04-miscellaneous.ipynb

{
    "cells": [
        {
            "cell_type": "code",
            "execution_count": 2,
            "id": "f3f2c92f-3486-4cfb-bf50-cd883d58a52d",
            "metadata": {},
            "outputs": [],
            "source": [
                "import warnings\n",
                "warnings.simplefilter('ignore')\n",
                "import sys\n",
                "sys.path.append('/home1/jrudoler/src/')\n",
                "import cmlreaders as cml\n",
                "import numpy as np\n",
                "import matplotlib.pyplot as plt\n",
                "from matplotlib.colors import Normalize\n",
                "plt.rcParams['pdf.fonttype'] = 42\n",
                "# plt.rcParams['savefig.facecolor']='white'\n",
                "import seaborn as sns\n",
                "import pandas as pd\n",
                "pd.options.display.max_rows = 50\n",
                "pd.options.display.max_columns = None\n",
                "from pd_to_pb import pandas_to_pybeh as pb\n",
                "import scipy as scp\n",
                "plt.rcParams['axes.labelsize'] = 16\n",
                "from classifier_io import ClassifierModel\n",
                "from sklearn.linear_model import LogisticRegression\n",
                "from sklearn import __version__ as sklearn_version\n",
                "import pickle\n",
                "from ptsa.data.filters import MorletWaveletFilter, ButterworthFilter\n",
                "from ptsa.data.timeseries import TimeSeries\n",
                "sns.set_palette('twilight')\n",
                "import cmldask.CMLDask as da\n",
                "from nicls_helpers import *\n",
                "import scipy as scp\n",
                "\n",
                "subject_list = np.loadtxt(\"final_subject_list.txt\", dtype=str)"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 2,
            "id": "e122aeaf-1d9e-49a1-b6b6-b8a9048e4ee0",
            "metadata": {},
            "outputs": [],
            "source": [
                "settings = da.Settings()\n",
                "settings['rel_start'] = 300\n",
                "settings['rel_stop'] = 1300\n",
                "settings['buffer_time'] = 500\n",
                "settings['freqs'] = np.logspace(np.log10(6), np.log10(180), 8)\n",
                "settings['width'] = 5\n",
                "settings['experiment'] = 'NiclsCourierReadOnly'\n",
                "settings['clean'] = 1\n",
                "settings['save'] = 1\n",
                "settings['reference'] = 'average'\n",
                "# settings['save'](\"NICLS_CL_encoding_features_cleaned.pkl\")"
            ]
        },
        {
            "cell_type": "markdown",
            "id": "f396553e-328d-4f6a-8328-66f22de3ef4e",
            "metadata": {},
            "source": [
                "### Cluster Setup"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 2,
            "id": "37d1fd9d-6732-4f07-8a76-b8bfb94aa54c",
            "metadata": {},
            "outputs": [
                {
                    "name": "stdout",
                    "output_type": "stream",
                    "text": [
                        "Unique port for jrudoler is 51360\n",
                        "{'dashboard_address': ':51360'}\n",
                        "To view the dashboard, run: \n",
                        "`ssh -fN jrudoler@rhino2.psych.upenn.edu -L 8000:192.168.86.142:51360` in your local computer's terminal (NOT rhino) \n",
                        "and then navigate to localhost:8000 in your browser\n"
                    ]
                }
            ],
            "source": [
                "client = da.new_dask_client(\"nicls_analysis\",\n",
                "                            \"100GB\",\n",
                "                           log_directory='/home1/jrudoler/logs/')"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 3,
            "id": "6e390ace-7b44-4fd2-9640-f7f4c611fd16",
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "text/plain": [
                            "'http://192.168.86.144:51360/status'"
                        ]
                    },
                    "execution_count": 3,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": [
                "client.dashboard_link"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 86,
            "id": "14b64778-b4d4-4d3e-98d0-fbc0d0c454bc",
            "metadata": {},
            "outputs": [],
            "source": [
                "client.shutdown()"
            ]
        },
        {
            "cell_type": "markdown",
            "id": "2760ae05-dfc5-4c39-a6ec-56d54b15c441",
            "metadata": {},
            "source": [
                "# Classification Performance"
            ]
        },
        {
            "cell_type": "markdown",
            "id": "6c749bae-234b-46f1-b527-5e0dc12c3780",
            "metadata": {},
            "source": [
                "## Read-Only"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 12,
            "id": "a83cb28b-90bd-4005-9764-4c63173798a9",
            "metadata": {
                "tags": []
            },
            "outputs": [],
            "source": [
                "data = cml.get_data_index(kind = 'ltp');\n",
                "data = data[data['experiment'] == 'NiclsCourierReadOnly']\n",
                "data = data.query(\"subject in @subject_list\")\n",
                "full_evs = None\n",
                "for i, row in data.iterrows():\n",
                "    if sum(data.subject == row['subject']) < 3:\n",
                "        continue\n",
                "    if row['subject']=='LTP480':\n",
                "        # subject dropped out before closed loop\n",
                "        continue\n",
                "    read = cml.CMLReader(subject=row['subject'], experiment=row['experiment'], session=row['session'], montage=0, localization=0)\n",
                "    evs = read.load('task_events')\n",
                "#     evs['phase'] = evs['phase'].astype(str)\n",
                "    evs.drop(index=evs[evs.type.str.contains('VV')].index, inplace=True)\n",
                "    # evs.drop(index=evs.query(\"phase in ['practice', 'movie', 'video']\").index, inplace=True)\n",
                "    full_evs = evs if full_evs is None else full_evs.append(evs)\n",
                "# only real delivery days\n",
                "full_evs = full_evs.reset_index().drop(columns='index')"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 14,
            "id": "0f6dbd04-feef-41f2-9582-1b9901b51c2e",
            "metadata": {},
            "outputs": [],
            "source": [
                "sub_recalls = full_evs.query(\"type=='WORD'\").groupby(\"subject\").agg({\"recalled\":\"mean\"}).reset_index()"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 15,
            "id": "0d45dff5-4bf9-47a6-9ddd-4ffee7862fa3",
            "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>subject</th>\n",
                            "      <th>recalled</th>\n",
                            "    </tr>\n",
                            "  </thead>\n",
                            "  <tbody>\n",
                            "    <tr>\n",
                            "      <th>0</th>\n",
                            "      <td>LTP448</td>\n",
                            "      <td>0.861667</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>1</th>\n",
                            "      <td>LTP449</td>\n",
                            "      <td>0.561111</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>2</th>\n",
                            "      <td>LTP451</td>\n",
                            "      <td>0.538596</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>3</th>\n",
                            "      <td>LTP453</td>\n",
                            "      <td>0.645614</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>4</th>\n",
                            "      <td>LTP455</td>\n",
                            "      <td>0.710526</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>5</th>\n",
                            "      <td>LTP457</td>\n",
                            "      <td>0.775439</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>6</th>\n",
                            "      <td>LTP458</td>\n",
                            "      <td>0.871930</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>7</th>\n",
                            "      <td>LTP462</td>\n",
                            "      <td>0.637427</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>8</th>\n",
                            "      <td>LTP468</td>\n",
                            "      <td>0.605674</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>9</th>\n",
                            "      <td>LTP470</td>\n",
                            "      <td>0.616374</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>10</th>\n",
                            "      <td>LTP473</td>\n",
                            "      <td>0.812935</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>11</th>\n",
                            "      <td>LTP474</td>\n",
                            "      <td>0.701493</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>12</th>\n",
                            "      <td>LTP475</td>\n",
                            "      <td>0.911111</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>13</th>\n",
                            "      <td>LTP476</td>\n",
                            "      <td>0.588304</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>14</th>\n",
                            "      <td>LTP477</td>\n",
                            "      <td>0.594152</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>15</th>\n",
                            "      <td>LTP478</td>\n",
                            "      <td>0.808658</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>16</th>\n",
                            "      <td>LTP479</td>\n",
                            "      <td>0.890043</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>17</th>\n",
                            "      <td>LTP481</td>\n",
                            "      <td>0.718615</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>18</th>\n",
                            "      <td>LTP484</td>\n",
                            "      <td>0.818182</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>19</th>\n",
                            "      <td>LTP486</td>\n",
                            "      <td>0.871861</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>20</th>\n",
                            "      <td>LTP488</td>\n",
                            "      <td>0.622511</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>21</th>\n",
                            "      <td>LTP491</td>\n",
                            "      <td>0.749550</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>22</th>\n",
                            "      <td>LTP493</td>\n",
                            "      <td>0.513420</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>23</th>\n",
                            "      <td>LTP494</td>\n",
                            "      <td>0.576623</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>24</th>\n",
                            "      <td>LTP495</td>\n",
                            "      <td>0.665801</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>25</th>\n",
                            "      <td>LTP496</td>\n",
                            "      <td>0.838095</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>26</th>\n",
                            "      <td>LTP497</td>\n",
                            "      <td>0.662281</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>27</th>\n",
                            "      <td>LTP498</td>\n",
                            "      <td>0.662338</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>28</th>\n",
                            "      <td>LTP499</td>\n",
                            "      <td>0.888312</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>29</th>\n",
                            "      <td>LTP500</td>\n",
                            "      <td>0.608658</td>\n",
                            "    </tr>\n",
                            "  </tbody>\n",
                            "</table>\n",
                            "</div>"
                        ],
                        "text/plain": [
                            "   subject  recalled\n",
                            "0   LTP448  0.861667\n",
                            "1   LTP449  0.561111\n",
                            "2   LTP451  0.538596\n",
                            "3   LTP453  0.645614\n",
                            "4   LTP455  0.710526\n",
                            "5   LTP457  0.775439\n",
                            "6   LTP458  0.871930\n",
                            "7   LTP462  0.637427\n",
                            "8   LTP468  0.605674\n",
                            "9   LTP470  0.616374\n",
                            "10  LTP473  0.812935\n",
                            "11  LTP474  0.701493\n",
                            "12  LTP475  0.911111\n",
                            "13  LTP476  0.588304\n",
                            "14  LTP477  0.594152\n",
                            "15  LTP478  0.808658\n",
                            "16  LTP479  0.890043\n",
                            "17  LTP481  0.718615\n",
                            "18  LTP484  0.818182\n",
                            "19  LTP486  0.871861\n",
                            "20  LTP488  0.622511\n",
                            "21  LTP491  0.749550\n",
                            "22  LTP493  0.513420\n",
                            "23  LTP494  0.576623\n",
                            "24  LTP495  0.665801\n",
                            "25  LTP496  0.838095\n",
                            "26  LTP497  0.662281\n",
                            "27  LTP498  0.662338\n",
                            "28  LTP499  0.888312\n",
                            "29  LTP500  0.608658"
                        ]
                    },
                    "execution_count": 15,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": [
                "sub_recalls"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 16,
            "id": "d2e0409d-4aef-41d8-b91a-a67b89ea9523",
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "text/plain": [
                            "array(['LTP449', 'LTP451', 'LTP453', 'LTP455', 'LTP457', 'LTP462',\n",
                            "       'LTP468', 'LTP470', 'LTP474', 'LTP476', 'LTP477', 'LTP481',\n",
                            "       'LTP488', 'LTP491', 'LTP493', 'LTP494', 'LTP495', 'LTP497',\n",
                            "       'LTP498', 'LTP500'], dtype=object)"
                        ]
                    },
                    "execution_count": 16,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": [
                "sub_recalls.query(\"recalled < .8\")['subject'].values"
            ]
        },
        {
            "cell_type": "markdown",
            "id": "a674d54f-3b52-47cd-90ba-e4fef72cb6e5",
            "metadata": {},
            "source": [
                "### LOSO Cross-validation with session-level zscore across all events"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 2,
            "id": "52ec6ab0-8646-4e81-920b-90847e9ebb72",
            "metadata": {},
            "outputs": [],
            "source": [
                "from nicls_helpers import train_read_only_class"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 46,
            "id": "07702aec-1a99-44a4-88f6-1477b7e8a10c",
            "metadata": {
                "tags": []
            },
            "outputs": [
                {
                    "data": {
                        "application/vnd.jupyter.widget-view+json": {
                            "model_id": "663688ff81c541dfaa7c2f01e6bada51",
                            "version_major": 2,
                            "version_minor": 0
                        },
                        "text/plain": [
                            "VBox()"
                        ]
                    },
                    "metadata": {},
                    "output_type": "display_data"
                }
            ],
            "source": [
                "#subject_list = full_evs.subject.unique() #np.array(['LTP462','LTP468', 'LTP470', 'LTP474', 'LTP476', 'LTP477', 'LTP481', 'LTP488'])\n",
                "subject_list = subject_list[(subject_list > 'LTP460')] # &(subject_list < 'LTP490')\n",
                "c_list = np.logspace(np.log10(2e-5), np.log10(1), 9)\n",
                "path = '/scratch/nicls_intermediate/read_only/bipolar_encoding_powers/'\n",
                "\n",
                "result_dict = {}\n",
                "\n",
                "futures = client.map(train_read_only_class, subject_list, c_list=c_list, path = path)\n",
                "from dask.distributed import progress\n",
                "progress(futures)"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 48,
            "id": "9beec5cc-21a8-4184-a57c-3e0f91e9d77f",
            "metadata": {},
            "outputs": [],
            "source": [
                "good_futures = da.filter_futures(futures)\n",
                "success_mask = np.isin(futures, good_futures)\n",
                "results = client.gather(good_futures)\n",
                "for i, subject in enumerate(subject_list[success_mask]):\n",
                "    result_dict[subject] = results[i]\n",
                "    \n",
                "with open(f\"loso_results_bipolar_read_only.pkl\", \"wb\") as f:\n",
                "    pickle.dump(result_dict, f)"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 50,
            "id": "1b8a358e-d267-46ed-bdda-0cf57bf0c562",
            "metadata": {},
            "outputs": [],
            "source": [
                "with open(f\"loso_results_clean_read_only.pkl\", \"rb\") as f:\n",
                "    clean_result_dict = pickle.load(f)"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 13,
            "id": "626ad75c-bd1b-4727-bad8-f27f4c1333a4",
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "image/png": 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",
                        "text/plain": [
                            "<Figure size 576x576 with 2 Axes>"
                        ]
                    },
                    "metadata": {},
                    "output_type": "display_data"
                }
            ],
            "source": [
                "with open(f\"loso_results_read_only.pkl\", \"rb\") as f:\n",
                "    result_dict = pickle.load(f)\n",
                "fig, ax = plt.subplots(1, 1, figsize=(8, 8))\n",
                "ax.plot([0, 1], [0, 1], '--k')\n",
                "base_fp = np.linspace(0, 1, 301)\n",
                "tp_list = []\n",
                "for subject in cl_result_dict:\n",
                "    fp, tp, auc, _ = result_dict[subject]\n",
                "    tp = scp.interp(base_fp, fp, tp)\n",
                "    tp_list.append(tp)\n",
                "    if np.mean(result_dict[subject][2] > result_dict[subject][3])>=0.95:\n",
                "        alpha=0.8\n",
                "    else:\n",
                "        alpha=0.3\n",
                "    ax.plot(base_fp, tp, color='indigo', label=f\"{subject}, AUC = {auc:.3f}\", alpha=alpha)\n",
                "\n",
                "ax.plot(base_fp, np.mean(np.vstack(tp_list), 0), color='k', lw = 4,\n",
                "        label=f\"{subject}, AUC = {auc:.3f}\", alpha=1)\n",
                "\n",
                "# ax.legend(fontsize=14, bbox_to_anchor=(1.05, 1))\n",
                "ax.set_ylabel('Hit Rate', fontsize=18)\n",
                "ax.set_xlabel('False Alarm Rate', fontsize=18)\n",
                "ax.grid(False)\n",
                "ax_pos = np.array(ax.get_position()).ravel()\n",
                "top_ax = fig.add_axes([0.5, .2, .4, .1])\n",
                "auc_dist = pd.DataFrame({\"auc\":[result_dict[k][2] for k in result_dict] +\n",
                "                         [np.mean(result_dict[k][3]) for k in result_dict],\n",
                "                         \"type\":[\"test\"]*len(result_dict)+[\"null\"]*len(result_dict)})\n",
                "sns.histplot(auc_dist, x=\"auc\", hue=\"type\", multiple=\"layer\", bins=5, ax=top_ax)\n",
                "sns.move_legend(top_ax, loc='upper right', title='')\n",
                "top_ax.grid(False)\n",
                "top_ax.set_title('AUC', fontsize=14)\n",
                "top_ax.set_xticks([.5, .6])\n",
                "top_ax.set_xlabel('')\n",
                "top_ax.set_ylabel('')\n",
                "ax.tick_params(labelsize=14)\n",
                "ax.spines['top'].set_visible(False)\n",
                "ax.spines['right'].set_visible(False)\n",
                "# top_ax.tick_params(labelsize=14)\n",
                "np.mean([result_dict[k][2] for k in result_dict])\n",
                "# plt.savefig('FinalReport/loso_read_only.pdf', dpi=200, bbox_inches='tight')"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 5,
            "id": "6404b1c1-34f1-4645-a037-9f1224cfdb2c",
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "text/plain": [
                            "array([ True, False,  True, False,  True,  True,  True,  True, False,\n",
                            "        True,  True,  True,  True,  True,  True,  True,  True, False,\n",
                            "        True, False,  True,  True])"
                        ]
                    },
                    "execution_count": 5,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": [
                "np.array([np.mean(result_dict[k][2] > result_dict[k][3])>=0.95 for k in result_dict])"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 114,
            "id": "369628ff-9295-41f5-a039-65b28ec7adf7",
            "metadata": {},
            "outputs": [
                {
                    "name": "stdout",
                    "output_type": "stream",
                    "text": [
                        "6.757080906380654 1.4256390018797065e-06 0.5566160878534587 0.008282556497288313\n"
                    ]
                }
            ],
            "source": [
                "t, p = scp.stats.ttest_rel(\n",
                "np.array([result_dict[k][2] for k in cl_result_dict]),\n",
                "np.array([np.mean(result_dict[k][3]) for k in cl_result_dict])\n",
                ")\n",
                "mean = np.array([result_dict[k][2] for k in cl_result_dict]).mean()\n",
                "sem = scp.stats.sem(np.array([result_dict[k][2] for k in cl_result_dict]), ddof=1)\n",
                "print(t, p, mean, sem)"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 115,
            "id": "0e2fdb5d-61da-43c4-9209-734cebb5bf7d",
            "metadata": {},
            "outputs": [
                {
                    "name": "stdout",
                    "output_type": "stream",
                    "text": [
                        "2.145548144232949 0.044366391989229044 0.5440762737672168 0.020485695383793613\n"
                    ]
                }
            ],
            "source": [
                "t, p = scp.stats.ttest_rel(\n",
                "np.array([cl_result_dict[k][2] for k in cl_result_dict]),\n",
                "np.array([np.mean(cl_result_dict[k][3]) for k in cl_result_dict])\n",
                ")\n",
                "mean = np.array([cl_result_dict[k][2] for k in cl_result_dict]).mean()\n",
                "sem = scp.stats.sem(np.array([cl_result_dict[k][2] for k in cl_result_dict]), ddof=1)\n",
                "print(t, p, mean, sem)"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 64,
            "id": "8048b90c-5898-442e-8aa9-1f5a6469cb18",
            "metadata": {},
            "outputs": [],
            "source": [
                "res = pd.DataFrame({\"subject\":result_dict.keys(),\n",
                "                    \"AUC\":[result_dict[k][2] for k in result_dict],\n",
                "                    \"clean_AUC\":[clean_result_dict[k][2] for k in result_dict],\n",
                "                    \"bipolar_AUC\":[bipolar_result_dict[k][2] for k in result_dict]\n",
                "                   })"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 66,
            "id": "cf958266-849b-4870-a03c-99845752fc00",
            "metadata": {},
            "outputs": [],
            "source": [
                "res.to_csv(\"read_only_AUC_comparisons.csv\")"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 81,
            "id": "40d6f40c-ba27-455d-a1fe-f268240a8759",
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "text/plain": [
                            "<AxesSubplot:xlabel='value', ylabel='Count'>"
                        ]
                    },
                    "execution_count": 81,
                    "metadata": {},
                    "output_type": "execute_result"
                },
                {
                    "data": {
                        "image/png": 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",
                        "text/plain": [
                            "<Figure size 432x288 with 1 Axes>"
                        ]
                    },
                    "metadata": {
                        "needs_background": "light"
                    },
                    "output_type": "display_data"
                }
            ],
            "source": [
                "sns.histplot(pd.melt(res, value_vars=['AUC', 'clean_AUC', 'bipolar_AUC'], var_name='type'), x='value', hue='type', kde=True)"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 18,
            "id": "03b970b5-0a34-4035-a8ca-c71d54f2d8ff",
            "metadata": {},
            "outputs": [],
            "source": [
                "res = pd.read_csv(\"read_only_AUC_comparisons.csv\", index_col=0)"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 22,
            "id": "402b7499-1b29-44ff-a41a-813af7aba372",
            "metadata": {},
            "outputs": [],
            "source": [
                "res = res[res.subject!=\"LTP499\"]"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 31,
            "id": "7dbd6508-a183-494e-936e-53b0a8dd5f05",
            "metadata": {},
            "outputs": [],
            "source": [
                "with open(\"AUC_comparison.tex\", \"w\") as f:\n",
                "    f.write(res.to_latex(float_format=\"%.3f\", longtable=True, \n",
                "                       label=\"AUC_comparison\",\n",
                "                       caption=\"\\\\textbf{Preprocessing Methods and Classifier Performance} \"+ \\\n",
                "                       \"We found no significant difference in classifier performance (AUC) between \"+ \\\n",
                "                       \"different pre-processing methods. Our default pre-processing was to just use a global \"+ \\\n",
                "                       \"average reference scheme coupled with a lowpass filter for drift and a bandpass filter \"+ \\\n",
                "                       \"to remove electrical line noise. We compared this approach to pre-processing with ICA-based \"+ \\\n",
                "                       \"localized component filtering ($t$(21)=0.193, $p$=0.85) \"+ \\\n",
                "                       \"and also to a bipolar reference scheme ($t$(21)=0.316, $p$=0.75).\"))"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 23,
            "id": "b24c0fd8-6891-46d7-bac8-13ee077c5c2d",
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "text/plain": [
                            "Ttest_relResult(statistic=0.19303756833282604, pvalue=0.8488763833468397)"
                        ]
                    },
                    "execution_count": 23,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": [
                "scp.stats.ttest_rel(res[\"AUC\"], res[\"clean_AUC\"])"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 24,
            "id": "3a93ce27-69e8-4c1a-bd3a-6d4469c028e7",
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "text/plain": [
                            "Ttest_relResult(statistic=-0.31642110379348315, pvalue=0.7549617093030289)"
                        ]
                    },
                    "execution_count": 24,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": [
                "scp.stats.ttest_rel(res[\"AUC\"], res[\"bipolar_AUC\"])"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 25,
            "id": "aee54acd-9636-41bb-8ced-c357a2323da2",
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "text/plain": [
                            "Ttest_1sampResult(statistic=6.835581244992977, pvalue=1.2096635104608003e-06)"
                        ]
                    },
                    "execution_count": 25,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": [
                "scp.stats.ttest_1samp([result_dict[k][2] for k in cl_result_dict], 0.5)"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 26,
            "id": "752fafbf-94a9-475b-8b66-a6a0e703e36f",
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "text/plain": [
                            "Ttest_1sampResult(statistic=2.1515634661875263, pvalue=0.04383090164736231)"
                        ]
                    },
                    "execution_count": 26,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": [
                "scp.stats.ttest_1samp([cl_result_dict[k][2] for k in cl_result_dict], 0.5)"
            ]
        },
        {
            "cell_type": "markdown",
            "id": "8d7dbb72-f84b-4299-942b-eeafba7a10dc",
            "metadata": {},
            "source": [
                "### Checking Read-Only Classifier With Varying Normalization Lists"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 19,
            "id": "b404131e-1484-48c4-b59f-c8e92df69f7f",
            "metadata": {},
            "outputs": [],
            "source": [
                "# subject_list = full_evs['subject'].unique()\n",
                "c_list = np.logspace(np.log10(2e-5), np.log10(1), 9)\n",
                "# path = '/scratch/nicls_intermediate/read_only/encoding_powers/'\n",
                "def n_list_normalization(n_lists,\n",
                "                         subject_list,\n",
                "                         c_list = np.logspace(np.log10(2e-5), np.log10(1), 9),\n",
                "                         path='/scratch/nicls_intermediate/read_only/encoding_powers/'):\n",
                "    result_dict = {}\n",
                "    for subject in subject_list:\n",
                "        ts = TimeSeries.from_hdf(path+subject+\"_feats.h5\", engine=\"netcdf4\")\n",
                "        evs = ts.indexes['event'].to_frame(index=False)\n",
                "        ts = ts.groupby('session').apply(norm_sess_feats, n_lists=n_lists)\n",
                "        scores_mat = computeCV(ts.data, evs, c_list)\n",
                "        best_c = c_list[scores_mat.mean(0).argmax()]\n",
                "        model = LogisticRegression(penalty='l2', C=best_c, class_weight='balanced', solver='liblinear')\n",
                "        prob = perform_normed_loso_cross_validation(model, events=evs, powers=ts, n_lists=n_lists)\n",
                "        fp, tp, _ = roc_curve(evs.recalled.values, prob)\n",
                "        auc = roc_auc_score(evs.recalled.values, prob)\n",
                "        result_dict[subject] = (fp, tp, auc)\n",
                "\n",
                "    with open(f\"{n_lists}_list_normalization.pkl\", \"wb\") as f:\n",
                "        pickle.dump(result_dict, f)\n",
                "\n",
                "futures = []\n",
                "for n_lists in range(1, 11):\n",
                "    futures.append(client.submit(n_list_normalization, n_lists, [s for s in cl_result_dict]))"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 16,
            "id": "0166d35e-c5d8-4e71-9c99-6876908d2349",
            "metadata": {},
            "outputs": [],
            "source": [
                "client.cancel(futures)"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 10,
            "id": "75e12bd4-2d12-48fa-bc47-2e13a447e659",
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "text/plain": [
                            "21"
                        ]
                    },
                    "execution_count": 10,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": [
                "len([s for s in cl_result_dict])"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 20,
            "id": "cfdcdbc6-2847-4f26-98af-a766ed7e3448",
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "application/vnd.jupyter.widget-view+json": {
                            "model_id": "73d9a203cf534bedacb82f47c6b76d7d",
                            "version_major": 2,
                            "version_minor": 0
                        },
                        "text/plain": [
                            "VBox()"
                        ]
                    },
                    "metadata": {},
                    "output_type": "display_data"
                }
            ],
            "source": [
                "from dask.distributed import progress\n",
                "progress(futures)"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 24,
            "id": "ff83d738-b2b7-4f3b-a97e-2854932da6ca",
            "metadata": {},
            "outputs": [
                {
                    "ename": "Exception",
                    "evalue": "None of the given futures resulted in exceptions",
                    "output_type": "error",
                    "traceback": [
                        "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
                        "\u001b[0;31mException\u001b[0m                                 Traceback (most recent call last)",
                        "\u001b[0;32m/tmp/2888767.1.jupyter.q/ipykernel_7639/2579271649.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mda\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_exceptions\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfutures\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m11\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
                        "\u001b[0;32m~/cmldask/cmldask/CMLDask.py\u001b[0m in \u001b[0;36mget_exceptions\u001b[0;34m(futures, params)\u001b[0m\n\u001b[1;32m    123\u001b[0m             ))\n\u001b[1;32m    124\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mexceptions\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 125\u001b[0;31m         \u001b[0;32mraise\u001b[0m \u001b[0mException\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"None of the given futures resulted in exceptions\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    126\u001b[0m     \u001b[0mexceptions\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconcat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mexceptions\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mT\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    127\u001b[0m     \u001b[0mexceptions\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_index\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"param\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minplace\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
                        "\u001b[0;31mException\u001b[0m: None of the given futures resulted in exceptions"
                    ]
                }
            ],
            "source": [
                "da.get_exceptions(futures, np.arange(1, 11))"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": null,
            "id": "8cdc98d3-7e41-4bc3-b140-21887dc20ce7",
            "metadata": {},
            "outputs": [],
            "source": [
                "sns.set_palette('twilight')\n",
                "plt.rcParams['figure.facecolor']='white'\n",
                "fig, ax = plt.subplots(5, 2, figsize=(15, 15))\n",
                "ax = ax.ravel()\n",
                "res_frame = []\n",
                "for i, n_lists in enumerate(range(1, 11)):\n",
                "    with open(f\"{n_lists}_list_normalization.pkl\", \"rb\") as f:\n",
                "        result_dict = pickle.load(f)\n",
                "    result_dict = {k:result_dict[k] for k in cl_result_dict if int(k[-3:])>460}\n",
                "    res_frame.append(pd.DataFrame({'subject':[s for s in cl_result_dict], 'auc': [result_dict[k][2] for k in cl_result_dict], 'nlists':n_lists}))\n",
                "    ax[i].plot([0, 1], [0, 1], '--k')\n",
                "    for subject in result_dict:\n",
                "        fp, tp, auc = result_dict[subject]\n",
                "        ax[i].plot(fp, tp, label=f\"{subject}, AUC = {auc:.3f}\", alpha=0.5)\n",
                "    # ax.legend(fontsize=14, bbox_to_anchor=(1.05, 1))\n",
                "#     ax[i].set_xlabel('FPR', fontsize=18)\n",
                "#     ax[i].set_ylabel('TPR', fontsize=18)\n",
                "    ax[i].grid(False)\n",
                "    ax_pos = np.array(ax[i].get_position()).ravel()\n",
                "    top_ax = fig.add_axes(np.array([ax_pos[0]+.25, ax_pos[1]+.01, .05, .05]))\n",
                "    sns.barplot(y=[result_dict[k][2] for k in result_dict], orient='vertical', ci=68, ax=top_ax)\n",
                "    mean = np.mean([result_dict[k][2] for k in result_dict])\n",
                "    top_ax.set_ylim(0.5)\n",
                "    top_ax.grid(False)\n",
                "    top_ax.set_title(f'Mean AUC: {mean:.3f}', fontsize=14)\n",
                "    ax[i].tick_params(labelsize=14)\n",
                "    top_ax.tick_params(labelsize=14)\n",
                "    ax[i].set_title(f'{n_lists}-list normalization', fontsize=15)\n",
                "    \n",
                "# plt.tight_layout()\n",
                "res_frame = pd.concat(res_frame)\n",
                "# plt.savefig('FinalReport/readonly_loso_varying_norm.pdf', dpi=200, bbox_inches='tight')"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 32,
            "id": "e58bafef-cfe9-41b7-bf88-f747336bb863",
            "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>subject</th>\n",
                            "      <th>auc</th>\n",
                            "      <th>nlists</th>\n",
                            "      <th>auc_less_mean</th>\n",
                            "    </tr>\n",
                            "  </thead>\n",
                            "  <tbody>\n",
                            "    <tr>\n",
                            "      <th>0</th>\n",
                            "      <td>LTP462</td>\n",
                            "      <td>0.515774</td>\n",
                            "      <td>1</td>\n",
                            "      <td>-0.016774</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>1</th>\n",
                            "      <td>LTP468</td>\n",
                            "      <td>0.456944</td>\n",
                            "      <td>1</td>\n",
                            "      <td>-0.014708</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>2</th>\n",
                            "      <td>LTP470</td>\n",
                            "      <td>0.577232</td>\n",
                            "      <td>1</td>\n",
                            "      <td>-0.013388</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>3</th>\n",
                            "      <td>LTP473</td>\n",
                            "      <td>0.506079</td>\n",
                            "      <td>1</td>\n",
                            "      <td>-0.018135</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>4</th>\n",
                            "      <td>LTP474</td>\n",
                            "      <td>0.554209</td>\n",
                            "      <td>1</td>\n",
                            "      <td>-0.019141</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>...</th>\n",
                            "      <td>...</td>\n",
                            "      <td>...</td>\n",
                            "      <td>...</td>\n",
                            "      <td>...</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>16</th>\n",
                            "      <td>LTP495</td>\n",
                            "      <td>0.536795</td>\n",
                            "      <td>10</td>\n",
                            "      <td>-0.000862</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>17</th>\n",
                            "      <td>LTP496</td>\n",
                            "      <td>0.492316</td>\n",
                            "      <td>10</td>\n",
                            "      <td>-0.001693</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>18</th>\n",
                            "      <td>LTP497</td>\n",
                            "      <td>0.549361</td>\n",
                            "      <td>10</td>\n",
                            "      <td>0.000366</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>19</th>\n",
                            "      <td>LTP498</td>\n",
                            "      <td>0.523560</td>\n",
                            "      <td>10</td>\n",
                            "      <td>-0.001825</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>20</th>\n",
                            "      <td>LTP500</td>\n",
                            "      <td>0.556858</td>\n",
                            "      <td>10</td>\n",
                            "      <td>-0.001951</td>\n",
                            "    </tr>\n",
                            "  </tbody>\n",
                            "</table>\n",
                            "<p>210 rows × 4 columns</p>\n",
                            "</div>"
                        ],
                        "text/plain": [
                            "   subject       auc  nlists  auc_less_mean\n",
                            "0   LTP462  0.515774       1      -0.016774\n",
                            "1   LTP468  0.456944       1      -0.014708\n",
                            "2   LTP470  0.577232       1      -0.013388\n",
                            "3   LTP473  0.506079       1      -0.018135\n",
                            "4   LTP474  0.554209       1      -0.019141\n",
                            "..     ...       ...     ...            ...\n",
                            "16  LTP495  0.536795      10      -0.000862\n",
                            "17  LTP496  0.492316      10      -0.001693\n",
                            "18  LTP497  0.549361      10       0.000366\n",
                            "19  LTP498  0.523560      10      -0.001825\n",
                            "20  LTP500  0.556858      10      -0.001951\n",
                            "\n",
                            "[210 rows x 4 columns]"
                        ]
                    },
                    "execution_count": 32,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": [
                "res_frame"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 45,
            "id": "229edff2-21ad-4570-ab5c-4dcc5ba57e8b",
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "text/plain": [
                            "(0.5, 0.5593870939785306)"
                        ]
                    },
                    "execution_count": 45,
                    "metadata": {},
                    "output_type": "execute_result"
                },
                {
                    "data": {
                        "image/png": 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CscUPBbYnPp8HVU8PqHp64IejF8ADMLKv8fLehLQVSiykU2vqsOcgbznAgCtmOuE9nGTo46uAv4czJFYPzz+Ljn+NRR5GD8+/INIl1Q95Npdc3B1lKCytQNotjdE+R5kEfXwVCPKSw9ZGbOJoQkhLUGIhnVpFVQ1+v3rH7H51YanBtnV9J3w3F7iYG1pFCGkVSiykU6plDHdLKpCSkWPy1tb9+Hwegv7shPeW23WKTnhCOjNKLKTD09XWQlNcjtyiMuQUluLO3RLcLa6AromPoT/WV4m+Src2jdFcX8+KHScMth/GKW4IaYgSC+lQanQ65N+rSyLqwhJkF5SgsKyyVVOZeMvtuQvQjIZT3BDSlVFiIa3CGMON3CLcLakAA8PZa2r06ubSpJFVVTU65N0rQ25RKdR3S5F9twT3TDyc2BCfx4OTrQSeTrZwc7TFxZt5uF1QYrJuT3cnONlaN/t1EUJajhILabFqrQ47T11GVt49fdnBP27gxOVbmDj4Ebg72erLK6pqkHuvDLlFZci+Wwx1YRlKTaxU2JCAz4PczubPJCKDwkEKZ1sb8Pl/9ZMEeDpj1++XcTPfcPXGbi52GBvak4NXSghpDkospMUOn880SCr1Kqq12Ho8DaE93ZFTVIqcwjKUm1g7vSGRkA9Xeyk8nGRwc7CFwkEKR5mk0c72xp5juZlfjC9/SgZAfRuEWBIlFtIiVTVaXLiZb36/Vtfog4tioQAKByk8ne2gcJBC4SiFnbW42SO2qG+DkI6HEgtpkey7pdA2ccVEG7EIbo5SeDjZQuEgg8JeCpm1ZSdnJIRYDiUW0mTVWh3Ss+/iQlYushr0Z5gS5C3HyD5KWlCKkC6GEgtplK62Fjdyi3A+Kw8Z6kLUNmPc78AAL0oqhHRBlFiIEcYYbmmKcSErH1fuaFBj4paXk8wa3Vzs8EdmLkw9+B7S3e2B65YQQh5OlFgIgL9WTky7lY8LWfmorNEa1ZFJrNDbxwVB3VzgbFuXNIK6ueDYxZu4pam7NWZrbfXnlO3uRscTQroGSixd3N3SCqTdrEsmxRXGDyeKRQL06uaCXt4ucHOUGY3a8nK2w4tDg/H94fNgjOGlYb3Bp7m4COnSKLF0QSUV1bh8W4PzmbnQlFQY7RcK+AjwcEawjwu8XeyblCjqHljkUVIhhKBdlseLjY2FUqmERCJBaGgojh07ZrZuZmYmeDye0U9iYqLJ+sePH4dQKERwcHBbhd8pVVZr8UdmLjYdPo+v9p/BofOZBkmFz+Ohp7sjnh0YgDfHDcCT/f3g4+pAiYIQ0mwWv2KJj4/HW2+9hdjYWERERCA2NhZjxoxBWloaunUz/6BbYmIi+vbtq992cnIyqlNYWIiXX34ZI0eOxJ075tfo6MyaumLi4EAvhPl7IiOnEOez8pCZV2RyIkdvZzv09nWFn4cTxCK6gO3saJZl0hHwGGvNvLHNFxYWhj59+mDt2rX6Mj8/P0ycOBHLli0zqp+ZmQmlUonTp09DpVI12vaECRPQt29fMMawfft2XLhw4YHxqFQqnDlzpvkvpB3pamux89QVXM8pNNo3NLgbXO2kuHgzH1fNLMOrsJeit68rAjydIZVw86Di5qN17/WLQ+lKkZCuzqJ/olZXV+Ps2bN4++23DcpHjx6NEydOmDmqzoQJE1BZWQk/Pz/MmTMHEydONNgfGxuLnJwcbNu2DR9//HGjbcXFxSEuLg4AkJ9vflqSjup0erbJpAIARy/cNFnuIBWjt48rHvF2gYNU0pbhEUK6OIsmFo1GA51OB4VCYVCuUChw4MABk8fIZDLExMQgPDwcQqEQe/bsQVRUFDZs2IDo6GgAwPnz57F48WKcOnUKAoHggXHMmDEDM2bMAIAHXgV1RKk3cptUz0YsQnA3F1qGlxBiUe1yU73hkFXGmNnJB+VyOebNm6ffVqlU0Gg0WLFiBaKjo1FVVYUXXngBMTExUCqVbRp3R6CrrUXxA9YskYqFGD8wEJ5OtrQMLyHE4iw6Kkwul0MgECAnJ8egPC8vz+gqpjFhYWFIT08HAKjVaqSlpeHvf/87hEIhhEIhlixZgosXL0IoFOKXX37h9DW0l7oFtQoRf+ziA+t6u9jDy5nWdieEtA+LXrFYWVkhNDQUSUlJeP755/XlSUlJeO6555rcTmpqKtzd657s9vT0xPnz5w32x8bGIikpCTt37oSvry8nsbeXGp0OaTc1OHXldpNWVwSA4G6ubRwVIYSYZ/FbYXPnzsXkyZMxYMAAhIeHY82aNcjOzsasWbMAAAsWLEBycjIOHjwIANiwYQNEIhFCQkLA5/Oxd+9erF69GsuXLwcAiEQio2dWXF1dIRaLO/WzLGWV1TiXocbZDDWqtYZzdTnJJKiq0aHMxOJZj3jLoVQ4WChKQggxZvHEEhUVhYKCAixduhRqtRrBwcFISEiAj48PgLpbWxkZGQbHLF26FFlZWRAIBPD398e6dev0HfcPm/x7ZUi+mo202/lGz530cHNAmL8XPJ1tUVmtxamrd3D2mhq1jEFuZ40QpRv6dnejW2CEkHZl8edYOprmPMfSnIcTm/PwGWMM13OLcOrKbdwpKDHYJ+Dz0NdXAZWfBxykkjaLobXoORZCSD1KLK14QLK1X6Y1Wh0u3szHqat3jEZ62VgJ0d/fA3193SCx6nhPxHfUBEcIaX8d7xurCyitqOs/OXfduP/Exc4GgwK94O/h/OfEjh0TrTVPCDGHEosF5RaVIfnqHVy+rUHDy8Se7o76/hNCCOnMKLG0McYYMnIKcerKbWTfLTXYJ+Dz0FepQP+eHrCnaVYIIQ8JSixtpLq+/+TKbZRUVBvssxGLMMDPA32VCppRmBDy0KFvtWZijCEr/x7+yMxFblEpBHw+0rPvoqe7I3g8HkoqqnDumhrnrucYrRWvsJdiYKAn/Nw7dv8JIYS0BiWWZmCM4fCFLJxOz76vtBY7T11GDzcHiAQCXLlTYNR/4udR13/i4UT9J4SQhx8llma4nlPYIKn8JSOnyGBbyOfj0e4KqHp6wM5GbIHoCCGkY6DE0gxNma5eKhZhYIAXgn1cIRY9eAp/Qgh52FBiaYbCsspG94uEfLw6VkXrxBNCujSLTpvf2UnFokb321mLKakQQro8SizN0KubS6P7gx+wnxBCugJKLM3Qq5sLfF0dTO5zd5QhpIe7ZQMihJAOiPpYmkHA52PCoECczVDjvzdyUVRWCQGfhzB/Twzw94SVkDrrCSGEEkszCQV8hPl7IszfUz+7MU3GSAghf6FbYYQQQjhFiYUQQginzCaWlJQUODs7Y/fu3WYP3r17N5ydnXHhwoU2CY4QQkjnYzaxrFq1Cn379sX48ePNHjx+/HiEhobiyy+/bJPgCCGEdD5mE8uhQ4cwefLkBzYwadIkHDx4kNOgCCGEdF5mR4VlZ2ejZ8+eD2yge/fuuHPnDqdBdVTm1nlfseOEwTat804I6crMJhZra2uUlpaa261XWloKiaRrrH5I67wTQsiDmb0V1qtXrybd4jpw4ACCg4M5DYoQQkjnZTaxvPTSS/jqq6+QnJxs9uBTp07h66+/xqRJk9okOEIIIZ0PjzHWcMFDAIBWq8WIESNw+vRpzJgxA0899RR8fHwAAFlZWdi7dy/i4uIQFhaGgwcPQiDonNOZqFQqnDlzpr3DIISQh4bZKxahUIjExERMmjQJsbGxePzxxxEYGIjAwEA8/vjjiI2NRXR0NPbt29fspBIbGwulUgmJRILQ0FAcO3bMbN3MzEzweDyjn8TERH2dI0eOYPDgwXB2doa1tTUCAwMRExPTrJgIIYRwo9G5wmxsbPDNN99g6dKlOHToEG7dugUA8Pb2xrBhw+Du3vzZfOPj4/HWW28hNjYWERERiI2NxZgxY5CWloZu3cx3jCcmJqJv3776bScnJ/3/y2QyvPnmm+jduzdsbGzw22+/YebMmbCxscFrr73W7BgJIYS0nNlbYW0lLCwMffr0wdq1a/Vlfn5+mDhxIpYtW2ZUPzMzE0qlEqdPn4ZKpWryeSZMmACxWIzNmzc3Wo9uhRFCCLfMXrH8+uuv5g8SCqFQKBAQENCsk1VXV+Ps2bN4++23DcpHjx6NEydOmDmqzoQJE1BZWQk/Pz/MmTMHEydONFs3JSUFJ06cwKJFi5oVHyGEkNYzm1hGjRoFHo8HUxc0vD+X33V3d8eyZcua9IQ+AGg0Guh0OigUCoNyhUKBAwcOmDxGJpMhJiYG4eHhEAqF2LNnD6KiorBhwwZER0cb1PXy8kJ+fj60Wi0WLlyIWbNmmWwzLi4OcXFxAID8/PwmxU4IIaRpzCaWQ4cOmT1Ip9MhOzsb27dvx9SpU+Ho6Ihx48Y1+aS8BuvCM8aMyurJ5XLMmzdPv61SqaDRaLBixQqjxHLs2DGUlpbi1KlTmD9/PpRKpcmkN2PGDMyYMUPfHiGEEO6YTSyRkZEPPDg6Ohp/+9vf8NlnnzUpscjlcggEAuTk5BiU5+XlGV3FNCYsLAzffvutUblSqQQA9O7dG7m5uVi0aFGTr6YIIYRwo9Xrsbz44otITU1tUl0rKyuEhoYiKSnJoDwpKQmDBw9u8jlTU1MfOCKttrYWVVVVTW6TEEIIN1q9NLGtrS2qq6ubXH/u3LmYPHkyBgwYgPDwcKxZswbZ2dn6/pAFCxYgOTlZP53Mhg0bIBKJEBISAj6fj71792L16tVYvny5vs2VK1dCqVTqBxMcPXoUMTExNNSYEELaQasTy++//65/Ir8poqKiUFBQgKVLl0KtViM4OBgJCQn6NtRqNTIyMgyOWbp0KbKysiAQCODv749169YZ9K/odDrMnz8fmZmZEAqF6NGjB/75z3+a7bwnhBDSdlr8HItWq8WePXswdepUzJ49G0uWLOE6Noug51gIIYRbZq9YvL29zY7U0ul00Gg00Gq1GDVqFD744IM2C5AQQkjnYjaxjBw50mxiEQqFcHV1xfDhwzFq1Kg2C44QQkjnYzaxrF+/vkkNHDlyBBs2bMC6deu4iokQQkgn1qLhxteuXcNHH30EpVKJ4cOHY+vWrVzHRQghpJNqcmK5d+8e4uLiEBERgYCAAHzyySdwdHTEV199hezs7LaMkRBCSCfSaGKpra1FQkICXnjhBbi7u2PWrFnIzMzE66+/DgD44osvMHPmTNjZ2VkkWEIIIR2f2T6Wt99+G99//z3y8vIgkUjw7LPPYsqUKRg1ahSKi4uxatUqS8ZJCCGkkzCbWD7//HPweDyMHTsW69evh7Ozs36fudFihBBCiNlbYf/zP/8DW1tb7Nu3DwEBAXjjjTeQnJxsydgIIYR0QmYTyzfffIOcnBxs2rQJoaGhWLNmDQYNGoRHHnkEy5cvp6sWQgghJjV5She1Wo3vvvsOGzduRFpaGgBg4MCBeO211zBx4kRIJJI2DbSt0JQuhBDCrRbNFXb69Gls2LAB8fHxKCgogL29PQoLC9sivjZHiYUQQrjVogck+/fvj1WrVulXkWzKomCEEEK6hhbPbvywoCsWQgjhVqtXkCSEEELuR4mFEEIIpyixEEII4RQlFkIIIZyixEIIIYRTlFgIIYRwihILIYQQTlFiIYQQwilKLIQQQjhFiYUQQginKLEQQgjhFCUWQgghnGqXxBIbGwulUgmJRILQ0FAcO3bMbN3MzEzweDyjn8TERH2dHTt2YPTo0XBxcYGtrS3CwsKwZ88eS7wUQgghDVg8scTHx+Ott97Ce++9h5SUFAwePBhjxozBzZs3Gz0uMTERarVa/zNixAj9viNHjmDEiBHYt28fUlJSMHbsWDz77LONJixCCCFtw+LT5oeFhaFPnz5Yu3atvszPzw8TJ07EsmXLjOpnZmZCqVTi9OnTUKlUTT7PgAEDMGTIEPzrX/9qtB5Nm08IIdyy6BVLdXU1zp49i9GjRxuUjx49GidOnGj02AkTJsDV1RXh4eHYvn37A89VUlICR0fHVsVLCCGk+SyaWDQaDXQ6HRQKhUG5QqFATk6OyWNkMhliYmKwdetWJCQkYOTIkYiKisKmTZvMnmf16tW4ffs2Jk+ebHJ/XFwcVCoVVCoV8vPzW/6CCCGEGBG2x0l5PJ7BNmPMqKyeXC7HvHnz9NsqlQoajQYrVqxAdHS0Uf0ff/wR77zzDrZs2QIfHx+Tbc6YMQMzZszQt0cIIYQ7Fr1ikcvlEAgERlcneXl5RlcxjQkLC0N6erpR+Y8//ojJkyfju+++w9NPP93qeAkhhDSfRROLlZUVQkNDkZSUZFCelJSEwYMHN7md1NRUuLu7G5Rt3boV0dHRWL9+PSZOnMhJvIQQQprP4rfC5s6di8mTJ2PAgAEIDw/HmjVrkJ2djVmzZgEAFixYgOTkZBw8eBAAsGHDBohEIoSEhIDP52Pv3r1YvXo1li9frm9zy5YtmDx5MmJiYjB06FD9FZGVlRWcnJws/RIJIaRLs3hiiYqKQkFBAZYuXQq1Wo3g4GAkJCTo+0PUajUyMjIMjlm6dCmysrIgEAjg7++PdevWGfSvrFmzBlqtFrNnz8bs2bP15ZGRkTh8+LAlXhYhhJA/Wfw5lo6GnmMhhBBu0VxhhBBCOEWJhRBCCKcosRBCCOEUJRZCCCGcosRCCCGEU5RYCCGEcIoSCyGEEE5RYiGEEMIpSiyEEEI4RYmFEEIIpyixEEII4RQlFkIIIZyixEIIIYRTlFgIIYRwihILIYQQTlFiIYQQwilKLIQQQjhFiYUQQginKLEQQgjhFCUWQgghnKLEQgghhFOUWAghhHCKEgshhBBOUWIhhBDCKUoshBBCOEWJhRBCCKfaJbHExsZCqVRCIpEgNDQUx44dM1s3MzMTPB7P6CcxMVFfR61W46WXXkJgYCAEAgGmTp1qgVdBCCHEFIsnlvj4eLz11lt47733kJKSgsGDB2PMmDG4efNmo8clJiZCrVbrf0aMGKHfV1VVBblcjnfffRdhYWFt/RIIIYQ0gscYY5Y8YVhYGPr06YO1a9fqy/z8/DBx4kQsW7bMqH5mZiaUSiVOnz4NlUr1wPbHjRsHuVyO9evXNykelUqFM2fONDl+QgghjbPoFUt1dTXOnj2L0aNHG5SPHj0aJ06caPTYCRMmwNXVFeHh4di+fXur4oiLi4NKpYJKpUJ+fn6r2iKEEGLIoolFo9FAp9NBoVAYlCsUCuTk5Jg8RiaTISYmBlu3bkVCQgJGjhyJqKgobNq0qcVxzJgxA2fOnMGZM2fg4uLS4nYIIYQYE7bHSXk8nsE2Y8yorJ5cLse8efP02yqVChqNBitWrEB0dHSbxkkIIaT5LHrFIpfLIRAIjK5O8vLyjK5iGhMWFob09HSuwyOEEMIBiyYWKysrhIaGIikpyaA8KSkJgwcPbnI7qampcHd35zo8QgghHLD4rbC5c+di8uTJGDBgAMLDw7FmzRpkZ2dj1qxZAIAFCxYgOTkZBw8eBABs2LABIpEIISEh4PP52Lt3L1avXo3ly5cbtJuamgoAKC4uBp/PR2pqKqysrBAUFGTR10cIIV2dxRNLVFQUCgoKsHTpUqjVagQHByMhIQE+Pj4A6h52zMjIMDhm6dKlyMrKgkAggL+/P9atW2fUvxISEmKwvXfvXvj4+CAzM7NNXw8hhBBDFn+OpaOh51gIIYRbNFcYIYQQTlFiIYQQwilKLIQQQjhFiYUQQginKLEQQgjhFCUWQgghnKLEQgghhFOUWAghhHCKEgshhBBOUWIhhBDCKUoshBBCOEWJhRBCCKcosRBCCOEUJRZCCCGcosRCCCGEU5RYCCGEcIoSCyGEEE5RYiGEEMIpSiyEEEI4RYmFEEIIpyixEEII4RQlFkIIIZyixEIIIYRTlFgIIYRwihILIYQQTrVLYomNjYVSqYREIkFoaCiOHTtmtm5mZiZ4PJ7RT2JiokG9I0eOIDQ0FBKJBN27d8eaNWva+mUQQggxweKJJT4+Hm+99Rbee+89pKSkYPDgwRgzZgxu3rzZ6HGJiYlQq9X6nxEjRuj33bhxA2PHjsXgwYORkpKCBQsW4B//+Ad+/PHHtn45hBBCGuAxxpglTxgWFoY+ffpg7dq1+jI/Pz9MnDgRy5YtM6qfmZkJpVKJ06dPQ6VSmWxz/vz52LFjB9LT0/Vlr7zyCi5evIiTJ082Go9KpcKZM2da+GoIIYQ0ZNErlurqapw9exajR482KB89ejROnDjR6LETJkyAq6srwsPDsX37doN9J0+eNGrz8ccfx5kzZ1BTU8NN8IQQQppEaMmTaTQa6HQ6KBQKg3KFQoEDBw6YPEYmkyEmJgbh4eEQCoXYs2cPoqKisGHDBkRHRwMAcnJyMGrUKKM2tVotNBoN3N3dDfbFxcUhLi4OAHD58mWzV0JNkZ+fDxcXlxYfz4WOEAPFQXF09BgoDm7jkMvlRn3d9SyaWOrxeDyDbcaYUVk9uVyOefPm6bdVKhU0Gg1WrFihTyzm2jRVDgAzZszAjBkzWhz//TrCrbSOEAPFQXF09BgoDsvFYdFbYXK5HAKBADk5OQbleXl5RlcxjQkLCzPoT3FzczPZplAohLOzc+uCJoQQ0iwWTSxWVlYIDQ1FUlKSQXlSUhIGDx7c5HZSU1MNbm8NGjTI6FZaUlISVCoVRCJR64ImhBDSLBa/FTZ37lxMnjwZAwYMQHh4ONasWYPs7GzMmjULALBgwQIkJyfj4MGDAIANGzZAJBIhJCQEfD4fe/fuxerVq7F8+XJ9m7NmzcKqVaswe/ZszJw5E7/99hvWr1+PzZs3t/nr4eqWWmePAaA4GqI4OlYMAMXRUJvFwdrB6tWrmY+PD7OysmL9+vVjR44c0e+bMmUK8/Hx0W+vX7+ePfLII8zGxobZ2tqy0NBQtnHjRqM2Dx8+zEJCQpiVlRXz9fVlX331lSVeCiGEkAYs/hwLIYSQhxvNFUYIIYRTlFgIIYRwihJLI5ozWWZlZSWmTp2KPn36QCQSYdiwYe0Sx+HDhzF+/Hi4u7vDxsYGffr0wbp16yweR1paGoYPHw6FQqGfGPS9995DdXW1ReO4X3p6OmxtbSGTyVodQ3PjaOpkqm0ZA1D3fNcXX3yBwMBAiMViuLu74913321VDM2NY9GiRSbfCx6Ph7y8PIvFAQA///wzBg0aBFtbW8jlcowfPx5Xr15tVQwtiWPr1q149NFHYWNjAx8fH3z22WetOv/Ro0fx9NNPw9PTEzweD+vXr3/gMefPn0dkZCSsra3h6emJJUuWoMU9Je3bxdNxbdmyhQmFQhYXF8fS0tLYG2+8waRSKcvKyjJZv7S0lM2cOZN9/fXXbPz48SwyMrJd4vjkk0/Y+++/z44fP84yMjJYbGwsEwgE7Pvvv7doHOnp6ezbb79lqampLDMzk+3evZu5urqyd955x6Jx1KuqqmL9+vVjY8eOZVKptFUxtCSOGzduMAAsMTGRqdVq/U9VVZXFYmCMsTlz5jA/Pz+2a9culpGRwc6dO8f27dvX4hhaEkdJSYnBe6BWq1lkZCQbNmyYReO4fv06E4vF7J133mHp6eksJSWFPfbYY6xHjx4WjSMhIYEJBAK2evVqlpGRwX766Sfm7u7OVq5c2eIY9u3bxxYsWMC2bdvGrK2t2bffftto/Xv37jGFQsGef/55dv78ebZ9+3Ymk8lYTExMi85PicWMAQMGsFdeecWgrGfPnuzdd9994LGvv/46Z4mlNXHUe/7559mECRPaPY45c+awgQMHtkscs2fPZlOnTmXffvstJ4mluXHUJ5bTp0+3+twtjeHy5ctMKBSytLQ0zmJoSRwN3bx5k/H5/Fb/8dPcOLZt28b4fD7TarX6sl9//ZUBYPn5+RaL48UXX2TPPPOMQdmXX37JvLy8WG1tbYvjqCeVSh+YWGJjY5mtrS0rLy/Xl3388cfMw8OjRTHQrTATWjNZZkeMo7i4GI6Oju0ax7Vr15CYmIjIyEiLx7Fv3z789NNP+PLLL1t8bi7iABqfTLWtY9i9eze6d++OxMREdO/eHb6+vpgyZUqrbj9x8dn4v//7Pzg4OOC5556zaBz1D1B/88030Ol0KCkpwYYNG9C/f3/I5XKLxVFVVQWJRGJQZm1tjdu3byMrK6tFcTTXyZMnMWTIEFhbW+vLHn/8cWRnZyMzM7PZ7VFiMaGxyTIbTh3T0eP46aefcPDgwVY9CNWaOAYPHgyJRAI/Pz9ERETg008/tWgcarUa06dPx8aNG2Fra9vic7c2jvrJVLdu3YqEhASMHDkSUVFR2LRpk8ViuH79OrKysrBlyxasX78eGzduxOXLl/HUU0+htrbWYnHcr7a2FuvWrcPLL78MsVjcohhaGoevry+SkpKwcOFCiMVi2Nvb4/z58/jpp58sGsfjjz+OXbt24ZdffkFtbS2uXr2Kf/3rXwDqPr+WkJOTYzLm+n3NRYmlEc2ZLLMjxvHbb7/hpZdewpdffokBAwa0Sxzx8fE4d+4cfvjhByQkJBjMmGCJOKKjo/Hqq69i4MCBrT5va+Kon0x14MCBUKlUWLJkCWbOnIkVK1ZYLIba2lpUVVVh48aNGDp0KIYMGYKNGzciOTkZp0+ftlgc99u/fz9u3bqFV155pVXnb0kcOTk5mDZtGl5++WWcPn0ahw8fhq2tLf72t7+1ONG2JI7p06fjzTffxPjx42FlZYWBAwfihRdeAAAIBIJWxdEczZnI90EosZjA1WSZ7RnH8ePHMWbMGCxZsgSvvvpqu8Xh7e2NoKAgvPjii/jnP/+JxYsXQ6vVWiyOX3/9FYsXL4ZQKIRQKMS0adNQVlYGoVCoXzrBEnGY0nAy1baOwd3dHUKhEP7+/voyPz8/CIXCB67gymUc94uLi8PgwYPRq1evFp2/NXGsXr0aUqkUK1asQEhICIYOHYpNmzbhyJEjLb7l3ZI4eDweli9fjtLSUmRlZSEnJ0f/h6Cvr2+L4mgucxP5AmjRdx4lFhO4miyzveI4evQoxowZg4ULF2L27NntFkdDtbW10Gq10Ol0Fovj/PnzSE1N1f8sWbIE1tbWSE1NxfPPP2+xOExpOJlqW8cQHh4OrVaLjIwMfdn169eh1Wrh4+NjsTjqZWdnY9++fZg+fXqLzt3aOMrLy42uCOq3W3rF0pr3QyAQwNPTE1ZWVti8eTMGDRoEV1fXFsXRXIMGDcKxY8dQWVmpL0tKSoKHh0fLkluzu/u7iC1btjCRSMTWrl3L0tLS2JtvvsmkUinLzMxkjDH27rvvshEjRhgcc/HiRZaSksKioqJYaGgoS0lJYSkpKRaN49ChQ8zGxoa9/fbbBsM58/LyLBrHd999x7Zu3couXbrEMjIyWHx8PPPw8GBRUVEWjaMhrkaFNTeO9evXs++//56lpaWxy5cvs88++4yJRCL2+eefWywGnU7H+vXrx4YOHcrOnTvHzp07x4YOHcrCwsKYTqezWBz1Pv74Y2ZnZ8fKyspafO7WxHHw4EHG4/HYokWL2NWrV9nZs2fZ448/zry9vVlpaanF4sjPz2exsbEsLS2NpaSksDfffJNJJBL2+++/tziGkpIS/fePtbU1W7x4MUtJSdEPeW4YQ1FREVMoFCwqKoqdP3+e/fjjj8zW1paGG7eF5kyWyRhjPj4+DIDRjyXjmDJliskYGsba1nH88MMPLCQkhMlkMiaVSllQUBD75JNPDIYzWiKOhrhKLM2No6mTqbZlDIwxlp2dzSZOnMhkMhlzcXFhL730EsvJybF4HLW1tczX15e9+uqrrT53a+LYvHkzCwkJYVKplMnlcjZu3Dh28eJFi8aRn5/PBg4cyKRSKbOxsWEjR45kp06datX5Dx06ZPJ7YMqUKSZjYIyxP/74gw0ZMoSJxWLm5ubGFi1a1OLhzjQJJSGEEE5RHwshhBBOUWIhhBDCKUoshBBCOEWJhRBCCKcosRBCCOEUJRZCCCGcosTSQaxfv95gwSNbW1v07dsXq1atavEUKO0pNTUVixYtwt27d4328Xg8LFq0yPJBdQCHDx8Gj8fD4cOHH1j3008/Rbdu3SAUCvHoo4+2eWwPi/p/S/fPyuvr64upU6dyfq7MzEwsWrQI169fN9rXVufsDITtHQAxtG3bNnh5eaG4uBjbtm3DP/7xD+Tl5WHJkiXtHVqzpKamYvHixYiOjoaTk5PBvpMnT8LLy6udIusckpOT8f777+Odd97BM888w9nMzF3Vzp07YWdnx3m7mZmZWLx4MSIiItC9e3eLnLMzoMTSwTz66KPo2bMngLo1HK5du4YvvvjCbGKpqamBUChsl1mXTdHpdA9czrQtZhp+2Fy6dAkAMGvWLKMvrJaoqqpq1bT0nf38ISEhXeKcHQXdCuvg+vfvj5KSEuTl5enXTY+NjcX//u//wsPDA2KxGEVFRWCM4d///jcCAgJgZWUFd3d3vPHGGyguLjZoj8fj4f3338cnn3wCLy8vWFtbY+jQoUhNTTWo19z2/vnPf0KpVMLKygorV67E3//+dwB1s+fW396rvzVh6lZYYmIiBg0aBGtra9jb2+OZZ57BlStXDOoMGzYMEREROHDgAPr16wcbGxsEBwdj165dD3wf69dZb2jq1KkGk+xptVp8+OGH6NGjByQSCeRyOSIiInD8+HGD49auXYu+ffvq60ybNs3otl9+fj5eeukl2NnZwcHBAS+//DKKiooeGOuwYcP0t1B69Ohh8H4VFxfjjTfe0P/uAwIC8O9//9sgmdffbtuxYwemT58OFxeXRmeorb91dOrUKUyaNAl2dnbw8PDAm2++aTApIVC3PsjLL78MuVwOsViMPn36GK0pU9/e0aNH8fzzz8PBwQFhYWEA6m4PRUdHY+PGjQgICIC1tTWGDBmC9PR0lJWVYebMmXB2doZCocC8efMMbgNXVlZizpw5CA4Ohkwmg5ubG5566ilcvnz5ge/p/bel6v8dmfoZNmyY/phVq1Zh0KBBcHJygoODAwYOHIh9+/YZvM/Dhw8HADz22GP6Nupvc5q6FZacnIxRo0ZBJpNBKpVi5MiRSE5ONqgzdepUeHl5ISUlBUOGDIGNjQ38/PywZs2aB77ODqPFk9EQTn377bcMAEtPTzconzhxIhMIBKysrEy/vK2HhwcbP34827t3L9u1axcrLy9nCxYsYADY66+/zhITE9nnn3/OpFIpi4iIMJhgEADz8vJigwcPZjt37mRbtmxh/v7+zMnJiRUUFOjrNac9Dw8PFhERwbZv387279/PcnJy2AcffMAAsG3btrGTJ0+ykydPssrKSv0xCxcu1Lexf/9+xufz2ahRo9ju3bvZ999/z3r06MHkcjm7ffu2vl5kZCRzc3NjQUFBbOPGjWz//v1s1KhRTCAQGL1vDS1cuNDkvG0N50xaunQpk0ql7IsvvmCHDx9me/bsYR999BHbvXu3vs78+fOZUChkc+fOZT///DNbt24d8/DwYAMGDDBY5jYiIoLZ2tqylStXssTERPb3v/+deXl5MQDs0KFDZmO9ePGi/v3fsWMHO3nyJLt16xbT6XQsIiKC2djYsJiYGPbzzz+zN998kwFgCxYs0B9fP0+Uh4cHmzZtGtu/fz/buXOn2fPVf/Z69uzJPvzwQ5aUlMSWLFnC+Hw+++ijj/T1SktLmZ+fH5PL5ezrr79mCQkJ7KWXXmIA2Ndff23UnpeXF3vnnXdYUlIS279/P2Osbj49b29vNnDgQLZz504WHx/P3N3dWe/evdn48ePZvHnz2C+//KL//KxevVrfblFREZs2bRrbvHkzO3z4MNuxYwcbNWoUs7e3Z2q12uj8N27c0Jf5+Pjo58mqrKzUfybrf7755hvG5/PZrFmz9MfMmzePffPNN+zAgQMsMTGRvf766wwAS0hIYIzVrRO/evVqBoB9+eWX+rbu3btndE7GGPvvf//LJBIJ69evH9u2bRvbvn07U6lUTCKRsNTUVH29KVOmMFtbWxYYGMjWrFnDfvnlF/biiy8yAOzXX381+3vsSCixdBD1/xguX77Mampq2N27d9maNWsYn89n48ePZ4z9tW56SEiIweRwBQUFTCwWG3yIGWNs48aNDIDBlyIA5uzsbDB7640bN5hQKGQffPBBi9pzd3c3mlzSXKKsP+b+xBIaGsp69uzJampq9GXXr19nQqGQzZkzR18WGRnJhEIhu3r1qr4sNzeX8fl89sknnxid535NTSxPPvkke/bZZ822c+PGDcbn89nixYsNyo8fP84A6L/Af/nlFwaAbd682aDeE0888cDEwhhja9euNfpy3Lt3LwNgtH75tGnTmJWVlX6d9vrE0nAddXPqf1f3JxHG6t4LPz8//fbKlStNxj5y5Ejm4uKiT6r17c2ePdvoXD4+PszR0ZEVFRXpy/7zn/8wAGzatGkGdUNCQtiwYcPMxq3VallZWRmTyWQGs0Q/KLE0lJeXx5RKJRs0aBCrqKgwWUen07Gamhr22GOPsaefflpfXv9eJyUlmXyt95/zueeeY/b29qywsFBfdu/ePebo6GjwmaufSPb+JFJZWcmcnZ3Z9OnTzb0dHQrdCutgAgMDIRKJ4OTkhNdeew2TJk3CunXrDOo888wzBrd1Tp06haqqKkRHRxvUe+GFFyAUCnHkyBGD8rFjx0Iqleq3fX19MXDgQJw8ebJF7T3xxBMGa2U3R1lZGc6dO4eoqCgIhX91+SmVSoSHhxudy8/PD35+fvptV1dXuLq6tnihqob69++PhIQEvP/++zh+/Diqq6sN9iclJaG2thaTJk2CVqvV/4SFhcHOzg5Hjx4FUDdAQSAQGK3jXr8yYEscPXoUfD4fL774okF5dHQ0qqur9b+/es8++2yz2n/yyScNtnv37m3wvh49ehSenp4Gt4vqz5+fn4+0tLQmnX/QoEGwt7fXbwcGBgKoW6L3foGBgbh165ZB2datWxEWFgYHBwcIhUJIpVKUlpYa3TZtqurqan2cu3fvNlh7/uzZsxg3bhwUCgWEQiFEIhGSkpJafK6jR49i3LhxcHBw0JfZ2dnh6aefNvqc29jY6G+zAYBYLIafnx9nn/O2Rp33HczOnTvh5eUFW1tb+Pj4GHzQ6zVcHKr+3n7DcqFQCGdnZ6N7/6butysUCly8eLFF7bV0sSoAKCwsBGPMZBtubm7IysoyKGs4wgyo+0fXsC+gpd577z1IJBJs2rQJn376KWQyGSZOnIjPPvsMcrlcv6pe/QCLhgoKCgDU9UU4OjpCJBIZ7G/NCqR3796Fk5OTUSe4m5ubfv/9mvt7afjeisViVFVVGZzf3O+pOed3dHQ02LaysjJbfv/vde/evYiKisKUKVOwcOFCyOVy8Pl8jB07tsW//+nTp+PChQs4ceIEXFxc9OW3bt3CyJEjERQUhJUrV+qHfX/44Yf6gRXN1dj7V1hYaFDW8L0AuP2ctzVKLB1McHCw2S+teg07oeu/EHJycgyWeNVqtSgoKICzs7NB/dzcXKM2c3Nz4enp2aL2WjMizdHRETwez2hZ1PrzNzxXS9Un6Orqav0XGfBXIqgnEokwf/58zJ8/Hzk5Ofjpp58wd+5clJeXIz4+Xh/PL7/8YvIff/1+d3d3FBYWoqamxiC5mHrvm8rJyQl37941eg317x2Xvxdz5zf117qlzr9lyxb07NkT69ev15fV1NSYfFaqKT799FP88MMPSEhIQFBQkMG+xMRE3Lt3D1u3bjUYGl9eXt6icwF175+5z7mpP5g6M7oV9hAYOHAgxGIxtmzZYlAeHx8PrVaLyMhIg/KEhASUlZXptzMzM3Hq1CkMGjSoRe2ZUv9XdUVFRaP1pFIpQkNDsW3bNoMli7OysnDixIkmnasp6pfevXDhgr6sqKio0bXN3dzc8Morr2DUqFH64x577DHw+XzcvHkTKpXK6EepVAKou92j0+nw448/GrTZ8D1tjsjISNTW1mLbtm0G5d9//z2srKzafBh3ZGQkbt++jd9++82g/IcffoCrqyseeeSRNj1/eXm5we1SANi4cWOLlrresWMHPvjgA6xatQqPPfaYyXMBMPij4OrVq0avvamfc6Du/du3bx9KSkr0ZSUlJdi7dy9nn/OOgq5YHgJOTk6YO3culi1bBqlUirFjx+LSpUv44IMPEBERYXTv3NraGqNHj8Y777yDqqoqLFy4EHZ2dpgzZ06L2jOl/i/A1atXY8qUKRCJROjTp4/BX9r1Pv74Yzz55JMYN24cXnvtNZSWlmLhwoWwt7fHvHnzOHiHgDFjxsDe3h7Tp0/H4sWLUVVVhRUrVkAmkxnUGz9+PPr27Yt+/frB0dERKSkpSExMxMyZMwHUDf+dP38+3njjDVy5cgWRkZGQSCS4desWkpKS8Morr2D48OF47LHHEBERgZkzZ0Kj0cDPzw/x8fEGia0lryEiIgKzZs1Cfn4+evXqhYSEBHzzzTdYsGAB5HJ5q96jB5k6dSr+85//YMKECfrh6t9//z2SkpLw9ddfG60fz7UnnngCu3btwpw5czBu3DicPXsWX375pUGfRVNcv34dkydPxujRo9G3b1+cOnVKv8/Ozg5BQUEYNWoUhEIhXn75ZcybNw9qtRoLFy5Et27dUFtbq6/v7+8PoVCIdevW6W9TBgQEmHyg9cMPP8RPP/2EkSNHYv78+eDxeFi+fDnKy8vx0Ucftfh96ZDae/QAqdPYKKp69aPC1q5da7SvtraWff7558zf35+JRCLm5ubGXnvtNf3Qx3oA2Hvvvcc++eQT5unpycRiMYuIiGApKSktbu/99983Ge+iRYuYh4cH4/P5BqN00GBUGGN1Q44HDhzIJBIJs7OzY08//TS7fPmyQZ3IyEgWHh5udJ7GRvzc79ixY0ylUjFra2vm5+fHNm7caDQqLCYmhoWFhTEnJycmkUiYv78/W7hwIauurjZo67vvvmNhYWHMxsaGSaVSFhgYyF5//XV269YtfZ28vDz2wgsvMJlMxuzt7dnkyZPZrl27WjwqjLG6UUSvv/46c3NzYyKRiPn5+bHPP//cYJRgYyOVTDH32TM1ki47O5tFR0czZ2dnZmVlxXr37m20xHJjn2UfHx82adIkgzJz8U6ZMoV5enrqt3U6HXv//feZu7s7s7a2ZkOHDmXnzp0z+v0/aFSYuWV7AbDIyEj9MfHx8SwgIICJxWIWFBTENm/ebHJJ3zVr1jClUskEAoHB79bU5/LUqVNs5MiR+mWIR4wYYbS2fcPXXS8yMtIgvo6MlibuYuofaFy6dGl7h0IIeUhRHwshhBBOUWIhhBDCKeq872LozichpK3RFQshhBBOUWIhhBDCKUoshBBCOEWJhRBCCKcosRBCCOHU/wMf4zKZZiKyHAAAAABJRU5ErkJggg==",
                        "text/plain": [
                            "<Figure size 432x288 with 1 Axes>"
                        ]
                    },
                    "metadata": {},
                    "output_type": "display_data"
                }
            ],
            "source": [
                "res_frame['auc_less_mean'] = res_frame.groupby('subject')[\"auc\"].apply(lambda series: series - series.mean()).values\n",
                "ax = sns.pointplot(data=res_frame, x='nlists', y='auc', ci=None)\n",
                "err = res_frame.groupby('nlists').agg({'auc':'mean', 'auc_less_mean':'sem'})\n",
                "plt.errorbar(range(0, 10), y=err['auc'], yerr=err['auc_less_mean'], capsize=5)\n",
                "ax.spines['top'].set_visible(False)\n",
                "ax.spines['right'].set_visible(False)\n",
                "ax.set_xticklabels([i/10 for i in range(1, 11)])\n",
                "plt.xticks(fontsize=14)\n",
                "plt.yticks(fontsize=14)\n",
                "ax.set_xlabel('Proportion used for normalization', fontsize=16)\n",
                "ax.set_ylabel('AUC', fontsize=16)\n",
                "plt.grid(False)\n",
                "plt.ylim(0.5, None)\n",
                "plt.savefig('figures/AUC_by_pnorm.pdf', dpi=200, bbox_inches='tight')"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 27,
            "id": "4502b5d2-29a6-4a57-b8d2-4d3f72debe62",
            "metadata": {},
            "outputs": [],
            "source": [
                "p_list = []\n",
                "for subject, df in res_frame.groupby('subject'):\n",
                "    p_list.append(np.polyfit(x=df['nlists'], y=df['auc'], deg=1))\n",
                "p_list=np.stack(p_list)"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 28,
            "id": "5c34d41a-2278-4b12-9bdd-762c25e977a6",
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "text/plain": [
                            "Ttest_1sampResult(statistic=3.18637608414388, pvalue=0.004638157101947827)"
                        ]
                    },
                    "execution_count": 28,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": [
                "scp.stats.ttest_1samp(p_list[:, 0], popmean=0)"
            ]
        },
        {
            "cell_type": "markdown",
            "id": "ecc9377e-3750-4227-921f-fc3ea480cbdf",
            "metadata": {
                "tags": []
            },
            "source": [
                "# CL Behavioral Data"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 3,
            "id": "1a857bfc-faa4-48d3-be02-9f92ddc4dcf3",
            "metadata": {},
            "outputs": [],
            "source": [
                "cl_data = cml.get_data_index(kind = 'ltp');\n",
                "cl_data = cl_data[cl_data['experiment'] == 'NiclsCourierClosedLoop']"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 6,
            "id": "172fc717-3713-4bec-bef7-12fdffbd8aeb",
            "metadata": {
                "tags": []
            },
            "outputs": [],
            "source": [
                "cl_data = cml.get_data_index(kind = 'ltp');\n",
                "cl_data = cl_data[cl_data['experiment'] == 'NiclsCourierClosedLoop']\n",
                "\n",
                "# Subjects with good classification protocol and low recall\n",
                "cl_data = cl_data.query(\"subject in @subject_list\")\n",
                "performance_stats = pd.read_csv(\"data/system_performance_stats.csv\", header=[0, 1], index_col=[0, 1])\n",
                "\n",
                "cl_data = cl_data[~performance_stats['bad_session'].values.ravel()]\n",
                "\n",
                "CL_evs = None\n",
                "for i, row in cl_data.iterrows():\n",
                "    if sum(cl_data.subject == row['subject']) < 1: \n",
                "        continue\n",
                "    read = cml.CMLReader(subject=row['subject'], experiment=row['experiment'], session=row['session'], montage=0, localization=0)\n",
                "    evs = read.load('task_events')\n",
                "#     evs['phase'] = evs['phase'].astype(str)\n",
                "    evs.drop(index=evs[evs.type.str.contains('VV')].index, inplace=True)\n",
                "    evs.drop(index=evs.query(\"phase in ['practice', 'movie', 'video']\").index, inplace=True)\n",
                "    \n",
                "    CL_evs = evs if CL_evs is None else CL_evs.append(evs)\n",
                "# only real delivery days\n",
                "CL_evs = CL_evs.reset_index().drop(columns='index')\n",
                "\n",
                "res = CL_evs.groupby(['subject','session', 'trial']).agg({'classifier':lambda x: list(np.unique([i for i in x if i!='X'])) or 'NoStim'})\n",
                "res = res.apply(lambda x: [i if isinstance(i, str) else i[0] for i in x])\n",
                "\n",
                "CL_evs['trial_type']=''\n",
                "for (subject, session, trial), row in res.iterrows():\n",
                "    idx = CL_evs.query(f\"subject=='{subject}' & session=={session} & trial=={trial}\").index\n",
                "    CL_evs['trial_type'][idx] = [row['classifier']]*len(idx)\n",
                "\n",
                "final = CL_evs.query('type==\"WORD\"')\n",
                "\n",
                "# subject_list = final.subject.unique()"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 7,
            "id": "56037ba0-2b9c-4d89-875b-1e7aaa4ae265",
            "metadata": {},
            "outputs": [],
            "source": [
                "class_evs = CL_evs[(CL_evs.type=='TIMEOUT')|(CL_evs.type=='CLASSIFIER')]\n",
                "# Show we have expected proportions\n",
                "types = class_evs.groupby('classifier').count()['item']\n",
                "plt.figure(figsize=(5, 5))\n",
                "_ = plt.pie(types, labels=types.index.values, autopct='%1.0f%%')"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 62,
            "id": "145be3f2-fc5b-4226-96c8-471fd5669604",
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "image/png": 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",
                        "text/plain": [
                            "<Figure size 576x288 with 1 Axes>"
                        ]
                    },
                    "metadata": {},
                    "output_type": "display_data"
                },
                {
                    "data": {
                        "image/png": 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",
                        "text/plain": [
                            "<Figure size 576x288 with 1 Axes>"
                        ]
                    },
                    "metadata": {},
                    "output_type": "display_data"
                }
            ],
            "source": [
                "# See how many items timed out vs. had action changed by classifier result\n",
                "props = class_evs.groupby(['subject','classifier', 'type']).agg({'item':'count'})\n",
                "\n",
                "plt.figure(figsize=(8, 4))\n",
                "sns.barplot(data = props.reset_index(), x='classifier', y='item', hue='type')\n",
                "plt.xlabel('Classifier Type', fontsize=18)\n",
                "plt.ylabel('Event Count', fontsize=18)\n",
                "# plt.savefig()\n",
                "\n",
                "plt.figure(figsize=(8, 4))\n",
                "sns.barplot(data = props.reset_index(), x='subject', y='item', hue='type', ci=None)\n",
                "plt.xlabel('Subject', fontsize=18)\n",
                "plt.ylabel('Event Count', fontsize=18)\n",
                "_ = plt.xticks(rotation=45)"
            ]
        },
        {
            "cell_type": "markdown",
            "id": "64ec8c57-f68f-4a42-8c09-50bea3f39685",
            "metadata": {},
            "source": [
                "### Correlating Recall and AUC"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 8,
            "id": "884c492b-22fa-4407-99f8-04dc36850095",
            "metadata": {
                "tags": []
            },
            "outputs": [],
            "source": [
                "avg = final.groupby(\n",
                "    ['subject', 'session']\n",
                ").agg(\n",
                "    {'recalled':'mean'}\n",
                ").mean(level=0)\n",
                "\n",
                "above_med = avg >= avg.median()\n",
                "\n",
                "final['above_avg'] = np.isin(final['subject'].values, above_med.index[above_med['recalled']])"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 9,
            "id": "b093a3e2-1539-4abd-8c69-8f27411b6481",
            "metadata": {
                "collapsed": true,
                "jupyter": {
                    "outputs_hidden": true
                },
                "tags": []
            },
            "outputs": [
                {
                    "ename": "NameError",
                    "evalue": "name 'result_dict' is not defined",
                    "output_type": "error",
                    "traceback": [
                        "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
                        "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
                        "\u001b[0;32m<ipython-input-9-2494af3723ca>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0mavg\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mavg\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreset_index\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mavg\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'RO_auc'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mavg\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'subject'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mapply\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;32mlambda\u001b[0m \u001b[0mk\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mresult_dict\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mk\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      3\u001b[0m \u001b[0mavg\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'CL_auc'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mavg\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'subject'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mapply\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;32mlambda\u001b[0m \u001b[0mk\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mcl_result_dict\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mk\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0mavg\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_index\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'subject'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
                        "\u001b[0;32m/usr/global/miniconda/py310_23.1.0-1/envs/workshop/lib/python3.7/site-packages/pandas/core/series.py\u001b[0m in \u001b[0;36mapply\u001b[0;34m(self, func, convert_dtype, args, **kwargs)\u001b[0m\n\u001b[1;32m   4355\u001b[0m         \u001b[0mdtype\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mfloat64\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   4356\u001b[0m         \"\"\"\n\u001b[0;32m-> 4357\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mSeriesApply\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mconvert_dtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mapply\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   4358\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   4359\u001b[0m     def _reduce(\n",
                        "\u001b[0;32m/usr/global/miniconda/py310_23.1.0-1/envs/workshop/lib/python3.7/site-packages/pandas/core/apply.py\u001b[0m in \u001b[0;36mapply\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m   1041\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mapply_str\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1042\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1043\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mapply_standard\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1044\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1045\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0magg\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
                        "\u001b[0;32m/usr/global/miniconda/py310_23.1.0-1/envs/workshop/lib/python3.7/site-packages/pandas/core/apply.py\u001b[0m in \u001b[0;36mapply_standard\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m   1099\u001b[0m                     \u001b[0mvalues\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1100\u001b[0m                     \u001b[0mf\u001b[0m\u001b[0;34m,\u001b[0m  \u001b[0;31m# type: ignore[arg-type]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1101\u001b[0;31m                     \u001b[0mconvert\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconvert_dtype\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1102\u001b[0m                 )\n\u001b[1;32m   1103\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
                        "\u001b[0;32m/usr/global/miniconda/py310_23.1.0-1/envs/workshop/lib/python3.7/site-packages/pandas/_libs/lib.pyx\u001b[0m in \u001b[0;36mpandas._libs.lib.map_infer\u001b[0;34m()\u001b[0m\n",
                        "\u001b[0;32m<ipython-input-9-2494af3723ca>\u001b[0m in \u001b[0;36m<lambda>\u001b[0;34m(k)\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0mavg\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mavg\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreset_index\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mavg\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'RO_auc'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mavg\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'subject'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mapply\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;32mlambda\u001b[0m \u001b[0mk\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mresult_dict\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mk\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      3\u001b[0m \u001b[0mavg\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'CL_auc'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mavg\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'subject'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mapply\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;32mlambda\u001b[0m \u001b[0mk\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mcl_result_dict\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mk\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0mavg\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_index\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'subject'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
                        "\u001b[0;31mNameError\u001b[0m: name 'result_dict' is not defined"
                    ]
                }
            ],
            "source": [
                "avg = avg.reset_index()\n",
                "avg['RO_auc'] = avg['subject'].apply(lambda k: result_dict[k][2])\n",
                "avg['CL_auc'] = avg['subject'].apply(lambda k: cl_result_dict[k][2])\n",
                "avg.set_index('subject')"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 65,
            "id": "265e1bef-9ddd-44b7-b93e-618f05b48d85",
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "text/plain": [
                            "<AxesSubplot:xlabel='recalled', ylabel='CL_auc'>"
                        ]
                    },
                    "execution_count": 65,
                    "metadata": {},
                    "output_type": "execute_result"
                },
                {
                    "data": {
                        "image/png": 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",
                        "text/plain": [
                            "<Figure size 432x288 with 1 Axes>"
                        ]
                    },
                    "metadata": {},
                    "output_type": "display_data"
                }
            ],
            "source": [
                "sns.regplot(data=avg, x=\"recalled\", y=\"CL_auc\", ci=68)"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 66,
            "id": "5a1b1058-6c75-4e19-b333-278bc0ed8ba1",
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "image/png": 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",
                        "text/plain": [
                            "<Figure size 576x144 with 1 Axes>"
                        ]
                    },
                    "metadata": {},
                    "output_type": "display_data"
                }
            ],
            "source": [
                "plt.figure(figsize=(8, 2))\n",
                "ax = sns.regplot(data=avg, x=\"RO_auc\", y=\"CL_auc\", ci=68)\n",
                "ax.spines['top'].set_visible(False)\n",
                "ax.spines['right'].set_visible(False)\n",
                "ax.tick_params(labelsize=14)\n",
                "ax.set_xlabel(\"Training AUC\")\n",
                "ax.set_ylabel(\"Closed-loop AUC\")\n",
                "# plt.savefig('FinalReport/auc_train_test_correlation.png', dpi=300, bbox_inches='tight')"
            ]
        },
        {
            "cell_type": "markdown",
            "id": "3c599061-6f1c-4c76-b0ca-cb298b097836",
            "metadata": {},
            "source": [
                "## Evaluating memory improvement"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 9,
            "id": "a16678ca-935d-4bbc-8ada-66602ca9fa29",
            "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></th>\n",
                            "      <th>recalled</th>\n",
                            "      <th>session</th>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>subject</th>\n",
                            "      <th>trial_type</th>\n",
                            "      <th></th>\n",
                            "      <th></th>\n",
                            "    </tr>\n",
                            "  </thead>\n",
                            "  <tbody>\n",
                            "    <tr>\n",
                            "      <th rowspan=\"3\" valign=\"top\">LTP462</th>\n",
                            "      <th>Neg</th>\n",
                            "      <td>0.477778</td>\n",
                            "      <td>2</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>Pos</th>\n",
                            "      <td>0.577778</td>\n",
                            "      <td>2</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>Sham</th>\n",
                            "      <td>0.400000</td>\n",
                            "      <td>2</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th rowspan=\"2\" valign=\"top\">LTP468</th>\n",
                            "      <th>Neg</th>\n",
                            "      <td>0.544444</td>\n",
                            "      <td>2</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>Pos</th>\n",
                            "      <td>0.622222</td>\n",
                            "      <td>2</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>...</th>\n",
                            "      <th>...</th>\n",
                            "      <td>...</td>\n",
                            "      <td>...</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th rowspan=\"2\" valign=\"top\">LTP498</th>\n",
                            "      <th>Pos</th>\n",
                            "      <td>0.622222</td>\n",
                            "      <td>3</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>Sham</th>\n",
                            "      <td>0.711111</td>\n",
                            "      <td>3</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th rowspan=\"3\" valign=\"top\">LTP500</th>\n",
                            "      <th>Neg</th>\n",
                            "      <td>0.655556</td>\n",
                            "      <td>4</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>Pos</th>\n",
                            "      <td>0.633333</td>\n",
                            "      <td>4</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>Sham</th>\n",
                            "      <td>0.533333</td>\n",
                            "      <td>4</td>\n",
                            "    </tr>\n",
                            "  </tbody>\n",
                            "</table>\n",
                            "<p>63 rows × 2 columns</p>\n",
                            "</div>"
                        ],
                        "text/plain": [
                            "                    recalled  session\n",
                            "subject trial_type                   \n",
                            "LTP462  Neg         0.477778        2\n",
                            "        Pos         0.577778        2\n",
                            "        Sham        0.400000        2\n",
                            "LTP468  Neg         0.544444        2\n",
                            "        Pos         0.622222        2\n",
                            "...                      ...      ...\n",
                            "LTP498  Pos         0.622222        3\n",
                            "        Sham        0.711111        3\n",
                            "LTP500  Neg         0.655556        4\n",
                            "        Pos         0.633333        4\n",
                            "        Sham        0.533333        4\n",
                            "\n",
                            "[63 rows x 2 columns]"
                        ]
                    },
                    "execution_count": 9,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": [
                "plot_data"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 8,
            "id": "513dc4c9-f9fc-438a-a450-7c5b27818b95",
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "image/png": "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",
                        "text/plain": [
                            "<Figure size 288x216 with 1 Axes>"
                        ]
                    },
                    "metadata": {
                        "needs_background": "light"
                    },
                    "output_type": "display_data"
                }
            ],
            "source": [
                "fig, ax = plt.subplots(1, 1, figsize=(4, 3))\n",
                "plt.rcParams['figure.facecolor']='white'\n",
                "sns.set_palette('twilight')\n",
                "plot_data = final.query(\"trial_type!='NoStim'\").groupby(['subject', 'trial_type', 'session']).agg('mean').reset_index()\\\n",
                "            .groupby(['subject', 'trial_type']).agg({\"recalled\":'mean', \"session\":\"count\"})\n",
                "sns.barplot(data=plot_data.reset_index(),\n",
                "            x = 'trial_type',\n",
                "            y='recalled',\n",
                "            ec='k',\n",
                "            ci=68,\n",
                "            ax=ax\n",
                ")\n",
                "plt.xlabel('', fontsize=18)\n",
                "plt.ylabel('Recall Probability')\n",
                "ax.set_xticklabels([\"Bad\", \"Good\", \"Random\"])\n",
                "ax.tick_params(labelsize=14)\n",
                "ax.spines['top'].set_visible(False)\n",
                "ax.spines['right'].set_visible(False)\n",
                "plt.ylim(0.4, .8)\n",
                "plt.grid(False)\n",
                "# plt.savefig('FinalReport/stim_effect.pdf', dpi=200, bbox_inches='tight')"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 62,
            "id": "e2013b23-1878-4ce6-811c-031ad72575f5",
            "metadata": {},
            "outputs": [],
            "source": [
                "avg[\"positive\"] = plot_data.xs('Pos', level=1)['recalled'].values\n",
                "avg[\"negative\"] = plot_data.xs('Neg', level=1)['recalled'].values\n",
                "avg[\"sham\"] = plot_data.xs('Sham', level=1)['recalled'].values\n",
                "avg[\"pct_change\"] = 100*(plot_data.xs('Pos', level=1)['recalled'] - plot_data.xs('Neg', level=1)['recalled']).values / plot_data.xs('Neg', level=1)['recalled'].values"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 63,
            "id": "ad0fa647-fe60-4e0c-9ee1-bb01b1f6cb1d",
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "image/png": 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",
                        "text/plain": [
                            "<Figure size 720x144 with 1 Axes>"
                        ]
                    },
                    "metadata": {},
                    "output_type": "display_data"
                }
            ],
            "source": [
                "plt.figure(figsize=(10, 2))\n",
                "ax = sns.histplot(avg, x=\"pct_change\", bins=range(-30, 51, 10))\n",
                "ax.set_xlabel(\"% Change In Memory Performance (Random vs. Optimized)\", fontsize=18)\n",
                "ax.set_ylabel(\"# of Subjects\", fontsize=18)\n",
                "# ax.set_yticks([1, 2])\n",
                "ax.spines['top'].set_visible(False)\n",
                "ax.spines['right'].set_visible(False)\n",
                "ax.tick_params(labelsize=14)\n",
                "plt.savefig('FinalReport/pct_change_memory.pdf', dpi=200, bbox_inches='tight')"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 64,
            "id": "d324e8f0-d86f-4c6b-9a92-f1fba956e627",
            "metadata": {},
            "outputs": [],
            "source": [
                "df = final.groupby([\"subject\", \"trial_type\", \"recalled\"]).agg({\"item\":\"count\"}).reset_index()\n",
                "df = df.pivot(index=[\"subject\", \"trial_type\"], columns=\"recalled\", values=\"item\")\n",
                "df.fillna(0, inplace=True)\n",
                "\n",
                "p_dict = {}\n",
                "for idx, frame in df.reset_index().groupby(\"subject\"):\n",
                "    frame = frame.query(\"trial_type in ['Pos', 'Neg']\")\n",
                "#     chi2, p, _, _= scp.stats.chi2_contingency(frame[[0, 1]].values)\n",
                "    chi2, p = scp.stats.fisher_exact(frame[[1, 0]].values)\n",
                "#     print(frame[[1, 0]].values)\n",
                "#     print(chi2, p)\n",
                "    p_dict[idx] = p\n",
                "\n",
                "avg['p'] = avg['subject'].map(p_dict)\n",
                "avg['sig'] = avg['p'] <= .05"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 67,
            "id": "924da7e6-eeca-4994-b196-6f31cf1c6f6f",
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "text/plain": [
                            "<matplotlib.collections.PathCollection at 0x2ae7caf308b0>"
                        ]
                    },
                    "execution_count": 67,
                    "metadata": {},
                    "output_type": "execute_result"
                },
                {
                    "data": {
                        "image/png": 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dS5cuhZeXF4qKiopts7S0RHp6uqhgGzZsUDlBIiIiql5EzfPw+PFjNG/evNRtBQUFEAShSpMiIiKi6kvUlQc7O7tiy2f/3fHjx+Hk5CQ64L1797B3716kp6ejoKCg2DaJRIJ58+aJPhYRERFpnqjiITAwEIGBgTA0NMSIESMAAA8ePMCPP/6IVatWISIiQlSwgwcPYtCgQXjy5Emp21k8EBERVX+iiofx48fjxo0bmDt3rmLZ7O7du0NHRweffvopRo4cKSrYxx9/DBcXF6xevRr29vbQ09OreOZERES1ROCUik+SmJ39WPHfyhxn9aoh5e/0P6LneVi0aBE++ugj/PHHH8jOzoaJiQm6d++OVq1aiQ6WmpqK5cuXq3Sbg4iIiKoXlWaYtLKywrhx44q9d+7cOYSFhWHnzp3ltndxceES3ERERDVcmU9bFBUVITY2Fjt27MC5c+eKbYuLi0Pfvn3h5uaGo0ePigq2bNkyLF68GGfOnKl4xiKFhITAxsYGEokEiYmJiveTkpLg5eUFW1tbeHl5ITk5We25EBER1SZKi4fbt2/Dw8MDXl5eGDJkCNzc3DB06FC8ePECH374ITw8PHDkyBF88sknuHnzpqhgHTp0gJ+fHzp37oxGjRrB0tKy2JeVlVWVnZi/vz9OnDhR4piTJk1CYGAgkpKSEBgYiIkTJ1ZZTCIiojeB0tsWn3/+Oa5evYqvvvoKrq6uSElJwYIFC9CpUyfEx8dj9OjRWLRoEczNzUUHCwkJwapVq+Di4gJ7e/tiM1VWtc6dO5d4Lzs7GwkJCfjjjz8AAMOHD8eUKVOQk5MDMzMzteVCRNWfqktqA1xWm95cSouHw4cPIzQ0FCEhIYr37Ozs0K1bNwQFBWHFihUqB9uwYQNmz56ttccx09PT0bx5c0ilUgCvlhq3sLBAeno6iwciIiKRlBYPOTk58PT0LPael5cXAGDw4MEVCiaRSNClS5cKtdWGiIgIxRwWOTk5Ws6GiIhKw89qzVM65kEul5e4rfD6+/r161co2ODBg7F///4Kta0KLVu2xJ07dxTrcxQVFSEjIwMtW7Ysdf8JEyYgLi4OcXFxvDJBRFRN8bNa88p8VHPPnj3FnlSQy+WQSCTYvXs3zp8/X2zfsWPHlhusV69emD59OvLz8/Huu+/CyMioxD6+vuq7h9ikSRPIZDJERkZi1KhRiIyMhIuLCzsbERGRCsosHr7++utS3w8LCyv2vUQiEVU8DBgwAACwbt06rFu3rlh7QRAgkUhKrNpZUVOnTsVvv/2GzMxMdOvWDSYmJrh06RLWrFmD0aNHIywsDEZGRti0aVOVxCMiInpTKC0eUlJSqjyY2PkgqsLKlSuxcuXKEu/b29sjJiZGY3kQERHVNkqLh6qcc+E1b2/vKj8mERERaVaZM0wSERER/ZNKa1tUxAcffCB6X4lEgo0bN6oxGyIiIqostRcPJ06cgEQiEbWv2P2IiIhIe9RePKSmpqo7BBEREWkQxzwQERGRSlg8EBERkUpE37Y4fvw4IiMjkZaWhoKCgmLbJBIJDh8+XOXJERERUfUjqnj4/vvv8dFHH8HExARt27aFvr5+se2CIKglOSIiIqp+RBUP3377LUaMGIH169eXWCyLiIiI3iyixjzcuXMHY8aMYeFARERE4oqHDh064ObNm+rOhYiIiFTUsJEZDA2boWEjza0QLeq2xcqVKzFy5EjY2dmhS5cuaklk/PjxkMvlxVbbJCIiorL16zdL4zFFFQ99+/bFw4cP0bVrV9SvXx9GRkbFtkskEty6datSifz4448QBIHFAxERUTUnqnjw8/NT+9TRhYWFaj0+ERERVQ1RxcOGDRvUnAYRERHVFGpf2+Lv5HI55HI5dHX/CnvgwAEkJibC19cXLi4umkyHiIiIKkBp8XDkyBGVDuTr61vuPsOHD4e+vj42bdoEAFizZg0mT54MANDT08PevXvRrVs3leISEb0WOGWbSvuvXjVETZkQ1W5Ki4du3bpBIpGUOXvk6+0SiQRFRUXlBouOjsY333yj+H7JkiUYN24cvv32W0yYMAFff/01iwciIqJqTmnxcPTo0SoPlp2djebNmwMArl+/jpSUFEyZMgUNGzbEmDFjMGLEiCqPSURERFVLafHg7e1d5cEaNWqEvLw8AMCxY8dgamoKZ2dnAIBUKi2x4BYRvVlW7lDtdunU98u/XUpEVU+jAybffvttLFq0CLq6uggPD0fv3r0V265fv44WLVpoMh0iIiKqAI0WD4sXL0afPn3Qr18/tGrVCqGhoYptW7duhZeXlybTISI14NUDotpPo8VD27ZtkZSUhLy8PJiYmBTbtmLFCjRt2lST6RAREVEFiFoYq6rs3r0bhYWFJQoHAHBycoKZmeYW9SAiIqKK0Wjx4O/vj2bNmiEoKAixsbGaDE1ERERVROXi4fHjx7h16xZevnypcrAzZ85g2LBhivENdnZ2+Prrryu9qBYRERFpjugxD1FRUZgzZw4uXLgAiUSC2NhYuLq6Yty4cfD19RU1R4OHhwc8PDywfPly7N+/Hz/99BO+/vprzJkzB507d8YHH3yADz/8sFInRESVwwGPRFQeUVcedu3ahf79+8PU1BTffPMN5HK5YpuNjQ02btyoUlBdXV307dsX27ZtQ1ZWFtauXYubN29i4sSJqmVPREREGieqeJg3bx7GjBmDgwcPIjg4uNg2R0dHJCYmVij4rVu3sHLlSixevBh37txBkyZNKnQcIiIi0hxRxcOVK1cwdOhQAK/Ws/g7IyMjxayRYuTn52Pt2rXo0qULWrVqhYULF6JDhw7Yv38/bt++rULqlZOUlAQvLy/Y2trCy8sLycnJGotNRERUk4kqHho1aoTc3NxSt6Wmpop+xHLw4MFo2rQpPvroI+jp6WH9+vXIzMzEli1b0LNnT+joaO7hj0mTJiEwMBBJSUkIDAzkLRMiIiKRRP217t69OxYuXIgHDx4o3pNIJHj+/DlWrVqFXr16iQp25coVhIaGIjU1FYcPH8bo0aNhYGBQocQrIzs7GwkJCRg+fDiAV0uFJyQkICcnR+O5EBER1TSinrb4+uuv0bFjR9jZ2aF3796QSCRYtGgRLl68iPz8fOzatUtUsIqOjahq6enpaN68OaRSKYBXi3JZWFggPT2dE1URERGVQyIIgiBmx9u3b2Pu3Lk4cOAAsrOzYWJignfffRdhYWFo2bKluvOsUvHx8fjggw9w6dIlxXsODg7YvHkzXF1dFe9FREQgIiICAJCTk1Op+Sgq+/hb4JRtKrVfvWpIhWP/M35lcmdszccmqs3c3NwQFxdX7L2yPqsr8v9iVaup/3+W9rN+TfQ8Dy1atMC6deuqLCltatmyJe7cuYOioiJIpVIUFRUhIyOjRBE0YcIETJgwAcCrHyIREVU//KzWPI1OT11dNGnSBDKZDJGRkQCAyMhIuLi48JYFERGRCKKuPIwdO1bpNh0dHRgaGqJDhw4YOHAg6tatW2XJqdOaNWswevRohIWFwcjICJs2bdJ2SkREVM3831ef4l5OJozNmmLy7MXaTqfaEFU8HD16FPn5+Xjw4AF0dXVhamqK3NxcFBYWonHjxgCA5cuXo3Xr1jh69ChatGihzpyrhL29PWJiYrSdBhERVWP3cjKRc/eOttOodkTdtvj5559haGiIX3/9FQUFBcjIyEBBQQG2b9+ORo0aISoqCjExMSgoKMAXX3yh7pyJiIhIi0RdeZg+fTo+++wzDBgwQPGejo4OBg0ahOzsbAQHByM2NhZffPEFwsLCirW1sbEpMStlWW7evCl6XyIiItI8UcXDhQsX0Lp161K3tW7dWjF/g4ODA+7fv19su7e3d7Hi4fDhw8jKykKnTp1gbm6OrKwsnD59Gk2bNoWfn19Fz4OIiIg0RFTx0LRpU+zYsQPdu3cvsW379u0wNzcHADx8+BBGRkbFtm/YsEHxOiIiAjExMfjzzz+LjYtIT09Hz5494eXlVZFzICIiIg0SVTwEBwdj+vTpyMjIwPvvv48mTZogOzsb27dvx759+xAeHg4AOHnyJFxcXJQeZ8mSJViwYEGJAZUtW7ZEaGgoZs6cifHjx1f8bIiIiEjtRBUP06ZNQ4MGDRAWFoa9e/cq3m/RogXWrl2LDz/8EAAQGBiIevXqKT3O7du3lT7Kqa+vjzt3OKKViIiouhM9w+S4cePw4Ycf4vbt27h79y6aNWuGFi1aFBvPYG1tXeYxHBwcsGTJEnTv3r1YEfHs2TMsWbIEDg4Oqp8BERERaZTo4gF4tZJmy5YtK7yWxeLFi9GnTx9YWlqid+/eigGT+/btQ35+Pvbv31+h4xIREZWmsutKrJpZHzkAGhvUr7FrVKiD6OLh4cOH2LdvH9LS0lBQUFBsm0QiwezZs8s9hp+fH86dO4f58+fj5MmTiisYPXr0wJdffgl7e3vVz4CoFuKHFBFVZ6KKh9OnT6Nv37548OBBqdvFFg8A0K5dO2zZskV0gkQ1Ef/4E1FtJmqGyeDgYFhbW+Ps2bMoKCiAXC4v9lVUVKRSULlcjsTERBw/fhxPnjypUOJERESkHaKKhytXrmD+/Pno0KED6tSpU6mAq1evRtOmTeHs7AxfX19cu3YNAODv74+VK1dW6thERESkfqKKB0tLSzx//rzSwdauXYtp06bB398f27ZtgyAIim3vvPMOfv3110rHICIiIvUSVTzMnTsXixYtwsOHDysVbNmyZfjkk08QERFRbJ0M4NUql6+vQhAREVH1JWrAZFRUFLKysmBjYwMvLy8YGxsX2y6RSLBx48Zyj5OSkoKePXuWuq1BgwZKB2QSERFR9SGqeDh16hQkEgkaNWqES5culdgudtVMU1NTpKamlrrt2rVraN68uajjEBERkfaIKh5SUlKqJFjfvn0RFhYGHx8fWFlZAXhVeOTm5mL58uXw9/evkjhERESkPirNMFlZ8+fPx5EjR+Do6AgPDw9IJBJMnToVV69eRZMmTTBnzhxNpkNUJs7VQESvl10ob/mFN43KxUN2dnaJGSaBV09klMfExARxcXEIDw/HgQMH0Lp1axQWFmLKlCmYPn06GjVqpGo6REREanPw4EFtp1AtiSoe5HI5vvzyS3z//fdKBzWKnSiqYcOGmD17tugZKYmIiKh6EfWoZnh4OFavXo1PPvkEgiBg5syZ+PLLL2FjY4PWrVtj7dq1ooKFhYVhx44dpW67c+cOwsLCxGdOREREWiGqePjxxx8xZ84cfPbZZwCAAQMGYN68ebhy5QqaN2+OtLQ0UcFCQ0MxdOhQzJgxo8S227dvY968eSqkTkRERNogqni4efMm3NzcIJVKoauri2fPngEA9PT0EBwcjPXr14sOGBgYiFWrVmHgwIGK4xAREVHNIap4MDQ0VAyStLCwKDYTZGFhIe7duyc64KhRo3Do0CGcPn0aXbp0QWZmpoopExERkTaJGjDp4uKCy5cvo2fPnujZsyfmzp2LevXqQVdXF7NmzYKrq6tKQTt16oSYmBi89957cHd3x969eyuUPFFZ+KglEZF6iCoegoODcfPmTQDAvHnzkJCQgJEjRwIArKyssGrVKpUDW1tb48yZMxg6dCg6d+6MqVOnqnwMqv1YABARVT+iiofu3bsrXjdt2hSxsbG4ceMGnj59inbt2kFPT69CwRs2bIioqChMnz4dCxYsED3NNREREWlPhWaYlEgkaNOmjcrtfvzxR7Ru3brYezo6OlixYgW8vLy4qiYREVENILp4kMvliI2NRVpaWqkzTH7wwQflHmP06NFKtw0bNkxsKkRERKRFooqHy5cvw9/fHzdu3IAgCCW2SyQSpcXDpk2b0KdPH5iYmGDTpk1lxpFIJPjXv/4lJqUybd68GYsXL8bly5cRHh6OKVOmKLY9ffoUY8aMQXx8PHR1dbF06VK89957lY5JRET0phBVPEyePBmFhYXYtm0bnJycoK+vLzpAQEAAoqOjYWJigoCAgDL3rariQSaT4ZdffsGiRYtKbFu6dCkaNmyI69evIzk5Ge+88w6uX78OAwODSselkjjgkYio9hFVPCQkJGDDhg0YOHCgygFSUlLQrFkzxWtNcHR0BPBqPMU/bd26FRs3bgQAtG3bFm5ubti/fz8GDx6skdyIiIhqOlHFg6mpKerUqVOhAFZWVqW+1pa0tLRieVhaWiI9PV2LGREREdUsooqH6dOnY/Xq1ejVqxekUmmVBZfL5SXeK+1qwT+5uroqXU8jKyurynKMiIhAREQEACAnJ6dKjlkT8dYDEVVn/KzWPKXFw5w5c4p9f/XqVTg4OKB79+4wNjYutk0ikYha1OrZs2eYN28etm/fjtu3b6OwsLDEcf75XmkSEhLK3UcZS0tL3Lp1C2ZmZgBeXYno2rVrqftOmDABEyZMAAC4ublVOKa28Y8/EdVmteWzuiZRWjzMnz+/1PeTk5NLvCe2eJg8eTK2bNmCvn37YtiwYRW+FVIZgwcPxvfffw83NzckJyfj7NmziIyM1HgeRERENZXS4qG0WwqVtXv3bixdulTtU1FHRkZixowZuH//Pn7//XcsWrQIBw8ehIODA2bMmIGAgAC0adMGUqkUERERaNiwoVrzISIiqk0qNMNkRenr66Ndu3ZqjzN8+HAMHz681G0NGjTA9u3b1Z4DERFRbaV0dGJmZiYGDRpU5oqX+/btw6BBg5CbmysqWEBAAH755RfVsyQiIqJqQ+mVhxUrViAxMRG9evVS2rhnz54ICQnBypUrERYWVm6wr776Ch999BF69OiBnj17wsjIqMQ+Y8eOFZk6ERERaYPS4mHv3r2YOHFimY9OSqVSjB8/Hhs3bhRVPMTHx2P37t3Izs7GoUOHSmyXSCQsHoiIiKo5pcXDjRs34OLiUu4BZDIZvvzyS1HBJk2aBBMTE6xduxb29vZaedqCiIiIKkdp8SCRSEQfROy+V69exY4dO9C7d2/RxyYiIqLqRek9CRsbG8THx5d7gLi4OFhbW4sKZmdnhydPnohOjoiIiKofpcXDe++9hxUrViAvL09p49zcXKxYsQL9+vUTFWzRokWYP38+bt26pXqmVDXu3gVGjAACA4HcXCA0FOjdG7h+XduZERFRDaH0tkVISAh++ukneHl54ZtvvkGvXr1Qt25dAEBBQQH279+Pzz77DBKJBCEhIaKCzZ8/H9nZ2bC1tYWtrW2Jpy0kEgmOHz9eidOhci1YALyeUbOgAFi//tXrAQOA//5Xe3kREVGNobR4MDExwcGDBzFo0CAMGjQIurq6ivUgcnJyUFRUBFtbWxw8eLDEWhfKSKVS2NvbV03mVDH/Wx4dANCkyV+vnZ01nwsREdVIZc4w6eDggMTERPz66684fPiwYunqli1bolu3bhg4cKBKK1geO3asUslSFZg5E+jeHWjYELC3B4KCgCtXAD8/bWdGREQ1RLnTU0ulUgwZMgRDhgzRRD6kCe7uf722sHj1RUREJJLa17Y4ceIEXF1dYWBggBMnTpS7f5cuXdSdEhEREVWC2osHHx8fREdHo2PHjvDx8VE6J4QgCJBIJCgqKlJ3SkRERFQJai8ejh49CgcHBwDAkSNHVJp8ioiIiKoftRcP3t7eitc+Pj7qDkdERERqpnzVKzVo1aoVLly4UOq2xMREtGrVSpPpEBERUQVU6MrD/fv3ER0dDUEQ4OnpKXqeh9TUVDx//rzUbQUFBZx5koiIqAZQuXg4fvw4BgwYAB0dHTx//hy6urrYsWMH/ETOE6BszENcXBwaN26sajpERESkYSoXD9OnT8eyZcsQEBCAwsJCTJ06FcHBwfivkqmNly9fjuXLlwN4VTj07du3xFLcz549w7179zBs2LAKnAIRERFpktLiISgoCAsWLEDDhg2LvZ+amqr4I6+rq4uBAwdi8+bNSgO0atVKcVVi48aNcHNzU0xz/Zq+vj4cHBwwbty4Cp8IEZEyL1++xO3bt1FQUKDtVEikunXrokWLFtDT09N2KlQKpcXDzZs3YWtri2XLlmH48OGK9z08PDB9+nQEBQXh8ePHWLBgATw8PJQG6N+/P/r376/4fs6cObCxsami9N8cq1dxhk+iirp9+zYaNmwIa2trPi5eAwiCgLy8PNy+fZt/L6oppU9b7N27F//3f/+HL774An5+fkhKSgIArFmzBhcvXoSjoyM8PT3x9OlTfP/996KC/fjjj8U6Qk5OTiXTJyIqX0FBAUxMTFg41BASiQQmJia8UlSNlfmo5oABA3DlyhW4u7vDzc0NX375JczNzXH69Gk8fPgQ+fn5iI6OVukRy+PHj8Pb2xv16tVD06ZNUa9ePfj4+IiaupqIqKJYONQs/H1Vb+XO81CvXj0sWrQIsbGxiI6OhoODA6KiomBgYFBiPER5tm/fDl9fX2RnZ2PGjBlYuXIlQkJCkJWVBV9fX+zYsaPCJ0JEpIqvv/4ab731FpydnSGTyRATEwMAGDduHC5fvlyhY2ZkZOD9999XfD98+HA4Oztj+fLlmDNnDg4dOlQlub8WHh6Op0+flrrt5cuX+Pzzz9G2bVs4OjqiY8eO2L9/PwDA2toaubm5VZLD7t27sWjRIgCvriZ7eHjAxcUFJ0+eRO/evfHgwYMqiUPVS5lPW8jlciQnJ6OgoAB2dnY4dOgQfv75Z0ycOBHu7u747rvv0LJlS9HB5syZgz59+mDXrl3Q0fmrbpk3bx769euH2bNnF/sfj4hIHc6cOYOoqCgkJCRAX18fubm5ePHiBQDghx9+qPBxLSwsFP8IyszMxJ9//qnW+WvCw8MxatQo1K9fv8S22bNn4+7du0hMTIS+vj6ysrJw/PjxKs+hX79+6NevHwDg8OHDsLe3x8aNGwEA77zzjkrHKioqglQqrfIcqeopvfJw8eJF2Nvbo127dnBxcUGLFi2wc+dOjBgxAlevXoWVlRWcnJzwzTffoLCwUFSwlJQUfPTRR8UKBwDQ0dHB5MmTkZqaWqmTISIS4+7duzA1NYW+vj4AwNTUFBb/W5rex8cHcXFxAIB169bB1tYWPj4+GD9+PKZMmQIACAgIwNSpU/H222+jVatWioIhNTUVjo6OAIAePXogOzsbMpkMJ0+eREBAgGK/s2fP4u2330b79u3RsWNHPHr0CKmpqXjnnXfg6uoKV1dX/PnnnwCAY8eOwcfHB++//z7s7e0xcuRICIKAlStXIiMjA127dkXXrl2Lnd/Tp0+xdu1afPfdd4pzNDc3x5AhJQde+/v7o0OHDnjrrbcQEREB4NUf8YCAADg6OsLJyUnxuP3KlSvh4OAAZ2dnxVN3GzZswJQpU3D+/Hl8+umn2LdvH2QyGZ49e1bsCsfmzZvRsWNHyGQyTJw4UbEIooGBAebMmQMPDw+cOXOmcr9Y0hilxcOECRPg4uKCu3fvIj8/H1OmTMEHH3yAgoICNGzYECtWrMDx48exZ88etG/fXlSwtm3bKh0kmZOTgzZt2lTsLIiIVNCjRw+kp6fD1tYWkydPLvVf5BkZGfjqq68QHR2NP/74A1evXi22/e7duzh16hSioqLw+eefl2i/e/dutG7dGufPny/2L/AXL15g6NChWLFiBS5cuIBDhw6hXr16aNKkCf744w8kJCRg69atmDp1qqLNuXPnEB4ejsuXL+PmzZs4ffo0pk6dCgsLCxw9ehRHjx4tFvv69euwtLREo0aNyv1ZrF+/HvHx8YiLi8PKlSuRl5eH8+fP486dO0hMTMR///tfjBkzBgCwaNEinDt3DhcvXsSaNWuKHUcmkyEsLAxDhw7F+fPnUa9ePcW2K1euYOvWrTh9+jTOnz8PqVSKLVu2AACePHkCR0dHxMTEoHPnzuXmS9WD0uLh8uXLmDBhAszNzdGwYUMEBwfjyZMnSEtLU+zTvn17nDp1CiEhIaKCff3115g7dy7Onj1b7P2YmBiEhoZi4cKFFTwNIiLxDAwMEB8fj4iICJiZmWHo0KHYsGFDsX1iY2Ph7e0NY2Nj6OnpYfDgwcW2+/v7Q0dHBw4ODsjKyhId+9q1a2jWrBnc3d0BAI0aNYKuri5evnyJ8ePHw8nJCYMHDy427qJjx45o0aIFdHR0IJPJqvQq7cqVK9G+fXt4enoiPT0dycnJaNWqFW7evImgoCD85z//URQhzs7OGDlyJDZv3gxdXfFzDB4+fBjx8fFwd3eHTCbD4cOHcfPmTQCAVCrFoEGDqux8SDOU/vbd3d2xaNEiNG7cGHXr1sWqVatgYmJS6pMVr6vS8ixZsgQFBQXw9PREy5YtYW5ujqysLKSnp8Pc3ByLFy/G4sWLAbwaaauO+3NERMCrP1o+Pj7w8fGBk5MTNm7ciICAAMV2QRDKbP/6doCYff9OEIRSnyRYvnw5zM3NceHCBcjlctStW7fUWFKptNxbxW3atEFaWhoePXpU5sD2Y8eO4dChQzhz5gzq168PHx8fFBQUwMjICBcuXMCBAwewevVqbNu2DevXr8fevXtx4sQJ7N69G1999RUuXbok+pxHjx5d6j8Q69aty3EONZDSKw/r1q3D8+fP4e7uDicnJxw5cgQ7duxQqdr8J6lUCnt7e3Tp0gU2NjaoX78+bGxs0KVLF9jZ2UEqlSq+/jkugoioqly7dg3JycmK78+fPw8rK6ti+3Ts2BHHjx/H/fv3UVhYiF9//bVKYtvb2yMjI0NxBfbRo0coLCxEfn4+mjVrBh0dHfz000+KMQFladiwIR49elTi/fr16+PDDz/E1KlTFQNB7969W2I24Pz8fBgZGaF+/fq4evUqoqOjAQC5ubmQy+UYNGgQvvrqKyQkJEAulyM9PR1du3bF4sWL8eDBAzx+/FjUOfv5+WHHjh3Izs4GANy7d48LIdZwSisBa2trnDhxAk+fPsWLFy+qZNGqY8eOVfoYYgQGBuLw4cPQ19eHgYEBVqxYATc3NwCvBhKNGTMG8fHx0NXVxdKlS/Hee+9pJK8qc/cu8MkngJERMG8esGoVEBsLrFwJcNwIUbkeP36MoKAgPHjwALq6umjTpo1isOBrzZs3x8yZM+Hh4QELCws4ODjA0NCw0rHr1KmDrVu3IigoCM+ePUO9evVw6NAhTJ48GYMGDcL27dvRtWtXNGjQoNxjTZgwAb169UKzZs1KjHuYP38+vvzySzg4OKBu3bpo0KABwsLCiu3z7rvvYs2aNXB2doadnR08PT0BAHfu3MGYMWMgl8sBAAsXLkRRURFGjRqF/Px8CIKA6dOni/674ODggPnz56NHjx6Qy+XQ09PD6tWrSxRsVHNIBFWut9UQUVFR6NmzJ/T09BAVFYVp06bhxo0bAICwsDCkpaXhhx9+QHJyMt555x1cv34dBgYGZR7Tzc1NMQK7IlbuOKLS/lPf91W+MSjoVcEAAGPHAuvXv3rt6AgoWaCM6E125coVtGvXTuV2jx8/hoGBAQoLCzFgwACMHTsWAwYMUEOGVJrSfm/lfRZX9rOa/lLWz1Jj9wbu37+PJUuWoFevXnB0dISjoyN69eqFpUuX4v79+1Ua67333lMspuLl5YXbt28rKuitW7di0qRJAF49/eHm5qaYOKXGaNbsr9dNmvz12tlZ87kQ1WKhoaGQyWRwdHSEjY0N/P39tZ0SUbVQ8QEMKjhy5AiGDRuG3NxcmJubw9raGoIg4Pz58zhw4AC++eYbREZGolu3blUee9WqVejTp49iDEVaWlqxS2WWlpZIT0+v8rhqNXMm0L070LAhYG//6krElSvA/1YvJaKqsXTpUm2nQFQtqb14SEpKQr9+/dC+fXvs3btX8XjSa7GxsQgODoa/vz/i4uJgb29f7jFdXV2LPTL6d1lZWYqRu7/88gt+/vnnCq+bERERobgPWu0W8fr7z9HC4tUXEdEbqFp/VtdSar9tMX/+fNjb2+Po0aMlCgfg1Yjmo0ePws7ODgsWLBB1zISEBOTm5pb69bpw2LlzJ2bNmoUDBw7A3Nxc0dbS0rLYKN+0tDSlU2xPmDABcXFxiIuLg5mZmSqnTUREGsLPas1Te/Fw5MgRBAcHo06dOkr30dfXR3BwMA4fPlwlMaOiovDxxx/jwIEDsLa2LrZt8ODBiiXEk5OTcfbsWbz77rtVEpeIiOhNoPbbFjk5OSX+gJfGxsYGeXl5VRJzzJgxqFOnTrFFtg4fPgwTExPMmDEDAQEBaNOmDaRSKSIiIlReHZSIiOhNpvbiwczMDKmpqeXOWZ6SkgJTU9MqiVnWPa8GDRpg+/btVRKHiKg6kUqlcHJyUny/a9cupf94MzAwED3JE9E/qb148PPzw7JlyzBkyBClty6eP3+O5cuXw49PCxBRLaHq3C7lKXPul/+pV68ezp8/X6VxiUqj9jEPs2bNQlJSEvz8/JCQkFBie3x8PPz8/JCUlISZM2eqOx0iojfG48eP4efnB1dXVzg5OeH3338vsc/du3fRpUsXxXwWJ0+eBAAcPHgQXl5ecHV1xeDBg3mVgopR+5UHW1tb7Nq1C8OHD4e7uzuaNm2quIyWmpqKzMxMNG7cGL/99hvs7OzUnQ4RUa317NkzyGQyAK/GkW3fvh07d+5Eo0aNkJubC09PT/Tr16/Ywlw///wzevbsiVmzZqGoqAhPnz5Fbm4u5s+fj0OHDqFBgwb45ptvsGzZMsyZM0dLZ0bVjUYmierWrRuuXbuGtWvX4siRI0hPT4cgCHjrrbcwdepUjBs3DiYmJppIhYio1vrnbYuXL19i5syZOHHiBHR0dHDnzh1kZWWhadOmin3c3d0xduxYvHz5Ev7+/pDJZDh+/DguX76MTp06AQBevHgBLy8vTZ8OVWMaKR4AwNjYGJ999hk+++wzTYUkInqjbdmyBTk5OYiPj4eenh6sra1RUFBQbJ8uXbrgxIkT2Lt3L/71r39hxowZMDIyQvfu3REZGamlzKm647rXRES1VH5+Ppo0aQI9PT0cPXq01GWwb926hSZNmmD8+PH48MMPkZCQAE9PT5w+fRrXr18H8Go14qSkJE2nT9WY2ouH/v3749y5c6L3LygowLJly7BmzRo1ZkVEVPuNHDkScXFxcHNzw5YtW0qd/v/YsWOQyWRwcXHBr7/+imnTpsHMzAwbNmzA8OHD4ezsDE9PT1y9elULZ0DVldpvW1haWsLT0xMymQwjR45E586d4ezsDF3dv0JnZGQgNjYWe/bswW+//YbmzZtj/etlpomIaiAxj1ZWtX8+EWFqaoozZ86Uue/o0aMxevToEtt9fX1x9uzZqk+SagW1Fw/fffcdgoODER4ejtDQUOTn50MikaBRo0bQ19fH/fv38fLlSwiCgI4dOyI8PBz/+te/FKtgEhERUfWikQGTrVu3xnfffYdvv/0WZ86cQUxMDDIyMlBQUAATExPY29ujS5cuxZbKJiIioupJY09bAECdOnXg7e0Nb29vTYatFrRxCZOIiEgdeG+AiIiIVMLigYiIiFTC4oGIiIhUotExD0REpB55eXmKlYkzMzMhlUphZmYGAIiNjVW6qjFRRbB4ICJSg8Ap26r0eKtXDSlzu4mJiWJdi9DQUBgYGCAkJESxvbCwsNj8OkSVwZ5ERFRLBQQEwNjYGOfOnYOrqysaNmxYrKhwdHREVFQUrK2tsXnzZqxcuRIvXryAh4cH/u///g9SqVTLZ0DVFcc8EBHVYklJSTh06BC+/fZbpftcuXIFW7duxenTp3H+/HlIpVJs2bJFg1lSTcMrD0REtdjgwYPLvYJw+PBhxMfHw93dHQDw7NkzNGnSRBPpUQ3F4oGIqBZr0KCB4rWuri7kcrni+9fLcwuCgNGjR2PhwoUaz49qJt620Ia7d4ERI4DAQCA3FwgNBXr3Bv63/C0RkTpYW1sjISEBAJCQkICUlBQAgJ+fH3bs2IHs7GwAwL1790pdvpvoNV550IYFC4DIyFevCwqA1yuIDhgA/Pe/2suLiGq1QYMGYdOmTZDJZHB3d4etrS0AwMHBAfPnz0ePHj0gl8uhp6eH1atXc70hUorFgzY0a/bX67/fV3R21nwuRKQW5T1aqU6hoaGlvl+vXj0cPHiw1G1Dhw7F0KFD1ZgV1SYsHrRh5kyge3egYUPA3h4ICgKuXAH+N8ELERFRdcbiQVv+N6oZAGBh8eqLiIioBuCASSIiIlIJiwcieiMIgqDtFEgF/H1VbyweiKjWq1u3LvLy8vgHqYYQBAF5eXmoW7eutlMhJTjmgYhqvRYtWuD27dvIycnRdiokUt26ddGiRQttp0FKsHggolpPT08PNjY22k6DqNbgbQsiIiJSCYsHIiIiUgmLByIiIlKJRODwY1FMTU1hbW1d5cfNycmBmZmZVtoztuZjV7Y9YzN2TWlf2djKpKamIjc3V+l2dX1Wv4nK/FkLpFUdOnTQWnvGrnntGZuxa0r7ysam6o23LYiIiEglLB6IiIhIJSwetGzChAlaa8/YNa89YzN2TWlf2dhUvXHAJBEREamEVx6IiIhIJSwetCQpKQleXl6wtbWFl5cXkpOTRbcNCQmBjY0NJBIJEhMTVY6dl5eH3r17w87ODs7Ozhg4cKBKc/77+/ujffv2cHFxwTvvvIPz58+rnMO8efMqlL+1tTXs7e0hk8kgk8lw4MABldoXFBTgo48+Qtu2beHk5CT60mpqaqoipkwmg7W1NYyNjVWKHRUVBRcXF8hkMjg7O+O3334T3Xbv3r1wdXWFk5MTvL29kZKSUub+yvqImH6nrK3YflfafmL7nLIYYvtceTmW1++UtRfT75S1FdvnSmsvtt8piy22zylrL6bflfW7rcznHFVz2n7c403VtWtX4aeffhIEQRB++uknoWvXrqLbnjx5UkhLSxOsrKyE//73vyrHzsvLE44ePar4PiQkRBg7dqzo9g8ePFC83rVrl+Di4qJS/Pj4eOHdd98VLC0tVc6/ouf8WlBQkBAcHCzI5XJBEAQhMzOzQseZNm2aEBgYKHp/uVwuNG7cWJH7hQsXBAMDA6GoqKjctvfu3RNMTEyEa9euCYLwqr/07NmzzDbK+oiYfqesrdh+V9p+Yvucshhi+1xZOYrpd8rai+l3ytqK7XNifr7K+l1pbVXpc6W1F9vvyvrdVuZzjqo3Fg9akJWVJRgaGgqFhYWCIAhCYWGhYGhoKGRnZ6t0nMr+IX1tx44dgp+fX4Xabty4UaXnuQsKCgRPT0/h5s2bFcq/Muf86NEjwdDQUHj06FGF2r/2/PlzwdTUVIiPjxfdRi6XC8bGxsKpU6cEQRCE48ePC23bthXVNjY2VnBwcFB8n5eXJwAQcnJyym3795+Xqv1O2c9a7O+grP3K63NltRXT5/7ZXtV+V5HiobR9K9LnlMUS0+/+WTyo2uf+3r6i/e7177aqPueoeuJtCy1IT09H8+bNIZVKAQBSqRQWFhZIT0/XeC5yuRz//ve/0a9fP5XajRs3DpaWlpg1axY2btwout2cOXMwatSoSq1wOHLkSDg7O2Py5Ml48OCB6HY3btyAiYkJ5s2bBzc3N/j4+ODUqVMqx9+9ezeaN28OV1dX0W0kEgm2bduG/v37w8rKCv7+/qJ/bra2tsjMzMTZs2cBAFu2bAEApKWlqZR3del3mu5zgPb6XVX1OUD1fleZPgdUrN/9/XdbXfobqQeLhzdcUFAQDAwMMGXKFJXa/fDDD0hLS8OCBQswY8YMUW3OnDmDs2fPYvLkyRVJFQBw8uRJXLhwAWfPnoUgCCrlXVhYiJs3b8LFxQVxcXH45ptvMHDgQDx8+FClHNavX4+xY8eq1KawsBALFy7E77//jlu3bmHPnj0YOnQoHj9+XG5bQ0NDbN26FdOnT4ebmxuys7PRuHFj6OnpqZRDdaHJPgdot99VVZ8DVO93lelzQMX6XUV/t1QDafvSx5uouty2+OSTT4Tu3bsLBQUFFT6GIAhC3bp1hdzc3HL3W7hwodCsWTPByspKsLKyEqRSqWBhYSEcOHCgQnEvXrwoWFtbi94/JydH0NXVVdx7FgRBaNeunXD27FnRx7hz545Qv359Uef7d2fPnhXatWtX7D17e3shNjZWpeMIwqt75vr6+sLjx4/L3be63bYQ2+fKi1Fen/t7+4r0u7Lil9fv/t62In2utNhi+93f21akz5V13uX1u3/+bnnbonbjlQctaNKkCWQyGSIjIwEAkZGRcHFxUcsiMsrMmjUL8fHx2LVrF/T19UW3e/z4cbHLjnv27IGxsbGoJw8+//xzZGRkIDU1FampqWjRogUOHDiAHj16iIr95MkT5OfnAwAEQcAvv/wCmUwmOndTU1N07doVf/zxB4BXI8Gzs7PRpk0b0cfYsGED+vTpAxMTE9FtAKBFixa4ffs2rl27BgC4cuUKMjMz0bp1a1HtMzMzAby6LDxz5kxMmjQJDRo0UCkHbfc7bfQ5QLv9rir6HFCxflfZPgeI73el/W613d9IzbRdvbyprly5InTs2FFo27at0LFjR+Hq1aui2wYFBQnNmzcXpFKpYG5uXmxQkxiJiYkCAMHW1lZo37690L59e8Hf319U28zMTMHDw0NwdHQU2rdvL3Tt2lWlgYN/p+qVkxs3bggymUxwcnISHBwchPfff1/IyMhQKeaNGzcEb29vwdHRUXBxcRH27dunUvu2bdsK+/fvV6nNa5s3bxYcHR0FZ2dnwdnZWdi5c6foth9++KFgb28vtGrVSpg0aZLw7NmzMvdX1kfE9DtlbcX2u9L2E9vnSmurSp8Tk2NZ/a609mL7nbLYYvtcWbmX1++UtRXb55S1F9PvyvrdVuZzjqo3zjBJREREKuFtCyIiIlIJiwciIiJSCYsHIiIiUgmLByIiIlIJiwciIiJSCYsHohrE2toaS5curfQ+RESVweKBSANycnIwefJkWFtbQ19fH+bm5vDz81NMHlSVKjsV8z/5+PioPN1wTk4O9PT08PTpUxQWFqJBgwYqr8VBRNWXrrYTIHoTDBo0CE+fPsW6devQpk0bZGdn4/jx48jLy6vyWNVhBr8zZ85AJpOhfv36iImJgbGxMSwtLbWdFhFVEV55IFKzBw8e4OTJk1i0aBH8/PxgZWUFd3d3hISEYNiwYYr9SrvdUNq/+h8/foxRo0bBwMAATZs2LdHmn8fJz8/HhAkT0KRJEzRs2BDe3t6Ii4sr1iY6Ohq+vr5o0KABDA0N4efnh4yMDAQEBOD48eNYvXo1JBIJJBIJUlNTyz3nP//8E506dQIAnDp1SvGaiGoHFg9EamZgYAADAwPs3r0bBQUFlT7esmXL0K5dOyQkJGDevHmYOXMmfvvtt1L3FQQBffr0wZ07dxAVFYVz586hS5cu8PX1xd27dwEAFy5cQNeuXdGmTRucPn0a0dHRGDJkCAoLC7FixQp4eXlhzJgxuHv3Lu7evYuWLVuWGistLQ2NGzdG48aNsWzZMnz//fdo3LgxZs6ciV27dqFx48ZVejuFiLSHty2I1ExXVxcbNmzA+PHjERERARcXF3Tq1AmDBw+Gh4eHysfz8PDArFmzAAC2trY4e/Ysli1bhoEDB5bY9+jRozh//jxycnJQr149AMBXX32FPXv24KeffsKnn36KxYsXo3379oiIiFC0a9euneJ1nTp1UL9+fTRt2rTMvCwsLHD+/Hk8fPgQbm5uiI6OhoGBAWQyGfbu3QtLS0sYGBiofL5EVP3wygORBgwaNAgZGRnYs2cPevXqhT///BOenp5YsGCBysfy8vIq8f3ly5dL3Tc+Ph5Pnz6FmZmZ4gqIgYEBEhMTcePGDQDAuXPn4Ofnp/pJ/YOuri6sra1x9epVuLu7o3379sjMzIS5uTm6dOkCa2trmJqaVjoOEWkfrzwQaUjdunXRvXt3dO/eHXPmzMG4ceMQGhqKkJAQ1KlTBzo6OvjnOnUvX76sVEy5XA5zc3OcPHmyxLZGjRoBQImYFfXWW2/h1q1bePnyJeRyOQwMDFBYWIjCwkIYGBjAysoKly5dqpJYRKRdLB6ItMTBwQGFhYUoKChAnTp1YGZmphiHAAAFBQW4evUqXFxcirWLjo4u8f3fbzP8naurK7KysqCjo4NWrVop3efIkSNK86xTpw6KiorKPZ99+/bh5cuX8PPzw+LFi9GhQwcMGzYMAQEBePfdd6Gnp1fuMYioZuBtCyI1y8vLg6+vLzZv3oyLFy8iJSUF27dvx+LFi+Hn56e4AuDr64stW7bg2LFjuHTpEsaOHVvqlYfo6GgsXLgQycnJWLt2LTZt2oTp06eXGrtbt27o1KkT+vfvj/379yMlJQVnzpzB3LlzFVcjZsyYgXPnzmHChAm4cOECrl27hh9++EExL4O1tTViY2ORmpqK3NxcyOXyUmNZWVnBwMAAWVlZ6N+/PywtLXH58mUMHDgQbdq0gZWVVVX8OImoGmDxQKRmBgYG8PT0xIoVK+Dt7Y233noLM2fOxIgRI7B161bFfl988QV8fX3Rv39/9OjRA507d4arq2uJ43388ce4ePEiXFxc8OWXXyIsLAzvv/9+qbElEgn27dsHX19fjB8/HnZ2dhgyZAiuXbsGCwsLAIBMJsOhQ4dw9epVeHp6wsPDA7/88oviSsHr2yoODg4wMzMrc7KnY8eOwd3dHXXr1kVMTAyaN2+uiENEtYdEqKobnkRULTRr1gxz587FpEmTtJ0KEdVSHPNAVEs8ffoUp0+fRlZWFhwdHbWdDhHVYrxtQVRLREREYNiwYZg2bRo6d+6s7XSIqBbjbQsiIiJSCa88EBERkUpYPBAREZFKWDwQERGRSlg8EBERkUpYPBAREZFKWDwQERGRSv4fHB1F0svzR2MAAAAASUVORK5CYII=",
                        "text/plain": [
                            "<Figure size 576x324 with 2 Axes>"
                        ]
                    },
                    "metadata": {},
                    "output_type": "display_data"
                }
            ],
            "source": [
                "avg_sorted = avg.sort_values(\"pct_change\").reset_index()\n",
                "fig = plt.figure(figsize=(8, 4.5))\n",
                "gs = fig.add_gridspec(1, 10)\n",
                "ax = fig.add_subplot(gs[0:8])\n",
                "sns.barplot(data=avg_sorted, ax=ax,\n",
                "            x=\"subject\", y=\"pct_change\", hue=\"good_class\", dodge=False)\n",
                "plt.xticks(ticks=np.arange(len(avg_sorted)), labels=np.arange(len(avg_sorted)))\n",
                "sns.move_legend(ax, 0, title=\"Significant Classifier\")\n",
                "avg_ax = fig.add_subplot(gs[8:10])\n",
                "avg_ax.sharey(ax)\n",
                "sns.barplot(data=avg_sorted, ax = avg_ax, x=\"good_class\", y=\"pct_change\", hue=\"good_class\", errcolor='black', ci=68, dodge=False)\n",
                "avg_ax.legend_.remove()\n",
                "avg_ax.set_xticks([])\n",
                "avg_ax.set_xlabel(\"\")\n",
                "ax.tick_params(labelsize=11)\n",
                "avg_ax.tick_params(labelleft=False, labelsize=12, labelrotation=60)\n",
                "avg_ax.set_ylabel(\"\")\n",
                "ax.set_xlabel(\"Subject #\", fontsize=14)\n",
                "ax.set_ylabel(\"% Change In Recall \\n(Optimized vs. Impaired)\")\n",
                "mask05 = avg_sorted['p'] <= 0.05\n",
                "mask1 = (avg_sorted['p'] <= 0.1)&(avg_sorted['p'] > 0.05)\n",
                "ax.scatter(np.arange(len(avg))[mask05], avg_sorted[\"pct_change\"][mask05]+np.sign(avg_sorted[\"pct_change\"])[mask05]*3, marker='$**$', c='r', s=30)\n",
                "ax.scatter(np.arange(len(avg))[mask1], avg_sorted[\"pct_change\"][mask1]+np.sign(avg_sorted[\"pct_change\"])[mask1]*3, marker='$*$', c='r', s=30)\n",
                "# plt.savefig(\"FinalReport/sorted_pct_change.pdf\", dpi=300, bbox_inches=\"tight\")"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 68,
            "id": "953fe99a-00eb-4b12-a8b1-38e8a43da006",
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "text/plain": [
                            "Ttest_1sampResult(statistic=1.8336557273067577, pvalue=0.12617294117100103)"
                        ]
                    },
                    "execution_count": 68,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": [
                "scp.stats.ttest_1samp(avg.query(\"good_class\")[\"pct_change\"], 0)"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 69,
            "id": "8ccfd238-740a-4997-a337-54acf1a17ac8",
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "text/plain": [
                            "Ttest_1sampResult(statistic=-1.33684105908533, pvalue=0.20259335569609468)"
                        ]
                    },
                    "execution_count": 69,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": [
                "scp.stats.ttest_1samp(avg.query(\"~good_class\")[\"pct_change\"], 0)"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 72,
            "id": "cc1f354b-4c88-43dd-ad2b-1fa5dcd992fc",
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "text/plain": [
                            "Ttest_indResult(statistic=2.73231671459519, pvalue=0.013232250607167324)"
                        ]
                    },
                    "execution_count": 72,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": [
                "scp.stats.ttest_ind(avg.query(\"good_class\")[\"pct_change\"], avg.query(\"~good_class\")[\"pct_change\"])"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 80,
            "id": "fe942d92-5362-4430-b580-52aa65a21984",
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "text/plain": [
                            "(0.37426300274990937, 0.09463227218196262)"
                        ]
                    },
                    "execution_count": 80,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": [
                "scp.stats.pearsonr(avg[\"CL_auc\"], avg[\"pct_change\"])"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 34,
            "id": "d3e1d629-3316-47c6-aada-07caa3ed3547",
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "image/png": 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",
                        "text/plain": [
                            "<Figure size 432x288 with 1 Axes>"
                        ]
                    },
                    "metadata": {},
                    "output_type": "display_data"
                }
            ],
            "source": [
                "plt.rcParams['savefig.facecolor']='none'\n",
                "plt.figure(figsize=(6, 4))\n",
                "ax = sns.scatterplot(data=avg, x=\"CL_auc\", y=\"pct_change\", ci=68, hue=\"good_class\")\n",
                "sns.regplot(data=avg, x=\"CL_auc\", y=\"pct_change\", ci=68, scatter=False, ax=ax)\n",
                "sns.move_legend(ax, 0, title=\"Significant Classifier\")\n",
                "# ax.legend_.remove()\n",
                "ax.spines['top'].set_visible(False)\n",
                "ax.spines['right'].set_visible(False)\n",
                "ax.tick_params(labelsize=14)\n",
                "ax.set_xlabel(\"Closed-loop AUC\")\n",
                "ax.set_ylabel(\"$\\Delta$ Recall\", labelpad=-10)\n",
                "plt.grid(True)\n",
                "r, p = scp.stats.pearsonr(avg[\"CL_auc\"], avg[\"pct_change\"])\n",
                "ax.annotate(f\"Pearson R: {r}\\n p:{p}\", xy=(.1, .1))\n",
                "plt.savefig('FinalReport/auc_test_pct_change_correlation.png', dpi=300, bbox_inches='tight')"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 79,
            "id": "6e39e8ab-8cf0-4a8d-878b-4aacddbaf4af",
            "metadata": {
                "tags": []
            },
            "outputs": [
                {
                    "data": {
                        "text/plain": [
                            "<AxesSubplot:xlabel='trial', ylabel='recalled'>"
                        ]
                    },
                    "execution_count": 79,
                    "metadata": {},
                    "output_type": "execute_result"
                },
                {
                    "data": {
                        "image/png": 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",
                        "text/plain": [
                            "<Figure size 432x288 with 1 Axes>"
                        ]
                    },
                    "metadata": {},
                    "output_type": "display_data"
                }
            ],
            "source": [
                "sns.lineplot(data=final, x = 'trial', y='recalled', ci=95)"
            ]
        },
        {
            "cell_type": "markdown",
            "id": "f38160f0-cd42-48c3-8271-070ece5808e0",
            "metadata": {},
            "source": [
                "## Closed-Loop Classifier Performance"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 3,
            "id": "0754c652-2e36-4597-aa3c-ecd58de420bf",
            "metadata": {},
            "outputs": [],
            "source": [
                "from compute_scalp_features import compute_scalp_features"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 4,
            "id": "163842f6-822f-4d45-ad74-4c6397fadcd3",
            "metadata": {},
            "outputs": [],
            "source": [
                "ro_futures = client.map(compute_scalp_features, subject_list, \n",
                "                        settings_path = \"settings/phase1_settings.pkl\",\n",
                "                        save_path = '/scratch/nicls_intermediate/read_only/encoding_powers/')"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 5,
            "id": "c52c1227-6300-45d0-a49e-cf17bd6d61b0",
            "metadata": {
                "collapsed": true,
                "jupyter": {
                    "outputs_hidden": true
                },
                "tags": []
            },
            "outputs": [
                {
                    "data": {
                        "text/plain": [
                            "[<Future: pending, key: compute_scalp_features-f6802bada31e1cd624adda28a0bce2b0>,\n",
                            " <Future: pending, key: compute_scalp_features-ed1e78f9e90d90404eeecf5cf5b2676d>,\n",
                            " <Future: pending, key: compute_scalp_features-e53559513f56f577a955fe7c2f92c7ff>,\n",
                            " <Future: pending, key: compute_scalp_features-152ad1c2e1b2fb2c0e73b6e852f51f1b>,\n",
                            " <Future: pending, key: compute_scalp_features-55adbcd600f8796976cabb735ebc0fa8>,\n",
                            " <Future: pending, key: compute_scalp_features-39a78b493de1b6b44bd52d90565a1a57>,\n",
                            " <Future: pending, key: compute_scalp_features-17e743540b095edb2247a982239f4b5d>,\n",
                            " <Future: pending, key: compute_scalp_features-a745e55d28222cb1cdf25961f62b5332>,\n",
                            " <Future: pending, key: compute_scalp_features-229df7e74d53468405a9c85a3b583e82>,\n",
                            " <Future: pending, key: compute_scalp_features-38bb26bc2d6a9e6d75420753d12588c3>,\n",
                            " <Future: pending, key: compute_scalp_features-9fc746dfbbb9a2e3ac981e168d4d6a08>,\n",
                            " <Future: pending, key: compute_scalp_features-afcdc1bfeafb4d8a866d1ceb424e8b8c>,\n",
                            " <Future: pending, key: compute_scalp_features-776cd489cfa3f8ca4707337715dc3ba8>,\n",
                            " <Future: pending, key: compute_scalp_features-7e440f165d712cfc113607904bb99d07>,\n",
                            " <Future: pending, key: compute_scalp_features-6ba2045e2ac14d5261434ed1211d10ab>,\n",
                            " <Future: pending, key: compute_scalp_features-20e61a80f405cde37942d4a5983887c3>,\n",
                            " <Future: pending, key: compute_scalp_features-95b784b7e203a4c9fef427b2f4fa1c2c>,\n",
                            " <Future: pending, key: compute_scalp_features-faef90390e293c370849fbfb7d8dc61e>,\n",
                            " <Future: pending, key: compute_scalp_features-ae885f4a4932c801bd649db2bc59d7ac>,\n",
                            " <Future: pending, key: compute_scalp_features-cc58ecb21b48edfba7b1bd2dac38070b>,\n",
                            " <Future: pending, key: compute_scalp_features-66d19a882b82d4955dc2cb0b46015aba>,\n",
                            " <Future: pending, key: compute_scalp_features-356610fcd8d3d681fe440d9965aff986>,\n",
                            " <Future: pending, key: compute_scalp_features-d5ea723544251afe944c8bc1312d38dc>]"
                        ]
                    },
                    "execution_count": 5,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": [
                "ro_futures"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 124,
            "id": "02247bca-ca1c-408a-b0b0-0b1c816fe51f",
            "metadata": {},
            "outputs": [],
            "source": [
                "client.shutdown()"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": null,
            "id": "ffa3f302-bc5f-4501-91b6-36b35750e84a",
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "text/plain": [
                            "Settings(rel_start=300, rel_stop=1300, buffer_time=500, freqs=array([  6.        ,   9.75368156,  15.85571732,  25.77526961,\n",
                            "        41.90062864,  68.11423148, 110.72742057, 180.        ]), width=5, experiment='NiclsCourierReadOnly', clean=0, save=1, reference='average')"
                        ]
                    },
                    "execution_count": 91,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": [
                "da.Settings.Load(\"settings/phase1_settings.pkl\")"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 98,
            "id": "f0354608-8885-4be0-a029-66fa1feb4ff3",
            "metadata": {},
            "outputs": [],
            "source": [
                "r = cml.CMLReader(\"LTP462\",\"NiclsCourierReadOnly\", 0)"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 101,
            "id": "5083adcf-7a9f-439e-8be8-3ae0efc79e2d",
            "metadata": {
                "collapsed": true,
                "jupyter": {
                    "outputs_hidden": true
                },
                "tags": []
            },
            "outputs": [
                {
                    "name": "stdout",
                    "output_type": "stream",
                    "text": [
                        "Extracting EDF parameters from /protocols/ltp/subjects/LTP462/experiments/NiclsCourierReadOnly/sessions/0/ephys/current_processed/LTP462_session_0.bdf...\n",
                        "BDF file detected\n"
                    ]
                },
                {
                    "ename": "FileNotFoundError",
                    "evalue": "[Errno 2] No such file or directory: '/protocols/ltp/subjects/LTP462/experiments/NiclsCourierReadOnly/sessions/0/ephys/current_processed/LTP462_session_0.bdf'",
                    "output_type": "error",
                    "traceback": [
                        "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
                        "\u001b[0;31mFileNotFoundError\u001b[0m                         Traceback (most recent call last)",
                        "\u001b[0;32m/tmp/2888767.1.jupyter.q/ipykernel_84349/1626103553.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0mevs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mload\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"events\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0mword_evs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mevs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mevs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtype\u001b[0m\u001b[0;34m==\u001b[0m\u001b[0;34m'WORD'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m&\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mevs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0meegoffset\u001b[0m\u001b[0;34m!=\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0mr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mload_eeg\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mword_evs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m100\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
                        "\u001b[0;32m~/anaconda3/envs/py38/lib/python3.8/site-packages/cmlreaders/cmlreader.py\u001b[0m in \u001b[0;36mload_eeg\u001b[0;34m(self, events, rel_start, rel_stop, scheme, clean)\u001b[0m\n\u001b[1;32m    313\u001b[0m             })\n\u001b[1;32m    314\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 315\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mload\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'eeg'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    316\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    317\u001b[0m     \u001b[0;34m@\u001b[0m\u001b[0mclassmethod\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
                        "\u001b[0;32m~/anaconda3/envs/py38/lib/python3.8/site-packages/cmlreaders/cmlreader.py\u001b[0m in \u001b[0;36mload\u001b[0;34m(self, data_type, **kwargs)\u001b[0m\n\u001b[1;32m    223\u001b[0m             )\n\u001b[1;32m    224\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 225\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mcls\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mload\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    226\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    227\u001b[0m     def load_eeg(self, events: Optional[pd.DataFrame] = None,\n",
                        "\u001b[0;32m~/anaconda3/envs/py38/lib/python3.8/site-packages/cmlreaders/readers/eeg.py\u001b[0m in \u001b[0;36mload\u001b[0;34m(self, **kwargs)\u001b[0m\n\u001b[1;32m    599\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    600\u001b[0m         \u001b[0mevents\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_eegfile_absolute\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mevents\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcopy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 601\u001b[0;31m         return self.as_timeseries(events, kwargs[\"rel_start\"],\n\u001b[0m\u001b[1;32m    602\u001b[0m                                   kwargs[\"rel_stop\"])\n\u001b[1;32m    603\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
                        "\u001b[0;32m~/anaconda3/envs/py38/lib/python3.8/site-packages/cmlreaders/readers/eeg.py\u001b[0m in \u001b[0;36mas_timeseries\u001b[0;34m(self, events, rel_start, rel_stop)\u001b[0m\n\u001b[1;32m    706\u001b[0m             \u001b[0;31m# if scalp EEG, info is an MNE Info object; if iEEG, info is a list\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    707\u001b[0m             \u001b[0;31m# of contacts\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 708\u001b[0;31m             \u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minfo\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mreader\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    709\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    710\u001b[0m             \u001b[0mattrs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m{\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
                        "\u001b[0;32m~/anaconda3/envs/py38/lib/python3.8/site-packages/cmlreaders/readers/eeg.py\u001b[0m in \u001b[0;36mread\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    412\u001b[0m         \u001b[0;31m# To read BioSemi data (.bdf)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    413\u001b[0m         \u001b[0;32melif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdtype\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'.bdf'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 414\u001b[0;31m             eeg = mne.io.read_raw_bdf(self.filename,\n\u001b[0m\u001b[1;32m    415\u001b[0m                                       \u001b[0meog\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'EXG1'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'EXG2'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'EXG3'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'EXG4'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    416\u001b[0m                                       \u001b[0mmisc\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'EXG5'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'EXG6'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'EXG7'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'EXG8'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
                        "\u001b[0;32m~/anaconda3/envs/py38/lib/python3.8/site-packages/mne/io/edf/edf.py\u001b[0m in \u001b[0;36mread_raw_bdf\u001b[0;34m(input_fname, eog, misc, stim_channel, exclude, infer_types, preload, verbose)\u001b[0m\n\u001b[1;32m   1447\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mext\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0;34m'bdf'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1448\u001b[0m         \u001b[0;32mraise\u001b[0m \u001b[0mNotImplementedError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mf'Only BDF files are supported, got {ext}.'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1449\u001b[0;31m     return RawEDF(input_fname=input_fname, eog=eog, misc=misc,\n\u001b[0m\u001b[1;32m   1450\u001b[0m                   \u001b[0mstim_channel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mstim_channel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mexclude\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mexclude\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1451\u001b[0m                   infer_types=infer_types, preload=preload, verbose=verbose)\n",
                        "\u001b[0;32m<decorator-gen-215>\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, input_fname, eog, misc, stim_channel, exclude, infer_types, preload, verbose)\u001b[0m\n",
                        "\u001b[0;32m~/anaconda3/envs/py38/lib/python3.8/site-packages/mne/io/edf/edf.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, input_fname, eog, misc, stim_channel, exclude, infer_types, preload, verbose)\u001b[0m\n\u001b[1;32m    138\u001b[0m         \u001b[0mlogger\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0minfo\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Extracting EDF parameters from {}...'\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minput_fname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    139\u001b[0m         \u001b[0minput_fname\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mos\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpath\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mabspath\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minput_fname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 140\u001b[0;31m         info, edf_info, orig_units = _get_info(input_fname, stim_channel, eog,\n\u001b[0m\u001b[1;32m    141\u001b[0m                                                \u001b[0mmisc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mexclude\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minfer_types\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    142\u001b[0m                                                preload)\n",
                        "\u001b[0;32m~/anaconda3/envs/py38/lib/python3.8/site-packages/mne/io/edf/edf.py\u001b[0m in \u001b[0;36m_get_info\u001b[0;34m(fname, stim_channel, eog, misc, exclude, infer_types, preload)\u001b[0m\n\u001b[1;32m    382\u001b[0m     \u001b[0mmisc\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmisc\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mmisc\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    383\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 384\u001b[0;31m     \u001b[0medf_info\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0morig_units\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_read_header\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfname\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mexclude\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minfer_types\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    385\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    386\u001b[0m     \u001b[0;31m# XXX: `tal_ch_names` to pass to `_check_stim_channel` should be computed\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
                        "\u001b[0;32m~/anaconda3/envs/py38/lib/python3.8/site-packages/mne/io/edf/edf.py\u001b[0m in \u001b[0;36m_read_header\u001b[0;34m(fname, exclude, infer_types)\u001b[0m\n\u001b[1;32m    369\u001b[0m     \u001b[0mlogger\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0minfo\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'%s file detected'\u001b[0m \u001b[0;34m%\u001b[0m \u001b[0mext\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mupper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    370\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mext\u001b[0m \u001b[0;32min\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;34m'bdf'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'edf'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 371\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0m_read_edf_header\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfname\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mexclude\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minfer_types\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    372\u001b[0m     \u001b[0;32melif\u001b[0m \u001b[0mext\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'gdf'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    373\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0m_read_gdf_header\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfname\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mexclude\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
                        "\u001b[0;32m~/anaconda3/envs/py38/lib/python3.8/site-packages/mne/io/edf/edf.py\u001b[0m in \u001b[0;36m_read_edf_header\u001b[0;34m(fname, exclude, infer_types)\u001b[0m\n\u001b[1;32m    586\u001b[0m     \u001b[0medf_info\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m{\u001b[0m\u001b[0;34m'events'\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    587\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 588\u001b[0;31m     \u001b[0;32mwith\u001b[0m \u001b[0mopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfname\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'rb'\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mfid\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    589\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    590\u001b[0m         \u001b[0mfid\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m8\u001b[0m\u001b[0;34m)\u001b[0m  \u001b[0;31m# version (unused here)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
                        "\u001b[0;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: '/protocols/ltp/subjects/LTP462/experiments/NiclsCourierReadOnly/sessions/0/ephys/current_processed/LTP462_session_0.bdf'"
                    ]
                }
            ],
            "source": [
                "evs = r.load(\"events\")\n",
                "word_evs = evs[(evs.type=='WORD')&(evs.eegoffset!=-1)]\n",
                "r.load_eeg(word_evs, 0, 100)"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 92,
            "id": "20d64326-d443-4115-9653-93b8295dbb28",
            "metadata": {},
            "outputs": [
                {
                    "name": "stderr",
                    "output_type": "stream",
                    "text": [
                        "  File \"/home1/jrudoler/anaconda3/envs/py38/lib/python3.8/site-packages/dask/utils.py\", line 41, in apply\n",
                        "    return func(*args, **kwargs)\n",
                        "  File \"/home1/jrudoler/NICLS/compute_scalp_features.py\", line 36, in compute_scalp_features\n",
                        "    eeg = r.load_eeg(word_evs,\n",
                        "  File \"/home1/jrudoler/anaconda3/envs/py38/lib/python3.8/site-packages/cmlreaders/cmlreader.py\", line 315, in load_eeg\n",
                        "    return self.load('eeg', **kwargs)\n",
                        "  File \"/home1/jrudoler/anaconda3/envs/py38/lib/python3.8/site-packages/cmlreaders/cmlreader.py\", line 225, in load\n",
                        "    return cls.load(**kwargs)\n",
                        "  File \"/home1/jrudoler/anaconda3/envs/py38/lib/python3.8/site-packages/cmlreaders/readers/eeg.py\", line 601, in load\n",
                        "    return self.as_timeseries(events, kwargs[\"rel_start\"],\n",
                        "  File \"/home1/jrudoler/anaconda3/envs/py38/lib/python3.8/site-packages/cmlreaders/readers/eeg.py\", line 708, in as_timeseries\n",
                        "    data, info = reader.read()\n",
                        "  File \"/home1/jrudoler/anaconda3/envs/py38/lib/python3.8/site-packages/cmlreaders/readers/eeg.py\", line 414, in read\n",
                        "    eeg = mne.io.read_raw_bdf(self.filename,\n",
                        "  File \"/home1/jrudoler/anaconda3/envs/py38/lib/python3.8/site-packages/mne/io/edf/edf.py\", line 1449, in read_raw_bdf\n",
                        "    return RawEDF(input_fname=input_fname, eog=eog, misc=misc,\n",
                        "  File \"<decorator-gen-215>\", line 24, in __init__\n",
                        "  File \"/home1/jrudoler/anaconda3/envs/py38/lib/python3.8/site-packages/mne/io/edf/edf.py\", line 140, in __init__\n",
                        "    info, edf_info, orig_units = _get_info(input_fname, stim_channel, eog,\n",
                        "  File \"/home1/jrudoler/anaconda3/envs/py38/lib/python3.8/site-packages/mne/io/edf/edf.py\", line 384, in _get_info\n",
                        "    edf_info, orig_units = _read_header(fname, exclude, infer_types)\n",
                        "  File \"/home1/jrudoler/anaconda3/envs/py38/lib/python3.8/site-packages/mne/io/edf/edf.py\", line 371, in _read_header\n",
                        "    return _read_edf_header(fname, exclude, infer_types)\n",
                        "  File \"/home1/jrudoler/anaconda3/envs/py38/lib/python3.8/site-packages/mne/io/edf/edf.py\", line 588, in _read_edf_header\n",
                        "    with open(fname, 'rb') as fid:\n"
                    ]
                }
            ],
            "source": [
                "da.print_traceback(da.get_exceptions(ro_futures, subject_list), 'LTP462')"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 6,
            "id": "f7ef6b25-cd4c-4f5b-b384-8fb0b824395e",
            "metadata": {},
            "outputs": [],
            "source": [
                "bipolar_ro_futures = client.map(compute_scalp_features, subject_list, \n",
                "                        settings_path = \"/home1/jrudoler/NICLS/NICLS_RO_encoding_features_bipolar.pkl\",\n",
                "                        save_path = '/scratch/nicls_intermediate/read_only/bipolar_encoding_powers/')"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 36,
            "id": "d160a9e7-44b6-4b75-91fb-53026c6222da",
            "metadata": {},
            "outputs": [],
            "source": [
                "cl_futures = client.map(compute_scalp_features, subject_list, \n",
                "                        settings_path = \"/home1/jrudoler/NICLS/NICLS_CL_encoding_features_cleaned.pkl\",\n",
                "                        save_path = '/scratch/nicls_intermediate/closed_loop/clean_encoding_powers/')"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 8,
            "id": "ddebbdb0-f567-4168-bcf1-34f6abf6f7d4",
            "metadata": {
                "tags": []
            },
            "outputs": [],
            "source": [
                "bipolar_cl_futures = client.map(compute_scalp_features, subject_list, \n",
                "                        settings_path = \"/home1/jrudoler/NICLS/NICLS_CL_encoding_features_bipolar.pkl\",\n",
                "                        save_path = '/scratch/nicls_intermediate/closed_loop/bipolar_encoding_powers/')"
            ]
        },
        {
            "cell_type": "markdown",
            "id": "2dae4c79-42d6-43db-9567-3da63390e720",
            "metadata": {},
            "source": [
                "Do this by averaging ROC / AUC within session?"
            ]
        },
        {
            "cell_type": "markdown",
            "id": "84e035f7-e293-4495-aa16-6d5fda216073",
            "metadata": {
                "tags": []
            },
            "source": [
                "### One-list normalization for closed loop"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 20,
            "id": "f98f4ce5-7911-4bf1-94a1-6ee9b69e9591",
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "image/png": 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",
                        "text/plain": [
                            "<Figure size 864x1152 with 22 Axes>"
                        ]
                    },
                    "metadata": {
                        "needs_background": "light"
                    },
                    "output_type": "display_data"
                }
            ],
            "source": [
                "path = '/scratch/nicls_intermediate/closed_loop/encoding_powers/'\n",
                "\n",
                "# with open(\"all_nicls_subjects.txt\", \"r\") as f:\n",
                "#     cl_sub_list = f.read().splitlines()\n",
                "cl_sub_list = CL_evs['subject'].unique()\n",
                "cl_sub_list = [s for s in cl_sub_list if int(s[-3:])>460]\n",
                "fig, ax = plt.subplots(len(cl_sub_list) // 2 + len(cl_sub_list) % 2, 2, figsize=(12, 16))\n",
                "ax = ax.ravel()\n",
                "cl_result_dict = {}\n",
                "for i, subject in enumerate(cl_sub_list):\n",
                "#     try:\n",
                "    if subject == 'LTP499':\n",
                "        continue\n",
                "    ts = TimeSeries.from_hdf(path+subject+\"_raw_feats.h5\", engine=\"netcdf4\")\n",
                "#     except:\n",
                "#         continue\n",
                "    evs = CL_evs.query(f'type==\"WORD\"&subject==\"{subject}\"').reset_index()\n",
                "    \n",
                "    ts = ts.groupby('session').apply(norm_sess_feats, n_lists=1)\n",
                "    \n",
                "    model = ClassifierModel(\n",
                "        LogisticRegression(),\n",
                "        sklearn_version=sklearn_version\n",
                "    ).load_json(f\"/data/eeg/scalp/ltp/NiclsCourierReadOnly/{subject}/nicls_{subject}_classifier.json\").model\n",
                "\n",
                "    sham_powers = ts.isel(event=evs.query('trial_type in [\"Sham\", \"NoStim\"]').index).data\n",
                "    prob = model.predict_proba(sham_powers)[:, 1]\n",
                "    evs = evs.query('trial_type in [\"Sham\", \"NoStim\"]')\n",
                "    fp, tp, _ = roc_curve(evs.recalled.values, prob)\n",
                "    auc = roc_auc_score(evs.recalled.values, prob)\n",
                "    auc_results = [roc_auc_score(np.random.choice(evs.recalled.values,\n",
                "                                                  size=len(evs),\n",
                "                                                 replace=False),\n",
                "                                 prob)\n",
                "                   for i in range(10000)]\n",
                "    cl_result_dict[subject] = (fp, tp, auc, auc_results)\n",
                "    p_val = (sum(auc_results>auc)+1) / (len(auc_results)+1)\n",
                "    ax[i].set_title(subject)\n",
                "    ax[i].hist(auc_results, ec='k', alpha=.7)\n",
                "    ax[i].axvline(auc, c='r')\n",
                "#     ax[i].annotate(f'AUC = {auc:.3f}', xy=(.05, .9), xycoords='axes fraction', fontsize=18)\n",
                "    ax[i].annotate(f'p = {p_val:.3f}', xy=(.05, .8), xycoords='axes fraction', fontsize=18)\n",
                "    ax[i].annotate(f'AUC = {auc:.3f}', xy=(.05, .9), xycoords='axes fraction', fontsize=18)\n",
                "plt.tight_layout()"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": null,
            "id": "db94b15d-bb21-49f7-a8b1-adecafdeb473",
            "metadata": {},
            "outputs": [],
            "source": [
                "with open(f\"clf_results_closed_loop.pkl\", \"wb\") as f:\n",
                "    pickle.dump(cl_result_dict, f)"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 3,
            "id": "7133c443-d77e-4030-9ed9-3edd63d8e3a9",
            "metadata": {},
            "outputs": [],
            "source": [
                "from scipy.stats import zscore"
            ]
        },
        {
            "cell_type": "markdown",
            "id": "7e69b9c1-fc78-4d38-848f-aebad0fd79ad",
            "metadata": {},
            "source": [
                "#### Full session normalization"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": null,
            "id": "2fc16b94-1ace-4666-bd43-1d37f5746496",
            "metadata": {},
            "outputs": [],
            "source": [
                "path = '/scratch/nicls_intermediate/closed_loop/encoding_powers/'\n",
                "# with open(\"all_nicls_subjects.txt\", \"r\") as f:\n",
                "#     cl_sub_list = f.read().splitlines()\n",
                "cl_sub_list = CL_evs['subject'].unique()\n",
                "cl_sub_list = [s for s in cl_sub_list if int(s[-3:])>460]\n",
                "cl_result_dict = {}\n",
                "for i, subject in enumerate(cl_sub_list):\n",
                "    try:\n",
                "        if subject == 'LTP499':\n",
                "            continue\n",
                "        ts = TimeSeries.from_hdf(path+subject+\"_raw_feats.h5\", engine=\"netcdf4\")\n",
                "    except:\n",
                "        continue\n",
                "    evs = CL_evs.query(f'type==\"WORD\"&subject==\"{subject}\"').reset_index()\n",
                "    \n",
                "    ts = ts.groupby('session').reduce(zscore, ddof=1)\n",
                "    \n",
                "    model = ClassifierModel(\n",
                "        LogisticRegression(),\n",
                "        sklearn_version=sklearn_version\n",
                "    ).load_json(f\"/data/eeg/scalp/ltp/NiclsCourierReadOnly/{subject}/nicls_{subject}_classifier.json\").model\n",
                "\n",
                "    sham_powers = ts.isel(event=evs.query('trial_type in [\"Sham\", \"NoStim\"]').index).data\n",
                "    prob = model.predict_proba(sham_powers)[:, 1]\n",
                "    evs = evs.query('trial_type in [\"Sham\", \"NoStim\"]')\n",
                "    fp, tp, _ = roc_curve(evs.recalled.values, prob)\n",
                "    auc = roc_auc_score(evs.recalled.values, prob)\n",
                "    auc_results = [roc_auc_score(np.random.choice(evs.recalled.values,\n",
                "                                                  size=len(evs),\n",
                "                                                 replace=False),\n",
                "                                 prob)\n",
                "                   for i in range(10000)]\n",
                "    cl_result_dict[subject] = (fp, tp, auc, auc_results)"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 33,
            "id": "5646157c-3c7d-4f7d-a101-568497229a77",
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "image/png": 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79+/njjvu4JJLLuHjjz/OOh59+/blnHPOoWHDhqxfv57/+Z//4bzzzuOLL77I+vYv3HVJ4X27aRNJ3XsU3DFCEhscz4vPzyq4o8QdM6sBvAqMds7ty+ffZW4LXD7tORudewZ4BqBDhw55n016lJv/sGvXLu69915eeOGFPOMwMxYuXEjfvn1p0aJFVszPPfccXbt2LdS6RKTE+P72PuXmP4Rzrrt7925q1KhB+fLlc6z/wIED/P7771SqVCnHuhcvXsy8efOYMWNGgfsnIuGL+wLG4cOHef3117nkkkuyksfAgQMZNGgQH3/8Meeee26R1luc/zn/5JNPCuzTpEmTQseQWX3OLzbnHM455s2bR926dYHAXBcXXHABS5YsoVu3bgA8/vjjWWPOO+88OnXqRLt27XjuuecYPXp0odYlhZfuHCMmPBaz7U0fPzpm25LYMbOKBIoXLzrnXvOafzKzBs65NO/2kMxrV38AGgUNbwj86LU3zKW9WJSbQ919992cffbZXHzxxXn2ycjIYNCgQezatYs5c+ZQv359UlNTuf7666lbty49e/YMe10iUjK8K98yr4Lbb2bBt/cled1mAe8CYwm6vQ/YbGaZt/dtwbu9D8DMMm/vK1YBQ7mZHH3COdfNbR35rX/Lli1cddVV9O3bN8/Ci4gUTdwXMBYuXMiePXu4+OKL2bNnDwBJSUlUrlyZ5OTkrERcoUIF0tPTc11Heno6FSr8cSgSExOL9cindu3aFdgnexU3WGbFOHN/MmW+z15hzj62adOmWUkYoHPnzlSqVIl169blWXQ49dRTadmyJZ9++mmx1yUi0eddSvwvYL1zbmrQojeAwcAU7+e8oPaXzGwqgW/5mgMrnHPpZrbfzM4hcAvKNUCxp0xXbv7D2rVrmTFjBu+//35W3wMHDgCwd+9eypcvT9WqVVmwYAFvvvkmX3/9ddalyklJSWzdupU77riDnj17hr0uESl5+d3eZ2bBt/d9HDQs8za+I4Rxe5+ZDSVwlUZYV0EoN+ccW9C5bp06ddi/fz/p6ekhcezZs4dq1arluILkl19+oVevXpx44om88MILBe6biBRO3BcwMu/Zu/zyy3Msmzt3Lo8++ijly5enXr167Nu3jwMHDlCtWrWQfmlpadSvXz/rfVJSEqmpqRw9ejQkQYeruJfCNWvWjIoVK7JhwwYuuOCCrPYNGzZQrly5rEuLc9O6dWsOHz6co905R7lyBU95ElxFLu66iuPqQYPZlra94I4RkvkoMZE48idgEPCFma322u4iULiYa2bXA98DlwM459aa2VxgHYFLnG/2LlEGuAmYCVQl8O1eRC5RBuVmgG+++YYjR47k+s1mw4YNuf7663n22WfZsGED1apVy3Gf9RlnnMEbb7xRqHWJSMmK1e19Rbm1D5SbM4VzrtuqVSvS09PZuHEjLVu2DFl/9nk3Dhw4QO/evfn999958803qV69eoH7JiKFE9cFjF9//ZUFCxYwcOBAhg4dGrIs85FOS5cupXv37px33nlkZGSwYMGCkFmEf/vtNxYvXsyQIUOy2kaMGMGsWbOYPHky9913X47tpqam5nvpbnEvhatcuTJdunTh5Zdf5sYbb8xqnzNnDueee26OGeqD9e7dm/vuu4+dO3dmzYb8/vvvc+TIEdq2bZvnuC+//JKvvvoqZHtFXVckbEvbHtNbLEZeFd0ZskUizTn3Abmf4ALkenmUc24yMDmX9pXAqZGKTbk5VOfOnVm6dGlI26JFi3jooYdITU2ladOmAJx00kkcOHCAr776KuQkedWqVTRu3LhQ6xKRkuPX2/uUm3MK51y3U6dO1KxZk5dffjnrcbMHDhxg/vz5Icfx6NGjXH755XzzzTd8+OGHIUUeEYmcuC5gzJs3jwMHDjBq1CjOPvvskGV/+tOfmDx5MsnJyXTv3p02bdpw5ZVXcv3117N582bat2/Pjh07eOSRR3DOMXLkyKyx7dq1Y+rUqYwePZp169YxYMAAEhIS2Lx5MzNmzGDv3r35JuIOHToUe9/uvfdekpKSGD16NP369SM1NZXU1FQWLVqU1ee7776jWbNmzJgxg2uuuQaAoUOHMm3aNPr06cNdd93F/v37GTt2LN27d6dz584AvPnmm7zwwgv07t2bE044gQ0bNjBp0iROPPHEkOp2OOsSEclOuTk0NyckJJCUlBSynsyrvs477zxq1KgBwMUXX8yJJ55Iv379GD9+PPXq1ePNN99k7ty5/OMf/wAIe10iUjL8fHufcnPRzpurVKnCuHHjmDhxInXq1KFVq1ZMnTqVjIwMRowYkbX+v/3tb6SmpvL444/zyy+/8PHHf9wZdMYZZ1C5cuVi76eIxHkBIzk5mebNm+dIwhC4HO2KK64gOTmZJ598ksqVKzN79mwmTZrEM888w/fff88xxxxDUlISL774IomJobcVjhw5ktNOO42///3v3HDDDezbt4/ExER69OjB7bffHvV969y5M6+88gr33HMPTz31FE2aNOGll17ioosuyurjnCM9PZ2MjIystpo1a7JkyRJGjhzJgAEDqFSpEn379uXRRx/N6tOoUSN27NjB6NGj2bNnT9bkcA888AA1a9Ys1LpERLJTbs6Zm8NxzDHHsHjxYu68805uvfVW9u3bR7NmzXj66adzfFsqIr7l29v7lJuLdt4MMG7cODIyMnjwwQfZtWsXHTp04O233+a4447L6vPWW28BMGrUqBzxbd68OetKOhEpHsvvucp+0KFDB7dy5cqSDkNiLKl7j5jfQjLtpQWldnvTx4/m3Xf+E7PtlZjMb6bffbcko8iTma1yzhX/qyYfUG4umyKVm88fPhiA95/I//HOypUSC6UlNysvl12l/bzZl7nZ5+ecpUFeuTm6MzGKiIiIiIiIiERAgbeQmFkjYDZwPJABPOOce9zMjgXmAI2BLcAVzrnd3pg7geuBdGCkc+4/Xnt7/rgULhUY5fx+CYiIiIiUCd9u2kRS9x4x215ig+N58fn8r0IRkfijp+mJRE84c2AcBW51zn1qZscAq8zsbeBaYLFzboqZjQPGAWPNrA0wADiFwGRE75hZC+9+vqcIPKv6YwIFjJ5E4HF9IiIiIsWV7lxML8OePn50zLYlIrGjp+mJRE+Bt5A459Kcc596v+8H1gOJQF8g82uDWUA/7/e+QIpz7rBzbjOwEejoPTKqpnNumXfVxeygMSIiIiIiIiIieSrUHBhm1hg4g8DjnI5zzqVBoMgBZD7sOBHYGjTsB68t0fs9e3tu2xlqZivNbOXPP/9cmBBFREREREREpBQKu4BhZjWAV4HRzrl9+XXNpc3l056z0blnnHMdnHMd6tWrF26IJWbevHmcdtppVKlShTZt2jBnzpywxq1bt45u3bpRrVo1TjjhBMaPH096enpIH+ccDzzwAI0aNaJq1aqcf/75rF69OqTPyy+/zF/+8hcSExOpUaMG7du3Jzk5OVK7JyISl4qam48ePcqUKVNo3rw5lStXpmHDhtxyyy1Zy9PS0rj99ttp27YtNWrUoFGjRgwePJgff/wxZD0bN27kxhtvpG3btpQvX56kzBnLRUTKsGidN//+++9cccUVNG3alKpVq1KvXj169erFqlWrQtaj82aR+BZWAcPMKhIoXrzonHvNa/7Juy0E7+cOr/0HoFHQ8IbAj157w1za49oHH3zApZdeSpcuXVi4cCF//vOfGThwYNazoPOye/duunfvjpkxb948xo8fzyOPPMJ9990X0m/KlClMnDiRsWPHMn/+fGrUqEH37t3Zvv2PiYGmTp1KjRo1ePTRR3njjTfo0qULV111FdOnT4/KPouI+F1RczPAddddx7Rp07jtttt46623mDJlClWrVs1avmrVKl5//XUGDhzI/Pnzefjhh1m+fDmdOnXi119/zeq3du1aUlNTadGiBS1atIjKfoqIxJNonjenp6djZtx55528+eab/N///R8HDhyga9eufPvtt1n9dN4sEt/CeQqJAf8C1jvnpgYtegMYDEzxfs4Lan/JzKYSmMSzObDCOZduZvvN7BwCt6BcA8R9ppg4cSLnn38+06ZNA6BLly6sXbuWCRMmcNFFF+U57umnn+bgwYO89tpr1KxZkwsvvJB9+/Zx//33c8cdd1CzZk0OHTrElClTuPPOOxk+fDgA5557Lo0bN+aJJ55g0qRJAMyfP5+EhISsdXft2pUff/yRqVOnMmLEiCjuvYiIPxU1Ny9atIiUlBTWrFlDmzZtcu3TuXNnNmzYQIUKf/wJPfPMM2nZsiWvvvoqgwcPBqBPnz707dsXgMsuu4ydO3dGavdEROJSNM+bq1atmuNqju7du1O3bl3+/e9/M2bMGEDnzSLxLpwrMP4EDAK6mtlq73UxgcLFhWb2DXCh9x7n3FpgLrAOWATc7D2BBOAm4FkCE3tuIgJPILn22mvp0KED//73v2nVqhVVqlShc+fOrFu3rrirLtDhw4dZunQpV1xxRUj7gAEDWLZsGXv37s1z7MKFC+nRowc1a9YMGXfw4EHee+89AD766CP27dsXsv7q1avTp08fFi7849AFJ+FMZ5xxBjt27MjRLiISC/Gam2fMmEHXrl3zLF4A1K5dO6R4AdCiRQuqVasWknfLlSvUNFMiIlEXr7k5nPPm3FSvXp0qVarw+++/Z7XpvFkkvoXzFJIPnHPmnDvdOdfOe6U653Y557o555p7P38JGjPZOdfMOdfSObcwqH2lc+5Ub9lw72kkxfbdd98xZswY7r33Xl566SX27t1Ljx49OHToUL7jjh49WuArvxA3bdrEkSNHaNWqVUh769atycjI4Ouvv85z7IYNG3KMO/HEE6lWrRobNmzI6lO+fHmaN2+eY/2ZffLy0Ucf5XsCLiISbfGYm5cvX06LFi0YPnw4NWvWpFq1avTv3z/H/BbZff755xw4cEB5V0R8Lx5zczjnzZmccxw9epTt27dzxx13UL58eQYOHJjvvum8WSR+lIqvh3bu3MkLL7zA1VdfTf/+/VmwYAFpaWnMnDkz33EVK1Ys8DVr1qw8x+/evRsIfBsXrE6dOiHL8xqbfVzm2Mxxu3fvpkaNGpQvXz5HnwMHDoRUk4MtXryYefPmcfPNN+e5fRGRaIvH3Lx9+3ZmzpzJ6tWrSUlJ4bnnnmPVqlVccskleZ6YZ2RkMGrUKJo3b57vJdAiIn4Qj7k5nPPmTA899BAVK1akQYMGzJo1i9TUVE466aQ8163zZpH4UuAcGPGgfv36dOrUKev9SSedRPv27VmxYgXDhg3Lc9wnn3xS4LqbNGlSYJ/ANCF/yDzJzd5e0LjMscHtefXJa9mWLVu46qqr6Nu3L9dee22BsYuIREs85mbnHM455s2bR926dQFo0KABF1xwAUuWLKFbt245xtx5550sW7aM9957j4oVKxYYl4hISYrH3JzX8uznzRC4TaZ79+6kpaXx5JNP0rt3b95///1cr7DQebNI/Ck1BYzc2tLS0vId165duwLXnf3qh2CZFeM9e/aEtGe+z61SHDw2+ziAvXv3Zo2rU6cO+/fvJz09PSSOPXv2UK1atRwnyr/88gu9evXixBNP5IUXXsh7p0REYiBec3PTpk2zihcQmLSzUqVKWY/wC/bkk0/y8MMPk5yczNlnn11g3CIiJS1ec3NB582Zjj/+eI4//ngAevXqxSmnnMKUKVOYPXt2SD+dN4vEp1JRwMht0p0dO3Zwyimn5DsunG/KnnvuuTwrss2aNaNixYps2LCBCy64IKt9w4YNlCtXLt/H5rVq1SrHPXtbt27lt99+y7rHr1WrVqSnp7Nx40ZatmwZsv7s9wEeOHCA3r178/vvv/Pmm29SvXr1AvdNRCSa4jE3t27dmsOHD+dod87lmJTz1VdfZcSIEfzv//4vV155ZYExR8PVgwazLW17wR0jZMuWLTHblohERzzm5nDOm3NToUIFTjvttJDHqILOm0XiWakpYHz00UdZl8N9//33fPrpp1x33XX5jivupXCVK1emS5cuvPzyy9x4441Z7XPmzOHcc8+lVq1aeY7t1asXDz/8MPv37+eYY47JGle1atWspN6pUydq1qzJyy+/zD333AMEEu78+fMZOnRo1rqOHj3K5ZdfzjfffMOHH36Ya2VdRCTW4jE39+7dm/vuu4+dO3dmzVT//vvvc+TIEdq2bZvV79133+Xqq69m+PDh3HbbbQXGGy3b0rYzYsJjMdveyKt6x2xbIhId8Zibwzlvzs2hQ4f49NNP+dOf/pTVpvNmkfhWKgoYCQkJDBo0iIkTJ1K1alXGjx9P/fr1C7yXrUOHDsXe9r333ktSUhKjR4+mX79+pKamkpqayqJFi7L6fPfddzRr1owZM2ZwzTXXADBs2DCmTZtG//79GTt2LN9++y33338/Y8aMyXpEVJUqVRg3bhwTJ06kTp06tGrViqlTp5KRkRHynOq//e1vpKam8vjjj/PLL7/w8ccfZy0744wzqFy5crH3U0SksOIxNw8dOpRp06bRp08f7rrrLvbv38/YsWPp3r07nTt3BmD9+vX069ePVq1aceWVV4bk3Hr16tGsWTMgUHBOTU0FYNu2bezbt49XXnkFgIsvvphq1aoVez9FRAorHnNzOOfNycnJLFy4kJ49e3LCCSdkzYGRlpbGmDFjstav8+b48+2mTSR17xGz7SU2OJ4Xn897QlopWaWigHHSSSdx1113MW7cOL777js6dOhAcnIyVapUifq2O3fuzCuvvMI999zDU089RZMmTXjppZdCZqJ3zpGenk5GRkZWW506dVi8eDHDhw+nT58+1K5dm1tuuYX7778/ZP3jxo0jIyODBx98kF27dtGhQwfefvttjjvuuKw+b731FgCjRo3KEd/mzZtp3LhxZHdaRCQM8Ziba9asyZIlSxg5ciQDBgygUqVK9O3bl0cffTSrz/Lly9m7dy9r1qwJ+VYPYPDgwVkz+e/YsYPLL788ZHnme+VmESkp8ZibwzlvbtmyJS+88AJjxoxh9+7dNGjQgLPPPpuVK1eG3B6j8+b4k+5cTK82nD5+dMy2JYVXKgoYAP3796d///4lsu1+/frRr1+/PJc3btw418fvtWnThiVLluS7bjPj7rvv5u67786zj+5JFhG/isfcfPLJJ2ddOZGba6+9NqzZ6vNav4hISYvH3FzQefOZZ57Jm2++WeD2dd4sEt/KFdxFRERERERERKRkqYAhIiIiIiIiIr4X97eQZN5rLCIi/qHcLCLiP8rNIhLvdAWGiIiIiIiIiPieChgiIiIiIiIi4nsqYIiIiIiIiIiI76mAISIiIiIiIiK+pwKGiIiIiIiIiPieChgiIiIiIiIi4nsqYIiIiIiIiIiI76mAISIiIiIiIiK+pwKGiIiIiIiIiPieChgiInHIzGaY2Q4z+zKo7Vgze9vMvvF+1gladqeZbTSzr8ysR1B7ezP7wls2zcws1vsiIiIiIhIOFTBEROLTTKBntrZxwGLnXHNgsfceM2sDDABO8cY8aWblvTFPAUOB5t4r+zpFRCRMKi6LiESXChgiInHIOfc+8Eu25r7ALO/3WUC/oPYU59xh59xmYCPQ0cwaADWdc8uccw6YHTRGREQKbyYqLouIRI0KGCIipcdxzrk0AO9nfa89Edga1O8Hry3R+z17e67MbKiZrTSzlT///HNEAxcRKQ1UXBYRiS4VMERESr/cLj12+bTnyjn3jHOug3OuQ7169SIWnIhIKRfV4rKISFmiAoaISOnxk/fNHd7PHV77D0CjoH4NgR+99oa5tIuISPQVu7isK+NEpKxRAUNEpPR4Axjs/T4YmBfUPsDMKptZEwL3U6/wvgncb2bneBPEXRM0RkREIiNqxWVdGSciZU2BBQzNpiwi4j9mlgwsA1qa2Q9mdj0wBbjQzL4BLvTe45xbC8wF1gGLgJudc+neqm4CniVw7/UmYGFMd0REpPRTcVlEJEIqhNFnJvAEgQmEMmXOpjzFzMZ578dmm035BOAdM2vhnShnzqb8MZBKYDZlnSiLiBSBc25gHou65dF/MjA5l/aVwKkRDE1EpMzyistJQIKZ/QDcR6CYPNcrNH8PXA6B4rKZZRaXj5KzuDwTqErgfFnnzCIihFHAcM69b2aNszX3JZCcITCb8rvAWIJmUwY2m1nmbMpb8GZTBjCzzNmUlYxFREREpFRQcVlEJLqKOgeGHtUnIiIiIiIiIjET6Uk89ag+EREREREREYm4cObAyM1PZtbAOZemR/WJ+N+3mzaR1L1HwR0jJLHB8bz4/KyYbU9EREREREq/ohYwMmdTnkLO2ZRfMrOpBCbxzJxNOd3M9pvZOcByArMpTy9W5BJTVw8azLa07THb3pYtW2K2rbIg3TlGTHgsZtubPn50zLYlIiIiIiJlQ4EFDM2mLADb0rbH9H+AR17VO2bbEhEREREREf8L5ykkmk1ZREREJMJ0e5+IiEjhFPUWEhEREREpBt3eJyIiUjiRfgqJiIiIiIiIiEjEqYAhIiIiIiIiIr6nAoaIiIiIiIiI+J4KGCIiIiIiIiLie5rEU0RERERESq2rBw1mW9r2mG1vy5YtMduWSFmjAoaIiIiIiJRa29K2x/SJPyOv6h2zbYmUNSpgiIiIRIi+5RMRERGJHhUwREREIkTf8omIiIhEjybxFBERERERERHf0xUYIiIiIiIiIsC3mzaR1L1Hvn0eW/M5AKML6BeOxAbH8+Lzs4q9nrJCBQwRERERERERIN25Am8HTRw+GCAit41OHz+62OsoS3QLiYiIiIiIiIj4ngoYIiIiIiIiIuJ7KmCIiIiIiIiIiO+pgCEiIiIiIiIivqcChoiIiIiIiIj4ngoYIiIiIiIiIuJ7KmCIiIiIiIiIiO+pgCEiIiIiIiIivqcChoiIiIiIiIj4ngoYIiIiIiIiIuJ7FUo6ABERERGJvm83bSKpe4+YbS+xwfG8+PysmG1PRERKPxUw4tTVgwazLW17zLa3ZcuWmG1L4p9OkkVE/CfdOUZMeCxm25s+fnTMtiUiImWDChhxalva9piehIy8qnfMtiXxTyfJIiIikptYfwkH+iJOpDRRAUNEREotXa0mIuIvsf4SDvRFnPibrlwunJgXMMysJ/A4UB541jk3JdYxiIhIqNKam3W1mojEs9Kam0XkD7pyuXBi+hQSMysP/APoBbQBBppZm1jGICIioZSbRUT8R7lZRCSnWD9GtSOw0Tn3rXPudyAF6BvjGEREJJRys4iI/yg3i4hkE+tbSBKBrUHvfwDOjnEMUaH7rEVKTua9g4+t+RyA0VG+jzDe7x3MRcxys3KlSNmh+7qLLSa5WXlZpGyJ99xszrmIrazAjZldDvRwzt3gvR8EdHTOjcjWbygw1HvbEvgqZkGGLwHYWdJBlDAdAx0D0DGA8I/BSc65etEOprBilJv99jlRPAXzW0yKJ3+KJ3/5xRO3ubkU5WW/xOKXOECx5MYvcYB/YvFLHBD5WHLNzbG+AuMHoFHQ+4bAj9k7OeeeAZ6JVVBFYWYrnXMdSjqOkqRjoGMAOgZQKo5B1HOz346R4imY32JSPPlTPPnzWzxhKjA3l5a87JdY/BIHKBY/xwH+icUvcUDsYon1HBifAM3NrImZVQIGAG/EOAYREQml3Cwi4j/KzSIi2cT0Cgzn3FEzGw78h8DjoGY459bGMgYREQml3Cwi4j/KzSIiOcX6FhKcc6lAaqy3GwW+vsUlRnQMdAxAxwBKwTGIQW722zFSPAXzW0yKJ3+KJ39+iycsUc7NfjomfonFL3GAYsmNX+IA/8TilzggRrHEdBJPEREREREREZGiiPUcGCIiIiIiIiIihaYCRgHMrKeZfWVmG81sXC7L+5rZ52a22sxWmlnnkogzmgo6BkH9zjKzdDO7LJbxRVsYn4EkM9vrfQZWm9n4kogzmsL5DHjHYbWZrTWz92IdY7SF8Tm4Pegz8KX3b+HYkog11oqTJ8PNLzGMZ4uZfZG5LBbxBPXLkUNL4vgUEE/Mj09+ObaEPj/5xVMin5+88m9JfX7yiSfixyecmPLLz9E4Rn7gl7zsp3zsl1zspxzsp/zrl9zrp5zrl3zruxzrnNMrjxeBCZM2AU2BSsAaoE22PjX441ac04ENJR13rI9BUL8lBO7TvKyk447xZyAJWFDSsZbwMagNrANO9N7XL+m4Y30MsvXvAywp6bj9cmzyypOFPa7Rjsd7vwVIiPVnJ7ccWlLHJ694Sur45JVjS/Dzk2fOL6HjU5tc8m8JHp9c44nG8SnKfhKUn6NxjPzwCvO/U9TzcnHiiPTnJdz9Isq5uDhxlMQxIUb5tzixRPK4hBlHbWKQc4sTS6yPSbb+Uc+xugIjfx2Bjc65b51zvwMpQN/gDs65X533XwioDpS2SUUKPAaeEcCrwI5YBhcD4e5/aRbOMbgKeM059z2Ac66sfw4GAskxiazkFSdPRuPfl9/ydnFyaIkcn3ziiYbi7GNJHp9YKU7+LanjE+u/B8XJz3777x0pfsnLfsrHfsnFfsrBfsq/fvm36Kec65d867scqwJG/hKBrUHvf/DaQpjZJWa2AXgTGBKj2GKlwGNgZonAJcDTMYwrVsL6DADnmtkaM1toZqfEJrSYCecYtADqmNm7ZrbKzK6JWXSxEe7nADOrBvQkcOJRFhQnT4Z9XGMUDwROnt/yPsdDixlLWPHkk0NL5PgUkNNjfnw8ueXYEvv85BEPlMzxySv/ltTxye/vQaSPT7gxAbnm52gcIz/wS172Uz72Sy72Uw72U/71S+71U871S771XY6N+WNU44zl0pajMuycex143czOByYC3aMdWAyFcwweA8Y659LNcuse18LZ/0+Bk5xzv5rZxcC/gebRDiyGwjkGFYD2QDegKrDMzD52zn0d7eBiJKxc4OkDfOic+yWK8fhJcfJkYY5rLOIB+JNz7kczqw+8bWYbnHPvRzmex8g9h5bU8ckrHiiZ45NXji2p45Nfzi+J45Nr/g1zbMzi8f4eRPr4hBtTpuz5ORrHyA/8kpf9lI/9kov9lIP9lH/9knv9lHP9km99l2N1BUb+fgAaBb1vCPyYV2fvQ9HMzBKiHVgMhXMMOgApZrYFuAx40sz6xSS66Ctw/51z+5xzv3q/pwIVy+Bn4AdgkXPuN+fcTuB9oG2M4ouFwuSCAZSd20egeHmyUGNjEA/OuR+9nzuA1wlc/hjtePLKoSV1fPLM6SVxfPLJsSVyfPLL+SX0+ckr/5bU5yfPvwdROD7hxpQpe36OxjHyA7/kZT/lY7/kYj/lYD/lX7/kXj/lXL/kW//lWFfMSTRK84tAVetboAl/TDxySrY+J/PH5ENnAtsy35eGVzjHIFv/mZSuSTzD+QwcH/QZ6Ah8X9Y+A0BrYLHXtxrwJXBqSccey2Pg9asF/AJUL+mY/XRs8sqThc0vMYinOnCM114d+AjoGYvPTlD/rBxaUscnn3hK5PjklWNL8POTVzwldXxyzb8leHzyiifix6cwn2lyyc/ROEZ+eIX53ynqebmYcUT081LY/SJKubiYccT8mBCj/FvMWCJ2XMKMIyY5t5ixxPSYeP1ilmN1C0k+nHNHzWw48B8Cs6jOcM6tNbNh3vKngUuBa8zsCHAQuNJ5/8VKgzCPQakV5v5fBtxkZkcJfAYGlLXPgHNuvZktAj4HMoBnnXNfllzUkVWIfweXAG85534roVBjrph5MtexJRWPmR1H4DJmCPzRfck5tygG8RRqbEnFA5TU8ckrx5bU8ck1npL6/OSXf0vi+OQVj5k1JcLHJ9yYvK458nM0/o35gV/ysp/ysV9ysZ9ysJ/yr19yr59yrl/yrR9zbGYVS0RERERERETEtzQHhoiIiIiIiIj4ngoYIiIiIiIiIuJ7KmCIiIiIiIiIiO+pgCEiIiIiIiIivqcChoiIiIiIiIj4ngoYIiIiIiIiIuJ7KmCIiIiIiIiIiO+pgCEiIiIiIiIivqcChoiIiIiIiIj4ngoYIiIiIiIiIuJ7KmCIiIiIiIiIiO+pgCEiIiIiIiIivqcChoiIiIiIiIj4ngoYEpfMbIuZdfd+/zXolWFmB4PeX21m95vZEe/9HjP7yMzOzWWdz5mZM7OTg9rWZlv/UTOb7y1LMLMPzWyXt95lZvan2B0FERF/8UNuzjZ2sDf2hujuuYiIP/klL3v9fwta/mxsjoCUNipgSNxzztXIfAHfA32C2l70us3xltcDPgBeMzPLXIeZdQaa5bLuU4LWfYy3/pe9xb8CQ7x11gEeAuabWYXo7KmISPwowdycObYOcCewNhr7JyISb0o6LwNtg7anwrIUiQoYUqY4544As4DjgboAXsFhOjC8gOHnA/WBV711HXLOfeWcywAMSCdQyDg2OtGLiJROkczNQR4EpgE7IxqsiEgZEKW8LFJsKmBImWJmlYFrgR+cc5kntbcA7zvnPi9g+GDgFefcb9nW+TlwCHgDeNY5tyOyUYuIlG6Rzs1m1hHoADwdhXBFREq9aJwzA++b2XYze83MGkc0YCkzdKm7lBVXmFlv4HfgS6AfgJk1Am4E2uc32MyqAZcBf8m+zDl3uplVAS4BKkU2bBGRUi3iudnMygNPAiOccxlBVz6LiEjBonXOfAHwMVANmAQsMLN2zrmjEY1eSj0VMKSsmOuc+2su7Y8BE5xzewsY3x/4BXgvt4XOuUNAspmtN7PVzrk1xYpWRKRsiEZu/hvwuXNuWWRCFBEpU6Jyzuyce9/79XczGwXsA1oDXxQvXClrdAuJlHXdgIe9y9m2e23LzOyqbP0GA7Odc66A9VUEmkY6SBGRMqY4ubkbcEnQ2E7AI2b2RPTDFhEptSJ9zuwIzCEnUii6AkPiWUXv1o1MRbkErQWhhbw0oA+QdQWFmTUEugDDggea2TkE/g2tAMoDI4HjgOVFiENEpLQo0dxM4J7t4O2/BrwC/KsIcYiIlAYlfc58CoEv+b4AqhK4hWQbsL4IcUgZpwKGxLPUbO8nF3YF2Sfc9O6V3umcOxjUPAhY5pzblG14ZQIz3DcFjhBIyn92zv1Y2DhEREqREs3Nzrk92cb+DuwL47JnEZHSqqTPmY8DngIaAr8BHwG9vSediBSKFXx1j4iIiIiIiIhIydIcGCIiIiIiIiLieypgiIjEITOrYmYrzGyNma01s//x2o81s7fN7BvvZ52gMXea2UYz+8rMegS1tzezL7xl00zPnRQRERERH1IBQ0QkPh0Gujrn2gLtgJ7exLLjgMXOuebAYu89ZtYGGACcAvQEnjSz8t66ngKGAs29V88Y7oeIiIiISFhUwBARiUMu4FfvbUXv5YC+wCyvfRbQz/u9L5DinDvsnNsMbAQ6mlkDoKZzbpn3yLPZQWNERERERHzD908hSUhIcI0bNy7pMCQefPVV4GfLliUbh0geVq1atdM5Vy9S6/OuoFgFnAz8wzm33MyOc86lATjn0sysvtc9Efg4aPgPXtsR7/fs7flSbhYJk/42+V6kc3NJUV6OU8oRIrnKKzf7voDRuHFjVq5cWdJhSDxISgr8fPfdkoxCJE9m9l0k1+ecSwfamVlt4HUzOzW/zee2inzac67AbCiBW0048cQTlZtFwqG/Tb4X6dxcUnTOHKeUI0RylVdu1i0kIiJxzjm3B3iXwNwVP3m3heD9zHxu+w9Ao6BhDYEfvfaGubTntp1nnHMdnHMd6tWL+y8rRURERCTOqIAhIhKHzKyed+UFZlYV6A5sAN4ABnvdBgPzvN/fAAaYWWUza0Jgss4V3u0m+83sHO/pI9cEjRERERER8Q3f30IiIiK5agDM8ubBKAfMdc4tMLNlwFwzux74HrgcwDm31szmAuuAo8DN3i0oADcBM4GqwELvJSIiIiLiKypgiIjEIefc58AZubTvArrlMWYyMDmX9pVAfvNniIiIiIiUOBUwxJeuHjSYbWnbCzXmsTWfAzC6e49Cby+xwfG8+PysgjuKiEjMFOVvQXHob4GIxCPlSilLVMAQX9qWtp0REx4r1JjE4YHb/gs7DmD6+NGFHiMiItFVlL8FxaG/BSISj5QrpSxRAUME+HbTJpKKcOVGUalyLSLxKNbf8m3ZsiVm2xIRERH/UwFDBEh3TpVrEZECxPpbvpFX9Y7ZtkRERMT/VMAQERERERGRsOjKZSlJKmCIiIiIiIhIWHTlspSkciUdgIiIiIiIiIhIQVTAEBERERERERHfUwFDRERERERERHxPBQwRERERERER8T0VMERERERERETE91TAEBERERERERHfUwFDRERERERERHxPBQwRERERERER8T0VMERERERERETE91TAEBERERERERHfUwFDRERERERERHyvwAKGmTUys6Vmtt7M1prZKK/9WDN728y+8X7WCRpzp5ltNLOvzKxHUHt7M/vCWzbNzCw6uyUiIiIiIiIipUk4V2AcBW51zrUGzgFuNrM2wDhgsXOuObDYe4+3bABwCtATeNLMynvregoYCjT3Xj0juC8iIiIiIiIiUkoVWMBwzqU55z71ft8PrAcSgb7ALK/bLKCf93tfIMU5d9g5txnYCHQ0swZATefcMuecA2YHjRERERERERERyVOh5sAws8bAGcBy4DjnXBoEihxAfa9bIrA1aNgPXlui93v29ty2M9TMVprZyp9//rkwIYqIiIiIiIhIKRR2AcPMagCvAqOdc/vy65pLm8unPWejc8845zo45zrUq1cv3BBFREREREREpJQKq4BhZhUJFC9edM695jX/5N0Wgvdzh9f+A9AoaHhD4EevvWEu7SIiIiIiIiIi+QrnKSQG/AtY75ybGrToDWCw9/tgYF5Q+wAzq2xmTQhM1rnCu81kv5md463zmqAxIiIiIiIiIiJ5qhBGnz8Bg4AvzGy113YXMAWYa2bXA98DlwM459aa2VxgHYEnmNzsnEv3xt0EzASqAgu9l4iIiIiIiIhIvsJ5CskHzjlzzp3unGvnvVKdc7ucc92cc829n78EjZnsnGvmnGvpnFsY1L7SOXeqt2y49zSSiHDO0aRJE8yMjRs35lh+//33k5CQkOvY2267jcaNG+dof/fdd+nduzcJCQlUqlSJxo0bM3LkSL7//vtIhZ2vefPmcdppp1GlShXatGnDnDlzCjV+27Zt1KhRAzPj119/zWp/9913MbNcXz169Mjq9/LLL/OXv/yFxMREatSoQfv27UlOTo7Y/olI6afcHDBz5sxcc+7TTz8d0i8tLY3rrrsuK++eccYZvPjiiyF9kpKS8szhy5Yti+i+ikjppNwc8Morr9CpUyfq1q1LlSpVaNmyJZMmTeL3338P6bdnzx6GDBnCscceS40aNejVq1eO46bzZpHYKNRTSPxs2bJlbNmyBYCUlJRir2/atGl07dqVqlWr8s9//pN33nmH++67j88++4y+ffsWe/0F+eCDD7j00kvp0qULCxcu5M9//jMDBw7krbfeCnsdt99+OzVq1MjRfuaZZ7Js2bKQV2aS79WrV1a/qVOnUqNGDR599FHeeOMNunTpwlVXXcX06dOLv4MiUiYoN4dasmRJSO7t379/1rKMjAz+8pe/8N577/G///u/zJs3j3POOYe//vWvvP7661n9nnzyyRw5/MILLyQhIYGzzjor4vssIuEzs0ZmttTM1pvZWjMb5bUfa2Zvm9k33s86QWPuNLONZvaVmfUIam9vZl94y6Z5t2BHhHJzwK5du+jSpQvPPvssCxcuZMiQIUyePJkxY8aE9Lvyyiv5z3/+w+OPP85LL73Erl276NatG/v2/fFcA503i8RGOLeQxIXk5GSqV6/OqaeeSnJyMvfcc0+R1/XZZ58xZswY7rnnHiZMmJDVfv7553PdddexYMGCSIScr4kTJ3L++eczbdo0ALp06cLatWuZMGECF110UYHj//vf/7Jo0SLuuusubr/99pBlNWvW5JxzzsnRv1y5clxxxRVZbfPnzw+pvnft2pUff/yRqVOnMmLEiOLsnoiUEcrNoc4666xcC8sAX3/9NStXruSNN96gT58+AHTr1o3ly5eTkpLCJZdcAkCbNm1Cxv3++++sXLmSK6+8kgoVSs2fdZF4dRS41Tn3qZkdA6wys7eBa4HFzrkpZjYOGAeMNbM2wADgFOAE4B0za+Hdfv0UMBT4GEgFehKh26+VmwNuvPHGkPddunRh3759/OMf/2D69OlZV7a99dZbLF68mK5duwJw9tln06RJE5555hluu+02QOfNIrFSKq7ASE9Pz7psa8iQIaxbt47PP/+8yOubPn06CQkJ3Hvvvbku7927d5HXHY7Dhw+zdOnSkGICwIABA1i2bBl79+7Nd3x6ejojRoxg/PjxeV7+l11ycjIXXHABJ5xwQlZbbmPPOOMMduzYkaNdRCQ75ebCOXLkCAC1atUKaa9duzb53XG5aNEidu/ezcCBA4u1fREpPudcmnPuU+/3/cB6IBHoC8zyus0C+nm/9wVSnHOHnXObgY1AR+8JfzWdc8u8W65nB40pFuXm/NWtWzfkFpLVq1dToUIFLrjggqy24447jtNPP50333wzq03nzSKxUSoKGEuWLOGnn35iwIABXHbZZVSsWLFY95y99957dOvWjYoVKxZp/NGjRwt85XcyumnTJo4cOUKrVq1C2lu3bk1GRgZff/11vtt/+umnOXToEDfffHNY8X7zzTd89tlnYZ38fvTRRzm+/RMRyY1yc07NmjWjQoUKtGzZkn/+858hy0499VTOPvtsxo8fzzfffMO+ffuYOXMmH374IcOGDctznSkpKSQmJnLeeecVuH0RiR0zawycASwHjvOeyIf3s77XLRHYGjTsB68t0fs9e3v2bQw1s5VmtvLnn38OKy7l5pzS09M5cOAAH3zwAdOmTeOmm24i846dQ4cOUaFCBcqXLx8ypnLlyqxfvz7f9eq8WSTySkUBIzk5mdq1a9OzZ0+OPfZYLrzwQlJSUvJNdvnZtm0bJ554YpHjqVixYoGvWbNm5Tl+9+7dQOBbt2B16tQJWZ6bXbt2ce+99zJ16tSw/5AkJydTsWJFLr300nz7LV68mHnz5oVdGBGRsk25+Q8NGjRg4sSJPP/888yfP5+zzz6bYcOG8eijj2b1MTMWLlxIRkYGLVq0oFatWgwdOpQZM2ZkXbac3YEDB5g/fz5XXnklEbw9XkSKycxqAK8Co51z+/Lrmkuby6c9tMG5Z5xzHZxzHerVqxdWbMrNOVWvXp3q1atz3nnnccEFF/Dwww9nLTv55JM5dOgQX3zxRVbbwYMH+fLLL/nll19yWx2g82aRaIn7m2UPHz7M66+/ziWXXEKlSpUAGDhwIIMGDeLjjz/m3HPPLdJ6i3Mi+MknnxTYp0mTJoWOIfMPS36x3X333Zx99tlcfPHFBa4/U0pKChdddBHHHntsnn22bNnCVVddRd++fbn22mvDXreIRIeZNSJwSfHxQAbwjHPucTM7FpgDNAa2AFc453Z7Y+4ErgfSgZHOuf947e354xHXqcCo4j4lSrk5VI8ePUKe8tSrVy8OHz7MpEmTGDVqFOXKlSMjI4NBgwaxa9cu5syZQ/369UlNTeX666+nbt269OzZM8d658+fz6+//qrbR0R8xMwqEihevOice81r/snMGjjn0rzbQzLvK/gBaBQ0vCHwo9feMJf2YlFuzt1HH33EgQMHWLFiBRMmTGD48OE8+eSTQCB/N2nShBtvvJHnnnuOmjVrMm7cOPbu3Zvnl4U6bxaJnrgvYCxcuJA9e/Zw8cUXs2fPHiDwiLnKlSuTnJyclYgrVKhAenp6rutIT08PmfgsMTGxWI98ateuXYF9sl+GFiyzYpy5P5ky32evMGdau3YtM2bM4P3338/qe+DAAQD27t1L+fLlqVq1asiYNWvWsH79eu6+++484/nll1/o1asXJ554Ii+88EI+eyUiMeTrieKUmwt22WWXMXfuXLZs2ULTpk1ZsGABb775Jl9//TXNmzcHAsds69at3HHHHbkWMFJSUjj55JPp0KFDobYtItHhPSnkX8B659zUoEVvAIOBKd7PeUHtL5nZVAK5uTmwwjmXbmb7zewcAregXAMU+3EWys25O/PMMwHo3LkzCQkJDB48mFtvvZVmzZpRqVIlUlJSGDhwYNZtKp07d+aaa65hyZIlOdal82aR6Ir7W0gy79m7/PLLqVOnDnXq1KFRo0YcPnyYuXPnZiXfevXqsW/fvqz/oQ+WlpZG/fr1s94nJSWxePFijh49WqSYinspXLNmzahYsSIbNmwIad+wYQPlypWjRYsWuY775ptvOHLkCOeee27Wsci8bK1hw4a5zoCckpJC1apV83zE1YEDB+jduze///47b775JtWrVw/3MIhIFPl9ojjl5vBlfju4YcMGqlWrllW8yHTGGWewadOmHOP27t3LwoULdfWFiL/8CRgEdDWz1d7rYgKFiwvN7BvgQu89zrm1wFxgHbAIuNkrLAPcBDxLIF9vIgJPIFFuLlhmMWPz5s1ZbR07dmTjxo1s2LCBjRs38t///pcdO3bkeKqfzptFoi+ur8D49ddfWbBgAQMHDmTo0KEhyzIf6bR06VK6d+/OeeedR0ZGBgsWLAiZpfi3335j8eLFDBkyJKttxIgRzJo1i8mTJ3Pffffl2G5qamq+t2gU91K4ypUr06VLF15++eWQxzvNmTOHc889N8cM9Zk6d+7M0qVLQ9oWLVrEQw89RGpqKk2bNs0xZs6cOfTp0yfXx/odPXqUyy+/nG+++YZzOnXmiqsGFbhfkZL5bHIRKVh+E8WZWfBEcR8HDcucEO4IYUwU521nKIErNfK931m5OTyvvvoqCQkJnHTSSQCcdNJJHDhwgK+++oqWLVtm9Vu1ahWNGzfOMf7111/n8OHDKmCI+Ihz7gNyn78CoFseYyYDk3NpXwmcGqnYlJvD8+GHH+a6TTPLys3ffPMN77zzDvPnz89aHnze/OGHH4YUeUQkcuK6gDFv3jwOHDjAqFGjOPvss0OW/elPf2Ly5MkkJyfTvXt32rRpw5VXXsn111/P5s2bad++PTt27OCRRx7BOcfIkSOzxrZr146pU6cyevRo1q1bx4ABA0hISGDz5s3MmDGDvXv35puII3Ep77333ktSUhKjR4+mX79+pKamkpqayqJFi7L6fPfddzRr1owZM2ZwzTXXkJCQQFJSUsh6MgsB5513Xo4ixccff8zmzZuZOnUqufnb3/5Gamoqjz/+ODOff5HL/98tWcuatGhNRe/eyWgYeVV0H7klUlpknygun3t9izVRHAQmiwOeAejQoUOec2QoN4fmZoBLL72Ujh07cvrpp5Oens6cOXOYM2cO06ZNo1y5wMWQF198MSeeeCL9+vVj/Pjx1KtXjzfffJO5c+fyj3/8I0csKSkptG3bltatWxd7v0Sk9FNuzpmbe/bsSffu3TnllFMoX748H374IY888ghXXnklzZo1yxo7ceJEWrVqRUJCAl988QUTJ05kwIABXHjhhVl9gs+bf/nlFz7++I/vDM444wwqV65c7P0UkTgvYCQnJ9O8efMcSRgCl6NdccUVJCcn8+STT1K5cmVmz57NpEmTeOaZZ/j+++855phjSEpK4sUXXyQxMfQLx5EjR3Laaafx97//nRtuuIF9+/aRmJhIjx49uP3226O+b507d+aVV17hnnvu4amnnqJJkya89NJLXHTRRVl9nHOkp6eTkZFRpG2kpKRQq1YtevXqlevyt956C4BRo0YB8NnKP77le/KVt6nfINcvaUUkRvw6UZxyc87c3LJlS2bMmMHWrVtxztGmTRtmz57NoEF/XNl2zDHHsHjxYu68805uvfVW9u3bR7NmzXj66adzfFu6c+dOFi9ezMSJE6O+zyJSOig358zNZ511FjNnzmTLli1UqFCBpk2b8uCDD+Z4dPWuXbsYPXo0O3fupFGjRtx2223ceuutIX2ynzcH27x5c65X0olI4VkxJ5qPug4dOriVK1eWdBhlXlL3HoyY8FjMtjfyqt5Me2lBocacP3wwAO8/kfd9kpHcXnFMHz+ad9/5T8y2J/5gZquccxGZbdGbKG4W8ItzbnRQ+8PArqBJPI91zt1hZqcALwEdCUwUtxho7k0U9wkwgsAtKKnAdOdcan7bV24um+Lhb0FxRCU3Z14Z+e67kV2vREwkc3NJUl6OUxHIEcrNUhrllZvj+goMEZEyLHOiuC/MbLXXdheBieHmmtn1wPfA5RCYKM7MMieKO0rOieJmEniM6kIiMFGciIiIiEikqYAhIhKH/DxRnIiIiIhINMT9Y1RFREREREREpPTTFRgiIiIiIiIRcvWgwWxL2x5W38fWfA7A6O49iry9zKcOipQFKmCIiIiIiIhEyLa07WFPqpnoTUJfnEk4R17Vu8hjReKNbiGJgHnz5nHaaadRpUoV2rRpw5w5cwocM3PmTMwsx+vpp58O6bdnzx6GDBnCscceS40aNejVqxcbN27Msb6UlBTOPPNMatSoQWJiItdccw0//lisJyFKFH27aRNJ3XvE7HX1oMElvcsiMVeU3Aywbt06unXrRrVq1TjhhBMYP3486enpWcvffffdXPO3mdGjR+g3aP/+9785/fTTqVy5Mk2aNGHq1KkR3UcRkXhT1Nxc2HPd0aNHY2bcdtttIe1JSUl55vBly5YVa99EJPp0BUYxffDBB1x66aX87W9/Y9q0aaSmpjJw4EDq1KkT8uzpvCxZsoSqVatmvW/atGnI8iuvvJIvv/ySxx9/nFq1ajFp0iS6devGF198Qc2aNQF44403GDhwIDfffDMPP/wwaWlp3HPPPfTu3ZuVK1dSrpzqVH6T7lxMH3c1ffzomG1LxA+Kmpt3795N9+7dadOmDfPmzWPTpk3ceuutZGRkMGnSJADOPPPMHCe533//PVdeeSW9evXKavvwww/p378/Q4YM4e9//zvLly9n7NixlCtXjtGjR0dlv0VE/Kyoubmw57rr1q1jxowZWefKwZ588kn27dsX0jZ+/Hg+++wzzjrrrMjsqIhEjQoYxTRx4kTOP/98pk2bBkCXLl1Yu3YtEyZMCKuAcdZZZ1GjRo1cly1btoy33nqLxYsX07VrVwDOPvtsmjRpwjPPPJNVUX7ppZc488wzeeKJJ7LG1qxZk759+/LVV1/RunXr4u6miEhcKWpufvrppzl48CCvvfYaNWvW5MILL2Tfvn3cf//93HHHHdSsWZOaNWtyzjnnhIz773//S7ly5bjiiiuy2iZMmEDnzp159tlnAbjooovYvXs3EyZM4G9/+xuVKlWKwp6LiPhXUXNzYc91R44cyahRo3j++edzrKtNmzYh73///XdWrlzJlVdeSYUK+l8jEb+L+6/mr732Wjp06MC///1vWrVqRZUqVejcuTPr1q2L+rYPHz7M0qVLQ05YAQYMGMCyZcvYu3dvsda/evVqKlSowAUXXJDVdtxxx3H66afz5ptvZrUdOXKEWrVqhYytXbs2AM65YsUgIlIU8ZqbFy5cSI8ePUK+tRswYAAHDx7kvffey3NccnIyF1xwASeccEJW2+rVq+nevXtIv8wihi5TFpGSEK+5uTDnuq+88grr169n3LhxYcW1aNEidu/ezcCBA8PqLyIlK+4LGADfffcdY8aM4d577+Wll15i79699OjRg0OHDuU77ujRowW+8isAbNq0iSNHjtCqVauQ9tatW5ORkcHXX39dYOzNmjWjQoUKtGzZkn/+858hyw4dOkSFChUoX758SHvlypVZv3591vshQ4bw3//+l9mzZ7Nv3z6+/vpr7rnnHrp06ZKjyiwiEivxmJs3bNiQY9yJJ55ItWrV2LBhQ65jvvnmGz777LMcJ7+HDh3KcZVF5cqVAUJyuIhILMVjbg73XPfgwYPceuutTJkyherVq4d1PFJSUkhMTOS8884Lq7+IlKxSUcDYuXMnL7zwAldffTX9+/dnwYIFpKWlMXPmzHzHVaxYscDXrFmz8hy/e/du4I8KcKY6deqELM9NgwYNmDhxIs8//zzz58/n7LPPZtiwYTz66KNZfU4++WQOHTrEF198kdV28OBBvvzyS3755Zestj//+c/MnDmToUOHUqtWLVq2bEl6ejqvvfZavvsvIhJN8Zibd+/enWNc5ti8xiUnJ1OxYkUuvfTSkPaTTz6ZTz75JKRtxYoVACE5XEQkluIxN4d7rvvggw/SoEED/vrXv+a7L5kOHDjA/PnzufLKKzGzsMaISMkqFTd61a9fn06dOmW9P+mkk2jfvj0rVqxg2LBheY7LfmKZmyZNmhTYJ3vCy6w+55cIe/ToETJbfa9evTh8+DCTJk1i1KhRlCtXjh49etCkSRNuvPFGnnvuOWrWrMm4cePYu3cvFStWzBq7dOlShg0bxqhRo+jVqxc//fQT999/P5dccgnvvPNOjis4RERiIR5zc17LnXN5jktJSeGiiy7i2GOPDWkfNmwYN910E//3f//HZZddxooVK3jkkUcAlJdFpMTEY24O51x38+bN/P3vf2fJkiVhFyPmz5/Pr7/+qttHfC7z6X2xktjgeF58Pu9inJSsUlPAyK0tLS0t33Ht2rUrcN35nWRmVoz37NkT0p75Prdv8fJz2WWXMXfuXLZs2ULTpk2pVKkSKSkpDBw4MOtyu86dO3PNNdewZMmSrHG33norf/nLX3jooYey2tq1a0erVq2YN28e/fv3L1QcIiKREI+5uU6dOjnGAezduzfXcWvWrGH9+vXcfffdOZYNGTKENWvWcNNNNzF06FCqVavGQw89xIgRIzjuuOPyjEFEJJriMTeHc647btw4evXqRatWrbLWmZGRweHDh9mzZw+1atXKUdhISUnh5JNPpkOHDgXum5QcPb1PgpWKAsaOHTtybTvllFPyHRd8FUNennvuOa699tpclzVr1oyKFSuyYcOGkIk2N2zYQLly5WjRokWB689NcHLt2LEjGzdu5Ouvv6ZChQo0a9aM3r17h8yAv2HDhhyV45YtW1K1alU2bdpUpBhERIorHnNzq1atcsx1sXXrVn777bcc921D4OS3atWq9O3bN8ey8uXL88QTTzBx4kR++OEHmjRpkrXu7E8xKaqrBw1mW9r2iKwrHFu2bInZtkQkOuIxN4dzrvvVV1+xZs2aHLeVPPHEEzzxxBNs3bqVhg0bZrXv3buXhQsXcscddxS4XyLiH6WmgPHRRx9lXQ73/fff8+mnn3LdddflO664l8JVrlyZLl268PLLL3PjjTdmtc+ZM4dzzz03x2zJBXn11VdJSEjgpJNOCmk3M1q2bAkEJot75513mD9/ftbyk046iU8//TRkzPr16zl48CCNGzcuVAwiIpESj7m5V69ePPzww+zfv59jjjkma1zVqlVDTriD19mnT588H4cNgW8dM795fPLJJ+nUqVOuxZCi2Ja2PabfSo28qnfMtlUSonGZ8mNrPgdgdC7r1WXKUhLiMTeHc6777LPP8uuvv4b0GTBgABdccAE33XQT9erVC1n2+uuvc/jwYd0+IhJnSkUBIyEhgUGDBjFx4kSqVq3K+PHjqV+/fp4V4EyRuFzs3nvvJSkpidGjR9OvXz9SU1NJTU1l0aJFWX2+++47mjVrxowZM7jmmmsAuPTSS+nYsSOnn3466enpzJkzhzlz5jBt2jTKlftjbtWJEyfSqlUrEhIS+OKLL5g4cSIDBgzgwgsvzOozbNgwbrnlFk444YSs+wInTJhA48aNufjii4u9jyIiRRGPuXnYsGFMmzaN/v37M3bsWL799lvuv/9+xowZE/JoVYCPP/6YzZs3M3Xq1Fxj+Pjjj/nggw9o164d+/btIzk5mf/85z988MEHxd4/iY5oXKacOHwwQK7r1WXKUhLiNTcXdK6bW3xVqlShUaNGJCUl5ViWkpJC27Ztad26dbH3S0Rip1QUME466STuuusuxo0bx3fffUeHDh1ITk6mSpUqUd92586deeWVV7jnnnt46qmnaNKkCS+99BIXXXRRVh/nHOnp6WRkZGS1tWzZkhkzZrB161acc7Rp04bZs2czaNCgkPXv2rWL0aNHs3PnTho1asRtt93GrbfeGtJn5MiRVKpUiaeeeoqnn36a2rVr07lzZx588MGwHyElIhJp8Zib69Spw+LFixk+fDh9+vShdu3a3HLLLdx///05tpGSkkKtWrXo1atXrjFUrFiROXPmcP/991OuXDnOO+88PvzwQ0477bSI76+ISLjiMTdH+lx3586dLF68mIkTJ0Zkv0QkdkpFAQOgf//+JTZZZb9+/ejXr1+eyxs3bpzjudgPPPAADzzwQIHrfuyxx3jsscfy7WNm3HTTTdx0003hhCsiEjPxlpsB2rRpEzJRcl4Kys/t27cP65JrEZFYi7fcXNRz3bzm7UlISODIkSOFWpeI+EO5gruIiIiIiIiIiJQsFTBERERERERExPfi/haSmTNnlnQIIiKSjXKziIj/KDeLSLzTFRgiIiIiIiIi4nsqYIiIiIiIiIiI76mAISIiIiIiIiK+pwKGiIiIiIiIiPieChgiIiIiIiIi4nsFFjDMbIaZ7TCzL4PajjWzt83sG+9nnaBld5rZRjP7ysx6BLW3N7MvvGXTzMwivzsiIiIiIiIiUhqFcwXGTKBntrZxwGLnXHNgsfceM2sDDABO8cY8aWblvTFPAUOB5t4r+zpFRERERERERHJVYAHDOfc+8Eu25r7ALO/3WUC/oPYU59xh59xmYCPQ0cwaADWdc8uccw6YHTRGRERERERERCRfRZ0D4zjnXBqA97O+154IbA3q94PXluj9nr09V2Y21MxWmtnKn3/+uYghioiIiIiIiEhpEelJPHOb18Ll054r59wzzrkOzrkO9erVi1hwIiIiIiLRornjRESiq6gFjJ+820Lwfu7w2n8AGgX1awj86LU3zKVdRERERKS0mInmjhMRiZqiFjDeAAZ7vw8G5gW1DzCzymbWhEDCXeHdZrLfzM7xKsjXBI0REREREYl7mjtORCS6wnmMajKwDGhpZj+Y2fXAFOBCM/sGuNB7j3NuLTAXWAcsAm52zqV7q7oJeJZAct4ELIzwvoiIlBm6TFlEJG5Ebe44zRsnImVNOE8hGeica+Ccq+ica+ic+5dzbpdzrptzrrn385eg/pOdc82ccy2dcwuD2lc65071lg33KsoiIlI0M9FlyiIi8azYc8dp3jgRKWsiPYmniIjEgC5TFhGJG5o7TkQkQlTAEBEpPfSIaxER/9HccSIiEaIChohI6adHXIuIxIDmjhMRia4KJR2AFM3VgwazLW17zLa3ZcuWmG1LRIrsJzNr4JxL02XKIiKx55wbmMeibnn0nwxMzqV9JXBqBEMTESkVVMCIU9vStjNiwmMx297Iq3rHbFsiUmSZlylPIedlyi+Z2VTgBP64TDndzPab2TnAcgKXKU+PfdgiIiIiIgVTAUNEJA55lyknAQlm9gNwH4HCxVzvkuXvgcshcJmymWVepnyUnJcpzwSqErhEWZcpi4iIiIgvqYAhIhKHdJmyiIiIiJQ1msRTRERERERERHxPBQwRERERERER8T0VMERERERERETE91TAEBERERERERHf0ySeImXAt5s2kdS9R8y2l9jgeF58flbMticiIiKSl6sHDWZb2vaYbW/Lli0x25ZIWaMChkgZkO4cIyY8FrPtTR8/OmbbEhEREcnPtrTtMT0PGnlV75htS6Ss0S0kIiIiIiIiIuJ7KmCIiIiIiIiIiO+pgCEiIiIiIiIivqcChoiIiIiIiIj4ngoYIiIiIiIiIuJ7KmCIiIiIiIiIiO+pgCEiIiIiIiIivlehpAMQEREpLa4eNJhtadtjtr0tW7bEbFsiIiJlwbebNpHUvUfMtpfY4HhefH5WzLYX71TAEBERiZBtadsZMeGxmG1v5FW9Y7YtiTydJIuI+E+6czH9Wz59/OiYbas0UAFDREREpAToJFlERKRwNAeGiIiIiIiIiPieChgiIiIiIiIi4nsqYIiIiIiIiIiI76mAISIiIiIiIiK+pwKGiIiIiIiIiPienkISIVcPGsy2tO0x296WLVtiti0RERERERGRkqYCRoRsS9se00ehjbyqd8y2JSIiIiIiIlLSdAuJiIiIiIiIiPiersAQkYj7dtMmkrr3iNn2Ehscz4vPz4rZ9kREREREJPZUwBCRiEt3Lqa3VE0fPzpm2xIRERERkZKhAoaIiIiIiMRErCe+B01+L1KaqIAhIiIiIiIxEeuJ70GT34uUJprEU0RERERERER8T1dgiIiIiIiIiJQATX5fODEvYJhZT+BxoDzwrHNuSqxjEBGRUKU1N8f6XmvdZy0ikVRac7OI/EGT3xdOTAsYZlYe+AdwIfAD8ImZveGcWxfLOERE5A+lOTfH+l5r3WctfqZv+eJLac7NIiJFFesrMDoCG51z3wKYWQrQF4h4Ita3biJlh07Kiy1muVlESo6+5Ys7ys0iItnEuoCRCGwNev8DcHY0NqRv3UTKDp2UF1vMcrOKyyJlh4rLxRaT3Ky8LFK2xHtuNudcxFZW4MbMLgd6OOdu8N4PAjo650Zk6zcUGOq9PRX4MmZBhi8B2FnSQeTCj3H5MSZQXIXlx7j8GBPkHddJzrl6sQ6mIEXMzS2Br2IaaN78+jmIBu1r6aR9LVlxm5vzyct+PM6KKTyKKTyKKTzxHFOuuTnWV2D8ADQKet8Q+DF7J+fcM8AzAGa20jnXITbhhU9xhc+PMYHiKiw/xuXHmMC/ceWj0LnZT+LweBeZ9rV00r5KHgrMzXnlZT8eZ8UUHsUUHsUUntIYU7lIBhOGT4DmZtbEzCoBA4A3YhyDiIiEUm4WEfEf5WYRkWxiegWGc+6omQ0H/kPgcVAznHNrYxmDiIiEUm4WEfEf5WYRkZxifQsJzrlUILUQQ3x3ubJHcYXPjzGB4iosP8blx5jAv3HlqQi52U/i7ngXg/a1dNK+Sq6KkZv9eJwVU3gUU3gUU3hKXUwxncRTRERERERERKQoYj0HhoiIiIiIiIhIofmmgGFmPc3sKzPbaGbjclneysyWmdlhM7vNR3FdbWafe6+PzKytD2Lq68Wz2sxWmlnnaMcUTlxB/c4ys3Qzu8wPcZlZkpnt9Y7XajMbX9IxBcW12szWmtl70Y4pnLjM7Pag4/Sl99/xWB/EVcvM5pvZGu94XeeDmOqY2evev8UVZnZqtGMqzfya96LBr7k0GvyYn6PFr3k/Gvz6t6Q08uP5s86dIxNTUL+Y5Xo/5mQ/5k4/5rgyda7snCvxF4GJiTYBTYFKwBqgTbY+9YGzgMnAbT6KqxNQx/u9F7DcBzHV4I/bg04HNvjhWAX1W0Lgfs7L/BAXkAQsiMVnqhAx1QbWASd67+v7Ia5s/fsAS/wQF3AX8JD3ez3gF6BSCcf0MHCf93srYHGsPmOl7eXXvFdS+xrUL2a5tAT/u8Y0P5fwvsY875fUvmbrH5O/JaXxFebnKqbnz2HGVObPnf2Y6/2Yk/2YO/2Y48I8TqXmXNkvV2B0BDY65751zv0OpAB9gzs453Y45z4Bjvgsro+cc7u9tx8TeEZ3Scf0q/M+CUB1IBYTnRQYl2cE8CqwIwYxFSauWAonpquA15xz30Pg8++TuIINBJJ9EpcDjjEzI3AS8gtwtIRjagMsBnDObQAam9lxUYypNPNr3osGv+bSaPBjfo4Wv+b9aPDr35LSyI/nzzp3jlBMnljmej/mZD/mTj/muDJ1ruyXAkYisDXo/Q9eW0krbFzXAwujGlGYMZnZJWa2AXgTGBLlmMKKy8wSgUuAp2MQT9hxec71LqlaaGan+CCmFkAdM3vXzFaZ2TVRjincuAAws2pATwJ/VP0Q1xNAa+BH4AtglHMuo4RjWgP0BzCzjsBJRP8krbTya96LBr/m0mjwY36OFr/m/Wjw69+S0siP5886d45QTCWQ6/2Yk/2YO/2Y48rUubJfChiWS5sfvj0LOy4z60IgCY+NakRhxuSce9051wroB0yMckwQXlyPAWOdc+nRDydLOHF9CpzknGsLTAf+7YOYKgDtgT8DPYB7zayFD+LK1Af40Dn3SxTjyRROXD2A1cAJQDvgCTOrWcIxTSHwB3U1gW9QPiO6le7SzK95Lxr8mkujwY/5OVr8mvejwa9/S0ojP54/69w5cjE9RmxzvR9zsh9zpx9zXJk6V65Q7NAi4wegUdD7hgSqQyUtrLjM7HTgWaCXc26XH2LK5Jx738yamVmCc25nCcfVAUgJXLlEAnCxmR11zv27JONyzu0L+j3VzJ6M8vEK51j9AOx0zv0G/GZm7wNtga+jFFO4cWUaQOwu+Q0nruuAKd7lnxvNbDOBe+lWlFRM3ufqOgDvcr3N3ksKz695Lxr8mkujwY/5OVr8mvejwa9/S0ojP54/69w5cjHFOtf7MSf7MXf6MceVrXNlV8SJOSL5IlBI+RZowh+TfJySR9/7id0kngXGBZwIbAQ6+Simk/ljIqIzgW2Z7/3w39DrP5PYTOIZzvE6Puh4dQS+j+bxCjOm1gTuCasAVAO+BE4t6WPl9atF4L656tH+71eI4/UUcL/3+3HeZz6hhGOqjTc5EvD/gNmxOF6l8eXXvFdS+5qtf0xyaQn+d41pfi7hfY153i+pffX6xfRvSWl8FSZfEKPz5zA/62X+3NmPud6POdmPudOPOS7M41RqzpV9cQWGc+6omQ0H/kNgxtIZzrm1ZjbMW/60mR0PrARqAhlmNprATKb78lpvLOICxgN1gSe9CulR51yHEo7pUuAaMzsCHASudN4no4Tjirkw47oMuMnMjhI4XgOiebzCick5t97MFgGfAxnAs865L6MVU7hxeV0vAd5ygUp31IUZ10Rgppl9QeCStbEuit/QhhlTa2C2maUTmB37+mjFU9r5Ne9Fg19zaTT4MT9Hi1/zfjT49W9JaeTH82edO0c0ppjyY072Y+70Y44ra+fKFsXPnIiIiIiIiIhIRPhlEk8RERERERERkTypgCEiIiIiIiIivqcChoiIiIiIiIj4ngoYIiIiIiIiIuJ7KmCIiIiIiIiIiO+pgCEiIiIiIiIivqcChoiIiIiIiIj4ngoYIiIiIiIiIuJ7KmCIiIiIiIiIiO+pgCEiIiIiIiIivqcChoiIiIiIiIj4ngoYIiIiIiIiIuJ7KmCIiIiIiIiIiO+pgCFxycy2mFl37/dfg14ZZnYw6P3VZna/mR3x3u8xs4/M7Nxc1vmcmTkzOzmobW229R81s/lBy8ub2SQz+9HM9pvZZ2ZWOyYHQUTEZ/yQm83svGzLfvXGXxq7IyEi4g9+yMve8q5m9qmZ7TOzb81saGyOgJQ2KmBI3HPO1ch8Ad8DfYLaXvS6zfGW1wM+AF4zM8tch5l1Bprlsu5TgtZ9jLf+l4O6/A/QCTgXqAkMAg5Ffi9FROJLSeVm59x/s227N/ArsCiKuysi4nsllZfNrCLwOvBPoBZwJTDVzNpGbWel1FIBQ8oU59wRYBZwPFAXwMwqANOB4QUMPx+oD7zqjasDjAb+n3PuOxfwpXNOBQwRkUKIZG7OxWDgFefcb5GJVkSk9ItwXj6WwBd9z3vny58A64E2UQhdSjkVMKRMMbPKwLXAD865nV7zLcD7zrnPCxie/ST4NOAocJmZbTezr83s5mjELSJSmkU4NwevtxpwGYGTcBERCVMk87Jz7icgGbjOu/36XOAkAld4iBRKhZIOQCRGrjCz3sDvwJdAPwAzawTcCLTPb3DQSfBfgpobErgMrgXQBGgOLDazr51zb0d6B0RESqFo5OZglwI7gfciFK+ISGkXrbycDDwLPO69v8k5tzVyYUtZoQKGlBVznXN/zaX9MWCCc25vAeP7A78QehJ80Ps5wTl3EPjczFKAiwEVMEREChaN3BxsMDDbOeeKHqKISJkS8bxsZq2AOcAlBM6RmwMLzOxH59ybEYlaygzdQiJlXTfgYe8WkO1e2zIzuypbv9xOgjMvn9OJsYhIZBUnNwNZ3xYmAbOjGqmISNlQnLx8KvCVc+4/zrkM59xXwJtAr+iHLaWNrsCQeFbRzKoEvT9ahHW0ILSQlwb0AdZkNphZQ6ALMCx4oHNuk5n9F7jbzEYCTQnMqjywCHGIiJQWJZqbgwwCPnLObSrC9kVESpOSzsufAc3NrCuwlMA5c2/goSLEIWWcChgSz1KzvZ9c2BU453YEv/eeErXTuyUk0yBgWR4nwQOBfwG7gB3Avc65xYWNQ0SkFPFDbga4Bni4sNsWESmFSjQve1/6DQGmEZi8cy/wIoFzaJFCMd0WKiIiIiIiIiJ+pzkwRERERERERMT3VMAQEREREREREd9TAUNEREREREREfE8FDBERERGRCDCzKma2wszWmNlaM/sfr/1YM3vbzL7xftYJGnOnmW00s6/MrEdQe3sz+8JbNs28WRNFRMoy30/imZCQ4Bo3blzSYYhILHz1VeBny5YlG0eUrFq1aqdzrl5JxxEJys0icaqU59miiGRu9ooM1Z1zv5pZReADYBTQH/jFOTfFzMYBdZxzY82sDZAMdAROAN4BWjjn0s1shTf2YwJPkZjmnFuY17aVl6VUU+4qc/LKzb5/jGrjxo1ZuXJlSYchIrGQlBT4+e67JRlF1JjZdyUdQ6QoN4vEqVKeZ4sikrnZBb4Z/NV7W9F7OaAvkOS1zwLeBcZ67SnOucPAZjPbCHQ0sy1ATefcMi/G2UA/IM8ChvKylGrKXWVOXrlZt5CIiIiIiESImZU3s9XADuBt59xy4DjnXBqA97O+1z0R2Bo0/AevLdH7PXu7iEiZpgKGiEgc0n3WIiL+5JxLd861AxoSuJri1Hy655ZvXT7toYPNhprZSjNb+fPPPxcpXhGReKIChohIfDoMdHXOtQXaAT3N7BxgHLDYOdccWOy9x7vPegBwCtATeNLMynvregoYCjT3Xj1juB8iIqWSc24PgVtFegI/mVkDAO/nDq/bD0CjoGENgR+99oa5tGffxjPOuQ7OuQ716pWKKZZERPKlAoaISBxyAXndZz3La59F4J5pCLrP2jm3Gci8z7oB3n3W3r3bs4PGiIhIIZhZPTOr7f1eFegObADeAAZ73QYD87zf3wAGmFllM2tCoIi8wrvNZL+ZneNdFXdN0BgRkTLL95N4Stl09aDBbEvbHrPtJTY4nhefn1VwRxEf8a6gWAWcDPzDObfczELuszaz4PusPw4annk/9RHCvM/azIYSuFKDE088MZK7InFCuVmkQA2AWV5+LgfMdc4tMLNlwFwzux74HrgcwDm31szmAuuAo8DNzrl0b103ATOBqgQm78xzAk8p25SbpSxRAUN8aVvadkZMeCxm25s+fnTMtiUSKd5Jbjvv277Xo3mftbe9Z4BnADp06ODvZ3BLVCg3i+TPOfc5cEYu7buAbnmMmQxMzqV9JZBfXhcBlJulbNEtJCIicS4W91mLiIiIiJQ0XYEhIhKHzKwecMQ5tyfoPuuH+OM+6ynkvM/6JTObCpzAH/dZp5vZfm8C0OUE7rOeHtu9ERERkXjx7aZNJHXvUXDHCElscDwvxmxr4ncqYIiIxCfdZy0iIiIxl+6cblmREqMChohIHNJ91iIiIiJS1mgODBERERERERHxPRUwRERERERERMT3VMAQEREREREREd9TAUNEREREREREfE8FDBERERERERHxPRUwRERERERERMT3VMAQEREREREREd9TAUNEREREREREfK9CSQcgIiIikptvN20iqXuPmG0vscHxvPj8rJhtT0RERApHBQwRdJIsIuJH6c4xYsJjMdve9PGjY7YtERERKTwVMETQSbKIiIiIiIjfaQ4MEREREREREfE9FTBERERERERExPdUwBARERERERER31MBQ0RERERERER8TwUMEREREREREfE9FTBERERERERExPdUwBARERERERER31MBQ0RERERERER8r9QUMJxzNGnSBDNj48aNOZbff//9JCQk5Dr2tttuo3Hjxjna3333XXr37k1CQgKVKlWicePGjBw5ku+//z7S4edq3rx5nHbaaVSpUoU2bdowZ86csMYdPXqUKVOm0Lx5cypXrkzDhg255ZZbcvT74osv6N27N7Vq1eKYY46hY8eOrFq1Kmv5K6+8QqdOnahbty5VqlShZcuWTJo0id9//z1i+ygipZty8x8Kys2///47V1xxBU2bNqVq1arUq1ePXr16heTlcNclIpIf5eaAmTNnYmY5Xk8//XSeY0aPHo2Zcdttt+VYptwsEn2lpoCxbNkytmzZAkBKSkqx1zdt2jS6du1K1apV+ec//8k777zDfffdx2effUbfvn2Lvf6CfPDBB1x66aV06dKFhQsX8uc//5mBAwfy1ltvFTj2uuuuY9q0adx222289dZbTJkyhapVq4b0Wb16NZ06daJ27drMmTOHl19+mT59+nDw4MGsPrt27aJLly48++yzLFy4kCFDhjB58mTGjBkT8f0VkdJJufkPBeXm9PR0zIw777yTN998k//7v//jwIEDdO3alW+//bZQ6xIRyY9yc6glS5awbNmyrFf//v1z7bdu3TpmzJhBzZo1c12u3CwSfRVKOoBISU5Opnr16px66qkkJydzzz33FHldn332GWPGjOGee+5hwoQJWe3nn38+1113HQsWLIhEyPmaOHEi559/PtOmTQOgS5curF27lgkTJnDRRRflOW7RokWkpKSwZs0a2rRpk2e/YcOG0adPH1544YWstp49e4b0ufHGG0Ped+nShX379vGPf/yD6dOnY2ZF2TURKUOUmwPCyc1Vq1bN8Y1h9+7dqVu3Lv/+97+zisfh5nkRkbwoN4c666yzqFGjRoH9Ro4cyahRo3j++edzLFNuFomNAq/AMLNGZrbUzNab2VozG+W1H2tmb5vZN97POkFj7jSzjWb2lZn1CGpvb2ZfeMumWYT+Dzg9PZ2XX36Zv/zlLwwZMoR169bx+eefF3l906dPJyEhgXvvvTfX5b179y7yusNx+PBhli5dyhVXXBHSPmDAAJYtW8bevXvzHDtjxgy6du2ab+Jct24dy5cvZ8SIEYWOrW7durqFRETCotz8h3Byc26qV69OlSpVQvJuUdclIgLKzUX1yiuvsH79esaNG5frcuVmkdgI5xaSo8CtzrnWwDnAzWbWBhgHLHbONQcWe+/xlg0ATgF6Ak+aWXlvXU8BQ4Hm3iv0K/8iWrJkCT/99BMDBgzgsssuo2LFiiQnJxd5fe+99x7dunWjYsWKRRp/9OjRAl/OuTzHb9q0iSNHjtCqVauQ9tatW5ORkcHXX3+d59jly5fTokULhg8fTs2aNalWrRr9+/fnxx9/DOkDsHv3btq2bUuFChVo1qwZ//rXv3JdZ3p6OlcOvJozOnTkrrvvpm69+nS5sCdJ3XtE7ZV5WaOIxC/l5j+Ek5szOec4evQo27dv54477qB8+fIMHDiwSOsSEclOuTmnZs2aUaFCBVq2bMk///nPHMsPHjzIrbfeypQpU6hevXqu61BuFomNAm8hcc6lAWne7/vNbD2QCPQFkrxus4B3gbFee4pz7jCw2cw2Ah3NbAtQ0zm3DMDMZgP9gIXF3Ynk5GRq165Nz549qVSpEhdeeCEpKSk88MADRbrNYdu2bZx44olFjiecBP7cc89x7bXX5rps9+7dANSuXTukvU6dOiHLc7N9+3ZmzpxJ27ZtSUlJYf/+/dxxxx1ccsklfPzxx5gZ27dvB+Caa67hjjvu4KyzzuKVV17hhhtuoEGDBlx88cUh66xevTqHDx8G4IKefbn57smUKxfd6VNGXhXdar2IRJ9y8x/Cyc2ZHnroIe68804A6tWrR2pqKieddFKR1iUikp1y8x8aNGjAxIkT6dixI+np6SQnJzNs2DAOHDgQMvnmgw8+SIMGDfjrX/+a57qUm0Vio1BzYJhZY+AMYDlwnFfcwDmXZmb1vW6JwMdBw37w2o54v2dvz207QwlcqVFgQjx8+DCvv/46l1xyCZUqVQJg4MCBDBo0iI8//phzzz23MLsYHEORxgF88sknBfZp0qRJoWPIrD7nF5tzDucc8+bNo27dukAgOV9wwQUsWbKEbt26kZGRAcANN9zAHXfcAQTuFVy/fj0PPvhgjgLGRx99xPVDh9G2Uxdefu4p/jV1Ev/vtvEFxi8iZZdyMzn6FJSbM1177bV0796dtLQ0nnzySXr37s3777+fdVlyYdYlIhJMuTlUjx496NEj6253evXqxeHDh5k0aRKjRo2iXLlybN68mb///e8sWbIkYnleRIou7AKGmdUAXgVGO+f25fMPOLcFLp/2nI3OPQM8A9ChQ4e8rxkDFi5cyJ49e7j44ovZs2cPAElJSVSuXJnk5OSsRFyhQgXS09NzXUd6ejoVKvxxKBITE4v1yKd27doV2Kd8+fJ5LsusGGfuT6bM99krzNnHNm3aNCtxAnTu3JlKlSqxbt06unXrxrHHHgsEihbBunbtyqOPPppjnWeeeSa1atehz4BrOaZWHZ6YdCd9BlzL8Q2LXm0XkdJNuTnn2IJyc6bjjz+e448/HgicTJ9yyilMmTKF2bNnF3pdIhJbZtYImA0cD2QAzzjnHjezY4E5QGNgC3CFc263N+ZO4HogHRjpnPuP194emAlUBVKBUS6/eynCoNxcsMsuu4y5c+eyZcsWmjZtyrhx4+jVqxetWrXKWmdGRgaHDx9mz5491KpVCzNTbhaJkbDuAzCzigSKFy86517zmn8yswbe8gbADq/9B6BR0PCGwI9ee8Nc2osl8569yy+/nDp16lCnTh0aNWrE4cOHmTt3blbyrVevHvv27ePAgQM51pGWlkb9+vWz3iclJbF48WKOHj1apJgqVqxY4GvWrFl5jm/WrBkVK1Zkw4YNIe0bNmygXLlytGjRIs+xrVu3zrXdOZd120c4ffLStGXgG8AdaT/k209Eyjbl5lBFzbsVKlTgtNNOC3mManFyuIhEna/njlNuDl/ml7VfffUVr732WtbxqlOnDlu3buWJJ56gTp06bNu2DVBuFomVcJ5CYsC/gPXOualBi94ABnu/DwbmBbUPMLPKZtaEQMJd4d1ust/MzvHWeU3QmCL59ddfWbBgAQMHDmTp0qUhr6lTp/LTTz+xdOlSAM477zwyMjJyPMrpt99+Y/HixZx33nlZbSNGjODnn39m8uTJuW43NTU137g++eSTAl99+vTJc3zlypXp0qULL7/8ckj7nDlzOPfcc6lVq1aeY3v37s3nn3/Ozp07s9ref/99jhw5Qtu2bQHo1KkTderUYfHixSFjFy9enNUnLxs+/xSA+g0a5ttPRMou5eacwsnNuTl06BCffvppyOXTRV2XiESfcy7NOfep9/t+IHjuuMz/C59FYB44CJo7zjm3GcicO64B3txx3lUXs4PGFIlyc3heffVVEhISsuYeevbZZ3Mcr+OOO44rrriCpUuXUq9ePUC5WSRWwrmF5E/AIOALM1vttd0FTAHmmtn1wPfA5QDOubVmNhdYR6AKfbNzLvMatJv441K4hRRzAs958+Zx4MABRo0axdlnnx0a9J/+xOTJk0lOTqZ79+60adOGK6+8kuuvv57NmzfTvn17duzYwSOPPIJzjpEjR2aNbdeuHVOnTmX06NGsW7eOAQMGkJCQwObNm5kxYwZ79+7NMU9EsA4dOhRntwC49957SUpKYvTo0fTr14/U1FRSU1NZtGhRVp/vvvuOZs2aMWPGDK655hoAhg4dyrRp0+jTpw933XUX+/fvZ+zYsXTv3p3OnTsDUKlSJcaPH88dd9xB7dq1Oeuss3j11Vd5//33ee+997LW37NnT7p3784pp5zCL7t2MufZ6cxPmUmnbr10+4hICfPzZcrKzUXLzcnJySxcuJCePXtywgknZM2BkZaWxpgxY7LWH866RKTkxWruuHApN+fMzZdeeikdO3bk9NNPJz09nTlz5jBnzhymTZuWddVEbvFVqVKFRo0akZSUlNWm3CwSG+E8heQDcp+/AiDXm7mcc5OBHGVY59xK4NTCBJif5ORkmjdvniMJQ+BytCuuuILk5GSefPJJKleuzOzZs5k0aRLPPPMM33//PccccwxJSUm8+OKLJCaG/k0YOXIkp512Gn//+9+54YYb2LdvH4mJifTo0YPbb789UruQp86dO/PKK69wzz338NRTT9GkSRNeeuklLrrooqw+zjnS09OzJuUEqFmzJkuWLGHkyJEMGDCASpUq0bdv3xxzW4wePZqMjAymT5/O/fffT8uWLXnllVdCKupnnXUWM2fOZMuWLRw6fJhfDxzkqhtv4aJLroz6/otIgTIvU/7UzI4BVpnZ28C1BC5TnmJm4whcpjw222XKJwDvmFkLr8CceZnyxwQKGD0pRoFZubloublly5a88MILjBkzht27d9OgQQPOPvtsVq5cySmnnFKodYlIyYrV3HGFmfheuTlnbm7ZsiUzZsxg69atOOdo06YNs2fPZtCgQYWOQblZJDasmHMBRV2HDh3cypUrSzqMMi+pew9GTHgsZtsbeVVvpr20oOCOcbq96eNH8+47/4nZ9uJG5jcZ775bklFEjZmtcs4V/6um3Nc9D3jCeyV53/A1AN51zrX0rr7AOfeg1/8/wP0ErtJY6pxr5bUP9MbfmN/2lJvLptL+t6BM5OZSnmeLItK52Zs7bgHwn8zbr83sK6Kcm5WXy64ykZuPHg68Ue4qM/LKzZpRRkQkzuV3mTIQfJny1qBhmZcjJ1KIR1yb2UozW/nzzz9HdB9EREoDP88dJyJSGqiAISISx7Jfppxf11zaCv2Ia+dcB+dch8xJy0REJETm3HFdzWy197qYwNxxF5rZN8CF3nucc2uBzLnjFpFz7rhnCUzsuYlizh0nIlIahDOJp4iI+FB+j7gOuky5RB5xLSJSFvl57jgRkdJAV2CIiMQhXaYsIiIiImWNrsAQEYlPvn3EtYiIiIhINOgKjAiYN28ep512GlWqVKFNmzbMmTMnrHHr1q2jW7duVKtWjRNOOIHx48eTnp4e0sc5xwMPPECjRo2oWrUq559/PqtXrw7p88orr9CpUyfq1q1LlSpVaNmyJZMmTeL333+P1C6KiM845z5wzplz7nTnXDvvleqc2+Wc6+aca+79/CVozGTnXDPnXEvn3MKg9pXOuVO9ZcOd3x9PFaaSzs0AR48eZcqUKTRv3pzKlSvTsGFDbrnllkjsnohIXCpKbt64cSM33ngjbdu2pXz58iRlPk0nF1988QW9e/emVq1aHHPMMXTs2JFVq1aF9Pn3v//N6aefTuXKlWnSpAlTp07NY20i4jcqYBTTBx98wKWXXkqXLl1YuHAhf/7znxk4cCBvvfVWvuN2795N9+7dMTPmzZvH+PHjeeSRR7jvvvtC+k2ZMoWJEycyduxY5s+fT40aNejevTvbt2/P6rNr1y66dOnCs88+y8KFCxkyZAiTJ09mzJgxUdlnERG/80NuBrjuuuuYNm0at912G2+99RZTpkyhatWqEd9fEZF4UNTcvHbtWlJTU2nRogUtWrTIs9/q1avp1KkTtWvXZs6cObz88sv06dOHgwcPZvX58MMP6d+/Px07dmT+/PkMGTKEsWPH8thjj0VqN0UkinQLSTFNnDiR888/n2nTpgHQpUsX1q5dy4QJE7jooovyHPf0009z8OBBXnvtNWrWrMmFF17Ivn37uP/++7njjjuoWbMmhw4dYsqUKdx5550MHz4cgHPPPZfGjRvzxBNPMGnSJABuvDH0keBdunRh3759/OMf/2D69OkEbmsXESk7/JCbFy1aREpKCmvWrKFNmzZR2c+rBw1mW9r2gjtGyJYtW2K2LREpfYqam/v06UPfvn0BuOyyy9i5c2eu/YYNG0afPn144YUXstp69uwZ0mfChAl07tyZZ599FoCLLrqI3bt3M2HCBP72t79RqVKlYu0jKDeLRFPcFzCuvfZavvzyS+655x7GjRvHli1b6NChA88880zUThgzHT58mKVLl2Yl4UwDBgzguuuuY+/evdSqVSvXsQsXLqRHjx7UrFkzZNzYsWN577336NOnDx999BH79u3jiiuuyOpTvXp1+vTpw8KFC7NOknNTt25d3UIiIiVGuRlmzJhB165do7q/29K2M2LCY1Fbf3Yjr+ods22JSOTFa24uV67gi8bXrVvH8uXLefTRR/Ptt3r1am6++eaQtosuuohHH32UZcuWccEFFxS4rYIoN4tET6m4heS7775jzJgx3Hvvvbz00kvs3buXHj16cOjQoXzHHT16tMBXfreCb9q0iSNHjtCqVauQ9tatW5ORkcHXX3+d59gNGzbkGHfiiSdSrVo1NmzYkNWnfPnyNG/ePMf6M/sES09P58CBA3zwwQdMmzaNm266SVdfiEiJKeu5efny5bRo0YLhw4dTs2ZNqlWrRv/+/fnxRz2lVkRKTjzm5nAsX74cCNwK2LZtWypUqECzZs3417/+FdLv0KFDOa6yqFy5MgDr168vVgwiEn2looCxc+dOXnjhBa6++mr69+/PggULSEtLY+bMmfmOq1ixYoGvWbNm5Tl+9+7dANSuXTukvU6dOiHL8xqbfVzm2Mxxu3fvpkaNGpQvXz5HnwMHDuS4wqJ69epUr16d8847jwsuuICHH344z+2LiERbWc/N27dvZ+bMmaxevZqUlBSee+45Vq1axSWXXJLvSb6ISDTFY24OR+YcRNdccw1XX301b7/9Nj179uSGG24gNTU1q9/JJ5/MJ598EjJ2xYoVAPzyyy+IiL/F/S0kAPXr16dTp05Z70866STat2/PihUrGDZsWJ7jsiev3DRp0qTAPtmvcsg8MS3o6ofcljvnQtrz6pPbso8++ogDBw6wYsUKJkyYwPDhw3nyyScLjF9EJBrKem52zuGcY968edStWxeABg0acMEFF7BkyRK6detW4D6IiERavObmgmRkZABwww03cMcddwCBOTbWr1/Pgw8+yMUXXwwE5sm46aab+L//+z8uu+wyVqxYwSOPPAKQozAtIv5TagoYubWlpaXlO65du3YFrju/RJZZMd6zZ09Ie+b73L7FCx6bfRzA3r17s8bVqVOH/fv3k56eHhLHnj17qFatGhUrVgwZe+aZZwLQuXNnEhISGDx4MLfeeivNmjXLMw4RkWgp67m5Tp06NG3aNKt4AYH8XKlSpaxHtYqIxFo85uZwHHvssUCgaBGsa9euIfNiDBkyhDVr1nDTTTcxdOhQqlWrxkMPPcSIESM47rjjihWDiERfqbiFZMeOHbm2NWjQIN9xxb0UrlmzZlSsWDHHfBQbNmygXLly+T7mqVWrVjnGbd26ld9++y3r3sBWrVqRnp7Oxo0bc6w/+/2D2WUWMzZv3pxvPxGRaCnrubl169a5bsM5F9aEdCIi0RCPuTkc4ebc8uXL88QTT/Dzzz/z+eef89NPP3HOOf+fvTsPj6JK+///vmVfRKJBxYAGEVncGImoDGoQFFAYEDfQL6A4P4QREJcRVEQekBEvHxkEBx0eBwFHAsroIA7gAiqyKKKDC4sKEhUEEWRTECGc3x9dCd1JJ+kkvVQnn9d19ZX06VNVd1Wau4u7T526CCDvp4j4V7kYgbF9+3aWL1+eNxzu22+/5eOPP+bWW28tcrmyDoWrVq0a7dq146WXXgq5lens2bO5+OKLC51JGaBz5848/vjj7Nu3j2OPPTZvuRo1auTNftymTRvq1KnDSy+9xIgRIwDYv38/8+bNo3///kXGvWzZsmLjFxGJpYqem7t06cLDDz/Mjh07SE1NBWDJkiUcOnSI8847r9h9FBGJhWTMzZFo06YNKSkpLFq0iI4dO+a1L1q0KGzOTUlJyRsVMnnyZNq0aVPsF4QiknjlooCRmppK7969GTNmDDVq1GDkyJGceOKJ3HLLLUUul5GRUeZtP/TQQ2RmZjJ06FC6d+/O/PnzmT9/PgsXLszr880339C4cWOmTp1Knz59gMD1dxMnTqRHjx4MGzaMr7/+mlGjRnH33Xfn3b6vevXqDB8+nDFjxpCSkkKzZs0YP348R44cYfDgwXnr79SpEx06dOCss86iUqVKLFu2jCeeeIIbb7xRl4+ISMJU9Nzcv39/Jk6cSNeuXXnggQfYt28fw4YNo0OHDrRt27bM+ygiUhrJmJv379+fNxHnli1b2Lt3L3PmzAHgqquuombNmlStWpWRI0dy3333UbduXS644AL+9a9/sWTJEt5999289b///vssXbqUli1bsnfvXrKysnj99ddZunRpmfdPRGKvXBQwTjvtNB544AGGDx/ON998Q0ZGBllZWVSvXj3m227bti1z5sxhxIgRPP300zRq1IiZM2dy5ZVX5vVxzpGTk5M3uRCQVyEeNGgQXbt2pW7dutx1112MGjUqZP3Dhw/nyJEjPProo+zcuZOMjAzefPPNkGv0LrjgAqZNm0Z2djaVK1fm9NNP59FHHy1yIiYRkVir6Lm5Tp06LF68mCFDhtCzZ0+qVq1Kt27dQq7FFhGJt2TMzdu3b+f6668PWVfu802bNpGeng7A0KFDOXLkCJMmTWLUqFE0bdqUOXPmcMkll+QtV6VKFWbPns2oUaM45phjuOSSS1i2bBnnnHNODPdcRKKlXBQwAHr06EGPHj0Ssu3u3bvTvXv3Ql9PT08Pe8u8Fi1asHjx4iLXbWY8+OCDPPjgg4X2GTNmDGPGjIk4XhGReKnIuRkCt+sLvn2fiIgfJFtuLixfh3P33Xdz9913F/p6q1atIrocRkT8SbOIiYiIiIiIiIjvqYAhIiIiIiIiIr6X9JeQTJs2LdEhiIhIPsrNIiL+o9wsyejrjRtZvfsnAIZ26FhM77JLq38yLzxf+C2BJbGSvoAhIiIiIiIi5VOOc6Q1OgOAwaMnxHx7k0YOjfk2pPR0CYmIiIiIiIiI+J4KGCIiIiIiIiLieypgiIiIiIiIiIjvaQ4MkQT4euNGMuMwCVEuTUYkIiIiIiLJTgUMkQTIcS4ukxDl0mREIiLFU3FZRETE31TAEBEREUHFZREREb/THBgiIiIiIiIi4nsqYIiIiIiIiIiI76mAISIiIiIiIiK+V2wBw8ymmtl2M/s8qO14M3vTzL7yfqYEvXa/mW0wsy/MrGNQeysz+8x7baKZWfR3R0RERERERETKo0hGYEwDOuVrGw4scs41ARZ5zzGzFkBP4CxvmclmVslb5mmgP9DEe+Rfp4iIiIiIiIhIWMUWMJxzS4Cf8jV3A3Lv+zUd6B7UPss5d9A5twnYALQ2s/pAHefcCuecA2YELSMiIiIikvQ0cllEJLZKOwfGSc65rQDezxO99jTgu6B+m722NO/3/O1hmVl/M1tlZqt+/PHHUoYoIiIiIhJX09DIZRGRmIn2JJ7hqsOuiPawnHNTnHMZzrmMevXqRS04EZHyQt/yiYj4j0Yui4jEVmkLGD94yRXv53avfTPQMKhfA+B7r71BmHYRESmdaehbPhGRZBDTkcsiIhVJaQsYrwJ9vd/7AnOD2nuaWTUza0TgZHill6z3mdlF3rd7fYKWERGREtK3fCIiSa/MI5d12bWIVDSR3EY1C1gBNDWzzWZ2GzAOuMLMvgKu8J7jnFsDvAisBRYCdzjncrxVDQSeJXDivBFYEOV9ERGp6DQ/kYiI/8Rs5LIuuxaRiqZycR2cc70Keal9If3HAmPDtK8Czi5RdFKom3v3ZcvWbXHbXnZ2dty2JSJRF7X5iYApABkZGYX2ExGRELkjl8dRcOTyTDMbD5zC0ZHLOWa2z8wuAj4gMHJ5UvzDFhHxn2ILGOJPW7ZuY/DoCXHb3pCbusRtWyJSaj+YWX3n3FbNTyQiEn/eyOVMINXMNgMPEyhcvOiNYv4WuB4CI5fNLHfk8mEKjlyeBtQgMGpZI5dFRFABQ0SkPNG3fCIiCaSRyyIisaUChohIEtK3fCIiIiJS0aiAISKShPQtn4iIiIhUNKW9jaqIiIiIiIiISNyogCEiIiIiIiIivqcChoiIiIiIiIj4ngoYIiIiIiIiIuJ7KmCIiIiIiIiIiO+pgCEiIiIiIiIivqcChoiIiIiIiIj4ngoYIiIiIiIiIuJ7lRMdgIiISHlxc+++bNm6LW7by87Ojtu2RERERBJNBQwREZEo2bJ1G4NHT4jb9obc1CVu2xIRERFJNBUwRERERESk3NLoOJHyQwUMEREREREptzQ6TqT80CSeIiIiIiIiIuJ7KmCIiIiIiIiIiO+pgCEiIiIiIiIivqcChoiIiIiIiIj4nibxFKkAvt64kcwOHeO2vbT6J/PC89Pjtj0RERERESn/VMAQqQBynIvr7NuTRg6N27ZERJJVQorLcduaiEhy0hd//qYChoiIiEgCqLgsIuI/ys3+pjkwRERERERERMT3VMAQEREREREREd9TAUNEREREREREfE8FDBERERERERHxPU3iGSU39+7Llq3b4ra97OzsuG1LREREREREJNFUwIiSLVu3xXW22iE3dYnbtkREREREREQSTZeQiIiIiIiIiIjvqYAhIiIiIiIiIr6nS0hERKTc0vxEIiIiIuVH3AsYZtYJeBKoBDzrnBsX7xhERCRUec3Nmp9IRJJZeczN8S4sg4rLIuVJXAsYZlYJ+BtwBbAZ+NDMXnXOrY1nHCISW19v3Ehmh44lXm7CJ58CMLSEy6bVP5kXnp9e4u1JgHKziIj/lNfcHO/CMqi4LFKexHsERmtgg3PuawAzmwV0A6KeiDVsWCRxcpwr1clJ2qC+ACVedtLIoSXeloSIW24WkcT5euNGVu/+CSh5obg0VFwuM+VmkQqgtF/8lVay5+Z4FzDSgO+Cnm8GLozFhjRsWKTiUOIvs7jlZhWXRRInxznSGp0BlLxQXBp33dxVubls4pKblZdFEqu0X/yVVrLnZnPORW1lxW7M7Hqgo3Puj97z3kBr59zgfP36A/29p02BL0q4qVRgRxnDLQ90HAJ0HAJ0HAISeRxOc87VS9C2CxXH3JxL78WjdCyO0rEI0HE4Kl7HImlzcynyst/eX36Kx0+xgOIpip9iAcVTnNLGEzY3x3sExmagYdDzBsD3+Ts556YAU0q7ETNb5ZzLKO3y5YWOQ4COQ4COQ4COQ1hxyc259Dc4SsfiKB2LAB2Ho3Qsis/NJc3LfjumforHT7GA4imKn2IBxVOcaMdzTLRWFKEPgSZm1sjMqgI9gVfjHIOIiIRSbhYR8R/lZhGRfOI6AsM5d9jMBgGvE7gd1FTn3Jp4xiAiIqGUm0VE/Ee5WUSkoHhfQoJzbj4wP8abKfMQ53JCxyFAxyFAxyFAxyGMOOXmXPobHKVjcZSORYCOw1EV/ljEIDf77Zj6KR4/xQKKpyh+igUUT3GiGk9cJ/EUERERERERESmNeM+BISIiIiIiIiJSYklbwDCzTmb2hZltMLPhYV7vZmafmtlqM1tlZm0TEWesFXccgvpdYGY5ZnZdPOOLlwjeD5lmtsd7P6w2s5GJiDPWInk/eMditZmtMbN34x1jPETwfvhz0Hvhc+/fxvGJiLU8U54OUJ4+Srn6KOXro5Szy64s+TbSHBXHeLLN7LPc1+IRT1C/Ank42senjLHE/dgUlZcT9N4pKp6EvHcKy9WJeO8UEUsi3juF5u4yHRvnXNI9CExktBE4HagKfAK0yNenNkcvkTkXWJ/ouBNxHIL6LSZwDeV1iY47Qe+HTOC1RMfqg+NQF1gLnOo9PzHRcSfiOOTr3xVYnOi4y9tDeTry4xDUr9zm6RK8J8p9ri7BsSj3+TrSY5Gvv3J2KY5hYfm2pMc/1vF4z7OB1Hi/x8Ll4Wgfn7J+JiTi2BSWlxP43in0cyJBx6cuYXJ1It47hcWSqGOTr39e7i7rsUnWERitgQ3Oua+dc78Bs4BuwR2ccz877wgBtYDyONlHscfBMxj4F7A9nsHFUaTHobyL5DjcBLzsnPsWwDlXHt8TJX0/9AKy4hJZxaI8HaA8fZRy9VHK10cpZ5ddWfJtLP5d+i3/lyUPR/v4+O0zoSz7l5D3TpyVJVcn4r0Tz8+NsuTuMh2bZC1gpAHfBT3f7LWFMLNrzGw98B+gX5xii6dij4OZpQHXAM/EMa54i+j9AFxsZp+Y2QIzOys+ocVVJMfhTCDFzN4xs4/MrE/cooufSN8PmFlNoBOBkwSJLuXpAOXpo5Srj1K+Pko5u+zKkm8jPv5xigcCxYw3vPd9/zLGElE8ReThaB+fsn4mxP3YeMLl5YS9dwqJBxJzfArL1XF/7xQRCyTuvRMud5fp2MT9NqpRYmHaClRunXOvAK+Y2aXAGKBDrAOLs0iOwwRgmHMuxyxc93IhkuPwMXCac+5nM7sK+DfQJNaBxVkkx6Ey0ApoD9QAVpjZ+865L2MdXBxFlB88XYFlzrmfYhhPRaU8HaA8fZRy9VHK10cpZ5ddWfJtSY5/POIB+L1z7nszOxF408zWO+eWxDieCYTPw9E+PmX9TEjEsSksLyfqvVPU50Qijk/YXB3hsnGJxfvcSMSxyZU/d5fp2CTrCIzNQMOg5w2A7wvr7P1xGptZaqwDi7NIjkMGMMvMsoHrgMlm1j0u0cVPscfBObfXOfez9/t8oEoFfT9sBhY6535xzu0AlgDnxSm+eClJfuiJhiLHivJ0gPL0UcrVRylfH6WcXXZlybclWjYO8eCc+977uR14hcBw81jHU1gejvbxKdNnQiKOTRF5OSHvnaI+JxL03iksVyfivVPo50aCjk2u/Lm7bMfGRWkij3g+CFSXvgYacXTij7Py9TmDo5MDnQ9syX1eXh6RHId8/adRDieHi/D9cHLQ+6E18G1FfD8AzYFFXt+awOfA2YmOPd7Hwet3HPATUCvRMZfHh/J05MchX/9ymadL8J4o97m6BMei3OfrSI+F1085u2zvp7D5tqQ5Kg7x1AKO9dprAcuBTvF4jwX1z8vD0T4+ZYwlIcemsLycwPdOYfEk6viEzdWJeO8UEUvC/l0RJneX9dgk5SUkzrnDZjYIeJ3ALKZTnXNrzGyA9/ozwLVAHzM7BBwAbnTeESsvIjwO5V6Ex+E6YKCZHSbwfuhZEd8Pzrl1ZrYQ+BQ4AjzrnPs8cVFHXwn+XVwDvOGc+yVBoZZrytMBytNHKVcfpXx9lHJ22ZUx34ZdNlHxmNlJBC4rgcB/cmY65xbGIZ4SLZuIWIBEHZvC8nKi3jth40nUe6eoXB3v905hsZjZ6STu31WB3F3Wf1e51SsREREREREREd9K1jkwRERERERERKQCUQFDRERERERERHxPBQwRERERERER8T0VMERERERERETE91TAEBERERERERHfUwFDRERERERERHxPBQwRERERERER8T0VMERERERERETE91TAEBERERERERHfUwFDRERERERERHxPBQwRERERERER8T0VMERERERERETE91TAEBERERERERHfUwFDkpKZZZtZB+/3n4MeR8zsQNDzm81slJkd8p7vNrPlZnZxmHU+Z2bOzM4IaluTb/2HzWxe0Otdzexz77XlZtYiPkdARMR/4pibjzez2Wa2w3u8YGZ1gl6fYmZfeNu9JS47LyLiQ37Jy0H9+nrL/jG2ey7llQoYkvScc7VzH8C3QNegthe8brO91+sBS4GXzcxy12FmbYHGYdZ9VtC6j/XW/5K3TBPgBWAAUBeYB7xqZpVjta8iIskilrkZeARIAU73Xj8JGBX0+ifAn4CPo7xbIiJJK8F5GTNLAe4H1kR3z6QiUQFDKhTn3CFgOnAycAKAV3CYBAwqZvFLgROBf3nPOwLvOeeWOucOA48BacBlMQhdRKTcKkVubgT82zm31zm3B3gFOCtofX9zzi0Cfo117CIi5VG087LnUWAisCNWcUv5pwKGVChmVg24BdjsnMtNnncBS5xznxazeF9gjnPul9zVeQ/yPT87ehGLiJR/pcjNfwO6mFmK943etcCCuAQrIlIBRDsvm1lrIAN4JqaBS7mnAoZUFDeY2W7gO6AV0B3AzBoCtwMji1rYzGoC1wHTgprfBC4zs0wzqwo8AFQFakY5dhGR8qq0ufljAvl2p/fIASbHOFYRkYog6nnZzCp5vw92zh2JYexSAaiAIRXFi865us65E51zlzvnPvLaJwCjvaFuRekB/AS8m9vgnFtPYFTGU8BWIBVYC2yOdvAiIuVUaXPzS8CXBOYmqgNsBP4Z82hFRMq/WOTlPwGfOudWxDBuqSBUwJCKrj3wuJltM7NtXtsKM7spX7++wAznnAtudM7Ncc6d7Zw7AXgYOA34MOZRi4iUb8Xl5vOAvzvnfnHO/UxgSPJViQhURKSCKEtebg9cE7RsG+AJM3sqnjsg5YPuliDJrIqZVQ96frgU6ziT0ELeVqArgRnsATCzBkA7AncbCWFmrYDVwPEERmLM80ZmiIhUVPHIzR8CfzSz+7zn/QnN21W95S0ont80dFlEKqhE5+VbgODtvwzMAf5RijikglMBQ5LZ/HzPx5Z0Bc657cHPvbtE7XDOHQhq7g2scM5tDLOKJwlUnA8RGDp3d0ljEBEpZ+KRm/sRmMl+M4EixUoCJ8i53uDoHaHaAFMIFKLfKWksIiLlQELzsnNud75lfwP2RnAJt0gBlm9EvIiIiIiIiIiI72gODBERERERERHxPRUwRERERERERMT3VMAQEREREYkCM6tuZivN7BMzW2Nm/+O1H29mb5rZV97PlKBl7jezDWb2hZl1DGpvZWafea9NNG/SARGRikwFDBERERGR6DgIXO6cOw9oCXQys4uA4cAi51wTYJH3HDNrAfQEzgI6AZPNrJK3rqcJ3MmhiffoFMf9EBHxJd/fhSQ1NdWlp6cnOgyR2Pvii8DPpk0TG4fEzEcffbTDOVcv0XFEg3JzklO+EckTzdzsArPj/+w9reI9HNANyPTapxO4I84wr32Wc+4gsMnMNgCtzSwbqOOcWwFgZjOA7sCCwratvCxSQVSQz/DCcrPvCxjp6emsWrUq0WGIxF5mZuDnO+8kMgqJITP7JtExRItyc5JTvhHJE+3c7I2g+Ag4A/ibc+4DMzvJObcVwDm31cxO9LqnAe8HLb7Zazvk/Z6/Pf+2+hMYpcGpp56qvCxSEVSQz/DCcrMuIRERERERiRLnXI5zriXQgMBoirOL6B5uXgtXRHv+bU1xzmU45zLq1SsXA/xERIqkAoaIiIiISJQ553YTuFSkE/CDmdUH8H5u97ptBhoGLdYA+N5rbxCmXUSkQlMBQ0REREQkCsysnpnV9X6vAXQA1gOvAn29bn2Bud7vrwI9zayamTUiMFnnSu9yk31mdpF395E+QcuIiFRYvp8DQ0REREQkSdQHpnvzYBwDvOice83MVgAvmtltwLfA9QDOuTVm9iKwFjgM3OGcy/HWNRCYBtQgMHlnoRN4iohUFCpgiAA39+7Llq3b4ra9tPon88Lz0+O2PSl/zKw6sASoRiCXz3HOPWxmxwOzgXQgG7jBObfLW+Z+4DYgBxjinHvda2/F0ZPk+cCd3kz6IiGUK0WK5pz7FPhdmPadQPtClhkLjA3Tvgooav4MkYTQZ4EkkgoYIsCWrdsYPHpC3LY3aeTQuG1Lyq2DwOXOuZ/NrAqw1MwWAD2ARc65cWY2HBgODDOzFkBP4CzgFOAtMzvT+6bvaQKz2L9PoIDRCX3TJ2EoV4qIiD4LJJE0B4aISBJyAT97T6t4Dwd0A3K/ppgOdPd+7wbMcs4ddM5tAjYQmB2/PlDHObfCG3UxI2gZERERERHfUAFDRCRJmVklM1tNYDb7N51zHwAneZO/4f080eueBnwXtPhmry3N+z1/e7jt9TezVWa26scff4zqvoiIiIiIFEeXkIiIJCnv8o+W3oz3r5hZUddKW7hVFNEebntTgCkAGRkZmiNDRETEB+I9J0V2dnbctiWSnwoYIiJJzjm328zeITB3xQ9mVt85t9W7PGS7120z0DBosQbA9157gzDtIiIikgTiPSfFkJu6xG1bIvnpEhIRkSRkZvW8kReYWQ2gA7AeeBXo63XrC8z1fn8V6Glm1cysEdAEWOldZrLPzC4yMwP6BC0jIiIiIuIbGoEhIpKc6gPTzawSgWL0i86518xsBfCimd0GfAtcD+CcW2NmLwJrgcPAHd4lKAADOXob1QXoDiQiIiIi4kMqYIiIJCHn3KfA78K07wTaF7LMWGBsmPZVQFHzZ4iIiIiIJJwuIRERERERERER31MBQ0RERERERER8TwUMEREREREREfE9FTBERERERERExPdUwBARERERERER31MBQ0RERERERER8TwUMEREREREREfE9FTBERERERERExPcqJzoAkXBu7t2XLVu3xW172dnZcduWiIiIiIiIlJwKGOJLW7ZuY/DoCXHb3pCbusRtWyIiIiIiIlJyuoRERERERERERHxPBQwRERERERER8T1dQiIiIiK+9PXGjWR26Bi37aXVP5kXnp8et+2JiIhIyaiAISIiIr6U41xc50OaNHJo3LYlIiIiJadLSERERERERETE94otYJhZQzN728zWmdkaM7vTaz/ezN40s6+8nylBy9xvZhvM7Asz6xjU3srMPvNem2hmFpvdEhEREREREZHyJJIRGIeBe5xzzYGLgDvMrAUwHFjknGsCLPKe473WEzgL6ARMNrNK3rqeBvoDTbxHp2jtiHOORo0aYWZs2LChwOujRo0iNTU17LL33nsv6enpBdrfeecdunTpQmpqKlWrViU9PZ0hQ4bw7bffRivsIs2dO5dzzjmH6tWr06JFC2bPnl3sMtOmTcPMCjyeeeaZvD7vvPNO2D5mRseOodcaHz58mHHjxtGkSROqVatGgwYNuOuuu6K+ryJSPik3F7RlyxZq166NmfHzzz8XeP2zzz6jS5cuHHfccRx77LG0bt2ajz76KO/1l156iT/84Q+kpaVRu3ZtWrVqRVZWVpn3S0QqDuXmgMzMzELPiVesWJHXzznHX/7yFxo2bEiNGjW49NJLWb16dYH1zZo1i/PPP5/atWuTlpZGnz59+P7776O5myIVXrEFDOfcVufcx97v+4B1QBrQDcid6Wo60N37vRswyzl30Dm3CdgAtDaz+kAd59wK55wDZgQtU2YrVqwgOzsbCCSPspo4cSKXX345NWrU4O9//ztvvfUWDz/8MP/973/p1q1bmddfnKVLl3LttdfSrl07FixYwNVXX02vXr144403Ilp+8eLFrFixIu/Ro0ePvNfOP//8kNdWrFiRl+Q7d+4csp5bb72ViRMncu+99/LGG28wbtw4atSoEb0dFZFyTbm5oD//+c/Url077GurV6+mTZs21K1bl9mzZ/PSSy/RtWtXDhw4kNdn/Pjx1K5dm7/+9a+8+uqrtGvXjptuuolJkyaVef9EpGJQbg6YPHlygXPiK664gtTUVC644IK8fuPGjWPMmDEMGzaMefPmUbt2bTp06MC2bdvy+rz66qv06tWLNm3aMHfuXB577DGWLFlCly5dOHLkSMz2XaSiKdEknmaWDvwO+AA4yTm3FQJFDjM70euWBrwftNhmr+2Q93v+9nDb6U9gpAannnpqRLFlZWVRq1Ytzj77bLKyshgxYkSku1XAf//7X+6++25GjBjB6NGj89ovvfRSbr31Vl577bVSrztSY8aM4dJLL2XixIkAtGvXjjVr1jB69GiuvPLKYpe/4IILCj1BrlOnDhdddFFI23vvvccxxxzDDTfckNe2cOFCZs2axSeffEKLFi3KsDciUlEpN4d67733WLhwIQ888AB//vOfC7w+YMAAunbtyj//+c+8tk6dQgcrzps3L+Sb0csvv5zvv/+e8ePHM3jw4NLumohUIMrNAfnPb3/77TdWrVrFjTfeSOXKgf8m/frrr4wbN47777+fQYMGAXDxxReTnp7OU089xSOPPALAzJkzOf/883nqqafy1lenTh26devGF198QfPmzaO6zyIVVcSTeJpZbeBfwFDn3N6iuoZpc0W0F2x0bopzLsM5l1GvXr1iY8vJyckbUtuvXz/Wrl3Lp59+WuxyhZk0aRKpqak89NBDYV/v0qVLqdcdiYMHD/L222+HFBMAevbsyYoVK9izZ0/Ut5mVlcVll13GKaecktc2depULr/8chUvRKRUlJtD5eTkMHjwYEaOHBl2aPbatWv54IMPii1ChFv2d7/7Hdu3b49gL0SkolNuLtzChQvZtWsXvXr1ymtbvnw5e/fuDVl/rVq16Nq1KwsWLMhrO3ToEMcdd1zI+urWrQsELkERkeiIqIBhZlUIFC9ecM697DX/4F0Wgvcz98xpM9AwaPEGwPdee4Mw7WW2ePFifvjhB3r27Ml1111HlSpVynQ98Lvvvkv79u2pUqVKqZY/fPhwsY+iEtnGjRs5dOgQzZo1C2lv3rw5R44c4csvvyw2hsaNG1O5cmWaNm3K3//+9yL7fvXVV/z3v/8NSdYAH3zwAWeeeSaDBg2iTp061KxZkx49euhaPhGJiHJzqGeeeYZff/2VO+64I+zrH3zwAQC7du3ivPPOo3LlyjRu3Jh//OMfxe7b8uXLVWwWkYgoNxdu1qxZpKWlcckll+S1rV+/nkqVKtGkSZMC61+/fn3e8379+vHee+8xY8YM9u7dy5dffsmIESNo166d8rNIFEVyFxID/gGsc86ND3rpVaCv93tfYG5Qe08zq2ZmjQhM1rnSu9xkn5ld5K2zT9AyZZKVlUXdunXp1KkTxx9/PFdccQWzZs0qdbVzy5YtEV+6Ek6VKlWKfUyfPr3Q5Xft2gUcrdrmSklJCXk9nPr16zNmzBief/555s2bx4UXXsiAAQP461//WugyWVlZVKlShWuvvTakfdu2bUybNo3Vq1cza9YsnnvuOT766COuueYaVZJFpFjKzUft3LmThx56iPHjxxd6kp97LXWfPn24+eabefPNN+nUqRN//OMfmT9/fqHrXrRoEXPnzi20MCIiEky5Obz9+/czb948brzxRoJvlLhr1y5q165NpUqVQvqnpKSwf/9+fvvtNwCuvvpqpk2bRv/+/TnuuONo2rQpOTk5vPzyy4hI9EQyB8bvgd7AZ2a22mt7ABgHvGhmtwHfAtcDOOfWmNmLwFoCdzC5wzmX4y03EJgG1AAWeI8yOXjwIK+88grXXHMNVatWBaBXr1707t2b999/n4svvrhU6y3LHV4//PDDYvs0atSoxDHkfrAUFVvHjh1D7iTSuXNnDh48yCOPPMKdd97JMccUrFnNmjWLK6+8kuOPP77A9pxzzJ07lxNOOAEIFEguu+wyFi9eTPv27YvdBxGpmJSbQz344INceOGFXHXVVYX2yZ3k7Y9//CP33XcfELiOe926dTz66KNhl83Ozuamm26iW7du3HLLLcXGLiKxZWYNCUxUfzJwBJjinHvSzI4HZgPpQDZwg3Nul7fM/cBtQA4wxDn3utfeiqPnzfOBO10Zv0FSbi7cvHnz+PnnnwuMSC5sHfnX//bbbzNgwADuvPNOOnfuzA8//MCoUaO45ppreOuttwoUQESkdIotYDjnlhJ+/gqAsP+Ddc6NBcaGaV8FnF2SAIuzYMECdu/ezVVXXcXu3buBwC2RqlWrRlZWVl4irly5Mjk5OWHXkZOTkzdRD0BaWlqZbvnUsmXLYvsUlcRyK8a5+5Mr93n+CnNxrrvuOl588UWys7M5/fTTQ1775JNPWLduHQ8++GDYOE4//fS84gVA27ZtqVq1KmvXrlUBQ0QKpdx81Jo1a5g6dSpLlizJ67t//34A9uzZQ6VKlahRo0ZeEbldu3Yhy19++eVhR9H99NNPdO7cmVNPPTVk0k8RSajDwD3OuY/N7FjgIzN7E7gFWOScG2dmw4HhwDAzawH0BM4CTgHeMrMzvS//niYwqf37BAoYnSjjl3/KzYWbNWsWZ5xxBhkZGQXWv2/fPnJyckLi2L17NzVr1swbVXfPPffwhz/8gcceeyyvT8uWLWnWrBlz584NuSOgiJReie5C4ke51+xdf/31BV578cUX+etf/0qlSpWoV68ee/fuZf/+/dSsWTOk39atWznxxBPznmdmZjJ//nwOHz4ckqAjFck1gM8991yh35Y1btyYKlWqsH79ei677LK89vXr13PMMcdw5plnljgmCF89njVrFjVq1Ah7i6vmzZtz8ODBAu3OubAjOSRyX2/cSGaHjiFtEz4JTKA1NF97NKTVP5kXni98+KVItCk3H/XVV19x6NChsN9sNmjQgNtuu41nn3220Bnqw+Xc/fv306VLF3777Tf+85//UKtWrWL3TURiz7tkOvcuffvMbB2Bu+51AzK9btOBd4BhXvss59xBYJOZbQBam1k2UMc5twLAzGYA3SljAUO5Obw9e/awYMGCvNFvwZo1a0ZOTg4bNmygadOmIesPnndj/fr1BUZvNG3alBo1arBx48ZiYxCRyCR1AePnn3/mtddeo1evXvTv3z/ktdxbOr399tt06NCBSy65hCNHjvDaa6+FzCL8yy+/sGjRIvr165fXNnjwYKZPn87YsWN5+OGHC2x3/vz5RQ4DLutQuGrVqtGuXTteeuklbr/99rz22bNnc/HFFxeY4bg4//rXv0hNTeW0004r8Nrs2bPp2rVr2FuudunShYcffpgdO3bkzXq/ZMkSDh06xHnnnVeiGCRUjnMMHj0hpC1tUGBKmfzt0TBp5NCor1OkMMrNodq2bcvbb78d0rZw4UIee+wx5s+fnzcyrk2bNqSkpLBo0aKQSwEXLVoUknMPHz7M9ddfz1dffcVFbdpyw029i92v/EpbMM3Ozi7xtkQqKjNLB34HfACc5BU3cM5tNbPcCkAagREWuTZ7bYe83/O3l5pyc+FeeeUVDh48GPbykTZt2lCnTh1eeumlvNvN5s6XEXwcTzvtND7++OOQZdetW8eBAwdIT08vNgYRiUxSFzDmzp3L/v37ufPOO7nwwgtDXvv973/P2LFjycrKokOHDrRo0YIbb7yR2267jU2bNtGqVSu2b9/OE088gXOOIUOG5C3bsmVLxo8fz9ChQ1m7di09e/YkNTWVTZs2MXXqVPbs2VNkIs4/9Kw0HnroITIzMxk6dCjdu3dn/vz5zJ8/n4ULF+b1+eabb2jcuDFTp06lT58+AFx77bW0bt2ac889l5ycHGbPns3s2bOZOHFigW/w3n//fTZt2sT48eMJp3///kycOJGuXbvywAMPsG/fPoYNG0aHDh1o27ZtmfdRRMon5ebQ3JyamkpmZmbIenILAZdcckleAblq1aqMHDmS++67j7p163LBBRfwr3/9iyVLlvDuu+/mLfunP/2J+fPn8+STTzLt+Re4/v+7K++1Rmc2p4p3XXtRSlswHXJTbG+HKFJemFltAnfwG+qc21vEPAzhXnBFtOffTn8Cl5kUO5GmcnPB8+Zcs2bN4rzzzgs7Eq569eoMHz6cMWPGkJKSQrNmzRg/fjxHjhwJue31gAEDuOuuuzjllFPy5sAYPXo06enpRe6/iJRMUhcwsrKyaNKkSYEkDIHhaDfccANZWVlMnjyZatWqMWPGDB555BGmTJnCt99+y7HHHktmZiYvvPACaWmhRe0hQ4Zwzjnn8L//+7/88Y9/ZO/evaSlpdGxY0f+/Oc/x3zf2rZty5w5cxgxYgRPP/00jRo1YubMmVx55ZV5fZxz5OTk5E38BoGhalOnTuW7777DOUeLFi2YMWMGvXsX/IZu1qxZHHfccXTu3DlsDHXq1GHx4sUMGTKEnj17UrVqVbp161bkHU1ERJSbC+bmSA0dOpQjR44wadIkRo0aRdOmTZkzZ07ILf3eeOMNAO68804A/rvq6DeGk+e8yYn1y/QlrYiUkZlVIVC8eME5l3sLih/MrL43+qI+sN1r3ww0DFq8AfC9194gTHsI59wUYApARkZGkRN8KjeHz807duxg0aJFjBkzptD1Dx8+nCNHjvDoo4+yc+dOMjIyePPNNznppJPy+gwZMoSqVavy9NNP88wzz1C3bl3atm3Lo48+qsv8RKLI/H47zIyMDLdq1apEhyFxltmhY0wupSjMkJu6MHHmawnd3qXeN6JLnor+XBWTRg7lnbdej/p6pWTM7CPnXNm/aiLxM90rN/tDaXNlafONH3JlLClXVkxRzs1GYI6Ln5xzQ4PaHwd2Bk3iebxz7j4zOwuYCbQmMInnIqCJcy7HzD4EBhO4BGU+MMk5V+g9lZWXK67yft6s3JxP7qjOd95JZBQxV1hu1kyMIiLJKXem++bARcAd3mz2wwnMdN+EwInwcIB8M913AiabWe506rkz3TfxHp3iuSMiIuXI74HewOVmttp7XAWMA64ws6+AK7znOOfWAC8Ca4GFwB3eHUgABgLPAhuAjZRxAk8RkfIgqS8hERGpqPw+072ISEXknFtK+PkrAMLef945NxYYG6Z9FXB29KITEUl+GoEhIpLkiprpHgie6f67oMVyZ7RPI8KZ7s2sv5mtMrNVP/74Y1T3QURERESkOCpgiIgksfwz3RfVNUxbxDPdQ2CyOOdchnMuo169eiUPVkRERESkDFTAEBFJUkXNdO+9HrWZ7kVEREREEk0FjCiYO3cu55xzDtWrV6dFixbMnj272GVeeukl/vCHP5CWlkbt2rVp1aoVWVlZBfqlp6djZiGPk08+uUC/WbNmcf7551O7dm3S0tLo06cP33+v/4OIlFfeTPf/ANY558YHvfQq0Nf7vS8wN6i9p5lVM7NGBCbrXOldZrLPzC7y1tknaJmkVprcnJmZWSDn5j5WrFiR12/37t3069eP448/ntq1a9O5c2c2bNhQYH3KzSIioUqTmwHWrl1L+/btqVmzJqeccgojR44kJycnpI9zjr/85S80bNiQGjVqcOmll7J69eqQPnPmzKFNmzaccMIJVK9enaZNm/LII4/w22+/RWsXRSSGVMAoo6VLl3LttdfSrl07FixYwNVXX02vXr144403ilxu/Pjx1K5dm7/+9a+8+uqrtGvXjptuuolJkyYV6HvTTTexYsWKvMf8+aF30Hr11Vfp1asXbdq0Ye7cuTz22GMsWbKELl26FLjXtYiUG5rpvgilzc2TJ08OybcrVqzgiiuuIDU1lQsuuCCv34033sjrr7/Ok08+ycyZM9m5cyft27dn796jV/EoN4uIhCptbt61axcdOnTAzJg7dy4jR47kiSee4OGHHw7pN27cOMaMGcOwYcOYN28etWvXpkOHDmzbti2vz86dO2nXrh3PPvssCxYsoF+/fowdO5a77747JvssItGlu5CU0ZgxY7j00kuZOHEiAO3atWPNmjWMHj2aK6+8stDl5s2bR2pqat7zyy+/nO+//57x48czePDgkL7169fnoosuKnRdM2fO5Pzzz+epp57Ka6tTpw7dunXjiy++oHnz5qXdPRHxKc10X7TS5uYWLVqEPP/tt99YtWoVN954I5UrBz4yV6xYwRtvvMGiRYu4/PLLAbjwwgtp1KgRU6ZM4d577wWUm0VE8ittbn7mmWc4cOAAL7/8MnXq1OGKK65g7969jBo1ivvuu486derw66+/Mm7cOO6//34GDRoEwMUXX0x6ejpPPfUUjzzyCAC33357yLrbtWvH3r17+dvf/sakSZMIDEYUEb9K+hEYt9xyCxkZGfz73/+mWbNmVK9enbZt27J27dqYb/vgwYO8/fbb3HDDDSHtPXv2ZMWKFezZs6fQZYOLF7l+97vfsX379jC9i3bo0CGOO+64kLa6desCgaF0IiLxlqy5Ob+FCxeya9cuevXqlde2evVqKleuzGWXXZbXdtJJJ3Huuefyn//8J69NuVlE/CZZc/OCBQvo2LEjderUCVnuwIEDvPvuuwAsX76cvXv3hqy/Vq1adO3alQULih5YeMIJJ+gSEpEkkfQFDIBvvvmGu+++m4ceeoiZM2eyZ88eOnbsyK+//lrkcocPHy72UdRJ5saNGzl06BDNmjULaW/evDlHjhzhyy+/LNF+LF++vMC3fwBTp06latWqHHfccVx33XV88803Ia/369eP9957jxkzZrB3716+/PJLRowYQbt27cKuT0QkHspDbp41axZpaWlccskleW2//vorlStXplKlSiF9q1Wrxrp16/KeKzeLiB8lY25ev359geVOPfVUatasyfr16/P6VKpUiSZNmhRYf26fYDk5Oezfv5+lS5cyceJEBg4cqNEXIkmgXFxCsmPHDubOnUubNm0AaNWqFY0bN2batGkMGDCg0OWqVKlS7Lqfe+45brnllrCv7dq1Czj6jVqulJSUkNcjsWjRIubOncvUqVND2rt168ZFF11EgwYNWLduHf/zP//DJZdcwmeffZb3zd7VV1/NtGnTuO222+jbNzB3X5s2bXj11Vcj3r6ISLQle27ev38/8+bNo3///iEntWeccQa//vorn332Geeccw4ABw4c4PPPP2ffvn15/ZSbRcSPkjE379q1q8ByucvmLrdr1y5q165doLickpLC/v37+e2336hatWpee61atTh48CAAffr04fHHHy9y3yRxvt64kcwOHeO2vbT6J/PC89Pjtj0pmXJRwDjxxBPzkjDAaaedRqtWrVi5cmWRifjDDz8sdt2NGjUqtk/+am1u9TnSKm52djY33XQT3bp1K5D0n3zyybzfL7nkEtq0aUPLli157rnnGDp0KABvv/02AwYM4M4776Rz58788MMPjBo1imuuuYa33nqrQCIXEYmHZM/N8+bN4+effw65fASgY8eONGrUiNtvv53nnnuOOnXqMHz4cPbs2RNygq/cLCJ+lKy5OdzrzrmQ9sL6hHtt+fLl7N+/n5UrVzJ69GgGDRrE5MmTi41f4i/HOQaPnhC37U0aOTRu25KSKzcFjHBtW7duLXK5li1bFrvuok4wcyvGu3fvDmnPfR6uUpzfTz/9ROfOnTn11FP55z//WWz/s88+m6ZNm/Lxxx/ntd1zzz384Q9/4LHHHstra9myJc2aNWPu3Ln06NGj2PWKiERbMudmCFw+csYZZ5CRkRHSXrVqVWbNmkWvXr3yhjS3bduWPn36sHjx4rx+ys0i4kfJmJtTUlIKLAewZ8+evOVSUlLYt28fOTk5IXHs3r2bmjVrFhhBcv755wOB/J2amkrfvn255557aNy4caFxiEjilYs5MMJNfLl9+3bq169f5HJVqlQp9jF9euHDhxo3bkyVKlUKXFe3fv16jjnmGM4888wit79//366dOnCb7/9xn/+8x9q1apVZP9gwVXk9evXF/hQadq0KTVq1GDjxo0Rr1NEJJqSNTdD4KR4wYIFBUZf5GrdujUbNmxg/fr1bNiwgffee4/t27eH3DFKuVlE/CgZc3OzZs0KLPfdd9/xyy+/5BWSmzVrRk5ODhs2bCiw/vzzZ+SXW8zYtGlTkf1EJPHKxQiM7du3s3z58rzhcN9++y0ff/wxt956a5HLlXUoXLVq1WjXrh0vvfRSyC2ZZs+ezcUXX1xg9vlghw8f5vrrr+err75i2bJlYavh4Xz++ed88cUXIds77bTTQkZkAKxbt44DBw6Qnp4e0XqLc3PvvmzZuq34jlGSnZ0dt22JSGwkY27O9corr3Dw4MFCCxgQKCQ3bdoUgK+++oq33nqLefPm5b0ej9wsIlJSyZibO3fuzOOPP86+ffs49thj85arUaNG3h2h2rRpQ506dXjppZcYMWIEEDqXUVGWLVtWbPwi4g/looCRmppK7969GTNmDDVq1GDkyJGceOKJhU4ilCv/sODSeOihh8jMzGTo0KF0796d+fPnM3/+fBYuXJjX55tvvqFx48ZMnTqVPn36APCnP/2J+fPn8+STT/LTTz/x/vvv5/X/3e9+R7Vq1fjPf/7DP//5T7p06cIpp5zC+vXreeSRRzj11FND9m3AgAHcddddnHLKKXnXWY8ePZr09HSuuuqqMu8jwJat2+J67dmQm7rEbVsiEhvJmJtzzZo1i/POO4/mzZuHXf+YMWNo1qwZqampfPbZZ4wZM4aePXtyxRVX5PWJR24WESmpZMzNAwYMYOLEifTo0YNhw4bx9ddfM2rUKO6+++68W6tWr16d4cOHM2bMGFJSUmjWrBnjx4/nyJEjDB48OG/9nTp1okOHDpx11llUqlSJZcuW8cQTT3DjjTfq8hGRJFAuChinnXYaDzzwAMOHD+ebb74hIyODrKwsqlevHvNtt23bljlz5jBixAiefvppGjVqxMyZM7nyyivz+jjnyMnJ4ciRI3ltb7zxBgB33nlngXVu2rSJ9PR0GjZsyPbt2xk6dCi7d+/mhBNOoFOnTvzlL38JuQ/2kCFDqFq1Kk8//TTPPPMMdevWpW3btjz66KMluixFRCSakjE3Q2CG/kWLFjFmzJhC179z506GDh3Kjh07aNiwIffeey/33HNPSB/lZhHxo2TMzSkpKSxatIhBgwbRtWtX6taty1133cWoUaNC1j98+HCOHDnCo48+ys6dO8nIyODNN9/kpJNOyutzwQUXMG3aNLKzs6lcuTKnn346jz76aJETmJaURi6LxE65KGAA9OjRI2ETonXv3p3u3bsX+np6enqB+2JHkmjOPfdcFi1aVGw/M2PgwIEMHDiw2L4iIvGUbLkZAt9OHjp0qMh1T5gwgQkTJhTZR7lZRPwqGXNzixYtQiZKDsfMePDBB3nwwQcL7TNmzJgiC9TRoJHLIrFTLibxFBEREREREZHyTQUMEREREREREfG9pL+EZNq0aYkOQURE8lFuFhHxH+VmEUl2GoEhIiIiIiIiIr6nAoaIiIiIiIiI+J4KGCIiIiIiIiLieypgiIiIiIiIiIjvqYAhIiIiIiIiIr6nAoaIiIiIiIiI+J4KGCIiIiIiIiLieypgiIiIiIiIiIjvFVvAMLOpZrbdzD4PajvezN40s6+8nylBr91vZhvM7Asz6xjU3srMPvNem2hmFv3dEREREREREZHyKJIRGNOATvnahgOLnHNNgEXec8ysBdATOMtbZrKZVfKWeRroDzTxHvnXKSIiIiIiIiISVrEFDOfcEuCnfM3dgOne79OB7kHts5xzB51zm4ANQGszqw/Ucc6tcM45YEbQMiIiIiIiIiIiRSrtHBgnOee2Ang/T/Ta04Dvgvpt9trSvN/zt4dlZv3NbJWZrfrxxx9LGaKIiIiIiIiIlBfRnsQz3LwWroj2sJxzU5xzGc65jHr16kUtOBERERGRWNHccSIisVXaAsYP3mUheD+3e+2bgYZB/RoA33vtDcK0i4iIiIiUF9PQ3HEiIjFT2gLGq0Bf7/e+wNyg9p5mVs3MGhFIuCu9y0z2mdlFXgW5T9AyIiIiIiJJT3PHiYjEVuXiOphZFpAJpJrZZuBhYBzwopndBnwLXA/gnFtjZi8Ca4HDwB3OuRxvVQMJVKVrAAu8h4jEwdcbN5LZoWPxHaMkrf7JvPD89OI7SqmZ2VSgC7DdOXe213Y8MBtIB7KBG5xzu7zX7gduA3KAIc651732VhzNzfOBO70TZhERiY6QuePMLHjuuPeD+uXOEXeICOeOM7P+BEZqcOqpp0Y5bBER/ym2gOGc61XIS+0L6T8WGBumfRVwdomiE5GoyHGOwaMnxG17k0YOjdu2KrBpwFMEvpnLlTtMeZyZDfeeD8s3TPkU4C0zO9MrMOcOU36fQAGjEyowi4jEQ5nnjnPOTQGmAGRkZKj4LCLlXrQn8RQRkTjQMGURkaShueNERKKk2BEYIiKSNGI2TBk0VFnKP11uJzGSO3fcOArOHTfTzMYTGB2XO3dcjpntM7OLgA8IzB03Kf5hi4j4jwoYIiLlX9RucY2GKks5psvtpKw0d5yISGypgCEiUn78YGb1vdEXGqYsIhJnmjtORCS2NAeGiEj5oVtci4iIiEi5pREYIiJJSMOURURERKSiUQFDRCQJaZiyP93cuy9btm6L2/ays7Pjti0RERGRRFMBQ0REJEq2bN0W10kgh9zUJW7bEhEREUk0zYEhIiIiIiIiIr6nAoaIiIiIiIiI+J4KGCIiIiIiIiLieypgiIiIiIiIiIjvqYAhIiIiIiIiIr6nu5AkKd2qT0RERERERCoSFTCSlG7VJyIiIiIiIhWJLiEREREREREREd/TCAwRERERERER4OuNG8ns0DFu20urfzIvPD89bttLdipgiIiIiIiIiAA5zsX1Uv1JI4fGbVvlgS4hERERERERERHfUwFDRERERERERHxPl5CIiIiIiEi5dXPvvmzZui1u28vOzo7btkQqGhUwRERERESk3NqydVtc5zQYclOXuG1LpKLRJSQiIiIiIiIi4nsqYIiIiIiIiIiI76mAISIiIiIiIiK+pwKGiIiIiIiIiPieChgiIiIiIiIi4nu6C4mIRN3XGzeS2aFj3LaXVv9kXnh+ety2JyIiIiIi8acChohEXY5zcb1d2aSRQ+O2LRERERERSQxdQiIiIiIiIiIivqcRGCIiIiIJoMvtRERESkYFDBERKbdu7t2XLVu3xW172dnZcduWJD9dbiciIlIyKmCIiEi5tWXrtrj+B3HITV3iti0RERGRikYFjCjRt3wiIiIiIiIisaMCRpToWz4REREREREpiZLOhzThk08BGFrKOZSSfT6kuBcwzKwT8CRQCXjWOTcu3jGIiEgo5WYREf8pj7k53qOWQSOXxd9KOh9S2qC+AKX+8jzZ50OKawHDzCoBfwOuADYDH5rZq865tfGMQ0TKF83kXzbKzSIi/lNec3O8Ry2DRi6LlCfxHoHRGtjgnPsawMxmAd2AqCdizUkhUnFoJv8yU24WqQBU7E06ccnNyssiFUuyfxaYcy5qKyt2Y2bXAZ2cc3/0nvcGLnTODcrXrz/Q33vaFPiihJtKBXaUMdxo8ls8oJgipZgio5iKlwrUcs7VS3Qg+cUxN5eW3/6WuRRXyfk1Nr/GBf6Nza9xQcljOy1Zc3MM87Kf/75FUdzxlYxxJ2PMUDHjDpub4z0Cw8K0FaigOOemAFNKvRGzVc65jNIuH21+iwcUU6QUU2QUU/G8eNITHUch4pKbS8tvf8tciqvk/BqbX+MC/8bm17jA37GVULG5OVZ5OVmPoeKOr2SMOxljBsUd7JhoriwCm4GGQc8bAN/HOQYREQml3Cwi4j/KzSIi+cS7gPEh0MTMGplZVaAn8GqcYxARkVDKzSIi/qPcLCKST1wvIXHOHTazQcDrBG4HNdU5tyYGm4r7EOdi+C0eUEyRUkyRUUzF81s8eeKYm0vLr8dOcZWcX2Pza1zg39j8Ghf4O7aIJTg3J+sxVNzxlYxxJ2PMoLjzxHUSTxERERERERGR0oj3JSQiIiIiIiIiIiWmAoaIiIiIiIiI+F7SFjDMrJOZfWFmG8xseJjXm5nZCjM7aGb3+iSmm83sU++x3MzO80FM3bx4VpvZKjNrm+iYgvpdYGY53n3QExqTmWWa2R7vOK02s5GJjCcoptVmtsbM3o1lPJHEZGZ/Djo+n3t/u+MTHNNxZjbPzD7xjtOtsYwnwphSzOwV79/dSjM7O9Yx+VVZ8pOZZZvZZ7mvxTOuoH4FclSkyyYotoQds6JyaKKPWTGxJfR9VlieT/QxKya2RL7PCv0civUxS1YRHNO4n7tGoiyfH4lSlvydSGXJoYlUljyWSGXJc4kUQdzROyd3ziXdg8BERhuB04GqwCdAi3x9TgQuAMYC9/okpjZAivd7Z+ADH8RUm6NzoZwLrE90TEH9FgPzgesSHROQCbwW6/dRCeKpC6wFTvWen5jomPL17wosTnRMwAPAY97v9YCfgKoJjulx4GHv92bAoni8r/z2KGt+ArKB1ETEFdQvJEeV9N9JPGNL9DErLIf64ZgVFpsPjlldwuR5nxyzsLEl+pjl65/3ORTrY5asjwj/1nE9d41i3HE9v41GzEH94nL+G8VjXWgO9XncheYxP8edr3/Mz7ejeLyjdk6erCMwWgMbnHNfO+d+A2YB3YI7OOe2O+c+BA75KKblzrld3tP3CdzPO9Ex/ey8dxJQC4j1rK7FxuQZDPwL2B7jeEoSU7xEEs9NwMvOuW8h8H73QUzBegFZPojJAceamRE4mfkJOJzgmFoAiwCcc+uBdDM7KYYx+ZUf81NEcXnC5ahY5xI/5s+SxBXtZf2w/tIqS573wzGL92dQpHEFC/4c8uv7INH8eO4aCb9+fhTFr/m7OMn6b8eveaw4fjzfjkRcz8mTtYCRBnwX9Hyz15ZIJY3pNmBBTCOKMCYzu8bM1gP/AfolOiYzSwOuAZ6JcSwRx+S52Bv2tMDMzkpwPGcCKWb2jpl9ZGZ9YhhPpDEBYGY1gU4EPoATHdNTQHPge+Az4E7n3JEEx/QJ0APAzFoDp+GPE8J4K2t+csAb3vu/fzzjKiJHxfqzqaz5M2HHzBMuhyb8mBURGyT2mBWW5/1wzIr6DEr0+yzc55Afzxv9wI/nrpHw4/ltcfx4/hsJv50jR8qP59KR8OP5diTiek5euTQL+YCFaUt0ZTXimMysHYEPgVhfjxdRTM65V4BXzOxSYAzQIcExTQCGOedyAkW6mIskpo+B05xzP5vZVcC/gSYJjKcy0ApoD9QAVpjZ+865LxMYU66uwDLn3E8xiiVXJDF1BFYDlwONgTfN7D3n3N4ExjQOeNLMVhNI4P8ltqNC/Kqs+en3zrnvzexEAn/X9c65JXGKawLhc1SsP5vKmj8TecwKy6F+OGZF5fdEHrOweT7CZRMSm/cZlMhjliv/55Afzxv9wI/nrpHw4/ltcfx4/hsJv50jR8qP59KR8OP5diTiek6erCMwNgMNg543IFDNSaSIYjKzc4FngW7OuZ1+iCmXd4LR2MxSExxTBjDLzLKB64DJZtY9kTE55/Y65372fp8PVInhcYrkGG0GFjrnfnHO7QCWAOfFKJ5IY8rVk/gMZ4skplsJDA90zrkNwCYC804kLCbvvXSrc64l0IfAdYCbYhiTX5UpPznnvvd+bgdeITB8MV5xFZajYv3ZVKb8mchjVkQOTfgxKyq/J/h9VlieT/gxKyK2RB+zXPk/h/x43ugHfjx3jYQfz2+L48fz30j47Rw5Un48l46EH8+3IxHfc3Lng4lWSvogUDH7GmjE0YlCziqk7yjiM4lnsTEBpwIbgDZ+OU7AGRyd5Oh8YEvu80T/7bz+04j9JJ6RHKeTg45Ta+DbWB2nCONpTmAehcpATeBz4OxE/92A4whc01Yrln+zEhynp4FR3u8nee/vqE8uV8KY6uJNWgT8f8CMWB8rPz7Kkp8IXM98rNdeC1gOdIpXXPn65+Woki4b59gSeswKy6F+OGZFxJboYxY2z/vkmBUWW8L/bRLmcyjWxyxZHxH+reN67hrFuON6fhuNmPP1n4Y/JvH01TlylOOO67l0NN8n4fKc3+MmiufkSXkJiXPusJkNAl4nMOvpVOfcGjMb4L3+jJmdDKwC6gBHzGwogdlQYzJ0PJKYgJHACQQqqgCHnXMZsYinBDFdC/Qxs0PAAeBG572zEhhTXEUY03XAQDM7TOA49YzVcYokHufcOjNbCHwKHAGedc59Hot4Io3J63oN8IZz7pdYxVLCmMYA08zsMwL/IRnmAlX2RMbUHJhhZjkEZr++LVbx+FlZ8pMFJj19xcujlYGZzrmFcYyrRMtGI66yxkbgZCGRx6ywHOqHYxY2tkS/z4rK84k+ZoXFZmank/h/mwU+h2L9bzNZ+fHcNRJ+PL+NUsy+47dz5Ej58Vw6En48345EvM/JLcHvLxERERERERGRYiXrHBgiIiIiIiIiUoGogCEiIiIiIiIivqcChoiIiIiIiIj4ngoYIiIiIiIiIuJ7KmCIiIiIiIiIiO+pgCEiIiIiIiIivqcChoiIiIiIiIj4ngoYIiIiIiIiIuJ7KmCIiIiIiIiIiO+pgCEiIiIiIiIivqcChoiIiIiIiIj4ngoYIiIiIiIiIuJ7KmBIUjKzbDPr4P3+c9DjiJkdCHp+s5mNMrND3vPdZrbczC4Os87nzMyZ2RlBbceb2Wwz2+E9XjCzOkGvVzKzR8zsezPbZ2b/NbO6cTkIIiI+46PcfLmZfWxme83sazPrH58jICLiL3HMy2lmNtfMfjKzzWY2IOi1VDNbZmY7vfWuMLPfx+cISHmjAoYkPedc7dwH8C3QNajtBa/bbO/1esBS4GUzs9x1mFlboHGY1T8CpACne6+fBIwKev1/gDbAxUAdoDfwazT3T0QkGSUqN5tZFeAV4O/AccCNwHgzOy8GuykikjRinJf/CWwikI+vBv5iZu28134G+nnrTAEeA+aZWeXo76WUdypgSIXinDsETAdOBk4A8JLnJGBQmEUaAf92zu11zu0hcFJ8lrdcCjAU+P+cc9+4gM+dcypgiIiUQDRzM3A8gYLy815e/hBYB7SI7V6IiJQfJcnLZlYbyATGOucOOec+AeYQKFrgnPvVOfeFc+4IYEAOgULG8fHZGylPVMCQCsXMqgG3AJudczu85ruAJc65T8Ms8jegi5mleAWLa4EF3mvnAIeB68xsm5l9aWZ3xHYPRETKn2jmZufcD0AWcKt3md/FwGkEvkkUEZEIlDAvW76fub+fnW+dnxIYqfwq8Kxzbnu045byT8N2pKK4wcy6AL8BnwPdAcysIXA70KqQ5T4GqgI7veeLgMne7w0IDE8+k8C3gU2ARWb2pXPuzRjsg4hIeROL3AyBAsazwJPe84HOue+iGrmISPlU4rzsnNtnZsuAh8zszwRGvF0L/Jiv37lmVh24hkAOFykxjcCQiuJF51xd59yJzrnLnXMfee0TgNHeEORwXgK+BI4lMCR5I4Fr/AAOeD9HO+cOeNXoWcBVMdkDEZHyJ+q52cyaAbOBPgROkM8C7jOzq2O3GyIi5UZp8/LNBL7Q+w54GngB2Jy/k3c5SRYwXHMTSWmogCEVXXvgce8SkG1e2wozu8n7/Tzg7865X5xzPwPPcLRAkTt8zsUvXBGRCqEsufls4Avn3OvOuSPOuS+A/wCd47kDIiLlTJF52ZsProtzrp5z7kIC82asLGJ9VQhMxCxSIrqERJJZFW8YWq7DpVjHmYQW8rYCXYFPvOcfAn80s/u85/1zX3PObTSz94AHzWwIgSR8I9CrFHGIiJQXCc3NwH+BJmZ2OfA2gdzchcCs9yIiFVHM87KZNScw4uIgcANwJdDce+0iAv/vXAlUAoYQuFvJB6WIQyo4FTAkmc3P93xsSVeQf/Ig7y5RO5xzuZeH9AMmEkjIRiDx3hK0SC/gHwSuw94OPOScW1TSOEREypGE5mavuJz7+mnAHgJDmf9R0jhERMqJeOTljsCDQE0CheROzrncOTCqEcjJpwOHgM+Aq51z35c0DhFzTqPfRURERERERMTfNAeGiIiIiIiIiPieChgiIiIiIiIi4nsqYIiIiIiIiIiI76mAISIiIiIiIiK+pwKGiIiIiIiIiPie72+jmpqa6tLT0xMdRul98UXgZ9OmiY1DRBLuo48+2uGcq5foOKIh6XOziJQvZTjfimZuNrPqwBICt42sDMxxzj1sZscDs4F0IBu4wTm3y1vmfuA2IAcY4px73WtvBUwDahC4DeadrojbByovS7ml/09VSIXlZt8XMNLT01m1alWiwyi9zMzAz3feSWQUIuIDZvZNomOIlqTPzSJSvpThfCvKufkgcLlz7mczqwIsNbMFQA9gkXNunJkNB4YDw8ysBdATOAs4BXjLzM50zuUATwP9gfcJFDA6AQsK27DyspRb+v9UhVRYbtYlJCIiIiIiUeACfvaeVvEeDugGTPfapwPdvd+7AbOccwedc5uADUBrM6sP1HHOrfBGXcwIWkZEpMJSAUNEREREJErMrJKZrQa2A2865z4ATnLObQXwfp7odU8DvgtafLPXlub9nr89/7b6m9kqM1v1448/Rn1fRET8RgUMEREREZEocc7lOOdaAg0IjKY4u4juFm4VRbTn39YU51yGcy6jXr1yMcWSiEiRfD8HhlRMN/fuy5at2+K2vbT6J/PC89OL7ygiInGjzwJJZs653Wb2DoG5K34ws/rOua3e5SHbvW6bgYZBizUAvvfaG4RpF0m4eOfmyevW06J5s7htT/xNBQzxpS1btzF49IS4bW/SyKFx25aIiERGnwWSbMysHnDIK17UADoAjwGvAn2Bcd7Pud4irwIzzWw8gUk8mwArnXM5ZrbPzC4CPgD6AJPiuzci4cU7N/929e/jti3xP11CIiKShMysupmtNLNPzGyNmf2P1368mb1pZl95P1OClrnfzDaY2Rdm1jGovZWZfea9NtHMwg1dFhGR4tUH3jazT4EPCcyB8RqBwsUVZvYVcIX3HOfcGuBFYC2wELjDuwMJwEDgWQITe26kiDuQiIhUFBqBISKSnBJ2qz4REQnPOfcp8Lsw7TuB9oUsMxYYG6Z9FVDU/BkiIhWORmCIiCQh3apPRERERCoaFTBERJJUPG/V521Pt+sTERERkYRRAUNEJEnF81Z93vZ0uz4RERERSRgVMEREkpxzbjfwDkG36gPQrfpEREREpDxRAUNEJAmZWT0zq+v9nnurvvUcvVUfFLxVX08zq2ZmjTh6q76twD4zu8i7+0ifoGVERERERHxDdyEREUlO9YHpZlaJQDH6Refca2a2AnjRzG4DvgWuh8Ct+sws91Z9hyl4q75pQA0Cdx/RHUhERERExHdUwBARSUK6VZ+IiIiIVDS6hEREREREREREfE8FDBERERERERHxPV1CIgJ8vXEjmR06xm17afVP5oXnp8dteyIiIiIiIslOBQwRIMc5Bo+eELftTRo5NG7bEhERERERKQ90CYmIiIiIiIiI+J4KGCIiIiIiIiLieypgiIiIiIiIiIjvqYAhIiIiIiIiIr6nAoaIiIiIiIiI+J4KGCIiIiIiIiLieypgiIiIiIiIiIjvqYAhIiIiIiIiIr5XOdEBiIiIiIiISOnc3LsvW7Zui9v2srOz47YtkfxUwBAREREREUlSW7ZuY/DoCXHb3pCbusRtWyL56RISEREREREREfG9clPAcM7RqFEjzIwNGzYUeH3UqFGkpqaGXfbee+8lPT29QPs777xDly5dSE1NpWrVqqSnpzNkyBC+/fbbaIcf1ty5cznnnHOoXr06LVq0YPbs2cUuM2fOHNq0acMJJ5xA9erVadq0KY888gi//fZbSL/Jkydz9dVXc8IJJ2BmvPPOO6Vel4hIYZSbjzp8+DDjxo2jSZMmVKtWjQYNGnDXXXcV2n/o0KGYGffee29I+4YNG7j99ts577zzqFSpEpmZmWXZHRGpgJSbA6ZNm4aZFXg888wzIf0iOW8G+Pe//825555LtWrVaNSoEePHj4/GrolIkGILGGbW0MzeNrN1ZrbGzO702o83szfN7CvvZ0rQMveb2QYz+8LMOga1tzKzz7zXJpqZRWtHVqxYkXc91qxZs8q8vokTJ3L55ZdTo0YN/v73v/PWW2/x8MMP89///pdu3bqVef3FWbp0Kddeey3t2rVjwYIFXH311fTq1Ys33nijyOV27txJu3btePbZZ1mwYAH9+vVj7Nix3H333SH9ZsyYwU8//UTHjh0LWVPk6xIRKYxy81G33norEydO5N577+WNN95g3Lhx1KhRI2zftWvXMnXqVOrUqVPgtTVr1jB//nzOPPNMzjzzzDLvk4hUPMrNoRYvXsyKFSvyHj169Ah5PZLz5mXLltGjRw9at27NvHnz6NevH8OGDWPChAll2TURySeSOTAOA/c45z42s2OBj8zsTeAWYJFzbpyZDQeGA8PMrAXQEzgLOAV4y8zOdM7lAE8D/YH3gflAJ2BBNHYkKyuLWrVqcfbZZ5OVlcWIESNKva7//ve/3H333YwYMYLRo0fntV966aXceuutvPbaa9EIuUhjxozh0ksvZeLEiQC0a9eONWvWMHr0aK688spCl7v99ttDnrdr1469e/fyt7/9jUmTJpFbM1q+fDnHHHMMn3/+OVlZWWVal4hIYZSbAxYuXMisWbP45JNPaNGiRbHbGTJkCHfeeSfPP/98gde6du2a9x+C6667jh07dpRyb0SkolJuDnXBBRdQu3btQl+P5Lx59OjRtG3blmeffRaAK6+8kl27djF69Gj+9Kc/UbVq1VLsmYjkV2wBwzm3Fdjq/b7PzNYBaUA3INPrNh14Bxjmtc9yzh0ENpnZBqC1mWUDdZxzKwDMbAbQnSgUMHJycnjppZf4wx/+QGZmJrfffjuffvop5557bqnWN2nSJFJTU3nooYfCvt6lS2wnrjl48CBvv/12XhLO1bNnT2699Vb27NnDcccdF/H6TjjhhAKXfRxzTOmuHgq3LhGRcJSbj5o6dSqXX355RMWLOXPmsG7dOubOnRu2gFHa/B0NmuleJPkpN5dcJHl39erV3HHHHSFtV155JX/9619ZsWIFl112WZliEJGAEt2FxMzSgd8BHwAnecUNnHNbzexEr1sagREWuTZ7bYe83/O3h9tOfwIjNTj11FOLjWvx4sX88MMP9OzZk7Zt2zJo0CCysrJKnYjfffdd2rdvT5UqVUq1/OHDh48+cS63MaRPpUqVCh3BsHHjRg4dOkSzZs1C2ps3b86RI0f48ssvueCCC4qMIScnh4MHD/Lxxx8zceJEBg4cWOoRE9Fcl4hUHL7OzYWIVW7+4IMP+MMf/sCgQYOYMWMGhw8fplOnTjz11FOccsopef0OHDjAPffcw7hx46hVq1YJ9i4+NNO9SPJTbi6ocePG7Ny5k8aNG3P33XcXGIUciV9//bXAKItq1aoBsG7dOhUwyuDAgQOs/uRThnYo/BKeaEqrfzIvPD89LtuSkou4gGFmtYF/AUOdc3uL+A9suBdcEe0FG52bAkwByMjICNsnWFZWFnXr1qVTp05UrVqVK664glmzZvGXv/ylVP/R3rJlS0SFk8KETeD52p577jluueWWsMvv2rULgLp164a0p6SkhLxelFq1anHw4EEA+vTpw+OPP17sMvFYl4hUHEmRm/OJVW7etm0b06ZN47zzzmPWrFns27eP++67j2uuuYb3338/73g8+uij1K9fn//3//5fBHskIlJyys1H1a9fnzFjxtC6dWtycnLIyspiwIAB7N+/v8hJlsM544wz+PDDD0PaVq5cCcBPP/1UonVJKAekNTojbgX0SSOHxmU7UjoRFTDMrAqB4sULzrmXveYfzKy+N/qiPrDda98MNAxavAHwvdfeIEx7mRw8eJBXXnmFa665Jq/q2atXL3r37s3777/PxRdfXKr1lmWEQUjyyq3g/v3vIX0aNWpU4hicN5ojktiWL1/O/v37WblyJaNHj2bQoEFMnjy52OVivS4RqRh8n5sLEavc7JzDOcfcuXM54YQTgMCJ82WXXcbixYtp3749mzZt4n//939ZvHixRrmJSEwoN4fq2LFjyMScnTt35uDBgzzyyCPceeedJbpkb8CAAQwcOJD/+7//47rrrmPlypU88cQTQGAEiYhER7EFDO9OIf8A1jnngu8F9CrQFxjn/Zwb1D7TzMYTmMSzCbDSOZdjZvvM7CICl6D0ASaVdQcWLFjA7t27ueqqq9i9ezcAmZmZVKtWjaysrLxEXLlyZXJycsKuIycnh8qVjx6KtLS0Mt3yqWXLlkef5E4IFNxG0Ykst2Kcuz+5cp/nrzCHc/755wPQtm1bUlNT6du3L/fccw+NGzcudtlYrktEKgbf5+ZCxCo3p6SkcPrpp+cVLyCQU6tWrcratWtp3749w4cPp3PnzjRr1ixvnUeOHOHgwYPs3r2b4447ToUNESkT5ebiXXfddbz44otkZ2dz+umnR7xcv379+OSTTxg4cCD9+/enZs2aPPbYYwwePJiTTjqpRDGISOEiKSv+HugNXG5mq73HVQQKF1eY2VfAFd5znHNrgBeBtcBC4A7vDiQAA4FngQ3ARqIwgWfuTMDXX389KSkppKSk0LBhQw4ePMiLL76Yl3zr1avH3r172b9/f4F1bN26lRNPPDHveWZmJosWLYromrxwqlSpcvSxZEngEdxWpQrTpxd+XVXjxo2pUqUK69evD2lfv349xxxzTIlvm5dbgNi0aVPJdyaG6xKR8sv3ubmQR6xyc/PmzcO2O+fyvuH74osvePnll/OOV0pKCt999x1PPfUUKSkpbNmypRR7LSLxZGYNzextM1tnZmvM7E6v/Xgze9PMvvJ+pgQtc7+ZbTCzL8ysY1B7KzP7zHttokWhgqncHLmSHu5KlSrx1FNP8eOPP/Lpp5/yww8/cNFFFwHk/RSRsovkLiRLCT9/BUD7QpYZC4wN074KOLskARbl559/5rXXXqNXr170798/5LXcWzq9/fbbdOjQgUsuuYQjR47w2muvccMNN+T1++WXX1i0aBH9+vXLaxs8eDDTp09n7NixPPzwwwW2O3/+fK666qpC4yrrJSTVqlWjXbt2vPTSSyGTCM2ePZuLL764xDMpL1u2rNhtJmJdIlI+JUVuLkSscnOXLl14+OGH2bFjB6mpqQAsWbKEQ4cOcd555wHw7LPP8vPPP4cs17NnTy677DIGDhxIvXr1io1fyubrjRvJjNMkcaCJ4sqpw8A9zrmPzexY4CMzexO4BVjknBtnZsOB4cAwM2sB9ATOIjBy+S0zO9P78u9pApPavw/MBzpRhi//lJsj869//YvU1FROO+20Ei2XK7cwBDB58mTatGlTYIJRESm9Et2FxG/mzp3L/v37ufPOO7nwwgtDXvv973/P2LFjycrKokOHDrRo0YIbb7yR2267jU2bNtGqVSu2b9/OE088gXOOIUOG5C3bsmVLxo8fz9ChQ1m7di09e/YkNTWVTZs2MXXqVPbs2VNkIs7IyDj65NhjcxtLtG8PPfQQmZmZDB06lO7duzN//nzmz5/PwoUL8/p88803NG7cmKlTp9KnTx8AOnXqRIcOHTjrrLOoVKkSy5Yt44knnuDGG28MueRj1apVZGdn89133wGBGaR37NhBenp6XvzB63r8f8fz9aav+e7bb6h34sncdvufSrQ/JaVb54kkr6TIzaVU2tzcv39/Jk6cSNeuXXnggQfYt28fw4YNo0OHDrRt27bQ+KpXr07Dhg3JzMzMa9u/fz/z588HApPn7d27lzlz5gBw1VVXUbNmzTLvZ0WV41xc77KiieLKH+8Ofbl36dtnZusI3HWvG5DpdZsOvAMM89pnOecOApvMbAPQ2syygTrOuRUAZjYD6E4ZChjKzQVz87XXXkvr1q0599xzycnJYfbs2cyePZuJEyeGzH8RyXnz+++/z9KlS2nZsiV79+4lKyuL119/naVLl5Z5/0TkqKQuYGRlZdGkSZMCSRgCw9FuuOEGsrKymDx5MtWqVWPGjBk88sgjTJkyhW+//ZZjjz2WzMxMXnjhBdLSQu/oOmTIEM455xz+93//lz/+8Y/s3buXtLQ0OnbsyJ///OeY71vbtm2ZM2cOI0aM4Omnn6ZRo0bMnDmTK6+8Mq+Pc46cnByOHDmS13bBBRcwbdo0srOzqVy5MqeffjqPPvooAwYMCFn/U089FTIcb9SoUQD07duXadOmFVjXrwcPcurpTbhl8DCuvOZGKlcu3a2yIqVb54kkL+Xmgrm5Tp06LF68mCFDhtCzZ0+qVq1Kt27d+Otf/1riGLZv3871118f0pb7fNOmTaSnp5du50QkqswsHfgdgbnfTvKKG3gT4Odeg5FGYIRFrs1e2yHv9/zt+bfRn8AojWLvBKLcXDA3N23alKlTp/Ldd9/hnKNFixbMmDGD3r17h6w/kvPmKlWqMHv2bEaNGsUxxxzDJZdcwrJlyzjnnHNit+MiFZDlztDrVxkZGW7VqlWJDqP0cr81e+edREZRZpkdOsb1W6khN3Vh4szXyu32Jo0cyjtvvR637Yk/mNlHzrmyf9UUWFdDYAZwMnAEmOKce9LMjgdmA+lANnCDc26Xt8z9wG1ADjDEOfe6194KmAbUIDBM+U5XzIdD0udmKRV9FkSXPguiqAznW9HMzUHrrA28C4x1zr1sZrudc3WDXt/lnEsxs78BK5xz//Ta/0EgD38LPOqc6+C1XwLc55zrWtg2lZcrrvKem+tffj5NWpzDkqfic8mdcrM/FJabI783kIiI+EnuddbNgYuAO7xrqYcTuM66CbDIe06+66w7AZPNLHda99zrrJt4j07x3BERkfLEzKoA/wJecM697DX/YGb1vdfrA9u99s1Aw6DFGwDfe+0NwrSLiFRoKmCIiCQh59xW59zH3u/7gODrrHO/ophO4JppCLrO2jm3icDdoFp7J9J1nHMrvFEXM4KWERGREvDuFPIPYJ1zbnzQS68Cfb3f+wJzg9p7mlk1M2tEoIi80rvcZJ+ZXeSts0/QMiIiFVZSz4EhIiLxuc7a207E11qLiFRQvwd6A5+Z2Wqv7QFgHPCimd1G4PKQ6wGcc2vM7EVgLYGRdXd4dyABGMjRy/sWUIYJPEVEygsVMEREkph3nfW/gKHOub1F3Lc+3AuuiPaCjc5NAaZA4FrrkkcrIlK+OeeWEj6vArQvZJmxwNgw7auAs6MXnYhI8tMlJFEwd+5czjnnHKpXr06LFi2YPXt2RMv9+9//5txzz6VatWo0atSI8ePHF+ize/du+vXrx/HHH0/t2rXp3LkzGzZsCOkzZ84c2rRpwwknnED16tVp2rQpjzzyCL/99ltU9k9E/EnXWRettLl57dq1tG/fnpo1a3LKKacwcuRIcnJyQvo45/jLX/5Cw4YNqVGjBpdeeimrV68u1bpERCqS0uTmSM91I83Ns2bN4vzzz6d27dqkpaXRp08fvv++XHz0iZR7KmCU0dKlS7n22mtp164dCxYs4Oqrr6ZXr1688cYbRS63bNkyevToQevWrZk3bx79+vVj2LBhTJgwIaTfjTfeyOuvv86TTz7JzJkz2blzJ+3bt2fv3r15fXbu3Em7du149tlnWbBgAf369WPs2LHcfffdsdhlEfEBXWddtNLm5l27dtGhQwfMjLlz5zJy5EieeOIJHn744ZB+48aNY8yYMQwbNox58+ZRu3ZtOnTowLZt20q8LhGRiqK0uTnSc91IcvOrr75Kr169aNOmDXPnzuWxxx5jyZIldOnSJeQWqyLiT7qEpIzGjBnDpZdeysSJEwFo164da9asYfTo0SH3ns5v9OjRtG3blmeffRaAK6+8kl27djF69Gj+9Kc/UbVqVVasWMEbb7zBokWLuPzyywG48MILadSoEVOmTOHee+8F4Pbbbw9Zd7t27di7dy9/+9vfmDRpEkUMKReR5KXrrItQ2tz8zDPPcODAAV5++WXq1KnDFVdcwd69exk1ahT33XcfderU4ddff2XcuHHcf//9DBo0CICLL76Y9PR0nnrqKR555JGI1yUiUpGUNjdHcq4baW6eOXMm559/Pk899VTe+urUqUO3bt344osvaN68ebR3W0SiKOlHYNxyyy1kZGTw73//m2bNmlG9enXatm3L2rVrY77tgwcP8vbbb3PDDTeEtPfs2ZMVK1awZ8+eQpddvXo1HTp0CGnLLWKsWLEir0/lypW57LLL8vqcdNJJnHvuufznP/8pMrYTTjhBl5CIlGPOuaXOOXPOneuca+k95jvndjrn2jvnmng/fwpaZqxzrrFzrqlzbkFQ+yrn3Nnea4O8u5GUSbLm5gULFtCxY8eQ4kLPnj05cOAA7777LgDLly9n7969IeuvVasWXbt2ZcGCBSVal4hIPCVrbg4n/7lupLn50KFDHHfccSHrqlu3LhC4BEVE/C3pCxgA33zzDXfffTcPPfQQM2fOZM+ePXTs2JFff/21yOUOHz5c7KOoRLZx40YOHTpEs2bNQtqbN2/OkSNH+PLLLwtd9tdff6Vq1aohbdWqVQNg3bp1eX0qV65MpUqVCvTL7RMsJyeH/fv3s3TpUiZOnMjAgQM1+kJEEiYZc/P69esLLHfqqadSs2ZN1q9fn9enUqVKNGnSpMD6c/tEui4RkXhLxtycq6hz3Uhzc79+/XjvvfeYMWMGe/fu5csvv2TEiBG0a9eOFi1aFBuDiCRWuShg7Nixg3/+85/cfPPN9OjRg9dee42tW7cybdq0IperUqVKsY/p06cXuvyuXbuAo1XbXCkpKSGvh3PGGWfw4YcfhrStXLkSgJ9++imvz6+//spnn32W1+fAgQN8/vnneX2C1apVi1q1anHJJZdw2WWX8fjjjxex9yIisZWMuXnXrl0FlstdNne5Xbt2Ubt27QLF5ZSUFPbv35/3jWAk6xIRibdkzM25ijrXjTQ3X3311UybNo3+/ftz3HHH0bRpU3Jycnj55ZcREf8rF3NgnHjiibRp0ybv+WmnnUarVq1YuXIlAwYMKHS5/AWEcBo1alRsn/yjHHKrz0WNfhgwYAADBw7k//7v/7juuutYuXIlTzzxBEBe4u3YsSONGjXi9ttv57nnnqNOnToMHz6cPXv2UKVKlQLrXL58Ofv372flypWMHj2aQYMGMXny5GLjFxGJhWTMzYW97pwLaS+sT/7XIlmXiEg8JWtuhuLPdSPJzW+//TYDBgzgzjvvpHPnzvzwww+MGjWKa665hrfeeqtAAURE/KXcFDDCtW3durXI5Vq2bFnsuotKYrkV4927d4e05z4P981brn79+vHJJ58wcOBA+vfvT82aNXnssccYPHgwJ510EgBVq1Zl1qxZ9OrVK2+4Xdu2benTpw+LFy8usM7zzz8/r09qaip9+/blnnvuoXHjxsXup4hItCVjbk5JSSmwHMCePXvylktJSWHfvn3k5OSExLF7925q1qyZV2COZF0iIvGWjLk5V1HnupHm5nvuuYc//OEPPPbYYyH71qxZM+bOnUuPHj2KjUNEEqdcXEKyffv2sG3169cvcrmyDoVr3LgxVapUKXAt8/r16znmmGM488wzC122UqVKPPXUU/z44498+umn/PDDD1x00UUAeT8BWrduzYYNG1i/fj0bNmzgvffeY/v27SF9wslN8Js2bSqyn4hIrCRjbm7WrFmB5b777jt++eWXvEJys2bNyMnJYcOGDQXWH3xtdyTrEhGJt2TMzeHkP9eNNDevX7++QDGmadOm1KhRg40bN5YoBhGJv3IxAmP79u0sX748bzjct99+y8cff8ytt95a5HJlHQpXrVo12rVrx0svvRRye6fZs2dz8cUXF5jhOJyUlJS8ivTkyZNp06ZNgRNbM6Np06YAfPXVV7z11lvMmzevyPUuW7as2PhFRGIpGXNz586defzxx9m3bx/HHnts3nI1atTIuyNUmzZtqFOnDi+99BIjRowAYP/+/cybN4/+/fuXaF0iIvGWjLk5nPznupHm5tNOO42PP/44ZF3r1q3jwIEDpKenlygGEYm/clHASE1NpXfv3owZM4YaNWowcuRITjzxRG655ZYil8vIyCjzth966CEyMzMZOnQo3bt3Z/78+cyfP5+FCxfm9fnm119pXLkyU6dOpU+fPgC8//77LF26lJYtW7J3716ysrJ4/fXXWbp0acj6x4wZQ7NmzUhNTeWzzz5jzJgx9OzZkyuuuCKvT6dOnejQoQNnnXUWlSpVYtmyZTzxxBPceOONunxERBLG97n5m29o3LhxSG4eMGAAEydOpEePHgwbNoyvv/6aUaNGcffdd+fdDrV69eoMHz6cMWPGkJKSQrNmzRg/fjxHjhxh8ODBeeuPZF0iIvGWjLk5knPdkuTmu+66i1NOOSVvDozRo0eTnp7OVVddVeZ9FJHYKhcFjNNOO40HHniA4cOH880335CRkUFWVhbVq1eP+bbbtm3LnDlzGDFiBE8//TSNGjVi5syZXHnllXl9HIHbPh05ciSvrUqVKsyePZtRo0ZxzDHHcMkll7Bs2TLOOeeckPXv3LmToUOHsmPHDho2bMi9997LPffcE9LnggsuYNq0aWRnZ1O5cmVOP/10Hn300SInYhIRiTXf52bnCuTmlJQUFi1axKBBg+jatSt169blrrvuYtSoUSHrHz58OEeOHOHRRx9l586dZGRk8Oabb+bNYVSSdYmIxFMy5uZIz3Ujyc1DhgyhatWqPP300zzzzDPUrVuXtm3b8uijj1KrVq2YHwMRKZtyUcAA6NGjR8Im3enevTvdu3cv9PX06tUL3Be7VatWEQ3FmzBhAhMmTCiyz5gxYxgzZkwkoYqIxJWvc3N6eoHcDNCiRYuwEyUHMzMefPBBHnzwwSL7RbIuEZF4S7bcHOm5biS52cwYOHAgAwcOjDhmEfGPclPAEEkmX2/cSGaHjnHbXlr9k3nh+cIn1hIREREREfE7FTBEEiDHOQaPnhC37U0aOTRu2xIREREREYmFpC9gTJs2LdEhiIhIPsrNIiL+o9wsIsnumEQHICIiIiIiIiJSHBUwRERERERERMT3VMAQEREREREREd9TAUNEREREREREfE8FDBERERERERHxPRUwRERERERERMT3VMAQEREREREREd9TAUNEREREREREfE8FDBERERERERHxPRUwRERERERERMT3ii1gmNlUM9tuZp8HtR1vZm+a2Vfez5Sg1+43sw1m9oWZdQxqb2Vmn3mvTTQzi/7uiIiIiIiIiEh5FMkIjGlAp3xtw4FFzrkmwCLvOWbWAugJnOUtM9nMKnnLPA30B5p4j/zrFBEREREREREJq9gChnNuCfBTvuZuwHTv9+lA96D2Wc65g865TcAGoLWZ1QfqOOdWOOccMCNoGRERERERERGRIpV2DoyTnHNbAbyfJ3rtacB3Qf02e21p3u/528Mys/5mtsrMVv3444+lDFFEREREJH506bWISGxVjvL6wiVXV0R7WM65KcAUgIyMjEL7iYiIiIj4yDTgKQKjjXPlXno9zsyGe8+H5bv0+hTgLTM70zmXw9FLr98H5hO49HpB3PZCyuTm3n3ZsnVb3LaXnZ0dt22JJFppCxg/mFl959xW7/KQ7V77ZqBhUL8GwPdee4Mw7SIiUgpmNhXoAmx3zp3ttR0PzAbSgWzgBufcLu+1+4HbgBxgiHPuda+9FYET7hoETpLv9C71ExGREnLOLTGz9HzN3YBM7/fpwDvAMIIuvQY2mVnupdfZeJdeA5hZ7qXXKmAkiS1btzF49IS4bW/ITV3iti2RRCvtJSSvAn293/sCc4Pae5pZNTNrRGCyzpXeZSb7zOwibwhcn6BlRESk5KahCZZFRJJBzC691mXXIlLRRHIb1SxgBdDUzDab2W3AOOAKM/sKuMJ7jnNuDfAisBZYCNzhDYMDGAg8S2Biz42oiiwiUmqaYFlEJOmV+dJr59wU51yGcy6jXr16UQ1ORMSPir2ExDnXq5CX2hfSfywwNkz7KuDsEkUnIiIlEfItn5kFf8v3flC/3G/zDlGCCZbFf3SdtUhS0KXXIiJREu1JPEVExH+iMsGymfUncLkJp556anQikzLRddYiSSH30utxFLz0eqaZjScwiWfupdc5ZrbPzC4CPiBw6fWk+IctIuI/pZ0DQ0RE/OcH79s9YvEtn4Yqi4gUTZdei4jElkZgJCkNGxaRMPQtn4hIAunSa5Hk9/XGjWR26Bi37aXVP5kXnp9efEcBVMBIWho2LFKxed/yZQKpZrYZeJhA4eJF7xu/b4HrIfAtn5nlfst3mILf8k0jcBvVBehbPhEREanAcpyL6/+zJo0cGrdtlQcqYIiIJCF9yyciIiIiFY0KGCIiIiIJoGHKIiIiJaMChoiIiEgCaJiyiIhIyeguJCIiIiIiIiLieypgiIiIiIiIiIjvqYAhIiIiIiIiIr6nAoaIiIiIiIiI+J4KGCIiIiIiIiLieypgiIiIiIiIiIjvqYAhIiIiIiIiIr6nAoaIiIiIiIiI+J4KGCIiIiIiIiLie5UTHYCIxN7XGzeS2aFj3LaXVv9kXnh+ety2JyIiIiIi5Z8KGCIVQI5zDB49IW7bmzRyaNy2JSIiIiIiFYMuIRERERERERER31MBQ0RERERERER8TwUMEREREREREfE9FTBERERERERExPdUwBARERERERER31MBQ0RERERERER8T7dRFRERERGRcuvm3n3ZsnVb3LaXnZ0dt22JVDQqYIiIiIiISLm1Zes2Bo+eELftDbmpS9y2JVLRqIARJYVVdid88ikAQzt0jOr2VNkVERERERGRikQFjCgprLKbNqgvQNSrvqrsioj4j4Ypi4iIiMSOChgiIiJRomHKIiIiUhJfb9xIZpRH6xclrf7JvPD89LhtL9pUwBARERGpAHSSLCLiPznOxfXLj0kjh8ZtW7GgAoaIiIhIBaCTZBERSXYqYIhI1OlbPhERERERiTYVMEQk6vQtn4iIiIiIRNsx8d6gmXUysy/MbIOZDY/39kVEpCDlZhER/1FuFhEJFdcRGGZWCfgbcAWwGfjQzF51zq2N9rZ0KzsRkcjEMzeLiEhkymtujvc5Oug8XaQ8ifclJK2BDc65rwHMbBbQDYh6Itat7EQqDs25UWZxy83xpmK2SOLEMzdP+ORTqlatSou4bC1uymVujvc5Oug8XSRYsp83m3MuaisrdmNm1wGdnHN/9J73Bi50zg3K168/0N972hT4Im5Bll4qsCPRQUSZ9ik5aJ+SQypQyzlXL9GB5JdkuTkZ3xuKOX6SMW7FHB9FxXxasubmKOflZPy7hqP98Bfth38k2z6Ezc3xHoFhYdoKVFCcc1OAKbEPJ3rMbJVzLiPRcUST9ik5aJ+Sg7dP6YmOoxBJk5uT8b2hmOMnGeNWzPGRjDETQW6OZl5O0mNUgPbDX7Qf/lEe9gHiP4nnZqBh0PMGwPdxjkFEREIpN4uI+I9ys4hIPvEuYHwINDGzRmZWFegJvBrnGEREJJRys4iI/yg3i4jkE9dLSJxzh81sEPA6UAmY6pxbE88YYiipLnmJkPYpOWifkoNv9ynJcrNvj2MRFHP8JGPcijk+ki7mBOTmpDtGhdB++Iv2wz/Kwz7EdxJPEREREREREZHSiPclJCIiIiIiIiIiJaYChoiIiIiIiIj4ngoYJWRmnczsCzPbYGbDw7zezcw+NbPVZrbKzNomIs6SKG6fgvpdYGY53n3JfS2Cv1Omme3x/k6rzWxkIuIsiUj+Tt5+rTazNWb2brxjLKkI/k5/Dvobfe69/45PRKyRimCfjjOzeWb2ifd3ujURcfpNWXKrmWWb2We5r/kl5qB+BXJnpMvGQhnj9uWxLiqnJ+pYlzFmXx7noLgLfM74/T1dRNwJOdbxlIz5NZxkzblh4ku6HFxIfEmXl8NJxlwdTrLm71JxzukR4YPABEobgdOBqsAnQIt8fWpzdG6Rc4H1iY67rPsU1G8xMB+4LtFxR+HvlAm8luhYo7xPdYG1wKne8xMTHXdZ9ylf/67A4kTHHYW/0wPAY97v9YCfgKqJjj0JjluhuRXIBlL9FnNQv5DcWdL3vl/i9vOxLiynJ+pYl/VzyMfHOeznjN/f04XFnahj7cO/q6/ya2n3I6ifb3JuNPcj2f4efsvL0d6PJPx7+C5/l/ahERgl0xrY4Jz72jn3GzAL6BbcwTn3s/PeDUAtwO+zpBa7T57BwL+A7fEMrpQi3adkEsk+3QS87Jz7FsA55/e/VUn/Tr2ArLhEVnqR7JMDjjUzI3DS+BNwOL5h+k4y5tay5M5E5qhkzPllOV6JOtbJ+DlUls8Zv7+nk+3zMZqSMb+Gk6w5N79kzMHhJGNe9nssZZGs+btUVMAomTTgu6Dnm722EGZ2jZmtB/4D9ItTbKVV7D6ZWRpwDfBMHOMqi4j+TsDFFhjGv8DMzopPaKUWyT6dCaSY2Ttm9pGZ9YlbdKUT6d8JM6sJdCLwYe5nkezTU0Bz4HvgM+BO59yR+ITnW2XNrQ54w3vf949ppEeVJXdG/N6PgbLmfF8ea0+4nJ6oY13WzyG/HufCPmd8/Z6m6M/HRBzreErG/BpOsubc/JIxB4eTjHk5nGTM1eEka/4ulcqJDiDJWJi2AlVq59wrwCtmdikwBugQ68DKIJJ9mgAMc87lBL409r1I9ulj4DTn3M9mdhXwb6BJrAMrg0j2qTLQCmgP1ABWmNn7zrkvYx1cKUX078nTFVjmnPsphvFEQyT71BFYDVwONAbeNLP3nHN7Yxybn5U1t/7eOfe9mZ1I4Hiud84tiV24QNlyZ0ne+9FW1pzv12NdWE5P1LEu6+eQX49z2M+ZCJeNlbJ+PibiWMdTMubXcJI15+aXjDk4nGTMy+EkY64OJ1nzd6loBEbJbAYaBj1vQOBb1LC8N3BjM0uNdWBlEMk+ZQCzzCwbuA6YbGbd4xJd6RS7T865vc65n73f5wNVysHfaTOw0Dn3i3NuB7AEOC9O8ZVGSf499cT/l49AZPt0K4EhfM45twHYBDSLU3x+Vabc6pz73vu5HXiFwHDIWCtL7izR/kZZmXK+X491ETk9Uce6TJ9Dfj3OFP454/f3dKGfjwk61vGUjPk1nGTNufklYw4OJxnzcjjJmKvDSdb8XTrOBxNxJMuDQOXqa6ARRyc5OStfnzM4OhHS+cCW3Od+fESyT/n6T8P/k3hG8nc6Oejv1Br4Ntn/TgQuS1jk9a0JfA6cnejYy/reA44jME9ErUTHHKW/09PAKO/3k7wckfAJoJLguIXNrQSu1z7Wa68FLAc6+SHmfP3zcmdJl/VR3L491oXl9EQd6zLG7OfjHPZzxu/v6SLiTsixjucjwuPjq/xa2v3I1z84dyXs/Rnl/Uiqv0cROS6p/h5F7Eey/T18l79L+9AlJCXgnDtsZoOA1wnM2DrVObfGzAZ4rz8DXAv0MbNDwAHgRue9a/wown1KKhHu03XAQDM7TODv1DPZ/07OuXVmthD4FDgCPOuc+zxxURetBO+9a4A3nHO/JCjUiEW4T2OAaWb2GYEPwGEuUAmvsMqSW83sJALDniHwITzTObfQJzGXaNlYx1zWuAkU3Px6rAvL6Qk51mWJ2c/v6aI+Z/z8ni4sbjM7nQQc63hKxvxahv0o0bLxiDvSWPycg8NJxrwc7f1Itn8ffszfpZVbTRIRERERERER8S3NgSEiIiIiIiIivqcChoiIiIiIiIj4ngoYIiIiIiIiIuJ7KmCIiIiIiIiIiO+pgCEiIiIiIiIivqcChoiIiIiIiIj4ngoYIiIiIiIiIuJ7KmCIiIiIiIiIiO+pgCEiIiIiIiIivqcChoiIiIiIiIj4ngoYIiIiIiIiIuJ7KmCIiIiIiIiIiO+pgCFJycyyzayD9/vPQY8jZnYg6PnNZjbKzA55z3eb2XIzuzjMOp8zM2dmZwS1pZnZXDP7ycw2m9mAQuLp6y37x9jttYiIv/klN3v9fwna3rOx33sREf/xUV6uZGaPmNn3ZrbPzP5rZnVjfgCk3FEBQ5Kec6527gP4Fuga1PaC122293o9YCnwsplZ7jrMrC3QOMzq/wlsAk4Crgb+YmbtgjuYWQpwP7Am2vsmIpKsEp2bgfOCtqfisohUeAnOy/8DtAEuBuoAvYFfo7uHUhGogCEVinPuEDAdOBk4AcDMKgOTgEHBfc2sNpAJjHXOHXLOfQLMAfrlW+2jwERgR0yDFxEpp2KUm0VEpJSimZe9L/uGAv+fc+4bF/C5c04FDCkxFTCkQjGzasAtwGbnXG7B4S5giXPu0/zd8/3M/f3soPW1BjKAZ2ISsIhIBRDt3OxZYmbbzOxlM0uPcsgiIuValPPyOcBh4DovL39pZnfEJnIp71TAkIriBjPbDXwHtAK6A5hZQ+B2YGT+BZxz+4BlwENmVt3MzgeuBWp6y1YCJgODnXNH4rAPIiLlTdRzs+cyIB1oBnwPvOZ9cygiIkWLRV5uABwHnAk0Aq4DRpnZFTHdEymX9GEuFcWLzrn/F6Z9AjDaObenkOVuBv5GIIl/DbwAtPBe+xPwqXNuRZRjFRGpKGKRm3HOLfF+/c3M7gT2As2Bz6IUt4hIeRWLvHzA+znaOXcA+NTMZgFXAW9GK3CpGDQCQyq69sDj3nC2bV7bCjO7CcC7Tq+Lc66ec+5CAtcArgxa9pqgZdsAT5jZU/HeCRGRcqYsuTkcR+jQZhERKZmy5OXcS05cfEOW8kgjMCSZVTGz6kHPD5diHWcSWsjbCnQFPgEws+bAZuAgcANwJYFv8SBwXWDw9l8mMGHRP0oRh4hIeZHQ3GxmZwFVCIy2qAE8AmwB1pUiDhGR8iChedk5t9HM3gMeNLMhwOnAjUCvUsQhFZwKGJLM5ud7PrakK3DObQ9+7t0laoc3vA2gI/AggWv4/gt0cs796C27O9+yvwF7ixhaJyJSESQ0NxO4hd/TBK65/gVYDnTxZtQXEamIEp2XIVCs+AewE9gOPOScW1TSOETMOY3kERERERERERF/0xwYIiIiIiIiIuJ7KmCIiIiIiIiIiO+pgCEiIiIiIiIivqcChoiIiIiIiIj4ngoYIiIiIiIiIuJ7vr+NampqqktPT090GCLJ54svAj+bNk1sHJLno48+2uGcqxeNdXn3c18CVCOQy+c45x42s+OB2UA6kA3c4Jzb5S1zP3AbkAMMcc697rW3AqYBNQjczZjoIQAAXPlJREFUau1OV8wtqpSbJYTyjSSxaObmRFJelqShzwyJQGG52fcFjPT0dFatWpXoMESST2Zm4Oc77yQyCgliZt9EcXUHgcudcz+bWRVgqZktAHoAi5xz48xsODAcGGZmLYCewFnAKcBbZnamcy4HeBroD7xPoIDRCVhQ1MaVmyWE8o0ksSjn5oRRXpakoc8MiUBhuVmXkIiIJCEX8LP3tIr3cEA3YLrXPh3o7v3eDZjlnDvonNsEbABam1l9oI5zboU36mJG0DIiIiIiIr6hAoaISJIys0pmthrYDrzpnPsAOMk5txXA+3mi1z0N+C5o8c1eW5r3e/52ERERERFfUQFDRCRJOedynHMtgQYERlOcXUR3C7eKItoLrsCsv5mtMrNVP/74Y4njFREREREpC9/PgSESDzf37suWrdvitr20+ifzwvPTi+8oEgHn3G4ze4fA3BU/mFl959xW7/KQ7V63zUDDoMUaAN977Q3CtIfbzhRgCkBGRkaRk3xK+VRYrpzwyacADO3QMarbU64UEfGfsp43l/QzQ58FEqzYAkaiZ7oXiYctW7cxePSEuG1v0sihcduWlE9mVg845BUvagAdgMeAV4G+wDjv51xvkVeBmWY2nsAknk2Alc65HDPbZ2YXAR8AfYBJ8d0bSRaF5cq0QX0Bop5HlStFRPynrOfNJf3M0GeBBIvkEpLcme7PA1oCnbwT3eEEZrpvAizynpNvpvtOwGQzq+StK3em+ybeo1P0dkVEpEKpD7xtZp8CHxKYA+M1AoWLK8zsK+AK7znOuTXAi8BaYCFwh3cHEoCBwLMEJvbcSDF3IBERERERSYRiR2B4IyQKm+k+02ufDrwDDCNopntgk5nlznSfjTfTPYCZ5c50rxNlEZEScs59CvwuTPtOoH0hy4wFxoZpXwUUNX+GiIiIiEjCRTQHhjeC4iPgDOBvzrkPzCxkpnszC57p/v2gxXNntD9EhDPdm1l/AiM1OPXUUyPfGxERERERkQok3nO5ZWdnx21bIvlFVMDwhhm3NLO6wCuxnuleE8WJiIiIiIgUL95zuQ25qUvctiWSX4luo+qc203gUpG8me4Boj3TvYiIiIiIiIhIsGILGGZWzxt5QdBM9+s5OtM9FJzpvqeZVTOzRhyd6X4rsM/MLjIzIzDT/VxERERERERERIoRySUk9YHp3jwYxwAvOudeM7MVwItmdhvwLXA9BGa6N7Pcme4PU3Cm+2kEbqO6AE3gKSIiIiIiIiIRiOQuJJrpXkRE/v/27jw+iirf///rY9hh1EhAEZBtWN1QogiCBkRBBMEV0AuIzg9lZBvcHUW/IFccruiAotfxIuASUFBxCYjDogOyCA6gLCpIVDCIoICYESE5vz+6E7uTTtJJd7qrw/v5ePQj3afq1PlUdfik+XTVKRERERGRuCrVHBgiIiIiIiIiIvGgAoaIiIiIiIiIeJ4KGCIiIiIiIiLieSpgiIiIiIiIiIjnqYAhIiIiIiIiIp6nAoaIiIiIiIiIeF6Jt1EVERERiYevtm8nrVv3mI1Xv94pvPzizJiNJyIiIqWjAoaIiIh4Uo5zjBj3ZMzGmzp2dMzGEhERkdLTJSQiIiIiIiIi4nkqYIiIiIiIiIiI56mAISIiIiIiIiKepwKGiIiIiIiIiHieChgiIiIiIiIi4nkqYIiIiIiIiIiI56mAISIiIiISBWbW0MyWmtkWM9tkZqP87SeZ2ftm9qX/Z3JAn/vMbJuZfW5m3QPa25nZp/5lU8zM4rFPIiJeogKGiIiIiEh0HAXucM61Bi4AbjezNsC9wGLnXHNgsf81/mX9gdOBHsA0M0vyb+sZYCjQ3P/oEcsdERHxogpTwHDO0aRJE8yMbdu2FVr+8MMPk5KSErLvnXfeSePGjQu1L1u2jF69epGSkkKVKlVo3LgxI0eO5Jtvvol2+CHNnz+fM888k2rVqtGmTRvmzJlTYp8ZM2ZgZoUezz77bJF9Ro8ejZlx5513Flr25ptvctZZZ1G1alWaNGnC5MmTI9onETm2KDcXtmvXLmrVqoWZcejQoaBlWVlZDBkyhPr161OrVi3OOeccXn755ULbOHr0KBMnTqR58+ZUrVqVBg0a8Je//CWi/RKRyDnnspxzn/if/wxsAeoDfYCZ/tVmAn39z/sAs51zh51zO4BtwPlmVg843jm30jnngFkBfaIRp3Iz4X1u/u2337j++utp2rQp1atXp06dOlx++eWsW7eu0Pb0uVmk/FWYAsbKlSvJzMwEYPbs2RFvb8qUKXTt2pXq1avzv//7v/zzn//koYce4t///jd9+vSJePslWb58Oddccw1dunRhwYIFXHHFFQwYMIBFixaF1X/JkiWsXLky/3H11VeHXG/z5s1Mnz6d448/vtCyFStWcPXVV3P++efz9ttvc/PNN3PPPffw5JNPRrJrInIMUW4u7K677qJWrVqF2nNzc7nyyiv54IMP+Nvf/sb8+fO54IIL+K//+i/eeOONoHWHDBnClClTuPPOO1m0aBETJ06kevXqEe+fiESPmTUGzgFWAyc757LAV+QA6vpXqw98G9Btp7+tvv95wfaCYww1s7VmtvaHH34IOzbl5mDFfW7OycnBzLjvvvt49913+cc//kF2djZdu3blq6++yl9Pn5tFYqNSvAOIlvT0dGrWrMkZZ5xBeno6DzzwQJm39e9//5sxY8bwwAMPMG7cuPz2iy66iCFDhvDOO+9EI+RijR8/nosuuogpU6YA0KVLFzZt2sS4ceO47LLLSux/3nnnhfyAXNDIkSMZNWoUL774YqFl48aNo1OnTjz//PMAXHbZZfz000+MGzeOP//5z1SpUqWUeyUixxrl5mD/+te/WLhwIffffz933XVX0LIvvviCtWvX8tZbb9G7d28ALrnkElavXs3s2bO56qqrAFi4cCGzZ89mw4YNtGnTJsp7KCLRYGa1gHnAaOfcwWKmrwi1wBXTHtzg3HPAcwCpqamFlhdFuTlYcZ+bq1evXuhsjm7dulG7dm3efPNNxowZA+hzs0isVIgCRk5ODq+99hpXXnklaWlp3HrrrWzcuJGzzjqrTNubOnUqKSkpPPjggyGX9+rVK5JwS3T48GGWLl2an4Tz9O/fnyFDhnDgwAFOOOGEiMeZO3cuW7ZsYf78+SELGOvXr+f2228Parvssst44oknWLlyJRdffHHEMRTlxoGD2ZW1u9y2X1DetxCx8tX27aR1617yihF4csNGAEZ36079eqfw8oszS+ghEl3KzcFycnIYMWIEY8eO5cQTTyy0/MiRIwCFtnHiiSfiO4PcZ/r06XTt2lXFCxGPMrPK+IoXLzvnXvc3f29m9ZxzWf7LQ/b423cCDQO6NwC+87c3CNEeMeXmyNWsWZNq1arx22+/5bfF83OzyLGkQhQwlixZwvfff0///v3p1KkTw4cPJz09vcyJ+IMPPuCSSy6hcuXKZep/9OjREtdJSkqiqGr89u3bOXLkCK1atQpqb926Nbm5uXzxxRecd955xW6/WbNm7Nu3j2bNmjFmzBhuvfXWoOX/+c9/uOOOO5g4cSI1a9YMuY1ff/21ULW4atWqAGzZsqVcE/GurN2MGPdkuW2/oJE3lO8f14JynCv3/as/fDAAI8Y9ydSxo8t1LJFQlJuDPfvss/z666/cfvvtIee1OOOMM2jfvj1jx47lH//4ByeffDKvv/46K1asYMGCBfnrrV69miuvvJLhw4cza9Ysjh49So8ePXjqqac49dRTS9xHESk//juF/B+wxTkXOAHCW8BgYKL/5/yA9lfMbDJwKr7JOtc453LM7GczuwDfJSiDgKnRiFG5ubCSPjeDb96QnJwc9u7dy+OPP05SUhIDBgzIXx7Pz80ix5IKMQdGeno6J554Ij169OCkk07i0ksvZfbs2UHfWJXGrl27OO2008ocT+XKlUt8zJxZ9LfhP/30E0Chb+iSk5ODlodSr149xo8fz4svvsjbb79N+/btue2223jiiSeC1nv00UepV68e//Vf/1Xktv74xz/y8ccfB7WtWbMGgB9//LHIfiIioNwcaN++fTz44INMnjy5yA/5ZsaCBQvIzc2lRYsWnHDCCQwdOjT/jIs8u3fvZsaMGaxfv57Zs2fzwgsvsG7dOq666qoyH1sRiZoLgYFAVzNb73/0xFe4uNTMvgQu9b/GObcJeBXYDCwEbnfO5fi3NQx4Ht/EntuBBUSBcvPvwv3cDPDYY49RuXJl6tWrx8yZM8nIyKBRo0b5y/W5WSQ2Ev4MjMOHD/PGG29w1VVX5Vc9BwwYwMCBA1m1ahUdOnQo03YjudV2weQVSpMmTUodQ94fluJi6969O927/35pwuWXX87hw4d55JFHGDVqFMcddxw7duzgf/7nf1iyZEmx27rtttsYNmwY//jHP7j22mtZs2YNjz/+OOCrhIuIFEW5Odhf//pX2rdvT8+ePYtcJzc3l4EDB7Jv3z7mzJlD3bp1ycjI4JZbbqF27dr06NEjfzznHPPnz6d27dqA70P4xRdfzJIlS7jkkktK3AcRKR/OueWEnr8CIOQ/TufcBGBCiPa1wBnRi065uaBwPjfnuemmm+jWrRtZWVlMmzaNXr168eGHH+ZfzqfPzSKxkfAFjAULFrB//3569uzJ/v37AUhLS6Nq1aqkp6fnJ+JKlSqRk5MTchs5OTlUqvT7oahfv35Et3xq27ZtiesUl8jyKsZ5+5Mn73Woa6eLc+211/Lqq6+SmZlJ06ZNuffee7n88stp1apV/jZzc3M5fPgw+/fv54QTTsDMuPnmm9mwYQPDhg1j6NCh1KhRg8cee4wRI0Zw8sknlyoGETm2KDf/btOmTUyfPp0PP/wwf93s7GwADhw4QFJSEtWrV+edd97h3Xff5YsvvqB58+aA75h9++233H333fkFjOTkZJo2bUrt2rXz5wtyzmFm3Pbn4dRvWPZvQksS6/mCRCS6lJtLVvBzc55TTjmFU045BfAVOk4//XQmTpzIrFmzAPS5WSRGEr6AkZ6eDsB1111XaNmrr77KE088QVJSEnXq1OHgwYNkZ2dTo0aNoPWysrKoW7du/uu0tDQyMjI4evRoUIIOVzjXAL7wwgvcdNNNIZc1a9aMypUrs3Xr1qDr5bZu3cpxxx1HixYtSh0T/F6B/vzzz9mwYQOvv/560PKnnnqKp556im+//ZYGDRqQlJTEU089xfjx49m5cydNmjRh69atAFxwwQVlikFEosPMGgKzgFOAXOA559zfzewkYA7QGMgErnfO/eTvcx9wC5ADjHTOvedvbwfMAKoDGcAoF+G1CMrNv/vyyy85cuRIyG82GzRowC233MLzzz/P1q1bqVGjRn7xIs8555zDW2+9lf+6devWHD58GPh9vqDc3Fw++rAtF/e6lsuvuaHE/SyrWM8XJCLRpdwcvuLO3KhUqRJnnnlm0G1U9blZJDYSuoBx6NAh3nnnHQYMGMDQoUODluXd0mnp0qV069aNzp07k5ubyzvvvMP111+fv94vv/zC4sWLufnmm/PbRowYwcyZM5kwYQIPPfRQoXEzMjKKPQ040lPhqlatSpcuXXjttdeCJhGaM2cOHTp0KPVMyvPmzSMlJSX/Or3nn3+eQ4cOBa3Tv39/Lr74YoYNG0adOnWCliUnJ+dXt6dNm0bHjh0LTZQkIjF3FLjDOfeJmf0BWGdm7wM3AYudcxPN7F7gXuAeM2sD9AdOxzdR3D/NrIX/WutngKHAKnwFjB5EcK21cnOwTp06sXTp0qC2hQsX8thjj5GRkZH/DV+jRo3Izs7m888/p2XLlvnrrlu3jsaNG+e/7tWrFw899BB79+7Nb9u8fi1Hjx6l8R9/7yciEki5OTwFPzeH8uuvv/LJJ59w4YUXFlqmz80i5SuhCxjz588nOzubUaNG0b59+6BlF154IRMmTCA9PZ1u3brRpk0b+vXrxy233MKOHTto164de/bs4fHHH8c5x8iRI/P7tm3blsmTJzN69Gg2b95M//79SUlJYceOHUyfPp0DBw4Um4hTU1Mj3rcHH3yQtLQ0Ro8eTd++fcnIyCAjI4OFCxfmr/P111/TrFkzpk+fzqBBgwC45pprOP/88znrrLPIyclhzpw5zJkzhylTpuRfxxcqvmrVqtGwYUPS0tLy21atWsXy5ctp27YtBw8eJD09nffee4/ly5dHvH8iEhnnXBaQ5X/+s5ltAeoDfYA0/2ozgWXAPf722c65w8AOM9sGnG9mmcDxzrmVAGY2C+hLBAUM5ebg3JySkhKUW+H3SzE6d+5MrVq1AOjZsyennXYaffv2ZezYsdSpU4d3332XV199laeffjq/79ChQ5kyZQq9e/fm5+xf+deid3jpmcmcldqB1me3i3gfRaRiUm4u2+fm9PR0FixYQI8ePTj11FPz58DIyspizJgx+dvX52aR2CjxLiRm1tDMlprZFjPbZGaj/O0nmdn7Zval/2dyQJ/7zGybmX1uZt0D2tuZ2af+ZVMskhl/8CWU5s2bF0rC4Dsd7frrr+f111/PP9V21qxZ/OUvf+G5557j8ssvZ/jw4TRq1Ijly5dTv379oP4jR45k8eLFHDp0iD/96U907dqVsWPH0rJlS1577bVIwg5Lp06dmDt3Lv/85z/p3r07b731Fq+88gqXXXZZ/jp5t3PKzc3Nb2vZsiXTp0/nmmuu4brrrmPz5s3MmjWLESNGlDqGypUrM2fOHPr27ctNN91EdnY2K1asKPNttkSkfJhZY+AcfLfaO9lf3MgrcuSd51sf+Dag205/W33/84LtocYZamZrzWztDz/8UGQ8ys2Fc3M4/vCHP7B48WLOOOMM7rjjDvr27cuSJUt49tlnGTZsWP56xx9/PEuWLCE5OZktn23k+cmPcGa7CxgzfnIxWxeRY51yc9k+N7ds2ZJ9+/YxZswYunfvzn333cepp57K2rVrOeecc/LX0+dmkdiwki5zNrN6QL3A05TxfTt3E/BjwGnKyc65vNOU04Hz8Z+mDLTw3896DTCK309TnuKcK/ZbvtTUVLd27dpI9lESUFq37owY92TMxht5Qy+mvPJOhRrvouGDAfjwqZlMHTuaZf98r1zHk5KZ2TrnXORfNQVvsxbwATDBOfe6me13zp0YsPwn51yymT0NrHTOveRv/z98efgb4FHnXDd/e2fgbudc7+LGVW72Bq/kysB8E4vxyoty5bGpPHJzPCgve0feBMuxkpmZyeOz3ozZeJHm5tL+zVBuPjYVlZtLvITEy6cpi4gcy8ysMjAPeNk5lzcr7/dmVs85l+UvQO/xt+8EGgZ0bwB8529vEKJdREREyiBvguVY0QTLciwp8RKSQF47TVlE5FjlvwTv/4AtzrnAawfeAgb7nw8G5ge09zezqmbWBGgOrPHn75/N7AL/NgcF9BERERER8YywCxj+05TnAaOdcweLWzVEmyumvXCjc88551Kdc6kF74ghIiIAXAgMBLqa2Xr/oycwEbjUzL4ELvW/xjm3CXgV2AwsBG7334EEYBjwPLAN2I7OjBMRERERDwrrLiQ6TVlExFucc8sJXRgGuKSIPhOACSHa1wJnRC86EREREZHoC+cuJDpNuQTz58/nzDPPpFq1arRp04Y5c+aU2Gfbtm3ceuutnH322SQlJRW6xR5AVlYWd911F2effTa1atWiYcOGDB48mO++K1z3OXr0KBMnTqR58+ZUrVqVBg0a8Je//CUauycikpDKkpuh5Hwabm4OJ8+LiBxrypKb586dS8eOHalduzbVqlWjZcuWPPLII/z2229B602bNo0rrriC2rVrY2YsW7as2O3u2rWLWrVqYWYcOnQokt0SkRgJ5xISnaZcjOXLl3PNNdfQpUsXFixYwBVXXMGAAQNYtGhRsf02bdpERkYGLVq0oEWLFiHXWbduHW+88QYDBgzg7bffZtKkSaxevZqOHTsWSrJDhgxhypQp3HnnnSxatIiJEydSvXr1qO2niEgiKWtuhpLzabi5OZw8LyJyLClrbt63bx9dunTh+eefZ8GCBdx8881MmDCBMWPGBK03a9YsfvzxR7p37x5WPHfddRe1atUq8/6ISOyFcxcSnaZcjPHjx3PRRRcxZcoUALp06cKmTZsYN25c0L2nC+rduzd9+vQB4Nprr2Xv3r2F1unUqRNbt26lUqXf36Zzzz2Xli1bMm/ePAYP9p0As3DhQmbPns2GDRto06ZNNHdPRCQhlTU3h5NPw83N4eR5EZFjSVlz86233hr0ukuXLhw8eJCnn36aqVOn4ju5Gz766COOO+44PvvsM9LT04uN5V//+hcLFy7k/vvv56677opwz0QkVkp1FxIvuummm0hNTeXNN9+kVatWVKtWjU6dOrF58+ZyH/vw4cMsXbqU66+/Pqi9f//+rFy5kgMHDhTZ97jjSj70J554YtAHZIAWLVpQo0YN9uzZk982ffp0unbtquKFiHhGoubmcPJpuLk5nDwvIhJLiZqbQ6ldu3ahS0jCzbs5OTmMGDGCsWPHkpKSUqpxRSS+wprE0+u+/vprxowZw/jx46levToPPfQQ3bt358svv6RatWpF9jt69GiJ205KSsqv6ha0fft2jhw5QqtWrYLaW7duTW5uLl988QXnnXde6XamBBs3biQ7Ozvow/Xq1au58sorGT58OLNmzeLo0aP06NGDp556ilNPPTWq44uIhCsRc3NZ82mo3Cwi4kWJmJvz5OTkcPjwYT755BOmTJnCsGHDihyvOM8++yy//vort99+Oy+//HKp+0tsfbV9O2ndwrssKBrq1zuFl1+cGbPxpHQqRAFj7969zJ8/n44dOwLQrl07mjVrxowZM7jtttuK7Fe5cuUSt/3CCy9w0003hVz2008/Ab5v4wIlJycHLY+W3NxcRo0aRfPmzYNOs9u9ezczZszg7LPPZvbs2fz888/cfffdXHXVVaxatapMiV1EJFKJmJvLkk+Lys0iIl6UiLk5T82aNTl8+DAAgwYNYtKkSSX2KWjfvn08+OCDvPTSS2Htk8RfjnOMGPdkzMabOnZ0zMaS0qsQBYy6devmJ2GARo0a0a5dO9asWVNsIv74449L3HaTJk1KXKfgB1rnXMj2SN13332sXLmSDz74ICjhOudwzjF//nxq164NQL169bj44otZsmQJl1wScqoSEZFylYi5uSz5tKjcLCLiRYmYm/N89NFHZGdns2bNGsaNG8fw4cOZNm1aif0C/fWvf6V9+/b07NmzVP1ExBsqTAEjVFtWVlax/dq2bVvitpOSkopcllcx3r9/f1B73uuCFeZITJs2jUmTJpGenk779u0LxdG0adP8D9vgm2SuSpUqbN68WQUMEYmLRMzNpc2nxeVmEREvSsTcnOfcc88FfHk5JSWFwYMHc8cdd9CsWbMS+4Lv7lDTp0/nww8/zB83OzsbgAMHDpCUlKS7+Il4XIWYYSxw0rTAtnr16hXbr3LlyiU+Zs4s+vqnZs2aUblyZbZu3RrUvnXrVo477rio3TZv3rx5jBgxgr/97W/069ev0PLWrVuH7Oec0yRyIhI3iZibS5NPS8rNIiJelIi5OZS8YsaOHTvC7vPll19y5MgROnToQHJyMsnJydx+++0ANGjQgBEjRpQqBhGJvQpxBsaePXv46KOP8k+H++abb/jkk08YMmRIsf0iPRWuatWqdOnShddeey3o9k5z5syhQ4cOnHDCCWHuQdGWLVvGjTfeyPDhw7nzzjtDrtOrVy8eeugh9u7dmz+T8ocffsiRI0c4++yzI45BRKQsEjE3h5tPw8nNIiJelIi5OZQVK1aUOGZBnTp1YunSpUFtCxcu5LHHHiMjI4OmTZuWKgYRib0KUcBISUlh4MCB+bMpjx07lrp16xY5iVCe1NTUiMd+8MEHSUtLY/To0fTt25eMjAwyMjJYuHBh/jpff/01zZo1Y/r06QwaNAjwna6WkZEBwK5duzh48CBz584FoGfPntSoUYMtW7bQt29fWrVqRb9+/Vi1alX+NuvUqZN/utzQoUOZMmUKvXv35v777+fnn3/mnnvuoVu3bnTq1CnifRQRKYtEzM3h5NNwc3M4eV5EJNYSMTf36NGDbt26cfrpp5OUlMSKFSt4/PHH6devX9DlI2vXriUzM5Nvv/0WgA8++IC9e/fSuHFjUlNTSUlJIS0tLSimzMxMADp37kytWrUi3kcRKV8VooDRqFEj7r//fu69916+/vprUlNTSU9PL/ZWUNHSqVMn5s6dywMPPMAzzzxDkyZNeOWVV4JmonfOkZOTQ25ubn7bnj17uO6664K2lfd6x44dNG7cmNWrV3PgwAE2bNjAhRdeGLTu4MGDmTFjBgDHH388S5YsYeTIkfTv358qVarQp08fnnjiiXLaaxGRkiVibg4nn4abm8PJ8yIisZaIufm8885jxowZZGZmUqlSJZo2bcqjjz5aaNLRp556KugylocffhgIzs0iktgqRAED4Oqrr+bqq6+Oy9h9+/alb9++RS5v3Lhx/gzLxbUVdNNNN5VYDc/zxz/+Mf+bPhERr0i03Awl59Nwc3M4eV5EJB4SLTePHz+e8ePHl7jtGTNmlLpQUZrP2yISf5rhUUREREREREQ8TwUMEREREREREfG8hL+ERNeziYh4j3KziIj3KDeLSKLTGRgiIiIiIiIi4nkqYIiIiIiIiIiI56mAISIiIiIiIiKepwKGiIiIiIiIiHieChgiIiIiIiIi4nkqYIiIiIiIiIiI56mAISIiIiIiIiKepwKGiIiIiIiIiHieChgiIiIiIlFgZtPNbI+ZfRbQdpKZvW9mX/p/Jgcsu8/MtpnZ52bWPaC9nZl96l82xcws1vsiIuJFKmCIiIiIiETHDKBHgbZ7gcXOuebAYv9rzKwN0B843d9nmpkl+fs8AwwFmvsfBbcpInJMUgFDRERERCQKnHMfAj8WaO4DzPQ/nwn0DWif7Zw77JzbAWwDzjezesDxzrmVzjkHzAroIyJyTKsU7wBEREREvOCr7dtJ69a95BWjpH69U3j5xZklryiJ7mTnXBaAcy7LzOr62+sDqwLW2+lvO+J/XrC9EDMbiu9MDU477bQohy0i4j0qYIiIiIgAOc4xYtyTMRtv6tjRMRtLPCnUvBaumPbCjc49BzwHkJqaGnIdEZGKRJeQiIgkIE0UJyKSML73XxaC/+cef/tOoGHAeg2A7/ztDUK0i4gc81TAEBFJTDPQRHEiIongLWCw//lgYH5Ae38zq2pmTfDl4DX+y01+NrML/EXlQQF9RESOaSUWMPQtn4iI92iiOBER7zGzdGAl0NLMdprZLcBE4FIz+xK41P8a59wm4FVgM7AQuN05l+Pf1DDgeXz5ejuwIKY7IiLiUeHMgTEDeArfB9s8ed/yTTSze/2v7ynwLd+pwD/NrIU/Ged9y7cKyMD3LZ+SsYhI9JTbRHGgyeJERErinBtQxKJLilh/AjAhRPta4IwohiYiUiGUeAaGvuUTEUl4EU8UB77J4pxzqc651Dp16kQtOBERERGRcJR1Doygb/mAwG/5vg1YL+/bvPqU8ls+M1trZmt/+OGHMoYoInLM0URxIiIiIlJhRXsST33LJyISP5ooTkREREQqrHDmwAjlezOr57/GWt/yiXjcV9u3k9ate8krRkn9eqfw8oszS15Rysw/UVwakGJmO4GH8E0M96p/0rhvgOvAN1GcmeVNFHeUwhPFzQCq45uXSHMTiYiIiIgnlbWAkfct30QKf8v3iplNxjeJZ963fDlm9rOZXQCsxvct39SIIheRsOU4x4hxT8ZsvKljR8dsrGOVJooTERERkWNNiQUMfcsnADcOHMyurN0xGy8zMzNmY4mIiIiIiIj3lVjA0Ld8ArAra3dMv8EfeUOvmI0lIiIiIiIi3hftSTxFRERERERERKKurHNgiIiIiIiIeJ4uhRapOFTAEBERiRJ9SBYR8R5dCi1ScaiAISIiEiX6kCwiIiJSfjQHhoiIiIiIiIh4ns7AEBEREREREQG+2r6dtG7dYzZe/Xqn8PKLM2M2XqJTAUNEREREREQEyHEuppeDTh07OmZjVQS6hEREREREREREPE8FDBERERERERHxPBUwRERERERERMTzVMAQEREREREREc9TAUNEREREREREPE8FDBERERERERHxPBUwRERERERERMTzVMAQEREREREREc9TAUNEREREREREPE8FDBERERERERHxPBUwRERERERERMTzVMAQEREREREREc9TAUNEREREREREPK9SvAMQERERORZ9tX07ad26x2y8+vVO4eUXZ8ZsPBERkWhTAUNEREQkDnKcY8S4J2M23tSxo2M2loiISHnQJSQiIiIiIiIi4nk6A0NEok6nRYuIiIiISLSpgJGgbhw4mF1Zu2M2XmZmZszGksSn06JFREQklFh/hgV9jhWpSFTASFC7snbH9D+II2/oFbOxRESiRcVeERFvifVnWNDnWJGKRAUMERGpsFTsFRERES/TpdelowKGiIiIiIiISBzo0uvSifldSMysh5l9bmbbzOzeWI8vIiKFKTeLiHiPcrOISLCYFjDMLAl4GrgcaAMMMLM2sYxBRESCKTeLiHiPcrOISGGxvoTkfGCbc+4rADObDfQBNsc4jqjTRHEi8aNrByMWs9ysXCkSP8qVCScmuVl5WeTYkuh/C8w5F7WNlTiY2bVAD+fcn/yvBwLtnXPDC6w3FBjqf9kS+Nz/PAXYG6Nwy0oxRk8ixKkYoycR4ow0xkbOuTrRCiZaopCbI5UI7z0ozmhKhBhBcUabV+NM2Nxchrzs1fcgXIo/vhR/fB1r8YfMzbE+A8NCtBWqoDjnngOeK9TZbK1zLrU8AosWxRg9iRCnYoyeRIgzEWIso4hyc8SDJ8hxVZzRkwgxguKMtkSJ00NKzM2lzcuJ/h4o/vhS/PGl+H1iPYnnTqBhwOsGwHcxjkFERIIpN4uIeI9ys4hIAbEuYHwMNDezJmZWBegPvBXjGEREJJhys4iI9yg3i4gUENNLSJxzR81sOPAekARMd85tKsUmon7qcjlQjNGTCHEqxuhJhDgTIcZSi0JujlSiHFfFGT2JECMozmhLlDg9oZxyc6K/B4o/vhR/fCl+YjyJp4iIiIiIiIhIWcT6EhIRERERERERkVJTAUNEREREREREPM8TBQwz62Fmn5vZNjO7N8TyPma20czWm9laM+sUbl+PxJhpZp/mLSuvGMOJM2C988wsx3+P8VL1jXOMMTmWYbzfaWZ2wB/HejMbG25fD8XpiWMZEOd6M9tkZh+Upq8HYozZv+9Eo7zpqTg9czyVP6MbZ0CsyqGSL4zf7xv9+XejmX1kZmfHI86iRPL3wwsiyddeEEl+9IJIcpIXhHH87wo49p/5f4dOikesoYQR/wlm9raZbfAf/yGlGsA5F9cHvkmJtgNNgSrABqBNgXVq8ft8HWcBW8PtG+8Y/a8zgRQvHMuA9ZYAGcC1XjuWRcUYq2MZ5vudBrxT1v2Ld5weO5YnApuB0/yv63rtd7KoGGN1HBPxobzpnTi9djyVP6Mep3KoHmV5TzoCyf7nlwOr4x13KeMv8u9HvB+R5ut4PyLNj/F+RJqT4v0obe4GegNL4h13KY///cBj/ud1gB+BKuGO4YUzMM4HtjnnvnLO/QbMBvoEruCcO+T8ewjUBFy4fT0QYyyFezxGAPOAPWXoG88YYyWSYxGr4xjrscoqnBhvAF53zn0D4JzbU4q+8Y5Riqa86Z04Y0n5M7qUQ6Uswsm/HznnfvK/XAU0iHGMxUmUvx9FSZR8XZREyY9FSfScVNrjPwBIj0lk4Qknfgf8wcwMXzHyR+BouAN4oYBRH/g24PVOf1sQM7vKzLYC7wI3l6ZvnGME35u0yMzWmdnQcogv7DjNrD5wFfBsaft6IEaIzbEM91h08J/6tMDMTi9l32iIJE7wzrFsASSb2TJ/LINK0TfeMULs/n0nGuXN6EqE3AnKn/GIUzlUCirte38LsKBcIyqdSP9+xFuk+TreIs2P8RZpToq3sP/9mlkNoAe+QphXhBP/U0Br4DvgU2CUcy433AEqRRphFFiItkJVVOfcG8AbZnYRMB7oFm7fKIgkRoALnXPfmVld4H0z2+qc+zBOcT4J3OOcy/EVvUrVNxoiiRFicyzDifEToJFz7pCZ9QTeBJqH2TdaIokTvHMsKwHtgEuA6sBKM1sVZt9oKHOMzrkviN2/70SjvBldiZA7w41T+TO6cSqHSkFhv/dm1gVfAcNLc0hE+vcj3iLN1/EWaX6Mt0hzUryVJnf3BlY4534sx3hKK5z4uwPrga5AM3y5/1/OuYPhDOCFMzB2Ag0DXjfAV40Jyf9HrZmZpZS2b5xixDn3nf/nHuANfKfWlIdw4kwFZptZJnAtMM3M+obZN94xxupYlhijc+6gc+6Q/3kGUDnGv5ORxumZY+lfZ6Fz7hfn3F7gQ+DsMPvGO8ZY/vtONMqb3onTU8dT+TO6caIcKoWF9d6b2VnA80Af59y+GMUWjoj+fnhARPnaAyLKjx4QUU7ygNL8/vfHW5ePQHjxD8F3CY9zzm0DdgCtwh7BxX+ij0rAV0ATfp/o4/QC6/yR3yfqORfYha+6U2JfD8RYE/iDv70m8BHQI17HssD6M/h9MjrPHMtiYozJsQzz/T4l4P0+H/gmlr+TUYjTS8eyNbDYv24N4DPgDC/9ThYTY8z+fSfaI8zjqrwZmzg9dTyLyUueOp7FxOm146kcqkdZ3pPTgG1Ax3jHW8b4Q/79iHfs4cZfYP38fO2FRyT5Md6xlyL+kDkp3rGX5vcHOAHf3BE14x1zGY7/M8DD/ucn+//9hj2Zc9wvIXHOHTWz4cB7+GYtne6c22Rmt/mXPwtcAwwysyPAf4B+zrfHIft6KUYzOxnf6W3ge0Nfcc4tjHaMpYizVH29FCO+X/ByP5ZhxngtMMzMjuJ7v/vH8ncy0jhj9XsZTozOuS1mthDYCOQCzzvnPgPwyu9kUTGaWVNi9O870ShveidOYpQ7SxGn8mcU41QOlYLC/P0eC9TG980/wFHnXGq8Yg4U4f8N4i7CfB13EebxuIs0b8ZbKX5/rgIWOed+iVOoIYUZ/3hghpl9iu+LgXuc70yYsJhHftdERERERERERIrkhTkwRERERERERESKpQKGiIiIiIiIiHieChgiIiIiIiIi4nkqYIiIiIiIiIiI56mAISIiIiIiIiKepwKGiIiIiIiIiHieChgiIiIiIiIi4nkqYIiIiIiIiIiI56mAISIiIiIiIiKepwKGiIiIiIiIiHieChgiIiIiIiIi4nkqYIiIiIiIiIiI56mAIQnJzDLNrJv/+aGAR66Z/Sfg9Y1m9rCZHfG/3m9mH5lZhxDbfMHMnJn9MaCtvpnNN7MfzWynmd0WsKxzgbEP+ftfE5ujICLiLV7Izf7lXc3sEzM7aGZfmdnQ8t97ERHviVZeNrM0f5/AbQwOGKeqmU33593dZjamQBxtzWydmWX7f7aN6YGQCkMFDEl4zrlaeQ/gG6B3QNvL/tXm+JfXAZYDr5uZ5W3DzDoBzUJs/iVgB3AycAXw32bWxT/uvwqM3Qs4BCwsp10VEUkY8crNZlYZeAP4X+AEoB8w2czOLpcdFRFJEFHIy98FbsM5NzNg8w8DzYFGQBfgbjPrAWBmVYD5+HJ3MjATmO9vFykVFTDkmOKcO4IvaZ4C1AYws0rAVGB44LpmVgtIAyY454445zYAc4Gbi9j8YGCuc+6X8oleRKRiinJuPgk4HnjR+XwMbAHaxGBXREQqhFB5uQSDgPHOuZ+cc1uAfwA3+ZelAZWAJ51zh51zUwADukY7bqn4VMCQY4qZVcWXTHc65/b6m/8CfOic21hw9QI/856fEWK7NYBr8SV6EREphWjmZufc90A6MMTMkvynPzfC902iiIiEoYi8XNfMvjezHWb2hJnV9K+bDJwKbAjYxAbgdP/z04GNzjkXsHxjwHKRsKmAIceK681sP/At0A7oC2BmDYFbgbEFOzjnfgZWAA+aWTUzOxe4BqgRYvvXAHuBD8ojeBGRCqq8cnO6v+9h4F/AX51z35bfboiIVBgh8zKwFWgL1MN35kQ7YLJ/WS3/zwMB2zkA/CFgeeCygstFwlYp3gGIxMirzrn/CtH+JDDOOVcwqea5EXgaXxL/CniZ0KchDwZmFagsi4hI8aKem82sFTAHuAp4H9812e+Y2XfOuXejG76ISIUTMi8753YDu/0vd5jZ3cC7+IrNh/ztxwO/Bjz/2f/8kP91oMDlImHTGRhyrLsEmOSfLTkvKa80sxsAnHNfO+d6OefqOOfa47sGcE3gBvzfFKYBs2IYt4hIRRZJbj4D+Nw5955zLtc59zm+D9mXx3onREQqMIf/Uj7n3E9AFhA4WfLZwCb/803AWYGTNANnBSwXCZvOwJBEVtnMqgW8PlqGbbQguJCXBfTGfw2fmbUGduI7Dfl64DKgdYFtDAQ+cs5tL8P4IiIVTbxz87+B5mbWFVgKNMV3l6jHyhCHiEhFEHFeNrM0fGe8fQs0ACbiu7NInlnAA2a2Ft8dov4/YIh/2TIgBxhpZs/6lwEsKW0cIipgSCLLKPB6Qmk34JzbE/jaXxje65z7j7+pO/BXfNdW/xvo4Zz7ocBmBgGTSju2iEgFFdfc7JzbbmY3A1PwTd55AN8lJv9X2jhERCqIiPMycC6+XJoM7APeBO4PWP4Q8AzwNfAf4DHn3EIA59xvZtYXeB5f4WML0Nc591sZ4pBjnOmSfRERERERERHxOs2BISIiIiIiIiKepwKGiIiIiIiIiHieChgiIiIiIiIi4nkqYIiIiIiIiIiI56mAISIiIiIiIiKe5/nbqKakpLjGjRvHOwyRxPD5576fLVvGNw4Jad26dXudc3XiHUc0KDdLzCm/STmpKLlZeVliRvlYYqCo3Oz5Akbjxo1Zu3ZtvMMQSQxpab6fy5bFMwopgpl9He8YokW5WWJO+U3KSUXJzcrLEjPKxxIDReVmXUIiIiIiIiIiIp6nAoaIiIiIiIiIeJ4KGCIiIiIiIiLieZ6fA0MkFm4cOJhdWbtjNl79eqfw8oszYzaeiEg0KFeKiHhPrHPztC1badO6VczGEwmkAoYIsCtrNyPGPRmz8aaOHR2zsUREokW5UkTEe2Kdm3+74sKYjSVSkC4hERERERERERHPUwFDRERERERERDxPBQwRERERERER8TzNgSEiIiIiIiJh+c9//sP6DRsZ3a17TMbThM4SSAUMERERERERCYsD6jf5Y8wmDtWEzhJIl5CIiIiIiIiIiOepgCEiIiIiIiIinqcChoiIiIiIiIh4ngoYIiIJyMyqmdkaM9tgZpvM7P/5208ys/fN7Ev/z+SAPveZ2TYz+9zMuge0tzOzT/3LppiZxWOfRERERESKowKGiEhiOgx0dc6dDbQFepjZBcC9wGLnXHNgsf81ZtYG6A+cDvQApplZkn9bzwBDgeb+R48Y7oeIiIiISFhUwBARSUDO55D/ZWX/wwF9gLx7jc0E+vqf9wFmO+cOO+d2ANuA882sHnC8c26lc84BswL6iIiIiIh4hgoYIiIJysySzGw9sAd43zm3GjjZOZcF4P9Z1796feDbgO47/W31/c8Ltocab6iZrTWztT/88ENU90VEREREpCQqYIiIJCjnXI5zri3QAN/ZFGcUs3qoeS1cMe2hxnvOOZfqnEutU6dOqeMVEREREYmEChgiIgnOObcfWIZv7orv/ZeF4P+5x7/aTqBhQLcGwHf+9gYh2kVEREREPKVSvAMQCeXGgYPZlbU7ZuNlZmbGbCyRaDCzOsAR59x+M6sOdAMeA94CBgMT/T/n+7u8BbxiZpOBU/FN1rnGOZdjZj/7JwBdDQwCpsZ2b0RERERESqYChnjSrqzdjBj3ZMzGG3lDr5iNJRIl9YCZ/juJHAe86px7x8xWAq+a2S3AN8B1AM65TWb2KrAZOArc7pzL8W9rGDADqA4s8D9ERERERDxFBQwRkQTknNsInBOifR9wSRF9JgATQrSvBYqbP0NEREREJO40B4aIiIiIiIiIeJ4KGCIiIiIiIiLieSpgiIiIiIiIiIjnqYAhIiIiIiIiIp6nAoaIiIiIiIiIeJ4KGCIiIiIiIiLieSpgiIiIiIiIiIjnVZgChnOOJk2aYGZs27at0PKHH36YlJSUkH3vvPNOGjduXKh92bJl9OrVi5SUFKpUqULjxo0ZOXIk33zzTbTDD2n+/PmceeaZVKtWjTZt2jBnzpwS+8yYMQMzK/R49tlnC6376aef0qtXL0444QT+8Ic/cP7557Nu3br85XPnzqVjx47Url2batWq0bJlSx555BF+++23qO6niFRcys0+4eTmrKws7rrrLs4++2xq1apFw4YNGTx4MN99912h7R09epSJEyfSvHlzqlatSoMGDfjLX/4S1f0UkdIzs4ZmttTMtpjZJjMb5W8/yczeN7Mv/T+TA/rcZ2bbzOxzM+se0N7OzD71L5tiZhatOJWbfcL93Oyc47//+79p2LAh1atX56KLLmL9+vVl2paIRKbCFDBWrlxJZmYmALNnz454e1OmTKFr165Ur16d//3f/+Wf//wnDz30EP/+97/p06dPxNsvyfLly7nmmmvo0qULCxYs4IorrmDAgAEsWrQorP5Llixh5cqV+Y+rr746aPn69evp2LEjJ554InPmzOG1116jd+/e/Oc//8lfZ9++fXTp0oXnn3+eBQsWcPPNNzNhwgTGjBkT1X0VkYpLuTlYcbl53bp1vPHGGwwYMIC3336bSZMmsXr1ajp27MihQ4eCtjNkyBCmTJnCnXfeyaJFi5g4cSLVq1eP6r6KSJkcBe5wzrUGLgBuN7M2wL3AYudcc2Cx/zX+Zf2B04EewDQzS/Jv6xlgKNDc/+gRrSCVm4OV9Ll54sSJjB8/nnvuuYe3336bWrVq0a1bN3bv3l3qbYlIZCrFO4BoSU9Pp2bNmpxxxhmkp6fzwAMPlHlb//73vxkzZgwPPPAA48aNy2+/6KKLGDJkCO+88040Qi7W+PHjueiii5gyZQoAXbp0YdOmTYwbN47LLrusxP7nnXcetWrVKnL5bbfdRu/evXnppZfy23r0CP67eOuttwa97tKlCwcPHuTpp59m6tSpRPGLABGpoJSbgxWXmzt16sTWrVupVOn3P83nnnsuLVu2ZN68eQwePBiAhQsXMnv2bDZs2ECbNm2isFciEi3OuSwgy//8ZzPbAtQH+gBp/tVmAsuAe/zts51zh4EdZrYNON/MMoHjnXMrAcxsFtAXWBCNOJWbgxWXm3/99VcmTpzIfffdx/DhwwHo0KEDjRs35qmnnuKRRx4Je1siErkKUcDIycnhtdde48orryQtLY1bb72VjRs3ctZZZ5Vpe1OnTiUlJYUHH3ww5PJevXpFEm6JDh8+zNKlS/OTcJ7+/fszZMgQDhw4wAknnFDm7W/evJnVq1fzxBNPlLpv7dq1dQmJiIRFubl0TjzxxEJtLVq0oEaNGuzZsye/bfr06XTt2pU2bdpw48DB7Moq/A1gecn7xlZESmZmjYFzgNXAyf7iBs65LDOr61+tPrAqoNtOf9sR//OC7QXHGIrvLA1OO+20sOJSbi6djz76iIMHD3L99dfnt9WsWZPevXuzYMGCQgUMESlfJRYwzKwhMAs4BcgFnnPO/d3MTgLmAI2BTOB659xP/j73AbcAOcBI59x7/vZ2wAygOpABjHLOuUh3YsmSJXz//ff079+fTp06MXz4cNLT08uciD/44AMuueQSKleuXKb+R48eLXGdpKSkIs9g2L59O0eOHKFVq1ZB7a1btyY3N5cvvviC8847r9jtN2vWjH379tGsWTPGjBkTdDbF6tWrAfjpp584++yz2bRpE40aNeL+++/nlltuKbStnJwcDh8+zCeffMKUKVMYNmyYzr4QkRIpNxdWXG4OZePGjWRnZwedabF69WquvPJKhg8fzpz0l0mqVJm27TvxpzEPcFKdusVsLXIjbyjf/4iIVBRmVguYB4x2zh0s5nNTqAWumPbgBueeA54DSE1NDesztXJzYcXl5q1bt5KUlETz5s0LbT/UPBulzfMiUjrhzIHh+Wv50tPTOfHEE+nRowcnnXQSl156KbNnz6astZFdu3aFXcUOpXLlyiU+Zs6cWWT/n376CSj8bVxycnLQ8lDq1avH+PHjefHFF3n77bdp3749t912W9DZFnnX6w0aNIgbb7yR999/nx49evCnP/2JjIyMQtusWbMmNWvWpHPnzlx88cVMmjQp7GMhIscu5ebfhZObC8rNzWXUqFE0b9486BTo3bt3M2PGDNavX0/rM87i9vsn8NXnm/jb/SPKfGxFJHrMrDK+4sXLzrnX/c3fm1k9//J6QN5pVTuBhgHdGwDf+dsbhGiPmHLz78LJzT/99BO1atUiKSkpqG9ycjLZ2dn5ZyaXJc+LSOmVeAaG16/lO3z4MG+88QZXXXUVVapUAWDAgAEMHDiQVatW0aFDhzJtN5IzDD7++OMS12nSpEmpY8j7w1JcbN27d6d79/wJrLn88ss5fPgwjzzyCKNGjeK4444jNzcXgD/96U/cfffdgO9awS1btvDoo4/Ss2fPoG1+9NFHZGdns2bNGsaNG8fw4cOZNm1aifGLyLFLuTlYOLm5oPvuu4+VK1fywQcfBH2z6ZzDOcf8+fO5pt8NXNjtcpJT6jD29kF8um4VZ6WW7diKSOT8dwr5P2CLc25ywKK3gMHARP/P+QHtr5jZZOBUfF/wrXHO5ZjZz2Z2Ab5LUAYBUyONT7k5WLi5OdQ2Cm6/LHleREqvVHNgxOJavtJasGAB+/fvp2fPnuzfvx+AtLQ0qlatSnp6en4irlSpEjk5OSG3kZOTEzRpWv369SO65VPbtm1LXKdgFTdQXsU4b3/y5L0OdZ10ca699lpeffVVMjMzadq0KSeddBLgK1oE6tq1a8gq8bnnngv4JphLSUlh8ODB3HHHHTRr1qxUcYjIsUO5uWQFc3OgadOmMWnSJNLT02nfvn2hOJo2bUrt2rXz21qddS6VKldm547tKmCIxNeFwEDgUzNb72+7H1/h4lUzuwX4BrgOwDm3ycxeBTbjO+v5dudcXlIcxu+XXi8gChN4KjeXrGBuTk5O5ueffyYnJycojv3791OjRo1iL50pLs+LSNmEXQoseC1fcauGaAv7Wj7/WEPNbK2Zrf3hhx+KjSs9PR2A6667juTkZJKTk2nYsCGHDx/m1VdfzU++derU4eDBg2RnZxfaRlZWFnXr/n7dcFpaGosXLw7rmrxQIj0VrlmzZlSuXJmtW7cGtW/dupXjjjuOFi1alCmuvApx69atQy53zpVYHc4rZuzYsaNMMYjIsUG5OXwFv9mbN28eI0aM4G9/+xv9+vUrtH5RORznMH3DJxJXzrnlzjlzzp3lnGvrf2Q45/Y55y5xzjX3//wxoM8E51wz51xL59yCgPa1zrkz/MuGR2PeOOXm8OXl5latWpGTk8O2bdsKbb/gvBslbUtEIhfWJ51YX8vnnHvOOZfqnEutU6dOkXEdOnSId955hwEDBrB06dKgx+TJk/n+++9ZunQpAJ07dyY3N7fQrZx++eUXFi9eTOfOnfPbRowYwQ8//MCECRNCjhtqnohAH3/8cYmP3r17F9m/atWqdOnShddeey2ofc6cOXTo0KHUMynPmzePlJQUGjVqBEDHjh1JTk5m8eLFQestXryYs88+u9htrVixAgjvVD4ROTYpN4enYG4GWLZsGTfeeCPDhw/nzjvvDNmvV69ebNy4kb179+a3bV6/lqNHj9L4jy1LFYOIHDuUm8MT6nPz8ccfH7T97Oxs3n77bS6//PJSbUtEIhfOXUg8ey3f/Pnzyc7OZtSoUYVOsb3wwguZMGEC6enpdOvWjTZt2tCvXz9uueUWduzYQbt27dizZw+PP/44zjlGjhyZ37dt27ZMnjyZ0aNHs3nzZvr3709KSgo7duxg+vTpHDhwoNA8EYFSU1Mj2S0AHnzwQdLS0hg9ejR9+/YlIyODjIwMFi5cmL/O119/TbNmzZg+fTqDBg0C4JprruH888/nrLPOIicnhzlz5jBnzhymTJmSf3ZFlSpVGDt2LHfffTcnnngi5513HvPmzePDDz/kgw8+yN9+jx496NatG6effjpJSUmsWLGCxx9/nH79+unyEREpknJz2XLzli1b6Nu3L61ataJfv36sWvX71Zh16tTJz7tDhw5lypQp9O7dm5+zf+Vfi97hpWcmc1ZqB1qf3S7ifRSRikm5uWy5uVq1atx7772MHz+e5ORkWrVqxeTJk8nNzWXEiBH52w9nWyISuXDmwPDstXzp6ek0b968UBIG3+lo119/Penp6UybNo2qVasya9YsHnnkEZ577jm++eYb/vCHP5CWlsbLL79M/frB03GMHDmSM888k//5n//hT3/6EwcPHqR+/fp0796du+66K5Kww9KpUyfmzp3LAw88wDPPPEOTJk145ZVXgmaid86Rk5OTPyknQMuWLZk+fTrffvstzjnatGnDrFmzGDhwYND2R48eTW5uLlOnTuXhhx+mZcuWzJ07N6iift555zFjxgwyMzOpVKkSTZs25dFHH+W2224r9/2v6L7avp20bt1LXrGUntywEYDRBbZdv94pvPxi0adfikSTcnPZcvPq1as5cOAAGzZs4MILLwwad/DgwcyYMQOA448/niVLljBy5EjWrHmPzB1fcV6nrtw08p7y3XkRSWjKzWX/3HzvvfeSm5vLo48+yr59+0hNTeX999/n5JNPLvW2RCQy5vVbrqWmprq1a9fGOwyJsbRu3Rkx7smYjTfyhl5MeeWdklf0+HgXDR8MwIdPBRcrpo4dzbJ/vhf18aR0zGydcy7yr5o8QLnZGyp6rgzKXWlpvp/LlsVsfDk2VJTcrLx87Ir134J6Xc+leZszC33eLC/6HHtsKio3l+ouJCIiIiKxEni2WlFnmEWTzlYTERHxNhUwRERExJNynMv/VrG+/wyz8vyWcerY0eW2bREREYmcZpQREREREREREc/TGRgiIiIiIiJRcuPAwezK2h2z8TIzM2M2lki8qYARBfPnz+eBBx7gyy+/pGnTpjz00EP069ev2D7btm1j0qRJrFq1is8++4zOnTuzrMDEZFlZWUyePJlFixaxfft2kpOT6dq1K48++iinnnpq/npz585l8uTJfP755/zyyy80atSIgQMHcvfdd1OlSpXy2GURiTMzawjMAk4BcoHnnHN/N7OTgDlAYyATuN4595O/z33ALUAOMNI5956/vR2/3yEqAxjlvD7DcxjKkptfe+01XnzxRdatW8eBAwdo2bIld955JwMGDAhar3Hjxnz99ddBbSeffDK7dwd/YH3zzTcZO3Ysn3/+OaeeeiojRoxgzJgx0dlBEZEEVJbcHGjXrl20bNmSX375hZ9//platWrlL/NKbt6VtTvmEyyLHCt0CUmEli9fzjXXXEOXLl1YsGABV1xxBQMGDGDRokXF9tu0aRMZGRm0aNGCFi1ahFxn3bp1vPHGGwwYMIC3336bSZMmsXr1ajp27MihQ4fy19u3bx9dunTh+eefZ8GCBdx8881MmDBBH5JFKrajwB3OudbABcDtZtYGuBdY7JxrDiz2v8a/rD9wOtADmGZmSf5tPQMMBZr7Hz1iuSPloay5efLkydSqVYsnnniCt956iy5dunDDDTcwderUQuvecMMNrFy5Mv+RkZERtHzFihVcffXVnH/++bz99tvcfPPN3HPPPTz55JPR3FURkYRR1twc6K677goqWhSk3CxSsekMjAiNHz+eiy66iClTpgDQpUsXNm3axLhx44LuPV1Q79696dOnDwDXXnste/fuLbROp06d2Lp1K5Uq/f42nXvuubRs2ZJ58+YxeLBvQrNbb701qF+XLl04ePAgTz/9NFOnTsXMIt5PEfEW51wWkOV//rOZbQHqA32ANP9qM4FlwD3+9tnOucPADjPbBpxvZpnA8c65lQBmNgvoCyyI1b6Uh7Lm5rfffpuUlJT81127duW7775j8uTJjBgxImjdevXqccEFFxS5rXHjxtGpUyeef/55AC677DJ++uknxo0bx5///GedIScix5yy5uY8//rXv1i4cCH3338/d911V8h1lJtFKraEPwPjpptuIjU1lTfffJNWrVpRrVo1OnXqxObNm8t97MOHD7N06VKuv/76oPb+/fuzcuVKDhw4UGTf444r+dCfeOKJQcULgBYtWlCjRg327NlTbN/atWvz22+/lTiGiCQ+M2sMnAOsBk72Fzfyihx1/avVB74N6LbT31bf/7xge0QSNTcHFi/ynHPOOSXm3FDWr19Pt27dgtryPiivXLmy1NsTEYlUouZmgJycHEaMGMHYsWND5upwKTeLJLaEL2AAfP3114wZM4YHH3yQV155hQMHDtC9e3d+/fXXYvsdPXq0xEdxl4Fv376dI0eO0KpVq6D21q1bk5ubyxdffBGV/Qu0ceNGsrOzadOmTaFlOTk5ZGdns3z5cqZMmcKwYcN09oVIBWdmtYB5wGjn3MHiVg3R5oppDzXWUDNba2Zrf/jhhxJjqyi5+aOPPgqZc6dPn06VKlU44YQTuPbaawtdd/3rr78W+iavatWqAGzZsqVUMYiIREui5uZnn32WX3/9ldtvv73Y9ZSbRSq2CnEJyd69e5k/fz4dO3YEoF27djRr1owZM2Zw2223FdmvcuXKJW77hRde4Kabbgq57KeffgJ8Z0oESk5ODloeLbm5uYwaNYrmzZuHPM2uZs2aHD58GIBBgwYxadKkqI4vIt5iZpXxFS9eds697m/+3szqOeeyzKwekHfqwE6gYUD3BsB3/vYGIdoLcc49BzwHkJqaWuIknxUhNy9evJj58+czffr0oPY+ffpwwQUX0KBBA7Zs2cL/+3//j86dO/Ppp59ywgknAPDHP/6Rjz/+OKjfmjVrAPjxxx/DjkFEJJoSMTfv27ePBx98kJdeeqnYOJSbRSq+ClHAqFu3bn4SBmjUqBHt2rVjzZo1xSbigskrlCZNmpS4TsGzHPKqz9E+++G+++5j5cqVfPDBByGT90cffUR2djZr1qxh3LhxDB8+nGnTpkU1BhHxBvMlmP8DtjjnJgcsegsYDEz0/5wf0P6KmU0GTsU3Weca51yOmf1sZhfguwRlEFB4xsoySPTcnJmZyQ033ECfPn0KfSD/+9//nv+8c+fOdOzYkbZt2/LCCy8wevRoAG677TaGDRvGP/7xD6699lrWrFnD448/DkBSUhIiIvGQiLn5r3/9K+3bt6dnz57Fblu5uWL6avt20rp1j9l49eudwssvzozZeFI6FaaAEaotKyur2H5t27YtcdvFJbK8ivH+/fuD2vNeF6wwR2LatGlMmjSJ9PR02rdvH3Kdc889F/BN/pmSksLgwYO54447aNasWdTiEBHPuBAYCHxqZuv9bffjK1y8ama3AN8A1wE45zaZ2avAZnx3MLndOZfj7zeM32+juoAoTeCZyLn5xx9/5PLLL+e0007jpZdeKnH9M844g5YtW/LJJ5/kt918881s2LCBYcOGMXToUGrUqMFjjz3GiBEjOPnkk0vcpohIeUi03Lxp0yamT5/Ohx9+mL9udnY2AAcOHCApKYnq1auH7KvcXDHkOBfT29JOHTs6ZmNJ6VWIOTBCTa62Z88e6tWrV2y/ypUrl/iYObPo6luzZs2oXLkyW7duDWrfunUrxx13XJG3Ry2tefPmMWLECP72t7+FfZ/svGLGjh07ohKDiHiLc265c86cc2c559r6HxnOuX3OuUucc839P38M6DPBOdfMOdfSObcgoH2tc+4M/7LhrriLmEshUXNzdnY2vXr14rfffuPdd9+lZs2axa4fKPAbxKSkJJ566il++OEHNm7cyPfff58/M35xM+SLiJSnRMvNX375JUeOHKFDhw4kJyeTnJycPw9GgwYNCt0hKhTlZpGKo0KcgbFnzx4++uij/NPhvvnmGz755BOGDBlSbL9IT4WrWrUqXbp04bXXXgu6lemcOXPo0KFD/rV2kVi2bBk33ngjw4cP58477wy734oVK4DwTuUTESkPiZibjx49ynXXXceXX37JihUrQn5TGcpnn33G559/Xui21kD+B27wnU3XsWPHQpPYiYjESqLl5k6dOrF06dKgtoULF/LYY4+RkZFB06ZNixxTuVmk4qkQBYyUlBQGDhzI+PHjqV69OmPHjqVu3bpFTiKUJzU1NeKxH3zwQdLS0hg9ejR9+/YlIyODjIwMFi5cmL/O119/TbNmzZg+fTqDBg0CfN/wZWRkALBr1y4OHjzI3LlzAejZsyc1atRgy5Yt9O3bl1atWtGvXz9WrVqVv806derkXxrSo0cPunXrxumnn05SUhIrVqzg8ccfp1+/frp8RETiJhFz85///GcyMjL4+9//zo8//hiUd8855xyqVq3Ku+++y0svvUSvXr049dRT2bp1K4888ginnXZa0L6tWrWK5cuX07ZtWw4ePEh6ejrvvfcey5cvj3j/RETKKtFyc0pKCmlpaUHbyczMBHzzXNSqVQtAuVnkGFEhChiNGjXi/vvv59577+Xrr78mNTWV9PR0qlWrVu5jd+rUiblz5/LAAw/wzDPP0KRJE1555ZWgu4Q458jJySE3Nze/bc+ePVx33XVB28p7vWPHDho3bszq1as5cOAAGzZs4MILLwxad/DgwcyYMQOA8847jxkzZpCZmUmlSpVo2rQpjz76aLETMYmIlLdEzM2LFi0CYNSoUYW2mZebGzZsyJ49exg9ejT79++ndu3a9OjRg//+7//m+OOPz1+/cuXKzJkzh4cffpjjjjuOzp07s2LFCs4888xy3HMRkeIlYm4Oh3KzyLGhQhQwAK6++mquvvrquIzdt29f+vbtW+Tyxo0bF7ovdqi2gm666aYSq+EA48ePZ/z48eGEKiISU4mWm/O+1SvOWWedxeLFi0tcr127dmGdci0iEmuJlpsLCvUZWblZ5NhQISbxFBEREREREZGKTQUMEREREREREfG8hL+EJG8eCBER8Q7lZhER71FuFpFEpzMwRERERERERMTzEv4MDImNGwcOZlfW7piNF85EeiIiIiIiInLsUAFDwrIrazcjxj0Zs/FG3tArZmOJiIiIiIiI9+kSEhERERERERHxPBUwRERERERERMTzVMAQEREREREREc9TAUNEREREREREPE8FDBERERERERHxPBUwRERERERERMTzVMAQEREREYkCM5tuZnvM7LOAtpPM7H0z+9L/Mzlg2X1mts3MPjez7gHt7czsU/+yKWZmsd4XEREvUgFDRERERCQ6ZgA9CrTdCyx2zjUHFvtfY2ZtgP7A6f4+08wsyd/nGWAo0Nz/KLhNEZFjkgoYIiIiIiJR4Jz7EPixQHMfYKb/+Uygb0D7bOfcYefcDmAbcL6Z1QOOd86tdM45YFZAHxGRY1qJBQydCiciIiIiUmYnO+eyAPw/6/rb6wPfBqy3099W3/+8YHshZjbUzNaa2doffvgh6oGLiHhNOGdgzECnwomIiIiIRFOoL/NcMe2FG517zjmX6pxLrVOnTlSDExHxohILGDoVTkRERESkzL73fxbG/3OPv30n0DBgvQbAd/72BiHaRUSOeWWdA6PcToUDnQ4nIiIiIhXGW8Bg//PBwPyA9v5mVtXMmuA7Q3mN/7P1z2Z2gf+S60EBfUREjmnRnsQz4lPhQKfDiYiIiEjiMbN0YCXQ0sx2mtktwETgUjP7ErjU/xrn3CbgVWAzsBC43TmX49/UMOB5fGczbwcWxHRHREQ8qlIZ+31vZvWcc1k6FU5EREREBJxzA4pYdEkR608AJoRoXwucEcXQREQqhLKegaFT4URE4kh3iBIRERGRY004t1HVqXAiIt4zA90hSkRERESOISVeQqJT4UREvMc596GZNS7Q3AdI8z+fCSwD7iHgDlHADjPLu0NUJv47RAGYWd4dolRgFhERERHPifYkniIiEj+6Q5SIiIiIVFgqYIiIVHy6Q5SIiIiIJLyy3oVERBLIV9u3k9ate8krRkn9eqfw8oszYzae5NMdokRERESkwlIBQ+QYkOMcI8Y9GbPxpo4dHbOxJEjeHaImUvgOUa+Y2WTgVH6/Q1SOmf1sZhcAq/HdIWpq7MMWERERESmZChgiIgnIf4eoNCDFzHYCD+ErXLzqv1vUN8B14LtDlJnl3SHqKIXvEDUDqI5v8k5N4BmBGwcOZlfW7piNl5mZGbOxREREROJNBQwRkQSkO0R5066s3TE922nkDb1iNpaISKJScVmk4lABQ0REREREKiwVl0UqDt2FREREREREREQ8TwUMEREREREREfE8FTBERERERERExPM0B4aIiIgI8NX27aR16x6z8erXO4WXX5wZs/FEREQSnQoYIiIiIkCOczGd6G/q2NExG0tERMKjYra3qYAhIiIiIiIigorZXqc5MERERERERETE81TAEBERERERERHPUwFDRERERERERDxPBQwRERERERER8TxN4pmgbhw4mF1Zu2M2XmZmZszGEhERERERESlIBYwEtStrd0xnxx15Q6+YjSUiIiIiIiJSkC4hERERERERERHPUwFDRERERERERDxPBQwRERERERER8TwVMERERERERETE81TAEBERERERERHPUwFDRERERERERDxPBQwRERERERER8TwVMERERERERETE8yrFOwARqXi+2r6dtG7dYzZe/Xqn8PKLM2M2noiIiJTNjQMHsytrd0zHzMzMjOl4IlJ+VMAQkajLcY4R456M2XhTx46O2ViSWGL9QVkfkkVEircra3dMPyMAjLyhV0zHEykNffFXOipgiIhIhRXrD8r6kCwiIiKloS/+SkcFDBEREZE40LduIiIipRPzAoaZ9QD+DiQBzzvnJsY6hvKg05RFJJFV1Nws4mX61k1KotwsIhIspgUMM0sCngYuBXYCH5vZW865zbGMozzoNGURSVQVOTeLiCQq5WYRkcJifQbG+cA259xXAGY2G+gDKBGLSJnpNOyIxSw362w1EZGwxSQ3Ky+LHFsS/XOzOeeitrESBzO7FujhnPuT//VAoL1zbniB9YYCQ/0vWwKfRymEFGBvlLYVDV6KR7GEpliK5qV4EiWWRs65OrEMJhzKzcXycmyg+CLh5djA2/F5OTYofXwJm5sjyMtefw+Lo9jjQ7HHx7Ece8jcHOszMCxEW6EKinPuOeC5qA9uttY5lxrt7ZaVl+JRLKEplqJ5KR7FEjHl5iJ4OTZQfJHwcmzg7fi8HBt4P75SKDE3lzUvJ/IxUuzxodjjQ7EXdly0N1iCnUDDgNcNgO9iHIOIiARTbhYR8R7lZhGRAmJdwPgYaG5mTcysCtAfeCvGMYiISDDlZhER71FuFhEpIKaXkDjnjprZcOA9fLeDmu6c2xTDEKJ+6nOEvBSPYglNsRTNS/EolggoNxfLy7GB4ouEl2MDb8fn5djA+/GFpZxzcyIfI8UeH4o9PhR7ATGdxFNEREREREREpCxifQmJiIiIiIiIiEipqYAhIiIiIiIiIp5XYQoYZtbDzD43s21mdm+I5X3MbKOZrTeztWbWKdy+MY4l08w+zVtW3rEErHeemeX47zleqr4xiiWqxyWceMwszcwO+Mdcb2ZjS7svMYol5r8z/njWm9kmM/ugNH1jGEtMj4uZ3RXw/nzm/x0+Kdz9qIi8lAvLEl/AeuWeH6McW9yPXSzzZznEV67Hz0s5thzii+uxUx72ft4tjpdzckm8nrOL4/V8XhIv5/uSeP3vQXHi+rfCOZfwD3wTG20HmgJVgA1AmwLr1OL3OT/OAraG2zdWsfhfZwIpsTouAestATKAa+N1XIqKJdrHpRTvUxrwTln3JRaxxON3BjgR2Ayc5n9dN47HJWQs8TguBdbvDSwpj+OSKI8w37+Y5MJI3tNQOam839NIYvPKsSsqZ8Xi30Mk8ZX38Yskr3no2IWMzwvHrsD6x1weDvP9i1vejcb7Gyrvxfv9jST2RDjuReXLeB/3SONPkGMfMt/G+9hHEns0jntFOQPjfGCbc+4r59xvwGygT+AKzrlDzn/EgJqAC7dvDGOJtnD3bQQwD9hThr6xiKU8RLJ/8To2sRBOLDcArzvnvgFwzu0pRd9YxRJtpd23AUB6GftWFF7KhWWKzy8W+TGascWCl/JnvMYoKy/l2GjHV96Uh0vm9bxbHC/n5JJ4PWcXx+v5PBFiKCuv/z0oTlz/VlSUAkZ94NuA1zv9bUHM7Coz2wq8C9xcmr4xigV8f0gWmdk6MxsaQRxhxWJm9YGrgGdL2zeGsUB0j0tY8fh1MLMNZrbAzE4vZd9YxAIx/p0BWgDJZrbMP+agUvSNVSwQ++MCgJnVAHrg+5BSqr4VjJdyYZnii2F+jGZs4IFj5xeL/Bnt+KB8j5+Xcmy044P4HzvgmM7DXs+7xfFyTi6J13N2cbyez0vi5XxfEq//PShOXP9WVCp1uN5kIdoKVZSdc28Ab5jZRcB4oFu4fWMUC8CFzrnvzKwu8L6ZbXXOfViOsTwJ3OOcyzELWj0ex6WoWCC6xyXceD4BGjnnDplZT+BNoHmYfWMVC8T+d6YS0A64BKgOrDSzVWH2jUkszrkviP1xydMbWOGc+7EMfSsSL+XCssb3JLHJjwV5LV+WJb5Y5c9QvJRTyxJbrHJsKF7Ku2WJLc+xmoe9nneL4+WcXBKv5+zieD2fl8TL+b4kXv97UJy4/q2oKGdg7AQaBrxuAHxX1Mr+A9TMzFJK27ecY8E5953/5x7gDXyn6JRnLKnAbDPLBK4FpplZ39LuRznHEu3jElY8zrmDzrlD/ucZQOV4/c4UE0s8fmd2Agudc7845/YCHwJnh9k3VrHE47jk6c/vpy2Xtm9F4qVcWNb4YpUfoxmbJ45dDPNntOMr7+PnpRwb7fi8cOzyHKt52Ot5tzhezskl8XrOLo7X83lJvJzvS+L1vwfFie/fCheHSUui/cBX4fkKaMLvE4mcXmCdP/L7pEXnArvwVY9K7BvDWGoCf/C31wQ+AnqUZywF1p/B7xMixfy4FBNLVI9LKd6nUwLep/OBb+L4O1NULDH/nQFaA4v969YAPgPOiNNxKSqWuPxbAk4AfgRqlvV3v6I8wnz/YpILI3lPA9afQTnlxyjH5oljR4zyZznEV67HL8zYYpJjyyG+uB87/3rHbB4O8/2LW96NxvsbsP4MYpSTyzl2zx934pjPyzn+RDj2cft7UI6xR3zcK1EBOOeOmtlw4D18s6JOd85tMrPb/MufBa4BBpnZEeA/QD/nO3Ih+8YjFjM7Gd8pfeB7s19xzi0s51hK1TcesQBRPS6liOdaYJiZHcX3PvWP4+9MyFji8TvjnNtiZguBjUAu8Lxz7jOAWB+XomIxs6bE59/SVcAi59wvJfUtayyJwku5MIL4StXXC7FRDvmyjPHFJH9GO77y/t3zUo6NdnzRzrtlic2/6jGbh72ed6MQe6n6xiLu4sb3Ss4ujtfzeXnGnwi/8/H8e1BesUfjb0VeNUpERERERERExLMqyhwYIiIiIiIiIlKBqYAhIiIiIiIiIp6nAoaIiIiIiIiIeJ4KGCIiIiIiIiLieSpgiIiIiIiIiIjnqYAhIiIiIiIiIp6nAoaIiIiIiIiIeN7/D8r17cLIIOL8AAAAAElFTkSuQmCC",
                        "text/plain": [
                            "<Figure size 1080x1152 with 21 Axes>"
                        ]
                    },
                    "metadata": {
                        "needs_background": "light"
                    },
                    "output_type": "display_data"
                }
            ],
            "source": [
                "with open(f\"clf_results_closed_loop.pkl\", \"rb\") as f:\n",
                "    cl_result_dict = pickle.load(f)\n",
                "fig, ax = plt.subplots(len(cl_sub_list) // 3 + len(cl_sub_list) % 3, 3, figsize=(15, 16))\n",
                "ax = ax.ravel()\n",
                "for i, subject in enumerate(cl_result_dict):\n",
                "    (fp, tp, auc, auc_results) = cl_result_dict[subject]\n",
                "    p_val = (sum(auc_results>auc)+1) / (len(auc_results)+1)\n",
                "    ax[i].set_title(subject)\n",
                "    ax[i].hist(auc_results, ec='k', alpha=.7)\n",
                "    ax[i].axvline(auc, c='r')\n",
                "#     ax[i].annotate(f'AUC = {auc:.3f}', xy=(.05, .9), xycoords='axes fraction', fontsize=18)\n",
                "#     ax[i].annotate(f'p = {p_val:.3f}', xy=(.025, .6), xycoords='axes fraction', fontsize=15)\n",
                "    ax[i].annotate(f'AUC = {auc:.3f}\\np = {p_val:.3f}', xy=(.025, .5), xycoords='axes fraction', fontsize=15)\n",
                "plt.tight_layout()\n",
                "plt.savefig(\"one_list_normalized_cl_auc_distributions.pdf\", dpi=200)"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 22,
            "id": "e9554330-2285-4cf5-a93c-73da3ecaf960",
            "metadata": {},
            "outputs": [],
            "source": [
                "with open(f\"clf_results_normalized_closed_loop.pkl\", \"wb\") as f:\n",
                "    pickle.dump(cl_result_dict, f)"
            ]
        },
        {
            "cell_type": "markdown",
            "id": "4b30c5f4-38ec-485d-9e95-bcc252ec8dad",
            "metadata": {},
            "source": [
                "Note: Try coloring this plot by recall rates / proportions"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 8,
            "id": "fd141cce-e4a5-408e-8e36-700bf5dea1eb",
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "image/png": 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",
                        "text/plain": [
                            "<Figure size 576x576 with 2 Axes>"
                        ]
                    },
                    "metadata": {},
                    "output_type": "display_data"
                }
            ],
            "source": [
                "with open(f\"clf_results_closed_loop.pkl\", \"rb\") as f:\n",
                "    cl_result_dict = pickle.load(f)\n",
                "plt.rcParams['figure.facecolor']='white'\n",
                "sns.set_palette('twilight')\n",
                "fig, ax = plt.subplots(1, 1, figsize=(8, 8))\n",
                "ax.plot([0, 1], [0, 1], '--k')\n",
                "base_fp = np.linspace(0, 1, 301)\n",
                "tp_list = []\n",
                "for subject in cl_result_dict:\n",
                "    fp, tp, auc, _ = cl_result_dict[subject]\n",
                "    tp = scp.interp(base_fp, fp, tp)\n",
                "    tp_list.append(tp)\n",
                "    if np.mean(cl_result_dict[subject][2] > cl_result_dict[subject][3])>=0.95:\n",
                "        alpha=0.8\n",
                "    else:\n",
                "        alpha=0.3\n",
                "    ax.plot(base_fp, tp, color='indigo', label=f\"{subject}, AUC = {auc:.3f}\", alpha=alpha)\n",
                "\n",
                "ax.plot(base_fp, np.mean(np.vstack(tp_list), 0), color='k', lw = 4,\n",
                "        label=f\"{subject}, AUC = {auc:.3f}\", alpha=1)\n",
                "\n",
                "# ax.legend(fontsize=14, bbox_to_anchor=(1.05, 1))\n",
                "ax.set_ylabel('Hit Rate', fontsize=18)\n",
                "ax.set_xlabel('False Alarm Rate', fontsize=18)\n",
                "plt.grid(False)\n",
                "top_ax = fig.add_axes([0.5, .2, .4, .1])\n",
                "auc_dist = pd.DataFrame({\"auc\":[cl_result_dict[k][2] for k in cl_result_dict] +\n",
                "                         [np.mean(cl_result_dict[k][3]) for k in cl_result_dict],\n",
                "                         \"type\":[\"test\"]*len(cl_result_dict)+[\"null\"]*len(cl_result_dict)})\n",
                "sns.histplot(auc_dist, x=\"auc\", hue=\"type\", multiple=\"layer\", bins=7, ax=top_ax)\n",
                "top_ax.grid(False)\n",
                "top_ax.set_title('AUC', fontsize=14)\n",
                "top_ax.set_ylabel('')\n",
                "sns.move_legend(top_ax, loc='upper left', title='')\n",
                "# top_ax.axvline(0.5, ls='--')\n",
                "ax.tick_params(labelsize=14)\n",
                "ax.spines['top'].set_visible(False)\n",
                "ax.spines['right'].set_visible(False)\n",
                "# top_ax.tick_params(labelsize=14)\n",
                "plt.savefig('FinalReport/cl_roc.pdf', dpi=200, bbox_inches='tight')"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 12,
            "id": "23b2b008-c414-446d-a02f-5448b611c518",
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "text/plain": [
                            "22"
                        ]
                    },
                    "execution_count": 12,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": [
                "len(result_dict)"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 66,
            "id": "89163996-3118-4f9c-b75d-fdccf2dcd79e",
            "metadata": {},
            "outputs": [],
            "source": [
                "good_class_subs = [k for k in cl_result_dict if np.mean(cl_result_dict[k][3]>cl_result_dict[k][2])<.05]\n",
                "good_class_mask = np.isin(list(cl_result_dict.keys()), good_class_subs)\n",
                "good_class_subs\n",
                "avg['good_class'] = good_class_mask"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 29,
            "id": "f01f1816-d2e6-4684-8b3d-c791cb85efe8",
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "text/plain": [
                            "array([ True, False,  True, False, False, False,  True,  True, False,\n",
                            "       False, False,  True, False,  True, False, False, False, False,\n",
                            "       False, False, False])"
                        ]
                    },
                    "execution_count": 29,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": [
                "np.isin(list(cl_result_dict.keys()), good_class_subs)"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 7,
            "id": "06d4e185-7e50-441d-b3be-cb4fbbdda4df",
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "text/plain": [
                            "6"
                        ]
                    },
                    "execution_count": 7,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": [
                "len([k for k in cl_result_dict if np.mean(cl_result_dict[k][3]>cl_result_dict[k][2])<.05])"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 11,
            "id": "c9ae97a8-115a-45d0-afcf-3be10731aa74",
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "text/plain": [
                            "16"
                        ]
                    },
                    "execution_count": 11,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": [
                "len([k for k in cl_result_dict if np.mean(result_dict[k][3]>result_dict[k][2])<.05])"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 10,
            "id": "b7bba916-ae71-4207-887f-765ea459253b",
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "text/plain": [
                            "21"
                        ]
                    },
                    "execution_count": 10,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": [
                "len(cl_result_dict)"
            ]
        },
        {
            "cell_type": "markdown",
            "id": "36dfcb0d-c738-4e70-b063-8c7f9ce6b545",
            "metadata": {},
            "source": [
                "## Compare AUC for RO and CL"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 33,
            "id": "5bb72373-1261-4713-a251-8b37bd94bc42",
            "metadata": {},
            "outputs": [],
            "source": [
                "auc_frame = pd.DataFrame({'Record-Only':[result_dict[k][2] for k in cl_result_dict], \n",
                "              'Closed-Loop':[cl_result_dict[k][2] for k in cl_result_dict]})\n",
                "auc_frame_melt = pd.melt(auc_frame, var_name='type', value_name='auc')"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 34,
            "id": "d324e103-c8e0-470e-bc1b-1f905981e647",
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "image/png": 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",
                        "text/plain": [
                            "<Figure size 288x288 with 1 Axes>"
                        ]
                    },
                    "metadata": {},
                    "output_type": "display_data"
                }
            ],
            "source": [
                "fig, ax = plt.subplots(1, 1, figsize=(4, 4))\n",
                "sns.barplot(data=auc_frame_melt,\n",
                "            x='type',y='auc', orient='vertical', ci=68, capsize=.1)\n",
                "# ax.set_ylim(0.5)\n",
                "ax.tick_params(labelsize=14)\n",
                "ax.set_xlabel('')\n",
                "ax.set_ylabel('AUC')\n",
                "ax.grid(False)\n",
                "plt.savefig('FinalReport/cl_ro_AUC_comparison.png', dpi=200, bbox_inches='tight')"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 35,
            "id": "2f52aad7-8688-4939-bf79-10410622e419",
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "text/plain": [
                            "Ttest_relResult(statistic=0.6202041740574749, pvalue=0.5421263377940593)"
                        ]
                    },
                    "execution_count": 35,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": [
                "scp.stats.ttest_rel(auc_frame['Record-Only'], auc_frame['Closed-Loop'])"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 70,
            "id": "fef841a1-4634-40db-9048-292bca040b50",
            "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>Record-Only</th>\n",
                            "      <th>Closed-Loop</th>\n",
                            "    </tr>\n",
                            "  </thead>\n",
                            "  <tbody>\n",
                            "    <tr>\n",
                            "      <th>0</th>\n",
                            "      <td>0.550459</td>\n",
                            "      <td>0.656436</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>1</th>\n",
                            "      <td>0.485519</td>\n",
                            "      <td>0.483852</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>2</th>\n",
                            "      <td>0.575867</td>\n",
                            "      <td>0.601562</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>3</th>\n",
                            "      <td>0.522095</td>\n",
                            "      <td>0.471555</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>4</th>\n",
                            "      <td>0.541850</td>\n",
                            "      <td>0.586807</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>5</th>\n",
                            "      <td>0.589504</td>\n",
                            "      <td>0.391741</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>6</th>\n",
                            "      <td>0.585922</td>\n",
                            "      <td>0.663475</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>7</th>\n",
                            "      <td>0.619852</td>\n",
                            "      <td>0.636016</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>8</th>\n",
                            "      <td>0.508076</td>\n",
                            "      <td>0.546335</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>9</th>\n",
                            "      <td>0.611040</td>\n",
                            "      <td>0.575720</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>10</th>\n",
                            "      <td>0.562206</td>\n",
                            "      <td>0.462465</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>11</th>\n",
                            "      <td>0.619012</td>\n",
                            "      <td>0.708444</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>12</th>\n",
                            "      <td>0.583892</td>\n",
                            "      <td>0.313337</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>13</th>\n",
                            "      <td>0.548136</td>\n",
                            "      <td>0.641498</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>14</th>\n",
                            "      <td>0.572789</td>\n",
                            "      <td>0.518840</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>15</th>\n",
                            "      <td>0.553830</td>\n",
                            "      <td>0.548589</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>16</th>\n",
                            "      <td>0.536795</td>\n",
                            "      <td>0.486328</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>17</th>\n",
                            "      <td>0.492321</td>\n",
                            "      <td>0.538800</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>18</th>\n",
                            "      <td>0.549361</td>\n",
                            "      <td>0.563465</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>19</th>\n",
                            "      <td>0.523560</td>\n",
                            "      <td>0.525643</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>20</th>\n",
                            "      <td>0.556852</td>\n",
                            "      <td>0.504691</td>\n",
                            "    </tr>\n",
                            "  </tbody>\n",
                            "</table>\n",
                            "</div>"
                        ],
                        "text/plain": [
                            "    Record-Only  Closed-Loop\n",
                            "0      0.550459     0.656436\n",
                            "1      0.485519     0.483852\n",
                            "2      0.575867     0.601562\n",
                            "3      0.522095     0.471555\n",
                            "4      0.541850     0.586807\n",
                            "5      0.589504     0.391741\n",
                            "6      0.585922     0.663475\n",
                            "7      0.619852     0.636016\n",
                            "8      0.508076     0.546335\n",
                            "9      0.611040     0.575720\n",
                            "10     0.562206     0.462465\n",
                            "11     0.619012     0.708444\n",
                            "12     0.583892     0.313337\n",
                            "13     0.548136     0.641498\n",
                            "14     0.572789     0.518840\n",
                            "15     0.553830     0.548589\n",
                            "16     0.536795     0.486328\n",
                            "17     0.492321     0.538800\n",
                            "18     0.549361     0.563465\n",
                            "19     0.523560     0.525643\n",
                            "20     0.556852     0.504691"
                        ]
                    },
                    "execution_count": 70,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": [
                "auc_frame"
            ]
        }
    ],
    "metadata": {
        "kernelspec": {
            "display_name": "optimizing-memory",
            "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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    "nbformat": 4,
    "nbformat_minor": 5
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