From 6d5cee8de0c8b63e5937101f98efcfb2f5a4815a Mon Sep 17 00:00:00 2001 From: Alan Mburu Kagiri Date: Mon, 19 Feb 2024 16:21:12 -0600 Subject: [PATCH 01/24] update on function to add nodes and their attributes to graph --- utils/linkage.py | 35 ++++++++++++++++++++++++++++++----- 1 file changed, 30 insertions(+), 5 deletions(-) diff --git a/utils/linkage.py b/utils/linkage.py index cae5024..9138c2a 100644 --- a/utils/linkage.py +++ b/utils/linkage.py @@ -1,14 +1,15 @@ -import textdistance as td -import usaddress -from names_dataset import NameDataset - """ Module for performing record linkage on state campaign finance dataset + """ + +import textdistance as td +import usaddress +from names_dataset import NameDataset import math import os.path import re - +import networkx as nx import numpy as np import pandas as pd @@ -633,3 +634,27 @@ def get_address_number_from_address_line_1(address_line_1: str) -> str: elif address_line_1_components[i][1] == "USPSBoxID": return address_line_1_components[i][0] raise ValueError("Can not find Address Number") + +def create_network_nodes(df: pd.DataFrame) -> nx.MultiDiGraph : + ''' Takes in a dataframe and generates a MultiDiGraph where the nodes are + entity names, and the rest of the dataframe columns make the node attributes + + Args: + df: a pandas dataframe (complete_individuals_table / + complete_organizations_table) + + Returns: + A Networkx MultiDiGraph with nodes lacking any edges + ''' + G = nx.MultiDiGraph() + # first check if df is individuals or organizations dataset + if 'name' in df.columns: + node_name = 'name' + else: node_name = 'full_name' + + for _, row in df.iterrows(): + G.add_node(row[node_name]) + for column in df.columns: + nx.set_node_attributes(G, row[column], name=column) + + return G From e92192bfd75950307ef781c1e298b79a6d0ce8bb Mon Sep 17 00:00:00 2001 From: Alan Mburu Kagiri Date: Mon, 19 Feb 2024 17:04:10 -0600 Subject: [PATCH 02/24] checking for issue with linter test --- utils/linkage.py | 23 +++++++++++++---------- 1 file changed, 13 insertions(+), 10 deletions(-) diff --git a/utils/linkage.py b/utils/linkage.py index 9138c2a..14c57f6 100644 --- a/utils/linkage.py +++ b/utils/linkage.py @@ -3,15 +3,16 @@ """ -import textdistance as td -import usaddress -from names_dataset import NameDataset import math import os.path import re + import networkx as nx import numpy as np import pandas as pd +import textdistance as td +import usaddress +from names_dataset import NameDataset from utils.constants import COMPANY_TYPES, repo_root @@ -635,8 +636,9 @@ def get_address_number_from_address_line_1(address_line_1: str) -> str: return address_line_1_components[i][0] raise ValueError("Can not find Address Number") -def create_network_nodes(df: pd.DataFrame) -> nx.MultiDiGraph : - ''' Takes in a dataframe and generates a MultiDiGraph where the nodes are + +def create_network_nodes(df: pd.DataFrame) -> nx.MultiDiGraph: + """Takes in a dataframe and generates a MultiDiGraph where the nodes are entity names, and the rest of the dataframe columns make the node attributes Args: @@ -645,16 +647,17 @@ def create_network_nodes(df: pd.DataFrame) -> nx.MultiDiGraph : Returns: A Networkx MultiDiGraph with nodes lacking any edges - ''' + """ G = nx.MultiDiGraph() # first check if df is individuals or organizations dataset - if 'name' in df.columns: - node_name = 'name' - else: node_name = 'full_name' + if "name" in df.columns: + node_name = "name" + else: + node_name = "full_name" for _, row in df.iterrows(): G.add_node(row[node_name]) for column in df.columns: nx.set_node_attributes(G, row[column], name=column) - + return G From 976d2af420317817143f45e6476ef41cbfaf0d43 Mon Sep 17 00:00:00 2001 From: Alan Mburu Kagiri Date: Wed, 21 Feb 2024 10:17:31 -0600 Subject: [PATCH 03/24] Saving notebook on networkx --- notebooks/Test.ipynb | 887 +++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 887 insertions(+) create mode 100644 notebooks/Test.ipynb diff --git a/notebooks/Test.ipynb b/notebooks/Test.ipynb new file mode 100644 index 0000000..457fb6f --- /dev/null +++ b/notebooks/Test.ipynb @@ -0,0 +1,887 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import numpy as np\n", + "import re\n", + "import networkx as nx\n", + "import matplotlib.pyplot as plt" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "orgs_sample = pd.read_csv(\"../output/complete_organizations_table.csv\",index_col=0).sample(10)\n", + "inds_sample = pd.read_csv(\"../output/complete_individuals_table.csv\",index_col=0, low_memory=False).sample(10)" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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The 'get_likely_name' function takes in 3 string inputs. The data is not clean and when there are NaN entries, the function is somehow inputing null values as strings, so a column that has \"Tim\", \"Walz\" and Nan in the first, last, and full name columns, is being combined as \"Tim Walz Nan\". When calling this function account for this possibility" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Playing Around with Graphs\n", + "\n", + "**Some considerations**\n", + "1. What attributes do we want each Node to Have?\n", + "- UUID, Name, Entity Type, Address, {from transactions table: money_donated and money_given}, affilition?\n", + "- Should transaction info also be included? If so, how would we show transaction info to multiple recipients / from multiple donors?" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Notes for Graphs\n", + "**Generating Graphs**\n", + "* nx.Graph() → the most simple undirected graph (edges going both ways)\n", + "* nx.DiGraph() → a graph with directed edges\n", + "* nx.MultiGraph() → multiple edges between nodes\n", + "* nx.MultiDiGraph() → the MultiGraph equivalent for directed graphs\n", + "\n", + "**Finding Centrality**\n", + "There are 4 main ways to find the centrality of a node (how important or frequent is a node / how influential are some donors potentially)\n", + "* nx.degree_centrality : based on the assumption that important nodes have many connections\n", + "* nx.closeness_centrality : based on the assumption that important nodes are close to other nodes. It is calculated as the sum of the path lengths from the given node to all other nodes. \n", + "* nx.eigenvector_centrality : assumes that important nodes connect other nodes. Considers the number of shortest paths between 2 nodes .For Graphs with a large number of nodes, the value of betweenness centrality is very high\n", + "* nx.betweeness_centrality : a measure of centrality in a graph based on shortest paths. For every pair of vertices in a connected graph, there exists at least one shortest path between the vertices such that either the number of edges that the path passes through (for unweighted graphs) or the sum of the weights of the edges (for weighted graphs) is minimized. The betweenness centrality for each vertex is the number of these shortest paths that pass through the vertex\n", + "* nx.pagerank : Page Rank Algorithm (developed by Google founders to measure the importance of webpages) assigns a score of importance to each node. Important nodes are those with many inlinks from important pages. It mainly works for Directed Networks\n", + "\n", + "**Finding Connections**\n", + "* nx.find_cliques (undirected graphs): finds the maximum subgraphs based on the number of interconnected nodes\n", + "* nx.k_core : A k-core is a maximal subgraph that contains nodes of degree k or more. Groups clusters meeting the threshold k (can be used as a toggle)\n", + "\n", + "**Sources**\n", + "* https://www.youtube.com/watch?v=VetBkjcm9Go\n", + "* https://www.activestate.com/blog/graph-theory-using-python-introduction-and-implementation/ \n" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " id first_name \\\n", + "891077 c94a0491-7ea1-45ce-a155-6153ea74da08 BELA \n", + "617571 c38816dd-8a47-4102-97cd-59d0f6bc42dc JANICE \n", + "\n", + " last_name \\\n", + "891077 LAHNER \n", + "617571 SHAPIRO \n", + "\n", + " full_name entity_type state \\\n", + "891077 BELA LAHNER ... Individual MI \n", + "617571 JANICE SHAPIRO ... Individual TX \n", + "\n", + " party company \n", + "891077 NaN NOT EMPLOYED \n", + "617571 NaN NaN " + ] + }, + "execution_count": 43, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "inds_sample.head(2)" + ] + }, + { + "cell_type": "code", + "execution_count": 100, + "metadata": {}, + "outputs": [], + "source": [ + "def add_notes_from_df(df):\n", + " G = nx.MultiDiGraph()\n", + " if 'name' in df.columns:\n", + " node_name = 'name'\n", + " else: node_name = 'full_name'\n", + " for index, row in df.iterrows():\n", + " # if nodes 1 and 2 don't exist, this both creates the nodes and adds the edges to them\n", + " # the weight can be added to show the magnitude of the edge\n", + " G.add_node(row[node_name])\n", + " for column in df.columns:\n", + " nx.set_node_attributes(G, row[column], name=column)\n", + " nx.draw_random(G, with_labels=True)\n", + " plt.show()\n", + " return G" + ] + }, + { + "cell_type": "code", + "execution_count": 105, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "{'id': 'da441d41-1050-4505-a834-99d6023001e1',\n", + " 'first_name': 'AARON ',\n", + " 'last_name': 'KRAUSS ',\n", + " 'full_name': 'AARON KRAUSS ',\n", + " 'entity_type': 'Individual',\n", + " 'state': 'MI',\n", + " 'party': nan,\n", + " 'company': nan}" + ] + }, + "execution_count": 105, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "x = add_notes_from_df(inds_sample)\n", + "x.nodes['BELA LAHNER ']" + ] + }, + { + "cell_type": "code", + "execution_count": 104, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['BELA LAHNER ',\n", + " 'JANICE SHAPIRO ',\n", + " 'RAMON HAWKINS ',\n", + " 'LEAH CYGAN ',\n", + " 'ALLISON HATT ^ ',\n", + " 'ELLEN FEINGOLD ',\n", + " 'KEVIN HERTEL FOR SENATE',\n", + " 'SARA LAFORGE ^ ',\n", + " 'LOIS TACK ',\n", + " 'AARON KRAUSS ']" + ] + }, + "execution_count": 104, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "inds_sample.full_name.tolist()" + ] + }, + { + "cell_type": "code", + "execution_count": 94, + "metadata": {}, + "outputs": [ + { + "ename": "KeyError", + "evalue": "'MALLORY MCMORROW FOR MICHIGAN'", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mKeyError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[94], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mG\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mnodes\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mMALLORY MCMORROW FOR MICHIGAN\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m]\u001b[49m\n", + "File \u001b[0;32m~/miniconda3/envs/climate_cabinet/lib/python3.11/site-packages/networkx/classes/reportviews.py:194\u001b[0m, in \u001b[0;36mNodeView.__getitem__\u001b[0;34m(self, n)\u001b[0m\n\u001b[1;32m 189\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(n, \u001b[38;5;28mslice\u001b[39m):\n\u001b[1;32m 190\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m nx\u001b[38;5;241m.\u001b[39mNetworkXError(\n\u001b[1;32m 191\u001b[0m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mtype\u001b[39m(\u001b[38;5;28mself\u001b[39m)\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__name__\u001b[39m\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m does not support slicing, \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 192\u001b[0m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mtry list(G.nodes)[\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mn\u001b[38;5;241m.\u001b[39mstart\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m:\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mn\u001b[38;5;241m.\u001b[39mstop\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m:\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mn\u001b[38;5;241m.\u001b[39mstep\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m]\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 193\u001b[0m )\n\u001b[0;32m--> 194\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_nodes\u001b[49m\u001b[43m[\u001b[49m\u001b[43mn\u001b[49m\u001b[43m]\u001b[49m\n", + "\u001b[0;31mKeyError\u001b[0m: 'MALLORY MCMORROW FOR MICHIGAN'" + ] + } + ], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "G = nx.petersen_graph()\n", + "subax1 = plt.subplot(121)\n", + "nx.draw(G, with_labels=True, font_weight='bold')\n", + "subax2 = plt.subplot(122)\n", + "nx.draw_shell(G, nlist=[range(5, 10), range(5)], with_labels=True, font_weight='light')\n" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'REPUBLICAN STATE LEADERSHIP COMMITTEE MICHIGAN PAC': Text(-0.071782758799796, -0.3387166453182715, 'REPUBLICAN STATE LEADERSHIP COMMITTEE MICHIGAN PAC'),\n", + " 'Paa Pac': Text(0.06023249378587841, -0.07946204618171311, 'Paa Pac'),\n", + " 'UNITED FOOD AND COMMERCIAL WORKERS ACTIVE BALLOT CLUB': Text(-0.12554712442237967, 0.08789304420689323, 'UNITED FOOD AND COMMERCIAL WORKERS ACTIVE BALLOT CLUB'),\n", + " 'COMMITTEE TO ELECT DR PATRICIA BERNARD': Text(-0.40486733116122986, -0.04769565353200762, 'COMMITTEE TO ELECT DR PATRICIA BERNARD'),\n", + " 'Pabar Pac (Pa Bar Assn)': Text(-0.6714326170558735, 0.21693950702464565, 'Pabar Pac (Pa Bar Assn)'),\n", + " 'Ugi Utilities Inc/Ugi Energy Services Llc Pac': Text(1.0, -0.38838038123915186, 'Ugi Utilities Inc/Ugi Energy Services Llc Pac'),\n", + " 'Pa Fraternal Order Of Police Pac': Text(0.5897482153166077, -0.2569656851069028, 'Pa Fraternal Order Of Police Pac'),\n", + " 'MICHIGAN ASSOCIATION OF NURSE ANESTHETISTS PAC': Text(-0.27784326029554446, 0.2828712220763738, 'MICHIGAN ASSOCIATION OF NURSE ANESTHETISTS PAC'),\n", + " 'Citizens For Kail': Text(-0.09850761736766293, 0.5235166380701339, 'Citizens For Kail')}" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "G = nx.from_pandas_edgelist(sample_df,source='name',target='donations_to',edge_attr=['donations','received'])\n", + "G.nodes()\n", + "pos=nx.spring_layout(G)\n", + "weights = list(nx.get_edge_attributes(G,'donations').values())\n", + "weights = [i/5000 for i in weights]\n", + "node_color = [G.degree(v) for v in G] \n", + "#node_size = [0.0005 * nx.get_node_attributes(G, 'donations')[v] for v in G] \n", + "nx.draw_networkx_nodes(G, pos, node_color=node_color)#, node_size=node_size) \n", + "nx.draw_networkx_edges(G, pos, width=weights)\n", + "nx.draw_networkx_labels(G, pos)" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{}" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "G.nodes['Citizens For Kail']" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "ename": "KeyError", + "evalue": "'REPUBLICAN STATE LEADERSHIP COMMITTEE MICHIGAN PAC'", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mKeyError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[8], line 7\u001b[0m\n\u001b[1;32m 4\u001b[0m node_color \u001b[38;5;241m=\u001b[39m [G\u001b[38;5;241m.\u001b[39mdegree(v) \u001b[38;5;28;01mfor\u001b[39;00m v \u001b[38;5;129;01min\u001b[39;00m G] \n\u001b[1;32m 5\u001b[0m \u001b[38;5;66;03m# node colour is a list of degrees of nodes \u001b[39;00m\n\u001b[0;32m----> 7\u001b[0m node_size \u001b[38;5;241m=\u001b[39m \u001b[43m[\u001b[49m\u001b[38;5;241;43m0.0005\u001b[39;49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43m \u001b[49m\u001b[43mnx\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_node_attributes\u001b[49m\u001b[43m(\u001b[49m\u001b[43mG\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mpopulation\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m[\u001b[49m\u001b[43mv\u001b[49m\u001b[43m]\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mfor\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mv\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;129;43;01min\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mG\u001b[49m\u001b[43m]\u001b[49m \n\u001b[1;32m 8\u001b[0m \u001b[38;5;66;03m# size of node is a list of population of cities \u001b[39;00m\n\u001b[1;32m 10\u001b[0m edge_width \u001b[38;5;241m=\u001b[39m [\u001b[38;5;241m0.0015\u001b[39m \u001b[38;5;241m*\u001b[39m G[u][v][\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mweight\u001b[39m\u001b[38;5;124m'\u001b[39m] \u001b[38;5;28;01mfor\u001b[39;00m u, v \u001b[38;5;129;01min\u001b[39;00m G\u001b[38;5;241m.\u001b[39medges()] \n", + "Cell \u001b[0;32mIn[8], line 7\u001b[0m, in \u001b[0;36m\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m 4\u001b[0m node_color \u001b[38;5;241m=\u001b[39m [G\u001b[38;5;241m.\u001b[39mdegree(v) \u001b[38;5;28;01mfor\u001b[39;00m v \u001b[38;5;129;01min\u001b[39;00m G] \n\u001b[1;32m 5\u001b[0m \u001b[38;5;66;03m# node colour is a list of degrees of nodes \u001b[39;00m\n\u001b[0;32m----> 7\u001b[0m node_size \u001b[38;5;241m=\u001b[39m [\u001b[38;5;241m0.0005\u001b[39m \u001b[38;5;241m*\u001b[39m \u001b[43mnx\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_node_attributes\u001b[49m\u001b[43m(\u001b[49m\u001b[43mG\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mpopulation\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m[\u001b[49m\u001b[43mv\u001b[49m\u001b[43m]\u001b[49m \u001b[38;5;28;01mfor\u001b[39;00m v \u001b[38;5;129;01min\u001b[39;00m G] \n\u001b[1;32m 8\u001b[0m \u001b[38;5;66;03m# size of node is a list of population of cities \u001b[39;00m\n\u001b[1;32m 10\u001b[0m edge_width \u001b[38;5;241m=\u001b[39m [\u001b[38;5;241m0.0015\u001b[39m \u001b[38;5;241m*\u001b[39m G[u][v][\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mweight\u001b[39m\u001b[38;5;124m'\u001b[39m] \u001b[38;5;28;01mfor\u001b[39;00m u, v \u001b[38;5;129;01min\u001b[39;00m G\u001b[38;5;241m.\u001b[39medges()] \n", + "\u001b[0;31mKeyError\u001b[0m: 'REPUBLICAN STATE LEADERSHIP COMMITTEE MICHIGAN PAC'" + ] + }, + { + "data": { + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "\n", + "# fixing the size of the figure \n", + "plt.figure(figsize =(10, 7)) \n", + "\n", + "node_color = [G.degree(v) for v in G] \n", + "# node colour is a list of degrees of nodes \n", + "\n", + "node_size = [0.0005 * nx.get_node_attributes(G, 'population')[v] for v in G] \n", + "# size of node is a list of population of cities \n", + "\n", + "edge_width = [0.0015 * G[u][v]['weight'] for u, v in G.edges()] \n", + "# width of edge is a list of weight of edges \n", + "\n", + "nx.draw_networkx(G, node_size = node_size, \n", + "\t\t\t\tnode_color = node_color, alpha = 0.7, \n", + "\t\t\t\twith_labels = True, width = edge_width, \n", + "\t\t\t\tedge_color ='.4', cmap = plt.cm.Blues) \n", + "\n", + "plt.axis('off') \n", + "plt.tight_layout(); " + ] + }, + { + "cell_type": "code", + "execution_count": 89, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'color': 'white'}" + ] + }, + "execution_count": 89, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "G = nx.Graph()\n", + "G.add_node(0)\n", + "nx.set_node_attributes(G, \"red\", name=\"color\")\n", + "nx.set_node_attributes(G, 4, name = 'size')\n", + "G.add_node(2)\n", + "nx.set_node_attributes(G, \"white\", name='color')\n", + "G.nodes[2]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "climate_cabinet", + "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": "3.11.7" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} From fa8c0da373b88cdde630347ff630d80654da1c68 Mon Sep 17 00:00:00 2001 From: Alan Mburu Kagiri Date: Thu, 22 Feb 2024 13:16:09 -0600 Subject: [PATCH 04/24] Saving work on networkx branch --- Makefile | 5 + notebooks/Test.ipynb | 1469 ++++++++++++++++++++++++++++++++++-------- utils/linkage.py | 22 +- 3 files changed, 1214 insertions(+), 282 deletions(-) diff --git a/Makefile b/Makefile index e210fb2..f0d93dd 100644 --- a/Makefile +++ b/Makefile @@ -19,6 +19,11 @@ project_dir := "$(current_abs_path)" build-only: docker build -t $(project_image_name) -f Dockerfile $(current_abs_path) + # these are called directives + # run-pipeline: + # docker build -t $(project_image_name) -f Dockerfile $(current_abs_path) + # docker run -e python pipeline.py + run-interactive: docker build -t $(project_image_name) -f Dockerfile $(current_abs_path) docker run -it -v $(current_abs_path):/project -t $(project_image_name) /bin/bash diff --git a/notebooks/Test.ipynb b/notebooks/Test.ipynb index 457fb6f..b17aeb7 100644 --- a/notebooks/Test.ipynb +++ b/notebooks/Test.ipynb @@ -10,7 +10,9 @@ "import numpy as np\n", "import re\n", "import networkx as nx\n", - "import matplotlib.pyplot as plt" + "import matplotlib.pyplot as plt\n", + "\n", + "from utils.linkage import deduplicate_perfect_matches" ] }, { @@ -19,8 +21,365 @@ "metadata": {}, "outputs": [], "source": [ - "orgs_sample = pd.read_csv(\"../output/complete_organizations_table.csv\",index_col=0).sample(10)\n", - "inds_sample = pd.read_csv(\"../output/complete_individuals_table.csv\",index_col=0, low_memory=False).sample(10)" + "orgs_sample = pd.read_csv(\"../output/complete_organizations_table.csv\",index_col=0)#,nrows=10000).sample(10)\n", + "inds_sample = pd.read_csv(\"../output/complete_individuals_table.csv\",index_col=0, low_memory=False)#, nrows=10000).sample(10)\n", + "transactions = pd.read_csv(\"../output/complete_transactions_table.csv\",index_col=0, low_memory=False)" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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11779679NaNNaNrm coulonindividualNaNNaNarea agency on aging
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" + ], + "text/plain": [ + " id first_name last_name full_name entity_type state party \\\n", + "0 1869727 NaN NaN william \bstoner individual NaN NaN \n", + "1 1779679 NaN NaN rm coulon individual NaN NaN \n", + "\n", + " company \n", + "0 NaN \n", + "1 area agency on aging " + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "inds_sample.head(2)" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(542368, 102, 248318, 3)" + ] + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "inds_ids = set(inds_sample.id.tolist())\n", + "orgs_ids = set(orgs_sample.id.tolist())\n", + "trans_donorids = set(transactions.donor_id.tolist())\n", + "trans_recepids = set(transactions.recipient_id.tolist())\n", + "ind_id_there, org_id_there = [], []\n", + "for ind_id in inds_ids:\n", + " if ind_id in trans_donorids:\n", + " ind_id_there.append(ind_id)\n", + " elif ind_id in trans_recepids:\n", + " ind_id_there.append(ind_id)\n", + "\n", + "for org_id in orgs_ids:\n", + " if org_id in trans_donorids:\n", + " org_id_there.append(org_id)\n", + " elif org_id in trans_recepids:\n", + " org_id_there.append(org_id)\n", + "\n", + "len(inds_ids), len(ind_id_there), len(orgs_ids), len(org_id_there)" + ] + }, + { + "cell_type": "code", + "execution_count": 99, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['100894', '100883']" + ] + }, + "execution_count": 99, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "a = []\n", + "for ind_id in inds_ids:\n", + " if ((ind_id in trans_donorids) and (ind_id in trans_recepids)):\n", + " a.append(ind_id)\n", + "a" ] }, { @@ -66,10 +425,10 @@ " REPUBLICAN STATE LEADERSHIP COMMITTEE MICHIGAN...\n", " MI\n", " committee\n", - " 503\n", - " Pabar Pac (Pa Bar Assn)\n", - " 5210\n", - " MICHIGAN ASSOCIATION OF NURSE ANESTHETISTS PAC\n", + " 4249\n", + " REPUBLICAN STATE LEADERSHIP COMMITTEE MICHIGAN...\n", + " 730\n", + " COMMITTEE TO ELECT DR PATRICIA BERNARD\n", " \n", " \n", " 1\n", @@ -77,10 +436,10 @@ " REPUBLICAN STATE LEADERSHIP COMMITTEE MICHIGAN...\n", " MI\n", " committee\n", - " 2969\n", - " REPUBLICAN STATE LEADERSHIP COMMITTEE MICHIGAN...\n", - " 5768\n", + " 426\n", " MICHIGAN ASSOCIATION OF NURSE ANESTHETISTS PAC\n", + " 853\n", + " Pabar Pac (Pa Bar Assn)\n", " \n", " \n", " 2\n", @@ -88,10 +447,10 @@ " REPUBLICAN STATE LEADERSHIP COMMITTEE MICHIGAN...\n", " MI\n", " committee\n", - " 4592\n", - " COMMITTEE TO ELECT DR PATRICIA BERNARD\n", - " 4274\n", - " UNITED FOOD AND COMMERCIAL WORKERS ACTIVE BALL...\n", + " 382\n", + " REPUBLICAN STATE LEADERSHIP COMMITTEE MICHIGAN...\n", + " 620\n", + " MICHIGAN ASSOCIATION OF NURSE ANESTHETISTS PAC\n", " \n", " \n", " 3\n", @@ -99,10 +458,10 @@ " UNITED FOOD AND COMMERCIAL WORKERS ACTIVE BALL...\n", " MI\n", " committee\n", - " 2459\n", - " REPUBLICAN STATE LEADERSHIP COMMITTEE MICHIGAN...\n", - " 2602\n", - " UNITED FOOD AND COMMERCIAL WORKERS ACTIVE BALL...\n", + " 2328\n", + " MICHIGAN ASSOCIATION OF NURSE ANESTHETISTS PAC\n", + " 4505\n", + " Paa Pac\n", " \n", " \n", " 4\n", @@ -110,10 +469,10 @@ " UNITED FOOD AND COMMERCIAL WORKERS ACTIVE BALL...\n", " MI\n", " committee\n", - " 4748\n", - " MICHIGAN ASSOCIATION OF NURSE ANESTHETISTS PAC\n", - " 4153\n", - " REPUBLICAN STATE LEADERSHIP COMMITTEE MICHIGAN...\n", + " 3421\n", + " Paa Pac\n", + " 672\n", + " Paa Pac\n", " \n", " \n", "\n", @@ -135,18 +494,18 @@ "4 UNITED FOOD AND COMMERCIAL WORKERS ACTIVE BALL... MI committee \n", "\n", " donations donations_to received \\\n", - "0 503 Pabar Pac (Pa Bar Assn) 5210 \n", - "1 2969 REPUBLICAN STATE LEADERSHIP COMMITTEE MICHIGAN... 5768 \n", - "2 4592 COMMITTEE TO ELECT DR PATRICIA BERNARD 4274 \n", - "3 2459 REPUBLICAN STATE LEADERSHIP COMMITTEE MICHIGAN... 2602 \n", - "4 4748 MICHIGAN ASSOCIATION OF NURSE ANESTHETISTS PAC 4153 \n", - "\n", - " donations_from \n", - "0 MICHIGAN ASSOCIATION OF NURSE ANESTHETISTS PAC \n", - "1 MICHIGAN ASSOCIATION OF NURSE ANESTHETISTS PAC \n", - "2 UNITED FOOD AND COMMERCIAL WORKERS ACTIVE BALL... \n", - "3 UNITED FOOD AND COMMERCIAL WORKERS ACTIVE BALL... \n", - "4 REPUBLICAN STATE LEADERSHIP COMMITTEE MICHIGAN... " + "0 4249 REPUBLICAN STATE LEADERSHIP COMMITTEE MICHIGAN... 730 \n", + "1 426 MICHIGAN ASSOCIATION OF NURSE ANESTHETISTS PAC 853 \n", + "2 382 REPUBLICAN STATE LEADERSHIP COMMITTEE MICHIGAN... 620 \n", + "3 2328 MICHIGAN ASSOCIATION OF NURSE ANESTHETISTS PAC 4505 \n", + "4 3421 Paa Pac 672 \n", + "\n", + " donations_from \n", + "0 COMMITTEE TO ELECT DR PATRICIA BERNARD \n", + "1 Pabar Pac (Pa Bar Assn) \n", + "2 MICHIGAN ASSOCIATION OF NURSE ANESTHETISTS PAC \n", + "3 Paa Pac \n", + "4 Paa Pac " ] }, "execution_count": 3, @@ -229,9 +588,19 @@ "* https://www.activestate.com/blog/graph-theory-using-python-introduction-and-implementation/ \n" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Things to think about\n", + "* Apply the deduplicated_uuids.csv info to the transactions table\n", + "* After doing a left join on the inds/orgs dataset with the transactions data, the recipient_id column needs to have a recipient_name column so that a new node can be created\n", + "* for ppl who have multiple donations {and so have various attributes like office_sought, purpose, transaction_type}, should this information be saved?" + ] + }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 97, "metadata": {}, "outputs": [ { @@ -256,172 +625,336 @@ " \n", " \n", " id\n", - " name\n", - " state\n", + " first_name\n", + " last_name\n", + " full_name\n", " entity_type\n", + " state\n", + " party\n", + " company\n", " \n", " \n", " \n", " \n", - " 1351658\n", - " 1ec10e00-c7a7-4bcc-861f-cd1ff43bfc04\n", - " Friends Of Freedom & Convenience\n", + " 0\n", + " 1869727\n", + " NaN\n", + " NaN\n", + " william \bstoner\n", + " individual\n", + " NaN\n", + " NaN\n", + " NaN\n", + " \n", + " \n", + " 1\n", + " 1779679\n", + " NaN\n", + " NaN\n", + " rm coulon\n", + " individual\n", + " NaN\n", + " NaN\n", + " area agency on aging\n", + " \n", + " \n", + " 2\n", + " 2277221\n", + " NaN\n", + " NaN\n", + " james engelson\n", + " individual\n", + " NaN\n", + " NaN\n", + " retired\n", + " \n", + " \n", + " 3\n", + " 2277156\n", + " NaN\n", + " NaN\n", + " marivic franciaskinner\n", + " individual\n", + " NaN\n", + " NaN\n", + " fibre source international corp\n", + " \n", + " \n", + " 4\n", + " 2341373\n", + " NaN\n", + " NaN\n", + " anthony grindle\n", + " individual\n", + " NaN\n", + " NaN\n", + " zimmerbiomet\n", + " \n", + " \n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " \n", + " \n", + " 861260\n", + " 6acfa74b-d5e1-4afd-b020-dbe429eb1c3f\n", + " NaN\n", + " NaN\n", + " Melissa Hart\n", + " Candidate\n", " PA\n", - " Committee\n", + " REP\n", + " NaN\n", " \n", " \n", - " 1158960\n", - " 6359974e-9e78-409c-b9dd-fe7415304560\n", - " GRETCHEN WHITMER FOR GOVERNOR\n", - " MI\n", - " committee\n", + " 861271\n", + " f111045d-bc3d-4050-9ad7-b3b1e6d72e56\n", + " NaN\n", + " NaN\n", + " Heather Miller\n", + " Candidate\n", + " PA\n", + " DEM\n", + " NaN\n", " \n", " \n", - " 474220\n", - " 9e43c101-03ef-4083-ab60-b7fd76dea7b5\n", - " TUDOR DIXON FOR GOVERNOR INC\n", - " MI\n", - " committee\n", + " 861277\n", + " d40859d7-b523-4ef5-895b-c3a947ab582f\n", + " NaN\n", + " NaN\n", + " Christopher M. Gebhard\n", + " Candidate\n", + " PA\n", + " REP\n", + " NaN\n", " \n", " \n", - " 25789\n", - " 5fb7cb16-912f-4fec-ba37-f201465a5725\n", - " LNAACK BEVERLEY\n", - " MI\n", - " corporation\n", + " 861775\n", + " f5d76d43-86f4-40f9-aeb9-3df97ca8cdf0\n", + " NaN\n", + " NaN\n", + " April Weaver\n", + " Candidate\n", + " PA\n", + " REP\n", + " NaN\n", " \n", " \n", - " 495642\n", - " 6359974e-9e78-409c-b9dd-fe7415304560\n", - " GRETCHEN WHITMER FOR GOVERNOR\n", - " MI\n", - " committee\n", + " 861920\n", + " 1a0cf90d-3252-4c8d-b109-dea084a01f69\n", + " NaN\n", + " NaN\n", + " Krista Paolucci\n", + " Candidate\n", + " PA\n", + " REP\n", + " NaN\n", " \n", " \n", "\n", + "

2505346 rows × 8 columns

\n", "" ], "text/plain": [ - " id \\\n", - "1351658 1ec10e00-c7a7-4bcc-861f-cd1ff43bfc04 \n", - "1158960 6359974e-9e78-409c-b9dd-fe7415304560 \n", - "474220 9e43c101-03ef-4083-ab60-b7fd76dea7b5 \n", - "25789 5fb7cb16-912f-4fec-ba37-f201465a5725 \n", - "495642 6359974e-9e78-409c-b9dd-fe7415304560 \n", - "\n", - " name state entity_type \n", - "1351658 Friends Of Freedom & Convenience PA Committee \n", - "1158960 GRETCHEN WHITMER FOR GOVERNOR MI committee \n", - "474220 TUDOR DIXON FOR GOVERNOR INC MI committee \n", - "25789 LNAACK BEVERLEY MI corporation \n", - "495642 GRETCHEN WHITMER FOR GOVERNOR MI committee " + " id first_name last_name \\\n", + "0 1869727 NaN NaN \n", + "1 1779679 NaN NaN \n", + "2 2277221 NaN NaN \n", + "3 2277156 NaN NaN \n", + "4 2341373 NaN NaN \n", + "... ... ... ... \n", + "861260 6acfa74b-d5e1-4afd-b020-dbe429eb1c3f NaN NaN \n", + "861271 f111045d-bc3d-4050-9ad7-b3b1e6d72e56 NaN NaN \n", + "861277 d40859d7-b523-4ef5-895b-c3a947ab582f NaN NaN \n", + "861775 f5d76d43-86f4-40f9-aeb9-3df97ca8cdf0 NaN NaN \n", + "861920 1a0cf90d-3252-4c8d-b109-dea084a01f69 NaN NaN \n", + "\n", + " full_name entity_type state party \\\n", + "0 william \bstoner individual NaN NaN \n", + "1 rm coulon individual NaN NaN \n", + "2 james engelson individual NaN NaN \n", + "3 marivic franciaskinner individual NaN NaN \n", + "4 anthony grindle individual NaN NaN \n", + "... ... ... ... ... \n", + "861260 Melissa Hart Candidate PA REP \n", + "861271 Heather Miller Candidate PA DEM \n", + "861277 Christopher M. Gebhard Candidate PA REP \n", + "861775 April Weaver Candidate PA REP \n", + "861920 Krista Paolucci Candidate PA REP \n", + "\n", + " company \n", + "0 NaN \n", + "1 area agency on aging \n", + "2 retired \n", + "3 fibre source international corp \n", + "4 zimmerbiomet \n", + "... ... \n", + "861260 NaN \n", + "861271 NaN \n", + "861277 NaN \n", + "861775 NaN \n", + "861920 NaN \n", + "\n", + "[2505346 rows x 8 columns]" ] }, - "execution_count": 5, + "execution_count": 97, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "orgs_sample.head(5)" + "sample_inds" ] }, { "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "id 1ec10e00-c7a7-4bcc-861f-cd1ff43bfc04\n", - "name Friends Of Freedom & Convenience\n", - "state PA\n", - "entity_type Committee\n", - "Name: 1351658, dtype: object\n", - "id 6359974e-9e78-409c-b9dd-fe7415304560\n", - "name GRETCHEN WHITMER FOR GOVERNOR\n", - "state MI\n", - "entity_type committee\n", - "Name: 1158960, dtype: object\n", - "id 9e43c101-03ef-4083-ab60-b7fd76dea7b5\n", - "name TUDOR DIXON FOR GOVERNOR INC\n", - "state MI\n", - "entity_type committee\n", - "Name: 474220, dtype: object\n", - "id 5fb7cb16-912f-4fec-ba37-f201465a5725\n", - "name LNAACK BEVERLEY \n", - "state MI\n", - "entity_type corporation\n", - "Name: 25789, dtype: object\n", - "id 6359974e-9e78-409c-b9dd-fe7415304560\n", - "name GRETCHEN WHITMER FOR GOVERNOR\n", - "state MI\n", - "entity_type committee\n", - "Name: 495642, dtype: object\n", - "id f1df070b-a91b-4aab-b943-4f80e5c41026\n", - "name MICHIGAN LABORERS POLITICAL LEAGUE\n", - "state MI\n", - "entity_type committee\n", - "Name: 1939825, dtype: object\n", - "id 57fbfb3e-835c-4096-9dc9-1555816aff0d\n", - "name PLUMBERS AND PIPEFITTERS LOCAL 333 PAC\n", - "state MI\n", - "entity_type committee\n", - "Name: 1643401, dtype: object\n", - "id 357e354f-d81b-4eb5-af6e-574afd175672\n", - "name MICHIGAN FARM BUREAU POLITICAL ACTION COMMITTEE\n", - "state MI\n", - "entity_type committee\n", - "Name: 2088505, dtype: object\n", - "id 1a5d85e2-0382-4064-9606-8ee0a2be5ea1\n", - "name ANEDOT INC \n", - "state MI\n", - "entity_type corporation\n", - "Name: 157224, dtype: object\n", - "id 6d8e2e79-72c1-487e-835f-ededfe0aafaa\n", - "name DEMOCRATIC LEGISLATIVE CAMPAIGN COMMITTEE\n", - "state MI\n", - "entity_type committee\n", - "Name: 854930, dtype: object\n" - ] - } - ], - "source": [ - "for index, row in orgs_sample.iterrows():\n", - " print(row)" - ] - }, - { - "cell_type": "code", - "execution_count": 38, + "execution_count": 63, "metadata": {}, "outputs": [ { "data": { + "text/html": [ + "
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idcompanyentity_typefirst_namefull_namelast_namepartystatetransaction_iddonor_idyearamountrecipient_idoffice_soughtpurposetransaction_typedonor_typerecipient_typedonor_office
025625730individualNaNvarious 0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
11617483aggregate cashindividualNaNcash _small donationsNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
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" + ], "text/plain": [ - "{'color': 'blue', 'size': 2}" + " id company entity_type first_name full_name \\\n", + "0 2562573 0 individual NaN various 0 \n", + "1 1617483 aggregate cash individual NaN cash _small donations \n", + "\n", + " last_name party state transaction_id donor_id year amount recipient_id \\\n", + "0 NaN NaN NaN NaN NaN NaN NaN NaN \n", + "1 NaN NaN NaN NaN NaN NaN NaN NaN \n", + "\n", + " office_sought purpose transaction_type donor_type recipient_type \\\n", + "0 NaN NaN NaN NaN NaN \n", + "1 NaN NaN NaN NaN NaN \n", + "\n", + " donor_office \n", + "0 NaN \n", + "1 NaN " ] }, - "execution_count": 38, + "execution_count": 63, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "G = nx.Graph()\n", - "G.add_node(0)\n", - "nx.set_node_attributes(G, \"red\", name=\"color\")\n", - "nx.set_node_attributes(G, 2, name=\"size\")\n", - "G.add_node(1)\n", - "nx.set_node_attributes(G, \"blue\", name='color')\n", - "G.nodes[0]\n" + "sample_inds = inds_sample.loc[inds_sample['id'].isin(ind_id_there)]\n", + "# apply dedup\n", + "sample_inds = deduplicate_perfect_matches(sample_inds)\n", + "\n", + "# map the uuids in transaction donor and recipient columns to the deduplicated uuids\n", + "deduped = pd.read_csv(\"../output/deduplicated_UUIDs.csv\")\n", + "transactions[['donor_id','recipient_id']] = transactions[['donor_id','recipient_id']].replace(deduped)\n", + "\n", + "# add recipient name to transactions df: \n", + "# this step took more than 16 minutes to run...think of alternative way\n", + "# id_to_name = {id: name for id, name in zip(inds_sample.id.tolist(), inds_sample.full_name.tolist())} #the same would be applied to orgs\n", + "transactions['recipient_name'] = transactions['recipient_id'].apply(lambda x: sample_inds.loc[sample_inds.id == x] )\n", + "\n", + "# left merge according to ind_id and transaction donor_id. This was entities that only received money will still be there, no info from ind_dataset\n", + "# is lost\n", + "merged_inds_sample = pd.merge(sample_inds,transactions,how='left',left_on='id',right_on='donor_id')\n", + "merged_inds_sample.head(2)" ] }, { "cell_type": "code", - "execution_count": 42, + "execution_count": 93, "metadata": {}, "outputs": [ { @@ -446,52 +979,76 @@ " \n", " \n", " id\n", - " name\n", - " state\n", + " company\n", " entity_type\n", + " first_name\n", + " full_name\n", + " last_name\n", + " party\n", + " state\n", " \n", " \n", " \n", " \n", - " 297930\n", - " e44b8553-0dff-4a6b-8335-d97849641ff8\n", - " FRIENDS OF DANA NESSEL\n", - " MI\n", - " committee\n", - " \n", - " \n", - " 945536\n", - " 4f5b8fc4-c871-4774-a436-1622b8e26a44\n", - " MALLORY MCMORROW FOR MICHIGAN\n", - " MI\n", - " committee\n", + " 27\n", + " 100894\n", + " none (is a candidate)\n", + " candidate\n", + " NaN\n", + " abdussamad, shams\n", + " NaN\n", + " democratic\n", + " AZ\n", " \n", " \n", "\n", "" ], "text/plain": [ - " id name \\\n", - "297930 e44b8553-0dff-4a6b-8335-d97849641ff8 FRIENDS OF DANA NESSEL \n", - "945536 4f5b8fc4-c871-4774-a436-1622b8e26a44 MALLORY MCMORROW FOR MICHIGAN \n", + " id company entity_type first_name full_name \\\n", + "27 100894 none (is a candidate) candidate NaN abdussamad, shams \n", "\n", - " state entity_type \n", - "297930 MI committee \n", - "945536 MI committee " + " last_name party state \n", + "27 NaN democratic AZ " ] }, - "execution_count": 42, + "execution_count": 93, "metadata": {}, "output_type": "execute_result" } ], "source": [ - ".head(2)" + "sample_inds.loc[sample_inds.full_name == 'abdussamad, shams']" ] }, { "cell_type": "code", - "execution_count": 43, + "execution_count": 87, + "metadata": {}, + "outputs": [], + "source": [ + "def add_notes_from_df(df):\n", + " G = nx.MultiDiGraph()\n", + " #inds or org...\n", + " if 'name' in df.columns:\n", + " node_name = 'name'\n", + " else: node_name = 'full_name'\n", + "\n", + " for _, row in df.iterrows():\n", + " G.add_node(row[node_name])\n", + " for column in df.columns:\n", + " # only add info that's present\n", + " if (row[column] != 'nan'):\n", + " nx.set_node_attributes(G, row[column], name=column)\n", + " #nx.set\n", + " nx.draw_random(G, with_labels=True)\n", + " plt.show()\n", + " return G" + ] + }, + { + "cell_type": "code", + "execution_count": 77, "metadata": {}, "outputs": [ { @@ -516,99 +1073,360 @@ " \n", " \n", " id\n", + " company\n", + " entity_type\n", " first_name\n", - " last_name\n", " full_name\n", - " entity_type\n", - " state\n", + " last_name\n", " party\n", - " company\n", + " state\n", + " transaction_id\n", + " donor_id\n", + " year\n", + " amount\n", + " recipient_id\n", + " office_sought\n", + " purpose\n", + " transaction_type\n", + " donor_type\n", + " recipient_type\n", + " donor_office\n", " \n", " \n", " \n", " \n", - " 891077\n", - " c94a0491-7ea1-45ce-a155-6153ea74da08\n", - " BELA\n", - " LAHNER\n", - " BELA LAHNER ...\n", - " Individual\n", - " MI\n", + " 27\n", + " 100894\n", + " none (is a candidate)\n", + " candidate\n", + " NaN\n", + " abdussamad, shams\n", + " NaN\n", + " democratic\n", + " AZ\n", + " 5088079\n", + " 100894\n", + " 2022.0\n", + " 5.00\n", + " 750413\n", + " state representative - district 11\n", + " e-qual online qc\n", + " ccec $5 qualifying contribution\n", + " NaN\n", + " NaN\n", + " NaN\n", + " \n", + " \n", + " 28\n", + " 100894\n", + " none (is a candidate)\n", + " candidate\n", + " NaN\n", + " abdussamad, shams\n", + " NaN\n", + " democratic\n", + " AZ\n", + " 5088080\n", + " 100894\n", + " 2022.0\n", + " 5.00\n", + " 2002235\n", + " state representative - district 11\n", + " NaN\n", + " ccec $5 qualifying contribution\n", + " NaN\n", + " NaN\n", + " NaN\n", + " \n", + " \n", + " 29\n", + " 100894\n", + " none (is a candidate)\n", + " candidate\n", + " NaN\n", + " abdussamad, shams\n", + " NaN\n", + " democratic\n", + " AZ\n", + " 5088081\n", + " 100894\n", + " 2022.0\n", + " 100.00\n", + " 1942680\n", + " state representative - district 11\n", + " NaN\n", + " receive loan from candidate or family member\n", + " NaN\n", + " NaN\n", + " NaN\n", + " \n", + " \n", + " 30\n", + " 100894\n", + " none (is a candidate)\n", + " candidate\n", + " NaN\n", + " abdussamad, shams\n", + " NaN\n", + " democratic\n", + " AZ\n", + " 5088083\n", + " 100894\n", + " 2022.0\n", + " 5.00\n", + " -1\n", + " state representative - district 11\n", + " NaN\n", + " in-state contributions $100 or less\n", + " NaN\n", + " NaN\n", + " NaN\n", + " \n", + " \n", + " 31\n", + " 100894\n", + " none (is a candidate)\n", + " candidate\n", + " NaN\n", + " abdussamad, shams\n", + " NaN\n", + " democratic\n", + " AZ\n", + " 5088084\n", + " 100894\n", + " 2022.0\n", + " 20.00\n", + " -1\n", + " state representative - district 11\n", + " NaN\n", + " in-state contributions $100 or less\n", + " NaN\n", + " NaN\n", + " NaN\n", + " \n", + " \n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " \n", + " \n", + " 597\n", + " 100883\n", + " none (is a candidate)\n", + " candidate\n", + " NaN\n", + " abeytia, anna lynn\n", + " NaN\n", + " democratic\n", + " AZ\n", + " 5084100\n", + " 100883\n", + " 2022.0\n", + " 10.00\n", + " 2017053\n", + " state representative - district 11\n", + " NaN\n", + " contribution from individuals\n", + " NaN\n", + " NaN\n", + " NaN\n", + " \n", + " \n", + " 598\n", + " 100883\n", + " none (is a candidate)\n", + " candidate\n", + " NaN\n", + " abeytia, anna lynn\n", + " NaN\n", + " democratic\n", + " AZ\n", + " 5084102\n", + " 100883\n", + " 2022.0\n", + " 180.00\n", + " 2017970\n", + " state representative - district 11\n", + " video production\n", + " in-kind cont. from individual\n", + " NaN\n", + " NaN\n", + " NaN\n", + " \n", + " \n", + " 599\n", + " 100883\n", + " none (is a candidate)\n", + " candidate\n", + " NaN\n", + " abeytia, anna lynn\n", + " NaN\n", + " democratic\n", + " AZ\n", + " 5084103\n", + " 100883\n", + " 2022.0\n", + " 51.99\n", + " 2008747\n", + " state representative - district 11\n", + " NaN\n", + " contribution from individuals\n", + " NaN\n", + " NaN\n", + " NaN\n", + " \n", + " \n", + " 600\n", + " 100883\n", + " none (is a candidate)\n", + " candidate\n", + " NaN\n", + " abeytia, anna lynn\n", + " NaN\n", + " democratic\n", + " AZ\n", + " 5084105\n", + " 100883\n", + " 2022.0\n", + " 10.80\n", + " 1193076\n", + " state representative - district 11\n", + " NaN\n", + " contribution from individuals\n", + " NaN\n", + " NaN\n", " NaN\n", - " NOT EMPLOYED\n", " \n", " \n", - " 617571\n", - " c38816dd-8a47-4102-97cd-59d0f6bc42dc\n", - " JANICE\n", - " SHAPIRO\n", - " JANICE SHAPIRO ...\n", - " Individual\n", - " TX\n", + " 601\n", + " 100883\n", + " none (is a candidate)\n", + " candidate\n", + " NaN\n", + " abeytia, anna lynn\n", + " NaN\n", + " democratic\n", + " AZ\n", + " 5084107\n", + " 100883\n", + " 2022.0\n", + " 51.99\n", + " 1691025\n", + " state representative - district 11\n", + " NaN\n", + " contribution from individuals\n", + " NaN\n", " NaN\n", " NaN\n", " \n", " \n", "\n", + "

575 rows × 19 columns

\n", "" ], "text/plain": [ - " id first_name \\\n", - "891077 c94a0491-7ea1-45ce-a155-6153ea74da08 BELA \n", - "617571 c38816dd-8a47-4102-97cd-59d0f6bc42dc JANICE \n", + " id company entity_type first_name \\\n", + "27 100894 none (is a candidate) candidate NaN \n", + "28 100894 none (is a candidate) candidate NaN \n", + "29 100894 none (is a candidate) candidate NaN \n", + "30 100894 none (is a candidate) candidate NaN \n", + "31 100894 none (is a candidate) candidate NaN \n", + ".. ... ... ... ... \n", + "597 100883 none (is a candidate) candidate NaN \n", + "598 100883 none (is a candidate) candidate NaN \n", + "599 100883 none (is a candidate) candidate NaN \n", + "600 100883 none (is a candidate) candidate NaN \n", + "601 100883 none (is a candidate) candidate NaN \n", + "\n", + " full_name last_name party state transaction_id donor_id \\\n", + "27 abdussamad, shams NaN democratic AZ 5088079 100894 \n", + "28 abdussamad, shams NaN democratic AZ 5088080 100894 \n", + "29 abdussamad, shams NaN democratic AZ 5088081 100894 \n", + "30 abdussamad, shams NaN democratic AZ 5088083 100894 \n", + "31 abdussamad, shams NaN democratic AZ 5088084 100894 \n", + ".. ... ... ... ... ... ... \n", + "597 abeytia, anna lynn NaN democratic AZ 5084100 100883 \n", + "598 abeytia, anna lynn NaN democratic AZ 5084102 100883 \n", + "599 abeytia, anna lynn NaN democratic AZ 5084103 100883 \n", + "600 abeytia, anna lynn NaN democratic AZ 5084105 100883 \n", + "601 abeytia, anna lynn NaN democratic AZ 5084107 100883 \n", "\n", - " last_name \\\n", - "891077 LAHNER \n", - "617571 SHAPIRO \n", + " year amount recipient_id office_sought \\\n", + "27 2022.0 5.00 750413 state representative - district 11 \n", + "28 2022.0 5.00 2002235 state representative - district 11 \n", + "29 2022.0 100.00 1942680 state representative - district 11 \n", + "30 2022.0 5.00 -1 state representative - district 11 \n", + "31 2022.0 20.00 -1 state representative - district 11 \n", + ".. ... ... ... ... \n", + "597 2022.0 10.00 2017053 state representative - district 11 \n", + "598 2022.0 180.00 2017970 state representative - district 11 \n", + "599 2022.0 51.99 2008747 state representative - district 11 \n", + "600 2022.0 10.80 1193076 state representative - district 11 \n", + "601 2022.0 51.99 1691025 state representative - district 11 \n", "\n", - " full_name entity_type state \\\n", - "891077 BELA LAHNER ... Individual MI \n", - "617571 JANICE SHAPIRO ... Individual TX \n", + " purpose transaction_type \\\n", + "27 e-qual online qc ccec $5 qualifying contribution \n", + "28 NaN ccec $5 qualifying contribution \n", + "29 NaN receive loan from candidate or family member \n", + "30 NaN in-state contributions $100 or less \n", + "31 NaN in-state contributions $100 or less \n", + ".. ... ... \n", + "597 NaN contribution from individuals \n", + "598 video production in-kind cont. from individual \n", + "599 NaN contribution from individuals \n", + "600 NaN contribution from individuals \n", + "601 NaN contribution from individuals \n", "\n", - " party company \n", - "891077 NaN NOT EMPLOYED \n", - "617571 NaN NaN " + " donor_type recipient_type donor_office \n", + "27 NaN NaN NaN \n", + "28 NaN NaN NaN \n", + "29 NaN NaN NaN \n", + "30 NaN NaN NaN \n", + "31 NaN NaN NaN \n", + ".. ... ... ... \n", + "597 NaN NaN NaN \n", + "598 NaN NaN NaN \n", + "599 NaN NaN NaN \n", + "600 NaN NaN NaN \n", + "601 NaN NaN NaN \n", + "\n", + "[575 rows x 19 columns]" ] }, - "execution_count": 43, + "execution_count": 77, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "inds_sample.head(2)" - ] - }, - { - "cell_type": "code", - "execution_count": 100, - "metadata": {}, - "outputs": [], - "source": [ - "def add_notes_from_df(df):\n", - " G = nx.MultiDiGraph()\n", - " if 'name' in df.columns:\n", - " node_name = 'name'\n", - " else: node_name = 'full_name'\n", - " for index, row in df.iterrows():\n", - " # if nodes 1 and 2 don't exist, this both creates the nodes and adds the edges to them\n", - " # the weight can be added to show the magnitude of the edge\n", - " G.add_node(row[node_name])\n", - " for column in df.columns:\n", - " nx.set_node_attributes(G, row[column], name=column)\n", - " nx.draw_random(G, with_labels=True)\n", - " plt.show()\n", - " return G" + "merged_inds_sample.loc[merged_inds_sample.donor_id.notnull()]" ] }, { "cell_type": "code", - "execution_count": 105, + "execution_count": 91, "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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zs+9/E1m956HgU46i9/zfB3XRoFKp5MSJE3h5efHs2bO/hZNYVpLSMolNTif3BJ2CEbDrDnXc7MQXnBw4e/Yshw4dYtiwYR+stevr60toaCgnTpwgMDCQVatWCTOfIjnzj86hzB4AzJs3D1Bp7gG0aNECgNmzZ2u0U48QtmzZ8jMfoSYdOnTg5cuXLF26VGtdSkoKSUlJBe7zyZMnPHnyJNc2Tk5OeHh48Oeff5KY+H4q+uTJk9y6dUvrGOVyORMmTNBYbmqoh4ulYYGmstVIDYwAUKTlfn5FbU0wNdR8x+nWrRsxMTH069ePxMTEPLUc4f3ocdY35PT0dBYsWKDRTi6X06lTJ9LT09m6dWu+dUMdHBywsrJi+/btpKenC8sTExPZvXs3pUuXFoLx7FJSoMrpnD17NnZ2dlStWlVYrivlITQ0lKNHj+aoIblu3TpMTExo27atzvW69r9v3z6uXLlCs2bNcj/RfxHzjz9i1LZbpGUq8gwksyNXKEnLVDBq2y3mH3/0mY5Q5N+IQcMBuPRb8kn7VBtBiOjm7NmzBAUF5Sm1JvLp+UePUD579gwfHx+aNWvGuXPnWLt2LV26dBH0CytWrEiPHj1YsmSJMA168eJFVq9eTZs2bfDy8vqix9utWzc2bdpE//79OX78OHXr1kUul3P//n02bdrEwYMHCyw+rZaTyevtadKkSXzzzTfUrVuXnj17EhMTw/z58/Hw8NAIMhs0aEC/fv2YPHky169fp2nTpujr6/Po0SOur1mPcYMfMHavW6BjNHD4CiRS4s5vQZGWjESmh1GxishMrYQ2MqkEr1LaeZuVK1fGw8NDKGiqUqVKnvurU6cO1tbW9OjRgyFDhiCRSFizZo3WFMyiRYs4duyY8PfIiqOjI02aNNHZv0wmw9/fn7Fjx1KrVi26d++OXC5n+fLlvHjxgrVr1wpt//jjD3bs2EHr1q0pWrQor1+/ZsWKFYSHh7NmzRqNILZ8+fJ8/fXXVKpUCWtrax49esTy5cvJyMjQsmkE1XT5/v37+fbbbzWKrbJfi8qVK1OtWjUsLS25evUqK1aswMXFhTFjxuR5LdV6nCdOnAB0T/npmuJydXWlYcOGrFq1Ks99fG4+tfe8espRIpEwfvx4AgMDP0nfX4Lsf89PSWpqKgYGBhppIH8nkpOTMTH5MqN6SUlJvEpUcCY0FqSf9jGb1QiihIOY36smKSkpz2JNkc/L3/Obn082btyIoaEho0aNYu/evQwaNIjly5drtFm2bBlBQUFcunSJYcOGcezYMUaPHs2GDRu++PFKpVJ27NjBlClTuHXrFv7+/sKxDR06lFKlSn22fbdu3Zr169eTnp7OqFGj2LZtG6tWrcLd3R0jIyONtosWLWLJkiVEREQwZswYRo8ezbFjx+jUuQv6RcrksIeckZlZY9NsIIrkOKL3zSFq13QyojQrFuUKJV1r6Z5OVFsW5qcYB8DW1pY9e/ZQuHBhxo4dy4wZM2jSpAnTpk3TaKcevVu0aBHdunXT+MmrAvqXX34hODgYfX19goKCGDduHBYWFmzZskVj+rlu3bo4ODiwbNkyBg4cyKxZs3B3d+fIkSNa09QDBgzg0aNHTJkyBT8/P4KDg2natCkXL16kTp06WsewefNmMjIyctSzBOjYsSOPHj1i0qRJDB48mAMHDtCnTx8uXbpUoJzLfyof4j2fF/9U73lQGSs8ePBAEPx3cXGhWrVqXLp0CVC9pPj7+1O+fHnMzMywsLCgefPm3LhxQ6MftZ/8hg0bGDt2LEWKFMHExEQogNu8eTNVq1bF2NgYOzs7unbtysuXL7/YeTZs2BAPDw+uXLlC/fr1MTExEV6gIiIi+OGHH3B0dMTIyIiKFSuyevVqrT5iY2Px9fXF0tISKysrevTooXPUy9fXFzMzM548eUKLFi0wNzfn+++/J/hCONF7Z/FiQS+N9vKUeKJ2/074zO8In9WRqD0zSX/7lLAprUi8+b5Q7k3wKN4Ej9LaX9SeWbxc2Iu159/fQ3Myo8haJKq2iNX1k598UF2cPn2a6tWrY2RkhJubG4sXL9ZqExoaikQi0flyKZFINF7IwsLC8PPzw93dHWNjY2xtbfnuu++0BkzUphQnT57Ez88PBwcHnJ2dCQwMZMSIEQAUL15cOL+s269du1b4bNrY2NCpUyeeP3/+Qecvosk/coQyMDBQ+BBu3rw517Z6enoEBATotLXLSk4jfNnf5H19fXV++Ro2bKg1AqZrFEBfX5+RI0cycuTIXI9HF7q+kAXJ6+jYsaNWJfC4ceNwdnbWatunTx/69Omjtbzb8gucfRqNXKHE2W+Fzv0U+l57NM28ojfmFbXdbZz9ViCTSqjzlW2Ob9sGBgZIJJIC5QnWqVOHc+fOaS3P+jfK+jn6ELp06ZJrMAcqD++cRjqzU9Dj6devH/369cu1zcSJE5k4cWK++8zOh/rRP3jw4G8xUvUh3vN5oZ5yTElJQU/vn3MLffXqFWFhYcTFxQk2oZcuXWL58uVcunSJ6tWr8/TpU3bs2MF3331H8eLFefv2reBcdffuXZycnDT6nDBhAgYGBvj7+5OWloaBgYGQ31i9enUmT57M27dvmTNnDmfOnOHatWtfzEM+Ojqa5s2b06lTJ7p27YqjoyMpKSk0bNiQx48fM2jQIIoXL87mzZvx9fUlNjaWoUOHAqr7xDfffMPp06fp378/ZcqUYfv27fTo0UPnvjIzM/H29qZevXrMmDEDExMTlj2I0KpNVCoVRG75lbRXDzGv3AJ9W2eSH10gau+sAp2bUgnHH0YQiMpwIyczimvXrnHmzBn09fVp166dltLHlStXmD17dp5GDrq4desWTZs2xd7ensDAQDIzMxk/fvxHvaheunSJs2fP0qlTJ5ydnQkNDWXhwoU0bNiQu3fvao0w+/n5YW9vT0BAAElJSTRv3pyHDx+yfv16Zs2ahZ2dHfA+V/23335j3LhxdOjQgd69exMZGcm8efOoX7/+F/1s/lv559wNRT6KjIwMJBIJaXKV1mN6poLrF89w48aNAgUck9qWp/GskwXOQ8sNPamESW3L61ynVCpZvnw5DRo00CnrJPJ5+VA/+qyWlH8Vn9t7/kV8BiUcjPLe4G/C6NGjefv2LRcuXBBSa6pWrcry5cuFnN/y5cvz8OFDjZeBbt26Ubp0aZYvX864ceM0+kxNTeXy5cvC9hkZGfz88894eHhw6tQpYfajXr16tGrVilmzZhEUFPTR55L1xV7XyzzAmzdvWLRokcZL15w5c7h37x5r164VXlD79+9PgwYNGDt2LL169cLc3Jxdu3Zx6tQppk2bJox4DRgwIMc0qbS0NL777jsmT54MQGJaJgGBB7XapTw8T9rL+1h/3QeL6t8AYFalBREbxpJRwGsQHp1MUlom1y6dZ9myZQQHB2u84Hp5edGsWTM2b95Mly5dqFChAhUqVBDWR0VF8csvv1C+fHnGjx9fwL1DQEAASqWSkJAQ4d787bffamkbF4SWLVvSvn17jWWtW7emdu3abN26VWuWysbGhqNHj2oUalapUoX169fTpk0bjSKtsLAwxo8fz8SJEzXSfdq1a0flypVZsGCBsDzr4M3HDjz8l/jrhxBEPjuP3iYwYtVxzAsVo8jX3an/w1gatO/JD53bIzO1Zm9GOQJ33eHR24Q8+3KxMSHIp1ye7QrCrz7ltCoWk5KSWL9+Pf369ePWrVv8+OOPn3Sf/yVu3ryJRCJh165dwrIrV64gkUi0clKbN29OzZo1hd91+dHnB1dXV42R/IJOpW7atImgoCCKFCmCubk57du3Jy4ujrS0NIYNG4aDgwNmZmb07NmTtLQ0nccQfCFcw3s+O5lxEUQfXMDLJf0In9GO57M7E7l9Mpmxb/M8P5lUQklHi4+arjtz5gzDhw/H3t4eU1NT2rZtq7OIasGCBZQrVw5DQ0OcnJwYOHCgxtTroEGDMDMzIzn5/TS8Wqy92TffYufgSGxiCjt27MDa2hp/f3/hWlevXh2AXr16IZFIMDIy4s8//9TwuS9evDhpaWkEBARo+dz36NFDQw3i8uXLRERE4Ofnp5FK07JlS0qXLq1lJvE5MTQ0pGfPnhrL9u3bR6FChejcubOwTF9fnyFDhpCYmMjJkyeFdnp6egwYMEBoJ5PJBPMMXWRtGxadpFMRI+XpFZDqYVbp/WyNRCLFvEqrgp6eYASRkxlF1apVMTMz02mYIZfL6dy5MwkJCWzfvr3AuYdyuZyDBw/Spk0bjRf9MmXK4O2tPROVX7J+ljIyMoiOjqZEiRJYWVlp6CWryY/qh5pt27ahUCjo0KGDxnUqVKgQJUuW/GzGIv8lxBHKfzFZNfck6clI7d1IvHkIeXIcUn0jjN2qYdXQl1dp+gXS3OtUvShRiWmfpNBhRFN3nZpqkZGRdOnSBSsrK8aMGYOPj89H7+u/ioeHB1ZWVpw6dUq4jiEhIUilUm7cuEF8fDwWFhYoFArOnj2bLy3OglLQqdTJkydjbGzMqFGjePz4MfPmzUNfXx+pVEpMTAyBgYGcP3+eVatWUbx4cZ0pLccfROQ6kp72+iFpL+9hWqY+MnNbMuMiSLy2jzfrRuPUZwFS/ZxHH3X1W9DpusGDB2Ntbc348eMJDQ1l9uzZDBo0iI0bNwpt1AVPjRs3ZsCAATx48ICFCxdy6dIlYSqzY8eO/PHHHywL3sI7x6qCWLs8I5UX+/dhVv5rKozZSnx8PJb2hUlJV0lalSlThl9//ZWAgAD69u2Lp6cnCoWChw8fsnbtWiQSicbIX/HixXn27JnG9GhW2TZ4b6Lg7u6udX1Kly7N6dOnc7ymn5oiRYpojbCHhYVRsmRJrXQMtayc+vjDwsIoXLiwVrGbrvMCVWpV1tShrAYNWcmMj0BmZq312dKzLpyPM9ImPVORpxmFLvUItenD3r17cXNzK/B+IyMjSUlJ0TIXAdU1UrvUFZSUlBQmT57MypUrefnypcbnT5d0WvbPX248evQIpVKp85hB9WIh8nGIAeW/lOyae0oDE+zb/Jxj+4Jq7g3yKomdmaGwj4JMgcukEvSkEn71KZejQK+rq6sojPuJkEql1K1bl5CQEGFZSEgIbdq0YefOnZw9e5ZmzZoJwWVW+9FPRUGnUjMzMzl58qRwk4+MjGTDhg00a9ZMeFj5+fnx+PFjVqxYoRVQJqZlEp5H4YyxW3VMS2u6FJmUqMGbNf4kPziLmUejPM8ra+BQ0Ok6W1tbDh06JGjSKhQK5s6dS1xcHJaWlkRGRjJ58mSaNm3K/v37hWtXunRpBg0axNq1a+nZsyfFylbBxNqe0b8vptC3vwjfxZQnl1FmpGJSxlMYLUtMy+TGi1i6Lb/ApLblad68OQEBAdSuXZuuXbsyceJEfvvtN3r16kWTJk2wsbFBKpUyZMgQnj9/jpOTE3PnzhXsS7OOKP3d+JLHZmhoqPHZzmrQ8MFIJLoNIpTvP3MGetIczSjUZNeb3bFjB1OnTmXChAlfRDosJ81lXVq9gwcPZuXKlQwbNozatWtjaWmJRCKhU6dOWoYUULC/sUKhQCKRsH//fp2jmjkpZYjkHzGg/BfypWy+OlUvSl03uzydR9So19f5ylZ0HvnCeHp6MnbsWEFa4/Tp00yaNImwsDBCQkJo1qwZISEhSCQSLSvIT0HWnEq5XE5sbCxmZma4u7vrnMrq3r27xohBzZo1Wb9+Pb16aVbM1qxZk7lz55KZmalRIJPTlGNWpPrvj0kpz0SRnoyedWGkhqakv3kC+QgoY1Pe65Bmn66Lj4/XmK7LHlD27dtX42Hr6enJrFmzCAsLo0KFChw5coT09HSGDRumEaz06dOHMWPGsHfvXow9vmb8rjvolahDwrUDZKQmIzVQHUfyvVPIzG0xdC4HKJEYmqDMSAXevzh2L6H5UN+yZQteXl5aahlhYWGkpKRw+PBhChUqJASU2VGbKDx48IBGjTSv34MHD764mUR2ihUrxs2bN1EoFBrXVH0+6uMrVqwYR48eJTExUSPQyK+7lKutqU47Bz0LB1LDbqHISNUYpcyMea3VVmpkRmbsG63lmXGqEUe1EUROZhS6ePjwIT169KBNmzb5kg3LCXt7e4yNjXn0SFuXNfs1UtsSZ6+Qz259DKrPX48ePfj999+FZampqQXSlMwpgHVzc0OpVFK8ePHPqqjyX0bModSBOn8oa+7Th+aS5cbn6PNTa+5tvBSeaxt5fARre9eik8F1utUsRjFbE60bqQQoZmuCy9M9hE1pxZofagrBZPZcO5HPg6enJ5mZmZw7d44HDx4QERGBp6cn9evXF0YuQ0JCKFu2LDY2Np98/wqFglmzZlGyZEkMDQ2xs7PD3t6emzdv6pzKyl6AZWlpCWjbk1paWqJQKLT6yGnKUeOYMtKIPbWWF3/4Ej69LS/mdOHF3O9RpCXlKcKvJusLVEpKCgEBAbi4uGicY2xsbL7OUf3gVUu95DR9bGBgwFdffcWl2w8EsXaT0p4oM9NIeaTykFekp5Dy5Aom7vX+nwspxaRkLRQpCSjSUwSx9vnHVU5Z6tkAmUymNTPQt29fkpOTKVGiRJ4+99WqVcPBwYFFixZp5Lbu37+fe/fufXEziey0aNGCN2/eaKQVZGZmMm/ePMzMzGjQoIHQLjMzk4ULFwrt5HK5YJ6RF6aGehTV8cJsVLwKKDJJvP6+YEepVJBwdY9WW32rwmREv0Ce/P6zk/72KWkv7wHvjSByMqNQn5s6GEtMTKRt27YUKVKE1atXf5Rbm0wmw9vbmx07dhAe/v4Zce/ePQ4e1CxGsrCwwM7OjlOnTmksz240oe43++dv3rx5eTqPZUWdD5o9CG3Xrh0ymUynLaRSqSQ6Ojrf+xDRjThC+S/ic2nu6bL5SkrLJDQ6idCXqpudhbE+Y3zKEUg5YV16pgIDPSmutqaYGuoRGHiSk5/06ETyS7Vq1TAyMuLUqVMULVoUBwcHSpUqhaenJwsWLCAtLY2QkJAcHXc+lkmTJjFu3Dh69erFhAkThKnUYcOG6ZzKyinRPqfl2R8QS+dMJWzmVIqN0n5Qq4k5vJjEW0cwr+aDYZHSSA1NQSIhaue0fHvRZy36Keh0XX7PReexJ6fzJj4DdeadYZHSyCwdSbp/GtNyDUl5fBFlZhqmZd6nL1g16E7S3VNkRDzl3ZGl6Nu5kPZaNcIUcu8lPYFWrVrx66+/0rNnT+rUqcPhw4fZvHkzBgYGOkejsqOvr8/UqVPp2bMnDRo0oHPnzoJskKura47FdRKJhIEDBzJ//vxc+1dL43yozWLfvn1ZvHgxvr6+XLlyBVdXV7Zs2cKZM2eYPXs25uYq6bLWrVtTt25dRo0aRWhoKGXLlmXbtm05WqDqwsvdgavZYjaTUrUwKFyKmGPLyYx5rZINenwBecr/zSWyBHlmFZoQf2kHERsDMKvQBHlyHAnX9qNvVxRlerJgBJGbGcXmzZuZM2cO7du3JygoiLt37zJ27Fh27typcVxubm7Url0bUFXQr169Os9rHBQUxIEDB/D09MTPz08IzMuVK8fNmzc12vbu3ZspU6bQu3dvqlWrxqlTp3j4UHvgo1WrVqxZswZLS0vKli3LuXPnOHLkCLa2tvm97IL72C+//EKnTp3Q19endevWuLm5MXHiREaPHk1oaCht2rTB3NycZ8+esX37dvr27SsUrIl8GGJAmU8+VI/vS/I5NffW/FCTR28TCL4QLiT9K0GoiJ1x6AEHZcfxcnfg+5pFKedkqdXX2LFjGTVKW6hX5PNjYGBAjRo1BIkPdZ6kp6cnaWlpBAcH8/btWw3v809JTlOpsbGxglbcp8TKOG+5o6QHZzD1aITN172FZcrM9ALZi2bdz6eYrstK1unjr776Slj+5E0sz8PCMHKtqNHetHQ94i/vQpGWTNK9EGSWjhgWKS2s1zO3w6CQG5mxb0m6ewJFWjIyYwsA9t2NVBXxjRlDUlIS69atY8OGDSgUCsqVK0dCQoLGSFRu+Pr6YmJiwpQpU/j555+FCvapU6f+5Tp/xsbGnDhxglGjRrF69Wri4+Nxd3fXcH4CVd7xrl27GDZsmFCg5OPjw++//07lypXzta/vaxZlRrbbsUQqw+G78bw7soTE20dBIsWkVG0s63bh7doRSPTep3no27lg2+pH4kKCeXdsGfq2RbFrPZykOydJDb+lYQSxaNEiqlatyuLFixkzZgx6enq4urrStWtX6tZVOZupFQR0ycT16NFDCCgTExMxNjbO829VoUIFDh48yPDhwwkICMDZ2ZmgoCBev36tFVAGBAQQGRnJli1b2LRpE82bN2f//v1ahURz5sxBJpMRHBxMamoqdevW5ciRIwWqHK9evToTJkxg0aJFHDhwAIVCwbNnzzA1NWXUqFGUKlVKQ77KxcWFpk2bioWfnwAxoMwnH6rH96X43Jp73y48w5Xw2BzzJJVA2LvkXKvF9fT0/lFC0P82PD09mTlzJk+ePOGnn34CwM7OjjJlyjB16lShzedA11TW5s2befnypZbY8qcgP0UREol2m/gruzWKHgqyn08xXZeVxo0bY2BgwNy5c2nWrJkwRdnFfxKKtCSM3aprtDcp40n8ha0k3jpKytMrWFTTfkBKZPro2zoL5gMZ0c95tXQAmWkpwovjjBkzmDp1Kk2bNuXOnTscPHiQpUuXauhH5qT9qKZDhw506NAh1/P7nPp+uVlLOjg4sGKFblOGrNjY2PDnn39qLc9+3qtWrdJpOlHS0Zy2w34TjCDUyEwssfcZodE2+aHKhEFmrvlyZVbOC7NymtqXZm5VdRpB5GRGkddxZickJISBAwfmK/ivX78+ly9f1lqe/e9qbGzMsmXLWLZsmcby7NfSyspK598mu/RWTgYjasaOHcvYsWN1rmvXrh3t2rXLcVuRD0fMocwn2fMds+rl/fbbbzg7O2NkZMTXX3/N48ePtbZfsmQJbm5uGBsbCyNF2UlPTycgIICqVatiaWmJqakpnp6e+dLHyklzLz3i2f+tun4gbHpbns/rStTe2chT4vN13srMdGJDgtn1SwfCprclbE5XIrb9RoaOJPKE6wcIX/ADYdPbsHlcN+qNWMqGLDmYauuvvIiNjWXYsGFCLlqJEiWYOnWq1rThhg0bqFq1Kubm5lhYWFC+fHnmzJmTr/P6L+Lp6UlKSgrPnz/XCBzr16/Pw4cPcXV11ema9Clo1aoVJ06coGfPnixdupQhQ4bQv39/jZG3z0FuOpTGJaqTdPsY744sIeH6AaL2zibh8m6k/x+1U6NUKlFkaGpd6upXPV03bNgwlixZQs+ePZk7d26BpuuyYm9vz+jRozlw4ADNmjXjjz/+oHvv/lxc9zsGhUtimi3QMCxUAj3rwsSe+hPkGZiUyfvlQM9KVYQUd3U/+7auY/bilTx79kzwuW/bti3Hjx8XRpzWrl3L4cOHP+h8/o4kJeUvV/ZDmdS2PHrZPivZP0tKhZyEK7uRGJpg4Ji3hE9uRhAfy507d0hJSeHnn3NWBBERyQkxoPxIpkyZwvbt2/H392f06NGcP39eyyJw+fLl9OvXj0KFCjFt2jTq1q2Lj4+Pln9ofHw8y5Yto2HDhkydOpXAwEAiIyPx9vbm+vXruR5HTpp7qc+ukRn7BtMKjbFp0g/TMvVJvhdCxKbAPHO1lAo5EZuDiDuzHoNCJbBp9APm1XxQpiWREalZoZd89yTxF7ZiVqkZVvW7kRH7lpebJ/Lz5mvMP5537pXQT3IyDRo0YO3atXTv3p25c+dSt25dRo8ezfDhw4V2hw8fpnPnzlhbWzN16lSmTJlCw4YNOXPmTL739V+jTp06yGQyzM3NqVjx/XRp1unvz8WYMWP46aefOHjwIEOHDuXq1avs3btXq8jmQ2ncuLFOP+Gs3wmlQk7smfW8XNSbsOltSAm7ib5dUZLunCDm2HLkie9w7DQRRWoiqWHXSXl6hderhhE+ox2J1w8AoEhN5N2RJYTNU1nwzZ07V3jZmTNnDt27dyc4OJiffvqJ8+fPY2hoSHR0NBs3btTyVgZVUVqrVq0Eiz+Azp07CyNjgYGBzJ8/n/DwcH788Ue2bd2CReVmOHacgESmPdpvUqY+yvQUJPqGRG75lbDpbXi5qDexZ9ajVGiPlEpketi2Go5EKuXdwT/4sX8vli5dKnjZq33ut2/fDqDlc58fX+RHjx7x7bffUqhQIYyMjHB2dqZTp0468xF37NiBh4cHhoaGlCtXjgMHDuT0J9dg//79eHp6Ympqirm5OS1btuTOHc188pw8tz8nuowgYg4vJnLXdOKv7Cb+wjbeBo8iNewmlrXaaygP5IQuI4hPRbly5YiPj89XGopaPP9aeAx3XsWRlJb5WY5J5J+DRCmK/WmhK/FbPTqpnkpRJy6XKVOG69evC1Pic+fOZejQody6dQsPDw8yMjJwdnbGycmJCxcuCO2WLl1K3759adCggdCnXC5HLpcLbVxdXalTpw7Hjh2jZcuWWvlnahYtXc6Avr0p0n85elaaPqqKjDStm1TS3ZNE7ZqO4/dTMHLxyPE6JN48TPS+OVg36o1FjTYa65RKJRKJhMzYt7xc9ANSYwuc+i1BZqSS2Eh+dIHIrROwbx+ASYkaTG1Xnnt7V2hV2Lm6utKwYUNhKmbixIlMmTKFa9euaQjQjh49munTp/Ps2TNcXFwYNmwYK1eu5N27d/l2SigIJ06cwMvLi+PHj3/ySnyRT8etW7eoWbMm9vb2DBgwgMzMTObPn4+joyM3b96k67LzwpRj1J5ZJN0+iol7XYyKVSDt1QOSbh/DuGQtHL59Pz32YkEvJDIZipQEzCo1Q8/SEX2bIhg4leLNn/7IE99RpuE33Nm/hkqVKnHjxg2GDBnC7NmzhT769Okj3EeqVq0qeCuXLVtWECQH1effyMiI2NhYfvjhB5ycnFixYgXXrl3j1q1blCunGYw0mH6csDz0NSO2TkQi08egcEmkBkakht0k+f5pLGq0w7pRr1y3tUt/w92Fg3O8nlm/u1l9kRs0aCD4IpuZmQm+yOnp6ZQuXZq0tDT8/PwoVKgQL1++ZM+ePWzevFnIE5VIJFSsWFFw2TE3N2fu3Lm8efOG8PBwYZRX1715zZo19OjRA29vb1q2bElycjILFy4kNjaWa9euCe18fX3ZsGEDzs7O1KtXj9q1a2NiYqIl5/Q5yCrllnTnBPEXt5MR+xplZjr61k6YVW6ORdXWefYzoqk7A70+fYpIftGVR69GAhS1MRHy6Es6mufUjci/FDGh7SPp2bOnRn6lepTn6dOneHh4CFZkv/76q0Y7X19fwSNWjUwmE4IjhUIh/FSrVk2nVp+a6ETd1nOQTWsvMx1FegqGTqpE/fQ3T0Ch4O36MRTprwpWXy76AcfOkzAqVoHkB2eRGltgXk37Rpd96tqkjKcQTAIYuqgehGodtYBdd2iRnrdb7fLlyylUqBDW1tZERb3PCW3cuDFTpkzh1KlTfP/991hZWZGUlMThw4e/iDhvXjRs2BBXV1dWrVqFr68voaGhueZxiXwa8vITVnvPp7x+QtLto5hVbIpt8yEAmFdpiczEiviL20gNu4lRsfc+x5kxr3HoEITxV1WFZbFnNpAZ+4Zifeaxon8Dau5fQ79+/QgLC2P69On89NNPuLi4cPr06Xx5K6t58OABp06dEu4dHTp0wMXFhZUrVzJjxgyhXX7E2gHsfEZofO/NK7cg+sB8Eq7txap+N43Cj+zc270sX/7M+fVFvnv3Ls+ePWPz5s0aou+6nI3u3bvH3bt3BecWLy8vKlasyPr16xk0aJDO401MTGTIkCH07t2bJUuWCMt79OiBu7s7kyZN0lie3XP7S5HVCEJW3gvTcg3zvW1+jCA+N1ld1z4mj17k34045f2R5FdLLrvdk76+vs78sdWrV1OhQgWMjIx4/vw5GzduZO/evbnKVeRW2S1PSeDd4cU8n9uV8BnteDH3e14u+gEARVruD6eMWJWshUSa9wignoWmG4M6uFRXzGYqlBy9l7dH8vPnz3ny5An29vYaP40bNwbe24j5+flRqlQpmjdvjrOzM7169cr39Fh+qF+/PikpKZ+t6lnk48mPn7B6yjHlqapwwKK6piySRQ3V7ylPLmks17N01AgmAZLvn8bQpRzeNjEEjBiMRCKhUqVKNG7cGLlcLujsFdRbuWzZshrpBvb29ri7u/P06VONdvkRawfNl0hFWjLy5DgMXcqhzEgjI/p5jtspFXJSnl3Dy7tlnv7M+fVFVuuHHjx4UMNrXBeNGzfWsAGsUKECFhYWWtchK4cPHyY2NpbOnTtrHIdMJqNmzZo688+zem5/STpVL8qRHxtQ5yvVaGtu+b1Z19f5ypYjPzb4y4LJDZfCaTzrJGefqnQa83JFy+66tiEPLWORfw/iCOVH8jFactlZu3Ytvr6+tGnThhEjRuDg4IBMJmPy5Mk8efIkx+2yJ31nJWrHFNJe3seiZjsMHIojMTAGpYKITeMLVM2aJzoqZgFBz0+uUOZrdEV93XJK/Fc7HDg4OHD9+nUOHjzI/v372b9/PytXrqR79+6sXr1aq8/U1NQC2XRJpVKMjHL2claTnJys5dEs8mXIr59wp+pFmW+RzhmJVMszWWZmjdTQVHAfUZM9dQRUo5YZkaEsnnhFWKaWWoH3LzsF9VbO/lIKqhfT7PmW+RFrB0iPDCP21BpSw2+izPbSmNtLpCI5HmVmGs7FtP2Rs/sz59cXuXjx4gwfPpyZM2cSHByMp6cnPj4+dO3aVQg21eT3OmRFrY2Z3ZVHjYWFZoFVds/tL42LjYmmBNvDCMKjdUwd25rgVcqBrrWKalVzf0myTtUnXNuPRCrDrGLTfG1bENc1kX8HYkCZD06fPs3Vq1dJTEzEzc2NkSNH6my3cuVKli5dCqhyqKZNmyY4Qzx69IhGjRrRqlUr7t69y4MHD3j27JlGccSQIUMwMDBg27ZtSCQSIb8wNTVVaHPnzh0GDx7MuXPnsLW1pX///tjaaz/8AOSpiaSG3cCy3vdY1essLM949zJf561vVZi0Vw9Ijwgl7uwGUsNuosxIRWZhj4l7XawbdBfaJj84S+KNg8jjI5HoGWpMHwr8P1gsWbIkz58/x9TUlKSkJF69egWo0gDUldxNmjTJsplqO7XX8dKlS3ny5AmWlpa0adOGKVOmMH/+fPz8/Fi8eDFHjx6lUqVKDB48mF9++YXbt28zZcoUhg0blq/zBt05lA0bNiQqKorVq1fz448/cvnyZfr27auROyfy98TDyZKzEjDUlyEnd4tQAIne+1E+9ZSjnhQaNmmS4/df/bJTUG/l/L6U5kcKSZGayNt1o5EaGGNV73v0rAsj0TMg/c1jYk+sytdLZF4jZ1AwX+Tff/8dX19fdu7cyaFDhxgyZAiTJ0/m/PnzGsHdh7ycq+8Xa9asoVChQlrrs8uUZfXcDgwMJCgoiMjIyM+ihZqdrOLtJR3NCczDCOKvJrvrmr5NEd5uHIfM0gFj10oF6mvGoYfYmxn+ZaOsoaGhFC9enOnTp4vi5Z+Rv/5T+zfn1q1bNG3aFIVCgaurKz179mT8+PGCvVNWFi5cKCSZd+zYkcjISGbMmIGZmRmLFi2iZ8+edOzYke7duxMYGKgheBwWFkZMTAw2NjZCwQuoRmHOnTtH0aJFefPmDV5eXmRmZjJq1ChMTU1ZsmRJjiNv73X2NG/I8Zd35evcTdzrkPLkEq9X/4jUwBizSt7oWTqSGfOalMcXNQLKzPgIzCt6IzO3JTMugsRrqtEMpfx95V9quEp6xMvLixo1ahAfH8+4ceMEy6t+/foREhLC06dPGTFiBBUqaAalPXv2ZN26dfTs2ZNevXoRGRnJ/PnzuXbtGmfOnBHaK5VKHjx4QOfOnenXrx99+vTRsq/7UKKjo2nevDmdOnWia9euODrqDuZFPj8F8RMuVqwYSoWCRa2cWHk3Q8gFS094hyItCT1L7dHE7N7zzba5kZiYKKRf5ERBvJULgtofOrdQODX8FoqUeOzbjsGo6PuCO12e0NmRmlgg0TMk6qX2FGX261lQX+Ty5ctTvnx5xo4dy9mzZ6lbty6LFi3SKbJdENRT5A4ODnn+Xf6umBrq6TSC+CvR5bpmVKwClnU7827/PAr/8AdSg7xncLKSk+taQUlOTmbatGk6rYv37dvHxYsXP5u+qUjuiDmUeaBO+q9UqRJFixZl7NixHD9+XEtoFeDkyZNMnz4dUOUDHTx4EG9vb4yMjLh+/TqNGjXi9evXyGQyfv/9d40cyk2bNiGRSHj37h1t27ZlyZIlxMTEcPjwYcqWLQvA1KlTiYyM5MCBA4wfPx5/f3/OnDkj5GlKs40sSA1NMHTxIP7CVmJOrSHh6j4itk4k7cVdoY1RsQoUG7UHPStH9KwcKTZqjzC6aOrRCImhKcgzMChSGj1zOxRpSaRHPMWynqbchkX1Nlh5fo95pWZYN+iOw3eBABo5WxkxqpHIWfMW0Lt3b4YPH469vb2Q8F+7dm1hZHLWrFmcPHmSxMREfv/9d5o3b86ff/7J/PnzWbJkCadPn+bcuXO0bduWS5cu0aFDB3755RcqVaqEvr4+jx8/Zv369UyePJl+/fp9skrtN2/eMGHCBObOnUvfvn355ptvANWIprpSfdWqVWJBzhegIH7CLVq0AGDjqsWs+aEmh4fVp1vNYnBrL4CWSLixgYxuNYtx5Mf6gvd8hw4dOHfunFbfoNJOzcxUvTzl11u5oOTkD62BjpdIpTyDhKv7dLfX2FSGjXs19uzemef1zK8vcnx8vHBd1JQvXx6pVKrh9f2heHt7Y2FhwaRJk8jI0C76U7vDiBSMnFzXLOt0RM/WWaV1WkDUrmsfS3JyMkFBQTrvsfv27dMQ4Bf5sogjlLmQNen/9ev3Qt5lypShevXqXLhwQaN91tGI5ORkoqKiaNCgAQcPHuT3339n/vz5BAQEYG5ujoGBgcZ0z8aNG6lVqxatW7dm8eLFHDx4EKVSSf369XF2dubEiRPs27ePWrVqUaNGDWE7e3t7vv/+exYsWIBCodR6Q7Dz8efd4cUkXtmDEjAuXhmHDkG8nN+dvFCkJqJMS8KgcEkyI8N49/QqMmNzDJ3LYuConWcFqhFJRXqykKsmT3yf/ySRqarcT166SYt6VXRur56iGjFiBJs3b+bPP//EwsICfX19jIyMaN26NVFRUfj4+PDnn38KhTiHDx+ma9euBAYGUqtWLYoXL14gu678YmhoSM+ePT95vyIfRn79hCtWrEiPHj1YsmQJsbGxNGjQgNCLF3lxYgOtfL5hygw/YcqxxTpjyn9lS2A2/cARI0awa9cuWrVqha+vL1WrViUpKYlbt26xZcsWQkNDsbOzy7e38ofg5e7AmgthOU7ZGzqXQWpkRtSeWVhUaw1ISLpznNzHNVXIpBK+6zucVT9/n+f1zK8v8rFjxxg0aBDfffcdpUqVIjMzkzVr1iCTyfj2228/6BpkxcLCgoULF9KtWzeqVKlCp06dsLe3Jzw8nL1791K3bt08/cE/Jx+Sv/0xJCUl6Zw9Kwi5ua5JJBIcO3xYwKZ2XXsckfCX5oWKfD7EEUod+Pr6olQqMTU1FZL+T5w4ofFGVKdOHQCNB8OZM2fo3bs3JiYmDBw4EHt7e0FSo3379jx9+pTU1FQWLlxIREQEkydP5sSJEzx58oQrV67QsWNH4QadmppK4cKFcXZ2ZtWqVYSGhhIWFpZjAQJAdVdrrfwnPXM7HNr9gsuPGyn640bs24xCz8yGYqP2YOWZu6iveprMrFIzigxYTrGRO3AevAb7tqPRt1LlK+lZOeLy01aUacm8+MOX8OlteTGnCy/mqvrWt3svXG3XajhSQ1NaelalfPnywgNalx3YpEmTePToEWlpaURGRlKpUiVSU1MpUqQI9vb29OrVixMnTgjSQl9//TWLFi0S8qiKF9cd8H4sRYoU+dvbcP6XUPsJ29vbExAQwIoVKq3Ttm3barVdtmwZQUFBXLp0iWHDhnHs2DFGjx7Nlk0bKedkSeWi1pRzsiQnMycTExNOnjzJiBEjOHHiBEOHDmXKlCk8evSIoKAgjSKTRYsWsWTJEiIiIhgzZgyjR4/m2LFjGt7KH8L3NYvmmv8pM7bAvv14ZGbWxJ5aS/zFbRi5VsLaK++XILlCyU+dmuT7eo4aNYqtW7cilUoJCgrC39+fXbt2afgiV6xYEW9vb3bv3s3w4cMJDAzEzMyM/fv3U6tWrQ++Dlnp0qULR48epUiRIkyfPp2hQ4eyYcMGKlWqlK+Xv6ioKDp06ICFhQW2trYMHTpUI28dVPnxjRo1wsHBAUNDQ8qWLcvChQu1+lIL1R88eJBq1aphbGysIbQPEBwcjLu7O0ZGRlStWlVQB1ATFhaGn58f7u7uGBsbY2try3fffac1K7Zq1SokEgknT57Ez88PBwcHYZAiv33oIifXtbyIDQkmbEorMt69JGr3DMJndeD5nC7EnlqDUqkkMz6SyK0TKOdamEKFCml43kP+nOJCQ0OFHOSgoCAkEgkSiYTAwEB8fX35448/AITlupzZ1K51hoaGVK9enUuXLmm1uX//Pu3bt8fGxgYjIyOqVavGrl3vU8ViY2ORyWTMnTtXWBYVFYVUKsXW1lZj1H7AgAEa+b0FEfr/pyGOUH4injx5wtdff03p0qWZOXMmLi4uGBgYsG/fPmbNmqVhG9i6dWtMTEzYtGkTderUYdOmTUilUr777ruPOoYR3qXpuTU0z4KDT03M4cUk3jqCeTUfDIuURmpoChIJUTunCYU4AEZFPXDqv4x+xaK5c1Gl1Tdr1iwWLVpE7969c91HQQsdPteIwJcaaRDJP/n1E9bT0yMgIECnBmJWcnvompmZMWnSJCZNmpTnceXlrZzbvnJKmSjpaI5nCTstf+isGDmXoXD337WWFxu1J8fjkEklgj90CYf8XU/I2xe5ePHiORoyZCWnwpv8ejjryqfLTk5e1h06dMDV1VUoFJo7dy4xMTEaPt4LFy6kXLly+Pj4oKenx+7du/Hz80OhUDBw4ECN/nLL3z558iQbN25kyJAhGBoasmDBApo1a8bFixfx8FDlvF66dImzZ8/SqVMnnJ2dCQ0NZeHChTRs2JC7d+9qqUr4+fkJLwBqK8mC9pGVnFzX8kvkjqno27lg3cCXlCeXiDu7EamROQnXD2BUrAJFm/XGMeIy/v7+VK9eXZBmUzvFde7cmT59+pCQkMDy5cvx9vbm4sWLVKpUCXt7exYuXMiAAQNo27at8NmrUKGCUOB5+PBh1qxZo/PY1q1bR0JCAv369UMikTBt2jTatWvH06dPBWWCO3fuULduXYoUKSLUKmzatIk2bdqwdetW2rZti5WVFR4eHpw6dYohQ1S6tqdPnxbS1u7evSuYEoSEhAiyYOnp6Xh7e5OWlsbgwYM1hP5jY2O1lA/+aYgBZS4UJOl/9+7dpKWlsWvXLg35C106aKamprRq1YrNmzczc+ZMNm7ciKenJ05OTrkeT7FixXI9FicrY4J8yjFq28fnqQDo/X8UMrvNYnaSHpzB1KMRNl+/DwqVmemCBqVGn8bm/OjXHtMfB5KYmEj9+vUJDAwUAsqcvL4/V6GDiMg/DbVY+6d8cfyc/tB/d4oXL87OnTsBGDhwIBYWFixYsAB/f3+h0O/kyZMa951BgwbRrFkzZs6cqRVQPn78mAMHDuhMubl9+zaXL1+malWVxmmnTp1wd3cnICCAbdu2AdCyZUutlIjWrVtTu3Zttm7dquXsY2Njw9GjRzWq5Avah5r8iufnhqFTKWybqYTozSp583LhD8QcW45Vwx5Y1mpPJrBx4VhKFi/KihUrhIDS2tqa0NBQjRmgPn36ULp0aebNm8fy5csxNTWlffv2DBgwgAoVKtC1a1eNfZcqVUpIf9JFeHg4jx49EvSi3d3d+eabbzh48CCtWrUCYOjQoRQtWpRLly5haKhSe/Dz86NevXr8/PPPwmi9p6cnW7ZsEfoOCQmhXr163L9/n5CQEMqVKycEl3379gUokND/PxFxyjsXCpL0r/4yZ33TjouLY+XKlTr77tixI69evWLZsmXcuHGDjh075nk8LVq04Pz581y8eFFYFhkZqTFq16l6Ufyb5l11mR9kJpYYuniQePOwlk5f1vOU6NCgjL+yW0uiRJ4ST1FbE0ESw8zMjBIlSmgk56vzf7IXLnyuQgcRkX8auvyhP5bP6Q/9dyd7QDh48GAADd3NrMFkXFyckB//9OlTranK3PK3a9euLQSToNLeVAc0crlca18ZGRlER0dTokQJrKysdDqm9enTR0tyqaB9qMmveH5uZNWplEhlGBQqASgxq6AquFQCsXJ9LfF+mUwmBJMKhYJ3796RmZmZp1NcQejYsaMQTIKmsx3Au3fvOHbsGB06dCAhIUEQyo+Ojsbb25tHjx7x8uVLYdu3b98KAzohISHUr18fT09PQkJCANWopVKpFPZTEKH/fyJiQJkH6ooxT09Ppk6dym+//YaXl5eWx27Tpk0xMDCgdevW/PHHH0ydOpWqVavmKG7cokULzM3N8ff3z3eC+siRI7G1taVZs2YEBQUxY8YM6tatK0gVqRnkVZIp7cpjqCctcC5M9tY2jVVvVq9XDSPm5GoSrh8g5uSfvF45RGhjXKI6SbeP8e7IEhKuHyBq72wSLu9GaqwpKvx6qR/RO6Yybdo0li1bRv/+/dmyZQudO7/XyFTfbIcMGUJwcDAbNmwA0Ch0aNGiBbNnz+aPP/5g2LBhFCtWjCNHjuTr/AIDA5FIJGIVtsg/mk/54jiiqftfpg/4dyB7XrqbmxtSqVRjuv3MmTM0btwYU1NTrKysNPLjdQWU+d0XqEbVkpOThYr0lJQUAgICcHFxwdDQEDs7O+zt7YmNjdWZZ6drfwXtQ01+xfNzI7trmtTQFImeATKT99O56ZkKLC0ttUTrszrF2draYm9vn6dTXEHIy9nu8ePHKJVKxo0bp+XWNn78eOC9MYE6SAwJCSEpKYlr167h6elJ/fr1hYAyJCQECwsLQW9aLfS/bNky7Ozs8Pb25o8//vhX5E+COOWdJ+qk/+HDhxMQEICzszNBQUG8fv1ao+rR3d2dLVu2MHbsWPz9/SlUqBADBgwQCkiyY2RkhI+PD8HBwTRu3DjHwDMrhQsX5vjx4wwePJgpU6YIwuZOTk788MMPGm07VS9KXTe7PP1X1ajXVy5qxdXwWGG5geNXFOo2g9iQtSRe3YdSnoHMwgHT0vWENjaN+4JEStKdEyjlGRgWKYNjp4m83ag5jG9erTWyhLtMnjyZtLQ0ihUrxsSJEzU8zdu1a8fgwYPZsGEDa9euRalU0qlTJ0BV6FC1alUWL17MmDFj0NPTw9XVtUCFDomJiUgkEp0iyCIi/ySy+kNn/t+VJL/8Hfyh/65kT7spSH48fHye9eDBg1m5ciXDhg2jdu3aWFpaIpFI6NSpk9a+ctpfQftQkx/x/DzR5ZqWbZnfuqu8jk7CIEMuLPtQp7iCkJd4vvra+Pv75zjKXKJECQCcnJwoXrw4p06dwtXVFaVSSe3atbG3t2fo0KGEhYUREhJCnTp1BDF9yL/Q/z8RMaDMB/lN+m/dujWtW7fWapdTpeHatWtZu3ZtjvvVlbBfvnx5naNruoLWD7X56rb8gkbSv4F9MRza/ZLjcUqNzLBrOUxrubPfCuH/MqmEVt0HsuaHmjn2AwiVc1mr57LyMYUOAKdOneLbb7+ldOnSufahnn7K6rQhjmqK/N340BdHtVj7f3WaOyuPHj3SGOV7/PixYGQBBcuPz8++svPw4UNMTEyEwsItW7bQo0cPjSro1NTUAqX1fGgf+RHP/xS8jkvlbXwa8uQkui2/wKS25dmyZQtfffWV4BSnRj0yqCanPPu81uUHtTa0vr5+voTyPT09OXXqFMWLF6dSpUqYm5tTsWJFLC0tOXDgAFevXtWpi/m5hP7/asSA8j9AQW2+/q1J//Hx8dy4cUPL61sXat3RL2HJJiLyMfzT/KH/bvzxxx80bfo+72/evHkANG/eHCh4fnxunDt3jqtXr1KlikqH9/nz5+zcuZNmzZoJ+5HJZFpV7/PmzRNecvPDh/ahFs8P+8jCnPygPr6zT6NpPOskVvFpwnJ1YHjhwgXBKU6NukJdV3CcNQffysqqwMfk4OBAw4YNWbx4MYMHD6Zw4cIa6yMjIzUURTw9Pfnzzz/ZuHGj8HmRSqXUqVOHmTNnkpGRIUyNg+oZZGJiojFQ8SmF/v9qxIDyP0Z+bL7USf+fqloc/h5J/xYWFnl+aZOSkggODmbOnDk4Ozvny1ZOROTvwD/BH/rvyLNnz/Dx8aFZs2acO3eOtWvX0qVLFyHvLWt+fL9+/UhMTGTp0qU4ODhoGF7kBw8PD7y9vTVkgwCNUaxWrVqxZs0aLC0tKVu2LOfOnePIkSPY2trmez8f00de4vmfGvn/0zWeGLsT/fQAbdu2pWXLljx79oxFixZRtmxZEhPfK4YYGxtTtmxZNm7cSKlSpbCxscHDwwMPDw+NHHxvb29kMpmQMpVf/vjjD+rVq0f58uXp06cPX331FW/fvuXcuXO8ePGCGzduCG3VweKDBw80pMTq16/P/v37Ba1LNZ9b6P+vRrzDiOikU/WiRCWmMePQw4/u65+U9B8ZGcngwYMpX748K1eu1Mh9ERH5p/B39If+u7Jx40YCAgIYNWoUenp6DBo0SLDQhYLnx+dGgwYNqF27NkFBQYSHh1O2bFlWrVolyBMBzJkzB5lMRnBwMKmpqdStW5cjR44UyPnrY/r4vmZRVp0LLdB5fQpMyzdGnhTD2UvHOHjwIGXLlmXt2rVs3rxZK91o2bJlDB48mB9//JH09HTGjx+Ph4eHkIO/atUqQYuyoAHlxYsXiYmJoUWLFqxatYro6GgcHByoXLmylryPu7s7Dg4OREREUK/e+7oCdaBZo0YNQXoINIX+X758iYmJCRUrVvykQv9/JRJlToqyIiLAhkvhYtK/iIiIyH+I7Hn0XxJDPSlHfmzwUTNaq1atomfPnjx79kzIhS3otpcuXaJatWq5tl2wYAEmJiY6xfb/i4jDLyK50ql6UY782IA6X6mmSvKSIVKvr/OVLUd+bCAGkyIiIiL/MCa1LY/eB9gvfgoyFUrGbP+4dKtu3bqRkpKiJan3qVmwYIFO96X/KuKUt0ieiEn/IiIiIv8dPkcefX6RK5SEPI7icURCgZ8jSUlJmJqaIpPJcpQIEvl8iCOUIvlGnfR/0t+L24He7B1cj+0D6rB3cD1uB3pz0t+LQJ9yf8tgMiktkzuv4rgWHsOdV3EkpWX+1YckIiIiIpCQkMCwYcNwdXXF0NAQBwcHmjRpouES4+rqmm8v87S0NMaPH0+JEiUwNDTExcWFkSNH5ruaOLt4fmZcBNEHF/ByST/CZ7Tj+ezORG6fTGbs23z1p1QqiL+0k1fL/Aib3pbnc7sSfWA+8mwWvS8W9CJySxCTVuygRo0aGBkZ8dVXX2l4q4NqaloikXDy5En8/PxwcHAQdBzV67JKyCkUCgIDA3FycsLExAQvLy/u3r2b4zVNS0tj+PDh2NvbY2pqStu2bQXxeVD9Le7cucPJkyeRSCRIJJI8/eT/7fwtRyjFCsW/P/+EpH9hRPVBBOHvdIyo2pjg5e7A9zWLUtLx7xcEi4iI/HdQO4cNGjSIsmXLEh0dzenTp7l3754gM5RfFAoFPj4+nD59mr59+1KmTBlu3brFrFmzePjwITt27MhXP1nF81PePCLt5T1My9RHZm5LZlwEidf28WbdaJz6LECqb5RrX+8OzCfx1lHMyjfGvGprMuPeknBlD+lvn1Co63QksvfP+PR3r1n721BGDBlAjx49WLFiBb6+vlStWlXLpc7Pzw97e3sCAgJISkrKcf+jR49m2rRptG7dGm9vb27cuIG3tzepqak62w8ePBhra2vGjx9PaGgos2fPZtCgQWzcuBGA2bNnM3jwYMzMzPjlF5VOs6OjY76u67+Vv02UJj78RT4Vz98l5yn0rATC3iWz5kIYq86F4lnCThR6FhER+cvYu3cvffr00RAjHzly5Af1tW7dOo4cOcLJkyc1qo89PDzo378/Z8+epU6dOvnqSy2eP3KjCefc62rcU01K1ODNGn+SH5zFzKNRjn2kPr9D4o1D2LX2x7RcQ2G5UdHyRGwaT/L90xrLM9+9oND3UxgT8BOmhnp06NABFxcXVq5cyYwZMzT6trGx4ejRo7lOcb99+5aZM2fSpk0btm/fLiwPCgrSMihRY2try6FDhwRNTIVCwdy5c4mLi8PS0pI2bdowduxY7Ozs6Nq1a477/i/xl095P3+XTLflF2gy+xRrLoQRli2YBM2Hf5PZp+i2/ALPv4Dwqsg/jw2Xwmk86yRnn0YD5FmlqF6vFtfdcCn8sx+jiIiISHasrKy4cOECr169+ui+Nm/eTJkyZShdujRRUVHCT6NGqqCvoC4/LjYmrB/QgMPD6tOtZjEczfSQp8SjZ10YqaEp6W9yt0ZMvn8aiaEpRsUrI0+OE34MCpVAYmBMavhNjfb6dkUxdPEgNFo14mhvb4+7uztPnz7V6rtPnz555ksePXqUzMxM/Pz8NJYPHjw4x2369u2r4bzj6emJXC4nLCws1339l/lLRyizStJAwR/+QT7l6CRWEYv8n/nHH32wbqZaXHfUtltEJaYxyKvkJz46ERERkZyZNm0aPXr0wMXFhapVq9KiRQu6d+8u2AEWhEePHnHv3j0NV5esREREFLjPlJQU1vwxnZUrV/Ly5UsNJx5FWs5TzQAZMa9QpiXxYu73OtfLk+I0fpdZqI47PfO977i1tTUxMTFa22a1zcwJdRCo9uFWY2Njg7W1tc5tsrrzqPcP6DwGERV/WUApPvw/H6GhoRQvXpyVK1cWSB8rMDCQoKAgIiMjv7jloHrfHyqLuuFS+CcRYQeYcegh9maGHyR5pE7KLqjvt/pvNn36dPz9/Qu8XxERkX82HTp0wNPTk+3bt3Po0CGmT5/O1KlT2bZtm2Drl5NXtVwu1xilUygUlC9fnpkzZ+ps7+LiUuDjGzx4MCtXrmTYsGE4u1dkyrFwkEiI2jkN8rpvK5VITayw89F9b5MZW2j8LpGoJk8N9DQnUXU9H4yNjQtwFvknp1FPUbo7Z/6SgPJDH/6xIcHEnVmP85BgZCaqgpCPefh/LB8bBIl8Gp6/S2b8rjuftM+AXXeo42b3r82pzEmQ9+7du2zatAlfX98CCwKLiIh8HIULF8bPzw8/Pz8iIiKoUqUKv/32mxBQWltb6/SwDgsL0xjJdHNz48aNG3z99dc5BqEFZcuWLfTo0YPff/+dpLRM5j47iCIzHUW2Km1d6FkXJjX0OoZFyiDVN8yzvRpXW9OPOWQBtR7l48ePNUY0o6OjP2rEMa9r+18rMP7iOZSf6+Ev5lS+p1ixYqSkpNCtW7e/+lC+CGO23xLSJj4Vn0JcNy+yShk9fBv/WfeVnZwEee/evUtQUJCG3IaIiMjnRS6XExenOe3r4OCAk5OThsyPm5sb58+fJz09XVi2Z88enj9/rrFthw4dePnyJUuXLtXaV0pKSq7V0Dkhk8mEwRNTQz2K2pgQf2U3KBV5bAmmpeuBUkHc2Q1a65QKeY5Baf+1Vz7Js/3rr79GT0+PhQsXaiyfP3/+R/VramqqFeA/eptA4K47NJh+HI/Ag7Scd5q2C8/Sct5pPAIP0mD6cQJ33eHR24SP2vffkS8eKn/Oh/+aH2p+0n7zYuzYsYwaNeqL7jM/SCQSjIxyl3D4t/DobQIhj6M+eb8fI66bGzmpGai13BaceExiqTsFVjNQC/qKiIj880hISMDZ2Zn27dtTsWJFzMzMOHLkCJcuXdKo+u7duzdbtmyhWbNmdOjQgSdPnrB27Vrc3Nw0+uvWrRubNm2if//+HD9+nLp16yKXy7l//z6bNm3i4MGDedoKZqdVq1asWbMGS0tLypYtS/yB/SRcPo0023S1LoyKlsesUjPiz20m/e1TjItXQSKVkRHziuT7p7Fu3FcVdGYja73Ex+Do6MjQoUP5/fff8fHxoVmzZty4cYP9+/djZ2f3waO4VatWZeHChUycOBHrQi7suJ/II33X/6y6yBcdoVQ//D+1P2jWh/+XRE9P77MEboGBgUgkEh4+fEjXrl2xtLTE3t6ecePGoVQqef78Od988w0WFhYUKlRI44YDqnw8iUSiNQJ1//59OnTogL29PcbGxri7uwv6WVmJjY3F19cXKysrLC0t6dmzJ8nJmm+JK1eupFGjRjg4OGBoaEjZsmW13v7U7N+/H09PT0xNTTE3N6dly5bcufNpRqmDL4SjSIj8NIK7yweqBHfndOHtxgAy3z5m7fnwAp9vVtLT0wkICKB8pcoYmJjh7mLP1IEdeXDtvJaaAUBMcgbz5s2mTCk39AyMqFXXk9u3b2u08fX1xczMjCdPntCiRQvMzc35/ntVsrtCoWD27NmUK1cOIyMjHB0d6devn8a0Tk6CvKtWreK7774DwMvLS1inzgfduXMnLVu2xMnJCUNDQ9zc3JgwYQJyuTxf11lEREQ3JiYm+Pn5cf36dcaPH8+PP/7IgwcPWLBgAcOHDxfaeXt78/vvv/Pw4UOGDRvGuXPn2LNnjyDorUYqlbJjxw6mTJnCrVu38Pf3JygoiEuXLjF06FBKlSqV/RDyZM6cOXTv3p3g4GB++uknzJUJOHaaiCQP/Uk1ts0GYdNsEIrkOGJP/knMydWkht3EtJwXhs5ldW4jVyhJy1QwatstXsamFPiYszJ16lTGjRvHpUuX8Pf35/Hjxxw6dAilUvnBz/GAgABatGjBpClTGdTHl9ObFwvHnRv/VnURiTKfCYA2NjYkJyfj4eFBQEAAPj4+Bd5Z4K47LFqzgfhrB0h/+1QlO2Buh2n5r7Gs3QGJNPfSf105lJlxEbxd/wsSPQOG/L6aGd0bsHLlStasWcPt27eJi4vDzc2NwYMHM2DAAI3+XF1d8fDwwN/fH39/f+7cuUOJEiWYN28eDRs2ZNu2bYwfP55Hjx5Rrlw5li1bRuXKld+fj44cSolEwsCBA2ncuDFjx47l0aNHlChRgt9//51mzZoJ7RISEhg3bhw7duzg9evXWFpaUrFiRaZOncquXbsICgqiUqVKlClTBk9PT/bu3cvevXuZOXMmixcvplGjRpQvX57g4GDOnDnDyZMnqV+/PqC7KOfmzZt4enqir69P3759cXV15cmTJ+zfv5+bN29qnE/lypUpXrw4jRs35urVqyxbtoyRI0cydepU4fhr1KhBuXLlqFixInp6euzevZtDhw4xf/58Bg4cKLRbs2YNPXr0wNvbm5YtW5KcnMzChQuJjY3l2rVrQp7eh+ajNph+nLtnDxF3diMmJWtpCO5KDEzyJbgbtXc2SbeOYPRVVYyLVwGFnNQXdzFyrUj5ph056e+V7/PNXpQTFRVFyTLlkLjVRWbtRGZaMok3DpMZ+4bCPWZi4KjKe8qMfcvLRT+gb++KIj0F88rNkcgziLu8CzMjAx7euyOI5vr6+rJhwwacnZ2pV68etWvXxsTEhG7dutGnTx9WrVpFz549qVq1Ks+ePWP+/PmULVuWM2fOoK+vz44dO3QK8rq5uTFnzhzmzp3LmDFjKFOmDABNmjTB0dGRtm3bYmBgQMXKVUlFn/OnT3J4706G/jic2TM1X2pEREQ+L+rv+bNnz/6yfOduyy9w9mn0Jx8kyomp7cp/UL2Er68vW7ZsITFRc2o9NjYWa2trJk6cqHNwJTdcXV1p2LAh1Xr8woxDD0kNu8nb9WNw7DwJo2IVCnyM/k1L/eMLjPM95T1q1ChMTU3ZtGkTbdq0YevWrbRt27ZAOzv+IIL4G0eQ6BtjXr0NUgMjUsNuEhcSjDItBetGvQrUX0bMa96uH4PUyBzHThO4FKHK5Vi4cCHlypXDx8dHePj7+fmhUCg0Hv6gStLt0qUL/fr1o2vXrsyYMYPWrVuzaNEixowZI+hWTZ48mQ4dOvDgwQOk0twHdk+fPs22bdvw8/PD3NycuXPn8u233xIeHo6trS2QuyuCmho1arB4seqNRx0E/vTTT0yePJmff/4ZgM6dO+Pk5MSKFSuEgFIXgwcPRqlUcvXqVQ05hClTpmi1rVy5MsuXLxd+j46OZvny5RoB5cmTJzWq6wYNGkSzZs2YOXOmcI0TExMZMmQIvXv3ZsmSJULbHj164O7uzqRJkzSWF5TEtEzC3yVj7FZda7ok34K7YTdJunUE86qtsWnST1huUbMdSqWS8OhkktIy83W+ulh3PQqLnouRyPSFZeYVvXm5tD/xV3Zj12KoRvvMmNc49VuMnrmqyt6weBXe/PkTnQeN4tjmlUK7tLQ0vvvuOyZPniwsO336NMuWLSM4OJguXboIy728vGjWrBmbN2+mS5cuuQryenp6MnfuXJo0aaJhI/bobQJlu4zldGg8F9VT9eVLYfYyjbnz/+CKfVO+LucsGg+IiPyHmNS2PI1nncwxoEy8eYTofbMp0n85elYf7yLzMcWSugYrZs+eDfDBlonPopI48ZnURT5UMSQnPlT9pSDkO6BUK/b7+flRr149fv755wIFlOqHv53PCI0qL/PKLYg+MJ+Ea3uxqt8NiZ5+Lr28JyP6OW/X/4LM3BaHjhOQGZl90MP/wYMHnD17ltq1awNQtmxZvL296dOnD/fv3xeCL2tra/r168epU6fy/PDdu3ePu3fvCnktXl5eVKxYkfXr1zNo0CAgd1cEtXJ/7969hXUymYxq1arx4sULfvjhB2G5lZVVjoKvaiIjIzl16hRDhw7V0tbSlTvSv39/jd/VUhbx8fFYWKjyZbJe37i4ODIyMmjQoAEHDx4UnAQOHz5MbGwsnTt3JirqfZ6jTCajZs2aBRbXzU5YdBJK0Pg8KeWZKNKTNQR3ExUKovfNplCPWRgW1nwDTH5wBpBgWa8L2ZFIJCiB0OgkDZvJnM43OxsuhTPz6BMhmFQqFShSk0CpwLBQSZ1iwMalagnBJIChkzsGTu6EHDvCxkvhGm/n2UfcN2/ejKWlJU2aNNG43lWrVsXMzIzjx48LgWZMTAx37twhNDQ019GNnFyHFGnJKOUZGLqUI/H6AZ48esiDswcI+mY2baZsY24fb1xsTD75TVFERERFt27d6NSpE4aG+a+a/tS42JgQ5FOOUds+bwGjmo+pl8jMzKRhw4a0aNECMzMzTp8+zfr162natCl169YtcH9yhZLrz2OxLl/gTXPkn64uku+AMusDytvbm/Hjx/Py5UuKFCmSr+11PfyzP5Qyop8LU4C5kREZRuTOaehbF8ahQxBSQ9XF/5CHf9myZYVgEqBmTdUHtVGjRhrBl3r506dP8wwoGzdurJEkXaFCBSwsLDSCvqyuCE5OTjr7yR78WVpaYmRkpKURaWlpSXR0dI7Ho96vh4dHrsed036zCrqqA8ozZ84wfvx4zp07p5Vfqb7Gjx49AhDcGbKj7utDUYveKjLSiD+3mfgru1FmE9jNU3A39g0ycxtkxjmPqqVnKvJ1vlnJqmaQeOso8Re3kxH9AhSZQhs9S+03dn1r7c+CvrUTSfdPCzcbUOXvZs+bevToEXFxcTg4OOg8j4KKGWc3Hkh5G0rsqTWkht9EmaZ5DRRpySj+X+x5JTz2kyTSi4iI5IxMJsvTIeZL0Kl6UaIS0z6ZDnBufEyxpFQqRU9Pj2nTphEfHy8U6kycOPGDjiUmOR2J1b+jwPhTke+AUpfifkRERL4DSvXDPz0yLNeHUn6I2DoBmYkVDh1/RWqgKWpa0Ie/rqANtIVf1cvzo1mVvU/QVvnPjyuCrpvFlxBbzWsfT5484euvv6Z06dLMnDkTFxcXDAwM2LdvH7NmzULx/8hC/e+aNWsoVKiQVn96eh8nMqAWvY05vJjEW0eQGpkis3XBpnHf/Avu5oM3L8Jom4/zzYpazSDx9nGi987CuGQtLGq2U+X+SmXEn9tERsybAh2H+mYjAwwNDbVSLxQKBQ4ODgQHB+vcPut32MrKCjc3N0GfLTvbr71gZ8T7YFyRmsjbdaORGhhjVe979KwLI9EzIP3NY2JPrNKQDlFkSaTXj02hiNXnER4WEfkvkz2HcufOnSxZsoRr164RHR2Ns7Mzvr6+jBkzRuOe3rBhQ6Kioli3bh2DBw/m0qVLODk5MWXKFNq3b8/JkycZOXIkN2/epGjRovzxxx80btxYY98vX75k3Lhx7N27l9jYWEqUKIF3O19O4EHm/41HAFJCr6vaLx2AVE8fPevCWFRvo+HbnV8Sbx4m8fZxMqLCKD0jmVIlS+isjQBITU3FwMBA4x4pk8lYvXo1AwcO5MiRI6SmpqKnp6cljD5jxgy2bdvGgwcPSE5OpmzZsowePZr27dsLbR69TSA1Q4HRJ04d/asKjD8V+X6iHz58WGtZdhuj3DDQkxbooZQbJqXqkHT7KEl3TmBeubnGuoI+/HMKnj4mcMvPtrm5Inxq1EFq9mrhD2X37t2kpaWxa9cujeA5+xS2epTWwcFB64b0KXC1NUUCJD04g6lHI9JfP0RqbIFx8coo8ym4q29ViNSnV5GnJOgcpZQAt84ezdf5qklJlwtSRskPzqBnVQj7dr9opBfEhegO+jJitH18M2JeoWfpKNxsqqRm6NzWzc2NI0eOULdu3RzdI9TyQlKpFKlUqpXyoP59zZ6TxBxbhl2bUZiWrkdq+C0UKfHYtx2DUdH3I92ZsbkHxc/fJaMvy1tMQv1wvHTpUq5yJjkl138M6uR6Xbqcuo7xryyCEBHJiVWrVmFmZsbw4cMxMzPj2LFjBAQEEB8fz/Tp0zXaxsTE0KpVKzp16sR3333HwoUL6dSpE8HBwQwbNoz+/fvTpUsXpk+fTvv27Xn+/Dnm5qr749u3b6lVqxYSiYRBgwZhb2/P/v37WTJxJAG/TeWpYwNCHkeRcP0AyXdPAGBZtxNSAyMyIkJJvHuSpLsn8izMfbGgF0ZFy2PX6kcAEq7tQ9+uGPL4SPSMzXBxccHPz4/79+8zd+5c1q9fz+3bt1m5ciWvX7/m3bt3KBQKJk2axM6dO0lKSqJo0aIULlyYYcOGcf/+fX7//Xfc3Nw0gtI5c+bg4+PD999/T3p6Ohs2bOC7775jz549tGzZkpCQEPqPmog8IYqkO8dIDbuBiXtdVUFnPkiPeMa7w4v//7wyx6xSc/TMbYneN4ci/ZdjaFNIUBfR2C49nYkTJ7J3714eP35MZmYmVapU4ddff8XLy0ujbWxsLMOGDWP79u1IJBK++eYbfvzxR402u3bt4ptvvuHGjRtUqKAqItq6dSvt27enbdu2HxSL5Dug/NiAwNXW9IMfStmxbtQLiVTGu0MLkRoYC287H/Lw/yvJyRXhUwdf9vb21K9fnxUrVjB8+HCN66JUKguswaUOmLMGyHFxcaxcuVKjnbe3NxYWFkyaNAkvLy/09d/nx4aFhREYGMjZs2cJDw8XjiF7Tp/6IX769Gm2bt3KmjVrSE5OpmnTpixZsoSiNiaES6Qk3z+NMiNV1feUVjqPWynP4N3RpSTdPo4yMw0j18qYeTQi4epe4k6vE4pyEq7uJeHqXjJiXmFgasnuCmW0zrdevXpcuHABUBVG3bhxA2trawwNDVGY2iKTSshITSbl8cX/y2ooUX1CIe3VA9Jeqoqv4s5twrJ2B6HflIfnyUyIEvIo0149IP3VA8yrfaO69lIJTyISSU9Px8nJidjYWGrWrMkff/zB1q1bkcvlTJgwgUmTJgnX7sSJE6xfv56tW7eSmZlJTEwMycnJ3L59W+N6u7q6YmWr2m/8xe0AvDu0EGVGGlIjM/VVBFQzDe8OLyQt/P/T+rePYZSD9EdoVBLP3yULeUFpaWlMmjSJ4OBgnj9/joODA+XLf8JEJBGR/yDr1q3TeJHs378//fv3Z8GCBUycOFEj1/LVq1esW7eOzp07Ayolh9KlS9OlSxfOnj0rpHiVKVMGb29vtm7dKhRy/PLLL8jlcm7duqVRZNq5c2dmTZ1Ev8Wq4CblyWVkZjbIE99hVraBUJQTsXUiEpl+gQtzHbtMQapvyJvgUSiBbbv28u03rdi8eTMAEyZMwMDAAH9/f9LS0jAwMODu3bvs2LEDZ2dnYmNjadiwIQ8ePGD+/PncvXuXp0+fsnz5co2A8uHDh1o1GFWqVGHmzJm0bNmSzZs38+ZdHBIDY/RtXTCwK0rCld2kv9XOic9OZkIUb9eNAYkEi1rfIdU3JOHmIY2iTblCyfGHEWR/KsfHx7Ns2TI6d+5Mnz59SEhIYPny5Xh7e3Px4kUqVaoEqJ5T33zzDadPn6Z///6UKVOG7du306NHD43+6tWrh0Qi4dSpU0JAGRISglQq5fTp00K7yMjIHD3hs/PBc44F2QmolPUdLIyJBMiiwKeUZ5BwdV+B92/TfBCK9BSi9s5CYmCMScmaFLU1wdjQQNVvHsHOX4lcLicxMVFj6l2XK8KnZO7cudSrV48qVarQt29fihcvTmhoKHv37uX69esF6qtp06YYGBjQunVr+vXrR2JiIkuXLsXBwYHXr18L7SwsLFi4cCHdunWjSpUqdOrUCXt7e8LDwwkODiYhIYGBAwfi7OzMn3/+yenTp2nYsCF3797FxEQzKXnw4MFYW1szfvx4QkNDmT17NoMGDcLr+wCulqxB4q1jSPSNVF9yayfSo8Ig22h0zOHFSI1MsazXGXlcBPGXdiKR6WNazouEK7tVo4NKJanPriI1scSkZC3KuRXl3IGNSCQSWrVqRf/+/UlMTOTKlStCv2XKlKF79+5s2bKFY8eOYVII7BVKpAbGGBQuSfqrB0RsmYhJyRpkxr4h8dp+pCZWKJJjMS3bUOMY9awL82btz5hXbq76blzahdTYAota3wKqm83juzfJyMigWrVqeHt7c+PGDby9vZHL5ZQqVYrJkydz/fp1rKysAGjXrh2JiYl07dpV0J9zdXXl6dOnzJs3j+rVqwt5l0+eq7Q7FfL3ThzRe2fj2HUqUiMzovbMwqz818Rf3I4yMx2pqSWKpFhSQ6+R/uaxzs+LEoS8IIVCgY+PD6dPn6Zv376UKVOGW7du5UvTU0REJGeyBkEJCQmkpaXh6enJ4sWLuX//PhUrVhTWm5mZ0alTJ+F3d3d3rKysKFKkiBBMgmbtAKieq1u3bqVDhw4olUqhtuJlTDJhxiVIiI8jeN8JDIqURWpoilzHLNGHFuZqWDYqFFx7FC7URoBqmvvy5csa16F8+fI8fPiQXr16cfv2bTZs2EBSUhKlS5dm+fLleHp6smbNmhyvY0xMDHK5HE9PT9avXw/AuF9/Y7fpKeIW9ELfpgi2LYagZ12Y2JN/6jzurMSf34oiNZHCPecI9SKmFZrwanFfjXbh0ck4K5TIpO/DSmtra0JDQzEwMBCW9enTh9KlSzNv3jxBmWXXrl2cOnWKadOmMWLECEBVwJl9FNPGxoayZcsSEhIiFAuHhITw7bffsnnzZu7fv0/p0qU5ffp0vguw8x1Qjh49mq+++oq3b99y7tw5Xrx4wY0bN/K7OQAtvm7IvQ2qh5JFtdaAhKQ7x0GnxHPuSCRS7Fr/ROS2FCJ3TKFwxyC8avvQ1D1/wc5fSV6uCPHxn96Gr2LFipw/f55x48axcOFCUlNTKVasGB06dMh742y4u7uzZcsWxo4di7+/P4UKFWLAgAHY29vTq5fmG2aXLl2E/Jzp06eTlpZGkSJFqFevHsOGDaNq1aqAKifn9OnThIWFsXXrVi3bSFtbWw4dOiSMZCoUCubOncvFib+z4us+KJGQdPsYysx0JA7FKdxtBm83Bmj0ITU2x6HjBKEPpVJBwuXdOA9Zi75DcRKu70ce8xqkeug7umFRqz3rpvbi4OY6DBo0iNjYWOF8nZychJtsQEAArq6u/PDDD5iZmZMa975AyrJeFyI3jSf99QNSQ69hYFcU29b+RO/5HWT66FlqFtCYejRCIpEQf3kX8qRYDAuXwqZpf2SmVqpp/LQkkt+GIZPJ2LVrl7BdUFAQgYGBtGzZEn9/fxYvXsyRI0cA1Y12yJAhDB8+nMKFCwPg4+PDsWPHWLx4MTNnzqRBgwZkypUkRr7AvGY71SguoEiOA6keKQ/PYd9+PDHHlhF3dhMo5ZiW88LUw4uIjQHYNO5H1L45Oj8vSuX7vKDzh3Zy5MgRTp48Sb169YRcp/j4eP78809u3LhRYAePfyof4m70X/MGFsk/d+7cYezYsRw7dkzrGZLd1tHZ2VlrZsrS0jLP2oHIyEhiY2NZsmRJjpJvmYmxGACWtdqT/Pg8ysx03qwbhXGJGpiWbaAxk1GQwtzUF3eJCwkm7cUdUCrxLK/pDtSjRw+tVJ+so7LqvHMzMzPc3d25evUqFStW1KqL2LNnDxMnTuT69esaAzzq6xWRrBAiFqVCjjw5DsMiZchPHJPy9AqGRUprnKPM2BzTcg1JuLJbWKYEUjPlmBq8/25nLcJSKBTExsaiUCioVq0aV69eFdrt27cPPT09jVFXmUzG4MGDCQkJ0TgeT09Pdu7cCajikhs3bjB16lSOHz9OSEgIpUuXJiQkJN8BZb6dclatWsXAgQNZtGgRUqmUgICAvDfKRp8mFbBvPx6ZmTWxp9YSf3EbRq6VsPbqWeC+ACQyPezajMbQyZ03Wybgof9WCHYkEgn+/v4sWrSIvn37MnTo0Lw7/ELk5YoQGBiIUqnUquZetWqVztyxEydOaORHurq6olQqtbSmypUrx7Zt24iJiSElJYX79+/z66+/Cutz2q+vry9KpVJjKrp169bcuHGDlJQUnj17xsiRI+nZs6dWO1AlgR84cIDY2FhSUlJ4/Pgxa9asEYLJjIwMBg8eTGRkJFZWVhpfDjV9+/bVuAF6enoil8vRS3lHAw9XHFv/iL6tM4bOZXHs+Cv6ts44+60Q8m8AzCo10+jDyLkcKBUkPzhH8r1TyGMjhHbGRUrzZtUwSjiY06dPHywsLATJpokTJ/LmjSpNY9u2bbi6uvLy5UuV3JIEFCnxvFrmR+KNQxgXr4zMzAbj4lUoNmI7hbrNIOXxRVV+p0LOiz98iTm+ApmZDcVG7cGyZjssarRFHh+JecWmmFVuRtSu6YRPb0vK0yukht4AlCxcu0Xj+gwePFj4f58+fbh8+TLjxo0DVJ+3efPmUblyZXr16kV0dLTwoLh9+zZKpZITJ06QmJ6Jvl1RbLx6Ye+jerO1+2YkUiNT4q/sIWLjWGQmluhZ2mPg5I5d658wLl4FmYUDyY8uaCXavwkeRXqEKuiWSSVMXLqFbt264eTkxLZt2yhUqBDGxsY8e/ZMEFHPOvIbExNDjRo1cHZ25sGDBxp9v3z5kjZt2mBmZoa9vT3+/v5ajj1JSUn89NNPuLi4YGhoiLu7OzNmzMhXHvSdO3do1KgRxsbGODs7M3HiRJ3FV5A/J6jc3I3y4r/sDSySP2JjY2nQoAE3btzg119/Zffu3Rw+fFjQDv5UtQPqfrp27crhw4cZOH0lDp0mavyoXW/07VywbuALgEHhUiQ/OMPbtSOJPriAiK0TCZ/VgeezOvBi7vdE71bJ5+VUmKvSnP5FlXNpVRh9+2IsXb9DIy+wePHiWtspFApmzZrFtm3bSEtLw87ODnt7e27evKkVZINqhM7HxwcjIyMWLFjAvn37OHz4MF26dBGuQVhYOFF7ZiGPjyT57glezP2et+vyZ8GcGR+BnnVhreW6lum6Ta1evZoKFSpgZGSEra0t9vb27N27V+NcwsLCKFy4MGZmZhrburu7a/Xn6enJ69evefz4MWfPnkUikVC7dm08PT2F4DN7EJob+X61/RSjeyUdzWnS0JOzRctqCaEWG7Unz+2tPL/HylPzJizVN6RIt6nU+cqW9s1Vw/OtW7emdevWWtv37KkZuIaGhurcj64HjjpIy0pgYKCgGZnbttn3ZWBgwLRp05g2bZrOtv8FUlJSmDx5MitXruTly5daKQrZyU3KaFLb6jSedTLPfepZaKZoqPMC3x1agMzcDiPXiqQ+u0ry/RD0zW2FdgYGBnz11Vdcv36dY8eOsWnTJhwcHEhJSaF48eIaSepflXDn0bNQ9KwKE71/Lor0FNXb57X9yNNTiNo+idSwmyCRYtXoB+Qxr4m/tJOMd69w+HasxvGlht0k6f5pzKu0QmZigZ6lIylPLwNQ2NlVo62NjY1wTdSogxofHx9q1arFnTt3WLJkCXfu3NHSGgVIzZAjy3Zjizu7CWVGKnrmdpiUrkvC5V0oM9MxcdK+Oenb5Kz4IFcoufYiFoAXL14wa9YsYV3W4r53794BKpmyJk2a8O7dO06ePKkhwyWXy/H29qZmzZrMmDGDI0eOaCXXK5VKfHx8OH78OD/88AOVKlXi4MGDjBgxgpcvX2rsPztv3rzBy8uLzMxMwdBhyZIlOgudsjpBTZ06VXCCqlevnoYTFKh08KpXr05MTAwzZszIUd5JTU4aoFn5t3sD/5v4HAVlak6cOEF0dDTbtm3TMLh49uzZR/Wb/flmb2+PRCLhzJkztBzyG3ui0zB2zTn1TZ0baOPVC5m5DRGbfyXx2j5klo55F+ZmeflPeXwR5BmYlvMi9vgK9B3d6Ny2JZNuXRTa6Pp+Tpo0iXHjxlGiRAnS09PZtWsXUqmUYcOG6XxB3Lp1K0ZGRhw8eFBjdFOdMieXyxnYtR0pryOQGJpg4OiGZZ0OyBOiid6b8z0lN94Ej0Ke8H5WS+2aJnErgZnz+3vq2rVr8fX1pU2bNowYMQIHBwdkMhmTJ0/myZO88zd1Ua+eyhDk1KlTPH36lCpVqjBw4ED27t2Lk5MTiYmJXLt2Ld/9fVEvb1Ap6+tJJXk3LAB6UgmT2opJ/f8kBg8ezG+//UaHDh3YtGkThw4d4vDhw9ja2ur8ouf25qwW180TSQ4fd4mUQl2nYVRU9Rmy+2YkmVG6vVUfPHjAiRMnKFasGHZ2dlSqVElIUr927Rqly3kg0TfGof04TMrUJ/b0OoxLe6JMTyH2+EpSn11HamSOSanaWFb/Bpum/bFp0p+UR+dJfXFPY18Z717i2GUyVvU6Y16lpcY0ib5e3t8htf7ngAED6NOnD7Nnz2bFihVcvHhR0AhVk5iWSaZciSTbNVKkJmDg6IbM3Bbrhr7YtlL5Cme+065Gz4u3caqiKQMDA/bs2cPhw4eFH39/fwDat2/PmzdvaNiwIfHx8Zw6dUojmATVFH7Hjh1Zvny54DiV3d1p165dHDt2jAkTJrB06VIGDhzIrl27aN++PXPmzMn1Bjx16lQiIyM5cOAA48ePx9/fnzNnzhAWFqZ5zbI4Qe3fv59BgwYxcuRIzp8/j1KpZNKkSRrt09LSKFtWNXrTo0cPrbSOrGy4FE7jWSc5+1T1oPmS3sAKhYJVq1bh4+ODi4sLpqameHh4MHHiRFJTU3Vus3z5csqUKYORkRElS5Zk3rx5Wm22bdtGx44d+eqrrzAxMcHd3Z2ffvqJ2NhYrbaJiYkMGzYMZ2dnDA0NKVOmzF+aZ9uwYUPB2z77T9Ziw+TkZAIDA7+4kL/6/rhixQph3+np6SxYsOCT78fExISwsDBGLdurtV6e/H4wQJ6iOe0ukekj+X8epE3TAVhU/waTEjUwdq2Upegvy76MzN7rCGe7LxnoSclMTcqzNmLLli14eXlRt25d9PT0aNq0KY0bN9b5mVOfn0Qi0ZjtCA0NZceOHQDcunWL0KePsWn0A1JDU/Qs7DF2rYTMzEZoHxsSzNtN43X2r2fhQGaM9uCcMjNda1l2dYwtW7bw1VdfsW3bNrp164a3tzeNGzfW+k4WK1aM169fa724ZJ/lAdVATdGiRQkJCSEkJARPT0/hOoSGhrJ582atmZ/c+OIBZb4f/gXgV59y4hv5P4wtW7bQo0cPfv/9d9q3b0+TJk2oV69ejl/0vOhUvSh25rl7dmdH+f+3YUPnsuiZ2wr5jN9WcKRFi/dyVOnp6cKbfoMGDYSgQNWHKkm9devWKJVK9JCDUoE8OQ7j4lVUQuvyDAwc3Uh+cBaZpT2K5FiMS9ZEnhyHPDlO8H1NC7+pcXyGLh4Y2GmOzOpZqI4xPVrzphQdHa2VC5Q1eTs1NZWoqChq1aoFaI/Oh0XrFoA39WikcTM3ca8LEqlueaN3L3X2kR0jIyNatGhB48aNhZ9y5VT3BD09PRo0aEBGRganTp3KUSdTl5tTVuOAffv2IZPJGDJkiEa7n376CaVSyf79+3M8vn379lGrVi1q1KghLLO3t9eaos7uBKX+yc0Jqnr16jnuV838448Yte0WaZmKAnsky7NogM4//ijvDXSQnJxMz549iYyMpH///syePZsaNWowfvx4mjdvrjULs3jxYnr37k25cuWYN28etWvXZsiQIRpWraBKW7l37x5du3Zl7ty5NGvWjPnz51O7dm1SUlLen8P/R6AXLlxIhw4dmD17Nu7u7vj5+WkF6V+KX375hTVr1mj8LFq0CFAVKapJTk4mKCjoiweUderUwdLSktWrVzN58mRmzZpFrVq1PlqfeOzYsVrLrK2tkeobEL7yR94dWULC9QPEndtM5I4pvFry3r42YsM44i/tACDxXggxx5aT8uQSkN3dTHdhrp5VIdJe3Ucpz8C4eGWQ6RF3TlXRLUmJo2rVqnmO8stkMq1rsHnzZl6+1H2vatmyJcnJyTRr1oxFixbx66+/UrNmTWEWRR2425i+v7cqlUoSLr/PZ09/8xgTN93fc+PiVUh7eZ/0t+/vVXbf/IwiQ7sYV5otx1WXusqFCxc4d+6cRrsWLVqQmZmp8QIml8t1vuSB6t557NgxLl68KASUUqkUc3NzpkyZkqMEnS7+kmzuT6msP6Kp+weZxYv8tej6os+bN69Ab0PZKepgjcG7BAz1pBriujmhSFUFUXrmKpkfC7cqxOob8OzkFkqXLi20W758uTANnz1PJ6ck9Rdz3wcfiqRYTMt5EXNsmbAses9MreORJ8Vq/K7L+9bItSISqYyVy5fQumUzYfn8+fO12qrfUJs2baoVbGZ9gMN744Hs6Fs7kZZl5FQikSAztUKe+I60Vw8w/P/UtzIzneRH53X2kZ34+HiWLl1K3759tdZ17doVfX197t27p1MMH1QBaXaFiezGAWFhYTg5OQnaeWrUuZrZRxuzEhYWplHpqiZ7DtKHOEEdO3ZM+P+JEyfw8vLi+PHjgvvWhkvhn8xxJLs3cH4xMDDgzJkz1KlTR1jWp08fXF1dGT9+PEePHhWkzVJSUvjll19o2bIlW7ZsEdoqFAomTJhA3759hVSMLVu2aLmMVa1alR49ehAcHCxYzW7bto2zZ8+yfPlyochvwIABtG/fngkTJtC7d+88A4lPTZMmTbSWrV27FiDfubCfE1tbW9atW0fLli05duwYt2/fpmvXrnz99dd4e3t/cL+6zCeUSDEoXh2ZiQXJD88hv7oPmbE5+nZFsWroK7Qzq9xckB+LC1mLnoU9ZpWakXznRL4Kc80qNlXlXW4cj0mpWhgVr0bqE5VUW2pCDH1//klnIWhWWrVqxa+//oqbmxsZGRkMGTKE4OBgDQORrDRq1Ijly5czadIkhg0bRvHixZk6dSqhoaHcvHmT0qVL4+bmxosDS1Cmpf/fAnqMhuaxMjMN4xwCSota35J05zhvN4zFvGprQTZIz9KBjIhEkEjIaQK3VatWbNu2jbZt29KyZUuePXvGokWLKFu2rMZoZOvWralbty6jRo0iNDSUsmXLsm3bNp2pZKAKKNVmGPXq1WP7dtXfrE6dOhw8eLBAPudffIRSzSCvkkxpVx5DPalGaXx+kEklGOpJmdquPAO98i+uLvL3oVWrVqxZs4Zhw4axZMkSevbsydy5cwVdsw+hatWqhD++h4/8PE5RV0kJvZHvz1adr2w5MdaHX8aM5sCBA8KXasiQIQwePFgYWcr+tpY9Sb1JkyaYWtlSqLNmkrppuQbCNsYlamglsjt0moh5lZYafUv1DMiOgbkNtXy6sn37dnx8fFiwYAH9+vVj+fLl2NnZaRQdqae7vv32W7Zt28ahQ4c4cOAAoJ3rq3Ydyg8yczuQSInYNJ7Y0+tQpCeT8vSyVo5qTkYFlStXFnTr5s+fz5w5c/jzT5XkRoMGDYiNjWXOHN0V45Bz+sOXJqsTVNbpe/WPunpSjXqKFFRFFFOmTAFU34WePXvy8EWUYNdZEDLjIog+uICXS/oRPqMdz2d3JnL7ZDJj3xKw6w7P3+XPgUyNgYGBRjCpRl3pee/e+xeM48ePEx0djZ+fn0bbgQMHkpSUxN6976dFdT2YdPWpLgLIKmuj/j01NVXruupCLeysLsgqUaIEU6dOzbGw6kNYt24dpqamfPONSiM2NDRUeNEJCgoS/t7Z8xDzU1Cmi8uXL+Pt7Y2dnR3GxsaCJIyenh6hoaG0bKm6f2RmZvLq1SumTZvG2bNnUSqVGtdeXcSZkZFBUFAQJUuWxMjIiISEBGJjYzVMTNTHnvWFNTE9E6m+ATZNB2DfdgwoMrFq6Itj598wr/T+JVfPwp7M/89aFOm7mCL9l2JZ+zsMnMsiT4wm5thyYk6sRGpipVGYmxp2k7AprVCkJmJUrCJpz28Rc2QJiuQYrOqqPhPlypblyZMn+Pv7CxJz2ad41VPXP/30E8nJychkMq5evYqBgQFJSUnC+a1cuRKJRMLJkyfx8/Nj1KhRREdHk5qayr1790hKSuLPP//EyMiIunXrMn78ePQkShTpKWTGvkHf2gm7/6cCGRWvgr5dUWEwQJ4YQ9Te2bz4owdh09vwZvVw9B2Ko2fpSNy5TcRf3oUyMwNluuoFX6KnjyLbQMiMGTOQSCR4eXkxadIkbty4wZAhQzh48CCNGzfm3r17Gp+fS5cuYWhoiEwmY8GCBQwdOhR9fX1Wr16t8XeVSCTcvXtXUAsxNDTUeP6qZ43OnDlDfvlL9SY6VS9KXTe7PBPP1ajX1/nKVkw8/4czZ84cZDIZwcHBpKamUrduXY4cOfJRb9MBAQGEhYWxeP4sEhISqFG7Hs07f8P6xwZkdzqXAIUdbIkCKljLBe/UwMBA7O3tGTVKVbW3adMm+vbty6RJk7SKXkA1FWpubo5cLqdx48asXbsWK7MH6BWrpNVWamKJIjkOi1rfYeRc5oPOUa5QsnLBbILLF2Xp0qUcOXKE2rVrc+jQIerVq4eRkWraPyYmRnhI9+vXT5DiyZ47qcbVVrd8TfapbaVSiTwhCgMndyRSGfHnt6BUyFWWatV8iN4/V2ibGR+ps8+RI0cSHh7On3/+yfbt2zExMRGqzkeMGEGtWrUICAjA0tJS+DsUlGLFinHkyBESEhI0Rinv378vrM9tW13XKXsOUkGdoExMTAR1hA4dOghVmGqXnotvMsms0jnvk8tG2uuHpL28h2mZ+sjMbcmMiyDx2j7erBuNS7+Fn8wbWK1skFUFQp2wn13qqWrVqkilUq5du0bXrl0L1GdaWhoymUwjZQMQAocrV67Qp0+fHPtMTk6mQYMGvHz5kn79+lG0aFHOnj3L6NGjef36NbNnz87H2eZOZGQkhw8fpmPHjoL0k729PQsXLmTAgAG0bduWdu3aAQii0ZC/gjJdRERE0LRpU+HeZGVlxdq1azl58iQ2NjYolco8952dwMBAJk+eTO/evalRowbx8fFcvnyZq1ev6hyRVZOaIUfv/49pw8Il0bMqRPL9EMzKf63RLuleCFIjM5wHr0Ui00OeFMObP/1BIsGybidkJpakPLlCyuMLGH9VRSjMTQ1Tpf7End0IUj2svX5AKc/AvFpr0h+qghuFQsG9e/cIDAzkwYMHLFy4kJUrV9KjRw+Nl2o9PT1mzJjBjBkzhGU5OWP5+flhb29PQECAEHAuXLiQQYMG4enpyY8//khoaCjDhg3D3s4WI8fimLWfKMQsxUbt4eXS/pi4vU+Vidw+ifSocCyqtkJm6YgiOZaUZ9exbvSDYO7yJngUmTGvkegZoG9qSUWrTF4Co0aNwtfXl/DwcEaOHMnmzZsZPXo0o0ePFvp3c3OjefPm7NmjunbHjh2jefPmVK1alSlTpiCVSlm5ciXHjx8nPT1dGEhQvyh89913lCxZkgULFmgMMsjlcnbv3k23bt10ztbkxF8uYOZiY8KaH2ry6G0CwRfCOf4wgvDoZI0BcAlQ1NYEr1IOdK1VtMCm8CJ/P6ysrFixYoXW8uy5fb6+vlryR6B6CGcfZXN0dBS+WFkJ9JlK0p+/6dDva0nrVyc4duwYr169wsnJCVA5/AwbNgx4/8DLTtYcqW+//ZZ169Zx+/Zt4SbVbfkFzj6NJj0xVuXfjWpkT5EcR0ZUmFZAqchIA6USqUHOeaAyqYQ6X9niXtiKX3/9VUPyKTY2VvDvhfejeIGBgRoPe/XDtFKlSkKiOaiMB+qO3UBYttGspNvHcOq9EKmh6mGe/OAM8sR3WNT8FovqqpGZyO2TSX1xB1MPL8wqqvLJkh9fRB4fiaGLB4W+V43EOVoa8RbVTX7kyJGMHDlS2I/a1cfMzIxx48YRHx/P6NGjsbS0zPVBmxMtWrRgyZIlzJ8/X+MGPGvWLCQSCc2bN89129mzZ3Px4kUhjzIyMlLLIz03Jyj1Ntmn5tV/l8qVK9OtWze8vLzw9/cnXSnl6PFduFTSHJXLD8Zu1TEtXU9jmUmJGrxZ40/CvTOEyBrxOCLho++b06ZNw8LCQuPavX79GplMpjUFbWBggK2tLa9e5V68NXXqVGQymYZPsru7O3K5nPPnzwtVqPB+5DKn/Dc1M2fO5MmTJ1y7do2SJUsCqpcqJycnpk+fLkhJfQwbN24kMzNTY7rb1NSU9u3bM2DAACpUqKAzkFYXlKklvfr370+VKlW03Fqyc/bsWWJiYjh06BAuLi5s2bKFx48fU6dOHSHQzmvf2dm7d6/wPckv6uK9rIGDSRlP4i9sR56aiOz/xTVKeQYpD89h7F4HiUzVOvbkGlAqKNxrPjJjVTqIeeUWRO6cRuzpdZhVaqaZW5mZTuEfZmksa1u5CIt2qz5fR48eFb5zxYoVY+TIkezevRsfH598n09WbGxsOHr0qPAdTU9PZ9y4cVSvXp1jx44J0/8VKlTA19eXWnU9eZdlECwj9g2Z0S8wbqoarVekJpL28h5WXr2wrNlO2I95tW80z1MhR54Ui1Gx8ujr6THCuwQ7srxHFy1alFq1arFx40ZhVBpUI5FPnz4VgkOlUkn//v3x8vJi//79QmDdr18/ypUrx9ixYzl06JDGOVesWJF169ZpXYvU1FSaNWvG3Llztdblxl825Z2dko7mBPqU46S/F7cDvdk7uB7bB9Rh7+B63A705qS/F4E+5cRgUuSDMDXUo5yTJZWLWlPOyVIQgw4MDCQzM5O6desybdo0Jk+eTIMGDfDw8Mijx/dMmTKFwoULU7NmTWEKv0jYQSK2TxaS1DMT35ERGYaetRPvDvxB5M5pxF/ZTfylnUQf/IOXf/iSEf081/2o1Qyy5z/C+0BRPbVlYWFB/fr1mTZtGmPHjmXhwoW0bds21+kLL3cHrRQBqZE5b9aOJP7STmJOrCJ6z0z0rAtjVun9SLJZxaYokmJ5u3E8Cdf2EXNsBe/2z0PP6r0EkUwqobKzVa7nl5Xp06fTp08fBg4cKOSqFYTWrVvj5eXFL7/8Qr9+/ViwYAFt2rRh48aNDB06VEuG6MyZMzg6OmJoaMjevXsxMzOjWbNmBAUFMWPGDKpUqSLkIM2ePZsiRYpQpEgR3N3dCQkJoXLlytSpUwdzc3MhmMqu1ZucnCwE8dmLiiSFyqBIiSdsqo9Gpaya6P3zCJ/VUWc1qGaBQ6ZKq8+6MFJDU9LfPEER94a5209rbXf//n3at2+PjY0NRkZGVKtWTUMs//Lly0gkElavXs2kSZM4cuQIU6ZMwcrKioMHDyKRSHj48CEGBgb4+vpq6c8aGRlx7dq1HK1d161bx/Lly/npp5+EwA9UZgiWlpb06tWLw4cPExoaypIlS4QUDl2f/6xs3rwZT09PrK2tNYqlGjdujFwu59SpU7lunx/WrVuHvb19riN5OZFXQZku1K5Xe/bs4datW4wYMYISJUrk6T+fV5937tzJcdZCF7qK90xLe4Iik+QHZ4VlKc+uoUhLwrSMqshDqVSS/OAMxiVqgFIpFCRmLV7Mbl1o6vG1xmd7RFN3ahZXTcn27dtX4wVuwIAB6OnpsW9fwV331PTp00cjneby5ctER0fTp08fjVzS77//XmWzqyfVKDBOeXIJiaEphi6qZRI9Q5DpkRZ+S8Mx6M0af1Ux07X9xJ5er3IYU8qxrNOJX33K4WSlXQTTsWNHrly5oqFOsXHjRgwNDYWUi+vXr/Po0SO6dOlCdHS08LlPSkri66+/5tSpU1opH7rk49So3XMKwl8+QqkL9cNfRORzU7VqVfbv34+/vz/jxo3DxcWFX3/9lXv37gnTo3nh6OjIxYsX+fXXX9m2bRsLFizA9n/snXVYFIn/x1+7dDeoGCg2doCFKAbY2K3YreedfWeehd3tid1xdhe2nt0YgKKS0rnx+2NvB4ZdBDzvfn7veD2Pzx2zszM7GzOf+cT7bWODUxFn3rm2I+HxBRIenEKio4NDl1kkPrtE4uPzJL28jlTPAF3LApjVaP1FHUfIUDPw9/fH39+f5s2bY2pqypUrV9i5cydNmzalbt26wvo7duxgxIgRrFy5EqVSSdOmTTlx4oSQic1Kd7ei+F8PEi2zqN2RtIggYq/vRZmWjGGxylg3HYJULyOTalSiOlae/Yi7fYjos+sxKFgKu45T+XwuYwhJrlDSpLwDm8k9a9asISEhgT59+mBmZiacOHODVCrl8OHDTJkyhd27d7Np0yacnJyEDJWasLAwPn78iIGBARMmTMDOzo4TJ04QGBiIs7Mzc+fOxcbGBi8vLyGjfvnyZSZMmMCrV69Yvnw5DRo04Pnz57x48QKJRIKhoSHR0dFfnLAtWrSoqG8wSH29USpIfHYZ8+oZOrpKeTpJL65iXKYOEi19tYr0VOKu7yXh0dk/9ewy9qtITeTD9kms2yVh2aCMbPuTJ0+oW7cujo6Ogtbmnj178PHxYf/+/bRt25YaNWpQokQJlixZwoMHD+jXr5+QRdu9ezdWVlaULFlSNGiUmZSUFJG9bGYCAgLo168fXl5ezJo1S/RYgQIFOHz4MD179hQmqM3NzVm+fDm9e/fWEGzOSmBgIA8fPszWGjg8PPyLz8+JN2/ecP36dYYPH651aOVL5GagTBseHh60b9+e6dOnY25uTpMmTfDx8dHQ580LM2bMoE2bNpQuXZqSZcpRr2FjBvfzxa1GtWyfo214T9+hBLo2hUl6FoCZukLxLACpkTmGxVR2j4qkWBSpiSTcP0nC/ZNat63QMpSoI5WgK5Uwo7ULnWsWxd9fdWOU+QYEVFaSBQsWzFZbOjdkHbhUD+5l1skFVYVFffOUecA4+fUdjIpXRSJVBaUSXT2sGvTh8/mNvF/WA4NCZTAqWRODIhVIfn2HhPunQKKSU9K1dmRy//Z0rllU6zF07NiRH3/8kd27dzNp0iSUSiV79+6lWbNmwvCf+sYgq2d3ZmJjY0WtW9rE4EF1/sxucOlLfJcB5d9JvnVZPlnx9PTUcOfx8fERysdqvhQg2Nvbs2LFCo1p6x7j/dg+bwI65nbYtBiNroUdFrU6YFGrQzZbUpFV6D+zmkGlSpXQ1dVl3rx5xMXF4eDgwKhRo5g5c6boOY6Ojhw4cEBj29kdRykHM9xL2nLtTRSGxSoJr8EED6w8sj9JAZi7tsXcVWzPpS51q0v13du60T2bfWtrbZBKpezYsUNUklEH01nRZjJgamrKokWLWLRIc6Jezc8//4y9vT2PHj0SGtLVA0MnTpwgOjoaIyMjLl68yG+//UaFChW4efOmkB2JiIhg165deHt7i7IjderUEQag1K87c5tE5kxIcpqcqARV5lHfoQSJTy6KAsrkV7dRpCRg6iL24lXz+cxaEh6dxaxGawwcyyI1MAGJhMjf5wl2GzK5ksRUmXCuGzVqFEWLFhUa+EHVQ1avXj3Gjx8vDMu4urqya9cuvLy8BJmctLQ0Dh48SLt27XB0dEQul2uqBqSlERUVpaEhCvDgwQNat25NhQoV2Ldvn9agrH79+rx584ZHjx6RmJhI5cqVhfK52o8+OxQKBU2aNBG1VGQmp+fnhPr7+DXT3V87UCaRSNi3bx83btzgyJEjnDp1ir59+7Jw4UJu3LiRY5CdGaG97IUcs96r0Qm8wYe399iy6Tf8166kVLvRdOvVh+5umsFqdsN7JmXdib2+B3lSLFJ9Y5Je3cSkXH0huFJ/D01cGmKSpddS2Ladk/iYdfX/lnmJ7Aag8iKPk5nhDUthpqugz4JH2HqLh9PMa7bBqKQryS+vk/z2HjEB25DqGuDQdTb6BZzRkUr4uG0CTrYmXxwwLlSoEO7u7uzZs4dJkyZx48YNQkJCRNJc6uzj/PnzqVKlitbtZP2efO0xZ8d/IpLK+AGFExKtpT/T2piGZezp7laUUg75JfX/GsnJyaIfVmBgIMePH//inV5u2eY3npYdujL18JNcSRllJuvduZpq1aoJPt3fmtltK9J48aU86x9+ie/VeECtIdqpUyeUSiWRkZHCY15eXuzatYu7d++Ksr69evUSldrc3NzYuXOnhnSJm5sby5YtQyaT5ZjF+hSXEYwZlalD7OVtpH/+iN6frkWJTy+iY26HQVHt72Hii6uYVPDEulH/jGOTpQlSJoWHqjKrQVGJuBSyIDo6mvPnzzNjxgzi4+OJj8+wbPTy8mLq1KmEhoby/v17YaK6TZs2wnGcPn2amJgYOnfuTHp6OqDSQc3MnTt3UCgUGtJPr1+/xtvbG3t7e44fP/7FQEhHR0d0YVR/53MagHJ2diYhISFXg1Jfw44dO3B2dhY0XTOTXXn/W1GrVi1q1arFrFmz2LFjB927d2fXrl30798/x31rc16SGplhWqkJppWaoEhLJmz7BN6c9mdrGU/8rwdh/uS9aBvZDe+ZlKtP7NWdKq1dE0uUqUmYlMtw7JEamyPRN0KpVGDkVCVXxzqlZXlGZDNMFhgYSMOGGTdYCQkJfPz4kebNmwvLrKysNHSN09LScu36px7ce/XqlWhfMpmMoKAg0cCTTWwgyNOp37AJdyKUogFjPauC6Lm1w9ytHenRoXzcNJL42wexaTWGOiVseFXEEoNcqGx07tyZoUOH8uLFC3bv3i0M+alR37yZm5v/5e++QqHgzZs3eb75+m56KP8O3kUn0XPjTZosuczWm8EEZwkmQWxd1mTJZXpuvJlnmY18/rcpUaIEEydOZP369fzyyy/UqlULfX39bDMceaVLzaKcHe1BnRKqDFhOUkbqx+uUsOHsaI9/VGf1/9t4IDFVxpMPsdwL+cyTD7Ekpsq+6WvJTGYNUTs7O9E/tU1r1vJo1hKjuqSbdcjDwsIChUKRrfZbZmTyjLOSkbMr6OiR+OQisthwUj+8JOnVbUzKe2QbMGR1NwKI++OIhmxTYnIqz58/F5x8Jk+erHHcU6eqHD5u3LhBixYtKFGiBKVKlRJktEBV7ra1tcXT0xNPT0+sra01JuBXr16NsbGx6IL06dMnmjZtilQq5dSpU9mWpLURERGBn58flSpVyvFi2alTJ65fv86pU6c0HouJiUEmy/hOvX79Ok+2dffu3ePZs2d069ZN6+PqAZmvNWjIjs+fP2tUFtTBdmpqao77/u3CY+r/vI0rT1UOSnKFUsPJRqpvhK5VQZSydCEYevdZdbOjdl4yMdBFV0fze6hnWwQ9OycSnwWQ+CwAHVNrDIpm9KFLpDoYl6lL0ourpEUEaTw/c9+wg4WqnaagRfbZs3Xr1gk3M6D6vslkMtHAmLOzs0a/7Lp163KtdVyjRg1sbGxYv3696Duzfft2jRaF48ePU6NGDfb92JwzP9Snp1sxCptJNXqe9awKomtgTFELfc6Ors/Wfm65CiZBNfypo6PDzp072bt3Ly1bthQUBkDVvuXs7MyCBQu02ntGRGhX3cgObdrGOfGvzVDuuh0iZIUg79Zl01u70CVfMP0/gbe3Nzt37uTTp08YGBhQu3ZtZs+erdGn81f4X1Iz+KeNB/6/KgiZNUSzy0ZnlV3JrmT5JWvQnMh8gdYxMMG4ZE0Sn14kJfgBqe8eA6pSYXYYlaxJ4uPzSA2M0bMtSmroc1KC7iM1Egurx0R+wq1WZSGLM2bMGK0yXUlJSQwbNozPnz8zduxYbt68ye+//87q1asxNDQUrN90dXXR1dXl119/ZdiwYRgbG7NhwwYCAgLYtm0bs2bNEpXCvb29efPmDePGjePKlStcuZIxKOTg4CAacPHw8KB27dqULFmST58+sW7dOhISEjh69ChS6ZcvwGPHjuXw4cO0bNkSX19fqlevTmJiIo8ePWLfvn0EBQUJMkWNGqnKr7ntvVNP+mdX7jYyMqJ8+fLs3r2b0qVLY21tTYUKFfI05KeNzZs3s2rVKtq2bYuzszPx8fGsX78ec3Nz4fPMbt8XIwyYtmAjUceXYNP8B0wrqQLyD+uHYli0IvoFnJEamZH2MZCk51cxq95S2K/6+zvhwCMiE1IZ3rAUhno6yLXc25iUcycmYDsSXT1MKzXVuNGxatCblOCHfNryE6aVvdCzLYoiOZ60sNekBN2nyA+7hOG9h5qbF5GWlkajRo3o1KkTL168YNWqVdSrV0804d2/f38GDx5M+/btadKkCQ8ePODUqVMiiaovoa+vz7Rp0xgxYgSenp506tSJoKAg/P39cXZ2Ft3gHT9+XLgJVQ8Y+xRNp1GjRjRu4YOTc2n09fW4du4EwQmfmfHjwDyf3+3t7WnYsCGLFi0iPj6ezp07ix6XSqVs2LCBZs2a4eLiQp8+fXB0dCQ0NJQLFy5gbm7OkSNHcrUvQ0NDTp48Se/evXFzc9PQmc2Of2VAueJC4FdfDOV/liUz/4Dy+XeTkx/st0R9spmGy3fdzzu8YSlsTQ2+aak+K9pKcFnJXEHwvx6Ee0nbb9ZTlVVD9P+LAubiTIxJhUYk7f8VyZ+DT/oOzujbZa+bad14IEikJD65iFKejoFjORy6zCRsd8aUuQQobKl6z9S6nHp6elqPOygoiPfvVaXOzDqgmS8qmUXHhw4dysGDBzl//jzDhg2jSJEiLF68mFGjRokC9QcPHgAq+aGseHh4iALK6tWrCxZ56iGUX3/9NVeDAsbGxly6dInZs2ezd+9etmzZgrm5OaVLl2b69OnZDgrlhEKhYNeuXVSrVk3DNSkzGzZsYMSIEYwePZq0tDSmTp36lwNKDw8Pbt26xa5duwgLC8PCwgJXV1e2b98uGqzIuu/2/X/gjq3277Z5jVYkBd4k5e1dlHIZOhZ2WNbviXkmiZvMqJ2XTPV1idFyOjAu507M5a0o01Mx/nO6OzM6JlYU7L2IqJMrVFaFEik6xhYih53cDu+tWLGC7du3M2XKFNLT0+natSvLli0TBXkDBgzg7du3bNy4kZMnT+Lu7s6ZM2eEm4jcMHz4cJRKJQsXLmTMmDFUrlyZw4cPM3LkSEHz98mTJwQHB4vK7aCqWnTt2pVz585x7MBudHV1KVu2LHv27KF9+/a5fg2Z6dy5M2fPnsXMzExjf6BS+bh+/Tq//vorK1asICEhgQIFCuDm5sagQYO0bFE7Ojo6nDx5kiFDhjB27NhcB5QS5V81+/ybUevTvX37VkOWQhu7bocw4cCjXG///aq+GBatiG3L0YBKVDVs5yQcus7GsFgl/NpVZPXYXkRGRvL48eOvPQwNshNX/StIJBKmTp2qMZyQTz5fS26CPjXqx3MT9GWuIHxNsPqtKgh9+vRhx44d/PHHHxoX/cw6kmqbxL1794o0E9Xnp9u3b4v0PqdNm8b06dOJiIgQMiJZf/OZrRen3lYKGqBKuYz3K3phWLQCSS9vYNWwj8bAU15I//wREwMdjk3qIGR4GzZsyMOHD3n8+DEFCxYUra9NP7NSpUrY2tri4ODApUuXeP/+vShTuHLlSoYPH86DBw+ErO7Hjx8pXbo0CQkJf9lTOp+88y46icaLL5Gaja3q12CgK+XsaA8mHXzEtTdRGr/d4LktMavWAuum2Wtqqq+xtj4TRPqp6uG9byHC/3eiUCiws7OjXbt2rF+/nnnz5rFo0SI+fvz4t/fQfu/8q3oo30UnfZV12ZeYcvjJN/1B/hdISUlhzpw5lC9fHmNjYxwdHenYsSNPnog/G39/f8GeLOs/bYLihw8fplq1ahgaGlK0aFGmTp0q6m3RxoABA5BIJLRs2VK0PCoqivnz51O/fn3s7OywtLQUxGPzyUBdqlf3BRWzMSbrKVMCFLMxpqdbMaEv6EvB5IoLgUw48IhUmSLPwz9yhZJUmYIJBx6x4kLu9fOyQ5uG6Ny5c+nUqdMXs1DfmswaoBIdXUzK1Rd0/YzLe3zpqTkStvNn3vhPEPWIq6WkKlasKPQPz5w5kxYtWmjNWnbu3JlLly5x+PBhOnTooFF27tKlCyYmJrRt25alS5cyZ84c3Nzc/vJE9dfwT/bhfs9MOvhIaPn6VsgUSiYdfMTsthXRzaNlck58j8N7KSkpGjdDW7ZsITo6WtD8dXJyEgwT/ut8H/W1b8TX/IAcB66FL3wRZAolbyISsNPLdpXvhuTk5Dzrov0ddO/encOHDzNgwACqVavGhw8fWLlyJbVr1+bRo0catnczZszQ0MNSC/mqOXHiBD4+PjRo0IDly5fz6NEjZs6cSXh4OKtXr9b6Ou7cuYO/v79QmsjM9evX+fnnn2nevDm//PILurq67N+/ny5duvD06VOmT5/+196Eb4y2jNc/ybcq1e+6HfJNejMhowT3V4aWxo8fj1wup0+fPiINURcXF5Ekx99NVg1Qk4qexN89iqFTZXRNrb/ZfjL3iN+5c4fp06fj7+9PVFQU9vb2VK1aVUOQHVQB5S+//EJSUpJG7xaAjY0NBw8e5Mcff2TcuHEUL16cOXPmEBgYqCHJ9XeQr+QhJjAsnoBXkTmvmEfkCiUBryJJlcmZ3tolT9XAnMjL8N4/xY0bNxg9ejQdO3bExsaGu3fvsnHjRipUqEDHjh0B1RBYPir+36MPhUJBWlqa1ot+XvjaH5BE98uRolyhJDY5HQvJ95+l/Kvv4bcgNDSUAwcOMGbMGObPny8sd3d3x9PTkwMHDjB69GjRc5o1a6bhBZyVMWPGUKlSJU6fPi0EzWrbu1GjRlG2bFnR+kqlkpEjR9KrVy/OnTunsT0XFxcCAwNFwe3QoUNp3Lgxfn5+jBs3TjRB91/lypUruLur+qHUwWxm44HQ0FD69BzA6dOnUSgUNGzYkMWLF2vtdfu7Kgh1nG3/0oVIR0dHq4ZoZrRZfUL21qDadDHVgx+rVq3C2NgYX19f0TbVGqByhRKJVPUd/9IwTm5RywaBuEd8TNPSbN6cO6n5kiVL5li2btKkCY8eaQYYf2cLzj/RhxsfH8/kyZM5dOgQHz9+xMLCgsqVK+Pn50e1aioR8OxamNRZrMwapMuXL2fNmjW8ffsWAwMDnJ2d+fHHH4XJ8eDgYPz8/Dh37hwhISEYGxvj6enJ/PnzRW1f6naLK1eusH//frZu3UpSUhJNmzalZLsfc2xR0YY8OZ6463tIfnsXWUwYSCQYFC6PlUdv9B1Uv2kdqYRtN0KY1tolV8N7MVd3ERuwHavGAzGvkSFzg1JJ7LXdxN87jiQlnnW36lF97VqRkHhAQADLli3j5s2bhIWFYW9vT4cOHZg9e7ZI6s3X15d9+/bx4sULhg0bxtmzZzEyMqJ3796CvefX4OTkRJEiRVi2bBnR0dFYW1vTq1cv5s6dq+E1n08eSt4//vij8P9yuRwzMzPGjx8vLFuwYAF16tTBxsYGIyMjqlevzr59+zS2I5FIGD58ONu3b8fFxQUDAwNBAPjJkyd4enpiZGRE4cKFmTlzpoZVkDYOHz5M6QLmyCKDhGWJz68SPLcl4QfELgyh6wcTcSgj8/B+VV8ijy7+4vYlEgkxyek8ffqUhg0bCmVcbc3lqampTJ06lZIlS2JgYECRIkUYN26cIO2QHery75UrVxg5cqRQhh00aBBpaWnExMTQq1cvrKyssLKyYty4cRoneIlEkuPJOy0tjSlTplC9enUsLCwwMTHB3d2dCxcuiNZr0KBBtuXoL/V9qjXtHBwcRMvVfVrZCanGx8dnK+fw9OlTnj59ysCBA0UZ2KFDh6JUKrV+z7Zu3crjx481XDjUFC9eXCNTKpFI8PHxITU1NUcrtP8CCoWCESNGZBtYJyQk0LBhQy5dusSkSZOYPn069+7dw8PDQ0OXEP7eEpw21L+pL03wrl+/XkPy5u9m1apVWn9DmcuICQ9OIdE3wrh0nb/tdfzgUw+Plh1zXC837+PfwcWLF5FIJKJgLDO7bofQePElrr1RfdfyquShlsLJicGDB7N69Wrat2/PqlWrGDNmDEZGRiKno9yyfv16Ro4cSfny5VmyZAnTp0+nSpUq3Lx5U1jn9u3bXLt2jS5durBs2TIGDx7MuXPnaNCgAUlJmpJ2I0aM4MGDB0ydOpUhQ4Zw5MgRNi+Y8lVasrKYTyS9vIGRc02sGvVXaSdGBPFpx0Rk8Rnv84WXKjmt4Q1LMbddRQx0pVrl0D5f3kpswHasvYeJg0kg7sY+kgOv073/MH75WSXWnXV6fu/evSQlJTFkyBCWL1+Ol5cXy5cvp1evXhr7ksvleHl5YWNjw4IFC/Dw8GDhwoV58irPipOTE4cPH+bTp0+kpaXx6dMnfvvtNw3v+nxU5DpDGRAQIPz/vXv3SEhIoH79DOHSpUuX0rp1a7p3705aWhq7du2iY8eOHD16lBYtWoi2df78efbs2cPw4cOxtbXFycmJT58+0bBhQ2QymWAFtm7dulwpuderVw8kEpKCH2Fu6wRA6vsnIJGS+v6psJ48KRZZ1HvMq7XMZkvaUSqVxMZ+xtvbm3bt2tGpUyf27dvH+PHjqVixoqB9pVAoaN26NVeuXGHgwIGUK1eOR48esXjxYl6+fCn4+H6JESNGUKBAAaZPn86NGzdYt24dlpaWXLt2jaJFizJ79myOHz/O/PnzqVChgtYf1peIi4tjw4YNdO3alQEDBhAfH8/GjRvx8vLi1q1bgrbZzz//TP/+/UXP3bZtG6dOnfrij8nZ2ZnChQuzcOFCypQpQ9WqVfnw4YNQBss8HaqmYcOGJCQkoK+vj5eXFwsXLhRJ9ty7dw9AI4tZqFAhChcuLDyuJj4+nvHjxzNp0iQNYeWcUPdu/n+Ulb831q1bx7t37+jfvz9Lly7VeHzVqlUEBgZy69YtatasCaiyzRUqVGDhwoXMnj1bWPfvLsG9Co/PkwxHYmIiJiYmIpHy/2+KWBvT2uojG44EEH//FGbVWyDV/3urDvffxfAuOumblxqTkpKYN28eDRo0ELJ035J/Usnj2LFjDBgwgIULFwrLvlaj9tixY7i4uLB3795s12nRooVo8AtU3vS1a9dm//799OzZU/SYjY0Np0+fFnr4UtJlrFy+nCKeQ5Aa5q3Kom/nRKFBa0WSP6YunoSuH0zCw9NY1u0KQEhUkuC81KVmUeo62zLp4CPekCHu/vn8RuJu/45Nix8wzeSOI/0z8DSSyvnj7j2cC1gCKiHyUaNG8fjxY2FAzs/PTxQDDBw4kJIlSzJp0iRCQkJEurApKSl07tyZyZMnA6obgWrVqrFx40bBMjSfv5dcZyjVQSSogkupVCpykHj58iUrV65k2LBhjB49mitXrlChQgWttmcvXrzg4sWLTJkyhaFDh1KlShX8/PyIiIjg5MmTTJ06lTFjxnD16lXBT/NL6JuYo2dThNR3GcFj6rsnGJepgyIplvSod8IyQDBvzwtpcVFMmTaDJUuWMGTIEE6cOEGBAgXYuHGjsM6OHTs4e/Ysp06dYvHixQwcOJDly5ezYsUKfv/9d65du5bjfhwcHDh+/DhDhw5ly5Yt1K5dWwget2/fzpAhQzh06BCFCxcWvIXzgpWVFUFBQSxcuJDBgwczduxYbty4gbW1NcuXLxfWa9KkCT169BD+lShRgvPnz9O3b1+tcgVq9PT02L9/PyYmJrRu3ZoiRYrg5uZGQkIC165dE/VGqst+K1eu5ODBg4wbN45z585Rp04d3r17J6yndjbIOo2qXqa2ZFMzY8YMjIyMNErrOREdHc2GDRtwd3fXuq/vgZiYGHx9fbG0tMTCwoI+ffpozVr8VaKjo/nll1+YMWOGRj+rmn379lGzZk0hmAQoW7YsjRo1Ys+ePaJ1t98M0chgBM9tSfTp1SQ+v8KH9UMIWdCOj1t+Ii08CID4eycIXTOA4Plt+bR9gqoEl4mUd4+JODiH96v6UNbRhiJFijB69GgNG8AxY8ZgamrK69evad68OWZmZkImxNfXF1tbW1EGTqFQsGTJElxcXDA0NMTBwYFBgwbl6LcMqhuSPn36ULhwYQwMDChYsCBt2rQRtu3k5MSTJ0+4dOmSkPHPHHDtXTaDxGs7MXKugWW9vFv75QmFnLiHZ/EZPCHndfNIUlIS06dPzza7mBvq16+Pu7u7ILau5lv34e7OIVNpaWkpnIv/KpaWlrx//57bt29nu07mACo9PZ0iRYqwZMkSLC0ttfajDhw4UDQQUqpSDVAqkMXl3bNcoqsnBJNKhRx5chwSfUP0rB1J+5QhAK9E5bykRj28B1Da3pT0y+uJu3MY21Y/CcGkenivaTlV9ern0UOFYBIQWmsyV4cyvxeJiYlERkZSp04dlEqlRiIBVEFkZtzd3fOrTf8guQ4olUol169fB1QBZYUKFUQXmswf/OfPn4mNjcXd3V3rD8DDw4Py5cuLlh0/fpxatWrh6uoqLLOzs8uVX2pwVCIGRVxUWUlAkZpEWvhbTKt4IzUyJ+XPQDLl/ROkBibofUHTLTsk+kbU8vIR/tbX18fV1VX0Zd27dy/lypWjbNmyREZGCv88PT0BNMrK2ujXr5/o5ODm5oZSqaRfv37CMh0dHWrUqPFVPxQdHR2h90OhUBAdHY1MJqNGjRrZNs9/+vSJDh06UKVKFVatWpXjPqysrKhSpQoTJkzg0KFDLFiwgKCgIDp27EhKSoqwXqdOndi0aRO9evXCx8eHX3/9lVOnThEVFSUqVasDBLXncGYMDQ1FAcTLly9ZunQp8+fP17p+digUCrp3705MTIwosP7e6NSpE/Hx8cyZM4dOnTrh7+//twwQTZ48mQIFCmSrXaZQKHj48KHW3ldXV1dev34tsvS78CJcawku5d0TPp/fiEnFRljU60Z61HvC900n/o+jxP9xBLNqzbFwa0fqh+dEHhdnSZOeX0UpS8WsajNKtBnxxXKYTCbDy8sLe3t7FixY8EUduEGDBjF27Fjq1q3L0qVL6dOnD9u3b8fLy0vkzqGN9u3bc/DgQfr06cOqVasYOXIk8fHxhISogpYlS5ZQuHBhypYty9atW9m6dSs///yz8PygoCDSUlP4bftujExMc3RV+ha8iUzkVXh8ziv+w0ilUqRSqeh8+Hf14X7JHW3evHkkJiZy48YNXF1dmTZt2lcHKePHj8fU1BRXV1dKlSrFsGHDuHr1qmid5ORkpkyZQpEiRTAwMOD9+/fs3r2bmJgYrc5LWd2bjE1V/c1q6828oFQqiLt1iNC1AwiZ35b3S7vxfll30iOCkMdHETy3JelRKo3S39as0Crl9zzgKB+u/c6SpUu5uPpnDg6pQ/O0AILmtuTyWE82/6KyKR03bpxwU3X16lWsrKwA1futrdXK1NQUOzs7PDxUqgdZ3wtDQ0MNuSsrK6tc3Qjm823IdUBZuXJloeyduVFfzdGjR6lVqxaGhoZYW1tjZ2fH6tWrtf4Ask70gqoRWZszSW6kO9JkCgwLuyBPiCb98wdSQ5+pmokLlVUFmn8GlKnvnmJQuLxWq7Kc0DGzIV0uviBm/bIGBgby5MkTDTsztXRGVhs3beTF2u1rfyibN2+mUqVKGBoaYmNjg52dHceOHdP6WclkMjp16oRcLufAgQM5BmnqG4natWszZ84c2rRpw08//cT+/fu5cuVKjiLi9erVw83NTeRVrb5Z0daHmpKSIrqZGTVqFHXq1MmzcOyIESM4efIkGzZsoHLlynl67j9J1apV2b9/P0OGDGH9+vW0bdtWlCX/Fjx8+JC1a9eyaNGibJvZo6OjSU1NzTZrDAiZ44RUGSHZXLDTo0Mp0G0uFrU6YFGrA1YN+yCPiyD22m4K9FyAuWtbldiya1tSQx6KspSWDXyx7zgNi9qdSC/lydKVa5g5cyb79+8XAjg1qampdOzYEX9/f4oWLcq6deswMTFh+/btJCYm8scff1CsWDGuXLnChg0b+PXXX0lLS8PPz48lS5agr6/P7du3v1gViImJ4dq1a/zyyy/8+uuv9OvXj4kTJ3L+/Hnq16/Ppk2bWLZsGR8/fuTFixfMnj2b+Ph4kaC3mqx2nRIQMrnB89vyYcNQkl5cI/LoYt6vEvuIK9JSiD63gfcrfQme70PoukHE3jyQ7VCNVALbbqjer9z2sd+5cwcvLy9sbW0xMjKiePHigp95UFCQcGGfPn26EBBk7u9+/vw5HTp0wNraGkNDQ2rUqMHhw4dF+7h48SKXLl0SWQnWqF2Pt2uGkBYZwqcdEwlZ0J73K3oRe0Ozjzq3fKkPF1Q3ca6urpQsWZJChQoxf/58XFxcOHHihLBOdpIxWfvCy5Urx4sXL9i1axf16tVj//791KtXT5SFHTFiBLNmzaJTp07s2bOHo0ePcuLECWxsbLR+Fll/o4Lz0ldof8Ze28Pn8xswKFIB21Y/Yd95BvZdZqpcbVITkRqaoWvtCMCTe3e0+pnXrVsXBwcH1q9ZTUFDOVWLWjG4dzfhBmrSpEnCcRYpUgQrKytRlQNUyYPNmzdToEABzMzM6Ny5M5MmTeLMmTNC/3HW9+JrB2/y+XbkuofS3d2dgIAAnj9/TkREhCigDAgIoHXr1tSvX59Vq1ZRsGBB9PT02LRpEzt27NDYVm76IvOCvq4Ug8KqjGdqyGPSY8PQd3BGqm+IYWEX4v44giItmbSw11h65K3nUI1EIkVfi+dm5pO0QqGgYsWKWsv8oBkUaiMv1m5fIxa8bds2fH198fHxYezYsdjb26Ojo8OcOXO0etqOHTuW69evc/bsWQoXLpzj9vfv309YWJjIAgtUWWlzc3OuXr2aYz9LkSJFRIMS6gDl48ePGu/hx48fhaz2+fPnOXnyJAcOHBANEMhkMpKTkwkKCsLa2hpzc7El3fTp01m1ahVz587V6E/63tBW0jl48CBxcXEax/W1jBw5kmbNmtG0adNs18kpa5x5neCoRLL7pho5VUbXMmOAy6Cg6ubLuEwdpAbGmZarbizTYz4J60v1MvYtT0vhXmDIF8thQ4YMYevWrfTu3RsvLy/8/Pzw9/fn/v37NGrUiHv37rF3714sLCxITU3lxYsXdO7cGXt7e54/f866deuYPHmyRolROBYjI/T19bl48SL9+vUTMi5qVq9ejYuLC3Z2dpiamlKkSBGGDh2KQqFg2LBhGtvLbNfZZPRiIg/5oWdXDCuP3ihSEog6sQwdMxvRc5RKJRH7Z5AS/AjTyk3Qty9B8tu7xFz4DXl8FNaNB6BUKkCeoc2oUMKFl+EM/mSTqz728PBwmjZtip2dHRMmTMDS0pKgoCAOHDgAICQThgwZQuvWrQV5FbXg+ZMnT6hbty6Ojo7Cfvbs2YOPjw/79++nbVvtIu6BYfHEJqejSIknfM9UjEvXxqSsO0kvrhBz0R99OyeMnL+sFqGN3PThGhgY4OjoyKFDhwgPD6datWrMmjVL6J+3srLS6qEdHBysoXhgYmJC586d6dy5M2lpabRr145Zs2YxceJEDA0N2bdvH7179xb1bKakpOTaHzyr81JeSHpxFYOilbBtPkq0PCo1EaVCgX6h0qobBODxvTv89NOPGtsoWbKk0D/r7e3NuXPnqFSpkvD5X7x4kdmzZ1O+fHlWrFhB//79NSamdXV1qVSpEp8+fWLz5s2iqsOZM2e++vjy+XvJdUBZv3591q9fL0iwZB7I2b9/P4aGhpw6dUp0gcmLpV2xYsUIDNQUKs7NBKaTjQl6FvbomNuR8v4psphPGP7ZJ2lQtALy8xtIen4FlAph+dfgZPPlBmdnZ2cePHhAo0aNvluR03379lGiRAkOHDggeo1Z+5QAdu3axZIlS1iyZIlQZsiJsDBVBinrnblSqUQul+coRA6qHprMpQv1oNCdO3dELREfPnzg/fv3DBw4EEDISrVrp2kdFhoaSvHixVm8eDE//PCDsHzlypVMmzaNH374QaRa8L2SNYOtDlo+f/78TQLK3bt3c+3atRxdoXLKGmdeJ+0LxgA65uISldTAJJvlquAycxlPFhtOTMB2kl/dRJGSgHum+7is2XZdXV0sLS0ZOXIk/fv3FyY/79y5w5s3b/j8+TMTJ04kNjaW2NhYIZt248YN0XYiIiLYs2cP+/bt49y5c6SkpFCpUiUmT55MixYt8PPz46effsLOzg65XE63bt0oUqQI27ZtIyIiAjMzM0xNTXF0dOTUqVOUK1eO4cOH0717d6GFSG3zNnr0aBYtWkRBSyM+nFVlRg2LVcbc1QcAfcdyROydChIpwfN90LVwwKBQWVKCH2JZvycWdVR6kdFn1qBrWYD4O7+T/PoWsthw7HzEfZMhUUnMnD2HiIgI2rRpw6xZs9i1axdXr14VKkctW7bk9evX6OjokJiYyJYtW0SGAeq++c2bNwtyRGFhYfTo0YM7d+4wduxY/vjjDz5//iy07aiHW4YOHUq9evUYP358tgHl9pshSCQS5AnR6BdxIfHpZRSpiehaFUJiYEzCw9OigFKpVBB/5wgJD0+THv0Bqb4R+gVKYlm/JwYFVceU8PAMCY8vkB4ZTNkFSZQuVZIRI0YIN71yuVyYHVBjb29PoUKFRN99Z2dnYSp906ZN+Pr6cvToUd69e0eJEiUEF7MRI0ZgY2Mj6MoGBgYSFBSEXC7H3t6e9u3bo6OjI0oWODk5YWtrKzqnvnnzhpUrVwKqqk6VKlWE76CRvir5kBb2RuVI02Y8ss8fiL93HHlSHIaFy2HtPRw9q0Ia77Gqepexb3lKAkkvriGPj0KiZ4C+bTHkSbE46KXwR+h7SpUqRWRkJIaGhpiamgrPq1SpEsePH6dJkya0atWKEydOaNyYXLlyBaVSmWNbW+b3QqlUah0QzOf7INe1X3d3d5KTk1m2bBnOzs6iUpeOjo7qh57pCx8UFJSrqWY1zZs358aNG9y6dUtYFhERwfbt23N8romBLkWtjTEs4kJK8APSPr7EoLAqcNS3L45E34jYG/uQ6BqgX6BkDlvTjp6ONEfx5k6dOhEaGsr69es1HktOTiYxMVHLs/5Z1JnOzD/SmzdvCv2xah4/fkz//v3p0aMHo0aJ71a/hLq8v2vXLtHyw4cPk5iYSNWqVYVlERERGs8/fvw4f/zxB97e3sIyFxcXypYty7p160TfsdWrVyORSISJSE9PTw4ePKjxz87Ojho1anDw4EFatcqQrti9ezcjR46ke/fu2WaVvzeyy2B/K2u7sWPH0rFjR/T19QkKCiIoKEjIjLx8/ZYLd59zL+QzH1N0MDAwEAamMqNeVqiQ6oKlLbOvJtv2k2zbUlTHqVTICds9meTXtzGv1QG7dr+wfuehbMthBgYGnDt3jpiYGLp27Sr0N2fu6b1x4wYKhQJ7e3scHBwoVKgQZmZmdOzYkYEDBwqlugEDBnDq1CmGDh3KrFmzSElJoXXr1hw8eJAffviBly9fMmDAAAB27tzJggUL6NKlC5MmqaRRQkNDkclkREZGCq40p06dEl6HeuhR3WJ0+8kr0iOCASWGTlVUx69UEn9rv+qt0tXH2rM/etaOJD4+B0gwqy6WaFH++X7oWjli3XgguhZiWS8lcPTYMWxtbTl58iQHDx6kXbt22NnZCRqvxYsXZ9GiRULGsUePHhq/4aioKGHSH1TBkDqjGRQUxMiRI1EqldSpU4fr168Ln0NUVBReXl4EBgYSGhqq9ZO/8CL8z+OQkPb+OSYuHlg17IuOoSnK1CRSP4iTD1HHl/H53Hp0zGyxauCLRa0OSHT1Sf3wXFgn/t5xdC3ssajdCaeWg4WssTpYi4+Px9HRkefPn/P+/XvWr19P586duX37Nl27dhW2079/fyIjVSoGFy5cYOzYsQwYMABnZ2fRa2ratCktWrQQPtu6devy7NkzKlasSOfOnfH396dgwYJs3bpVcG6KjIzk2bNn2NiostFhYWHUqVNHuOkbOnSo6DuoRvJn/23cjX0kvbyOuWs7LGp3JPXDCyIPL9D6HhuVdCU15BGRx5YQf/8koav6EH1imeo7kp5K3K0DvF/WnT8Wqnr6W7VqhZ2dHcOHD9fYVq1atfj999+5ceMGHTp00Og/DggIoEiRIqLklJqkpCTq1FFJZvXp04datWqxcOFCPD09BZ/5fL4/cp2htLe3p0yZMrx48UJDyLdFixYsWrQIb29vunXrRnh4OCtXrqRkyZI8fPgwV9sfN24cW7duxdvbm1GjRgnllmLFiuVqGw3L2PO0SAUSn1wEJBgUUZXAJVIdDBzLkfL2LgZFKyLRybtMiEQiyZUTSM+ePdmzZw+DBw/mwoUL1K1bF7lczvPnz9mzZw+nTp3KUcD776Zly5YcOHCAtm3b0qJFC96+fcuaNWsoX7686E68T58+gCoTvW3bNtE26tSpo1W4GlQnGBcXF2bMmEFwcDC1atXi1atXrFixgoIFC4qGi+rUqUPVqlWpUaMGFhYW3L17l99++40iRYoIF2818+fPp3Xr1jRt2pQuXbrw+PFjoVxSrlw5QJW9y5rBA/jhhx9wcHDAx8dHWHbr1i169eqFjY0NjRo10rhx+dIx/pt59+4dO3bs0NqqUreWK3r2xSnUVzW0pLQqyraj57H3fiJyILl58yYlSpTAzEz1t5ONCRLItuz9NaRHBCOLDsWmxWhMKzZCAnRt68W1y9kPvqkrIOohuaxERUXh7OzM2bNnKVCgAKGhoVhaWmpIvMTHxxMQEKCSK0MVYFaqVIkff/yRNm3a4OzsTOfOnVmzZg0lSpTgw4cPfPr0ibZt27Jjxw5evnzJ1atXRVn4ixcv0rlzZ5RKJVeuXKF9+/YcOHCAhISETEoXEgwLq77ryYE3SQl+iJ5NERTpqZhVb4lZ9Za8W9YDRVIM8qRYUcuAPE4V+OnbFsWsmljGDVQB+vugt0gkEo4fPy60OwQHBws3+cuXL8fJyYnBgwcTGhrKmTNnKFKkCF5eXvj4+KBQKPj06RNr1qyhffv2bNiwgbJly3Lt2jU+f/4siN9PnTqVS5cuAWgMUYCqpO7o6Chapu7DlSdEA0psWv6A6Z+i72ZVm/F+ZS/k8dEoUpOQGhiTEvyQxEdnMaveCusmGYNl5m7tRDdfDt3mCq0TcuDAtEW0b9OSRYsWMWzYMIyNjRk6dCirVq0iMjKS0aNHU7JkSVatWiVq3fHy8uLnn39m1qxZ7NixAzc3N44ePcpPP/0kOo5Bgwaxfft2Yco7OTmZn376iV9++QVzc3OioqK4ePEivXr1Yvv27aSkpCCXy/H29hbaOObOnUtYWBgTJ05kzpw5dOvWjRkzZgjfQXVPtfLPITilLI2CfZcJ1z6poSmfz64jLSIIfTsn0euzqN0JZXoKiU8vkfQsAF2rgphW9iLu1gHkcRHYd5oOUh3KRVwiMfYzc+bMATJuHrPi6enJnj17aN++PT179hSdV0JCQoTBHNFrsLBg3LhxVKtWjXfv3rFw4UJu3rzJH3/8Qe/evRk5cuR33ef+XyZPTjnu7u68ePFCYyDH09OTjRs3MnfuXH744QeKFy+On58fQUFBuQ4oCxYsyIULFxgxYgRz587FxsaGwYMHU6hQIVEQkh3d3Yqy7ogqiNSzKYyOUUb5z7CICylv72JY+OvK3UqlEkvjnANRqVTKoUOHWLx4MVu2bOHgwYMYGxtTokQJRo0a9f/ia5sVX19fPn36xNq1azl16hTly5dn27Zt7N27VyTxERERQWJiolBOzsymTZuyDbb09fUJCAjg119/5dixY+zcuRMzMzN8fHyYPXu2SN+xc+fOHDt2jNOnT5OUlETBggUZMGAAU6dO1RBGVwfC06dPZ8SIEdjZ2TFp0iStNnG54enTp6SlpRERESEME+T2GL93QkJCSEpK0nAPyg3qDEdkfCrbbgbz9GMcSc8CSHx2GZuWP6JrlvH5GZetS8xFfzYcOov/9ZK4l7SlXwUDzp8/z5gxY4T11BWE4C9M0uaZLBnMojbGGOvrfLEcps5abt26VdAnnTdvHjdu3CA+Pp5169ZRsGBBVq1axadPn5BIJIwcOZIqVapgampKeno6LVq0oFChQkIwCWBqasrAgQOZOHEi9+7do3r16sJjAwcOZNGiRURFRdGoUSNBTN/e3p7t27dz/PhxFi9eLJTWnz17RlRUFBMmTGD//v1cv34dPR3VseqYWSM1VJUVk9/cAYkUXauCgswSgK6lA2lJMSS/uYNepiylfuHypL3PZjpaLiPi4BwUCgVNmjQR9c4eOJAxzBMdHS2UNXfs2CEMUoSGhtK3b1+MjY3R19enT58+xMXFCdtQl/KPHj0qZGTHjBmDl5eX1peT2SlFjboPV54SDxIpJpn8zSU6uirZuKQnpLx7jHFJV5JeXAUkWNTrprGtzAGMqA83JZF7gSF4eHhw6tQpYmNjsbCwYN68eUJQ/SUZpP79+zNr1izWr18vJF3U66v3OXDgQAYOHCiUvM+dOycaSFH3RC9ZskQYAHNycsLCwkLoCy9Tpgyurq7Mnj1bpPWq/g7a29ujVCppMnYVZ8+DSaXGokSK+jooiwnTCCglunpYefbDylN8zZXFfiLtwwtMS9agTgkb7i3eSceOHTV837VVSlq3bi3KTjZo0EAIhjOXu52cnLQ+/6effmL27Nn8/PPPNG7cmEqVKmms5+/vr9UsQJtjVT5/H3kKKNevX6+1nAvQt29frRfmrB/ml0pzFStW1PqD1bbdrJRyMKNRrWpcsz+mIU9iUaez0E+Ulcy2ZACGxSpRbMJR4W8dqYSOUzcIGluZ0fYF1tPTY9y4cTkK32Z1nciLhZt631n3n5uyp0QiYeLEiUycOFG0PKv4/F9xxbCysmLRokU5lpFnzpzJzJkzc71dHx8fUZYxt2g7luze738DvXr14tKlS19VBvfx8WHX7RDmH36CzKICxmZK0sJUEilGJaqjY2whrGtWtTkJ90/xcfc0zN3acuKOLrtuHcLc2lYjM9OwjD1bbwZ/lXuHNvRsCqNrWZDP5zeiSIjCpExhPD1nfFH5QF1+tLe3Fy6E27ZtEwYCatWqhZOTE76+vvj7+2NkZISVlRUfPnwgMDCQnTt3AtpF79VZ8gYNGtCjRw9BMH3btm2EhYVRoEABUlNT+e2336hXrx7h4eHcuHGDt2/fAqoWk+TkZAICAihYsCDVqlUTlDV69VEZDOgYWwr7k8WGo2NmgyxWrByhZ12YtA8vkEWLtVnVAtc6FpoZwdgbe1GmJVPI0VEk9QSI+tozB8pqKlWqxJ07d9ixYwfdu3fH1tYWfX19UdDm4eFB+/btmT59ujBo8uzZM2bOnJlraS91H65SloZER0+jVULdeyv/8/1Ij/mEjpk1OkZfFrtPef+U2IDtpH54jjI9FfclGY+pA8q/k6/piQ4ODsbNTfN6pP4OBgcHU6FCBQa4l+DsAtDN2o/8501JTrJCipRElApVz3tK0AMMi1VGkhJH/4qONHjyhJkzZxIZGYmenl6e3ielUsmOHTuoUKGCMKiTE6NHj2by5MmcPXtWqzlGPt8HedfP+Y7JbF32rdCVSpjdtuI33WY++XyvrLgQyIQDj0iVKXIM/qQGxjh0m4NhERdir+0m+vI29OycMGr3K7sfx4jW7e5W9JsFk6DKStl3mIK+Qwliru/lxv61lCpVii1btmT7HC8vL8H/XZuepNouUp3lVCgUTJo0SZD+yVqG1YaHhwcXL14UbryTk5PZs2eP4PmsDvIrVaqEn5+f0Gcuk8m4fv06AQEBQgVIrawhS1VldmWx4SjSMjRXlbI00iOCRPs3Kq7qUVbfBKiRRb0DJBiV0Gy5MSpeFam+IVKJRKOPPXPf99atWzlz5gwHDhzg9OnTnDlzhrVr1wIZg3Nqa1RjY1W5PSYmBolEwr59+7h+/TojR47E1NSUY8eOUalSJY2BF2191fDlPtyvJf3zR8J2/ow8OQ4rz/7Yd5zK+p2HBEOE3Nj+Zia30kGZ+Tt7ou3N/3RZyq4fOYd9hO//lffLugs6lPF3fuf1oq40cKsCQNu2bbGzs6NNmzZ5el1qw5LcaEyrMTIywsbGhujo6DztK59/ljxlKL93ilgbM721CxMOZK8plldmtHb55rZk+eSjxtfXl4sXL+aYEc4uU60t0/q17iTZOZBYunfH0l37yV/X3Ba7thM1li84/ZLQR9c58dsinj9/TmpqKp2WneHOpzQhsMxcCRC2Z+mgdXnWygGAnm0RCnWbRZ0SNqIKgvpiPGGCapLZ09NT8HtfvXo1PXv2pFq1anTp0oU6depw//59oqKimD17Nvv378fc3ByJREJqaio///wzjo6OnD59WsgmqocvMvP8uWrQY+7cuVSoUIGLFy/SsGFD5syZQ4cOHXjx4gX6+vqCnWnp0qVJTEwUlCH09PQICAggICCAsWPHAprKGoqUBD5tHYtppSbIk2JRJMWqeihlacLr0PlTUin1/ROiTq5A316l+Sv7/BGzGm3Qs9LUDTV0LEv7bn3YPWsYenp6oj7248ePC+vVq1cPJycnlixZwqpVq2jbti3Ozs5cvXqV9evXo6OjI/TNGhkZUb58eXbv3k3p0qWxtramQoUKzJo1i+7du+Pq6srLly/p1KkTbdu2JSwsjOvXr/P+/XsePHig8RrVfbgSXX2UqUkolQpRllLxZ9CtY6GyhdWzLEDKm7vIk+OzzVImv7oF8nTs209G18Je6MOd/eiW1vVzQp1dzCrvkxu3t7xQrFgxreon6u+guq1CTevKhTinKcaQI1aN+qNISSA19BmxV3YydM562lYvyvLlywkNDWXu3Lmq9bLIY+XE9u3bkUgkdOum2Y6QHfHx8URGRmrtuc3n++FflaEElSDwmKbfpldxbNMydK6pOeSRTz5/F0lJSUybNu0vWdZ9Dd/agUSeHMe8sYPR0TNg5cqVbN26lTmdav6/VxC6devGuXPncHR0ZP78+YwaNUpwPclsKaqnp4eenh4rV65k4sSJ6OnpCULWHz58EKkiJCYmsm7dOpycnDQcwNSUKVOGffv2CVmsixcvMnDgQEFBoVKlSuzcuZOQkBBRhlKtrFHUqTi2bcaiVMj4fNEfRfKfPYoSCRLdjP64+DsqcXCTio1JfnWL6LOqTKmBY1msGvXX+toUSpgyuDO7d+9GoVCgq6vL3LlzWbJkCT179hReszpQ9/DwoEaNGuzatYuRI0cyb948SpUqReXKlUV6ghs2bMDR0ZHRo0fTtWtXIagvX748+/erJtSvXr3KsGHDWLNmDVKpNNueaHUfro6hGSgVJD0LEB5TKuR/2utKMCyimi43LlMXUBJ7RXO4TMj+aenDlaUk5knuLjPm5ubY2tpy+fJl0fLcuIvlhebNm3Pr1q1cfwebVSjI3HYVMdCV5sl5yaBASUxLVEUHJcVKlmXl+L40btyYsLAwGjduLPzT1gqRHenp6ezdu5d69eppHaBMSUnRaLsA+PXXX1EqlSL1j3y+P/5VGUo1wxuWwtbUgKmHnyBTKPNUatORStCVSpjR2iU/mMznH0ftgQyIPJ7/biYdfITsG5ak0z4GokhLxqp+D9FQ3T9ZQfD29sbPzw97e3vR8gYNGoje2/79+7Nx40aRK1eBAgVwcXERZehAZUNauXJlmjVrxsiRI7G2tmbz5s28ffuW/fv3I5Vmf4/eqlUrKlasSPHixZk/f76QWe7Tpw8TJ05k7ty5WFhYULGiKkDOqqwhq9eOay4eyBVKlEoFYTt/ITXkITqmNsTfPUby23skB97ArEYbrBsPEPYbPLcl+g7OGiXZAj3mEbqmHyVsTShpb0ZJHx/8/f3p1asXAwYMEMrZ9vb2TJw4kR49euDj44OZmZlgCzhz5kxhAKtBgwYibcbatWtz584dIaOZnJzMunXriI+PZ/369Zibm3P//n2tzmnq7WXWv21Yxp6QASsJ/W0UkccWk/bpFToW9iQ9v4oiKRarRgOEyXbDYpUwcWlI/B9HSP/8AaMS1UGpJPXdEwyKVcS8eiuMilfls44u4ftmYF61GXoFDKlefQD29vZa5bByQ//+/Zk7dy79+/enRo0aXL58mZcvv43vuJoJEyawc+fOPH0Hu9QsSl1nWyYdfMSF6E857kNHKkGuUFKnhA1vr3ygUmPV55CSksK9e/c0VDhyi9paN7ty96dPn6hatSpdu3YVhgpPnTrF8ePH8fb2znN5PZ9/ln9lQAniH1DAq0jhB5IdmX9As9tWzC9z5/OvIjExERMT7cL8gWHxBLzSLOP+FeSJMQA8iZKLHEi61CxKZEKqqLSuSE9BqmeY5338nRUEiUSi9cLs4ODAtWvXGD9+PMuXLxeEzY8cOaIx2JYX3N3dmTt3LnXq1BHtV62sUbt2bZq0Kof38qvIFUokEinmbu2ICHmIIi2J6LPr0bWwx7JhX8xdtQuDZ0ezCgWE/+/Rowfx8fEMHToUc3Nz5s+fz4QJEyhdujSLFy8WbnaKFClC06ZNNRyxtOHh4cGtW7fYtWsXYWFhWFhY4Orqyvbt27MNJrXR3a0o/teDcOg2h88X/Ul4dA5FWhJ61oWxaf4DppXEE8c2LX5Az744CQ9P8/nCb0gNTNAvUBIDR9Xwip5NYex8JhJzeSvR5zfyomBBRgwfip2dXa4GQbUxZcoUIiIi2LdvH3v27KFZs2acOHFC46bmr/C130G189KFSiZ4rgEbU33SEMt5SVBlahuWtqdHraIUtzHG8oc7DB6guin8448/SEtLo3bt2l/12rdv346enp6gZZoVS0tLWrZsyZkzZ9i8eTNyuZySJUsye/ZsxowZ88Ubtnz+/5Eov5Ui8ndMYFg822+GcOFlOCFRSV/8AWVnvZXPf4+HDx9SuXJlfv/9d+HC+ccff1CjRg2qVq3K3bt3hXWbNWtGdHQ0N2/eBOD3339n3bp13Lt3j6ioKAoXLoyvry+TJk0SNeJn7qEMCgrSeoGdOnWq0D/5/PlzfvnlF86fP09SUhIVKlRgypQpogu7v78/ffr04eLFi+zevZt9+/aRnp6e7QT0tMNPtE5gZ3YRUaQmomdZELPqrTCr1lzrdtR82j6B1Hdip53evXvj7+9PgwYNiIyMpNs4P2b+Mp6Uj68wreKFdeOBJL28QcKDk6SFvUGeHIeumS0mFRthUbsTEqnqPdORSvi4bQI2uqmcOnKQYcOGcfPmTaysrBg1apRIXUHdx7h3715B/D41NZX27dsTEBDAiRMnqFOnjvB+vX37FicnJyAjO6xuPVBva/fu3QQGBrJ69WoiIyOpW7cua9eu1Sp18y0JCgqicePGVGnYigvvZaRHvSPh3kmkBsYU7L9SJJOWF/zaVfyfqsT03HiTa2+ivumAl45UotGH+18hMVVGUFQiaTIF+rpSnGxMcqW5nE8+2vhPfHNKOZgxrbUL03DJ/wHlk2sqVKiApaUlly9fFgI2tYvJgwcPBP9shULBtWvXRJqd/v7+mJqa8uOPP2Jqasr58+eZMmUKcXFxzJ8/X+v+Mnsgt23bVrCQ/FoP5KFDVdmWKVOmfNGl6cKLcK0X6Ph7x9GzLYZxKTeQSlU9eadXgVKBWfWWWrakwqJOZ5JeFCbh/kks3LtTwLEogwZlaCJGRUWxZEwfOvt04FlKK94k6qIjlZDw6CwSPSPMavog1TckJfghsQHbUaYmY9OoLwolVChkjpmDKeGhUXh7e9OuXTs6derEvn37GD9+PBUrVhT8lbOSnJxMmzZtuHPnDmfPnhXp/+WWuXPnIpVKGTNmDLGxscybN4/u3bsLNxJ/F1ZWVlSvXp2rJ/YSFx6OQscAI+caWDbw/epg8n+xR3x224o0XnzpmwaU/2UlDxMDXVwK/b3SSPn8d/jXR1LaAsj8H1A+uUEqlVK3bl3BJg1UAaWPjw+///47165dw9vbWwguMwv+79ixQ+RdO3jwYAYPHsyqVauy1d8zMTGhQ4cODBkyhEqVKtGjRw/R46NGjaJo0aLcvn1beP6XPJCtra05d+5cttIkkOFAoo3MLiIA5tVbEbZ7CnG3D30xoDQqXhV5fBQJ909iVKIGqQVLUalaRvCmdlMZNEjlYKKuIJw1nUponFyoIJhVbU7UyRXE3zuGZf2eSHT1ePA+lk+f4kn98IEynSdg6dmDxm5F6devH8WKFWPjxo1aA8qEhARatmzJkydPOH/+vCBzk1dSUlK4f/++MHyizow+fvxYsBv8O7CwsGD37t3C37tuh/wne8TzlTzyyef75V8ZUAol7hfhhERrKXFbG9OwjL3ILi6ffLTh7u7OL7/8IvQgXrlyhdmzZxMcHExAQADe3t4EBAQgkUhE7imZg8n4+HhSU1Nxd3dn7dq1PH/+PM/WYdHR0Zw/f54ZM2YQHx8vmoT08vJi6tSphIaGirQSBwwY8MVgEjIcSLSROZhUixwbFq1AzNu7KFISBbHsnFACQVGJwo2cgYGBYO0JmSoIrVUVhBtvolh0/AEPQyIxKlKBhPsnSY96h75DhnORRN+IZKe6bL0ZjP/1INxL2lKxSnVhYjszsbGxNG3alDdv3nDx4kVcXL7OMQtUAzSZJ5nVNxFv3rz5WwPKrPyXe8S19eF+Lf+LWdp8tJNfffz/51/1br+LTsrxBKsEgqOTRBei//UTbD5/H+7u7oLodJEiRQgPD8fd3Z0nT54ImcuAgADKly+PtbW18LwnT54IvY6ZbehAFeDklVevXqFUKpk8eTKTJ0/Wuk5WD+TcDDyoHUi0kdVFJDOK1NwHlFn34+joKArK1Dx58gTfYT9x90aAoCuYsT/x3zpmNkgkGb/xa2+iiPyQioEWYewffvhBmE79K8EkfNnZ5J9GPWTxX+wRz1fyyAe+v+TRfz2o/dccaeYSEJDjCSbzhajx4ktMb+1Cl/yTSz5ZqFGjBoaGhly+fJmiRYtib29P6dKlcXd3Z9WqVaSmphIQECAqN8fExODh4YG5uTkzZszA2dkZQ0ND7t69y/jx4/PswAEZrh158UDOnCXNjuwcSNQuIno2hbHy7I+uuS3o6JH8+jbxt3/P0WXjS/vR9rpiYmJwq1OPVIkBFvW6o2tVEImuPmmfXhFz0R+U4vcsq/We/M+gIjoxjRUXAhnesJTwWJs2bdi1axdz585ly5Ytf2lS9O90Nvla/qs94v/lLO1/ne8pefS9BbX/n/wrzjQrLgR+dflDfSGacOARkQmpogtRPvno6+vj6upKQEAA1tbWhIeH4+/vT8uWLUlNTWX79u2EhYVRv3594TkXL14kKiqKAwcOiJarnVa+RHb2bSVKqMq9enp6gg/1t0DtQJL5JPhp+wTk8VGCiwhKJaFr+mHT/AckurnzXs6M5M/9fIlf1+8lMS4Gh25zMSyaUTqWxeSsmZeVBadfYmdqgMOff/v4+NC0aVN8fX0xMzNj9erVed7mtya3Dkl55b82ZPFfztL+V/lekkffU1D7vfBVt+pBQUFIJBIWLFjwrV+PVpycnGjZUvsQQHZ2cV/DgtMv2X075KufP23atGwDgv8Psgo45/N1uLu7c/PmTZEzha2tLeXKlcPPz09YR406i5U5a5WWlqbhmPHhwwfu379PWlqGdV5mD+TM2Nvb06BBA9auXatVdDk7D+ScUDuQ5AZleiqJj87meR9FbYxFWbL09HSmT5+Oq6srVlZWWNvYsmL+LPVeMvYnTyf2hspRJeJ3P4Lnt+Xd8h6kRQShyFKCl8WEkfj4HPK4cILntqSLazEaNmwIQMeOHQkICGDZsmWsWbOG8ePHk5qayvjx4ylUqJAwnZ95+Cqf3PHx40cGDhxI8eLFMTIywtnZmR9//FHwRc/MihUrKFeuHAYGBjg6OvLjjz9+UX0gL6iztJfGNOTxNC+OjajHwSF1ODaiHo+neXFpTEOmtXbJDyb/x1lxIZAJBx6RKlPkedJfrlCSKlMw4cAjVlwI/EuvY9ftEBovvsS1N1HCtnPaN2QEtbv+QpzxPfPdqIQ+ffqUadOm5emO/VvbxQFMOfyEd9lMvebz30Rtgffx40euXbtGz549AZXX8suXL3FycqJw4cLC+nXq1MHKyorevXuzaNEiFi9eTK1atTTKoh8+fODBgweigDKzB/KqVavYtWsXjx+rNB1XrlyJUqmkYsWKTJw4kfXr1zNz5kxatGjxl7KWDcvYa1iySY1M4U8XkYTHKi/pmCs7kBrnLfulI5XQsLRY1DkuLg4/Pz9KlizJzJkzcW7aS7DBCz8wm7hbB4m7dYhPW8aAPB0Ao5KuWDcdgrlrO5DLkMeGkfzmj4zXa2yBQeHySAzNsWn5E3atf8KleW/h8aZNmzJ8+HBmzZrFvHnzcHV1ZdGiRXTv3l3wFO7bty9XrlzJ0/F9LevXr9fqx/y/REJCArVr1+bgwYP06tWL5cuX07x5c1asWEHjxo1FrR3jx49nxIgRVKhQgaVLl9K+fXuWL18uSGN9S9RZ2qpFrXApZPGvLfnnFXXCQ5sX/bfE398fiUTCnTt3vul2vzZ5lBL8kOC5LUl8nvHb/ivJo28d1Pr6+mJqavpVr+V747sKKKdPn56ngPJb28UByBRKJh38dpIU+fzv4+rqilQqxczMDFdXVyEDmdlzOTM2NjYcPXqUggUL8ssvv7BgwQKaNGnCvHnzcrW/L3kg37lzhxYtWuDv758rD+Tc0N2tqMaJUaJrgJ3PRABir+8FwKBoBcxr5OyMkhm5QkmPWuLykomJCSEhIezYsYOmHXoR4dSIAr0WoWNujzItmZjL24i7dQBDpyrYNB8JgKlLQ8wqN8XCrR169iVAIiXu9u/CNqX6huhaFkSqb4hphYYYl29IpIXKStHIyIhWrVQ6mJMmTaJnz548fPiQli1bMn/+fCGL7+joKBJG/ztQZ+T09PS0Skf9L3H48GGCg4Px9/dn+vTp9O/fn+XLlzNx4kTu37/PgwcPAFUWc9GiRfTs2ZO9e/cyePBgli1bxuLFizl9+jRHjhz5fz6SfL6GVatW4e/v/4/s659IHvn6+grGBtnx/1kRTUpKwsnJKU/uUv80301AmVfUdnHfUuAWVBfAgFeRvArXNKjP5/8P9d31y5cv6dGjBxYWFtjZ2TF58mSUSiXv3r2jTZs2mJubU6BAARYuXCh6flpaGlOmTKF69epYWFhgYmKCu7s7Fy5cEK2XuZ1jyZIlODs7Y2tryx9//MHDhw/R1dUVTqLdu3fnwYMHSKVSSpQogaGhIQUKFKBv376UKVOG69evk5SURGhoKH5+fjRt2hSlUkmDBg24ePGiIKwdFRWFRCJBIpHg7+8veCBv27aNatWq4efnh62tLT169MDAwIDNmzfz8eNH0tLSaNy4MRcuXKBWrVr4+PhgamrK2LFj+emnn6hataro2BQKBUuWLMHFxQVDQ0McHBwYNGgQtvoy3EvaamQpjUu5UajfChwHrFH97VwT00pNKDbhKLqWDiQHPeDTtnGELGxPyOLOhO/7lfTIdwCYVmpMiUnHaOxeW1RmvHjxIq9evcLW1haA7TdD0JFKkOjqYVymDihkFB6xlcLDt2DVsA9GxatRbMJRDItVErZRsOc89GyLoEgVl0ttW46m8NDfhL8N7IogkUrp3LkzhoYZ1o4FChRAR0eHzZs3A6oLiVKpZNCgQVy/fp13795x8eJFwSUHVO0jSqWSDh06EBAQQMeOHSlatChlypShcOHCPHjwgOTkZNHrUWceXr9+TfPmzTEzMxM8jLNevBo0aCB8B7L+y3zRfvPmDR07dsTa2hpjY2Nq1arFsWPHRPu9ePEiEomEPXv2MGvWLAoXLoyhoSGNGjXi1atXonUzH4uBgQFFihRh9OjRGseiDbV6gYODg2h5wYIFgYzhq+vXryOTyejSpYtoPfXfu3btEpZFR0czZswYKlasiKmpKebm5jRr1kwITvP5fvgnA8pvlTySxUcRE7CdtLA3eU4e/V1BbWKqLFfrJiUlERwc/M3aRP4O/nJAuXjxYooVK4aRkREeHh5CeS4zz58/p0OHDlhbW2NoaEiNGjU4fPiw8Li/v7/g7dmwYUPhRJr5hA5w5coVXF1dMTQ0xLVyeZKenP/q15308gbhe6fxfkUvguf7ELqmPzFXd6JUyNGRSth248t3DleuXKFmzZoYGhri7OzM2rVrta4nk8n49ddfcXZ2xsDAACcnJyZNmkRqqrgHTCKRCPZ6mXFycsLX11e07OHDh3h4eGBkZEThwoWZOXMmmzZtQiKRfDHDm9ugClQn+erVq2NmZoa5uTkVK1Zk6dKlX3xP/gk6d+6MQqFg7ty5uLm5MXPmTJYsWUKTJk1wdHQUSqljxozh8uXLwvPi4uLYsGEDDRo0wM/Pj2nTphEREYGXlxf379/X2M+mTZtYvnw5AwcOZOHChSJJoMycOXOGN2/e0KdPH5YvX06XLl3YtWsXzZs3/+Lkb7ly5ZgxYwYAAwcOZOvWrWzdulUY4vH396dTp07o6OgwZ84cBgwYwIEDB6hXr55Gf6VcLsfLywsbGxsWLFiAh4cHCxcuZN26daL1Bg0axNixY6lbty5Lly6lT58+bN++HS8vL6a3LIuuNPf9v8lB9wnfMwV5UiwW9bphXtOH1NBnfNo2FllMGJA7B5LMLj3yxM9I9AyQ6Glm7hSpSciTYkmPesfnS5tJjwjGsNiXtTzjH19CqVAIQZyae/fuUbp0aczNxQ4zrq6uAFq/D5nZu3cvSUlJDBkyhOXLl+Pl5cXy5cvp1auXxroymQwvLy/s7e1ZsGAB7du317rNn3/+WfgOqP+pp/nVPtBhYWHUqVOHU6dOMXToUGbNmkVKSgqtW7fm4MGDGtucO3cuBw8eZMyYMUycOJEbN25ovBd5OZas1K9fH6lUyqhRo7hx4wbv37/n+PHjzJo1Cx8fH8qWLQsgnOuyTver+4X/+COjdeHNmzccOnSIli1bsmjRIsaOHcujR4/w8PDgw4cPOb6mfP59fMvkkTwhmtirO1X2rlmSRzm1ofxdFdG7If+85NjfRa6bS+7fv6/hLrFlyxbi4+MZNmwYKSkpLF26FE9PTx49eiTctebGLq5+/fqMHDmSZcuWMWnSJMqVKwcg/BdUOnwdOnSgX79+9O7dmwlzlhJzZDEF7Z3RtyuW5wP/ks2blWdfLrwMZxraNesePXpE06ZNsbOzY9q0achkMqZOnapxpw7Qv39/Nm/eTIcOHfjpp5+4efMmc+bM4dmzZ1ovAjkRGhoqBN0TJ07ExMSEDRs25Kp8pg6qunbtyoABA4iPj2fjxo14eXlx69Yt4fM9c+YMXbt2pVGjRsLQybNnz7h69SqjRo3K82v+lri6ugrB+8CBA3FycuKnn35izpw5jB8/HoCuXbtSqFAhfvvtNyFAs7KyIigoSKR/OGDAAMqWLcvy5cvZuHGjaD/v37/n1atX2NnZCcu0BetDhw7lp59+Ei2rVasWXbt25cqVKxrlcDUODg40a9aMKVOmULt2bZErTnp6OuPHj6dChQpcvnxZyK7Vq1ePli1bsnjxYqZPny6sn5KSQufOnQV9ysGDB1OtWjU2btzIkCFDANUN0IYNG9i+fbvQMwiqGzhvb29unDvG9Nb1cu1A8vn8b0gNzSjQcwE6RqoMpHHpWnzcNIqYK9uxbfljjg4kmV160j9/IPnldYzL1BV8uzMTcWguKW//9E7X0cW0ijeWdbtorJeZxKcX0TG1xq1ufdHyjx8/Clm0zKiX5RS4+Pn5iYKjgQMHUrJkSSZNmkRISIhIqzI1NZWOHTsyZ86cL26zSZMmor+vXbvG+fPn6du3L82bq7zT586dS1hYGAEBAYKI/oABA6hUqRI//vgjbdq0EUki5cbVJy/HkpXy5cuzbt06xowZQ+3atYXlvXv3ZsOGDcLfZcqoWg+uXr0qDEpBxhBUaGiosKxixYq8fPlSdBw9e/akbNmybNy4MVsN1ny+juDgYBo1aoShoSHnzp3DwcGBTZs24b95C48fPyY+Po4ixYozYsRwfhgxXHiek5MTwcHBQIYyhYeHhygJlJqayo8//sjWrVtJSkqiadOmrFu3Tjin9u7dm2PHjvHx40f09PREr6tp06YEBwfz4sULoYrxlwNKhVz4X6VcRvje6aS8f8Icy7VsHN+DWbNmMX36dAIDA5k5cyaHDh1CqVTSrl07Rk+ZS8Arce9pwuMLxN8+RHrUOyS6+hgWr4pVw77omtuJ1kv98IKYKztIC32OUiFD17IgppWaYF6zDXKFksi4VJRK1e9g2LBhnD17FiMjI3r37o2fnx86OjoEBQUJpe6IiAjhPZ86darWRNT/F7kOKLVddF+9ekVgYKAgpuzt7Y2bmxt+fn4sWrQIyJ1dXIkSJXB3d2fZsmU0adJE62TyixcvuHz5Mu7u7iSkyvB7aUXCSl8SH51F37Nfng/ctvVYkRNIVpu3kKgkElNlWhu6p0yZglKpJCAgQDjhtm/fnooVxdmYBw8esHnzZvr378/69euFY1dnKy5cuCA6weYGPz8/Pn/+zN27d4UAsE+fPpQqlbPcUW6DqmPHjmFubs6pU6dydFr5p+nfv7/w/zo6OtSoUYP379/Tr1/Gd8DS0pIyZcqIXFN0dHSEY1EoFMTExKBQKKhRowZ3797V2E/79u1FwWR2ZL4Yp6SkkJCQQK1atQC4e/dutgHll7hz5w7h4eFMmzZNVKpt0aIFZcuW5dixY6KAElRBZGbc3d3ZunWr8PfevXuxsLCgSZMmoqb86tWrY2pqyoULF1jfrRuRCamM2f7l1ydLiCY9/A3mbu2FYBJA3744hk5VSH59J1cOJGqXHkV6ChEH5yLR1ceyga/Wda0a+CJ3bYs8LoKEx+dRymUoFXKyy6mmR4eS9ukVZjXbEPI5GRejjO98cnKy1hsw9XudU7k382eemJhIcnIyderUQalUcu/ePY0gTB3U55ZPnz7RoUMHqlSpIlIGOH78OK6uriJHJlNTUwYOHMjEiRN5+vSpyK0nN64+eT2WrDg6OuLq6krz5s0pVqyYME1va2srqIBUq1ZNuC44OjrSsGFDnj17xpAhQ9DT0xO935k/F7lcTkxMDKamppQpU0br7zSfr+f169d4enpibW3NmTNn+Cw3YPXhJyya6ofSsgh6VdpiKpXy4dUtRo8cwcrzgXTvM5DubkVZsmQJI0aMwNTUlJ9//hnQbH0YMWIEVlZWTJ06laCgIBYtWsShQ4d48eIFM2bM4ODBgyQlJdGzZ0927tzJ+/fvGT58OOfOnSMxMVHI0KurGEp5OrFXd5P8+jbpMR9BIUffwRlL9x6ilhhZTBiha/ph2bAvEqkOsTdUPejJb+6S+Hg+ANGnVgjrX7mruok+dOgQAJ06daJ48eKMGTOGyZMn4+/vT8CDl4S+fYMsPhJ9u+LoFyxJwr2TGJerh2llL+RJscT/cYRP2ydQqM9SpIaqIZvkt/cI3zcdHRNrzGq0RsfUioQnF4m5sp3Yq7tQylJVUmyyVLy8vHBzc2PBggWcPXuWhQsX4uzszJAhQ7Czs2P16tUMGTIEMzMz4bxQqVIlTp8+jY+PD23atGHr1q3o6uoikUgYNmwYjRs35pdffiEwMJCSJUuycOFCvL29RZ/TvXv3mDRpElevXkWhUODm5sasWbOE69j58+fx9PTM1Xcq1yVvbT9mHx8fkTOHq6srbm5uHD9+HMiwi+vUqRPx8fFERkYSGRlJVFQUXl5eBAYGiu5Ov0T58uWFE2JwVCJSYwv0rB1J/wqdOshiK/dnOc2giAvK9FTSo94JdnFZkcvlnDp1Ch8fH9HJtly5chqC0+r34ccffxQtV2e0svY+5YaTJ09Su3ZtUbbY2tpao5SlDR0dHeECo1AoiI6ORiaTaQRVlpaWJCYmcubMmTy/vr+brBc4CwsLDA0NhZ68zMuzupds3ryZSpUqYWhoiI2NDXZ2dhw7dkyrc01uG5+jo6MZNWoUDg4OGBkZYWdnJzz3axxxAOHOX53ZyUzZsmWFx9UYGhpqBL9WVlai4w8MDCQ2NhZ7e3vs7OxE/xISEggPDwdUDiQl7EyRSiQaPZVq5LGqdfWsHUXLdaQSDO2KokiOw9dVMwOYlTSZAqVCTuTv80iPCsHWZyK6ZjZa19V3KIFR8aqYVm6KQ5dfSfv4kqhji7PdduKTiwCYuDTUcAMyMjLSaDkB1Q2B+vEvERISgq+vL9bW1piammJnZ4eHhweg+Znr6uqKFAByQiaT0alTJ+RyOQcOHBAFWMHBwVq/E+pKTtbvRW5cffJyLFm5evUqLVu2ZNasWYwaNQofHx8WLlzIL7/8wqJFi3j69Kmw7v79+6lcuTJ9+/alePHitGrVik6dOlG1alXRhKtCoWDx4sWUKlUKAwMDbG1tsbOz4+HDh1/9e8pHk+fPn1O/fn0cHBzYsv8oo39/TZMll9l6MxjLTrOxafED5jXbYF69FQ6df8WweDWCLu5h681gmiy5zP6oghibmuHg4ECPHj3o0aOHRpbdxsaGs2fPMnz4cBYsWICbmxsAHTp0QKFQMG/ePAwNDdm9e7eobUkd8Jw6dYqTZy8IVQxFahIJD09jULQiVg18sajXDXlSHGG7p5AWpmm5mvjoLPF/HMGopKqVxaBQGczrdFI9qKOHhXtPbFr+RKJ1aVEfY9WqVdm/f79QNbKwsOD1/Ruqqkj9nqR//kjCvRNYuHfDrs14zKo1x7JeVxy6zUYeH0n8XdV1X6mQE31yBTom1hTquwxL9+6YVW2OPDYc4/INsHTvjmX9XiCRoJDJqFKlChs3bmTw4MHs27ePqlWrCkkeExMTOnToAKjO9+r3PCQkhNatW9OxY0e2bduGrm5GAuzKlSsMHTqULl26MG/ePFJSUmjfvr1I0uvJkye4u7vz4MEDxo0bx+TJk3n79i0NGjTg5s2bALkOJiEPGUptP2ZtWbHSpUuzZ88e4Ovs4rIj88lRfYGQGpqiSEnI1evPSlpEMDGXt5IS8hBlNjZv2mzpIiIiSE5O1nrsZcqUEYJIUJ3gpVKphoNJgQIFsLS01LgA5Ibg4GBReUlN1n1kx+bNm1m4cCHPnz8nPT1dWJ45gBo6dCh79uyhWbNmODo60rRpUzp16qRxZ/P/gbaMaW7cS7Zt24avry8+Pj6MHTsWe3t7oT/x9evXGs/NjcsMqO5mr127xtixY6lSpQqmpqYoFAq8vb2/yhHna8hNFlmhUGBvb8/27drTj5kDUnszAyyM9ChZwoaAV5FIc+itzOxAYlDNkY23cve69XWlRJ1YTvKr29i2HoORU+78zSU6ehiVciPu+j4U6amim0M1iU8vomtdGIMCJTXcgAoWLKj1Rlat71moUKFs9y2Xy2nSpAnR0dGMHz+esmXLYmJiQmhoKL6+vhqfuYGBQZ6cecaOHcv169c5e/ZsngJRbeT0u8jrsWRl7dq1ODg4UKNGDdHy1q1bM23aNK5du0b58uUBVSbzypUrBAYG8unTJ0qVKkWBAgUoVKgQpUuXFp47e/ZsJk+eTN++ffn111+xtrZGKpXyww8//GO/p387jx8/pnPnzpQsWZJ+M9fRYdNDkUi4KNmSkohSIcOwaAVi3t4lPSkBqaEJ195E8T4qCR1jzRszNQMHDhTpMhctWpQbN25QunRpduzYAaiuZwsWLODHH39k7ty5jB8/nho1auDm5sbDhw9Zs249SmdVskRqaIrjkI1IdDLK42aVvQhdP5i4P45g21zcjiWLj8Rx0HrSI4JJfHAKdHRJDlSdnCzqdMGybmdh3czJo6zVnvT0dFAqMKvaHKmBMelR70l8eAYdEyvkSRlxkY6JFXpWhUgJeYhFnU6qwZ/YMKwaDRAylgCFBq4VvcdpYa9JfHKBN2+DRPvNWmXKyoEDB+jSpQu+vr6C2kdmnj17xtOnT3F2dgZU7U2VK1dm586dDB+ual/45ZdfSE9P58qVK4J5Rq9evShTpgzjxo3j0qVL2e5fG7kOKP8pu7jsyHxyFF0gvsLyTJGSQNiOiUj1jbD8gs1bdrZ0eeWviJ3L5fKcV8oluQ2q7O3tuX//PqdOneLEiROcOHGCTZs20atXL2Ey9n+Nffv2UaJECQ4cOCD6PKZOnfrV2/z8+TPnzp1j+vTpItmewMDcieZm970oVkzVE/zixQuNu8MXL14Ij+cFZ2dnzp49S926dXMVLBvoSgUHkpWHr5N5HEvHQjUkkh4dSrEsDiTN9k3D1tYWE5Ocfb7XL5hB4qOzWDUagEl5jzwdjzI9DVCiTEuGLAFl6ocXyD5/xMK9u1aXnipVqnDhwgXi4uJEgznqO/KsveKZefToES9fvmTz5s2iwZVvkc3ftWsXS5YsYcmSJUKWMDPFihXTOjTw/Plz4fG88FePJSwsTOv5SX2jKpNpTq+WKlVKuBl/+vQpHz9+FA0d7tu3j4YNG2q0V8XExGhUIfL5Olq1aoWDgwPtfl7JjNNBGo+nvH9KbMB2Uj88R5nFQECRmojU0ERVglYqeRORoGF1qiZrhlx93smcyfT19WX+fFUZul+/frx48YI//viDNWvWsGbNGkKCg0AVD/3ZW602jVCgSEkEpQKDAqVI+6SZFDAuXQcdYwvUaZPP59YLx5O1EpI5eZT1dVeoWoNbVy+jSElAapDREx59cgXRJ1egwZ894LIY1Q2qXpYZj8zBpDwlAaVCBhIpT56IB5qzVpkys3PnTnr16iVIcGm7ljRu3FgIJkFVHjc3NxdaweRyuVAuVweToLrh7tatG+vXr9c4R+bEX1J81XbhVAs9Q97s4vISdKnt4r6WlJBHKJLjsGs7KVubt+zs4uzs7DAyMtJ67FlP9sWKFUOhUBAYGCgaMAoLCyMmJkZ0AbCystKY3k1LS9NwRSlWrJiG9AegdVlW8hJU6evr06pVK1q1aoVCoWDo0KGsXbuWyZMn5/om4Hsis3uN+tjV7jc59YnlZpuZWbJkSa6erw66sn7uNWrUwN7enjVr1tC3b1+h7HnixAmePXv2VZqTnTp1YtWqVfz666/Mnj1b9JhMJiMhIQFLS0uN55VyMOOHJqVZCszyqUC9FvVIkynodqEi0UFX+L3/BuF5jx8/5vTp06IBo+yYP38+Sxcvoohnd6Q122S7njwxBh0T8etSpCSQ9PIaOuZ2Go9BpnJ3+QYaLj2gKrktWLBAGCgB1QDBpk2bcHNzo0iRItm+Hm2fuVKp/MsKCI8fP6Z///706NEj28G35s2bs2TJEq5fvy5UKRITE1m3bh1OTk5CNjC3/NVjKV26NKdPn+bE6XMUrVBD8A/fsk2VBc8qW5UZhULBuHHjMDY2FmWEdHR0NH5Pe/fuJTQ09H/yvJMbLl68SMOGDblw4cI/4mzWvn17Nm/ezOxlGzCr2kz0WPrnj4Tt/Bk9m8JYefZH19wWdPRIuHeCpBdXSX3/DF0LsVGB2uo0a890dhnyzNWQ8uXLY2Njw+fPn7G1tWXp0qXo6+vTqVMndu7cyYewCFKCHxK2cxIOXWcji4sg7tZB0qPegyLjhkXXQnMgVtdSvMy4lBuJT1XKH1m/Y5mTR1lft7mFqlVEnbxSDw0auzTAtKJmXCPVM9RYlpmkV7eIvbZbVaaXZ1QJ4/+U4cqJz58/06NHDzp27Mjy5cuzXU/bdS1zkBoREUFSUlK2bTQKhYJ3797h4qJ9OFkbfymgPHToEKGhoULJ+tatW9y8eZMffvgBENvFjRgxQmOyMiIiQvhyZXdx1YbaLk7TgC6XSNRfHrHNm7r3ATTt4tTo6Ojg5eXFoUOHRFOQz54949SpU6J1mzdvzqRJk1iyZIlIVkg9sNSiRQthmbOzs0jmBmDdunUaGQAvLy9WrlwpmrqPjo7OtpSZ9bVDzkFVVFQUNjYZd3BSqZRKlVRNz9p6z/4XaNmyJQcOHKBt27a0aNGCt2/fsmbNGsqXL09Cwte1TZibm1O/fn3mzZtHeno6jo6OnD59Olee3aD6zC0tLVmzZg1mZmaYmJjg5uZG8eLF8fPzo0+fPnh4eNC1a1fCwsJYunQpTk5OjB49Os+v1cPDg0GDBjFnzhzu379P06ZN0dPTIzAwkL1797J06VKhRyc7DPR0BJ/o5UsW0axZM2rXrk2/fv1ITk5m+fLlWFhY5Dh1ePDgQcaNG0epUqWoULkCV55eIHMBxKh4FXRMVCfx8D1T0TGzxaBQGaQmFshjI0h4dBZ5QjS2bTRFyJUKOYnPA9AvVAZDm0IaLj0Abm5udOzYkYkTJxIeHk7JkiXZvHkzQUFBGpmxrJQtWxZnZ2fGjBlDaGgo5ubm7N+/P9tMQm7p06cPoJLi2bZtm+ixOnXqUKJECSZMmMDOnTtp1qwZI0eOxNrams2bN/P27Vv279+fp9L6Xz2WwLB4lOW8kOpvoEWrVphVb4muuT0p7x6T9PQSlqWqcyLMFOuweEo5mDFq1ChSUlKoUqUK6enp7Nixg1u3brF582bRuadly5bMmDGDPn36UKdOHR49esT27dtFGZR8/ho/TJrOwQcfiT69Gqm+ESYuDYTHkl/dAnk69u0niwLHxIdaLFczJSWmHH5CHWfbXHlUZw3YnJ2duXXrFh8/fmTHjh20aNFC6PfNLGWW/PYecTf2YlSqFuZu7dAxtgCpDnHX95D+WXOWQqIrrlwYlaqFrlUhYq/sIPHJecwqqzKl2SWPhNebpVKpa6WKY6QGJhg5Vcn2ebqWqvXSI4KF9VLePSZi368YFHHBpukQdEytibv9OynBD1Aqv1wFVl+zzczMcHFx4fjx49y5c0ej5UR43bloBfvW/KWAsmTJktSrV48hQ4aQmprKkiVLsLGxEblNrFy5knr16lGxYkUGDBhAiRIlCAsL4/r167x//14QrK1SpQo6Ojr4+fkRGxuLgYEBnp6eggZbVhqWseeWRPJVb45B4XJIDU2JPLoY8xqtAAmJTy6gDjClWuziMjN9+nROnjyJu7s7Q4cORSaTsXz5clxcXHj48KGwXuXKlenduzfr1q0jJiYGDw8P4STq4+MjmvDu378/gwcPpn379jRp0oQHDx5w6tQpjTLPuHHj2LZtG02aNGHEiBGCbFDRokWJjo7+YqY3t0FV//79iY6OxtPTk8KFCxMcHMzy5cupUqWKKNP6v4Svry+fPn1i7dq1nDp1ivLly7Nt2zb27t2roXeaF3bs2MGIESMEW8SmTZty4sSJL/bhqdHT02Pz5s1MnDiRwYMHI5PJ2LRpE8WLF8fX1xdjY2Ohr8jExIS2bdvi5+enNZOYG9asWUP16tVZu3YtkyZNQldXFycnJ3r06EHdunXztK3GjRtz8uRJpk6dypQpU9DT08PDwwM/P78cB5rUv/nAwEACF0/UeNyh62whoDSt1ITEZ5eJu33oz3Kbqaq5vvUYDItU0HhuStB9FIkxmNTurNWlR82WLVuYPHkyW7du5fPnz1SqVImjR48KMlPZoaenx5EjRxg5ciRz5szB0NCQtm3bMnz4cCpXzl0PqDYiIiJITEwUfMUzs2nTJkqUKIGDgwPXrl1j/PjxLF++nJSUFCpVqsSRI0dEN6e55WuO5V10EpMOPiLgVSQ6UikFei8h5vJWEp9cRJ7wGR1Ta8xd22Hm3o2tN4Pxvx6Ee0lbqpYqz5YNq9m+fTtSqRRXV1fOnTunoXIxadIkEhMT2bFjB7t376ZatWocO3aMCRMm5Pn4/leoX78+ycnJoon8v5NZx59j02wEspRkIo8tRqJvhHEp1cBMRrIlA0VKIikhquuavmNGv6tEz1AwF1CLhG/t55bn11O8eHFu3brFqFGjePPmjVACB5BKoHRlVwwcDxDxux+6lgWwa/ez6DoXG5BzMkWNUUlXYq/sIDXkEZ8vbMKqYZ9sk0dqTPXFjxmXrkPMhU2khjwWJWdAFawpUuLRMTJHv4AzuhYOxN3+HdOKjZAampL04hoSXX3sO81Aqqf6vBOfBeTqtat1WyUSCUePHsXT0xNvb28uXbqUpyyiGjs7O4yNjbNto5FKpV+s1mhDosxlRObk5CTo8Kk1kebPn49UKmXJkiWEh4fj6urKihUrhGyWmjdv3jB9+nROnz5NVFQU9vb2VK1aFV9fX5HQ74YNG5gzZw7BwcHI5XKhBODk5ESFChU4evSosG5gWDwVa9YBoED3uXk6aICU98/4fH4D6eFBSA1NMHFpiKFTZcJ3T8Gh62yuLBkucvjIyuXLl/nxxx959OgRhQsXZty4cXz8+JHp06eLglyZTMbs2bPx9/fn/fv3FChQgB49ejB16lTRBKdCoWDSpEls3LiRpKQk3N3dWblyJY0aNaJBgwYiR4L79+8zcuRIbt26hZ2dHcOGDcPExISRI0fy6dMnQb5BXT5RB0xKpZK5c+eydu1aPn36RPny5fn111+FoEr9+e7fv59169Zx//59YmJiKFCgAM2aNWPatGkUKFAgz+91Pvl8iZ4bb3LtTdQ3db3SkUpwc7Lm5xblhFKsk41Jvq/zX2DX7RCmHn6CTKHM02elI5WgK5UwvbULXXKQkvo3kJiYmKse4n+aadOmMX36dAqP3I6OsQVKuYyIAzNJDnqAfcdpGDlVJj3qPR9+G46edWHMqnijSE8h4f4pJPqGpIe/xXHwRqGUHHV6NQl3j2Ph3h09q4JIjS25unQEV47vp0+fPty+fVuUPfP19WXz5s0cOnSINm3aiJZv27YNuVyOpaUlnz59wsDAgAYNGhAZGUmH2bvZejOYT/tmkRb+hkKD1iH5M/BN/fCCT1vGoGNuJ7hkZZYNsnBrJ5TMbX0moG9XjA/rh2DoXIOU13ew8ujFyB/HMa21C1WqVOHBgwdERERga2srxDmdOnViz549omMPntsSAAPHchiVroVU3whZTBhJL69jWsUbCzeVR33ymz8I3/crOqbWmFZqTMq7p6S+e4RhkQo4dJ0FQMSB2SS9vAaIs4fqzyvzMhMTE9LS0li6dCl6enrMmTOHlJQUAgICRP2SatmgFSvEPZ5OTk6ieKJt27acOHGC58+fC62KYWFhlC5dmipVquR5KCfXAeX3yN91IapTwuar7rT+P/nhhx9Yu3YtCQkJ3512ZD75fIl30Uk0XnyJVC2qCn8FCZmbWlR/F7U2pmEZe7q7FaWUQ/Y3jPmIWXEh8Jt4GI9pWlrrAMf3SmhoKFOmTOHEiRNERUVRqFAhvL29hV4/f39/+vTpw8WLF9m9ezf79u0jPT2dz58/ExwcjJ+fH+fOnSMkJARjY2M8PT2ZP3++yHYzux7KlStXsnDhQj5+/EjFihVZuHChoJbyNVWVPn364O/vj9TUBkVyLDrGlhiVqkV6+FvSwt/g0GUmBoXKkBR4k5jLW5F9/oDUxAqzqs1QJMcTd3M/tq3GYuKiGhhLfHmd6FOrUCTGAErQ0cWteVf6tqzHoEGDhIDS19eXffv24e3tzf79+zEwMMDGxoZJkyYxbNgw2rRpw9GjR1EoFJiamrJ27Vq6desmBJQTZsyjZ/sWmLu1J+7mfoxKuqFfsCSJjy8i+/xBtW+JFOOydbHxGo4iJUEVUDbwFVrZFImfkRqZY1KpCQn3T6JjYoWuhQMpb//AzNyC+u71eP78Oa9fv0ZfXx9HR0eGDRvGmDFjhICyyNDfkJqrKpfBc1tiXLYe8oRoQbJIx9wWo2KVMavRWiSplvL+KbFXdpL64Tko5ChlaehaFsDcrR3yxFjiru8BhQylUpljQFm1alWeP3+OQqEgLS2N0aNHCwYpV65cEdoPcxtQPnnyBDc3NywtLRk6dCi6urqsXbuW0NBQLl26hJubGykpKSI95C/xP327PrttRRovvvRNA8rc2MX9f5OcnCya1I2KimLr1q3Uq1cvP5jM53+OItbGTG/tkmuXntyS9aygBIKjk0Sl2NltK+aq7+vvRlsVJq+og5u3b9+KApbcoO3ipWbX7ZBvEkyC5gBH5mqXekDqe2Hfvn107NgRAwMDhgwZQtmyZQkNDWXfvn0kJSWJStRDhw7Fzs6OKVOmCF7Lt2/f5tq1a3Tp0oXChQsTFBTE6tWradCgAU+fPhVKmNpYvXo1w4cPx93dndGjRxMUFISPjw9WVlZfLSdlWXzF8QABAABJREFUZmaGdVk3FA5lkBqZk/bhJQn3jmNcujZFf9wrrGdcyi2jBP4nKcEPibu5Hx1TK2FZavBDDAo4Y1C4vLC9m8e2U8QgWeN7JJfLhWzXjBkz+P333xk+fDgmJiacOXNGKBvb2trSq1cvkTRe4T9/n8bO1ZEamhB/7wTJr26CVAcTlwakx3wiPTIEWcwnoQQPkPT8CmmfXmFcpi6GxSqR+uEF8Tf3o1+wNMr0FFKC7wOgr6fLq1evCA0NxcLCglmzZvHbb78J38eaNWsyc9kGmiwRzzjo2RTBzifnVgzDwuUx7PKr8HfCg9PE3thH9Nn16Fo6YO01jG7lDVm+QFxpnTZtmkY/uoWFBc7OziKL62HDhuHu7k6TJk24fPlyntQQXFxcCAgIYOLEicyZM0cQNt+2bZugGzpp0iRh7iMn/qcDyr/jQpSTXdz3QO3atWnQoAHlypUjLCyMjRs3EhcXl29Lls//LF1qFiUyIfWbBS5fQn0Deu1NFI0XX/rPlGK/hnfRSUw9/OSbbjMvAxz/n6jdzaZPny7YuoIqIMoaMFlbW3Pu3DnRDX2LFi00Bt1atWpF7dq12b9/Pz179tS637S0NCZPnkzNmjU5f/68IFZdqVIlfH19vzqgnDxjFkdMLmfcaFXxRteqIDGXtiCLDdeY3s4Jywa+Yg3YP7e3f/8WDdvOlJQUpkyZwsSJqp7pgQMHUqhQIfr27cvOnTvZunUrz54949ixY5QrV47NmzcLWVj1f3WlUixqd8KoRA0+bhqJbeuxmJTNcI3KTME+y/i4aSSmlZti02wkAGbVWqBjbEncrQM4dJ2NhXMVzo72wL1aeZETH6hUMYoUKcLw4cOFwNK9pK1QES024etv/EwrN8W0clMgoyK6rJ8by+Z/2Z4183uRGWdnZw272OwKz9rsg6tWrcrJkyez3Wdug0nIg1PO90qXmkUZ07R0zivmgtzYxX0PNG/enOPHjzN69Gj8/PwoWrQoJ06cyHGgIJ98vmeGNyzF3HYVMdCVZuvS8y2RK5SkyhRMOPCIFRdypx36PdOzZ0+Sk5O/Sqc0OyYdfCSIXn8r1AMc3zMKhYKrV68CiHrT1GQdfhwwYIBGdShzFSk9PZ2oqChKliyJpaXlF20k79y5Q1RUFAMGDBA5n3Tv3l2Yfv4awpMUQjCpSEtRucM5lgOUWp1mckIkgJ5pe2rbzqxkts1V2+Pq6+tz//59jh07xqhRoyhbtiyWlpYi21w1feo6ASD5Uwsy5e1dFOkpWl9b8ps7AJjXbCtabu6q+jv59W1R8iizEx+oBlay2vfObltRNHX+LfhfqIjmhf/pDKWa4Q1LYWtq8Jcaxme0dvmfCCZB5SaRVUswn3z+DXSpWZS6zraZJokl37SlJTuy09L7X0A9BJLZr/5bEBgWT8CryJxXzCNyhZKAV5G8Co//bi9A6qn73KJN2SA5OZk5c+awadMmQkNDRVmjL9lIqh3UsupuqlUZvpbg4BAijy4m+dVNDYe5zKXi3CKLDScmYLvW7WU9Pm32sBYWFqSmprJixQr69evH0KFDheXapKs8yzpgX8aRBafBrKYP8bcPkfjkEgZFymNc0g0Tl4ZIDU2E14ZEKkj8qNExtUJqYIKzYbLot56TZiP8dyuieeF/PkOppkvNopwd7UGdEir9xJwyHOrH65Sw4exoj//JC0k++ajx9/dHIpFoLWlkxcnJSeRO8r1RxNqYrf3cOPNDfXq6FaOYjbFWI4NvncOccvgJ76KTcl4RVempRo0aGBoa4uzszNq1a5k2bZpG5mrTpk2C/JmBgQHly5dn9erV2W739OnTVKlSBUNDQ8qXL0+vXr0oUaIEOjo6VKlSRficu3btiqmpKRKJRJCRkkgkWr8DJ06cwN3dHRMTE8zMzGjRogVPnuRcxt5+MyRXmWJZbDhRp1YRum4QIQva8W5JVyIOzkEWE5btc3SkErbdCBEtW7x4McWKFcPIyAgPDw9RnxioVCu0CX/7+vpqBFoKhYKlS5dSsWJFIZjx9vbmzp07wjpnzpyhXr16WFpaYmpqSpkyZZg0adIXjzU1NZWWLVtiYWHBtWvXhOVr1qyhSJEiGBgYUKZMGRYsWMDw4cOZNWuWMNRx+vRpzpw5g42NzT9uIymXyxnWox3Jr29jXqsDdu1+wb7LTGxa/Klpm8fZXKVCTtjuydluL+vxZXej4+LiQnx8PBs2bBBlY7Mr2aqrGAW9BlK4/0rMa3dEmZ5G9Nl1fNg4FFlclhugTL9HHakEA10pRvo6ONubilbLrWbjf7Eimhe+1xvEr0J9IQoMi2f7zRAuvAwnJCpJc9Izi11cPvn815k1axY3b97k5s2bhIeHM3Xq1GwFys+ePcusWbN49OgRMpmM0qVLM2LEiGx7wnJCPRCSFQMDA1JSUpiGC4mpMoKiEkmTKTiydxsLFywkPuIDOua2mFVv/aeerCZp4UF8/G04BXotxKBQGd6v6os8LlxjPdMq3tg3HyHS0ouJiWHcuHEcPHiQpKQkXF1dWbhwIRKJBG9vbwoWLMj06dORy+XMmDFDIwMDquEKFxcXWrduja6uLkeOHGHo0KEoFAqGDRsmWjcwMJDOnTszePBgevfuzbJly9i6dSuNGjVi2jSVpWV4uOq179q1C0dHR9q1a/dFK82tW7fSu3dvvLy88PPzIykpidWrV1OvXj3u3bv3xYzXhRfhucoOp358SWroM0zK1UfHzAZZbDgJ947zacdECg1YpdU5RK5QcuFlOL6VVJm9LVu2EB8fz7Bhw0hJSWHp0qV4enry6NEjQQYtL/Tr1w9/f3+aNWtG//79kclkBAQEcOPGDWrUqMGTJ09o2bIllSpVYsaMGRgYGPDq1SuhzG1nZ4eJiYkoS5mcnEybNm24c+cOZ8+epWbNmoKG344dO+jfvz9VqlTh1KlTjB07Fn19fXr37s3ChQuFbaSkpORo3qFuWXj16pVIp1MmkxEUFKQhy5cbHj16RNCbV9i2GI1JxUYZx/RWszSdG9IjgpFFh2LTYjSmmbaX8pXbywsZVQxrAmyLoePelcSQp4RtG0v8/RNY1e+p6gdVKpBFf8DQvihyhZI6JWwYXdee6jPj/lJbyH+tIpoX/lUBpZpSDmZMa+2icSHK16LLJx/t/PLLLxQoUICqVatqOD5l5vDhw/j4+FC7dm0hI7dnzx569epFZGTkV7n4qFm9ejWmphmZg8xZAxMDXVwKWbB27VqmjhmJcZk6WFVpReq7J3w+uxalLBWLWppOP8mvbyM1tkS/YEZWQc++hNBLJSyzLiQqxZawNaFFixY8ePCAsWPHYmtry6pVq2jQoAE1atRAR0eHq1evCgL2nTp10ir6f+nSJVHAN3z4cLy9vVm0aJFGQPny5Uv2799Pu3YqDbuQkBCWLFlCZGSk4LWtlvvQ19cnODj4iyXuhIQERo4cSf/+/Vm3bp2wvHfv3pQpU4bZs2eLlouemyojJJfZWiPnmhrDEcYlXfm0dQxJL65hWsFT6/NCopJISlXZ57169YrAwEBB9sTb2xs3Nzf8/PzyNBQAcOHCBfz9/Rk5cqTIRvKnn34SMk5nzpwhLS2NEydOaJ2KlUql1K1bl9OnT/P69WsSEhJo2bIlT5484fz584Lwu7pXcMiQIaxatQpQTd127NiRffv2EZfFTm/58uVa/c8zU6NGDWxsbFi/fj19+vQRMnfbt2//akcm9ffE2kQftdeZUqkk/s7hr9qeNgF0pVJJ+sNjX7e9PBAXF0dBc3Hy6IyRkjCJFGQqK0OjEjWIubQFxaNj9Jw0V0geqYervsYIIDN5bc1RP16nhM13oyzxd/Cvj6zUF6J88skne9RSM5GRkVozbWpWrFhBwYIFOX/+vCDMP2jQIMqWLYu/v/9fCig7dOjwRcmL5ORkfv75Z0rVqI+86XjkCiVmVbwBJbFXd2FaxRsdQ3EpK/n1HYxKVBeVonXNbDCt0BBtqEux5VOecO3aNfbu3StM6nbq1InSpUtz+fJlOnfuLHJDKlmyJM2aNePIkSOi7amDSaVSSVhYGLq6unh4eHDq1CliY2OxsMg4NxUqVIi2bTMC3djYWPT09Hjw4AGfPn0SmQrY2dnl2C955swZYmJi6Nq1K5GRGaVAHR0d3NzcuHDhQrbPDY5K1JBdyo7MwxlKuQxFWhK6VgWRGpiQ9uk1ZBNQKoH3Maqg1cfHRwgmAVxdXXFzc+P48eN5Dij379+PRCJh6tSpGo+pvwfqNoHff/+dPn36aLWt7N+/P6dPn2bKlCksXbqUhIQEevbsSY8ePbhy5QqWlpaCM1rnzp1Fz/3pp5/Yt28fBw8e5IcffqB8+fJcv36ds2fPimxttaGvr8+0adMYMWIEnp6edOrUiaCgIPz9/XF2dv6iG1p2qG02359ch0HVj6BvRNKLaxq9j7lFz6YwupYF+Xx+I/L4KCQGRiS/uIaFTtpXbS8vnD9/nuHDh9OxY0dKly6NjUxG+uGt6OnqsPqXIZSrXB193XrMSb/N9m1bebbdmNPvPZiZjUvd15JfEdXkXx9Q5pPPfxWlUsmsWbNYs2YN0dHRuLm5aQjdqslts39cXBxWVlYilyddXd08aZ996fXGxcVhZmam9aJ54cIFoqKiKNjam/hM2QCzai1IfHKR5Fe3RYGiIiWB1NBnmGkphyvl6SjlcqT64nKsuhT79NY+HBwchGwhqIK4li1bsm3bNq3vV+YhCrWupKenJ9OmTSM+Pl5jfWdnZ+Lj44X/VqhQQTjurMdfsGBBNm3aJHh+h4aGCutkFcRWExiomlz39NQe0Jmbm2tdDpCWB5F5RXoqcdf3qjzW46PIrACa07BHuky1bqlSmmLnpUuXZs+ePbl+HWpev35NoUKFsLa2znadzp07s2HDBvr378+ECRNo1KgR7dq1o0OHDkJwqb6xUigUfPz4EX19fU6ePEmzZs0EDcmoqCgADWccdba6fPnybN++nZSUFOrWrcvZs2fx8vLK8RiGDx+OUqlk4cKFjBkzhsqVK3P48GFGjhyZa5HpzKhtNvsPHsb163uR6OhhXLo2ZtVb8vG3EXnenkRHF/sOU4g+u5bYGxnb27B4Kq086+R5e3mhcuXKeHl5ceTIEUJDQzE2NqZy5cqcOHGCRo0yyu/+m36jdKmS+Pv7c/DgQQoUKMDEiRO13mj8FfIrohn8d440n3z+Y0yZMoWZM2fSvHlzmjdvzt27d2natClpaV+fRWjQoAF+fn5MnjyZ3r17I5FI2LFjB3fu3Pmqi39mSpQoQUJCAiYmJvj4+LBw4UJR/5y6vBhrWlQ0TahfoCRIpKSFvYZMAWXy27sgkWDkVFW0n5Tgh4QsaA9KBTrm9pjXbIN5zQwruJCoJELv3qNatWoamasqVaqwbds2IZD4Eo8fP+bYsWPY29vTqlUrKlasSI0aNejRowdhYWEMHToUe3t7jhw5wrNnz0Raclu3bmXdunVcv34dmUzG1KlTqVmzJgMGDGD9+vXY29sLvXnaSu2QMWlrY2MjDJLcvn2b69evM2TIEDp16pTta9fXzf285ucza0l4dBazGq0xcCyL1MAEJBIif5+X47CHnm7us20SiUTrsEZOJWRtGBkZcfnyZS5cuMCxY8c4efIku3fvxtPTk9OnT4uyv507d2bXrl107NiRLVu2iL4Tjo6OxMbGiiwGM+Pt7S3ypgZNLcAGDRpoPa4RI0YwYkRGsKdQKHj79i1Vq1bVWDc3lCtXjquXzms4zOVGU1Gp/PMGQ5rxvujZFsGhy0wgQ0+xZUM3jWPx9/cXWQeryc7tJ6f3p3jx4mzcuDHH16yrq8uUKVOYMmXKF9fLbpDxa9yI/usV0fyAMp98/oVEREQwb948WrRowZEjR4Rs1s8///yXJKcmT57M27dvmTVrFjNnqi4mxsbG7N+/X+TPmxesrKwYPnw4tWvXxsDAgICAAFauXMmtW7e4c+eOkEn7+PEjOjo6SE0sRc+X6OghNTJDnhAtWp786jYGjuUEKREAfXsnDAqXR8+6MIrkOBIenePzufXIE6KxaqjK/imBDx8/4uGhqetaurSqF1M9jJGZV69eif5Wy7/cvn1bJEvSq1cv5s+fT9++fXFycmL48OEYGRnx9u1blEolEomEHj16cPbsWW7cuAHA4MGDKVCgAHXq1GH9+vWYmprSo0ePL76v6ueuWbNGJLDdtWtXduzYIRoWyYqTjYmGdWV2JL64ikkFT6wbZegMKmVpOZZTJUBhS1WmT51NzczLly9FmWArKyut+oTq91mNs7Mzp06dIjo6+otZSqlUSqNGjWjUqBGLFi1i9uzZ/Pzzz1y4cIHGjRsL6/n4+NC0aVN8fX0xMzMTTekXK1aMs2fPEh8fj5lZRjnz+fPnwuNfQ0pKCgYGBqJM9ZYtW4iOjtaajc4LX+MwJ09Q9W7qGGvPav/b9BTz+Tr+NbJB+eSTTwZnz54lLS2NESNGiC5KP/zww1/aroGBAaVLl6ZDhw7s3LmTbdu2CVk3dQCTV0aNGsXy5cvp1q0b7du3Z8mSJWzevJnAwEBh0AFUPZR6evpatyHR1Ucpy8i8KpUKkt/exahkTdF69h2mYFGrA8ala2FauSkO3ediWLwacbcPiSRHUlOSRWV9NerS5rVr10QZxVevXnHixAnRuupeuaz6g9u2bRP9HRkZiaGhIXK5XPRYeno6MpmMKlWqiPonc4NSqeSPP/5AT0+PxYsX8/HjRyIjI4mMjMTLy4vY2FjOnTuX7fNNDHQpmsvBAYmWAY24P46A8stl86I2xhj/WQ48dOgQoaGhwmO3bt3i5s2bNGvWTFjm7OzM8+fPiYiIEJY9ePBAmMxW0759e5RKpVblAPVnER0drfFYlSpVAJU0UFZ69erFsmXLWLNmjcg1p3nz5sjlco1WksWLFyORSESvPy/cuHGDatWqMXv2bNauXcugQYPo378/FSpUoGPHjl+1TTVqPcXcoEhLIf7+SeJu7EXHzBbdTB7Vmfm36Snm83XkZyjzyedfiDprk7U3zc7O7i+5bQwfPpwbN25w9+5dofTXqVMnXFxcGDVqFDdv3vz6F52Jbt268dNPP3H27FkmTFD55RoZGZGerr1cr5SlIdHNCDbTPgaiSIrFyLmm1vXVSCQSzGu2IeXtXVJCHgk9mAaGRloDi5QUlTOHQqGgbt26DBkyRAgoKlSowP3794V1S5UqRXx8PK1atWLQoEEkJCSwfv16ISgtX748ycnJou0PHz6cp0+f4uDgwPHjx1EqlcydK/b4zQ0RERFCyfvatWuiASI1q1atomXLltluo2EZe7beDM4xk2VUsiaJj88jNTBGz7YoqaHPSQm6j9Qo+x5NHamEhqUzrP5KlixJvXr1GDJkCKmpqSxZsgQbGxvGjRsnrNO3b18WLVqEl5cX/fr1Izw8nDVr1uDi4iKapm7YsCE9e/Zk2bJlBAYG4u39f+yddXRU19rGf+MzcU+AhAS34BSH4FCsuFtokeJatHiR4i0EKQ7B7eJWHFqkuHuCEyGuM7O/P4Y5ZDIJVnrb2y/PWlkwR/bZcs7e737tqYfRaOTEiRNUr16dPn36MGHCBI4fP06DBg3w9fXl1atXBAUF4e3tTeXKGdP59enTh5iYGEaNGoWjoyMjR46kUaNGVK9enVGjRvHo0SOKFy/OgQMH+M9//sOAAQMyZNn5EPj5+eHj48NPP/0kaVo7derE1KlTLTjEPxUfSnVqTIgm8uBC1O5+uNfvn+Hm4d+YTzELn4YsgTILWcjCByElJYWlS5fy3XffWfiRqVQqvvzyS+bNm0dKSspnWfAAfHx8LDRJ2bJlw2AwYIyPsjB7C0MqxsRYFHZvzZuJ98+jcPRE7fb+hU7h8Cb4IskUOCMDsmfLxvPnz62uNR9bsGABQUFBfP/99/j4+DBhwgRu3rwpmTrBpKHcvHkzo0ePZsiQIXh5edGqVSspann06NGUKFECtVpNs2bNiI2N5eeff2bmzJncvn0bjUaDRqP5oCCO9DAnlu7QoQMlSpRg/fr13Lx5k5SUFNzc3ChatOh7I/Lbl8vJit8evfdZLrW6g0xO/PWjCEMqmhyF8GwziZcbMvddMxgFHcrnhASTL2qnTp2Qy+XMmTOHV69eUbZsWSmjgBmFChVi1apVjBkzhkGDBlG4cGFWr17N2rVrrfzdli9fTrFixVi6dClDhw7F0dGRMmXKULGiKWCkcePGPHr0iGXLlhEeHo6bmxsBAQGMHz/eIvI+PUaOHEl0dLQkVPbu3ZsdO3YwZswYNmzYwPLly/Hz82P69OkMHjz4vX2XGfz8/Nix4xNT+nwgPiSfotLJE9+h262O/9vzKWbh05AlUGYhC/9CmH237t69S+7cuaXjYWFhn5zLLiIiAr1en2EQRGpqKkaj8ZMCJDKCEIJHjx5ZBCCYTZKOcaHEphEok5/fA2FE7fm2nYn3z6HLk3GgRHroo14AoLAxCRI5XW3wKFmCEydOYDQaLYTnM2fOYGNjQ8eOHS24icHka+ft7W1xrFGjRjRq9DbKfM6cOej1ekJCQiz8Kvv27cvkyZOpWrWqlHOyS5cubN682aK8Ll26sGLFCos0QGkxduxY/Pz8MBgM2NvbYzAYGDx48HuFm3Hjxlklss/naU+VvG4WARwZQa61w63BAKvj3r2WZXi9OYDDlELF3sIlYNCgQe+sZ/v27Wnfvr3FsTp16lg/Q6FgyJAhDBkyJMNyatSokWn0uxmZBctMmzaNadOmSb/t7OyYNWvWR6c3+icgK59iFj4nsnwos5CFfyFq1aqFSqXi559/tlgU58yZ88llenh44OTkxLZt2ywixePi4ti5cycFCxZ8J2tLZkjrE2fGggULCAsLo169etKxGjVq4OLiQsq1/RaUgHEX9yBTaSTztiH+NSkv7mOTTqA0JMYijJYCrzDoifl9MyiUaHMWk0yxLVq04OXLl2zdulW6Njw8nE2bNtGoUSMrarm7d++yZ8+e9wZMmKOH0/tVLl++/J33fSwUCgXNmzdny5YtVhSGkHGfZ4TJTYui/AD6xY9BVgDHPwsfQnUqA3xdbehYzpdDA6uy+utyWcJkFqyQpaHMQhb+hXB3d2fIkCFMmTKFhg0bUr9+fS5evJgpM8jq1asJCQkhIcGUaPr48eNSFHfHjh3x9fWVtD6jR4+mfPnydOrUCYPBwNKlS3ny5IlFQMnHwNfXl9atW0u8yydPnmT9+vWUKFGCHj16SNfpdDomTpxI7969sYnRo81diuTH14m/fgSnqp1Q6ExRton3zyNTqtHktKSoS7x7hujTG7ApWAmloyfGpFjibxwjNSwEp4BOKOycJVNsLteClC9fnsDAQG7cuCEx5RgMBsaPH0/u3Lnp0qULuXPnJiQkhAULFqBWqy18/jJCnTp1UKvVVn6VHh4eGZrYzUib3y4+Rc+HBOhOnTqVI0eOUK5cObp160bhwoWJjIzkwoULHDp0KMPAlPQwB3AM33r1/Q/8QPx/COD4X8xHmJVPMQt/FllvSBb+NmRNWn8tJk2ahFarZeHChZJgceDAgQxpx5YuXcqxY8ek30eOHJGYVCpXriyZ0EeNGkWuXLmYO3cu48ePJzk5mWLFirF582aaN2/+SfVs3749p0+fZsuWLSQlJeHr68t3333HqFGjpATSZvTq1QuVSsWQMT8QeeAMSnt3nGt2w75MY+maxPvn0foWtWBwAVB5+KFy8yH++hEMCdHIFCrUHrlwazIc24KV05liYc+ePQwdOpSffvqJxMREvvjiC1asWEGBAgWoV68e69at48WLF2g0GipUqMDkyZMzTNCdFgUKFLDyq/z2229xd3ena9euFtfGJKaSYjASMP0IoZFvGThePInGmBhLwPQjVC/gQftyOcnnac3A4enpydmzZ5kwYQJbt24lKCgIV1dXihQpYmGyfR8+NIDjQ/BvDuCQGFNuv7IYL3jDmOJi887x+ifh/3s+xSx8GmQiIyeRLGThL8K/adLNwt+Hx5EJ1Jp9jOR0jC7CaODx3LY4B3TGvtTH8fVqlHIODQz427VnjyMTPtqnrUpet7/cp239udB3BnC8q47/5gCOf+p4ZSEL/21kCZRZ+K8ga9LNwufG+nOhVqZYQ3wUsRd2Y1fyS5R2mSe1zgjTmhX92wWePyu0jW9chDZ/YRuyvmNL/NPHKwtZ+G8iS6DMwl+OrEn3n4N/m5vBvCN3P5sptnf1vO+/8C/E52rLkDr56VP93ab3PwvJ0nDnFaERGVgaXG2ont+DDuVzSi4E/zb8L41XFrLw30CWQJmFvxRZk+7fj/+Wm8FfLaxWq1aN8PBwq6jlv8oUK5PJGDt2rFU6nb8CGWlb/wz+m9rWDx33R48ekStXLpYvX06XLl0+6hldunTh6NGjFrzL48aNY/z48Rmm9vkUnDt3jv79+3P58mUSEhK4ePGilKoqPf7O8fLz86NatWoZ8mP/WZj7NCwsLMPgPTMyGo8sZOF/VzXxL8CKFSsIDAzk4cOHFpy1/xasPxf6WYRJgBkH7uBup/nLF8l/05h8iHlSACGRCaw+E8KK3x59tHnycwurz549Y/HixTRp0iTTxTw9/tdz6T2OTGDsjuuftcwxO65TMY/bf6VtmQVwJCQk8OOPP1KtWrU/zT/9VyM1NZWWLVui1WqZPXs2NjY2mfJw/6+PVxay8FchS6DMwl+CrEn370VarR3wXs2d+fzpBxHUmn3svW4Gf5Ww+uzZM8aPH4+fn997Bcr0mrGFHUrzLCrxs5liExMTUSr/+ily5Lar0jh9LuiNgpHbrrL663KftdyPQUJCgsSnXa1aNXx9fd/wsas+uqxffvnFKvfn6NGjJVrOP4v79+8TEhLCL7/8YpWwPj3+7vG6ffu2RbL9vwMZjUcWspAlUP6N6NixI23atEGj0bz/4v8hxCfr6bf+IqmGzzvh/BMWyf8F/Bk3A8Mbs/HwrVcJj0vO0M3grxZW34UUvZFxO66/VyO6pGMZsjvpPtoEbzQaSUlJQavVotVqP6mOH4O7L2M5cS9j1ps/A4NRcOJeOPdexf7tPozmPlWr1Z/cp2mF0Pj4eGxtbVEqlZ9N4H/16hUATk5O77zu7stYjt14glz9ed+Njxmvf8J68Smbgv8mhBAkJSV9EtFCFj4dWUw5fyMUCgVarRaZ7PMyUfwduPsylnE7rhMw/QhFxu3n4uOoD0q+/DFIO+l+DLp06fLB5uvt27d/fMXeYMWKFchksr/Vr+hzuxlsOBdqcWzekbsM33qVZL3RQpDUx4YTvmcuT+Z1ImR6E54s+JqI/fMRhlTAxFITfmgJDxd9S7tKBdDZ2vHll19y+fJlqYyjR4/yxRcmtpvAwEBkMhkymYxvvvmGx5EJ3Hwew71HoUxoXpoz8/oTMqM5T+Z1Ivp3Ez1hWo1orRm/UqFlTxpULkX5fF7UK+fP+O9HkpycbNEemUxGnz59CA4OpkiRImg0Gvbt2yedS+s/OW7cOGQyGffu3aNLly44OTnh6OhIYGCglBA+LdasWUPp0qXR6XS4uLjQpk0bHj9+bHFN8JlQC9afzCAMqUQdX8Pz5f0Jnd2K0JnNebHmO5JCrmR6j0IuY83voRZ1L1u2LCqVCplMhkqlomnTpgghePz4MV999RU6nQ6VSoVKpcLNzY0OHTrw9OlTUlJSGDNmDKVLl0atVktlVa5cGTs7OymR/v3795HJZIwcORJ3dxNH+sSJE9FoNNJ4rlixgitXrlCmTBlkMhkajQYvLy+6du1KRISJ23vEiBGo1WqGDRuGTCajSZMm2Nra4uzsTOXKlQETzaRMJpMS4ru7uyOTyWjVqhXbt2/H398fjUZDkSJFpDFNi4sXL/Lll1+iVqsJCAgAoGXLlshkMslE36VLF+zs7Lh//z7169fH38+LiF0zAEh6fI2wbVN4EhRoeufndyHy0C8YUy3fsfBdswmd2QJ9bDivtkwidGYLHs9tx+vDSy2YmxRyGatPP2Lu3LkWbapXrx7nz5+XrvPz87PwQY2MjGTIkCEULVoUOzs7HBwcrL4tM37++WeKFCmCjY0Nzs7OlClThrVr12b6DgGEhISQN29e/P39efnypdQvaefUR48eIZPJmDFjBosXLyZPnjxoNBq++OILzp07Z1GeuU+fPn1KkyZNLN6f9NStRqOROXPmUKRIEbRaLZ6envTo0cOKPtbPz4+GDRuyf/9+ypQpg06nY9GiRe9sVxY+P7IEyr8R6QWQ//znPzRo0IDs2bOj0WjIkycPEydOtPrIqlWrhr+/P1euXCEgIAAbGxvy5s0r8f4eO3aMcuXKodPpKFCgAIcOHbJ69tOnT+natSuenp7SpLtsmTX37vsmoMeRCXRceobac46z+kwIIZHWC2taxF07YloUZzTj8Zw2hP1nGvoYSxq4F8HDebakFynhobxYO4LQNIJD2kUSTJNd48aNsbW1xcPDg4EDB7J//35kMhlHjx61KPfMmTPUq1cPBwcHVCoVxYsX59SpU++sL8D58+epW7cubm5u6HQ6cuXKZZWEOj4+nvXr1wOQP39+ChQowIwZMxBCSIt5eHi4JMB8yIKXGfz8/KTFOe1fh8Bv/hI3g8dvxjQzYVUfG8GLlYNIuHkcm4JVcKnVA7si1UkOvYZ4s7jGXztM7OWD6PJ8gXPNb9CUbsLZC5cICAjg2bNnrFixgurVq0vMON27d5e4kRNsPKg1+xgxSXowpCL0yag9cuFc42tUrt5EHV1B4v23C67eYODVlglc27eGaPdidBo8niZNmjB79mxat25tVf/58+fTvXt3Wrduzdy5c9+7+WjVqhWxsbFMmTKFVq1asWLFCsm0a8YPP/xAp06dyJcvH7NmzWLAgAH8+uuvVK1alaioKOm6I7dffVAgkTE5gbgrB9DkLIpztS44Vm6HISGGlxvGkPLyQYb3GIyCI3dMmrdLly4BcPXqVQoXLkzr1q3Jli0b27dvZ86cOdSuXZvY2FiSkpLQarXo9Xrq1avH1q1bqVy5Mo8fP2bJkiVUq1aN0qVLo1QquXz5MqdPn6Z///4EBAQwc+ZM1q1bB8C2bdukoI6iRYsye/ZsC67rgwcPSjSUjRo1ok2bNqxfv5769esjhGDjxo3UqVNH0jAdPXoUIQSTJ0+mW7dugGm+BPDx8WHatGmS+fv06dP06tWLNm3a8OOPP5KUlETz5s0lYRXg+vXrVKlShcuXL9OlSxeqV68OmDb5Y8eOZdSoUdK1er2eunXr4uHhQc76PdDlrwhAwq1TCH0y9iW/xKV2T3S5ShH7x04idmXA6S2MvNowBrnOHucaXdHm9Cfm7DbiLu23GK9FPwxlwIABFm3SarX8/vvvmb4bDx48YPv27TRs2JBZs2YxdOhQrl69Kn1bZvzyyy/069ePwoULM2fOHMaPH0+JEiU4c+ZMpmXfv3+fqlWrYm9vz9GjR/H09Mz0WoC1a9cyffp0evTowaRJk3j06BHNmjUjNTXV4jqDwUDdunVxdXVlxowZ0vuzePFii+t69OjB0KFDqVSpEnPnziUwMJDg4GDq1q1rVebt27dp27YttWvXZu7cuR/sg/1PR3yynuvPorkY+prrz6KJT9b/3VXKFFkm738QVqxYgZ2dHYMGDcLOzo7Dhw8zZswYYmJimD59usW1r1+/pmHDhrRp04aWLVuyYMEC2rRpQ3BwMAMGDKBnz560a9eO6dOn06JFCx4/foy9vcmU8vLlS8qXLy8JN+7u7uzdu5evv/6amJgYBgwYALydgFq0aEH//v1JSkriypUrnDlzhnbt2n206TP69Aaijq/BplBl7IrXxZAQTewfO3kRPJzsgXORa+2ka41JcbzaOBab/BWwLViFhNsniTq6ArW7H0ecdSZ6sPh4atSowfPnz+nfvz9eXl6sXbtWYnhJi6SkJKpWrUrp0qUZMmQIY8eO5cWLF9SoUYOVW/dSoGhJ1Eo5derVlxYqMJnC6tSpg7u7O8OHD8fJyYlHjx5ZcDwLIWjcuDGHDx8GTL5d58+fZ+jQoTx9+hRHR8uAhZMnT7J161Z69eqFvb09P/30E82bNyc0NBRXV9f3vSYAlChRgsGDB1scC76Z8pf5dk1uWjRTYTXq2EoM8VF4dZqJJttbE7lT1Q5SBG5qxBNEchzO1bpI5+XFa/Hsl54sXboUHx8fACpWrMiiRYuoUKECnTt3hsK1mXP4ATK9ESEEQp8CChUutboDYFe8Nk+DuhJ35QC6N9zd8dePkfToMp7tpqD1KcJBAUOaNWeevz89e/bk9OnTVKxY0aINCQkJjBkzBoDJkye/U8tcsmRJli5dKv2OiIhg6dKlEvtMSEgIY8eOZdKkSYwcOVK6rlmzZpQsWZKgoCBGjhxJXLKe0PdswKS+0tqR49ulyBRvTY32xevy9JeexPyxE7f6/TO8LzQigWevIti7dy8A7dq1k+puMBjw9fVl8ODBTJo0iblz5+Lv78/BgwfJnTs3SqWSTZs20bBhQ1asWMGjR49Qq9V06dKF33//nZEjR7J06VJevHjB5s2bKVWqFBs3bgRMvrBnzpyhUKFCNGvWjAEDBvDo0SMGDRoEmBiPBg8eTMWKFXn06BGbN2+mfPnytG3bll9++YUHDx4wbtw47t+/D4CLiwtGo5Fvv/0WMDE5mTVwe/bskdo7ZMgQwsLCuHHjBnny5AGgevXqFC9enHXr1tGnTx/A9I2mpqZy8uRJcufOzdGjRzly5AhqtZojR45YaKaTk5Np2bIlo8ZNpOi4/ZhnKadqXSzZmErUQ+mcjahjq9BHv0Lp6CGdEvoUbApVwalSW9PYlazP8+X9ibtyAPtS9QFICrnCy/P7+bZ3H4Lm/SzdO3jw4HdGshctWpQ7d+5Y+FV27NiRggULsnTpUr7//nsAdu/eTZEiRdi0aVOmZaXFrVu3qFmzJjly5GD//v04Ozu/957Q0FDu3r0rXVugQAG++uor9u/fT8OGDaXrkpKSaN26tVS3nj17UqpUKZYuXSqN8cmTJ1myZAnBwcG0a9dOurd69erUq1ePTZs2WRy/d+8e+/bto27dusBbQex/MUXa/yoByD+6d/9tOfPeh7Vr11r4fPTs2ZOePXsSFBTEpEmTLHxnnj17xtq1a2nb1jRB1a5dm4IFC9KuXTtOnz5NuXImP8NChQpRt25dtmzZIplJRo0ahcFg4OrVq5IA07NnT9q2bcu4cePo0aMHOp3unRPQx/rp6aNfEXUiGKeqHXGs2Eo6blOgAs+X9yf2wh6L44a4SFwbDsLOvwZgKTiE5ilDfLKeRYsWSbvzr776CjDtaEuWLGn1/IiICGrWrMnevXs5e9O04Gv8a2P84xCBfYbg2WYiAHFXbgIw5+Adejd25fpvp3n9+jUHDhygTJkyUnlmnmuAHTt2cPjwYZo1a8bWrVvp1KkTY8aMoWXLlsydO1daxMy4efPmexe89yFHjhx06NBB+n33ZSyjrx8HPkygfBFs0uZ4tZ8KgD7qJU8Xfo1r/QHYFasFQNSJYKJPrcMwfBcDNlxEbxQ8CeqKNmdR3BoOBEAIIwl3f0eXt6yFMGmG2Z1DlmaxE0YDxuR4UGrQuftw4cIFSaA0Q6/Xs+H8Y+YeDUEmV7w9IVdY/JYpVKiz5Ucf9VI6lnDrJCpXb1Su3hgSogGYtv0cg6sWBEzCSFqBMnv27BaanMmTJ9OiRYtM+65nz54Wv6tUqcK2bduIiYnBwcGBrVu3YjAY+OqrrwgPf+sf6eXlRb58+Thy5AgjR44kJCL+A0eLN202tVsII8akeBBGNF75SHlxP9P7BLBh+25SUlKs6q5QKPjiiy/Yvn07JUuW5NWrV4wbNw4vLy8KFCjAgwcPaNCgAQULFmTv3r1MnDjxzfNNte7YsSOXL1/mwoULUj+sXLkSgObNm78z7Yx5nmvdujUDBgzg3LlzlC9fHoD169ej0Wj46quvJK1m/vz5uXXrlnT/li1bMi27Vq1a0rcFUKxYMRwcHHjwwKTJNRgMHDhwgCZNmpA7d26LeytXrsyvv/4qjaUZ3377rdV4pRUmjSlJCH0ymhyFAEHKywcWAiWYhMi00HgXJv76281vwu1TgIwO31puFOHtd5QR0q4LBoOBqKgo7OzsKFCggDQ2YPIPffLkCefOnZNcSzLDtWvXaN26NXnz5mXv3r0WffEutG7d2kLwrFKlCoDU92mR0Xe0evVq6femTZtwdHSkdu3aFt9R6dKlsbOz48iRIxYCZa5cuchdomKmPtYAXg4avvBzoVvl3BTzcfqgNv038d/IzPFX4h8nnf2vSuYfA3NqmrRaR7PPjtlMGxsbS3JyMlWqVGHRokXcunWL4sWLS9fb2dnRpk0b6XeBAgVwcnIiR44ckjBZrVo19HqTetz8QQsh2LJlC61atUIIYfGh1q1bl/Xr13PhwgUqVaqU6QSU1vQZvms2CbdPkXPw5ne2OeH2aRBG9PGRGBKiif59C7FnTVo+mUpL1Ik1xJzbjkylwZAUByoNCbdOEXlgATKVBseyzSTBQQBzghYzevBgPD09adzYxONs9rVKu/CAycdIr9dTt3FzGoxdxd6JXQB4fDgYAH30S16fWINzlQ4k3PkNgK2XnrL9wXHy6U2CRq9evejduzeTJ08mJCSEIkWKMHv2bKpWrcqePXtQKBTUrl3bQnM5ePBgNm/ezG+/mco0pyFxdHQkKSlJui79gpcWaYNE0iMlJYXU1FRsbW0lX7wPMp8mxaF//RxDYgwh05ugsHFE7WUpDBrio0i4azKFhU5vyjOXHDiUbWpxjVkIBRAGPU8XfoM+Nhy1ey5c6n6LJlt+wPSOxF/7FYCQqQ1Jj+1P7qFWqwGThgng5r1HbLt8mJB5gRZCrkylAaOR1KgXRO6fT/KTGyAEKFQIIZDJZOhfPyM14jFPfmpv8ZyBP5n+HT16NDly5JA2WGZhMqNF++LFi1SvXp179+7x/PlzAHbt2iX5/wHSAtq3b1/q1KkjCV7+/v4Z9v/Dhw+ZO3cuVZt2yvB8Zoi7+isxZ7eRGvEEjG/NXkrHd5shHz18+17lzGkZDOXo6IhWqyU62iR4FyhQQDpuNhEXLFiQkydPsnLlSmbOnCnlAS1UqBBgWsjB1A/mcszHMkNkZCTjx4+X3GfKli0rnbtw4QJffvmlhRBjtq6Ycf/+fezt7YmNtfanTt9Gc93MfndhYWEkJCRIbU2LHDlyYDQaefz4MUWKFAFAqVTi7e3N5SfRFteaN8mJ985gTIqzOGdMjrf4LVOqUdhYWirkWjuL+1KjXqCwd0Fn/3Ec2kajkblz5xIUFMTDhw8tXKTSWjyGDRvGoUOHKFu2LHnz5qVOnTq0a9eOSpUqWZXZqFEjPD092b9/P3Z2dlbnM0P6vjd/G+l9Hs3+oemvTXvd3bt3iY6OxsPDUjA3wxxIBSbrWLLOldpzjr9zHnwRk8zOK8/ZeeU5WqWcBsWy0bNqnn+ELPF3Bjt+LvxjBMr/dcn8cyA+Pp6mTZty+PBhYmJiLM6ZJ2ozvL29rRZAR0dHK02POQoy7WQaFRXF4sWLrfxVzDB/qBlNQDUbNmPs6ZSPblvqa9OiHffHLuL+2GVxTqQmIbdzQeOZh8T750ChAiFQ2LviXC2Q+BtHeX1kGdpcpUzCJkgfna+v73uDmsx9Oah390yv0fpZajWNRoEcuK8yCYHnzp0jMDCQ/Pnz07hxY8kf8+zZs4SEhJA9e3Yroc+84Jod6gcNGsSkSZMICwujaNGidO7cmQULFqDVaqXJVCaT0bt3bypUqMDkyZO5c+cOmzZtwsnJierVq0vm/MOHD2NjYyMtHu4lamBTb9Db/o54TNSJYJJCriBSk1A4uGNToBKOFVryIngYhrhIdIUqY4gOIzXsEYl3TT5aSY+vYVOoCi/XjjAJLoBT9a4k3DpJxO7ZOFXvikOZxlb9l/LsFjYFq5D89AYpL+/xYuUg5Hau2BasjG3RmiQ/uYE+6jkaH39UbjmRq3Voc5Ukcv8C9K+fcvLkScCkrd24cSNHH8SQYm8SFiIP/4Imh0m7iDCZD58t6g4I5LbOCH0KIimO6BPB2BSqQmpEmsAXmRyEOduAyddUiIyzD6xevZqOHTsCSJq2HTt2ACbNy4YNGwCYMGECEyZMsLr/9OnT7N27lzx58nD+/Hnmz59PbGwsw4cPp2TJklIwyfPnzzl16hS1WnbJsB4ZIe7aESJ2z0aXrzwO5ZqZhBO5gpjfNpL6+sU7700b9GP2W7Q4n8ExwMLMmpycTJcuXWjSpAmOjo6cP3+enTt3MmXKFMksnRbvi65t1aoVp0+fZujQoWzZsoXExESCgoKoV68e0dHRVr6umdUxI3xIez4GGo0GuVyOWmmpZX+54XuMibE4lG+BysUbmVqLITaCiN2zTZuctJB9eLhC2ud8CCZPnsz3339P165dmThxIi4uLsjlcgYMGGCR2qdQoULcvn2bXbt2sW/fPrZs2UJQUBBjxoyx8gFu3rw5K1euJDg4WPJr/hB8aN9/yHgajUY8PDwIDg7O8LxZIF1/LpQXMUmoNTLceb8gZkaS3siWC0/ZcuHp3y5L/NWZOf5b+EcIlP8GyfzPYuPGjRQqVIiIiAgmTJhAnjx50Gq1XLhwgWHDhlnl/Mrsg3zfB20up0OHDiYftQxQrFgxIPMJyKlyWxwrt8/w3kzxZhF3bzEGmVJN3NVfSXhj7nFtOBiVczbU2fIROrM5GFKR2zjhWrcXALaFq/JkXmf0r59LE7XyAyJjzXjy2uSn5lS9K2rP3BiT4wnfNgWbItWxK1oTAG12a20FYBGpXq5uM1LDHrFp0yby5cuHEELyu8sIcXEm4dfDw4NXr15Jvpnly5fnjz/+YMWKFRgMBlatWmXqojdtO3z4MBs3bqRPnz64ubnh5+dnEchRrFgxKleuTIECBbh9+zbDhg0j7NJhHDTOOFcPJOXVQ14ED0MmV2JXoi5KR0/0r5+TeO8sMrmC1LAQXOv3J+pEMMhkOJRvDjIZ0cfXEH/1EMakeFIjHqPLX4HEO7/hUKYR9iW/5OXa4USfWod9iXrIFJZThzp7QWQKJUoHd9QeuYm/fgS1W05i/9iJITYcY7KpL8wmdql/U0yaHPOGp2HDhmzcuJGHr1OIPWzS4DuWb4nK1RthNCJSE0EIVB65sCtWh4RbJ0l+ch3kCqLPbEabqwQyjQ0i2TTmchtHHL74CqFPJfb8DivNUVqYXQi++eYbypYty4kTJwCTNWDp0qWkpKSwbds2SpYsyaVLl5gyZQpTpkzBaDQSGxvLgwcPuHr1Krt37+b8+fPUqlWLoKAgHBwcOHfunNW3GZ+sR8aHOSkk3D6F0skL92ajLDZQ0SeC3/RjUoapbGRAKf+C7y3frD2/ffs2NWrUsDhnznuYO3dutm7dSmBgIBcvXqRWrVqMHTs20zIz2+jFx8fz66+/Mn78eMaMGYOXlxe9evWSziuVSho1avTO+ubJk+ejAtnSwt3dHRsbG27fvm117unTp8jlcquNOYCfq600XqlhIegjn+LaYKA0hwAkPrz4SXUCUDl5kfTgAg6y5PdfnAabN2+mevXqFn69AFFRUVZuB7a2trRu3ZrWrVuTkpJCs2bN+OGHHxgxYoTFhnj69OkolUrJzzutafm/hTx58nDgwAFq167NuXPnLFyOzDALYkJYy/Afg5P3wz9YlvgzjE8Z4X+RACQz/O1R3pmlIfkQGIyCZL2R4VuvMu/I3b+ohv8dnD59msjISFasWEH//v1p2LAhtWrV+iBHaDCZ0UNCQkhMTHznde7u7tjb22MwGKhVq1aGf2lNDOYJaPny5fx67jq6PF8QdWqDKTjiI6B0zgaAyiUHOr8SqJy8pHNa78JochREJlcgV5s0G3KtLWByVH88pw0KOxeMKaa2he+azaxJJkEuJCTknZqH9edCefpGhpBrbND5lUDrYzJFqpy80PmVQOdXwkpASg9NjoI8LxHI0AVbCQ4O5u7duxQtWpT9+/fj4+PDs2fPLMzYYEobA0hRw9mzZwegcOHCVK9eHUdHR1avXs2VK5apX27fvs3Ro0cZM2YMvXr1sopW3LFjB9999x25cuVi2LBhpra4+RJzbjsJ987zfFlfREoi2brMwTmgM/Yl6pES9giZUk3C7VOoPHLx+tgqDPFRZAuci1OlttgVNkW5qrPlJ/HeWQAMCW+15DKFEvvSjRApibza+oNV/yQ9OE/c5f0kPb6OPi4SAF3eL3Cs2oGE26cRelNEZvzNE0SdXMuTeZ0IndEMY3wUAMWLm9po9iGNOroc4xuhUGHjZKpPfKS0arg3GY5DmUZ4tv0Buc4RjAYw6El+essiuMuldnccy7fAqXJbPFqNS6OtxMpc6uHhwcqVK5HJZBa+dXFxcdjZ2bFt2zbApBESQkh+k2YtsVar5dy5czRr1gyFQiHRAsbExNCjRw+rdCo3rlwkZzqNSGrEY8K2TebxnDaETG/K8xUDSLh7Bpmk4RLEXTlEyNSGxF7YQ/LTmxhiw3g6P+PNYU5XGxo3+FJyKUj/jpq/nTJlyuDh4cHChQstUivt3buXmzdvki1bNovrwZQ1wezOkRFsbExtS7sZAqTgEXNZzZs3R6FQMHr0aMDkL2lra5tpueZ7MsP7NJEKhYI6derwn//8xyr46uTJk1SuXDlDn0FbjfLteGWgcRRCEHt+xzuf/S7YFKgECGZMtf6+3tUmhUJhdX7Tpk08ffrU4ljaKHcAtVpN4cKFEUJYRUzLZDIWL15MixYt6Ny5s6Sp/2/C7JaVEfR6PUsPX/1sgpgQWMkSa9euZc6cOYDJFU0mk0mZVN6Fj0lT91cRgDz+wGC/z42/VUP5b5LM/yzM0W7mD+jo0aNUr15d0hy0bNmS2NhYKlWqlKHQaPa9PHr0KGXLlmXmzJlW16SkpDBp0iTUajXr1q1j+/btlC5dmgkTJkhpM8LCwiRTQkREhIUPzqaLL1C75yTxwR8Igx6ZUi2d08eGE3lgIUmPLiFTqrErWhOnal2kAAqb/BWJOrKcV1snk/3reRb1EggMiTEodA4gM11vEYgBGFMSMSZGgxAk3j6F4c3ply9fsmPHDikoJ63/UJ06dZC7+iJ7U27Mma3YFg6QzsddP0rMue2m4IbsBXCqau3Tlvom2CPl5UNCpjeh4yJ3OrcxLWaurq4kJCRQqVIllixZYpWeyewfdvasSUDbv9+UIsScnsnsm5Q2ShUgICCAwoULW9UlPfz9/XFwcCAmJgZtrpKkhocQd/WA6aQQkmAlhJHkpzexL16P2Au70OWvSGpYCHIbRxACQ0I0hiSTcKVy9yXluembTK9hUrmatDYixfr9k6m0CKMetVtu6f7Xh966VMjUNojUJCL3z0eutUPpnJ3kZ281ROY+atWqFUuWLEHoU1A5eZESFyEF1kj+ZjI5yjcbEplCicrVm+Q3/m2GuEgUOgcM0Sa3jdgLezHER5n8LiOfWNQ5fVDAkCFDOHnypMUiJpfLcXFxYfbs2WzdupVt27aRP7/JN/TMmTMoFAoqV67MgQMHsLGxoUuXLsyZM4dJkyYxYsQIihYtCsDSpUtZtmwZarWa4sWL8/DhQ5o1a0aXuTtZG5WIwShICQvh+bK+yBQqHCu3Ra7SEn/rJGFbJmFXqj76Oy8I2/KDNK6RBxcgU2pAqcKhvHUQkUIuo3p+DxwcHKhbty47d+6kTp06dOzYEWdnZy5fviylzVKpVEybNo3AwEACAgIIDw8nOjqaFi1a4OfnR58+fejTpw9NmzYlLCyMlJQU6tWrR+HChSVNfHrodDoKFy7Mhg0byJ8/Pzt37pSOV61alR9//JHU1FRy5MiBi4uLFECSke9pejeg6tWrU6xYMa5cuUL9+vWpV6+eZH25evX9HNuTJk3i4MGDVK5cmV69ehESEgKYBJUff/wx0/uqF/Bg9ZkQVK7eKJ2y8frwUgyxEcg0OhJun7bypfwY2OYqTrFqjfjpp5+4e/eu1KYTJ05QvXr1TAP2GjZsyIQJEwgMDKRixYpcvXqV4OBgq4CjOnXq4OXlRaVKlfD09OTmzZvMmzePBg0aWPmogundX7NmDU2aNKFVq1bs2bPHSnv9VyIgIIBq1apx9OhR+vfvT8uWLVGpVNy9e5f1GzYirxSIOl/F9xf0kTDLEmvXruXatWsMGDDAIl7hzzA+pcfHsC6F75pNUuhVvHtZp/dLiz9LACKTyRg7dqxFpoMPxd+moXyfZP4ieDjhu2YDpo40R6W+C3+VZJ6QkMC4ceOs8hp+Tjg4OKBUKuncuTOzZs2SIqvN/oytW7dmxIgR/P7779y8edPi3qVLl7Jy5UrUajW5c+fm3LlzfPnllzx+/Jhjx45J18XExLBkyRJatGiBi4sLer2e69evU7t2bfr160erVq0sHNXr1KlDgwYNmDx5MkuXLmXpzPFEn9uBLk8Z5Jo0mpUPyLGmeqOh1IeH8HLNdyQ9fduGl2tHEnfFOlcmIPm7GROiQa5ArrOnVK2vpKjVHDly0LZtW0aMGAG8FdoA/Co0wJAYQ9KjS8i1Nhhiw3m2pBevjywHwBAThlxrj8LeHX3US16sHSH5aAIYU5N4uWYoYAoGsSlQGZnGliUL5qFWq8mXL5/UT9WrV5ciT1evXk2TJk0kzWPTpqZgFrNpu3Hjxhw8eJBt27Yhl8uttCTvC2gwQy6XS2Yguda0IKS+emg6KZOR/KaPU18+RCQnoPExBRlgSAFhxBj/mic/tefJT+15sWIAAPFXDn7Qs9Mj+zdB6PKUJeXl/QwFToWtE2DaGOhjI0EY8Wo3Raq32fC7fv16ZCotKNWkPDHND5IgqE/JUDMk02SuzdJHveD14WVEn1pHyvO7oDBtgpJTDezevdvi2u+++44dO3ZYsK/I5XJsbW3p0KGD5Apy/frbeWvlypVSpoW6detSoUIFRo8eTe/evdmyZYtFBK69vT1qtZpz585RtGhRnjx5gk/8Hcky8/rQYmRKFSqvPDiWb4F96YZ4tpuCJkdBkh5exCmgEymvHhJ//SgAShcfdAUqIVfb4JiBQGkwCjqUN22wS5UqBZg2MRMnTmTYsGFcuHCBHDlySNd36dKFDRs2kJKSwoMHD3j9+jVNmzbl5MmT9OrVi8mTJ3P58mXOnDmDwWBgzZo1GZoh02LJkiXkyJGDgQMHWpio165dS926dZk/fz4jRowgZ86ckkBo/q7eB/Mm8uHDhwwdOpTJkycDSNrUd6FIkSKcOHECf39/pkyZIn2bY8eOlYIaM0L7cjkxGAUyhRKPFmNQe+Ym+vdNRJ9ch8o5O24NB2V67/tgMAo2rl3F9OnTLdqUmJholeYqLUaOHMngwYPZv38//fv358KFC+zevdvKbN+jRw/i4uKYNWsWvXv3Zvv27fTr10+ypGQElUolpXT66quv2LFjB+PHj2fXrl08fvwYNzc3qlWrJvlAp4dZOz19+nRsbW2pXr26labUDLMLglarJWfOnIwdO1ZyQ4mMjGTkyJGMGDGC3bt3E2+Q83LvfEJntuDZ0j4YkxM+2Uc2I4zZcZ3EVEOG52QyGVqtNlP3sl9++SVDd4r0MLNkfaxl9n34VAKQz4G/TUP5d/OhfgzSc9L+FVCpVBQtWhSNRsPo0aMlc5G7uzuhoaG0aNGCatWq4ezsLOWEBEhNTWXkyJGUKFGCyMhIfHx8uHfvHj179rTSUjo7O0u55MyBBdu3b+f169csWbKEihUrSrn0wDQBBQcHM2vWLOLi4jDoXHAo0wjHim0syv2QHGtm2BSsjCEukuTQt1oErU8RbPJlPGZJj0y55pROXhgSY5CpdKxb8QuzxrmwYMEC+vfvz7Fjx5g7dy6AJGgCxPpVwbNIY54u/AaRmoJXxxlEn1pP/LU3qToUKjTZC2BXoh4qNx+eLe5BavjbpOkxZ7dLGgdjSgIJt08h19iidPJCHxPGo0ePsLGxwdPTkx07dtC8eXMOHDjAhAkTyJUrFx06dGDNmjWS+c4clOHj40OtWrWkCPz0yCigISN/tPj4eEqXLs3hw4cxxJomaUOcyRdRYe9O0uPr6HKXJunJdUCGxrswSicvKdhGbuOEW+Mh0n0Ru2ZiX7YZyaFXSHlxz2qCNgt2MrV1/RT2rpJZ3cyOo7B3w6lqR1OQwpv6OwV0wrHcW3OlfakGRJ9ej3+J0ly5cI4a9RqyY/N61F55SXl5H4RA620ShBUOHhiT4jAmRKGPeoHKxSQMOVftwPP7Jg2nws7Fol4utbphk7+C9Dt0ViuEIYXwuGScnJyQyWRWJlkzunfvzuLFi6V+GDduHP3795c0aPb29rRt2xa5XE5gYCBOTk7069ePtm3bcuzYMZo1a0apUqXIlSsXvXr1Yv78+RiNRnr16iWxeCS9fkGVvOU4cf0RSSFXcKzcVsrRaoY2VymiTwZj618DxwqtiLtyiIg9c3As39zCf89iPOQyKuZ2lWj8xo0b90Eah1atWtGqVasMz40YMULauJnRoEED6f8ZPaNChQpSUNq4ceMYP3685HeWNiNCZjCXmfa7NiOz4KiMkFFe0ZIlS77XD3PFihWsWLFC+p3P054qed04/SAC3HzwbDPJ6h7f4ZZBh24NB8KbNFtp4VSlPU5VTL7o5vEqkM2JAkOGSNkOPqQtGo2GGTNmMGPGDIvj6RUg3bt3p3v3zAMTIeMx1Ol0Ulnz5s1j2rRpNGnShEqVKqHX61m1ahUdO3Zk2bJlBAYGSvcZjUYaNGiAra0tQ4cOxc3NjaCgIP744w927dpFr169+PXXXwkNDUWpVBIXF0eFChUIDAzk6tWrTJo0SWIwWrRoEcHBwaxfv5579+4h09iagtN0DiQ++IPUsEeonLOReP880b9tNM0dMjlanyI4VQtE7W6y9MVdOUjEnrlk6zIXtVcei3ZGn95I1Ik15Oi1jJc7Z3LnzRqVfu7NyIfyypUrzJo1i+PHj/Ps2TOcnJyoX78+06dPzzS38PsycyTc+Z24y/tIefnAtPbJlSBXIIwGCwvei+DhGBNjcGsynMgDC0h5dge51o5vn3fm4Iq3MoDZ6rlhwwbu3r3LggULCA8Pp1KlSixatIi8efNK14aEhNCyZUvOnDnDy5cvrRjGMsPfoqH8N0rmnwKzedasEXFwcOC3334jISFB8tXo06cPQghJkDXn9TLnZzt//jyvXr3C39+f0NBQaYK1t7eXylUoFOzdu5fJkydLuyo3NzcmTJjApUuXqFSpEkqlkj/++IMBAwZQrFgx5s6dS/fu3Tl27Bjh4eH8GLQUmVJFzPmdvFg9hITbp6WUQWASIpNCrhAytSFJIVfQeBdGH2WKPtVHvZTSxahcffDq8COOFd4uWgl3z/BsaR+erxhgEkZkcrJ/EwRA8mNTihKZUoXSzpmm41cxul83C1agefPmSW4AaTVCkfvmYUiIQuHghtAno3L3xa3JMDDnRDTqSbj7O1FHl5Py/C62hQMQb0y/SidPEm6dRJOz6JvB0uPRcizZv5mP+5d9MRoN7Nu3jzp16rBr1y5at24tMU5ky5aNjh078tNPP+Hg4CAFd9y9exchBPPmmUz+Z8+exWg0cunSJTQajRRFvG/fPqtF4/jx4wAcOHCAnj174uHhgbe3t6SFiLuyD2QKRKppo2GIiyD+xrE3fXgdlZsPCp092lylJaHZmBDF61+XYIiNQOttMrGr3XJiW6T6m/NvhRphNBB92pS0OjnkCk8XfkPcjaPS+acLvsHw+hkibdCLXP5WwHszASq0b01rxpQkSTh1cTQdb9W5G45V2pPy4p6Vl73S0R2RYrJAxKbJFJBijuqWyd9Gg79B0pv3R2rHm/7RGwU//vijhRl1+PDhPHjwQFpA6tc3bYbM1xiNRtq3by+l2ipYsKBFMml4G9lvNqGag43M6VTkcrmk6TSfn9y0KIbwEEAQfXItT+d3ljTHT35qT/TJN4E38ZaZHpROmacLUsplTG5aNNPz/2tQq9WSH2hmSEpKsgpe/CswuWnRjwoK/BD808YrNjaWAQMG4Ofnh0ajwcPDg9q1a+Pp6UloaKjEiHPx4kVOnz5NwYIFpQDFatWqUa1aNTZv3szp06dZsWIFLi4uBAUF8eDBA+Li4mjYsCG7d++mTZs2/PTTTxId6NmzZ+nbty/BwcHkz59fEmT79OnDzZs3Jf9+kRxP3MU9vD62iqRHF5Hr7Im9vJ9Xm8YjU+twqtYFx4qtSQl/zMs130l5am0KVEKm1BCfZu4CkztNzNltIFfwdOE3pIQ9Qq61w8nZhdWrV0vkFEajUZq/u3XrRs2aNbl37x4HDx7kwYMHBAYGUqpUKRISEiTmp4cPH2ZISzmzd3MSnr7VZAp9CmE7Z0k0nRH755P44ALGpHi0Pv7IdQ6I5Hiijq60Gi8zEchbBrEcHFo5SyI0SIupU6eybds2hgwZIlk927e3DLK9ceMGCQkJfPvtt/z8889WZWQK8YGoVq2a0Ol0Inv27GLatGnS8djYWGFjYyP69etndc/jx4+FXC4XkydPtjg+9j/XRO6Ru4Xv8F2Z/ml8/IWtf03hO3yXsPWvKTQ+/hbncw7bIZxrdhMqd1+BQiXkOgehzVVK5AicI8b+55oQQojU1FQxYcIEkTt3bqFWq4Wvr68YMWKESEpKsqjPuXPnRJ06dYSrq6vQarXCz89PBAYGCiGEePjwocBkj7P4Gzt2rBBCiMuXL4vOnTuLXLlyCY1GIzw9PUVgYKAIDw/PtC+XL18uADFo0CAhk8lEfHy8CAgIEAEBAVJ9ypQpIwChUqkyrc/06dNFx44dBSBkMpkAxIkTJwQgKlWqJJycnAQgOnXqJFq2bCkAUa9ePVG0aFGhUqks2qNUKsXYsWPFggULRL9+/UStWrWk+u7fv1/I5XKhcvcVzjW+EQ4VWwuZxlao3HIKmUorZEq18B2+S3i2nSwA4dl2snCs1FYAwnf4LpGj51LpOY6V2grf4buEXcn60jH7cs2FS+2eQuPj/+aYTBpnhYN7hv2ftt79+/eXfsvlctO/Glsh1zkIhb2bULn7CUB491ktPN7UEZlcyNQ6YVOwslC6egtA2BatJZXjO3yXkCk173325cuXRZMmTUSrVq1Eq1atBCDq1ze1bciQISI4OFgam4IFC4pFixaJUaNGiRIlSgh/f1N78+fPL8aMGSMWL14sAKHRaISvr6+Ij4+XxmD+/PmmOtraCq1WK2rVqiWaNm0qChUq9LZOSrWQKdVC6ZxdgOmZ7s1GC7mtk7Ar+aXwHrRZKF1yWLRBbu9m+tfOVQDCtf4AkaPXcotrnGv3EGqvvNLYqDzzCMcqHYRMZy9do8tXIcM+kmvtLZ6j9spnGg+FSujylRdO1QKlNgNi/Z4jwnf4LqHOXvBtOTKFkNs4CXWON8fkSqF0zi5s/WsImcZOIFeYnmHrJJArBQqlNMbIFcL+iybCpc63QuWRSyrTwcFJTJw4UdSpU0c6plAohEqlEhqNRri7uwsXFxfpXP78+cWXX375tk+cnUWVKlWk8TGPe+XKlaWycufOLfLkySMAUbt2bbFkyRLx/fffC6VSKbRarQBEzpw5hU6nE+VrNTTd9+Z99WgzSXi0mSTkNk6Zvn8udXpJ30m2LnOFNndpIVPrhEylFUXKVBS//fZbhvPOyZMnxcCBA4Wbm5uwsbERTZo0Ea9evbK4dvv27aJ+/foiW7ZsQq1Wi9y5c4sJEyYIvV6f6byWFidOnBBlypQRGo1G5M6dWyxcuFCMHTtWpF9yli1bJqpXry7c3d2FWq0WhQoVEkFBQVblpZ0fhRDiyJEjAhDr1q0To0aNEtmzZxcymUz88ccfAhCzZs2yKuPUqVMCEGvXrhVCCKk+t2/fFu3btxcODg7Czc1NjB49WhiNRhEaGioaN24s7O3thaenp5gxY4ZU1rqzIaZ1aMg24ViprVA6ZRMolEJh7yYcyjUTOYdss1ivAGFfqoFwbzZKqNxyChRKoXLLKTxajRe+w3eJ9WdDMu1Lc1vXr18vRowYITw9PYWNjY1o1KiRCA0Ntbj2+PHjokWLFsLHx0eo1Wrh7e0tBgwYIBISEqzKvXnzpmjZsqVwc3MTWq1W5M+fX4wcOVIIIUS7du2EWq0WgwYNEkuWLBHTpk0TjRo1EmvWrJHu9/X1FZ07dxZCCDFo0CABiJiYGGmsWrZsKTw9PcXChQsFIFq0aCEWLVokKlasKJRKpejVq5cQQojr16+b5ip301wfGBgoRo4cKRwdHaV3vXTp0iIlJUWULVv2zbfubJoPi9cVDmWbStfZFa9r0e/efVcLmcZWOu7Vefabe2VCplQLhaOnsC1S3TR+IDQ+/sKlbi/hWKWDkGtshZ2zuxBCCE9PTwGIfPnyCZ1OJwBhY2Mj1Gq1KFu2rNS/ERERonDhwtI8AogSJUoIQJQsWVLkzZtXTJs2TXT9pptp3dbaC4fyLYXCzkWATNgUqmqql72bkGlsBXKFkKlNz1NnL2Bab1Ua4TN4q1A4eAhd3nLSuunacNCbd3KrkKlthEypFl81bWb1HhUqVEgkJyeLpKQkMWDAAGFraysAUa1aNfH48WMBSO+B+Tv5UHywybt48eK0atWKzZs3M2zYMIoWLcqXX36JnZ0dTZs2ZcOGDcyaNcvCr2DdunUIIayk3w/lr30XIvb8RPzVQ2hzl8auWB0wGkh6coOEJzc5cqcY4yjCN998w8qVK2nRogWDBw/mzJkzTJkyhZs3b0oRm++j1nN3d2fBggV8++23NG3alGbNmgFvU+uk3Zl4eXlx/fp1Fi9ezPXr1/n9998zNFWac0oGBwdToUIFybydtj7mgI2vv/4aV1fXDM1Dy5cvl7Ql1apVs6Ac/P777xkxYoSUmLlLly7UqFGDffv20bhxY4YOHYqrqytff/01UVFRuLu7W5g6RBrt0LBhw3B190Db9kcp+lrr48+rDd+b/N0+IseaGYkP/pD+71CyPkonT+xKNSB0ehNTxO7bigCgcPLCRadk7arl/Pjjj1y+fJkZM2bQr18/ix2UVqslISEBu1IN0GQvQNiWiW81kkD0CZO/kE3haugjQkm4+zsY9Cjs3aTo5rePNqL1K0nSo4vo8pVD5epD/PUjGOKjUTq6s/SnGRQrVkxiOFqxYgUbN25k/vz5TJ06laCgICIjIzlw4AArV67kzp07fPvtt7i4uEiJzFu3bi1xgIPJLNWwYUO2bNnCli1b6NixI/HJel6/sTjEx8fjlysX58+fJzo62uL90njlRWHrhGPFNjxfPRT0yUTsn4cxPgpD3GueL+6OIS4S57q9iD65DmP8a4yx4SCTYYwzmcxjL+zm9eElgMkkbkyI4vXhpcgUapArUNg6oXb3w6lSGzTZC/Jqgyky173JMJ4u/BpjYhxC/6aycgVyrS3GpFhJE5ny4h688Ze0KVARkRBl0efZHbXIiEcY3po41TkKoPUuTMz5naZ3zahH6+NP0tMbiDfpiJArsS1aG5lMRuy5/yDQo3BwxxAfZdJmymRgeBvJWrtObVatWiV9i507d2bXrl2Sf1d4eDgNGjTg0qVLvHz5kvv373Pnzh10Oh1fffUV165d48qVK6xatUrSUt68eVPiOu7RowdhYWGSL/Rvv/3GsWPH8PLywt3dHb1eT1JSEl5eXowaNQqNRsPvh0xaV7nWDp1fCQBc6/XGmGIZmR11fBWGmDBkb77FlLAQXgQPQ66xwaFcc6oV9OLKoS1Uq1aNY8eOWfkE9u3bF2dnZ8aOHcujR4+YM2cOffr0kTTk8HE0sOlx9epVaU4dN24cer2esWPHZsgDvWDBAooUKULjxo1RKpXs3LmTXr16YTQa6d279zufAzBx4kTUajVDhgwhOTmZggULUqlSJYKDgxk40NLMHBwcjL29veR7aUbr1q0pVKgQU6dOZffu3UyaNAkXFxcWLVpEjRo1mDZtGsHBwQwZMoQvvviCqlWr0uaLnLyKSWRYt7YkP7mBXXGT20zqq0fEnPsPqZHP8Gg+2uI5SU9ukHDnN+xK1keu1hH7x07Ctk1mysYTHxRM+sMPPyCTyRg2bBivXr1izpw51KpVi0uXLkluMps2bZI0S66urpw9e5aff/6ZJ0+eWDCeXblyhSpVqqBSqejevTt+fn7cv3+fnTt38sMPP7B79266detm4Tb13XffZVq3Fy9eYGNjY7GWXbx4kVKlSrF3714LxjWFQsHp06eloLhz584BpqwYrVu3xtHRkR9++IFGjRpRoYLJXaV79+6oVCry5MsvBfAhkyNSk1B65YE3CZ10ectauIsgk6PJnp+k0CsY4qPerFkaQGBXsj4ylYa4i3sxJsbAG79Yc3xAyrPbpEa+dYECE0FIt27dWLhwoeSOdvbsWe7fv4+/vz8PHjwgNDQUrVbL8OHDGTVqlJSn9dGjR9y/fx9nZ2eWb97FsiW/IJJiSbh1AoeyzRCGVFLCQhD6FAyxEajcc2JXtDaGpFhifttEyssHyNU6jIkx6COfYOtfnZjft6D2yotMrZMsSwn3ziJSElDnKMSdu/esxiowMBC1Wk3Hjh1Zs2YNX375JXv37iU5OVlyYUkbcJSRu0mm+GDR8w2Sk5OFl5eXaN68uXRs//79AhB79+61uLZYsWIWu0ohhIhNShV+79BMfsifWRNmX7qR1bmcw3YKv+G7xG9nzwtAfPPNNxbPHzJkiADE4cOHhRBCbNu2TQDi3LlzmbY5LCxMABlK6hnt/NatWycAcfz48QzLGzZsmABEuXLlxJ07d4QQb3fg5vqYd3WbNm2yuDethtLBwUHs3r1bgEkLCYiHDx8KIYRISUkRTk5OIiAgQMTExIiwsDBRunRpAYiLFy8KIUyaUED4+fkJX1/fDOv67NkzAYjBQ76zGjdJQ6nSfpSG0rv/OgEyoctv0mp5dZkjvPsFC+9+wULh4CEAkaP3ijeaqgICEH1nrpbq1LlzZ6m+s2fPFvnz55fKt7W1FdODlpl2pwPWC0CoPHMLmUojcvQNfqPhUghdvnLSM737BQvHyu3faLVkwmfgRql9mjdaMftSDazetWtPo6z6y9zXa9asEYC4dOmSpAm5ceOGaNGihbC3txfOzs6iT58+IjExUbo3JSVFAKJr167CwdFJlGvUUVT98bDwG75LuNYf8EbDl1fIVBqh1NmL0nVbit1Hz0htt3HxFK61ups0Vl/PN2nozNo3Jy/Tn62zyDlsh9D4+At1joLCvlQDIU+jaVS6+giXen2k3Togmsw7JmRKjbApVFUoHDwky4Hv8F1CpraRxjn7NwuE2jOvVJbc1llk6/qzqVy3nG80meUt+tDcLrM24ty5c6L4t3Pfaj7zVxTIFcK92Wjh0Wq8VC+5zsGkiXyjnfRsP/VNu+dJmkiNj79wrvG1Scv9pi+UTtnEF8NWC71eL+7duydpEczfTd68eSVtglkToVarpd19Zn/msa9UqZKws7MTMTExQgiTpgcQU6ZMkcY5ICBAui/tnFKtWjWhUCiFNkdBKwuOd79g4Tt8l3BrMly616vzbOE7fJfQ5SsvUCiFX++lkqbr2bNnwt7eXlStWlUq36yhrFWrljAajdLxgQMHCoVCIaKi3r7PGc1rPXr0EDY2NlYWnvRo0qSJ0Gq1IiTkrdbtxo0bQqFQWGkoM3pO3bp1Re7cuS2OZaahzJ07t1UZixYtEoC4efOmdCwlJUW4ublJGjUh3moou3fvLh3T6/XC29tbyGQyMXXqVOn469evhU6ns7h/9erVQiaXC59O0y3Gy6Vub9M72WG6hYYShVJk7/GL8B2+y3R9N5PV4eeff35nf5rbmiNHDum9EkKIjRs3CkDMnTv3nf05ZcoUIZPJLMajatWqwt7e3uKYEEJ6L3x9fUWZMmXE06dPM62XWUN59+5dodVqRceOHYUQb8fK1tZWdO3aVXTu3Fk4OjqKs2fPCiGEtGa1a9dOeHt7W31LnTt3FuHh4SIsLEyy7pjX6wkzfn4zT7+d25DJhTpb/nd+nzKNjXBvNso0Lh1nCIWdi7AtWkv4Dt8llK4+ArlC2BSqavHN6fJ8IRQOHiIuKVXSUH777bfSGjx37lyhVqsFINasWSP69esnaVnT/nXt2lUAomzZslLfLV6/QzrvVL2r9Exb/5pv5k4n4dlxhtDlKy9kGhurMj3bTRXZu5vec6VzdqF09X5b77zlhMLRU9gUqSGye+e0eo/Wr18vLl26JADRq1cvqT0rVqyQ5qsBAwaIzp07C2dnZ6tv9l34aNWSWq2mbNmyFjRxtWrVInv27BYZ7c07+LR8w8BH8ddmBjPnqWNl62SrMpkMAazfasqbNWiQZdTd4MEmnlRzhKeTkxNgolNLn4vrQ5A2gCIpKYnw8HCJkzYtj2paFCxo8vNav369VUSjuT7vyu9mRvPmzalduzbu7u5WDtiTJ08mKiqKkydP4uDggLu7O3/8YdIKmoMQzDsns79XRjCfK1yogFXOPKVLjoxueS/0r58BgsQ3NIcvVgyQ/MUMMaaodmN8NAq5DFtfkyZY9fSiVTmJiYl069bNIrI7Pj6e7DlNUdKKN+lVUsNC0PqVxBD9hlHEaCDx7pkM/dTUHrmlHapNwcokP7WkcTTDmBRHQpJp53b9+nWaNm2Ko6Oj1Nfm9z46Oppx48YhhKBQoUJs2rSJmJgYIiMj+fnnn6Xk6D4+PpL/57Jly4iJjuJ6yAtC0tGPOtf8hpyDt5Cj/zpel+5Cr31hOOYwOZcnRL7E1tcUMKJ290XpZIp2VTh64t1zCTK5EqVzdimnoUyuxKXOt3i2eZv3zrFcc+xL1LNo6+ga3gh9MmqX7Fb9oHIz+XCG755D0pPrKBxNKafkNk6oXHKg9siF7/BdKO1MCZZ1uUpJ9yrkMvJ7msZIp9MhhKBMmTJoHp81sSVhCuLx7rMKjXchaSeucsmOT/+1+A7djsLOFZVbTim3qKldJiuJITEWh7JN8e61HKVzNtTZC6CwcaB8LldiY2MpXry4lC9u3bp1tG7dmnv3TDv6xo0bEx0dzY8//khKSgo7duxg7dq17NixAyEECQkJFCpUCLVazeDBg5k3bx6NGjXi1KlTEs97eHi4RFmaliYOLH19zZg/fz4yGehf3kN3cQOxl/YRc3oDrzaN4+W6UaSEhxKxZy4qz7fpYOQYSXp0Ed+SARyf0EbSdGXLlo127dpx8uRJK9at7t27W2i2q1SpgsFgsJgH0s5rsbGxhIeHU6VKFRISEqyoTdPCYDCwf/9+mjRpYkHDV6hQIerWrWt1fdrnREdHEx4eTkBAAA8ePLBiB8sInTt3tgpia9WqFVqt1mI92r9/P+Hh4VbrEZgS2ZuhUCgoU6YMQgi+/vpr6biTk5PEb27Gpk2bKFyoEDtHt6KUu8KkGUuKQftmzkoOtcwvq/MrgdbV9A1VzO3KialdMqVczQidOnWySO/TokULsmXLZpF2LG1fxMfHEx4eTsWKFRFCcPGiaQ4NCwvj+PHjdO3a1Yoq0fxe/Pjjj1y7dg0fHx/Kli3LuHHjMqynXq+nZcuW6HQ6pk61JC1ITExEo9EwbNgw7OzsKFu2LPny5ZMC0tatW0erVq0kVqTmzZsjl8tZuXIlbm5uuLu7WwUGGjHVT5OjIDKtPSr3XOjyljVlcAAcKrTCo80k679mo5G/yQaR9OAPbApWIeHOaYQ+xUSYYTRIGr70eBTx1i/cHCQEppgHc2qmyZMn88svv/Dtt99SrVo1PDw8JBnDHMuQNr2WUvH2+zP7dQMYU03WHdtCAYRtnkDqq4c4VW6PTaGqpvExZ7QQRtP8mr0AhvgoaU43JMaS+OAPbItUezOW1tKWQqGQ3pl+/fq9rYcQDBgwADBlJNm9ezfDhg2zYq16Fz4pKCc956ZcLqd9+/Zs376dhAST03xwcDBarZaWLVta3Jui//NO02bOU4Uuc/7NJ49DkcvlFpFLAF5eXjg5OUmTZ0BAAM2bN2f8+PG4ubnx1VdfsXz58g+OaoqMjKR///54enqi0+lwd3eX0r58yISYHub6mKnfpk2blml9cuXKhUqlYtKkSYSGmlTzK1asoHfv3owfPx6lUomfnx87d+7k4MGDtGhhSi0yatQoFi9ezLp164C3LBnvQ/UCHhY0blbIhBnDiuruzSShzVUaMDHlmD98lbsfKg8/lM7ZqJjblaDvugAwe/Zs2rZty7x587hx4wYRERF4e3tbpVDy9/enT6cWRJ1aR/Tvb5PQOlVpLz3X1r8WMqUauc4B26K1sC1eF7mtM8iVOFXvKt3jUK4Zak+TsJYUcoXYi3uIObOV8F2zeTK/C8kJsURFRREQEMDly5eZMGGC1NfmaPn3BQn07duXH374gVatWtF/8nxytJuEV9tJJgfsDO6VKd8KIma3kSSZ6ZjO1o4p37zlyjan6jEH3HwqcjibBGx5BuOrzmbaECU9vMDrQ7+gjzRRbMoz+TbTHlfKZbQo5W11jTbhlWSefv5LTwvBX6QkYkgXnKJwcLcqA3hresfEu6xyzo4AviqRDRsbG3r16iVN8pMmTeL27dvMnz/fYj44evQo5cuXZ/ny5bRr146+ffsCbyNf27dvz8qVKxk8eDDPnj2jZMmSjBkzRtpYmM2u6ROKp03XY0bhwoUpVaoUrq6uRFw8QOyvi9BfP4BOrcSxXDPCtk5GYeeKfXGTwJ/NUUuzgg6I1GQ6fVnRijauUKFCEi91WnwI3/L7NkmZISwsjMTExAxT/2TEnX3q1Clq1aqFra0tTk5OuLu7M3LkyPc+x4yMUmw5OTnRqFEjKQ8smNajHDlyZJhHMTN+8/QsM46OjlZc09evX6dEfl82D6zHk5/aEzKnHc8Wm6gKDW+S9kv1cs9Gx3K+HBpYldVfl8PHxcZqLX0X0vepTCYjb968FgF8oaGhdOnSBRcXF+zs7HB3d5eEIHN/mgXDzLjmwSSUP3jwgJ9//pns2bMzffp0ihQpYhXkcezYMW7cuMHmzZsl4gZzsKlOpyM5OVliXFu/fj2VK1eWAgxz5crFzJkzpdRTW7ZsQQhBQEAABw4c4ODBg5I7yd27d3kcmcCiNaY53b5Ca0RKApocBfBoPlqaA2QqnURWkfZP61sMTc6i2BSoSPSpdcRd3o9ITiDy1yUmFyuFCl3uUmSEtDJL+lRB5mCdGzduMHz4cMaOHUtcXByRkZE0bmyiqDVTHKddw70c0myC0gjNZuFSGPUYE2NwbTAQhy++kjJayNK5l9n510CkJCAMpowhCbdOglGP3RvhWCnPWMQLCQlBLpeTJ49lpLv5G42MjGTmzJkMGzZMUoB9CD45bVD6nUOnTp2YPn0627dvp23btqxdu5aGDRtKHW7Gx/KUfirMi9/7eJ7N2e9///13du7cyf79++natSszZ87k999/l3wZM0NaTtoSJUpgZ2eH0Wi0SLT7MTDXJygoiN69exMZGWlRn7Qw70a7d+/OyZMnWb16NVOmTCFnzpwIIfD398fR0ZGGDU1CxoMHD9i8eTP379+nX79+knbG3d3dSoNihlnYvHv3Ll0btGTFb4+kc/pISyYGc8Ll9PR2+mjLss1Jqc2+jVrvwlLEarK9I042Kg4Nr0deD3tJ89qtWzdOnTrFtm3bpHdv6NCh5M+fn8jISKnsgIAASpUpS49BI9DHhAFgWzgAtUcuaYJX2Dvj1WE6r4+tNGm7hRF1tgI4B3REk6PQ2/aotHi2n8rjWS3Rx0UQeWgxcrUNKpccOFVuh79fNo4e2k9ERARbt26latWq0r0PHz7MsD/TY/PmzXTu3JlcDXsy48AdlDlzotCnfFyCZJkclVtOPL4JIjJRz5A6+U1lvUlv5NbIpJVXOrqT+uoRQhgtKBDN6YDcW4zBJm9Z6XiN6tXZs/IqBoMBnU5HAZt4nqVLqmt4E8nv3ceUxy8p5Aov143EqUp7bAtWlq5T2DkjU2mxLVRFOjahcRFaf/ElV87/ZqFh16nkaO2dcWo4mIxcrRU6SxaT9JOshHT3ymQyHLRKfF3tUKvV/Pjjj3h7e9O/f3+uX78ufQ+9evWymA/++OMPihQpwuPHjy3mAw8PDylJfVRUFHnz5kWv12dInZo+FY9ZI5seOp2OggULWlkcGjdpyuGUaFb95yAFChbEz3UutholL168wJrC4N14Hz2reZPk4ODwQTSwn4r79+9Ts2ZNChYsyKxZs/Dx8UGtVrNnzx5mz579Qc/JjDO8U6dObNq0idOnT1O0aFF27NhBr169rKLy4dP5zY1GI0WLFpUybqRFUooB7FzJ4ZcHtVKO/1RoVjon4xoXeWeZfwYGg4HatWsTGRkpCQK2trY8ffqULl26fPS4ZcuWjV69etGrVy9evXpFqVKl+OGHH/jyyy8Bk+Y6MjKStWvXWgjqISEh5M6dm2zZsvH8+XPAkvKxfPny9OzZkwcPHpCUlCTlDS1YsCC3bt3Cz8+P2rVrW0TtT509jxmPsvHylsnKlvzgPBgN6HKbhFGtXwnirxwg/tqvOJZrasV8ZkiIRmHjiHvTkSQ/vUXCvbPEnttO3EWTpk6m0lqRacjUWkRy/AfLLEIIJk+ezPnz57G1taVixYocPHiQpk2bWqxbADr1e7jM0zBjpYUxXY5fm0JViTywUFpz468fQe2VD5WrN3YaZWY6ng9uz8fis+Wh9Pf3p2TJkgQHB+Pt7U1oaGiG4eZp+VA/FWbOU0NibIZaShlQJH8eNhmN3L17V0rlASZmlaioKCutXPny5Slfvjw//PADa9eupX379qxfv55vvvkmU6H09evXFpy0Zty9+24ayC5dulhxgKZfQMwfMmBVn4cPH1rtzGvUqMHq1au5ffs2V69epXHjxsyZM0fanaakpLBgwQLAZGqvVq0aRqORvHnz8urVKy5dumRRnhACmUxGtmzZKFGiBCtXrmT48OFSDra4+xdIDQ81MaC8SeWidPAAmZykx9dwqdlNyrEWd9Gk+netPwC7YrVMD1CoSHp4gRx9VqG2d6G4txNrvi6H7ZQGb9h6LMe1bdu2kqmkS5cuEjUhIAmU06dPl/K3rQzzISQygZCpDVE6moRVha0TmpxFibu4D/vSjVDYOiPX2kvMAxbO3G8gV+us8soB+Lra4GSnkxaetB9fSkoKQUFBVvdkBIVCwYOwOA6nYYyK+WMnpNfqfiBmHLjDtGZFmdqsKD32yCwmFF3uMiQ9vEjCzRMSY5AwGoj9YxcytQ6tjz8KuUxKibJ31c9sblSVFi1aULduXfbt28eIwEEsu2wSdlPDH5P4IGO3jvdhaJ0CmQYi5MmTh0OHDmGXswipMks2iidBXVG7++LRcuxHPU/p6IH+9TN8vCzfq8wSEL9rPsgIR48e/VMbi3dh6tSp7NrxH7Zu3UqTGpYBNu/ipb5161amvNTvwp9pi7u7OzqdLsM5MH0dd+7cSXJyMjt27LDQEqYNLvxU1KtXD3d3d4KDgylXrhwJCQl07NjxT5ebFnny5OHy5cvUrFnzvYqLz4H0fSqE4N69e1KA6NWrV7lz5w4rV66kU6e3rF8HD1qSFZjNtNeuWabUMsNgMBAXF2ehDPLw8CB79uySlm3o0KFERkZib29vQYNpTnieO3duSpQowYkTJyzY1wD++OMPk2uaEAwaNEhKBXT//n2LnI1m1xGAJxGxyFePwPgmCC/2wi403oXRvclfbN5U6iOf8HxFf2wLVUVu44g+JozEe+fQehfCpc63gMlcrslREIXWjtdHTHO/SIol4fZpbAq8TSCv9spLws0TLJw21srCkB4FChTgxx9/xM7ODgcHB1JTUyUlTUZuBcWLF8+wHJlKK10j19oRvms2DmUaZep6pdDZI9fZY0xOQB/9iuQnN3Gu1Q2FXIaXo5ao8Izr6+vri9Fo5P79+xbuN+Zv1NnZmSFDhvD06dO3QVAfgM+qLuzYsSMHDhxgzpw5uLq6SjuZtLDgQ/1EmDlPo0+utTonhCCnqw1NGpu0cmYuTjPMu0lzNNPr16+tJHEzd7L548mMkzYjYSKjZ34MPqQ+70PFihVxdnaWWHdmz55N+fLlrcqVy+UsWLCAZ8+eUaJECcaPH8/ixYsZNGgQ9eq99aObMmUKL1++pHLlyuR+cZSo46sJ2z4VlZvlhyLX2mJTsDKxf+wi8tclxF7YzcuNY600lIC0EXi+tA9Rx1ZSPuUSs6dPpUGDBtSqVeuD2vkuZGaeN00qgudL+5iSd6ckEnVqveSn9iFQyGXkTg2lcePGUqRijRo1KFiwIP369cuwr0+fPk3lypWxsbHBy8uLfv36ERcXR/Xa9Ti2ewuRhxbz+kQwT4K6SnnG4m8e58WaYSTcPWNVh9hL+3gRPJzHP3Ug+fF1UiOfEr57Dvqol4zZcZ1KedyoU9gTzZvdtUIuMyVwd/UhfPdsXh9eSswfO3m5bhTJT27gUrUjco0NFXO7cmhggNXzzIn9lw7rRMXYk8T9vpGX60ZYvQPvg0YpZ1qzovSunjfTa1q1aoXBYKDIy0zYk4wZJ4Q3I+XlA5DJMcS8InR2a0Jnt0Yf/ZKkp7e4cumtABwWFmbhZwef/v392Y1FZjh06BCjR49m1KhRNGnSJMPnZsRL/fLlS9auXZspL/W78GfaolAoqFu3Ltu3b5fccMAUAZ/W1zmz50RHR7N8+fKPqm9GUCqVtG3blo0bN7JixQqKFi1qkf/zc6BVq1Y8ffqUX375xepcYmIi8fHxGdz16Vi1apUF//zmzZt5/vy5tM5m1J9CCIn0wQx3d3eqVq3KsmXLLMbIfH1sbCzu7u7IZDImTpzIL7/8QuvWrTl37hxt27Zl+vTpzJgxgzZt2hAbG0u9evVYuHAhQ4cOpVu3bpIZtUWLFrx8+ZJy5cpJjGtz5sxh1apVyGQyfHx82LRpEzNnzkSpVJKamkpycjJ37twhMDCQn376SaqXS+2eJn/tN03T5CyGe/PvTYKp0UDSY1MScqdqXVHYuRJ9ZiuvDy0m4cZx1J65sS1WG0NSnEXfmHwmTWuE3NaZsO1Tidj7M5G/LiFkakNiL+7FuWBZ1qxa+V73ix49elC3bl0iIiKIjY1FJpNJ7gFpNwJmFxsXF5cMy5GrNICMhFsncW38HQo7Z6KOr3lDTvHWCmhxj40TGFJNwrFcjm2hqhiMgtzumTOImd+ZtH0Mb2WXtm3bUqJECaZMmfJRDIGflSmnXbt2fPfdd2zbto1vv/02U65LMx/qp6YO0voWw7ZIdWL/2Enq62focpcGIUh+fB2dXzGq9+5D8eJF6Ny5M4sXL5ZMOGfPnmXlypU0adJE4q5euXIlQUFBNG3alDx58hAbG8svv/yCg4ODlNg4PSeti4sL/v7++Pv7W3HSHjhwINOdvEwmIyAg4J0D9CH1eR9cXV3ZtWsXgwcPZvTo0Tg7O9OhQwdq1qxp5Rhft25djhw5wvjx45k5cyZGo5E8efLQrVs36Zp69eqxadMmRo8ezbSJY3HLnhNF/f4k3D2DITkRn35vqbtcavcAo564S3uRKVTYFKyMffWuPF+aLgWIXInGuwhKBzfE7aN8/912PDw8JB+0P4v25XJamOfNULvlxKvLHKJPriXh1kmEPoW4S3tRe+TG8Q3Tz/tgMAryaWJ5LZdLfnhr167lzp073L59mxYtWtCtWzepry9dukTNmjUpVKgQs2bN4smTJ8yYMYO7d+/iUH8wdjfDiL9+FGNqEnKVFvuS9Ym7cQzVG9eAsC0TsfW3ZERJefkApaMnNnnLEntpP8akWBIfnCfx3lm8v5nHyG1XsdUocbPTcHBAVYLPhHLkzitoN4XIoyuIu/orxpQEVC7e5G/1HW3bd6JD+ZwSs0p6FCtWjP379zNo0CC2/DILr+w5yNOsB7cfhBIdZt3PaWEW7BVyGYcGBlj5+qVHQEAAPXr0YNGi+RQue5Xn9gWQyRWkvn6GITY8U/9MALW7H77DdkhUrWbTfvfSzszq2Rij0UhwcDC2trYsXrwYX19fiSITPv37S7uJ69evHzKZjNWrV/9pc2bbtm1xd3cnX758VhR55kTT6XmplUolixYtIjk5+Z281H9VW8aPH8++ffuoUqUKvXr1Qq/X8/PPP1OkSBGLvq5Tpw5qtZpGjRpJtIC//PILHh4ekqn0z6BTp0789NNPHDlyxIIB7HOhY8eObNy4kZ49e3LkyBEqVaqEwWDg1q1bbNy4kf3797+XmvJj4OLiQuXKlQkMDOTly5fMmTOHvHnzSnN1wYIFyZMnj6RZcnBwYMuWLRn6aP70009UrlyZUqVK0b17d3LlysWjR4/YvXs3Z8+e5YsvvuD06dNSv+XNm5egoCC8vLxo1qwZ+fLlo0GDBsjlcvbu3cuJEyfIlSsXK1eulCgwW7RoQfny5bl48SJGo5Fjx45JMRZdu3Zl1qxZ0mZn/fr1tG3bluTkZC5cuMDTp0+Jj49HptJiU6AS2pz+aHP6g4C4y/vQRz4l7vJ+ZEoNCTePSe5Xmmx5cSzfLMP+izn3H2Iv7MYmfwWUztkwxEUCAhQqPFqOI/7KQRLu/oYh3tRfKgc3vhk1ix87VMLLy4uXL03J0f38/KRvYeHChYBJo7d161bGjh3LhAkTkMlk7Nq1i759+7J69WqpDsePH0cmk9GhQwdJE2+l3VYoMcRHEXVkKXZFa2P0K0H0GVPaQJlSbWUxy951Lk/mdSbh1km0uUuj1Oqwv76NfoO6UW392w1ztWrVLL7htm3bEhQURHR0NPPnz2fnzp1SUKK7uzvz588H+ChO788qUHp6elKnTh327NnzTvNCZov9x8C1wQBUHrmIu3KA10eWIdfYovbKiyp7QYm/dsmSJeTOnZsVK1awbds2vLy8GDFiBGPHvjWXmQXN9evX8/LlSxwdHSlbtizBwcEWZuUlS5bQt29fBg4cSEpKCmPHjsXf35+1a9fSt29f5s+fjxCCOnXqsHfvXslB2QzzzuR9PLMfWp/0SM+6U7FixQwjxTNaFCpVqsSBAwfeWa9mzZpJOTgBZu65zNhhZ5DJZMiUbxkszH4q6ZGR2Viu0jDtp0Xv1Fal/wgACyo0sPzAzcjnaU9FX3vkI3ZiFJYfrMrJC7eGgwhHRlLoVcnk/SEwU6SN/LoBDOojHZ8yZQoJCQnkzp2b2NhY6tSpI9Wpfv36ODs7c/ToUWkC9fPzo1u3bng4VsS1fn/Sk3O51DFpPoXRwPMVA0h+dtuiD13r9pL+71DONC7JL+7xYsUAYq78yglbFw79+DMrzNR7jYswjiLEJ+t5FNGQFL0RtVKOn6stthrraeDIkSPSpsuMqlWrSjR6Ztx9GUvwmVEcufOK0IgEi3MyIKerDdXze3DlvicHHsqthMnMtDgLFy6kdOnSLFq0iPiTq9ELGUpHT2QqbYa79Iwgk8nQKOVvfDVz0qjAEfr27cvUqVNxdXWlZ8+eZM+e3SKa91O/v4/ZxH0MzHlmO3fubHXuyJEjeHp6SrzUI0aMYMqUKRiNRsqVK8eaNWveyUv9V7Ul7eZjzJgxeHt7M378eJ4/f24hUBYoUIDNmzczevRohgwZgpeXF99++y3u7u507dr1HU/4MJQuXZoiRYpw8+ZNq1zInwNyuZzt27cze/ZsVq1axbZt27CxsSF37tz079+f/Pnzf9bnjRw5kitXrjBlyhRiY2OpWbMmQUFBkgVNpVKxc+dO+vXrx5QpU9BqtTRt2pQ+ffpYmViLFy/O77//zvfff8+CBQtISkrC19eXVq1aoVarqV27NqdPn+bRo0cWwUlm4eLu3btWa/zdu3dRq9UWCpM9e/YwdOhQtm/fjhCCqlWrMmPGDCtBu02bNmi1WsaPH8/NmzcxGo0Urt+Z+CJNMMre+hu6ftkHmwIViP5tEzFntkqbYpe6vbEvaW0RTQutjz/Jz+8Qf/O4KTL6jc+ke5PhaLzyoPHKg0udnpIfuF2phnSvY9Jqa7VaGjRoIAW2SmVqtQQEBEgubCNHjiQ+Pp61a9fSv39/KQ/n8OGmDe6RI0do06aN5HawYPkaZt93IflN4I9bw4G4NRxIwu3TRB1fzetjK1E5Z8O98VAS7p4hKQ1lsRkyhQqbQlWIu7AbO/8aKAwpXN25lKOlvN9JFb1s2TLJLWT79u3UqFEjQw74j4FMfC6P4Ddo2rQpV69elSTdzNBx6RlOP4j4rPSL5sX+c3N5fw7s2bOHhg0bcvnyZYoW/fwUW5MmTWLMmDHExcVZJJf9HDBz8F6/fp1Jkyaxd+9e/Pz8cMiRlxOH9iCSE6wExrhrR4j9YwepYaHIFEpU7n44VmqNLlcpFHIZofMDKervz09TxzNo0CCuXLlC9uzZGTdunIX/T2RkJJMnT2b//v08fPgQuVxOpUqVmDp1qsUkaeYpXbduHdeuXWP58uU8f/6c3IM3oFfakHDnN6KOryb19XNUztlwqtJB+kA/RqDUKOXv1LIVLVoUW1tbKYAqJiYGV1dXBg4caKEtSklJwd7RGU2Byrh82S/Dssx4tWk8yS/u4tN3zTuvMyTG8GRuOxzKt8CtRiAdy/lmGAQAJr+q8ePHc+3aNfR6PTly5KB58+aSduFjeF/BlEJl8hvSAJ3OhkrVazF7+o/ky/3WV7lLly5s3ryZy5cv07dvX06cOEHNmjXZvn07RqORn376iV9++YX79+/j6OhIkyZNmDp1Ks7OzjyOTGDktqusG9QYjYcv9uVa8PrwElJePUJp54Jj5XYSr7VCLiMlPgb7mzsxPr7Ek9CQ9743GzZs4ObNmyxevJiYmBjq1q3L0qVL0Wq1DBs2jLVr15KQkEDLli1ZuHBhhml/svDPQ8mSJXFxceHXX3/9u6vyyTC/o5s2bbISaP4qmOf8sLAwq2j3zwUhBElJSRkGVt19GUvtOcf/kucmPrpMakQo0cfXoPEthkczS1cns0BZpedkji8YkUkpnw/rz4UyfKu1oPgxiDz0C7GX9+PdaxljGhSie52SjB079qO0i58Dn9WH8vnz5+zevfuDnJ//P/ChpoV5Z/K5hcmXL18yf/58Fi5caMW687nRsmVLEhISmDx5Mt26dSOXmy12abRbZtNm1Mm1ROyaiUyuxLFKexyrtEfh4CblZauY2xUvBy0J4U9p0aIFtWvXZubMmTg7O9OlSxeuX78ulfngwQO2b99Ow4YNmTVrFkOHDuXq1asEBATw7NkzqzpOnDiR3bt3M2TIECZPnsz3jYuR+PACYdumgEyGc0BndPkrEL5nLikv3h08lREmNC5iIUzGxMQQHh7OrVu3GDlyJNeuXaNmzbfm6atXr6LX66125Gq1Gq1XHpJf3Ld6hjElCUNCNKmvnxNzdjuJD/5A65uxE7chMQZDfBTJz+8SsXsOAFrf4hiMwmTizgDXr1+nYcOGJCcnM2HCBGbOnEnjxo05deqU1bUfwvu6YsUKWrVqhUqpZOqUKfTs0Z3De3dSp2Y1K79jvV5P3bp18fDwYMaMGZJDf48ePRg6dCiVKlVi7ty5BAYGEhwcTN26dUlNTcXHxYbVX5fDy0GLLjGcyP9MRedXEpcaXyPX2hGxew6pYSH4utrQsZwvP9XPRsr9MzT9qvEHvTdTpkxh//79DB8+nK5du7J161Z69uxJ165duXPnDuPGjaNZs2asWLHiLzGfZuHz4/z581y6dMlig/q/iBcvTJkUWrZsabIIpfsz48SJE7Rs2ZKcOXOi0Wjw8fFh4MCBJCYmWpV569YtWrVqJQVRFShQgFGjrH3Io6Ki6NKlC05OTjg6OhIYGCiZrc1Yvnw5NWrUwMPDA41GQ+HChaUA0LTw8/OjYcOGkiuATqdj0aJFBAQEWGlQg8+EopDLeLq4By83fP/RfZby6iHhu2bzdMHXhExvyuOfOxC+ew6GxBiiT63j9a9LUXnmNrloZYLWZXIwcuRIvLy8sLW1pXHjxlYpuMC0mS5dujQ6nQ43Nzc6dOjA06dPM7yucOHCaLVa/P392bZtG126dGF4y6oMqfNWmx19ZisvVg/h8Zy2hM5oxvPl/Ym/ddKqvJCpDYk8sIC4KweJ/WMnpCZTLvUa3euUBEyuJ+Z35L8lWH4Wk/fDhw85deoUS5YsQaVS0aNH5oNkho+LDeMbF/nTknlapF/s/0l4H13Zp+LmzZsMHTqUsmXLZugc/jlRvHhxi9xuZ8+eldIzmf309py+SMip9ejyV8C96QhkMrlk+qyWz52OFXzJ62GP30QZt2/f5vjx41SpYkol06pVK3x8fFi+fDkzZswATBq/O3fuWKT76NixIwULFmTp0qV8/73lZJOUlMT58+ctdr2ju65DYeuEV3tr6kiFg8cHtz+jyORWrVpJwQZqtZoePXpY1MnsC5be1SEuWY9e64gh7InVc14fXkLcpX2mHzI5NvkrSBGK6fFkXmcpZ6Nc54BzrR7ocpkmlNCIBOKT9VYm7YMHD5KSksLevXvfq31ISkri0qVLqNUmtwZnZ2f69+/PtWvX8Pf3JzU1lWHDhuHv78/x48fRak1RipUrV6Zhw4bMnj1bCugBU2BLy5YtmTJlinTs5MmTLFmyhODgYNq1e0tWUL16dcl/13xcqZAREfKQ48ePU6psBR5FxPPy5SC+rFCUFvb3mDukl/Scrz7ivdHr9Rw7dkzy+w4LC2P9+vXUq1dPSgLcq1cv7t27x7Jlyz6Ln28W/hpcu3aNP/74g5kzZ5ItW7aPSsz8T4Q52rpv376ULWtK65WamsrAgQOl7xI+H+1iWrRq1YpcuXIxZcoULly4wJIlS/Dw8LDYVH0Mfebt27dp27YtPXr0oFu3bhQoUAA7Ozu6desmzSlgomdOeHobfeRTHCt+/PglPbyIPuoFtsVqobB1JjU8lLhL+0kND8Wr08wPispfNHfGe6kuV6xYQWBgIF988YUUvDp37lxOnTrFxYsXJaKS3bt307p1a4oWLcqUKVN4/fo1X3/9tZSPtk/1fLjZaRi74zqx53egy1cO28LVEAY98TePE759KrIWY7HJ+4VFHRPu/Ebs5f0gjHzVuRcD29WnVlGfTKmi/2p8FoHy2LFjBAYGkjNnTlauXImXl9cH3dfmi5yExyUzI03KlE/Fu9KQ/JtRrVo1qx3jXwVzRLMZK1askEwj+TztGde4CHZ39nJOGFn98zRyF/R/p59e4cKFJWESTI7AZjYKk69fvIWvn1YpIyoqCjs7OwoUKJAhE1F65oznz5/z5N5NGnfuxS07e/RGgcEo0OUqicotpxVPcnqY0+iY/fDSY+rUqQwePJjHjx+zcuVKUlJS0OvfRiGbtQPpTaQhEfHIFCqMemueVIcvvsKmYGUMsRGmwCFhtOChTgvPVuMR+hRSIx4Tf/2oBeuCwMTyUCS7ZS5Y8yT3n//8h8DAwAxz85lh5n01wzxeDx48wN/fn/Pnz/Pq1SvGjRsnCZNgyqJQsGBBdu/ebSFQAnz7raVwvGnTJhwdHaldu7bkMwgmHzg7OzuOHDliIWimfW+KZHekSHZHChYowOOQR9I1afvbYDC8973p1KmTRRBhuXLlWLdunZUvX7ly5fjpp5/Q6/WSv3IW/lnYvHkzEyZMoECBAqxbt87ivfxfhHk+q1q1qmTy7t27N3FxcRYpgaZNm2Yx93Xv3p28efMycuRIQkNDpfQ1ffv2RQjBhQsXLFLapGe6AZPLwNKlS6XfERERLF261EKgPHbsmMVz+/TpQ7169Zg1a5aVQHnv3j327dtn4YtbsmRJ+vbty5o1a5g6dSpxyXpCIxOIv3bEFJSTvyIfC7tSDSTfcjM02QsQvmM6yU+uWzBrpUfrL3Ly0zqTu9XNmzcldqJSpUrRqlUrfvnlF/r16/dRm+kRI0aQI0cOTp06JeWyrVmzJtWqVZNSGLb5IieV8rjxXfbN/BYah0Iuw2AU2JduyPMV/Yk9t81KoDTERaK2cWD0mLF8P8zECligQAG+/fZbihUrliEz1F+Jz2Ly7tKlC0IIQkJCPtrHo0/1fExtVhSNUv5uFpYMoJDLPigNSRY+D94VlGDG/fv3kcvlNKpWjpI5nSmS3TFDYRKs83PdfRlLtFHN4csP8R+3nwY/n6RJ0EmqdByCU7acKNVqiZLrypUrGaZySF9HMyPSV1VLc2hgABVzm8JfFHLZO6kjze+iOY1OZpuVEiVKULt2bbp27crBgwc5e/asRY5R80SbPuVMit6IMKQiTxPQZIbK1QedXwnsitbEo+VYREoSrzZPyDCgSutbDF2eMjiUbYpbk+FEn1pnymOZ5jnp0bp1aypVqsQ333yDp6cnbdq0YePGjRkmPn4fq4q5fzNiQSlYsKAVradSqcTb25Id5+7du0RHR+Ph4YG7u7vFX1xcnFXS/fR1MtcrbTSr0Whk9uzZ5MuXD41G8973JiO2FMDKQd3R0RGj0fhJLFhZ+O9g3LhxGI1Gbt68aUGV978Kc2CieW1dtWoVQUFB/PjjjxaBc5+LdjEt0isRqlSpQkREhAWd58fQZ+bKlcsqsMvR0ZGvvvqKdevWmeSIiHiMRgPxt05gk788cvXHbwhM6XdMEPoUDAnRaLKbGF9SMnAzSitLNC1pWhfeR3Vp3kz36tUr0800wLNnz7h69SqdOnWyIEYICAiwcoHzcbFh3bcBHBxQlY7lfMmuMyCS49F4F5HqLcOUBxmgfKWqJMdHS8Lk341/xBbbLJmP3HaVE/fCJck8M5jPV8ztyuSmRf+xZu5/GzJjpfhUmPOmmQMuTtwL52VMMkIIKfF99OmNRJ9Yg22x2jhV6YDSxgEjMpKPLyMxxTof4bvqaPbDM0UmhzJvh4L0+sG0kcnvSqOTEdRqNY0bN2bq1KkkJiai0+kkU3f6NChqpRxDXCQKu4zzkaWFTcFKRO6bhz7yKSpXa6pCM1TO2VB75ib++lEcSjeSnpMeOp2O48ePc+TIEXbv3s2+ffvYsGEDNWrU4MCBAxYsIR/CGPIx0Gg0VhpRo9GIh4eHVU5IM9ImRf7QOk2ePJnvv/+erl27MnHiRFxcXJDL5QwYMCBDwTmzMj93+7OQhT+DS5cu0bNnT9q2bcugQZZCRGhoKGPGjGHHjh1WqYI+hnYxLd61oTRnrDh16hRjx47lt99+s7KWRUdHWyRIz0wp0alTJzZs2MCJEyew9ytK0qNLGOOjMuXXfh8MibFEn1xL/M0TGBOiLM4Zk9/WMSNZ4uhRUx+9j+ryfZvpkydPWlyXPpDRfCy9xWTXrl1MmjSJS5cuWSgiZDIZu/tWlix+sqFQuMA/S5H2jxAowXqxN6chSTtt/5nFPgv/HeTJkwej0ciNGzekhNDvwvpzoYzdcR39mw1E+oU64fYpNDmL4Va/v8XxiN3RXH5sy/pzobR5h6tDWupIM8zm+U0jIrF10rG7b+X3ptH5UCQmJkrJgXU6Hf7+/iiVSs6fP29Bv5fdXkXKq4fYpKEnzAwi1TSppKe0zAjG1BTJPC7DxEyVEeRyOTVr1qRmzZrMmjWLyZMnM2rUKI4cOfJRieXN/Xv79m0rnuTbt29/EE+8mRmnUqVKn23TsnnzZqpXr25hrgNTkMFfFbWahSz8lXj9+jXNmzcnf/78LFmyxOLc56ZdNON9G6qPpc/M7PuuW7cunp6erFmzhv7jphN//SgKW2e0fiU+qd7h26eS/PQWDuWaofbIhUytA2Hk1caxEgtZNkctdQt7/aNkiRMnTtC4cWOqVq1KUFAQ2bJlQ6VSsXz5ctauXWvlvvS5lTx/Fv8YgdIM82L/Nmde/Gdb7LPw16NJkyYMGzaMCRMmsHnzZgttlJnO0YxHEfHvDcoyUWpZCpnxt05iiI3A6JSN4VuvEh6XjH8m3hLpqSPNu+WDBw9y48YNfH19rT7SD8GrV68kyjAzoqKi2LJlCz4+PtI5R0dHatWqxZo1a/j+++8lE8rWjesQKYkWfNeG+CgUtk4WZQqDnvhrh5EpNRIrjTAaMKYkokiXjzH52W1Swx5JtIo5XW0y/F4iIyOtmBo+lo3JjDJlyuDh4cHChQvp2rWr5Lu4d+9ebt68+UHBK61atSIoKIiJEydKaYvM0Ov1xMXFSX6fHwqFQmG1Odm0aRNPnz7NUFOQhSz8k2E0Gmnfvj1RUVEcOnTIKpvH56Jd/Fh8LvpMhUJBu3btWLFiBWMm/EDC3d+xK17Hil/7Q2BIiiMp5DKOldvjVPktWUVq5NvIaxlwaGDAO+WJ91Fdfuhm2vxvRqkU0x/bsmULWq2W/fv3W/iBfwx71H+DBjQz/KOlM1uN8pMW+yz8fcibNy+jRo1i4sSJVKlShWbNmqHRaDh37hzZs2eXonvjUwy8CovnfTHWurxliT61jvDdc9DkKEhqWAjx14+idHob+DXjwB06+cZlWsaUKVNo0KABlStXpmvXrkRGRkrMHeaE8x+LL7/8Em9vb8qVK4eHhwehoaEsX76cZ8+esWHDBotrf/jhBypWrEhAQADdu3fnyZMnzJw5kzwlKiLylpHcOyL2zUOkJKDx8Udh74oh7jXxN46ij3iCc42vkatNu1GRksjT+V2wKVQFtVtOZCotqWGPiLt6CLnGFsdKbVDIZVTPn3HvTpgwgePHj9OgQQN8fX159eoVQUFBeHt7U7ny+zWmaaFSqZg2bRqBgYEEBATQtm1bKdLRz8+PgQMHvrcMMzPOlClTuHTpEnXq1EGlUnH37l02bdrE3LlzP9o3u2HDhkyYMIHAwEAqVqzI1atXCQ4OlhbULGThfwnjx49n//797N27N0Oz8afQLg4aNMhCCEy/4f8QfE76zI4dOzJ79mwG9ett2mx/ornbzOudXhERc36H9P/MNttpsWrVKkaMGCEpAcxUl8OGDQM+fDOdPXt2/P39pfLMfpTHjh3j6tWrFlYchUKBTCaTiEoAHj16xPbt2z+4/ZlRRf838I8WKLPw8TAnwc0Iv/32G+XLl7c4dvr0ab777jsuXLiAg4MDrVq1YvLkyRbOw2YYjUbc3d0ZOnQo3333HV26dGHlShPvdNqJqECBAixbtoyff/6ZUaNGYWNjQ7FixVCr1eTKlYvnz5+TnKpH5W4dkJIejhVaIVKTiL9xjISbJ1B75cGj5VgiD/1C8uNrhEw1cbZPfHN9y5YtWbduHW3atJHKqFevHrNnz2bkyJEMGjQIuVxO5cqVyZYtm5SAPD1Kly5NuXLlCAoKkiLZ0+PixYucPn2aqKgonJ2dKV++vETBWKhQIR4+fIiPjw/9+vXj0KFDDBs2jIEDB2Jvb8/XX3/N1wNH0nTJRak820JVTDnFLu7BmBiLXK1D7ZUX52qB2OR7m6xfptJgV7wOSSFXSLh9CpGagsLOBdtCAThWbI3SyRODUUiMUenRuHFjHj16xLJlywgPD8fNzY2AgADGjx9v4e/0oejSpQs2NjZMnTqVYcOGYWtrS9OmTZk2bdoHaxbTMuOMHDkSpVKJn58fHTp0oFKlSh9dp7SMFRs2bKBUqVLs3r1bYqzIQhb+V3D16lUmTpxI1apVefXqlRUFZ4cOHT4b7eKlS5c+qm6fkz6zZMmS+Pv7s2nTJty8c2OTPd8nEZ/INTZofPyJObMFYTSgtHMl8eEF9NEm6kSZLPPNdlq8j+ryYzbTkydP5quvvqJSpUoEBgby+vVr5s2bh7+/v4VSo0GDBsyaNYt69erRrl07Xr16xfz588mbN68Fy9S78C6q6L8an50pJwt/L8wCZb9+/fjiC8sUA/Xq1bPwH7t06RIVKlSgUKFCkuZsxowZVK9eXSK2T4vff/+dChUqcO3aNYoUKUKXLl1Yv369lT+Po6MjjRo1sjg2YsQIpk6dSrdu3biS7MrV07+ScO8cbo2HSibaj4E+6iVPF36NTeEAdLnLIJdDPnc7vq2WlypVqljs+p48eULJkiVxdHSkX79+xMXFMWPGDHLmzMnZs2ct0uKAKYAmR44c7Ny5kwYNGkgC5YIFCywEbYVCQdu2lvzfixYtomfPnjRv3py6dety4sQJVq9eLQlb6fH/jTEqC1nIwsfhXUoCeKsdvHnzJv369eP333+3ol1cvny5RfaJ69ev8/3333PkyBEL2sUJEyYAmTPlmPMuPnz4ED8/P8Bk9h49ejR37tyxos9Me52fnx/+/v7s2mVNw2vG9OnT+e677xg0cixbjF9ket37oI8NJ/LgIpJDriAAXa6SONfqztN5nXCs1JbzWxdl6jeZlnXtypUrLF26lNjYWGrUqEFQUJBVoNLGjRuZOnUqN27cwNbWli+//JJp06ZJOSbN2LBhA+PGjePhw4fky5ePyZMns3LlSq5fv87Nmzel65YtW8bUqVMJDQ0lV65cDBs2jEePHjF+/HgLTbBMJqN3797MmzfPqg2//fYbffv25erVqxJV9H8juXmWQPkvw8fQdNWvX59Lly5x69YtKWJvyZIldOvWjf3791OnTh2L68eMGcOqVaukKDczld77zMZPnz4lV65cdO/enf7fT6H2nOMIIXgZPBx99EtyfLv0o31lzAKlU/WuOKbJN3ZoYFWriaJXr16sWLGCW7duSZPBoUOHqF27NosWLaJ79+4W1y9btow+ffoQERGBTqf7YBqyxMREfHx8KF++vMWk2aFDB7Zv387jx4+lKEkzHkcmUGv2MYnL9XPgffSQWchCFrLwT8TcuXMZOHAgjx49YtTB5//6zXaJEiVwd3e38nX9X8VnpV7Mwj8LsbGxFkm20yImJoaDBw/SoUMHSZgEpFxZGzdutLpn9+7dNGjQwOq4wWCwyEuWHv/5z39ITU2lV69eEqWWTCbDvlR9DLHhJD+99QmtewtjShLCkIpCLmPN76FW57ds2ULDhg0tdpa1atUif/78mbazevXqVhF0QghiYmIyTRlz5MgRIiIi6NWrl8Xx3r17Ex8fL+UlSwszY9TnxD+ZMSoLWchCFjKCEIKlS5cSEBBAzpw5/1X0zKmpqVZr8dGjR7l8+TLVqlX7r9fnr0KWQPkvRWBgIA4ODmi1WqpXr8758+ctzr+LY7pEiRJSIlwzXrx4wcWLF6lfv77F8YSEBBwcHHB0dMTFxUVicEiLixcvYmtrS6FChThy+5W041RnM/GXprx88MntjD61jsezWhA6vRlPlg1gy05Loe3p06e8evXKqp0AZcuWtWpnamoqhw4dsmonmCIkHR0dsbe3p0OHDrx8+dKqnYDVs0qXLo1cLrd6lhltvshpweX6Z/D/lTEqC1nIwv8m4uPjWbduHT169ODq1auS7+G/abP99OlTChYsyLhx41i8eDGDBg2ifv36eHl5WSWP/19GVlDOvwxqtZrmzZtTv3593NzcuHHjBjNmzKBKlSqcPn2akiVNPM+ZcUybj504ccLi2J49e9BqtRbpEbJly8Z3331HqVKlMBqN7Nu3j6CgIC5fvszRo0clarrnz5/j6elJfIqB0Mg0SWXtTOZfQ1zExzdUJkObqyQ2+SugsHNFH/WCmHPbubZ0BFsaFKR5k8Yf1M7IyEiSk5OlCL0TJ04QExNjoYl1dnamT58+VKhQAY1Gw4kTJ5g/fz5nz57l/Pnzkob3+fPnKBQKq3RCarUaV1dXnj17lmlz0nK5mukhPxTvo4fMQhaykIV/KsLCwmjXrh1OTk6MHDmSxo0bS+f+LfTMzs7OlC5dmiVLlhAWFoatrS0NGjRg6tSpuLq6/i11+iuQJVD+y1CxYkUqVnzLfdq4cWNatGhBsWLFGDFiBPv27QMy55gG0Gq10nkz9uzZY2UGNqcAMqNNmzbkz5+fUaNGsXnzZinSOjExEY1GQ0hEvGWi+je0gyIDPuv3QenogWfriRbHbP1r8GzJtwwdMkQSKN/XzrT1M7ezcOHCkiM5QP/+lknVmzdvTtmyZWnfvj1BQUFS5HBiYqJVgE/aZ6Xv0/TIYozKQkbIysebhX8z/Pz83sk89W/YbDs6Olqlk/s3ImtW+n+AvHnz8tVXX7F161YMBgMKhSJTjmmApKQkC8ExNTWVgwcPWgmQGWHgwIF8//33HDp0SBIodTodycnJVrzSZkFSlgGf9adAobPHrmgtHv6+mSdPnuDt7f3edprrZ8bu3butItQzQrt27Rg8eDCHDh2SBEqdTkdKSsbCcfo+zQxZjFFZAN6O/+1XhEZmMP4uNlQv4EH7cjnJ55k1/ln4dyNrs/2/gSyB8v8JfHx8SElJIT4+HgcHh0w5ps3HsmfPLv0+efIkMTExGfoVpodOp8PV1ZXIyEjpWLZs2Thy5AgqhaWDtSHOlCNNYff5VP5KBxPvc2RkJN7e3u9tp4uLi6SdfPjwIbdu3WLBggUf9CwfHx+rdhoMBisWnZSUFCIiIiz69H3IYoz6/4m0vPaZLZoCCIlMYPWZEFb89ogqed2yFs0s/OuRtdn+5yNrVfp/ggcPHqDVaqU8iplxTKekpHDp0iWLY7t377YyA2eG2NhYwsPDcXd3l46VKFGCJUuWkBQWioy3/AUpz24DoPa0Zn74VOijXgBIz8+RIwfu7u5WQUkAZ8+eteAb3717N46Ojh/EFiOE4NGjR5JPKrylLzx//ryF8H3+/HmMRuMHcZtnhCzGqP8fSM9r/z7Tnvn86QcR1Jp9jPGNi7yT1z4LWfg3IGuz/c9FVpT3vwxhYWFWxy5fvsyOHTuoU6eOxK2dlmM6NjZWunb16tXExcXRsmVL6diePXus0gUlJSVZ3GfGxIkTEUJQr1496dhXX32FSqVixZLF5HyjRRFCEHtxLwp7VzQ5Cn10Ow0J0VbH9LHhJFw9RLFixSyCcJo3b86uXbt4/PixdOzXX3/lzp07Vu2sU6eOFExkRkZ9umDBAsLCwizaWaNGDVxcXKw0nAsWLMDGxibDlEtZyALAvCN3Gb71Ksl640fn3TMYBcl6I8O3XmXekbvvvyELWfiXwLzZLpnTmSLZHbOEyb8ZWYnN/2WoUaMGOp2OihUr4uHhwY0bN1i8eDEqlYrffvuNQoXeCm8XLlygYsWKFC5c2IJjumrVquzfvx8wmYFz587N0aNHCQh4y2hj1s61bduWggULArB//3727NlDvXr12L17tyS8Anz33XdMnz6dUnVbEqrIRvzt30i8fw63RkOwLVLto9sZvnsO+tfP0foVR2Hngj76FXGX9iFLTeTQwQMWub0eP35MyZIlcXJyon///sTFxTF9+nS8vb05d+4cGo2GxMREXFxcWLhwIZ07d7Z4lo2NDa1bt6Zo0aJotVpOnjzJ+vXrKV68OKdOnZK4UwGCgoLo3bs3LVq0kJhyVq1axQ8//MDIkSM/up1ZyJid49+E9edCGb716mcrr222CPYum8WtW7dITk7m9evXH0yBaU7i/99YFswkDEeOHPlX5eLLQhb+vyJLnP+XoUmTJgQHBzNr1ixiYmJwd3enWbNmjB07lrx581pcW6pUqQw5ptMG3+zZswdHR0crPmUnJycaNmzIwYMHWblyJQaDgbx58zJ58mSGDBliIUwCTJ06FWdnZ+YHLST8+TNUztlxbTT4k4RJMFFpxUa9IPbCboxJccg1tmh8irBu/jSqVatica2Pjw/Hjh1j0KBBDB8+HLVaTYMGDZg5c6bkP3n48GGSk5P58ssvrZ7Vvn17Tp8+zZYtWySasu+++07iKU+LXr16oVKpmDlzJjt27MDHx4fZs2dbRYpnIQtg8pkcu+P6ZyvPkBjDj0N7UqJYUebPn49Go8HW1vazlZ+FLGQhC5khS0OZhXeifv36mTLnfCr+ifzVvXr14vz585w9e/az1SkLnwcGg4HU1FQ0Gg0y2edlzvi78bm/hcQHf/Bq41hqDvqJQzP7fvT9er0evV4vpdT6K5GlocxCFv5dyNJQZuGdqFatGlWqVHn/hR+ByU2LUmv2sc8qUP5ZSq0SJUp8ULqgLHx+vM+xXqFQoFB8HNf7/wLuvozlxL3wz1qmIT4KgOsRBu69iv3oKFelUmnlQ/ypEEJ8cLqsLGQhC//7yArKycI78d1331GhQoXPWuY/kVKre/fuGZq7s/DX4O7LWMbtuE7A9CP4j9tPg59P0nTBaRr8fBL/cfsJmH6EcTuuc/dlLCtWrEAmk/Ho0SPAxA3foEEDsmfPjkajIU+ePEycOBGDwWDxjGrVquHv78+VK1cICAjAxsaGvHnzsnnzZgCOHTtGuXLl0Ol0FChQgEOHDlnV8+nTp3Tt2hVPT080Gg1FihRh2bJlVtf9/PPPFClSBBsbG5ydnSlTpgxr1659Zx+Yee3T4//YO+uwqBK3Dd8TMEOXtApioIKKi13Y3YHdnWuuudbauna3a3d3d3esLaAg3Q0z8/0xzoFhUMH19627O/d1ealnTk+988bzxD86TfC2cbxf3BH/uc0JWtOfuHvHvnpPg7eOIeLoAvW/Nw2jsL053bp1A9QOUG3atCF//vzIZDLy5cvHsGHDdMT2J0+erJMF3rBhAzVr1sTOzg6ZTEbx4sWzldZydXWlcePGnDx5kjJlymBkZMSqVasA+PDhA82bN8fExAQ7OzuGDRuWrTYswO7du/H29sbIyIg8efLQqVMnAgMDv3r9evTo+XvRZyj1/C38Wyy19OSOb9FZdAp5o/X4xo0bMTU1Zfjw4ZiamnLu3DkmTpxIbGwsc+fO1Vo3KiqKxo0b065dO9q0acOKFSto164dW7duZejQofTr148OHTowd+5cWrduzfv37zEzU2f1QkJCqFChAiKRiEGDBmFra8vx48fp2bMnsbGxDB06FIA1a9YwZMgQWrduzc8//0xycjKPHj3i5s2bdOjQ4bP3IrOvfWbi7h/DII8LxoXLg1hM0utbRJ5aDiolZt6NP7s/i0ptSXyRl/gHJ7Co2hEH5/z07avOuu/evZvExET69++PjY0Nt27dYsmSJXz48IHdu3d/dp+gVinw8PCgadOmSKVSDh8+zIABA1AqlQwcOFBr3RcvXtC+fXv69u1L7969cXd3JykpiVq1ahEQEMCQIUNwcnJi8+bNnDt3TudYmgGssmXLMnPmTEJCQli0aBFXr17l/v37OR4u0qNHz/8/+h5KPX8rmbX3/omWWnpyzrc+14lPzhB2ZCGLD1xhcLPKJCUl6ZRR+/Xrx+bNm4mMjBQGrapXr87FixfZtm0b7du3B9QBT9GiRRGLxVy7do3y5dU9t6dOnaJevXps2LBByOr16tWLY8eO8fjxYy2/3fbt23P8+HE+fvyIkZERzZs35/Xr1zx58iTH1xSfkk6JySfJ7i4o01IQG2hbhYbsnEh6VBDO/dZ+eb+PzhBxbCEOXRcgdyzMk8n1MJFJs71ns2bNYty4cfj5+ZE/v/o9lN2Ud3bb1q9fn1evXvHmTUaw7+rqir+/PydOnKBevXrC8kWLFjF06FB27dolyHQlJiZSqlQpXr9+LfRQpqWlkTdvXuzs7Lh9+7bQx3n06FEaN27MxIkTmTJlyhevX48ePX8f+pK3nr+VdmXzc2aYD5Xc1F/Y2ZUAM6N5vJKbDWeG+eiDyX8If0VnUfnJsXP2iRcsPf9KK7jRCOlXrVqVxMREnj9/rrWtqampYAEK4O7ujqWlJcWKFROCSUD499u3bwF1/9/evXtp0qQJKpWK8PBw4U+9evWIiYnh3r17gFrx4MOHD9y+fTvH15TV1z4zmYNJZXICisQY5Pk9SY8ORpmckONjqAC/CPX6me9ZQkIC4eHhVKpUCZVKxf3797+4n8zbxsTEEB4ejo+PD2/fviUmRlsPtkCBAlrBJKiVIhwdHWndurWwzNjYmD59+mitd+fOHUJDQxkwYIDWUFCjRo0oWrQoR48ezdmF69Gj529BX/LW87ejt9TKnn+L/uKO2wHfpbUhcGVPpsXMIDmkLFd3LuPcuXPExsZqrZM1wMmbN69OT6CFhQX58uXTWQbqEjmoxeyjo6NZvXo1q1evzvZ8QkNDARg9ejRnzpyhXLlyFCpUiLp169KhQwcdqa3MZPW1z0zyh2fEXN5KStBzVGnafYbKlATE8pzLAGmOExAQwMSJEzl06JBwjRqy3rOsXL16lUmTJnH9+nUSExN1ttXcO1AHlFnx9/enUKFCOs+Du7u7znrZLQcoWrQoV65c+eJ56tGj5+9FH1Dq+WHQW2r9+/jeOovK1CRG92iFs501U6dOpWDBgsjlcu7du8fo0aNRKrUDtc9Nh39uuabUq9lPp06ddITuNZQsWRKAYsWK8eLFC44cOcKJEyfYu3cvy5cv/2KJ1lCaURxKDQ8g8c/LmJaojUqlJGT7eAxs8mJVsxdS8zwgMSDpzW3ibh+EXHYoGUrFKBQK6tSpQ2RkJKNHj6Zo0aKYmJgQGBhIt27ddO5ZZt68eUOtWrUoWrQo8+fPJ1++fBgaGnLs2DEWLFigs61+oluPnv8u+m9oPT8kev9q6Ny5M+3atRN6Av+JjNv/WPCm/h6khr5DmRRL0baz+fnnfsLyd+/efbdjgNoL3szMDIVCQe3atb+6vomJCW3btqVt27akpqbSsmVLpk+fztixY7PVdHS1MRF87dPCA4i5uh15/hKkhr4DRRp2rX5FamEnrJ/s/yjX1yD6dJzHjx/z8uVLNm3aRJcuXYTHT58+/dV9HD58mJSUFA4dOiT0WQKcP38+x+fh4uLCkydPUKlUWlnKFy9e6KynWV6zZk2tx168eCE8rkePnh8TfQ+lHj0/GAkJ6r43iUSCXC7/x4p5a3QWv6feqOZePPoQzetQtZd8amoqy5cv/27HAPW9b9WqFXv37s122Cazv3tERITWY4aGhhQvXhyVSkVaWlq2+zeRSQVfey1Euh/JyuQEEh7rShp9jfw2xpjIpEI2NvOgjUqlYtGiRV/dR3bbxsTEsGHDhhyfR8OGDQkKChLkmkA9lJO1laBMmTLY2dmxcuVKLUmh48eP8+eff9KoUaMcH1OPHj3//+gDSj16/gf4+/szYMAA3N3dMTIywsbGhjZt2ghaiho0GosXL15kwIAB2NnZkTdvXq3HMm+TWw3GZ8+eUaNGDYyNjXF2dmbOnDla66WmpjJx4kS8vb2xsLDAxMSEqlWr5ioDlZVHjx7RrVs3ypUqTsDcFrxf0onwowtRJMV+fWMgPTac0L3TCPi9Fe8XdyTh2UXhMYM8LojlpkQcXUCH7n0pVaoUpqamPHz4EIClS5fqaCsGBwfTuUtXHBydMZTJeP/+Pbdv39a6r5rS7YYNGzA2NqZGjRp0794dhUJB6dKlGTp0KKtXr6Z27dqIRCKtPr+6detSqlQpRCIRs2fPZuTIkSxevBiZTEb79u05deoUXl5eyOVyihcvzr59+wCo4W5H4pMzhB+YBUDI9nFEnVHrNoZsG0fc3SOEH57Ph6VdUCSq+xyDt44m+up2VErt5zsrErGIGkXUGc6iRYtSsGBBRo4cyYwZM1i6dCk1a9bkw4cPX30u6tati6GhIU2aNGHZsmXMnj0bb29v7Ozsvrqtht69e1OoUCG6dOnCmDFjWLRoEdWqVdOxLTUwMGD27NmCbuiiRYsYN24crVu3xtXVlWHDhuX4mP9VElLSeRoUw/2AKJ4GxZCQkv53n5Ke/xD6krcePf8Dbt++zbVr12jXrh158+bFz8+PFStWUL16dZ49e5atB7itrS0TJ04UMpTZkVsNxvr169OyZUt8fX3Zs2cPo0ePpkSJEoKIe2xsLGvXrqV9+/b07t2buLg41q1bR7169bh16xZeXl65vvbTp0/z9u1bLErVQSw2IS08gPgHJ0kLD8Chy+9fzLgq01II2TEeRWwYZt5NkJhaE3v7oPC4WGaMbetJRJ1by50TuzA0kFKxYkVKly7NokWL2L9/P126dGH37t0kpSoIjUvBvUJt4oL9MPdujFlpe6LOrSM8Nolmsw/QokEdOpbPz9r50wCws7Nj5MiRPHz4kI4dO2JhYYGtrS379u1j+fLlQvvB7NmzhXPq27cv8+fPB2DixInky5ePIUOGsGPHDl69ekXbtm3p168fXbt2ZcOGDbRp04YTJ07QsXwF1hz3xMy7CXF3D2Ne0RcDm3ykhrwh+e1dos6vB0RIrRyR2uQl6fkVDGxdibm8FVVKElY1e3z2PiqUKjpVUJeoDQwMOHz4MEOGDGHmzJnI5XJatGjBoEGDKFWq1BefS3d3d/bs2cOECRMYOXIkDg4O9O/fH1tbW3r0+PzxM2NsbMzZs2cZPHgwS5YswdjYmI4dO9KgQQPq16+vtW63bt0wNjZm1qxZjB49GhMTE1q0aMHs2bP1GpSfQRhmfBFKQGQ2w4zWxtRwt6Nj+fwUtv/3DzPq+fvQ61Dq0fM/IDvtvhs3blCxYkX++OMPOnfuDGRMclepUoULFy5oDYtkN+WdWw3GzMdKTU3FxcWFypUrC+VHhUKBQqHA0NBQ2F90dDRFixalUaNGrFu37puuXSE20NJZTHh2kfBDc7HvOAt5Ps/Pbht7+yBRZ9eQp/kYTIpWUS+7d5SoU2pnFvv2M5C7qIdhVGkpPJveVGtYS6Ot2HzWfu5FSSE1Af/5bbGs0QOL8i11jicRi0iNiyRweTfq1mvIiaOHhMemTJnC5MmT6dq1Kxs3bgSy12mE7J8rjS7j3r17adlSfezY2FiKFi2Kg4MD9+7do/O6m5w6epDQ/TO1rk1DdpqUESeWkvD0PPl+3oFIapDtNX2rr/2vv/7KzJkzSU/XZ7Z+dHJiEqBB83jVQnmY0aLEX3IV06Pnc+hL3nr0/A/IHPSlpaURERFBoUKFsLS0FPQLM9O7d+8c+VXnVoOxU6dOwv8NDQ0pV66coLUI6h45TTCpVCqJjIwkPT2dMmXKZHueOcHIyEjQWVSlp6JIjEHmVBSA1OA3X9w26e0dJKbWGLtnSO4ok+KyXVdkIBN0FjXaiklWBVGpVFy7fQcAldgQJFJSAh6jSI7X2YdCqSLZ7yEqhYInluXZcTtAeGzw4MG5uu7scHJyokWLFsL/zc3N6dKlC/fv3yc4OJgZLUp8UXtVS5MyJVF9L/N5oEpLIS3ifbbb/BVf+48fP5InT55v2lZPzvgeZekdtwOoveAi196q+3e/1qesefza2whqL7io9TrXo+d7oS9569HzPyApKYmZM2eyYcMGAgMDdYYaspKdfl92PH36lAkTJnyzBqOVlRWPHmlPDG/atInff/+d58+faw2R5PScMvMqJI61Zx6zadlcwh5eQJkYrfW4MiUx+w0/kR4ThtTSEZFIhCIhisTnV4l/cBypTV7SIz5kWTeUsT/358rZEzraiulJ6kBTJDXAqnp3os6t48PiTsic3DEqVBZTz1pITK3U68aq9SRV5g6M2feY8PgUBtUojLW1NVZWVrm+B5nJTn+xSJEiAPj5+VGhQgXalsnLwr3Zb58a5k/0pc0kBzxCleXefe5efouv/du3b9m/fz+7d++mcePP2zvq+Ta+Z1l66flX36zrqvjkUpX5da5Hz/dCn6HUo+d/wODBg5k+fTq+vr7s2rWLU6dOcfr0aWxsbLLV/cuJfl90dDQ+Pj48fPiQqVOncvjwYU6fPi308+VUgzFzcLtlyxa6detGwYIFWbduHSdOnOD06dPUrFnzi/qEWXkfmUjndTeps/ASSyYMIPT2ccxKN8C2xTjs2v6Gne8nPUZVzveZFv6eqPMbkFo6YubdVPsalApCdv7KlXOnGD16NCPmrsGu3TRsGg3TXKSwrnnZZjj1WYWVTxdEUkOiL28haE2/z2ZL5516yc7PZHCyBocXLlxAJBLx9Om3a21WLmSb7XJlcjwh28aSFvoOyyodsW09Ebt207Cs3k29Qjb38lt97S9dusSUKVPw8fER+kH/TWQ34Pb/Qeb3xeab/vhHJhJ1eSv+szKCdhXgFx7HwpmTKFbYDZFYTL2GTbLd3/cyCYAvv86/F35+fohEIqFlJLfEx8fTq1cvHBwcEIlEDB06FICQkBBat26NjY0NIpGIhQsXfrdz1vPt6DOU/zH0guH/P+zZs4euXbvy+++/C8uSk5OJjo7+5n1euHCBiIgI9u3bR7Vq1YTlf0WDcc+ePbi5ubFv3z6tYGnSpEk53oeWR3dyPEl+D7Go0hHLKu2FddIiA3O0L6mFLWlh/qhUKuQuJck/Up26i7mpncJLC/MnPTKQ2WvXU79FW2ovuIiRqyNJ77K3ETSwcsSgfEvMy7ckLTKQjxuGEHt7P3majERqrp5YTo/6iIGlAwATDz2lmJVIJ/OpyVhGR0drDYmEhIRke9zXr1/r6C++fKkOCDS9lprH+lRzY2egWPA6Tw54jDIpFtsW45Dnz+g7TY8O1jrG9/C179atm+Bhruf7kPl9AV8uS8c/Ok3Mjb2YlWmG3LEQzyzt2HE7gHaZns/vbRIA6td5pYJ5ftieyhkzZrBx40Z+/fVXChYsSLFixQAYNmwYJ0+eZNKkSTg4OFCmTJm/+Uz1gD6g/E+gnwL8/0cikegMbixZskRH3ie3+wTtDONf1WDMvE9NYHPz5k2uX7+uJWT9ObKW30SCjqL2tcfeOUROMHIrQ/K7+yS+uCoM5SjTkol/cEJ7xU/HMZSKBfF0lUpFXJbjKNOSEYnEiKQZQ0dSK0dEhkao0tXlfblrKRBLiLt/DKMCpQFIV6roPGKqzvkVLFgQUGf0mjbNyJqeOnUq2+sJCgpi//79WkM5f/zxB15eXjg4qINXExO1leJPDjJ6+/oIgxZiIcOcST9SkUbcvWMAiDP52usHLb7M/7dJwJfK0haV22FRsY3WsmT/h0jMbLCu3VtYlrUs/b1NAkD9Oh+3//E3DXD9f3Du3DkqVKig8wP33LlzNGvWjJEjR/5NZ6YnO/QB5b+YnEwBqgD/yEQ23/Rn43W//+QUoGZyNyws7LsNJDRu3JjNmzdjYWFB8eLFuX79OgcPquVvwsPDv2mflSpVwsrKiq5duzJkyBBEIhGbN2/WCVw1vHv3jurVq3PhwoUvnue+ffto0aIFjRo14t27d6xcuZLixYsTH687xJKZ7MpvYpkxsnyexN7Yi0qpQGpqQ9K7e6THZJ/By4qpVz3i7h0h4sh8UoNfIzGxIuHpeRSJ2v2ictt8WDnkY9jwEag8GyGSGZH44hrKLIM36ZFBhGwfj3GxKhjY5EcklpD48jrKhGiMi6mzvBITK8zLNCX21n5C90zFyM2b1NB3+L+9i9WnkpqGunXrkj9/fnr27MmoUaNYv349oPYCzy5LWaRIEXr27Mnt27ext7dn/fr1hISEaAmDe3l5IZFImD17Nv1iYmhkImNgx3Lse2DJgiMLCD+yAPMyTQARCU/Powkw6xazZ0Lvav8JX/uc4OrqiqenJ0eOHNF5TCKR5Gjo7XvwtbK0SCwBsfa5KBJiEMt0PdrnnXqJramMn/Jbcfl1zj83VCoVqvRUHYWArCiUKi6/Dud1aNwP+ToKDQ2lePHi2S7Xy0j9eOh7KP+l6KcA/z6WL19O+fLl6dKlC1u3bmXEiBF8/PiRUaNG/aX92tjYcOTIERwdHZkwYQLz5s2jTp06OmLluaFbt27MmDGDhw8fMmTIEE6ePMmWLVu+WkLKXH6L/tQTlhoeQNihuaSGvAGJhPi7R4g8tYLUoBdYVGoLQMy1nQStGyhYCSa+uEbQuoH4z23Bxw0/kx4RiH276chdSxN39zAx13Yiy1scmaP28IBSJGHb7n1Y5StMzI3dxFzZjtTKEbmrOsMYcXwJ7xd1IPLsWuQupUgOeEz0xU1EXdxEWkQAYmNLwg/P5cPyHkRd3IRFlY5YVGpH6sdXRJ1bT/yDExgXKkdyqkLLOrFw4cKULFmSggUL8uuvv+Lv7w+gNckNsHv3bj5+/Mjr169RKBSsXLmSMWPGkJaWxs6dO6lXrx7dunXD1NQUhUJBiRIluHnzJt26daN9+/YkhLxjTqfKXDxzAu9iBUi6tg3p06N0atWYPeuWqZ+7ygV+yCDgRyS7Hsrjx49TtWpVTExMMDMzo1GjRjq9sJrnKDAwkObNm2NqaoqtrS39+vVj4sSJPHjwAMjoFZwwdQY/T55H4Mpe+M9tzseNw0j5qB1cRmfqoUyPDsF/VmNSAh6RFh6A/6zG+M9qLLw/VColg8dPp2IZL7VJwOJORJxYqqNY8GF5D0J3TyHp7V0+bhxKwLyWuln9zyARi9hyI4B9+/ZRtmxZChUq9EUzhs8RHR1Nt27dsLCwwNLSkq5du2bb4lO9enWqV6+us7xbt25CG4imN/ndu3ccPXoUkUgk9GKKRCJUKhXLli0Tluv5MdBnKP+F6KcA/16WL19Onjx5dDKDmbUMNXypdy27xypVqsT169d11s2apbxw4QKpqak662VtjheJRIwdO5axY8dqLf+azV125bfwA7OQWjlhVb07oMLsp0Z8WN4DkdSQqDOrsajSAbHMhNhb+wjd+xvW9QYQffEPzH5SHyvm+m7CDs7Cqc8q7Fr/qr3vIwsQGciRu5QUdBbrVylD3o4zUESqp53Djy4k7tY+5G7eGBX4CZQKkj88Q5bfA9sWY4T9JDw5i7F7ZeQuJUkJekHs9d2khb/HrtUELKupZZb8ZzVGZCAnKS5acC7SYGNjw+HDhwE4c+YMderUoX79+syaNUu4x927d8fQ0JCiRYvStGlTFi1ahKOjI9evX9fKrCgUCurVq0f58uXp27cvZ86cYe/evbx8+ZJatWpRqVIlbt64oXP/9fLBf43NmzfTtWtX6tWrx+zZs0lMTGTFihVUqVKF+/fvC4ENaD9H8+bN48yZM6xapXY0cnNz0xL/X75uE3Fx8Zh61QeRiNgbewnbNwPnfmsRSXS/bsXGFtg0HkHM9Z2oUpOx9OkKgEGefABEnlhK/OOzWJSqg5VHA9JjQoi7e4TUkDc4dJqrtc+0yA+EH5qLqVd9TEvVw8DaOUf3QqFUcf5lKG8fHeDOnTsMGTIET0/Pr5oxZEalUtGsWTOuXLlCv379KFasGPv376dr1645OoesFCtWjM2bNzNs2DDy5s3LiBEjAChdujSbN2+mc+fO1KlTR8ubXs/fjz6g/M5ovkxu3779lxqFXV1dqV69eq6n4773FKCtqeybG/31/D0kJiZibGysJVb+PdF4dGfFwK4Atk11s7DpkR9w6DwXmbO6od4gTz5Cd04k8vhSnHqvQGqhHooRy02JPLGUlIAnOgLfmdHoLManpBPwKZhM9n9EwuMzmHk3wbpOX2Fd8/ItheArNeQtCU/OYlqqLjYNhgBg9lMjJMaWxN7aR7L/I63jpgS9AKB8pSo655CQkICJiQlSqfZHaFpaGqNHj8bT05PY2FgKFCjAzJkzqVKlCo0bN2bBggVMmTJFWD85OZm2bdvy66/qALpfv3789NNPrFu3jv79+3/2HvwX0Nzj7018fDxDhgyhV69eWn7iXbt2xd3dnRkzZmgtz/wcpaen06NHD0qWLMmLFy909h0TFoxT39VI5KYAGFjnJWzvbyS9u4dxoXI664sN5Zh61iD+4UmUolhMPWtkHPf9U+IfniJPk5GYeFQXlsvzlyB01yQSn1/RWp4e9RE73ykYuXnn+p4ERCRSs7DaTnTYsGFCQN2kSRMqVqzI3r17BYOE7Dh06BCXLl1izpw5QiWmf//+1KhR47PbfAl7e3s6derEhAkTcHZ21tLT9fDwoHPnzhQpUkRruZ6/H33J+19EbqYAlalJRJ5ZzYdlXfGf25zANf2EZv/MTDz0lCfvgujTpw+2traYmJhQo0aNz4peHzp0iJ9++gm5XE7+/PmZNGmSjuvG2bNn6dGjB0WKFMHY2Bg3Nzd69erFx48fdc9TqWTlypV4eXlhamqKvb09DRo04Nq1azm6zm/F39+fQoUK4enpKfTGbdiwgZo1a2JnZ4dMJqN48eKsWLFCaztXV1eePn3KxYsXhXJM1vJOSkoKw4cPF+5nixYtCAsL01ont57dd+/eFfyRx40bJzyW+diaMtKuXbuYPn06efPmRS6XU6tWLV6/fq1zD27evEn9+vWxsLDA2NgYHx8frl69ytabAdmKcZt5Ncj2XhrkyY/MuRjxj04TvG0c4YfmqR8QiUh6c0dYT+ao1mfMOsWcmfS4cMwvLaRYfjvyOzkQeW4dKqWCxBdXAREWVTqgUimJvX2QoLUD8J/bgg9LOqvdZV5cBcC8bEZ5OunNHZLfPwEgZPt4Alf3I+rSZgBS3j9BXqA0S1eswtTUlDdv3hASEsLWrVvp2LEjgJCV1HD48GFCQ0MpXLgw8fHxnD9/HplMxuTJk3FxceHo0aM617Rlyxbkcjmenp7s37+f5ORk7t/XnlafN28elSpVwsbGBiMjI7y9vQW3o38DkydPRiQS8ezZMzp06ICVlRVVqqgD+fT0dH777TcKFiyITCbD1dWVcePGkZKSku2+PuedruH06dNER0cTERGBs7MzMpmMAgUKsGzZMsqVKyf42Pv5+bFp0yZAncnXHH/58uVCMNm9e3dEIpGg2WparKoQTALI8nmoryGb17RKkUb0pS183PAzKR+ekRb+nuAtv2S0gzy/gkhmgrxAaRSJMcIfQ4dCiAyNSA54RNKbOwRv+QVFbCggIu7uYVLD/LWOkxr6jvAjCwhc0RP/uS14v6QT4UcXokjK6EuOuryVyRMnAGr9Wc1nl6GhIZaWlty5c+eLz8GxY8eQSqUsXbqUxo0bc+XKFSpWrMiNT9n1q1evfvH51/PvQJ+h/EF58eIFYnHu4v2cTgGqlApCd04kJfgVZj81wsDKmaR394g8tRxlcjwWlXyFddMUCmrWqU9i8FtGjRpFnjx5WL58OdWrV+fu3bsULpxREj9+/DjNmzenevXqLFmyhMePHzNt2jRCQ0O1Aq/Ro0cTGRlJmzZtKFy4MG/fvmXp0qUcOXKEBw8eCNOvAKNGjWL+/Pl06tSJAQMGEB0dzapVq4Tgplw53V/9f5U3b95Qs2ZNrK2tOX36tDCos2LFCjw8PGjatClSqZTDhw8zYMAAlEolAwcOBGDhwoUMHjwYU1NTxo8fD6h/bWdm8ODBWFlZMWnSJPz8/Fi4cCGDBg1i586dwjq58eyOiIigQYMGtGvXjk6dOukcLyuzZs1CLBYzcuRIYmJimDNnDh07duTmzZvCOufOnaNBgwZ4e3szadIkxGKxEFAX77MAhamLzn6lltkfV2Ku1lmMu38MgzwuGBcuT9TZNeqM5KnloFJi5t1YGErIOlgjoFKiPDqdkjWr0rP9PPYcOsbZ4/uRWjqSFh2MxMwaiZEZEccXE//4LKYlamPm3UQoE4oMZGi8sQHin5wj4sgC5G4/gViKSCIlPSqQ2Gvq58HQsQi2LcahDNhHeno69erVQyKR4OXlRatWrbI9xcBAtTzS48ePiY2NpVChQvTo0YM5c+aQkJCgNej04YNaqF0ulzNz5kyioqLo2bMnBgYGOhqgixYtomnTpnTs2JHU1FR27NhBmzZtOHLkyFfbE/5JaD4TZsyYIWSWe/XqxaZNm2jdujUjRozg5s2bzJw5kz///JP9+/drbf857/Thw4cL62j6JDMHmn5+fvz222+A2s0oMyKRiA0bNtCnTx9kMhktWrTg4MGDXLhwgT59+lC1alXCwsIYPnw4YnNtTVFNcJnda1qZkkj8o1MYF6uGMjXpkxNSLCE7J+LYdT5pUUGoUhL4sLhjtvcq5eNr4h+eRu72EyK5KRK5Kanh7wnZ8guO3RcL78fkd/dJjw7GpGRtJCZWpIUHEP/gJGnhATh0+R2RSISxeyXKWSVx+sgBLCwsBJMEb291tvPYsWO8fv36s8+Bv78/jo5qQwLNej179qR27drMnDmTdevWMXToUDw8PL709Ov5h6MPKH8gVCoVycnJGBkZ5Vre4nNlyOxIfHmdlMA/sWkwBNNSdQEw+6khYftnEHNtB6al6iIxsQQg7tkVwt88ZvHaPxjcU13y8PX1pUiRIkyaNIlt27YJ+x05ciQlS5bk1KlTQinQ3NycGTNm8PPPP1O0qNp+b/78+VSpUkUrYK5fvz4+Pj4sXbqUadOmAerMxIoVK2jdujWbN28W1m3Tpg1ubm5s3br1uweUz58/p1atWjg7O3Py5Ektp5SLFy9qCZAPGjSI+vXrM3/+fCGgbN68ORMmTCBPnjyfLcfY2Nhw6tQpoZlcqVSyePFiYmJisLCwAGDbtm1ax+rXrx/9+vVj+fLlTJs2Tev1ERwczMqVK+nbty85ITk5mQcPHgglcSsrK37++WeePHmCp6cnKpWKfv36UaNGDY4fPy6cZ9++fSle3IPnR9Zi3+43nf1mlubRWv5J4se+wyxh6jTq7BqMC5cnLeojsbcPYOatLfScGYlYhEQsQpWeSv8enYXycNWm7fnpJ2/iH51CbKy+b18rE4IIRCKUqUlEnV71qfw9mPcL2iJ39cK6bj8CV/dDlZKAkZs3YkMjxCJ1VrlNmzZs2bIFd3f3L5b/QD2FGhoaKrx+3N3dadasGQYGGd7b9+7dQyQScfXqVUxN1YFHrVq1sh1YePnypc5r76effmL+/Pn/qoCyVKlSWp8pDx8+ZNOmTfTq1Ys1a9YAMGDAAOzs7Jg3bx7nz5/XKqu+fPlSyzu9Z8+eFC1alF27dgnrXLp0CYC5c+dq9T+uW7eOXbt2CcfRoFKpuHHjBra2GcFi4cKFuXDhAhUrVqRTp048ffEahg8X5Kx0yKbnVSw3xbn/OkQSA4KDXyOSSHHoNIfANf2IvXsYVCrExpbkaaorjaNMSyHi8Dzh9ftheQ8MbPJh03AIgav7EXN9FzYN1Nahpj81wjyLj73MyZ3wQ3NJ+fAUeT5PDO0K8PGeehCzdevWQmVCJBLRunVrXr9+/cXnIDMvXrzg0qVLVK1alUePHjFz5kwkEgkbNmxg3rx5wlBNVv6KpJqeHwN9yTuXBAYG0rNnT6EUWaBAAfr3768zAJGTsqarqyuNGzfm5MmTlClTBiMjI6HZ29XVVWsgIy0tjSlTplC4cGHkcjk2NjZUqVKF06dPA3y2DJkdKe/Vv9CNi1fTWm5crBqq9FQSX2UMASS+uIrExJJwGy9hma2tLb6+vhw8eFAoeTx79oxnz57Rp08frb6yAQMGoFKptMpz1apV08m+VqtWDWtra/7880+ta05KStLJuNnZ2SEWi3PkLpMbnjx5go+PD66urpw5c0bHdi/z8WJiYggPD8fHx4e3b99ma6f4Ofr06aM1mVi1alUUCoUwMZz1WF/z7JbJZHTv3j3Hx9cMjGQ+PiB4fD948IBXr17RoUMHIiIiCA8PJzw8nISEBLwrVSX5/RNUuXC80ZBVwkSZnoo8vyfp0cEokxN01pdk0lmsW1z9GujXr5/wuKuNCbK8xUmPDsbA0gFFXCQJT89/tkyIRAqoSI8MIvndfZQpCZgU9yE1PABlSoI6KBWJkTkVUQedKQmIANNPwv89e/bMti0jM87O6kEILy8vrdeP5h5rfjAEBQURFRWFVCoVgkkAHx8f7OzsdPab+fUQFRVFTEwMVatW/Wa/9R+VzM8vqDNjgFaGERCGNLK2EHzOOz0gIEO1QiMs7+LigpeXl/CnV69eKJVKLftRAKlUqhVMZkdg9JctRbNDJJYgkqh/YKhUKlRKBSqlAplDYVKD3yC1ckSZFIvMuRhGrl5af0RKBarUJEyK+6BIjEGlVKJSpAmv3+SADHvVzO87VXqq2g/eSf3jXuMWJQJeP38GwIQJE2jdujV16tShSpUqgsXrl54DFxcXPn78iEqlonjx4sLrXdMa4OjoKHy+WFlZZTv9nfnzT88/E32GMhcEBQVRrlw5oqOj6dOnD0WLFiUwMJA9e/aQmJio9SWdk7ImqN9w7du3p2/fvvTu3Rt3d/dsjz158mRmzpxJr169KFeuHLGxsdy5c4d79+5Rp04dzr8I/ao0kAbNB4/mw0yD5oMnNTijny415A0G9gW5kCX7Wa5cOVavXs3Lly8pUaKE0POVdRDJycmJvHnz6vSEZSU+Pp74+HgtHUgjIyPKly/Pxo0bqVixIlWrViU6OprffvsNKysr+vTpk6PrzSlNmjTB3t6ekydPan3Ja7h69SqTJk3i+vXrJCZqf4Fkzi5+jayC4ZrAI7MrS248u52dnXM1gPO147969QrgixOaypRErV6xnJD84Rkxl7eSEqQOiBMenc60P+2A0sFcRn0PRzpVyE8hOzO6XV6BXC7X+mI3kUmxsrIiLjkeY/fKxN07StLbe18sEwLE3jmI9JMjTsj2ccLy+PvHiL//qY9YJCbl/VPy2xgj/ShGKpVy7Nixr2ZRSpQoAaiDlpSUFCGTrOklc3NzAzK+PLOTPLG2tiY0NFRr2ZEjR5g2bRoPHjzQ6h38t0mmZPWP9/f3RywWU6hQIa3lDg4OWFpa6gQhX/JO16C5t76+vmSHJvDRkJN7nJb+bVP38Y/PEntrP2lh/oBKeN1KLewxKVqF+HtHibm2Aysf7feixnkq8+s3OT5c2F4ky5jIViTFEXNlGwl/XkaZGK21H40ffH4bY0Kyuc4lS5YIpgdfeg46derE6tWriYuLE3QjFQoFS5YsAcDY2Fj4fClYsCDHjh0jLCxMeD8/fPiQq1evki9fvpzfPD0/HPqAMheMHTuW4OBgbt68qRU4TZ06VSeFn5OyJqit2U6cOEG9evW+eOyjR4/SsGFDrelDDZmnXXOCgbUzqJSkBD5Hni+jpyX5U+ZSERchLFPERyHL50lARCIJKemCTaOjo7oPLSgoiBIlSgiZG83yzDg6OhIUFPTFc1q4cCGpqam0bdtWa/mWLVto27atVvnYzc2Nq1evCl/O34tWrVqxadMmtm7dqlM+fvPmDbVq1aJo0aLMnz+ffPnyYWhoyLFjx1iwYEGufK+/5rGt8ew2Nzdn6tSpFCxYELlczr179xg9erTOsXKbqf3a8TX7z1oSBPALT2D8wSeIDeRZN/8iaVEfCdk+HgObvFjV7EXkyWUYFSqP1MqBuNsHdUqC67qWxcNJO0DP7rwL5DEhAJC7lMTEo4Za+FsixbSkupUjLdQPA/sCGBepCEDs7UPEPziBga0rALK8xUn58AxD52JYVs0IQpPe3CHu9gHC9s0gVaXuf1u4cOFXhe81Je3g4GB8fHxo3749ISEhLFq0CIAKFSrk8I5lcPnyZZo2bUq1atVYvnw5jo6OGBgYsGHDBq3y8L+Bz72Wv2fgrFQq8fT05OnTp7i4uFC9enUsLS0JDQ3l5s2bOgNqOTm2gTT35xf/5DwRRxdgVLgCKkUaqvRUbBr+TOz1XaRFBSPPXwJTr/rEXt9NashbjAr8hEgsIS0qiPhPP8ZsGo9AYmpF+KF5SC3ssPTp8umcMypA4QdmkRL4HPPyLTG0K4DI0AhUSnULiEqJRCyiRhE7EooX5+7du0ydOpUKFSpw/fp1zpw5g0wmIzU19Yv3oUmTJlSuXJmrV6/y559/smzZMvbt26f141fz+dKjRw/mz59PvXr16NmzJ6GhoaxcuRIPDw+dH896/lnoA8ocolQqOXDgAE2aNMlWDijrmy27suaCBQvw9/enZMkMaZICBQp8NZgEsLS05OnTp7x69UprEAbAPyJBp+/sS5gUr0701R1EHFuEdd3+GFg5keR3n7hP2RlVekb5XpWeikhigArwi0gQvuQ1Ys9JSUlaf2fX+ymXy7/4QXHp0iWmTJmCr68vNWvW1HrMzMwMDw8PKlasSK1atQgODmbWrFk0b96cy5cvfzdnG1AHUFKplAEDBmBmZkaHDh2Exw4fPkxKSgqHDh3SyvBl7R+Cv/7l97/w7M4NGntBc3NzateurfVYQko60x9Kc/V6A0h6fQsUadi1+hWphR2RJ5chNc+DSKr7ehGhLmfnhBLOFlz89G+bRkNJi/5IauBz4h+eRCw3xdChEKYl6yBzUGdX5PlLEHNtF3H31G4qaZGBmFdsg2XlDoikGRl7uUtJRBIpwS/P8/JTee706dPUqlUrR+fVqVMnnj59yujRo4WWl61btwrvGxcX9VBTdr1kkZGRWv/fu3cvcrmckydPar2/Mrvt/FtxcXFBqVTy6tUrwccZ1N7p0dHRwn3U8CXvdA0FCxbEzMyMc+fOMWvWLPbv309KSgrOzs5UrVqVQYMG5fo881rm3lks8cVVpJYO2LYcT8i2sSiTYjFy9SLm8lZhHZv6gzB0KET8gxNEX/wDxGKkFvbI85ck6fVNJCYW6hK41BCJsfrfmVEkx5Ps/xCLKh2xrNJeWK7JcIJah7JThfxImzfn7t27HDhwgN27d1O5cmXOnDlDhQoVSElJ+eJzIBaLhc/GwMBAxo8fT9OmTfn9998pXbq01jkVK1aMP/74g4kTJzJ8+HCKFy/O5s2b2bZt2xddvfT8+OgDyhwSFhZGbGwsnp6eOVo/J2VN0C3xfI6pU6fSrFkzihQpgqenJ/Xr16dz586ULFmS1PTc9bNJTK2wa/Ur4Ud+J3SnesBBJDPGunZfIo4uUP+C/YRIaqgukYPWcZKTk4GMjILm7+ykPDSDRtnx/PlzWrRogaenJ2vXrtV6LD09ndq1awtT4xpq166Nh4cHc+fOZfbs2bm69i8hEomEsk3Xrl0xNTUV/Jqz89GOiYnJ9kvdxMQk2x6hnPK/8OzODd7e3hQsWJB58+bRoUMHrfK/iUyKg2EqH1MzSuyWVTtqZfcyk3eA2pow9s5hreUuY46gTE4gaG2G1qLU0h6XMUdwsTEWMuFfw8ZUHWBJxCIUSLDy6UrItrGYl2+pUyZUfeo7s6zSHvOyzfiwvBsGNvmwrNJRR3RalBxP014j2dxzN926dWPPnj0ULFhQxzXEwcFByHJlplSpUlqDZABbt2YECk5OTnh6ehIdHU18fLxwjy9evEhoaKhWoCSRSBCJRFrldj8/Pw4cOJCje/RPpmHDhowbN46FCxcK/eWgHuwDXQH+z3mn58uXj/fv3yOVSvH19WXy5MmkpKRw4oS2m0x0dLROu8v06dN1zmv48OGsWbNGeJ8Xdy9EtTnn8M+mUuQyRtsKUvN+Cds349MSFQ4d1fJTKUEvSAl8LigjAJh51cfMqz5yAzFpCrXxhDIlkQ/LuxFzbRfy/CWF95kGRWIMEmOLTJlK7R8usVk87+/6Rwk/zs+dO6dVmbhw4QJeXl5ffQ6sra3JkyePjv2lSqXSeX907NhRkN7SULduXbLyOZcevbD/j4k+oPwf8bWyooacliurVavGmzdvOHjwIKdOnWLt2rUsWLCAlStXUrFhm1yfnzy/J8791pIW5o8yLRlDuwIo4tWZEQNrp4zrMLUSlhtKM8oomhK3k5N6XU2p++PHjzp9MB8/fsx2Gvv9+/fUrVsXCwsLjh07hpmZto3cpUuXePLkifDBpaFw4cIUK1bsf6JtJhaL2bJlC82bN8fX15djx45Rs2ZN6tati6GhIU2aNKFv377Ex8ezZs0a7OzsdAY1vL29WbFiBdOmTaNQoULY2dnpZF6/RG49u783YrGYtWvX0qBBAzw8POjevTvOzs4EBgZy/vx5IpNESOr8kuOeXQCjAqWJkkgJ3TMVM6/6KNOSiX9wErGxhfD6AoTyW26RfvKq/1KZMPH5Faxq98GkaBXEMmNs6g4g/Mh8Pm78GZNi1RAbW5AeG0bS69sY5y/OjIk7v37gv8CMGTNo1qwZlStXpnv37kRFRbF06VI8PT215IUaNWrE/PnzqV+/Ph06dCA0NJRly5ZRqFAhHj169IUj/PMpVaoUXbt2ZfXq1UIryK1bt9i0aRPNmzfXEc7+nHd6p06d2Lx5M9bW1owaNYpDhw7RuHFjunXrhre3NwkJCTx+/Jg9e/bg5+f31cpHwYIFsbS0ZOXKlZiZmWFiYoKXpRUfokU5fl8YFSpL4strhO2djlGhsqRHBxN//zgGefKhTE3WWlciFtGohCNHHn1EoVR99fUrz1sM67r9EcuMkeXzJPbmXlRKBVJTG5Le3SM9Rq2tG/fwFLF3DtFhkZhihdQtROPHj6ddu3YYGBhQsmRJIbhcvXq1TstV5ufg1q1bREZGcvnyZQwMDEhPT//sZ1ZISAhjxozh6NGjxMXFUaxYMcaOHUubNl/+LqtTpw5nzpxh4MCBLF26NEf3Wc//H/op7xxia2uLubk5T548+dvOwdramu7du7N9+3bev39PyZIlmTx5Mq42JnxLkVUklmBo74Y8b3HEhkYk+z0AQJ6pbGJo56b2ZlYptcqQN2/exNjYWGh413zo3LmTIVQN6ozBhw8fdHrxIiIiqFu3LikpKZw8eTLb3kuNoHh2gxBpaWk6gunfCwMDA/bs2UOFChVo1qwZN2/exN3dnT179iASiRg5ciQrV66kT58+/PzzzzrbT5w4kYYNGzJnzhzat2/P1KlTc3X8/4Vnd26pXr06169fp0yZMixdupTBgwezceNGHBwcGD96ZK6CSQADm7zYNlfbO0adX0/c/eOYetXDvExTrfU05bfcMqVpRi+wTf1BWNcfhDIxhuiLfxB1cRPJ/o8w8aiBLG9xYT0Tj+rYt5uGxNSGmJv7iDqzmsRnlzC0d2P04H7kszYmISWd6MRUlCp4GhRDQsr3e801adKE7du3k5qaypgxY9i3bx8bN27E3d1dyz+8Zs2arFu3juDgYIYOHcr27duZPXu2jn/4v5W1a9cyZcoUbt++zdChQzl37hxjx45lx44dOusWLlyYnTt3cuzYMcaMGUNycjLdu3fn7NmzVKxYEWNjY4yNjbl48SKjRo3iwoUL/Pzzz8yaNYtXr14xZcqUHA3XGRgYsGnTJiQSCf369aN9+/Y4Jb7L1fvCpERtLH26kBr6jsjTq0h+dx+bJiMxdNC1u1UoVfT3Kaj1Ov/S69ekZB1hvTxNRyIv8BPxd48QdXETIokUY/fKgHoQ06p6V6wqtyPo06T6jRs3BE95TftF27ZtadmyJba2tkgkEmxsbBgwYIDWc3Ds2DHi4uIQiURf7G+PjY2lSpUq7N27l759+zJv3jzMzMzw9fX9Yk/wvn37srWd1fPjIFLpc8c5pmvXrmzZskVnKAcQ+nY+Z7144cIFatSowfnz54X0v6urq055QENW68WIiAhsbGy01vH19eX8+fOEhYXhM/d8tuWWnKJIjOHjxqGIZSY49lgslEoS/rxE+ME5FOs8iWd/TAYgPDycwoULU69ePa0PlGLFiiGTybh7966Qof3111+ZPn06T58+FfpvEhISqFmzJn/++Sfnz58XxHOzcvfuXcqUKUPXrl21LCjv3btH2bJl6dOnj45TjZ7/Hzqvu8m1txG5Diy/hMaje3PP8t+0/V/xsM9M90quqFRw/kUoAZGJWsVCEZDf2pga7nZ0LJ+fwvZmn9vNN+Pl5YWtra0gCabn27lw4QINGzakXLlyrFmzRqf//Hvzv35fvAqJo87CS395n4Gr+iCWm+DQZX7G4GhKIpGre1CrVk0OHjwIqEvOBQoUYO7cuYwcqauHmZmQkBDMzc0xMjJi0KBBLFu2LNsM5dy5c/nll184e/asULlRKpVUqFCB9+/f4+/vr6NakZycTLFixejRowcTJ07UZyh/UPQl71wwY8YMTp06hY+PD3369KFYsWJ8/PiR3bt3c+XKFSwtLf9nxy5evDjVq1fH29sba2tr7ty5w549e4QG8hrudmy+6Z/jD7LgrWOQORdFauWIIj6K+IcnUaUmkaf1JK0JQWP3ysici/Jm91ymFhILTjkKhULLkxjUHxRNmzalbt26tGvXjidPnrB06VJ69eql1czdsWNHbt26RY8ePfjzzz+1tCdNTU1p3rw5oC4d16lTh02bNhEbG0vdunX5+PEjS5YswcjIiKFDh37j3dTzV5nRogS1F1z8rl+cGo/ub2VQjcLkMZUx6dBT0pWqXJ2bRCxCIgIXGxM2XPNT92Rms70K8I9MZPNNfzZe96NqoTzMaFGCfNa5H8pIS0tDJBJp6bZeuHCBhw8fCuL+ev4a1atX15H4ysrkyZOZMmUKr169Ytq0aRw4cACVSkXLli1ZtmwZxsYZz+2GDRvYvHkzT548ISYmhoIFCzJ48GDBd13zvvBf2h1DWxfMK7Qm6txaUkP9kJpaY1GlA6YltAe7lMnxRF/ZTuLL6ygSIpEYWyDPXxKrWr0QSQ0JWNIJn+49oGd5QW9YoVSRHhtO4IoeWFbrhEXF7CWQPocyNREDa2et4SUDIxNEhvLPtmElJCRgYGDwWYmyrzl0abh8+TK2trZabUBisRhfX19GjRrFxYsXqVOnjtY2c+bMQalUMnLkSCZOnJij4+j5/0cfUOYCZ2dnbt68ya+//srWrVuJjY3F2dmZBg0aaH3o/C8YMmQIhw4d4tSpU6SkpODi4sK0adMYNWoUAB3L52fjdb8c78/QoSCJz6+QHheBWGaM3NULy2qdMbB00FpPJJZg22YyFUOPsXjxYpKSkihbtqxQmstM48aN2bdvH1OmTGHw4MHY2toybtw4nQ+ABw8eALB+/XrWr9duJndxcRECSlB7Ws+bN48dO3Zw4sQJDA0NqVq1Kr/99ttnNTv1/O/JZ23MlKYejNn3+Lvtc2pTj28KzDLTrmx+KhfMw7j9j7n8OvyzgaEGzeNuNib4RyXyNlyth/m1YFTz+LW3EdRecJEpTT1oVzZ3pfrAwEBq165Np06dcHJy4vnz56xcuRIHBwcdgW89/3t8fX0pUKAAM2fO5N69e6xduxY7Ozutwb+v2a9q3hedFqulssIOzMS0ZF1MPWsR/+g0EUcXYuhQCENb9dCVMjWJ4K2jSQt/j2nJOhg6FESRGEvS65so4iIwtHejcq0GHDu4D8XyJVp6wwl/XgSVCpPi1XN9rfL8JUh8fpXYO4cxLlQOlSKViLtHSIyPy7aNZ8qUKYwaNQqRSIS3tzfTp0/PdogmJ6SkpGQbtGq+Q+/evasVUAYEBDBr1izWr1//3c0s9Hxf9CXvfxE/YhlSz7+TVyFxbL0ZwP4HgcQkpX19g68wqq47A2sU+vqKuUBzjudfhhIQkU3p2saYGkXsEIlgwzW/v3y8kXWLMKhGzkuqMTEx9OnTh6tXrxIWFoaJiQm1atVi1qxZgnSTnv89mgxljx49WLdunbC8ZcuWXLp0ifDwDFOHpKQknaCmfv36vHr1ijdv3gjLbBzyEhkSiH3HWcjzqZVBFIkxfFjWDXPvxljV7AlA9OWtxFzdjm2LcRi7V9Lar0ql4pd6RSmc9pZ69eqx79ARhl/LmNcOWjcIsdxUmBDPDYqEaMIPzSXZ/6GwTGxkjl3rX3m9ZqigshAQEEDv3r1p0aIFzs7OvH37lvnz5/PhwwcOHTr0WdvPL5W8hwwZwrJly3j79q2WmkH79u3ZsWMHgwYN0lL1aNOmDUFBQcIQpkgk0pe8f1D0Gcp/ET9iGfLfTEJKOn4RCaSmKzGUinG1Mcmx3M3fgeaL81t+Q1avXp3w8HCOX7qV4+zf15CIRUjFIqY29aBtLrN78PX+rsL2Zkxu6sFkPLJ9rnZv30L3ZjVx6LoAmeNf762bd+oltqayHF+LhYWFjnPW/5Ksfdl6tMmaFa5atSr79+8nNjYWc3NzQNd+NS0tDR8fH06ePKllWmEml2JUoDAmBUoK7RcSYwsMrJ1Jiw4W9pH44ioGdgW0gsms7wul0g0nJyfWb/wDVRG1cHlqmB9pYX5Yf/Lrzi0iAxlSm7yYmOXBqFBZVKlJxN4+SNi+GVzqW4sGldXakfnz5+fkyZNa23bu3JnixYszYsSIb/KR79WrFytXrsTX15cFCxZgb2/Prl272L9/P5ChaQxqnd+9e/dy8+bNb7pOPf+//LjffnpyzY9ahvw3IWS9/qaBjb+TmKQ0ai+4SPqnIPJbg0lNIFrJzeab+w9zi4lMquO8Exmvq5n6V5l46CmVCub5T75nnj17xq5du+jWrRuurq5ajy1fvhxjY2O6dev2t5xbTviSdrAmoMyN/WoJ94KsHuaj9QNMLDdFmZwhCZUeHSwEk597X4jFYjp27Miy5SuwKeCL2EBOwtMLiKSGmLhX+aZrDTswC5FIjF2bScIyo8IVCFrVhwWzptLg8P7PbqtRG5k1axYfPnwgb968uTp2yZIl2bZtG/369aNyZfW0uYODAwsXLqR///6CDmh6ejpDhgyhc+fOlC1b9huuUs//N3rZoH8Z7crmZ2TdIl9fMQeMquv+TZmjfyPvIxPpvO4mdRZeYvNNf/yzBJOgPbBRZ+ElOq+7yfu/MHn/IxEYnURIbDIp6cq/lJW0NDKgc3kXzgyrxuae5f/WwGvPvQ/ffZ/pShXj9n+/H3T/JJ49e8aUKVOyFaNevnz5D58Z/Zp2sMZ+NTw8nPnz53P06FFOnz7NsGHDAHQsUSUSCfmsjdncszynh1ajc3kX5AYSHZtRABcb4y++L7p06UJiQjxJL2+gUqlIeHYRo4JlEctz5iiVmbToYJLf3sWosHYbk8TIDFne4jy6c+ur+9BoDWd1dcoprVu3JigoiFu3bnH9+nX8/f0FqSGNFN0ff/zBixcv6Nu3L35+fsIfgLi4OPz8/L46cKXn/xd9QPkvZFCNwsxqWQKZVIxEnDuFSolYhEwqZnbLEt+9p+2fyo7bAdRecJFrb9Ue57kd2NhxOyBXx5s8efJ39S7OjgsXLiASiXJkdbbjdoBWYBz/6Az+sxqTHh2S6+NGJ6VRzNGMQna62dv09HRSU1Oz2Up7nV9++YV8+fJpad2JRCImT56c4/N4FRLHy5D4r6+YSxRKFZdfh/M6NO6771vP30tm+9W+ffvSsGFDateunaNBEU37hVc+S8q6WnN0cBX2969EwYIFcZNEcnFkDSY39cj2fQHg6elJKS8vEp5dIOX9UxSxYZh45twsITPKhE9ubapsHNaU6ahUurq/WXn79i2g1mf+VgwNDSlbtiwVKlTA0NCQM2fOAAh2rwEBAaSlpVG5cmUKFCgg/AF1sFmgQAFOnTr1zcfX8/3RB5T/UtqVzc+ZYT5UclNrV34tsNQ8XsnNhjPDfPSZyU8sPf+KMfsef1NmTqFUkZKuZMy+xyw9/+p/dIbZc+XKFcqWLYtcLqdgwYJalmlZ2bJlC97e3hgZGWFtbU27du14//49oM7MTjr0VFg3Jfg1MdfU2qMft44W/N8zo0iIJvzYIt4v7oT/3BYErRtE/OOzwuMTDz3l2oM/EYlEzJs3j4ULF1KwYEFkMhnPnj0jNTWViRMn4u3tjYWFBSYmJlStWpXz58+zfv165s6dS+vWrZk3b57WcRctWoRMJqNs2bLcvn37i/dn680AxNl8+imS4/m4aRgflnUlLUKdwUx8eYPQ3ZP5sLQL/nObE7iyF9FXt6NSZnzxRl/eiv+cZmrLO7GILTcyfkT06dMHS0tLwa4U4Pjx41StWhUTExPMzMxo1KgRT59m3OfcolQqWbRoESVKlEAul2Nra0v9+vV1jAY0vH37FpFIxIIFC3Qeu3btGiKRiO3btwPg7+/PgAEDcHd3x8jICBsbG9q0aaOVidy4caPgclKjRg1EIpHwg8XV1ZWnT59y8eJFYXlWK75/ArmxX/3ifsQiPJwsKJ3fig5t2/D40SOhfzAzWXudu3bpQvK7+8TeOYjYyBwjt+z1e7+G1MoJRGIS/rysdYz02HBSA5/xUybv7bCwMJ3tAwMDWb9+PSVLlszWkOJbePXqFStXrqRx48ZChrJdu3bs379f5w+oLTn3799P+fL6YdEfCX0P5b8YTbklp9OunSrk/+wv5P8PNEMjYWFhX7U++15oBjs2bNig09+143bAdxHKhtwPbPwVHj9+TN26dbG1tWXy5Mmkp6czadIkQSeuevXqqFQqkpKSmDt3LpMmTcLX15devXoRFhbGkiVLqFatGldu3GbI3hekKdSZDGVyPKG7JmNo60J6dDBSE0siTy5HJJZiWkotIaJMSyFk21jSoj5i5t0IqYUDic+vEHF0AcrkeMzLNiNdqWLuyeeAWtcvOTmZPn36IJPJsLa2JjY2lrVr19K+fXt69+5NXFwc69ato169etSoUQNnZ2cWLFiAn58fI0aMYNu2bbi5udGrVy+kUilz5syhZcuWvH37FgMDg2zv0fkXoWSpUKJIjCFkx68ok+Ow7zALAyv1l2X84zOIDIwwK9scsaGcZP9HxFzeiiolCauaPQAw8axBzNXtJPx5CYl3E86/DGUyHqSmprJnzx5atWoluN9s3ryZrl27Uq9ePWbPnk1iYiIrVqygSpUq3L9/X+g/zM3QV8+ePdm4cSMNGjSgV69epKenc/nyZW7cuKFjwgDg5uZG5cqV2bp1q1Cy1bB161bMzMxo1qwZALdv3+batWu0a9eOvHnz4ufnx4oVK6hevTrPnj3D2NiYatWqMWTIEBYvXsy4ceME3dlixYqxcOFCBg8ejKmpKePHjwdyrln4I5Eb+9WcMmrUKPbs2UObNm3o0aMH3t7eREZGcujQIVauXEmpUqWEdTt06MDIUb+Q9PI6pqUb6vjP5xSJsQWmJWsT//AUIdvHY+xeEVVqEnH3jqFMS2Xs2LHCur/88otQ6ndycsLPz49Vq1aRkJDAokWLtPbr7+8v+Ndrfsho9FRdXFzo3LmzsG7x4sVp06YN+fPn5927d6xYsQJra2tWrlwprFO0aFGKFi2a7TUUKFBAS15Oz4+BPqD8D/C1adcfeTL57yJrZu6vkvzhT3p1ncXwWD9ioqMoWbIkM2fOxMfH57sdQ8PEiRNRqVRcvnxZGDRo1aoVJUpkTOuLRCJCQkKYMmUK06ZNY9y4cYC6FBxsVZKVQ1vj6TsCi0oZgsmK+EisavZELDcj2f8heZqOIuzAbKIu/oGJZ01EEinxD06QFvEemyYjMPVQe/yalW5AyLYxRF/egmnJOiAz5rafuuz24cMHXr9+rVU6UygU+Pn5aQko9+7dm6JFi/L48WOsra21rjcgIIBXr14JQxTu7u40a9aMkydP0rhxY537E5+STkCW3lZFfBQhO8ajSk/FoeNspBYZfuJ5mo5CbCAT/m9WuiERJ5YSd/8oltU6I5IaYGDlhMy5KAlPL2Du3YSAiEQSUtI5dewoUVFRwpdpfHw8Q4YMoVevXlq+yF27dsXd3Z3Rv06hWJuRuRr6On/+PBs3bmTIkCFaX/IjRoz44kR/ly5d6Nu3L8+fPxe+uNPS0ti1axctW7YUdAEbNWpE69attbZt0qQJFStWZO/evXTu3Bk3NzeqVq3K4sWLqVOnjlYGsnnz5kyYMIE8efLQqVOnz57Pj47GfnXChAmMHDkSBwcH+vfvj62tLT169PimfZqamnL58mUmTZrE/v372bRpE3Z2dtSqVUtn2MXe3p6q1Wty8ewpTD1rfGaPOcO63kAM7AoQ//A00Rf/AMDQoTBrN26kWrVqwnp169Zl5cqVLFu2jKioKCwtLalWrRoTJkzgp59+0trnu3fv+PXXX7WWaf7v4+OjFVCWKlWKDRs2EBISQp48efD19WXKlCnY2dmh55+LPpL4j5HdtOt/GRcXF5KSknQyWeP2Pxammb8H4YfnYWBpj1PVNvxSwYVly5ZRv3591q5dy8KFC3n8+DHOzs788ssv2W7/NYcODa6urrx//54aNWrQunVrHj16hJOTE5MnT6ZevXocO6YuUUdHRwuZMF9fXx6+CmDa0T+5eP0mcTf3olIpib68maR397D0UUuVIJZg6tWAxOdXABCJpZiVrk/kyeWkBr9G5lyUpLd3kJhYYVI8I1AWSaSYeTch/NBcEt/cJi34NUmv1Y3/SUlJdOnShVmzZgnZGIlEIpQXlUol0dHR+Pn5Cd7uHz9+1Ooxbdu2LdbW1kyaNInJkydTtWpVQB30ZOd+MnDCDK1ATREXTuieqcL07ccNQ5AXKI1VjR5IzW21gkllSiIqRRqyfB5C8Gxor+7lNPGsSeTJ5aRFfcTAyhG/iAS2bt1Kvnz5hB8Op0+fJjo6mvbt22vpGwbHpmCctyj7jp4kf/42uXLp2bt3LyKRiEmTJuls86VeXF9fX37++We2bt3Kb7/9BsDJkycJDw/XCvwy9wimpaURGxtLoUKFsLS05N69e1qBwj+RyZMnZ9t/261bN52qRZMmTWjSpInOut27d9f6f3aDSUC2PcvW1tYsWbJES3vxc1iZGWNqmxfjfMX/0nCcSCzB3LsJ5t7qa9HoDXdqqV1Cbt++Pe3bt8/RPjWVj5ygaaf4FvTS2T8u+oBSz38akUgklCI1vAqJ4/Lr8M9s8W3YNvsFmZM7YUCj9tVo0KAB7u7udO/eHUdHx2xL05n5mkOHBoVCgVKp5MaNG/z888907dqV9evX061bt89mhz7nbyw2sUKZHE/I9nEY2ORHYmqN2FD7XkmtnAFIjwlB5lyU9JgwpFZOWvadAAY26qnQtOA3JL68gbxAadIiA6latSqPHz/Gx8eHZ8+e4eTkBMCmTZv4/fffef78OWlpGcLpBgYG2NvbM3PmTMLCwhg+fPhn5V4ge/cTldwcLDNcPsIOzgZFOkaFy2NUwBtFYgxxdw8TvHUMTt0XkR4XQfSlzSQHPEKVop3ZVGb6v3GxakSeWUPC0wtYVmlPRGQ0R44cYdiwYUJg9+qVupc2s+1cZkQy41wPfRnde4KTk5NO5vZrWFpa0qRJE7Zt2yYElFu3bsXZ2Vnr/JKSkpg5cyYbNmwgMDBQp4dQz/8PHz9+5OjRowwa/gtH/6IGbFb0esN6vgf6oRw9PzT+/v4UKlQIT09PIUMVHR3N0KFDyZcvHzKZjEKFCjF79mxBtiMtLU3QSstKbGwscrlcEML28/NDJBJpSZq06dCZgN9bkx4XTujeaQT83pr3izoQdW6d1iBGbpA5qW0iNQMbmiBWU5oeM2YMEyZMYPTo0Tx69AhAKBEGBgZy8eJFNmzYgIWFBYMHD2bWrFnUq1eP+fPnC8eYMWMGHz6oB0lat27N9OnTMTAwICIiApVKxd69e7M9N7OyzQF1AGnoUBjbtr9h124adi3H49Dld6SmNqTH5H6iOzskZjY49V2FRflWANSqU48rV66QnJwsuJRUrVqVbt26UbBgQRo1aoSdnR2nT5+mZs2aiMViUlNTMTU1pUWLFup9fkbuBaB06dLs3buX/v37s2bNGlq0aMHBnVu0V1Kon1MDm3yY/dQQyyrtse8wA0VcOLG39qt7QkPfYVmlI7atJ2LXbhqW1bupt800KSuRm2JcqCwJzy4AcOjAPlJSUrQCec1rdPPmzZw+fZqBczeo77XmT8sJOb6XmqGvFyHxJKSk53i7zHTp0oW3b99y7do14uLiOHToEO3bt0ecaWJp8ODBTJ8+HV9fX3bt2sWpU6c4ffo0NjY2OlI5uWXjxo1CsK1RHvhcdu+/yrt379iyZQvt27fHwMCAX4YOYkpTj+96DL3esJ7vgT6g1PPD8ubNG6pVq4aZmRkXLlzA3t6exMREfHx82LJlC126dGHx4sVUrlyZsWPHMnz4cECdxWrRogUHDhzQkaE5cOAAKSkptGvX7rPHDY5JBpWS0J0TERuZYVWzB/L8nsTe2k/8g5Of3S4nKJQqzj0PFs61Xr165M+fH1dXV0qVKsXw4cMFceTevXuzb98+qlSpQkqKWoS7devWyOVy1qxZg4+PD2/fvhWyRFu3bkUulyMSiUhJSWHdunX07dsXZ2dnIfMHCBPcGiRG6uMpE6Iw826MzN4NQ7sCSK0cUaUlI3cthSolEUVcBMrUZK1t06MCAZBa2H/625b0qCBUWSRJ0iLVga7UylErezl+7GgiIyNxd3fn3r17gPpHhFwuZ9++fZQqVQojIyNq164tTEmHhYVx4MABnXv74MEDnYDkwYMHWv18VatWJToqUifTaFqmKbE39vB+UQf1pLaJFQZWTiS+voUyKRabRsMwL9sM40LlMHL1Qiw31Tk+gIlnLdIjA0n5+JLNW7ZSunRpPDwyvvw1lop2dnaEWxThSIQtRq5ewh+5S0lhXY08U8rHLysEGFg6EB0eyrozj764XnbUr18fW1tbtm7dSrly5UhMTNQpYe/Zs4euXbvy+++/07p1a+rUqUOVKlWIjo7WWk8TGGqehz179ug8lpW1a9fm+pz/a1y8eJHOnTvz7t07Nm3ahIODg15vWM8Pib7kreeH5Pnz59SqVQtnZ2dOnjwplDHnz5/PmzdvuH//vlCq7du3L05OTsydO5cRI0aQL18+2rZty/r16zl16pTWYMbOnTtxc3PLdvIV1AMb8SnpqNJTMS5WFcvK6v4hs9IN+bjhZ+IfncLsp4Z/6drub59H/IMTAEJju0ql4vnz53h6elKjRg2WLFnCzJkzqVKlCo0bN2bYsGG8f/+e69evk5yczIoVK4T9xcTE8PbtW549e4aNjQ3m5uYcOHCA06dP4+Xlxfnz56lSpQpBQUGAWjImj62m+V1E4usbwr4ijurKyAioVMQ/OI5Yrh4GUSTFEX15K4jEBG8bi8TEEompNYqEKBL/vCz0UUZd2kzsNbXFYNieqTq79fZWy5+YmpoyZcoUQkJCSE1NJU+ePJiZmZGcnMzNmze5fv26MKjz+vVrQZNu1KhRABw8eFB9jExSJzKZjNevX/PTTz/h5+dHXJxaHzLp3EpUDpoJUhXxdw4BoEyK5cPijsL2EmE4J6O8qFKkEXdPVy4JwMjNG7GRObE39pD48j49J0/TerxevXqYm5szaeo0IqqNIutvekViDBLj3PU4G7tXJu7eUUaO+5W6P23XyjSpVKov9lGmpKTQvn17tm3bRnp6OjKZjJIlS2qtI5FIdPrWlixZgkKhna03MVGLbMfH6+p7mpiY6ASgenJGdr2coNYbzmMqY9Khp4K9Y075q7aner7Of3EAVp+h1PPD8eTJE3x8fHB1deXMmTNaPXG7d++matWqWFlZER4eLvypXbs2CoWCS5cuAeoetTx58mh5JUdFRXH69Gnatm372WP7RyQI/zYrrR04yvIWJz2TD++3EPfwFPEPTjBgxBit5ampqaSmpjJgwACk0owPnUaNGuHm5samTZsEh44pU6YA6sltUE85d+/eHZFIREREBJGRkaSmphIRESF4XT948ED4wpdIJISHhQJgYO9GatAL4XhGRSpiXLQKYrkZYrM86uBRLAaxBAzkxNzYQ8LTCwAEb/0FZWIMIqkhFhXV0+CafYUf/p2oc+uIvXuYhCfnATAuWhkjd7XVmqGjOrvSvG0nTp06hYeHB6GhocyePVuwuXNyciIqKoqQkBBq1qxJ8eLFSUlJQaVScfXqVeGcNcNFmuB81qxZwmPh4eHI5XKqVq3KlClTqF+/PgBhD88RfWWb1nNgkCe/+joBk1L1MXAojCI+GsQSwo8sIOLUCoLWDSLg9zakhaqFnWPvHkWZlmHhmPLhGcqkWBJfXAOVkpmTxgnai66urpibm7NixQpuXLvC69/bEjC/Df6zmxKwoC3vl3RWB+hZiDq/gfdL1JqeH5Z1JezgbJTJGa9TuUtJTDxqEH37EBV8arN48WIWLlxIq1atWLZsmbBefHw8mzZt4uLFiwwYMAA7Ozvy5s1Lly5dCA8PJzo6GqVSqaON2bhxYzZv3kydOnUQiUQ0bdqUxYsXY2NjI+x7xowZNGzYEIlEwrZt6vu6Z88eatSogYWFBXfu3OHhw4f07t2bHTt2cO7cOYAf2orxn4Beb/jH4lVIHJMPPcVn7nk8J5+k0ZIrtFhxjUZLruA5+SQ+c88z+dBTXoX8O40P9AGlnh+OJk2aYGZmxsmTJ4XgQsOrV684ceIEtra2Wn807gqhoepASSqV0qpVKw4ePCiUi/ft20daWtoXA8rUdHWpViQ11MkUZfXh/RJZS8MaEp6cQ2rtTPeBIzAyMhKGNNLT1T1w7u7uvHjxQmsbY2NjVCqV4NAxfvx4HB0dhW0fPHjA06dPKV68ODY2Nri6ugqZvMOHD7N+/XpcXV2FwDxztkmREI3EPEOyJ+nNbVLD/BDJjDG0dcG8Yhus6/RDLDOBtGSMCvxEesynoDo9FbGROWKZMfGPTmFcpCImn6SCUCmJe3BS3Xearr7/1nUHkB4VhCx/SWybjQagrFcJ6tSpQ3R0NJaWlgQEBNCwYUMKFChAXFwcCQnqwMnIyIgyZcoglUoRiUSC+DEgZKA10kPXr18XHnNzc+PNmzcUKFCAwYMHa0mdKBOihX+blW2OWG6KyEAOIhEJD08gNjTCunZvzCu0QWJqRfz946RFBiJzdsesTFP1/Xp5jYgjGX2sBnnyYVFFneE0sHXBvd1YlixZgoGBgSCJUrZWEwydiiIykKt7ckUiUCpQJkShSk0S9qVxLEkL88PcuwnWdftjWqo+6dHBKFMyAkoAm0ZDsazRg9CP7xk5ahQzZswgKSmJSpUqkZUBAwbw7NkzJk6cyJgxYyhatCjOzurhKgMDA2bPns2vv/7Ks2fPqFKlCiNGjKBLly7cv38fUOucnjlzRvBcBnW7RY0aNVi5cqWQidy5cyehoaFMmjSJSZMmYWZmJuiLTp2qzlRn/vH0OXLioPRfJqu9o4uNMVnDShFft3fU8+381615Nfy78696/pG0atWKTZs2sXXrVvr27av1mFKppE6dOp+V18kcaLRr145Vq1Zx/Phxmjdvzq5duyhatKiWWHBWDKWffmOJcv5bK/ryVmKubsex13Jiru0k+c0dJBb2OPVYjEqpIOb6LhIenyU9LhxUKiSm1khQUq9ePQ4cOEBAQIarytGjRwVpn+LFizNt2jShZKkJBOPi4rCzs+Phw4cATJkyhfT0dGbOnMngwYNxd3enSZMmLFiwgKpVq9K0aVPGjh0r9CBmHqRQxkcI/5aY2yGSSHHsugBEYi25HGP3ykRf2kzC0/NY1epD5Am1xInY2IL0iPfYNB6OqWdNkt7eJeHpecRyU+QuJbFtMU64PxmokFraU8T3F8aPHY2FmQmBgYEEBgaydOlSNm3apHV/LSwsiIiIYMmSJfj5+XHp0iUh2yqTydi3bx8Af/75p3APDx48yJQpU4iLixME3osVKyYEMtqIUCREZTznKhUSc1tSg56TFvEe87LNcOzyO8q0FK17Yl27DzHXdxN98Q/SY0KRWtghMbHCuHB5Yq5sxbx8K6LFFkyfPp309HQePHhAvnz5cC5dA4c2ExHJtU0EIk4sJeHpeVTpQxBJDVDEqX2Szbwbkx4bRuKtA6iU6RgXroBIph0MiMQSDPPkR2pkgSI+gqSkJEQiETJZxvnOmzeP7t278/z5c/bv38/QoUO5fPkyly9fJjg4GENDQwoUKMCgQYMAtTamm5sbP/30E23atCE4OJguXbpw8uRJevbsSXR0NHv27OHChQv4+/szatQoYZiqZs2alCpVitatWzN//nzCw8OpWLEir1+/plixYoJl3oULF3BxcQHUsjPv3r0TsupSqVR4zu/evYuXlxfPnz9nwoQJnDt3jsTERDw9PZk4cSJNmzbN5nn9b6HXG/572HE7QGg7gNxb805p6kG7f0mmWP8K0/PDofkyGTBgAGZmZnTo0EF4rGDBgsTHxwsZyS9RrVo1HB0d2blzJ1WqVOHcuXOCU8fncLUx+ebzDj8wC6mVE5Y+XdH03EUcW0zCk7MYu1fGvFwL4h6cJC30LROH9mH6b1M5ceIEVatWFYK9+fPnY2trS1hYGFKplDZt2mBvb49IJKJJkyZ0796dOXPmCJlYQBCgPn36NKAun2v64G7duoW/vz+Ojo58+PABDw8PHj15hjJdnfExr+BLyscXpPg/xMqnKxHHFxG0dgCmJWojMbNBERdBst8DRAZyTErUJP7BCRTx6kAHqSHpMSGIpIbI8pcgOeAxUec3YOhYGImJlY7Xd+DKXkIGLnBlL9w9vQAYP348bm5ugj9wVhQKBRKJRLhOlUrFkydPhH9rekM1gblKpSIxUf3LXzPx/+LFC8LCwqhfvz4nTqj7VyUSCea1+5IeFUzcrX2IDI2FbKrY0BhVeipiAznRFzZiaOuKUcGMvltlajKq9BRkzsUAFakhbwUx9PiHJxEZGmFcpBIh28eTGhxMhw4d8PHx4datW6zbsAHjwm+xbaF2JPmctqVIqs4yx905hIFNPiyqdCA98gNx94+THhuGfYeZwo+N+CfniDiyALnbT+Rv0Is+FfNm676jue6GDRtSpUoV5s2bx/nz51EoFDg5OaFQKAR9zLNnz5KcnIyJiQlbtmxBIpHg4+PD9u3bCQsLY+rUqchkMtauXcv79++FFozXr18DCAFnv379iIuLY+nSpZiYmHDp0iWUSqXWJHlWsnNQevr0KZUrV8bZ2ZkxY8ZgYmLCrl27aN68OXv37hWm/vX8c/SG/+mB79Lzr77ZTU3xqe91zL7HhMenMKhG9vJt/yT+Oc+cnv8MIpGI1atXExcXR9euXTE1NRUyEL6+vkyePJmTJ09Sr149re2io6MxNTUVymhisZjWrVuzfv16ypUrR3p6+hfL3aD+IDaVSfmWQoSBXQFsm44S/p8a8paEJ2cxLVUXmwZDAJAX+AnlgwMcOXSQ4UN/5uTJkwwfPlwIhqytrenTpw/Tp0/nypUruLq68vHjR9q2bcuff/7JL7/8gkKhYOTIkWzfvp0PHz4QERGBo6OjIIzs7+8vBJcJCQlcunSJjh074ufnh4GhTAgmAYwKeCExtSTF/yEy56I4dJ5HzNUdxN45hCo18VNcrA6Ok/3UJU+hhF2nP5Enl6FSphO0vLtwfbYtxhJ5aiWpYf7q50FuitlPjZE5F0WFiNhb+0gLecPTK+rAbsOGDSxatCjbgPL169ckJCTg5uamJQ+UlpZGo0aN8PX1xdzcXCuY0Az5AFqe3lmnl7v0HcI581rq83YuSvjRhfBJFkqlVGBStAqmpRsSumMC8Y9OYZAnP9GXt5L0+qZO64MyJYHEVzdJi3hP3IOTmHk3IuXDU1I/vqT7wGGsX6oui3fo2oO9r9OJvvgHwTt+JfXji89qW2paLpTJ8aQEv0FkKMe4UHksKrcn5spWkl7fwrhweZSpSUSdXvXpdTYYBTDw53qC+86MGTO0XHnS0tJo06YNHTt25O7du1y8eBHImP7P7FqkQXPvNS0I3t7eDBkyBKVSydSpU/H19cXMTJ1x1UhX+fv74+/vL2TSASHQj4mJ0eqNzkp2Dkq1a9cmf/783L59W8i8DhgwgCpVqjB69Oj/dEC5ceNGunfvzrt377R+PPyICFbAuXCDygkXLlygRo0anD9//v/FK/7vtub9/77enKAPKPX8kIjFYrZs2ULz5s3x9fXl2LFj1KxZk1GjRnHo0CEaN25Mt27d8Pb2JiEhgcePH7Nnzx78/Py0fMDbtm3LkiVLmDRpEiVKlBA8hr+Eg4Wc0K+upYuZVwOt/ye9VfvZmpfN+KIL3TEeBwe1R/TRo0eZN28ed+7cwdXVlZiYGCIjIzlz5gwLFy5k1qxZwsTslClTcHd3p1SpUjg5OfHLL79gamrKpEmTSEhIEL6ohwwZQuPGjQkJCRECqLZt29K2bVuuXLnCg/v3kJjaoIiPwKbhUOQuJUkN8xPOz9DeDZuGQwhc1QeJuR1mZZqqpX6khqQGvyb6wi1AWRkAAQAASURBVEaMCvyEVfVupAS9UAudi6XY1B9EauhbYm/uJeLoQnUv4qcSvXnZZlr3xaJEdRzD7nB1jdrZpXTp0oIGYWYSExNp06YNYrFYCBIvXLiAWCwmT548HDlyRFj348ePODqq72uNGjUEK8kRI0YAMHXqVFJTUwkICBA0SMWijK8yY/dKGN45RErgnyCW4twnw1PY0LEIaVHBhOz8FWVSHOYVWmNgnReRoRxFXIR6Ml6lIvL0KpSJ0RgVLINJiTqEbhuLvEBpho1R288lJCTw4F0IBraugIq00LdYVumoc3/JIrlkUakdSKQkv7tP5JnViE0sQSwh6c1tjAuXJ/ndfZQpCZgU90GRqJaQuv8qgKIO5pQvX57z58+Tlf79+7N+/XqmTp0qvF+KFi1KcnIy7du3Z86cOTRu3JiBAwdquUhpxNP3799PYmIi169fJygoSEtrU9Oa0ahRI4YOHSosf/36Nf3792fKlCla/ZfZ0apVK61gMjIyknPnzjF16lTi4uKEaX1QT85PmjSJwMBAoRdUz4/H+8hExu1/zOXX4Ug+I8z+OTeoH63fM6s1b8LTCygSY3Q+63LDxENPqVQwzw93rblBH1Dq+WExMDBgz549NGjQgGbNmnHmzBnKly/PxYsXmTFjBrt37+aPP/7A3NycIkWKMGXKFEHDUUOlSpXIly8f79+//2p2UoNbHhNyr+gHUktth5v0mFAQiZFaOQrLVCpwsjEj3tISf39/rfVLlizJwIEDmTVrFqNHj8bExARvb29u3LhBVJTa+/rVq1c8evRIJ4uk+RJ3dXWlTp06wmBP3rx5CQoKYtSoUUilUpzzF+B9bBqKTL2TaZGBWvtKDniMMikW2xbjkOf3zLieTBPuadHBhGwfh8TcHpEIjItUwLhIBaQW9kQcXYDc5fN9qlKxiO2zRuK583diY2OzXUehUNCuXTuePXuGra2tUO7WXKsmG6bB1tYWmUxGSkoKb968EVoiNFmwZs3UH/R37twRtlm/fBGO3dww+GSdCCAykEEWsW6x3BRl8CsU8ZHYNBqGaYlawmNJ7+4L/847YD0AyrQUgjePQiw3wapGD2aP/ZljR48Iz6EG05J1tb6APqcgIHcpqdanrNyO5A9/ErJlFCJDY/XrC0iLUpf8Q7aPE7apujhj+6yDbRKJhLx58wqWg7t378bX1xdDQ0MCAgKYPXs2bdq0YceOHTrn0rZtWxYtWsSDBw9wcHAQpJ3q1KkjrKPRPHV0dNRqTSlUqBCgtjvNanWaFY0klIbXr1+jUqn49ddfdfyiNYSGhv5nA8rOnTvTrl07rZ7ZH4lv7TO8+iacWvMv0s/Hjb7VCn6xHF6tWjWSkpKEgcT/JVmteROeXSQ1zP8vBZTpShXj9j9mc8/yX1/5B0UfUOr5YcjOU9fIyEjH/9bU1JQZM2YwY8aMr+5TJBJpDb1kxdXVVUdjb9/OrXRed5NrbyO0Pvgsq3bEsmrHrLvIOJb0Mx9knzJvErGI9vMPsbln+c+W+3x9ffH19RX+v379em7cyNCJzDyUFBMTQ9u2bZFKpfj4+DBq1ChhKEnj/GNvby8EUcOGDWPhwoVYNxxK5LGFgFr3UCMDlHG+mt62z2svJjw6gyo9DYmRGcrkjGyRUeHywn4/h8aVw8bG5rMBZe/evTly5Ahbt27NdgAr69SvRCKhVKlS3Lp1i8jISJ31w8LCsi/l+l2HTAElOvOxn0elUhH3Sb8yM5Enl5EeFYh9xzlEH57FSVUyo0ePpmjRooQlwfDf13/a7svalqp03clmQ1uXT89Pptfsp9evTeMRSEzVr6vpzTxxzaPuB846SW1oaKjVv6jRxtT02jo6OnLs2DHu3LlDmTJltO6dkZERd+/epVChQiiVSvz9/VEqlTRo0IBTp04hkUiE1+CRI0eIj4/XyUZ+7jnPTGb/cMgYJBs5cqROq4sGTcD6XyIhIQETExMkEskXHaP+Tv5Kn6FSBakKJYvPvWbxude4fKEcLhaLdWx0/xf8VWteVXoqSKQ69rQKpYrLr8N5HRpHIbucl/p/JPSyQXr0ZMP38LWVWtiBSkl6pDqDpPHLDQkJITo6Wphu1aDJwmTm5Uv1B7GmLyrzUNK7d+9QKpU0atSIs2fPcuTIEU6cOEH37t11JqUBfvnlF6xtbIg6o+6nS3p7l+DNo5CaawdasrzFEMtNCT+ygNhb+4m9dYDgP0aSOYhRJEZ/CmSyZBoUnywAVdlnIDSuHCqVShj+0GBlZUV0dDSjRo1iw4YNLFiwgFatWvHx40et9UxMTAgKCmL+/Pns2LGDmzdvAuqpeFDrLQ4dOpTVq1cTEBDAs2fPcHdXW19mLqurVCrs5cqvavcBIJYgtXQk6tw6Yq7tIvbuYUK2j1NP7mci8fVtEp6cw8jNm5R3d0gK+0CrVq1wdnZGpVLRvkUj5HnVzjlxD05+9v4CpEWofwhFX9tJ3L1jxN45TMiO8YAIVVqKMASUGqweglHER2Lk6oWxqxftWzSidu3a1K5d+6v9VRptzJiYGD58+ED16tWxsrKiWrVqFC1aVNA9FW6FWMzAgQOFYLJ///6cO3dOKK1rgtWIiAg8PDyYPHkya9asYeHChQCCxWZucHNTB/0GBgbCdWX9kzVr/U/B39+fAQMG4O7ujpGRETY2NrRp00bL8UljESsSiXR0RCHDwlKzzeTJk4X1s/7JrP2ZkJAgmEHIZDLc3d2ZN2+ezueQSCRi0KBBHDhwAE9PT2QyGR4eHsKA2+euxczCitH9u+sM6OWGD8t7ELp7Cknv7nF9Xk+mtlS7T/kMmKUlu6Npm8mcgKhevTqenp7cvXuXSpUqYWRkRIECBVi5cqXOcVJSUpg0aRKFChVCJpORL18+fvnlF0F2TsPWmwFanxnBW8eQ9OY2ithQ/Gc1xn9WY94vUice4u4dUy+b2+LT8g4EzGtJwOymKJLiiDq3jqB1Awn4vTUB89sQumsS87af0r0HHz7QvHlzTExMsLOzY9iwYTrnpeHmzZvUr18fCwsLjI2N8fHx0dLtBbVSyNChQ3F1dUUmk2FnZ0edOnUEt7JvRZ+h1PO38a2N5NWrVyc8PFyY9P1fcPVNeK6cJ7LDyK0M0Rf/IPbOQWzqD2JqUw+q/lRcyCQ0atRIa/2goCD2799Py5YtAXUm548//sDLywsHBwcgYyhp4MCBbNu2jTp16gj2jFu2bCElJQV7e3udIAzUJcgL589TqWEb4j+8IOndPczLtUBqak3E8YwaqcTIHNvWk4g6t5boS1sQy00w8aiB3LUUoTsnAmBg5QSoUCTGIBJnZEYSnqkHPMRGZig1mopJscjMLLVcOVasWKHVBwfqYHn37t0EBQUxbtw4fv75Z5YuXarjyNK6dWs2bdrEuHHjSElJEeRtdu/eDah7Afft28fy5csBdQA6Z84c4d+ZaVmnGqtCc/I8i7BrPZHIM6uIubEbkcQA4yIVMfNuzMf1g4W1lJ8ys4kvrqnFzYFVq1axatUqXFxceNesGYrnnwIvucln7y+AxEwd6Kd8eEbKh2eIDeQY2BXApGRtEh6exMhNPXWuyUomvriCedlmuNiZa5UGP5edzUyHDh2YM2cOb968YcmSJSQnJ6NUKgkICNAqZ0dGRmJtbU3Hjh0ZPXo0BQsWpHHjxqxYsULnC2769OncuHGDpUuXEh8fL/Rq1q1bNwf3Wxs7OzuqV6/OqlWrGDx4sNAvm5tr/FG5ffs2165do127duTNmxc/Pz9WrFhB9erVefbsmVa7B6gHkWxtbZk4caIwJJWVli1b6mRs7969y8KFCwU9VJVKRdOmTTl//jw9e/bEy8uLkydPMmrUKAIDA1mwQNs168qVK1pC+c+ePaNBA3Xf+MyZMxkzZozWtaSJDPl96SpSX1wl8cU15AW9sa7TDwNLh2zPOWzfDJTpKdj7TiH+0RkiPlVRAJJiQ0l6ox6ws6jcgcSX17i0cjwVYxTMG9oZ448PhN7otm3b0q9fP6E1IioqioYNG+Lr60v79u3ZtWsX/fv3x9DQkB49egDqDHjTpk25cuUKffr0oVixYjx+/JgFCxbw8uVLLavX8y9Ctb4bLCq1JWzvK7W7mkcNxAYyDB0KoVKkE31VLfIvMTIDsQTTUvVJDnhEiv9D0qODSXx5A+OilZFaOqBIiCL+wQnWjunKxDYVhdaRpKQkatWqRUBAAEOGDMHJyYnNmzcL5gCZOXfuHA0aNMDb25tJkyYhFovZsGEDNWvW5PLly5QrVw6Afv36sWfPHgYNGkTx4sWJiIjgypUr/Pnnn1pavblFH1Dq0ZOFrA3X34qhvRsmnrWIf3ACVUoCr61bEh4eTkJCAs2bN6dGjRpa6xcpUoSePXty+/Zt7O3tWb9+PSEhIWzYsEFYZ9SoUcybN4/ly5fj6OiIl5cXp06dQqVSkZycjJ+fH0uXLhWySlk9y0uUKMHQRTuY0bEKYmMrJKbWiAxkOPVbq9UDKs9bDMcuv+tck8sY9SCMgZ0rsbf2kx4TgplXA+IenCA1+DXxD9XT0PbtfkNqYIhCqeLDiu60bNWGwMulWX5bzpUrV9ixY4fQ26rB29ubO3fuCBnIWrVq8fjxY8zMzHjz5g0hISHY29szc+ZMNm/ejFKppHbt2sTFxVG4cGHhy/WPP/6gbNmyAHh6evL69Wv+/PNPVqxYQVJSEgYGBqSlpeHi4kI+W3NKp6TyKE7deyY1s8Gp1/Jsn0+DPPmwbzdNZ7nmngCYlqyNacnaSMQiKuS34ObcrsTExDBs2DDMzc2pWbMmBinqXkrLSu0wLVn7s/vSTHkbWDsjlplgXKwK6RGBxN0/hixvcaG9ICXoBYaORUgNfs3HjT9TsG5TVq9+RUBAAEePHqVy5cosXbqUbt26ceHCBS2P7cxYWlri4uIi/FALDAykSpUqDB06lDJlyuDs7MzUqVO5dOkSNWrUQKVSkS9fPvr06UPevHmpUqWK1v4KFCgg2GKCOstWoEABIZOcW5YtW0aVKlUoUaIEvXv3xs3NjZCQEK5fv86HDx+0psn/STRq1IjWrVtrLWvSpAkVK1Zk7969dO7cGRcXF1avXk2fPn2wtrbm7NmzXyxxlyxZUstCMzw8nPHjx1OiRAkmTVIPwx06dIhz584xbdo0QU5t4MCBtGnThkWLFjFo0CDBex4ydF7r1KlDly5dCAgIYPz48XTp0oUmTZpoXUt8fDxOhYqjSIrD9KdGxN87SmrgC0K2jsGxx2IkRtp9vSpFOkl+97Hy6aq13KJqR2JvH0aVHIuZd1MMHQtjUrQy5uWaE7SmH2FnN/BzWjphe6bg5eUFQOXKlZk2bZogrRYUFMTvv//O8OHDAbVVb/ny5Rk7diydO3fGwMCAbdu2cebMGS5evKj1Ovb09KRfv35cu3aNSpUqEZ+STkAWMXJZ3uKo0lMQGRpj22S4sDwt4r1gniCSGuLYcxliAxnRynRS/B9iaOuKU99VWqVvU4+aBK7px4pVa/htivp5Wr16NS9fvmTXrl20adMGULcEZdVTVqlU9OvXjxo1anD8+HGhGtO3b188PDyYMGGCoP969OhRevfuze+/Z3zGf07bOTfoS956/jY6d+5MUlKSTun37yZrw/VfwabhECyqdCTl40smjh1FcnIyBQsWzHbgoXDhwuzcuZNjx44xZswY0tLS2Llzp1bPmLGxMR8/fmTs2LGYmJgIWYSQkJBsh5Kyo0vlgtg0Go5ILCby5DLCD80lJSB32V6JkTkO3RZg4lGDpNe3iDy9kqTXtzAtWQf7DjNxtbMQXDm6d+nM04f3mDx5MkOHDuX27dv88ssvwiS2Bnt7dUCbkJDAxo0bOXfuHGFhYcTFxQm/nkGdae3ZsyeGhoacOXNGEDbXlHYzZ6pMTEywtLTk4MGDjBgxgvHjx+Pg4ICtrS2BgYF07tyZqqahSHNS9s4FUrGIWW1Kc/jwYby8vJg5cyZTpkyhcOHCrFm34es7yIR1nX4Y5MlHzOWtxD85i0nxati2+hWRSIQyJZHU0HfkaTQM+3bTkJjacOfwH/z888/s2LEDLy8vunfv/k3X4OzszJkzZ0hJSaFOnTqEh4fTtGlT8ufPz9q1a1EqlTx58oRq1apx7ty5HL32/grFixfnzp07NGrUiI0bNzJw4EBWrlyJWCxm4sSJX9/BD0rmftG0tDQiIiIoVKgQlpaWQglSJBIJg0y9e/fOVb+kQqGgffv2xMXFsX//fiFLf+zYMSQSCUOGDNFaf8SIEahUKo4fP661XDNgVaRIETp16sS4ceMwNzfHxsYGDw8PrWuZOnsBcSHvsWkyAssqHRDLTJAXLIsiLoIPizqQleT3T1GlJmFUsKz2vXErg9hQjsTUGqvavTH1rIFIaohYZoyJZ01SQ94QeXYNUltXGvZUZyiHDBnC2LFjWbVqFYmJiUilUi2DDENDQ/r27UtoaCh3794F1Ja+xYoVo2jRovj7+wuWvjVr1gQQ2jn8IxJ0HHA0VYmsZhiKhIw+chPPWlqmCAAiqYEQTKqUChRJsYgM5RhYO3PtVobc2bFjx3B0dNT60WFsbEyfPn209vfgwQNevXpFhw4diIiIEK4hISGBWrVqCfqvoP7xePPmTUHD93uhz1Dq+dv4ERvJv6Xh+kvDOiKxBMsq7bGs0h6A9K0DKFq0qM40ZuZ+qa+VBDMPJWmyPuPHj9fxRc7aB6WhsL0Z9Ro04pp7hb9U1pea5SFPw5+F/4tFUCqvJVt6ltcqua5Zsybb7TW9dJqhkSlTpuj0632O1atXa2krfg5Nf+XXcLodwJj0Wdk+lqfxsBztIzOawSOsiwmaoJnptPaGztBXVjTZTgB5fk9s6g/SWUcsM8Zl1AH1Onb5qVOr5henRDdu3CgMbGUl6/AbqNsQsn7p1K5dm4sXL9KiRQshmM9M9erVs33tZTcAl/VcslsnMxpf+38akydPZsqUKbx48YKpU6dy+PBhDA0N6devH2PHjmXs2LGsXr1aMDjQEBOjDkr8/PyEHwaaCfjg4GDGjh3L6dOnCQlR9yj27t2bNWvWaLUQtW/fnjNnzmBkZETp0qVxd3dn2LBh+Pv74+TkpNN7qpFWy6pCkT9/hkaixonJyspKS70gKSmJmTNn8vvMmQCE7syYyBeJxUgt7bNVM0h6cxuDPPl1lDIAVColUktHHVkxddsNpEcGYl23P5tuZFQ7BgwYwPTp0wkLC8PJyUmn1UUzOLZp0yYqVqxI/vz5CQgI+GzbxMSJE5k0aRJKlQqRsSVmpephUbkdMVd3CC5gqpR4/Gc1RmJuhzx/CRKenBW2j7mylWT/hzh0zPiMUamUxN0+RNz9o+oe00xyYRdPf6Bdu3bMnTsXf39/ChUqpHP9mr5wDRor3q5dtbO8mdHov86ZM4euXbuSL18+vL29adiwIV26dBF6lb8VfYZSz99G1kZygOXLl+Ph4YFMJsPJyYmBAwcK3sBZefbsGTVq1MDY2BhnZ2ehT06Dpkl7165dTJ8+nbx58yKXy6lVq5bg5pGVrA3XWVEp0oi+tIWPG34mYIEvAb+3InjLLyT7f11oSCIWEZ+iHlq5cuUK5cqVQy6X4+bmxh9//KGz/tu3b2nTpg3W1tYYGxtToUIFjh49+tXjZMXHx0enPDKjRQmkYhGBq/sSsjN7GZbcYiARs7hd6Rw7XXz8+BGRSCRoG/6dtCubn5F1i3x9xRygGTz6Epr7/z3RDH39L5k6dSrDhw/Hy8tLENLXk3Patm2LUqlk1qxZlC9fnmnTplGzZk0WL16Mu7s7vXr1ErJ95ubmWjapGjRZwFatWrF//366d+8uaM4mJCRoqVoMGTKE3bt3Y29vz4QJE5g1axZeXl46wzQ5QfPjf+PGjZiYmGBkZERQUBBv3rwR1hk8eDDTpk1DqVQiL1Aau7a/YdduGmIjc1CpMC5WDcgQ79eQ9PaO0BOcmZDt41DGhZP64Rmhe6bqSJxpMHQohCLTDxEnJyfy5s0r6Ph+jeDgYExMTBg4cCA9e/bk9OnT/Prrr7i5uVGjRg2USiV58+ZFJpOjSk0i5uoOIk+vxNi9Ela1egMgksqwaTwC69q9MStdH/OKGWodpt6NsaikLVsXc20XUefWIsvnibF7JUCELH8JxCZW2Ds6cvbsWapVqyb0kCekpPM0KIb7AVE8DYohOU27t1zzWpk7dy6nT5/O9o9GccHX15e3b9+yZMkSnJycmDt3Lh4eHjpZ6dyiz1Dq+WHQ/IqvXbs2/fv358WLF6xYsYLbt29z9epVLe26qKgo6tevT8uWLfH19WXPnj2MHj2aEiVKCI3iGmbNmoVYLGbkyJHExMQwZ84cOnbsmG32KmvDdVaUKYnEPzqFcbFqmHrVQ5maRPzD04TsnIhj1/kY2n/+F55CqSI5TcHr169p3bo1PXv2pGvXrqxfv14Qadd8mYSEhFCpUiUSExMZMmQINjY2bNq0iaZNm7Jnz55cuYJ07tyZ3r178+TJEzw91bqS+ayN6VIonQmRgTofdN+KkJX7CiEhIezZs4eVK1dSsWJFnaGDv4tBNQqTx1Qm6OXlJnsrEYuQikVag0dfIp+1MVOaejBm3+O/cspa5PT+/xWyy2LqyTnlypVj1apVAPTp0wdXV1du3rwp9A+D2vHLyclJZ2gtM9HR0Vy7do25c+cycuRINm7cyIYNG9i2bZuQnbx79y5LlizBysqKd+/eaZXWNf12Z86cIS4uTitL+fz5c4BsW5EqVaqEr68vBQoUICgoiCFDhnD58mVWrFhB//792bNnD63a+LJn1051n2+B0qjSUwVnKamZDaBWJBB/8qNPiw4mPeIDRnUHCMcRGcgwKVEbef4SRJ1bjyo9hWT/hwRvHoVj90VIzW0F/VUAiak16VHqQcTAKHWw6ujoyPPnz4mOjhbklTRo1DM0g2KaAHnJkiVCJrBy5cpMnToVDw8PSpUqxZ07d7h87QY1faoiL1Se+PsnMC/TFOPCFYg6uwYkBph6ZvTFq9LTiL2+CwB5Pk+MCpTWupeJL64iy18Sy8rtCVzZC8tqnbGo5MuHZV1xK1CAFcuXUbp0acxtnQh4+AyPSScECTqA2JuHAdh49R3OxbyFfldzc/McWRM7OjoyYMAABgwYQGhoKD/99BPTp0/X+f7MDfoMpZ4fgrCwMGbOnEndunU5efIkgwYNYsmSJSxatIjbt2+zZcsWrfWDgoKYPn06CxcupH///hw/fhwHB4dsJUmSk5O5du0aQ4cOZdKkScycOZNbt27pTIln13CdFbHcFOf+67Cu1Quz0g2xKN8Kxy7zEBuZEnv38FevM12h4sWLF+zevZvp06czcOBATpw4gaGhodbwzaxZswgJCeHYsWNMmzaNYcOGceXKFVxcXBg+fHi2mYvP0aZNG+Ryuc49DL13BkO5McZFKuV4X58jJ1k5DX/++SejRo2iUKFCny2//l20K5ufM8N8qOSm/uL7mqSQ5vFKbjacGeaTK+u0/++sqJ6/n169egn/lkgklCmjzsoVLpzh42xpaYmVldUXS/9GRkYYGhpy4cIFHcF8UEtnNW/eHFAPM2XV9RSJRDRs2BCFQoG5ublgvwmwYMECRCIR0dHRiEQi4XMyPDycwoULs2jRIkFtwtDQEAsLC8aNG0dSUhISiYR7D9Ri/8rkBMIOzeX9grZCOTfprbpnMbPGatLrWyAxIPzYIvznNufD8h6khr7Fpt5ATEvUQmQgQ5WWjCotBWVSLDHXdqJMSSThyTnEJmqFg6hz64X9nXgcxJQpU3j8+DFxcXGkp6fj6ekptJ6kpqayatUqbG1theC7devWBAUFabXnGBkZ8ezZM549e0anTp2IiYmhRPGiSI1MkZhYACoSnmeS48lGN/ZLiD7pySa+uPYpe1uFuIenUMRFkJSSypQzgYgsHIlLUZAaG0HCi4xjKdOSiXugzjKf+jOEOgsvsehBOi4F3Jg3b162mdmwsDBA3VOraaXQYGdnh5OT02eliHKKPkOp54fgzJkzpKamMnToUC3R5d69ezNu3DiOHj2qNVxgamqqZfdmaGhIuXLlsvWD7t69u5Z7QtWqVQF1SVmTsYPsG66zopbIUZd+VColyuQEUCmRORQmNfjNlzf+RMEiRYVzAPUQibu7u9a5Hzt2jHLlymlNHJqamtKnTx/Gjh3Ls2fPtM79S1hYWNCsWTO2b9/OzJkzEYlEKBQKdu7cSZtWLWjcruz/S1ZOQ/Xq1QWryB+RfNbGbO5ZPsNz+GUoARHZeA7bGFOjiB2dKuT/ZiHi/8+sqJ6/n8x9iKB+b0okEqEsXbx4ca5fv054ePgX+8tlMhmzZ89mxIgR2NvbC32VYWFhuLq6MmXKFMFTPSgoSOvHZMGCBalYsSJNmjTBx8dH0LUcOHAgp06d4uDBgwwdOpRz587h4eEhfM68f/8eiURC9+7dcXBw4OnTpyxevBhjY2MSEhK4e/cujRs3Ftp34h+eRCwzVRs+fJIWU336IZzZBCL2xl5QpCFzKITcpSUpQS+Ivb6btPD32LWaAIDU2pn0yEBEBkYkvrhGSuBzFAnRmJaoTfyjU8J+AU4e2MnWB2eF7KOhoSEfP35k8uTJvHjxgp07d/LgwQNWr14t3OOOHTsSGBhIv379OH/+PJUrVyYpKUmQHvvll1+0FAvkKhUSszykhbwFD3VWUqVIJfLsGmSORRAZyBEbagfxWTEqVI6Yq9tRxEcBKoJWZQzZ3L11g7u36gNgYOuC1MqRiCPzSQ1+jcTEioSn5xFJ1X34yk+fGdf9olBU7oPf7kl4eHjQvXt3nJ2dCQwM5Pz585ibm3P48GHi4uLImzcvrVu3plSpUpiamnLmzBlu376tNfX9LegDSj0/BJoG8KyNxoaGhri5uek0iOfNm1enSdnKyopHj3R7GbN+iGtcarL+sk9Nz1nWL/7xWWJv7Sct4gMo04XlUgvdhvLssHfUtYfL2tzu7+9P+fK6wxWZG+ZzGlACdOnShZ07d3L58mWqVavGmTNnBL/vemXzU7lgnq/67GrQPF7JzeaH9Nn9XhS2N2NyUw8m40FCSjp+EQmkpisxlIpxtTHJca/o12inv///GbILEuVyOb6+vmzdupXk5GQqV65MqVKlePDgwRf3NXToUJo0acKBAweE6kbt2rW5cOGCkI0CBH1GDV27dqVixYqIxWKOHDlCmTJleP78uSB0PXfuXDp27CjYc2ooUaKEjiD41q1bBYOCyMhIFi1axM0793jx7Amq1GQMnIthXbsPIZ/0VVWftGklpuq+6ZTA5yjiI5Dl88C2xVgAzH5qhMTYkthb+4TedAMrJ9IjA1Ep01ElJSE2NidPs19QKdLUAWV6RmYt8t0T6tVvwJPHj9TmBfb2bNq0icGDBzNq1Cjs7e1ZunQpvXv3FiokJiYmHDhwgAULFvDHH3+wf/9+VCqV4Mj1+++/4+npiUgkok3btqQplUhMrUjPZGFrYOdGwqMzxN0+iMTcjjyNhn7x+bOo6IsqLVlwyDLIkx9Tr/rEP1RL+1jVUmezxQbqKffI06uIu3sYkVSGiUd1jNy8Cd01SdifQqlCkteTPB3mYvX6iKD/6uDgQPny5YVJd2NjYwYMGMCpU6fYt28fSqWSQoUKsXz5cvr37//Fc/4a+oBSzz+Sz/16z65MlNN1DaVf7wCJf3KeiKMLMCpcAfPyLdVagWIJsdd3kRaVvRdzVrJa4X3ufL4n9erVw97eni1btlCtWjW2bNmCg4OD0Gvz/5mV+ydiIpPi4fS/k8XR3///Bs+DYzFJlAg/SjSsX79ea73q1atTpEgRnZaQDRs2CGVyUGcbR4wYwYgRI3j16hVeXl78/vvvbNmyhcaNG9OmTRv2798vlL+zYmpqyuzZs2nevDmnTp2iVi21T/3SpUtRKpW0bavur8762ZScnEx8fDy3b98WsqO2trZYWlrSskULZj57gtTaGfu2U4EMn/v3nySDlElxiGXGxN1XB1OWVbto7d+8XAtib+0TxMw1SORmiGRGOPdWB7apYf7C/uQuJXEZc4TgrWN48PABwUFBQknb29uba9euZXsPNBgYGPDLL78IeoyWlpZ4eHhw//59OnbsiJ2dHcnJycTFxOBSVEZQjCGKTMNFJh7VsSjfMuMefQqG8zQfg0lRbY1WUMsGWdXsidjEkujzG7BrOR6ptTPmZZpme352rXWHJzPr1mowtHcj2n4Iy34vkW3lwtDQkDlz5ugMsX4P9AGlnh8CTQP4ixcvtKQLUlNTeffuXY6ajP8qrjYmiNAxE9Qi8cVVpJYO2LYcr5Uhjbm8NcfHMTb8ulSSi4sLL1680Fn+pYb5LyGRSOjQoQMbN25k9uzZHDhwIFs9u/+vrJye7NHf/38Xmh8IG66+A6D9mhuCYL0ISHoSTKpCyauQOB1v6i+RmJio411dsGBBzMzMhD64unXrYmZmxsyZM6lfv77WuiqVSvj80tj07dy5Uwgod+7ciZeXlyCvA+pBlmXLlrFjxw5BNFyDsbEx3t7eQMYP8/TID6R8fIXMUd0fmhbxAWWSto97yke11I3MWbsyhViMWGbC/7F3ltFRnV0bvmYiE3dBQhIIECC4BSdooFiw4K5FCgWKS5DiDoVCcQgUp1AsUIJ7cQ2WBALE3Ue+H9M5ZDITw0rfb661WG3OPMdH9tnP3vctjVffjyw5BvMybYS/De1dQKyHNDYMhVymlGmr35OYfcrMnap7+969e2pC7/khq6yd6pquWrUKmUxGVWdrwoKfq03dfywmpesQd3YrsRd3YtdmnIY9rDwtUUMIPj9MP/yQOm52X3UGQ/ftpOOboGnTphgaGrJy5UpatGghfKg2btxIfHy8hk3hl8BUoo+zjQkhuTTmfHA1UKD8SVA6laSHPUHPIm/rN309Ub68o7/77juWL1/OlStXqF27NqCUBFm/fj2urq4f5TbSq1cvli1bxpAhQ0hKSlKrQdXGl87K6cgd3fX/7/I6JkWthCE+NVNjjAJlI6BUpqDZ8vPUL2mX7xKGoKAgmjRpgq+vL+XKlUNfX5+DBw8SHh4uuGNZWFiwbNkyBg4cSI0aNejevTvW1tbcvXuXlJQUQc9TIpHg4+PDwYMHWbNmDeHh4Vy6dIm5c+eq7bNFixYEBwdTr149OnbsSHJyMidOnCAiIgJvb2+1OnVQlgBF7J2JhWd7RGJ9Em4cAj0DkH24FrKESECk1r0M8H77OOSZacogNCOVjMhgAESGJljW8VUbK5aYIk9NIGL3NEzKNiAzMgSpNJMGDRrw+PFjoqOjqVq1Kr/++qtaU1RetG7dWqg9XbNmDe/fv+f06dPY2tpiKtHHVJpImlXxfG8vJwysC2PVoBdx57YSHh+BcelaiA2NkcaFkxJ0BbPKLdQyn/lFKlcw+eD9XHVpPze6gFLHN4G9vT2TJk1i5syZtGjRgrZt2/L06VPWrFlDjRo18gx+PheN3B3Yfi0kxxo245I1SAm6TOT+nzEuWQNp3HuSbh/HwK4Y8ow0reuo0BOLMDLIn5D7xIkT2bVrFy1btuSHH37AxsaGrVu38urVK/bv36/WuJRfqlSpQvny5QVXiE/xbNWhQ4d2fr8RKjRZAflutLr8Mpqmy84xs61HnmOLFStGt27d+Ouvv9i+fTv6+vqUKVOGPXv20LFjR2HcgAEDcHBwYP78+cyePRsDAwPKlCnDjz+qi/V36dKFrVu38tdff/H48WMUCoUw3Q3KevNXr17h5ubGs2fPuHr1KqamppQvX56IiAit2T+7DlNIuLyb+Mu7QaHAqFh55FYZpIcqLTIzIkNQZCq/M6UxbzGwKyasa+xWg8Sbh5XSQLJM5NJ00NPHpGw99P7p7AalLrA8LQmJc0VkqUnEnFqHnokl48ZPZM4sP5o1a0ZERARGRkb4+fkxcOBApFIpcrlcIwDOzooVK0hISBBqK728vDh9+jTe3t6kpKQQE/GOVq18uZPn3coby9qdMbApSsKNQ8RfVAql61nYYVy8CialPi4glMkVXHgexfOIxK9WHqMLKHV8M/j5+WFvb8/q1av58ccfsbGxYfDgwcydO1dNg/JL0sPTmS1XgnN83bRCU2TJsSTePkHqq1sY2jlj22YcKU8ukhaau6agTK7ALJ9Tlo6Ojly+fJkJEyawatUq0tLSqFixIkeOHPmkbG3v3r0ZP368IISsQ4eOz8fqwGcsDgjSWJ6Tm5Zd6x/hHycm2T9d/hMP3GfcjN8Y0eiDlFB2ByFbW1tWr16dr2Nq06aN4LWdE02bNsXGxobdu3fz+PFjatasKdRGwoc69F69egle4KD0/r548aLWbdarWJpbhSerBdQR+2YL/5/64gZiY3PkqYmE75kO/9gP6pvbCU07jr4zMXKpSMLNw8SeXk/G2w/XNuHaAWIDlbWZ+hb22HWfi0Iu483yLiCXYmBgwJYtWyhevDgVK1YkLi4ONzc3goOD+fvvv6lcuTK1atWiY8eONG/enJSUFMqXL8/06dNp27YtVlZWHDx4kMKFC/P+/XsmT57MunXrSE5O5tChQwBMGtCZ14bFmGF3TFBpSH1xk/ir+8gIf4HI0PifLnYpph5egPK9YOxWnYg900kPe4JIIcPAsRSWDXtTqOfH1TUm3TtF0oNAMqNCkKcnY2BVGPNqbbCq3oodV0Pxy+Mh5cmTJ0yfPp3AwECSkpJwdnamU6dO/PzzzwU6Dl1AqeNfQ+UAkLVJZfjw4QwfPjzX9XISV85ewF4QCzgVpRzNqV/SLkdbPJFIhGVtXyxrq0+7mJSsoTE2K3piEXVK2LI9NETr69rOqUSJEuzduzfX7Wo7Fz8/P7XuzKwYGhoiEono0UO7VaQOHf81RCIRM2bMEN7zW7ZsoV+/frx69UrNgvBL8/uNUK3BpDberOmPob0LDp1naH19cUAQ9maSryYHZWBgQEZGBtu3b0cmk7F48WLOnj1Lo0aNCAwMxMvLiwYNGrBw4UIyMzMpWrQoAQEBvHr1KsdtTm1VFt9tD3PM0OpbOmLTbCixgVuQJURiYF8cSbHypIc9If3NI/StCmPkosx8GjkpA6LMyGAiD8zFqHhlEm7905CSxUNbFvkKeYZSd3jhwoWCXu+9e/ewsLBg8ODBSCQSbGxsePjwIXXr1qVo0aJMnDgRU1NT9uzZg4+PD/v37xfMI7p06cKKFSto1aoVTk5OeHh4cO7cOUQiEStXrmT37t2CSsPxA78TfWwFBvbOWNbqjNjIlIzwF6S+/FsIKDNC7/F+93TsXMsybuo0tl8NJez6ccJ3TaZQjwVIimSrJ80HibePYWDnosxmisWkPr9OTMAaUMgJtPbFj5wDynv37lG/fn0MDAwEsf0XL15w5MgRXUCp47/Dt2S9l5W57SvQdNm5T/K5zs7XsMXLC4VCwcaNG2nYsKGGlJIOHTo+ntcxKcw4/PCzbvNrN1WYmpqSlJSESCTC19dXw552586d+Pj4sGTJEiQSCc2bN+f48eMUKVJE6/aKWufuBmVaVqnFa+RWg8Qbf5B8/zQpTy+jZ2aNgWMJpNFhKKSZiPQNMHAsjsjQBANbJ9JePyDl5U2QyzAqUY20l3+jkCnl21JCHiASiUhJSWHevHmkpipliiQSCY8fP1Y71qZNm+Ls7MyNGzeQSJSajsOGDaNevXpMmDBBCCgrV64srPPy5UuSkpKYOnUqiYmJrF69mvj4eIrZWLK6UxmKjt5I0dIVKN53IW8SpEKDp0KhQAQUszHm4bZ11G/QkHN/nWLrlRAUyQ8pVKYp7zYMI+78Dhy7fsji5hfH7vMRG0iEvy2qtSF893QSbhwitFprktOlOTb0jRw5EoVCwa1bt9R+F+bPn691fG7oAkodX51v1XpPxX/VFi8nkpOTOXz4MIGBgdy/f58//vjjXzkOHTq+Br169aJr165CkPA1mHzwvlAz+bn42k0VISEhiMViobwoe0BZtGhRHB0diYyMJDg4WFie2wxJVzuISkoXMrcmpWuT+lzd8lZPYoJVvW5Y1euGPD0FhSyT1Fe3iD6yhMzo1xg6lkAkEiNxKos8LYlio3aSERXKuw3DsGrQi/evbmNWsSl6YhFmsc8oVqECf/+tdOQJDg6mePHidO/eXS2YjImJ4cyZM8yaNYvExEQ1m0tvb29mzJhBWFgYRYt+0AzetGkTnTt3Fv4+ePAgK1asICQkhIoVK3Lq1CmSkxLZsWAmPj7eWlUagh7dp+r4EAbOm8WCP67zS+AHMwwj10okPQhEoZBnaf7MH1mDSXlaMgq5FCPn8sS9uoUsLZng6GStDX6RkZGcP3+eUaNGaSQZVI2xWRUB8kIXUOr46qis92rWrKlmdfUt0bWGs9oX4afwb9viRUZG0r17d6ysrJg8eTJt22rXOdOh43+BrHIvefE5pJmehSdy4XnUxxxqrnztpoovFYBndYNK0RInZUSGEHd+u1JzUi5Te02eRefRyMmDuIs7kWemExuoFHKXFCqJoUNx0l8/xKpUNeJfP6RtlmYiFVnrQVUlEQDTpk1j2jRNfUeAiIgItYAyL4OMFy+UwaHKcEKbSsOzZ0qZpD59+mjdp+qc9YzMcnxdG2lvHhF/wZ/0t09QZKrbJ8rTk3M07VC5s+VmkhEZGYmDg0O+jkMXUOr46nzr1nufG0Weho5fltxqRnXo+BL4+fkxc+ZMnj59yqxZszhy5AiGhoYMHTqUWbNm8ebNG0aMGEFgYCAmJib89NNPjB07Vlg/IyODOXPmcPToUZ4/f45UKqVq1arMmjWLRo0a5bpvbTWUf/zxB+vXr+f27dtERUdjZuOARcVmUNlHsAWEf8TjbUxo5O5AD0/nHLUhsx7fwydBZGRmYujohlX9nkLdX35Ie/2Q2DMbyIgIRt/MBst63TGr0OTDgPQkeg8ZSeLzv3n16hVisZi6desyf/58KlWqJAxT1TuqmmrWr19PQkIC3t7ebNy4ESMjIyZMmMDOnTtJSUmhc+fO/Prrr2pBpKurK15eXhq16Cq8vLwEz29VxsrFxUXIVkZERDBp0iT+/PNP4uPjcXd3Z8yYMfTp00dwg2o35BiRKG0Z9SwdiL+yB1l8BOjpo29dBHlqAnZtfyLj/XPizm4h6V4AKOQYuVRE4lQO5FKlu07CB31KiVM50t48ZEhFCWOjotRsbVVk9zJXMW7cOLy9vbW+VrJkSbW/C2KmAfDjjz9y7tw5goODSUtLw8XFBQ8PZS2jXZMBiO21Sw6JDYy0Ls+JzNh3hO+agoGtE9aNB6JvYQd6BqS+uEHijT9AociXaQfA5cuXCQgIYPTo0VhZWQEIdaj5QRdQ6vgkRCIRw4cPz3fH4X+FghTY50VeBfaqH8AbN26ouWBow8vLC8i5MUmHjm+JLl26ULZsWebPn8/Ro0eZM2cONjY2rFu3jsaNG7NgwQL8/f0ZN24cNWrUoEGDBgAkJCSwYcMGunXrxqBBg0hMTGTjxo14e3tz/fp1tbq2/LBlyxbEhsYUrteJ9AQZ6SH3CAnYjEVcPNaN+wvjFEBITArbr4Ww5UpwjtqQWY8vwaUBUXHxJN09Rfju6RTusxRDxxLkRWbsOyIPzcOsYnPMyjch6d4poo8ux7BQSaVgN5Ae847bF04xelBvihcvTnh4OOvWraNhw4Y8evRIo35x3rx5GBsbM3HiRJ4/f86qVaswMDBALBYTGxuLn58fV69eFbqfp0+fnu9rOGXKFOLj43nz5g3Lli0DlE47AKmpqXh5efH8+XNGjBhB8eLF2bt3L3379iUuLo5Ro0ZRzMaE7p4u3N4Hhm9ukfgsFaNi5UmOP4NIX4IsPgKxiQXGrpWRxildx5IfnEHf0lEZUBYpDXr6pL95iCwtGavGAwCQFCtP2oNTSCIeAwjvofxgYGDw2Uwz3NzcAHjw4AElS5bkxo0b1K9fn379+mFkZMTt27fZuHEjAAoDI4xdK3+W/aY+vw6yTBw6TkPf8kMmUeXUA6i5MmVFZSLy4MEDQBlQzpw5k759+woBpb193vrKKnQBpY480fbU8r/M/0KBvQ4d3wI1a9Zk3bp1AEIH6dixY5k3bx4TJkwAoFu3bhQpUoRNmzYJwYC1tTXBwcFqWoGDBg2iTJkyrFq1Svhhzi/txyzg54AXSOUKzOUKzKt8h+jEahJvH8WqQS9E+uqyZKqGvKzakF2zPBCqji9DIeaA30ksAfNK3oT9NpSEv49g992oPI9JGvMGxx7zMSqmnG40KVufN7/0Jfn+aQz/CZYM7V1xHPgrU/1aClPxvXr1okyZMmzcuFFjulYqlXLu3DmhDjIyMpLff/+dFi1acOyY0uZw2LBhPH/+nE2bNhUooGzWrBlFixYlNjZWQxd4/fr1PH78mB07dggKEkOHDqVhw4ZMnTqV/v37Y27+IdsrTYrm7YsXnA48h2/HM1jX6kjMuW0opBkoZJmCv3VWRPqGSAqXJvnReeSJUZgWr4xEX8zkoZ0YcmgeK1euxM3NjcKFC+frfDw9PVm3bh0jR47UWCcyMrJAgRRoOhNdvHhRKKlIz5RRp5UvRjaFWfbzdOIu7caknBdiQ/XMqSwlXnBSyjda6i3lackk3z8NQFFr4xzLOOzt7WnQoAGbNm1izJgxaq+paifzWz4CUHB1ZB3/71A9tcTFxf3bh/JV+JIF9p9KQEAAAQEBn+GIdOj48mR1JtHT06N69eooFAoGDBggLLeyssLd3V2o51KNVQWTcrmcmJgYpFIp1atX59atWwU6htWBz5h+7BnpUjkyuQJ5egqylHgkxTxQZKaTGf06x3VlcgXpUjkTD9xndeAzjeMLiU5GrpAjS01EIZchKVSKjPcvctxeVgzsnIVgEkDPxBIDm6Jk/pOdA6XfMyIxwdHJyGQyoqOjMTMzw93dXet16N27t5pmr6enJwqFgv79+6uN8/T05PXr10il0jyPs1GjRgQFBdGzZ09OnjzJ69evmTZtGgqFgtevX9OuXTvGjh2LWCzm3bt3H87PwIBKlSqRlJTEnj171LZZrVo1bGxsMNQTYW1tjdHzMwAoMlJ5v20cClmGMDb+0i5C5rcmZH5rFAoF0pg3oGdA2MaRnP6xIYO9q+Lu7k5QUBAGBgb4+/vj7u6OkZFRrvqbs2fPRqFQUKFCBSZNmsQPP/yAq6sr+vr6FCpUiFatWvHwYf4TCypnouvXr+NSpiLFWw7Cud0oarXuRoPWnWm16iLbHyq7zmXJcbzdMIy4C/4k3jlB3AV/3vtPJProcmF7CmkGcRf8CVs3mJBF7XmzqhcRB34mM/bDNZZnpJERrny/ha3tz+uVPQjf48fbzaMQ/xOY1i5ui0gkYsSIERw6dIjy5csjkUjw8PDgxIkTrFy5EoVCQZkyZfjpp58AZc2pWCxGJBKpNWDlhS6g1KEjC6oC+88pGQTqBfafgqGhYZ4ODzp0fG68vLxyLdzPieyNDJaWlhgZGWFnZ4erqyt9+/YVlr958waRSCSUc2zdupWKFStiZGSEra0t9vb2HD16lPj4+HzvX1W6khEZQsT+OYQu8+X1Ml/erOxB9JElgHrjR24sDghi941Q4e+tW7fi06QOoYs68GZFN96s7EHqixso0pN57z+RtxuG5bo9bVatYiMz5GlJwt8KhZyE64do5lkBfX197OzssLe35969e1qvg7brDUpnnezL5XJ5vq9lly5dkMvllClTBolEwpw5c1i+fLmQtbS1tcXMzIyffvqJ8+fPC+upMn9hYWFq21Nl/ywtLfnzzz9xLuakfEGWSfW6DSndeogw1rh0bWxbj8Wu9Vicayinp12cleejmvFR1U0mJiYyevRoevbsyaxZs4SGmffvPwTpKkqVKsXNmzdp1aoVa9euZdWqVbx//54yZcrQvXt3Hj16RL169YiKyl/D1euYFM5SHvuO04iX6RNyejsxgZtJfxuEYRF3Ul/dIu7CdkSGxjh2nY2kUCkSbx0l5tQ6ku7/hZ6pNeY1fABQyGVE7J1J/KVdGBYqiU3jAZhXb4siPZnMSKWWsUKhIHL/LJLv/4WRaxXEZjbIUxNIe3kTfXMbLKormy99qijLIi5evMiwYcPo2rUrCxcuJC0tjY4dO+Lk5MTVq1epXbu28DBib29Pu3bt2L59e4EytbqAUkeu+Pn5qT21iEQirU8t2p58snP79m1atmyJhYUFZmZmNGnShKtXr6qN2bJlCyKRiIsXL/LDDz9gb2+PlZUVQ4YMISMjg7i4OHr37o21tTXW1taMHz9eoyh68eLF1KlTB1tbW4yNjalWrRr79u3TOJ5Tp05Rr149rKyshKf+/iPG5MtrOztprx8QeXAeb9b0I2SRD29+6UvM6d+QZ+m40xOL2HE1NMdtpKSkMGTIEGxtbbGwsKB3797CF6IKLy8voY4SlM0B06dPp1q1alhaWmJqakr9+vUJDAxUWy84OBiRSMTixYtZv349bm5uSCQSatSowY0bNzSO5cmTJ/j6+mJvb4+xsTHu7u5MmTJFbUx+7qeO/99omy7LT3PDjh076Nu3L25ubmzcuJETJ05w6tQpGjdunO8mgbdxqcw4/BB5WhLhOyeRGfEKq3o9sO80HYeuc7Dy6vvPjvPfdDD98ENex6QIx+fsWhzb737AwXcmDl3nYORSMd8NcDlKw2RZP/7yHmLPbKBilWqAMuN76tQpPDw8tF6HnK5tQRtKslOzZk127tyJi4sL9vb2ODk5MXbsWPr168eaNWuwtrbGw8MDY2NjNm3alON2OnXqBHwIdAHq1KnDlStXAGWW79L+jTzcMpXgN8pMXK/v6nNu3TSC983n2UFlNq1vb3WnL5VaSFhYGCdOnGDatGmMHz+ey5cvY2RkpJb9zkqJEiX45ZdfEIlEDBo0iLS0NB48eMD27du5evUqCoWCoKAgFAqFRn27yjjDy8uL32+E0nTZOS6/jMaklCeFei7Cedx+CnWZTWb4C2ID1hKxezoowKHjNIycK2DfYTLFRu3E5aeDOA3bhL3PBIxdlY1WyQ/OkBZyF+vGA7FvNx7zaq2xrN0Zx25zMf7HijH12TXSQu5h1aAnjl1nU2zENlwmHMHEvS7pbx5j4lKBnhuu4lVd+SD4+PFjLly4wNSpUxk1ahQHDx4kJSWFXbt24eHhwV9//SX4t1+/fp1Dhw7Rs2dPTE21119qQ1dDqSNXOnToQFBQELt27WLZsmXY2dkB6oW6Fy9e5MCBAwwbNgxzc3NWrlxJx44dCQ0NxdbWFoCHDx9Sv359LCwsGD9+PAYGBqxbt07oHPT0VNdaGzlyJIUKFWLmzJlcvXqV9evXY2VlxeXLl3F2dmbu3LkcO3aMRYsWUb58eXr37i2su2LFCtq2bUuPHj3IyMjg999/p3Pnzvz555+CbeHDhw9p3bo1FStWZNasWUgkEp4/f85v+wOwKulT4OuU8uQSCmk65lVaIja2IONtEIl/H0GWGIV9+0mAMksZGBSRo2vBiBEjsLKyws/Pj6dPn7J27VpCQkI4e/ZsjjpgBW1e2LlzJ4mJiQwZMgSRSMTChQvp0KEDL1++FJ5O8+OcUND7qUNHQdi3bx8lSpTgwIEDau/9rLZ/ebHo5BOkcglpofeRpyZg334yRs4fsqzSOM2sVV6oSlcSjyiP74+DB6kwM0DQcYi/4F/gbeZGytNLGDlXZOmiRXgcOYS7uztNmzYlLi5O+C7+GqhKF1QJherVq/PmzRuhdMHFxYV79+5plC6opsCzyu/kF1XdX2FLY60aitqoXbs21apVE/52dnamXbt2HDlyBJlMpjWwPnXqFHFxcXTr1k0tG6mnp4enp6fGw3l2crLbBGVZg0PXOSgy0kgPe0xa8B3k//iX50bK08uIjS0wr645Za/6PKS+vAkiMebV1MdY1GxPytNLpL38m7lzugnLmzZtKjQOAVSsWBELC4scg+2PQRdQ6siVihUrUrVqVXbt2oWPj49WK7PHjx/z6NEj4c3aqFEjKlWqxK5duxgxYgQAU6dOJTMzk4sXLwqdZb1798bd3Z3x48cLchQqHB0dOXbsGCKRSCgiX7RoEUOGDGHt2rXAhyL/TZs2qQWUQUFBajIRI0aMoGrVqixdulQIKE+dOkVGRgbHjx8XvpiT0qXs0/f6KJEfK6++auKyVG6BvnVh4s5tQxofIXTfhUan5OhaYGhoyF9//SUEdi4uLowfP54jR47kqB1Z0OaF0NBQnj17Jmioubu7065dO06ePEnr1q2B/DknFPR+6tBREFQ//FlFla9du8aVK1fy7fJ0IzgWfSvHLE0LHz7ZOTV+5IWqdMVdqtyWiaEezjYmhMSkkP72KelhT7ROZX8sIpEYiYEYkyzfF3v37iUsLExD1uZTyS1bqbrmpqamxMfHq5UuAHz33XcEBARgbW0t1GVKpVJOn1Y2hhTkAVMlORUZGSksy0vSSEWpUqU0lpUuXZqUlBQiIyMpVKiQxusqbcjGjRtr3aaFhUWO+8tLDUQsMRG6uU1K1yL54Vki98+hcN/luSoBZMa9QyGT8m7TSIoMXKN1jDQ+Aj1zW8QS9UZPA1tlOYCnvUytCVTb58ba2lpjFuxT0E156/hk8nrykclkBAQE4OPjIwQfoKyv6d69OxcvXiQhIUFtmwMGDFDLTKiKy7MW86uK/LM/YWUNJmNjY4mPj6d+/fpqReyqbvU//vhDmDoKiU7+aMVINaeCjDRl0X/RsoCCjPAPx6cAgqOTtW5j8ODBagX133//Pfr6+kJ3pjYK2rzQpUsXIZiED7VHqmuock7o379/js4JH3M/dXy7qOrOXF1dkUgkODg40KxZM63vn0ePHtGoUSNMTEwoWrQoCxcu1BiTnp4uZHWKFClCsWLFGD9+POnp6Rpjc6J169a8fPmS9u3bM2nSJEqUKEGtWrUAZT3cpUuX1MbPnDmTgwcPqi0Ti0X//IDPRmRoTNSfy0i4fpCE64d4v20c5PFpj792gPfbx/F6eTdCF3fg3eZRJD+5iJ5YhFGJGsLxWQSfI/7cViJ2T8fATr1WMf39c95vH0fo4g68WTuAxNvqn2WFLJO48zt4t3kUoct8SX/zkIzwl4Lki0mpmsS/uCOUHR06dIihQ4cKn7vBgwdjaGioVre4Y8cOqlWrhrGxMcOHDxeuWVZUgdndu3dp0KABJiYmuQYWqgC/WrVqxMXFcePGDRQKBUeOHBGOo2zZsjx58oR3796xevVqmjZtKrjtqOSFVBRE2/BLozqW7du3c+rUKY1/OTmLfYwaiIl7HQCSH5/PY+SnU6aQeiD8qWUP+UEXUP7LJKdLefg2ntuhsTx8G09yet5dd98aeT35REZGkpKSgru7pul92bJlkcvlvH6t3mlZkOLy7F+Ef/75J7Vq1cLIyAgbGxvs7e1Zu3atWgF6ly5dqFu3LgMHDsTR0ZGuXbvy56H9KApQT5UVaXwEUX8u4/Xyrrxe2ok3K3sQvnMioHQqyEpOrgXZn67NzMwoXLhwnl12BWleyMvtIb/OCQW9nzq+XYYOHcratWvp2LEja9asYdy4cRgbG/P48WO1cbGxsbRo0YJKlSqxZMkSypQpw4QJEzh+/LgwRi6X07ZtWy5fvgwos9o+Pj4sW7aMLlocTHKib9++zJ07l2vXrjF//nwiIiIYMGAA1atXRy6X07hxY65fvy6Mt7CwwN9ffbpZLleQ/Ogs+laFcfCdhZ6ZNXHnd5Bw/QBGrpWxbtQv12NIvHkYA0c3rOr3wKpBbxDrEXVoPolB14ktVoe5c+dy9+5dAjbOJ+XlLWzbjMOw0IfPsDwtiYg9fhg6lsTKqx/65rbEnFxD0t0PKg3y9BSS7gUgca6AtVdf9CwdUcilhO+eTkb4Syxq+TLg+5FCwBgWFsbRo0dxcnLiyZMnbNu2jYMHDwpyS/v376d3796UKlWKpUuX0rx5c0AZ8GlT6ejSpQuVK1dm+fLlGBnlLag9bNgwunfvzrNnz0hPT2fkyJGA8iH+7NmzODo6EhcXx9ixY4mJieGHH34A0Nh3frJiWRMKT58+zZermirbmJWgoCBMTExybC5RJUMcHBxo2rSpxr+sNetZ+Rg1EIU0ExTyPBvBDKwKo5Cm5xrs6Vs6IEuM1thWP48PM1wFJb8Wizke0yetreOjeBaeiP+1UAKfRhAak6L2nJxfp4ZviS/x5FOQ4vKs+7lw4QJt27alQYMGrFmzhsKFC2NgYMDmzZvZuXOnMM7Y2Jjz588TGBjI0aNHOXHiBLt378bIpSIOXWYjEudfe0shlxG+exry1EQsanXCwMYJkaERssRooo8uUyuyB/LtWpAfVM0BPj4+/PTTTzg4OKCnp8e8efMEK7CsfI2nVB3/LY4ePcqgQYNYsmSJsGz8+PEa496+fcu2bdvo1UvZEDFgwABcXFzYuHEjLVu2BJQ1uqdPn+bcuXPUq1dPWLd8+fIMHTqUS5cuaZ22PHv2rOD2AsoftokTJ7J582YqV67M8ePHP9SOpabi4eHB1KlTCQgIQKFQMHnyZJYuXUp8fDyduvVk5lN7pCnxpL66jWVtX4ycylK49xKN/bpM/DPH61Jk8Dq1mQfzaq15t2UUiTcO8rpkDX7w+4lJk5T10b02XlM2ZJSsAcB7/4nIkmKwbjwAi5rtletXacG7rWOJPbcNp+FbEOnpo5DLKPr9RkR6Bv+M+Q55WhJhvw0l6dYR2o2czYYBKwkeP4bixYszfPhwqlevTuHChblx4waHDx8Wgsbg4GDc3NyYM2cOkydPBpSzHA8ePKBKlSqsWbNGWO7q6kpISAg///wzQ4YoO6oHDx6sdv5eXl7MmDGDmTNnCstMTU3x9/fHwMCAffv2qT3sOjg44O7ujq2trSCU/fDhQ1auXMn58+epXLkyrq6uSKVS6tatq3G9+/Tpo2bYYGKinK6Ni4vLty3klStXuHXrFlWrVgXg9evX/PHHH7Ro0SLH7z5vb28sLCyYO3cujRo1UpslAu2alHnZbcrTkhAZGCHSUw+xku6eBJSWkblh4l6H1Bc3kKdpqoKoykCMS1Qn6c4JEm/9iWVtXwCKWhnz9PR2RCKR8JksCKoGnI+VCNRlKL8ir2NS6LXxGs2Wn2f7tRBCsgWToO7U0Gz5eXptvMbrmH/XpvBTn1rs7e0xMTHh6dOnGq89efIEsViskXn8WPbv34+RkREnT56kf//+tGzZMkcnBLFYTJMmTVi6dCmPHj1ixszZpIXcIy20YHqRmZEhSGPCsG48AMtanTApXQtj18romdlojBWRs2tB9qfrpKQk3r17p7VuVUXW5oVevXrh7e1N06ZNSUvLu/BbG9mdE7TxNe+nji+PlZUV165d4+3bt7mOMzMzUxO0NjQ0pGbNmmolJ3v37qVs2bKUKVOGqKgo4Z+qPi2vBoes3Llzh2fPntG9e3eio6OFbSUnJ9OkSRPOnz8vTFf27t2b9PR09u3bJ5SuJD8+D3IZpuVzt2rMiazBpCwtCXl6MhInDzLev9AoXZnbvgL62dUhxHqYVf7woy7SM8C8SgvkKXFkvFdOBYvEekIwqdCiZzm3fQW1TWZkZAgNhseOHROCSYADBw4gl8vx9fVVu/aFChWiVKlSGtdeIpEIntZfCg8PD2rVqsWkSZPw8/Nj5cqV1K9fX6izvH//PjVq1MDIyIj9+/eTmPghgDI2NqZcuXLs3r0bW1tbGjZsKHwvpaYq9RwrVKiAmZmZUOdYsmRJvL29mT17NgsXLqRGjRqkpaVRp04dfv75Z5ycnBg0aBCAEAxbWFiwdu1aLly4gJ2dHVZWVujr62NhYYGDg4NWr2//a6G5qoGkhd4nbN0g3u+YQNjaAYQu7kjIgjbEBm5C36pQnu9J0/KNEUlMkSfHEb7HT6lFubAdIYt8lEkKwLhUTSTOFYk7t53oYyuJ2OvHjRmt2b17N6ampqxbt06jzCS7HmVISAiHDh0SVFlUDU2+vr6CKoivr2+ux5oVXUD5lcgqKwDkqXOY3anh9xs5y818aT71qUVPT4/mzZvzxx9/qD3RhoeHs3PnTurVq5dr4XNB9yUSiZDJZMKy4OBgDh06pDYuJiZGY92a1ZVPtQppZsF2qkX+Q6FQkHjzsMZyZ1uTHF0L1q9fT2bmh32vXbsWqVSa65Nm1uYFFarmhY8hq3NCaKj6e061j695P3V8eRYuXMiDBw8oVqwYNWvWxM/PT2vnp5OTk8bDZfai/mfPnvHw4UPs7e3V/pUuXRpQ+j3nF9UDVp8+fTS2t2HDBtLT04WyjjJlylCjRg38/f2FkpLkh2cxLOKOgXWRHPeRGynPr/Nu21ilqPTyrrxZ2YOk28eEKcaspSvFbEyY2VZdvUHPzAaxofo0sr61sttZGh8uLEu6/xdvN47Q0LO01MvUcNaaN28ehw4dYt++fRpTsc+ePUOhUFCqVCmN6/X48WONa1+0aNGvomnr7+9PnTp1mD9/vpAFVDX4jRs3joiICPz8/ChVqhRxcXFqtbAbNmygaNGixMTEcP78eUH+TfWea926NUuXLhVqTENDQ5kxYwbbt29n+vTpwvfQrl27OHjwIOPGjRMaM0ePHi3sp3v37rRr1w5DQ0PS09MRi5Xf6VFRUUIdaFYCn0bk+htuYO+KkXMF0sMeI02IRCGXIjaxQmxmgzTuPamv7uR6zURiPQzsXBAZGJH26hbSuHeI9CWIJaYkPzhD6oubiERiHDpNw7x6W5IeBpL64ibGRoZ07NiRPn365FhmklWP0traGplMRseOHYmOjqZGjRrMnj1bqLn96aefCvS7r5vy/grkJiuQFzK5AplcwcQD94lKSmdEI80uti+N6qllypQpdO3aFQMDA9q0aVMgfao5c+YIuo/Dhg1DX19feILSVtj/sbRq1YqlS5fSokULunfvTkREBL/88gslS5bk3r0P3qazZs3i/PnztGrVChcXFyIiIlizZg0Wto6YOnsUqDnHwNYJfavCxJ7ZiCwxGpHEmJSnl9UEikGpQ9motEMOW1FmH5o0aYKvry9Pnz5lzZo11KtXL8cOb1B+oR44cID27dvTqlUrXr16xa+//kq5cuVISkrKcb3cWLlyJfXq1aNq1aoMHjyY4sWLExwczNGjR7lz5w7w9e6nji+Pr68v9evX5+DBgwQEBLBo0SIWLFjAgQMH1B5m8lMuIZfLqVChAkuXLtU6tiCZa1X2cdGiRTl6d2dt9ujduzejRo0iJuIdmbHvyHj7FJtmQ/O9v6ykvX5A5L7ZSIp5YNv8e+Vsg54+SfdOkfJIqWCQvXSlaw1nopLSC/Rdn/QgkOijyzAuVQsLzw5K2z2xHlZPj5Ieo5kx9vb25sSJEyxcuBAvLy+1uke5XI5IJOL48eNa71X2xpiszYs54efnh5+fn8byLVu25Fi6kJ0SJUpw6tQpjeU+Pj6cOHGCCxcu4OzszMSJE3n8+DEVKnzIytauXZubN28KXd6qY1m1ahW//vqrEPgBTJ8+HalUSmxsLEFBQcLxNGrUiLS0NO7cuYOhoSGjR49m5cqVjBo1igcPHgj14v7+/hrXZP78+UyePJnQ0FCh/jwpXUpoHrOGBtaFsWs9Bhvv4WqZboVMKpRNqMojckIkFqPITMO29RjMyjf+Z/1Mwtb0J+leAMZu1REbGmNYqCTIZTSf8Csn538QhK9QoQJDhw7l8uXLwmf0l19+UVNlmTp1Kvfu3VNTZZk6dSpSqZSZM2fSrVs3tVKxvNAFlF+YvGQFCsLigCDszSR0qaHZBPMlUT21/Prrr5w4cQK5XM6rV68KFFB6eHhw4cIFJk2axLx585DL5Xh6erJjx47PqlnYuHFjNm7cyPz58xk9ejTFixdnwYIFBAcHqwWUbdu2JTg4mE2bNhEVFYWdnR0NGzak38ifGHQ496m/7Ij09HHoNJ2Y0+uIv7oXkZ4BJqVrK2uuNo0UxsnkCnrWyvnerV69Gn9/f6ZPn05mZibdunVj5cqVuZYc9O3bl/fv37Nu3TpOnjxJuXLl2LFjB3v37tX65Z4fKlWqxNWrV5k2bRpr164lLS0NFxcXtamPr3U/dXwdChcuzLBhwxg2bBgRERFUrVqVn3/+ucB1WG5ubty9e5cmTZp8cqmMqlnCwsIix7KVrHTt2pUxY8Zw9dQRUh7eB7E+JmXrf9S+U55eRqRviGOX2Wo+30n3lIFRTqUrIxqVws5MQv+dImRJMcgz0tSylNJYpWOMvqXjP/u5hL5VIew7TEFfT4y+WMSsth6sHH2MMI2tQ61atRg6dCitW7emc+fOHDx4EH195c+4m5sbCoWC4sWLCxnhb5HkdCkvIhI4ceIkjbxbYev4IYNctmxZvL29c1W2ANRqKmUymZBFs7a21qpO0K9fP7VsbFZ1C1VAmTWYTE5OJjU1lTp16qBQKLh9+7YQUBZEDSR72QRyGRInD+GhJC9EhsaYenyYHhfpGWBYuDTSuA8Z7pQnFzG0K8bCAS3VdDSzlpnUqVNHWF4QPcqhQwv2QPY/HVDu3LmTiIgItdT21+RjZAXyYvrhh9Rxs9OYCvnSTJ06lalTp2osz6mZQ1tncpUqVThx4gRv375l/fr1+Pj4aGQe+vbtK9ixZaUgT8r9+/fX8K5VbUNF48aNc9Qdq/8og8svowtkv2hgVwzHrnM0lquK/vXEIuqUsKWkg2aTVdZzbtCgAevWrctxP9mDRJFIxKRJk4TmABWqaR0Vrq6uOd4rbcs9PDw4cOBAjscBH+6njv8uMpmMpKQkNecSBwcHihQpUiCZHxW+vr4cO3aM3377TaPJIzU1Fblcnu8H0WrVquHm5sbixYvp3r27RoYte7OEnZ0dLVu2ZM/vO0l/G4NxiarKjN/HIBKDCBQKGSKUAaU0LpzUZ0onqNxKV7rWcGZFMSuuhspIunMc61odkMkVSu3L2ycQm1gqs0pkdctRUKeELXPbV+Dts/u56m02bdpUMGvo1asX/v7+iMViOnTowKRJk5g5cyY7duxQC+gVCgUxMTGC0cTXJnsjqjQplrS0VC5HGlDe76RaI6q7u3ueAaVcLmfFihWsWbOGV69eCSVO0dHRH6VuAcrp8unTp3P48GGNLvSs28xJpUMbKc+vE395t1I6Tpa1lCp/D1t65rYaD2ZiIzMyIoOFv6Wxb8mMfk3l0tq7urOXOhREj7J48eL5Ok4V//MB5YMHD/61gPJjZAXyQuXUsH3AfzcL9PbtW2bOnImrq2uOU1n/JnPbV6DpsnOf1c9bXyzSKLDXoePfJjExEScnJzp16kSlSpUwMzPj9OnT3LhxQ63rO7/06tWLPXv2MHToUAIDA6lbty4ymYwnT56wZ88eTp48qWFhlxNisZgNGzbQsmVLPDw86NevH0WLFiUsLIzAwEAsLCwEHUQVvXv3Fuz9HOr11LbZfGHsVoPEG4eI2D0DU4+GyJLjSbx1FH2rwmRGBudaugIg0Rcrg/J7h7EzTiXawJY3N/8iM+IlNi1GINLTRwQ4Va5L0J7LlLrzK/VLtGPNokP5Klnx8fFh8+bN9O7dGwsLC9atWyd0eE+aNIng4GB8fHwwNzfn1atXHDx4kMGDBzNu3DiNbSWnSwmOTiZDKsdQX4yrrWmOwXJBeR2TwuSD97nwPAo9sUjrd2rWRtQtV4IxfvAuz+3OnTuXadOm0b9/f2bPno2NjQ3e3t7Y2NgUyJJS9TAtk8lo1qwZMTExTJgwgTJlymBqakpYWBh9+/ZV22Z+VTryUzaRF/mx57QxMcChSP7LTAqi9JGfsois/E8HlF8CuVxORkZGnppdeckKfCwqp4bnEYlas106Ph1Vgf3EAwXr9s6NWW09vnpWWYeOvDAxMWHYsGEEBAQIXcIlS5ZkzZo1fP/99wXenlgs5tChQyxbtkzQSDQxMaFEiRKMGjWqwFOxXl5eXLlyhdmzZ7N69WqSkpIoVKgQnp6egtxNVtq0aaN0a5HJMSpZs8DHr8LYtRK2LX8g/uo+Yk7/hr6VI9ZefZHGhxMfGZxr6YoKa2trtm7dysiRIwm7fQBHB0d6/byIjj0HCIGbieF3zK9szbp16/jhh78KVLLSs2dPEhMTGTZsGBYWFixatIiJEydSunRpli1bJsj9FCtWjObNm6vVYqdmyIhITKfhosAvJl33+41QZhx+KCRVsgaTYhMLRPoSMmM/lBepXg95+VxYv2sO5V379u2jUaNGak5gCoUCJyenjzrW+/fvExQUxNatW9Vc17TVfrramiIiL1n8vMsmPgWRSPnQMqutB9vulflsZSafyjfT5e3n54dIJOLJkyf4+vpiYWGBra0to0aN0iqBktUNwMbGhq5du6qJKXt5eXH06FFCQkIE/9Gs8ivp6enMmDGDkiVLIpFIcnRzULXZ+/v74+HhgUQiydc0X1ZZgYyIV0T9uYywtQMIWdSe16t6EnV0ObLU/LmJKKSZxF3wJ+zXQYQs8uHNL33pM3RUjse6d+9eypUrh7GxMbVr1+b+fWVgtG7dOkqWLImRkRFeXl4a09JeXl6UL1+ee/fu0bBhQ0xMTChZsqTQWafyaDY2Nsbd3V2w1cpKWFgY/fv3x9HREYlEgoeHB5s2bRJeP3v2LDVqKIuR+/XrJ9ybvCy1vjZdazgzrvnnqUP6qbn7V6971aEjPxgaGrJw4ULu3LlDQkICSUlJ3LlzRyOYPHv2rFYpqS1btmh8jxgYGDB+/HgePHhAWloaMTEx3Lx5U63rFpRlMVk/915eXigUCo3u5cqVK7N//36ioqJIS0sjODiY3bt3ay1ZEYvF6Ovr07lTRxqUKZKrtEtemFVqTtEh63H56SBFB/2KWcWm2DbsSc8NV/N8mFddr2rVqnH58mVSU1MJCQlmzuRxVHG2xqOIJaYSfaFkJTg4mLS0NG7dukWrVq00rquqZCV7hvH7779HoVCwaNEiYVmHDh24cOECSUlJJCUl8fjxY1avXk3p0qUF6brwhhMx77nii0nXrQ58xsQD90mXyrVmJUViPYyKVyH12VWk8R+mZDOjXpP6UlkDOfHAfVYHagqVgzLLlj2jprKk/Bi0qWUoFApWrFihMdZUoo9zfpIDWcomVGQtm/io4/zn/SzRF3P6x4Z0qeGMr68vYWFhWoXfU1NTSU7W7sz2JfjmMpS+vr64uroyb948rl69ysqVK4mNjWXbtm3CmJ9//plp06bh6+vLwIEDiYyMZNWqVTRo0IDbt29jZWXFlClTiI+P582bNyxbptRtUtXgqNwcLl68KFhG3b9/n2XLlhEUFKQhMXPmzBn27NnDiBEjsLOzy1UXUEVWWYG0V7eRxr3HtGJT9EytyYwKJenOSTKjQinUe0muTxUKhZyI/bNIf/MIs0otMLArRmZEMFf+2EaXLjEax3rhwgUOHz4sWG7NmzeP1q1bM378eNasWcOwYcOIjY1l4cKF9O/fnzNnzqitHxsbS+vWrenatSudO3dm7dq1dO3aFX9/f0aPHs3QoUPp3r07ixYtolOnTrx+/Rpzc+WXa3h4OLVq1RICW3t7e44fP86AAQNISEhg9OjRlC1bllmzZjF9+nQGDx4sFEdnLRr+VlAV2KuesgsyBa4nFgkF9rpgUoeOr8OhQ4eIjIykd+/elKjw5UtXsvpOq3ytvwRbtmyhX79+3LhxI98lA9nJLWOojezSdTPbeuSYMcy+n/w0olrV78H7V7d47z8B86qtQC4j4e8jGNg5k/lPjaCqEfX9+/dqFoitW7dm1qxZ9OvXjzp16nD//n38/f3VrGDzw6ZNm5g4cSLh4eFYWloybtw4nj59yrFjx3j48KGgl5mdRu4ObL8Wkus1zKtsoiCIUNbtNirtwL0XjtxKfC7MeH3OMpNP5ZsLKIsXLy68cYYPH46FhYVgB1axYkVCQkKYMWOGmhsAKJ/KsroBNGvWjKJFixIbG6smxgt5uzlcvnxZLcB5+vQp9+/fp1y5cvk6h+yyAmZVW2Hh2UFtjKSIO1GHF5H+5iFGxXK2uUt+eI604Ls4dp+HUbEPOmeG9i788ccvWo/1yZMnQtBrbW3NkCFDmDNnDkFBQULwJ5PJmDdvHsHBwWoB8tu3b9m5cyfdunUDoFmzZpQpU4bu3btz+fJloYNX1Y23f/9+oaFkypQpyGQy7t+/LxR/Dx06lG7duuHn58eQIUNwdHSkZcuWTJ8+ndq1a2vcm2+NrjWcqetml2cdkArV66oCe900tw4dX55r165x7949Zs+eTZUqVWjYsCHAf650Zc2aNZiYmGhtTPwUvpZ0XUEaUQ0diuPgO4vYMxuIu7ADfXM7rOr1QJYUQ3yWgGv64YcaWdTJkyeTnJzMzp072b17N1WrVuXo0aNMnDixQOd28eJFZsyYgZubG0ZGRqxbt45FixYhlUrx9PSkbdu2TJkyRWO9Hp7ObLkSrLnBLORVNpEbR4bXpf85S2Kjpfwxsp5aXWvfA+pOPp+7zORT+OYCSlVmTcXIkSNZs2YNx44do2LFihpuACqyugFkDTS1kd3NQUVObfYNGzbMdzAJmrICajpU0gzkGalIipQBIOP9i1wDypQnFzGwdcLA1glZyodOM4lLRa3H2qRJE7UAURUAduzYUQgmsy5/+fKl2ngzMzO6du0q/O3u7o6VlRVFixZVk4PJuj4opwf279+Pr68vCoVC7bp6e3vz+++/c+vWLa2WW986xWxM2D7A80OnYlAEodFa6o7+eYLsWctZV9+qQ8dXZO3atezYsYPKlSurTaN/jDZkTuRWuhIaGoq9vT2bN28uUDCYPcO5Zs0a7OzsPmtAmVPGMO6CP/GXduVqP5mdvKTr8tOIKo0LJ+zXAVg16o+lZwcK912uMcaqfo8P4+UKrFzKYW+QISyTSCQsXryYxYsXq62Xve5UVUahImum19PTE319fWbMmCG83qlTJwoVKkTTpk3ZsWOH8py0xBOlHM2pX9IuTzUQs0rNMavUXGN51vPLikoNpIKTFTcuX9Q6Rlt5mKrMRJtlalbyq8qSk6pKXnxzAWWpUupPP25ubojFYuGEs7oBaCO7D6c2nj17xuPHj3M0i8/eZl/Q1vnssgKy1ETiL+4k+fEF5Clxaq/lZRKvkgR4s1L7GzAvSQCVHEj2Ti/V8uxSAdrcMCwtLfNcPzIykri4ONavX8/69evzdaz/NUo5muPX1gM/PL5oZ6QOHToKRk5C2/D/u3TldUwK0/94gDwzXS2x8SnkJF33JRtR41MzsRTlX64nP0RERGhNFEVERGBlZZXn+jo1EE2++V/A7MFNQd0AtFFQN4eCts5nlxWIOjSf9LAnWHh2wNChOCJDY1DIidgzAxS5f0gUCgUG9q5YNxmo8drP7cpTv0oZtWU5SQLkVyrgY9dXySr07NmTPn36aB1bsWJFrcv/i5hK9PEo8pEadzp06PiqfOnSlWLFipGamoqBgQEhISE0adIEIyMj/vrrLxwdHdm8eTPbt2/nwYMHxMfH4+bmxsiRI9W24erqSkhICPDhd69hw4ZqWbf09HTGjBnD9u3bSUlJoXnz5qxfv14tOeLq6kr58uUZOXIk3YeMJvbNC6y8+pISdAV5WhJFBqzWOP6w9UPQt3TAsctstQyiWGJCwtV9SBOjMLQvjo339+gVddcqXadqRM3pusZfO0Bq0GUyIpXNs4k3/kDf0gHTMvW0js+KSCQiLjWTR48eMXz4cK5du4a1tTWjRo3SyMpFREQwadIk/vzzT+Lj43F3d2fMmDFqv0uqxtBXr14J13rz5s2Ct/kvv/zCL7/8Aih/41SZzQsXLrBnzx527dpFZmYm1Ru34lnJzigy04k5tY7U59cBMKvsjZVXP7X4RXX+mdFhKKTpGNgWw6J2Z7Xzn9XWA2dbU4YPH07Tpk2ZOnUqz549o2TJkixZsoQWLVrkea3+Tb65gPLZs2dqGcHnz58jl8uFadmCuAHk1OzyOd0ctJFVVkCWlkRayF0s6/XAql43YUxmTP660QysC5ER8Qojl0pqxyoCurX3/mayYvb29pibmyOTyfJ0tfi3pQ106NDx/48vWboiEokwMjLixYsXNG7cGBsbG06dOiU06qxduxYPDw/atm2Lvr4+R44cYdiwYWpORMuXL2fkyJGYmZkJdXuOjo5q+xk5ciTW1tbMmDGD4OBgli9fzogRI9i9e7fauKdPn9Kla1cUZZpiXaYJBjZFERkaEXN8FRmRwRjauwpj098FIY0Jw7KOuu9zyqNzyDNSMKvcAkQiEq7uJ/LAXIoO3aBVui4vf+vEm4cxLuWJUfGqxF/cCSIRUYfmI+o0I08bQoVCQXx8LC1atKBDhw74+vqyb98+JkyYQIUKFYTrmJqaipeXF8+fP2fEiBEUL16cvXv30rdvX+Li4oSZtZkzZ7Jq1SqcnJwYO3YsoDRq2L59O7169aJZs2Zq8kFZr3+hQoWYOXMmV69eZfv27TTtasqFi5fQs7DHqmEfUl/cIOHaAQzsXDCr0ETj/E3LeaGQSUl+fF7t/FUlFV1R1nYeOHCAYcOGYW5uzsqVK+nYsSOhoaH/mjh9fvhmZINUqJ4KVKxatQpAeMN06NABPT09Zs6cqZFdUygUREdHC3+bmppqVc3/0m32WWUFsjohZCXh5uF8bcukTH1kidEk3T2pttzZ1gSxPPOrSgLkhp6eHh07dmT//v1a5UUiIyOF/1c5ZRTEdF6HDh06Pgeq0pVz4xrxwM+boyPr4Z12npD5rTnYozjF7mxkWa861PYowbRp01AoFLx+/Zp27dphYWFBoUKFNETfQ0NDEYlEVK9eHUdHR86cOYOdnZ0gg/fq1St+//131q5dy7t37zh58iTe3t5cuXIFUH4XHjp0iLdv3/Ls2TP++usvOnToQLNmzdT2ExwczP379xk7dizHjh2jYcOG7N+/X+N37vnz52RIrEj8+wixgZtIuH4AQzsXRPqGJD88qzY2+UEgIgMjTEqrK21IEyIp1GcZlrU6YenZEdvvRiFLjCL11S3kiZF06zsId3d3pXSfrS03N05TswTMTpHB67Bt/j1m5ZVBlnnV1ohNrYk8OJfQxR147z9RzQFGRWrwXTLCX5KREE1UVBSvXr3Cy8uL48ePU6hQIUGL8vbt21StWpXHjx8jFou5e/cuNWrU4K+//qJ27dpMnTpVkCD87rvvMDU1pWjRorRq1YqVK1fi7e0tZC5Lly5Nz549NZpGHR0dOXbsGMOGDWPbtm3Url2bv3ZvoH7Nqjh1mIBV9VbYd5yKnrmdht6k6vzNq7XGoqYPhXouxMDehaSbh1jQoQLDG5UUxj5+/JgLFy4wdepURo0axcGDB0lJSWHXrl05Xt9vgW8jvZWFV69e0bZtW1q0aMGVK1fYsWMH3bt3p1KlSgAFcgOoVq0au3fvZsyYMdSoUQMzMzPatGnzVdrsVbICSEyQFCtPwrX9KOQy9M1sSX11C2l8zh+8rJiWb0TKkwvEnPiFtJB7SJzKIlYoSNaPw2lep68qCZAX8+fPJzAwEE9PTwYNGkS5cuWIiYnh1q1bnD59mpiYGEB5D62srPj1118xNzfH1NQUT0/PAteq6tChQ8enoCpdKWypLGvq16sHZcuWZf78+Rw9epQ5c+ZgY2PDunXraNy4MQsWLMDf359x48YJwQdAUJCy6cXOzo7Tp09jYWHBvXv3qF+/PgYGBgwePBhXV1devHjBoUOH+PHHH2nYsCEnTyoTBb6+vhQvXhxHR0dhitXBwYEFCxaoHW+lSpVo166dkOUMCAgAICQkRCgpio5T6hsrTG2xquiNIjOdxNvHidgzA4lLJZIfncOq4Yfp3+QnFzApXUvNcxzApGx99Iw+lJBJ/lEZkca9J1WaweM7Nxk7qAdOTk7cuP+Ujb+t4/3OSRQZtAaxgabxR/YazuQHf6HITEMEWNTqTOLNw4TvmkKRAavRM1VaI6YG3yFizwxEYj3Q06ffsNHs2fobdevW5datW9SsWZOXL1/y8OFD6tevj1QqFTK869evx8vLi3PnzvHDDz/QrVs3njx5onYMGRkZNG7cmJiYGM6dO6fmca2NAQMGqM2weXp6cuXKFeZNGoVjiXJCSYWkcCnS3z/P+fzTk5DLZBT3qMb722c06nML4rf9LfHNBZS7d+9m+vTpTJw4EX19fUaMGKEm2grk2w1g2LBh3Llzh82bN7Ns2TJcXFxo06bNV2mzzyorYNd2HDGn1pH0958oAOPiVXDwnUnYas2UenZEIjH2HaaScOMQyQ/OkBJ0BbGBhMLupb66JEBeODo6cv36dWbNmsWBAwdYs2YNtra2eHh4qH0xGhgYsHXrViZNmsTQoUORSqVs3rxZF1Dq0KHjX6VmzZqsW7cOQAgCx44dy7x585gwYQIA3bp1o0iRImzatEkoxRowYAAAY8eOFcTbR44ciUKh4NatW7x+/ZoZM2Zw5coVUlJSNBpCq1SpwsaNGylfvjx2dnbUrl2bjRs3agSU8+fPFxQ2RowYQc2aNblx44bQHLnk2B2S4uPQM7PB0XemsJ5ZhSaErR8KchmyhEjSX3+Q9pEnx2Hq0UjjWuhbqB+jKriUpyVhVskbszL1GD9ZWXZVPTSWI3GFeb99HClPL2NWXlN0XvC1fv8CgMxolRGJCKt63TAuUZX328YSf3UfNk0GARB7ZhNiI3P0rQohT0/Cu+dwhvTuSpUqVZgxYwbW1tbcu3ePqVOnkpmZSdGiRXFycmLixIn06dMHd3d3xo8fz8qVKwHUZjBlMhlXrlzBwcGB8+fP4+Ki3Qs7K7k1vTpmKanofP03HoTcUXPUUZ1/ZsRLFNIPvt7aSsAK4rf9LfHNBZT29vbs3bs3z3EdOnSgQ4cOuY4xNTXF399f62uf2mafF1llBTC3w6GDppZVfuUaRHr6ymmHWp0EWQFtXt7ajlXlsJCd7HIKoCm5oCK7pEBu+3NwcGD16tWsXq1Z+J2Vtm3bqgX/OnTo+LyoJGk+9jssP7i6uuLl5ZWn05WqqeHVq1f5Mob4nIhEImbMmJEvGZSBAz80P27fvp03b94AHwJGACsrK9zd3dUk11q2bMn+/fu5cuUKQ4cOJTIykvPnzzNq1CgyMzNp0qQJZcqUYenSpRQrVgxDQ0OOHTsmmG4MHTpU7Tjq16/PwYMHSUhIUHMXUjVHxsfHk5mZSaVKlbhx4waJiYn8fiOUBRv2Agr0LOzVZOYQiZEUKY009h1iUyuSHwYqvaUBPVNrjFwra7lwOftIiw0kKIDg6GRK25uQkhCHvnVhxBJTZcCYLaDM6mttVb8ncee2IHGuiJ6ZteBrLSnijmERd1Jf/A1NBiFNiiEz4iUWnh1Jf/sUkUjMD7vvUL+kHfW9GnPs2DFatWqFXC4nICAAHx8f7t69K+yzcOHCdO/end9++03DF/3Nmze8f/8eiUTC+fPnKVq0qNZT7du3L2fPnhXeO/lpWi3laE7lYlY81xfzwM+b4Ohkrl6+xKAFs6lbrz79Zo+ncOHCGBgYsHnzZnbu3Jnr9tQv/Zf7LH8OvrmA8n8JnayADh06vlX+q9JXX0r8G7RnhgwNDTVccCwtLdWyXZMmTWL//v1s374db29vYbqyfPnyHDlyhPT0dA4fPqy2/cDAQI39qrJV1tbKKd9mzZoRHBwslAu1bNmSpKQkDTvi0PAYVlx7KHhjZ7x9ql1qzsAIA5siavV9Rm7VlVPK2cgIf0nUkcWkv32KNPYdkix6yfLMdBKu7KXJ1sFEvH+nFujI0zXr+rP6WsuSYog7twWjYh5qXt4ABtZFSH6i1F+U/WPJaGBTlPS3T4Uxl19GE5NkRlxUFJmZmcjlclJSUnB3dycuLo579+4hl8sRi8WULVsWuVwu1KqqGlp69eqFXC6nVq1aOQaTnwNVScVvFwMwMjLi9KkAJJIPU9+bN2/+Yvv+N/j2vz3+wxSzMfnPOTXo0KHjfxehw/lpBKExWjqcbUxo5O5AD09nSjnm3eH89OlTxOKv29tZUPHv1NRU9PXz91OnLTOU0/llDaJUgWD16tXp06cPc+fO1dhm1vHx8fFqwYRqjKmpqVqzYrFixfD19eXq1avs27eP2NhYjI2N2bZtG4ULF2bdunXs27ePnddCkNrbofhHvg2RCBQK7NpPQiwxFbaX9uo2CdcPgEIBegYgk2NcqpbW80t/+wR5SjyGhUohT01Uey321DqS7p/GZ9D3tG7mhaWlJWP33uXhjtnKbWdHi6+1PC3po3ytZVm0RJ+Gqx/Xd999R0BAALt37xbc3kDpjmdmZkaZMkqZvQ4dOrBt27YcZ99U/Pbbb8jl8k9uhtHT00MkEiGTfTj/4OBgDevk/zrfXJf3/xpdazgzrvnnqXPMzalBhw4dOnLidUwKvTZeo9ny82y/FkJItmASlLVeITEpbL8WQrPl5+m18RqvY3I3XpBIJPkyk/g3MTIyyndA+akMGTKE5s2bM3XqVAAePHhA8+bNMTQ0pE2bNvzyyy8sWLCAatWq4eDgoLZucHAw165d4+7duxw+rFQBUSgUjBs3TrjGixcvJikpibCwMJo2bSpku56+T0QmVwjNnmJjZW2fUbHyGLtWFv5Z1OpEsdG7MbB3AZmyji8jTL1RRYVp2QYU+3E3hbrPFabHVSQ/vYRZ+cb8snI5nTp1olmzZrRt3hh5WpLWbRm71UCRmU7YL31JeqTMzCbeOYG+VWG1cZmxb9G3VEol6Vkqr482ib3MmDeIjS14Ep5CqlSBiYkJT58+ZfDgwZQtW5a+ffsybtw49u3bByg7wOfMmYORkbJZaOTIkUgkEl68eMG0adO0HjMoS+OyZhQ/llatWpGSkkKLFi349ddfmTVrFp6enpQsWTLHdbZs2YJIJMoz6P2W+GYCSj8/PxQKhcbUwv8CIxqVYn6HCkj0xeiJC6bBqCcWIdEXa8gK6NChQ4eKixcvUqNGDYyMjHBzcxMaS0Bpvdd02Tkuv4wm6d4pwnZM4vXKHoQs8uHtb9+TeOuY2rbe7fYjbO0ALr+Mpumyc/x+I1R4rXbt2mqqEq6urhqZwocPH9K4cWOMjY1xcnJizpw5gvFBXrx//55+/frh5OSERCKhcOHCtGvXTvhRdXV15eHDh5w7dw6RSIRIJMLLyyvXbYpEIo36ydu3b9OyZUssLCwwMzNj27ZtuW5jzJgx2NvbY2pqSvv27cnIyFB7vXXr1gC8ePGC8PBwMjOVwdratWsxNjZm3759iEQixo0bx9q1axk8eDCjRo3Suq+KFSty/PhxQOlRDh+ypKpmnri4OOLj4zlx4oTyHMUiZKmJpD69DOTiwKaQI5aYYKqlaSY7YiOzLLJ36ohEYkwlemolEpn3joFCTkb4S6Rx4STcPMLbDcMIXdyRqINzQSRGnplG/MXf/zkWBUYulYT1098+JePtU4xcKxN3wZ9w/4mAiITrB8iIDEGhCoAjg0l7eQuA5MfniH4fhr6+AQcOHCA8PJyzZ8/So0cPNm/ezMWLF9HX12fYsGGsW7eOwYMHA0pZJVVwOWfOHAwMDPDy8tII3vr27atW8/v27VtEIhGLFy9m/fr1QrNPs2bNuHHjhsZ1OnPmDPXr16dNmzYYGxtz69YtfvjhB3bt2sWCBQto3759jtf/9OnTAJQpUwZra2vevXun0eUdFhZG//79cXR0RCKR4OHhwaZNmzS2lZaWhp+fH6VLl8bIyIjChQvToUMHXrx4IYxJTk5m7NixFCtWDIlEgru7O4sXLy5Q3aZuyvsr8aWdGr41/qv1WTp0/Ne4f/8+zZs3x97eHj8/P6RSKTNmzBBEsbOW3CTePoaBnQsmpTxBLCb1+XViAtaAQo55NWVQZFK2PtF/LiUl7CmSwqWZeOA+UUnptClhyNWrVzVUN7Ly/v17GjVqhFQqZeLEiZiamrJ+/fp8u4117NiRhw8fMnLkSFxdXYmIiODUqVOEhobi6uqaL/HvvFBJzFhYWDB+/HgMDAyYP38+AH///Tfe3t5q4zMyMrh7966amLiNjQ0ODg6C57GTkxOg1FEeMWIE/fr145dffuHx48dUrFiRYcOGMWLECIKDgzl69KjQDBoSEiIolaj48ccfAejXrx9nz57F2NiYkJAQdu/ezffffw9AVFQU1apVw8nJiejoaAwLuxN3YQd6ZtZYNx1E9J9KF7iEG4fQt3REmhBJ6vMbGDmVxab594jEOX8X61s55tgwqlouS4gg4tZpRo8eTbly5bhy5QqnT59G38iEzMhXJPx9hMQbhzBxr4t59bYopBnEnduG2MSCQt3mEfbrAAxsnUh5dhUrr77EXdpF4o3DiIzMyYx4SdKbR5iUbYBRiaok3ToK0gwkRcspx/2tPAajEtUwKuIOCinyx4FIE+KpXr06Y8aMoUyZMpw7d46YmBhcXV05cuQIw4cP5+bNm+zbt48ff/yROnXqcPz4capWrcqtW7e4dOkS/fv31xpA9e3bl759+woB586dO0lMTGTixImIRCIWLlxIhw4dePnyJQYGBmzZsoWePXvi7e1NiRIl8PPzIzU1lVWrVmFoaMjx48eFQDX7w45CoeC3335j8ODBtG/fHm9vb9LT07l3756g4QwQHh5OrVq1EIlEjBgxAnt7e44fP86AAQNISEhg9OjRynslk9G6dWv++usvunbtyqhRo0hMTOTUqVM8ePBAMItp27YtgYGBDBgwgMqVK3Py5El++uknwsLChOaxvND9wn9FvqRTw7fA567P0qHjc6Dqdo6MjPziMyD57Xr+nEyfPh2FQsGFCxeE5o6OHTtSvoJm855j9/lqengW1doQvns6CTcOfQgoS9UiWs+AlMcXkBRWlussDgjiavIlRCIRvr6+gHKKNiQkRM3IYMGCBURGRnLt2jVq1qwJQJ8+fShVqlSe5xEXF8fly5dZtGiRoCUMyoYXPz8/GjZsiEKhYOrUqdjZ2WmITucXlcTMxYsXKVGiBKC061u6dCkzZ87UCChBmS1S1UnK5XKWLVum1bFk+PDh/Pzzz4BSW9LJyQl7e3vWrl1LWloaLi4uwvXLD/r6+ri5uZGeng5ASkoK+vr6/PXXX3z//feYW9kwbMggMqNfk3T7OA6+fhgXr0pa8B2SH5wh4e+jiORS9MxskRTzwLRiMxQKhbIpx9AEMnIvacgJ6yaD8PYojL+/P2lpadStW5fTp09Tp249YtJSSH/7FAM7Z+zbTxLWSbh+CEO7D9I8puUbIxKJSLh5GFlyHJLCpTFyrUz8RX+sGw/EoqYPoHw/xl/cSfLjC4j09TEqVh6rej0wLPRBp1FRpTUWe8eQlBDHvHnzkMvleHp68vLlS16/fs2TJ09wdXVly5Yt7Nu3j/fv33P+/HkAjh8/zujRo/n9998JDAwkODg4TyWC0NBQnj17JjRPubu7065dO06ePClkq3/66SdsbGy4cuUKNjbKkgEfHx9B8mjr1q05bv/o0aN4eHhw4MCBHMdMmTIFmUzG/fv3hffi0KFD6datG35+fgwZMkSot/3rr79YunSp8LACSvlFVfB8+PBhzpw5w5w5c4QHteHDh9O5c2dWrFiR74Dym5ny/v+ENqeGg9/X4ejIejzw8+bcuEb4tfX4zwSTX6o+S4cOHbkjk8k4efIkPj4+ah3EZo4uGBevqjE+azApT0tGlhKPkXN5pHHvkacpu3PFEhOMS1Qj+clFtWzNvn17qVq9ptZOaBXHjh2jVq1aQjAJymnaHj20dBxnw9jYGENDQ86ePfvF9PZkMpkgMaMKJgGWLFnCkCFDuHbtGgkJCWrr/P7772pagfXr1wdQszvU19enXLlyas049vb2lClThgoVKvDgwQO6detGbGwsCxYsEKYmcyr16tu3ryBwnp6ezrFjx1iyZAmWlpZIpVKCg4OZMGECw4Yo9Roj9vqhb1UI4+JVUcgyBccaEQoQidAzs8bEvR7SmDBiTqwmMzIY4+JVhP0lXD/EmzX9cnWskWemE3lwLq+XdyVsdW+Cnz1m48aNJCYmcuLECa5cuUJMdBQAGWGPyYwKJWR+a9JC7qltR5oYhWHhUsSd307i339iVb8nLj8dpFDPBWS8C0JsbIF59TbCeGPXyhTquRCXnw7g/OMeHDpNF4LJlKCrhP8+jbA1fXkdEkxcXBzjx48nISGBM2fOANCkSRMhQOzbty937twBoFy5coCynGDnzp0cPHgQgLp161K+fHnCw5XXMDMzk86dO2NjY0PZsmUBpaC5KpjM+p5YvXo1JUuWRCKRcOfOHVxdXdWyihUrVkQul7Nnzx4OHTpE+fLlhalqVfkCKKWpXr58qVFDKZfL8fPzo0iRIoJD0JMnT3B2dqZr165ERUXh7e1NfHw8t24pywL279+PnZ2dhnc8fGgmO3bsGHp6evzwww9qr48dO7ZAU966gPJfRiUrUMXZGo8ilv+5aeGs9VlAnhJJqte11Wfp0KGjYERGRpKamqqRAZx88D76NppyKGlvHhG+awqhSzryenkX3qzsQdw5Zf1gVrkX07L1lQLY/zRsZMa+I/3dc0RudTS2mZWQkBCt2Uh3d/c8z0UikbBgwQKOHz+Oo6MjDRo0YOHChbx//z7PdfNLZGSkIDGTHZXEzOvXr9WWZw+gVYFE9qA3JzFq1dTk6dOnGTFiBCtWrKBkyZIMGDCA5cuX5+u4W7ZsyZgxY/j1118xMDCgd+/ebN++ndnLfkXipAyMTMs1BJT1kxnhylo7y7pdsazXHVlKApEHZhN1eBEpTy5iUdsXfesiAKQEXSHh7yOYV22FRa3OZEaGEL5rCrLkD+enkGaQGf6CzOg32NTpzMyf52NqaoqPj48QiDVo0EAISOq07oLoHyH0qGPLiQ5Yi0KWSWbsOyIPzcPItQo2jQcgNjIj+uhyMiJDAMiMe4eBrZNWGaOspDy/zrttY4k88DNpwbeRJcehUMiRy+WCMUpO90UlRp5V3xMQtCpVDzWOjo6kpqby/v17Tp48ybBhw4TM+fHjx4XzzrrNM2fO0KZNGyET+Pfff9Oli7pHOihrGr///nu6du3KwoULSUtLo2PHjoIU1YQJE4RmIC8vL4YPH86lS5eYNGkSM2fOpMI/sw/v3r2jXr16vH79mt27d2Nvb0+/fv0AZdYdlHW97u7uuTamhYSEUKRIEczN1ZNYqgA6v+gCSh0fzerAZ0w8cJ90qbzAWpsyuYJ0qZyJB+6zOvDZFzpCHTr+//EsPJELz6OQZ8ssZMa+UwYKqQlYNx6IQ+cZOHSdg3mNdsoBWcYbl/REZCAh5ckFAOV/RWLe2VbmeYS6VMvnZPTo0QQFBTFv3jyMjIyYNm0aZcuW5d27d19sn3mRX5HpnMa9fPkSmUzG7du3mTZtGkOHDuWPP/6ga9euQm1dfuncuTPVq1cnKCiInj17Uq1GTdLfPEZsao2pR2OkceHI01IwraD0yzZ2qYxpOS8K916M2Ngc04rNKPbjbqwbfnBpkyVEUKjnAixrdcKqXjccfP2Qp8QTf3XfhzHJsYj0DSjcdwWr5/sxdfyPnD17ltq1awsOQiVKlBAydT+PHcqmkzco3H4Cxi4VSXl6CXlyLNKYN9j7TMS6YW/Mq7XGocss0NMn+f7pfF8DlUi6SM8Am+bf49DZD4euc4SAesiQIaxZs0YoE8jpvmSVg3ry5IlQczhjxgwhY3z//n3kcjnHjh1jzpw5gsC9tbU1Y8aMEZrNVOLkPXv2ZNmyZfj4+ADQvXt3/vjjDy5fvqyx/4CAgBy9ulX2n6CUotq/fz/16tVj8eLF+Pj4CNPlPXv2pHdv5b1s1qwZp06dEv7VrVs339f0c6ELKP9FtHVI/lf4/UYoiwOCPsu2FgcEsfsbylSePXsWkUiUo3OQjv8mcXFx9O3bFysrKywtLenXrx8pKeplF1KplNmzZ+Pm5oZEIsHV1ZXJkycLP04qFAoFc+bMwcnJCRMTExo1asTDhw/JTmZmJjNnzqRUqVIYGRlha2tLvXr1OHXqlNo4VTeoqakpVlZWtGvXjsePH6uN8fPzQyQS8fz5c+E83N3d0dPTUxvrfy0UPbEIaTa5ldTn10GWiUPHaZhXaYmxWw0MrIuQFqqsgQz7bSivl3cj8uA85CnxGLvVJOXJRRQKOcmPLyBxKofE0o4dVzU/q+vXr8fNzY2MjAwOHDig0fH69KlSmLpz5865niMosy6///4758+fx9HRkeTkZEGYGj5M0506dYp69ephZWWFmZkZ7u7uTJ48WW1bqizN4sWLMTIywtvbG0NDQ+F4QFkLKhKJhKnt1q1bI5FINJplPgaFQkFUVBRt2rQR/l/1L/vUZH5JTU0lPj6e5ORkxg3pBSiQJ8fydv0gwn4dwNv1g0i6pWxcebdlFOG7p6GQy5AUKiXYHmbFyLUK+uYfptzVHGsAWWoiisx0xMaWDKtbhCbFTYiKiiI6Ohpvb2+ePXtGWJimtE/fBmW4tmE6Pj/MwWnYZkT6hsrtF/4go6dnYomBTVEy45RZaAOrwmRGv0Ehk+Z4/llF0s2rfoexW3WMXSsj/2ed+vXrk5KSouHbnRMPHjygYcOGQnOXmdkH//KwsDAMDQ2pV6+e2jqenp4EBwfz6NEjAMHdz87OjqioKGEbqixjVhF7UMoRVchS46zNq1u17uLFiwkNDaVy5crI5XIGDhyIvb095ubmyGQyob6xSJEiNG3aVPinkqVyc3Pj6dOngvKANlxcXHj79i2JieoPi/m9hip0AeX/MKovypz+DRo0SBj78OFDOnfuTIkSJTAxMcHOzo4GDRpw5MgRje2+jklhzPy1vNs2ltBlXXi9vBvv/SeS8lxTNiG/TD/88P91TeXly5fx8/NTEzXW8Xnx9fUlMTGRefPm4evry5YtWzSChoEDBzJ9+nSqVq3KsmXLaNiwIfPmzaNr165q46ZPn860adOoVKkSixYtokSJEjRv3pzkZHWXEFVDUKNGjVi9ejVTpkzB2dlZLYg4ffo03t7eRERE4Ofnx5gxY7h8+TJ169bVqkGX/TxkMhkHDx4kNFQZ6AU+jSAtIpTUl9kCFS0SMGmh98n8p17Oqm43zKq0JC3kLu93TsKkVC1kSTEk3Q0gM+IVpmUbIJMrCAyKUNvGq1evWLRoEUOGDKFu3bokJyfTunVr4QcsMjJSyKhER0fneI4pKSncvHmT5s2bC9di0KBByGQy4dxAKf79/v17WrduTXp6OrNmzWLJkiW0bduWS5cuCeNSU1MFSaEKFSqwaNEiLC0tycjIYP/+/RrX9vr160gkEr7//nvmzJlDVJSyHlAqzTm4yYvMzEykUinr16/H3t5e7V/2qcmsSKVSrXWk169f5/79+1SvXp1Bgwbx6uVLSrT+HvsOU9T+GaqCNpEYaVQob1b2IPXFDRRaXGz0LR00lhlYFxE0LaX/uNnIEiKY2N5T7RxmzJih9Rzi45W2j6pG1FXda8A/neXZg0WxkZmgX2niXgd5aoLQyZ0VISOcRSQ9IzKEiP1zCF3amdSnynuvatZSHUNetGnTBnNzc63qBUlJSVp1VlXBWkiIcqr+2TPlLNuiRYuwt7cXgsUNGzYAH66PqoGtePHiGtvM6tWd1YUJlNPwquloZ2dn9PT06NixI/v37+ft27dq9Zyg/Myp6NixI1FRUVrtkFXX9LvvvkMmk2mMWbZsmVav8Zz4bxXs6SgQ9vb2bN++XWP5iRMn8Pf3p3nz5sKykJAQEhMT6dOnD0WKFCElJYX9+/fTtm1bNf0ugA4jpvHuwFKM3Wpg5tUHhTSTpPunidw3E/v2kzFxz73OShtSuYLJB+9r9Sj//8Dly5eZOXOmkHnS8fmpUqWKUMgOyi/tjRs3smDBAgDu3r3L1q1bGThwIL/99hsAw4YNw8HBgcWLFxMYGEijRo2IjIxk4cKFtGrViiNHjghfuFOmTFFrygBlt+Z3333H+vXrczyugnaDZj+Ply9fChnOAYOHci/gMQl/H8HAzlkIFgGMi1chVk+fiH2zMK/cAnlmGom3TyjHRbzCtFxD9K0cMSlZk/fbx6GQpiMyNCb2zEYQiYXPdWh0CsnpH4KC5ORknj9/jrW1Nb169cLd3Z2IiAj69OlD1apVWb9+veAQcuDAASpXrqz1HIOCgqhTpw5yuZz+/ftjaWnJwYMHkcvlapmTatWqsWbNGgAGDRpEyZIladxYU1dx/fr1QgbU29ubkSNHMnToUGrUqMHdu3epU6cOI0aMEJpw5HI5hw8fplmzZoDS73nlypVcuXKFWrW0u8nkl549e9KnTx+tr1WsWFFjWVJSEsWKFRPqUdetW8f9+/fZvHkzlpaWODs78/PPP7Njxw6emVdm+7UQoewo6UEgGe+Us0fWTQdjYF0YxHokXNlDZsw74i4pdSDTw5TXJv3dM+Iu/Y7YyBSLah+aYVAoiLv0O5I0pe2jhYUFHTp0AJQBetbjzi7Q/dNPP7F+/Xrq1q2Lo6MjF27cQZGRgtjEErFEiwTeP4GNafnGJD04Q+yZDaS/C8KomAfyzDTSgu9gXqUVJqVrYexWg8QbhwjfNVX5/hbrgwL0LByQJUSwYMECJkyYkG/t044dO7J161aNWYOCoNpXr169hCnoW7duMWnSJIoWLYpEImH27NmsWrUKUGY4taEK8Jo3by48yOzevZvIyEhh2lyV/Zw/fz6BgYF4enoik8kICgpi/vz53Lp1i9OnTwt2nb1792bbtm2MGTOG69evU79+fZKTkzl9+jTDhg2jXbt2tGnThkaNGjFlyhSCg4OpVKkSAQEB/PHHH0IpQH7QBZT/w5iammqV1tiyZQsWFha0afPhy+O7777ju+++Uxs3YsQIqlWrxtKlS4WA8ll4IndO/I5h4VLYd5ou/JiaVWzGm1/6kHT/r48KKGVyBReeR/E8IvE/092u47/F0KFD1f6uX78+Bw8eJCEhAQsLC44dUwp8jxkzRm3c2LFjWbx4MUePHqVRo0acPn2ajIwMRo4cqfb0Pnr0aI2A0srKiocPH/Ls2TOtzSrv3r3jzp07jB8/XggmQRlkNGvWTDim3M6jdevWnDlzBhsbG+bM8kNhYoNVvR7IkmKIzxJQGtg6Ye8zibjz24kN3ITY1BrzKi3RM7Ek+tgKFDIpstQE9K0LI5aYkhEZgkkpT5IfnsXItTJ6plaAUrEhODoZVe+qq6urkCEpXLgwx48fp169euzdu5fz58/To0cPFi5cKFyPnM6xSJEiKBRK15NFixahr69PmTJl2LNnD1u2bBHGTZ8+nYsXL3Lv3j2GDBlCgwYNtAaUx44do1ChQmpNPQYGBkycOJFu3bpRqFAh5s2bJ/xwd+jQQQgmAUqXVmb5tE3n5hcDAwP09PSQyWQ0bdo03+uZmJgwcOBAoaZu5MiRFClShG7dutG8eXN69OjB6NGj6dGjB8/CE9lyJVhYN+XpJSHrZ1q2PnomyoaR+Av+oJATf2GH2r4y3j4h4+0T9CwchIAyM/YtYomx2tiEhARBDmvGjBlaz0f1eWjTpg03b95k6dKlJCUlUbSoEyJDEwwd3TTWUVtfrIdDZz/ir+wh5eFZUp5eRs/YHIlTOQwcXAEwdq2EbcsfiD2/HUVmGnoW9ljV64E0Ppz4S7s0snV5oXqvrVixQuM1MzMzDd90+JABdHFRyiCpppWLFy8uXJemTZtSrVo1ZsyYwerVqzEwMKBhw4YcOXJEoyEoO0OGDGHJkiWA8v1erFgxWrRowdGjR3n+/DnFixfH0dGR69evM3nyZDZu3MjVq1d59eoVHh4ewkMyKGtIjx07xs8//8zOnTvZv3+/UHqjyqSKxWIOHz7M9OnT2b17N5s3b8bV1ZVFixYxduzYfF9L3ZR3NkJCQhg2bBju7u4YGxtja2tL586dNaZHVLZIly5d0nBRyJpuhvzVW6kkArTpPV2+fBmRSPTJfqKg/AELDAykQ4cOglNATujp6VGsWDG1aVj/a6EoMlLQM7FS+zEVS0wQGxghMjDM8xgUCjkJN/7g7YZhhCxqz+uVPYk+sRrSk7TWZ+VGQa5bfu9tTly7do0WLVpgaWmJiYkJDRs2VJtiy41Vq1bh4eGBiYkJ1tbWVK9eXSjk9vPz46effgKUUyGqkoT/kuXWf4G8unVDQkIQi8Ua2ZZChQphZWUlTG+p/ps9QLS3t9f4MZs1axZxcXGULl2aChUq8NNPP3Hv3gcZFdW2cuo6joqK0phGz+k8Dh06xLVn7yk6dAPmVVpiVb+HhkC1SSlPigxYjfO4Azh9vxHzam2QxoWjZ27H29+G8mZFd96s7IE8PRl5ejJ2bcbhMvFPHLvOUdtOhvRD9kc1datC1QwwdepU3rx5I7iBbNy4UUPfL+s5yuVypFIpo0ePJikpibi4OK5evUrnzp3Vro+joyNXr14V9vPo0SO6du3Knj171LJSISEhwr3M2uGq6lwdMGAAiYmJQhYzq9QRKHX4AI3OVy8vLxQKhZpDT3BwMH/+qTlNe+7cOXr16sX+/fvV9DpVZP+tUGFoaMjy5csFIfOMjAyCg4OZPXs2o0aNol69esIUbSlHc+qXtBNc2EQiMWIjM5wnHBaCyfS3T5Ud+2I9XCb+icvEPyk6VJnlFulLKDp8C07DNgljM94+xax8Y3puuCqcq42NDW/fvkWhUKiJcWc9B5VETv369Tl37hxRUVGkpaXx4sVzDE3N8+zeBqW0lXWDXhT9fiMu4w/hNHI79u0nYWBVSBhjVqk5ti1GAGDXegxmFZtSyWcQ6enpQvYalL+92adxXV1dUSgU1KlT55/rJWL9+vV06tQJfX19DA0//Ib179+fjIwMoYbX1dWVpKQkQddSJT2k0hYtWlRdWaFJkyacOnWKyMhI4uPjBUvNvBg8eDCTJik1PJ8+fcrz58/ZuHEj+vr6rF27Vhjn4OBAsWLFAGUm8t27d5w+fVqtnA2Uklxz5szh5cuXZGRk8O7dO/bu3asmnWVmZsbSpUsJCwsjIyODoKAgxo0bp5vy/hRu3LjB5cuX6dq1K05OTgQHB7N27Vq8vLx49OgRJibq6fqRI0dibW2t5qIwYsQINY2y6dOnM2fOHCELeOvWLZo3b65m31WiRAnq1q2Lv7+/mvgogL+/P+bm5rRr1+6Tz+/3339HLpfnqAuXnJwsFH0fPnyY48ePq8keBD6NQOJcgZQnl0i4eQSTkjVRyDJI+PtP5OkpWFRvm+cxxJxYTdL9vzCr0FT5gxYfTuLff5IR/oK/HH/Br61Hvs+nINetoPc2K2fOnKFly5bCE6dYLGbz5s00btyYCxcuaPwYZeW3337jhx9+oFOnTowaNYq0tDTu3bvHtWvX6N69Ox06dCAoKIhdu3axbNkyocNQZbOm4/OQ327dgnyB5kWDBg148eIFf/zxBwEBAWzYsIFly5bx66+/MnDgwI/aZm7nYWhQsBxB7Kl1JN0/jXn1tkiKlkEsMQWRiKg/Fqp1fWfHUF8MGXkfz5fC2NiY8+fPExgYyNGjRzlx4gS7d++mcePGBAQECMek+o7Nj6D9lzqPrFOTgwYNoly5csTExGhMTeaHH374gcjISMaPH8/vv/8uLK+emc6FyHSwdcG4ZA1Sgi4Tuf9njEvWQBr3nqTbxzGwK4Y8QzPbpm9dmPc7JmBepSUKWSaJNw4jNrbArk5n5rZXZrB++eUXIaM1aNAgSpQoQXh4OFeuXOHNmzfcvXsXgMqVK6Onp8eCBQuIj49HIpHQuHFjHBwcMDLQI/PzfbSQOJVFbGRG1J/LsKzRFiMXG2rVKphuogqxWMyOHTvw8fHB19eXY8eO0bhxYyZOnMiuXbto2bIlP/zwAzY2NmzdupVXr16xf/9+oVO8V69e7Nmzh6FDhxIYGEjdunWRyWQ8efKEPXv2cPLkSTXL0o/B0dGRUaNGCfXCLVq04O7duxw/fhw7O7vP+r31segCymy0atWKTp06qS1r06YNtWvXZv/+/fTq1UvtNVtbWwICAtRcFFauXEl8fDyWlpYFqrfq3bs3Q4YM4cmTJ5QpUwZQFnXv2bOHDh065Brw5Bd/f38KFy6sdYoIlNN7Kh9gsVhMhw4dhCe8pHQpoTEp2DQdgjwlgdjT64g9/c9YYwscu81BUjR33aq01w9JuhuAXZtxmHp4CcuNnCsQsWcGTy4FkJzeqEB6nPm9bgW9tyoUCgVDhw6lUaNGHD9+XLiHQ4YMwcPDg6lTpxIQEJDj8alcD1SdgNmpWLEiVatWZdeuXfj4+OTp0qDjy+Di4oJcLufZs2dq+mvh4eHExcUJ01uq/z579kztCT8yMlJrI4WNjQ39+vWjX79+JCUl0aBBA/z8/Bg4cKCwraxdxyqePHmCnZ2dmjByXrjamiICDWOBnEh+egnT8o2xafIhuFVIM4QmCW2I/tlP5Lv8iY/n9xyNjIwwNjYWGhyyom1dsVhMkyZNaNKkCUuXLmXu3LlMmTKFwMBAmjZtipGRkRDoNGrUSG2fWY/rS6Oampw1axYHDhxgzZo12NraakxN5ofIyEhkMplGWQZAx4GjuYkLphWaIkuOJfH2CVJf3cLQzhnbNuNIeXKRtND7Gutpc6yxaT6UuT3qC5a/5cqV4+bNm8ycOZMtW7YQHR2Ng4MDVapUYfr06cK2ChUqxK+//sq8efMYMGAAMpmMwMBAHBwcMDPUJ+YzPmPoGVtg32kGsWc2EHtuO3ftbOjTuxdNmjTR6naUFwYGBuzbt4+WLVvSrl07Tp8+jaenJ5cvX2bChAmsWrWKtLQ0KlasyJEjR2jVqpWwrlgs5tChQyxbtoxt27Zx8OBBTExMKFGiBKNGjRLKJz6VBQsWYGJiwm+//cbp06epXbs2AQEB1KtXL88Zx6+BLqDMRlbP2czMTBISEihZsiRWVlbcunVLI+gYPHiwhovCsmXLCAkJoWLFigWqt/L19WXUqFH4+/sze/ZsAE6ePElUVNRH24xlJSgoiL///psff/xRTYMrK6NHj6ZTp068ffuWPXv2IJPJhKf8kOhkFIDIQIK+rROm5nYYl6yBIiOVhBt/EHlgLo49F2Dwj2CuNlKeXEQkMcWoeBVkKR+68AwLlURkaExa6D2Co5PxKGKZ7/PK73Ur6L1VcefOHZ49e8bUqVM1uu+aNGnC9u3bkcvlOV5TKysr3rx5w40bN6hRo0a+z0vH1+W7775j8uTJLF++XHioAli6VOmLrPoBadq0KQYGBqxatYrmzZsLn2ttItXR0dFqFn1mZmaULFlSEM8uXLgwlStXZuvWrUyaNEmoMXzw4AEBAQEF/tybSvRxtjEhJJ+KCSItnd8Jfx8BRc4NDc62JphK9NE+WatJfs9RT08Pb29vDh06RGhoqDC1//jxY06ePKm2zZiYGLWaU0Bo9lFJPL1+/Zr09HQGDx4sTJlLpVJWrVqFmZkZDRs2zOcZfDoODg6sXr1aa6etCtVUbFZUXuEq8pIyWx34jMUBQVjW9sWytrrFo0lJ9e+e7J7dFjXbC///U3N3utRQL60oUaJErnaBKgYOHKg1+/7mdQi9Nl7j8stoNd3iQj3m57nNnDByKotT36XUKWGr1tCZnyxl9msLyt+I7Ne4RIkSOSYDsmJgYMD48eMFn/acyOnYspc4qfzDs6Knp8esWbOYNWuWsCwuLo7o6GjBT/7fRBdQZiM1NZV58+axefNmwsLC1G6+NhmC/NRlQf7qraysrGjTpg07d+4UAiN/f3+KFi2aY0axIPj7+wPkaoNWpkwZIcvXu3dvmjdvTps2bbh27ZpQNxV5aD4ikRiHzjOE9YxL1eLtusHEnduOvc+EHLefGfsWRXoyb1ZqPwZZcrxafVZ+yO91K+i9VaHKmOTUpalaP6di8AkTJnD69Glq1qxJyZIlad68Od27d/9XhGd15EylSpXo06cP69evJy4ujoYNG3L9+nW2bt2Kj4+PkOWyt7dn3LhxzJs3j9atW/Pdd99x+/ZtYeopK+XKlcPLy4tq1aphY2PDzZs32bdvHyNGjBDGLFq0iJYtW1K7dm0GDBhAamoqq1atwtLSUuMHLz80cndQ6/rNDeOSNUh+cAaxxAQDO2fSw56QFnwHsbH2pgE9sYhGpTVlZvIiv+c4c+ZMTpw4Qf369Rk2bJgQAHp4eKjVns6aNYvz58/TqlUrXFxciIiIYM2aNTg5OQmagW/evKFatWps2bIFc3NzXF1d2bdvH5cuXWL58uUatZH/C4xoVAo7MwkzDj9EKlcUyHBCTyxCXyxiVlsPjWDyczG3fQWaLjtXYCOM3NAXi4Sp+f91UlNT1RIj8OFBNmtN77+FLqDMxsiRI9m8eTOjR4+mdu3aWFpaIhKJ6Nq1q1YZgs9dd9O7d2/27t3L5cuXqVChAocPH2bYsGE5Zr8Kws6dO3F3d6datWr5XqdTp04MGTKEoKAgDM0LkRn3nrSXf2PTYoTaOFUnXnrYo9w3qFAgNrHCru04rS/rGVso67MKSH6uW0HvrQrVa4sWLRKyINnJKoabnbJly/L06VP+/PNPTpw4wf79+1mzZg3Tp0//LOLJOZGcLiU4OpkMqRxDfTGutqb/OWvPr82GDRsoUaIEW7Zs4eDBgxQqVIhJkyYJensq5syZg5GREb/++qtQHxcQEKA2DQbKmrfDhw8TEBBAeno6Li4uzJkzR2jCAmXG88SJE8yYMYPp06cL3aALFizQqleXFz08ndW6fnPDpulgEIlJfngWhSwTSdGyOHadQ/ju6VrHy+QKetYqeLCR33OsWLEiJ0+eZMyYMUyfPh0nJydmzpzJu3fv1ALKtm3bEhwczKZNm4iKisLOzo6GDRsyc+ZMwQZPlW2aOHEiW7duJSEhAXd3dzZv3vyfNZTID11rOFPXzY7JB+9z4XkUemJRrgGc6vU6JWyZ276CMM39JShmY8LMth5MPKA59f6xzGrr8UWP+Vti9+7dbNmyhe+++w4zMzMuXrzIrl27aN68+TeRoBApvmTl9H8QKysrOnTowKZNm4RlaWlpmJmZ0bNnT0E2YcuWLfTr148bN26oFduePXuWRo0aERgYiJeXF7t27aJ79+6cOHFCra4jMjISBwcH+vTpI2wTlFMyRYoUoXPnznh6etKnTx/u3r2rVausIFy7do1atWoxa9Yspk2blu/1VqxYwejRo7l27RoelapSctBy3m//CRvvYZhXUZcZCt8zg4zwFxQbuSOHrUF0wFqSbh+n2Ji9iA0kGq+LgAd+3gUOfPJz3fJ7b7Pfwxs3blCzZk0NPc6PJSMjgw4dOnDixAmSkpIwMjJiyZIljBs3jlevXn1SDeWz8ET8r4US+DSC0JgUtVo6EeBsY0Ijdwd6eDpTyvF/L0OjQ4m2qcVPRU8s0pha1PFtI3wfBEUQGq3l+8DWhEalHehZy/mryrWppuY/lZ+auzO8Ucm8B/6PcOvWLcaPH8+dO3dISEjA0dGRjh07MmfOnFyTGl8LXboiG3p6ehrZxVWrVgnCvAWlIPVWoJS36NatGzt37uTx48caArIfi0qipnv37lpfj4iIENT/VWRmZrJt2zaMjY0pV64cphJ9XIu78V4kJvnxBcwqtxTOR5oQRfqbR0icyuV6HKZl6pF06yjxl3/HuqH6FLJCLqOomUgIJt+9e0d8fDxubm5a3Qqykp/r9rH3tlq1ari5ubF48WK6d++u8cGNjIzMtSM7ex2doaEh5cqV4/jx42RmZmJkZCQ0XmhzygkNDSUlJUUoRdDG65iUPDMSCiAkJoXt10LYciWY+iXtvnhGQse/g25qUQcoJYX82nrgh8c3NWPxrU/Nf6tUrVqV06fz73v+tdEFlNlo3bo127dvx9LSknLlynHlyhVOnz6tFhAUhILUW6no3bs3K1euJDAwsMBdgNqQyWTs3r2bWrVq4eamXVh2yJAhJCQk0KBBA4oWLcr79+/x9/fnyZMnLFmyRAiivKuV5mGlZiTeOUn4rimYuNdGkZFK4q1jKDLTsazVOddjMXKugFnlFiRc2UtG+EuMi1dFJNYjM/YtKU8u4TlsijB20qRJgkRDfrJ2eV23j723YrGYDRs20LJlSzw8POjXrx9FixYlLCyMwMBALCwstFpUqmjevDmFChUSXCMeP37M6tWradWqlVDHpSpDmDJlCl27dsXAwIA2bdpgampK7969OXfuXI5lFL/fCBW+mIE8v5xVr19+GU3TZeeY2daDrv/Pvpj/19FNLerIjqlEv0DNjl+ab3lqXsfHoQsos7FixQr09PTw9/cnLS2NunXrCl67H0t+661UVKtWDQ8PDx4/fpxrA01+OX36NOHh4UyZMiXHMV26dGHjxo2sXbuW6OhozM3NqVatGgsWLKBt2w/akj08ndncfBj69q4k3T1F3LltABgWKoVd6zEYOZfP83hsW4zAsFBJku6cUK4vFqNv6Yiphxeje7b+6PPM67p9yr318vLiypUrzJ49m9WrV5OUlEShQoXw9PRkyJAhua47ZMgQ/P39BdcIJycnfvjhB6ZOnSqMqVGjBrNnz+bXX3/lxIkTyOVyXr16ladkzKdMHcn+yQxMPHCfqKR0RjTSdHLR8d+law1nopLSP9vU4v+3bJCOL4/K6/tbnZrXUTB0NZTfKFWqVMHGxoa//vrr3z4UDb7l+qxv+bp9bn6/EfpZM1ALOlQoUNDg6uqKl5dXjrWn/wYikYgZM2Z8VHf0/ypZM9i6qUUd2viWpsO/pWPRUTB0d+kb5ObNm9y5c0etWedb4lutz/rWr9vH4OXlhaurK1u2bKFv374EBwdz9uxZXsekMOPww7w3UACmH35IHTe7/7dTSTld6/86uqlFHdr4Vhv4vrWpeR35RxdQfkM8ePCAv//+myVLllC4cGE1y8NviW+tPuu/ct0+J5MP3hdqJj8XUrmCyQfv5ztL/PTp088iZ/U5SU1NVfNt1qFEN7WoQ4WugU/Hl0L3zfsNsW/fPmbNmoW7uzu7du36JqyUcuJbqs/6ktdNKpUil8sxNDT8bNv8VJ6FJ3LhedRn365MruDC8yieRyTmK5iQSDRlnz4nycnJBbIdBPJ17z9mu/8rfKtdvzq+DroGPh1fkm8rvfD/HD8/P+RyOY8fP/6qtmAfy4hGpZjfoQISfTF64oIZ0+uJRUj0xSzoUCFPHTE/Pz9EIhFBQUH07NkTS0tL7O3tmTZtGgqFggEDBtCmTRvCwsLo0qULS5YsUVs/IyOD6dOnU61aNSwtLTE1NaV+/foEBgaqjQsODkYkErF48WKWL1+Om5sbEomER4+UYu1PnjzB19cXe3t7jI2NcXd312h0un37Ni1btsTCwgIzMzOaNGnC1atXC3Rt8sL/WqhwveOvHeD99nG8Xt6N0MUdeLd5FMlPLmqsI89MJ+bUOl6v6E7o0s5E7JuFNDGKkPmtibvgL4yLPrqM6hU05YlU9yArrq6ueQpEX7hwgc6dO+Ps7IxEIqFYsWL8+OOPpKamqo3r27cvZmZmvHjxgu+++w5zc/OPakgTiURq9ZOq43706BHdu3fH2tpacFL5/45qarGKszUeRSx1weT/OKsDnzHxwH3SpfIClyvJ5ArSpXImHrjP6kBNr3UdOkAXUOr4RLrWcOb0jw2pU0IpvZNXYKl6vU4JW07/2LBAmckuXbogl8uZP38+np6ezJkzh+XLl9OsWTOKFi3KggULKFmyJOPGjeP8+fPCegkJCWzYsAEvLy8WLFiAn58fkZGReHt7c+fOHY39bN68mVWrVjF48GCWLFmCjY0N9+7dw9PTkzNnzjBo0CBWrFiBj4+PmlzQw4cPqV+/Pnfv3mX8+PFMmzaNV69e4eXlxbVr1/J9nnkR+DRC+EFIvHkYA0c3rOr3wKpBbxDrEXVoPinPb6itE310OYl/H8G4RDWsvfoi0pcQsVfTpUehgLTMj9Nc1cbevXtJSUnh+++/Z9WqVXh7e7Nq1Sp69+6tMVYqleLt7Y2DgwOLFy+mY8eOn+04OnfuTEpKCnPnzmXQoEGfbbs6/vfJ+qD5X+X3G6GfZTYJYHFAEA1bd9aQcsv+MPets2XLFkQikYaHto6PR/dIquOT+Vr1WSq3GoDBgwfj6urK2LFjmTdvHhMmKP3Du3XrRpEiRdi0aRMNGjQAlP7qwcHBatPWgwYNokyZMqxatYqNGzeq7efNmzc8f/5cTay8V69eKBQKbt26pebfPn/+fOH/p06dSmZmJhcvXqREiRKAUhvT3d2d8ePHc+7cuQKfc9amkC1btpCULqWC30lhWZHB69Qch8yrtebdllEk3jiISckaAKS/f07KkwuYV2+HTVNlMGVetRVRR5eTGfFKY59SmYLkdOlnyVgtWLBAzXt28ODBlCxZksmTJxMaGqp2LdPT0+ncuTPz5s375P1mp1KlSoK4f05kv9Y6dPyXUKksvHql/EwXL16cwMBA3CrW/OwNfHdex2HxmWu4v3VUMzJ+fn5qDXw6PqALKHV8Nr50fdbAgQOF/9fT06N69eq8efOGAQMGCMutrKxwd3fn5cuXamNVnutyuZy4uDjkcjnVq1fn1q1bGvvp2LGjWjAZGRnJ+fPnGTVqlFoABAjTwDKZjICAAHx8fIRgEqBw4cJ0796d3377jYSEBCwsLD7pGoREJ6sF6lmDSVlaEshlSJw8SHn0IXhNe6k8R/Oq6laZ5tVak3xfu+tCcHTyZ+m0zBpMJicnk5qaSp06dVAoFNy+fVvjen7//fefvE9tDB069ItsV4eOb50v0cAnVyiITclQW/Zfa4jr1asXXbt2/eK14P+f+O/cfR3/Kb6E9EP24MPS0hIjIyMNxyFLS0uio6PVlm3dupUlS5bw5MkTMjMzheXFixfX2E/2ZargtHz5nEXbIyMjSUlJwd3dXeO1smXLIpfLef36NR4eHjluIz9kSOVqf6c8v0785d1khL8EWWaWVz6UHkgTIkAkRt+qkNq6BtZF8r2fjyU0NJTp06dz+PBhYmNj1V6Lj49X+1tfXx8nJ6fPst/saLvPOv5/8v+pKetNTAoXnid/9u0qS2Pkag1833ITqTayJhpyQqFQkJaWpvZgrCNndDWUOv4zaPvw5/SFkFWvf8eOHfTt2xc3Nzc2btzIiRMnOHXqFI0bN0Yu1wycvuUvD0P9Dx/ZtNcPiNw3G5GeAbbNv8ehsx8OXedgUq4h8JEZiX8yrln3A3yUl71MJqNZs2YcPXqUCRMmcOjQIU6dOiVME2W/9hKJ5IvJEH3L91THlyO3piwvLy+tAvx9+/bN0ep12bJluLi4YGxsTMOGDXnw4IHGmCdPntCpUydsbGwwMjKievXqHD58WG2Mqn7v0qVLjBkzBnt7e0xNTWnfvj2RkZGffN4qTj8OF+rWZUmxRB1dzptf+hCyyIc3q3oRsW820rhwYXxK0FUi9vrxZnVvQhb5EPbrQOIu7UIh1/z8i0Sw42polr+1N8Q9f/6cvn37YmVlhaWlJf369SMlJUVtW5s3b6Zx48Y4ODggkUgoV64ca9eu1djnzZs38fb2xs7ODmNjY4oXL07//v0/6tpoq6F0dXWldevWnDx5kurVq2NsbCyUWenIG12GUsf/PPv27aNEiRIcOHBArVN5xowZ+VpfNYWt7cdDhb29PSYmJjx9+lTjtSdPniAWiylWrFgBj1wTV1tTRCjDxZSnlxHpG+LYZTYifQNhTNK9U2rr6Fs4gEKONO49BjZFheWZsW81ti82MkWenoyrrXoGJyQkpMDHev/+fYKCgti6dataE86pU6dyWUuHjs9P586dKVWqFHPnzuVjzeG2bdtGYmIiw4cPJy0tjRUrVtC4cWPu37+Po6MjoGzMq1u3LkWLFmXixImYmpqyZ88efHx82L9/P+3bt1fb5siRI7G2tmbGjBkEBwezfPlyRowYwe7duz/5nAFuv45DZu0AQOTBuWREhWJRrTV6lo7IU+JIfXUHaUIk+lbK40+6fxqRgTHmNXwQGxqRFnKP+Av+KNJTsW6sHrgpFBAYFIEfuc+6+Pr6Urx4cebNm8etW7fYsGEDDg4OLFiwQBizdu1aPDw8aNu2Lfr6+hw5coRhw4Yhl8sZPnw4ABERETRv3hx7e3smTpyIlZUVwcHBHDhw4LNcKxVPnz6lW7duDBkyhEGDBmmdddKhHV1AqeN/HlUWU6FQCAHltWvXuHLlisY0ujbs7e1p0KABmzZtYsyYMWrrqLapp6dH8+bN+eOPPwgODhYyHOHh4ezcuZN69ep9cv0kKEsJnG1MCIlJAZEYRKBQyBChDCilceGkPlOXKTIqURXObyPx1jGhKQcg8e8/NbZvYFUYRXoyL54+omLFigC8e/eOgwcPFvhYs153FQqFghUrVhR4W5+bFy9eAODm5vYvH4mOr0F+mrLy4vnz5zx79oyiRZUPZS1atMDT05MFCxawdOlSAKHO+saNG0Jt3rBhw6hXrx4TJkzQCChtbW0JCAgQvpfkcjkrV64kPj4eS8uClQx5eXmpfdYS0zKFBj55WhLpYY+xatQfS88OwhjL2r5q27Br+5N6k1+V74g+sZrE20exatBL7cEVIDQ6Jc8GvipVqqg1PkZHR7Nx40a1gPLcuXNqswgjRoygRYsWLF26VAgoL1++TGxsLAEBAVSvXl0YO2fOnNwvTAF5/vw5J06cwNvbW2151kzm/4KD1pdAN+Wt43+e1q1b8/LlS9q3b8/69euZNGkSLVq0oFy5cvnexsqVK1EoFFStWpXJkyfz22+/MWXKFKpUqSKMmTNnDvr6+tSrV4+5c+eycOFC6tSpQ3p6OgsXLvxs59PI3QE9sQhjtxooMtOJ2D2DxNvHiLu4i3fbxqJvVVhtvKRQSUzc65B48w+ijiwh8dZRIg8tUNZdgjDNDWDu0RADI2Pat2/PihUrmDdvHp6enpQuXbrAx1mmTBnc3NwYN24cc+fOZfXq1TRu3Jg3b94UaDtnz5797JIkTZo0oUmTJp9te/8GyelSHr6N53ZoLA/fxpOcLv23D+mb5XM0Zfn4+AjBJChVJzw9PTl27BgAMTExnDlzBl9fXxITE4mKiiIqKoro6Gi8vb159uwZYWFhatscPHiw2qxJ/fr1kclkHzUjkJ2sDXwifQno6ZMeel/ZvJcDWYNJeXoKspR4JMU8UGSmkxn9WmO8AmUDX25kv/b169cnOjqahIQEYVnWYDI+Pp6oqCgaNmzIy5cvhVprKysrAP7880+1OvjPTfHixTWCSR35Q5eh1PE/T9++fXn//j3r1q3j5MmTlCtXjh07drB37958P2lWqlSJq1evMm3aNNauXUtaWhouLi74+n54wvfw8ODChQtMmjSJefPmIZfL8fT0ZMeOHXh65s/OMD/08HRmy5VgjF0rYdvyB+Kv7iPm9G/oWzli7dUXaXw48ZHBauvYth6Dnqk1yY/Ok/LsKkYulf6PvbOOjuJq4/CzG9m4EiFIgkOCFgnFkmDB3d2KuxXaAgmlaHFK0SIBCjS4SwkUlxbXYAmaEHfd+f7Y7pDNbgwCBb55zsk57OydmTtzl9137n3f3w+7Nt/yctUgZPoZXICMzFm18XfmT/+BiRMniktVgYGBOivis8PAwIB9+/YxcuRIZs2ahZGREW3btmX48OFUqlQp18eJi1P9ABYsWDCHll8+n6r/8qdOfhRllSpVSmtb6dKl2b59O6Ca2RIEgSlTpjBlyhSdxwgNDdUISjOvkFhbWwNoFbC9CxkL62T6Blh79iXyxFqeL+mBwqkMxiWrY1a+AXpm1m/3eRNE1F9+JAXfQEjWzHNUZnqt6zy6yO4a1as2Z8+eZdq0aZw/f14rv1I9W+vh4UH79u3x9fVl4cKFeHp60qZNG7p165avldpSAd+7IwWUEp88Pj4+Omen1q9fr1MHLHOQKJPJmDx5MpMnT9bY3rx5c43XLi4u2eZXubm55ZivU6VKFQ4fPpxtm/ellIM5dUsW4NzjcMwqNcasUmOtNlZ1NV1m5AZG2DQegk3jt7I86hlKPfO3ovS1itvSp6M7fTq21jpm5jHILAiceckNVBXuunImM7fLaiwB/vrrLwoXLpyjK4+6cCijdElWn53PTcxY8l9+P3QVZclkMp3/39+lAA3eFpmNHz8+yxmukiU1XcFyU1T4rmQurLOo3hrjkjVIfHCexCdXiTq9iZjzf+DQdSaGjiVQJsURsmUyckNjrOp0R9+6IDJ9Q1JePyTq5HoQdAeOmc+TmZyu8dGjRzRo0ICyZcuyYMECihQpgqGhIQcPHmThwoXifZXJZPj7+3PhwgX27dvHkSNH6NevH/Pnz+fChQuYmZnl8Q7pRirge3ekgFJC4jNkZtsKNFx4KtcWasrUZI3lLICYy3tAJseoiEoOSV8uY2bbCvne1/clICCAKVOm5DgL8erVKwAtGanPHcl/+cNgbW2toVerJqvl5sBAbcvBBw8eiPnS6uI9AwMDGjZsmH8dfUcyFvCpMbAuiIF7Oyzc25Ea8YJX60YSc3kXBVqOJyn4JsrEGOzafodR0bcSaWlRr7M8h+zf87wP+/btIzk5mb1792rMZma2xlVTs2ZNatasyU8//cSWLVvo3r07W7du1dAplvhvkHIoJSQ+Q4rYmODbKvealjEXdxDqP52YS7uJubKPkO3TiL/1J2aVGqNvoRJxn97K7ZOczbp8+TIDBw7Mto2/vz9Dhw5FJpPh5eX1kXr24ZH8lz8cJUqU4N69exoyPdevX+fs2bM62+/evVsjB/LSpUtcvHiRpk2bAmBvb4+npycrV64UH24ykp9yQLlBXcAHoExNQkjTFCLXty6IzNAYIe3ffESZOhzIUESXnkrsPwezPEdRW5P3NqzQVbwXHR3NunXrNNpFRkZqzdxWrlwZULlsSfz3SDOUEhKfKV2qFyUsLjlXHr2KQuVIenqN6HNbUaYkoW9hh2WdbljW6gzAhMZl8uSr/qkxceJEZDIZa9euzbPMR58+fTh58mSulsF9fHzw9fV9pyXJ9evX07dvX548eZKlzmFG8tt/2c5M8c5jnNe+fw7069ePBQsW4O3tTf/+/QkNDWXFihW4ublpFIyoKVmyJHXq1GHIkCEkJyezaNEibG1tmThxotjml19+oU6dOlSoUIFvvvmG4sWLExISwvnz53n+/DnXr1//mJeIVxl7/C4GkRLxkpDfv8ekXB0MbIsik+uR8OA8yvgoTMqpLGoVhcshNzIjbP9CLKq1BGTE3w4gK01bmQy8Stu/dx8bN26MoaEhLVu2ZNCgQcTFxbF69Wrs7e01AvMNGzawfPly2rZtS4kSJYiNjWX16tVYWFjQrNlbF7A+ffqwYcOGL+qz+rkgBZQSEp8xw71KUcBMIS6JZjWLZVysCsbFqmhs05PL0JfLmN7K7bMOJgGdS5fvSkJCAnPnzs1S+Ppj8Cwi4Z39l58v70d6TKjW9kFHm1LrmP8nOQv9X1CuXDk2btzI1KlTGTt2LK6urvj5+bFlyxadxXq9evVCLpezaNEiQkNDqVGjBsuWLdMoFnN1deXKlSv4+vqyfv16wsPDsbe3p0qVKkydOvUjXp0KdQGfnkUBTF3rkRh0nfhbASDXw8C2MAXaTMK0bG0A9IwtsOswjcgTa4j6axNyI1NM3bwwcqlE6DbtvgsC9Kj5/t8bZcqUwd/fnx9++IHx48fj6OjIkCFDsLOz0xAt9/Dw4NKlS2zdupWQkBAsLS2pUaMGmzdv1iikiYuLw9jYWKwKl/h4yIT8yP6VkJD4T8lN0YYa9ftS0YaKzDOUYWFh2NnZMW3aNK2CnrS0NNLS0t7JZi4vs3w9117k3OPwPC9zgyqglBuZYVFDU/NQUaAQ9evUwq9/3hUHvsQZyv8X3uezlBXqAr53+Sx9aBwcHOjVqxfz5s3Ltt3atWsZMGAAz549+2CWr/9vSDOUEhJfAEVsTPDr7/5WVuZBKMHhOmRlbE3wKm1Pj5pFRQ9eidyjr6+vUUX+IQgMieX0w7D3Ooa+uS1m5bVzSU8/DNPwX/5SSEtLQ6lUYmhomHPj/zPyWsCXGz7VAr7bt2+TmJjIt99+m2PbV69eIZPJsLGx+Qg9+/9AKsqRkPiCKOVgjk8rN06N9+KWjzcHRtRh15BaHBhRh1s+3pwa74VPK7dPPqBQ+wA/ePCAHj16YGlpiZ2dHVOmTEEQBJ49e0br1q2xsLDA0dGR+fPna+yvy6cX3oqkZ6U/+vTpU+zsVEVKvr6+yGQyDVF1db8yIpPJGD58OJs3b6ZMmTIYGRlRtWpV/vrrr1xd66FDh6hbty6mpqaYm5vTvHlz0sODc94xB4T0VJQpSRrb9OQyDf9lXdy+fZv69etjbGxM4cKFmTFjhk7Pe4Dly5fj5uaGQqHAycmJYcOGERUVpdHG09OT8uXLc+fOHby8vDAxMaFQoUI6xf5DQ0Pp378/Dg4OGBkZUalSJTZs2KDR5unTp8hkMn7++WcWLVpEiRIlUCgU3LlzB4ClS5fi5uaGiYkJ1tbWVKtW7b1dct6FT0V4Pq8FfLnhUy3gU+e/Zqf0EBISwi+//MKKFSv4+uuvMTH59K7jc0WaoZSQ+EIxVejj5pQ3+7ZPjc6dO1OuXDlmz57NgQMHmDFjBjY2NqxcuZL69eszZ84cNm/ezPjx46levTr16tV7r/PZ2dnx66+/MmTIENq2bUu7diqbOrUNZVacOnWKbdu2MXLkSBQKBcuXL6dJkyZcunSJ8uXLZ7mfn58fvXv3xtvbmzlz5pCQkMDU2QtJvTWBgn2XiB7LeSUp6AbBP7cHQYmehT0W1VtjUb016UohW//l169f4+XlRVpamuhFvWrVKp3afOoCpYYNGzJkyBDu37/Pr7/+yuXLlzl79iwGBm9t+iIjI2nSpAnt2rWjU6dO+Pv78+2331KhQgWxSjoxMRFPT08ePnzI8OHDKVasGH/88Qd9+vQhKiqKUaNGaZx/3bp1JCUlMXDgQBQKBTY2NqxevZqRI0fSoUMHRo0aRVJSEjdu3ODixYt069btne5lXvhUhefzUsCXE597Ad/du3eZMGECNWrUYPXq1f91d74opIBSQkLik6VGjRqsXLkSUNnUubi4MG7cOGbNmiUua3Xt2hUnJyd+++239w4oTU1N6dChA0OGDKFixYr06NEjV/vdunWLK1euULVqVQC6dOlCmTJlmDp1apZi+HFxcYwcOZIBAwawatUq1bbkNJY9d+L5qsFEn9+ObdMReb4GQ3sXFIVdMbApjDIxhribfxL552rS4yKw9uqbrf/ynDlzePPmDRcvXqRGjRoA9O7dW8sl5s2bN8yaNYvGjRtz6NAh5HLVYlfZsmUZPnw4mzZtom/fvmL7ly9fsnHjRnr27AlA//79cXZ2Zu3atWJAuWrVKu7evcumTZvo3l0lzD948GA8PDz44Ycf6NevH+bmb4Ow58+f8/DhQ3FGGeDAgQO4ubnxxx9/5Pm+vQ+fg/B8bgv4dPElFfB5enpqufFI5A/SkreEhMQnS0axYj09PapVq4YgCPTv31/cbmVlRZkyZfK10juvfP3112IwCSq7udatW3PkyJEsnVeOHTtGVFQUXbt2FX2frwUGI8jkKJxKkxR84536Yt9hKpY1O2BSuiZmlRrj0H02RsW+IubybtJiwrL1Xz548CA1a9YUg0lQzdqqAzw1x48fJyUlhdGjR4vBJMA333yDhYUFBw4c0GhvZmamEZwbGhpSo0YNjTE7ePAgjo6OdO3aVdxmYGDAyJEjiYuL49SpUxrHbN++vUYwCarPwvPnz7l8+XIOdyn/2Ho5mIYLT3HucTiQd+H5rZffP70ht3SpXpTjYzyoVfytO1Z2qN+vVdyW42M8PvtgUuLDIs1QSkhIfLJk9gG2tLTEyMhIK0fK0tKS8PDwj9k1DbLyeU5ISODNmzc4Ojpqva92Xqlfv77OY8oU+TNzJZPJsKjemqQn/5AUfBOz8l5Z+i8HBQXp9J3PrO2pdpPJvN3Q0JDixYtruc0ULlxYK/fU2tqaGzfeBs1BQUGUKlVKI0AFlbxPxnOq0eW5/O2333L8+HFq1KhByZIlady4Md26daN27do6r/d9WRYQ+M7LyOn/zhJO2nmTsLhkhntpf4Y+BFIBn8SHQgooJSQkPll0+QDnxv84c/Ci5l19mj8E6kIXPz8/MeB8GhbP93tuASCT5d8Ckt6/bkjKpFggZ//l/OZDeFbryussV64c9+/fZ//+/Rw+fJgdO3awfPlypk6diq+v7zufSxe6hOfTokJ4saI/ts1GY1Yx9/aL7ys8/y6oC/h8cCM+OY2n4fGkpCkx1JfjYmv63g44Ev9/SJ8YCQmJLw5ra2sArYrjrHyaM5JVMJodWfk8m5iYaC3LqilRogSgsuxTez/HJ6fx03X9LLxJ3h21H7OeiWW2/svOzs46r+X+/fta7dTb1R7WACkpKTx58oSGDRuyZcsWQkO1BdazwtnZmRs3bqBUKjVmKe/du6dxzpwwNTWlc+fOdO7cmZSUFNq1a8dPP/3E5MmT30k/VBfvIzyfFVP33qZWiQL/SfX0l1DAJ/HfI+VQSkhIfHGog7WM0j3p6eli8Ut2qGVEMgej2XH+/Hn++ecf8fWzZ8/Ys2cPjRs3znJ2ztvbGwsLC2bOnElqqspPOaP/cnpCdK7PryY9MRZBqTkLK6SnEXPBH/T0MSpaMVv/5WbNmnHhwgUuXbokbnvz5g2bN2/WaNewYUMMDQ1ZsmSJxizj2rVriY6Opnnz5mzZsoVFixbluu/NmjXj9evXbNu2TdyWlpbG0qVLMTMzw8PDI8djZE57MDQ0xNXVFUEQxHucH3y36yZpOnIl9SztKTp+J6Y6NEBzIk0p8N2um/nRPQmJ/wRphlJCQuKLw83NjZo1azJ58mQiIiKwsbFh69atpKXlrAVobGyMq6sr27Zto3Tp0tjY2FC+fPls5X/Kly+Pt7e3hmwQkO0yq4WFBb/++is9e/bkq6++okuXLtjZ2SFc/ptXfx1HUagcNo2H5Om6EwMvEn1uGyZla6Nv6YAyKZb4O6dIfROElUcvDC1ssvVfnjhxIn5+fjRp0oRRo0aJskHq2UM1dnZ2TJ48GV9fX5o0aUKrVq24f/8+y5cvp3r16vTo0YMdO3bkqe8DBw5k5cqV9OnTh7///hsXFxf8/f05e/YsixYt0qjwzorGjRvj6OhI7dq1cXBw4O7duyxbtozmzZvnav/ckJ3wvEwmA/13E1dPVwpawvPx8fGYmuqeTZaQ+NSQZiglJCS+SDZv3kytWrWYPXs2M2fOxMvLi9mzZ+dq3zVr1lCoUCHGjBlD165d8ff3z7a9h4cHixYtws/Pj6lTp2JjY8OhQ4dy1K/s1q0bf/75J4UKFWLevHmMGjWKoMvHMLAvjmnFRrm+VjUG9i4YFChC/O0AIo6vJPr8H8gVphRoMwnLrzuRrhSy9V8uWLAgAQEBVKxYkdmzZ7No0SJ69eqlpQEZGxtLVFQUNjY2HD16lOHDh7Ny5Uratm3L0aNHadSoEQcOHCAoKIhTp05x+/Zt0bIxJSWFqVOnsm/fPoKDgzE1NaVu3bpcuHCBkydP0r17dzZs2MC4ceOIiIiga9eurFmzBiMjI+zs7Ojdu7fGfa9UqZL4etCgQcTFxbFgwQK++eYbli9fzsiRI9m0aVOu7+GNGzfo06cPxYsXx8jICEdHR/r16yfOfm6+GJxldXRaVAhBs1sQd+O4xvbEp9d5vWkiwfPbE7ywM6H+P5Ia9kyjTdTpzQTNbsHC7QF069YNa2tr6tSpA6jybX18fHBycsLExAQvLy/u3LmDi4sLPXr20hBQfxESxujRoylSpAgKhYKSJUsyZ84cDXH6jOLwq1atEsXhq1evrlUh//r1a/r27UvhwoVRKBQULFiQ1q1ba5kG6BLov31bMy2gT58+mJmZ8eLFC9q0aYOZmRl2dnaMHz/+k8pvlng3JC9vCQkJifdAJpMxbNgwli1blm/H/NT9l7t3746/vz/Dhw/H1dWV8PBwzpw5Q+fOnenevTvHjh1j4sSJPH/+nIULFwIq6aA2bdoQFhZGxYoV6dq1K6VKlSI2Npa1a9fy+PFjLl26ROXKlcXz9O3bl/Xr19O0aVO8vb1JS0vj9OnTNGzYkOHDh7NmzRq++eYbbt68qTGDfPnyZWrUqKGhfZlb5s+fz549e2jUqBGOjo7cvn2bVatWUaFCBS5cuIDnzycJitCtY6irKCfx6TVCt09D38oRs0qNEVJTiP17HwhKCvZZLIrXR53eTPTZ3zFxcKZRzcp4e3sjCAJDhw7l22+/Ze7cubRs2RJvb2/+unCFw4cOkZiUhGGxahRoMQYAZWoSrzeOR4iPoGazzjSu4cbDW//g5+fHyJEjxRSEp0+fUqxYMapUqUJsbCzffPMNMpmMuXPnYmRkxOPHj0Vh+tq1a3P79m1GjBiBi4sLoaGhHDt2DB8fH1H3NaNAf/PmzUlISODXX38lKiqKq1evig8Tffr0Ydu2bZQoUQJ3d3eqV6/O8ePHxeKpIUPyNiMv8WkhBZQSEhIS78GHCCifRSTQcOEpkrOQ93kXFPpyjo/xyJeiDysrK3r06JHtNbdo0YJbt25pzWSlp6eTnp6u4bsdFRVF2bJlad68OWvXrgUgICCA+vXrM3LkSBYvXqxxDEEQkMlkREdH4+joyKhRozRmn0eNGsXatWsJCQnJ85JxYmKiVgX51q1b6dq1K4ePBzD4eHyWRVO6AsqXv40kPS4Cp29+Rc9YtZSdEvqEV+tGYermSYEWY1X34N+A0tTVg5B/jot5riEhIRQuXJgWLVqwZO1mUUA95uzvRJ7ejGn5BmJAGXV2KzEX/CnYdzFGBQqTrhSoW7IAFjf/YOWyRTx58oQiRYqIAaWtrS2BgYFiEdvevXtp3bo1+/bto0WLFkRFRWFtbc28efMYP368zmuOi4ujSJEidOzYUSNHOSQkhDJlytCpUydxe58+fdiwYQPTp09nypQpYtuvvvoKuVzOlStX8jJUEp8Y0pK3hISExCfGp+6/bGVlxcWLF3n58mWe99XT0xODSaVSSUREBGlpaVSrVk2jsGnHjh3IZDKmTZumdQx1Jb6lpSWtW7fm999/F4uD0tPT2bZtG23atHmn/MOMwWRSUhJhYWHUrFkTgICzF/JUgZ8WF0Fq6GPMKjQQg0kAQ/tiGLlUJvGRdgBlVrmphvD8n3/+SVpaGuUadNAQUDf9qoXWvgn3zqAo4obcyIyUuCjSE6I5fesxe8NsSU9P1/KX79y5sxhMAtStWxdAFJw3NjbG0NCQkydPEhkZqfMadQn0h4WFoaenh7u7OwEBAVr7DB48WON13bp1/1NjAon8QSrKkZCQkPgE+ZT9l+fOnUvv3r0pUqQIVatWpVmzZvTq1UtDQig7NmzYwPz587l3755G9XVGsfJHjx7h5OSEjY1Ntsfq1asX27Zt4/Tp09SrV4/jx48TEhKS56VuNREREfj6+rJ161Yt2aPo6GjIQ81NerRqfwObQlrvGdgWIenJPyhTkpAbvpUz0rdy0BCeV0tdbbidhIHV2+16xubIjcw0jpkW+YrUN095vkTT2UjNjrO3yWh6lNk4QB1cqoNHhULBnDlzGDduHA4ODtSsWZMWLVrQq1cvUTs1J4F+CwsLjdfqXNjM580qYJX4fJACSgkJCYn34ENmDX2q/sudOnWibt267Nq1i6NHjzJv3jzmzJnDzp07RW/urNi0aRN9+vShTZs2TJgwAXt7e/T09Jg1axaPHj3Kc1+8vb1xcHBg06ZN1KtXj02bNuHo6Chqe77LtZ07d44JEyZQuXJlzMzMOHz4MAsWLODq3UdQqc47HTe3yPQNGdilJaaG+pw8eZLrz6Nyva8gKDFyqYJFzfY637+EE9suB+P+bzyXG8H50aNH07JlS3bv3s2RI0eYMmUKs2bN4sSJE1SpUkWnQH9Gbt26hUwmE2cqszqnxOePFFBKSEhIfMJ0qV6U2iUKiLlzenJZtoGl+v1axW2Z2bbCBxPKLliwIEOHDmXo0KGEhoby1Vdf8dNPP4kBZVYC8f7+/hQvXpydO3dqtMm8tF2iRAmOHDkiyj5lhZ6eHt26dWP9+vXMmTOH3bt3880337xT4BIZGcmff/6Jr68vU6dOFbf//fffANx6GUOBSlntraNvliqJptSIF1rvpUY8R25soTE7CSrbQyN9Vd+fRSRw6qVqrNMiX2Fg9TZgS0+MQZkUp7GvgXVBhNREjF0qZ9mnqXtvs669S+4vAtVYjBs3jnHjxhEYGEjlypWZP38+mzZt0inQnxF9fSnM+H9ByqGUkJCQ+MRR+y8fG12Pnu7OONuakDlckwHOtib0dHfm+Jh6+PV3/yDBZHp6umrpNwP29vY4OTlx+fJlWrRQ5faZmppqtYO3M1QZZ8EuXrzI+fPnNdq1b98eQRB0anlmnhXu2bMnkZGRomxQjx493unadPUNYNOOfTrbP1/ej9A/stYa1TezwcC+OHG3/tQI/lLePCXpyVWMS1TT2qewjYkoS/TdrpsYFq0Ecj1irx7UaBf7936tfU3K1iH5xT1ebRir9Z4yKQ5BmU6aUmDekXtZ9jkjCQkJJCUlaWwrUaIE5ubmJCcnA7oF+jNSrlw5EhMTxYpwgPXr1yOTyaQinC8M6dFBQkJC4jPhU/Bfjo2NpXDhwnTo0IFKlSphZmbG8ePHuXz5skaBR9WqVdm2bRtjx46levXqmJmZ0bJlS1q0aMHOnTtp27YtzZs358mTJ6xYsQJXV1fi4t4GXV5eXvTs2ZMlS5YQGBhIkyZNUCqVnD59Gi8vL9q1a8eqVato06YNVapUoXz58vzxxx+UK1eOr7766p2uzcLCgnr16jF37lxSU1MpVKgQe/Yf4v4DbTvK3GJdvx+h26fxym88ZhUbI6QlE/v3fuQKEyzrdNNqX6dEAS4FQGJKOqcfhiEzscKiWitiLu0i1H86xsWrkhL6hMTHfyM3toAMs7wW7u2IPredlFeBhB9agqFjSYSUJFLeBJFw/yyFhqwl3cSSy09zl6/44MEDGjRoQKdOnXB1dUVfXx9/f39CQkLo0qWLeM90CfQHBwdz4MABateuna8KCBKfLlJAKSEhIfEZ8l/5L5uYmDB06FCOHj3Kzp07USqVlCxZkuXLlzNnzhyx3dChQ7l27Rrr1q1j4cKFODs707JlS/r06cPr169ZuXIlR44cwdXVlU2bNvHHH39w8uRJjXOtW7eOihUrsnbtWiZMmIClpSXVqlWjVq1avHz5El9fX1xcXKhcuTK9evVi4sSJ71SM8+LFC6ZOncqhQ4cICwvDwMCAefPmYWxsjGWpaljUaEPE8dWgTCfiz9XE3wpASEtGEASEdCet48Vc3k3UXxtJT4xB37wApq6epEW+JPr0ZtDTw6hIedLiInjjPx3bFmOJPL6a5BeqWcMOVQtzaQ2ExCaJ6QtWnn1Ark/s3/tIfHgJkCE3sURITUImf7u0LzcwQqYwRUhJIO7GMbh+FGRy9MyssarTDblCVfUuz0KYPTPDhg0D4PDhw6xatQqlUomjoyPbt2+nQ4cOTJs2DR8fH7p164aTkxOzZ89mypQpyOVyXFxcqFu3LhUqVNDIoQSVWDqoZjfj4+PFqv/o6GgsLSVP8c8VKaCUkJCQkNCJLus/Q0ND5s6dy9y5c7XaZwwoTU1NtTzAQZVbOXnyZCZPnqyxvXnz5lpt9fT0GD9+vIYGYlpaGkqlUsMKUt0vmUxG9+66K5yz4uXLl9SoUYOoqCgGDhxI2bJlefHiBf7+/vjtPkL7dTeQ/et8kxr6BLmRKZZ1upIeHUrMpV2kvgkSjyUIqgIVmb4C8+ptkBsakRR0g/hbf2JRox2OPeeJbV9vnkRabDih26dhWq4e5hW8cCtelBrlXACISkjFSp0rK5OREvIQEDCv1hqDAkVIeRlI3I0jJD2/Ix4z5U0QQmI0MoUJVnX7ItMzIC3yFcmvHmDh3k5sJ7ewp968E4wfr+05nnG538DAAENDQ+Lj4xk2bBjly5fHwcGB1q1ba+3n6emJp6cnLi4ueHp6sn79egCNh4T169ezatUqChcuDKhki6pWrcqLFy/Yv38/UVFRUkD5GSPlUEpISEhI4OPjg0wm486dO1rWf2lpafz444+iRZ+LiwvfffedmEeXmaNHj1K5cmWMjIxwdXVl586dWm2ioqLyZBG4aNEi8fxqz3BQuenIZDJGjx5NmTJltKRwsiMlJYWmTZvy8uVLZDIZq1atYtOmTaK14d670Ro2i3Jjc+y7/IRF1ZZY1++PzNCY9PhI4u+f4+VvI3i5WqWvaFysMpbu7TCv0gy7NpMwq9yE2H8OEH70V57/0oegeW1IfvmA9PhILOt2x6bRIGyqNmPmgBbIZDKCgp8RHxnKi1/7E/xze16uGUbS02vYdfJFbmxG9Nnfibt5TDU24c9Ien4XgKSn1wBQFCyNgW1hYq8eJObvfSgTokm4f07j2oPDE3Jl0/j69WvevHkj3texY8eiUCgA1cxuZlJTUzl58iQ2NjYYGRkxaNAgjffv3LnDmzdvAOjXrx/9+/dn6tSpHDlyhI4dO1K4cGHu37+f6zGU+HSQZiglJCQkJEQ6duxIqVKlmDlzpjhbNWDAADZs2ECHDh0YN24cFy9eZNasWdy9e5ddu3Zp7B8YGEjnzp0ZPHgwvXv3Zt26dXTs2JHDhw/TqJHKnzwhIQEPDw9evHjBoEGDKFq0KOfOnWPy5Mm8evVKtAhUs27dOpKSkhg4cCAKhYK2bdsSGxvL1KlTqV+/PkqlkpMnTzJmzJg8XWtUVBQ3b96kRIkSjB8/XrSB9Pb25tKlS5x8EKlRUW9WuYlGZbpMzxCBRML3L0RRxBVlcgLp0aFEn9uOokh5FE5lENJTMXQqTdy1w8TfPon5V83Rt7Aj6vRmlAkppL4JBlTC807/6kyGhISQnpKGVb2eKJNiiT63DZARdXIj6bFvkBuZkx4bLvYjOfgGRoXLiUvaKW+e8mbXbMy/aoZZ+QbE3TzOm92zse/ki3GxKgCkpyZR38uT8NDX2Y6BoaEhKSkpbNmyhdjYWAYNGoRMJmPixIls27aN5cuXizaNt2/f5tWrV1hYWPDdd99hamoquuScPn0aT09PjRlIdcFPWFgYjRo1IiIiglOnTomV4xKfGYKEhISExP8906ZNEwCha9euGtuvXbsmAMKAAQM0to8fP14AhBMnTojbnJ2dBUDYsWOHuC06OlooWLCgUKVKFXHbjz/+KJiamgoPHjzQOOakSZMEPT09ITg4WBAEQXjy5IkACBYWFkJoaKhG28uXLwuAAAhWVlbCd999l+drfvHihQAI33//vbgtMjJScHBwEHr27iO4TNovOE/aL9g2Gy0AgmOv+YLzv9ucJ+0X5CZWAiBY1u0uIJMLBrZFBOv6AwT0DARkcrF/6j/bluPEfRVFygsyhamATC78uPUvjevV19cXFIVcxbYyhYnWsTL+mX3VXHCetF8oMm6H6twgyAyNBZNydYUCrb8VCo/eKuiZ2QiGDiXEY1rW7SEYm2Q/Bh4eHkLRokVVfbe1FSIiIsR26nPv27dP3NagQQPBwMBA6NGjh7jtxIkTAiAUKlRI3Obt7S0AgkKhEDw8PARHR0fBxcVFePr0aZ7HUOLTQVrylpCQkJAQyWyLd/CgSq5m7FhNKZpx48YBcODAAY3tTk5OtG3bVnxtYWFBr169uHr1qliM8ccff1C3bl2sra017PoaNmyo0yKwffv2Wu4qatatW0dkZCQ//fRTnq81o1ZlZhvIy1f+1rZZlOn+yVQUdsP527049pxH9PntyPQNQVBi23ws9l1mIDdVVb/LDIxIT4gmPSEaQZmO3MgUBCXF0oI1jmdtWwDkb88lN1DZQZpW8sa+ywzxz6xKMwBMSn/9bzsFchMr5EbmmFVsTGroU8L2zCFs10xM3LxICXlEepyqwjvh3hmqun+d4xgYGal0MjPbNKpRWyZGRERw4sQJjI2NSU1NFY8XExMDqJbH1Uvk6grx9u3bc+nSJV6/fk1SUpIkev6ZIy15S0hISEiIZLQ/BJX1n1wup2TJkhrbHR0dsbKyEq0B1ZQsWVJL1Lx06dKAKifS0dGRwMBAbty4kWWQmNnyMHOf8gs7OzuMjY1ZunSpKBWkplAR51z/QKqvNin4JsrEGCzc2xNzcQcGNk4oCpVFmRgLQNhO3UFv5us1NTYmJv3taz2LAqTHhWPkXFFDtDw9Joy4qwc1LBhlMhn6ds7YNPwGgOhz24n6ayMG9ipbzLToEPTMrEmLfMWZgKe5HgNdNo2RkZGiZeLDhw8RBIGYmBi2bdvGtm3bdB6zUKG3NpR79+5FoVCwbds2WrVqxYoVK5gxY4bO/kh8+kgBpYREHvmv9P8kJD4GxsbGOrdn5XzzLiiVSho1asTEiRN1vq8OQHPq0/uyZcsWEhMTSUxMxMfHh5o1a4o2kPceBCIIQt6uWz2DmaFSWkhPBWUaAFZe/TB0UAV2kX+uQZmSwIoVq2hcq4rGYeRyGUYZ5ICMilQg5eV9koNvYubqoXVaZYoqFzH938A1I4YO/wbjyvRM7+Q8Bnv27BFfZ549LFGiBFeuXBHzbDMW8mQ87vXr1xk/fjw///wzJUuWJCYmhvR0VV/atWvHxo0bCQgIQC6XZ1nkJfF5IP0KSkjkgsCQWDZfDCbgfijBEQkaS2EyoKiNCV5l7OnuXpRSDub/VTclJPIdZ2dnlEolgYGBlCtXTtweEhJCVFQUzs7OGu3VM1UZA7EHDx4A4OLiAqiCkbi4uHf224b8CXD9/f0pWrQoKSkpzJ07l4EDB1KuXDkCAwN5E/Iax+R4ZBlm/7JC/X2gKFwOuZEZsdcOA5D45B8ijv4KegaQnorCsSRGzhUBiDYyA0FJ17bNdT6QWpkYiDqURsUqE3PRn7hrh1EmJ6AoXA6UShICLwCQHh0ClCf67O+kx0WgTIwh9tphlAkxxF49gJ55ATHY1bd0QE8uo0DBou81BgMGDODKlSts374dR0dHLlxQ9cXY2BgnJyfxuGrrxapVq2Jubs7u3buZMGECoLLvbNasGQsXLkRPT4/27XV7kEt8Hkg5lBIS2fAsIoGeay/SaNFf+F0MIihTMAmqH5OgiAT8LgbRaNFf9Fx7kWcRCf9FdyUk8p1mzVR5epkrrxcsWABo60e+fPlSo/I7JiaGjRs3UrlyZRwdVV7UnTp14vz58xw5ckTrfFFRUaSlpeXYL7U+ZlRUVK6vJTN6enro6+tz/vx5OnTowObNmxkxYgTPnj3DyMgIZ3vtnEFdJD+7rTqesQUFWn+LkJYMyIi7dhgjl8qYlFXJLyW/eqCxn4GenNTEOJ3X62BuJFaYy/4NBk3cPEl985TIE78RffZ30qJCVOe1UHmGG5d0B7kcITWJiKO/EvvPfhRFymPXfgoJ905jYF8cPTNr0pUCXbu83xh8841qST0oKIhx48bx+vVr3N3dSU5OJjExUecxASpVqkT58uUB1WfqwoULFClShPT0dK5evZrtOSU+baQZSgmJLNh6OZhpe2+T9u+Xekb5EF2o3z/3OJyGC0/h28qNLtVzr4knIfEpUqlSJXr37s2qVauIiorCw8ODS5cusWHDBtq0aYOXl6Y4dunSpenfvz+XL1/GwcGB3377jZCQENatWye2mTBhAnv37qVFixb06dOHqlWrEh8fz82bN/H39+fp06cUKFAg236VKFECKysrVqxYgbm5Oaampri7u+cp31JtAzlq1CiaN2+Ok5MTK1asoFSpUsTFxdHArRB+F4Mwq9gQs4raM3kyfUP0bQoRe3k3QloyeqbWKocaQcC+kw/GxasCoExNIi0smKi//EiLfImhY0lMS7tTRAijcOHCWtc7ZMgQxo8fT8+1Fzn3+K08kEmpmpi2fCvyHnfjOOEHFyE3VBXOGLtUQs/ECpm+Acr4KEzL10fP1JrwAwtJj4/Cttko9OQyahW3ZVbXHzjz55Fsx+DkyZM8ffpU5z2V/1s0NGHCBHx8fACVxmSdOnX4888/mTx5MsWLFyckJIRmzZoxbdo02rRpQ7FixejXrx+nT5/mzJkzVKum8jMfOHAgw4YNw9zc/J292CX+W6SAUkJCB8sCAvn56IOcG+ogXSmQrhSYtPMmYXHJDPcqlc+9k5D4uKxZs4bixYuzfv16du3ahaOjI5MnT2batGlabUuVKsXSpUuZMGEC9+/fp1ixYmzbtg1vb2+xjYmJCadOnWLmzJn88ccfbNy4EQsLC0qXLo2vr2+u3FIMDAzYsGEDkydPZvDgwaSlpbFu3bo8BZQ52UB2dy/K+vNPs++HtRPmjQYReeI3UiNeoG/lQIHWE8VgElSWiA7dZxN9fjsJ984Qd+sEckMTXMqXy/Z6Z7atQMOFp3J9Pbnpk75cxsy2FfJlDDLj6urKlStX8PX1Zf369YSHh2Nvb0+VKlWYOnVqtvuuWLGCuLg4+vbti7m5uU43HolPG5kgCNlPu3xm+Pj44Ovry5s3b3J8wn1fMltMfc6cPHkSLy8vAgIC8PT0zLLdx7y/78L69evp27cvT548EfO18srWy8FM2nkz3/oUNLuF6Hn7peDp6cmpU6ofuubNm7N//37xPZlMlu/Xm/mY7zPOuf1/mx+fpZxQz/6sW7eOPn36ADB69GgWL14MqJZ14+LiPsi5JXKHepYwpxWKvKCeJfTr755j2/z+PprTrgKdpZUTiQ+AlEP5kXj+/DldunTB3t4eCwsL3N3d8xyIzpw5k927d3+Q/kmoeBaRwLS9t/P9uNGJqTk3yob27duLuWyfCmXLlsXPz0/DZ1ni/enZsyd+fn7UrVv3v+6KBKpZQn15/lW4A+IsYW7oUr0o4xuXzrlhLpjQuIwUTEp8MKSA8iOgVCpp1aoV+/fvZ+DAgcyePZty5crp1OnKjg8ZUNarV4/ExETq1av3QY7/sejZsyeJiYlalae55btdN8Wcyfzkz7sh77xvamoqx44d0yp++K9xcHCgR48eWjPaiYmJ/PDDDx/03O8zzvfv32f16tUfoFf5Q9WqVenRowfFixf/r7siARSxMcG3lVu+HnN6KzeK2Jjkuv1wr1LMblcBhb5cw1s8N+jJZSj05cxpV4FhXiVz3kFC4h2RcijziCAIJCUl5UkX7f79+1y9epW5c+eKcglDhw79JDS3kpKSMDQ0RC6Xi44InzN6enrv7LYQGBLL6Ydh+dwjFcERCTwMjaWkfd4lhU6fPk1sbOwnF1Bmxcf4HL3POCsUijy1fxASQ6Q8UtIc/T+mS/WihMUlv3NedUbedZawS/Wi1C5RgO923eT0wzBRUigr1O/XKm7LzLYV8hTASki8C/8XM5RBQUGULFmS8uXLExKimilat24d9evXx97eHoVCgaurK7/++qvWvi4uLrRo0YIjR45QrVo1jI2NWblyZZ7Or66Gy5yumpcfNplMRnx8PBs2bEAmkyGTycScK1DZWvXr1w8HBwcUCgVubm789ttvGsc4efIkMpmMrVu38sMPP1CoUCFMTEyIiYkR3zt58mSerg1039/Hjx/TsWNHbGxsMDExoWbNmloWbQDJyclMmzaNkiVLolAoKFKkCBMnTtQKtmUyGcOHD2f37t2UL19evMbDhw9rtFu/fj0ymYynT5+K25RKJT4+Pjg5OWFiYoKXlxd37tzBxcVF4x5uvhis8+k/+uJOXvuN59mirgT/3I5X60YRf++MVjshLZWI46t5trgbwQs6Euo/nbSYMLH/my68tVfLzXipOXDgAK6urhp5fPfu3aNDhw7Y2NhgZGREtWrV2Lt3r8Z+qamp+Pr6UqpUKYyMjLC1taVOnTocO3ZMbPP69Wv69u1L4cKFUSgUFCxYkNatW2vcv7wik8k08id9fHyQyWQ8fPiQPn36YGVlhaWlJX379iUhQVNeKTk5mTFjxmBnZ4e5uTmtWrXi+fPnWufIPM4tWrTIckbv66+/FitJAa1xB7h9+zb169fH2NgYR6dC1O8xklkH7wDQb/0V2v56juZLz2BmZIBzo9747L1NYEhslseMiIhg/PjxVKhQATMzMywsLGjatCnXr1/PzS2U+AT5FGYJi9iY4NffnWOj69HT3RlnWxMy90QGONua0NPdmeNj6uHX310KJiU+Cl/8o/ajR4+oX78+NjY2HDt2TCwk+fXXX3Fzc6NVq1bo6+uzb98+hg4dilKpZNiwYRrHuH//Pl27dmXQoEF88803lClTJk99KFOmDLVq1WL+/Pl06dJFy8IqN/j5+TFgwABq1KjBwIEDAZVsBqgEhmvWrCkGXXZ2dhw6dIj+/fsTExPD6NGjNY71448/YmhoyPjx40lOTsbQ0DDP/VGj6/6GhIRQq1YtEhISGDlyJLa2tmzYsIFWrVrh7+8v+vyqUwHOnDkjCgrfvHmThQsX8uDBA63l/TNnzrBz506GDh2Kubk5S5YsoX379gQHB2Nra5tlHydPnszcuXNp2bIl3t7eXL9+HW9vb5KSkjTaBdwP1fnEH3tlL8al3DF19URITyP+7l+E7Z6NrMM0TEpWF9uFH1pC/O0ATFw9MCpUjqSgG4T+4QOoHiYCHoTig1uex+vgwYO0aNFCfH379m1q165NoUKFmDRpEqampmzfvp02bdqwY8cO8f76+Pgwa9Ys8XMTExPDlStX+Oeff2jUqBGgys28ffs2I0aMwMXFhdDQUI4dO0ZwcHC+F6J06tSJYsWKMWvWLP755x/WrFmDvb09c+bMEdsMGDCATZs20a1bN2rVqsWJEydyNTPbuXNnevXqxeXLl6le/e2YBAUFceHCBebNm5flvq9fv8bLy4uU1DRKN+5BcKySM4d2gJ7u/xfRian4XQxi/fmn1C1ZQGcu3OPHj9m9ezcdO3akWLFihISEsHLlSjw8PLhz5w5OTk45XpPEp8enMktYysEcn1Zu+OAmOXdJfDoIXxjTpk0TAOHNmzfC3bt3BScnJ6F69epCRESERruEhAStfb29vYXixYtrbHN2dhYA4fDhw1rtnZ2dhd69e+fYp9evXwuVKlUSDA0NhTJlygihoaF5u6h/MTU11Xm+/v37CwULFhTCwsI0tnfp0kWwtLQUrzUgIEAAhOLFi2tdv/q9gICAbPuQm/s7evRoARBOnz4tbouNjRWKFSsmuLi4COnp6YIgCIKfn58gl8s12gmCIKxYsUIAhLNnz4rbAMHQ0FB4+PChuO369esCICxdulTctm7dOgEQnjx5IgiC6t7r6+sLbdq00TiHj4+PAIj3MzYpVXCZtF9w1vFXZNwOjddFJ+wWDOycBSPniuK2gn2XCIBg9lVzjbYmrh4CIFjW7iq4TNovxCWl5nq8BEEQHj9+rDUuDRo0ECpUqCAkJSWJ25RKpVCrVi2hVKlS4rZKlSoJzZs31z2QgiBERkYKgDBv3rws22SFh4eH4OHhofM9QJg2bZr4Wv2Z6devn0a7tm3bCra2tuLra9euCYAwdOhQjXbdunXTOmbmcY6OjhYUCoUwbtw4jX3nzp0ryGQyISgoSNyW+f+t+vNapO9Cofh3BwTnSfuFwiM3CzKFqQAIhQavFcdTPZbq18W/OyCU/uGgUKBgYY1jJiUliZ9zNU+ePBEUCoUwffp0jW2AsG7dOq372Lt3b8HU1FRru8SnwYPXMcK0PbeEevNOaH13uEzaL9Sbd0KYtueWEBgS8193VULio/DFLnnfunULDw8PXFxcOH78ONbWmo4HGXMgo6OjCQsLw8PDg8ePHxMdHa3RtlixYhoaankhLS2NVq1aYWpqys2bN4mNjaVx48Ya7g6///47MpmMR48e5fn4giCwY8cOWrZsiSAIhIWFiX/e3t5ER0fzzz//aOzTu3fv9/bGze7+Hjx4kBo1alCnTh1xm5mZGQMHDuTp06fcuaNaSvzjjz8oV64cZcuW1eh3/fr1AQgICNA4Z8OGDcVZWYCKFStiYWHB48ePs+znn3/+SVpaGkOHDtXYPmLECI3XQeHxWg44auQGb1MT0pPiUCbHoyjsRsrrt+OV+PgKABZVW2rsa1H9rZaaADwJi8vTeB04cABLS0vxXkZERHDixAk6depEbGysuG94eDje3t4EBgby4sULAKysrLh9+zaBgYE6r8vY2BhDQ0NOnjxJZGRkFleffwwePFjjdd26dQkPDycmJgZQfW4ARo4cqdEu84ytLtRLytu3b9dILdm2bRs1a9bMdlVgi/8eDJ3KIHcoJc426ZlYYurmmeN505UCyWlKIuNTuPs6RtyuUCjEVJf09HTCw8MxMzOjTJkyWv8fc0t8chq3X0ZzNTiS2y+jiU/O2U1G4sOhniU8Nd6LWz7eHBhRh11DanFgRB1u+XhzarwXPq3c3ilvWkLic+SLnRdv2bIlDg4OHDlyBDMzbS/Ws2fPMm3aNM6fP6+VxxUdHa0h6poXodzM+Pv7c+nSJS5dukTp0qU5cuQIdevWpVmzZhw7dgxTU1Nu3bqFnZ3dO53nzZs3REVFsWrVKlatWqWzTWhoqMbr97keNdnd36CgINzdtfXV1D7AQUFBlC9fnsDAQO7evYudnV2u+q0rKLC2ts42GAoKCgKgZEnNvCUbGxuNIDglTZnlMRIeXiL63DZSQh5Dekb5n7fZS2nRoSCTo29dUGNfA5vCGq9DQkLzNF4HDhygcePGoh+u2id5ypQpTJkyJcv9CxUqxPTp02ndujWlS5emfPnyNGnShJ49e1KxospLWKFQMGfOHMaNG4eDgwM1a9akRYsW9OrVS7TIy08yj5/6/q9atYoJEybQrVs35HK5xkMDkOsUk86dO7N7927Onz9PrVq1ePToEX///Tddu3ZFJpPp1JPcejmY0FfPMS1bR+t4BjaF8nB1cPtFDNsuB9O5elGUSiWLFy9m+fLlPHr0SCPItbW1FXU1M+dyZkYQwGfvbQ0P+aDZLTD/qjm2jYdIHvKfCKYKfdyc8i4ELiHxJfHFBpTt27dnw4YNbN68mUGDBmm89+jRIxo0aEDZsmVZsGABRYoUwdDQkIMHD7Jw4UKUSs3g4n1m886dO4e+vr5YFFC+fHn27t1L48aNad26NTt37mTDhg107dpVnNHIC+q+9ujRg969e+tsow4g1Lzv7CRkf39zi1KppEKFCqIncGaKFCmi8Tqrql4hH7T5DfV13/ukZ7d44/8jiiJu2DYegp6ZDejpE3fjGAl38u5goT5NbsYrISGBkydPahSLqcd7/PjxWc6aq4PnevXq8ejRI/bs2cPRo0dZs2YNCxcuZMWKFQwYMABQzf61bNmS3bt3c+TIEaZMmcKsWbM4ceIEVapUyfP1Zce7VmXnlpYtW2JiYsL27dupVasW27dvRy6XU716dX7//Xet9vmqOSqoxmXq3tvUKlGADcsXMGXKFGrXrs3Dhw9p27YtpUqVYvv27VrfL7p4FpHA6cA3JKam43cxSGeeXkYP+Yz5nFIBhoSExH/BFxtQzps3D319fbGAo1u3buJ7+/btIzk5mb1792rMmmReYs0PZDIZaWlpvHr1SkzEr1u3Llu3bqV9+/ZUqlSJ6OhoUU4op2NlRl0Nm56eTsOG2l6zH4rs7q+zszP379/X2ufevXvi+6AqKrp+/ToNGjTQeW35gfpcDx8+1JiZDQ8P15jZdLE1RQZay94J988h0zfEofOPyPQNxO1xN45ptNO3tAdBSVrkKwxs385Kpka8rVCWAVVKO+d6vE6cOEFycjJNmzYVt6krmQ0MDHI13jY2NvTt25e+ffsSFxdHvXr18PHxEQNKUI3DuHHjGDduHIGBgVSuXJn58+ezadOmHI+fH7Rt25bhw4ezYMEClEoljx490piV1PVZ0oWpqSktWrTgjz/+YMGCBWzbto26detqpbuoUWuO6lvakxr5Uuv91IgXWtvkRmYok+M1tgnpqaTHRQCQphT4btdNbvr74+Xlhb29PYUKFWLnzp0AbN68GVBpderr6+usYFd7yIfGqpQOJA95CQmJz4EvNodSJpOxatUqOnToQO/evTUkVdQzJRlntqKjo1m3bl2+90P9o5/Zx7R169YMGDCAp0+fUr16dQoXLqxrdw1MTU01ci9BdS3t27dnx44d3Lp1S2ufN2/evHvnsyG7+9usWTMuXbrE+fPnxW3x8fGsWrUKFxcXXF1dAVXV74sXL3SKTCcmJhIfH6+1Pa80aNAAfX19LUmoZcuWabw2VehTVNfMjkwOMhCEdHFTWlQIiYEXNJoZF1fNQMf8vU9je8zlPeK/i9qaYGGiyPV4HTx4kGrVquHg4CBus7e3x9PTk5UrV/Lq1ats9w8PD9d4z8zMjJIlS4qSTAkJCVqV7iVKlMDc3PyjaqTq6elhZGQkOgEtWbJE4/1Fixbl+lidO3fm5cuXrFmzhuvXr9O5c2ed7WISUzn9MIx0pYBx8WqkvLxP8su3gWt6QjTxt09q7advVZDkZ5qzmrHXDoszlOlKgdMPw0gTZAiCQGhoKFZWVoAqZ1id32pkZCSmMWRkWUAgk3beJDlNSV4n3tX5nJN23mRZgO68WQkJCYkPxRc7Qwkq/cdNmzbRpk0bOnXqxMGDB6lfvz6NGzfG0NCQli1bMmjQIOLi4li9ejX29vY6f6TfhxYtWtC6dWvWrl3Lw4cPadOmDQqFgsOHD7Nv3z7q1atHQEAAU6dOZfr06Rr7ZpaDqFzlK44fP86CBQtwcnKiWLFiuLu7M3v2bAICAnB3d+ebb77B1dWViIgI/vnnH44fP05ERES+XpOarO7vpEmT+P3332natCkjR47ExsaGDRs28OTJE3bs2CEu7ffs2ZPt27czePBgAgICqF27Nunp6dy7d4/t27eL2p/vg4ODA6NGjWL+/Pm0atWKJk2acP36dQ4dOkSBAgU0Zka9ythrLS8al6hO7OXdhG6bhqmbB+nx0cT+cwB9q4KkvnkqtjN0KI6Jqwdx/xxASI5HUagcSU+vizNfMpkMr9L2ALker4MHD9K3b1+ta/rll1+oU6cOFSpU4JtvvqF48eKEhIRw/vx5nj9/Lmodurq64unpSdWqVbGxseHKlSv4+/szfPhwAB48eECDBg3o1KkTrq6u6Ovrs2vXLkJCQujSpct73fe84O/vz4QJE3jy5Aldu3Zl+fLlXL58mWfPnhESEiI+AGYOfgGuXr3K4MGDOX/+PKmpqVStWhUTExPGjx8vPmypi30y8jgsHr2SKkkXi5rtib8dQOj2aZhXa4XMwIi4a4fRt7Aj9U080ee3kfzyPmlRIQjKdEhPJeT37zEpW5uU0CckPf4HubGFeGw9uQzTom6c3L9Z3Kb+nKlXKTLnUO7evZu+ffviNGgV0We3kRB4ASFVdb3K1CTkBtmLxUed3Ur06c1YNxyIRbWW/Hz0AXZmio9msyeTyRg2bJjWg5qEhMT/D190QAmqpUF/f3+aNm1K69atOX78OO7u7vj7+/PDDz8wfvx4HB0dGTJkCHZ2dvTr1y9fzy+TydixYwcLFy5k/fr1fPvttxgbG1OzZk2OHDlCo0aN6N69Oz/++CMlS5bka++2bL4YrJGEryatSGv07V/w7eTvSUtJonfv3ri7u+Pg4MClS5eYPn06O3fuZPny5dja2uLm5qah8fchyOr+njt3jm+//ZalS5eSlJRExYoV2bdvn4amoFwuZ/fu3SxcuJCNGzeya9cuTExMKF68OKNGjaJ06fzxr50zZw4mJiasXr2a48eP8/XXX3P06FHq1Kmj4erS3b0o688/1djX2KUStk1HEn3Bn4jjq9G3csDasw9p0SFEv9FsW6DZKCKNLYi/c4qEBxcwcq6IfUcfXizvgyAI9Kip+nHPzXjdvn2boKAgnf7drq6uXLlyBV9fX9avX094eDj29vZUqVJFYyZ85MiR7N27l6NHj5KcnIyzszMzZswQ0yuKFClC165d+fPPP/Hz80NfX5+yZcuyfft22rdvnx+3Ps/89ttvPHr0iEuXLqGnp4ebmxvVqlVj/fr1+Pn58eOPP2Jg8Db1oHPnzlSrVo1p06Yhl8tZt24dSUlJKJVKGjZsiL29vc7zvI5OwujfBwd9Mxscus4k4tgKYi74Izc2x6xyU/TNbAg/tITEx1cxdfNA38qRtLgIYi/tJCnoOskv7qIoUh77LjMI+f178djpSgG96h1pmh7BsWPHSE9Pp1SpUnTt2pWjR4/q7E9CimoGPGz3HPQtHbD26E3M5d2kRb4k+szvWHtpP1ioifzLj5hz27FpMgzzyk3E7ep8TimnUkJC4mMgE/KjokHivXkWkZBnsVwpCf/diYqKwtramhkzZvD992+DgZ5rL3LucXiOeWt5QU8uo1ZxW/z6a1e+Z8XcuXNZsGABr169+mD5pe+Dp6cnqamp7NmzB0NDQywsLHLeKQvWr19P3759efLkCaamphQuXBhPT08OHTokzmb/8ssvDB8+nN9++42+ffsiCAJlypShePHiHDp0SLxHiYmJuLm5UbJkSTF4y3h8FxcX4pLTqOBzJEuZqIwIaamgp4dM9jY7KC0qhBerB2NZqxNWtbvq3E8G3PLxprl3Q8LCwrTSG9QzlGpHoYqtBnBz31pMKzbCpuEghLRkIo6tJOHeGeRGZhQZtUXcV13lbdN4CJEn1hJzeQ+2zUZhVqGBxjne5XP3rkgzlBISEl9sDuXnxNbLwTRceIpzj1U5b3lNwt96OTjb9v/vJCYmam1T5+V5enpqbJ/ZtgL6ebRVywl9uUynm0p2uLi4sHDhwk8ymFRz7tw57OzsNAqy3pfjx4+TkpLC6NGjNVQPvvnmGywsLET7zmvXrhEYGEi3bt0IDw8X9Tjj4+Np0KABf/31l85qaqVSyYMX4bkKJgFk+gZiMCko00lPjEFmaISBTSENHdLMCMDT8NzlAAeGxBL0RqVhaV6lKVF/beT5ku4k3P0L5HKUiTEokzWlzQQg4uivxFzZS4GW47SCSXibz/kwNFbrPQkJCYn8Rgoo/2MyJuHndVZMSsLPHdu2bcPT05O5c+eyfPlyunXrhq+vL40bN6Z27doabYvYmODbyi1fzz+9lVueZ5E7depE1666Z78+BebPn8+xY8c4duwYP/30U74dV60bmll70tDQkOLFi4vvq205e/fujZ2dncbfmjVrSE5OJjo6GplMhp+fHwCNGjVCoVBw8k9VhX5abBhhBxbxbEkPgua14eWaocRd11ySFgQlUX9tInh+e4Lntub54m48X9Kd1DdPSQy8QFLQDY328ffO8GrdKIJ/bked8sW5e/cuaWmaAuTqvMmIiAiaNWtGeRdHEh6cA+D1xvGYlKuHfZcZ2HeZgUU1lTB++OFlBC/sjJCWojrPrRPE/nMAm0aDMHX1yPJ+Jtw6TikHC86cOcPIkSOxs7PDysqKQYMGkZKSQlRUFL169cLa2hpra2smTpyoJcOl1tSsUKECRkZG2NnZ0aRJE65cuZLleQFmzJiBXC5n6dKlgGpshw4dSpkyZTA2NsbW1paOHTtq+MY/fvwYmUzGwoULtY537tw5ZDKZTgkoCQmJ/558CSjXr1+PTCbT+GLIL/r06aNTmPxdePr0KTKZjPXr1+fL8d6XrZeD+fnog3w51s9HH7DtcvAHHYt3wdPTk/Lly/+nfahYsSL6+vrMnTuX0aNHc/r0aUaNGsWOHTt0tu9SvSjjG+dP/uaExmU+WmHEx6Rq1ao0bNiQhg0bUqlSpY967qtXrzJz5kxApSjQt29fbG1tRUkldaCr/t64e/cuoCqQW7x4Mc7OzqTHR/J643iSgq5jXrU5Ng0Hom9VkPBDSzQq86NObyH63FaEtFSMS7qrcittCoGeAZmJu3GcsN2zQS7HyqM37bv3JiwsjMePH2upMwBs2rQJe3t7ijYbhIHtv5qrgpKU14EYu1TG2KWyuD3x0RVMytRCpq/yF1cUKofc1IrYv/eTnpj1DKR6knbEiBEEBgbi6+tLq1atWLVqFVOmTKFly5akp6czc+ZM6tSpw7x588QAXE3//v0ZPXo0RYoUYc6cOUyaNAkjIyMuXLig44wqfvjhB6ZOncrKlStFV6rLly9z7tw5unTpwpIlSxg8eDB//vknnp6eorlE8eLFqV27tiivlJHNmzdjbm5O69attd6TkJD47/nii3I+VfJVVPlfpu69zSCHjyf38qmRkJDA3Llz8fT01FjK/uorVXV8XhjuVYoCZgqm7b1NmlLI0+yxnlyGvlzG9FZuX2Qw+SFR64bev39fDBABUlJSePLkCQ0bNmTatGno6emRlpZG69atGThwIA8fPhSdmDLrc75+/RqAUaNG4eLiQnxyGlETZoCgpGC/Zej9W6FtXqUZb/bMJerMFswqN0FuoCD+hmrG0q7td5iUrgmAkJZC8IKOGucQ0tOIPLkeAztnHLvPQa5vyGIfby6ePMatW7dYuHAhvr6+Gvu4urqybOUaKvgcwTDkNYmBFzB0LEn87ZNaFp5CSgJmbl7iawPrglh79SVky2RCt0/FoctPyBVZz4IXsLPn4MGDyGQyhg4dysOHD5k3bx6DBg0SJbUGDhyIi4sLv/32G7169QJU2rzr169n5MiRLF68WDzeuHHjsjQUGD9+PAsXLmTdunUa4v3NmzenQ4cOGm1btmzJ119/zY4dO+jZsycAvXr1YtCgQdy7d4+yZcsCkJqayvbt22nXrh0mJlLOuMS7k1k9xcXWFFOFFArlB/kyQ9mzZ08SExPFHwOJnFGLKucnaUoB/3+0hZL/X0hISMDX15eTJ0/my/G6VC/K8TEe1CpuC6gCxexQv1+ruC3Hx3hIweQ70LBhQwwNDVmyZIlGwLJ27Vqio6Np2rQpx48fp127dpQoUYKff/6ZuLg4SpYsqSEAn1GPM/PyuYmhHkmB5zAuWQMEgfSEaPHPuNhXCMnxpISo8iOVKYmgp49xqbeFLQkPL4EyXeOYKa8DUSZEYV6lOTJ9Q4rammCq0MfW1hZDQ0Mx9zMj1atX1/KQNylXl5SX90mN1JQvk5vZoCiqmYdraF8M+44+pIY/J9R/OsrUrB8mm7bvppGP6+7ujiAI9O/fX9ymp6dHtWrVePz4sbhtx44dYgFRZjLn9wqCwPDhw1m8eDGbNm3ScoLK6NCVmppKeHg4JUuWxMrKSsPfvFOnThgZGWnMUh45coSwsDB69OiR5TVKSGRFYEgsPntv4zEvgPI+R2i+9Axtfz1H86VnKO9zBI95AfjsvU1giJRv/D7kS1iup6f3wW3VviQCQ2I5/TAs34+brhR4EBKX78f91FEqlaSkpHyQYxexMcGvvzuBIbEqOacHoQSHa8o5yVCJlnuVtqdHzaKUtJc8ld8VOzs7Jk+ejK+vL02aNKFVq1bcv3+f5cuXU716dRo1akRiYiKlSpVi4MCBNG3aFDc3N/r27SsWX3l4eGBhYcG+ffvEY6pdmkAVbKYlxhF37TBx1w7r7IcyPkr1D7k+pCQSfnAxikJlSX0TRPztk8hNrVHGv3VaSotW+a/r2xRCT/5WcxRUnunq3M+MWFhYaHnIG5f+mqi/NhF/+yRWdboi/BskmpR011mgpShUFrt2PxD6hw9hu2dh1+4HZHraX+sFHJ00XltaqnynM9ubWlpaajhIPXr0CCcnJ2xsbLSOmZmNGzcSFxfHr7/+qjP/NzExkVmzZrFu3TpevHihZSyhxsrKipYtW7JlyxZ+/PFHQLXcXahQIerXr59jPyQk1ORGPUWyMM0/PkgO5Z49e2jevDlOTk4oFApKlCjBjz/+SHp6uta+Fy9epFmzZlhbW2NqakrFihU1llZ0ce3aNezs7PD09CQuThVAvXjxgn79+uHg4IBCocDNzY3ffvst2+OsW7cOmUzG1atXtd6bOXMmenp6orOFOhfwzp07eHl5YWJiQqFChZg7d25ubpEGmy8GoyeXqXyid83i+fK+BM1rw/Nf+hBxfHW2Mw0ZSXkTxOst3xH8czue/9KbqLNbkcm0/8PkdjxcXFzEgoGMZF5CBlWCfatWrTA1NcXe3p4xY8Zw5MgRZDKZzhnC3Ny35ORkpk2bRsmSJVEoFBQpUoSJEydqubbIZDKGDx/O5s2bcXNzQ6FQsGLFCuzs7ADw9fVFJpMhk8nw8fHJ0zhnRSkHc3xauXFqvBe3fLw5MKIOu4bU4sCIOtzy8ebUeC98WrlJwWQ+4OPjw7JlywgODmbMmDFs376dgQMHcvToUQ0NSk9PT86fP0+1atVYtmyZaJ3q6OjImDFjxHYZ94G3fuimbl5i8UvmP0VhlZuT3NgcPXNbkp78Q+Tx1aSEPMK+4zRxmVwX6cq3mqPZIZPJtDzk9RSmmJSsTvydkwAkv1YV2xmX/jrL4xi7VMKuzSQSn1wlbP98BEG7ul1hqJ3zCbr91d9VSa527do4ODiwbNkynWYKI0aM4KeffqJTp05s376do0ePcuzYMWxtbbUq8nv16sXjx485d+4csbGx7N27l65du2pU/ktIZIeknvLx+SCJA+vXr8fMzIyxY8diZmbGiRMnmDp1KjExMcybN09sd+zYMVq0aEHBggUZNWoUjo6O3L17l/379zNq1Cidx758+TLe3t5Uq1aNPXv2YGxsTEhICDVr1hQDDTs7Ow4dOkT//v2JiYlh9OjROo/VoUMHhg0bxubNm6lSpYrGe5s3b8bT05NChQqJ2yIjI2nSpAnt2rWjU6dO+Pv78+2331KhQgWN5bacCLgfSrpSIOHeWYS0ZMyrNEVubEHKywfE/r2P9Ngw7NpOzvYY6XGRhPz+HSjTsajZQXT3UCftZyS345Fb4uPjqV+/Pq9evRLHbcuWLVl6oefmvimVSlq1asWZM2cYOHAg5cqV4+bNmyxcuJAHDx6IVb1qTpw4wfbt2xk+fDgFChSgUqVK/PrrrwwZMoS2bdvSrl07QFWQU6xYsTyNc06YKvRxc7LMwx2TyA71g01GK8Jhw4YxbNgwrbbm5uYYGRnx8OFDACpXriwWV7Vq1Yp9+/axbds2jX3KlSvHn3/+Kb62s7PD3NwcW1MD5MWrZPtDY2DpQGp4MIWGbdCYITSr2JDIE2vF1/qWqhlJZeQL6jZsID5YnDx5EldXV628P1NTU3x8fIhPTkMGWNXtjlXd7qr3yjcgYcePJL96QFp0KIYOJTAp/pW4r/Ok/Vr9NCnljvPEPVrb1ThZZu+0kxUlSpTgyJEjRERE5DhLWbJkSTGHuUmTJvz555+Ym799wPL396d3797Mnz9f3JaUlKSzYKlJkybY2dmxefNm3N3dSUhIEHMsJSRyYllA4DsXvKb/mzM/aedNwuKSGe5VKp979+XyQQLKLVu2aOTLDB48mMGDB7N8+XJmzJiBQqEgPT2dQYMGUbBgQa5duyb63ULWT8hnz56lWbNm1K1blx07dqBQKAD4/vvvSU9P5+bNm9ja2orn7Nq1Kz4+PgwaNEijP2rMzc1p06YNv//+O3PnzhWffq9evcqdO3dERxE1L1++ZOPGjeIXW//+/XF2dmbt2rW5DijjktMIjlBVNFp59kFuoHj7ZuUm6FsXJOrURtKiQ8UfKV1EX/BHmRCNY6/5KJxUOWJmFRrwYuVAABKS30qV5GY88sLKlSt5/Pgxu3fvFisuBw0apBVFdyRaAABJaUlEQVSsqcnNfduyZQvHjx/n1KlT1KlTR9y3fPnyDB48mHPnzlGrVi1x+/3797l586boCw6qXLkhQ4ZQsWJFrVyrvIyzxMdFLd6em2VVPT09GjZsyO7du3n58qVoZfjw4UMOHTqUq/OpLRm3bNlCwcptwEZzNjE9IRo9E9UDg3GxKiQ9vUpi4EWNopzY60c09jF0LIXcxIqYq4eYtm6GuP3QoUPcvXtXw8EoI2oP+aCItzqTxsWrIje2IOaCP8nPbmXrkpNbjA3f7au+ffv2/PLLL/j6+mqtHAmCoLUMX7FiRQ4ePEijRo1o2bIlhw4dEr979PT0tL7bly5dqnPlSl9fn65du7Jlyxbu3r1LhQoVqFix4jtdw5fAyZMn8fLyIiAgQFwt8vT01Cma//9ORvWUoNktsKzdVXxYi7txnPCDiyg0eC36Vg45HutjW5h+7nyQ9YOMwUtsbCxhYWHUrVuXhIQEMZfp6tWrPHnyhNGjR2sEk6Cd7A2qakNvb28aNGjAzp07xSBIEAR27NhBy5YtEQRBFDgOCwvD29ub6OhojYTvzPTq1YuXL19qzK5t3rwZY2NjLfs5MzMzjUDF0NCQGjVqaCSx50TGJPyMwaQyJYn0hGgUhcoBAikh2R8z8fEVDJ3KiMEkgJ6JJaZungA8j8rwA5WL8cgLhw8fplChQrRq1UrcZmRkxDfffKOzfW7u2x9//EG5cuUoW7asxhiqc6Yyz356eHhoBJM5kZdxlvg4hISE8Msvv7BixQq+/vrrXFfv+vj4kJaWRu3atZk7dy6zZs3Cw8MjT/JUs2fPpmDBgrzeOI6I46uIvXaY6PN/8Gb3bF6uGiS2M6vSFD1LB8L2ziPy5Hpiruzl9eZJyPT+XQn497tKpqePtWcfUkKf0KNtUxYvXsx3331Hhw4dcHFx0ViCz4xXGXuNgi+Znj6m5eqRcF+lTWmSjc5kTrzvCrGXlxc9e/ZkyZIlNGvWjCVLlrBo0SIx0NRFzZo12bNnDxcuXKBDhw6kpqYCKtkmPz8/Ro8ezapVq+jbty9LliwRJwEy06tXL8LCwggICJCKcf5jEhIS8PHxybeCx5xwcXERXaQ8PT11pmLp4kOppzyLSMi5ocSHCShv375N27ZtsbS0xMLCAjs7O/ELQZ18/eiRqooyNz8CSUlJNG/enCpVqrB9+3YMDd8u675584aoqChWrVqlJXDct6/qyT40NDTLYzdq1IiCBQuKFYVKpZLff/+d1q1bayzXABQuXFgr2LW2ttZIYs+JjEn4adGhhO1fyLNFXXi2oAPPl3QnZMskVT+Ss3fZSIsOxcDaSWu7gY1q6TY17e1MQG7GIy8EBQVRokQJrXtRsmRJne1zc98CAwO5ffu21hiq/bwzj2GxYsXy1Oe8jLPEx+Hu3btMmDCBkiVL5kkbtmrVqhw6dAhra2umTJnC2rVrmT59Og0aNNDwZs8OtZ/6gP790A++TMTRFcRe2YsyMRYrzz5iO7mhMQ5df8LIuSKxV/YRfW4bRkXcsKrdBUAjxcR3/DC2bdtGSkoK3377LStXrqRt27acOXNG66E5I93di2otu5tWUD1IGblUQt8s55nbrNBhFpRn1q1bx7x583jy5AkTJkxg5syZJCYmaqwYZKZ+/fpinmTPnj1FcfRevXqxefNmxo0bx6tXrzh+/HiWOsNVq1bFzc0NuVxO9+7d3/9CJN6Z/FbQ+FDkpJ5iWt6LouN3opfN6l9m0pQC3+26mR/d++LJ9yXvqKgoscpy+vTplChRAiMjI/755x++/fZbnXZoOaFQKGjcpCn79+3lFz9/Gno3FbWj1Mfr0aOHlkyFmuyWSvT09OjWrRurV69m+fLlnD17lpcvX+p8Is6qkj0vSezqJHxBmU7ItikoE2OxqNkBA5vCyAyNSI8NJ/zAQnhPi3UDfVUAl5fxyMrmLz09/b2q+HNz35RKJRUqVGDBggU622auRtWVwpBTH3I7zhIfh4yC1nmlfv36WisPbdq0oXDhwhrbsvu/aW9vz7Jly1i2bBlbLwdnqTlqYOWIfUdN2Ry1+LmhZQEU+vIMmqMl6dSpU7Z9X79+vUYAXcrBnLolC2h4yMvkqq9m0wzak3lFTy6jabsu+B3Qdp3x8fERZ4Cy6xuo/u+MHz+e8ePHZ3kuXfe5VatW4uwkqKq3dRVKZmfCYGBgkOccZ4n840MqaOQ3uVFPkcn1QJ6337KMFqZfatFlQkJCvui75vsM5cmTJwkPD2f9+vWMGjWKFi1a0LBhQ6ytrTXalShRAiDb/I/AkFiuPYsiKU3J1RI90C9SkTEDe9Ng/HJRO2r5+VBMzcxJT08XnTsy/9nbZ/800qtXL2JiYti3bx+bN2/Gzs4Ob2/v978ZOnCxNUUGpL4JIi3iBdb1+2NZswMmpWti7FIZvVzORuhb2pMa+VJre2qEqlq5sJXqw5Hb8QDVrKGuBPnMkifOzs48evRI60dEXSjxLpQoUYKIiAgaNGigcwwzawnqIiff6485zhIflsz+7IGBgRw8eFBLjSC3ZKc5mll1QUhLIe7aIfStnahXqUy+aI5m9pCPu34EmaExJqWzngXMiXfxkP+UuHLlCteuXRNF1r9EcmNHmRN///03tWrVwtjYmGLFirFixQqtNh9KQUPNiRMnqFu3LqamplhZWdG6dWvRoepDEBoaSv/+/XFwcMDIyIhKlSoxYdbSHLWC424cJ2h2C9KiQlTH+cOXF7/219n21cZxvFo/GlB9H4ybsZj69etjb2+PQqHA1dVVNAXIyJUrV/D29qZAgQLimPTr1++drtPHxweZTMa9e/fo1KkTFhYW2NraMmrUKJKSkrTab9q0iapVq2JsbIyNjQ1dunTh2bNnGm3UijV///039erVw8TEhO+++w7I+nOSW/I9oFTPRmUMNlJSUli+fLlGu6+++opixYqxaNEirSAmODyenmsv0mjRXzx6E6earNMzwK7tdxg6liJ0x48kvbxPUEQCm688h2LubPvDn+NnL2v1J6PAcVZUrFiRihUrsmbNGnbs2EGXLl00Kk7zE3USPjLtWy8IArFX9ubqOMbFq5Hy8j7JL++L29IToom/fRIAk3+V/3M7HqAK6i5cuKDxRLp//36tD6S3tzcvXrxg7963fU1KSmL16tW56rsuOnXqxIsXL3QeIzExkfj47FMAAPEJS1dQDB93nCU+LMWLF2fy5MmsXr2aH374gZo1a2JoaJinL7/MqDVHj42uR093Z5xtTZABb3bNJPzwMmL/OUj0+T8I8xtLavhz5vw0Hb/+7vmiV6f2kE8IvEj0BX9irx3BrFJj5IbvVp0N7+Yh/ylw69YtNmzYQL9+/ShYsCCdO3f+r7v0wciNHWV2REZG0qxZM6pWrcrcuXMpXLgwQ4YM0ZgJVito/Pzzz7Rs2ZKlS5fSpk0bFi5cqPPenjhxgjFjxtC5c2cWL15M9erVxcCpbdu2+Pn54efnJyppHD9+HG9vb0JDQ/Hx8WHs2LGcO3eO2rVrfxAL4MTERDw9PfHz86N79+7MmzcPS0tL9iz+nsiLu/N0LJNydUmLDiH5lWZFeFp0KCkv72NSrh6gmqUM2L0JZ2dnvvvuO+bPn0+RIkUYOnSoRi5xaGgojRs35unTp0yaNImlS5fSvXv3bG1Kc0OnTp1ISkpi1qxZYi7zwIEDNdr89NNP9OrVi1KlSrFgwQJGjx7Nn3/+Sb169bR+E8PDw2natCmVK1dm0aJFeHl5Zfs5yS35/mtaq1YtrK2t6d27NyNHjkQmk+Hn56c1myWXy/n1119p2bIllStXpm/fvhQsWJA9Jy9x8uJVHLuoBG0z7iY3UGDfcRohv39H6HYfHLrPwtDOBWvP3iQH36CxV12823enrZc7ERER/PPPPxw/flynJlpmevXqJS7pfOhlUK8y9jwLj0XfqiCRJ9aSHhuOTGFMwv1zKJNyJ0xuUbM98bcDCN0+DfNqrUTZIANLe1JCn4jtcjseAAMGDMDf358mTZrQqVMnHj16xKZNm8TZZDWDBg1i2bJldO3alVGjRom5ier8tZxmCnXRs2dPtm/fzuDBgwkICKB27dqkp6dz7949tm/fzpEjR6hWrVq2xzA2NsbV1ZVt27ZRunRpbGxsKF++vEae7sccZ4kPR5MmTfj99995/fo1CoWCr7/+mpkzZ1Kq1PtLfKg1R31wIz45jR+NbuC/ZSMvz5xGUKbj6urKxMWz8z3Q6VK9KMPO/EZ0+BuMS1TDqs675w1+zh7y/v7+TJ8+nTJlyvD777/nOi/2cyS3dpRZ8fLlS+bPn8/YsWMB1Xezu7s7kydPpmfPnhgYGHxwBY0JEyZgY2PD+fPnRaWGNm3aUKVKFaZNm8aGDRvyfF8yBqKZ8zZXrVrF3bt32bRpk5hb26PvAAqW/Yqo05swq9goWxvSjJiUqkm4ngEJd0+jKFha3B5/7zQgw7Ts2/tl3Wkmy35qJdo0Dh8+nCZNmrBgwQJR4uzcuXNERkZy9OhRjd+rGTPeKj+8C8WKFWPPHlWazbBhw7CwsGD58uWMHz+eihUrEhQUxLRp05gxY4Y42wjQrl07qlSpwvLlyzW2v379mhUrVjBo0NsCxE2bNmX5Ockt+T5DaWtry/79+ylYsCA//PADP//8M40aNdIpZO3t7U1AQAClS5dm/vz5jBg1hmPH/0RRonqW2nByhQn2naejZ2ZN6NYppEa+RM/UGsdeCzCr0JBjB/cxdJjK/isiIoI5c+bkqt/du3dHT0+P0qVLU6NGjfe6Bzmey70oSpke9h2mYuhQnOgLfxB95ncMrJ0o0GJsro6hb2aDQ9eZGNi5EHPBn9grezAtXx/zTB7AeR2P+fPn8+DBA0aPHs358+fZv3+/Vl6aWsuyfv36LF68mBkzZlC3bl2mTJkC8E4/AHK5nN27dzN79mxu3rzJ+PHj8fX15fLly4waNUoszsmJNWvWUKhQIcaMGUPXrl3x9/fXeP9jjrPEh2PdunU8ffqUpKQkoqOjOXz4MF999VXOO+YRU4U+s6dN5uH9uyTEx5GYmMjff//9wWbNwl8/Z8u5QIp0noqBsWme9tWTy1Doy5nTrgLDvHQXyH0O+Pj4oFQquXv3Lh4e717h/jmQWzvKrNDX19cICgwNDRk0aBChoaH8/fffwIdV0Hj16hXXrl2jT58+GrJfFStWpFGjRhw8eDBXx8kLBw8exNHRUcON6WVMCuZVWyKkJJL0LPcySnKFCcbFqxJ/74zGJEvC3dMoCpXRkO6TGSh4Gq5aKYuOjiYsLAwPDw8eP34sFreqi+/279+vkT/8vmTW5B0xYgSAeH937tyJUqmkU6dOGmPs6OhIqVKltMZYoVCIRctqcvqc5IZ8maHMLExcq1Ytzp8/r9VO16xY7dq1OXr0KFsvBzNpp3YlVYEWY6CFpuSGnrEFTgM0l2z1TK2waTwEm8ZDAJjTroLWE7qLi4vYBxcXFzw9PcUEdH19fWQyWZazVllVt2VVnSqTyRg2bBjLli3Tek9MwpfLcOii/eSiS7hYF4b2Ljh2ny2+1pPLqFXcFr+DmnpxeRmPsWPHik+7anRde7Fixdi/X7OfixYtAtAIQPNy3wwMDJg4cWKOy5bZFVp8/fXXXLlyJcv3cxpnCYn/mi7Vi1K7RIEcLePUqN+vVdxWsoz7zMitHWVWODk5YWqq+eChfvh++vQpNWvWJDAwkLt374p5kJl5HwUNdX69rhz3cuXKceTIEeLj47X6+D4EBQVRqlQpDdeklDQlBraqws306KxVXXRhWq4uiYEXSH5xD6PC5UiNfEXK64dYN9CUwUt6fod+neZx6+plrXSE6OhoLC0t8fDwoH379vj6+rJw4UI8PT1p06YN3bp1y7Pec0Yyr7yUKFECuVwuzuQGBgYiCEKWKzSZ3cIKFSqkoZajPkZWn5PcFh7nS0CZF2FiXXwo7ahaJQrk+st1/fr1pKenfzQ3hpltK9Bw4akc7aDywsdMwk9MTNR4uk5KSmLlypWUKlXqk67I/NjjLCHxLkge8v8fjBgxgnXr1jF69Gi+/vprLC0tkclkdOnS5Z0UUXTxoRU0PgUyW5jmBeOS7sgMFCTcO41R4XIk3DsNMjkmGZa7UyNfEfL791iVKcOCBQsoUqQIhoaGHDx4kIULF4pjJZPJ8Pf358KFC+zbt48jR47Qr18/5s+fz4ULF7KUyMormdPKlEolMpmMQ4cO6VRVyXxeXWOc0+ckN7xXQBkSEoK/v3+ehYkzk5N21Lug1o7y6++ebbsTJ05w584dfvrpJ9q0aYOLi0u+9iMr1En4umZl35WPmYTfrl07ihYtSuXKlYmOjmbTpk3cu3dP1Hn81PivxllC4n3InM/5NDyelDQlhvpyUTpN4vMlL3aUunj58qXWDOCDB6oCE/V3XIkSJbh+/ToNGjR4p/x2yDov3tnZGVDlXWbm3r17FChQIF9nJ9XnvHHjBkqlUpyldLE1JS3iOUCeNCYB5IZGGJeoQcK9M1g3GED83dMoCruib/5WcD/x4SVIT2X37j2ULVVc3J6V3XDNmjWpWbMmP/30E1u2bKF79+5s3bqVAQMG5PVyAdXsYcaZ44cPH6JUKjXGWBAEihUrluv0sMzkx+fkvXIo31WYOCNq7aj8nKkDTe2o7Jg+fTpjx46lcuXKLF26NF/7kBNdqhdlfON3G/zMfOwkfG9vb86ePcuECRPw9fVFoVCwdetWunXr9tH6kBf+y3GWkMgP1B7yVYpa4+ZkKQWTXwB5saPURVpaGitXrhRfp6SksHLlSuzs7KhatSrwYRU0ChYsSOXKldmwYYPGe7du3eLo0aM0a9YsV9eRF5o1a8br16/Ztm2buE2hB8nXDyIzNMaoSO6LSNSYlqtLelwEcdePkhr6BNN/q7tF/lVlMTZ8O/sXHR3NunXrNJpFRkZqjWflypUBtCSa8kJmVyr1b5jaurhdu3bo6enh6+urdX5BEAgPD8/xHNl9TnJLnr6Rtm7dyrx583jw4AEymQxnZ2dmzZrFqFGjAIiIiGDmzJkcOXKEJ0+eIJfLqV27NrNnz6ZSpUricdS+pNu2bWP57lM837cNZUoCxsWqYNt0FDJ9QyJPriP+zimE1GRMytbB1nsYMv23eQBxN44RdyuA1LAglMnxGFgVxLxqS8y/evsB1pPL8DsfhMHN3axYsYKIiAjc3d018hrfRfk/MDCQSZMmcfbsWaKioihQoAB16tRh5cqVWFpaarTdvXs3P/zwA4GBgZQsWZL58+fTpEkT8f2WxQ3ZE7iVUwEBpMW8QaavwMi5ItZe/TS8RtUepA7d55Bw7wzxd06BMh1z13r8smwJzV0L0KtXL/bt2wfAN998w5w5czSeNHSN34ABA8TxywujR49m9OjRed7vv+JTd3iQkJD4/0NtR2lpaYmrqyvnz5/n+PHjWdpRZsbJyYk5c+bw9OlTSpcuzbZt27h27RqrVq0S8+Y+tILGvHnzaNq0KV9//TX9+/cnMTGRpUuXYmlpqVM8/30ZOHAgK1eupE+fPvz999+4uLjg7+9PzNNb2DYcmOsKb43rK1ENmaExkSfWqpa7y2jqv5qW+IqYkwa0bNmSQYMGERcXx+rVq7G3t+fVq1diuw0bNrB8+XLatm1LiRIliI2NZfXq1VhYWGgE13369GHDhg08efIkV6tlT548oVWrVjRp0oTz58+zadMmunXrJsZVJUqUYMaMGUyePJmnT5/Spk0bzM3NefLkCbt27WLgwIHZGhNA9p8TXXqbush1QHns2DG6du1KgwYNxMrpu3fvcvbsWTEgefz4Mbt376Zjx44UK1aMkJAQVq5ciYeHB3fu3MHJSdMqcNasWTyOTMGiZgfSIl8S+/d+kOuDTIYyKQ6rOt1IfnGP+JvH0bd0wKrO26qu2KsHMSjgjEkpd5DLSXx4iYijy0FQYl61BaCapVy/dA7Bf26iWbNmNGvWjH/++YfGjRu/s/p/SkoK3t7eJCcnM2LECBwdHXnx4gX79+8nKipKI6A8c+YMO3fuZOjQoZibm7NkyRLat29PcHCw+IVx+fJl3jy8ydD+vbgQInA38DFxVw/yestknL5ZjtxAs2I64thK9M2ssarbHcvYpzw5f4gbe1az4ttzFC1alJkzZ3Lw4EHmzZtH+fLlRVHg3IyfhISEhMTHY/Hixejp6bF582aSkpKoXbu2qOuYG6ytrdmwYQMjRoxg9erVODg4sGzZMr755m1BiVwuZ/M2f36cPY89f/zOzl27MDUxoXjx4nlW0BgxYgRjxowhJSWFadOmUb58eRo2bMjhw4eZNm0aU6dOxcDAAA8PD+bMmZNni9zcYGxszMmTJ5k0aRIbNmwgJiaGMmXKMHvRr/z6ukjOB9CBTN8Qk1LuxN8+iZFLZfRMrTTe17MuxC+/bWL5zz8xfvx4HB0dGTJkCHZ2dhqi5R4eHly6dImtW7cSEhKCpaUlNWrUYPPmzRr3Ii4uDmNj42wtWTOybds2pk6dyqRJk9DX12f48OHMmzdPo82kSZMoXbo0CxcuxNfXF1DlxzZu3JhWrVrleA610srChQvZuHEju3btwuTfz0lukQm5LN8ZPXo069atIyIiIksrveTkZAwMDDSqr54+fUrZsmX5/vvvRVkZ9Qylq5sb8c1+Aj1VXPtm7zwS7vyFUfGvcOjkKx7jtd940mIjKDw0g1hrajJyA82qqZBtU0mLfEmhwWsAldD3819606RxYw4e2C/O1n3//ffMnDmT3r1753mp/tq1a1SpUoU//vhDSz8sIzKZDENDQ+7cuSPqON64cYNKlSqxdOlShg8fDmgXtwSGxDJn4z7WTuxOgRZjMS2vKtlXz1Bal67OiLm/0fNrZ0ram1OrVi0uXLjAoEGDxKeI9PR0XFxcKFGihDgzl5vxk5CQkJD4MhALuu6HEhyho6DLxgSvMvZ0dy9KKYcvo6Cr59qLGham+YGonpJDPUZecHBwoFevXlpBYWZ8fHzw9fXlzZs3FChQIN/O/6HIdQ6llZUV8fHxHDt2LMs2CoVCDCbT09MJDw/HzMyMMmXK6NTUatK2sxhMAv8KiwqYVWyk0c6wYBnSY8MQlG/zSjIGk8qkeNITojEqWp60qNcok1Q5IUlPr0F6Gm179NdY+n2fpVr1DOSRI0dydDJo2LChhih4xYoVsbCw4PHjx+K2zDpkNvopzO7bGCsrKzoXS+PAiDrsGlKLMQ1VcgArZ0zAt3V5saLT3d0dQRDo3/+tfZSenh7VqlXTOE9uxk9CQkJC4vPmWUSC6DTndzGIoEzBJIAABEUk4HcxiEaL/qLn2os8i8jZmedTJ7OFaX6Q3+opt2/fJjExkW+//TbfjvmpkOuAcujQoZQuXZqmTZtSuHBh+vXrx+HDhzXaKJVKFi5cSKlSpVAoFBQoUAA7Oztu3LihU1PLzlFzCVyuUFWD6VvYZdpuAoISZfLb5OGk53cI+f17gue359mizjxf0p2oUxtV/fi3Xdq/elQFi2pOu9vZ2en0ss4NxYoVY+zYsaxZs4YCBQrg7e3NL7/8ovP6ihbVLpKxtrYmMjJSfJ2YmMjUqVMpUqSIxj2LiooiIS5WTMIvZG2i85jqADez9IOlpaXGeXIzfhISEhISny9bLwfTcOEpzj1WFWHkNFOnfv/c43AaLjzF1svBH7yPHxK1ekp+kt/qKW5ubsTExHwWM455JdcBpb29PdeuXWPv3r20atWKgIAAmjZtSu/evcU2M2fOZOzYsdSrV49NmzZx5MgRjh07hpubm05NLUUmsU0RHT7XgOjDqNaESk+Mwbr+AOw7TsO+ywzMq7fWaKfGQC9/DYHmz5/PjRs3+O6770hMTGTkyJG4ubnx/PlzjXZZLS1nzDIYMWIEP/30E506dWL79u0cPXqUY8eOYWtrq/OeZXVMXdsznic34ychISEh8XmyLCCQSTtvkpymzPOSb7pSIDlNyaSdN1kWEPiBevhx+JzVUz538hRpGRoa0rJlS5YvX86jR48YNGgQGzdu5OHDh4BKU8vLy4u1a9fSpUsXGjduTMOGDbPU1LIzV/Auk9NqTSj79lMwr9IU4xLVMXapjExfM6dSbZuUHP5CY/ubN280Zu/ehQoVKvDDDz/w119/cfr0aV68eMGKFSvyfJyMOmQdOnSgUaNG1KlTJ9c6ZHkhp/GTkJCQ+FLx8fFBJpMRFhb2X3cl39l6OZifjz7Isd3z5f0I279QfJ0UdIOg2S1ICrohbvv56AO25XKmUn1PPzWGe5VidrsKKPTl6MllhO1fyPPl/XLekU/LwtTHxwdBED6b2cxcB5SZdYzkcjkVK1YE3uor6dLU+uOPP3jxQjOgU2NkoEfRd5lK1jGDqUyKJ/7mcc3ju1RGpqfP6hXLNfqltgh8F2JiYkhLS9PYVqFCBeRy+TvpTL2vDlluyc34fWzik9O4/TKaq8GR3H4ZTXxyWs47SUhISEiIfCinuc8tp/Lly5f4+Phw7do1QDVTeXyMB7WKqxRVcop79f7NvaxV3JbjYzykmcl3INeyQQMGDCAiIoL69etTuHBhgoKCWLp0KZUrV6ZcuXKASlNr+vTp9O3bl1q1anHz5k02b96cbdm5Vxl7/C4G5WmK3rhYFSL19An1n4555SYoU5OIu3YEuYkl6XERYjtDMytqt+3DAf81tGjRgmbNmnH16lUOHTqkM+JX60Gp/TF1ceLECYYPH07Hjh0pXbo0aWlp+Pn5oaenR/v27XN9DWreV4cst+Rm/D4G/4+VhxISEhIfiv/Sae5T4uXLl/j6+uLi4iKKiastTO94b2fLxSBOP4mSLEw/ILkOKHv06MGqVatYvnw5UVFRODo60rlzZ3x8fMTK7u+++474+Hi2bNnCtm3b+Oqrrzhw4ACTJk3K8rjd3Yuy/vzTPHXawLYwdm0mE/WXH5EBvyE3tca8SlP0TCwJP7hYbJeuFFi79Ge2VijCihUrCAgIwN3dnaNHj9K8eXOt48bHx1OyZPZT3JUqVcLb25t9+/bx4sULTExMqFSpEocOHaJmzZp5ug54fx2y3JKb8fuQPItI4LtdNzn9MAw9uUznA0TGysP1559St2QBZrat8NHsJCUkJCQ+J9ROc/lNRqe5LyHAci1sw4zCNgCShekHJNc6lB+ST0E76s6dO7i5ubF//36dwabEu7P1cjDT9t4mTSnkaYz15DL05TJ8W7nRRVp+kJD44njx4gVTp07l0KFDhIeH4+TkRJMmTVi8eDGGhoa5dl8DVarQihUrePLkCQqFghIlSjB27FjRDlat6RcYGMiMGTPYvXs3giDQrl07fvnlF9FeML/JyaFs/fr19O3bl9OnT7N9+3Z+//13UlNT6dy5M0uXLiUhIYGRI0fqdEHz2Xsbv4tBRJzfQeKDc6SGv0BIS8bAtggWX3fEtGwdjb48X94Po6IVKNBiDKDKoQz5/Tscus7EyLmiRls9uYye7s74/Fs1febMGcaMGcPNmzcpVKgQEydO5NWrV1p2f2lpacyaNYv169fz/PlzChYsSLdu3Zg2bRoKxds6BxcXF8qXL8+kSZMYO3YsN27cwMnJCR8fH9GQA3LnwKfWts7MunXr6NOnD3369OHkyZMaq4/x8fFMnTqV7du3ExoaiouLC9988w3jxo3TyAuVyWQMGzaMhg0bZut6Fxsby5QpU9i9ezevXr3C0tKSSpUqMWfOHL766qtcfFI+fz6JsHxm2wo0XHgqXwPKvGpHBQQE8PXXX0vBZD6zLCAwV8niukj/NwCdtPMmYXHJDPcqlc+9k5CQ+K94+fIlNWrUICoqioEDB1K2bFlevHiBv78/CQkJGBoa5tp9bfXq1YwcOZIOHTowatQokpKSuHHjBhcvXhQDSjWdOnWiWLFizJo1i3/++Yc1a9Zgb28vOojlJ3lxKFM7r/n6+nLhwgVWrVqFlZUV585l7YIWcD+UdKVA7JW9GJdyx9TVEyE9jfi7fxG2ezayDtMwKVn9nfqerhQIeBCKD27cvHmTxo0bY2dnh4+PD2lpaUybNg0HBwet/QYMGMCGDRvo0KED48aN4+LFi8yaNYu7d++ya9cujbYPHz6kQ4cO9O/fn969e/Pbb7/Rp08fqlatipubKpDNzWegXLlyTJ8+nalTpzJw4EDq1q0LQK1atbT6ByoFFLXaSf/+/alcuTJHjhxhwoQJvHjxgoULF2q0z43r3eDBg/H392f48OG4uroSHh7OmTNnuHv37v9NQPlJzFCCahZr0s6b+Xa8Oe0qfDZJteon1Nz6emZE/dT9XwxjTv3+kGOqfmrM6Mv+oenTpw/+/v7ExcVl287T0xOQ/MMlJLKjd+/ebNq0iYsXL2p5SQuCgEwmy7X7Wps2bXj48CG3bt3K8nzq78p+/fqxdu1acXu7du3466+/Pkj1d24cytTfo97e3hw6dEicHcvJBW3/keNU8DmCgLZznJCexqv1o9AzscSh60xxe15mKEGVX3jLx5seXTpy+PBh7t+/L2oh3717lwoVKpCeni7+/ly/fp3KlSszYMAAVq9eLR5nwoQJ/Pzzz5w4cUKcSXRxcSEoKIi//vpLDADfvHlDkSJFGD58OD///DOQewe+K1euUL16dXFWMiOZZyj37NlDmzZtmDFjBt9//73YrmPHjuzYsYPAwEDRlCS3rndWVlb06NHjo/4mfWp8+OS5XCJpR31ZSJWHEhISWaFUKtm9ezctW7bUCiYBMajKrfualZUVz58/5/Llyzmee/DgwRqv69atS3h4ODExMe9zSTrJi0NZ//6ajm45uaAFhceLxSUZg8n0pDiUyfEoCruR8vrRe/VfAB6FxnDkyBHatGmjYaxRrlw5rVz/gwcPAjB27FiN7ePGjQPgwIEDGttdXV3FYBJUpiNlypTRcHnLqwNfbjh48CB6enqMHDlSq5+CIHDo0CGN7blxvbOysuLixYu8fPnynfr0JfDJBJSgrR2VFz4l7SiJD1t5+Klz9OhRjh49+l93Q0Lik+XNmzfExMRQvnz5bNvl1n3t22+/xczMjBo1alCqVCmGDRvG2bNndR4zs9uY2jXtfbWJdZEXh7K8uqClpL01vkh4eIlXG8cRNK8tzxd14fmS7sRdPYgy+f0fwENCQklMTKRUKe2UozJlymi8DgoKQi6XaxW3Ojo6YmVlRVBQkMb23LjJ5dWBLzcEBQXh5OSEublmwZFa8eRd+jl37lxu3bpFkSJFqFGjBj4+PhoB5/8Dn1RACdraUTkFlpJ21KeHuvIwP3NiQbPy8FPG0NAQQ0PD/7obEhKfPbl1XytXrhz3799n69at1KlThx07dlCnTh2mTZumdczcOJjlF3lxKMurC5qhvurnO+nZLd74/4hMzwDbxkOw7+iDfZcZmLh6gJaLd95Rnycv5FbsPDdjkVcHvg9BbvrZqVMnHj9+zNKlS3FycmLevHm4ublpzXZ+yXxyASW81Y46NroePd2dcbY10XLUkQHOtib0dHfm+Jh6+PV3/+LkZQ4dOkTdunUxNTXF3Nyc5s2bc/t2zsvI69ato379+tjb26NQKHB1dRVzcDLi4uJCixYtOHPmDDVq1MDIyIjixYuzceNGrba3b9+mfv36GBsbU7hwYWbMmJHlf+bNF4NzfBBIT4wl8sRaXq4dRvD8DgQv6EjI9mmkhGT/RKcnl7HpwlsXh927d1O+fHkUCgVubm46n/6vXr1K06ZNsbCwwMzMjAYNGnDhwgWNNqmpqfj6+lKqVCmMjIywtbWlTp06OS5VXbt2DTs7Ozw9PcXcSk9PTzGPElS5lDKZjO3bt/PTTz9RuHBhjIyMaNCggeRSJPF/iZ2dHRYWFtnmPELe3NdMTU3p3Lkz69atIzg4mObNm/PTTz+RlJT0ga4id3wohzIXW1NkQML9c8j0DXHo/CNmlRpjXKIaxi6V86XvMqBKaWeMjY0JDNS2ZLx//77Ga2dnZ5RKpVbbkJAQoqKicHZ2znMfcvsZyItjj7OzMy9fviQ2VnNy4t69e+L770LBggUZOnQou3fv5smTJ9ja2vLTTz+907E+Rz7JgFJNKQdzfFq5cWq8F7d8vDkwog67htTiwIg63PLx5tR4L3xauX0ROlmZ8fPzo3nz5piZmTFnzhymTJnCnTt3qFOnTrbC6wC//vorzs7OfPfdd8yfP58iRYowdOhQfvnlF6226iq7Ro0aMX/+fKytrenTp49G4Pr69Wu8vLy4du0akyZNYvTo0WzcuJHFixdrHQ8QKw+zIy3qNQkPLmBcojrWDQZg4d6O1DdPeb1lMmmx4Vnup648BFXl3dChQ+nSpQtz584lKSmJ9u3ba7gC3b59m7p163L9+nUmTpzIlClTePLkCZ6enly8eFFsp07Y9/LyYtmyZXz//fcULVo02xydy5cvU79+fapUqcKhQ4cwMzPL9ppnz57Nrl27GD9+PJMnT+bChQt07949230kJL5E5HI5bdq0Yd++fVy5ckXrffXMT27d1zI7gRkaGuLq6oogCKSmpuZz73PPh3QoM1Xoq5zmZHKQgSC8dVdLiwohMfBCNnvnjqK2JliYKPD29mb37t0EB799mL979y5HjhzRaN+sWTNA241uwYIFAO+kopLbz4CpqSlArmyLmzVrRnp6ulYBzcKFC5HJZDRt2jRPfUxPT9dafre3t8fJyek/c6L7L/gkZINyg6lCHzcny/+6Gx+FuLg4Ro4cyYABA1i1apW4vXfv3pQpU4aZM2dqbM/MqVOnMDY2Fl8PHz6cJk2asGDBAoYNG6bR9v79+xpVdp06daJIkSKsW7dOrLKbM2cOb9684eLFi9SoUUPsi66cmrjkNIJzUThjaOeC06CVyDLYaJq51efF6sHE3TiKVe2uWe4bHK46/t27dzUq77y8vKhUqRK///67WHn3ww8/kJqaypkzZ0THpl69elGmTBkmTpzIqVOnAFWyeLNmzbK9rxk5e/YszZo1o27duuzYsUNDXy0rkpKSuHbtmrgcbm1tzahRo7h161aOuWQSEl8aM2fO5OjRo3h4eDBw4EDKlSvHq1ev+OOPPzhz5gxWVla5dl9r3Lgxjo6O1K5dGwcHB+7evcuyZcto3ry5Vp7cx+RDO5R5lbEnsFQNYi/vJnTbNEzdPEiPjyb2nwPoWxUk9c3Tdz62nlyGV2l7AHx9fTl8+DB169Zl6NChpKWlsXTpUtzc3Lhx460PeKVKlejduzerVq0iKioKDw8PLl26xIYNG2jTpo1OrcicyO1noESJElhZWbFixQrMzc0xNTXF3d2dYsWKaR2zZcuWeHl58f333/P06VMqVarE0aNH2bNnD6NHj9YowMkNsbGxFC5cmA4dOlCpUiXMzMw4fvw4ly9fZv78+Xm+5s+Vzyag/H/i2LFjREVF0bVrVw0pCz09Pdzd3QkICMh2/4zBZHR0NKmpqXh4eHDkyBGio6PFZG/IXZXdwYMHqVmzphhMqtt1796d5cuXa5w7Y+Vhdsj0DcR/C8p0lMnxyAyNMLAplGNlovr4OVXepaenc/ToUdq0aaPx5aMW2l29ejUxMTFYWFhgZWXF7du3CQwM1BkoZyQgIICWLVvSuHFjtm7dmut8yb59+2q0Vd/3x48fSwGlxP8dhQoV4uLFi0yZMoXNmzcTExNDoUKFaNq0qSgynlv3tUGDBrF582YWLFhAXFwchQsXZuTIkfzwww//xaWJfGiHMpXTXEVsm44k+oI/EcdXo2/lgLVnH9KiQ4h+j4AyXSnQo6aqJqFixYocOXKEsWPHMnXqVAoXLoyvry+vXr3SCCgB1qxZQ/HixVm/fj27du3C0dGRyZMn68xnzQ25/QwYGBiwYcMGJk+ezODBg0lLS2PdunU6A0q5XM7evXuZOnUq27ZtY926dbi4uDBv3jyxIj0vmJiYMHToUI4ePcrOnTtRKpWULFmS5cuXM2TIkHe67s+RT0aH8v+ZzHqOc+fO5dtvv82yvYWFhTi9rkuH8uzZs0ybNo3z58+TkKA5WxgUFCRWrLm4uFCuXDmtpGFPT09kMpkYuBoZGdGpUyet3MolS5YwatQoDR3Kq8GRtP31XI7XLAhKYi/vJfbqAdKiQkB4m4+pKFoRx24zs9kbgma3YPDgwVq5oS4uLnh5ebFu3Tpev35NwYIFmTJlCtOnT9dot3jxYkaPHs2tW7dwc3Pjr7/+onXr1kRFRVG+fHmaNGlCz549xeUpUGmZbdq0CUNDQ6pWrUpAQAD6+trPZJl1KNUuDlu3bqVz585iu6dPn1KsWDHWr1+vM0lfQkJCIic+Bac5CQmQZig/SdTFLn5+fjg6Omq9ryuIUfPo0SMaNGhA6TJlGD/1Jwo4OGFspOD6+QCWLVmsVUiT3xWPua0IjD63nejTmzCt2Airuj2QG5uDTE7k8VUawWV25Gff69Wrx6NHj9izZw9Hjx5lzZo1LFy4kBUrVjBgwACxnUKhoFmzZuzZs4fDhw/TokWLXJ/jY1aXSkhI/H/wKTjNSUiAFFB+kqiXce3t7WnYsGGu9wsMiWXErNUkJycTWXs0G6LsIUr1XtTlVwAsOvaAYa1sKeWQ+7wiZ2fnXFX4wdvKw5y+2hLun0VRtCIFmmnaj4UnxyM3tsh239zW8tnZ2WFiYqKzn/fu3UMul2tovNnY2NC3b1/69u1LXFwc9erVw8fHRyOglMlkbN68mdatW9OxY0cOHTqkUdEtISHx5ZOens6bN2+ybWNmZpZjoV5+UMTGBN9WbvnqSja9ldsXp5oi8eH5pKu8/1/x9vbGwsKCmTNn6qxQ1PVF1nPtRRot+osrwVGAZkCnTIon7uZxAHZee0GjRX/Rc+3FXD/RNmvWjAsXLnDp0iWNPmzevFmrrVh5mAOqYhzN88ffO0N6NhXeaora5u6LTk9Pj8aNG7Nnzx6NyviQkBC2bNlCnTp1sLBQBa+ZqzHNzMwoWbKkzgo9Q0NDdu7cSfXq1WnZsqXGfZGQkPjyefbsGQULFsz2T13U+DGQnOYkPgWkGcpPEAsLC3799Vd69uzJV199RZcuXbCzsyM4OJgDBw5Qu3ZtUe7g1gtVLuW5x6qASOFSBfT0CfWfjnnlJihTk4i7dgS5iSXpcREolQLyf9u/jknCLioxx/5MnDgRPz8/mjRpwqhRozA1NWXVqlU4OztrJWSDqvLQ72JQtgGrcckaRJ/9nbADi1AUKkvqmyDib59E30p7iT8j6srDv3LstYoZM2Zw7Ngx6tSpw9ChQ9HX12flypUkJyczd+5csZ2rqyuenp5UrVoVGxsbrly5gr+/v1gtrtV/Y2P2799P/fr1adq0KadOnZIKayQk/k9wdHTMUaM2cxXyh2a4VykKmCmYtvc2aUohT0vgenIZ+nIZ01u5ScGkxDsjBZSfKN26dcPJyYnZs2czb948kpOTKVSoEHXr1qVv374ALAsI5PjdEADxy8PAtjB2bSYT9ZcfkQG/ITe1xrxKU/RMLAk/+FY3Ml0pIAhw73UsywICGe6VdWVzwYIFCQgIYMSIEcyePRtbW1sGDx6Mk5OThs+sGlXl4dNsr8/y604IqUnE3zlFwt3TGDqWwL7jNCJPrs92P3XloW+2rd7i5ubG6dOnmTx5MrNmzUKpVOLu7s6mTZtwd3+bcD5y5Ej27t3L0aNHSU5OxtnZmRkzZjBhwoQsj21hYcGRI0eoV68ejRo14vTp01qWYxISEl8eRkZGeUpH+lh0qV6U2iUK8N2um5x+GIaeXJZtYKl+v1ZxW2a2rSAtc0u8F1KV92fK1svB+ZozM6ddhXx9MpUqDz8P4pPTeBoeT0qaEkN9OS62ppgqpOdMCYnPncCQWDZfDCbgQSjB4QkaCUYyVKlDXqXt6VGz6BdpDiLx8ZECys+QZxEJNFx4iuS0vPmYpifGEHfjGImBl0gNf4agTMfAtjAW1VtjU8GT42M8NJ5Qk5OTmTp1Kn5+fkRGRlKxYkVmzJhBo0aNNI47c+ZM9u7dy6NHj4iNjaVIkSLUa9CYkyZ1SVdoflEJgpKYizuJvXqI9LgIDGwKYfl1R0xdPXLsv0JfrtVHibwj/tDcDyU4QscPjY0JXmXs6e5eNE/FWxISEp8m0oOjxMdACig/Q9519i/h4SXe7JyJcYlqGBWtgEyuR/z9cyQH38CqTlda9BmlMfvXtWtX/P39GT16NKVKlWL9+vVcvnyZgIAA6tSpI7Zr3749dnZ2lC1bFnNzc+7evcvq1asxsbTBoOPPyA2NxLaRJ9cTc8Efs0reGBYsRWLgRRIfXaZAqwk5BpX5PYv6/8aziIQ8L4XVLVlAWgqTkJCQkMgRKaD8BImPjxd9STMTGBJLo0W5LUnRJDXqNTKZHH1Le3GbIAiEbv2epOd3KTLqd05825iS9uZcunQJd3d35s2bx/jx4wGVdWD58uWxt7fn3Lnsxct37NhBhw4d6PPDAgLSVNWHabFhvPh1AOaVvbFpPEQ8f8jmSaRFh1BoyFpkct1ajRMal2GYl5Sf+K5svRz8Xsn6vq3c6CIF8xISEhISWSDJBr0DsbGxjB49GhcXFxQKBfb29jRq1Ih//vlHbHP69Gk6duxI0aJFUSgUFClShDFjxpCYqFlV3adPH8zMzHj06BHNmjXD3Nyc7t27Z3mMnt8MQ5ae8k79NrBy1AgmQaWraFzqa0hPRYgJYdOFYAD8/f3R09Nj4MCBYlsjIyP69+/P+fPnefbsWbbnUjvnuBcyZna7Cij05SQ9vAjKNMy+aq5xfvOvmpEeG0byi3sax9CTy1Doy/lfe/ceFdV1L3D8OzyHN4IoIKgNKCKIpGJAjcUHSqJG6huMUQyJRmOpRo1iRdBlERjTay+5JHJjhaA2tg1SDMZXFWOj16up5kpiArmK2PhA8QnDG+4fc2d0HEBkSBrl91nLtZxz9tnnnDm6+LHP/v12yuQBEkwa4d3DxazMOUtNfeNjj2o3NDZRU9/IypyzvHvYsBapEEIIARJQtot2yb8pU6aQnp7OsmXLsLKy4ty5c7o2f/7zn1Gr1SxYsIC0tDTCw8NJS0tj9uzZBv3V19cTHh5Ot27d2LhxI1OmTGmxjxP52ynL69jF5hsqbwHQpLTjcFEZAKdPn6Zv3766Oo1a2vW8z5w5o7e9qamJGzducPXqVY4ePUpsbCympqaMGDGCyME9ObgklC7q71GYK1G66I90WbhpRjBrr2nW4DY10ZQuH/qMMweXhP7kXnNHR0frAuZHSUxMRKFoayl2fZmZmSgUCr0amo/ro5OlbNxf1O7jH7RxfxE7T5Z2SF8PGjFihBSHF0KIJ5zMym2H/Px8Xn/9dd55535g9/bbb+u1SUlJwcrKSvd53rx5eHt7s2rVKkpLS3XraYMm+WXatGls2LCh1T5mznmVj7+r5/aRD6m/U2Yw2tgeDVX3qPhyP5YefpjZOlFarqaypp4rV67g5uZm0F677fLly3rbr127ptfew8ODHTt20K9fP0CzmoOPfQM17q7MDumtl3loatsFgMaKcno9gZmHarWa1NTUn1xgdOmmmoS8rx7ZrvLcZ1R999/UXP6W+ltXsPT0x/XlZIN21Rf/h8jnJhDZTB/Hjx8nJCSkA65aCCHEk0gCynZwdHTkxIkTXL58GXd392bbPBgIVlZWUlVVxdChQ2lqauL06dN6ASXAggULHtnHmQvXsOzhCzRRe+280QFlU1MjN3ZvpLGmAqcx8zXbgJJyzfVaWloaHKNUahJsHn517+TkxIEDB6iurub06dPk5ORQUVGh16aqqgpbaysSJ/qRiJ8u87C6tp6gd+DlIDf+Y9lIo+7pX0GtVrN2raYy5sMB5erVq1m5cuW/4Kpg1a6z1LfhFfe9f+yh9tr/YuHah8aqe49s7zN6OqujX9LbJvU3hRCic5OAsh1SU1OZM2cOnp6eDBo0iHHjxjF79my9lRFKS0tZs2YNeXl53Lp1S+/4O3fu6H02MzPDw8PD4Dyt9dFYU2n0fdw8sJnq81/gPOEtLLrfv/ba+kasrKyaXXawuroa0A92QbMcobbQ74QJExg9ejTDhg2jW7duTJgwQXfMg33aWJrh5+6AWq0GwN62+USkJ5mZmRlmZj/+f7Pia/c4+t2NNrXt+tJSTO2cUShMuPzBwke2L7d7hpCxEU/MCLKxWkuSE0IIoSFzKNth+vTpnD9/nrS0NNzd3VGpVPj5+fHpp58C0NDQwJgxY8jPz2fFihXk5uZy4MABMjMzAWhs1K8faWlpiYmJ/qNoro///GMuzuOXaBoYmZx/++87qPhHPo4jorH1H6W3z8LMBDc3N65cuWJwnHZbSyOzWkOHDsXNzU1vvW83NzeuXr3Kw4UF2tpna7RzFYuKipg1axYODg64uLgQHx9PU1MTly5dIiIiAnt7e1xdXfWmK0DL8xULCgpQKBQUFBQ0e96SkhJcXFwAWLt2LQqFAoVCQWJiot51PUihULBo0SK2b9+Oj48PSqWSQYMG8dlnbcve//TTTxk+fDg2NjbY2dkxfvx4vvpK/9X29hOlurmoj2Jm7/L/a6u3jYlCwZZD56ivr2/zMVoZGRl4eXlhZWXFc889x9GjR5ttV1ZWRkxMDN27d0epVDJw4ECysrL02pSUlKBQKNi4caOuX0tLSwYPHszJkycN+jx06JDue3N0dCQiIkJv3jPcf15ff/01M2fOpEuXLroSWVevXmXu3Ll4eHhgaWmJm5sbERERRs1xFUKIp4UElO3k5ubGwoULyc3N5cKFCzg7O/Pb3/4WgLNnz1JUVMQ777zDihUriIiIICws7LECpub6iJo0HjNbJ6Ov/d4Xn3Dn7zuwC4rAIWSq3j4F0NvZhsDAQIqKirh7967e/hMnTgAQGBj4yPNUV1frjcYGBgaiVqsNfog/Tp+PMmPGDBobG0lOTiY4OJj169ezadMmxowZQ48ePUhJScHb25tly5a1OYBrjYuLC++99x4AkyZNIjs7m+zsbCZPntzqcUeOHGHx4sXMmjWLdevWUV5ezgsvvEBhYWGrx2VnZzN+/HhsbW1JSUkhPj6er7/+mueff14vsDn8bVmHrlL0oOv5m0iOCkapVDJy5EhOnTrVpuO2bNnC/PnzcXV1JTU1lWHDhjFx4kSDigFVVVWMGDGC7OxsXn75ZVQqFQ4ODkRHR/P73//eoN8dO3agUqmYP38+69evp6SkhMmTJ1NXV6drc/DgQcLDwykrKyMxMZG33nqLY8eOMWzYsGYDwmnTpqFWq0lKSuL1118HNPVWd+3axdy5c0lPTyc2NpZ79+5RWtrxiUpCCPGkkVfej6mhoYGKigocHBx027p164a7u7vuda6pqaaW4oMjcU1NTc3+MGxJc31YW5hS92W+Uddfee4zbh7MwMZvBF1Gv2awv6ezNTaWZkydOlU38qOtQ1lTU8PWrVsJDg7G09NT019lJQqFAmtr/cLXH3/8Mbdu3SIoKEi3LSIigiVLlpCens67776ru7/333+fHj16MHToUKPuDTRZ6Js3bwY0iVC9e/dm6dKlbNiwgRUrVgCagu3u7u784Q9/4Be/+IVR57OxsWHq1KksWLCAgIAAZs2a1abjCgsLOXXqFIMGDQIgMjISHx8f1qxZQ05OTrPHVFRUEBsby2uvvUZGRoZu+5w5c/Dx8SEpKYmMjAwqauopvak26r6aZWqOtc9QrJ4JwtTagTcDlfz7pt8xfPhwjh07xrPPPtvioXV1daxatYrAwEAOHz6MhYUFAP3792fevHm6f0+gGcU8d+4c27Zt05XQeuONNwgNDWX16tW8+uqr2Nndf91eWlpKcXExXbpokrt8fHyIiIhg3759uukWy5cvx8nJiePHj+PkpPml7Je//CXPPvssCQkJBqOfAwcOZMeOHbrPt2/f5tixY3p1WQHi4uLa9VUKIcTTRgLKx3Tv3j08PDyYOnUqAwcOxNbWloMHD3Ly5Enda9R+/frh5eXFsmXL+P7777G3t9cFWG3VUh/mNW3v42E1l7/lxie/w8TKDmWvgVR+VaC339rTl5FDNJm6wcHBTJs2jbi4OMrKyvD29iYrK4uSkhK2bNmiO6a4uJiwsDBmzJhBv379MDEx4dSpU2zbto3evXvz61//WtfWw8ODxYsXo1KpqKurY/DgweTm5nL06FG2b9+uC6KN8dpr94NkU1NTgoKC+Oc//0lMTIxuu6OjIz4+Ppw/f97o87XXkCFDdMEkQM+ePYmIiGD37t00NDQ0+10cOHCA27dvExUVxY0b9+dHmpqaEhwczOHDhwG4WF7JDzE2qfTwRenhq/s85dXnmRk5nYCAAOLi4ti7d2+Lx546dYqysjLWrVunCyZBU4Jp+fLlem337NmDq6srUVFRum3m5ubExsYSFRXFkSNHdIEiaEaltcEkwPDhwwF0z/fKlSucOXOGt99+WxdMAgQEBDBmzBj27NljcL1vvPGG3mcrKyssLCwoKCggJiZG73xCCCEkoHxs1tbWLFy4kP3795OTk0NjYyPe3t6kp6frMrXNzc3ZvXs3sbGxbNiwAaVSyaRJk1i0aBEDBw5s03la6uOtNUlMHNW+kby6G5egoZ5G9R3K9zQzWjpuMbNCpus+fvjhh8THx+ut5f3JJ5/ojep5eHgwZcoUDh06RFZWFnV1dfTq1YtFixbxm9/8BmdnZ71TJCcn06VLFzZv3kxmZiZ9+vRh27ZtzJw5s1339LCHs+cdHBxQKpV07drVYHt5eXmHnLM9+vTpY7Ctb9++qNVqrl+/jqurq8H+4mJNYfFRo0YZ7AN0NUNrH3ON9/aqrW/Ez9ubiIgIcnJyWgyEAS5evAgY3re5ubleMpu2bZ8+fQzmFfv6+ur1pfXwM9cGe9pf4LTtfXx8DK7L19eXffv2GSTe/OxnP9NrZ2lpSUpKCkuXLqV79+6EhIQwYcIEZs+e3eyzEkKIzkYCysdkYWFBamoqqamprbbz9fXlwIEDBtsfTkjJzMzUJeu0tY9ZH/xXu9bytg0IwzYgrNl9piYKhj7jrJe5q1QqUalUqFSqFvvs2rWr7hVzW5iYmBAXF/eDvSpsLqBpKch58Fm0VHy8oaGhYy6sA2iTubKzs5sNYrTZ5BZmP87UaO15PD09qa2tpbKy0qAQ/o+hLc/3cT1cxQBg8eLFvPTSS+Tm5rJv3z7i4+PZsGEDhw4davV1vxBCdAaSlPMESpo0ALM2ZvC2lZmJgqRJAzq0zyeJdlTr9u3betsfHg1rTntWwtGONj6oqKgIa2trXdb4w7y8vADNnN2wsDCDP9oamL2dbejYfx2GtMlboHm1rFQqsbW1bbF9r169AMP7rqur48KFCwZti4uLDaohfPPNN3p9tZW2/bfffmuw75tvvqFr165tLgvk5eXF0qVL2b9/P4WFhdTW1hpUDBBCiM5IAsonkKeTNWsn+nVon+sm+uHpZP3ohk8pbbD2YOZ3Q0ODXvJLS7QJSQ8Ho605fvy43trvly5d4q9//Stjx45tccQtPDwce3t7kpKS9DKYta5fvw5o6nv2/AGeZYP6fsa+Nnnryy+/JC8vj7Fjxxq8on5QUFAQLi4uvP/++9TW3l+LPjMz0+B7GzduHFevXmXnzp26bfX19aSlpWFra0toaOhjXbebmxuBgYFkZWXpnauwsJD9+/czbty4R/ahVqt1NVi1vLy8sLOza7ZeqxBCdDbyyvsJFTm4JzcqajpkneblY31+cutl/9j8/PwICQkhLi6Omzdv4uTkxEcffdSmWotWVlb079+fnTt30rdvX5ycnPD398ff37/FY/z9/QkPDyc2NhZLS0vS09MBdCvuNMfe3p733nuPV155hZ///OdERkbi4uJCaWkp+fn5DBs2TJc9P9KnG9knLrZpWkR1aSHVlzTlihrUd2msq+H25x8BoPT0R9lTcx/Xc1MwMbdA6eGLvb8XS5bkkZGRgbW1NcnJhks1Psjc3Jz169czf/58Ro0axYwZM7hw4QJbt241mEM5b948Nm/eTHR0NF988QW9e/fmL3/5C59//jmbNm3Sy/BuK5VKxYsvvsiQIUOIiYmhqqqKtLQ0HBwcdDVDW1NUVMTo0aOZPn06/fv3x8zMjF27dnHt2jUiI5tbjFIIIToXCSifYItG9qGrrSUJeV9R39j0WHMqTU0UmJkoWDfRr9MHk1rbt29n/vz5JCcn4+joSExMDCNHjmTMmDGPPPaDDz7gV7/6FUuWLKG2tpaEhIRWA8rQ0FCGDBnC2rVrKS0tpX///mRmZhIQENDqeWbOnIm7uzvJycmoVCpqamro0aMHw4cPZ+7cubp2Lwf3JPN4SZvuu/ril9z5/I962+4c3ab5y7AoXUBp3TeEyq8KuHMil72fV+Hi4sLkyZNJSEho09KL8+bNo6GhAZVKxfLlyxkwYAB5eXnEx8frtbOysqKgoICVK1eSlZXF3bt38fHxYevWrURHR7fpnh4WFhbG3r17SUhIYM2aNZibmxMaGkpKSopBAk5zPD09iYqK4m9/+xvZ2dmYmZnRr18//vSnPzFlypR2XZMQQjxNFE3GzFwXPwmXbqpZtessR7+7gamJotXAUrt/uHdXkiYN6NSvuf9VFAoFb775pm408YfyypYT7Ureao02eSs7JrjD+hRCCPHkkxHKp4CnkzXZMcEUX7vH9hOlHC4qo7RcrVeLUIFm3tvIvt2YFdKz06zD3JklTRpA2L8d6dCAsrMnbwkhhGieBJRPkT7d7Uic6EciflTW1FNSXkltfSMWZib0drbBxlIed2eiTd5amXO2w/rs7MlbQgghmicRxlPKxtIMP3eHRzcUTzVJ3hJCCPFjkDmUQnQCH50sleQtIYQQPxgJKIXoJCR5SwghxA9FAkohOhlJ3hJCCNHRJKAUohOT5C0hhBAdQQJKIYQQQghhFFnLWwghhBBCGEUCSiGEEEIIYRQJKIUQQgghhFEkoBRCCCGEEEaRgFIIIYQQQhhFAkohhBBCCGEUCSiFEEIIIYRRJKAUQgghhBBGkYBSCCGEEEIYRQJKIYQQQghhFAkohRBCCCGEUSSgFEIIIYQQRpGAUgghhBBCGEUCSiGEEEIIYRQJKIUQQgghhFEkoBRCCCGEEEaRgFIIIYQQQhjl/wANCa2AG97CjQAAAABJRU5ErkJggg==", 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" ] @@ -619,74 +1437,159 @@ { "data": { "text/plain": [ - "{'id': 'da441d41-1050-4505-a834-99d6023001e1',\n", - " 'first_name': 'AARON ',\n", - " 'last_name': 'KRAUSS ',\n", - " 'full_name': 'AARON KRAUSS ',\n", - " 'entity_type': 'Individual',\n", - " 'state': 'MI',\n", + "{'id': '1869727',\n", + " 'company': nan,\n", + " 'entity_type': 'individual',\n", + " 'first_name': nan,\n", + " 'full_name': 'william \\x08stoner',\n", + " 'last_name': nan,\n", " 'party': nan,\n", - " 'company': nan}" + " 'state': nan,\n", + " 'transaction_id': nan,\n", + " 'donor_id': nan,\n", + " 'year': nan,\n", + " 'amount': nan,\n", + " 'recipient_id': nan,\n", + " 'office_sought': nan,\n", + " 'purpose': nan,\n", + " 'transaction_type': nan,\n", + " 'donor_type': nan,\n", + " 'recipient_type': nan,\n", + " 'donor_office': nan}" ] }, - "execution_count": 105, + "execution_count": 91, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "x = add_notes_from_df(inds_sample)\n", - "x.nodes['BELA LAHNER ']" + "x = add_notes_from_df(merged_inds_sample)\n", + "x.nodes['abdussamad, shams']" ] }, { "cell_type": "code", - "execution_count": 104, + "execution_count": 79, "metadata": {}, "outputs": [ { "data": { + "text/html": [ + "
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idcompanyentity_typefirst_namefull_namelast_namepartystatetransaction_iddonor_idyearamountrecipient_idoffice_soughtpurposetransaction_typedonor_typerecipient_typedonor_office
6631869727NaNindividualNaNwilliam \bstonerNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
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" + ], "text/plain": [ - "['BELA LAHNER ',\n", - " 'JANICE SHAPIRO ',\n", - " 'RAMON HAWKINS ',\n", - " 'LEAH CYGAN ',\n", - " 'ALLISON HATT ^ ',\n", - " 'ELLEN FEINGOLD ',\n", - " 'KEVIN HERTEL FOR SENATE',\n", - " 'SARA LAFORGE ^ ',\n", - " 'LOIS TACK ',\n", - " 'AARON KRAUSS ']" + " id company entity_type first_name full_name last_name \\\n", + "663 1869727 NaN individual NaN william \bstoner NaN \n", + "\n", + " party state transaction_id donor_id year amount recipient_id \\\n", + "663 NaN NaN NaN NaN NaN NaN NaN \n", + "\n", + " office_sought purpose transaction_type donor_type recipient_type \\\n", + "663 NaN NaN NaN NaN NaN \n", + "\n", + " donor_office \n", + "663 NaN " ] }, - "execution_count": 104, + "execution_count": 79, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "inds_sample.full_name.tolist()" + "merged_inds_sample.loc[merged_inds_sample.full_name == 'william \\x08stoner']" ] }, { "cell_type": "code", - "execution_count": 94, + "execution_count": 65, "metadata": {}, "outputs": [ { - "ename": "KeyError", - "evalue": "'MALLORY MCMORROW FOR MICHIGAN'", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mKeyError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[0;32mIn[94], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mG\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mnodes\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mMALLORY MCMORROW FOR MICHIGAN\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m]\u001b[49m\n", - "File \u001b[0;32m~/miniconda3/envs/climate_cabinet/lib/python3.11/site-packages/networkx/classes/reportviews.py:194\u001b[0m, in \u001b[0;36mNodeView.__getitem__\u001b[0;34m(self, n)\u001b[0m\n\u001b[1;32m 189\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(n, \u001b[38;5;28mslice\u001b[39m):\n\u001b[1;32m 190\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m nx\u001b[38;5;241m.\u001b[39mNetworkXError(\n\u001b[1;32m 191\u001b[0m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mtype\u001b[39m(\u001b[38;5;28mself\u001b[39m)\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__name__\u001b[39m\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m does not support slicing, \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 192\u001b[0m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mtry list(G.nodes)[\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mn\u001b[38;5;241m.\u001b[39mstart\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m:\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mn\u001b[38;5;241m.\u001b[39mstop\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m:\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mn\u001b[38;5;241m.\u001b[39mstep\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m]\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 193\u001b[0m )\n\u001b[0;32m--> 194\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_nodes\u001b[49m\u001b[43m[\u001b[49m\u001b[43mn\u001b[49m\u001b[43m]\u001b[49m\n", - "\u001b[0;31mKeyError\u001b[0m: 'MALLORY MCMORROW FOR MICHIGAN'" - ] + "data": { + "text/plain": [ + "{'color': nan, 'size': 2}" + ] + }, + "execution_count": 65, + "metadata": {}, + "output_type": "execute_result" } ], - "source": [] + "source": [ + "G = nx.Graph()\n", + "G.add_node(0)\n", + "nx.set_node_attributes(G, \"red\", name=\"color\")\n", + "nx.set_node_attributes(G, 2, name=\"size\")\n", + "G.add_node(1)\n", + "nx.set_node_attributes(G, np.nan, name='color')\n", + "G.nodes[0]" + ] }, { "cell_type": "code", @@ -831,7 +1734,7 @@ }, { "cell_type": "code", - "execution_count": 89, + "execution_count": 26, "metadata": {}, "outputs": [ { @@ -840,13 +1743,13 @@ "{'color': 'white'}" ] }, - "execution_count": 89, + "execution_count": 26, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "G = nx.Graph()\n", + "G = nx.MultiDiGraph()\n", "G.add_node(0)\n", "nx.set_node_attributes(G, \"red\", name=\"color\")\n", "nx.set_node_attributes(G, 4, name = 'size')\n", @@ -855,6 +1758,28 @@ "G.nodes[2]" ] }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'color': 'white', 'age': 4}" + ] + }, + "execution_count": 28, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "G.add_node(2)\n", + "nx.set_node_attributes(G, 4, name='age')\n", + "G.nodes[2]" + ] + }, { "cell_type": "code", "execution_count": null, diff --git a/utils/linkage.py b/utils/linkage.py index e955f94..9346751 100644 --- a/utils/linkage.py +++ b/utils/linkage.py @@ -449,21 +449,23 @@ def name_rank(first_name: str, last_name: str) -> list: def convert_duplicates_to_dict(df: pd.DataFrame) -> None: - """Saves to the "output" directory a file where each row represents a string - matching to another string + """For each uuid, maps it to all other uuids for which it has been deemed a + match. - Given a dataframe where each row contains one string in a column and a list - of strings in another column, the function maps each string in the list to - the single string. + Given a dataframe where the uuids of all rows deemed similar are stored in a + list and all but the first row of each paired uuid is dropped, this function + maps the matched uuids to a single uuid. Args: - A pandas dataframe + A pandas df containing a column called 'duplicated', where each row is a + list of all uuids deemed a match. In each list, all uuids but the first + have their rows already dropped. Returns None. However it outputs a file to the output directory, with 2 - columns. The first, which indicates the duplicated UUIDs, is labeled - 'duplicated_uuids', and the 2nd, which shows the uuids to which the - deduplicated entries match to, is labeled 'mapped_uuids'. + columns. The first lists all the uuids in df, and is labeled 'all_uuids' + The 2nd shows the uuids to which each entry is mapped to, and is labeled + 'mapped_uuids'. """ deduped_dict = {} for i in range(len(df)): @@ -474,7 +476,7 @@ def convert_duplicates_to_dict(df: pd.DataFrame) -> None: # now convert dictionary into a csv file deduped_df = pd.DataFrame.from_dict(deduped_dict, "index") deduped_df = deduped_df.reset_index().rename( - columns={"index": "duplicated_uuids", 0: "mapped_uuids"} + columns={"index": "all_uuids", 0: "mapped_uuids"} ) deduped_df.to_csv( repo_root / "output" / "deduplicated_UUIDs.csv", From 1e4a550703613ff389835021faa18b83015a9d91 Mon Sep 17 00:00:00 2001 From: Alan Mburu Kagiri Date: Thu, 22 Feb 2024 13:16:28 -0600 Subject: [PATCH 05/24] Saving work on networkx branch --- utils/linkage.py | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/utils/linkage.py b/utils/linkage.py index 9346751..49f10bf 100644 --- a/utils/linkage.py +++ b/utils/linkage.py @@ -459,13 +459,13 @@ def convert_duplicates_to_dict(df: pd.DataFrame) -> None: Args: A pandas df containing a column called 'duplicated', where each row is a list of all uuids deemed a match. In each list, all uuids but the first - have their rows already dropped. + have their rows already dropped. Returns None. However it outputs a file to the output directory, with 2 - columns. The first lists all the uuids in df, and is labeled 'all_uuids' + columns. The first lists all the uuids in df, and is labeled 'all_uuids' The 2nd shows the uuids to which each entry is mapped to, and is labeled - 'mapped_uuids'. + 'mapped_uuids'. """ deduped_dict = {} for i in range(len(df)): From cd94c0863510d5e4eb4f87a73e8397fadc537590 Mon Sep 17 00:00:00 2001 From: Alan Mburu Kagiri Date: Sat, 24 Feb 2024 14:47:36 -0600 Subject: [PATCH 06/24] pipeline progress so far on network linkage --- utils/linkage.py | 27 ------------ utils/network.py | 104 +++++++++++++++++++++++++++++++++++++++++++++++ 2 files changed, 104 insertions(+), 27 deletions(-) create mode 100644 utils/network.py diff --git a/utils/linkage.py b/utils/linkage.py index 49f10bf..f71a2b5 100644 --- a/utils/linkage.py +++ b/utils/linkage.py @@ -2,7 +2,6 @@ import os.path import re -import networkx as nx import numpy as np import pandas as pd import textdistance as td @@ -638,29 +637,3 @@ def get_address_number_from_address_line_1(address_line_1: str) -> str: elif address_line_1_components[i][1] == "USPSBoxID": return address_line_1_components[i][0] raise ValueError("Can not find Address Number") - - -def create_network_nodes(df: pd.DataFrame) -> nx.MultiDiGraph: - """Takes in a dataframe and generates a MultiDiGraph where the nodes are - entity names, and the rest of the dataframe columns make the node attributes - - Args: - df: a pandas dataframe (complete_individuals_table / - complete_organizations_table) - - Returns: - A Networkx MultiDiGraph with nodes lacking any edges - """ - G = nx.MultiDiGraph() - # first check if df is individuals or organizations dataset - if "name" in df.columns: - node_name = "name" - else: - node_name = "full_name" - - for _, row in df.iterrows(): - G.add_node(row[node_name]) - for column in df.columns: - nx.set_node_attributes(G, row[column], name=column) - - return G diff --git a/utils/network.py b/utils/network.py new file mode 100644 index 0000000..b0d1e90 --- /dev/null +++ b/utils/network.py @@ -0,0 +1,104 @@ +import networkx as nx +import pandas as pd + +from utils.linkage import deduplicate_perfect_matches + + +def deduplicate_datasets( + ind_df: pd.DataFrame, org_df: pd.DataFrame, transactions_df: pd.DataFrame +) -> tuple: + """Deduplicates the uuids in the inds and orgs dfs and updates the uuids in + transactions dataset to match those in the new inds and orgs dfs + + Args: + ind_df: A pandas df with individual information + org_df: A pandas df with organization information + transactions df: A pandas df with info on transactions between entities + + Returns: + A tuple of the ind_df, org_df, and transactions_df + """ + # apply dedup to both inds and orgs + inds_df = deduplicate_perfect_matches(ind_df) + orgs_df = deduplicate_perfect_matches(org_df) + + # update the deduplicated uuids in transaction donor and recipient columns + # to the uuids they are mapped to + deduped = pd.read_csv("../output/deduplicated_UUIDs.csv") + transactions_df[["donor_id", "recipient_id"]] = transactions_df[ + ["donor_id", "recipient_id"] + ].replace(deduped) + + return inds_df, orgs_df, transactions_df + +def name_identifier(uuid:str, orgs_df, inds_df) -> str: + '''Returns the name of the entity given the entity's uuid + + Args: + uuid: the uuid of the entity + orgs_df and inds_df: the dataframes from which the entities uuid + is queried + + Return: + The entity's name + ''' + # first, check orgs df: + name_in_org = orgs_df.loc[orgs_df['id']==uuid] + if len(name_in_org)> 0: + return name_in_org.iloc[0]['name'] + # theoretically it must be in inds if not in orgs, but for the sample data + # this might not be the case + name_in_ind = inds_df.loc[inds_df['id']==uuid] + if len(name_in_ind)> 0: + return name_in_ind.iloc[0]['full_name'] + else: return None + + +def network_prep_pipeline( + ind_df: pd.DataFrame, org_df: pd.DataFrame, transactions_df: pd.DataFrame +) -> tuple: + '''Pipeline for preparing the orgs, inds, and transactions dataframes for + network linkage + + Args: + ind_df, org_df, transactions_df: pandas dataframes with information + regarding campaign contributions between donors and recipients + + Returns: + a tuple containing the 3 dataframes ready for network building + ''' + + ind_df, org_df, transactions_df = deduplicate_datasets( + ind_df, org_df, transactions_df + ) + + # add recipient_name to the transactions dataset + transactions_df['recipient_name'] = transactions_df['recipient_id'].apply(name_identifier, args=(org_df, ind_df)) + return ind_df, org_df, transactions_df + + + +def create_network_nodes(df: pd.DataFrame) -> nx.MultiDiGraph: + """Takes in a dataframe and generates a MultiDiGraph where the nodes are + entity names, and the rest of the dataframe columns make the node attributes + + Args: + df: a pandas dataframe (complete_individuals_table / + complete_organizations_table) + + Returns: + A Networkx MultiDiGraph with nodes lacking any edges + """ + G = nx.MultiDiGraph() + # first check if df is individuals or organizations dataset + if "name" in df.columns: + node_name = "name" + else: + node_name = "full_name" + + for _, row in df.iterrows(): + G.add_node(row[node_name]) + for column in df.columns: + nx.set_node_attributes(G, row[column], name=column) + + return G From 22607e7420cfd5ed80752110ea024a629945c76d Mon Sep 17 00:00:00 2001 From: Alan Mburu Kagiri Date: Sat, 24 Feb 2024 15:01:27 -0600 Subject: [PATCH 07/24] saving changes in networkx, no need for review --- notebooks/Test.ipynb | 1052 ++++++++++++++++++++++++++++++++++-------- utils/network.py | 39 +- 2 files changed, 887 insertions(+), 204 deletions(-) diff --git a/notebooks/Test.ipynb b/notebooks/Test.ipynb index b17aeb7..d188b44 100644 --- a/notebooks/Test.ipynb +++ b/notebooks/Test.ipynb @@ -8,7 +8,6 @@ "source": [ "import pandas as pd\n", "import numpy as np\n", - "import re\n", "import networkx as nx\n", "import matplotlib.pyplot as plt\n", "\n", @@ -21,8 +20,8 @@ "metadata": {}, "outputs": [], "source": [ - "orgs_sample = pd.read_csv(\"../output/complete_organizations_table.csv\",index_col=0)#,nrows=10000).sample(10)\n", - "inds_sample = pd.read_csv(\"../output/complete_individuals_table.csv\",index_col=0, low_memory=False)#, nrows=10000).sample(10)\n", + "orgs_df = pd.read_csv(\"../output/complete_organizations_table.csv\",index_col=0)#,nrows=10000).sample(10)\n", + "inds_df = pd.read_csv(\"../output/complete_individuals_table.csv\",index_col=0, low_memory=False)#, nrows=10000).sample(10)\n", "transactions = pd.read_csv(\"../output/complete_transactions_table.csv\",index_col=0, low_memory=False)" ] }, @@ -89,7 +88,7 @@ } ], "source": [ - "orgs_sample.head(2)" + "orgs_df.head(2)" ] }, { @@ -136,7 +135,7 @@ " 0\n", " 4640650\n", " 100592\n", - " 2021.0\n", + " 2021\n", " 25.0\n", " 1869727\n", " none\n", @@ -150,7 +149,7 @@ " 1\n", " 8185257\n", " 201800301\n", - " 2020.0\n", + " 2020\n", " 100.0\n", " 1779679\n", " none\n", @@ -165,17 +164,13 @@ "" ], "text/plain": [ - " transaction_id donor_id year amount recipient_id office_sought \\\n", - "0 4640650 100592 2021.0 25.0 1869727 none \n", - "1 8185257 201800301 2020.0 100.0 1779679 none \n", + " transaction_id donor_id year amount recipient_id office_sought purpose \\\n", + "0 4640650 100592 2021 25.0 1869727 none wr 9.13 \n", + "1 8185257 201800301 2020 100.0 1779679 none ab \n", "\n", - " purpose transaction_type donor_type recipient_type \\\n", - "0 wr 9.13 contribution from individuals NaN NaN \n", - "1 ab contribution from individuals NaN NaN \n", - "\n", - " donor_office \n", - "0 NaN \n", - "1 NaN " + " transaction_type donor_type recipient_type donor_office \n", + "0 contribution from individuals NaN NaN NaN \n", + "1 contribution from individuals NaN NaN NaN " ] }, "execution_count": 4, @@ -187,58 +182,6 @@ "transactions.head(2)" ] }, - { - "cell_type": "code", - "execution_count": 43, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
idnamestateentity_type
\n", - "
" - ], - "text/plain": [ - "Empty DataFrame\n", - "Columns: [id, name, state, entity_type]\n", - "Index: []" - ] - }, - "execution_count": 43, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "orgs_sample.loc[orgs_sample['id']=='201800301']" - ] - }, { "cell_type": "code", "execution_count": 5, @@ -273,6 +216,10 @@ " state\n", " party\n", " company\n", + " occupation\n", + " address\n", + " zip\n", + " city\n", " \n", " \n", " \n", @@ -286,6 +233,10 @@ " NaN\n", " NaN\n", " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", " \n", " \n", " 1\n", @@ -297,6 +248,10 @@ " NaN\n", " NaN\n", " area agency on aging\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", " \n", " \n", "\n", @@ -307,9 +262,9 @@ "0 1869727 NaN NaN william \bstoner individual NaN NaN \n", "1 1779679 NaN NaN rm coulon individual NaN NaN \n", "\n", - " company \n", - "0 NaN \n", - "1 area agency on aging " + " company occupation address zip city \n", + "0 NaN NaN NaN NaN NaN \n", + "1 area agency on aging NaN NaN NaN NaN " ] }, "execution_count": 5, @@ -318,28 +273,28 @@ } ], "source": [ - "inds_sample.head(2)" + "inds_df.head(2)" ] }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "(542368, 102, 248318, 3)" + "(541803, 541150, 77611, 77611)" ] }, - "execution_count": 22, + "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "inds_ids = set(inds_sample.id.tolist())\n", - "orgs_ids = set(orgs_sample.id.tolist())\n", + "inds_ids = set(inds_df.id.tolist())\n", + "orgs_ids = set(orgs_df.id.tolist())\n", "trans_donorids = set(transactions.donor_id.tolist())\n", "trans_recepids = set(transactions.recipient_id.tolist())\n", "ind_id_there, org_id_there = [], []\n", @@ -360,16 +315,16 @@ }, { "cell_type": "code", - "execution_count": 99, + "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "['100894', '100883']" + "['100883', '100894']" ] }, - "execution_count": 99, + "execution_count": 7, "metadata": {}, "output_type": "execute_result" } @@ -600,7 +555,7 @@ }, { "cell_type": "code", - "execution_count": 97, + "execution_count": 11, "metadata": {}, "outputs": [ { @@ -632,63 +587,87 @@ " state\n", " party\n", " company\n", + " occupation\n", + " address\n", + " zip\n", + " city\n", " \n", " \n", " \n", " \n", - " 0\n", - " 1869727\n", + " 102\n", + " 100894\n", " NaN\n", " NaN\n", - " william \bstoner\n", - " individual\n", + " abdussamad, shams\n", + " candidate\n", + " AZ\n", + " democratic\n", + " none (is a candidate)\n", + " NaN\n", " NaN\n", " NaN\n", " NaN\n", " \n", " \n", - " 1\n", - " 1779679\n", + " 103\n", + " 100894\n", + " NaN\n", + " NaN\n", + " abdussamad, shams\n", + " candidate\n", + " AZ\n", + " democratic\n", + " none (is a candidate)\n", " NaN\n", " NaN\n", - " rm coulon\n", - " individual\n", " NaN\n", " NaN\n", - " area agency on aging\n", " \n", " \n", - " 2\n", - " 2277221\n", + " 104\n", + " 100883\n", + " NaN\n", + " NaN\n", + " abeytia, anna lynn\n", + " candidate\n", + " AZ\n", + " democratic\n", + " none (is a candidate)\n", " NaN\n", " NaN\n", - " james engelson\n", - " individual\n", " NaN\n", " NaN\n", - " retired\n", " \n", " \n", - " 3\n", - " 2277156\n", - " NaN\n", + " 105\n", + " 100883\n", " NaN\n", - " marivic franciaskinner\n", - " individual\n", " NaN\n", + " abeytia, anna lynn\n", + " candidate\n", + " AZ\n", + " democratic\n", + " none (is a candidate)\n", " NaN\n", - " fibre source international corp\n", - " \n", - " \n", - " 4\n", - " 2341373\n", " NaN\n", " NaN\n", - " anthony grindle\n", - " individual\n", " NaN\n", + " \n", + " \n", + " 0\n", + " b8fbed14-0766-49ab-8516-97952c654a12\n", + " FREDERICK\n", + " BERG\n", + " FREDERICK BERG ...\n", + " Individual\n", + " MI\n", " NaN\n", - " zimmerbiomet\n", + " BUTZEL LONG\n", + " ATTORNEY\n", + " 1033 YORKSHIRE\n", + " 48230-0000\n", + " GROSSE POINTE PARK\n", " \n", " \n", " ...\n", @@ -700,117 +679,826 @@ " ...\n", " ...\n", " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " \n", + " \n", + " 17734\n", + " 75b99f42-e0d4-4c3c-89a6-16e11f6dd810\n", + " NaN\n", + " NaN\n", + " Rodriguez, Adrian\n", + " Individual\n", + " MN\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", + " \n", + " \n", + " 17735\n", + " 8b634b74-a6be-4280-a2c4-63e46a8f9bc9\n", + " NaN\n", + " NaN\n", + " O'Connor, Timothy J\n", + " Individual\n", + " MN\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", + " \n", + " \n", + " 17736\n", + " d7d5b121-015f-474f-8b76-7c6c865da557\n", + " NaN\n", + " NaN\n", + " Frenzel, Robert C\n", + " Individual\n", + " MN\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", + " \n", + " \n", + " 17737\n", + " b2eaaec4-30d5-46f4-9922-efc8d79c16d2\n", + " NaN\n", + " NaN\n", + " Enzminger, Peter\n", + " Individual\n", + " MN\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", + " \n", + " \n", + " 17738\n", + " de34f2c7-fa2f-4fa5-abea-b67f6c8fe35f\n", + " NaN\n", + " NaN\n", + " Bowler, Erin\n", + " Individual\n", + " MN\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", + " \n", + " \n", + "\n", + "

1760156 rows × 12 columns

\n", + "" + ], + "text/plain": [ + " id first_name \\\n", + "102 100894 NaN \n", + "103 100894 NaN \n", + "104 100883 NaN \n", + "105 100883 NaN \n", + "0 b8fbed14-0766-49ab-8516-97952c654a12 FREDERICK \n", + "... ... ... \n", + "17734 75b99f42-e0d4-4c3c-89a6-16e11f6dd810 NaN \n", + "17735 8b634b74-a6be-4280-a2c4-63e46a8f9bc9 NaN \n", + "17736 d7d5b121-015f-474f-8b76-7c6c865da557 NaN \n", + "17737 b2eaaec4-30d5-46f4-9922-efc8d79c16d2 NaN \n", + "17738 de34f2c7-fa2f-4fa5-abea-b67f6c8fe35f NaN \n", + "\n", + " last_name \\\n", + "102 NaN \n", + "103 NaN \n", + "104 NaN \n", + "105 NaN \n", + "0 BERG \n", + "... ... \n", + "17734 NaN \n", + "17735 NaN \n", + "17736 NaN \n", + "17737 NaN \n", + "17738 NaN \n", + "\n", + " full_name entity_type state \\\n", + "102 abdussamad, shams candidate AZ \n", + "103 abdussamad, shams candidate AZ \n", + "104 abeytia, anna lynn candidate AZ \n", + "105 abeytia, anna lynn candidate AZ \n", + "0 FREDERICK BERG ... Individual MI \n", + "... ... ... ... \n", + "17734 Rodriguez, Adrian Individual MN \n", + "17735 O'Connor, Timothy J Individual MN \n", + "17736 Frenzel, Robert C Individual MN \n", + "17737 Enzminger, Peter Individual MN \n", + "17738 Bowler, Erin Individual MN \n", + "\n", + " party company occupation address \\\n", + "102 democratic none (is a candidate) NaN NaN \n", + "103 democratic none (is a candidate) NaN NaN \n", + "104 democratic none (is a candidate) NaN NaN \n", + "105 democratic none (is a candidate) NaN NaN \n", + "0 NaN BUTZEL LONG ATTORNEY 1033 YORKSHIRE \n", + "... ... ... ... ... \n", + "17734 NaN NaN NaN NaN \n", + "17735 NaN NaN NaN NaN \n", + "17736 NaN NaN NaN NaN \n", + "17737 NaN NaN NaN NaN \n", + "17738 NaN NaN NaN NaN \n", + "\n", + " zip city \n", + "102 NaN NaN \n", + "103 NaN NaN \n", + "104 NaN NaN \n", + "105 NaN NaN \n", + "0 48230-0000 GROSSE POINTE PARK \n", + "... ... ... \n", + "17734 NaN NaN \n", + "17735 NaN NaN \n", + "17736 NaN NaN \n", + "17737 NaN NaN \n", + "17738 NaN NaN \n", + "\n", + "[1760156 rows x 12 columns]" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# for now only work with datasets \n", + "sample_inds = inds_df.loc[(inds_df['id'].isin(transactions.donor_id.tolist()))]\n", + "sample_inds\n" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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transaction_iddonor_idyearamountrecipient_idoffice_soughtpurposetransaction_typedonor_typerecipient_typedonor_office
212637NaNb8fbed14-0766-49ab-8516-97952c654a122022100.001d4ae24b-2814-4d0d-995e-28fd4c26785dNaNNaNDIRECTNaNNaNNaN
212667NaNb8fbed14-0766-49ab-8516-97952c654a12202250.001d4ae24b-2814-4d0d-995e-28fd4c26785dNaNNaNDIRECTNaNNaNNaN
440542NaNb8fbed14-0766-49ab-8516-97952c654a12202250.001d4ae24b-2814-4d0d-995e-28fd4c26785dNaNNaNDIRECTNaNNaNNaN
440573NaNb8fbed14-0766-49ab-8516-97952c654a12202250.001d4ae24b-2814-4d0d-995e-28fd4c26785dNaNNaNDIRECTNaNNaNNaN
440607NaNb8fbed14-0766-49ab-8516-97952c654a12202250.001d4ae24b-2814-4d0d-995e-28fd4c26785dNaNNaNDIRECTNaNNaNNaN
440642NaNb8fbed14-0766-49ab-8516-97952c654a12202250.001d4ae24b-2814-4d0d-995e-28fd4c26785dNaNNaNDIRECTNaNNaNNaN
636312NaNb8fbed14-0766-49ab-8516-97952c654a12202250.001d4ae24b-2814-4d0d-995e-28fd4c26785dNaNNaNDIRECTNaNNaNNaN
636346NaNb8fbed14-0766-49ab-8516-97952c654a12202250.001d4ae24b-2814-4d0d-995e-28fd4c26785dNaNNaNDIRECTNaNNaNNaN
636382NaNb8fbed14-0766-49ab-8516-97952c654a12202250.001d4ae24b-2814-4d0d-995e-28fd4c26785dNaNNaNDIRECTNaNNaNNaN
839846NaNb8fbed14-0766-49ab-8516-97952c654a12202283.33f9fa8506-bfbb-4ef0-9e08-5c9c3e948121NaNNaNDIRECT/FUND RAISERNaNNaNNaN
840051NaNb8fbed14-0766-49ab-8516-97952c654a12202283.34389fe2ba-828a-41d4-815c-8efb2499ea11NaNNaNDIRECT/FUND RAISERNaNNaNNaN
8612606acfa74b-d5e1-4afd-b020-dbe429eb1c3f968402NaNb8fbed14-0766-49ab-8516-97952c654a12202283.33043a03b7-af31-4830-b12e-446b93fca9a0NaNNaNDIRECT/FUND RAISERNaNNaNMelissa HartCandidatePAREPNaN
861271f111045d-bc3d-4050-9ad7-b3b1e6d72e561414338NaNb8fbed14-0766-49ab-8516-97952c654a122022250.00ba06baf6-eae6-459f-b3a9-7261e4baa33eNaNNaNDIRECT/FUND RAISERNaNNaNHeather MillerCandidatePADEMNaN
861277d40859d7-b523-4ef5-895b-c3a947ab582f1502742NaNb8fbed14-0766-49ab-8516-97952c654a12202250.001d4ae24b-2814-4d0d-995e-28fd4c26785dNaNNaNDIRECTNaNNaNChristopher M. GebhardCandidatePAREPNaN
861775f5d76d43-86f4-40f9-aeb9-3df97ca8cdf01502777NaNb8fbed14-0766-49ab-8516-97952c654a12202250.001d4ae24b-2814-4d0d-995e-28fd4c26785dNaNNaNDIRECTNaNNaNApril WeaverCandidatePAREPNaN
8619201a0cf90d-3252-4c8d-b109-dea084a01f691502812NaNb8fbed14-0766-49ab-8516-97952c654a12202250.001d4ae24b-2814-4d0d-995e-28fd4c26785dNaNNaNDIRECTNaNNaNKrista PaolucciCandidatePAREPNaN
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2505346 rows × 8 columns

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" ], "text/plain": [ - " id first_name last_name \\\n", - "0 1869727 NaN NaN \n", - "1 1779679 NaN NaN \n", - "2 2277221 NaN NaN \n", - "3 2277156 NaN NaN \n", - "4 2341373 NaN NaN \n", - "... ... ... ... \n", - "861260 6acfa74b-d5e1-4afd-b020-dbe429eb1c3f NaN NaN \n", - "861271 f111045d-bc3d-4050-9ad7-b3b1e6d72e56 NaN NaN \n", - "861277 d40859d7-b523-4ef5-895b-c3a947ab582f NaN NaN \n", - "861775 f5d76d43-86f4-40f9-aeb9-3df97ca8cdf0 NaN NaN \n", - "861920 1a0cf90d-3252-4c8d-b109-dea084a01f69 NaN NaN \n", + " transaction_id donor_id year amount \\\n", + "212637 NaN b8fbed14-0766-49ab-8516-97952c654a12 2022 100.00 \n", + "212667 NaN b8fbed14-0766-49ab-8516-97952c654a12 2022 50.00 \n", + "440542 NaN b8fbed14-0766-49ab-8516-97952c654a12 2022 50.00 \n", + "440573 NaN b8fbed14-0766-49ab-8516-97952c654a12 2022 50.00 \n", + "440607 NaN b8fbed14-0766-49ab-8516-97952c654a12 2022 50.00 \n", + "440642 NaN b8fbed14-0766-49ab-8516-97952c654a12 2022 50.00 \n", + "636312 NaN b8fbed14-0766-49ab-8516-97952c654a12 2022 50.00 \n", + "636346 NaN b8fbed14-0766-49ab-8516-97952c654a12 2022 50.00 \n", + "636382 NaN b8fbed14-0766-49ab-8516-97952c654a12 2022 50.00 \n", + "839846 NaN b8fbed14-0766-49ab-8516-97952c654a12 2022 83.33 \n", + "840051 NaN b8fbed14-0766-49ab-8516-97952c654a12 2022 83.34 \n", + "968402 NaN b8fbed14-0766-49ab-8516-97952c654a12 2022 83.33 \n", + "1414338 NaN b8fbed14-0766-49ab-8516-97952c654a12 2022 250.00 \n", + "1502742 NaN b8fbed14-0766-49ab-8516-97952c654a12 2022 50.00 \n", + "1502777 NaN b8fbed14-0766-49ab-8516-97952c654a12 2022 50.00 \n", + "1502812 NaN b8fbed14-0766-49ab-8516-97952c654a12 2022 50.00 \n", "\n", - " full_name entity_type state party \\\n", - "0 william \bstoner individual NaN NaN \n", - "1 rm coulon individual NaN NaN \n", - "2 james engelson individual NaN NaN \n", - "3 marivic franciaskinner individual NaN NaN \n", - "4 anthony grindle individual NaN NaN \n", - "... ... ... ... ... \n", - "861260 Melissa Hart Candidate PA REP \n", - "861271 Heather Miller Candidate PA DEM \n", - "861277 Christopher M. Gebhard Candidate PA REP \n", - "861775 April Weaver Candidate PA REP \n", - "861920 Krista Paolucci Candidate PA REP \n", + " recipient_id office_sought purpose \\\n", + "212637 1d4ae24b-2814-4d0d-995e-28fd4c26785d NaN NaN \n", + "212667 1d4ae24b-2814-4d0d-995e-28fd4c26785d NaN NaN \n", + "440542 1d4ae24b-2814-4d0d-995e-28fd4c26785d NaN NaN \n", + "440573 1d4ae24b-2814-4d0d-995e-28fd4c26785d NaN NaN \n", + "440607 1d4ae24b-2814-4d0d-995e-28fd4c26785d NaN NaN \n", + "440642 1d4ae24b-2814-4d0d-995e-28fd4c26785d NaN NaN \n", + "636312 1d4ae24b-2814-4d0d-995e-28fd4c26785d NaN NaN \n", + "636346 1d4ae24b-2814-4d0d-995e-28fd4c26785d NaN NaN \n", + "636382 1d4ae24b-2814-4d0d-995e-28fd4c26785d NaN NaN \n", + "839846 f9fa8506-bfbb-4ef0-9e08-5c9c3e948121 NaN NaN \n", + "840051 389fe2ba-828a-41d4-815c-8efb2499ea11 NaN NaN \n", + "968402 043a03b7-af31-4830-b12e-446b93fca9a0 NaN NaN \n", + "1414338 ba06baf6-eae6-459f-b3a9-7261e4baa33e NaN NaN \n", + "1502742 1d4ae24b-2814-4d0d-995e-28fd4c26785d NaN NaN \n", + "1502777 1d4ae24b-2814-4d0d-995e-28fd4c26785d NaN NaN \n", + "1502812 1d4ae24b-2814-4d0d-995e-28fd4c26785d NaN NaN \n", "\n", - " company \n", - "0 NaN \n", - "1 area agency on aging \n", - "2 retired \n", - "3 fibre source international corp \n", - "4 zimmerbiomet \n", - "... ... \n", - "861260 NaN \n", - "861271 NaN \n", - "861277 NaN \n", - "861775 NaN \n", - "861920 NaN \n", + " transaction_type donor_type recipient_type donor_office \n", + "212637 DIRECT NaN NaN NaN \n", + "212667 DIRECT NaN NaN NaN \n", + "440542 DIRECT NaN NaN NaN \n", + "440573 DIRECT NaN NaN NaN \n", + "440607 DIRECT NaN NaN NaN \n", + "440642 DIRECT NaN NaN NaN \n", + "636312 DIRECT NaN NaN NaN \n", + "636346 DIRECT NaN NaN NaN \n", + "636382 DIRECT NaN NaN NaN \n", + "839846 DIRECT/FUND RAISER NaN NaN NaN \n", + "840051 DIRECT/FUND RAISER NaN NaN NaN \n", + "968402 DIRECT/FUND RAISER NaN NaN NaN \n", + "1414338 DIRECT/FUND RAISER NaN NaN NaN \n", + "1502742 DIRECT NaN NaN NaN \n", + "1502777 DIRECT NaN NaN NaN \n", + "1502812 DIRECT NaN NaN NaN " + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "transactions.loc[transactions['donor_id'] == 'b8fbed14-0766-49ab-8516-97952c654a12']" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'BUTZEL LONG POLITICAL ACTION COMMITTEE'" + ] + }, + "execution_count": 28, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "x = orgs_df.loc[orgs_df['id']=='1d4ae24b-2814-4d0d-995e-28fd4c26785d']\n", + "x.iloc[0]['name']" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "# apply dedup to both inds and orgs\n", + "inds_df = deduplicate_perfect_matches(inds_df)\n", + "orgs_df = deduplicate_perfect_matches(orgs_df)\n", + "\n", + "# map the uuids in transaction donor and recipient columns to the deduplicated uuids\n", + "deduped = pd.read_csv(\"../output/deduplicated_UUIDs.csv\")\n", + "transactions[['donor_id','recipient_id']] = transactions[['donor_id','recipient_id']].replace(deduped)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "# add recipient name to transactions df: \n", + "def name_identifier(uuid:str, orgs_df, inds_df) -> str:\n", + " # 1st check orgs df:\n", + " name_in_org = orgs_df.loc[orgs_df['id']==uuid] \n", + " if len(name_in_org)> 0:\n", + " return name_in_org.iloc[0]['name']\n", + " # theoretically it must be in inds if not in orgs, but for the sample data\n", + " # this might not be the case\n", + " name_in_ind = inds_df.loc[inds_df['id']==uuid]\n", + " if len(name_in_ind)> 0:\n", + " return name_in_ind.iloc[0]['full_name']\n", + " else: return None" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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transaction_iddonor_idyearamountrecipient_idoffice_soughtpurposetransaction_typedonor_typerecipient_typedonor_officerecipient_name
884875NaN6c2b94a2-4247-4bc4-b784-6b5a9a2ae9f220225.00533f6de5-5140-4799-be24-1d5f4e228d1bNaNNaNDIRECTNaNNaNNaNFRIENDS OF DANA NESSEL
122735NaNb906d3eb-3874-4789-b523-e2eaab41532820229.16f2fad7aa-a782-4d56-8343-049d2150c16fNaNMERCHANT SVCS FEESNaNNaNNaNNaNTHE JULIE BRIXIE BLUE WAVE FUND 2
458788NaN88740001-952f-477e-b2da-9b24f747f6ce20221.00a0619eff-155f-442f-ab71-b5c0ee942223NaNNaNDIRECTNaNNaNNaNCOMERICA INC POLITICAL ACTION COMMITTEE
1522918NaNda538e73-c823-48f8-b4ec-920aa1da458f202220.0081169dce-331e-44ad-b870-1b376d49cf2fNaNNaNDIRECTNaNNaNNaNWASTE MANAGEMENT EMPLOYEES BETTER GOVERNMENT F...
1933218NaN33814139-8442-4050-b15a-40aed1aa9db7202235.00abdf0530-e2fb-40b6-9a52-dea386cd60f4NaNNaNDIRECTNaNNaNNaNGRETCHEN WHITMER FOR GOVERNOR
465682NaN133431dd-41ef-4161-97ef-02d23fc05b4220227.50d582fba6-2a0c-4864-9fb2-5a4f898f26c2NaNNaNDIRECTNaNNaNNaNMI ASSOC OF COMMUNITY BANKERS OF MICHIGAN POLI...
761674NaN668d8471-ade6-469b-9e6b-71ddbfd1d8ba202225.001d05ca29-e97f-43cd-bd9e-f313573b324bNaNNaNDIRECTNaNNaNNaNEND CITIZENS UNITED NON-FEDERAL MI
993543NaNb9b66f08-4e99-43e7-9161-c75db92b0bb4202210.00ecebf482-f298-4777-bea6-e3451c75e3fcNaNNaNDIRECTNaNNaNNaNRESCARE INC DBA BRIGHTSPRING HEALTH SERVICES L...
1196687NaNf4942707-0d7f-4617-b478-56af7504123e202212.00a24e305e-a49b-4cb3-a857-d629f1162ce8NaNNaNDIRECTNaNNaNNaNMARATHON PETROLEUM CORPORATION EMPLOYEES PAC
334698NaN96be56db-56b6-48d0-9cf7-9d47da307388202211.809fc94e93-b6aa-400d-9a4a-d6501afb84dcNaNNaNDIRECTNaNNaNNaNMICHIGAN REGIONAL COUNCIL OF CARPENTERS POLITI...
\n", + "
" + ], + "text/plain": [ + " transaction_id donor_id year amount \\\n", + "884875 NaN 6c2b94a2-4247-4bc4-b784-6b5a9a2ae9f2 2022 5.00 \n", + "122735 NaN b906d3eb-3874-4789-b523-e2eaab415328 2022 9.16 \n", + "458788 NaN 88740001-952f-477e-b2da-9b24f747f6ce 2022 1.00 \n", + "1522918 NaN da538e73-c823-48f8-b4ec-920aa1da458f 2022 20.00 \n", + "1933218 NaN 33814139-8442-4050-b15a-40aed1aa9db7 2022 35.00 \n", + "465682 NaN 133431dd-41ef-4161-97ef-02d23fc05b42 2022 7.50 \n", + "761674 NaN 668d8471-ade6-469b-9e6b-71ddbfd1d8ba 2022 25.00 \n", + "993543 NaN b9b66f08-4e99-43e7-9161-c75db92b0bb4 2022 10.00 \n", + "1196687 NaN f4942707-0d7f-4617-b478-56af7504123e 2022 12.00 \n", + "334698 NaN 96be56db-56b6-48d0-9cf7-9d47da307388 2022 11.80 \n", + "\n", + " recipient_id office_sought \\\n", + "884875 533f6de5-5140-4799-be24-1d5f4e228d1b NaN \n", + "122735 f2fad7aa-a782-4d56-8343-049d2150c16f NaN \n", + "458788 a0619eff-155f-442f-ab71-b5c0ee942223 NaN \n", + "1522918 81169dce-331e-44ad-b870-1b376d49cf2f NaN \n", + "1933218 abdf0530-e2fb-40b6-9a52-dea386cd60f4 NaN \n", + "465682 d582fba6-2a0c-4864-9fb2-5a4f898f26c2 NaN \n", + "761674 1d05ca29-e97f-43cd-bd9e-f313573b324b NaN \n", + "993543 ecebf482-f298-4777-bea6-e3451c75e3fc NaN \n", + "1196687 a24e305e-a49b-4cb3-a857-d629f1162ce8 NaN \n", + "334698 9fc94e93-b6aa-400d-9a4a-d6501afb84dc NaN \n", + "\n", + " purpose transaction_type donor_type \\\n", + "884875 NaN DIRECT NaN \n", + "122735 MERCHANT SVCS FEES NaN NaN \n", + "458788 NaN DIRECT NaN \n", + "1522918 NaN DIRECT NaN \n", + "1933218 NaN DIRECT NaN \n", + "465682 NaN DIRECT NaN \n", + "761674 NaN DIRECT NaN \n", + "993543 NaN DIRECT NaN \n", + "1196687 NaN DIRECT NaN \n", + "334698 NaN DIRECT NaN \n", + "\n", + " recipient_type donor_office \\\n", + "884875 NaN NaN \n", + "122735 NaN NaN \n", + "458788 NaN NaN \n", + "1522918 NaN NaN \n", + "1933218 NaN NaN \n", + "465682 NaN NaN \n", + "761674 NaN NaN \n", + "993543 NaN NaN \n", + "1196687 NaN NaN \n", + "334698 NaN NaN \n", + "\n", + " recipient_name \n", + "884875 FRIENDS OF DANA NESSEL \n", + "122735 THE JULIE BRIXIE BLUE WAVE FUND 2 \n", + "458788 COMERICA INC POLITICAL ACTION COMMITTEE \n", + "1522918 WASTE MANAGEMENT EMPLOYEES BETTER GOVERNMENT F... \n", + "1933218 GRETCHEN WHITMER FOR GOVERNOR \n", + "465682 MI ASSOC OF COMMUNITY BANKERS OF MICHIGAN POLI... \n", + "761674 END CITIZENS UNITED NON-FEDERAL MI \n", + "993543 RESCARE INC DBA BRIGHTSPRING HEALTH SERVICES L... \n", + "1196687 MARATHON PETROLEUM CORPORATION EMPLOYEES PAC \n", + "334698 MICHIGAN REGIONAL COUNCIL OF CARPENTERS POLITI... " ] }, - "execution_count": 97, + "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "sample_inds" + "ex = transactions.sample(10)\n", + "ex['recipient_name'] = ex['recipient_id'].apply(name_identifier, args=(orgs_df, inds_df))\n", + "ex" ] }, { @@ -933,15 +1621,7 @@ } ], "source": [ - "sample_inds = inds_sample.loc[inds_sample['id'].isin(ind_id_there)]\n", - "# apply dedup\n", - "sample_inds = deduplicate_perfect_matches(sample_inds)\n", "\n", - "# map the uuids in transaction donor and recipient columns to the deduplicated uuids\n", - "deduped = pd.read_csv(\"../output/deduplicated_UUIDs.csv\")\n", - "transactions[['donor_id','recipient_id']] = transactions[['donor_id','recipient_id']].replace(deduped)\n", - "\n", - "# add recipient name to transactions df: \n", "# this step took more than 16 minutes to run...think of alternative way\n", "# id_to_name = {id: name for id, name in zip(inds_sample.id.tolist(), inds_sample.full_name.tolist())} #the same would be applied to orgs\n", "transactions['recipient_name'] = transactions['recipient_id'].apply(lambda x: sample_inds.loc[sample_inds.id == x] )\n", diff --git a/utils/network.py b/utils/network.py index b0d1e90..443f240 100644 --- a/utils/network.py +++ b/utils/network.py @@ -22,7 +22,7 @@ def deduplicate_datasets( inds_df = deduplicate_perfect_matches(ind_df) orgs_df = deduplicate_perfect_matches(org_df) - # update the deduplicated uuids in transaction donor and recipient columns + # update the deduplicated uuids in transaction donor and recipient columns # to the uuids they are mapped to deduped = pd.read_csv("../output/deduplicated_UUIDs.csv") transactions_df[["donor_id", "recipient_id"]] = transactions_df[ @@ -31,9 +31,10 @@ def deduplicate_datasets( return inds_df, orgs_df, transactions_df -def name_identifier(uuid:str, orgs_df, inds_df) -> str: - '''Returns the name of the entity given the entity's uuid - + +def name_identifier(uuid: str, orgs_df, inds_df) -> str: + """Returns the name of the entity given the entity's uuid + Args: uuid: the uuid of the entity orgs_df and inds_df: the dataframes from which the entities uuid @@ -41,23 +42,24 @@ def name_identifier(uuid:str, orgs_df, inds_df) -> str: Return: The entity's name - ''' + """ # first, check orgs df: - name_in_org = orgs_df.loc[orgs_df['id']==uuid] - if len(name_in_org)> 0: - return name_in_org.iloc[0]['name'] + name_in_org = orgs_df.loc[orgs_df["id"] == uuid] + if len(name_in_org) > 0: + return name_in_org.iloc[0]["name"] # theoretically it must be in inds if not in orgs, but for the sample data # this might not be the case - name_in_ind = inds_df.loc[inds_df['id']==uuid] - if len(name_in_ind)> 0: - return name_in_ind.iloc[0]['full_name'] - else: return None + name_in_ind = inds_df.loc[inds_df["id"] == uuid] + if len(name_in_ind) > 0: + return name_in_ind.iloc[0]["full_name"] + else: + return None def network_prep_pipeline( ind_df: pd.DataFrame, org_df: pd.DataFrame, transactions_df: pd.DataFrame ) -> tuple: - '''Pipeline for preparing the orgs, inds, and transactions dataframes for + """Pipeline for preparing the orgs, inds, and transactions dataframes for network linkage Args: @@ -65,19 +67,20 @@ def network_prep_pipeline( regarding campaign contributions between donors and recipients Returns: - a tuple containing the 3 dataframes ready for network building - ''' - + a tuple containing the 3 dataframes ready for network building + """ + ind_df, org_df, transactions_df = deduplicate_datasets( ind_df, org_df, transactions_df ) # add recipient_name to the transactions dataset - transactions_df['recipient_name'] = transactions_df['recipient_id'].apply(name_identifier, args=(org_df, ind_df)) + transactions_df["recipient_name"] = transactions_df["recipient_id"].apply( + name_identifier, args=(org_df, ind_df) + ) return ind_df, org_df, transactions_df - def create_network_nodes(df: pd.DataFrame) -> nx.MultiDiGraph: """Takes in a dataframe and generates a MultiDiGraph where the nodes are entity names, and the rest of the dataframe columns make the node attributes From d0f36b6c8c13708109f4be8ee8c7791dd30fea4c Mon Sep 17 00:00:00 2001 From: Alan Mburu Kagiri Date: Mon, 26 Feb 2024 09:47:58 -0600 Subject: [PATCH 08/24] saving Networkx work before merge...no need to review --- utils/network.py | 26 ++++++++++++++++++++++++-- 1 file changed, 24 insertions(+), 2 deletions(-) diff --git a/utils/network.py b/utils/network.py index 443f240..8b4d461 100644 --- a/utils/network.py +++ b/utils/network.py @@ -99,9 +99,31 @@ def create_network_nodes(df: pd.DataFrame) -> nx.MultiDiGraph: else: node_name = "full_name" + transact_info = [ + "office_sought", + "purpose", + "transaction_type", + "recipient_id", + "transaction_id", + "recipient_type", + "donor_office", + "recipient_name", + "amount", + ] + for _, row in df.iterrows(): + # add node attributes based on the columns relevant to the entity G.add_node(row[node_name]) - for column in df.columns: - nx.set_node_attributes(G, row[column], name=column) + for column in df.columns.difference(transact_info): + if not pd.isnull(row[column]): + G.nodes[row[node_name]][column] = row[column] + + # link the donor node to the recipient node. add the attributes of the + # edge based on relevant nodes + for column in transact_info: + if not pd.isnull(row[column]): + G.add_edge( + row[node_name], row["recipient_name"], column=row[column] + ) return G From f8df69fcd07046f05899375f5705d66973bd3b62 Mon Sep 17 00:00:00 2001 From: Alan Mburu Kagiri Date: Mon, 26 Feb 2024 11:35:32 -0600 Subject: [PATCH 09/24] saving work for merge, no need to review --- utils/network.py | 87 ++++++++++-------------------------------------- 1 file changed, 17 insertions(+), 70 deletions(-) diff --git a/utils/network.py b/utils/network.py index 8b4d461..6e9dcd9 100644 --- a/utils/network.py +++ b/utils/network.py @@ -1,84 +1,31 @@ import networkx as nx import pandas as pd -from utils.linkage import deduplicate_perfect_matches - -def deduplicate_datasets( - ind_df: pd.DataFrame, org_df: pd.DataFrame, transactions_df: pd.DataFrame -) -> tuple: - """Deduplicates the uuids in the inds and orgs dfs and updates the uuids in - transactions dataset to match those in the new inds and orgs dfs - - Args: - ind_df: A pandas df with individual information - org_df: A pandas df with organization information - transactions df: A pandas df with info on transactions between entities - - Returns: - A tuple of the ind_df, org_df, and transactions_df - """ - # apply dedup to both inds and orgs - inds_df = deduplicate_perfect_matches(ind_df) - orgs_df = deduplicate_perfect_matches(org_df) - - # update the deduplicated uuids in transaction donor and recipient columns - # to the uuids they are mapped to - deduped = pd.read_csv("../output/deduplicated_UUIDs.csv") - transactions_df[["donor_id", "recipient_id"]] = transactions_df[ - ["donor_id", "recipient_id"] - ].replace(deduped) - - return inds_df, orgs_df, transactions_df - - -def name_identifier(uuid: str, orgs_df, inds_df) -> str: +def name_identifier(uuid: str, dfs: list[pd.DataFrame]) -> str: """Returns the name of the entity given the entity's uuid Args: uuid: the uuid of the entity - orgs_df and inds_df: the dataframes from which the entities uuid - is queried - + List of dfs: dataframes that have a uuid column, and an 'name' or + 'full_name' column Return: The entity's name """ - # first, check orgs df: - name_in_org = orgs_df.loc[orgs_df["id"] == uuid] - if len(name_in_org) > 0: - return name_in_org.iloc[0]["name"] - # theoretically it must be in inds if not in orgs, but for the sample data - # this might not be the case - name_in_ind = inds_df.loc[inds_df["id"] == uuid] - if len(name_in_ind) > 0: - return name_in_ind.iloc[0]["full_name"] - else: - return None - - -def network_prep_pipeline( - ind_df: pd.DataFrame, org_df: pd.DataFrame, transactions_df: pd.DataFrame -) -> tuple: - """Pipeline for preparing the orgs, inds, and transactions dataframes for - network linkage - - Args: - ind_df, org_df, transactions_df: pandas dataframes with information - regarding campaign contributions between donors and recipients - - Returns: - a tuple containing the 3 dataframes ready for network building - """ - - ind_df, org_df, transactions_df = deduplicate_datasets( - ind_df, org_df, transactions_df - ) - - # add recipient_name to the transactions dataset - transactions_df["recipient_name"] = transactions_df["recipient_id"].apply( - name_identifier, args=(org_df, ind_df) - ) - return ind_df, org_df, transactions_df + for df in dfs: + # first, check orgs df: + if "name" in df.columns: + name_in_org = df.loc[df["id"] == uuid] + if len(name_in_org) > 0: + return name_in_org.iloc[0]["name"] + # theoretically it must be in inds if not in orgs, but for the sample + # data this might not be the case + + if "full_name" in df.columns: + name_in_ind = df.loc[df["id"] == uuid] + if len(name_in_ind) > 0: + return name_in_ind.iloc[0]["full_name"] + return None def create_network_nodes(df: pd.DataFrame) -> nx.MultiDiGraph: From 609220d143cc710ad9404ee6e54fda16464e72eb Mon Sep 17 00:00:00 2001 From: Alan Mburu Kagiri Date: Sun, 3 Mar 2024 16:26:17 -0600 Subject: [PATCH 10/24] saving work for graph work. No need to review yet --- utils/network.py | 29 ++++++++++++++++++++++------- 1 file changed, 22 insertions(+), 7 deletions(-) diff --git a/utils/network.py b/utils/network.py index 6e9dcd9..8a076ef 100644 --- a/utils/network.py +++ b/utils/network.py @@ -50,14 +50,11 @@ def create_network_nodes(df: pd.DataFrame) -> nx.MultiDiGraph: "office_sought", "purpose", "transaction_type", - "recipient_id", + "year", "transaction_id", - "recipient_type", "donor_office", - "recipient_name", "amount", ] - for _, row in df.iterrows(): # add node attributes based on the columns relevant to the entity G.add_node(row[node_name]) @@ -67,10 +64,28 @@ def create_network_nodes(df: pd.DataFrame) -> nx.MultiDiGraph: # link the donor node to the recipient node. add the attributes of the # edge based on relevant nodes + edge_dictionary = {} for column in transact_info: if not pd.isnull(row[column]): - G.add_edge( - row[node_name], row["recipient_name"], column=row[column] - ) + edge_dictionary[column] = row[column] + G.add_edge(row[node_name], row["recipient_name"], **edge_dictionary) + + # the added 'recipient_name' node has no attributes at this moment + # for the final code this line won't be necessary, as each recipient + # should ideally be referenced later on. For now, all added nodes for + # the recipient will only have one default attribute: classification + G.nodes[row["recipient_name"]]["classification"] = "neutral" + + edge_labels = {(u, v): d["amount"] for u, v, d in G.edges(data=True)} + entity_colors = {"neutral": "green", "c": "blue", "f": "red"} + node_colors = [ + entity_colors[G.nodes[node]["classification"]] for node in G.nodes() + ] + + nx.draw_planar(G, with_labels=False, node_color=node_colors) + nx.draw_networkx_edge_labels( + G, pos=nx.spring_layout(G), edge_labels=edge_labels, label_pos=0.5 + ) + # nx.draw_planar(G, with_labels=False) return G From 5a81b23c3e928b8d9b3aaf30a233e77af473d64f Mon Sep 17 00:00:00 2001 From: Alan Mburu Kagiri Date: Mon, 4 Mar 2024 02:39:54 -0600 Subject: [PATCH 11/24] graph work so far with plotly --- utils/network.py | 92 ++++++++++++++++++++++++++++++++---------------- 1 file changed, 62 insertions(+), 30 deletions(-) diff --git a/utils/network.py b/utils/network.py index 8a076ef..88572af 100644 --- a/utils/network.py +++ b/utils/network.py @@ -1,5 +1,6 @@ import networkx as nx import pandas as pd +import plotly.graph_objects as go def name_identifier(uuid: str, dfs: list[pd.DataFrame]) -> str: @@ -28,7 +29,7 @@ def name_identifier(uuid: str, dfs: list[pd.DataFrame]) -> str: return None -def create_network_nodes(df: pd.DataFrame) -> nx.MultiDiGraph: +def create_network_graph(df: pd.DataFrame) -> nx.MultiDiGraph: """Takes in a dataframe and generates a MultiDiGraph where the nodes are entity names, and the rest of the dataframe columns make the node attributes @@ -37,7 +38,7 @@ def create_network_nodes(df: pd.DataFrame) -> nx.MultiDiGraph: complete_organizations_table) Returns: - A Networkx MultiDiGraph with nodes lacking any edges + A Networkx MultiDiGraph with nodes and edges """ G = nx.MultiDiGraph() # first check if df is individuals or organizations dataset @@ -46,7 +47,7 @@ def create_network_nodes(df: pd.DataFrame) -> nx.MultiDiGraph: else: node_name = "full_name" - transact_info = [ + edge_columns = [ "office_sought", "purpose", "transaction_type", @@ -55,37 +56,68 @@ def create_network_nodes(df: pd.DataFrame) -> nx.MultiDiGraph: "donor_office", "amount", ] + for _, row in df.iterrows(): # add node attributes based on the columns relevant to the entity - G.add_node(row[node_name]) - for column in df.columns.difference(transact_info): - if not pd.isnull(row[column]): - G.nodes[row[node_name]][column] = row[column] - - # link the donor node to the recipient node. add the attributes of the - # edge based on relevant nodes - edge_dictionary = {} - for column in transact_info: - if not pd.isnull(row[column]): - edge_dictionary[column] = row[column] - G.add_edge(row[node_name], row["recipient_name"], **edge_dictionary) - - # the added 'recipient_name' node has no attributes at this moment - # for the final code this line won't be necessary, as each recipient - # should ideally be referenced later on. For now, all added nodes for - # the recipient will only have one default attribute: classification + G.add_node( + row[node_name], + **row[df.columns.difference(edge_columns)].dropna().to_dict(), + ) + # add the recipient as a node G.nodes[row["recipient_name"]]["classification"] = "neutral" - edge_labels = {(u, v): d["amount"] for u, v, d in G.edges(data=True)} - entity_colors = {"neutral": "green", "c": "blue", "f": "red"} - node_colors = [ - entity_colors[G.nodes[node]["classification"]] for node in G.nodes() - ] + # add the edge attributes between two nodes + edge_attributes = row[edge_columns].dropna().to_dict() + G.add_edge(row[node_name], row["recipient_name"], **edge_attributes) + + return G + - nx.draw_planar(G, with_labels=False, node_color=node_colors) - nx.draw_networkx_edge_labels( - G, pos=nx.spring_layout(G), edge_labels=edge_labels, label_pos=0.5 +def plot_network_graph(G: nx.MultiDiGraph): + """Given a networkX Graph, creates a plotly visualization of the nodes and + edges + + Args: + A networkX MultiDiGraph with edges including the attribute 'amount' + + Returns: None. Creates a plotly graph + """ + edge_trace = go.Scatter( + x=[], y=[], line=dict(color="#888"), hoverinfo="text", mode="lines" ) + hovertext = [] - # nx.draw_planar(G, with_labels=False) - return G + for edge in G.edges(data=True): + # donor = edge[0], recipient = edge[1] + hovertext.append(f"Amount: {edge[2]['amount']:.2f}") + + edge_trace["hovertext"] = hovertext + + node_trace = go.Scatter( + x=[], + y=[], + text=[], + mode="markers", + hoverinfo="text", + marker=dict(showscale=True, colorscale="YlGnBu", size=10), + ) + + for node in G.nodes(): + node_info = f"Name: {node}
" + for key, value in G.nodes[node].items(): + node_info += f"{key}: {value}
" + node_trace["text"] += tuple([node_info]) + + # Define layout settings + layout = go.Layout( + title="Network Graph Indicating Campaign Contributions from 2018-2022", + titlefont=dict(size=16), + showlegend=False, + hovermode="closest", + margin=dict(b=20, l=5, r=5, t=40), + xaxis=dict(showgrid=False, zeroline=False, showticklabels=False), + yaxis=dict(showgrid=False, zeroline=False, showticklabels=False), + ) + + fig = go.Figure(data=[edge_trace, node_trace], layout=layout) + fig.show() From b377acdceb702ca0bd252699e3febd14f3bbb163 Mon Sep 17 00:00:00 2001 From: Alan Mburu Kagiri Date: Mon, 4 Mar 2024 10:50:25 -0600 Subject: [PATCH 12/24] Test notebook with functions for merging datasets, no need to review, will delete later --- notebooks/Test.ipynb | 12403 +++++++++++++++++++++++++++++++++++------ 1 file changed, 10703 insertions(+), 1700 deletions(-) diff --git a/notebooks/Test.ipynb b/notebooks/Test.ipynb index d188b44..b9ac176 100644 --- a/notebooks/Test.ipynb +++ b/notebooks/Test.ipynb @@ -10,8 +10,8 @@ "import numpy as np\n", "import networkx as nx\n", "import matplotlib.pyplot as plt\n", - "\n", - "from utils.linkage import deduplicate_perfect_matches" + "import plotly.express as px\n", + "import plotly.graph_objects as go\n" ] }, { @@ -20,9 +20,9 @@ "metadata": {}, "outputs": [], "source": [ - "orgs_df = pd.read_csv(\"../output/complete_organizations_table.csv\",index_col=0)#,nrows=10000).sample(10)\n", - "inds_df = pd.read_csv(\"../output/complete_individuals_table.csv\",index_col=0, low_memory=False)#, nrows=10000).sample(10)\n", - "transactions = pd.read_csv(\"../output/complete_transactions_table.csv\",index_col=0, low_memory=False)" + "orgs_df = pd.read_csv(\"../data/classified_data/classified_organizations_v1\").sample(10000)\n", + "inds_df = pd.read_csv(\"../data/classified_data/classified_individuals_v1\", low_memory=False).sample(10000)\n", + "transactions = pd.read_csv(\"../data/classified_data/transactions_v1\", low_memory=False)" ] }, { @@ -55,31 +55,53 @@ " name\n", " state\n", " entity_type\n", + " classification\n", " \n", " \n", " \n", " \n", - " 0\n", - " 1022\n", - " #1022 arizona committee of automotive retailers\n", - " AZ\n", - " pac\n", + " 63128\n", + " 422065cd-0262-4ac9-a2a4-74136ddb99e2\n", + " floyd workman\n", + " MI\n", + " corporation\n", + " neutral\n", " \n", " \n", - " 4\n", - " 100112\n", - " 314 action victory fund (fec id c00689828)\n", - " DC\n", - " pac\n", + " 98258\n", + " dfd160b5-9389-44ef-a632-c08dc1a1d201\n", + " front 43\n", + " MI\n", + " corporation\n", + " neutral\n", + " \n", + " \n", + " 1712\n", + " 858415ce-d53f-4843-aee0-85560117bdc6\n", + " arizona federation of democratic women\n", + " NaN\n", + " vendor\n", + " neutral\n", " \n", " \n", "\n", "" ], "text/plain": [ - " id name state entity_type\n", - "0 1022 #1022 arizona committee of automotive retailers AZ pac\n", - "4 100112 314 action victory fund (fec id c00689828) DC pac" + " id \\\n", + "63128 422065cd-0262-4ac9-a2a4-74136ddb99e2 \n", + "98258 dfd160b5-9389-44ef-a632-c08dc1a1d201 \n", + "1712 858415ce-d53f-4843-aee0-85560117bdc6 \n", + "\n", + " name state entity_type \\\n", + "63128 floyd workman MI corporation \n", + "98258 front 43 MI corporation \n", + "1712 arizona federation of democratic women NaN vendor \n", + "\n", + " classification \n", + "63128 neutral \n", + "98258 neutral \n", + "1712 neutral " ] }, "execution_count": 3, @@ -88,13 +110,33 @@ } ], "source": [ - "orgs_df.head(2)" + "orgs_df.head(3)" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array(['neutral'], dtype=object)" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "orgs_df.classification.unique()" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, "outputs": [ { "data": { @@ -117,74 +159,36 @@ " \n", " \n", " \n", - " transaction_id\n", - " donor_id\n", - " year\n", - " amount\n", - " recipient_id\n", - " office_sought\n", - " purpose\n", - " transaction_type\n", - " donor_type\n", - " recipient_type\n", - " donor_office\n", + " id\n", + " name\n", + " state\n", + " entity_type\n", + " classification\n", " \n", " \n", " \n", - " \n", - " 0\n", - " 4640650\n", - " 100592\n", - " 2021\n", - " 25.0\n", - " 1869727\n", - " none\n", - " wr 9.13\n", - " contribution from individuals\n", - " NaN\n", - " NaN\n", - " NaN\n", - " \n", - " \n", - " 1\n", - " 8185257\n", - " 201800301\n", - " 2020\n", - " 100.0\n", - " 1779679\n", - " none\n", - " ab\n", - " contribution from individuals\n", - " NaN\n", - " NaN\n", - " NaN\n", - " \n", " \n", "\n", "" ], "text/plain": [ - " transaction_id donor_id year amount recipient_id office_sought purpose \\\n", - "0 4640650 100592 2021 25.0 1869727 none wr 9.13 \n", - "1 8185257 201800301 2020 100.0 1779679 none ab \n", - "\n", - " transaction_type donor_type recipient_type donor_office \n", - "0 contribution from individuals NaN NaN NaN \n", - "1 contribution from individuals NaN NaN NaN " + "Empty DataFrame\n", + "Columns: [id, name, state, entity_type, classification]\n", + "Index: []" ] }, - "execution_count": 4, + "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "transactions.head(2)" + "orgs_df.loc[orgs_df.classification == 'f']" ] }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 6, "metadata": {}, "outputs": [ { @@ -208,47 +212,44 @@ " \n", " \n", " \n", - " id\n", - " first_name\n", - " last_name\n", - " full_name\n", - " entity_type\n", - " state\n", - " party\n", - " company\n", - " occupation\n", - " address\n", - " zip\n", - " city\n", + " transaction_id\n", + " donor_id\n", + " year\n", + " amount\n", + " recipient_id\n", + " office_sought\n", + " purpose\n", + " transaction_type\n", + " donor_type\n", + " recipient_type\n", + " donor_office\n", " \n", " \n", " \n", " \n", " 0\n", - " 1869727\n", - " NaN\n", - " NaN\n", - " william \bstoner\n", - " individual\n", - " NaN\n", - " NaN\n", - " NaN\n", - " NaN\n", + " 7773a71e-9f67-438e-8313-80b1b75deeb4\n", + " 4544b60d-da6b-4dd5-9efe-334152ccf1f1\n", + " 2018\n", + " 1000.0\n", + " 981a0414-b738-4e20-91b8-a29ee2cc7edf\n", + " none\n", + " bob worsley for state senate\n", + " contribute to a candidate committee\n", " NaN\n", " NaN\n", " NaN\n", " \n", " \n", " 1\n", - " 1779679\n", - " NaN\n", - " NaN\n", - " rm coulon\n", - " individual\n", - " NaN\n", - " NaN\n", - " area agency on aging\n", - " NaN\n", + " 95f74915-a945-491f-8751-8c970a76fc24\n", + " 946d7561-42a3-4a4b-b410-3a10271c9f18\n", + " 2018\n", + " 1000.0\n", + " 981a0414-b738-4e20-91b8-a29ee2cc7edf\n", + " none\n", + " drew john for state house\n", + " contribute to a candidate committee\n", " NaN\n", " NaN\n", " NaN\n", @@ -258,36 +259,64 @@ "" ], "text/plain": [ - " id first_name last_name full_name entity_type state party \\\n", - "0 1869727 NaN NaN william \bstoner individual NaN NaN \n", - "1 1779679 NaN NaN rm coulon individual NaN NaN \n", + " transaction_id donor_id \\\n", + "0 7773a71e-9f67-438e-8313-80b1b75deeb4 4544b60d-da6b-4dd5-9efe-334152ccf1f1 \n", + "1 95f74915-a945-491f-8751-8c970a76fc24 946d7561-42a3-4a4b-b410-3a10271c9f18 \n", "\n", - " company occupation address zip city \n", - "0 NaN NaN NaN NaN NaN \n", - "1 area agency on aging NaN NaN NaN NaN " + " year amount recipient_id office_sought \\\n", + "0 2018 1000.0 981a0414-b738-4e20-91b8-a29ee2cc7edf none \n", + "1 2018 1000.0 981a0414-b738-4e20-91b8-a29ee2cc7edf none \n", + "\n", + " purpose transaction_type \\\n", + "0 bob worsley for state senate contribute to a candidate committee \n", + "1 drew john for state house contribute to a candidate committee \n", + "\n", + " donor_type recipient_type donor_office \n", + "0 NaN NaN NaN \n", + "1 NaN NaN NaN " ] }, - "execution_count": 5, + "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "inds_df.head(2)" + "transactions.head(2)" ] }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "(541803, 541150, 77611, 77611)" + "array(['neutral', 'f'], dtype=object)" ] }, - "execution_count": 6, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "inds_df.classification.unique()" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(9926, 9919, 10000, 10000)" + ] + }, + "execution_count": 8, "metadata": {}, "output_type": "execute_result" } @@ -315,16 +344,40 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "['100883', '100894']" + "['242d019c-e0ab-405e-8e77-abae7418b87f',\n", + " '8b2ad550-64a1-4975-8b77-5eb1f24a8871',\n", + " 'aee69307-194f-4c40-af3d-a55a34e1068e',\n", + " '55e5e946-6261-4f19-9752-fb58219b2e99',\n", + " '4faf251a-73d9-46ef-9e17-d3cf0a3052ae',\n", + " '3b5c0a9e-c6f2-44e9-ad05-fde071447564',\n", + " '3936bdf5-9a7a-462c-9e8c-9124f2bd7f57',\n", + " '13882059-3c74-4d9e-825d-a03a72b43b08',\n", + " '50c78f1a-3e9b-4996-a319-eef4fe01ccfb',\n", + " 'ae96f38f-68c8-47e3-95b3-c6f096d3c22e',\n", + " '74ba8a8a-7256-4eb3-b0f8-995f7a6319fb',\n", + " '12823a76-78e2-4b09-b606-859efaa5c8ef',\n", + " '9de9bf03-8c4a-4d2f-9a95-283b230ddfad',\n", + " '588593b9-9bba-4597-94d9-1b3a7fd5b402',\n", + " '5277b642-6bf0-4423-9350-3602ae51c6ac',\n", + " 'd98985b4-f55d-4ada-b279-0497e3176512',\n", + " 'c8586d36-f188-4684-aa99-193407d4d068',\n", + " '3798fda1-83cd-4e48-974a-e1a390060198',\n", + " 'a536b509-f052-4984-a35d-10397308daec',\n", + " '80996477-ce99-4f34-b5fc-bab4d676fc77',\n", + " 'cd1a740c-b1d7-4334-b335-925bd5708753',\n", + " '46af8908-f4e4-4041-9d1e-5b442d051921',\n", + " '2969075a-86d2-4b04-a991-a81832e096a0',\n", + " 'd0337f72-b701-4524-891b-c48ef6f771ec',\n", + " '591aa72b-511b-4dbb-a161-80458f257471']" ] }, - "execution_count": 7, + "execution_count": 9, "metadata": {}, "output_type": "execute_result" } @@ -339,135 +392,9 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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250c7d9a1-b448-46a5-8e2d-cd15b3097360REPUBLICAN STATE LEADERSHIP COMMITTEE MICHIGAN...MIcommittee382REPUBLICAN STATE LEADERSHIP COMMITTEE MICHIGAN...620MICHIGAN ASSOCIATION OF NURSE ANESTHETISTS PAC
362ea1e9c-ac12-400c-b3dc-519389c0f7d3UNITED FOOD AND COMMERCIAL WORKERS ACTIVE BALL...MIcommittee2328MICHIGAN ASSOCIATION OF NURSE ANESTHETISTS PAC4505Paa Pac
462ea1e9c-ac12-400c-b3dc-519389c0f7d3UNITED FOOD AND COMMERCIAL WORKERS ACTIVE BALL...MIcommittee3421Paa Pac672Paa Pac
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MI committee \n", - "\n", - " donations donations_to received \\\n", - "0 4249 REPUBLICAN STATE LEADERSHIP COMMITTEE MICHIGAN... 730 \n", - "1 426 MICHIGAN ASSOCIATION OF NURSE ANESTHETISTS PAC 853 \n", - "2 382 REPUBLICAN STATE LEADERSHIP COMMITTEE MICHIGAN... 620 \n", - "3 2328 MICHIGAN ASSOCIATION OF NURSE ANESTHETISTS PAC 4505 \n", - "4 3421 Paa Pac 672 \n", - "\n", - " donations_from \n", - "0 COMMITTEE TO ELECT DR PATRICIA BERNARD \n", - "1 Pabar Pac (Pa Bar Assn) \n", - "2 MICHIGAN ASSOCIATION OF NURSE ANESTHETISTS PAC \n", - "3 Paa Pac \n", - "4 Paa Pac " - ] - }, - "execution_count": 3, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "data = {'id':['50c7d9a1-b448-46a5-8e2d-cd15b3097360','50c7d9a1-b448-46a5-8e2d-cd15b3097360','50c7d9a1-b448-46a5-8e2d-cd15b3097360',\n", " '62ea1e9c-ac12-400c-b3dc-519389c0f7d3','62ea1e9c-ac12-400c-b3dc-519389c0f7d3','62ea1e9c-ac12-400c-b3dc-519389c0f7d3',\n", @@ -555,7 +482,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 10, "metadata": {}, "outputs": [ { @@ -579,274 +506,180 @@ " \n", " \n", " \n", - " id\n", - " first_name\n", - " last_name\n", - " full_name\n", - " entity_type\n", - " state\n", - " party\n", - " company\n", - " occupation\n", - " address\n", - " zip\n", - " city\n", + " transaction_id\n", + " donor_id\n", + " year\n", + " amount\n", + " recipient_id\n", + " office_sought\n", + " purpose\n", + " transaction_type\n", + " donor_type\n", + " recipient_type\n", + " donor_office\n", + " recipient_name\n", " \n", " \n", " \n", " \n", - " 102\n", - " 100894\n", - " NaN\n", - " NaN\n", - " abdussamad, shams\n", - " candidate\n", - " AZ\n", - " democratic\n", - " none (is a candidate)\n", - " NaN\n", + " 0\n", + " 7773a71e-9f67-438e-8313-80b1b75deeb4\n", + " 4544b60d-da6b-4dd5-9efe-334152ccf1f1\n", + " 2018\n", + " 1000.0\n", + " 981a0414-b738-4e20-91b8-a29ee2cc7edf\n", + " none\n", + " bob worsley for state senate\n", + " contribute to a candidate committee\n", " NaN\n", " NaN\n", " NaN\n", + " #1022 arizona committee of automotive retailers\n", " \n", " \n", - " 103\n", - " 100894\n", - " NaN\n", - " NaN\n", - " abdussamad, shams\n", - " candidate\n", - " AZ\n", - " democratic\n", - " none (is a candidate)\n", - " NaN\n", + " 1\n", + " 95f74915-a945-491f-8751-8c970a76fc24\n", + " 946d7561-42a3-4a4b-b410-3a10271c9f18\n", + " 2018\n", + " 1000.0\n", + " 981a0414-b738-4e20-91b8-a29ee2cc7edf\n", + " none\n", + " drew john for state house\n", + " contribute to a candidate committee\n", " NaN\n", " NaN\n", " NaN\n", + " #1022 arizona committee of automotive retailers\n", " \n", " \n", - " 104\n", - " 100883\n", - " NaN\n", - " NaN\n", - " abeytia, anna lynn\n", - " candidate\n", - " AZ\n", - " democratic\n", - " none (is a candidate)\n", - " NaN\n", + " 2\n", + " d05f1763-132d-4717-addc-8ff6239ad4d9\n", + " c8f98436-9562-48ed-b51f-45b2b217aad1\n", + " 2018\n", + " 1000.0\n", + " 981a0414-b738-4e20-91b8-a29ee2cc7edf\n", + " none\n", + " elect karen fann ld1\n", + " contribute to a candidate committee\n", " NaN\n", " NaN\n", " NaN\n", + " #1022 arizona committee of automotive retailers\n", " \n", " \n", - " 105\n", - " 100883\n", - " NaN\n", - " NaN\n", - " abeytia, anna lynn\n", - " candidate\n", - " AZ\n", - " democratic\n", - " none (is a candidate)\n", - " NaN\n", + " 3\n", + " 3dc3da30-6562-4755-bfad-6a26f1baec15\n", + " b9965bc2-c94d-4f69-98d1-bc4f5ad701c5\n", + " 2018\n", + " 1000.0\n", + " 981a0414-b738-4e20-91b8-a29ee2cc7edf\n", + " none\n", + " elect noel campbell for house\n", + " contribute to a candidate committee\n", " NaN\n", " NaN\n", " NaN\n", + " #1022 arizona committee of automotive retailers\n", " \n", " \n", - " 0\n", - " b8fbed14-0766-49ab-8516-97952c654a12\n", - " FREDERICK\n", - " BERG\n", - " FREDERICK BERG ...\n", - " Individual\n", - " MI\n", - " NaN\n", - " BUTZEL LONG\n", - " ATTORNEY\n", - " 1033 YORKSHIRE\n", - " 48230-0000\n", - " GROSSE POINTE PARK\n", - " \n", - " \n", - " ...\n", - " ...\n", - " ...\n", - " ...\n", - " ...\n", - " ...\n", - " ...\n", - " ...\n", - " ...\n", - " ...\n", - " ...\n", - " ...\n", - " ...\n", - " \n", - " \n", - " 17734\n", - " 75b99f42-e0d4-4c3c-89a6-16e11f6dd810\n", - " NaN\n", - " NaN\n", - " Rodriguez, Adrian\n", - " Individual\n", - " MN\n", - " NaN\n", - " NaN\n", - " NaN\n", - " NaN\n", - " NaN\n", - " NaN\n", - " \n", - " \n", - " 17735\n", - " 8b634b74-a6be-4280-a2c4-63e46a8f9bc9\n", - " NaN\n", - " NaN\n", - " O'Connor, Timothy J\n", - " Individual\n", - " MN\n", - " NaN\n", - " NaN\n", - " NaN\n", - " NaN\n", - " NaN\n", - " NaN\n", - " \n", - " \n", - " 17736\n", - " d7d5b121-015f-474f-8b76-7c6c865da557\n", - " NaN\n", - " NaN\n", - " Frenzel, Robert C\n", - " Individual\n", - " MN\n", - " NaN\n", - " NaN\n", - " NaN\n", - " NaN\n", - " NaN\n", - " NaN\n", - " \n", - " \n", - " 17737\n", - " b2eaaec4-30d5-46f4-9922-efc8d79c16d2\n", - " NaN\n", - " NaN\n", - " Enzminger, Peter\n", - " Individual\n", - " MN\n", - " NaN\n", - " NaN\n", - " NaN\n", - " NaN\n", - " NaN\n", - " NaN\n", - " \n", - " \n", - " 17738\n", - " de34f2c7-fa2f-4fa5-abea-b67f6c8fe35f\n", - " NaN\n", - " NaN\n", - " Bowler, Erin\n", - " Individual\n", - " MN\n", - " NaN\n", - " NaN\n", - " NaN\n", - " NaN\n", - " NaN\n", + " 4\n", + " a4340a2c-7b8a-4eeb-8290-746f0f436c83\n", + " 946d7561-42a3-4a4b-b410-3a10271c9f18\n", + " 2018\n", + " 1000.0\n", + " 981a0414-b738-4e20-91b8-a29ee2cc7edf\n", + " none\n", + " closed to new donations\n", + " refund from contrib to a cand committee\n", " NaN\n", + " NaN\n", + " NaN\n", + " #1022 arizona committee of automotive retailers\n", " \n", " \n", "\n", - "

1760156 rows × 12 columns

\n", "" ], "text/plain": [ - " id first_name \\\n", - "102 100894 NaN \n", - "103 100894 NaN \n", - "104 100883 NaN \n", - "105 100883 NaN \n", - "0 b8fbed14-0766-49ab-8516-97952c654a12 FREDERICK \n", - "... ... ... \n", - "17734 75b99f42-e0d4-4c3c-89a6-16e11f6dd810 NaN \n", - "17735 8b634b74-a6be-4280-a2c4-63e46a8f9bc9 NaN \n", - "17736 d7d5b121-015f-474f-8b76-7c6c865da557 NaN \n", - "17737 b2eaaec4-30d5-46f4-9922-efc8d79c16d2 NaN \n", - "17738 de34f2c7-fa2f-4fa5-abea-b67f6c8fe35f NaN \n", - "\n", - " last_name \\\n", - "102 NaN \n", - "103 NaN \n", - "104 NaN \n", - "105 NaN \n", - "0 BERG \n", - "... ... \n", - "17734 NaN \n", - "17735 NaN \n", - "17736 NaN \n", - "17737 NaN \n", - "17738 NaN \n", + " transaction_id donor_id \\\n", + "0 7773a71e-9f67-438e-8313-80b1b75deeb4 4544b60d-da6b-4dd5-9efe-334152ccf1f1 \n", + "1 95f74915-a945-491f-8751-8c970a76fc24 946d7561-42a3-4a4b-b410-3a10271c9f18 \n", + "2 d05f1763-132d-4717-addc-8ff6239ad4d9 c8f98436-9562-48ed-b51f-45b2b217aad1 \n", + "3 3dc3da30-6562-4755-bfad-6a26f1baec15 b9965bc2-c94d-4f69-98d1-bc4f5ad701c5 \n", + "4 a4340a2c-7b8a-4eeb-8290-746f0f436c83 946d7561-42a3-4a4b-b410-3a10271c9f18 \n", "\n", - " full_name entity_type state \\\n", - "102 abdussamad, shams candidate AZ \n", - "103 abdussamad, shams candidate AZ \n", - "104 abeytia, anna lynn candidate AZ \n", - "105 abeytia, anna lynn candidate AZ \n", - "0 FREDERICK BERG ... Individual MI \n", - "... ... ... ... \n", - "17734 Rodriguez, Adrian Individual MN \n", - "17735 O'Connor, Timothy J Individual MN \n", - "17736 Frenzel, Robert C Individual MN \n", - "17737 Enzminger, Peter Individual MN \n", - "17738 Bowler, Erin Individual MN \n", + " year amount recipient_id office_sought \\\n", + "0 2018 1000.0 981a0414-b738-4e20-91b8-a29ee2cc7edf none \n", + "1 2018 1000.0 981a0414-b738-4e20-91b8-a29ee2cc7edf none \n", + "2 2018 1000.0 981a0414-b738-4e20-91b8-a29ee2cc7edf none \n", + "3 2018 1000.0 981a0414-b738-4e20-91b8-a29ee2cc7edf none \n", + "4 2018 1000.0 981a0414-b738-4e20-91b8-a29ee2cc7edf none \n", "\n", - " party company occupation address \\\n", - "102 democratic none (is a candidate) NaN NaN \n", - "103 democratic none (is a candidate) NaN NaN \n", - "104 democratic none (is a candidate) NaN NaN \n", - "105 democratic none (is a candidate) NaN NaN \n", - "0 NaN BUTZEL LONG ATTORNEY 1033 YORKSHIRE \n", - "... ... ... ... ... \n", - "17734 NaN NaN NaN NaN \n", - "17735 NaN NaN NaN NaN \n", - "17736 NaN NaN NaN NaN \n", - "17737 NaN NaN NaN NaN \n", - "17738 NaN NaN NaN NaN \n", + " purpose transaction_type \\\n", + "0 bob worsley for state senate contribute to a candidate committee \n", + "1 drew john for state house contribute to a candidate committee \n", + "2 elect karen fann ld1 contribute to a candidate committee \n", + "3 elect noel campbell for house contribute to a candidate committee \n", + "4 closed to new donations refund from contrib to a cand committee \n", "\n", - " zip city \n", - "102 NaN NaN \n", - "103 NaN NaN \n", - "104 NaN NaN \n", - "105 NaN NaN \n", - "0 48230-0000 GROSSE POINTE PARK \n", - "... ... ... \n", - "17734 NaN NaN \n", - "17735 NaN NaN \n", - "17736 NaN NaN \n", - "17737 NaN NaN \n", - "17738 NaN NaN \n", + " donor_type recipient_type donor_office \\\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", - "[1760156 rows x 12 columns]" + " recipient_name \n", + "0 #1022 arizona committee of automotive retailers \n", + "1 #1022 arizona committee of automotive retailers \n", + "2 #1022 arizona committee of automotive retailers \n", + "3 #1022 arizona committee of automotive retailers \n", + "4 #1022 arizona committee of automotive retailers " ] }, - "execution_count": 11, + "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "# for now only work with datasets \n", - "sample_inds = inds_df.loc[(inds_df['id'].isin(transactions.donor_id.tolist()))]\n", - "sample_inds\n" + "from utils.network import name_identifier\n", + "from utils.linkage import deduplicate_perfect_matches\n", + "transactions = transactions.loc[(transactions.recipient_id.isin(inds_df.id)) | \n", + " (transactions.recipient_id.isin(orgs_df.id)) |\n", + " (transactions.donor_id.isin(inds_df.id)) |\n", + " (transactions.donor_id.isin(inds_df.id))]\n", + "inds = deduplicate_perfect_matches(inds_df) \n", + "orgs = deduplicate_perfect_matches(orgs_df)\n", + "transactions[\"recipient_name\"] = transactions[\"recipient_id\"].apply(name_identifier, args=([orgs, inds],))\n", + "\n", + "transactions.head(5)" ] }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "87" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "x = transactions.loc[transactions.donor_id.isin(inds_df.id)]\n", + "len(x.recipient_name.unique())" + ] + }, + { + "cell_type": "code", + "execution_count": 12, "metadata": {}, "outputs": [ { @@ -870,8 +703,17 @@ " \n", " \n", " \n", - " transaction_id\n", - " donor_id\n", + " id\n", + " first_name\n", + " last_name\n", + " full_name\n", + " entity_type\n", + " state\n", + " party\n", + " company\n", + " occupation\n", + " address\n", + " ...\n", " year\n", " amount\n", " recipient_id\n", @@ -881,361 +723,229 @@ " donor_type\n", " recipient_type\n", " donor_office\n", + " recipient_name\n", " \n", " \n", " \n", " \n", - " 212637\n", - " NaN\n", - " b8fbed14-0766-49ab-8516-97952c654a12\n", - " 2022\n", - " 100.00\n", - " 1d4ae24b-2814-4d0d-995e-28fd4c26785d\n", - " NaN\n", - " NaN\n", - " DIRECT\n", - " NaN\n", - " NaN\n", - " NaN\n", - " \n", - " \n", - " 212667\n", - " NaN\n", - " b8fbed14-0766-49ab-8516-97952c654a12\n", - " 2022\n", - " 50.00\n", - " 1d4ae24b-2814-4d0d-995e-28fd4c26785d\n", - " NaN\n", - " NaN\n", - " DIRECT\n", - " NaN\n", - " NaN\n", - " NaN\n", - " \n", - " \n", - " 440542\n", - " NaN\n", - " b8fbed14-0766-49ab-8516-97952c654a12\n", - " 2022\n", - " 50.00\n", - " 1d4ae24b-2814-4d0d-995e-28fd4c26785d\n", - " NaN\n", - " NaN\n", - " DIRECT\n", - " NaN\n", - " NaN\n", - " NaN\n", - " \n", - " \n", - " 440573\n", - " NaN\n", - " b8fbed14-0766-49ab-8516-97952c654a12\n", - " 2022\n", - " 50.00\n", - " 1d4ae24b-2814-4d0d-995e-28fd4c26785d\n", - " NaN\n", - " NaN\n", - " DIRECT\n", - " NaN\n", - " NaN\n", - " NaN\n", - " \n", - " \n", - " 440607\n", - " NaN\n", - " b8fbed14-0766-49ab-8516-97952c654a12\n", - " 2022\n", - " 50.00\n", - " 1d4ae24b-2814-4d0d-995e-28fd4c26785d\n", - " NaN\n", - " NaN\n", - " DIRECT\n", - " NaN\n", - " NaN\n", - " NaN\n", - " \n", - " \n", - " 440642\n", - " NaN\n", - " b8fbed14-0766-49ab-8516-97952c654a12\n", - " 2022\n", - " 50.00\n", - " 1d4ae24b-2814-4d0d-995e-28fd4c26785d\n", - " NaN\n", - " NaN\n", - " DIRECT\n", - " NaN\n", - " NaN\n", - " NaN\n", - " \n", - " \n", - " 636312\n", - " NaN\n", - " b8fbed14-0766-49ab-8516-97952c654a12\n", - " 2022\n", - " 50.00\n", - " 1d4ae24b-2814-4d0d-995e-28fd4c26785d\n", - " NaN\n", - " NaN\n", - " DIRECT\n", - " NaN\n", + " 55243\n", + " 0e24b503-b209-48b5-8edb-cca0cdaca78c\n", + " M.\n", + " TANG\n", + " m. tang ...\n", + " Individual\n", + " MD\n", " NaN\n", " NaN\n", - " \n", - " \n", - " 636346\n", " NaN\n", - " b8fbed14-0766-49ab-8516-97952c654a12\n", - " 2022\n", - " 50.00\n", - " 1d4ae24b-2814-4d0d-995e-28fd4c26785d\n", + " 6614 23RD PLACE\n", + " ...\n", + " 2022.0\n", + " 2.0\n", + " 49a2d46f-5e75-433c-94fa-f910e66d1a1e\n", " NaN\n", " NaN\n", - " DIRECT\n", + " direct\n", " NaN\n", " NaN\n", " NaN\n", + " None\n", " \n", " \n", - " 636382\n", - " NaN\n", - " b8fbed14-0766-49ab-8516-97952c654a12\n", - " 2022\n", - " 50.00\n", - " 1d4ae24b-2814-4d0d-995e-28fd4c26785d\n", - " NaN\n", - " NaN\n", - " DIRECT\n", - " NaN\n", + " 55244\n", + " 0e24b503-b209-48b5-8edb-cca0cdaca78c\n", + " M.\n", + " TANG\n", + " m. tang ...\n", + " Individual\n", + " MD\n", " NaN\n", " NaN\n", - " \n", - " \n", - " 839846\n", " NaN\n", - " b8fbed14-0766-49ab-8516-97952c654a12\n", - " 2022\n", - " 83.33\n", - " f9fa8506-bfbb-4ef0-9e08-5c9c3e948121\n", + " 6614 23RD PLACE\n", + " ...\n", + " 2022.0\n", + " 95.0\n", + " 49a2d46f-5e75-433c-94fa-f910e66d1a1e\n", " NaN\n", " NaN\n", - " DIRECT/FUND RAISER\n", + " direct\n", " NaN\n", " NaN\n", " NaN\n", + " None\n", " \n", " \n", - " 840051\n", - " NaN\n", - " b8fbed14-0766-49ab-8516-97952c654a12\n", - " 2022\n", - " 83.34\n", - " 389fe2ba-828a-41d4-815c-8efb2499ea11\n", - " NaN\n", - " NaN\n", - " DIRECT/FUND RAISER\n", - " NaN\n", + " 55245\n", + " 0e24b503-b209-48b5-8edb-cca0cdaca78c\n", + " M.\n", + " TANG\n", + " m. tang ...\n", + " Individual\n", + " MD\n", " NaN\n", " NaN\n", - " \n", - " \n", - " 968402\n", " NaN\n", - " b8fbed14-0766-49ab-8516-97952c654a12\n", - " 2022\n", - " 83.33\n", - " 043a03b7-af31-4830-b12e-446b93fca9a0\n", + " 6614 23RD PLACE\n", + " ...\n", + " 2022.0\n", + " 10.0\n", + " 49a2d46f-5e75-433c-94fa-f910e66d1a1e\n", " NaN\n", " NaN\n", - " DIRECT/FUND RAISER\n", + " direct\n", " NaN\n", " NaN\n", " NaN\n", + " None\n", " \n", " \n", - " 1414338\n", - " NaN\n", - " b8fbed14-0766-49ab-8516-97952c654a12\n", - " 2022\n", - " 250.00\n", - " ba06baf6-eae6-459f-b3a9-7261e4baa33e\n", - " NaN\n", - " NaN\n", - " DIRECT/FUND RAISER\n", - " NaN\n", + " 55246\n", + " a23037f6-741c-43a5-8a6d-0f1db4371e1d\n", + " OLIVIA N\n", + " DALMASSO\n", + " olivia n dalmasso ...\n", + " Individual\n", + " IL\n", " NaN\n", " NaN\n", - " \n", - " \n", - " 1502742\n", " NaN\n", - " b8fbed14-0766-49ab-8516-97952c654a12\n", - " 2022\n", - " 50.00\n", - " 1d4ae24b-2814-4d0d-995e-28fd4c26785d\n", + " PO BOX 574\n", + " ...\n", + " 2022.0\n", + " 12.6\n", + " 6b33721f-3f6a-47c0-bce2-284fc58e0d2a\n", " NaN\n", " NaN\n", - " DIRECT\n", + " direct\n", " NaN\n", " NaN\n", " NaN\n", + " None\n", " \n", " \n", - " 1502777\n", - " NaN\n", - " b8fbed14-0766-49ab-8516-97952c654a12\n", - " 2022\n", - " 50.00\n", - " 1d4ae24b-2814-4d0d-995e-28fd4c26785d\n", - " NaN\n", - " NaN\n", - " DIRECT\n", - " NaN\n", + " 55247\n", + " a23037f6-741c-43a5-8a6d-0f1db4371e1d\n", + " OLIVIA N\n", + " DALMASSO\n", + " olivia n dalmasso ...\n", + " Individual\n", + " IL\n", " NaN\n", " NaN\n", - " \n", - " \n", - " 1502812\n", " NaN\n", - " b8fbed14-0766-49ab-8516-97952c654a12\n", - " 2022\n", - " 50.00\n", - " 1d4ae24b-2814-4d0d-995e-28fd4c26785d\n", + " PO BOX 574\n", + " ...\n", + " 2022.0\n", + " 4.2\n", + " 6b33721f-3f6a-47c0-bce2-284fc58e0d2a\n", " NaN\n", " NaN\n", - " DIRECT\n", + " direct\n", " NaN\n", " NaN\n", " NaN\n", + " None\n", " \n", " \n", "\n", + "

5 rows × 25 columns

\n", "" ], "text/plain": [ - " transaction_id donor_id year amount \\\n", - "212637 NaN b8fbed14-0766-49ab-8516-97952c654a12 2022 100.00 \n", - "212667 NaN b8fbed14-0766-49ab-8516-97952c654a12 2022 50.00 \n", - "440542 NaN b8fbed14-0766-49ab-8516-97952c654a12 2022 50.00 \n", - "440573 NaN b8fbed14-0766-49ab-8516-97952c654a12 2022 50.00 \n", - "440607 NaN b8fbed14-0766-49ab-8516-97952c654a12 2022 50.00 \n", - "440642 NaN b8fbed14-0766-49ab-8516-97952c654a12 2022 50.00 \n", - "636312 NaN b8fbed14-0766-49ab-8516-97952c654a12 2022 50.00 \n", - "636346 NaN b8fbed14-0766-49ab-8516-97952c654a12 2022 50.00 \n", - "636382 NaN b8fbed14-0766-49ab-8516-97952c654a12 2022 50.00 \n", - "839846 NaN b8fbed14-0766-49ab-8516-97952c654a12 2022 83.33 \n", - "840051 NaN b8fbed14-0766-49ab-8516-97952c654a12 2022 83.34 \n", - "968402 NaN b8fbed14-0766-49ab-8516-97952c654a12 2022 83.33 \n", - "1414338 NaN b8fbed14-0766-49ab-8516-97952c654a12 2022 250.00 \n", - "1502742 NaN b8fbed14-0766-49ab-8516-97952c654a12 2022 50.00 \n", - "1502777 NaN b8fbed14-0766-49ab-8516-97952c654a12 2022 50.00 \n", - "1502812 NaN b8fbed14-0766-49ab-8516-97952c654a12 2022 50.00 \n", + " id first_name \\\n", + "55243 0e24b503-b209-48b5-8edb-cca0cdaca78c M. \n", + "55244 0e24b503-b209-48b5-8edb-cca0cdaca78c M. \n", + "55245 0e24b503-b209-48b5-8edb-cca0cdaca78c M. \n", + "55246 a23037f6-741c-43a5-8a6d-0f1db4371e1d OLIVIA N \n", + "55247 a23037f6-741c-43a5-8a6d-0f1db4371e1d OLIVIA N \n", + "\n", + " last_name \\\n", + "55243 TANG \n", + "55244 TANG \n", + "55245 TANG \n", + "55246 DALMASSO \n", + "55247 DALMASSO \n", + "\n", + " full_name entity_type state \\\n", + "55243 m. tang ... Individual MD \n", + "55244 m. tang ... Individual MD \n", + "55245 m. tang ... Individual MD \n", + "55246 olivia n dalmasso ... Individual IL \n", + "55247 olivia n dalmasso ... Individual IL \n", + "\n", + " party company occupation address ... year amount \\\n", + "55243 NaN NaN NaN 6614 23RD PLACE ... 2022.0 2.0 \n", + "55244 NaN NaN NaN 6614 23RD PLACE ... 2022.0 95.0 \n", + "55245 NaN NaN NaN 6614 23RD PLACE ... 2022.0 10.0 \n", + "55246 NaN NaN NaN PO BOX 574 ... 2022.0 12.6 \n", + "55247 NaN NaN NaN PO BOX 574 ... 2022.0 4.2 \n", "\n", - " recipient_id office_sought purpose \\\n", - "212637 1d4ae24b-2814-4d0d-995e-28fd4c26785d NaN NaN \n", - "212667 1d4ae24b-2814-4d0d-995e-28fd4c26785d NaN NaN \n", - "440542 1d4ae24b-2814-4d0d-995e-28fd4c26785d NaN NaN \n", - "440573 1d4ae24b-2814-4d0d-995e-28fd4c26785d NaN NaN \n", - "440607 1d4ae24b-2814-4d0d-995e-28fd4c26785d NaN NaN \n", - "440642 1d4ae24b-2814-4d0d-995e-28fd4c26785d NaN NaN \n", - "636312 1d4ae24b-2814-4d0d-995e-28fd4c26785d NaN NaN \n", - "636346 1d4ae24b-2814-4d0d-995e-28fd4c26785d NaN NaN \n", - "636382 1d4ae24b-2814-4d0d-995e-28fd4c26785d NaN NaN \n", - "839846 f9fa8506-bfbb-4ef0-9e08-5c9c3e948121 NaN NaN \n", - "840051 389fe2ba-828a-41d4-815c-8efb2499ea11 NaN NaN \n", - "968402 043a03b7-af31-4830-b12e-446b93fca9a0 NaN NaN \n", - "1414338 ba06baf6-eae6-459f-b3a9-7261e4baa33e NaN NaN \n", - "1502742 1d4ae24b-2814-4d0d-995e-28fd4c26785d NaN NaN \n", - "1502777 1d4ae24b-2814-4d0d-995e-28fd4c26785d NaN NaN \n", - "1502812 1d4ae24b-2814-4d0d-995e-28fd4c26785d NaN NaN \n", + " recipient_id office_sought purpose \\\n", + "55243 49a2d46f-5e75-433c-94fa-f910e66d1a1e NaN NaN \n", + "55244 49a2d46f-5e75-433c-94fa-f910e66d1a1e NaN NaN \n", + "55245 49a2d46f-5e75-433c-94fa-f910e66d1a1e NaN NaN \n", + "55246 6b33721f-3f6a-47c0-bce2-284fc58e0d2a NaN NaN \n", + "55247 6b33721f-3f6a-47c0-bce2-284fc58e0d2a NaN NaN \n", "\n", - " transaction_type donor_type recipient_type donor_office \n", - "212637 DIRECT NaN NaN NaN \n", - "212667 DIRECT NaN NaN NaN \n", - "440542 DIRECT NaN NaN NaN \n", - "440573 DIRECT NaN NaN NaN \n", - "440607 DIRECT NaN NaN NaN \n", - "440642 DIRECT NaN NaN NaN \n", - "636312 DIRECT NaN NaN NaN \n", - "636346 DIRECT NaN NaN NaN \n", - "636382 DIRECT NaN NaN NaN \n", - "839846 DIRECT/FUND RAISER NaN NaN NaN \n", - "840051 DIRECT/FUND RAISER NaN NaN NaN \n", - "968402 DIRECT/FUND RAISER NaN NaN NaN \n", - "1414338 DIRECT/FUND RAISER NaN NaN NaN \n", - "1502742 DIRECT NaN NaN NaN \n", - "1502777 DIRECT NaN NaN NaN \n", - "1502812 DIRECT NaN NaN NaN " + " transaction_type donor_type recipient_type donor_office \\\n", + "55243 direct NaN NaN NaN \n", + "55244 direct NaN NaN NaN \n", + "55245 direct NaN NaN NaN \n", + "55246 direct NaN NaN NaN \n", + "55247 direct NaN NaN NaN \n", + "\n", + " recipient_name \n", + "55243 None \n", + "55244 None \n", + "55245 None \n", + "55246 None \n", + "55247 None \n", + "\n", + "[5 rows x 25 columns]" ] }, - "execution_count": 14, + "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "transactions.loc[transactions['donor_id'] == 'b8fbed14-0766-49ab-8516-97952c654a12']" + "# left merge according to ind_id and transaction donor_id. This was entities that only received money will still be there, no info from ind_dataset\n", + "# is lost\n", + "merged_inds_sample = pd.merge(inds_df,transactions,how='left',left_on='id',right_on='donor_id')\n", + "merged_inds_sample.dropna(subset = ['amount'], inplace=True)\n", + "merged_inds_sample.tail(5)" ] }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 13, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "'BUTZEL LONG POLITICAL ACTION COMMITTEE'" + "Index(['id', 'first_name', 'last_name', 'full_name', 'entity_type', 'state',\n", + " 'party', 'company', 'occupation', 'address', 'zip', 'city',\n", + " 'classification', 'transaction_id', 'donor_id', 'year', 'amount',\n", + " 'recipient_id', 'office_sought', 'purpose', 'transaction_type',\n", + " 'donor_type', 'recipient_type', 'donor_office', 'recipient_name'],\n", + " dtype='object')" ] }, - "execution_count": 28, + "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "x = orgs_df.loc[orgs_df['id']=='1d4ae24b-2814-4d0d-995e-28fd4c26785d']\n", - "x.iloc[0]['name']" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "# apply dedup to both inds and orgs\n", - "inds_df = deduplicate_perfect_matches(inds_df)\n", - "orgs_df = deduplicate_perfect_matches(orgs_df)\n", - "\n", - "# map the uuids in transaction donor and recipient columns to the deduplicated uuids\n", - "deduped = pd.read_csv(\"../output/deduplicated_UUIDs.csv\")\n", - "transactions[['donor_id','recipient_id']] = transactions[['donor_id','recipient_id']].replace(deduped)\n" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "# add recipient name to transactions df: \n", - "def name_identifier(uuid:str, orgs_df, inds_df) -> str:\n", - " # 1st check orgs df:\n", - " name_in_org = orgs_df.loc[orgs_df['id']==uuid] \n", - " if len(name_in_org)> 0:\n", - " return name_in_org.iloc[0]['name']\n", - " # theoretically it must be in inds if not in orgs, but for the sample data\n", - " # this might not be the case\n", - " name_in_ind = inds_df.loc[inds_df['id']==uuid]\n", - " if len(name_in_ind)> 0:\n", - " return name_in_ind.iloc[0]['full_name']\n", - " else: return None" + "merged_inds_sample.columns" ] }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 14, "metadata": {}, "outputs": [ { @@ -1259,630 +969,149 @@ " \n", " \n", " \n", - " transaction_id\n", " donor_id\n", - " year\n", - " amount\n", " recipient_id\n", + " full_name\n", + " recipient_name\n", + " address\n", + " amount\n", + " city\n", + " classification\n", + " company\n", + " donor_office\n", + " ...\n", + " occupation\n", " office_sought\n", + " party\n", " purpose\n", - " transaction_type\n", - " donor_type\n", " recipient_type\n", - " donor_office\n", - " recipient_name\n", - " \n", - " \n", - " \n", - " \n", - " 884875\n", - " NaN\n", - " 6c2b94a2-4247-4bc4-b784-6b5a9a2ae9f2\n", - " 2022\n", - " 5.00\n", - " 533f6de5-5140-4799-be24-1d5f4e228d1b\n", - " NaN\n", - " NaN\n", - " DIRECT\n", - " NaN\n", - " NaN\n", - " NaN\n", - " FRIENDS OF DANA NESSEL\n", - " \n", - " \n", - " 122735\n", - " NaN\n", - " b906d3eb-3874-4789-b523-e2eaab415328\n", - " 2022\n", - " 9.16\n", - " f2fad7aa-a782-4d56-8343-049d2150c16f\n", - " NaN\n", - " MERCHANT SVCS FEES\n", - " NaN\n", - " NaN\n", - " NaN\n", - " NaN\n", - " THE JULIE BRIXIE BLUE WAVE FUND 2\n", - " \n", - " \n", - " 458788\n", - " NaN\n", - " 88740001-952f-477e-b2da-9b24f747f6ce\n", - " 2022\n", - " 1.00\n", - " a0619eff-155f-442f-ab71-b5c0ee942223\n", - " NaN\n", - " NaN\n", - " DIRECT\n", - " NaN\n", - " NaN\n", - " NaN\n", - " COMERICA INC POLITICAL ACTION COMMITTEE\n", - " \n", - " \n", - " 1522918\n", - " NaN\n", - " da538e73-c823-48f8-b4ec-920aa1da458f\n", - " 2022\n", - " 20.00\n", - " 81169dce-331e-44ad-b870-1b376d49cf2f\n", - " NaN\n", - " NaN\n", - " DIRECT\n", - " NaN\n", - " NaN\n", - " NaN\n", - " WASTE MANAGEMENT EMPLOYEES BETTER GOVERNMENT F...\n", - " \n", - " \n", - " 1933218\n", - " NaN\n", - " 33814139-8442-4050-b15a-40aed1aa9db7\n", - " 2022\n", - " 35.00\n", - " abdf0530-e2fb-40b6-9a52-dea386cd60f4\n", - " NaN\n", - " NaN\n", - " DIRECT\n", - " NaN\n", - " NaN\n", - " NaN\n", - " GRETCHEN WHITMER FOR GOVERNOR\n", - " \n", - " \n", - " 465682\n", - " NaN\n", - " 133431dd-41ef-4161-97ef-02d23fc05b42\n", - " 2022\n", - " 7.50\n", - " d582fba6-2a0c-4864-9fb2-5a4f898f26c2\n", - " NaN\n", - " NaN\n", - " DIRECT\n", - " NaN\n", - " NaN\n", - " NaN\n", - " MI ASSOC OF COMMUNITY BANKERS OF MICHIGAN POLI...\n", - " \n", - " \n", - " 761674\n", - " NaN\n", - " 668d8471-ade6-469b-9e6b-71ddbfd1d8ba\n", - " 2022\n", - " 25.00\n", - " 1d05ca29-e97f-43cd-bd9e-f313573b324b\n", - " NaN\n", - " NaN\n", - " DIRECT\n", - " NaN\n", - " NaN\n", - " NaN\n", - " END CITIZENS UNITED NON-FEDERAL MI\n", - " \n", - " \n", - " 993543\n", - " NaN\n", - " b9b66f08-4e99-43e7-9161-c75db92b0bb4\n", - " 2022\n", - " 10.00\n", - " ecebf482-f298-4777-bea6-e3451c75e3fc\n", - " NaN\n", - " NaN\n", - " DIRECT\n", - " NaN\n", - " NaN\n", - " NaN\n", - " RESCARE INC DBA BRIGHTSPRING HEALTH SERVICES L...\n", - " \n", - " \n", - " 1196687\n", - " NaN\n", - " f4942707-0d7f-4617-b478-56af7504123e\n", - " 2022\n", - " 12.00\n", - " a24e305e-a49b-4cb3-a857-d629f1162ce8\n", - " NaN\n", - " NaN\n", - " DIRECT\n", - " NaN\n", - " NaN\n", - " NaN\n", - " MARATHON PETROLEUM CORPORATION EMPLOYEES PAC\n", - " \n", - " \n", - " 334698\n", - " NaN\n", - " 96be56db-56b6-48d0-9cf7-9d47da307388\n", - " 2022\n", - " 11.80\n", - " 9fc94e93-b6aa-400d-9a4a-d6501afb84dc\n", - " NaN\n", - " NaN\n", - " DIRECT\n", - " NaN\n", - " NaN\n", - " NaN\n", - " MICHIGAN REGIONAL COUNCIL OF CARPENTERS POLITI...\n", - " \n", - " \n", - "\n", - "" - ], - "text/plain": [ - " transaction_id donor_id year amount \\\n", - "884875 NaN 6c2b94a2-4247-4bc4-b784-6b5a9a2ae9f2 2022 5.00 \n", - "122735 NaN b906d3eb-3874-4789-b523-e2eaab415328 2022 9.16 \n", - "458788 NaN 88740001-952f-477e-b2da-9b24f747f6ce 2022 1.00 \n", - "1522918 NaN da538e73-c823-48f8-b4ec-920aa1da458f 2022 20.00 \n", - "1933218 NaN 33814139-8442-4050-b15a-40aed1aa9db7 2022 35.00 \n", - "465682 NaN 133431dd-41ef-4161-97ef-02d23fc05b42 2022 7.50 \n", - "761674 NaN 668d8471-ade6-469b-9e6b-71ddbfd1d8ba 2022 25.00 \n", - "993543 NaN b9b66f08-4e99-43e7-9161-c75db92b0bb4 2022 10.00 \n", - "1196687 NaN f4942707-0d7f-4617-b478-56af7504123e 2022 12.00 \n", - "334698 NaN 96be56db-56b6-48d0-9cf7-9d47da307388 2022 11.80 \n", - "\n", - " recipient_id office_sought \\\n", - "884875 533f6de5-5140-4799-be24-1d5f4e228d1b NaN \n", - "122735 f2fad7aa-a782-4d56-8343-049d2150c16f NaN \n", - "458788 a0619eff-155f-442f-ab71-b5c0ee942223 NaN \n", - "1522918 81169dce-331e-44ad-b870-1b376d49cf2f NaN \n", - "1933218 abdf0530-e2fb-40b6-9a52-dea386cd60f4 NaN \n", - "465682 d582fba6-2a0c-4864-9fb2-5a4f898f26c2 NaN \n", - "761674 1d05ca29-e97f-43cd-bd9e-f313573b324b NaN \n", - "993543 ecebf482-f298-4777-bea6-e3451c75e3fc NaN \n", - "1196687 a24e305e-a49b-4cb3-a857-d629f1162ce8 NaN \n", - "334698 9fc94e93-b6aa-400d-9a4a-d6501afb84dc NaN \n", - "\n", - " purpose transaction_type donor_type \\\n", - "884875 NaN DIRECT NaN \n", - "122735 MERCHANT SVCS FEES NaN NaN \n", - "458788 NaN DIRECT NaN \n", - "1522918 NaN DIRECT NaN \n", - "1933218 NaN DIRECT NaN \n", - "465682 NaN DIRECT NaN \n", - "761674 NaN DIRECT NaN \n", - "993543 NaN DIRECT NaN \n", - "1196687 NaN DIRECT NaN \n", - "334698 NaN DIRECT NaN \n", - "\n", - " recipient_type donor_office \\\n", - "884875 NaN NaN \n", - "122735 NaN NaN \n", - "458788 NaN NaN \n", - "1522918 NaN NaN \n", - "1933218 NaN NaN \n", - "465682 NaN NaN \n", - "761674 NaN NaN \n", - "993543 NaN NaN \n", - "1196687 NaN NaN \n", - "334698 NaN NaN \n", - "\n", - " recipient_name \n", - "884875 FRIENDS OF DANA NESSEL \n", - "122735 THE JULIE BRIXIE BLUE WAVE FUND 2 \n", - "458788 COMERICA INC POLITICAL ACTION COMMITTEE \n", - "1522918 WASTE MANAGEMENT EMPLOYEES BETTER GOVERNMENT F... \n", - "1933218 GRETCHEN WHITMER FOR GOVERNOR \n", - "465682 MI ASSOC OF COMMUNITY BANKERS OF MICHIGAN POLI... \n", - "761674 END CITIZENS UNITED NON-FEDERAL MI \n", - "993543 RESCARE INC DBA BRIGHTSPRING HEALTH SERVICES L... \n", - "1196687 MARATHON PETROLEUM CORPORATION EMPLOYEES PAC \n", - "334698 MICHIGAN REGIONAL COUNCIL OF CARPENTERS POLITI... " - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "ex = transactions.sample(10)\n", - "ex['recipient_name'] = ex['recipient_id'].apply(name_identifier, args=(orgs_df, inds_df))\n", - "ex" - ] - }, - { - "cell_type": "code", - "execution_count": 63, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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11617483aggregate cashindividualNaNcash _small donationsNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
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" - ], - "text/plain": [ - " id company entity_type first_name full_name \\\n", - "0 2562573 0 individual NaN various 0 \n", - "1 1617483 aggregate cash individual NaN cash _small donations \n", - "\n", - " last_name party state transaction_id donor_id year amount recipient_id \\\n", - "0 NaN NaN NaN NaN NaN NaN NaN NaN \n", - "1 NaN NaN NaN NaN NaN NaN NaN NaN \n", - "\n", - " office_sought purpose transaction_type donor_type recipient_type \\\n", - "0 NaN NaN NaN NaN NaN \n", - "1 NaN NaN NaN NaN NaN \n", - "\n", - " donor_office \n", - "0 NaN \n", - "1 NaN " - ] - }, - "execution_count": 63, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "\n", - "# this step took more than 16 minutes to run...think of alternative way\n", - "# id_to_name = {id: name for id, name in zip(inds_sample.id.tolist(), inds_sample.full_name.tolist())} #the same would be applied to orgs\n", - "transactions['recipient_name'] = transactions['recipient_id'].apply(lambda x: sample_inds.loc[sample_inds.id == x] )\n", - "\n", - "# left merge according to ind_id and transaction donor_id. This was entities that only received money will still be there, no info from ind_dataset\n", - "# is lost\n", - "merged_inds_sample = pd.merge(sample_inds,transactions,how='left',left_on='id',right_on='donor_id')\n", - "merged_inds_sample.head(2)" - ] - }, - { - "cell_type": "code", - "execution_count": 93, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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idcompanyentity_typefirst_namefull_namelast_namepartystate
27100894none (is a candidate)candidateNaNabdussamad, shamsNaNdemocraticAZ
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" - ], - "text/plain": [ - " id company entity_type first_name full_name \\\n", - "27 100894 none (is a candidate) candidate NaN abdussamad, shams \n", - "\n", - " last_name party state \n", - "27 NaN democratic AZ " - ] - }, - "execution_count": 93, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "sample_inds.loc[sample_inds.full_name == 'abdussamad, shams']" - ] - }, - { - "cell_type": "code", - "execution_count": 87, - "metadata": {}, - "outputs": [], - "source": [ - "def add_notes_from_df(df):\n", - " G = nx.MultiDiGraph()\n", - " #inds or org...\n", - " if 'name' in df.columns:\n", - " node_name = 'name'\n", - " else: node_name = 'full_name'\n", - "\n", - " for _, row in df.iterrows():\n", - " G.add_node(row[node_name])\n", - " for column in df.columns:\n", - " # only add info that's present\n", - " if (row[column] != 'nan'):\n", - " nx.set_node_attributes(G, row[column], name=column)\n", - " #nx.set\n", - " nx.draw_random(G, with_labels=True)\n", - " plt.show()\n", - " return G" - ] - }, - { - "cell_type": "code", - "execution_count": 77, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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idcompanyentity_typefirst_namefull_namelast_namepartystatetransaction_iddonor_idyearamountrecipient_idoffice_soughtpurposetransaction_typedonor_typerecipient_typedonor_officezip
27100894none (is a candidate)candidateNaNabdussamad, shamsNaNdemocraticAZ508807910089400007b184-4e1d-401a-ba51-99733d2e13e7d461f2bd-9074-44b3-8948-e659bead3e58graham filler ...saginaw county republican committee12705 WARM CREEK500.00DEWITTneutralNoneNone...NoneNoneNoneNoneNoneMINonedirect2022.05.00750413state representative - district 11e-qual online qcccec $5 qualifying contributionNaNNaNNaN48820-0000
28100894none (is a candidate)candidateNaNabdussamad, shamsNaNdemocraticAZ5088080100894100523627-46c7-4f76-ab42-fb2c1fbac1b16126e78b-4e80-4361-a019-9d99aa1623eddaniel millstone ...rooted in community leadership pac10518 ROUNTREE RD0.77LOS ANGELESneutralNoneNone...NoneNoneNoneNoneNoneCANonedirect2022.05.002002235state representative - district 11NaNccec $5 qualifying contributionNaNNaNNaN90064-0000
29100894none (is a candidate)candidateNaNabdussamad, shamsNaNdemocraticAZ5088081100894200934782-86e5-4941-94cf-0a700100a2c02d1a0919-218e-4692-98ec-c4a73a126482josie petersheim ...mi greenstone pac7196 W. BRIGGS RD.25.00STANTONneutralNoneNone...NoneNoneNoneNoneNoneMINonedirect2022.0100.001942680state representative - district 11NaNreceive loan from candidate or family memberNaNNaNNaN48888-0000
30100894none (is a candidate)candidateNaNabdussamad, shamsNaNdemocraticAZ5088083100894300f22bdd-96bf-4074-9620-4737e8444958af8417ee-5bca-49f5-91e9-d2de65d73631robert doerfler ...michigan senate democratic fund1534 NE 5TH AVE50.00FORT LAUDERDALEneutralNoneNone...NoneNoneNoneNoneNoneFLNonedirect2022.05.00-1state representative - district 11NaNin-state contributions $100 or lessNaNNaNNaN33304-1006
31100894none (is a candidate)candidateNaNabdussamad, shamsNaNdemocraticAZ508808410089440138403b-b5b9-453a-a1d2-b6ed9fa5fe586126e78b-4e80-4361-a019-9d99aa1623edjoseph martinez ...rooted in community leadership pac139 HURON AVE1.65MOUNT CLEMENSneutralNoneNone...NoneNoneNoneNoneNoneMINonedirect2022.020.00-1state representative - district 11NaNin-state contributions $100 or lessNaNNaNNaN48043-0000
..................
597100883none (is a candidate)candidateNaNabeytia, anna lynnNaNdemocraticAZ50841001008831120fdccce6b-e55f-4f1d-bd95-1714f2a667eda3fe20e2-8019-448e-9b54-bfdce4d87f2fmichael olthoff ...bumstead leadership fund1499 MIDDLEBROOK DR1000.00NORTON SHORESneutralnicholsNone...ceoNoneNoneNoneNoneMINonedirect2022.010.002017053state representative - district 11NaNcontribution from individualsNaNNaNNaN49441-0000
598100883none (is a candidate)candidateNaNabeytia, anna lynnNaNdemocraticAZ50841021008831121fe969829-b8a4-4d38-88e2-8314b340d5676126e78b-4e80-4361-a019-9d99aa1623edjoanna simon ...rooted in community leadership pac1546 POPLAR GROVE DR3.82RESTONneutralNoneNone...NoneNoneNoneNoneNoneVANonedirect2022.0180.002017970state representative - district 11video productionin-kind cont. from individualNaNNaNNaN20194-1731
599100883none (is a candidate)candidateNaNabeytia, anna lynnNaNdemocraticAZ50841031008831122ff1423ba-ff5e-4bc1-b864-303a9dcc9b326126e78b-4e80-4361-a019-9d99aa1623edadriana p{on ce ...rooted in community leadership pac9 BIRCH CT3.82NORMALneutralNoneNone...NoneNoneNoneNoneNoneILNonedirect2022.051.992008747state representative - district 11NaNcontribution from individualsNaNNaNNaN61761-3900
600100883none (is a candidate)candidateNaNabeytia, anna lynnNaNdemocraticAZ50841051008831123ff24644e-d64a-4a8a-a87f-cdb53b86dd636126e78b-4e80-4361-a019-9d99aa1623eddavid friedman ...rooted in community leadership pac8823 MOUNTAIN PATH CIR0.15AUSTINneutralNoneNone...NoneNoneNoneNoneNoneTXNonedirect2022.010.801193076state representative - district 11NaNcontribution from individualsNaNNaNNaN78759-0000
601100883none (is a candidate)candidateNaNabeytia, anna lynnNaNdemocraticAZ50841071008831124ffb25947-c03f-43b2-abb4-23531cdb73247f272fe4-d592-453c-9ca1-315ea3fdcff1dennis starner ...bill g schuette for state representative4612 CONGRESS DRIVE525.00MIDLANDneutralretiredNone...retiredNoneNoneNoneNoneMINonedirect/fund raiser2022.051.991691025state representative - district 11NaNcontribution from individualsNaNNaNNaN48642-0000
\n", - "

575 rows × 19 columns

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1125 rows × 25 columns

\n", "
" ], "text/plain": [ - " id company entity_type first_name \\\n", - "27 100894 none (is a candidate) candidate NaN \n", - "28 100894 none (is a candidate) candidate NaN \n", - "29 100894 none (is a candidate) candidate NaN \n", - "30 100894 none (is a candidate) candidate NaN \n", - "31 100894 none (is a candidate) candidate NaN \n", - ".. ... ... ... ... \n", - "597 100883 none (is a candidate) candidate NaN \n", - "598 100883 none (is a candidate) candidate NaN \n", - "599 100883 none (is a candidate) candidate NaN \n", - "600 100883 none (is a candidate) candidate NaN \n", - "601 100883 none (is a candidate) candidate NaN \n", - "\n", - " full_name last_name party state transaction_id donor_id \\\n", - "27 abdussamad, shams NaN democratic AZ 5088079 100894 \n", - "28 abdussamad, shams NaN democratic AZ 5088080 100894 \n", - "29 abdussamad, shams NaN democratic AZ 5088081 100894 \n", - "30 abdussamad, shams NaN democratic AZ 5088083 100894 \n", - "31 abdussamad, shams NaN democratic AZ 5088084 100894 \n", - ".. ... ... ... ... ... ... \n", - "597 abeytia, anna lynn NaN democratic AZ 5084100 100883 \n", - "598 abeytia, anna lynn NaN democratic AZ 5084102 100883 \n", - "599 abeytia, anna lynn NaN democratic AZ 5084103 100883 \n", - "600 abeytia, anna lynn NaN democratic AZ 5084105 100883 \n", - "601 abeytia, anna lynn NaN democratic AZ 5084107 100883 \n", - "\n", - " year amount recipient_id office_sought \\\n", - "27 2022.0 5.00 750413 state representative - district 11 \n", - "28 2022.0 5.00 2002235 state representative - district 11 \n", - "29 2022.0 100.00 1942680 state representative - district 11 \n", - "30 2022.0 5.00 -1 state representative - district 11 \n", - "31 2022.0 20.00 -1 state representative - district 11 \n", - ".. ... ... ... ... \n", - "597 2022.0 10.00 2017053 state representative - district 11 \n", - "598 2022.0 180.00 2017970 state representative - district 11 \n", - "599 2022.0 51.99 2008747 state representative - district 11 \n", - "600 2022.0 10.80 1193076 state representative - district 11 \n", - "601 2022.0 51.99 1691025 state representative - district 11 \n", - "\n", - " purpose transaction_type \\\n", - "27 e-qual online qc ccec $5 qualifying contribution \n", - "28 NaN ccec $5 qualifying contribution \n", - "29 NaN receive loan from candidate or family member \n", - "30 NaN in-state contributions $100 or less \n", - "31 NaN in-state contributions $100 or less \n", - ".. ... ... \n", - "597 NaN contribution from individuals \n", - "598 video production in-kind cont. from individual \n", - "599 NaN contribution from individuals \n", - "600 NaN contribution from individuals \n", - "601 NaN contribution from individuals \n", - "\n", - " donor_type recipient_type donor_office \n", - "27 NaN NaN NaN \n", - "28 NaN NaN NaN \n", - "29 NaN NaN NaN \n", - "30 NaN NaN NaN \n", - "31 NaN NaN NaN \n", - ".. ... ... ... \n", - "597 NaN NaN NaN \n", - "598 NaN NaN NaN \n", - "599 NaN NaN NaN \n", - "600 NaN NaN NaN \n", - "601 NaN NaN NaN \n", - "\n", - "[575 rows x 19 columns]" + " donor_id \\\n", + "0 0007b184-4e1d-401a-ba51-99733d2e13e7 \n", + "1 00523627-46c7-4f76-ab42-fb2c1fbac1b1 \n", + "2 00934782-86e5-4941-94cf-0a700100a2c0 \n", + "3 00f22bdd-96bf-4074-9620-4737e8444958 \n", + "4 0138403b-b5b9-453a-a1d2-b6ed9fa5fe58 \n", + "... ... \n", + "1120 fdccce6b-e55f-4f1d-bd95-1714f2a667ed \n", + "1121 fe969829-b8a4-4d38-88e2-8314b340d567 \n", + "1122 ff1423ba-ff5e-4bc1-b864-303a9dcc9b32 \n", + "1123 ff24644e-d64a-4a8a-a87f-cdb53b86dd63 \n", + "1124 ffb25947-c03f-43b2-abb4-23531cdb7324 \n", + "\n", + " recipient_id \\\n", + "0 d461f2bd-9074-44b3-8948-e659bead3e58 \n", + "1 6126e78b-4e80-4361-a019-9d99aa1623ed \n", + "2 2d1a0919-218e-4692-98ec-c4a73a126482 \n", + "3 af8417ee-5bca-49f5-91e9-d2de65d73631 \n", + "4 6126e78b-4e80-4361-a019-9d99aa1623ed \n", + "... ... \n", + "1120 a3fe20e2-8019-448e-9b54-bfdce4d87f2f \n", + "1121 6126e78b-4e80-4361-a019-9d99aa1623ed \n", + "1122 6126e78b-4e80-4361-a019-9d99aa1623ed \n", + "1123 6126e78b-4e80-4361-a019-9d99aa1623ed \n", + "1124 7f272fe4-d592-453c-9ca1-315ea3fdcff1 \n", + "\n", + " full_name \\\n", + "0 graham filler ... \n", + "1 daniel millstone ... \n", + "2 josie petersheim ... \n", + "3 robert doerfler ... \n", + "4 joseph martinez ... \n", + "... ... \n", + "1120 michael olthoff ... \n", + "1121 joanna simon ... \n", + "1122 adriana p{on ce ... \n", + "1123 david friedman ... \n", + "1124 dennis starner ... \n", + "\n", + " recipient_name address \\\n", + "0 saginaw county republican committee 12705 WARM CREEK \n", + "1 rooted in community leadership pac 10518 ROUNTREE RD \n", + "2 mi greenstone pac 7196 W. BRIGGS RD. \n", + "3 michigan senate democratic fund 1534 NE 5TH AVE \n", + "4 rooted in community leadership pac 139 HURON AVE \n", + "... ... ... \n", + "1120 bumstead leadership fund 1499 MIDDLEBROOK DR \n", + "1121 rooted in community leadership pac 1546 POPLAR GROVE DR \n", + "1122 rooted in community leadership pac 9 BIRCH CT \n", + "1123 rooted in community leadership pac 8823 MOUNTAIN PATH CIR \n", + "1124 bill g schuette for state representative 4612 CONGRESS DRIVE \n", + "\n", + " amount city classification company donor_office ... \\\n", + "0 500.00 DEWITT neutral None None ... \n", + "1 0.77 LOS ANGELES neutral None None ... \n", + "2 25.00 STANTON neutral None None ... \n", + "3 50.00 FORT LAUDERDALE neutral None None ... \n", + "4 1.65 MOUNT CLEMENS neutral None None ... \n", + "... ... ... ... ... ... ... \n", + "1120 1000.00 NORTON SHORES neutral nichols None ... \n", + "1121 3.82 RESTON neutral None None ... \n", + "1122 3.82 NORMAL neutral None None ... \n", + "1123 0.15 AUSTIN neutral None None ... \n", + "1124 525.00 MIDLAND neutral retired None ... \n", + "\n", + " occupation office_sought party purpose recipient_type state \\\n", + "0 None None None None None MI \n", + "1 None None None None None CA \n", + "2 None None None None None MI \n", + "3 None None None None None FL \n", + "4 None None None None None MI \n", + "... ... ... ... ... ... ... \n", + "1120 ceo None None None None MI \n", + "1121 None None None None None VA \n", + "1122 None None None None None IL \n", + "1123 None None None None None TX \n", + "1124 retired None None None None MI \n", + "\n", + " transaction_id transaction_type year zip \n", + "0 None direct 2022.0 48820-0000 \n", + "1 None direct 2022.0 90064-0000 \n", + "2 None direct 2022.0 48888-0000 \n", + "3 None direct 2022.0 33304-1006 \n", + "4 None direct 2022.0 48043-0000 \n", + "... ... ... ... ... \n", + "1120 None direct 2022.0 49441-0000 \n", + "1121 None direct 2022.0 20194-1731 \n", + "1122 None direct 2022.0 61761-3900 \n", + "1123 None direct 2022.0 78759-0000 \n", + "1124 None direct/fund raiser 2022.0 48642-0000 \n", + "\n", + "[1125 rows x 25 columns]" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "attribute_cols = merged_inds_sample.columns.difference(['donor_id','recipient_id','full_name','recipient_name'])\n", + "agg_functions = {col: 'sum' if col == 'amount' else 'first' for col in attribute_cols}\n", + "grouped_sample = merged_inds_sample.groupby(['donor_id','recipient_id','full_name','recipient_name']).agg(agg_functions).reset_index()\n", + "grouped_sample" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [], + "source": [ + "def create_network_nodes(df: pd.DataFrame) -> nx.MultiDiGraph:\n", + " G = nx.MultiDiGraph()\n", + " # first check if df is individuals or organizations dataset\n", + " if \"name\" in df.columns:\n", + " node_name = \"name\"\n", + " else:\n", + " node_name = \"full_name\"\n", + " \n", + " transact_info = ['office_sought', 'purpose', 'transaction_type', 'year','transaction_id','donor_office','amount']\n", + " for _, row in df.iterrows(): \n", + " # add node attributes based on the columns relevant to the entity\n", + " G.add_node(row[node_name])\n", + " for column in df.columns.difference(transact_info):\n", + " if not pd.isnull(row[column]):\n", + " G.nodes[row[node_name]][column] = row[column]\n", + " \n", + " # link the donor node to the recipient node. add the attributes of the\n", + " # edge based on relevant nodes \n", + " edge_dictionary = {}\n", + " for column in transact_info:\n", + " if not pd.isnull(row[column]):\n", + " edge_dictionary[column] = row[column]\n", + " G.add_edge(row[node_name], row['recipient_name'], **edge_dictionary)\n", + "\n", + " # the added 'recipient_name' node has no attributes at this moment\n", + " # for the final code this line won't be necessary, as each recipient\n", + " # should ideally be referenced later on. For now, all added nodes for\n", + " # the recipient will only have one default attribute: classification\n", + " G.nodes[row['recipient_name']]['classification'] = 'neutral' \n", + " \n", + " edge_labels = {(u,v):d['amount'] for u,v,d in G.edges(data=True)}\n", + " entity_colors = {'neutral': 'green', 'c':'blue', 'f':'red'}\n", + " node_colors = [entity_colors[G.nodes[node]['classification']] for node in G.nodes()]\n", + "\n", + " nx.draw_planar(G, with_labels=False,node_color=node_colors)\n", + " plt.figure(3,figsize=(12,12)) \n", + " nx.draw_networkx_edge_labels(G, pos=nx.planar_layout(G),edge_labels=edge_labels, label_pos=0.5)\n", + "\n", + " #nx.draw_planar(G, with_labels=False)\n", + " plt.show()\n", + " return G" + ] + }, + { + "cell_type": "code", + "execution_count": 122, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{}" + ] + }, + "execution_count": 122, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#for u,v in G.nodes(data=True):\n", + " #print(u)#['classification'])\n", + " \n", + "G.nodes['michigan association of health plans political action committee']#['classification'])#['nancy davis ']['classification']" + ] + }, + { + "cell_type": "code", + "execution_count": 66, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array(['neutral', 'f'], dtype=object)" ] }, - "execution_count": 77, + "execution_count": 66, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "merged_inds_sample.loc[merged_inds_sample.donor_id.notnull()]" + "grouped_sample.classification.unique()" ] }, { "cell_type": "code", - "execution_count": 91, + "execution_count": 16, "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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", 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"text/plain": [ - "{'id': '1869727',\n", - " 'company': nan,\n", - " 'entity_type': 'individual',\n", - " 'first_name': nan,\n", - " 'full_name': 'william \\x08stoner',\n", - " 'last_name': nan,\n", - " 'party': nan,\n", - " 'state': nan,\n", - " 'transaction_id': nan,\n", - " 'donor_id': nan,\n", - " 'year': nan,\n", - " 'amount': nan,\n", - " 'recipient_id': nan,\n", - " 'office_sought': nan,\n", - " 'purpose': nan,\n", - " 'transaction_type': nan,\n", - " 'donor_type': nan,\n", - " 'recipient_type': nan,\n", - " 'donor_office': nan}" + "
" ] }, - "execution_count": 91, "metadata": {}, - "output_type": "execute_result" + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{'address': '3836 BRISTOL CT', 'city': 'CLARKSTON ', 'classification': 'neutral', 'donor_id': 'c7f7a9e5-2e9e-47d1-92f6-2238c7ce301a', 'entity_type': 'Individual', 'first_name': 'THERESA ', 'full_name': 'theresa fougnie ', 'id': 'c7f7a9e5-2e9e-47d1-92f6-2238c7ce301a', 'last_name': 'FOUGNIE ', 'recipient_id': '520c9ce3-c702-4926-8688-750984ee6c0d', 'recipient_name': 'friends of sarah may seward', 'state': 'MI', 'zip': '48348-3610'}\n", + "{'classification': 'neutral'}\n", + "{'address': '330 BROAD ST APT 1', 'city': 'SPRING CITY ', 'classification': 'neutral', 'donor_id': '318b9b37-369b-45ba-9802-27177198e694', 'entity_type': 'Individual', 'first_name': 'ERIC ', 'full_name': 'eric oconnor ', 'id': '318b9b37-369b-45ba-9802-27177198e694', 'last_name': 'OCONNOR ', 'recipient_id': '6126e78b-4e80-4361-a019-9d99aa1623ed', 'recipient_name': 'rooted in community leadership pac', 'state': 'PA', 'zip': '19475-1763'}\n", + "{'classification': 'neutral'}\n", + "{'address': '15 W260 FILLMORE ST', 'city': 'ELMHURST ', 'classification': 'neutral', 'donor_id': '283c7a56-1298-4003-b4b3-e4519b6077b0', 'entity_type': 'Individual', 'first_name': 'EVELYN ', 'full_name': 'evelyn pape ', 'id': '283c7a56-1298-4003-b4b3-e4519b6077b0', 'last_name': 'PAPE ', 'recipient_id': 'af8417ee-5bca-49f5-91e9-d2de65d73631', 'recipient_name': 'michigan senate democratic fund', 'state': 'IL', 'zip': '60126-5349'}\n", + "{'classification': 'neutral'}\n", + "{'address': '16190 DOBBINS DR', 'city': 'ALBION ', 'classification': 'neutral', 'donor_id': '306d7309-ccc7-457e-a263-394b1143dacb', 'entity_type': 'Individual', 'first_name': 'STEPHANIE ', 'full_name': 'stephanie dobbins ', 'id': '306d7309-ccc7-457e-a263-394b1143dacb', 'last_name': 'DOBBINS ', 'recipient_id': 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senior', 'recipient_id': '5a56136a-8ea1-4027-918f-be7d7a66c373', 'recipient_name': 'blue cross blue shield of michigan political action committee', 'state': 'MI', 'zip': '48306-0000'}\n", + "{'classification': 'neutral'}\n", + "{'address': '4608 OAKRIDGE DR', 'city': 'MIDLAND ', 'classification': 'neutral', 'donor_id': '4c1803dc-2633-4432-9d19-005d82aedf68', 'entity_type': 'Individual', 'first_name': 'JAMES ', 'full_name': 'james allen ', 'id': '4c1803dc-2633-4432-9d19-005d82aedf68', 'last_name': 'ALLEN ', 'recipient_id': 'af8417ee-5bca-49f5-91e9-d2de65d73631', 'recipient_name': 'michigan senate democratic fund', 'state': 'MI', 'zip': '48640-1914'}\n", + "{'address': '1919 CURTIS ST', 'city': 'BERKELEY ', 'classification': 'neutral', 'donor_id': '514931c3-da83-44dd-bc30-4fece766d85e', 'entity_type': 'Individual', 'first_name': 'JOAQUIN ', 'full_name': 'joaquin carbonell ', 'id': '514931c3-da83-44dd-bc30-4fece766d85e', 'last_name': 'CARBONELL ', 'recipient_id': 'af8417ee-5bca-49f5-91e9-d2de65d73631', 'recipient_name': 'michigan senate democratic fund', 'state': 'CA', 'zip': '94702-1648'}\n", + "{'address': '39842 GOLFVIEW DR.', 'city': 'NORTHVILLE ', 'classification': 'neutral', 'donor_id': '739bc866-c9cc-4360-ae52-9b15c22ca6b6', 'entity_type': 'Individual', 'first_name': 'DONALD ', 'full_name': 'donald gates ', 'id': '739bc866-c9cc-4360-ae52-9b15c22ca6b6', 'last_name': 'GATES ', 'recipient_id': 'e9e8bf7f-2d34-42c9-b155-b95481ca238f', 'recipient_name': 'committee to elect dave staudt', 'state': 'MI', 'zip': '48167-0000'}\n", + "{'classification': 'neutral'}\n", + "{'address': '7300 KRAENZLEIN ROAD', 'city': 'BAY CITY ', 'classification': 'neutral', 'donor_id': '4ae0900b-eac4-4e41-b4a2-6727561db273', 'entity_type': 'Individual', 'first_name': 'JOAN ', 'full_name': 'joan wilson ', 'id': '4ae0900b-eac4-4e41-b4a2-6727561db273', 'last_name': 'WILSON ', 'recipient_id': 'c5bc157e-1eff-4db0-b26a-eea376cc3fd0', 'recipient_name': 'tamara d carlone for state board of education', 'state': 'MI', 'zip': '48706-0000'}\n", + "{'classification': 'neutral'}\n", + "{'address': '753 PATRICIA PLACE DR', 'city': 'WESTLAND ', 'classification': 'neutral', 'company': 'blue cross blue shield of mich', 'donor_id': '184e5f13-aba5-44da-be09-572ac083b3e9', 'entity_type': 'Individual', 'first_name': 'SHUNDA ', 'full_name': 'shunda jones ^ ', 'id': '184e5f13-aba5-44da-be09-572ac083b3e9', 'last_name': 'JONES ^ ', 'occupation': 'manager - administrative', 'recipient_id': '5a56136a-8ea1-4027-918f-be7d7a66c373', 'recipient_name': 'blue cross blue shield of michigan political action committee', 'state': 'MI', 'zip': '48185-0000'}\n", + "{'address': '3830 33RD AVE SW UNIT A', 'city': 'SEATTLE ', 'classification': 'neutral', 'donor_id': '9a5a86bb-a480-42ad-913a-17f80efbfb86', 'entity_type': 'Individual', 'first_name': 'JAMES ', 'full_name': 'james sims ', 'id': '9a5a86bb-a480-42ad-913a-17f80efbfb86', 'last_name': 'SIMS ', 'recipient_id': '6126e78b-4e80-4361-a019-9d99aa1623ed', 'recipient_name': 'rooted in community leadership pac', 'state': 'WA', 'zip': '98126-2514'}\n", + "{'address': '204 HURON ST', 'city': 'BAY CITY ', 'classification': 'neutral', 'donor_id': '298c73fa-495f-4df0-a348-16a62d6464ee', 'entity_type': 'Individual', 'first_name': 'MATHEWS ', 'full_name': 'mathews gavin ', 'id': '298c73fa-495f-4df0-a348-16a62d6464ee', 'last_name': 'GAVIN ', 'recipient_id': 'a9c205c4-6e86-465d-b9f8-55400317be37', 'recipient_name': 'sheet metal workers local 7 pac', 'state': 'MI', 'zip': '48706-4931'}\n", + "{'address': '740 HEWITT LN', 'city': 'NEW WINDSOR ', 'classification': 'neutral', 'donor_id': 'a41724c3-f42d-42a0-bc7d-8973c2e3a0c8', 'entity_type': 'Individual', 'first_name': 'MARY ', 'full_name': 'mary washburn ', 'id': 'a41724c3-f42d-42a0-bc7d-8973c2e3a0c8', 'last_name': 'WASHBURN ', 'recipient_id': 'af8417ee-5bca-49f5-91e9-d2de65d73631', 'recipient_name': 'michigan senate democratic fund', 'state': 'NY', 'zip': '12553-5462'}\n", + "{'address': '100 ROCKVIEW ST', 'city': 'JAMAICA PLAIN ', 'classification': 'neutral', 'donor_id': '1755fe5d-6210-4ecd-8075-de785b4a8a73', 'entity_type': 'Individual', 'first_name': 'TIMOTHY ', 'full_name': 'timothy havel ', 'id': '1755fe5d-6210-4ecd-8075-de785b4a8a73', 'last_name': 'HAVEL ', 'recipient_id': '097002ca-1bbd-417a-bad9-9fd54887ebab', 'recipient_name': 'movement voter pac mi', 'state': 'MA', 'zip': '02130-4660'}\n", + "{'address': '2260 POLISH LINE RD.', 'city': 'CHEBOYGAN ', 'classification': 'neutral', 'donor_id': '46b3649a-e403-4bd0-8ee2-d65a34d191f9', 'entity_type': 'Individual', 'first_name': 'STEVE ', 'full_name': 'steve downing ', 'id': '46b3649a-e403-4bd0-8ee2-d65a34d191f9', 'last_name': 'DOWNING ', 'recipient_id': 'b92fe9af-a5f5-4f15-8f35-d5536eb946eb', 'recipient_name': 'friends of marie fielder', 'state': 'MI', 'zip': '49721-0000'}\n", + "{'classification': 'neutral'}\n", + "{'address': '10698 BEAR LAKE TRL', 'city': 'PORTAGE ', 'classification': 'neutral', 'donor_id': 'b0dafcd3-4ba2-4aa1-ac43-2298edc705e4', 'entity_type': 'Individual', 'first_name': 'MICHAEL ', 'full_name': 'michael anderson ', 'id': 'b0dafcd3-4ba2-4aa1-ac43-2298edc705e4', 'last_name': 'ANDERSON ', 'recipient_id': 'af8417ee-5bca-49f5-91e9-d2de65d73631', 'recipient_name': 'michigan senate democratic fund', 'state': 'MI', 'zip': '49024-6206'}\n", + "{'address': '150 MARINE AVE', 'city': 'BROOKLYN ', 'classification': 'neutral', 'donor_id': '58988e4c-4376-4fd7-8c13-10bc9fc65335', 'entity_type': 'Individual', 'first_name': 'PAMELA L ', 'full_name': 'pamela l landberg ', 'id': '58988e4c-4376-4fd7-8c13-10bc9fc65335', 'last_name': 'LANDBERG ', 'recipient_id': '6126e78b-4e80-4361-a019-9d99aa1623ed', 'recipient_name': 'rooted in community leadership pac', 'state': 'NY', 'zip': '11209-7744'}\n", + "{'address': '1701 PORTER SW SUITE 6', 'city': 'WYOMING ', 'classification': 'neutral', 'company': 'self emp;oyed', 'donor_id': '3dfd0b64-eb59-4475-9abc-8be958bd8182', 'entity_type': 'Individual', 'first_name': 'DANIEL ', 'full_name': 'daniel hibma ', 'id': '3dfd0b64-eb59-4475-9abc-8be958bd8182', 'last_name': 'HIBMA ', 'occupation': 'property management', 'recipient_id': 'b4b49f06-2c4d-42e4-83e8-fc63c95fad04', 'recipient_name': 'committee to protect voters rights', 'state': 'MI', 'zip': '49519-0000'}\n", + "{'classification': 'neutral'}\n", + "{'address': '1501 BRIDGEWATER DR', 'city': 'MELBOURNE ', 'classification': 'neutral', 'donor_id': 'd71d895c-b18c-45ed-9a13-ec025564fedb', 'entity_type': 'Individual', 'first_name': 'JUDITH ', 'full_name': 'judith behrendt ', 'id': 'd71d895c-b18c-45ed-9a13-ec025564fedb', 'last_name': 'BEHRENDT ', 'recipient_id': '6126e78b-4e80-4361-a019-9d99aa1623ed', 'recipient_name': 'rooted in community leadership pac', 'state': 'FL', 'zip': '32934-3215'}\n" + ] } ], "source": [ - "x = add_notes_from_df(merged_inds_sample)\n", - "x.nodes['abdussamad, shams']" + "matplot_G = create_network_nodes(grouped_sample.sample(50))\n", + "for v,d in matplot_G.nodes(data=True):\n", + " #print(u)\n", + " #print(v)\n", + " print(d)" ] }, { "cell_type": "code", - "execution_count": 79, + "execution_count": 118, "metadata": {}, "outputs": [ { "data": { - "text/html": [ - "
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'green',\n", + " 'green',\n", + " 'green',\n", + " 'green',\n", + " 'green',\n", + " 'green',\n", + " 'green',\n", + " 'green',\n", + " 'green',\n", + " 'red',\n", + " 'green',\n", + " 'green',\n", + " 'green',\n", + " 'green',\n", + " 'green',\n", + " 'green',\n", + " 'green',\n", + " 'green',\n", + " 'green',\n", + " 'green',\n", + " 'green',\n", + " 'green',\n", + " 'green',\n", + " 'green',\n", + " 'green',\n", + " 'green',\n", + " 'green',\n", + " 'green',\n", + " 'green',\n", + " 'green',\n", + " 'green',\n", + " 'green',\n", + " 'green',\n", + " 'green',\n", + " 'green',\n", + " 'green',\n", + " 'green',\n", + " 'green',\n", + " 'green',\n", + " 'green',\n", + " 'green',\n", + " 'green',\n", + " 'green']" ] }, - "execution_count": 79, + "execution_count": 118, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "merged_inds_sample.loc[merged_inds_sample.full_name == 'william \\x08stoner']" + "#for a,b in G.nodes(data=True):\n", + " #print(G[node])#['classification'])\n", + "# print(b)#['classification'])\n", + "entity_colors = {'neutral': 'green', 'c':'blue', 'f':'red'}\n", + "node_colors = [entity_colors.get(G.nodes[node].get('classification', 'neutral'), 'green') for node in G.nodes()]\n", + "node_colors" ] }, { "cell_type": "code", - "execution_count": 65, + "execution_count": 36, "metadata": {}, "outputs": [ { "data": { + "image/png": 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", "text/plain": [ - "{'color': nan, 'size': 2}" + "
" ] }, - "execution_count": 65, "metadata": {}, - "output_type": "execute_result" + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "('William Stoner', 'KALAMAZOO ANESTHESIOLOGY PC', {'amount': 10.0, 'year': 2017})\n", + "('KALAMAZOO ANESTHESIOLOGY PC', 'Bob Kushman', {'amount': 1530})\n", + "('Bob Kushman', 'KALAMAZOO ANESTHESIOLOGY PC', {'amount': 530})\n", + "('James Engelson', 'Bob Kushman', {'amount': 90.0, 'year': 2019})\n", + "('Allen Wolf', 'William Stoner', {'amount': 111.5, 'year': 2018})\n", + "('Allen Wolf', 'William Stoner', {'amount': 11100.5, 'year': 2018})\n" + ] } ], "source": [ - "G = nx.Graph()\n", - "G.add_node(0)\n", - "nx.set_node_attributes(G, \"red\", name=\"color\")\n", - "nx.set_node_attributes(G, 2, name=\"size\")\n", - "G.add_node(1)\n", - "nx.set_node_attributes(G, np.nan, name='color')\n", - "G.nodes[0]" + "G = nx.MultiDiGraph()\n", + " \n", + "G.add_node(\"William Stoner\", Age=10, Weight=110)\n", + "G.add_edge(\"William Stoner\",\"KALAMAZOO ANESTHESIOLOGY PC\",amount=10.00, year=2017)\n", + "G.add_node(\"KALAMAZOO ANESTHESIOLOGY PC\", Age=50, Weight=180)\n", + "G.add_edge(\"KALAMAZOO ANESTHESIOLOGY PC\",\"Bob Kushman\",amount=1530)\n", + "G.add_node(\"Bob Kushman\", Age=90, Weight=111)\n", + "G.add_edge(\"Bob Kushman\",\"KALAMAZOO ANESTHESIOLOGY PC\",amount=530)\n", + "G.add_node(\"James Engelson\", Age=40, Weight=10)\n", + "G.add_edge(\"James Engelson\",\"Bob Kushman\",amount=90.00, year=2019,)\n", + "G.add_node(\"Allen Wolf\", Age=30, Weight=1710)\n", + "G.add_edge(\"Allen Wolf\",\"William Stoner\",amount=111.50,year=2018)\n", + "G.add_edge(\"Allen Wolf\",\"William Stoner\",amount=11100.50,year=2018)\n", + "\n", + "\n", + "\n", + "edge_labels = {(u,v):d['amount'] for u,v,d in G.edges(data=True)}\n", + "nx.draw(G, with_labels=True,node_color='red')\n", + "pos = nx.planar_layout(G)\n", + "for edge, label in edge_labels.items():\n", + " nx.draw_networkx_edge_labels(G, pos=pos, edge_labels={edge: label}, label_pos=0.5, verticalalignment='center', horizontalalignment='center')\n", + "plt.show()\n", + "for edge in G.edges(data=True):\n", + " print(edge)" ] }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 35, "metadata": {}, "outputs": [ { "data": { - "image/png": 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Network graph made with Plotly" + }, + "xaxis": { + "showgrid": false, + "showticklabels": false, + "zeroline": false + }, + "yaxis": { + "showgrid": false, + "showticklabels": false, + "zeroline": false + } + } + }, + "text/html": [ + "
" ] }, "metadata": {}, @@ -2288,41 +2729,1184 @@ } ], "source": [ - "G = nx.petersen_graph()\n", - "subax1 = plt.subplot(121)\n", - "nx.draw(G, with_labels=True, font_weight='bold')\n", - "subax2 = plt.subplot(122)\n", - "nx.draw_shell(G, nlist=[range(5, 10), range(5)], with_labels=True, font_weight='light')\n" + "G = nx.MultiDiGraph()\n", + "\n", + "G.add_node(\"William Stoner\", Age=10, Weight=110)\n", + "G.add_node(\"KALAMAZOO ANESTHESIOLOGY PC\", Age=50, Weight=180)\n", + "G.add_node(\"Bob Kushman\", Age=90, Weight=111)\n", + "G.add_node(\"James Engelson\", Age=40, Weight=10)\n", + "G.add_node(\"Allen Wolf\", Age=30, Weight=1710)\n", + "\n", + "G.add_edge(\"William Stoner\", \"KALAMAZOO ANESTHESIOLOGY PC\", weight=10.00, amount=10.00, year=2017)\n", + "G.add_edge(\"KALAMAZOO ANESTHESIOLOGY PC\", \"Bob Kushman\", weight=1530, amount=1530, year=2017)\n", + "G.add_edge(\"Bob Kushman\", \"KALAMAZOO ANESTHESIOLOGY PC\", weight=530, amount=530, year=2017)\n", + "G.add_edge(\"James Engelson\", \"Bob Kushman\", weight=90.00, amount=90.00, year=2017)\n", + "G.add_edge(\"Allen Wolf\", \"William Stoner\", weight=111.50, amount=111.50, year=2017)\n", + "\n", + "# Create Plotly graph\n", + "edge_trace = go.Scatter(x=[], y=[], line=dict(color='#888'), hoverinfo='text', mode='lines')\n", + "hovertext = []\n", + "\n", + "for edge in G.edges(data=True):\n", + " x0, y0 = G.nodes[edge[0]]['Age'], G.nodes[edge[0]]['Weight']\n", + " x1, y1 = G.nodes[edge[1]]['Age'], G.nodes[edge[1]]['Weight']\n", + " edge_trace['x'] += tuple([x0, x1, None])\n", + " edge_trace['y'] += tuple([y0, y1, None])\n", + " hovertext.append(f\"Amount: {edge[2]['amount']:.2f}, Weight: {edge[2]['weight']:.2f}\")\n", + "\n", + "edge_trace['hovertext'] = hovertext\n", + "\n", + "node_trace = go.Scatter(x=[], y=[], text=[], mode='markers', hoverinfo='text', marker=dict(showscale=True, colorscale='YlGnBu', size=10))\n", + "\n", + "for node in G.nodes():\n", + " x, y = G.nodes[node]['Age'], G.nodes[node]['Weight']\n", + " node_trace['x'] += tuple([x])\n", + " node_trace['y'] += tuple([y])\n", + " node_info = node + '
' + 'Age: ' + str(G.nodes[node]['Age']) + '
' + 'Weight: ' + str(G.nodes[node]['Weight'])\n", + " node_trace['text'] += tuple([node_info])\n", + "\n", + "fig = go.Figure(data=[edge_trace, node_trace],\n", + " layout=go.Layout(\n", + " title='
Network graph made with Plotly',\n", + " titlefont=dict(size=16),\n", + " showlegend=False,\n", + " hovermode='closest',\n", + " margin=dict(b=20,l=5,r=5,t=40),\n", + " xaxis=dict(showgrid=False, zeroline=False, showticklabels=False),\n", + " yaxis=dict(showgrid=False, zeroline=False, showticklabels=False)))\n", + "\n", + "fig.show()\n" ] }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 58, "metadata": {}, "outputs": [ { "data": { - "text/plain": [ - "{'REPUBLICAN STATE LEADERSHIP COMMITTEE MICHIGAN PAC': Text(-0.071782758799796, -0.3387166453182715, 'REPUBLICAN STATE LEADERSHIP COMMITTEE MICHIGAN PAC'),\n", - " 'Paa Pac': Text(0.06023249378587841, -0.07946204618171311, 'Paa Pac'),\n", - " 'UNITED FOOD AND COMMERCIAL WORKERS ACTIVE BALLOT CLUB': Text(-0.12554712442237967, 0.08789304420689323, 'UNITED FOOD AND COMMERCIAL WORKERS ACTIVE BALLOT CLUB'),\n", - " 'COMMITTEE TO ELECT DR PATRICIA BERNARD': Text(-0.40486733116122986, -0.04769565353200762, 'COMMITTEE TO ELECT DR PATRICIA BERNARD'),\n", - " 'Pabar Pac (Pa Bar Assn)': Text(-0.6714326170558735, 0.21693950702464565, 'Pabar Pac (Pa Bar Assn)'),\n", - " 'Ugi Utilities Inc/Ugi Energy Services Llc Pac': Text(1.0, -0.38838038123915186, 'Ugi Utilities Inc/Ugi Energy Services Llc Pac'),\n", - " 'Pa Fraternal Order Of Police Pac': Text(0.5897482153166077, -0.2569656851069028, 'Pa Fraternal Order Of Police Pac'),\n", - " 'MICHIGAN ASSOCIATION OF NURSE ANESTHETISTS PAC': Text(-0.27784326029554446, 0.2828712220763738, 'MICHIGAN ASSOCIATION OF NURSE ANESTHETISTS PAC'),\n", - " 'Citizens For Kail': Text(-0.09850761736766293, 0.5235166380701339, 'Citizens For Kail')}" - ] - }, - "execution_count": 11, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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zip: 48706-0000
", + "Name: matthew burgess
donor_id: de98dec5-b8d3-4701-a9dd-a254aca2c4cf
recipient_id: 4844870e-39f8-41d7-8a41-a824d5dd9998
full_name: matthew burgess
recipient_name: reproductive freedom for all
address: 8823 SPECTRUM CENTER BLVD 2313
city: SAN DIEGO
classification: neutral
entity_type: Individual
first_name: MATTHEW
id: de98dec5-b8d3-4701-a9dd-a254aca2c4cf
last_name: BURGESS
state: CA
zip: 92123-0000
", + "Name: teresa robertson
donor_id: dcf2b3a5-ddf4-4027-8a75-4477893854ff
recipient_id: 4844870e-39f8-41d7-8a41-a824d5dd9998
full_name: teresa robertson
recipient_name: reproductive freedom for all
address: 7101 RIVER GLEN DR SE
city: CALEDONIA
classification: neutral
entity_type: Individual
first_name: TERESA
id: dcf2b3a5-ddf4-4027-8a75-4477893854ff
last_name: ROBERTSON
state: MI
zip: 49316-8136
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" ] }, "metadata": {}, @@ -2330,65 +3914,7506 @@ } ], "source": [ - "G = nx.from_pandas_edgelist(sample_df,source='name',target='donations_to',edge_attr=['donations','received'])\n", - "G.nodes()\n", - "pos=nx.spring_layout(G)\n", - "weights = list(nx.get_edge_attributes(G,'donations').values())\n", - "weights = [i/5000 for i in weights]\n", - "node_color = [G.degree(v) for v in G] \n", - "#node_size = [0.0005 * nx.get_node_attributes(G, 'donations')[v] for v in G] \n", - "nx.draw_networkx_nodes(G, pos, node_color=node_color)#, node_size=node_size) \n", - "nx.draw_networkx_edges(G, pos, width=weights)\n", - "nx.draw_networkx_labels(G, pos)" + "def create_network_nodes(df: pd.DataFrame) -> nx.MultiDiGraph:\n", + " G = nx.MultiDiGraph()\n", + " \n", + " # Define columns for edge attributes\n", + " edge_columns = ['amount', 'donor_office', 'office_sought', 'party', 'purpose', 'transaction_id', 'transaction_type', 'year']\n", + " # Define columns for node attributes\n", + " node_columns = ['donor_id', 'recipient_id', 'full_name', 'recipient_name', 'address', 'city', 'classification', 'company', 'donor_type', 'entity_type', 'first_name', 'id', 'last_name', 'occupation', 'recipient_type', 'state', 'zip']\n", + " \n", + " for _, row in df.iterrows(): \n", + " # Add nodes\n", + " G.add_node(row['full_name'], **row[node_columns].dropna().to_dict())\n", + " G.add_node(row['recipient_name'], classification='neutral') # Adding recipient nodes with default classification\n", + "\n", + " # Add edges\n", + " edge_attributes = row[edge_columns].dropna().to_dict()\n", + " G.add_edge(row['full_name'], row['recipient_name'], **edge_attributes)\n", + " \n", + " return G\n", + "\n", + "def plot_network_graph(G: nx.MultiDiGraph):\n", + " edge_trace = go.Scatter(x=[], y=[], line=dict(color='#888'), hoverinfo='text', mode='lines')\n", + " hovertext = []\n", + "\n", + " for edge in G.edges(data=True):\n", + " source = edge[0]\n", + " target = edge[1]\n", + " hovertext.append(f\"Amount: {edge[2]['amount']:.2f}\")\n", + "\n", + " edge_trace['hovertext'] = hovertext\n", + "\n", + " node_trace = go.Scatter(x=[], y=[], text=[], mode='markers', hoverinfo='text', marker=dict(showscale=True, colorscale='YlGnBu', size=10))\n", + " node_trace['marker']['color'] = []\n", + "\n", + " for node in G.nodes():\n", + " node_info = f\"Name: {node}
\"\n", + " for key, value in G.nodes[node].items():\n", + " node_info += f\"{key}: {value}
\"\n", + " node_trace['text'] += tuple([node_info])\n", + " # Get the classification value for the node\n", + " classification = G.nodes[node].get('classification', 'neutral')\n", + " # Assign a color based on the classification value\n", + " if classification == 'c':\n", + " color = 'blue'\n", + " elif classification == 'f':\n", + " color = 'red'\n", + " else:\n", + " color = 'green' # Default color for unknown classification\n", + " node_trace['marker']['color'] += tuple([color])\n", + "\n", + " # Define layout settings\n", + " layout = go.Layout(\n", + " title='Network Graph Indicating Campaign Contributions from 2018-2022',\n", + " titlefont=dict(size=16),\n", + " showlegend=True,\n", + " hovermode='closest',\n", + " margin=dict(b=20, l=5, r=5, t=40),\n", + " xaxis=dict(showgrid=True, zeroline=True, showticklabels=False),\n", + " yaxis=dict(showgrid=True, zeroline=True, showticklabels=False)\n", + " )\n", + "\n", + " fig = go.Figure(data=[edge_trace, node_trace], layout=layout)\n", 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" ] }, - "execution_count": 12, "metadata": {}, - "output_type": "execute_result" + "output_type": "display_data" } ], "source": [ - "G.nodes['Citizens For Kail']" + "G = nx.random_geometric_graph(200, 0.125)\n", + "edge_x = []\n", + "edge_y = []\n", + "for edge in G.edges():\n", + " x0, y0 = G.nodes[edge[0]]['pos']\n", + " x1, y1 = G.nodes[edge[1]]['pos']\n", + " edge_x.append(x0)\n", + " edge_x.append(x1)\n", + " edge_x.append(None)\n", + " edge_y.append(y0)\n", + " edge_y.append(y1)\n", + " edge_y.append(None)\n", + "\n", + "edge_trace = go.Scatter(\n", + " x=edge_x, y=edge_y,\n", + " line=dict(width=0.5, color='#888'),\n", + " hoverinfo='none',\n", + " mode='lines')\n", + "\n", + "node_x = []\n", + "node_y = []\n", + "for node in G.nodes():\n", + " x, y = G.nodes[node]['pos']\n", + " node_x.append(x)\n", + " node_y.append(y)\n", + "\n", + "node_trace = go.Scatter(\n", + " x=node_x, y=node_y,\n", + " mode='markers',\n", + " hoverinfo='text',\n", + " marker=dict(\n", + " showscale=True,\n", + " # colorscale options\n", + " #'Greys' | 'YlGnBu' | 'Greens' | 'YlOrRd' | 'Bluered' | 'RdBu' |\n", + " #'Reds' | 'Blues' | 'Picnic' | 'Rainbow' | 'Portland' | 'Jet' |\n", + " #'Hot' | 'Blackbody' | 'Earth' | 'Electric' | 'Viridis' |\n", + " colorscale='YlGnBu',\n", + " reversescale=True,\n", + " color=[],\n", + " size=10,\n", + " colorbar=dict(\n", + " thickness=15,\n", + " title='Node Connections',\n", + " xanchor='left',\n", + " titleside='right'\n", + " ),\n", + " line_width=2))\n", + "\n", + "node_adjacencies = []\n", + "node_text = []\n", + "for node, adjacencies in enumerate(G.adjacency()):\n", + " node_adjacencies.append(len(adjacencies[1]))\n", + " node_text.append('# of connections: '+str(len(adjacencies[1])))\n", + "\n", + "node_trace.marker.color = node_adjacencies\n", + "node_trace.text = node_text\n", + "\n", + "\n", + "fig = go.Figure(data=[edge_trace, node_trace],\n", + " layout=go.Layout(\n", + " title='Network graph made with Python',\n", + " titlefont_size=16,\n", + " showlegend=False,\n", + " hovermode='closest',\n", + " margin=dict(b=20,l=5,r=5,t=40),\n", + " annotations=[ dict(\n", + " text=\"graphs\",\n", + " showarrow=True,\n", + " xref=\"paper\", yref=\"paper\",\n", + " x=0.005, y=-0.002 ) ],\n", + " xaxis=dict(showgrid=False, zeroline=False, showticklabels=False),\n", + " yaxis=dict(showgrid=False, zeroline=False, showticklabels=False))\n", + " )\n", + "fig.show()" ] }, { "cell_type": "code", - "execution_count": 8, + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "G = nx.Graph()\n", + "G.add_node(0)\n", + "nx.set_node_attributes(G, \"red\", name=\"color\")\n", + "nx.set_node_attributes(G, 2, name=\"size\")\n", + "G.add_node(1)\n", + "nx.set_node_attributes(G, np.nan, name='color')\n", + "G.nodes[0]" + ] + }, + { + "cell_type": "code", + "execution_count": 16, "metadata": {}, "outputs": [ { - "ename": "KeyError", - "evalue": "'REPUBLICAN STATE LEADERSHIP COMMITTEE MICHIGAN PAC'", + "ename": "NetworkXError", + "evalue": "Invalid edge_attr argument: ['donations', 'received']", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mKeyError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[0;32mIn[8], line 7\u001b[0m\n\u001b[1;32m 4\u001b[0m node_color \u001b[38;5;241m=\u001b[39m [G\u001b[38;5;241m.\u001b[39mdegree(v) \u001b[38;5;28;01mfor\u001b[39;00m v \u001b[38;5;129;01min\u001b[39;00m G] \n\u001b[1;32m 5\u001b[0m \u001b[38;5;66;03m# node colour is a list of degrees of nodes \u001b[39;00m\n\u001b[0;32m----> 7\u001b[0m node_size \u001b[38;5;241m=\u001b[39m \u001b[43m[\u001b[49m\u001b[38;5;241;43m0.0005\u001b[39;49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43m \u001b[49m\u001b[43mnx\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_node_attributes\u001b[49m\u001b[43m(\u001b[49m\u001b[43mG\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mpopulation\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m[\u001b[49m\u001b[43mv\u001b[49m\u001b[43m]\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mfor\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mv\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;129;43;01min\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mG\u001b[49m\u001b[43m]\u001b[49m \n\u001b[1;32m 8\u001b[0m \u001b[38;5;66;03m# size of node is a list of population of cities \u001b[39;00m\n\u001b[1;32m 10\u001b[0m edge_width \u001b[38;5;241m=\u001b[39m [\u001b[38;5;241m0.0015\u001b[39m \u001b[38;5;241m*\u001b[39m G[u][v][\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mweight\u001b[39m\u001b[38;5;124m'\u001b[39m] \u001b[38;5;28;01mfor\u001b[39;00m u, v \u001b[38;5;129;01min\u001b[39;00m G\u001b[38;5;241m.\u001b[39medges()] \n", - "Cell \u001b[0;32mIn[8], line 7\u001b[0m, in \u001b[0;36m\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m 4\u001b[0m node_color \u001b[38;5;241m=\u001b[39m [G\u001b[38;5;241m.\u001b[39mdegree(v) \u001b[38;5;28;01mfor\u001b[39;00m v \u001b[38;5;129;01min\u001b[39;00m G] \n\u001b[1;32m 5\u001b[0m \u001b[38;5;66;03m# node colour is a list of degrees of nodes \u001b[39;00m\n\u001b[0;32m----> 7\u001b[0m node_size \u001b[38;5;241m=\u001b[39m [\u001b[38;5;241m0.0005\u001b[39m \u001b[38;5;241m*\u001b[39m \u001b[43mnx\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_node_attributes\u001b[49m\u001b[43m(\u001b[49m\u001b[43mG\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mpopulation\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m[\u001b[49m\u001b[43mv\u001b[49m\u001b[43m]\u001b[49m \u001b[38;5;28;01mfor\u001b[39;00m v \u001b[38;5;129;01min\u001b[39;00m G] \n\u001b[1;32m 8\u001b[0m \u001b[38;5;66;03m# size of node is a list of population of cities \u001b[39;00m\n\u001b[1;32m 10\u001b[0m edge_width \u001b[38;5;241m=\u001b[39m [\u001b[38;5;241m0.0015\u001b[39m \u001b[38;5;241m*\u001b[39m G[u][v][\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mweight\u001b[39m\u001b[38;5;124m'\u001b[39m] \u001b[38;5;28;01mfor\u001b[39;00m u, v \u001b[38;5;129;01min\u001b[39;00m G\u001b[38;5;241m.\u001b[39medges()] \n", - "\u001b[0;31mKeyError\u001b[0m: 'REPUBLICAN STATE LEADERSHIP COMMITTEE MICHIGAN PAC'" + "File \u001b[0;32m~/miniconda3/envs/climate_cabinet/lib/python3.11/site-packages/pandas/core/indexes/base.py:3653\u001b[0m, in \u001b[0;36mIndex.get_loc\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m 3652\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m-> 3653\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_engine\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_loc\u001b[49m\u001b[43m(\u001b[49m\u001b[43mcasted_key\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 3654\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m err:\n", + "File \u001b[0;32m~/miniconda3/envs/climate_cabinet/lib/python3.11/site-packages/pandas/_libs/index.pyx:147\u001b[0m, in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n", + "File \u001b[0;32m~/miniconda3/envs/climate_cabinet/lib/python3.11/site-packages/pandas/_libs/index.pyx:176\u001b[0m, in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n", + "File \u001b[0;32mpandas/_libs/hashtable_class_helper.pxi:7080\u001b[0m, in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n", + "File \u001b[0;32mpandas/_libs/hashtable_class_helper.pxi:7088\u001b[0m, in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n", + "\u001b[0;31mKeyError\u001b[0m: 'donations'", + "\nThe above exception was the direct cause of the following exception:\n", + "\u001b[0;31mKeyError\u001b[0m Traceback (most recent call last)", + "File \u001b[0;32m~/miniconda3/envs/climate_cabinet/lib/python3.11/site-packages/networkx/convert_matrix.py:455\u001b[0m, in \u001b[0;36mfrom_pandas_edgelist\u001b[0;34m(df, source, target, edge_attr, create_using, edge_key)\u001b[0m\n\u001b[1;32m 454\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 455\u001b[0m attribute_data \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mzip\u001b[39m(\u001b[38;5;241m*\u001b[39m\u001b[43m[\u001b[49m\u001b[43mdf\u001b[49m\u001b[43m[\u001b[49m\u001b[43mcol\u001b[49m\u001b[43m]\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mfor\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mcol\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;129;43;01min\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mattr_col_headings\u001b[49m\u001b[43m]\u001b[49m)\n\u001b[1;32m 456\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m (\u001b[38;5;167;01mKeyError\u001b[39;00m, \u001b[38;5;167;01mTypeError\u001b[39;00m) \u001b[38;5;28;01mas\u001b[39;00m err:\n", + "File \u001b[0;32m~/miniconda3/envs/climate_cabinet/lib/python3.11/site-packages/networkx/convert_matrix.py:455\u001b[0m, in \u001b[0;36m\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m 454\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 455\u001b[0m attribute_data \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mzip\u001b[39m(\u001b[38;5;241m*\u001b[39m[\u001b[43mdf\u001b[49m\u001b[43m[\u001b[49m\u001b[43mcol\u001b[49m\u001b[43m]\u001b[49m \u001b[38;5;28;01mfor\u001b[39;00m col \u001b[38;5;129;01min\u001b[39;00m attr_col_headings])\n\u001b[1;32m 456\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m (\u001b[38;5;167;01mKeyError\u001b[39;00m, \u001b[38;5;167;01mTypeError\u001b[39;00m) \u001b[38;5;28;01mas\u001b[39;00m err:\n", + "File \u001b[0;32m~/miniconda3/envs/climate_cabinet/lib/python3.11/site-packages/pandas/core/frame.py:3761\u001b[0m, in \u001b[0;36mDataFrame.__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m 3760\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_getitem_multilevel(key)\n\u001b[0;32m-> 3761\u001b[0m indexer \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcolumns\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_loc\u001b[49m\u001b[43m(\u001b[49m\u001b[43mkey\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 3762\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m is_integer(indexer):\n", + "File \u001b[0;32m~/miniconda3/envs/climate_cabinet/lib/python3.11/site-packages/pandas/core/indexes/base.py:3655\u001b[0m, in \u001b[0;36mIndex.get_loc\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m 3654\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m err:\n\u001b[0;32m-> 3655\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m(key) \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01merr\u001b[39;00m\n\u001b[1;32m 3656\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m:\n\u001b[1;32m 3657\u001b[0m \u001b[38;5;66;03m# If we have a listlike key, _check_indexing_error will raise\u001b[39;00m\n\u001b[1;32m 3658\u001b[0m \u001b[38;5;66;03m# InvalidIndexError. Otherwise we fall through and re-raise\u001b[39;00m\n\u001b[1;32m 3659\u001b[0m \u001b[38;5;66;03m# the TypeError.\u001b[39;00m\n", + "\u001b[0;31mKeyError\u001b[0m: 'donations'", + "\nThe above exception was the direct cause of the following exception:\n", + "\u001b[0;31mNetworkXError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[16], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m G \u001b[38;5;241m=\u001b[39m \u001b[43mnx\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfrom_pandas_edgelist\u001b[49m\u001b[43m(\u001b[49m\u001b[43msample_df\u001b[49m\u001b[43m,\u001b[49m\u001b[43msource\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mname\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43mtarget\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mdonations_to\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43medge_attr\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mdonations\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mreceived\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 2\u001b[0m G\u001b[38;5;241m.\u001b[39mnodes()\n\u001b[1;32m 3\u001b[0m pos\u001b[38;5;241m=\u001b[39mnx\u001b[38;5;241m.\u001b[39mspring_layout(G)\n", + "File \u001b[0;32m~/miniconda3/envs/climate_cabinet/lib/python3.11/site-packages/networkx/utils/backends.py:412\u001b[0m, in \u001b[0;36m_dispatch.__call__\u001b[0;34m(self, backend, *args, **kwargs)\u001b[0m\n\u001b[1;32m 409\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m__call__\u001b[39m(\u001b[38;5;28mself\u001b[39m, \u001b[38;5;241m/\u001b[39m, \u001b[38;5;241m*\u001b[39margs, backend\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs):\n\u001b[1;32m 410\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m backends:\n\u001b[1;32m 411\u001b[0m \u001b[38;5;66;03m# Fast path if no backends are installed\u001b[39;00m\n\u001b[0;32m--> 412\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43morig_func\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 414\u001b[0m \u001b[38;5;66;03m# Use `backend_name` in this function instead of `backend`\u001b[39;00m\n\u001b[1;32m 415\u001b[0m backend_name \u001b[38;5;241m=\u001b[39m backend\n", + "File \u001b[0;32m~/miniconda3/envs/climate_cabinet/lib/python3.11/site-packages/networkx/convert_matrix.py:458\u001b[0m, in \u001b[0;36mfrom_pandas_edgelist\u001b[0;34m(df, source, target, edge_attr, create_using, edge_key)\u001b[0m\n\u001b[1;32m 456\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m (\u001b[38;5;167;01mKeyError\u001b[39;00m, \u001b[38;5;167;01mTypeError\u001b[39;00m) \u001b[38;5;28;01mas\u001b[39;00m err:\n\u001b[1;32m 457\u001b[0m msg \u001b[38;5;241m=\u001b[39m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mInvalid edge_attr argument: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00medge_attr\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m--> 458\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m nx\u001b[38;5;241m.\u001b[39mNetworkXError(msg) \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01merr\u001b[39;00m\n\u001b[1;32m 460\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m g\u001b[38;5;241m.\u001b[39mis_multigraph():\n\u001b[1;32m 461\u001b[0m \u001b[38;5;66;03m# => append the edge keys from the df to the bundled data\u001b[39;00m\n\u001b[1;32m 462\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m edge_key \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n", + "\u001b[0;31mNetworkXError\u001b[0m: Invalid edge_attr argument: ['donations', 'received']" ] - }, - { - "data": { - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" } ], + "source": [ + "G = nx.from_pandas_edgelist(sample_df,source='name',target='donations_to',edge_attr=['donations','received'])\n", + "G.nodes()\n", + "pos=nx.spring_layout(G)\n", + "weights = list(nx.get_edge_attributes(G,'donations').values())\n", + "weights = [i/5000 for i in weights]\n", + "node_color = [G.degree(v) for v in G] \n", + "#node_size = [0.0005 * nx.get_node_attributes(G, 'donations')[v] for v in G] \n", + "nx.draw_networkx_nodes(G, pos, node_color=node_color)#, node_size=node_size) \n", + "nx.draw_networkx_edges(G, pos, width=weights)\n", + "nx.draw_networkx_labels(G, pos)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "\n", "# fixing the size of the figure \n", @@ -2414,20 +11439,9 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{'color': 'white'}" - ] - }, - "execution_count": 26, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "G = nx.MultiDiGraph()\n", "G.add_node(0)\n", @@ -2440,20 +11454,9 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{'color': 'white', 'age': 4}" - ] - }, - "execution_count": 28, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "G.add_node(2)\n", "nx.set_node_attributes(G, 4, name='age')\n", From 96b8e0b33b6d7f9cd9b9ac70d5de1f1ededca7b8 Mon Sep 17 00:00:00 2001 From: Alan Mburu Kagiri Date: Mon, 4 Mar 2024 12:21:22 -0600 Subject: [PATCH 13/24] updated network graph work --- utils/network.py | 83 ++++++++++++++++++++++++++++++++++++++++++------ 1 file changed, 73 insertions(+), 10 deletions(-) diff --git a/utils/network.py b/utils/network.py index 88572af..d6222cc 100644 --- a/utils/network.py +++ b/utils/network.py @@ -29,24 +29,77 @@ def name_identifier(uuid: str, dfs: list[pd.DataFrame]) -> str: return None +def combine_datasets_for_network_graph(dfs: list[pd.DataFrame]) -> pd.DataFrame: + """Combines the 3 dataframes into a single dataframe to create the graph + + Given the inds, orgs, and transactions dataframes, the func first finds the + recipient_id in the transaction dataframe in either the org or inds + dataframes and adds the name of the recipient to the transaction df. Then, + the inds and orgs dfs are merged with the transaction df and concatenated + with the contributions amount aggregated, making a final dataframe of the + merged transactions and entity dataframes. + + Args: + list of dataframes in the order: [inds_df, orgs_df, transactions_df] + Transactions dataframe with at least column: 'recipient_id' + Individuals dataframe with at least column: 'full_name' + Organizations dataframe with at least column: 'name' + + Returns + A merged dataframe with aggregate contribution amounts between entitites + """ + + inds_df, orgs_df, transactions_df = dfs + + # first update the transactions df to have a recipient name tied to id + transactions_df["recipient_name"] = transactions_df["recipient_id"].apply( + name_identifier, args=([orgs_df, inds_df],) + ) + + # next, merge the inds_df and orgs_df with the transactions_df + inds_trans_df = pd.merge( + inds_df, transactions_df, how="left", left_on="id", right_on="donor_id" + ) + inds_trans_df = inds_trans_df.dropna(subset=["amount"]) + orgs_trans_df = pd.merge( + orgs_df, transactions_df, how="left", left_on="id", right_on="donor_id" + ) + orgs_trans_df = orgs_trans_df.dropna(subset=["amount"]) + orgs_trans_df = orgs_trans_df.rename(columns={"name": "full_name"}) + + # concatenated the merged dfs + merged_df = pd.concat([orgs_trans_df, inds_trans_df]) + + # lastly, create the final dataframe with aggregated attributes + attribute_cols = merged_df.columns.difference( + ["donor_id", "recipient_id", "full_name", "recipient_name"] + ) + agg_functions = { + col: "sum" if col == "amount" else "first" for col in attribute_cols + } + aggreg_df = ( + merged_df.groupby( + ["donor_id", "recipient_id", "full_name", "recipient_name"] + ) + .agg(agg_functions) + .reset_index() + ) + + return aggreg_df + + def create_network_graph(df: pd.DataFrame) -> nx.MultiDiGraph: """Takes in a dataframe and generates a MultiDiGraph where the nodes are entity names, and the rest of the dataframe columns make the node attributes Args: - df: a pandas dataframe (complete_individuals_table / - complete_organizations_table) + df: a pandas dataframe with merged information from the inds, orgs, & + transactions dataframes Returns: A Networkx MultiDiGraph with nodes and edges """ G = nx.MultiDiGraph() - # first check if df is individuals or organizations dataset - if "name" in df.columns: - node_name = "name" - else: - node_name = "full_name" - edge_columns = [ "office_sought", "purpose", @@ -60,7 +113,7 @@ def create_network_graph(df: pd.DataFrame) -> nx.MultiDiGraph: for _, row in df.iterrows(): # add node attributes based on the columns relevant to the entity G.add_node( - row[node_name], + row["full_name"], **row[df.columns.difference(edge_columns)].dropna().to_dict(), ) # add the recipient as a node @@ -68,7 +121,7 @@ def create_network_graph(df: pd.DataFrame) -> nx.MultiDiGraph: # add the edge attributes between two nodes edge_attributes = row[edge_columns].dropna().to_dict() - G.add_edge(row[node_name], row["recipient_name"], **edge_attributes) + G.add_edge(row["full_name"], row["recipient_name"], **edge_attributes) return G @@ -102,11 +155,21 @@ def plot_network_graph(G: nx.MultiDiGraph): marker=dict(showscale=True, colorscale="YlGnBu", size=10), ) + node_trace["marker"]["color"] = [] for node in G.nodes(): node_info = f"Name: {node}
" for key, value in G.nodes[node].items(): node_info += f"{key}: {value}
" node_trace["text"] += tuple([node_info]) + classification = G.nodes[node].get("classification", "neutral") + # Assign a color based on the classification value + if classification == "c": + color = "blue" + elif classification == "f": + color = "red" + else: + color = "green" # Default color for unknown/neutral classification + node_trace["marker"]["color"] += tuple([color]) # Define layout settings layout = go.Layout( From cdf035a93e0ebac357bf73e9d45842af7ffc6770 Mon Sep 17 00:00:00 2001 From: Alan Mburu Kagiri Date: Mon, 4 Mar 2024 16:15:40 -0600 Subject: [PATCH 14/24] updated visualizations for the graph --- utils/network.py | 39 +++++++++++++++++++++++++++++++-------- 1 file changed, 31 insertions(+), 8 deletions(-) diff --git a/utils/network.py b/utils/network.py index d6222cc..3fa86ea 100644 --- a/utils/network.py +++ b/utils/network.py @@ -84,7 +84,7 @@ def combine_datasets_for_network_graph(dfs: list[pd.DataFrame]) -> pd.DataFrame: .agg(agg_functions) .reset_index() ) - + aggreg_df = aggreg_df.drop(["id"], axis=1) return aggreg_df @@ -136,16 +136,34 @@ def plot_network_graph(G: nx.MultiDiGraph): Returns: None. Creates a plotly graph """ edge_trace = go.Scatter( - x=[], y=[], line=dict(color="#888"), hoverinfo="text", mode="lines" + x=(), + y=(), + line=dict(color="#888", width=1.5), + hoverinfo="text", + mode="lines+markers", ) hovertext = [] + pos = nx.spring_layout(G) for edge in G.edges(data=True): - # donor = edge[0], recipient = edge[1] + source = edge[0] + target = edge[1] hovertext.append(f"Amount: {edge[2]['amount']:.2f}") + # Adding coordinates of source and target nodes to edge_trace + edge_trace["x"] += ( + pos[source][0], + pos[target][0], + None, + ) # None creates a gap between line segments + edge_trace["y"] += (pos[source][1], pos[target][1], None) edge_trace["hovertext"] = hovertext + # Define arrow symbol for edges + edge_trace["marker"] = dict( + symbol="arrow", color="#888", size=10, angleref="previous" + ) + node_trace = go.Scatter( x=[], y=[], @@ -154,13 +172,14 @@ def plot_network_graph(G: nx.MultiDiGraph): hoverinfo="text", marker=dict(showscale=True, colorscale="YlGnBu", size=10), ) - node_trace["marker"]["color"] = [] + for node in G.nodes(): node_info = f"Name: {node}
" for key, value in G.nodes[node].items(): node_info += f"{key}: {value}
" node_trace["text"] += tuple([node_info]) + # Get the classification value for the node classification = G.nodes[node].get("classification", "neutral") # Assign a color based on the classification value if classification == "c": @@ -168,18 +187,22 @@ def plot_network_graph(G: nx.MultiDiGraph): elif classification == "f": color = "red" else: - color = "green" # Default color for unknown/neutral classification + color = "green" # Default color for unknown classification node_trace["marker"]["color"] += tuple([color]) + # Add node positions to the trace + node_trace["x"] += tuple([pos[node][0]]) + node_trace["y"] += tuple([pos[node][1]]) + # Define layout settings layout = go.Layout( title="Network Graph Indicating Campaign Contributions from 2018-2022", titlefont=dict(size=16), - showlegend=False, + showlegend=True, hovermode="closest", margin=dict(b=20, l=5, r=5, t=40), - xaxis=dict(showgrid=False, zeroline=False, showticklabels=False), - yaxis=dict(showgrid=False, zeroline=False, showticklabels=False), + xaxis=dict(showgrid=True, zeroline=True, showticklabels=False), + yaxis=dict(showgrid=True, zeroline=True, showticklabels=False), ) fig = go.Figure(data=[edge_trace, node_trace], layout=layout) From 74996a56dc8e04007bef8986f139a9f658bdf7d5 Mon Sep 17 00:00:00 2001 From: Alan Mburu Kagiri Date: Mon, 4 Mar 2024 17:47:39 -0600 Subject: [PATCH 15/24] updates to the README files under the output and data directories --- data/README.md | 9 +++++++++ output/README.md | 1 + 2 files changed, 10 insertions(+) diff --git a/data/README.md b/data/README.md index 5326bff..9c154f7 100644 --- a/data/README.md +++ b/data/README.md @@ -160,3 +160,12 @@ contribution data and READMEs in a Google Drive for the duration of this project 3. The Finance Report states that a record must be kept for any contribution over \$10.00, but “Contributions and receipts of \$50.00 or less per contributor, during the reporting period, need not be itemized on the report” … this might mean that if 1,000 people for instance donate \$50 or less, there could be potentially thousands/tens of thousands of \$ not shown on the data, even though this information is recorded. This means that the total contributions that filers itemize does not necessarily reflect the total contributions they received. 4. Transparency USA has aggregated data on the contributions of individuals and committees. This could be a helpful source to cross-check the data and potentially help alleviate the debt-contribution issue. Pennsylvania' Dept. of State also offers a detailed website that shows all the aggregated contributions made and received, expenditures made, debts, and receipts. The catch is one must know which candidate they are looking for as it's a searchable database, but it can be very helpful for cross-matching and verification. Here's the link :https://www.campaignfinanceonline.pa.gov/Pages/CFReportSearch.aspx + +## classified_data +### Summary +- The classified_data subdirectory consists of 3 files: 'classified_individuals_v1', 'classified_organizations_v1' & 'transactions_v1'. These files are derived from the record_linkage pipeline, which mainly adds a classification column to the individuals and organizations entities that reflects the entity's affiliation with the fossil-fuel industry, clean-energy industry, or neutrality. These take the form of 'f' for fossil-fuel, 'c' for clean-energy, and 'neutral' for neutrality' + +### Format +- The 'classified_individuals_v1' dataset comprises of the following columns: ['id', 'first_name', 'last_name', 'full_name', 'entity_type', 'state','party', 'company', 'occupation', 'address', 'zip', 'city','classification']. As noted, the 'classification' column is the added column. + +- The 'classified_organization_v1' dataset comprises of the following columns: ['id', 'name', 'state', 'entity_type', 'classification']. As noted, the 'classification' column is the added column diff --git a/output/README.md b/output/README.md index 932298f..5c511d5 100644 --- a/output/README.md +++ b/output/README.md @@ -1,2 +1,3 @@ # Output README --- +'deduplicated_UUIDs.csv' : Following record linkage work in the record_linkage pipeline, this file stores all the original uuids, and indicates the uuids to which the deduplicated uuids have been matched to. From 0cebc4ce893ba8fea4bbcd1767ef987af2aa0d6a Mon Sep 17 00:00:00 2001 From: Alan Mburu Kagiri Date: Mon, 4 Mar 2024 18:31:46 -0600 Subject: [PATCH 16/24] latest version of networkx work --- utils/network.py | 46 ++++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 46 insertions(+) diff --git a/utils/network.py b/utils/network.py index 3fa86ea..5b95b2b 100644 --- a/utils/network.py +++ b/utils/network.py @@ -207,3 +207,49 @@ def plot_network_graph(G: nx.MultiDiGraph): fig = go.Figure(data=[edge_trace, node_trace], layout=layout) fig.show() + + +# create pipeline + + +def construct_network_graph( + start_year: int, end_year: int, dfs: list[pd.DataFrame] +): + """Runs the network construction pipeline starting from 3 dataframes + + Args: + start_year & end_year: the range of the desired data + + Returns: + """ + inds_df, orgs_df, transactions_df = dfs + transactions_df = transactions_df.loc[ + (transactions_df.year >= start_year) + & (transactions_df.year <= end_year) + ] + + aggreg_df = combine_datasets_for_network_graph( + [inds_df, orgs_df, transactions_df] + ) + G = create_network_graph(aggreg_df) + plot_network_graph(G) + nx.write_adjlist(G, "Network Graph Node Data") + + +def main(): + """""" + text = input( + "Provide a range of desired years to extract data. Format is year1, \ + year2. Ex: 2018, 2023" + ) + + assert len(text == 2) + start_year, end_year = text.split(",") + construct_network_graph( + start_year, + end_year, + ) + + +if __name__ == "__main__": + construct_network_graph(1998, 2023) From 9c5ff3cfebaef805ee700263579c9c54ec3280b2 Mon Sep 17 00:00:00 2001 From: Alan Mburu Kagiri Date: Mon, 4 Mar 2024 20:52:51 -0600 Subject: [PATCH 17/24] making revisions to data/README and network.py per Avery's feedback --- data/README.md | 9 --------- utils/network.py | 28 ++++++++++------------------ 2 files changed, 10 insertions(+), 27 deletions(-) diff --git a/data/README.md b/data/README.md index 9c154f7..5326bff 100644 --- a/data/README.md +++ b/data/README.md @@ -160,12 +160,3 @@ contribution data and READMEs in a Google Drive for the duration of this project 3. The Finance Report states that a record must be kept for any contribution over \$10.00, but “Contributions and receipts of \$50.00 or less per contributor, during the reporting period, need not be itemized on the report” … this might mean that if 1,000 people for instance donate \$50 or less, there could be potentially thousands/tens of thousands of \$ not shown on the data, even though this information is recorded. This means that the total contributions that filers itemize does not necessarily reflect the total contributions they received. 4. Transparency USA has aggregated data on the contributions of individuals and committees. This could be a helpful source to cross-check the data and potentially help alleviate the debt-contribution issue. Pennsylvania' Dept. of State also offers a detailed website that shows all the aggregated contributions made and received, expenditures made, debts, and receipts. The catch is one must know which candidate they are looking for as it's a searchable database, but it can be very helpful for cross-matching and verification. Here's the link :https://www.campaignfinanceonline.pa.gov/Pages/CFReportSearch.aspx - -## classified_data -### Summary -- The classified_data subdirectory consists of 3 files: 'classified_individuals_v1', 'classified_organizations_v1' & 'transactions_v1'. These files are derived from the record_linkage pipeline, which mainly adds a classification column to the individuals and organizations entities that reflects the entity's affiliation with the fossil-fuel industry, clean-energy industry, or neutrality. These take the form of 'f' for fossil-fuel, 'c' for clean-energy, and 'neutral' for neutrality' - -### Format -- The 'classified_individuals_v1' dataset comprises of the following columns: ['id', 'first_name', 'last_name', 'full_name', 'entity_type', 'state','party', 'company', 'occupation', 'address', 'zip', 'city','classification']. As noted, the 'classification' column is the added column. - -- The 'classified_organization_v1' dataset comprises of the following columns: ['id', 'name', 'state', 'entity_type', 'classification']. As noted, the 'classification' column is the added column diff --git a/utils/network.py b/utils/network.py index 5b95b2b..0dcc5a8 100644 --- a/utils/network.py +++ b/utils/network.py @@ -14,13 +14,10 @@ def name_identifier(uuid: str, dfs: list[pd.DataFrame]) -> str: The entity's name """ for df in dfs: - # first, check orgs df: if "name" in df.columns: name_in_org = df.loc[df["id"] == uuid] if len(name_in_org) > 0: return name_in_org.iloc[0]["name"] - # theoretically it must be in inds if not in orgs, but for the sample - # data this might not be the case if "full_name" in df.columns: name_in_ind = df.loc[df["id"] == uuid] @@ -30,20 +27,18 @@ def name_identifier(uuid: str, dfs: list[pd.DataFrame]) -> str: def combine_datasets_for_network_graph(dfs: list[pd.DataFrame]) -> pd.DataFrame: - """Combines the 3 dataframes into a single dataframe to create the graph + """Combines the 3 dataframes into a single dataframe to create a graph - Given the inds, orgs, and transactions dataframes, the func first finds the - recipient_id in the transaction dataframe in either the org or inds - dataframes and adds the name of the recipient to the transaction df. Then, - the inds and orgs dfs are merged with the transaction df and concatenated - with the contributions amount aggregated, making a final dataframe of the - merged transactions and entity dataframes. + Given 3 dataframes, the func adds a 'recipient_name' column in the + transactions df, merges the dfs together to record transaction info between + entities, then concatenates the dfs into a final df of the merged + transactions and entity dfs. Args: list of dataframes in the order: [inds_df, orgs_df, transactions_df] - Transactions dataframe with at least column: 'recipient_id' - Individuals dataframe with at least column: 'full_name' - Organizations dataframe with at least column: 'name' + Transactions dataframe with column: 'recipient_id' + Individuals dataframe with column: 'full_name' + Organizations dataframe with column: 'name' Returns A merged dataframe with aggregate contribution amounts between entitites @@ -56,7 +51,7 @@ def combine_datasets_for_network_graph(dfs: list[pd.DataFrame]) -> pd.DataFrame: name_identifier, args=([orgs_df, inds_df],) ) - # next, merge the inds_df and orgs_df with the transactions_df + # next, merge the inds_df and orgs_df ids with the transactions_df donor_id inds_trans_df = pd.merge( inds_df, transactions_df, how="left", left_on="id", right_on="donor_id" ) @@ -208,10 +203,6 @@ def plot_network_graph(G: nx.MultiDiGraph): fig = go.Figure(data=[edge_trace, node_trace], layout=layout) fig.show() - -# create pipeline - - def construct_network_graph( start_year: int, end_year: int, dfs: list[pd.DataFrame] ): @@ -219,6 +210,7 @@ def construct_network_graph( Args: start_year & end_year: the range of the desired data + dfs: dataframes in the order: inds_df, orgs_df, transactions_df Returns: """ From 18a52ff3acbb8f2819de6cb6c9eef7cc69fe35c9 Mon Sep 17 00:00:00 2001 From: Alan Mburu Kagiri Date: Mon, 4 Mar 2024 20:54:30 -0600 Subject: [PATCH 18/24] making revisions to data/README and network.py per Avery's feedback --- utils/network.py | 1 + 1 file changed, 1 insertion(+) diff --git a/utils/network.py b/utils/network.py index 0dcc5a8..c391003 100644 --- a/utils/network.py +++ b/utils/network.py @@ -203,6 +203,7 @@ def plot_network_graph(G: nx.MultiDiGraph): fig = go.Figure(data=[edge_trace, node_trace], layout=layout) fig.show() + def construct_network_graph( start_year: int, end_year: int, dfs: list[pd.DataFrame] ): From 083f92f84554b5d60cbc5c7e38e4a8a1562f288a Mon Sep 17 00:00:00 2001 From: Alan Mburu Kagiri Date: Tue, 5 Mar 2024 01:23:19 -0600 Subject: [PATCH 19/24] last minute modifications to network file. final version --- utils/network.py | 21 +++++++++++++++++---- 1 file changed, 17 insertions(+), 4 deletions(-) diff --git a/utils/network.py b/utils/network.py index c391003..5f1ada0 100644 --- a/utils/network.py +++ b/utils/network.py @@ -2,6 +2,16 @@ import pandas as pd import plotly.graph_objects as go +from utils.constants import BASE_FILEPATH + +inds_path = BASE_FILEPATH / "output" / "cleaned_individuals_table.csv" +orgs_path = BASE_FILEPATH / "output" / "cleaned_organizations_table.csv" +transactions_path = BASE_FILEPATH / "output" / "cleaned_transactions_table" + +inds_df = pd.read_csv(inds_path, low_memory=False) +orgs_df = pd.read_csv(orgs_path, low_memory=False) +transactions_df = pd.read_csv(transactions_path, low_memory=False) + def name_identifier(uuid: str, dfs: list[pd.DataFrame]) -> str: """Returns the name of the entity given the entity's uuid @@ -112,7 +122,7 @@ def create_network_graph(df: pd.DataFrame) -> nx.MultiDiGraph: **row[df.columns.difference(edge_columns)].dropna().to_dict(), ) # add the recipient as a node - G.nodes[row["recipient_name"]]["classification"] = "neutral" + G.add_node(row["recipient_name"], classification = "neutral") # add the edge attributes between two nodes edge_attributes = row[edge_columns].dropna().to_dict() @@ -236,11 +246,14 @@ def main(): year2. Ex: 2018, 2023" ) - assert len(text == 2) + assert len(text == 2), ( + "Wrong input for range of years. Format should be" + + " year1, year2. Ex: 1998,2023" + ) + start_year, end_year = text.split(",") construct_network_graph( - start_year, - end_year, + start_year, end_year, [inds_df, orgs_df, transactions_path] ) From 09aca55f9d39d4b7707d00b53dd55bb083444a4c Mon Sep 17 00:00:00 2001 From: Alan Mburu Kagiri Date: Tue, 5 Mar 2024 11:34:34 -0600 Subject: [PATCH 20/24] removing main() from file --- utils/network.py | 24 +----------------------- 1 file changed, 1 insertion(+), 23 deletions(-) diff --git a/utils/network.py b/utils/network.py index 5f1ada0..3978512 100644 --- a/utils/network.py +++ b/utils/network.py @@ -122,7 +122,7 @@ def create_network_graph(df: pd.DataFrame) -> nx.MultiDiGraph: **row[df.columns.difference(edge_columns)].dropna().to_dict(), ) # add the recipient as a node - G.add_node(row["recipient_name"], classification = "neutral") + G.add_node(row["recipient_name"], classification="neutral") # add the edge attributes between two nodes edge_attributes = row[edge_columns].dropna().to_dict() @@ -237,25 +237,3 @@ def construct_network_graph( G = create_network_graph(aggreg_df) plot_network_graph(G) nx.write_adjlist(G, "Network Graph Node Data") - - -def main(): - """""" - text = input( - "Provide a range of desired years to extract data. Format is year1, \ - year2. Ex: 2018, 2023" - ) - - assert len(text == 2), ( - "Wrong input for range of years. Format should be" - + " year1, year2. Ex: 1998,2023" - ) - - start_year, end_year = text.split(",") - construct_network_graph( - start_year, end_year, [inds_df, orgs_df, transactions_path] - ) - - -if __name__ == "__main__": - construct_network_graph(1998, 2023) From 269998cf46a29cf362c8ae13f658542e308163e6 Mon Sep 17 00:00:00 2001 From: Alan Mburu Kagiri Date: Tue, 5 Mar 2024 11:36:44 -0600 Subject: [PATCH 21/24] removing main() from file --- utils/network.py | 10 ---------- 1 file changed, 10 deletions(-) diff --git a/utils/network.py b/utils/network.py index 3978512..90f12a6 100644 --- a/utils/network.py +++ b/utils/network.py @@ -2,16 +2,6 @@ import pandas as pd import plotly.graph_objects as go -from utils.constants import BASE_FILEPATH - -inds_path = BASE_FILEPATH / "output" / "cleaned_individuals_table.csv" -orgs_path = BASE_FILEPATH / "output" / "cleaned_organizations_table.csv" -transactions_path = BASE_FILEPATH / "output" / "cleaned_transactions_table" - -inds_df = pd.read_csv(inds_path, low_memory=False) -orgs_df = pd.read_csv(orgs_path, low_memory=False) -transactions_df = pd.read_csv(transactions_path, low_memory=False) - def name_identifier(uuid: str, dfs: list[pd.DataFrame]) -> str: """Returns the name of the entity given the entity's uuid From d6167df68bd2e86a1f267807f044b6656ed88707 Mon Sep 17 00:00:00 2001 From: Alan Mburu Kagiri Date: Tue, 5 Mar 2024 12:03:07 -0600 Subject: [PATCH 22/24] updated README.md to show networkX portion of the pipeline --- README.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/README.md b/README.md index 879a41e..0a5fe1a 100644 --- a/README.md +++ b/README.md @@ -45,8 +45,8 @@ If you prefer to develop inside a container with VS Code then do the following s ### Record Linkage and Network Pipeline 1. Save the standardized tables "complete_individuals_table.csv", "complete_organizations_table.csv", and "complete_transactions_table.csv" (collected from the above pipeline or data from the project's Google Drive) in the following format: repo_root / "output" / "file" -2. **UPDATE:** Run the pipeline by calling ```make run-linkage-pipeline```. This pipeline will perform conservative record linkage, attempt to classify entities as neutral, fossil fuels, or clean energy, and an interactive network visual -3. The pipeline will output the deduplicated tables saved as "cleaned_individuals_table.csv", "cleaned_organizations_table.csv", and "cleaned_transactions_table.csv". A mapping file, "deduplicated_UUIDs" tracks the UUIDs designated as duplicates. +2. **UPDATE:** Run the pipeline by calling ```make run-linkage-pipeline```. This pipeline will perform conservative record linkage, attempt to classify entities as neutral, fossil fuels, or clean energy, convert the standardized tables into a NetworkX Graph, and show an interactive network visual. +3. The pipeline will output the deduplicated tables saved as "cleaned_individuals_table.csv", "cleaned_organizations_table.csv", and "cleaned_transactions_table.csv". A mapping file, "deduplicated_UUIDs" tracks the UUIDs designated as duplicates. The pipeline will also output "Network Graph Node Data", which is the NetworkX Graph object converted into an adjecency list. ## Repository Structure From c9752a0faf07d0a6a98b53be7754f435dd5a44f7 Mon Sep 17 00:00:00 2001 From: Avery Schoen <33437601+averyschoen@users.noreply.github.com> Date: Tue, 5 Mar 2024 12:06:54 -0600 Subject: [PATCH 23/24] Delete notebooks/Test.ipynb --- notebooks/Test.ipynb | 11495 ----------------------------------------- 1 file changed, 11495 deletions(-) delete mode 100644 notebooks/Test.ipynb diff --git a/notebooks/Test.ipynb b/notebooks/Test.ipynb deleted file mode 100644 index b9ac176..0000000 --- a/notebooks/Test.ipynb +++ /dev/null @@ -1,11495 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "import pandas as pd\n", - "import numpy as np\n", - "import networkx as nx\n", - "import matplotlib.pyplot as plt\n", - "import plotly.express as px\n", - "import plotly.graph_objects as go\n" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "orgs_df = pd.read_csv(\"../data/classified_data/classified_organizations_v1\").sample(10000)\n", - "inds_df = pd.read_csv(\"../data/classified_data/classified_individuals_v1\", low_memory=False).sample(10000)\n", - "transactions = pd.read_csv(\"../data/classified_data/transactions_v1\", low_memory=False)" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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idnamestateentity_typeclassification
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" - ], - "text/plain": [ - "Empty DataFrame\n", - "Columns: [id, name, state, entity_type, classification]\n", - "Index: []" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "orgs_df.loc[orgs_df.classification == 'f']" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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transaction_iddonor_idyearamountrecipient_idoffice_soughtpurposetransaction_typedonor_typerecipient_typedonor_office
07773a71e-9f67-438e-8313-80b1b75deeb44544b60d-da6b-4dd5-9efe-334152ccf1f120181000.0981a0414-b738-4e20-91b8-a29ee2cc7edfnonebob worsley for state senatecontribute to a candidate committeeNaNNaNNaN
195f74915-a945-491f-8751-8c970a76fc24946d7561-42a3-4a4b-b410-3a10271c9f1820181000.0981a0414-b738-4e20-91b8-a29ee2cc7edfnonedrew john for state housecontribute to a candidate committeeNaNNaNNaN
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" - ], - "text/plain": [ - " transaction_id donor_id \\\n", - "0 7773a71e-9f67-438e-8313-80b1b75deeb4 4544b60d-da6b-4dd5-9efe-334152ccf1f1 \n", - "1 95f74915-a945-491f-8751-8c970a76fc24 946d7561-42a3-4a4b-b410-3a10271c9f18 \n", - "\n", - " year amount recipient_id office_sought \\\n", - "0 2018 1000.0 981a0414-b738-4e20-91b8-a29ee2cc7edf none \n", - "1 2018 1000.0 981a0414-b738-4e20-91b8-a29ee2cc7edf none \n", - "\n", - " purpose transaction_type \\\n", - "0 bob worsley for state senate contribute to a candidate committee \n", - "1 drew john for state house contribute to a candidate committee \n", - "\n", - " donor_type recipient_type donor_office \n", - "0 NaN NaN NaN \n", - "1 NaN NaN NaN " - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "transactions.head(2)" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array(['neutral', 'f'], dtype=object)" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "inds_df.classification.unique()" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(9926, 9919, 10000, 10000)" - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "inds_ids = set(inds_df.id.tolist())\n", - "orgs_ids = set(orgs_df.id.tolist())\n", - "trans_donorids = set(transactions.donor_id.tolist())\n", - "trans_recepids = set(transactions.recipient_id.tolist())\n", - "ind_id_there, org_id_there = [], []\n", - "for ind_id in inds_ids:\n", - " if ind_id in trans_donorids:\n", - " ind_id_there.append(ind_id)\n", - " elif ind_id in trans_recepids:\n", - " ind_id_there.append(ind_id)\n", - "\n", - "for org_id in orgs_ids:\n", - " if org_id in trans_donorids:\n", - " org_id_there.append(org_id)\n", - " elif org_id in trans_recepids:\n", - " org_id_there.append(org_id)\n", - "\n", - "len(inds_ids), len(ind_id_there), len(orgs_ids), len(org_id_there)" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "['242d019c-e0ab-405e-8e77-abae7418b87f',\n", - " '8b2ad550-64a1-4975-8b77-5eb1f24a8871',\n", - " 'aee69307-194f-4c40-af3d-a55a34e1068e',\n", - " '55e5e946-6261-4f19-9752-fb58219b2e99',\n", - " '4faf251a-73d9-46ef-9e17-d3cf0a3052ae',\n", - " '3b5c0a9e-c6f2-44e9-ad05-fde071447564',\n", - " '3936bdf5-9a7a-462c-9e8c-9124f2bd7f57',\n", - " '13882059-3c74-4d9e-825d-a03a72b43b08',\n", - " '50c78f1a-3e9b-4996-a319-eef4fe01ccfb',\n", - " 'ae96f38f-68c8-47e3-95b3-c6f096d3c22e',\n", - " '74ba8a8a-7256-4eb3-b0f8-995f7a6319fb',\n", - " '12823a76-78e2-4b09-b606-859efaa5c8ef',\n", - " '9de9bf03-8c4a-4d2f-9a95-283b230ddfad',\n", - " '588593b9-9bba-4597-94d9-1b3a7fd5b402',\n", - " '5277b642-6bf0-4423-9350-3602ae51c6ac',\n", - " 'd98985b4-f55d-4ada-b279-0497e3176512',\n", - " 'c8586d36-f188-4684-aa99-193407d4d068',\n", - " '3798fda1-83cd-4e48-974a-e1a390060198',\n", - " 'a536b509-f052-4984-a35d-10397308daec',\n", - " '80996477-ce99-4f34-b5fc-bab4d676fc77',\n", - " 'cd1a740c-b1d7-4334-b335-925bd5708753',\n", - " '46af8908-f4e4-4041-9d1e-5b442d051921',\n", - " '2969075a-86d2-4b04-a991-a81832e096a0',\n", - " 'd0337f72-b701-4524-891b-c48ef6f771ec',\n", - " '591aa72b-511b-4dbb-a161-80458f257471']" - ] - }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "a = []\n", - "for ind_id in inds_ids:\n", - " if ((ind_id in trans_donorids) and (ind_id in trans_recepids)):\n", - " a.append(ind_id)\n", - "a" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "data = {'id':['50c7d9a1-b448-46a5-8e2d-cd15b3097360','50c7d9a1-b448-46a5-8e2d-cd15b3097360','50c7d9a1-b448-46a5-8e2d-cd15b3097360',\n", - " '62ea1e9c-ac12-400c-b3dc-519389c0f7d3','62ea1e9c-ac12-400c-b3dc-519389c0f7d3','62ea1e9c-ac12-400c-b3dc-519389c0f7d3',\n", - " 'd31df1ca-714e-4a82-9e88-1892c0451a71','d31df1ca-714e-4a82-9e88-1892c0451a71','62ea1e9c-ac12-400c-b3dc-519389c0f7d3',\n", - " '4db76e6e-f0d5-40eb-82de-6dbcdb562dd7','f71341d7-d27e-47eb-9b66-903af39d6cb5','c875d7de-94be-42f1-b994-dd89b114d51e',\n", - " '910c4d36-b036-469e-aa2a-ea4ff8855a6c','60d454d1-3773-4d88-80e9-132c161da0f0','1d2b5bc0-9385-4cd7-ac48-df43b3eca6fd',\n", - " '1d2b5bc0-9385-4cd7-ac48-df43b3eca6fd','1d2b5bc0-9385-4cd7-ac48-df43b3eca6fe','1d2b5bc0-9385-4cd7-ac48-df43b3eca6ff',\n", - " '1d2b5bc0-9385-4cd7-ac48-df43b3eca6fd'],\n", - " 'name':['REPUBLICAN STATE LEADERSHIP COMMITTEE MICHIGAN PAC','REPUBLICAN STATE LEADERSHIP COMMITTEE MICHIGAN PAC',\n", - " 'REPUBLICAN STATE LEADERSHIP COMMITTEE MICHIGAN PAC','UNITED FOOD AND COMMERCIAL WORKERS ACTIVE BALLOT CLUB',\n", - " 'UNITED FOOD AND COMMERCIAL WORKERS ACTIVE BALLOT CLUB','UNITED FOOD AND COMMERCIAL WORKERS ACTIVE BALLOT CLUB',\n", - " 'COMMITTEE TO ELECT DR PATRICIA BERNARD','COMMITTEE TO ELECT DR PATRICIA BERNARD','UNITED FOOD AND COMMERCIAL WORKERS ACTIVE BALLOT CLUB',\n", - " 'Ugi Utilities Inc/Ugi Energy Services Llc Pac','Pabar Pac (Pa Bar Assn)','Pa Fraternal Order Of Police Pac','Citizens For Kail',\n", - " 'Paa Pac','MICHIGAN ASSOCIATION OF NURSE ANESTHETISTS PAC','MICHIGAN ASSOCIATION OF NURSE ANESTHETISTS PAC',\n", - " 'MICHIGAN ASSOCIATION OF NURSE ANESTHETISTS PAC','MICHIGAN ASSOCIATION OF NURSE ANESTHETISTS PAC','Paa Pac'],\n", - " 'state':['MI','MI','MI','MI','MI','MI','MI','MI','MI','PA','PA','PA','PA','PA','MI','MI','MI','MI','PA'],\n", - " 'entity_type':['committee','committee','committee','committee','committee','committee','committee','committee','committee',\n", - " 'Organization','Organization','Organization','Organization','Organization','committee','committee','committee','committee','Organization']}\n", - "\n", - "sample_df = pd.DataFrame(data)\n", - "sample_df['donations'] = np.random.randint(100, 6000, sample_df.shape[0])\n", - "sample_df['donations_to'] = np.random.choice(sample_df.name.tolist(), size=len(sample_df))\n", - "sample_df['received'] = np.random.randint(0, 6000, sample_df.shape[0])\n", - "sample_df['donations_from'] = np.random.choice(sample_df.name.tolist(), size=len(sample_df))\n", - "sample_df.head(5)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Some Considerations to Remember Moving Forward:\n", - "1. The 'get_likely_name' function takes in 3 string inputs. The data is not clean and when there are NaN entries, the function is somehow inputing null values as strings, so a column that has \"Tim\", \"Walz\" and Nan in the first, last, and full name columns, is being combined as \"Tim Walz Nan\". When calling this function account for this possibility" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Playing Around with Graphs\n", - "\n", - "**Some considerations**\n", - "1. What attributes do we want each Node to Have?\n", - "- UUID, Name, Entity Type, Address, {from transactions table: money_donated and money_given}, affilition?\n", - "- Should transaction info also be included? If so, how would we show transaction info to multiple recipients / from multiple donors?" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Notes for Graphs\n", - "**Generating Graphs**\n", - "* nx.Graph() → the most simple undirected graph (edges going both ways)\n", - "* nx.DiGraph() → a graph with directed edges\n", - "* nx.MultiGraph() → multiple edges between nodes\n", - "* nx.MultiDiGraph() → the MultiGraph equivalent for directed graphs\n", - "\n", - "**Finding Centrality**\n", - "There are 4 main ways to find the centrality of a node (how important or frequent is a node / how influential are some donors potentially)\n", - "* nx.degree_centrality : based on the assumption that important nodes have many connections\n", - "* nx.closeness_centrality : based on the assumption that important nodes are close to other nodes. It is calculated as the sum of the path lengths from the given node to all other nodes. \n", - "* nx.eigenvector_centrality : assumes that important nodes connect other nodes. Considers the number of shortest paths between 2 nodes .For Graphs with a large number of nodes, the value of betweenness centrality is very high\n", - "* nx.betweeness_centrality : a measure of centrality in a graph based on shortest paths. For every pair of vertices in a connected graph, there exists at least one shortest path between the vertices such that either the number of edges that the path passes through (for unweighted graphs) or the sum of the weights of the edges (for weighted graphs) is minimized. The betweenness centrality for each vertex is the number of these shortest paths that pass through the vertex\n", - "* nx.pagerank : Page Rank Algorithm (developed by Google founders to measure the importance of webpages) assigns a score of importance to each node. Important nodes are those with many inlinks from important pages. It mainly works for Directed Networks\n", - "\n", - "**Finding Connections**\n", - "* nx.find_cliques (undirected graphs): finds the maximum subgraphs based on the number of interconnected nodes\n", - "* nx.k_core : A k-core is a maximal subgraph that contains nodes of degree k or more. Groups clusters meeting the threshold k (can be used as a toggle)\n", - "\n", - "**Sources**\n", - "* https://www.youtube.com/watch?v=VetBkjcm9Go\n", - "* https://www.activestate.com/blog/graph-theory-using-python-introduction-and-implementation/ \n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Things to think about\n", - "* Apply the deduplicated_uuids.csv info to the transactions table\n", - "* After doing a left join on the inds/orgs dataset with the transactions data, the recipient_id column needs to have a recipient_name column so that a new node can be created\n", - "* for ppl who have multiple donations {and so have various attributes like office_sought, purpose, transaction_type}, should this information be saved?" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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transaction_iddonor_idyearamountrecipient_idoffice_soughtpurposetransaction_typedonor_typerecipient_typedonor_officerecipient_name
07773a71e-9f67-438e-8313-80b1b75deeb44544b60d-da6b-4dd5-9efe-334152ccf1f120181000.0981a0414-b738-4e20-91b8-a29ee2cc7edfnonebob worsley for state senatecontribute to a candidate committeeNaNNaNNaN#1022 arizona committee of automotive retailers
195f74915-a945-491f-8751-8c970a76fc24946d7561-42a3-4a4b-b410-3a10271c9f1820181000.0981a0414-b738-4e20-91b8-a29ee2cc7edfnonedrew john for state housecontribute to a candidate committeeNaNNaNNaN#1022 arizona committee of automotive retailers
2d05f1763-132d-4717-addc-8ff6239ad4d9c8f98436-9562-48ed-b51f-45b2b217aad120181000.0981a0414-b738-4e20-91b8-a29ee2cc7edfnoneelect karen fann ld1contribute to a candidate committeeNaNNaNNaN#1022 arizona committee of automotive retailers
33dc3da30-6562-4755-bfad-6a26f1baec15b9965bc2-c94d-4f69-98d1-bc4f5ad701c520181000.0981a0414-b738-4e20-91b8-a29ee2cc7edfnoneelect noel campbell for housecontribute to a candidate committeeNaNNaNNaN#1022 arizona committee of automotive retailers
4a4340a2c-7b8a-4eeb-8290-746f0f436c83946d7561-42a3-4a4b-b410-3a10271c9f1820181000.0981a0414-b738-4e20-91b8-a29ee2cc7edfnoneclosed to new donationsrefund from contrib to a cand committeeNaNNaNNaN#1022 arizona committee of automotive retailers
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" - ], - "text/plain": [ - " transaction_id donor_id \\\n", - "0 7773a71e-9f67-438e-8313-80b1b75deeb4 4544b60d-da6b-4dd5-9efe-334152ccf1f1 \n", - "1 95f74915-a945-491f-8751-8c970a76fc24 946d7561-42a3-4a4b-b410-3a10271c9f18 \n", - "2 d05f1763-132d-4717-addc-8ff6239ad4d9 c8f98436-9562-48ed-b51f-45b2b217aad1 \n", - "3 3dc3da30-6562-4755-bfad-6a26f1baec15 b9965bc2-c94d-4f69-98d1-bc4f5ad701c5 \n", - "4 a4340a2c-7b8a-4eeb-8290-746f0f436c83 946d7561-42a3-4a4b-b410-3a10271c9f18 \n", - "\n", - " year amount recipient_id office_sought \\\n", - "0 2018 1000.0 981a0414-b738-4e20-91b8-a29ee2cc7edf none \n", - "1 2018 1000.0 981a0414-b738-4e20-91b8-a29ee2cc7edf none \n", - "2 2018 1000.0 981a0414-b738-4e20-91b8-a29ee2cc7edf none \n", - "3 2018 1000.0 981a0414-b738-4e20-91b8-a29ee2cc7edf none \n", - "4 2018 1000.0 981a0414-b738-4e20-91b8-a29ee2cc7edf none \n", - "\n", - " purpose transaction_type \\\n", - "0 bob worsley for state senate contribute to a candidate committee \n", - "1 drew john for state house contribute to a candidate committee \n", - "2 elect karen fann ld1 contribute to a candidate committee \n", - "3 elect noel campbell for house contribute to a candidate committee \n", - "4 closed to new donations refund from contrib to a cand committee \n", - "\n", - " donor_type recipient_type donor_office \\\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", - " recipient_name \n", - "0 #1022 arizona committee of automotive retailers \n", - "1 #1022 arizona committee of automotive retailers \n", - "2 #1022 arizona committee of automotive retailers \n", - "3 #1022 arizona committee of automotive retailers \n", - "4 #1022 arizona committee of automotive retailers " - ] - }, - "execution_count": 10, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "from utils.network import name_identifier\n", - "from utils.linkage import deduplicate_perfect_matches\n", - "transactions = transactions.loc[(transactions.recipient_id.isin(inds_df.id)) | \n", - " (transactions.recipient_id.isin(orgs_df.id)) |\n", - " (transactions.donor_id.isin(inds_df.id)) |\n", - " (transactions.donor_id.isin(inds_df.id))]\n", - "inds = deduplicate_perfect_matches(inds_df) \n", - "orgs = deduplicate_perfect_matches(orgs_df)\n", - "transactions[\"recipient_name\"] = transactions[\"recipient_id\"].apply(name_identifier, args=([orgs, inds],))\n", - "\n", - "transactions.head(5)" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "87" - ] - }, - "execution_count": 13, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "x = transactions.loc[transactions.donor_id.isin(inds_df.id)]\n", - "len(x.recipient_name.unique())" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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idfirst_namelast_namefull_nameentity_typestatepartycompanyoccupationaddress...yearamountrecipient_idoffice_soughtpurposetransaction_typedonor_typerecipient_typedonor_officerecipient_name
552430e24b503-b209-48b5-8edb-cca0cdaca78cM.TANGm. tang ...IndividualMDNaNNaNNaN6614 23RD PLACE...2022.02.049a2d46f-5e75-433c-94fa-f910e66d1a1eNaNNaNdirectNaNNaNNaNNone
552440e24b503-b209-48b5-8edb-cca0cdaca78cM.TANGm. tang ...IndividualMDNaNNaNNaN6614 23RD PLACE...2022.095.049a2d46f-5e75-433c-94fa-f910e66d1a1eNaNNaNdirectNaNNaNNaNNone
552450e24b503-b209-48b5-8edb-cca0cdaca78cM.TANGm. tang ...IndividualMDNaNNaNNaN6614 23RD PLACE...2022.010.049a2d46f-5e75-433c-94fa-f910e66d1a1eNaNNaNdirectNaNNaNNaNNone
55246a23037f6-741c-43a5-8a6d-0f1db4371e1dOLIVIA NDALMASSOolivia n dalmasso ...IndividualILNaNNaNNaNPO BOX 574...2022.012.66b33721f-3f6a-47c0-bce2-284fc58e0d2aNaNNaNdirectNaNNaNNaNNone
55247a23037f6-741c-43a5-8a6d-0f1db4371e1dOLIVIA NDALMASSOolivia n dalmasso ...IndividualILNaNNaNNaNPO BOX 574...2022.04.26b33721f-3f6a-47c0-bce2-284fc58e0d2aNaNNaNdirectNaNNaNNaNNone
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5 rows × 25 columns

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" - ], - "text/plain": [ - " id first_name \\\n", - "55243 0e24b503-b209-48b5-8edb-cca0cdaca78c M. \n", - "55244 0e24b503-b209-48b5-8edb-cca0cdaca78c M. \n", - "55245 0e24b503-b209-48b5-8edb-cca0cdaca78c M. \n", - "55246 a23037f6-741c-43a5-8a6d-0f1db4371e1d OLIVIA N \n", - "55247 a23037f6-741c-43a5-8a6d-0f1db4371e1d OLIVIA N \n", - "\n", - " last_name \\\n", - "55243 TANG \n", - "55244 TANG \n", - "55245 TANG \n", - "55246 DALMASSO \n", - "55247 DALMASSO \n", - "\n", - " full_name entity_type state \\\n", - "55243 m. tang ... Individual MD \n", - "55244 m. tang ... Individual MD \n", - "55245 m. tang ... Individual MD \n", - "55246 olivia n dalmasso ... Individual IL \n", - "55247 olivia n dalmasso ... Individual IL \n", - "\n", - " party company occupation address ... year amount \\\n", - "55243 NaN NaN NaN 6614 23RD PLACE ... 2022.0 2.0 \n", - "55244 NaN NaN NaN 6614 23RD PLACE ... 2022.0 95.0 \n", - "55245 NaN NaN NaN 6614 23RD PLACE ... 2022.0 10.0 \n", - "55246 NaN NaN NaN PO BOX 574 ... 2022.0 12.6 \n", - "55247 NaN NaN NaN PO BOX 574 ... 2022.0 4.2 \n", - "\n", - " recipient_id office_sought purpose \\\n", - "55243 49a2d46f-5e75-433c-94fa-f910e66d1a1e NaN NaN \n", - "55244 49a2d46f-5e75-433c-94fa-f910e66d1a1e NaN NaN \n", - "55245 49a2d46f-5e75-433c-94fa-f910e66d1a1e NaN NaN \n", - "55246 6b33721f-3f6a-47c0-bce2-284fc58e0d2a NaN NaN \n", - "55247 6b33721f-3f6a-47c0-bce2-284fc58e0d2a NaN NaN \n", - "\n", - " transaction_type donor_type recipient_type donor_office \\\n", - "55243 direct NaN NaN NaN \n", - "55244 direct NaN NaN NaN \n", - "55245 direct NaN NaN NaN \n", - "55246 direct NaN NaN NaN \n", - "55247 direct NaN NaN NaN \n", - "\n", - " recipient_name \n", - "55243 None \n", - "55244 None \n", - "55245 None \n", - "55246 None \n", - "55247 None \n", - "\n", - "[5 rows x 25 columns]" - ] - }, - "execution_count": 12, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# left merge according to ind_id and transaction donor_id. This was entities that only received money will still be there, no info from ind_dataset\n", - "# is lost\n", - "merged_inds_sample = pd.merge(inds_df,transactions,how='left',left_on='id',right_on='donor_id')\n", - "merged_inds_sample.dropna(subset = ['amount'], inplace=True)\n", - "merged_inds_sample.tail(5)" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Index(['id', 'first_name', 'last_name', 'full_name', 'entity_type', 'state',\n", - " 'party', 'company', 'occupation', 'address', 'zip', 'city',\n", - " 'classification', 'transaction_id', 'donor_id', 'year', 'amount',\n", - " 'recipient_id', 'office_sought', 'purpose', 'transaction_type',\n", - " 'donor_type', 'recipient_type', 'donor_office', 'recipient_name'],\n", - " dtype='object')" - ] - }, - "execution_count": 13, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "merged_inds_sample.columns" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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donor_idrecipient_idfull_namerecipient_nameaddressamountcityclassificationcompanydonor_office...occupationoffice_soughtpartypurposerecipient_typestatetransaction_idtransaction_typeyearzip
00007b184-4e1d-401a-ba51-99733d2e13e7d461f2bd-9074-44b3-8948-e659bead3e58graham filler ...saginaw county republican committee12705 WARM CREEK500.00DEWITTneutralNoneNone...NoneNoneNoneNoneNoneMINonedirect2022.048820-0000
100523627-46c7-4f76-ab42-fb2c1fbac1b16126e78b-4e80-4361-a019-9d99aa1623eddaniel millstone ...rooted in community leadership pac10518 ROUNTREE RD0.77LOS ANGELESneutralNoneNone...NoneNoneNoneNoneNoneCANonedirect2022.090064-0000
200934782-86e5-4941-94cf-0a700100a2c02d1a0919-218e-4692-98ec-c4a73a126482josie petersheim ...mi greenstone pac7196 W. BRIGGS RD.25.00STANTONneutralNoneNone...NoneNoneNoneNoneNoneMINonedirect2022.048888-0000
300f22bdd-96bf-4074-9620-4737e8444958af8417ee-5bca-49f5-91e9-d2de65d73631robert doerfler ...michigan senate democratic fund1534 NE 5TH AVE50.00FORT LAUDERDALEneutralNoneNone...NoneNoneNoneNoneNoneFLNonedirect2022.033304-1006
40138403b-b5b9-453a-a1d2-b6ed9fa5fe586126e78b-4e80-4361-a019-9d99aa1623edjoseph martinez ...rooted in community leadership pac139 HURON AVE1.65MOUNT CLEMENSneutralNoneNone...NoneNoneNoneNoneNoneMINonedirect2022.048043-0000
..................................................................
1120fdccce6b-e55f-4f1d-bd95-1714f2a667eda3fe20e2-8019-448e-9b54-bfdce4d87f2fmichael olthoff ...bumstead leadership fund1499 MIDDLEBROOK DR1000.00NORTON SHORESneutralnicholsNone...ceoNoneNoneNoneNoneMINonedirect2022.049441-0000
1121fe969829-b8a4-4d38-88e2-8314b340d5676126e78b-4e80-4361-a019-9d99aa1623edjoanna simon ...rooted in community leadership pac1546 POPLAR GROVE DR3.82RESTONneutralNoneNone...NoneNoneNoneNoneNoneVANonedirect2022.020194-1731
1122ff1423ba-ff5e-4bc1-b864-303a9dcc9b326126e78b-4e80-4361-a019-9d99aa1623edadriana p{on ce ...rooted in community leadership pac9 BIRCH CT3.82NORMALneutralNoneNone...NoneNoneNoneNoneNoneILNonedirect2022.061761-3900
1123ff24644e-d64a-4a8a-a87f-cdb53b86dd636126e78b-4e80-4361-a019-9d99aa1623eddavid friedman ...rooted in community leadership pac8823 MOUNTAIN PATH CIR0.15AUSTINneutralNoneNone...NoneNoneNoneNoneNoneTXNonedirect2022.078759-0000
1124ffb25947-c03f-43b2-abb4-23531cdb73247f272fe4-d592-453c-9ca1-315ea3fdcff1dennis starner ...bill g schuette for state representative4612 CONGRESS DRIVE525.00MIDLANDneutralretiredNone...retiredNoneNoneNoneNoneMINonedirect/fund raiser2022.048642-0000
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1125 rows × 25 columns

\n", - "
" - ], - "text/plain": [ - " donor_id \\\n", - "0 0007b184-4e1d-401a-ba51-99733d2e13e7 \n", - "1 00523627-46c7-4f76-ab42-fb2c1fbac1b1 \n", - "2 00934782-86e5-4941-94cf-0a700100a2c0 \n", - "3 00f22bdd-96bf-4074-9620-4737e8444958 \n", - "4 0138403b-b5b9-453a-a1d2-b6ed9fa5fe58 \n", - "... ... \n", - "1120 fdccce6b-e55f-4f1d-bd95-1714f2a667ed \n", - "1121 fe969829-b8a4-4d38-88e2-8314b340d567 \n", - "1122 ff1423ba-ff5e-4bc1-b864-303a9dcc9b32 \n", - "1123 ff24644e-d64a-4a8a-a87f-cdb53b86dd63 \n", - "1124 ffb25947-c03f-43b2-abb4-23531cdb7324 \n", - "\n", - " recipient_id \\\n", - "0 d461f2bd-9074-44b3-8948-e659bead3e58 \n", - "1 6126e78b-4e80-4361-a019-9d99aa1623ed \n", - "2 2d1a0919-218e-4692-98ec-c4a73a126482 \n", - "3 af8417ee-5bca-49f5-91e9-d2de65d73631 \n", - "4 6126e78b-4e80-4361-a019-9d99aa1623ed \n", - "... ... \n", - "1120 a3fe20e2-8019-448e-9b54-bfdce4d87f2f \n", - "1121 6126e78b-4e80-4361-a019-9d99aa1623ed \n", - "1122 6126e78b-4e80-4361-a019-9d99aa1623ed \n", - "1123 6126e78b-4e80-4361-a019-9d99aa1623ed \n", - "1124 7f272fe4-d592-453c-9ca1-315ea3fdcff1 \n", - "\n", - " full_name \\\n", - "0 graham filler ... \n", - "1 daniel millstone ... \n", - "2 josie petersheim ... \n", - "3 robert doerfler ... \n", - "4 joseph martinez ... \n", - "... ... \n", - "1120 michael olthoff ... \n", - "1121 joanna simon ... \n", - "1122 adriana p{on ce ... \n", - "1123 david friedman ... \n", - "1124 dennis starner ... \n", - "\n", - " recipient_name address \\\n", - "0 saginaw county republican committee 12705 WARM CREEK \n", - "1 rooted in community leadership pac 10518 ROUNTREE RD \n", - "2 mi greenstone pac 7196 W. BRIGGS RD. \n", - "3 michigan senate democratic fund 1534 NE 5TH AVE \n", - "4 rooted in community leadership pac 139 HURON AVE \n", - "... ... ... \n", - "1120 bumstead leadership fund 1499 MIDDLEBROOK DR \n", - "1121 rooted in community leadership pac 1546 POPLAR GROVE DR \n", - "1122 rooted in community leadership pac 9 BIRCH CT \n", - "1123 rooted in community leadership pac 8823 MOUNTAIN PATH CIR \n", - "1124 bill g schuette for state representative 4612 CONGRESS DRIVE \n", - "\n", - " amount city classification company donor_office ... \\\n", - "0 500.00 DEWITT neutral None None ... \n", - "1 0.77 LOS ANGELES neutral None None ... \n", - "2 25.00 STANTON neutral None None ... \n", - "3 50.00 FORT LAUDERDALE neutral None None ... \n", - "4 1.65 MOUNT CLEMENS neutral None None ... \n", - "... ... ... ... ... ... ... \n", - "1120 1000.00 NORTON SHORES neutral nichols None ... \n", - "1121 3.82 RESTON neutral None None ... \n", - "1122 3.82 NORMAL neutral None None ... \n", - "1123 0.15 AUSTIN neutral None None ... \n", - "1124 525.00 MIDLAND neutral retired None ... \n", - "\n", - " occupation office_sought party purpose recipient_type state \\\n", - "0 None None None None None MI \n", - "1 None None None None None CA \n", - "2 None None None None None MI \n", - "3 None None None None None FL \n", - "4 None None None None None MI \n", - "... ... ... ... ... ... ... \n", - "1120 ceo None None None None MI \n", - "1121 None None None None None VA \n", - "1122 None None None None None IL \n", - "1123 None None None None None TX \n", - "1124 retired None None None None MI \n", - "\n", - " transaction_id transaction_type year zip \n", - "0 None direct 2022.0 48820-0000 \n", - "1 None direct 2022.0 90064-0000 \n", - "2 None direct 2022.0 48888-0000 \n", - "3 None direct 2022.0 33304-1006 \n", - "4 None direct 2022.0 48043-0000 \n", - "... ... ... ... ... \n", - "1120 None direct 2022.0 49441-0000 \n", - "1121 None direct 2022.0 20194-1731 \n", - "1122 None direct 2022.0 61761-3900 \n", - "1123 None direct 2022.0 78759-0000 \n", - "1124 None direct/fund raiser 2022.0 48642-0000 \n", - "\n", - "[1125 rows x 25 columns]" - ] - }, - "execution_count": 14, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "attribute_cols = merged_inds_sample.columns.difference(['donor_id','recipient_id','full_name','recipient_name'])\n", - "agg_functions = {col: 'sum' if col == 'amount' else 'first' for col in attribute_cols}\n", - "grouped_sample = merged_inds_sample.groupby(['donor_id','recipient_id','full_name','recipient_name']).agg(agg_functions).reset_index()\n", - "grouped_sample" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [], - "source": [ - "def create_network_nodes(df: pd.DataFrame) -> nx.MultiDiGraph:\n", - " G = nx.MultiDiGraph()\n", - " # first check if df is individuals or organizations dataset\n", - " if \"name\" in df.columns:\n", - " node_name = \"name\"\n", - " else:\n", - " node_name = \"full_name\"\n", - " \n", - " transact_info = ['office_sought', 'purpose', 'transaction_type', 'year','transaction_id','donor_office','amount']\n", - " for _, row in df.iterrows(): \n", - " # add node attributes based on the columns relevant to the entity\n", - " G.add_node(row[node_name])\n", - " for column in df.columns.difference(transact_info):\n", - " if not pd.isnull(row[column]):\n", - " G.nodes[row[node_name]][column] = row[column]\n", - " \n", - " # link the donor node to the recipient node. add the attributes of the\n", - " # edge based on relevant nodes \n", - " edge_dictionary = {}\n", - " for column in transact_info:\n", - " if not pd.isnull(row[column]):\n", - " edge_dictionary[column] = row[column]\n", - " G.add_edge(row[node_name], row['recipient_name'], **edge_dictionary)\n", - "\n", - " # the added 'recipient_name' node has no attributes at this moment\n", - " # for the final code this line won't be necessary, as each recipient\n", - " # should ideally be referenced later on. For now, all added nodes for\n", - " # the recipient will only have one default attribute: classification\n", - " G.nodes[row['recipient_name']]['classification'] = 'neutral' \n", - " \n", - " edge_labels = {(u,v):d['amount'] for u,v,d in G.edges(data=True)}\n", - " entity_colors = {'neutral': 'green', 'c':'blue', 'f':'red'}\n", - " node_colors = [entity_colors[G.nodes[node]['classification']] for node in G.nodes()]\n", - "\n", - " nx.draw_planar(G, with_labels=False,node_color=node_colors)\n", - " plt.figure(3,figsize=(12,12)) \n", - " nx.draw_networkx_edge_labels(G, pos=nx.planar_layout(G),edge_labels=edge_labels, label_pos=0.5)\n", - "\n", - " #nx.draw_planar(G, with_labels=False)\n", - " plt.show()\n", - " return G" - ] - }, - { - "cell_type": "code", - "execution_count": 122, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{}" - ] - }, - "execution_count": 122, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "#for u,v in G.nodes(data=True):\n", - " #print(u)#['classification'])\n", - " \n", - "G.nodes['michigan association of health plans political action committee']#['classification'])#['nancy davis ']['classification']" - ] - }, - { - "cell_type": "code", - "execution_count": 66, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array(['neutral', 'f'], dtype=object)" - ] - }, - "execution_count": 66, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "grouped_sample.classification.unique()" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", 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"{'classification': 'neutral'}\n", - "{'address': '15 W260 FILLMORE ST', 'city': 'ELMHURST ', 'classification': 'neutral', 'donor_id': '283c7a56-1298-4003-b4b3-e4519b6077b0', 'entity_type': 'Individual', 'first_name': 'EVELYN ', 'full_name': 'evelyn pape ', 'id': '283c7a56-1298-4003-b4b3-e4519b6077b0', 'last_name': 'PAPE ', 'recipient_id': 'af8417ee-5bca-49f5-91e9-d2de65d73631', 'recipient_name': 'michigan senate democratic fund', 'state': 'IL', 'zip': '60126-5349'}\n", - "{'classification': 'neutral'}\n", - "{'address': '16190 DOBBINS DR', 'city': 'ALBION ', 'classification': 'neutral', 'donor_id': '306d7309-ccc7-457e-a263-394b1143dacb', 'entity_type': 'Individual', 'first_name': 'STEPHANIE ', 'full_name': 'stephanie dobbins ', 'id': '306d7309-ccc7-457e-a263-394b1143dacb', 'last_name': 'DOBBINS ', 'recipient_id': 'af8417ee-5bca-49f5-91e9-d2de65d73631', 'recipient_name': 'michigan senate democratic fund', 'state': 'MI', 'zip': '49224-9689'}\n", - "{'address': '3685 CREEKSIDE DRIVE', 'city': 'DORR ', 'classification': 'neutral', 'donor_id': '57069727-fd76-4630-9d36-b786d0992b4a', 'entity_type': 'Individual', 'first_name': 'ANNETTE ', 'full_name': 'annette magyar ', 'id': '57069727-fd76-4630-9d36-b786d0992b4a', 'last_name': 'MAGYAR ', 'recipient_id': '097002ca-1bbd-417a-bad9-9fd54887ebab', 'recipient_name': 'movement voter pac mi', 'state': 'MI', 'zip': '49323-0000'}\n", - 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'94702-1648'}\n", - "{'address': '39842 GOLFVIEW DR.', 'city': 'NORTHVILLE ', 'classification': 'neutral', 'donor_id': '739bc866-c9cc-4360-ae52-9b15c22ca6b6', 'entity_type': 'Individual', 'first_name': 'DONALD ', 'full_name': 'donald gates ', 'id': '739bc866-c9cc-4360-ae52-9b15c22ca6b6', 'last_name': 'GATES ', 'recipient_id': 'e9e8bf7f-2d34-42c9-b155-b95481ca238f', 'recipient_name': 'committee to elect dave staudt', 'state': 'MI', 'zip': '48167-0000'}\n", - "{'classification': 'neutral'}\n", - "{'address': '7300 KRAENZLEIN ROAD', 'city': 'BAY CITY ', 'classification': 'neutral', 'donor_id': '4ae0900b-eac4-4e41-b4a2-6727561db273', 'entity_type': 'Individual', 'first_name': 'JOAN ', 'full_name': 'joan wilson ', 'id': '4ae0900b-eac4-4e41-b4a2-6727561db273', 'last_name': 'WILSON ', 'recipient_id': 'c5bc157e-1eff-4db0-b26a-eea376cc3fd0', 'recipient_name': 'tamara d carlone for state board of education', 'state': 'MI', 'zip': '48706-0000'}\n", - "{'classification': 'neutral'}\n", - "{'address': '753 PATRICIA PLACE DR', 'city': 'WESTLAND ', 'classification': 'neutral', 'company': 'blue cross blue shield of mich', 'donor_id': '184e5f13-aba5-44da-be09-572ac083b3e9', 'entity_type': 'Individual', 'first_name': 'SHUNDA ', 'full_name': 'shunda jones ^ ', 'id': '184e5f13-aba5-44da-be09-572ac083b3e9', 'last_name': 'JONES ^ ', 'occupation': 'manager - administrative', 'recipient_id': '5a56136a-8ea1-4027-918f-be7d7a66c373', 'recipient_name': 'blue cross blue shield of michigan political action committee', 'state': 'MI', 'zip': '48185-0000'}\n", - "{'address': '3830 33RD AVE SW UNIT A', 'city': 'SEATTLE ', 'classification': 'neutral', 'donor_id': '9a5a86bb-a480-42ad-913a-17f80efbfb86', 'entity_type': 'Individual', 'first_name': 'JAMES ', 'full_name': 'james sims ', 'id': '9a5a86bb-a480-42ad-913a-17f80efbfb86', 'last_name': 'SIMS ', 'recipient_id': '6126e78b-4e80-4361-a019-9d99aa1623ed', 'recipient_name': 'rooted in community leadership pac', 'state': 'WA', 'zip': '98126-2514'}\n", - "{'address': '204 HURON ST', 'city': 'BAY CITY ', 'classification': 'neutral', 'donor_id': '298c73fa-495f-4df0-a348-16a62d6464ee', 'entity_type': 'Individual', 'first_name': 'MATHEWS ', 'full_name': 'mathews gavin ', 'id': '298c73fa-495f-4df0-a348-16a62d6464ee', 'last_name': 'GAVIN ', 'recipient_id': 'a9c205c4-6e86-465d-b9f8-55400317be37', 'recipient_name': 'sheet metal workers local 7 pac', 'state': 'MI', 'zip': '48706-4931'}\n", - "{'address': '740 HEWITT LN', 'city': 'NEW WINDSOR ', 'classification': 'neutral', 'donor_id': 'a41724c3-f42d-42a0-bc7d-8973c2e3a0c8', 'entity_type': 'Individual', 'first_name': 'MARY ', 'full_name': 'mary washburn ', 'id': 'a41724c3-f42d-42a0-bc7d-8973c2e3a0c8', 'last_name': 'WASHBURN ', 'recipient_id': 'af8417ee-5bca-49f5-91e9-d2de65d73631', 'recipient_name': 'michigan senate democratic fund', 'state': 'NY', 'zip': '12553-5462'}\n", - "{'address': '100 ROCKVIEW ST', 'city': 'JAMAICA PLAIN ', 'classification': 'neutral', 'donor_id': '1755fe5d-6210-4ecd-8075-de785b4a8a73', 'entity_type': 'Individual', 'first_name': 'TIMOTHY ', 'full_name': 'timothy havel ', 'id': '1755fe5d-6210-4ecd-8075-de785b4a8a73', 'last_name': 'HAVEL ', 'recipient_id': '097002ca-1bbd-417a-bad9-9fd54887ebab', 'recipient_name': 'movement voter pac mi', 'state': 'MA', 'zip': '02130-4660'}\n", - "{'address': '2260 POLISH LINE RD.', 'city': 'CHEBOYGAN ', 'classification': 'neutral', 'donor_id': '46b3649a-e403-4bd0-8ee2-d65a34d191f9', 'entity_type': 'Individual', 'first_name': 'STEVE ', 'full_name': 'steve downing ', 'id': '46b3649a-e403-4bd0-8ee2-d65a34d191f9', 'last_name': 'DOWNING ', 'recipient_id': 'b92fe9af-a5f5-4f15-8f35-d5536eb946eb', 'recipient_name': 'friends of marie fielder', 'state': 'MI', 'zip': '49721-0000'}\n", - "{'classification': 'neutral'}\n", - "{'address': '10698 BEAR LAKE TRL', 'city': 'PORTAGE ', 'classification': 'neutral', 'donor_id': 'b0dafcd3-4ba2-4aa1-ac43-2298edc705e4', 'entity_type': 'Individual', 'first_name': 'MICHAEL ', 'full_name': 'michael anderson ', 'id': 'b0dafcd3-4ba2-4aa1-ac43-2298edc705e4', 'last_name': 'ANDERSON ', 'recipient_id': 'af8417ee-5bca-49f5-91e9-d2de65d73631', 'recipient_name': 'michigan senate democratic fund', 'state': 'MI', 'zip': '49024-6206'}\n", - "{'address': '150 MARINE AVE', 'city': 'BROOKLYN ', 'classification': 'neutral', 'donor_id': '58988e4c-4376-4fd7-8c13-10bc9fc65335', 'entity_type': 'Individual', 'first_name': 'PAMELA L ', 'full_name': 'pamela l landberg ', 'id': '58988e4c-4376-4fd7-8c13-10bc9fc65335', 'last_name': 'LANDBERG ', 'recipient_id': '6126e78b-4e80-4361-a019-9d99aa1623ed', 'recipient_name': 'rooted in community leadership pac', 'state': 'NY', 'zip': '11209-7744'}\n", - "{'address': '1701 PORTER SW SUITE 6', 'city': 'WYOMING ', 'classification': 'neutral', 'company': 'self emp;oyed', 'donor_id': '3dfd0b64-eb59-4475-9abc-8be958bd8182', 'entity_type': 'Individual', 'first_name': 'DANIEL ', 'full_name': 'daniel hibma ', 'id': '3dfd0b64-eb59-4475-9abc-8be958bd8182', 'last_name': 'HIBMA ', 'occupation': 'property management', 'recipient_id': 'b4b49f06-2c4d-42e4-83e8-fc63c95fad04', 'recipient_name': 'committee to protect voters rights', 'state': 'MI', 'zip': '49519-0000'}\n", - "{'classification': 'neutral'}\n", - "{'address': '1501 BRIDGEWATER DR', 'city': 'MELBOURNE ', 'classification': 'neutral', 'donor_id': 'd71d895c-b18c-45ed-9a13-ec025564fedb', 'entity_type': 'Individual', 'first_name': 'JUDITH ', 'full_name': 'judith behrendt ', 'id': 'd71d895c-b18c-45ed-9a13-ec025564fedb', 'last_name': 'BEHRENDT ', 'recipient_id': '6126e78b-4e80-4361-a019-9d99aa1623ed', 'recipient_name': 'rooted in community leadership pac', 'state': 'FL', 'zip': '32934-3215'}\n" - ] - } - ], - "source": [ - "matplot_G = create_network_nodes(grouped_sample.sample(50))\n", - "for v,d in matplot_G.nodes(data=True):\n", - " #print(u)\n", - " #print(v)\n", - " print(d)" - ] - }, - { - "cell_type": "code", - "execution_count": 118, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "['green',\n", - " 'green',\n", - " 'green',\n", - " 'green',\n", - " 'green',\n", - " 'green',\n", - " 'green',\n", - " 'green',\n", - " 'green',\n", - " 'green',\n", - " 'green',\n", - " 'green',\n", - " 'green',\n", - " 'green',\n", - " 'green',\n", - " 'green',\n", - " 'green',\n", - " 'green',\n", - " 'green',\n", - " 'green',\n", - " 'green',\n", - " 'green',\n", - " 'green',\n", - " 'green',\n", - " 'green',\n", - " 'green',\n", - " 'green',\n", - " 'green',\n", - " 'green',\n", - " 'green',\n", - " 'green',\n", - " 'green',\n", - " 'green',\n", - " 'green',\n", - " 'green',\n", - " 'green',\n", - " 'green',\n", - " 'green',\n", - " 'green',\n", - " 'green',\n", - " 'green',\n", - " 'green',\n", - " 'red',\n", - " 'green',\n", - " 'green',\n", - " 'green',\n", - " 'green',\n", - " 'green',\n", - " 'green',\n", - " 'green',\n", - " 'green',\n", - " 'green',\n", - " 'green',\n", - " 'green',\n", - " 'green',\n", - " 'green',\n", - " 'green',\n", - " 'green',\n", - " 'green',\n", - " 'green',\n", - " 'green',\n", - " 'green',\n", - " 'green',\n", - " 'green',\n", - " 'green',\n", - " 'green',\n", - " 'green',\n", - " 'green',\n", - " 'green',\n", - " 'green',\n", - " 'green',\n", - " 'green',\n", - " 'green',\n", - " 'green',\n", - " 'green',\n", - " 'green']" - ] - }, - "execution_count": 118, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "#for a,b in G.nodes(data=True):\n", - " #print(G[node])#['classification'])\n", - "# print(b)#['classification'])\n", - "entity_colors = {'neutral': 'green', 'c':'blue', 'f':'red'}\n", - "node_colors = [entity_colors.get(G.nodes[node].get('classification', 'neutral'), 'green') for node in G.nodes()]\n", - "node_colors" - ] - }, - { - "cell_type": "code", - "execution_count": 36, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "('William Stoner', 'KALAMAZOO ANESTHESIOLOGY PC', {'amount': 10.0, 'year': 2017})\n", - "('KALAMAZOO ANESTHESIOLOGY PC', 'Bob Kushman', {'amount': 1530})\n", - "('Bob Kushman', 'KALAMAZOO ANESTHESIOLOGY PC', {'amount': 530})\n", - "('James Engelson', 'Bob Kushman', {'amount': 90.0, 'year': 2019})\n", - "('Allen Wolf', 'William Stoner', {'amount': 111.5, 'year': 2018})\n", - "('Allen Wolf', 'William Stoner', {'amount': 11100.5, 'year': 2018})\n" - ] - } - ], - "source": [ - "G = nx.MultiDiGraph()\n", - " \n", - "G.add_node(\"William Stoner\", Age=10, Weight=110)\n", - "G.add_edge(\"William Stoner\",\"KALAMAZOO ANESTHESIOLOGY PC\",amount=10.00, year=2017)\n", - "G.add_node(\"KALAMAZOO ANESTHESIOLOGY PC\", Age=50, Weight=180)\n", - "G.add_edge(\"KALAMAZOO ANESTHESIOLOGY PC\",\"Bob Kushman\",amount=1530)\n", - "G.add_node(\"Bob Kushman\", Age=90, Weight=111)\n", - "G.add_edge(\"Bob Kushman\",\"KALAMAZOO ANESTHESIOLOGY PC\",amount=530)\n", - "G.add_node(\"James Engelson\", Age=40, Weight=10)\n", - "G.add_edge(\"James Engelson\",\"Bob Kushman\",amount=90.00, year=2019,)\n", - "G.add_node(\"Allen Wolf\", Age=30, Weight=1710)\n", - "G.add_edge(\"Allen Wolf\",\"William Stoner\",amount=111.50,year=2018)\n", - "G.add_edge(\"Allen Wolf\",\"William Stoner\",amount=11100.50,year=2018)\n", - "\n", - "\n", - "\n", - "edge_labels = {(u,v):d['amount'] for u,v,d in G.edges(data=True)}\n", - "nx.draw(G, with_labels=True,node_color='red')\n", - "pos = nx.planar_layout(G)\n", - "for edge, label in edge_labels.items():\n", - " nx.draw_networkx_edge_labels(G, pos=pos, edge_labels={edge: label}, label_pos=0.5, verticalalignment='center', horizontalalignment='center')\n", - "plt.show()\n", - "for edge in G.edges(data=True):\n", - " print(edge)" - ] - }, - { - "cell_type": "code", - "execution_count": 35, - "metadata": {}, - "outputs": [ - { - "data": { - "application/vnd.plotly.v1+json": { - "config": { - "plotlyServerURL": "https://plot.ly" - }, - "data": [ - { - "hoverinfo": "text", - "hovertext": [ - "Amount: 10.00, Weight: 10.00", - "Amount: 1530.00, Weight: 1530.00", - "Amount: 530.00, Weight: 530.00", - "Amount: 90.00, Weight: 90.00", - "Amount: 111.50, Weight: 111.50" - ], - "line": { - "color": "#888" - }, - "mode": "lines", - "type": "scatter", - "x": [ - 10, - 50, - null, - 50, - 90, - null, - 90, - 50, - null, - 40, - 90, - null, - 30, - 10, - null - ], - "y": [ - 110, - 180, - null, - 180, - 111, - null, - 111, - 180, - null, - 10, - 111, - null, - 1710, - 110, - null - ] - }, - { - "hoverinfo": "text", - "marker": { - "colorscale": [ - [ - 0, - "rgb(255,255,217)" - ], - [ - 0.125, - "rgb(237,248,177)" - ], - [ - 0.25, - "rgb(199,233,180)" - ], - [ - 0.375, - "rgb(127,205,187)" - ], - [ - 0.5, - "rgb(65,182,196)" - ], - [ - 0.625, - "rgb(29,145,192)" - ], - [ - 0.75, - "rgb(34,94,168)" - ], - [ - 0.875, - "rgb(37,52,148)" - ], - [ - 1, - "rgb(8,29,88)" - ] - ], - "showscale": true, - "size": 10 - }, - "mode": "markers", - "text": [ - "William Stoner
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Weight: 110", - "KALAMAZOO ANESTHESIOLOGY PC
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Network graph made with Plotly" - }, - "xaxis": { - "showgrid": false, - "showticklabels": false, - "zeroline": false - }, - "yaxis": { - "showgrid": false, - "showticklabels": false, - "zeroline": false - } - } - }, - "text/html": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "G = nx.MultiDiGraph()\n", - "\n", - "G.add_node(\"William Stoner\", Age=10, Weight=110)\n", - "G.add_node(\"KALAMAZOO ANESTHESIOLOGY PC\", Age=50, Weight=180)\n", - "G.add_node(\"Bob Kushman\", Age=90, Weight=111)\n", - "G.add_node(\"James Engelson\", Age=40, Weight=10)\n", - "G.add_node(\"Allen Wolf\", Age=30, Weight=1710)\n", - "\n", - "G.add_edge(\"William Stoner\", \"KALAMAZOO ANESTHESIOLOGY PC\", weight=10.00, amount=10.00, year=2017)\n", - "G.add_edge(\"KALAMAZOO ANESTHESIOLOGY PC\", \"Bob Kushman\", weight=1530, amount=1530, year=2017)\n", - "G.add_edge(\"Bob Kushman\", \"KALAMAZOO ANESTHESIOLOGY PC\", weight=530, amount=530, year=2017)\n", - "G.add_edge(\"James Engelson\", \"Bob Kushman\", weight=90.00, amount=90.00, year=2017)\n", - "G.add_edge(\"Allen Wolf\", \"William Stoner\", weight=111.50, amount=111.50, year=2017)\n", - "\n", - "# Create Plotly graph\n", - "edge_trace = go.Scatter(x=[], y=[], line=dict(color='#888'), hoverinfo='text', mode='lines')\n", - "hovertext = []\n", - "\n", - "for edge in G.edges(data=True):\n", - " x0, y0 = G.nodes[edge[0]]['Age'], G.nodes[edge[0]]['Weight']\n", - " x1, y1 = G.nodes[edge[1]]['Age'], G.nodes[edge[1]]['Weight']\n", - " edge_trace['x'] += tuple([x0, x1, None])\n", - " edge_trace['y'] += tuple([y0, y1, None])\n", - " hovertext.append(f\"Amount: {edge[2]['amount']:.2f}, Weight: {edge[2]['weight']:.2f}\")\n", - "\n", - "edge_trace['hovertext'] = hovertext\n", - "\n", - "node_trace = go.Scatter(x=[], y=[], text=[], mode='markers', hoverinfo='text', marker=dict(showscale=True, colorscale='YlGnBu', size=10))\n", - "\n", - "for node in G.nodes():\n", - " x, y = G.nodes[node]['Age'], G.nodes[node]['Weight']\n", - " node_trace['x'] += tuple([x])\n", - " node_trace['y'] += tuple([y])\n", - " node_info = node + '
' + 'Age: ' + str(G.nodes[node]['Age']) + '
' + 'Weight: ' + str(G.nodes[node]['Weight'])\n", - " node_trace['text'] += tuple([node_info])\n", - "\n", - "fig = go.Figure(data=[edge_trace, node_trace],\n", - " layout=go.Layout(\n", - " title='
Network graph made with Plotly',\n", - " titlefont=dict(size=16),\n", - " showlegend=False,\n", - " hovermode='closest',\n", - " margin=dict(b=20,l=5,r=5,t=40),\n", - " xaxis=dict(showgrid=False, zeroline=False, showticklabels=False),\n", - " yaxis=dict(showgrid=False, zeroline=False, showticklabels=False)))\n", - "\n", - "fig.show()\n" - ] - }, - { - "cell_type": "code", - "execution_count": 58, - "metadata": {}, - "outputs": [ - { - "data": { - "application/vnd.plotly.v1+json": { - "config": { - "plotlyServerURL": "https://plot.ly" - }, - "data": [ - { - "hoverinfo": "text", - "hovertext": [ - "Amount: 5.00", - "Amount: 100.00", - "Amount: 15.00", - "Amount: 151.76", - "Amount: 75.00", - "Amount: 11.12", - "Amount: 1.00", - "Amount: 1.00", - "Amount: 5.88", - "Amount: 250.00", - "Amount: 15.00", - "Amount: 273.00", - "Amount: 25.44", - "Amount: 100.00", - "Amount: 50.00", - "Amount: 400.00", - "Amount: 300.00", - "Amount: 1020.00", - "Amount: 100.00", - "Amount: 100.00", - "Amount: 5.00", - "Amount: 15.00", - "Amount: 100.00", - "Amount: 13.00", - "Amount: 750.00", - "Amount: 15.00", - "Amount: 500.00", - "Amount: 2.50", - "Amount: 1.00", - "Amount: 250.00", - "Amount: 35.00", - "Amount: 40.00", - "Amount: 9.29", - "Amount: 5.00", - "Amount: 19.00", - "Amount: 75.00", - "Amount: 25.15", - "Amount: 15.78", - "Amount: 1.00", - "Amount: 250.00", - "Amount: 1000.00", - "Amount: 2.87", - "Amount: 67.18", - "Amount: 150.00", - "Amount: 29.40", - "Amount: 1.00", - "Amount: 500.00", - "Amount: 60.00", - "Amount: 10.00", - "Amount: 76.32" - ], - "line": { - "color": "#888" - }, - "mode": "lines", - "type": "scatter", - "x": [], - "y": [] - }, - { - "hoverinfo": "text", - "marker": { - "color": [ - "green", - "green", - "green", - "green", - "green", - "green", - "green", - "green", - "green", - "green", - "green", - "green", - "green", - "green", - "green", - "green", - "green", - "green", - "green", - "green", - "green", - "green", - "green", - "green", - "green", - "green", - "green", - "green", - "green", - "green", - "green", - "green", - "green", - "green", - "green", - "green", - "green", - "green", - "green", - "green", - "green", - "green", - "green", - "green", - "green", - "green", - "green", - "green", - "green", - "green", - "green", - "green", - "green", - "green", - "green", - "green", - "green", - "green", - "green", - "green", - "green", - "green", - "green", - "green", - "green", - "green" - ], - "colorscale": [ - [ - 0, - "rgb(255,255,217)" - ], - [ - 0.125, - "rgb(237,248,177)" - ], - [ - 0.25, - "rgb(199,233,180)" - ], - [ - 0.375, - "rgb(127,205,187)" - ], - [ - 0.5, - "rgb(65,182,196)" - ], - [ - 0.625, - "rgb(29,145,192)" - ], - [ - 0.75, - "rgb(34,94,168)" - ], - [ - 0.875, - "rgb(37,52,148)" - ], - [ - 1, - "rgb(8,29,88)" - ] - ], - "showscale": true, - "size": 10 - }, - "mode": "markers", - "text": [ - "Name: rachel puthuff
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\"\n", - " for key, value in G.nodes[node].items():\n", - " node_info += f\"{key}: {value}
\"\n", - " node_trace['text'] += tuple([node_info])\n", - " # Get the classification value for the node\n", - " classification = G.nodes[node].get('classification', 'neutral')\n", - " # Assign a color based on the classification value\n", - " if classification == 'c':\n", - " color = 'blue'\n", - " elif classification == 'f':\n", - " color = 'red'\n", - " else:\n", - " color = 'green' # Default color for unknown classification\n", - " node_trace['marker']['color'] += tuple([color])\n", - "\n", - " # Define layout settings\n", - " layout = go.Layout(\n", - " title='Network Graph Indicating Campaign Contributions from 2018-2022',\n", - " titlefont=dict(size=16),\n", - " showlegend=True,\n", - " hovermode='closest',\n", - " margin=dict(b=20, l=5, r=5, t=40),\n", - " xaxis=dict(showgrid=True, zeroline=True, showticklabels=False),\n", - " yaxis=dict(showgrid=True, zeroline=True, showticklabels=False)\n", - " )\n", - "\n", - " fig = go.Figure(data=[edge_trace, node_trace], layout=layout)\n", - 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"color": "#2a3f5f" - } - }, - "ternary": { - "aaxis": { - "gridcolor": "white", - "linecolor": "white", - "ticks": "" - }, - "baxis": { - "gridcolor": "white", - "linecolor": "white", - "ticks": "" - }, - "bgcolor": "#E5ECF6", - "caxis": { - "gridcolor": "white", - "linecolor": "white", - "ticks": "" - } - }, - "title": { - "x": 0.05 - }, - "xaxis": { - "automargin": true, - "gridcolor": "white", - "linecolor": "white", - "ticks": "", - "title": { - "standoff": 15 - }, - "zerolinecolor": "white", - "zerolinewidth": 2 - }, - "yaxis": { - "automargin": true, - "gridcolor": "white", - "linecolor": "white", - "ticks": "", - "title": { - "standoff": 15 - }, - "zerolinecolor": "white", - "zerolinewidth": 2 - } - } - }, - "title": { - "font": { - "size": 16 - }, - "text": "Network graph made with Python" - }, - "xaxis": { - "showgrid": false, - "showticklabels": false, - "zeroline": false - }, - "yaxis": { - "showgrid": false, - "showticklabels": false, - "zeroline": false - } - } - }, - "text/html": [ - 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "G = nx.random_geometric_graph(200, 0.125)\n", - "edge_x = []\n", - "edge_y = []\n", - "for edge in G.edges():\n", - " x0, y0 = G.nodes[edge[0]]['pos']\n", - " x1, y1 = G.nodes[edge[1]]['pos']\n", - " edge_x.append(x0)\n", - " edge_x.append(x1)\n", - " edge_x.append(None)\n", - " edge_y.append(y0)\n", - " edge_y.append(y1)\n", - " edge_y.append(None)\n", - "\n", - "edge_trace = go.Scatter(\n", - " x=edge_x, y=edge_y,\n", - " line=dict(width=0.5, color='#888'),\n", - " hoverinfo='none',\n", - " mode='lines')\n", - "\n", - "node_x = []\n", - "node_y = []\n", - "for node in G.nodes():\n", - " x, y = G.nodes[node]['pos']\n", - " node_x.append(x)\n", - " node_y.append(y)\n", - "\n", - "node_trace = go.Scatter(\n", - " x=node_x, y=node_y,\n", - " mode='markers',\n", - " hoverinfo='text',\n", - " marker=dict(\n", - " showscale=True,\n", - " # colorscale options\n", - " #'Greys' | 'YlGnBu' | 'Greens' | 'YlOrRd' | 'Bluered' | 'RdBu' |\n", - " #'Reds' | 'Blues' | 'Picnic' | 'Rainbow' | 'Portland' | 'Jet' |\n", - " #'Hot' | 'Blackbody' | 'Earth' | 'Electric' | 'Viridis' |\n", - " colorscale='YlGnBu',\n", - " reversescale=True,\n", - " color=[],\n", - " size=10,\n", - " colorbar=dict(\n", - " thickness=15,\n", - " title='Node Connections',\n", - " xanchor='left',\n", - " titleside='right'\n", - " ),\n", - " line_width=2))\n", - "\n", - "node_adjacencies = []\n", - "node_text = []\n", - "for node, adjacencies in enumerate(G.adjacency()):\n", - " node_adjacencies.append(len(adjacencies[1]))\n", - " node_text.append('# of connections: '+str(len(adjacencies[1])))\n", - "\n", - "node_trace.marker.color = node_adjacencies\n", - "node_trace.text = node_text\n", - "\n", - "\n", - "fig = go.Figure(data=[edge_trace, node_trace],\n", - " layout=go.Layout(\n", - " title='Network graph made with Python',\n", - " titlefont_size=16,\n", - " showlegend=False,\n", - " hovermode='closest',\n", - " margin=dict(b=20,l=5,r=5,t=40),\n", - " annotations=[ dict(\n", - " text=\"graphs\",\n", - " showarrow=True,\n", - " xref=\"paper\", yref=\"paper\",\n", - " x=0.005, y=-0.002 ) ],\n", - " xaxis=dict(showgrid=False, zeroline=False, showticklabels=False),\n", - " yaxis=dict(showgrid=False, zeroline=False, showticklabels=False))\n", - " )\n", - "fig.show()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "G = nx.Graph()\n", - "G.add_node(0)\n", - "nx.set_node_attributes(G, \"red\", name=\"color\")\n", - "nx.set_node_attributes(G, 2, name=\"size\")\n", - "G.add_node(1)\n", - "nx.set_node_attributes(G, np.nan, name='color')\n", - "G.nodes[0]" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [ - { - "ename": "NetworkXError", - "evalue": "Invalid edge_attr argument: ['donations', 'received']", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mKeyError\u001b[0m Traceback (most recent call last)", - "File \u001b[0;32m~/miniconda3/envs/climate_cabinet/lib/python3.11/site-packages/pandas/core/indexes/base.py:3653\u001b[0m, in \u001b[0;36mIndex.get_loc\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m 3652\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m-> 3653\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_engine\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_loc\u001b[49m\u001b[43m(\u001b[49m\u001b[43mcasted_key\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 3654\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m err:\n", - "File \u001b[0;32m~/miniconda3/envs/climate_cabinet/lib/python3.11/site-packages/pandas/_libs/index.pyx:147\u001b[0m, in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n", - "File \u001b[0;32m~/miniconda3/envs/climate_cabinet/lib/python3.11/site-packages/pandas/_libs/index.pyx:176\u001b[0m, in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n", - "File \u001b[0;32mpandas/_libs/hashtable_class_helper.pxi:7080\u001b[0m, in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n", - "File \u001b[0;32mpandas/_libs/hashtable_class_helper.pxi:7088\u001b[0m, in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n", - "\u001b[0;31mKeyError\u001b[0m: 'donations'", - "\nThe above exception was the direct cause of the following exception:\n", - "\u001b[0;31mKeyError\u001b[0m Traceback (most recent call last)", - "File \u001b[0;32m~/miniconda3/envs/climate_cabinet/lib/python3.11/site-packages/networkx/convert_matrix.py:455\u001b[0m, in \u001b[0;36mfrom_pandas_edgelist\u001b[0;34m(df, source, target, edge_attr, create_using, edge_key)\u001b[0m\n\u001b[1;32m 454\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 455\u001b[0m attribute_data \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mzip\u001b[39m(\u001b[38;5;241m*\u001b[39m\u001b[43m[\u001b[49m\u001b[43mdf\u001b[49m\u001b[43m[\u001b[49m\u001b[43mcol\u001b[49m\u001b[43m]\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mfor\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mcol\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;129;43;01min\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mattr_col_headings\u001b[49m\u001b[43m]\u001b[49m)\n\u001b[1;32m 456\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m (\u001b[38;5;167;01mKeyError\u001b[39;00m, \u001b[38;5;167;01mTypeError\u001b[39;00m) \u001b[38;5;28;01mas\u001b[39;00m err:\n", - "File \u001b[0;32m~/miniconda3/envs/climate_cabinet/lib/python3.11/site-packages/networkx/convert_matrix.py:455\u001b[0m, in \u001b[0;36m\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m 454\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 455\u001b[0m attribute_data \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mzip\u001b[39m(\u001b[38;5;241m*\u001b[39m[\u001b[43mdf\u001b[49m\u001b[43m[\u001b[49m\u001b[43mcol\u001b[49m\u001b[43m]\u001b[49m \u001b[38;5;28;01mfor\u001b[39;00m col \u001b[38;5;129;01min\u001b[39;00m attr_col_headings])\n\u001b[1;32m 456\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m (\u001b[38;5;167;01mKeyError\u001b[39;00m, \u001b[38;5;167;01mTypeError\u001b[39;00m) \u001b[38;5;28;01mas\u001b[39;00m err:\n", - "File \u001b[0;32m~/miniconda3/envs/climate_cabinet/lib/python3.11/site-packages/pandas/core/frame.py:3761\u001b[0m, in \u001b[0;36mDataFrame.__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m 3760\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_getitem_multilevel(key)\n\u001b[0;32m-> 3761\u001b[0m indexer \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcolumns\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_loc\u001b[49m\u001b[43m(\u001b[49m\u001b[43mkey\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 3762\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m is_integer(indexer):\n", - "File \u001b[0;32m~/miniconda3/envs/climate_cabinet/lib/python3.11/site-packages/pandas/core/indexes/base.py:3655\u001b[0m, in \u001b[0;36mIndex.get_loc\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m 3654\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m err:\n\u001b[0;32m-> 3655\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m(key) \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01merr\u001b[39;00m\n\u001b[1;32m 3656\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m:\n\u001b[1;32m 3657\u001b[0m \u001b[38;5;66;03m# If we have a listlike key, _check_indexing_error will raise\u001b[39;00m\n\u001b[1;32m 3658\u001b[0m \u001b[38;5;66;03m# InvalidIndexError. Otherwise we fall through and re-raise\u001b[39;00m\n\u001b[1;32m 3659\u001b[0m \u001b[38;5;66;03m# the TypeError.\u001b[39;00m\n", - "\u001b[0;31mKeyError\u001b[0m: 'donations'", - "\nThe above exception was the direct cause of the following exception:\n", - "\u001b[0;31mNetworkXError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[0;32mIn[16], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m G \u001b[38;5;241m=\u001b[39m \u001b[43mnx\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfrom_pandas_edgelist\u001b[49m\u001b[43m(\u001b[49m\u001b[43msample_df\u001b[49m\u001b[43m,\u001b[49m\u001b[43msource\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mname\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43mtarget\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mdonations_to\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43medge_attr\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mdonations\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mreceived\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 2\u001b[0m G\u001b[38;5;241m.\u001b[39mnodes()\n\u001b[1;32m 3\u001b[0m pos\u001b[38;5;241m=\u001b[39mnx\u001b[38;5;241m.\u001b[39mspring_layout(G)\n", - "File \u001b[0;32m~/miniconda3/envs/climate_cabinet/lib/python3.11/site-packages/networkx/utils/backends.py:412\u001b[0m, in \u001b[0;36m_dispatch.__call__\u001b[0;34m(self, backend, *args, **kwargs)\u001b[0m\n\u001b[1;32m 409\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m__call__\u001b[39m(\u001b[38;5;28mself\u001b[39m, \u001b[38;5;241m/\u001b[39m, \u001b[38;5;241m*\u001b[39margs, backend\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs):\n\u001b[1;32m 410\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m backends:\n\u001b[1;32m 411\u001b[0m \u001b[38;5;66;03m# Fast path if no backends are installed\u001b[39;00m\n\u001b[0;32m--> 412\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43morig_func\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 414\u001b[0m \u001b[38;5;66;03m# Use `backend_name` in this function instead of `backend`\u001b[39;00m\n\u001b[1;32m 415\u001b[0m backend_name \u001b[38;5;241m=\u001b[39m backend\n", - "File \u001b[0;32m~/miniconda3/envs/climate_cabinet/lib/python3.11/site-packages/networkx/convert_matrix.py:458\u001b[0m, in \u001b[0;36mfrom_pandas_edgelist\u001b[0;34m(df, source, target, edge_attr, create_using, edge_key)\u001b[0m\n\u001b[1;32m 456\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m (\u001b[38;5;167;01mKeyError\u001b[39;00m, \u001b[38;5;167;01mTypeError\u001b[39;00m) \u001b[38;5;28;01mas\u001b[39;00m err:\n\u001b[1;32m 457\u001b[0m msg \u001b[38;5;241m=\u001b[39m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mInvalid edge_attr argument: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00medge_attr\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m--> 458\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m nx\u001b[38;5;241m.\u001b[39mNetworkXError(msg) \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01merr\u001b[39;00m\n\u001b[1;32m 460\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m g\u001b[38;5;241m.\u001b[39mis_multigraph():\n\u001b[1;32m 461\u001b[0m \u001b[38;5;66;03m# => append the edge keys from the df to the bundled data\u001b[39;00m\n\u001b[1;32m 462\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m edge_key \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n", - "\u001b[0;31mNetworkXError\u001b[0m: Invalid edge_attr argument: ['donations', 'received']" - ] - } - ], - "source": [ - "G = nx.from_pandas_edgelist(sample_df,source='name',target='donations_to',edge_attr=['donations','received'])\n", - "G.nodes()\n", - "pos=nx.spring_layout(G)\n", - "weights = list(nx.get_edge_attributes(G,'donations').values())\n", - "weights = [i/5000 for i in weights]\n", - "node_color = [G.degree(v) for v in G] \n", - "#node_size = [0.0005 * nx.get_node_attributes(G, 'donations')[v] for v in G] \n", - "nx.draw_networkx_nodes(G, pos, node_color=node_color)#, node_size=node_size) \n", - "nx.draw_networkx_edges(G, pos, width=weights)\n", - "nx.draw_networkx_labels(G, pos)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "\n", - "# fixing the size of the figure \n", - "plt.figure(figsize =(10, 7)) \n", - "\n", - "node_color = [G.degree(v) for v in G] \n", - "# node colour is a list of degrees of nodes \n", - "\n", - "node_size = [0.0005 * nx.get_node_attributes(G, 'population')[v] for v in G] \n", - "# size of node is a list of population of cities \n", - "\n", - "edge_width = [0.0015 * G[u][v]['weight'] for u, v in G.edges()] \n", - "# width of edge is a list of weight of edges \n", - "\n", - "nx.draw_networkx(G, node_size = node_size, \n", - "\t\t\t\tnode_color = node_color, alpha = 0.7, \n", - "\t\t\t\twith_labels = True, width = edge_width, \n", - "\t\t\t\tedge_color ='.4', cmap = plt.cm.Blues) \n", - "\n", - "plt.axis('off') \n", - "plt.tight_layout(); " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "G = nx.MultiDiGraph()\n", - "G.add_node(0)\n", - "nx.set_node_attributes(G, \"red\", name=\"color\")\n", - "nx.set_node_attributes(G, 4, name = 'size')\n", - "G.add_node(2)\n", - "nx.set_node_attributes(G, \"white\", name='color')\n", - "G.nodes[2]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "G.add_node(2)\n", - "nx.set_node_attributes(G, 4, name='age')\n", - "G.nodes[2]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "climate_cabinet", - "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": "3.11.7" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} From 0f7d07ef00da198d3aa9e28b0ddcbdb830270dba Mon Sep 17 00:00:00 2001 From: Avery Schoen <33437601+averyschoen@users.noreply.github.com> Date: Tue, 5 Mar 2024 12:07:31 -0600 Subject: [PATCH 24/24] Update Makefile --- Makefile | 7 +------ 1 file changed, 1 insertion(+), 6 deletions(-) diff --git a/Makefile b/Makefile index 273bc9c..07383c3 100644 --- a/Makefile +++ b/Makefile @@ -19,11 +19,6 @@ project_dir := "$(current_abs_path)" build-only: docker build -t $(project_image_name) -f Dockerfile $(current_abs_path) - # these are called directives - # run-pipeline: - # docker build -t $(project_image_name) -f Dockerfile $(current_abs_path) - # docker run -e python pipeline.py - run-interactive: docker build -t $(project_image_name) -f Dockerfile $(current_abs_path) docker run -it -v $(current_abs_path):/project -t $(project_image_name) /bin/bash @@ -39,4 +34,4 @@ run-notebooks: #still waiting on linkage_pipeline completion to get this into final shape output network_graph: all_individuals.csv all_organizations.csv all_transactions.csv - python linkage_pipeline.py \ No newline at end of file + python linkage_pipeline.py