modular_threat_attribution.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "822be3b0-db1e-4113-acad-e5854c359220",
   "metadata": {},
   "outputs": [],
   "source": [
    "import copy\n",
    "import random\n",
    "import math\n",
    "import time\n",
    "import pickle\n",
    "import pprint\n",
    "\n",
    "import tqdm\n",
    "\n",
    "import numpy\n",
    "import pandas\n",
    "\n",
    "import sklearn\n",
    "import sklearn.metrics\n",
    "import sklearn.svm\n",
    "\n",
    "import xgboost\n",
    "\n",
    "import matplotlib\n",
    "import matplotlib.pyplot\n",
    "import seaborn"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "6e85e311-1d71-4eef-9b27-1e9d1ba6fc6b",
   "metadata": {
    "tags": []
   },
   "source": [
    "# Utility functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "6992db6a",
   "metadata": {},
   "outputs": [],
   "source": [
    "def equal_pdf(size):\n",
    "    return numpy.ones((size,)) / size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "49dff043-df4c-4eae-8f4b-12759623db00",
   "metadata": {},
   "outputs": [],
   "source": [
    "def normalize_pdf(x):\n",
    "    x = x / x.sum()\n",
    "    x = numpy.nan_to_num(x, copy=True, nan=0.0, posinf=None, neginf=None)\n",
    "    if x.sum() == 0.:\n",
    "        x = equal_pdf(x.shape[0])\n",
    "    return x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "268e8234-1bac-4e02-97a5-4864d7555f4b",
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_k_accuracy_for_target(x, target):\n",
    "    x = pandas.Series(x).sort_values(ascending=False).reset_index()\n",
    "    return x[x['index']==target].index[0] + len(x[x[0]==x[x['index']==target].iloc[0][0]].index)/2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "9462572c-ebb8-4088-aa98-dde1af854aa5",
   "metadata": {},
   "outputs": [],
   "source": [
    "def target_pdf(size, target):\n",
    "    x = numpy.zeros((size,))\n",
    "    x[target] = 1\n",
    "    return x"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "1b80f535-4cfd-4bfe-a3b9-f30085c25c4e",
   "metadata": {
    "tags": []
   },
   "source": [
    "# Opinion pools"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "fb42bfc7-8340-4970-bec5-ac35bed9e836",
   "metadata": {},
   "outputs": [],
   "source": [
    "def majority_vote(X):\n",
    "    votes = X.argmax(axis=1)\n",
    "    vote = pandas.Series(votes).value_counts().sort_index().idxmax()\n",
    "    return target_pdf(X.shape[1], vote)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "2e15553c-38a6-4ec5-a168-a1cb1cc1276c",
   "metadata": {},
   "outputs": [],
   "source": [
    "def linear_opinion_pool(X):\n",
    "    x = X.sum(axis=0)\n",
    "    return normalize_pdf(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "1c1230a1-149b-4fae-96c4-724f7d77d814",
   "metadata": {},
   "outputs": [],
   "source": [
    "def logarithmic_opinion_pool(X):\n",
    "    x = X.prod(axis=0)\n",
    "    return normalize_pdf(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "6b40c619-4193-499e-9d84-f19c973cdb41",
   "metadata": {},
   "outputs": [],
   "source": [
    "def paired_opinion_pool(X, pool1, pool2):\n",
    "    Y = []\n",
    "    for i in range(X.shape[0]):\n",
    "        for j in range(i+1, X.shape[0]):\n",
    "            Y.append(pool1(numpy.array([X[i, :], X[j, :]])))\n",
    "    Y = numpy.array(Y)\n",
    "    return pool2(Y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "98f3e82f-d992-44ab-83aa-8edd25e66ae7",
   "metadata": {},
   "outputs": [],
   "source": [
    "class PairedOpinionPool:\n",
    "    def __init__(self, pool1, pool2):\n",
    "        self.pool1 = pool1\n",
    "        self.pool2 = pool2\n",
    "        \n",
    "    def __call__(self, X):\n",
    "        return paired_opinion_pool(X, self.pool1, self.pool2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "ea98bf7c-739f-43f2-8dc6-05cd0317954f",
   "metadata": {},
   "outputs": [],
   "source": [
    "logarithmic_linear_opinion_pool = PairedOpinionPool(logarithmic_opinion_pool, linear_opinion_pool)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "1d793e75-cff2-4b07-9eaf-d16dbcf8179d",
   "metadata": {
    "tags": []
   },
   "source": [
    "# Configuration"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "a5a2673d-8f74-437b-958c-f60b2a5d5b2d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 100000\n",
    "EXPERIMENT_SIZE = 100000\n",
    "ROLLING_WINDOW_SIZE = EXPERIMENT_SIZE//10\n",
    "# 128\n",
    "ACTORS = 128\n",
    "# 8\n",
    "FEATURES = 8\n",
    "\n",
    "# 0.1\n",
    "BASE_ACTIVITY = 0.1\n",
    "BASE_STD = 1/ACTORS\n",
    "\n",
    "ACTIVITY_SHIFT_STD = 1e-2\n",
    "MEAN_SHIFT_STD = 1e-2\n",
    "STD_SHIFT_STD = 1e-2\n",
    "\n",
    "TRAIN_SIZE = 0.5\n",
    "\n",
    "PROB_FALSE_FLAG = 0.4\n",
    "\n",
    "MODELS = {\n",
    "    # 'Naive Bayes': sklearn.naive_bayes.GaussianNB(),\n",
    "    'Linear SVM': sklearn.svm.SVC(kernel='linear', probability=True),\n",
    "    # 'SVM with RBF': sklearn.svm.SVC(probability=True),\n",
    "    # 'MLP': sklearn.neural_network.MLPClassifier(early_stopping=True),\n",
    "    'XGBoost': xgboost.XGBClassifier(n_estimators=1000, n_jobs=1, objective='multi_softmax')\n",
    "}\n",
    "MODEL = MODELS['Linear SVM']\n",
    "\n",
    "FIGSIZE = (8, 4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "e03adfbf-83a2-4659-b0c9-896e5022736c",
   "metadata": {},
   "outputs": [],
   "source": [
    "pools = {\n",
    "    # 'Majority Vote': majority_vote, \n",
    "    'Linear Opinion Pool': linear_opinion_pool, \n",
    "    'Logarithmic Opinion Pool': logarithmic_opinion_pool,\n",
    "    'Pairing Aggregator': logarithmic_linear_opinion_pool,\n",
    "}"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "2d583518-00a4-4609-a71c-2d177594ed52",
   "metadata": {},
   "source": [
    "# Experiment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "9cf24d69",
   "metadata": {},
   "outputs": [],
   "source": [
    "feature_names = [f'x{i}' for i in range(FEATURES)]"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "42c4457e-d5e0-48e6-b8c4-d27766cd4e32",
   "metadata": {},
   "source": [
    "## Prepare dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f1aa8e1c-d86e-4d3a-af47-74628969a2f0",
   "metadata": {},
   "outputs": [],
   "source": [
    "columns = [\n",
    "    'Name',\n",
    "    'start',\n",
    "    'end',\n",
    "    'activity',\n",
    "]\n",
    "\n",
    "for i in range(FEATURES):\n",
    "    columns.append(f'x{i}_mean')\n",
    "    columns.append(f'x{i}_std')\n",
    "\n",
    "df_actors = pandas.DataFrame(columns=columns)\n",
    "\n",
    "for i in range(ACTORS):\n",
    "    start = random.uniform(0, EXPERIMENT_SIZE * (TRAIN_SIZE - 0.1))\n",
    "    end = random.uniform(EXPERIMENT_SIZE * (TRAIN_SIZE + 0.1), EXPERIMENT_SIZE)\n",
    "\n",
    "    values = [\n",
    "        'APT'+str(i),\n",
    "        start,\n",
    "        end,\n",
    "        random.uniform(0.0001, BASE_ACTIVITY),\n",
    "    ]\n",
    "    for _ in range(FEATURES):\n",
    "        values.append(random.uniform(-1, 1))\n",
    "        values.append(random.uniform(0, BASE_STD))\n",
    "\n",
    "    df_actors.loc[i] = pandas.Series(\n",
    "        values,\n",
    "        index=df_actors.columns,\n",
    "        name=i,\n",
    "    )\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "aad655ec-ec96-41fe-90e2-a274be7abc22",
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>start</th>\n",
       "      <th>end</th>\n",
       "      <th>activity</th>\n",
       "      <th>x0_mean</th>\n",
       "      <th>x0_std</th>\n",
       "      <th>x1_mean</th>\n",
       "      <th>x1_std</th>\n",
       "      <th>x2_mean</th>\n",
       "      <th>x2_std</th>\n",
       "      <th>x3_mean</th>\n",
       "      <th>x3_std</th>\n",
       "      <th>x4_mean</th>\n",
       "      <th>x4_std</th>\n",
       "      <th>x5_mean</th>\n",
       "      <th>x5_std</th>\n",
       "      <th>x6_mean</th>\n",
       "      <th>x6_std</th>\n",
       "      <th>x7_mean</th>\n",
       "      <th>x7_std</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>APT0</td>\n",
       "      <td>28706.74534</td>\n",
       "      <td>71522.773839</td>\n",
       "      <td>0.077005</td>\n",
       "      <td>-0.820592</td>\n",
       "      <td>0.002832</td>\n",
       "      <td>0.27332</td>\n",
       "      <td>0.005723</td>\n",
       "      <td>0.407798</td>\n",
       "      <td>0.002934</td>\n",
       "      <td>-0.240162</td>\n",
       "      <td>0.005786</td>\n",
       "      <td>-0.306415</td>\n",
       "      <td>0.003061</td>\n",
       "      <td>-0.354689</td>\n",
       "      <td>0.000621</td>\n",
       "      <td>-0.677331</td>\n",
       "      <td>0.004643</td>\n",
       "      <td>0.004811</td>\n",
       "      <td>0.00569</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>APT1</td>\n",
       "      <td>22885.572764</td>\n",
       "      <td>75358.394044</td>\n",
       "      <td>0.011253</td>\n",
       "      <td>-0.318926</td>\n",
       "      <td>0.004806</td>\n",
       "      <td>0.967</td>\n",
       "      <td>0.006445</td>\n",
       "      <td>0.482932</td>\n",
       "      <td>0.006152</td>\n",
       "      <td>0.696349</td>\n",
       "      <td>0.003473</td>\n",
       "      <td>0.175878</td>\n",
       "      <td>0.004689</td>\n",
       "      <td>0.800283</td>\n",
       "      <td>0.005346</td>\n",
       "      <td>-0.67634</td>\n",
       "      <td>0.005998</td>\n",
       "      <td>-0.961339</td>\n",
       "      <td>0.00058</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>APT2</td>\n",
       "      <td>13686.340629</td>\n",
       "      <td>87194.596239</td>\n",
       "      <td>0.053374</td>\n",
       "      <td>0.02996</td>\n",
       "      <td>0.005948</td>\n",
       "      <td>0.763969</td>\n",
       "      <td>0.00645</td>\n",
       "      <td>0.26125</td>\n",
       "      <td>0.001815</td>\n",
       "      <td>0.939208</td>\n",
       "      <td>0.003766</td>\n",
       "      <td>0.977583</td>\n",
       "      <td>0.006245</td>\n",
       "      <td>-0.753842</td>\n",
       "      <td>0.001502</td>\n",
       "      <td>0.246647</td>\n",
       "      <td>0.00314</td>\n",
       "      <td>0.96053</td>\n",
       "      <td>0.000863</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>APT3</td>\n",
       "      <td>15469.753789</td>\n",
       "      <td>67852.004096</td>\n",
       "      <td>0.059287</td>\n",
       "      <td>-0.624815</td>\n",
       "      <td>0.003561</td>\n",
       "      <td>-0.344747</td>\n",
       "      <td>0.000783</td>\n",
       "      <td>-0.336424</td>\n",
       "      <td>0.006418</td>\n",
       "      <td>0.419702</td>\n",
       "      <td>0.003588</td>\n",
       "      <td>-0.055239</td>\n",
       "      <td>0.004288</td>\n",
       "      <td>0.886934</td>\n",
       "      <td>0.006477</td>\n",
       "      <td>0.094761</td>\n",
       "      <td>0.004823</td>\n",
       "      <td>0.306553</td>\n",
       "      <td>0.002409</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>APT4</td>\n",
       "      <td>2513.049464</td>\n",
       "      <td>60474.967226</td>\n",
       "      <td>0.022255</td>\n",
       "      <td>0.732821</td>\n",
       "      <td>0.005297</td>\n",
       "      <td>-0.72749</td>\n",
       "      <td>0.000851</td>\n",
       "      <td>0.158977</td>\n",
       "      <td>0.002643</td>\n",
       "      <td>-0.177384</td>\n",
       "      <td>0.004338</td>\n",
       "      <td>0.966511</td>\n",
       "      <td>0.002831</td>\n",
       "      <td>0.651822</td>\n",
       "      <td>0.001268</td>\n",
       "      <td>-0.859898</td>\n",
       "      <td>0.00633</td>\n",
       "      <td>-0.191439</td>\n",
       "      <td>0.002715</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>123</th>\n",
       "      <td>APT123</td>\n",
       "      <td>4746.413984</td>\n",
       "      <td>63921.918377</td>\n",
       "      <td>0.070892</td>\n",
       "      <td>0.348162</td>\n",
       "      <td>0.000862</td>\n",
       "      <td>-0.984142</td>\n",
       "      <td>0.005359</td>\n",
       "      <td>0.580728</td>\n",
       "      <td>0.007765</td>\n",
       "      <td>-0.448492</td>\n",
       "      <td>0.000902</td>\n",
       "      <td>0.878032</td>\n",
       "      <td>0.000874</td>\n",
       "      <td>0.990198</td>\n",
       "      <td>0.006893</td>\n",
       "      <td>0.167481</td>\n",
       "      <td>0.000807</td>\n",
       "      <td>0.851596</td>\n",
       "      <td>0.00069</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>124</th>\n",
       "      <td>APT124</td>\n",
       "      <td>328.686421</td>\n",
       "      <td>62304.434566</td>\n",
       "      <td>0.034007</td>\n",
       "      <td>-0.638509</td>\n",
       "      <td>0.001475</td>\n",
       "      <td>0.510368</td>\n",
       "      <td>0.007755</td>\n",
       "      <td>0.319787</td>\n",
       "      <td>0.005712</td>\n",
       "      <td>0.538152</td>\n",
       "      <td>0.002927</td>\n",
       "      <td>0.7046</td>\n",
       "      <td>0.006245</td>\n",
       "      <td>-0.756329</td>\n",
       "      <td>0.002538</td>\n",
       "      <td>0.741324</td>\n",
       "      <td>0.000657</td>\n",
       "      <td>0.860774</td>\n",
       "      <td>0.005362</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>125</th>\n",
       "      <td>APT125</td>\n",
       "      <td>34360.803777</td>\n",
       "      <td>63463.481633</td>\n",
       "      <td>0.082748</td>\n",
       "      <td>-0.039965</td>\n",
       "      <td>0.005816</td>\n",
       "      <td>0.359279</td>\n",
       "      <td>0.001522</td>\n",
       "      <td>0.483778</td>\n",
       "      <td>0.001153</td>\n",
       "      <td>0.627422</td>\n",
       "      <td>0.002602</td>\n",
       "      <td>0.732864</td>\n",
       "      <td>0.00374</td>\n",
       "      <td>-0.027154</td>\n",
       "      <td>0.002697</td>\n",
       "      <td>0.822893</td>\n",
       "      <td>0.000383</td>\n",
       "      <td>-0.040601</td>\n",
       "      <td>0.000819</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>126</th>\n",
       "      <td>APT126</td>\n",
       "      <td>19682.10523</td>\n",
       "      <td>64356.315571</td>\n",
       "      <td>0.077507</td>\n",
       "      <td>0.639574</td>\n",
       "      <td>0.002925</td>\n",
       "      <td>0.538404</td>\n",
       "      <td>0.001846</td>\n",
       "      <td>-0.630967</td>\n",
       "      <td>0.00343</td>\n",
       "      <td>0.879191</td>\n",
       "      <td>0.003559</td>\n",
       "      <td>0.751754</td>\n",
       "      <td>0.001549</td>\n",
       "      <td>0.514178</td>\n",
       "      <td>0.004466</td>\n",
       "      <td>0.830439</td>\n",
       "      <td>0.004617</td>\n",
       "      <td>0.090496</td>\n",
       "      <td>0.00129</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>127</th>\n",
       "      <td>APT127</td>\n",
       "      <td>5653.618083</td>\n",
       "      <td>77889.568543</td>\n",
       "      <td>0.023476</td>\n",
       "      <td>-0.631359</td>\n",
       "      <td>0.002869</td>\n",
       "      <td>0.264258</td>\n",
       "      <td>0.007596</td>\n",
       "      <td>0.814928</td>\n",
       "      <td>0.000323</td>\n",
       "      <td>0.563475</td>\n",
       "      <td>0.003172</td>\n",
       "      <td>0.279146</td>\n",
       "      <td>0.001853</td>\n",
       "      <td>0.895142</td>\n",
       "      <td>0.000507</td>\n",
       "      <td>-0.72194</td>\n",
       "      <td>0.007474</td>\n",
       "      <td>0.793048</td>\n",
       "      <td>0.006323</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>128 rows × 20 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Name         start           end  activity   x0_mean    x0_std   \n",
       "0      APT0   28706.74534  71522.773839  0.077005 -0.820592  0.002832  \\\n",
       "1      APT1  22885.572764  75358.394044  0.011253 -0.318926  0.004806   \n",
       "2      APT2  13686.340629  87194.596239  0.053374   0.02996  0.005948   \n",
       "3      APT3  15469.753789  67852.004096  0.059287 -0.624815  0.003561   \n",
       "4      APT4   2513.049464  60474.967226  0.022255  0.732821  0.005297   \n",
       "..      ...           ...           ...       ...       ...       ...   \n",
       "123  APT123   4746.413984  63921.918377  0.070892  0.348162  0.000862   \n",
       "124  APT124    328.686421  62304.434566  0.034007 -0.638509  0.001475   \n",
       "125  APT125  34360.803777  63463.481633  0.082748 -0.039965  0.005816   \n",
       "126  APT126   19682.10523  64356.315571  0.077507  0.639574  0.002925   \n",
       "127  APT127   5653.618083  77889.568543  0.023476 -0.631359  0.002869   \n",
       "\n",
       "      x1_mean    x1_std   x2_mean    x2_std   x3_mean    x3_std   x4_mean   \n",
       "0     0.27332  0.005723  0.407798  0.002934 -0.240162  0.005786 -0.306415  \\\n",
       "1       0.967  0.006445  0.482932  0.006152  0.696349  0.003473  0.175878   \n",
       "2    0.763969   0.00645   0.26125  0.001815  0.939208  0.003766  0.977583   \n",
       "3   -0.344747  0.000783 -0.336424  0.006418  0.419702  0.003588 -0.055239   \n",
       "4    -0.72749  0.000851  0.158977  0.002643 -0.177384  0.004338  0.966511   \n",
       "..        ...       ...       ...       ...       ...       ...       ...   \n",
       "123 -0.984142  0.005359  0.580728  0.007765 -0.448492  0.000902  0.878032   \n",
       "124  0.510368  0.007755  0.319787  0.005712  0.538152  0.002927    0.7046   \n",
       "125  0.359279  0.001522  0.483778  0.001153  0.627422  0.002602  0.732864   \n",
       "126  0.538404  0.001846 -0.630967   0.00343  0.879191  0.003559  0.751754   \n",
       "127  0.264258  0.007596  0.814928  0.000323  0.563475  0.003172  0.279146   \n",
       "\n",
       "       x4_std   x5_mean    x5_std   x6_mean    x6_std   x7_mean    x7_std  \n",
       "0    0.003061 -0.354689  0.000621 -0.677331  0.004643  0.004811   0.00569  \n",
       "1    0.004689  0.800283  0.005346  -0.67634  0.005998 -0.961339   0.00058  \n",
       "2    0.006245 -0.753842  0.001502  0.246647   0.00314   0.96053  0.000863  \n",
       "3    0.004288  0.886934  0.006477  0.094761  0.004823  0.306553  0.002409  \n",
       "4    0.002831  0.651822  0.001268 -0.859898   0.00633 -0.191439  0.002715  \n",
       "..        ...       ...       ...       ...       ...       ...       ...  \n",
       "123  0.000874  0.990198  0.006893  0.167481  0.000807  0.851596   0.00069  \n",
       "124  0.006245 -0.756329  0.002538  0.741324  0.000657  0.860774  0.005362  \n",
       "125   0.00374 -0.027154  0.002697  0.822893  0.000383 -0.040601  0.000819  \n",
       "126  0.001549  0.514178  0.004466  0.830439  0.004617  0.090496   0.00129  \n",
       "127  0.001853  0.895142  0.000507  -0.72194  0.007474  0.793048  0.006323  \n",
       "\n",
       "[128 rows x 20 columns]"
      ]
     },
     "execution_count": 121,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_actors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3bf5e939-c609-4470-b66c-d1e8232b4b67",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 100000/100000 [14:54:06<00:00,  1.86it/s]  \n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 122,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "columns = [\n",
    "    'Name',\n",
    "    'Timestep',\n",
    "] + feature_names\n",
    "\n",
    "df_incidents = pandas.DataFrame(columns=columns)\n",
    "\n",
    "incident_i = 0\n",
    "for i in tqdm.tqdm(range(EXPERIMENT_SIZE)):\n",
    "    for j in range(ACTORS):\n",
    "        if df_actors.iloc[j]['start'] <= i and df_actors.iloc[j]['end'] >= i and random.uniform(0, 1) < df_actors.iloc[j]['activity']:\n",
    "            feature_values = [\n",
    "                random.normalvariate(\n",
    "                    df_actors.iloc[j][f'x{k}_mean'], df_actors.iloc[j][f'x{k}_std']\n",
    "                )\n",
    "                for k in range(FEATURES)\n",
    "            ]\n",
    "\n",
    "            # If generating test data already, introduce false flags corresponding to a sample from another actor present in the training data\n",
    "            if i > EXPERIMENT_SIZE * TRAIN_SIZE:\n",
    "                # if len(df_incidents[(df_incidents['Name'] != df_actors.iloc[j]['Name'])].index) > 0:\n",
    "                blamed_actor = df_incidents[\n",
    "                    (df_incidents['Name'] != df_actors.iloc[j]['Name'])\n",
    "                    & (df_incidents['Timestep'] < EXPERIMENT_SIZE * TRAIN_SIZE)\n",
    "                ]['Name'].sample(1)[0]\n",
    "                for k in range(FEATURES):\n",
    "                    if random.uniform(0, 1) < PROB_FALSE_FLAG:\n",
    "                        feature_values[k] = df_incidents[\n",
    "                            (df_incidents['Name'] == blamed_actor)\n",
    "                            & (df_incidents['Timestep'] < EXPERIMENT_SIZE * TRAIN_SIZE)\n",
    "                        ][f'x{k}'].sample(1)[0]\n",
    "\n",
    "            values = [\n",
    "                df_actors.iloc[j]['Name'],\n",
    "                i,\n",
    "            ] + feature_values\n",
    "\n",
    "            series_index = str(i)+str(',')+str(j)\n",
    "\n",
    "            df_incidents.loc[series_index] = pandas.Series(\n",
    "                values,\n",
    "                index=df_incidents.columns,\n",
    "                name=series_index,\n",
    "            )\n",
    "            incident_i += 1\n",
    "\n",
    "    for k in range(FEATURES):\n",
    "        df_actors.loc[j, 'activity'] = min(\n",
    "            max(\n",
    "                df_actors.loc[j, 'activity'] +\n",
    "                random.normalvariate(0, ACTIVITY_SHIFT_STD),\n",
    "                0\n",
    "            ),\n",
    "            BASE_ACTIVITY\n",
    "        )\n",
    "\n",
    "        df_actors.loc[j, f'x{k}_mean'] = min(\n",
    "            max(\n",
    "                df_actors.loc[j, f'x{k}_mean'] +\n",
    "                random.normalvariate(0, MEAN_SHIFT_STD),\n",
    "                -1\n",
    "            ),\n",
    "            1\n",
    "        )\n",
    "\n",
    "        df_actors.loc[j, f'x{k}_std'] = min(\n",
    "            max(\n",
    "                df_actors.loc[j, f'x{k}_std'] +\n",
    "                random.normalvariate(0, STD_SHIFT_STD),\n",
    "                0\n",
    "            ),\n",
    "            BASE_STD\n",
    "        )\n",
    "\n",
    "    if i % 1000 == 0:\n",
    "        df_incidents = df_incidents.copy()\n",
    "0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f7b23e46-5b2d-46a7-87fc-8a7a9397a366",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>start</th>\n",
       "      <th>end</th>\n",
       "      <th>activity</th>\n",
       "      <th>x0_mean</th>\n",
       "      <th>x0_std</th>\n",
       "      <th>x1_mean</th>\n",
       "      <th>x1_std</th>\n",
       "      <th>x2_mean</th>\n",
       "      <th>x2_std</th>\n",
       "      <th>x3_mean</th>\n",
       "      <th>x3_std</th>\n",
       "      <th>x4_mean</th>\n",
       "      <th>x4_std</th>\n",
       "      <th>x5_mean</th>\n",
       "      <th>x5_std</th>\n",
       "      <th>x6_mean</th>\n",
       "      <th>x6_std</th>\n",
       "      <th>x7_mean</th>\n",
       "      <th>x7_std</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>APT0</td>\n",
       "      <td>28706.74534</td>\n",
       "      <td>71522.773839</td>\n",
       "      <td>0.077005</td>\n",
       "      <td>-0.820592</td>\n",
       "      <td>0.002832</td>\n",
       "      <td>0.27332</td>\n",
       "      <td>0.005723</td>\n",
       "      <td>0.407798</td>\n",
       "      <td>0.002934</td>\n",
       "      <td>-0.240162</td>\n",
       "      <td>0.005786</td>\n",
       "      <td>-0.306415</td>\n",
       "      <td>0.003061</td>\n",
       "      <td>-0.354689</td>\n",
       "      <td>0.000621</td>\n",
       "      <td>-0.677331</td>\n",
       "      <td>0.004643</td>\n",
       "      <td>0.004811</td>\n",
       "      <td>0.00569</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>APT1</td>\n",
       "      <td>22885.572764</td>\n",
       "      <td>75358.394044</td>\n",
       "      <td>0.011253</td>\n",
       "      <td>-0.318926</td>\n",
       "      <td>0.004806</td>\n",
       "      <td>0.967</td>\n",
       "      <td>0.006445</td>\n",
       "      <td>0.482932</td>\n",
       "      <td>0.006152</td>\n",
       "      <td>0.696349</td>\n",
       "      <td>0.003473</td>\n",
       "      <td>0.175878</td>\n",
       "      <td>0.004689</td>\n",
       "      <td>0.800283</td>\n",
       "      <td>0.005346</td>\n",
       "      <td>-0.67634</td>\n",
       "      <td>0.005998</td>\n",
       "      <td>-0.961339</td>\n",
       "      <td>0.00058</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>APT2</td>\n",
       "      <td>13686.340629</td>\n",
       "      <td>87194.596239</td>\n",
       "      <td>0.053374</td>\n",
       "      <td>0.02996</td>\n",
       "      <td>0.005948</td>\n",
       "      <td>0.763969</td>\n",
       "      <td>0.00645</td>\n",
       "      <td>0.26125</td>\n",
       "      <td>0.001815</td>\n",
       "      <td>0.939208</td>\n",
       "      <td>0.003766</td>\n",
       "      <td>0.977583</td>\n",
       "      <td>0.006245</td>\n",
       "      <td>-0.753842</td>\n",
       "      <td>0.001502</td>\n",
       "      <td>0.246647</td>\n",
       "      <td>0.00314</td>\n",
       "      <td>0.96053</td>\n",
       "      <td>0.000863</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>APT3</td>\n",
       "      <td>15469.753789</td>\n",
       "      <td>67852.004096</td>\n",
       "      <td>0.059287</td>\n",
       "      <td>-0.624815</td>\n",
       "      <td>0.003561</td>\n",
       "      <td>-0.344747</td>\n",
       "      <td>0.000783</td>\n",
       "      <td>-0.336424</td>\n",
       "      <td>0.006418</td>\n",
       "      <td>0.419702</td>\n",
       "      <td>0.003588</td>\n",
       "      <td>-0.055239</td>\n",
       "      <td>0.004288</td>\n",
       "      <td>0.886934</td>\n",
       "      <td>0.006477</td>\n",
       "      <td>0.094761</td>\n",
       "      <td>0.004823</td>\n",
       "      <td>0.306553</td>\n",
       "      <td>0.002409</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>APT4</td>\n",
       "      <td>2513.049464</td>\n",
       "      <td>60474.967226</td>\n",
       "      <td>0.022255</td>\n",
       "      <td>0.732821</td>\n",
       "      <td>0.005297</td>\n",
       "      <td>-0.72749</td>\n",
       "      <td>0.000851</td>\n",
       "      <td>0.158977</td>\n",
       "      <td>0.002643</td>\n",
       "      <td>-0.177384</td>\n",
       "      <td>0.004338</td>\n",
       "      <td>0.966511</td>\n",
       "      <td>0.002831</td>\n",
       "      <td>0.651822</td>\n",
       "      <td>0.001268</td>\n",
       "      <td>-0.859898</td>\n",
       "      <td>0.00633</td>\n",
       "      <td>-0.191439</td>\n",
       "      <td>0.002715</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>123</th>\n",
       "      <td>APT123</td>\n",
       "      <td>4746.413984</td>\n",
       "      <td>63921.918377</td>\n",
       "      <td>0.070892</td>\n",
       "      <td>0.348162</td>\n",
       "      <td>0.000862</td>\n",
       "      <td>-0.984142</td>\n",
       "      <td>0.005359</td>\n",
       "      <td>0.580728</td>\n",
       "      <td>0.007765</td>\n",
       "      <td>-0.448492</td>\n",
       "      <td>0.000902</td>\n",
       "      <td>0.878032</td>\n",
       "      <td>0.000874</td>\n",
       "      <td>0.990198</td>\n",
       "      <td>0.006893</td>\n",
       "      <td>0.167481</td>\n",
       "      <td>0.000807</td>\n",
       "      <td>0.851596</td>\n",
       "      <td>0.00069</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>124</th>\n",
       "      <td>APT124</td>\n",
       "      <td>328.686421</td>\n",
       "      <td>62304.434566</td>\n",
       "      <td>0.034007</td>\n",
       "      <td>-0.638509</td>\n",
       "      <td>0.001475</td>\n",
       "      <td>0.510368</td>\n",
       "      <td>0.007755</td>\n",
       "      <td>0.319787</td>\n",
       "      <td>0.005712</td>\n",
       "      <td>0.538152</td>\n",
       "      <td>0.002927</td>\n",
       "      <td>0.7046</td>\n",
       "      <td>0.006245</td>\n",
       "      <td>-0.756329</td>\n",
       "      <td>0.002538</td>\n",
       "      <td>0.741324</td>\n",
       "      <td>0.000657</td>\n",
       "      <td>0.860774</td>\n",
       "      <td>0.005362</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>125</th>\n",
       "      <td>APT125</td>\n",
       "      <td>34360.803777</td>\n",
       "      <td>63463.481633</td>\n",
       "      <td>0.082748</td>\n",
       "      <td>-0.039965</td>\n",
       "      <td>0.005816</td>\n",
       "      <td>0.359279</td>\n",
       "      <td>0.001522</td>\n",
       "      <td>0.483778</td>\n",
       "      <td>0.001153</td>\n",
       "      <td>0.627422</td>\n",
       "      <td>0.002602</td>\n",
       "      <td>0.732864</td>\n",
       "      <td>0.00374</td>\n",
       "      <td>-0.027154</td>\n",
       "      <td>0.002697</td>\n",
       "      <td>0.822893</td>\n",
       "      <td>0.000383</td>\n",
       "      <td>-0.040601</td>\n",
       "      <td>0.000819</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>126</th>\n",
       "      <td>APT126</td>\n",
       "      <td>19682.10523</td>\n",
       "      <td>64356.315571</td>\n",
       "      <td>0.077507</td>\n",
       "      <td>0.639574</td>\n",
       "      <td>0.002925</td>\n",
       "      <td>0.538404</td>\n",
       "      <td>0.001846</td>\n",
       "      <td>-0.630967</td>\n",
       "      <td>0.00343</td>\n",
       "      <td>0.879191</td>\n",
       "      <td>0.003559</td>\n",
       "      <td>0.751754</td>\n",
       "      <td>0.001549</td>\n",
       "      <td>0.514178</td>\n",
       "      <td>0.004466</td>\n",
       "      <td>0.830439</td>\n",
       "      <td>0.004617</td>\n",
       "      <td>0.090496</td>\n",
       "      <td>0.00129</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>127</th>\n",
       "      <td>APT127</td>\n",
       "      <td>5653.618083</td>\n",
       "      <td>77889.568543</td>\n",
       "      <td>0.092858</td>\n",
       "      <td>0.971876</td>\n",
       "      <td>0.001848</td>\n",
       "      <td>-0.362926</td>\n",
       "      <td>0.000939</td>\n",
       "      <td>0.361583</td>\n",
       "      <td>0.003224</td>\n",
       "      <td>-0.991907</td>\n",
       "      <td>0</td>\n",
       "      <td>0.35973</td>\n",
       "      <td>0</td>\n",
       "      <td>0.235998</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.903778</td>\n",
       "      <td>0.005033</td>\n",
       "      <td>-0.369704</td>\n",
       "      <td>0.001571</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>128 rows × 20 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Name         start           end  activity   x0_mean    x0_std   \n",
       "0      APT0   28706.74534  71522.773839  0.077005 -0.820592  0.002832  \\\n",
       "1      APT1  22885.572764  75358.394044  0.011253 -0.318926  0.004806   \n",
       "2      APT2  13686.340629  87194.596239  0.053374   0.02996  0.005948   \n",
       "3      APT3  15469.753789  67852.004096  0.059287 -0.624815  0.003561   \n",
       "4      APT4   2513.049464  60474.967226  0.022255  0.732821  0.005297   \n",
       "..      ...           ...           ...       ...       ...       ...   \n",
       "123  APT123   4746.413984  63921.918377  0.070892  0.348162  0.000862   \n",
       "124  APT124    328.686421  62304.434566  0.034007 -0.638509  0.001475   \n",
       "125  APT125  34360.803777  63463.481633  0.082748 -0.039965  0.005816   \n",
       "126  APT126   19682.10523  64356.315571  0.077507  0.639574  0.002925   \n",
       "127  APT127   5653.618083  77889.568543  0.092858  0.971876  0.001848   \n",
       "\n",
       "      x1_mean    x1_std   x2_mean    x2_std   x3_mean    x3_std   x4_mean   \n",
       "0     0.27332  0.005723  0.407798  0.002934 -0.240162  0.005786 -0.306415  \\\n",
       "1       0.967  0.006445  0.482932  0.006152  0.696349  0.003473  0.175878   \n",
       "2    0.763969   0.00645   0.26125  0.001815  0.939208  0.003766  0.977583   \n",
       "3   -0.344747  0.000783 -0.336424  0.006418  0.419702  0.003588 -0.055239   \n",
       "4    -0.72749  0.000851  0.158977  0.002643 -0.177384  0.004338  0.966511   \n",
       "..        ...       ...       ...       ...       ...       ...       ...   \n",
       "123 -0.984142  0.005359  0.580728  0.007765 -0.448492  0.000902  0.878032   \n",
       "124  0.510368  0.007755  0.319787  0.005712  0.538152  0.002927    0.7046   \n",
       "125  0.359279  0.001522  0.483778  0.001153  0.627422  0.002602  0.732864   \n",
       "126  0.538404  0.001846 -0.630967   0.00343  0.879191  0.003559  0.751754   \n",
       "127 -0.362926  0.000939  0.361583  0.003224 -0.991907         0   0.35973   \n",
       "\n",
       "       x4_std   x5_mean    x5_std   x6_mean    x6_std   x7_mean    x7_std  \n",
       "0    0.003061 -0.354689  0.000621 -0.677331  0.004643  0.004811   0.00569  \n",
       "1    0.004689  0.800283  0.005346  -0.67634  0.005998 -0.961339   0.00058  \n",
       "2    0.006245 -0.753842  0.001502  0.246647   0.00314   0.96053  0.000863  \n",
       "3    0.004288  0.886934  0.006477  0.094761  0.004823  0.306553  0.002409  \n",
       "4    0.002831  0.651822  0.001268 -0.859898   0.00633 -0.191439  0.002715  \n",
       "..        ...       ...       ...       ...       ...       ...       ...  \n",
       "123  0.000874  0.990198  0.006893  0.167481  0.000807  0.851596   0.00069  \n",
       "124  0.006245 -0.756329  0.002538  0.741324  0.000657  0.860774  0.005362  \n",
       "125   0.00374 -0.027154  0.002697  0.822893  0.000383 -0.040601  0.000819  \n",
       "126  0.001549  0.514178  0.004466  0.830439  0.004617  0.090496   0.00129  \n",
       "127         0  0.235998         0 -0.903778  0.005033 -0.369704  0.001571  \n",
       "\n",
       "[128 rows x 20 columns]"
      ]
     },
     "execution_count": 123,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_actors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d1d9e349-ee6b-459d-a867-a5f50c32309e",
   "metadata": {},
   "outputs": [
    {
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>157,5</th>\n",
       "      <td>APT5</td>\n",
       "      <td>157</td>\n",
       "      <td>0.165575</td>\n",
       "      <td>0.478021</td>\n",
       "      <td>0.54325</td>\n",
       "      <td>-0.93793</td>\n",
       "      <td>-0.931223</td>\n",
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       "      <td>-0.841161</td>\n",
       "      <td>0.181773</td>\n",
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       "      <th>168,5</th>\n",
       "      <td>APT5</td>\n",
       "      <td>168</td>\n",
       "      <td>0.165628</td>\n",
       "      <td>0.477952</td>\n",
       "      <td>0.548967</td>\n",
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       "      <td>-0.924607</td>\n",
       "      <td>-0.900543</td>\n",
       "      <td>-0.850106</td>\n",
       "      <td>0.165962</td>\n",
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       "    <tr>\n",
       "      <th>208,5</th>\n",
       "      <td>APT5</td>\n",
       "      <td>208</td>\n",
       "      <td>0.164336</td>\n",
       "      <td>0.477998</td>\n",
       "      <td>0.537964</td>\n",
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       "      <td>-0.941115</td>\n",
       "      <td>-0.901665</td>\n",
       "      <td>-0.831019</td>\n",
       "      <td>0.176477</td>\n",
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       "    <tr>\n",
       "      <th>243,5</th>\n",
       "      <td>APT5</td>\n",
       "      <td>243</td>\n",
       "      <td>0.162881</td>\n",
       "      <td>0.47797</td>\n",
       "      <td>0.537589</td>\n",
       "      <td>-0.941488</td>\n",
       "      <td>-0.938519</td>\n",
       "      <td>-0.901864</td>\n",
       "      <td>-0.843723</td>\n",
       "      <td>0.171911</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>333,124</th>\n",
       "      <td>APT124</td>\n",
       "      <td>333</td>\n",
       "      <td>-0.637978</td>\n",
       "      <td>0.514327</td>\n",
       "      <td>0.321225</td>\n",
       "      <td>0.539044</td>\n",
       "      <td>0.706843</td>\n",
       "      <td>-0.755271</td>\n",
       "      <td>0.741007</td>\n",
       "      <td>0.856741</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>99758,20</th>\n",
       "      <td>APT20</td>\n",
       "      <td>99758</td>\n",
       "      <td>-0.980243</td>\n",
       "      <td>-0.903211</td>\n",
       "      <td>0.035211</td>\n",
       "      <td>-0.114349</td>\n",
       "      <td>0.9851</td>\n",
       "      <td>-0.456833</td>\n",
       "      <td>-0.807037</td>\n",
       "      <td>0.896355</td>\n",
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       "    <tr>\n",
       "      <th>99796,37</th>\n",
       "      <td>APT37</td>\n",
       "      <td>99796</td>\n",
       "      <td>0.310684</td>\n",
       "      <td>-0.498707</td>\n",
       "      <td>-0.454661</td>\n",
       "      <td>0.75608</td>\n",
       "      <td>-0.640071</td>\n",
       "      <td>0.25557</td>\n",
       "      <td>0.24839</td>\n",
       "      <td>-0.832962</td>\n",
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       "    <tr>\n",
       "      <th>99810,37</th>\n",
       "      <td>APT37</td>\n",
       "      <td>99810</td>\n",
       "      <td>-0.852118</td>\n",
       "      <td>-0.507844</td>\n",
       "      <td>0.933745</td>\n",
       "      <td>-0.469913</td>\n",
       "      <td>0.030024</td>\n",
       "      <td>0.257686</td>\n",
       "      <td>0.248039</td>\n",
       "      <td>-0.833692</td>\n",
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       "    <tr>\n",
       "      <th>99845,37</th>\n",
       "      <td>APT37</td>\n",
       "      <td>99845</td>\n",
       "      <td>-0.042967</td>\n",
       "      <td>0.360498</td>\n",
       "      <td>0.921839</td>\n",
       "      <td>0.625446</td>\n",
       "      <td>-0.649429</td>\n",
       "      <td>0.252656</td>\n",
       "      <td>0.246344</td>\n",
       "      <td>-0.040976</td>\n",
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       "      <th>99848,37</th>\n",
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       "      <td>-0.506527</td>\n",
       "      <td>0.926321</td>\n",
       "      <td>0.756452</td>\n",
       "      <td>0.189032</td>\n",
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       "      <td>0.029819</td>\n",
       "      <td>-0.833801</td>\n",
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       "            Name Timestep        x0        x1        x2        x3        x4   \n",
       "157,5       APT5      157  0.165575  0.478021   0.54325  -0.93793 -0.931223  \\\n",
       "168,5       APT5      168  0.165628  0.477952  0.548967 -0.936848 -0.924607   \n",
       "208,5       APT5      208  0.164336  0.477998  0.537964 -0.938837 -0.941115   \n",
       "243,5       APT5      243  0.162881   0.47797  0.537589 -0.941488 -0.938519   \n",
       "333,124   APT124      333 -0.637978  0.514327  0.321225  0.539044  0.706843   \n",
       "...          ...      ...       ...       ...       ...       ...       ...   \n",
       "99758,20   APT20    99758 -0.980243 -0.903211  0.035211 -0.114349    0.9851   \n",
       "99796,37   APT37    99796  0.310684 -0.498707 -0.454661   0.75608 -0.640071   \n",
       "99810,37   APT37    99810 -0.852118 -0.507844  0.933745 -0.469913  0.030024   \n",
       "99845,37   APT37    99845 -0.042967  0.360498  0.921839  0.625446 -0.649429   \n",
       "99848,37   APT37    99848  0.325897 -0.506527  0.926321  0.756452  0.189032   \n",
       "\n",
       "                x5        x6        x7  \n",
       "157,5    -0.902985 -0.841161  0.181773  \n",
       "168,5    -0.900543 -0.850106  0.165962  \n",
       "208,5    -0.901665 -0.831019  0.176477  \n",
       "243,5    -0.901864 -0.843723  0.171911  \n",
       "333,124  -0.755271  0.741007  0.856741  \n",
       "...            ...       ...       ...  \n",
       "99758,20 -0.456833 -0.807037  0.896355  \n",
       "99796,37   0.25557   0.24839 -0.832962  \n",
       "99810,37  0.257686  0.248039 -0.833692  \n",
       "99845,37  0.252656  0.246344 -0.040976  \n",
       "99848,37  0.428336  0.029819 -0.833801  \n",
       "\n",
       "[392577 rows x 10 columns]"
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     },
     "execution_count": 124,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "df_incidents"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "fe1d045e",
   "metadata": {},
   "source": [
    "# Describe dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b2366137",
   "metadata": {},
   "outputs": [
    {
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       "      <th>freq</th>\n",
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       "      <td>6.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>19.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>6.000000</td>\n",
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      "text/plain": [
       "          Name  Timestep        x0             x1             x2   \n",
       "count   392577    392577  392577.0  392577.000000  392577.000000  \\\n",
       "unique     128     89905  318327.0  318126.000000  318034.000000   \n",
       "top      APT81     59210      -1.0       0.645827       0.553069   \n",
       "freq      7529        21      10.0       7.000000       6.000000   \n",
       "\n",
       "                   x3        x4        x5             x6             x7  \n",
       "count   392577.000000  392577.0  392577.0  392577.000000  392577.000000  \n",
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       "freq         7.000000      19.0      10.0       6.000000       6.000000  "
      ]
     },
     "execution_count": 125,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_incidents.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a20ba043",
   "metadata": {},
   "outputs": [
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       "      <td>21394.174586</td>\n",
       "      <td>0.589163</td>\n",
       "      <td>0.565915</td>\n",
       "      <td>0.548706</td>\n",
       "      <td>0.593997</td>\n",
       "      <td>0.605565</td>\n",
       "      <td>0.630383</td>\n",
       "      <td>0.582353</td>\n",
       "      <td>0.574928</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>157.000000</td>\n",
       "      <td>-1.020642</td>\n",
       "      <td>-1.016341</td>\n",
       "      <td>-1.016064</td>\n",
       "      <td>-1.005816</td>\n",
       "      <td>-1.010904</td>\n",
       "      <td>-1.013426</td>\n",
       "      <td>-1.004727</td>\n",
       "      <td>-1.009938</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>32623.000000</td>\n",
       "      <td>-0.563940</td>\n",
       "      <td>-0.398511</td>\n",
       "      <td>-0.529141</td>\n",
       "      <td>-0.315189</td>\n",
       "      <td>-0.401703</td>\n",
       "      <td>-0.547926</td>\n",
       "      <td>-0.635384</td>\n",
       "      <td>-0.479469</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>48404.000000</td>\n",
       "      <td>-0.027718</td>\n",
       "      <td>0.018178</td>\n",
       "      <td>-0.061391</td>\n",
       "      <td>0.178514</td>\n",
       "      <td>0.181388</td>\n",
       "      <td>0.154160</td>\n",
       "      <td>-0.107864</td>\n",
       "      <td>0.076330</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>63972.000000</td>\n",
       "      <td>0.509634</td>\n",
       "      <td>0.557532</td>\n",
       "      <td>0.370586</td>\n",
       "      <td>0.625330</td>\n",
       "      <td>0.644630</td>\n",
       "      <td>0.645416</td>\n",
       "      <td>0.524748</td>\n",
       "      <td>0.545567</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>99848.000000</td>\n",
       "      <td>1.009356</td>\n",
       "      <td>1.003642</td>\n",
       "      <td>1.009983</td>\n",
       "      <td>1.017823</td>\n",
       "      <td>1.015125</td>\n",
       "      <td>1.019453</td>\n",
       "      <td>0.968629</td>\n",
       "      <td>1.015917</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Timestep             x0             x1             x2   \n",
       "count  392577.000000  392577.000000  392577.000000  392577.000000  \\\n",
       "mean    48487.939136      -0.030761       0.069582      -0.052340   \n",
       "std     21394.174586       0.589163       0.565915       0.548706   \n",
       "min       157.000000      -1.020642      -1.016341      -1.016064   \n",
       "25%     32623.000000      -0.563940      -0.398511      -0.529141   \n",
       "50%     48404.000000      -0.027718       0.018178      -0.061391   \n",
       "75%     63972.000000       0.509634       0.557532       0.370586   \n",
       "max     99848.000000       1.009356       1.003642       1.009983   \n",
       "\n",
       "                  x3             x4             x5             x6   \n",
       "count  392577.000000  392577.000000  392577.000000  392577.000000  \\\n",
       "mean        0.125891       0.128176       0.070693      -0.054770   \n",
       "std         0.593997       0.605565       0.630383       0.582353   \n",
       "min        -1.005816      -1.010904      -1.013426      -1.004727   \n",
       "25%        -0.315189      -0.401703      -0.547926      -0.635384   \n",
       "50%         0.178514       0.181388       0.154160      -0.107864   \n",
       "75%         0.625330       0.644630       0.645416       0.524748   \n",
       "max         1.017823       1.015125       1.019453       0.968629   \n",
       "\n",
       "                  x7  \n",
       "count  392577.000000  \n",
       "mean        0.048388  \n",
       "std         0.574928  \n",
       "min        -1.009938  \n",
       "25%        -0.479469  \n",
       "50%         0.076330  \n",
       "75%         0.545567  \n",
       "max         1.015917  "
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_incidents[['Timestep'] + feature_names].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "55daba60",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = matplotlib.pyplot.subplots(figsize=FIGSIZE)\n",
    "\n",
    "fg = seaborn.violinplot(data=df_incidents[feature_names], inner='box', color=seaborn.color_palette()[0])\n",
    "\n",
    "fig.savefig('feature-distributions.pdf', dpi=600, bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "fd6b64e2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "n_actors_shown = 4\n",
    "\n",
    "shown_actors = [f'APT{i}' for i in range(n_actors_shown)]\n",
    "\n",
    "fig, ax = matplotlib.pyplot.subplots(figsize=FIGSIZE)\n",
    "\n",
    "fg = seaborn.kdeplot(\n",
    "    data=df_incidents[df_incidents['Name'].apply(lambda x: x in shown_actors)],\n",
    "    x='Timestep',\n",
    "    hue=\"Name\",\n",
    "    hue_order=shown_actors,\n",
    ")\n",
    "\n",
    "fg.set_ylabel('Activity of threat actor')\n",
    "\n",
    "fig.savefig('threat-actor-activity.pdf', dpi=600, bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "79151e8c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = matplotlib.pyplot.subplots(figsize=FIGSIZE)\n",
    "\n",
    "fg = seaborn.histplot(\n",
    "    data=df_incidents['Name'].value_counts(), \n",
    "    kde=True,\n",
    ")\n",
    "\n",
    "fg.set_xlabel('Number of incidents associated with threat actor')\n",
    "fg.set_ylabel('Number of threat actors')\n",
    "\n",
    "fig.savefig('threat-actor-hist.pdf', dpi=600, bbox_inches='tight')"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "8ea7d433-e970-4b01-982f-63bc7ec6ebec",
   "metadata": {},
   "source": [
    "# Load/save dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8308da6b-edfd-4267-8741-3cb837a7a098",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_incidents.to_csv('df_incidents.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "8863fce5-26cd-47f7-8bba-a0788675f8d3",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_incidents = pandas.read_csv('df_incidents.csv')"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "56d50a90-30c6-4b19-af42-98cedd98b2e8",
   "metadata": {},
   "source": [
    "# Split dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "id": "f97ea3e3-cee1-4fb2-8ee0-c84cae4bc1b0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(206511, 186066)"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train = df_incidents[df_incidents['Timestep'] < EXPERIMENT_SIZE * TRAIN_SIZE]\n",
    "df_test = df_incidents[df_incidents['Timestep'] >= EXPERIMENT_SIZE * TRAIN_SIZE]\n",
    "\n",
    "len(df_train), len(df_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "id": "a3514cf4-b411-427f-b3bb-5991caa8a8bd",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\20173892\\AppData\\Local\\Temp\\ipykernel_19428\\4255208019.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df_train['Name'] = df_train['Name'].apply(lambda x: int(x[3:]))\n",
      "C:\\Users\\20173892\\AppData\\Local\\Temp\\ipykernel_19428\\4255208019.py:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df_test['Name'] = df_test['Name'].apply(lambda x: int(x[3:]))\n"
     ]
    }
   ],
   "source": [
    "df_train['Name'] = df_train['Name'].apply(lambda x: int(x[3:]))\n",
    "df_test['Name'] = df_test['Name'].apply(lambda x: int(x[3:]))"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "b1ea0dcb-e5b6-4143-89d9-730c7f1df894",
   "metadata": {},
   "source": [
    "## Run experiment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "id": "4c692448",
   "metadata": {},
   "outputs": [],
   "source": [
    "runtimes = {}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "id": "9717da16-2f36-495a-bc0b-f86ece0389a7",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 8/8 [56:13<00:00, 421.69s/it]\n"
     ]
    }
   ],
   "source": [
    "models = [\n",
    "    copy.deepcopy(MODEL)\n",
    "    for _ in range(FEATURES)\n",
    "]\n",
    "\n",
    "t1 = time.process_time()\n",
    "for i in tqdm.tqdm(range(FEATURES)):\n",
    "    models[i].fit(df_train[[f'x{i}']].to_numpy(), df_train['Name'])\n",
    "t2 = time.process_time()\n",
    "runtimes['Training Modules'] = t2 - t1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "id": "4732ffda-dff4-4ccc-aea7-7feab4df96d1",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 8/8 [10:59:42<00:00, 4947.79s/it] \n"
     ]
    }
   ],
   "source": [
    "preds = []\n",
    "t1 = time.process_time()\n",
    "for i in tqdm.tqdm(range(FEATURES)):\n",
    "    preds.append(models[i].predict_proba(df_test[[f'x{i}']].to_numpy()))\n",
    "t2 = time.process_time()\n",
    "runtimes['Prediction Modules'] = t2 - t1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "id": "103eff0d-11be-497d-a4ef-bac8d851301a",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_pools = []"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "id": "989734f6-855f-405d-8c3c-8bfb63ffb6ad",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 50%|█████     | 1/2 [07:04<07:04, 424.09s/it]c:\\Users\\20173892\\AppData\\Local\\Programs\\Python\\venv\\Lib\\site-packages\\xgboost\\data.py:520: FutureWarning: is_sparse is deprecated and will be removed in a future version. Check `isinstance(dtype, pd.SparseDtype)` instead.\n",
      "  if is_sparse(data):\n",
      "100%|██████████| 2/2 [42:14<00:00, 1267.31s/it]\n"
     ]
    }
   ],
   "source": [
    "for model_name, model_base in tqdm.tqdm(MODELS.items()):\n",
    "    model = copy.deepcopy(model_base)\n",
    "    t1 = time.process_time()\n",
    "    model.fit(df_train[feature_names].to_numpy(), df_train['Name'])\n",
    "    t2 = time.process_time()\n",
    "    runtimes[f'Training {model_name}'] = t2 - t1\n",
    "    \n",
    "    probs_correct = numpy.zeros(len(df_test))\n",
    "    probs_incorrect = numpy.zeros(len(df_test))\n",
    "    k_accurates_correct = numpy.zeros(len(df_test))\n",
    "    times = numpy.zeros(len(df_test))\n",
    "    names = numpy.zeros(len(df_test))\n",
    "\n",
    "    t1 = time.process_time()\n",
    "    \n",
    "    for i in range(len(df_test)):\n",
    "        if not isinstance(df_test.iloc[i]['Name'], str):\n",
    "            apt_i = df_test.iloc[i]['Name']\n",
    "        else:\n",
    "            apt_i = int(df_test.iloc[i]['Name'][3:])\n",
    "\n",
    "        c = model.predict_proba(df_test.iloc[i][feature_names].to_numpy().reshape(1, FEATURES)).reshape(ACTORS)\n",
    "        target = target_pdf(ACTORS, apt_i)\n",
    "\n",
    "        probs_correct[i] = c[apt_i]\n",
    "        probs_incorrect[i] = c[c != c[apt_i]].max() if c.max() != c.min() else c[apt_i]\n",
    "        k_accurates_correct[i] = get_k_accuracy_for_target(c, apt_i)\n",
    "\n",
    "        times[i] = df_test.iloc[i]['Timestep']\n",
    "        names[i] = apt_i\n",
    "        \n",
    "    t2 = time.process_time()\n",
    "    runtimes[f'Prediction {model_name}'] = t2 - t1\n",
    "\n",
    "    df_pool = pandas.DataFrame(\n",
    "        numpy.array([probs_correct, probs_incorrect, k_accurates_correct, times, names]).transpose(), \n",
    "        columns=['Probability of correct attribution', 'Probability of incorrect attribution', 'k-Accuracy', 'Timestep', 'Name']\n",
    "    )\n",
    "    df_pool['Method'] = model_name\n",
    "    df_pools.append(df_pool)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "id": "7ba53b39-6497-4e19-91fe-d68514a2c9b1",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 3/3 [09:52<00:00, 197.51s/it]\n"
     ]
    }
   ],
   "source": [
    "for pool_name, pool in tqdm.tqdm(pools.items()):\n",
    "    entropies_x = numpy.zeros(len(df_test))\n",
    "    entropies_y = numpy.zeros(len(df_test))\n",
    "    entropies_z = numpy.zeros(len(df_test))\n",
    "    entropies_c = numpy.zeros(len(df_test))\n",
    "    joint_entropies = numpy.zeros(len(df_test))\n",
    "    conditional_entropies = numpy.zeros(len(df_test))\n",
    "    probs_correct = numpy.zeros(len(df_test))\n",
    "    probs_incorrect = numpy.zeros(len(df_test))\n",
    "    k_accurates_correct = numpy.zeros(len(df_test))\n",
    "    times = numpy.zeros(len(df_test))\n",
    "    names = numpy.zeros(len(df_test))\n",
    "    \n",
    "    t1 = time.process_time()\n",
    "    \n",
    "    for i in range(len(df_test)):\n",
    "        if not isinstance(df_test.iloc[i]['Name'], str):\n",
    "            apt_i = df_test.iloc[i]['Name']\n",
    "        else:\n",
    "            apt_i = int(df_test.iloc[i]['Name'][3:])\n",
    "\n",
    "        c = pool(numpy.array([preds[j][i] for j in range(FEATURES)]))\n",
    "        target = target_pdf(ACTORS, apt_i)\n",
    "        \n",
    "        probs_correct[i] = c[apt_i]\n",
    "        probs_incorrect[i] = c[c != c[apt_i]].max() if c.max() != c.min() else c[apt_i]\n",
    "        k_accurates_correct[i] = get_k_accuracy_for_target(c, apt_i)\n",
    "        \n",
    "        times[i] = df_test.iloc[i]['Timestep']\n",
    "        names[i] = apt_i\n",
    "        \n",
    "    t2 = time.process_time()\n",
    "    runtimes[f'Prediction {pool_name}'] = t2 - t1\n",
    "\n",
    "    df_pool = pandas.DataFrame(\n",
    "        numpy.array([probs_correct, probs_incorrect, k_accurates_correct, times, names]).transpose(), \n",
    "        columns=['Probability of correct attribution', 'Probability of incorrect attribution', 'k-Accuracy', 'Timestep', 'Name']\n",
    "    )\n",
    "    df_pool['Method'] = pool_name\n",
    "    df_pools.append(df_pool)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "id": "e4486e34-3ee7-432d-b083-9aa58e50fc11",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pandas.concat(df_pools)\n",
    "\n",
    "df['prediction'] = df.apply(lambda x: 'correct' if x['Probability of correct attribution'] > x['Probability of incorrect attribution'] else 'incorrect', axis=1)\n",
    "df['correct'] = (df['prediction'] == 'correct')\n",
    "df['incorrect'] = (df['prediction'] == 'incorrect')"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "923a9417",
   "metadata": {},
   "source": [
    "# Load/save results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "id": "b8e56ddf",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.to_csv('df_results.csv')\n",
    "with open('runtimes.pickle', 'wb') as file:\n",
    "    pickle.dump(runtimes, file)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "id": "9adc9a8a",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pandas.read_csv('df_results.csv')\n",
    "with open('runtimes.pickle', 'rb') as file:\n",
    "    runtimes = pickle.load(file)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "124f46ea-4e24-4659-9548-0ee081cf9547",
   "metadata": {},
   "source": [
    "# Results"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "e31e51e9",
   "metadata": {},
   "source": [
    "## Precision-Recall"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "id": "032c0124",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 5/5 [10:20<00:00, 124.06s/it]\n"
     ]
    }
   ],
   "source": [
    "# Precision-Recall\n",
    "df_pools = []\n",
    "\n",
    "for pool in tqdm.tqdm(df['Method'].unique()):\n",
    "    thresholds = set(df[(df['Method']==pool) & (df['correct']==True)]['Probability of correct attribution'])\n",
    "    thresholds = thresholds.union(set(df[(df['Method']==pool) & (df['incorrect']==True)]['Probability of incorrect attribution']))\n",
    "    thresholds = set([round(x, 3) for x in thresholds])\n",
    "    thresholds = [0] + sorted(list(thresholds)) + [1]\n",
    "    # thresholds = numpy.linspace(1, 100) / 100\n",
    "    precisions = []\n",
    "    recalls = []\n",
    "    for threshold in thresholds:\n",
    "        # t = len(df[(df['Method'] == pool) & (df['correct']==True)].index)\n",
    "        t = len(df[(df['Method'] == pool)].index)\n",
    "        tps = (df[(df['Method'] == pool) & (df['correct']==True)]['Probability of correct attribution'] >= threshold).sum()\n",
    "        fns = (df[(df['Method'] == pool) & (df['correct']==True)]['Probability of correct attribution'] < threshold).sum()\n",
    "        fps = (df[(df['Method'] == pool) & (df['incorrect']==True)]['Probability of incorrect attribution'] >= threshold).sum()\n",
    "        if (tps + fps) > 0:\n",
    "            precisions.append(tps / (tps + fps))\n",
    "        else:\n",
    "            precisions.append(0)\n",
    "        if t > 0:\n",
    "            recalls.append(tps / (tps + fns))\n",
    "        else:\n",
    "            recalls.append(0)\n",
    "\n",
    "    df_pool = pandas.DataFrame(\n",
    "        numpy.array([thresholds, precisions, recalls]).transpose(), \n",
    "        columns=['threshold', 'Precision', 'Recall']\n",
    "    )\n",
    "    df_pool['Method'] = pool\n",
    "    df_pools.append(df_pool)\n",
    "\n",
    "pr_df = pandas.concat(df_pools)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "id": "ab771a19",
   "metadata": {},
   "outputs": [],
   "source": [
    "pr_df = pr_df[(pr_df['Precision'] > 0) | (pr_df['Recall'] > 0)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "id": "cf1891d1",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\20173892\\AppData\\Local\\Programs\\Python\\venv\\Lib\\site-packages\\seaborn\\_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "c:\\Users\\20173892\\AppData\\Local\\Programs\\Python\\venv\\Lib\\site-packages\\seaborn\\_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "c:\\Users\\20173892\\AppData\\Local\\Programs\\Python\\venv\\Lib\\site-packages\\seaborn\\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
      "  with pd.option_context('mode.use_inf_as_na', True):\n",
      "c:\\Users\\20173892\\AppData\\Local\\Programs\\Python\\venv\\Lib\\site-packages\\seaborn\\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
      "  with pd.option_context('mode.use_inf_as_na', True):\n",
      "c:\\Users\\20173892\\AppData\\Local\\Programs\\Python\\venv\\Lib\\site-packages\\seaborn\\_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "c:\\Users\\20173892\\AppData\\Local\\Programs\\Python\\venv\\Lib\\site-packages\\seaborn\\_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "c:\\Users\\20173892\\AppData\\Local\\Programs\\Python\\venv\\Lib\\site-packages\\seaborn\\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
      "  with pd.option_context('mode.use_inf_as_na', True):\n",
      "c:\\Users\\20173892\\AppData\\Local\\Programs\\Python\\venv\\Lib\\site-packages\\seaborn\\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
      "  with pd.option_context('mode.use_inf_as_na', True):\n",
      "c:\\Users\\20173892\\AppData\\Local\\Programs\\Python\\venv\\Lib\\site-packages\\seaborn\\_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "c:\\Users\\20173892\\AppData\\Local\\Programs\\Python\\venv\\Lib\\site-packages\\seaborn\\_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "c:\\Users\\20173892\\AppData\\Local\\Programs\\Python\\venv\\Lib\\site-packages\\seaborn\\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
      "  with pd.option_context('mode.use_inf_as_na', True):\n",
      "c:\\Users\\20173892\\AppData\\Local\\Programs\\Python\\venv\\Lib\\site-packages\\seaborn\\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
      "  with pd.option_context('mode.use_inf_as_na', True):\n",
      "c:\\Users\\20173892\\AppData\\Local\\Programs\\Python\\venv\\Lib\\site-packages\\seaborn\\_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "c:\\Users\\20173892\\AppData\\Local\\Programs\\Python\\venv\\Lib\\site-packages\\seaborn\\_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "c:\\Users\\20173892\\AppData\\Local\\Programs\\Python\\venv\\Lib\\site-packages\\seaborn\\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
      "  with pd.option_context('mode.use_inf_as_na', True):\n",
      "c:\\Users\\20173892\\AppData\\Local\\Programs\\Python\\venv\\Lib\\site-packages\\seaborn\\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
      "  with pd.option_context('mode.use_inf_as_na', True):\n",
      "c:\\Users\\20173892\\AppData\\Local\\Programs\\Python\\venv\\Lib\\site-packages\\seaborn\\_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "c:\\Users\\20173892\\AppData\\Local\\Programs\\Python\\venv\\Lib\\site-packages\\seaborn\\_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "c:\\Users\\20173892\\AppData\\Local\\Programs\\Python\\venv\\Lib\\site-packages\\seaborn\\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
      "  with pd.option_context('mode.use_inf_as_na', True):\n",
      "c:\\Users\\20173892\\AppData\\Local\\Programs\\Python\\venv\\Lib\\site-packages\\seaborn\\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
      "  with pd.option_context('mode.use_inf_as_na', True):\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = matplotlib.pyplot.subplots(figsize=FIGSIZE)\n",
    "ax.set_xlim(0,1)\n",
    "ax.set_ylim(0,1)\n",
    "\n",
    "for pool in pr_df['Method'].unique():\n",
    "    best_f = 0\n",
    "    best_i = 0\n",
    "    last_fpr = 0\n",
    "    last_tpr = 0\n",
    "    for i, row in pr_df[pr_df['Method']==pool].sort_values('Recall').iterrows():\n",
    "        if (row['Precision'] + row['Recall']) > 0:\n",
    "            f = 2 * (row['Precision'] * row['Recall']) / (row['Precision'] + row['Recall'])\n",
    "            if f > best_f:\n",
    "                best_f = f\n",
    "                best_i = i\n",
    "    seaborn.lineplot(data=pr_df[pr_df['Method']==pool], x='Recall', y='Precision', ax=ax, label=f\"{pool} (Best F-measure {best_f:.3f})\")\n",
    "    # seaborn.scatterplot(data=pr_df[(pr_df['Method']==pool) & (pr_df.index==best_i)], x='Recall', y='Precision', ax=ax, marker='x', s=50, legend=False)\n",
    "\n",
    "f_scores = numpy.linspace(0.2, 0.9, num=8)\n",
    "lines, labels = [], []\n",
    "for f_score in f_scores:\n",
    "    x = numpy.linspace(0.01, 1)\n",
    "    y = f_score * x / (2 * x - f_score)\n",
    "    (l,) = matplotlib.pyplot.plot(x[y >= 0], y[y >= 0], color=\"gray\", alpha=0.2)\n",
    "    matplotlib.pyplot.annotate(\"f1={0:0.1f}\".format(f_score), xy=(0.9, y[45] + 0.02))\n",
    "    \n",
    "# add the legend for the iso-f1 curves\n",
    "handles, labels = ax.get_legend_handles_labels()\n",
    "handles.extend([l])\n",
    "labels.extend([\"iso-f1 curves\"])\n",
    "\n",
    "matplotlib.pyplot.legend(loc='lower left')\n",
    "\n",
    "# https://scikit-learn.org/stable/auto_examples/model_selection/plot_precision_recall.html\n",
    "\n",
    "fig.savefig('pr-curve.pdf', dpi=600, bbox_inches='tight')"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "caa9c694",
   "metadata": {},
   "source": [
    "## k-Accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "id": "eb03f1b8-7375-4bfa-ba12-402ffa012eae",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\20173892\\AppData\\Local\\Programs\\Python\\venv\\Lib\\site-packages\\seaborn\\_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "c:\\Users\\20173892\\AppData\\Local\\Programs\\Python\\venv\\Lib\\site-packages\\seaborn\\_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "c:\\Users\\20173892\\AppData\\Local\\Programs\\Python\\venv\\Lib\\site-packages\\seaborn\\_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "c:\\Users\\20173892\\AppData\\Local\\Programs\\Python\\venv\\Lib\\site-packages\\seaborn\\_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "c:\\Users\\20173892\\AppData\\Local\\Programs\\Python\\venv\\Lib\\site-packages\\seaborn\\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
      "  with pd.option_context('mode.use_inf_as_na', True):\n"
     ]
    },
    {
     "data": {
      "image/png": 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YJaI32rHoZITGPC53fMcmtmhVz7IKMyIiojeJxovZlStXYsGCBUhKSoKnpyeWL1+OVq1Kv5XjkiVLsGrVKiQkJKBWrVr4+OOPERgYCH19fTVmTUSqcPrWE1x/mKo0ZtOZeACAd10LjOrQQGmsjYk+mtY2VVV6RESkDgW5QNw/QH72/7ZlZJX7cI0Ws9u3b0dAQABWr16N1q1bY8mSJfDz80NMTAxsbGyKxW/duhVTp07F+vXr0aZNG9y8eRP+/v4QiURYvHixBl4BEVVWanY+/DecR4FMKFe8k5Uh3mtiW8VZERGR2p1aBJz8SXFbbvl+NwAaLmYXL16MESNGYOjQoQCA1atXY//+/Vi/fj2mTp1aLP7MmTNo27YtBgwYAABwdnZG//79ce7cuVL7yM3NRW7u/+7ik5aWpuJXQUSvEgQBD1NzIFNSqCam5qBAJkAkAvq0qKO0PamOGMPaOqs4SyIiqlK5GcDukUBqGbf9fnjpf987+RR9zS4AEFKubjRWzObl5SEiIgLTpk2TbxOLxejUqRPCw8NLPKZNmzb4v//7P5w/fx6tWrXCnTt3cODAAQwaNKjUfgIDAzF79myV509Epftu93VsPZdQrlgdsQgLP/Gs4oyIiEilEq8Cd8OUx9w6CtwuX0EKAPhwDeD5adH3aWnAGLNyHaaxYvbJkycoLCyEra3iaUNbW1vcuHGjxGMGDBiAJ0+e4J133oEgCCgoKMDIkSPx7bffltrPtGnTEBAQIH+elpYGR0fld/Qhotdz7b+VBaQScZmrBvTwsFdHSkREVB752UDcKaAwV3nc9s8q1u6AP5Tv1zcHHEu/ZkoZjV8AVhGhoaH48ccf8csvv6B169aIjY3F+PHjMXfuXEyfPr3EY/T09KCnp6fmTImqp9TsfGw6E4+0nHylcdceFBWzawa1RIcmxee/ExGRmhUWANd3ABmPlMcdnVGxduv5AkbWpe8XS4AWQwDnthVrtwI0VszWqlULEokEjx4pvqmPHj2CnV3J9zmfPn06Bg0ahM8//xwA0KxZM2RmZuKLL77Ad999B7G49Lv5ENHr2xFxH4uO3ix3vJGeVv29TET0ZinIAx5cBGQFyuMOf1s0P1WZ53EV79/x7TL2vwW8/0PF21Uxjf2mkUqlaNmyJUJCQtC7d28AgEwmQ0hICMaMGVPiMVlZWcUKVolEAqDoghMiqlpZuUX/oTaxM4FvYyV/iQNwMDeAd10LdaRFRPTmKMgDMpOVx2SnAOEryi5Sr/2psrQUeA5Qvt/ICmg3CTAwr5r+VUyjwyYBAQEYMmQIvL290apVKyxZsgSZmZny1Q0GDx4MBwcHBAYGAgB69uyJxYsXo3nz5vJpBtOnT0fPnj3lRS0RVVxuQSH6rDqDm0nK/7LPK5QBAJo7mWNaV1d1pEZEVPUexwA5yte8xt0zQOIVQKJbekxhHvDvX6rN7QXrMv7PNbQCOs1UHiPWAew9i079VyMaLWb79euHx48fY8aMGUhKSoKXlxcOHTokvygsISFBYST2+++/h0gkwvfff48HDx7A2toaPXv2xLx58zT1EoiqhfgnWbj+oPzL1nnWMa+6ZIiIypL6ALh9HICSs7KCDDi5ADBWfhYJDy+rNDU5SRnX6xTmAvZegEdf5XHGtkDTD6tdAapKIqGGnZ9PS0uDmZkZUlNTYWpa/jsFHdy5HpMzfobtczGOTbhShRkSqZYgCHickav0//yYR+kYtO48zA11cXB8O6Xt6elIYGkkVXGWRKS1cjOKRiyVEoBLmwBJGf933AkFUu8BUL4KitL/0F6HRT3l+1PvFZ1+1zNRHlevXdEIKFVaReo1Xp1BVM1N/OMKdl0uY8Hq/0hEItibGVRxRkRUZQoLgNQy1ngWBODGPqBQ+aokSDgLJEcD+mUM/CRHVSzHcilnsVqnFWBoqaQZGWBYq2hkUxldg6LF+iUsi7QRPzWiai4i4TkAQCwCRCLlox1dm5W8kggRVYIgAHmZZcQUAtH7yjFfMwx4FgdIDZXH3b9QsRzLo7wzkAxrAQZKLvoUCgGIAK8yLj4SS4AmPcsuovXNAB0uvUksZom02oOUbPkKA6W5+zQLAPDnSB+0rKtkBIOoppPJgPh/gKynyuMSzgHP7pRdSN3Yp7rcKkokBnSNSt8vyy+6WKl5GQvf52UCTXoUXVykjFUDwEz5bamJqgqLWSIttTPiPib+yfnbVI1lPQNy05XHPLoOxJ8uKt5KI8iAs78UXcmtTFnLJFW1Zp8o35+dUnS6vKzlkmxcAcv6qsqK6I3HYpZIS91IKjr3p6cjhqFU+VWuda2M4GZfvntcE1VaQS6QdK3o9HqpBODUoqICU5nYY2XHVFRFilVn5RdCIuNR0ely/TJ+rmybAXbuymNEYp4uJ3oNLGaJ3kC5BYVIz1H+i/dpRh4AwL+NM6Z145qvVEmPbwIpd5XHJEcBV7YDesbK4+6dU11eL9MtY55oflbR6XJlp8IFAbB1B+q9q7wtibRowXgi0hosZoneMM8y89BxUSieZ5VxpTHVTAV5QNxJID9bedy5NUVzP5Wdfk9JAPLKOI1fGSIxYO5U+n5BVjSfs81Y5e3oGgAufoBUydxPIqrxWMwSvWFuP84odyFrrKeDtg1rVXFG9NryMoHkG8pjCrKB4/PKXhoo7h/V5fUyey/l+3PTAI9PAdumyuMs65UdQ0SkQixmid5Q9WoZ4cSk9ppOo+aSFRY9lHkcDVz4rWhtT2WubFVdXi9zfFv5flk+8N73ULoAvUgM1PHm6CcRaS0Ws0RqtCr0NnZeuq80JjY5Q03Z1FAPLwP3LyqPeXobOLdK9X3rmwF6Si4YkuUDtVyAFoPLaMccqN+eC7wTEYHFLJFa/XbqDp5m5pUr1tGyjIteahJZYdlXtt88DIQt/W9h9lIU5hVdba9qTT8EajdXHmPvBdT3VX3fREQ1HItZIjWS/bdk0cJPPFHHovTbxopFInjUqQFLaeWkAs/LuJI+6SqwZ7Tq+27So+hOQ6USAS0GAXXeUt6Ojj6XVSIi0iAWs0QqkFcgQ/idp8jJVz7H8sWFXZ51zNDI1kQdqWlG5lPgzgnlc04LcoC/x6m+785zi07VK2PvAZjWVn3fRESkdixmiVRg5YlYLA25Ve54sVjJBTlvsrxM4Mq2su8jHzK7Yu2a2CvfLxIDHb4DmnRXHqdrwFFSIqIahsUskQokpeYAAGqb6cPevPTpAwDQ2M4E9Wu9gVeOX1gHJEeXEbO2Ym2Kdcq+k1KT7kCrERVrl4iI6D8sZolUaODbdTG6Q0NNp6HoTihw9U8ASm4x+vwucPd0xdpt/pny/WaOQLtJvOKeiIiqFH/LECmRW1CIwAM38DBF+d2WjkQ9UlNGL0m9D5xeUnTqX5mKrnH67mTl+3UNigpZY5uKtUtERFQFWMwSKXHuzjMEn4kvd7yVkVQ1Hf+7G3h2R3nMiXmArIzF+l/mNRCo1Uh5TMPOgJ17+dskIiLSMBazRErkFxatbepgblDm9AFTAx10drNV3mDqfeDuGeUxydHA6cXlT1LHAOgwTXmMZX3AtWf52yQiItISLGaJyqGWsRQDWjspD8pJBXKeKY/5tT2Q+bj8HXuVMS9VzxhoO57LTBERUY3FYpZqrD2RD3DiRrLSmJAy9std2gzsHQulF1m9zKQ2YN249P0icdEtTZv2Ll97RERENRSLWaqxpu26hqw85Tc5eMH8aSSwdkbpAQ8iyt+xTVPgixNcD5WIiEgFWMxSjZX73926xjdIholuKUXtkxhIUu6isywCePCk7EY/XAN4fqrCLImIiEgZFrNU/RTkAffOFn1VRigEIMGA+7NgK0opPe7FT0n/7crb0zcDHFtXIFEiIiJ6XSxm/yPLylK6X8jLVVMm9LoyD8/FqbPhyC3jn3chxhZ907AzYGFSeqBIAngNABxaqDBLIiIiUgUWswAeTJyEtP37lcbk1gEwiG+XRqUkAJt6AxnKL8qan/kRNhZ+Xe5mJR+uBIw5f5WIiEgbsToDkBkWVu5YGS/aqRpxp4qmBihz9Q/g2e0ym0oWzAEA9c3EsKtloTS2hZMFarGQJSIi0losZgEUFMggAhDfey5yzUter/Py40gAq1Cgp+R0NFVOQR6w5ROgQPktY+Vs3IBPt5S+f/d94GY6hrZ3xSAfZ5WkSERERG8mFrMACvJk0AWQ9CAXWSk5JcbITHQBAGKJSI2ZVQOXtwDH5yq/7Wpe5v8K2WZ9AV2D0mMlukDLoUV3tCqN9DmA9EqlS0RERNqFxSwACEUL3bd43wlGbiUvZB+VKWBPHGBkxlPSFXJ1O5CeWL5YC+eipa3E4hJ3p2bnY+ae63i8LwNA6VMSwmKfVjxPIiIi0kosZl9i38AcNi1tStyX9sgCiANEYo7MAgAe/Qts/gjIKqNwlOUXfe04E3DxUx5rUa/UQhYA/rn5GLsjH5Y7RWsT/XLHEhERkXZiMUuVEx8GZCSVL1bHAHDpAti6vVaXBTIZAKCxrQlGdWigNNbSSIq2DWq9Vn9ERET05mMxS8Wd+BG4f1F5zO2Qoq+N/ICeS5TH6pkCesYqSQ0AbEz10MvLQWXtERERkfZiMUuKMpKBkz+VP97CGTAteQUIIiIioqrGYpYUFf43x1UkBnqvUh4rkQKNOquk24DtkTgV+0RpzON03oWNiIiIFLGYrUkurgdO/wz8N/e0RGkPir6KJIDnp2pJKyuvALsuPyh3fCMbrvVLRERERVjMAtjQMR8nm0mA2CEQ3S55tYI8WZ6as6oClzYV3RK2PGq5VG0uL/lvZTQAwK5RbaCvIyk1VlciQkMb1c2/JSIiIu3GYhbA2cYy5OuIACEfEJTHutdyV09SVeFF1dglCHBsrTzWuknV51MCVztTGEhLL2aJiIiIXsZi9iXzHabB6+0Ope4XiUSwNbRVY0bllJ8N/PsXkJOqPC4xsuirZQPAoUWVp0VERERU1VjMvsRKYgF7Y3tNp1FxkVuA/RPLH6+jvruY3XuWhb+vPkRhYelD3pl5hWrLh4iIiKoXFrPVQdbzoq8WzoCDt/JYszpA3TZVntILPx6IxsHr5bu5gq5EpOwGYERERETFsJitTur5Ah8s03QWCtJyipb68qlvBedahkpj365vBT0lF38RERERvYrFLKnFp60cedcuIiIiUjkWs2+y/BxgbQcgOUrTmRARERG9kThD8U327E4FClkR4NiqStMhIiIietNwZFYbGFgCo88rj9GRAvpm6skHwNOMXPx06AaeZeYrjQuLfaqmjIiIiKgmYjGrDURiwNha01koOPzvI/xx8X65462M1LccGBEREdUcLGapUvILZQCAZg5mGNjaSWmsjake2jSwUkdaREREVMOwmKXX4mRpiE9bKS9miYiIiKoKi1lNCf8FuLAWEEq/Mxaex6kvHyIiIiItpPHVDFauXAlnZ2fo6+ujdevWOH9e+YVOKSkpGD16NOzt7aGnpwcXFxccOHBATdmq0IW1RasVPI8r/fGCVQPN5UlERET0BtPoyOz27dsREBCA1atXo3Xr1liyZAn8/PwQExMDGxubYvF5eXno3LkzbGxssGPHDjg4OODu3bswNzdXf/Kv68WIbI+fAVt35bF2zao+n5ckp+cgNjlDaUz4ba5SQERERJqn0WJ28eLFGDFiBIYOHQoAWL16Nfbv34/169dj6tSpxeLXr1+PZ8+e4cyZM9DV1QUAODs7qzNl1bNp+katD5udV4iOC08iPbegXPEiURUnRERERKREpYrZwsJCBAcHIyQkBMnJyZDJZAr7jx8/XmYbeXl5iIiIwLRp0+TbxGIxOnXqhPDw8BKP2bt3L3x8fDB69Gjs2bMH1tbWGDBgAKZMmQKJRFLiMbm5ucjNzZU/T0tLK89LrLFSsvPkhWwjG2OlsVIdMfp6O6ojLSIiIqISVaqYHT9+PIKDg9G9e3e4u7tDVInhuSdPnqCwsBC2trYK221tbXHjxo0Sj7lz5w6OHz+OgQMH4sCBA4iNjcWoUaOQn5+PmTNnlnhMYGAgZs+eXeH8ajpdiQhHA3w1nQYRERGRUpUqZrdt24Y//vgD3bp1U3U+SslkMtjY2ODXX3+FRCJBy5Yt8eDBAyxYsKDUYnbatGkICAiQP09LS4OjI0cTiYiIiKqDShWzUqkUDRs2fK2Oa9WqBYlEgkePHilsf/ToEezs7Eo8xt7eHrq6ugpTClxdXZGUlIS8vDxIpdJix+jp6UFPj3efIiIiIqqOKrU018SJE7F06VIIytZILYNUKkXLli0REhIi3yaTyRASEgIfH58Sj2nbti1iY2MV5ujevHkT9vb2JRayRERERFS9VWpk9vTp0zhx4gQOHjyIpk2bylcWeGHXrl3laicgIABDhgyBt7c3WrVqhSVLliAzM1O+usHgwYPh4OCAwMBAAMBXX32FFStWYPz48Rg7dixu3bqFH3/8EePGjavMy6gaqfeB0z8DucqXttLEDRHyCmS49iAVMiV/hCSm5qgxIyIiIqLXU6li1tzcHB9++OFrd96vXz88fvwYM2bMQFJSEry8vHDo0CH5RWEJCQkQi/83eOzo6IjDhw/j66+/hoeHBxwcHDB+/HhMmTLltXNRmYhg4MJv5Y83sKiyVF4V8Eck9l1NLFdsZS7qIyIiIlK3ShWzGzZsUFkCY8aMwZgxY0rcFxoaWmybj48Pzp49q7L+VS4/u+hr3XcAFz/lsZb1AWuXqs/pP3efZgEAbE31YCRV/tF3bVbyvGUiIiKiN8lr3TTh8ePHiImJAQA0btwY1tbWKkmqWnBoAbR9g6Y/vCToIw90aFL8DmtERERE2qZSF4BlZmZi2LBhsLe3x7vvvot3330XtWvXxvDhw5GVlaXqHImIiIiISlSpYjYgIAAnT57E33//jZSUFKSkpGDPnj04efIkJk6cqOociYiIiIhKVKlpBjt37sSOHTvQvn17+bZu3brBwMAAffv2xapVq1SVHxERERFRqSo1MpuVlVXsNrQAYGNjw2kGRERERKQ2lRqZ9fHxwcyZM7Fp0ybo6+sDALKzszF79uxSb3hAVSspNQcZuflKY2KTy1j7loiIiEjLVKqYXbp0Kfz8/FCnTh14enoCAK5cuQJ9fX0cPnxYpQlS2Y78m4QvNkdoOg0iIiIitatUMevu7o5bt25hy5YtuHHjBgCgf//+GDhwIAwMDFSaIJUtJikdACCViGGkJ1EaW9vcAM2dzNWQFREREVHVq/Q6s4aGhhgxYoQqc6HX1KelAwI/8tB0GkRERERqU+5idu/evejatSt0dXWxd+9epbEffPDBaydGRERERFSWchezvXv3RlJSEmxsbNC7d+9S40QiEQoLC1WRGxERERGRUuUuZmUyWYnfExERERFpSqXWmd20aRNyc3OLbc/Ly8OmTZteOykiIiIiovKoVDE7dOhQpKamFtuenp6OoUOHvnZSRERERETlUanVDARBgEgkKrb9/v37MDMze+2k3kg5acDa94Bnd5THCZwvTERERKQuFSpmmzdvDpFIBJFIhI4dO0JH53+HFxYWIi4uDl26dFF5km+ER/8CT2+VL1YkBhxavnaXMpmAKTuv4uajdKVxV+4XHyUnIiIiqgkqVMy+WMUgMjISfn5+MDY2lu+TSqVwdnZGnz59VJrgG8e8LjCsjLuc6eoDBhav3dWdJxn4M+J+uePtzXjDCiIiIqpZKlTMzpw5E4WFhXB2dsb7778Pe3v7qsrrzSXWAUzV87oL/1s0wkRPB0s+9VIaa6ArQat6llWfFBEREdEbpMJzZiUSCb788ktER0dXRT5UAqmOGB1dbTWdBhEREdEbp1KrGbi7u+POnTIuhCIiIiIiqmKVKmZ/+OEHTJo0Cfv27UNiYiLS0tIUHkRERERE6lCppbm6desGAPjggw8Uluh6sWQXb2dLREREROpQqWL2xIkTqs6DiIiIiKjCKlXM+vr6qjoPIiIiIqIKq1QxCwApKSlYt26dfFWDpk2bYtiwYdX3DmBERERE9Map1AVgFy9eRIMGDfDzzz/j2bNnePbsGRYvXowGDRrg0qVLqs6x2krLyUdqdumPtJx8TadIRERE9Ear1Mjs119/jQ8++ABr166V39K2oKAAn3/+OSZMmIB//vlHpUlWR9N2XcXv5+9pOg0iIiIirVapYvbixYsKhSwA6OjoYPLkyfD29lZZctXZmdtPyx3boYlNFWZCREREpL0qVcyampoiISEBTZo0Udh+7949mJiYqCSxmmLbF2+jhZOF0hipTqVmgxARERFVe5UqZvv164fhw4dj4cKFaNOmDQAgLCwM33zzDfr376/SBKs7XYmIxSoRERFRJVWqmF24cCFEIhEGDx6MgoICAICuri6++uorBAUFqTRBIiIiIqLSVKqYlUqlWLp0KQIDA3H79m0AQIMGDWBoaKjS5IiIiIiIlKn0OrMAYGhoCHNzc/n3RERERETqVKnJmgUFBZg+fTrMzMzg7OwMZ2dnmJmZ4fvvv0d+PtdGJSIiIiL1qNTI7NixY7Fr1y7Mnz8fPj4+AIDw8HDMmjULT58+xapVq1SaJBERERFRSSpVzG7duhXbtm1D165d5ds8PDzg6OiI/v37s5glIiIiIrWo1DQDPT09ODs7F9ter149SKXS182JiIiIiKhcKlXMjhkzBnPnzkVubq58W25uLubNm4cxY8aoLDkiIiIiImUqNc3g8uXLCAkJQZ06deDp6QkAuHLlCvLy8tCxY0d89NFH8thdu3apJlMtkp6Tj9wCmdKYpxl5asqGiIiIqPqqVDFrbm6OPn36KGxzdHRUSULa7uC1RIz5/TIKZYKmUyEiIiKq9ipVzG7YsEHVeVQblxKel7uQrWtlCBdbkyrOiIiIyquwsJBLTBKpiVQqhVhcqRmvCl7rpgmPHz9GTEwMAKBx48awtrZ+7YSqiy/frY9p3Vw1nQYREZWDIAhISkpCSkqKplMhqjHEYrFKFg+oVDGbmZmJsWPHYtOmTZDJiuaGSiQSDB48GMuXL+fdwIiISKu8KGRtbGxgaGgIkUik6ZSIqjWZTIaHDx8iMTERTk5Or/UzV6liNiAgACdPnsTff/+Ntm3bAgBOnz6NcePGYeLEiVxnloiItEZhYaG8kLWystJ0OkQ1hrW1NR4+fIiCggLo6upWup1KFbM7d+7Ejh070L59e/m2bt26wcDAAH379mUxS0REWuPFHFmeVSRSrxfTCwoLC1+rmK3UrNusrCzY2toW225jY4OsrKxKJ0NERKQpnFpApF6q+pmrVDHr4+ODmTNnIicnR74tOzsbs2fPho+Pj0oSIyIiIiIqS6WmGSxZsgRdunQpdtMEfX19HD58WKUJEhERESnTvn17eHl5YcmSJSptd9asWdi9ezciIyNV2i6pVqVGZps1a4Zbt24hMDAQXl5e8PLyQlBQEG7duoWmTZuqOkciIiLSUv7+/hCJRBg5cmSxfaNHj4ZIJIK/v3+52goNDYVIJOISaqSgwiOz+fn5aNKkCfbt24cRI0ZURU5ERERUjTg6OmLbtm34+eefYWBgAADIycnB1q1b4eTkpOHsSNtVeGRWV1dXYa4sERERkTItWrSAo6Mjdu3aJd+2a9cuODk5oXnz5vJtMpkMgYGBqFevHgwMDODp6YkdO3YAAOLj49GhQwcAgIWFRbERXZlMhsmTJ8PS0hJ2dnaYNWuWQg4JCQno1asXjI2NYWpqir59++LRo0cKMUFBQbC1tYWJiQmGDx/OekdLVGqawejRo/HTTz+hoKBAJUmsXLkSzs7O0NfXR+vWrXH+/PlyHbdt2zaIRCL07t37tfpvmJ+H4SmpsE/YA5xaXPLj9M+v1QcREVFNNmzYMGzYsEH+fP369Rg6dKhCTGBgIDZt2oTVq1fj33//xddff43PPvsMJ0+ehKOjI3bu3AkAiImJQWJiIpYuXSo/duPGjTAyMsK5c+cwf/58zJkzB0ePHgVQVOj26tULz549w8mTJ3H06FHcuXMH/fr1kx//xx9/YNasWfjxxx9x8eJF2Nvb45dffqnKt4RUpFIXgF24cAEhISE4cuQImjVrBiMjI4X9L//lVZbt27cjICAAq1evRuvWrbFkyRL4+fkhJiYGNjY2pR4XHx+PSZMmoV27dpV5CQp+TH0Cx8IC4PnvQFwZwboGr90fERFRTfPZZ59h2rRpuHv3LgAgLCwM27ZtQ2hoKAAgNzcXP/74I44dOyZfGal+/fo4ffo01qxZA19fX1haWgIoWgrU3NxcoX0PDw/MnDkTANCoUSOsWLECISEh6Ny5M0JCQnDt2jXExcXB0dERALBp0yY0bdoUFy5cwFtvvYUlS5Zg+PDhGD58OADghx9+wLFjxzg6qwUqVcyam5ujT58+Kklg8eLFGDFihPyvs9WrV2P//v1Yv349pk6dWuIxhYWFGDhwIGbPno1Tp0699kRwk/9uyfvM6i1YOjYuPVAkAjz6vlZfRERENZG1tTW6d++O4OBgCIKA7t27o1atWvL9sbGxyMrKQufOnRWOy8vLU5iKUBoPDw+F5/b29khOTgYAREdHw9HRUV7IAoCbmxvMzc0RHR2Nt956C9HR0cUuUvPx8cGJEycq/FpJvSpUzMpkMixYsAA3b95EXl4e3nvvPcyaNUs+mbui8vLyEBERgWnTpsm3icVidOrUCeHh4aUeN2fOHNjY2GD48OE4deqU0j5yc3ORm5srf56WllZq7P16/WDZgxe1ERERVYVhw4ZhzJgxAIqmGL4sIyMDALB//344ODgo7NPT0yuz7VfvICUSiSD7b7CKqrcKzZmdN28evv32WxgbG8PBwQHLli3D6NGjK935kydPUFhYWOxuYra2tkhKSirxmNOnT2PdunVYu3ZtufoIDAyEmZmZ/PHyX2VERESkPl26dEFeXh7y8/Ph5+ensM/NzQ16enpISEhAw4YNFR4vfne/fPvTinB1dcW9e/dw7949+baoqCikpKTAzc1NHnPu3DmF486ePVvh10jqV6GR2U2bNuGXX37Bl19+CQA4duwYunfvjt9++w1icaWuJauQ9PR0DBo0CGvXrlU4NaHMtGnTEBAQIH+elpZWqYK2UCZgweEY3H2aqTTu4PWSi3AiIqKaTiKRIDo6Wv79y0xMTDBp0iR8/fXXkMlkeOedd5CamoqwsDCYmppiyJAhqFu3LkQiEfbt24du3brBwMAAxsbGZfbbqVMnNGvWDAMHDsSSJUtQUFCAUaNGwdfXF97e3gCA8ePHw9/fH97e3mjbti22bNmCf//9F/Xr11f9G0EqVaFiNiEhAd26dZM/79SpE0QiER4+fIg6depUuPNatWpBIpEUWxrj0aNHsLOzKxZ/+/ZtxMfHo2fPnvJtL04h6OjoICYmBg0aNFA4Rk9Pr1ynJ8py/UEqVp+8Xe54SyPpa/dJRERU3Ziampa6b+7cubC2tkZgYCDu3LkDc3NztGjRAt9++y0AwMHBAbNnz8bUqVMxdOhQDB48GMHBwWX2KRKJsGfPHowdOxbvvvsuxGIxunTpguXLl8tj+vXrh9u3b2Py5MnIyclBnz598NVXX/HOplpAJAiCUN5giUSCpKQkWFtby7eZmJjg6tWrqFevXqUSaN26NVq1aiX/ByWTyeDk5IQxY8YUuwAsJycHsbGxCtu+//57pKenY+nSpXBxcZGfgihNWloazMzMkJqaKv+BSpltAXNBhqveC+FRypzZC/HP8MnqcFgZSTGhUyOlfRjp6aCruz0MpBKlcUREpHk5OTmIi4tDvXr1oK+vr+l0iGoMZT97JdVrpanQyKwgCPD391cY6czJycHIkSMVlueqyNJcAQEBGDJkCLy9vdGqVSssWbIEmZmZ8tUNBg8eDAcHBwQGBkJfXx/u7u4Kx79YmuPV7VXF1EAXg3yc1dIXERERESlXoWJ2yJAhxbZ99tlnr5VAv3798PjxY8yYMQNJSUnw8vLCoUOH5BeFJSQkqGU+LhERERFpnwoVsy/fuUOVxowZI1+q41UvFlMuTXnmyhARERFR9cQhTyIiIiLSWixmiYiIiEhrsZglIiIiIq3FYpaIiIiItBaLWSIiIiLSWixmiYiIiEhrsZglIiKqhkQiEXbv3q3pNIiqHItZIiIiLeTv74/evXuXuj8xMRFdu3ZVX0IVdPLkSbz33nuwtLSEoaEhGjVqhCFDhiAvLw87d+6ERCLBgwcPSjy2UaNGCAgIAAC0b98eIpEIQUFBxeK6d+8OkUiEWbNmVeVLIQ1jMUtERFQN2dnZKdx+XhMEQUBBQUGx7VFRUejSpQu8vb3xzz//4Nq1a1i+fDmkUikKCwvxwQcfwMrKChs3bix27D///IPY2FgMHz5cvs3R0bHYTZQePHiAkJAQ2Nvbq/x10ZuFxSwREVE19PI0g/j4eIhEIuzatQsdOnSAoaEhPD09ER4ernDM6dOn0a5dOxgYGMDR0RHjxo1DZmamfP/mzZvh7e0NExMT2NnZYcCAAUhOTpbvDw0NhUgkwsGDB9GyZUvo6enh9OnTxXI7cuQI7OzsMH/+fLi7u6NBgwbo0qUL1q5dCwMDA+jq6mLQoEEl3uVz/fr1aN26NZo2bSrf1qNHDzx58gRhYWHybRs3bsT7778PGxubyr6FpCVYzBIREb1CEARk5RWo/SEIQpW+ru+++w6TJk1CZGQkXFxc0L9/f/nI6e3bt9GlSxf06dMHV69exfbt23H69GmF283n5+dj7ty5uHLlCnbv3o34+Hj4+/sX62fq1KkICgpCdHQ0PDw8iu23s7NDYmIi/vnnn1JzHT58OG7duqUQk5GRgR07diiMygKAVCrFwIEDsWHDBvm24OBgDBs2rNzvDWkvHU0nQERE9KbJzi+E24zDau83ao4fDKVV96t50qRJ6N69OwBg9uzZaNq0KWJjY9GkSRMEBgZi4MCBmDBhAoCieanLli2Dr68vVq1aBX19fYXisH79+li2bBneeustZGRkwNjYWL5vzpw56Ny5c6l5fPLJJzh8+DB8fX1hZ2eHt99+Gx07dsTgwYNhamoKAHBzc8Pbb7+N9evX49133wUA/PHHHxAEAZ9++mmxNocNG4Z27dph6dKliIiIQGpqKnr06MH5sjUAR2aJiIhqiJdHSV/MJX0xTeDKlSsIDg6GsbGx/OHn5weZTIa4uDgAQEREBHr27AknJyeYmJjA19cXAJCQkKDQj7e3t9I8JBIJNmzYgPv372P+/PlwcHDAjz/+iKZNmyIxMVEeN2zYMOzYsQPp6ekAiqYYfPLJJzAxMSnWpqenJxo1aoQdO3Zg/fr1GDRoEHR0OGZXE/BTJiIieoWBrgRRc/w00m9V0tXVlX8vEokAADKZDEDRKfwvv/wS48aNK3ack5MTMjMz4efnBz8/P2zZsgXW1tZISEiAn58f8vLyFOKNjIzKlY+DgwMGDRqEQYMGYe7cuXBxccHq1asxe/ZsAMCnn36Kr7/+Gn/88QfeffddhIWFITAwsNT2hg0bhpUrVyIqKgrnz58vVw6k/VjMEhERvUIkElXp6f43UYsWLRAVFYWGDRuWuP/atWt4+vQpgoKC4OjoCAC4ePGiyvq3sLCAvb29wgVnJiYm+OSTT7B+/Xrcvn0bLi4uaNeuXaltDBgwAJMmTYKnpyfc3NxUlhu92WrWTyoREVE1kpqaisjISIVtVlZW8mKzIqZMmYK3334bY8aMweeffw4jIyNERUXh6NGjWLFiBZycnCCVSrF8+XKMHDkS169fx9y5cyuV95o1axAZGYkPP/wQDRo0QE5ODjZt2oR///0Xy5cvV4gdPnw42rVrh+joaEyZMkVpuxYWFkhMTFQYgabqj3NmiYiItFRoaCiaN2+u8Hhxir6iPDw8cPLkSdy8eRPt2rVD8+bNMWPGDNSuXRsAYG1tjeDgYPz5559wc3NDUFAQFi5cWKm+WrVqhYyMDIwcORJNmzaFr68vzp49i927d8vn4b7wzjvvoHHjxkhLS8PgwYPLbNvc3Lzc0xyoehAJVb0OyBsmLS0NZmZmSE1NlV8xmTLbAuaCDFe9F8Kjx4gSj7sQ/wyfrA5HvVpGODGpvRozJiKiqpSTk4O4uDjUq1cP+vr6mk6HqMZQ9rNXUr1WGo7MEhEREZHWYjFLRERERFqLxSwRERERaS2uZgDg1/ze2FfYDnkXrSCNOlFiTMKzLDVnRURERERlYTEL4PeC9/EcpkAOgBzlRauzlaF6kiIiIiKiMrGYBSCg6C4oE12eo03H7qXGiUWAW23lV9QRERERkfqwmH1JPeN8tKxroek0iIiIiKiceAEYEREREWktFrNEREREpLVYzBIRERGR1mIxS0REpIUKCwvRpk0bfPTRRwrbU1NT4ejoiO+++06+befOnXjvvfdgYWEBAwMDNG7cGMOGDcPly5flMcHBwRCJRPKHsbExWrZsiV27dqntNQFA+/btMWHCBLX2SdqNxSwREZEWkkgkCA4OxqFDh7Blyxb59rFjx8LS0hIzZ84EAEyZMgX9+vWDl5cX9u7di5iYGGzduhX169fHtGnTFNo0NTVFYmIiEhMTcfnyZfj5+aFv376IiYlR62sjqggWs0RERFrKxcUFQUFBGDt2LBITE7Fnzx5s27YNmzZtglQqxdmzZzF//nwsXrwYixcvRrt27eDk5ISWLVvi+++/x8GDBxXaE4lEsLOzg52dHRo1aoQffvgBYrEYV69elcc8f/4cgwcPhoWFBQwNDdG1a1fcunVLoZ2dO3eiadOm0NPTg7OzMxYtWqSw/5dffkGjRo2gr68PW1tbfPzxxwAAf39/nDx5EkuXLpWPEMfHx1fNm0fVBpfmIiIiepUgAPkauPOjriEgElXokLFjx+Kvv/7CoEGDcO3aNcyYMQOenp4AgN9//x3GxsYYNWpUiceKlPRVWFiITZs2AQBatGgh3+7v749bt25h7969MDU1xZQpU9CtWzdERUVBV1cXERER6Nu3L2bNmoV+/frhzJkzGDVqFKysrODv74+LFy9i3Lhx2Lx5M9q0aYNnz57h1KlTAIClS5fi5s2bcHd3x5w5cwAA1tbWFXo/qOZhMUtERPSq/Czgx9rq7/fbh4DUqEKHiEQirFq1Cq6urmjWrBmmTp0q33fz5k3Ur18fOjr/+3W/ePFizJgxQ/78wYMHMDMzA1A039bY2BgAkJ2dDV1dXfz6669o0KABAMiL2LCwMLRp0wYAsGXLFjg6OmL37t345JNPsHjxYnTs2BHTp08HUDR6HBUVhQULFsDf3x8JCQkwMjJCjx49YGJigrp166J58+YAADMzM0ilUhgaGsLOzq6i7x7VUJxmQEREpOXWr18PQ0NDxMXF4f79+0pjhw0bhsjISKxZswaZmZkQBEG+z8TEBJGRkYiMjMTly5fx448/YuTIkfj7778BANHR0dDR0UHr1q3lx1hZWaFx48aIjo6Wx7Rt21ahz7Zt2+LWrVsoLCxE586dUbduXdSvXx+DBg3Cli1bkJWlgVFwqjY4MktERPQqXcOiUVJN9FtBZ86cwc8//4wjR47ghx9+wPDhw3Hs2DGIRCI0atQIp0+fRn5+PnR1dQEA5ubmMDc3L7HoFYvFaNiwofy5h4cHjhw5gp9++gk9e/as/Ot6iYmJCS5duoTQ0FAcOXIEM2bMwKxZs3DhwgWYm5urpA+qWTgyS0RE9CqRqOh0v7ofFZwvm5WVBX9/f3z11Vfo0KED1q1bh/Pnz2P16tUAgP79+yMjIwO//PJLpd8KiUSC7OxsAICrqysKCgpw7tw5+f6nT58iJiYGbm5u8piwsDCFNsLCwuDi4gKJRAIA0NHRQadOnTB//nxcvXoV8fHxOH78OABAKpWisLCw0vlSzcORWSIiIi01bdo0CIKAoKAgAICzszMWLlyISZMmoWvXrvDx8cHEiRMxceJE3L17Fx999BEcHR2RmJiIdevWQSQSQSz+37iWIAhISkoCUDRn9ujRozh8+LB8jm2jRo3Qq1cvjBgxAmvWrIGJiQmmTp0KBwcH9OrVCwAwceJEvPXWW5g7dy769euH8PBwrFixQl5Q79u3D3fu3MG7774LCwsLHDhwADKZDI0bN5a/hnPnziE+Ph7GxsawtLRUyJGoGKGGSU1NFQAIqamp8m2eU34X6k7ZJ/y9fb0GMyMiIk3Izs4WoqKihOzsbE2nUiGhoaGCRCIRTp06VWzf+++/L7z33nuCTCYTBEEQtm/fLrRv314wMzMTdHV1hTp16ggDBgwQzp49Kz9mw4YNAgD5Q09PT3BxcRHmzZsnFBQUyOOePXsmDBo0SDAzMxMMDAwEPz8/4ebNmwr979ixQ3BzcxN0dXUFJycnYcGCBfJ9p06dEnx9fQULCwvBwMBA8PDwELZv3y7fHxMTI7z99tuCgYGBAECIi4tT1VtGbxhlP3sl1WulEQnCSzO/a4C0tDSYmZkhNTUVpqamAACvqduQAhOsaJGMHn2HajhDIiJSp5ycHMTFxaFevXrQ19fXdDpENYayn72S6rXScNyeiIiIiLQWi1kiIiIi0losZomIiIhIa7GYJSIiIiKtxWKWiIiIiLQWi1kiIiIi0losZomIiIhIa7GYJSIiIiKtxWKWiIiIiLQWi1kiIqJqSCQSYffu3ZpOQy2Cg4Nhbm5eoWPat2+PCRMmVEk+6lSZ117dsJglIiLSQv7+/ujdu3ep+xMTE9G1a1f1JVQJ+/btg6+vL0xMTGBoaIi33noLwcHBFW6nX79+uHnzZoWO2bVrF+bOnVvhvioiPj4eIpFI/rCyssL777+Py5cvV2m/Nc0bUcyuXLkSzs7O0NfXR+vWrXH+/PlSY9euXYt27drBwsICFhYW6NSpk9J4IiKimsjOzg56enoazUEQBBQUFJS4b/ny5ejVqxfatm2Lc+fO4erVq/j0008xcuRITJo0qUL9GBgYwMbGpkLHWFpawsTEpELHVNaxY8eQmJiIw4cPIyMjA127dkVKSopa+q4JNF7Mbt++HQEBAZg5cyYuXboET09P+Pn5ITk5ucT40NBQ9O/fHydOnEB4eDgcHR3x/vvv48GDB2rOnIiIqitBEJCVn6X2hyAIKnsNL08zeDFCuGvXLnTo0AGGhobw9PREeHi4wjGnT59Gu3btYGBgAEdHR4wbNw6ZmZny/Zs3b4a3tzdMTExgZ2eHAQMGKPy+Dg0NhUgkwsGDB9GyZUvo6enh9OnTxXK7d+8eJk6ciAkTJuDHH3+Em5sbGjZsiIkTJ2LBggVYtGgRzp07p9Dm/v374eHhAX19fbz99tu4fv26vL1XT7XPmjULXl5e2Lx5M5ydnWFmZoZPP/0U6enp8phXpxk8f/4cgwcPhoWFBQwNDdG1a1fcunWrWB+HDx+Gq6srjI2N0aVLFyQmJpb5WVhZWcHOzg7e3t5YuHAhHj16JH99O3fuRNOmTaGnpwdnZ2csWrRI4diy8iJAR9MJLF68GCNGjMDQoUMBAKtXr8b+/fuxfv16TJ06tVj8li1bFJ7/9ttv2LlzJ0JCQjB48GC15ExERNVbdkE2Wm9trfZ+zw04B0Ndwypr/7vvvsPChQvRqFEjfPfdd+jfvz9iY2Oho6OD27dvo0uXLvjhhx+wfv16PH78GGPGjMGYMWOwYcMGAEB+fj7mzp2Lxo0bIzk5GQEBAfD398eBAwcU+pk6dSoWLlyI+vXrw8LColgeO3bsQH5+fokjsF9++SW+/fZb/P7772jd+n+fwTfffIOlS5fCzs4O3377LXr27ImbN29CV1e3xNd6+/Zt7N69G/v27cPz58/Rt29fBAUFYd68eSXG+/v749atW9i7dy9MTU0xZcoUdOvWDVFRUfI+srKysHDhQmzevBlisRifffYZJk2aVKw2UcbAwAAAkJeXh4iICPTt2xezZs1Cv379cObMGYwaNQpWVlbw9/cvd141nUaL2Rcf5LRp0+TbxGIxOnXqVOyvxdJkZWUhPz8flpaWJe7Pzc1Fbm6u/HlaWtrrJU1ERKSlJk2ahO7duwMAZs+ejaZNmyI2NhZNmjRBYGAgBg4cKB+tbNSoEZYtWwZfX1+sWrUK+vr6GDZsmLyt+vXrY9myZXjrrbeQkZEBY2Nj+b45c+agc+fOpeZx8+ZNmJmZwd7evtg+qVSK+vXrF5sDO3PmTHmbGzduRJ06dfDXX3+hb9++JfYhk8kQHBwsn0owaNAghISElFjMvigWw8LC0KZNGwBFg2eOjo7YvXs3PvnkEwBFxfzq1avRoEEDAMCYMWMwZ86cUl/nq1JSUjB37lwYGxujVatWCAgIQMeOHTF9+nQAgIuLC6KiorBgwQKFIrasvGo6jRazT548QWFhIWxtbRW229ra4saNG+VqY8qUKahduzY6depU4v7AwEDMnj37tXMlIqKaw0DHAOcGnNNIv1XJw8ND/v2LQjI5ORlNmjTBlStXcPXqVYVRRkEQIJPJEBcXB1dXV0RERGDWrFm4cuUKnj9/DplMBgBISEiAm5ub/Dhvb2+V5+7j4yP/3tLSEo0bN0Z0dHSp8c7OzgpzYu3t7UudwhgdHQ0dHR2FkWArK6tifRgaGsoL2bLafFmbNm0gFouRmZmJ+vXrY/v27bC1tUV0dDR69eqlENu2bVssWbIEhYWF5c6rptP4NIPXERQUhG3btiE0NBT6+volxkybNg0BAQHy52lpaXB0dFRXikREpIVEIlGVnu7XlJdPS4tEIgCQF6QZGRn48ssvMW7cuGLHOTk5ITMzE35+fvDz88OWLVtgbW2NhIQE+Pn5IS8vTyHeyMhIaR4uLi5ITU3Fw4cPUbt2bYV9eXl5uH37Njp06FCp1/jCq6fgRSKR/LWqss3yzHPevn073NzcYGVlVeOX0aoKGr0ArFatWpBIJHj06JHC9kePHsHOzk7psQsXLkRQUBCOHDmi8Jfmq/T09GBqaqrwICIiIkUtWrRAVFQUGjZsWOwhlUpx48YNPH36FEFBQWjXrh2aNGlSrlHJkvTp0we6urrFLnYCiq6dyczMRP/+/RW2nz17Vv798+fPcfPmTbi6ulaq/1e5urqioKBAflEWADx9+hQxMTEKI86V5ejoiAYNGhQrZF1dXREWFqawLSwsDC4uLpBIJFWeV3Wh0ZFZqVSKli1bIiQkRL5WnkwmQ0hICMaMGVPqcfPnz8e8efNw+PDhKjmVQUREpA1SU1MRGRmpsM3KyqpSZyCnTJmCt99+G2PGjMHnn38OIyMjREVF4ejRo1ixYgWcnJwglUqxfPlyjBw5EtevX6/0Oq1OTk6YP38+Jk6cCH19fQwaNAi6urrYs2cPvv32W0ycOFHh1DpQNA/XysoKtra2+O6771CrVi2l6+xWRKNGjdCrVy+MGDECa9asgYmJCaZOnQoHB4di0wBUaeLEiXjrrbcwd+5c9OvXD+Hh4VixYgV++eUXjealbTS+NFdAQADWrl2LjRs3Ijo6Gl999RUyMzPlqxsMHjxY4QKxn376CdOnT8f69evh7OyMpKQkJCUlISMjQ1MvgYiISCNCQ0PRvHlzhUdlrxPx8PDAyZMncfPmTbRr1w7NmzfHjBkz5NMArK2tERwcjD///BNubm4ICgrCwoULK537hAkT8Ndff+HUqVPw9vaGu7s7tm7dilWrVpXYblBQEMaPH4+WLVsiKSkJf//9N6RSaaX7f9WGDRvQsmVL9OjRAz4+PhAEAQcOHKjSFQNatGiBP/74A9u2bYO7uztmzJiBOXPmyFcy0FRe2kYkqHJRu0pasWIFFixYgKSkJHh5eWHZsmXyv8jat28PZ2dn+R1BnJ2dcffu3WJtzJw5E7NmzSqzr7S0NJiZmSE1NVU+5cBr6jakwAQrWiSjR9+hKntdRET05svJyUFcXBzq1atX6vUXpDmhoaHo0KEDnj9/zvmm1Yyyn72S6rXSvBEXgL1Yx64koaGhCs/j4+OrPiEiIiIi0goan2ZARERERFRZb8TILBEREVFJ2rdvr9Lb/FL1w5FZIiIiItJaLGaJiIiISGuxmCUiIiIircViloiIiIi0FotZIiIiItJaLGaJiIiISGuxmCUiIiKVmjVrFry8vJTGxMfHQyQSITIyUuX9Ozs7Y8mSJSpvtzJCQ0MhEomQkpJS7mP8/f3Ru3fvKstJXSrz2iuDxSwREZEWepMLnkmTJiEkJET+XN25XrhwAV988cVrtXHmzBl069YNFhYW0NfXR7NmzbB48WIUFhZWqJ02bdogMTERZmZm5T5m6dKlCA4OrmDGFScSieQPMzMztG3bFsePH6/yflWNxSwRERGphCAIKCgogLGxMaysrDSWh7W1NQwNDSt9/F9//QVfX1/UqVMHJ06cwI0bNzB+/Hj88MMP+PTTTyt0EwepVAo7OzuIRKJyH2NmZgZzc/NKZF5xGzZsQGJiIsLCwlCrVi306NEDd+7cUUvfqsJiloiI6BWCIECWlaX2hyrvdHXy5Em0atUKenp6sLe3x9SpU1FQUCDfn56ejoEDB8LIyAj29vb4+eef0b59e0yYMEEes3nzZnh7e8PExAR2dnYYMGAAkpOT5ftfnEY+ePAgWrZsCT09PZw+fVphmsGsWbOwceNG7NmzRz4KGBoaKm/jzp076NChAwwNDeHp6Ynw8HD5vuDgYJibm2Pfvn1o3LgxDA0N8fHHHyMrKwsbN26Es7MzLCwsMG7cOIUR01enGaSkpODLL7+Era0t9PX14e7ujn379pX4vmVmZmLEiBH44IMP8Ouvv8LLywvOzs74/PPPsXHjRuzYsQN//PEHgP9Nldi2bRvatGkjb/vkyZPF3qMXp9pfvKbDhw/D1dUVxsbG6NKlCxITE+XHvDqSnZubi3HjxsHGxgb6+vp45513cOHChWJ9hISEwNvbG4aGhmjTpg1iYmJKfI0vMzc3h52dHdzd3bFq1SpkZ2fj6NGjAMr+N1RWXurC29kSERG9QsjORkyLlmrvt/GlCIheY0TxhQcPHqBbt27w9/fHpk2bcOPGDYwYMQL6+vqYNWsWACAgIABhYWHYu3cvbG1tMWPGDFy6dElhrmt+fj7mzp2Lxo0bIzk5GQEBAfD398eBAwcU+ps6dSoWLlyI+vXrw8LCQqFYnTRpEqKjo5GWloYNGzYAACwtLfHw4UMAwHfffYeFCxeiUaNG+O6779C/f3/ExsZCR6eoRMnKysKyZcuwbds2pKen46OPPsKHH34Ic3NzHDhwAHfu3EGfPn3Qtm1b9OvXr9h7IZPJ0LVrV6Snp+P//u//0KBBA0RFRUEikZT43h05cgRPnz7FpEmTiu3r2bMnXFxc8Pvvvyv09c0332DJkiVwc3PD4sWL0bNnT8TFxZU6Op2VlYWFCxdi8+bNEIvF+OyzzzBp0iRs2bKlxPjJkydj586d2LhxI+rWrYv58+fDz88PsbGxsLS0lMd99913WLRoEaytrTFy5EgMGzYMYWFhJbZZEgMDAwBAXl5euf4NlTevqsZiloiIqJr55Zdf4OjoiBUrVkAkEqFJkyZ4+PAhpkyZghkzZiAzMxMbN27E1q1b0bFjRwBFp5tr166t0M6wYcPk39evXx/Lli3DW2+9hYyMDBgbG8v3zZkzB507dy4xF2NjYxgYGCA3Nxd2dnbF9k+aNAndu3cHAMyePRtNmzZFbGwsmjRpAqCooF61ahUaNGgAAPj444+xefNmPHr0CMbGxnBzc0OHDh1w4sSJEovZY8eO4fz584iOjoaLi4v8tZTm5s2bAABXV9cS9zdp0kQe88KYMWPQp08fAMCqVatw6NAhrFu3DpMnTy6xjfz8fKxevVr+msaMGYM5c+aUGJuZmYlVq1YhODgYXbt2BQCsXbsWR48exbp16/DNN9/IY+fNmwdfX18ARX9gdO/eHTk5OdDX1y/19b6QlZWF77//HhKJBL6+vmX+G8rOzi53XlWNxSwREdErRAYGaHwpQiP9qkJ0dDR8fHwU5mm2bdsWGRkZuH//Pp4/f478/Hy0atVKvt/MzAyNGzdWaCciIgKzZs3ClStX8Pz5c8hkMgBAQkIC3Nzc5HHe3t6VztXDw0P+vb29PQAgOTlZXswaGhrKiz4AsLW1hbOzs0IxbWtrqzD94WWRkZGoU6eOvJAtr4pM+fDx8ZF/r6OjA29vb0RHR5ca/+prsre3LzX/27dvIz8/H23btpVv09XVRatWrYr1Udp76eTkVGou/fv3h0QiQXZ2NqytrbFu3Tp4eHhg1qxZSv8NpaSklDuvqsZiloiI6BUikUglp/u1WWZmJvz8/ODn54ctW7bA2toaCQkJ8PPzQ15enkKskZFRpfvR1dWVf/+icHpRNL+6/0VMSdtePuZlBhX8A+FF0RsdHY02bdoU2x8dHa1QyFdGSfmrYr50We9lSX7++Wd06tQJZmZmsLa2fu0cNIEXgBEREVUzrq6uCA8PVyiQwsLCYGJigjp16qB+/frQ1dVVuFgnNTVV4fT5jRs38PTpUwQFBaFdu3Zo0qRJqaOHZZFKpRVe0kpVPDw8cP/+/WJTA0rz/vvvw9LSEosWLSq2b+/evbh16xb69++vsP3s2bPy7wsKChAREVHqNIWKatCgAaRSqcLc1/z8fFy4cOG1i2oAsLOzQ8OGDYsVsmX9G6rqvCqCxSwREZGWSk1NRWRkpMLj3r17GDVqFO7du4exY8fixo0b2LNnD2bOnImAgACIxWKYmJhgyJAh+Oabb3DixAn8+++/GD58OMRisXxEz8nJCVKpFMuXL8edO3ewd+9ezJ07t1J5Ojs74+rVq4iJicGTJ0+Qn5+vyrdBKV9fX7z77rvo06cPjh49iri4OBw8eBCHDh0qMd7IyAhr1qzBnj178MUXX+Dq1auIj4/HunXr4O/vj48//hh9+/ZVOGblypX466+/cOPGDYwePRrPnz9XmG/8OoyMjPDVV1/hm2++waFDhxAVFYURI0YgKysLw4cPV0kfJSnr35Cm8ioJpxkQERFpqdDQUDRv3lxh2/Dhw/Hbb7/hwIED+Oabb+Dp6QlLS0sMHz4c33//vTxu8eLFGDlyJHr06AFTU1NMnjwZ9+7dk18sZG1tjeDgYHz77bdYtmwZWrRogYULF+KDDz6ocJ4jRoxAaGgovL29kZGRgRMnTsDZ2fm1XntF7Ny5E5MmTUL//v2RmZmJhg0bIigoqNT4jz/+GCdOnMC8efPQrl075OTkyFdbmDBhQrE1Y4OCghAUFITIyEg0bNgQe/fuRa1atVSWf1BQEGQyGQYNGoT09HR4e3vj8OHDsLCwUFkfr3JwcCjz35Am8iqJSFDlonZaIC0tDWZmZkhNTYWpqSkAwGvqNqTABCtaJKNH36EazpCIiNQpJycHcXFxqFevXrmu+q6uMjMz4eDggEWLFql9ZE1bxcfHo169erh8+XKZt++l4pT97JVUr5WGI7NEREQ10OXLl3Hjxg20atUKqamp8qWhevXqpeHMiCqGxSwREVENtXDhQsTExEAqlaJly5Y4deqUSk+PE6kDi1kiIqIaqHnz5oiIUP9autWJs7OzSm9BTJXD1QyIiIiISGuxmCUiIiIircViloiIiIi0FotZIiIiItJaLGaJiIiISGuxmCUiIiIircViloiIqAYKDg6Gubl5mXHt27fHhAkTqjwfospiMUtERKSF/P39IRKJIBKJIJVK0bBhQ8yZMwcFBQXlOr5fv364efNmmXG7du3C3LlzXzfdcvv9998hkUgwevRotfWpTvHx8RCJRIiMjNR0KtUGi1kiIiIt1aVLFyQmJuLWrVuYOHEiZs2ahQULFpTrWAMDA9jY2JS6Py8vDwBgaWkJExMTleRbHuvWrcPkyZPx+++/IycnRy19vnit2iY/P1/TKbwRWMwSERG9QhAE5OcWqv1R0btJ6enpwc7ODnXr1sVXX32FTp06Ye/evQCAxYsXo1mzZjAyMoKjoyNGjRqFjIwM+bGvTjOYNWsWvLy88Ntvv6FevXrQ19cHUHyagbOzM3788UcMGzYMJiYmcHJywq+//qqQ15kzZ+Dl5QV9fX14e3tj9+7d5RqNjIuLw5kzZzB16lS4uLhg165dxWLWrl0LR0dHGBoa4sMPP8TixYuLTZf44YcfYGNjAxMTE3z++eeYOnUqvLy85Pv9/f3Ru3dvzJs3D7Vr10bjxo0BAPfu3UPfvn1hbm4OS0tL9OrVC/Hx8fLjCgoKMG7cOJibm8PKygpTpkzBkCFD0Lt3b3nMoUOH8M4778hjevTogdu3b8v316tXD0DRHdhEIhHat28PAJDJZJgzZw7q1KkDPT09eHl54dChQ/LjXozobt++Hb6+vtDX18eWLVuUvp81BW9nS0RE9IqCPBl+HX9S7f1+sdQXunqSSh9vYGCAp0+fAgDEYjGWLVuGevXq4c6dOxg1ahQmT56MX375pdTjY2NjsXPnTuzatQsSSel5LFq0CHPnzsW3336LHTt24KuvvoKvry8aN26MtLQ09OzZE926dcPWrVtx9+7dcs+53bBhA7p37w4zMzN89tlnWLduHQYMGCDfHxYWhpEjR+Knn37CBx98gGPHjmH69OkKbWzZsgXz5s3DL7/8grZt22Lbtm1YtGiRvIh8ISQkBKampjh69CiAolFOPz8/+Pj44NSpU9DR0cEPP/yALl264OrVq5BKpfjpp5+wZcsWbNiwAa6urli6dCl2796NDh06yNvNzMxEQEAAPDw8kJGRgRkzZuDDDz9EZGQkxGIxzp8/j1atWuHYsWNo2rQppFIpAGDp0qVYtGgR1qxZg+bNm2P9+vX44IMP8O+//6JRo0by9qdOnYpFixahefPm8j84ajoWs0RERFpOEASEhITg8OHDGDt2LAAUG0394YcfMHLkSKXFbF5eHjZt2gRra2ul/XXr1g2jRo0CAEyZMgU///wzTpw4gcaNG2Pr1q0QiURYu3Yt9PX14ebmhgcPHmDEiBFK25TJZAgODsby5csBAJ9++ikmTpyIuLg4eSG6fPlydO3aFZMmTQIAuLi44MyZM9i3b5+8neXLl2P48OEYOnQoAGDGjBk4cuSIwqg0ABgZGeG3336TF5P/93//B5lMht9++w0ikQhAUXFtbm6O0NBQvP/++1i+fDmmTZuGDz/8EACwYsUKHDhwQKHdPn36KDxfv349rK2tERUVBXd3d/l7a2VlBTs7O3ncwoULMWXKFHz66acAgJ9++gknTpzAkiVLsHLlSnnchAkT8NFHHyl9L2saFrNERESv0JGK8cVSX430WxH79u2DsbEx8vPzIZPJMGDAAMyaNQsAcOzYMQQGBuLGjRtIS0tDQUEBcnJykJWVBUNDwxLbq1u3bpmFLAB4eHjIvxeJRLCzs0NycjIAICYmBh4eHgqjhq1atSqzzaNHjyIzMxPdunUDANSqVQudO3fG+vXr5RegxcTEyAvJl9t+uZiNiYmRF9ovxxw/flxhW7NmzeSFLABcuXIFsbGxxeYH5+Tk4Pbt20hNTcWjR48UXotEIkHLli0hk8nk227duoUZM2bg3LlzePLkiXxfQkIC3N3dS3ztaWlpePjwIdq2bauwvW3btrhy5YrCNm9v7xLbqMlYzBIREb1CJBK91ul+denQoQNWrVoFqVSK2rVrQ0en6Nd6fHw8evToga+++grz5s2DpaUlTp8+jeHDhyMvL6/UYtbIyKhc/erq6io8F4lECgVdZaxbtw7Pnj2DgYGBfJtMJsPVq1cxe/ZsiMWqvczn1deakZGBli1bljgPtTwF/gs9e/ZE3bp1sXbtWtSuXRsymQzu7u4qu8isvJ9RTcILwIiIiLSUkZERGjZsCCcnJ3khCwARERGQyWRYtGgR3n77bbi4uODhw4dqyalx48a4du0acnNz5dsuXLig9JinT59iz5492LZtGyIjI+WPy5cv4/nz5zhy5Ii87VfbevV5eWJK0qJFC9y6dQs2NjZo2LChwsPMzAxmZmawtbVVaKuwsBCXLl1SeB0xMTH4/vvv0bFjR7i6uuL58+cK/bwYDS4sLJRvMzU1Re3atREWFqYQGxYWBjc3tzJzr+lYzBIREVUzDRs2RH5+PpYvX447d+5g8+bNWL16tVr6HjBgAGQyGb744gtER0fj8OHDWLhwIQDI56K+avPmzbCyskLfvn3h7u4uf3h6eqJbt25Yt24dAGDs2LE4cOAAFi9ejFu3bmHNmjU4ePCgQrtjx47FunXrsHHjRty6dQs//PADrl69WmrfLwwcOBC1atVCr169cOrUKcTFxSE0NBTjxo3D/fv35W0HBgZiz549iImJwfjx4/H8+XN52xYWFrCyssKvv/6K2NhYHD9+HAEBAQr92NjYwMDAAIcOHcKjR4+QmpoKAPjmm2/w008/Yfv27YiJicHUqVMRGRmJ8ePHV+JTqFlYzBIREVUznp6eWLx4MX766Se4u7tjy5YtCAwMVEvfpqam+PvvvxEZGQkvLy989913mDFjBgCUevX9+vXr8eGHH5ZYcPbp0wd79+7FkydP0LZtW6xevRqLFy+Gp6cnDh06hK+//lqh3YEDB2LatGmYNGkSWrRogbi4OPj7+5d55b+hoSH++ecfODk54aOPPoKrqyuGDx+OnJwcmJqaAii62K1///4YPHgwfHx8YGxsDD8/P3nbYrEY27ZtQ0REBNzd3fH1118XW/dXR0cHy5Ytw5o1a1C7dm306tULADBu3DgEBARg4sSJaNasGQ4dOoS9e/cqrGRAJRMJFV3UTsulpaXBzMwMqamp8n+cXlO3IQUmWNEiGT36DtVwhkREpE45OTnyK+a51FHV2LJlC4YOHYrU1FSFObGqMGLECNy4cQOnTp0qNaZz586ws7PD5s2bVdq3TCaDq6sr+vbtq9a7pFUXyn72SqrXSsMLwIiIiEilNm3ahPr168PBwQFXrlzBlClT0LdvX5UUsgsXLkTnzp1hZGSEgwcPYuPGjQrLjWVlZWH16tXw8/ODRCLB77//jmPHjsnXk30dd+/exZEjR+Dr64vc3FysWLECcXFxCmvhkvqxmCUiIiKVSkpKwowZM5CUlAR7e3t88sknmDdvnkraPn/+PObPn4/09HTUr18fy5Ytw+effy7fLxKJcODAAcybNw85OTlo3Lgxdu7ciU6dOr1232KxGMHBwZg0aRIEQYC7uzuOHTsGV1fX126bKo/FLBEREanU5MmTMXny5Cpp+48//lC638DAAMeOHauSvh0dHYutOECaxwvAiIiIiEhrsZglIiJC0S1hiUh9VPUzx2KWiIhqtBd3s8rKytJwJkQ1y4u7okkkr3e3Pc6ZJSKiGk0ikcDc3BzJyckAitYbLWuBfSJ6PTKZDI8fP4ahoaHC3esqg8UsERHVeHZ2dgAgL2iJqOqJxWI4OTm99h+PLGaJiKjGE4lEsLe3h42NDfLz8zWdDlGNIJVKIRa//oxXFrNERET/kUgkrz1/j4jU6424AGzlypVwdnaGvr4+WrdujfPnzyuN//PPP9GkSRPo6+ujWbNmOHDggJoyJSIiIqI3icaL2e3btyMgIAAzZ87EpUuX4OnpCT8/v1LnLZ05cwb9+/fH8OHDcfnyZfTu3Ru9e/fG9evX1Zw5EREREWmaxovZxYsXY8SIERg6dCjc3NywevVqGBoaYv369SXGL126FF26dME333wDV1dXzJ07Fy1atMCKFSvUnDkRERERaZpG58zm5eUhIiIC06ZNk28Ti8Xo1KkTwsPDSzwmPDwcAQEBCtv8/Pywe/fuEuNzc3ORm5srf56amgoASEtLk28rzM2CDBJkZWUrbCciIiIi9XtRj5XnxgoaLWafPHmCwsJC2NraKmy3tbXFjRs3SjwmKSmpxPikpKQS4wMDAzF79uxi2x0dHYtt6wcAQ0eXL3kiIiIiqlLp6ekwMzNTGlPtVzOYNm2awkhuSkoK6tati4SEhDLfHKoaaWlpcHR0xL1792BqaqrpdGokfgaax89A8/gZaB4/A817Uz8DQRCQnp6O2rVrlxmr0WK2Vq1akEgkePTokcL2R48eyRewfpWdnV2F4vX09KCnp1dsu5mZ2Rv1odVEpqam/Aw0jJ+B5vEz0Dx+BprHz0Dz3sTPoLyDjhq9AEwqlaJly5YICQmRb5PJZAgJCYGPj0+Jx/j4+CjEA8DRo0dLjSciIiKi6kvj0wwCAgIwZMgQeHt7o1WrVliyZAkyMzMxdOhQAMDgwYPh4OCAwMBAAMD48ePh6+uLRYsWoXv37ti2bRsuXryIX3/9VZMvg4iIiIg0QOPFbL9+/fD48WPMmDEDSUlJ8PLywqFDh+QXeSUkJCjc6qxNmzbYunUrvv/+e3z77bdo1KgRdu/eDXd393L1p6enh5kzZ5Y49YDUg5+B5vEz0Dx+BprHz0Dz+BloXnX4DERCedY8ICIiIiJ6A2n8pglERERERJXFYpaIiIiItBaLWSIiIiLSWixmiYiIiEhr1bhiduXKlXB2doa+vj5at26N8+fPazqlauuff/5Bz549Ubt2bYhEIuzevVthvyAImDFjBuzt7WFgYIBOnTrh1q1bmkm2GgoMDMRbb70FExMT2NjYoHfv3oiJiVGIycnJwejRo2FlZQVjY2P06dOn2E1JqPJWrVoFDw8P+WLkPj4+OHjwoHw/33/1CwoKgkgkwoQJE+Tb+DlUrVmzZkEkEik8mjRpIt/P9189Hjx4gM8++wxWVlYwMDBAs2bNcPHiRfl+bf6dXKOK2e3btyMgIAAzZ87EpUuX4OnpCT8/PyQnJ2s6tWopMzMTnp6eWLlyZYn758+fj2XLlmH16tU4d+4cjIyM4Ofnh5ycHDVnWj2dPHkSo0ePxtmzZ3H06FHk5+fj/fffR2Zmpjzm66+/xt9//40///wTJ0+exMOHD/HRRx9pMOvqpU6dOggKCkJERAQuXryI9957D7169cK///4LgO+/ul24cAFr1qyBh4eHwnZ+DlWvadOmSExMlD9Onz4t38f3v+o9f/4cbdu2ha6uLg4ePIioqCgsWrQIFhYW8hit/p0s1CCtWrUSRo8eLX9eWFgo1K5dWwgMDNRgVjUDAOGvv/6SP5fJZIKdnZ2wYMEC+baUlBRBT09P+P333zWQYfWXnJwsABBOnjwpCELR+62rqyv8+eef8pjo6GgBgBAeHq6pNKs9CwsL4bfffuP7r2bp6elCo0aNhKNHjwq+vr7C+PHjBUHgz4E6zJw5U/D09CxxH99/9ZgyZYrwzjvvlLpf238n15iR2by8PERERKBTp07ybWKxGJ06dUJ4eLgGM6uZ4uLikJSUpPB5mJmZoXXr1vw8qkhqaioAwNLSEgAQERGB/Px8hc+gSZMmcHJy4mdQBQoLC7Ft2zZkZmbCx8eH77+ajR49Gt27d1d4vwH+HKjLrVu3ULt2bdSvXx8DBw5EQkICAL7/6rJ37154e3vjk08+gY2NDZo3b461a9fK92v77+QaU8w+efIEhYWF8juLvWBra4ukpCQNZVVzvXjP+Xmoh0wmw4QJE9C2bVv53fKSkpIglUphbm6uEMvPQLWuXbsGY2Nj6OnpYeTIkfjrr7/g5ubG91+Ntm3bhkuXLslvi/4yfg5Vr3Xr1ggODsahQ4ewatUqxMXFoV27dkhPT+f7ryZ37tzBqlWr0KhRIxw+fBhfffUVxo0bh40bNwLQ/t/JGr+dLRFVvdGjR+P69esK89RIPRo3bozIyEikpqZix44dGDJkCE6ePKnptGqMe/fuYfz48Th69Cj09fU1nU6N1LVrV/n3Hh4eaN26NerWrYs//vgDBgYGGsys5pDJZPD29saPP/4IAGjevDmuX7+O1atXY8iQIRrO7vXVmJHZWrVqQSKRFLtC8tGjR7Czs9NQVjXXi/ecn0fVGzNmDPbt24cTJ06gTp068u12dnbIy8tDSkqKQjw/A9WSSqVo2LAhWrZsicDAQHh6emLp0qV8/9UkIiICycnJaNGiBXR0dKCjo4OTJ09i2bJl0NHRga2tLT8HNTM3N4eLiwtiY2P5c6Am9vb2cHNzU9jm6uoqn+6h7b+Ta0wxK5VK0bJlS4SEhMi3yWQyhISEwMfHR4OZ1Uz16tWDnZ2dwueRlpaGc+fO8fNQEUEQMGbMGPz11184fvw46tWrp7C/ZcuW0NXVVfgMYmJikJCQwM+gCslkMuTm5vL9V5OOHTvi2rVriIyMlD+8vb0xcOBA+ff8HNQrIyMDt2/fhr29PX8O1KRt27bFlma8efMm6tatC6Aa/E7W9BVo6rRt2zZBT09PCA4OFqKiooQvvvhCMDc3F5KSkjSdWrWUnp4uXL58Wbh8+bIAQFi8eLFw+fJl4e7du4IgCEJQUJBgbm4u7NmzR7h69arQq1cvoV69ekJ2draGM68evvrqK8HMzEwIDQ0VEhMT5Y+srCx5zMiRIwUnJyfh+PHjwsWLFwUfHx/Bx8dHg1lXL1OnThVOnjwpxMXFCVevXhWmTp0qiEQi4ciRI4Ig8P3XlJdXMxAEfg5VbeLEiUJoaKgQFxcnhIWFCZ06dRJq1aolJCcnC4LA918dzp8/L+jo6Ajz5s0Tbt26JWzZskUwNDQU/u///k8eo82/k2tUMSsIgrB8+XLByclJkEqlQqtWrYSzZ89qOqVq68SJEwKAYo8hQ4YIglC0FMj06dMFW1tbQU9PT+jYsaMQExOj2aSrkZLeewDChg0b5DHZ2dnCqFGjBAsLC8HQ0FD48MMPhcTERM0lXc0MGzZMqFu3riCVSgVra2uhY8eO8kJWEPj+a8qrxSw/h6rVr18/wd7eXpBKpYKDg4PQr18/ITY2Vr6f7796/P3334K7u7ugp6cnNGnSRPj1118V9mvz72SRIAiCZsaEiYiIiIheT42ZM0tERERE1Q+LWSIiIiLSWixmiYiIiEhrsZglIiIiIq3FYpaIiIiItBaLWSIiIiLSWixmiYiIiEhrsZglIiIiIq3FYpaIqILat2+PCRMmaDoNIiICi1kiIrUJDAyERCLBggULNJ0KEVG1wWKWiEhN1q9fj8mTJ2P9+vWaTgV5eXmaToGISCVYzBIRvab9+/fDzMwMW7ZsKTXm5MmTyM7Oxpw5c5CWloYzZ84o7JfJZJg/fz4aNmwIPT09ODk5Yd68efL99+/fR//+/WFpaQkjIyN4e3vj3LlzAAB/f3/07t1bob0JEyagffv28uft27fHmDFjMGHCBNSqVQt+fn4AgMWLF6NZs2YwMjKCo6MjRo0ahYyMDIW2wsLC0L59exgaGsLCwgJ+fn54/vw5Nm3aBCsrK+Tm5irE9+7dG4MGDSr3+0dE9DpYzBIRvYatW7eif//+2LJlCwYOHFhq3Lp169C/f3/o6uqif//+WLduncL+adOmISgoCNOnT0dUVBS2bt0KW1tbAEBGRgZ8fX3x4MED7N27F1euXMHkyZMhk8kqlOvGjRshlUoRFhaG1atXAwDEYjGWLVuGf//9Fxs3bsTx48cxefJk+TGRkZHo2LEj3NzcEB4ejtOnT6Nnz54oLCzEJ598gsLCQuzdu1cen5ycjP3792PYsGEVyo2IqLJEgiAImk6CiEibtG/fHl5eXmjUqBG+++477NmzB76+vqXGp6Wlwc7ODuHh4fD09ERkZCTatWuHxMREGBsbIz09HdbW1lixYgU+//zzYsf/+uuvmDRpEuLj42FpaVlsv7+/P1JSUrB79275tgkTJiAyMhKhoaHynNPS0nDp0iWlr23Hjh0YOXIknjx5AgAYMGAAEhIScPr06RLjR40ahfj4eBw4cABA0UjvypUrERsbC5FIpLQvIiJV4MgsEVEl7NixA19//TWOHj0qL2RPnToFY2Nj+ePFtIPff/8dDRo0gKenJwDAy8sLdevWxfbt2wEA0dHRyM3NRceOHUvsKzIyEs2bNy+xkK2Ili1bFtt27NgxdOzYEQ4ODjAxMcGgQYPw9OlTZGVlyfsuLS8AGDFiBI4cOYIHDx4AAIKDg+Hv789ClojUhsUsEVElNG/eHNbW1li/fj1enODy9vZGZGSk/PHBBx8AKJpi8O+//0JHR0f+iIqKkl8IZmBgoLSvsvaLxWK8epItPz+/WJyRkZHC8/j4ePTo0QMeHh7YuXMnIiIisHLlSgD/u0CsrL6bN28OT09PbNq0CREREfj333/h7++v9BgiIlViMUtEVAkNGjTAiRMnsGfPHowdOxZAUeHXsGFD+cPExATXrl3DxYsXERoaqlDohoaGIjw8HDdu3ECjRo1gYGCAkJCQEvvy8PBAZGQknj17VuJ+a2trJCYmKmyLjIws8zVERERAJpNh0aJFePvtt+Hi4oKHDx8W67u0vF74/PPPERwcjA0bNqBTp05wdHQss28iIlVhMUtEVEkuLi44ceIEdu7cWepNFNatW4dWrVrh3Xffhbu7u/zx7rvv4q233sK6deugr6+PKVOmYPLkydi0aRNu376Ns2fPyi8S69+/P+zs7NC7d2+EhYXhzp072LlzJ8LDwwEA7733Hi5evIhNmzbh1q1bmDlzJq5fv15m/g0bNkR+fj6WL1+OO3fuYPPmzfILw16YNm0aLly4gFGjRuHq1au4ceMGVq1aJZ9TCxTNq71//z7Wrl3LC7+ISO1YzBIRvYbGjRvj+PHj+P333zFx4kSFfXl5efi///s/9OnTp8Rj+/Tpg02bNiE/Px/Tp0/HxIkTMWPGDLi6uqJfv35ITk4GAEilUhw5cgQ2Njbo1q0bmjVrhqCgIEgkEgCAn58fpk+fjsmTJ+Ott95Ceno6Bg8eXGbunp6eWLx4MX766Se4u7tjy5YtCAwMVIhxcXHBkSNHcOXKFbRq1Qo+Pj7Ys2cPdHR05DFmZmbo06cPjI2Niy0RRkRU1biaARERvbaOHTuiadOmWLZsmaZTIaIahsUsERFV2vPnzxEaGoqPP/4YUVFRaNy4saZTIqIaRqfsECIiopI1b94cz58/x08//cRClog0giOzRERERKS1eAEYEREREWktFrNEREREpLVYzBIRERGR1mIxS0RERERai8UsEREREWktFrNEREREpLVYzBIRERGR1mIxS0RERERa6/8BBKYAkNjEmLwAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 800x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = matplotlib.pyplot.subplots(figsize=FIGSIZE)\n",
    "seaborn.ecdfplot(data=df, x='k-Accuracy', hue='Method', ax=ax)\n",
    "ax.set_xlim(0, ACTORS/2)\n",
    "fig.savefig('k-accurate-correct.pdf', dpi=600, bbox_inches='tight')"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "db9d2af8-3bf0-4dd6-a6fd-59ee7f8e3a96",
   "metadata": {},
   "source": [
    "## Probability of attribution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "id": "8ee5fdb1-3c97-4114-b24b-632879cecff2",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\20173892\\AppData\\Local\\Programs\\Python\\venv\\Lib\\site-packages\\seaborn\\_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "c:\\Users\\20173892\\AppData\\Local\\Programs\\Python\\venv\\Lib\\site-packages\\seaborn\\_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "c:\\Users\\20173892\\AppData\\Local\\Programs\\Python\\venv\\Lib\\site-packages\\seaborn\\_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "c:\\Users\\20173892\\AppData\\Local\\Programs\\Python\\venv\\Lib\\site-packages\\seaborn\\_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "c:\\Users\\20173892\\AppData\\Local\\Programs\\Python\\venv\\Lib\\site-packages\\seaborn\\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
      "  with pd.option_context('mode.use_inf_as_na', True):\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Probability of correct attribution', ylabel='Proportion'>"
      ]
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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13xSEQ6w5ysCIqRkMPzETq80Y4QpFWyiKwvsrC3nov5vxBoJkxll4++qJ9EvqOSPrEmaFEKKT1XvqeXX9q/zvtv8loASIMcZwx7g7OH/A+Wg1shuS6Fx+X4DtP5ex9psC6ivUEGuy6hlzWjYjpmZiNEtU6K6K61w8+MkmFm2rAODkwck89YdRxEf1rBcm8hsqhBCd6Os9X/PYiseocdcAcGrOqdw36T4SLYkRrkz0Nn5fgK0/lbL6yz001XsBtb3W+DNyGXJsmoTYbswXCPL2sj08880OnN4ABp2G208dyHUn9EOr7XoLuI6W/KYKIUQnqHHX8Nef/xpe4NXH3od7JtzDsRnHRrgy0dsEgwo7V5az4tM8GqrdAETFmhh9ShZDj0uXENuNBYMKX20u48mF29hT7QRgfE4cj18wgoEp7bS5inQzEEKI3uebvd8wZ/kc6j316DV6rhp+FdeNug6jrmed6hNdWzAQZMcv5axeuJe6cjXoRMWaGDcjh6FT0tEZZIpLd/ZzXjVPfLmNdYV1ACREGbnjtEH8cUJW+4zGdr0MGyZhVgghOojb7+aplU/x4Y4PARgUN4g5U+YwLGFYhCsTvUkwqLBjRRkrF+TjqFJHYk1WPaNPyWbUyVkYTLLRQXe2em8tz3yznZ92qT2Ao4w6Zh3Xhz+d2I9oU0fEvK6XaiXMCiFEB9hdt5s7l9zJrrpdAFw9/GpuGnMTBm333WVHdC8BX5BtP5eyflEhtWXqSKw52sCYU7MZfmKGTCfoxvyBIN9ureCfP+axam8tAAadhgvHZ3HLyQNIsfWuXQLlN1kIIdqRoih8uvtTHlvxGC6/iwRzAo8d9xhTMqZEujTRSwR8QbYuVxd2NdZ6AHUkduz0HEZMy8QgW852Ww1uH/+3qojXl+ZTXKd2njDoNJw/JoObTxpAVnzv7AEsYVYIIdpJo7eRh5Y9xNd7vwbgmLRjmHv8XOlUIDqFq8HL5h+L2bSkONydIMpuZPSp2QyZko7JIv/yuyNFUVhbWMf/rSrkk7UluHwBAOKjjFw0IYuZx+b2upHYX5PfbCGEaAe763Zz2/e3scexB71Gzw2jb+Dq4Vej08oomOhYVUUNbP6xhK3LSgn4goAaYsdMz2HYcenoZSS2WyqqdfLlxjI+Wl3E9vKG8PH+ydHMPDaX34/LxGyIwM9WuhkIIUTP899d/w1PK0i2JvM/J/4Po5NHR7os0YMFA0F2r6lkw/eFlOU5wseTc2IYOS2T/uNSpDtBN1Tb5OWLTaV8sraYlXtqw8dNei1njEjjjxOymNgnHk0XDJSRJGFWCCHayBf08eQvT/LB9g8AmJQ6iSdPeJIES0KEKxM9lbvJx5afStj8Q3G4M4FWq6HP6ESGnZBB5qA4CTrdTIPbx5IdlXy2voTvt1XiDaij61oNTMiN56yRaZwzKgO7VRaPHoyEWSGEaIMqVxV3/XAXK8tWokHDdaOu488j/yzTCkSHqCxsYNOSYnasKMMfmkpgjjYw4sQMhp2QQZTdFOEKxeFSFIXt5Q38tKuaxdsrWL67Gn9QCV8/NM3GuaPTOWd0Oml2SwQr/TXl0DeJEAmzQghxhLZUb+Hm726mwlmBRW/hbyf8jalZUyNdluhhFEWhaFsta77aS9G2llPOCZnRjJyWyYDxKdIjtpuod/r4cVcly3dX88POSgprXK2u75sYxanDUjhvdAZD0mwRqvIwdcGRfwmzQghxBBYXLuauH+7C5XeRa8vluZOeo6+9b6TLEj2I3xtgx8py1i8qpKakCVCnEvQdm8SIEzNI6x8rUwm6uHqXjzUFtazaU8OKvBrWFtYR2Gf01WzQMqlPAsf2S+C0Yan0SYyKYLXdn4RZIYQ4DIqi8M6Wd3hm9TMElSCT0ibx7NRniTG2037notdzN/rYuKSIDd8X4W70AWAw6Rh0TCpjTs3GltiVTjmLfZXVu/lpVxWr9taytqCW7eUNKL86Kz8gOZrjBiQyuW8CU/onEtUhu3N1gi74QqqbfieFEKLzeANeHv35UT7Z9QkAvxvwO+4/5n7ZzUu0C6fDy9pvCti0pAi/V50PGxNvZvjUDIYdl45JFv50KV5/kO1lDawrqmPVnhrWFNTuN20AIDveysQ+8YzPiWNK/8Reu6FBZ5AwK4QQv6HSWckdS+5gbcVatBot/2/8/+PSIZfKaV5x1BprPaz7poDNS4vDITYxK5oxp2bTf1wyWp201uoKKhxuVu+tZeWeWn7ZU832sgZ8gdbDrloNDM+wc0zfBMZkxTIuN47kmN69kUFnkjArhBAHsbN2J9d/ez3lznKiDdE8deJTHJdxXKTLEt2co8rFum8K2PJTKQG/GmKTc2KYcFYfcoYnyAulCKps8LCpuJ4NRfWsLaxlc4mDygbPfreLtRoYnm5nXE4c43LiGJMdS4xZRtAjRcKsEEIcwPrK9Vz/7fU0eBvIteXy4skvkmPLiXRZohurq3Cy9qu9bPu5jGBoZC+tv51xp+eSPVQa4Xe2CoebjcX1bC5xsKGons0l9ZTWu/e7nVYDA1NimJAbz/jcOMZmx5EZZ5GfVxciYVYIIX5lWckybvv+Nlx+F6OSRvHSyS9hN9kjXZbophpq3KxakM+25WUEQyvaMwbFMe70HNnkoBMoikJxnYv1hfVsLXWwrUwNrxUHGHHVaKBPYhQjM+yMyY5jeIadQakxRHfXxVodouv9vspPRwgh9vHp7k958KcHCSgBJqdNZt60eVgNsnBDHLm6cidrvy1g27LS8Ehs9rB4xp+eS1r/2MgW10MFggr5VU2sL6xjZ0UjO8sb2FBcf8CpAloN9E2KZmSGnWEZdkZk2BmSFiPTBbohCbNCCBHy7pZ3eXLlkwCc3ud0/jrlrxh1xghXJbqbpjoPK7/Yw5alJSjhkdhYJp7dl3QJse2mtsnL9vIG8qua2FxSz7bSBraUOnB6A/vdVqfVMDTNxrB0G4NSYxiRYWdoug2rUWJQTyA/RSFEr6coCs+ueZY3N70JwGVDLuP/Tfh/aDWymlwcPp83wPpvC1n95Z7wlrM5wxMYOz2H9AGxkS2uG6tt8rKrspGd5Y3srGhgW2kDOysaqGr0HvD2Jr2WkZnq9ICBKTEMTbMxPMOO2SC7pfVUEmaFEL2aoig8vuJx3t/+PgC3jr2VWcNnyTxGcdgURSFvbSVL/28njbXq6ezUvjaOOa8fGQPjIlxd96AoCuUOD/lVTeyqbCS/somdFQ1sL2s44NzWZplxFvokRjE0zcaQ0Mhr36RodFr5++1NJMwKIXotX8DH/T/dz5f5X6JBwwOTH+APA/8Q6bJEN1Kx18FPH+2iZGcdoG52MOncvgycmCIviA4gEFQornWxpdTBrooGtpc3squikT1VTbh8+08PaJYRa6FfcjQDkqMZlBLDkDQbuYlWmd8qAAmzQoheyhf0MXvJbBYXLkav0TNnyhzO6XdOpMsS3YS7yceKT/PY/EMxigI6g5Yxp2YzdkYOBmPvPp2tKArVTV4Ka5zkVTaxvbyBvMom8qoaKaxx7rfhQDOdVkNmnIX+SdHkJkYxMCWa/skx0k2gq+mCr9Hkt0MI0esEggHu+/E+Fhcuxqg18vxJzzMlY0qkyxLdgKIobFteyrKPd+Nu8gEwYEIKk8/vR0x879nxSVEUSurdFFQ7KahpIr/KSWGtk4JqJ/lVTTR6/Ae9r1GvpV9SNENSYxiQEsOA5Gj6JEWRFWfFqJd56uLISZgVQvQqzXNkF+5ZiF6r59lpz0qQFYelprSJxe9uo3R3PQDx6VEcf9FAMgf1zHmxgaBCmcNNca2Lwhonuysbya9qoqjWdcjAqtFAqs1MVryVIakx9EuOpm9iNLmJVtLtFrQyp7Xb6cqzZiTMCiF6DUVReHb1s3y440M0aJh7/FxOyDwh0mWJLi7gD7Lqyz2sWbiXYEBBb9Ix4YxcRp2cha6bjyTWO33srWmipM5NcZ2LvdVNFNQ42VvtpLjWhTcQPOh9DToNGbEWsuKt9EmMIjveSnbz5QQrJn3vnm7Rc3W9VCthVgjRa7y28TXe3Ky233pg8gPMyJ0R4YpEV1ex18Hi97ZTWdAAQO6IBI7/40BsCZYIV3Z4Gj1+yurdFNU62VPVxJ5qJ0W1LorrXBTXOnG4Dz66CmpgTbNbwl0D+iVFkxUKrX2TojDouneYFz2DhFkhRK/w3tb3eGHtCwDcOf5O6VogfpPPG2Dl5/ms+6YARQGTVc+JFw9iwISUSJfWiscfoLDGRUFNE8V1amgtDoXVPVVN1Dp9h/wcSTEm0mMtpNvN5CREkZNgJSfBSlaclfRYi7S5EqoDr9vrEiIeZl966SWeeuopysrKGDVqFC+88AITJ0486O3nzZvHK6+8QkFBAYmJifz+979n7ty5mM29Z+K9EOLIfLDtA5745QkA/jTyT1w57MoIVyS6ssqCBr5+fTN15U4ABoxPZsofBhBlN3V6Lf5AkMpGD4U1LvZUN6nzV2vV0dW91U2UOw7eg7VZjElPWqyZ3IQo+iRGkRlvJTPWQkacOuIqu2CJI9EVW85F9Df4gw8+YPbs2bz66qtMmjSJefPmMX36dLZv305ycvJ+t//3v//NPffcwxtvvMGxxx7Ljh07mDlzJhqNhmeeeSYCX4EQoqv7sehHHv/lcQCuHn41N42+KcIVia5KCSqs/baAFZ/kEQwqWO1GTrx4EH1HJ3XYY3r8AXW+aq2L/OomSutclIamBZTUuSl3uPEHf3tILMqoIzshioxYM5lxVjJi1ZCanWAlJyFK2lqJHi+iv+HPPPMM1157LVdddRUAr776KgsWLOCNN97gnnvu2e/2y5YtY8qUKVxyySUA5ObmcvHFF7NixYqDPobH48HjaXnl6nA42vmrEEJ0VTtqd3DnkjsJKkHO638et429rUuOKojIczq8fPPGZoq21QLQZ1QiJ10+BHP00TXlb/T4Q8FUDaklda7wYqviWhcl9S6UQ5y+1Ws1pMWayYmPIiveQmaclcw4CzkJ6qKrOKtBfq9FrxaxMOv1elm9ejX33ntv+JhWq+WUU05h+fLlB7zPsccey7vvvssvv/zCxIkTycvL44svvuDyyy8/6OPMnTuXOXPmtHv9QoiuraihiOu/uR6n38mE1Ak8eMyD8g9fHNDuNRUsfm877iYfeoOW4y4cwNDj0g/r98XtC1DuUMNpYXMXgNDl/MOcs2ox6EiPNdMnMYr0WEt4wZV62UyqzSytrETkdeFfwYiF2aqqKgKBACkprSfTp6SksG3btgPe55JLLqGqqorjjjsORVHw+/1cd9113HfffQd9nHvvvZfZs2eHP3Y4HGRlZbXPFyGE6JLq3HXcsOgGKlwV9LP349mpz2LQybaXojWv289PH+9iy48lACRkRHParGHEp0cBEAwq1Di9lNW3hNXSejel9S4KapwU1riodx06rMZaDWSEgmmaXQ2p6bFmMmLVqQBJ0SZ5oSW6jy74u9qtJtIsXryYxx9/nJdffplJkyaxa9cubr31Vh599FEeeOCBA97HZDJhMnX+pH0hRGS4/W5u/u5m8uvzSbGm8Oqpr2I32SNdluhiyvfU88mbmymsctJgULAPiaWxTwyPLd1FmcMVnsf6W31Wm5kNWtLslnCf1cw4dSpAbqLMWRU9kYTZsMTERHQ6HeXl5a2Ol5eXk5qaesD7PPDAA1x++eVcc801AIwYMYKmpib+9Kc/cf/996PVSr87IXozRVG4f+n9rKtcR4wxhldPeZXUqAM/n4iezeMPUFSrjqAW1TipbvJSWOOizOFib2kjZY0e/BogJnSHggr17QASo42kx1rIirOSEWch1WYmM85CbmIUKTYzNrNeRlaFiKCIhVmj0ci4ceNYtGgR5513HgDBYJBFixZx000HXm3sdDr3C6w6nbrDiHKoGfRCiB6teXevr/d+jV6r57lpz9E/rn+kyxIdIBhUqGr0hE/5N6/6Lwl1ASiscVHVeIiWVRp1fCk5xkR6nDoFIDHaRIpNnaOaHuoIkGIzY+zmu3wJ0dNF9NzH7NmzufLKKxk/fjwTJ05k3rx5NDU1hbsbXHHFFWRkZDB37lwAzj77bJ555hnGjBkTnmbwwAMPcPbZZ4dDrRCid3pl/Svh3b0ePOZBJqROiHBFoq0cbh+loYBaVu+mMLQRQEkouJbWu/AFDj2AYTXqyIqzkp1gJUrR0LC9DmNDABtapp6aw0mn98VkkP8dQnR3EQ2zF110EZWVlTz44IOUlZUxevRoFi5cGF4UVlBQ0Gok9i9/+QsajYa//OUvFBcXk5SUxNlnn81jjz0WqS9BCNEFfLTjI15Z/woAd024i/MHnB/hisSBKIqCw+WnzOGmzOGmsMZJWb07FFJdVDV6Ka930+D57S1WAbQaSI4xk2o3kx5rJsVmJt2ubgSQHa/uXBVnVRf9bfi+iGXzdxH0a7AlRnPqrGGk9pF51EL0FBqll52fdzgc2O126uvrsdlskS5HCHGUvi/4ntsW30ZQCfLnkX/mpjGyKUIk+ANByhs8FNe6KKp1Uu7wUNngoaLBTYXDQ3mDOtLq9h16QRWA3WIg1WYmxW4mK04NqRmhtlXpsepUAL3ut0//e91+Fr29lby1lUCod+wVQzBHSWcLIY6U8qAdjRb8M5ehzx3W4Y93JHlNllgKIbqt7TXbufvHu8ObItw4+sZIl9Qj+QJBKho8lNW7qWzwhOaruiioUYNrca2LykbPIZv/N4u1GkiJMYd7qaba1ctJMSaSY0yk2S1EHWUHgNqyJr54ZSN15U60eg3HXtCfkdMyZaGWEEerC/4NSZgVQnRL9Z56bl98Oy6/i0mpk3hwsmyK0BZuXyB8qr+s3k1pvTqCWt3oVacDhBZZHWJHVQAMOo0aTGOtpNnNJMWYSIoJLaqym0kOXTZ38DzVom01LPzHJjxOP1F2IzP+PILUvjKtQIg2CwbRNJ8I0Xa9eeYSZoUQ3Y4v6OP2xbdT2FBIelQ6T5/4NAatnDr+teZV/+UOTzisFtepo6kFNU5K69xUN3kP63MZdBqSY9SAmhhtIs1uJit+n3ZVdjOJUaaI71S1fUUZ3/1rK0G/QkofG2dcPxKrzRjRmoTo9gL7PE/out7fk4RZIUS3oigKj/38GCvLVhJliOL5k54n1hwb6bIiwusPqq2oal0U17oorXdRVKsupipzuCmtcx920/90u4W00EKqNLuZhCgTqXZ1RDUj1kJSdOSD6m8JBhV++TSP1Qv3AtBvbBKnXDUUvXQrEOLoBfZpdafrehtRSZgVQnQrH27/kI93fowGDU8e/ySD4gdFuqQOoSgKDR4/xbXqiGq5w92yCUCtk+I6FxUNh56nqtVAQrSJ9H22Us2Is5ATWvGfHmvGbjF06ykaXpefr/65mYLN1QCMnZ7DMef2RdOFw7cQ3Yp/35HZrncWTMKsEKLbWF6ynCd+eQKA28bdxolZJ0a4orZzev0UhhZQNc9NLaxxUlLnpqLBTbnDg8sXOOTnsRh0ZMerp/rT7Obwqv/mxv9p9kOv+u/Oakqa+PLv6kIvnUHL1EsHMfiYtEiXJUTP4m0EIOjX0DJ5tuuQMCuE6BaKGor4fz/8P/yKnzP6nMFVw66KdEkHpSgKlQ0eiurUUdWSOhd7qpvCQbWs3k1V4+HNVY2zGki1W0i1mUiLVUdUs+KtZMSqo6yJ0cZuPap6NIq21fDlqxvxugNExZo44/oRJOdIy0Uh2p2rBoCAV9Mln28kzAohujyX38Wt399Kvaee4QnDeWTKIxF9QlUUhZomL4WhnqrN81SLal3sqWqiqM6F13/ouap2iyEUStW5qplxVjLjLOFV/8k2E1ajPE0fyNZlpSx+dxvBoEJafzun/3kElpiutzBFiB6hoQwAv0tH15tkIGFWCNHFKYrCo8sfZUftDuLN8Tw77VlMHbwAQVEUqpu84YCaX9WkTgcIta7aW+085BQArQZSQy2p0mIt5CZYyYi1kmJTg2pWvBW7pSv+W+jalKDCsv/sZt03BQD0H5/MyVcOkYVeQnSk2j0A+JokzAohxBF7d+u7fJb3GVqNlqdPfJrUqNR2+by+QJCSOjWo5lU2sbOikcLQ4qrSejeeQ4ysajSQHGMKt6bKCC2syk2IIjveSorNjFHf9eaWdWd+X4BFb29l16oKAMafkcvEs/rIQi8hOlrZRgA8Dj3WCJdyIBJmhRBd1ury1Tyz6hkA7hh3BxNSJxz2fd2+AHur1XBaUudS21fVtbSwOlQngOawmh5roU9iFFlx6kYAKXYzOaEFVya9jAZ2FleDly9f3Ujp7nq0Og3TLh8sC72E6Ax+L+z8BgBnpYm4CJdzIBJmhRBdUkljCbMXz8av+JmeO53Lh17e6nq3L0BxndqqqnmRVX5VE4U1atuqw1lgZdJryUmw0icxigHJMeQkWFvmrdpMEla7iLpyJ5+9sA5HlRujRc/pfx5O5uD4SJclRO+w/t/grMLn0uKs6Jrz0iXMCiG6HG/Ay+2Lb6fa6aCP9Rimxd/C60vzKahRd67aXdlIce2ht1i1mfVkhfqpZsZZyIyzkhGrtqzqKjtWid9WsrOWL17ZiMfpx5Zo5swbRxGfFhXpsoToHRyl8PWDAFRvjQZFo5626mIkzAohIqre5QuPpu6ubKSg2smPe7ZSUns2ij+OjcCNazce8L5Wo9pjtbmfak6ClZyEKDJC4bW7bwbQ2+34pYxFb20lGFRIzrVx5g2yNa0QncbrhPnXgqceJW00tbvKI13RQUmYFUJ0qEBQobTeRUG1GliL69T2VflVTeypdlLv8h3gXtHhS1FGHbmJUeQmRJEVbyU73krfpCj6JkWRFG2SsNoDKYrC2q8LWP6f3YC6Ne3JM4diMMq0DyE6RU0evH0u1BeA3gLnvQLP/i7SVR2UhFkhxFFr3iQgr6qJvdVqSN0T6hKQV9WIL/Db8wESooxkxllItCksr/qUoK6C0waM4aETric+qvduCtAbKUGFH/9vJxu/LwJg1ElZTPl9f+lYIERncNfDT8/Dj0+rHxuj4cK3IXlIZOs6BAmzQojD0tx7Na+yibzKRvZUOymoaSK/ysne6iac3oP3XTXoNOEWVml2M7mJUfRNjCI7PorcRCtWox6X38UlCy5Bp+zimNQJ/M8pN2LognuAi44TDCosfncbW5eVggam/K4/o07OkhczQnS02r1qgN36Gbhq1WPRqXD1QojvA8FDbwITSRJmhRCtBIIKxbUutpc3sK3UQV5VEzvKGyiodtLg8R/0floNZMRZ6JMYTU68lZwEK/2SohmQEk2a3YLuECNr/7Pqf9hVt4tESyJ/O+FvEmR7mYA/yLdvbmHX6go0Gjj5yiEMktZbQnSMgA9K1sGub2DVG+CsBiUUWGOz4YT/B6MuAV33iIndo0ohRLtzev3kVTaxp7qJ3RVN7KhoYHdFI3mVTXgDB34VrtFARqjvat9EdQ5rn8QockN9WNu6ScDneZ/zwfYPAHjk2EdItCS2+esS3Y/H6eOrf26mcEsNWq2GU2cNo/+45EiXJUTP4KqDyu1Qmw97l0HxGqjaAQFP69vlHg8jL4RRF0M3G0yQMCtED1fT5GVbmSM0PUCdw7qzvJHiOtdB72PUaemXHM3g1Bj6JUUxMCVG3Tgg3oq5nbcNzavP45HljwBw7YhrOT7z+Hb9/KJrczV4+fT5dVQVNqI3aDn9+hFkD02IdFlCdD8+N1TvgootULEVyjepIbZu74Fvb46FvidCnxNh4HSwZ3Zque1JwqwQPURNk5fdlY1sKq5na6mDnaFR1gN3C1DFRxnJTbDSNymaAcnqlID+STFkxB16WkB78AQ83PPDPbj8LiamTuTG0Td2+GOKrsNR5eKzF9ZTV+7EEmPg7JtHk5QdE+myhOh6FEWdCtBQCg1l6vv6IqgrVMNq7R5wFB/8/rYMiO8LKcMh9zhIGQqxuaDtGVtuS5gVoptp8vjZVtbAjvIGtpY62FHewK6Kxt/c8So73kr/5Gh1ekBSFP2SohmUEkNcVGR7dj698mm21mzFbrIz9/i56LTSeqm3qC5u5PMX19NY6yE6zsQ5t44mLlU2QxC9SDCoBtSa3eB2gKsGnDXgrIKmSmisUN+aL/96WsCBmOxq54HkwWpwTRqkvrf27B3zJMwK0UUFggp7q5vYXtbAzopGNpfUs7nEQVHtwacHpNvNDEmzMSzdxsDUGPolRZMdbyXK1PX+1L/M/5L3t78PwOPHPU6yVeZI9halu+tZ8NJ6PE4/calWzrl1DNFxpkiXJUT7CAbU0VNHiRpMGyugsVxte9VUqR6v3QsNJRA8+KLaA4pKgphUtdOALV1drBWbA3E56sirNaFjduhSDrHdYoR1vf9wQvRC9S5fKLQ2sLnEwdZSB9tKG3D5DtzuKjnGxKDUGAanxjAo1RaeImA1do8/6bKmslbzZE/IPCHCFYnOkr+hiq/+sYmAP0hqXxtn3jgKc1T3WmwieqGAH8rWQ2OlGkJr8kGjVdtYuevV985qNbTu2xngkDRqALUmgC2t5XJUkvoWnQzRKRCVCDHpoI/8DnhdsVVe9/jPJ0QPUlbvZmNxPZtCb5tLHJQ53Ae8rdmgZWBKDP2ToxmSamN4hp3BqZGfHnA0gkqQe3+8l0ZfIyMSR3DD6BsiXZLoJNuWl/L9v7YRDCrkjkjgtGuGYzDJ1BIRAYoC3kY1hNbkg8cBVTvVMKooakB1FKvB1FWrzlE9Elo9xKSpITQqSQ2k1niwxKvzV2OzwJ6lhtVu1jmgK5IwK0QHCQQV8iob2VLqYGNRPVvL1NHW6qYDz23NiLXQP9RBYFiGnaFpMfRJjO6UhVid6f+2/x+ryldh0VuYe/xc9Fp5GurpFEVhzVd7+fmTPAAGTkzhpCuHoNP1jMUnIsICvn1GR2taLrvroHQDmKLV9lRNFVAfCqjuuiM/xQ/qaf3EQWCMgoAX0seoXQHMdnVENSZVDbDWxG7To7UnkO+0EO2kwuFmbWEdG4rqWF9Yz/qiOhrc+z9ZajUwIDmGYRk2RmTYGZlpZ0BKDDZzz391nleXx1OrngLg1rG3kmPLiXBFoqMpisKyj3ex7ttCAMacms3k8/vJ9rRif4rSMs/UXQ+ehn2Cab0aTC2xodHSOij6BfQW8B98HcEh6Uxq+PQ2Qfpo9XGyJqkBNaGfep05NhRSkyWgdlHyUxGiDZxeP1tLHawvrGdtYR1r9tYesG+r1ahjcGoMwzPsDEu3MTjVxsCUGCzG3ndq1RfwcdcPd+EJeJicNpmLB18c6ZJEBwsGFZb8eztblpYAcNwfBjDq5KwIVyU6lKKop+xdtWrgdNWCz6mGRUeJumLf06Cu3vc2qu/ddWpv1LbYN8iabGCJC42SxqvvLXHqaG36GPWUvi1DPe1viVODsVE6aPQEEmaFOAwVDW5+2lXFqj21rC+qY1tpA/5g69WdGg0MSI5mbHYcwzPsjM6KZXBqDHo5lQrAqxteZXvtduJMcTx23GNoNfJ96ckCgSCL3tzCzlUVoIFplw5m6HHpkS5LHErzXFJnjRoym0OnpxG8DeB1quG0uedpUzV46lsCqsfRttP3+9JoIWEAmG2hgBqrjo6aotU60kaFwmqsOi/VGq/eTkZNO450MxCie1EUhT3VTlbm17A8r5o1BbXsrXbud7ukGBMjMuyMyYplTHYcY7Jju2QLrK5gU9UmXt/4OgD3H3M/SdakCFckOpLPG2Dh3zdSsLkGrU7DqVfL9rSdTlHU0VB3nTo6WlcQmltap67Id9Wq4XTfU/nOamgoP7x+poeiN6th0xKnjn4arWo7qehkMEarQdUYDaYYNaxa4tXb2tJlQVRXJ90MhOh6Gj1+1uytZW1BHRuL61lXWLvfBgQaDQxLtzG5bwKjQuE1I9YSoYq7F5ffxQM/PUBACTAjdwbTc6dHuiTRgbxuPwte2kDJzjr0Ri3Trx1O7ojESJfVfSiKOvLpaVSDpsfRMn/U42gZIXXXq6fxPQ37XNegBtjm0/fKgVv7HRadUQ2YZpsaOI1RYIxRQ6nBqgbPmFT1lH3zqKnZro6QWuPBIM+PovNImBW9TlWjh2W7q1m2q4p1hXXsKG/gVzMGMOq0jMy0c0zfBCb0iWd0Zix2q4wWtMW81fPYVbeLBHMC9026L9LliA7kbvTx+UvrKc93YDTrOPOmUaT3j410WZ0nGFBHOB3FoYDZqM7pbChX+44G/WrY9Dha5oo2h9HmNlFH1KP0MGgN6sinNUFdyZ87JbTaPkENp6bo0Ahq6DbRyepCJ4OlS47ACXEgEmZFj+dw+1i2q5plu6v4Oa+aHeWN+90mM87C+Jw4RmTGMirTzohMOyZ971uk1d6Wlyzn39v+DcBjxz1GnDkuwhWJjuJ0ePn0uXVUFzdisuo5+5bRpOTaIl3W4fF7Wo9oehtbTr97HC3zRJtD6L4joM2jpc0r7mnHuYWm0JzR5rmjplD4NEar4dNkbxk5NdnU60wxYIhSR0nNNnUUVUKp6OEkzIoeJxBU2FRcz5IdlSzdVcWavbX7LdYanBrDcf0TmdAnnlGZsaTazRGqtudy+pw8tOwhAC4ceCFTMqZEuCLRURxVLj57YT115U4sMQbOvX0MCenRHfeAAd8+I5uhsOltUk+/+1yhjxtan6rfN4D6XPucyneo/ULbkyVO7TNqjFLDpMGsTgmISgJ7RktANdvV0VGzveU0vTVRvU5vAa0skhTicEiYFT1CbZOXH3dV8fXmMpbuqqLO6Wt1fZ/EKE4YkMgxfROY2CeehGjZB76jPbP6GUqbSsmIzuCO8XdEuhzRQWpKmvjvvLU4HV6i40yce9sYYlOs6pXNi5Cag6TPGQqSTaHA2dhyit3TuH/Y9Da2BNV97+M/8I55R01nDAXM6JbT7yZbKJSaW4+UGqPUt+YRU0tcqC1UbJfYclSIdiXdDIRofy5vgGW7q/hxZxXLd1ezo6Kh1d9ajEnP5H4JnDgoieP6J5KTIL0EO9OK0hV8sP0DAB4+9mGsBmuEKxJtoiitg6WnUT2V7q6H+iIai4uoWLWVU3WlaFKtpKaD7uMGdRTS26i+tef8z19rXg1vjFYXJoUXKFla5oOGT8833y4UQg2W1mHUGC2tnYQ4HF1w2or85YpuQVEUdlc2sWx3Fd9vq2Dprip8gdavFAemRDNtcDKnDU1hVGas9HeNEKfPycPLHgbU6QXHpB0T2YJEC58bavPVveibQ2lDKTSUqZe9ja1Xzrvrf7NnaDQweN9ByJKD3FCjVedxNgdNQ9Q+I5vRoeAZ09LCyRAaCTXuc8wYHQqg0S3hVFo4CSGQMCu6MEVR2FTs4ItNpXy1qYy8qqZW12fEWpg6KIlj+yUyoU8cyTEy77UreHXDqxQ1FpEalcpt426LdDk9T8APTZXQWN66XZMnNIfUVae+d9aEdmGqbQmtR0NvCYdItxJNVTn4gkYMNhupEyegtyWGtv60t+xV33y6XhYhCSE6kIRZ0aUoisK2sgYWbCjl0/UlFNS0bFZg1GmZ0CeOY/slctrQFPonR6ORf5BdypbqLbyz+R0A7pt4HzHGmAhX1EUFAy2Lkpob1u/75qxWd1Zy17WMoJZvUk+JexxH//iGKMie1LLgyJ4Ram4f3Xr1vCW2ZXV8aDHSlp9KWPzuNhQFckYkMONPw9EbpPOHECJyJMyKLqGo1sl/1hTz3/Ul7KpoaZ1lMeiYNjiJ6cNSOWlwMjFmOa3YVfmCPu5fej8BJcCpOacyLXtapEvqeAF/KGzWhXZWqlBP2Qe80FSlhtJfj5Y6q9seSMP304BWp4bRuD77tGuKa9n60xraUcnSvEd9bMuCpjaukl/7TQHLPt4FwOBj05h26SC0Mp1HiJ5PFoAJcWBOr59vtpTz3ooCfsmvCR836rWcODCJs0amcerQFKxG+TXtDt7Z/A676nYRZ4rjL8f8JdLlHDmvs2VU1FmlBtOmKmiqUC83j5A2B9PmOaZHQ28OrYSPbwmizavio5JCq+PtLafujVFqY3trvBpmO9HqhXv4+ZM8AMZOz+aY8/rJmREheqMu+HcvKUF0Kn8gyM95NXyyrpgvNpbi9LZstzi5bwIXjM1g+vBUbDIC263k1+fz0rqXAJg9fjbx5vgIV4Q6ktA8Wlq7R+1LWrRSvc5VA47SlpHTo51Pagz1CLXEgVavLkxKHakGz+ZA2hxWm0OqydYtWjgpisLPn+Sx5qu9AEw4qw8TzsyVICuE6DIkzIoOFwwqrNpbyyfrilm4qYyappYG5VnxFs4fk8kfJ2SRHit7eXdHQSXIQ8sewhf0MSVjCuf2O7fjHsznCo2YVqrvndXqmyu02KmpWl0YVV+kjq7+xkr8g4pKVhcyRSerwdMaumyJU+eSNvcSDY+Y2nvsqnolqPDDBzvYtKQYgMnn92Ps9JwIVyWEEK1JmBUdprLBw4erCnl/ZQGFNa7w8VirgTNGpHH+mAzG58TJCE839/HOj1lbsRar3sqDxzx4ZD9Pv1cNn40VaiBtqoS6QvVYU2XLfFRXrTrftC2n9Y3Nze+jIWW42nA/axLEpKkhtXnU1Bqv3lZ+HwEIBoJ8/952ti0rBQ2cePEghp+QEemyhBBiPxJmRbvLq2zktR/zmL+mGI9fbZgeY9Jz2rBUzhuTzuS+CdIDtocoaiji6ZVPA3DTmJtIj05XT+97GtQw2lCmvm+qBEdxaJV+FdQVqKHVU3/kD6oz7TNqmtAyh9QSr76PTgZ7ZsuoqkFath2pgC/IV//cRP76KjRaDafMHMLAiamRLksIIQ5IwqxoF4qisHpvLX//IY9vt5aHFz6OyrRz2TE5nDUyHYtR2vd0W8GAOjraWKGewm8sQ2msYOvW9/mLo5hsfQwjf/wnLJyrBtcj2eteq4foVDWIWhPUIBqTpp7q33e+aXNwNdtl9LQDed1+vnx1I0XbatHqNUy/Zjh9RydFuiwhRAR17V4GEmbFUQoEFb7cVMqrS3azqbil3dDJg5P50wl9mdgnXqYRdHXBINTtBUeJ+r52LziKoKFcPdZQqgbZXz2daYBTwx85ob689ec1RodGUENzUG0Z6ntrPNizIC43tEAqvs2tokT7cjf5WPDSBsry6tGbdJzx5xFkDe0Ci/mEEF1HF/yfLmFWtInbF+D/Vhfx5tL88M5cZoOWc0dlcO0JfemfHB3hCgWgnvJ31qhzUKt3qqOqDWUtW5jWF6mn/w93JNUSDzFpuKMT+bZqHeUahVE50xife6oaVJuDa1Si2kZKdBtNdR4+e2E91cWNmKx6zrp5FKl97JEuSwghDknCrDgivkCQj1YX8fyinZTWuwF1QdeVk3O58thc4qO6fquhHsXTAOVboGq7Oge1qQIaK6GxTB1Zbao4vKCqNUDQB9nHQmJ/deQ0JhVi0sGWHjrlHw86PYqicNui6/lJyWdU0ihmzniz03ueivZVV+Hks+fX4ahyY4kxcO5tY0jIkBekQojuQcKsOCyBoMLHa4p44bud4c4EaXYz1xzfl4smZBFtkl+lduf3qmHUUQr1oRX+jmKoL1YXUNXuUdtPHQ5zLMT3VU/tx6TuE1TT1OBqSz/s9lKf7PqEn4p/wqg18sixj6CTINutVRc38unz63DWe7Elmjn3tjHYEqVNnhCi+4h4AnnppZd46qmnKCsrY9SoUbzwwgtMnDjxoLevq6vj/vvvZ/78+dTU1JCTk8O8efM444wzOrHq3kNRFJbsqOSJL7exrawBgIQoIzdM68+lk7Ixy57sbRMMqKv6m0dQ6wvV+ap1hWpIdZSo4fVwpt1Hp0Dy0JagGpWoLqhqbj0VnQx6U7uUXdxYzJMrnwTU7gV9Y/u2y+cVkVGWX8/nL67H0+QnPj2Kc24dTZS9fX5XhBCis0Q0zH7wwQfMnj2bV199lUmTJjFv3jymT5/O9u3bSU5O3u/2Xq+XU089leTkZD766CMyMjLYu3cvsbGxnV98L7C7spGH/ruZpbvU0T+bWc+N0/pzxeRc6UxwKAGfOora3IKqvih0ea/6Vl8MSuDQn6d5pb89Qw2ntnR1IVVsFsT1gdhsdVepThAIBvjL0r/Q5GtidNJorhh6Rac8rugYBZur+fIfm/B7AqT0sXHWTaMwR/XMzR+EEEdJ6dr9DCIaZp955hmuvfZarrrqKgBeffVVFixYwBtvvME999yz3+3feOMNampqWLZsGQaD+qSbm5vbmSX3CvUuHy99v4s3f8rHF1Aw6rRcMTmHG6f1J07mxKoURR1Zrd0DtfmhoBo69V+7Rw2vhwyrGrUXakyKGlDjctW2VHG56sf2LHWUtYusHH1367usKl+FVW/lseMek+kF3djWZSUsfnc7waBC5uA4Tr9uBEZzxE/UCSG6ha7xP2lfbXr2CgQCvPXWWyxatIiKigqCwWCr67/77rtDfg6v18vq1au59957w8e0Wi2nnHIKy5cvP+B9Pv30UyZPnsyNN97If//7X5KSkrjkkku4++670ekO/I/V4/Hg8XjCHzscjgPeTrTMi/3bwm1UNaqLhqYNSmLOOcPJTrBGuLoI8DpbRlELV6hh1VEceitRd5L6LTqjOnJqzwy9z4LYHPVyXI668l/XPQJEaWMpL617CYC7JtxFti07whWJtlr7TQHLPt4FwMCJKZx0xRB0emmNJoTovtr0n/TWW2/lrbfe4swzz2T48OFt6iNaVVVFIBAgJSWl1fGUlBS2bdt2wPvk5eXx3Xffcemll/LFF1+wa9cubrjhBnw+Hw899NAB7zN37lzmzJlzxPX1NrsqGvh/H21gbUEdAP2SovjLmUOZNnj/6R49hqKo/VObR1Srd0H1bqjcBjV56jaqhxKTri6sit03qOZCfB91ekAP6J+qKAp/XfFXXH4XY5PHcv6A8yNdkmiDYFBh2fxdrP+2EIDRp2Rx7AX90Wi73iiLEEIciTaF2ffff58PP/yw0xddBYNBkpOT+cc//oFOp2PcuHEUFxfz1FNPHTTM3nvvvcyePTv8scPhICsrq7NK7vIUReHdFQU8tmALbl+QKKOOW04ewFVT+mDsKaM1PpcaUss2qiG1chtU7VAvH6ptlcnWMrqa0F8NqslD1GkAtvR2W1jVlX2R/wU/FP2AQWvggWMeQKvpIb8XvYjPG+DbN7aQt64SgMnn92PMadmyoYkQokdoU5g1Go3079//qB44MTERnU5HeXnrXYPKy8tJTT3wHuBpaWkYDIZWUwqGDBlCWVkZXq8Xo3H/+ZwmkwmTqecHjrYoq3dz33828t22CgCOH5DI334/kjR7N23L46xRQ2rzW3UouNbmgxI8+P2siepIanxfSByghtbEQWpY7eVbp1a7qnnyF7V7wZ9G/on+cUf3dy86n7vJx5evbqRkZx1avYaTrxzCwAkHfo4VQogD6okLwO644w6ee+45XnzxxTa/sjcajYwbN45FixZx3nnnAerI66JFi7jpppsOeJ8pU6bw73//m2AwiDZ0+nbHjh2kpaUdMMiKg1u8vYLbPlhHndOHUafl/00fxDXH9+keIzXeJjWklm+Biq1Quh4qt4Kz+uD3MdkgdSQk9FMDa9Jg9X10KhjMnVd7N/PUqqeo9dQyMG4gs4bPinQ54gg5ql18/sJ6asucGMw6zrpxFOkDYiNdlhCiG+uKMaFNYXbp0qV8//33fPnllwwbNizcWaDZ/PnzD+vzzJ49myuvvJLx48czceJE5s2bR1NTU7i7wRVXXEFGRgZz584F4Prrr+fFF1/k1ltv5eabb2bnzp08/vjj3HLLLW35MnqlYFDh+e928tyinSgKDM+w8cyFoxmYEhPp0g7M71WnBxSvgpK1ULRSnTJwsP6r9ix1ZDVpUGikdaA6LSA6pWv+BXZhy4qXsSBvAVqNlocnP4zhMDdVEF1DZWEDC17aQFOdhyi7kbNuHkViZhf9OxdCiKPQpjAbGxvL+ecf/SKQiy66iMrKSh588EHKysoYPXo0CxcuDC8KKygoCI/AAmRlZfHVV19x++23M3LkSDIyMrj11lu5++67j7qW3qDJ4+fujzfw+YZSAC6emM3D5wzFpO8iLZYURV2MVbIWilerwbVkHfhd+9/Wmgipw9UR1tSR6uWE/mCM6vSyeyJPwMPcX9QXkZcMvoQRSSMiXJE4Ens2VvHVPzbh9wWJS7Vyzq2jiY6TMxBCiJ5JoyhdfCJEO3M4HNjtdurr67HZbJEup9OU1ruY+cZKtpc3oNdqeOz84Vw0IcLtlZqqoXQtFPwMRaugdJ3aXeDXLPGQMQ4yxkLGeEgfA9FJnV5ubzJv9Txe3/Q6iZZE/nvef7EZe8/fSne3+cdifvjfHQSDCllD4zlt1jDZDEEIcVSCbjfbR48BYNDqVWijOn7g6Ejy2lE1uaysrGT79u0ADBo0iKQkCRhd0abiema9vZJyh4ekGBMvXTKWiX3iO7+QxkooWA5530P+D2orrF/T6tWtWTPGQuZEyByvThWQKQKdptBRyNtb3gbgL8f8RYJsN7L26wKWzW/pIXvylUPQ6qT7hBCiZ2tTmG1qauLmm2/mnXfeCW+YoNPpuOKKK3jhhRewWnthg/0uavnuav70r1U0uP30T47mzZkTyIrvpJ+Pz63OdV37rtpdoHgN+811je8LWZPUkdfMCeq0AVmQFTGKovDYisfwB/1MTpvMydknR7okcRgURWH5f3az9usCAMbNyGHSuX27x4JOIUTX18VP4rcpzM6ePZslS5bw2WefMWXKFEBdFHbLLbdwxx138Morr7RrkaJtPt9Qwq3vryMQVJiYG88/Z47HZu7A043BgDrfdfd3kLcYCn+BoK/1bZKHQs4U6HcS5EwGS1zH1SOO2MI9C/mp5CeMWiP3Trr30HcQEef3Bfju7a3sXKW22JMeskKIDtUFn1vaFGY//vhjPvroI6ZOnRo+dsYZZ2CxWLjwwgslzHYBH68u4v99tJ6gAmeOTON//jAKs6EDFno1VsKub2DXt7D7e3DVtL4+Khn6HK+Ovg45B2xp7V+DaBc17hrmrlAXfV0z8hr62PtEuCJxKD5PgC9f3UDh1lq0Wg0nXjKIocelR7osIYToVG0Ks06nc79taAGSk5NxOp1HXZQ4OvPXFHHH/60H4MLxmcy9YCS69tyysqEMtn0OWz6FPUtBCbRcZ7Kr4bXfSdB3qjqNoAu+ihP7e3rl09R6ahkQN4Brhl8T6XLEIXicPha8vIHSXfXojVrOvGEkmYMjMBdeCCEirE1hdvLkyTz00EO88847mM3q/EaXy8WcOXOYPHlyuxYojsyCDaXhIHvppGwePXc42vYIsl4nbPkvrHtPDbD7zn1NGwX9Tob+p6gjsLqjWlcoImBZyTI+y/sMDRrmTJ4jPWW7OKfDy2cvrKOqsBGjRc/ZN48ita890mUJIUREtCl1PPfcc0yfPp3MzExGjRoFwPr16zGbzXz11VftWqA4fF9uLOWW99eiKHDR+KyjD7IBP+QvgfX/C9u+AF9Ty3UZ42HI2TD0HHX0VXRbbr+bv/78VwD+OPiP0lO2i6submTBSxtoqHFjsRk55xbZDEEI0bu1KcwOHz6cnTt38t5777Ft2zYALr74Yi699FIsFku7FigOz8951eHFXheMzeDxC0a0PchW7YR1/4b170NDScvx2GwYcwWMuki9LHqEl9e/TGFDIcnWZG4ZI7vpdWUlO+tY8PIGvC4/tiQLZ980itgU6R4jhOhgPbGbAYDVauXaa69tz1pEG+VXNfHnf63GGwgyY1gqT/1+1JHPkQ0G1UVcPz4NhStajlviYdj5MOpiteerzH/tUXbX7eZfm/8FwAPHPEC0MTrCFYmD2bOxioX/2ETAFyS1r50zbxwpmyEIITpfF8wBhx1mP/30U04//XQMBgOffvrpb972nHPOOerCxOFpcPu45u2V1Lt8jMqK5dmLRh9ZkG2qUvvArv1XyyYGWj30nQZjLoOBM6Tvaw+lKAqPr3gcv+JnauZUpmZNjXRJ4iC2Litl8bvbCAYVckckcNq1wzEYu8g21EIIEWGHHWbPO+88ysrKSE5O5rzzzjvo7TQaDYFA4KDXi/ajKAq3f7CO3ZVNpNrMvHbFOCyH8w9OUaB0Pax6Hda803LcZIOxV8CxN0NMascVLrqEb/Z+wy9lv2DSmbh74t2RLkccgKIorPw8n5UL9gAwcFIKJ10xBJ3s6iWEEGGHHWabd/r69WUROf/6eS/fbq3AqNfyjyvGkRxziBFUv0ftSLD8JShd13I8Jg2m3gvDLwCTLCTpDZw+J0+tegqAmcNmkhmTGeGKxK8pQYUfP9zJxsVFAIydnsMx5/ZF055t9oQQogdo05zZd955h4suugiTydTquNfr5f333+eKK65ol+LEweVVNvLYgq0A3DNjMCMzYw9+44ZydRR21ZvQpO4ShM4Eg06HSX+G7Mldcg6M6Dh/3/B3yprKSI9KZ9aIWZEuR/xKMBDk27e2snNlOWjghIsGMmKqvOAQQogDadO5qquuuor6+vr9jjc0NHDVVVcddVHit7m8AW54bw0ef5Dj+idy1ZTcA9/Q2wRL58H/DIQlT6pBNiYNpt0Ps7fAhW9DzrESZHuZvY69vLNFnV5yz8R7sOilA0lXsm+Q1Wo1nDJzqARZIURk9cRuBoqiHHDf76KiIux2adzd0Z75ZjvbyhpIjDby9B9G7f+z8Lnhl3/AsuehqVI9ljwMjrlO7UogDfF7LUVRmLN8Dv6gn+MyjmNa9rRIlyT24fcG+Pr1zeSvr0Kr1TD92uH0HZMU6bKEEKJFFxwAO6IwO2bMGDQaDRqNhpNPPhm9vuXugUCA/Px8ZsyY0e5Fihabiut5fWk+AE/+biSp9n3mySoKbPkEvrofHMXqsbhcOPFuGHGh7Mwl+Dzvc1aWrcSit3DfpPsiXY7Yh88TYMFL6yneUYdOr+W0a4bRd7QEWSGEOJQjSjfNXQzWrVvH9OnTiY5u6UlpNBrJzc3ld7/7XbsWKFr4A0Hu/ngDQQXOGpnGyUNSWq6sL4YFs2HHQvVjWwZMuw9GXiQjsQKAek89T696GoBrR1xLVkxWhCsSzbxuPwte2kDJzjoMZh1n3jCSjIFxkS5LCCG6hSMKsw899BCBQIDc3FxOO+000tLSOqoucQD/+nkvm0sc2C0GHjxrqHow4FcXdy16BLyNoDXA8bPhuNnSH1a08szqZ6hx19DX3pcrh10Z6XJEiLvJx+cvrqc834HRrOOsm0eT1k+mawkhxOE64vPOOp2OP//5z2zdurUj6hEHUef08uw3OwC4a8Ygkm1mtVfsJzdA+Sb1RpkT4eznIGVoBCsVXdGa8jXM3zkfgAcnP4hRZ4xwRQLUIPvpc+uoLGjAZNVzzq2jSc6xRbosIYRopYuv/2rbArDhw4eTl5dHnz592rsecRAvL96Nw+1nUEoMfxyfBT+/Ct88AAEvWOLUDgXjZ4FWmqmL1oJKkKdWqj1lfzfgd4xLGRfhigSAq9HLp8+to6qwEXO0gfNuH0NChmwnLITo4rr7ArBmf/3rX7nzzjt59NFHGTduHFFRUa2ut9lkZKE9FdY4eWvZHgDunpqG7v0/ws6v1CsHnQFnPw/RslBEHNhnuz9jU/UmrHorN425KdLlCMBR7eLzF9ZTW+bEEmPg3NvHkJAuQVYIIdqiTWH2jDPOAOCcc85p1RaquWWXbGfbvp79dgdef5Bj0zVM++o0cNeAzginPgKTruuSr5JE19DgbeCZ1c8A8OdRfybRkhjhikR1cSOfPrcOp8NLVKyJc28bTVxq1KHvKIQQ4oDaFGa///779q5DHERhjZNP1qpttu6pug+NtkZtt/XHf0PKsMgWJ7q81za8Ro27hj72Plw+5PJIl9PrleXX8/kL6/E4/cSnR3HWTaOIiZeFmkIIcTTaFGZPPPHE9q5DHMRrS3YSVOB47QZGavNh/NVw2mNgtEa6NNHFFTgKeHfruwDcOf5ODNKiLaLK8uv59Ll1+NwBUvvaOPPGUZij5GcihBBHq81d9Ovq6nj99dfDXQ2GDRvG1VdfLTuAtaOK8jLe/2UPoOd640L43esw4veRLkt0A4qiMPeXufiCPqakT+H4jOMjXVKvVrKzjgUvrcfnDpA+IJYzbxyJ0SybmAghuouu3c6gTUvfV61aRb9+/Xj22WepqamhpqaGZ555hn79+rFmzZr2rrF3ctbwrzdfwKvoGavbzbFXzJEgKw7bV3u/YmnxUgxaA3dPvPuA20+LzlG8o5bPnl+HV4KsEKK78vvDFzU6XQQLObA2PaPefvvtnHPOObz22mvhLW39fj/XXHMNt912Gz/88EO7Ftnr1BXgevsi3q27FYBrZkyCvpMiXJToLhq9jTz1i9qK65oR19DHLi30IqVkZy2fv7gevy9I9rB4Zvx5BAZj1/tHIIQQv0XZJ8zSU8LsqlWrWgVZAL1ez1133cX48ePbrbheqaEM3jmX+ZW51BJDpk3PacdOiHRVohv5x4Z/UOGqICsmi1kjZkW6nF6rdFcdn72wHr83SNbQeE7/8wj0EmSFEN2Q4vMBoDEYuuSZvjZNM7DZbBQUFOx3vLCwkJiYmKMuqtdy1sA750FNHv/mdABmHj8AvU42QhCHZ0v1Ft7Z8g4Ad024C5POFOGKeqeyvHo+e3GfIHudBFkhRPcVHpk1dM1Fq21KSRdddBGzZs3igw8+oLCwkMLCQt5//32uueYaLr744vausXcI+OH9S6FyK9ssY9jsz0Cv1XDB2MxIVya6iaAS5IlfniCgBJieO52pWVMjXVKvVLq7nk+fV7sWZAyM5fTrZGqBEKJ7U3xqmNXou+Z8/zZV9fTTT6PRaLjiiivwh9K6wWDg+uuv54knnmjXAnuN7x6FgmVgsvFe9qNQ6+DUoSnERxkjXZnoJhbkLWBtxVosegt3jr8z0uX0SiU7a8NTC9IHxHLmTaMkyAohuj3F5wXUaQZdUZvCrNFo5LnnnmPu3Lns3r0bgH79+mG1Su/TNslbDD/NA8B75vN8+h8XAJdMyo5cTaJbafI18ezqZwH408g/kRqVGuGKep/yPQ4+e3FDqzmyEmSFED2B4nYDoDV3zU1ejmq82Gq1EhsbG74s2sDTCJ/cqF4eN5PvdZOpd60mOcbEsf1k61FxeP6+4e9UuirJjM7kiqFXRLqcXqdir4PPXliH3xMgc3AcZ8gcWSFEDxJ0qWFWY+maYbZNc2b9fj8PPPAAdrud3NxccnNzsdvt/OUvf8EXWvEmDtPSZ8FRBPZsOO0xPlpdBMD5YzLQabveikHR9eyu282/Nv8LgLsn3o1RJ1NTOlPFXgefPrcOT5OflD42WewlhOhxgi4nAFqzJcKVHFibRmZvvvlm5s+fz9/+9jcmT54MwPLly3n44Yeprq7mlVdeadcie6yGclj+knp5+mM0KCaW7KgE4PyxGREsTHQXiqLwt5V/w6/4mZo5VRZ9dbLyPQ4+e34dHqef1L42zr5ltGyIIITocYJNoTAbHR3hSg6sTc+6//73v3n//fc5/fTTw8dGjhxJVlYWF198sYTZw7XsefC7IHMCDDmbr9cU4/UH6ZcUxaAUaXEmDu2bvd+wrGQZBq2B/zfh/0W6nF6lqqhhnyBr5+ybR0mQFUL0SMHGBgC0UVERruTA2vTMazKZyM3N3e94nz59MBrlFOdh8TTCGrUfKCfcBRoNCzeXAXDmyPQu2ZRYdC1uv5unVz0NwNXDrybbJgsGO0t5vjpH1uNUpxacfYsEWSFEzxWoqwdAZ7dHuJIDa9Oc2ZtuuolHH30Uj8cTPubxeHjssce46aab2q24Hm3jh+BxQHxf6H8Kbl+AH3eqUwymD0uJcHGiO3hz85uUNpWSYk2Rnb46UemuOv773NqWICsjskKIHi5QVweALrTov6tp0zPw2rVrWbRoEZmZmYwaNQqA9evX4/V6Ofnkk7ngggvCt50/f377VNqTKAqsekO9PH4WaLWs3F2J2xck1WZmaJotsvWJLq+8qZw3N70JwB3j78Ci75qT8nuasrx6PnthPT5PgIxBsZxx/UgJskKIHs9fUw2APj4uwpUcWJuehWNjY/nd737X6lhWVla7FNQrlK6Hso2gM8HoSwD4blsFACcMTJQpBuKQ/mf1/+DyuxiTPIYZuTMiXU6vUL7HwecvqUE2c3AcZ9wwUvrICiF6BX+FeuZYn5QU4UoOrE1h9s0332zvOnqXdf9W3w8+E6zxAPy4swqAqYOSI1WV6CY2VG7gy/wv0aDh3on3youfTlBZ0MCnz63D61KnFpxxvQTZnioQCEiLSSF+xe33EUxLI5CWhju0gUJ7MBqNaLVtmvHaylGdH6usrGT79u0ADBo0iKQumti7lIAPNv9HvTzqYgDKHW52VTSi0cCx/RIiWJzo6oJKkMdWPAbAOf3OYUjCkAhX1PNVFjTw33lr8br8pPWzc9bNozCYJMj2NIqiUFZWRl1obqAQIkRR8F11FQBlFgua/Px2+9RarbZdmge0Kcw2NTVx880388477xAMBgHQ6XRcccUVvPDCC7Ib2G/Z8yM0VYA1EfpNA2DJdnX4fmSGnVirdIMQB/e/2/6XLdVbiDZEc9u42yJdTo9Xnu/g0+fW4nUHSO1r46ybZLFXT9UcZJOTk7FarXLGQ4iQoNeL1+8HrRZTv37t9rcRDAYpKSmhtLSU7Ozso/q8bXpWnj17NkuWLOGzzz5jypQpACxdupRbbrmFO+64Q/rM/pYdX6nvB80AnQGAH0JdDE4cKCPb4uDKmsp4bs1zANw29jYSLbLdcUeq2Ovg0+fX4XUHSOtv58wbR2G0SJDtiQKBQDjIJiTI2TEh9hXweNBotWgtFkyW9l1snJSURElJCX6/H4PB0ObP06Zn5o8//piPPvqIqVOnho+dccYZWCwWLrzwQgmzB6MosGOhenngjNAhhZ/zagCY0l/CiTi4p1c9jcvvYmzyWP4w6A+RLqdHa96iNjy1QEZke7TmObJyVlGI/QWd6u5fmnYOskB4ekEgEDiqMNumWbdOp5OUlP17oSYnJ+MMfdHiAKp3Qe0etYtBv5MAKKhxUtXowaDTMCorNqLlia7ru4Lv+GrPV+qir0n3otUc/YR5cWBVRY18+lzLzl5nSpDtNWRqgRD7CzY1AaDtgBd77fU316b/iJMnT+ahhx5qtaLN5XIxZ84cJk+e3C6F9UgFP6vvM8aBUd0SbtWeWgBGZNgxG2RRidif0+fksZ/VRV8zh81kcPzgCFfUc9WVO/k0tCFCal91QwSTTC0QQvRSis9HMJT1dF10K1to4zSDefPmMWPGjP02TTCbzXz11VftWmCPsvcn9X32MeFDawvVMDs2u2s2IhaR98+N/6TCVUFGdAY3jL4h0uX0WI4qF588uxZXg4/ErGiZIytENzJ16lRGjx7NvHnz2vXzPvzww3zyySesW7euXT9vdxFwOADQmi1ojmIaQEdr08jsiBEj2LlzJ3PnzmX06NGMHj2aJ554gp07dzJs2LD2rrHnaA6zuVPCh9YV1gHIFANxQDtrd/LmZrWv853j78SsN0e4op7JUe3ik2fW0lTnIS7Vytk3j8Yc1XWfuIXoTmbOnIlGo+G6667b77obb7wRjUbDzJkzD+tzLV68GI1GIy3UOoGiKARq1QE3Xaw9wtX8tiMedvD5fAwePJjPP/+ca6+9tiNq6pnqi6GuADRayJwIgMsbYGtpAwBjc2RkVrSmKAqP/vwo/qCfqVlTOTn75EiX1CM11nr47Pn1NNS4sSdbOOfWMVht0iJPiPaUlZXF+++/z7PPPosltJDI7Xbz73//m+zs7AhXJw4k2NSkTjHQaNHFxka6nN90xCOzBoOhXXd/6DVK1qjvU4aB2QbA5pJ6AkGFpBgT6XYZcROtfZ73OWsr1mLRW7h/0v2yOKUDNNS4+c8za6grdxIdb+K828cQHWeKdFlC9Dhjx44lKyuL+fPnh4/Nnz+f7OxsxowZEz4WDAaZO3cuffr0wWKxMGrUKD766CMA9uzZw7Rpan/2uLi4/UZ0g8Egd911F/Hx8aSmpvLwww+3qqGgoIBzzz2X6OhobDYbF154IeXl5a1u88QTT5CSkkJMTAyzZs3qtXlHURT8FRUA6ONi0ei79pSrNk0zuPHGG3nyySfx+/3tUsRLL71Ebm4uZrOZSZMm8csvvxzW/d5//300Gg3nnXdeu9TRoeoK1PcJA8KHNhbXAzAq0y5BRbTS4G3g2dXPAvCnkX8iNSo1whX1PI21bj55di2OShcxCWbOnz2W6Dh5USlER7n66qt58803wx+/8cYbXBXaWarZ3Llzeeedd3j11VfZvHkzt99+O5dddhlLliwhKyuLjz/+GIDt27dTWlrKc889F77v22+/TVRUFCtWrOBvf/sbjzzyCN988w2gBt1zzz2XmpoalixZwjfffENeXh4XXXRR+P4ffvghDz/8MI8//jirVq0iLS2Nl19+uSO/JV1WoK5Obcml0aLrBru7tilqr1y5kkWLFvH1118zYsQIon61wm3fV16H8sEHHzB79mxeffVVJk2axLx585g+fTrbt28nOTn5oPfbs2cPd955J8cff3xbvoTO16RujEB0y9e0uUSdWD00vWvPRRGd78W1L1LpqiTHlsPlQy+PdDk9jtPh5b/z1uGodGFLNHPe7LHExEuQFaIjXXbZZdx7773s3bsXgJ9++on333+fxYsXA+DxeHj88cf59ttvw52R+vbty9KlS/n73//OiSeeSHx8PKC2Ao391anvkSNH8tBDDwEwYMAAXnzxRRYtWsSpp57KokWL2LhxI/n5+WRlZQHwzjvvMGzYMFauXMmECROYN28es2bNYtasWQD89a9/5dtvv+11o7NBtxtfaSkAhuQktF144VezNoXZ2NhYfve737VLAc888wzXXntt+NXZq6++yoIFC3jjjTe45557DnifQCDApZdeypw5c/jxxx+7x0RwlzqJGkvL3NhtZaEwm2aLREWii9pZu5MPtn8AwP2T7sekk9Pe7cnr8vPpc+vCUwvOvX2MBFkhOkFSUhJnnnkmb731FoqicOaZZ5KY2LJZ0K5du3A6nZx66qmt7uf1eltNRTiYkSNHtvo4LS2NitCp8q1bt5KVlRUOsgBDhw4lNjaWrVu3MmHCBLZu3brfIrXJkyfz/fffH/HX2l0FfT68e/dCMIg2OhpdYvfYzOmIwmwwGOSpp55ix44deL1eTjrpJB5++OHwZO4j5fV6Wb16Nffee2/4mFar5ZRTTmH58uUHvd8jjzxCcnIys2bN4scff/zNx/B4PHg8nvDHjlCbiU7nVqcUYI4FwBcIsqOsEYAhaTGRqUl0OUElyJzlcwgoAU7KOonJ6dK3uT153X6+eGUD1cWNWGxGzr11DLaE9t/VRghxYFdffTU33XQToE4x3Fdjo/o/ccGCBWRkZLS6zmQ69Iv6X+8gpdFoCAaDR1NurxL0ePDu2Yvi86ExGjFmZnabKZBHNGf2scce47777iM6OpqMjAyef/55brzxxjY/eFVVFYFAYL/dxFJSUigrKzvgfZYuXcrrr7/Oa6+9dliPMXfuXOx2e/ht31dlncodCtEmNbjmVzXhDQSJNunJjpctFIXq87zPWV+5Hqveyn2T7ot0OT2K3xfgi1c2UryjDoNJx1k3jiQ2Rf72hOhMM2bMwOv14vP5mD59eqvrhg4dislkoqCggP79+7d6a/7fve/2p0diyJAhFBYWUlhYGD62ZcsW6urqGDp0aPg2K1asaHW/n3/++Yi/xu4o0NiINz8fxedVg2xubpdf9LWvI6r0nXfe4eWXX+bPf/4zAN9++y1nnnkm//znP9FqO357zYaGBi6//HJee+21Vqcmfsu9997L7Nmzwx87HI7IBFqv+oqzuZPBjnK1JdeAlOhu88pHdKxqVzVPrXwKgGtHXktK1P5bRou2CfiCfPnqRoq312Iw6TjnttEk58j0HiE6m06nY+vWreHL+4qJieHOO+/k9ttvJxgMctxxx1FfX89PP/2EzWbjyiuvJCcnB41Gw+eff84ZZ5yBxWIhOjr6kI97yimnMGLECC699FLmzZuH3+/nhhtu4MQTT2T8+PEA3HrrrcycOZPx48czZcoU3nvvPTZv3kzfvn3b/xvRRSjBIP6qqnDnAq3ZjDEnp0tvkHAgRxRmCwoKOOOMM8Ifn3LKKWg0GkpKSsjMzDziB09MTESn0+3XGqO8vJzU1P1Xb+/evZs9e/Zw9tlnh481n0LQ6/Vs376dfv36tbqPyWQ6rNMTHc6jhleM6h/djnI13A5IPvQfoegdnln9DHWeOgbGDeTKYVdGupweIxAI8tU/N1GwuQa9QcuZN44ktY8suhQiUmy2g7+QfPTRR0lKSmLu3Lnk5eURGxvL2LFjue8+9UxVRkYGc+bM4Z577uGqq67iiiuu4K233jrkY2o0Gv773/9y8803c8IJJ6DVapkxYwYvvPBC+DYXXXQRu3fv5q677sLtdvO73/2O66+/vkfubKooCsHGRnylZShedSqmLjYWQ1oaml+9yOgONIqiKId7Y51OR1lZGUn7tGmIiYlhw4YN9OnTp00FTJo0iYkTJ4Z/oYLBINnZ2dx00037LQBzu93s2rWr1bG//OUvNDQ08NxzzzFw4MDwKYiDcTgc2O126uvrf/MPqt09OxzqC+Ga7yBzHDe8t5ovNpZx/xlDuPaEnvuqTxyeNeVruHKhGmDfO+M9RiaNPMQ9xOEIBhW+fWMzO1dVoNOrQTZrSHykyxJdjNvtJj8/nz59+mA2y2JA0XMpikKwqQl/RYXaegvQ6PXoU1PR2Tu/Tehv/e0dSV47opFZRVGYOXNmq5FOt9vNdddd16o915G05po9ezZXXnkl48ePZ+LEicybN4+mpqZwd4MrrriCjIwM5s6di9lsZvjw4a3u39ya49fHu5zmkVmTOhK7s3lkNkVGZns7X8DHI8sfAeCCARdIkG0nwaDCore3sHNVBVqthhl/Hi5BVgjRKynBIAGHg0BVlbqrF4BGgz4+Hn1ycrccjd3XEYXZK6/c/9TnZZdddlQFXHTRRVRWVvLggw9SVlbG6NGjWbhwYXhRWEFBQafMx+1w3ib1vTGKQFBhb436iqhvooTZ3u6dLe+wu3438eZ4Zo+bfeg7iEMKBhW+e3srO1aUo9VqOO2aYeSO6B4tZoQQoj0oioLichGoqyNQX4/SvGhOq0UfG4suqXv0kD0cRxRm9925oz3ddNNN4VYdv9bcTPlgDmeuTMT5vRD0qZeNURTVOvH6g5j0WjLipC1Qb1bUUMSr618FYPa42dhNMpfzaAWDCove2sKOX8rRhIJsv7EH34BFCCF6CiUYJNjkJNjgINDQgOLzha/TGAzo4uLQx8d3q04Fh6NnfTVdla+p5bIhirzKOgD6JEah00ong95KURQeWf4I7oCb8SnjOaffOZEuqdtTFIUl/94eDrLTJcgKIXo4xe8n0NBAsKGBYGMjyj69dTVaLdqYGHSxsWije273JAmznaF5ioHWAHoje6rVj3MSpMdlbzZ/53yWly7HqDXy0OSHeuyTTGdRFIVlH+9iy9IS0MBpsyTICiF6FkVRULxegk4nQZcLxelsmQMbotHr1QAbE6MG2J4wVfMQJMx2huYwG1r8lV+lftxH5sv2WtWuap5Z/QwAt4y9hVx7bmQL6gFWf7mXdd+qDdGnXTaY/uMkyAohuq9wcHW5UNzu0HsPSsC/3221ZnM4wGosll43OCJhtjN4QhsmhHrM7q1WF3/lyshsr/XkyidxeB0MihvEpUMujXQ53d6ar/ay4tM8AKb8vj9Dp6RHuCIhhDh8SiBA0ONBcbvVAOt2ozhdKMED7HSm0aC1WNBarer7qKgeNwf2SPXur76zeFtvmFAY6mQg29j2TsuKl/Fl/pdoNVrmHDsHvVb+DI/G+kWFLP/PbgAmnt2H0adkR7giIYTYn6IoKD4fisejvnm9aoD1elst1GpFo0FrtqCxmNXRV7MZjdncK6YOHAn5L9oZ9mnLFQwqFNW6AMiWkdlexxf0MfeXuQBcMvgShiUOi3BF3dv6RYUs/b+dAIw/M5cJZ7Zt8xYhhGgPSjCoBlavD8XrCV32EnR7UHxe+I19qjR6PRqzGa3RqL63WNCYTBJcD4OE2c6wz5zZ8gY33kAQnVZDqk12mult3t78Nnsce4g3x3PD6BsiXU63tuH7onCQHXd6DhPPkiArxL40Gg3/+c9/OO+88yJdSo+gKAoEAmpA9fvDQTX83us74HzWVjQaNayaTGjC701oTcZeP1XgaEjc7wzeljmzxaFR2TS7Gb1Ovv29yV7HXv6+/u8A3DH+DmKMMRGuqHtSFIWVC/L58YMdAIydns2kc/r2ugUPQsycOfM3g2ppaSmnn3565xV0hJYsWcJJJ51EfHw8VquVAQMGcOWVV+L1evn444/R6XQUFxcf8L4DBgxg9mx1k5mpU6ei0Wh44okn9rvdmWeeiUaj4eGHH/7NWprnrAYaG/HX1eGvrMRXWoq3oADP7t24t2/HvWUL7m3b8OzejXfvXnwlJfirqgjU16uLs5qDrFaL1mRCFxODPiEBQ2oaxpxcTAMHYh46FNOAARizszGkpqKPi0MXZZUge5Tku9cZmheAGawU16lhNiNWNkvoTQLBAA/89ADugJtJaZM4u+/ZkS6pW1IUhRX/zWP1wr0AjD8jl4ln95EgK8QBpKamRroEFEUhEAig/1VY27JlCzNmzODmm2/m+eefx2KxsHPnTj7++GMCgQDnnHMOCQkJvP3229x3332t7vvDDz+wa9cuZs2aFT6WlZXFW2+9xT333BM+VlRYyKJFi0hLTSXo8eCvrUXx+8HvR2keYQ2NsrJPb9ZD0eh0aAyG0JsRjdGgjrKGjqHTyXNSJ5Ohwc4QnmYQI2G2l5q/az5rK9Zi1Vt59NhH5YmuDZr7yDYH2WN/119GZIX4DRqNhk8++QSAPXv2oNFomD9/PtOmTcNqtTJq1CiWL1/e6j5Lly7l+OOPx2KxkJWVxS233EJTU8vGP//6178YP348MTExpKamcskll1BRURG+fvHixWg0Gr788kvGjRuHyWRi6dKl+9X29ddfk5qayt/+9jeGDx9Ov379mDFjBq+99hoWiwWDwcDll18e3uVTURQUv5+gx8Pr//gHE8ePZ1BGBv6qKhSvjzNOOomqigq+/7//w7NzJ+6tW3n96ac5efJkEu12AvX1+IqL8ZeX46+uJlBXR7CpCcXrDQdZjVaLxmhEGxWFzh6LPjERQ1oaxuxsTH37Yho0CPPQoZiHDMHUvz/GnBwM6WnoExPR2WzqHFe9Xp6TIkDCbGcITzOIoiQUZtMlzPYaFc4Knl31LAA3jbmJtOi0CFfU/SiKws//zQv3kT3hjwMZc6p0LRAdR1EUnF5/p78pv7FAqD3cf//93Hnnnaxbt46BAwdy8cUX4/erp8d3797NjBkz+N3vfseGDRv44IMPWLp0aavt5n0+H48++ijr16/nk08+Yc+ePcycOXO/x7nnnnt44okn2Lp1KyNHjmz1fVX8fpLj4yktLeX7hQsJ1Nfjr6lRT+2Xl+MtLsZbUMDlM2awc+dOvv3Xv8Kn+KvXr+fj+fO54swz8RUV4SsrQ/F50QcCXHTGGbz9/vtqh4BAgHf/+1+uOP98NBqNupFAdDS62FBITUnBkJmJMbcPpgEDMA8ZogbVgQMx9emDMStTnQaQkKAGVasVrcEgi7G6KJlm0Bk8odZcphjKStWdOlLtsvirt5i7Yi4NvgaGJwzn4sEXR7qcbkdRFH75LJ81oRHZE/44kBFTMyNclejpXL4AQx/8qtMfd8sj07EaO+5f85133smZZ54JwJw5cxg2bBi7du1i8ODBzJ07l0svvZTbbrsNUOelPv/885x44om88sormM1mrrrqKggGUYJBctPTefbJJznmhBOoKyoi2mLBX1sHwAO33MqJAwehBAJQU4O7shICwXDf1HNGjeIPM2Zw0umnk5KYyMSRI5k6aRKXnnMOtmi1jeWgtDQmjhzJ2x99xJQxYwCY//XXKMBF552n7m6l06HRG9CaLVx56SVMO+ccnn/5ZdZs3IjD5eKC669n7htvoI+Px5Sb22HfVxFZ8hKjM4Rbc0VT0eABkE4GvcQPRT/wbcG36DQ6Hj72Yekpe4SaR2RXfbEHUDdEkCArRNuNGDqUYGhXqeQYGwClu3fjr65m3erVvPXWW0RHRYXfpp92GsFgkG2LFuHesoVlH37ImSefTE52NvaEBE6aPh2AvNWr8ZWWEqirBWB0bg6BBgdBZ5O6AYDP12oDAL3ByGtP/o3dP/7I4/feS0ZmJk+//jrjL7iASkXBkJaGITOTq6+5hv8sWoQ3LQ3z0KH8a+FC/nDhhSSOHo0pNxdjVhYakxFtlJVxU6cyYMAA/rNwIW+9+y6XX345BoOh87/JotPJf9bOsE+f2QqHGmaTbaYIFiQ6Q72nnkeWPwLAZUMuY1D8oAhX1L0oisLPn+Sx5it1RPa4Pwxg1MlZEa5K9BYWg44tj0yPyOP+mqIo4dFQAoGW96Gm+/7KKjUoBoIEXU40BoN6G8BbUqKuwC9Up+gEi4vxxKidVHwOh3qbigp8paU01tcz6/e/54ZL99+VMCstjcbGRs697jpOOfZY3nziCZISEyksL+fsWbPwGwzq6fjQqKo9JwdDfDwanQ60WvW9Ttfyceh0fd9BA+l73HFcDTxeW8vAgQN5/aOPmDNnDgCXzJzJHffey0effMIJJ5zATz/9xNy5cw/6/bv66qt56aWX2LJlC7/88ksbfwqiu5Ew2xlCc2aDhigqG0NhNkZGZnu6J355gnJnOdkx2dJT9ggpQYUl7+9g8w9qW57jLhzAqJMkyIrOo9Fo2nS6X1EUtTF+MBgOoeEg2nw5EFBPvweCBN0utS1T6DbeX90+fL8DCDY1oXg8+MrLWl/hcrXU09wTdd/PodGop+eN6qCKxmJBZ7MzZtQothcWMmj8eDRaHej2CaFaLavXraO6ro6/vfQSWTk5aDQaNr77LgDG9HS13VSeuq20ITERfWzsEX3v4uLiSEtLa7XgLCYmhj/84Q+88cYb7N69m4EDB3L88ccf9HNccskl3HnnnYwaNYqhQ4ce0eOL7kvCbGcIjczWKlYCQXVyf0K0MZIViQ72Y9GPfJ73ORo0PH7841gNstvb4QoGgix+bztbl5WCBk7840CGnyhTC0T7UxQFxeXCX1enrpR3uwkEAq1C534joq1CpgLKPpebj9NBi7g0GnVEs3mkU2+gweViY14eGrMZtDrQakiIjSW7b18ADMkpmPr1wxg63W7q2xfLMHXnQXNdHQDG1FSM2VncO2cOxxxzDLc/8gjXXHMNUVFRbNmyhW+++YYXX3yR3P79MRqNvPjyy1x33XVs2rSJRx99tE1fyt///nfWrVvH+eefT79+/XC73bzzzjts3ryZF154odVtZ82axfHHH8/WrVu5++67f/PzxsXFUVpaKtMLehkJs50hFGYrfBbASXyUEYNsmNBjNXgbePRn9Qn+sqGXMSppVIQr6j6UoML3725j2/IyNBo4+cohDDpGuj/0ZkogQNDlIuh0ojid6mWXm6DLieLxqM3qw8dc6nGnk6DTqR5zu1Cc6v2DTU3qbdzu0H1cEAwSTEsj8Jf78SpK+65W12jQaLSg1bScWg+Ncu57yl0JBNCaTC230WpBq0PTfL/Q7dTP19L2SRcTzZKff2bSuee2ethZs2bxz3/+EwCtRd0WVWsKjcL+xtc3cuRIlixZwv3338/xxx+Poij069ePiy66CICkpCTeeust7rvvPp5//nnGjh3L008/zTnnnHPE35qJEyeydOlSrrvuOkpKSoiOjmbYsGF88sknnHjiia1ue9xxxzFo0CB27drFFVdcccjPHXuEI8Ki+9MoHd0HpItxOBzY7Xbq6+ux2Wyd86DPDANHEUtO+5IrP61lcGoMC287oXMeW3S6v/78Vz7Y/gGZ0Zl8fM7HMip7mAL+IN+9s5Udv5Sj0Wo4bdYw+o9LjnRZ4iCUYFANk243ittN0OVGcbtaPna7CTpd6gIgp1M95vGguD1qwPR4Q/dztQROtxt/TTWByiq0NhuKy4Xi83X41xJMTyfwl/vJSU3DpNe1Hv1sDpY67a/C5j6X9xkx3fe49BsV4re53W7y8/Pp06cPZnPr6ZdHktdkZLYzhObMVvvUqQWJ0bL4q6daW7GWD7d/CMBDxz4kQfYw+b0BFv5jE3s3VaPVajh55hAJskeh+ZR5c6hUQiObzaOVaugMhUq3B8XjDo1chm7TfF+POzSq2aQGU68nHEIVr7dDv4ZgaHFSmEajNqWPsqK1WNGazWgsZrRmi3rZalEvW61orep7jSV0zGJGGxUVui503GQKH/NoNOzZuxfTAf6hCiG6PgmzHU1Rwn1mqzzqKlWZL9szeQNe5iybg4LCef3P45i0YyJdUrfgdfv54pWNFG+vRW/QMuO6EeQMS4h0WR1GURR1UU74lLlLDZEudfTSV1qGxmhUP24+Je72oHg9BJ1OvAWFKF4vWos5fGq9+ZR58yl0OmE0sxWDIXwqW2O1oDWZ0ZjN4fCotVjU4GkyozGZ1PBpNqM1m9CYzGr4tFjUkGmxoGn+fFFR4c+jMRo7bKRT43Z3yOcVQnQOCbMdze8BRe2tV+1Rn4gTomRktif658Z/srt+N/HmeO4cf2eky+kW3E0+PnthPRV7HBhMOs66aRTpA2IjVo+iKGq7I6dzn4DoRvG4w6fUgy5XS7hsHtV0hUY2XU6CjU0EnU3qiGYoXKrBtPk0u/uI9oE/WuropDpvUmO1oLVGhcKkSQ2XoZCptYbCY2g0U2M2he8XDpVmsxo+Tcbw6KbGZFJX4wshRITIM1BH8znDF2tCL/5lZLbnyavL47UNrwFw78R7sZvsEa6o63NUu1jw0gZqSpowRxk486aRpPY5+PetOWi2GoUMnQJvFTybT6E3B0h3y7HwqXO3p+UUevMp9Sa1uXtnBk0MhvDIZfObxmLBW7AX65ixaC1mNGaL2hQ+FDg1FgsooLVY0CXEq6fRo5pPu4c+h8kUfi/zNoUQPZ2E2Y7WvJWt3kxNk3rqLz5KwmxPElSCPL7icfyKnxMzT2RGnxmRLililGDwV/MzXaGRTHfLCnS3m/qiWrYtycfe5CJZ76fv0BgCL31KodulBtDmleeh1eeBmppO/1o0BoMaDq3WlhHI5lPlZpN6zGzZ51R581zNKLTR0WrwNJvV+Z0Wc+uRTWtonqe0DxJCiKMmYbaj7bOVba1TXTARZ5V/YD3Jv7f+mxVlKzDrzNw14a5Il3NY1HZH6unzYFNTS3h0qivLg01NLaE0FCoDjY2hBUJNKC43rg0bMKSnq6flm0PrPs3aDyV7n8vu3XBEsxZ/PaJptapB02JtHS4tzYFSDZHhEc59w+U+o6Iti4bMcupcCCG6CXm27mihTgaYoql1qiOzcVYZme0pChsKeX7t8wDcOf5Osm3Zh7jHkVEURZ2ruW/gbGxUg2VjE8HGhpYV6I2NuHdsRxdjI+h04tmxA31aastIp1ud96mE9klvD978/ANf0bzyvDlwmtVRzEanhrraAAGdCWtiDBkj0zDEhOZpWqNC4dOE1mJFFxPdMofTYkFjNKmn000mGdEUQggRJmG2ozWHWUMUdfXqyGyshNkeIRAMcP/S+3H5XYxPGc8fBv0BCAVQp5NAQwPBxkY1fDocBOodLaE0tFioVbN3Z1NLe6TGRvW2DQ1HFTz9FRWHvI3GalVDojXU8qj51HrzafOoqNBb9D7tjSxoTGZQgugTEtBYWtohNc/73HeuZiAQ5Pt/bWP7z2WQBSOmZTL+DwPQamU+pxBCiKMjYbaj+dTTrkGDlXpX88isjCp1VYqihEc/gw0N4fAZaGwk2NBIoK6OQH09wcZG3jetZ23qdix+LVfNXc+uh6cAEGhsBL+/3WvThAKmLio0JzMmBm1UFLro0OhlKHQqTif69HR0UVEoQQVDSnLrhUEmU6uPO3qBkLvRxxevbKB0dz0arYYTLx7IsOMzOvQxhRBC9B4SZjtaKMw2aG0EQ3ut2SwSZjta0O1Wg6fDQbChITQyWq8eaw6k9Q4CdbX4a+sIOhzhkdTDWc2elwpvXaEDNFzxtY+kci8BfjVfVK9HFx0azbTb0MXY1BAate8IqKWlsbvVGgqk1tAoqFW9v92uzuVsz202O0l9pZPPX9xAXbkTo1nHqbOGkTsiMdJlCSGE6EEkzHY0v7qspV6rthyyGHSYDbpIVtTtKH6/GkQdDoL19fhravFXVxGoriFQW4u/toZgvQN/TY16vKZW7eV5NJqDaHS0GjqbR0FjY/HFRfNy6jcEdHWcoBvM5TOvRhdjQ2MwoE+IV29ns6GxWnt1W6S9m6v59o0tuJt8RMeZOPvm0cSnR0W6LCF6jEAgwPHHH09qairz588PH6+vr2f48OFcccUVPPbYYwB8/PHHvPTSS6xduxa32012djZTpkzh5ptvZsyYMQC89dZbXHXVVeHPExUVxaBBg7j//vu54IILOu3rmjp1KqNHj2bevHmd9piie5Mw29Gaw6yi/hO3WeRbrvj9+Kuq8FdU4K+qJlBTjb+2lkBNrXq5efS0ppZAXR3Bhoa2PZBejy4mBq0tBl2MDZ3Nhi42Fl1cHDq7DZ3djtZuRx8fj85mUwNrTAy6mBg0ZvNBg+hff/4rBdvrSLIk8eg5f8dujj+K70bPowQVVi/cw4rP8kGB5JwYzrhhJFF22SxEiPak0+l46623GD16NO+99x6XXnopADfffDPx8fE89NBDANx99938z//8D7fccgtz5swhJyeHyspKvvzyS+69914WLlwY/pw2m43t27cD0NDQwJtvvsmFF17I5s2bGTRoUOd/kUIcBklWHc2vLvpyEA2AvYdOMVAURV3oVFODr6w8FFSr8FdW4q+qxF9ZSaAqFFqrq9VtfttAn56GPj4BXUI8+vgE9Anx6GJj1VCakIA+IQFdfDy6uDh1JXw7j4z+VPwTH2z/AIBHpzxKvATZVnzeAIve2sLuNZUADDs+neMuHIBezkYI0SEGDhzIE088wc0338xJJ53EL7/8wvvvv8/KlSsxGo38/PPP/O1vf+O5557jlltuCd8vOzubcePGofzquVij0ZCamgpAamoqf/3rX3n66afZsGFDOMzW1tZy66238tlnn+HxeDjxxBN5/vnnGTBgQPjzfPzxxzz44IPs2rWLtLQ0br75Zu64447w9S+//DLPPvsshYWF2O12jj/+eD766CNmzpzJkiVLWLJkCc899xwA+fn55ObmdtS3UPQAEmY7WsADgEOxAmAzd78wq/j9+MrK8JeW4istxVdSqgbVqir8ZWXq6f2KiiM7ta/Xo09MVMNnYoIaUGNj0cXHqSOldrs6ghoXp1622SLejqneU89Dy9SRjksGX8KUjCkRraeraahx88UrG6gqbESr1XDipYMYOiU90mUJ0TaK0moHx05jsMIRvgi/+eab+c9//sPll1/Oxo0befDBBxk1ahQA//u//0t0dDQ33HDDAe/7Wy/4A4EA77zzDgBjx44NH585cyY7d+7k008/xWazcffdd3PGGWewZcsWDAYDq1ev5sILL+Thhx/moosuYtmyZdxwww0kJCQwc+ZMVq1axS233MK//vUvjj32WGpqavjxxx8BeO6559ixYwfDhw/nkUceASApKemIvh+i95Ew29GCAQAagmo7rhhz1/yWB+rr8e7di3fvXnxFRfhKSvAWFeErLsFXWgqH2R5KY7WiT0rEkJKqhtWkJPTJSeiTktAlJKCPj1cvx8Wh0XWv0bq5v8yl3FlOji2H28bdFulyupSibTV8/fpmXA0+LDEGZvxpBOkDYiNdlhBt53PC4xF4MXZfCRiPbG65RqPhlVdeYciQIYwYMYJ77rknfN2OHTvo27cv+n02AXnmmWd48MEHwx8XFxdjt6vrOurr64mOVs8kulwuDAYD//jHP+jXrx9AOMT+9NNPHHvssQC89957ZGVl8cknn/CHP/yBZ555hpNPPpkHHngAUEePt2zZwlNPPcXMmTMpKCggKiqKs846i5iYGHJycsLzdu12O0ajEavVGh4hFuJQumay6oEcfvVbHclOBkGnE+/evXjy8vAVFOAtLFID7K5dBOrrf/O+GoMBfVoahrQ0DOnpakhNiEefkqqG1VBw1VosnfTVdK6v9nzFgrwFaDVaHj/ucSz6nvl1HilFUdi4uIil/7cLJaiQmBXN6deNwJYg3x8hOtMbb7yB1WolPz+foqKi3zwtf/XVV3POOeewYsUKLrvsslZTDWJiYlizZg0ATqeTb7/9luuuu46EhATOPvtstm7dil6vZ9KkSeH7JCQkMGjQILZu3QrA1q1bOffcc1s95pQpU5g3bx6BQIBTTz2VnJwc+vbty4wZM5gxYwbnn38+Vqu1Hb8jojeRMNvh1CcJR0D9VnfGyKwSCOArLsa9aRPuLVvw7NyFe/t2/GVlv3k/fXIyxpwcDJmZGDIyMGRkYMxU3+tTU7tla6j2UO+p54lfngBg1vBZjEwaGeGKugafN8D3/9rGzpXlAAycmMK0ywajN3avEXchDshgVUdJI/G4R2jZsmU8++yzfP311/z1r39l1qxZfPvtt2g0GgYMGMDSpUvx+XwYQlO1YmNjiY2NpaioaL/PpdVq6d+/f/jjkSNH8vXXX/Pkk09y9tlnt/3r2kdzYF68eDFff/01Dz74IA8//DArV64kNja2XR5D9C4SZjuJI6A+ibT3nFklGMSzcyeuDRtwb96MZ+s23Dt2oLhcB7y9LjYWY24uxj59MGZnYcjMxDRgAMasLLRR0jbpQJ5d/SxVrir62Ptw3ajrIl1Ol1BT0sTC1zZRW9qEVqvhmPP7MfqUrF7dikz0MBrNEZ/ujwSn08nMmTO5/vrrmTZtGn369GHEiBG8+uqrXH/99Vx88cW88MILvPzyy9x6661tegydTocr9D9lyJAh+P1+VqxYEZ5mUF1dzfbt2xk6dGj4Nj/99FOrz/HTTz8xcOBAdKHpZXq9nlNOOYVTTjmFhx56iNjYWL777jsuuOACjEYjgUCgrd8S0QtJmO0kjaFpBtFHOTKrKArePXto+mkZTcuX41q16oBTBDRGI8b+/bCOHo2xXz/MgwZhGjAAXWhelDg8n+3+jI93fgzAA8c8gFHXu7ciVhSFrctK+eH9HQR8QSw2IzOuHUb6gLhIlyZEr3TvvfeiKApPPKGePcrNzeXpp5/mzjvv5PTTT2fy5Mnccccd3HHHHezdu5cLLriArKwsSktLef3119FoNGj3OeumKAplobN4LpeLb775hq+++io8x3bAgAGce+65XHvttfz9738nJiaGe+65h4yMjPDUgjvuuIMJEybw6KOPctFFF7F8+XJefPFFXn75ZQA+//xz8vLyOOGEE4iLi+OLL74gGAyGuyXk5uayYsUK9uzZQ3R0NPHx8a1qFOLXJMx2tNBUpIbQNIO2jMwGXS6aVqygcfFiGn/4AX9JaavrNVYrlhEjMA8fhnnIUMxDBmPMze12C6y6mh21O5izfA4A14y4hgmpEyJcUWT5PAGW/Hs721eo/+iyhsRxylXDsNp6d8AXIlKWLFnCSy+9xOLFi1vNN/3zn//M/Pnzw9MNnn76aSZOnMgrr7zCG2+8gdPpJCUlhRNOOIHly5djs9nC93U4HKSlpQFgMpnIycnhkUce4e677w7f5s033+TWW2/lrLPOwuv1csIJJ/DFF1+EpzGMHTuWDz/8kAcffJBHH32UtLQ0HnnkEWbOnAmo0xzmz5/Pww8/jNvtZsCAAfzv//4vw4YNA+DOO+/kyiuvZOjQobhcLmnNJQ5Jo/y6yVwP53A4sNvt1NfXt/oD7jBLnoLv/8olphdYVp/AvItGc96YQ+9LH/R6afzuexxfLaTx+8Wt2l5pDAYsY8cSdeyxRE2aiHnYsIi3reppHF4Hly64lD2OPUzJmMLLJ7+MVtN7Rwaa6jwseHkDlQUNaDQw8Zy+jJ2eg1Yr0wpE9+d2u8nPz6dPnz6YzeZIlyNEr/Fbf3tHktdkZLaTNE8zONQCMNe6ddR9PJ+Gr79uNX1An5ZG9IknEDNtGtYJE9DKqs8OoygK9/14H3sce0i2JvPYlMd6dZAt3l7LV69vxuXwYo42MONPw8kYKNMKhBBCdA0SZjucOvDdGFRP+UeZDvwtd65ZQ8Uzz+BatTp8TJ+cjO3ss7DNOB3z8GGyuKaTvLPlHZYULcGoNfLCSS+QYEmIdEkRoSgKm38s4cf3dxAMKsSnR3HG9SOxJ0nbLSGEEF2HhNlO0hSaMxv9qzDrKy+n4m9P4ViwQD1gMGA/4wzs556DddIkmffaybbX/P/27jssiuvrA/h3F1iW3qsiRRBBRJSiiIpGDGqsMcH2qiSWWNAYgooVbBEVe40V40+DiS3GEBuKRtRoVCyhCYJYKIJILwt73z8Ioyugguyu6Pk8D4/szJ25Z/bOytk7d+4kYN2NqkcoznCdAXs9ezlHJB+iskqc+9+LabdsXAzRY5QdlJTpfCSEEPJ+oWRWRqqT2Zd7ZvN+/x0Zi5dAnJ8P8PnQHvI59P38oGRkJK8wP2rFomIEnA+ASCxC9+bdMdR2qLxDkovnmcX488c7ePakCDw+D50GWqH9py3oygAhhJD3EiWz0sYYGAOKKv8bZiBQABOLkbViJZ6FhQEAhG3bwjg4CCr/3clJZI8xhvnR85GanwojVSMs8lj0USZvSdezEPlTHCrKKqum3ZrgAFNrbXmHRQghhNSJklkZKIUADFWJkapAARnBC/H8l18AAPqTJ0F/8mTwFKkp5Ck8IRynHpyCIk8RoZ6h0BF+XDc4VVaIcfGXe7h74TEAoFkrbfQa2wZqWspyjowQQgh5PcqgZKAIL6abKNqxrSqR5fNhsnQptAcPkl9gBABwL/ceQq+FAgC+c/4OToZO8g1IxgpzS/Hn1jvIelAAAGj/aQt0HGgFBYWPdwYHQgghTQcls1LHUMKqereECsCzjRsBAMYL5lMi+x4oqShBwPkAlIvL4dHMA6PsR8k7JJl6ci8XJ7bdRUmBCMpqivDytYdFW315h0UIIYS8NUpmZaC6Z1ZYWgwA0P7yS+gMGybPkMh/lv29DPfz7sNAxQBLPZZ+NONkmZjh5pk0XDl6H0zMoNesatotTX2adosQQkjTQtcRZaAYVT2zyuWlUNDXh+FLjwUk8nPx8UUcSToCHngI6Rry0cwnW15SgT+23Mblw8lgYgYbVyMMmelCiSwhHxgej4ejR4/KOwyZCAsLg7a2dr226d69O6ZPny6VeGSpIcf+oaFkVtoYQymrena9sLIcel9/DQV1NTkHRbJLsjH34lwAwAi7EXAzcZNzRLLxNK0AB364hgd3cqCgxIfnCFv0+tqe5o8lpAny9fXFoEGD6lyfnp6OPn36yC6gBjh+/Dg8PT2hoaEBVVVVuLq6Iuy/mX7qY+jQoUhMTKzXNocPH8bixYvrXVd9pKamgsfjcT96enr49NNPcfPmTanW+7F5L5LZTZs2wcLCAkKhEB07dsTVq1frLLt9+3Z07doVOjo60NHRgZeX12vLvw9KqntmxSJo0ThZuRMzMeZdnIdnpc9go2OD75y/k3dIUlf1NK/HOLTiOvKflkBdRxmfB3SAQ7dmH83QCkI+NsbGxlBWlu+MJIwxVFRU1Lpuw4YNGDhwIDw8PPD333/j9u3bGDZsGCZOnIiAgIB61aOiogJDQ8N6baOrqwsNDY16bdNQZ86cQXp6Ok6ePInCwkL06dMHz58/l0ndHwO5J7MHDhyAv78/goKCcOPGDbRr1w7e3t7IysqqtXxUVBSGDx+Oc+fO4fLlyzAzM8Onn36Kx48fyzjyt1eCqp5ZVVUhFHU+rimf3kdbbm1B9JNoKCsoY2W3lVBW+LCnnxKVV+L0rlhE7UtAZYUY5m31MHSeGwzNNeUdGiHvLcYYikXFMv9hjDXaMbw8zKC6h/Dw4cPo0aMHVFVV0a5dO1y+fFlim4sXL6Jr165QUVGBmZkZpk2bhqKiIm793r174eLiAg0NDRgbG2PEiBESf6+joqLA4/Hw559/wtnZGcrKyrh48WKN2B4+fIjvv/8e06dPxw8//AB7e3tYW1vj+++/x8qVK7Fq1Sr8/fffEvv8448/4OjoCKFQiE6dOuHu3bvc/l691B4cHAwnJyfs3bsXFhYW0NLSwrBhw1BQUMCVeXWYQW5uLkaPHg0dHR2oqqqiT58+uHfvXo06Tp48CTs7O6irq6N3795IT09/Y1vo6enB2NgYLi4uCA0NRWZmJnd8hw4dQps2baCsrAwLCwusWrVKYts3xUXegxvAVq9ejfHjx+Orr74CAGzduhV//PEHdu3ahcDAwBrl9+3bJ/F6x44dOHToECIjIzF69GiZxFw/L2YzUNVSl3Ms5GzaWfx460cAwLxO89BSu6WcI5Ku/OwSRGy5g5zHheDzeeg4yArtvVqAx6feWEJep6SiBB33d5R5vX+P+BuqSqpS2//cuXMRGhoKGxsbzJ07F8OHD0dSUhIUFRWRnJyM3r17Y8mSJdi1axeePn0KPz8/+Pn5Yffu3QAAkUiExYsXw9bWFllZWfD394evry8iIiIk6gkMDERoaCisrKygU0snzsGDByESiWrtgf3mm28wZ84c/Pzzz+jY8UUbzJgxA+vWrYOxsTHmzJmD/v37IzExEUpKSrUea3JyMo4ePYrjx48jNzcXPj4+CAkJwdKlS2st7+vri3v37uHYsWPQ1NTErFmz0LdvX8TGxnJ1FBcXIzQ0FHv37gWfz8f//d//ISAgoEZu8joqKlX3J5SXl+P69evw8fFBcHAwhg4dikuXLmHy5MnQ09ODr6/vW8f1sZNrMlvdkLNnz+aW8fl8eHl51fi2WJfi4mKIRCLo6urWur6srAxlZWXc6/z8/HcLugFK/+uZVdOSzeUMUrvk58kI/CsQDAzDbIdhkPUgeYckVal3snFmdyzKiiugoqEE7/EOaNaKrgwQ8jELCAjAZ599BgBYuHAh2rRpg6SkJLRu3RrLli3DyJEjud5KGxsbrF+/Hp6entiyZQuEQiG+/vprbl9WVlZYv349XF1dUVhYCHX1Fx02ixYtQq9eveqMIzExEVpaWjAxMamxTiAQwMrKqsYY2KCgIG6fe/bsQfPmzXHkyBH4+PjUWodYLEZYWBg3lGDUqFGIjIysNZmtThajo6PRuXNnAFWdZ2ZmZjh69Ci+/PJLAFXJ/NatW9GyZVVHiJ+fHxYtWlTncb7q+fPnWLx4MdTV1eHm5gZ/f3/07NkT8+fPBwC0atUKsbGxWLlypUQS+6a4PnZyTWazs7NRWVkJIyMjieVGRkaIj49/q33MmjULpqam8PLyqnX9smXLsHDhwneOtaGYWMwls6radFlXXvLK8vDtuW+reltMOmKW24c7o4RYzHDtjxT8E5EKMMDIUhO9JzhAXUf4xm0JIVVUFFXw94i/5VKvNDk6OnK/VyeSWVlZaN26NW7duoXbt29L9DIyxiAWi5GSkgI7Oztcv34dwcHBuHXrFnJzcyEWiwEAaWlpsLe357ZzcXFp9Njd3d2533V1dWFra4u4uLg6y1tYWEiMiTUxMalzCGNcXBwUFRUleoL19PRq1KGqqsolsm/a58s6d+4MPp+PoqIiWFlZ4cCBAzAyMkJcXBwGDhwoUdbDwwNr165FZWXlW8f1sZP7MIN3ERISgvDwcERFRUEorP0P9ezZs+Hv78+9zs/Ph5mZmaxChLiokEtmVTRoFgN5EDMx5kXPw4P8BzBRM8HyrsuhyG/Sp36dSgrKcXp3LB7GPgMAOHRrhi4+NlBQlPvweEKaFB6PJ9XL/fLy8mXp6ps/qxPSwsJCfPPNN5g2bVqN7Vq0aIGioiJ4e3vD29sb+/btg4GBAdLS0uDt7Y3y8nKJ8mpqr/9716pVK+Tl5eHJkycwNTWVWFdeXo7k5GT06NGjQcdY7dVL8DwejzvWxtzn24xzPnDgAOzt7aGnp/fRT6MlDXL9C6evrw8FBQVkZmZKLM/MzISxsfFrtw0NDUVISAhOnTol8U3zVcrKytDU1JT4kSVxURHKWNXJL1Si6Y/kYeutrYh6GAUBX4DV3Vd/sPPJZtzPwy8/XMPD2GdQVOKjp68dPEfYUiJLCHkrHTp0QGxsLKytrWv8CAQCxMfHIycnByEhIejatStat279Vr2StRkyZAiUlJRq3OwEVN07U1RUhOHDh0ssv3LlCvd7bm4uEhMTYWdn16D6X2VnZ4eKigrupiwAyMnJQUJCgkSPc0OZmZmhZcuWNRJZOzs7REdHSyyLjo5Gq1atoKCgIPW4PhRy7Z4SCARwdnZGZGQkN1eeWCxGZGQk/Pz86txuxYoVWLp0KU6ePCmVSxmNSVxayvXMKlNSIXPnH57H1ltbAQAL3BfAQd9BzhE1PnGlGNf+SMX1P1PBGKBlqII+37SFXjO64ZCQD11eXh5iYmIklunp6TXoCuSsWbPQqVMn+Pn5Ydy4cVBTU0NsbCxOnz6NjRs3okWLFhAIBNiwYQMmTpyIu3fvNnie1hYtWmDFihX4/vvvIRQKMWrUKCgpKeG3337DnDlz8P3330tcWgeqxuHq6enByMgIc+fOhb6+/mvn2a0PGxsbDBw4EOPHj8ePP/4IDQ0NBAYGolmzZjWGATSm77//Hq6urli8eDGGDh2Ky5cvY+PGjdi8ebNc42pq5J5d+fv7Y/v27dizZw/i4uIwadIkFBUVcbMbjB49WuIGseXLl2P+/PnYtWsXLCwskJGRgYyMDBQWFsrrEF6LlZWhDFU9s5TMylbCswTMvDATDAw+rXww0PrD++AXPCvF0TU38U9EVSJr42oEn9mulMgS8pGIiopC+/btJX4aep+Io6Mjzp8/j8TERHTt2hXt27fHggULuGEABgYGCAsLw6+//gp7e3uEhIQgNDS0wbFPnz4dR44cwV9//QUXFxc4ODhg//792LJlS637DQkJwbfffgtnZ2dkZGTg999/h0AgaHD9r9q9ezecnZ3Rr18/uLu7gzGGiIgIqc4Y0KFDB/zyyy8IDw+Hg4MDFixYgEWLFnEzGcgrrqaGxxpzUrsG2rhxI1auXImMjAw4OTlh/fr13Dey7t27w8LCgnsiiIWFBR48eFBjH0FBQQgODn5jXfn5+dDS0kJeXp5MhhyUbBiBoAxT/FLZAzO8bTGlh7XU6yRAVnEWRvwxApnFmXAzdsPWXluhxP+wPviZqfmI2HIbxXnlEAgV0H1ka9i4Gr15Q0KIhNLSUqSkpMDS0rLO+y+I/ERFRaFHjx7Izc2l8aYfmNd99uqTr70Xd8FUz2NXm6ioKInXqamp0g+oEbEK0YvH2dKYWZkoFhXDL9IPmcWZsNC0wOruqz+oRJYxhluRD3H5cDLEYgZdUzX0+aYttI0+vJtVCCGEkDd5L5LZD1plJTfMQEDDDKROzMSY/ddsxD2Lg46yDjZ7bYaWspa8w2o0ZSUViNoXj6R/qm66aNnBEN1H2kKo9uEk64QQQkh9UDIrbYyhjG4Ak5m1N9bi7MOzUOIrYd0n62CmIbtp2KTtaVoBTmy7g/zsUvD5PHh8aYO23Ztx0+sQQsiHqHv37o36mF/y4aFkVgbK/3ubKZmVrl8SfsHuu1WPXFzssRjtDdvLOaLGwRjDvxce4+LBJFSKxFDXVUavr9vA1Fpb3qERQgghckfJrNQxlDOazUDazqWdw5IrSwAAE9tNxGdWn8k5osZRWihC5J5YpN7JAQC0aKOHXl/b07ACQggh5D+UzMpAdc8sjZmVjn9z/uWm4BpiMwST202Wd0iNIi02B2d/ikfR8zIoKPLRaZAV2n1iBh6fhhUQQggh1SiZlYHy6hvAFGg2g8b2IP8BJp+ZjNLKUniYemBep3lNfgxpRXklLh1Oxp2oRwAAbSNVfDq2DQxaaLxhS0IIIeTjQ8mstDFGPbNSklmUiW9Of4Nnpc9gp2uHlZ4rochv2qd0bkYRTm7/FzmPqx4C4uDZDJ2HWENJQF+ECCGEkNo07b/8TUQZo6m5GltBeQEmRU7C48LHMNMww2avzdAQNO2ey4S/MxD1v3hUiMRQ0VBCT197mLfRk3dYhBBCyHuNsitpYwyi/74zKCk07cvf74tiUTGmnp2Ke7n3oK+ij+2fboe+ir68w2qw0kIRTu38F2d2x6JCJEbz1jrwmeNGiSwhpMkKDg6Gk5PTa8ukpqaCx+MhJiam0eu3sLDA2rVrG32/DREVFQUej4fnz5+/9Ta+vr4YNGiQ1GKSlYYce0NQMisDNDVX4xFVivBd1He4nnkdakpq2NxzM5qpN5N3WA2WcjsbB5Zexb1rmeDxANfPLNB/mhPUdZTlHRoh5D33Pic8AQEBiIyM5F7LOtZr165hwoQJ77SPS5cuoW/fvtDR0YFQKETbtm2xevVqVFZW1ms/nTt3Rnp6OrS03v4BPuvWrUNYWFg9I64/Ho/H/WhpacHDwwNnz56Ver2NjbIrGXjRM0tv97uoFFdi1l+zcOnJJagoqmCr11bY6dnJO6wGKS0UIfKnOERsvo3C3DJoGahgyEwXuPW3Ap9mKyCENFGMMVRUVEBdXR16evK7umRgYABV1YY/4vvIkSPw9PRE8+bNce7cOcTHx+Pbb7/FkiVLMGzYsHo9xEEgEMDY2LheNydraWlBW1u7AZHX3+7du5Geno7o6Gjo6+ujX79+uH//vkzqbiyUXckAJbPvjjGGJX8vwekHp6HEV8LaHmvhZOgk77AaJPVONvYFX0H8pXSABzh5mWHofDcYWWrKOzRCyH8YYxAXF8v8pzGfdHX+/Hm4ublBWVkZJiYmCAwMREVFBbe+oKAAI0eOhJqaGkxMTLBmzRp0794d06dP58rs3bsXLi4u0NDQgLGxMUaMGIGsrCxuffVl5D///BPOzs5QVlbGxYsXJYYZBAcHY8+ePfjtt9+4XsCoqChuH/fv30ePHj2gqqqKdu3a4fLly9y6sLAwaGtr4/jx47C1tYWqqiq++OILFBcXY8+ePbCwsICOjg6mTZsm0WP66jCD58+f45tvvoGRkRGEQiEcHBxw/PjxWt+3oqIijB8/HgMGDMC2bdvg5OQECwsLjBs3Dnv27MHBgwfxyy+/AHgxVCI8PBydO3fm9n3+/Pka71H1pfbqYzp58iTs7Oygrq6O3r17Iz09ndvm1Z7ssrIyTJs2DYaGhhAKhejSpQuuXbtWo47IyEi4uLhAVVUVnTt3RkJCQq3H+DJtbW0YGxvDwcEBW7ZsQUlJCU6fPg3gzefQm+KSFboBTMqYmHFTc1Ey2zCMMay5sQYHEw+Cz+Njebfl6GzaWd5h1Vt5aQWiDyUh9q8nAABdUzV0H2ELE3qSFyHvHVZSgoQOzjKv1/bGdfDeoUex2uPHj9G3b1/4+vrip59+Qnx8PMaPHw+hUIjg4GAAgL+/P6Kjo3Hs2DEYGRlhwYIFuHHjhsRYV5FIhMWLF8PW1hZZWVnw9/eHr68vIiIiJOoLDAxEaGgorKysoKOjI5GsBgQEIC4uDvn5+di9u+opjbq6unjypOr/wrlz5yI0NBQ2NjaYO3cuhg8fjqSkJCgqVqUoxcXFWL9+PcLDw1FQUIDPP/8cgwcPhra2NiIiInD//n0MGTIEHh4eGDp0aI33QiwWo0+fPigoKMD//vc/tGzZErGxsVCoY7rMU6dOIScnBwEBATXW9e/fH61atcLPP/8sUdeMGTOwdu1a2NvbY/Xq1ejfvz9SUlLq7J0uLi5GaGgo9u7dCz6fj//7v/9DQEAA9u3bV2v5mTNn4tChQ9izZw/Mzc2xYsUKeHt7IykpCbq6uly5uXPnYtWqVTAwMMDEiRPx9ddfIzo6utZ91kZFRQUAUF5e/lbn0NvGJW2UzEqZ6KXObwEls/XGGMPya8uxL67qAz6341z0Mu8l56jq78m95zi7Nw55WSUAAMdPmsN9cEsoKtGUW4SQxrd582aYmZlh48aN4PF4aN26NZ48eYJZs2ZhwYIFKCoqwp49e7B//3707NkTQNXlZlNTU4n9fP3119zvVlZWWL9+PVxdXVFYWAh1dXVu3aJFi9CrV+3/N6urq0NFRQVlZWUwNjausT4gIACffVb11MaFCxeiTZs2SEpKQuvWrQFUJdRbtmxBy5YtAQBffPEF9u7di8zMTKirq8Pe3h49evTAuXPnak1mz5w5g6tXryIuLg6tWrXijqUuiYmJAAA7u9qHsbVu3ZorU83Pzw9DhgwBAGzZsgUnTpzAzp07MXPmzFr3IRKJsHXrVu6Y/Pz8sGjRolrLFhUVYcuWLQgLC0OfPn0AANu3b8fp06exc+dOzJgxgyu7dOlSeHp6Aqj6gvHZZ5+htLQUQqGwzuOtVlxcjHnz5kFBQQGenp5vPIdKSkreOi5po2RWyirwYoyMkiKNhayPSnElll1dhgMJB8ADD3M6zoGPrY+8w6qX0kIRLh1JQlx01eUjdR1lePnao5mtjpwjI4S8Dk9FBbY3rsul3sYQFxcHd3d3iXGaHh4eKCwsxKNHj5CbmwuRSAQ3NzduvZaWFmxtbSX2c/36dQQHB+PWrVvIzc2FWCwGAKSlpcHe3p4r5+Li0uBYHR0dud9NTEwAAFlZWVwyq6qqyiV9AGBkZAQLCwuJZNrIyEhi+MPLYmJi0Lx5cy6RfVv1GfLh7u7O/a6oqAgXFxfExcXVWf7VYzIxMakz/uTkZIhEInh4eHDLlJSU4ObmVqOOut7LFi1a1BnL8OHDoaCggJKSEhgYGGDnzp1wdHREcHDwa8+h58+fv3Vc0kbJrJRVsBcngSKfembflqhShJkXZuJM2hkAQJB7EIa0GiLnqN6euFKM2+ce4Z+IVJQVV40vsvcwgfvn1hCqKck5OkLIm/B4vEa53N+UFRUVwdvbG97e3ti3bx8MDAyQlpYGb29vlJeXS5RVU1NrcD1KSi/+T6xOnKqT5lfXV5epbdnL27xMpZ5fEKqT3ri4OHTuXHNIW1xcnEQi3xC1xd8Y46Xf9F7WZs2aNfDy8oKWlhYMDAzeOQZ5oOxKykR4cRmZ5pl9O6UVpZh6birOpJ2BEl8JK7utbFKJ7JN7z/HLsn8QfTAJZcUV0GumhsEBHdBjlB0lsoQQmbCzs8Ply5clEqTo6GhoaGigefPmsLKygpKSksTNOnl5eRKXz+Pj45GTk4OQkBB07doVrVu3rrP38E0EAkG9p7RqLI6Ojnj06FGNoQF1+fTTT6Grq4tVq1bVWHfs2DHcu3cPw4cPl1h+5coV7veKigpcv369zmEK9dWyZUsIBAKJsa8ikQjXrl1756QaAIyNjWFtbV0jkX3TOSTtuOqDemalTMSqvi8oQFyvaTk+VsWiYvid9cO1jGsQKgix7pN1TeZmr8LcUlw5eh8Jf2cAAJRVFeE+uCXsPExpui1CiFTk5eXVeOiAnp4eJk+ejLVr12Lq1Knw8/NDQkICgoKC4O/vDz6fDw0NDYwZMwYzZsyArq4uDA0NERQUBD6fz/2tatGiBQQCATZs2ICJEyfi7t27WLx4cYPitLCwwMmTJ5GQkAA9Pb16zbn6rjw9PdGtWzcMGTIEq1evhrW1NeLj48Hj8dC7d+8a5dXU1PDjjz9i2LBhmDBhAvz8/KCpqYnIyEjMmDEDX3zxBXx8JIe8bdq0CTY2NrCzs8OaNWuQm5srMd74XaipqWHSpElcW7Vo0QIrVqxAcXExxo4d2yh11OZN55C84qoNJbNSVj1mVhGv7+YnQF5ZHiafmYzb2behpqSGjZ9shItxw8dhyYqorBJ3LzzGtT9SICqt6nmw8zCB+6CWUNEQyDk6QsiHLCoqCu3bt5dYNnbsWOzYsQMRERGYMWMG2rVrB11dXYwdOxbz5s3jyq1evRoTJ05Ev379oKmpiZkzZ+Lhw4fczUIGBgYICwvDnDlzsH79enTo0AGhoaEYMGBAveMcP348oqKi4OLigsLCQpw7dw4WFhbvdOz1cejQIQQEBGD48OEoKiqCtbU1QkJC6iz/xRdf4Ny5c1i6dCm6du2K0tJSbraF6dOn1+icCgkJQUhICGJiYmBtbY1jx45BX7/xnkwZEhICsViMUaNGoaCgAC4uLjh58iR0dKR3/0WzZs3eeA7JI67a8FhjTmrXBOTn50NLSwt5eXnQ1JT+vJ6xS4ajb+H/QR1luBvyudTra6qeFj/FxDMTkZibCC1lLWzpuQVtDdrKO6zXKisW4U7UY9yKfIjSIhEAwMhSE92GtYKhOc0ZS0hTUVpaipSUFFhaWr7VXd8fqqKiIjRr1gyrVq2Sec9aU5WamgpLS0vcvHnzjY/vJTW97rNXn3yNemalrHpqLuqZrVv8s3hMOTMFWSVZ0BPqYdun29BKp353ncpSaZEIMafTcPvcI4jKqnpiNfWFcO5jATt3E/BoSAEhpAm4efMm4uPj4ebmhry8PG5qqIEDB8o5MkLqh5JZKav8bzYDBUpma3Uz6yb8Iv2QX54PSy1LbPpkE8w0zeQdVq1Ki0S4FfkQt88+RPl/wwl0TdXQwdscNq5GNC6WENLkhIaGIiEhAQKBAM7Ozvjrr78a9fI4IbJAyayUVfzXM6vEo2T2VacfnEbghUCUi8vRzqAdNntthqbg/bs8X1JQjtvnHuH2uUcoL6maZkuvmRrc+lnB0kmfbuwjhDRJ7du3x/Xrsp9L90NiYWHRqI8gJg1DyayUVTIaZlCbQ4mHsPDyQjAwdDfrjhXdVkBFsXEmC28shblluHEiFbHR6aisqGo/XVM1uH5miZbtDWg4ASGEEPIeoGRWyqpnM6BhBlXETIzNMZvx4+0fAQBDbIZgfqf5UOC/P491zXlSiJun0nDvaibE4qpv3IbmGmj/qTklsYQQQsh7hpJZKat4aZ7Zj11OSQ4WXFqAC48uAADGtx2Pqe2nvheX6RljeBSfi9tnHyL1Tg633NRGG679LNGslfZ7ESchhBBCJFEyK2WV1bMZfORjZu88vYPp56YjqyQLSnwlzO80H4NtBss7LJSXViAuOh13zj9CXlYJt9yqvQE6fGoOI8v3bwwvIYQQQl6gZFbKKrmHJnycA8QZYwhPCMeKqytQwSpgqWWJld1WwlbXVq5xFeaW4s75x/j3wmOUFVfd1KWorAC7TsZw/MQM2kYf9zPZCSGEkKaCklkpq/yIx8xmFWch6FIQLj6+CADwauGFxR6LoS5Ql1tMGffzEHMmDfdjssH+Gw+rZagCp55maNXRGAIhfSQIIYSQpoT+ckvZxzpm9nrmdcw4PwNPS55CwBdgWodpGG0/Wi7jTkVllbh/Mwt3LzxBxv08brmpjTYcP2kOy3YGNEcsIeSjExYWhunTp+P58+evLde9e3c4OTlh7dq1MomLkPriyzuAD93HNsygUlyJ9TfW4+uTX+NpyVNYa1vj1/6/YkybMTJNZBljePqwABcOJGLXzIs4ExaHjPt54Cvy0NrdGMPmu2Hw9x3Qsr0hJbKEkCbJ19cXPB4PPB4PAoEA1tbWWLRoESoqKt5q+6FDhyIxMfGN5Q4fPozFixe/a7hv7eeff4aCggKmTJkiszplKTU1FTweDzExMfIO5YNBPbNSVt0zy/8IbgB7kP8Acy/Oxa2ntwAAA1oOwNyOc6GqJLvxp6VFIsRfTkdsdDpy04u45Zr6QrR2N4F9F1OoaSnLLB5CCJGm3r17Y/fu3SgrK0NERASmTJkCJSUlzJ49+43bqqioQEWl7vm9y8vLIRAIoKur25ghv9HOnTsxc+ZM/Pjjj1i1ahWEQqHU66w+1qZGJBJBSUlJ3mHIHfXMStnH0DPLGMPBxIP48vcvcevpLagpqWFZ12VY2mWpTBJZcaUYqXeycXrXv9gzOxrRB5OQm14EBSU+rJwMMGCaE/5vsTtcP7OkRJYQ8lYYYxCVVcr8p75Pk1JWVoaxsTHMzc0xadIkeHl54dixYwCA1atXo23btlBTU4OZmRkmT56MwsJCbtuwsDBoa2tzr4ODg+Hk5IQdO3bA0tKSSyK7d++O6dOnc+UsLCzwww8/4Ouvv4aGhgZatGiBbdu2ScR16dIlODk5QSgUwsXFBUePHn2r3siUlBRcunQJgYGBaNWqFQ4fPlyjzPbt22FmZgZVVVUMHjwYq1evljgOAFiyZAkMDQ2hoaGBcePGITAwEE5OTtx6X19fDBo0CEuXLoWpqSlsbatuSn748CF8fHygra0NXV1dDBw4EKmpqdx2FRUVmDZtGrS1taGnp4dZs2ZhzJgxGDRoEFfmxIkT6NKlC1emX79+SE5O5tZbWloCqHoCG4/HQ/fu3QEAYrEYixYtQvPmzaGsrAwnJyecOHGC2666R/fAgQPw9PSEUCjEvn37Xvt+fiyoZ1bKqh9n+6GOmc0tzcWiy4twJu0MAMDN2A2LPRbDVN1U6nXnZ5fg9tlHSLyWgZICEbdcr5kaHDybw8bVCMoqdIoTQuqvolyMbd+el3m9E9Z5Qkm54Q+RUVFRQU5O1VzZfD4f69evh6WlJe7fv4/Jkydj5syZ2Lx5c53bJyUl4dChQzh8+DAUFOqOY9WqVVi8eDHmzJmDgwcPYtKkSfD09IStrS3y8/PRv39/9O3bF/v378eDBw8kkuHX2b17Nz777DNoaWnh//7v/7Bz506MGDGCWx8dHY2JEydi+fLlGDBgAM6cOYP58+dL7GPfvn1YunQpNm/eDA8PD4SHh2PVqlVcElktMjISmpqaOH36NICqXk5vb2+4u7vjr7/+gqKiIpYsWYLevXvj9u3bEAgEWL58Ofbt24fdu3fDzs4O69atw9GjR9GjRw9uv0VFRfD394ejoyMKCwuxYMECDB48GDExMeDz+bh69Src3Nxw5swZtGnThusRXrduHVatWoUff/wR7du3x65duzBgwAD8+++/sLGx4fYfGBiIVatWoX379jLptW4K6C+9lIn/65nlf4A9s5eeXELghUDkluVCkacIv/Z++MrhK/B50uvwLy+twP2bTxF/JQNPEnNR3YkhVFdCK1cjWLsYwdhKkx5wQAj5qDDGEBkZiZMnT2Lq1KkAUKM3dcmSJZg4ceJrk9ny8nL89NNPMDAweG19ffv2xeTJkwEAs2bNwpo1a3Du3DnY2tpi//794PF42L59O4RCIezt7fH48WOMHz/+tfsUi8UICwvDhg0bAADDhg3D999/j5SUFC4R3bBhA/r06YOAgAAAQKtWrXDp0iUcP36c28+GDRswduxYfPXVVwCABQsW4NSpUxK90gCgpqaGHTt2cMnk//73P4jFYuzYsYP7G7J7925oa2sjKioKn376KTZs2IDZs2dj8OCqedI3btyIiIgIif0OGTJE4vWuXbtgYGCA2NhYODg4cO+tnp4ejI2NuXKhoaGYNWsWhg0bBgBYvnw5zp07h7Vr12LTpk1cuenTp+Pzzz9/7Xv5saFkVsoq2X9Tc/E+nGQ2pyQHK66tQERK1QfYWtsaS7sshb2evVTqY4whIzkPd/96jPs3n6Ki/EUvt5m9Lhx7NIeZnS4UFGnUDCGkcSgK+JiwzlMu9dbH8ePHoa6uDpFIBLFYjBEjRiA4OBgAcObMGSxbtgzx8fHIz89HRUUFSktLUVxcDFXV2oeAmZubvzGRBQBHR0fudx6PB2NjY2RlZQEAEhIS4OjoKNFr6Obm9sZ9nj59GkVFRejbty8AQF9fH7169cKuXbu4G9ASEhK4RPLlfb+czCYkJHCJ9stlzp49K7Gsbdu2EuNkb926haSkJGhoaEiUKy0tRXJyMvLy8pCZmSlxLAoKCnB2doZY/OLv0r1797BgwQL8/fffyM7O5talpaXBwcGh1mPPz8/HkydP4OHhIbHcw8MDt27dkljm4uJS6z4+ZpTMStmH1DMrZmIcTDyIdTfWIb88Hzzw4GPrgwCXAAgVG/9SR2mhCHGX0xEX/QS5GcXcck0DFdi5G6OVmzE09eu+eYEQQhqKx+O90+V+WenRowe2bNkCgUAAU1NTKCpW/VlPTU1Fv379MGnSJCxduhS6urq4ePEixo4di/Ly8jqTWTU1tbeq99Wbjng8nkRC1xA7d+7Es2fPJG5KE4vFuH37NhYuXAg+v3E7LF491sLCQjg7O9c6DvVtEvxq/fv3h7m5ObZv3w5TU1OIxWI4ODigvLz8nWMG3r6NPiaUzErZi4cmNO1k9mHBQyyIXoB/Mv8BALTWbY1g92C00W/TqPVUVojxMPYZEq9l4n7MU1SKqv5zVFTiw9rVCG26mMLIkoYREEIIUJXYWFtb11h+/fp1iMVirFq1iksCf/nlF5nEZGtri//9738oKyuDsnLVTbfXrl177TY5OTn47bffEB4ejjZtXvxdqaysRJcuXXDq1Cn07t0btra2Nfb16uvqMqNHj66zTG06dOiAAwcOwNDQEJqatT/K3MjICNeuXUO3bt24+G7cuMHdXJaTk4OEhARs374dXbt2BQBcvHhRYh/VvcGVlZXcMk1NTZiamiI6Ohqeni+uCERHR79Vr/bHjpJZKRP/dwMYv4neAFYsKsbe2L3YcWcHSitLIVQQYlqHaRjeejgU+Y1z+jAxQ2ZqPuIupSPpehbKS17Mkahvpg6Hbs1g7UI3cxFCyNuytraGSCTChg0b0L9/f0RHR2Pr1q0yqXvEiBGYO3cuJkyYgMDAQKSlpSE0NBQA6uyI2Lt3L/T09ODj41OjTN++fbFz50707t0bU6dORbdu3bB69Wr0798fZ8+exZ9//imxzdSpUzF+/Hi4uLigc+fOOHDgAG7fvg0rK6vXxj1y5EisXLkSAwcO5GYVePDgAQ4fPoyZM2eiefPmmDp1KpYtWwZra2u0bt0aGzZsQG5uLle/jo4O9PT0sG3bNpiYmCAtLQ2BgYES9RgaGkJFRQUnTpxA8+bNIRQKoaWlhRkzZiAoKAgtW7aEk5MTdu/ejZiYGJqx4C1QdiBl1SmsYhMbMytmYhxNOop1N9bhWekzAICLkQsWeyxGc43m77x/xhiyHxYi8Vomkq9noeBZKbdOVVMAa2dDtOpoDENzDeqFJYSQemrXrh1Wr16N5cuXY/bs2ejWrRuWLVsm0VspLZqamvj9998xadIkODk5oW3btliwYAFGjBhR5933u3btwuDBg2v9/37IkCEYNWoUsrOz4eHhga1bt2LhwoWYN28evL298d1332Hjxo1c+ZEjR+L+/fsICAhAaWkpfHx84Ovri6tXr742blVVVVy4cAGzZs3C559/joKCAjRr1gw9e/bkempnzZqFjIwMjB49GgoKCpgwYQK8vb25mR/4fD7Cw8Mxbdo0ODg4wNbWFuvXr+em3wIARUVFrF+/HosWLcKCBQvQtWtXREVFYdq0acjLy8P333+PrKws2Nvb49ixYxIzGZDa8Vh9J7Vr4vLz86GlpYW8vLw6LyM0pg3zZ2GVqBsGKsVj3eLvpV5fY/gn4x+suLYCcc/iAABmGmbwc/JDH8s+75RYView965lIumVBFZJWQEWjvqw72KKZjba4NFTuQghMlJaWsrdMU9THUnHvn378NVXXyEvL++1D2poiPHjxyM+Ph5//fVXnWV69eoFY2Nj7N27t1HrFovFsLOzg4+Pj0yfkvaheN1nrz75GvXMSlllE7oBLP5ZPDbe3Ijzj6rmVlRXUsfEdhMxovUIKCk07AkjYjHD48Rc3L/xFA/u5kgksApKfFg46MHG1QgtHPSgJHj/b7YghBDyZj/99BOsrKzQrFkz3Lp1C7NmzYKPj0+jJLKhoaHo1asX1NTU8Oeff2LPnj0S040VFxdj69atXI/pzz//jDNnznDzyb6LBw8e4NSpU/D09ERZWRk2btyIlJQUiblwiexRMitlTWE2g4cFD7E5ZjOO36+a2kSBp4AhNkMwpf0U6Arr9xhDJmZ4ll6Ex4m5eHLvOR4l5KKs6MUYWAUlPiza6sHa2QjmbSmBJYSQD1FGRgYWLFiAjIwMmJiY4Msvv8TSpUsbZd9Xr17FihUrUFBQACsrK6xfvx7jxo3j1vN4PERERGDp0qUoLS2Fra0tDh06BC8vr3eum8/nIywsDAEBAWCMwcHBAWfOnIGdnd0775s0HCWzUvY+98ymF6Zj592dOHTvECrEVQlnb4vemOw0GZZalm/YukqFqBLZDwuR9aAAGffz8DgxF8V5ktOPKKsqomV7A1i2M0AzW50mMd0NIYSQhps5cyZmzpwplX2/aVYGFRUVnDlzRip1m5mZITo6Wir7Jg1HyayUsf8emvA+DQGNzYnF/rj9+CPlDy6JdTdxx7fO36KNXt1TbTExw/OsYqQn5yHrQQEyU/KQ87gITCyZqCsK+DBpqQVTGx00a6UNI0tN8BXogQaEEEIIaXyUzErZ+zLMoLyyHFEPoxCeEI5rGS/m23M1dsVEx4lwM5Gcx44xhsLcMmQ/LOB6XbMeFEhMm1VNRUMJBi00YWylCZOWWjBuqQVFJep9JYQ0LR/Z/dCEyF1jfeYomZWy6qm55JXMJuUmISIlAgcTDyK3LBdA1ZjYTy0+xUi7kWhn0A7iSjGyHxUi51EBsh8XIedRVQJbVlwzcVVU4sPAXAPGllowstSEoYUm1HWUafosQkiTVf00q+Li4ka/054QUrfqp6JVT23WUJTMShn7r2dWlqleSl4KzqadxYnUE4h/Fs8tN1QxxADrAeijPxAK2arIOJ+Hg8n/IOdRISpENR/qwOPzoGOsCkNzDRhZasHIQhN6zdRoyAAh5IOioKAAbW1tZGVlAaiab5S+oBMiXWKxGE+fPoWqqir3GOaGomRWyrhhBlJ8aEKRqAj/Zv+LK+lX8NfjvyQSWEWeItprusBZ1A0mj+3w7EoxThffr7EPJaECDMw0oGeqBr3m6jBooQE9U3UoKFHiSgj58BkbGwMAl9ASQqSPz+ejRYsW7/zlkZJZKWvMMbOMMeSU5uBe7j0kPU/C/bz7iMuJQ9yzOIjZi55VBSjABg6wyG4L4xR7CCvUAADpyK+KRZEHPVN1GJhrwLSlFowstaBloEIPKiCEfLR4PB5MTExgaGgIkUgk73AI+SgIBALw+e/eaUbJrJTVN5kVMzEeFjxE8vNkPMh/gIyiDDwpfIInRU/wqOARiiuKa91OvUIbJnnWMHvWGs3zbKEqevG0DE19IUyttWHcUguG5prQNVWDgiL1uBJCyKsUFBTeefweIUS23otkdtOmTVi5ciUyMjLQrl07bNiwAW5ubnWW//XXXzF//nykpqbCxsYGy5cvR9++fWUY8dsTszcnsyKxCBceXcDJlJO4lH4JeWV5dZblMR60RQbQLjSCdrEx9IpNYVxgBfVybQCAUE0JelbqMLLQhJFl1ewCKhqCRj0mQgghhJD3hdyT2QMHDsDf3x9bt25Fx44dsXbtWnh7eyMhIQGGhoY1yl+6dAnDhw/HsmXL0K9fP+zfvx+DBg3CjRs34ODgIIcjeL3qFLa2ftAnhU9wNOE3/HrvV2SXPeWWKzIl6ItMoFlkCNViLWiU6Vb9lOpBo1wHiuKq5FRZTRFG5pow7KRZNd61mTo09YV04wIhhBBCPho8JueJ9Tp27AhXV1ds3LgRQNXdbWZmZpg6dSoCAwNrlB86dCiKiopw/PhxblmnTp3g5OSErVu3vrG+/Px8aGlpIS8vD5qamm8s/65mzA3Gr5WuGKBwHsq2qigSFSO3IgdP+Gl4JsjgyqmUq6NVthssnrWFYaE5FNh/l7l4gIauENpGqtAxUoWmgQr0mqlDx1gVqpoCSlwJIYQQ8sGpT74m157Z8vJyXL9+HbNnz+aW8fl8eHl54fLly7Vuc/nyZfj7+0ss8/b2xtGjR2stX1ZWhrKyMu51Xl7VJfz8/Px3jP7tPC0tgFhcjHTVHPxb/CIBRyWAYh6MCyxhn9sJjjxXaOuqQ721EJr6Qmjqq0BTr+pfRUHN8VuVKENBQVmN5YQQQgghTV11nvY2fa5yTWazs7NRWVkJIyMjieVGRkaIj4+vdZuMjIxay2dkZNRaftmyZVi4cGGN5WZmZg2MumEe1rE8DrE4hz9kGgshhBBCSFNQUFAALS2t15aR+5hZaZs9e7ZET65YLMazZ8+gp6cnk0v0+fn5MDMzw8OHD2UyrIE0PmrDpo/asOmjNmzaqP2aPlm3IWMMBQUFMDU1fWNZuSaz+vr6UFBQQGZmpsTyzMxMbgLrVxkbG9ervLKyMpSVlSWWaWtrNzzoBtLU1KQPcBNHbdj0URs2fdSGTRu1X9MnyzZ8U49sNblONioQCODs7IzIyEhumVgsRmRkJNzd3Wvdxt3dXaI8AJw+fbrO8oQQQggh5MMl92EG/v7+GDNmDFxcXODm5oa1a9eiqKgIX331FQBg9OjRaNasGZYtWwYA+Pbbb+Hp6YlVq1bhs88+Q3h4OP755x9s27ZNnodBCCGEEELkQO7J7NChQ/H06VMsWLAAGRkZcHJywokTJ7ibvNLS0iQedda5c2fs378f8+bNw5w5c2BjY4OjR4++l3PMAlXDHIKCgmoMdSBNB7Vh00dt2PRRGzZt1H5N3/vchnKfZ5YQQgghhJCGkuuYWUIIIYQQQt4FJbOEEEIIIaTJomSWEEIIIYQ0WZTMEkIIIYSQJouS2UawadMmWFhYQCgUomPHjrh69epry//6669o3bo1hEIh2rZti4iICBlFSupSnzbcvn07unbtCh0dHejo6MDLy+uNbU6kr76fw2rh4eHg8XgYNGiQdAMkb1TfNnz+/DmmTJkCExMTKCsro1WrVvT/qRzVt/3Wrl0LW1tbqKiowMzMDN999x1KS0tlFC151YULF9C/f3+YmpqCx+Ph6NGjb9wmKioKHTp0gLKyMqytrREWFib1OGvFyDsJDw9nAoGA7dq1i/37779s/PjxTFtbm2VmZtZaPjo6mikoKLAVK1aw2NhYNm/ePKakpMTu3Lkj48hJtfq24YgRI9imTZvYzZs3WVxcHPP19WVaWlrs0aNHMo6cVKtvG1ZLSUlhzZo1Y127dmUDBw6UTbCkVvVtw7KyMubi4sL69u3LLl68yFJSUlhUVBSLiYmRceSEsfq33759+5iysjLbt28fS0lJYSdPnmQmJibsu+++k3HkpFpERASbO3cuO3z4MAPAjhw58try9+/fZ6qqqszf35/FxsayDRs2MAUFBXbixAnZBPwSSmbfkZubG5syZQr3urKykpmamrJly5bVWt7Hx4d99tlnEss6duzIvvnmG6nGSepW3zZ8VUVFBdPQ0GB79uyRVojkDRrShhUVFaxz585sx44dbMyYMZTMyll923DLli3MysqKlZeXyypE8hr1bb8pU6awTz75RGKZv78/8/DwkGqc5O28TTI7c+ZM1qZNG4llQ4cOZd7e3lKMrHY0zOAdlJeX4/r16/Dy8uKW8fl8eHl54fLly7Vuc/nyZYnyAODt7V1neSJdDWnDVxUXF0MkEkFXV1daYZLXaGgbLlq0CIaGhhg7dqwswiSv0ZA2PHbsGNzd3TFlyhQYGRnBwcEBP/zwAyorK2UVNvlPQ9qvc+fOuH79OjcU4f79+4iIiEDfvn1lEjN5d+9TPiP3J4A1ZdnZ2aisrOSeVlbNyMgI8fHxtW6TkZFRa/mMjAypxUnq1pA2fNWsWbNgampa40NNZKMhbXjx4kXs3LkTMTExMoiQvElD2vD+/fs4e/YsRo4ciYiICCQlJWHy5MkQiUQICgqSRdjkPw1pvxEjRiA7OxtdunQBYwwVFRWYOHEi5syZI4uQSSOoK5/Jz89HSUkJVFRUZBYL9cwS8g5CQkIQHh6OI0eOQCgUyjsc8hYKCgowatQobN++Hfr6+vIOhzSQWCyGoaEhtm3bBmdnZwwdOhRz587F1q1b5R0aeQtRUVH44YcfsHnzZty4cQOHDx/GH3/8gcWLF8s7NNIEUc/sO9DX14eCggIyMzMllmdmZsLY2LjWbYyNjetVnkhXQ9qwWmhoKEJCQnDmzBk4OjpKM0zyGvVtw+TkZKSmpqJ///7cMrFYDABQVFREQkICWrZsKd2giYSGfA5NTEygpKQEBQUFbpmdnR0yMjJQXl4OgUAg1ZjJCw1pv/nz52PUqFEYN24cAKBt27YoKirChAkTMHfuXPD51Nf2vqsrn9HU1JRpryxAPbPvRCAQwNnZGZGRkdwysViMyMhIuLu717qNu7u7RHkAOH36dJ3liXQ1pA0BYMWKFVi8eDFOnDgBFxcXWYRK6lDfNmzdujXu3LmDmJgY7mfAgAHo0aMHYmJiYGZmJsvwCRr2OfTw8EBSUhL3RQQAEhMTYWJiQomsjDWk/YqLi2skrNVfTBhj0guWNJr3Kp+R+S1nH5jw8HCmrKzMwsLCWGxsLJswYQLT1tZmGRkZjDHGRo0axQIDA7ny0dHRTFFRkYWGhrK4uDgWFBREU3PJWX3bMCQkhAkEAnbw4EGWnp7O/RQUFMjrED569W3DV9FsBvJX3zZMS0tjGhoazM/PjyUkJLDjx48zQ0NDtmTJEnkdwketvu0XFBTENDQ02M8//8zu37/PTp06xVq2bMl8fHzkdQgfvYKCAnbz5k128+ZNBoCtXr2a3bx5kz148IAxxlhgYCAbNWoUV756aq4ZM2awuLg4tmnTJpqaqynbsGEDa9GiBRMIBMzNzY1duXKFW+fp6cnGjBkjUf6XX35hrVq1YgKBgLVp04b98ccfMo6YvKo+bWhubs4A1PgJCgqSfeCEU9/P4csomX0/1LcNL126xDp27MiUlZWZlZUVW7p0KauoqJBx1KRafdpPJBKx4OBg1rJlSyYUCpmZmRmbPHkyy83NlX3ghDHG2Llz52r921bdbmPGjGGenp41tnFycmICgYBZWVmx3bt3yzxuxhjjMUb9+YQQQgghpGmiMbOEEEIIIaTJomSWEEIIIYQ0WZTMEkIIIYSQJouSWUIIIYQQ0mRRMksIIYQQQposSmYJIYQQQkiTRcksIYQQQghpsiiZJYQQQgghTRYls4QQmfD19cWgQYPeaR+pqang8XiIiYmps0xUVBR4PB6eP38OAAgLC4O2tja3Pjg4GE5OTu8UR0NlZGSgV69eUFNTk4iJvLtXz41XzwNp1kUIkS9KZgkhEnx9fcHj8cDj8SAQCGBtbY1FixahoqJC3qG9lc6dOyM9PR1aWlq1rg8ICEBkZCT3ujGS7Le1Zs0apKenIyYmBomJiTKpU5pe/aLwLiwsLLB27doG79/MzAzp6elwcHBolHiq1XZ+SKsuQkjDKMo7AELI+6d3797YvXs3ysrKEBERgSlTpkBJSQmzZ8+uUba8vBwCgUAOUdZOIBDA2Ni4zvXq6upQV1eXYUQvJCcnw9nZGTY2NjKrs672EYlEUFJSklkc0lR9jK9r98akoKAgs7oIIW9GPbOEkBqUlZVhbGwMc3NzTJo0CV5eXjh27BiAFz1VS5cuhampKWxtbQEAd+7cwSeffAIVFRXo6elhwoQJKCwsrLHvhQsXwsDAAJqampg4cSLKy8u5dSdOnECXLl2gra0NPT099OvXD8nJyTX2ER8fj86dO0MoFMLBwQHnz5/n1r3p8vLLwwyCg4OxZ88e/Pbbb1xvdFRUFD755BP4+flJbPf06VMIBAKJXt1XbdmyBS1btoRAIICtrS327t3LrbOwsMChQ4fw008/gcfjwdfXt8797Nq1C23atIGysjJMTEwkYklLS8PAgQOhrq4OTU1N+Pj4IDMzs8bx7dixA5aWlhAKhQAAHo+HLVu2YMCAAVBTU8PSpUsBAL/99hs6dOgAoVAIKysrLFy4UKIX/vnz5/jmm29gZGTEvd/Hjx9HVFQUvvrqK+Tl5XHvXXBwcK3Hk5ycjIEDB8LIyAjq6upwdXXFmTNnuPXdu3fHgwcP8N1330m0Q137t7CwwOLFizF69GhoampiwoQJdV76j46OhqOjI4RCITp16oS7d+/WeK9etnbtWlhYWHDrazs/aqvr/PnzcHNz49osMDBQ4n3s3r07pk2bhpkzZ0JXVxfGxsZ1vl+EkHpihBDykjFjxrCBAwdKLBswYADr0KEDt15dXZ2NGjWK3b17l929e5cVFhYyExMT9vnnn7M7d+6wyMhIZmlpycaMGSOxX3V1dTZ06FB29+5ddvz4cWZgYMDmzJnDlTl48CA7dOgQu3fvHrt58ybr378/a9u2LausrGSMMZaSksIAsObNm7ODBw+y2NhYNm7cOKahocGys7MZY4ydO3eOAWC5ubmMMcZ2797NtLS0uDqCgoJYu3btGGOMFRQUMB8fH9a7d2+Wnp7O0tPTWVlZGdu3bx/T0dFhpaWl3HarV69mFhYWTCwW1/q+HT58mCkpKbFNmzaxhIQEtmrVKqagoMDOnj3LGGMsKyuL9e7dm/n4+LD09HT2/PnzWvezefNmJhQK2dq1a1lCQgK7evUqW7NmDWOMscrKSubk5MS6dOnC/vnnH3blyhXm7OzMPD09JY5PTU2N9e7dm924cYPdunWLMcYYAGZoaMh27drFkpOT2YMHD9iFCxeYpqYmCwsLY8nJyezUqVPMwsKCBQcHc/V16tSJtWnThp06dYolJyez33//nUVERLCysjK2du1apqmpyb13BQUFtR5TTEwM27p1K7tz5w5LTExk8+bNY0KhkD148IAxxlhOTg5r3rw5W7RokUQ71LV/c3NzpqmpyUJDQ1lSUhJLSkrizo2bN29KnAd2dnbs1KlT7Pbt26xfv37MwsKClZeX1zgXqq1Zs4aZm5u/9vx4ta5Hjx4xVVVVNnnyZBYXF8eOHDnC9PX1WVBQELdfT09PpqmpyYKDg1liYiLbs2cP4/F47NSpU7W+Z4SQt0fJLCFEwsvJrFgsZqdPn2bKysosICCAW29kZMTKysq4bbZt28Z0dHRYYWEht+yPP/5gfD6fZWRkcNvp6uqyoqIirsyWLVuYuro6l6y+6unTpwwAu3PnDmPsRTIbEhLClRGJRKx58+Zs+fLljLH6JbOvHm+1kpISpqOjww4cOMAtc3R05JK82nTu3JmNHz9eYtmXX37J+vbty70eOHCgRIJfG1NTUzZ37txa1506dYopKCiwtLQ0btm///7LALCrV69yx6ekpMSysrIktgXApk+fLrGsZ8+e7IcffpBYtnfvXmZiYsIYY+zkyZOMz+ezhISEWuN59b2tjzZt2rANGzZwr83Nzbmk/U37Nzc3Z4MGDZJYVlcyGx4ezpXJyclhKioqXLu+KZllrPbz49W65syZw2xtbSW+6GzatEni3Pb09GRdunSR2I+rqyubNWtWjeMjhNQPDTMghNRw/PhxqKurQygUok+fPhg6dKjEJdG2bdtKjMOMi4tDu3btoKamxi3z8PCAWCxGQkICt6xdu3ZQVVXlXru7u6OwsBAPHz4EANy7dw/Dhw+HlZUVNDU1ucu9aWlpEvG5u7tzvysqKsLFxQVxcXGNcuwAIBQKMWrUKOzatQsAcOPGDdy9e/e1QwPi4uLg4eEhsczDw6NecWVlZeHJkyfo2bNnnXWYmZnBzMyMW2Zvbw9tbW2JeszNzWFgYFBjexcXF4nXt27dwqJFi7hxxOrq6hg/fjzS09NRXFyMmJgYNG/eHK1atXrrY6hNYWEhAgICYGdnB21tbairqyMuLq5Gu9bHq8dSl5fPFV1dXdja2jbquQJUtYu7uzt4PB63zMPDA4WFhXj06BG3zNHRUWI7ExMTZGVlNWoshHyM6AYwQkgNPXr0wJYtWyAQCGBqagpFRcn/Kl5OWhtT//79YW5uju3bt8PU1BRisRgODg4S42plZdy4cXBycsKjR4+we/dufPLJJzA3N5dqnSoqKo2yn7ra59XlhYWFWLhwIT7//PMaZYVCYaPFExAQgNOnTyM0NBTW1tZQUVHBF1988U7t2hjnIJ/PB2NMYplIJHrn/dbl1RvueDwexGKx1Ooj5GNBPbOEkBrU1NRgbW2NFi1a1Ehka2NnZ4dbt26hqKiIWxYdHQ0+n8/dIAZU9QSWlJRwr69cuQJ1dXWYmZkhJycHCQkJmDdvHnr27Ak7Ozvk5ubWWt+VK1e43ysqKnD9+nXY2dk15FAhEAhQWVlZY3nbtm3h4uKC7du3Y//+/fj6669fux87OztER0dLLIuOjoa9vf1bx6KhoQELC4s6bzKzs7PDw4cPuZ5sAIiNjcXz58/rVU+1Dh06ICEhAdbW1jV++Hw+HB0d8ejRozqnEavrvXtVdHQ0fH19MXjwYLRt2xbGxsZITU19477edv+v8/K5kpubi8TERO5cMTAwQEZGhkRC++oNZG8Tg52dHS5fviyxn+joaGhoaKB58+bvFD8h5M0omSWEvLORI0dCKBRizJgxuHv3Ls6dO4epU6di1KhRMDIy4sqVl5dj7NixiI2NRUREBIKCguDn5wc+nw8dHR3o6elh27ZtSEpKwtmzZ+Hv719rfZs2bcKRI0cQHx+PKVOmIDc3943JZl0sLCxw+/ZtJCQkIDs7W6Jnbty4cQgJCQFjDIMHD37tfmbMmIGwsDBs2bIF9+7dw+rVq3H48GEEBATUK57g4GCsWrUK69evx71793Djxg1s2LABAODl5YW2bdti5MiRuHHjBq5evYrRo0fD09PzrS+7v2zBggX46aefsHDhQvz777+Ii4tDeHg45s2bBwDw9PREt27dMGTIEJw+fRopKSn4888/ceLECQBV711hYSEiIyORnZ2N4uLiWuuxsbHB4cOHERMTg1u3bmHEiBE1eiQtLCxw4cIFPH78GNnZ2fXa/+ssWrQIkZGR3DARfX19bt7Y7t274+nTp1ixYgWSk5OxadMm/PnnnzXiquv8qDZ58mQ8fPgQU6dORXx8PH777TcEBQXB398ffD79mSVE2uhTRgh5Z6qqqjh58iSePXsGV1dXfPHFF+jZsyc2btwoUa5nz56wsbFBt27dMHToUAwYMIAbi8vn8xEeHo7r16/DwcEB3333HVauXFlrfSEhIQgJCUG7du1w8eJFHDt2DPr6+g2Kffz48bC1tYWLiwsMDAwkeleHDx8ORUVFDB8+nJviqi6DBg3CunXrEBoaijZt2uDHH3/E7t270b1793rFM2bMGKxduxabN29GmzZt0K9fP9y7dw9A1WXp3377DTo6OujWrRu8vLxgZWWFAwcO1Pu4AcDb2xvHjx/HqVOn4Orqik6dOmHNmjUSwykOHToEV1dXDB8+HPb29pg5cybXU9m5c2dMnDgRQ4cOhYGBAVasWFFrPatXr4aOjg46d+6M/v37w9vbGx06dJAos2jRIqSmpqJly5bceN+33f/rhISE4Ntvv4WzszMyMjLw+++/c+O97ezssHnzZmzatAnt2rXD1atXa3z5eN35Ua1Zs2aIiIjA1atX0a5dO0ycOBFjx47lvhQQQqSLx14dMEQIIQQAuOTq2rVrNZIvQggh7wdKZgkh5BUikQg5OTkICAhASkpKrb1xhBBC3g80zIAQQl4RHR0NExMTXLt2DVu3bpV3OIQQQl6DemYJIYQQQkiTRT2zhBBCCCGkyaJklhBCCCGENFmUzBJCCCGEkCaLkllCCCGEENJkUTJLCCGEEEKaLEpmCSGEEEJIk0XJLCGEEEIIabIomSWEEEIIIU3W/wN6A5aodU/IDgAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 800x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = matplotlib.pyplot.subplots(figsize=FIGSIZE)\n",
    "seaborn.ecdfplot(data=df, x='Probability of correct attribution', hue='Method', ax=ax)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "id": "e3a701e3-f5cb-4aa1-b865-b30253b69e35",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\20173892\\AppData\\Local\\Programs\\Python\\venv\\Lib\\site-packages\\seaborn\\_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "c:\\Users\\20173892\\AppData\\Local\\Programs\\Python\\venv\\Lib\\site-packages\\seaborn\\_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "c:\\Users\\20173892\\AppData\\Local\\Programs\\Python\\venv\\Lib\\site-packages\\seaborn\\_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "c:\\Users\\20173892\\AppData\\Local\\Programs\\Python\\venv\\Lib\\site-packages\\seaborn\\_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "c:\\Users\\20173892\\AppData\\Local\\Programs\\Python\\venv\\Lib\\site-packages\\seaborn\\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
      "  with pd.option_context('mode.use_inf_as_na', True):\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Probability of incorrect attribution', ylabel='Proportion'>"
      ]
     },
     "execution_count": 118,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = matplotlib.pyplot.subplots(figsize=FIGSIZE)\n",
    "seaborn.ecdfplot(data=df, x='Probability of incorrect attribution', hue='Method', ax=ax)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "7e971c86-48b7-40b1-b44f-cc1a1068de7e",
   "metadata": {},
   "source": [
    "## Non-stationarity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "id": "0ac77336-bb2b-4db8-bed8-6fe72d34a6b0",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\20173892\\AppData\\Local\\Programs\\Python\\venv\\Lib\\site-packages\\seaborn\\_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "c:\\Users\\20173892\\AppData\\Local\\Programs\\Python\\venv\\Lib\\site-packages\\seaborn\\_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "c:\\Users\\20173892\\AppData\\Local\\Programs\\Python\\venv\\Lib\\site-packages\\seaborn\\_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "c:\\Users\\20173892\\AppData\\Local\\Programs\\Python\\venv\\Lib\\site-packages\\seaborn\\_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "c:\\Users\\20173892\\AppData\\Local\\Programs\\Python\\venv\\Lib\\site-packages\\seaborn\\_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "c:\\Users\\20173892\\AppData\\Local\\Programs\\Python\\venv\\Lib\\site-packages\\seaborn\\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
      "  with pd.option_context('mode.use_inf_as_na', True):\n",
      "c:\\Users\\20173892\\AppData\\Local\\Programs\\Python\\venv\\Lib\\site-packages\\seaborn\\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
      "  with pd.option_context('mode.use_inf_as_na', True):\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Timestep', ylabel='Probability of correct attribution'>"
      ]
     },
     "execution_count": 119,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = matplotlib.pyplot.subplots(figsize=FIGSIZE)\n",
    "seaborn.lineplot(data=df[['Method', 'Timestep', 'Probability of correct attribution']].groupby('Method').rolling(ROLLING_WINDOW_SIZE).mean(), x='Timestep', y='Probability of correct attribution', hue='Method', ax=ax)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "id": "b033a0a2-9012-4171-ac94-af98606257bf",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\20173892\\AppData\\Local\\Programs\\Python\\venv\\Lib\\site-packages\\seaborn\\_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "c:\\Users\\20173892\\AppData\\Local\\Programs\\Python\\venv\\Lib\\site-packages\\seaborn\\_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "c:\\Users\\20173892\\AppData\\Local\\Programs\\Python\\venv\\Lib\\site-packages\\seaborn\\_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "c:\\Users\\20173892\\AppData\\Local\\Programs\\Python\\venv\\Lib\\site-packages\\seaborn\\_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "c:\\Users\\20173892\\AppData\\Local\\Programs\\Python\\venv\\Lib\\site-packages\\seaborn\\_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "c:\\Users\\20173892\\AppData\\Local\\Programs\\Python\\venv\\Lib\\site-packages\\seaborn\\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
      "  with pd.option_context('mode.use_inf_as_na', True):\n",
      "c:\\Users\\20173892\\AppData\\Local\\Programs\\Python\\venv\\Lib\\site-packages\\seaborn\\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
      "  with pd.option_context('mode.use_inf_as_na', True):\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Timestep', ylabel='Probability of incorrect attribution'>"
      ]
     },
     "execution_count": 120,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = matplotlib.pyplot.subplots(figsize=FIGSIZE)\n",
    "seaborn.lineplot(data=df[['Method', 'Timestep', 'Probability of incorrect attribution']].groupby('Method').rolling(ROLLING_WINDOW_SIZE).mean(), x='Timestep', y='Probability of incorrect attribution', hue='Method', ax=ax)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "13ed163d",
   "metadata": {},
   "source": [
    "## Runtime"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "id": "0375d38c",
   "metadata": {},
   "outputs": [],
   "source": [
    "temp = [\n",
    "    'Training Linear SVM',\n",
    "    'Prediction Linear SVM',\n",
    "    'Training XGBoost',\n",
    "    'Prediction XGBoost',\n",
    "    'Training Modules',\n",
    "    'Prediction Modules',\n",
    "    'Prediction Linear Opinion Pool',\n",
    "    'Prediction Logarithmic Opinion Pool',\n",
    "    'Prediction Pairing Aggregator',\n",
    "]\n",
    "\n",
    "runtimes = {x: runtimes[x] for x in temp}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "id": "c0d3265b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'Prediction Linear Opinion Pool': 164.109375,\n",
      " 'Prediction Linear SVM': 398.515625,\n",
      " 'Prediction Logarithmic Opinion Pool': 160.125,\n",
      " 'Prediction Modules': 39571.296875,\n",
      " 'Prediction Pairing Aggregator': 268.1875,\n",
      " 'Prediction XGBoost': 836.09375,\n",
      " 'Training Linear SVM': 25.359375,\n",
      " 'Training Modules': 3369.171875,\n",
      " 'Training XGBoost': 1291.875}\n"
     ]
    }
   ],
   "source": [
    "pprint.pprint(runtimes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "id": "8276e303",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'Prediction Linear Opinion Pool': 2.73515625,\n",
      " 'Prediction Linear SVM': 6.641927083333333,\n",
      " 'Prediction Logarithmic Opinion Pool': 2.66875,\n",
      " 'Prediction Modules': 659.5216145833333,\n",
      " 'Prediction Pairing Aggregator': 4.469791666666667,\n",
      " 'Prediction XGBoost': 13.934895833333334,\n",
      " 'Training Linear SVM': 0.42265625,\n",
      " 'Training Modules': 56.15286458333333,\n",
      " 'Training XGBoost': 21.53125}\n"
     ]
    }
   ],
   "source": [
    "pprint.pprint({k:(v/60) for k,v in runtimes.items()})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "id": "52229305",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\20173892\\AppData\\Local\\Programs\\Python\\venv\\Lib\\site-packages\\seaborn\\_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "c:\\Users\\20173892\\AppData\\Local\\Programs\\Python\\venv\\Lib\\site-packages\\seaborn\\_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "c:\\Users\\20173892\\AppData\\Local\\Programs\\Python\\venv\\Lib\\site-packages\\seaborn\\_oldcore.py:1765: FutureWarning: unique with argument that is not not a Series, Index, ExtensionArray, or np.ndarray is deprecated and will raise in a future version.\n",
      "  order = pd.unique(vector)\n",
      "c:\\Users\\20173892\\AppData\\Local\\Programs\\Python\\venv\\Lib\\site-packages\\seaborn\\_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "c:\\Users\\20173892\\AppData\\Local\\Programs\\Python\\venv\\Lib\\site-packages\\seaborn\\_oldcore.py:1765: FutureWarning: unique with argument that is not not a Series, Index, ExtensionArray, or np.ndarray is deprecated and will raise in a future version.\n",
      "  order = pd.unique(vector)\n",
      "c:\\Users\\20173892\\AppData\\Local\\Programs\\Python\\venv\\Lib\\site-packages\\seaborn\\_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n"
     ]
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 800x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "colors = [\n",
    "    'Linear SVM',\n",
    "    'Linear SVM',\n",
    "    'XGBoost',\n",
    "    'XGBoost',\n",
    "    'Modules',\n",
    "    'Modules',\n",
    "    'Aggregation',\n",
    "    'Aggregation',\n",
    "    'Aggregation',\n",
    "]\n",
    "colors = [str(x) for x in colors]\n",
    "\n",
    "fig, ax = matplotlib.pyplot.subplots(figsize=FIGSIZE)\n",
    "fg = seaborn.barplot(\n",
    "    x=list(runtimes.keys()),\n",
    "    y=list(runtimes.values()),\n",
    "    ax=ax,\n",
    "    hue=colors,\n",
    "    dodge=True,\n",
    ")\n",
    "fg.set_yscale(\"log\")\n",
    "ax.set_xticklabels(ax.get_xticklabels(), rotation=35, ha='right')\n",
    "ax.set_ylabel('Runtime (seconds)')\n",
    "fig.savefig('runtime.pdf', dpi=600, bbox_inches='tight')\n"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "venv",
   "language": "python",
   "name": "venv"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
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