x3/wN4+ZVXsWfPHhw5cgQvvvgiTRGaDtQZ1xHMWMLFAg7s+h3Wprq4/aZrwEaOh+cbGeqhu6tzeNLQcXNjPdTV1YfOhTkwMTFGfW3VmEUbG2phaKA/Zh4lih+BQY2KuYEZE9Ox11gNlllon7KcN7KnqwMs8uFc9E0gEP3f/VzoTW0QASIASBEEIrDYCPj5+eFMePSIbsfEJaGhsQlLAn1HpF98smHtcrz07GN45uW3cM/DzyDqXMLFRfjzlcuWoqW5FSlpmZwX5ks+Tdhdcmo677XJKSrDDz/+CCcnJzJqhIU3i+XYegBRis/KSEdtVSmu2L5xTLGhwUuQdT5lVF5nZyc0NTTARvFHZU6QwN79oaD4n3E/WJQZ651tzWCBBAbT6FP8CRQWFqKts5d72FcTf2VFqKGCggL0jSywa88+sQkGIsLukaiZEaDaRGAEATJyRuCgk8VAQE5OboQXJvJcPN796Au8+tzjMDacfDTbxsoCO379Gs8++SD2HTmGh554ATl5BSPQKcjL46tP3sYDd9+CjMzsEXkTnRSVlOHTr3/Bt999jwcffHCiopQ3xwQ01NXQ3i6aNTnn01Lw6w9f4OH7bhu3F0pKClBUkEddXc1QmbZWznBOjIezk8Moz89QoXEOZFjUtIGBUbnFhflwcrQblU4J4k2goqoaRiZm4q3kLGlna+8AdS0DsDVJs9QEiSUCRGABECAjZ/Ai0ueiIcBCArM1DbW1dXyf8/IK4ebiCCvLqU37MDMxwuvPPwk/b3e8/OYHeP+Tr3l5gzuBQICq6hruwXjsKUeD5dhnY2MzDh87hXc//hpfffUVS6JNzAgY6Ouivu7Cd2Y81R5/4HZ8/9XHI7LramuQkf6fR6a6qgIHd/+Oj955EcwYHlH4ohM2bVFKIIXq6iokJZxDQXYqAnxcYGRocFHJyU/NzMzQWF85FMygvr4O59OSUVtdBksLi8kFUAmxImCgp4sa7nshVkrNoTJW1rZIO5+Fg4cOgXk357BpaooIEAEJIUBGjoRcKFJTtASMjY3R3nHB+JCVlR436hSE+HfNldvx89cfYqCvHx9e9GLRxqYmSEtLITImfkJJZyKisXPvEXz00cgH5AkrzVEmNXOBgLS0NPr7ei+cjLNnwQlSkuJQW1PNl2AL+v/86UukJ0Tgs4/exrnIs9j9z2/YsGYZlBQV+TLj7f7asQ/nMzIgkBKgrrIY/l6uWLN6BQxmEAFt2+aN6OtqQWpyPBqqSuBsZ46tm9Zz31Hp8dSgdDEk0N3djdKyMnS2t4ihdnOjkqKSEmJiY8GckwoKCnPTKLVCBIiARBEgI0eiLhcpKyoC/X19UFFR4sXZ2lghMTWNP57JLio2Hpdtu3h9hYBfO/HSa/9DZ1fXuOI1NdSgrfNfkINxC1LGvBFgD1JdXR0Tts9GlLdsXIvEhFi+3PHD+7EiJAAP3HUjVgZ5obzwPPzc7bBq+VI+f7zdidORKKyox9oNm1CYk4GVy0Ogra09XvEppft6e8DXwwlLgwNhaGjIB9yYkgAqPEhg3j7z8vMRExeP8rLSedNBHBpeErQUwUFLxEEV0oEIEAExJEBGjhheFFJp9glISUujovLCaDtrzXCGkaXOcp4YSwszGBuNnEZUUFQMG2sLONjZIj7xvylLrM3hW29vHzQ1NYcn0bGYEdDS0kJ35+RGzrm4ZM4g0UH4mWMQ9LVjReiFh7DlIUtw7+3XY8PalRP2rLq6FodPnMV1N98NL58laGppRVFR0YR1pprJ+jLVOlRefAjExSWgs7MbTq4e4qPUHGvS0dGB3p6uKUcZnGM1F2lz1G0iIB4EyMgRj+tAWswxASkIhiKWeXm4cj+Wfdh/6Ni0tdh36Di0OCMlOiYe3d09vJyU9AwcPxWOZ594GOxFjBeHp+YL/btztLdFTk4OBtjci3/T6EO8CLB3ZQwM9I0IWjFcQ/ZCTSkpKSwP8kN26jn0tNbgsQduH15EqGMNzqvX0dGOfbv/5oMTHNz7D/ed6haqLhVaHATWrlkFDy9fmJlbLY4Oj9HL8rIS2HIDSGNkURIRIAJEgCcgxe/FaEeqEIG5IJCekQG2cJe19c4Hn0NPVwub1q9mp9PaXn3+KTQ0NOCr73/FXQ8+hZvufJg/fv+NF7jjB3Hl9i3w8XIbV7aCgjwU5OUgEAjGLUMZ80/A1NgQ1VWVYyrS1dUJNqXtkq3r8ch9t+HaK7aNWW6yRBb973+vPY3O5mrcesMV3Ih9F2xtbSerRvmLiADzxPV1dy2iHo/uag/nxVFRHh0SfXRJSiECRGCxEiAjZ7Fe+UXc79bWVpiaGENNTZWnEBufhJuuvYI/nu5OTk4Gb736LL77/D1uexdfffwOPnjzRdxyz6P44cv3sWp58ISiNTXU0dXVPdF7HyasT5lzQ8DO1gaVFcWcZ6ULn7z3Ovd5IXgFa72rsxPynKHKjme6KcjL48G7b0Z9fSPc3MY3jmfaDtWXTAJdXV2Qkl7cP999vb00KCSZX1/SmgjMGYHFfZecM8zUkDgRUFJS4h5G5flpR/sPHoOjgx2MuRF6UerIjJ5Lrr4Vf/38BdjI/GSyy8oroaauNjSFbrLylD8/BNh3x8/TFV9+9DZszAzw8Xtv8ZHKCgtysX/XH9i0burewDNno/DKmx+O2aGYhBRs2LBhzDxKHCSw+D6VOQ9Gb08PH9Rk8fX+3x7399P98l8U9EEEiMDYBMjIGZsLpS5gAgcOHICFuSlkZGTAXqjXy40Iirq7Dz7xAu669XoocwbVZLI7uVHZF994H++887/JilK+GBDQ09ODNDeKrq2jCV93e0SHHcWhXb8hdIkHrrvqkilpeOREGE5GJGHD9hvw3KvvjliTdcf9T+Le+x+Et7f3lGRS4cVBwNBAD3W1tYujsxf1sqWlGbLSgA5FpLyIzEWndEoEFjkBMnIW+RdgMXZ/8+bNKKuoQlpGFvewKg09XdGGbk5Nz+CnnW1ct0oovN/++Ae2bt1GUy+EoiUeha699lqcjYzFVZdtwQtPPYhXnnsMS/ymZozsO3QCRRUNuPP+R2FobIzunj5+ehrr4dMvvoW777kX+pxBxc5pIwIXE+jq7oasnOzFyQv+nAVnyc85jwB/nwXfV+ogESACMyMwnpEzM6lUmwiIOQEBBOjp6oGfjwfSOWNHVOqy96Q898r/0NLSCrb2ZyK5bIrarr2HkJCUinXr1k1UlPLEjICjoyOyc/LQ0to6bc2qq6vR0zuA8tIivP/mC7h081poa2tiz/4jkFNQgqen57RlU8WFT4Ct19LQWHxh50tLi2FqpM8H+Vj4V5l6SASIwEwIkJEzE3pUVyIJsChoFZUV8PRwQURUHFwd7afQj4mL/vHPXpiaGOF/rz+HW+55DFHn4set8PWPv+OvnfvBokazqXPjFqQMsSTg4+OD+MTUaet2641Xo7q8CD9+8wnuuPFyzuB259dY7DlwDC+88MK05VLFhU+gvb0dcnLyC7+jF/WQ9bu5rgIuzk4X5dApESACRGA0ATJyRjOhlAVOgL10s6+vDwWFxSgpLYejo53IelxdUwcjA33o6+ni128/wXc//4EHHnsOWbn5o9poampBbV0dLrv88lF5lCD+BLZt24Zd+w7PSNFnH78Xzz5+H2xtLrzv5N6Hn8E1114LgUAwI7nzUpkanTMCioqKYO9SmrMGxaShyrJS2FhZiIk2pAYRIALiToCMHHG/QqTfrBAICFyCxJR0tLa1QktLU2RttHe0Q1lZkZcnKyuDrz95B9dccQlef+sjfPPDb3w627357qfIy8+Hl5cnRc9iQCRwY6GdlZRVcepMxLS1Z98RZhAzAe998g3uvPserFq1ip3SRgTGJSAQCMBC4Le1tY1bZjoZNdVVSE9NREJMJOJjIhB++hiSE86BTcOdjjxR1qmvr0N/bxssLS1FKXZOZFEjRIAIzA8BMnLmhzu1Os8ElgYHY//hE7jj5uvwyRffi0wbKe7hY/ChdVBogJ8Xfvz6AzS3tOGDT7/hk7Nz86CgoIi33nqbP6edZBJYsWIFzkbFzlj5F19/H/KKKvDw8JixLBKwOAh0dHZCTk5u2p1tampEbm42SoqLkJWRjsTYCLQ3VcHdyQbBgd5YtSwIl2/fjCB/L+ScT0BM5BmuXOq025tJRRYBMy8jBSHBS2YihuoSASIgXgRmXRsycmYdMTUgjgTYKDz74WTrZ7q7uxERPfMHVdZPXR0d1NTWscNR2yP3347i0jJEnovjPEjt+N///jeqDCVIFoHU1FTOc6c0I6WffO51rN2wCffee++M5FDlxUOgpqYGCkqq03pPTFVlBVKT4lBRnAMTXVWoyPXBxlwfq5YvRWCAP/T09KChoQE2JU4gEHAeIzWsWbUCa1eFwtJED4lx50aBrq2tRkND/ah0USQwuVHhJ7Fm9XJRiCMZRIAILCICZOQsoou9oLo6w84wA0dbSxsD3H9XcKOVv/6xc4YSL1S3tjZHVU3thZMx9qHBAdi57xAMDQ1hZmY2RglKkhQC7DuUkpLMeQOvmZbKlZVVeOK517COM3ACAwKmJYMqLU4COTl5MDSe+v2DGTcdLTXwdnfEimUhsLKygrOzM0xNTSEvLz8uTIFAwBs9Fdx3VvaigAf1dXWIjTiDdhFPnWPKlJQUojA7BVs2rOHbZ2m0EQEiQASEJUBGjrCkqNyCIsCimVVVV6G7uxdbN61F/8AAqqtrZtzHI8dOw8Zy/IWx7ME4P68Id91194zbIgHzS4B9hzZt2oxvfvhjTEV6e/v49Lb2Dv6T7RqbmvHZ1z/i25/+wGvvfoY77rwHy5eLdoSatUPbwiTQ09ODffsOADJK0NbWGbOTjY0NqKutQUlJEVgYe+YJYZ8JMVFwsDHDEs5bo8N5nMesPEmin68P6mvK0dvby5fMz81GbmYy9I2MYWxiyqeJase8QwNdLdi4YT0ZOKKCSnKIwCIjILXI+kvdJQI8AfYj3d/XDy1Ndf5cV0cbsQnJ/PFMdpdv24SjJ8KQV1AE9kB7sSwBBLCytgJ7z8rFeXQueQTU1NSQmZ0HNsLNtG9qbkFkdBzik1Lw4BPPY/Xmq/DiG+/jsWdew7OvvINHn34FJuY2UFbTxscffwIbGxtWjTYiIBSB0tJStPcIYGFtO6o8m3abGBeNqtI8tDZUQEW2HyX5magqyUN5USaWh/jD3Nx8VL2pJEhLS8PczBRNnCEVdy4cOuoK3EBRNzS19aYiZtKyLPplfVUJAvz9Ji07hQJUlAgQgUVGgIycRXbBqbsXCLS3t6Ovvw9SUlLo44yd1PQMrFwWfCFzBnt9Az0YGurjjXc/BVtM/ulXP+JUWAQOHj2JmLgknDgT/ACv1wAAEABJREFUiVdeeXUGLVBVcSKwZs0a3HPvvXjz/S844/YM2Itg9x85jb2HTuP1N97CsWPH8Oqrr+OJJ5/C1ddcjy+//ApbtmzBVVddJU7dIF0khACLLKanrYaC/FwMcN5npjaLfJaSFIvkuEiEBvlheehSfm2Ns7MTVi5fihXLQ7AsZCmUlZVZ8RlvyampyGJBAJb4oo27j7LpueUlBTOWOyiA9Sv81BFO55nfjwdlzuQzITEJRUVFMxFBdYmAmBNYuOqRkbNwry31bAICXV1dMDDQ50vU1dejv78fCgoK/PlMdpbmpnj2iQfg4mSPJUFBUFBWR0JqDjJzS7Bj31G89fY7NPViJoDFsK67uzs+/PAjpGYW4CPOO/P6G2/gpZdegrr6BS8hW+vAFnM7OzuDTXETwy6QShJEYHloMJRk+hF55ihiI0+DRT7z93LF1s3roaIiGkNmIhzMYPLz9sDJ02cRHhmN4EBfqKhpTlRlSnnnIsKwYZ34hFFnRteBw8fR0NAwpX5QYSJABOafABk5838NJFoDSVV+//79WOLnxauvqaEBDe6BlBk6fMIMdxrqanjonlsRey4aLi4uePTRR/HII4/grbfe4h5CVGYonaqLKwF2ncVVN9JrYRHw9HTH5Zduw+aNa7F29UpoaorOyJiMlIO9HQQC8Ot9Any9EBMTD3k5mcmqCZVfkJOBpUu8hwYIhKo0y4WampoRFLoKe/cfmuWWSDwRIAKiJkBGjqiJkjyJIMBG5VRVLhgc7IWMmprqOBebKFLd5eTlkJ+fL1KZJExiCJCiRGDWCbDptrPeyEUNsLU/52LieM93V3cPrrryMuhoqKC4uPCiklM7LcjLgZKCFHR1dadWcZZLq3ODVkcO7IW5qdEst0TiiQAREDUBMnJETZTkSQSBmppqKP47PW3vgaPQ09FBoL+3SHV/7fkneG/OgQMHRCqXhBEBIkAE5ouAnJwcLtu+DVdcdglClgbzXhd57l7a29MrpEqji3W0t6OjpR6eHh6jM+c5xcrSAlJSAzAzMwNb/zTP6lDzRIAITIEAGTlTgEVFFwYB5sWpq6uDppYG36GKqmp0dnXyx6LeOTrYorZ2/PfmiLo9kkcEiAARmEsCLDx1YXEZrKynHynwfFoSPNxd5lJtodtikd7UNLSxY/c+ZGRkCF2PChKBKROgCiInQEaOyJGSQHEnkJCQAGMjA3j9+6Pq6eaMltY2kavNIg/t2X8YN954o8hlk0AiQASIgDgQUFFRQWNjw7iqtLW2jsqrrqrkI8TlZGciJTkepsb6mO67e0YJF3FCbHwiloaugLWNPVjI+ObmZpwOO4szZyOG3hck4iZJHBEgAiIiQEaOiEDOsxhqfgoEVq5cifMZWUPvNvHz8URlZTVyC0QXJpQFMfj48x9w+RVXTEEzKkoEiAARkDwCS4MCceLofmRmnEdd3X+e68KCfJw8shcx0ZF8p5hXJDEuBm1NNdDTUICxrio8nGzg4SaeXhzmpWpr74K6ugb8ApeiuLwWkecSUV3bDBNjI4qWyF9V2hEB8SVARo74XhvSbBYJLFu2HHv2Hx1q4dort+Ob738dOhf2oLSsgjOYslFQVMJ/Hjl+Gu99/DXWbbsWWnoGuPGGG4QVReVmhQAJJQJEYLYJGBka4JortsPaVAdtDZXIzTqP+JgISPW1w5Qt2O/rQVJ8NJLjIuDlZoegQD/Y2trCxsYGBgYGs63etOTX1dXh9NlILAlZyddnQR5sHVygoaUND1cHWFtZ8um0IwJEQHwJkJEjvteGNJtFAnfffTd27j041MI/u/fj+aceGjoX9uDn33fg7Q++wHOvvINvf/4baVmFsHdyxdGjR3HH7bcLK4bKEQEiQATmloCIW5OWloa5uTkC/H1hbqyDZcH+/PGG9ethqK8JFQUZbNqwFoaGhiJuWbTimBf+zNkIRCekwctv6ZDwpMRYnOa8VWZGOrCztR5KpwMiQATElwAZOeJ7bUizWSbg5OSE8KgYsJe9qamqorKqZsotPv7QXZCVk8U333yLd997j38nzsaNG6cshyoQASJABBYKgfqGJrS0tAx1x9/fHyEhS1FZWYWenp6hdHE8YBHUSssq4ep24T1qTMeC/FzYmBngumuvBPNasTTaFi4B6tnCIUBGzsK5ltSTKRBgEdY6O9rh5+0BgUCArJw87sdLfwoSLhSNiU+Cvr4B/86ICym0JwJEgAgsXgLs3lpQUAAjo9Hvldl34CBYnjjSaWu7EHxGSUkJm9avQmzUWV7NlKR46GupwM7Ojj+nHREgApJDgIwckV4rEiYpBDQ1NcGmV7CX2XVzI4uW5mZoGSMK0MX9YV6fr7//jfcAsbwl/j7o4oylM2fOsFPaiAARIAKLmgC7t65cuRzJKalDHJqampCYlIzrrrlK7IyFjo4OfPfDz8jNzQMLNMCUjow6BwdnN6QkxcHSVB821pYsmTYiQAQkjAAZORJ2wUhd0RBg8657env5qWpdXV2w4IycU2GRkwrPyStASnoWdu07iqeefwMPP/UybLgRvtDQ0EnrLuoC1HkiQAQWBQFmNBw9GYZuKGD3/sM4cOgYIs8lobi8DtnZOWLFgHlvdu87AlUNbaSdz8QPP/+KCM7AUdcxQkFeBnw9nGBvZytWOpMyRIAICE+AjBzhWVHJBUSARcrx8fHFI0++hEefegXGRvo4cjxs0h7a2Vihr68Ht9x6G+69/0E88uhjuPPOuyatRwWIABEgAmMRWGhpjY2N0NLUgbGxCXz8l8LRzQfu3n7o7myBu7ub2HS3oLAIh4+fRsiKNfDy9oeGtj42rF2Ngf5+DPS0Ys2KUGhra4uNvqQIESACUydARs7UmVGNBUKgoqICvf29MLe0RF5+odDrau667QZ8/vnn+L//ewpJyckLhAZ1gwgQASIwcwJsAGlg4IIcdqygoID4cxFYt3rFhUQx2IedPYvymmYEBq/g12QW5GXBWF8TVlZWCFoSgKDAAMjJyYH+zSsBapwIzJgAGTkzRkgCJJUAG6Xr6+2Duro6urq7Ud9QL1RX7GwsUV1dhbaWVvz5xx9C1aFCRIAIEIHFQCAnNx9GJmZ8V1uam/mXhPr7uEFVVZVPm88dWxt0OiwcOoaWsLSyATuPjQ6Ds70lPP71MgkEgvlUkdomAkRAhATEwshhi7lF2CeAhBEBIQgMvq+htLQUysrKcLC1QUR07KQ1FeTlERIUiCXBwXj11VcnLU8FiAARIAKLhYC8vBwqK8uRl5uNlIRI9Ha1Q09Pb16739jYiKioc4iMTYKesRU0NLVRXVWBqtJ8bFy7Uuzf3TOv8KhxIiDBBMTCyGE3oCwxW5AowdeUVBeSwKWXXoptl2zHa6+9BvaOnEcfvBNffPPzpLXPZ2ajqKQcjz/+OCwsLCYtL04FSBciQASIwGwScHF2gvRAN1QUBLhs+zbcfNONs9ncuLL7+vpQWFiIU2fCEBWXDCUNfbh7+qGrsxNpyQnISInHspAlkJGRAf0jAkRgYRIQCyNHU1MT2dnZC5Mw9UqsCWzduhXp6elobmlBaWkFmptb8Mb/PplQZycHO7S2tuD8+fMTlqNMIkAEJIYAKSoiAoqKighdugSe7m78ehcRiZ2SmPr6euw/dAyF5XXQN7HhjRsNTS1EhZ1AZ2stnO0tsH7d6inJpMJEgAhIHgGxMHIYts2bNrIP2ojAnBL4/fff8eqrr6C3txdPv/QGvDxdERufiPjElHH1OHbiDKSkpWFlZTVuGcogAkSACBCBuSXAXkQadjYciamZ8A0MhZ29EzQ0NFFXW4O0hChs2bQWgf5+MDY25tI15lY5iW2NFCcCkktAbIwcyUVImksygSuuuIIzcPrQ29OL+++6BbfdeA0fiKC9o3PMbv302w6cPZcIXz9/oaOxjSmIEokAESACREBkBFhI6KjYJOhxnhtHF88hudWVlWiqLcfGDWshKys7lE4HRIAILHwCs2rkLHx81ENJJ7Br1y5oa2lBUUkRujo60NTQgBJ3vHSJ36iuRcXEIz0rDy+//ApuunF+5pmPUooSiAARIAKLnEBSUjJSM3Lh4R0ANTX1IRp5uVnoaK5GcFDAUBodEAEisHgIkJGzeK419XQMAsnJiQhe4gNFBQXOMyPPGTgK0OeMnT//2TOqdGtrG1RUVEalTyOBqhABIkAEiIAICPABBoqL0d/Xh5ycTHR2dqK4qACxUadhqq+BwEB/EbRCIogAEZBEAlKSqDTpTAREQYC9DFRGRhYxccnw8faAjdWFSGneXm5ISh0dVGD1ihAs8fPEU089haNHj/I/pqLQg2QQASIwSIA+icDUCEhLSyMkOAiNdVWQF/QiKz0eqvLA5g1rYWFhMTVhVJoIEIEFRYCMnAV1OakzUyHA3pPz0ksv49nnnsepMxEoK6/8t/oA+gcG/j0e+bEyNBAbVofwIUifeeYZMnRG4qEzIkAEiMC8EAgNDUGAvy/Wr1kFZ2cnSEktsMebeaFKjRIBySZAdwHJvn6k/QwJsB9CVVVV1NXV4eEnX8SpsCgcPn4Gzzx237iSQ4L88cj9t2OgrwdxcXHjlqMMIkAEiAARmH0CWlpasLQwn/2GqAUiQATEjsBECklNlEl5RGChE+jt7cXVV1+F/v5+MN9NSmo6brn+Sjz69KsTdj0sPAp6BkYIDg6esBxlEgEicIEA+xu7cER7IkAEiAARIAKzT4CMnNlnTC2ILQFARkYGX3zxJdi8bikBcMct1yEzJw96utrjah0RHYcd+47ya3PGLUQZRIAIDBFgi8F/+OkXtLa2orJycFroUDYdEAEiQASIABEQOQEpkUskgURAwgi0tbWBvaWbvRvnrgeeQvr5LLz2wpPj9uJUWCSCgsiDMy4gypB8AiLuQWlpKTo6+/Diiy8hPCJKxNJJHBEgAkSACBCB0QTIyBnNhFIWGQE7OztcddVVYO/HkZeXRUra+QkJaKiroba2dsIylEkEiMB/BGxsbLB21VKsWLkKWzZv/C+DjoiAhBEgdYkAEZAcAmTkSM61Ik1nkcCVV16JNWvWQkpaBksC/PDR59/h6x9+HdEiW1Pwfy+8CQtre9xzzz0j8uiECBCBiQkwQ2fD+rWQk5ObuCDlEgEiQASIgKQREEt9ycgRy8tCSs0HgVtvvRXbLtmO5uZmSEtJIS+vEJ9++f2QKl1d3WhqbsWmTZuG0qZ60NPTA/byuqnWo/JEgAgQASJABIgAESACwhMgI0d4VlRytgiIkdx169ahb0AKvb19yMrJQ3RswpB2iooK2LB2OR599NGhNGEOiouL8e13P+HnX37Dn//swm+//4WBcd7DI4w8KkMEiAARIAJEgAgQASIwMQEycibmQ7mLkMBzzz2HhOQ0tLV3oK2tHdfdej+2XHETbrrzYRw6cgK5ubn4/IsvhCZTW1ePrt5e9EMACKRgbmbKh6wG/SMCkxCgbCJABIgAESACRNmIlX0AABAASURBVGB6BMjImR43qrWACaioqCAkNBTq6mp8iOmGhkZ4urtwXpwVaG5pRV9fLw7s3895e3qFosCMmq0b12JpUCAu3boRISHBYCGrhapMhYgAESACROBiAnROBIgAEZiUABk5kyKiAouRgIODA5qamtHc3AIDAz0kJqXhx1//4iOw3Xz9VVBTU8F9990nFBptbW0YGxvDytKCD1UtVCUqRASIABEgAkSACBCBKRGgwsMJkJEznAYdE4F/CQQFBWHFihWQkpZGaVkFHB3sOMNGDVXVtfjhl7+wevlS1NXV4oMPP/y3hmR89Pb20nogybhUpCURIAJEgAgQASIwAwJk5MwA3kKrSv0ZSeCpp57C5ZdfDlVVFaSmnUdtbR0/RU1VRQXrVi9HZ2cXjHQ0sHXrVvz555983kgJ4ncWFnYWn3z2JXJy88RPOdKICBABIkAEiAARIAIiIkBGjohAkpiFSeDmm2/GH3/8ifc/+IAzdlT5dTpqnNHD3qMjJyuLsxFR8HJ3QWJ8HC699FJcffXVSE9PF1sYy5cvw+pVy3Hs+ElERZ9DX1+f2OoqRoqRKkSACBABIkAEiICEESAjR8IuGKk79wRkZGRgb2+PnTt34sUXX4KDkzPiE1PQ3NKC/KJinItLRElZKZydnLBmzWo4OzvPvZJCtigQCODA9eWuO26Fh7sbBUAQkhsVIwJEYCwClEYEiAAREF8CZOSI77UhzcSQgK2tLR566CG8/vrr+O2337Bq1Wo8+OCDePXV1/H6G2/g5ptvEUOtR6skJSVFQRBGY6EUIkAEiAARIAIzJ0ASxIIAGTlicRlICUkj4OPjA11dXTz88MNYu3YtLC0tJa0LpC8RIAJEgAgQASJABBYsATJyxO/SkkZEgAgQASJABIgAESACRIAIzIAAGTkzgEdViQARmEsC1BYRIAJEgAgQASJABIQjQEaOcJyoFBEgAkSACBAB8SRAWhEBIkAEiMAoAmTkjEJCCUSACBABIkAEiAARIAKSToD0X9wEyMhZ3Nefek8EiAARIAJEgAgQASJABBYcATJyxr2klEEEiAARIAJEgAgQASJABIiAJBIgI0cSrxrpTATmkwC1TQSIABEgAkSACBABMSdARo6YXyBSjwgQASJABCSDAGlJBIgAESAC4kOAjBzxuRakCREgAkSACBABIkAEFhoB6g8RmBcCZOTMC3ZqlAiMJNDW1objJ06ht7d3ZAadEQEiQASIABEgAkSACEyZgPgbOVPuElUgApJBoKioCImJibyyuXl5qK6pxsDAAH9OOyJABIgAESACRIAIEIHpEyAjZ/rsqCYRmBGB7u5u5HDGTXt7O9zd3HDNVVeir68PpaWlYGlxcXHo6OgYtw3KIAJEgAgQASJABIgAERibABk5Y3OhVCIw6wRsbW1xxWWXQV5eHo2NjTh1Ogw///on9u47hIOHDiMlNR3FJaWzrgc1QAQWGAHqDhEgAkSACBABkJFDXwIiMMcE8jjvDfPYsGaZp+bg4eM4cjwM9S3daKirQ09PDzzc3XDLzTfC3s6WFaONCBABIkAEiMAMCVB1IrC4CJCRs7iuN/V2ngmUV1TgbFQ8os/F4PjJMzh+OgKGphYw0NeB9EAXNm/eAHc3Z5ibm8+zptQ8ESACRIAIEAEiQAQkl4DQRo7kdpE0JwLiQaCzsxN6urro6mhBaXk19E2s0NHRDll0I3TpEmzbshGOjg5YtiwUsrKy4qE0aUEEJJxAbW0tUlNTZ9SL+QgIUlZWjpycnBnpTZWJABEgAouZABk5i/nqU99FQUAoGSxE9N87dqGmpgba2roIXrYaSkrKqKmsgLycLHJzc/mgA0IJo0JEQAgCbNpjSUkJkpNTEB0dg/CIaERERiM5JUWI2pJdhE0HPXDoCA4cPIpPPv8Sp8PCkZiYxPf//PkMsCmjLMBHV1fXmB1l9bOzcxATG4/I6Fj8+sc/KC0tG7Ps8EQWAv6vv3aA/b0PT2fHYxlKKdy1YFEWW1tbwba6ujqwjRk3R46fRkFRCViAElafNiJABIgAEZgaATJypsaLShOBKRNgD5uKioqwsbLAyTNRCFi6EmkpCTgXfgqtLS38A1ga9+AlLS09ZdlUgQhcTIA9TJ+LjcOBwyeQU1jB+QkVoKCmC019U6hoGqAHCth/8AhyOMP64rqiPZ+ZNOb5rKysRH19PZgxwo4zs7I4wyMOefn5vKFyPiMTCYnJnOGWiszMTGRnZ/PHh48chY6+CYwsbOHl5QNnF0/0SilBXdcEzZ1AaXUzMvPLcfREGM5GRCE1LY1XlhkcbNu5ay+qGzshq6QFFQ09hK5cj5iEFBw+egJ79x/i9eEr/LtjzJkxUlBQgOr6Jt4Dw4wVJis+PgGHDh/DoSMn0NjU9G8NgOWlZ+Ujr6QaZ8JjcYK7N8QkpCEu6TyKKurhG7gU7R1dOHLsJMLDI3lvFJPPgpS0cPcNZogxYaxt9sk2xol90kYEiAARIAKgwAP0JSACs0kgNzcPv/3xN6KizyE6NgF2js78yGxJYR6WhQRi2bKluOuO27Bty+bZVINkL1AC7KG2oaGB793xEyexe+9+HDl6DKUVtfBbEgoHJ1cYm5hCT98Amppa0NXTh4GhMSqra3HyVBj34JzGexA6OjpGPbjzQudoV15ejuhzsZyhkoVzMXGcgXYMx06GIT2nELGJaTh+6ixSM/JQ29gFOWUdzkhp4beG1l70SSmie0AedS29qGnuQVe/HMxtXGBqbgV9rt8+ASFwcHHnOWhpacPM3AK2dg5wcnaFt38wNPVM0dErw7V5HAVldcgvqYVv0ApYWdtA38AAOrp6YAMQAUHLOLlOcHTzxf5DRznDIwqHjh7H/oPH8Nc/e/Dr738jMzsXXZ2tKCqp4IyVdBSV1wHy6rBz9oKTuy/+2bEXYWcjcPDIMRSXNyB0xVo4OLrA0zcAvgHBcPfyg6uHD6ebG+fx1YEPp7uHbzC0DC3R3iuL0qomREQn4mRYNMfnDJge5zhvE5uOt3PXHvz25984w3mt5uiyUTPTJUD1iAARmBMC5MmZE8zUyGIlkM0ZOaoaWkhISoKRoRk0tXQQG3kKt9x0PYyMjODv779Y0VC/p0mAeTjYiH5sXDz2HDiC3fsO8UZ0bl4BtLgHdhVNQ9hzD87jiZeSkkLI8jW8R7G+pQf7uIf0YyfOgN+On+IewiNx4uQZHDh0jPcyxick8p6JwsJClHPGCDOq2DHzXIzXxnjpTHe2RqaETaNLSUV4RDT+2rEb6dlFUFTTQ31rD2fEaHFGiTdngCyFo5M799DvyxkBQXB284KVjS1nsOnD1taOM1TsYWllzRsvzJBjx5aWVjAxNYOWts54KoxK1+IMHxNTc65NL9jZO3LsnCAnJzeqHEtQV9eAsrIyApeugqa+GeycvODq5Y+lnLGydMU6MGPmimtuge+SZbzeNnaOMDQ0gqKSEi9z+eoN0DYwh7WdG7fZM5ETbuxaMQNLQ0MDpmZcPVt7jsUFg8ieM5wcXX2gpK6HM5FxyM3Lh4mRMQz0dfmBlAkFUyYRIAJEQAwJiFolMnJETZTkEYFhBNavXY0VSwNw1+23YaCvkw80ICUtw41Wx2L4NJNhVehwkRBg0xiF6Sqb9pSdnYPDR45jx659OHzsNOfFaMKAjAr8AkMRwj1cyyprY93my2DDPfwzDwR7cJ9ItoqKCnR19WBr74DQlevgw8nx8l8KKwd36BlbwdDcFs4eftA1ssSArCqqOQ9KYVkt0rMKcS4+hdvSkJ5+fqImhvJYPzOzsvnpWoePn0FsQjpyCqvQ2SfLG2XBy9bC2dWD8zLpwcLSmvOeGIJN7xwSMEcHU21TU0uL13MwSIgSZ8ioqqrxXp/xVGbGjiZnVKmqqY1XROh09n4tprOOji5Clq3E2k3b0d0vQFRcMqI5z3FxcTHY1tfXh6qqKjDDND8/X2j5VJAIEAEiIOkEyMiR9CtI+v9LQHw+2EPF+fPnudHw0ygpKQUbhWUPQj4+nigtLoTfkmWoqGnEn3/vxC+//sE/fLBpR+LTA9JktgmwUfede/bj5KnTYIvVx2qPGRFHj53kpz1VN3XCnhu1D1i6kvv+hMLaxp73WDCPA3vQNeJG8NnxWHKmksZkqamrc99ZTd7zwKa4MdnMS2Ln4Mx5Uzzh4OwBAfo4b4rNhKL7+/u5738J9nCeptrGTji4enNGWQjcvf3g4OTMeyY0OENhQiGUKRQBZugwo9XT2x8rVq1Hr5Qi0jLzsffAYbA1RGmZBcgtrMDhoyfB1i6xexRb1yOUcCpEBIgAEZBQAmTkSOiFI7XFhwCbfsNGTAcfVtmUnBNnImBgaoviinqwtQZskXF+QRHaWxohIyODgCUh8AkIhaffUkSei8cnn36OjMws8ekUaSJSAuyBn30v2HSv6upqZGRkws0rAPrcd4QFCEhKTuanGLW3t/PtxsXFoaCkBpb2bvy0JysrG7CRe4FAwOdPaSeiwuzBOCLsOGIiTyM40BfMGzRcNHtoZsY6iyDIopnt2L0fyedz4B+8AlY2trz+w8vT8ewQYPcXB0dneHAGz9ZLr4Ff0Ao4urjDifOW2XOfbB3TvsPHER4dxxk9x1BZWTU7ipBUIkAEiMA8EyAjZ54vADUv+QQio85h196DOHjoKN8ZNmd//ZpVSE2Oh5KKKr8wOiklFSVl5XDzDoRAcOFBVUlZGZpaWli2agNcPPzw199/UxhpnqBwO/ZAPdmUv8bGRn6qDivHDA1mYBQWFvLeM2Z4sshdg62xtSasPHtYZ4YrM0hKS0tRVlbGr0VhD+8sNDCbfsUW6rOH/sG67JMZMYP1cnJywBbQH+Y8MTt278Muzmtz4nQ4cgrKkJCSCS09E6ira/DBAHwClnIj78o4cuwUToVF4WxEFFo7++Du5c2v/2Cy53tj/Q0/cxw+Hq7Ytnk9v56M6cQYxHIG2Z59Bzm9Y/kAAcnpuTDkjLeg0NXw8gmccPoWk0Hb7BFg63kEggv3G9aKo6MLzC2t4RfAedQ4I9vSzh1hUbH8d5yFsGZlFtNGfSUCRGBhEyAjZ2FfX+rdHBDYvGkDbrnxWsjLyeDX3/4AeyA0MTaEk50FEuPO8Yuj3byDISsrh6y0OLCH5IvVcnX3hLGJJfbvPwj28H5x/mI7ZwbDICe2wJ1N/0tLSxtix4yV9z78BCxtLDbMIGFTdaLjkpGeXYA9+w9x22GkZOSisLwe+aW13FaHuMR07sH8DCKiYhCTkIrwqHicjYxHLJeelVeKnMJyZOeXIiuvBImpmTjJeegOHjnOGSMR/DSgvdzD/eEjx7D/0DEcPHoSiWlZyMwtQVV9O2SVtWBt5wq/JcsRGLIanr5BsHVwgZunLyytbYfUFggEnNFgDG/uwdPTdwm0DSxg5+gmVsZBZ2cH2lpbwQxBuX8X5TODLy4uHrn5ZfDl+ujhG8j3kXkMRLHmZAgQHcwaEPNOAAAQAElEQVQaAVVVVc7gWYqI6Hjs2H0Ae/cdwKnTYYiK5v4eYuMRl5CE5ubmWWufBBMBIiCWBBaMUlILpifUESIwjAAbYR52OquHAoEA7GGhuroGdXW1/PS0k2fCkcc9IBsYGvFts6lGK9duRmdnF04e2cenXbxbv/kStHT24vCRExictnRxmYV8zoya9PR03pDZuXsP9h04whuMSckpKCyr44yMHERGRoEZPPHx8bwhcOjoMbDF1I2cx4Z5Uc6cjcCf/+xCUlo2vPyWcgaFHxyc3Pmpgf5By+Hk4slH0GLTeewdHDkPmg/MrJygqqnPTwvz8lsCDx9/sFC+9k6usHfkNic32HObi7sPvP2XwjdwGVcmCP7BK+HBtWHn4gNXT38EcPKdXb3g4OwGKxs7GBgYQllFZcrGioaGhthdZrY2Z+XaTSgur+bfU8OmVp44E8kZctoIXblG7PQlhYQnwIzW4GWrsWrdZlg7esDAzJaP2CatqIEBaSX8/c9u7r7Vyf8tCi+VShIBIkAE5p8AGTnzfw0Wlgbz1Bs29SguPgGxcWzjRiV37sHpM2H8e0COHz8JFnKXTVmaTfUsrSxRW9eIXshDQ9sQVZWV6O5o4aetsXYFAgEsbJ1hYmLIr2tg06dY+uAmEAgQunw1GppbuQeLnYPJi+aTGXZHT5zGp59/DQ/fpWARv/bsO4TCkrILhomTMyCjhOZOATT1LRC4JBjrNl7Kv0xx78GjnPflPHQMLbB0+TqwcMMs/O4gvOHHg2mDn2xtiY6u3uDplD5lZGSgoKDAeelksdD/MUPdLzCEuwRafKhnH/9gGPxrxC/0vi+G/gkE3N+VphbU1TXA3qfEQl+z0Nx+wcvx829/48+//6GIkIvhi0B9JAILiIDUAuoLdWURE2hpbeXXQCSnpCH1fBYUlNVQ09DGL3zuHpDB2agEhIdHjCLEvACVnDHCMtj6jJKSEnY4pa2zs5Of0hEctAQPPXAPtNTk0NVah2uvvpSf4pMYG42Txw6juroSeTlZsLa05BduJyfEjGqHzaG3sLKGQEaefzfJqAILNIEZfGyanreHG7x8A/joXsyA8A9eAd+AUCgpK4M9cNk6OPMvc2QvdGQomHHi7OKOVZyXzN3Ll1/jwtLFcVsoOjEPlaWl9ULpDvVjAgK1NTXIOZ8Mfx8PXLb9kqH1hBNUoSwiQASIgNgQkBIbTUiRBUGAeUtSUtMQdS4WhUVFc9IntmaDzSFnU2r0dHXQ3d6CgZ52KMoMoKujFR0tdTAx0IS1tRVvOGRmZSMlJRUZGRk4fz4DX339Lb7//ge89sZb/Nz0AwcP8Xns3RJszQGbRoWL/rF56rv37MWxY8fw3gcf458dO/noWKlc3/OLSvkRT2XuwXz5sqXw8/eDhooCSvIzYaCtirr6Opw8HQ4HZ/eLpF44tbN34h7aNyG/qBI///IHL/dCzsLYswXOzLMWHR2DfQcO4x/O67Z3/2EkpmVDQVUXNnaOQx2Vl5cH24YS6IAIEIFZJcDud1kZ55EYH42mulJs37YJbm6uo6LpiVAJEkUEiAARmBUCZOTMCtbFK1QgEKC2tg7ZObkIi4zDV9/8CLYwm0Wp+vb7H3Hk2EmcPnMWUdGx3BbDL2RmtGpra8E8KeyYGS2FhYVgo/vsfLKNzSnfvm0LLC1MERt7DjV1DXwbusZWWLl2C+xdfSGnooPzOcX8Sw3rmrvRNSCPhrZ+aBlYIHTlOhib2+KSy69DUOgqmFg6oqljAKmZ+ZBV1gEL8bt770E+gtrZ8Egw3fbtP4DquiZo6JnBwMCQf4EhW5CemZWFEs6423/wMA4dOY5zcYlobeuAgoo6VDV0ICWvChlFbSwJXQ1FRcUJu6ZvZApNTXUwj8aEBSUgs62tjTccDx89jjPh0SiuqIeCmi48fIP5Rfl+Qcvh6u4NEzNzCegNqUgEJJ8Au8/W1lQjPy8XaSkJiI08jYSYcKTER8LKVAehS3yxlPNOS35PqQdEQFIJkN4zJUBGzkwJLqL6bDoRex8M21gEMdb1pqYm/PLrb2Cj8+ycbSYmRhD098LG3BBenq5oamrB7j37YGyoj5KScuQVFuN8ZjZiOAMgOTkF+fkF+OTTL/DTz7+Ara357fc/kZ5VhF+5z8zMTGRnZ2P/gUOIiIwC86709fXx7TU2NmLwn7q6Ory9PPF/Tz2JLZvWw9nJESrKKvwLDbW0tGFlbQsnF3dYWlnzG5v6ZGFpBbbZcp4TF3cvrowNv0iczUc3M7fkyntAV08PfktC+fUh/tyDuKaeKfeA3gBzayes3bAVxiZmWLf5Utg6eYG998bJ3Q8hK9biuptug5mVA/yDVnDpIXBy9YSdgwusbR04g8gAUlJSg6qP+xkTHYHOznahyo4rZB4z2HeEeWzCzobj5JkINLb1wYEzOD1ZlDGOuZ6+Ac9bIBDMo5bUNBFYHATYfbOyogJVlRXIyc5E+KnDnKemHJoqMvBwtsWmDWuwZmUoNm9cB3Nzc/LcLI6vBfWSCCxoApM/aS3o7i/czomqZ8ywYe8JYYbNu++9j/ySGuQXV+HYyTDOK3MCJ06FoaS0Aj//8jtniBzGiZNnkJySjmIuLSu3GCVlVZBTVIKJuTW6+qShrKoGRQVlKCoqc0aFLXogj5KqRlx2zU3w8AlCQWkNrOycYWxqzoelrW3sQkZWHoo5OWo6JkhOz8O+g8dwMiwSUbFJ+GfHHlRXV2Pn7j3Ys/8I2HtJ6hoaoaKqgrOnjyA1OUEkKAaNEi1tHdjYOfDRs2RlZYdks8XrrIy1jR2MjE1gYGAEbR3dac1hLy4qQGlJMbo7m+Do4DDUhiQdMOP3yLFTvEHIQiJ7+y8Fez8H87pJUj9IVyKwEAjExUTh5NH96OloQHdbPTSVpXHNVZdznppAfkBIjxvMYesB6e9zIVxt6gMRIAKDBMjIGSSxSD/ZGhrWdX7qQm0tsrKyEHY2AmfCwvmF/PsOHuHfEZJTWMF7JewcnGDn6MJ7J5j3wo/zVNx42z3YtP1qOHsGwNzWBeydILfceT/WbtzK1VkGf84T4he4FL4BwQhdsRrLVq3lN28/7gfW1R22nNHAvC28bAdnzohwhJ6+PtTU1GFla8e/QHPr9iugra0DF3dP+AaGwId7aHb38od3YCjORsXDwNgSAcHLuW0ljMztYe3oDms7R87IkGbdk4itqakRxUWFOHpwD/IyE3H3nXfC2dlZInQfVLKurg5Z2Tk4FRYFFh3N1t4Rmlrag9ni8Ek6EIFFQ4B5bzLSkhETeQZtrS0wNzWGv58P3NzcJNZDDPpHBIgAERCSgJSQ5ajYAiQQF5/Iv1Nkx67deP3Nt/jQvcWVjdAxsoSOoQXklbU5g2IZ2AJ5R2c3ODq7jqCgoKAw9EPJRgHl5eWhpKQ0lDai8AxOmFyB4L8pTQLBf8csLzB4GWw4g4bpwJphxpGxsRmcXT3h4ubOkuZlYw8YbBuv8eLiQsTHRiM1KRbhnNepsiQHqvL9uPXmG3D1VVfxU7nGqysu6Wwt1akzZ3Hg0FHs2ncQMQlpqGnqBJvixzxb4qIn6UEEFhsB5sWOjzoNFwdLPPXk47j80m3Q4zw243OgHCJABIjAwiIgtbC6Q70ZJMAePgcX8peXl4Ods4hgbPrZYJn6+joU5OVgoG8ABvoGkOGcHtHhpzhDRZkffdczMFgU7/8Y5DGdTxaJiE0tKyspRnJiHCLPnsa+XX/j799+wKG9/yCK49na2jJKNKvX0tiAmqoyVFWUwdfbA8tDQ3jPjbGx8ajy4pLAjLaWlhY+iMDRYycQFhkDNW0jOLh4w3/Jcrh7+cHKykZc1CU9iMCiJFBZXgp1FQVs3bIR+pxXnE2n1dXVFfkA1KKES51efASoxxJLgIwcib10EyteW1vHe2n+2rEXSel5iE06zz+QHj91FocOH8OBw0fR1NzKGzGqqkpISz+P3t4BSMvKIyUpYWLhlDtEoKqiHNnpiaiuKISRrhoqyothZmkDT78lvHfJ2tIUaQlRSIqPQmlpMVg0o/a2Np67s5sn1m26FCErNyApNYtfWzQkWEQHTU1N/ItQCwsLUVNTA/bCzamKZjJyc3Nx8nQY9h44wn2P4tDU3g8re3cEBC2HoaERFJWUpiqWyhMBIjALBCrKyxBx5jgC/LwhEAi4+/1RfPzpF/j0s6/w4UefLLiQ9LOAkEQSASKwQAiQkTO7F3LepDs42GP71k3o7+1GQ101jIxN4OUbBBbZyt7VB87u/vxal5CVGyGnpI1rrrsZV1x3C2669S74+AXMm96S1jALeezg5oOOjk7OSOyFpaUlertaUVNRgvaOdiQmp6Gce+hY4ucFZZletNZXIC8zGTGRpxAVdgzR4SeQFBsBWWnwDx85OTlISUmZEYaGhgZkZmXh5MkziDyXCBauubCsDnFJGTh6MpyfWsYCSQjTSF1dPaJjE1FR2wIDUxv4c0aNp08AzCysoKSsLIwIKkMEiMAcETh+5CB2/f07+vp6h1o0MzXG+rWrsGL5Uly6fRsfcXIokw6IABEgAguYABk5C+jixsbG4a8de8CCBYSFR4C9m2TVymVYt3oZstKTkRAbhfNpyajkvA/tra2QkZGFiakZ7J2cYWFtCxaGeQHhmLOuGBkZw8s/BJV1zVBSkMf6NSuwbGkggvw8cOWlW3HnHbdDQ0ODn4q2ZEkA1q5ZiW2bN+ASzgjdvGEtdLQ00NzcgiPHTuPI8VNIO58xLd3ZNDIWjvvE6XDUNnXD0MIO7t7+YCGybewckBAfg6TEeBw5egyffPYl/vp7Bx+Ku6+vb9z2wiOjIJCSRU9vH5o5rxAz2NhWwX2Hqqoq0dnZOW7diTMolwgQgekSaGpqRGFBHrflc1su4s5FIOzEYdRzA1rGxoawsrLiBl46ePHOzs6wsbGBo6MjTExM+DTaEQEiQAQWAwGpxdDJxdJHNTU1yMvJcQ+kA6htaEV4dAJn8BzHvgNHICMlQHCAF+ytjKGpIo366hJkpMRynoRTSE9N5l8Ix6ZTdXRc+GFcLMxE1U8FBQV4egdAWU2H85Qcwd87duGPv3chJzcPlZWVvJfn4rakpKTAh6EWDMDGygLr1izHfXffgWuuuhKT/WPX6ejxk3zI7N37uNHbPQew/9BxVDe0Y0nIKlhZ23BGq8aQGNbWpq2X4arrbkZQcCjUNTSRV1DAfzf++OPvoXIXH2zdvBEeLrawNNaGkkwfBrqa0d/ZhN72BuRnpWDPjj8vrkLnRIAIzITABHWbm5uQEBeNipI8aKnKQlNZCjpq8li1bAmuuuISPHT/3bj7ztuwhfu7VVRUnEASZREBIkAEFj4BMnIW0DW2t7fD1s3rsXp5EAJ93HijxtvThRvBs4epqQmYN8HU1BTs3SvBQQHYxHkRtm1eB2c7M+iqy3EPsb3ISI1DYlwUYqPO8FvcuXCkJMRyhlAS8nKzUV5WuoCIib4rTDMVtwAACJhJREFUphZWcPEMxLLVm7Fu03b0y6jgt792Yu/+gzgXEwu2tqWiogJsSlkT5xlh3jYPd3fY29uCPZSwdTPMKCovL0dhYSH/ItTUtDSubhzCI6Nx4uRpHOe2Q0dPQlFFG7390hiANPr6+6GmqozujiZEhh3jr1dRYQHXTj0aGxvQxI38yssroLurC9Z2Dli2ah0MDUxQVVkOB0cHsEAIY9Fg+jVxepaUVaK0vALFpWUoLilFZnY29HV1YGCoN27dseRRGhEgAtMjUFtbg+zziQj298SK0GA42NuDeWfs7OygRGvipgeVakkUAVKWCEyVABk5UyUmAeVVVVVhaGgINmXBzdUF/r7e8PX1GVNzNsJvZGTEPWTbw8XZGRvXrYamqgLUVBQhLyuF3u4O1NdVoSg/GwkxETh57AC/dmRMYZTIE2DGCu9Vk5eHkZExrr3hNrhyho+csjYq69qQnlWA6LgUhEXE4sSZKH47dOQUdu8/jL0HjuHgkZM4eiIMp8PPITo+DSnn81FQWoXquhY0tXejq2cAcsxg4QwadycrrAgJwGWXbMbG9WsQErwEl2zZCBd7C6jID6ChqhhVJbmoLM5BVVkuvyZIqqcFWsrSWMbVe/CB++Dt5QH2viRmeBUVFSGNM6rYu5L2cB6i02ejeZ11DM3h6ObLhxQPDFmN4GXroKCuD/8lyy94o/ie044IEAFREyjjBpYSYyPQVFOKLRvXQ0NDQ9RNkDwiQASIwIIksEiMnAV57WalU3v3H0BFbSsGpJVh5eAJd59gLAldgzUbt+Oyq2/CdTfdCTk5uVlpe6EKZYaksooK9A0MYWVjBydXjquXH7z9g+DDbW6evmhuroeelhqszA3h5mwHXy9XLA30wdIAT27k1h1LfFzh6+GAQG/u09MZy5cGYOXyEM5DZwp1dfUR6Hp7e3nDQ1lJESoqypCVY9dLgM6OLjS2tKK8qgYFxeVIScvEvoNH8dc/u3CY8wydi0vmjKlqtPXIQNvAHB6+wfD0XQJrW3toaGiCvZNIRkaGj9jEDDkjzoBTUFAY0TadEAEiIBoCbOChp6cH51MToaOljqXcAIZoJJMUIkAEiMDiIEBGzuK4zkL3csumjfBwtoay/ADSkmJQUV6G5qZGvr5AIOA/aSdaAsx4WL56M3QMrTAgo4TapnbkFJQgJT0TCUmpSEhOAzNIEpPTERufzG9HT5zGjl17sZfz/hw/cYqf2sa8MMeOn8SBIyeQmJqFkqpG9EABmnqmMLKwh5W9G+ydPOHo6sNt3nDx9IebVwCCl6+DT2Ao3Lz8YefgAlMzc/49SbKysqB/RIAIzA+BxPhYfPPFh8jLycb+g4exZ98BPgx8a2vr/ChErRIBIkAEJIwAGTkSdsFmW12BQABra2v4+/li0/rV0NNQQFNdOZLjI5GaFIe62hpMFI1rtvVbqPKZp8fMwhIOTm5w8/CFH2d0+AetgB+3eXIeFSd3X94oYYaJo5sPvPyWXsjzC4aKph5nFJWhsLQWlvbu/LtrnN28YGvnCGMTU2hqaoFNYWRtsPfZMC8MM6yYF4a8cgv1G0X9mimB+a7v4eWDW+64H7fe9QDuvPcRuHsH4cWXX8f3P/zEDXQk8qHm8/PzUV9fz08hZh7c+daZ2icCRIAIiBMBMnLE6WpMU5eExCREREYjPCIKZyMiER0dg9i4eCQlpyA9PR2ZmZlg719hP4hstL+0tJSP+FVXV8eHEO7u7h6zZfYAbGdni6VBgWChjj1d7VFTXoCYyNOIj4lAckIMzqenID8vB8VFhfz7YKqrKtFQX4eWlmZ0tLfzIYaZfGYYsekXwxu6+Hx4Hh1fICAtLc1PD2QGyfCNeVlYHtvMzK3AGzUOTrQAGfSPCCwMAmyaKxuMYH/r7FhOXh4PP/4Mlq3eBCl5NfQIlFDKeWtjE1L56aaHj53E7r0HcDY8fGEAoF6IKwHSiwhIDAEyciTmUo2vqJGhASwsLGDKPezqGppBQU0XUgoa6IECWrqkUNfSi8r6du4HsYlfc5GdX4rzuUVgP46nwqKw/9AxPtzx/oNHse/AYRw5dgJnwsLBjKfUtAtGEjOU6rkRQyNDfTg72MBYXwsaKnKQ6e9AW0MF6isLUFGUhay0eBw7uAu7/voFf/z8DX794Uv8+M2n+PbLD/H15x/ii0/e444/wVefsfOPUFtbPX7HKIcIEAEiQASGCCgpK0NbRxeGhkYwNDKGDeetdfX0w5KQ5Vi2bAXWr1sLXx+fofJ0QASIABFYzASk5rTz1NisEDAwMICxkQHMTfThYGUCd0dLONtZwN7WEjY2VrCytoa1jR33g+jAr7lwcHaHg6Mb2BoMb/9gBASvQMiKdXD1CoCzhz9sHD2hY2SJPmlltHVLob61jzeUmLE0uHX0yWFAVg0yStpQ0jCAipYR1HSMYWLpgM2XXovLr74JV11/G67mtmtuuB3X3nAHrrnhNu78Flxy+dW47EquzFXXQUFBaVaYkFAiQASIwEIkIBAAcrJSUFGUgZaqHAy1FPl35bBzeTkZ7p6qAPpHBIgAESACABk5C/BbwH4EFeSkoaYky//4sR9BtraGeV7YDyHLk5WRgpSUYETvZWVl+R9IRUVFsHUcxsYmMDO3gIWlFSytrEdtLJ29dJI3oGztYcttrByrr6ikBLaxkUe2FoRtKioqUFfXgJaWNrS0dfjF7SxthBKL9IS6TQSIABEYToDdn9l9mt2v2X2b3b/ZfZzdz9kLQNW4+zvLY/f74fXomAgQASJABC4QICPnAocFv5eRFkBJXpo3fNjon666PAw0FWCkrQh97lOHO9fkRgXVlWX5EUJFrix7Tw6rRz+iC/7rQR0kAuJKYMHpxe6n7L7K7q/sPssMGHbfZfdfdh9m92N2X2b3Z3afZvdrZtCw+zert+CAUIeIABEgArNE4P8BAAD//yxbIsQAAAAGSURBVAMAT8b5wlAjMJ8AAAAASUVORK5CYII="
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
},
|
||
{
|
||
"ename": "RuntimeError",
|
||
"evalue": "\n\nKaleido requires Google Chrome to be installed.\n\nEither download and install Chrome yourself following Google's instructions for your operating system,\nor install it from your terminal by running:\n\n $ plotly_get_chrome\n\n",
|
||
"output_type": "error",
|
||
"traceback": [
|
||
"\u001b[31m---------------------------------------------------------------------------\u001b[39m",
|
||
"\u001b[31mChromeNotFoundError\u001b[39m Traceback (most recent call last)",
|
||
"\u001b[31mChromeNotFoundError\u001b[39m: ",
|
||
"\nThe above exception was the direct cause of the following exception:\n",
|
||
"\u001b[31mChromeNotFoundError\u001b[39m Traceback (most recent call last)",
|
||
"\u001b[36mFile \u001b[39m\u001b[32m/opt/python/lib/python3.13/site-packages/plotly/io/_kaleido.py:398\u001b[39m, in \u001b[36mto_image\u001b[39m\u001b[34m(fig, format, width, height, scale, validate, engine)\u001b[39m\n\u001b[32m 388\u001b[39m height = (\n\u001b[32m 389\u001b[39m height\n\u001b[32m 390\u001b[39m \u001b[38;5;129;01mor\u001b[39;00m fig_dict.get(\u001b[33m\"\u001b[39m\u001b[33mlayout\u001b[39m\u001b[33m\"\u001b[39m, {}).get(\u001b[33m\"\u001b[39m\u001b[33mheight\u001b[39m\u001b[33m\"\u001b[39m)\n\u001b[32m (...)\u001b[39m\u001b[32m 395\u001b[39m \u001b[38;5;129;01mor\u001b[39;00m defaults.default_height\n\u001b[32m 396\u001b[39m )\n\u001b[32m--> \u001b[39m\u001b[32m398\u001b[39m img_bytes = \u001b[43mkaleido\u001b[49m\u001b[43m.\u001b[49m\u001b[43mcalc_fig_sync\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m 399\u001b[39m \u001b[43m \u001b[49m\u001b[43mfig_dict\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 400\u001b[39m \u001b[43m \u001b[49m\u001b[43mopts\u001b[49m\u001b[43m=\u001b[49m\u001b[38;5;28;43mdict\u001b[39;49m\u001b[43m(\u001b[49m\n\u001b[32m 401\u001b[39m \u001b[43m \u001b[49m\u001b[38;5;28;43mformat\u001b[39;49m\u001b[43m=\u001b[49m\u001b[38;5;28;43mformat\u001b[39;49m\u001b[43m \u001b[49m\u001b[38;5;129;43;01mor\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mdefaults\u001b[49m\u001b[43m.\u001b[49m\u001b[43mdefault_format\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 402\u001b[39m \u001b[43m \u001b[49m\u001b[43mwidth\u001b[49m\u001b[43m=\u001b[49m\u001b[43mwidth\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 403\u001b[39m \u001b[43m \u001b[49m\u001b[43mheight\u001b[49m\u001b[43m=\u001b[49m\u001b[43mheight\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 404\u001b[39m \u001b[43m \u001b[49m\u001b[43mscale\u001b[49m\u001b[43m=\u001b[49m\u001b[43mscale\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;129;43;01mor\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mdefaults\u001b[49m\u001b[43m.\u001b[49m\u001b[43mdefault_scale\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 405\u001b[39m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 406\u001b[39m \u001b[43m \u001b[49m\u001b[43mtopojson\u001b[49m\u001b[43m=\u001b[49m\u001b[43mdefaults\u001b[49m\u001b[43m.\u001b[49m\u001b[43mtopojson\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 407\u001b[39m \u001b[43m \u001b[49m\u001b[43mkopts\u001b[49m\u001b[43m=\u001b[49m\u001b[43mkopts\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 408\u001b[39m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 409\u001b[39m \u001b[38;5;28;01mexcept\u001b[39;00m ChromeNotFoundError:\n",
|
||
"\u001b[36mFile \u001b[39m\u001b[32m/opt/python/lib/python3.13/site-packages/kaleido/__init__.py:171\u001b[39m, in \u001b[36mcalc_fig_sync\u001b[39m\u001b[34m(*args, **kwargs)\u001b[39m\n\u001b[32m 170\u001b[39m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[32m--> \u001b[39m\u001b[32m171\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43m_sync_server\u001b[49m\u001b[43m.\u001b[49m\u001b[43moneshot_async_run\u001b[49m\u001b[43m(\u001b[49m\u001b[43mcalc_fig\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43margs\u001b[49m\u001b[43m=\u001b[49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m=\u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
|
||
"\u001b[36mFile \u001b[39m\u001b[32m/opt/python/lib/python3.13/site-packages/kaleido/_sync_server.py:131\u001b[39m, in \u001b[36moneshot_async_run\u001b[39m\u001b[34m(func, args, kwargs)\u001b[39m\n\u001b[32m 130\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(res, \u001b[38;5;167;01mBaseException\u001b[39;00m):\n\u001b[32m--> \u001b[39m\u001b[32m131\u001b[39m \u001b[38;5;28;01mraise\u001b[39;00m res\n\u001b[32m 132\u001b[39m \u001b[38;5;28;01melse\u001b[39;00m:\n",
|
||
"\u001b[36mFile \u001b[39m\u001b[32m/opt/python/lib/python3.13/site-packages/kaleido/_sync_server.py:122\u001b[39m, in \u001b[36moneshot_async_run.<locals>.run\u001b[39m\u001b[34m(func, q, *args, **kwargs)\u001b[39m\n\u001b[32m 121\u001b[39m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[32m--> \u001b[39m\u001b[32m122\u001b[39m q.put(\u001b[43masyncio\u001b[49m\u001b[43m.\u001b[49m\u001b[43mrun\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[43m*\u001b[49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m)\n\u001b[32m 123\u001b[39m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mBaseException\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m e: \u001b[38;5;66;03m# noqa: BLE001\u001b[39;00m\n",
|
||
"\u001b[36mFile \u001b[39m\u001b[32m/opt/python/lib/python3.13/asyncio/runners.py:195\u001b[39m, in \u001b[36mrun\u001b[39m\u001b[34m(main, debug, loop_factory)\u001b[39m\n\u001b[32m 194\u001b[39m \u001b[38;5;28;01mwith\u001b[39;00m Runner(debug=debug, loop_factory=loop_factory) \u001b[38;5;28;01mas\u001b[39;00m runner:\n\u001b[32m--> \u001b[39m\u001b[32m195\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mrunner\u001b[49m\u001b[43m.\u001b[49m\u001b[43mrun\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmain\u001b[49m\u001b[43m)\u001b[49m\n",
|
||
"\u001b[36mFile \u001b[39m\u001b[32m/opt/python/lib/python3.13/asyncio/runners.py:118\u001b[39m, in \u001b[36mRunner.run\u001b[39m\u001b[34m(self, coro, context)\u001b[39m\n\u001b[32m 117\u001b[39m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[32m--> \u001b[39m\u001b[32m118\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m_loop\u001b[49m\u001b[43m.\u001b[49m\u001b[43mrun_until_complete\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtask\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 119\u001b[39m \u001b[38;5;28;01mexcept\u001b[39;00m exceptions.CancelledError:\n",
|
||
"\u001b[36mFile \u001b[39m\u001b[32m/opt/python/lib/python3.13/asyncio/base_events.py:725\u001b[39m, in \u001b[36mBaseEventLoop.run_until_complete\u001b[39m\u001b[34m(self, future)\u001b[39m\n\u001b[32m 723\u001b[39m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mRuntimeError\u001b[39;00m(\u001b[33m'\u001b[39m\u001b[33mEvent loop stopped before Future completed.\u001b[39m\u001b[33m'\u001b[39m)\n\u001b[32m--> \u001b[39m\u001b[32m725\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfuture\u001b[49m\u001b[43m.\u001b[49m\u001b[43mresult\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n",
|
||
"\u001b[36mFile \u001b[39m\u001b[32m/opt/python/lib/python3.13/site-packages/kaleido/__init__.py:101\u001b[39m, in \u001b[36mcalc_fig\u001b[39m\u001b[34m(fig, path, opts, topojson, kopts)\u001b[39m\n\u001b[32m 100\u001b[39m kopts[\u001b[33m\"\u001b[39m\u001b[33mn\u001b[39m\u001b[33m\"\u001b[39m] = \u001b[32m1\u001b[39m \u001b[38;5;66;03m# should we force this?\u001b[39;00m\n\u001b[32m--> \u001b[39m\u001b[32m101\u001b[39m \u001b[38;5;28;01masync\u001b[39;00m \u001b[38;5;28;01mwith\u001b[39;00m \u001b[43mKaleido\u001b[49m\u001b[43m(\u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[43mkopts\u001b[49m\u001b[43m)\u001b[49m \u001b[38;5;28;01mas\u001b[39;00m k:\n\u001b[32m 102\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;01mawait\u001b[39;00m k.calc_fig(\n\u001b[32m 103\u001b[39m fig,\n\u001b[32m 104\u001b[39m path=path,\n\u001b[32m 105\u001b[39m opts=opts,\n\u001b[32m 106\u001b[39m topojson=topojson,\n\u001b[32m 107\u001b[39m )\n",
|
||
"\u001b[36mFile \u001b[39m\u001b[32m/opt/python/lib/python3.13/site-packages/kaleido/kaleido.py:164\u001b[39m, in \u001b[36mKaleido.__init__\u001b[39m\u001b[34m(self, page_generator, n, timeout, width, height, stepper, plotlyjs, mathjax, *args, **kwargs)\u001b[39m\n\u001b[32m 163\u001b[39m \u001b[38;5;28;01mexcept\u001b[39;00m ChromeNotFoundError:\n\u001b[32m--> \u001b[39m\u001b[32m164\u001b[39m \u001b[38;5;28;01mraise\u001b[39;00m ChromeNotFoundError(\n\u001b[32m 165\u001b[39m \u001b[33m\"\u001b[39m\u001b[33mKaleido v1 and later requires Chrome to be installed. \u001b[39m\u001b[33m\"\u001b[39m\n\u001b[32m 166\u001b[39m \u001b[33m\"\u001b[39m\u001b[33mTo install Chrome, use the CLI command `kaleido_get_chrome`, \u001b[39m\u001b[33m\"\u001b[39m\n\u001b[32m 167\u001b[39m \u001b[33m\"\u001b[39m\u001b[33mor from Python, use either `kaleido.get_chrome()` \u001b[39m\u001b[33m\"\u001b[39m\n\u001b[32m 168\u001b[39m \u001b[33m\"\u001b[39m\u001b[33mor `kaleido.get_chrome_sync()`.\u001b[39m\u001b[33m\"\u001b[39m,\n\u001b[32m 169\u001b[39m ) \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mChromeNotFoundError\u001b[39;00m\n\u001b[32m 171\u001b[39m \u001b[38;5;66;03m# do this during open because it requires close\u001b[39;00m\n",
|
||
"\u001b[31mChromeNotFoundError\u001b[39m: Kaleido v1 and later requires Chrome to be installed. To install Chrome, use the CLI command `kaleido_get_chrome`, or from Python, use either `kaleido.get_chrome()` or `kaleido.get_chrome_sync()`.",
|
||
"\nDuring handling of the above exception, another exception occurred:\n",
|
||
"\u001b[31mRuntimeError\u001b[39m Traceback (most recent call last)",
|
||
"\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[19]\u001b[39m\u001b[32m, line 178\u001b[39m\n\u001b[32m 166\u001b[39m fig.update_layout(\n\u001b[32m 167\u001b[39m margin=\u001b[38;5;28mdict\u001b[39m(l=\u001b[32m0\u001b[39m, r=\u001b[32m0\u001b[39m, t=\u001b[32m40\u001b[39m, b=\u001b[32m0\u001b[39m),\n\u001b[32m 168\u001b[39m legend_title_text=\u001b[33m\"\u001b[39m\u001b[33mTotal absolute net flows (€)\u001b[39m\u001b[33m\"\u001b[39m,\n\u001b[32m (...)\u001b[39m\u001b[32m 174\u001b[39m )\n\u001b[32m 175\u001b[39m )\n\u001b[32m 177\u001b[39m fig.show()\n\u001b[32m--> \u001b[39m\u001b[32m178\u001b[39m \u001b[43mfig\u001b[49m\u001b[43m.\u001b[49m\u001b[43mwrite_image\u001b[49m\u001b[43m(\u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mtotal_flow_intensity_world.png\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mscale\u001b[49m\u001b[43m=\u001b[49m\u001b[32;43m3\u001b[39;49m\u001b[43m)\u001b[49m\n",
|
||
"\u001b[36mFile \u001b[39m\u001b[32m/opt/python/lib/python3.13/site-packages/plotly/basedatatypes.py:3895\u001b[39m, in \u001b[36mBaseFigure.write_image\u001b[39m\u001b[34m(self, *args, **kwargs)\u001b[39m\n\u001b[32m 3891\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m kwargs.get(\u001b[33m\"\u001b[39m\u001b[33mengine\u001b[39m\u001b[33m\"\u001b[39m, \u001b[38;5;28;01mNone\u001b[39;00m):\n\u001b[32m 3892\u001b[39m warnings.warn(\n\u001b[32m 3893\u001b[39m ENGINE_PARAM_DEPRECATION_MSG, \u001b[38;5;167;01mDeprecationWarning\u001b[39;00m, stacklevel=\u001b[32m2\u001b[39m\n\u001b[32m 3894\u001b[39m )\n\u001b[32m-> \u001b[39m\u001b[32m3895\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mpio\u001b[49m\u001b[43m.\u001b[49m\u001b[43mwrite_image\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
|
||
"\u001b[36mFile \u001b[39m\u001b[32m/opt/python/lib/python3.13/site-packages/plotly/io/_kaleido.py:528\u001b[39m, in \u001b[36mwrite_image\u001b[39m\u001b[34m(fig, file, format, scale, width, height, validate, engine)\u001b[39m\n\u001b[32m 524\u001b[39m \u001b[38;5;28mformat\u001b[39m = infer_format(path, \u001b[38;5;28mformat\u001b[39m)\n\u001b[32m 526\u001b[39m \u001b[38;5;66;03m# Request image\u001b[39;00m\n\u001b[32m 527\u001b[39m \u001b[38;5;66;03m# Do this first so we don't create a file if image conversion fails\u001b[39;00m\n\u001b[32m--> \u001b[39m\u001b[32m528\u001b[39m img_data = \u001b[43mto_image\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m 529\u001b[39m \u001b[43m \u001b[49m\u001b[43mfig\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 530\u001b[39m \u001b[43m \u001b[49m\u001b[38;5;28;43mformat\u001b[39;49m\u001b[43m=\u001b[49m\u001b[38;5;28;43mformat\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[32m 531\u001b[39m \u001b[43m \u001b[49m\u001b[43mscale\u001b[49m\u001b[43m=\u001b[49m\u001b[43mscale\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 532\u001b[39m \u001b[43m \u001b[49m\u001b[43mwidth\u001b[49m\u001b[43m=\u001b[49m\u001b[43mwidth\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 533\u001b[39m \u001b[43m \u001b[49m\u001b[43mheight\u001b[49m\u001b[43m=\u001b[49m\u001b[43mheight\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 534\u001b[39m \u001b[43m \u001b[49m\u001b[43mvalidate\u001b[49m\u001b[43m=\u001b[49m\u001b[43mvalidate\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 535\u001b[39m \u001b[43m \u001b[49m\u001b[43mengine\u001b[49m\u001b[43m=\u001b[49m\u001b[43mengine\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 536\u001b[39m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 538\u001b[39m \u001b[38;5;66;03m# Open file\u001b[39;00m\n\u001b[32m 539\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m path \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[32m 540\u001b[39m \u001b[38;5;66;03m# We previously failed to make sense of `file` as a pathlib object.\u001b[39;00m\n\u001b[32m 541\u001b[39m \u001b[38;5;66;03m# Attempt to write to `file` as an open file descriptor.\u001b[39;00m\n",
|
||
"\u001b[36mFile \u001b[39m\u001b[32m/opt/python/lib/python3.13/site-packages/plotly/io/_kaleido.py:410\u001b[39m, in \u001b[36mto_image\u001b[39m\u001b[34m(fig, format, width, height, scale, validate, engine)\u001b[39m\n\u001b[32m 398\u001b[39m img_bytes = kaleido.calc_fig_sync(\n\u001b[32m 399\u001b[39m fig_dict,\n\u001b[32m 400\u001b[39m opts=\u001b[38;5;28mdict\u001b[39m(\n\u001b[32m (...)\u001b[39m\u001b[32m 407\u001b[39m kopts=kopts,\n\u001b[32m 408\u001b[39m )\n\u001b[32m 409\u001b[39m \u001b[38;5;28;01mexcept\u001b[39;00m ChromeNotFoundError:\n\u001b[32m--> \u001b[39m\u001b[32m410\u001b[39m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mRuntimeError\u001b[39;00m(PLOTLY_GET_CHROME_ERROR_MSG)\n\u001b[32m 412\u001b[39m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[32m 413\u001b[39m \u001b[38;5;66;03m# Kaleido v0\u001b[39;00m\n\u001b[32m 414\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m ENABLE_KALEIDO_V0_DEPRECATION_WARNINGS:\n",
|
||
"\u001b[31mRuntimeError\u001b[39m: \n\nKaleido requires Google Chrome to be installed.\n\nEither download and install Chrome yourself following Google's instructions for your operating system,\nor install it from your terminal by running:\n\n $ plotly_get_chrome\n\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"# ============================================================\n",
|
||
"# GEOGRAPHIC COVERAGE — TOTAL FLOW INTENSITY (LATAM + USA FIXED)\n",
|
||
"# ============================================================\n",
|
||
"\n",
|
||
"import pandas as pd\n",
|
||
"import numpy as np\n",
|
||
"import plotly.express as px\n",
|
||
"import pycountry\n",
|
||
"\n",
|
||
"# ------------------------------------------------------------\n",
|
||
"# Helper: country name → ISO-3\n",
|
||
"# ------------------------------------------------------------\n",
|
||
"\n",
|
||
"def country_to_iso3(name):\n",
|
||
" try:\n",
|
||
" return pycountry.countries.lookup(name).alpha_3\n",
|
||
" except:\n",
|
||
" return None\n",
|
||
"\n",
|
||
"# ------------------------------------------------------------\n",
|
||
"# 0. Harmonize country labels (USA + special buckets)\n",
|
||
"# ------------------------------------------------------------\n",
|
||
"\n",
|
||
"stocks[\"RegistrarAccount - Country\"] = stocks[\"RegistrarAccount - Country\"].replace({\n",
|
||
" \"US OFFSHORE\": \"UNITED STATES\"\n",
|
||
"})\n",
|
||
"\n",
|
||
"flows[\"RegistrarAccount - Country\"] = flows[\"RegistrarAccount - Country\"].replace({\n",
|
||
" \"US OFFSHORE\": \"UNITED STATES\"\n",
|
||
"})\n",
|
||
"\n",
|
||
"# ------------------------------------------------------------\n",
|
||
"# 1. Aggregate flows at Account × Date level (robust)\n",
|
||
"# ------------------------------------------------------------\n",
|
||
"\n",
|
||
"flows_acc_day = (\n",
|
||
" flows\n",
|
||
" .assign(abs_flow=lambda x: x[\"Value € - NetFlows\"].abs())\n",
|
||
" .groupby([\"Registrar Account - ID\", \"Centralisation Date\"], as_index=False)\n",
|
||
" .agg(abs_flow=(\"abs_flow\", \"sum\"))\n",
|
||
")\n",
|
||
"\n",
|
||
"# ------------------------------------------------------------\n",
|
||
"# 2. Map each Registrar Account to a country (from STOCKS)\n",
|
||
"# ------------------------------------------------------------\n",
|
||
"\n",
|
||
"account_country = (\n",
|
||
" stocks\n",
|
||
" .groupby(\"Registrar Account - ID\")[\"RegistrarAccount - Country\"]\n",
|
||
" .agg(lambda x: x.mode().iloc[0])\n",
|
||
" .reset_index()\n",
|
||
")\n",
|
||
"\n",
|
||
"flows_acc_day = flows_acc_day.merge(\n",
|
||
" account_country,\n",
|
||
" on=\"Registrar Account - ID\",\n",
|
||
" how=\"left\"\n",
|
||
")\n",
|
||
"\n",
|
||
"# ------------------------------------------------------------\n",
|
||
"# 3. Aggregate TOTAL absolute flows per country\n",
|
||
"# ------------------------------------------------------------\n",
|
||
"\n",
|
||
"country_flows = (\n",
|
||
" flows_acc_day\n",
|
||
" .groupby(\"RegistrarAccount - Country\", as_index=False)\n",
|
||
" .agg(\n",
|
||
" total_abs_flows=(\"abs_flow\", \"sum\"),\n",
|
||
" n_accounts_active=(\"Registrar Account - ID\", \"nunique\")\n",
|
||
" )\n",
|
||
")\n",
|
||
"\n",
|
||
"# ------------------------------------------------------------\n",
|
||
"# 4. Handle LATAM explicitly (visual redistribution)\n",
|
||
"# ------------------------------------------------------------\n",
|
||
"\n",
|
||
"latam_countries = [\n",
|
||
" \"ARGENTINA\", \"BRAZIL\", \"CHILE\", \"COLOMBIA\", \"MEXICO\",\n",
|
||
" \"PERU\", \"URUGUAY\", \"PARAGUAY\", \"BOLIVIA\",\n",
|
||
" \"ECUADOR\", \"VENEZUELA\"\n",
|
||
"]\n",
|
||
"\n",
|
||
"latam_row = country_flows[\n",
|
||
" country_flows[\"RegistrarAccount - Country\"] == \"LATAM\"\n",
|
||
"]\n",
|
||
"\n",
|
||
"if not latam_row.empty:\n",
|
||
" latam_flow = latam_row[\"total_abs_flows\"].iloc[0]\n",
|
||
" latam_accounts = latam_row[\"n_accounts_active\"].iloc[0]\n",
|
||
"\n",
|
||
" latam_df = pd.DataFrame({\n",
|
||
" \"RegistrarAccount - Country\": latam_countries,\n",
|
||
" \"total_abs_flows\": latam_flow / len(latam_countries),\n",
|
||
" \"n_accounts_active\": latam_accounts / len(latam_countries)\n",
|
||
" })\n",
|
||
"\n",
|
||
" country_flows = pd.concat(\n",
|
||
" [\n",
|
||
" country_flows[\n",
|
||
" ~country_flows[\"RegistrarAccount - Country\"].isin(\n",
|
||
" [\"LATAM\", \"INTERNATIONAL\", \"UNKNOWN\"]\n",
|
||
" )\n",
|
||
" ],\n",
|
||
" latam_df\n",
|
||
" ],\n",
|
||
" ignore_index=True\n",
|
||
" )\n",
|
||
"\n",
|
||
"else:\n",
|
||
" country_flows = country_flows[\n",
|
||
" ~country_flows[\"RegistrarAccount - Country\"].isin(\n",
|
||
" [\"INTERNATIONAL\", \"UNKNOWN\"]\n",
|
||
" )\n",
|
||
" ]\n",
|
||
"\n",
|
||
"# ------------------------------------------------------------\n",
|
||
"# 5. Convert country names to ISO-3 (with diagnostics)\n",
|
||
"# ------------------------------------------------------------\n",
|
||
"\n",
|
||
"country_flows[\"iso3\"] = country_flows[\"RegistrarAccount - Country\"].apply(country_to_iso3)\n",
|
||
"\n",
|
||
"unmapped = country_flows[country_flows[\"iso3\"].isna()]\n",
|
||
"if not unmapped.empty:\n",
|
||
" print(\"Unmapped country labels:\")\n",
|
||
" print(unmapped[\"RegistrarAccount - Country\"].unique())\n",
|
||
"\n",
|
||
"country_flows = country_flows.dropna(subset=[\"iso3\"])\n",
|
||
"\n",
|
||
"# ------------------------------------------------------------\n",
|
||
"# 6. Discretize flows (order-of-magnitude legend)\n",
|
||
"# ------------------------------------------------------------\n",
|
||
"\n",
|
||
"bins = [0, 5e8, 2e9, 1e10, 3e10, np.inf]\n",
|
||
"labels = [\n",
|
||
" \"< €0.5bn\",\n",
|
||
" \"€0.5–2bn\",\n",
|
||
" \"€2–10bn\",\n",
|
||
" \"€10–30bn\",\n",
|
||
" \"> €30bn\"\n",
|
||
"]\n",
|
||
"\n",
|
||
"country_flows[\"flow_bin\"] = pd.cut(\n",
|
||
" country_flows[\"total_abs_flows\"],\n",
|
||
" bins=bins,\n",
|
||
" labels=labels,\n",
|
||
" include_lowest=True\n",
|
||
")\n",
|
||
"\n",
|
||
"# ------------------------------------------------------------\n",
|
||
"# 7. World map — total flow intensity\n",
|
||
"# ------------------------------------------------------------\n",
|
||
"\n",
|
||
"fig = px.choropleth(\n",
|
||
" country_flows,\n",
|
||
" locations=\"iso3\",\n",
|
||
" color=\"flow_bin\",\n",
|
||
" hover_name=\"RegistrarAccount - Country\",\n",
|
||
" hover_data={\n",
|
||
" \"total_abs_flows\": \":,.0f\",\n",
|
||
" \"n_accounts_active\": True\n",
|
||
" },\n",
|
||
" category_orders={\"flow_bin\": labels},\n",
|
||
" color_discrete_sequence=px.colors.sequential.OrRd\n",
|
||
")\n",
|
||
"\n",
|
||
"fig.update_layout(\n",
|
||
" margin=dict(l=0, r=0, t=40, b=0),\n",
|
||
" legend_title_text=\"Total absolute net flows (€)\",\n",
|
||
" geo=dict(\n",
|
||
" showframe=False,\n",
|
||
" showcoastlines=True,\n",
|
||
" coastlinecolor=\"rgba(0,0,0,0.3)\",\n",
|
||
" projection_type=\"natural earth\"\n",
|
||
" )\n",
|
||
")\n",
|
||
"\n",
|
||
"fig.show()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 22,
|
||
"id": "36b93cfb-d7f5-4ba4-a86b-024247bddc5a",
|
||
"metadata": {
|
||
"scrolled": true
|
||
},
|
||
"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>RegistrarAccount - Country</th>\n",
|
||
" <th>n_accounts</th>\n",
|
||
" <th>iso3</th>\n",
|
||
" <th>account_bin</th>\n",
|
||
" <th>total_abs_flows</th>\n",
|
||
" <th>n_accounts_active</th>\n",
|
||
" <th>share_accounts</th>\n",
|
||
" <th>share_flows</th>\n",
|
||
" <th>flow_per_account</th>\n",
|
||
" </tr>\n",
|
||
" </thead>\n",
|
||
" <tbody>\n",
|
||
" <tr>\n",
|
||
" <th>15</th>\n",
|
||
" <td>LUXEMBOURG</td>\n",
|
||
" <td>331.0</td>\n",
|
||
" <td>LUX</td>\n",
|
||
" <td>High (200–999)</td>\n",
|
||
" <td>6.445076e+10</td>\n",
|
||
" <td>310.0</td>\n",
|
||
" <td>0.047619</td>\n",
|
||
" <td>0.228139</td>\n",
|
||
" <td>1.947153e+08</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>12</th>\n",
|
||
" <td>ITALY</td>\n",
|
||
" <td>487.0</td>\n",
|
||
" <td>ITA</td>\n",
|
||
" <td>High (200–999)</td>\n",
|
||
" <td>6.008376e+10</td>\n",
|
||
" <td>478.0</td>\n",
|
||
" <td>0.070062</td>\n",
|
||
" <td>0.212681</td>\n",
|
||
" <td>1.233753e+08</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>24</th>\n",
|
||
" <td>SPAIN</td>\n",
|
||
" <td>340.0</td>\n",
|
||
" <td>ESP</td>\n",
|
||
" <td>High (200–999)</td>\n",
|
||
" <td>4.995133e+10</td>\n",
|
||
" <td>330.0</td>\n",
|
||
" <td>0.048914</td>\n",
|
||
" <td>0.176814</td>\n",
|
||
" <td>1.469157e+08</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>6</th>\n",
|
||
" <td>FRANCE</td>\n",
|
||
" <td>2672.0</td>\n",
|
||
" <td>FRA</td>\n",
|
||
" <td>Very high (≥ 1,000)</td>\n",
|
||
" <td>4.753435e+10</td>\n",
|
||
" <td>2631.0</td>\n",
|
||
" <td>0.384405</td>\n",
|
||
" <td>0.168259</td>\n",
|
||
" <td>1.778980e+07</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>7</th>\n",
|
||
" <td>GERMANY</td>\n",
|
||
" <td>90.0</td>\n",
|
||
" <td>DEU</td>\n",
|
||
" <td>Medium (50–199)</td>\n",
|
||
" <td>1.960362e+10</td>\n",
|
||
" <td>80.0</td>\n",
|
||
" <td>0.012948</td>\n",
|
||
" <td>0.069392</td>\n",
|
||
" <td>2.178180e+08</td>\n",
|
||
" </tr>\n",
|
||
" </tbody>\n",
|
||
"</table>\n",
|
||
"</div>"
|
||
],
|
||
"text/plain": [
|
||
" RegistrarAccount - Country n_accounts iso3 account_bin \\\n",
|
||
"15 LUXEMBOURG 331.0 LUX High (200–999) \n",
|
||
"12 ITALY 487.0 ITA High (200–999) \n",
|
||
"24 SPAIN 340.0 ESP High (200–999) \n",
|
||
"6 FRANCE 2672.0 FRA Very high (≥ 1,000) \n",
|
||
"7 GERMANY 90.0 DEU Medium (50–199) \n",
|
||
"\n",
|
||
" total_abs_flows n_accounts_active share_accounts share_flows \\\n",
|
||
"15 6.445076e+10 310.0 0.047619 0.228139 \n",
|
||
"12 6.008376e+10 478.0 0.070062 0.212681 \n",
|
||
"24 4.995133e+10 330.0 0.048914 0.176814 \n",
|
||
"6 4.753435e+10 2631.0 0.384405 0.168259 \n",
|
||
"7 1.960362e+10 80.0 0.012948 0.069392 \n",
|
||
"\n",
|
||
" flow_per_account \n",
|
||
"15 1.947153e+08 \n",
|
||
"12 1.233753e+08 \n",
|
||
"24 1.469157e+08 \n",
|
||
"6 1.778980e+07 \n",
|
||
"7 2.178180e+08 "
|
||
]
|
||
},
|
||
"execution_count": 22,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"# ------------------------------------------------------------\n",
|
||
"# 8. Merge accounts & flows at country level\n",
|
||
"# ------------------------------------------------------------\n",
|
||
"\n",
|
||
"country_summary = (\n",
|
||
" country_accounts\n",
|
||
" .merge(\n",
|
||
" country_flows[[\n",
|
||
" \"RegistrarAccount - Country\",\n",
|
||
" \"total_abs_flows\",\n",
|
||
" \"n_accounts_active\"\n",
|
||
" ]],\n",
|
||
" on=\"RegistrarAccount - Country\",\n",
|
||
" how=\"inner\"\n",
|
||
" )\n",
|
||
")\n",
|
||
"\n",
|
||
"# Totals (for shares)\n",
|
||
"total_accounts = country_summary[\"n_accounts\"].sum()\n",
|
||
"total_flows = country_summary[\"total_abs_flows\"].sum()\n",
|
||
"\n",
|
||
"country_summary[\"share_accounts\"] = country_summary[\"n_accounts\"] / total_accounts\n",
|
||
"country_summary[\"share_flows\"] = country_summary[\"total_abs_flows\"] / total_flows\n",
|
||
"country_summary[\"flow_per_account\"] = (\n",
|
||
" country_summary[\"total_abs_flows\"] / country_summary[\"n_accounts\"]\n",
|
||
")\n",
|
||
"\n",
|
||
"country_summary.sort_values(\n",
|
||
" \"total_abs_flows\", ascending=False\n",
|
||
").head()\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 23,
|
||
"id": "fa687277-05fe-405e-967d-9ba91dce2a95",
|
||
"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>RegistrarAccount - Country</th>\n",
|
||
" <th>n_accounts</th>\n",
|
||
" <th>total_abs_flows</th>\n",
|
||
" <th>share_accounts</th>\n",
|
||
" <th>share_flows</th>\n",
|
||
" </tr>\n",
|
||
" </thead>\n",
|
||
" <tbody>\n",
|
||
" <tr>\n",
|
||
" <th>30</th>\n",
|
||
" <td>UNITED STATES</td>\n",
|
||
" <td>1166.0</td>\n",
|
||
" <td>3.891997e+08</td>\n",
|
||
" <td>0.167746</td>\n",
|
||
" <td>0.001378</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>29</th>\n",
|
||
" <td>UNITED KINGDOM</td>\n",
|
||
" <td>622.0</td>\n",
|
||
" <td>3.576470e+09</td>\n",
|
||
" <td>0.089484</td>\n",
|
||
" <td>0.012660</td>\n",
|
||
" </tr>\n",
|
||
" </tbody>\n",
|
||
"</table>\n",
|
||
"</div>"
|
||
],
|
||
"text/plain": [
|
||
" RegistrarAccount - Country n_accounts total_abs_flows share_accounts \\\n",
|
||
"30 UNITED STATES 1166.0 3.891997e+08 0.167746 \n",
|
||
"29 UNITED KINGDOM 622.0 3.576470e+09 0.089484 \n",
|
||
"\n",
|
||
" share_flows \n",
|
||
"30 0.001378 \n",
|
||
"29 0.012660 "
|
||
]
|
||
},
|
||
"execution_count": 23,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"# Countries with many accounts but low flow intensity\n",
|
||
"low_flow_high_accounts = (\n",
|
||
" country_summary\n",
|
||
" .query(\"share_accounts > 0.05 and share_flows < 0.05\")\n",
|
||
" .sort_values(\"n_accounts\", ascending=False)\n",
|
||
")\n",
|
||
"\n",
|
||
"low_flow_high_accounts[\n",
|
||
" [\n",
|
||
" \"RegistrarAccount - Country\",\n",
|
||
" \"n_accounts\",\n",
|
||
" \"total_abs_flows\",\n",
|
||
" \"share_accounts\",\n",
|
||
" \"share_flows\"\n",
|
||
" ]\n",
|
||
"]\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"id": "bbebadf0-19a3-426a-bfb1-96b48a94832f",
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"import pandas as pd\n",
|
||
"import numpy as np\n",
|
||
"import matplotlib.pyplot as plt\n",
|
||
"import seaborn as sns\n",
|
||
"\n",
|
||
"# =========================\n",
|
||
"# STYLE — PUBLICATION\n",
|
||
"# =========================\n",
|
||
"\n",
|
||
"sns.set_theme(\n",
|
||
" style=\"whitegrid\",\n",
|
||
" context=\"paper\",\n",
|
||
" font_scale=1.2\n",
|
||
")\n",
|
||
"\n",
|
||
"# =========================\n",
|
||
"# DATA PREP\n",
|
||
"# =========================\n",
|
||
"\n",
|
||
"stocks[\"Centralisation Date\"] = pd.to_datetime(stocks[\"Centralisation Date\"])\n",
|
||
"flows[\"Centralisation Date\"] = pd.to_datetime(flows[\"Centralisation Date\"])\n",
|
||
"\n",
|
||
"snapshot_date = stocks[\"Centralisation Date\"].max()\n",
|
||
"\n",
|
||
"aum = (\n",
|
||
" stocks.loc[stocks[\"Centralisation Date\"] == snapshot_date]\n",
|
||
" .groupby(\"Registrar Account - ID\")[\"Value - AUM €\"]\n",
|
||
" .sum()\n",
|
||
" .rename(\"aum_eur\")\n",
|
||
")\n",
|
||
"\n",
|
||
"flow_activity = (\n",
|
||
" flows\n",
|
||
" .groupby(\"Registrar Account - ID\")[\"Value € - NetFlows\"]\n",
|
||
" .apply(lambda x: x.abs().mean())\n",
|
||
" .rename(\"avg_abs_flow\")\n",
|
||
")\n",
|
||
"\n",
|
||
"df = pd.concat([aum, flow_activity], axis=1).dropna()\n",
|
||
"df = df[(df[\"aum_eur\"] > 0) & (df[\"avg_abs_flow\"] > 0)]\n",
|
||
"\n",
|
||
"# =========================\n",
|
||
"# ECDF FUNCTION\n",
|
||
"# =========================\n",
|
||
"\n",
|
||
"def ecdf(x):\n",
|
||
" x = np.sort(x)\n",
|
||
" y = np.arange(1, len(x) + 1) / len(x)\n",
|
||
" return x, y\n",
|
||
"\n",
|
||
"x_aum, y_aum = ecdf(df[\"aum_eur\"])\n",
|
||
"x_flow, y_flow = ecdf(df[\"avg_abs_flow\"])\n",
|
||
"\n",
|
||
"# =========================\n",
|
||
"# PLOT\n",
|
||
"# =========================\n",
|
||
"\n",
|
||
"fig, ax = plt.subplots(figsize=(7.5, 5))\n",
|
||
"\n",
|
||
"ax.plot(x_aum, y_aum, label=\"Assets under management (AUM)\", linewidth=2)\n",
|
||
"ax.plot(x_flow, y_flow, label=\"Average absolute net flows\", linewidth=2)\n",
|
||
"\n",
|
||
"# Percentiles to annotate\n",
|
||
"percentiles = [0.5, 0.75, 0.9]\n",
|
||
"\n",
|
||
"for p in percentiles:\n",
|
||
" q_aum = np.quantile(df[\"aum_eur\"], p)\n",
|
||
" q_flow = np.quantile(df[\"avg_abs_flow\"], p)\n",
|
||
"\n",
|
||
" ax.scatter(q_aum, p, s=25)\n",
|
||
" ax.scatter(q_flow, p, s=25)\n",
|
||
"\n",
|
||
" ax.text(\n",
|
||
" q_aum, p,\n",
|
||
" f\"{int(p*100)}%\",\n",
|
||
" ha=\"left\", va=\"bottom\", fontsize=9\n",
|
||
" )\n",
|
||
"\n",
|
||
"ax.set_xscale(\"log\")\n",
|
||
"ax.set_xlabel(\"€ (log scale)\")\n",
|
||
"ax.set_ylabel(\"Cumulative share of registrar accounts\")\n",
|
||
"ax.set_title(\"Empirical Distributions of Account Size and Flow Activity\")\n",
|
||
"ax.legend(frameon=False)\n",
|
||
"\n",
|
||
"sns.despine()\n",
|
||
"plt.tight_layout()\n",
|
||
"plt.show()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"id": "9d647926-4bea-415d-9eb4-9745d76565e7",
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"import pandas as pd\n",
|
||
"import numpy as np\n",
|
||
"import matplotlib.pyplot as plt\n",
|
||
"import seaborn as sns\n",
|
||
"\n",
|
||
"# =========================\n",
|
||
"# STYLE — PUBLICATION\n",
|
||
"# =========================\n",
|
||
"\n",
|
||
"sns.set_theme(\n",
|
||
" style=\"whitegrid\",\n",
|
||
" context=\"paper\",\n",
|
||
" font_scale=1.2\n",
|
||
")\n",
|
||
"\n",
|
||
"# =========================\n",
|
||
"# DATA PREP\n",
|
||
"# =========================\n",
|
||
"\n",
|
||
"stocks[\"Centralisation Date\"] = pd.to_datetime(stocks[\"Centralisation Date\"])\n",
|
||
"flows[\"Centralisation Date\"] = pd.to_datetime(flows[\"Centralisation Date\"])\n",
|
||
"\n",
|
||
"snapshot_date = stocks[\"Centralisation Date\"].max()\n",
|
||
"\n",
|
||
"# AUM snapshot\n",
|
||
"aum = (\n",
|
||
" stocks.loc[stocks[\"Centralisation Date\"] == snapshot_date]\n",
|
||
" .groupby(\"Registrar Account - ID\")[\"Value - AUM €\"]\n",
|
||
" .sum()\n",
|
||
")\n",
|
||
"\n",
|
||
"# Flow activity\n",
|
||
"flow_activity = (\n",
|
||
" flows\n",
|
||
" .groupby(\"Registrar Account - ID\")[\"Value € - NetFlows\"]\n",
|
||
" .apply(lambda x: x.abs().mean())\n",
|
||
")\n",
|
||
"\n",
|
||
"df = pd.concat([aum, flow_activity], axis=1)\n",
|
||
"df.columns = [\"aum_eur\", \"avg_abs_flow\"]\n",
|
||
"df = df.dropna()\n",
|
||
"df = df[(df[\"aum_eur\"] > 0) & (df[\"avg_abs_flow\"] > 0)]\n",
|
||
"\n",
|
||
"# =========================\n",
|
||
"# QUANTILES\n",
|
||
"# =========================\n",
|
||
"\n",
|
||
"quantiles = [0.5, 0.75, 0.9, 0.95, 0.99]\n",
|
||
"\n",
|
||
"rows = []\n",
|
||
"for q in quantiles:\n",
|
||
" rows.append({\n",
|
||
" \"Percentile\": f\"{int(q*100)}%\",\n",
|
||
" \"Variable\": \"Assets under management (AUM)\",\n",
|
||
" \"Value\": df[\"aum_eur\"].quantile(q)\n",
|
||
" })\n",
|
||
" rows.append({\n",
|
||
" \"Percentile\": f\"{int(q*100)}%\",\n",
|
||
" \"Variable\": \"Average absolute net flows\",\n",
|
||
" \"Value\": df[\"avg_abs_flow\"].quantile(q)\n",
|
||
" })\n",
|
||
"\n",
|
||
"q_df = pd.DataFrame(rows)\n",
|
||
"\n",
|
||
"# =========================\n",
|
||
"# PLOT\n",
|
||
"# =========================\n",
|
||
"\n",
|
||
"plt.figure(figsize=(8, 5))\n",
|
||
"\n",
|
||
"sns.barplot(\n",
|
||
" data=q_df,\n",
|
||
" x=\"Percentile\",\n",
|
||
" y=\"Value\",\n",
|
||
" hue=\"Variable\"\n",
|
||
")\n",
|
||
"\n",
|
||
"plt.yscale(\"log\")\n",
|
||
"plt.ylabel(\"€ (log scale)\")\n",
|
||
"plt.xlabel(\"Registrar account percentile\")\n",
|
||
"#plt.title(\"Account Size vs Flow Activity Across the Distribution\")\n",
|
||
"plt.legend(frameon=False)\n",
|
||
"\n",
|
||
"sns.despine()\n",
|
||
"plt.tight_layout()\n",
|
||
"\n",
|
||
"plt.savefig(\n",
|
||
" \"aum_vs_flow_activity_concentration_top10.png\",\n",
|
||
" dpi=300,\n",
|
||
" bbox_inches=\"tight\"\n",
|
||
")\n",
|
||
"\n",
|
||
"plt.show()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"id": "dc2aed5b-67a1-453d-9d74-d47a25135658",
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"# ------------------------------------------------------------\n",
|
||
"# 5. Convert country names to ISO-3\n",
|
||
"# ------------------------------------------------------------\n",
|
||
"\n",
|
||
"def country_to_iso3(name):\n",
|
||
" try:\n",
|
||
" return pycountry.countries.lookup(name).alpha_3\n",
|
||
" except:\n",
|
||
" return None\n",
|
||
"\n",
|
||
"country_flows[\"iso3\"] = country_flows[\"country\"].apply(country_to_iso3)\n",
|
||
"\n",
|
||
"# Diagnostic: unmapped countries\n",
|
||
"unmapped = country_flows[country_flows[\"iso3\"].isna()]\n",
|
||
"if not unmapped.empty:\n",
|
||
" print(\"Unmapped country labels:\")\n",
|
||
" print(unmapped[\"country\"].unique())\n",
|
||
"\n",
|
||
"country_flows = country_flows.dropna(subset=[\"iso3\"])\n",
|
||
"\n",
|
||
"# ------------------------------------------------------------\n",
|
||
"# 6. Discretize total absolute flows (order of magnitude)\n",
|
||
"# ------------------------------------------------------------\n",
|
||
"\n",
|
||
"bins = [0, 5e8, 2e9, 1e10, 3e10, np.inf]\n",
|
||
"labels = [\n",
|
||
" \"< €0.5bn\",\n",
|
||
" \"€0.5–2bn\",\n",
|
||
" \"€2–10bn\",\n",
|
||
" \"€10–30bn\",\n",
|
||
" \"> €30bn\"\n",
|
||
"]\n",
|
||
"\n",
|
||
"\n",
|
||
"country_flows[\"flow_bin\"] = pd.cut(\n",
|
||
" country_flows[\"total_abs_flows\"],\n",
|
||
" bins=bins,\n",
|
||
" labels=labels,\n",
|
||
" include_lowest=True\n",
|
||
")\n",
|
||
"\n",
|
||
"# ------------------------------------------------------------\n",
|
||
"# 7. World map — total absolute net flows\n",
|
||
"# ------------------------------------------------------------\n",
|
||
"\n",
|
||
"fig = px.choropleth(\n",
|
||
" country_flows,\n",
|
||
" locations=\"iso3\",\n",
|
||
" color=\"flow_bin\",\n",
|
||
" hover_name=\"country\",\n",
|
||
" hover_data={\n",
|
||
" \"total_abs_flows\": \":,.0f\",\n",
|
||
" \"n_accounts_active\": True\n",
|
||
" },\n",
|
||
" category_orders={\"flow_bin\": labels},\n",
|
||
" color_discrete_sequence=px.colors.sequential.OrRd,\n",
|
||
" title=\"Geographic distribution of total absolute net flows (€)\"\n",
|
||
")\n",
|
||
"\n",
|
||
"fig.update_layout(\n",
|
||
" margin=dict(l=0, r=0, t=60, b=0),\n",
|
||
" legend_title_text=\"Total absolute net flows (€)\",\n",
|
||
" geo=dict(\n",
|
||
" showframe=False,\n",
|
||
" showcoastlines=True,\n",
|
||
" coastlinecolor=\"rgba(0,0,0,0.3)\",\n",
|
||
" projection_type=\"natural earth\"\n",
|
||
" )\n",
|
||
")\n",
|
||
"\n",
|
||
"fig.show()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"id": "a37023a0-388c-4883-a193-36d4c3eeead2",
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"import pandas as pd\n",
|
||
"import matplotlib.pyplot as plt\n",
|
||
"import seaborn as sns\n",
|
||
"\n",
|
||
"# =========================\n",
|
||
"# STYLE\n",
|
||
"# =========================\n",
|
||
"sns.set_theme(style=\"whitegrid\", context=\"paper\", font_scale=1.2)\n",
|
||
"\n",
|
||
"# =========================\n",
|
||
"# DATA PREP\n",
|
||
"# =========================\n",
|
||
"stocks[\"Centralisation Date\"] = pd.to_datetime(stocks[\"Centralisation Date\"])\n",
|
||
"flows[\"Centralisation Date\"] = pd.to_datetime(flows[\"Centralisation Date\"])\n",
|
||
"\n",
|
||
"snapshot_date = stocks[\"Centralisation Date\"].max()\n",
|
||
"\n",
|
||
"# AUM snapshot par account\n",
|
||
"aum = (\n",
|
||
" stocks.loc[stocks[\"Centralisation Date\"] == snapshot_date]\n",
|
||
" .groupby(\"Registrar Account - ID\")[\"Value - AUM €\"]\n",
|
||
" .sum()\n",
|
||
")\n",
|
||
"\n",
|
||
"# Flow activity totale par account\n",
|
||
"flows_activity = (\n",
|
||
" flows\n",
|
||
" .groupby(\"Registrar Account - ID\")[\"Value € - NetFlows\"]\n",
|
||
" .apply(lambda x: x.abs().sum())\n",
|
||
")\n",
|
||
"\n",
|
||
"df = pd.concat([aum, flows_activity], axis=1)\n",
|
||
"df.columns = [\"aum_eur\", \"abs_flows\"]\n",
|
||
"df = df.dropna()\n",
|
||
"df = df[(df[\"aum_eur\"] > 0) & (df[\"abs_flows\"] > 0)]\n",
|
||
"\n",
|
||
"# =========================\n",
|
||
"# TOP 10 % VS BOTTOM 90 %\n",
|
||
"# =========================\n",
|
||
"top_pct = 0.10\n",
|
||
"df_sorted = df.sort_values(\"aum_eur\", ascending=False)\n",
|
||
"cutoff = int(len(df_sorted) * top_pct)\n",
|
||
"\n",
|
||
"top = df_sorted.iloc[:cutoff]\n",
|
||
"bottom = df_sorted.iloc[cutoff:]\n",
|
||
"\n",
|
||
"aum_top = top[\"aum_eur\"].sum()\n",
|
||
"aum_bottom = bottom[\"aum_eur\"].sum()\n",
|
||
"\n",
|
||
"flows_top = top[\"abs_flows\"].sum()\n",
|
||
"flows_bottom = bottom[\"abs_flows\"].sum()\n",
|
||
"\n",
|
||
"# Normalisation\n",
|
||
"aum_total = aum_top + aum_bottom\n",
|
||
"flows_total = flows_top + flows_bottom\n",
|
||
"\n",
|
||
"plot_df = pd.DataFrame({\n",
|
||
" \"Metric\": [\"AUM\", \"Flow activity\"],\n",
|
||
" \"Top 10%\": [aum_top / aum_total, flows_top / flows_total],\n",
|
||
" \"Bottom 90%\": [aum_bottom / aum_total, flows_bottom / flows_total],\n",
|
||
"})\n",
|
||
"\n",
|
||
"# =========================\n",
|
||
"# PLOT — STACKED BAR\n",
|
||
"# =========================\n",
|
||
"fig, ax = plt.subplots(figsize=(6, 4))\n",
|
||
"\n",
|
||
"ax.bar(\n",
|
||
" plot_df[\"Metric\"],\n",
|
||
" plot_df[\"Top 10%\"],\n",
|
||
" label=\"Top 10% of registrar accounts\",\n",
|
||
" color=\"steelblue\"\n",
|
||
")\n",
|
||
"\n",
|
||
"ax.bar(\n",
|
||
" plot_df[\"Metric\"],\n",
|
||
" plot_df[\"Bottom 90%\"],\n",
|
||
" bottom=plot_df[\"Top 10%\"],\n",
|
||
" label=\"Bottom 90% of registrar accounts\",\n",
|
||
" color=\"lightgrey\"\n",
|
||
")\n",
|
||
"\n",
|
||
"ax.set_ylim(0, 1)\n",
|
||
"ax.set_ylabel(\"Share of total\")\n",
|
||
"ax.set_title(\"Concentration of AUM and Flow Activity\\nTop 10% vs Bottom 90% of Accounts\")\n",
|
||
"\n",
|
||
"ax.legend(frameon=False)\n",
|
||
"sns.despine()\n",
|
||
"plt.tight_layout()\n",
|
||
"plt.show()\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "7f64f406-6f06-4d56-9684-834aaccefe5c",
|
||
"metadata": {},
|
||
"source": [
|
||
"# MERGE"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"id": "e9bb67ab-9029-4ace-b960-b3d6e0b8683c",
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"# 1. Merge behavior (flows) with static diversification (stocks)\n",
|
||
"client_master = client_behavior.merge(\n",
|
||
" account_static,\n",
|
||
" on=\"Registrar Account - ID\",\n",
|
||
" how=\"left\"\n",
|
||
")\n",
|
||
"\n",
|
||
"# 2. Add geographic info\n",
|
||
"client_master = client_master.merge(\n",
|
||
" geo,\n",
|
||
" on=\"Registrar Account - ID\",\n",
|
||
" how=\"left\"\n",
|
||
")\n",
|
||
"\n",
|
||
"# 3. Create additional engineered features\n",
|
||
"client_master[\"log_total_aum\"] = np.log1p(client_master[\"total_aum\"].clip(lower=0))\n",
|
||
"client_master[\"log_median_aum\"] = np.log1p(client_master[\"median_aum\"].clip(lower=0))\n",
|
||
"\n",
|
||
"\n",
|
||
"# 4. Replace NaN flow volatility with 0 (inactive accounts)\n",
|
||
"client_master[\"flow_volatility\"] = client_master[\"flow_volatility\"].fillna(0)\n",
|
||
"\n",
|
||
"# 5. Fill missing diversification metrics with 0 (for accounts without stocks)\n",
|
||
"client_master[[\"n_isin_held\",\"n_funds_held\",\"n_asset_types\",\"n_strategies\"]] = \\\n",
|
||
" client_master[[\"n_isin_held\",\"n_funds_held\",\"n_asset_types\",\"n_strategies\"]].fillna(0)\n",
|
||
"\n",
|
||
"# 6. Fill missing geography as “UNKNOWN”\n",
|
||
"client_master[\"country\"] = client_master[\"country\"].fillna(\"UNKNOWN\")\n",
|
||
"client_master[\"region\"] = client_master[\"region\"].fillna(\"UNKNOWN\")\n",
|
||
"\n",
|
||
"# 7. Export\n",
|
||
"client_master.to_csv(\"client_master.csv\", index=False)\n",
|
||
"\n",
|
||
"print(client_master.head())\n",
|
||
"print(client_master.describe(include='all'))"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 24,
|
||
"id": "ea64d610-7816-4ead-8af5-33999fee87b4",
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"stocks.to_csv('stocks.csv')\n",
|
||
"flows.to_csv('flows.csv')"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"id": "4808a08d-57cd-4a6b-af8e-e1cc461ea4ca",
|
||
"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": "3.13.11"
|
||
}
|
||
},
|
||
"nbformat": 4,
|
||
"nbformat_minor": 5
|
||
}
|