From 38b7a23f8b61994ee8d96d793c627fc8d5296aab Mon Sep 17 00:00:00 2001 From: Marcela Caetano Date: Fri, 29 Nov 2024 13:59:45 +0000 Subject: [PATCH] Done --- Final_Version_code.ipynb | 2857 +++++++++++++++++++++++++ IronHack_Payments_Cohort_Analysis.pdf | Bin 0 -> 912183 bytes 2 files changed, 2857 insertions(+) create mode 100644 Final_Version_code.ipynb create mode 100644 IronHack_Payments_Cohort_Analysis.pdf diff --git a/Final_Version_code.ipynb b/Final_Version_code.ipynb new file mode 100644 index 0000000..2937400 --- /dev/null +++ b/Final_Version_code.ipynb @@ -0,0 +1,2857 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Business Understanding" + ] + }, + { + "cell_type": "code", + "execution_count": 57, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "\" 1. how often users from different cohorts use the cash advance services.(Frequency of service usage over the time - trend analysis - bar chart)\\n 2. Incident rate for payment-related issues across cohorts (trend variation to understand in which periods this appear and for each cohort)\\n 3. Revenue Analysis over the time: total revenue generated by each cohort over months to assess the financial impact of user behavior (trend)\\n \\n \\n4.New Metric to track this:\\n\\n- how long time to happen the first incident rate / min and max \\n- avg of users that need the cash advance\\n-measure of insights into user behavior or the performance of IronHack Payments' services.\\n\\nColumns_to_use: \\n track the data of first request - [id,amount,created_at,updated_at,user_id,status]\\n revenue analysis = transfer_type,cash_request_received_date,\\n incident rate = [deleted_account_i, reimbursement_date (charge_date),recovery_status,reco_last_update]\\n\"" + ] + }, + "execution_count": 57, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# objective \n", + "\"\"\" 1. how often users from different cohorts use the cash advance services.(Frequency of service usage over the time - trend analysis - bar chart)\n", + " 2. Incident rate for payment-related issues across cohorts (trend variation to understand in which periods this appear and for each cohort)\n", + " 3. Revenue Analysis over the time: total revenue generated by each cohort over months to assess the financial impact of user behavior (trend)\n", + " \n", + " \n", + "4.New Metric to track this:\n", + "\n", + "- how long time to happen the first incident rate / min and max \n", + "- avg of users that need the cash advance\n", + "-measure of insights into user behavior or the performance of IronHack Payments' services.\n", + "\n", + "Columns_to_use: \n", + " track the data of first request - [id,amount,created_at,updated_at,user_id,status]\n", + " revenue analysis = transfer_type,cash_request_received_date,\n", + " incident rate = [deleted_account_i, reimbursement_date (charge_date),recovery_status,reco_last_update]\n", + "\"\"\"" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Data Colection" + ] + }, + { + "cell_type": "code", + "execution_count": 58, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(21061, 13)" + ] + }, + "execution_count": 58, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#Libraries used for this project \n", + "\n", + "import pandas as pd # painel data, dataframes\n", + "import numpy as np # numerical data \n", + "import os # manage directories folders\n", + "import seaborn as sns # visualization\n", + "from datetime import datetime # format dates\n", + "\n", + "#dataset1\n", + "cash_req = pd.read_csv(\"extract - cash request - data analyst.csv\")\n", + "df1 = cash_req.copy()\n", + "df1 = pd.DataFrame(df1)\n", + "df1\n", + "\n", + "#dataset2\n", + "fee = pd.read_csv(\"extract - fees - data analyst - .csv\")\n", + "df2 = fee.copy()\n", + "df2 = pd.DataFrame(df2)\n", + "df2.shape" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Joining the Dataframes" + ] + }, + { + "cell_type": "code", + "execution_count": 59, + "metadata": {}, + "outputs": [], + "source": [ + "# merging the dataframe \n", + "merged_df = pd.merge(df1,df2,how='left',left_on='id',right_on='cash_request_id') # Isi remarks: should be a inner join # left join based on the id\n", + "\n", + "# Improvemnts needed\n", + "#remove user id, request id\n", + "#rename suffix - name of columns on the marge\n", + "# freq count with the fees type\n", + "# use everything on a subset to use later - fees per cast request total fees per cash request # flag to business\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Data Cleaning and Manipulation" + ] + }, + { + "cell_type": "code", + "execution_count": 60, + "metadata": {}, + "outputs": [], + "source": [ + "merged_df2 = merged_df.copy()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "id_x 0\n", + "amount 0\n", + "status_x 0\n", + "created_at_x 0\n", + "updated_at_x 0\n", + "user_id 0\n", + "moderated_at 0\n", + "deleted_account_id 0\n", + "reimbursement_date 0\n", + "cash_request_received_date 0\n", + "money_back_date 0\n", + "transfer_type 0\n", + "send_at 0\n", + "recovery_status 0\n", + "reco_creation 0\n", + "reco_last_update 0\n", + "id_y 0\n", + "cash_request_id 0\n", + "type 0\n", + "status_y 0\n", + "category 0\n", + "total_amount 0\n", + "reason 0\n", + "created_at_y 0\n", + "updated_at_y 0\n", + "paid_at 0\n", + "from_date 0\n", + "to_date 0\n", + "charge_moment 0\n", + "dtype: int64" + ] + }, + "execution_count": 61, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# checking the Data\n", + "\n", + "merged_df.head()\n", + "merged_df2.info\n", + "merged_df2.columns\n", + "merged_df2 = merged_df.copy()\n", + "merged_df2.isnull().sum()\n", + "\n", + "# Cleaning Duplicates, nulls and the empty spaces\n", + "merged_df2.isnull().sum()\n", + "merged_df2.duplicated().sum()\n", + "(merged_df2es =='').sum() # no spaces on the dataframe\n", + "(merged_df2 =='').sum() # no space the dataframe\n" + ] + }, + { + "cell_type": "code", + "execution_count": 62, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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05100.0rejected2019-12-10 19:05:21.596873+002019-12-11 16:47:42.40783+00804.02019-12-11 16:47:42.405646+00NaN2020-01-09 19:05:21.596363+00NaN...NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
170100.0rejected2019-12-10 19:50:12.34778+002019-12-11 14:24:22.900054+00231.02019-12-11 14:24:22.897988+00NaN2020-01-09 19:50:12.34778+00NaN...NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
27100.0rejected2019-12-10 19:13:35.82546+002019-12-11 09:46:59.779773+00191.02019-12-11 09:46:59.777728+00NaN2020-01-09 19:13:35.825041+00NaN...NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
31099.0rejected2019-12-10 19:16:10.880172+002019-12-18 14:26:18.136163+00761.02019-12-18 14:26:18.128407+00NaN2020-01-09 19:16:10.879606+00NaN...NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
41594100.0rejected2020-05-06 09:59:38.877376+002020-05-07 09:21:55.34008+007686.02020-05-07 09:21:55.320193+00NaN2020-06-05 22:00:00+00NaN...NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
..................................................................
3208922357100.0money_back2020-10-20 07:58:04.006937+002021-02-05 12:19:30.656816+0082122.0NaNNaN2021-02-05 11:00:00+002020-10-21...acceptedNaN5.0Instant Payment Cash Request 223572020-10-20 07:58:19.637461+002020-10-20 07:58:19.637491+002021-02-05 12:19:30.685779+00NaNNaNafter
3209020256100.0money_back2020-10-10 05:40:55.700422+002021-02-05 13:14:19.707627+0064517.0NaNNaN2021-02-05 11:00:00+002020-10-12...acceptedNaN5.0Postpone Cash Request 202562020-10-30 17:08:16.906449+002020-10-30 17:08:21.967966+002020-10-30 17:08:21.416796+002020-11-06 11:00:00+002020-12-07 11:00:00+00before
3209120256100.0money_back2020-10-10 05:40:55.700422+002021-02-05 13:14:19.707627+0064517.0NaNNaN2021-02-05 11:00:00+002020-10-12...acceptedNaN5.0Instant Payment Cash Request 202562020-10-10 05:42:11.679401+002020-10-13 14:25:03.460352+002021-02-05 13:14:19.731397+00NaNNaNafter
3209219886100.0direct_debit_sent2020-10-08 14:16:52.155661+002021-01-05 15:45:52.645536+0044867.0NaNNaN2021-02-05 11:00:00+002020-10-10...acceptedNaN5.0Postpone Cash Request 198862020-10-10 21:22:00.083739+002020-10-13 14:25:18.501716+002020-10-10 21:22:04.456828+002020-11-06 22:00:00+002020-12-06 11:00:00+00before
3209319886100.0direct_debit_sent2020-10-08 14:16:52.155661+002021-01-05 15:45:52.645536+0044867.0NaNNaN2021-02-05 11:00:00+002020-10-10...acceptedNaN5.0Instant Payment Cash Request 198862020-10-08 14:17:09.126909+002020-10-13 14:25:16.470702+002021-02-11 04:24:07.529815+00NaNNaNafter
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32094 rows × 29 columns

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" + ], + "text/plain": [ + " id_x amount status_x created_at_x \\\n", + "0 5 100.0 rejected 2019-12-10 19:05:21.596873+00 \n", + "1 70 100.0 rejected 2019-12-10 19:50:12.34778+00 \n", + "2 7 100.0 rejected 2019-12-10 19:13:35.82546+00 \n", + "3 10 99.0 rejected 2019-12-10 19:16:10.880172+00 \n", + "4 1594 100.0 rejected 2020-05-06 09:59:38.877376+00 \n", + "... ... ... ... ... \n", + "32089 22357 100.0 money_back 2020-10-20 07:58:04.006937+00 \n", + "32090 20256 100.0 money_back 2020-10-10 05:40:55.700422+00 \n", + "32091 20256 100.0 money_back 2020-10-10 05:40:55.700422+00 \n", + "32092 19886 100.0 direct_debit_sent 2020-10-08 14:16:52.155661+00 \n", + "32093 19886 100.0 direct_debit_sent 2020-10-08 14:16:52.155661+00 \n", + "\n", + " updated_at_x user_id moderated_at \\\n", + "0 2019-12-11 16:47:42.40783+00 804.0 2019-12-11 16:47:42.405646+00 \n", + "1 2019-12-11 14:24:22.900054+00 231.0 2019-12-11 14:24:22.897988+00 \n", + "2 2019-12-11 09:46:59.779773+00 191.0 2019-12-11 09:46:59.777728+00 \n", + "3 2019-12-18 14:26:18.136163+00 761.0 2019-12-18 14:26:18.128407+00 \n", + "4 2020-05-07 09:21:55.34008+00 7686.0 2020-05-07 09:21:55.320193+00 \n", + "... ... ... ... \n", + "32089 2021-02-05 12:19:30.656816+00 82122.0 NaN \n", + "32090 2021-02-05 13:14:19.707627+00 64517.0 NaN \n", + "32091 2021-02-05 13:14:19.707627+00 64517.0 NaN \n", + "32092 2021-01-05 15:45:52.645536+00 44867.0 NaN \n", + "32093 2021-01-05 15:45:52.645536+00 44867.0 NaN \n", + "\n", + " deleted_account_id reimbursement_date \\\n", + "0 NaN 2020-01-09 19:05:21.596363+00 \n", + "1 NaN 2020-01-09 19:50:12.34778+00 \n", + "2 NaN 2020-01-09 19:13:35.825041+00 \n", + "3 NaN 2020-01-09 19:16:10.879606+00 \n", + "4 NaN 2020-06-05 22:00:00+00 \n", + "... ... ... \n", + "32089 NaN 2021-02-05 11:00:00+00 \n", + "32090 NaN 2021-02-05 11:00:00+00 \n", + "32091 NaN 2021-02-05 11:00:00+00 \n", + "32092 NaN 2021-02-05 11:00:00+00 \n", + "32093 NaN 2021-02-05 11:00:00+00 \n", + "\n", + " cash_request_received_date ... status_y category total_amount \\\n", + "0 NaN ... NaN NaN NaN \n", + "1 NaN ... NaN NaN NaN \n", + "2 NaN ... NaN NaN NaN \n", + "3 NaN ... NaN NaN NaN \n", + "4 NaN ... NaN NaN NaN \n", + "... ... ... ... ... ... \n", + "32089 2020-10-21 ... accepted NaN 5.0 \n", + "32090 2020-10-12 ... accepted NaN 5.0 \n", + "32091 2020-10-12 ... accepted NaN 5.0 \n", + "32092 2020-10-10 ... accepted NaN 5.0 \n", + "32093 2020-10-10 ... accepted NaN 5.0 \n", + "\n", + " reason created_at_y \\\n", + "0 NaN NaN \n", + "1 NaN NaN \n", + "2 NaN NaN \n", + "3 NaN NaN \n", + "4 NaN NaN \n", + "... ... ... \n", + "32089 Instant Payment Cash Request 22357 2020-10-20 07:58:19.637461+00 \n", + "32090 Postpone Cash Request 20256 2020-10-30 17:08:16.906449+00 \n", + "32091 Instant Payment Cash Request 20256 2020-10-10 05:42:11.679401+00 \n", + "32092 Postpone Cash Request 19886 2020-10-10 21:22:00.083739+00 \n", + "32093 Instant Payment Cash Request 19886 2020-10-08 14:17:09.126909+00 \n", + "\n", + " updated_at_y paid_at \\\n", + "0 NaN NaN \n", + "1 NaN NaN \n", + "2 NaN NaN \n", + "3 NaN NaN \n", + "4 NaN NaN \n", + "... ... ... \n", + "32089 2020-10-20 07:58:19.637491+00 2021-02-05 12:19:30.685779+00 \n", + "32090 2020-10-30 17:08:21.967966+00 2020-10-30 17:08:21.416796+00 \n", + "32091 2020-10-13 14:25:03.460352+00 2021-02-05 13:14:19.731397+00 \n", + "32092 2020-10-13 14:25:18.501716+00 2020-10-10 21:22:04.456828+00 \n", + "32093 2020-10-13 14:25:16.470702+00 2021-02-11 04:24:07.529815+00 \n", + "\n", + " from_date to_date charge_moment \n", + "0 NaN NaN NaN \n", + "1 NaN NaN NaN \n", + "2 NaN NaN NaN \n", + "3 NaN NaN NaN \n", + "4 NaN NaN NaN \n", + "... ... ... ... \n", + "32089 NaN NaN after \n", + "32090 2020-11-06 11:00:00+00 2020-12-07 11:00:00+00 before \n", + "32091 NaN NaN after \n", + "32092 2020-11-06 22:00:00+00 2020-12-06 11:00:00+00 before \n", + "32093 NaN NaN after \n", + "\n", + "[32094 rows x 29 columns]" + ] + }, + "execution_count": 62, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "merged_df2" + ] + }, + { + "cell_type": "code", + "execution_count": 73, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "user_id float64\n", + "created_at_x object\n", + "status_x object\n", + "status_y object\n", + "recovery_status object\n", + "amount float64\n", + "cash_request_id float64\n", + "total_amount float64\n", + "reimbursement_date object\n", + "type object\n", + "dtype: object" + ] + }, + "execution_count": 73, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "subset_merged_df_approach2 = merged_df2[['user_id','created_at_x','status_x','status_y','recovery_status',\n", + " 'amount','cash_request_id','total_amount','reimbursement_date','type']]\n", + "subset_merged_df_approach2.loc[:, 'recovery_status'] = subset_merged_df_approach2['recovery_status'].fillna(\"Not Applicable\")\n", + "subset_merged_df_approach2.isnull().sum()\n", + "subset_merged_df_approach2 = subset_merged_df_approach2.dropna(subset=['user_id','cash_request_id'])\n", + "subset_merged_df_approach2.isnull().sum()\n", + "subset_merged_df_approach2.dtypes\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Data Analysis Exploration" + ] + }, + { + "cell_type": "code", + "execution_count": 74, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "user_id float64\n", + "created_at_x object\n", + "status_x object\n", + "status_y object\n", + "recovery_status object\n", + "amount float64\n", + "cash_request_id float64\n", + "total_amount float64\n", + "reimbursement_date object\n", + "type object\n", + "dtype: object" + ] + }, + "execution_count": 74, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "subset_merged_df_approach2.dtypes" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Converting dates" + ] + }, + { + "cell_type": "code", + "execution_count": 75, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "user_id float64\n", + "created_at_x object\n", + "status_x object\n", + "status_y object\n", + "recovery_status object\n", + "amount float64\n", + "cash_request_id float64\n", + "total_amount float64\n", + "reimbursement_date object\n", + "type object\n", + "dtype: object" + ] + }, + "execution_count": 75, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "subset_merged_df_approach2.loc[:, 'created_at_x'] = subset_merged_df_approach2['created_at_x'].apply(lambda x: pd.to_datetime(x, errors='coerce'))\n", + "subset_merged_df_approach2.loc[:, 'reimbursement_date'] = subset_merged_df_approach2['reimbursement_date'].apply(lambda x: pd.to_datetime(x, errors='coerce'))\n", + "subset_merged_df_approach2.dtypes\n" + ] + }, + { + "cell_type": "code", + "execution_count": 66, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "user_id float64\n", + "created_at_x object\n", + "status_x object\n", + "status_y object\n", + "recovery_status object\n", + "amount float64\n", + "cash_request_id float64\n", + "total_amount float64\n", + "reimbursement_date object\n", + "type object\n", + "dtype: object" + ] + }, + "execution_count": 66, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "subset_merged_df_approach2.dtypes" + ] + }, + { + "cell_type": "code", + "execution_count": 95, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "user_id float64\n", + "created_at_x datetime64[ns, UTC]\n", + "status_x object\n", + "status_y object\n", + "recovery_status object\n", + "amount float64\n", + "cash_request_id float64\n", + "total_amount float64\n", + "reimbursement_date datetime64[ns, UTC]\n", + "type object\n", + "dtype: object" + ] + }, + "execution_count": 95, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "subset_merged_df_approach2.loc[:, 'created_at_x'] = subset_merged_df_approach2['created_at_x'].apply(lambda x: pd.to_datetime(x, errors='coerce'))\n", + "subset_merged_df_approach2.loc[:, 'reimbursement_date'] = subset_merged_df_approach2['reimbursement_date'].apply(lambda x: pd.to_datetime(x, errors='coerce'))\n", + "subset_merged_df_approach2.dtypes\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Data Exploration" + ] + }, + { + "cell_type": "code", + "execution_count": 86, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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countmeanstdmin25%50%75%max
user_id20151.037988.6027987.1247.014868.031538.056357.5103503.0
amount20151.081.8026.971.050.0100.0100.0200.0
cash_request_id20151.016514.396587.491456.011968.017403.021921.527010.0
total_amount20151.05.000.005.05.05.05.05.0
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" + ], + "text/plain": [ + " count mean std min 25% 50% \\\n", + "user_id 20151.0 37988.60 27987.12 47.0 14868.0 31538.0 \n", + "amount 20151.0 81.80 26.97 1.0 50.0 100.0 \n", + "cash_request_id 20151.0 16514.39 6587.49 1456.0 11968.0 17403.0 \n", + "total_amount 20151.0 5.00 0.00 5.0 5.0 5.0 \n", + "\n", + " 75% max \n", + "user_id 56357.5 103503.0 \n", + "amount 100.0 200.0 \n", + "cash_request_id 21921.5 27010.0 \n", + "total_amount 5.0 5.0 " + ] + }, + "execution_count": 86, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Descriptive statitics : Checking the measures of the data \n", + "statitics_table = round(subset_merged_df_approach2.describe(),2).T\n", + "statitics_table" + ] + }, + { + "cell_type": "code", + "execution_count": 94, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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Wtddeq/fff1+SNG3aNA0ZMkTXXXedHn74YaWlpem9995Thw4dHPdTXYy/v7/eeustPfLII+rVq5fuvfdeBQUFafv27crJydG8efPUtm1btWrVSs8++6yOHz8uf39//fe//y11X9G+ffs0YMAA3X333Wrfvr1cXV315ZdfKjk5WcOHD7+i7/+v7du3Tx999JEMw1BmZqa2b9+uBQsWKCsrS2+++aYGDRp0yfeWp84ePXpo1qxZ+uMf/6irrrpKoaGhpXqZyuvqq6/W6NGjtWnTJoWFhenDDz9UcnKy5syZ42gzcOBARUVFafTo0Xruuefk4uKiDz/80PF9v1B5a3Nzc9P06dP10EMP6frrr9eIESMc0943b95cTz/9dKWOBwCqhXkTPAIADOP89N6bNm0qta64uNho1aqV0apVK8dU4wcPHjQeeOABIzw83HBzczOaNm1q3Hrrrcbnn3/u9N4dO3YY119/veHp6Wk0bdrU+MMf/mD885//LDWVeHFxsfHCCy8YISEhhre3txEXF2ccOHCg1LT3hlEyLf3kyZONq666ynB3dzdCQkKMvn37Gq+//rpRUFBgGIZhfP7558bAgQON0NBQw93d3YiKijIeffRR4+TJk07b+vvf/260bNnScHFxKTUF/sqVK424uDgjICDA8PT0NFq1amU8+OCDxubNm5228d///tdo166d4eHhYbRv39744osvjFGjRl122nu7RYsWGX379jW8vLwMf39/o3fv3sZ//vMfx/rdu3cbsbGxhq+vrxESEmKMGTPG2L59u9N08qmpqca4ceOMtm3bGj4+PkZAQIDRp08f47PPPnPaV3R0tDFkyJBSNVx//fVlTuX+a5IcL6vVagQGBhrdunUzJkyYYOzatatU+19Pe1/eOpOSkowhQ4YYfn5+hiRHbZe6Ti827f2QIUOMpUuXGp07dzY8PDyMtm3bGgsWLCj1/vj4eKNPnz6O6+XNN98sc5sXq+3X097bffrpp0a3bt0MDw8PIzg42Bg5cqRx7NgxpzajRo0yfHx8StV0sen4AaCqWQyjCu4kBgDUCfaH5R46dMjpgcUAAMAc3EMGAAAAACYhkAEAAACASQhkAAAAAGAS7iEDAAAAAJPQQwYAAAAAJiGQAQAAAIBJTH0w9OrVq/Xaa68pPj5eJ0+e1JdffqmhQ4eW2faxxx7T3/72N7311luaOHGiY3laWpqefPJJffXVV7JarRo2bJjeeecd+fr6Otrs2LFD48aN06ZNm9S4cWM9+eSTev755522v2DBAr388ss6fPiwWrdurenTp2vw4MHlPhabzaYTJ07Iz89PFoulQucBAAAAQP1hGIbOnj2riIgIWa2X7gMzNZBlZ2erS5cuevjhh3XnnXdetN2XX36p9evXKyIiotS6kSNH6uTJk1q2bJkKCwv10EMPaezYsfr4448lSZmZmRo4cKBiY2M1e/Zs/fzzz3r44YcVGBiosWPHSpLWrl2rESNGaNq0abr11lv18ccfa+jQodqyZYs6duxYrmM5ceKEIiMjK3EWAAAAANRHR48eVbNmzS7ZptZM6mGxWMrsITt+/Lj69OmjpUuXasiQIZo4caKjh2zPnj1q3769Nm3apJ49e0qSlixZosGDB+vYsWOKiIjQrFmz9Lvf/U5JSUlyd3eXJL344otauHCh9u7dK0m65557lJ2drcWLFzv2e80116hr166aPXt2uerPyMhQYGCgjh49Kn9//ys8GwAAAADqqszMTEVGRio9PV0BAQGXbGtqD9nl2Gw23X///XruuefUoUOHUuvXrVunwMBARxiTpNjYWFmtVm3YsEF33HGH1q1bp/79+zvCmCTFxcVp+vTpOnPmjIKCgrRu3TpNmjTJadtxcXFauHDhRWvLz89Xfn6+4+uzZ89Kkvz9/QlkAAAAAMp1K1OtntRj+vTpcnV11VNPPVXm+qSkJIWGhjotc3V1VXBwsJKSkhxtwsLCnNrYv75cG/v6skybNk0BAQGOF8MVAQAAAFRUrQ1k8fHxeueddzR37txaOUnG5MmTlZGR4XgdPXrU7JIAAAAA1DG1NpD9+OOPSklJUVRUlFxdXeXq6qojR47omWeeUfPmzSVJ4eHhSklJcXpfUVGR0tLSFB4e7miTnJzs1Mb+9eXa2NeXxcPDwzE8kWGKAAAAACqj1gay+++/Xzt27NC2bdscr4iICD333HNaunSpJCkmJkbp6emKj493vG/FihWy2Wzq06ePo83q1atVWFjoaLNs2TK1adNGQUFBjjbLly932v+yZcsUExNT3YcJAAAAoAEzdVKPrKwsHThwwPH1oUOHtG3bNgUHBysqKkqNGjVyau/m5qbw8HC1adNGktSuXTsNGjRIY8aM0ezZs1VYWKjx48dr+PDhjiny7733Xv3+97/X6NGj9cILL2jnzp1655139NZbbzm2O2HCBF1//fV64403NGTIEH3yySfavHmzPvjggxo4CwAAAAAaKlN7yDZv3qxu3bqpW7dukqRJkyapW7dumjJlSrm3MX/+fLVt21YDBgzQ4MGDdd111zkFqYCAAH333Xc6dOiQevTooWeeeUZTpkxxPINMkvr27auPP/5YH3zwgbp06aLPP/9cCxcuLPczyAAAAACgMmrNc8jquszMTAUEBCgjI4P7yQAAAIAGrCLZoNbeQwYAAAAA9R2BDAAAAABMQiADAAAAAJMQyAAAAADAJAQyAAAAADAJgQwAAAAATEIgAwAAAACTEMgAAAAAwCQEMgAAAAAwCYEMAAAAAExCIAMAAAAAk7iaXQAAALVVYmKiUlNTK/y+kJAQRUVFVUNFAID6hkAGAEAZEhMT1bZdO+Xm5FT4vV7e3tq7Zw+hDABwWQQyAADKkJqaqtycHI184TWFRbUq9/uSEw9q/vTnlJqaSiADAFwWgQwAgEsIi2qlZq07mF0GAKCeYlIPAAAAADAJgQwAAAAATEIgAwAAAACTEMgAAAAAwCQEMgAAAAAwCYEMAAAAAExCIAMAAAAAkxDIAAAAAMAkBDIAAAAAMAmBDAAAAABMQiADAAAAAJMQyAAAAADAJAQyAAAAADAJgQwAAAAATEIgAwAAAACTEMgAAAAAwCQEMgAAAAAwCYEMAAAAAExCIAMAAAAAkxDIAAAAAMAkBDIAAAAAMAmBDAAAAABMQiADAAAAAJMQyAAAAADAJAQyAAAAADAJgQwAAAAATEIgAwAAAACTEMgAAAAAwCQEMgAAAAAwCYEMAAAAAExCIAMAAAAAkxDIAAAAAMAkBDIAAAAAMAmBDAAAAABMQiADAAAAAJMQyAAAAADAJAQyAAAAADAJgQwAAAAATEIgAwAAAACTEMgAAAAAwCSmBrLVq1frtttuU0REhCwWixYuXOhYV1hYqBdeeEGdOnWSj4+PIiIi9MADD+jEiRNO20hLS9PIkSPl7++vwMBAjR49WllZWU5tduzYoX79+snT01ORkZGaMWNGqVoWLFigtm3bytPTU506ddI333xTLccMAAAAAHamBrLs7Gx16dJFM2fOLLUuJydHW7Zs0csvv6wtW7boiy++UEJCgn7zm984tRs5cqR27dqlZcuWafHixVq9erXGjh3rWJ+ZmamBAwcqOjpa8fHxeu211zR16lR98MEHjjZr167ViBEjNHr0aG3dulVDhw7V0KFDtXPnzuo7eAAAAAANnsUwDMPsIiTJYrHoyy+/1NChQy/aZtOmTerdu7eOHDmiqKgo7dmzR+3bt9emTZvUs2dPSdKSJUs0ePBgHTt2TBEREZo1a5Z+97vfKSkpSe7u7pKkF198UQsXLtTevXslSffcc4+ys7O1ePFix76uueYade3aVbNnzy6zlvz8fOXn5zu+zszMVGRkpDIyMuTv73+lpwMAYLItW7aoR48emjTzCzVr3aHc7zu2f5feHHen4uPj1b1792qsEABQW2VmZiogIKBc2aBO3UOWkZEhi8WiwMBASdK6desUGBjoCGOSFBsbK6vVqg0bNjja9O/f3xHGJCkuLk4JCQk6c+aMo01sbKzTvuLi4rRu3bqL1jJt2jQFBAQ4XpGRkVV1mAAAAAAaiDoTyPLy8vTCCy9oxIgRjpSZlJSk0NBQp3aurq4KDg5WUlKSo01YWJhTG/vXl2tjX1+WyZMnKyMjw/E6evTolR0gAAAAgAbH1ewCyqOwsFB33323DMPQrFmzzC5HkuTh4SEPDw+zywAAAABQh9X6QGYPY0eOHNGKFSucxmCGh4crJSXFqX1RUZHS0tIUHh7uaJOcnOzUxv715drY1wMAAABAdajVQxbtYWz//v36/vvv1ahRI6f1MTExSk9PV3x8vGPZihUrZLPZ1KdPH0eb1atXq7Cw0NFm2bJlatOmjYKCghxtli9f7rTtZcuWKSYmproODQAAAADMDWRZWVnatm2btm3bJkk6dOiQtm3bpsTERBUWFuq3v/2tNm/erPnz56u4uFhJSUlKSkpSQUGBJKldu3YaNGiQxowZo40bN2rNmjUaP368hg8froiICEnSvffeK3d3d40ePVq7du3Sp59+qnfeeUeTJk1y1DFhwgQtWbJEb7zxhvbu3aupU6dq8+bNGj9+fI2fEwAAAAANh6mBbPPmzerWrZu6desmSZo0aZK6deumKVOm6Pjx41q0aJGOHTumrl27qkmTJo7X2rVrHduYP3++2rZtqwEDBmjw4MG67rrrnJ4xFhAQoO+++06HDh1Sjx499Mwzz2jKlClOzyrr27evPv74Y33wwQfq0qWLPv/8cy1cuFAdO3asuZMBAAAAoMEx9R6yG264QZd6DFp5HpEWHBysjz/++JJtOnfurB9//PGSbe666y7dddddl90fAAAAAFSVWn0PGQAAAADUZwQyAAAAADAJgQwAAAAATEIgAwAAAACTEMgAAAAAwCQEMgAAAAAwCYEMAAAAAExCIAMAAAAAkxDIAAAAAMAkBDIAAAAAMAmBDAAAAABMQiADAAAAAJMQyAAAAADAJAQyAAAAADAJgQwAAAAATEIgAwAAAACTEMgAAAAAwCQEMgAAAAAwCYEMAAAAAExCIAMAAAAAkxDIAAAAAMAkBDIAAAAAMAmBDAAAAABMQiADAAAAAJMQyAAAAADAJAQyAAAAADAJgQwAAAAATEIgAwAAAACTEMgAAAAAwCQEMgAAAAAwCYEMAAAAAExCIAMAAAAAkxDIAAAAAMAkBDIAAAAAMAmBDAAAAABMQiADAAAAAJMQyAAAAADAJAQyAAAAADAJgQwAAAAATEIgAwAAAACTEMgAAAAAwCQEMgAAAAAwCYEMAAAAAExCIAMAAAAAkxDIAAAAAMAkBDIAAAAAMAmBDAAAAABMQiADAAAAAJMQyAAAAADAJAQyAAAAADAJgQwAAAAATEIgAwAAAACTEMgAAAAAwCQEMgAAAAAwiamBbPXq1brtttsUEREhi8WihQsXOq03DENTpkxRkyZN5OXlpdjYWO3fv9+pTVpamkaOHCl/f38FBgZq9OjRysrKcmqzY8cO9evXT56enoqMjNSMGTNK1bJgwQK1bdtWnp6e6tSpk7755psqP14AAAAAuJCpgSw7O1tdunTRzJkzy1w/Y8YMvfvuu5o9e7Y2bNggHx8fxcXFKS8vz9Fm5MiR2rVrl5YtW6bFixdr9erVGjt2rGN9ZmamBg4cqOjoaMXHx+u1117T1KlT9cEHHzjarF27ViNGjNDo0aO1detWDR06VEOHDtXOnTur7+ABAAAANHgWwzAMs4uQJIvFoi+//FJDhw6VVNI7FhERoWeeeUbPPvusJCkjI0NhYWGaO3euhg8frj179qh9+/batGmTevbsKUlasmSJBg8erGPHjikiIkKzZs3S7373OyUlJcnd3V2S9OKLL2rhwoXau3evJOmee+5Rdna2Fi9e7KjnmmuuUdeuXTV79uxy1Z+ZmamAgABlZGTI39+/qk4LAMAkW7ZsUY8ePTRp5hdq1rpDud93bP8uvTnuTsXHx6t79+7VWCEAoLaqSDaotfeQHTp0SElJSYqNjXUsCwgIUJ8+fbRu3TpJ0rp16xQYGOgIY5IUGxsrq9WqDRs2ONr079/fEcYkKS4uTgkJCTpz5oyjzYX7sbex76cs+fn5yszMdHoBAAAAQEXU2kCWlJQkSQoLC3NaHhYW5liXlJSk0NBQp/Wurq4KDg52alPWNi7cx8Xa2NeXZdq0aQoICHC8IiMjK3qIAAAAABq4WhvIarvJkycrIyPD8Tp69KjZJQEAAACoY2ptIAsPD5ckJScnOy1PTk52rAsPD1dKSorT+qKiIqWlpTm1KWsbF+7jYm3s68vi4eEhf39/pxcAAAAAVEStDWQtWrRQeHi4li9f7liWmZmpDRs2KCYmRpIUExOj9PR0xcfHO9qsWLFCNptNffr0cbRZvXq1CgsLHW2WLVumNm3aKCgoyNHmwv3Y29j3AwAAAADVwdRAlpWVpW3btmnbtm2SSiby2LZtmxITE2WxWDRx4kT98Y9/1KJFi/Tzzz/rgQceUEREhGMmxnbt2mnQoEEaM2aMNm7cqDVr1mj8+PEaPny4IiIiJEn33nuv3N3dNXr0aO3atUuffvqp3nnnHU2aNMlRx4QJE7RkyRK98cYb2rt3r6ZOnarNmzdr/PjxNX1KAAAAADQgrmbufPPmzbrxxhsdX9tD0qhRozR37lw9//zzys7O1tixY5Wenq7rrrtOS5Yskaenp+M98+fP1/jx4zVgwABZrVYNGzZM7777rmN9QECAvvvuO40bN049evRQSEiIpkyZ4vSssr59++rjjz/WSy+9pP/7v/9T69attXDhQnXs2LEGzgIAAACAhqrWPIesruM5ZABQv/AcMgBAZdWL55ABAAAAQH1HIAMAAAAAkxDIAAAAAMAkBDIAAAAAMAmBDAAAAABMQiADAAAAAJMQyAAAAADAJAQyAAAAADAJgQwAAAAATEIgAwAAAACTEMgAAAAAwCQEMgAAAAAwCYEMAAAAAExCIAMAAAAAkxDIAAAAAMAkBDIAAAAAMAmBDAAAAABMQiADAAAAAJMQyAAAAADAJAQyAAAAADAJgQwAAAAATEIgAwAAAACTEMgAAAAAwCQEMgAAAAAwCYEMAAAAAExCIAMAAAAAkxDIAAAAAMAkBDIAAAAAMAmBDAAAAABMQiADAAAAAJMQyAAAAADAJAQyAAAAADAJgQwAAAAATEIgAwAAAACTEMgAAAAAwCQEMgAAAAAwCYEMAAAAAExCIAMAAAAAkxDIAAAAAMAkBDIAAAAAMAmBDAAAAABMQiADAAAAAJMQyAAAAADAJAQyAAAAADAJgQwAAAAATEIgAwAAAACTEMgAAAAAwCQEMgAAAAAwCYEMAAAAAExCIAMAAAAAkxDIAAAAAMAkBDIAAAAAMAmBDAAAAABMQiADAAAAAJMQyAAAAADAJLU6kBUXF+vll19WixYt5OXlpVatWukPf/iDDMNwtDEMQ1OmTFGTJk3k5eWl2NhY7d+/32k7aWlpGjlypPz9/RUYGKjRo0crKyvLqc2OHTvUr18/eXp6KjIyUjNmzKiRYwQAAADQcNXqQDZ9+nTNmjVL77//vvbs2aPp06drxowZeu+99xxtZsyYoXfffVezZ8/Whg0b5OPjo7i4OOXl5TnajBw5Urt27dKyZcu0ePFirV69WmPHjnWsz8zM1MCBAxUdHa34+Hi99tprmjp1qj744IMaPV4AAAAADYur2QVcytq1a3X77bdryJAhkqTmzZvrP//5jzZu3CippHfs7bff1ksvvaTbb79dkvSvf/1LYWFhWrhwoYYPH649e/ZoyZIl2rRpk3r27ClJeu+99zR48GC9/vrrioiI0Pz581VQUKAPP/xQ7u7u6tChg7Zt26Y333zTKbgBAAAAQFWq1T1kffv21fLly7Vv3z5J0vbt2/XTTz/plltukSQdOnRISUlJio2NdbwnICBAffr00bp16yRJ69atU2BgoCOMSVJsbKysVqs2bNjgaNO/f3+5u7s72sTFxSkhIUFnzpwps7b8/HxlZmY6vQAAAACgImp1D9mLL76ozMxMtW3bVi4uLiouLtaf/vQnjRw5UpKUlJQkSQoLC3N6X1hYmGNdUlKSQkNDnda7uroqODjYqU2LFi1KbcO+LigoqFRt06ZN0+9///sqOEoAAAAADVWt7iH77LPPNH/+fH388cfasmWL5s2bp9dff13z5s0zuzRNnjxZGRkZjtfRo0fNLgkAAABAHVOre8iee+45vfjiixo+fLgkqVOnTjpy5IimTZumUaNGKTw8XJKUnJysJk2aON6XnJysrl27SpLCw8OVkpLitN2ioiKlpaU53h8eHq7k5GSnNvav7W1+zcPDQx4eHld+kAAAAAAarFrdQ5aTkyOr1blEFxcX2Ww2SVKLFi0UHh6u5cuXO9ZnZmZqw4YNiomJkSTFxMQoPT1d8fHxjjYrVqyQzWZTnz59HG1Wr16twsJCR5tly5apTZs2ZQ5XBAAAAICqUKsD2W233aY//elP+vrrr3X48GF9+eWXevPNN3XHHXdIkiwWiyZOnKg//vGPWrRokX7++Wc98MADioiI0NChQyVJ7dq106BBgzRmzBht3LhRa9as0fjx4zV8+HBFRERIku699165u7tr9OjR2rVrlz799FO98847mjRpklmHDgAAAKABqNVDFt977z29/PLLeuKJJ5SSkqKIiAg9+uijmjJliqPN888/r+zsbI0dO1bp6em67rrrtGTJEnl6ejrazJ8/X+PHj9eAAQNktVo1bNgwvfvuu471AQEB+u677zRu3Dj16NFDISEhmjJlClPeAwAAAKhWFsMwDLOLqA8yMzMVEBCgjIwM+fv7m10OAOAKbdmyRT169NCkmV+oWesO5X7fsf279Oa4OxUfH6/u3btXY4UAgNqqItmgVg9ZBAAAAID6jEAGAAAAACYhkAEAAACASQhkAAAAAGASAhkAAAAAmIRABgAAAAAmqVQga9mypU6fPl1qeXp6ulq2bHnFRQEAAABAQ1CpQHb48GEVFxeXWp6fn6/jx49fcVEAAAAA0BC4VqTxokWLHH9funSpAgICHF8XFxdr+fLlat68eZUVBwAAAAD1WYUC2dChQyVJFotFo0aNclrn5uam5s2b64033qiy4gAAAACgPqtQILPZbJKkFi1aaNOmTQoJCamWogAAAACgIahQILM7dOhQVdcBAAAAAA1OpQKZJC1fvlzLly9XSkqKo+fM7sMPP7ziwgAAAACgvqtUIPv973+vV199VT179lSTJk1ksViqui4AAAAAqPcqFchmz56tuXPn6v7776/qegAAAACgwajUc8gKCgrUt2/fqq4FAAAAABqUSgWyRx55RB9//HFV1wIAAAAADUqlhizm5eXpgw8+0Pfff6/OnTvLzc3Naf2bb75ZJcUBAAAAQH1WqUC2Y8cOde3aVZK0c+dOp3VM8AEAAAAA5VOpQLZy5cqqrgMAAAAAGpxK3UMGAAAAALhyleohu/HGGy85NHHFihWVLggAAAAAGopKBTL7/WN2hYWF2rZtm3bu3KlRo0ZVRV0AAAAAUO9VKpC99dZbZS6fOnWqsrKyrqggAAAAAGgoqvQesvvuu08ffvhhVW4SAAAAAOqtKg1k69atk6enZ1VuEgAAAADqrUoNWbzzzjudvjYMQydPntTmzZv18ssvV0lhAAAAAFDfVSqQBQQEOH1ttVrVpk0bvfrqqxo4cGCVFAYAAAAA9V2lAtmcOXOqug4AAAAAaHAqFcjs4uPjtWfPHklShw4d1K1btyopCgAAAAAagkoFspSUFA0fPlyrVq1SYGCgJCk9PV033nijPvnkEzVu3LgqawQAAACAeqlSsyw++eSTOnv2rHbt2qW0tDSlpaVp586dyszM1FNPPVXVNQIAAABAvVSpHrIlS5bo+++/V7t27RzL2rdvr5kzZzKpBwAAAACUU6V6yGw2m9zc3Eotd3Nzk81mu+KiAAAAAKAhqFQgu+mmmzRhwgSdOHHCsez48eN6+umnNWDAgCorDgAAAADqs0oFsvfff1+ZmZlq3ry5WrVqpVatWqlFixbKzMzUe++9V9U1AgAAAEC9VKl7yCIjI7VlyxZ9//332rt3rySpXbt2io2NrdLiAAAAAKA+q1AP2YoVK9S+fXtlZmbKYrHo5ptv1pNPPqknn3xSvXr1UocOHfTjjz9WV60AAAAAUK9UKJC9/fbbGjNmjPz9/UutCwgI0KOPPqo333yzyooDAAAAgPqsQoFs+/btGjRo0EXXDxw4UPHx8VdcFAAAAAA0BBUKZMnJyWVOd2/n6uqqU6dOXXFRAAAAANAQVCiQNW3aVDt37rzo+h07dqhJkyZXXBQAAAAANAQVCmSDBw/Wyy+/rLy8vFLrcnNz9corr+jWW2+tsuIAAAAAoD6r0LT3L730kr744gtdffXVGj9+vNq0aSNJ2rt3r2bOnKni4mL97ne/q5ZCAQAAAKC+qVAgCwsL09q1a/X4449r8uTJMgxDkmSxWBQXF6eZM2cqLCysWgoFAAAAgPqmwg+Gjo6O1jfffKMzZ87owIEDMgxDrVu3VlBQUHXUBwAAAAD1VoUDmV1QUJB69epVlbUAAAAAQINSoUk9AAAAAABVh0AGAAAAACYhkAEAAACASQhkAAAAAGASAhkAAAAAmIRABgAAAAAmIZABAAAAgElqfSA7fvy47rvvPjVq1EheXl7q1KmTNm/e7FhvGIamTJmiJk2ayMvLS7Gxsdq/f7/TNtLS0jRy5Ej5+/srMDBQo0ePVlZWllObHTt2qF+/fvL09FRkZKRmzJhRI8cHAAAAoOGq1YHszJkzuvbaa+Xm5qZvv/1Wu3fv1htvvKGgoCBHmxkzZujdd9/V7NmztWHDBvn4+CguLk55eXmONiNHjtSuXbu0bNkyLV68WKtXr9bYsWMd6zMzMzVw4EBFR0crPj5er732mqZOnaoPPvigRo8XAAAAQMPianYBlzJ9+nRFRkZqzpw5jmUtWrRw/N0wDL399tt66aWXdPvtt0uS/vWvfyksLEwLFy7U8OHDtWfPHi1ZskSbNm1Sz549JUnvvfeeBg8erNdff10RERGaP3++CgoK9OGHH8rd3V0dOnTQtm3b9OabbzoFNwAAAACoSrW6h2zRokXq2bOn7rrrLoWGhqpbt276+9//7lh/6NAhJSUlKTY21rEsICBAffr00bp16yRJ69atU2BgoCOMSVJsbKysVqs2bNjgaNO/f3+5u7s72sTFxSkhIUFnzpwps7b8/HxlZmY6vQAAAACgImp1IPvll180a9YstW7dWkuXLtXjjz+up556SvPmzZMkJSUlSZLCwsKc3hcWFuZYl5SUpNDQUKf1rq6uCg4OdmpT1jYu3MevTZs2TQEBAY5XZGTkFR4tAAAAgIamVgcym82m7t27689//rO6deumsWPHasyYMZo9e7bZpWny5MnKyMhwvI4ePWp2SQAAAADqmFodyJo0aaL27ds7LWvXrp0SExMlSeHh4ZKk5ORkpzbJycmOdeHh4UpJSXFaX1RUpLS0NKc2ZW3jwn38moeHh/z9/Z1eAAAAAFARtTqQXXvttUpISHBatm/fPkVHR0sqmeAjPDxcy5cvd6zPzMzUhg0bFBMTI0mKiYlRenq64uPjHW1WrFghm82mPn36ONqsXr1ahYWFjjbLli1TmzZtnGZ0BAAAAICqVKsD2dNPP63169frz3/+sw4cOKCPP/5YH3zwgcaNGydJslgsmjhxov74xz9q0aJF+vnnn/XAAw8oIiJCQ4cOlVTSozZo0CCNGTNGGzdu1Jo1azR+/HgNHz5cERERkqR7771X7u7uGj16tHbt2qVPP/1U77zzjiZNmmTWoQMAAABoAGr1tPe9evXSl19+qcmTJ+vVV19VixYt9Pbbb2vkyJGONs8//7yys7M1duxYpaen67rrrtOSJUvk6enpaDN//nyNHz9eAwYMkNVq1bBhw/Tuu+861gcEBOi7777TuHHj1KNHD4WEhGjKlClMeQ8AAACgWlkMwzDMLqI+yMzMVEBAgDIyMrifDADqgS1btqhHjx6aNPMLNWvdodzvO7Z/l94cd6fi4+PVvXv3aqwQAFBbVSQb1OoeMgAA6orjZ3Ll5e5idhkAgDqGQAYAwBU6kZ6rz7cck6erVTeXPTkvAABlqtWTegAAUNsZhqGfDqRKkvKKbNqXSS8ZAKD8CGQAAFyBQ6nZOpmRJ8u5rw+ctcrqE2hmSQCAOoRABgBAJdkMQ2sPnpYkdY8OUri/p4oNiwJi7jG5MgBAXUEgAwCgkhKSzup0doE8XK3qGR2kvq0aSZL8ug5SclaRydUBAOoCAhkAAJVQZLNp3S8lvWM9o4Pk6eaiyGBvhXraZHFx06e7skyuEABQFxDIAACohJ3HM3U2r0g+Hi7qEhnoWN4hoFiS9MORXO1PPmtSdQCAuoJABgBAJew7F7Z6RgfLzeX8f6fBHoZy9q+XIWnR9hMmVQcAqCsIZAAAVJBhGErNypckRQZ5lVqfd3ibJGn3icyaLAsAUAcRyAAAqKD03EIVFhtysVoU5O1ean1B8kFJ0i4CGQDgMghkAABU0KmzJb1jIb7uslotpdYXnDosi6SkzDydPteTBgBAWQhkAABUkD2QNfb1KHO9UZCrcF8XSdLuk/SSAQAujkAGAEAFnTrX69XYr+xAJkktAt0kcR8ZAODSCGQAAFSQvYcs1M/zom1aBJUEMu4jAwBcCoEMAIAKyM4vUk5BsSySGvmWntDDrkWgqySGLAIALo1ABgBABaSc6x0L8nZ3ev7Yr9mHLP5yKku5BcU1UhsAoO4hkAEAUAHluX9MkoK8XBTi6yGbIe1NopcMAFA2AhkAABXgmGHxMoFMkjpE+EviPjIAwMURyAAAqICKBLL25wIZ95EBAC6GQAYAQDnlFxUrI7dQEj1kAICqQSADAKCcUs8WSJJ8PVzl5eZy2fbtm5QEsr0nM1VUbKvW2gAAdROBDACAcirvhB52zRv5yNvdRflFNh1Kza7O0gAAdRSBDACAcko5myep/IHMarWoXRPuIwMAXByBDACAcrJP6BFazkAmnR+2yH1kAICyEMgAACiHYpuhtOySe8ga+5Y/kNkn9thNIAMAlIFABgBAOaRlF8hmSB6uVvl5upb7fe0dMy1myDCM6ioPAFBHEcgAACiHMzklvWPBPu6yWCzlft/VYX7n3l+o9JzCaqkNAFB3EcgAACiHzHPPHwvwcqvQ+zzdXBz3nB09k1PldQEA6jYCGQAA5WB/ILR/BQOZJEUFe0uSEtMIZAAAZwQyAADKISPvXA+ZZ8UDWSSBDABwEQQyAADKITO3SFLFhyxK5wPZ0bTcKq0JAFD3EcgAALgMm83Q2Tz7kMXyz7BoF+UIZPSQAQCcEcgAALiMrPwi2QzJxWKRr0fFA1lkkJckhiwCAEojkAEAcBn2CT38vFwrNOW9XVSjkh6yE+m5Kiq2VWltAIC6jUAGAMBlZFRyynu7MD9PubtYVWQzdDIjrypLAwDUcQQyAAAuI/MKZliUJKvVombnhi1yHxkA4EIEMgAALuNKnkFm55hpkYdDAwAuQCADAOAyrnTIoiRFBjOxBwCgNAIZAACXcSXPILOLcjwcmmeRAQDOq/jcvQBQBRITE5Wamlqp94aEhCgqKqqKKwLKVmiTcguLJVXuGWR2PIsMAFAWAhmAGpeYmKi27dopN6dyH0y9vL21d88eQhlqRE5RyTT3nq5Webi6VHo7zYIIZACA0ghkAGpcamqqcnNyNPKF1xQW1apC701OPKj5059TamoqgQw1IqtktOIVTeghnX8W2ensAmXnF8mnEg+YBgDUP/xvAMA0YVGt1Kx1B7PLAC4p+1wP2ZXcPyZJ/p5uCvByU0ZuoY6eyVHbcP+qKA8AUMcxqQcAAJeQU0WBTLpgYo/TDFsEAJQgkAEAcAn2HrIrHbIoXTCxxxlmWgQAlCCQAQBwCVU1ZFGSmp17FhkTewAA7AhkAABclEXZ5yb1qNIhiwQyAMA5BDIAAC7CxTdINllksUi+VTArIs8iAwD8GoEMAICLcA0IlyT5ebjKxWq54u1FBp3vITMM44q3BwCo+whkAABchGtgSSCrigk9JCki0EtWi5RfZNOps/lVsk0AQN1GIAMA4CLsgawq7h+TJHdXq5oEnJvY4wzDFgEABDIAAC7KNSBMUtUFMkmKPDfTIhN7AAAkAhkAABflGLLoWXWB7PzDoXkWGQCAQAYAwEW5+jeWJPl7XfkMi3bNzk3scTydHjIAQB0LZH/5y19ksVg0ceJEx7K8vDyNGzdOjRo1kq+vr4YNG6bk5GSn9yUmJmrIkCHy9vZWaGionnvuORUVFTm1WbVqlbp37y4PDw9dddVVmjt3bg0cEQCgtiq2GXLxayRJ8qvCHrKIwJIhiycz8qpsmwCAuqvOBLJNmzbpb3/7mzp37uy0/Omnn9ZXX32lBQsW6IcfftCJEyd05513OtYXFxdryJAhKigo0Nq1azVv3jzNnTtXU6ZMcbQ5dOiQhgwZohtvvFHbtm3TxIkT9cgjj2jp0qU1dnwAgNrlTJ5NFquLLDLk4+5SZduNCPSUJB1PZ8giAKCOBLKsrCyNHDlSf//73xUUFORYnpGRoX/+85968803ddNNN6lHjx6aM2eO1q5dq/Xr10uSvvvuO+3evVsfffSRunbtqltuuUV/+MMfNHPmTBUUFEiSZs+erRYtWuiNN95Qu3btNH78eP32t7/VW2+9ZcrxAgDMdyqnWJLk5SJZLFf+DDK7iHOzLJ5Iz+VZZACAuhHIxo0bpyFDhig2NtZpeXx8vAoLC52Wt23bVlFRUVq3bp0kad26derUqZPCwsIcbeLi4pSZmaldu3Y52vx623FxcY5tlCU/P1+ZmZlOLwBA/ZF6LpB5u1ZtaAoPKOkhyyu06UxOYZVuGwBQ99T6QPbJJ59oy5YtmjZtWql1SUlJcnd3V2BgoNPysLAwJSUlOdpcGMbs6+3rLtUmMzNTubllDymZNm2aAgICHK/IyMhKHR8AoHY6lX0ukLlUbSDzdHNRiK+HpJJeMgBAw1arA9nRo0c1YcIEzZ8/X56enmaX42Ty5MnKyMhwvI4ePWp2SQCAKmTvIavCCRYdmnIfGQDgnFodyOLj45WSkqLu3bvL1dVVrq6u+uGHH/Tuu+/K1dVVYWFhKigoUHp6utP7kpOTFR5e8uyY8PDwUrMu2r++XBt/f395eXmVWZuHh4f8/f2dXgCA+sMxZLGKe8ikC2ZaJJABQINXqwPZgAED9PPPP2vbtm2OV8+ePTVy5EjH393c3LR8+XLHexISEpSYmKiYmBhJUkxMjH7++WelpKQ42ixbtkz+/v5q3769o82F27C3sW8DANDwnKqme8ik84HsBFPfA0CDVw0DMaqOn5+fOnbs6LTMx8dHjRo1ciwfPXq0Jk2apODgYPn7++vJJ59UTEyMrrnmGknSwIED1b59e91///2aMWOGkpKS9NJLL2ncuHHy8CgZw//YY4/p/fff1/PPP6+HH35YK1as0Geffaavv/66Zg8YAFBrnO8hq/pt2wMZQxYBALU6kJXHW2+9JavVqmHDhik/P19xcXH661//6ljv4uKixYsX6/HHH1dMTIx8fHw0atQovfrqq442LVq00Ndff62nn35a77zzjpo1a6Z//OMfiouLM+OQAAAmy8ovUnZhSc+YV3X0kJ2baZFJPQAAdS6QrVq1yulrT09PzZw5UzNnzrzoe6Kjo/XNN99ccrs33HCDtm7dWhUlAgDqOPu9XcV5WXKzulf59h1DFglkANDg1ep7yAAAMIN9KGFx5qlq2b49kKWczVdhsa1a9gEAqBsIZAAA/MqJ9JLJNoqqKZA18nGXu6tVhiElMbEHADRoBDIAAH7lhKOHLOUyLSvHarVwHxkAQBKBDACAUuwhqbp6yKQLp74nkAFAQ0YgAwDgV47XQCBrEmCf2IMhiwDQkBHIAAD4FXuvVXVN6iFJTQMZsggAIJABAOCk2GY4JtqokSGLBDIAaNAIZAAAXCA1K1+FxYasFqk4K63a9nM+kDFkEQAaMgIZAAAXsPdYBXu5SEb1PSOMHjIAgEQgAwDAib3HqrF39f4XGXHuHrKz+UXKzCus1n0BAGovAhkAABew91iFeLtU63683V0V5O0mSTrJsEUAaLAIZAAAXMA+5X3jag5k0oVT3zNsEQAaKgIZAAAXsIejRjUQyOz3kR0nkAFAg0UgAwDgAvZnkNVEDxnPIgMAEMgAALiAfVKP6r6HTGKmRQAAgQwAAIfcgmKlZRdIqpkeMkcgy2BSDwBoqAhkAACcYx+u6OvhKm83S7Xvjx4yAACBDACAc+zBKCLQUxZLTQSyknvIkjLyVGwzqn1/AIDah0AGAMA5x8/YA5lXjewv1M9TrlaLimyGUs4ybBEAGiICGQAA59inn29aQ4HMxWpReAAzLQJAQ0YgAwDgHHsPWdOgmglk0vnwd+wMgQwAGiICGQAA5xyr4R4y6Xz44+HQANAwEcgAADjH3kPWrAZ7yJqdC3/H6SEDgAaJQAYAgKSiYpuSMksm1mga6F1j+7VPIEIPGQA0TAQyAAAkJZ/NV7HNkJuLRaF+HjW2X8eQRXrIAKBBIpABAKDzgahJgJes1up/Bpld0wt6yAyDZ5EBQENDIAMAQNLx9BxJNTuhh3R+yGJOQbHScwprdN8AAPMRyAAAkDlT3kuSp5uLQnxLhkhyHxkANDwEMgAAVPMPhb6QPQTyLDIAaHgIZAAA6HwYqukeMumCqe/pIQOABodABgCAzoehZib2kDHTIgA0PAQyAECDZxiGTqSb10N2fqbFnBrfNwDAXAQyAECDdzq7QHmFNlksJdPe17SmDFkEgAaLQAYAaPDsQwVD/Tzk7lrz/zXae+VOpOfV+L4BAOYikAEAGjwzZ1iUzj+LLC27QDkFRabUAAAwB4EMANDgnX8Gmbcp+w/wcpOfh6skOe5lAwA0DAQyAECDd+xMyWQaZvWQSTyLDAAaKgIZAKDBO27iDIt2TOwBAA0TgQwA0ODZe6WamRnIeBYZADRIBDIAQINn5kOh7eghA4CGiUAGAGjQMvMKdTavZGZDU4cs0kMGAA0SgQwA0KDZA1CQt5u83V1Nq4MeMgBomAhkAIAG7fyU9+b1jl24/+TMPBUW20ytBQBQcwhkAIAGzeyHQtuF+HjI3dUqmyElZeSZWgsAoOYQyAAADdr5QGbOQ6HtrFaLIxTyLDIAaDgIZACABq22DFmUpIhAT0ncRwYADQmBDADQoB2rJUMWL6yBmRYBoOEgkAEAGrRjaTmSzH0otJ192OTx9ByTKwEA1BQCGQCgwcrMK9Tp7AJJUvMQH5OrkaIblQSyI6cJZADQUBDIAAAN1pHUkuAT4ushXw/znkFmRyADgIaHQAYAaLAOn86WJDVvZO4Mi3YtzvXSJWXmKbeg2ORqAAA1gUAGAGiwDqeeC2S1YLiiJAV6uyvAy02SdCQt2+RqAAA1gUAGAGiwDp8bGlhbesik87UcTmXYIgA0BAQyAECD5RiyWEt6yKTztdhrAwDUb7U6kE2bNk29evWSn5+fQkNDNXToUCUkJDi1ycvL07hx49SoUSP5+vpq2LBhSk5OdmqTmJioIUOGyNvbW6GhoXruuedUVFTk1GbVqlXq3r27PDw8dNVVV2nu3LnVfXgAAJMdcdxDVnsCWfS5Wo4QyACgQajVgeyHH37QuHHjtH79ei1btkyFhYUaOHCgsrPP/yf19NNP66uvvtKCBQv0ww8/6MSJE7rzzjsd64uLizVkyBAVFBRo7dq1mjdvnubOnaspU6Y42hw6dEhDhgzRjTfeqG3btmnixIl65JFHtHTp0ho9XgBAzTmbV6jUrJIp76MZsggAMIn5c/xewpIlS5y+njt3rkJDQxUfH6/+/fsrIyND//znP/Xxxx/rpptukiTNmTNH7dq10/r163XNNdfou+++0+7du/X9998rLCxMXbt21R/+8Ae98MILmjp1qtzd3TV79my1aNFCb7zxhiSpXbt2+umnn/TWW28pLi6uxo8bAFD97FPLh/i6y8/TzeRqzrMPWaSHDAAahlrdQ/ZrGRkZkqTg4GBJUnx8vAoLCxUbG+to07ZtW0VFRWndunWSpHXr1qlTp04KCwtztImLi1NmZqZ27drlaHPhNuxt7NsoS35+vjIzM51eAIC6w36PVnQtGq4onR8+eSIjT3mFTH0PAPVdnQlkNptNEydO1LXXXquOHTtKkpKSkuTu7q7AwECntmFhYUpKSnK0uTCM2dfb112qTWZmpnJzc8usZ9q0aQoICHC8IiMjr/gYAQA1xzHlfS0LZEHebvLzLBnAkpjGsEUAqO/qTCAbN26cdu7cqU8++cTsUiRJkydPVkZGhuN19OhRs0sC6iTDMLQ/5azmrj2s/8YfU2pWvtkloYGojVPeS5LFYnGERHtoBADUX3UikI0fP16LFy/WypUr1axZM8fy8PBwFRQUKD093al9cnKywsPDHW1+Peui/evLtfH395eXl1eZNXl4eMjf39/pBaBicoqkxTtO6pufk5SRW6hj6bn6z8ZErT2YqqJim9nloZ6rbQ+FvtD5+8joIQOA+q5WBzLDMDR+/Hh9+eWXWrFihVq0aOG0vkePHnJzc9Py5csdyxISEpSYmKiYmBhJUkxMjH7++WelpKQ42ixbtkz+/v5q3769o82F27C3sW8DQNXzah2jZSfd9EtqtqwWqWd0kFqG+MhmSJsOn9FHGxKVnlNgdpmox873kNXCQHau1+4QE3sAQL1Xq2dZHDdunD7++GP973//k5+fn+Oer4CAAHl5eSkgIECjR4/WpEmTFBwcLH9/fz355JOKiYnRNddcI0kaOHCg2rdvr/vvv18zZsxQUlKSXnrpJY0bN04eHh6SpMcee0zvv/++nn/+eT388MNasWKFPvvsM3399demHTtQn53Nt6nRLU+pyLCoSYCnBrQNVSPfkn+PB1KytGpfijJyC7Uy4ZSGdo2QxWIxuWLUNyVT3pcMj40OqV1DFiWeRQYADUmt7iGbNWuWMjIydMMNN6hJkyaO16effupo89Zbb+nWW2/VsGHD1L9/f4WHh+uLL75wrHdxcdHixYvl4uKimJgY3XfffXrggQf06quvOtq0aNFCX3/9tZYtW6YuXbrojTfe0D/+8Q+mvAeqyae7zsrFy0/+bjb9tnszRxiTpKtCffXb7s3kYrEoMS3H0YsBVCX7UMBGPu7yr0VT3tu1COFZZADQUNTqHjLDMC7bxtPTUzNnztTMmTMv2iY6OlrffPPNJbdzww03aOvWrRWuEUDFHEjJ0pKDJR8yuwQVy2ot3fsV6O2urlGBij9yRqv3n1JUsLdcymgHVJZ9yvvaeP+YdL6H7ERGrvKLiuXh6mJyRQCA6lKre8gA1D9//maPbIaUs3+DQj0v/kuXXs2D5OXmovScQu04ll5zBaJBsPeQRdeyGRbtGvm4y9fDVYYhHWXqewCo1whkAGrM6n2ntGJvilws0pmV/7xkWw9XF/Vt1UiStOFQmnJ5QC6qkH2GxRa1cEIP6dzU9wxbBIAGgUAGoEYUFdv0x693S5JuucpHRWdOXPY97SP8FeLrrvwim9b/crq6S0QDYh+yGF1LhyxK54ctHmZiDwCo1whkAGrE0l3J2pecpUBvN93dwbdc77FaLOrfurEkaefxDGXnF1VniWhA7JPF1NYeMun81PcEMgCo3whkAGrEJ5sSJUn39YmWr3v5f/REBnsrzN9DNkPak5RZXeWhAcnKL9KpsyVT3kfV0nvIpAunvmfIIgDUZwQyANXuaFqOftyfKkm6p1dkhd/fsWmAJGnn8UyVY/JV4JLsz/YK9nFXgFftm/LerkUIQxYBoCEgkAGodp9uOipJ6tc6RJHBFe+RuDrUT24uFmXkFupUPtPf48rYJ8loXot7x6TzM0AeP5OrgiKbydUAAKoLgQxAtSoqtmlBfEkgG94rqlLbcHe1qk24nyTpcBY/tnBl9iWflSS1bFy+exnN0tjXQz7uLrIZUiJT3wNAvcUnGwDVamXCKSVn5quRj7tubh9W6e10jCgZtng8xyqrp19VlYcGaM/JknsR2zfxN7mSS7NYLGodVnKt7+X+SQCotwhkAKrVJxtLJvMY1qOZ3F0r/yMnzN9ToX4esskin443VVV5aIB2nwtk7Wp5IJPO12gPkQCA+odABqDanMzI1cqEFEnS3T0rPpnHr3WIKPlw6tslTgaze6ASMnILdexMrqTa30MmSe2blPSQ7T5BIAOA+opABqDaLNh8TDZD6t08WFeFXvn9Om3C/eRiMeQeEqW9pwuroEI0NHvP9TQ1DfRSgHftnWHRrn2EvYfsrMmVAACqC4EMQLUwDEOfxx+TJA3vfeW9Y5Lk4eqiSO+S2eZWHGKSA1Tcnjo0XFGS2oSX1JmUmae07AKTqwEAVAcCGYBqse1ouhLTcuTl5qJBHcOrbLtRPiWBbN2xPOUXFVfZdtEw7HZM6FE3Jobx9XB1TH/PfWQAUD8RyABUi0XbT0iSBnYIk7e7a5VtN8TDUFHmKeUUGlq591SVbRcNgyOQRdSNHjJJahfOxB4AUJ9V3ackoJ5LTExUampqhd8XEhKiqKjKPX+rriq2Gfpq+0lJ0m+6RFTpti0WKXvPagX0GaZF249Xae8b6rfCYpv2JWdJqjtDFqWS8LhkV5IjTAIA6hcCGVAOiYmJatuunXJzKn7fkpe3t/bu2dOgQtm6g6eVmpWvQG839WvduMq3n717lQL6DNP3e1KUmVcof8/aPzkDzPfLqWwVFNnk6+GqyCBvs8spN3t4ZKZFAKifCGRAOaSmpio3J0cjX3hNYVGtyv2+5MSDmj/9OaWmpjaoQLZo+3FJ0uBOTa7o2WMXU5hySM38XXUss0hLdybpriqYUh/1n33IX9twP1mtFpOrKb925+53O3gqSwVFtmr5NwUAMA+BDKiAsKhWata6g9ll1Gr5RcX6dmeSpKofrnih/lGe+nhnlv637QSBDOVSF+8fk0qm6Pf3dFVmXpEOpGTVufoBAJfGr9kAVKlVCad0Nq9I4f6e6t08uNr2c12UlyRp7cFUpWTmVdt+UH/Yh/zVpfvHJMlisZwftsh9ZABQ7xDIAFSpRdtKZle8rUuTah0WFu7rqm5RgbIZ0lc7TlbbflA/GIbhGLLYvo4FMul8iGSmRQCofwhkAKpMVn6Rvt+TLEm6vWvTat/f0HP7WLTteLXvC3Vbytl8nc4ukNUitQmvG88gu1B7AhkA1FsEMgBVZunOJOUX2dQyxEcdauA+lyGdm8jFatH2Yxk6eCqr2veHuss+1K9lY195urmYXE3FXdhDZhiGydUAAKoSgQxAlflya0lP1dBuTWWxVP8sdiG+HurfOkSS9MWWY9W+P9Rd9vvH6uJwRUlqHeYrF6tFZ3IKlcQ9kwBQrxDIAFSJkxm5WnOw5MHZd3Sr/uGKdsN6NJMkfbnluGw2eg5QNvtQv7o2oYedp5uLWjX2kcSwRQCobwhkAKrE/7adkGFIvZoHKTK45h66G9suTH6erjqRkaf1v5yusf2ibqmrU95f6PywxbMmVwIAqEoEMgBXzDAMfbmlZLjind2b1ei+Pd1cdGvnkued/XcLk3ugtNSsfP1yKluS1LEOBzL7cMudxzNMrgQAUJUIZACu2O6TmUpIPit3V6sGd2pS4/sf1r1kiOS3O08qO7+oxveP2m3twZKe07bhfmrk62FyNZXXLSpIkrTxUBrDcwGgHiGQAbhiX5zrmYptF6oAL7ca33+P6CBFN/JWTkGxlu5KqvH9o3Zbs7/k3sbrrgoxuZIr0zUyUN7uLjqdXaC9SQxbBID6gkAG4IoUFdv0v3MPg76jW80OV7SzWCy689y+/8tsi7iAYRj66cC5QNa6bgcyd1er+rQIliStOXdMAIC6j0AG4Ir8dCBVqVn5CvZx1/VXNzatjjvPDVtce/C0TqTnmlYHapfDp3N0PD1Xbi4W9T4XZuqya8/18tlnNAUA1H0EMgBXxP7ssds6N5G7q3k/UiKDvdW7RbAM43xNgL13rHtUkLzdXU2u5srZA9mGX9JUUGQzuRoAQFUgkAGotLTsAn27s+SerTtqeHbFstzdM1KS9NH6Iyos5sMqzt8/1q+OD1e0axPmpxBfd+UWFmtr4hmzywEAVAECGYBK+8/GRBUU2dS5WYC6NAswuxzd2rmJGvm462RGHpN7QMU2Q2vPDe27to5P6GFntVrUt9W5YYvcRwYA9QKBDEClFBbb9NH6I5KkB/s2l8ViMbmikmeSjbwmWpI0Z81hc4uB6X4+nqHMvCL5ebqqU1Pzf2FQVeyzRf5EIAOAeoFABqBSvtuVrJMZeQrxddeQzjX/7LGLue+aKLm5WBR/5Iy2HU03uxyYyN6D1LdVI7m61J//7q49N/xy+7EMnc0rNLkaAMCVqj//QwGoUXPXHpIk3dsnWh6uLiZXc16on6du6xIhSZqz5pDJ1cBMP+4/JanuP3/s15oGeqlFiI+KbYY2/JJmdjkAgCtEIANQYTuPZ2jT4TNytVp0X58os8sp5eFrW0iSvt5xUkkZeSZXAzPkFBRpy5F0SfXn/rEL9W3VSBLDFgGgPiCQAWUoKrbpu11Jen1pgsb+a7PGfZOiyAmfaEWSq5bvSdb2Y+k6k1Ngdpmmmbv2sCRpSOcmCvX3NLeYMnRsGqDeLYJVZDP07/WHzS4HJth0+IwKim2O3qT6xt7rx8QeAFD31f2HsgBVqKDIpi+2HNOsHw7qyOkcp3VWT1+dKZDOnMh0LLs6zFd9WjRSsI97TZdqmtSsfC3adkJSyWQetdXD17bQxkNpmr8hUU/ccJV8PPhx15B8s+OkpJLgUhsmnKlqMa0ayWKR9qdkKSkjT+EBte8XIwCA8qGHDJBkGIa+2HJMN7y2Ui9+8bOOnM5RkLebRvSO1Cu3tdcr1wfrxD/HqU+jIvVqHqTIIC9J0r7kLP17/RF9u/Nkg7m5/h8/HlJBsU1dmgWoW1SQ2eVc1M3twxTdyFvpOYWa/cNBs8tBDUrPKdD/tpc8HPy3Pc1/Pl51CPR2V9fIQEnS/7bxIHQAqMv4lTEavPScAv3flz/rm59LnlsV6uehsf1b6t4+UfJ2L/knsmXLGRWmHlEzH5uanXsGUMrZPG08lKaDp7K1LzlLR07nKLZdmK4K9TXtWKrb0bQcffhTyUQZT97U2uRqLs3FatHkW9rpsY/i9bfVv+junpGKDPY2uyzUgAWbjymv0Ka24X7qGV17f2lwpUb0itLWxHR9tOGIHunXUi7W+tcTCAANAT1kaNDWHEjVoLd/1Dc/J8nVatEzN1+t1c/fqEf6tXSEsYsJ9fPUrZ0jdG/vKIX6eSi/yKavfz6p5XuSVVhsq6EjqFnTl+xVQbFN117VSAPahZpdzmXFdQhT31aNVFBk05+/2WN2OagBNpuhjzaUPB/vgZja8Xy86nJblwgFeLnpaFquVu87ZXY5AIBKIpChQbLZDL27fL/u++cGJWXmqWWIj754oq+eHNBanm4Vm8K9sZ+H7u4ZqR7nfhO/80SmPtl4tN5N+hF/5IwW7zgpi0X63eD2deKDrsVi0ZTb2stqkb7dmaS1B5kAob77Yf8pHTmdIz9PVw3tFmF2OdXKy91Fd/UoGZL5r3WHzS0GAFBpBDI0OFn5RXp8frzeXLZPhiGN6B2pxU9dp87NAiu9TRerRdddFaI7uzWVj4eL0nIK9Mmmo0rKrf2hpTxsNkN/WLxbknR3j0i1j/A3uaLyaxvur/uuiZYkvfrVbhXV095LlPjXuRlA7+oRedle7vpg5Llre9W+U0r81UREAIC6gUCGBuVQarbumLlGS3cly93FqhnDOmvanZ2r7INbZLC3RvSKUpMATxUU2bTmlKv8+wyTYRhVsn2zfLXjhLYdTZe3u4ueibva7HIq7OnYqxXg5aa9SWf10fojZpeDapJ4Okerzg3duz8m2uRqakaLEB/1v7qxDEOav4FrGwDqIgIZGoyVCSn6zfs/aX9KlsL8PfTpo9fo7l6RVb4fHw9X3dm9qTpG+EuyKOiGh/TW+nTlFhRX+b5qQmpWvqZ9s1eS9MQNrRTqV/em1w7ycdczA0uC5J++2aONh9JMrgjV4aMNR2QYUr/WIfXy2WMXc/+5XrJPNx9VXmHd/DkDAA0ZgQz1nmEY+uuqA3p47iadzStS96hAfTX+umqdst3VatWAdmHqFlQko7hIPx3N07BZa3XsTN0aUlRQZNMTH21x3Gf3SL+WZpdUaff1idbgTuEqLDb06L8368jpbLNLQhVKOZunTzYmSpJGxTQ3t5gadlPbUDUN9FJ6TqG+Pvf8NQBA3UEgQ72WlV+k8f/ZqhlLEs7dLxal/4y9RqH+NdPL09LPpuRPfid/D6t2n8zUb95fo3UHT9fIvqvC1K92aePhNPl5uOqDB3pWeMKT2sRqteiNu7qqU9MAnckp1Oh5m5WR2zCeHVff2WyGnl2wQ5l5RWob7qcb29b+GUCrkovVonv7REmSZq46UGd74wGgoSKQod6KP3JGg9/5UV/vOCk3F4v+dEdHTbuzkzxcazZU5B/bpddiQ9Sxqb/Ssgs08h/r9cZ3CbV+avyP1h/RxxsSZbFI747oVi+er+bl7qJ/jOqpcH9PHUjJ0viPt/DhtR6Ys/awVu87JQ9Xq94d0a1BPo9rZJ+Sx2/8ciqbRzwAQB1DIEO9U1Rs01vL9unuv61TYlqOmgZ66T9jrtHIPubd5N/Yx0ULHu2rYd2byWZI7604oGGz1urgqSzTarqU73YlaeqiXZKk5+Pa1qsehzB/T/1jVE95ubnox/2pGjpzjQ6k1M7vAy5v94lMTf+25B7Hl4a009VhfiZXZI5Ab3e9cXcXSdK/1x/R8j3JJlcEACgvAhnqlQ2/nNbQv67RO8v3q9hm6I5uTfXtxH7q2TzY7NLk5e6iN+7uopn3dleAl5t2HMvQkHd/1MyVB2rNjfjFNkNvfJegsf+OV5HN0G+6ROix6+vufWMX07FpgOY81Eshvh5KSD6r37z/k77YcszsslBBuQXFeuqTrSootim2Xajj8QYNVb/WjfXIdS0kSc9/vkMpZ/NMrggAUB4EMtQLR05n67F/x+ueD9Zr5/FM+Xm66t0R3fTWPV3l7+lmdnlOhnRuoqUT+6tf6xDlFdr02tIE3fT6Kv03/phsNvOmxz+TXaAH52zUeysOSJIe7Ntcr9/VpU48ALoyrmnZSN9MuE59WzVSTkGxJn22XY/M26SNh9Lq/GMKGoKUzDw9OGejDqRkqbGfh6YP61xvr9WKeG5QG7UN99Pp7AI9t2CHqT9TAADlU/+fmol6bdvRdP1r7WF9teOECosNWS3S8N5RmnTz1Qrx9TC7vIsKD/DUvId663/bj+v1pft0PD1XzyzYrr//+Ivuj4nWb7pEyK+GgmRGTqE+2nBEc9YcUmpWgTzdrPrLnZ01tFvTGtl/TUpMTFRqaqrTsqe7uSnK01ef7c7S93tS9P2eFF0V7KZbW/uoexMP+bpbFRISoqioKJOqrn5lnZfyMOu8rDmQqgmfbFVqVoF83F307vBualSL/73XJA9XF707optue+8n/bDvlMb+e7Nev6uLAr3dzS4NAHARBDLUOSczcrVib4r+/dNB7T2V61jeJcxdD3bxV3RgoRL37VLiRd5vxofIPXvKvsk+WtLrNwXo6/2u+mJvlvYmndXvvtypP3y1S7GtA3VXTGv1ah5UZQ+utisqtmnH8Qwt3n5Sn2xKVM65iS1ahvho5sjuatfEv0r3VxskJiaqbbt2ys0p+9EDro2ayb/nUPl2vEkH0qS3N6TLsBWrIOmAio7v0nuvPK1rO7ZU00AvWevRpBGXOy+X4uXtrb179tTYv6eTGbmau/awPlj9iwxDahvup7+O7K6Wjev+hDNV6eowP834bWc99/kOfb8nRUPe/UkzR3ZX18hAs0sDAJSBQPYrM2fO1GuvvaakpCR16dJF7733nnr37m12WQ2WzWYoMS1He05matuxdP2QcEp7k8461htFhcres1pnt36tIyf3aVE5tlmTHyIz005Jku67777LtrV6+sqn4wD5dR0kNYrU4j1ntHjPRrm5WNQtMki9WgTpqlBftQjxVYtGPgrwLrsH7cLeDsMwlFNoKCm7WMlZRTqZVay9qQXafapAuUXnhzJFB7jq/p7hGnVTJ7m51M+RzKmpqcrNydHIF15TWFSri7bLK5Z+ySrSsWwXnS1ykUdEG3lEtNGL3xyRvjkiH3cXXRXqq2ZB3ooI9FTTQC819vNUoLebArzcHH/6erhWeghdTfZYpaamKje/QHe/8KaCIpqryGZRkSEV2XTuz5KvbYZkqORlkZSTnqoN33yihVuOqu1Zd3m6ucjTzUXe7i7y93STv5er/DzdrmjGQ8MwdDIjTxsOndZ/449rzcFU2UeTjugdqVdu61CnH8VQnW7v2lStGvtq3MdbdOR0ju6avVaP9m+le3pFKjLY2+zyAAAXIJBd4NNPP9WkSZM0e/Zs9enTR2+//bbi4uKUkJCg0ND6M8tcdTly5IiSUlJVbBgqtkmFtpI/iw2j5MPdBV8X26Sic3+6e/vJyz9QWXlFysgtVFJmnpIy8pWUmatfTmU7em/srBapdbCb1n/+gQbdeK2aDeknDelXrhqTEw9q/vTnlJqaWiOBLDcrU5I05NHfqU3nHuV6j2FI+48e1trtCWoRM1ipOcXaeDhNGw+nObVzd7XK39NV/p5u8vYo+VCan1+g3Xv2SC7usnr4yOrpI4tL2cGtOPes8hJ3KGv7Uh05tEWbvL01sAZ7O8wSFtVKzVp3uGSbq879eTavUNv3HNAPq1erw7UDdSKrWNkFxdp+LEPbj2VcchuuVosCvNzk5e4iV6tFLlaLXK1WuZz7u4vVIqvlfNCRYchQyfdw565dstlsJYHOci4gWyySLCVpSJJsNsmwybD/adhklaE+vXrJ29tTVkvJPopthvKLbCo49yostqmguOTvOQXFys4vVPSzC7VBkk5U5EyGK2TwRL25Pl1aH3/RVr4eriXXqZebI6j5e7rJz9NVHm4u585LyUFl5xcrp6BIZ/OLdCwtRwdSspT9q3//HRq7a0hrb13TrEi7f95erkrr+5DTi+nYNEBfPXmdXvh8h77dmaT3Vx7Q+ysPqG+rRhraram6RQaqeYhPvf0lDADUFQSyC7z55psaM2aMHnroIUnS7Nmz9fXXX+vDDz/Uiy++aHJ15ff97mQdPJWlIpuhomJDxTbbuTBkOP4sLLY5fV3yp02FxYaKikvaFxSV/FlYfH654+/n2hYW21RUbKiguFiVf6xWmqQjF13r7iJF+rupeaCrOod5qGuYh479kqDvNv5XzUaNvOyH69qgUUR0heq0WKTF08frnZF9FBzVWj8nF+jAmQKdPFusE1lFSsst+UCdmlWg1KwCp/e6NW5RanseVkM+roZ8XQ0FuBsK9TQU4OYhS5te0s29riioVqY352JDOKtzG5XZp5+nm5r72vTF12/qrVdHqlOXrjpyOlsHUrJ1Ij1Xx9NzdSI9V6ezC5SRU6j03AKdySl0/Ns5nV0gZVd4t3ILrfzMlltPZKtSO1XJA4bdXaxyc7HI3dUqNxer3F2scnWxyMVikcVqkVUlAfJsRob274xX75hr5ebprbxCm/ILi5WVX6SzeUXKPTdzaFZ+kbLyi3Qio3Iz/rlaLWrV2Fd9o7z02vi79E3SEX1TwW14eHrqv59/riZNmlTofVVxnZrN39NNfx3ZXYt3lAxPXnPgtNYeLHlJkptLyfltFuSlQG93BZ7r5XVzscr13LXgai25Bux/L0/Hr0WXb1S+7dSMqpz6pCrnBDKqsLKqqqtqz1XtnHSmNn4Pq7SmKj2+KtxWFRXmYrXozu7NqmRbNYVAdk5BQYHi4+M1efJkxzKr1arY2FitW7euVPv8/Hzl5+c7vs7IKPlteWZmZvUXexmfrNmr73anmF2GJMliGLLI+aUL/m7Lz1PWmRTZCnJkK8iTkZ+t4uwzKs5KU3FWmgrTk1R85qT2X+Sf/L4dm5WfW/57X04dOyRJio+PV1ZW+Z89lZCQIEk6tn9XhfaXnHhQkpR0eJ8O+pR/mNDhPVslSffff5Ghjq7usnr6lfSCeXjL4u5ZsvzcD7O+g+9Ws6jmcrMacrNIrr/+BXixdGHHQ2F+yYflip6X5ORk3f/AA8rPq9yH7Yp+/6Tz56Y8w0CrYp9lXTPeKulFu8pfktPtdu6S3FVQZCir0KacIimv0KZio+Q/GpshFRvne4ntw/8sF3R8nTh2TO+89676D71fQSHhJQsv8qnUsA8jNKSM08la89UnGvvY4wpvEiHbuf25WEv+c3KzWuRq1bk/S772cJWOJ/6iZydO0F1PTFZos9Jh/vzOJF1wzZxKP6S1n/9etw/8QG3atDm31Oo4B0XFhnKKbMopNJRdWDJ0NqfQppwCQ7lFhrIKi1VkK+noKz533Xq6WuTpapGHi1XB3lY183NVqI+LXK1SQsI2ZScd0Y13jVZg4/IHq5OH92n9N5/p1ltvLfd7fq2mfs5IJf/v2GwV/+3W5d4XIOnRq6U7m3npp8Q8bU/O19HMYuXnG9p9JFu7L/47MQCoU9xcLIq9aqDZZTgyQXmCpsWorb+eqGEnTpxQ06ZNtXbtWsXExDiWP//88/rhhx+0YcMGp/ZTp07V73//+5ouEwAAAEAdcfToUTVrdukeO3rIKmny5MmaNGmS42ubzaa0tDQ1atTIlGfhZGZmKjIyUkePHpW/f/2bIc9snN/qxzmuXpzf6sc5rl6c3+rHOa5enN/qV5vOsWEYOnv2rCIiIi7blkB2TkhIiFxcXJScnOy0PDk5WeHh4aXae3h4yMPD+bk3gYGB1Vliufj7+5t+AdZnnN/qxzmuXpzf6sc5rl6c3+rHOa5enN/qV1vOcUBAQLnaMbXSOe7u7urRo4eWL1/uWGaz2bR8+XKnIYwAAAAAUFXoIbvApEmTNGrUKPXs2VO9e/fW22+/rezsbMesiwAAAABQlQhkF7jnnnt06tQpTZkyRUlJSeratauWLFmisLAws0u7LA8PD73yyiulhlGianB+qx/nuHpxfqsf57h6cX6rH+e4enF+q19dPcfMsggAAAAAJuEeMgAAAAAwCYEMAAAAAExCIAMAAAAAkxDIAAAAAMAkBLJ6YObMmWrevLk8PT3Vp08fbdy40eyS6qxp06apV69e8vPzU2hoqIYOHaqEhASnNjfccIMsFovT67HHHjOp4rpl6tSppc5d27ZtHevz8vI0btw4NWrUSL6+vho2bFiph7Xj0po3b17qHFssFo0bN04S129FrV69WrfddpsiIiJksVi0cOFCp/WGYWjKlClq0qSJvLy8FBsbq/379zu1SUtL08iRI+Xv76/AwECNHj1aWVlZNXgUtdulznFhYaFeeOEFderUST4+PoqIiNADDzygEydOOG2jrOv+L3/5Sw0fSe10uWv4wQcfLHXuBg0a5NSGa/jSLneOy/qZbLFY9NprrznacA1fXHk+m5Xn80NiYqKGDBkib29vhYaG6rnnnlNRUVFNHspFEcjquE8//VSTJk3SK6+8oi1btqhLly6Ki4tTSkqK2aXVST/88IPGjRun9evXa9myZSosLNTAgQOVnZ3t1G7MmDE6efKk4zVjxgyTKq57OnTo4HTufvrpJ8e6p59+Wl999ZUWLFigH374QSdOnNCdd95pYrV1z6ZNm5zO77JlyyRJd911l6MN12/5ZWdnq0uXLpo5c2aZ62fMmKF3331Xs2fP1oYNG+Tj46O4uDjl5eU52owcOVK7du3SsmXLtHjxYq1evVpjx46tqUOo9S51jnNycrRlyxa9/PLL2rJli7744gslJCToN7/5Tam2r776qtN1/eSTT9ZE+bXe5a5hSRo0aJDTufvPf/7jtJ5r+NIud44vPLcnT57Uhx9+KIvFomHDhjm14xouW3k+m13u80NxcbGGDBmigoICrV27VvPmzdPcuXM1ZcoUMw6pNAN1Wu/evY1x48Y5vi4uLjYiIiKMadOmmVhV/ZGSkmJIMn744QfHsuuvv96YMGGCeUXVYa+88orRpUuXMtelp6cbbm5uxoIFCxzL9uzZY0gy1q1bV0MV1j8TJkwwWrVqZdhsNsMwuH6vhCTjyy+/dHxts9mM8PBw47XXXnMsS09PNzw8PIz//Oc/hmEYxu7duw1JxqZNmxxtvv32W8NisRjHjx+vsdrril+f47Js3LjRkGQcOXLEsSw6Otp46623qre4eqCs8ztq1Cjj9ttvv+h7uIYrpjzX8O23327cdNNNTsu4hsvv15/NyvP54ZtvvjGsVquRlJTkaDNr1izD39/fyM/Pr9kDKAM9ZHVYQUGB4uPjFRsb61hmtVoVGxurdevWmVhZ/ZGRkSFJCg4Odlo+f/58hYSEqGPHjpo8ebJycnLMKK9O2r9/vyIiItSyZUuNHDlSiYmJkqT4+HgVFhY6Xc9t27ZVVFQU13MlFRQU6KOPPtLDDz8si8XiWM71WzUOHTqkpKQkp2s2ICBAffr0cVyz69atU2BgoHr27OloExsbK6vVqg0bNtR4zfVBRkaGLBaLAgMDnZb/5S9/UaNGjdStWze99tprtWYoUl2watUqhYaGqk2bNnr88cd1+vRpxzqu4aqVnJysr7/+WqNHjy61jmu4fH792aw8nx/WrVunTp06KSwszNEmLi5OmZmZ2rVrVw1WXzZXswtA5aWmpqq4uNjp4pKksLAw7d2716Sq6g+bzaaJEyfq2muvVceOHR3L7733XkVHRysiIkI7duzQCy+8oISEBH3xxRcmVls39OnTR3PnzlWbNm108uRJ/f73v1e/fv20c+dOJSUlyd3dvdSHrLCwMCUlJZlTcB23cOFCpaen68EHH3Qs4/qtOvbrsqyfwfZ1SUlJCg0NdVrv6uqq4OBgrutKyMvL0wsvvKARI0bI39/fsfypp55S9+7dFRwcrLVr12ry5Mk6efKk3nzzTROrrRsGDRqkO++8Uy1atNDBgwf1f//3f7rlllu0bt06ubi4cA1XsXnz5snPz6/UcHyu4fIp67NZeT4/JCUllfmz2r7ObAQy4CLGjRunnTt3Ot3jJMlp3HynTp3UpEkTDRgwQAcPHlSrVq1qusw65ZZbbnH8vXPnzurTp4+io6P12WefycvLy8TK6qd//vOfuuWWWxQREeFYxvWLuqqwsFB33323DMPQrFmznNZNmjTJ8ffOnTvL3d1djz76qKZNmyYPD4+aLrVOGT58uOPvnTp1UufOndWqVSutWrVKAwYMMLGy+unDDz/UyJEj5enp6bSca7h8LvbZrK5jyGIdFhISIhcXl1KzyCQnJys8PNykquqH8ePHa/HixVq5cqWaNWt2ybZ9+vSRJB04cKAmSqtXAgMDdfXVV+vAgQMKDw9XQUGB0tPTndpwPVfOkSNH9P333+uRRx65ZDuu38qzX5eX+hkcHh5eapKloqIipaWlcV1XgD2MHTlyRMuWLXPqHStLnz59VFRUpMOHD9dMgfVIy5YtFRIS4viZwDVcdX788UclJCRc9ueyxDVclot9NivP54fw8PAyf1bb15mNQFaHubu7q0ePHlq+fLljmc1m0/LlyxUTE2NiZXWXYRgaP368vvzyS61YsUItWrS47Hu2bdsmSWrSpEk1V1f/ZGVl6eDBg2rSpIl69OghNzc3p+s5ISFBiYmJXM+VMGfOHIWGhmrIkCGXbMf1W3ktWrRQeHi40zWbmZmpDRs2OK7ZmJgYpaenKz4+3tFmxYoVstlsjjCMS7OHsf379+v7779Xo0aNLvuebdu2yWq1lhpqh8s7duyYTp8+7fiZwDVcdf75z3+qR48e6tKly2Xbcg2fd7nPZuX5/BATE6Off/7Z6ZcL9l/utG/fvmYO5FJMnlQEV+iTTz4xPDw8jLlz5xq7d+82xo4dawQGBjrNIoPye/zxx42AgABj1apVxsmTJx2vnJwcwzAM48CBA8arr75qbN682Th06JDxv//9z2jZsqXRv39/kyuvG5555hlj1apVxqFDh4w1a9YYsbGxRkhIiJGSkmIYhmE89thjRlRUlLFixQpj8+bNRkxMjBETE2Ny1XVPcXGxERUVZbzwwgtOy7l+K+7s2bPG1q1bja1btxqSjDfffNPYunWrY4a/v/zlL0ZgYKDxv//9z9ixY4dx++23Gy1atDByc3Md2xg0aJDRrVs3Y8OGDcZPP/1ktG7d2hgxYoRZh1TrXOocFxQUGL/5zW+MZs2aGdu2bXP6uWyfGW3t2rXGW2+9ZWzbts04ePCg8dFHHxmNGzc2HnjgAZOPrHa41Pk9e/as8eyzzxrr1q0zDh06ZHz//fdG9+7djdatWxt5eXmObXANX9rlfk4YhmFkZGQY3t7exqxZs0q9n2v40i732cwwLv/5oaioyOjYsaMxcOBAY9u2bcaSJUuMxo0bG5MnTzbjkEohkNUD7733nhEVFWW4u7sbvXv3NtavX292SXWWpDJfc+bMMQzDMBITE43+/fsbwcHBhoeHh3HVVVcZzz33nJGRkWFu4XXEPffcYzRp0sRwd3c3mjZtatxzzz3GgQMHHOtzc3ONJ554wggKCjK8vb2NO+64wzh58qSJFddNS5cuNSQZCQkJTsu5fitu5cqVZf5MGDVqlGEYJVPfv/zyy0ZYWJjh4eFhDBgwoNR5P336tDFixAjD19fX8Pf3Nx566CHj7NmzJhxN7XSpc3zo0KGL/lxeuXKlYRiGER8fb/Tp08cICAgwPD09jXbt2hl//vOfnQJFQ3ap85uTk2MMHDjQaNy4seHm5mZER0cbY8aMKfVLXa7hS7vczwnDMIy//e1vhpeXl5Genl7q/VzDl3a5z2aGUb7PD4cPHzZuueUWw8vLywgJCTGeeeYZo7CwsIaPpmwWwzCMaup8AwAAAABcAveQAQAAAIBJCGQAAAAAYBICGQAAAACYhEAGAAAAACYhkAEAAACASQhkAAAAAGASAhkAAAAAmIRABgAAAAAmIZABAAAAgEkIZAAA1BEPPvighg4danYZAIAqRCADAAAAAJMQyAAADcqSJUt03XXXKTAwUI0aNdKtt96qgwcPSpIOHz4si8Wizz77TP369ZOXl5d69eqlffv2adOmTerZs6d8fX11yy236NSpU45t2mw2vfrqq2rWrJk8PDzUtWtXLVmyxLF+1apVslgsSk9Pdyzbtm2bLBaLDh8+LEmaO3euAgMDtXTpUrVr106+vr4aNGiQTp48KUmaOnWq5s2bp//973+yWCyyWCxatWpVtZ8vAED1IpABABqU7OxsTZo0SZs3b9by5ctltVp1xx13yGazOdq88soreumll7Rlyxa5urrq3nvv1fPPP6933nlHP/74ow4cOKApU6Y42r/zzjt644039Prrr2vHjh2Ki4vTb37zG+3fv79CteXk5Oj111/Xv//9b61evVqJiYl69tlnJUnPPvus7r77bkdIO3nypPr27Vs1JwUAYBpXswsAAKAmDRs2zOnrDz/8UI0bN9bu3bvl6+srqST8xMXFSZImTJigESNGaPny5br22mslSaNHj9bcuXMd23j99df1wgsvaPjw4ZKk6dOna+XKlXr77bc1c+bMctdWWFio2bNnq1WrVpKk8ePH69VXX5Uk+fr6ysvLS/n5+QoPD6/cwQMAah16yAAADcr+/fs1YsQItWzZUv7+/mrevLkkKTEx0dGmc+fOjr+HhYVJkjp16uS0LCUlRZKUmZmpEydOOMKa3bXXXqs9e/ZUqDZvb29HGJOkJk2aOPYDAKif6CEDADQot912m6Kjo/X3v/9dERERstls6tixowoKChxt3NzcHH+3WCxlLrtwiOPlWK0lv/80DMOxrLCwsFS7C/dh38+F7wEA1D/0kAEAGozTp08rISFBL730kgYMGKB27drpzJkzV7RNf39/RUREaM2aNU7L16xZo/bt20uSGjduLEmOCTqkkkk9Ksrd3V3FxcWVLxYAUOvQQwYAaDCCgoLUqFEjffDBB2rSpIkSExP14osvXvF2n3vuOb3yyitq1aqVunbtqjlz5mjbtm2aP3++JOmqq65SZGSkpk6dqj/96U/at2+f3njjjQrvp3nz5lq6dKkSEhLUqFEjBQQElOpVAwDULfSQAQAaDKvVqk8++UTx8fHq2LGjnn76ab322mtXvN2nnnpKkyZN0jPPPKNOnTppyZIlWrRokVq3bi2pZCjif/7zH+3du1edO3fW9OnT9cc//rHC+xkzZozatGmjnj17qnHjxqV65QAAdY/FYHA6AAAAAJiCHjIAAAAAMAmBDAAAAABMQiADAAAAAJMQyAAAAADAJAQyAAAAADAJgQwAAAAATEIgAwAAAACTEMgAAAAAwCQEMgAAAAAwCYEMAAAAAExCIAMAAAAAk/w/XCQGRC8dupAAAAAASUVORK5CYII=", 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Assign the `x` variable to `hue` and set `legend=False` for the same effect.\n", + "\n", + " sns.barplot(x='year_month_str', y='accepted_user_count', data=grouped_df, palette='viridis')\n" + ] + }, + { + "data": { + "image/png": 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year_monthincident_count
02020-040
12020-0542
22020-06501
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62020-1048
72020-110
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" + ], + "text/plain": [ + " year_month incident_count\n", + "0 2020-04 0\n", + "1 2020-05 42\n", + "2 2020-06 501\n", + "3 2020-07 604\n", + "4 2020-08 467\n", + "5 2020-09 368\n", + "6 2020-10 48\n", + "7 2020-11 0" + ] + }, + "execution_count": 184, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import pandas as pd\n", + "\n", + "subset_merged_df_approach2['incident_flag'] = subset_merged_df_approach2['type'].apply(lambda x: 1 if str(x).startswith('incident') else 0)\n", + "grouped_df_incident = subset_merged_df_approach2.groupby('year_month')['incident_flag'].sum().reset_index()\n", + "grouped_df_incident= grouped_df.rename(columns={'incident_flag': 'incident_count'})\n", + "grouped_df_incident\n", + "\n", + "\n", + "\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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year_monthincident_counttotal_countincident_rate
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22020-0650115440.32
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72020-1101130.00
\n", + "
" + ], + "text/plain": [ + " year_month incident_count total_count incident_rate\n", + "0 2020-04 0 1 0.00\n", + "1 2020-05 42 241 0.17\n", + "2 2020-06 501 1544 0.32\n", + "3 2020-07 604 1878 0.32\n", + "4 2020-08 467 3357 0.14\n", + "5 2020-09 368 4440 0.08\n", + "6 2020-10 48 8577 0.01\n", + "7 2020-11 0 113 0.00" + ] + }, + "execution_count": 193, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "subset_merged_df_approach2['incident_flag'] = subset_merged_df_approach2['type'].apply(lambda x: 1 if str(x).startswith('incident') else 0)\n", + "grouped_df_incident = subset_merged_df_approach2.groupby('year_month')['incident_flag'].sum().reset_index()\n", + "grouped_df_incident = grouped_df_incident.rename(columns={'incident_flag': 'incident_count'})\n", + "\n", + "grouped_df_total = subset_merged_df_approach2.groupby('year_month')['type'].count().reset_index()\n", + "grouped_df_total = grouped_df_total.rename(columns={'type': 'total_count'})\n", + "\n", + "grouped_df_incident = pd.merge(grouped_df_incident, grouped_df_total, on='year_month')\n", + "\n", + "grouped_df_incident['incident_rate'] = round(grouped_df_incident['incident_count'] / grouped_df_incident['total_count'],2)\n", + "\n", + "# Exibir o DataFrame final\n", + "grouped_df_incident\n" + ] + }, + { + "cell_type": "code", + "execution_count": 219, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\meite\\AppData\\Local\\Temp\\ipykernel_12752\\817313753.py:6: FutureWarning: \n", + "\n", + "Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `y` variable to `hue` and set `legend=False` for the same effect.\n", + "\n", + " sns.barplot(x='incident_count', y='year_month', data=grouped_df, palette='viridis')\n" + ] + }, + { + "data": { + "image/png": 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2014682122.02020-10-20 07:58:04.006937+00:00money_backacceptedNot Applicable100.022357.05.02021-02-05 11:00:00+00:00instant_payment2020-10202010NaN0
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2014864517.02020-10-10 05:40:55.700422+00:00money_backacceptedNot Applicable100.020256.05.02021-02-05 11:00:00+00:00instant_payment2020-10202010NaN0
2014944867.02020-10-08 14:16:52.155661+00:00direct_debit_sentacceptedNot Applicable100.019886.05.02021-02-05 11:00:00+00:00postpone2020-10202010NaN0
2015044867.02020-10-08 14:16:52.155661+00:00direct_debit_sentacceptedNot Applicable100.019886.05.02021-02-05 11:00:00+00:00instant_payment2020-10202010NaN0
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20151 rows × 15 columns

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Assign the `x` variable to `hue` and set `legend=False` for the same effect.\n", + "\n", + " sns.barplot(data=monthly_revenue, x='year_month', y='total_revenue', palette='viridis')\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import seaborn as sns\n", + "import matplotlib.pyplot as plt\n", + "\n", + "plt.figure(figsize=(12, 6)) # Ajusta o tamanho da figura\n", + "sns.barplot(data=monthly_revenue, x='year_month', y='total_revenue', palette='viridis')\n", + "\n", + "\n", + "plt.title('Trend Analysis of Monthly Revenue', fontsize=16)\n", + "plt.xlabel('Year-Month', fontsize=12)\n", + "plt.ylabel('Total Revenue', fontsize=12)\n", + "plt.xticks(rotation=45) # Rotaciona os rótulos do eixo x para melhor visibilidade\n", + "plt.tight_layout()\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 213, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "subset_merged_df_approach2['new_metric'] = (\n", + " subset_merged_df_approach2['reimbursement_date'] - subset_merged_df_approach2['created_at_x']\n", + ").dt.days\n", + "\n", + "subset_merged_df_approach2['new_metric']\n", + "\n", + "import matplotlib.pyplot as plt\n", + "import seaborn as sns\n", + "\n", + "# Histograma da nova métrica\n", + "plt.figure(figsize=(10, 6))\n", + "sns.histplot(data=subset_merged_df_approach2, x='new_metric', kde=True, color='blue', bins=30)\n", + "\n", + "# Configurações estéticas\n", + "plt.title('Distribution of New Metric', fontsize=16)\n", + "plt.xlabel('New Metric (Days)', fontsize=12)\n", + "plt.ylabel('Frequency', fontsize=12)\n", + "plt.grid(axis='y', linestyle='--', alpha=0.7)\n", + "plt.tight_layout()\n", + "\n", + "# Exibir o gráfico\n", + "plt.show()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 216, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\meite\\AppData\\Local\\Temp\\ipykernel_12752\\803856511.py:6: FutureWarning: \n", + "\n", + "Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `y` variable to `hue` and set `legend=False` for the same effect.\n", + "\n", + " sns.boxplot(data=subset_merged_df_approach2, x='new_metric', palette='viridis')\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "import seaborn as sns\n", + "\n", + "# Criar o boxplot com a paleta viridis\n", + "plt.figure(figsize=(10, 6))\n", + "sns.boxplot(data=subset_merged_df_approach2, x='new_metric', palette='viridis')\n", + "\n", + "# Configurações estéticas\n", + "plt.title('Boxplot of New Metric', fontsize=16)\n", + "plt.xlabel('New Metric (Days)', fontsize=12)\n", + "plt.grid(axis='y', linestyle='--', alpha=0.7)\n", + "plt.tight_layout()\n", + "\n", + "# Exibir o gráfico\n", + "plt.show()\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Valores de 'time_to_reimbursement' após o cálculo:\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Remover linhas com valores nulos nas colunas 'created_at_x' e 'reimbursement_date'\n", + "merged_df2_clean = merged_df2.dropna(subset=['created_at_x', 'reimbursement_date'])\n", + "merged_df2_clean\n", + "\n", + "(merged_df2_clean['reimbursement_date'] - merged_df2_clean['created_at_x']).dt.days\n", + "\n", + "print(\"Valores de 'time_to_reimbursement' após o cálculo:\")\n", + "merged_df2_clean['time_to_reimbursement'].head(20)\n", + "\n", + "\n", + "plt.figure(figsize=(10, 6))\n", + "sns.boxplot(x=merged_df2_clean['time_to_reimbursement'], color='green')\n", + "plt.title('Boxplot do Tempo até o Reembolso', fontsize=14)\n", + "plt.xlabel('Tempo até o Reembolso (dias)', fontsize=12)\n", + "plt.show()\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "import seaborn as sns\n", + "\n", + "plt.figure(figsize=(10, 6))\n", + "sns.histplot(merged_df2_clean['time_to_reimbursement'], kde=True, color='blue', bins=30)\n", + "plt.title('Distribuion of the time until reimbursement', fontsize=14)\n", + "plt.xlabel('Tempo até o Reembolso (dias)', fontsize=12)\n", + "plt.ylabel('Frequência', fontsize=12)\n", + "plt.show()\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "\"# Plotando o gráfico de linha\\nplt.figure(figsize=(10, 6))\\nsns.lineplot(x=monthly_avg_reimbursement_time.index, y=monthly_avg_reimbursement_time.values, marker='o', color='red')\\nplt.title('Tempo Médio até o Reembolso por Mês', fontsize=14)\\nplt.xlabel('Mês', fontsize=12)\\nplt.ylabel('Tempo Médio até o Reembolso (dias)', fontsize=12)\\nplt.xticks(ticks=range(12), labels=['Jan', 'Feb', 'Mar', 'Apr', 'May', 'Jun', 'Jul', 'Aug', 'Sep', 'Oct', 'Nov', 'Dec'])\\nplt.show()\"" + ] + }, + "execution_count": 141, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "\"\"\"# Plotando o gráfico de linha\n", + "plt.figure(figsize=(10, 6))\n", + "sns.lineplot(x=monthly_avg_reimbursement_time.index, y=monthly_avg_reimbursement_time.values, marker='o', color='red')\n", + "plt.title('Tempo Médio até o Reembolso por Mês', fontsize=14)\n", + "plt.xlabel('Mês', fontsize=12)\n", + "plt.ylabel('Tempo Médio até o Reembolso (dias)', fontsize=12)\n", + "plt.xticks(ticks=range(12), labels=['Jan', 'Feb', 'Mar', 'Apr', 'May', 'Jun', 'Jul', 'Aug', 'Sep', 'Oct', 'Nov', 'Dec'])\n", + "plt.show()\"\"\"\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'Histogram and boxplot are good for exploring the distribution and identifying outliers.\\nThe line plot is useful for analyzing the trend over time, such as the evolution of the time to reimbursement by month.\\nThe scatter plot helps to investigate the relationship between the creation date and the time to reimbursement.'" + ] + }, + "execution_count": 142, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "\n", + "\n", + "\"\"\"Histogram and boxplot are good for exploring the distribution and identifying outliers.\n", + "The line plot is useful for analyzing the trend over time, such as the evolution of the time to reimbursement by month.\n", + "The scatter plot helps to investigate the relationship between the creation date and the time to reimbursement.\"\"\"" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'import seaborn as sns\\nimport matplotlib.pyplot as plt\\n\\n# Assuming \\'merged_df2_clean\\' has columns \\'created_at_x\\' and \\'time_to_reimbursement\\'\\nplt.figure(figsize=(10, 6))\\n\\n# Scatter plot with regression line\\nsns.scatterplot(x=merged_df2_clean[\\'created_at_x\\'], y=merged_df2_clean[\\'time_to_reimbursement\\'], \\n color=\\'blue\\', s=80, edgecolor=\\'black\\', alpha=0.6, label=\\'Time to Reimbursement\\')\\n\\n# Optionally, add a regression line\\nsns.regplot(x=merged_df2_clean[\\'created_at_x\\'].map(lambda x: x.timestamp()), \\n y=merged_df2_clean[\\'time_to_reimbursement\\'], scatter=False, color=\\'red\\', line_kws={\"color\": \"red\", \"lw\": 2})\\n\\nplt.title(\\'Scatter Plot of Time to Reimbursement vs Creation Date\\', fontsize=14)\\nplt.xlabel(\\'Creation Date\\', fontsize=12)\\nplt.ylabel(\\'Time to Reimbursement (days)\\', fontsize=12)\\n\\n# Rotate x-axis labels if needed\\nplt.xticks(rotation=45)\\n\\n# Show the plot\\nplt.tight_layout()\\nplt.legend()\\nplt.show()'" + ] + }, + "execution_count": 143, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "\"\"\"import seaborn as sns\n", + "import matplotlib.pyplot as plt\n", + "\n", + "# Assuming 'merged_df2_clean' has columns 'created_at_x' and 'time_to_reimbursement'\n", + "plt.figure(figsize=(10, 6))\n", + "\n", + "# Scatter plot with regression line\n", + "sns.scatterplot(x=merged_df2_clean['created_at_x'], y=merged_df2_clean['time_to_reimbursement'], \n", + " color='blue', s=80, edgecolor='black', alpha=0.6, label='Time to Reimbursement')\n", + "\n", + "# Optionally, add a regression line\n", + "sns.regplot(x=merged_df2_clean['created_at_x'].map(lambda x: x.timestamp()), \n", + " y=merged_df2_clean['time_to_reimbursement'], scatter=False, color='red', line_kws={\"color\": \"red\", \"lw\": 2})\n", + "\n", + "plt.title('Scatter Plot of Time to Reimbursement vs Creation Date', fontsize=14)\n", + "plt.xlabel('Creation Date', fontsize=12)\n", + "plt.ylabel('Time to Reimbursement (days)', fontsize=12)\n", + "\n", + "# Rotate x-axis labels if needed\n", + "plt.xticks(rotation=45)\n", + "\n", + "# Show the plot\n", + "plt.tight_layout()\n", + "plt.legend()\n", + "plt.show()\"\"\"\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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id_xamountstatus_xcreated_at_xupdated_at_xuser_idmoderated_atdeleted_account_idreimbursement_datecash_request_received_date...status_ycategorytotal_amountreasoncreated_at_yupdated_at_ypaid_atfrom_dateto_datecharge_moment
05100.0rejected2019-12-10 19:05:21.596873+002019-12-11 16:47:42.40783+00804.02019-12-11 16:47:42.405646+00NaN2020-01-09 19:05:21.596363+00NaN...NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
170100.0rejected2019-12-10 19:50:12.34778+002019-12-11 14:24:22.900054+00231.02019-12-11 14:24:22.897988+00NaN2020-01-09 19:50:12.34778+00NaN...NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
27100.0rejected2019-12-10 19:13:35.82546+002019-12-11 09:46:59.779773+00191.02019-12-11 09:46:59.777728+00NaN2020-01-09 19:13:35.825041+00NaN...NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
31099.0rejected2019-12-10 19:16:10.880172+002019-12-18 14:26:18.136163+00761.02019-12-18 14:26:18.128407+00NaN2020-01-09 19:16:10.879606+00NaN...NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
41594100.0rejected2020-05-06 09:59:38.877376+002020-05-07 09:21:55.34008+007686.02020-05-07 09:21:55.320193+00NaN2020-06-05 22:00:00+00NaN...NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
..................................................................
3208922357100.0money_back2020-10-20 07:58:04.006937+002021-02-05 12:19:30.656816+0082122.0NaNNaN2021-02-05 11:00:00+002020-10-21...acceptedNaN5.0Instant Payment Cash Request 223572020-10-20 07:58:19.637461+002020-10-20 07:58:19.637491+002021-02-05 12:19:30.685779+00NaNNaNafter
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32094 rows × 29 columns

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" + ], + "text/plain": [ + " id_x amount status_x created_at_x \\\n", + "0 5 100.0 rejected 2019-12-10 19:05:21.596873+00 \n", + "1 70 100.0 rejected 2019-12-10 19:50:12.34778+00 \n", + "2 7 100.0 rejected 2019-12-10 19:13:35.82546+00 \n", + "3 10 99.0 rejected 2019-12-10 19:16:10.880172+00 \n", + "4 1594 100.0 rejected 2020-05-06 09:59:38.877376+00 \n", + "... ... ... ... ... \n", + "32089 22357 100.0 money_back 2020-10-20 07:58:04.006937+00 \n", + "32090 20256 100.0 money_back 2020-10-10 05:40:55.700422+00 \n", + "32091 20256 100.0 money_back 2020-10-10 05:40:55.700422+00 \n", + "32092 19886 100.0 direct_debit_sent 2020-10-08 14:16:52.155661+00 \n", + "32093 19886 100.0 direct_debit_sent 2020-10-08 14:16:52.155661+00 \n", + "\n", + " updated_at_x user_id moderated_at \\\n", + "0 2019-12-11 16:47:42.40783+00 804.0 2019-12-11 16:47:42.405646+00 \n", + "1 2019-12-11 14:24:22.900054+00 231.0 2019-12-11 14:24:22.897988+00 \n", + "2 2019-12-11 09:46:59.779773+00 191.0 2019-12-11 09:46:59.777728+00 \n", + "3 2019-12-18 14:26:18.136163+00 761.0 2019-12-18 14:26:18.128407+00 \n", + "4 2020-05-07 09:21:55.34008+00 7686.0 2020-05-07 09:21:55.320193+00 \n", + "... ... ... ... \n", + "32089 2021-02-05 12:19:30.656816+00 82122.0 NaN \n", + "32090 2021-02-05 13:14:19.707627+00 64517.0 NaN \n", + "32091 2021-02-05 13:14:19.707627+00 64517.0 NaN \n", + "32092 2021-01-05 15:45:52.645536+00 44867.0 NaN \n", + "32093 2021-01-05 15:45:52.645536+00 44867.0 NaN \n", + "\n", + " deleted_account_id reimbursement_date \\\n", + "0 NaN 2020-01-09 19:05:21.596363+00 \n", + "1 NaN 2020-01-09 19:50:12.34778+00 \n", + "2 NaN 2020-01-09 19:13:35.825041+00 \n", + "3 NaN 2020-01-09 19:16:10.879606+00 \n", + "4 NaN 2020-06-05 22:00:00+00 \n", + "... ... ... \n", + "32089 NaN 2021-02-05 11:00:00+00 \n", + "32090 NaN 2021-02-05 11:00:00+00 \n", + "32091 NaN 2021-02-05 11:00:00+00 \n", + "32092 NaN 2021-02-05 11:00:00+00 \n", + "32093 NaN 2021-02-05 11:00:00+00 \n", + "\n", + " cash_request_received_date ... status_y category total_amount \\\n", + "0 NaN ... 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