From f59fa76cd46bb4de9c4d61bcf258b222498e8f0d Mon Sep 17 00:00:00 2001 From: Eric Pugh Date: Mon, 19 Feb 2024 09:46:44 -0500 Subject: [PATCH 1/2] Tweaks from David and Scott --- .../examples/Jaccard and RBO Comparison.ipynb | 69 +++++++++++++++++-- 1 file changed, 62 insertions(+), 7 deletions(-) diff --git a/jupyterlite/files/examples/Jaccard and RBO Comparison.ipynb b/jupyterlite/files/examples/Jaccard and RBO Comparison.ipynb index 6f8112d..e86fc58 100644 --- a/jupyterlite/files/examples/Jaccard and RBO Comparison.ipynb +++ b/jupyterlite/files/examples/Jaccard and RBO Comparison.ipynb @@ -23,12 +23,12 @@ "cells": [ { "cell_type": "markdown", - "source": "# Jaccard and RBO Comparison \nTo understand the magnatude of changes to your query result sets, you can compare multiple snapshots together, either from the same case or different cases. \n\nThis notebook provides both Jaccard and Rank Biased Overlap (RBO) metrics.\n\nPlease copy this example and customize it for your own purposes!", + "source": "# Jaccard and RBO Comparison \nTo understand the magnatude of changes to your query result sets, you can compare multiple snapshots to each other.\n\nThis notebook provides both Jaccard and Rank Biased Overlap (RBO) metrics.\n\nPlease copy this example and customize it for your own purposes!", "metadata": {} }, { "cell_type": "code", - "source": "from js import fetch\nfrom typing import List, Optional, Union\n\nimport json\n\nimport matplotlib.pyplot as plt\nimport numpy as np\nimport pandas as pd\n\nimport piplite\nawait piplite.install('seaborn')\nawait piplite.install('rbo')\n\nimport rbo\nimport seaborn as sns\n\nimport os\n\nos.environ[\"TQDM_DISABLE\"] = \"1\"", + "source": "from js import fetch\nfrom typing import List, Optional, Union\n\nimport json\n\nimport matplotlib.pyplot as plt\nimport numpy as np\nimport pandas as pd\n\nimport piplite\nawait piplite.install('seaborn')\nawait piplite.install('rbo')\n\nimport rbo\nimport seaborn as sns\n\nimport os", "metadata": { "trusted": true }, @@ -72,16 +72,30 @@ } ] }, + { + "cell_type": "code", + "source": "os.environ[\"TQDM_DISABLE\"] = \"1\"", + "metadata": { + "trusted": true + }, + "execution_count": 5, + "outputs": [] + }, { "cell_type": "code", "source": "def jaccard(l1, l2, max_n):\n if len(l1) == 0 and len(l2) == 0:\n return 1\n max_len = min(len(l1), len(l2), max_n)\n set1 = set(l1[:max_len])\n set2 = set(l2[:max_len])\n intersection = len(set1.intersection(set2))\n union = len(set1) + len(set2) - intersection\n return float(intersection) / union\n\nasync def load_snapshots(case_id1, snapshot_id1, case_id2, snapshot_id2):\n df_a = await load_snapshot(case_id1, snapshot_id1)\n df_b = await load_snapshot(case_id2, snapshot_id2)\n return df_a.merge(df_b, on='query')\n\nasync def compare(case_id1, snapshot_id1, case_id2, snapshot_id2):\n df = await load_snapshots(case_id1, snapshot_id1, case_id2, snapshot_id2)\n \n df['jaccard'] = df.apply(lambda row: jaccard(row['docs_x'], row['docs_y'], 10), axis=1)\n df['rbo'] = df.apply(lambda row: rbo.RankingSimilarity(row['docs_x'], row['docs_y']).rbo(), axis=1)\n df['score_delta'] = df['score_y'] - df['score_x']\n df.name = f\"Case {case_id1} snapshot {snapshot_id1} vs. case {case_id1} snapshot {snapshot_id2}\"\n return df\n\n\n\nawait compare(case_id1=6789, snapshot_id1=2471, case_id2=6789, snapshot_id2=2472)", "metadata": { "trusted": true }, - "execution_count": 7, + "execution_count": 6, "outputs": [ { - "execution_count": 7, + "name": "stderr", + "text": "/lib/python3.11/site-packages/rbo/rbo.py:129: TqdmMonitorWarning: tqdm:disabling monitor support (monitor_interval = 0) due to:\ncan't start new thread\n for d in tqdm(range(1, k), disable=~self.verbose):\n", + "output_type": "stream" + }, + { + "execution_count": 6, "output_type": "execute_result", "data": { "text/plain": " num_results_x score_x \\\nquery \nprojector screen 1 1.0 \nnotebook 1 1.0 \niphone 8 1 1.0 \nprinter 1 1.0 \ncomputer 1 1.0 \n... ... ... \nwindows 10 1 1.0 \nmicrowave 1 1.0 \nbluetooth speakers 1 1.0 \ncoffee 1 1.0 \nvans 1 1.0 \n\n docs_x \\\nquery \nprojector screen [1069226, 47471, 490523, 1229109, 1229118, 325... \nnotebook [3851056, 3959000, 1550833, 1684763, 1675257, ... \niphone 8 [2048598, 1648546, 79524888, 1857711, 3613408,... \nprinter [3849563, 2225354, 1569761, 798960, 377837, 13... \ncomputer [560468, 532095, 560475, 523407, 693956, 56047... \n... ... \nwindows 10 [4481689, 3902727, 1560529, 1797902, 3155116, ... \nmicrowave [79513345, 4020048, 1768856, 2936032] \nbluetooth speakers [1993197, 3537784, 279672, 2663204, 558184, 33... \ncoffee [1996660, 2102472, 79583150, 1357989, 656359, ... \nvans [78503576, 79118095, 77388459, 78322005, 79013... \n\n num_results_y score_y \\\nquery \nprojector screen 1 1.0 \nnotebook 1 1.0 \niphone 8 1 1.0 \nprinter 1 1.0 \ncomputer 1 1.0 \n... ... ... \nwindows 10 1 1.0 \nmicrowave 1 1.0 \nbluetooth speakers 1 1.0 \ncoffee 1 1.0 \nvans 1 1.0 \n\n docs_y \\\nquery \nprojector screen [1069226, 47471, 490523, 1229109, 1229118, 325... \nnotebook [3851056, 3959000, 1550833, 1684763, 1675257, ... \niphone 8 [2048598, 1648546, 79524888, 1857711, 3613408,... \nprinter [3849563, 2225354, 1569761, 798960, 377837, 13... \ncomputer [560468, 532095, 560475, 523407, 693956, 56047... \n... ... \nwindows 10 [4481689, 3902727, 1560529, 1797902, 3155116, ... \nmicrowave [79513345, 4020048, 1768856, 2936032] \nbluetooth speakers [1993197, 3537784, 279672, 2663204, 558184, 33... \ncoffee [1996660, 2102472, 79583150, 1357989, 656359, ... \nvans [78503576, 79118095, 77388459, 78322005, 79013... \n\n jaccard rbo score_delta \nquery \nprojector screen 1.0 1.0 0.0 \nnotebook 1.0 1.0 0.0 \niphone 8 1.0 1.0 0.0 \nprinter 1.0 1.0 0.0 \ncomputer 1.0 1.0 0.0 \n... ... ... ... \nwindows 10 1.0 1.0 0.0 \nmicrowave 1.0 1.0 0.0 \nbluetooth speakers 1.0 1.0 0.0 \ncoffee 1.0 1.0 0.0 \nvans 1.0 1.0 0.0 \n\n[135 rows x 9 columns]", @@ -93,11 +107,45 @@ }, { "cell_type": "code", - "source": "import matplotlib\nmatplotlib.rc_file_defaults()\n\ndef plot_compare(df):\n figure, axes = plt.subplots(1, 3, figsize=(10, 4))\n figure.suptitle(df.name)\n\n sns.barplot(ax=axes[0], x=df['score_delta'], y=df.index, width=0.3, color='darkgrey')\n axes[0].set(xlim=(-1, 1))\n axes[0].set_xlabel('Change in Score')\n axes[0].set_ylabel('')\n axes[0].set_facecolor((0.90, 0.90, 0.90))\n axes[0].grid(True)\n axes[0].spines['top'].set_visible(False)\n axes[0].spines['right'].set_visible(False)\n axes[0].spines['bottom'].set_visible(False)\n axes[0].spines['left'].set_visible(False)\n axes[0].set_axisbelow(True)\n axes[0].xaxis.grid(color='w', linestyle='solid')\n axes[0].yaxis.grid(color='w', linestyle='solid')\n \n sns.heatmap(df[['jaccard']], ax=axes[1], cmap='crest', annot=True, xticklabels=False, yticklabels=False)\n axes[1].set_xlabel('Jaccard Similiarity')\n axes[1].set_ylabel('')\n \n sns.heatmap(df[['rbo']], ax=axes[2], cmap='crest', annot=True, xticklabels=False, yticklabels=False)\n axes[2].set_xlabel('Rank Biased Overlap')\n axes[2].set_ylabel('')\n \n plt.show()\n \ndf = await compare(case_id1=6789, snapshot_id1=2471, case_id2=6789, snapshot_id2=2473)\nplot_compare(df)", + "source": "import matplotlib\nmatplotlib.rc_file_defaults()\n\ndef plot_compare(df):\n figure, axes = plt.subplots(1, 3, figsize=(10, 4))\n figure.suptitle(df.name)\n\n sns.barplot(ax=axes[0], x=df['score_delta'], y=df.index, width=0.3, color='darkgrey')\n axes[0].set(xlim=(-1, 1))\n axes[0].set_xlabel('Change in Score')\n axes[0].set_ylabel('')\n axes[0].set_facecolor((0.90, 0.90, 0.90))\n axes[0].grid(True)\n axes[0].spines['top'].set_visible(False)\n axes[0].spines['right'].set_visible(False)\n axes[0].spines['bottom'].set_visible(False)\n axes[0].spines['left'].set_visible(False)\n axes[0].set_axisbelow(True)\n axes[0].xaxis.grid(color='w', linestyle='solid')\n axes[0].yaxis.grid(color='w', linestyle='solid')\n \n sns.heatmap(df[['jaccard']], ax=axes[1], cmap='crest', annot=True, xticklabels=False, yticklabels=False)\n axes[1].set_xlabel('Jaccard Similiarity')\n axes[1].set_ylabel('')\n \n sns.heatmap(df[['rbo']], ax=axes[2], cmap='crest', annot=True, xticklabels=False, yticklabels=False)\n axes[2].set_xlabel('Rank Biased Overlap')\n axes[2].set_ylabel('')\n \n plt.show()\n \ndf = await compare(case_id1=6789, snapshot_id1=2471, case_id2=6789, snapshot_id2=2473)\n", "metadata": { "trusted": true }, - "execution_count": 6, + "execution_count": 7, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": "## Overall Jaccard and RBO Scores", + "metadata": {} + }, + { + "cell_type": "code", + "source": "print(f\"Overall Jaccard Score: {df['jaccard'].mean()}\\nOverall RBO Score: {df['rbo'].mean()}\")", + "metadata": { + "trusted": true + }, + "execution_count": 8, + "outputs": [ + { + "name": "stdout", + "text": "Overall Jaccard Score: 1.0\nOverall RBO Score: 1.0\n", + "output_type": "stream" + } + ] + }, + { + "cell_type": "markdown", + "source": "## Query Level Jaccard and RBO Scores", + "metadata": {} + }, + { + "cell_type": "code", + "source": "plot_compare(df)", + "metadata": { + "trusted": true + }, + "execution_count": 9, "outputs": [ { "output_type": "display_data", @@ -111,8 +159,15 @@ }, { "cell_type": "markdown", - "source": "_This notebook was last updated 16-FEB-2024_", + "source": "_This notebook was last updated 19-FEB-2024_", "metadata": {} + }, + { + "cell_type": "code", + "source": "", + "metadata": {}, + "execution_count": null, + "outputs": [] } ] } \ No newline at end of file From 98a0ccce7f1f915293ba844a04b340e90fed003b Mon Sep 17 00:00:00 2001 From: Eric Pugh Date: Mon, 19 Feb 2024 10:19:41 -0500 Subject: [PATCH 2/2] Tweaks from the team.. --- jupyterlite/files/examples/Fleiss Kappa.ipynb | 29 +-- .../examples/Multiple Raters Analysis.ipynb | 171 +++++++++--------- 2 files changed, 92 insertions(+), 108 deletions(-) diff --git a/jupyterlite/files/examples/Fleiss Kappa.ipynb b/jupyterlite/files/examples/Fleiss Kappa.ipynb index e9df73b..2e1f9fe 100644 --- a/jupyterlite/files/examples/Fleiss Kappa.ipynb +++ b/jupyterlite/files/examples/Fleiss Kappa.ipynb @@ -23,7 +23,7 @@ "cells": [ { "cell_type": "markdown", - "source": "# Fleiss' Kappa \nTo understand how much your raters what? Scott, need some text!\n\nPlease copy this example and customize it for your own purposes!", + "source": "# Fleiss' Kappa \nTo understand how much your judges agree with each other. It is meant to be used with more than two judges.\n\nRead https://www.datanovia.com/en/blog/kappa-coefficient-interpretation/ to learn more.\n\nPlease copy this example and customize it for your own purposes!", "metadata": {}, "id": "bd7e4efa-eb00-451e-984d-ed6646d8e25f" }, @@ -51,11 +51,11 @@ }, { "cell_type": "code", - "source": "QUEPID_BOOK_NUM = 25\n\n# Not needed if running within Quepid JupyterLite\n# QUEPID_API_TOKEN = \"\"", + "source": "QUEPID_BOOK_NUM = 25", "metadata": { "trusted": true }, - "execution_count": 3, + "execution_count": 2, "outputs": [], "id": "71803a49-4065-4adf-a69e-cb0fe2d00f22" }, @@ -71,7 +71,7 @@ "metadata": { "trusted": true }, - "execution_count": 4, + "execution_count": 3, "outputs": [], "id": "31193536-98eb-4b46-ab98-af04ee07c6d3" }, @@ -81,7 +81,7 @@ "metadata": { "trusted": true }, - "execution_count": 5, + "execution_count": null, "outputs": [], "id": "8fef6231-daa8-467f-ac57-13a144e8a356" }, @@ -97,7 +97,7 @@ "metadata": { "trusted": true }, - "execution_count": 6, + "execution_count": null, "outputs": [], "id": "9a8561fd-2dbf-477e-9ac1-4df6d5ebdc91" }, @@ -113,7 +113,7 @@ "metadata": { "trusted": true }, - "execution_count": 7, + "execution_count": null, "outputs": [], "id": "a7598308-129b-4628-ad3a-fc3d703f8205" }, @@ -129,22 +129,13 @@ "metadata": { "trusted": true }, - "execution_count": 8, - "outputs": [ - { - "output_type": "display_data", - "data": { - "text/plain": "", - "text/markdown": "## Fleiss' Kappa: -0.3333" - }, - "metadata": {} - } - ], + "execution_count": null, + "outputs": [], "id": "25a613f9" }, { "cell_type": "markdown", - "source": "_This notebook was last updated 17-FEB-2024_", + "source": "_This notebook was last updated 19-FEB-2024_", "metadata": {}, "id": "5704579e-2321-4629-8de0-6608b428e2b6" }, diff --git a/jupyterlite/files/examples/Multiple Raters Analysis.ipynb b/jupyterlite/files/examples/Multiple Raters Analysis.ipynb index 1a262b4..53c3283 100644 --- a/jupyterlite/files/examples/Multiple Raters Analysis.ipynb +++ b/jupyterlite/files/examples/Multiple Raters Analysis.ipynb @@ -61,7 +61,7 @@ }, { "cell_type": "code", - "source": "# You need to get your book_id from Quepid UI. You should be able to see its content if you open /api/books/1.json\nBOOK_ID = 25", + "source": "# You need to get your book_id from Quepid UI. You should be able to see it's content if you open /api/books/1.json\nBOOK_ID = 25", "metadata": { "trusted": true }, @@ -75,10 +75,10 @@ "metadata": { "trusted": true }, - "execution_count": 4, + "execution_count": 66, "outputs": [ { - "execution_count": 4, + "execution_count": 66, "output_type": "execute_result", "data": { "text/plain": " query docid charlie@flax.co.uk \\\n0 projector screen 325961 NaN \n1 projector screen 47471 NaN \n2 projector screen 126679 NaN \n3 projector screen 254441 NaN \n4 projector screen 325958 NaN \n... ... ... ... \n2415 power supply 1667352 NaN \n2416 power supply 1667804 NaN \n2417 power supply 1667752 NaN \n2418 power supply 1667821 NaN \n2419 power supply 1667357 NaN \n\n epugh@opensourceconnections.com eschramma@cas.org dtaivpp@gmail.com \\\n0 3.0 NaN 3.0 \n1 3.0 NaN 3.0 \n2 3.0 NaN 3.0 \n3 3.0 NaN NaN \n4 3.0 NaN NaN \n... ... ... ... \n2415 0.0 NaN NaN \n2416 0.0 NaN NaN \n2417 0.0 NaN NaN \n2418 0.0 NaN NaN \n2419 0.0 NaN NaN \n\n aarora@opensourceconnections.com cmcollier@gmail.com \\\n0 NaN NaN \n1 NaN NaN \n2 NaN NaN \n3 NaN NaN \n4 NaN NaN \n... ... ... \n2415 NaN NaN \n2416 NaN NaN \n2417 NaN NaN \n2418 NaN NaN \n2419 NaN NaN \n\n ben.w.trent@gmail.com jeff@vin.com cmarino@enterprise-knowledge.com \\\n0 NaN NaN NaN \n1 NaN NaN NaN \n2 NaN NaN NaN \n3 NaN NaN NaN \n4 NaN NaN NaN \n... ... ... ... \n2415 NaN NaN NaN \n2416 NaN NaN NaN \n2417 NaN NaN NaN \n2418 NaN NaN NaN \n2419 NaN NaN NaN \n\n msfroh@gmail.com peter@searchintuition.com maximilian.werk@jina.ai \\\n0 NaN NaN NaN \n1 NaN NaN NaN \n2 NaN NaN NaN \n3 NaN NaN NaN \n4 NaN NaN NaN \n... ... ... ... \n2415 NaN NaN NaN \n2416 NaN NaN NaN \n2417 NaN NaN NaN \n2418 NaN NaN NaN \n2419 NaN NaN NaN \n\n ryan.finley@ferguson.com \n0 NaN \n1 NaN \n2 NaN \n3 NaN \n4 NaN \n... ... \n2415 NaN \n2416 NaN \n2417 NaN \n2418 NaN \n2419 NaN \n\n[2420 rows x 15 columns]", @@ -95,13 +95,13 @@ "metadata": { "trusted": true }, - "execution_count": 5, + "execution_count": 28, "outputs": [], "id": "98ad3844-d67f-44a2-8bae-034223de6c68" }, { "cell_type": "code", - "source": "df.dropna(inplace=True)\ndf.shape", + "source": "#df.dropna(inplace=True)\ndf.shape", "metadata": { "trusted": true }, @@ -124,10 +124,10 @@ "metadata": { "trusted": true }, - "execution_count": 7, + "execution_count": 29, "outputs": [ { - "execution_count": 7, + "execution_count": 29, "output_type": "execute_result", "data": { "text/plain": "Empty DataFrame\nColumns: [query, docid, charlie@flax.co.uk, epugh@opensourceconnections.com, eschramma@cas.org, dtaivpp@gmail.com, aarora@opensourceconnections.com, cmcollier@gmail.com, ben.w.trent@gmail.com, jeff@vin.com, cmarino@enterprise-knowledge.com, msfroh@gmail.com, peter@searchintuition.com, maximilian.werk@jina.ai, ryan.finley@ferguson.com]\nIndex: []", @@ -144,10 +144,10 @@ "metadata": { "trusted": true }, - "execution_count": 8, + "execution_count": 30, "outputs": [ { - "execution_count": 8, + "execution_count": 30, "output_type": "execute_result", "data": { "text/plain": "['charlie@flax.co.uk',\n 'epugh@opensourceconnections.com',\n 'eschramma@cas.org',\n 'dtaivpp@gmail.com',\n 'aarora@opensourceconnections.com',\n 'cmcollier@gmail.com',\n 'ben.w.trent@gmail.com',\n 'jeff@vin.com',\n 'cmarino@enterprise-knowledge.com',\n 'msfroh@gmail.com',\n 'peter@searchintuition.com',\n 'maximilian.werk@jina.ai',\n 'ryan.finley@ferguson.com']" @@ -157,13 +157,23 @@ ], "id": "72d4481e-ae12-4fff-bbbd-1888a894f69a" }, + { + "cell_type": "code", + "source": "# We need to filter to raters that we THINK might have some overlap\nraters = [\n 'epugh@opensourceconnections.com',\n 'aarora@opensourceconnections.com',\n 'ben.w.trent@gmail.com'\n]", + "metadata": { + "trusted": true + }, + "execution_count": 67, + "outputs": [], + "id": "dcd60629-44fc-4122-95dd-98fe2558489d" + }, { "cell_type": "code", "source": "nb_raters = len(raters)", "metadata": { "trusted": true }, - "execution_count": 9, + "execution_count": 68, "outputs": [], "id": "4c1fc91f-cda6-4e76-8372-3062e6975adb" }, @@ -179,14 +189,14 @@ "metadata": { "trusted": true }, - "execution_count": 10, + "execution_count": 69, "outputs": [ { - "execution_count": 10, + "execution_count": 69, "output_type": "execute_result", "data": { - "text/plain": "Empty DataFrame\nColumns: [query, docid, rating_0, rating_1, rating_2, rating_3, rating_4, rating_5, rating_6, rating_7, rating_8, rating_9, rating_10, rating_11, rating_12, rater_0, rater_1, rater_2, rater_3, rater_4, rater_5, rater_6, rater_7, rater_8, rater_9, rater_10, rater_11, rater_12]\nIndex: []\n\n[0 rows x 28 columns]", - "text/html": "
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querydocidrating_0rating_1rating_2rating_3rating_4rating_5rating_6rating_7...rater_3rater_4rater_5rater_6rater_7rater_8rater_9rater_10rater_11rater_12
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" + "text/plain": " query docid charlie@flax.co.uk rating_0 \\\n0 projector screen 325961 NaN 3.0 \n1 projector screen 47471 NaN 3.0 \n2 projector screen 126679 NaN 3.0 \n3 projector screen 254441 NaN 3.0 \n4 projector screen 325958 NaN 3.0 \n... ... ... ... ... \n2415 power supply 1667352 NaN 0.0 \n2416 power supply 1667804 NaN 0.0 \n2417 power supply 1667752 NaN 0.0 \n2418 power supply 1667821 NaN 0.0 \n2419 power supply 1667357 NaN 0.0 \n\n eschramma@cas.org dtaivpp@gmail.com rating_1 cmcollier@gmail.com \\\n0 NaN 3.0 NaN NaN \n1 NaN 3.0 NaN NaN \n2 NaN 3.0 NaN NaN \n3 NaN NaN NaN NaN \n4 NaN NaN NaN NaN \n... ... ... ... ... \n2415 NaN NaN NaN NaN \n2416 NaN NaN NaN NaN \n2417 NaN NaN NaN NaN \n2418 NaN NaN NaN NaN \n2419 NaN NaN NaN NaN \n\n rating_2 jeff@vin.com cmarino@enterprise-knowledge.com \\\n0 NaN NaN NaN \n1 NaN NaN NaN \n2 NaN NaN NaN \n3 NaN NaN NaN \n4 NaN NaN NaN \n... ... ... ... \n2415 NaN NaN NaN \n2416 NaN NaN NaN \n2417 NaN NaN NaN \n2418 NaN NaN NaN \n2419 NaN NaN NaN \n\n msfroh@gmail.com peter@searchintuition.com maximilian.werk@jina.ai \\\n0 NaN NaN NaN \n1 NaN NaN NaN \n2 NaN NaN NaN \n3 NaN NaN NaN \n4 NaN NaN NaN \n... ... ... ... \n2415 NaN NaN NaN \n2416 NaN NaN NaN \n2417 NaN NaN NaN \n2418 NaN NaN NaN \n2419 NaN NaN NaN \n\n ryan.finley@ferguson.com rater_0 \\\n0 NaN epugh@opensourceconnections.com \n1 NaN epugh@opensourceconnections.com \n2 NaN epugh@opensourceconnections.com \n3 NaN epugh@opensourceconnections.com \n4 NaN epugh@opensourceconnections.com \n... ... ... \n2415 NaN epugh@opensourceconnections.com \n2416 NaN epugh@opensourceconnections.com \n2417 NaN epugh@opensourceconnections.com \n2418 NaN epugh@opensourceconnections.com \n2419 NaN epugh@opensourceconnections.com \n\n rater_1 rater_2 \n0 aarora@opensourceconnections.com ben.w.trent@gmail.com \n1 aarora@opensourceconnections.com ben.w.trent@gmail.com \n2 aarora@opensourceconnections.com ben.w.trent@gmail.com \n3 aarora@opensourceconnections.com ben.w.trent@gmail.com \n4 aarora@opensourceconnections.com ben.w.trent@gmail.com \n... ... ... \n2415 aarora@opensourceconnections.com ben.w.trent@gmail.com \n2416 aarora@opensourceconnections.com ben.w.trent@gmail.com \n2417 aarora@opensourceconnections.com ben.w.trent@gmail.com \n2418 aarora@opensourceconnections.com ben.w.trent@gmail.com \n2419 aarora@opensourceconnections.com ben.w.trent@gmail.com \n\n[2420 rows x 18 columns]", + "text/html": "
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
querydocidcharlie@flax.co.ukrating_0eschramma@cas.orgdtaivpp@gmail.comrating_1cmcollier@gmail.comrating_2jeff@vin.comcmarino@enterprise-knowledge.commsfroh@gmail.competer@searchintuition.commaximilian.werk@jina.airyan.finley@ferguson.comrater_0rater_1rater_2
0projector screen325961NaN3.0NaN3.0NaNNaNNaNNaNNaNNaNNaNNaNNaNepugh@opensourceconnections.comaarora@opensourceconnections.comben.w.trent@gmail.com
1projector screen47471NaN3.0NaN3.0NaNNaNNaNNaNNaNNaNNaNNaNNaNepugh@opensourceconnections.comaarora@opensourceconnections.comben.w.trent@gmail.com
2projector screen126679NaN3.0NaN3.0NaNNaNNaNNaNNaNNaNNaNNaNNaNepugh@opensourceconnections.comaarora@opensourceconnections.comben.w.trent@gmail.com
3projector screen254441NaN3.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNepugh@opensourceconnections.comaarora@opensourceconnections.comben.w.trent@gmail.com
4projector screen325958NaN3.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNepugh@opensourceconnections.comaarora@opensourceconnections.comben.w.trent@gmail.com
.........................................................
2415power supply1667352NaN0.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNepugh@opensourceconnections.comaarora@opensourceconnections.comben.w.trent@gmail.com
2416power supply1667804NaN0.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNepugh@opensourceconnections.comaarora@opensourceconnections.comben.w.trent@gmail.com
2417power supply1667752NaN0.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNepugh@opensourceconnections.comaarora@opensourceconnections.comben.w.trent@gmail.com
2418power supply1667821NaN0.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNepugh@opensourceconnections.comaarora@opensourceconnections.comben.w.trent@gmail.com
2419power supply1667357NaN0.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNepugh@opensourceconnections.comaarora@opensourceconnections.comben.w.trent@gmail.com
\n

2420 rows × 18 columns

\n
" }, "metadata": {} } @@ -205,20 +215,30 @@ "metadata": { "trusted": true }, - "execution_count": 11, + "execution_count": 70, "outputs": [ { - "execution_count": 11, + "execution_count": 70, "output_type": "execute_result", "data": { - "text/plain": "Empty DataFrame\nColumns: [query, docid, rating, rater]\nIndex: []", - "text/html": "
\n\n\n \n \n \n \n \n \n \n \n \n \n \n
querydocidratingrater
\n
" + "text/plain": " query docid rating rater\n4063 iphone 11 1423 NaN aarora@opensourceconnections.com\n6483 iphone 11 1423 NaN ben.w.trent@gmail.com\n1643 iphone 11 1423 NaN epugh@opensourceconnections.com\n4065 iphone 11 1424 NaN aarora@opensourceconnections.com\n6485 iphone 11 1424 NaN ben.w.trent@gmail.com\n... ... ... ... ...\n2383 windows 10 79583170 NaN epugh@opensourceconnections.com\n4803 windows 10 79583170 NaN aarora@opensourceconnections.com\n5879 samsung 79659021 NaN ben.w.trent@gmail.com\n3459 samsung 79659021 3.0 aarora@opensourceconnections.com\n1039 samsung 79659021 3.0 epugh@opensourceconnections.com\n\n[7260 rows x 4 columns]", + "text/html": "
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
querydocidratingrater
4063iphone 111423NaNaarora@opensourceconnections.com
6483iphone 111423NaNben.w.trent@gmail.com
1643iphone 111423NaNepugh@opensourceconnections.com
4065iphone 111424NaNaarora@opensourceconnections.com
6485iphone 111424NaNben.w.trent@gmail.com
...............
2383windows 1079583170NaNepugh@opensourceconnections.com
4803windows 1079583170NaNaarora@opensourceconnections.com
5879samsung79659021NaNben.w.trent@gmail.com
3459samsung796590213.0aarora@opensourceconnections.com
1039samsung796590213.0epugh@opensourceconnections.com
\n

7260 rows × 4 columns

\n
" }, "metadata": {} } ], "id": "97b0bc0c-a20e-49b2-a65a-ef09eb7e6a58" }, + { + "cell_type": "code", + "source": "df_overall.dropna(inplace=True)", + "metadata": { + "trusted": true + }, + "execution_count": 71, + "outputs": [], + "id": "d58d14a6-6bfb-4c77-8145-514c0030bc53" + }, { "cell_type": "markdown", "source": "### Rating distribution per query\nHe we just want to plot the distribution of ratings for each query:\n", @@ -231,14 +251,14 @@ "metadata": { "trusted": true }, - "execution_count": 12, + "execution_count": 72, "outputs": [ { - "execution_count": 12, + "execution_count": 72, "output_type": "execute_result", "data": { - "text/plain": "Empty DataFrame\nColumns: [(rating, count), (rating, mean), (rating, std)]\nIndex: []", - "text/html": "
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
rating
countmeanstd
query
\n
" + "text/plain": " rating \n count mean std\nquery \n120v power supply 2 0.000000 0.000000\naa 10 1.800000 1.549193\naa battery 10 1.800000 1.135292\naaa 7 1.285714 1.603567\nadapter 10 1.300000 0.674949\n... ... ... ...\nwireless headphones 5 0.800000 1.303840\nwireless mouse 14 2.000000 1.109400\nxbox 15 0.000000 0.000000\nxbox one 7 0.428571 0.786796\nyoutube 12 0.000000 0.000000\n\n[137 rows x 3 columns]", + "text/html": "
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
rating
countmeanstd
query
120v power supply20.0000000.000000
aa101.8000001.549193
aa battery101.8000001.135292
aaa71.2857141.603567
adapter101.3000000.674949
............
wireless headphones50.8000001.303840
wireless mouse142.0000001.109400
xbox150.0000000.000000
xbox one70.4285710.786796
youtube120.0000000.000000
\n

137 rows × 3 columns

\n
" }, "metadata": {} } @@ -251,32 +271,15 @@ "metadata": { "trusted": true }, - "execution_count": 13, + "execution_count": 73, "outputs": [ { "output_type": "display_data", "data": { "text/plain": "
", - "image/png": 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\n" 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VS+666y7Jy8uTJUuWSFxcnMmDIDExMfLII49IVlaW/Prrr9KzZ09xd3eXo0ePysaNG2XUqFFyzz33WFQ+f39/6dKli7z11lvi5eVV7UEu0XVVcw88EtUs7VUAX375ZZnfiouLERMTg5iYGBQVFaGkpASPPfYYIiIiYDAY0KBBA7z33nvlPvr++eefo1GjRnB0dDR5VN7c6x3Gjx9fZvkREREYPHiwybRt27ahQYMGcHR0RExMDJYtW4apU6fCycnJovVdvHgxoqKiYDAYkJ6ejp07d6Jt27YVvt7h9OnTGD9+PBISEuDq6gpPT080bdoUa9euNcn75MmT6N69O9zd3U1eGVFRHZt7vUP37t2RnZ2N1NRUGAwGJCQk4K233ioz/759+9C0aVM4OjoiPDwczzzzTLl5mitb6dc7aNasWYMGDRrAYDDAx8cHAwYMwB9//GGSZvDgwXB1dS1TJnOvnSitKut54cIFZGVlITY2Fo6OjvDz80OLFi3w9NNPo6CgAMD/2q2y13AYu3z5Mu666y74+vrC1dUVN910E37//fdyX0+Sk5ODlJQUODo6Ij4+HqtWrTK7ruvXr0erVq3g6uoKV1dXJCQkYPz48Th8+LBK07ZtWyQnJ1dYvrVr10JEMGrUKIvXiehGxL91SFSL9ezZU77//nv56aefarooVAW2+JuO1UWn05X7tzqvt7ffflt69uwpO3fuVH+tgag24hgtolri8uXLJt9/+ukn2bx5s/qTMkT/Ji+99JJER0dLq1atarooRNeEY7SIaono6GgZMmSIREdHy2+//SZLliwRR0dHs4/+E9VGb775pnzzzTfy/vvvy8KFC23yJC/R9cRAi6iW6NKli7zxxhty8uRJMRgM0rx5c3nsscfKvIiUqDbr16+fuLm5yfDhw2XcuHE1XRyia8YxWkREREQ2wjFaRERERDbCQIuIiIjIRv5zY7RKSkrkzz//FHd3dw6yJCIiqiUAyIULFyQkJETs7GpPP9F/LtD6888/JSwsrKaLQURERFb4/fffJTQ0tKaLYbH/XKDl7u4uIlcbysPDo4ZLQ0RERJbIzc2VsLAwdR6vLf5zgZZ2u9DDw4OBFhERUS1T24b91J6bnERERES1DAMtIiIiIhthoEVERERkIwy0iIiIiGyEgRYRERGRjTDQIiIiIrIRBlpERERENsJAi4iIiMhGGGgRERER2QgDLSIiIiIbqdFAa8mSJZKamqr+HE7z5s3lgw8+qHCeHTt2SMOGDcVgMEhsbKysXLny+hSWiIiIqIpqNNAKDQ2Vxx9/XPbt2yd79+6V9u3byy233CLff/99uemPHj0q3bt3l3bt2smBAwdk0qRJMmLECMnOzr7OJSciIiKqXI0GWjfddJN069ZN6tatK3FxcfLoo4+Km5ubfPHFF+WmX7p0qURFRcm8efMkMTFRJkyYILfddpvMnz//OpfcOnkFReV+tjRd6XmsSWfJ9KqU1Zzf/7lkUTpr8q4oj+qoB0vzu9a8bVmG6q5XS6ZXRxksrYeq5HGt6SwtjzX1ZbyfXM9jgiXLsbQMNZVfddSXNa71mGBp3tYu15oyGKeraJu09LhOpm6YMVrFxcXy5ptvyqVLl6R58+blptm1a5d07NjRZFpmZqbs2rXLbL75+fmSm5tr8k9EpLCw8Lr/S5qZXe5n439/n78kSTOz5fKVfLmYd0WSZmbLPxfz1DyXLudLYWGhnDx3NV1BQYHkXrosSTOzJffSZSkoKJCkmdly8twlKSwslD/PXjS7LOPpf5y5IMdOX5DCwkI5k5unynDp8tUynLlwtQy//Z0rf5y5WOm6tn5yR6Vp8vOvlvWv81fLmvf/61zZfHlX8iU/v0AKCwvl7MU8s/WaNDNb/jx7tawnzl4qtx60+jr1//V1/J//1dc//5+3Vqakmdly9v/b4nTuJTmTm1dpeybNzFZl1erSXLpzly5fbTOjMpw1KsOVUvVl3GbGZbh0Od+kzaz5d/lKfrll1dpMqy8tbWXb+LHTF+SPMxcqXa5xm2nbeGFhoZw32sYrmr90m5n7dzr3at55RmXX/h3/56LJ+mn/Lly62n7n/78MSTOz5cTZSyZlr2wb+OPMRZM20/YT1Wb/v039efaiamfjvLVjwlmjY4JWX1odFRYWyjmj+irdZsf/uSi//Z1b7n6VNDNbTueWXffyjktaGcz9y62gzYy38YrqztxxqXS9/vZ3rhz/52o9nPr/7SY/v0C12TmjNtOOodb+K7Of/X+bld7Gza2TJXmXt02aOy4Z/7t0OV+tn9Zm5W3jxv9OGZ1LjLfJ8rZxS47rtv5XG+kAoCYL8O2330rz5s3lypUr4ubmJqtXr5Zu3bqVmzYuLk6GDh0qWVlZatrmzZule/fukpeXJ87OzmXmmT17tsyZM6fM9NWrV4uLi0v1rUgF8otFpu9xEBGRJ5tcvUIw/m6wrzzdI42K5MF9ZT9XJZ22nNLLsibv0vmVdvcuB1nYvOKrKXNlqCjviuapiXqwJm9bl8E4XWXtVJ7S2+H12m4szduS+a1NVx3rXpVtQNtPqnMbsLYtzK27pccvS+v5WveL6jg2Vsd+YWkZLFmONdudtekqW25522Tp32pKXl6e9O/fX86fPy8eHh41Vo6qcqjpAsTHx8uBAwfk/Pnzsm7dOhk8eLB8/PHHkpSUVC35Z2VlyZQpU9T33NxcCQsLk86dO1+3hsorKJLpe7aLiEhmZmcREZPvLo4OZdK169BJCouLRfbsFBGRDh07yIP7Pi7zOTOzs5zLKxTZ80ml6bTllF5WRfPkFRSL7Kk8v9LqNcuTMJ+KA9lL+eWXoaK8zZW7dL1WZz20bddRnBztqi1va8rQrkMnsdOJyJ6PKi2DcT1U1k7lKb29WrPdGJfhWtvW+HOTVu2kjnfZC6rS87fKaC/+7k4WLKeTeDjrLVr3vy9csWh/rMo2oO0n5/IKRPbsqDTv3MuV7+vGn5u2bideLnqL2uL85UK1fVV0XCoqKVHHhIra889zl1VZzeVnbd2JWLavm6uv6tgv8gtLLGozS5ZjzT7Xul0H8XMzVJquXYeO4uXiaPFytW2yot9qinZHqrap8UDL0dFRYmNjRUSkUaNG8uWXX8rChQvlhRdeKJM2KChITp06ZTLt1KlT4uHhUW5vloiIwWAQg8FQZrperxe9Xl/OHNVPD53JcsuWw6FMOkdHB5EiO6N0DmY+68XRKMuK0pl8NylTBXnDXBlM8ystOtDT7G8aR7NlMJ+3+XKXrtfqqwcXZ0ext7OsvizLu+plcHV2lJISVJquou3LUqW3V2u2G9MyXGvb/u+zYwX7bdlyV57O2cmxgvUzLauLk2X1X5VtQNtPXIxiworydii0JJ3RZwfTeqiorE7FUu5vpY9LxqfsitrTYCgqN9217xdV2NeLzaW79v2iRFds9N2y7cHSvC3d50zatsJ6tXSf+d82WdFvNeV6nbOr2w0zRktTUlIi+fn55f7WvHlz2bZtm8m0LVu2mB3TVZvZ63QmJ/cK09pbls4adjfcFnL9WdoOtmZ3g5SjJtlZeLvHFm3m6GC7nUGns6y8uioWoSrpLdm+7HU60dtblqmjhelsycGG+4xDDR8c7SzcZnjYqHk1uqVkZWXJzp075ddff5Vvv/1WsrKyZMeOHTJgwAD1+6BBg1T6MWPGyJEjR2T69Oly6NAhWbx4saxdu1YmT55cU6tgM3Z2OosPEnobBlo1fTAhMqa3cHu05Qm2JlV1vaq7HqoS7N8IgZalQaE1avoCzNK2relyUg3fOvzrr79k0KBBcuLECfH09JTU1FTJzs6WTp06iYjIiRMn5NixYyp9VFSUvP/++zJ58mRZuHChhIaGyrJlyyQzM7OmVsGmLN1BLD352LIMRNeDg4UXFQ43wEneFiztxdDU5P57I/TA2jLQqmmWbuOW9paS7dRooPXyyy9X+Ht5b33PyMiQr776ykYlurFYuoPcCAc0ouvh39pTZamqBk68UCKqef/ecJ+I/nX+61fnNX3rkIiqjoEWEVEtUdVA878emBLdCBhoEREREdkIAy0iIiIiG2GgRURERGQjDLSIiIiIbISBFhEREZGNMNAiIiIishEGWkREREQ2wkCLiIiIyEYYaBERERHZCAMtIiIiIhthoEVERERkIwy0iIiIiGyEgRYRERGRjTDQIiIiIrIRBlpERERENsJAi4iIiMhGGGgRERER2QgDLSIiIiIbYaBFREREZCMMtIiIiIhshIEWERERkY0w0CIiIiKyEQZaRERERDbCQIuIiIjIRhhoEREREdkIAy0iIiIiG2GgRURERGQjDLSIiIiIbISBFhEREZGNMNAiIiIishEGWkREREQ2wkCLiIiIyEYYaBERERHZCAMtIiIiIhthoEVERERkIwy0iIiIiGyEgRYRERGRjTDQIiIiIrIRBlpERERENsJAi4iIiMhGGGgRERER2QgDLSIiIiIbYaBFREREZCM1GmjNnTtXGjduLO7u7hIQECA9e/aUw4cPVzjPjh07RKfTlfl38uTJ61RqIiIiIsvUaKD18ccfy/jx4+WLL76QLVu2SGFhoXTu3FkuXbpU6byHDx+WEydOqH8BAQHXocRERERElnOoyYV/+OGHJt9XrlwpAQEBsm/fPmnTpk2F8wYEBIiXl5cNS0dERER0bWo00Crt/PnzIiLi4+NTadq0tDTJz8+XlJQUmT17trRs2bLcdPn5+ZKfn6++5+bmiohIYWGhFBYWVkOpK1dYWGT0ubDUb4VSqIMF6YrMfLY8nbacitNZmrdpftawtKyWzmNN3tdeD9bkbdsymKarejvV1HZjad6WzW9duupY95rfBqxrC3O/Wbt9WZafdXVnfjm2O35VxzHB0ryrdxuq/n2hplyvc3Z1u2ECrZKSEpk0aZK0bNlSUlJSzKYLDg6WpUuXSnp6uuTn58uyZcskIyNDdu/eLQ0bNiyTfu7cuTJnzpwy03NycsTFxaVa18Gc/GIRraqzs3P+f+r/vhvsK0+3beu2cj9XJZ22nNLLsibv0vlZw1wZKsq7onnMldWW9WBN3rYug3E6a9qp9HZ4vbYbS/O2ZH5r01XHutf0NmBtW5hbd0uPX6VZkp+1dWdJPVT38cva47Mly7Fmu7M2nTXLrY7jfXXIy8ur6SJY5YYJtMaPHy/fffedfPrppxWmi4+Pl/j4ePW9RYsW8ssvv8j8+fPltddeK5M+KytLpkyZor7n5uZKWFiYdO7cWTw8PKpvBSqQV1Ak0/dsFxGRzMzOIiIm310cHSpN16FjB3lw38dlPlclnbac0suyJu/S+V1rvViad0Xz1EQ9WJO3rctgnM6adiq9HV6v7cbSvC2Z39p01bHuNb0NWNsW5tbd0uOXpfV8rXVSHcfG6tgvLC2DJcuxZruzNp01y62O43110O5I1TY1X3MiMmHCBHnvvfdk586dEhoaWuX5mzRpYjZAMxgMYjAYykzX6/Wi1+urvCxr6KEzWW7ZcjhYkM7BzGfL05l8N1mWNXmb5mcN82Uwn3dF85gvqy3rwZq8bVsG03RVb6fS2+H12m4szduy+a1LVx3rXvPbgHVtYW7dLT1+lWZZftbVnfl1st3xy9rjsyXLsW67sy6ddcu99uN9dbhe5+zqVqM1B0AmTpwoGzdulB07dkhUVJRV+Rw4cECCg4OruXRERERE16ZGA63x48fL6tWr5e233xZ3d3f1LixPT09xdnYWkau3/o4fPy6vvvqqiIgsWLBAoqKiJDk5Wa5cuSLLli2T7du3S05OjtnlEBEREdWEGg20lixZIiIiGRkZJtNXrFghQ4YMERGREydOyLFjx9RvBQUFMnXqVDl+/Li4uLhIamqqbN26Vdq1a3e9ik1ERERkkRq/dViZlStXmnyfPn26TJ8+3UYlIiIiIqo+/FuHRERERDbCQIuIiIjIRhhoEREREdkIAy0iIiIiG2GgRURERGQjDLSIiIiIbISBFhEREZGNMNAiIiIishEGWkREREQ2wkCLiIiIyEYYaBERERHZCAMtIiIiIhthoEVERERkIwy0iIiIiGyEgRYRERGRjTDQIiIiIrIRBlpERERENsJAi4iIiMhGGGgRERER2QgDLSIiIiIbYaBFREREZCMMtIiIiIhshIEWERERkY0w0CIiIiKyEQZaRERERDbCQIuIiIjIRhhoEREREdkIAy0iIiIiG2GgRURERGQjDLSIiIiIbISBFhEREZGNMNAiIiIishEGWkREREQ2wkCLiIiIyEYYaBERERHZCAMtIiIiIhthoEVERERkIwy0iIiIiGyEgRYRERGRjTDQIiIiIrIRBlpERERENsJAi4iIiMhGGGgRERER2QgDLSIi+lcqKi6p6SIQMdAiIqJ/p8Ji1HQRiGo20Jo7d640btxY3N3dJSAgQHr27CmHDx+udL4dO3ZIw4YNxWAwSGxsrKxcudL2hSUiolqlsIQ9WlTzajTQ+vjjj2X8+PHyxRdfyJYtW6SwsFA6d+4sly5dMjvP0aNHpXv37tKuXTs5cOCATJo0SUaMGCHZ2dnXseRERHSjA+MsugE41OTCP/zwQ5PvK1eulICAANm3b5+0adOm3HmWLl0qUVFRMm/ePBERSUxMlE8//VTmz58vmZmZNi9zZX7/55KE+bjWdDFqRF5Bkbg4Vr5JVXc6a9gy79qkptrCOD9r8rZleapzWcUlN/6tq3/zvlBZ9d/I634jl42q5oYao3X+/HkREfHx8TGbZteuXdKxY0eTaZmZmbJr165y0+fn50tubq7JPxGRwsJCm/xr/eSOcqdryvuedyVfLl/JryRdkZnPVUtXfpmsyds0ryv5BZI0M1vy/n89KvqXNDPboryN01Ve7qqtU+VlsGUdV3dbVJTO8raoaHutvvq6ur0b52dN3paW29w/y9bP+vL9b7+oiW2gavuwuXW/mHdFLl2+Um6dVLQelrTHtdWJ5fVVUEEef5+/ZHY7Mv53Me+K5F66XEk9WH5MuLZtsnqON5buM9ash63/1UY3TLhcUlIikyZNkpYtW0pKSorZdCdPnpTAwECTaYGBgZKbmyuXL18WZ2dnk9/mzp0rc+bMKZNPTk6OuLi4VE/hTTjI5s2bTabkF1+dLiKSnZ2j0omIvLs5Rx7cd/XzI42KzKbbtnVbuZ8rSvfJR9tkYXORu3c5yI6tWrqyZapoHnPpsrNzxGBfNr+k2VvlySZFJr8Zp5m+5+r8G9/V6qhs3sZ1svHdzeUupyr1UHneDrKweZHs2fm/df/ko6rnba5OrE1nzfr9L++r61S63Y1py607I8ekzYzLU7q+LC2DuXTm8tPKUJW2qKjcpeu4vPUuLz/jfdDada/pbeBa26L0bxUdl6pSz8bpzLWtuTqp6Lik/SYiJttNRcevio4xpddBO2ZZe3w2l3dFdaXlXXqbrI5traLlmsvP0vWwtby8vJouglVumEBr/Pjx8t1338mnn35arflmZWXJlClT1Pfc3FwJCwuTzp07i4eHR7UuS0SkXrM8CfMxDeDyCopk+p7tIiKSmdlZRER979Cxgzy47+Myn6sjXWZmZ3FxdJCMjmW7oI3LVNE8laXT5F4uFNnzUbm/XWs9GOdVUXksyc+S+snoWGSSly3Kakk6a7cHc21eWun2ML6Vdy11bG26zMzOkpkpVWoLc+U2tw2Wl66618l4uZfyr/82cL3bzNJ6Lp2uvLY1VycVHZfM5e3i6CAnz18W2fNJpetkyTpUx/Zgjq2Pjda0kaX70/Wk3ZGqbWq+5kRkwoQJ8t5778nOnTslNDS0wrRBQUFy6tQpk2mnTp0SDw+PMr1ZIiIGg0EMBkOZ6Xq9XvR6/bUVvBzRgZ5llwWdyXJNy+Fg5nN1pNOLXu8gnuWsp2mZzM9TWTqNk9Gg09K/lb9My9fP5HsF5bEsv8rrx1Ovl7yCIpN01V1Wy9JZtz2Ya/PSSreHls+117G16fRlDuaVt4W5cpe/DZaXrrrXyfi7Y41sA9e3zSyt59Lpymtb83VS0XGp/LxFRPSOxvlb1mbm1+HatwdzbH1stKaNLN2fridbnLOvhxodowVAJkyYIBs3bpTt27dLVFRUpfM0b95ctm3bZjJty5Yt0rx5c1sVkyyg0+kqT0REdB052PG4RDWvRgOt8ePHy6pVq2T16tXi7u4uJ0+elJMnT8rly5dVmqysLBk0aJD6PmbMGDly5IhMnz5dDh06JIsXL5a1a9fK5MmTa2IViIjoBqW3v6Ge96L/qBrdCpcsWSLnz5+XjIwMCQ4OVv/WrFmj0pw4cUKOHTumvkdFRcn7778vW7Zskfr168u8efNk2bJlN8SrHYiI6MbBQItuBDV60xWo/B0z5b31PSMjQ7766isblIiIiIio+jDcJyIiIrIRBlpERERENsJAi4iIiMhGGGgRERER2QgDLSIiIiIbYaBFREREZCMMtIiIiIhshIEWERERkY0w0CIiIiKyEQZaRERERDbCQIuIiIjIRhhoEREREdkIAy0iIiIiG2GgRURERGQjDLSIiIiIbISBFhEREZGNMNAiIiIishEGWkREREQ2wkCLiIiIyEYYaBERERHZCAMtIiIiIhthoEVERERkIwy0iIiIiGyEgRYRERGRjTDQIiIiIrIRBlpERERENsJAi4iIiMhGGGgRERER2YhVgdaHH34on376qfq+aNEiSUtLk/79+8vZs2errXBEREREtZlVgda0adMkNzdXRES+/fZbmTp1qnTr1k2OHj0qU6ZMqdYCEhEREdVWDtbMdPToUUlKShIRkfXr10uPHj3ksccek/3790u3bt2qtYBEREREtZVVPVqOjo6Sl5cnIiJbt26Vzp07i4iIj4+P6ukiIiIi+q+zqkerVatWMmXKFGnZsqXs2bNH1qxZIyIiP/74o4SGhlZrAYmIiIhqK6t6tJ5//nlxcHCQdevWyZIlS6ROnToiIvLBBx9Ily5dqrWARERERLWVVT1a4eHh8t5775WZPn/+/GsuEBEREdG/hVWBlrlxWDqdTgwGgzg6Ol5ToYiIiIj+DawKtLy8vESn05n9PTQ0VIYMGSKzZs0SOzu+E5WIiIj+m6wKtFauXCkPPPCADBkyRJo0aSIiInv27JFXXnlFHnzwQfn777/l6aefFoPBIPfff3+1FpiIiIiotrAq0HrllVdk3rx5cscdd6hpN910k9SrV09eeOEF2bZtm4SHh8ujjz7KQIuIiIj+s6y6r/f5559LgwYNykxv0KCB7Nq1S0SuvgLi2LFj11Y6IiIiolrMqkArLCxMXn755TLTX375ZQkLCxMRkTNnzoi3t/e1lY6IiIioFrPq1uHTTz8tt99+u3zwwQfSuHFjERHZu3evHDp0SNatWyciIl9++aX06dOn+kpKREREVMtYFWjdfPPNcujQIXnhhRfkxx9/FBGRrl27yqZNmyQyMlJERMaOHVtthSQiIiKqjawKtEREoqKi5PHHH6/OshARERH9q1gdaJ07d0727Nkjf/31l5SUlJj8NmjQoGsuGBEREVFtZ1Wg9e6778qAAQPk4sWL4uHhYfLyUp1OZ3GgtXPnTnnqqadk3759cuLECdm4caP07NnTbPodO3ZIu3btykw/ceKEBAUFVXk9iIiIiGzJqqcOp06dKsOGDZOLFy/KuXPn5OzZs+rfP//8Y3E+ly5dkvr168uiRYuqtPzDhw/LiRMn1L+AgICqrgIRERGRzVnVo3X8+HG56667xMXF5ZoW3rVrV+natWuV5wsICBAvL69rWjYRERGRrVkVaGVmZsrevXslOjq6ustjkbS0NMnPz5eUlBSZPXu2tGzZ0mza/Px8yc/PV9+1P4hdWFgohYWFNi/r1WUVGX0urOC36k5XKIU6WFAm8/NYl6785Vq7fpaWx7L8rKmf6i+rZemuvQwVMTefbbfJitJZs92YK3f1t62l6Wp+G6j5NitvPkva1tJjyrUf5679OGlpOkv2x+u5rVW0XGvq+Hq6Xufs6mZVoNW9e3eZNm2a/PDDD1KvXj3R6/Umv998883VUrjSgoODZenSpZKeni75+fmybNkyycjIkN27d0vDhg3LnWfu3LkyZ86cMtNzcnKuuUfOUvnFIlpVZ2fn/P/Uq9+3bd1W7ufqSJednSMG+8rLVNE81qQzt1xr68HS8liSnzX1Y4uyWpLO2u3B3DpVto7afNdax9ams2a7MVduW7Stpelqehu4Edqs9DpZ2raWHlOsaetrPQ7Ycn+8nsebipZrTR1fT3l5eTVdBKtYFWiNHDlSREQeeuihMr/pdDopLi6+tlKZER8fL/Hx8ep7ixYt5JdffpH58+fLa6+9Vu48WVlZMmXKFPU9NzdXwsLCpHPnzuLh4WGTcpaWV1Ak0/dsFxGRzMzOIiLqe4eOHeTBfR+X+Vwd6TIzO4uLY/lNbFymiuaxJp255VpbD5aWx5L8rKkfW5TVknTWbg/m1qmyddTmu9Y6tjadNduNuXLbom0tTVfT28CN0Gbl1bMlbWvpMcWatr7W44At98frebypaLnW1PH1pN2Rqm2sqrnSr3OoSU2aNJFPP/3U7O8Gg0EMBkOZ6Xq9vkxPnK3o8b+nMksvU693MPO5OtLpTb6bL5P5eaxLV/5yra0HS8tjWX7W1E/1l9WydNZtD+bWqTRzbXbtdWxtOmu2G3Plrv62tTRdzW8DNd9mItYdEyw9pljX1td6HLDd/ng9jzcVLdeaOr6ertc5u7pZ9dThjeTAgQMSHBxc08UgIiIiKsPiEPXZZ5+VUaNGiZOTkzz77LMVpr3rrrssyvPixYvy888/q+9Hjx6VAwcOiI+Pj4SHh0tWVpYcP35cXn31VRERWbBggURFRUlycrJcuXJFli1bJtu3b5ecnBxziyAiIiKqMRYHWvPnz5cBAwaIk5OTzJ8/32w6nU5ncaC1d+9ekxeQamOpBg8eLCtXrpQTJ07IsWPH1O8FBQUydepUOX78uLi4uEhqaqps3bq13JeYEhEREdU0iwOto0ePlvv5WmRkZAhg/pHRlStXmnyfPn26TJ8+vVqWTURERGRrVo3Reuihh8p9zPLy5cvlPolIRERE9F9kVaA1Z84cuXjxYpnpeXl55b6zioiIiOi/yKpAC4DJH5LWfP311+Lj43PNhSIiIiL6N6jSizG8vb1Fp9OJTqeTuLg4k2CruLhYLl68KGPGjKn2QhIRERHVRlUKtBYsWCAAZNiwYTJnzhzx9PRUvzk6OkpkZKQ0b9682gtJREREVBtVKdAaPHiwiIhERUVJixYtau1bWomIiIiuB6veqd+2bVv1+cqVK1JQUGDy+/X6G4JERERENzKrBsPn5eXJhAkTJCAgQFxdXcXb29vkHxERERFZGWhNmzZNtm/fLkuWLBGDwSDLli2TOXPmSEhIiPpzOURERET/dVbdOnz33Xfl1VdflYyMDBk6dKi0bt1aYmNjJSIiQl5//XUZMGBAdZeTiIiIqNaxqkfrn3/+kejoaBG5Oh7rn3/+ERGRVq1ayc6dO6uvdERERES1mFWBVnR0tPp7hwkJCbJ27VoRudrT5eXlVW2FIyIiIqrNrAq0hg4dKl9//bWIiNx3332yaNEicXJyksmTJ8u0adOqtYBEREREtVWVx2gVFhbKe++9J0uXLhURkY4dO8qhQ4dk3759EhsbK6mpqdVeSCIiIqLaqMqBll6vl2+++cZkWkREhERERFRboYiIiIj+Day6dXjnnXfKyy+/XN1lISIiIvpXser1DkVFRbJ8+XLZunWrNGrUSFxdXU1+f+aZZ6qlcERERES1mVWB1nfffScNGzYUEZEff/zR5DedTnftpSIiIiL6F7Aq0Proo4+quxxERERE/zpWjdEiIiIiosox0CIiIiKyEQZaRERERDbCQIuIiIjIRhhoEREREdkIAy0iIiIiG2GgRURERGQjDLSIiIiIbISBFhEREZGNMNAiIiIishEGWkREREQ2wkCLiIiIyEYYaBERERHZCAMtIiIiIhthoEVERERkIwy0iIiIiGyEgRYRERGRjTDQIiIiIrIRBlpERERENsJAi4iIiMhGGGgRERER2QgDLSIiIiIbYaBFREREZCMMtIiIiIhshIEWERFVu5IS1HQRpKi4pKaLUGWFtbDMVDEGWtcZUPMHH7qKbWE7rFsquAEChsLi2rcdMtD696nRQGvnzp1y0003SUhIiOh0Otm0aVOl8+zYsUMaNmwoBoNBYmNjZeXKlTYvZ3WyxY7Pk5p18ov+fQe0G2VbKLawN6M29jiQZW6IQKuk5stQVbUxOKSK1WigdenSJalfv74sWrTIovRHjx6V7t27S7t27eTAgQMyadIkGTFihGRnZ9u4pNWnxAYnwhugh94q1RkUWHOb4t8YaBXdIBtDsYVt+29sg9rIFrf5iourPcsqQw1vXlYd426MXZiqkUNNLrxr167StWtXi9MvXbpUoqKiZN68eSIikpiYKJ9++qnMnz9fMjMzbVXMamXplX5VWBO8VVdPQl5Bkbg4Vr4Z5RUUlZlWnVVRDIhUcZUKi2xzRKusTmzZ62TJ9mVpm10LS1exyMZX79W1rsUlEHs73XVf7vVysaBIPJz01ZqntReV1Vl3NX3dYcnySx8bi0tQpfNEVY7B1blNGudX27b3661WjdHatWuXdOzY0WRaZmam7Nq1y+w8+fn5kpuba/JPRKSwsPC6/Lt8JV8KCwtVeQr+f7qmsLDIzGfL0xUUVJxHef+u5JvLu2zaitIlzcwuJ53p/Gdy81Q64zT5BZbXQ2XlKSoskgKzeZj5XFRx25Uug6VlLb9O/peuoKDIwjqu+vZQUFB2PfKu5EtBQUGV2uxaynB1m7Rmnaxpi4rTGa/rxbwrcuHSFavaVqtXS9YpP7+g0m3A8v3Mmvqv+jxFhUVWlsF8mznoSixKVzqNJdunpWUoqMI6VV6+qtfrlfzKzxGlj40FhUVVatukmdmSn19QaVmTZmabHAeupY7PXbosSTOz5WLelTJtZut/tVGtCkFPnjwpgYGBJtMCAwMlNzdXLl++LM7OzmXmmTt3rsyZM6fM9JycHHFxcbFZWfOLRabvuVq9jzQqEq2qP9ux/f9TXP2+beu2cj9nZ+dcc7rs7Bwx2JsvnyXzmEv37uYceXDf1c8b391sUgbjPIzr4d3NOVatn7nyfPLRNlnYXOTuXQ7y6UdbzJbV3Od9n+2Q78zUj4iovHdszbEob+N1qjsjx6Tdjcu6c3uO8WLMrtOenduqXIadH20rU1/G26GlbWarbbKidOa2V+PyVDSPuXRaW2jr/mSToirX67atW/5/OQ6ysHmRfPJR+ekq2i8q3zcrztvSeq1oHm370j5r0z//2PLjknE97Nhqui2XVXG6ytrMkuOANce56jhOamWtKO+Ptm2pMO/yjo17P/vIoryNyxA/a4tJfZlLFzdzizzZpMikTMb7grl1N3dM3/yhNmznapuVzru65eXl2S5zG6pVgZY1srKyZMqUKep7bm6uhIWFSefOncXDw8Nmy80rKJLpe64evDp07CAP7vtYREQy2ncUg96u3N+MP2dmdhYRsShdcYmIlJMuM7Oz2e5cc+UrPY+5dBWV1TiPipYDWLZ+5sqj/ZbR8Wq3tSVlNf7cok17CfR0Krd+NFXJu6I2K11Wc21RXrqqlKF9x07i6awvN+/qaDNz61c6XUFRicieHZWmyysoFtlT8fZaun4sKXdV2sLStrVmnsrKYKyyvC2tf2vauWOnzmJvZ/n+WN52XJ7K0lW1zczNZ24ZJ89fFtnziUXrVFn5rGmLzp07i6uhank3a91OPF301XqOME5X0bZnrnyW7mcV1WV10O5I1Ta1KtAKCgqSU6dOmUw7deqUeHh4lNubJSJiMBjEYDCUma7X60Wvr94xCSb543/jOfT6/1Wzo6Ne9A525f5m+tm0bBWlczAaC1H6N+PvlpSv9Dzm01VU1v/lUdFyLF0/8+W5+pvn/+dlWVn/99nF2bHSbaBqeVe0TqZlNUlXwTpVtQzOBtPt2vL6r94209mVWJTO1c7e5Ht522vp+rGs3Ja3haVta808lZXBWOV5W1r/VW9nJ0PV9sfytuPyVJauqm1mbj6zxznHIqN0lh1jzJev6m3h6GhF3o6l9uFqOEeYpjO/7Zkvn6X7mfn1rQ62PGfbUq0ao9W8eXPZtm2bybQtW7ZI8+bNa6hEVae3t3xAraV0uurP87/A4FCrNn+L3CjbgqUDxx3s/31tQDcOhyo8wHCjsMU5gmpWjR7lLl68KAcOHJADBw6IyNXXNxw4cECOHTsmIldv+w0aNEilHzNmjBw5ckSmT58uhw4dksWLF8vatWtl8uTJNVF8q9woJ0JiW9gS65ZuBPpaGMjXxjJTxWq0Rffu3SsNGjSQBg0aiIjIlClTpEGDBjJz5kwRETlx4oQKukREoqKi5P3335ctW7ZI/fr1Zd68ebJs2bJa82oHIiK6fmpj0FIbe+GoYjU6RisjI6PCdwqV99b3jIwM+eqrr2xYKiIioprB3uB/n9oX7hMRERHVEgy0iIiIiGyEgRYRERGRjTDQIiIiIrIRBlpERERENsJAi4iIiMhGGGgRERER2QgDLSIiIiIbYaBFREREZCMMtIiIiIhshIEWERERkY0w0CIiIiKyEQZaRERERDbCQIuIiIjIRhhoEREREdkIAy0iIiIiG2GgRURERGQjDLSIiIiIbISBFhEREZGNMNAiIiIishEGWkREREQ2wkCLiIiIyEYYaBERERHZCAMtIiIiIhthoEVERERkIwy0iIiIiGyEgRYRERGRjTDQIiIiIrIRBlpERERENsJAi4iIiMhGGGgRERER2QgDLSIiIiIbYaBFREREZCMMtIiIiIhshIEWERERkY0w0CIiIiKyEQZaRERERDbCQIuIiIjIRhhoEREREdkIAy0iIiIiG2GgRURERGQjDLSIiIiIbISBFhEREZGNMNAiIiIishEGWkREREQ2UuOB1qJFiyQyMlKcnJykadOmsmfPHrNpd+zYITqdrsy/kydPXscSExEREVmmRgOtNWvWyJQpU2TWrFmyf/9+qV+/vmRmZspff/1V4XyHDx+WEydOqH8BAQHXqcRERERElqvRQOuZZ56RkSNHytChQyUpKUmWLl0qLi4usnz58grnCwgIkKCgIPXPzq7GO+aIiIiIynCoqQUXFBTIvn37JCsrS02zs7OTjh07yq5duyqcNy0tTfLz8yUlJUVmz54tLVu2NJs2Pz9f8vPz1ffc3FwRESksLJTCwsJrXAvzCguLzHwuvI7pCqVQhyqXz3ge8+kqKsP/8qiO9TNfHmvKaln9lGZdPVjTFta0mTV1Yts2sy5d+etuXbktb4vqrVfb7puWpavu+rJun7FE9bRZ9e4zls1/7fV1o+1nFZfP0u2merePsmWy3Tnblmos0Dp9+rQUFxdLYGCgyfTAwEA5dOhQufMEBwfL0qVLJT09XfLz82XZsmWSkZEhu3fvloYNG5Y7z9y5c2XOnDllpufk5IiLi8u1r4gZ+cUiWvVu27pNfc7Ozvn/FGV/q+502dk5YrCvevmM5zGXrqIyGOdRHfVgrjzWlNXS+inNmnqwpi2saTNr2s/WbWZNOnPrXrp+LCl3VdqiOuvV1vumufyudd+0dn+8VtXRZtW9z1gyf3XU1422n1VUPku3m+rePkrLy8uzXeY2VGOBljXi4+MlPj5efW/RooX88ssvMn/+fHnttdfKnScrK0umTJmivufm5kpYWJh07txZPDw8bFbWvIIimb5nu4iIdOjYQR7c97GIiGRmdhYRKfe36k6XmdlZXBzLb+KKymc8j7l0FZXBOI/qqAdz5bGmrJbWj6X1Vd1tYU2bWdN+tm4za9KZW/fS9WNJuavSFtVZr7beNy2pV2va2dr98VpVR5tV9z5jyfzVUV832n5WUfks3W6qe/soTbsjVdvUWKDl5+cn9vb2curUKZPpp06dkqCgIIvzadKkiXz66admfzcYDGIwGMpM1+v1otfrLS9wFemhM1qWg9Fn02Wa/lbd6fQm3y0tn8l3s+kqKsP/8qiOejBfHmvKaln9lGZdPVjTFta0mTXtZ9s2sy5d+eteun4sK7flbVG99WrbfdN8fte6b1q3P16r6mmz6t1nLJv/2uvrRtvPKi6fpdtN9W4fZcpkw3O2LdXYKHJHR0dp1KiRbNu2TU0rKSmRbdu2SfPmzS3O58CBAxIcHGyLIhIRERFdkxq9dThlyhQZPHiwpKenS5MmTWTBggVy6dIlGTp0qIhcve13/PhxefXVV0VEZMGCBRIVFSXJycly5coVWbZsmWzfvl1ycnIqWgwRERFRjajRQKtPnz7y999/y8yZM+XkyZOSlpYmH374oRogf+LECTl27JhKX1BQIFOnTpXjx4+Li4uLpKamytatW6Vdu3Y1tQpEREREZtX4YPgJEybIhAkTyv1t5cqVJt+nT58u06dPvw6lIiIiIrp2fNMnERERkY0w0CIiIiKyEQZaRERERDbCQIuIiIjIRhhoEREREdkIAy0iIiIiG2GgRURERGQjDLSIiIiIbISBFhEREZGNMNAiIiIishEGWkREREQ2wkCLiIiIyEYYaBERERHZCAMtIiIiIhthoEVERERkIwy0iIiIiGyEgRYRERGRjTDQIiIiIrIRBlpERERENsJAi4iIiMhGGGgRERER2QgDLSIiIiIbYaBFREREZCMMtIiIiIhshIEWERERkY0w0CIiIiKyEQZaRERERDbCQIuIiIjIRhhoEREREdkIAy0iIiIiG2GgRURERGQjDLSIiIiIbISBFhEREZGNMNAiIiIishEGWkREREQ2wkCLiIiIyEYYaBERERHZCAMtIiIiIhthoEVERERkIwy0iIiIiGyEgRYRUS0BoKaLQERVxECLiKiWKCphoEVU2zDQIiKqJYoZaBHVOgy0qFrcCCcA3la5MbAdbKeEdSsiNX+8qU3beG0q678VAy2qFoXFJTZNb1metjuglNwAgWR1q+oB2NL0vL1lOzUdYNhyPyiqwjGhqKT6jx9VUZu28VpU1H+tGg+0Fi1aJJGRkeLk5CRNmzaVPXv2VJh+x44d0rBhQzEYDBIbGysrV668PgWtRlU5oNQWN0KgZcuDb7EFQUZtu3LML6pafRVY2GaWBgO1rb5uBDUcX1i0H1irKsFLjddDLYpealNZ/61qNNBas2aNTJkyRWbNmiX79++X+vXrS2Zmpvz111/lpj969Kh0795d2rVrJwcOHJBJkybJiBEjJDs7+zqX/Nr8G7f7qh74LO19qkpAZssDiiW3bIpLUKuCh6oGuwUWBmaW3t66UU4Atgj6RSyvL0vdCL2qtty8q7I91PQt1Fq0mwsENtvGyTI1Gmg988wzMnLkSBk6dKgkJSXJ0qVLxcXFRZYvX15u+qVLl0pUVJTMmzdPEhMTZcKECXLbbbfJ/Pnzr3PJr01NHiR+/+eSTfKt8knTwuRavpaU25ZXuZY0WTEglwuLLc7TVm1hqdL1VVl5LK1fSzeF6owbrqUubXUSulJk+bZgiRKgxk/w5R27Ste9ubaorI2KYfmFii2Oocblq3RfqOLyq5J3VViy7QJle69r+tjzX1NjgVZBQYHs27dPOnbs+L/C2NlJx44dZdeuXeXOs2vXLpP0IiKZmZlm04uI5OfnS25ursk/EZHCwkKb/9MUFhYZfS6UYqMDsOlvpunM52FpurJlav3kjirPU1kZ8vMLxV5XYjaP8ubRodiivIuLiioot+ly7FBSaX6Vrau5fyXFlbdZcVGxlJhtW0vbwppt6n+fCwosm6egoFB0MC5r5duGpW0mxVVrW0vW3ThNQUHZebSyG0+/km/ZNm6+za5tv6i4HqrezkVFxWInlpS1ouOD+Xq1ZJ1K/n+dzG3H5X2vbLpGV1wi+QWW1b+uxPzxpqrtVF75KtsXYOE2bk3eVSt35dtASXGxSLH5fd3S/c7c9Px8y49f1fGvNtKhhu51/Pnnn1KnTh35/PPPpXnz5mr69OnT5eOPP5bdu3eXmScuLk6GDh0qWVlZatrmzZule/fukpeXJ87OzmXmmT17tsyZM6fM9NWrV4uLi0s1rY1l7t7lIAubF5X5bGm60vNYms6aMpibx9brVJ1ltbS+LHWjrV9VfrOmrLZcp+vVtras1+oug6XluxHarKLplqazpgyW5mdp3jWxjVtbhuos6/U89lenvLw86d+/v5w/f148PDxsvrzq8q8PtPLz8yU/P199z83NlbCwMDl9+vR1b6i8giJxcXQo89nSdKXnsTSdsd//yZMwH5dK05krj63Xydw8xuW2tKyW1pelqnv9bNkWluZRUR1fa5tVRzpL5zFXl7as1+rYHy1ly2OCNemMld5uzO2rFe3D1d1mluZtbvuvjrJamndNtK01x1NL87al3Nxc8fPzY6BlqYKCAnFxcZF169ZJz5491fTBgwfLuXPn5O233y4zT5s2baRhw4ayYMECNW3FihUyadIkOX/+vEXLzc3NFU9Pz1rXUERERP9ltfX8XWNjtBwdHaVRo0aybds2Na2kpES2bdtm0sNlrHnz5ibpRUS2bNliNj0RERFRTarRpw6nTJkiL730krzyyity8OBBGTt2rFy6dEmGDh0qIiJZWVkyaNAglX7MmDFy5MgRmT59uhw6dEgWL14sa9eulcmTJ9fUKhARERGZZfubqhXo06eP/P333zJz5kw5efKkpKWlyYcffiiBgYEiInLixAk5duyYSh8VFSXvv/++TJ48WRYuXCihoaGybNkyyczMrKlVICIiIjKrxsZo1ZTaeo+XiIjov6y2nr9r/E/wEBEREf1bMdAiIiIishEGWkREREQ2wkCLiIiIyEYYaBERERHZCAMtIiIiIhthoEVERERkIwy0iIiIiGykRt8MXxO097Pm5ubWcEmIiIjIUtp5u7a9Z/0/F2hduHBBRETCwsJquCRERERUVRcuXBBPT8+aLobF/nN/gqekpET+/PNPcXd3F51OV+355+bmSlhYmPz+++8iIuV+9vDwqJF0LMONU4baVFaWofaV9UYoQ20qK8tgm7JW95/JASAXLlyQkJAQsbOrPSOf/nM9WnZ2dhIaGmrz5RhvYKU/V/SbLdOxDDdOGWpTWVmG2lfWG6EMtamsLINtymr8vbrUpp4sTe0JCYmIiIhqGQZaRERERDbyn7t1aGsGg0FmzZolBoNBRMTs55pKxzLcOGWoTWVlGWpfWW+EMtSmsrIMtikr/QcHwxMRERFdL7x1SERERGQjDLSIiIiIbISBFhEREZGNMNAiIiIishEGWmSR0s9MaH/KqLqsX79evv7662rN80ayfft2i9JV9GxKSUlJudO3bNliVZksWeZ/xbZt22q6CGWwXa6q7mONuf3IWidPnrRJvsa4LdRuDLT+pYqLi9VnS3bSkpISGTBggDzxxBPl/qb9uaI//vhDHnzwQWnfvr2cPn3a6vIZH5R++OEHefTRR2XGjBny/fffW51n6XxLr7ctD4QVefbZZ2XMmDGyYsWKStNqB+3SZR85cqR8+OGHJu0qcjVAzczMlBdeeEFEyl/HitofgGpb47qvqO6uXLlS6XqUJy8vr8Lfyyun8bSXXnpJTpw4YdWyRaRM3Wm09nn55ZfLLNuabcbccrQ8LWG8zx07dsziZZcur7Y8S9ajdJqK5qlKvZS3zitXrpSCgoJK5+3du7dMmzZNzpw5Y/HyzFm9erX8/vvvYmdnZ9JGFbWXOdr6b9y4UYKDg2Xv3r1iZ2dnUb0Y10dldQBA3nvvPdm6davJcrXfLNmeSm8D1xK0Gc9raT61LUg0tw9dE9ANqbi4GABQUlKCkpKSKs+vzfPss8/iyy+/VPkZ/wYAzzzzDADg7NmzmDRpEjw9PbFo0aJy85wyZQpGjBiBhQsXolWrVujWrRv++usvs8u2xBNPPIG1a9fitddeQ/v27dGrVy988803Fs+vLa+4uFgt9+jRo9i9e7fZ9A8//DCee+65Ki1DW47GuD5L/1Z62oYNGzBo0CC0bNkSy5YtU7+/99572LBhg/r+1ltvISoqCl988UWZPNPS0hAaGootW7agqKjI5LdHHnkEer0eS5cuxYsvvog1a9ZgxYoVOH/+vErz999/48yZMwCATZs2YdOmTSZ53HfffWjcuDGOHz9usm7aPJrBgwfjkUcewaVLl8yud3n1MHbsWDzxxBM4e/YsAODIkSNYuHAhnn32Waxfv77C+bdt24Zly5ZBp9Nh4sSJOHXqVKXL/O2339R6PPvss2bLWVxcjG+++QZDhgxBamoq5s6dCwB45513MGnSJHTr1g0ffPCByfZVet2MFRUVqc9btmxBfn6+SmNcr6dPnzZJa859992HoUOH4u+//8aVK1cqTGtcloULF+LEiRNlpr/33ns4d+5chfmMGTMGO3fuLFNmrbzGx6YdO3bg66+/NpnfeL1K7ycAsGvXLuh0OkydOhVXrlxR5dPmMy7vsmXLYGdnh3vvvRd///13heU2VrpdDh48iLS0NLRp0wa///67Kptx+RYvXoxPP/20TLlL52X8/cUXX0SvXr3g6emJGTNm4OGHH8bnn39eZr8pz0svvYQlS5aUyXPEiBFIT08HAKxcuRIigqSkJBQUFJTJo7i4GMuWLcNnn31mUj7jNtL8+OOPOHfuHJ5++ml8/vnnAGB2GyxvW9fy3L59O3744Qez66XNe/78eeTl5QEAsrOz8dNPP5nN35pznC0Yt/u2bdtU+a8VA60bhPGGpgVXJSUlOHbsWJU2wtIHDnt7e6xZswbZ2dn46KOPTJbx8ccfQ6/Xo3///rh06RJOnTqFGTNmwMvLq0yw9fXXXyMhIQG7du1CcXEx1q5di8zMTDRq1Ag5OTkAgAsXLuDy5cu4dOkSSkpK8Pvvv+OXX37BoUOHsH//frz11luqfIWFhfj7778RFxeHffv2AQBWrFiBHj16oFevXjh8+HCl66jVS25uLoqKilBcXIwVK1YgOjoaIlLuQfP1119HaGgovvzyy3LrzBzjNli/fj0OHjwIANi8eXO5eWjpN27ciJCQEKxbtw4jR45EmzZtsGrVKuzevRseHh5YsWKFmjc7Oxs33XQTGjdurAJF4wNhp06dEBISgpycHPz000947bXX8PLLL+Puu+/GtGnTYGdnBxGBTqeDl5cXLly4gJKSEpw7dw6+vr546KGHsHz5cuh0Orzxxhuq3F988QU6duyIzz//XJXlxRdfxPDhwzF58mR8++232Lt3Lx577DGICAYOHIjPPvtMreOePXuwePFiLFy4EB9++GG59deuXTu4u7tj0aJF+PTTT+Ht7Y3WrVsjPj4enp6euPXWW/Hrr7+WmS8vLw+33347brvtNmzYsAEODg6YMGEC/vzzT7Nt9fHHHyMpKQnZ2dm46667oNPp8NNPP2Ht2rV44IEHkJWVhW3btqG4uBiFhYUArgYXvr6+SEpKwqBBg+Dp6Ylhw4ahXbt2MBgM+OGHH8rso6Xb55133kHbtm0BAJMmTUJqaqoKdoqLi/HPP/8AuBroDxs2DN98843Z7aa4uBifffYZEhMTsXv3bmzduhVPPPGE2u7Km0dr7yNHjiApKQlTp041Sbd7927ExMSobau8k522fbRp00aVo3TQ8dprr6G4uBjTp09HfHw8HnnkEaxevRoPPvgg9u3bpwLCjz/+WAW8U6ZMwZNPPqnyWb9+PRwdHTF58mRcuXIFU6ZMwcCBA7Fnzx4VCGlt8+abb0JEkJWVpeqzItqx87PPPsPkyZMxevRoZGdnY/Xq1ejQoQPat2+PP/74A0OGDEGHDh0AXA3GQ0JCsHfvXpP1XbVqFbZt24YLFy6UqbNHH30UoaGhePXVV2EwGGBvbw8/Pz8kJCSgS5cu+O6773Dx4kWz5czMzETr1q1Nyg1cPcGHhYUhOTkZ9vb2GDduHJKTk5Gdna3aRPv/119/RWhoKBITE3Hu3DlcvnxZ5Xf06FGV/6ZNmxAfH4933nkHwcHBePHFF00uWN577z1s2rQJGzduBACTfIyXuWbNGkRGRqJ9+/bqfFLednTixAmEh4fjgw8+wGuvvQadToe3334bANTFR3FxMQ4dOoT8/HyTdqspxst+4IEHEB4ejhUrVlh0QVQZBlrXWWFhoWrQkydP4syZM+pKxfiquaioCGPHjoW/vz9Onz5tsvNXFBjMnz8fQ4cOxbZt2/DSSy9hwoQJ8Pb2RlhYGMLCwjB06FB88sknOH36NM6fP49Vq1bB19cXderUQZcuXXD//fejX79+0Ol0WLFiBQDg8ccfx7hx4zBy5EhVvs2bN6NRo0YQETRs2FBd2aWkpGDw4MGYMWMGoqKiEB0djdTUVLi4uEBEMHbsWABXr+amT58OV1dXLF++HOvXr8fEiRNRr1492NnZoVevXvj+++/LrJ+2/JKSEgwaNAhxcXGoV68e9u3bh+nTpyMgIAD29vYICAjAyJEjsX37dixevBizZs3CwoULMXr0aCxYsEDVo3Fdnjx5Er/99luZZRqn+/zzzxEfH48OHTpgypQpiIiIMDn5vfnmm+oA9uuvv6Jfv35YunQp3n33XYwePRp+fn6IjIzELbfcgpkzZ5ZZ1meffYZ+/fqhQYMG2Ldvn9omSkpKcPHiRSQnJyMoKAi+vr7Q6XRwcHCAXq9X9Wtvb4/mzZujUaNGJvW1Zs0aODo6wt7eXgXRxcXFeO2119C7d2+0bt0a586dQ3FxMaZNmwZXV1e4urpixowZuOeeeyAiCAkJgYggOjoafn5++Oabb7B27Vr4+Pjg5ptvRseOHRETE4N77rmnTHtt3rwZzs7OCAkJQWxsLIYPHw7gas/O559/jvDwcDRv3rzcNl+4cCFiYmLwxx9/YNOmTRAR3H///WaDraKiImRmZiI4OBju7u7Yu3cvpk2bBjc3N/j4+ECn08HFxQVdu3bFp59+igEDBsDV1RXdunWDq6srdDodevfujY8++ghz585FSEgI1q9fXybA3759OyZOnIjBgwfjwQcfxCeffIKAgADUrVsXnp6e+OGHH9Q8jz32GDIyMhAeHg5PT0+88MILqvxaft9//z127dqFt99+G8888wwmTZqE22+/HY8//jicnZ0xc+bMMj0JxvV7yy23oF69eggMDETv3r2RkZGhji3aiS01NRUDBw4st94A4MSJE+jRowf0ej2GDh0K4GpPyMSJEwEAzz//PEQEzZo1Q0BAAB566CGEh4cjMDAQBoMBBoMBnTp1QteuXaHT6dCtWzcMHz4cnp6e+Pbbb01OWhs3boROp0Pz5s1hb2+Ppk2bqkD66NGjKCkpwalTp7BgwQLUqVMH9vb2uPPOO3HXXXdh8ODB2LJlS7n7K3A1kHN3d4ednR0SEhIgIpgxYwZeeeUVNGzYEA0bNkTjxo2xfft2fPXVVxgzZgzWrl1rcrK99957ERQUhJdffrlMD9XevXvRr18/vP/++0hPT0f79u0hIggKCsIjjzyCtLQ0uLi4ICkpCc8884xJUKPVwffff4/o6Gh10WO87GnTpkFE0LhxYwBAYmIi7rjjjnLbXjsWJCQkoFmzZti9ezeKi4sxadIkiAiioqJUr2CLFi0gImjUqBHefPNNAEDr1q3h5OQEBwcHGAwGREdHY9SoUeVe7E6ZMgXe3t5455138Mknn1TYsz106FDVBi+99BLGjx+P+++/Hw8//DD++usvjBw5Eq6urmjXrp3JHQZLeo5tacaMGfD398cnn3xSpV7UijDQuk6WLl1q0mgbN25EbGws6tWrh+bNm5tcqe3YsQPDhg1DXFwcMjIy0KtXLwwcOBCPP/64SlNesJWfn4+FCxfC3d1d9WzExcXh1VdfxcGDB7Fq1Sp4enqqE+imTZuQkZEBEVE7mYuLC6Kjo+Hj4wODwYBFixZh+vTp0Ol0SE9Px7lz5zBp0iQEBATA2dkZDRo0gJOTE3Q6HWbMmIHVq1dj2LBhEBG1gxUUFGDt2rXQ6XSwt7dHq1atEBQUhAkTJsDV1RUeHh4QETz11FP44IMPMGvWLNSvX79MsGW8zpMmTYKjoyNEBAaDAXXq1EFERATuuusuxMbGIj4+HjqdDu7u7rC3t1e9PSKCfv36qXy0Hfj+++9HYmIiPDw80LVrV7z88ssoLCw0Wf7cuXPRr18/2NnZwcHBAXXq1MHrr7+urrxzcnIQERGB/Px8fPHFFxg4cCBatWqF4cOHIygoCE8++SSmTp0KJycn6PV63HLLLaoMWh7fffcdHnroIfj7+6Nhw4aqt+/uu+9Gz549ERkZqep28ODBqvdO+6fT6aDT6ZCYmKhuMRYXF+Po0aPqt0ceeUTd8r3//vvh6uoKBwcHrFixAk888QTc3d0REBCAESNGoE6dOqqeRQTu7u6YMmUKdDod+vfvj5CQECxevBgAsH//fri5uWHSpEll2uzYsWNo3rw5IiMj4eTkhAEDBpjc2jx16hSioqLQrl07dTVtfIWbmJgIPz8/pKWlqWDo7rvvxsmTJ032Aa0e582bBycnJyQkJOD++++Hh4cHPDw88Prrr+Ouu+6Cvb09vLy84ObmBnt7e4gIAgICVN4ODg4QEbRo0QIODg5wc3NDQkICnn76aRw8eBAbNmyAq6srJk6ciHvvvRfx8fGoX78++vTpA51Oh6SkJIwaNQpdu3bFTTfdBHd3dwwbNgyOjo5ITEzEtGnT8M8//+DMmTN4+OGHccstt8DV1RUJCQnw9fVFYGCg2rbt7Ozg7u6OxMREtGrVCitWrMCePXtU/b799ttwdnZG37594ezsjMjISBgMBri7u2P+/PkAoAKu9957D/Xq1cMXX3xR5uSl9UTt3r0bYWFhCA0NxcyZM/Haa6/B2dkZUVFREBE4OzurCyxPT0/cfPPNCAwMxIQJEyAiaNeuHR5//HE0bNgQIgInJyds3ry5zO3urKwsGAwGFby3adMGTk5O6NChA+6++24sXLgQDg4OcHd3V8GxiCAmJgZpaWkICwvDwIED8dVXX5msx6FDhxAaGoq2bduqPO3s7JCVlYX7779ftXtAQABWrlwJNzc3+Pr6mtzGf+qppxAQEFCmh6uwsBCrVq1Cs2bNUK9ePTzzzDOws7NDt27dMHjwYHVR6ezsjNDQUPj5+cHBwQGZmZlYtGgRNm3apPI6ffo0unXrhnHjxmHHjh2YPHkyxo4di+HDh0NEcOedd0Kn06FPnz5Ys2YN6tSpg5ycHHXxVVRUhKlTp8LOzg729va444470LNnT7i6umLt2rUAoC6G/fz84OjoCEdHR7i4uKBjx45wdXVFRkYGdDod5s6di2effVYdXwICAtSdCm39t2zZgujoaHz55Zf47rvv0LBhQ2RlZeGdd95BQUFBmVvke/bsgU6ng5OTk8o3IiICiYmJ8Pf3V8dBDw8P6PV6jB49GsDVIHbt2rV48cUX8fvvv5tcLNjaH3/8gSZNmqh2+uuvv/DVV18hKysLOTk5Ft0SLg8DrevgzJkziImJQWxsLM6ePYtff/0V3t7eePLJJ7Fo0SK0adMGQUFB+Prrr7F+/Xq4uLjA09MTLi4usLOzQ3JyMm6++WbExMSYdDWXdx/+/PnzWLx4Mdzd3dUGfvr0aQBXA7F3330XBoMBrq6u0Ov1iImJQd26daHX6yEiCAsLQ5MmTdC3b1+MGDECbm5uWLJkCR5//HHodDpMnjwZ/v7+SExMxGuvvYZ//vkHvr6+ameeOnUq6tSpg7CwMISEhAAAfvrpJ9StWxfDhg3D2LFjISIYNWoU8vLy1MG7adOmJnW2bNkyJCcno1evXvjxxx9Nfjt9+jRuvvlm+Pn54ZZbblFX097e3njooYfQtm1bDBs2TAUWgwYNwpNPPmkSLMybN0/lt2TJEgQGBmLlypV455130KNHD7Ro0QL9+vVDSkoKVq9erQKQt956Cz4+PnBzc4OIoEuXLjhy5AhKSkrw+eefo0GDBigoKMAbb7yBmJgYGAwGhISEmBxgJ02aBGdnZ7i6uqreNQBYu3YtgoODMXbsWPTo0QOhoaFo0KABHnjgAXh6euLTTz9Fy5Yt0bdvX3h4eCAkJAQRERGq7bTAUuu1efnll03Gdfzyyy+qG//BBx/E6dOnkZWVBXd3dwQGBmLo0KG47bbb0L17d8TExMDX1xdt2rSBi4uLCjxEBF27doWfnx88PT0REhKC6OhoTJ8+HeHh4Rg9erTJLUWNFgBrJwW9Xo9Fixbh3LlzqoxHjx6Fr68v+vTpg2PHjqnbLhs2bFDrqJ0YunTpAhHBhAkTcPLkyTIXHvv378f333+Pbt26ISgoCG5ubli6dCnWrVsHNzc39OzZE25ubqqnJDY2FiKCunXrqrrUTjhhYWFISEiAq6srQkJCMGnSJNSrV0+N/fr1118RHByMESNGYN26dbj77rtV78ZNN90EZ2dnNGrUCFOnTkXbtm2RlZWF1NRUtGjRQl3x29nZITAwEO7u7hg3bpxJsBcUFKSC3ZYtW8LV1RVBQUEYMmQILl68iE6dOuGWW26Bn58fHn/8cURGRmLUqFG477770LBhQ/z222+qF+XHH39EcnIynnrqKXXsePXVV7Fjxw5Vdy+//DLq1q0Le3t7GAwGPPDAA6p+9Ho9/Pz8VHvExsaicePGmD17NhwdHdGoUSO88cYb6thhMBjg7OyMPn364JdffkFRUREefvhh+Pj44PXXX0eXLl3QuHFj6HQ6eHh4oEmTJggLC4PBYEBQUBBEBB4eHhg4cCAcHR1VAHrvvffi+eefR8eOHTF48GAcP35clX/FihWqLM888wzc3d2h1+vRrVs3BAQEoGvXrvD29oaHhwfat2+PMWPGwMHBAWPHjsXff/+NwsJC3HHHHZgzZw6Aqz3T77zzDrp27YpJkyZhyZIlqhcoNDQUUVFRGDNmDIqKirBjxw7Ur18fBoMB/fv3R4MGDRAXF6cCDnt7e2RkZOCjjz5CSUmJuh3u4uKCkSNH4q677oKrqysMBgOmT5+OzMxMODk5oU2bNoiPj8fMmTPx+uuv46mnnkLPnj2h1+uRmpoKg8GA7t27AwDGjx8PvV6P999/H6tWrUJKSgpEBK6urmjfvj2Sk5MREBCAzMxMFRDm5ORg/vz56gIqJiYGt99+OwCoi7KNGzciIiICxcXFOHDgAFJSUmAwGODo6IjWrVubXBhpvZGff/45+vbtq/YnEcG4ceMQHh4OEUFoaCieeuopBAYGQqfTISMjA56enmjatClcXFyQmpqKhx9+uEpjQq/FkSNH4OHhgbVr12LXrl0YNmwY0tLSEBUVhZCQEBWMV7UcDLSuk4MHD6Jp06bqXvusWbPUb6dPn0b37t3h6+uLkJAQ9O3bFwkJCdDr9fD09ISjoyN69+6NnTt3IjY2Fp07d1bzGgdb+fn5KCgoQG5uLl588UX4+flBRHDXXXcBuHo1duXKFbzxxhuqV8bOzg6jR4+Gk5MTXF1dERgYiLS0NKSkpCAzMxMdO3aEvb09XnvtNTzwwAOws7ODi4sLGjdujPvuuw9vvvkm3Nzc4OrqqnakhIQEpKamIi4uDqtXr4a/vz9GjRqFoqIirFq1Sh1AtQOss7MzfHx80L59ezRs2BCtW7fGvHnzcN9998FgMKB9+/aqZ+nFF19ESEgIUlNTkZiYiC+//BI+Pj4mvTrR0dFo3bq1OiG4uLioKyetd8vHxwcvvvgitm/fjieffNJkkPr58+cxZcoUJCYmonXr1mjTpg1SUlLwwAMP4MKFC6hTpw6io6PVybBnz5749ddf8e6776qAsbi4GO+99x4SExNhMBgwa9Ysk50zKysL9vb2auzUn3/+icTERDz99NMqzdtvv40ePXogKCgI9evXx6VLl9C6dWsVCGo9BikpKUhJSTEZo2Vvbw9nZ2f06NEDs2bNwlNPPYUnn3wSP/74IyZPnqyCxISEBLzyyiuoV68ePDw84ObmBi8vL1VvBoMBnp6eEBEVXNrZ2cHJyQktW7aETqdDcHAwgoKCVBuXlJRg6dKlaNGiBV5//XUsW7YMXl5e+PLLL9G+fXt06dIFzs7O8PDwwPPPP4+zZ8+qcRpPPvmkCoAGDBiAFStWICwsDHFxcfDy8sLUqVPh5uYGOzs7FWxNmTJFnWi/+eYbfP/99+oW2+XLlxEXFwd7e3u0bdsWrq6ueOSRR1C/fn1MmjRJXWlr/26//XaTHjytJwQAunXrBnd3d7i6uiIyMhKXLl3CH3/8gdDQUAwbNgx5eXk4deoUYmJiMGHCBPj6+sLFxQVBQUFo2LCh6l3o3bs3XF1d1cVQhw4d4OLiom4/ubi4ICYmBsuXL0daWpq6Jezj44Pk5GTVy+Pi4oJFixYhODgYzs7OKiDu0aMHnnnmGXWC9fLyQlZWljphLl26FIGBgTh48CDWr1+PAQMGQKfTYfTo0ejWrZu6CKxbty7S09NRp04ddRHm5+eHxo0bw83NTdWTp6cnPDw8YDAY0K5dOwQFBanA4qGHHsLPP/8MJycn3HTTTTh48CA6deqEnj17IjY2Vl0kabel9Xo9Jk6ciLi4OFUXDg4O8PLyQnJyMgIDAxEbGwt7e3s88sgj6rbi559/rvavdevWwdnZGTqdDr6+vrjzzjsxfvx42NnZoXv37ujQoQNGjx6t8k1JScGIESMQGhqK+fPn448//kCbNm1w++23Y8mSJejWrRs6deqEbt26oU2bNhgwYAB27NiBtm3bwtHREc2aNUNqaireeOMN1fOmHWsiIyPh6OiIBQsWqKDEyckJgYGB+OCDD3Dw4EF4eHigU6dOKCgowLp169QtV+DqOFtXV1d4enoiISEB7u7u8Pb2Nrnw0QI5Ozs79O7dG88995y6kP7hhx9w/PhxODk5mfSQ6vV6BAQEqLsZzs7OcHBwQEREBADgueeeg5eXF9q0aYO4uDg88cQT+P7779WxJjAwEEOGDEHbtm2RkpICnU6HNWvW4P3338cff/yBn3/+Gb/99htWrFgBHx8f+Pn5wc7OTpXTxcVF9Wj9+eefOHHihCpPq1atcPHiReTn5+Puu+9G69at8eijj6re6upibgjOxIkT4eLiAldXV0yePFmNw23RogWmTZtm1bIYaNmY8cn10KFDSE9Ph06nw5133gngf419+vRp1K9fH66urmrcg4ggMDAQixcvhpOTE4YNG4b33nsP8fHxqgtZ8+STT6Jv375ISkrCjBkz8MUXX+DFF19U43Luvvtuk0Gz2hWrdtXatm1bBAcH47HHHoNer1dXX1qwJiK47777MGjQIIgI0tLS1NiKuLg4DBo0SF21REREYMiQIXB1dYWjoyPatGmDRYsW4bnnnsPYsWMRGBioerI8PT3Rt29feHp6ol27dmjevDlmzZqFwsJCvPHGG0hNTcWAAQPw+++/o7i4GBs3bkTjxo3h7e0NJycnJCcnw9fX1+TkqN3e0G5JasFHnz59MHv2bJOeLePbacbtlZ+fj8TERPTt2xd9+vSBj48PMjIycOLECYSEhGDt2rVISkpSy7311lvx+OOPIzk5GQcOHFBt+/LLL8PJyQleXl5o3bo1JkyYoAbWJiYmwsnJCWlpafjkk0/g7++vnsDTrg43bdoEBwcHNW6oT58+SExMhJeXl8n6NmjQAAkJCSYHYO3A5ujoCFdXV7i4uGDChAmIjo42CTC0ddAOwNp0e3t7FYxrt5K1+jK+Tam1oxbQv/XWW9DpdPDx8YGvr6+6NaM98RQXF4f+/fvDz88PBoMB99xzD06fPo21a9eiSZMmiI2NxYIFC9C3b1/Y2dmhUaNGiIuLQ7du3eDg4IABAwbg9ddfh52dHdLS0uDg4ICRI0di6tSpSE1NRd26dVXvKnC1p1BbJ61nRuu90urRw8MD48ePVz1+2klJr9ejVatWWLx4MYqLi9XFQVJSEt566y2Eh4cjPT0dHTt2RL169fD444/Dzc0NM2fOxEcffQRnZ2fY29sjMzMTDg4OCA4OxsSJE/H666+rHoVmzZqZ9EpqdT9v3jwMGDBAlVcLeCIjIzF27FjodDpERkYiICAAt99+u+q1cHR0VD1Cbm5uqrx+fn5YsGABXn31VfTt2xddu3ZFbGwsTp48ic2bN2PAgAGwt7dHYmIinnjiCTRv3hwjR45E06ZNodPp0KhRIzz55JPw9/dHvXr1oNfr1W1XrdzarbKQkBD4+flh1KhR+PXXX9GtWzc4OTmhV69e6kKrYcOG6NChAxo1aqRuC2rboRZ4ab2KWk/mzJkz1cBzEcHixYuRmJiIPn36oEuXLigoKMD58+eRmZmpLuL279+PFStWqGEEnp6eCA8PV7ctHR0dER4ejqFDhyIiIgLPPfecukiqU6cOHnroIcycOROPPvoo0tPTkZSUhB49emD37t3qNm98fDzq1auH2bNnQ6fTITQ0VNWLXq/Hl19+iezsbOh0OjRt2hTOzs5wdHTEwIED1UXLwYMH4efnB2dnZ3UX4sUXX8S4ceMQEhICnU4HT09PzJ8/H9OmTVPBk4eHhwqUtMBJ2zednZ2xcOFCdO7c2WTb1+l0CAsLU8cIEcGQIUMQHR2NqVOnIioqCg4ODkhPT8eYMWOg0+mwdOlSDB06FA0bNsT48eNx5swZFBYWYvDgweo2ZEREBBo3boyIiAg0a9YMHTp0QEZGBry8vNC7d2+T45N2fhMRfPvtt3jhhRdUu957770AgIsXL2L8+PFIS0tTD5NUB+Nz59q1a7FgwQLcf//96kGMPXv2lLkl3a5dO5ML4apgoGVj2olbG/9w6NAhdOrUCeHh4eoqvKSkBFu3bsXKlSsRGRmJsLAw1UsTGhqKHj16IDY2FjqdTh2ojHtg7r33Xvj5+WHChAmIjY1FWFgYmjZtimPHjmHx4sVwc3ODg4MDBg0ahI8//hjLli1TPRjajjp69GikpaWhSZMmaidNTk5Gz549ISIYP368Cla0neO+++7DDz/8gKysLHU1qp20fXx81E6l9SJpV+Hajt24cWPo9Xo8//zzaNu2LVxcXNCjRw81dueee+7Bvffeqw46x48fxx9//IFVq1apMQf29vYYOnQo5s+fbxJ8eHt7Iz4+Hnq9Xg2IPXr0KObNm4d27drBzc0N7u7uaNCgAXx9fdGlSxf8/fffJjvgmDFjcOutt2Lv3r0IDQ2FXq9H165dkZCQgNmzZ2Ps2LGqd0ULWB0dHeHp6YmoqCjMnz8fw4cPVz1NkZGRcHd3R6tWrXD06FHccccd6Nu3L9LT0zFo0CA0bdoUDz/8sMlj/KdPn1brpdPpsGjRInVL2fiApR1UnZyc1BgakauDXi9fvoyNGzeqXintictRo0aZ5JGeno5+/fqpnitt7IR2UvX29oa7uzs8PT1VGxoHs/b29ujTp4864WtjhrTtr2fPnvj666/h7e2NoUOHonPnzmpe7fZC165d0bx5c7zwwgu46aaboNPp0LFjRzXu0M7ODs899xwuXbqkThzh4eEICwuDr68vPv74Y0yZMkWduLWxKuPGjVPBk/FtDOMAJioqCm5uboiIiFDpPDw80K1bN4wfPx5vvvkmEhMTVVo7Ozukp6fD398fU6dOxR133KG2hcmTJ+PBBx9ERESEyQn31ltvVWNsHn/8cSQkJKiLGUdHR/Tt2xetWrVSFwJaMNmvXz+1DWm9mFrAERkZCTc3N6Snp+O2225Dt27dVG+uFig/+uijePjhh9G1a1c1rs/Hx0c95g/875ZbUlKSCtxERJ3QtX28Xr16qjfOuB61fV/b3x0dHdGiRQuMGTMGaWlpyMnJgZeXlzp++Pj4qLE9X3zxBZycnFQeWjDm4OAAJycnDBw4EK1bt0ZwcDCio6Ph6uqKJk2a4MiRI2jZsiW6deuGyMhIvPLKK/jjjz/w3HPPISYmBn5+fupWq3YS146rQ4cOVW3j6uqKPn36ICAgAAEBAZg5cyZOnTqFkydPYurUqQgPD0dmZqYKzoYMGYKioiJMmzYNHh4eiIuLQ3h4ONLS0tR+pV3M2tnZYdiwYfj2229VL9arr74Kd3d39O3bVx0nPTw8cOeddyIgIAAvvfQSDh48iDvvvBNvvvkmBg0apNJ5eXnh9OnT8PDwQHh4uMmxQKs/vV4PNzc3Nci9bdu2ar/UlhUeHm7Sk5iVlYWsrCykpaXByckJBoMBKSkpCAkJgaOjIxwcHHDHHXdg6tSp0Ol0GDZsGI4fP47u3bsjICAA8fHxsLe3x4IFC/D222+rBycefPBBhIWFITw8HAMGDFBDTbRtxcvLC76+vvD29laBX3p6uho6kJubC71ejzVr1lTvyRlXHzqIiIhAjx490KlTJzg5OZmM1bt48SK+/vpr9OjRA6mpqVb3qjHQsiEtyMrJycF9992nxqwcPHgQjRs3RkJCAj7//HMMGjQI/v7+aN26NQIDA9G0aVPUqVMHjRo1QmxsLCIjIxEeHo5ly5bhu+++Q1JSkgq0vvrqK6SkpODJJ5+Ek5MTRo4cCYPBgOXLlwO4ehts6dKl6iTs4+ODMWPGqLFS2gGmUaNGyMzMVAfnevXqqav30NBQLFy4EAsWLFDdvU5OTkhNTUWfPn3QqlUrFQzY29ujRYsW6oSjnYiDgoKQlpYGLy8vBAQEwMnJCZmZmZgxY4bqsnZ1dUVYWBgaNmyI9u3bw9HREd9++y0+/PBDdTshLi5O3cqys7ODs7Mzzpw5gz179qBVq1bqQKMd4LVy+fr6Yvv27bj55pvRtWtXTJkyBatXr0avXr3UE3GDBw/GsWPHUFRUhJ9//hmpqakYOnQo7rnnHnXg1/6lpaWhfv36cHd3N7lK065wtd4gZ2dnJCcnqxOlvb094uPj0apVK0RHR+PZZ5/Ffffdh1atWmHUqFGIiorC1KlTMX36dAwcOFBdVbdr165Mj5NxT4L2uWPHjipwcHR0RGRkJL744guEh4ejXr16uPPOO7Fy5UoMGDDApBdBO/h6eHggIiICkZGRaj2Mr5KND+RaQBcWFmYyjsvb2xszZ86EyNVbcX/88QeCg4Ph4eGB4OBg6HQ6tG7dGm+88Qbq1aunlh8cHAxfX1+sX78ednZ2aNq0KXbs2IEzZ87gjTfegIODAxwcHLBhwwbk5uZi3LhxSEtLw+LFi9GzZ0+sXr0aDzzwgLrCj42NRVpaGvbv349vv/3WZFnG/+bMmaMCMW271cbJaN+1hx8SExMxdepUODo6wsPDA5GRkRg2bBj0ej3uuOMOFfxrQW12djbOnz+vtp/09HR89dVXyM7OVum1OtRusRgHBPb29sjOzsaFCxcQHByMkJAQk1u4BoMBPj4+SEhIQHJyMjIyMpCWloa4uDgV1Ds4OKBXr14Arg72nTNnDnx8fODo6IjFixerk8euXbtwxx13oHfv3rjnnntUT6jxiVzb3hISEkyml75tHRoaCg8PD/j7+8POzg6PPfYYNmzYgNtuu03dLtRufV66dAlnzpyBs7MzvLy81EBpbVnadnjnnXciOTkZdnZ2CA4OhqOjI9q1a6d6jLUgKCMjA2PHjoWPj49JuerUqQODwaDGRE2cOBFOTk4qYOzduzfmzJkDvV6PiIgI/PPPP1i3bh2Cg4PxwAMPqPxFBA888ABKSkrUcUc7htWrV89kKIO2T4SEhGDMmDH44YcfsH//fjRu3BhRUVH4+uuv1RCA4OBgXLx4EUOGDEHHjh3RpUsXtGjRAqdOncJHH31kEqCMGjUKu3btwqBBg9SDFFpQrdWbcSDs7e0Nb29vtU05ODggMjJSpdWO6dpAfgcHB/j6+uLzzz/HqFGjVD3u3LkTFy9ehJubGzIzM9GmTRs0adIEFy5cQFxcHCIjI/Huu++qHu2xY8di/PjxcHd3VwHp7t271cWEiGD48OH47bffkJOTA29vb/Tu3Ru//PKL2i5PnjyJtLQ0bN26tVrPz2+88QaCgoJUz9X27duh0+lMAq23334b7dq1Q/v27dVYUmte98BAy8bWr18PJycnPProo+qWEnC1ZysmJkbdvnNwcECjRo3Qu3dv9XqC/v37IyMjQ/WaZGRk4Oabb0ZcXJzaCD/77DPExMSgVatWGDBgANzd3dVL8HJzc7FmzRosXbpU7XRNmjTBxo0bTU6MycnJ6kkQETHpdbKzs0Pbtm3RtWtXeHp6qkCsf//+aNu2reqp8vb2RkhICFxcXODv74/09HR1oElISMD48eOxZMkSfPbZZ6hXr54a2+Pt7Y3g4GD4+PggPz8fK1aswMyZM3Hffffh4MGDmDZtGnx9fdVgYe1RZuMDvKOjo7q/bzyI2N7e3uTkql1d2tvbo3HjxlixYgWOHDkC4Oo7kFxcXBAfH4+EhAR4eHjAzs4OcXFxcHV1xe7du/Huu++qIE87AURFRWHkyJGq58zOzg73338/+vfvrw7qZ8+exe+//w5XV1dER0ercSLaE3oHDx5UT7PUrVsXdnZ2qFOnjroatbOzw9NPP61OsMYnPS34atmyJTp06IC2bduib9++aNasmXopZGhoKEaMGIGQkBB4enqqniA/Pz8MHjxY3VIxPmFGRkbitttuU8syGAzqMf6+ffsiKipK3ZK6cuUKvvrqKzRs2BBxcXFISUnBoEGDEBsbqx5dT0tLU3Wk3ULKyMjAgAEDEBoaCm9vbwQFBeHRRx9V77GqX78+/vzzT5SUlKhxPtq7hUJCQqDX6xEfH4+TJ08iICAAq1evVlfV999/P95++200a9YMDg4OasyMtm1o+4Px7VWtJ1c74TRs2FD1bNWvXx+TJ09W+92kSZPUoP6goCD1Ogzt1RtavfXt2xdjx45VgaqHhwdcXFxQt25dxMXFqVt7pW/5anlrAXBqaqq6/Vu3bl21D7i5ual3hd10000IDg6Gg4MDvL290a1bNwQGBmLu3LlqUHxJSQnmzp2LhIQENYD6ueeeUy8x/eSTT1Tw5+LiAm9vb1UOrVzOzs7Q6/VqujbmyNnZWfV2ubu7IykpSfVsa/XcoUMHDBgwwOQ2VnR0tMl+JXL19RHakAltX9MG1tvb26vxVxEREeqYt2rVKoSEhKBp06YmFwNaXWnHtccffxy9evVS+4Ber8fkyZOxYcMG7N69GzqdDq+++iqAq2OVevbsidDQUJNbc7feeiseeOAB+Pv7o1GjRjAYDPDz8zMJxrVxYI0bN1bBYd++ffHQQw/B3t4e9evXx4IFC3DzzTer/W/cuHF4/fXXVbDWtWtXrFmzRvVcaRcbWh399ttvuHTpkrrta3xb38vLC5mZmYiPj1cXVV5eXqp3VUTg7++PHj16wMXFBSEhIYiPj0fv3r3Rt29fTJ06FSNHjlQ96C+++CKOHDkCLy8veHh4YMOGDXjmmWewe/dulJSUoHXr1pgxYwaOHj2qzlnt2rVT6+bk5ISQkBCkp6fj6NGjaNOmjUmwBQAffvgh3NzcMGjQIHz99df45Zdf8OCDDyI4OBjHjh2r1nPzvHnz1OuG3nzzTZNz5/nz55Gfn4+LFy+ql0QDYI/WjejQoUOIiorC0qVLy/z24YcfwsPDQ53ktFsjIlcHN86aNQtBQUEICAhQB74uXbrgiSeeQElJCbKzs5GXl4cvv/wSDRs2hL+/v7pCNH7PT2ZmJtzc3NRjx4mJiejQoQMmT54MR0dHpKSkmAyk1U6E2jtx+vbti6KiIjzyyCPqYPvQQw+p24R169ZF48aN0bx5cwwaNAiPPvoo6tWrh02bNqluae2N49p7h5566inUr18f0dHReOutt3Dy5Em1ARs/Jde9e3f4+PjglltuwYMPPogHHnhAlaFjx47o3r27OkAbB18DBw5EmzZtsGXLFnz66afq1pVWj87OzupVAV26dFEDp3NycuDs7Aw7Ozu4ubmp2w06nQ7Tp08HcPXpOS0vf39/XLlyBXfffbe6bZicnIzU1FQsW7ZMHRBTUlIQGRmJ0aNH4+LFi/jss8/g7u6OhIQE9RLCp556Cps3b8Ztt92GkSNHokGDBqpXRQu8H330UcTFxakThnHvgfbuJgcHB3Tr1g0zZsxAenq6el3F8uXL4e7ujri4OPUkq4hg5syZqF+/fpleHq3HplevXmrdhgwZgi5duuDdd99VPaJ16tTBpUuX1IMW27dvVwfW0NBQrFmzBrNnz0bnzp2xdu1aNG7cWF19R0VFoaCgQL3OoUGDBmo7DA4OhpubGw4dOqS2jeeeew46nU4FNlOmTFG/jR8/XgXVOp0ODRo0QO/evU2CnoCAAAQGBqoLivr168Pe3l491RUbGwtXV1cMHz4cjz32GDZv3ozHH38cTk5OaNy4sbqlm5+fjzlz5qinC6dPn47OnTtDr9fjrbfewoULF1RQrAVYPj4+JsFddHQ0hg8froIs40BLp9MhNTUVUVFRqtewc+fO6NOnD2JjY9Vtb+3Wq7OzM4YNG4bi4mJ8++23GD16NPR6PVq0aIHvv/8ehw4dQmBgIL7++mv1WoDly5eX6Rlbvnw57r///jIBvVamgIAA1RNl3FMSHR2NOnXqQK/XIyUlBe+//z7OnTuH9evXIzU1VT004eDggOjoaCxYsADDhg1TQVvjxo3VO6e0uujQoQP+/PNPrFmzRj2hGx8fjx49euD06dNITU2FXq9HdHQ0wsPDUVBQgKNHj6JFixZq/KeTkxOSkpIQEBCgbsOGhYXhqaeeUoPSIyIi0Lx5c3z88cfq3VeLFy9GXl4ezp8/j2HDhiEzM1P1sGu999r7+urVq4e8vDx8//33KpjTHixq3LgxOnXqBE9PT0yZMkVd6NWrVw8BAQEICgqCj48Phg8fru4eaE+CJyQkqLsH2oMHr7/+OmJjYzFixAjV29quXTsUFxdj8uTJJj1uer0ew4YNQ0FBAb799lt4eXmpQejanQVPT0906tQJOTk5eO2115CQkICRI0di//79qF+/Pnr27Ik2bdqoXtPs7GxER0cjPj4ekZGRaNasGXbs2IFOnTrhm2++wZw5czB48GCEhYVh8ODB6N+/v7rAjY2NRZMmTdTDNZ06dcLw4cNVQGlnZ4dPPvkEwNUXtoaEhCAsLAxRUVGIiopSr7mpTpMmTcKtt96KnJwcuLu7q9fUAMDTTz+NKVOmlPuXEazBQMuGtmzZgtjYWPz8889qWklJCc6ePYshQ4Zg9uzZyMjIgIuLi7ploPXONGzYEOPGjcM999yD7du3Iy0tDX5+fnjttdfUW6c/++wz/PHHH2jdujVErg7IBq5uEJcuXUK3bt0QGxur3nkVGBiIGTNmwNPTU71Y0NfXFx4eHujevTtcXFzQuXNneHp6ws/PD7169YJOp0PPnj1NAhlnZ2fVDa3X61GnTh01ZiI8PBxOTk64//770bBhQ8TExECn06Fdu3ZIS0tD27ZtodPpMG7cOHh4eCA+Pl69dND4T8to79rq3bs36tevj3HjxqkTlYODA+bPn4+DBw8iOjoavr6+8PT0VOVwd3fHtm3bAFy9xx4eHg4vLy9ERUUhJycHBw4cQFFREdatW4fu3bujd+/e+Ouvv7BlyxYEBwdDRNCmTRu8/vrrqsdReyorJSUF99xzD8LDw9Uj1Lfccgt0uqsvXvz000/VmIaxY8eqg19oaKh6sm7RokWoW7cuGjVqhMOHD2P16tVwcXGBj48PYmNjsXnzZtx9991ISkoyOemFhYXhwQcfNBmDpQU7Xbp0gY+Pjwq89u7di5SUFOj1etx+++2YNWsWevXqpdpNOymUHt9h/F2v12PBggVISEiAvb09XFxc0Lx5c1y5cgXHjh1DvXr1kJqaioCAABUsnTt3TgUn2vvNBg8ejB49emDEiBEoKSnBd999px7cKC4uVo9uA0BsbKwa16WND8nMzFTbx969e7Fz507MmTMHXbp0AXD1KvPRRx9VvVHau7BCQ0MRHBysbl8Z15l2Ra/X6+Hv768C1/r166tej7///hsTJkxAYmKi6vlct24d6tevj8aNG2PPnj0YP348HBwcUL9+fQQEBGDo0KHq1olx72CTJk1U0K31KmiBR/fu3dV2cueddyIpKUn14hpfRLi7uyMiIgKBgYGYPXs2Ll++rPaJpk2b4p9//sHKlSuRk5ODcePGITY2FsuWLcPLL78Mg8GAF154Aa+//joAqIdetOBIOxEHBgZi+PDh6n1Lxr3B2kWNdsESExOjAhmt7qKiojBz5kwsX74cU6ZMwcyZM/Hnn3/C2dkZvr6+MBgMcHNzg7+/P8aMGQORq7fh586di/bt26Np06Zqf+3WrRuOHz+OxYsXw87ODn379lW3lDp37gydTqcCjgEDBgCAOrlr48C08VfaMUu7GJkyZQqSkpIwevRoREdHq7sLzz//PEaPHo0BAwbg3XffRadOndRt3xkzZuDMmTPo16+f6qmLjo7G6tWr1cMi2gM22otpXVxc0LRpUxw9ehSPPfYY3NzcEBwcjLS0NDg6OqJLly5o1qyZGmIRHh6Om2++GaNGjVK9rFobdOrUCVFRUfjmm28wfPhwdRyYNm0aPvzwQ/XwiJa+oKAAb775pnrKtHTgrN3Ka9myJa5cuYLVq1cjISEBo0ePRo8ePRAQEAA3Nzc8//zzuO222+Dq6oru3bujW7dumDx5Mho0aIDg4GC0bNlS9RJq767bv38/brvtNrVde3l54fDhw3j66aeh1+uh0+lw1113Yfny5WjcuDEcHR2RmZmJ7777DsDVVyLt3LkTO3fuNHl1hzXMPV24c+dONGzYEA4ODnj++efV9AsXLuCmm25SL+mtDgy0rlF5jai9fHTNmjUIDg7GP//8g61bt6o3Rd933324+eabsXjxYvXWbRHB6tWrsXr1anUF7OnpqV7++Oqrr5rcelmyZAk8PT2xf/9+9R4fe3t7DBkyBJMnT0b79u0REhKiAoeUlBT1OLNxD5o2MDYgIACtW7dGWloaHnjgAXWbRhvj5ObmhiNHjqgXnGoBj6enpzrYJyYmqq5ig8GA8ePHq/y1J2Zefvll9OzZUw0cTUxMRPPmzXH06FFVf4WFhVi4cCFGjhyJ5557Dp06dVK9N9oJx9nZGampqQgMDISHhwccHBxUUOnn5wcPDw98+umnOHbsGJKTk1G/fn34+PioHRm4euJeuXIlYmJisGLFCnWbwNPTUwW9EydORExMjDoxOzg4qMDK398fzz//PNLT09G1a1d10PL09MT48eORlZWl5tXe2P7SSy+pwPjJJ59Ex44d0bVrVzRo0MDk1oSXlxcGDhyIRx991OSEp/UqeHl5wdnZGZ6enqottdskWq/nzz//DC8vLzg6OqpeEC2f0oGH9l6h0j0Z2i0OLQiLiIhQfzvzzJkz+OKLL9CmTRt8++23KCwsxKVLl3D27Fk89dRTqodU613YuXOnCqTXr1+PJk2a4OLFiygsLMTevXtx5MgR/Prrr+ppTi0QbNCgAQ4fPqxe7LplyxZMnjwZOp0Od9xxB2bPno3CwkJ1YHd2dla9VSJi0qtlfNWv3do1LuPevXsxZMgQeHt74+GHH8abb74JT09PvPnmmzh8+DBcXV3VgPno6Gjcf//9GD9+POzt7XHrrbciNjYWXl5eKijSgipPT0/1Vm7tJKctNz09Xb2qwvgWmnarTeTqWEptu3ZwcFC9nI0aNVLt5urqisTERNx3333qbe7x8fFITk5GcHAwWrVqBX9/f7Ro0QJpaWlITk7GPffcg1atWiE9PV31lGZnZ6Nx48YqENVuz2s9gVqwFRUVBScnJ7XtNGvWDBMnTkTDhg3Vcadbt244deoUWrVqhZkzZ+LNN99Utw2Ntzc/Pz/VTu3bt1f12K1bN/z222+YMmUKDAYDMjIy1In53XffVb3cWlm1PLSeMW1AeEJCAt58803VQxQYGIjo6GisWrVKHeMiIiLQtWtXeHl5Yd++fWjatKl6+auXlxdGjRqFDRs24P3334evry/0ej3CwsLg5eUFb29vpKWlqacJ09PT0adPH9SpUwd+fn5wd3fHmjVrMG3aNBgMBiQlJalbgW+99RaOHDmC+Ph41KlTBx4eHggLC4OHhwdmzJih6lLrVVy+fDkee+wxNGvWTAXHQ4cOxalTp9C9e3eTY4UWYGoXEsZj1erUqYOEhATExcXhs88+w88//4xXXnkFAQEBaNy4Mezt7ZGUlIRffvkFq1evRlJSEgIDA5GcnIzt27fj9ttvh729Pfz9/eHs7IybbrpJHasiIiLg4+OjBt/b29vj2WefRXFxsRqa0alTJ7zxxhsoKirCsmXLkJSUhF69elX4dxSryvip/7fffhsrVqxQf4IqNzcX48ePR2JiIh577DH89ddf2L17N7p164YGDRqo3vLqeHcXA61q8OOPP+LZZ58FcPVR0R49euDs2bM4ceIEvLy8MGTIEGRkZKBp06YYNmyYGrekvbdKp9PBz88PX375JV577TU0a9ZMDURNSUlRgVvz5s3VSVKv12Pt2rUq0NP+PpZ2wtB6LhwdHdU7TLQrCe1Fe1pabYcPCQlB27ZtkZycjPnz52P+/PnqgGBnZ4dJkybh/vvvVz0099xzD0aNGoUWLVrA399fDXDUrsLatGkDNzc3jBgxAj169FBd53q9HsHBwSgoKMDx48cRFBSE9u3bq7+BdfnyZeTm5qqnVox7dIYOHVrmNovxVd/DDz+MzMxMBAYGwsfHBw8//DDmzZun/vTLBx98AMD0D+T6+PggPj4e7dq1U7fUUlNT4ebmhv3792P48OHqhGncS9GlSxecPXsW58+fV491G98ecnV1xVtvvYUOHTqgfv36apxGp06dkJmZiYSEBAwZMgQ7duxQT2Rp65GUlITWrVuroKz0k3JpaWlo1KiRWp7WzvHx8cjPz1cPXvz111+IjIxEaGioyqNBgwbqalgLYLX1atasmbq1oB0gnZyc0KVLF9V7kJaWZvLHgJ9++mnExsYiIiICMTExeOmll/Ddd99h/vz56l1cMTEx2L9/v+q9uvvuu9GxY0ds3LgRiYmJcHZ2hrOzM9LS0rB8+XIMHToUMTExiImJQcuWLdGuXTscOnQI99xzD0JDQxEQEKBOQFqw8uKLL+Luu+9WJ6nSj5JrJ3Tj71oaLQBp06aNOmH7+Pjg22+/Rd++fREaGoouXbqofUwLSuzs7NS2op3sQ0JC4OzsjEceecTk1RnGAZ9229pgMKj6TUxMRGRkpBqMrAUEWrtpLyvWxj86OzsjICAAGzZswMCBA1V7an+v7vDhw2jfvj10Oh0WLlwI4H9/pLhOnTrqKr5Lly6q50cLgLRbbdq6GteZXq9XQapWh05OTujfvz9KSkpw1113ISgoSAXK7dq1Q58+feDo6IiXXnoJx48fLxPU6/V6vPzyy+opzT59+iAuLg4GgwGTJk1Sgai2zIkTJ6oe4rvuuqtMO2t1oR0XtaEQWr05OTnB3d0d7du3R0ZGBtLT09UA/JtvvhlpaWmws7NTr6PR6/WqfhITE9X4Qm2MlzYGzdvbG02aNEHHjh1x5MgR9Z4pR0dH+Pr6on379rjlllvUNhEYGIjVq1dj69atcHJygq+vr+pFDwoKwqlTp7Bo0SKTC6QGDRqgbt26iI+PN7nt3KZNG/z5558mF8POzs5o0qSJGsPl4OCATz/9FAsWLICvry+cnJzg4eEBX19fxMbGokGDBnjxxRdx+PBhNb5Re6VFTEyM6hFMTU1FkyZN8OKLL8Lb2xsGgwGJiYkYMmSIak/t9r+2DaWmpiIvL0/9qaXAwEA8/PDDKqAp772J1SUrKwsuLi7qnV+zZ89GUVERTp8+jbFjx6oxiw0bNkTHjh2vaeB7eRhoXaP8/HzMmzcPOp0OQ4YMgU6nw8qVK9Xvy5cvh6urq3rxpPYWXZ1Oh+effx6ZmZkqqNIeda1Tpw6aNWuGlStXolGjRrjvvvsAXL1tcdNNN6kASdsYtY3i8OHDmDhxojow+/r6YvHixQgMDMSGDRvQqlUrhIWFmVyZilx927T2TqeAgADUq1cPSUlJeO211/Dkk0/C3t7e5JaTln/79u3RrFkzeHt7o3///mjXrp0KtrQnt/r06QPg6ng17WkiEcEzzzyjXlT5559/4scff8TmzZsxZswYJCcno0GDBmjWrJk6mWoH/tLvjzJ+f1ZsbCw2bdqEpKQkNGnSRI0FWrp0KW655Ra0a9cOKSkpJldMn332GerWrYuVK1eqF/9ptxhmzJgB4OpTohERESa9QNr4jKioKEyePBlBQUFISkrChg0b4OPjg7CwMLi7uyMlJQWtW7eGs7MzFixYgO+++04FG9999x2OHDmC5557DgMHDkR0dLR62aNOp1Ovw7Czs0Pr1q3x7rvvqoNqq1atAFwNsP39/dG8eXM0aNAAvXr1QoMGDdQbo9evX48ZM2YgKSnJpHdFO+jGxsaaBCQxMTHqb1Rq66vVeVRUVJm/TjB69Gh1wtDGiXl4eGDIkCH4+OOP8corr6jb0/7+/qhfv74a57Jw4UI1YHnmzJmIi4tTAdLzzz+PTZs2ITMzE5GRkUhMTETPnj3h5eWFzZs3q8HBjo6OcHd3h7+/P+Lj49GzZ0+0aNHCZKxd6ROwNkZK2xb9/Pxw6623Ys2aNVi5cqW6daUN5NeeMnRxcUFoaKi6DWNvb4/WrVurF+Ia9ximp6er+tYe+mjatClatmxpEsBoF1UODg7YunUrnn32WZWP8SBiEUHPnj2xcuVK9RJU7WTct29fdWtSe/+QdkwYNGgQ2rdvj6KiIrzxxhvw9PRUt4KSkpLUOLNGjRph3bp1iImJUXU0YMCAMvtbvXr1TJ7ALX1768SJE+oWljYGSxsaoQX32ji8rl27moz5srOzQ0xMDG677TbodFff29W8eXMUFBSgT58+CA4OVg9A+Pn54bPPPsOaNWtw0003lXlpsVZ3QUFB6m362m+tW7dGnTp10KpVK/XXEzZs2ICgoCAVUBkMBjW29vvvv1f7ZHh4OHJycpCXl4dHHnkEycnJ6mJVu/3aunVr5ObmArj68kvt5axjx45VwYT21zicnJzQvXt3dUGqvahVRLB06VKsXr0abm5u6N+/v8mfPnrsscfUA0DGx+SMjAwVXJfe5g0GA5o0aYKUlBSsWrVKPTiivaV/2rRp6k7KwYMHcf78eQQGBqJz586YNWsW3nrrLezduxcbN27E9u3bsX37duzYsQPdu3cvs83WqVMHcXFxSEhIUO+EtLOzQ1RUlAp4tTGYWmdB6fcmXgvjN9T/+eefaN++PXbt2oULFy5g5cqV6kGhoqIi5Ofn4/Tp08jJycFPP/2kylOdL0hloGWlJ598Ur148sqVK+pvnBn/HT0AOHv2LNatWwcfHx/1xJ7xH2zdv3+/OllojxovXbpUDRJevHgxvvjiC+Tm5qKkpAQffPABRo8eDWdnZ7i7u6tuUO1AeuLECQQHB6vxAl26dEF4eDgOHz6MyMhITJ8+XfWS6PV6PPHEE5gxY4a64tJemRAUFITExEQsXbpUXUGLiOq+Nz45a4Mce/bsqd5BYzzg/JlnnlF/LqZTp07qXTGPP/64OiBNmzZNBXPGf38uKCiozFgVrefIOMhq1aoVTp06hT179sDf3189Xp6Tk4NLly6huLgY99xzD5KTkxEXF4eHHnoIXbp0Ub18wcHBiIyMRHp6uno03t3dHWlpafDw8FB/mPuxxx5Tg66dnJxUD4mdnZ0KiGfNmoWYmBgVPDdq1AhTpkxRY+d+/PFHtG/fHnXr1lVjVrQ8Bg4caHJS0Kbfdttt6u87auOf2rVrh59//hmurq5ITk5GnTp1TP50k/Y0XdeuXdXJ2bhHR3sHU+lApGvXrsjOzlYHee3K+KeffsLmzZsRGBiIW265BQsXLoSHhwfq168P4OqfbtGedoqPj0ffvn2xa9cuLFmyBI0aNVLv4hK5OhapXr16CA8Px8cff4wrV66oJ9y6dOmi3kW1fv163HLLLXjzzTexbt06hISE4JNPPkFYWBhcXV2RlJSk3t0VGBiogp7AwEAkJCQgMDCwzLgzrU61W63aIGxtbJizszM6deoELy8vvPPOO7jzzjvRqlUrtGzZUvUaavl5e3tj9OjRaryR8SP02nKef/55hISEwNvbG6GhoSZj4YyfHvT09MSSJUsQHh6unlbUes369eunArInnngCzz//PAwGA5o1a4a6devC0dERs2fPBnB1jMn8+fPx1VdfoXPnznjiiSewadMm2Nvbo3fv3gCu/j05LcDR6XRo0qQJ7rnnHhUYaUGUdszS9n0XFxe4ubmhT58+ePPNN7F27Vp4eXnB1dVV/XF6bX9s3ry5CuS1CxjtfXRaXWk9W8a9qkuXLlU9xElJSWjQoIEK9Js2bape8Km9IV170jMoKEgNjdACKq23XitHREQEzp49i59++gm33347WrZsiddee02dmL/++mu0atUKgYGBeOmll3D48GGEhISonl2tHAMHDoSzs7N6elF72lOn06Fu3brIysrCsmXLVOAYEBCA5ORk9O3bFxMmTMCoUaNMxlrefPPNWLRokckFrbYPT5w4Efv378djjz1W7sXuRx99ZPKCV3t7e/WEsFan2nGjVatW6j12/fr1w9ixY+Ht7Y277roLI0aMUAGli4uLeqN+ly5d8P7772Pt2rXw9fVVxwzttRo6nc7kCUInJydMnz4dqamp8PT0RHR0tBq7ZrwthYWFYefOneoccO+99+Lee++1+u8J/h97/x1eZbWufcNzpvfeeyW9kYT0hBQICaFDINQQQofQe6/SiwpIkSoISu8KgiBNwNBREZEiSFksKUpJ+31/5BvXk6nr2c/ea6/9Put9D8dx5BAJyZzzvsc9xjmu6yxq1KXzPHz4kO+++44RI0YIjxRg48aNaLVaRowYIT6N/7vf8a8YfwGtf2L88ssvJCQk6FRG+vTpQ4sWLbC2tmb27Nl/ulG//PILn376qbQeXF1d+e6774Daqkpubi6urq4EBgZKq7CqqoopU6YICbJVq1YC7hYtWiQbw5o1a8jIyCAtLY327duL4aLiGZmZmbFz507c3d1FHq7K5MuXL6dJkybMnDkTe3t7AVuqvRgWFkZsbCxeXl4EBQXh7OyMjY0NpaWljB8/Xh6ugoICSkpK2L9/P7/99pvEUQQEBODh4UFwcDD79++nurqaMWPG4OjoiIODA3//+99ZuXKlOL3XrXq1bNmSHj16SMaceojrehupOJLw8HBGjRpF9+7dCQ4Ols1DPcSPHj3C2tqapKQkCgsLxUxQlbBVBSk1NZVGjRrpVCfU4lBSUsKxY8cYMWIEgwYNwsXFRZy5NZraCmLfvn357bffuHXrFqdPnxZ1Xk5ODk+ePKF///5SHbOxsaFZs2Z/MlotLy+nf//+f6rc6enpYWtrK60MPT09WrZsyXfffcfAgQOZM2cOy5YtIzQ0lI4dO0rLV1UH6gI4KysrUlJSZK7UBc7W1tYcO3aM169f07x5c1l8Hz9+zMuXL/n88885e/YsvXv3pmvXrkRERLBlyxYsLCyE1KwW5OHDh/PDDz/QoEED1qxZw82bN9m2bZsA+969e3Pjxg3s7e0xNTVl+fLl3L9/Xzb7vn378vz5c2pqarhx4wZ2dnY0btyYoKAgzpw5Q0xMjFRP1QKuPnf//v3Zv38/Pj4+oriqe01V602p1JSa9Pz58wC0adMGGxsbQkJC0NPTY+TIkUybNo2kpCSCgoIE6Ds5OQkHqUuXLjptRSsrK6ytrXF1dUVPr9ZhX91/BTjqErcdHR2ZOnWqVH2U07tqrerp6ZGVlSV5l5aWlhgbGzNq1Cgxou3Tpw/16tVj3bp1bNiwQaeqZ2xsTGFhIZs2bdLhp6kqg1arxdnZWZ5BrVYr80TNQ42mVnhz//597t27R2pqqlxztaE2adKEa9eu8dlnn+mYhTo6OtKxY0eJB1Jh1XUBnUaj4aOPPmLbtm306tWLtLQ0unbtqrPWKk9A9e8XL15M+/bt8fPzw9bWVl6vWbNmWFhYCK9O+Vy9fPmSmzdvkpCQgKenJ+3bt+fYsWPU1NSwY8cOgoODSU1NJSoqiry8PKKjo2nVqpVOu3TevHmUlZXRo0cPhg8fTpcuXYSvpEC9Ai1aba3BpyLSZ2dnM3PmTMzNzXFycpLWu56enk62pboHBgYGJCYmyv8ruolWq6VLly5otVod4FavXj25h3l5eTRu3BhPT0/hz+bm5kqOX/369QkNDeXChQv06dOH7t276/i5qbg1Dw8Pli5dyurVq8UzKyMjQ3i46v2oz96tWzfS09MlG9bU1JR69erx4Ycfcv/+fdLS0sQ3MTs7GyMjIx0O7X93jB49mqioKKysrAgICKC8vFzn+5s2bUJfX58+ffqIvcn/1PgLaP2TQ0m9v/rqK9nMf/31V2bMmIGlpSWzZs0CaoM4P/74Y9asWQPUttASEhIwNjYmPj5ewFZRURHjx48nODiYTp06UVNTw6JFi3B0dGTatGliapeamsqTJ0+A2jBkdYJXVg16erUuxEOGDBHVoEZTy29SXkVK5q4W/tjYWE6cOCHvS3nV2NjYkJSUJKfRefPmyQYxduxYfv31V502pOJVLFq0iJcvXzJ8+HAxwPPz89PxIPrqq6/k5DJy5Ejp7dc1axw0aBAPHz6URT40NPRPnCx7e3vMzc15//33adq0KcHBwcTHx7Nx40b8/Pxo2LAhDx8+BODmzZsEBweTkZHB0KFDMTExEVVR//79haeRkpKCiYkJsbGxOgag6nWNjY359NNPOXv2LI0bNyYqKkpIzF27dtWxqIBaDoxybPfx8cHd3Z3jx4/Tq1cvndO92vRGjhzJBx988KdYoaZNm/LgwQN+/fVXzp8/j7+/P4MGDeLKlSsUFxcLSG7bti2jR4/GwsJCx5RQbZR1r7HiXNSNU9HT0yMoKIiqqiqOHj3KihUrsLCw4N69ezrE0CNHjrB8+XKMjIxwdXVlyZIlvH37Vipb6n526dKFbt268euvvwK1B4idO3ei1dY6QKvA9cGDB1NZWcndu3elYmVoaMiJEyeoqanh6NGjpKSkYGZmxtSpU6murqZdu3Y4ODhgY2MjG74CW7a2tuJxpNzs1WakqlJ1fbVsbGy4fPkyUHv4efnyJS1atJB/s3PnTmlFTJkyBUdHR7l/SpygFI8DBgwgMDBQwJBWqxX3awMDAxo0aEBpaakQlVVlR5llqiicjz/+GHd3dwHMCsApaxVTU1NiYmLIyMggPz+fDh06oNHUtpeqqqqYM2eOBAdnZ2cL/0dxlIKCguReqc+pDnAaTa3H3o8//kifPn0EzCrwcPPmTTZu3IiXl5d8X80hKysrOnbsiJeXl6QJWFhYSOu4TZs24lEWFhbG7NmziY2NlQrVmzdv+P333yXk28nJSdTb1dXVdO/eXadConivI0aMkBag+p56NpWfoXJ1Hz58uPCTFChq3rw5tra29O/fn5ycHIyNjbG3tycqKoqHDx+KFYpSPCpzWGtra3bs2MGuXbswMTERsKTsVKytrfH09CQwMJAhQ4bg4eFBbm6uiA7q3gMfHx/Wr1+vU31WINbCwoLNmzcTHByMRqORlnFISAiVlZV/SmowNTWlbdu2jB8/XviYSkyRmppKUFAQpqam4ro+c+ZM+vXrx+nTp/H19dWpehYUFLB161a6dOnCiBEj+Prrr/Hz88PT0xNXV1fi4+OZOnWq0EdUnuLq1atp3Lix3HMVjQWwcuVKJk+eLL6J/51Rt7ih4rGWL1/O5MmTsbS0pE+fPnz//fc6P7Nq1SqSk5P/x8Oq/wJa/8VR94b8/vvv1KtXDz8/PwFbDx48YObMmVhZWZGeno6VlZU8MP369aO6upqbN29Kb9/Ozo6UlBQ8PT3F86SqqkrKne+99x5XrlyhoqKCNm3aoNVqSU5OZvz48dy8eZPDhw9L8KwCASqs99q1a4wYMUJnkzUwMGDatGnk5OTomJb26dNHqkbu7u6yYHbq1ElOu9bW1vj7+zN58mSWLl3K3r17ad26tVQE1AlSTeYZM2bQoEEDiTYZN24cHTt2xNLSku+//579+/fz9u1bioqKKCsr01kklBomMjJSDC21Wu2fWl2GhoYUFxeTmpoqJnmHDh2ipqaG7777Dm9vbxo2bCjg9M6dO/I5lZLr3Llz/P7773z//fdiETFq1CgBmVptrXeTEiNoNBoyMzPZvn07W7ZsYfHixQwbNkwcyfft26fT3+/SpQtFRUWkpKQQGRlJixYtuHPnDgUFBcIJ+WOLS0+vNt6lLh9GX1+fBQsWcPPmTXJzc4mLi2Ps2LFCxlb3UlVGVGhy3Wy4f/SlAnvrKt6MjY3Zvn071dXV7Nu3j5CQEAkl/uNo0KABRkZGwhncunUrPXv2FC84jaa2gnvjxg2uX7/OiRMnePbsGT169JAqR1BQEMOHD9eJNZk5cyYJCQmEhITQtWtXvL29iYmJwcvLi5SUFMaPHy/mnQpAqGtX97/6+vr4+vrqbFy9e/fGxcVFXMLj4+Np0KABffv2ZdWqVQQEBDBz5kxevHiBlZWVtF42btzI3/72N6nCRUVFye9VVcZFixaxefNmli1bxs2bN3XaYwqsREZGcvDgQVHv+fv7/4m8b25uzt69e5k7d65OVUr9DgcHB+bPn4+3t7eAM42mVvFVUVEhvnV9+vShbdu2f1Ke6uvrixeXq6srDg4Of4rVefToEe+99x7h4eF88MEH9O/fX57F9u3bs2XLFvr06cP+/fulWl8X/ChbAPV6LVu2pKSkhICAANnEbW1t+fTTT2Ud8fHxoVOnTrx584Y7d+4QExODVlubevDTTz9RU1PD27dvdQQbddcNZ2dnbG1tSUtLkza/2vyDg4PZs2cP5eXlFBQUcPbsWd5//3309fWlRdmzZ08Arl+/TlhYGFlZWZw6dUq4jpMnTxYg1717d169ekWPHj0wMDCgS5cu9OvXj0uXLkmcmTKfdXBwIDk5mZiYGPLy8rCyspLDRF2rFdVO37x5s87zrw65T5484dKlS8yZM0c+f2RkJLdu3RIxj1oHevXqJe09MzMzoqKiKCgowNTUlNjYWBFRGBsbM336dKqrq4mNjWXEiBE8evSIv/3tb0IdcXNzIzs7G3Nzcxo1asSkSZPknltbWxMSEkJycjJr1qyRn9HT0yM5ORlra2vy8vJ49913MTQ0ZOjQof/6zfn/P7744gv69u3LypUr5e82bdqEh4cHAwYM+BPYUuN/Emz9BbT+yXH79m2gligZHR1NZGSkZPTdv39f2mpqMVcS+dLSUgBu3bpFTEyMVJRUWbOyspLPP/8cjUYj3xs+fDhQ6y3Spk0bWWRv377N3r17dQCT+t60adN49uwZFy9elAdYpcDXr1+f4OBgmjRpohO7ozLj1MaiuEt9+vSRzaJ+/foUFhZib29PRkYGnp6enD9/XioEenp6DBkyhPv37+Ps7Ex6ejpQW8WKj48nMzOT8vJy4TFdunSJFStW6JBAFSn2jwRXMzMzrK2tJTS37r9XwcW2trbs3btXgM6NGzfw9fUlOTmZPXv20LhxYyGxa7Va8d9R1793795oNBrWrVtHbGws9vb2tGvXjsePH0tlQb2uqakp4eHhTJ06lZcvXxISEkJwcDCurq7s2LGD06dPU15ezvTp0zE2NqZZs2b06NEDHx8fIeqHhYX9SUVZF7QC9OzZU2dzTEpKIjExkQkTJuDo6MjBgwf55ptvpLWg0dSKB+Li4qQyqRzJ64JuBR4sLCwICQnht99+E76TRqMRUu+IESOIiYlh5syZ5OTkkJycrHMqHThwIHp6tW72Fy9epEWLFgwdOpQ3b94QGxtLz549RXWn3M19fX3p2LEj6enpaLVaEhMTsba2Jjw8HH19fezt7cnOziYtLU2H+2JpaUnz5s0ZM2aMqJ3+CBy9vLxYuXKltHQVH0u5yWs0GtnU27dvj5mZGVlZWQwYMIAGDRrQq1cv2rdvT0JCAjNmzMDf35+EhAThNiqekr29PYmJiUycOFE2e6W2cnZ2Jicnh08++UQI/iEhIVK9MjQ0lIzC5s2bY2ZmJqkHdYFiamoqixcvlva5paUlCxcu5IMPPuDKlSv8/vvvlJeX07hxY8zNzcWHadeuXdKyGj58OI8ePaJbt24S+lx3PtnZ2WFmZiZVkrpfjRs3JjY2ViJwVq1axaNHj4SGoCo8qrWdkpKiYwVR161cHeLy8/Pp2rWrjpJPASyVm+rv7y9E6b1790q2ZHBwMO3bt+fy5cvk5eUJAM3IyBChgeK6Ll68WAxzNZpaw+cGDRoQHR1NaGgoSUlJXLx4kcDAQJYtW8b06dPx9/fHzc2NixcvsmfPHjHRtbKywsXFhVGjRhEVFUV4eLjwRPv06cOmTZvEpX7u3Ln8/vvv+Pv7C3CNiIjAxsYGb29vUbEq2oOpqSmjR4+mY8eOcq3q1avHxYsXWbVqlQ61wMrKijlz5jBp0iR+/fVXmc/29vZ8++23BAUFiSI3MDCQrVu30r17d/T09CSYWlk0tG3blrt373L+/Hm8vLzEuuaLL74gJiaGffv2AehU8ubNm0dxcbEY8NrZ2elYR7i6ulJSUsLYsWPls7i5uWFiYsI333zDixcvWLFiBWZmZvTr10/WkH8VyLl+/TqBgYGYmZkxbdo0ne8psFVWVvYvVzX+n8ZfQOufGLdu3SIqKoqdO3cCcPjwYTFvfP78OTNnzqRFixYih23Xrp3EZPwRbDVo0AA9PT0ePHhAZWWlTDhFBs3JycHV1VX4YE+ePJFolICAAJydnencuTPnzp3TkZg7OjoyaNAgRo4cKRUlFxcX9uzZw+eff86iRYvo06ePTsuorhS97oOtfGLqLsCqgqCMEgMCAmjVqpW8BwWGXr16RUVFBZ988onwfq5cuSIJ70FBQSQnJwtHqmnTpv9Qlq8UgRrN//KH0Wq1dO3alS1btnD69Gk8PT1xc3MTSa66lvfu3SM8PJzIyEjS0tKoqKhg48aNAkiWLVsmEvjY2FgsLS3Ztm0bEydOxM/Pj71791KvXj1pASpTVvW+lMt7REQEFRUVkmJvZ2fHgAEDRFpta2tLYWGhVNRU6yMwMFBOrn80FiwsLCQvLw9PT08hmi9ZsoTTp0+TlJTE+vXr+fHHH/noo4+wtLTUiRzS09MTa4+IiIg/eTRpNBpx6g4ICGD69Ol069ZNx9FeKQQLCgrQ09PT4aDEx8cDtcHXihenuCMTJ06kR48e2NrasmXLFpljdnZ2BAYGSk5aVlYWI0eOxNvbGw8PDxwdHYmMjMTS0pIzZ85w8eJFIiIiCA8PFzNaZ2dnmjZtioODA9bW1sTFxem0lHNzcyXWRZm4FhYWcu/ePd577z3Z3D09PXn27BmbN28mLy+P3Nxc+vbtS0xMDKWlpTpcPgWsO3XqRGZmpvi3tW7dmsGDB4tnlmqTK9J8SkqKtKeMjIxISUkhNTVV2on6+vo0b96c8PBw4uPjad++vVxjdb/q1avHzp07mTt3LsHBwYwaNYo9e/Zw+fJlqqqqGDNmDO3bt+fFixfs27eP6OhosrOzWbt2LZ6ensTHxwtZv3nz5owaNUqn6hQTEyOtxLpVQTXHXV1dxbTYwsJC7EHy8vKE16aeSVNTU1xdXTEzM5OcRDV31f9bWVnJvQ4MDMTPz4/u3bsLHSMnJwcDg9q81VmzZmFpaUmXLl3ksKcUrJ988glGRkZ4enpiYWGhQ7RX64QKlp44cSJv3rzh7NmzBAQEYGlpibm5OQcPHsTLy4tz586xZMkSESWkpqbStGlTWrZsKUpMZYeg0gWOHTvG7NmzZZ6Fhoair6/PO++8w6FDh/D19RXwbGxsTFBQEEOGDOH69euixFMHqqqqKqqqqmjdurX8fWpqKqdPnxYrnrpfCqD/ETSrg4CJiQkhISF4eHiIZYSPjw+hoaFoNLWq2FOnTgk36d69e3KQcHFxwdvbm9LSUp49e8bPP/9MVFQUrVu3xsDAgP79+8u9DA0N1elE6OnpsXDhQu7evStt0YKCAm7evCnr8u+//867776Lo6Mjjx49+pdXknbt2kVERARpaWmcOXNG53sff/yxdAb+nxx/Aa1/Yty8eZOCggKGDBlCcXExLVu2lDaGWhwUb0r5GX322WdyWjU2NqZdu3ZC8O3Tpw9VVVVs3ryZI0eOcP78eT7//HNZECMjI4mJiRHfmDVr1siCojaYkpISITX7+/tLu0gZJzo4OMiD7+7uzsuXL/n2229xdHSkadOm+Pn5/SlvTam3IiMjGT58uATv6uvr06RJEyZMmIC3tzfW1tZ4eXnRunVr9u3bx+TJk+nZs6dk4A0ePJiYmBgKCgqYNGmSeEo5OzvrAAtLS0s8PT0xMTGRRUNtRnUl8ao1oKoZSUlJNGzYEFtbW3r16gX8L3n727dvqa6u5vz58yxfvpwdO3bw+vVrKisriY2Nlc9ad+NxdnamVatWbNiwATs7OwoKCkhNTSUyMhIrKyumTZsmmYgK8Pr5+QkYrqys5N69exw4cABbW1uMjIzEyVxtXOr+KcFCXSKx4n38cXFVpNOOHTty8uRJ0tPTJQPP0NAQX19fkpKShNdSt3r1x5aQ+nJ0dCQ+Pp7AwEAxmoyLi8PJyYmxY8fyzjvvMHfuXPT19cUn6enTp3Tr1g19fX3JKHvw4AGLFi2SfEBlDFm/fn3mzZtHSEiIgLW0tDRiYmJo27Yt9evXZ/bs2SLy+O2335g4cSIFBQVUVlZy7tw57O3t2bNnD/Xr1xcAoDhM48ePZ968efJ51OdMTk6WE7efnx+pqam8evWK3377DQcHBzlUpKWl8fjxY/bs2UNubq5UmZXVSWhoKK6ursK9UhFJSmWsqhpHjhxh0qRJeHh4SDKA8sLSaGo5YT169MDExIR69eqRl5cnwLAuD0bNw9atW+uQm/Pz82ncuDF5eXkEBwfj6ekphyxDQ0M++ugjuQ+Ojo64urrSpEkTVqxYIWbGNjY24sz/xypqXfWsCmo2MDDAyckJQ0NDJkyYoGM+3L59ewYMGMCyZcvo2rUrubm5OgcG1aLUarXMnDmT69evs27dOuGUKk6Tr68vzs7OzJo1i82bN4utiBLruLq60rhxY6m8ent7i0WJyhq1tLTEx8eH6OhoWRPMzc1p0aIFP/zwA0VFRVhbW3Pp0iWZU+7u7lLJ1Wg0nD59mg8//FDWIy8vL3r27MmCBQtwdHTEy8uLw4cPM336dPEIa9KkCa9evRLH9wYNGsi/TUlJwd3dXcjvigxeXFzMrFmzBLypr7y8PE6fPs3du3d1gEtdSwxfX1+dCqC6Dop7aGZmRvv27bl69SplZWXk5uayadMmpk2bRlhYmKgn1c8GBATQuXNnHjx4AMDr168ZM2YMnTt31jF6bdq0KR07duSXX37RMUSta+FhaWmpM6ciIiKwsLCgV69eWFhYsGjRIp099NWrV8LZ/GfHf6QM3LZtG3FxcXTu3Fn2YDXqZhf+PzX+Alr/iaEQ9507d2QD37VrF0ZGRmzevJnvvvsODw8PmjdvLqeItm3boqenx6RJk9izZw9WVlasXLmShQsXoqdXG1asTnBQK8l2cHAgJycHjaa2/Tdo0CCGDx8uLaYZM2bw3XffYW9vT0lJCStXrmTRokXSBlOn5rr2CNbW1ri5ueHk5ISHhwd6enqEhIRw/vx5Tp8+Le27169fC0dBfQ0ZMkSsHry8vEhPT5eF0cnJiSZNmpCQkCCBulZWVnzwwQcCCKGW6B4TE8Py5ctp1KgRWVlZUmVRpyuNppbMWfeUX/d9KANIY2Nj4uLisLe3F0NStfgXFhYybdo0iouLmTBhArm5udy4cYMff/xR+FOKh+Lu7s6XX37J0aNHdQBIXfPPuLg4qqurxZHa2NiYevXqyeY2Z84c/va3vzF58mRpwx05ckSHCP/111/j4eEh4bz6+vqyyXfs2JHo6Gidtkp4eDht2rQhLCyMn3/+WefUGxcXh7OzM5MmTcLOzk5A3969e0lJSSE0NBRDQ0Ps7Ozw8fHB1dVVh7z/R+80FxcXWezd3NwwMDCQNkBhYaGcBKurq4Vno9SwAKdPn5ZWn4rI+PLLLzE3N+edd97h8ePH0gZS7dpu3bqxZs0atNraSKaYmBiioqLEHkK93qhRo/D29mbKlClER0djYGCAvb29AH21kRsZGREfHy+xR8qvy9bWFjMzM2nRenl5kZSUxG+//SbP2vbt2ykoKCA8PJzBgwfz5MkT4eoobqDa1FQ1NSIigi5dupCVlUVwcPCfqgkODg5yDRVfS31fqTx9fX2lfa/aqBqNhsDAQD799FMB2KpSpOaeuj+3b99mxowZYumgBC2qIgLwzTff4O/vj7u7O46OjlKNVrw/VRmsW9ms2650cHCQ7EYzMzOpkqqDgkajkfieoqIiCbtXZpDqQJmdnY2JiYkYqFZXV/PJJ58IqHNwcCAsLIyysjK8vb0xMjKiWbNmzJw5k6dPnzJixAi0Wq3kClpZWdG1a1exyklMTBSu0eDBgxkyZAhOTk6kpKTIGqBscrKzswkPDxdBxyeffCKB4Eo4YWJiIhWw1q1bi9+VAmOffPIJUBuhZW1tjYuLC40aNeL8+fN07twZPT09AgMDWbJkiRxaFDdQvYZKWlD2LOp6KqPTL7744k8HJWWpc+7cObGVqDvvOnXqxIIFC1i+fDlQe1iZOnUqtra2vPvuu+zdu5e1a9fSsWNHqUipuBxTU1NycnL4+eefAdi+fTs2Njbk5eUJf0ujqeW2jR8/HkNDQ6l8qf1Cq63N5qzLMzQwMGD79u08f/6cKVOmYGlpKabe/4pRF2StX7+eUaNGMW7cOI4ePSp/v2XLFgFbSpFbd/w/Cbb+Alr/yXHs2DEMDQ1p06aNqNgmTJhAQEAA3333HdOmTcPAwIBGjRoRGxvL119/zdSpU9HT08PJyUlKleXl5XJaW7NmjWzmhw4d4vLlywQHB+Pn50d8fDxeXl40a9aM3Nxc9PX1qV+/Phs2bBADVAUAy8vLZVNwc3OTk4ZS/KnSularJSEhgcDAQFxdXSXW59q1a1RWVor5olqEO3XqxMcffywycKVeUnyayZMnc/LkSUaPHo2npydeXl5ERUXRpk0bJk2aRIcOHbCwsBC+w7Jly2jUqJE4BqtcOlVZqbu4KH6NkiOryCLlXK5Iqjdu3GDKlClSgQsLCxP1neK1WFtbS+6ji4uLZD3Onz+f5s2bi5zdy8tLNgHldD1p0iQsLS0JDQ0lPz9f+Gt1S9KDBg1CT09Ph+B58eJFAHbv3i3X1NLSku7duwvvo6SkhJycHB1PpXnz5vHbb78xd+5c8vPzZcNNSUmhWbNmODo6MmrUKKna9OzZUxRTdfP7IiMjpeqi0dS2GpSLtUZT28IrKipi+/btxMXFCSn7ypUr8hnUYrZgwQJsbW3/FOzq7u6OVqvlyy+/BGrb3X379gVg+fLl0mrSamtjPxTvZsOGDVy/fp1x48bh4OCAj4+PgCCAK1euCDH7008/ZcKECeJgnZGRQYsWLXRIwkOHDqVLly4SvmtkZCRVLBW27uPjw7p16zh+/LgOGXbChAk0aNCA9u3bs3HjRjp37kxubi5NmjShX79+cmhR9gUrVqzg+PHjYjhpZ2enwyVUViFarZaMjAwROSiwq2wgVCVMVaaysrLECkQZrqoq5sCBA2nWrBk3btwAajmgS5YswdzcXID1hQsXqK6uls2jvLwcT09PqXDXVbC6urqyceNGHad6BQZ9fX0FdDg5OUkMzciRIzl69Cjnzp2TVpaqSqt5oLJOlXJuzZo1BAQEEBERQXFxMR988AHPnj3js88+E8Xe1atXefDggZiXenl5sXr1ark/KqJMKeUMDQ2xsrLi8OHD/PDDD2RlZaHR1PK7wsLCpO2lno1du3YxefJkUf+1bNmSTz75hIcPHzJ//nypOCtA06tXLz777DNKSkp0lLMtWrTAyMhIQuAbN26Mvr6+zDlVHQ8JCRHzZjc3N+Li4pg5cyaDBw/WyYd1dHTk1q1bOvxLdT1VtFldbyxjY2OWLl0qFIm6YEu1mBV/6+7du3Tq1ImAgAAxeFVh1QMGDABqEyNUddHPz49GjRpx6NAh/Pz8JFz5jw7+6rD6008/MXToUM6dO6djqzJr1ixiY2PF5uG9996jpqaGhw8fMm3aNLRaLcuWLftP77n/mTFy5EicnJzo1q0bSUlJpKamMn/+fPn+J598QkJCAvn5+f9tVeN/Z/wFtP6T47vvvsPJyUmiBJYvX86WLVvo0aMH3bp148aNG4SGhmJiYkKbNm34/vvvqaioYNiwYWg0Gsks/PbbbykrK2PXrl1UVlayZ88eXFxcxE3+m2++wd3dHTc3N1avXs2cOXPo1auXLIqK2Kivr8/69eupqamhurpajPMUJ6LuKVU9jE5OTtjY2BAbG0teXh5VVVXk5OQQFxfHw4cPad26NXp6elI5cHZ25unTp3LKNjMzo169enz00UfcvXtXrs0HH3yAlZUV3bt3Z+HChXTo0EFOEpcvX2bUqFG4uroycuRIPv30U2JjY3Fzc8Pc3JycnBwSEhKEx6BOfWpjs7W1FU+mJ0+esHz5cjw9PfHz82PVqlXU1NTw8uVLFixYgKGhIePHj+fp06fk5uZiaWmJnZ0djo6O2Nvbc/jwYQCKi4vlNaysrBg6dCiLFy9m1KhRwg9Rn1e1bYYNGyb2EWZmZowePZpTp07x6NEjRo0aJWTm58+fM2HCBOLi4vjoo494//33JYhaVetUa0pdY1XJU4t6UFAQJiYmREZGYmRkRFlZGVVVVbx584aCggIdjxsDAwNp1dWtsCj1aV3OSt0KhvpZ1dIIDg7m+fPnVFdX/4kz8e233+Lv70+/fv1EBALg7e2Nr6+vgM6CggL69u3LpUuXJM7i0KFDzJw5E39/f3l2zp07x9/+9jd+++03fHx8hIuiANDPP/+Mt7c3AQEB9OrVi/fee0/sDRwcHOjRo4cAcwVqjIyM5ABQd863a9dO5pQSBcTFxbFq1Sr5HCNGjJAWX0lJCRs2bCA7O5vGjRszdOhQ4S4p8DdmzBgBsoGBgaSkpOgof+vXr8/o0aMlx27t2rXY2dlJdVNfXx83NzfKy8vp0qULzZs3p3nz5hgbG0v1IywsTADAsGHD5HBXV82q/pyZmYm3tzfffPMNNTU1VFVVMXv2bOrXry9eYfv27RPrBwMDAxo3bqzjLB4cHExmZqaID9TGunPnTvz8/LCysuLAgQNUV1dz584dQkNDMTMzIyAggIKCAiwsLPD19ZXqn7JbUIkORkZGuLm50b59exo1akS9evVIT0/n559/pqqqir1794oNQufOnXny5AkXL14U8KnmtBJGxMXFAbWWOnWrniqU3NHRkW+++UZ+bvr06Xh6epKRkSG+XopUXjcL0MLCAn9/fwIDAykoKJC2+7Fjx+jRowf6+vp89tlnIhoxMzPDzMyMoKAgIiIiJPlDHZJsbW1p27YtQ4YMEZK++jwxMTH89NNPor5VX8o2ROUmKnBma2vLmjVr6Nmzp1TiVOVUCRP8/PwwNzeXdIvU1FSOHz/OuHHjhOx+7NgxwsLC6N27N1lZWVLZDgoKYuDAgfz6669cunQJHx8f+vTpwzvvvKPz/lasWMGbN2949eqVCHBMTU3lYGNjY0Pv3r1lHVFRN7NnzxY7o3/F+OCDD/Dx8ZFq1UcffSRUmxkzZsi/W7t2LT169PiXm5D+V8ZfQOv/MNTNqampYfbs2UybNo0JEyZQWlpKbm4ujo6OWFhYcOzYMZ48eSJgq7CwkG+//ZZvvvlGNrdp06bRtGlTWrRoAdRWO3r37i3qsYYNG3LhwgW++eYbbG1tsbOz48SJExw4cICWLVtKJSI/P5/Q0FACAwPFXPHhw4fCq7C1taVfv346m7Genh7x8fFotVqR6V6+fJljx46RlpaGv78/Q4cOFQCiuEK7du0iKSlJ4iI8PT0pKCjgk08+4cmTJ+zatUtaNqptVlNTw5s3b6ioqGD27NmYmppiY2PDqFGjmDhxIoMHDxanbUUar7sAKdKpaqnUq1cPJycn/Pz8GDBggCiARo4cKffpt99+Y/r06Wg0teRSa2trUlJSBKBGRERw8eJFtm/fjoWFBcOGDROQYWVlRWhoKCtWrMDT05Mvv/xS5/18+umnsqm9evWK5cuXExERga2tLaGhoXh4eFBeXs7jx48ZPXq0gLpHjx7x2Wef4ebmJoBbo9GICaX6f+UEXld67+XlRUJCAk5OTpibm3Pp0iWZi3l5ecIrU7Ez/0h9V/fEreaC8lBSVR9fX1+mTJlCZWUlFRUVVFRUsHLlSsaOHUtZWZlU5g4ePCitmw8//JAvv/xSlGplZWViPaJiPRRx3tbWlj59+tCgQQNcXFzEsNPX15e4uDgcHBzw8vKSVtiCBQv44YcfsLW1pbi4WDzU6raTVdbeihUrdBRZao4qpa+hoSGrV6/G2tqaRo0a0apVK06ePMmgQYOwt7dn4cKFYl/RsmVL2QhVpcjExAQ/Pz+GDRum4y2nNhYbGxtMTEywtrZm3rx50koxNjbm4sWLZGZmUlZWxtu3b/noo4/QaDRkZ2fj6elJr169+Pbbb9m7dy9JSUlYWlpKi7N3796sWbMGV1dXaY2PHDmSX375hfPnz5OdnS05hVDbCs3MzMTFxYWzZ8/qtPgUGE1PT6ekpORP3mzqeVOV8D+mEri7u2NjY0N4eDh+fn7cunWL6upq5syZI63Nuj9T1xpGVbmSkpIkX9De3p7mzZvz9u1bXr9+rdO+SU9Px93dnfDwcNLT07G2thZvrpSUFBGjqPkcHR1NeHg4tra2LF++nJkzZzJq1CjS0tJITk6mbdu2oo50d3cX4Km4lXZ2dgwcOJAOHTrIGunt7c2hQ4eYPn06O3fupF27dsIdPXDgAJ06dUKrrXWs9/Lykggj9XmdnZ3p2rUrVVVVfPbZZzI/lBGpCnlXP5OQkMDt27clPFyjqaVBKLK5iodSv9/ExITS0lJR8CoerapoK+GSk5OTVFpVykHPnj0xMzMjOTmZuLg4Hj16BMDEiRMJDw+nsLCQ4uJikpKSsLa2xsPDg8WLF7Ny5UodGoOpqSlPnjzh7du3bNmyReezKx6jWi937NjBwYMHgX9tq66yspIpU6Ywc+ZM4H+1PGfOnElRUREeHh7MmzfvTz/3fwts/QW0/oNx/vx5fH19+eKLL3j58iVXrlwhJSWFL774gufPn4tsX6PRiE1AXbDVrl07bt68yYwZM9Bqtfj6+hITE0NFRQWXL18mPj4ea2trZsyYwZUrV0hOTiYxMZFRo0bxxRdfiLpGLfIaTa0svWvXrkRGRspDfe7cOUaNGoW9vT0rVqygZcuWREVF4e3tLQaI6pTq7u4urtxhYWHs2rWLxMREaYkpSXjdzVqjqfWN6ty5s+RXBQcHY2tri4WFBVZWVpLfV/fE/fvvvxMREYFWqyUpKYmOHTvK4uLq6oqnp6fwP8LDw3V4RQoQREZGkpeXh4ODg1RovvnmG/bs2UNFRQWPHj3i66+/Fl8mVcKfNm0aT58+Zf78+fK7srKyhAdy48YNXFxcSEhIwN7eHkdHR/z8/EhLS6OmpkYHaHXo0IFHjx5x9epVeVBv3rzJiRMn+Pzzz7l//75kf4WGhnLy5EkAnj17Js7kqgKg0dT6VtWtJnh4ePD06VOOHz9O48aNMTIykjDy9957T/hAxcXFbNu2jREjRugoAxW/5Y+ARIG6ujwcxelR7TwF6Ddv3swPP/zAkCFDsLGxoVGjRrIxqVL8oUOHaNSokcwdExMT5s+fj6enJ3p6ehK1pNHUtioPHDjApk2b5HQ+evRo0tPTSUxMZNasWbRo0QJXV1fhtjVt2hRjY2OGDBkiwc6mpqY0a9ZM4pHU53B2duaTTz6RaoCat+Hh4Xz//fesWrUKPb3aEO+YmBiOHDlCREQEd+7coaSkROwv5s2bh1arJS8vDxsbG8mBVK1erVbLuHHjWLBgAYGBgVhYWMgGZmhoSP/+/fHx8SE+Pp6ysjKZN127dqVly5Z07tyZFy9eEBcXR1pamvjAmZqakpmZSXFxMc2aNcPe3h4nJycBe2pzvnTpEps3b8bQ0JCAgACJxTE2NmbRokXibdazZ0+5D+r61/XeUj5Ke/bsEc6Rmi+mpqY4OzszduxYgoKCxNMpPDwcZ2dnysvLOXfuHOHh4bRu3VrEOoWFhdja2gr41Gq1tGvXjjFjxkgrsKysjDt37tCuXTuMjY1p2rSpbLgnT55k0aJFLF26lAMHDrB//36Sk5Oxs7PTqcYbGRnh7OyMn5+fPMNmZmbY2dlJ6LECuGfOnOH169eMGzdOPqdSPZqamrJy5UquX7+OlZWVKD5btGiBh4eHHBBU3qu9vT0FBQWikFUB4GquqcPW/Pnz//T8DRs2jEuXLuk8i66urjRr1kyAk/rKz88XoKL+rmHDhmLREB8fT0BAgHzPyspKqlkxMTE8ffqUkydPkpGRQePGjXny5AnvvvsuEydORKOppUG88847QC2oUlSDmTNnikirXbt2DBs2DBcXF+bOnStzXb1meHg4PXv2FFAdGBjI5s2bqa6uxsvLSw51HTp0kKibV69eUVRUpBMc/c+Of6RMfPjwIffv3+fWrVsEBwfLOnX27FlsbW3x9vaWyvX/tCHp/2n8BbT+MNQNuXjxIteuXaOwsJCIiAhKSkq4efMmmzdvltTx9PR06tevT3FxMdeuXZNN+I+VrZ9++ombN29y6tQp3rx5Q01NDZWVlYwcORIrKyu8vb25ePEilZWVzJ8/n6ysLJycnMjIyMDIyIhx48ZRVlYmbREl81cbt5+fH/7+/lJCPXPmDEFBQTg6OlJcXMzKlStlEWjbti0LFy7ExcVFeDuZmZm8ffuWc+fOyWKnHrC6i3dwcLDIqIcMGcKiRYtYtGiRkPpfvXqlcy1//vlnkTU7OTnJibl9+/b07t0bDw8PrK2tsba2xt7eHn9/f3ktExMTLly4wNSpU4mOjqZNmza88847REdH8+jRI65du8aECRPw8fEhICBACLBqE5s3bx4zZszgyJEjhIWFyQLo4eHB/PnzJQaofv365OfnC7na0NBQjAbnz59PQkICGo2GoqIiBgwYIHJoRR6tqanh4sWLovy0tLRk2rRpzJs3jwEDBhAQEPAna4x/9JWQkICFhQUZGRkYGxuLsaCFhYVwYBTI8PX15ZtvviEyMlIHWNUFyHVPwCqHzdPTUywRVBZmdHS0KJZOnz5Nhw4dpAUFtQuznZ0dK1askLn9888/S9aeAhZubm4EBwfToUMHqTpYWFjQqFEjsViYNm0aM2bMIDc3lxMnTtC0aVN27tzJrFmzJJJFARGVp+ni4sK4ceOYNWsWN2/elNcLDQ0lJCREODaLFy8W0NSwYUPOnDmDk5OTtFkXLVpE8+bNBVwqsOrr6yttvxEjRtCpUyfhIC1dupT3339fDBrV3Dhz5ozYHSiOlL+/vzw36jo7OjoyYMAA3r59q2N9ogyFu3btiqWlpQAoIyMjyXs0MDBgzpw5VFRUUFlZKRmYqlrQr18/jI2NGT9+vBhYXr9+HWtrayIjI3nz5g0ZGRk688HIyIjt27dTWVnJt99+S/PmzQWkKiCQkZFBaWmp+G05ODjw/PlzvvvuO4YNG0ZAQICIcY4fP05QUJAOx1BVvceMGcOOHTswMjKS1pO+vr74r23ZskUEAso7bdiwYQwYMEDHAFl9jR49Wow31bU3NTXlq6++AmqdwFXF8dSpU5IrOnHiRDp37izzplGjRmzdulWud1xcHE+ePCEyMlKenz/yRj/++GO+++47aZMpkKOvr0/Xrl1p1KgRY8eOlXtvZGREy5YtuXHjBt27d5dr07lzZ/Ly8tBoNDrGogok1vUc8/HxwdraGh8fH2mLqoQKJWjKzs7m559/lmd1x44d0lX4+9//DtTGSOnr69OzZ0+ePn3KtGnTCA4OljgnxR9cuXIl4eHhHDt2jF9//VV4xH98j4qDp/YDJZgKDg7Gzc1NJ8x57NixeHt788MPP/xL9mSozRhWB1kF2Hfs2EFYWJioJ48cOUKrVq149913/6+2C+uOv4BWnaFu6Pbt23F1dRXDs/Xr14tE+P3336d58+YUFhaKFcHu3bvld9QFW66urtjZ2dG6dWtJI1cl823btmFpaUlhYSHR0dG0bNmSK1euUFNTw88//8zQoUOF8Dhq1CjGjh2Lnp4ezZs3R19fHycnJ3kQLC0tZQG7cOEC+/btw9raWocwqdRLpqam2NvbS9uoQYMGrFu3Dqi1RPj999+5dOkSjRo1wsvLi9jYWExNTcnPz0dfX5/Zs2fTrl074uPjWbhwoXzuuiDr4MGD/Pjjj/z973/HxsYGR0dHnTBRFXysFn+NRiMtT3WSNzQ0xMPDg0uXLjFlyhTi4uLIy8vD2dmZ/Px8AZErVqxg27Zt+Pv7M3DgQAGjin+VlpbG5s2bdbyrlM9RfHw8YWFh7N+/n7Fjx+pkdal7//LlSwYOHChVuQcPHjB9+nTq1asnYdUAq1evlvK9akO4urry0UcfUV1d/Q8rhY0bN2bVqlVyLdLS0nj58qUsqObm5rRp0wY/Pz9Wr15NTk6O+JelpaXxzjvvyKakr18bJFv3NRSvQ2U31lXDabVajhw5QmVlJYcOHSIsLIzAwEBiYmLYu3evDmF8xIgR2NnZSfjqF198IRUTAwMDCbtNSEjAzs6Otm3b0q1bNzw8PAgPD6e8vFxI58+ePePhw4c8ePCA9evXM378eCwsLHQqFHUrsApI+vv7SytYhRi7uroKsGjWrBllZWV07tyZJk2a8PjxYyIiIhg1apQAkj59+qDVauW5dXBwYMKECSQkJMjrqHaMAvD9+/cXgKScvfPz84mMjJTrqCohdZ35AwMDMTAwID8/n7Fjx4phrJOTkwgDjIyMyMjIkKqznZ2dkK49PT0JCwtjx44dXLt2jaZNm/Lpp58CtRuLra2ttLEmTJjAggULePbsmeTy/bFqUnfe7du3j5qaGmpqali3bp2Q+pWnVWhoKGfPnhUgpmwyOnfuzOzZs9m4cSN6enocPXpUqigajYaysjKJksrMzOTkyZNSiYqPjxf17ZUrV3BxcWHJkiWyVm7YsEE8qhQHS4EjAwMD+vTpIyBYeXJptVr69esnqRy7d++WdeTgwYPs27cPExMTyXGty4UMDg4mPz+foqIi1q1bh7e3N3l5eRLfo+6j8iq8fPkyP/zwg3hDqUPb+vXrad++PYsXL5Zn0dHRETc3N3r16iWqZQXC7O3t6dChA2ZmZtIWV4Cnbdu2ZGVlyVxUlUIFZB0dHenSpQuZmZmcOnVKLFGgtvvi7e1Njx49BIBUVVVRWloqyQ19+vShoKCAHTt2CJVBT0+P2bNnc/r0aebOnUtFRYWoDRWYVZ9XtbHrfu3du5fbt2+zbds2XFxciIiIoFWrVrRs2RJ7e/s/5Qv+V0ddoHTr1i3i4+Oxs7PjwoUL8ve7du2SbM/Hjx/TrFkzBg8erMMR+789/gJafxh79+6VErMifFdXV/PixQtWrlwpPXBHR0f27NmDn58fBQUFOvJRNTmePXvGqVOnKC4uprq6mlmzZpGVlSWGhYpnsXnzZho2bEhOTg5Xr15l586d4mWj0WgYN24cDRs2RKOpbQkow1KlyFMLZWRkJMnJyWzatIkePXr8KZg0Pz+fmJgYDAwMsLGx4fvvv6dZs2bUr1+fTz75hLFjxwqHJCMjg+LiYtzc3AgKCuK3335j/PjxmJiYMG3aNFq2bImnpyfLli3T4bEpbtiAAQP429/+Jqdq5Y1T13qi7ulRo9HQo0cPHjx4IDJi5cW0adMmysrKKC4u5rPPPmPChAls3rxZYnUeP34sZOupU6fKtVKLg/LvqutbpMj39erV44MPPuD8+fOMHDlSOF329vYcOHCAffv20aRJEzFkjIuLw8bGRu63ktXPnTtXQF6nTp0oLS1Fq9USHBzM1q1bhWuiNi0lqc7KyhKCr52dHRcvXsTOzk54KHZ2drRr107Azh9PmUqdqhZjZeRa975bW1sTHx/PkSNH6Nq1K8OGDcPMzEzHX+bo0aOkpaVhamqKra0tx44dk3bw/fv3cXJyYv/+/QCMGzeO4uJiUXQZGxtjY2MjrvEmJibs3LmTDRs2kJycTLNmzQQsHzx4UBa+06dPM3nyZFq1akWnTp1Yvny5BCk7ODiIYlHlEHbp0oUpU6bofEZlnJmRkYGtrS1FRUVs27aNnJwcIiMj2b17txinFhcXCz/LwsICW1tbTp06JXwXNW/y8vLYtWsXwcHBUmmtm/NXr149MYJUbauQkBDJWKw7hywtLbG1taV58+ZMnjyZzZs3ExgYKPwbZfKp0WiIjo7G3t6etWvXUlFRgYuLCxkZGSxbtoy1a9fy+vVrTpw4gbu7u/gS9ejRQ4DBqVOnGD9+/J9Creuq17RaLc2aNePMmTO8evWKmpoaZs6ciZ6eHqNHj+bu3bs8e/aMESNG4OzsTFxcnNyHjh07AnD16lUiIiLYunUrbdu2Zfbs2URGRuLj48N7770n83fw4MG4u7szfvx4qqqqyM3NxcjIiNzcXIKCgnSqMQCFhYViU2NlZUVYWBj169cnPj5euEgODg6S+6oUwvPnz6empoZdu3YJZ8rW1lYc6F++fElpaSnR0dHyM1qtlhEjRmBkZISXl5fwB4cMGYKxsTHu7u7MnDlTvLw0Gg0pKSnCQVTzUs3BzMxM2TsUOFSVXQcHB1EKGxsbk5OTI+1RZSkRFBREZmYms2fPFrClOI/Tp0+XqCk/Pz9SUlLYu3evXLdFixZRXFwsbvMq5xJqiwQmJiZipWNra8uvv/7KrVu32LVrl9hYrFy5kidPnvDVV19J1fWdd95h3759MnfS09N11h6tVitFhsrKSm7fvs2gQYPo1asX06dPF5Xsv2KMGTOGjIwMcnJyhB+p1q/bt2/TunVrPD09cXd3F3qO2pP+HcZfQKvOeP36Ne3atWPs2LFALcfoxo0bvPPOO2zfvp3hw4fj5+eHiYkJxsbGLF++nFu3buHn50e7du2EmA6IGhBqJ+Hs2bOxtLRk9OjRtG3bFn19fRITE7l8+TLLli2TeA51Wu/du7d4cZmZmeHq6iocFj09PfH5uXbtGlOmTJGydmZmJr/88guPHj0iKCiIjh07EhsbKyduxS/x9PTk+vXrfPvttxQWFlJUVIShoSHdu3cXWwBlzqfiHQAGDBhAeno6V65ckbanWvRVoGubNm1wd3cnMzMTR0dHgoODsbGxISEhQaeN5uvry4ABA3Qe3vHjx/Po0SPKysoEmDg6OhIbG0ufPn149OiRLCKVlZWsXbuW8PBwrK2tadu2LTU1NZw6dUrH1Vr5iDk6OuLo6EhRUZFcL09PT2bPns3Tp0/5+9//Lq0v5dsTExNDixYtqKioYP/+/WzcuJEff/xR5z7fu3ePjIwMBg0apGOvUZfnZWtrS/369YUsrHIRzc3NsbGxYdasWdy5c0d4ekqdpoChsbGxALQ/nirr/ltVYVGnaFVRtLOzw8HBQdSvERERrFu3jiVLlkiJ/fPPPyc4OBgTExOR2NfU1PDDDz/g7u7Oxo0bAWjXrh3Tpk1jx44dUpWcMGECxcXFmJiYEBQUhL+/P9u3b2fNmjXSOk5KSiIsLIxJkyZx4MABtNrasPK4uDi6devG69evpVqpNpW6n9HBwQFzc3MCAgLEA0hfX5+wsDA8PT3FaiEhIYG8vDzu3LmDr68vRUVF9OrVS8wuFUdFKQl79uxJdna22BLo6ekxePBgunfvTmhoKPXr18fT05O8vDwaNWqERlNLpC4pKcHU1JTGjRtTWVnJpEmTMDQ0FBGDnp4enTt3RqvVsmHDBrFfyMvLw9HRkby8PIYNG0ZBQYG0NMeMGSPKz4yMDMzMzOjTp4+0ggYOHEinTp14/fo11dXVNG3aFCcnJzw9PXnz5g2XL1/WOWDVbVmqLwW+GjRogIeHB40aNWLdunXo6ekxYcIEjh07hr+/v7Tltm3bhqmpKe+//z7Pnj3D399frFRUFFFRUZEAiIEDB0rFydTUlLKyMpo1aya+VOp9KM+j6upq5s+fLxw5FxcXOZAoikZ0dLRw544ePcq2bduEW/TgwQNRNs+ZM4e8vDyxtvHx8aGkpIR33nmHLl264OzsrCMMUQcaVREODw8XQUbdnFj1bEVFRYloyN3dXaqYhoaGzJo1S+cQoF7HxcWFCxcuyGdQQg21TqiEjbi4ODIzM5kxY4ZwONUBWbULJ02aRFZWFoGBgbx584Zp06ZhZWXFjh07+OKLL+RgGx0dTWVlJb///jsbN25k7Nix5OTkoNVq6d27N48fP+by5cvCw1PP/I0bNwREKtGOm5sbZmZmtGjRgoULFzJgwADpjty9e1eoMP9TY9WqVZibm3P69GmePHnC119/TYsWLSQ9Amo9Lo8dO8b27dt19od/l/EX0KozXr16RVxcHAMHDuTp06f079+fjIwMPDw8JDl9586dLF26VPLd1q1bx48//oi/vz9FRUWcPn36T79XeU2pisCdO3fkFK0cjEePHk1paSkaTS35Wo2lS5dKhp+SIOvr14bTbtmyhSdPnoiT94wZM7C2tiYrK4u2bduSkpIi/fE+ffqID42xsTGenp6Eh4fzww8/MGzYMCIiIkhPT8fW1pasrCyys7NFeWhsbMyIESOE5FhTU8O1a9fIyspi6NCh3Llzh61bt+Lk5CRgMysrCz09PWxsbBg4cCAnTpxg1qxZOvyLukRt5Xek0WiYNWuWXN8pU6ZQXV1N586dRYUJtSrDpKQkqYAoEDl+/Hjc3Nzo0qWLKHlUy0IBH8W9qGuHoNq4yu4iPj6eL7/8kocPH8qDW9eIte5Q0vTJkyczfPhwOdUqubh6jaKiIgmKVpEYCoxev36dI0eOEB4ezrVr1/jkk08k11Gr1ZKamipB4Oq9180uVBUtRW5OT0/HyclJ2nJqQb9w4QKVlZW0adOG8vJy2rVrR/v27Xnz5g1v377l0KFD0sbbunUrO3fuJC8vD19fX2xsbHjy5AkrV66U9l5GRgZxcXHk5+cL587AwIAGDRqg0dS65jds2FCctp2cnHBychIVrVKmqc2hrh1FYGAgcXFxcrhQFV3VUklNTcXKyoqCggI6duxI7969mT9/Pk+ePJGTbI8ePTA1NcXOzo7U1FSSkpJkg1XPQk5ODpaWllhbW4uvkHoPip9mbGyMm5sbkyZNkorVxx9/LG2ZxYsX8/btW9zd3cnNzRW1p4GBAQMGDOCXX36R+aN4SbGxsSQmJkrFJDQ0lEWLFlFdXc2ePXvIzMwUZSTUtvZzc3Pp1q0bVVVVQnZWlZGQkBDWr18v3lJ//FLvW7VP1XOjDpZr1qwRHmdsbCyAqHTfffdd1q1bJ55Ma9euFUNRf39/Hjx4wOnTp2natCnm5uZER0fj6ekpSQ56enp069aN999/n8mTJ4vFxfjx4+nZsyfh4eGMGDECPz8/evfuTXx8vNgbKKNhNQ/69+9PixYt6NevH1qtluLiYpydndm1axdPnjzht99+Y8mSJbJeqsiskpISli5dKnxArVYrAgSl4vb19WXo0KHiou7g4MC2bdvIzMwUJWpERASHDh3i1q1bNG3aVJ7R8PBwcWdXnFq15kRGRrJy5UqsrKwIDAwULp5qGxoaGsp7UHFTde9deno6ly5dEsugsLAwbty4QVpaGosWLeLp06f89ttvvHz5ks6dO6PR1IaLK9/HmpoaVq5cKXtF3759adOmDbGxsfK86+vrS4xQ3TVFBWKbmZmRnZ3N1q1bWbJkCY6OjpLEAbWAaMuWLbJH/KvGqFGjaN26tc7f3blzR/iff/T3g3+PdmHd8RfQ+sNYt26dSP5btWrFunXrOHr0KOHh4fj7+wtKfvHiBYsXL8bExISTJ09SXl6OmZkZEydO1Pl9hw8fxtXVFVtbW44fPw7UArqDBw/StGlTTExMmD59Ok+fPiUuLk5MUU+cOMHXX3/NzZs3ef/99yVfq+6GWlxcTJMmTYiIiMDBwYGzZ89KHInysnFycuLChQu4u7vj5+cnhqPDhg0jJiZGSummpqasW7dODDqNjY3/lAQ/dOhQvvnmG54+fcqYMWNo3bo1T58+BaBt27Y0atQIqCWm1j3ZGRkZ0atXLyZOnEh+fr6cltQio0BHXT5AYGAgbm5ubNu2jf3792Npacny5cu5d+8eDx48YMuWLZibm5OZmcnQoUPlc6iWwuzZs+nZs6eO+7K3tzehoaGSg9ehQwf69eunw8nQaDR069ZN1FxqqPteXV3NuXPnOHDgANevX+fJkycUFhYKf8Xd3R0TExP5nQYGBvj5+cniPnbsWCGjmpqa4u/vz0cffURVVRWHDh3C29ubjRs3Ul1dTYsWLaS6ZmRkpGOMqfydlLeQRqPR4aEFBgaSn59Pv379SE9Pp2/fvhgaGrJmzRoqKytl8z506JAozPr06cP06dPJy8uT1yopKWHo0KF07NiRJUuWUFVVJSHGVlZWfPHFF2zcuBEjIyMCAgJwcnIiLi4OMzMzaZG3bt0aOzs71q1bJ7mC0dHRNGnSROaY8ltSm6OqApqYmAhhX80X9X0VZK5sMYYNGyYtIag9pDg7OzNkyBCxfKgbfaLVakWZqzyhbG1tCQwMFDm7sbExRkZGOi0h9WdjY2MsLCzEd65nz554e3szb968PwWiN2nShD179jB06FABdKqFb2RkJLJ6pcpUrXNVZVFtmEWLFgnwrssLU+8pMDCQ9evXM2XKFHltY2NjunfvLgpgHx8fTp48yZ49exg/fjzW1taUlZUBtZ5DChgsX74cExMTJkyYwG+//UZ4eDhOTk7ExMTwww8/iDVCQEAACQkJbN++nVatWuHq6krbtm0BmDt3rnD5FOl/9erVkv2qqvN1wa2Xlxfu7u5ywNVqteJlVtfj7rfffmPTpk3y+WfOnEnz5s358MMPCQwMpGfPnnK4dHR0xN/fn969e9OnTx/69evHyZMnqV+/PikpKXIY02hqDWO7dOkiHlqKJpCVlSXvZ9myZVRWVnLr1i1at26tQ4fo1KkTycnJhISEyGFarUuNGjUSI1FnZ2eGDRtGamqqkM8NDQ2l+q4OR+Hh4dy5cweAR48eYWFhQUBAAFVVVcTHx5OXl0dycjKBgYFMmjSJy5cvi9WImZkZnTt35uHDh4wdO5YPP/yQ5cuXY2Njw9dff01RURERERGydlhaWspBVKne1XOn5tLdu3f5/fffWbNmDU5OTuTm5jJo0CC0Wu2/1CdLjXHjxuHp6fmnDNu1a9dKVVwZLf+7kN//OP4CWv9gXLt2jc8//xyo5aeoUnl0dLRObM7f//53mjdvTv/+/YFaIvofkfT169dF9j1lyhQAMRW8efMmoaGhLFiwgG3btpGXl4e+vr5kAFpYWAhhumvXrlK1Ue00JQl+9OgRTZs2lYWpcePGtGrVSlLnp06dire3N0uWLOHHH3+kffv2ZGdn8+GHH2JmZibmoY0aNcLExISVK1dSXl7O+PHjxX4hJCRELBn8/Pyws7MTouP169dlkWnWrJmc4jp06PCnTC97e3siIyMFiKgML1V5CQgIoHv37lhZWREUFMT+/fuxsLBg6dKlPH/+nMaNGxMSEiKndlXp2rx5M+np6bI4GRgYsGjRIjp27Cibs6enJ/Xr15f2UlxcnFQc+vfvj5eXF1ZWVpLfV5d7pv47evRo/Pz8cHV1xdHRkbi4OCIiIoiNjZV2oPqsittiZGREQkICzZs3l9dUNgsuLi7k5+fz888/M3jwYJFuq5gR9e/rXsP58+ezbNkyad8ognBdsKjVaiUypKqqimfPnpGQkMCOHTt0TElfv35NRkYGsbGxtGzZksTERPz8/KTt4+joiK+vL35+fmzatIlDhw7Rtm1bli1bRmZmJg4ODlKhMzc3p7y8nF69euHo6IiTkxMDBw4kIiKCNWvWsG3bNpYvX87UqVMpLS3F39+fUaNGkZqaKoeHumDL09OT0NBQbG1tpcKalpaGoaEhw4YNw8HBQVrAGk0tMVcdlMaMGcOHH37I1q1bATh37pxscL6+vlIN7Nu3L8OGDSMjI0M+c9euXbl3755sOKqaojbMugDH3NycIUOG4Ofnh76+Pt7e3pw8eRJvb29JAFA8ysjISBwcHPj000/p37+/bIahoaEkJiYyevRooqOjsbS0JDU1ldu3b3PhwgUyMjLw8vLi8ePHVFdXs2TJEuLj4/Hx8SE7O5uGDRsK5ysxMZGCggKmTJkim6eJiQllZWXk5+eLs/p7770ncn4FVEePHg3AsmXLxKRY2bioKolGUxtJpFTTfn5+bN68WThvycnJBAcHExoaypUrV/jb3/6GnZ0dTk5O7N69m6lTp2JgYEDnzp3/YXi8Vqtl2LBhbN68WaxHNJraFvi7775L+/bt5dn4/vvvWbdunbir6+npMX36dL788ku8vb3ZtWuXtCpVUoaiYly9ehWoNblUoKZp06akpqai1WpFeerr68u5c+dkLSgoKECj0dC7d2/xnXvw4IHErGk0GrHsuXTpEvPmzcPW1lbWBdWqVNywXbt2sXnzZgFq6pCmPBHd3Nx48uQJ9+/fZ9WqVdLZ6Nq1K999953E6gwYMICysjJiYmJo3bo1PXv2FKsbFfptZWXFlStXGDNmDKmpqVRXV7Nq1So5pKpkgGXLlkmkkLqn6uCh1Wr58MMPZe3Yv38/WVlZNGnSRDz3/tXj1KlT1K9fn4kTJ4rqG2rVhb1796Z9+/aEh4fLof/fcfwFtP4P49tvv6W0tFR65H9UUfTo0YPc3Fydv/sj2Lp16xYDBgzAzc2NxYsXA7UgTRF41WahQJGJiQktWrRg2bJl9OjRAycnJywtLenatSvHjh3j/fffp1u3bqJkGj16NDdv3uTjjz/miy++4M2bN/zyyy8MGjQIExMT2rdvT7169QQQ3rp1i/Xr14tJZN3yulrgf//9d3744QemTp1KSkoKTk5OnD17li+//JLt27fLCQtqAchXX30li4gqjdd16a4LAupaRmi1WqZNm4aNjY1wZ+Lj43F2dqZevXpYW1uzcuVKua51ydDOzs507NiRsWPH8urVK3FfVouLm5sbLi4uhISEyGLi7e0tXkiHDx/m0KFD0tJ59uwZJiYmhIeH/2ke1NTU8N577+Hi4kKnTp1EQabagwYGBkyfPp2BAwfqfF71mRctWkS3bt0wMzMjLy9PwKq5uTkjR45k/vz5ODo6MnPmTDIzM3F2dhajxT/K9JOTk9m+fbtwKZQnUN0qoQIBc+fO5bvvvmP06NE4OjrSs2dPBg0axPfff8+WLVtITExk0aJFeHh4iDx6xYoVWFhYMH/+fDIzM6XaNG7cOGnfhIeHM2zYMObOnYu7uzshISHY29tz6dIl+vfvz8qVK3n69CmvXr0iLCxMXKOTkpIwMTHBw8MDDw8P8TKrW/1U4GbGjBmEh4dLMHV2drbkRFZUVMj8SEhIwMzMjMOHDwtvT8WLDBkyhBcvXjB79mwKCgrknpmZmYkFS1hYGG3btmXYsGG4ublRr149YmNjadasmYAQ1W5XVbSysjLatm0rQgZF8DYyMqJnz5506NCBixcvkpWVhZWVFcbGxmi1td5Lyvy4Z8+ebNy4EY2mNg+yYcOGtG3bltatW0sFtbKyksePHxMfH09OTg7Lly9nxIgReHl50ahRI1FMKgARGBiIt7e3XFul4lORV3WNbb29vcXjTc1VZQK8Z88eDAwMaN++Pba2thgYGBAaGoqxsbGsBSdOnKCgoEDWK1NTU/r06cPRo0fx9/fH1taWM2fO0LhxYwwMDMjNzeXq1assWLBARBpBQUE6fK4mTZqgp6fHwoULGTFihKRFZGZmYmFhwd27dzl48KBUbIODg4mPj8fExAQfHx/mzp1LeXm5+Fu1b9+ezz//nMWLF0tAdnZ2NvC/DsGRkZEEBwcL72f69Ols27ZN7EFOnTol68CbN2+kzazCieu2yVRV97PPPpPvBQUFYWpqKuthYWGhGBmr7Na6z23dg5qpqSkODg5S4be3t+fkyZPExcXRt29fBg0ahKWlJW3btuXs2bPs3r1b3O8nTZpEdnY2lpaWWFlZ0a5dOx49esTUqVOJi4ujR48ecqBRDvf6+vpkZGQwduxYWrRogZ2dHQ0aNKBdu3asX7+eLl26YGJiwqZNm3TWx7oRWv/qoUB9SkoKgwYN4vbt29y6dUuSKA4cOICLi4vYPvw7jr+A1n8wzp8/T1FRESEhIXzyySdERUXRtWtXkZa+ePFCMrXqliwVEb6yslJ4TTdv3pSHolu3bowaNYrIyEhZqNPT00U90qNHD+rXr09paakoxOrVq6cTyvnixQuaNGmCj4+PpKSr137z5g3r16/Hy8sLe3t7goKCKCgoICoqSiwZ3r59K22zgoICysvL8fDwkI2/uLhY+GmOjo64uroybty4f3idampqOHLkiPgxabVahgwZQteuXWXRUC08pV46fvy4TrVLVaGCg4NZv349V69eley0WbNm8fr1a3r37i2hzCqqxMPDg2bNmgG1FgtqIw0ODhbn/KKiIpo0aULz5s1lszMzM2PSpEn079+fixcv8vXXX5Obm4unpyfBwcE6EUOVlZXU1NQIDygtLQ1PT08JdFWfQU9Pjy5duuDm5qbDzSopKeH169dSnUpPT+eLL74Q49hdu3YxePBgpk+frhMHo77qSsH/0ZdSOP0xckkRdn19fQkMDOSrr74S9ZEyh128eDE1NTU0adKEHj16CLnfw8ODK1euUF1dzcaNG/Hz88PU1JTx48fz4MED+vXrR2ZmJjY2NjRs2FCqe5mZmbi6uvL9999TU1PD77//Ljy/48ePc+HCBVJTU0XF6ezsLK1BAwMDDAwMSEhIwNvbm6SkJJHYl5aW8vvvv8vPlZSUoKenR3FxMXfu3JHK5uHDh6moqGDjxo2YmZlhbm7OrFmzpAKYmpoqlZSkpCRat25NSEgIq1evJj09Ha1Wy4IFC6SioXzeFNh69913mTdvHgYGBkyZMoVOnTpJNUIR4NV8njVrloAtVYnQaGq9nAwMDDh37hw1NTWUlJSQkZFB37598fLyIjo6Wp6riooKqqqqWL16tQgbEhMTdQjvintlZWUlLShzc3OMjY0ZNWoUnTt3xt7ensDAQJkXDRo0wMLCgk6dOonxsKrA9u3bV4KXFQ9S/W51L/38/Dh//jzV1dVcu3YNgAcPHlBVVUWPHj2wtraWypafnx/169cXI9xFixZJqzAyMlKqLQ4ODoSEhOiYK6uKcFJSEnl5efzwww+MHj1aWnLGxsZ4eHhgampKgwYNCA4OxtfXV6dCpHijP/zwgyi09+3bB8CPP/7I48eP2bVrF66urtSrV4/Ro0fTpEkTfH19CQoKkmia9evXM2LECAE+KuJm3rx5wh2F2hajq6sr06dPlzBrxZGNjo5m0qRJLFq0SFTPGk0tb3TJkiXy+XNzc/noo48ICgqif//+LF26lA8//FAA+OnTp2nQoAHe3t6UlJQInzE6Ohp/f39MTU3lwGppaSnvt6SkhKNHjwq1RKOpTSvo1auXmLaq2DKtVktycjLPnj2TfQygrKwMS0tL1q5d+7/lrf6rRl1+4uTJk+VQFhAQIAfin376CT8/P77++uv/0ffy3xl/Aa3/YLx69Yrjx4/LplteXk5oaCguLi4UFBTQunVrYmJipJ1YU1MjoaP79u2jTZs2xMTE0K9fP/bu3cuPP/7IgAEDpBy7ePFi6tevj5ubG6WlpVLhKi4upry8nAYNGhAUFERqaioDBw4kLi6OGTNmSOvnt99+ExJyeHi4TPr+/ftL5uGuXbuYMGGCkIHDw8MpKSnh448/pmfPnkKinTt3LitWrJCqkr+/P++99x7V1dUMHjxYQqQBJk2apOO4W1VVxaBBg+jZsyenT5/GyckJV1dXqbAo/oNqC6nctidPntC0aVNxvY6IiMDS0pKgoCCRtKtyvGqpBQYG0qtXLwmx1mpr8++WLl3K+fPnpV0SGhpKTEwMd+/eZffu3ejp6ZGQkCCxHmqB9/Dw4P3336dVq1Y0atSIXbt24ePjw927d6V9rE6sUVFRZGRkkJCQgLGxMd7e3rRq1UraoIqIrgKE64IeBSZUS9XFxYXk5GTZfFWcjlKEDRo0SMfPRv1+VfVR1QnVhgwODtbJu9RoNGzfvp0rV65w6tQpfvnlF5nXiYmJaLVarKysSExMpG3btvTt21dcpdeuXYu+vj6lpaWywHbs2BFjY2OSk5P58ccfqamp4fnz58yZM4e0tDS5T2ZmZrIBq9GrVy9pd9y7d48vv/ySvLw8Wezd3d3FN01V51SrR7VZGjZsSE1NDVevXtVRc6pDz/Xr1yVp4YsvvqCiokJigZydnZk6daq0ypV3lTo8dOvWDW9vbxo2bEhQUBB79uzRydgbNGiQhFgHBwdz9uxZJk6cSEpKCs+fP8fHx0fk+B06dNBx+J40aRLl5eUkJSXp2CR8++23co1GjBhBTk4Ojx49YsiQIdjb29O+fXtpxVRXV0tOoZ2dHRYWFhQXF8vcUApmVRVSAMPGxkbAdnp6OmfPnqWkpARbW1upGHfq1InU1FQSExMpLy8XQD9s2DCWLl1Kz549ycrKYvny5WJzoipIfn5+XLhwQSqh58+fp7CwUCpsKtLF0NCQdu3aiXBAPSMaTS23KjAwkClTpmBsbCz8xfr163P79m2ysrJwcHAgKiqK33//ndmzZ8t9i4yMZNasWXINlHegApMKSJSUlLBjxw65hiYmJsTGxrJnzx6g1h4gLi4OOzs7IiIiWLZsGSNGjGDkyJFSUdJoNJJWoIQ6dXlL/v7+zJ49W+a8ArJZWVm8ffuWRYsWodHURoH5+vpKVbVu+3TUqFECtsPDw+nYsSNWVlbye9U6pMDWpUuX8PX1JSUlhUOHDrF+/XqGDx/OnDlzWLJkiRjO5ubm8urVKyZOnEhCQgJJSUlSRaxXrx7vv/8+mzdvlkBo1VZv0qQJhoaGNGzY8E9dmpKSElxcXMTD7J8ZdZX5fxx1X0/9G2WztH//fk6fPi1/P3ToUDGy/ncdfwGt/+K4cuUKvr6+pKWlsWzZMpn8FRUVTJw4EWtra5YvX46RkREDBw5k0qRJ1K9fn9TUVBYtWsTNmzcZOnSonMJOnz7N0qVLiY6OprCwkLy8PDw9Pfnoo484e/YsdnZ2NG/enJs3b5KcnEx2drZUy+bNm0fnzp3JzMwkNDSUjRs38t133+Hu7i7KpKqqKm7duiVE0YYNG+Ls7CwckrNnz9KrVy/i4+OZN28e58+fl6BbNdmV+eH58+fZsmULnp6eUqZVJpaKr3Hs2DHhjCjXYQU2NJpaU8tXr16xe/dudu3axfvvvy+n802bNnH//n1ycnLIyMhg9erVfP/991JdSE1NZffu3RQUFPD555+zdu1aWbSMjIxwcXHBxcVFnM8Vx+jkyZMsXLhQ+CXK+V1PTw9/f3/Gjh3L8ePHefr0KePGjSM6OpoBAwZQWloqxo6Ajllh165dBQSoTVijqfVgUryQ8PBweW+enp5ySlRg6Y+B4QYGBkRHR4sdR+vWrXU4X2qxVzYE6vSdmJgolg7q97dq1Yrr169TXV3NmjVrmDBhAp07d2bFihUS6GtjY0NZWRkdO3aUn+vdu7eQ9BVnZ+zYsaLC0tfXJyAggKNHj8p12blzJx9++KEQoZctWwbUtt2nTJmCp6enOGlHRkaSlZVFQECAAMe65rqKbKwAup6eHqampri5ubFo0SKuXr0qpGE3NzdR+T5+/FgMglXVSE9Pj2nTpqGnp4e7uzvTpk0TsJWWloaJiYkcXlatWkXDhg3x8fHhyy+/ZNCgQSxevFi4KcOHDxeytK+vL1999RWJiYmUlpZSUFAgB6awsDA+/vhjHbuN0tJS9u3bR0REBDY2NkRERHDp0iVqamp49eoVGRkZFBYWyucoLS3FyMiI0NBQvv76a/bs2SPAQbUiVS5fXRWvAqDNmjUjKipK7AgUh0+1orZt2yY8PAsLCwExNTU17NixQ0dtlpWVRevWrXFycmL48OE0btwYFxcXUasZGRkxdOhQrl+/zuDBg/H19RWgs23bNqqrq1m6dCkeHh706tWLKVOmSNyQg4MDTZs2RaPRsHr1asaPHy+AtUmTJtTU1PDgwQMSEhJITk5mwYIFFBUVsWzZMknlMDc3JyIiQg5x9vb2fPDBB1y9epV69ephbGyMmZkZLi4uODk5UVRURG5uLvn5+Tr0h/fffx8vLy/i4+P57rvvOHPmDGlpaRw7doyKigoOHjxIv379cHR0JCMjAwcHB8le1Gg04sWlqCFQa6HSqFEjrl27hru7u06SglI81jVvVr6Bu3btori4WLiAzZs35/nz58Cf1XyXLl3C1dWV/Px84Z1BrahLiTUuX74MIGrjuuuJ8lpT89XT01M6DUoZaWNjI1YfdYc6MP8rxtatW1m/fj3btm2Tv6sLtv6RivGrr76if//+2NjY6BiY/juOv4DWPzEuXLhAQkICPXv2lEl89uxZiouL2b9/P5mZmeIsDrUTsm/fviQnJ3Po0CFOnTqFt7c3Li4uTJ8+nZcvX7JgwQKio6Pp2LEjeXl5pKWl0bFjR/r164epqSkHDx6U08tnn33GBx98gIGBASUlJZw5c4bAwEBiY2MZPHgw9erV4/bt2+LbU11dTZ8+fYT7ogDDmDFjgNo2Ys+ePUlMTGTSpEkSE/Ttt98yZswYrK2tOXr0KEePHqV3797imzV06FACAgK4fPkyp0+fpqCgAGNjYzp27Chqo7qLirm5OS4uLnz88ce0adOGhg0bkpGRQUpKik4e1r1794iPj8ff358NGzZQUFCgk6fm7OwsMTEzZswgMDBQImXqLhAKPJiZmZGUlERgYCCDBg2iRYsW4ngfGxuLjY0Nbm5uREVF4eDgQHl5OYcPH+b48eMSS1ReXs6hQ4fE7ywyMlLHXkHlMCoQpTYrtZAp/szixYvRamvNUmfPns3WrVtp3bq1zuIXFRUlIE25n6uTrwJ2ig935swZrl69Snh4OMbGxrRo0YKAgABWrFhBVVUVI0aMwN3dnZKSEjEcLC4u5uXLl5J2sG3bNg4fPkz37t2xt7entLSUOXPmcPXqVebNm4e9vb14KalWR7NmzWSeDBo0iPHjx7NhwwaioqIoKipi0KBBhIaGYmdnx+zZszE2NpbKj6+vrw6B38jIiJSUFBE4KL6Qra0tMTExFBUVUVZWRsOGDbl//z7nzp3j2LFjhIaG0rlzZ7p06UKzZs0kvNjJyYmmTZui1dbGteTn50v1ZObMmcIRUmIN1Wq2sbGRKBR9fX1pSytvrTFjxsihwMLCAkNDQ5KSkoTHMnToUM6dO0dYWBhNmzbVMQqNiIigYcOG7Nu3j/T0dKmqdO/enejoaCoqKgTUP3z4kKKiIgGZYWFhODs76/CrLC0t5VBhYWEhm3J8fDze3t7Y29vL9e3fv79w+YKCgrh+/Tp79+6VqkZqaipFRUW8ePGCK1eu6FTkFHjRaGpVrQMHDpRsPgWEd+zYAdTGlkVHR2NtbY2zszONGzeWDXrs2LE4OTnRu3dvyT00MDDQSTRQa4SpqSnDhg2T+fXw4UMxwvXw8KBp06YSUm5gYMCqVasYPny4VGnj4+OlYm9gYEBUVJR4r2VmZjJo0CBevHjBkSNHmDdvntjuqBiatLQ08vPzyc/PZ+/evVI5+fTTT3UAypEjR7h8+TIHDx7Ey8sLV1dXkpOTuX//PoDkCC5fvpyYmBhmzpwp1ehevXpx5swZWrduLfc5MjISX19funfvzoABA2QeqsOAOtTWFefcvXuXli1bEhoaSo8ePbh27RpVVVUMGTIEIyMjlixZIl2H3377jcLCQp3weq22NvN0yJAhzJgxQw4S5ubmDBw4kOvXr9O6dWscHBx02qP/ndGtWze6dOki/z948GBJJnB3d6dz587yvf/IpuHIkSOUlpZK6/rfefwFtP7JUV5ejp+fH56enkydOpXY2FgiIyP5+eefqV+/PtOnTwf+V9nz8ePHhIWFUVRURMuWLWnYsCFRUVFyIsvKymLUqFHEx8frqDzGjBlD27ZtqVevHps2baJBgwaEh4dLTEjdqpOjoyNNmjTB09OT27dvy+sXFRVhYmIilZeUlBTmzZuHoaGhlKXfvn1LYWGh8HTq8tMuXrzIpUuX8PPzw8LCgpkzZ3L48GF5j0lJSVI9UZN+y5Yt9OjRQ05KDg4O9O3bl6ioKIyMjLC2tsbBwYEhQ4YQFxcnET7Kjfzw4cOYmZnh4+ODkZERCxcupEmTJrJAJCUlMWjQIPz8/HBycpLoloEDB7J69WrS0tJ0vGj09PTkdPrmzRumTp0qTsM///wzK1euZPv27fz00098+eWX0qZRp0JLS0uioqJ0cgf/0ZfiyKgKjfLIUeT8/fv3y2aiFHBpaWmyUajfU69ePakKWltbExoaire3NzY2NhLvY2BggIeHBx9//DFDhgzh6tWr/P777wQHB7NixQp2796Nu7s7Fy9e5O7du7Rr1w6NRiNRLlDbErSxsWHr1q1s3bpVyLGKG9SpUydJMFDqrrCwMD777DPKy8sZOXIkNjY2HDx4kNGjR2NgYCBVhNDQUIl3WrNmDUZGRtJiVBE16jqFh4dTXFwsnMXGjRuTkpLC2LFjefr0KVu3bsXY2FgqQVAbvaEiU1avXs3t27f55ptv5PlR7bQWLVqIO3xoaChDhw4VLl+/fv3kPs+fP58hQ4bIfXB3d6dt27Z4eXlJRl1dtVz79u2ZNGkSkZGRmJiYSBs2KyuLx48fc+jQIamwWVtbS/v2888/p2nTplhaWrJ7924dY2P1PD958oROnTqJrceAAQOksmtlZcVHH30kIgmtVitikqZNm0qLb+jQobRv3x4rKysaN27Me++9Jy3a69evM2vWLDF+NTIyok2bNqSlpWFhYcHAgQMpKytjyJAh8poKIE2aNIl58+YxfPhweS5WrlxJRUUF5eXlBAYGEhwcTHh4OO3bt6dLly7CEVKV2zlz5hAUFISbmxvGxsYEBAQQFhaGtbW18DkXLVokdIgnT57QsGFDfH19WbNmDTNmzCAsLIywsDAqKip477336NKlC+np6Xh5eWFtbY2trS1bt24Vrzol5Ni1axdbt27F1tZW4orUnFqyZIlUb01MTGjdurV879dffxVXen19fZmLat1TvNHPPvuMFy9e8PPPP9O8eXNatWpFbm4ux44dE5FG//796dGjh4DY8PBwiXfT09MjLy+PqKgorl69ypgxY4iOjmbhwoU8ffqU9evX6+xFr169ory8nJCQENLT0/n222+prKwkJiaG5ORk7t27x6effkpsbCx5eXk0btxYOiempqa4u7vToUMHDh06JM9n69atpSVcVVUlrv3/yCfyvzJevXrFwoULcXJyYtCgQbx8+ZLMzEyuXLnC3bt32bp1K/b29jq+Wf8R2KrLHft3Hn8Brf/G6NevH5aWlqSlpcmJa/v27aSmplJaWgogFSWo5U4FBgbi7+8v0RyzZ89m7ty5ZGdnY2Njw+DBg+nZsyfJyck0adKEgIAA5s6dK6RbDw8PMbrbvXs3z58/Z+3atXh4eGBra0vfvn1F3g6wcOFCsU3o1KkTnTt3xtfXl+TkZCZPnoy9vb1OaVm9199++43jx4/rlNcVOdPV1VWqOSrHMCwsjOvXrwMIWVURrtXmqSo+yjtr8eLFXLhwAQMDAyZPnqxTHu7fv7/kjNWvX58bN27w6tUrhg4dKhUPIyMjEhMTyc7OFtDn4+PDrFmzWLBgAatWrWLKlCnismxhYcH27duprq6mVatWHD58+B/mYZ08eVJI9927d8fd3R1zc3MBuF5eXtIOU8BISfsTExOJjo5Gq62Nz+jSpQs//fQTqamp2NraCldHBeTa2dmRnZ3Nvn37/mSFUde/SVUtZsyYIaRxT09PAXcFBQUcPXqUcePGYW1tzY0bN9i4cSNt2rTh/Pnzks03cOBAoHbjUmC8U6dOWFtbi/o1IiKCN2/e8ObNG/z9/dm2bRsbNmzAyMiIkpISqUza2Njg6elJ9+7diY2Nxd7eXlojXbp0wdLSku3btwO1Bw3FIVPXS7VcIiIisLOzIz4+XvL72rRpQ69evYiLi6NFixby/uPi4pg5c6ZswC1atMDKyori4mJpZz98+JDo6GiMjIzE72vChAlUVFTQvXt33NzcxOri+fPnJCYmStRReno63bt3JyEhASMjIzp06MD8+fNxcnKSzWjQoEHCCfL19SUnJwdvb2/Mzc1xdnYmJCQEQKp9RUVF/P3vf5eK1YMHD1i3bh0jRozQcbGuOxdXr15Nr169SEpKwtPTk5SUFCFUK5PeulYeSgxx+PBhDh8+TGpqKu3atcPLywtLS0vxrlPXX7UqFyxYINViBahyc3O5ceMGt2/fJioqiuLiYjHvVAKDdevWcfbsWWJiYrC1tcXBwYFWrVqxdOlSLl26RHR0tLQZjY2NWbhwoSjt9PT06NGjB+Xl5cTGxuLi4oKenh45OTnk5OTg6urKxIkT/2QUfOPGDerXr09SUhK5ubkEBAQQFBTE/fv3KSgokFzRVatWoa+vT0hICMuWLeOXX37hwIEDjB49WgjjdnZ2ErOm4lrUOrhkyRJ8fX0xMjKiU6dOfP/99/K9a9euydzNycnhxo0bVFZW8sMPP1BUVCTtt9LSUp49e8Z7771HSEiIHMAcHR1F5ad84iIiImjRooXQUQoKCmjZsiVxcXGsXLmSyspKhg8fLs+YIoHX5Tc9f/6c3NxcjI2NWbt2rTwHnp6euLm5SXyXUpg/efKEs2fPkp6eLnQEdRjfsGGDBFHXBVtdu3bVyUD9Z8fz589ZuXIljo6OkjepDtoVFRXs2bMHe3t72rRpIz/z7+qP9Z8dfwGt/+L44w1fsGABmZmZ5ObmkpSURGpqqmRkKYWfWkDbtWtH586dmTZtGvr6+iQnJ8vvefv2LV27dsXGxob79+/z5s0bSkpKcHV1xdfXl+joaMaPH8/HH38sUR8ODg5YW1tjZ2eHra0tK1euRKutNWw0NDRkxIgRtGzZkpSUFFEOffvtt1y/fh0vLy8aNGiAo6Pjn04pdaML1J/VoqfCrYOCgoRzEhISgqGhIREREWzevJn69etTWFhIUlISO3bsYOLEiTg5OVFcXMzChQs5ffo0ERERohZcs2YNBgYGDB8+nLNnz0rJ39/fX9olZmZmJCYmsmHDBnr16iUbg/I4MjExkTBm1eZo1KgRb968YePGjToSai8vL0JDQ3nx4gWXL1/W8Ub75JNPKC4uZty4cUycOBEDAwPGjRuHt7e3qARbtGghbTD1/oyNjQkJCSExMVGCsTWaWjL0/Pnz6d69O2FhYejr65OSkoKDg4MA0JCQEDZu3EhiYiIxMTE6LScFSgwNDQkMDGTcuHE0a9YMf39/3NzcRA1mYmJCcnIyZWVl9OzZk+XLlzN37lxSU1NZv369tCEmTZoE1Lo49+rVi71797Jo0SJmz55Nly5dMDAwwNHRke7duwO1wgdlIeDj48OmTZvQ19enqKiIqKgoWrVqhZOTExs2bOCdd97B0tJSzFjr1atHu3bt+PXXX6murqa6ulqc2tW/c3R0xMLCAjc3N0xMTKhXrx42NjY4ODhQVFQkrvFKmt+5c2fi4+MpKSnh7du3lJSU4OXlhZubG0lJSXTu3Jm8vDx69+6NRqMR4rS5uTk9evRg69atWFpaioHmy5cviY6OJiMjA3Nzc2xtbRk8eDBnz56Vlu3MmTMxMzMjJCRE2u6q0hgeHi7qXVdXV5kP6enptG3bFjMzMw4ePCjP0ujRo4V03LFjRzZs2CDPlrpGU6ZMkfabqqzW5c6oqoOqlA0ZMkTiiBR4jY6OFlGMpaWlxA+plm1cXBy//vorUOsxpowpVYXjzp07nD59mtDQUM6dO8euXbtkvnfq1Ilnz55RVFQkh6TTp09TVFQkxPby8nJiYmJEUVhSUsLTp09JSkqiW7dusj6eOXOGrl27kpCQQGJiIocPHyYvL4+cnBwRNUBtDFB2drZUvZR3m1oDIiIi5N+eOnUKLy8v4UB17txZsgH37t1LREQEVlZWXLp0SSwYNm3aRExMDGvXrqW6upp58+bh7++PlZUVLi4umJmZkZ+fT05ODn5+ftJC9vT0ZOzYsdL+V4cvNzc3Me9s0aKF8PsaN27M2LFjadCggRjlKsFKhw4dCAkJYc2aNTRt2hRfX1+xoqiqqsLBwQEbGxvGjBkjgdJ196MTJ07QuXNnQkND2b17NzU1Nbz77rtyMKtb3Z8wYQLffPMNFhYWYrej1WrJycnhzp07nDx5En19ffr06cPPP//8H+6J/9lR972+fPmSFStW4OPj8yc7nYqKCvbu3YuTkxMNGzb8l7z2/+3xF9D6L4y6E2XTpk28//779OvXj9LSUnx9fSkoKCArK4vc3FzJrxwkBQAAst9JREFU8OvcuTNlZWX06dMHc3Nzrl69yqVLl6Qt1rdvX06dOsWBAwe4ePEiYWFhjBw5kqlTp2JnZ8cnn3zCqlWraNGiBVqtlnHjxvHy5Utmz56Nr68vYWFhtG/fnoqKCl6+fElSUhLHjx9ny5YtcmJS7YFmzZrJyWzOnDloNLVeS4qjoBZ6Nd59913atGlD69atGTZsGCdOnMDNzU1O0EoBZW5uLuDAwcGB9PR0rl27Ju7qd+7cYfDgwWRmZgp/4Y+vtXXrVrGRMDExISAggA0bNkjlx8HBQUj2CxcuFA6OkqW3bt0aGxsbqaio6l9UVBRv375ly5YtzJgxg+joaKysrDhz5gyDBg0iMDBQiKYPHjygSZMm2Nrakpuby/r168VIdezYsdK2Uw7+2dnZkl+mp6cnC3BddaCSrW/atIlWrVpJq7NuxpkyllWVt8zMTB2rhqioKJGsm5mZ0bVrVyoqKli7di1dunRhwYIFJCQk0LhxY16+fEl6ejpHjhyRyo7yKlOGqR9++CHNmjUjOzsbY2NjBg8ezPfff8+ZM2c4cuSIWIK0adOGrl27Ymtri76+vkjip02bhomJCZ6envTt25cPP/yQ6upqZs+eTd++faVNZ2BggJeXFxMnTmTx4sUMHTpUjEHbtm1LcXGxjjrTxsaGwMBAGjRowHvvvSeAws/Pj9LSUqmyDR48mOjoaObPn8/XX3+Nn5+fKFb79OlDVlaWbI5WVlYkJCTw8ccf6/DwlLJv9OjR0narV68eAQEBFBUV8eDBAwmZVrxGc3NzSkpKJA9y5syZeHl50bJlSzIzM6Vqq1rH/v7+6OvrS36nctL+6KOPxFfNw8NDOI81NTW8ffuWDh060KlTJ4lQUpUrU1NT4T0q4K8Up4rQbGxsTGxsLI0bN8bS0hJzc3OCg4OFpqDRaGjRogX16tUjISGB58+fM2TIEGxtbXFyciI9PR0zMzN69uzJZ599hrOzM2vXruXcuXPExcVhZGSEt7c3Xl5eGBgYiAv/ixcvcHV1JSoqioiICEaOHEl5eblYeKjqcbdu3fD19aWsrIzg4GCKi4sZMGAArVq1omnTplRVVbF//36aNm1Ko0aNOHLkCHv37pV0iVatWklrUXkYKqPSe/fu8ezZMwlHHzJkCObm5nh6etKgQQMBDCUlJRgaGhIcHMyDBw+orq7mhx9+IDc3lwYNGrBmzRqqq6spKysTjzwFUBwcHLh//z6lpaUCqrVaLXl5eTocOn19fQoLC5k+fTpnzpwRPqiVlRUeHh6EhIRga2uLpaWlvK9Tp04xYcIEqqqq+OmnnwgNDcXT05OnT58SGRlJeno6vXv3JiEhgZEjR/Ly5UsqKip01tHz58/TsWNHQkNDGTdunHjPTZgwgR07dtCiRQv5LEVFRUyZMoWpU6cSEhJCQEAAaWlpNG3alDt37nDmzBm0Wi2DBg36b0fa1H2P169f5+XLl7x+/ZoVK1ZgZmZGv379dP59RUUFn376KXl5ef+vr2bBX0DrnxojRozAw8ND8rhMTEyEtNy4cWNycnJo0qQJM2fOJD4+HkdHRxwcHCgsLOT69evU1NQQFRVFTEwMjo6OWFlZUa9ePUJDQ3F2dqawsJC0tDQ++OADoBaEmJub06RJE7RaLV988QWgm71XVVXF2LFj8fX1ZcaMGQwdOpSBAwcyffp0HB0dad++PcnJyTRt2pTt27czbtw4PDw8MDY2lriMumP06NHY29szcuRIaZ+pEr9SPP0xj0t9GRgYUF5erqPY279/v3Bs6o66D9GMGTNEGeXs7ExMTAwlJSUMGjQIOzs7rK2tGTt2LMbGxhQVFVGvXj20Wq1U/Ozs7IiKiiIxMVEI6G5ubvTp00de4/Lly7Ru3VpA2tdff63Tsjx37hzBwcFoNBratWtHXFwcbm5uGBgY0LdvXyEB9+jRQ0CPihMJCgrC29tbQpwDAgKwt7fn2LFjABQXF5OdnU1MTIx44ERGRgpPo+71U9WuuqdQ9eeOHTvy5s0bfvzxR1FzLVu2jDt37vD69WsCAgLYvXs3FRUVzJkzh8jISEpLS/nyyy9JSEgQp26l8JswYYIs9i9evMDDw0MsBxITE4mPjycyMlKCiGNjY3F3d8fd3R0LCwuR7puYmDBt2jSOHj1KYmIijRo1wsjISMeRWykKjY2NWbNmDQsWLJAqkZKbq81j5cqVGBkZERkZya1bt6iurub06dNotVrCwsLIz8/n+fPnDBo0CHt7e9q1a8ff//53fvrpJ6KiooTHZmxszBdffMGzZ8+4d+8eISEhBAYG0rlzZ2xtbQkPD8fV1VWqPkZGRiKFj4yMFBCYnJxMTEwMHh4euLm54e3tzdy5c8WpvHnz5jRp0oQTJ06QlZVFQkICnTp1wsPDg5kzZ9KvXz9x1YbaA8jEiRPx8/Pjs88+49KlS3z//fekpKTg5ubGqlWrxMBT3XsLCwusrKyIi4vD29tbWssqvNnV1RUfHx+6du3KtWvXmDdvnnhBqVithg0b0q5dOznA6OvrM2zYMG7evMmdO3eYPXs2+vr6NG7cmIyMDJycnESRd/fuXQ4cOMCKFSsYO3YsP/30E7/88gv+/v7069ePZ8+eCZm6T58+xMbGotVqhcpw9+5dIiMjRUzQu3dvWrdujYWFBZGRkXJtDhw4QLNmzYiOjiY7O5v8/Hysra0JCAjA399fRBm7du1i2bJlmJiY4OvrKweS999/n9evX7N3715MTEx0rBeOHz+Om5sbmZmZcsiqqqrixx9/FMPasWPHyhypGydma2vL0qVL+fbbb/H09NQR3CgPOCMjI4yNjZkxYwZt27bFw8MDIyMjoqOj+eKLLzhy5AgXLlzg8ePHAkqhVvk4d+5ceT/btm3Dw8MDf39/0tLSpL3WrVs3SkpK2L9/P61atSI7O1ssQ2pqarhw4YJwyRToGz58OCdOnNAx3dVqtYwePZqKigrOnz/P/fv32blzJxkZGbRs2ZKffvqJc+fOCSXknx1119dx48aRmZnJ1q1bqa6u5vnz5yxfvhwHBwehNajxjywe/t86/gJa/8WxadMmXF1d2bVrlxBOtVotO3fuZNKkSTRo0IDc3Fyio6Np1qwZ5ubmjBs3jqFDh5KdnU29evW4cuUKq1atwt3dHSMjIxwdHRk6dCiDBw+Wk5CdnR2rVq3i/PnzODk5sWLFCt68eUN+fj4DBgzg3r17MhH3799PSUkJDg4O4tScm5uLjY0NUVFRFBYWkpiYSPfu3WnXrp0o4fz9/dm4cSPOzs468QlXrlzB29tbWh6PHj3i9OnTWFtb4+XlJbws1fP39fWVk7wqqcfHx3Pjxg15QL7//nsCAwMlbf0fDeXfpDZHrbY2qHXBggUSV1I3727Pnj2EhoZKud7CwoKmTZuyb98+iYGxsrIiJSVFSJOVlZVS/VLE/b///e+8fPmSmpoa9u3bJy1IGxsbMRhVC3jLli0lJWD69OmYmJjg5eVFYGAgFy9e5NWrV9y7d4/vv/9elIkrVqwAatsWGo1GMtOUmaDy31LXT19fX5yw1YKo3o/aVMPCwujfvz9dunQhJCSEnj17yucJDg7m0KFDcu/mzZsngc1hYWE4OjoSHBxMcHAwc+bMwcPDgw4dOpCbm0tOTg729vaUl5dTXV1NRUUFlZWVnDt3jtWrVzNjxgzJZly+fLkox1TWoaurK5MnTxb3exWYrMDVwIEDCQsLE1NEZ2dn4eJptVpiYmJYsWIFlZWVVFZWkpycjLGxMWVlZcIPmT9/Pnp6ejg7O7N06VIBc4oHo/IKlyxZgpWVlagKmzRpIkTnd999l5SUFKZMmYK7uzthYWFCKlfX28LCQipYqn3p7e0tCsrY2Fi8vb3JyckRD6jIyEjevn3L7du3adCgAYGBgXTo0EFAaV3T4ZqaGm7dukVKSgqpqanSejQyMpIKiWoX1gXdDg4OUi1T31MZqxs3biQuLo4uXbpIioUKoG7RogWXLl0iPz+fsLAw6tWrR1JSkgCF+fPn065dO+Lj4wkICECr1ZKVlcXx48c5ffr0n/hkCqQowc6vv/7KyJEjJTVBWT00aNCAjIwMMWgdNmyYiESSkpLw8fER7lBZWZn4BK5evZrhw4cTGhpKZGQk69at4+XLl3z66adkZ2djZ2dHo0aNeP36NZcvX+bTTz9l/vz5wpFTgdjTpk1Dq9UyY8YMNm/ezJMnT2jVqhUJCQm8++67PHv2jPv37zN+/HipnIeHh+Pi4kJGRgbTp0/HyMhIgshdXFxEPZmfny8iowYNGnDs2DH5POqejBw5UqpdS5YskWtYWVlJ48aNGTp0qLj9v/POO9y7d0/mSOPGjWnatOmfPKJ27tyJkZER/fv3Z/To0YSHhxMYGMjOnTs5evSoztxxcXGRwPcOHTrw4sULec9GRkY6thBQ6wEZFRVFhw4ddGgk/90xadIkHB0d2b9/vygooZaztWLFCiHI/39x/AW0/otj3rx59OjRgxMnTmBlZSU5fABPnz5l+PDhmJiYkJCQQEhICB988AFjxoyRmAsLCwscHR05d+6ckIC9vb3JzMxEX18fV1dXKisrad++PVlZWSxatIi4uDhqamp4/fo1SUlJuLm54e/vz+TJk8Wnpri4mO7du0vavYWFBWPGjNE5vdvb2+Pi4oK7uztBQUFUV1dz+PBhvL29dZzQv/rqK+zt7bl79y5TpkwR7lC3bt2wt7enVatWsvCrOAq1CVhaWkpLo6ioiB07dnDixAkJEq5rPld33Lx5kylTptC3b1/8/PxEkabMRa2srOREq15r2LBhZGdn63AjbGxsqKyspH///qxYsYKFCxcSFxfHs2fPqKqqYtGiRZiamsomNGPGDJKSkoiOjsbPz0+CtxU/RsnfFelcuZKriBNlZjh8+HBAV1BQVlaGqakp6enprFy5krFjx5KVlYW+vj5+fn5SdVM8N6UCMjY2Jj09nRMnTpCWloaDgwPFxcV8+OGH6Ovr06hRIyZPnkxxcTFhYWHs2rWLmJgYevTowdmzZ2nWrBlXrlzR4f5ALdB7+fIlt27d0lGQBgYGYmJiQlBQEJ06dcLExETahHV/XrUrlVnt48ePiY6OxtzcHG9vb27cuEHfvn0FNKnNJz8/X2w0hg4dKrE8fn5+LF68mI8//pjKykqio6Px9fXF3t6eiRMnSktHEb2HDx8uC39CQgIaTa3Jo3IZ79q1K+3atSMgIIAWLVpw4cIFzMzM8PDwYODAgXTr1k3auL/88guLFi3i0aNHzJo1Szhba9asITo6WoQeJiYmOma7qi2j3LuVea2qbvj6+krm2uHDh0VEoWxamjVrxs2bN3Uk+tnZ2VhYWHDkyBHWrVtHgwYNcHBwEBCteGh1q55r1qwhISGBAQMGYGBgIGvEli1baNu2Lb6+vuTn54tAoE+fPvj6+opCTpHpHRwcBOxrtVoKCws5cOAAgKga58yZA9Q6q3/zzTc6QACgQ4cOtGjRgsOHD+Pr60vbtm2ZMGECERERbNu2jV27dtG8eXPMzc2Ji4vD2dkZBwcH8QE0NTVl1qxZjBo1CgsLC8aNG8e2bduIjY3l8OHDAhKUVURNTY2ons3Nzbl165asXz///DPu7u4MGjQIGxsbli5dSmVlJZMnTxae47Fjx3j16hVdunQhIiJCQLK7uzvHjx/n1q1b+Pj4yN9bWlqydOlSli1bJgbBFhYWdO/eHR8fHwwMDAS42NraSnSWoaEhR48eBRAVtFon1OdITk6moKAAR0dHcbBX34PauDRVyVLry6+//kpKSooAOfVvmzVrhrOzs+SqfvXVV2RlZeHk5CTxSYMHD+b58+cSRWZiYkKnTp3+BLY+++wzEcv8d0dNTQ03b94kIiKCnTt36nxPrS0vXrwQjrHiNv9/afwFtP6D8Y/Klb1796ZTp06sX78ePT09BgwYACCbuIqdUTyjNWvWSOZYXFwcLi4uWFhY4OXlRUZGBnPmzBGQlZiYSGVlpaijgoKCaNWqlZz0UlJSsLe3JzQ0VJyGv/nmG37//Xc++OADoqKiaN68OVeuXGHu3LkEBgYyZMgQQkJCCAsLY+fOnbz//vts375dNqyePXuSmprK06dP+eyzz6iurubJkye4ubnRoUMHMQBs06YNERERooSr686tNtXs7Gzatm0rXBHFLyksLKS0tFTIquq/UKskUiolJycnRo0axfnz5/Hw8BB+mapY+Pj4SD5bWFgYxsbGNGrUiBEjRuDv7y8tlsDAQFxdXfnmm29o1KgRhYWFshjt3r1b4mEmTpyIvb09y5YtY+HChSQnJ4tBn4WFBd7e3hgYGGBvb0/37t2lgmBubs7du3c5f/48n3zyiVgFTJ06VT5XYmKiKDONjIwICwsjOTkZNzc32ZQVv02109Rm7urqSqNGjTh58iT79u3D2NiYjz/+mLS0NIyNjblw4YLMTQWmysvLiY+PFxCs3KWjoqIEbA8ZMkTe35UrV2jWrJn4wfXv3x8fHx8h9Xp5eYlBbmJionBEvLy8WLBggbSk4+Pjpb1lbW2tE6ytAJLiLikRwciRI6moqCAqKgonJyeuX7/O8uXLpS3r7u5OYWGhzNFBgwahp6eHvb29tOMsLCwYNmyYVLAiIyPx8fFh5cqV6Ovr4+/vL9f5nXfeoaamhkuXLmFnZ8d7771HQkIC27dvp6KigiFDhpCTk0NgYCANGzZkypQppKenY2RkxJAhQ6hfvz6ff/457733nnigeXt7o6enh5ubmwBLY2Njzp49K9d4yJAhREVFMXbsWAmDdnFxYcSIEdy4cYOamhqWLFmCvb09/v7+vPvuuzx48IDPP/8cf39/AgMD8fPz49atW+zevVtex8DAgLKyMubMmUNBQQHOzs4EBwcTEhKCpaWlZJMaGBjQpEkTfvnlF65cuYKrqyvu7u5irTF8+HBWrVpFZWUlP/30Ew0aNMDDw4NDhw4xbdo0wsPD6datG5WVlWzbtg07Ozv8/PywtLRk48aN4nM1fvx4UlNTady4MXZ2dlhZWbFnzx7MzMwkFuXmzZscOHCAqVOnsnjxYu7du8ft27cJCwuTdtmcOXPkOvr5+bFixQrWrl0rB69evXrpHAibN2+Os7OzPH+qojtjxgzMzc1p06YNX331FXfv3uX169eEhISg0Wjk9SoqKvj222/JysqiRYsWjBs3Ttbztm3byvqm/j3UKhK9vLxkjilwVVBQIEIYxXdTP5+Wloa+vj59+/YVQcXAgQNJSkoiJCSE3r17M2zYMKCWv/Thhx+KybXqKvzRsDM0NJRVq1ZRXV3N69evqamp4eDBg1J1U1W9ly9fsnr1ahFLGBoakp+fj7OzM+PHj+fkyZMS9/av8qNSvl11x/fff4+Tk5PMh7qf582bNzx//pzXr1+za9eu/zYf7N9x/AW0/jejLsg6c+YMP/30k/xZldWVTcK9e/f4/fffadq0qaSmW1lZSaZhamqqENYPHjwo3BgVbmxoaIijoyP29vZ06tSJU6dOkZaWJuRlOzs7HBwccHJyws/PjxMnTnD//n0aNGggYEKBEq1Wy7Jly6isrGThwoUEBgYydOhQQkJCZFMDuHr1Kq1atcLAwICNGzdK21J9lpYtW+Lg4EDz5s3ZvXs3UFvG1mq1uLm5YWlpKUn2iguVmZlJfn6+AK0ZM2bg6OjIlClTuHXrFqCraJw8ebKAACV7TklJ4erVq5w/f16neqWME83NzeW/KqJl27ZtfP311xQWFkply8TEhJiYGCIjI3n16hUPHz7UebgfPnxIZGQkGzZsoLq6mrdv31JTU0OrVq3Q19cXLkzLli0xMjIS9Z2KjBk+fDg1NTX8/PPPlJaW8vnnn8sCMWHCBCwtLfH395fKi6WlJR06dCA+Ph4bGxtcXFyYN28e69ato3///tIm1NPTw8bGBg8PD2bNmsV3332Hs7MzDRs2JCcnh+DgYJYsWSJqqbqfqby8nPDwcPGf+vzzzzlw4AAfffQRGzZsEE6fep9VVVUiWIDalpyxsbGYFaqfW7hwIa1bt8bR0VGARP/+/XFzc8PU1FRAm1ImFhcXo9VqheytnOyVp5GZmZkA7y5dulBVVcWhQ4ekFXj//n15/oYNG4aVlRU9e/aU5yAoKIgRI0bwxRdfSLzPsGHDhLNXUFAgc9XU1JTjx48DtW01lak4ffp0bt26RWVlpVRbR40aJaHAY8aMwdfXl23btjFlyhQADh48KIeHkSNHShvSyclJAqaHDx/O6dOnKS0txcTEhKysLAEH1dXVzJ8/HwcHB8LCwmjXrh0eHh7o6elhZ2eHv78/Tk5Owp9Tru8jRozghx9+EEK+skdJTExk6dKl/O1vf2PChAmYmprSqFEjoHZj69y5MyYmJmLlcPXqVSZPnszo0aNxcXHhyy+/pKqqirdv31JVVcXLly/x9vaW51Gj0bB161bu3btHYGAgH3zwAadPn2b8+PHo6emJC/rdu3fp2bMnvr6+cr1v3bpFvXr1WL16NdXV1fTr108A/aBBg1ixYgXHjx8nICBAKmR79+4lJSWFJk2a0LhxY/72t79RWVnJ5s2bRUEYGhpK7969KSwslG6CanmraumPP/5ITk6OqDL19fUJDg4mKCiIuXPnotVqmT9/vjw7ly5doqKigq1bt6LRaKSipyJ7wsPD+fDDD/nkk09o1KgR4eHhZGVl4ePjI3uBjY0NrVq1Yty4cVhYWFC/fn2hP/j6+rJv3z4qKys5fPgwHTp0oEOHDpSVlUlgspWVFQsXLiQ+Pp6CggImTZokz7tSRa9evVqU2jExMTrGnsphv+5Xbm4ud+/epbKykhEjRqDVasUA1crKiu+++463b99K3FuHDh349ttv/4Nd8T83njx5In/+/PPPefToEbdu3cLExISPPvpI1h51/U+cOMGHH36ocwD/V7Ys/x3GX0DrH4y6IGv06NE0aNCAMWPGUFpaysCBA2nTpg1BQUGMGzcOd3d3wsPDiYmJwdfXl759+4pqyt3dHVNTUzw9PVm4cCGnTp3izJkzpKen4+npSfPmzfHz80NfX5/169ezZs0aUWsZGRlRWFjIuHHjmD59Ot26dWPy5Mn07t2bAQMGCFnz008/ldNTcHAwCQkJxMXFsXv3bqmyhYaGUlpaStu2bWWCr1u3juzsbCIjI7G2tsbS0lIess8//1wUX8opu379+qJi8vHxkXah4mSp1peRkRHp6elERETw5MkTFi1ahLu7O2PGjNHxYJk4cSLOzs7s3r2b27dvk5ubi6WlJT4+PvTq1YurV69y9uxZgoKCMDIyEnNOKysrwsPDOXXqlFRIDAwMGD16NKdPn+bGjRsMGjRInL+rqqoYMGAAQ4cO5c2bN7z33ns0adKE27dvY29vL9mUb9++ZdmyZZJFqaoySp1kYGCAoaHh/4+9s46u6tq6+L1xd3fiCVHiSkIIEIEAQYJLEtwhOAS34t7iDoUihVIoVoprW9y9uDux3/dHxtnfvUmgtAX62jLHeOOVmytH9tl77bXmmlMQ4tXV1enatStVq1alevXqYtLYt2+f0GiaNGmSKBlL19HY2BgjIyNcXFwwMjIiISFBcOji4uLQ0tISXU6+vr4kJSXh6+vL1q1bKSwsJCQkRBCLy8KRI0cICQkhKyuLc+fOAcULidTKL01gigFaYWEh69atEzIBlpaWDB48mLt37wpjaKmDKj8/n6ysLAwMDHB0dERHRwc/Pz+ePHnCsGHDhFGzvr4+oaGhohmhYsWKhIaGkpKSQnR0NK1atVJ6zrZv346vry+FhYU8fPiQGTNmCBPuypUr8/DhQx48eEBAQIAwE5c6orKzswkLCyMsLEwsfC1bthRWUz179qRnz54iSJEaCi5evMirV6/o06cPmpqaWFhY4OHhgaqqKtHR0airq3PgwAHu3LnD69evqV69Ou3btxf6cO7u7hgYGGBsbMzdu3dZtWqVCAhUVFQEsV+63tI1lzqtgoODiYyMFNkxSWepQYMGeHt7o6urq2SN4u/vz/Lly4Wdi5eXl8iK1apVCz8/P7y8vKhatSpQXEarWrUqMTEx3Lt3T3CqoJjH16hRI/Hv/Px8Xr58SWhoqLCvatmyJTo6OkyfPr0UUXn06NFKZZ43b95w/PhxISHy5s0bKleuTFBQEPXq1UNTU5NKlSqRnp6Ojo4O5cuXZ8+ePTg4OPDFF19w48YNkpOT8fHxEby68ePHi2NbvHgx3t7euLi4EBsbS3Z2ttBoe/HiBdOnT8ff35+DBw/y4MEDKlSoQKNGjYTLgkwmY8SIEdy6dYsvvvgCTU1NRo0apfRMLFiwQDR8SPqBkoSFqampsEnr2LGj0Cxs1qyZMLc2NDSkfPnyDBo0iN27d9O5c2d8fHzw8fEhIyNDqTSoKLR5/fp1WrZsiYuLC2PGjBFCqD/++CMxMTHcv3+f27dv4+/vz/Dhw4HiBik7Oztyc3OZNm0ampqaODg4iKqG1KgUGxvLrVu3eP78uXCbcHJyEpkyCQcPHqRixYpCN+vPQjKuV7Sak76zU6dO2NvbizkXirOKiYmJtGrV6i/97v86Pgda78CAAQMwNzdn6NChgqAaFhYmRP0kMrOurq4QfVNRUaFGjRr88ssvfP3115iYmGBjY4OpqakgkWtra5OVlSUE2ywtLTE3Nxc8DFVVVTw8PIQH3e7du8UkPWPGDDIzMzExMaFXr140b96cnJwcBg0ahIWFBYMHDyYqKorQ0FCRhh06dCgZGRlKpqRFRUU8e/ZM/EZsbCx79+4FiidRxe4miYckkZUlTRhJHFSayFRVVYUVTb9+/ZRMP7W0tBg8eLAgVsfExAjT5nXr1mFkZETFihVRU1OjXLlyIpV94MAB4QspZQJNTU0ZPHgwkZGRZGdn07dvX+RyOdWrVxcE7unTp6OlpSX4NkePHqWgoICFCxeKsmiFChWIiopi8uTJ5OTkiMYERXK9dF8lYry7u7vI6Ekm03l5eezcuZOioiLu3LnDkiVLGDVqFN7e3kLGoVmzZpibm6OtrY2VlZUYM7q6uujr69OkSRPWr1/PxIkTRaBVt25dpZJrQUEBU6dO5fjx4+8ctxs2bEBPT486deowc+ZMbGxslEpaJZGXl0eXLl2oVq2aKDs7ODjQuXNnMUm2aNECV1dX6tati5qaGpGRkbRt21b4yTk5OdGkSRNhaaOqqoq/v7+wd3n8+DFxcXGYmprStm1bEfBLY+Ty5cvI5XLq1q1L+fLlRZldyvisXLmShw8fUrlyZdG52ahRI2xtbWnbti0bN27E3t6eRYsWkZCQgJubGykpKSxatIjLly9TqVIl1qxZg5eXlygJS3xFqdOzYsWKdO7cmYkTJ/LNN9+Ihf/ixYvcunVLlLPat2/PyJEjqVKlCnv37hUcmqKiIpFNyczMpKCggHPnzrF48WKR7T1//jx5eXmMGDGC4OBgjIyMGDRoEN7e3ujo6GBtbS3kPzw9PYWnpbGxMQcOHKBHjx40bNiQ8ePHC3PpUaNGoampiZmZGX369KFBgwZs374d+P9u3549e9KhQwfOnz8PFDs3ODs7i5KVNL4aNWrE119/jaenJ02bNqV9+/bI5XICAgKUAjVpntDU1GTkyJEicPjyyy9RV1enU6dOrFmzRpQ7DQwMGDZsGJ6ensjlchYtWsTTp0/p2LGj0LwKCgri5cuX5ObmiuYGif+an5/PokWLiIyMpHr16qLMmJuby5kzZygqKhLWWi9fvqRv3748fPhQBFETJ05ELpeLYGv48OGYmJgwYMAAKlasyHfffcfmzZuFNqDidenSpQt2dnYMGzaMQYMGiaC8SpUqDB8+nA0bNghOl1wuJyQkRHDjpJKej48PjRs35scff2Ts2LGkpqaSnp7OsGHDxLwsEcQl6ZvExERq1arFnj17aNu2LU2aNBHOGQ8ePGDkyJFCj09DQwNnZ2e0tbU5efIkO3bsENQFSSjZ0NCQwYMH4+DgQGZmJhcvXlS6nx9CZX379u3Url0bW1tbjI2NlUq9R48epVmzZhgYGNCzZ0969epFfHw8Pj4+StmsfyM+B1pvwalTp/Dy8mLlypV89dVXjBs3jl9++YWBAwfStGlTkX7evHkza9aswcbGhg4dOjBixAgcHR2pUqWKILdKwoeGhobY29uTlpaGnZ0dgYGBlC9fnm7dujFhwgSRDv7tt9+wsbFBQ0OD+vXri2N6/fo1o0aNomHDhgwaNAhjY2M0NDTEDrZHjx44OjoyfPhwoqOjiYyMZMWKFaKLB5RTtq9evWL16tUsXbqUihUrEhgYSNeuXYmLi6Nfv3706tVLGNhKRPBatWqRlJSk1LItlUednJwYN24cgYGBdOzYkfz8fIYPH46vry/Z2dkiw3L69GmhHbRt2zasrKyYMmUKRUVFwuzY2dmZjIwMTp48yZEjR5RU2KX/GRoa8uuvv9KzZ0/B5ZIMjZ8+fSoUvyXtsOvXr3Pp0iVWrlyJg4ODMAKWOsq0tbX54YcfMDMzw9/fH09PT9Gd5e7uTr169ViwYIEos0ilpxEjRlC+fHkWL15MUVERo0aNEp5mEqdNahCwt7enZcuWQi3e0NAQJycnfH19qVChAqGhoaSmpqKiokLz5s3FNZPKfWWZq5bEyZMnRfZPLpeLdD0Ui8Mqqv0rmqJLJG4oLutWqFBBfLZFixbi+9TU1Jg5cyanTp0SjgXSPZG4QTo6OkyaNIkhQ4bg7+9P7dq1iYiIQFNTk8zMTKC42zMvL4/Hjx8DxZnUWrVq0b9/f7Zu3UpycjL6+vq4uroyZcoUzp8/T3BwMOXLlxe8vZCQEGxsbET2Iisri/nz52NsbIyuri7bt2/nypUrdOzYkbt37+Lq6oqfn5/omjU1NSUpKQlXV1fCwsIICQnB0tJS+C/6+Pjg6enJzp07iYmJEd2iERERhIWFiYVcMTsnidN+9dVXxMbGkpiYSPv27QkICMDR0ZF69eqRkZFBXFwcERERtGzZUnRvSrw2mUwmDJAln0J9fX3WrVtHXl4eT58+ZeLEiUIUc968eZiamtK6dWu6du2Kr68vixcvFt2+7du3x8LCgr59+3Lz5k1evXrFxIkTsbGxITY2ls6dOxMeHo63tzdFRUW0aNGCGjVqAMUixaqqqixfvrzUWMvNzcXExISHDx+K8bR27VocHBywtbUVnZTx8fGUL18edXV1YemUn5/PqlWrmDRpEu7u7gwbNowOHTrw7NkzOnXqhLm5ORoaGsycOVNojC1ZsgRvb2/CwsJEVrFdu3YMGzaMGTNmYGhoKLpqJUFSCRMnThQ0gJs3b3LlyhUyMjJEBrROnTps3LgRBwcHYb4tQRKBVlVVRV9fn2HDhtG0aVOCgoJo2bIl69atE+NeS0uL+vXri8ClsLCQzZs3Y2FhQeXKldHT06NXr17UqVMHDw8PKlSoIMaRVAmJj4/Hz8+P+/fv061bN8zMzAgMDFS69g8ePKBJkyaCI6upqcny5csFRWDbtm1io+fm5saPP/4IFHs6ent7fzBT6GrVqonsIyBsrLy8vEqR7K9evcrEiRMJCgqiWrVqtG7dWpz7v61cqIjPgdZbcO3aNTw8PET7fpUqVYS3lqqqqhAQnTVrFleuXKFLly588cUXpKamkpSUhKWlJQkJCQwbNoyoqCh0dXVp0qQJ+/fvp2rVqoLEq62tjZubG99++y1+fn5s3LiRmzdvYmVlhVwuF0JuS5cu5dSpU7x+/Zpnz56xc+dOgoODhZnp1KlTKSgooHv37jg5OTFy5Eg8PT3Jzs4W56Q46YwYMYLWrVsL/aRVq1aJLkqpi0sy+ZUWO6lNWgoY3rx5Q15enlBxlnDjxg2x8Jw6dYrU1FQSEhJYsmSJeI9Ux2/QoAHt27cXu7iGDRvi4uJCYGCgSGWPHj1aqNFLJT1pobSzs8PDw4OjR4/SsWNHYmNjKSwsFN50km5Xnz598Pf3x9jYmJycHCZNmoS9vb2QH5DJZMLHUENDg3379tGtWzesrKxISUkRZSJpF6i4A5s7dy5BQUGiSSE5ORkDAwPCw8Np2bIlzZs3F0FKYGAgS5YsYcmSJcLiREdHB09PT8aOHUubNm3w9fUVGbU+ffqILMQfgUTcdnJyEvfi1q1baGpqkpycrNQ5VrKMCMUt1xUqVCAyMpKMjAxR5m3fvr2Qc5CU6SW1e3V1dWFNFBMTQ3h4OEOHDmX48OHUrFmTli1b8vjxYwoLC1mzZg0JCQn4+/sTHx8vFjXFY7l27Rpubm5CKFLKGI8ePZrz58/j7u6OnZ0dY8aMwdHRkaCgIBHMREZGYmpqKspgjo6OnDhxQngI2tvbU69ePezs7ISumpTp8vHxoVOnTqSlpVGhQgWCgoJEZ6G2tjZeXl7UrFlTjIHCwkIWL14sxvdPP/1EvXr1MDc3Z9iwYaJklJKSgqamJt27d6dbt25CAX/QoEGCOyap8MtkxX6XUkZcKvPb2tqyd+9eli9fLpTMFe+5jY0NqampBAcHC/9VqdtX+nvPnj25e/cu+fn57Nmzhxo1alCzZk2aNWsmFunatWvTunVrsUnr2LEj2trawsJKEYqt+opejWfPnmX06NE0bNiQjRs3oqenJzZCEsE+PT0dbW1txo4dS8+ePSlfvrx4Ztu0aYOFhQUaGhois3X//n1WrFjB5cuXGTBgACkpKcyYMYOEhAQx3iWz6c6dOyt1zhUVFTFy5EiMjIxEkCGJ7zZo0IDevXtjY2NDnz59hNju+vXrKSws5PXr1wQHB1O/fn10dXX54YcfeP36NWPGjCE4OJgJEybg6elJkyZNMDU1VdokSZg/fz4NGjQQAtFQzPn19fUlOjqaoqIivvrqK8zNzbGwsBDZq19//ZWcnBxUVVWViPm//vorBgYGwjRdygifOnWK/Px8Nm3aRHp6Os7Ozujq6iptsEpmJ/8snj17phTcQXG37YIFC6hbty6BgYGCAK84bkoS3v/NQRZ8DrSAsrsLb9++TUJCgiCFVq5cmQ0bNnD//n3i4+MZNGgQTZo0EZpRQUFBSjsVV1dXNDU1MTU1FZ1Akjmtr68vNjY2+Pr6CmHF+Ph44uLiGDp0KOXKlSM7O5sHDx5QUFDAb7/9RosWLZg/fz7Hjh3jxIkTnDx5kt27dxMREYGtra3w9SosLCQnJwdNTU0WLFhQ5rn17NkTa2trpk+fzpUrV/j5558ZPHgwWVlZInslcbTU1NSIi4vDzMyMqKgotLW1hcBmfHw8jo6OWFpaClJ1WYv2pUuXSElJIT4+Xim78vjxY/z8/Bg5ciTw/xpXkt7Oq1evOHDgAHFxcZQvX567d+9y8OBBgoKCRCAyfvx4pZ1ZUVER69atQy6X06hRI65evcrSpUuxtbVl4cKFIuNYt25dxo0bJ0qF0q5QMgFetWoVt27dwsDAgNmzZwu1aWnCl9C9e3fs7OyIiopCS0sLIyMjTExMsLS0pEWLFgQEBFCnTh0lcVepS65ixYps3LhRlEX9/f358ssvuXDhAuPHj0dHRwdNTU1Rcn0fSNd/27ZtjBkzhsDAQPz9/enRowdNmzYVnLHY2Fhu3LihJDMAxUGCVNK7ceMGoaGh6OvrC47OunXr0NTUFIGjuro6xsbGlCtXTlxDV1dXbt26RZ06dQgNDeXLL79UGofffvstGhoaDBs2jOHDh9OmTRvkcjljx44Vx7J27VoyMjIwMDAQbfYWFhZYW1tTt25dpkyZgq6uLrNmzRIl4bS0NHJzc6lWrRrx8fGiYULiTMXExHD69GnOnTtH1apVqVSpEjVr1sTIyAhNTU0yMjLo27cvLVu2JDw8nEGDBtGwYUOioqJEierWrVvcuXNHqQz/7NkzEZRKVi/SHCJh6dKl6OjoEBAQABRbPUmdu+bm5tSpU4cWLVoIKxzJikmSo7h+/ToNGzZERUUFW1tbjh8/LkjgiibDknaYTFZsP2Rtbc3cuXOB4sVt8uTJ2NjY0KtXL65fvy44ixJevHhB9+7dMTc3L0WM7tChA1paWqJFf968eXTs2JGcnBylgE9xET19+jQaGhrI5XLmzZsnuGovX74kPj4eS0tLJc5hbGws0dHRSr8pdbLWr1+fxMRE5s2bBxSXoiTZk4EDB1KxYkXc3NyQy+XUr18fOzs7evfuXUqm4Msvv6Rnz5507twZMzMz4eEoldpr1qwp5rjo6GiWLl0KFGduGjduTLNmzTA2NqZnz56kpKRga2uLi4sLHh4eLF26lLlz5wqJCWmTJFmTubi48PPPPytdqy1btuDp6cn3339PXl4eq1evJiQkhLCwMFGWPn/+PF27dsXNzY1evXoxe/ZsMjMzhcRD//79BX0jJSWFH374geTkZIYPH86pU6eQy+VCW096xj40xowZo6SDtXnzZmrWrElgYCCHDh0Sry9btkw4hHysY/lfw38+0FJcAM6ePculS5fEBHn9+nVWrVpFSkoKWlpaYhINCwsTpGgp66WlpSXq+kVFRezfvx8PDw/U1dWxtbXFzs6O+Ph4DA0N8fDwoH///nh4eODm5kadOnUIDAzEzc1NZJKkTpPCwkJ69eqFm5sbVatWxcrKCnt7e4yNjRkwYADbt28nPDwcGxsbvL29xaI2ZcoUpe4yCd999x12dnYijTx37lxcXV2JiIjg+vXrxMXFKZlASyVCSZxUKlN+8803tG3bloCAADIyMnj+/Pk723IVgy1p4oJiuQx9fX1ycnIIDQ3F399ffM/u3buFGW7NmjXFZwoKChg+fDgyWbENh1R6klBUVMS8efOwt7enX79+9OzZk0WLFvHo0SPevHnD2rVrKV++POnp6QwfPlxw7ezs7ESg4OTkxLfffouDgwPLli3j4sWLuLi4sGLFCjExzJ49G2traw4fPsy9e/cYO3asuG4WFhZ8//33dOrUiYyMDCU1Zk1NTaHy7+HhQXJyMu3atSMsLEyUXK9fv05wcDBt2rQptTMuC2+brI4dO4a1tbUwHD527Bg//PADVlZWSrYkUNwW7+npqZR5bN68OZqamqirq+Ps7Cz4K5KllBRwh4SEiGynvr4+1apVY9q0aQQHBwshRan8k56eTqdOnZSOc9q0acjlctatWwcUT9JJSUnUqVOHPn36kJSUhKGhIc2aNcPd3R01NTV69uwp2ttnz55NmzZtMDU1pXLlyqIknJmZyfbt20VXVmRkJKdPn+bixYv4+/uLQFtSWW/RogV37twhMzOT8PBw+vfvT6NGjYiNjWXcuHFK11rxmt+4cYPExERiY2NZtWqV+NuTJ0/YtGkTnp6eooEkMTERTU1NkfWTyuBt27YVHbv16tUTG4qMjAyePXtG27ZtWbdunRCvVHymFIOtuXPnYm5uLmyv4uPjlQKhyZMnY2trS+/evRkzZozgLJ4+fZp+/frh7u4udOZKokuXLsjlctGF2rBhQ2JjY7G2tqZhw4bifYrz6sqVK1FTU6NevXrs2LGD7du3k5iYiJeXF+bm5sTHx7Np0yYKCwu5evUqrq6uSibzXbt2pX79+lhZWVGrVi2ioqKoVasW9+7dIzMzU2QG+/bty44dOxg1ahT5+flMnToVW1tb+vXrJ5wFCgoK6Ny5M5GRkcIce9euXSQkJFCuXDmqV6/O/v37mTZtGuHh4Zibm+Pi4kJ6ejpBQUHEx8dz7tw58TzHxcVha2srNqhSuXDevHk4ODjQrFkzrl+/zvnz50lJSUFNTY2hQ4cqXdP79+8rWTH9/PPPIiPt6upKr169KCws5MyZM9SoUUNJNzAkJEQEYyNGjBDZUMmb89WrV1y5cgU3N7dSrhx/FSU38WPGjEFPT0/YMgFs2bKF2rVr4+3tzbJly6hWrRqBgYH/eKX3P4r/dKClOFHm5uaKzjkjIyMyMjI4cuQIr1694v79+zRv3hx9fX1RIhowYAAzZsxg6dKllC9fHm1tbcLDw1m+fDkPHz7k8ePHQg+nX79+ws4lMDAQfX19Ro4cyfjx40V57+rVq4SEhAiOS8eOHenSpYvgxkhWFjY2NkIFWF1dnSNHjvDTTz8REREhrEEUReEUg58ePXpQtWpVfH19hQbO7NmzqVevHnl5eRQUFLBs2TJq1KiBsbGx6K6Sy+Xo6+sjk8lYu3YtDx484PTp0/Tq1QszMzNOnjz5XhkXxYVh0aJFQDHvrGPHjlSpUoXGjRsrEb+h2JZCMqW9evWq0iInlVcl7sjq1auVCJ4LFiwQnZ/Z2dlUqVKFqKgoYmNjqVu3rvBWlCQXdHV1xTlbWFigqamJpqamaLn28fERKfIVK1aQkJAg7pPUvTlixAh8fHyEvMTp06e5evUqDRs2FNo8Upt+u3bt0NXVxdbWlqdPn3Lz5k3x/ZKAp2QC/T7jeO/evXzxxRcMGjRIaQdZs2ZNTExM8PPz4/Dhw/Tq1QtbW1tsbGyoXLmyKCMOGTIEY2NjgoKCWLJkCYMHD8bCwoIvvvgCHx8fIdR48uRJrl69KpwNpEyd1KF25MgR9u7dK1ruJf82KUDw8/MTWQwpe1ZYWEjz5s2pWbMmb9684cyZM8TGxmJoaEjPnj05ffo0Ojo6QqSyXLlyNGrUiJ9++olz584RERFBo0aNWLJkCe7u7mhpaYnAecGCBRQVFVG3bl1kMhlRUVEcP36cAwcOCP/KNm3aULVqVXx8fFi1ahX37t2jdevWeHt7M3ToUJKSkmjbtq3SnFFSuPPGjRtUqlSJihUrsmbNGqCYd9a+fXvq1avH/v37qVevnsg2ZWRkKJWuJVkSU1NTLCwsqFChAnK5nPj4eCGnIcnMvOuZAkS3b9u2bYmLiysVjE2ZMgVVVVUmTZokOIt169blyJEj3Lx5852bpoYNG2JhYSFkM168eMHatWsxMzMT/DtFPH/+nKSkJMzNzbG1tSUoKIjq1avz4MEDEXyamJiQm5vL+fPnGT58OJUqVVLSpTt//jy3b9/m6dOnbN26laCgIHx8fBg3bpzoCpTuq4T8/HymTZuGlpYWubm5Yo66fPkyP/30E9bW1pibm5OTk0NaWhrBwcEEBwdTvXp1Hj9+TPPmzVFVVaV58+bUqVMHZ2dnVFRUSE5OpmbNmtSrVw8/Pz8sLCwwNTUVWnnStZs5cyZpaWkiqLh+/To1atQgLCxMZOWk6+fj48P06dP5+uuvcXJyIjs7W9hayWQyBg8ezJ07d3B1dRU8L6nbvE6dOjx//hwozhabmZmhrq5O586defPmDb1798bT05Nbt2699Z7+FaxYsYITJ07w+vVrpk2bhomJiVIzwc6dO2natKngLit24v5X8J8NtBQjaklqoHfv3ujr6wtn+HLlytGhQwfy8/MZOHAgdnZ2qKqqCoL0pk2blHYqbm5uwocrICCAmJgYLC0tmTp1Kj///LPwhAsKCiI2NlYol5cvX57s7GwuXbpEixYtGDduHHXq1CE2NpYOHTqwe/duYW3y5MkTWrdujaqqKr6+vjx+/JhXr16xa9cu3NzclB70kmjfvj3lypVDS0tL8EbGjh0rtIy+/PJLIdUgkViNjY1xdXVFTU1NGJQqWmy8bef7NkgLQ1xcHMuWLROvP336VPy3oro6wIQJE7CxsWHgwIFcv36dvLw8ioqK8PPzo0uXLuTn59OvXz9kMhlXrlxROv+VK1eip6eHqqoq7du3Jy0tTewGJdKxtAuUuialEpj0vubNmzNkyBAxUb969YohQ4Ygk8lE1lJVVZWgoCC++uorVFVVRbnKz8+PiRMnkpubS3R0tAhYJK0sKfPi7OwsJsvCwkI2bdqEubl5KTLp2/DNN99gYWFBxYoVSUpKQkVFhRUrVgCQnJwsyNv29vY4Oztz+PBh0blVtWpVnj17xq+//kq5cuWwsLDAx8cHFxcX0UI/YsQIEUwNHz6ctWvXoq2tTXp6ughocnJyuHDhAqdOncLJyQkvLy9xzSSl74cPH9K1a1fCwsIEZ0QqJ+Xk5BAZGSnOad68ecTFxVGuXDm2bNnC0qVLsbCwoGrVqnz77bc4OTnRu3dvgoODRQlu27ZtYtevpaVFixYtRJdbUVGRCHarVasm/OoiIyOJjIxkzJgxJCQkUK1aNdasWcOdO3fo1KkTly9f5vbt22JMFhUV8eWXXxIdHS2CDQlXr14lMDCQgIAAMjMzadeuHZs3b6aoqIhDhw5haWmJjo4Oenp6NG7cWMg6SDpq9vb2tGjRggYNGghvUTs7OwICAjhy5Mhbn6nU1FQqV67Ml19+KV6XPAC7dOlCcnJyqWBs5cqVFBQU8PLlS5YvXy6CLQklnQUkrFu3DgcHBzFeoVjeYeHChXh4eJR5nIWFhdy9e5dZs2Yxf/58QQ1ISkrCzMyMZs2aiQyu1FUtyR00a9aMmJgYpk+frsQz6tatG2lpaRgZGWFubo6Hh4eQhlE87tmzZzNlyhTGjx9P1apVMTc3x9/fH0dHR6ZMmULTpk0FLzIsLIyoqCjB16tfvz5HjhyhoKCA/fv3CyqJlpYWFStWFLSAw4cPixKglJUtef5QHORJnM8WLVowduxYYfJ96dIl7OzshGRGXl4eK1asEMF4Wlqa2DT269ePadOm4evri1wuVwq2Dh48yNixY9m0aZPgjCmWKz8kLl68SFhYmKB/PHjwgKlTp5YKtl69eqWkj/dv52SVxH8u0Co5MUpSA7NmzcLR0ZEOHTpgZGQkUvqS4fJvv/3GjBkzqFatGjo6OhgYGNCvXz/g/3cqlpaWqKqq4uDgQIsWLYiLi0NFRYV27dpRVFTEgQMHSE1Nxd7eHj8/P+Lj41mwYAGTJk3Cx8dHaIkUFhby9OlT4YV25MgRNDU1Wbt2Lbt27RJt3eHh4aSkpNCpUyeOHDnCL7/8IiZHRbK24s5Bcp2Piori1KlTDB48mLp16wpbFCkFLnXdmZmZ0bt3b1Eqlcim69atUyo7/RFIC0NCQoLwASzrWBXVz8eOHYutrS25ubkis+Xn58cPP/zAyJEj0dbWFlkcxTbls2fPKnmuSYKTEo9FS0uL9PR03N3d0dbWFiKBgYGBLFy4kISEBDw8PETn4rlz58TOUBKUlNSfo6KiUFVVpU2bNmhqaqKvr4+xsTHm5ua4ubkRExPDmDFjREfqjh07mD17Ns2aNSMrK0spQDx79qxSa3RZkK7P3r17sbS0FIvstWvXxH2cOXMm69evx87OjpkzZ7J//35RGl+8eDFZWVnCNgWKmxdcXV0xNzdHVVWV5ORk1q1bJ7pOZbJir0V1dXW0tLSIjo5GW1tb2CRZWVnh7++PgYGB6C47f/488fHx4l6vXbuWiIgIunTpwrVr1ygsLGTs2LE0aNCAtLQ0Hj16RN++ffHw8MDS0hIHBwfCw8PZtm0b48ePFx584eHhxMTE4O7uLrwws7KyePDgAenp6chkMgIDA8nOzlYSomzUqBHa2tq0bt2aTp06KWWFRo4cKYKtdevWlQoypH8fO3YMd3d3YXWiiO3btwtZkAEDBogs1IIFC8jIyMDKykpIoqirqxMYGCgycFKGR09PDzU1NQ4ePMiFCxeUCOdl4dKlS4SHh4vSs4TZs2dz7tw5pWBMMqtX7ER+/vw5K1aseGuwBTBq1CiWL1/O0aNHsba2VtJDksaOgYGBkn2TIvLz82nTpg0ymUx4Gp4/fx5LS0ssLS3p3LkzTZs2FYbRUkm1ffv2NG/eHBMTExo1aqR0vSW9qpiYGA4fPlxK5bywsJAePXpgZmaGr68vKioqlCtXjpiYGJydnQkJCeH48eMsW7ZMSHzUqVNHNGBIzz0U883mz58vzNF9fX1p27at+K179+5hZ2cnxv3bcOXKFdLS0lBRUSEpKUlwE3/99VcsLCw4evSo2Hy8evWKJUuWCMkXfX19Dh48KIzX16xZQ2BgIKqqqiQlJYlgKz8/n6NHj9K9e/f33qy9D8oq+Q0ZMgQrKyshSn3v3j2mTp2KqampUhnxXd/xb8d/KtCSpAYUOUKS1MAPP/yAm5sbVlZWDBgwAAcHB6ysrDA1NWXu3LmsWrWKa9eucfXqVfbt20fLli0JCAgQ5OiDBw+ir6+PmZkZTk5O9OvXj7S0NOHRd/PmTVavXi189CSD5rCwMKZPn86ECRPw9fWlTZs24tj69u2LjY0N/v7+6OvrExkZiba2NnPnziU/P5+FCxcSEhKCgYGBUGkuKipSCrJmz55Nnz59aNasGVu3bqWoqIhKlSqhrq4upATc3d1xdHQU3TeTJ09GW1ubtLQ0WrVqhZeXF8OHD2fjxo3CUBj+2q5EWhgky4u3QTqXq1evUq9ePezt7enSpQt3794lOjoaZ2dnNDQ0RJAladRUrFiR3Nxcjh07Rvfu3enduzfq6upoamqio6ND7969RYehZEthY2ODjY0N6enpwih627ZtREZG4uXlRWZmJuXLl8fU1FT4LEqeiJJ8QLNmzYTwpJWVFdbW1qSnp3P+/HkKCwtp27Yt5cqVAxAK/Iqcjd+7posWLVKayF+9esW0adPo378/UBxkmZubExcXR3Z2tpBiaN26NeHh4WKc3L59m9TUVEaPHs3y5cuxtLTk+PHjNG/enIyMDEFul+QcpCxnamoqgYGB6OnpYWhoiJGRkRAU3bRpE8uWLRMBUEFBAfv27aNz586kpaUpBQsTJkwgIiICX19fqlevLmREJMkTLS0tFixYQP/+/QkKCsLExITw8HC2bt3K/v37ycrKomvXrowbN46BAwdibGxMbGws2traoqtL6saqUKEC/fv3F2Pp9evXItOVkJAAFO/Mk5OTSUxMZPDgwSQmJhIcHFxqY6aIU6dO4e3tTUpKitL7hg0bJixg8vLyRPdYUVERu3btYuDAgSJrKpPJ6NatmyBxS8KulpaW9OzZ873GhISbN2++M2NQ8pl78uQJ+fn5YnF++vSpUrCl+F3z58/HxsaGQ4cOce3aNSpWrEjjxo2VRDjv3LmDn5+fCGjLwu3bt8UmNj4+nt27d3P58mXR6ODt7U1CQgJVqlRBVVVVZGWhmLcnySkoSpFI1xaKN86KKudr167F2tqa/v37M2vWLEaPHk3fvn0JCgoiKyuLgIAAIiIiePPmDb/99hsFBQUsWrQIfX191NTU6NOnDzNmzKBSpUro6OhQr149LC0t8fX1JSwsjDlz5ohjUCwB/h6uXbtGamoqNWvWFJzIR48eie5j6Xy2bNlCmzZtBHVDJpPRt29fTp8+zZgxY9i0aRMjR44kNDQUdXV1wb2V8LH0qZYtW6ZEUalWrRoxMTGC+vDgwQOmT5+OXC4XRtr/ZfynAq0zZ86Ukhq4desW9+7dE15iWVlZ2Nvbk5WVRUZGBi4uLsKAs1OnTqIt9sKFC7Rs2VIESleuXMHIyIjIyEhUVFSoWrWqeGD8/PyoX7++MCeFYi0TmUyGi4sLHTt25PHjx4wfPx5bW1u6devGrFmzMDc3Z9myZQwcOBALCwu0tLSoXLmyUPm+e/cu5cuXx8rKqswHKicnR4mDYGdnR9++fUVqV/HhlWw9JLkJKUORnp5OQECAUHeX8CH8qBRTySX1nRR/4+LFi5iamtK+fXsGDRpEWloaL168EM0DEi9j1KhR6OnpCW6PjY0NgYGBnD17lpMnT+Lq6irKhVJHpaRub2JigqqqKn379sXR0ZGYmBjkcjn29vYcOXJEyBZ8/fXXDB48WJBhTU1NRXbC2NhYdJSqq6vTvHlzbGxs6Nixo/BX/Pnnn7GxscHW1lbIhfyRhTQxMZGIiAgljoekov/ixQvs7OxQV1enXLly6OjoiI6vcePG0a1bN2Es7uTkhI+PjzDoLVeuHJs2baJ8+fJs2rSJ06dPC5kPKfMiWUZZW1sTGRnJlClThC2Oi4sLlStXJiQkhAoVKogdd7du3TA2NsbGxqaUbs/3339Pv379SEhIwMHBATU1NQICAoQnZLly5Thw4ADr168nJCQEU1NTUZqaPXs2/fr1o1GjRjg7O1OuXDlsbGwExzExMZHFixcTFRWFTCYTatXStX748CH9+vVDRUVFzAUXL14kOjqaGjVq8OOPP4qsy7tw8uRJypcvT/Xq1Zk/fz5Xr14VHo6PHj1iy5YtdO3alV69epGTk0N8fDwNGjTA3t4eT09PTExM0NLSwtfXl0WLFnHp0iWuXr1KcnIySUlJpQKK98G7jll65jZu3EjVqlWJiooiOTlZ6HE9e/ZMBFsNGjQAijOmXbp0USpLbt68mfLly1OjRg2hJ1i5cmUqVKhQSvPt4cOHgp8HxQFe3bp1hdZS586d2bBhA2lpaWzatImXL19y6NAhbGxs+OWXX5S+a+PGjWhoaJTKIiq+R1HlfNy4cfj7+wshXUlDq3Xr1lSqVIkNGzYQGBhIZGSkaEC6cOECMplMWEwFBgbSt29fVq1ahYqKCg4ODgwdOvStJcD3eZ4lcdvk5GQqVarEnDlzePnyJc2bNyc+Pp7vv/+eb775Bm1tbfr27YuhoSGJiYkMHDhQiEVbW1tjZ2fH2LFjmT9/PklJSaV4gx8DP//8szAkz87O5vXr10L3btq0aeI+PHjwgG+++eZf6V34R/GfCrSguO29fPnyxMfH07FjR1JTU3n48CEXLlwQXJ3u3bsLqYEJEyaIHb2UIpYG0sWLF2nZsiVRUVGMGzeOoKAgatSoIXYqCxcu5PXr11SpUoXg4GCaNm0KFNfpXV1dadWqldiFFhUV8eTJE6ZPny7kFHR1dWnUqBE3btxgy5Yt2NjYoKuri7Ozc6lFDZR3Lxs3bsTJyUnwJRo2bCjObcOGDZw/f57w8HBBxjY3N0dTU5OgoCBu3bpFQUEBP/30EyYmJpQrV45y5cqRlpam5I33V6C4GCjqO0m+Y1L57/79+zg7O5OZmSmuu/TZXbt2iRb0Q4cOCb2enTt3Eh8fT4UKFbCzsyMyMpIXL17g5eWFtrY2crlcmN9GRkYSFxeHg4ODMMFduHAhNWvWZOnSpfj4+NC5c2fat2/PqFGjWLhwoch4SIr5Q4YMoWPHjqioqAjjW8lwViKed+zYURD1T5w4QW5uLmPHjhWT8u9NRllZWfTs2ZNr165Rt25doqKimD17ttLnVqxYgZ6eHjNnzhS8NQMDA2JiYtizZw+FhYVcunSJ6dOnKxmLd+3aFVdXV9LT02nfvr34vmPHjgk9MqnMKtkRRUZGcvToUaZOnSo4RH379uWrr76ioKCAmzdviu8fOXKkCDgVF1zpXkqaWO3atSMhIQEzMzOmTJlCpUqVcHV1JS8vjw0bNgjduAYNGmBhYUH37t1FZ56TkxMrV64U/pgqKioYGxszYcIEqlWrhkwmY8aMGezbt4/bt2+LY+vUqROqqqqCvH7p0qVScgC/F2ydPn2aatWqie5iyVFgzpw5aGtriw63atWqic2MgYEBdevWJTo6GmNjY1RUVJRsbq5du/anS/O/h7Vr16Kjo0Nubi5z584V7hRSdurZs2esXLkSXV1datSoIWyhRo8erfQ927dvp3nz5pibm1OhQgUlorM0LlevXo2/vz8eHh5kZWWJsvvTp09p2rSp4MJK1l4SHePAgQPo6ekJ9wgpCIJi+6DfK89JWbq5c+fi6+vLpk2bqFixIh4eHjx48IC9e/cil8vZv38/W7duxcrKiqysLCXx3CpVquDq6kq5cuVo3rw5Y8eOxdTUFGtra/Lz899aAlQ8/7KgGDSmp6cLs/QnT55w/fp1oqOjCQkJwdjYmK5du9KlSxesrKyE7E5AQIBQuZcaiaT79jGgyE2UfqdNmzY0bNgQLy8v0tLSmDBhAqmpqTRt2rTMMvd/jZNVEv+pQKugoIBx48Yhk8kEgVexVCcZ0QYEBBAYGIirq6uwGPn111958eIFFy5cYNiwYXzzzTdCtbxu3bpUqVKFyZMnExQURJs2bUhOTiYhIYFx48YRGRmJv78/EydOJD8/HxsbG1q3bi0G7vLly4Vg48KFC0WmJT09HRsbG6Kiojh79ixbtmzBzc1N+LdJGkJjx44V9XEJCxcuJD4+HoD09HTkcjnt27fnxo0bPH36lAEDBqCrqyv83Xr27EloaCgymYzq1auLAHDr1q3Y29szfPjwDxZkKaJfv340bdqU8uXLC1KwpPB98eJF8vPzhUiiIn9L8b8VNWrGjRsnxEUtLS0xMzNDT0+PmJgYrKysMDAwwNraGjU1NdTU1HB0dMTc3Bw/Pz9CQkIoKChQWlgPHTpEcHAwxsbGxMXFoa+vT0pKCi4uLlhbW6OtrY2qqqowfJV4FGZmZowbN478/Hyhq9OpU6dSthfw+0HWsmXLMDMzE+XRBw8eiA7K2bNnA8X+eRIJ/Pvvv+fZs2cMGDCAcuXK4eLiQk5OTqnfPnHiBJ06dRIG4SYmJtSqVUvp/G/fvi1kB+RyOXFxcVy/fh1bW1siIiI4cuQIY8aMoW7duuI8vv32W4KDg5WUxPv160eFChUYMGCAGEeKfCdTU1OOHj3KwoUL8fLyIjQ0lKNHj2Jvb8+YMWOAYmkSDw8P9PX1OXDgAG/evKFKlSrIZDJhZaSnp0eVKlWoXbs2VlZWtGzZklevXhEREYGHhwfGxsYkJCTQrFkzXr58yatXr+jatSvq6uqiFPJnuqHu3bvHkSNH+OGHH1i6dKlorhg1ahR16tRh0qRJjBgxAicnJ6pUqYKVlRV6enpYW1ujrq4ugtWP3YklmVNLgcr169dxdHTEysoKfX199u/fDxQHQmvWrOHcuXOsWLECc3NzqlWrJjabEgoKCnj48CG3bt1S0haD4vtqY2PDgAEDmDRpEpaWlsTGxgpj+6dPn9KuXTsx16mpqWFhYSEaY5o3b46xsbGSntfDhw/x9PRUon/A/4+lkkHxoUOH0NXVpXfv3hw+fBg/Pz/8/f3Zs2cPvr6+nDp1ijVr1hASElKmeK5iMOXi4oKBgYEIxAsKCsosAUqffRc2b96Mrq4u9erVIyYmBhUVFeG9eOPGDapXry5EgaUs7vHjx0XguXDhQuRyOZGRkTRq1EhJMPRjYevWreK/V69eTWpqKmfPnmX69Om0b99elL/fp1P6v4b/VKAFxTuj+vXri9SwYifO7du3SUxMRF1dXZTQJKG8yZMn07BhQypWrIidnR06OjpYWVkJc9G9e/fy+vVrhgwZgpeXF/7+/hgZGSGTyXBzc2PUqFFYWlpiZWVF586dlTIZTZo0oUuXLrx584Y9e/bQvXt3sfs6deoUtra2Qv9n69at2NjYCOL8qlWrlBY5CdOmTaNmzZqsXLkSuVxOvXr1gOIJYPny5URHR+Pt7U1wcLAguH/11VdUqFABmUwmMnDNmjVDR0eHixcvfnAS44QJEzAyMmLPnj188803aGlpidJUTEwMAQEBQvDvXb+t2PlpYmIiWtYlpXfFDkIjIyP09PRQUVHBxMREaIZZWlqyfft2obWjiGPHjmFlZSWkDDIyMnjw4AGDBg3Cw8NDGEbr6OjQokULwekKDAzk5cuX5OfnK+nqlCyR/h7GjBmDp6cnUDzBVaxYkezsbGrXrk1MTAxz5swRop9mZmbI5XJ8fHwwNDTk6NGjwli8ZAZ1wYIFNGjQgGPHjvHzzz/TqlUrNDQ0hMDllClTaNu2LT4+PjRt2pT+/fvj6enJ0KFD+e2337Czs6NixYrs2bNHLCxSN+LYsWNL6fb06dOHwMBAcnNzuXPnjiiLFRQU4OTkRI8ePYDiRSQ6OlqUraU2/6KiIlFqhOIAW01NjSZNmnDgwAEsLCyoVasWUBxA+Pn5Ua9ePcaMGYOZmRk7duwgLy+PFi1aoKury+7du4HiBV+yDdm5c+cfujdvQ4MGDZRcFaTMoJaWFk+fPqVHjx6YmpqydOlSNm/eTEJCAuHh4Xz33XcfNdg6ceIE3bt359WrV1y/fh03NzexqZEoAnv37uXVq1dKxyF5hHbq1EnJqUDxuSwqKlJ6ds6dO0ffvn3Fv2/fvo2trS3R0dGcOnWKoqIiHj9+TLdu3cjJySE8PJzk5GShofX48WNSUlLQ1tZm9OjRIjvp5+en9DuKmW5JlDg9PZ0WLVqQmZnJkCFDkMvldOrUiRkzZuDm5iZMz9evX68kntu6dWsl8VwopnnY2NigqakpTJ0Vf/fixYukpKSQkJCgxNl6G+7fv8/48eOVuEuSbpb0/d98843QO3z48KHSRnPHjh1899131KpVC319/d/1Pv2zULz/x44dE80C0nPToUMHIS7722+/CT/JuLi4/5R0w/vgPxdo5eXl0a5dO9LS0tDX18fT07OU1MDly5fZsWMHv/zyC5MmTRILrIuLC+PHj2fp0qXo6enh6enJsGHDRDnj5MmT5OXlsW3bNmrVqoWDgwOhoaFCgFLqTJS6kJ4/f06fPn3Erq9Nmzb4+PjQrVs3jh8/LnZf58+fF4vaoUOHOHTokNJEI01233//vdj9Xbt2TQR6KioqooT48uVLwsPDsbW1xd7ennXr1pGQkICLiwvnzp1jy5YtYoEICwujdu3af1jC4X3RokULsrOzOXLkCLNmzWLIkCGcPn0aBwcHgoKCiIuLw8fHR/BH3pX52bZtGx4eHkIJe/PmzUJbR5JTkKQU6tatS6tWrYRp64wZM3BycmLAgAFKKW7FyWLEiBFoampStWpVUdK9c+cO3bt3x8LCQpSMJGNuyVpDUa6gpK7O++LgwYN4eHhQqVIl5HI5I0eOJC8vj9u3b1O1alViYmKYPXu2MBZv1KgRM2fOVMpySsbiitfw5s2bIstVVFTEuXPnyMrKwt3dnfr166OpqUmjRo0wMjLCzc2NxYsX069fP7y8vBg2bBh37twRXbVQTOaNiYkptaNVtKoZMGAATk5OQpdKGpejR48mKChIiILOnTuXmJgYTExM6N27tyg5TpkyhejoaAYNGoS+vj4hISHUqFEDZ2dnKlWqRKdOndi4cSPVqlXD0NCQ3NxcUlJSRPD4/fffo6enx6xZs4DijVdBQQEvXrxg0qRJH6zEsXr1aoyMjMTc4eTkhLOzsxAA3rlzJ4aGhqxcuRKAI0eOkJaWVqps+TEgjYvWrVtTu3ZtEVRJyvMGBgZCLDY3N1eM17IUzxUhPS8//vgjw4YNo0aNGkoVA/j/YKtixYoiQBg5cqSSq4anp6eYNwsLC+nTpw9+fn5EREQIzT9Q9m0FhK2RlLFWVVUlOjoaFRUVunXrhoeHB1ZWVoK/aGVlRc2aNZVKtlC2eG7jxo2pWLHiW4Mpybu0Ro0a77S3OXPmjOh8XLBggdLfpGBrzJgxnDx5UvCzSqJBgwaUL18eLy8vwWH70CiZ1YbihqTKlSsTGRlJ+/btuXz5Mg0aNBDPLBTPe3/El/W/gv9EoFXWDX/06BHDhw8XAZOi1IDEjZA+d/LkSXr27Im9vT19+/alV69eVK5cmaZNm/L69WumTp2Kj48PGRkZHD16VEhGxMTEcPbsWaA4nb5+/Xri4uIwMjIiISGBSpUqYWVlRUJCAqqqqri4uKCvr4+bmxubNm3ixIkT+Pr6EhAQwMWLF1FVVRWLmvSdEg4dOoSDgwNt2rQRbvZLlizB0NAQfX19unXrxogRI4iMjMTY2JiQkBDMzc2Jjo5m9erVVKpUidjYWJYuXUpOTg5VqlTBw8Pjo9b9k5OTiY2NJSYmBl1dXaG7Iuk7hYaGkpiYSEBAQJm7Nun+3Lx5k4oVK6Kvr4+FhQXu7u6iVChZsUiecRJRffbs2UraXVK2o6zjlK6ltbU1vr6+grfSqFEjJkyYIDJi0vGCsrXGu3R13hft2rUTpQIJCxYsIDw8nMjISKKjo5k7d64wFh83bpzwkyx5vaTMkIeHB+XKlSM8PFx0Kx06dIhatWphaGgoOhnv3LlDYGAglStXZvny5eTm5qKpqUmPHj14+PChmFhv3bqFg4ODcFAome2Q/j8qKgpTU1OWLVsmFv1r167RtWtXIZkwYMAA3N3d0dHRISYmBjMzM0aOHCmaSGQyGcuXL+fs2bNYW1vj6upKlSpVSEpKIjU1FWdnZxwdHTl//jyxsbHs2bOH9evXK/ntvXnzhq+++krJpxP+HJ+k5LleunQJLS0tEQR6eXnx4MEDOnXqhEwmIyUlBXt7e6GILh3Ph4R0za9du8b58+dF9yMUd8jFxsYyYsQI8b62bdvStGlTdHV1RdAjZWalZ6Ok4nlJbNq0SchvaGtrY29vXypLd+fOHbS0tEhKSmLv3r2CWylh//79lC9fntjYWPGaZEBesjwp4dy5c6SlpTFo0CDs7e3Zt2+fKAlL3cUjR47kzJkzHD16lC1bttCkSRN8fX3fKZ6bn59PXl4eb968+d1g6sqVK78ryQLFdjlyuVx0GyteG6lTb9KkScyePRt1dXVycnI4fvw4p06domfPnhgaGrJo0aL3+q0/A8WxPGzYMOLj44UOV35+Pl9++SWJiYkYGhpSuXJlatasWYpP+JkAr4x/faClmG4dMWIEAwcOFITH+/fvM3z4cPT19XFwcKBjx47k5uZSs2ZN8SApPgQLFizA0tISNTU1tLS0OH78OKtXr8ba2ppVq1bh4+ND48aN6d27N0FBQRgYGJSajK5du8akSZPo1q0bU6dOpXbt2ujo6FCpUiXhxO7k5ETlypXZunUrJ06cICAgAGtra27cuPHWAVxYWMjEiRMJDg6mXbt2tG3blgkTJrBgwQJ0dHREiaxcuXJs2LABXV1dbGxsMDQ0JCMjg9mzZxMWFoapqSlNmzbl/Pnz6OnpCZLwX4E0eUGxHIF0DhMmTMDOzo7s7GwiIiJwdXXlzp07SvpO8fHxREZGEhERoTTRSli2bBl9+vRhx44dVK1aFVtbW2EHI5MVG0XXqFGDqKgooWelqqpKaGgo27dvL3WPSwZbI0eOpEOHDjx+/FiUmkNCQoiLi1PKcKmqqtKvXz+lSUqy1vg94u7v4eXLl1SqVImsrCy8vb1p1KgRUKy4HBERIVqrpc41yVh8woQJIhOkSGidP38+xsbGzJ07V9hi2NrakpGRQWxsLC4uLhgbG2NgYCC0kq5cuSKCrfnz51O5cmWl1v6CggKePXuGp6cngwcPVnodYN++fcyfP190OCoGN9ICd/PmTb7++msqVqxIjRo1yMzMpHHjxvj7+5OQkICamhqampp07NhRlOwPHz5Mbm4uKioqaGtrk5qaSmJiIlpaWowfP55Xr15RuXJloqKiMDY2FkGWdE6JiYmlMgt/FIr3/MyZM1y4cIG5c+cKnTWJ/yQFg1IZu1y5ckqcwI+RAVi1apXgpqWlpSnJJWRkZGBvb8/atWtp3749ZmZmpKWllWl6/D6Z2WvXrtGhQwelDWtgYCCJiYlK/B4o7pieMGGC4FYq6lUpblK+/fZb8ZqEktdp0aJFlC9fnuDgYAYOHCi6I7/99lvU1dUxMjKiW7duaGpqsmjRIiGee//+fbp27UpoaKiSeC4Ud2wrei5KeN9g6vfQp08f1NXVS3HNoLhUeerUKQoLC/n6668xNjbGzs4OV1dXPDw8PlqFoSRycnKwsrJi+fLlXLhwQbxeVFTE06dPGTx4MKampsjlcqWO1M8ojX99oAXFD5zkLSa1tkslngcPHogMSkBAAPr6+oJ0rCjoB3D48GEhSlm5cmXOnDnD9evXRTQvKVcPGDCA5cuX4+bmRnR09FvFBtu0aSNa8c+fP8/3338vvPLs7e2pXLky27Zt4+effxbq7VA6GFCc8GrXro2hoSEaGhpC+fjChQsEBwdjb2+Pjo4O4eHhWFhYoK+vz4wZM3B3d0dfXx9TU1N0dHRITk7mwoULODs7s2/fvr907RWPbc6cOXh5ebF9+3aWLFkiBGHDw8PJzMwkIiKC2NhYKleuzOjRo1mxYgWWlpaldm/Sfblz5w6mpqbExcWRn59PRkYGOjo62NvbC86Uo6Mjrq6u2NvbCw6anp4ekZGRBAYGKqlYKx7rkiVL6NOnD7Vr1xbaNbt27cLCwoKQkBAqVapE06ZNhahitWrVhLWG9D1/RFfn9yBlFufMmYOHh4cItnbv3k1MTAyJiYlUqlRJlJ4kY3HFkjjAmjVrmDp1qpI8BCDEanv37i2ygDVq1FBa4CSbKH9/fw4ePAjAhg0bxML86tUrmjVrRmRkpJKW0s6dOwXvcNq0aVhZWZGUlESLFi3YvHlzmQrkkjm6g4MDjRo1IiMjQ3hF5ubmsnTpUuzt7YWER1hYmNDMioiIwMDAQBC3jx8/jq2tLVFRUUBxufDhw4ckJycLva8/C8VFf9CgQXh5eWFjY4NcLqdNmzZCnV1RTqRmzZqEhYUhl8vZsGHDR9v9nz59GhcXF8FZrFq1KrGxsSLYPH/+PFWrVsXR0RFHR0c8PT3fGfS8KzN7+PBhkpKS8Pf3FxweKC6pBQYGkpCQUCpz+D7+f+/apEgBuuSpaWZmxoEDB7h8+TKnT5/Gy8uLbt26YWtry6xZs8Tmy8fH563iuRLatWtHenq6kvjxH4U0Ns6cOcOhQ4dKabLl5OS8NdhSxG+//cbevXtFx+ynwNatW3F0dBTNEYWFhTx69Ig9e/YoXZNt27YxcODA/3xX4e/hXxtoSYP8+fPntGrVirlz54pdd3JyMlZWVmLg5+XlsW/fPiZNmlQm+VoaWC9fvuSnn35i/Pjx2NjY0LlzZ2H4e/LkSXR0dISvW35+PkuWLCEiIoKUlBQePnwofks6rh49euDr64u+vj7Z2dno6+vz1Vdf0a9fPzQ0NHB2dsbf318pc6AYZM2dO5eOHTuSk5PDnDlz6NmzJyYmJkRHRwsPN1NTU1q2bMn06dMZMmSI6KKUjKl37dpFfn4+27Zt4+jRo6xfv56rV6/Su3dvfH19uXnz5ge5Hzk5OUKUUrIdmTJlCtevXxf6TkZGRmhqaqKrq8utW7dE9qOsHeTmzZsZN24coaGh+Pj4iGzcV199JQxVnZycMDQ0xNfXF19fX2xtbYmNjSUkJIS1a9fSo0ePUos7FHtCWlpais4+Sbizd+/e/PLLLwQFBQmrGgsLCypVqiQ0cf6srs67oLiYz58/n0aNGmFnZ0f9+vWBYreDmJgYUlNTRdkOUDIWh2KBTWNjY1GagOKxLRFv3d3dyczM5Nq1a6SkpBASEsLSpUuVvkOyiSosLBRK33K5XARbFy9eJDw8nOjoaHJycqhTpw5GRkZoaWnx66+/kp6eLqQMvLy8iIyM5IsvvqCwsJCVK1dy4MAB4P/Nz/ft28fp06dFJ192djaqqqoMGTKEx48fc/z4cS5fvkxeXh6LFy9GTU0NAwMDUZKUnpdVq1ahpaVFSEgIYWFhREdHExAQUEqO4M9C0rpbs2aN0N8zNDRkxowZQrR2/vz5WFtbk52dzatXr+jTp49SUPMhcfLkSXJzc5VsUK5du0ajRo2Ijo4W3apQrBt15MiRvxT0XLhwgYSEBLS0tIQdi4SrV68K67GSzQbv4/9XFhSfiUWLFtGkSRPR0fn8+XO2b99OYGAgmzdvxtbWlvnz59OgQQOqVq1KjRo1yhTP9fPzo02bNtSrVw99ff2/dG+k41uzZo2wo5JMyxW7f3NyctDV1RUcwv8VSJ6hUKxYn5ubi6urK6qqqtStW7dMSsnnYOvt+FcFWlu2bFGqne/ZswcPDw/i4+NLZWakYGvXrl2lUtGK/5aUxssihkrB1unTp3ny5AnZ2dmYmJiwZMkSEWwtWrSIyMhIqlevXiqzdeLECfbs2UNCQgI6Ojo0adIEQGi8yGQyKlWqVCafRxIjbdCgAbGxsZiamqKrqyuCx0mTJmFrayvKLXK5nNq1a/PFF18wZ84cbGxsKF++PAEBAUoPzc8//0ybNm0wNDT8S/5YigvXvn37BE9m8eLFogswKyuLO3fuKOk79ejRg/j4eIKDg2ndujXR0dGlRBsLCgpEl1j58uWFLEezZs3Yv38/RkZGREREiC4qiQSvrq6Oi4uLENNUzKRI13jz5s2YmJgwYcIEzp8/z2+//cbs2bNRVVVFRUWFnJwczpw5w6lTpwRnCYonmUuXLv0pXZ33hWTiXa5cOWxtbTE0NCQpKQkoDrZiY2NJS0tj1apV4rwUf/vly5csWbIEV1dX0bkHxZw4yZS5Vq1ajB49mnLlygk7qbftuAsKCrh37x4dOnRAXV2d9evXA8XllS5dulChQgXc3NywsLAgODhYGJirqKiQm5vL06dPRdl46NChpKWlMXXqVAoLC5k1axY1a9bk1KlTmJiYoKOjw5dffsnr168ZP348qqqqDB8+XJzjnDlzhNq/ioqKkIRQxKVLlxg8eDADBgxgzpw5f8nhQJFzdvDgQWJjY0XrfXh4ODo6OkIstVKlSuJz06ZNE+WwD10qVOQsxsXFYWhoSI0aNZTec+XKFdE9LXnqSfizQY/i56tXr05kZGSpMXP58mUqVqxYZsftu/z/yro3ivPh8ePHCQwMJCgoiFatWhEQEEBUVBSjRo1CLpcTEhJC+fLlSUlJESVUGxubUpwiRfHc7OzsD2Jbs3nzZoyMjPjyyy958+aNqFjUr19fbNChOHtmYWHxThL9p8bp06cxNzcnNDQUa2trWrRowfz58zlw4AByuVzQLj7j/fCvCLQKCwvZuXMnenp6SoKI9+7dEyl6qZSh+JDWqFEDDQ0NUUYE5clPUhp/FzHU0tIST09PsSh37twZfX19oamSn5/P4sWL8fb2pl69eixZsoTJkycrHf+SJUswMDAgIiKCpUuX8vPPP9OlSxcWLVpUpqBlkyZNMDAwEEHVixcvGDt2LCoqKmJi7dq1K35+fmhqaiKTybCwsCAuLg5PT088PT3JzMzE2NgYe3t7oRsDxWWevn37ltLLeV9IqWYJX331Fa1atSIrKwsontDmzZuHmZkZmpqaNGvWTKn+DzB+/HgcHBwwMDAos6umqKiI+/fv07dvX+RyOampqWRnZxMUFETXrl2xt7enSZMmpKWlsWfPHi5dusTJkyepUKGCkAp4m1BoSkoKGhoaGBsb4+7uTmJiIhcuXGDChAno6+ujoaFBuXLlcHJyws/Pr5QB9p/V1SkLitydR48eUaNGDY4dO8aTJ0+YMmUKTk5OGBkZiU7TXbt24eXlRc+ePUuVbNesWcPLly958eIFy5cvx8zMjJSUFKC4tC7x9QwNDWncuDETJkwQmnPh4eHMnj37nUrfUhlcCrby8/N58+YNjx494syZM9SoUYPY2Fi8vb0ZPHgw6urqdOrUiVWrVpGamiq6xWbMmMGpU6dYvHgxiYmJzJ49G7lcTpcuXcTvLV26lLCwMNTU1GjatKn47b1793Lp0iUWLFggsl6/d+3/SgAsbZzOnz9P27ZtWblyJdu3bxdK787OzlhbW2NsbMysWbN48uQJTZo04YsvvvhoukfLli0jNzeXHTt2UK1aNVxdXUVno4SrV6+SmppKUlKScJmQ8EeDnpJQ9I1UHP/w7oD298Q/y9ps9ujRg/T0dCIjIzExMREZWUdHRzQ1NXFychJG3f7+/rx69eqd4rnSMX4IGZsnT57QqlUrwVe8dOkSLi4uIsOblpYmuqmBMo/l70ReXh67d++mU6dOrFy5UjTWPHr0iPDwcKU18zN+H/+KQEuCJIR48eJFMYHcu3eP8PBwEQyB8sRbr149pd2FBEWlcQllEUPr1q2Lvr6+yP5cvHiRLl26iGBLKrGsWLGCzZs3ExgYSHBwMIsXLxbf8e233wrn+MjISKH/I0FxUn706BE+Pj5oamoqEXt37doljGlzc3MxNjbmyJEjDB8+XNhGhIaGMmnSJDIzM6lSpYqQfyjZ3vxnF4G2bdvSsWNHpesrlZaCgoKUBE8fP36MqakpWlpa1KlTh99++w0o7lpasGABdevWFal76fvu3bvHhQsXePbsmegG6tSpE3K5nGrVqtGuXTv8/f2JiYnBz89PKFmfPHmSwsJCQkJCRJkK/l89GooXmOnTp6Onp4empiZbt25l6dKlVK1aFV9fX9auXYulpSWLFy9m9uzZzJo1663B2h/V1SkLJYO3c+fOkZiYKDga+fn5zJo1i+DgYGrUqCEaPH755RelBa1nz55YWlry1Vdfic++fPmSJk2aoKWlhbu7O9WqVSMiIgI9PT3mzJkjSrVnz54lICCAgIAAMUbepfQtBTwlve7u3bvH/fv3RePHuHHjWLt2LQ4ODtja2uLp6YmdnZ0oHx84cIB9+/YREhKCpqYmJiYmotT14sULUlJSyMrKYvXq1YSGhhIcHIyhoaFQD3/9+jUzZsxAVVWVESNGiOPo3r27eJ7/bDZJui+PHj3CwsKCkSNH8u2336KiosK3335Lo0aNaNCgAdbW1ri5udGoUSPRBdunTx+cnJzKlEb4K1DkLNra2oqy8MmTJ0lISCApKUnJlw7erTz/ZxXPJUjBVmJi4h8qib3PJmXOnDlcuXKFefPmYWRkxJEjR3j48CE3b96kSpUqhIeHk5WVJboDBw4ciIeHB/PnzwcQjgmSeK4UQHxojcA3b97w9ddfc+HCBR48eEBgYCCZmZlAcTAsl8sFF/afgLy8PB4+fEhqairh4eGfuwr/IP4VgZYiaf3KlSuCMCsFW/fv3ycoKIjy5cuXGWyVhKLSeEli6A8//ICnpydff/21eF2SKJAe1kuXLtGlSxdMTEzEQlty9+Xh4aE0CWVmZiKTydDU1MTMzKxMqYGRI0eyfPlytm7dipaWFgEBAYLUCVCrVi3ROh4VFUVhYSEPHjxAXV2devXqERoaipubG/PmzePXX39l6tSpuLq68ssvv3yQiUayzwGUgtfc3FwsLCwYNWoUd+/eFYHAkydPMDExwcTEhJUrV4oM1ZUrV0Q5U5HrEBISgp2dHZUqVSI6Oprk5GSsra2FavmXX37J8OHD8fPzIyAgAAcHB8zNzbG3txfZFOm3N2/eTNeuXcnPz6ddu3aCYN6iRQvi4+Np3rw5p06dYvfu3VSsWJGmTZvi4eHBjh07lM75bRPO++rq/B4UjcUdHR2VJCkkY/GoqCiioqKUAseCggImTJiAhYWFUgm4oKBAlJ07d+6Mq6srTk5OHDt2TIyB169f8+zZM1JSUoiLi+PGjRsUFha+l9J3+/btkcvlwri4S5cuVK1aFSjuQqtZsybR0dEsXLhQBM7nzp1j586dhIaGsmLFCrZu3UrdunVJTU3F2NgYT09PmjdvzoYNG6hatarIJEq/efbsWWxtbYUIJhQvdDNmzEBFRYX09HSio6P/Ml9O8RnZuHEjnTp1EqX5FStW8Pz5c/z8/Bg6dCirV69GW1sbc3NzKlasSL169cTm52NA4ix26NBBSWz0119/JSEhgapVq5ZJZn8b/mpm9s+O/3dtUvLy8jA1NaVDhw7069eP6OhopbL/9evXCQsLw9nZmZYtW4oO42HDhvHrr78qPat9+vShQoUKQjz3Y0Di9i5atIiIiAjRgb5s2TLi4uJwdHT8w+LFHwp/ZKPx+vVrFi1aJKyBFHXxPuP98K8ItEpi9OjRqKurM2LEiFLBlr+/v1LKtiz8XjeMmZkZISEhovX96tWruLq6KrW1X716VRhUT58+XWn3devWLapUqUJERITYaUGxd9iqVavEQFbMLElE2kOHDnHt2jUCAgKwsLDA19dXtKcfO3YMfX19VFVVsbOzY9SoUURGRqKurs7JkyfZunUrOjo6+Pj4CNmGkgrQHwILFiwgNjaW9evXs2XLFtavX0+3bt2U9J0UF8rg4GACAwNxcnJSIv5L+OGHH9DR0WHixIlcv36dyMhIZDIZCQkJWFpaCvV9mUzG+vXrGTNmDP7+/piZmWFgYEBqaiqZmZlKxOdBgwbh6+srSPRnzpyhYcOGVKlShTlz5hAdHU1KSgqrV68mIyNDiGP+kcnlz7SCK37/t99+i7W1tTAWd3Z2VprooDjYmjlzJq1atSql5dSyZUt69uwJFAf/X3/9NR4eHujo6DBlyhSgeFPh6OhIcnIyUJztWrBgAXFxcQQGBooxWFhYyJkzZ96p9A3FXbwSN6lq1aro6ekplX+vXbtGjRo1qFixonBkWLNmDSYmJlhaWrJ582Z69+5NgwYNiIqKwsDAQEnV39HRkTFjxrB+/XqxaXr27BnGxsalsjYFBQVs3LiRGjVq0LZt2w9GfO/Zsydubm7CWFwmk4nr3LlzZ3R1denatSs+Pj6YmprSpEkTBg8erGQl8yGhyFn08fHh5cuX4nUoDraqVq0qlOffF381M/tnpRDKCtKkOWrmzJlER0fTsWNHgoODRTAjSb9s27ZNaK9ZW1tjYmIiPGal90mQxHMVuX4fA0OGDMHHx0c0RPXu3ZspU6aUuZn+FFA812fPnpXS2yt5LZ49e8aGDRsYPXq0mLc/E9//GP7xgVZJCQYJ48ePF8abisGWs7Oz0GR6F95GDH3+/Lno3DIxMSE3N5dLly4xfPhw6tWrJ6QhpO+4fft2mbuvGzduEBYWhouLi1KwJZ2P4vHt3buXLl26CK2Sbt26ERERgba2Nmpqapibm9OjRw8qV66Mv7+/yI7Z2dlRq1Yt0Tn47bffUqNGDfr27ftRtXskfaeYmBhkMhmrVq0iPz+/lL6T9LC+efOGU6dOlfJSLCws5M2bN2RlZdG7d2/x3WpqatSsWZO5c+fi5OSElpYW06dPp0+fPoLEeuLECSWVYsUJQjr3pKQk5HI5TZo04cmTJ0yYMAFfX1+2bdvGokWLqF+/PioqKjg5OaGvry9Kb58ibT5v3jxmzZolysNSm31AQAARERFKQbiiQrYksPrmzRvBlZk0aZIoIfn7+2NiYkJqairHjx9n1KhR2NraoqKigqurK1C8mPXt21eMwW3btv2u0ndcXJwQlS0qKqJChQrI5XJhEqzYfCCVpipVqsTMmTMpKiqiVatWqKqqkpOTI8qgz58/5+HDh3To0AFDQ0PGjRtH9erVKV++PJaWlgQHB9OnTx9GjhxJYmIiEyZMKNU4AcqGxH91gZgxYwZmZmasWrWKO3fuMGDAAOG+MGTIEJ4/f063bt2oUqUKzZo1E9fwYy9M9+/fZ+DAgaioqIgAVnG+kZTn/2gG5UNlZv8oLl26VGaQduLECRwdHenbty+qqqrk5ubSrVs30YiwadMm0tPTiYqKQlNTk65du3Lp0qUyxXMBBg8eXMon9kPj6NGjaGpqEhUVRUJCAgYGBqXsqT4VFM996NChVKxYUbhJKFZpSgZbinPe57LhH8c/OtCSBs22bdto3749ffr0YdeuXeLv48aNKxVsPXjw4L0fLIkY6ubmpkQMtbe3JyMjg+HDhxMcHEybNm3IysrCy8tLqZNHOr4hQ4aU2n1BcQZLR0eHhIQEJQNexc//+uuvaGlpoaamxujRo1mwYIEoQaxZs4Z69eqhrq4uMlXSd0+aNAl1dXW6d+/OwYMHOXPmDMnJySJggQ+T+n3bdyxbtgxPT088PDzYsGGDkhCgor5TyYBP0sZRRK1atZg7dy43btzAzMwMY2NjAFq1aoVcLic7OxsoJmmvWrWKoqIiVq5cKYLekt2Fb9684fnz50RGRmJmZoa7uzs9evTg0qVLREREEBQUxJo1a7h79y5HjhwhOjqamjVrUlRU9El2cvfu3cPFxaWUQWt+fj5btmwhMDCQqKgoEUCUvF6SAviZM2cIDw/Hzc2NESNGcODAARYtWoS5uTnm5uZ4enrSsGFDBg4cKDTBSurkfPfdd39I6fvNmzfk5+fTvn17oWYvyQIUFRWJ77527Rpubm5CSX/fvn1CVLR79+5KnbD79+/H3t6eR48eCWJ0//796dmzJ/Xq1cPPzw99fX3U1dWpUKECDRo0oGvXrkyaNEmJh/QhNhXdunWja9eu2NjYMGPGDAwMDJg6darghEledc+fP2fOnDlik/EhNzSKnMVLly7x7NkzMQY6deqEqqqqyFgrBlt/lnv5oUQ6/wy+++47UTmQMGrUKOzt7Rk9ejRqamoEBQWxZMkSkYEbPnw4Bw8eRCaTKWWzFMVzFTmynwJ79+6lcePGtG/f/oN0NP5RlBx/ubm5mJqaMnXqVObMmSOcTCR+32d8WPyjAy0o5iaoqqpSr149rKysiImJUfJeGj9+POrq6vTv31/slP8IpN23lJYfO3YshYWFdOjQgRYtWvD06VOmT59OVlaW0Fsq2Xl37NgxVFVVGTRokNLr0u6rUqVKVK5cmTdv3pQ5Ia9YsQJzc3OqVatG27ZtBf9KMnE9ceIEgYGBuLm5MX/+fBEMrFq1Svjw2dnZKXVMfoiJX/E7li9fztSpU1m/fj3Xrl0T3Y62trZUr179nfpOEhTlL7Zs2SJ2WI0bNxZWJvXr18fPz49Bgwahq6uLTCYjMTGRgoIC9u7dS0xMDPv27RNSARIUg5G8vDz69OmDlZUV69evp3fv3vj7+4tgq1q1anh5eWFgYECFChXw9/cXkhCfyr/rl19+EcbiilmagoICJWNxxfMaO3YsDRo0wMPDg5EjR3L16lUKCgo4fvw4169f5+nTpzx79gxvb2/s7OyYNWuW2HTs2bOH4OBgJa+991X6Liws5O7du2U2lQwdOhS5XK5koAvFPnt2dnb06tWLWbNmCYcCc3NzNDU1qVixogi2fv75Z/T19ZVKkIpWLK9evSI7OxsvLy/WrFlD+/btiYqKIi0t7S/tvksGsEVFRSQnJ5OTk4OpqSna2tpC8uD169fUrFkTFRUV0QmppaUlSqofCoqcxdDQUMFZ7NChAy9evODNmzd07doVVVVVUUr9mNnrj4nTp0/j4+ODvb09jRs3Zt++fbx69Yq7d+8SGRkpAllzc3PRTOHs7MyrV684c+YM+vr6BAQElGrOkMRzP7WcwqecPxQhrXvS71+7dg1/f38hAwPFoqitW7cmMjJSaNl9xofDPzrQksQupfLK/fv3admypRBBlDBs2DCMjY3fqtD+e7h8+TKenp7IZDKio6PZvXu3KI9ILeRPnjyhQ4cO2NraltlVNG/ePOFbdfjwYaXd16lTp5DL5Uo7t5KKxAsWLMDGxobIyEh8fX0FD0PKamzduhVdXV18fX2FTg8UP0AHDx5kx44df0k36F2Q9J1cXFzw8fGhYcOGrF69GisrK4KCgoiIiFDSd5KguAi+rYsLoF+/fpiYmODq6sqDBw9o1KgROjo6hIaGYmpqirq6Ou3btycsLEyopCsSphUXzAkTJhAXF4eWlhapqak8fvyYp0+f0qNHD5ycnGjWrBmHDx8mLCwMV1dXVq1a9dGuW8ljU/y3FCBJxuKKi0J+fn4pY/HevXtjaWnJ+PHjmTlzJkZGRqSnp9O9e3eio6MxNzcnICAAHx8ffHx8xPjKz8/n+fPnpKamUrly5T+s9K14/AsXLmTw4MF07tyZ/fv3i0Bp2LBhqKqqMn78eO7evYu/vz+ampocPXqUEydO4ODgIAKWU6dOoaqqiqmpKZMnTxYEf09PT9q0aUOvXr3o0KFDKcmCtWvX4uXlpfSaYjn1r0AxKzZr1iwhGdO4cWPx+sOHD0lKSqJBgwaEhoaSlJT0l3To3oWSnEWpiUSy13ny5Andu3cXyvP/FPz444/Mnz+fcePGCeL4nTt32LlzJ/7+/oSHh1O1alUuXLhAvXr1iIiIoKioiBs3brBr1y6xId64cSO9e/fG2dmZoKAgIZ67YMECMjMz/9bS3adGnz59qFWrlhLh//79+5QrV46FCxcC//983L9/H3t7e6VO3c/4MPjHBlrSQhAQEKCkNnznzh0yMzOJjIxUymyVxd34I7h48SIxMTFiwe/cuTPff/+90GqSUFKXRhGKGSZbW1sCAwN59eoVV65cwdXVVTz87xJJtbCwQEVFhU6dOil993fffVeKf1UWPkR9/W36Tvfv3+err74iLCyM6tWrs2rVKrS1talevTqenp6CMFwW3rx5w6JFi9DQ0BBdXFCcfZJI0RoaGsTHxxMTE4OpqSlqamq0bduWrKwsUV719vamSpUqIgAt2WlkZWVFhw4dMDExQS6XU7FiRdq1a4e3tzdaWlro6upiaWlZJg/qQ0PxPs2dO5cuXbqQmZmpNJ4UjcUVOw4Vj+vQoUO4u7sLbZtDhw6hqqpKrVq1MDExYdOmTWzatEkIt0pcqsePH7No0SISEhKUVNILCwv/sNJ39+7dMTU1pXbt2ri4uODh4SFMp+H/y/je3t6YmJgIuYiNGzcSGBgoRGsdHR1p1aoVKSkpZGRkcPjwYV6/fo2rqytmZmZ069aNunXr4uLioqSt9eOPP6Ktra2kug1/Tb8MioMauVzO9OnTqVu3Lv3798fDwwNDQ0MaNGjAo0ePOH/+PMnJyYSEhJCfn8+zZ8+UOkA/FMriLN67dw87OzvR5Sm9T1Ke/9AZtY8FKasZEhKCo6MjRkZGjB07VnDKXrx4wTfffEP16tVxdHSkWrVqyOVyJWHUe/fu0bBhQ1RUVITkjqJ4rre3N1WrVv3PBFlQnFGOiooiMzNTcEzv3LmDl5cXnTt3BpTLy3Xq1CnFw/yMv45/bKAlLQTa2tqlFoJ79+7RunVrvL29hTjoh0jZXr58mdDQUAIDA/H19UVXV5dy5coJwu/74MaNG+zbt4+ffvpJDO5evXrh6enJrVu33ksk1cTEBBUVFVq3bs3Bgwe5cOHCR+FflYWS+k5nz54lMTGR5cuX06pVKypXrkz16tXx8/OjRo0arFy5Em1tbdLT03938fnll1+En11JZe/q1asL09umTZvSokULWrRogb29PdWqVaNRo0acOXOGc+fOiQBJkRtz7Ngx4bMI4ODgIHwl27Vrx9q1a7l58yaWlpb069fvo2axSqJXr17Y2dmRkZFBkyZNUFFRUVpAFI3FJf6VIg4ePEhISAhQzI3T09Nj9OjRxMfHs2rVKrZs2cKmTZvQ09MTDRWvX7/m5cuXfPHFF+Tk5JTZTfS+St/ff/89dnZ2StIFgwcPJjo6mtzcXDF2f/31V7799lsSExNJS0sjPz+fH374geTkZC5fvoytrS01a9YU137Xrl3k5OSwbNkyypUrJ0oaX3/9NVpaWoL0DcWBurW1NT/++OOfvxEoj+/Zs2czYsQI5HI5urq6JCYmEhUVJYzlJbcBCwsLnJycWLNmDVeuXPkgQfn7chZtbW1p1aqV+NvatWtFZ+E/pVR48uRJHB0dWbVqlRjfvXr1wt3dnc6dO5eqECxfvpz27dsTHx/P3bt3RbYmPz+fn376CX9/f9TU1JTEc6F4EyxVAv7tULz3kydPJjExkRYtWohrtXLlSlRUVBg/frx435s3bwgODlbqnv+MD4N/bKAF714I7ty5Q6dOnbh8+fIH/U2JGJqXl0fXrl3FRFtWtuH3cOLECZo0aYKpqSk///zze4ukzpw5k5CQkI/Kv/o9KOo7mZubo6WlRb9+/Rg0aBCVK1dGT0+PkJAQoqKiWLt2LQYGBjRs2PCtwdbp06e5c+cO+/btY8GCBaKLS5GLk5KSgqWlJT/99JN4vaTnljSpSlkbKL4eu3btwtfXl4KCAtasWYOenh5NmjRhx44d3Lt3T6jjR0VFiS7TT9FdM3fuXOzt7QVxX7Lp0NDQYObMmeJ9krF4WYFf3bp10dbWFlmBadOmcffuXZycnJg9ezYRERHo6OiIEvvLly+ZMmUK58+fV+puLet830fpW7L1uXv3rlIXcLdu3fD09OTFixdcuXJFBAz9+/cnODiYo0ePcuHCBXR0dJDL5TRv3py4uDhRquzUqROJiYlMnjyZ2NhYoHiBkMzQoZh0vmvXLp49e0arVq0+2D3r2bMnNjY2zJw5kwEDBhAdHY2enh5bt27l4cOHtGrVCkNDQ5o1a0ZUVBTly5fHx8fng4qRluQsSuVSRc5idna2uOaKyvP/pM6wo0ePYmtrW6rUOn78eLy9vYVdk+K89vr167eK5z548OCt4rn/FSgG57t27aJZs2ZYWVnRqlUrEWxNnDhROGs0bNiQuLg4Ja3Bz/hw+EcHWvDuheBjZXUUH/gtW7YoEYjfF/n5+Rw9epTu3btz4sSJd4qkbtmyBU9Pz1KCg7/99hv79+//qPwrCW/Td+revTuampo4ODiQl5fHrVu3sLKyom3btkr6TuvWrcPa2lpMhhKKiooYOnQo5ubm4jrOmDEDQ0NDVFRUGD58uLjec+bMwc/PD2dnZ3766adS5yq9r3PnzoSEhCiVlC9evChI9AYGBkpBzPbt2wkLCyMqKoqAgIBPtkg9f/6c0aNHM2vWLKD4uioai2tqaiplbRT5WxLH6sWLFwwdOhRDQ0NkMpnYjd67d4/KlSvj5OSEurq6Ehn99OnT1KhR4735O29T+paOZ8mSJTg6OgpejdQ4cP/+fTQ0NGjYsCGxsbEiI3X58mWsra1JTU1l+/btrFixAh0dHVxdXQkODuaXX36hR48eGBkZcfz4cRYsWECjRo3YuHEjenp6So4Ia9asoUePHkoctj9z/xSf6YsXL+Li4sKwYcOYOXMmQ4YMYdGiRaSnp6Ovr8+BAweUOiGl61BSj+iv4F2cxbNnzwrlecVnoG/fvh9Fef5j48CBA1hbW4uSuSI/dejQoZiamoqxI91bSTy3f//+vyueu2XLlk98Rv876Ny5MxUqVKBRo0YEBwdjbm5OZmamkNLZsWMHLVu2pGHDhkLAGT5LOHxo/OMDLfjzlg9/BR8qayRlFH5PJNXOzk6pi64s/B36TpcvX8bS0hJvb2+CgoKwtbUVcgsFBQWsXbuW27dvU1hYWCqbVVRUxI8//oiqqiq6uroiqLh79y5+fn7Y2tqiqqpK06ZNad26NVpaWsL7rmQXmiJOnDiBn58f3t7ejB8/nvz8fJ48eSJI9F27dhXvffnyJf7+/hgbGxMVFfXBRC3LQllj5sSJE1y+fJlLly7h5eXFxIkTgf83FpfL5axevVrpsxMmTCAtLY3s7Gx++OEHHj9+TLdu3dDV1UVfX58FCxYwceJEIeLarFkzoDgwevLkCcnJySQkJPyhc3yXntLz58+xsbGhVq1aSsd55swZTE1NMTU15euvvxY2S1DsHBAQEICXlxcmJiY4OjqioqKCnZ0dXl5e+Pv7c/ToUaA4MNTQ0EAulytp2r18+ZKqVavSsmXLv/Q8Km4iHjx4wJ07d1BTU0NXV5eMjAwiIyMJDg6mbt26ODg4YGlpyYoVK9DX11cql37ITPK7OIuAUJ6Pj4+nZs2aQnleumb/NCQmJuLj4yP+rah9VrFiRdLT0ykqKhLX+Ny5c78rnvvkyRO6dev2j+GpfWhs3rwZc3NzpS7CoUOHEhwcTFZWlshslZT9+JzR+vD4VwRa8PcJ631IvE0k9cWLF/j4+IjOrL8LZek73blzh8TERAYOHIiGhgaWlpaCZ3Hx4kUyMzPZtm2b0veUXJAkE+CEhATOnj0LFC94FSpUwN7eHj8/P9HFtWPHDnr37k3Pnj3LDBSkCfrixYvo6OigoaEheHq7d+8mIiKC6Ohohg0bJoQ8PTw8mD17ttBo+hgTjeI5L1u2rJSx+Pbt2wkMDBRyC4rG4orlvTFjxmBgYEC3bt3w8PAgMjKSqVOn8vjxY3r06IGxsTG6urrEx8eTmZlJ165d0dDQoG7dutSvX5+KFSvi6+v7h200CgsLRdl8yZIl9OnTh6FDh4oy9759+7CwsCAxMZHVq1ezbds2IiIi0NDQELZFr1+/5tatW2zcuJGnT5/y/Plzdu/ezdSpU/n222+5cuUKu3bt4tChQ6WyQxLXr2fPnuzYsYPt27eX6i79M4GO4mdycnKoVauWEJiMiIjg2bNnnDp1Cm1tbXr16kVqaiouLi4YGRnh4uJSpv7dh8Ivv/wigu2SnEUoDkDbtm1L06ZNGTx4sHh2/tfx22+/cf36daXA+9SpU7i7u4sSMfz/2GzVqpXo8Pzxxx//sHjufxXffPMN1tbWSl2zBQUF9OzZEx0dHdq0aSPErD/j4+JfE2jB3yus96EgiaS6u7sriaT+VZ+2PwtFzk1RUVGZ+k4pKSnIZDIqVaok9J2gmOcSEBCgNKFK2Lp1K3Xq1GHy5MksW7aMsLAw9PX1adu2rVgwJNskNzc3Vq1axbFjx0hLS6Nbt27ie0qKkUo4ePAgffv2RUVFBQ0NDVE627lzJ127dhUk+szMTCUi+MfuLpQ0z8oyFpfL5WzatInz58+TmpqqZCyen5/PkSNHaNWqlZBVePToEZmZmYSFhTF58mQePnzIhAkT8PDwUCJIz5w5k/bt29OsWbO/bKPRo0cPLC0tqVKlClFRUcjlcoYNGwYUk5qDgoJwdnbG3d2dwMBAXF1defjwIXv37qVnz564u7tjaGhI5cqVyxRufNv1LygoYOnSpdja2mJra0tQUBDVq1f/SxlIxUV4586dhISEsG/fPjZv3oytrS0RERF07dpVdEK+ePGC2rVrM2HCBKKjoylXrpySv+KHREnOoqqqqpCSgdL37p8SUCxevJiQkBDKlSuHqampEC5+8+YN33//Pa6uroSEhHDt2jUhUBsTE0OHDh3YtGnTHxbP/a9A8RpI883WrVtxc3MT3cglZRwcHBzEs/sZHxf/qkDr3wJJJFVFRYWkpCTGjh0r/vapaufSQynxxaSJfePGjaSmpmJqaoqjoyNPnjyhoKCA0NBQPDw86Nu3L3PnzqVdu3ZllvekCaFPnz40aNCAyMhI7OzsMDMzQy6Xo6mpib+/P71792b9+vWcOXOGhIQETExMsLOzIzg4+J32SQ8fPqRnz55YWVkxePBgOnbsiLq6OhoaGsLfDyjVvPApgtj3MRaXy+U4OzsLkVQJEofP29tbyTNv48aNVKtWDT8/P8aNG8ebN2+YNGkSPj4+tGrVqkxLJ/hz42jz5s1YWFiwb98+oLhkOHv2bNTV1UXGJS8vjytXrnDp0iVu376NlpYWQUFB6Ovrk52dzddff83+/fsxNTUVXWF/BJIw6tWrV5UaJf4KVq5cSZMmTcjKygKKJR2SkpLIzs4WzS49e/YkNDQUT09PevToQUpKCrVq1fpLv1sWioqKOHXqFDY2NoKz+Pr161LK84AQ7JQ+97+OxYsXo6ury5w5c/juu+/Izc1FRUVFzBH5+fns37+f0NBQzM3N8fHxEWP+0qVL7yWeC7xVPPffCsWNnKLVVV5eHr6+vkRHRyslIM6cOUPt2rX56quvPhtDfyJ8DrT+R3Ht2jVSU1OpWbOmEsn/U0yo0sO3fft2obyemZnJuHHj0NDQID09nZCQEFRUVLC2tubp06c8ffqUGjVqiImxRo0aSno1isetuNO8f/8+Dx8+pHv37piYmNCsWTO0tLQwMTHBzc1NdI3u2rWLffv2KZH+58yZoxR0zJ49G3Nzc8zMzPjhhx/Eb54+fRotLS20tLSYNm3a35INmDdv3nsbi5fV3CBx+PT19YVNRu/evXFycsLX1xdtbW309fUZM2YMz58/Z8KECfj5+dG2bds/fcwlg7GFCxfi7+9f6vUvvvgCU1NTjh8/zr59+/j555+Fcfu5c+cYNmwYGzZsEMGtFJivXr36Tx+bhL+6ULx584a0tDR0dXWJiIgAirPKUidkcnIyzZs3p0qVKrRs2ZL27dtTpUoVatWqRefOnZV8Jv8qpO958eIFNjY2QopEOs4ZM2agpqampDz/sYyqPzROnDhBhQoVlMypX7x4QYUKFZQaUyTMmzePKVOmMGPGDPbv309SUhJ+fn7vFM/9r2Ps2LHUrFmTpk2bCleA3377DXt7e0JDQ5k2bRobNmwgMTFRiU/5Odj6+PgcaP0PQ1KPT0hIUJqgPhXatm2Lnp4e6urqREZGIpfLadGiBVDMh+jZsycymQx9fX3By7pz5w6PHz9W0quRHugZM2aILq6tW7cqCbNmZmZiZ2fHo0ePmD17Nra2trRu3brMhaSgoECQ6BV1du7evYunpyfq6uqiJCFxtr788ktkMhlqamql7GA+Bf6MsXjJgObatWtUr16d2NhYmjdvjpWVlejU6ty5M+rq6qLD6smTJ6IbS9El4X2h2LggBbsbNmxAW1tblPyaNWvG1q1bOXz4MJaWljRq1AgzMzPs7e3x8PAoZQHz+vVr7t+/T7Vq1QgODv5bOpvKCoqePXsmLIHGjBnDmzdvWLt2Lbq6uvTq1Ytz584pdUK2aNECU1PTDxbklDymgoICnJ2dmT17ttLr+fn5wnrnYyrPfwwcOHCAoKCgUuXiKlWq0KNHD6B0+V/ChQsXqFSp0nuL5/5XoHithgwZgpmZGdnZ2VSrVg09PT2R/bt//z4pKSn4+fnh4uJCXFzcJ5UC+ozPgdb/PP4ukr+k77R+/XpycnLQ0dERgYq0A3369Cndu3dHJpP9bq2/QYMGqKioEBERIcqF6urqQpjV2dkZFRUVoSc1b948HBwcaNasmZANKImFCxdib29Pp06dBK/r2LFjqKmpYWVlxblz5ygoKKCgoEB04MjlcjGxfwr8VWNxCdKkKmW2rKysSE9PB4odBwwNDUUn6LNnzygoKODx48esWLHiDwc0mzdvFq3e7dq1w8PDg2fPnnHlyhXi4+Np3rw5P/74I8nJyZibm7N69WqcnZ2xsbFh3759fPfdd3Ts2BG5XC70yd68ecPkyZMJDw8nPDz8o3Z3vg2KC9PLly/Jy8srpUEVHh7OlClTePXqFfPmzcPAwEB0Qnp5eVGrVi08PT0/eHff1q1bqVu3ruAs1q9fn4EDB5bSiQM+mvL8x4ZiYChltSW1fUWUJcr7vuK5/0WcOXOGMWPG8NNPPwHFm+D+/fsjl8uFQHFBQQF3795V0rP73F346fA50PoH4FOR/KUH8PHjx0Lf6eXLl6xZswYNDQ3U1NRISEhQ0nd6+vSpEL5TtDxSxKlTp7C2tsbX15f4+HiGDh0qjMAl7N+/H01NTdzd3cVrM2fOJC0trUxzX+m1RYsWYW1tTfv27UWn0ZAhQ5DJZNjY2LBhwwaOHTtGamoqPj4+bN26VQRfnxJ/xFi8JBR3nWfPnuXSpUuYmZkREBBAv3790NPTE8FvXl4eEyZMKMV/+iPnO3jwYHx9fQkLC8PU1FSUAaGYFxQdHU1KSgpTpkyhSpUqqKurY2Njw8CBA8X77ty5Q5cuXYQ8BRQvtOPGjfukqvsSFMfQF198Qa1atfDz82PixImcPHkSKB73jRs3JiIigmnTppGXl8f169fZtWsXP//8M/fu3ePQoUNvDfz/DKR727dvXxo0aEBUVJTgLKqqqlKtWjU6derEuHHjWL9+/Z/S7Pu7UZafp/RagwYNhNxKUVERaWlpSk0iingf8dz/GiSBY3t7e6VA9sGDByLYKpkZhc/lwk+Nz4HWZ5QSa5w0aRInTpxg8uTJGBoa4unpSW5uLj169EBNTU1J3wmKswHTp09/q17N5s2bhXSBZOhsaGgoiPZHjhzhxYsXREZG4uzsXEqYFZT9FeH/A4dBgwZhb2+Puro6oaGhosOmY8eOyGQyNDU1MTAwQF9fHz8/PyXRz0+N9zEWLymuqHhvOnTogIGBAQDt27dHVVUVVVVVpYXp0aNHxMfHM3r06L90rElJSUKtvWQmdcmSJdSvXx8VFRU8PT2xsrJCQ0ODrKwsJc7S3bt36dKlC2pqaqUWxr9LELFPnz6YmZkxefJk+vfvT4UKFUhPTxcZqsePH9O0aVNhKC7hQ5dY3vZ9z58/V+IsfvXVV1SvXl0oz39op4uPhZ9++om9e/f+7oJev3594XuZlJSEjY3NO5td/g7NxP9lnD59mg4dOqChocHXX38N/P/YevjwIQMHDvzHmYv/G/E50PqPo6S+U0pKCoaGhjRp0gQtLS169OghgqTffvuNZs2aoa6uTnZ2ttIu8l0LkaKfnZWVFY6OjsjlcoYOHcqePXvIyckRnmXvEmZ98+aN+J1Xr16xaNEizMzMmD9/PtHR0WhoaODq6ip4S4sXL8bPz4/g4GDS09PJy8sjPz//b93NvctY3M3NTamBQPE4z58/T5MmTYSX3/nz56lSpQpOTk7cvHmTgoICbt26RbVq1QgLC/vDgYx0Xd+8ecPz58/p3bs3HTt2JCgoiB49eggtHsVjOnz4MDdu3ODs2bM0bdoUDQ0NUb6QcPfuXZo3b05UVJTS7/wdWLlyJW5ubqI8vWPHDlRVVfHx8VFq3nj48CGDBw/+aMGgdA327dunxFlURFnK85JB9/86li1bhlwuJyAggMOHD5d5z6Vr27RpU3JycsjIyMDNzU0EWe/KUP0bNBP/DN42b924cYPmzZujo6NTKqC6f/8+s2bN+s9m/P5X8DnQ+g/jbfpO4eHhyOVy6tWrx6pVq5T0nSpXroyHhwcmJibMnDnzvR5gxS6uTp06ce3aNSHMWrlyZapUqcLDhw/fKsxacvEeO3YsYWFheHh4ULt2bXEeLVq0QENDAxcXF0GMVeSyFBQU/E9MOGUZi/fu3VsYi5dcdJcsWYKHhwfh4eFK/nfr168XmkJ+fn5UqFCB0NDQP8x/UhwHJbMJw4YNw9/fnx49eiiVzDp16kRWVpboAjt16hQNGzbE1NS01P1SDBb+TmzdupUBAwYAsG7dOoyNjZk1axaLFi1CX1+fmjVrCukKCR8r2JJ4dYrK84raZ7/++mspeZR/AnH5+PHjBAUFMXDgQHx8fPD19eXQoUOljl0aD40bN0Yul1OhQoX3CrIk/Bs0E/8ISpqd9+3bl2bNmrFlyxZevHjBvXv3yM7OxsjISJiKl8T/wtz3X8XnQOszhL5TeHg4JiYmGBgYEBgYiJOTE6NHj6Zhw4ZK+k6XLl2ibdu22NnZ8fjx4/daAEp2cW3atEkQ4GvWrClak0sKsw4fPhxfX19BgB07diw6OjqYm5ujoaFRylA7MzMTTU1NHB0d2b9/v9Ix/C8uVCWNxSdOnEhERARFRUWiDPfll1+KeyOJxEp48OABixcvZsaMGaxevfoP858UJ/AJEyZQvXp1IiIiaNOmDY8fPwaK70FwcDAdO3bk8OHDODk5oaamxooVK5SUpc+cOUPjxo0xNzdXasMv67c+Nsr6rVevXnHv3j0ePXpEVFQUo0aNEu/18fHB0dGRXr16AR93rJw6dQoHBwfBq5OU5/v06aP0Pk9Pz4+qPP8xsG/fPjp37szVq1cpLCzE29sbPz+/MoMtKHY68Pf3/0siuv8l5OTkYGFhQU5ODmlpabi6upKTkwMUd2e2bt0aU1NTpbL3Z/z9+Bxo/cfxLn2n5ORkHBwcGDVqFKtXrxb6To8fPyY/P5/bt2+/9+8UFBSU2cUVHx+Puro6cXFxfPHFF6VMTc+dO0dqairx8fHMnj2bBg0asG7dOkaMGIGjoyPR0dF4e3sTGRkpfqtZs2bo6enRu3fvD3uxPjBKGosD3Lp1S5y79Fp+fj7Lly/Hw8OD+Ph4EWy9LXD5M1mYPn36YGVlxZgxY1i/fj1yuZzatWuLLskxY8YQEhKChYUFmpqawlYHSpP1pSyFYhn0U0Lxuty8ebNU5uP8+fPY29uLMsu1a9do0KABixYt+iTBoMRZhGLOkaQ8L+HIkSO8fv2amjVr/uOEN1++fClspKA4uFUMtiQodlN+KOHZfzs2btxIuXLlhL/mxo0bUVNTE1I2UNydWbduXapUqfJ3HeZnlIHPgdZ/HH379iU6Opo9e/YwevRoxo4dy5dffin0nVJSUnB0dGTkyJFcvHiRgQMHYm5urmT6+kdQsovr+vXrRERE0K9fv7fqR126dInk5GTc3d2xsrLi2LFj5OfnM2rUKEJCQqhRowbu7u5KJPrvvvtOeBf+r6Ms8q9kNyJ1d+bn57N48WIiIyNJSUkRfJ13EYffF8eOHcPLy0sIZP74449oa2sza9YspfedOHGC8ePH4+zszI0bN0r9tnStz549y6BBgz454X3ixIlKnJ2+ffvi4eGBjY0NLVq0EGXXixcvEh4eTuvWrVm3bh3JyckkJSV9MgFHRc6inZ0drVq1EtdKkbP4dzUMfChIHbRv3rwRwdaRI0e4efMmjRs3ZuHCheK9/4vZ5r8bJcfhvHnziI+PB4qdIvT19QXN4tmzZxw8eBAo3lz8E+a9/xI+B1r/USjqO7m4uKCrq0uVKlVwd3fHzs6OSpUqCX2nWrVqUa5cOXx8fLC2ti5VkvuruHHjRqmuwpI4fPgwdnZ2yGQyMjIygOIgY9iwYQQFBaGnp6dkVSThn7hYvXr1ikePHtG+fXuMjY1Fx15+fj6LFi0iKiqK6tWrK/G1/gp27dqFr68vAGvWrBFyEYWFhTx+/FhoYQHMnz8fPT09kVVTDLa2bNlSip/1qbIUUtdmkyZNeP36NfPmzcPe3p65c+cyc+ZMrK2tqVy5sshuTZw4kZCQEBwdHT+5gGNJzqIiOnXqJDiL/wZI9//NmzeCs+Xl5YWbm9vnDNZ7QhLGnTFjBjVr1mTnzp3o6+srCS8vX76cHj16KI2bz8HW/w4+B1r/cXz33XfIZDKSk5OBYs6PRNS1t7cX+k4bN25k7dq1SmWBD42Snl0lsXXrVpycnNDQ0KBdu3ZA8UKfm5uLjo4OISEh/0ghR0UsXbqUtm3bkp+fz40bN+jSpQv6+vpKwdbixYtxd3f/U8KrZQUSly5dws/Pj0GDBmFgYKBkibJ37168vLyEyvSzZ8/w8fEhOTlZaaF8/vw5VapU+cuyEn8Fu3btwtjYmKysLKZNm8aCBQvE3y5duoSdnR1xcXGi5H379m0uXLjwtwg4luQsHj9+XCjPS3pw/xZIm51r164hl8uJjIz8WwRr/ylYvXq14A927tyZevXqUVRUxPXr1zE2NkYulyuVC1+9ekVSUhItW7b8nBn8H8XnQOs/jqNHj2Jqaqqk73T69GkqVKiAkZERs2bNKlPf6VOhJL9my5YtODg4oKamRnx8PGPHjqVGjRqYmJiQlZX1j9vFlZwYBw8eTEBAgLAVunjxYqlgKy8vj02bNv2lRWrSpEls27aN/Px8njx5QqNGjdDR0RHikVBsmZOQkIC2tjY1atQQ2aoVK1YQEBBAeHg4W7duZcmSJcLY+u/OUuzcuRMjIyMlAV3pGl+6dElsHkpuGD71uCmLs+jv7//BFef/V3D37l1CQkLw9vb+THx/B169esXQoUNRV1cnISEBPT09oTcIxdIZJiYmtGnThkOHDrF582aqVq2q9Ox9Drb+9/A50PqPYuPGjSxatIiLFy9iampKjx49lPSdfHx8sLGxYfTo0aX0nT4VVq9ejb29fSkPsy1btmBra4tMJsPPz48vvviC169fi069f0qwpTghKnYThoSEkJCQIP59+fJlunbtKuQIFPFng62oqCjMzMzYtWsXALt37yYiIoKoqCiGDRvGpEmTSEhIwMfHhx9//JGgoCBq1qwpeCA//fQTCQkJWFhY4OfnR1pa2t9uqyNlM/fu3YulpSUpKSk8evQI+P9rffnyZVRVVUuV7P4ulOQs/lshaa39EQmH/ypevnxJaGgocrmcbt26Kf3t0aNHLF++HEdHR2xsbAgMDPzbnr3PeH98DrT+g9i3bx+GhoYsXryY27dvk5SUREZGBt9//73Qd3rz5g1RUVEkJSUJfadPjW3btlGrVi0qVKhQivszdepUZDIZMpmMH374AUBJlfyfhOHDh5OcnCxsc06fPo2np6dSGe7q1au0aNGCxMRE4I/tWt8WeFavXh1LS0txbXfu3Em3bt1E1iczM1MsiDt37iQgIEAp2ILijNvDhw//ls4xxfOaOnUq/fv3FxnQ3bt3Y2hoSKNGjXj69Cnw/9dMsbPzMz4NFMfr5yDr3Xj69CldunShffv2GBoaKs0D0nV89uwZZ8+e5dq1a5+7Nv8B+Bxo/cdw+fJlhg8frmTkumXLFsqXL0+dOnVYuHAhx44do3nz5mhoaGBsbKzkofWx8LZgYM+ePaSnp+Pv76+U2dq1axc1a9ZkxowZ/9gAC4qDw7p16yKXy9HT06Nv374cPXqUvn370rhxYyHxAMWcor+SrZOIsorXKiUlRSnYgmKtsoyMDCV5CSi+5vb29lSrVq1UlhH+PvJtTk4OlpaWzJ07V8miZteuXSLYkuQEFM/9c7D1Gf8LeNtz8+jRI4YNG4aBgYHgbEkoyeP7p2Tx/6v4HGj9R1BUVMRvv/2GjY0NBgYGdO/eXenvW7dupVatWhgbG+Pq6oqjoyNNmjRRWug/FhQnidWrV/Pll18yduxYQVo+cuQI6enpeHp6smrVKs6fP09aWhqdO3cWBtP/5EVz+/btNG3alOnTpxMXF0fr1q2pV68eTk5OzJgxo9T732dSnTNnjuhWgmI1aWtra6HLpBhwJCYm4uzszK5du8jPz+frr7/GysqK1q1bl7r/s2bNQl9fn8TExP8JPtG6deuws7MTHpcSpPPbvXs3JiYmJCUl8fLly7/jED/jM94Kxedw8uTJtG/fnuzsbO7cuQMUUwqGDx+OoaEhQ4cO5dWrVyQnJ5Odnf13HfJn/Al8DrT+A1B8mBcvXoy5uTmxsbGlFtFnz55x48YNTp48yf379z+IRtMfQbdu3TA3NyciIgITExO8vb2ZM2cOhYWF/Prrr2RlZSGXy3F3dycgIOCTtuR/aIwfP16QtQsLC2nRogUtW7YkLy+PBQsWiHOVy+V/uAvtxx9/RFVVlc6dOwtS/d27d/H398fPz08EW4WFhRQWFrJ582bkcrmS5cs333yDvb09WVlZSuNk3rx5pKSkkJmZ+T+xix49ejSxsbFKY7WkHtuWLVtITEz8nzjez/gMCYrjceDAgRgZGVG/fn2cnZ1xcHAQ7goPHjxg3LhxqKur4+7uTvny5T/53PwZfw2fA61/KfLz898qALpo0SKsra3p2LGjWIjh700/S1mUX375RRDbGzduTHBwsHClz8vL4/Dhw+zcufMPW838L0HS/1JVVSUjI4MtW7ZQUFBAhQoVGDNmjHhP165dqVKlyp/K1i1atAh7e3s6derE2bNngeIJOygoiPLly3Pu3Dlxv3fs2EFGRgZpaWmsWbNGqMGvWbNGBFubNm3iyZMnpKWlMWfOHPE7f9eYka7JgAEDCA8PL7XwFBQU8M0333DlyhWl1z8HW5/xv4Y7d+7QrFkzwX3Mz88nOTkZKysrUdIvLCzk5MmTrFy58h899/1X8TnQ+pfh5MmTSv/esmULbdq0oX379kyePFkEX3PnzsXW1pZOnTopBVufCiWDh3HjxhEWFsbLly/FBJKXl0f16tUJDQ19r+/4p+HEiRPUqlWL0NBQWrRoweLFi6lTp46w2IDS2Znfg2J2b9GiReIeS8HW/fv3CQoKwsPDgw0bNnDs2DFcXV3R19fHwcEBJycnHB0dRRZt3bp1hIWFYWVlhZOTE35+fn9LJvFtAZJkF6SoKwTw+PFjatWqxeLFiz/F4X3GZ7wXFi5cqGQ/NHv2bAwMDAgODubMmTNK701OTsba2ppdu3aVGv//9Lnvv4bPgda/CCtWrCAwMFDYtmzbtg25XE5GRgZRUVF4enoSFBQkFsq5c+fi5OREZmYmFy9e/FuO+csvv+TBgwcMHToUb29v8brEpzl58iTa2tocPnz4H1ki/D3cu3eP1atXExwcjIaGBqampgwdOlTpPe973mUFZQsWLCgVbL18+ZKEhARMTEwwNjZGVVWVvXv3cvPmTU6ePCl201I26MSJE2zevJlly5b9LbtpxUVmxYoVjB8/nt69e4vj69WrFxoaGkyfPp3Dhw/z66+/UrVqVQIDAz8vSJ/xP4Ovv/4aPz8/pfF8584dKlWqhJqamigVKv69evXqyOVyUdL/jH8mPgda/yKcP3+eqlWrUqlSJRYsWECbNm2YMGECUJwd2rNnD76+voSEhIhF+auvvsLb2/sPGUT/FShOIhMnTkQul3Pp0iUuXryInp6ekmAmFGsieXp6/i1Zt0+Nfv36oa2tTVxc3B/+rOQrB4jSnwTF7OWpU6fE67t27aJp06Y0aNAA+P/g6cmTJ8TGxhIdHV1mQPV3BS85OTk4ODhQvXp1qlatioaGBqtXr+bp06eMHDkSIyMjzM3NKV++vBJv63Ow9Rn/K5Dmv927d4su4Hv37hEaGoqXl5eY5xQ3Vz169Pg8hv/h+Bxo/ctw+fJlkpOTSUlJwd/fn40bN4q/FRQU8NNPP+Hl5cXcuXPF64pGvJ8Ku3btYsqUKUpG0AsWLEBPT4/s7Gx+/fVXfvnlF1JSUoiJiflXc2sUJ9UDBw6ISfV9Mlkl9cXGjh1LamoqderUITc3V1y3uXPnYmNjQ+fOnTl9+rQIQtq0aYO3t7f4Ten/FyxYgIeHx9+in1YWli1bhrW1tZAa2b59O3K5nDVr1oj3nDx5kkOHDnHo0KG/xVbnMz7jbVA0LD948CByuZyhQ4cKQV2ppF++fPkygy34vGH4J+NzoPUvxMWLF6lRowZyuZzevXsr/e3Fixf4+fnRt29f8dqnKMkpBkqHDh1CTU0NbW1tIdIJxcreEgHb2toaV1dXoqKiRFDwXwm24P0m1eHDh+Pr68vSpUuB4iBLX1+fXr16UadOHby8vAgMDCQvL4/t27fTqlUrHBwccHd3p0mTJsD/a6hNmDBBKSj5/vvv8fLyKmWB9KlQ8l6PGTOGNm3aAMUGuvr6+kL64tGjR2VKN/ybx8tn/HNQ1vw6btw41NTUGDFihFKwFRwcjJ+fn5I0y2f886Em+4x/HZydnWXTpk2TyWQy2ebNm2Wenp6yZs2ayWQymUxHR0dmZWUlKyoqkgEymUwmk8vlH/2YVFRUZDKZTDZ58mSZmpqabOTIkbJhw4bJ9uzZI0tNTZXJZDKZrq6urGbNmrKEhATZ2bNnZWpqajI/Pz+ZioqKrKCgQKam9u8driXvgaqq6u9+pm7durJ9+/bJZs2aJXv58qXsyJEjshUrVsiSkpJkMplMduDAAVlWVpYsJiZGZmBgIMvLy5MZGRnJTp06Jfv6669lMplMFhwcLIuMjJStW7dO9uTJE1mXLl1kDx8+lE2ePFnm5OQks7Oz+/An+zsAxHhZvHixrFatWrIHDx7I7ty5I9uyZYssOztbNnr0aFmbNm1kMplMNm/ePNnVq1dl48aNU7pu0nd8xmf8XSgqKhLj8PXr1zItLS2ZTCaTdevWTSaXy2Xdu3eXyWQyWdu2bWWmpqayTZs2ySpUqCAbPny4bNGiRX/bcX/GB8bfHel9xsfD5cuXSUlJwdfXl65du7Js2TJ69uyJpqbmJ9sxKWYVvvrqK2xsbDh27Bi3b99m+PDhaGhoKFlMKHKNyvqOz1DGpUuXSE5OplKlSri5uSkZ0BYUFLBlyxY8PT1ZvHgxHh4eyOVyoTItXde7d+/SpUsXvLy80NDQwMfHh+Dg4L8lk6i4+x8zZgyWlpacPHmSXbt2ERQUhJqaGlOnThXvefbsGdWrV6djx46f7Bg/4zPeB4pjecKECaSlpZGdnc2mTZvE6+PHj0culzNy5EiR2Xr8+PHnMuG/DJ8DrX85Ll++TFpaGqqqqgQGBtKtW7dSEhCfAocOHaJTp05K3LB79+4xYsQIDA0NhX4U/DMFSP+vvXuNieJq4wD+X65Vdg0ligFpDTS4GmikGKWlilRdbTAIXmLtEpFLReKFqM3WIOFDRWMQQdkqJla6apTKB68xattgKF5iK2AxFlTEXUWkiMHQroAQ9vQDr/OK+OZF3HEX9v/7tDtnZudMGGaec+Y5Z2yprq5OxMTESHkfL3r8+LHw8/MT27ZtE1FRUSIiIkJoNJo+0x60t7eLx48fi6NHj4qysjKbz9Vz9epVER8fL86ePSuE6MkjXL16tQgKChKbN28WTU1N4sqVKyIqKkp89NFHUj157pA9eLnBMGLECLF+/XqhVqvF1KlTxfbt26XyvLw84ezsLNLT03tN/cBga+hgoOUA6uvrRUREhNBqtdJIl7fFYrGIy5cvC3d3dzFs2LA+r5R59OiR2Lp1q/Dy8hKZmZlvtW5Dyf3798W8efNEWFiYMBgM0vKnT5+K4OBgUVBQIIToeaFyVFSU+Oyzz/oEW/YyV8+RI0dEaGioGDduXK9GQVNTk1i5cqUICgoS7u7uIjQ0VMyaNYujC8luVVRUiJSUFFFSUiKE6MknTE5OFuHh4SInJ0dab9OmTSI8PJwNhSFKIcR/EnVoSDOZTHB2dsZ7770n+76EEH1yjgoKCpCeno7Zs2cjOzsbAQEBUllzczN27NiB8vJy/PTTT28lZ2woMplMWL16NWpra/Hpp58iKCgIFy9eRHV1Nf78808px81oNGLNmjXo7OyEVqvF0qVLodFoEBYWhq1bt9r4KIA7d+5g7dq1KCkpwbfffotvvvlGKmtvb0dbWxuqqqowduxY+Pv7O0QOHw0+xcXF2LZtGzo6OnD8+HGMGzcOQM/1Lj09HTdv3sT8+fOlPK3n181XXT9pkLNpmEdD2sGDB0VeXp70PT8/X/j4+IiMjIw+o9mePHkitebYqhs4k8kkYmNjhaurq4iMjBQ5OTnSY7UXe3zu3r0rFixYICZMmCACAgJEcHDwK/PjbKW+vl5ER0eLTz75RBw+fFha/qp8MebwkT2qra0Vc+fOFSqVSuTn5/cqa25uFikpKSIwMFA6vy0WC699QxR7tEgWra2tiIuLQ0tLCxISEpCSkgIAyM3NxY4dO5CYmIgVK1b0GdUm2Jp7Yw8ePMDixYsxY8YMZGVlQaFQoLu7u89IxsbGRlRUVKCpqQnLli2Di4uLXfUMPe95a2trw/Lly/Hll18C4DlCg0d9fT1WrVqF1tZWpKamSucwADQ1NcFgMECn0/VrlDENXgy0yCpedfMzGo3IzMzE/fv3odVqpeH4eXl5yM/PR2xsLDIyMuDt7W2LKg9pDQ0N8PHxgZOTU78Dk1cFY7ZmNBqRlpaGjo4OLFmyBMnJybauEtFr+V8NhhfZ4/8eWQ8nmiGreH4jr62tlZb5+/tj8+bN8PPzQ1FREb7//nsAPXPIJCUl4d69exg1apRN6jvUjRkzBk5OTrBYLP3u/bHHC72/vz/0ej3MZjOqqqpsXR2i1+bv74/vvvsOHh4e+OGHH1BYWNhnHXv83yPrYY8WWU1RURF27dqFDRs2ICYmRlp+9+5drFixAg0NDdDpdEhMTATA5E/qv8bGRowePZqTkNKgZTQaodVqMXnyZOj1eltXh94iBlo0YC/OegwAv//+O3Q6HVQqFVJSUjBv3jyprLS0FLGxsfD19UVWVhYWLlz4Vmemp6Hh5XOOaDBhg8Ex8a9NA/LiDe/YsWMwmUyYMmUK9Ho92tvbsWfPHpw6dUpav7u7GxqNBlqtFvPnzwfQE2AxyKLXwRsUDWbP8yYtFoutq0JvEa9a9NrEC++i27hxI9LS0nDixAm0tbVh4sSJyM3NRVdXF3bv3g29Xg+j0YidO3di/PjxyMjI4IWGiBwaGwyOhY8OacCysrKg1+tx5swZTJgwAUqlUsq3qqmpQXZ2Ns6ePYt33nkHo0ePxqVLl+Dq6sqcLCIichgMtGhAWlpa8MUXXyAhIQFxcXFoaGhAXV0dCgsLERERgYULF8LZ2RkPHz5EY2Mjpk2bBmdnZ7uap4mIiEhuvOPRgCgUClRXV6OmpgZlZWUoKCiA0WiEQqHA6dOn8ffff2PdunVQq9VQq9UAevK0GGQREZEjYY8WDVhhYSF0Oh26u7uRmpoKjUaDWbNmIT4+HgBw8OBBG9eQiIjItti9QAOWnJwMjUaDZ8+eITAwEEDPaMSHDx/i448/tnHtiIiIbI89WmQVZrMZf/zxB7Kzs3Hv3j1UVlbyMSERETk83gnpjQkhUF5eLk3rUFFRARcXF76/i4iIHB57tMgqnj17hurqakycOBFOTk4cXUhERAQGWiQDviaFiIioBwMtIiIiIpmw24GIiIhIJgy0iIiIiGTCQIuIiIhIJgy0iIiIiGTCQIuIiIhIJgy0iIiIiGTCQIuIiIhIJgy0iIiIiGTCQIuIHFZnZ6etq0BEQxwDLSKyC0+fPkV8fDyUSiV8fHyQm5uLyMhIrF27FgCgUChw4sSJXtt4enpi//790vf6+nosXrwYnp6e8PLyQkxMDEwmk1SekJCA2NhYbNmyBb6+vlCr1di0aROCg4P71CckJASZmZkyHCkRORIGWkRkF3Q6HX799VecPHkSP//8M0pLS1FZWdnv7bu6ujBnzhyoVCpcuHABly5dglKpxOeff96r56qkpAS3bt3CL7/8gtOnTyMpKQk1NTW4evWqtM61a9dw/fp1JCYmWvUYicjxuNi6AkREZrMZhYWFOHToEGbOnAkAOHDgAPz8/Pr9G8XFxbBYLNi3bx8UCgUAwGAwwNPTE6WlpZg9ezYAwMPDA/v27YObm5u07Zw5c2AwGDB58mRpu+nTpyMgIMBah0hEDoo9WkRkc3V1dejs7ERYWJi0zMvLC2q1ut+/UVVVhTt37kClUkGpVEKpVMLLywsdHR2oq6uT1vvwww97BVkAsHz5cvz444/o6OhAZ2cnioqKkJSU9OYHRkQOjz1aRDQoKBQKCCF6Levq6pI+m81mTJo0CYcPH+6z7ahRo6TPHh4efcqjo6Ph7u6O48ePw83NDV1dXVi0aJEVa09EjoqBFhHZ3AcffABXV1f89ttveP/99wEAT548we3btzF9+nQAPcFSY2OjtE1tbS3a2tqk76GhoSguLoa3tzdGjBjxWvt3cXHBsmXLYDAY4ObmhiVLlmDYsGFWODIicnR8dEhENqdUKpGcnAydTofz58/jxo0bSEhIgJPTfy9RM2bMwK5du3Dt2jWUl5cjNTUVrq6uUnlcXBxGjhyJmJgYXLhwAUajEaWlpUhLS8ODBw/+bx2++uornD9/HufOneNjQyKyGvZoEZFdyMnJgdlsRnR0NFQqFb7++mu0trZK5bm5uUhMTMS0adPg6+uL/Px8VFRUSOXDhw9HWVkZNmzYgAULFuCff/7BmDFjMHPmzH71cAUGBiI8PBwtLS29csWIiN6EQryc9EBEZCciIyMREhKCnTt3yr4vIQQCAwOxcuVKrF+/Xvb9EZFjYI8WETm85uZmHDlyBH/99RfnziIiq2KgRUQOz9vbGyNHjsTevXvx7rvv2ro6RDSE8NEhERERkUw46pCIiIhIJgy0iIiIiGTCQIuIiIhIJgy0iIiIiGTCQIuIiIhIJgy0iIiIiGTCQIuIiIhIJgy0iIiIiGTyL73IEKyhbvFcAAAAAElFTkSuQmCC\n" }, "metadata": {} - }, - { - "ename": "", - "evalue": "zero-size array to reduction operation minimum which has no identity", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[0;32mIn[13], line 7\u001b[0m\n\u001b[1;32m 3\u001b[0m dataset \u001b[38;5;241m=\u001b[39m [df_overall[df_overall[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mquery\u001b[39m\u001b[38;5;124m'\u001b[39m] \u001b[38;5;241m==\u001b[39m q][\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mrating\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;28;01mfor\u001b[39;00m q \u001b[38;5;129;01min\u001b[39;00m queries]\n\u001b[1;32m 5\u001b[0m nb_queries \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mlen\u001b[39m(queries)\n\u001b[0;32m----> 7\u001b[0m \u001b[43maxes\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mviolinplot\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdataset\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m \u001b[49m\u001b[43mdataset\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mshowmeans\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mbw_method\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m0.05\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m 8\u001b[0m axes\u001b[38;5;241m.\u001b[39mset_xlabel(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mquery\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[1;32m 9\u001b[0m axes\u001b[38;5;241m.\u001b[39mset_ylabel(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mratings\u001b[39m\u001b[38;5;124m'\u001b[39m)\n", - "File \u001b[0;32m/lib/python3.11/site-packages/matplotlib/__init__.py:1412\u001b[0m, in \u001b[0;36m_preprocess_data..inner\u001b[0;34m(ax, data, *args, **kwargs)\u001b[0m\n\u001b[1;32m 1409\u001b[0m \u001b[38;5;129m@functools\u001b[39m\u001b[38;5;241m.\u001b[39mwraps(func)\n\u001b[1;32m 1410\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21minner\u001b[39m(ax, \u001b[38;5;241m*\u001b[39margs, data\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 1411\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m data \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m-> 1412\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[43max\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;28;43mmap\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43msanitize_sequence\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43margs\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[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 1414\u001b[0m bound \u001b[38;5;241m=\u001b[39m new_sig\u001b[38;5;241m.\u001b[39mbind(ax, \u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[1;32m 1415\u001b[0m auto_label \u001b[38;5;241m=\u001b[39m (bound\u001b[38;5;241m.\u001b[39marguments\u001b[38;5;241m.\u001b[39mget(label_namer)\n\u001b[1;32m 1416\u001b[0m \u001b[38;5;129;01mor\u001b[39;00m bound\u001b[38;5;241m.\u001b[39mkwargs\u001b[38;5;241m.\u001b[39mget(label_namer))\n", - "File \u001b[0;32m/lib/python3.11/site-packages/matplotlib/axes/_axes.py:7938\u001b[0m, in \u001b[0;36mAxes.violinplot\u001b[0;34m(self, dataset, positions, vert, widths, showmeans, showextrema, showmedians, quantiles, points, bw_method)\u001b[0m\n\u001b[1;32m 7935\u001b[0m kde \u001b[38;5;241m=\u001b[39m mlab\u001b[38;5;241m.\u001b[39mGaussianKDE(X, bw_method)\n\u001b[1;32m 7936\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m kde\u001b[38;5;241m.\u001b[39mevaluate(coords)\n\u001b[0;32m-> 7938\u001b[0m vpstats \u001b[38;5;241m=\u001b[39m \u001b[43mcbook\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mviolin_stats\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdataset\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m_kde_method\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mpoints\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mpoints\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 7939\u001b[0m \u001b[43m \u001b[49m\u001b[43mquantiles\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mquantiles\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 7940\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mviolin(vpstats, positions\u001b[38;5;241m=\u001b[39mpositions, vert\u001b[38;5;241m=\u001b[39mvert,\n\u001b[1;32m 7941\u001b[0m widths\u001b[38;5;241m=\u001b[39mwidths, showmeans\u001b[38;5;241m=\u001b[39mshowmeans,\n\u001b[1;32m 7942\u001b[0m showextrema\u001b[38;5;241m=\u001b[39mshowextrema, showmedians\u001b[38;5;241m=\u001b[39mshowmedians)\n", - "File \u001b[0;32m/lib/python3.11/site-packages/matplotlib/cbook/__init__.py:1447\u001b[0m, in \u001b[0;36mviolin_stats\u001b[0;34m(X, method, points, quantiles)\u001b[0m\n\u001b[1;32m 1444\u001b[0m stats \u001b[38;5;241m=\u001b[39m {}\n\u001b[1;32m 1446\u001b[0m \u001b[38;5;66;03m# Calculate basic stats for the distribution\u001b[39;00m\n\u001b[0;32m-> 1447\u001b[0m min_val \u001b[38;5;241m=\u001b[39m \u001b[43mnp\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmin\u001b[49m\u001b[43m(\u001b[49m\u001b[43mx\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1448\u001b[0m max_val \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39mmax(x)\n\u001b[1;32m 1449\u001b[0m quantile_val \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39mpercentile(x, \u001b[38;5;241m100\u001b[39m \u001b[38;5;241m*\u001b[39m q)\n", - "File \u001b[0;32m<__array_function__ internals>:200\u001b[0m, in \u001b[0;36mamin\u001b[0;34m(*args, **kwargs)\u001b[0m\n", - "File \u001b[0;32m/lib/python3.11/site-packages/numpy/core/fromnumeric.py:2946\u001b[0m, in \u001b[0;36mamin\u001b[0;34m(a, axis, out, keepdims, initial, where)\u001b[0m\n\u001b[1;32m 2829\u001b[0m \u001b[38;5;129m@array_function_dispatch\u001b[39m(_amin_dispatcher)\n\u001b[1;32m 2830\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mamin\u001b[39m(a, axis\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m, out\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m, keepdims\u001b[38;5;241m=\u001b[39mnp\u001b[38;5;241m.\u001b[39m_NoValue, initial\u001b[38;5;241m=\u001b[39mnp\u001b[38;5;241m.\u001b[39m_NoValue,\n\u001b[1;32m 2831\u001b[0m where\u001b[38;5;241m=\u001b[39mnp\u001b[38;5;241m.\u001b[39m_NoValue):\n\u001b[1;32m 2832\u001b[0m \u001b[38;5;250m \u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 2833\u001b[0m \u001b[38;5;124;03m Return the minimum of an array or minimum along an axis.\u001b[39;00m\n\u001b[1;32m 2834\u001b[0m \n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 2944\u001b[0m \u001b[38;5;124;03m 6\u001b[39;00m\n\u001b[1;32m 2945\u001b[0m \u001b[38;5;124;03m \"\"\"\u001b[39;00m\n\u001b[0;32m-> 2946\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43m_wrapreduction\u001b[49m\u001b[43m(\u001b[49m\u001b[43ma\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mnp\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mminimum\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mmin\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43maxis\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mout\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 2947\u001b[0m \u001b[43m \u001b[49m\u001b[43mkeepdims\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mkeepdims\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43minitial\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43minitial\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mwhere\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mwhere\u001b[49m\u001b[43m)\u001b[49m\n", - "File \u001b[0;32m/lib/python3.11/site-packages/numpy/core/fromnumeric.py:86\u001b[0m, in \u001b[0;36m_wrapreduction\u001b[0;34m(obj, ufunc, method, axis, dtype, out, **kwargs)\u001b[0m\n\u001b[1;32m 83\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 84\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m reduction(axis\u001b[38;5;241m=\u001b[39maxis, out\u001b[38;5;241m=\u001b[39mout, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mpasskwargs)\n\u001b[0;32m---> 86\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mufunc\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mreduce\u001b[49m\u001b[43m(\u001b[49m\u001b[43mobj\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43maxis\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdtype\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mout\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[43mpasskwargs\u001b[49m\u001b[43m)\u001b[49m\n", - "\u001b[0;31mValueError\u001b[0m: zero-size array to reduction operation minimum which has no identity" - ], - "output_type": "error" } ], "id": "70b93e5d-425d-4925-97fd-1b062f7c373f" @@ -293,32 +296,15 @@ "metadata": { "trusted": true }, - "execution_count": 14, + "execution_count": 74, "outputs": [ { "output_type": "display_data", "data": { "text/plain": "
", - "image/png": 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\n" 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\n" }, "metadata": {} - }, - { - "ename": "", - "evalue": "zero-size array to reduction operation minimum which has no identity", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[0;32mIn[14], line 5\u001b[0m\n\u001b[1;32m 2\u001b[0m raters \u001b[38;5;241m=\u001b[39m df_overall[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mrater\u001b[39m\u001b[38;5;124m'\u001b[39m]\u001b[38;5;241m.\u001b[39munique()\n\u001b[1;32m 3\u001b[0m dataset \u001b[38;5;241m=\u001b[39m [df_overall[df_overall[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mrater\u001b[39m\u001b[38;5;124m'\u001b[39m] \u001b[38;5;241m==\u001b[39m r][\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mrating\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;28;01mfor\u001b[39;00m r \u001b[38;5;129;01min\u001b[39;00m raters]\n\u001b[0;32m----> 5\u001b[0m \u001b[43maxes\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mviolinplot\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdataset\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m \u001b[49m\u001b[43mdataset\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mshowmeans\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mbw_method\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m0.05\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m 6\u001b[0m axes\u001b[38;5;241m.\u001b[39mset_xlabel(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mrater\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[1;32m 7\u001b[0m axes\u001b[38;5;241m.\u001b[39mset_ylabel(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mratings\u001b[39m\u001b[38;5;124m'\u001b[39m)\n", - "File \u001b[0;32m/lib/python3.11/site-packages/matplotlib/__init__.py:1412\u001b[0m, in \u001b[0;36m_preprocess_data..inner\u001b[0;34m(ax, data, *args, **kwargs)\u001b[0m\n\u001b[1;32m 1409\u001b[0m \u001b[38;5;129m@functools\u001b[39m\u001b[38;5;241m.\u001b[39mwraps(func)\n\u001b[1;32m 1410\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21minner\u001b[39m(ax, \u001b[38;5;241m*\u001b[39margs, data\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 1411\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m data \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m-> 1412\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[43max\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;28;43mmap\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43msanitize_sequence\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43margs\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[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 1414\u001b[0m bound \u001b[38;5;241m=\u001b[39m new_sig\u001b[38;5;241m.\u001b[39mbind(ax, \u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[1;32m 1415\u001b[0m auto_label \u001b[38;5;241m=\u001b[39m (bound\u001b[38;5;241m.\u001b[39marguments\u001b[38;5;241m.\u001b[39mget(label_namer)\n\u001b[1;32m 1416\u001b[0m \u001b[38;5;129;01mor\u001b[39;00m bound\u001b[38;5;241m.\u001b[39mkwargs\u001b[38;5;241m.\u001b[39mget(label_namer))\n", - "File \u001b[0;32m/lib/python3.11/site-packages/matplotlib/axes/_axes.py:7938\u001b[0m, in \u001b[0;36mAxes.violinplot\u001b[0;34m(self, dataset, positions, vert, widths, showmeans, showextrema, showmedians, quantiles, points, bw_method)\u001b[0m\n\u001b[1;32m 7935\u001b[0m kde \u001b[38;5;241m=\u001b[39m mlab\u001b[38;5;241m.\u001b[39mGaussianKDE(X, bw_method)\n\u001b[1;32m 7936\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m kde\u001b[38;5;241m.\u001b[39mevaluate(coords)\n\u001b[0;32m-> 7938\u001b[0m vpstats \u001b[38;5;241m=\u001b[39m \u001b[43mcbook\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mviolin_stats\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdataset\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m_kde_method\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mpoints\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mpoints\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 7939\u001b[0m \u001b[43m \u001b[49m\u001b[43mquantiles\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mquantiles\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 7940\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mviolin(vpstats, positions\u001b[38;5;241m=\u001b[39mpositions, vert\u001b[38;5;241m=\u001b[39mvert,\n\u001b[1;32m 7941\u001b[0m widths\u001b[38;5;241m=\u001b[39mwidths, showmeans\u001b[38;5;241m=\u001b[39mshowmeans,\n\u001b[1;32m 7942\u001b[0m showextrema\u001b[38;5;241m=\u001b[39mshowextrema, showmedians\u001b[38;5;241m=\u001b[39mshowmedians)\n", - "File \u001b[0;32m/lib/python3.11/site-packages/matplotlib/cbook/__init__.py:1447\u001b[0m, in \u001b[0;36mviolin_stats\u001b[0;34m(X, method, points, quantiles)\u001b[0m\n\u001b[1;32m 1444\u001b[0m stats \u001b[38;5;241m=\u001b[39m {}\n\u001b[1;32m 1446\u001b[0m \u001b[38;5;66;03m# Calculate basic stats for the distribution\u001b[39;00m\n\u001b[0;32m-> 1447\u001b[0m min_val \u001b[38;5;241m=\u001b[39m \u001b[43mnp\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmin\u001b[49m\u001b[43m(\u001b[49m\u001b[43mx\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1448\u001b[0m max_val \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39mmax(x)\n\u001b[1;32m 1449\u001b[0m quantile_val \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39mpercentile(x, \u001b[38;5;241m100\u001b[39m \u001b[38;5;241m*\u001b[39m q)\n", - "File \u001b[0;32m<__array_function__ internals>:200\u001b[0m, in \u001b[0;36mamin\u001b[0;34m(*args, **kwargs)\u001b[0m\n", - "File \u001b[0;32m/lib/python3.11/site-packages/numpy/core/fromnumeric.py:2946\u001b[0m, in \u001b[0;36mamin\u001b[0;34m(a, axis, out, keepdims, initial, where)\u001b[0m\n\u001b[1;32m 2829\u001b[0m \u001b[38;5;129m@array_function_dispatch\u001b[39m(_amin_dispatcher)\n\u001b[1;32m 2830\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mamin\u001b[39m(a, axis\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m, out\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m, keepdims\u001b[38;5;241m=\u001b[39mnp\u001b[38;5;241m.\u001b[39m_NoValue, initial\u001b[38;5;241m=\u001b[39mnp\u001b[38;5;241m.\u001b[39m_NoValue,\n\u001b[1;32m 2831\u001b[0m where\u001b[38;5;241m=\u001b[39mnp\u001b[38;5;241m.\u001b[39m_NoValue):\n\u001b[1;32m 2832\u001b[0m \u001b[38;5;250m \u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 2833\u001b[0m \u001b[38;5;124;03m Return the minimum of an array or minimum along an axis.\u001b[39;00m\n\u001b[1;32m 2834\u001b[0m \n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 2944\u001b[0m \u001b[38;5;124;03m 6\u001b[39;00m\n\u001b[1;32m 2945\u001b[0m \u001b[38;5;124;03m \"\"\"\u001b[39;00m\n\u001b[0;32m-> 2946\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43m_wrapreduction\u001b[49m\u001b[43m(\u001b[49m\u001b[43ma\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mnp\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mminimum\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mmin\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43maxis\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mout\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 2947\u001b[0m \u001b[43m \u001b[49m\u001b[43mkeepdims\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mkeepdims\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43minitial\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43minitial\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mwhere\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mwhere\u001b[49m\u001b[43m)\u001b[49m\n", - "File \u001b[0;32m/lib/python3.11/site-packages/numpy/core/fromnumeric.py:86\u001b[0m, in \u001b[0;36m_wrapreduction\u001b[0;34m(obj, ufunc, method, axis, dtype, out, **kwargs)\u001b[0m\n\u001b[1;32m 83\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 84\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m reduction(axis\u001b[38;5;241m=\u001b[39maxis, out\u001b[38;5;241m=\u001b[39mout, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mpasskwargs)\n\u001b[0;32m---> 86\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mufunc\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mreduce\u001b[49m\u001b[43m(\u001b[49m\u001b[43mobj\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43maxis\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdtype\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mout\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[43mpasskwargs\u001b[49m\u001b[43m)\u001b[49m\n", - "\u001b[0;31mValueError\u001b[0m: zero-size array to reduction operation minimum which has no identity" - ], - "output_type": "error" } ], "id": "f954846d-54a9-4cf1-a9da-ab0faaa46df9" @@ -341,13 +327,13 @@ "metadata": { "trusted": true }, - "execution_count": 15, + "execution_count": 75, "outputs": [ { "output_type": "display_data", "data": { "text/plain": "
", - "image/png": 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\n" + "image/png": 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\n" }, "metadata": {} } @@ -366,13 +352,13 @@ "metadata": { "trusted": true }, - "execution_count": 16, + "execution_count": 76, "outputs": [ { "output_type": "display_data", "data": { "text/plain": "
", - "image/png": 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\n" + "image/png": 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\n" }, "metadata": {} } @@ -385,10 +371,10 @@ "metadata": { "trusted": true }, - "execution_count": 17, + "execution_count": 77, "outputs": [ { - "execution_count": 17, + "execution_count": 77, "output_type": "execute_result", "data": { "text/plain": "Text(0.5, 1.0, 'Agents agreements')" @@ -399,7 +385,7 @@ "output_type": "display_data", "data": { "text/plain": "
", - "image/png": 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\n" + "image/png": 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\n" }, "metadata": {} } @@ -418,7 +404,7 @@ "metadata": { "trusted": true }, - "execution_count": 18, + "execution_count": 78, "outputs": [ { "name": "stdout", @@ -426,11 +412,11 @@ "output_type": "stream" }, { - "execution_count": 18, + "execution_count": 78, "output_type": "execute_result", "data": { - "text/plain": "Empty DataFrame\nColumns: [query, docid, rating_0, rating_1, rating_2, rating_3, rating_4, rating_5, rating_6, rating_7, rating_8, rating_9, rating_10, rating_11, rating_12, rater_0, rater_1, rater_2, rater_3, rater_4, rater_5, rater_6, rater_7, rater_8, rater_9, rater_10, rater_11, rater_12, nb_distinct_ratings]\nIndex: []\n\n[0 rows x 29 columns]", - "text/html": "
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querydocidrating_0rating_1rating_2rating_3rating_4rating_5rating_6rating_7...rater_4rater_5rater_6rater_7rater_8rater_9rater_10rater_11rater_12nb_distinct_ratings
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0 rows × 29 columns

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" + "text/plain": " query docid charlie@flax.co.uk rating_0 \\\n0 projector screen 325961 NaN 3.0 \n1 projector screen 47471 NaN 3.0 \n2 projector screen 126679 NaN 3.0 \n3 projector screen 254441 NaN 3.0 \n4 projector screen 325958 NaN 3.0 \n... ... ... ... ... \n2415 power supply 1667352 NaN 0.0 \n2416 power supply 1667804 NaN 0.0 \n2417 power supply 1667752 NaN 0.0 \n2418 power supply 1667821 NaN 0.0 \n2419 power supply 1667357 NaN 0.0 \n\n eschramma@cas.org dtaivpp@gmail.com rating_1 cmcollier@gmail.com \\\n0 NaN 3.0 NaN NaN \n1 NaN 3.0 NaN NaN \n2 NaN 3.0 NaN NaN \n3 NaN NaN NaN NaN \n4 NaN NaN NaN NaN \n... ... ... ... ... \n2415 NaN NaN NaN NaN \n2416 NaN NaN NaN NaN \n2417 NaN NaN NaN NaN \n2418 NaN NaN NaN NaN \n2419 NaN NaN NaN NaN \n\n rating_2 jeff@vin.com cmarino@enterprise-knowledge.com \\\n0 NaN NaN NaN \n1 NaN NaN NaN \n2 NaN NaN NaN \n3 NaN NaN NaN \n4 NaN NaN NaN \n... ... ... ... \n2415 NaN NaN NaN \n2416 NaN NaN NaN \n2417 NaN NaN NaN \n2418 NaN NaN NaN \n2419 NaN NaN NaN \n\n msfroh@gmail.com peter@searchintuition.com maximilian.werk@jina.ai \\\n0 NaN NaN NaN \n1 NaN NaN NaN \n2 NaN NaN NaN \n3 NaN NaN NaN \n4 NaN NaN NaN \n... ... ... ... \n2415 NaN NaN NaN \n2416 NaN NaN NaN \n2417 NaN NaN NaN \n2418 NaN NaN NaN \n2419 NaN NaN NaN \n\n ryan.finley@ferguson.com rater_0 \\\n0 NaN epugh@opensourceconnections.com \n1 NaN epugh@opensourceconnections.com \n2 NaN epugh@opensourceconnections.com \n3 NaN epugh@opensourceconnections.com \n4 NaN epugh@opensourceconnections.com \n... ... ... \n2415 NaN epugh@opensourceconnections.com \n2416 NaN epugh@opensourceconnections.com \n2417 NaN epugh@opensourceconnections.com \n2418 NaN epugh@opensourceconnections.com \n2419 NaN epugh@opensourceconnections.com \n\n rater_1 rater_2 \\\n0 aarora@opensourceconnections.com ben.w.trent@gmail.com \n1 aarora@opensourceconnections.com ben.w.trent@gmail.com \n2 aarora@opensourceconnections.com ben.w.trent@gmail.com \n3 aarora@opensourceconnections.com ben.w.trent@gmail.com \n4 aarora@opensourceconnections.com ben.w.trent@gmail.com \n... ... ... \n2415 aarora@opensourceconnections.com ben.w.trent@gmail.com \n2416 aarora@opensourceconnections.com ben.w.trent@gmail.com \n2417 aarora@opensourceconnections.com ben.w.trent@gmail.com \n2418 aarora@opensourceconnections.com ben.w.trent@gmail.com \n2419 aarora@opensourceconnections.com ben.w.trent@gmail.com \n\n nb_distinct_ratings \n0 3 \n1 3 \n2 3 \n3 3 \n4 3 \n... ... \n2415 3 \n2416 3 \n2417 3 \n2418 3 \n2419 3 \n\n[2148 rows x 19 columns]", + "text/html": "
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querydocidcharlie@flax.co.ukrating_0eschramma@cas.orgdtaivpp@gmail.comrating_1cmcollier@gmail.comrating_2jeff@vin.comcmarino@enterprise-knowledge.commsfroh@gmail.competer@searchintuition.commaximilian.werk@jina.airyan.finley@ferguson.comrater_0rater_1rater_2nb_distinct_ratings
0projector screen325961NaN3.0NaN3.0NaNNaNNaNNaNNaNNaNNaNNaNNaNepugh@opensourceconnections.comaarora@opensourceconnections.comben.w.trent@gmail.com3
1projector screen47471NaN3.0NaN3.0NaNNaNNaNNaNNaNNaNNaNNaNNaNepugh@opensourceconnections.comaarora@opensourceconnections.comben.w.trent@gmail.com3
2projector screen126679NaN3.0NaN3.0NaNNaNNaNNaNNaNNaNNaNNaNNaNepugh@opensourceconnections.comaarora@opensourceconnections.comben.w.trent@gmail.com3
3projector screen254441NaN3.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNepugh@opensourceconnections.comaarora@opensourceconnections.comben.w.trent@gmail.com3
4projector screen325958NaN3.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNepugh@opensourceconnections.comaarora@opensourceconnections.comben.w.trent@gmail.com3
............................................................
2415power supply1667352NaN0.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNepugh@opensourceconnections.comaarora@opensourceconnections.comben.w.trent@gmail.com3
2416power supply1667804NaN0.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNepugh@opensourceconnections.comaarora@opensourceconnections.comben.w.trent@gmail.com3
2417power supply1667752NaN0.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNepugh@opensourceconnections.comaarora@opensourceconnections.comben.w.trent@gmail.com3
2418power supply1667821NaN0.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNepugh@opensourceconnections.comaarora@opensourceconnections.comben.w.trent@gmail.com3
2419power supply1667357NaN0.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNepugh@opensourceconnections.comaarora@opensourceconnections.comben.w.trent@gmail.com3
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2148 rows × 19 columns

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" }, "metadata": {} } @@ -445,11 +431,11 @@ }, { "cell_type": "code", - "source": "print('All agree:')\ndf[df['nb_distinct_ratings']==1].sample(5)", + "source": "# We have none that everyone agrees on\nprint('All agree:')\ndf[df['nb_distinct_ratings']==1].sample(5)", "metadata": { "trusted": true }, - "execution_count": 19, + "execution_count": 79, "outputs": [ { "name": "stdout", @@ -462,7 +448,7 @@ "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[0;32mIn[19], line 2\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mAll agree:\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[0;32m----> 2\u001b[0m \u001b[43mdf\u001b[49m\u001b[43m[\u001b[49m\u001b[43mdf\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mnb_distinct_ratings\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m]\u001b[49m\u001b[38;5;241;43m==\u001b[39;49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m]\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msample\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m5\u001b[39;49m\u001b[43m)\u001b[49m\n", + "Cell \u001b[0;32mIn[79], line 3\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[38;5;66;03m# We have none that everyone agrees on\u001b[39;00m\n\u001b[1;32m 2\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mAll agree:\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[0;32m----> 3\u001b[0m \u001b[43mdf\u001b[49m\u001b[43m[\u001b[49m\u001b[43mdf\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mnb_distinct_ratings\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m]\u001b[49m\u001b[38;5;241;43m==\u001b[39;49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m]\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msample\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m5\u001b[39;49m\u001b[43m)\u001b[49m\n", "File \u001b[0;32m/lib/python3.11/site-packages/pandas/core/generic.py:5773\u001b[0m, in \u001b[0;36mNDFrame.sample\u001b[0;34m(self, n, frac, replace, weights, random_state, axis, ignore_index)\u001b[0m\n\u001b[1;32m 5770\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m weights \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[1;32m 5771\u001b[0m weights \u001b[38;5;241m=\u001b[39m sample\u001b[38;5;241m.\u001b[39mpreprocess_weights(\u001b[38;5;28mself\u001b[39m, weights, axis)\n\u001b[0;32m-> 5773\u001b[0m sampled_indices \u001b[38;5;241m=\u001b[39m \u001b[43msample\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msample\u001b[49m\u001b[43m(\u001b[49m\u001b[43mobj_len\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43msize\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mreplace\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mweights\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mrs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 5774\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mtake(sampled_indices, axis\u001b[38;5;241m=\u001b[39maxis)\n\u001b[1;32m 5776\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m ignore_index:\n", "File \u001b[0;32m/lib/python3.11/site-packages/pandas/core/sample.py:150\u001b[0m, in \u001b[0;36msample\u001b[0;34m(obj_len, size, replace, weights, random_state)\u001b[0m\n\u001b[1;32m 147\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 148\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mInvalid weights: weights sum to zero\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m--> 150\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mrandom_state\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mchoice\u001b[49m\u001b[43m(\u001b[49m\u001b[43mobj_len\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43msize\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43msize\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mreplace\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mreplace\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mp\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mweights\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241m.\u001b[39mastype(\n\u001b[1;32m 151\u001b[0m np\u001b[38;5;241m.\u001b[39mintp, copy\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m\n\u001b[1;32m 152\u001b[0m )\n", "File \u001b[0;32mmtrand.pyx:928\u001b[0m, in \u001b[0;36mnumpy.random.mtrand.RandomState.choice\u001b[0;34m()\u001b[0m\n", @@ -481,11 +467,11 @@ }, { "cell_type": "code", - "source": "print('Majority agree:')\ndf[df['nb_distinct_ratings']==2]", + "source": "# We have none\nprint('Majority agree:')\ndf[df['nb_distinct_ratings']==2]", "metadata": { "trusted": true }, - "execution_count": 20, + "execution_count": 80, "outputs": [ { "name": "stdout", @@ -493,11 +479,11 @@ "output_type": "stream" }, { - "execution_count": 20, + "execution_count": 80, "output_type": "execute_result", "data": { - "text/plain": "Empty DataFrame\nColumns: [query, docid, rating_0, rating_1, rating_2, rating_3, rating_4, rating_5, rating_6, rating_7, rating_8, rating_9, rating_10, rating_11, rating_12, rater_0, rater_1, rater_2, rater_3, rater_4, rater_5, rater_6, rater_7, rater_8, rater_9, rater_10, rater_11, rater_12, nb_distinct_ratings]\nIndex: []\n\n[0 rows x 29 columns]", - "text/html": "
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querydocidrating_0rating_1rating_2rating_3rating_4rating_5rating_6rating_7...rater_4rater_5rater_6rater_7rater_8rater_9rater_10rater_11rater_12nb_distinct_ratings
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0 rows × 29 columns

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" + "text/plain": " query docid charlie@flax.co.uk rating_0 \\\n6 projector screen 549808 NaN 3.0 \n19 laptop 77031393 NaN 3.0 \n20 iphone 8 79283963 NaN 0.0 \n21 iphone 8 79284190 NaN 0.0 \n24 iphone 8 77911774 NaN 0.0 \n... ... ... ... ... \n1330 coffee 656359 NaN 3.0 \n1331 coffee 77265396 NaN 2.0 \n1334 coffee 2102472 NaN 2.0 \n1340 vans 77129498 NaN 0.0 \n1342 vans 77388459 NaN 0.0 \n\n eschramma@cas.org dtaivpp@gmail.com rating_1 cmcollier@gmail.com \\\n6 NaN NaN 3.0 NaN \n19 NaN NaN NaN NaN \n20 NaN NaN NaN NaN \n21 NaN NaN NaN NaN \n24 NaN NaN 0.0 NaN \n... ... ... ... ... \n1330 NaN NaN 3.0 NaN \n1331 NaN NaN 2.0 NaN \n1334 NaN NaN NaN NaN \n1340 NaN NaN 0.0 NaN \n1342 NaN NaN NaN NaN \n\n rating_2 jeff@vin.com cmarino@enterprise-knowledge.com \\\n6 NaN NaN NaN \n19 3.0 NaN NaN \n20 0.0 NaN NaN \n21 0.0 NaN NaN \n24 NaN NaN NaN \n... ... ... ... \n1330 NaN NaN NaN \n1331 NaN NaN NaN \n1334 2.0 NaN NaN \n1340 NaN NaN NaN \n1342 0.0 NaN NaN \n\n msfroh@gmail.com peter@searchintuition.com maximilian.werk@jina.ai \\\n6 NaN NaN NaN \n19 NaN NaN NaN \n20 NaN NaN NaN \n21 NaN NaN NaN \n24 NaN NaN NaN \n... ... ... ... \n1330 NaN NaN NaN \n1331 NaN NaN NaN \n1334 NaN NaN NaN \n1340 NaN NaN NaN \n1342 NaN NaN NaN \n\n ryan.finley@ferguson.com rater_0 \\\n6 NaN epugh@opensourceconnections.com \n19 NaN epugh@opensourceconnections.com \n20 NaN epugh@opensourceconnections.com \n21 NaN epugh@opensourceconnections.com \n24 NaN epugh@opensourceconnections.com \n... ... ... \n1330 NaN epugh@opensourceconnections.com \n1331 NaN epugh@opensourceconnections.com \n1334 NaN epugh@opensourceconnections.com \n1340 NaN epugh@opensourceconnections.com \n1342 NaN epugh@opensourceconnections.com \n\n rater_1 rater_2 \\\n6 aarora@opensourceconnections.com ben.w.trent@gmail.com \n19 aarora@opensourceconnections.com ben.w.trent@gmail.com \n20 aarora@opensourceconnections.com ben.w.trent@gmail.com \n21 aarora@opensourceconnections.com ben.w.trent@gmail.com \n24 aarora@opensourceconnections.com ben.w.trent@gmail.com \n... ... ... \n1330 aarora@opensourceconnections.com ben.w.trent@gmail.com \n1331 aarora@opensourceconnections.com ben.w.trent@gmail.com \n1334 aarora@opensourceconnections.com ben.w.trent@gmail.com \n1340 aarora@opensourceconnections.com ben.w.trent@gmail.com \n1342 aarora@opensourceconnections.com ben.w.trent@gmail.com \n\n nb_distinct_ratings \n6 2 \n19 2 \n20 2 \n21 2 \n24 2 \n... ... \n1330 2 \n1331 2 \n1334 2 \n1340 2 \n1342 2 \n\n[272 rows x 19 columns]", + "text/html": "
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
querydocidcharlie@flax.co.ukrating_0eschramma@cas.orgdtaivpp@gmail.comrating_1cmcollier@gmail.comrating_2jeff@vin.comcmarino@enterprise-knowledge.commsfroh@gmail.competer@searchintuition.commaximilian.werk@jina.airyan.finley@ferguson.comrater_0rater_1rater_2nb_distinct_ratings
6projector screen549808NaN3.0NaNNaN3.0NaNNaNNaNNaNNaNNaNNaNNaNepugh@opensourceconnections.comaarora@opensourceconnections.comben.w.trent@gmail.com2
19laptop77031393NaN3.0NaNNaNNaNNaN3.0NaNNaNNaNNaNNaNNaNepugh@opensourceconnections.comaarora@opensourceconnections.comben.w.trent@gmail.com2
20iphone 879283963NaN0.0NaNNaNNaNNaN0.0NaNNaNNaNNaNNaNNaNepugh@opensourceconnections.comaarora@opensourceconnections.comben.w.trent@gmail.com2
21iphone 879284190NaN0.0NaNNaNNaNNaN0.0NaNNaNNaNNaNNaNNaNepugh@opensourceconnections.comaarora@opensourceconnections.comben.w.trent@gmail.com2
24iphone 877911774NaN0.0NaNNaN0.0NaNNaNNaNNaNNaNNaNNaNNaNepugh@opensourceconnections.comaarora@opensourceconnections.comben.w.trent@gmail.com2
............................................................
1330coffee656359NaN3.0NaNNaN3.0NaNNaNNaNNaNNaNNaNNaNNaNepugh@opensourceconnections.comaarora@opensourceconnections.comben.w.trent@gmail.com2
1331coffee77265396NaN2.0NaNNaN2.0NaNNaNNaNNaNNaNNaNNaNNaNepugh@opensourceconnections.comaarora@opensourceconnections.comben.w.trent@gmail.com2
1334coffee2102472NaN2.0NaNNaNNaNNaN2.0NaNNaNNaNNaNNaNNaNepugh@opensourceconnections.comaarora@opensourceconnections.comben.w.trent@gmail.com2
1340vans77129498NaN0.0NaNNaN0.0NaNNaNNaNNaNNaNNaNNaNNaNepugh@opensourceconnections.comaarora@opensourceconnections.comben.w.trent@gmail.com2
1342vans77388459NaN0.0NaNNaNNaNNaN0.0NaNNaNNaNNaNNaNNaNepugh@opensourceconnections.comaarora@opensourceconnections.comben.w.trent@gmail.com2
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272 rows × 19 columns

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" }, "metadata": {} } @@ -516,7 +502,7 @@ "metadata": { "trusted": true }, - "execution_count": 21, + "execution_count": 81, "outputs": [ { "ename": "", @@ -524,7 +510,7 @@ "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[0;32mIn[21], line 6\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;241m1\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mmax\u001b[39m(ratings) \u001b[38;5;241m-\u001b[39m \u001b[38;5;28mmin\u001b[39m(ratings) \u001b[38;5;241m>\u001b[39m\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m2\u001b[39m \u001b[38;5;28;01melse\u001b[39;00m \u001b[38;5;241m0\u001b[39m\n\u001b[1;32m 5\u001b[0m df[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mbig_discrepancy\u001b[39m\u001b[38;5;124m'\u001b[39m] \u001b[38;5;241m=\u001b[39m df\u001b[38;5;241m.\u001b[39mapply(big_discrepancy, axis\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m1\u001b[39m)\n\u001b[0;32m----> 6\u001b[0m \u001b[43mdf\u001b[49m\u001b[43m[\u001b[49m\u001b[43mdf\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mbig_discrepancy\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m]\u001b[49m\u001b[38;5;241;43m==\u001b[39;49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m]\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msample\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m2\u001b[39;49m\u001b[43m)\u001b[49m\n", + "Cell \u001b[0;32mIn[81], line 6\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;241m1\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mmax\u001b[39m(ratings) \u001b[38;5;241m-\u001b[39m \u001b[38;5;28mmin\u001b[39m(ratings) \u001b[38;5;241m>\u001b[39m\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m2\u001b[39m \u001b[38;5;28;01melse\u001b[39;00m \u001b[38;5;241m0\u001b[39m\n\u001b[1;32m 5\u001b[0m df[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mbig_discrepancy\u001b[39m\u001b[38;5;124m'\u001b[39m] \u001b[38;5;241m=\u001b[39m df\u001b[38;5;241m.\u001b[39mapply(big_discrepancy, axis\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m1\u001b[39m)\n\u001b[0;32m----> 6\u001b[0m \u001b[43mdf\u001b[49m\u001b[43m[\u001b[49m\u001b[43mdf\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mbig_discrepancy\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m]\u001b[49m\u001b[38;5;241;43m==\u001b[39;49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m]\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msample\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m2\u001b[39;49m\u001b[43m)\u001b[49m\n", "File \u001b[0;32m/lib/python3.11/site-packages/pandas/core/generic.py:5773\u001b[0m, in \u001b[0;36mNDFrame.sample\u001b[0;34m(self, n, frac, replace, weights, random_state, axis, ignore_index)\u001b[0m\n\u001b[1;32m 5770\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m weights \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[1;32m 5771\u001b[0m weights \u001b[38;5;241m=\u001b[39m sample\u001b[38;5;241m.\u001b[39mpreprocess_weights(\u001b[38;5;28mself\u001b[39m, weights, axis)\n\u001b[0;32m-> 5773\u001b[0m sampled_indices \u001b[38;5;241m=\u001b[39m \u001b[43msample\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msample\u001b[49m\u001b[43m(\u001b[49m\u001b[43mobj_len\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43msize\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mreplace\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mweights\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mrs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 5774\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mtake(sampled_indices, axis\u001b[38;5;241m=\u001b[39maxis)\n\u001b[1;32m 5776\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m ignore_index:\n", "File \u001b[0;32m/lib/python3.11/site-packages/pandas/core/sample.py:150\u001b[0m, in \u001b[0;36msample\u001b[0;34m(obj_len, size, replace, weights, random_state)\u001b[0m\n\u001b[1;32m 147\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 148\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mInvalid weights: weights sum to zero\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m--> 150\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mrandom_state\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mchoice\u001b[49m\u001b[43m(\u001b[49m\u001b[43mobj_len\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43msize\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43msize\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mreplace\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mreplace\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mp\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mweights\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241m.\u001b[39mastype(\n\u001b[1;32m 151\u001b[0m np\u001b[38;5;241m.\u001b[39mintp, copy\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m\n\u001b[1;32m 152\u001b[0m )\n", "File \u001b[0;32mmtrand.pyx:928\u001b[0m, in \u001b[0;36mnumpy.random.mtrand.RandomState.choice\u001b[0;34m()\u001b[0m\n", @@ -547,7 +533,7 @@ "metadata": { "trusted": true }, - "execution_count": 22, + "execution_count": 82, "outputs": [], "id": "6d8a99b9-f823-4829-aed8-9e376a0dfa73" }, @@ -557,15 +543,22 @@ "metadata": { "trusted": true }, - "execution_count": 23, + "execution_count": 83, "outputs": [ { - "output_type": "display_data", - "data": { - "text/plain": "
", - "image/png": 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\n" - }, - "metadata": {} + "ename": "", + "evalue": "Input y_true contains NaN.", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[83], line 2\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01msklearn\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mmetrics\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m confusion_matrix, ConfusionMatrixDisplay\n\u001b[0;32m----> 2\u001b[0m cm \u001b[38;5;241m=\u001b[39m \u001b[43mconfusion_matrix\u001b[49m\u001b[43m(\u001b[49m\u001b[43my1\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43my2\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mlabels\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mrange\u001b[39;49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mlen\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mratings\u001b[49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 3\u001b[0m disp \u001b[38;5;241m=\u001b[39m ConfusionMatrixDisplay(confusion_matrix\u001b[38;5;241m=\u001b[39mcm,\n\u001b[1;32m 4\u001b[0m display_labels\u001b[38;5;241m=\u001b[39mratings)\n\u001b[1;32m 5\u001b[0m plt\u001b[38;5;241m.\u001b[39mfigure(figsize\u001b[38;5;241m=\u001b[39m(\u001b[38;5;241m8\u001b[39m,\u001b[38;5;241m8\u001b[39m))\n", + "File \u001b[0;32m/lib/python3.11/site-packages/sklearn/metrics/_classification.py:317\u001b[0m, in \u001b[0;36mconfusion_matrix\u001b[0;34m(y_true, y_pred, labels, sample_weight, normalize)\u001b[0m\n\u001b[1;32m 232\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mconfusion_matrix\u001b[39m(\n\u001b[1;32m 233\u001b[0m y_true, y_pred, \u001b[38;5;241m*\u001b[39m, labels\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m, sample_weight\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m, normalize\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m 234\u001b[0m ):\n\u001b[1;32m 235\u001b[0m \u001b[38;5;250m \u001b[39m\u001b[38;5;124;03m\"\"\"Compute confusion matrix to evaluate the accuracy of a classification.\u001b[39;00m\n\u001b[1;32m 236\u001b[0m \n\u001b[1;32m 237\u001b[0m \u001b[38;5;124;03m By definition a confusion matrix :math:`C` is such that :math:`C_{i, j}`\u001b[39;00m\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 315\u001b[0m \u001b[38;5;124;03m (0, 2, 1, 1)\u001b[39;00m\n\u001b[1;32m 316\u001b[0m \u001b[38;5;124;03m \"\"\"\u001b[39;00m\n\u001b[0;32m--> 317\u001b[0m y_type, y_true, y_pred \u001b[38;5;241m=\u001b[39m \u001b[43m_check_targets\u001b[49m\u001b[43m(\u001b[49m\u001b[43my_true\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43my_pred\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 318\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m y_type \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m (\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mbinary\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmulticlass\u001b[39m\u001b[38;5;124m\"\u001b[39m):\n\u001b[1;32m 319\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;132;01m%s\u001b[39;00m\u001b[38;5;124m is not supported\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;241m%\u001b[39m y_type)\n", + "File \u001b[0;32m/lib/python3.11/site-packages/sklearn/metrics/_classification.py:87\u001b[0m, in \u001b[0;36m_check_targets\u001b[0;34m(y_true, y_pred)\u001b[0m\n\u001b[1;32m 60\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"Check that y_true and y_pred belong to the same classification task.\u001b[39;00m\n\u001b[1;32m 61\u001b[0m \n\u001b[1;32m 62\u001b[0m \u001b[38;5;124;03mThis converts multiclass or binary types to a common shape, and raises a\u001b[39;00m\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 84\u001b[0m \u001b[38;5;124;03my_pred : array or indicator matrix\u001b[39;00m\n\u001b[1;32m 85\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 86\u001b[0m check_consistent_length(y_true, y_pred)\n\u001b[0;32m---> 87\u001b[0m type_true \u001b[38;5;241m=\u001b[39m \u001b[43mtype_of_target\u001b[49m\u001b[43m(\u001b[49m\u001b[43my_true\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43minput_name\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43my_true\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m 88\u001b[0m type_pred \u001b[38;5;241m=\u001b[39m type_of_target(y_pred, input_name\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124my_pred\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m 90\u001b[0m y_type \u001b[38;5;241m=\u001b[39m {type_true, type_pred}\n", + "File \u001b[0;32m/lib/python3.11/site-packages/sklearn/utils/multiclass.py:381\u001b[0m, in \u001b[0;36mtype_of_target\u001b[0;34m(y, input_name)\u001b[0m\n\u001b[1;32m 379\u001b[0m data \u001b[38;5;241m=\u001b[39m y\u001b[38;5;241m.\u001b[39mdata \u001b[38;5;28;01mif\u001b[39;00m issparse(y) \u001b[38;5;28;01melse\u001b[39;00m y\n\u001b[1;32m 380\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m xp\u001b[38;5;241m.\u001b[39many(data \u001b[38;5;241m!=\u001b[39m data\u001b[38;5;241m.\u001b[39mastype(\u001b[38;5;28mint\u001b[39m)):\n\u001b[0;32m--> 381\u001b[0m \u001b[43m_assert_all_finite\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdata\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43minput_name\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43minput_name\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 382\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mcontinuous\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;241m+\u001b[39m suffix\n\u001b[1;32m 384\u001b[0m \u001b[38;5;66;03m# Check multiclass\u001b[39;00m\n", + "File \u001b[0;32m/lib/python3.11/site-packages/sklearn/utils/validation.py:161\u001b[0m, in \u001b[0;36m_assert_all_finite\u001b[0;34m(X, allow_nan, msg_dtype, estimator_name, input_name)\u001b[0m\n\u001b[1;32m 144\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m estimator_name \u001b[38;5;129;01mand\u001b[39;00m input_name \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mX\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;129;01mand\u001b[39;00m has_nan_error:\n\u001b[1;32m 145\u001b[0m \u001b[38;5;66;03m# Improve the error message on how to handle missing values in\u001b[39;00m\n\u001b[1;32m 146\u001b[0m \u001b[38;5;66;03m# scikit-learn.\u001b[39;00m\n\u001b[1;32m 147\u001b[0m msg_err \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m (\n\u001b[1;32m 148\u001b[0m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;132;01m{\u001b[39;00mestimator_name\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m does not accept missing values\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 149\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m encoded as NaN natively. For supervised learning, you might want\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 159\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m#estimators-that-handle-nan-values\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 160\u001b[0m )\n\u001b[0;32m--> 161\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(msg_err)\n", + "\u001b[0;31mValueError\u001b[0m: Input y_true contains NaN." + ], + "output_type": "error" } ], "id": "1a4f7484-185c-42a3-8931-32a28a6d6964"