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] 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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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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" - ], - "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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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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IUt7XgmKD8Cnuk+jlZmAZFjc/3JSsfELym7h/4hFCCCny4lPjccD3AK6/v47bQbdF6fXjGA5u9m7Y0mGLCC1U9zDkoSTlZlAICtwNuitpHYTkJwqUhBBCRBGbEovZ12Zj48ONSEpLAsdwkAtyUcpuWLoh/u75N0z1TUUp71vvo9+DZVjJbnkDwMfYj5KVTUh+o0BJCCFEY5cCL6H/8f4ISwhTTmzRNExyDAcZK8OiFosw1nOspPtjy3k5GIi/DNHXFLw0s94J0QUUKAkhhGhkq89WDD01FAzDaNTDJ2NlkPPpIdTGyAYja43E8FrD4WjuKFZTs2Sqbypp7yQAGOsZS1o+IfmJAiUhhJDvduj5IXidSl8TUpON1xgwYBkW85rNw08VfkIV2yrQ4/TEaqYKQRAQFBQEHx8fPHz4ED4+Prjz6Q6ETtJuHFeteDVJyyckP1GgJIQQ8l0+xnzE4L8Hi1KWAAEKXoHjL45jasOpmS4LxPM8Ll68iObNm0Mmy93blyAIePfunUp49PHxQXh4OADA1tYWHh4eGPLTECzFUgiQJlTKWBnqlKwjSdmE6AIKlIQQQr6L1ykvpChSRCtPISjgE+KD5XeWY3KDySrnIiMj0atXL1y8eBF79+5F79691Z7P8zwCAwOVwTHjc1RUFADAwcEBHh4eGDlyJDw8PODu7o6SJUsqt3B8tOsRrr67Ksni5nJejg4VO4heLiG6ghE0uUdBCCGkSPIJ8YHHJg9JyrYytELIxBAYyAwAAPfv30enTp3w+fNnCIKAX375BUuXLsWrV69UwuOjR48QE5O+G02pUqWUodHDwwNubm5wcHDItt6/X/yNTgc7if79MGDgbO2MgDEBkuw/ToguoEBJCCEkz7xOemHnk53KSTRi29N5D3pX641NmzZhzJgx4HkePJ8+acbMzAyCICA+Ph4AULZsWZXw6O7uDltb2zzXKeflqL6+Ol5GvhS9l3J3593oW72vqGUSoksoUBJCCMkTQRBgvdga0cnRkpTPMRy6uHZB0PIg3LlzR/08x2Hu3LmoXbs23N3dYW1tLVrdPiE+qL25tmgzvmWsDC2dWuJM7zPUO0kKNdp6kRBCSJ58iPkgWZgE/ttV5vPnz8pjHMf9d16hQNeuXdGiRQtRwyQAuDu4Y1GLRaKUxTEcbI1tsaXDFgqTpNCjQEkIISRPfMN8Ja/jY+xHPPV/iujoaBw7dgxeXl5wdPxvPUp/f3/J6p5UfxJmNZkFAN+92LmMlaG4SXFcH3gdJcxKiNk8QnQS3fImhBCSJwd8D6DX0V6S1xM6MRR2pnbKx4Ig4M2bN/D29ka7du1gbm4uaf17n+7FqLOjkJCakOcxla3Kt8K2jtsoTJIig3ooCSGE5MmXpC9aqefbhc0ZhoGzszN69+4teZgEgD7V++DF6BfoW70v9Fg9sAyb5faPGetmOlk5YUfHHTjX5xyFSVKkUA8lIYSQXPsQ8wEemzwQkRghaT1GMiPET4+XdP/uvIhIjMCep3tw48MN3Au6h5D4EPACD0OZIaoWrwrPkp7oVKkTmpdrTuMlSZFEgZIQQkiupCpS4bHJA/7h/pIs/v21BqUa4Obgm5LWoSlBECg8EvL/aKccQgghubLgxgI8D3su2faEGTiGQ+MyjSWtQwwUJgn5D/VQEkIIyVFwXDDKrCgj2ULmX2PA4NXYVyhvXV7yuggh4qAeSkIIITna4rNFtMW+s5OxEDiFSUIKFuqhJIQQkiPHZY74FPdJ8noMOAP4jvKFs7Wz5HURQsRDPZSEEEKyFRwXrJUwCQCLWy4udGFSEAREJEYgRZECQ5khbIxsaPwlKXQoUBJCCMmWT4iPVuoZXXs0xtYZq5W6pPY5/jO2PtqKC28uwCfEB3Gpccpz5gbmqF2iNlo7t8bAmgNRzLhYPraUEHHQLW9CCCHZ2vRwE4afHi55PfKZcnAsl/OFOiw8IRyTLkzCPt994AU+y3GnDBgwDAOO4TDIbRAWtVgES0NL7TaWEBHpxoqxhBBCdJaCV3z3ntZ5IfVyRFI78eIEKq6piL3P9kLOy7OdxCRAAC/wSOPTsNVnKyquqYgLby5osbWEiIsCJSGEkGxZGFpIHvZYnsW0qdOwefNm/PvvvwgNDUVBuoG24cEGdD7YGdHJ0Xle9F0hKBCRGIE2e9tgz9M9ErWQEGnRLW9CCCHZeh72HFXXV5W0Dot4CxQ7Xgxv374Fz6f37Jmbm6NixYqoUKGCymcXFxeYmJhI2p68OPT8EHoc6SFKWQwYnO59Gm1d2opSHiHaQoGSEEJIthS8AqZ/miJZnixJ+TJWhhEeI7C67WqkpKTgzZs3ePnyJQICAhAQEKD8OiLiv/3DHR0dVUJmxtdlypQBx2lvHOan2E+otLYSElITROnFZcDA2sgaAWMCYGNsI0ILCdEOCpSEEEJy1O94PxzwPSDZTjn/DvwXjco0yvaaL1++KMPl14Hz1atXSElJAQDo6+vD2dlZJWRmfG1jk/eAtm/fPkRERGD06NGZBtWO+zvizKszou5tzjEc+lTrg52dd4pWJiFSo0BJCCEkR3eD7qLe1nqil8uAQcViFeE3yu+712bkeR4fPnxQC5ovX77Ehw8flNdZW1tnGjTLly8PQ0PDTMt2dXXFixcv0KBBA+zduxdlypRRnguICECltZW+q805YRkWH8Z/QEnzkpKUT4jYKFASQgjJkSAI+HHPj7j69qqovXEAcKT7EXSt3FXUMjMkJibi9evXarfPAwICEBMTAwBgGAZly5ZVC5rOzs6oUKECUlNTIZPJYGBggI0bN6JPnz4AgAnnJ2CN9xrIBfF7bVmGxczGMzG76WzRyyZEChQoCSGE5MrHmI+otLYSEtMSRSmPYzh0ce2CQ90PiVJeXgiCgPDw8Ex7Nd+8eYO0tLQsn9uyZUvs2rULDQ41QGBUoGRtdHdwx8NhDyUrnxAxUaAkhBCSozdf3uBO0B0c9D2I069Oa1yejJGhjGUZ3PW6q3M7xcjlcrx9+xYHDhzAH3/8kek1FatXRECXAEnbocfqIWF6AvQ4PUnrIUQMtPUiIYSQTPECj6N+R7Hq3irc/HgTQHrIYcBoNKOZYziUtSqLawOu6VyYBACZTAYXFxcYGRkpj7EsC57n4erqiiZNmqBJ7ybodaWXpO1I49MQGBWIisUqSloPIWKgQEkIIUTNmy9vMPDvgbj54SY45r/ZzWl81reCc8IxHBSCAv1r9MeyVst0fqvB8+fPA0ifOd6wYUNMmjQJbdq0AQBcf3ddK21IkidppR5CNEU75RBCCFFx7tU5VFtfDXeD7gKARpNwOIZTBtIGpRvgn77/YFvHbTofJgGgffv2AIDU1FRcv34dbdu2hbOzM4YPH46QTyFaaYM+p6+VegjRFPVQEkIIUfrn9T/ocKADFLxC44W6zQ3M8aPTj6hVohbaV2yPyraVRWqldORyOT58+IDAwEC8fv1aeVyhSA/Vb968wZs3b3DryS2gjbRtYRkWZS3LSlsJISKhQEkIIQRA+izuroe6gud5UXZ9iU2JRRuXNhjsNliE1oknLi4OgYGBynCY8XVgYCDev38PuTx9GaCs1sVs3749tm7dimo7q+FzwmfJ2uls7QxjPWPJyidETBQoCSGEQBAEeJ30Qoo8BTx40codd24cWjq1RCmLUqKVmROe5xEaGpppYHzz5g3Cw8OV15qamqJ8+fJwcnJC586d4eTkpHxsb28Pc3NzCIIAjuNgZWWF7du3o127dgCAFk4tcPD5QUl2D5KxMrR0ail6uYRIhZYNIoQQgkuBl9Byt/gBRsbKMKjmIGxqv0nUcpOTk/H27Vu1sBgYGIjAwEAkJ/+373iJEiWUIfHbz7a2ttnu0FOiRAmEhISge/fuWL9+vcr2jbc+3ELD7Q1F/b6+9nTEU1SzqyZZ+YSIiQIlIYQQdDrQCWdenpFk1xdDzhChk0JhYWiR6+cIgoDIyMhMexgDAwPx6dMnZLx9GRgYoFy5cpmGxrJly8LY+PtvGx8+fBgcx6FLly6ZttFtoxt8w3xF38u7QakGuD5IOzPJCREDBUpCCCniopOjYbPYBrwg3q3urzFgsKXDFrWxlGlpafj48WOmt6YDAwMRGxurvNbGxgbly5fPNDSWKFECLJs/i5Y8DH6IOlvqiPqz02P18GzkM1p/khQoNIaSEEKKuIfBDyULkwDAsRwO3DiAiIsRahNgMmZPcxyH0qVLo3z58vD09ESvXr2UodHJyQkWFrnv3dQmjxIe+KPxH5h9fbZoZf7R5A8Kk6TAoR5KQggp4pbcWoJpl6eJettWTQhgutdU2cv4bU9j6dKloadXMLcY5AUew04Nw9ZHW0Urs4RZCbR0aonhHsNR17FutuM8CdEF1ENJCCFFXHhiOFiGlTRQ2jvbIzg2uFAGI5Zhsan9Jtib2mPBjQWi/CyD44Kx99le7HyyE+4O7tjaYStq2tcUp8GESIB2yiGEkCJOGzeqGIYplGEyA8uwmPfDPNwechvO1s4A0me4ayJjOaInoU9Qa1MtzL0+VyuvFSHfgwIlIYQUcdZG1pKOoQQAG2ObnC8qBOo61oXfaD/80/cftHVpCxM9E43LVAgKKAQF/rj2B4afHk6hkugkCpSEEFLEuTm4SXq7W8bKUKdEHcnK1zUsw+LH8j/i755/I3ZaLNa3XS9a2Zt9NmPWtVmilUeIWChQEkJIEVerRC1Jy1fwCtQuWVvSOnTVl6QvmHF1BhiId7t/3r/zcC/onmjlESIGCpSEEFLEFTMuhhZOLcAxnCTlcyyHrq5dJSlb1025OAUxyTGi7I2egWVYDDgxQPJhCoTkBQVKQgghGFtnrCS3vWWsDD9X+Rm2Jrail63rwhLCsPvpbtF/rgpBgYDIAFwKvCRquYRoggIlIYQQ/OTyEzxLekLGiLuaHMuwmNWkaI752/Zom2S9iDJWhrX310pSNiHfgwIlIYQQcCyHXZ13ib6F4Z/N/0QFmwqilllQXHxzUbJAKefluBx4mW57E51BgZIQQoq4VEUqzrw8gz1P96ByscqilMmAQTfXbvjF8xdRyitoBEHA/eD7ktaRkJaA119eS1oHIblFO+UQQkgR9SXpC5bdWYb1D9bjS9IXyFgZFLw44/26V+6O3V12g2Olmeij6yISIxCXGid5PS8jXxbZHmCiWyhQEkJIEXQq4BQGnxyMqKQo5aSRjJ1ZvpeMlUGP1cPyVssxzGNYod4ZJycpihTt1CPXTj2E5IQCJSGEFCGCIGDWtVmY++9csGDB4/vH4DFgwLEc5LwcJnomGOI2BL/W+xVlLMuI2GLdJQgCwsPDERQUhI8fP6p8DgwNBBpJ3wZDmaH0lRCSCxQoCSGkCPnf9f9h7r9zAUCjMAkAZSzLoE+1PvBw8MCP5X+Eib7m2wzqCkEQEBERkWlYzPgcFBSElJT/egj19PTg6OgIR0dHlC1VFj6CD1IYaXsQKxarKGn5hOQWI9CmoIQQUiRceHMBrfa0ErXMI92PoGvlgrVouSAIiIyMzDEsJicnK5+jp6eHkiVLwtHREaVKlUKpUqWUX2d8trW1VZkl32JXC1x5e0XURc2/ZqpvitjfYov00AKiO6iHkhBCioDYlFgMPDEQLMOKttQMAwZDTw1Fk7JNUMy4mChlakoQBERFReHjx4/ZhsWkpCTlc2QymUpYrFWrllpoLF68eJ6XVGrt3BpX312FFP02MlaGlk4tKUwSnUGBkhBCioBNDzfhc8JnUdctFCAgNiUWK++uxNwf5qqdj4yMxPLlyzFgwAC4uLhoXp8gIDo6OsewmJiYqHwOx3EqYdHd3T3TsMhx4s9GH1hzIKZfni7JWpFyXo4xdcaIXi4h34tueRNCSCHHCzzKrSyHDzEfJCnfxsgGwRODoc/pA0gPfocPH8aIESMQFRWFOXPmYObMmdmWIQgCYmJiVAJiZqHx27BYokQJtVvPX9+WtrOzkyQs5tawU8Ow7dE2Ubdf5BgOlYpVwrORz6iHkugM6qEkhJBC7v6n+5KFSQCITIrE1bdX0cq5FT59+oQRI0bg9OnTYBgGHMfhw4cPamExs9CYkJCgLJNlWZWwWL16dbXQaG9vD5lMt9/GFrVYhBMvTiAyKVK0nkoBAnZ13kVhkugU6qEkhJBCbo33Gow7N06yySEcw+F/Tf+HxH8SsXTpUqSlpamMG+Q4DgrFfz10DMPAwcEhy8ktjo6OcHBw0PmwmFvnX59H271tRfv5D3UfihWtV8BYz1iU8ggRAwVKQggp5Ib8PQS7nu7SeOHyrLAMi9ZlWuPswLOZnre1tcXq1atVwqKenp4kbdFVu57swsATAwFAlGDJMiyq21XHyFoj0btab5jqm2pcJiGaoL28CSGkkItKjpIsTALpYzQThAQ8fPgQv/32G0qUKAEAyh7GpKQk9OjRA/Xr10fp0qWLXJgEgP41+uNkr5OwNLQEx2g+ppMXeDwJfYIRp0egxF8lsO3RNklmkxOSWxQoCSGkkGMYBgykHW/HMizc3d3x559/4uPHj7h69Sr69u0LIyMjpKamQi6XLtAWFO0qtEPAmAD0qNojfZchDYOl8P//xaXGYcjJIWi7ty1ikmNEai0heUOBkhBCCrnixsUhY6Ubj8gxHOxN7ZWPWZZF06ZNsX37doSHh8PPz6/QjIfUlK2JLfZ22Yv3499jWsNpqGFXQ7TX5mLgRTTZ0QTRydGilEdIXlCgJISQQs7dwR1pfJpk5QsQ4OHgkek5ExMTlC9fXrK6C6pSFqUw94e5eDziMU72PClKmQpBAd8wX3Q+2FmStS8JyQ4FSkIIKeTqOtaVtHxe4FGnZB1J6yis4lLi4HXKCywjztuxQlDg2rtr2PBggyjlEZJbFCgJIaSQq1q8KqoVrwZWol/5ZSzKoEHpBpKUXdjN/XcuPseLu4MRAEy6MAkRiRGilklIdihQEkJIIccwDMZ5jgMP8W+DsgyLsXXGitbDVpQkpiViw4MNou6ikyFFkYJtj7aJXi4hWaHfAIQQUgT0r9EfVWyriLJkTQaO4VDaojRG1h4pWplFyaHnhxCXGidJ2bzAY633WlpKiGgNBUpCCCkC9Dl97OmyR9QyeYHH7s67aceW73T9/XVJZ99/iP2AT3GfJCufkK/ROg6EEFLIvY9+jyN+R3A/+D6sjawRnhguSrkrW69Ew9INRSmrKLobdFfSBecB4GHwQziaO0paByEABUpCCCm0fEJ88MfVP3D21Vnl4uaajtfjGA68wGNN2zUYVXuUSC0tmt5Fv5O0fJZhERgVKGkdhGSgQEkIIYVMqiIVc67PwZ83/wQDJn0/FQ3H0rFgwYOHs7Uzdnfejdola4vU2qKH53l8+fIFcoW0vZMMGEnXHyXkaxQoCSGkEElITUC7/e1w/d11CPj+EMmAgYyVKQNJleJVMM5zHPpV7wcDmYFYzS1UkpKSEBoaitDQUISEhGT59efPn9O3opwGQMIfJS/wNL6VaA0FSkIIKSTSFGnoeKAjbry/oVGYBNJ3v6lhVwNjPceidonaqFSsEhhG2v3AdRHP84iMjMw2IGZ8HROjuo+2TCaDvb298sPNzQ1t2rSBvb09HBwcMPP9TDyPeS5Z2wUIqGJbRbLyCfkaBUpCCCkkFt1ahCtvr2gcJjM8CHkAB1MHuNq6ilKeLklMTMx1b6JCoTru1NLSUhkKS5QoAXd3dzg4OCiDY8bX1tbWYNmsF1O5du4aAh4ESDoxx93BXbKyCfkaI9AiVYQQUuD5hvnCbaObqOGEZVjYmdghYEwAzAzMRCtXKjzPIyIiIseQGBoaitjYWJXn6unpqfQmZhYQMz4MDQ1Fae/FNxfx454fRSnrWxzDoU7JOrg95LYk5RPyLeqhJISQQmDBjQUQqWNSiRd4hMaHYsfjHRjrOVbtfGpqKo4ePYp27drBzEy6wJmYmJirW86Z9SZaWVkpQ2HJkiXh4eGRaVi0srLKtjdRCs2dmqOcZTm8i34nWq9yBoWgwNg66q8ZIVKhHkpCCCngwhLCUHJZSUlunTJg4GztjIAxASpjKG/cuIEhQ4bg1atX2LhxI4YNG5anchUKRa57E+PiVHeT0dfXz7Y30c7OTnnMwEC3JxBtfrgZw07n7WeXE47h4GjuiJdjX0Kf0xe1bEKyQj2UhBBSwJ1+eVqycXgCBLz68gp+4X6oUrwKvnz5gsmTJ2Pbtm3gOA4cxyEkJER5fUJCQq56E8PCwtR6E62trZWhsFSpUqhdu3aWvYmFZYLQEPch2PNsD25/vC3aa6gQFNjVeReFSaJV1ENJCCEF3Kgzo7DFZ4ukaw7u7LQTny98xpw5c5CYmAie5wEADMPAzs4OpqamCA0NRXx8vMrz9PX1sxyP+PXXdnZ2Ot+bKJV30e/gsckDMckxGi88DwDTGk7DguYLRGgZIblHPZSEEFLA+YT4SBom9Vg9nPQ+iaNTjqqdEwQBMpkMnTp1UgmJGZ8tLS0LTW+iVMpalsWV/lfQbGczxKbEahQqR9Yaifk/zBexdYTkDvVQEkJIAVdpTSUERAZIVr6MlWFgjYGo87kODh8+jCtXrkAQBGUvpZubG3x8fCSrv6h48+UN+hzrg3uf7uXpeTJWBo7hsKjFIozzHEcBnuQL7U5pI4QQIrrYlNicL9IAAwYcy2Ho0KG4cOECPn36hIULF6J8+fIAgKioKEnrLyrKW5fHrcG3sLzVchQzLgYgfYJNVjiGAwMGLZ1a4tnIZ/il7i8UJkm+oR5KQggpwNbdX4fRZ0dLWgfHcJjVZBZmNpmpclwQBNy+fRtyuRxNmjSRtA1FTZoiDcdfHMdx/+O4++ku3kW/U54z1jOGh4MHGpdpjMFug+Fk5ZR/DSXk/1GgJISQAupS4CW03N1SK3Wd6X0GbV3aaqUuoi4pLQkJaQmQsTKYG5iDZegGI9EtNCmHEEIKoNiUWAw4MQAsw4IXeEnrYhkWtUvUlrQOkj0jPSMY6RnldzMIyRIFSkIIKYAW3VyEz/GfJQ+TMlaGdhXawdbEVtJ6CCEFG/WZE0JIAZMiT8G6B+tEWbMwJ3JeTlv4EUJyRD2UhBBSwJwMOIno5GjJ6+EYDh0qdkCzss0kr4sQUrBRDyUhhBQwNz/chB6rJ2kdLMPC3MAcG9ptoKVoCCE5okBJCCEFjPcnb0l3xgHSeyfP9jmL4ibFJa2HEFI4UKAkhJAC5n3Me8nr6FWtF+o61pW8HkJI4UCBkhBCCphURaqk5csYGexM7CStgxBSuFCgJISQAiQgIkDyCTm8wMPXxxcXL15EUFAQaP8LQkhOaKccQggpIILjguGxyQOh8aGS1yU7IoPcVw4AMDMzQ6VKleDq6qry4eTkBJmMFgshhNCyQYQQUiAIggCvk14ITwjXSn2v/30N+Rc5/P394efnB39/f/j7++PEiROIjY0FAOjr68PFxUUtaFasWBFGRrSrCyFFCfVQEkJIAbD36V70Pd5X8npYhkV1u+p4NPxRpucFQUBISIgyYH79ERqa3nPKMAzKli2rFjRdXV1hZWUl+fdACNE+CpSEEKLjBEFApbWV8CryFQRI/yt7e8ftGFhzYJ6fFxUVhRcvXqgFzbdv3yrHYdrb22caNB0cHGi9S0IKMAqUhBCi466/u46mO5tKXg/HcHA0d4T/aH8Y6Yl3yzopKQkvX75Uu33+8uVLpKWlr6dpbm6eadAsV64cOI4TrS2EEGnQGEpCCNFxp16egoyVQc7LJa2HF3js7rxb1DAJAEZGRqhRowZq1KihclwulyMwMFClN/P58+c4cuQI4uPjAQAGBgaoUKGCWtCsUKECDA0NRW0nIeT7UQ8lIYTouMbbG+PGhxuS1zOrySzMbjpb8npyIggCPn36lOk4zbCwMAAAy7JwcnJSC5qVKlWChYVFPn8HuuNT7CfcD74PnxAfRCRGQBAEFDMuBjcHN9QuURulLErldxNJIUGBkhBCdJz1ImtEJUdJWkfzcs1xsd9FnR/H+OXLl0yD5rt375TXlChRItPb53Z2djr//YlBwStw4sUJrPZejevvrwMAZKwMDNK/dwGCsre7QakGGFtnLLpW7goZSzctyfejQEkIITrOcJ4hUhQpkpXPMixmN5qNmc1mSlaH1BISEhAQEKAWNF+9egW5PD08WVpaZho0y5YtC5YtHPt8BEQEYMCJAbj36R44hoNCUGR7Pcuw4AUe7vbu2NV5F6oUr6KllpLChgIlIYToONMFpkhIS5CuAh7ARcD0mSmKFSuW7YeNjY3K13p6etK1SwRpaWl48+aNWtB88eIFEhLSf6aGhoaoWLEiXF1dUblyZWXQdHFxgb6+fj5/B7l3+Plh9D3eF7zA53m8LcdwYBgG2zpsQ78a/SRqISnMKFASQoiOc13rihcRLyStY7TtaDglOyEiIiLTj8jISPA8r/Y8CwuLXIXPjA9ra2udmLXN8zyCgoJUQmbGDPTIyEgAAMdxKF++fKbjNM3MzPL5O1B10Pcgeh3tBQAaLy31vctGkaKNAiUhhOi4QX8Pwp6neySd5f32l7coa1k2y/M8zyM6OhqRkZFZhs6vw2dERAS+fPmitg84wzCwsrLKMXh+/WFpaanVW9Lh4eGZjtP8+PGj8hpHR0e1oFm5cmXY2tpqrZ0ZfMN84bbRDQpeIco6pSzD4p7XPdQqUUuE1pGiggIlIYTouO2PtmPwycGSle9g6oBPv34SfcKKQqFAVFRUtqHz24/o6Gi1cliWVYbOnMJnxoe5ubno3098fHymC7e/fv0aCkX6WEUbG5tMx2mWKlXqu0Oxt7c3atSoAQMDA7VzaYo01N5cG8/Dn4v2BwfHcHC2dsaTEU9gIFOvk5DMUKAkhBAdl5CaALuldpKMo2QZFv9r+j/83vh30cv+Hmlpafjy5Uu2ofPbYJqxt/jXZDJZrsLn19eYmpp+VwhNTU3F69ev1W6dBwQEICkpCQBgbGyMSpUqqQVNZ2fnbMehBgQEoFKlSqhSpQoOHz4MV1dXlfPbHm3DkJND8tzmnDBgsLrNaoyuM1r0sknhRIGSEEIKgF//+RWr7q3KcdZuXumxeng//j0czBxELVebUlNTsw2fmZ3LmJDzNX19/TyNBy1WrBiMjY2zbBfP83j//n2mt8+jotKXgZLJZHB2ds50nKaJiQmmTZuGhQsXgmVZ6OnpYeXKlRg2bBgYhoEgCKi5sSZ8w3zBC+rjWzXBgIGLjQtejH5RJJZaIpqjQEkIIQVATHIMKq2thLCEMFHDw5KWSzCp/iTRyisokpKSsgyhmR0PDw9HSor60k1GRkZ5Gg9qY2MDAwMDhIWFZRo0P336pCy7TJkyiIqKUuuB7dChA7Zt24ZgeTCqb6gu6c/pntc91ClZR9I6SOFAgZIQQgqIs6/Oot2+dqJMvOAYDrVK1MKtwbfAsfk/61rXCYKAxMTEPI0HjYiIUO5V/jVTU9Msg6eJiQmSk5MRExODsLAw7Nu3L9Mgq6+vj98O/4Y5j+ZI9j2zDIvlrZZjnOc4yeoghQcti08IITosPCEc2x5tw8mAk3gU+ki0MFneujxO9TpFYTKXGIaBiYkJTExMUKZMmVw9RxAExMXF5RhAP378iEePHinPZUzwyY5cLoffFz9J93hnGRYPgh9IUjYpfChQEkKIDopJjsHUS1Ox9dFW8AIv6m3ueo71cLzncRQzLiZamUQdwzAwNzeHubk5nJyccvUcnucRExOjDJv169fP9Dpzc3MkcomSLiUl5+UIiQ+RrHxSuFCgJIQQHXPl7RX0OdYH4QnhokzCydjD2UBmgMUtFmN0ndFgmcKx1WBhw7IsrKysYG5uDmtra+Xkm68xDAMnJyetrM0p9mQfUnhRoCSEEB1y1O8oehzpAQGCaG/m5a3LY2StkRhYcyCsjaxFKZPkjiAISEhIQGRkpPLjy5cv2T6OjIxEdHS0WpD8ukw/Pz84c86QMTLIBeluedP/LyS3KFASQoiOuP7uOnoe7Qle4EUZK+nl5oWFLRbCxthGhNaR1NTUXIXBr499+fIFqampamXJZDJYW1vDxsZG+VG5cmWVx9bW1vjrr79w584dAOlbQbIsi+nTp2Pq1KnY8HgDjrw8Itn3y4BBTbuakpVPChcKlIQQogPiUuLQ51gf0cIkAOx4sgNjPcdSoPxGxjaSOYXBbx/Hx8dnWp6lpaVKOCxVqhRq1KihFg6/fmxmZpar9R1Pnz6tDJRt2rTBypUrleMxa5WoJektaYWgoO0XSa5RoCSEEB0w48oMhMSHiBoQBEFA/+P98Wj4o0K5OHXGUj65vZWc8TgqKgo8r/5zNjIyUgt/5cuXVwuDX19jZWUFmUy6t9K6deviwYMHWLRoEdq2batyrl6perA3tUdofKgkdVsbWaNp2aaSlE0KH1qHkhBC8ll0cjTsl9ojRaG+3qAYrg64qvPBIGPLxbyMM/zy5UumazRyHKcMfNmFwW8fGxkZ5cN3rpl5/87DrGuzRO+p5BgOUxtMxfzm80UtlxReFCgJISSfrbq3CuPPjxftVvfXZKwMnSp2wuGfD4tedmYEQUBMTEyeJqF8+fIl0/24gfTlcXITBr9+bG5urpUZ0LrgS9IXVFxTEV8Sv4CHOKGSAQMLAwu8GPMCdqZ2opRJCj+65U0IIfns3OtzkpUt5+U4/+Y8BEHI823vjO0J8xIOo6KiMl2Y28DAQC34lSlTJttwaGVlBT09PbF+FIWStZE1tnbYio4HOopWpgAB69utpzBJ8oR6KAkhJB8JgoBiS4rhS9IXSeu52+cuLOQWeQqHycnJauVkrJOYXRDM7JiRkVGhHMepKyb+MxHL7i4TpazyVuUxxG0IapesjcZlGkOf0xelXFK4UaAkhJB8FJUUBevFWljrbx+Al6qHzMzMcryF/O0xCwuLInM7uSDhBR6//vMrVt5bCQaMRsMnWLBgGAYKQQFrI2uM8BiBcZ7jqMeSZIsCJSGE5KPguGCUXFZS8nomlZmEDuU7qExW0dennqfCRBAE7Pfdj5FnRiIhNUGUXZaA9Ak6pvqmWPfTOvSq2ot6mkmmKFASQkg+ikyMRLEl0u+pfaLHCXSsJN44O6K7QuND8eeNP7Ht8TbEp8ZDj9VDGp+mUZkZvZ5D3IZgY7uN4FhOpNaSwoICJSGE5CNBEGCx0AJxqXGS1vN81HNUtq0saR1EtySkJuDUy1O4/+k+zr0+B/8If43LZMBgQM0B2NZhG/VUEhU0EIYQQvIRwzDwKOEhaR2GMkNUtKkoaR1E95jom6Bn1Z4YVXsU3ka/BQPNA6AAATse78Cmh5tEaCEpTChQEkJIPmvp1BIsI82vY47h0LRMU7pFWUTxAo+Bfw+EnJeLus7phH8m4F30O9HKIwUfBUpCCMlng90Gi9J7lBmFoMCo2qMkKZvovrOvzuLmh5uQ83JRy03j0zDn+hxRyyQFGwVKQgjJZ/am9uhdrTc4RtxeRI7hUM6yHNq6tM35YlIorfFeI/r/V0D6gvn7nu1DVFKU6GWTgokCJSGE6IClPy6FmYGZqD2VCkGBHZ120O3uIioiMQIX3lwQbfmgb6UqUnHM/5gkZZOChwIlIYToAFtjWyxqsUi0cW4MGPxa91c0LtNYlPJIwfMg+IEk+8Nn4FgO94PvS1Y+KVhoL29CCMkngiDg9sfbWPdgHf55/Q8ikyJFKZcBgx5Ve2Bxy8WilEcKpkchj8AxnGQ9lHJeDu9P3pKUTQoeCpSEEJIPnn5+ikF/D4JPiA9krEyUSRMcw4EXeEyuPxkLmi+gW91FXERiBFiGlSxQAkB4YrhkZZOChQIlIYRokSAIWHxrMX6/+jsy7kZqGiYzAml56/LY3nE76peqL0JLiS6Ry+WIjo5GdHQ0oqKi1D4yO/66/GsoKisg0QIChKigQEkIIVoiCAIm/DMBK++tFK1MCwML/FDuB4yqPQo/lPtBsvUsieZSU1NzHQa/PR4Xl/lOSizLwtLSElZWVsoPGxsblC9fHqa2prjJ3JT0e7I1tpW0fFJwUKAkhBAtWXp7qahhck6zOfi90e+0BZ6WCIKApKSkPAXBrz+SkpIyLVdPT08lEFpaWqJEiRKoUqWK2vGvH1tZWcHMzCzL1//86/Nos7eNZD8PGStDnZJ1JCufFCy0lzchhGiBb5gv3Da6ibrAtAFnAN9RvnC2dhatzMJOEATEx8d/d09hampqpuUaGhqqhb2sQuC3x42NjSX5oyAiMQLFlxSXbKY3Awab22/GEPchkpRPChYKlIQQogV1t9TFg+AHok6QkLEyNC7dGJcHXBatzIKA53nExMR8Vy9hdHQ0FIrMXwNTU9M8BcGvjxkaGmr5p5A7rfe0xqXAS5JMzDHgDBAyMQRWRlail00KHrrlTQghErv/6T7ufbonerlyXo4r767gedhzVCleRe18amoqAgMDUalSJdHr1lTGJJO89hBGRUUhJiYGWfWFWFhYqAW+0qVL5xgQLS0toaenp+WfgvTG1BmDf978I3q5MlaG3tV6U5gkShQoCSFEYusfrBdtaaBvyVgZNj7ciFVtVqkcP3v2LMaMGYMPHz7g8+fPsLGxEb3uvE4y+fpcVpNMOI5TCXkZk0ycnZ1z7Ck0NzcHx9FSSV9r69IWDUs3xN2gu6L+/6fH6mFWk1milUcKPgqUhBAisQtvLkgSJoH0Xsqve6BevXqFcePG4fz582AYBoIgICwsLNNA+fUkk7zeOs7LJBMrK6tMJ5lkFgyzm2RC8o5lWOzstBNV1lWBgleINp5yeavlKGNZRpSySOFAYygJIURCkYmRKLakmKR1sAyLd8Pf4Y/f/sDu3bshCAJ4nlee79KlCwwNDTMNh3mdZJKb8YVSTTIh3++Y/zF0O9QNADQOlVaGVuhYsSNqlaiFdhXaUbAkAChQEkKIpLw/ecNzi6fk9dS4XQNPLjzJ9FyZMmVQpkyZPAVDXZ1kQr7fEb8j6HW0FyAAckHzxfQVfPpEn7YubTG90XRaUL+Io0BJCCESuvH+BhrvaCx5PWc6nMHxdcdx7NgxfPnyBSzLKnsp9+zZgz59+kjeBqL7fMN80f94fzwKfQQWLHjwOT8pBxlbfo6tMxYLmi+Aib6JCC0lBQ1tqUAIIRIy0jPSSj1lHcti8+bNCA0NxcGDB+Hp+V+vaFRUlFbaQHRf1eJV4T3UG1s7bEVVu6oA0teTlLHfP6VCIaSPzVxzfw3qba2H8ATa37sooh5KQgiRUGxKLCwWWkhah4yVIX5aPAxkBirHnz59in379mHw4MGoUKGCpG0gBY8gCHj6+SnuBt3F8RfHRVleSMbIULFYRdwafAsWhtL+f090CwVKQgiRWPmV5REYHShZ+dWKV8PTkU8lK58UbsFxwai0phLiU+NFmQXOMRz6Vu+LHZ12aN44UmDQLW9CCJFYuwrtNLqlmB2O4fCTy0+SlE2KhmGnhiFJniTakkIKQYGdT3bi7KuzopRHCgYKlIQQIrERtUZItg4lL/AYXmu4JGWTwu/+p/s48+qM6P9/sgyLaZenZbmjESl8KFASQojEXG1d0dq5tei9lDJGhq6Vu6KsZVlRyyVFx7r76yTpPecFHk8/P8X94Puil010EwVKQgjRgo3tNkKf0xetPAYMTPRNsLrNatHKJEWLglfg4PODkvWey1gZDvgekKRsonsoUBJCiBZYGFhgXJ1xopUnQMCWDltgb2ovWpmk8Hnx4gXOnTsHhUKhfi7iBZLkmW+fKQY5L4f3J2/Jyie6hQIlIYRIJC4lDuvvr0e19dVgucgSC28t1LhMBulbGq7/aT26Ve6mcXmkcJs/fz7atm2LsmXLYunSpSprkj75nPnOSmJ6HPpY8jqIbqBASQghIhMEAbue7ILjckeMPjsaz8Oei1Iux3CwMLTAsZ+PYUStEaKUSQo3Y2NjsCyLoKAgTJ06FQ4ODhg2bBiePXuG6ORo5R8oUklISwAvaL4bD9F9tA4lIYSIKD41Hr2P9sapl6fAgBFtXT8BAnpU6YEVrVeguElxEVpKCipBEJCYmIiYmBjlR3R0dKaPr169Cn9//0xnW3f5swuOpxwXbbmgzDBgoPhDAYaRNriS/CfNwmiEEFIExafGo/nO5ngY8hAANH6jNuAMUN2uOtq6tMVQ96EoaV5SjGaSfJaSkpJpEMwqFGb2WC7PfCINwzAwNzeHpaUlLCwskJCQoHZeEATUq1cPPzX5CccuHJP0e7U2sqYwWURQoCSEEBEIgoA+x/rgYchDKAT1CRDfY13bdRjsPliUsog45HI5YmNjcx0EMzuXkpKSZfmmpqawsLCAhYWFMhTa2dmhQoUKysdfn/v2sampKVj2v9Fsa9euxdixY5VBslatWli7di1q166NDzEfgAvS/axYsKhdsrZ0FRCdQoGSEEJEsPfZXpwMOClqmb/88wtaObeinkmR8DyPuLi47w6CMTExaj1+XzM0NFQLe5aWlihbtmyOQdDCwgLm5uaQycR9W7aysoIgCLCzs8Nff/2F3r17K3sMS5mXgq2xLcITw0WtMwPDMKjvWF+SsonuoTGUhBCioYTUBJRcVhKxKbGijkeTsTJ0de2KA91oLb+vxw1+TxCMiYlBbGxslju3yGSyXPUAZvY442t9ffHWGRVLQkICjhw5gm7dusHExETt/IzLM7Do1iLRetW/xoDB21/eooxlGdHLJrqHAiUhhGhoi88WDD01VJKyOYbDxwkf4WDmIEn52pKSkvLd4wUzvs5sLUUAYFkW5ubm2Ya9nEKhkZFRkRzr9yHmA8quKCv6xBwZK0Or8q1wuvdpUcsluosCJSGEaMh9ozuehD4BD/GXR2EZFnObzcX0RtNFLzu35HK5RkEwJiYm23GDZmZmue4NzCwkmpqaFskwKJYpF6fgrzt/ibq8jx6rhycjnsDV1lW0Moluo0BJCCEaiE+Nh8VCC8nW2mPAoKVTS/zT75/ven7GuMHvDYI5jRs0MjLKcwj8+rG5uTk4jvveHw8RQbI8GdXWV8PbqLei3fpe1GIRpjSYIkpZpGCgSTkkV55+forDzw/DO9gbPiE+iE+NB8uwsDOxQ13HuqjnWA+9qvVCMeNi+d3UIosXeFx5ewVnX53FvaB7eB7+HCmKFMgYGcpalkVdx7poUrYJurp2hZGeUX43t9B4HPpY0oWbBQi49+kenj9/rja7ODfjB+Pi4rIcN6inp5fpLWEHB4dc3S42NzfXyXGDJG8MZYY40/sM6m+tj5iUGI339paxMhz1O4pPsZ/QyrkVWpVvBY6lPxoKO+qhJNm6HHgZM6/OxJ2gO5CxMih4hdpYm4xFlzmGQ69qvTD/h/lwNHfMpxYXPYIgYNujbVhwcwECowIhY2WZviFkHLcwsMCo2qMwo9EMmOirD9InebP36V70Pd5X+ormA0j77yHLsrmeLJLVY0NDQ7pVTJQCIgLQYncLBMcFi/JHkh6rhzQ+DaXMS2FKgykYWWskBctCjAIlyVRcShwmXpiIzT6bwTJsrn+5yFgZDDgDrGqzCoNqDqI3K4m9j36PAScG4Pr763nalYVlWDiaO2J3591oXKaxxK0s3HY+3omBfw+UvJ5/mv6DksVKKkOhiYkJ/fsiootLicOki5Ow6eGmLP84/V6eJT2xq/MuVLCpIFqZRHdQoCRqIhIj0GJXCzwLe6bRX6kT6k7AXz/+RW96EnkS+gQ/7PoBscmxkAt5/6Wf0bO8s9NO9K2uhR62QuqI3xF0P9xd0joYMEj+PRn6HN1eJtrxIPgB1nqvxT7ffUhVpIJj0nsWNRljKWNkMNIzwoV+F1DXsa5YTSU6ggIlUZGQmoAG2xrAN8xXlMHZMxrNwLwf5onQMvK1N1/eoPbm2ohNidX4dWLA4OjPR9HZtbNIrSta/MP9UXldZUnrKG9VHq/HvZa0DkIyE5sSi4fBD7Hfdz+2+GzReHkhjuFgpGeE24Nvo5pdNZFaSXQBm/MlpCiZfnk6noU9E22m3/wb83H17VVRyiLpFLwCvY/1RlxKnGiv04ATAxAcFyxKWUVNBZsKMJJJN8mJYzjqzSH5xtzAHNXtquOYvzh7fisEBZLSktDzaE+kKlJFKZPoBgqUROnmh5tY5b1K1BmrLMOi/4n+SExLFK3Mom7VvVXw/uT9Xbe5MyNAQJI8CcNPDRelvKKGYzm0r9AeMlaaRTMUggLtK7SXpGxCcuOX878gOjlatMXPFYIC/uH+WHBjgSjlEd1AgZIozb0+VzlORiy8wCMoNgj7nu0TtdyiKlWRink3xB9CIOflOP3qNJ5+fip62UXB6DqjRZ288DUbIxsajkDyTWBUIPY92yf61owCBCy9vRTxqfGilkvyDwVKAiB9TN6FwAuS7OfKgsXKeyuzXAuP5N4x/2P4kvRFkrJlrAzr7q+TpOzCrlHpRvAs6Sl6LyUDBlMbTKXJOCTfbHywESwjTVRITEvE3qd7JSmbaB8FSgIA+Dvgb8l+afDg4Rvmi3fR7yQpvyg57n9c9F7kDHJejkPPD1Hw/w4Mw2Bnp52i/huSsTK4O7hjQr0JopVJSF4d8jskSUdDhqP+RyUrm2gXBUoCIH2JCAbSLu/zMOShpOUXBXeC7kj6yz0qOQpBsUGSlV9YpchTEJcah+6VxVk+iGM4GOsZY0+XPZKNzSQEAHx8fDBlyhT4+fmpnYtJjpG0I0CAgPvB9+mP2EKCAiUBAPiE+EgaVGSsDM8+P5Os/KIgITUBH2M/Sl7PszB6nXLLN8wXo86MgtUiK9TeXBv7nu3TuJdSxshgom+CS/0uoVKxSiK1lJDMnTp1CkuWLEGVKlXQvHlznDlzBjyfPjHTN8xX8vqjk6MRGh8qeT1EevSnLwGQvjuClBgwiEuVto7CTluD16X+f6EwiE+Nx5SLU7D+wXqV3UQECBr3trg5uGF3592oWKyiGE0lJFvm5uZgWRY8z+P69eu4cuUKHB0d0bdvX9TuVVsrbYhLjYMDHLRSF5EOBUoCQRCQkJYgeT10604zCl66HuSv0euUvRcRL9BqTyvl0ABNZ3dnBNLiJsXxW4PfMM5zHO13THJNEASkpKQgNjb2uz4+fvyo7JFUKNJ/xwQFBWHhwoVoGNIQKCf990C/cwoHehUJZl+bjZiUGEnrkPNyOJo7SlpHYZamSIPXSS+t1EWvU9ZeRLxA/a31EZsSq/F6rQwYVCxWEXUd66KdSzt0qNgBepyeSC0luo7neSQkJHxXCIyLi1N5nJaWlmU9MpkMFhYWMDc3V/mwt7dHhQoVULJkSZw8eVJ5PcuyYFkW/fv3x8iZI1F7p7S9lCzDwsGUeicLAwqURdxB34OY8+8cyesRIMDDwUPyegqrqZem4vyb85LXwzIsqttVl7yegig+NR6t9rQSZbtLIP3fxPwf5qOLaxcRWke0RS6XqwW67w2F2Q2PMDY2VgmAZmZmMDc3R7ly5dTCYXYfBgYGYJisJ1z+888/OHnypPK2d/fu3bF48WKULl0avMDDSGaEJHmSFD9KAP+/05SedDtNEe2hQFmEfY7/jBGnR4ABI9oOCFkx4AxQ076mpHUUVjfe38CKuyskf41YsKhpX5N+uWdh6qWpCIoNEm0nKQYMhp4aisZlGqOYcTFRyiSZ+97bwpkFx8TErHf9YhhGGfy+/XB0dMx1CDQzM4NMpp23ZweH9N7BmjVrYvXq1ahfv77yHMuwqF+qPq69uybJpE0ZK0PTMk1FL5fkDwqURdisa7MQnxoveVCRsTL0rd4XJvomktZTGAmCgFFnR4FlWEln4QPp64WOrDVS0joKqudhz0Vf9F2AgJjkGMy5Pger2qwStezCQhCE774t/O1Hbm8Lfx0IixcvDmdn51wHQRMTE7BswVo8pXr16vD390eFChUybftwj+G4/PayJHXLeTmGegyVpGyifYxAC0AVSTHJMbBfao9kRbJW6vMZ5gM3Bzet1FWY3P54Gw22NZC8HgYMzAzMEPxrMAX/TIw5OwYbH26UZHtFYz1jfJ70Gab6pqKXnV9ye1s4N9dk9xZlZGSUae9eZmMGNbktXJSlKdLguNwR4QnhonY+cAwHNwc33B96X7QySf6iHsoi6uDzg0hRpEheD8dw8HL3ojD5nbb6bFVZlkYqAgSsbL2SwmQmUhWp2P54u2SvQVJaEg4/P4xBboMkKT8vNJktnNvbwgCyvC1cokSJPN0W1tOjSUxS0+P0sP6n9eh6qKuo5QoQsOGnDaKWSfIXBcoi6vbH2+BYTtKgwjEcHMwcsKTlEsnqKOyuv78ueZjkGA4/lv8RA2oMkLSegup52HMkpmUfkDTBsRzuBN357kCZ1W3h75k4kpqamnU7OS7Tnr+icFu4qOvi2gU9q/TEIb9DoowhZsBgesPp8ChBEzULEwqURdTdoLuSBxUDmQEu9L0AMwMzSesprOJT4xEYFSh5Pc7WztjfdT/d8svCg+AHkpYv5+W4/uY6bt++/d2zhTPWEcyMoaFhpsGudOnSebotbGhoSP+PFGFbOmzB+5j38P7krdF4bgYMulfujtlNZ4vXOKITKFAWUcFxwZLXMcx9GFxtXSWvp7AKiQuRfMIUACxuuRgWhhaS11NQBccFQ4/VQxqf9aQOTb0MeYkGDVTHyubltnBW15qZmUFfX1+ydpOiw0TfBBf7XUSPIz1w5tWZPD+fZVjwAo+hHkOxtu1aWry/EKJAWURp4zaqjbGNpHUUdlLP6s5gJKNlgrKTxqdJ/lqYmpvi3vN7yiBoampKt4WJzjHRN8GpXqew/fF2jDs3TjkUJLs/fFmw4MHDxsgG2ztux08VftJWc4mWUaAsgu5/uo9kubSzuwUIMDcwl7SOwu7CmwtaqYdep6ylKlJx/MVx0daezIqVsRUqV64saR2EiIFhGAx2G4xOlTph5+OdWO29Gm+j3wJI70hgGAa8wCv/zVS1q4pxdcahZ9WeNOmvkKNAWcS8jHyJFrtbSH4rlRd41LCrIWkdhdnJgJP45fwvktfDMiyqFq8qeT0FkSAIGHBiAPzD/SWth2VY2kWKFDjWRtaYUG8Cxtcdj+C4YDwMeYhXka+QqkiFkZ4RXIu5wt3BHbYmtvndVKIlFCiLEAWvQN9jfSWdsZqBAUNLBX2nsIQwDDwxUCs7GJW3Kk+9BlnY92wfDvgekLweBgxqlagleT2ESIFhGJQ0L4mS5iXzuykkn1GgLELWeK/B/WDpF5FlweIHpx/oVup3+uXcL4hNiZV+q0WGRa+qvSSto6CKSIzAqLOjtBLqFYICXSuLu8YfIYRoGwXKIkLOy7Hw1kKt1MWDx9g6Y7VSV2HzLvodDj4/qJXZ3QIv0LZnWdjqs1Ur25JyDIeGpRuiUrFKktZDCCFSo2mERcTpl6cRGh8qeT0sWCAUqKpP4/K+x8YHG8Ey0v+zZAQGRi+M4GDiIHldBY2CV2DN/TWST8QB0nsn/9f0f5LXQwghUqNAWUScfnkaMlb6DmmGYWD8jzF27dwleV2F0fEXxyVfooZlWNgY2iDxRCIuXNDOTPKCxC/cD0GxQZLXwzIsRtUehSZlm0heFyGESI1ueRcR2tgZBwBWt1mNx58eY9u2bZg5cyY4jhavza3EtES8+vJK8npkrAzHex3H6P2jsWXLFrRp00byOguShyEPJa+DAYNqxathUYtFymPv3r3D6tWr8fjxY5w/f572qSaFUoo8BfeD7+NB8AP4hfshMS0RepweylmWg4eDB+o61qU1jAsoCpRFxMvIl5LX0bNKT4ysPRLegjc2bdqEixcvonXr1pLXW1i8jHypldusa9uuRcMyDeHl5YVff/0VYWFhKF68uOT1FhR+4X6S74yjz+njUv9LMNEzwa1bt/DXX3/hxIkTEIT0MZvJyckUKEmhEhQbhDXea7Dx4UZEJ0eDZViwDAtBENK39BQAuSCHjJWhe+Xu+MXzF3g6euZ3s0ke0C3vIkDBKyR9c8zQsHRDAEDt2rVRrVo1bNmyRfI6CxNtLOcEADXtawIA+vTpA47jsGsXDU/4WkJqguR1FDcpjm1rtqFcuXJo2LAhTp48qQyTQPrQka8fE1JQ8QKPdffXwWW1C5beXoro5GjlcTkvh0JQQM7LIRfS76DJeTkO+x1G3a11Mfz0cMSmxOZj60leUA9lESD1rjgZ9Lj0HhWGYeDl5YVJkyZR71ceRCZGaqUePTb9dbK2tkaXLl2wZcsWTJw4Mb2XgCj/P5aSocwQs6bPQnJy+r9NhUJ13KyZmRk4joOpqSnMzMyUH98+/vYju/MGBgb0GhOtSkxLRPdD3XH29dk8PS9jeNYWny049+ocLve/DBcbFymaSEREgbKQS5GnoP3+9lqpy8nKSfl13759MWXKFOzevRsTJ07USv0F2afYTxhxeoRW6iprWVb5tZeXF5o3b45bt26hYcOGWqlf1zlZOUk6MYoBAxcbF9z6cAszZ87E5s2bwTCMMlSam5tj3bp1iIuLU/mIj49Xfv3+/Xu180lJSdnWK5PJNAqk3543MDCQ7GdECr4UeQra72uPa++vfXcZvMAjOC4YDbY1wJ0hd1Deurx4DSSiYwS6r1KojT8/Hqu9V2tlbF7klEhYG1krH/fq1QuPHz+Gn58f9YxkQ8Er0GBbAzwMeSj5xKnS5qXxfsJ75WOe5+Hi4oLGjRtj+/btktZdUNz6cAsNt0sXrmWsDDMazcDsprMBAAEBAZg4cSLOnDkDAChTpgzevXuX53LlcjkSEhLUgmZ2wTS78xm9p1nR09MTrffUzMyMxowWMhPOT8Aq71WivPfIWBkq2lSEz3Af6HP6IrSOSIF6KAuxG+9vYNW9VdIvki0ALtYuKmESSO/9atGiBW7fvo0GDRpI24YCbOW9lbj36Z70FSkAmxjV2ZMsy2LIkCGYP38+VqxYAQsLC+nboePcHNxgrGcs2ZhWOS9HkzL/LRVUsWJFnD59GpcvX8b48eNRpkyZ7ypXJpPBwsJCtNcwLS0ty/CZUygNCQlRuyY1NTXb+vT19UXrPTUzM4NMRm9v+eXG+xtYeW+laO89cl4Ov3A/LLixQPmHGNE91ENZiHls8sCT0CeSr2sIAWjLtMWZWWdUDvM8D2dnZzRp0oR6v7IQmxIL+6X2SJJnf7tSLCb7TBB0LwiWlpbKY8HBwShVqhTWrVuH4cOHa6Udum70mdHY5LNJ9B5jBgzKWZXDq7GvtLKAvS5JTU3NUyjN6Rq5PPvXxtDQULQeVFNTU1oCLQ9qbaqFx6GPRX/vkbEyBE0Igp2pnajlEnFQoCykHgQ/QO3NtSWvhwEDGS+D+RZzfHz9EUZGRirn58+fjwULFiAkJATm5rS397fW3V+HMWfHSN6LLGNkqFqsKgImBWDKlCmYPXu2yvn27dsjNDQU9+9Lv9d7QeAX7oeq66qK/rowYLCqzSqMqTNG1HKLGkEQkJKSonEo/fr8txOjvmVsbJxjKM1NcDUzM4OJiQlYtnD+QfEw+CFqba4lSdksw2Jes3mY1miaJOUTzVCgLKTGnRuH9Q/Wa2Ux88X1FmNq66lYv369Wg/Xp0+fULp0aaxfvx7Dhg2TvC0FjccmDzwKeaSVPaN9hvtg5+Kd2LJlC969ewcrKyvl+b///hudOnXC48ePUaNGDUnbUlBMvTgVS+8sFW38McdwqG5XHd5DvbWyaxXJPUEQkJycrFEg/fYYz2f//42JiYlGofTra0xMTHRmnLrU7z1Olk5488sbScommqFAWUi5b3THo9BHktbBMRxaO7fGqV6n0L17dzx9+hT+/v5qt4batWuHsLAweHt7S9qegiZFngLTP021EvrnNZuHGY1nIDQ0FE5OTpg8eTL+97//9pBOS0tD6dKl0b17d6xatUry9hQESWlJqLq+Kt5Hv9f41h0LFnqcHh4Oe4gqxauI1EKiqwRBQGJi4ncH0m/PxcfHZ7suKcMwmYbQ773Nb2Rk9N0B1WOTB3xCfL73R5crUVOjYGloKWkdJO8oUBZCcl4O4/nGki9mXqlYJdwfeh+m+qa4d+8e6tati+PHj6NTp04q1504cQKdO3fGkydPUL16dUnbVJBIeWvoawNrDsS2DtuUbxATJ07MtJdy2rRp2LBhA4KDg9WGLhQVKfIUHPU/il1PduFu0F3EpMRoXCbLsOAYDid7nURrZ9o5iuQdz/PKgKpp72lcXBwSErJfvJ9l2e/qLTU2NUbnB52Vi5RL5XL/y/ih3A+S1kHyjgJlIfQl6QtsFku/F+qOjjswoOYA5eNGjRpBEATcvHlT5bq0tDSUKlUKPXr0wMqVKyVvV0Fx7tU5tN3XVvJ6AscFopxVOeXjz58/o1y5cpg0aRLmzJmjPP7q1StUqFABe/fuRe/evSVvly4RBAGbfTbjt0u/ISo5CizDinKrm2M4WBlZ4VC3Q2hWrpkILSVEczzPIz4+XqPe06/PJyb+/4oIRgCmSt/+/V33o2fVntJXRPKEBvIUQtr6G8FAprqw8eTJk9GxY0fcuXMH9erVUx7X09PDgAEDsHnzZixatAiGhoZaaZ+uk3z2fRbs7OwwatQorFy5EuPHj4e1dfpyTy4uLmjSpAm2bNlSpAJlaHwoeh/tjavvriqPaRomZYwMckGOnlV7YlWbVWpLahGSn1iWhbm5uWgTJRUKBeLj4/Eh/AOq75X+LhT1g+mmwjnNrIjb77tfK/VYGVqpPG7Xrh0qVKiApUuXql07ZMgQREVF4fjx41ppm67jBR4bH2zUSl2pserr/02ePBlpaWlYsWKFynEvLy9cvXoVb94UjUHvQbFBqLe1Hm58uCFambbGthhTZwwCxgRgT5c9FCZJocdxHCwsLFCxXEWtLIdlZWSV80VE6yhQFjLH/I9h7LmxWqnLzcFN5THLspg4cSKOHz+O169fq5yrUKECGjdujC1btmilbbpuxuUZOP3qtOT1sPEsZkycoXbczs4Oo0ePxsqVK/Hlyxfl8a5du8LCwgLbtm2TvG35LSE1AT/s/AFBsUEaT4xiGRYlTEvg9djXCJschuWtl6OCTQWRWkpIwaDP6aOiTUXJ63Gzd8v5IqJ1FCgLkbCEMAw5OQQMpF8+gkviYG2g3vPSv39/2NraYtmyZWrnvLy8cOXKFQQGBkrePl1268MtLLq1SPJ6OIZDbYfaOHr0KI4ePap2PqOXcvny5cpjRkZG6NOnD7Zv357jwtEF3bTL0/Am6o0os+x5gUdoQihW3aMZ8qRoq1+qvqTLYtmb2tPC5jqKAmUhMv78eMSlxEm+piHLsFA8VGDnzp1q5wwNDTFmzBhs374d4eHhKueKUu9XVhS8AgNODNDKbSGFoMCUNlPQqVMnjB49GlFRUSrnixcvnmkvpZeXF0JCQnDu3DnJ25hf7gXdE32Pe17gscp7Fbw/0fJYpOjqXa23ZEuhcQyH/tX7S1I20RwFykLiU+wnHHx+UCsTPQRBQPsS7TF79mwkJalvGThq1CgwDIN169apHDc2Nkbv3r2LRO9XVs6+Oos3UW+08jrZmdihQ8UOWLt2LZKTk/Hrr7+qXTN58mQoFAqVXko3Nze4u7tj69atkrcxvyy/u1ySXhQZK8PyO8tzvpCQQqpZ2WYob1VekjtlvMBjeC3aHlZXUaAsJLb4bNHKrW6WYeHl7oVlM5chNDRULTQCgI2NDQYPHow1a9aoBU4vLy8EBwfj/PnzkrdVF625vwYco509gRc0XwAZK0OJEiXw119/YceOHbhw4YLKNdn1Up4+fRohISFaaas2hSWE4aj/UUl6UeS8HEf8jyAsIUz0sgkpCBiGweKWi0W/U8YxHLzcveBk5SRquUQ8FCgLibOvz0re68UyLOxN7bH0x6VwdnbG0KFDsWDBAsTEqC/+PGHCBHz58gW7du1SOe7u7g43N7ciOTknTZGGq2+vSv46yVgZWpVvhUE1BymPDR48GM2bN8ewYcMQHx+vcv2kSZOgUChUxr326tULenp6mQ5rKOiuvbsm6e5Ecl6O6++uS1Y+Ibqui2sX/Fz5Z9H+eGYZFnamdlj6o/oKIkR3UKAsBBS8Ak8/P5W8Hn1WH8d+PgZzg/S1y2bOnImkpKRMlwkqX748unTpgr/++gsKhWqAyuj9Cg0NlbzNusQv3E/y3YsAoKRZSezotENl6zSGYbBp0yaEh4dj+vTpKtcXL14cY8aMwapVqxAZGQkAsLS0RPfu3bF169ZCt+bbw+CH0GP1JCtfj9XDw5CHkpVPSEGwod0GuNi4iDO0RACqFa+GrT5b4f3Ju9D9TiosKFAWAkGxQUiWJ0tez8IWC+Hp6Kl87ODggPHjx2PZsmX4/Pmz2vWTJk3Cq1evcOrUKZXjvXv3LrS9X9kJiAzQSj39mf6wN7VXO+7k5IT58+djzZo1uHXrlsq5zHopvby88Pr1a1y/Xrh62wIiAyTvodTWa02IrrIyssK1AddQ0UbztSl58LgUeAmTLk6C5xZPVFlXBRsfbJT03zHJOwqUhYA2wiQAVCymvr7YlClTYGBggHnz5qmd8/T0RKNGjbBkyRKV45aWlujWrRu2bNlSpP7S1NbrNG/6PJw9ezbTc2PHjoWnpye8vLyQnPxfe2xtbdV6KRs1agQXF5dCNzknKS1J0pUQBAhISlOfrEZIUWNnagfvod4YV2ccGDAa3QJXCArlqgwvIl5g5JmRqLWpFnzDfMVqLtEQBcpCICReOxMnDDgDtWOWlpb47bffsHHjxkzXl5w0aRJu376N27dvqxzP6P36999/JWuvrnkR8UIr9bRp2QY9evTAkydP1M5xHIetW7ciMDAQc+fOVTk3adIkCIKg7KVkGAZeXl44cuSI2pJDBZmhnvRbfxrKaHtRQgDAWM8Yy1svx+0ht9G+Yntlb6Umw06E///PN8wX7hvdcdyfdmDTBRQoC7j30e/R43APrdQV/yE+0+NjxoyBra0tZs2apXauXbt2qFixIv766y+V440bN4azs3Oh6/3KytW3V7H41mLJ6yluUhyH9h5ChQoV8NNPP+HTp09q11SuXBm///47Fi1ahMePHyuPZ9ZL2b9/f6SlpWHfvn2St11bKtpUlHwMJe2SQ4iquo51cbzHcbwf/x5bO2zFELchsDS01KhMhaCAnJej2+FuOBVwKucnEElRoCzAFLwCPx/5GV+Sv+R8sYa4VA6///J7putHGhsbY9asWdi7dy+ePlWdHPT1doyvXr1SHmcYBkOGDMHhw4cRHR0tdfPzVXhCOLoe6irqItqZYRkWniU9YWJiglOnToFhGLRv315tVjcATJ06FZUrV8bgwYNVXtOJEydCEATlHwD29vZo3759oZqV7+HgIenkqDQ+DbVK1JKsfEIKMkdzRwx2G4ziJsURk6y+QkheCRAgCAJ6He2FjzEfRWgh+V4UKAuwlfdWwvuTt+QDk2WsDC3KtoDvM1+sXLky02sGDRoEZ2dntRnEANCvXz/Y2tqqLJ4NAAMGDCh0vV+ZGX12NGJTYiXfwUgQBLR0agkAKFGiBM6cOYNXr16hd+/eajPt9fX1sXXrVjx58kSl9zijl3L16tWIiIgAkD484fHjx/Dx8ZG0/drSpGwTSXcq4hgOjcs0lqx8Qgq6RyGPMP/GfNF+JwoQkCxPxpCTQ4rUuHxdQ4GygEpMS8Tsa7O1Upecl2Nmq5kYM2YM/vjjD7x//17tGj09PcybNw9nzpzBjRs3VM4ZGhpi7NixatsxOjg4oF27doWq9+tbj0Mf47DfYa3sjGPAGaBfjX7Kx9WrV8ehQ4dw5swZTJw4Ue362rVrY+LEiZg1axYCAv6blZwxljIjaLZq1QolS5YsNK+Tvak9OlfqLNlOOZ0rdc50lj0hJN3MqzNFL1MhKHAx8CKuvy9cq1IUJBQoC6j9z/YjLjVO8no4hoObvRvql6qPuXPnwsrKCmPGjMn0r8Bu3brB3d0d06ZNUzs/cuTITLdj9PLywqNHjwpN79e31t5fK0lw+VbGLhLfjklq06YNVq9ejZUrV2LNmjVqz5s9ezZKlSoFLy8v8Hz6LflixYph7NixWLNmDSIiIiCTyTBw4EDs3bsXiYmJkn8v2jCh7gTJdsqZUG+C6OUSUli8i36Hs6+k2YhDxsqw7r767m1EOyhQFlC7nu7SylaLDMMoF8k2NzfHqlWrcPr0aRw/rj6rjmVZ/Pnnn7h16xbOnDmjci6r7Rhbt24NBweHQjk5R8ErsO/ZPsmHJLBgYWtsi3k/qC/dBKTvrT5hwgT88ssvaq+LsbExNm/ejJs3b2LDhg3K4xk9mhm9lIMHD0ZsbCyOHDki0XehXQ1KN8Awj2Gi3vrmGA7DPYajfqn6opVJSGFz0PegZENO5Lwcx/yPITGtcPzhW9BQoCyAeIHHg+AHko/JA4B5zeahul115ePOnTujffv2GDt2LGJjY9Wub9myJZo1a4Zp06apjdvL2I7x6wXNZTIZBg0aVKh6vzIERAZo5Rcbz/Mo/bA02LSs/zkvWbIE7dq1Q8+ePVVmdgNA06ZNMXz4cEydOlU5nCGjlzJjLKWTkxOaN29eoIJ/cFwwLry5gGP+x3Ay4CQehTxCmuK/yTjTGk5DcZPiory5yVgZSluUxpKWS3K+mJAizDvYW9L3LoWgwJNQ9SXTiPQYgUawFjivv7yGy2oXyesZWHMgtnXYprKFHwC8f/8elStXhpeXV6aTdO7du4e6deti9+7d6Nu3r8q57t2748mTJ/D39wfHpS9y++bNGzg7O2PXrl3o16+fWnkF1d6ne9H3eN+cL9TQFOcp2DBqA8qVK4dTp06hVKlSmV6XkJCAxo0b4/Pnz7h37x5KliypPBcbG4sqVaqgatWqOHv2LBiGQUREBMqVK4cxY8bgzz//xIEDB9CrVy8EBASgQgXdXBbn2ednWP9gPY74HUF4YrjaeRkjg52pHZLkSfiSJM7qCDJWBntTe9wcdBNlLMuIUiYhhVXp5aXxMVa62dgMGKxpuwajao+SrA6SOeqhLIDEeiPMydCaQ9XCJACUKVMGc+bMwerVq3H//n21856enujcuTNmzpyJ1NRUlXMZ2zGePHlSeax8+fL44YcfCs2kjwxfkr5IOps4w8LeC3Hr1i1ER0fD09MTDx9mvo90dssJmZubY8OGDTh//jx2794NQLWXMjw8HJ06dYK1tbVO9lIGxwWj/b72qL6hOjb7bM40TAKAXJDjU9wnUf8NeZb0xN0hdylMEpIJuVyOmJgYBAcH4+XLl4hMjJS0Po7ltPYeSVRRD2UBdDfoLuptrSd5PYPlg7F1bubhQS6Xo1atWmBZFt7e3pDJVCee+Pn5oVq1ali5ciXGjBmjcq5x48ZQKBQq+0nv378fvXv31uner7xafGsxpl6aKmkdLMNCPlMOhmEQGhqKjh07wtfXF/v27UPHjh0zfc7Tp0/RoEEDNGvWDMePH1f2FANAnz59cP78efj5+cHOzg6RkZEoW7YsRo8ejYULF+KXX37BgQMHEBQUBD096RYHz4tj/scw8MRAJKYlamU2Pcuw4AUe5gbmmNdsHkbXGa2VPxwIkVJqaioSEhIQHx8v2uf4+HikpKSoVjQNgPqma6KRsTLMbjIbMxrPkK4SkikKlAWMgleg08FOOP3ytPSVrQAuHbmE5s2bZ3ra29sbdevWxbJlyzB+/Hi184MHD8aZM2fw5s0bmJqaKo+fPHkSHTt2xK1bt1C/fvoEhuTkZJQoUQLDhg3DwoULpfhutCoqKQo1NtSQ9NYOANgY2SBiSoTycWJiIvr3749jx45h6dKlmDBhQqa9zOfOnUO7du0wduxYrFixQnk8PDwclStXRrNmzXDo0CEAwIwZM7By5Uq8ffsWISEhqFGjBo4dO4bOnTtL+r3lxs7HOzHo70FaGU8MpG8jV6tELQysMRA9qvaAsZ6xVuolBEhfazYlJUX00JeQkIC0tJwX+zc0NISJiQlMTU2Vn7/+Ojefe3v3RnBisGQ/IwYMNrTbgGEewySrg2SOAmUBM+XiFCy5Lf3Af3MDc9S+XBvPfZ/jyZMnKF68eKbXjRkzBjt27IC/v7/a2L0PHz6gQoUK+P333/H7778rj/M8j8qVK6Ny5co4duyY8vi4ceNw6NAhfPz4UWd6v74HL/BouqMpbn24BR7S7Y7DgMGP5X/E+b7nVevnecyYMQMLFy7EiBEjsHr1arUeZABYv349Ro0ahVWrVmHs2LHK4xljJTNC47e9lJ6enrC1tcXp01r4oyYbV95eQYtdLbQSJjlwcLJ2wtORT2mfbpIjQRCQmJgoWtj7+nPG8l7ZMTY2zlXAy2sY/PpuxvfqdaSX5Gvz3h96n3arygcUKAuQmx9uovH2xpK/gbIMixblWmBny52oUaMG3NzccPbsWbCs+m29mJgYuLq6wtPTM9OlhCZOnIgtW7bgzZs3KFasmPL45s2bMXz4cAQEBMDFJX2C0dOnT1GjRg0cP34cnTp1kuz7k9qKuysw4R/p1yLkGA6/N/4ds5vOzvT81q1bMWLECDRv3hyHDh2Cubm52jW//vorVq5ciZMnT+Knn34CkP5m2LFjR9y/fx9+fn6wsrJS6aU8ceIERowYgffv38PR0VHKbzFLcSlxcF3ripD4EMm3tMzAgMGMRjMw94e5WqmPSE+hUCAxMVG0sJfxOTExMccdWxiGyXOgy00YNDY2zvR3ta5YcXcFJl6YKNm/W31OH7G/xcJAJuF9dZIpCpQFBC/wqLSmEgKjArUyTmx/1/3oWbUnLly4gFatWmHx4sWYPHlyptcePnwYP//8c6ZBMGPJmWHDhmHp0qXK48nJyShTpgy6dOmC9evXK4/XqVMHdnZ2OHXqlCTfl9RC4kJQbmU5pChScr5YBK/HvkZ56/JZnr98+TK6du2KUqVK4fTp0yhTRnXiiEKhQNeuXXHp0iXcvHkTNWvWBAB8+vQJlStXRrdu3bB161ZERkaiXLlyGDlyJGbMmAEHBwdMmzZNpedZmyZfmIxld5dpLUxmYBkWfqP8ULFYRa3WW9TJ5XLRQ19CQoLKmrhZ4ThOlN69bz8bGRllOhylsAuND4XjMkfJFjbvW60vtnfaLnrZJGcUKAuIC28uoNWeVlqpy9rIGiETQ6DP6QMApk2bhqVLl+LGjRuoW7eu2vWCIKBdu3Z4+vQp/Pz8YGZmpnJ+7ty5mD9/Pl69eqVyW3zevHmYP38+Pnz4AFtbWwDAxo0bMWrUKHz48EFlWZuCYu71uZh9fbbkQYcFi1o2tXBvzL0cr/X398dPP/2ExMREnDp1CrVr11Y5n5CQgCZNmiAkJATe3t7Kn/vmzZsxbNgwXLx4ES1atMDvv/+O5cuX4927d5g6dSquXr2KN2/eaL03JCE1AfZ/2SM+NT7ni0UmY2QYXWc0VrReofW6C4LU1NRcBbm8hsBvV4vIjL6+vsa9e5l91tfXL5LBT0o9DvfAsRfHJNn04Z7XPdQpWUf0cknOKFAWEJ0OdMKZV2ck33UFALZ12IZBboOUj9PS0pSB49GjR7C0tFR7zrt371C5cmWMGDECy5YtUzkXHx8PJycntG/fXmXJmcjISJQuXRpTpkzBrFmzAKSvh+jg4IDp06djxoyCNUuPF3iUXFYSofGh0lcmANgIDGw9EAsXLoSdnV22l4eFhaFTp054/Pgxdu/eja5du6qcDw4ORt26dVGsWDH8+++/MDU1hSAIaN68Od6+fQtfX18kJycreyk7duyIBg0aKMOmNm17tA1DTg7Rap1fM9U3RfjkcJWxlIIg4Pz585g1axbGjh2r0+upCoKA5OTk7+7Vy+6zXJ7z7ydDQ0NJxvcV5HHXRc2ryFeour4qUhU5/6GQWxzDoatrVxzsflC0MkneUKAsAARBgMVCC8n37pYxMjR3ao5zfc6p/UX+7t07uLm5oUWLFjh06FCmf7EvXrwY06ZNw/379+Hu7q5ybvXq1Rg/fjx8fX3h6uqqPD527FgcOHAA79+/h7Fx+ozZQYMG4fr163j9+rVOjwX61qvIV6iwRvoljxgwmNl4Jkq8LIHp06dDLpdjzpw5GDVqVLZvqklJSRg0aBAOHjyIxYsXY9KkSSqv49OnT9GwYUM0adIEJ06cAMdxePPmDapVq4Zhw4ZhxYoVyl7KwMBANGvWDDVq1MD+/fsl/56/1v94f+x7tk8rQz+ycmfIHdR1TO+tf/LkCSZMmICrV68CSN/qcu3atRrXwfM8kpKSRL3Fm/E5NxM7TExMRAt7GZ/FmthBCr7ld5bj1wu/ilIWy7CwMLDAy7EvUcy4WM5PIJKgQFkAvI16C6dVTtJWIgDlLMvBe5h3lv8gjx07hq5du2LDhg0YPny42vm0tDR4eHjAwMAAd+/eVXnjSElJQaVKleDu7o6jR48qjwcGBsLFxQVr167FiBEjAAC3bt1Cw4YNcfnyZfzwww8if6PSOeB7AL2O9pK8nvYV2uNYj2OQsTJERkZi5syZ2LBhAypXrozVq1ejWbNmWT6X53n88ccfmD9/PoYOHYq1a9eqhNDz58+jXbt2GD16tHIXpGXLlmHSpEm4desWKlasiLJly2LEiBGwt7fHtGnTEBwcDBsbm0zrS5Gn4PTL07j54Sa8P3njQ8wHyAU5zPTN4O7gjlolaqFjxY5wscn9zk8uq13w+svrXF8vNgYMVrdZjc6OnfH7779jx44dYFkWCoUCHMehbdu2mDJlisa3ehMSEnJsC8uyoo/tMzU1hZGRUYH6Y44UPLzAY8DxAdj7bK9GE01ZhoUeq4dL/S+hYemGIraQ5BUFygLgUuAltNzdUvJ6qt+tjnvH78HQMOtlUUaPHo1t27bB29sb1apVUzt/584dNGjQACtXrlRZigYAdu/ejf79++PevXuoU+e/MS4///wzHj16hBcvXoDjOAiCAFdXV7i7u2Pfvn3ifYMSm//vfMy+PlvSYQl6rB7ipsWpzWD08fHB2LFjcfv2bfz8889YunRpllswAsDOnTsxdOhQNGnSBIcPH1YZxvDtckIKhQL169dHXFwcHj16hHnz5mHZsmW4f/8+atasiSVLluCXX35RKT8+NR6Lbi7Cugfr8CXpC/RYPaTxquvccQwHAQJ4gUeLci0ws8lMNC7TOMefgf5cfbWytEmP1UP3Mt2xf+D+HGfyZpDJZKKHPhMTExgaGtL4PlJgyXk5hp0ahu2Pt4MBk+dgKWNkMNQzxJneZ3L1u4NIiwJlAXD21Vn8tO8nyesx2GSADg064MCBA1n2TiQnJ8PT0xNpaWm4f/8+TExM1K4ZOXIk9u7dC39/f5WJNQqFAjVr1oStrS0uX76sfCPM2Pv768Wyly5dihkzZmTb+6VrZl2dhfk35kt6K9ZUzxRx0zMf+iAIAvbs2YPJkycjLi4OM2bMwMSJE2FgkPnyGdeuXUOXLl3g4OCA06dPo1y5cspzEydOxIoVK5TLCfn6+sLd3R2//fYbxo8fj3LlymH48OF4+/YtXrx4gadPnypfzytvr2DAiQEIjgvO9eQkjuGgEBQYVXsUFrVYBFN90yyvZf/Ham0h88zosXoYVH0QXq54iZs3b0KhUKgEy7p162Lbtm1qEzsIIeoEQcAB3wMYeWYk4lPjc/X7M+P3xY9OP2JLhy0oZZH1H89Ee+ieRgHgE+KjlXrWLF+DI0eOYMqUKVleY2hoiIMHD+L9+/cYN25cptf8+eefMDY2Vuu14jgOCxYswNWrV3Hp0iXlcU9PTzRq1AhLlvy3YHv//v3B8zz27t2r4XelHYIg4HbQbcnH9RnpGWV5jmEY9OvXDy9fvsTIkSMxa9YsVK1aFWfOnMn0+qZNm+LOnTtISUmBp6cn7t69qzy3ePFitG/fHj169MDjx49RtWpVTJ8+HX/++Sc+fvyIX375BWvXrkW3bt3g6+ur3NN97rm5aLGrRZ7CJADlz23Dgw2ov7U+whMy34sbgE6sL2duZI6rV68iLCwMf/75p9qkKFdXV5QqVQrW1tYUJgnJBsMw6FWtF16OfYmZjWeiuEn6Jhosw0LGysCAUd7WztC0bFP83fNvnO97nsKkDqEeSh33z+t/0HZvW0l3XAHSZ67G/haLNWvWYNy4cWq7p3xrx44dGDRoEPbu3YvevXurnT948CB69uyJU6dOoV27dsrjgiCgYcOGSElJgbe3t7In9NSpU+jQoYPKdozdunXDy5cv8eTJE52/rbfw5kJMuzxN0joYMPih3A+41P9SzhcjfbmgcePG4dKlS2jXrh2WL18OZ2dntesiIiLQqVMnPHz4ELt27UL37t0BqC8nZGtrqxwje/bsWbi4uMDLy0s5KSepYhKim0Zr/H1yDIfKtpVxc/BNmBuoL8buttENj0Mfa1yPJrZ33I6BNQcqH6elpeHw4cNYsmQJqlWrhl27duVf4wgpwNIUabj36R4eBj/E089PEZsaCxkrQ0mzkqhVohbqOdZDGcsyORdEtI4CpQ4LTwhHxTUVEZ0cLfktvsZlGuP6wOsAgEmTJmHZsmU4duxYljvWCIKA/v3748SJE3j06JFaUBEEAW3atIG/vz/8/PxUbo3fuHEDjRs3xqFDh5Thhed5VKlSBa6ursrtGM+dO4e2bdvC29tbbe1EXXL742003NZQ8tdIxsowsd5ELGyR+73OBUHAsWPH8OuvvyI0NBSTJ0/GtGnT1IYqpKSkYPDgwdi3bx8WLFiA3377DQzDICQkBJ6ensrlhPz8/FCvXj0sXLgQ169f/6/30wbASAAcABGyP8dwGOQ2CJvbb1Y7N/L0SGx5tEUrS2hl5emIp6hmpz6GmBBCiiq65a3DRp8djdiUWK1stdjO5b9exMWLF6Nbt27o1auXym3QrzEMg3Xr1sHe3h49evRASkpKpufDwsIwe/ZslXONGjVC27ZtMWPGDKSlpU+uYFkWEydOxIkTJ/Dy5UsAwI8//ghHR0ds3qweKnRFsjwZ/Y73A8tI/09Jzsux73/pge/Dhw+5eg7DMOjatSv8/f0xdepULF26FK6urjh8+LDKuD8DAwPs2bMHs2bNwvTp0zFkyBCkpqbCwcEBZ86cwevXr9GrVy94eHhg4MCBmDJlyn9hkgHQ+f8/i9SRrBAU2OKzBRffXFQ718alTb6GSQdTB1S2rZxv9RNCiC6iQKmjnoc9x2G/w1pZay+jNygDy7LYtWsXPDw80L59e7x+nfkSLWZmZjh48CB8fX3x22+/qZ13cnLCH3/8geXLl+PJkycq5xYsWIBXr15hx44dymN9+/aFra0tli9fnt4ujsPgwYOxf/9+xMdrf1eU3Nj/bL9WtsNkwKCUUSk0KdcE8+fPR5kyZdCsWTNs374dsbGxOT7f2NgYc+bMgZ+fH9zc3PDzzz+jefPmeP78+X91MAxmz56NXbt2Yc+ePWjdujWioqJQrVo1HD58GOfOnUP37t3Vb+c6AXBEeu+kiFiGxR/X/lA7/pPLTyhhVkLcynKJZViMqTMGHEtrKRJCyNcoUOqo9Q/WQ8bKJK+HYzgMcR+itvakoaEh/v77b9jY2KBNmzaIiIjI9Pnu7u5YsmQJVqxYken+2xMnTkSlSpUwfPhwKBT/ha4aNWqgd+/emD17tnI/XUNDQ4wdOxY7duxAWFgYgPRFzhMSEnD48GGxvmVRrfJepZXeSQEC5v44F7t37cbnz5+xc+dOcByHIUOGwN7eHn369MH58+dz3KnEyckJf//9N86ePYugoCDUqFEDv/76K2JiYpTX9OvXD5cuXcKTJ09Qv359BAYGolWrVmjTpg2OHz+uXkcdABLkaV7gcTfoLp6Eqv4xwrEcpjaYKn6FOWDAwFjPGF7uXlqvmxBCdB0FSh0kCAL2++6X/LYey7AoblIci1osyvS8jY0Nzp49i9jYWHTo0EEZ/L41duxYdOjQAYMGDUJQUJDKOX19fWzcuBH37t3Dxo0bVc7NmTMHYWFhWLNmjfLYyJEjwbIs1q1bBwAoW7YsWrZsqbJlo654F/0Oj0MfS75vt4yVoYVTC/Sv0R8AYGpqiv79++PSpUv48OEDZs2ahUePHqFNmzYoVaoUJk2ahKdPn2ZbZps2bfDs2TPMnz8fmzZtQsWKFbFz507lDiqNGzfG3bt3oVAo4OnpiQkTJuD06dPqBekBcIHovZMZZKwMh/3U/5gYXXs06pSoo5U/ujIIELCmzRrlLFRCCCH/oUk5OuhjzEeUXlFa+op4YHfL3ejbsG+2l92/fx9NmzZFq1atcPjw4Uy3TouMjETNmjXh5OSEy5cvQyZTfaMfNmwYDh48CH9/f5Qo8d/tytGjR2P//v0IDAxULq797XaMhw4dQo8ePeDn56eybWN+O+J3BN0Pd5e0DgYMrI2s8Wj4o2yXxxAEAT4+Pti1axf27duHiIgI1KhRA/369UPv3r3h4OCQ5XODgoIwefJkHDhwAPXq1cOaNWuUW2dGRkaiSZMmKrfGVZQCIOG22gwYtHRqiX/6/aN27mXkS9TeXBuxKTnf8hejHV1cu+Bw98PKFQfu3r2LFStWKAM3ISR/KHgFXkS8gE+IDyKTIsGAQTHjYnB3cEcFmwo0REVLqIdSBz39nH3vkljs79hjcvfJ8PX1zfa62rVr48CBA/j7778xceLETK+xsbHBvn37cPPmTcybN0/t/MKFC2FoaKj2xvv7778jJSUFixcvVh6bMGECvnz5gp07dwIAOnbsCBsbG53rpXz6+ankPWQsw+LqgKs5rrXGMAw8PDywcuVKBAcH4+TJk3BxccH06dPh6OiINm3aYP/+/UhMTFR7rqOjI/bv34+rV68iLi4OtWrVwogRIxAZGQme5xEQEJB1+xxYSDlnTIAAn9DM12GtYFMBF/tdBCPWTKBsVLCpgL1d9oLneRw7dgx169ZFvXr1cPDgQVy+fFny+gkh6h6HPsbQU0Nh9qcZqq6viv4n+mPShUn49cKv6Hu8LyqvqwyLhRYYdWYUnn1+lt/NLfQoUOqg6ORordRzdctV2Nvbo0mTJsqFqbPSvn17rFmzBitXrsSKFSsyvaZRo0b43//+h7lz5+LatWsq56ytrbF8+XIcOnQI586dUx53cHDA+PHjsWLFCoSEhABIH+fXtWtXLFu2DAqFAgYGBujfvz927tyJ1NRUjb5nMUUnR0seZoqbFM/z8jR6enpo3749Dh8+jNDQUKxfvx5xcXHo3bs37O3tMXjwYFy7dk15eztD06ZN8ejRI6xcuRIHDhyAi4sL3NzcMh2XmbF+qGAgSP4ziE/NekJWnZJ1YKKvvluTmFiw6FKxC36b/BtKliyJrl27Kv+9sCwLQ0PDXG/BSAjRXERiBHoe6Qm3jW7Y8XgHkuT/DcdSCAqVYUgJaQnY7LMZ1TdUR//j/RGVFJUfTS4S6Ja3Dtr0cBOGnx4ueT1RU6OAZKBt27Z49uwZTp8+jSZNmmT7nN9++w2LFy/G4cOH0bVrV7XzCoUCP/74I168eIHHjx/D1tZWeU4QBPz44494/fo1nj9/DmNjYwBAdHQ0nJyc0LNnT+XYSW9vb3h6euLo0aPo0qULnj9/jqpVq+Lw4cPo1q2biD+F7zfh/ASsvb9W0n2ly1qWxdtf3opS1uvXr7F7927s3r0bb9++RenSpdGvXz/069cPFStWVLk2LCwMffv2xcWLqsv2MAyjDE9DhgzBZeYy3jm+E6V9WTHWM0bC9IQsz1svskZUsnRvEnqsHjpZd8LhMVlPDGMYBsbGxjAxMVF+fPs4s2O5fayvr6/zi/sTog3/vv8XnQ92RkxyTJ5X1+AYDjbGNvi759+o61hXohYWXRQodUx4QjjcN7ojKC4o54s1YCgzRPy0eHAsh/j4eHTq1Am3bt3CsWPH0KZNmyyfx/M8+vTpg+PHj+PKlSvKXW2+FhwcjJo1a6J27do4deqUyr7gr1+/RtWqVTF+/HgsXPjfAt1Lly7FtGnT4O/vr1wkvUmTJkhLS8Pt27cBAPXq1YOlpaVKD2d+WnJrCX67/Jukk3LMo83RO7k3qlatiipVqqBq1aooVqxYzk/MhiAIuHXrFnbv3o2DBw8iJiYGnp6e6NevH3r27KncO93W1lZtdj/LsrCzs0NISAja9mmLm1VvSj6GsZxlOQT+Epjl+arrquJ5eBZjPEWyqd0mWLyxwIwZM/D69WtlsOY4Dj/88AO6deuGhIQEJCQkIDExUfl1bh5/21OcGY7jNA6l2R37dswzIbroytsraLM3fR3a7/29yzEc9Dg9XOx3EQ1LNxS5hUUbBUodouAVaLy9Me4F3YNCinVYvlLXsS7uDLmjfJycnIwePXrg3Llz2Lt3r3IHm8ykpKSgVatW8PX1xe3bt1GhQgW1a86fP482bdrgr7/+wq+//qpybv78+Zg9ezZ8fHxQrVr67dykpCS4uLigcePG2LdvHwD17Ri3bt2KoUOH4t27dyhdWguTlnJwOfAyWuxuIVn5HMPBOdIZ+lf18eLFC+Ui8HZ2dqhatapKyKxSpQrMzdW3KcxJcnIyTp06hV27duHcuXNgWRY//fQTKlWqpBL4v1a2bFlcu3UNzQ43w4foD5L+v8oxHH6u8jP2dd2X5TVD/h6CXU93SboqwsNhD+Hu4A6FQoFdu3bht99+Q0REBARBwJQpU7L8WeVEEASkpqaqBMzvCaXZXZMbenp6kvWuGhsbZzqRj5C8eBv1FlXWVUGKIkXjP+JZhoWJngn8RvvB0dxRpBYSCpQ6ZPmd5fj1wq85X6ghGSvDeM/xWPLjEpXjaWlpGDhwIA4cOIAtW7Zg0KBBWZQAREVFoUGDBkhJScGdO3dQvLj6UipTpkzBihUrcOvWLZWtE1NTU1GzZk1YWlri5s2byh7MLVu2YOjQoXj06BFq1qyp3I6xUqVKOH78OOLj4+Hg4IBJkyZh1qxZIv00vl90cjRsl9hKGmQOdz+MbpW7IS0tDa9evYKvry+eP38OX19f+Pr64vXr18oertKlSysDZsaHq6srjIyMclVXWFgY9u/fj927d+Phw4dq542NjZGUlARBEDD40GDs8N8h+ZJJLMNiRasVGOuZ9b7yW322YuipoZLtKGUkM0L0b9HQ5/SVxxISErBs2TIsXLgQy5Ytw/Dh0g9R+R6CICApKUnjUJrV4+Tk5Fy1w9DQULLeVSMjIxoOUMjxAo+mO5rizsc7kAvi/L6VsTI0L9cc5/qco/9/REKBUkdEJEbAcZkjUhQpOV8sAv/R/qhUrJLacYVCgVGjRmHTpk1YuXIlxo0bl2UZ79+/R926dVG6dGlcuXJFbX/o1NRUNGrUCBEREfDx8YGFhYXy3L///osmTZpgw4YNyjdjuVyOKlWqwMnJSXlbe8uWLRg2bBhevHiBChUqYOjQobhw4QICAwN1otej55GeOOp/VJJQaWVohZCJITCQGWR5TVJSEl68eKESMn19ffH+/XsA6WP7ypcvrxIyq1SpggoVKkBfXz/TMhUKBfT09DKdaLJs2TI079kcNTfVlHxLUCB9/GLIxBDYGNtkeU1sSizsltohWZ67cJMXMlaGoe5Dse6ndZmeVygUYFm2yL4h8Tyf5xCalyCb20l4X4dMsceyGhgYFNnXV1fse7YPfY71kaTs4z2Oo1OlTpKUXdRQoNQRi28txrTL0yTv8eEYDg1LN8S1gdeyvCbjNt7SpUsxb948TJ8+PctfqD4+PmjcuDGaN2+OY8eOqYW8t2/fombNmspla74ux8vLC0ePHoW/vz/s7e0BAEeOHEH37t1x7do1NGnSBMnJyShbtiw6deqEDRs24O7du6hXrx7++ecf/Pjjj5r/QDR088NNNNreSPRyOYbDlAZTsKD5gu96flxcHPz8/FRC5vPnz5Uz6WUyGSpWrKh269zJyQlnzpxBx44dlWVlvGYZvyqGnRiGbU+3Sb7wvoyVoU+1PtjRaUeO1446MwqbHm6SZAvMpyOe5nmmPRGHXC6XrHc1ISFBZfeurLAsK0lQzfjQ09PTwk+yYPPc7IkHIQ9Ef3/kGA6NyzTGlQFXRC23qKJAqSPKrSiHdzHvJK+HBQuf4T6oYV8j2+sEQcD8+fMxc+ZM5RixrELluXPn0L59e4wYMQKrV69Wu+7w4cP4+eefsXnzZnh5/bdtXWRkJCpVqoSWLVsqx00KgoDatWtDX18ft27dAsMwmD9/PubNm4f379/D1tYW1apVQ+XKlXHo0CENfxri6HG4B476HxUtzGTsYPRi9AtYGFrk/IQ8iIyMVOnNfP78OZ49e4aoqPRZ0oaGhrC0tERoaKjK84oXL46hQ4fCuZIzRn8YjcQ09fUsxcSAgYm+CV6MfoGS5iVzvP5z/GdUWlMJMSkxovWcsgwLL3cvbGy3MeeLSYEjCALS0tJE603N7HFu3l5lMpnGt/2ze6wLd3I08TzsOaquryppHW/GvYGTlZOkdRQFFCh1QGh8KBz+ynonE9EIAHOdwYJWCzBlyhSV2ddZWblyJcaPH48RI0Zg7dq1WT5n8+bNGDZsGJYsWYJJkyapnR8xYgR27dqF+/fvo0qVKsrju3fvRv/+/VV6HC9duoSWLVvi77//RocOHRAZGYnSpUtj0qRJ+N///ocVK1ZgypQp+PTpk8qyRPklIjECFddU/K5lLLJyrs85tHZuLUpZOREEAaGhocqQuW/fPjx48EDtOpZlMXvzbPzx8Q+ttGtbh20Y5KY+jpcX+Ez3Tz/oexA9j/YUpW6O4WBvag//0f4wMzATpUxStAiCgOTkZNGHAWR8ZLUV7rcMDAwk62E1MjLK1fuIJjY+2IiRZ0ZKOsRmV6dd6Fejn2TlFxUUKHXAmZdn0G5/O8nr6V65O5yfOOPP+X+iffv22LlzJ6ysrHJ83rZt2zB06FD07t0b27dvz3KJkd9//x3z58/HgQMH0KNHD5VzSUlJqFOnDgRBgLe3t3INSkEQ0KJFC7x79w6+vr7KCSQtWrRAaGgonjx5Ao7jMG7cOOzbtw8fPnxAYmIiSpYsiT///FNtBnl+8f7kjaY7miJVkapxqFzacikm1s98RyJt6NOnDw4cOACe58GyrHLSj7W1Nbov6o5NnzZJPn6SBQtXW1fUKVEHlkaWiEiMgPcnb7yJegM5LwfHcHCyckJdx7po4dQC3St3h5GeEWZemYl5N9R3asoLjuFgqm+Km4NvompxaXtGCPlePM8rJ1xJ0cOakpK78fxGRkaS9bAaGhpi+Onh2P54u2RDbPRYPYyqPQorWq+QpPyihAKlDtjwYANGnhkpeT2pv6dCj9PDmTNn0K9fP1haWuLo0aNwc3PL8bmHDh1Cnz590K5dOxw4cAAGBuoTRQRBQP/+/XHo0CFcunQJjRqpji308/NDrVq10LdvX2zatEl5/OXLl6hWrRomTZqE+fPnA0jfP7xOnTrYuXMn+vfvj8DAQLi4uGDNmjUYOXIkevbsiWfPnsHX11dnBsx7f/JG271tEZ0c/V0L7gLA8lbLs53RrA0eHh7w8fGBvr4+mjdvjk6dOqFdu3YoUaIEpl+ejqW3l0q6mHteyFgZ5LwcFgYWGFNnDKY1mIZ5V+ZhkfcisAz7Xa9DcZPiuNDvAoVJUqTJ5XJluJSihzWzHbi+xbIsmP4MFGWlXUavc6XOONbjmKR1FAUUKHXA6nurMe581rOpxaL4Q6G8Vfju3Tt069YNvr6+WLNmDYYMGZJjMDtz5gy6deuGRo0a4fjx42qzuoH0md2tW7fG48ePcevWLbi6uqqc37p1K7y8vNR6MefMmYN58+bh0aNHylvi3bp1w4MHDxAQEAADAwP8/PPP8PHxQUBAAK5cuYIff/wRt2/fRr169TT90YgmMjESY86OwYHnB5RhJzssw4IXeFS2rYw9nffAzSHncC+169evIyoqCi1btlR7jadcnIIVd1foTKD8GgMGXCwH+SE5Tp85jXHXxiEwKlD5M85Oxms1xG0I/vrxL9HHrhJCVH07fjWrELokbAleK15L2pZ2FdrhVK9TktZRFFCgzGeCIOCnfT/h3Gtpd38xlBkiaYbqmJvk5GSMHz8eGzduxMCBA7F27VrlreisXLt2De3bt0f16tVx5swZWFpaql0THR2Nhg0bIiEhAXfu3FHO4AbSv9/evXvj7NmzePToEZyc0gdCp6SkoEaNGrC1tcX169fBsixevHiBKlWqYPny5Rg3bpyy1/Lo0aPo1KkTnJyc0Lx5c2zdulXzH5DI7ny8g7X31+Lg84OQ83KwDKvshRQgKINmfcf6GOs5Fl1cu6isc6ir5lyfg7n/zpV8hvd34wEIwNGeR9G2Qlscen4Iq+6twsOQ9HU1ZaxMufe4nJdDgAADzgB9qvfB6Nqj4e7gno+NJ4R8q/OBzvg74G/JhtnkZvMEkjsUKPPZnOtzMOua9It0ezh44MEw9YkWQPrEmOHDh8PFxQVHjx5Vbn2YFW9vb7Ru3Rply5bFP//8k+nEmI8fP6Ju3bpwcHDAtWvXYGpqqjwXGxsLNzc32NjY4ObNm8r1EK9du4ZmzZqpzAb38vLCyZMn8ebNG5iZmaFJkyZITU3F7du3MW/ePCxcuBAhISHftUuMNsSnxuNRyCM8DHmI0PhQKHgFLAwtUNO+JjwcPOBgpoXJWCI67n8cXQ51ye9mZE8AZJwM5/ucR3On5gCAoNggPAx+iCefnyA2JRYyVoaSZiXhUcIDNe1rwlgv+z+kCCH5Y+aVmVh4a6Fkf8RyDIe5zeZiWqNpkpRflFCgzEe3P95Gw20NJZ/gIGNlGOY+DGt/WpvlNc+ePUPXrl3x+fNn7NixA507d862zGfPnqFly5awsrLCpUuXULKk+tIujx8/RqNGjdCkSROcOHFCZTLPgwcPUL9+ffzyyy9YsuS/HXsGDRqEv//+Gy9evEDx4sXx8eNHuLi4YNq0aZg1axZOnz6N9u3b4+bNmyhdujTKli2LDRs2YOjQod/xkyF59SHmA8qsKJPfzcgRy7AoZlwML0a/gJVRzhPPCCG66cSLE+h8MPv3I0390/cf/Fg+/9c1Luikne9PspQiT0G/4/0yXf5EbHJejo6VOmZ7TbVq1fDgwQO0bNkSXbp0weTJk7MdNF2tWjXcuHEDiYmJaNSoEQIDA9WuqVmzJo4ePYrz589jzJgxKmuy1apVC4sWLcLSpUtx9uxZ5fElS5aAZVlMnJg+y7lUqVIYM2YMli5divDwcLRt2xaVKlXC0qVLUapUKbRq1Uonb3kXVqXMS6Fa8WpgdfxXBy/wiEyMxMQL+TdbnhCiuR/K/QAjWe62j/0e5gbmaFRa/M0piiLdflcoxI76H0VgVKAkO3t8q5xlObRwapHjdebm5jh8+DCWLVv2f+3dd3yN5//H8dd9zsmS2CN2zYrQ2NHWCrFqb0prlCI1So1Qbewde9OapbWKqp1ErVYQxJ61KgMhYmSec//+8HO+IkPinJNEfJ6PRx+Jc+5zX9c5lHeu+7o/H2bNmkW9evWMnVUSU7p0aQ4dOoROp6NmzZpcuHAhwTENGzZk2bJlLFmyhKlTp8Z7btCgQTRt2pRu3boRFBQEQJ48efD29uaXX37Bx8cHgJEjR6LRaJg0aZIxbG7bto0rV67Qq1cv/P39OXv2bGo+EvEGz549MxY9P336NAEBARw7dgw/Pz+6lOqCAct2dDIHvapnVeAq/ov4L72nIoR4S9lsstG9Ynd0msTL1ZlCq2jpXbk3dlaWC6zvE7nknU4++fkTjt09ZvFWiwAVblRgy+gtFC9ePMWvOXz4MB07dkSv1/Pbb7/h5uaW5LGhoaE0aNCAoKAg9uzZQ5UqVRIcM2bMGMaOHcvatWvp3Lmz8fEHDx5QoUIFypQpw759+9BqtaiqSt26dbl79y5nz57F1taWCRMmMH78eK5cuYKjo6OxHePcuXMpUqQIn3/+ObNnz07NxyKS0aJFC7ZvT/yux8LFC8MACHoSlCZ/fk2hVbR8X+t7xtUdl95TEUK8pWsPr1FuYTli9Cnr7Z5Sdjo7LvW/RNHsRc163veVrFCmg7DnYRz976jF/zHWKToqOFTgvs99ypUrx6RJk1JcrLZmzZqcPHkSZ2dn3N3dmTp1apJtxBwdHfnrr78oVaoU9erV4/DhwwmOGT16NN27d6d79+7s37/f+HiePHlYt24dBw4cYNKkF32rFUVh8eLF3Lp1y/jYoEGDyJEjB2PGjMHW1pYBAwawcuVKwsPD6datG2vWrEnxexNv1qRJk0QfVxSFpQuWsqrVqgwfJuHFKuXmi5vTexpCCBOUylWKye6TzX7emY1mSpg0IwmU6eBlCRNLy2Ofhx1f7+DSxUt88803eHl5UaFCBfz8/FL0ekdHR/bu3YunpycjRoygVatWhIeHJ3psrly52LdvH1WqVKFhw4bs3bs33vOKorB06VLc3Nxo3bo158+fNz5Xp04dvLy8GDNmDAcPHgTAycmJkSNHMmXKFC5evIiDgwM//vgjq1ev5vz583h4eKDValmwYAE9e/bk4cOHbN269a0+J5HQ559/TvbsCWsxTp48mc8++4x6xesxosaIdJhZ6l1+cJmouKj0noYQwgTfVv+WRiUbmeW+A42ioWWZlvSp0scMMxMvySXvdDDzn5kM3zfcovsnNWi40O8CZfKUMT529uxZPDw8OHLkCJ07d2bGjBnxakQm588//+TLL78kV65cbNq0KcnuOpGRkbRv3559+/bx22+/JbhbPCIiglq1avHo0SOOHj1KwYIFAdDr9bi7u3Pt2jUCAwPJnTs3UVFRuLi4GEsPxcbGUqZMGSpWrMiWLVvitWNs1KgRtra27Nu37y0/MQEv6oH+/PPPTJw4keDgYOOqtFarpU6dOuzbt8/Yu1dVVb7d/S3zjs1LzymnyKk+p6iYv2J6T0MIYYLnsc9p8WsL/G74vXV1FAWFz0p9xuaOm7HV2Zp5hu83WaFMB09jnlq8XaCtlW28MAkv7sw+ePAgy5cvZ8+ePZQpU4b58+ej17852DZr1oyTJ0+SI0cOPvnkkyTvrLazs2PLli20bt2a9u3bs2bNmnjPZ8uWjR07dmAwGGjatClPnjwBXgSWtWvXEhUVRffu3VFVFVtbWxYtWsTBgwdZuXIl1tbWjB8/nq1bt3L06FEGDx7Mo0ePWLlyJb169cLHx4cbN2685Sf2fouNjeXnn3/mww8/pH///ri7u3PhwgXjvtscOXKwbt06Y5iEF6vOcxrPYUmzJdjp7Cyyad5cnsY8Te8pCCFMlMUqCzs678CzhueLzlj/3ywiJbSKFq2ixauOF1s7bZUwaQESKNNB0JMgi3casdJYJfq4RqOhR48eXL58mY4dOzJgwABcXV05duzYG89ZvHhxjhw5QteuXenVqxdfffUVkZGRCY6zsrJi7dq19OjRg65du7Jw4cJ4zxcuXJhdu3bx77//0r59e2JjX7TxK1SoEKtWreLPP/9k7ty5ALi7u/Pll18ydOhQ7t+/T+fOnXFxcWHEiBEUK1aMdu3aMXPmTFq3bk22bNlYvnx5aj+q95per2fNmjWULVuWXr168fHHH3P+/HlWr16Nk5MTU6ZMQafT8euvv+Lo6Jjg9Yqi0LtKby72u0i7su3QaXRoFE2alMNKjYwcdoUQKWejs2Fy/ckc7XWUWh+8KPejVbTGDlivejV01itej+NfH2eM2xistIn/+yhMI5e809jR/45Se0Vti/dCdsnnQqBH4Jvnc/QoHh4eBAYG0qdPHyZNmkTOnG8uBL1q1Sr69u1LmTJl2LRpU6LddVRVZciQIcyaNYspU6bg6ekZ73lfX18aN25Mt27dWLZsmXHV9rvvvmP+/Pn8888/VKlShXv37uHk5ESLFi1YuXIlO3bsoFmzZuzevZtcuXLh6urKpk2b8PHxYfv27dy6dQutNuU/ub6PDAYDmzZtYsyYMVy8eJGWLVsyduxYKlSokODYiIiIFHciCn0ayi9nfuHv//7G/z9/Qp+GEqemf5vGoO+C3rmuREKIN7v84DLrzq7D/64/J4JOEB4VjqIo5LDNgWtBV1wLudLFpQulciXfAU6YTgJlGrr/7D5OC5wIjwy3aB0/K40V3St2Z2nzpSk6Pi4ujoULF/LDDz9ga2uLt7c3X3755Rsvy585c4a2bdty//59Vq5cSatWrRIco6oqY8eOZezYsXz//fdMmDAh3nlXr15Nt27dGD9+PD/88AMAMTExfPrpp4SHh3Py5EmyZcvGzz//TK9evfDz88PNzY3atWvz7NkzTpw4Qb169YiKimLevHm4urqyY8eOJO9Sft+pqsoff/yBl5cXZ86c4bPPPmPcuHFUrVrVIuNFx0XjMNkhXXt/582Sl3vD7qXb+EII8T7IWNelMrn+O/vzOOqxxYtCxxpiU1X5X6fTMXDgQC5duoS7uzvdunXDzc0t3p3YiXFxcTEGutatWzN8+PAE3XUURWHMmDF4e3szadIkBg4ciMHwv/fftWtXxo0bZ7yDG8Da2pr169dz7949PDw8UFWVHj16UKtWLfr27UtMTAyTJ0/m1KlTbNy4kaFDh+Lv709UVBQVKlTgp59+SsWn9X5QVZXdu3fj6upKq1atyJMnD0eOHGHnzp0WC5Pw4vJUtYLV0u0SuE6jw62YW7qMLYQQ7xMJlGnkyO0jbLiwIU0642jjtGgua1J0s82rChYsyK+//srevXsJDg6mYsWKeHp68uzZsyRfkz17djZv3oy3tzczZ87E3d090e46Q4YMYcmSJSxYsICvvvoqXvD84Ycf6NmzJz179sTX1xeAkiVLsnTpUtatW8fKlSvRaDQsXryYGzduMGXKFGrWrEmzZs344YcfaNCgAWXLlmXGjBn06tWL7du3ExISkqr3npnt37+fmjVr8tlnn2FjY4Ofnx++vr58+umnaTK+R1WPdKtZGWeIk9IgQgiRBiRQppH5x+anyY0BGkVDoeBCfNHxC8qWLcvSpUuJikpdDb4GDRpw9uxZRo8ezdy5cylbtixbt25NsrC5oigMGTKE/fv3c/XqVSpXrsyBAwcSHNe7d2/WrVvH2rVr6dSpk7EQuaIoLFq0CHd3d9q0aWNso9ipUyd69uxJ//79uXjxIs7OzgwfPpxJkyZx+fJlJk6cyPXr11m5ciVDhgzhjz/+wNXVFa1Wa1ztfJ8dOXKEevXqUa9ePWJiYti9ezeHDh2ibt26aTqP9uXak8suV6Kb5i1Jq2gpnas09YrXS9NxhRDifSR7KNNARHQEuafltvg+Mo2iIZ99Pi71u8SVs1eYOnUqv//+O46OjgwaNIi+ffsmWqw6Of/++y/9+/dn165dNG3alHnz5iXbwjEkJITPP/+cQ4cOMXnyZIYOHZpgL+b27dtp3749bm5u/P7772TJkgWAJ0+eULt2be7fv8/Ro0cpXLgwz58/p2rVquh0Ovz9/YEX5Y+KFi2Kr68vXbt2xdfXl3PnzuHs7EzLli15+vQpJ06c4NKlSxYvz5QRHT9+HC8vL3bv3o2Liwvjx4+nefPm6fpZrD+3nk6bO6X5uH91+4s6xeqk+bhCCPG+kRXKNBAQFJAmNyUYVAMrW64ku212qlWrxqZNm7h06RLNmjXDy8uLokWLMmLEiFRdDi5RogQ7duxg8+bNBAYG4uzszMSJE5Nsc5g/f3727dvHsGHDGD58OG3atEnQXad58+bs2LGDw4cP07hxYyIiIgDImjUrO3bsQKvV0rRpUyIiIsiSJQvr16/n6tWrfPfdd9jZ2bFw4UL279/PmjVrGDt2LA8ePGDZsmUMHDiQVatW0bZtW65cuZJoC8jMLDAwkJYtW+Lq6sqtW7fYsGEDp06dokWLFukerDuU60Dbsm3RpNFfOQoKA10HxguTly9fZsyYMbIdQggBQFRcFHce3+Fm+E0eRT5K7+m88yRQpoGA4IA0uSlhQZMFNCrVKN5jH374IcuWLePGjRv07duXhQsXUqxYMfr06cO1a9dSdF5FUWjTpg0XL15kwIABjBkzhgoVKhj3O75Op9MxefJktm3bxv79+6latSqnT5+Od4y7uzv79u3j7NmzuLu7ExYWBrzYx7lz505u3bpF27ZtiY2N5aOPPmL27NksXryYTZs20bBhQzp37syQIUPInj07ffr0YcqUKXTq1AmdTsepU6coWbLke3NzzoULF+jQoQMVK1bkwoULrFmzhrNnz9K+fft4hcjTk6IorGq1ChdHlzQZr1GpRng39EZVVQ4dOkTz5s1xcnJi7NixSf65FUJkfgFBAXyz4xvKLSyHwyQHis4uSvE5xck1LRf5vfPT6rdWrD2zlui4xBdNRNLkkncaGLp3KHP951q09mQuu1yEDQ9743Hh4eEsXryY2bNnc//+fdq2bYunpydVqlRJ8Vjnzp3Dw8ODw4cP8/nnnzNjxgwKFEi8xt/169dp164dly5dYuHChfTo0SPe86dPn6Zhw4bky5ePffv2Gc+zf/9+GjVqROfOnVmxYgUAHTt2ZO/evZw6dYosWbLg5OREmzZtmDRpEiVLlqR///5ERkbyyy+/MHDgQKZOnUpwcHCqL/O/K65du8bYsWNZu3YtRYsWxcvLi65du6LTZdwi3juv7qTpuqYWH+f016e5ePAi3t7eBAQEoNVqjTep/f777wlaggohMjf///zpt7MfAcEB6DS6JK8aahQNBtVATtuc/FD7B76t/i1ajdQ1TgkJlGmg/87+LDqxyKJ3uhbOWpg7391J8fFRUVGsWrWK6dOnc/36derXr4+npyfu7u4pujyqqiqrVq1i2LBhxMTEMGHCBL755ptEC4pHRkYycOBAfvrpJ3r27Mm8efOws7MzPn/58mXq16+PjY0NPj4+FCtWDIB169bRpUsXvLy8GDt2LOHh4VSqVAlHR0cOHTrEihUr6NOnD3/99Rc+Pj54e3vj5+dHzZo1GT9+PF5eXsyfP5++ffum+HN5F9y8eZPx48ezatUqHB0djXfJW1tbp/fU3sj3X1/qr6lv8XGqHq3Kid0nEn2uZMmS5M+fnyxZsmBnZ5eiryk5xspKum8IkdHEGeL4we8Hph2ZhkbRpLrSimtBV9a1XUfJXCUtNMPMQwKlhT2NeUrZBWX5L+I/i45TPl95znqcTfXr9Ho9mzdvZurUqZw8eZIqVarg6elJmzZtUtRt5uHDh3z//fcsXbqUihUrsnjxYlxdXRM9dsWKFXzzzTc4OTmxefNmSpQoYXzu1q1buLu7ExUVhY+PD05OTgBMnjyZ77//np9//pmvvvoKf39/atasyXfffcfkyZOpVasWDx8+5MCBA5QtW5b27dvz6NEjAgICKFu2LMHBwZw4kXiweNfcvXuXiRMn8tNPP5EzZ05GjhxJnz594oXzjO5E0AmqLatm8XG21d7GqIGjOHfuHIqixKtQ0KJFC3LmzMnz58+JjIx849eU0mq1qQqpqQmrr3/NyKvQQmQUsfpY2m1sx/bL21F5u6ijU3Rkt83Oge4HKJevnJlnmLlIoLQgg2qg4ZqG7L+x36LFzHUaHV+4fMGKlive+hyqquLj48PUqVPx9fWlVKlSDBs2jK5du2Jra/vG17/awrF3795Mnjw50RaOp0+fpl27djx48IDVq1fTokUL43PBwcE0bNiQ0NBQ9u7dS8WKFVFVFQ8PD3766Sd27NhBo0aNmD59OsOHD2f37t0ULFiQypUrM3r0aOzs7PD09GTDhg3GS/kvg3KlSpXe+rNJb6GhoUyZMoVFixZhb2+Pp6cn/fr1w97ePr2nlmqRsZE4THaw6Gp9TtuchA0PQ1EUfH19+e677zhz5owxWB47doxq1VIWalVVJSoqKkHQTGkYTc3X1JT30ul0Fgurr361s7OT8CreWV9u+ZJ1Z9eZ/PeNVtGS0y4np/ucplC2QmaaXeYjgdKCZh+dzeA9gy0/kAqtbFoxu/NsPvjgA5NPd+LECaZOncrmzZtTVXLo9RaO06dPp2vXrgkuoYeHh9OjRw+2bt2Kp6cnEyZMMP6jFRYWRuPGjbl69Sq7du3ik08+IS4ujlatWnHgwAEOHTqEi4sLTZs2JSAggMDAQObOncusWbM4fvw4TZo0oUaNGoSEhBAZGcmdO3do06YN8+fPN/lzSWthYWFMnz6defPmodPpGDJkCIMGDUpxX+2MqtyCclx4cMEi59YoGhqUaMDuL3YbHzMYDPz2228MHz6cu3fvcvXq1UR7z6c3g8FAVFSURcLq60E4qSoNibGysrJoaH01vKbkqogQKbHh/AY6bupotvPpNDrqF6/Pzi47071qRkYlgdJCboXfosz8MkTr0+BOMRWs51sTExaDq6srHTp0oF27diaHyytXruDt7c2qVauwtbWlb9++DBo0KMkbcF4KDg7mu+++47fffqNWrVosWrSIcuXiXypQVRVvb29GjhxJrVq1+PXXX8mfPz8AERERNG/enICAALZt24a7uztPnz7Fzc2NoKAgjh49iq2tLRUrVjQWXa9QoQIlSpSgU6dOfP3118yePZtBgwbxxRdfsH37doKDg9+ZS8Ph4eHMnDmT2bNnYzAYGDRoEEOGDEl0xfddNPHgRLz+8rLYKuWKlivoXrF7gsejo6MJDAxMckvG+0Sv15s9vCb1XExMTIrnZW1tbdHQ+mp4zSgVEIT5hUeFU2JOCcKjwt/6UndS1rZZS+ePOpv1nJmFBEoL8dznyYx/Zli81aJW0dLKqRUrPlvB9u3b2bhxI7t27SI6Oprq1asbw2XRokXfeozg4GDmzJnDokWLiIqKolu3bgwbNozSpUsn+zofHx/69evHv//+y+DBg/Hy8sLBwSHeMQcPHqRjx44oisL69eupVetFD/Lnz5/Ttm1b/Pz82LhxIy1atCAkJIRPPvkEe3t7Dh8+zMmTJ6lfvz7jx4+ncuXKNGnShFWrVjFp0iQ++OAD7ty5Q4ECBfDz8+OXX36hS5cub/0ZpIUnT54wd+5cvL29iY6Opl+/fgwfPpy8efOm99TMKuRpCEVmFbFIbdZsNtkIGRKCndW78cPD+0Cv11ssrL7+NTY25ZU0bGxsLBJWX/9qa2sr4TWNzTk6h8F7Bps9TGrQ4JzPmTN9z8gqZSIkUFpAVFwU+b3z8zj6scXH0ipaAnoHUCF/BeNjERER/Pnnn2zYsIFdu3YRExPDxx9/bAyXRYoUeauxHj9+zOLFi5k1axb37t0z7lOsWrVqkq+Jjo5m+vTpTJw4kbx58zJnzhxatWoV73/GkJAQOnbsyJEjR5gyZQpDhgxBURRiYmLo0qULW7ZsYfXq1XTu3JmLFy9So0YNKlasyO7duxk/fjyTJ0/mr7/+Yv78+fj5+TFt2jR69OjBsGHDmD59Oq6urmTJkoX9+/e/1fu2tOfPn7Nw4UKmTp1KREQEffr0YeTIkW9cCX6XDd49mLnH5pp1lVJBYWr9qQyrMcxs5xTvlri4uDQJr8+fPzeWoUoJW1tbi664vhpe3/ego6oqpeaV4sajG2YPlC/9/dXffFLkE4uc+10mgdICDt8+TK0VtSw+joKCVx0vxriNSfKYiIgItm/fzoYNG9i9ezcxMTF88sknxnBZuHDhVI8bFRXF6tWrmTZtGtevX8fd3Z0RI0YkW3Lo33//ZcCAAezcuZOmTZsyd+7ceHd5x8XFMWrUKKZNm0br1q1ZsWIF2bNnJy4ujq+//ppVq1axePFievfuzaFDh6hfvz4dOnRg+fLl1KtXj1u3brF7927jezt9+jSKonD79m2cnZ3Zv38/V65ceeOqalqKiopi6dKlTJ48mQcPHtCzZ09GjRr11oH/XfI89jnOC5z5L+I/s6zi6zQ6KjpW5Givo1IzTqSJ2NhYi6+4vjzWYEjZD16KoqRZeLWxscmQ4fXfR/9Scq7lSvzoNDq+r/k9Y+uOtdgY7yoJlCkQFRfFkdtHCAgO4Oy9szyJfoJOo6NItiJUKViFGkVqUDzn//pbzzk6h+/2fmfRO1kVFNyKubH7i91Ya1NWf/Dx48fGcLlnzx5iYmL49NNPjeGyUKHU3b2m1+v5/fffmTJlCidPnqRy5cp4enrStm3bRDfXq6rK1q1b+fbbb7l//z6jRo1i2LBh2NjYGI/Ztm0b3bp1I2/evGzevBkXFxfjPsJ58+Yxffp0hg4dyvr16+nUqROjRo2iT58+VKhQgdq1a9OwYUP69evHrFmzGDx4MF26dGHjxo3Y2dnh4eHB5MmTk3w/MfoY/rnzDyeCTnDm3hkeRz1Gq9FSKGshqhSowqdFPqV0btMDaWxsLCtWrGD8+PEEBQXRtWtXfvzxx3gB+30QEBRArRW1iNZHm/T/ilbRksM2B0d7HaVUrox3s40QplBV1aTwmtoQm5rwaum9ri+/Wltbpzi8bjy/kQ6bOpjykSf/vlFoXKoxO7vstNgY7yoJlMm4G3GXecfmsSRgCeFR4WgUDQoKelWPgoJOozN2v6lbrC7fVv+WFmVa4LHDg+Wnllu0M04262zcHXIXB2uHNx+ciMePH/PHH38Yw2VsbCw1atQwhsuCBQum+FyqquLr68vUqVPx8fGhVKlSDB06lG7duiVacujp06eMHz+emTNnUqJECRYuXIi7u7vx+evXrxv7cS9atIhu3bqhqio//vgjEydOxMvLizFjxuDt7c3w4cNZsmQJ+fPnp2XLlsyePZtff/2VJ0+eUKBAAe7cucPdu3dxdnbmzp073LlzJ0EZlHvP7jH/2HwWnVjEg+cPkv19/rTwpwysPpD25dqnup1mXFwcv/zyC+PGjePmzZt06tSJ0aNHU6ZMmVSdJzM5cvsIjdc2Jio2ijg19XsqdYqOnHY58evmR/l85S0wQyHeH6qqEhMTk2bhNaXxQ6PRpDi8ns9zngD7APRY7v6FwtkKc2dwyhuJvC8kUCZCVVV+PvUz3+7+lui46BRdktMqWvSqnsYlG2Nvbc/WS1stekNOubzlOPfNObOcKzw83Bgu9+7dS1xcHDVr1qR9+/a0bds2VeEyICCAqVOnsmnTJvLly8egQYPw8PBItOTQuXPn+Oabbzh06FCCFo6RkZH079+f5cuX06tXL+bNm4etrS3Tpk3D09OTQYMGMWPGDAYOHMjixYv5448/2LNnD4sXL2b16tV06dKFPn36sHDhQurXr8+xY8eIiIhg27ZtxtqXqqry27nf8NjhwdOYp6n6fa5VtBarWq2KtzKdFIPBwPr16xkzZgxXrlyhTZs2jB07lvLlJQABXH94nW5bu3HkzhEUlBTte3rZHq1p6aYsa76MAlkz735TITIjVVWJjo42e5WBa8WvEVY6DINiuSuEue1y82D4A4ud/10lgfI10XHRfLnlSzZe2PhWr9dpdBhUg0UvdwNULVCV472Pm/28jx49ihcu9Xo9tWrVMobLlN4ocvXqVby9vVm5ciU2NjZ4eHgkWnJIVVXWrFnD0KFDiY6OZsKECXh4eBhXEZcvX06/fv0oW7YsmzZtokSJEixatIh+/frx1VdfsXDhQtq3b4+vry/79u2jX79+PH36lCZNmrBkyRLq1q1LYGAgISEhFCpUiIoVK/LHH38QZ4ijz/Y+LD+9PMUh5lU6jQ4rjRVbOm6hUalGiR6jqipbtmzBy8uL8+fP06xZM8aNG/dOF1m3FINqYPmp5Uz/ezpXwq4k+v/RqyvHlfJXYkTNEbR3bp8h93EJIdKH134vphyeYtErhAUcChA0JMhi539XSaB8RZwhjnYb2rH9ynaLB0JTaNDgonFhZs2ZVK1alaxZs1pknEePHrFt2zY2bNjAvn370Ov11K5d2xguX9aNTE5KSw49fPiQUaNGsWTJEipWrMiiRYuoXr068KK7Ttu2bXn48CGrV6+mefPm/PLLL3Tv3p22bduyZMkSGjVqxO3bt1m3bh0tWrSgWbNm/PPPPxQuXJijR4/i4uLCnTt3CAsLo5prNU4XP01MmRgwIYto0KDRaNjVZRf1S/yvP7WqquzYsQMvLy9OnTpFgwYNGDduHB9//PHbD/aeUFWVw7cP43fDjxNBJ7jw4AIxcTHYWdlRPl95qhasSsOSDalaMOnKAkKI99fqwNV029rNomPU/qA2B7ofsOgY7yIJlK+YcHACXvu9LFZqwFwUVcFqvxUxB2NQFAVnZ2eqV69u/K9cuXJmb5f28OFDY7j08fFBr9dTp04dY7h0dHRM9vUvSw7Nnj2b0NDQJEsO+fv74+HhwenTp+nduzeTJk0iV65chIeH0717d7Zt28bIkSMZN24c27dvp1OnTjRo0IAFCxZQr149bGxs+Pbbb+nbty+DBw9m1qxZ1K1bl8OHD/+vRp0r0MQ8n4tG0WBvZc/l/pfJ75AfHx8ffvzxR/z9/alduzbjx4+ndu3a5hlMCCFEss7fO0/5RZbbTmSlsWLQx4OY1mCaxcZ4V0mg/H9nQ89SeWllixRbtoQTvU5gG26Lv7+/8b9z586h1+vJkiULVapUoXr16ri6ulK9enWKFClitkuDDx8+ZOvWrcZwqaqqMVy2adMm2XAZFRXFmjVrmDZtGteuXaNevXqMGDGC+vXrG+en1+tZtGgRo0aNwtramunTp9Ot24ufOKdPn87IkSOpU6cOv/76K4GBgbRq1Yrq1aszY8YMGjRoQPny5SlatChbtmyhVKlSBAYG/m8COYFvAB0mrU6+SqfoqJarGrqNOg4dPMTHH3/M+PHjky2jJIQQwvz0Bj2O3o6ERYZZbIwdnXfQpLSZViUyEQmU/6/p2qbsub7H4p1tTKVRNFTOXznR/ZPPnj3j5MmTxoB57Ngxbt++DUD+/PnjBcxq1aqZpSd0WFiYMVz6+vqiqipubm506NCBNm3aJNnlRa/Xs2XLFqZMmUJAQACVKlXC09OTdu3aGUsOBQcHM3ToUNatW0fNmjVZtGgR5cuX58CBA3Ts2BGNRsOGDRsAaNq0KU5OTowZM4bWrVvTrFkzdu7cSWRkZPyB2wDlAAuUKix1uBSzB82mSZMmEiSFECKd/Oj3I5MPT7bIv+eFshbi1qBbUu82ERIogZvhNykxp0SGv9T90ro26/j8o89TdGxwcDDHjh3j2LFj+Pv7c/z4cSIiIlAUhbJlyxoDZvXq1SlfvjxWVlZvPa8HDx6wZcsWNm7ciJ+fH6qqUrduXTp06EDr1q0TDZeqquLn58eUKVPw8fGhZMmSDB06lO7duxtLDvn6+tKvXz+uXbvG4MGDGT16NE+ePKFjx478/fffTJs2jTp16tC4cWMKFCjAgAED6N27d8IJZgGGYJEwqVW0tC3blvXt15v/5EIIIVLsv4j/KDGnhNlvzFFQmN5gOkM+HWLW82YWEiiBiQcnMvqv0Rl+dVKn0eH2gRt7v9z71itgBoOBS5cuGQOmv78/Z86cQa/XY2dnR+XKlePtxyxatOhbjfUyXG7YsAE/Pz8URYkXLvPkyZPgNS9LDm3evJm8efPy7bff4uHhQY4cOYiOjsbb25sJEyaQJ08e5syZQ/PmzRk1ahTTp0837sls1aoVoaGhibdFq8aLvZMWWjzUKlrCR4S/dW1QIYQQ5uH9tzfD9pmvDatW0eKc15mA3gFYad9+4SUzk0AJtPi1BTuu7sjQd3YrKGS1ycpZj7MUzV7UrOd+/vw5p06dircf89atWwA4OjoaVzFdXV2pVq0aOXLkSNX579+/bwyX+/fvR1EU6tWrZwyXuXPnjnf8tWvXjCWHrK2t6du3L4MGDaJgwYLcuHGDAQMGsGPHDpo0acK8efM4c+YM3bp1w9HRkZiYGOPcE2gNlMciK5QvHepxiJpFa1puACGEEG+kN+iptaIWx+4eM3mxSEHBSmvFsV7HqJC/gplmmPlIoAQKzChAyNOQ9J5GsnQaHYd7HKZ64eppMl5oaGi8Vczjx4/z+PFjAJycnOLtx3RxcUnxpfJ79+4Zw+Vff/2Foii4u7vToUMHWrVqFS9choSEMGfOHBYuXEhUVBRdu3Y1lhzatm0bAwcO5P79+3z//fe0bduWunXrcu/evaQH7w8kXBg1G42iYVajWQysPtBygwghhEiRh5EPqb2iNpceXHrrUKlRNGgUDX90+oPPSn9m5hlmLhIoAZsJNsToY9J7GslyLeSKfy//dBvfYDBw5coV480+/v7+BAYGEhcXh62tLZUrV463H7NYsWJvvFQeGhpqDJcHDhxAo9HEC5e5cuUCXpQcWrJkCbNmzSI0NJQ2bdrg6emJs7Mz48aNY+bMmRQqVCjplcmXhvNiH6WFWGmsGFFzBOPqjrPcIEIIIVLsUeQjOm/uzO7ru1P9Wq2iJXeW3Kxvtx63Ym7mn1wmI4ESsB5vbdGq+ubgZOPEDJcZFCpUiIIFC5I7d240mtT1kja3yMhITp06FW8l88aNGwDkzZs3XsCsVq0aOXPmTPJcoaGh/P7778ZwqdVqqV+/vjFc5syZ01hyaPr06Vy9epW6desyYsQIChYsSJUqVYiJSfqHAp1Oh/K9QqzGcr/PVhorPGt4Mr7eeIuNIYQQInVUVWXl6ZUM3jOYx9GPja1bk6JVtBhUA10rdGVWo1nktEv63y7xPxIoAUdvR+49S+ZSaTpTUFDPqrD5f49ZWVlRoEABY8AsWLBgot9nzZo1TUvY3Lt3j+PHj8crXRQeHg7Ahx9+aAyYrq6uVKhQAWtr6wTnCAkJMYbLgwcPotVqadCgAR06dKBly5Zky5aNLVu2MHXqVE6cOEGpUqW4du1aknOyt7en6XdN2aDdYKm3Dby4NDKj4QwGfTzIouMIIYRIvcjYSNafX8+SgCUEBAUkupBUJFsROn/UmT5V+lA8Z/F0mOW7SwIlL2pQ7rq2K8OWDdIoGia4TeDLEl9y9+5dgoKCjP+9/utHjx7Fe629vX2CsJnYr1+W6DE3g8HAtWvX4gXM06dPExsbi42NDZUqVYq3H7NEiRLxAnBwcDCbN29m48aNHDp0CJ1OZwyXLVq04NSpUzRp0oTo6OhEx7e1taVO5zr4FvNNk6L1f3X7izrF6lh8HCGEEG8vVh/L+fvnCXoShN6gJ5tNNlwcXWQ10gQSKIFxB8Yx7sC4DF02yK+rH3WL133jcc+fPyc4ODjJwHn37l3u3r2boOB3rly5kl3pLFiwII6OjmZp6RgVFcXp06fj7ce8fv06ALlz505wqfzljTpBQUHGcHn48GF0Oh116tTBx8cnybHyF86Pw3AHbjy6YfHfX42i4ZHnI7LZmF4wXgghhHiXSKAEroZd5cP5H6b3NJJUwKEAtwffRqcxT39uVVWJiIhIEDhfD59BQUHExf1vVU+j0eDo6JjkSuer+ztTe5n9wYMH8QqwHzt2jIcPHwJQqlSpeJfKK1asyIMHD9i8eTOTJk0iNDQ0yfOO2D2Caf7TLF4SSqfR0bR0U7Z22mrRcYQQQoiMSALl/2uwugH7b+7PcKuUGkXDWLex/FD7hzQf22Aw8ODBgzeudt6/f59X/xhZW1sn2N+Z2GX25Fo/qqrK9evX49XGPH36NDExMVhbW1OxYkWqV6/O6tWrjeWMXteybUsOVjvIo6hHiT5vbnu/2EuDkg3SZCwhhBAiI5FA+f9OBJ2g+k/VM1Rxc42iIZ99Pi71u0R22+zpPZ0kxcbGEhoamuxq5927d40357zk4OCQZNh8+esCBQoY93dGR0cTGBgYbz/m1atXE52TVqulxagWbNFssfTbR6fRUfuD2vh86SM9vIUQQryXJFC+4nvf75lyeEqGujlnV5ddNC7VOL2nYRYv93cmd2NRUvs7EwucBQoUoHXr1kmO12VDF9ZfWm/Rm3EUFOys7LjY76LZOxgJIYQQ7woJlK+I0cfQdG1TfG4kfZNHWvKq7cXYumPTexppSlVVHj9+nOQq58vvg4OD4+3vfJ2zszNKP4Xz989bdL4KCls7baVFmRYAPH36lFWrVjFv3jzatWvHhAkTLDq+EEIIkRGY5y6PTMJaa822z7eRa2ouovWJl6GxtJcFV3+s/SNj3MakyxzSk6Io5MiRgxw5cuDs7JzkcQaDgcWLF9OvX78Er1dVFRsbGy4+umjp6fJVpa9oUaYFt27dYt68eSxZsoRnz56hqio3b960+PhCCCFERiCB8jVZrLKQxSpLugRKjaIhb5a8LG+5nCalm6T5+O8SjUZD9uwJ95WWL1+eESNG0KlTJ6wmpKy/+FvPAQ15yEO5cuW4ePEiiqJgMLzYg6vVaomKiuLq1atky5aNrFmzYmdnJ3sshRBCZEpyyTsRhWYWIuhJkMXH0SgaFBT0qh5He0e+qfYNA6sPJIdtDouP/a6Kjo4mNDSU4OBg9u/fz8iRIxM9rkCBAoT3CycyLjLR581BQaFbvm6s/GZlio7XarVkzZqVrFmzGkPmy6+pfSxLliwSToUQQmQYskKZCOe8zgQ/Cbb4zTku+VzoWL4j1QpWo06xOmarM/muUVWVhw8fEhISQkhICMHBwUl+/3onoNcpioKVlRU9e/Zke+7tBIYGWm7eqHzR6AtG3xjNsGHD2LRpE1qtFr1ej06n4+uvv6ZDhw48efKEiIiIeF9ffywoKCjBY3p90iWsNBqNMWCmNIwm9Zy9vb2EUyGEECZ5PxPMG7gWdOWvm39ZvFXfmXtnWFZiGVULVrXoOOklKirKuJr4prAYGxu/p2rWrFkpUKAA+fPnJ3/+/Hz00UfG7199vFy5coSFhaHRaDAYDHTq1IlZs2bh6OjIve33OH//vEV/HysXqExOu5zG7j0DBgzg9OnTxMXF4eTkhJub21udV1VVoqKiUhRGX38sJCQk3mMRERHJ3sCkKAoODg4mr5pmzZoVBwcHNBrNW36aQggh3lVyyTsRB24ewG2Vm8XH0SpaSuUqRWDfQGx0NhYfzxxUVSUsLCxFq4mv153UarU4OjomCIWvf+/o6Ii9vX2K5lO+fHnOnz/PBx98wLJly2jQ4H+FxTdf2Ey7je3M+faNNIoGF0cXTvU5Fe9xg8HA2rVrmT59OgsXLqRmzZoWGT81VFUlOjo6VaE0ucdiYmKSHc/BwSFVK6RJPebg4IBWq02jT0kIIYQpJFAmQlVVyswvw7WH19KkJuWqVqvoWqGrxcdJTmRkZIpWE0NDQxOsJmbLli3ZgPjy+zx58ph99WrZsmUEBwczbNgw7Ozs4j0Xq4+l0MxC3H9+36xjvrS8xXJ6VOphkXNnZC/DqTkCanR08je/2dvbm7xq+vKrhFMhhLAcCZRJ+Pnkz/Ta3svi42gUDZXyV+JE7xNmP7fBYDCuJr4pKL7evlCn0yW6mvjqr1+uJmbJksXsczeXqYenMtJ3pFl/MNAoGvJkycONb2+QxSrjvvd3QWxsbKLB822C6usF8V9nZ2dn0l7TV/esWllZtoKAEEK8ayRQJsGgGnBb6cY///1j8b2UAP8O/JfiOYun6Njnz58nGg5f/3VoaGiCvXM5cuR440pigQIFyJUrV6bYCxerj6Xy0spcvH/RrH3a//z8T5p+2NRs5xOmi42N5enTp2a5rP/8+fNkx7K1tTXLqmnWrFmxtrZOo09ICCEs570NlLH6WC7cv8ClB5eIiovCRmdD6Vyl+cjxI6y1L/6Cv/HoBpWWVCIiOsLil75/a/sbdfPVTdENLBEREfFea2VllegK4uvfOzo6Jrgs/D44E3qG6j9VJ0YfY3KvdgWFXpV7sbT5UjPNTmREcXFxPH361ORV04iICJ49e5bsWDY2NmYpJZU1a1ZsbN6NvdhCvOui46I5d+8cVx9eJTouGludLU55nHDO64yV9v28gvFeBUq9Qc+ua7tYcGwBvjd8iTXEJjhGp9FRo0gN+rv2p2WZlgSGBlL9p+omB5HkKKoCR0D1if9bkTNnzjeuJObPn5+cOXNmitVES/K74UfTdU2J1ce+9UqlgkKbsm34rd1v722JJ5F6er3eGE5TGkaTeu7p06fJjmVtbW22Wqc2NjZSTkqIV8TqY9l2eRvzj83nyJ0jiV69tNJY4V7cnX6u/fis1GdoNe/P3u33JlAe/e8oXbd05erDq2gVbbKh4uXzhbMVZlS5UQw9NJRnuuRXGUyhU3R8YvsJQ8oMibfSKKsN5nXs7jE6burI7ce3U/UDglbRYlANDK8xnIn1Jr5Xf0GIjMVgMPDs2TOTV01ffp/cX/86nc6kvaavPmZrayvhVLzTfP/1pfu27vwX8V+KM0TpXKVZ3Xo1Hxf+OA1nmn4yfaBUVRWv/V5MPDQRjaJJ3eqUCihAJGDBK8VWGiu+rvw1C5ousNwgAoDnsc/50e9H5h+fT6w+NtmtDDqNjjhDHC6OLixptuS9+UtBvB8MBgPPnz83edX05WMv244m5mWXKHNc1pcuUSItxRniGLxnMPOPzUejaN5qMWJUrVGMqzsu0/+5zdSBUlVV+u7oy9IAE/e7vQyWFqLT6Pih1g+MdhttuUFEPA8jH7Ly9Eo2X9jMqZBTCVo0FstRDLdibvSu3JuPC3+c6f8iEMIUqqomCKem7D9NaZcoUwOqdIkSydEb9Hy++XM2Xdhk8n0Ufar0YVHTRZn6z1umDpRTD09lhO+I9J5Gimz/fDvNPmyW3tN4LxlUAzfDb/Ik+gk6jY7C2QqT3TZ7ek9LiPfSyy5R5risn5IuUa+HzrcNqvb29rKXPZMZvm843n97m+2m3CnuU/Cs6WmWc2VEmTZQnrt3jkpLKqVJyR9TKSgEDwnG0cExvacihBCZRmJdot72sn5ERESCpg6vM2cLUynEn76O3D5CrRW1zFrhRafRcarPKcrnK2+2c2YkmTZQ1vi5BseCjmX4QKlVtDQs2ZCdXXam91SEEEIkw5wtTFPbJcqUG6QknKaOqqqUXVCWaw+vmbV+sU6jw7WgK0d6HjHbOTOSTFn75HTIaf7+7+/0nkaK6FU9/ar1S+9pCCGEeAMbGxtsbGzIkyePyeeKiYl5qxamd+7cSfBYVFRUsmNlyZLFbC1MdbpMGRvi8bvhx+Wwy2Y/b5whjr//+5vAkEAq5K9g9vOnt0z5J2NpwFLjHboZmU6jo84HdWhSukl6T0UIIUQasra2Jnfu3OTOndvkc73eJSqlq6Z3797l0qVL8R5LTZcoUwJqRu4StfjEYotlCJ1Gx5KAJSxsutDs505vmTJQ+vzrk+HDpAYN1lprlrdcnqnv+hJCCGFZVlZW5MyZk5w5c5p8rpddolJ7CT8kJIQrV67EeywlXaLM1cLUXHWbVVVl/839FssQcYY4fG/4WuTc6S3TBcqnMU+5/uh6ek8jWRo0aDQaNnfYTNHsRdN7OkIIIQTwoqB9jhw5yJEjh8nnerVLVGoC6r1797h+/fpbdYkyNaBGEEFYZJjJ7z051x5e41nMM+yt7S06TlrLdIHyzuM7Fm2T+Ko3VctPjE6jw0pjxe8df6dxqcYWmpkQQgiRvrRaLdmzZyd7dtPLsKWmS9Srz4WFhXHz5s14jyXbJaoo8JXJ003+vagG7kTcwSmPk2UHSmOZLlAm1p/bEmY1msXso7O5/fg2wBtLC7wMnzWL1mRFyxUUy1EsDWYphBBCvPteLWhvqpddohILpUdDjzIleIoZZpy8GH2MxcdIa5kuUNpbpc0S8qdFPqV3ld6sDlzNXP+5XHxwEXjRRvHVcPlyH0bd4nUZ4DqA5h82lz2TQgghRDrRaDQ4ODjg4OCQ4LkCdwsw5SfLB8q0yippKdPVoYwzxOEwyYFoffI1vkyhoPB4xGOy2rz4SUlVVS4+uMiJoBOcDjlNeFQ4GkWDo70jVQtWxbWQK4WyFbLYfIQQQghhuojoCLJPsWynNFudLU9HPkWryVz1QTPdCqVOo8PF0YXjQcctNkbxnMWNYRJetO9yzuuMc15nulboarFxhRBCCGE52WyyUTxHcW6E37DYGBUcK2S6MAmQKRuPtnZqjUaxzFvTKlpalWllkXMLIYQQIn21dmqNVrFM4NMoGlqWaWmRc6e3THfJG+Des3sUmlnIYnWkrg64SqlcpSxybiGEEEKkn6thV/lw/ocWObdOo+Pud3fJZ5/PIudPT5lyhTKffT6+qvSV2X/C0Cpa2pZtK2FSCCGEyKRK5y5Nm7Jt0Cnm3RWoVbT0rNQzU4ZJyKQrlACPox7jtMCJe8/umaUupYJCdpvsXOp/CUcHRzPMUAghhBAZUcjTEJzmOxERHfHGsoAp8fJG3Uv9L5HNJpsZZpjxZMoVSoDsttlZ12YdGkWDgnnK9KxotULCpBBCCJHJ5XfIz8pWK81yLgUFjaJhbZu1mTZMQiYOlPCi9uOGdhvQarRvffn7ZSBd3nI5rZxamXeCQgghhMiQWjm1YnnL5cZA+Da0ihatRsuGdhuoW7yumWeYsWTaS96vOnz7MF1+78J/Ef+l6vK3VtGSJ0seVrVaRaNSjSw4QyGEEEJkRHuu7aHr1q6EPQ9LVbtljaKhSLYirG2zlhpFa1hwhhnDexEoAZ7FPGPMX2NYeGIhkbGRKIqSaLjUKBpUVcVaa81Xlb5ikvskctjmSPsJCyGEECJDCI8K53vf71l+ajkx+pg3Zgg7Kzv6VevH6DqjsbfOfF1xEvPeBMqXnkQ/Yd3Zdey5vgf/u/4EPQkyPpfPPh8fF/oY9xLudK3QVYKkEEIIIYweRT5izZk1+Pzrg/9df+49u2d8rmDWglQvVJ1GJRvRxaULDtYJWztmZu9doHxdVFwUUXFRWGutyWKVJb2nI4QQQoh3xPPY58ToY7DV2WKrs03v6aSr9z5QCiGEEEII02Tqu7yFEEIIIYTlSaAUQgghhBAmkUAphBBCCCFMIoFSCCGEEEKYRAKlEEIIIYQwiQRKIYQQQghhEgmUQgghhBDCJBIohRBCCCGESSRQCiGEEEIIk0igFEIIIYQQJpFAKYQQQgghTCKBUgghhBBCmEQCpRBCCCGEMIkESiGEEEIIYRIJlEIIIYQQwiQSKIUQQgghhEkkUAohhBBCCJNIoBRCCCGEECaRQCmEEEIIIUwigVIIIYQQQphEAqUQQgghhDCJBEohhBBCCGESCZRCCCGEEMIkEiiFEEIIIYRJJFAKIYQQQgiTSKAUQgghhBAmkUAphBBCCCFMIoFSCCGEEEKYRAKlEEIIIYQwiQRKIYQQQghhEgmUQgghhBDCJBIohRBCCCGESSRQCiGEEEIIk0igFEIIIYQQJpFAKYQQQgghTCKBUgghhBBCmEQCpRBCCCGEMMn/ARG2g56FSHrWAAAAAElFTkSuQmCC", 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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 -}