{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "d4b3629ddef32e4a",
   "metadata": {},
   "source": [
    "# Kruskal Wallis across all conditions and pairwise between\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c79a2aaa4b595af9",
   "metadata": {},
   "source": [
    "#### Recap - Conditions\n",
    "**B:** Baseline, No communication</br>\n",
    "<br>**RTE (previously RTT):** Real-Time Textual Explanation</br>\n",
    "<br>**RV (previously RTV):** Real-Time Visual Communication</br>\n",
    "<br>**RVE:** Real-Time Visual Communication + Explanations</br>\n",
    "<br>**OTE (previously ST):** Textual Summary of Explanations</br>\n",
    "<br>**OVE (previously SVE):** Visual Summary of Explanations</br>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1392f701ce82597f",
   "metadata": {},
   "source": [
    "#### 1. Prep variables"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a31504ce344b9887",
   "metadata": {},
   "source": [
    "##### Import data and packages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "2068147d39a16a2b",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-12-28T22:29:10.207896Z",
     "start_time": "2024-12-28T22:29:10.150654Z"
    }
   },
   "outputs": [],
   "source": [
    "# Imports\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from scipy import stats\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.colors as mcolors\n",
    "import seaborn as sns\n",
    "\n",
    "# Allow for viewing the whole table\n",
    "pd.set_option('display.max_columns', None)\n",
    "pd.set_option('display.max_rows', None)\n",
    "\n",
    "# Import dataset\n",
    "file_path = 'capped_dataset.csv'\n",
    "# file_path = 'capped_dataset_no_baseline.csv'\n",
    "data = pd.read_csv(file_path)\n",
    "\n",
    "# Fix metrics and conditions\n",
    "metrics = ['Compliance', 'Natural_Trust', 'Satisfaction', 'Artificial_Competence', 'Artificial_Willingness', 'Artificial_Trust', 'Communication_Rate']\n",
    "conditions = ['B', 'RTE', 'RV', 'RVE', 'OTE', 'OVE']\n",
    "# conditions = ['SVE', 'RVE', 'ST', 'RTV', 'RTT']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aaef4e58fda43e57",
   "metadata": {},
   "source": [
    "###### Aggregate metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "d3a3a632b2692b30",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-12-28T22:29:10.236216Z",
     "start_time": "2024-12-28T22:29:10.227791Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "    }\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ID</th>\n",
       "      <th>Condition</th>\n",
       "      <th>Age</th>\n",
       "      <th>Region</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Education Level</th>\n",
       "      <th>CS Major</th>\n",
       "      <th>MATRX Experience</th>\n",
       "      <th>Gaming Experience</th>\n",
       "      <th>Operating System</th>\n",
       "      <th>Trust 1</th>\n",
       "      <th>Trust 2</th>\n",
       "      <th>Trust 3</th>\n",
       "      <th>Trust 4</th>\n",
       "      <th>Trust 5</th>\n",
       "      <th>Trust 6 (negative)</th>\n",
       "      <th>Trust 7</th>\n",
       "      <th>Trust 8</th>\n",
       "      <th>Satisfaction 1</th>\n",
       "      <th>Satisfaction 2</th>\n",
       "      <th>Satisfaction 3</th>\n",
       "      <th>Satisfaction 4</th>\n",
       "      <th>Satisfaction 5</th>\n",
       "      <th>Satisfaction 6</th>\n",
       "      <th>Satisfaction 7</th>\n",
       "      <th>What information would you have liked the RescueBot to provide but was missing?</th>\n",
       "      <th>What did you like most about your collaboration with RescueBot?</th>\n",
       "      <th>What did you like least about your collaboration with RescueBot?</th>\n",
       "      <th>What do you think RescueBot thinks of you? How does that make you feel?</th>\n",
       "      <th>Completeness</th>\n",
       "      <th>Score</th>\n",
       "      <th>Ticks</th>\n",
       "      <th>Agent actions</th>\n",
       "      <th>Human actions</th>\n",
       "      <th>Joint actions</th>\n",
       "      <th>Human messages</th>\n",
       "      <th>Compliance</th>\n",
       "      <th>Search competence</th>\n",
       "      <th>Victim competence</th>\n",
       "      <th>Obstacle competence</th>\n",
       "      <th>Search willingness</th>\n",
       "      <th>Victim willingness</th>\n",
       "      <th>Obstacle willingness</th>\n",
       "      <th>Search confidence</th>\n",
       "      <th>Obstacle confidence</th>\n",
       "      <th>Victim confidence</th>\n",
       "      <th>Natural_Trust</th>\n",
       "      <th>Satisfaction</th>\n",
       "      <th>Artificial_Competence</th>\n",
       "      <th>Artificial_Willingness</th>\n",
       "      <th>Artificial_Trust</th>\n",
       "      <th>Artificial_Confidence</th>\n",
       "      <th>Communication_Rate</th>\n",
       "      <th>Ratio_Joint_Human</th>\n",
       "      <th>Ratio_Joint_Agent</th>\n",
       "      <th>Ratio_Human_Agent</th>\n",
       "      <th>Human_Action_Rate</th>\n",
       "      <th>Agent_Action_Rate</th>\n",
       "      <th>Joint_Action_Rate</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
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       "      <td>B1</td>\n",
       "      <td>B</td>\n",
       "      <td>18-24</td>\n",
       "      <td>Europe</td>\n",
       "      <td>Woman</td>\n",
       "      <td>Bachelor</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Very little experience</td>\n",
       "      <td>Macbook</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>Maybe one or two</td>\n",
       "      <td>We get alung well</td>\n",
       "      <td>Sometimes I have to wait to send a message</td>\n",
       "      <td>Fast learner</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>27</td>\n",
       "      <td>4854</td>\n",
       "      <td>238</td>\n",
       "      <td>322</td>\n",
       "      <td>212</td>\n",
       "      <td>15</td>\n",
       "      <td>3</td>\n",
       "      <td>0.2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.4</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.30</td>\n",
       "      <td>0.35</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.875</td>\n",
       "      <td>4.428571</td>\n",
       "      <td>0.733333</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>0.766667</td>\n",
       "      <td>0.550000</td>\n",
       "      <td>0.030902</td>\n",
       "      <td>0.658385</td>\n",
       "      <td>0.890756</td>\n",
       "      <td>1.352941</td>\n",
       "      <td>0.663370</td>\n",
       "      <td>0.490317</td>\n",
       "      <td>0.436753</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>B2</td>\n",
       "      <td>B</td>\n",
       "      <td>18-24</td>\n",
       "      <td>Europe</td>\n",
       "      <td>Man</td>\n",
       "      <td>High School</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Some experience</td>\n",
       "      <td>Windows</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>The robot should tell me to try and use it as ...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.833333</td>\n",
       "      <td>24</td>\n",
       "      <td>5387</td>\n",
       "      <td>229</td>\n",
       "      <td>272</td>\n",
       "      <td>170</td>\n",
       "      <td>13</td>\n",
       "      <td>3</td>\n",
       "      <td>-0.4</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.4</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.730403</td>\n",
       "      <td>-0.002227</td>\n",
       "      <td>0.65</td>\n",
       "      <td>0.20</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.250</td>\n",
       "      <td>4.142857</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.276059</td>\n",
       "      <td>0.304696</td>\n",
       "      <td>0.616667</td>\n",
       "      <td>0.024132</td>\n",
       "      <td>0.625000</td>\n",
       "      <td>0.742358</td>\n",
       "      <td>1.187773</td>\n",
       "      <td>0.504919</td>\n",
       "      <td>0.425097</td>\n",
       "      <td>0.315575</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>B3</td>\n",
       "      <td>B</td>\n",
       "      <td>18-24</td>\n",
       "      <td>Europe</td>\n",
       "      <td>Woman</td>\n",
       "      <td>Bachelor</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No experience at all</td>\n",
       "      <td>Windows</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>that he was informing me of his next action at...</td>\n",
       "      <td>maybe the text input received can highlight th...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>27</td>\n",
       "      <td>5270</td>\n",
       "      <td>282</td>\n",
       "      <td>297</td>\n",
       "      <td>260</td>\n",
       "      <td>14</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.2</td>\n",
       "      <td>0.6</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.20</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.500</td>\n",
       "      <td>4.714286</td>\n",
       "      <td>0.466667</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.566667</td>\n",
       "      <td>0.733333</td>\n",
       "      <td>0.026565</td>\n",
       "      <td>0.875421</td>\n",
       "      <td>0.921986</td>\n",
       "      <td>1.053191</td>\n",
       "      <td>0.563567</td>\n",
       "      <td>0.535104</td>\n",
       "      <td>0.493359</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>B4</td>\n",
       "      <td>B</td>\n",
       "      <td>18-24</td>\n",
       "      <td>Europe</td>\n",
       "      <td>Woman</td>\n",
       "      <td>Bachelor</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Very little experience</td>\n",
       "      <td>Macbook</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>The fact that RescueBot was informing me at al...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>27</td>\n",
       "      <td>5292</td>\n",
       "      <td>281</td>\n",
       "      <td>359</td>\n",
       "      <td>239</td>\n",
       "      <td>21</td>\n",
       "      <td>4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.1</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.875</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.700000</td>\n",
       "      <td>0.683333</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.039683</td>\n",
       "      <td>0.665738</td>\n",
       "      <td>0.850534</td>\n",
       "      <td>1.277580</td>\n",
       "      <td>0.678382</td>\n",
       "      <td>0.530990</td>\n",
       "      <td>0.451625</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>B5</td>\n",
       "      <td>B</td>\n",
       "      <td>18-24</td>\n",
       "      <td>Europe</td>\n",
       "      <td>Man</td>\n",
       "      <td>Bachelor</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>A lot of experience</td>\n",
       "      <td>Macbook</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>That it was always falling back to me for guid...</td>\n",
       "      <td>That it would get stuck performing a task. I w...</td>\n",
       "      <td>I think the RescueBot trusts me. That makes me...</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>27</td>\n",
       "      <td>4184</td>\n",
       "      <td>240</td>\n",
       "      <td>257</td>\n",
       "      <td>210</td>\n",
       "      <td>28</td>\n",
       "      <td>2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.70</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.000</td>\n",
       "      <td>4.428571</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.900000</td>\n",
       "      <td>0.066922</td>\n",
       "      <td>0.817121</td>\n",
       "      <td>0.875000</td>\n",
       "      <td>1.070833</td>\n",
       "      <td>0.614245</td>\n",
       "      <td>0.573614</td>\n",
       "      <td>0.501912</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "   ID Condition    Age  Region Gender Education Level CS Major  \\\n",
       "0  B1         B  18-24  Europe  Woman        Bachelor      Yes   \n",
       "1  B2         B  18-24  Europe    Man     High School      Yes   \n",
       "2  B3         B  18-24  Europe  Woman        Bachelor       No   \n",
       "3  B4         B  18-24  Europe  Woman        Bachelor      Yes   \n",
       "4  B5         B  18-24  Europe    Man        Bachelor      Yes   \n",
       "\n",
       "  MATRX Experience       Gaming Experience Operating System  Trust 1  Trust 2  \\\n",
       "0               No  Very little experience          Macbook        5        4   \n",
       "1               No         Some experience          Windows        5        3   \n",
       "2               No    No experience at all          Windows        4        5   \n",
       "3               No  Very little experience          Macbook        5        3   \n",
       "4               No     A lot of experience          Macbook        4        4   \n",
       "\n",
       "   Trust 3  Trust 4  Trust 5  Trust 6 (negative)  Trust 7  Trust 8  \\\n",
       "0        5        5        4                   1        3        4   \n",
       "1        4        5        5                   4        4        4   \n",
       "2        5        4        4                   5        4        5   \n",
       "3        3        4        4                   5        3        4   \n",
       "4        5        5        4                   5        2        3   \n",
       "\n",
       "   Satisfaction 1  Satisfaction 2  Satisfaction 3  Satisfaction 4  \\\n",
       "0               5               5               5               4   \n",
       "1               4               4               4               4   \n",
       "2               4               5               5               5   \n",
       "3               4               4               2               2   \n",
       "4               5               4               5               5   \n",
       "\n",
       "   Satisfaction 5  Satisfaction 6  Satisfaction 7  \\\n",
       "0               4               4               4   \n",
       "1               3               5               5   \n",
       "2               5               5               4   \n",
       "3               3               4               2   \n",
       "4               5               5               2   \n",
       "\n",
       "  What information would you have liked the RescueBot to provide but was missing?  \\\n",
       "0                                   Maybe one or two                                \n",
       "1  The robot should tell me to try and use it as ...                                \n",
       "2                                                NaN                                \n",
       "3                                                NaN                                \n",
       "4                                                NaN                                \n",
       "\n",
       "  What did you like most about your collaboration with RescueBot?  \\\n",
       "0                                  We get alung well                \n",
       "1                                                NaN                \n",
       "2  that he was informing me of his next action at...                \n",
       "3  The fact that RescueBot was informing me at al...                \n",
       "4  That it was always falling back to me for guid...                \n",
       "\n",
       "  What did you like least about your collaboration with RescueBot?  \\\n",
       "0         Sometimes I have to wait to send a message                 \n",
       "1                                                NaN                 \n",
       "2  maybe the text input received can highlight th...                 \n",
       "3                                                NaN                 \n",
       "4  That it would get stuck performing a task. I w...                 \n",
       "\n",
       "  What do you think RescueBot thinks of you? How does that make you feel?  \\\n",
       "0                                       Fast learner                        \n",
       "1                                                NaN                        \n",
       "2                                                NaN                        \n",
       "3                                                NaN                        \n",
       "4  I think the RescueBot trusts me. That makes me...                        \n",
       "\n",
       "   Completeness  Score  Ticks  Agent actions  Human actions  Joint actions  \\\n",
       "0      1.000000     27   4854            238            322            212   \n",
       "1      0.833333     24   5387            229            272            170   \n",
       "2      1.000000     27   5270            282            297            260   \n",
       "3      1.000000     27   5292            281            359            239   \n",
       "4      1.000000     27   4184            240            257            210   \n",
       "\n",
       "   Human messages  Compliance  Search competence  Victim competence  \\\n",
       "0              15           3                0.2                1.0   \n",
       "1              13           3               -0.4                1.0   \n",
       "2              14           1               -0.2                0.6   \n",
       "3              21           4                0.0                1.0   \n",
       "4              28           2                1.0                1.0   \n",
       "\n",
       "   Obstacle competence  Search willingness  Victim willingness  \\\n",
       "0                  1.0                 0.4            1.000000   \n",
       "1                  0.4                 0.1            0.730403   \n",
       "2                  1.0                 0.0            1.000000   \n",
       "3                  1.0                 0.1            1.000000   \n",
       "4                  1.0                 1.0            1.000000   \n",
       "\n",
       "   Obstacle willingness  Search confidence  Obstacle confidence  \\\n",
       "0              1.000000               0.30                 0.35   \n",
       "1             -0.002227               0.65                 0.20   \n",
       "2              1.000000               0.20                 1.00   \n",
       "3              1.000000               1.00                 1.00   \n",
       "4              1.000000               1.00                 0.70   \n",
       "\n",
       "   Victim confidence  Natural_Trust  Satisfaction  Artificial_Competence  \\\n",
       "0                1.0          3.875      4.428571               0.733333   \n",
       "1                1.0          4.250      4.142857               0.333333   \n",
       "2                1.0          4.500      4.714286               0.466667   \n",
       "3                1.0          3.875      3.000000               0.666667   \n",
       "4                1.0          4.000      4.428571               1.000000   \n",
       "\n",
       "   Artificial_Willingness  Artificial_Trust  Artificial_Confidence  \\\n",
       "0                0.800000          0.766667               0.550000   \n",
       "1                0.276059          0.304696               0.616667   \n",
       "2                0.666667          0.566667               0.733333   \n",
       "3                0.700000          0.683333               1.000000   \n",
       "4                1.000000          1.000000               0.900000   \n",
       "\n",
       "   Communication_Rate  Ratio_Joint_Human  Ratio_Joint_Agent  \\\n",
       "0            0.030902           0.658385           0.890756   \n",
       "1            0.024132           0.625000           0.742358   \n",
       "2            0.026565           0.875421           0.921986   \n",
       "3            0.039683           0.665738           0.850534   \n",
       "4            0.066922           0.817121           0.875000   \n",
       "\n",
       "   Ratio_Human_Agent  Human_Action_Rate  Agent_Action_Rate  Joint_Action_Rate  \n",
       "0           1.352941           0.663370           0.490317           0.436753  \n",
       "1           1.187773           0.504919           0.425097           0.315575  \n",
       "2           1.053191           0.563567           0.535104           0.493359  \n",
       "3           1.277580           0.678382           0.530990           0.451625  \n",
       "4           1.070833           0.614245           0.573614           0.501912  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Map trust and satisfaction values to numbers\n",
    "likert_mapping = {\n",
    "    'Strongly agree': 5,\n",
    "    'Agree': 4,\n",
    "    'Neither agree or disagree': 3,\n",
    "    'Disagree': 2,\n",
    "    'Strongly disagree': 1\n",
    "}\n",
    "\n",
    "for col in data.columns[10:25]:\n",
    "    data[col] = data[col].map(likert_mapping)\n",
    "\n",
    "# Inverse Trust 6 (negative)\n",
    "data['Trust 6 (negative)'] = data['Trust 6 (negative)'].apply(lambda x: 6-x)\n",
    "\n",
    "# Natural Trust\n",
    "trust_columns = ['Trust 1', 'Trust 2', 'Trust 3', 'Trust 4', 'Trust 5', 'Trust 6 (negative)', 'Trust 7', 'Trust 8']\n",
    "data['Natural_Trust'] = data[trust_columns].mean(axis=1)\n",
    "\n",
    "# Overall Satisfaction\n",
    "satisfaction_columns = ['Satisfaction 1', 'Satisfaction 2', 'Satisfaction 3', 'Satisfaction 4', 'Satisfaction 5', 'Satisfaction 6', 'Satisfaction 7']\n",
    "data['Satisfaction'] = data[satisfaction_columns].mean(axis=1)\n",
    "\n",
    "# Artificial Trust\n",
    "competence_columns = ['Search competence', 'Victim competence', 'Obstacle competence']\n",
    "willingness_columns = ['Search willingness', 'Victim willingness', 'Obstacle willingness']\n",
    "\n",
    "data['Artificial_Competence'] = data[competence_columns].mean(axis=1)\n",
    "data['Artificial_Willingness'] = data[willingness_columns].mean(axis=1)\n",
    "data['Artificial_Trust'] = data[['Artificial_Competence', 'Artificial_Willingness']].mean(axis=1)\n",
    "\n",
    "confidence_columns = ['Search confidence', 'Victim confidence', 'Obstacle confidence']\n",
    "data['Artificial_Confidence'] = data[confidence_columns].mean(axis=1)\n",
    "\n",
    "# Communication rate (Human messages / second (Ticks/10))\n",
    "data['Communication_Rate'] = data['Human messages'] / (data['Ticks']/10)\n",
    "\n",
    "# Ratio Joint vs Human actions\n",
    "data['Ratio_Joint_Human'] = data['Joint actions'] / data['Human actions']\n",
    "# Ratio Joint vs Agent actions\n",
    "data['Ratio_Joint_Agent'] = data['Joint actions'] / data['Agent actions']\n",
    "# Ratio Human vs Agent actions\n",
    "data['Ratio_Human_Agent'] = data['Human actions'] / data['Agent actions']\n",
    "\n",
    "# Human action rate (Human actions / seconds)\n",
    "data['Human_Action_Rate'] = data['Human actions'] / (data['Ticks']/10)\n",
    "# Agent action rate (Agent actions / seconds)\n",
    "data['Agent_Action_Rate'] = data['Agent actions'] / (data['Ticks']/10)\n",
    "# Joint action rate (Joint actions / seconds)\n",
    "data['Joint_Action_Rate'] = data['Joint actions'] / (data['Ticks']/10)\n",
    "\n",
    "# Establish the numeric columns for calculating the mean/count/standard deviations\n",
    "numeric_cols = data.select_dtypes(include='number').columns\n",
    "\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "408d9c73",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['B', 'OVE', 'RVE', 'OTE', 'RV', 'RTE'], dtype=object)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['Condition'].unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c60b844c570e7d5a",
   "metadata": {},
   "source": [
    "#### 2. Check normality"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "fb2be05618f6fe8d",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-12-28T22:29:10.380638Z",
     "start_time": "2024-12-28T22:29:10.233557Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 700x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create an empty matrix to store results\n",
    "normality_matrix = np.zeros((len(metrics), len(conditions)))\n",
    "\n",
    "# Perform normality tests and fill the matrix for visualization purposes\n",
    "for i, metric in enumerate(metrics):\n",
    "    for j, condition in enumerate(conditions):\n",
    "        metric_per_condition = data[data['Condition'] == condition][metric]\n",
    "        normality = stats.shapiro(metric_per_condition)\n",
    "        \n",
    "        if(normality.pvalue < 0.05): \n",
    "            normality_matrix[i, j] = 0  # Not normal distribution (Red)\n",
    "        if normality.pvalue >= 0.05:\n",
    "            normality_matrix[i, j] = 1  # Normal distribution (Green)\n",
    "\n",
    "# Plot the matrix\n",
    "fig, ax = plt.subplots(figsize=(7, 5))\n",
    "\n",
    "# Create a color map: Green (#97ff85) for normal, Red (#ff7069) for not normal\n",
    "cmap = mcolors.ListedColormap(['#ff7069', '#97ff85'])\n",
    "\n",
    "# Create the heatmap\n",
    "cax = ax.imshow(normality_matrix, cmap=cmap, aspect='auto')\n",
    "\n",
    "# Add gridlines\n",
    "ax.grid(False)\n",
    "ax.set_xticks(np.arange(-0.5, len(conditions), 1), minor=True)\n",
    "ax.set_yticks(np.arange(-0.51, len(metrics), 1), minor=True)\n",
    "ax.grid(which=\"minor\", color=\"grey\", linestyle='-', linewidth=0.5)\n",
    "ax.tick_params(which=\"minor\", bottom=False, left=False)\n",
    "\n",
    "# Set the tick labels\n",
    "ax.set_xticks(np.arange(len(conditions)))\n",
    "ax.set_yticks(np.arange(len(metrics)))\n",
    "ax.set_xticklabels(conditions)\n",
    "ax.set_yticklabels(metrics)\n",
    "\n",
    "# Rotate the tick labels for better visibility\n",
    "plt.xticks(rotation=45)\n",
    "\n",
    "# Add a color bar to show the meaning of the colors\n",
    "cbar = fig.colorbar(cax, ticks=[0, 1])\n",
    "cbar.ax.set_yticklabels(['Not Normal', 'Normal'])\n",
    "\n",
    "# Set labels and title\n",
    "plt.xlabel('Conditions')\n",
    "plt.ylabel('Metrics')\n",
    "plt.title('Normality Test Results')\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f248fd7003b2ee0e",
   "metadata": {},
   "source": [
    "#### 3. Check outliers (box plots)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "cb30eeae75d55265",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-12-28T22:29:11.990242Z",
     "start_time": "2024-12-28T22:29:10.626408Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2000x2500 with 78 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(len(metrics), len(conditions), figsize=(20, 25))\n",
    "for i, metric in enumerate(metrics):\n",
    "    for j, condition in enumerate(conditions):\n",
    "        metric_per_condition =  data[data['Condition'] == condition][metric]\n",
    "        ax = axes[i, j]\n",
    "        if j == 0:\n",
    "            ax.set_ylabel(metric)\n",
    "        if i == len(metrics) - 1:\n",
    "            ax.set_xlabel(\"Condition \" + condition)\n",
    "        ax.boxplot(metric_per_condition)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "952fb66bd7873419",
   "metadata": {},
   "source": [
    "#### 4. Check variances (Levene)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "63a304d3252ad30e",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-12-28T22:29:12.106870Z",
     "start_time": "2024-12-28T22:29:11.997790Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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jammi52hEcckU6stBJlSFEELojSQhIYQQeiNJSAghhN5IEhJCCKE3koSEEOIl5Ovry4gRI/QdxhNJEhJCiDIUHByMSqXKtwUEBOg7tOeSLNEWQogyFhAQwPLly3XK1Gr5LFpBZCQkhBBlTK1W4+DgoLOVL18egKSkJJo3b46JiQkeHh7s2rULlUrFxo0bAYiJiUGlUnHz5k2lvbi4OFQqFcnJyQBcu3aN7t27U7lyZczMzPDy8mLNmjXP+CzLhoyEhBAvlczMTO7du1embWq12nwfjlWr1cUe3eTm5vLOO+9QsWJFDh8+TFpaWonu22RmZuLj48PHH3+MpaUlW7dupWfPnlSvXp1GjRoVuz19kiQkii05OZlq1arx66+/UrduXWJiYvDz8+PGjRtYW1vrOzzxCsvMzKRiNTvS/84o03Y1Gg0ZGbptTpo0idDQ0ALrb9myBY1Go1P2ySef0KBBA37//Xd27tyJo6MjAFOnTqVNmzbFiqdy5cqEhIQoj4cNG8bOnTtZu3atJCFRdv7++2+mTJnC1q1buXTpEvb29tStW5cRI0bQsmVLfYenaNKkCampqVhZWek7FPGKu3fvHul/Z5Tp9wHmfVfdxYsXsbS0VMofNwry8/Nj0aJFOmU2NjasWrUKJycnJQEBNG7cuNgx5eTkMHXqVNauXculS5e4d+8eWVlZmJmZFbstfZMk9JxKTk6madOmWFtbM3PmTLy8vLh//z47d+5kyJAh/P777/oOUWFsbIyDg4O+wxBC8TS+D9DS0lInCT2Oubk5NWrUKFE/eT9PodVqlbL79+/r1Jk5cybz5s1j7ty5eHl5YW5uzogRI8p8GvJZkIUJz6nBgwejUqk4cuQIgYGBuLm5Ubt2bUaNGsUvv/wCQEpKCh06dECj0WBpaUmXLl34559/lDZCQ0OpW7cuy5Yto0qVKmg0GgYPHkxOTg4zZszAwcEBe3t7pkyZotO3SqVi0aJFtGnTBlNTU1xcXPjuu+8KjfXRG6lFuWnq6+vL8OHDGTNmDDY2Njg4OOSb2rh58yYDBgygYsWKmJiY4OnpyZYtW5T9Bw4coFmzZpiamuLk5MTw4cO5fft2SS63EM+Eu7s7Fy9eJDU1VSnL+3vOY2dnB6BTJy4uTqdObGwsHTp0ICgoiDp16uDi4sLZs2efXuBPkSSh59D169fZsWMHQ4YMwdzcPN9+a2trcnNz6dChA9evX2ffvn3s2rWLP/74g65du+rUPX/+PNu3b2fHjh2sWbOGyMhI2rVrx19//cW+ffv49NNPGT9+PIcPH9Y5bsKECQQGBhIfH0+PHj3o1q0bCQkJRYo/76bp1q1bOX36NB988AE9e/bkyJEjOvVWrFiBubk5hw8fZsaMGYSHh7Nr1y7gwQ3cNm3aEBsby9dff82ZM2eYPn06hoaGynkFBAQQGBjIyZMn+fbbbzlw4ABDhw4tNK6srCzS09N1NiGehqysLP7++2+d7erVq7Rq1Qo3Nzd69+5NfHw8+/fv53//+5/OsTVq1MDJyYnQ0FCSkpLYunUrs2fP1qnj6urKrl27OHjwIAkJCQwYMEDnDeiLRKbjnkPnzp1Dq9VSq1atQuvs2bOHU6dOceHCBeWH2VauXEnt2rU5evQoDRs2BB68mC9btgwLCws8PDzw8/MjMTGRbdu2YWBgQM2aNfn000/Zu3cvr7/+utJ+586def/99wGIiIhg165dfP7553zxxRdPjL+oN029vb2ZNGkS8OCPasGCBezZs4fWrVuze/dujhw5QkJCAm5ubgC4uLgox06bNo0ePXooK4tcXV2ZP38+LVq0YNGiRZiY5J+KmTZtGmFhYU+MX4jS2rFjB5UqVdIpq1mzJr///jvff/89/fr1o1GjRjg7OzN//nydD7KWK1eONWvWMGjQILy9vWnYsCGTJ0+mc+fOSp3x48fzxx9/4O/vj5mZGR988AEdO3YkLS3tmZ1jWZEk9Bx6eC64MAkJCTg5Oen8MqiHhwfW1tYkJCQoScjZ2Vnnx78qVqyIoaGhzs8iV6xYkStXrui0/+jN0saNG+ebEihMUW+aent76zyuVKmSEkdcXByvvfaakoAeFR8fz8mTJ1m9erVSptVqyc3N5cKFC7i7u+c7Zty4cYwaNUp5nJ6eLr+sKspcVFQUUVFRhe53c3Nj//79j22jadOmnDx5Uqfs4dcFGxsb5XNFhYmJiXlSqM8FSULPIVdXV1QqVZksPihXrpzOY5VKVWBZbm5uqfvKU9Sbpo+Lw9TU9LF9ZGRkMGDAAIYPH55vX5UqVQo8piSf6xBCPF1yT+g5ZGNjg7+/PwsXLizwRvvNmzeVG5wXL15Uys+cOcPNmzfx8PAodQyP3iz95ZdfChxdFKQsbpp6e3vz119/FXpc/fr1OXPmDDVq1Mi3GRsbF6svIYT+SBJ6Ti1cuJCcnBwaNWrE+vXrSUpKIiEhgfnz59O4cWNatWqFl5cXPXr04MSJExw5coRevXrRokULGjRoUOr+161bx7Jlyzh79iyTJk3iyJEjj73p/7CyuGnaokULmjdvTmBgILt27eLChQvKAguAjz/+mIMHDzJ06FDi4uJISkpi06ZNRY5RiOeJVqulY8eO+g5DLyQJPadcXFw4ceIEfn5+jB49Gk9PT1q3bs2ePXtYtGgRKpWKTZs2Ub58eZo3b06rVq1wcXHh22+/LZP+w8LCiI6Oxtvbm5UrV7JmzZoij7DGjx9P/fr18ff3x9fXFwcHhxL9ga1fv56GDRvSvXt3PDw8GDNmDDk5OcCDkdK+ffs4e/YszZo1o169ekycOFHnQ4BCiOefSluUu+DilaJSqfj+++9f+ndm6enpWFlZMSdtQpl/sFE8PXfTMxllFUFaWlq+D48+jef0cf2J0pORkBBCCL2RJCSEEEJvZIm2yEdmaIUQz4qMhIQQQuiNJCEhhBB6I0lICCGE3kgSEkIIoTeShIQQQuiNJCEhhBB6I0u0xSvv5rpsMs2y9R2GKKLMO/JcvUwkCYlXnnVnI0wt5U/hRXE33Qje13cUoqzIdJwQQgi9kSQkhBBCbyQJCSGE0BtJQkIIIfRGkpAQQgi9kSQkhBBCbyQJCSGE0BtJQkIIIfRGkpAQQgi9kSQkhBBCbyQJCSGE0BtJQuKJkpOTUalUxMXF6TsUIcRLRpLQMxQcHIxKpWL69Ok65Rs3bkSlUhW5HV9fX0aMGFHG0ZVeaGgoKpXqsVtZkwQpxItNktAzZmJiwqeffsqNGzf0HQr37t0r0/ZCQkJITU1Vttdee43w8HCdsqfZvxDixSNJ6Blr1aoVDg4OTJs2rcD9165do3v37lSuXBkzMzO8vLxYs2aNsj84OJh9+/Yxb948ZXSRnJxMVFQU1tbWOm09OsIKDQ2lbt26fPXVV1SrVg0TExMAduzYwZtvvom1tTW2tra0b9+e8+fPF/vcNBoNDg4OymZoaIiFhYXyuFu3bgwdOpQRI0ZQoUIF/P39CxzJ3Lx5E5VKRUxMDAA3btygR48e2NnZYWpqiqurK8uXLwegWrVqANSrVw+VSoWvr2+x4xZC6I8koWfM0NCQqVOn8vnnn/PXX3/l25+ZmYmPjw9bt27l9OnTfPDBB/Ts2ZMjR44AMG/ePBo3bkz//v2V0YWTk1OR+z937hzr169nw4YNygv/7du3GTVqFMeOHWPPnj0YGBjQqVMncnNzy+ScH7ZixQqMjY2JjY1l8eLFRTpmwoQJnDlzhu3bt5OQkMCiRYuoUKECgHJddu/eTWpqKhs2bCi0naysLNLT03U2IYR+yS956UGnTp2oW7cukyZNIjIyUmdf5cqVCQkJUR4PGzaMnTt3snbtWho1aoSVlRXGxsaYmZnh4OBQ7L7v3bvHypUrsbOzU8oCAwN16ixbtgw7OzvOnDmDp6dnsft4HFdXV2bMmKE8Tk5OfuIxKSkp1KtXjwYNGgDg7Oys7Ms7D1tb2ydej2nTphEWFlb8oIUQT42MhPTk008/ZcWKFSQkJOiU5+TkEBERgZeXFzY2Nmg0Gnbu3ElKSkqZ9Fu1alWdBASQlJRE9+7dcXFxwdLSUnmRL6s+H+bj41PsYwYNGkR0dDR169ZlzJgxHDx4sER9jxs3jrS0NGW7ePFiidoRQpQdSUJ60rx5c/z9/Rk3bpxO+cyZM5k3bx4ff/wxe/fuJS4uDn9//yfexDcwMECr1eqU3b9/P189c3PzfGVvv/02169fZ+nSpRw+fJjDhw8DT2fhwKP9Gxg8+C/4cOyPxt2mTRv+/PNPRo4cyeXLl2nZsqXOaLGo1Go1lpaWOpsQQr8kCenR9OnT+eGHHzh06JBSFhsbS4cOHQgKCqJOnTq4uLhw9uxZneOMjY3JycnRKbOzs+PWrVvcvn1bKSvKsuVr166RmJjI+PHjadmyJe7u7s905V7eqOzhlXMFxW1nZ0fv3r35+uuvmTt3Ll9++SXw4FoA+a6HEOLFIPeE9MjLy4sePXowf/58pczV1ZXvvvuOgwcPUr58eebMmcM///yDh4eHUsfZ2ZnDhw+TnJyMRqPBxsaG119/HTMzMz755BOGDx/O4cOHiYqKemIM5cuXx9bWli+//JJKlSqRkpLC2LFjn8bpFsjU1JQ33niD6dOnU61aNa5cucL48eN16kycOBEfHx9q165NVlYWW7Zswd3dHQB7e3tMTU3ZsWMHr732GiYmJlhZWT2z+IUQpSMjIT0LDw/XWYU2fvx46tevj7+/P76+vjg4ONCxY0edY0JCQjA0NMTDwwM7OztSUlKwsbHh66+/Ztu2bcqy7tDQ0Cf2b2BgQHR0NMePH8fT05ORI0cyc+bMMj7Lx1u2bBnZ2dn4+PgwYsQIJk+erLPf2NiYcePG4e3tTfPmzTE0NCQ6OhoAIyMj5s+fz5IlS3B0dKRDhw7PNHYhROmotI/eSBDiFZGeno6VlRVz0iZgammi73BEEd1Nz2SUVQRpaWn57us9jef0cf2J0pORkBBCCL2RJCSKZeDAgWg0mgK3gQMH6js8IcQLRhYmiGIJDw8vdHm0TFUIIYpLkpAoFnt7e+zt7fUdhhDiJSHTcUIIIfRGkpAQQgi9kSQkhBBCbyQJCSGE0BtJQkIIIfRGVseJV55lxG3M1Nn6DkMUUbmsLH2HIMqQJCHxyut8Ow3LbGN9hyGKKD3rHu/rOwhRZmQ6TgghhN5IEhJCCKE3koSEEELojSQhIYQQeiNJSAghhN5IEhJCCKE3koSEEELojSQhIYQQeiNJSAghhN5IEhJCCKE3koReMc7OzsydO7dIde/cuUNgYCCWlpaoVCpu3rz5VGJKTk5GpVIRFxf3VNoXQjy/JAk9p/79918GDRpElSpVUKvVODg44O/vT2xsbJGOj4qKwtraOl/50aNH+eCDD4rUxooVK9i/fz8HDx4kNTUVKyur4pxCgYKDg+nYsaNOmZOTE6mpqXh6epa6fSHEi0W+wPQ5FRgYyL1791ixYgUuLi78888/7Nmzh2vXrpWqXTs7uyLXPX/+PO7u7k89ORgaGuLg4PBU+xBCPJ9kJPQcunnzJvv37+fTTz/Fz8+PqlWr0qhRI8aNG8d///tfAObMmYOXlxfm5uY4OTkxePBgMjIyAIiJiaFPnz6kpaWhUqlQqVSEhoYCutNxWq2W0NBQZbTl6OjI8OHDAfD19WX27Nn8/PPPqFQqfH19AVi1ahUNGjTAwsICBwcH3nvvPa5cuaIT/2+//Ub79u2xtLTEwsKCZs2acf78eUJDQ1mxYgWbNm1S4oqJiSlwOm7fvn00atQItVpNpUqVGDt2LNnZ//dzC76+vgwfPpwxY8ZgY2ODg4ODco5CiBeHJKHnkEajQaPRsHHjRrIK+e0UAwMD5s+fz2+//caKFSv46aefGDNmDABNmjRh7ty5WFpakpqaSmpqKiEhIfnaWL9+PZ999hlLliwhKSmJjRs34uXlBcCGDRvo378/jRs3JjU1lQ0bNgBw//59IiIiiI+PZ+PGjSQnJxMcHKy0eenSJZo3b45areann37i+PHj9O3bl+zsbEJCQujSpQsBAQFKXE2aNMkX16VLl2jbti0NGzYkPj6eRYsWERkZyeTJk3XqrVixAnNzcw4fPsyMGTMIDw9n165dhV7XrKws0tPTdTYhhH7JdNxzyMjIiKioKPr378/ixYupX78+LVq0oFu3bnh7ewMwYsQIpb6zszOTJ09m4MCBfPHFFxgbG2NlZYVKpXrsNFdKSgoODg60atWKcuXKUaVKFRo1agSAjY0NZmZmGBsb67TRt29f5d8uLi7Mnz+fhg0bkpGRgUajYeHChVhZWREdHU25cuUAcHNzU44xNTUlKyvrsXF98cUXODk5sWDBAlQqFbVq1eLy5ct8/PHHTJw4EQODB++dvL29mTRpEgCurq4sWLCAPXv20Lp16wLbnTZtGmFhYYX2K4R49mQk9JwKDAzk8uXLbN68mYCAAGJiYqhfvz5RUVEA7N69m5YtW1K5cmUsLCzo2bMn165d486dO0Xuo3Pnzty9excXFxf69+/P999/rzPlVZDjx4/z9ttvU6VKFSwsLGjRogXwIKEBxMXF0axZMyUBlURCQgKNGzdGpVIpZU2bNiUjI4O//vpLKctLyHkqVaqUb2rwYePGjSMtLU3ZLl68WOIYhRBlQ5LQc8zExITWrVszYcIEDh48SHBwMJMmTSI5OZn27dvj7e3N+vXrOX78OAsXLgTg3r17RW7fycmJxMREvvjiC0xNTRk8eDDNmzfn/v37Bda/ffs2/v7+WFpasnr1ao4ePcr333+v06+pqWkpz7roHk10KpWK3NzcQuur1WosLS11NiGEfkkSeoF4eHhw+/Ztjh8/Tm5uLrNnz+aNN97Azc2Ny5cv69Q1NjYmJyfniW2ampry9ttvM3/+fGJiYjh06BCnTp0qsO7vv//OtWvXmD59Os2aNaNWrVr5Rh7e3t7s37+/0ERWlLjc3d05dOgQWq1WKYuNjcXCwoLXXnvtieckhHhxSBJ6Dl27do233nqLr7/+mpMnT3LhwgXWrVvHjBkz6NChAzVq1OD+/ft8/vnn/PHHH6xatYrFixfrtOHs7ExGRgZ79uzh6tWrBU7TRUVFERkZyenTp/njjz/4+uuvMTU1pWrVqgXGVaVKFYyNjZV+N2/eTEREhE6doUOHkp6eTrdu3Th27BhJSUmsWrWKxMREJa6TJ0+SmJjI1atXC0xWgwcP5uLFiwwbNozff/+dTZs2MWnSJEaNGqXcDxJCvBzkL/o5pNFoeP311/nss89o3rw5np6eTJgwgf79+7NgwQLq1KnDnDlz+PTTT/H09GT16tVMmzZNp40mTZowcOBAunbtip2dHTNmzMjXj7W1NUuXLqVp06Z4e3uze/dufvjhB2xtbQuMy87OjqioKNatW4eHhwfTp09n1qxZOnVsbW356aefyMjIoEWLFvj4+LB06VJl6qx///7UrFmTBg0aYGdnV+CHbytXrsy2bds4cuQIderUYeDAgfTr14/x48eX9JIKIZ5TKu3Dcx5CvELS09OxsrIibVA/LNXG+g5HFFF61j2sFkWSlpaW775e3nM6J20CppYmZdLf3fRMRllFFNifKD0ZCQkhhNAbSUJCCCH0RpKQEEIIvZEkJIQQQm8kCQkhhNAbSUJCCCH0RpKQEEIIvZEkJIQQQm8kCQkhhNAb+T0h8cqLnO5YZp+uF0/f3fRMWKTvKERZkSQkXnk312WTafb431ESz4/MO/JcvUwkCYlXnnVnI0wt5U/hRXE33Qje13cUoqzIPSEhhBB6I0lICCGE3kgSEkIIoTeShIQQQuiNJCEhhBB6I0lICCGE3kgSEkIIoTeShIQQQuiNJCEhhBB6I0lICCGE3rxUSUilUrFx40bl8e+//84bb7yBiYkJdevWJTk5GZVKRVxcXJHaCw4OpmPHjkXuv7jtCyHEq+65SEKHDh3C0NCQdu3aFal+aGgodevWzVeemppKmzZtlMeTJk3C3NycxMRE9uzZg5OTE6mpqXh6ehapn3nz5hEVFVWkuiW1fv16fH19sbKyQqPR4O3tTXh4ONevX3+q/ZYFSbpCiNJ6LpJQZGQkw4YN4+eff+by5cuF1tNqtWRnF/4Nug4ODqjVauXx+fPnefPNN6latSq2trYYGhri4OCAkVHRvqzSysoKa2vrIp9Hcf3vf/+ja9euNGzYkO3bt3P69Glmz55NfHw8q1atemr9CiHE80LvSSgjI4Nvv/2WQYMG0a5dO52RR0xMDCqViu3bt+Pj44Narebrr78mLCyM+Ph4VCoVKpVKOebh6TiVSsXx48cJDw9HpVIRGhpa4Dv33377jfbt22NpaYmFhQXNmjXj/PnzQP7puB07dvDmm29ibW2Nra0t7du3V+oW15EjR5g6dSqzZ89m5syZNGnSBGdnZ1q3bs369evp3bu3UnfRokVUr14dY2NjatasmS9BqVQqlixZQvv27TEzM8Pd3Z1Dhw5x7tw5fH19MTc3p0mTJjqx5o0mlyxZgpOTE2ZmZnTp0oW0tDSdtr/66ivc3d0xMTGhVq1afPHFF8q+atWqAVCvXj1UKhW+vr5FOi7vediwYQN+fn6YmZlRp04dDh06pNN3bGwsvr6+mJmZUb58efz9/blx4wYAubm5TJs2jWrVqmFqakqdOnX47rvvSvRcCCH0R+9JaO3atdSqVYuaNWsSFBTEsmXL0Gq1OnXGjh3L9OnTSUhIoHXr1owePZratWuTmppKamoqXbt2zdduamoqtWvXZvTo0aSmphISEpKvzqVLl2jevDlqtZqffvqJ48eP07dv30JHW7dv32bUqFEcO3aMPXv2YGBgQKdOncjNzS32ea9evRqNRsPgwYML3J83Avv+++/58MMPGT16NKdPn2bAgAH06dOHvXv36tSPiIigV69exMXFUatWLd577z0GDBjAuHHjOHbsGFqtlqFDh+occ+7cOdauXcsPP/zAjh07+PXXX3XiWb16NRMnTmTKlCkkJCQwdepUJkyYwIoVK4AHiRRg9+7dpKamsmHDhiIdl+d///sfISEhxMXF4ebmRvfu3ZVrHxcXR8uWLfHw8ODQoUMcOHCAt99+m5ycHACmTZvGypUrWbx4Mb/99hsjR44kKCiIffv2FXrNs7KySE9P19mEEPql9x9RiYyMJCgoCICAgADS0tLYt2+fzrvq8PBwWrdurTzWaDQYGRnh4OBQaLt5024ajUapd/XqVZ06CxcuxMrKiujoaMqVKweAm5tboW0GBgbqPF62bBl2dnacOXOmyPeZ8iQlJeHi4qL0W5hZs2YRHBysJIdRo0bxyy+/MGvWLPz8/JR6ffr0oUuXLgB8/PHHNG7cmAkTJuDv7w/Ahx9+SJ8+fXTazszMZOXKlVSuXBmAzz//nHbt2jF79mwcHByYNGkSs2fP5p133gEejHzOnDnDkiVL6N27N3Z2dgDY2trqPBdPOi5PSEiIch8wLCyM2rVrc+7cOWrVqsWMGTNo0KCBzgiqdu3awINkMnXqVHbv3k3jxo0BcHFx4cCBAyxZsoQWLVoUeC2nTZtGWFjYY6+3EOLZ0utIKDExkSNHjtC9e3cAjIyM6Nq1K5GRkTr1GjRo8FT6j4uLo1mzZk9MBHmSkpLo3r07Li4uWFpa4uzsDEBKSkqx+350tFeYhIQEmjZtqlPWtGlTEhISdMq8vb2Vf1esWBEALy8vnbLMzEydd/9VqlRREhBA48aNyc3NJTExkdu3b3P+/Hn69euHRqNRtsmTJz92CrI4xz0cc6VKlQC4cuUK8H8joYKcO3eOO3fu0Lp1a50+Vq5c+djYxo0bR1pamrJdvHix0LpCiGdDryOhyMhIsrOzcXR0VMq0Wi1qtZoFCxYoZebm5k+lf1NT02LVf/vtt6latSpLly7F0dGR3NxcPD09uXfvXrH7dnNz48CBA9y/f7/ISfBxHm5DpVIVWlbUqcOMjAwAli5dyuuvv66zz9DQsEyOe1x8j3tu8vrYunWrThIFdBamPEqtVj92vxDi2dPbSCg7O5uVK1cye/Zs4uLilC0+Ph5HR0fWrFlT6LHGxsbKvYHS8Pb2Zv/+/dy/f/+Jda9du0ZiYiLjx4+nZcuWuLu7KzfJS+K9994jIyNDZ7rpYTdv3gTA3d2d2NhYnX2xsbF4eHiUuO88KSkpOqsRf/nlFwwMDKhZsyYVK1bE0dGRP/74gxo1auhseQsSjI2NAXSei6IcVxTe3t7s2bOnwH0eHh6o1WpSUlLy9eHk5FSSSyGE0BO9jYS2bNnCjRs36NevH1ZWVjr7AgMDiYyMZObMmQUe6+zszIULF4iLi+O1117DwsKiRO9whw4dyueff063bt0YN24cVlZW/PLLLzRq1IiaNWvq1C1fvjy2trZ8+eWXVKpUiZSUFMaOHVvsPvO8/vrrjBkzhtGjR3Pp0iU6deqEo6Mj586dY/Hixbz55pt8+OGHfPTRR3Tp0oV69erRqlUrfvjhBzZs2MDu3btL3HceExMTevfuzaxZs0hPT2f48OF06dJFub8TFhbG8OHDsbKyIiAggKysLI4dO8aNGzcYNWoU9vb2mJqasmPHDl577TVMTEywsrJ64nFFMW7cOLy8vBg8eDADBw7E2NiYvXv30rlzZypUqEBISAgjR44kNzeXN998k7S0NGJjY7G0tNS57ySEeL7pbSQUGRlJq1at8iUgeJCEjh07xsmTJws8NjAwkICAAPz8/LCzs3vsqOlxbG1t+emnn8jIyKBFixb4+PiwdOnSAqfHDAwMiI6O5vjx43h6ejJy5MhCk2RRffrpp3zzzTccPnwYf39/ateuzahRo/D29lZeSDt27Mi8efOYNWsWtWvXZsmSJSxfvlxn4UZJ1ahRg3feeYe2bdvyn//8B29vb52R2fvvv89XX33F8uXL8fLyokWLFkRFRSkjGiMjI+bPn8+SJUtwdHSkQ4cORTquKNzc3Pjxxx+Jj4+nUaNGNG7cmE2bNimf8YqIiGDChAlMmzYNd3d3AgIC2Lp1a7H6EELon0pb1Dvk4qUSGhrKxo0bX+lvO0hPT8fKyoo5aRMwtTTRdziiiO6mZzLKKoK0tDQsLS119j2N5/Rx/YnS0/vnhIQQQry6JAk9JQMHDtRZPvzwNnDgQH2HJ4QQzwW9f1j1ZRUeHl7gtzQAz8WQPjQ0lNDQUH2HIYR4xUkSekrs7e2xt7fXdxhCCPFck+k4IYQQeiNJSAghhN5IEhJCCKE3koSEEELojSQhIYQQeiNJSAghhN7IEm3xyru5LptMs4J/TVc8fzLvyHP1MpEkJF551p2NMLWUP4UXxd10I3hf31GIsiLTcUIIIfRGkpAQQgi9kSQkhBBCbyQJCSGE0BtJQkIIIfRGkpAQQgi9kSQkhBBCbyQJCSGE0BtJQkIIIfRGkpAQQgi9eSWTkEqlYuPGjcrj33//nTfeeAMTExPq1q1LcnIyKpWKuLi4IrUXHBxMx44di9x/cdsvCV9fX0aMGKE8dnZ2Zu7cucrjh6/Bs4hHCCEK8kIloUOHDmFoaEi7du2KVD80NJS6devmK09NTaVNmzbK40mTJmFubk5iYiJ79uzBycmJ1NRUPD09i9TPvHnziIqKKlLd4qpUqRLTp0/XKRs7diwqlYqYmBidcl9fX3r27AnAhg0biIiIKFIfxT1fIYQoKy9UEoqMjGTYsGH8/PPPXL58udB6Wq2W7OzCv2nXwcEBtVqtPD5//jxvvvkmVatWxdbWFkNDQxwcHDAyKtqXWlpZWWFtbV3k8ygOX1/ffMlm7969ODk56ZRnZmbyyy+/8NZbbwFgY2ODhYVFkfoo7vkKIURZeWGSUEZGBt9++y2DBg2iXbt2OiOPmJgYVCoV27dvx8fHB7Vazddff01YWBjx8fGoVCpUKpVyzMNTUSqViuPHjxMeHo5KpSI0NLTA6anffvuN9u3bY2lpiYWFBc2aNeP8+fNA/um4HTt28Oabb2JtbY2trS3t27dX6haXn58fsbGxSlK9desWv/76Kx9//LFOEjp06BBZWVn4+fkB+afjHufR8827nnv27KFBgwaYmZnRpEkTEhMTdY6bPHky9vb2WFhY8P777zN27FidkWfedZk1axaVKlXC1taWIUOGcP/+faVOVlYWISEhVK5cGXNzc15//XWd8/rzzz95++23KV++PObm5tSuXZtt27YBcOPGDXr06IGdnR2mpqa4urqyfPnyIl5ZIcTz4IVJQmvXrqVWrVrUrFmToKAgli1bhlar1akzduxYpk+fTkJCAq1bt2b06NHUrl2b1NRUUlNT6dq1a752U1NTqV27NqNHjyY1NZWQkJB8dS5dukTz5s1Rq9X89NNPHD9+nL59+xY62rp9+zajRo3i2LFj7NmzBwMDAzp16kRubm6xz9vPz4+MjAyOHj0KwP79+3FzcyMwMJDDhw+TmZkJPBgdOTs74+zsXOw+CvO///2P2bNnc+zYMYyMjOjbt6+yb/Xq1UyZMoVPP/2U48ePU6VKFRYtWpSvjb1793L+/Hn27t3LihUriIqK0nkDMXToUA4dOkR0dDQnT56kc+fOBAQEkJSUBMCQIUPIysri559/5tSpU3z66adoNBoAJkyYwJkzZ9i+fTsJCQksWrSIChUqFHo+WVlZpKen62xCCP16YeZfIiMjCQoKAiAgIIC0tDT27duHr6+vUic8PJzWrVsrjzUaDUZGRjg4OBTabt40lEajUepdvXpVp87ChQuxsrIiOjqacuXKAeDm5lZom4GBgTqPly1bhp2dHWfOnCn2fRdXV1cqV65MTEwMjRs3JiYmhhYtWuDg4ECVKlU4dOgQfn5+xMTEKKOgsjJlyhRatGgBPEjw7dq1IzMzExMTEz7//HP69etHnz59AJg4cSI//vgjGRkZOm2UL1+eBQsWYGhoSK1atWjXrh179uyhf//+pKSksHz5clJSUnB0dAQgJCSEHTt2sHz5cqZOnUpKSgqBgYF4eXkB4OLiorSdkpJCvXr1aNCgAcATE/C0adMICwsrk2sjhCgbL8RIKDExkSNHjtC9e3cAjIyM6Nq1K5GRkTr18l6MylpcXBzNmjVTEtCTJCUl0b17d1xcXLC0tFReHFNSUkrU/8P3hWJiYpTE26JFC2JiYrh79y6HDx8u8yTk7e2t/LtSpUoAXLlyBXjwnDRq1Ein/qOPAWrXro2hoaFOO3ltnDp1ipycHNzc3NBoNMq2b98+Zfpy+PDhTJ48maZNmzJp0iROnjyptDVo0CCio6OpW7cuY8aM4eDBg489n3HjxpGWlqZsFy9eLM7lEEI8BS/ESCgyMpLs7Gzl3TI8WHygVqtZsGCBUmZubv5U+jc1NS1W/bfffpuqVauydOlSHB0dyc3NxdPTk3v37pWofz8/Pz788EOuXbvGr7/+qoxOWrRowZIlS2jevDn37t1TFiWUlYeTrkqlAij2lOKjiVulUiltZGRkYGhoyPHjx3USFaBMub3//vv4+/uzdetWfvzxR6ZNm8bs2bMZNmwYbdq04c8//2Tbtm3s2rWLli1bMmTIEGbNmlVgLGq1WmdBihBC/577kVB2djYrV65k9uzZxMXFKVt8fDyOjo6sWbOm0GONjY3JyckpdQze3t7s379f54Z6Ya5du0ZiYiLjx4+nZcuWuLu7c+PGjVL17+fnx+3bt5kzZw6urq7Y29sD0Lx5c44cOcL27duVabtnpWbNmsp9qjyPPn6SevXqkZOTw5UrV6hRo4bO9vAUqpOTEwMHDmTDhg2MHj2apUuXKvvs7Ozo3bs3X3/9NXPnzuXLL78s3YkJIZ6p5z4JbdmyhRs3btCvXz88PT11tsDAwHxTcg9zdnbmwoULxMXFcfXqVbKyskoUw9ChQ0lPT6dbt24cO3aMpKQkVq1alW+1GDy4B2Jra8uXX37JuXPn+Omnnxg1alSJ+s3j4uJClSpV+Pzzz5VREDx4cXZ0dOTLL78s86m4Jxk2bBiRkZGsWLGCpKQkJk+ezMmTJ5URU1G4ubnRo0cPevXqxYYNG7hw4QJHjhxh2rRpbN26FYARI0awc+dOLly4wIkTJ9i7dy/u7u7Ag/tQmzZt4ty5c/z2229s2bJF2SeEeDE890koMjKSVq1aYWVllW9fYGAgx44d07lP8Oj+gIAA/Pz8sLOze+yo6XFsbW356aefyMjIoEWLFvj4+LB06dIC7xEZGBgQHR3N8ePH8fT0ZOTIkcycObNE/T7Mz8+PW7du6SzEgAdTcrdu3XrmSahHjx6MGzeOkJAQ6tevz4ULFwgODsbExKRY7SxfvpxevXoxevRoatasSceOHTl69ChVqlQBICcnhyFDhuDu7k5AQABubm588cUXwIOR7rhx4/D29qZ58+YYGhoSHR1d5ucqhHh6VNpH1zkLUUKtW7fGwcGBVatW6TuUIklPT8fKyoo5aRMwtSxe8hT6czc9k1FWEaSlpWFpaamz72k8p4/rT5TeC7EwQTx/7ty5w+LFi/H398fQ0JA1a9awe/dudu3ape/QhBAvkOd+Ou5lN3DgQJ3lyQ9vAwcO1Hd4hVKpVGzbto3mzZvj4+PDDz/8wPr162nVqpW+QxNCvEBkJKRn4eHhBX5LA/BcD/1NTU3ZvXu3vsMQQrzgJAnpmb29vbLkWgghXjUyHSeEEEJvJAkJIYTQG0lCQggh9EaSkBBCCL2RJCSEEEJvJAkJIYTQG1miLV55N9dlk2lW8K/kiudP5h15rl4mkoTEK8+6sxGmlvKn8KK4m24E7+s7ClFWZDpOCCGE3kgSEkIIoTeShIQQQuiNJCEhhBB6I0lICCGE3kgSEkIIoTeShIQQQuiNJCEhhBB6I0lICCGE3kgSEkIIoTeShF4AKpWKjRs3Ko9///133njjDUxMTKhbty7JycmoVCri4uKK1F5wcDAdO3Yscv/FbV8IIYpKktBTdOjQIQwNDWnXrl2R6oeGhlK3bt185ampqbRp00Z5PGnSJMzNzUlMTGTPnj04OTmRmpqKp6dnkfqZN28eUVFRRapbHKGhoahUqsduZU0SpBAvNklCT1FkZCTDhg3j559/5vLly4XW02q1ZGcX/s3ADg4OqNVq5fH58+d58803qVq1Kra2thgaGuLg4ICRUdG+hNPKygpra+sin0dRhYSEkJqaqmyvvfYa4eHhOmUPu3fvXpnHIIR4sUgSekoyMjL49ttvGTRoEO3atdMZecTExKBSqdi+fTs+Pj6o1Wq+/vprwsLCiI+PV0YNecc8PB2nUqk4fvw44eHhqFQqQkNDCxwN/Pbbb7Rv3x5LS0ssLCxo1qwZ58+fB/JPx+3YsYM333wTa2trbG1tad++vVK3ODQaDQ4ODspmaGiIhYWF8rhbt24MHTqUESNGUKFCBfz9/QuM/ebNm6hUKmJiYgC4ceMGPXr0wM7ODlNTU1xdXVm+fDkA1apVA6BevXqoVCp8fX2LHbcQQn/k++ufkrVr11KrVi1q1qxJUFAQI0aMYNy4cTpTUmPHjmXWrFm4uLhgYmLC6NGj2bFjB7t37wYejFgelZqaSqtWrQgICCAkJASNRsPVq1d16ly6dInmzZvj6+vLTz/9hKWlJbGxsYWOtm7fvs2oUaPw9vYmIyODiRMn0qlTJ+Li4jAwKNv3KStWrGDQoEHExsYW+ZgJEyZw5swZtm/fToUKFTh37hx3794F4MiRIzRq1Ijdu3dTu3ZtjI2NC20nKyuLrKws5XF6enrJT0QIUSYkCT0lkZGRBAUFARAQEEBaWhr79u3TeaceHh5O69atlccajQYjIyMcHBwKbTdv2i1v1AHkS0ILFy7EysqK6OhoypUrB4Cbm1uhbQYGBuo8XrZsGXZ2dpw5c6bI95mKytXVlRkzZiiPk5OTn3hMSkoK9erVo0GDBgA4Ozsr++zs7ACwtbV97HUDmDZtGmFhYcUPWgjx1Mh03FOQmJjIkSNH6N69OwBGRkZ07dqVyMhInXp5L6plLS4ujmbNmikJ6EmSkpLo3r07Li4uWFpaKi/yKSkpZR6bj49PsY8ZNGgQ0dHR1K1blzFjxnDw4MES9T1u3DjS0tKU7eLFiyVqRwhRdmQk9BRERkaSnZ2No6OjUqbValGr1SxYsEApMzc3fyr9m5qaFqv+22+/TdWqVVm6dCmOjo7k5ubi6en5VBYOPHrOedN9Wq1WKbt//75OnTZt2vDnn3+ybds2du3aRcuWLRkyZAizZs0qVt9qtVpngYcQQv9kJFTGsrOzWblyJbNnzyYuLk7Z4uPjcXR0ZM2aNYUea2xsTE5OTqlj8Pb2Zv/+/flezAty7do1EhMTGT9+PC1btsTd3Z0bN26UOoaiyptOe3jlXEHLre3s7Ojduzdff/01c+fO5csvvwRQ7gGVxXUTQjx7koTK2JYtW7hx4wb9+vXD09NTZwsMDMw3JfcwZ2dnLly4QFxcHFevXtW5iV4cQ4cOJT09nW7dunHs2DGSkpJYtWoViYmJ+eqWL18eW1tbvvzyS86dO8dPP/3EqFGjStRvSZiamvLGG28wffp0EhIS2LdvH+PHj9epM3HiRDZt2sS5c+f47bff2LJlC+7u7gDY29tjamrKjh07+Oeff0hLS3tmsQshSk+SUBmLjIykVatWBa5sCwwM5NixY5w8ebLAYwMDAwkICMDPzw87O7vHjpoex9bWlp9++omMjAxatGiBj48PS5cuLfAekYGBAdHR0Rw/fhxPT09GjhzJzJkzS9RvSS1btozs7Gx8fHwYMWIEkydP1tlvbGzMuHHj8Pb2pnnz5hgaGhIdHQ08uN82f/58lixZgqOjIx06dHimsQshSkelfXgyXohXSHp6OlZWVsxJm4CppYm+wxFFdDc9k1FWEaSlpWFpaamz72k8p4/rT5SejISEEELojSQhUSwDBw5Eo9EUuA0cOFDf4QkhXjCyRFsUS3h4OCEhIQXuk6kKIURxSRISxWJvb4+9vb2+wxBCvCRkOk4IIYTelCgJnThxglOnTimPN23aRMeOHfnkk0/k6/mFEEIUWYmS0IABAzh79iwAf/zxB926dcPMzIx169YxZsyYMg1QCCHEy6tESejs2bPKL4CuW7eO5s2b88033xAVFcX69evLMj4hhBAvsRIlIa1WS25uLgC7d++mbdu2ADg5OeX7WQEhhBCiMCVaHdegQQMmT55Mq1at2LdvH4sWLQLgwoULVKxYsUwDFOJpu7kum0yzwn9eXTxfMu/Ic/UyKVESmjt3Lj169GDjxo3873//o0aNGgB89913NGnSpEwDFOJps+5shKmlfFrhRXE33Qje13cUoqyU6C/P29tbZ3VcnpkzZ2JoaFjqoIQQQrwaSnRP6OjRoxw+fDhfeXx8PPHx8aUOSgghxKuhREloyJAhBf408qVLlxgyZEipgxJCCPFqKFESOnPmDPXr189XXq9ePc6cOVPqoIQQQrwaSpSE1Go1//zzT77y1NRUjIzkBq8QQoiiKVES+s9//sO4ceN0fkr55s2bfPLJJ7Ru3brMghNCCPFyK9GwZdasWTRv3pyqVatSr149AOLi4qhYsSKrVq0q0wCFEEK8vEqUhCpXrszJkydZvXo18fHxmJqa0qdPH7p37065cuXKOkYhhBAvqRLfwDE3N+eDDz4oy1iEEEK8YoqchDZv3kybNm0oV64cmzdvfmzd//73v6UOTAghxMuvyEmoY8eO/P3339jb29OxY8dC66lUKnJycsoiNiGEEC+5Iq+Oy83NVX7WOTc3t9BNElDxqVQqNm7c+FT7iIqKwtra+qn2IYQQxVXsJdr379+nZcuWJCUlPY14+Pvvvxk2bBguLi6o1WqcnJx4++232bNnz1Pp73mQmppKmzZtyqw9Z2dn5s6dq1PWtWtX5YcIn4Xk5GRUKpWy2djY0KJFC/bv31+sdmJiYlCpVNy8efPpBCqE0KtiJ6Fy5cpx8uTJpxELycnJ+Pj48NNPPzFz5kxOnTrFjh078PPze6m/DsjBwQG1Wv1U+zA1NVVGss/S7t27SU1N5eeff8bR0ZH27dsX+EFnIcSrqUQfVg0KCiIyMrKsY2Hw4MGoVCqOHDlCYGAgbm5u1K5dm1GjRvHLL78AkJKSQocOHdBoNFhaWtKlSxedF7XQ0FDq1q3LsmXLqFKlChqNhsGDB5OTk8OMGTNwcHDA3t6eKVOm6PStUqlYsmQJ7du3x8zMDHd3dw4dOsS5c+fw9fXF3NycJk2acP78eeWY4ODgfPfHRowYga+vr/LY19eX4cOHM2bMGGxsbHBwcCA0NDRf3w9Px/311190794dGxsbzM3NadCggfKFsefPn6dDhw5UrFgRjUZDw4YN2b17t05/f/75JyNHjlRGIVDwdNyiRYuoXr06xsbG1KxZM99nvFQqFV999RWdOnXCzMwMV1fXJy5KeZStrS0ODg54enryySefkJ6ervPlt6tWraJBgwZYWFjg4ODAe++9x5UrV4AHb0r8/PwAKF++PCqViuDgYODBlPC0adOoVq0apqam1KlTh++++65YsQkh9K9ESSg7O5tFixbRoEEDBgwYwKhRo3S2krh+/To7duxgyJAhmJub59tvbW1Nbm4uHTp04Pr16+zbt49du3bxxx9/0LVrV52658+fZ/v27ezYsYM1a9YQGRlJu3bt+Ouvv9i3bx+ffvop48ePz/dN4BEREfTq1Yu4uDhq1arFe++9x4ABAxg3bhzHjh1Dq9UydOjQYp/bihUrMDc35/Dhw8yYMYPw8HB27dpVYN2MjAxatGjBpUuX2Lx5M/Hx8YwZM0b5JduMjAzatm3Lnj17+PXXXwkICODtt98mJSUFgA0bNvDaa68RHh5OamoqqampBfbz/fff8+GHHzJ69GhOnz7NgAED6NOnD3v37tWpFxYWRpcuXTh58iRt27alR48eXL9+vdjX4O7du6xcuRIAY2Njpfz+/ftEREQQHx/Pxo0bSU5OVhKNk5OT8nPxiYmJpKamMm/ePACmTZvGypUrWbx4Mb/99hsjR44kKCiIffv2FRpDVlYW6enpOpsQQr9K9Dmh06dPK19gWlb3Gc6dO4dWq6VWrVqF1tmzZw+nTp3iwoULODk5AbBy5Upq167N0aNHadiwIfDgXfKyZcuwsLDAw8MDPz8/EhMT2bZtGwYGBtSsWZNPP/2UvXv38vrrryvt9+nThy5dugDw8ccf07hxYyZMmIC/vz8AH374IX369Cn2uXl7ezNp0iQAXF1dWbBgAXv27CnwK46++eYb/v33X44ePYqNjQ2A8qOBAHXq1KFOnTrK44iICL7//ns2b97M0KFDsbGxwdDQUBlZFGbWrFkEBwczePBgAGW0OWvWLGX0AQ9Ge927dwdg6tSpzJ8/nyNHjhAQEFCkc2/SpAkGBgbcuXMHrVaLj48PLVu2VPb37dtX+beLiwvz58+nYcOGZGRkoNFolGtgb2+vjOSysrKYOnUqu3fvpnHjxsqxBw4cYMmSJbRo0aLAWKZNm0ZYWFiR4hYvtn5jL2OpNn5yxSJIz7pHyd5ai6IoURJ69N1yWdBqtU+sk5CQgJOTk5KAADw8PLC2tiYhIUFJQs7OzlhYWCh1KlasiKGhIQYGBjpledM+eby9vXX2A3h5eemUZWZmkp6ejqWlZZHP7eF2ASpVqpSv7zxxcXHUq1dPefF9VEZGBqGhoWzdupXU1FSys7O5e/euMhIqqoSEhHwfNm7atKky0igodnNzcywtLQuNvSDffvsttWrV4vTp04wZM4aoqCidb9U4fvw4oaGhxMfHc+PGDWXEl5KSgoeHR4Ftnjt3jjt37uRL4vfu3VO+Rqog48aN0xmpp6en6/xfEkI8eyVKQn379mXevHk6L/QAt2/fZtiwYSxbtqzYbbq6uqJSqfj9999LEpKOR786SKVSFViW94JX0HF591IKKss7zsDAIF/yvH//fpHiebTvPKampgWW5wkJCWHXrl3MmjWLGjVqYGpqyrvvvsu9e/cee1xJFSf2gjg5OeHq6oqrqyvZ2dl06tSJ06dPo1aruX37Nv7+/vj7+7N69Wrs7OxISUnB39//seeTkZEBwNatW6lcubLOvsct8FCr1U99AYgQonhKdE9oxYoV3L17N1/5w/P+xWVjY4O/vz8LFy7k9u3b+fbfvHkTd3d3Ll68qPODemfOnOHmzZuFvmt+muzs7PLdc4mLiytVm97e3sTFxRV63yU2Npbg4GA6deqEl5cXDg4OJCcn69QxNjZ+4ue13N3diY2Nzdf207yO7777LkZGRnzxxRcA/P7771y7do3p06fTrFkzatWqlW+UlXf/6OHz8fDwQK1Wk5KSQo0aNXQ2GdkI8WIpVhJKT08nLS0NrVbLrVu3dG7w3rhxg23btpVqGfDChQvJycmhUaNGrF+/nqSkJBISEpg/fz6NGzemVatWeHl50aNHD06cOMGRI0fo1asXLVq0oEGDBiXut6Teeustjh07xsqVK0lKSmLSpEmcPn26VG12794dBwcHOnbsSGxsLH/88Qfr16/n0KFDwIMR44YNG4iLiyM+Pp733nsv38jE2dmZn3/+mUuXLnH16tUC+/noo4+Iiopi0aJFJCUlMWfOHDZs2EBISEip4n8clUrF8OHDmT59Onfu3KFKlSoYGxvz+eef88cff7B582YiIiJ0jqlatSoqlYotW7bw77//kpGRgYWFBSEhIYwcOZIVK1Zw/vx5Tpw4weeff86KFSueWvxCiLJXrCRkbW2NjY0NKpUKNzc3ypcvr2wVKlSgb9++pfo8j4uLCydOnMDPz4/Ro0fj6elJ69at2bNnD4sWLUKlUrFp0ybKly9P8+bNadWqFS4uLnz77bcl7rM0/P39mTBhAmPGjKFhw4bcunWLXr16lapNY2NjfvzxR+zt7Wnbti1eXl5Mnz4dQ0NDAObMmUP58uVp0qQJb7/9Nv7+/vl+5TY8PJzk5GSqV6+OnZ1dgf107NiRefPmMWvWLGrXrs2SJUtYvny5zvLyp6F3797cv3+fBQsWYGdnR1RUFOvWrcPDw4Pp06cza9YsnfqVK1cmLCyMsWPHUrFiRWV1YkREBBMmTGDatGm4u7sTEBDA1q1bqVat2lONXwhRtlTaoqwI+P/27duHVqvlrbfeYv369To3z42NjalatSqOjo5PJVAhylp6ejpWVlbMSZuAqaWJvsMRRXQ3PZNRVhGkpaXlWyCU95ymDepXpqvjrBZFFtifKL1iLUzIW/p64cIFqlSpotyoF0IIIUqiRAsTqlatyoEDBwgKCqJJkyZcunQJePDp9wMHDpRpgOL5NHDgQDQaTYHbwIED9R2eEOIFUaIl2uvXr6dnz57KAoGsrCwA0tLSmDp1Ktu2bSvTIMXzJzw8vNBFDDJlIYQoqhIlocmTJ7N48WJ69epFdHS0Ut60aVMmT55cZsGJ55e9vb1evhBVCPFyKdF0XGJiIs2bN89XbmVlJV+5L4QQoshKlIQcHBw4d+5cvvIDBw7g4uJS6qCEEEK8GkqUhPr378+HH37I4cOHUalUXL58mdWrVxMSEsKgQYPKOkYhhBAvqRLdExo7diy5ubm0bNmSO3fu0Lx5c9RqNSEhIQwbNqysYxRCCPGSKlESUqlU/O9//+Ojjz7i3LlzZGRk4OHhgUajKev4hBBCvMSKlYQe/u2XxynJt2gLoS8312WTaZat7zBEEWXekefqZVKsJBQVFUXVqlWpV69ekX7/R4gXgXVnI0wtSzQpIPTgbroRvK/vKERZKdZf3qBBg1izZg0XLlygT58+BAUFFfrja0IIIcSTFGt13MKFC0lNTWXMmDH88MMPODk50aVLF3bu3CkjIyGEEMVW7CXaarWa7t27s2vXLs6cOUPt2rUZPHgwzs7Oyi9eCiGEEEVRos8JKQcbGKBSqdBqtU/8JU8hhBDiUcVOQllZWaxZs4bWrVvj5ubGqVOnWLBgASkpKbJEWwghRLEUa2HC4MGDiY6OxsnJib59+7JmzRoqVKjwtGITQgjxkitWElq8eDFVqlTBxcWFffv2sW/fvgLrbdiwoUyCE0II8XIrVhLq1auX/JqqEEKIMlPsD6sKIYQQZaVUq+OEEEKI0pAkVAwxMTGoVCq9/XBfVFQU1tbWeulbCCGehpcyCQUHB6NSqVCpVJQrV45q1aoxZswYMjMzi9yGr68vI0aM0Clr0qQJqampWFlZlUmcBfXxOF27duXs2bNPtY/k5GRUKhVxcXGlbksIIZ7kpf3WxoCAAJYvX879+/c5fvw4vXv3RqVS8emnn5a4TWNjYxwcHMowyuIxNTXF1NRUb/0LIURZeylHQvDg64UcHBxwcnKiY8eOtGrVil27dgFw7do1unfvTuXKlTEzM8PLy4s1a9YoxwYHB7Nv3z7mzZunjKiSk5MLnI5bv349tWvXRq1W4+zszOzZs0sc840bN+jVqxfly5fHzMyMNm3akJSUpOx/dDouNDSUunXrsmrVKpydnbGysqJbt27cunXrsedRVlQqFRs3btQps7a2Vhaw5I2q1q5dS7NmzTA1NaVhw4acPXuWo0eP0qBBAzQaDW3atOHff/9V2jh69CitW7emQoUKWFlZ0aJFC06cOJGv76+++opOnTphZmaGq6srmzdvLrNzE0I8Gy9tEnrY6dOnOXjwIMbGxgBkZmbi4+PD1q1bOX36NB988AE9e/bkyJEjAMybN4/GjRvTv39/UlNTSU1NxcnJKV+7x48fp0uXLnTr1o1Tp04RGhrKhAkTSryKMDg4mGPHjrF582YOHTqEVqulbdu23L9/v9Bjzp8/z8aNG9myZQtbtmxh3759TJ8+vVjn8bRNmjSJ8ePHc+LECYyMjHjvvfcYM2YM8+bNY//+/Zw7d46JEycq9W/dukXv3r05cOAAv/zyC66urrRt21ZJrnnCwsLo0qULJ0+epG3btvTo0YPr168XGkdWVhbp6ek6mxBCv17a6bgtW7ag0WjIzs4mKysLAwMDFixYAEDlypUJCQlR6g4bNoydO3eydu1aGjVqhJWVFcbGxpiZmT12+m3OnDm0bNmSCRMmAODm5saZM2eYOXMmwcHBxYo3KSmJzZs3ExsbS5MmTQBYvXo1Tk5ObNy4kc6dOxd4XG5uLlFRUVhYWADQs2dP9uzZw5QpU4p8HgVp0qQJBga671Hu3r1L3bp1i9UOQEhICP7+/gB8+OGHdO/enT179tC0aVMA+vXrp5O433rrLZ3jv/zyS6ytrdm3bx/t27dXyoODg+nevTsAU6dOZf78+Rw5coSAgIAC45g2bRphYWHFjl8I8fS8tCMhPz8/4uLiOHz4ML1796ZPnz4EBgYCkJOTQ0REBF5eXtjY2KDRaNi5cycpKSnF6iMhIUF5Ic3TtGlTkpKSiv2FrgkJCRgZGfH6668rZba2ttSsWZOEhIRCj3N2dlYSEEClSpW4cuVKsfouyLfffktcXJzO1qBBgxK15e3trfy7YsWKAHh5eemUPRzzP//8Q//+/XF1dcXKygpLS0syMjLyPT8Pt2tubo6lpeVjz33cuHGkpaUp28WLF0t0PkKIsvPSjoTMzc2pUaMG8ODnxuvUqUNkZCT9+vVj5syZzJs3j7lz5+Ll5YW5uTkjRozg3r17eo66+MqVK6fzWKVSkZubW+p2nZyclOuX59FFEXnfoP6wgqYOH44x7xs3Hi17OObevXtz7do15s2bR9WqVVGr1TRu3Djf81Pcc1er1ajV6kL3CyGevZd2JPQwAwMDPvnkE8aPH8/du3eJjY2lQ4cOBAUFUadOHVxcXPItfTY2Nn7iaMbd3Z3Y2FidstjYWNzc3DA0NCxWjO7u7mRnZ3P48GGl7Nq1ayQmJuLh4VGsth5WlPMoKTs7O1JTU5XHSUlJ3Llzp9TtxsbGMnz4cNq2bass+rh69Wqp2xVCPH9eiSQE0LlzZwwNDVm4cCGurq7s2rWLgwcPkpCQwIABA/jnn3906js7O3P48GGSk5O5evVqge+wR48ezZ49e4iIiODs2bOsWLGCBQsW6NxvKipXV1c6dOhA//79OXDgAPHx8QQFBVG5cmU6dOhQ4vMuynmU1FtvvcWCBQv49ddfOXbsGAMHDsw3OikJV1dXVq1aRUJCAocPH6ZHjx6yNF2Il9Qrk4SMjIwYOnQoM2bMYPTo0dSvXx9/f398fX1xcHCgY8eOOvVDQkIwNDTEw8MDOzu7Au8X1a9fn7Vr1xIdHY2npycTJ04kPDy8yIsScnNzMTL6vxnR5cuX4+PjQ/v27WncuDFarZZt27aV6oW9KOdRUrNnz8bJyYlmzZrx3nvvERISgpmZWanbjYyM5MaNG9SvX5+ePXsyfPhw7O3tyyBiIcTzRqV9dFJfPDO1atXi/fffL9HISZReeno6VlZWzEmbgKmlib7DEUV0Nz2TUVYRpKWlYWlpqbMv7zlNG9QPS7VxmfSXnnUPq0WRBfYnSu+lXZjwPLty5Qrbt28nMTGRli1b6jscIYTQm1dmOu5Z279/PxqNpsDNwcGB0NBQ5s+fT7169Z5pXAMHDiw0roEDBz7TWIQQQkZCT0mDBg0K/BLQPI8uf35WwsPDC53+k6kGIcSzJknoKTE1NdVbonkce3t7uckvhHhuyHScEEIIvZEkJIQQQm8kCQkhhNAbSUJCCCH0RpKQEEIIvZEkJIQQQm9kibZ45d1cl02mWba+wxBFlHlHnquXiSQh8cqz7myEqaX8Kbwo7qYbwfv6jkKUFZmOE0IIoTeShIQQQuiNJCEhhBB6I0lICCGE3kgSEkIIoTeShIQQQuiNJCEhhBB6I0lICCGE3kgSEkIIoTeShIQQQuiNJKEyFhMTg0ql4ubNm3rpPyoqCmtra730LYQQxfXKJqHg4GBUKhUqlYpy5cpRrVo1xowZQ2ZmZpHb8PX1ZcSIETplTZo0ITU1FSsrqzKJs6A+Hqdr166cPXv2qfbxMH9/fwwNDTl69GiJji8LwcHBdOzYUW/9CyFK7pVNQgABAQGkpqbyxx9/8Nlnn7FkyRImTZpUqjaNjY1xcHBApVKVUZTFY2pqir29/TPpKyUlhYMHDzJ06FCWLVv2TPoUQrxcXukkpFarcXBwwMnJiY4dO9KqVSt27doFwLVr1+jevTuVK1fGzMwMLy8v1qxZoxwbHBzMvn37mDdvnjKiSk5OLnA6bv369dSuXRu1Wo2zszOzZ88uccw3btygV69elC9fHjMzM9q0aUNSUpKy/9HpuNDQUOrWrcuqVatwdnbGysqKbt26cevWrceeR1EsX76c9u3bM2jQINasWcPdu3d19t+6dYsePXpgbm5OpUqV+Oyzz/KNurKysggJCaFy5cqYm5vz+uuvExMTk+98du7cibu7OxqNRnnzkHd+K1asYNOmTUr8Dx8vhHi+vdJJ6GGnT5/m4MGDGBsbA5CZmYmPjw9bt27l9OnTfPDBB/Ts2ZMjR44AMG/ePBo3bkz//v1JTU0lNTUVJyenfO0eP36cLl260K1bN06dOkVoaCgTJkwgKiqqRHEGBwdz7NgxNm/ezKFDh9BqtbRt25b79+8Xesz58+fZuHEjW7ZsYcuWLezbt4/p06cX6zwepdVqWb58OUFBQdSqVYsaNWrw3Xff6dQZNWoUsbGxbN68mV27drF//35OnDihU2fo0KEcOnSI6OhoTp48SefOnQkICNBJrHfu3GHWrFmsWrWKn3/+mZSUFEJCQgAICQmhS5cuSmJKTU2lSZMmBcaclZVFenq6ziaE0K9X+kdUtmzZgkajITs7m6ysLAwMDFiwYAEAlStXVl7oAIYNG8bOnTtZu3YtjRo1wsrKCmNjY8zMzHBwcCi0jzlz5tCyZUsmTJgAgJubG2fOnGHmzJkEBwcXK96kpCQ2b95MbGys8kK7evVqnJyc2LhxI507dy7wuNzcXKKiorCwsACgZ8+e7NmzhylTphT5PB61e/du7ty5g7+/PwBBQUFERkbSs2dP4MEoaMWKFXzzzTe0bNkSeDBycnR0VNpISUlh+fLlpKSkKOUhISHs2LGD5cuXM3XqVADu37/P4sWLqV69OvAgcYWHhwOg0WgwNTUlKyvrifFPmzaNsLCwIp+jEOLpe6VHQn5+fsTFxXH48GF69+5Nnz59CAwMBCAnJ4eIiAi8vLywsbFBo9Gwc+dOUlJSitVHQkICTZs21Slr2rQpSUlJ5OTkFLstIyMjXn/9daXM1taWmjVrkpCQUOhxzs7OSgICqFSpEleuXClW349atmwZXbt2xcjowfuY7t27Exsby/nz5wH4448/uH//Po0aNVKOsbKyombNmsrjU6dOkZOTg5ubGxqNRtn27duntANgZmamJKDSxD9u3DjS0tKU7eLFi8VuQwhRtl7pkZC5uTk1atQAHryo1qlTh8jISPr168fMmTOZN28ec+fOxcvLC3Nzc0aMGMG9e/f0HHXxlStXTuexSqUiNze3xO1dv36d77//nvv377No0SKlPCcnh2XLljFlypQitZORkYGhoSHHjx/H0NBQZ59Go3ls/Fqttthxq9Vq1Gp1sY8TQjw9r/RI6GEGBgZ88sknjB8/nrt37xIbG0uHDh0ICgqiTp06uLi45Fv6bGxs/MTRjLu7O7GxsTplsbGxuLm55XvhfRJ3d3eys7M5fPiwUnbt2jUSExPx8PAoVlsPK8p5PGz16tW89tprxMfHExcXp2yzZ88mKiqKnJwcXFxcKFeunM7S7bS0NJ1rWK9ePXJycrhy5Qo1atTQ2YozNVjc+IUQzw9JQg/p3LkzhoaGLFy4EFdXV3bt2sXBgwdJSEhgwIAB/PPPPzr1nZ2dOXz4MMnJyVy9erXA0cXo0aPZs2cPERERnD17lhUrVrBgwQKd+01F5erqSocOHejfvz8HDhwgPj6eoKAgKleuTIcOHUp83kU5j4dFRkby7rvv4unpqbP169ePq1evsmPHDiwsLOjduzcfffQRe/fu5bfffqNfv34YGBgoy9fd3Nzo0aMHvXr1YsOGDVy4cIEjR44wbdo0tm7dWqz4T548SWJiIlevXn3sIg0hxPNFktBDjIyMGDp0KDNmzGD06NHUr18ff39/fH19cXBwyPeByJCQEAwNDfHw8MDOzq7A+0X169dn7dq1REdH4+npycSJEwkPDy/yooTc3Fzlvgs8uLnv4+ND+/btady4MVqtlm3btuWbsiqOopxHnuPHjxMfH6/cO3uYlZUVLVu2JDIyEniwKKNx48a0b9+eVq1a0bRpU9zd3TExMdE5n169ejF69Ghq1qxJx44dOXr0KFWqVCly/P3796dmzZo0aNAAOzu7fCNPIcTzS6UtyeS6eGZq1arF+++/X6KR0/Pm9u3bVK5cmdmzZ9OvXz99h0N6ejpWVlbMSZuAqaXJkw8Qz4W76ZmMsoogLS0NS0tLnX15z2naoH5Yqo3LpL/0rHtYLYossD9Req/0woTn2ZUrV9i+fTuJiYnKEucXza+//srvv/9Oo0aNSEtLU5ZVl2bqUAjxcpEkpEf79++nTZs2Be67c+cOVatWZf78+dSrV++ZxjVw4EC+/vrrAvcFBQWxePHiIrc1a9YsEhMTMTY2xsfHh/3791OhQoWyClUI8YKTJKRHDRo0IC4urtD9ecvHn7Xw8PBCp/+KMx1Rr149jh8/XlZhCSFeQpKE9MjU1FRvieZx7O3tn9mXoAohXm2yOk4IIYTeSBISQgihN5KEhBBC6I0kISGEEHojSUgIIYTeSBISQgihN7JEW7zybq7LJtMsW99hiCLKvCPP1ctEkpB45Vl3NsLUUv4UXhR3043gfX1HIcqKTMcJIYTQG0lCQggh9EaSkBBCCL2RJCSEEEJvJAkJIYTQG0lCQggh9EaSkBBCCL2RJCSEEEJvJAkJIYTQG0lCQggh9OaVT0IxMTGoVCpu3ryp71CEEOKV88IkoeDgYFQqFSqVinLlylGtWjXGjBlDZmZmkdvw9fVlxIgROmVNmjQhNTUVKyurMomzoD4AoqKisLa2LpM+nhdr1qzB0NCQIUOG6C0GeRMhxIvthUlCAAEBAaSmpvLHH3/w2WefsWTJEiZNmlSqNo2NjXFwcEClUpVRlK+OyMhIxowZw5o1a4r1ZkAIIfK8UElIrVbj4OCAk5MTHTt2pFWrVuzatQuAa9eu0b17dypXroyZmRleXl6sWbNGOTY4OJh9+/Yxb948ZUSVnJxc4Dvp9evXU7t2bdRqNc7OzsyePbvMzyU4OJiOHTvqlI0YMQJfX1/lsa+vL8OGDWPEiBGUL1+eihUrsnTpUm7fvk2fPn2wsLCgRo0abN++XTkmJyeHfv36Ua1aNUxNTalZsybz5s0rsO9Zs2ZRqVIlbG1tGTJkCPfv3y9y/BcuXODgwYOMHTsWNzc3NmzYkK/O0qVLcXJywszMjE6dOjFnzpx8o8FNmzZRv359TExMcHFxISwsjOzs//uqfpVKxVdffUWnTp0wMzPD1dWVzZs3A5CcnIyfnx8A5cuXR6VSERwcXORzEELo3wuVhB52+vRpDh48iLGxMQCZmZn4+PiwdetWTp8+zQcffEDPnj05cuQIAPPmzaNx48b079+f1NRUUlNTcXJyytfu8ePH6dKlC926dePUqVOEhoYyYcIEoqKinuXpKVasWEGFChU4cuQIw4YNY9CgQXTu3JkmTZpw4sQJ/vOf/9CzZ0/u3LkDQG5uLq+99hrr1q3jzJkzTJw4kU8++YS1a9fqtLt3717Onz/P3r17WbFiBVFRUcU6x+XLl9OuXTusrKwICgoiMjJSZ39sbCwDBw7kww8/JC4ujtatWzNlyhSdOvv376dXr158+OGHnDlzhiVLlhAVFZWvXlhYGF26dOHkyZO0bduWHj16cP36dZycnFi/fj0AiYmJpKam5ku4D8vKyiI9PV1nE0Lol0qr1Wr1HURRBAcH8/XXX2NiYkJ2djZZWVkYGBiwdu1aAgMDCzymffv21KpVi1mzZgEPRhZ169Zl7ty5Sp2YmBj8/Py4ceMG1tbW9OjRg3///Zcff/xRqTNmzBi2bt3Kb7/99sQ4fX19dZJjnuzsbExMTJQRV3BwMDdv3mTjxo1KnREjRhAXF0dMTIzSVk5ODvv37wcejHKsrKx45513WLlyJQB///03lSpV4tChQ7zxxhsFxjR06FD+/vtvvvvuO6XvmJgYzp8/j6GhIQBdunTBwMCA6OjoJ55jbm4uzs7OfP7553To0IGrV69SuXJlfv/9d6pVqwZAt27dyMjIYMuWLcpxQUFBbNmyRbkGrVq1omXLlowbN06p8/XXXzNmzBguX74MPBgJjR8/noiICABu376NRqNh+/btBAQE5Hv+Hic0NJSwsLB85XPSJmBqafLE8xbPh7vpmYyyiiAtLQ1LS0udfenp6VhZWZE2qB+WauNCWiie9Kx7WC2KLLA/UXov1EjIz8+PuLg4Dh8+TO/evenTp4+SgHJycoiIiMDLywsbGxs0Gg07d+4kJSWlWH0kJCTQtGlTnbKmTZuSlJRETk5Okdro0aMHcXFxOlt4eHix4sjj7e2t/NvQ0BBbW1u8vLyUsooVKwJw5coVpWzhwoX4+PhgZ2eHRqPhyy+/zHcdateurSQggEqVKum08Ti7du3i9u3btG3bFoAKFSrQunVrli1bptRJTEykUaNGOsc9+jg+Pp7w8HA0Go2y5Y1U80Z2j14Dc3NzLC0tixzrw8aNG0daWpqyXbx4sdhtCCHK1gv1c5Lm5ubUqFEDgGXLllGnTh0iIyPp168fM2fOZN68ecydOxcvLy/Mzc0ZMWIE9+7de+ZxWllZKXHmsbe313lsYGDAo4PQgu7JlCtXTudx3urAhx/Dg9EJQHR0NCEhIcyePZvGjRtjYWHBzJkzOXz48BPbzWvjSSIjI7l+/TqmpqZKWW5uLidPniQsLAwDg6K9t8nIyCAsLIx33nkn3z4Tk/8bmZQm1oep1WrUanWxjxNCPD0vVBJ6mIGBAZ988gmjRo3ivffeIzY2lg4dOhAUFAQ8eFE8e/YsHh4eyjHGxsZPHM24u7sTGxurUxYbG4ubm5vOyKG07OzsOH36tE5ZXFxcvhfc4oqNjaVJkyYMHjxYKTt//nyp2nzYtWvX2LRpE9HR0dSuXVspz8nJ4c033+THH38kICCAmjVrcvToUZ1jH31cv359EhMT8yXs4sib9izqKFUI8Xx5oabjHtW5c2cMDQ1ZuHAhrq6u7Nq1i4MHD5KQkMCAAQP4559/dOo7Oztz+PBhkpOTuXr1aoHvpkePHs2ePXuIiIjg7NmzrFixggULFhASElKmsb/11lscO3aMlStXkpSUxKRJk/IlpZJwdXXl2LFj7Ny5k7NnzzJhwoR8L/6lsWrVKmxtbenSpQuenp7KVqdOHdq2bassUBg2bBjbtm1jzpw5JCUlsWTJErZv366zFH7ixImsXLmSsLAwfvvtNxISEoiOjmb8+PFFjqdq1aqoVCq2bNnCv//+S0ZGRpmdqxDi6Xuhk5CRkRFDhw5lxowZjB49mvr16+Pv74+vry8ODg75lkCHhIRgaGiIh4cHdnZ2Bd4vql+/PmvXriU6OhpPT08mTpxIeHh4mS/99ff3Z8KECYwZM4aGDRty69YtevXqVep2BwwYwDvvvEPXrl15/fXXuXbtms6oqLSWLVtGp06dCvxcVWBgIJs3b+bq1as0bdqUxYsXM2fOHOrUqcOOHTsYOXKkzjSbv78/W7Zs4ccff6Rhw4a88cYbfPbZZ1StWrXI8VSuXJmwsDDGjh1LxYoVGTp0aJmcpxDi2XhhVseJF1///v35/fffldV++pa3kkpWx71YZHXcy+WFvScknn+zZs2idevWmJubs337dlasWMEXX3yh77CEEM8RSULFsH//ftq0aVPo/pfhfkRZnuORI0eYMWMGt27dwsXFhfnz5/P++++XRZhCiJeEJKFiaNCgAXFxcfoO46kqy3N89FsahBDiUZKEisHU1LRUy4lfBK/COQohnh8v9Oo4IYQQLzZJQkIIIfRGkpAQQgi9kSQkhBBCbyQJCSGE0BtZHSdeeTfXZZNplv3kiuK5kHlHnquXiSQh8cqz7myEqaX8Kbwo7qYbgXzm+aUh03FCCCH0RpKQEEIIvZEkJIQQQm8kCQkhhNAbSUJCCCH0RpKQEEIIvZEkJIQQQm8kCQkhhNAbSUJCCCH0RpKQEEIIvZEkJIQQQm8kCb0ioqKisLa21ncYQgih44VLQsHBwXTs2DFfeUxMDCqVips3bz7zmJ6GQ4cOYWhoSLt27Yp9rLOzM3PnztUp69q1K2fPni2j6J4sOTkZlUqlbDY2NrRo0YL9+/cXq52X7XkVQuh64ZLQqyIyMpJhw4bx888/c/ny5VK3Z2pqir29fRlEVjy7d+8mNTWVn3/+GUdHR9q3b88///zzzOMQQjyfXsokFBoaSt26dXXK5s6di7Ozs/I4b0Q1depUKlasiLW1NeHh4WRnZ/PRRx9hY2PDa6+9xvLly3Xa+fjjj3Fzc8PMzAwXFxcmTJjA/fv38/W9atUqnJ2dsbKyolu3bty6davI8WdkZPDtt98yaNAg2rVrR1RUVL46P/zwAw0bNsTExIQKFSrQqVMnAHx9ffnzzz8ZOXKkMgqBgqfjFi1aRPXq1TE2NqZmzZqsWrVKZ79KpeKrr76iU6dOmJmZ4erqyubNm4t8HgC2trY4ODjg6enJJ598Qnp6OocPH1b2r1q1igYNGmBhYYGDgwPvvfceV65cAR6Mpvz8/AAoX748KpWK4OBgAHJzc5k2bRrVqlXD1NSUOnXq8N133xUrNiGE/r2USaiofvrpJy5fvszPP//MnDlzmDRpEu3bt6d8+fIcPnyYgQMHMmDAAP766y/lGAsLC6Kiojhz5gzz5s1j6dKlfPbZZzrtnj9/no0bN7Jlyxa2bNnCvn37mD59epHjWrt2LbVq1aJmzZoEBQWxbNkytFqtsn/r1q106tSJtm3b8uuvv7Jnzx4aNWoEwIYNG3jttdcIDw8nNTWV1NTUAvv4/vvv+fDDDxk9ejSnT59mwIAB9OnTh7179+rUCwsLo0uXLpw8eZK2bdvSo0cPrl+/XuRzyXP37l1WrlwJgLGxsVJ+//59IiIiiI+PZ+PGjSQnJyuJxsnJifXr1wOQmJhIamoq8+bNA2DatGmsXLmSxYsX89tvvzFy5EiCgoLYt29foTFkZWWRnp6uswkh9OuF/CWvLVu2oNFodMpycnKK3Y6NjQ3z58/HwMCAmjVrMmPGDO7cucMnn3wCwLhx45g+fToHDhygW7duAIwfP1453tnZmZCQEKKjoxkzZoxSnpubS1RUFBYWFgD07NmTPXv2MGXKlCLFFRkZSVBQEAABAQGkpaWxb98+fH19AZgyZQrdunUjLCxMOaZOnTrKORkaGioji8LMmjWL4OBgBg8eDMCoUaP45ZdfmDVrljL6gAcjxu7duwMwdepU5s+fz5EjRwgICCjSuTRp0gQDAwPu3LmDVqvFx8eHli1bKvv79u2r/NvFxYX58+fTsGFDMjIy0Gg02NjYAGBvb6+M5LKyspg6dSq7d++mcePGyrEHDhxgyZIltGjRosBYpk2bpnPNhBD690KOhPz8/IiLi9PZvvrqq2K3U7t2bQwM/u8SVKxYES8vL+WxoaEhtra2yvQQwLfffkvTpk1xcHBAo9Ewfvx4UlJSdNp1dnZWEhBApUqVdNp4nMTERI4cOaK88BsZGdG1a1ciIyOVOnFxcTov5CWRkJBA06ZNdcqaNm1KQkKCTpm3t7fyb3NzcywtLYt8LvDgev3666+sX7+eGjVqEBUVRbly5ZT9x48f5+2336ZKlSpYWFgoCeTRa/qwc+fOcefOHVq3bo1Go1G2lStXcv78+UKPGzduHGlpacp28eLFIp+HEOLpeCFHQubm5tSoUUOn7OEpMwMDA53pK0Dnvk2eh18M4cE9kILKcnNzgQcr1nr06EFYWBj+/v5YWVkRHR3N7Nmzn9huXhtPEhkZSXZ2No6OjkqZVqtFrVazYMECrKysMDU1LVJbZaE05wIPptRcXV1xdXUlOzubTp06cfr0adRqNbdv38bf3x9/f39Wr16NnZ0dKSkp+Pv7c+/evULbzMjIAB5MS1auXFlnn1qtLvQ4tVr92P1CiGfvhRwJPYmdnR1///23TiKKi4srdbsHDx6katWq/O9//6NBgwa4urry559/lrrdPNnZ2axcuZLZs2frjPLi4+NxdHRkzZo1wIPRyZ49ewptx9jY+InTk+7u7sTGxuqUxcbG4uHhUfoTKcS7776LkZERX3zxBQC///47165dY/r06TRr1oxatWrlG2Xl3T96+Hw8PDxQq9WkpKRQo0YNnc3JyempxS+EKHsv5EjoSXx9ffn333+ZMWMG7777Ljt27GD79u1YWlqWql1XV1dSUlKIjo6mYcOGbN26le+//76Mon5wr+vGjRv069cPKysrnX2BgYFERkYycOBAJk2aRMuWLalevTrdunUjOzubbdu28fHHHwMPpgN//vlnunXrhlqtpkKFCvn6+uijj+jSpQv16tWjVatW/PDDD2zYsIHdu3eX2fk8SqVSMXz4cEJDQxkwYABVqlTB2NiYzz//nIEDB3L69GkiIiJ0jqlatSoqlYotW7bQtm1bTE1NsbCwICQkhJEjR5Kbm8ubb75JWloasbGxWFpa0rt376d2DkKIsvVSjoTc3d354osvWLhwIXXq1OHIkSOEhISUut3//ve/jBw5kqFDh1K3bl0OHjzIhAkTyiDiByIjI2nVqlW+BAQPktCxY8c4efIkvr6+rFu3js2bN1O3bl3eeustjhw5otQNDw8nOTmZ6tWrY2dnV2BfHTt2ZN68ecyaNYvatWuzZMkSli9frix+eFp69+7N/fv3WbBgAXZ2dkRFRbFu3To8PDyYPn06s2bN0qlfuXJlwsLCGDt2LBUrVmTo0KEAREREMGHCBKZNm4a7uzsBAQFs3bqVatWqPdX4hRBlS6V99OaJEK+I9PR0rKysmJM2AVNLE32HI4robnomo6wiSEtLyze7kfecpg3qh6XauJAWiic96x5WiyIL7E+U3ks5EhJCCPFikCT0DKWkpOgsKX50e9yy5OfNwIEDCz2PgQMH6js8IcQL4qVcmPC8cnR0fOwqvYeXZT/vwsPDC73PJlMWQoiikiT0DBkZGeX7fNOLyt7eXi9fiCqEeLnIdJwQQgi9kSQkhBBCbyQJCSGE0BtJQkIIIfRGkpAQQgi9kdVx4pV3c102mWbZ+g5DFFHmHXmuXiaShMQrz7qzEaaW8qfworibbgTv6zsKUVZkOk4IIYTeSBISQgihN5KEhBBC6I0kISGEEHojSUgIIYTeSBISQgihN5KEhBBC6I0kISGEEHojSUgIIYTeSBISQgihN5KEXhExMTGoVCpu3ryp71CEEELxUiehQ4cOYWhoSLt27fTSf3JyMiqViri4uGIf+9dff2FsbIynp2exj/X19WXEiBE6ZU2aNCE1NRUrK6tit1dSKpVK2SwtLWnYsCGbNm0qVhuluYZCiOffS52EIiMjGTZsGD///DOXL1/WdzjFEhUVRZcuXUhPT+fw4cOlbs/Y2BgHBwdUKlUZRFd0y5cvJzU1lWPHjtG0aVPeffddTp069UxjEEI8v17aJJSRkcG3337LoEGDaNeuHVFRUTr7N2/ejKurKyYmJvj5+bFixYp801UHDhygWbNmmJqa4uTkxPDhw7l9+7ay39nZmalTp9K3b18sLCyoUqUKX375pbK/WrVqANSrVw+VSoWvr2+RYtdqtSxfvpyePXvy3nvvERkZma9ObGwsvr6+mJmZUb58efz9/blx4wbBwcHs27ePefPmKaOQ5OTkAqfj1q9fT+3atVGr1Tg7OzN79mydPp50fkVhbW2Ng4MDbm5uREREkJ2dzd69e5X9O3bs4M0338Ta2hpbW1vat2/P+fPnlf2Pu4ZfffUV7u7umJiYUKtWLb744otixSaE0L+XNgmtXbuWWrVqUbNmTYKCgli2bBlarRaACxcu8O6779KxY0fi4+MZMGAA//vf/3SOP3/+PAEBAQQGBnLy5Em+/fZbDhw4wNChQ3XqzZ49mwYNGvDrr78yePBgBg0aRGJiIgBHjhwBYPfu3aSmprJhw4Yixb53717u3LlDq1atCAoKIjo6Wif5xcXF0bJlSzw8PDh06BAHDhzg7bffJicnh3nz5tG4cWP69+9PamoqqampODk55evj+PHjdOnShW7dunHq1ClCQ0OZMGFCvmT9uPMrjuzsbCWZGhsbK+W3b99m1KhRHDt2jD179mBgYECnTp3Izc0FCr+Gq1evZuLEiUyZMoWEhASmTp3KhAkTWLFiRaExZGVlkZ6errMJIfRLpc17ZX7JNG3alC5duvDhhx+SnZ1NpUqVWLduHb6+vowdO5atW7fqTAuNHz+eKVOmcOPGDaytrXn//fcxNDRkyZIlSp0DBw7QokULbt++jYmJCc7OzjRr1oxVq1YBD0YwDg4OhIWFMXDgQJKTk6lWrRq//vordevWLXLsPXr0wN7ens8++wyAunXrMmLECIKDgwF47733SElJ4cCBAwUe7+vrS926dZk7d65SFhMTg5+fn3J+PXr04N9//+XHH39U6owZM4atW7fy22+/ATzx/J5EpVJhYmKCoaEhd+/eJTc3F2dnZ44fP46NjU2Bx1y9ehU7OztOnTqFp6dnodewRo0aRERE0L17d6Vs8uTJbNu2jYMHDxbYdmhoKGFhYfnK56RNwNTS5InnI54Pd9MzGWUVQVpaGpaWljr70tPTsbKyIm1QPyzVxoW0UDzpWfewWhRZYH+i9F7KkVBiYiJHjhxRXqCMjIzo2rWr8k48MTGRhg0b6hzTqFEjncfx8fFERUWh0WiUzd/fn9zcXC5cuKDU8/b2Vv6tUqlwcHDgypUrJY795s2bbNiwgaCgIKUsKChIZ0oubyRUGgkJCTRt2lSnrGnTpiQlJZGTk6OUlfb8PvvsM+Li4ti+fTseHh589dVXOgkoKSmJ7t274+LigqWlJc7OzgCkpKQU2ubt27c5f/48/fr103l+Jk+erDOV96hx48aRlpambBcvXizyeQghno6X8uckIyMjyc7OxtHRUSnTarWo1WoWLFhQpDYyMjIYMGAAw4cPz7evSpUqyr/LlSuns0+lUilTSSXxzTffkJmZyeuvv66UabVacnNzOXv2LG5ubpiampa4/eIq7fk5ODhQo0YNatSowfLly2nbti1nzpzB3t4egLfffpuqVauydOlSHB0dyc3NxdPTk3v37hXaZkZGBgBLly7VuU4AhoaGhR6nVqtRq9VFjl0I8fS9dCOh7OxsVq5cyezZs4mLi1O2+Ph4HB0dWbNmDTVr1uTYsWM6xx09elTncf369Tlz5ozyAvrw9vA9jcfJq/fwyOJJIiMjGT16dL7YmzVrxrJly4AHo5M9e/Y8tt8n9enu7k5sbKxOWWxsLG5ubo99IS+NRo0a4ePjw5QpUwC4du0aiYmJjB8/npYtW+Lu7s6NGzd0jinoGlasWBFHR0f++OOPfM9N3kIGIcSL4aUbCW3ZsoUbN27Qr1+/fJ+JCQwMJDIykrVr1zJnzhw+/vhj+vXrR1xcnHJDPm8J88cff8wbb7zB0KFDef/99zE3N+fMmTPs2rWryKMpe3t7TE1N2bFjB6+99homJiaP/ZxOXFwcJ06cYPXq1dSqVUtnX/fu3QkPD2fy5MmMGzcOLy8vBg8ezMCBAzE2Nmbv3r107tyZChUq4OzszOHDh0lOTkaj0RR4/2X06NE0bNiQiIgIunbtyqFDh1iwYMFTX2E2YsQIOnXqxJgxY6hUqRK2trZ8+eWXVKpUiZSUFMaOHatTv7BrGBYWxvDhw7GysiIgIICsrCyOHTvGjRs3GDVq1FM9ByFE2XnpRkKRkZG0atWqwBf7wMBAjh07xq1bt/juu+/YsGED3t7eLFq0SFkdlzdd4+3tzb59+zh79izNmjWjXr16TJw4UWeK70mMjIyYP38+S5YswdHRkQ4dOjwxdg8Pj3wJCKBTp05cuXKFbdu24ebmxo8//kh8fDyNGjWicePGbNq0CSOjB+8pQkJCMDQ0xMPDAzs7uwLvr9SvX5+1a9cSHR2Np6cnEydOJDw8XFn88LQEBARQrVo1pkyZgoGBAdHR0Rw/fhxPT09GjhzJzJkzdeoXdg3ff/99vvrqK5YvX46XlxctWrQgKipKRkJCvGBe2tVxxTVlyhQWL14sN6tfIXkrqWR13ItFVse9XF666bii+uKLL2jYsCG2trbExsYyc+bMfJ8BEkII8XS9dNNxRZWUlESHDh3w8PAgIiKC0aNHExoa+tT7fXhJ8aPb/v37n3r/ZWXq1KmFnkebNm30HZ4Q4gXxyo6EPvvsM+XDoM/S476Is3Llys8ukFIaOHAgXbp0KXDfs1xCLoR4sb2ySUhfatSooe8QyoSNjU2h33oghBBF9cpOxwkhhNA/SUJCCCH0RpKQEEIIvZEkJIQQQm8kCQkhhNAbSUJCCCH0RpZoi1fezXXZZJpl6zsMUUSZd+S5eplIEhKvPOvORphayp/Ci+JuuhG8r+8oRFmR6TghhBB6I0lICCGE3kgSEkIIoTeShIQQQuiNJCEhhBB6I0lICCGE3kgSEkIIoTeShIQQQuiNJCEhhBB6I0lICCGE3kgSKqKYmBhUKhU3b97Udygl4uvry4gRI/QdhhBC6Hilk1BwcDAdO3YsUt0mTZqQmpqKlZXVU2n/UQMGDMDQ0JB169YV67jCkuWGDRuIiIgoUSwlERoaikqlQqVSYWhoiJOTEx988AHXr18vVjuluYZCiOffK52EisPY2BgHBwdUKtVT7+vOnTtER0czZswYli1bViZt2tjYYGFhUSZtFVXt2rVJTU0lJSWF5cuXs2PHDgYNGvRMYxBCPN8kCf1/WVlZDB8+HHt7e0xMTHjzzTc5evSosv/REUZUVBTW1tbs3LkTd3d3NBoNAQEBpKamAg9GAitWrGDTpk3KiCAmJqZIsaxbtw4PDw/Gjh3Lzz//zMWLF/PF+vHHH+Pk5IRaraZGjRpERkaSnJyMn58fAOXLl0elUhEcHAzkn467ceMGvXr1onz58piZmdGmTRuSkpKU/U86v6IwMjLCwcGBypUr06pVKzp37syuXbuU/Tk5OfTr149q1aphampKzZo1mTdvnrL/cdfw4sWLdOnSBWtra2xsbOjQoQPJyclFjk0I8XyQJPT/jRkzhvXr17NixQpOnDhBjRo18Pf3f+z00Z07d5g1axarVq3i559/JiUlhZCQEABCQkLo0qWL8sKdmppKkyZNihRLZGQkQUFBWFlZ0aZNG6KionT29+rVizVr1jB//nwSEhJYsmQJGo0GJycn1q9fD0BiYiKpqak6L+oPCw4O5tixY2zevJlDhw6h1Wpp27Yt9+/fL9L5FVdycjI7d+7E2NhYKcvNzeW1115j3bp1nDlzhokTJ/LJJ5+wdu1aoPBreP/+ffz9/bGwsGD//v3ExsYqSfLevXuFxpCVlUV6errOJoTQL/kRFeD27dssWrSIqKgo2rRpA8DSpUvZtWsXkZGRfPTRRwUed//+fRYvXkz16tUBGDp0KOHh4QBoNBpMTU3JysrCwcGhyLEkJSXxyy+/sGHDBgCCgoIYNWoU48ePR6VScfbsWdauXcuuXbto1aoVAC4uLsrxNjY2ANjb22NtbV1oH5s3byY2NlZJjKtXr8bJyYmNGzfSuXPnJ55fUZw6dQqNRkNOTg6ZmZkAzJkzR9lfrlw5wsLClMfVqlXj0KFDrF27li5duhR6Db/++mtyc3P56quvlOnR5cuXY21tTUxMDP/5z38KjGfatGk6/Qkhyp5KpeL7778v8r1cGQkB58+f5/79+zRt2lQpK1euHI0aNSIhIaHQ48zMzJQXaIBKlSpx5cqVUsWybNky/P39qVChAgBt27YlLS2Nn376CYC4uDgMDQ1p0aJFiftISEjAyMiI119/XSmztbWlZs2aOudb2vOrWbMmcXFxHD16lI8//hh/f3+GDRumU2fhwoX4+PhgZ2eHRqPhyy+/JCUl5bHtxsfHc+7cOSwsLNBoNGg0GmxsbMjMzOT8+fOFHjdu3DjS0tKU7dFpTiHKQnBwMCqViunTp+uUb9y4sdj3lJ2dnZk7d26R6uVNWT+8PRrD80hGQqVQrlw5nccqlQqtVlvi9nJyclixYgV///03RkZGOuXLli2jZcuWmJqalrj94irt+RkbG1OjRg0Apk+fTrt27QgLC1NW6UVHRxMSEsLs2bNp3LgxFhYWzJw5k8OHDz+23YyMDHx8fFi9enW+fXZ2doUep1arUavVRY5fiJIyMTHh008/ZcCAAZQvX/6Z9BkeHk7//v11yp71YqSSkJEQUL16dYyNjYmNjVXK7t+/z9GjR/Hw8Chxu8bGxuTk5BS5/rZt27h16xa//vorcXFxyrZmzRo2bNjAzZs38fLyIjc3l3379hXaJ/DYft3d3cnOztZ5sb927RqJiYmlOt8nGT9+PLNmzeLy5csAynTg4MGDqVevHjVq1Mg3kinoGtavX5+kpCTs7e2pUaOGzlacJfRCPC2tWrXCwcGBadOmPbbe+vXrqV27Nmq1GmdnZ2bPnq3s8/X15c8//2TkyJHKyOZxLCwscHBw0NnMzc2V/du2bcPNzQ1TU1P8/PyIiorSWWwVGhpK3bp1ddqcO3cuzs7OyuOjR4/SunVrKlSogJWVFS1atODEiRNFuyiFkCQEmJubM2jQID766CN27NjBmTNn6N+/P3fu3KFfv34lbtfZ2ZmTJ0+SmJjI1atXdW76FyQyMpJ27dpRp04dPD09lS1vFdjq1atxdnamd+/e9O3bl40bN3LhwgViYmKUm/lVq1ZFpVKxZcsW/v33XzIyMvL14+rqSocOHejfvz8HDhwgPj6eoKAgKleuTIcOHUp8vk/SuHFjvL29mTp1qhLHsWPH2LlzJ2fP/r/27i+kqTaOA/jX7UKNUhO7qPnawD8xKIyxLrpIll4UYtik1DBzhdFYYPWKzcS5NUGsSKyWRrArISKoJlGrq9YsFEESBkFdBUpoJcudyD/QfC96d3DOZTrtOPb9wLnYOWdnz+bY7zzn+T7HDzCbzSGJRGDxz7CqqgoZGRkoLS1FX1+f+BnU1dVhdHR0zdpPscM/Owv/zCot/4ddFoZaZmZmIr6+XC5HW1sbbt26FfE7OTQ0hPLyclRWVsLr9cJqtcJsNotBpEePHiEzMxM2m00M5qzUyMgIysrKcOjQIQwPD6O2thaNjY3LPo4gCKipqcHr168xMDCA3NxcFBcXQxCEFbctri/HBQIB8bJXe3s7AoEAqqurIQgCNBoNXrx4EVVX+vTp03C73dBoNPj+/TtevnwJrVa76L7j4+N4+vQp7t27F7ZNJpNBp9PB4XDg7Nmz6O7uRlNTE4xGIyYmJpCVlYWmpiYAgEKhwOXLl9HY2IiTJ0/ixIkTYek64NdA/rlz51BSUoLZ2VkUFBTg2bNnYZfgVtuFCxeg1+thMplw5swZvH37FhUVFUhISMCxY8dgNBrhcrnE/SN9hh6PByaTCWVlZRAEAQqFAkVFRUhJSVnT9tP6FpzP94+jZ1WPG0yfzmexWGC1WiM+R6fTYffu3bBYLHA4HGHbOzo6UFRUBLPZDADIy8vDu3fvcO3aNej1eqSnp0Mul4s9nKWYTCY0NzeHrHO5XNi3bx+6u7uRnZ0t9rR27NgBr9eLK1euLHnc+QoLC0Me3717F2lpaXj16hVKSkqWdayghLloBjFi3MGDB5GTkwO73S51U0gCfr8fqamp6Jg0IzklSerm0B+a8k/j39RWTE5OLnrSMT09/duo/krMzc2FXQ6LNMao1+vx7ds3OJ1OeDweFBYWwuv14v3799DpdOK4qlqtRmlpKSwWi/jc3t5eHD16FFNTU5DL5VAqlTh//vySt9xSKpU4fvy4OC8wSKFQIDk5GTqdDps3bw6Z/N7b24vDhw/D5/MhLS0NVqsVTqcTw8PD4j6dnZ3o7OwU5+CNj4+jubkZbrcbnz9/xs+fP/Hjxw/Y7XYYjUYAy0/HxWVPyOfz4c2bN3C73TAYDFI3h4hWUVJSEpKS1sdJRUFBAQ4cOIBLly6FFYjVlpGRIQaBVkImk4UFjxYOIdTU1GBiYgI3btzA9u3bkZiYiL1790ZV9ONyTOjUqVMwGAyor69f0zGQhdra2sRI8cIlOD8pVkR6Hxs3bkRfX5/UzSNaN9rb2/HkyRP09/eHrFepVCFhKOBXWCcvLw9yuRzA8sNNkahUKgwODoasGxgYCHm8ZcsWjI2NhRSi+b2iYPvq6upQXFwsBiq+fv0aVdvisif0+PFjSV7XYDCgvLx80W1/M3q9GhZ+OedTKBR/ryFE69yuXbtQVVWFmzdvhqyvr6/Hnj170NraioqKCvT398Nut6Orq0vcR6lUwuPxoLKyEomJieL8wcUIgoCxsbGQdRs2bEBKSgoMBgOuX7+OhoYG1NbWYmhoKGysWKvV4suXL7h69SqOHDmC58+fw+VyhVzyzM3NRU9PDzQaDfx+PxoaGqL+7YrLnpBU0tPTwyLFwSXWfrgjvY+cnJyYK6hEa81msyEQCISsU6vVePDgAe7fv4+dO3eipaUFNpst5LKdzWbDx48fkZ2d/ds5cADQ0tKCrVu3hiwXL14EAGRlZeHhw4dwOp3Iz8/HnTt3xJRqkEqlQldXF27fvo38/HwMDg6G3abL4XDA5/NBrVajurpavN9mNOI6mEDxjcGE2LRUMIH+jNvtxv79+8VgglTYEyIiIsmwCBERkWTiMphARBTvtFptVPe6XC3sCRERkWTYE6K4FTwLnPZHvgcYrT/Bv9d6OIun6DEdR3FrdHQ07H5gFDtGRkaQmZkpdTMoSixCFLcCgQA+ffqETZs2LfufjZF05ubmIAgCtm3bBpmMIwqxjkWIiIgkw9MIIiKSDIsQERFJhkWIiIgkwyJERESSYREiIiLJsAgREZFkWISIiEgyLEJERCQZFiEiIpIMixAREUmGRYiIiCTDIkRERJL5D8NrjT7z2h+bAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 400x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create an empty matrix to store results\n",
    "variance_list = np.zeros(len(metrics))\n",
    "\n",
    "# Perform variance test and fill in the list for visual representation\n",
    "for i, metric in enumerate(metrics):\n",
    "    conditions_each_metric = []\n",
    "    for j, condition in enumerate(conditions):\n",
    "        conditions_each_metric.append(list(data[data['Condition'] == condition][metric]))\n",
    "    var_test = stats.levene(*conditions_each_metric)\n",
    "    if(var_test.pvalue < 0.05): \n",
    "        variance_list[i] = 0  # Unequal variances (Red)\n",
    "    else:\n",
    "        variance_list[i] = 1  # Equal variances (Green)\n",
    "\n",
    "# Plot the matrix\n",
    "fig, ax = plt.subplots(figsize=(4, 5)) \n",
    "\n",
    "# Add gridlines\n",
    "ax.grid(False)\n",
    "ax.set_xticks(np.arange(-0.5, len(conditions), 1), minor=True)\n",
    "ax.set_yticks(np.arange(-0.51, len(metrics), 1), minor=True)\n",
    "ax.grid(which=\"minor\", color=\"grey\", linestyle='-', linewidth=0.5)\n",
    "ax.tick_params(which=\"minor\", bottom=False, left=False)\n",
    "\n",
    "# Create a color map: Green (#97ff85) for normal, Red (#ff7069) for not normal\n",
    "cmap = mcolors.ListedColormap(['#ff7069', '#97ff85'])\n",
    "\n",
    "# Create the heatmap as a vertical column\n",
    "cax = ax.imshow(variance_list[:, np.newaxis], cmap=cmap, aspect='auto')\n",
    "\n",
    "# Set the tick labels\n",
    "ax.set_yticks(np.arange(len(metrics)))\n",
    "ax.set_yticklabels(metrics)\n",
    "ax.set_xticks([])\n",
    "ax.set_xticklabels([])\n",
    "\n",
    "# Add a color bar\n",
    "cbar = fig.colorbar(cax, ticks=[0, 1])\n",
    "cbar.ax.set_yticklabels(['Not Equal', 'Equal'])\n",
    "\n",
    "plt.ylabel('Metrics')\n",
    "plt.title('Variance Test Results')\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b9bd52c184cbe43d",
   "metadata": {},
   "source": [
    "#### 5. Run Kruskal-Wallis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "fb628e25",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Natural_Trust - p-value: 0.023096058704354137, stats: 13.030344710566288\n",
      "Satisfaction - p-value: 0.006105951908478124, stats: 16.273161942215005\n",
      "Artificial_Competence - p-value: 0.030373450078054805, stats: 12.343415795943432\n",
      "Ratio_Human_Agent - p-value: 0.0005160632047120885, stats: 22.033113699262817\n",
      "Human_Action_Rate - p-value: 2.0981223081643133e-09, stats: 49.1192561983471\n",
      "Agent_Action_Rate - p-value: 6.381787724864391e-08, stats: 41.828016528925616\n",
      "Joint_Action_Rate - p-value: 0.00047841947745193465, stats: 22.206033057851243\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x1400 with 8 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Perform Kruskal-Wallis tests for each metric across all conditions\n",
    "valid_metrics = [] \n",
    "for metric in metrics:\n",
    "    conditions_each_metric = []\n",
    "    for condition in conditions:\n",
    "        conditions_each_metric.append(list(data[data['Condition'] == condition][metric]))\n",
    "    \n",
    "    kruskal = stats.kruskal(*conditions_each_metric)\n",
    "    \n",
    "    if kruskal.pvalue <= 0.05:  # Null hypothesis that the medians are equal is accepted\n",
    "        valid_metrics.append(metric)\n",
    "        print(metric + \" - p-value: \" + str(kruskal.pvalue) + \", stats: \" + str(kruskal.statistic))\n",
    "        \n",
    "fig, axes = plt.subplots((len(valid_metrics) + 1) // 2, 2, figsize=(10, len(valid_metrics) * 2))\n",
    "axes = axes.flatten()\n",
    "\n",
    "for ax, metric in zip(axes, valid_metrics):\n",
    "    format_dataframe = data[[\"Condition\", metric]]\n",
    "    sns.boxplot(data=format_dataframe, x=\"Condition\", y=metric, ax=ax)\n",
    "    \n",
    "    ax.set_xlabel(\"Group Condition\")\n",
    "    ax.set_ylabel(metric.replace(\"_\", \" \"))\n",
    "    ax.set_title(metric.replace(\"_\", \" \"))\n",
    "    sns.despine(ax=ax)\n",
    "\n",
    "    \n",
    "# Turn off unused subplots\n",
    "if len(valid_metrics) < len(axes):\n",
    "    for ax in axes[len(valid_metrics):]:\n",
    "        ax.axis('off') \n",
    "        \n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "e78bcffa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Compliance',\n",
       " 'Natural_Trust',\n",
       " 'Satisfaction',\n",
       " 'Artificial_Competence',\n",
       " 'Artificial_Willingness',\n",
       " 'Artificial_Trust',\n",
       " 'Communication_Rate',\n",
       " 'Ratio_Joint_Human',\n",
       " 'Ratio_Joint_Agent',\n",
       " 'Ratio_Human_Agent',\n",
       " 'Human_Action_Rate',\n",
       " 'Agent_Action_Rate',\n",
       " 'Joint_Action_Rate']"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "ab7231b9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "P-value for Natural_Trust: 0.023096058704354664\n",
      "P-value for Satisfaction: 0.006105951908478124\n",
      "P-value for Artificial_Competence: 0.030373450078054805\n",
      "P-value for Ratio_Human_Agent: 0.0005160632047120885\n",
      "P-value for Human_Action_Rate: 2.0981223081643133e-09\n",
      "P-value for Agent_Action_Rate: 6.381787724864575e-08\n",
      "P-value for Joint_Action_Rate: 0.00047841947745193465\n",
      "Saved plot for Natural_Trust as Natural_Trust_kruskal_wallis.png\n",
      "Saved plot for Satisfaction as Satisfaction_kruskal_wallis.png\n",
      "Saved plot for Artificial_Competence as Artificial_Competence_kruskal_wallis.png\n",
      "Saved plot for Ratio_Human_Agent as Ratio_Human_Agent_kruskal_wallis.png\n",
      "Saved plot for Human_Action_Rate as Human_Action_Rate_kruskal_wallis.png\n",
      "Saved plot for Agent_Action_Rate as Agent_Action_Rate_kruskal_wallis.png\n",
      "Saved plot for Joint_Action_Rate as Joint_Action_Rate_kruskal_wallis.png\n"
     ]
    }
   ],
   "source": [
    "valid_metrics = [] \n",
    "for metric in metrics:\n",
    "    conditions_each_metric = []\n",
    "    for condition in conditions:\n",
    "        conditions_each_metric.append(list(data[data['Condition'] == condition][metric]))\n",
    "    \n",
    "    kruskal = stats.kruskal(*conditions_each_metric)\n",
    "    \n",
    "    if kruskal.pvalue <= 0.05:  # Null hypothesis that the medians are equal is rejected\n",
    "        valid_metrics.append(metric)\n",
    "        print(\"P-value for \" + metric + \": \" + str(kruskal.pvalue))\n",
    "\n",
    "# Save individual plots for each valid metric\n",
    "for metric in valid_metrics:\n",
    "    plt.figure(figsize=(6, 4))\n",
    "    format_dataframe = data[[\"Condition\", metric]]\n",
    "    sns.boxplot(data=format_dataframe, x=\"Condition\", y=metric)\n",
    "    \n",
    "    plt.xlabel(\"Group Condition\")\n",
    "    plt.ylabel(metric.replace(\"_\", \" \"))\n",
    "    plt.title(metric.replace(\"_\", \" \") + \" Distributions\")\n",
    "    sns.despine()\n",
    "\n",
    "    # Save the plot as a PNG file\n",
    "    plot_filename = f\"{metric}_kruskal_wallis.png\"\n",
    "    plt.savefig(plot_filename, bbox_inches=\"tight\")\n",
    "    print(f\"Saved plot for {metric} as {plot_filename}\")\n",
    "    plt.close()\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "557a02ad1beff61e",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-12-28T22:29:12.713550Z",
     "start_time": "2024-12-28T22:29:12.707972Z"
    }
   },
   "outputs": [],
   "source": [
    "import itertools\n",
    "from scipy.stats import ttest_ind, mannwhitneyu, shapiro\n",
    "\n",
    "\n",
    "# Generate all pairs of conditions\n",
    "condition_pairs = list(itertools.combinations(conditions, 2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "5ef4314a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "            Condition 1             Condition 2 Test Used  Statistic  p-value\n",
      "0  B (M=3.66, STD=0.59)   OVE (M=4.1, STD=0.64)     M-W U     111.50    0.017\n",
      "1  B (M=3.66, STD=0.59)  RVE (M=4.24, STD=0.32)    t-test      -3.82    0.001\n",
      "2  B (M=3.66, STD=0.59)  OTE (M=4.09, STD=0.44)     M-W U     109.50    0.015\n",
      "3  B (M=3.66, STD=0.59)   RTE (M=4.12, STD=0.4)    t-test      -2.86    0.007\n"
     ]
    }
   ],
   "source": [
    "# Convert to DataFrame\n",
    "df = pd.DataFrame(data)\n",
    "\n",
    "# List of unique conditions and metrics\n",
    "conditions = df['Condition'].unique()\n",
    "metrics = [col for col in df.columns if col != 'Condition']\n",
    "\n",
    "# Perform pairwise tests for Natural_Trust (example metric)\n",
    "condition_pairs = itertools.combinations(conditions, 2)\n",
    "results = []\n",
    "\n",
    "for cond1, cond2 in condition_pairs:\n",
    "    group1 = df[df['Condition'] == cond1]['Natural_Trust']  # Example column 'Metric1'\n",
    "    group2 = df[df['Condition'] == cond2]['Natural_Trust']\n",
    "    \n",
    "    # Calculate averages and standard deviations\n",
    "    mean1, std1 = group1.mean(), group1.std()\n",
    "    mean2, std2 = group2.mean(), group2.std()\n",
    "    \n",
    "    # Check for normality using Shapiro-Wilk test\n",
    "    stat1, p1 = shapiro(group1)\n",
    "    stat2, p2 = shapiro(group2)\n",
    "    \n",
    "    if p1 > 0.05 and p2 > 0.05:\n",
    "        # Both groups are normally distributed, use t-test\n",
    "        stat, p = ttest_ind(group1, group2, equal_var=False)  # Assuming unequal variance\n",
    "        test_used = 't-test'\n",
    "    else:\n",
    "        # At least one group is not normally distributed, use Mann-Whitney U test\n",
    "        stat, p = mannwhitneyu(group1, group2, alternative='two-sided')\n",
    "        test_used = 'M-W U'\n",
    "    \n",
    "    if p <= 0.05:\n",
    "        results.append({\n",
    "            'Condition 1': str(cond1) + \" (M=\" + str(round(mean1,2)) + \", STD=\" + str(round(std1,2)) + \")\",\n",
    "            'Condition 2': str(cond2) + \" (M=\" + str(round(mean2,2)) + \", STD=\" + str(round(std2,2)) + \")\",\n",
    "            'Test Used': test_used,\n",
    "            'Statistic': round(stat,2),\n",
    "            'p-value': round(p,3)\n",
    "        })\n",
    "\n",
    "# Convert results to a DataFrame\n",
    "results_df = pd.DataFrame(results)\n",
    "\n",
    "# Display the results\n",
    "print(results_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "60ad29ea",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "            Condition 1             Condition 2 Test Used  Statistic  p-value\n",
      "0  B (M=3.79, STD=0.68)  OVE (M=4.28, STD=0.53)    t-test      -2.51    0.017\n",
      "1  B (M=3.79, STD=0.68)  RVE (M=4.31, STD=0.42)    t-test      -2.87    0.007\n",
      "2  B (M=3.79, STD=0.68)  OTE (M=4.51, STD=0.41)     M-W U      72.00    0.001\n",
      "3  B (M=3.79, STD=0.68)  RTE (M=4.41, STD=0.47)    t-test      -3.31    0.002\n"
     ]
    }
   ],
   "source": [
    "# Convert to DataFrame\n",
    "df = pd.DataFrame(data)\n",
    "\n",
    "# List of unique conditions and metrics\n",
    "conditions = df['Condition'].unique()\n",
    "metrics = [col for col in df.columns if col != 'Condition']\n",
    "\n",
    "# Perform pairwise tests for Natural_Trust (example metric)\n",
    "condition_pairs = itertools.combinations(conditions, 2)\n",
    "results = []\n",
    "\n",
    "for cond1, cond2 in condition_pairs:\n",
    "    group1 = df[df['Condition'] == cond1]['Satisfaction']  # Example column 'Metric1'\n",
    "    group2 = df[df['Condition'] == cond2]['Satisfaction']\n",
    "    \n",
    "    # Calculate averages and standard deviations\n",
    "    mean1, std1 = group1.mean(), group1.std()\n",
    "    mean2, std2 = group2.mean(), group2.std()\n",
    "    \n",
    "    # Check for normality using Shapiro-Wilk test\n",
    "    stat1, p1 = shapiro(group1)\n",
    "    stat2, p2 = shapiro(group2)\n",
    "    \n",
    "    if p1 > 0.05 and p2 > 0.05:\n",
    "        # Both groups are normally distributed, use t-test\n",
    "        stat, p = ttest_ind(group1, group2, equal_var=False)  # Assuming unequal variance\n",
    "        test_used = 't-test'\n",
    "    else:\n",
    "        # At least one group is not normally distributed, use Mann-Whitney U test\n",
    "        stat, p = mannwhitneyu(group1, group2, alternative='two-sided')\n",
    "        test_used = 'M-W U'\n",
    "    \n",
    "    if p <= 0.05:\n",
    "        results.append({\n",
    "            'Condition 1': str(cond1) + \" (M=\" + str(round(mean1,2)) + \", STD=\" + str(round(std1,2)) + \")\",\n",
    "            'Condition 2': str(cond2) + \" (M=\" + str(round(mean2,2)) + \", STD=\" + str(round(std2,2)) + \")\",\n",
    "            'Test Used': test_used,\n",
    "            'Statistic': round(stat,2),\n",
    "            'p-value': round(p,3)\n",
    "        })\n",
    "\n",
    "# Convert results to a DataFrame\n",
    "results_df = pd.DataFrame(results)\n",
    "\n",
    "# Display the results\n",
    "print(results_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "486a75eb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "            Condition 1             Condition 2 Test Used  Statistic  p-value\n",
      "0  B (M=0.66, STD=0.18)  OVE (M=0.82, STD=0.15)     M-W U      96.00    0.005\n",
      "1  B (M=0.66, STD=0.18)    RV (M=0.8, STD=0.16)     M-W U     104.00    0.010\n",
      "2  B (M=0.66, STD=0.18)   RTE (M=0.8, STD=0.09)    t-test      -2.94    0.007\n"
     ]
    }
   ],
   "source": [
    "# Convert to DataFrame\n",
    "df = pd.DataFrame(data)\n",
    "\n",
    "# List of unique conditions and metrics\n",
    "conditions = df['Condition'].unique()\n",
    "metrics = [col for col in df.columns if col != 'Condition']\n",
    "\n",
    "# Perform pairwise tests for Natural_Trust (example metric)\n",
    "condition_pairs = itertools.combinations(conditions, 2)\n",
    "results = []\n",
    "\n",
    "for cond1, cond2 in condition_pairs:\n",
    "    group1 = df[df['Condition'] == cond1]['Artificial_Competence']  # Example column 'Metric1'\n",
    "    group2 = df[df['Condition'] == cond2]['Artificial_Competence']\n",
    "    \n",
    "    # Calculate averages and standard deviations\n",
    "    mean1, std1 = group1.mean(), group1.std()\n",
    "    mean2, std2 = group2.mean(), group2.std()\n",
    "    \n",
    "    # Check for normality using Shapiro-Wilk test\n",
    "    stat1, p1 = shapiro(group1)\n",
    "    stat2, p2 = shapiro(group2)\n",
    "    \n",
    "    if p1 > 0.05 and p2 > 0.05:\n",
    "        # Both groups are normally distributed, use t-test\n",
    "        stat, p = ttest_ind(group1, group2, equal_var=False)  # Assuming unequal variance\n",
    "        test_used = 't-test'\n",
    "    else:\n",
    "        # At least one group is not normally distributed, use Mann-Whitney U test\n",
    "        stat, p = mannwhitneyu(group1, group2, alternative='two-sided')\n",
    "        test_used = 'M-W U'\n",
    "    \n",
    "    if p <= 0.05:\n",
    "        results.append({\n",
    "            'Condition 1': str(cond1) + \" (M=\" + str(round(mean1,2)) + \", STD=\" + str(round(std1,2)) + \")\",\n",
    "            'Condition 2': str(cond2) + \" (M=\" + str(round(mean2,2)) + \", STD=\" + str(round(std2\n",
    "                                                                                            ,2)) + \")\",\n",
    "            'Test Used': test_used,\n",
    "            'Statistic': round(stat,2),\n",
    "            'p-value': round(p,3)\n",
    "        })\n",
    "\n",
    "# Convert results to a DataFrame\n",
    "results_df = pd.DataFrame(results)\n",
    "\n",
    "# Display the results\n",
    "print(results_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0ea79ea4",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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