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+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# MNE Loading and visualizing from sample datasets (Example: LIMO dataset)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Requirement already satisfied: mne in c:\\users\\sange\\anaconda3\\lib\\site-packages (1.6.0)\n",
+ "Requirement already satisfied: numpy>=1.21.2 in c:\\users\\sange\\anaconda3\\lib\\site-packages (from mne) (1.24.3)\n",
+ "Requirement already satisfied: scipy>=1.7.1 in c:\\users\\sange\\anaconda3\\lib\\site-packages (from mne) (1.11.1)\n",
+ "Requirement already satisfied: matplotlib>=3.5.0 in c:\\users\\sange\\anaconda3\\lib\\site-packages (from mne) (3.7.2)\n",
+ "Requirement already satisfied: tqdm in c:\\users\\sange\\anaconda3\\lib\\site-packages (from mne) (4.65.0)\n",
+ "Requirement already satisfied: pooch>=1.5 in c:\\users\\sange\\anaconda3\\lib\\site-packages (from mne) (1.8.0)\n",
+ "Requirement already satisfied: decorator in c:\\users\\sange\\anaconda3\\lib\\site-packages (from mne) (5.1.1)\n",
+ "Requirement already satisfied: packaging in c:\\users\\sange\\anaconda3\\lib\\site-packages (from mne) (23.1)\n",
+ "Requirement already satisfied: jinja2 in c:\\users\\sange\\anaconda3\\lib\\site-packages (from mne) (3.1.2)\n",
+ "Requirement already satisfied: lazy-loader>=0.3 in c:\\users\\sange\\anaconda3\\lib\\site-packages (from mne) (0.3)\n",
+ "Requirement already satisfied: defusedxml in c:\\users\\sange\\anaconda3\\lib\\site-packages (from mne) (0.7.1)\n",
+ "Requirement already satisfied: contourpy>=1.0.1 in c:\\users\\sange\\anaconda3\\lib\\site-packages (from matplotlib>=3.5.0->mne) (1.0.5)\n",
+ "Requirement already satisfied: cycler>=0.10 in c:\\users\\sange\\anaconda3\\lib\\site-packages (from matplotlib>=3.5.0->mne) (0.11.0)\n",
+ "Requirement already satisfied: fonttools>=4.22.0 in c:\\users\\sange\\anaconda3\\lib\\site-packages (from matplotlib>=3.5.0->mne) (4.25.0)\n",
+ "Requirement already satisfied: kiwisolver>=1.0.1 in c:\\users\\sange\\anaconda3\\lib\\site-packages (from matplotlib>=3.5.0->mne) (1.4.4)\n",
+ "Requirement already satisfied: pillow>=6.2.0 in c:\\users\\sange\\anaconda3\\lib\\site-packages (from matplotlib>=3.5.0->mne) (10.0.1)\n",
+ "Requirement already satisfied: pyparsing<3.1,>=2.3.1 in c:\\users\\sange\\anaconda3\\lib\\site-packages (from matplotlib>=3.5.0->mne) (3.0.9)\n",
+ "Requirement already satisfied: python-dateutil>=2.7 in c:\\users\\sange\\anaconda3\\lib\\site-packages (from matplotlib>=3.5.0->mne) (2.8.2)\n",
+ "Requirement already satisfied: platformdirs>=2.5.0 in c:\\users\\sange\\anaconda3\\lib\\site-packages (from pooch>=1.5->mne) (3.10.0)\n",
+ "Requirement already satisfied: requests>=2.19.0 in c:\\users\\sange\\anaconda3\\lib\\site-packages (from pooch>=1.5->mne) (2.31.0)\n",
+ "Requirement already satisfied: MarkupSafe>=2.0 in c:\\users\\sange\\anaconda3\\lib\\site-packages (from jinja2->mne) (2.1.1)\n",
+ "Requirement already satisfied: colorama in c:\\users\\sange\\anaconda3\\lib\\site-packages (from tqdm->mne) (0.4.6)\n",
+ "Requirement already satisfied: six>=1.5 in c:\\users\\sange\\anaconda3\\lib\\site-packages (from python-dateutil>=2.7->matplotlib>=3.5.0->mne) (1.16.0)\n",
+ "Requirement already satisfied: charset-normalizer<4,>=2 in c:\\users\\sange\\anaconda3\\lib\\site-packages (from requests>=2.19.0->pooch>=1.5->mne) (2.0.4)\n",
+ "Requirement already satisfied: idna<4,>=2.5 in c:\\users\\sange\\anaconda3\\lib\\site-packages (from requests>=2.19.0->pooch>=1.5->mne) (3.4)\n",
+ "Requirement already satisfied: urllib3<3,>=1.21.1 in c:\\users\\sange\\anaconda3\\lib\\site-packages (from requests>=2.19.0->pooch>=1.5->mne) (2.1.0)\n",
+ "Requirement already satisfied: certifi>=2017.4.17 in c:\\users\\sange\\anaconda3\\lib\\site-packages (from requests>=2.19.0->pooch>=1.5->mne) (2023.7.22)\n"
+ ]
+ }
+ ],
+ "source": [
+ "# Install MNE\n",
+ "!pip3 install mne"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import mne \n",
+ "import numpy as np"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Download sample datasets\n",
+ "\n",
+ "MNE has a selection of sample datasets in various formats (FIF, EEG, etc) which can be downloaded from their methods \n",
+ "\n",
+ "Some examples are shown here in commented out code (they are huge datasets and take a while!)."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# # Neuromag raw fif files\n",
+ "\n",
+ "# sample_data_folder = mne.datasets.sample.data_path()\n",
+ "# sample_data_raw_file = (\n",
+ "# sample_data_folder / \"MEG\" / \"sample\" / \"sample_audvis_filt-0-40_raw.fif\"\n",
+ "# )\n",
+ "# raw = mne.io.read_raw_fif(sample_data_raw_file)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Large sample dataset: 3 GB after extraction"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# ### EEG\n",
+ "\n",
+ "# # BrainVision\n",
+ "# ssvep_folder = mne.datasets#.ssvep.data_path()\n",
+ "# ssvep_data_raw_path = (\n",
+ "# ssvep_folder / \"sub-02\" / \"ses-01\" / \"eeg\" / \"sub-02_ses-01_task-ssvep_eeg.vhdr\"\n",
+ "# )\n",
+ "# ssvep_raw = mne.io.read_raw_brainvision(ssvep_data_raw_path, verbose=False)\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Just for learning, let's take only a single subject from the [LIMO dataset](https://datashare.ed.ac.uk/handle/10283/2189)\n",
+ "\n",
+ "We can select subjects numbered between 1 and 18\n",
+ "\n",
+ "\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import numpy as np\n",
+ "import matplotlib.pyplot as plt\n",
+ "\n",
+ "from mne.datasets.limo import load_data\n",
+ "from mne.viz import plot_events, plot_compare_evokeds\n",
+ "from mne import combine_evoked\n",
+ "\n",
+ "subj = 1 # subject to use"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Lets see how long it takes to load this dataset"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "c:\\Users\\sange\\anaconda3\\Lib\\site-packages\\paramiko\\transport.py:219: CryptographyDeprecationWarning: Blowfish has been deprecated\n",
+ " \"class\": algorithms.Blowfish,\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "CPU times: total: 2.05 s\n",
+ "Wall time: 2.27 s\n"
+ ]
+ }
+ ],
+ "source": [
+ "%time limo_epochs = load_data(subject=subj)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "
\n",
+ " \n",
+ " Number of events | \n",
+ " 1055 | \n",
+ "
\n",
+ " \n",
+ " Events | \n",
+ " \n",
+ " Face/A: 524 Face/B: 531 | \n",
+ " \n",
+ "
\n",
+ " \n",
+ " Time range | \n",
+ " -0.300 – 0.500 s | \n",
+ "
\n",
+ " \n",
+ " Baseline | \n",
+ " off | \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ ""
+ ]
+ },
+ "execution_count": 7,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "limo_epochs"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "The loaded dataset is of the type **EpochsArray**"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "mne.epochs.EpochsArray"
+ ]
+ },
+ "execution_count": 8,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "type(limo_epochs)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "You can see the channel names, events, and their ID"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "{'A1',\n",
+ " 'A10',\n",
+ " 'A11',\n",
+ " 'A12',\n",
+ " 'A13',\n",
+ " 'A14',\n",
+ " 'A15',\n",
+ " 'A16',\n",
+ " 'A17',\n",
+ " 'A18',\n",
+ " 'A19',\n",
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+ " 'A20',\n",
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+ " 'B1',\n",
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+ " 'B3',\n",
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+ " 'B31',\n",
+ " 'B32',\n",
+ " 'B4',\n",
+ " 'B5',\n",
+ " 'B6',\n",
+ " 'B7',\n",
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+ " 'B9',\n",
+ " 'C1',\n",
+ " 'C10',\n",
+ " 'C11',\n",
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+ " 'C13',\n",
+ " 'C14',\n",
+ " 'C15',\n",
+ " 'C16',\n",
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+ " 'C5',\n",
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+ " 'C7',\n",
+ " 'C8',\n",
+ " 'C9',\n",
+ " 'D1',\n",
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+ " 'D11',\n",
+ " 'D12',\n",
+ " 'D13',\n",
+ " 'D14',\n",
+ " 'D15',\n",
+ " 'D16',\n",
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+ " 'D18',\n",
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+ " 'D2',\n",
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+ " 'D25',\n",
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+ " 'D27',\n",
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+ " 'D29',\n",
+ " 'D3',\n",
+ " 'D30',\n",
+ " 'D31',\n",
+ " 'D32',\n",
+ " 'D4',\n",
+ " 'D5',\n",
+ " 'D6',\n",
+ " 'D7',\n",
+ " 'D8',\n",
+ " 'D9',\n",
+ " 'EXG1',\n",
+ " 'EXG2',\n",
+ " 'EXG3',\n",
+ " 'EXG4'}"
+ ]
+ },
+ "execution_count": 9,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "set(limo_epochs.ch_names)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "array([[ 0, 0, 0],\n",
+ " [ 1, 0, 0],\n",
+ " [ 2, 0, 0],\n",
+ " ...,\n",
+ " [1052, 0, 1],\n",
+ " [1053, 0, 1],\n",
+ " [1054, 0, 1]])"
+ ]
+ },
+ "execution_count": 10,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "limo_epochs.events"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 11,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "{'Face/A': 0, 'Face/B': 1}"
+ ]
+ },
+ "execution_count": 11,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "limo_epochs.event_id"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Plotting events is easy!"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 12,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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mKCQkRJI0cuRI/eUvf1FSUpL69++vjIwMnT17Vpdddpm6d++up59+Wk8++aR8fHy0c+dONWnSRFdeeaXWrVunN998s9r5Kioq9NFHH6lJkyaKjY2tst7Hx0e9e/dWSkqK7rjjDknSqlWr1Lt3bwUGBmrDhg1avHix1q5dK0nauHGjAgIC1LlzZxUUFOjRRx9Vjx49HM8ILS0tVXl5uSoqKlRaWqozZ87Ix8fHEaBSUlI0efLkOvWwxkd+hgwZosGDB8uyLMXHx2vw4MGOr3vuuUfz5s3TokWL6lQMAABux95Siv1rowQfSZo2bZoyMjIUEhKi4cOHa+jQoY51sbGxWrFihZ566inZ7Xb17dvXcQ+dxYsX6+DBg4qLi1N4eLimT5+u06dPKy0tTTExMY4Adc6ECRMUEBAgu92uuXPn6tNPP1VoaGi1Nd17771aunSp4/WSJUsUGxsru92u6dOn67333tN1110nSTp16pRGjx4tu92udu3a6fTp0/r4448d2953333y8/NTSkqKxo0bJz8/P23cuFFS5ZVue/bs0YABA+rUwxof+XnmmWckVd5wacSIEee9BA0AANSP6u7rEx0drc2bN593m1tuuUXffvttleWhoaFVLm+XpMTExCqf3GzYsKFWdQ4bNkyJiYk6ePCgWrduXeXmhL81YMCAPwwvCxYs0IIFC6pd9/bbb+vxxx//0weX/plaP9V9/Pjxkiqv7jpx4oQqKpzPdI+JialTQQAAoPHExcWpa9euddqHzWarcjVXQ5g5c2a97KfW4eenn37S3/72tyqp89yJ0OXl5fVSGAAAaHgjRoxwdQmNrtbhZ8KECfLy8tLq1asVGRlZq0fIAwAAuFqtw09aWppSU1PVvn37hqgHAACgQdX6Pj8dOnRQVlZWQ9QCAADQ4GodfmbNmqXHH39cGzZsUHZ2tgoKCpy+AAAALma1/tjrlltukST17dvXaTknPAMAAHdQ6/Czfv36hqgDAACgUdQ6/PTu3bsh6gAAAG6qqKhIPXr00DfffFPlCe/1acKECbr77rvrfIfnWp/zI1U+V2PMmDHq2bOnMjIyJEkfffSRNm3aVKdiAADAr9q0aSN/f38FBAQoICBAbdq0abC5ysvLFRkZqTNnzjiW3XzzzYqKiqpyQ+PfmzNnjoYMGeIIPhMmTJCvr6+j7quuusoxdvXq1br++utlt9vVqlUrPfvss9Xuc9myZbLZbE53i37kkUccT5yoi1qHnxUrVig+Pl5+fn7asWOHSkpKJFU+aO2ll16qc0EAALiTzPzT2nwgS5n5pxtk/+vWrdOpU6d06tSpah93UV+2bNmizp07Ox5flZGRoZSUFBUXF2vdunV/uO3//M//aNSoUU7Lnn32WUfdv30afGFhoV544QWdOHFCmzdv1rJly/TRRx85bVtUVKQXXnjBKTRJUqdOnVRUVKSdO3fW5a3WPvy88MILmjt3rt5//32nZ2v07NlTO3bsqFMxAAC4k+X/PqQbXl6nUe9/oxteXqfl/z7U4HNmZ2erf//+CgsLU/PmzXX//fc7DkRIlWGpW7duCgoKUtu2bZWSkiJJysnJ0ahRoxQeHq64uLgqz/lau3at4uPjHa+XLFmiG264QSNGjNDixYvPW096erqysrKqBJXzGTlypPr27StfX1/FxMRo2LBh2rZtm9OY559/XpMmTVJYWFiV7Xv16qU1a9bUaK7zqXX4+fHHH9WrV68qy4OCgpSXl1enYgAAcBeZ+aeVsHKXKqzK1xWW9PeVuxvsCNA5FRUVmjp1qjIyMrRz505t375d7777riTp559/1tChQzVz5kzl5ubqq6++UmRkpCRp7Nixio6O1uHDh5WUlKSEhAR99913jv2uWbPGKfwsWrRII0aM0IgRI7Ry5Uqnj8N+a8+ePWrbtm2V5a+++qpCQ0PVs2dPx1PZq7N582an4LRv3z794x//0NSpU6sd365dO+3atesPOvTnah1+IiMjtX///irLN23apLi4uDoVAwCAu0jPKnIEn3PKLUu/ZBXX6zz9+vVTcHCwgoODlZCQoObNm2vQoEHy9fVVZGSkJk+e7DjndunSpRo8eLAGDRokT09PxcTE6PLLL9exY8eUkpKil156Sb6+vmrfvr1GjRqllStXSpKysrJ08uRJdejQQZK0e/du7dmzR8OHD9dNN90kPz8/ffHFF9XWl5+fX+Uk52nTpmn//v3KzMzUgw8+qNtvv12HDx+usu28efOUmZnpeGj6uW1nzZp13ie3BwYG1vlgS63Dz+TJkzVt2jR98803stlsOnr0qBYvXqxHH31UU6ZMqVMxAAC4i9iwpvL43eMtPW02tQnzr9d5kpOTlZeXp7y8PCUmJqqwsFDjxo1Tq1atFBQUpBkzZig7O1uSdOTIkWoPRBw6dEhFRUUKDQ11BKl58+bp+PHjkqQvv/xS/fr1c4xftGiR+vTpo/DwcHl4eOjOO+8870dfdrtdhYWFTsu6dOmikJAQ+fj4aPTo0br++uuVnJzsNGb16tV67rnntHr1avn5+UmS/vd//1deXl7q37//eftRWFio4ODgP2/cH6j1pe6PP/648vPz1adPH505c0a9evWSr6+vHn300fMeogIA4FITafdT4rBO+vvK3Sq3LHnabHppWEdF2v0adN7Zs2crJydHaWlpCgsL07x587R06VJJUnR0tH788ccq27Rs2VLBwcGOkPR7a9as0R133CGp8qbFS5cuVVZWliIiIiRJp0+f1pkzZ5STk6NmzZo5bdupUyft27fPcbPj6nh4OB9r2bhxoyZNmqSkpCRdfvnljuXr16/Xxo0bHfOee5/79u3T008/LUn64Ycf1KlTpz/t0x+5oEvdX3zxRWVlZWnbtm3aunWrTp48qeeff75OhQAA4G5GdI/Rpif7aOl912nTk300ontMg89ZWFgoPz8/2e12HTx4UHPmzHGsGzlypD7//HMlJSWpoqJChw8f1oEDB9SyZUt1795dTz/9tIqLi1VWVqYdO3Zo7969sixL69atczy5YePGjcrKytLOnTuVlpamtLQ0/fjjj2rTpo0+/fTTKvW0bt1aERERTufhrFixQkVFRSorK9Py5cu1adMm3XzzzZIqH5B+7khS165dnfb1/PPP68cff3TM261bN82aNUsPP/ywY0xKSorTuUkXotbhZ+HChSoqKpK/v7+6deumHj16NOgNjQAAuJhF2v10/WWhDX7E55xp06YpIyNDISEhGj58uIYOHepYFxsbqxUrVuipp56S3W5X3759lZmZKUlavHixDh48qLi4OIWHh2v69Ok6ffq00tLSFBMTo5CQEMe4u+++W5dddpkiIiIcX/fff78WLVpUbU333nuv4+iTJL3++uuKiopSWFiYZs+erc8++8xxj6I333xT2dnZjvsCBQQE6LbbbpNUeT7Pb+f08fGR3W5XYGCgJGnXrl3y9/dX586d69RDm2VZ1p8P+1Xz5s1VXFys22+/XWPGjFH//v3l5VXrT8/qRUFBgex2u7KyshQaGuqSGtxFaWmpkpKSNGDAgPOeRIZf0a+ao1c1R69qp777de5nRn5+voKCghzLz5w5o/T0dMXGxjrucWOSxMRElZaWOj5WuhBFRUXq3r27tm3b1uB3eL7rrrs0cODAKutq8/tY6yM/mZmZWr58uTw9PXXPPfcoMjJSU6ZM0ebNm2u7KwAA4GJxcXFVblBYW02bNtXevXsb/JOgBQsWVBt8aqvWh2y8vLw0aNAgDRo0SMXFxfrss8+0ZMkS9enTR61atdKBAwfqXBQAAGgcI0aMcHUJja5On1f5+/srPj5eubm5OnjwoL7//vv6qgsAAKBBXNDVXsXFxVq8eLEGDBigqKgovf766xoyZIh2795d3/UBANAoankKLC4ytfn9q/WRn5EjR+qLL76Qv7+/7rrrLm3YsEE9e/as7W4AALgoeHt7y2az6eTJk2revPl571WDi5dlWTp58qRsNluNTo6vdfix2Wxavny54uPjXXaVFwAA9cXT01OtWrXSkSNHGvSp6WhYNptNrVq1kqen55+OrXV6WbJkyQUVBQDAxSogIEBt27ZVaWmpq0vBBfL29q5R8JFqEX4GDBigpUuXym63S6q8y/ODDz7oeL5Gdna2/vrXv2rv3r21rxgAABfz9PSs8Q9PuLcan/C8du1alZSUOF7PmjVLOTk5jtdlZWXVPk8EAADgYlLj8PP7s6g5Kx4AALijC7rUHQAAwF3VOPzYbLYql/9xOSAAAHA3NT7h2bIsTZgwQb6+vpIqHyD2wAMPqGnTppLkdD4QAADAxarG4Wf8+PFOr8eMGVNlzLhx4+peEQAAQAOqcfiZP39+Q9YBAADQKDjhGQAAGIXwAwAAjEL4AQAARiH8AAAAoxB+AACAUQg/AADAKIQfAABgFMIPAAAwCuEHAAAYhfADAACMQvgBAABGIfwAAACjEH4AAIBRCD8AAMAohB8AAGAUwg8AADAK4QcAABiF8AMAAIxC+AEAAEYh/AAAAKMQfgAAgFEIPwAAwCiEHwAAYBTCDwAAMArhBwAAGIXwAwAAjEL4AQAARiH8AAAAo7g8/MyZM0exsbFq0qSJunbtqpSUFFeXBAAALmFerpx8+fLlmj59uubMmaMbbrhB8+bN02233aa9e/cqJibmT7c/ln9aknTfh6myfPzV1MdTRWfLJalevy+rsOTlYWuQfTfW/KdKypSba9OyY//WmbIK495/XfplSW7z/ssqLLUObapR18aoc3SIAABVuTT8zJ49W5MmTdK9994rSXrjjTe0du1avfvuu0pMTPzDbZf/+5AeW/KNJGn7oXx5+JY2eL3uz1MH03NdXYQbcc9+bU3P1fLtRzT8mpZ67e6/uLocALjouCz8nD17VqmpqXryySedlt96663avHlztduUlJSopKRExwpK9MSKXY1RJuC2VuzI0MjurdS5lb1B5yktLXX6FedHr2qnvvtF33GOy8JPVlaWysvL1aJFC6flLVq00LFjx6rdJjExUc8++6x8YzopYuQfHxkCIC1as1kZUVajzJWcnNwo81wK6FXt1Fe/iouL62U/cH8u/dhLkmw2m9Nry7KqLDsnISFBM2bM0LGCEt36/7Y1RnmAWxvTv2ejHPlJTk5Wv3795O3t3aBzuTt6VTv13a+CgoJ6qAqXApeFn7CwMHl6elY5ynPixIkqR4PO8fX1la+vr4KCpFnDOznO+QFQ1fBrWqpbbFijzeft7c0P9BqiV7VTX/2i5zjHZeHHx8dHXbt2VXJysoYOHepYnpycrMGDB//p9iO6x6hLhK/avSF1j7HL8vWX//9d+WKT6vX7315tU9/7brT5z5QpNzdXUS3CdLqswrz3X4d+VUhu8/4dV3v14GovADgfl37sNWPGDI0dO1bdunXT9ddfr/fee0+HDh3SAw88UKPtI+x+kqT3xnVVaGhoQ5bq9kpLS5WUlKQBA7rzv58aoF8AcOlyafgZMWKEsrOz9dxzzykzM1MdO3ZUUlKSWrdu7cqyAADAJczlJzxPmTJFU6ZMcXUZAADAEC5/vAUAAEBjIvwAAACjEH4AAIBRCD8AAMAohB8AAGAUwg8AADAK4QcAABiF8AMAAIxC+AEAAEYh/AAAAKMQfgAAgFEIPwAAwCiEHwAAYBTCDwAAMArhBwAAGIXwAwAAjEL4AQAARiH8AAAAoxB+AACAUQg/AADAKIQfAABgFMIPAAAwCuEHAAAYhfADAACMQvgBAABGIfwAAACjEH4AAIBRCD8AAMAohB8AAGAUwg8AADAK4QcAABiF8AMAAIxC+AEAAEYh/AAAAKMQfgAAgFEIPwAAwCiEHwAAYBTCDwAAMArhBwAAGIXwAwAAjEL4AQAARiH8AAAAoxB+AACAUQg/AADAKIQfAABgFMIPAAAwCuEHAAAYhfADAACMQvgBAABGIfwAAACjEH4AAIBRCD8AAMAohB8AAGAUwg8AADAK4QcAABiF8AMAAIxC+AEAAEYh/AAAAKMQfgAAgFEIPwAAwCiEHwAAYBTCDwAAMArhBwAAGIXwAwAAjEL4AQAARiH8AAAAoxB+AACAUQg/AADAKIQfAABgFMIPAAAwCuEHAAAYhfADAACMQvgBAABGIfwAAACjEH4AAIBRCD8AAMAohB8AAGAUwg8AADAK4QcAABiF8AMAAIxC+AEAAEYh/AAAAKMQfgAAgFEIPwAAwCiEHwAAYBTCDwAAMArhBwAAGIXwAwAAjEL4AQAARiH8AAAAoxB+AACAUQg/AADAKIQfAABgFMIPAAAwCuEHAAAYhfADAACMQvgBAABGIfwAAACjEH4AAIBRCD8AAMAohB8AAGAUwg8AADAK4QcAABiF8AMAAIxC+AEAAEYh/AAAAKMQfgAAgFEIPwAAwCiEHwAAYBTCDwAAMArhBwAAGIXwAwAAjEL4AQAARiH8AAAAoxB+AACAUbxcXUBdWJYlSSosLJS3t7eLq7m4lZaWqri4WAUFBfSqBuhXzdGrmqNXtVPf/SooKJD0688OmMutw092drYkKTY21sWVAADcRWFhoex2u6vLgAu5dfhp1qyZJOnQoUP8Qf4TBQUFio6O1uHDhxUUFOTqci569Kvm6FXN0avaqe9+WZalwsJCRUVF1UN1cGduHX48PCpPWbLb7fxDUkNBQUH0qhboV83Rq5qjV7VTn/3iP8qQOOEZAAAYhvADAACM4tbhx9fXV88884x8fX1dXcpFj17VDv2qOXpVc/SqdugXGorN4po/AABgELc+8gMAAFBbhB8AAGAUwg8AADAK4QcAABjFrcPPnDlzFBsbqyZNmqhr165KSUlxdUmNKjExUd27d1dgYKDCw8M1ZMgQ/fjjj05jLMvSzJkzFRUVJT8/P910003as2eP05iSkhI99NBDCgsLU9OmTXXHHXfoyJEjjflWGl1iYqJsNpumT5/uWEavnGVkZGjMmDEKDQ2Vv7+//vKXvyg1NdWxnn5VKisr03/9138pNjZWfn5+iouL03PPPaeKigrHGFN7tXHjRt1+++2KioqSzWbT559/7rS+vvqSm5ursWPHym63y263a+zYscrLy2vgdwe3ZrmpZcuWWd7e3tb7779v7d2715o2bZrVtGlT6+DBg64urdHEx8db8+fPt3bv3m2lpaVZAwcOtGJiYqxTp045xrz88stWYGCgtWLFCmvXrl3WiBEjrMjISKugoMAx5oEHHrBatmxpJScnWzt27LD69Oljde7c2SorK3PF22pw27Zts9q0aWNdffXV1rRp0xzL6dWvcnJyrNatW1sTJkywvvnmGys9Pd365z//ae3fv98xhn5VeuGFF6zQ0FBr9erVVnp6uvXJJ59YAQEB1htvvOEYY2qvkpKSrKeeespasWKFJcn67LPPnNbXV1/69+9vdezY0dq8ebO1efNmq2PHjtagQYMa623CDblt+OnRo4f1wAMPOC1r37699eSTT7qoItc7ceKEJcn6+uuvLcuyrIqKCisiIsJ6+eWXHWPOnDlj2e12a+7cuZZlWVZeXp7l7e1tLVu2zDEmIyPD8vDwsNasWdO4b6ARFBYWWm3btrWSk5Ot3r17O8IPvXL2xBNPWDfeeON519OvXw0cOND629/+5rRs2LBh1pgxYyzLolfn/D781Fdf9u7da0mytm7d6hizZcsWS5L1ww8/NPC7grtyy4+9zp49q9TUVN16661Oy2+99VZt3rzZRVW5Xn5+vqRfH/ianp6uY8eOOfXJ19dXvXv3dvQpNTVVpaWlTmOioqLUsWPHS7KXDz74oAYOHKhbbrnFaTm9crZq1Sp169ZNd911l8LDw9WlSxe9//77jvX061c33nijvvrqK+3bt0+S9N1332nTpk0aMGCAJHp1PvXVly1btshut+vaa691jLnuuutkt9sv2d6h7tzywaZZWVkqLy9XixYtnJa3aNFCx44dc1FVrmVZlmbMmKEbb7xRHTt2lCRHL6rr08GDBx1jfHx8FBISUmXMpdbLZcuWaceOHfr3v/9dZR29cvbzzz/r3Xff1YwZM/T3v/9d27Zt03/+53/K19dX48aNo1+/8cQTTyg/P1/t27eXp6enysvL9eKLL2rkyJGS+LN1PvXVl2PHjik8PLzK/sPDwy/Z3qHu3DL8nGOz2ZxeW5ZVZZkppk6dqp07d2rTpk1V1l1Iny61Xh4+fFjTpk3Tl19+qSZNmpx3HL2qVFFRoW7duumll16SJHXp0kV79uzRu+++q3HjxjnG0S9p+fLlWrRokZYsWaKrrrpKaWlpmj59uqKiojR+/HjHOHpVvfroS3XjTegdLpxbfuwVFhYmT0/PKqn+xIkTVf4XYYKHHnpIq1at0vr169WqVSvH8oiICEn6wz5FRETo7Nmzys3NPe+YS0FqaqpOnDihrl27ysvLS15eXvr666/11ltvycvLy/Fe6VWlyMhIdejQwWnZlVdeqUOHDkniz9ZvPfbYY3ryySd1zz33qFOnTho7dqwefvhhJSYmSqJX51NffYmIiNDx48er7P/kyZOXbO9Qd24Zfnx8fNS1a1clJyc7LU9OTlbPnj1dVFXjsyxLU6dO1cqVK7Vu3TrFxsY6rY+NjVVERIRTn86ePauvv/7a0aeuXbvK29vbaUxmZqZ27959SfWyb9++2rVrl9LS0hxf3bp10+jRo5WWlqa4uDh69Rs33HBDldsm7Nu3T61bt5bEn63fKi4uloeH8z+lnp6ejkvd6VX16qsv119/vfLz87Vt2zbHmG+++Ub5+fmXbO9QD1xxlnV9OHep+wcffGDt3bvXmj59utW0aVPrl19+cXVpjeY//uM/LLvdbm3YsMHKzMx0fBUXFzvGvPzyy5bdbrdWrlxp7dq1yxo5cmS1l5K2atXK+uc//2nt2LHDuvnmm93+Etua+O3VXpZFr35r27ZtlpeXl/Xiiy9aP/30k7V48WLL39/fWrRokWMM/ao0fvx4q2XLlo5L3VeuXGmFhYVZjz/+uGOMqb0qLCy0vv32W+vbb7+1JFmzZ8+2vv32W8ctSeqrL/3797euvvpqa8uWLdaWLVusTp06cak7/pDbhh/Lsqx33nnHat26teXj42Ndc801jku8TSGp2q/58+c7xlRUVFjPPPOMFRERYfn6+lq9evWydu3a5bSf06dPW1OnTrWaNWtm+fn5WYMGDbIOHTrUyO+m8f0+/NArZ1988YXVsWNHy9fX12rfvr313nvvOa2nX5UKCgqsadOmWTExMVaTJk2suLg466mnnrJKSkocY0zt1fr166v9N2r8+PGWZdVfX7Kzs63Ro0dbgYGBVmBgoDV69GgrNze3kd4l3JHNsizLNcecAAAAGp9bnvMDAABwoQg/AADAKIQfAABgFMIPAAAwCuEHAAAYhfADAACMQvgBAABGIfwAAACjEH4AyGaz6fPPP3d1GQDQKAg/QCM5ceKEJk+erJiYGPn6+ioiIkLx8fHasmWLq0sDAKN4uboAwBTDhw9XaWmpFi5cqLi4OB0/flxfffWVcnJyXF0aABiFIz9AI8jLy9OmTZs0a9Ys9enTR61bt1aPHj2UkJCggQMHSpJmz56tTp06qWnTpoqOjtaUKVN06tQpxz4WLFig4OBgrV69Wu3atZO/v7/uvPNOFRUVaeHChWrTpo1CQkL00EMPqby83LFdmzZt9Pzzz2vUqFEKCAhQVFSU3n777T+sNyMjQyNGjFBISIhCQ0M1ePBg/fLLL471GzZsUI8ePdS0aVMFBwfrhhtu0MGDB+u3aQDQQAg/QCMICAhQQECAPv/8c5WUlFQ7xsPDQ2+99ZZ2796thQsXat26dXr88cedxhQXF+utt97SsmXLtGbNGm3YsEHDhg1TUlKSkpKS9NFHH+m9997Tp59+6rTdq6++qquvvlo7duxQQkKCHn74YSUnJ1dbR3Fxsfr06aOAgABt3LhRmzZtUkBAgPr376+zZ8+qrKxMQ4YMUe/evbVz505t2bJF999/v2w2W/00CwAamqsfKw+Y4tNPP7VCQkKsJk2aWD179rQSEhKs77777rzjP/74Yys0NNTxev78+ZYka//+/Y5lkydPtvz9/a3CwkLHsvj4eGvy5MmO161bt7b69+/vtO8RI0ZYt912m+O1JOuzzz6zLMuyPvjgA6tdu3ZWRUWFY31JSYnl5+dnrV271srOzrYkWRs2bKh9EwDgIsCRH6CRDB8+XEePHtWqVasUHx+vDRs26JprrtGCBQskSevXr1e/fv3UsmVLBQYGaty4ccrOzlZRUZFjH/7+/rrsssscr1u0aKE2bdooICDAadmJEyec5r7++uurvP7++++rrTM1NVX79+9XYGCg44hVs2bNdObMGR04cEDNmjXThAkTFB8fr9tvv11vvvmmMjMz69oeAGg0hB+gETVp0kT9+vXT008/rc2bN2vChAl65plndPDgQQ0YMEAdO3bUihUrlJqaqnfeeUeSVFpa6tje29vbaX82m63aZRUVFX9ay/k+pqqoqFDXrl2Vlpbm9LVv3z6NGjVKkjR//nxt2bJFPXv21PLly3XFFVdo69atteoFALgK4QdwoQ4dOqioqEjbt29XWVmZXnvtNV133XW64oordPTo0Xqb5/fBZOvWrWrfvn21Y6+55hr99NNPCg8P1+WXX+70ZbfbHeO6dOmihIQEbd68WR07dtSSJUvqrV4AaEiEH6ARZGdn6+abb9aiRYu0c+dOpaen65NPPtErr7yiwYMH67LLLlNZWZnefvtt/fzzz/roo480d+7cepv/X//6l1555RXt27dP77zzjj755BNNmzat2rGjR49WWFiYBg8erJSUFKWnp+vrr7/WtGnTdOTIEaWnpyshIUFbtmzRwYMH9eWXX2rfvn268sor661eAGhI3OcHaAQBAQG69tpr9frrr+vAgQMqLS1VdHS07rvvPv3973+Xn5+fZs+erVmzZikhIUG9evVSYmKixo0bVy/zP/LII0pNTdWzzz6rwMBAvfbaa4qPj692rL+/vzZu3KgnnnhCw4YNU2FhoVq2bKm+ffsqKChIp0+f1g8//KCFCxcqOztbkZGRmjp1qiZPnlwvtQJAQ7NZlmW5uggADadNmzaaPn26pk+f7upSAOCiwMdeAADAKIQfAABgFD72AgAARuHIDwAAMArhBwAAGIXwAwAAjEL4AQAARiH8AAAAoxB+AACAUQg/AADAKIQfAABglP8PJBMRaV3Mh3IAAAAASUVORK5CYII=",
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