analysis-validation_dataset-validation.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import pingouin as pg\n",
    "from scipy import stats\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.lines import Line2D\n",
    "import seaborn as sns\n",
    "from IPython.display import display\n",
    "\n",
    "from dual_data_utils import make_clean_concat_data\n",
    "from stopsignalmetrics import StopData, SSRTmodel"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Read in and Compute Metrics on Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset='validation'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "OUTLIER_THRESH  = 3\n",
    "SSD_THRESH = 200\n",
    "\n",
    "dual_dict = {\n",
    "    'stop_signal_with_cued_task_switching': {\n",
    "        'dual_col': 'cue_task_switch',\n",
    "        'DC': 'cue_stay_task_stay',\n",
    "        'DE': 'cue_switch_task_switch',\n",
    "        'OTHER': ['cue_switch_task_stay']\n",
    "    },\n",
    "    'stop_signal_with_directed_forgetting': {\n",
    "        'dual_col': 'directed_forgetting_condition',\n",
    "        'DC': 'con',\n",
    "        'DE': 'neg',\n",
    "        'OTHER': ['pos']\n",
    "    },\n",
    "    'stop_signal_with_flanker': {\n",
    "        'dual_col': 'flanker_condition',\n",
    "        'DC': 'congruent',\n",
    "        'DE': 'incongruent'\n",
    "    },\n",
    "    'stop_signal_with_go_no_go': {\n",
    "        'dual_col': 'go_nogo_condition',\n",
    "        'DC': 'go',\n",
    "        'DE': 'nogo'\n",
    "    },\n",
    "    'stop_signal_with_n_back': {\n",
    "        'dual_col': 'delay_condition',\n",
    "        'DC': 1.,\n",
    "        'DE': 2.,\n",
    "        'OTHER': [3.]\n",
    "    },\n",
    "    'stop_signal_with_predictable_task_switching': {\n",
    "        'dual_col': 'predictable_condition',\n",
    "        'DC': 'stay',\n",
    "        'DE': 'switch'\n",
    "    },\n",
    "    'stop_signal_with_shape_matching': {\n",
    "        'dual_col': 'shape_matching_condition',\n",
    "        'DC': 'CONTROL',\n",
    "        'DE': 'DISTRACTOR',\n",
    "        'OTHER': ['DSD', 'SSS']\n",
    "    },\n",
    "}\n",
    "\n",
    "stopmetrics_var_dict = {\n",
    "    'columns': {\n",
    "        'ID': 'worker_id',\n",
    "        'block': 'current_block',\n",
    "        'condition': 'SS_trial_type',\n",
    "        'SSD': 'SS_delay',\n",
    "        'goRT': 'rt',\n",
    "        'stopRT': 'rt',\n",
    "        'response': 'key_press',\n",
    "        'correct_response': 'choice_correct_response',\n",
    "        'choice_accuracy': 'choice_accuracy'},\n",
    "    'key_codes': {\n",
    "        'go': 'go',\n",
    "        'stop': 'stop',\n",
    "        'correct': 1,\n",
    "        'incorrect': 0,\n",
    "        'noResponse': -1}}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_query_str(col, condition):\n",
    "    if type(condition)==str:\n",
    "        return \"%s=='%s'\" % (col, condition)\n",
    "    elif type(condition) in [float, int]:\n",
    "        return \"%s==%s\" % (col, condition)\n",
    "    elif type(condition)==list:\n",
    "        return \"%s in %s\" % (col, condition)\n",
    "\n",
    "def stop_summarize(curr_data, thresh=SSD_THRESH, query_str=None):\n",
    "    data_df = curr_data.query(query_str) if query_str is not None else curr_data\n",
    "    \n",
    "    sum_df = SSRTmodel().fit_transform(data_df, level='group')\n",
    "    sum_df_threshSSDs = SSRTmodel().fit_transform(data_df.query(f'SSD>={thresh} or condition==\"go\"'), level='group').add_suffix('_wThresh_SSDs')\n",
    "    sum_df_threshSubs = sum_df.query(f\"mean_SSD >= {thresh}\").add_suffix('_wThresh_subs')\n",
    "    return pd.concat([sum_df, sum_df_threshSSDs, sum_df_threshSubs],\n",
    "                       1,\n",
    "                       sort=True\n",
    "                      )\n",
    "\n",
    "def get_inhib_func(curr_data, cond):\n",
    "    inhib_df = pd.DataFrame(curr_data.groupby(['ID', 'SSD']).apply(lambda x: x.stopRT.notnull().sum()/len(x))).reset_index()\n",
    "    inhib_df['condition'] = cond\n",
    "    return inhib_df\n",
    "\n",
    "def get_gng_presp(curr_data):\n",
    "    curr_data = curr_data.copy()\n",
    "    stop_trials = curr_data.query(\"stop_signal_condition=='stop'\")\n",
    "    out={}\n",
    "    out['p_resp'] = (stop_trials.rt != -1).sum()/len(stop_trials)\n",
    "    out['p_resp_go'] = (stop_trials.query(\"go_nogo_condition=='go'\").rt != -1).sum()/len(stop_trials.query(\"go_nogo_condition=='go'\"))\n",
    "    out['p_resp_nogo'] = (stop_trials.query(\"go_nogo_condition=='nogo'\").rt != -1).sum()/len(stop_trials.query(\"go_nogo_condition=='nogo'\"))\n",
    "    return out"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "stop_data_dict = make_clean_concat_data(stop_subset=True, dataset=dataset)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Check P(resp|signal) in Go/NoGo "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "        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>p_resp</th>\n",
       "      <th>p_resp_go</th>\n",
       "      <th>p_resp_nogo</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>33.000000</td>\n",
       "      <td>33.000000</td>\n",
       "      <td>33.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.433333</td>\n",
       "      <td>0.501768</td>\n",
       "      <td>0.022727</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.017097</td>\n",
       "      <td>0.016765</td>\n",
       "      <td>0.058751</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.371429</td>\n",
       "      <td>0.433333</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.428571</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.435714</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.442857</td>\n",
       "      <td>0.508333</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>0.471429</td>\n",
       "      <td>0.525000</td>\n",
       "      <td>0.300000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          p_resp  p_resp_go  p_resp_nogo\n",
       "count  33.000000  33.000000    33.000000\n",
       "mean    0.433333   0.501768     0.022727\n",
       "std     0.017097   0.016765     0.058751\n",
       "min     0.371429   0.433333     0.000000\n",
       "25%     0.428571   0.500000     0.000000\n",
       "50%     0.435714   0.500000     0.000000\n",
       "75%     0.442857   0.508333     0.000000\n",
       "max     0.471429   0.525000     0.300000"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gng_presp_data = pd.DataFrame(list(stop_data_dict['stop_signal_with_go_no_go'].groupby('worker_id').apply(get_gng_presp)))\n",
    "gng_presp_data.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "del stop_data_dict['stop_signal_with_go_no_go']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Get SSRT metrics for remaining tasks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# build dict\n",
    "ssrtc_out = {}\n",
    "for task_name in stop_data_dict.keys():  # [k for k in stop_data_dict.keys()][:1]:  # development loop using just 1st task\n",
    "    curr_data = StopData(stopmetrics_var_dict).fit_transform(stop_data_dict[task_name].drop(columns=['correct_response']))\n",
    "    short_name = task_name.split('_with_')[-1]\n",
    "    ssrtc_out[short_name] = stop_summarize(curr_data)\n",
    "    \n",
    "    if 'with' in task_name:\n",
    "        dual_col = dual_dict[task_name]['dual_col']\n",
    "        for key in [k for k in dual_dict[task_name] if k!='dual_col']:\n",
    "            query_str = get_query_str(dual_col, dual_dict[task_name][key])\n",
    "            ssrtc_out['%s_%s' % (short_name,key)] = stop_summarize(\n",
    "                curr_data,\n",
    "                query_str=query_str\n",
    "            )\n",
    "\n",
    "ssrtc_out['SE'] = ssrtc_out.pop('stop_signal_single_task_network')\n",
    "metrics = [c for c in ssrtc_out['SE'].columns if 'max_RT' not in c]\n",
    "\n",
    "#flatten data, remove outliers w/ values > 3 SDs from the mean\n",
    "flattened_data = pd.concat([ssrtc_out[k].add_prefix(k+'_') for k in ssrtc_out], axis=1, sort=True)\n",
    "flattened_data = flattened_data.filter(regex='SE|DC|DE', axis=1)\n",
    "z_df = flattened_data.apply(stats.zscore).apply(np.abs)\n",
    "flattened_data[z_df>OUTLIER_THRESH] = None\n",
    "flattened_data.index.name='ID'\n",
    "\n",
    "\n",
    "\n",
    "SSRTs = flattened_data.loc[:, ~flattened_data.columns.str.contains(\"wThresh\")].filter(regex='SSRT')\n",
    "SSRTs_ThreshSSDs = flattened_data.loc[:, flattened_data.columns.str.contains(\"wThresh_SSDs\")].filter(regex='SSRT')\n",
    "SSRTs_ThreshSubs = flattened_data.loc[:, flattened_data.columns.str.contains(\"wThresh_subs\")].filter(regex='SSRT')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Validation Analyses"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2 subs dropped for nan cell\n"
     ]
    }
   ],
   "source": [
    "def filter_out_nans(SSRTs):\n",
    "    old_nSubs = len(SSRTs)\n",
    "    SSRTs = SSRTs.dropna()\n",
    "    new_nSubs = len(SSRTs)\n",
    "    print(f'{old_nSubs-new_nSubs} subs dropped for nan cell')\n",
    "    return SSRTs\n",
    "SSRTs = filter_out_nans(SSRTs)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# One-way Anova"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>cued_task_switching</th>\n",
       "      <th>SE</th>\n",
       "      <th>directed_forgetting</th>\n",
       "      <th>flanker</th>\n",
       "      <th>predictable_task_switching</th>\n",
       "      <th>n_back</th>\n",
       "      <th>shape_matching</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ID</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>s009</th>\n",
       "      <td>168.00</td>\n",
       "      <td>193.916667</td>\n",
       "      <td>296.0</td>\n",
       "      <td>208.25</td>\n",
       "      <td>221.75</td>\n",
       "      <td>248.219758</td>\n",
       "      <td>219.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>s019</th>\n",
       "      <td>257.00</td>\n",
       "      <td>233.666667</td>\n",
       "      <td>366.0</td>\n",
       "      <td>243.75</td>\n",
       "      <td>261.00</td>\n",
       "      <td>197.663734</td>\n",
       "      <td>294.833333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>s031</th>\n",
       "      <td>349.50</td>\n",
       "      <td>237.000000</td>\n",
       "      <td>322.5</td>\n",
       "      <td>292.00</td>\n",
       "      <td>292.75</td>\n",
       "      <td>273.458333</td>\n",
       "      <td>261.583333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>s034</th>\n",
       "      <td>233.75</td>\n",
       "      <td>198.500000</td>\n",
       "      <td>390.5</td>\n",
       "      <td>258.00</td>\n",
       "      <td>254.00</td>\n",
       "      <td>320.218750</td>\n",
       "      <td>264.083333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>s038</th>\n",
       "      <td>223.75</td>\n",
       "      <td>206.750000</td>\n",
       "      <td>309.5</td>\n",
       "      <td>223.50</td>\n",
       "      <td>194.00</td>\n",
       "      <td>253.545363</td>\n",
       "      <td>223.916667</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      cued_task_switching          SE  directed_forgetting  flanker  \\\n",
       "ID                                                                    \n",
       "s009               168.00  193.916667                296.0   208.25   \n",
       "s019               257.00  233.666667                366.0   243.75   \n",
       "s031               349.50  237.000000                322.5   292.00   \n",
       "s034               233.75  198.500000                390.5   258.00   \n",
       "s038               223.75  206.750000                309.5   223.50   \n",
       "\n",
       "      predictable_task_switching      n_back  shape_matching  \n",
       "ID                                                            \n",
       "s009                      221.75  248.219758      219.333333  \n",
       "s019                      261.00  197.663734      294.833333  \n",
       "s031                      292.75  273.458333      261.583333  \n",
       "s034                      254.00  320.218750      264.083333  \n",
       "s038                      194.00  253.545363      223.916667  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def mean_SSRTs_within_task(SSRTs):\n",
    "    SSRTs_taskMeaned = pd.DataFrame()\n",
    "    for task_name in stop_data_dict.keys():\n",
    "        short_name = task_name.split('_with_')[-1]\n",
    "        if short_name == 'stop_signal_single_task_network':\n",
    "            SSRTs_taskMeaned['SE'] = SSRTs.filter(regex='SE')\n",
    "        else:\n",
    "            SSRTs_taskMeaned[short_name] = SSRTs.filter(regex=short_name).mean(1)\n",
    "    return SSRTs_taskMeaned\n",
    "SSRTs_taskMeaned = mean_SSRTs_within_task(SSRTs)\n",
    "SSRTs_taskMeaned.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ID</th>\n",
       "      <th>variable</th>\n",
       "      <th>value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>s009</td>\n",
       "      <td>cued_task_switching</td>\n",
       "      <td>168.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>s019</td>\n",
       "      <td>cued_task_switching</td>\n",
       "      <td>257.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>s031</td>\n",
       "      <td>cued_task_switching</td>\n",
       "      <td>349.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>s034</td>\n",
       "      <td>cued_task_switching</td>\n",
       "      <td>233.750000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>s038</td>\n",
       "      <td>cued_task_switching</td>\n",
       "      <td>223.750000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>212</th>\n",
       "      <td>s514</td>\n",
       "      <td>shape_matching</td>\n",
       "      <td>296.833333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>213</th>\n",
       "      <td>s528</td>\n",
       "      <td>shape_matching</td>\n",
       "      <td>288.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>214</th>\n",
       "      <td>s541</td>\n",
       "      <td>shape_matching</td>\n",
       "      <td>198.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>215</th>\n",
       "      <td>s548</td>\n",
       "      <td>shape_matching</td>\n",
       "      <td>305.416667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>216</th>\n",
       "      <td>s560</td>\n",
       "      <td>shape_matching</td>\n",
       "      <td>280.916667</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>217 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       ID             variable       value\n",
       "0    s009  cued_task_switching  168.000000\n",
       "1    s019  cued_task_switching  257.000000\n",
       "2    s031  cued_task_switching  349.500000\n",
       "3    s034  cued_task_switching  233.750000\n",
       "4    s038  cued_task_switching  223.750000\n",
       "..    ...                  ...         ...\n",
       "212  s514       shape_matching  296.833333\n",
       "213  s528       shape_matching  288.666667\n",
       "214  s541       shape_matching  198.500000\n",
       "215  s548       shape_matching  305.416667\n",
       "216  s560       shape_matching  280.916667\n",
       "\n",
       "[217 rows x 3 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def melt_taskMeaned_SSRTs(SSRTs_taskMeaned):\n",
    "    return SSRTs_taskMeaned.reset_index().melt(id_vars='ID')\n",
    "melted_SSRTs = melt_taskMeaned_SSRTs(SSRTs_taskMeaned)\n",
    "melted_SSRTs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "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>Source</th>\n",
       "      <th>SS</th>\n",
       "      <th>DF</th>\n",
       "      <th>MS</th>\n",
       "      <th>F</th>\n",
       "      <th>p-unc</th>\n",
       "      <th>p-GG-corr</th>\n",
       "      <th>np2</th>\n",
       "      <th>eps</th>\n",
       "      <th>sphericity</th>\n",
       "      <th>W-spher</th>\n",
       "      <th>p-spher</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>variable</td>\n",
       "      <td>288913.843232</td>\n",
       "      <td>6</td>\n",
       "      <td>48152.307205</td>\n",
       "      <td>34.753595</td>\n",
       "      <td>1.039136e-27</td>\n",
       "      <td>3.682862e-19</td>\n",
       "      <td>0.536705</td>\n",
       "      <td>0.662985</td>\n",
       "      <td>False</td>\n",
       "      <td>0.178864</td>\n",
       "      <td>0.000482</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Error</td>\n",
       "      <td>249396.223688</td>\n",
       "      <td>180</td>\n",
       "      <td>1385.534576</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Source             SS   DF            MS          F         p-unc  \\\n",
       "0  variable  288913.843232    6  48152.307205  34.753595  1.039136e-27   \n",
       "1     Error  249396.223688  180   1385.534576        NaN           NaN   \n",
       "\n",
       "      p-GG-corr       np2       eps sphericity   W-spher   p-spher  \n",
       "0  3.682862e-19  0.536705  0.662985      False  0.178864  0.000482  \n",
       "1           NaN       NaN       NaN        NaN       NaN       NaN  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def run_anova_on_melted_SSRTs(melted_SSRTs):\n",
    "    return pg.rm_anova(data=melted_SSRTs, dv='value', within='variable', subject='ID', detailed=True)\n",
    "anova_res = run_anova_on_melted_SSRTs(melted_SSRTs)\n",
    "anova_res"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Planned Contrasts\n",
    "\n",
    "Guidelines from [here](http://web.pdx.edu/~newsomj/uvclass/ho_planned%20contrasts.pdf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_degOfFree(anova_res):\n",
    "    return anova_res.loc[anova_res.Source=='Error', 'DF'].values[0]\n",
    "\n",
    "df = get_degOfFree(anova_res)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_standardError(anova_res):\n",
    "    MS = anova_res.loc[anova_res.Source=='Error', 'MS'].values[0]\n",
    "    return np.sqrt(MS * (2/len(SSRTs)))\n",
    "std_err = get_standardError(anova_res)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "directed_forgetting: Mean=113.67, t=12.023, p=0.000000, d=1.84\n",
      "n_back: Mean=35.91, t=3.798, p=0.000100, d=0.57\n",
      "cued_task_switching: Mean=15.36, t=1.624, p=0.053041, d=0.30\n",
      "shape_matching: Mean=34.85, t=3.686, p=0.000151, d=1.01\n"
     ]
    }
   ],
   "source": [
    "def run_planned_contrasts(SSRTs_taskMeaned, std_err, df):\n",
    "    planned_tasks = ['directed_forgetting', 'n_back', 'cued_task_switching', 'shape_matching']\n",
    "    test_res_df = pd.DataFrame(index=planned_tasks, columns=['mean', 't', 'p', 'd'])\n",
    "    for task in planned_tasks:\n",
    "        diffs = (SSRTs_taskMeaned.filter(regex=task).values - SSRTs_taskMeaned.filter(regex='SE').values)\n",
    "        t = np.mean(diffs)/std_err\n",
    "        pval = stats.t.sf(t, df) \n",
    "        print(f'{task}: Mean={np.mean(diffs):.2f}, t={t:.3f}, p={pval:.6f}, d={np.mean(diffs)/np.std(diffs):.2f}')\n",
    "        test_res_df.loc[task, ['mean', 't', 'p', 'd']] = np.mean(diffs), t, pval, np.mean(diffs)/np.std(diffs)\n",
    "    return test_res_df\n",
    "\n",
    "test_res_df = run_planned_contrasts(SSRTs_taskMeaned, std_err, df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Planned related-samples t-test for n-Back load"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "def subtract_SE_SSRTs(SSRTs):\n",
    "    diff_SSRTs = SSRTs.sub(SSRTs.filter(regex='SE').values)\n",
    "    del diff_SSRTs['SE_SSRT']\n",
    "    return diff_SSRTs\n",
    "diff_SSRTs = subtract_SE_SSRTs(SSRTs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "n-Back DE > DC: Mean=26.16, t=2.277, p=0.015052, d=0.42\n"
     ]
    },
    {
     "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>mean</th>\n",
       "      <th>t</th>\n",
       "      <th>p</th>\n",
       "      <th>d</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>directed_forgetting</th>\n",
       "      <td>113.670699</td>\n",
       "      <td>12.022802</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.843494</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>n_back</th>\n",
       "      <td>35.910473</td>\n",
       "      <td>3.798204</td>\n",
       "      <td>0.0001</td>\n",
       "      <td>0.57305</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cued_task_switching</th>\n",
       "      <td>15.356183</td>\n",
       "      <td>1.624203</td>\n",
       "      <td>0.053041</td>\n",
       "      <td>0.30466</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>shape_matching</th>\n",
       "      <td>34.850806</td>\n",
       "      <td>3.686124</td>\n",
       "      <td>0.000151</td>\n",
       "      <td>1.013786</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>n_back: DE &gt; DC</th>\n",
       "      <td>26.159022</td>\n",
       "      <td>2.276704</td>\n",
       "      <td>0.015052</td>\n",
       "      <td>0.415667</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                           mean          t         p         d\n",
       "directed_forgetting  113.670699  12.022802       0.0  1.843494\n",
       "n_back                35.910473   3.798204    0.0001   0.57305\n",
       "cued_task_switching   15.356183   1.624203  0.053041   0.30466\n",
       "shape_matching        34.850806   3.686124  0.000151  1.013786\n",
       "n_back: DE > DC       26.159022   2.276704  0.015052  0.415667"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def compare_nBack_load(diff_SSRTs, test_res_df):\n",
    "    diffs = (diff_SSRTs.n_back_DE_SSRT.values - diff_SSRTs.n_back_DC_SSRT.values)\n",
    "    t, pval = stats.ttest_rel(diff_SSRTs.n_back_DE_SSRT.values, diff_SSRTs.n_back_DC_SSRT.values, nan_policy='raise', alternative='greater')\n",
    "    print(f'n-Back DE > DC: Mean={np.mean(diffs):.2f}, t={t:.3f}, p={pval:.6f}, d={np.mean(diffs)/np.std(diffs):.2f}')\n",
    "    test_res_df.loc['n_back: DE > DC', ['mean', 't', 'p', 'd']] = np.mean(diffs), t, pval, np.mean(diffs)/np.std(diffs)\n",
    "    return test_res_df\n",
    "\n",
    "test_res_df = compare_nBack_load(diff_SSRTs, test_res_df)\n",
    "test_res_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Visualize"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "formal_tasks = {\n",
    "    'All': 'All',\n",
    "    'SE': 'Single Task',\n",
    "    'single_task': 'Single Task',\n",
    "    'go_no_go': 'Go/No-Go',\n",
    "    'predictable_task_switching': 'Predictable Task Switching',\n",
    "    'cued_task_switching': 'Cued Task Switching',\n",
    "    'flanker': 'Flanker',\n",
    "    'shape_matching': 'Shape Matching',\n",
    "    'n_back': 'n-Back',\n",
    "    'directed_forgetting': 'Directed Forgetting' \n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "sns.set(style=\"whitegrid\", palette=\"colorblind\", color_codes=True)\n",
    "palette ={\"DC\":\"C0\", \"DE\":\"C1\", \"SE\":\"C2\",\n",
    "         \"Dual Low Demand\":\"C0\", \"Dual High Demand\":\"C1\", \"Single\":\"C2\"}\n",
    "\n",
    "task_order_dict = {\n",
    "    'SE': 'aa',\n",
    "    'go_no_go': 'ab',\n",
    "    'predictable_task_switching': 'ba',\n",
    "    'cued_task_switching': 'bb',\n",
    "    'flanker': 'ca',\n",
    "    'shape_matching': 'cb',\n",
    "    'n_back': 'da',\n",
    "    'directed_forgetting': 'db'\n",
    "}\n",
    "\n",
    "trial_order_dict = {\n",
    "    'Single': 'aa',\n",
    "    'Dual Low Demand': 'ab',\n",
    "    'Dual High Demand': 'ba',\n",
    "}\n",
    "\n",
    "def context_str(string):\n",
    "    if 'DC' in string:\n",
    "        return 'Dual Low Demand'\n",
    "    elif 'DE' in string:\n",
    "        return 'Dual High Demand'\n",
    "    elif 'SE' in string:\n",
    "        return 'Single'\n",
    "    elif 'OTHER' in string:\n",
    "        return 'POS/3BACK/DSD|SSS/CUE_SWITCH'\n",
    "    else:\n",
    "        return 'COMBINE'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "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>ID</th>\n",
       "      <th>variable</th>\n",
       "      <th>SSRT</th>\n",
       "      <th>Task</th>\n",
       "      <th>Trial Type</th>\n",
       "      <th>task_order</th>\n",
       "      <th>trial_order</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>372</th>\n",
       "      <td>s009</td>\n",
       "      <td>SE_SSRT</td>\n",
       "      <td>193.916667</td>\n",
       "      <td>Single Task</td>\n",
       "      <td>Single</td>\n",
       "      <td>aa</td>\n",
       "      <td>aa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>373</th>\n",
       "      <td>s019</td>\n",
       "      <td>SE_SSRT</td>\n",
       "      <td>233.666667</td>\n",
       "      <td>Single Task</td>\n",
       "      <td>Single</td>\n",
       "      <td>aa</td>\n",
       "      <td>aa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>374</th>\n",
       "      <td>s031</td>\n",
       "      <td>SE_SSRT</td>\n",
       "      <td>237.000000</td>\n",
       "      <td>Single Task</td>\n",
       "      <td>Single</td>\n",
       "      <td>aa</td>\n",
       "      <td>aa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>375</th>\n",
       "      <td>s034</td>\n",
       "      <td>SE_SSRT</td>\n",
       "      <td>198.500000</td>\n",
       "      <td>Single Task</td>\n",
       "      <td>Single</td>\n",
       "      <td>aa</td>\n",
       "      <td>aa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>376</th>\n",
       "      <td>s038</td>\n",
       "      <td>SE_SSRT</td>\n",
       "      <td>206.750000</td>\n",
       "      <td>Single Task</td>\n",
       "      <td>Single</td>\n",
       "      <td>aa</td>\n",
       "      <td>aa</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>119</th>\n",
       "      <td>s514</td>\n",
       "      <td>directed_forgetting_DE_SSRT</td>\n",
       "      <td>258.000000</td>\n",
       "      <td>Directed Forgetting</td>\n",
       "      <td>Dual High Demand</td>\n",
       "      <td>db</td>\n",
       "      <td>ba</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>120</th>\n",
       "      <td>s528</td>\n",
       "      <td>directed_forgetting_DE_SSRT</td>\n",
       "      <td>356.000000</td>\n",
       "      <td>Directed Forgetting</td>\n",
       "      <td>Dual High Demand</td>\n",
       "      <td>db</td>\n",
       "      <td>ba</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>121</th>\n",
       "      <td>s541</td>\n",
       "      <td>directed_forgetting_DE_SSRT</td>\n",
       "      <td>369.500000</td>\n",
       "      <td>Directed Forgetting</td>\n",
       "      <td>Dual High Demand</td>\n",
       "      <td>db</td>\n",
       "      <td>ba</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>122</th>\n",
       "      <td>s548</td>\n",
       "      <td>directed_forgetting_DE_SSRT</td>\n",
       "      <td>429.500000</td>\n",
       "      <td>Directed Forgetting</td>\n",
       "      <td>Dual High Demand</td>\n",
       "      <td>db</td>\n",
       "      <td>ba</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>123</th>\n",
       "      <td>s560</td>\n",
       "      <td>directed_forgetting_DE_SSRT</td>\n",
       "      <td>280.500000</td>\n",
       "      <td>Directed Forgetting</td>\n",
       "      <td>Dual High Demand</td>\n",
       "      <td>db</td>\n",
       "      <td>ba</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>403 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       ID                     variable        SSRT                 Task  \\\n",
       "372  s009                      SE_SSRT  193.916667          Single Task   \n",
       "373  s019                      SE_SSRT  233.666667          Single Task   \n",
       "374  s031                      SE_SSRT  237.000000          Single Task   \n",
       "375  s034                      SE_SSRT  198.500000          Single Task   \n",
       "376  s038                      SE_SSRT  206.750000          Single Task   \n",
       "..    ...                          ...         ...                  ...   \n",
       "119  s514  directed_forgetting_DE_SSRT  258.000000  Directed Forgetting   \n",
       "120  s528  directed_forgetting_DE_SSRT  356.000000  Directed Forgetting   \n",
       "121  s541  directed_forgetting_DE_SSRT  369.500000  Directed Forgetting   \n",
       "122  s548  directed_forgetting_DE_SSRT  429.500000  Directed Forgetting   \n",
       "123  s560  directed_forgetting_DE_SSRT  280.500000  Directed Forgetting   \n",
       "\n",
       "           Trial Type task_order trial_order  \n",
       "372            Single         aa          aa  \n",
       "373            Single         aa          aa  \n",
       "374            Single         aa          aa  \n",
       "375            Single         aa          aa  \n",
       "376            Single         aa          aa  \n",
       "..                ...        ...         ...  \n",
       "119  Dual High Demand         db          ba  \n",
       "120  Dual High Demand         db          ba  \n",
       "121  Dual High Demand         db          ba  \n",
       "122  Dual High Demand         db          ba  \n",
       "123  Dual High Demand         db          ba  \n",
       "\n",
       "[403 rows x 7 columns]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def clean_SSRTs_for_Barplots(SSRTs):\n",
    "    melted_SSRTs = SSRTs.reset_index().melt(id_vars='ID', value_name='SSRT')\n",
    "    melted_SSRTs['Task'] = [i.split('_SSRT')[0].replace('_DC', '').replace('_DE', '') for i in melted_SSRTs['variable']]\n",
    "    melted_SSRTs['Trial Type'] = [context_str(i) for i in melted_SSRTs['variable']]\n",
    "\n",
    "    melted_SSRTs['task_order'] = melted_SSRTs['Task'].map(task_order_dict)\n",
    "    melted_SSRTs['trial_order'] = melted_SSRTs['Trial Type'].map(trial_order_dict)\n",
    "\n",
    "    melted_SSRTs = melted_SSRTs.sort_values(['task_order', 'trial_order'])\n",
    "    melted_SSRTs['Task'] = melted_SSRTs['Task'].map(formal_tasks)\n",
    "    return melted_SSRTs\n",
    "melted_SSRTs = clean_SSRTs_for_Barplots(SSRTs)\n",
    "melted_SSRTs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "title_dataset = dataset if dataset != 'all' else 'combined'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x468 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def plot_SSRTs_barplot(melted_SSRTs, save_fig=False):\n",
    "\n",
    "    f, (a0, a1) = plt.subplots(1, 2, gridspec_kw={'width_ratios': [1, 8]}, sharey=True, figsize=(12,6.5))\n",
    "\n",
    "    # Single Task\n",
    "    bar = sns.barplot(x='Task', y='SSRT', hue='Trial Type', data=melted_SSRTs[melted_SSRTs['Trial Type'].isin(['Single'])], palette=palette, errcolor='0', ax=a0)\n",
    "    a0.axhline(SSRTs['SE_SSRT'].mean()-3, color='k', linestyle=':', linewidth=4)\n",
    "    a0.set_ylabel('Stop Signal Reaction Time', fontweight='bold', fontsize=15)\n",
    "    a0.set_xlabel('')\n",
    "    a0.set_xticks([-.25])\n",
    "    a0.set_xticklabels(['Simple Stop'], rotation=31, fontweight='bold', fontsize='11')\n",
    "    a0.set_yticks(np.arange(0, 450, 50))\n",
    "    a0.set_ylim([0, 400.5])\n",
    "    a0.set_yticklabels(np.arange(0, 450, 50), fontdict={'fontweight':'bold', 'fontsize':'11'})\n",
    "\n",
    "\n",
    "    custom_lines = [Line2D([0], [0], color=sns.color_palette()[2], lw=6),\n",
    "                    Line2D([0], [0], color=sns.color_palette()[0], lw=6),\n",
    "                    Line2D([0], [0], color=sns.color_palette()[1], lw=6)]\n",
    "    a0.set_zorder(1)\n",
    "    a0.legend(custom_lines, ['Single Task', 'Dual Low Demand', 'Dual High Demand'], prop={'weight': 'bold', 'size':9.75}, loc=(1.1,.85), frameon=True)\n",
    "\n",
    "\n",
    "    # Dual Tasks\n",
    "    sns.barplot(x='Task', y='SSRT', hue='Trial Type', data=melted_SSRTs[melted_SSRTs['Trial Type'].isin(['Dual Low Demand', 'Dual High Demand'])], palette=palette, errcolor='0', ax=a1)\n",
    "    a1.axhline(SSRTs['SE_SSRT'].mean()-3, color='k', linestyle=':', linewidth=4)\n",
    "    a1.set_ylabel('')\n",
    "    a1.set_xlabel('')\n",
    "    a1.get_legend().remove()\n",
    "\n",
    "    # _ = plt.legend(prop={'weight': 'bold'})\n",
    "    _ = plt.xticks(rotation=31, ha='right', fontweight='bold', fontsize='11')\n",
    "    _ = plt.yticks(fontweight='bold', fontsize='12')\n",
    "\n",
    "\n",
    "\n",
    "    _ = f.text(0.5, 0.01, 'Task', ha='center', fontweight='bold', fontsize=13)\n",
    "    _ = plt.title(f'Interference of SSRTs in Dual Tasks in {title_dataset.capitalize()} Dataset', fontsize='18', fontweight='bold',  y=1.1, x=.44, pad=-20)\n",
    "    f.tight_layout()\n",
    "    plt.subplots_adjust(wspace=.01)\n",
    "    if save_fig:\n",
    "        plt.savefig(f'figures/dataset-{dataset}_metric-SSRT_barplot.png', dpi=300)\n",
    "    plt.show()\n",
    "    plt.close()\n",
    "    \n",
    "plot_SSRTs_barplot(melted_SSRTs, save_fig=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "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>ID</th>\n",
       "      <th>variable</th>\n",
       "      <th>Dual Task SSRT - Single Task SSRT</th>\n",
       "      <th>Task</th>\n",
       "      <th>Trial Type</th>\n",
       "      <th>task_order</th>\n",
       "      <th>trial_order</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>186</th>\n",
       "      <td>s009</td>\n",
       "      <td>predictable_task_switching_DC_SSRT</td>\n",
       "      <td>25.083333</td>\n",
       "      <td>Predictable Task Switching</td>\n",
       "      <td>Dual Low Demand</td>\n",
       "      <td>ba</td>\n",
       "      <td>ab</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>187</th>\n",
       "      <td>s019</td>\n",
       "      <td>predictable_task_switching_DC_SSRT</td>\n",
       "      <td>39.333333</td>\n",
       "      <td>Predictable Task Switching</td>\n",
       "      <td>Dual Low Demand</td>\n",
       "      <td>ba</td>\n",
       "      <td>ab</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>188</th>\n",
       "      <td>s031</td>\n",
       "      <td>predictable_task_switching_DC_SSRT</td>\n",
       "      <td>48.500000</td>\n",
       "      <td>Predictable Task Switching</td>\n",
       "      <td>Dual Low Demand</td>\n",
       "      <td>ba</td>\n",
       "      <td>ab</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>189</th>\n",
       "      <td>s034</td>\n",
       "      <td>predictable_task_switching_DC_SSRT</td>\n",
       "      <td>-2.000000</td>\n",
       "      <td>Predictable Task Switching</td>\n",
       "      <td>Dual Low Demand</td>\n",
       "      <td>ba</td>\n",
       "      <td>ab</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>190</th>\n",
       "      <td>s038</td>\n",
       "      <td>predictable_task_switching_DC_SSRT</td>\n",
       "      <td>-18.750000</td>\n",
       "      <td>Predictable Task Switching</td>\n",
       "      <td>Dual Low Demand</td>\n",
       "      <td>ba</td>\n",
       "      <td>ab</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>119</th>\n",
       "      <td>s514</td>\n",
       "      <td>directed_forgetting_DE_SSRT</td>\n",
       "      <td>5.833333</td>\n",
       "      <td>Directed Forgetting</td>\n",
       "      <td>Dual High Demand</td>\n",
       "      <td>db</td>\n",
       "      <td>ba</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>120</th>\n",
       "      <td>s528</td>\n",
       "      <td>directed_forgetting_DE_SSRT</td>\n",
       "      <td>88.500000</td>\n",
       "      <td>Directed Forgetting</td>\n",
       "      <td>Dual High Demand</td>\n",
       "      <td>db</td>\n",
       "      <td>ba</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>121</th>\n",
       "      <td>s541</td>\n",
       "      <td>directed_forgetting_DE_SSRT</td>\n",
       "      <td>181.916667</td>\n",
       "      <td>Directed Forgetting</td>\n",
       "      <td>Dual High Demand</td>\n",
       "      <td>db</td>\n",
       "      <td>ba</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>122</th>\n",
       "      <td>s548</td>\n",
       "      <td>directed_forgetting_DE_SSRT</td>\n",
       "      <td>194.916667</td>\n",
       "      <td>Directed Forgetting</td>\n",
       "      <td>Dual High Demand</td>\n",
       "      <td>db</td>\n",
       "      <td>ba</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>123</th>\n",
       "      <td>s560</td>\n",
       "      <td>directed_forgetting_DE_SSRT</td>\n",
       "      <td>24.333333</td>\n",
       "      <td>Directed Forgetting</td>\n",
       "      <td>Dual High Demand</td>\n",
       "      <td>db</td>\n",
       "      <td>ba</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>372 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       ID                            variable  \\\n",
       "186  s009  predictable_task_switching_DC_SSRT   \n",
       "187  s019  predictable_task_switching_DC_SSRT   \n",
       "188  s031  predictable_task_switching_DC_SSRT   \n",
       "189  s034  predictable_task_switching_DC_SSRT   \n",
       "190  s038  predictable_task_switching_DC_SSRT   \n",
       "..    ...                                 ...   \n",
       "119  s514         directed_forgetting_DE_SSRT   \n",
       "120  s528         directed_forgetting_DE_SSRT   \n",
       "121  s541         directed_forgetting_DE_SSRT   \n",
       "122  s548         directed_forgetting_DE_SSRT   \n",
       "123  s560         directed_forgetting_DE_SSRT   \n",
       "\n",
       "     Dual Task SSRT - Single Task SSRT                        Task  \\\n",
       "186                          25.083333  Predictable Task Switching   \n",
       "187                          39.333333  Predictable Task Switching   \n",
       "188                          48.500000  Predictable Task Switching   \n",
       "189                          -2.000000  Predictable Task Switching   \n",
       "190                         -18.750000  Predictable Task Switching   \n",
       "..                                 ...                         ...   \n",
       "119                           5.833333         Directed Forgetting   \n",
       "120                          88.500000         Directed Forgetting   \n",
       "121                         181.916667         Directed Forgetting   \n",
       "122                         194.916667         Directed Forgetting   \n",
       "123                          24.333333         Directed Forgetting   \n",
       "\n",
       "           Trial Type task_order trial_order  \n",
       "186   Dual Low Demand         ba          ab  \n",
       "187   Dual Low Demand         ba          ab  \n",
       "188   Dual Low Demand         ba          ab  \n",
       "189   Dual Low Demand         ba          ab  \n",
       "190   Dual Low Demand         ba          ab  \n",
       "..                ...        ...         ...  \n",
       "119  Dual High Demand         db          ba  \n",
       "120  Dual High Demand         db          ba  \n",
       "121  Dual High Demand         db          ba  \n",
       "122  Dual High Demand         db          ba  \n",
       "123  Dual High Demand         db          ba  \n",
       "\n",
       "[372 rows x 7 columns]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def clean_diffs_for_Seaborn(diff_SSRTs):\n",
    "    melted_diffs = diff_SSRTs.reset_index().melt(id_vars='ID', value_name='Dual Task SSRT - Single Task SSRT')\n",
    "    melted_diffs['Task'] = [i.split('_SSRT')[0].replace('_DC', '').replace('_DE', '') for i in melted_diffs['variable']]\n",
    "    melted_diffs['Trial Type'] = [context_str(i) for i in melted_diffs['variable']]\n",
    "\n",
    "    melted_diffs['task_order'] = melted_diffs['Task'].map(task_order_dict)\n",
    "    melted_diffs['trial_order'] = melted_diffs['Trial Type'].map(trial_order_dict)\n",
    "\n",
    "    melted_diffs = melted_diffs.sort_values(['task_order', 'trial_order'])\n",
    "    melted_diffs['Task'] = melted_diffs['Task'].map(formal_tasks)\n",
    "    return melted_diffs\n",
    "\n",
    "melted_diffs = clean_diffs_for_Seaborn(diff_SSRTs)\n",
    "melted_diffs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def plot_diffs_barplot(melted_diffs, save_fig=False):\n",
    "\n",
    "    fig = plt.figure(figsize=(12,6))\n",
    "\n",
    "    bar = sns.barplot(x='Task', y='Dual Task SSRT - Single Task SSRT', hue='Trial Type', data=melted_diffs, palette=palette)\n",
    "    plt.legend(prop={'weight': 'bold'})\n",
    "\n",
    "    _ = plt.xticks(rotation=31, ha='right', fontweight='bold', fontsize='11')\n",
    "    _ = plt.yticks(fontweight='bold', fontsize='12')\n",
    "\n",
    "    bar.set_xlabel('Dual Task', fontsize = 18, fontweight='bold')\n",
    "    bar.set_ylabel('Dual Task SSRT - Single Task SSRT', fontsize = 16, fontweight='bold')\n",
    "\n",
    "    lowx, highx = plt.xlim()\n",
    "    plt.plot([lowx, highx],[0,0],color='k',linestyle=':',linewidth=3)\n",
    "    _ = plt.title(f'Interference of SSRTs in Dual Tasks in {title_dataset.capitalize()} Dataset', fontsize='18', fontweight='bold')\n",
    "    plt.tight_layout()\n",
    "    lowy, highy = plt.ylim()\n",
    "    _ = plt.axis([lowx, highx, lowy, highy])\n",
    "    if save_fig:\n",
    "        plt.savefig(f'figures/dataset-{dataset}_metric-SSRT_differences_barplot.png', dpi=300)\n",
    "    plt.show()\n",
    "    plt.close()\n",
    "    \n",
    "plot_diffs_barplot(melted_diffs, save_fig=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Repetitions with Exclusions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "def pipeline(SSRTs, strip_str=''):\n",
    "    SSRTs = SSRTs.copy()\n",
    "    SSRTs.columns = [c.replace(strip_str, '') for c in SSRTs.columns]\n",
    "    SSRTs = filter_out_nans(SSRTs)\n",
    "    \n",
    "    # One-way ANOVA\n",
    "    SSRTs_taskMeaned = mean_SSRTs_within_task(SSRTs)\n",
    "    melted_SSRTs = melt_taskMeaned_SSRTs(SSRTs_taskMeaned)\n",
    "    anova_res = run_anova_on_melted_SSRTs(melted_SSRTs)\n",
    "    display(anova_res)\n",
    "    \n",
    "    # Planned Contrasts\n",
    "    df = get_degOfFree(anova_res)\n",
    "    std_err = get_standardError(anova_res)\n",
    "    test_res_df = run_planned_contrasts(SSRTs_taskMeaned, std_err, df)\n",
    "    \n",
    "    # n-Back t-test\n",
    "    diff_SSRTs = subtract_SE_SSRTs(SSRTs)\n",
    "    test_res_df = compare_nBack_load(diff_SSRTs, test_res_df)\n",
    "\n",
    "    # Visualizations    \n",
    "    # All 7 tasks\n",
    "    melted_SSRTs = clean_SSRTs_for_Barplots(SSRTs)\n",
    "    plot_SSRTs_barplot(melted_SSRTs, save_fig=False)\n",
    "    # Diffs Dual SSRTs - Single SSRTs\n",
    "    melted_diffs = clean_diffs_for_Seaborn(diff_SSRTs)\n",
    "    plot_diffs_barplot(melted_diffs, save_fig=False)\n",
    "\n",
    "\n",
    "    return test_res_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Exclude Short SSD trials"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "7 subs dropped for nan cell\n"
     ]
    },
    {
     "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>Source</th>\n",
       "      <th>SS</th>\n",
       "      <th>DF</th>\n",
       "      <th>MS</th>\n",
       "      <th>F</th>\n",
       "      <th>p-unc</th>\n",
       "      <th>p-GG-corr</th>\n",
       "      <th>np2</th>\n",
       "      <th>eps</th>\n",
       "      <th>sphericity</th>\n",
       "      <th>W-spher</th>\n",
       "      <th>p-spher</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>variable</td>\n",
       "      <td>40237.252607</td>\n",
       "      <td>6</td>\n",
       "      <td>6706.208768</td>\n",
       "      <td>4.509974</td>\n",
       "      <td>0.000313</td>\n",
       "      <td>0.002045</td>\n",
       "      <td>0.152829</td>\n",
       "      <td>0.676243</td>\n",
       "      <td>False</td>\n",
       "      <td>0.187343</td>\n",
       "      <td>0.008991</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Error</td>\n",
       "      <td>223045.911874</td>\n",
       "      <td>150</td>\n",
       "      <td>1486.972746</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Source             SS   DF           MS         F     p-unc  p-GG-corr  \\\n",
       "0  variable   40237.252607    6  6706.208768  4.509974  0.000313   0.002045   \n",
       "1     Error  223045.911874  150  1486.972746       NaN       NaN        NaN   \n",
       "\n",
       "        np2       eps sphericity   W-spher   p-spher  \n",
       "0  0.152829  0.676243      False  0.187343  0.008991  \n",
       "1       NaN       NaN        NaN       NaN       NaN  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "directed_forgetting: Mean=37.07, t=3.784, p=0.000111, d=0.62\n",
      "n_back: Mean=25.51, t=2.605, p=0.005062, d=0.41\n",
      "cued_task_switching: Mean=15.67, t=1.600, p=0.055858, d=0.28\n",
      "shape_matching: Mean=34.87, t=3.560, p=0.000249, d=0.91\n",
      "n-Back DE > DC: Mean=5.14, t=0.255, p=0.400413, d=0.05\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x468 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "thresh_SSD_tests = pipeline(SSRTs_ThreshSSDs, strip_str='_wThresh_SSDs')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "thresh_SSD_tests.to_csv(f'result_csvs/dataset-{dataset}_metric-SSRT_wThresh_SSDs_tests-validationContrasts.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Exclude Short SSD subjects"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "22 subs dropped for nan cell\n"
     ]
    },
    {
     "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>Source</th>\n",
       "      <th>SS</th>\n",
       "      <th>DF</th>\n",
       "      <th>MS</th>\n",
       "      <th>F</th>\n",
       "      <th>p-unc</th>\n",
       "      <th>p-GG-corr</th>\n",
       "      <th>np2</th>\n",
       "      <th>eps</th>\n",
       "      <th>sphericity</th>\n",
       "      <th>W-spher</th>\n",
       "      <th>p-spher</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>variable</td>\n",
       "      <td>59893.475178</td>\n",
       "      <td>6</td>\n",
       "      <td>9982.245863</td>\n",
       "      <td>9.292882</td>\n",
       "      <td>3.381992e-07</td>\n",
       "      <td>0.000141</td>\n",
       "      <td>0.481674</td>\n",
       "      <td>0.514028</td>\n",
       "      <td>False</td>\n",
       "      <td>0.008501</td>\n",
       "      <td>0.016836</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Error</td>\n",
       "      <td>64450.915355</td>\n",
       "      <td>60</td>\n",
       "      <td>1074.181923</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Source            SS  DF           MS         F         p-unc  p-GG-corr  \\\n",
       "0  variable  59893.475178   6  9982.245863  9.292882  3.381992e-07   0.000141   \n",
       "1     Error  64450.915355  60  1074.181923       NaN           NaN        NaN   \n",
       "\n",
       "        np2       eps sphericity   W-spher   p-spher  \n",
       "0  0.481674  0.514028      False  0.008501  0.016836  \n",
       "1       NaN       NaN        NaN       NaN       NaN  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "directed_forgetting: Mean=86.23, t=10.358, p=0.000000, d=1.79\n",
      "n_back: Mean=27.58, t=3.313, p=0.000783, d=0.50\n",
      "cued_task_switching: Mean=4.45, t=0.535, p=0.297281, d=0.09\n",
      "shape_matching: Mean=28.94, t=3.476, p=0.000476, d=1.35\n",
      "n-Back DE > DC: Mean=29.86, t=1.124, p=0.143609, d=0.36\n"
     ]
    },
    {
     "data": {
      "image/png": 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jR44AT8c0bd26lXnz5lm28+effwJPu3U+ePAAZ2dnFi5cyNatW5kwYQL29vY8evTI8ll29epVy3i7Ro0asWHDBn788UcyZsyIn5+f5fsgpnhEXhe1UIkksBQpUjBy5EgcHR3JkycPv/76KxcuXLB0V0iXLp1lwLKtra1lHMrvv/9u6RbVsWNHnJ2dcXZ2pnnz5owcOZJNmzYxYsSIKN28XFxc6NChAyaTybLs2RPPDBky4OLiQkBAgKVrX5MmTciRIwcAnTp1YuvWrVy7do3jx4+/cOWzV69evPfee+TOnZvIyEjLyc9HH31k+TLr2LEjK1as4NGjR+zateuFq9Q9e/akbNmywNOJFcaNG4dhGAQEBJAqVSrLyV22bNnw9vbGZDKRO3dunjx5gp2dnSUm83oVK1a0DEL+5JNPWLlyJf7+/mzcuJGOHTtGWyerVq0iPDwce3t7pk2bRpYsWYCnV0KrVavG/fv3+e233yhfvjwuLi7Y2Dy99mQeK/MyBQsWZMOGDSxYsIANGzZw+fJly2N37tyha9eurF692jI2JLbrv0zZsmVp1qxZnAbym1+Dp06dwtfXl9KlSzN16lTLlOz/xcCBA3n//fcBqFevHosXLyYwMJDw8HDs7OL2NfTsazwus1aOHz+eAQMGEBYWRubMmQkKCuK9996zPB4UFGQZpxVfnJ2dOXjwIJcvXyZbtmzY29tjGAYuLi74+/tbEkPz8Q4PD2fPnj2Wq/qbN28mTZo0Ufbd7NChQ5aT60aNGtGiRQvLY8/W3759+8iSJQvFihVj1apVpEiR4j9NlvGqz7eXqVWrFnPnzuXq1av4+vqSOXNmy8m4uXXKzs6O7du3c+3aNdKmTUvKlCm5ceMGbm5unDx58oUucq/y77//Wlq1vby8LFPvDx48mN27d0dpFbR22zF9hkfHz8/Pso+dO3e2tPbkzp2bq1evsnTpUjZt2sSwYcOivP5y5MhhaTXv2bMnCxYs4PHjx1YdZ2uYW2qtfY1mzJjR0l3QwcHBss/58uXj6NGjXL58mTx58mAYBo8fPyZ9+vQEBQVZnm/ukhwUFMT+/fupVq0aderUoXz58lEuMJnH2NnY2PDFF1/g4OBAvnz5qFevHnPnzsXHx4euXbvGGI/I66KESiSBZc2aNcp4FnM3hvDw8Jc+78qVK5a/o7tn1OPHj7l27ZolGYKnV3WjO9l63rOtN+PHj2f8+PEvrHPu3LkoCZW9vX2UE86AgABLq9KCBQtYsGBBtGU8n1A9mxiYjwVgGY9gji1PnjxR9sXcemB29epV4OnV7WevcD+77ZicP38eeFo35mQKsNz/6c8//7SM44iLdOnS8dVXX/HVV19x7do1du/ezW+//caZM2cIDQ3l119/5Ztvvonz+vA0GW3Xrh2HDh1i+PDhPH78mKtXr1KgQIE4xezt7U2/fv34+++/6d69OyaTCXd3d2rWrEmrVq2i1FVsxVTnERERcU6onm1BetXsZDG1rp07d47169dz5MgRLl++HCUxi2kmx//qyZMn7Nu3jzFjxnD8+PEoJ8Tm7ZcuXZqaNWvy+++/88svv/DLL7+QPHlySpUqRaNGjaKdSvzWrVuWv//++28iIiIsiVL27Nn57LPP+OWXX1i7di1r167FwcGBYsWK8fHHH9OwYcM4J1Vx/XyD/yVU8PQCSbZs2SzPe/aiQFhYGIcOHeKPP/7g6NGjUfY1tsn0jRs3LH8XKlTI8retrS358+d/YXxqfG4b/vfZA7xw37fSpUuzdOlSwsPDuXjxIkWKFLE8litXLsvfJpOJ1KlT8/jxY6uO88uYL9qlTp3assya1+jLPHjwgB07djBy5EhOnDhh+Z549vn16tVjxYoVHD16lGnTpjFt2jTSpEljuShkvuhm/h6MjIykRo0aL2zr8uXLr2UWR5FXUUIlksCev7pv7YmLNSea5hnizKy9om5NDM+X/Xw/dWvKeHaMgNmzJ1/RlWE++X3VicKrtv98/NY+17x9axLT561du5YdO3YQGBjI7NmzsbW1JVu2bLRo0YJPPvmE6tWrc/PmTUsrVGzXf1aKFCnIli0b2bJlw8HBgV69euHv70/nzp1ZuXIlyZIli1XsderUoWjRoqxZs4Zdu3Zx8uRJzp49y9mzZ1m7di0rV66M81iFZ98D8TV1+LNj56K7efGzJ37m8T3PmjRpEjNnzsTe3p4aNWrg5eWFyWT6T+O6XuXhw4d8+umn+Pn54erqyscff0zx4sVZs2ZNlLFDNjY2TJs2jX379rFhwwb27t3L9evX2bFjBzt27KBjx45RuvKa5c6dGz8/Py5evMiKFSuiTMQwYMAAPvroI9atW8eePXvw8/Nj//797N+/n7179zJx4sQ47VNcP98AihYtiqurK//++y9bt261XLB57733LF1WIyIiaNu2LQcPHiRt2rR8+OGHFC9enMOHD/Prr7/GOt6XtWw+H3t8bxte/rn+bDzPf/78l+Mck/DwcEvCYr65r7Wv0Zj8+++/NGnShNu3b5MrVy5atGhBsWLFmDFjhuX2E+b9WbRoEX/88QebN29m37593L59m02bNlla6Jo1a/bK78HIyEgCAwPf2Bk05e2hhEokCTB/eT57JT1r1qyWv7du3Uq2bNmAp7PqhYeHky1bthe+dK292v9s2d99952l9efhw4dcv36dXLlyvXDF7/mynZ2dSZkyJQ8fPqRLly50794dePolffr0afLmzRvrk3p42tXv0qVL+Pr6RukSNn78eM6fP0+JEiXo0KEDWbNm5dy5czRo0CDK5BLHjx8nV65cL00uza16169f599//8XV1RV4erXWPN24+QQjNm7cuIGPjw/wdKxBlSpVLI/Z2NhYTpjMV4Nju35M6taty7p16/jjjz+4ePEi33//fZQp1F8lJCSEs2fPcuHCBRo1asSXX37JkydPWLlyJcOHD+fy5cscOHDAMiYnsT158oQdO3YAkD9/fkv3HnOXH3hal+bX37OtEubnz5kzB3jazdU89m7Tpk0JGvfGjRstieDChQst78PnJwC4ffs2586d486dO3z77beWfRg4cKCl9fL5hCpbtmwsXLiQIUOG8PvvvzNt2jQ+/vhjnJycuH//PmfPnuXy5ct8/fXX2NjYcO/ePcaNG8fKlSvZsGEDw4cPj/cujq9iMpmoWbMmv/zyC6dPn+bixYtA1Nap/fv3W7onT5kyxdKqE92tBKzx7Gff6dOnLeNswsPDLe/9uGw7us/w6Dzbo+DAgQOWrrCAZVv29vbkzJnTuh36D7Zt22aZ9dL82WPtaxSi3+dff/2V27dv4+TkxKpVqyyJoHnMqpm/vz++vr5ERkZaJtO5fPkyPXv25PTp0/z22280a9bMsn17e3uOHDli+V66fPkyDg4OUXoYWFsHIglBk1KIJAHmLwnzic+VK1coW7aspfvMwIEDOXHiBEeOHOHLL7+kRo0a1KlTJ05dTgBL1wp4OovVrl27OHv2LEOHDqV+/foUL178hSnKo2M+8VmwYAGbNm3iwoULjB8/niZNmliu5MaWeTrlmzdvMnz4cC5cuMDmzZuZP38+O3bs4ObNm1G27ePjw7Jly/Dz82POnDk0adKEEiVKRJn+/Hn16tXDZDIRFhZG9+7dOXz4MCdPnqRnz56Wlq3np1m2Rt26dS0n9d988w2rV6/m0qVLHD16lJ49e1oGvZsHsMd2/ZcZPHgwyZMnB2DevHnRtmrF5N69ezRv3pwBAwbQv39/Tp48yY0bN6J0b4pr17z4cPPmTW7cuMH169fZv38/X375JXfu3AGIMk7u2avUv/76K5GRkezcufOFSRfCwsIsXUz37NmDr68ve/bsYdKkSZZ1EqLL37NTxa9du5ZLly7x888/Wwb6m1tlV6xYweeff463tzc//vgjV65c4datW5bxJ88mjmaFCxcmXbp09OjRAxsbG27dusX8+fOBp8n6Z599xpAhQxg9ejQXL17k5s2blq5ctra2cWqRjQ/Pdl8MDg5+Ydmzs5Bu3LiRy5cvs3LlSlatWgVY17XwWRkyZKBw4cIAzJ8/n5UrV3L+/HmGDRv2Qne/2Gw7us/w6OTJk8cy3vSHH35g+fLl+Pn5MXfuXFasWAE8HZMa38ltcHAwN27c4MaNG1y6dIlVq1ZZWmPTp0/PJ598Alj/Gn12n//9918uXLjAv//+azlmwcHB+Pj4cPHiRSZMmGDp6mh+/syZM+ncuTO9evVi+fLlXL16lVu3blm68ppf49WrV8fOzo6wsDD69+/PuXPn2LdvH23atOGDDz6IMvtodPGIvC5qoRJJAsw3in38+DH169enefPmDB06lK+++orhw4fz119/Wb7w4OkJUK9evSyTJcRFr1698PLywt/fnw4dOkR5rGXLllGu5Mbkiy++YNu2bQQEBLwwTXm1atXw9PSMdVwNGzZkzZo1HDx4kKVLl0a5D5KLi4vlBNrLy4vVq1dz5cqVKLOLwdMZxKIbZ2JWoEABunbtyrRp0zh+/HiUAfzwtNWiTJkysY7dzc0Nb29vRowYwZ07d6Kdev2DDz6wXBWP7fovkyVLFrp168aYMWMICwtjzJgxUaYafhlXV1c6duzIDz/8wL59+14Yr+bh4WFJwBNDdGMn4Om4urp161r+r1ChAsmTJ+fx48dMmzaN6dOnYxgGhQoVitL6kDp1akqUKMGhQ4c4evQo9erVe6Hse/fuRRkzGB8qVqzI+PHjCQsLY8qUKUyZMuWFbcLT8XE+Pj74+vpGO8bx+ffrs3Lnzs3HH3/M6tWr+fHHH2natCkffvghy5cv56+//rKMyXpWy5YtE23q6eLFi5MpUyZL8p41a9YoY5uKFy+Os7Mz9+/fZ8mSJS9MZx8YGGiZtMBa/fv3p02bNgQHB/P1119blufPn5+zZ8/GadsxfYZHZ9SoUXh5efHgwYMXxkbmyZMnSkzxZfv27ZYbJj/LycmJsWPHWhI4a1+j5lgPHDjA9evXqVu3Ln369KFq1aqWRH7AgAEvbM/cDfyLL75gx44d3Lp164VjYDKZaN++PfD09dC6dWvLBBTmFn14OkmQuXU5pnhe9l4RiU9qoRJJApo0acIHH3xAypQpcXZ2JkOGDMDTE53JkydTrFgxkidPTqpUqShVqhQ//vhjjCeZ1ipSpAhLliyhatWqODs74+TkRL58+Rg8eDD9+/e3qoysWbOybNkyPvroI9KnT4+DgwM5cuSgR48ecR6TYWdnx48//kjnzp0ts0xlyZKFhg0bsnjxYjJlygQ8nYhgyZIlNGvWjMyZM2Nvb4+bmxvt2rVj7ty5rxyk3LVrV2bNmkX58uVJnTo1Tk5OFC1alIkTJ9KrV684xQ5PE7158+bxwQcfkC5dOuzs7EiTJg3FihVj8ODBfP/991FO/mK7/su0bt3aMinF9u3bLVMPW6NHjx6MHz+eYsWK4ezsjL29PdmyZaNNmzbMmzcvUVuozGxsbEidOjWlSpViwoQJfPfdd1Eez5w5MzNmzKBQoUI4ODjw3nvvMXjwYL744osXypo0aRK1a9fG2dmZ1KlTU6xYMWbPnm3pPhiX6bhfJXfu3Hz//fcUKlQIJycnXFxc+Oijjxg7dizwtBvTtWvXSJ06NfPnz6d9+/bkyJEDR0dHkidPzvvvv8/YsWP57LPPXrqdrl27Ym9vz4MHDyzjxH744Qd69uxJ3rx5SZ48OY6OjhQoUIBvvvnG6vd7QjCZTFEufjx/ISRdunTMnj0bT09PkidPToYMGfjggw+YPXs28LQl5Nl71lmjRIkSzJs3j6JFi+Lg4EDOnDkZNWoUDRs2jPO2Y/oMj07+/PlZu3YtTZs2xdXVNcpn16+//vqfJoCxhr29Pa6urjRo0ICVK1dGuXG4ta9RgM8//5xSpUpZjo2zszNly5ZlzJgx5M6dGycnJ9zc3GjevLklSTx8+DCPHz8mS5YsLF26lKZNm1o+51OmTEnp0qWZPXt2lO+3fv36MXToUAoUKICTkxPOzs5UrlyZBQsWROkyGV08Iq+LyVBnUxERERERkThRC5WIiIiIiEgcKaESERERERGJIyVUIiIiIiIicaSESkREREREJI6UUImIiIiIiMSREioREREREZE4UkIlIiIiIiISR0qoRERERERE4ui1J1S9e/fG3d0dgD179lCrVi1Kly7N6NGjMd9j+PTp09SvXx9PT0+8vb0JDg5+3WGKiIiIiIi80mtNqDZs2MD69esBCAkJoWfPnuTOnZsvvviCn3/+mY0bNwLQp08fkiVLxoABA/Dx8WHBggWvM0wRERERERGr2L2uDd28eZNhw4aRN29ezp8/z7FjxwgMDMTLy4uyZcsyZ84cdu/eTdGiRfHz82PEiBE0btyYZcuWsWvXLjp06GDVdiIjI3n06BH29vaYTKYE3qt3j2EYhIWFkSJFCmxsbHS8RUREROSt9vz57/NeS0JlGAYDBgygVKlS5MuXj/Pnz3Pz5k0AnJ2dAUiXLh23bt2yLE+bNq1luZ+fn9XbevToEb6+vvG7A/KCfPnykSpVKh1vEREREXknmM9/n/daEqpFixZx6tQpVq9ezZIlSwAIDw8HsLRqmMdPmX+bPf//q9jb2wNPd9jBweE/xS0vCg0NxdfX13KcdbxFRERE5G32/Pnv815LQrV582YCAgKoXLmyZdmUKVMACAgIACAwMJBChQqRKVMmAO7fv29Zbl5mDXOC5uDggKOjY3yEL9EwH2cdbxERERF5F8Q0vOW1JFRDhgzh4cOHACxbtozly5czYcIEOnbsyPz58zl//jw3btygYsWKZMuWjezZs7N8+XJsbGw4ceIE3bt3fx1hSjwJCwvj+vXrmp3xLefk5ETWrFljvFojIiIi8i54LQlVnjx5LH/v3r0bAE9PT6ZOncrw4cM5fPgwbdq0oXbt2sDT1qsBAwYwcuRIateuTevWrV9HmBJPrl+/TqpUqciRI4cmqnhLGYbB3bt3uX79Ojlz5kzscEREREQSzWub5c+sW7dudOvWDYDy5cuzefPmF9YpWLAgq1evfs2RSXwJDg5WMvWWM5lMpE+fntu3byd2KCIiIiKJ6rXf2FfeDUqm3n6qYxERERElVCIiIiIiInH22rv8ybsh0ohk2NHfWeR3hEsP78Vr2TlTpqNl7uIMKVYTG9OL1wQiIyMZM2YMv//+O/fv38fDw4ORI0eSPXt2vLy8cHNzY/To0bHerru7O6NGjaJRo0YvXa9///6sWrUqyjI3Nze2b99u1XauX79OtWrVmD9/PqVLl451nCIiIiLy+qiFShLEsKO/M+LY1nhPpgAuPbzHiGNbGXb092gf3717N7/88guTJ09m48aNPHr0iPHjxwPw448/Mnz48HiP6VnDhw/nyJEjdOrUiSxZsnDkyBF8fHwSdJsiIiIikjiUUEmCWOR3JNG2kTp1agzDYMqUKezevZuZM2cydepUADp06MDgwYOBpy1OAwcOpGLFilSrVo3jx48DMG3aNEqWLEmzZs1o2rQp/fv3j1J+YGAgnTt3plixYjRr1oyrV69GedzBwYEUKVJgb2+PyWQiRYoUJEuWjOnTp1OyZEmKFClC165diYyMZMeOHVStWpXChQvToEED/Pz8opTVv39/qlatys2bN+PlmImIiIhI/FJCJW+dYsWKMXjwYK5cucLAgQOpUqUKv/76a7TrPnjwgMWLFxMREcGKFSs4c+YM06dPZ/DgwQwZMgRfX98XnjNr1iyuXr3Kxo0byZcvH999990rY4qIiCA8PJzp06czcuRItmzZwqVLl/jtt99wcXFh2bJl1K1b13Kja4AZM2bwxx9/MG/ePDJnzhz3AyIiIiIiCUYJlSSIlrmLJ9o2zp49S5YsWdi2bRsbNmygQoUKjBs3Ltp1zTeTzpo1KyEhIVy6dAmAWrVqUaBAAfLmzfvCc86fP8/Vq1epX78+69at4+jRo6+M1WQyERYWxqRJk9i4cSMAoaGhdO/enRQpUtC8eXPWrFlDUFCQ5TnHjx/n0aNHREZGvrJ8EREREUkcSqgkQQwpVpOBRauTM2W6eC87Z8p0DCxanSHFakb7+KlTp+jWrRt79uzB2dmZ1KlTkz59+mjXtbW1Bf43BXiOHDkA2LJlC6dPn+b8+fMvbj9nTrJnz873339Pu3btaNOmzStj9vX15aeffqJLly5UqVIFeHpz3G3btlG4cGF8fHxImzYtc+fOtTxn1KhRlCtXjlGjRr2yfBERERFJHJrlTxKEjcmGYcU/ZFjxD1/7ths2bMiFCxfo168fgYGB5MuXL8YWqucVLFiQL774gqFDh5I/f35cXV1fuN9S586duXr1Kh06dCBdunQMGjToleXmyJGD0qVL06VLF4oXL06yZMnw9/enePHifPvtt8yePZts2bJZxncBpE2bFm9vbz7++GN27txJ5cqVY3cgRERERCTBmQzDMBI7iPgUEhLCyZMn8fDwwNHRMbHDees8f3yjO95nzpyhQIECiRxp3Ny8eZMpU6ZQtWpVPDw8aN68OfXr16dHjx6JHVqS9CbXtYiIiIg1XpVfqIVK5BkZM2bEwcGBAQMGEBoaSpEiRfDy8krssEREREQkiVJCJfIMGxsbhg4dytChQxM7FBERERF5A2hSChERERERkThSQiUiIiIiIhJHSqhERERERETiSAmViIiIiIhIHGlSCkkQkZEGQ38/x6Ij/ly69zhey86ZLjkti7sxtKY7NjamFx7fv38/rVu3Bp5OMpE5c2Z69OhBgwYNYr0tLy8v3NzcGD169Avlb9u2jaxZs8Z5P6Lj7u4OPL3RcKpUqWjcuDHe3t4v3Avrdbh+/TrVqlVj/vz5lC5d+rVvX0RERORNoBYqSRBDfz/HiK3n4z2ZArh07zEjtp5n6O/nXrqej48Pe/fupV69egwcOBA/P794jyUhDBs2jIMHDzJy5Ejmz5/P8uXLEzskEREREYmBEipJEIuO+Cf6NpycnEibNi09evTA3t6etWvXsn//ftzd3bl+/TrwtEVo5cqVBAQE0KpVKwoXLkzZsmVZtWpVrON5/Pgx/fr1o1ixYlSqVIl58+YRGRlJuXLlWLNmDTdv3sTd3Z05c+bw5MkTihQpws6dO18ox8HBgVSpUlGzZk3KlSvHypUrAZg3bx5ly5alUqVKlmX9+/enefPmNG7cmHLlyjF37lw+/vhjKlSowLZt2wCYPn06JUuWpEiRInTt2pXIyEhWrlxJ6dKlGTBgAJ6ennTs2JHw8HBu376Nl5cXnp6ejB8/PtbHQERERORdo4RK3nq2trakTp2agICAGNe5efMmxYsXZ+vWrRQrVowlS5bEejszZ87kr7/+Yvny5QwfPpzRo0dz8OBBqlWrxs6dO9m3bx+2trb89ddf7N27FwcHB8qWLfvSMtOmTcu9e/c4f/48o0ePZtSoUYwfP57Bgwdz8+ZNAC5evMjYsWPJnTs3s2bNYtKkSeTMmZPFixcTERFBeHg406dPZ+TIkWzZsoVLly4BcP/+fcqXL8+3337Lzp07OXXqFLNmzeLff/9l9erVFCxYMNbHQERERORdozFUkiBaFndjxNbzCb4NaxiGwYMHD3BxcYmyLCIiwvJ/ihQpOH36NP379ycwMDDKY9Y6deoUxYsXJ3fu3OTOnZt06dJx6tQpatSoQZ8+fbCxsaFhw4Zs2LCBDBky8MEHH+Dg4PDSMgMDA3F1deX8+fMYhoG3tzcAYWFhnDp1CoA8efKQO3du3NzciIyMJHfu3GTPnp2rV69iMpkICwtj0qRJpEuXDoDQ0FBL+R9++CH//PMPAMHBwVy5coWiRYuSLVs2qlWrxoQJE2J9HERERETeJWqhkgQxtKY7A6vnJWe65PFeds50yRlYPS9Da7q/dL3g4GCCgoL45ZdfCA4OplKlSiRLlgyAK1eucODAAcu6CxYs4J9//uHbb78lQ4YMGIbxyjj++ecfrly5wpUrV7h79y758+fn8OHD+Pn58ccff3Dv3j0KFy5MmTJliIyMZNOmTTRt2pT06dOzbt06atWqFW25oaGhPHz4kP3793PgwAGqVKlCrly5AOjXrx8jRoygcePG5M+fH3g68YbZs38D+Pr68tNPP9GlSxeqVKkCEGXfbG1to0x4kStXLo4ePcrVq1fZsmXLK4+BiIiIyLtOLVSSIGxsTAz/MD/DP8yfaDHUrVsXGxsbXF1d+e677/Dw8CAsLIxatWrRo0cPqlevjrOzMwA1atRg48aNNGjQAA8PD27evEl4ePhLy/fy8rL83bBhQwYNGsTNmzf55JNPSJUqFQMGDKBkyZIAVK5cmV27dlGoUCHKly/P2rVrqVChQrTlDhkyhCFDhpAmTRqaNWtG69atsbGxoW/fvkyaNInHjx/TsGFDXF1dX3kMcuTIQenSpenSpQvFixcnWbJk+PvHPPasU6dOnDlzhkaNGvHxxx+/snwRERGRd53JsOZS/BskJCSEkydP4uHhgaOjY2KH89Z5/vhGd7zPnDlDgQIFEjlSeR1U1yIiIvK2e1V+oS5/IiIiIiIicaSESkREREREJI6UUImIiIiIiMSREioREREREZE4UkIlIiIiIiISR0qoRERERERE4kj3oZIEYRiR3P9rOA/PLCH8waV4LdsudU5SFmiOc5nBmEwvXhPYv38/rVu3Bp7e6DZz5sz06NGDBg0axHpbXl5euLm5MXr06BfK37ZtG1mzZuX69etUq1aN+fPnA0R5LDrTpk1j1apVbN++/aXbNd942MnJibJlyzJq1CjSpk0b632ID1WrVqVhw4Z069YtUbYvIiIiklS9thaqyMhIxo0bR/ny5alQoQLz5s0DYNKkSbi7u1t+hg8fDsDp06epX78+np6eeHt7Exwc/LpClXhw/6/h3N//XbwnUwDhDy5xf/933P9r+EvX8/HxYe/evdSrV4+BAwfi5+cX77E8r0SJEhw5cgQ3N7f/XFa9evU4fPgwixcv5ty5c3z33XfxEKGIiIiIxKfXllBt2rSJn376ibZt21KuXDlGjRrF2bNnOXHiBGXKlGHu3Ln8/PPPtGrVCoA+ffqQLFkyBgwYgI+PDwsWLHhdoUo8eHhmSaJvw8nJibRp09KjRw/s7e1Zu3Yt+/fvx93dnevXrwPg7u7OypUrCQgIoFWrVhQuXJiyZcuyatWqOMV06NAhihcvjr+/P5cuXaJRo0aULl2ab775Jsp2Q0JC6NKlC56engwbNizasuzs7EiZMiWFChWiefPmbNiwgZCQEPbt20etWrUoWbIk3377LYZhsHLlSsqWLctXX31FyZIlGT16NF988QUlS5ZkypQpAGzfvp0PPvgADw8PGjRowL///sv169ctFzLKlClDvXr1uHbtGpGRkQwYMABPT08+//xznjx5EqfjISIiIhIXPj4+VKlSBR8fn8QO5ZVeW0JVo0YNNm/eTOvWrcmePTsmkwl7e3tOnjzJqVOn6NixIz/++CPOzs74+/vj5+dH48aNady4MR4eHuzatet1hSpvGVtbW1KnTk1AQECM69y8eZPixYuzdetWihUrxpIlr04IzclSo0aNon18/Pjx2NrasnbtWmxtbaM8dv/+fT7//HO6devG4sWLuXfv3ku3lTZtWsLDwwkMDKRPnz7UqlWLVatWsXXrVn7//XcA7t27R7169WjRogU///wzjRs3pkWLFvz4448ABAQE0LVrV9asWcPly5fZsWOHpXwbGxsWLVrE1atX2bhxI1u3bmX16tXMnDmTTp06cf/+/VceDxEREZH4MnjwYHbu3MngwYMTO5RXem1jqOzt7cmRIwcjRoxgwYIFtGrVirRp05I1a1YqVKhAgQIFGDBgABMnTrScoJrHi6RLly7W3bVOnjwZ7/sgMXv2eNvZ2eGQ+xPCj45L0G065P6ER48evbDc3D30yZMnPHr0CMMwePDgAenTp7c89vjxYx48eAA8bS2ysbHhxIkT9O3bl8DAQCIjI3n06BERERGEh4dH2Y65jClTppA5c2Zu3rxJmzZtonRLffLkCRcuXOCDDz4gZcqUVKhQgaVLl/LkyRNCQ0PJkCED7u7uBAUFAXD37l0cHR0tz39+u7dv38be3p7g4GDu3LnDokWLWLp0KQ8fPuTAgQPkyZMHgNKlS3P79m3L3/fu3SMsLMyyL8uXL2fLli3Y2toSFBRkaXkqV64cLi4uODs7ExQUxPnz50mbNi0FCxYEnr4XQ0NDXzjeoaGhHD58OE71JyIiIhKTO3fuWH4n9XON1z4pRZMmTXjvvfcYMWIEJUqUYOXKlZbHfHx8OHDgAA0bNozyHMMwYr0dDw+PKCeoEj9CQkKiTVafPd5nzpwhU6Vvue9gnyiTUjg5OQFgMpmIjIxkxYoVBAcHU61aNSIjIwG4desWd+/eBcDR0ZHly5dz8+ZNZs+ezfDhw7lx4wYpUqTA1tYWOzs7UqRI8UL52bJlI2vWrDg4OERZDpAsWTJy5crFkSNHePjwIbt377Ysd3BwwNbWlhQpUliekyxZsijbeLZF69atW2zcuJGKFSvi5uaGs7MzH330ER999BG//fYbNWrU4J9//gEgVapUlnp49u8UKVIwbtw4WrZsSbVq1WjdujUODg4kS5bM8rh5fx0cHMifPz/37t3j5MmTGIZBQEAADg4OUWIEcHBwoGjRorGoPREREZFXM5/DODo64unpmaixxHT+a/baEio/Pz98fX2pXbs27u7uTJgwgVWrVnH58mUaNGiAq6sr4eHh2NnZkSlTJgBLN6PAwEDLMnkzmEw2pC07lLRlhyZaDHXr1sXGxgZXV1e+++47PDw8CAsLo1atWvTo0YPq1avj7OwMPO2SunHjRho0aICHhwc3b94kPDz8P22/X79+9OjRg/r161O+fHngadc6a61bt45169ZZZvkbOXIktra2TJgwgREjRrBs2TJKlChBvnz5LAnVy3zyySf8/PPPbN68mcyZM1vGc0WnatWqNGnShC+//JLixYuTM2dOq+MWEREReZeYjLg0/8TBqlWr6N+/P3369CEkJIRp06YxZswYBg4cSJUqVahatSpDhw6lffv2dO/enRo1apA+fXo+/fRThgwZQvfu3enQocMrt2POINVClTCeP77RHe8zZ85QoECBRI408S1YsIAbN27Qpk0b1qxZw+TJkzl06FCUlqw3nepaREREEkK+fPk4f/48efPmxdfXN1FjeVV+8dpaqBo0aICvry8//fQTtra2dOvWjQYNGuDo6Mj48ePZvXs3tWvXpnPnzsDT8SkDBgxg5MiR1K5d23JfIZE3hYeHB7/99hvz58/H2dmZgQMHvlXJlIiIiIi8xoTKZDLh7e2Nt7d3lOW1a9emdu3aL6xfsGBBVq9e/ZqiE4l/xYoVY926dYkdhoiIiIgkoNc2bbq8W15TT1JJRKpjERERESVUkgCcnJy4e/euTrjfYoZhcPfuXXVhFBERkXfea582Xd5+WbNm5fr165b7IcnbycnJiaxZsyZ2GCIiIiKJSgmVxDt7e3tNsy0iIiIi7wR1+RMREREREYkjJVQiIiIiIiJxpIRKREREREQkjpRQiYiIiIiIxJESKhERERERkThSQiUiIiIiIhJHSqhERERERETiSAmViIiIiIhIHCmhEhERERF5S/n4+FClShV8fHwSO5S3ll1iByAiIiIiIglj8ODBHDlyhKCgIOrWrZvY4byV1EIlIiIiIvKWCgoKivJb4p8SKhERERERkThSQiUiIiIiIhJHSqhERERERETiSAmViIiIiIhIHCmhEhERERERiSMlVCIiIiIiInGkhEpERERERCSOrL6xb0REBDt27ODYsWO4urpSpkwZUqZMScaMGRMyPhERERERkSTLqoTqzp07fP755/j6+gJQrVo17t27xy+//ML8+fPJnz9/ggYpIiIiIiKSFFnV5W/UqFH4+vry0UcfYRgGAMmSJePBgweMHTs2QQMUERERERFJqqxKqHbv3k3hwoUZN26cZVm7du0oXLgwx44dS7DgREREREQk6YoMD0nsEOIsvmK3qstfWFgYkZGRLyx//PgxJpMpXgIREREREZE3i42dI3+OyRnv5T4JuP7/vy8lSPkAFbwvxUs5VrVQlSlThlOnTtGjRw8ALl68SPv27bl48SKlSpWKl0BERERERETeNFa1UA0cOJBLly6xadMm4GlCdfHiRTJlyoS3t3eCBigiIiIiIpJUWZVQubm5sX79etavX8+5c+cIDw8nX7581KtXDycnp4SOUUREREREJEmy+j5UdnZ2NGjQIAFDERERERERebNYfR+qsWPHcubMGUJDQ194fPPmza8sIzIykgkTJrB69WpMJhPt27enTZs27Nmzh+HDh3P//n0aNmyIt7c3JpOJ06dP8/XXX3P9+nWqV6/OsGHD1BomIiIiIiJJilUJVf/+/dmzZ4/lHlTPsnaWv02bNvHTTz/Rt29ffH19GTVqFGXKlKFnz56UKFGCUqVKMWrUKIoUKUKdOnXo06cPqVOnZsCAAQwZMoQ8efLQoUOH2O2diIiIiIhIArIqoTp48CCpUqViwIABuLi4YGNj1eSAUdSoUYPNmzeTJUsWZs+ejclk4sGDBwQGBuLl5UXZsmWZM2cOu3fvpmjRovj5+TFixAgaN27MsmXL2LVrlxIqERERERFJUqxKqFxcXHB1df1PY6js7e3JkSMHI0aMYMGCBbRq1YqbN28C4OzsDEC6dOm4deuWZXnatGkty/38/OK8bRERERERkYRgVULVu3dv+vTpw8KFCylbtizJkiWL8niWLFms3mCTJk147733GDFiBB4eHsD/ug2auxQ+37Uwuq6Gr3Ly5MlYP0fiTsdbREREJOkJCQmx/D58+HC8l+/p6RnvZb5O8XFMrEqonJ2dcXBwYOTIkS88Zp5A4lX8/Pzw9fWldu3auLu7M2HCBH788UcAAgICAAgMDKRQoUJkypQJgPv371uWm5dZy8PDA0dHx1g9R14tJCQk2uRJx1tEREQk6TGfnzk6Or7xyU9CsOaYxHT+a2ZVQjV48GAePnwIPE2g4jKG6vjx4/Tv35/r168TEhLCkydP6Ny5M8OGDWP+/PmcP3+eGzduULFiRbJly0b27NlZvnw5NjY2nDhxgu7du8d6myIiIiIiIgnJqoTqxo0bZM+enUWLFpExY8Y4bahBgwb4+vry008/YWtrS7du3fj4449Jnz49w4cP5/Dhw7Rp04batWsDMGXKFAYMGMDIkSOpXbs2rVu3jtN2RUREREREEopVCVWFChW4fPlynJMpeNqy5e3tjbe3d5Tl5cuXj/Y+VgULFmT16tVx3p6IiIiIiEhCsyqhqlixIn/++SfNmzendOnSL9xgt3PnzgkSnIiIiIjI2y44LBwne6tOyyUJsqrmhgwZAsDRo0f5+++/LcsNw8BkMimhEhERERGJIyd7O1y6z0mQsu/cfgDAxdsPEmQbN6Z+Hu9lvmmsSqgaNGhgmdpcROKXj48P48aNo2/fvtStWzexwxERERGRWLAqoRo9enRCxyHyzho8eDBHjhwhKChICZWIiEgc6OKkJKYYE6qZM2eSM2dOatWqxcyZM2MswGQy0alTpwQJTuRdEBQUFOW3iIiIxI4uTkpiijGhmjx5MtWrV6dWrVpMnjw52i5/5jFUSqhEREREJLHo4qQkphgTqoYNG1KoUCFAY6hERERERESiE2NC1ahRI9KmTQtoDJWIiIiIiEh0bGJ6oHXr1kyZMuV1xiIiIiIiIvJGiTGhMgwDwzBeZywiIiIiIiJvlJdOm/7kyRP++eefVxaSJUuWeAtIRERERETkTfHShGrv3r1Uq1btpQWYTCZOnz4dr0GJiIiIiIi8CV55Y18bmxh7BYqIiIiIiLzTXppQVatWjenTp7+uWERERERERN4oan4SERERERGJIyVUIiIiIiIicRRjl7/58+dbbuwrIiIiIiIiL4qxhapUqVLkzZv3dcYiIiIi8lr4+PhQpUoVfHx8EjsUEYlGckebKL+TslfO8iciIiLythk8eDBHjhwhKCiIunXrJnY4IvKcz2s48+uuBzSrlDqxQ3klJVQiIiLyzgkKCoryW0SSlnL5k1Muf/LEDsMqSb8NTUREREREJImyuoXq8ePHXLp0idDQUAzDiPJY8eLF4z0wEREREXm9fHx8GDduHH379lVXSBErWZVQbd26la+//pqHDx++8JjJZOL06dPxHpiIiIiIvF4aWyYSe1YlVFOnTiUoKAg7OzvSp0+Pra1tQsclkuREhodgY+eY2GHEyZscu4iIvD4aWyYSe1YlVFevXsXV1ZUVK1aQLl26hI5JJEmysXPkzzE5473cJwHX///3pQQpH6CC96UEKVdERETkXWdVQlW4cGHCwsKUTImIiIiIvEFM9o5Rfkv8s2qWv6+++oqLFy8yfPhw/vzzTw4fPsyRI0csPyIi8U033RQREfnvUpZugL2bOylLN0jsUN5aVrVQtWrVCpPJxJIlS1iyZEmUxzQphYgkBA2MFkn6NCOcxNabPKb3TY3dMWcRHHMWSeww3mpWJVRZsmRJ6DhERKLQwGiRpE8XPiS2NB5Z3kZWJVTbt29P6DhERETkDaMLHyIisbixr2EYbNy40TJmqlSpUtSoUQOTyZRgwYmIiIiIiCRlVk1K8eTJE7y8vOjduzcLFy5k4cKFfPXVV7Rp04aQkJBXPt8wDMaMGUOZMmUoXbo0I0aMIDIykkmTJuHu7m75GT58OACnT5+mfv36eHp64u3tTXBw8H/bSxGR10gTaoiIiLw7rGqhmjJlCocOHSJLlizUqlULwzD4/fffOXDgAFOmTKFfv34vff6GDRuYO3cu3t7ehIWFMXHiRDw8PDhx4gRlypShY8eOmEwmXFxcAOjTpw+pU6dmwIABDBkyhDx58tChQ4f/vrciIq+BxpWIiIi8O6xqodq4cSMZMmRg7dq1eHt7079/f1avXk369OmtugLr4uJCz549adeuHS1btgTA39+fkydPcurUKTp27MiPP/6Is7Mz/v7++Pn50bhxYxo3boyHhwe7du36b3spIvIaaVyJiIjIu8OqhCogIIA8efKQMmVKy7LUqVOTJ08e7t+//8rne3p60rlzZwBmzJgBQOnSpcmaNSstWrRg/Pjx/P3330ycOJGbN28CkDZtWgDSpUvHrVu3YrVT8m5SNysRkbdLZPirhxUkVW9y7CISO1Z1+cuWLRvHjh3j9OnTFCxYEIBTp07x999/kz17dqs3Nn36dObMmUPTpk0pUaIEK1eutDzm4+PDgQMHaNiwYZTnGIZhdfnPOnnyZJyeJ3GTFI53nz59OHv2LDdv3rR0H41Pnp6e8V7m63T48OHEDiFWzOMzQ0JCFLtIEpXQr3VPT88EmwI7oafZruB96Y18/7+OOn2TJVSdvunH5U0WH3VqVULVvHlzRowYwaeffoq7uzsA586dIyIigiZNmli1odmzZzNt2jQaNWrE0KFDuXbtGuvXr6dBgwa4uroSHh6OnZ0dmTJlArC0fAUGBlqWxYaHhweOjm/ezdeSupCQkGiTp6RwvCMiIiy/9cH0ojftmJhfT46OjopdJInSa/3l3sRjojp9OR2Tt481dRrT+a+ZVQlVq1at8Pf3Z/78+Zw6dQoAk8lE06ZNad269Suff/DgQSZOnEiuXLn46KOP+Ouvv3BwcOD777/n1KlTVK1alb1799K+fXuyZctG9uzZWb58OTY2Npw4cYLu3btbE6aIiIiIiMhrZfV9qLy9vWnbti3Hjh3DZDLh4eFhdbeqRYsWYRgGFy9epF27dgC0a9eOcePGMX78eHbv3k3t2rUt46ymTJnCgAEDGDlyJLVr17YqaRMRkaTNx8eHcePG0bdvX81+KCIibw2rEyqATJkyUaNGjVhvZPLkyUyePDnax2rXrv3CsoIFC7J69epYb0dERJIuTScvIiJvoxgTKg8PD6pVq8aUKVPw8PB4aSFJYUICERFJ2jSdvIiIvI1iTKjCw8Mtg/zDw8NfW0AiIiIiIiJvihgTqm3btpEsWTLL3yIiIvJmCg4Lx8k+Vr38RUTESjF+urq5uVn+PnjwIK6urpQuXTrKOuvXr+fJkydWT50uIiIir5+TvR0u3efEe7l3bj8A4OLtBwlS/o2pn8d7mSIi8c3GmpX69+/PwoULX1i+cOFCxo4dG+9BibxLkjvaRPktIiIiIm+OGFuoZs+ezaRJkyz/b926lQIFCrywnrOzc4IE9l+VKVOGW7duAeDv7x/tOgsXLsTb29vyf8uWLWNMED/88ENOnDhh+X/jxo0UKVLkhfWOHz8eZebCwoULs2nTpmjL7NevH4sWLbL8P2bMGFq1ahXtus+2GELi7VOmTJn46aefXlgnqRzvDBkyRFseJMzx3noygh+3R1j+r1bIho7Von9b9V8SxqXbhuX/Uc3syJXJhs9rOPPrrgc0q5QagIu3Ivn61/+NW8yZ0cTo5vbRljl7WzjbTkVa/u9Q1ZbqHrb/aZ+S0vsiS5YshIWFRbsevJn7BG/Gex0SZp+yZMkS7Trw5u4TvBn1ZAtEFvsYI1eJaNe1XTE4yv8RjYdHu57p4iFsjq4FIHNyWx4ZaXgY7Zpgs+0HTPf//V+ZVTtD2mheAwH/YLt9puVfw9mVyGpfRFtmbD73mk4NjfL/0u4O0a4XH5/lz3uTX3v29tF/5yTUPiVmPcXmOzeh68kWiMzhieFZP9p1E+L9ZDq8BpvLhy3/x8dnRGLXU0KcG61evZo8efJE+xi8pIWqTZs2ZMmSBcMwMJlMGIbxwo+trS1t27aNsXARebVy+ZMztaML5fInT+xQRERERCSWYmyhcnBwYPXq1Tx69IgqVapQoUIFRo4caXncZDKROnVqnJycXkugIiIiiUU3JRYRkZi8dMqfVKlSkSpVKs6ePQvAw4cPSZkyJQBXrlwhU6ZMCR+hiIhIItNNiUVEJCYmwzCMV6304MEDunbtSrZs2SytVOXKlSNnzpxMnz6dtGnTJnig1goJCeHkyZN4eHjg6OiY2OG8Ur58+Th//jx58+bF19c3scN5peePb1I63q/jWP45JmeClJvQKnhfSuwQYu1Ne288602OPSG9ycflTY7dLEFm+VvwDRH3b2LrnJkMXiNf/YRYujH18wT73G0+/jrX74STNYMdS/pkjffy38TPXXhzv0sTuj4h4es0Id6jr0NCvk8TmrV1+qrzXaumFRs9ejQHDhzgn3/+sRRqb2/PkSNHmDBhQizCFhEREUl8mmH17aL6lMRk1atu165dZM+enR9++AEAR0dHfv/9d7Jnz87OnTsTNEARERGR+PZ5DWeK5XLi8xrOiR1KrAWHhb96pXfMm1yf8uaz6rbpDx48IFeuXFEmoHB0dCRjxowcP348wYITERERSQjl8id/Y2dXTagbNcObe7PmN7k+5c1nVUKVO3duDh8+zMKFC6lQoQIRERH88ccfHD58mPz58yd0jPIWCQ4Lx8neqpediIiIiEiSZ9WZ7Zdffkm3bt2iTJsOYBgGX3wR/Y3CRKKjq2oiIiIi8jaxagxVjRo1mDFjBkWKFMHR0RFHR0eKFi3K999/T82aNRM6RhERERERkSTJ6r5XVatWpWrVqgkZi4iIiIiIyBvF6oTq7NmzXLp0iZCQEMuyR48ecejQISZNmpQgwYmIyOv3Jo91fJNjFxGRN5NV3zqLFy/m22+/jfFxJVQi7643+QQ2IiwYW3unV6+YxESGh2Bjl3A30k6osY4JPc4RNNbxdTPZO0b5LSLyLrLqLGjBggWYTCYqVarEjh07qFmzJpcuXeL8+fN06NAhoWMUkSTsTT/5Toi7uz8JuP7/vy8lSPnW3tldJKGlLN2AR0c3k6JYrcQORUQk0ViVUPn7+1OiRAlmzpxJtWrVaNq0KSVLluTDDz/k2LFjCR2jiIiIJEGOOYvgmLNIYochIpKorJrlz8nJiSdPngDw/vvvc/DgQRwcHHBzc+P06dMJGqCIiIiIiEhSZVULVaFChfjrr79YuHAhnp6ejB8/nvPnz3Po0CHSp0+f0DEmiODwMJzs7BM7jCRHx0VERERExHpWJVTe3t60b98eOzs7PvzwQ3788Ue2bdsGQOPGjRM0wITiZGdPpvFdEjsM7gXcAuBiwK0kEc+tPt8ndggiIiIiIm8MqxKq/Pnzs337doKDg0mdOjWLFy9m48aNZMuWjRo1aiR0jCIiIiIiIkmS1XMdOzg48OjRI/7880+cnJz49NNPSZkyZULGJiIiIiIikqRZlVCFh4czYsQIli9fTkREBNWqVaNEiRJs3LiRWbNm4ezsnMBhioiIvFpC36MrobypcYuIiJUJ1dSpU/n111/Jli0b165dA+D69escO3aMUaNGMWbMmAQNUkRExBo2do66t5iIiLxWVk2bvnr1arJnz86GDRssy/r370/27NnZtWtXggUnIiIiIiKSlFmVUAUEBODm5oa9/f+m07azsyNDhgyW+1OJiIiIiIi8a6xKqPLly8fBgwdZvHgx8DTBmj17NkeOHMHd3T1BAxSxlsneMcpvEREREZGEZlVC1bdvX0wmE99++y0AR44cYdKkSdja2tKtW7dXPt8wDMaMGUOZMmUoXbo0I0aMIDIykj179lCrVi1Kly7N6NGjMQwDgNOnT1O/fn08PT3x9vYmODj4P+yivCtSlm6AvZs7KUs3SOxQREREROQdYdWkFGXKlGHdunXMmjULX19fwsPDyZs3L23atMHDw+OVz9+wYQNz587F29ubsLAwJk6ciIeHB9999x0lSpSgVKlSjBo1iiJFilCnTh369OlD6tSpGTBgAEOGDCFPnjx06NDhP++svN0ccxbBMWeRxA5DRGKgVmQREXkbWdVCBZAjRw5GjRrFihUrWLNmDePHj8fDw4OzZ8++8rkuLi707NmTdu3a0bJlS+DpLIGBgYF4eXnRpk0bMmXKxO7du/H398fPz4/GjRvTuHFjPDw8NPGFiMhbQK3IIiLyNnppC9X58+dZv349jx49omjRonz00UeYTCYAwsLCmD59OnPmzOHkyZMv3Yinpyeenp4AzJgxAwBbW1sAyz2s0qVLx61bt7h58yYAadOmtSz38/OL4+6JiEhSoVZkERF5G8WYUP3xxx9069aNiIgIABYtWsSWLVuYOnUqFy9epGfPnvj6+sZqY+YErGnTpri5uQFYEjTz+Cnzb7Pn/7eWNUmeRO/w4cOxfs6rjreZjnviiUu9WkN1mjgSqj5BdZpYVKdvH33uvn1Up2+f+KjTGBOqWbNmER4ejru7O9myZePgwYNs2bKFrVu3MnToUO7cuYO9vT09evSwakOzZ89m2rRpNGrUiKFDh7J//37g6YyBAIGBgRQqVIhMmTIBcP/+fcty87LY8PDwwNEx6ffTNzk6RPmdFLzsTR0SEhJt8vSmHO93mT6s3y6qz7eP6vTtozp9+6hO3z7W1GlM579mMSZUFy5cIG/evKxatQobGxuOHz/Op59+Sq9evQgNDSV37tyMHz+eAgUKvDKIgwcPMnHiRHLlysVHH33EX3/9Rbp06UiZMiXz58/n/Pnz3Lhxg4oVK5ItWzayZ8/O8uXLsbGx4cSJE3Tv3v2V23hTJa9Vkic7/yZZ5fcTOxQREREREYmlGCelePz4MdmzZ8fG5ukqBQsWBJ6OnWrYsCErV660KpmCp90FDcPg4sWLtGvXjrZt27JmzRpL98Hp06fTpk0bateuDcCUKVMIDg5m5MiR1K5dm9atW//X/UyyHAvkwLlzAxwL5EjsUEREREREJJZibKGKjIy0jG8CsLN7umqOHDn47rvvojz2KpMnT2by5MnRPrZ58+YXlhUsWJDVq1dbXb6IiIiIiEhisHradLOcOXPGKpkSERERERF5W7102vTt27dHuXGvyWRix44dL9zM19oZ3kRERERERN4mL02oIiMjiYyMjLLMMIwXlomIiIiIiLyLYkyotm3b9jrjEBF5ayR3tInyW0RERN5eMSZU5hvviohI7Hxew5lfdz2gWaXUiR2KxBMlyfKuMNk7RvktIq/20i5/IiISe+XyJ6dc/uSJHYbEIyXJ8q5IWboBj45uJkWxWokdisgbQwmViIjIKyhJlneFY84iOOYskthhiLxR1HdBREREREQkjpRQiUiSpH78IiIi8iaIscvfoEGDrCrAZDIxfPjweAtIRATUj19ERETeDDEmVMuWLcNkMmEYxksLUEIlIglB/fhFRETkTRBjQtW1a9fXGYeIiIiIiMgbRwmViIiIiIhIHFk9bfrZs2e5dOkSISEhlmWPHj3i8OHDTJw4MUGCExERERERScqsSqgWL17Mt99+G+PjSqhERERERORdZNW06QsWLMBkMlG5cmUMw6BGjRrkyZMHwzBo3759QscoIiIiIiKSJFmVUPn7+1OiRAlmzpyJm5sbTZs2ZcWKFWTJkoVjx44ldIwiIiIiIiJJklUJlZOTE0+ePAHg/fff5+DBgzg4OODm5sbp06cTNEAREREREZGkyqoxVIUKFeKvv/5i4cKFeHp6Mn78eM6fP8+hQ4dInz59QscoIiIiIiKSJFnVQuXt7U369Omxs7OjTp06pEmThm3btmEYBo0bN07oGEVERERERJIkq1qo8ufPz/bt2wkODiZ16tQsXryYjRs3ki1bNmrUqJHQMYqIiIiIiCRJVt+HysHBAQcHBwBcXV1p165dggUlIiIiIiLyJrAqofLz8+Pbb7/l+PHjhIaGvvD4yZMn4z0wERERERGRpM6qhGrYsGEcOHAgoWMRERERERF5o1iVUJ08eZIUKVIwduxY8ubNi62tbULHJSIiIiIikuRZlVBlzpyZzJkzU61atYSOR0RERERE5I1h1bTpffv25dixY2zatCnaMVQiIiIiIiLvIqtaqCZNmoSNjQ09e/YEwMbGBpPJZHlck1KIiIiIiMi7yKqE6vz581H+j4iISJBgRERERERE3iRWJVTbtm1L6DhERERERETeOFYlVG5ubgkdh4iIiIiIyBvHqoTqZbP7OTg4kCFDBqpXr07r1q2jjK0SERERERF5m1k1y5+/vz///PMP/v7+L/xcunSJgwcPMnr0aGbOnPnKsg4cOIC7uzt//PEH8HTCC3d3d8vP8OHDATh9+jT169fH09MTb29vgoOD/8NuioiIiIiIxD+rEqqFCxeSIkUK2rVrx6pVq1i9ejVt2rQhWbJkTJ06ldmzZ5MsWTJWrVoVYxmGYbBp0ya6du0aZfmJEycoU6YMc+fO5eeff6ZVq1YA9OnTh2TJkjFgwAB8fHxYsGDBf9hNERERERGR+GdVQvXdd9+RO3du+vXrR4ECBcifPz/9+/cnd+7c/Pzzz1SqVIlixYrx77//xljGuXPn6NOnDxUrVrQsMwyDkydPcurUKTp27MiPP/6Is7Mz/v7++Pn50bhxYxo3boyHhwe7du3673srIiIiIiISj6waQ3XhwgWyZcv2wvLg4GDLlOqBgYHY2MScn7m6urJ161auXLnC+vXrAQgICCBr1qxUqFCBAgUKMGDAACZOnEijRo0ASJs2LQDp0qXDz88vVjv2qntjeXp6xqq8d8nhw4dj/Rxr70Wm45544lKv1lCdJo6Eqk9QnSYW1enbR5+7bx/V6dsnPurUqoQqT548nDlzhvbt21OtWjUMw2Dbtm34+fnh7u7O6tWrOXnyJPny5YuxjDRp0pAmTRquXLliWZYuXTpWrlxp+d/Hx4cDBw7QsGHDKM81DCO2+4WHhweOjo6xfp68/E0dEhISbfKk45306cP67aL6fPuoTt8+qtO3j+r07WNNncZ0/mtmVUI1YMAAOnbsyJ9//smePXuAp0mOeYzT0aNHAfDy8rKmOItr166xfv16GjRogKurK+Hh4djZ2ZEpUyYA7t+/Dzxt/TIvExERERERSSqsSqhKlCjB5s2bWbhwIZcuXSI8PJy8efPSvHlzXFxcMAyD+fPnU6pUqdht3M6O77//nlOnTlG1alX27t1L+/btyZYtG9mzZ2f58uXY2Nhw4sQJunfvHqcdFBERERERSShWJVQAGTNmpGfPntE+Vrp06Tht3NXVlXHjxjF+/Hh2795N7dq16dy5MwBTpkxhwIABjBw5ktq1a9O6des4bUNERERERCShxJhQDRo0iIIFC9K8eXMGDRoUYwEmk8ly7yhrlC5dmnPnzln+r127NrVr135hvYIFC7J69WqryxUREREREXndYkyoli1bRvXq1WnevDnLli3DZDJFOzlEbBMqERERERGRt0WMCVXXrl3JlSsXAF26dMFkMr22oERERERERN4EL02ozLp16/ZaghEREREREXmTvHJSirNnz+Lm5kaqVKkIDw9n1apVnDlzBhcXFz755BPSpUv3OuIUERERERFJcmJMqB49ekTHjh05cuQIixYtolixYnTq1Im9e/da1lmwYAFLliwha9asryVYERERERGRpMQmpgfmzp3L4cOHsbOzI1myZGzfvt1yU98GDRpQsmRJbt++zffff//aghUREREREUlKYmyh2rRpEylTpmTNmjW4ubkxZ84cAMqXL8+oUaMICwujRo0aUVqsRERERERE3iUxtlD9888/FC1aFDc3NwD27duHyWSiSpUqANjb25MnTx4CAgJeS6AiIiIiIiJJTYwJla2tLUFBQQAcP36cu3fvAlC2bFnLOtevX8fZ2TlhIxQREREREUmiYuzyV6BAAQ4dOsSsWbP4448/AMiZMye5c+fm3r17zJw5kytXrlChQoXXFqyIiIiIiEhSEmNC1bFjRw4fPszkyZMxDAMbGxt69OgBQJ8+fdi7dy+2trZ06tTpdcUqIiIiIiKSpMSYUFWsWJE5c+bw66+/Ym9vT+PGjS3d/TJmzEjBggXp1asXJUqUeG3BioiIiIiIJCUvvbFv2bJlo4yZMhs5ciR2dq+8J7CIiIiIiMhbLcZJKV5GyZSIiIiIiEgcEyoRERERERFRQiUiIiIiIhJnSqhERERERETiyOrBUHfv3mXx4sX4+flha2tLnjx5aNasGWnTpk3I+ERERERERJIsqxKqQ4cO0alTJx4/foxhGACYTCZ+/vlnfvrpJ4oUKZKgQYqIiIiIiCRFViVUI0eO5NGjR5QuXZrq1atjGAY7duxg7969DB8+nOXLlyd0nCIiIiIiIkmOVQmVn58fefLkYd68eZhMJgC8vLz4+OOP8fX1TdAARUREREREkiqrEqr8+fMTERFhSabgaZc/W1tbChYsmGDBiYiIiIiIJGVWJVT9+vWjc+fODB48mJo1axIWFoaPjw8XLlxgyJAhHDlyxLJu8eLFEyxYERERERGRpMSqhKpVq1aYTCaWLVvGsmXLojw2ePBgy98mk4nTp0/Hb4QiIiIiIiJJlFUJVZYsWRI6DhERERERkTeOVQnV9u3bEzoOERERERGRN47VN/Y1DIONGzdaxkuVKlWKGjVqRJmoQkRERERE5F1iVUL15MkTOnTowOHDhy039l20aBGlSpVi9uzZODo6JmiQIiIiIiIiSZGNNStNmTKFQ4cO4erqStu2bWnTpg2urq4cOHCAKVOmJHSMIiIiIiIiSZJVLVQbN24kQ4YMrF27lpQpUwLw5ZdfUqdOHXx8fOjXr1+CBikiIiIiIpIUWdVCFRAQQJ48eSzJFEDq1KnJkycP9+/fT6jYREREREREkjSrEqps2bJx7NixKPeYOnXqFH///TfZs2eP1QYPHDiAu7s7f/zxBwB79uyhVq1alC5dmtGjR1vGaJ0+fZr69evj6emJt7c3wcHBsdqOiIiIiIhIQrMqoWrevDlPnjzh008/pXHjxjRu3JimTZsSEhJCkyZNrNqQYRhs2rSJrl27WpaFhITQs2dPcufOzRdffMHPP//Mxo0bAejTpw/JkiVjwIAB+Pj4sGDBgjjsnoiIiIiISMKxKqFq1aoVbdu2xTAMTp06xalTp4iIiKBp06a0bt3aqg2dO3eOPn36ULFiRcuyY8eOERgYiJeXF23atCFTpkzs3r0bf39//Pz8LMmbh4cHu3btitseioiIiIiIJBCr70Pl7e1N27ZtOXbsGCaTCQ8PD1xcXKzekKurK1u3buXKlSusX78egJs3bwLg7OwMQLp06bh165Zledq0aS3L/fz8rN4WwMmTJ1/6uKenZ6zKe5ccPnw41s951fE203FPPHGpV2uoThNHQtUnqE4Ti+r07aPP3beP6vTtEx91alVC9fXXX+Ph4UHLli2pUaOGZfnYsWMJDAxk5MiRrywjTZo0pEmThitXrliWmcdLmW8ObP7f/Pv59WLDw8ND98eKo5e9qUNCQqJNnnS8kz59WL9dVJ9vH9Xp20d1+vZRnb59rKnTmM5/zWJMqC5cuEBAQAAAq1at4urVq+TLl8/yeGRkJDt37sTf39+qhCo6GTNmBLBsJzAwkEKFCpEpUyYAywyCgYGBlmUiIiIiIiJJxUsTqh49elhaj44cOfLCeCnDMMiaNWucN160aFFSpkzJ/PnzOX/+PDdu3KBixYpky5aN7Nmzs3z5cmxsbDhx4gTdu3eP83ZEREREREQSQoyTUnz44YdUr16dbNmyAeDk5ES2bNksP9mzZ6dYsWIMGTIkzhtPnjw5U6dO5eLFi0yfPp02bdpQu3ZtAKZMmUJwcDAjR46kdu3aVk9+ISIiIiIi8rq8dAzV9OnTAfDy8sLT05MePXr85w2WLl2ac+fOWf4vX748mzdvfmG9ggULsnr16v+8PRERERERkYRi1aQUz94D6uHDh1y6dIlMmTKROXPmBAtMREREREQkqXvpfaiWLVtGgwYNOHHihOX/ihUr8umnn/LBBx/wzTffEBER8VoCFRERERERSWpibKHavn07gwYNwmQyERYWxqVLlxg2bBjh4eEkT56c0NBQVq5cSc6cOWnfvv3rjFlERERERCRJiLGFau7cuQD06NGD/Pnzs3LlSsLDw8mWLRt//vknq1evxt7ennXr1r22YEVERERERJKSGBOqixcvUqRIETp16kTy5MnZtWsXJpOJOnXqkDx5cvLkycP777/P1atXX2e8IiIiIiIiSUaMCdXDhw9JlSoVAHfu3MHX1xeAMmXKWNYxDAM7O6vmtRAREREREXnrxJhQubm5cfz4cW7dusUvv/yCYRikSJECT09PAI4fP87Ro0f/0419RURERERE3mQxNi/VrVuX6dOnU7lyZQBMJhONGzfGwcGBUaNGsXjxYiIiIqhfv/5rC1ZERERERCQpibGFqmPHjjRs2BCTyYStrS3169end+/eANy4cYOwsDDq1atHy5YtX1uwIiIiIiIiSUmMLVTmlqiBAwdia2uLk5OT5TEvLy++/PJL3N3dX0uQIiIiIiIiSdErZ5RIkSLFC8tKlCiRIMGIiIiIiIi8SWLs8iciIiIiIiIvp4RKREREREQkjpRQiYiIiIiIxJESKhERERERkThSQiUiIiIiIhJHSqhERERERETiSAmViIiIiIhIHCmhEhERERERiSMlVCIiIiIiInGkhEpERERERCSOlFCJiIiIiIjEkRIqERERERGROFJCJSIiIiIiEkdKqEREREREROJICZWIiIiIiEgcKaESERERERGJIyVUIiIiIiIicaSESkREREREJI6UUImIiIiIiMSREioREREREZE4ShIJVZ8+fXB3d7f8zJkzhz179lCrVi1Kly7N6NGjMQwjscMUERERERGJwi6xAwA4ceIEH330EY0aNcJkMpEjRw4aNGhAiRIlKFWqFKNGjaJIkSLUqVMnsUMVERERERGxSPQWqgcPHnDlyhV27dpFp06dWLZsGdevXycwMBAvLy/atGlDpkyZ2L17d2KHKiIiIiIiEkWit1DduHGDfPny0aBBA1KlSsWgQYPIly8fAM7OzgCkS5eOW7duxarckydPvvRxT0/POMX7Ljh8+HCsn/Oq422m45544lKv1lCdJo6Eqk9QnSYW1enbR5+7bx/V6dsnPuo00ROqfPnysXbtWsv/ixcvZvr06QCYTCaAOI2f8vDwwNHRMX6CfMe87E0dEhISbfKk45306cP67aL6fPuoTt8+qtO3j+r07WNNncZ0/muW6F3+zp49y/Tp03n8+DEA4eHh2Nk9zfMCAgIACAwMJFOmTIkWo4iIiIiISHQSvYUqODiYadOmcfv2bXLnzo2vry+DBw9m4sSJzJ8/n/Pnz3Pjxg0qVqyY2KGKiIiIiIhEkegJ1fvvv8/AgQOZNWsW69ato127drRo0YIcOXIwfPhwDh8+TJs2bahdu3ZihyoiIiIiIhJFoidUAF5eXnh5eUVZVr58eTZv3pxIEYmIiIiIiLxaoo+hEhEREREReVMpoRIREREREYkjJVQiIiIiIiJxpIRKREREREQkjpRQiYiIiIiIxJESKhERERERkThSQiUiIiIiIhJHSqhERERERETiSAmViIiIiIhIHCmhEhERERERiSMlVCIiIiIiInGkhEpERERERCSOlFCJiIiIiIjEkRIqERERERGROFJCJSIiIiIiEkdKqEREREREROJICZWIiIiIiEgcKaESERERERGJIyVUIiIiIiIicaSESkREREREJI6UUImIiIiIiMSREioREREREZE4UkIlIiIiIiISR0qoRERERERE4kgJlYiIiIiISBwpoRIREREREYkjJVQiIiIiIiJxpIRKREREREQkjpRQiYiIiIiIxJESKhERERERkThSQiUiIiIiIhJHSTqhWrNmDZUrV6ZcuXL89NNPiR2OiIiIiIhIFEk2obpx4wbffPMNlSpV4pNPPmHcuHEcO3YsscMSERERERGxSLIJ1b59+wgLC6NDhw506dIFGxsbdu/endhhiYiIiIiIWNgldgAxuXnzJgDOzs44OjqSIkUKbt269crnGYYBQGho6CvXzeSU8r8F+RYKCQl56ePm42o+zrE53maZUjjEMbrEFRISgskpY2KHESevqtf/SnX6eiV0fYLq9HVTnUbvTa1P0OduTFSnMVOdvn7W1unz57/PMxkxPZLIfvjhByZPnsyhQ4dIlSoVnp6e1K1bl+HDh7/0eUFBQfj6+r6mKN9d+fLlI1WqVDreIiIiIvJOMJ//Pi/JtlBlzPg0071//z6Ojo48efKETJkyvfJ5KVKkIF++fNjb22MymRI6zHeOYRiEhYWRIkUKQMdbRERERN5uz5//Pi/JJlRlypTB1taW2bNnky5dOiIiIqhUqdIrn2djYxNt5ijxx8nJyfK3jreIiIiIvO2ePf99XpLt8gdPp02fPHkyISEhtGvXjvbt2yd2SCIiIiIiIhZJOqESERERERFJypLstOkiIiIiIiJJnRIqERERERGROFJCJSIiIiIiEkdKqEREREREROJICZWIiIiIiEgcKaESeUZERATw9AZu8naIjIxM7BBERN4ZkZGR+g59C6lOX04JVSIzn8BL4jLXg62tLQCPHz9OzHAkHhiGgWEY2Nj872NOydWbT3X4dlF9vj1CQkIAsLGxwWQyERwcnMgRSXyJjIzEZDJF+V+iUkKVSMwvRltbWwIDAzl79mwiR/TuioyMtCRSp0+f5vPPP2fChAmJHJX8FxEREZhMJkwmEw8ePKBXr148fPgQGxsbXWV7g0VGRloS5K1bt3L9+vVEjkjiytyKYa7PVatWsXr16sQNSuJsz549VKpUiUOHDgEwZcoUunTpYkmy5M1kPlc1v09nz57NjRs39F0aDbvEDuBdZX5xrl+/nmHDhmFjY0O5cuVo3749hQoVSuTo3i02NjbcvHmTYcOG8eTJEw4ePMjVq1c5fvw4RYoUSezwJA7MCfKcOXO4cuUKGzZsIHXq1AwdOhTDMKJcaZM3h/m9unTpUn777Te8vb3JmjVrYoclcWD+Dty/fz/79+9n1qxZlCxZkpIlS+Lm5pbI0UlsBQcHY29vzw8//MCcOXPYsGED9erVw9HRMcqFEHmzmOtt7969HDp0iBkzZvD3338zY8YMfY8+R6/w1+jZbP7cuXN88803LF26lHbt2vHJJ5+wd+9eZsyYoW6ACSy64zt9+nTOnj1Ly5Yt+fzzz7l16xa//PJLIkQncWHu3mf++9GjR/Tu3Zs5c+aQNm1aXF1d+fXXXzly5Ag2NjbqrvCGeP69eu/ePQYNGsSMGTOoU6cOlSpVSqTIJC6ev6K9cOFCPvvsM27dukX+/PnZv38/69evT6ToJLbCw8Mtf1erVo0mTZrw119/MWHCBGxtbQkICABQMvWGefa7NDIykpkzZ9K5c2du3bpFhgwZ2L59Ozt27ADU9e9ZepW/BuaTgmezeT8/P9atW8f58+dp1KgRffv25aOPPuL48eMcOHAgsUJ9q5lPus2tF3fv3rX08b58+TLOzs5Ur16dnj17UrFiRbZv387atWsTM2SxwrPd+8x/h4WFceTIEerUqUPPnj0ZP3482bNnZ8SIEYC+4N8Ez75Xjx8/zuXLl0mbNi316tUjbdq0PH78mDRp0qjbyRvA/Nn7/BXtw4cPU6BAAby9vVmxYgW5cuVi1apVHD58OJEildiws3vayenQoUPcuXOHRo0aUbx4cRYsWMDFixdZsWIFvXr1Yt++fYkcqVjDnByZ36fm79Vdu3ZRpkwZhg0bxtSpUylYsCDffvstoO/SZ+lIvAbmk4IlS5YwefJktm3bRp06dWjdujWPHj1i7969AJQoUYK7d++SLFmyxAz3rWM+4TJ/ONy7dw8vLy9atGjB2LFjAciRIwf3799n165dALi6uhIWFsayZcu4c+dOosUuL/fs+Leff/6Z8ePHc/r0aRwdHUmZMiW+vr4AFC1alAwZMnD69GkWLVpkea4kPc++X+/fv0/Hjh3p0qUL3bt3p2fPnuTNm5dq1aqxYsUK/Pz8MJlMqsskLDw83PLZu3v3biZNmsTBgwcBCAoKIjw8nNu3bwNQo0YNLl++zOrVq3ny5Elihi1WOHDgAFWrVqVjx45UqFABgLp165IqVSqyZMlC3759OXv2LG3btmXs2LGq0yQsLCzMkhzt27ePn376iVOnTmEymbC3t8ff3x9bW1uKFy9Orly58Pf3Z+bMmYC+S81Mhi7vxbu7d+8SGBhIrly5iIiI4M6dO/Tu3ZurV6+SKlUq/Pz88PLyolOnTrRu3Zrbt2/TrFkzzp8/z9mzZ/nll1947733Ens33gozZ87k/v379O/fH3g6oBLgyJEjPH78mAMHDjB48GAqVqxIixYtCA8Pp379+vj7+xMaGsrff//N2LFjqVy5cmLuhjzj0KFDGIZByZIlgaeti/PmzcPHx4eHDx/i4uLC8uXL+eWXX5g9ezZt27bF09OT9evXc+PGDa5du8bWrVt14SIJer4VY/Xq1SxevJg2bdowevRo7t27x9KlS3nw4AGDBg3C1dWVBQsWJGLEEp1Hjx5x/PhxypYtC8D9+/c5fPgwAwYM4NGjR6RIkYLvv/+e0NBQ2rVrx8cff8ynn36Kj48PBw4c4OHDh4wZM4YyZcok8p6IWXh4uKVFCp72DOjYsSPJkyencePG2NraUrFiRQICAvjuu+/YunUrGzZswNXVlUWLFlGlShWNjUtiHj58iJ+fH0WLFrX8v2LFCsaPH09YWBhp06ZlzZo1rFmzhgkTJtC+fXtq1qzJ8uXLOXv2LL6+vmzdupUMGTIk8p4kDWqhimf3799nxowZ7Nmzh8DAQGxtbTl9+jQXL15k7NixzJw5k549e7JgwQLL1dfg4GB27txJzpw5mT17tpKpePLw4UMAPDw8CAgI4OTJk/zwww9MnTqVTz75hLFjx1KpUiW+//57UqRIwdixY/nggw/YunUr7733HtWrV8dkMpElS5ZE3hMxu3//Pj169ODff/8lNDSUTZs28eGHH3LixAl++OEHRo4cyf3791mwYAHNmjWjcePGrFu3jq+//honJyfy5MlDqlSpuH//fmLvivw/Pz8/OnXqxMGDBzGZTGzZsoXevXsTHBzMmTNnOH/+PN7e3uTJk4fp06dz/PhxChcuTN26dTl48CDnz59P7F2QZwQFBdGzZ0+WLFlCZGQkFy5c4KOPPmLUqFF06dKFGTNmkDx5cqZMmULZsmVp2bIlO3bsoEuXLly4cIEWLVpw9+7dKCfvknjM19zN9eHv709ISAgBAQH4+vqSLl06qlSpYukmf/DgQRo1akR4eDhjxowBoGXLlkqmkpjQ0FAaNGjA9u3bCQkJ4dSpU5QpU4aNGzcyatQovv32W8LCwpg1axb16tWjUaNGzJ8/ny+//JKLFy9SrFgxnJyc+OeffxJ7V5IMJVTxxDw1qLOzM5cuXWLMmDE0bdqUffv2cfXqVe7fv4+bmxvZsmWjZMmSJEuWjD179vDhhx9SqlQpwsLCqFu3Lvny5SM0NDSR9+bNFhYWBkDKlCnx8vLir7/+omvXrri6utK1a1cMw8DOzg4XFxcaNGiAjY0NAwcOpGzZsnTq1IlixYpx9+5dvvvuO2rUqKEENwkwj0N0dnZm+fLlXLx4kTVr1lCqVCkKFSrElStXyJQpE/Xr16dRo0bMmzePu3fvMnLkSEaOHEn16tXx9PRk7969FCxYkHTp0iXyHomZr68v+/fvZ/HixQB8//33ODg44OTkRO7cuXny5Ak1atRg1KhR+Pn5MWzYMIKCgmjVqhV//vknefPmTeQ9EPjfiXeqVKkYPHgwtWrVYu7cubi5uVGiRAlu3LhBoUKFqFSpEg0aNMDX15dffvmFQYMGsWLFCqpUqULDhg3Zv38/bm5ueo8mEeYW499//52qVavSvn17ZsyYQYYMGciYMSN+fn6cPXuWR48eMXPmTH777TfKli3L7NmzmTx5cuIGLy/w9/dn586dODg4MGvWLHLmzImPjw9Zs2alSJEi/P3337i5udGkSRNatmzJr7/+ypUrV/juu++YPXs2ZcqUoUWLFly8eBEXFxfNsvoMJVT/0enTp+ncuTNdunRhxIgRhIeHExYWhmEYpEqVirx585IlSxacnJyYOXMmgYGBBAUFkSJFCgoWLIiTkxOtWrXi2rVr/PDDDxiGgYODQ2Lv1hvp+PHjPHr0CHt7e+DpjGApUqQgT548HD58mD179lCjRg0KFy7MhAkTePz4MbVq1aJ48eJcu3aNoKAgHj16RM6cObl37x4zZszg22+/VX0koqCgIOB/4xABXFxc2LBhAz/99BMBAQF069aNhw8fsmXLFgCaNGmCvb093333HYZhEBISwvXr15k2bRoff/wxEydOxNHRMVH2R56KiIiwnIDXrl2bjz/+mD179jB79mwiIiJIkSIFAJ9++ilFihRh69atjBgxgvnz51OjRg3SpUtHxowZ1dUkCYiMjLSMkzLLmDEju3fvZvz48fzzzz+0adOGlClTMnfuXADatGlD5syZmTlzJlevXuXOnTsEBQUxceJEHj58yPz588mVK1di7dI77eHDhyxbtoyjR49aLu7u27ePQYMGUb9+fdKkScOCBQvYt28fAwYM4NixY3z11VeMGDGCs2fPUrduXQBLd09JGsyft3v37mXs2LGcP38eJycnNm3axJgxY/D396dXr14A/Pnnn4SGhlK/fn2yZMnC119/bfnMNnfpjIiI4Mcff9SFj2cZEmuRkZGGYRjG4sWLjaJFixrt2rUzOnbsaHzxxRfG3bt3jf379xve3t5GoUKFjK1btxqGYRhDhgwxChQoYNSsWdMoVqyY0aVLF+P+/fuWMpcsWWLcvHkzUfbnbTBx4kTD3d3d2L9/v7F3716jfv36xieffGLs2LHDCAoKMtq3b29UrVrV8Pf3NzZs2GB4eHgY48ePNwzDMG7cuBGlrIiIiMTYBfl/kZGRRmRkpDFq1CijU6dOhmEYxoMHD4wRI0YYCxYsMIKDg41Dhw4ZBQsWNMaOHWsEBQUZX331lfHBBx8Yhw8fNgzDMLZt22ZcvnzZUuaFCxeivN8kcZjr1uyff/4xDONp/dSrV8+oXr264e7ublStWtUYNWqUERQUZNy9e9f44YcfjE6dOhkTJ05MrNAlGn/88YfRpk0by3fX0qVLjYULFxp37941Ll68aFStWtXw8vIyDMMwJk2aZLi7uxtbtmwxDMMwfv/9d8vfhmEYV69eNc6cOfP6d0IsZs+ebRQuXNgoU6aM8f777xurVq0yDMMwZs2aZTRq1MhYunSp0alTJ6NOnTpGixYtjNDQUGPTpk1G7969DS8vL8PHxydxd0CidezYMWPv3r2GYRjGb7/9Zri7uxvu7u7GjBkzjDNnzhhFihQxhgwZYgQFBRkDBw40ypYta1l/w4YNxqZNmwzDMIywsDDjzJkzxokTJxJtX5IyTUoRR+Hh4bRt25asWbMycuRIAHbv3o2bmxvZs2fHMAxq1apFlixZGDp0KLa2tty9e5f9+/eTO3du6tSpAzy9Uvvs1XeJHfMNA69cucKnn35KhQoVePLkCfb29hw9epSQkBDWrFmDn58f3bt3p0WLFnz22WcMGzYMOzs7xo0bZ+kb/vygW0lc/fr1448//sDLy4sjR45w7tw5AgICaNeuHd26dWPcuHGsW7eOadOmkTFjRurWrUubNm3o27ev5T2l91fSFBQURI8ePTh37hxVq1bF29ubtWvXMmrUKBwcHPjkk09YunQpAF26dKF9+/aEhYVZWp8ladiwYQO9evWiR48e+Pv7s27dOoKDg/Hw8LBMDDNq1CgmTpxIiRIlaNasGREREezcuTOxQ5dnnDlzBm9vb27cuMGXX35J/vz5GThwIOXKlWP48OE8ePCAJ0+esGLFCvLnz8+hQ4eYO3cuDRs2ZNSoUYkdvrzCmDFjOH36NB06dODOnTsMGzaMyMhIpk6dSuXKlRk5ciTLli1j0qRJuLu7U7VqVapVq8aECRNwcnJK7PDfGEqo4ujevXt06NCBu3fvUr9+fX7//XeuXbtGZGQk5cuXZ9iwYZw6dYpu3brh5uZGtWrVGDBgQJQydLIXd9Edu59++onx48eTN29e5s6dy4ULF+jRowe1a9fG29ubCRMmsHDhQlavXk2WLFlInTp1IkUv0TH+/yaC5noNCAigWbNmhIaGUr58eby8vFi0aBHr169nypQp5MuXj8aNG5MjRw6mTp3KhQsXKFWqVCLvhTzPfNHDbN68edy+fZsrV65gGAbbtm2jc+fOdOvWjY4dO3Lq1ClWrlyJjY0Ns2bNolKlSnzwwQeJuAfyrGcvPD158oShQ4fy+++/8/7779O1a1f27NnDjBkzGDJkCI0bN6ZTp04cPXqUP//8k+PHj+Pi4qLufEmMt7c3a9asoVevXnTs2JG///6bXr16kTZtWipUqMBHH33EnTt3aNu2Lb169eL69escPnyYnDlzMnbsWJycnHQ/oiTm2c/dhQsXWu7BOGvWLEJCQhg9ejQ5cuRg7ty5PHz4kEaNGuHs7MyUKVM4c+YMuXLlIkeOHIm4B28evQPiKHXq1LRu3RqAxYsXkzp1aj755BO6d+/O7t27+f3336lRowZffvklhQoVomnTppbnmnNYJVNxYzxzw88dO3YwefJktm/fTv369SlZsiShoaGEhoZSokQJmjZtytKlSzl27BjNmzfnm2++IX/+/JZkyjzZgSQu4/+ny7a1tbXMHpU2bVr69u3Lv//+S0BAAO7u7vTu3RtnZ2cWLlyInZ0dnTp1wtHREQcHB0sypTpNGgzDICIiIsqJ1unTp5k3bx7z5s3jww8/ZNKkSVSsWJFVq1Zx/vx5vvjiC+7fv8+oUaNwcXFhyJAhSqaSGHMydf78eRwdHfnss8/InDkz169fp0CBArRq1YrKlSszfvx4QkNDadWqFalTp+b69euUK1eOXLly6b41SYS5Htq3b0+BAgXYtGkT/fv3p0WLFuTIkYP33nuP2bNnM27cODJnzkymTJmYOnUqf/31F0OHDmX69OkkT55cyVQSEhkZiWEYUerkzp072NjYkDJlSjJkyEDNmjWpW7cuR48e5YcffuDBgwf06dOH69evExISQtWqVcmRI4fep7GVOD0N3xwRERFR+vybl5kFBwcbDx48MJ48eWIYxtPxOKVKlTJmz55tGMbTPqdmz5cj1jOPmzGMp8c0NDTUGDFihFGkSBGjfPnyxieffGIEBAQYO3bsMNzd3Y2pU6caoaGhhq+vr1GxYkVjzJgxibwH8jLm99Ts2bON999/3yhatKjRqlUrwzAMo23btkbVqlWNnTt3Gobxvz7gK1asMMLDwxMtZrGOv7+/MXXqVGPNmjWGYRjGzz//bOTPn9/47bffDMMwjJ07dxqVK1c22rVrZxjG0/Gk169fT7R4JaqgoCDDMAzLe+3w4cNGrVq1jPfff99o1KiRcfLkSWPOnDlGwYIFjd9//90wjKdjGN3d3Y3evXsbhqHvvqTMXK9z5swxPD09DU9PT2P37t1GcHCwYRiG0aFDB6NKlSrGv//+a5w6dcryPpakbfv27Ua/fv2MuXPnGjdv3jR2795tlC9f3vj6668NwzAMX19fo3nz5oa7u7tRtmzZF8aSS+xpwEg0jP+/Wv5sk+nNmzdJliwZqVOnjpL53717l7lz52Jvb0+xYsVYuXIlTk5OFC9eHEBjOeLJtm3bWLp0KRMnTsTW1hbDMPjjjz/o1q0bdevWJWPGjNjZ2VG2bFmaN2/O3LlzKV68OOXLl2fp0qW4urpayjKeu3moJI7x48eTM2dOGjdujI2NDWfPnmX+/Pl88cUXpE2blhIlSgDw1Vdf0alTJ1auXMn7779PkyZNAGjUqJGlLL2/koaVK1cSERFBkyZNiIyM5Ndff2XMmDG4uLhw5coVQkNDadasGdu2bWPChAnUqVOHSpUqUbx4cc6fP8/Nmzdp1qxZYu+G/L/jx4+zfv16BgwYgK2tLQ8ePGDcuHHkzJmTTp064eTkRKFChciUKRNbt25l1qxZFC1alHLlyjFo0CDLjXlNJpPeo0lAdOOEzXVSq1Yt/vzzTy5fvkzq1KlxdHTk2rVrBAYGkiZNGtKnT4+LiwsFCxZMjNDlJcz1an6PLVq0iB9++IG6desyZswYNm/eTP/+/fnss8+YNGkS+fLlw9PTk969e3Py5EkqVKhA5syZo5Qlsad22ueEh4dz+/ZtAGxsbHj48CFdunTh888/5/PPP8fHx4fHjx9b1ndwcCAsLIzly5czbNgw7ty5w6xZs/D09AT+dw8HfZHE3uHDh+nevTuPHj0iPDyc/fv3U6dOHWbOnElQUBDXr18nTZo0uLq6EhwczLfffsv06dNp27Yt8L8pt83JlLkrmJKpxPP48WM2bNgAPK2PoUOH0qxZM86fP8/Zs2e5d+8exYsXp0mTJuTMmZMlS5aQNWtWGjZsyKZNmzh06BCAJakyd0nQ+ytx7d+/Hy8vL2bNmsX48eN5+PAhNjY2rFu3jvr169O2bVucnJwYOXIkERERdOrUiSdPnjBo0CAAhgwZwrp16yxf6pI0nDt3Dh8fH7y9vZkzZw4PHjzgxIkTlntG1a5dm0OHDnHixAk6derEyZMn2bBhA05OTrRs2ZLcuXPrPZoEmL/7zCfKGzZsYM+ePQAEBwfTqVMnfv75Z2rWrIlhGMyePZu1a9fSrl07rl+/Ts+ePTUhTBL0fL2az01PnjyJh4cHN27cAKBy5cq4uLhQs2ZNSpYsyejRo5k6dSq5c+fms88+I3fu3C+UJbGnhOoZ9+7do1mzZpYTcng6gC8oKIgvvviCEydO8P3333P58mXL4xkyZODrr79m6dKl/PDDDyxfvpz8+fNb+rFK3JkHRgYGBhIcHEyyZMkIDQ2lePHiuLi44OnpyU8//YSvry9Xr15l/fr1pE+fnuzZs7Nr1y4+/PDDKOXpCz1xPXz4kL59+7Jx40bg6ZdBWFgYp06dIn369OTLlw/DMDh37hyPHj3ir7/+YsSIERw8eJAvv/ySH3/8kapVq0YpU333E9fDhw/p3r07n332GS4uLpbWpYkTJwLg6elJsWLF8Pf3p0ePHtja2jJ+/HhKlSpFrVq1ePDgAaGhoZogJokw/n/cm5mrqyt3795lzZo1pEyZEjs7O9zc3Ni6dSvwtIdGv3792Lt3L+XKlWPs2LF4eXlFKU/v0cRn/u67efMmzZs3p1evXnz++ed8/fXXfP311/j6+vLBBx/QrFkzKlasyNatW+nXrx8VKlRgz549VK5cOZH3QJ5nPDOW/MqVK3Ts2JHPP/8cgMDAQHbs2MG1a9eYP38+KVKkoE+fPri6ujJhwgRGjhzJ7NmzcXZ2fqEsiTt90j0jVapUVKtWDT8/P1auXAk8faFevXqVIUOGUKRIEby9vS0tH2ZOTk7kypWLIkWKAFgGYqslJG7MVzRbtWpF5syZ6dq1K8HBwYwcORIHBwc2b96Mg4MDnTt35sGDB3h5edGhQwecnZ2pUKEC8LQuldQmHeHh4aRMmZLBgwdTv359Bg8eTLVq1ShVqhRhYWH4+flRsGBBateuzbfffsuAAQP46aefyJQpE4ULFyZVqlRUrFgRQHWaRKxevZoSJUpw+fJlZs6cybhx42jYsCGVK1dm1apVnDx5kj59+uDv78+VK1coUqQIadOmZcmSJezYscNSxw4ODvqsTAIiIiIsE8M8fPiQwMBAAgMDqVWrFvD0gqOLiwv16tVj9+7ddO7cmenTp/PgwQOKFy+Ovb09H3/8Mba2tpbPcNVr0mDuDdC+fXuyZcvGzJkzKV++PKtXr6ZSpUr88ccflCxZEoAWLVrQtGlTNm/ezJAhQxI5cnnesz1tgoKC6NmzJz179uT06dMcP36cv//+m/r16+Pg4EDOnDlJnjw5W7ZsIVmyZBiGQYYMGWjcuLGlG665LPnv3vmE6tmrcfb29tStW5dSpUoxceJEnjx5QsqUKblz5w516tRh1qxZ7Ny5k969e/PkyZMYy1SmH3vPXhm1sbHh6NGjDBkyBGdnZy5cuMCpU6eoU6cO9erV488//2TJkiUULFiQFStW0KtXL7788ks2b95M7ty5LWUqqU185hMrOzs7AgICOHToEFu2bOG3334jNDSUUaNG8d577zF8+HAAxo0bh5eXFw8ePACeToXv5uYWpUzVaeILCwvj6NGjAAwYMIAqVaqwfPlyhg4dSu7cucmcOTOjR48G4P/au/O4nPL+8eOv9kWF7C1TKmRQlrLOlBka2WtIdhFZMqFMzFhGlkm27ESI5A7ZjbEvM2MkY8aMfWgiSbYSSqmu8/vD77pujbnv7z0GFe/nf811XZ/HOc6ccz7vz/J+79q1i5MnTzJ37lwsLS0JCwvjk08+0YyOipKlXhakfm8tWbKENm3aMGDAAFQqFfPnz8fZ2Znt27dz/vx5evfuzZdffkl2djanT5/miy++0NRVVJNZqdJFW1sbW1tbrly5QlFREa1bt2bIkCFUqlSJvXv3As+2L6hUKhwdHQkLC8PGxqaEj1o876+Wzm7fvp3vvvuOwYMHM2rUKGxsbAgNDaVdu3Z4eXlx+PBhgoKCSE5OZtCgQRgYGGh+K7NSr0EJJMIoFf6cve/AgQPKL7/8ohQWFioHDx5U6tWrpyxcuFC5ePGi0qJFC8XDw0OJiopSPvzwQ2XatGnFsveJVyc7O1tRFEX55ptvlDp16ijZ2dnKzJkzlcaNGyv79u1Tbt++rXh4eCgNGzZU2rdvr9y4caPY7+W6lF6DBg1SfH19lVOnTimdO3dWunTpoiiKosTFxSl16tRRevXqpcTGxiqKoii5ubma3z2fVVOUHqmpqcrHH3+s9O3bV/H19VXq1q2rLF++XMnJyVEiIyOVevXqKUePHlVOnz6t9OvXTxk0aJCSkpJS0oct/r/k5GSlU6dOSkBAgHL16lVFURRl+fLlSqtWrZTY2FilZcuWSrdu3ZQbN24ov/76q1KnTh3Fz89PmTdvnnL//n0lJyen2L0p92nplp2drQwaNEjp2LGjcvPmTSU/P1+JjIxU6tSpoxw4cKCkD0/8j86cOaN069ZN2bt3r7Jo0SKlYcOGmr5sTEyMUqdOHWXt2rWKoijK5cuXlf3798u9+Ya8s8NI6tmL+/fvExAQwMiRI+nduzchISE4ODjQvXt3lixZgomJCVOmTOG9995j165duLm5ERoaKhv3XoE/L8k7dOgQTZs2ZceOHZibm1O7dm327dtHQEAA5cuXJzY2Fn19fSIiIujUqRNjxozByspK83tFUeS6lDDl/xfnVfv555+ZNGkSmZmZdO/encuXL+Po6EivXr24evUqMTEx9OjRg379+nHx4kVu3rwJgJGREcALdYxE6WFlZcXQoUM5deoUeXl5JCQkMHToUIyNjenatSv16tUjMDCQunXrEhUVxapVq6RQZCmyZ88erly5QlJSEomJiQCkpKTQqFEjCgsLef/997l8+TJxcXE4OTkxYsQIzp8/z/nz5wE09YeeX1kgSi8zMzMCAgJIS0sjPj4efX19OnToQN++fXF0dCzpwxN/Qf0uValU5OfnM3fuXD7//HNq1aqFg4MDVatWJS8vj82bNwPP7kldXV0iIiK4efMmtWvXxsPDA21tbQoLC0vyVN4J71Tv88/pIBctWsS2bduoX78+sbGx/PTTTyxZsoTGjRvTpUsXfvjhB8aOHUt8fDyffPIJmZmZmJubA8WrUIu/JzMzk/Lly2ummy9duoS1tbXm77Vr19KxY0fy8/NJSUmhYsWK9O/fn5kzZxITE8Po0aNp1KjRC+3KUrCSp6WlhZaWFvn5+RgYGLBz5042b95Mfn4+Li4u1KxZk3379tGxY0e+++47IiIi+Pjjj5kwYQLDhw/X3F9qsiSh9NLS0sLT05P9+/eTnp6OsbGx5jN1Onw3NzdNcCxK3rFjx6hSpQrvv/8+jo6OODg4kJqaysaNG3F2diYkJITHjx+TkJDAxIkTmTZtGtu3b6datWoEBQXRuXNnatasWaxNuUfLjiZNmuDl5cWKFSto27YtTk5OTJw4saQPS/yJSqVCS0tL08fU1tZGW1ubM2fOkJqaSs+ePbG3t0dbWxs3NzcmT57M8ePHycrKokuXLuzdu5ft27cTGBioaVMGm1+/dyoiUP8PlZaWBoCdnR137twhKysLZ2dnhg8fTps2bYiNjcXZ2RkvLy+uXLnC9evXURQFc3NzzQi8BFN/3+PHj/niiy/w9fXFy8uLnTt3cu7cOby8vJg+fTotW7bExsaGzMxM0tLSuH79umbk1M/Pj4EDB+Lr66tpT6p4lw7bt28nIiKCFStWUFBQwN69e/H19eXixYt88MEH6OnpkZqaSlRUFEVFRdy6dQszMzPatWuHg4MD9+7dA8Dc3ByVSiXXtQwxMzNjyJAhpKens3379mIlJXx8fIq90EXJyc3NZdiwYQwdOpSZM2eSmJiIs7MzWlpadO7cmZSUFE0NxZ07d7Jq1SrOnDlDQUHBC4EyFN97LMoOXV1dBg0axNixYzVJtETpo15BdeLECSZPnsyqVatITU0lLCyMypUrc/jwYYqKiqhZsyZjx44lKCiI+/fvY2lpSevWrTEwMJBZxxLw1kcFz09zXrlyBR8fH/r06UN8fDzNmjXDy8uLlJQULl68yOPHjzE2NkZHR4eCggJ69+5NYmIiNjY2mtmP50cNxP8uOTkZX19fUlJS6NChA/Xr1yc/P5/69eszfvx4EhMTGTduHK6urtja2uLq6oqJiQkXLlzQbH4fN24cNWrU0CwTlOtQsm7dukXPnj35+uuv+f7771m9ejW//vorurq6GBoaEhgYSLly5dDR0aF9+/Y4Oztz+fJlvvvuOwA6derErl27NEWw4d8jcaLsUI96L1++nCtXrmj+u8wYlx4qlQoLCwsA0tPTNdnb3nvvPR49ekRAQACbNm0iKSmJ6tWrU6FCBcaNG4eJiQkxMTH4+fkVa09mpcqu9957j8GDB5f0YYjn5OXlMXXqVM6cOaMZtF++fDlDhw7lxo0bLFu2jF69elGuXDlN3cb169cDUKtWLfr27YutrS1Pnjxh4sSJODs706xZsxI+q3fPWzsHWFBQgJ6eHrq6ujx+/JgbN26we/duypcvj76+PrNmzaJmzZr07t2b7777Dj8/Pzp27MjevXvx9vZGT09Ps/xIKkf/c8nJyWRlZREWFoaLiwtJSUlkZWVx5coV/Pz8qF27NoGBgejq6lKrVi0aNWrElClT+P3334st71MURTpqpcTOnTvJzc1lzZo1vP/++5w/f55y5crRqFEjWrRogb+/PwsXLkSlUpGcnMz06dO5fPkyRkZG5OXlaQpFqqu7i7JJPeptZWWFs7NzSR+O+AsmJib07t2bEydOoKWlhYmJCQMHDqRly5Zs3LiRwMBAjhw5woYNGwgJCWHlypXcvXtXU/dNvQRJnr1CvFpxcXHMmzcPIyMjPv30U7S0tMjJyWHv3r34+PgQGhrKzz//zMSJE5k9ezZTpkzh0KFDLFu2DA8PDywsLCgoKMDFxYVjx44xc+bMF+o1ijdDS1HerqIuly5dok6dOpoH/48//sjo0aMpLCxEW1ub5cuXY2FhodmIOWnSJA4dOsT06dPp2bMnTZo0oXPnziV8Fm+ftWvXMmvWLBo1aoSiKJw+fVrz2ahRoxg+fDgHDx5kzpw5XLt2jU2bNhVbkiCBVOmhvhZhYWFs3ryZ6dOnk5KSwqFDh1AUhZo1azJmzBgqVqzIrFmz2LlzJxUqVGDPnj2oVKoX9kkJIV6tv3peFhYWsnHjRmbMmMH06dOJi4sjIyOD+/fv89VXX2FmZkZISAixsbGamkQgAx5CvA43btxg2LBh3Lx5k2HDhtG3b19MTEwoKCggKysLLy8vPD09mTx5MgUFBYSEhHD16lX27NlDXFwcmZmZBAYGyoqOUuStmnYZNWoU6enpLF26FF1dXQICAqhatSp+fn5kZGSwadMmzpw5g4uLCwEBAUyfPp0jR47g7u7ON998w6VLlxg1ahQgL5F/4vl/O/WLvUOHDmRmZrJt2zZsbGzo378/jo6OHD9+nOjoaAYOHEjbtm0xNzcnNze3WDAle9ZKF3VHrXnz5nz//feMHz8eQ0NDbGxssLGx0RQRnDVrFp9//jmVKlVCpVIVqzsks75CvHrqZ+9fDT7p6uri6enJrl272L59O5999hlbt25l//79nD59msmTJ3PkyBFq1KhR7HfyHhTi1UtNTSU5OZlevXppAquxY8fSqlUrunXrhr29PYcPH+bjjz/G1taWrKwszfL4Pn36lPDRi7/yVsxQqTOKpaenk5KSwp07d3Bzc2PkyJH88ssvLFmyhKZNmzJmzBh+//131q9fj7W1Nd7e3tjb2/P1119z+PBhxowZQ2RkJJ6enjIb8hKePHlSLKOXOhB6frRUpVLx4MEDzSzFpEmT+OWXX1i1ahXVqlUr1p7MSpUO/y34uXPnDhkZGRgYGGBpaYmJiQn9+vVDS0uLdevWveEjFULAs2VEDx8+xNXVFScnJ/T19TWf/fDDD4wcOZLhw4fTrFkz4uPjcXd3p3379oAs7xPiTfn88885ceIEzZo145tvvqFVq1aMGzdOUzJm8eLF3LhxgwoVKqBSqZg3bx4uLi6avpEMNpcuZTqg2rFjBzt37kRHR4cePXrQtm1bpk2bRlxcHIsWLcLS0pJu3boxZMgQgoODOXDgAJMmTaJevXqsWrWKe/fuUblyZeBZFqSUlBTq1atXwmdVtiiKgqIozJ8/n0uXLrFixQqePHlCUFAQHTp0wNvbu9j34+LiOHDgAE5OThQVFbF+/Xr8/PwICgqSkdBSZvv27dSsWVOzL2bz5s00b94ca2trAE1ykO+//54rV67g4eHBzz//zJw5c/D19WXkyJHFOmXy8Bfi9crOziY0NJSbN29iZWXF0aNHCQ4OZsiQIZp7MS8vjwkTJnDo0CF27dqluZ+FEK/H1atXOXbsGN7e3pps0VpaWty4cQNfX18yMzOZPHkyPXv2BP6dcOv69eucPn2aJ0+e4OvrK6s6Srky2bvJzc1l3LhxTJ06FSMjI7Kzszl79iwqlYqxY8diZmbGhg0bqFixIgMGDGDDhg2cOHGCNm3a4O7ujoODAyqVShNMFRUVYWxsLMHUS1BnPbx27Rq//vorBw8eJDk5me+//55WrVq98H17e3vy8vLYtWsXR48eZerUqYwZM0aCqVImOTmZ2bNnExoaCsDq1asJCwsr9h0tLS2ePn3K6dOniYyMpE+fPkyePBkPDw8CAwNfGOGWYEqIV+evUpffunWLa9eu0b17d9LS0qhSpQr29vbF0tkbGhoSHBzMli1bXhgcEUK8WoqisGvXLmbPns3p06c1wZSiKFhbWxMQEICWlpamUPbzxbJtbGz49NNP6dOnD7q6ulKct5QrkzNUZ8+eZcyYMQwZMgRfX1+ysrL47bffsLOzw9ramu3btzN+/HjGjRtHhw4d6NSpE3Z2dsTExKCtrY2BgUFJn0KZp07tqQ6Erly5QnBwMMbGxjRs2JA9e/YQGRmJi4sLUHx2oqioiPT09GIjozJ7UTqo92AoisKGDRuYNm0a06dPJy0tTTOirX4hqL97//59/vjjD1JSUnB1ddXUqpFrKsSr9+cleXv27EFfXx9XV1euXr3KiBEjyM7OxsfHB29vb+Li4nBzc6Nr164v3JNyjwrx+qjflY8ePcLX15caNWowceLEYsWx8/Pz6dGjB0ZGRoSFhVGnTp3/2pYovcrk/KGWlhZpaWl8//33XLx4kYMHD5KTk4O+vj6+vr4EBweTkJBAbGwsjRs3JiwsjNzc3L/c3yP+PvW/nY6ODg8fPiQ/P59atWrRrVs31q1bx8aNG9HS0iI0NJSgoCDatm2LiYkJ8OyhoKOjowmm1J1yuRYlS91JUwfIWlpatG3blu+++45p06ZhZ2dHamoqsbGxtGrVCjs7O801q1SpEpUqVdJkBlMX5pVrKsSrp76v7t69y9SpUzl06BAArq6uDBo0iFq1apGWloanpycPHz7k0KFDtGnTpthv/9yWEOLV09LSIikpiY0bN1KtWjUSExM5evQoNWrUwNDQEJVKhYGBAaGhofj7+7Nnzx5q1qxZbM/j822J0q1MzlA9efKERYsWERsbS/ny5alduzYNGjTgwoULnDp1is2bN5OXl4ePjw9z5syhU6dOJX3Ib6X58+ezceNGbGxsCAgIoHnz5nz22WccP36cdu3aYWxszDfffIOpqSnLly+nQYMGJX3I4v9w/PhxEhISsLe3x9/fn6SkJGbOnElKSgp169YlPT2dcuXKMXjwYNq1a0elSpWK/V5G0YR49f6cdXblypWsWrWK+vXrM3z4cG7cuEFYWBj9+vWjevXqxMTEkJOTQ2FhIY6OjkRGRkq5AiFes+dXeKgH/nv37k2TJk2oW7cuu3fv5tGjR0RGRtKwYcNiv126dCmenp7Y2dmVzMGLf6xMzlAZGRkRGhrKkCFDKCwspEqVKsCzTfQ///wzRUVFNGjQgEOHDmFpaVnCR1v2/Xl5X2FhIcuXL2f37t2MHDmSlJQUatWqhbGxMSNGjODChQtoa2sTHh6Ot7c3p06dkmCqFHp+llalUrFgwQLWrVtH06ZN+fXXX8nNzaVx48a0bt2a69evExISgpWVFTNmzODAgQN4enq+0KYEU0K8OurxTvWz9/r169jY2NC8eXMWL15MWloaNjY2NGnShHPnzpGQkMCePXv48MMP+emnnzAzM9PMTgkhXo8/36fq92BycjJ37tzho48+okuXLrRv357OnTuze/durKysqFy5sib4GjFihKYteY+WTWUyoFK7fv060dHRtG3bFoCoqCicnZ01o+aWlpaSAvYVUC8Fe/z4Mbq6uhgaGvLLL7+gUqk4efKkJsDNzMxkz549tGnThoSEBLp3706rVq1o2rRpSZ+CeE5RUZEmmQg8e4A/fvyYU6dO0bFjR+rUqUNOTg7R0dE0adIELy8vDh8+zOTJkzl8+DArV64s4TMQ4t2gfm+dOnWK6dOn8+DBA3x8fPDz82PIkCFER0dz8uRJPvzwQwwNDdHR0eHRo0dYW1sX26MqdRWFeH3U9+mJEydYt24dtra2uLm54ejoiL6+PhkZGTx9+pTCwkJMTExYv349jRs3pkOHDsX6phJMlW1lOqAyNTXl3r17hIeHo1KpaN26tSbzn5qsEX85a9as4ffffyc8PByABQsWsGXLFho0aMCwYcMYNmwYEyZMwMzMjOrVq/PkyRPOnj3LpUuXGDJkCJ6ensWy/MmDouRduHABfX19HBwcgGcP/4MHD+Ls7EyXLl3Q1dUlISEBZ2dnqlWrxnfffcfWrVv59ttv6du3Lzdu3Cg2WymdNCFevYKCAvT09DR/nzp1ijFjxuDh4cGtW7eIiYmhVq1a9O7dm927dxMSEkLHjh354YcfaN26NVWrVn2hTblPhXi1tm/fjq2tLQ0bNkRRFH744Qe++OILOnfuzN69e4mLi2PBggX07t2bBQsWkJqaSqVKlWjdujW5ubk4OTm90Kb0kcq2Mh1Q2dvbExUVxbVr16hQoQI2NjaAjMb9E5cuXeLLL78kNTWV4cOH8/DhQ5YsWcLevXvx9fVl6dKlAERERLB//37i4uI0HWw7OzsqV65MtWrVNNdCHUjJg6LkFBQUMG3aNDZt2kRwcDC2trbMnDmThIQErKysiI+PR19fn9WrV3P37l0uXbqEgYEBZmZmXL9+HUNDQ/r06aMZnFDfW3KPCfHqJCcnY2trqwmmHj16hKmpKXfv3kVfXx8nJyfu37+PkZERMTExNGzYkNDQUEaMGIGenh6TJk2S/cJCvAF5eXn8+OOPbNu2jQ4dOtC5c2fOnDlD9erVycjI4NatWwwePBhLS0tGjhzJvXv3SEpKIjs7m1mzZuHu7l7SpyBegzIdUAGUL19eU3hUXWRWOnp/35MnTxg/fjz79u0DICwsDF9fX548eUJSUhJt27ZFT0+PWrVqcf78eRISEnBzc2P+/PkYGBhgZWVFZGQk1apVK9auBFIlKyEhgTlz5vDgwQMWLlyIu7s7GRkZnDx5kqFDh6JSqVizZg1z587FxcWF8+fPM3LkSCpXrkx+fj7h4eEYGxtr2pPsmEK8epMnT+bbb79l9erVGBkZMX36dAwMDBg1ahTNmzdn7dq17N27l08//ZTmzZszY8YMVq5cyZAhQ2jTpg2//PIL/fr1A2RAUYjX4fnVGYaGhuTn53Py5El+/vlnXFxcKFeuHOfOnaOoqIioqCgePnzImDFjWLx4MV9//TXZ2dlUrFhRU5xX3qVvn7fqaj6/L0T877Kzs/nyyy+5fPkyU6ZMQVdXl59++kkzGjp16lS8vb15+vQpYWFh6OjokJCQwK1bt0hKSiIuLo74+Hjs7e01KbNFyXr8+DEdOnRg+vTpVKlShUqVKpGdnY2BgQE6Ojp4enpibW1NYWEhQ4cO5eHDh8TFxWk2uwcGBnL8+HHN/kQ1ub+EeHXy8/MB8Pb25tGjR2zevJnw8HAKCgo4deoUX3zxBQUFBTx48IC5c+eSnJzMtWvXMDEx4d69e5QrV45hw4Zx/fp1zpw5Uyx5kBDi1Xi+rEh+fj6PHj3CysoKU1NTzM3NMTMzo3HjxlhYWGBgYIC2tjaJiYkUFBRgYWGBvr4+VapUKVacV96lb58yP0Ml/jkzMzM+++wzypUrR7Vq1UhPTyc6OpqmTZvi4+NDgwYNGDBgAJmZmdjZ2VG+fHlycnK4du0arVq1kqWWpZCJiQm9evWiRYsWqFQqPv/8c/71r3/h5uZGjRo18Pf3Z8KECZiamuLi4oKuri7Lli2jadOmxYKowsJCzYiaEOLV2LFjBzt37kRHRwcfHx88PDzw8fFh06ZNuLq6Mm/ePM6ePUtISAjbtm2jYcOG2NvbM3v2bKpVq8a8efM0e1QbNGjA7t27NXsjhRCvljr4WbVqFd9++y0mJiaMHTuWWrVqER4ezqpVqxg/fjyjRo1ixowZTJw4kby8PKZMmYKBgUGxtuR9+vYqk3WoxOv3wQcfYGlpydSpU6lTpw4zZ85k/fr1moKv48aNo3r16iV9mOI5GRkZVK9eXRMEqW9tLS0tYmJiWL58Od7e3owbN46nT5/i6upKxYoVMTY25r333qN37964ublp2pNEIkK8Wrm5uYSFhXHw4EFatGjB3bt3adasGaNGjaKgoAB3d3cqVapETEwMVatWZeLEiRw9epT58+dTsWJFzp49S7t27TSJl2QQS4jX48/vv6VLlxIbG4u7uztZWVmEh4djYGDA5MmTOXLkCIGBgbRo0QI7OzvOnTuHk5PTXxboFW8vCajeMUVFRWhra//HjrI6w9T+/fsJCgpi5MiRDB8+nMLCQnbs2EHFihXx8PAAkJT0pUR+fj7Tpk3j+++/58CBA+jr62vWZ6tfCrm5uYwaNYqrV68SERFB06ZN2bVrlyZT0bhx4zQPfwmkhHg9zp49y5gxYxgyZAi+vr5kZWXx22+/YWdnh7W1Ndu3b2f8+PGMHTsWf39/fv/9dz799FP8/f0JDg7WtCMzx0K8Hn81SJGfn09AQABGRkZERkZqBjQyMjK4ffs2X331FZcuXcLT05P58+drfif36btFAqp3yPMPiqtXr1K+fHnKly+Pvr7+Xz5E/P39SUxMZOnSpS9kpZGR0dJBfR2WLl3K2rVr6dOnD0FBQS8U7dXW1ubYsWNMmzYNa2tr1qxZAzxLRiKj3UK8GefOnaN79+60bduWypUrc/DgQXJyctDX18fX15fg4GD69etHWloas2fPxsXFhatXrxZbzicDHkK8fhs2bODOnTs0b96c5s2b06tXL7S0tFi4cCGVK1cmIiKCXbt2cfDgQdLS0rh79y4tWrQo6cMWJUh2xb3lnj59yqlTpwA0RR+HDx/O4MGDCQkJYfLkyZrP1IqKigCYNGkSLi4u1K1b94V2peNdOqivg5WVFQ4ODvzrX/8iJSUFbW1tzXVUB1bu7u44OzuTnp7OtWvXADAyMiqWvUgI8frY29szaNAgjh07xsGDB6lduzb9+/fHycmJdevWceXKFUJDQ7l16xYZGRkAmmBKfT9LMCXEq6W+t+DZIOOwYcNYt24dFy5cwM/Pj5UrVxIYGMjPP//MnDlzOHbsGD/++CNubm4YGhri4OCgCaaeb0u8W2SG6i3Xo0cP0tLSWL58OU5OTmzdupUtW7bQp08fwsLCyM7OZsOGDTRu3LjYyKfMVpQNGRkZjB49moKCAgwNDTl9+jTt2rVjwYIFxb6nvp7379/HzMysWOFQIcSblZWVRWFhIVWqVAGeFQmdNm0acXFxODo6cvPmTSwtLUv4KIV4uz2/kuPRo0dcvHgRc3NzQkND8fT0ZNu2bTx58oTPPvuMbt26MWvWLHbt2sXTp0+xtLRk7ty51KxZs4TPQpQWsrjzLaQoCkVFRejq6jJ69GgGDRrEt99+S926dUlKSuK3337j0qVLODg4MGzYMNLS0rC3t6d8+fKaNv48YyXBVcn7q/XYv/zyC5cvXyYyMpLatWuzcOFC9u3bx4EDB/Dw8NBcO/X1q1SpEiDXVIiSdP36daKjozUZNaOionB2dtbcn5aWlrJHVYjXZPPmzTRo0ABHR0cA9u/fT2xsLFWrViUoKIgLFy5w4cIFOnbsSO/evdmyZQsPHz4kNDQUf39/bt68iZOTEyBLcMW/yZK/t4i6BpSWlpYmy1vLli1p164d8fHxJCYm4u7uTkFBAZ06dWLBggWcP3+e8PBwcnJy/mO70vEuHdTB1MmTJ/njjz+AZ/WmCgoK0NbWxsLCgq5du/LkyRMWLVpEfn7+f7x2ck2FKDmmpqbcu3eP8PBwZsyYQf369VmyZIlmxgr4r8mDhBAv59KlS/zrX//i9u3bFBYWsnHjRoKCgsjLy6Nnz56UL1+etm3bUqVKFTp37szTp0/ZuXMnVlZWwLNBSXUwVVRUJPeo0JAZqrfEsWPHWLlyJYsXL6ZChQps3rwZlUqFp6cnM2bMoGXLlsTHx9OjRw9atWrF1q1bKSws5KeffsLZ2ZmqVauW9CmI/0NycjKjR4+mqKiIvLw8OnXqhLOzM1ZWVkRHR2NnZ0d+fj62trbcvn2bH374gTZt2pT0YQsh/sTe3p6oqCiuXbtGhQoVpJafEK+RoiikpaVhbW2No6MjixcvZv78+eTk5ODs7EzTpk3JyMjA0dERU1NT/Pz8CAwM1BTWdnJyolGjRi+0K/eqeJ7soXpLrFixgsWLF+Pl5YWenh67d+8mJyeHmjVrsnjxYk6dOsXEiROZNGkSbdq0YdGiRdy6dQsbGxsmTJggqT3LgMjISC5fvoyHhwcTJkygVq1aLFu2jF27dhEdHU25cuVQqVR07twZPz8/qlWrVtKHLIT4HyiKgqIomv0cQohXY8uWLURFRVFQUICNjQ3Dhg3D1taWESNGkJuby9q1azlx4gRffvkloaGh+Pn5Ac+W5aakpFBYWFis2L0Q/4kEVGXY83tqnj59yujRozl37hy2trYEBwdz7do1IiMjNctJvL29yczMZOnSpdSrV4+8vDwMDQ0BGRktLZ7fJAvPXgaZmZkMGTKEYcOGkZSURF5eHl27dqV9+/ZkZGTg5eXF2bNnOXbsGHZ2dnh5eQEUK+wrhBBCvCuuXbvG+PHjuXz5Mj4+PiiKwokTJ7h79y5z584lLy+Pzz//HH9/f3r27El4eDgnT55k/fr12NravtCe9JHE/0WGw8og9WimOpj6448/0NfXx9/fn6dPn3Lz5k0aNmyIl5cXbm5unDlzhrt37zJ27FgeP36sacfQ0FBSZpcyzwdTDx48IDo6mtjYWO7du0ejRo3Izc1lwIABhIaGsm/fPhYuXEhOTg5NmjQhODhYE0zJhnYhhBDvory8PCIiIjhz5gwRERGMGzeOCRMmEBERgZmZGWvWrMHCwgJvb29Wr17NvXv36N69O/fu3WPv3r1/2ab0kcT/RQKqMuTp06cAmo7yiRMnaNeuHf7+/oSFhdGoUSN8fX25f/8+MTExqFQqrK2tUalUqFQqWrVqRVJSEvXq1dO0qaWlJctMSpCiKCQnJ5OZmQk8u8YTJ05k69atVKhQgeHDh1NUVMTixYsZNGgQFhYWbNq0ialTp7Jnzx66detWLDujelZKrqkQQoh3kaGhIf3798fY2Jjz589TUFAAPKvpNnDgQI4fP052dja9evWiXLlyfP3119StW5etW7cybNiwEj56UVZJr6uMOHbsGKtXr9b8feHCBUJDQ3F3d8fR0ZGEhAS2bNnCoEGDsLW1ZebMmQQFBREfH0+zZs0wNTXVzEQVFhaW4JkItR07duDh4cGAAQP46KOP+PHHH7l37x7nzp1j2bJlZGdn06VLF1q0aMHu3bu5dOkSq1aton///uTl5TF+/HjGjBlTLHiSGSkhhBDvOldXV7y8vNiwYQNnz55FURQMDAxwcXGhXLlynDx5klq1auHn54e9vT1mZma8//77gBTnFS9HMhGUEQUFBSxcuJCUlBTatGlDYWEhZmZm2NnZcfv2bezt7YmNjeXjjz9myJAhjB07FnNzc2bMmKGp4K0mCShK3s6dO4mIiMDHx4dKlSrx4MEDjIyMNHUwQkJCiI6OJiQkhHbt2rF7924mTJjAhg0bGDVqVLG9VrK2WwghhPg3XV1dBg4cyNGjR1m7di329vaYm5tz584ddHV1qV27NgD9+vVDX1+/2G/lfSpehvSsS6k/723Kz89HpVKxY8cO3NzcaNu2LS4uLuzZs4dPPvmE27dvM2vWLCZOnMiSJUvIzc3l008/1fxeOt2lg7oI4IEDB7C0tGTw4MGa2UN1gNSoUSO8vLxYs2YN7733Ho8fP8bV1RULCwtyc3MxNjZGW1tb8xu5rkIIIURxVlZWDB48mGnTpuHu7o65uTmLFi2iWrVq1K9fH0ATTP05IZQQf5cEVKXQ8x3lx48fk5OTg42NDS4uLvz0009kZWVhYGDA9evXmTlzJv7+/qhUKuzt7cnPzycnJwcfH58X2hIlT70k7+LFizRp0gRTU9Ni/33Pnj04ODjQt29fzp49S1hYGNWrV2fq1Km0bNmyWFvy8BdCCCH+mpaWFl26dOHw4cNMmjQJbW1t2rVrx4wZMzA2Ni72XXmfin9K0qaXIteuXSuWrjMmJoYVK1bw9OlTevfujZeXF3PmzOG3335j27ZtKIrCgAEDuHHjBhUqVGDatGl89NFHJXcC4n+iKApjx44lKSmJhIQETb2otLQ0+vfvj7u7O1999RXZ2dkkJSXx4YcfSnp7IYQQ4iX88MMPrF69ms8++0xToFfepeJVkxmqUqCwsJCQkBDS09PZuHEj2tramiQUPXv2REdHB09PT+zs7OjZsyenT58mODiY1q1bM2/ePK5cuYKnp6dm6loeFKWblpYW7du35/DhwyxZsoTQ0FBMTExIT0/n4cOH1K1bF4Dy5cvj4eEB/PuaynUVQggh/nctW7bkgw8+AJ6t2gHZJyVePQmoSpBKpWLlypX06dOH4cOHk5WVxa5du+jatSvJyck8evSILl26YGtrS1FREYcOHcLKyoqhQ4cyb9488vPz8fX11XTA1YV+5UFR+rm5udGvXz9WrFhBSkoKDRo0IDExkRo1atC0adMXvi/XVAghhPj7JIGTeBNk0WgJ2rlzJ5GRkcyePVuT+nzcuHGkpqZStWpV9PX1OXnyJPBsyjowMJDs7GwGDRrEjh072LRpEyYmJgDFCv2K0k9fX5/Ro0czfvx4DAwMSExMxNHRkY0bN/5llXYhhBBCvDwJpsTrJHuoSkBBQQF6enoAhISEcPz4cWJiYnjy5AlBQUE0bNiQ+fPn4+PjQ3p6Or6+vpw/f57bt28TFRWFhYWFpi31rJQo27KzszUFemUUTQghhBCi7JAZqhKgp6dHdnY2c+fOJTs7mwcPHjBv3jwaNWqEt7c3Bw4cIDExkblz59KiRQsOHjxIbm4us2fPLhZMgdSUKuvU4xnly5d/IVW+EEIIIYQo/WSG6g34c32D3NxcRo8ezfXr1zXL986cOcPXX3+Nm5sbQ4cO5f79+3z77bcYGBiQkZFB9erV/7ItIYQQQgghRMmRnvlrVlRUpAmA1NllVCoVV69epUmTJvj6+mr2UC1btgxDQ0PNUr9jx44BaIKp59sSQgghhBBClDxZL/aa6ejo8OjRIyIiIjA1NaVu3bp88MEH1KhRg99//x0AS0tLqlatytGjR5k2bRrh4eG0atUKS0vLF9oSQgghhBBClB4y3fGKKYrC86so79y5Q69evbh79y4ZGRmEhoaSmJiIq6srN27cwN/fn/Xr11NQUICPjw8ffvgh8CzIUqlUyIpMIYQQQgghSi+ZoXqFns+4d+bMGW7cuMF7772HtrY2FhYW7N69GycnJxwcHGjWrBkGBgZs3bqV+fPn4+vry+jRozXZ/wBZ3ieEEEIIIUQpJ0kp/iFFUUhLS8Pa2hqAx48fs3PnTpYsWUJAQABGRkZMnjwZU1NTBg8eTPPmzZk0aRLDhg2jQ4cO3L59m8LCQs3yPkk6IYQQQgghRNkhM1T/wNatW1m6dClFRUW4u7szcuRI1q1bx4oVK2jSpAm9evVCX1+flStXoq2tTbVq1Th37hy3bt2iRo0aAFSrVg34d8IKCaaEEEIIIYQoOySgegnXrl1j/PjxXL58mQ4dOnD//n3i4+MxMzMjKCiIrVu38uDBA27evEnNmjWZOHEiX3/9NeHh4RQVFdG5c2ecnJyKtSmBlBBCCCGEEGWPLPn7m/Ly8hgzZgxHjhxh0aJFtG7dGj09Pdq1a4eZmRmbN28mPj6eKVOmEBwczODBg9HW1ubevXskJydTqVIlHBwcSvo0hBBCCCGEEK+ATIv8TYaGhvTv3x9jY2POnTuHnp4eeXl5FBQUYGxsDEDPnj1xdnZm06ZNnDx5EoDKlSvTrFkzHBwcUKlUmiV+QgghhBBCiLJLlvy9BBcXF7p27Up8fDx6enps2LABc3NzJk2apPnOl19+ia+vL5mZmS/8Xpb3CSGEEEII8XaQJX8vKTU1lcDAQK5cuUK/fv0YMWIEFStW1NSO0tHRIT09HQsLi5I+VCGEEEIIIcRrIlMlL8nKyor+/fsDUL9+fczMzIBns086OjoAWFhYSHFeIYQQQggh3mISUL0kbW1t2rdvT8uWLZkzZw7Xr1//j9/T0tJ6w0cnhBBCCCGEeBMkoPoHTExMGDhwINbW1pQrV66kD0cIIYQQQgjxhskeqn9IURSZgRJCCCGEEOIdJVn+/iF1MFVUVKTZOyWEEOJF/fr1Iykp6S8/8/b2ZubMmS/d5okTJzA3N/+nhyiEEEL8bRJQvSISTAkhxH/XrVs3WrRoAcDSpUsxMjJi4MCBADg6OpbkoQkhhBAvTfZQCSGEeCO8vLwYMWIEI0aMQE9PD1NTU83fqampfPTRR9SvXx93d3eioqI0v4uKitJ89tFHHxEdHf2X7R8/fpx69erRoUMHHjx48IbOSgghxLtOAiohhBAl6qeffiI8PJyqVavi7++Pjo4O8+bNIzU1lcTERObNm4eFhQUBAQGYmpoye/ZsEhMTi7Xxxx9/MGrUKMzNzYmOjqZChQolczJCCCHeObLkTwghRIlq1KgRe/bsoWLFiqSlpXHhwgVu3rzJnTt30NV99poyMDDAwcGBTp06YWJiQtWqVYu1MWrUKB49esTixYuloLoQQog3SgIqIYQQJSo/P5+oqCgOHDiAtbW1phh6UVERzZo1Y9KkSaxdu5YxY8agpaWFq6sr4eHhWFlZadrIysoCYN++fTRv3rxEzkMIIcS7SZb8CSGEKFFRUVHs2LGDiIgIdu7cSbNmzTSf3bx5k0qVKrF69WoOHjxIcHAwSUlJLFu2rFgbs2fPpmnTpmzatInk5OQ3fQpCCCHeYTJDJYQQokSpZ6RWr15NUlISGzduBKCgoIDz588zevRo7Ozs8PT05OzZswDUqFGjWBstWrTA2tqaHj16MHv2bJYvX/5mT0IIIcQ7S2aohBBClCg/Pz9atmzJxYsXOXLkCN7e3gCcP3+eTz75hClTpqAoCtHR0Vy+fJkBAwYQEBDwQjtOTk54enpy5MgRTp48+aZPQwghxDtKS1EPDQohhBBCCCGE+FtkhkoIIYQQQgghXpIEVEIIIYQQQgjxkiSgEkIIIYQQQoiXJAGVEEIIIYQQQrwkCaiEEEIIIYQQ4iVJQCWEEEIIIYQQL0kCKiGEEEIIIYR4SRJQCSGEEEIIIcRLkoBKCCGEEEIIIV7S/wO89YrOnrVfMgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 864x468 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "thresh_subs_tests = pipeline(SSRTs_ThreshSubs, strip_str='_wThresh_subs')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "thresh_subs_tests.to_csv(f'result_csvs/dataset-{dataset}_metric-SSRT_wThresh_subs_tests-validationContrasts.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
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