notebooks/06_sw_detection_multi.ipynb

{
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    "# Multi-channel slow-wave detection\n",
    "\n",
    "This notebook demonstrates how to use YASA to perform **multi-channel slow-waves detection** from a NumPy array (example 1) or a MNE Raw object (example 2). If you haven't done so already, check out the notebook on [single-channel slow-waves detection](05_sw_detection.ipynb).\n",
    "\n",
    "Please make sure to install the latest version of YASA first by typing the following line in your terminal or command prompt:\n",
    "\n",
    "`pip install --upgrade yasa`\n",
    "\n",
    "**Important**\n",
    "- The data must be a numpy array of shape *(n_channels, n_samples)*.\n",
    "- The sampling frequency `sf` must be the same for all channels.\n",
    "- A list of the channel names (`ch_names`) must be provided as well.\n",
    "- The unit of the data must be $\\mu V$.\n",
    "\n",
    "## Example 1: Using NumPy\n",
    "\n",
    "To illustrate the multi-channel slow-waves detection, we load a full-night 3-channels dataset (Cz, Fz, Pz) sampled at 100 Hz. The data is in compressed NumPy format (*.npz*)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(3, 2161058) ['Cz' 'Fz' 'Pz']\n"
     ]
    }
   ],
   "source": [
    "import mne\n",
    "import yasa\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Load data\n",
    "f = np.load('data_full_6hrs_100Hz_Cz+Fz+Pz.npz')\n",
    "data, chan = f['data'], f['chan']\n",
    "hypno = np.load('data_full_6hrs_100Hz_hypno.npz').get('hypno')\n",
    "sf = 100.\n",
    "times = np.arange(data.size) / sf\n",
    "\n",
    "print(data.shape, chan)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To apply the multi-channel detection, we use the [sw_detect](https://raphaelvallat.com/yasa/build/html/generated/yasa.spindles_detect.html#yasa.spindles_detect) function. Note that we pass the hypnogram and restrain the detection to N2 or N3 sleep."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
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       "      <td>646.03</td>\n",
       "      <td>0.88</td>\n",
       "      <td>-48.10</td>\n",
       "      <td>48.93</td>\n",
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       "      <td>441.03</td>\n",
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       "      <td>647.29</td>\n",
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       "      <td>647.71</td>\n",
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       "      <td>52.30</td>\n",
       "      <td>118.95</td>\n",
       "      <td>566.41</td>\n",
       "      <td>1.20</td>\n",
       "      <td>2</td>\n",
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       "      <td>20500.38</td>\n",
       "      <td>20500.69</td>\n",
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       "      <td>1.34</td>\n",
       "      <td>-78.05</td>\n",
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       "      <td>158.14</td>\n",
       "      <td>564.79</td>\n",
       "      <td>0.75</td>\n",
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       "      <td>20501.76</td>\n",
       "      <td>20502.05</td>\n",
       "      <td>1.00</td>\n",
       "      <td>-41.65</td>\n",
       "      <td>45.12</td>\n",
       "      <td>86.77</td>\n",
       "      <td>394.42</td>\n",
       "      <td>1.00</td>\n",
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       "      <td>-64.73</td>\n",
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       "      <td>156.78</td>\n",
       "      <td>746.59</td>\n",
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       "      <td>0.79</td>\n",
       "      <td>2</td>\n",
       "      <td>Pz</td>\n",
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       "         Start   NegPeak  MidCrossing   PosPeak       End  Duration  \\\n",
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       "1       644.28    644.54       644.76    644.96    645.15      0.87   \n",
       "2       645.15    645.37       645.59    645.82    646.03      0.88   \n",
       "3       646.03    646.26       646.45    646.68    646.88      0.85   \n",
       "4       646.88    647.08       647.29    647.48    647.71      0.83   \n",
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       "\n",
       "      ValNegPeak  ValPosPeak     PTP   Slope  Frequency  Stage Channel  \\\n",
       "0         -43.37       34.23   77.60  323.32       1.00      2      Cz   \n",
       "1         -59.70       47.28  106.98  486.29       1.15      2      Cz   \n",
       "2         -48.10       48.93   97.03  441.03       1.14      2      Cz   \n",
       "3         -53.67       70.08  123.74  651.28       1.18      2      Cz   \n",
       "4         -66.64       52.30  118.95  566.41       1.20      2      Cz   \n",
       "...          ...         ...     ...     ...        ...    ...     ...   \n",
       "6992      -78.05       80.09  158.14  564.79       0.75      2      Pz   \n",
       "6993      -41.65       45.12   86.77  394.42       1.00      2      Pz   \n",
       "6994      -64.73       92.05  156.78  746.59       0.85      2      Pz   \n",
       "6995      -80.46       52.00  132.47  473.10       0.57      2      Pz   \n",
       "6996      -52.14       28.16   80.30  211.32       0.79      2      Pz   \n",
       "\n",
       "      IdxChannel  \n",
       "0              0  \n",
       "1              0  \n",
       "2              0  \n",
       "3              0  \n",
       "4              0  \n",
       "...          ...  \n",
       "6992           2  \n",
       "6993           2  \n",
       "6994           2  \n",
       "6995           2  \n",
       "6996           2  \n",
       "\n",
       "[6997 rows x 14 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sw = yasa.sw_detect(data, sf, ch_names=chan, hypno=hypno, include=(2, 3))\n",
    "\n",
    "# Get the full detection dataframe\n",
    "sw.summary().round(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
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       "      <th rowspan=\"3\" valign=\"top\">2</th>\n",
       "      <th>Cz</th>\n",
       "      <td>477</td>\n",
       "      <td>3.019226</td>\n",
       "      <td>1.203354</td>\n",
       "      <td>-78.654530</td>\n",
       "      <td>57.765269</td>\n",
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       "      <th>Fz</th>\n",
       "      <td>416</td>\n",
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       "      <td>-88.028501</td>\n",
       "      <td>56.238414</td>\n",
       "      <td>144.266915</td>\n",
       "      <td>545.816723</td>\n",
       "      <td>0.900314</td>\n",
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       "      <th>Pz</th>\n",
       "      <td>416</td>\n",
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       "      <td>-73.346148</td>\n",
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       "      <td>130.547023</td>\n",
       "      <td>476.516677</td>\n",
       "      <td>0.836252</td>\n",
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       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">3</th>\n",
       "      <th>Cz</th>\n",
       "      <td>1936</td>\n",
       "      <td>21.628264</td>\n",
       "      <td>1.156271</td>\n",
       "      <td>-76.036749</td>\n",
       "      <td>63.328056</td>\n",
       "      <td>139.364805</td>\n",
       "      <td>539.105399</td>\n",
       "      <td>0.921905</td>\n",
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       "    <tr>\n",
       "      <th>Fz</th>\n",
       "      <td>1884</td>\n",
       "      <td>21.047340</td>\n",
       "      <td>1.147707</td>\n",
       "      <td>-72.681406</td>\n",
       "      <td>60.168087</td>\n",
       "      <td>132.849492</td>\n",
       "      <td>524.391816</td>\n",
       "      <td>0.924978</td>\n",
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       "    <tr>\n",
       "      <th>Pz</th>\n",
       "      <td>1868</td>\n",
       "      <td>20.868594</td>\n",
       "      <td>1.173185</td>\n",
       "      <td>-78.509145</td>\n",
       "      <td>63.952058</td>\n",
       "      <td>142.461203</td>\n",
       "      <td>538.441727</td>\n",
       "      <td>0.908249</td>\n",
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       "               Count    Density  Duration  ValNegPeak  ValPosPeak         PTP  \\\n",
       "Stage Channel                                                                   \n",
       "2     Cz         477   3.019226  1.203354  -78.654530   57.765269  136.419800   \n",
       "      Fz         416   2.633120  1.181635  -88.028501   56.238414  144.266915   \n",
       "      Pz         416   2.633120  1.273101  -73.346148   57.200875  130.547023   \n",
       "3     Cz        1936  21.628264  1.156271  -76.036749   63.328056  139.364805   \n",
       "      Fz        1884  21.047340  1.147707  -72.681406   60.168087  132.849492   \n",
       "      Pz        1868  20.868594  1.173185  -78.509145   63.952058  142.461203   \n",
       "\n",
       "                    Slope  Frequency  \n",
       "Stage Channel                         \n",
       "2     Cz       515.759692   0.883919  \n",
       "      Fz       545.816723   0.900314  \n",
       "      Pz       476.516677   0.836252  \n",
       "3     Cz       539.105399   0.921905  \n",
       "      Fz       524.391816   0.924978  \n",
       "      Pz       538.441727   0.908249  "
      ]
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     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get the average per channel and stage\n",
    "sw.summary(grp_chan=True, grp_stage=True, aggfunc='mean')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
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\n",
      "text/plain": [
       "<Figure size 432x324 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot an average template of the detected slow-waves, centered around the negative peak\n",
    "ax = sw.plot_average(center=\"NegPeak\", time_before=0.4, \n",
    "                     time_after=0.8, palette=\"Set1\")\n",
    "ax.legend(frameon=False)\n",
    "sns.despine()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x324 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Same but grouped by stage, using the `hue=\"Stage\"` input parameter (default is \"Channel\")\n",
    "# Note that we're also disabling the time-consuming bootstrapped confidence \n",
    "# intervals with `ci=None`.\n",
    "ax = sw.plot_average(center=\"NegPeak\", hue=\"Stage\", time_before=0.4, \n",
    "                     time_after=0.8, ci=None,\n",
    "                     palette=[\"xkcd:cerulean\", \"xkcd:midnight blue\"])\n",
    "ax.legend(frameon=False)\n",
    "sns.despine()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Grouped by both stage and channel**\n",
    "\n",
    "While YASA does not directly support grouping by both channel and stage, it is possible with just a few lines of code."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "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>Time</th>\n",
       "      <th>Event</th>\n",
       "      <th>Amplitude</th>\n",
       "      <th>Stage</th>\n",
       "      <th>Channel</th>\n",
       "      <th>IdxChannel</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-0.40</td>\n",
       "      <td>0</td>\n",
       "      <td>11.871379</td>\n",
       "      <td>2</td>\n",
       "      <td>Cz</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.39</td>\n",
       "      <td>0</td>\n",
       "      <td>7.850428</td>\n",
       "      <td>2</td>\n",
       "      <td>Cz</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-0.38</td>\n",
       "      <td>0</td>\n",
       "      <td>6.414374</td>\n",
       "      <td>2</td>\n",
       "      <td>Cz</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.37</td>\n",
       "      <td>0</td>\n",
       "      <td>8.616323</td>\n",
       "      <td>2</td>\n",
       "      <td>Cz</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-0.36</td>\n",
       "      <td>0</td>\n",
       "      <td>14.647750</td>\n",
       "      <td>2</td>\n",
       "      <td>Cz</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>846632</th>\n",
       "      <td>0.76</td>\n",
       "      <td>2283</td>\n",
       "      <td>10.897506</td>\n",
       "      <td>2</td>\n",
       "      <td>Pz</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>846633</th>\n",
       "      <td>0.77</td>\n",
       "      <td>2283</td>\n",
       "      <td>7.598260</td>\n",
       "      <td>2</td>\n",
       "      <td>Pz</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>846634</th>\n",
       "      <td>0.78</td>\n",
       "      <td>2283</td>\n",
       "      <td>9.997711</td>\n",
       "      <td>2</td>\n",
       "      <td>Pz</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>846635</th>\n",
       "      <td>0.79</td>\n",
       "      <td>2283</td>\n",
       "      <td>17.296041</td>\n",
       "      <td>2</td>\n",
       "      <td>Pz</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>846636</th>\n",
       "      <td>0.80</td>\n",
       "      <td>2283</td>\n",
       "      <td>26.593912</td>\n",
       "      <td>2</td>\n",
       "      <td>Pz</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>846637 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        Time Event  Amplitude  Stage Channel  IdxChannel\n",
       "0      -0.40     0  11.871379      2      Cz           0\n",
       "1      -0.39     0   7.850428      2      Cz           0\n",
       "2      -0.38     0   6.414374      2      Cz           0\n",
       "3      -0.37     0   8.616323      2      Cz           0\n",
       "4      -0.36     0  14.647750      2      Cz           0\n",
       "...      ...   ...        ...    ...     ...         ...\n",
       "846632  0.76  2283  10.897506      2      Pz           2\n",
       "846633  0.77  2283   7.598260      2      Pz           2\n",
       "846634  0.78  2283   9.997711      2      Pz           2\n",
       "846635  0.79  2283  17.296041      2      Pz           2\n",
       "846636  0.80  2283  26.593912      2      Pz           2\n",
       "\n",
       "[846637 rows x 6 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# First, we need to get a long-format dataframe of peak-locked event.\n",
    "df_sync = sw.get_sync_events(center=\"NegPeak\", time_before=0.4, time_after=0.8)\n",
    "df_sync"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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bD2OVKuCHHeeYszGS+5KP89Sf07Ht1w+XTz7G5OTCKz8fIv1iHF8kbCT3/Qn8eWEVzzYfj16j55dTP/PLqZ+4v+4DPBnyVKX7PU/IyGfiT4eITs7h0U51eKpbXXTa0n0eTskuZNq6A4QXLUBvm8Kw5Rl0PZCG+6xZ2PTozr74vbTyDkWn0WE0G++209Hq9NStKDKambL8GPc19aVDfU+0mqu/f8bz50keOJh1nZ0IGPsiXZsMLKPEilL2/jhwic9WneCezCjGr5yOx7TPsX3gAUxmyaQlR9h2Momxe7+lX4ArXl9/jdAU/9FdHvkH3x//lh61ejKhxQuV9u7p/CIjX6w5xcpDsTSq4UT3YB+a13ZFqxHEpuWRmlOIg40eV3srGvo64X7FvR5SSn7dd4ZFkdOxcrxEm30ZDF4RR83HxuH04gsIGxuiMqL4v/0fMbHVyzR2D7ZgSyuUKhq3Iioxmxd/PEhydiE+LjaMaBfA/a1qYmv17yeNoogIUh4aitavJl5r1xCdc4E66s5SpZLZfDyBSUuO0MacxsTv3sRz+lTshjyE2Sz56M8I/g6P44kDv3OhTyKyaSM+vOdTNELD2ug1zDkyq7jvrvVrVWIq1fXH4vl2axQXUnKvu45eK+jfwo9HOgVSw/XfIUrCopNZdXoRR3I3oy8y02dtEt3O2+HzwcektAjko30fkJibyISWL9DNv3tFNMfSVNG4VUaTme2nk/htzwWOXMzAyVbPC72D6HfFAGuF+/djTktjX7A1Xxycyuut36RjzU5lFV9R7sjB6DRe/DGM+lYG3p7zPG6PP4LL+5ORUjJj7Sl+23uREZGb8Xbcy899HBjb5Gn6172fNdGr+frIbEK9W/Nm20noNXpLN+WWpOYUcuxSBgKo6WaHp6M1OQVGUnMKWR0ex6rwWKSECfc2YFi72ledcotJPceklR+Q7pKEfa6JHpuSaV6jNTWfm8isqO84lnKMkQ0fZljQ8Ep3qq6MVZ2iIYTwB34AvCmey2WelPJLIcRk4CkguWTVt6SUN+ylKqs+jaMX0/lhZzQDW/nRKciLS6m5ONnqcbazAqDQWMhby57ggk0uH3X+hCC3hne8T0W5E9HJOYyZvw8Pa3h/wUt4NGmM+8+LETodZrPk01Un0OzfS49N0/jkjQaEeDblvfbv81fUn3wbMZ9Q79a80eYtrLRWlm5KmUvKLGDq6pNsP5VEzxAf3ro/GDvr4rMIZrPkwxURrD97kFoO68mpkXn5fT444+pWk5NpJ+gd0Jdnmz9nqSZUhCpVNHwBXynlISGEI3AQGAgMBXKklFNLu63yGhr9jV8Pczwmk0kDQ2hXzwNTQgJnB9zLp8/VxODpwtSuM/C29y7z/SpKaRQYTDw5by+pOYVM3TgD95RYvNavRevpSXpuEa72VkgpKczN5s2tE0nS5DKz+2x2xu5gwbF5dKjRkZdDX61yRxi3wmyWxTcxbook0NOBaaNaXh5RV0rJ99vOMW/LWZrVMjIkKJu4Fd+zt7aBGH9bnLQONPIMoY1vWxq4NqC2U4BlG1M+qs4lt1LKeCnloZLn2cBJoFLd1z/6nro42Oh48ceDfL7qBAY3TwLmfMez31ygKCeLD/dMpvAas5IpSkWYseYUUUk5vJy4C9dTR3CbNROtpydbTyYyeMZ2jmw/jOnCBXam7iPKGM9zzcZzPjOa744toJ1ve14Nfb1aFwwAjUbwaOc6TH+kFQmZBTw5fy+n4oqPKoQQPNG1Lu8OCiEiRs+CMF9annLig0uhjPk5CefzyYTF7WPm4S+ZsPk5Zh+ehdFstHCLKkalLBpXEkIEAC2AfSWLxgshjgohvhNCXHO8ciHEWCFEmBAiLDk5+Vqr3NSu2J1Epkde9/WGNZxY+HR7RrSvzbIDlxg9bw8X/BoQ/P4MnvjuPIa4WNJzS3fduKKUpQ0R8aw4GMNwLwONf5uH48SXsO7YgbOJ2bz/xzECPexwnfw6qaOfoJtfNya3n0KAcwCfh31KLafaTGz1yl11M1vbuh7MH9MWvU7DuO8OsDvy378ZfZvX5KtHW2Gn12BtLKTgl9/o0nwwH1xoxZcvHuX1b1PRSw3rLqzhhS3jOZcRZcGWVIxKeXrqH0IIB2Ab8JGU8g8hhDeQQnE/xwcUn8J64kbbuJ3TU2ZpZuyGMSTlJdLAtQF9A/tzj1+X616jvT8qlSnLj/FQm1qMvqcOuYt/IvXNN3AZPwGn11+7pX0ryp1IyMjn4a93U9vFmslzn8favyaef64gs8DE4/P2YjCZmWk4hJg7g5xvPyWk5wjyDHm8un1iyXhS0/Gx97F0MywiNbuQl346SFRiDpMfbHLVgIhSSmReHtFvvsexA6foVNsJh3Fjyfrsc2LjTjFtYj2yHHUgoLF7MAPq3E+HGh2rekd51enTABBC6IFVwDop5RfXeD0AWCWlvOFY5bfbp5FnyGPzpU2sPreKmJwYajrU5OFGj173FyEzrwgHGz1ajWDv2RTqbf+boh6t+D5+Oc+1mICLtcstZ1CUW2E2S8YvOsCpuCxmp2/FaclPeK1ZjaZhQ174MYyjFzP4qqsnnqMGsv6le1gRmMZX3Wbz86nF7EvYy/sdPqSZZzNLN8OicgoMvPzTIY5eymB8ryCGtq2FXvfvCZlPVx5nRdglRoct44GMk3iu/hvjiRMkrVnBVI+DRNeyxsGgJUdvop1ve8Y3n4CTtbMFW3RHqk6fhij+q/wtcPLKglHSQf6PQUBEeWWwTs2iX+2+zO4xl0lt30EjtHx64P94dftETqSe+M/6znZWaDWCpKwCXv35EBML6xOv03Eo8SBztnxUXjEV5bJf9pzn0Pl0JtTX4/TLIhzGPY2+cSPOJmZz7GIGL/cJoubU90mq687KOpl0qtmZ/Yn72BO/m9HBT9z1BQPAwUbPl4+E0qG+JzPXn+bBL3fw657zXErNRUrJC70b0qWRN9+HPsTcfs9TZGuPdfv2+E/5lE8f+53W6S7Yp+YycGMWYbF7mbB+HHvidlNZP5zfjkp5pCGE6ATsAI4B5pLFbwEjgOYUn546DzwtpYy/0bZu50hDmkwk3dcHodHgPGUy1u3aYZImtlzczOKTP5JWkEqnmp15uukzOF/jU8TuM8m8s/QoNnoN3cVittVL4JVaT3JPy8G3lENRSisqMZvR3+yhQ113Xpo7EUxGvDdtRNgWXw2UkJGP66UokgYOZObnnYmxzmNc02eZdvBzOtXszCuhr1X1UyllSkrJvqhUFu04x+Hz6QA42OhoVsuVPs18OR2fzeJd0dRyt+dttxTq+zhh06M7Zmkm9cAuNLO/I+LCPn4e4kN8TVuC9H480e4FGpXjZFXloGqdniort1U0pCT/r5VkffgRprg4bPr2wX74cKw7dqBQB8vP/sHSM7/jZuPGO+3eo5ZT7f9sIzoph5d/PkRKVi51fL/GaGdkVt/vcHG9O88XK+VHSsn4RWGcTcxmgTYCzfTPcf95MYWt27N0/0Ue7VTn8imWDeFLmHl+IY8HP8HSyKW42bjx+T3TsNHdfGjxu9XZxGwiLmVwOj6LXWdSSMoqwNXeCk9Ha86n5FIvPYZHti6i9WvP4jB8KACFpkJe3fwivmlmAjae4O9OtmQ56+nn1IHHu7xaVe59UUXjVpnz88mZ+w05c79B5uQg7O2x6dkD24EDudjcl48PfkKBqYBXQ18n1Kf1f96fnlvEa78c5mJGJLaBP9M2wZ43nv1dfaJTytTWk4m88Ws4E9t40nH8EGz79MZtzmzeXnKELScSWfh0O/yPh2HVsQMTdr2Erc4WndBxLjOKL7rOwM/R39JNqDJMZsnesymsOxpHQmYBaTlFJGTkYTSDc14mAzxMPPb8Qzja6lly5nd+PLGI1p6hPHHSiyWRv7G5vSN+OVa81PZ16tdvZ+nm3IwqGrdLFhZSuGs3+WvXUrB6Deb0dISLM/n9u/JlhxwuyBQeDR7N4HoP/qcgFBhMXErNY9+69zBu2Mqg9mNwfu7ZO8qjKP8oMpoZMWsn1joN07fPxnTsKN7btrA50cQ7S4/ydPd6jHLJIbnfABxfeRmefZxfTv7E39GreLHlRLrX6mHpJlR5OQUGVoVd5Mv1kUghsMHEkE71ePyeOmyNXcu8o99Qx6UObwVP5MQvs5nncoQcey29cmvz2MAPcHDysHQTrkcVjbIgDQYKt+8g78+/KNi4kYL8LH58tDYHWzjRIdubCe1fx75e0H/eZzSZef2tRTjZ6Hjr3ZHotXfPdfBK+flnuPPP6hRQ963xOH/8EXmDhjJq9i5qe9jz9eOhZAx+iOSMGPyW/8V5czLv7JpEN//uvNhqoqXjVythZ5N59Yf9GM0Sg1ZPDVdb3ro/GGkXxecHPsXRypGpXaZDfCIL/3qXrf45OORLWic50Er606xBNxzv643QV5qbKlXRKGvSYKBwzx4Ktm1jef5eVoRKGp7O4aUzdXB95lms27T5d10pmbvxDEsif6eGbzrf9f8MK/1dNTa/UsYy84oYPGMHLWo68vKMZ9DW8MXjzxVM/OUI4RfS+eGZ9nhsXkPaiy/xzcw+xDsYMZiKsNPb80XXGdjqbG++E+WWnE/O4eVF+0nMM+JorSM9z8CAFjXp11bLkbR9PNr4sctnI05sXcrvJ34mwrOAIr3AK7GQp/7KpVGPoVi1aI4uMBCtvz/Cch8wVdG4U1n5BjJyi/BxscVK99+rldcdW8rsqO8Jjizg6dlncXtuAo4TX7rqh/7O+oUcyVuC95kmzHlxCnqrKtEhplRCs9af5qfd5/naHI7nd7Px/HslpkbBvLfsGK0C3RgS7EbiPV053q4GX91nxs/Bj8S8RD6/Z5oawr8cZeQW8d6yoxyPjKdr+hnWeDbBwUbHMz3q0695TY6nHcHNxp1aTrUAKDIVERa/j3kHZ5FlzOHBZbF02ZaKALR+fji99Sa29w+wRF+oKhq3y2gys3T/ReZtOUteoQkhwNvJhvb1PenVxIdmtVwvT9K0/vw6ZoV/RZM0e8ZM2Ytj2464fPIxusBAoPiIY8yScSRqk2gcNZD/e+XR/0zwpCg3k5xVwENf7aBrDRvGvvcw9qNG4vLJ/11+XUpJ3o+LSXnnbT6e3YU8rZHMwkyebvoM/er0t2Dyu4PZLIn8/S8c3niJ6GYd+O7esZxIysfVXotT0A/odAY+7PQRgc51Lr8nqzCTGYemE5Z4gC62zRiTFkzhdz9gOHECqzatsXvoIazatkFXt25FFRBVNG7FgXOpXEjOJSYtj/3nUjmXlEO7eh70DPEhISOfc0k57I5MocBgwtPJmt5Na9C3WQ0CPO3ZcGE9c8JnUc/szth3d2OfUYB1t244Pj0W604dicuO5dkN49AnevFth5dwanbDm9oV5T8+X3WCFQdjmHv8JzxOhuO9fSvfHEyhWW0XOtT3BIoLx9Kdc/gxdTU6jZ6WXi2Y1PZddfVeBSrYvoN3v93OCe96jO5Sj32Zgj0XzuBSdzlWVmbeazeFZj6NLq8vpeTX07/wy6mfCHFvwuuhr6P7Yw1ZX0zHnJAAgMbLC5se3bHp1RObLl0QNuV2ubQqGrdixKydRCfnYqPXEuBpz+h76tCloddV/+Hyi4zsPJ3M2qPx7D2bgskscbTREeDpgJfvBU4Yf6KGjTevnQzEatFSzElJOIx/DqfXXuXXo4v45cIynl9ppN2M5Tg42pVls5VqLC49n6Ezd9DHuYjRnz2Dy7TPOdmmF+MXhfFY5zqM614Xw+FwjE0b8uS60ei1VuQb85jT4xs87TwtHf+uE7H9EK+uiqJQq+eTPgH4NWnAzM37OWr8Bo2ugMH+43m8zdVXsW2L2cpXh2bgYevJO+3epaaDH8Zz0RTt20fh9u0UbN2GzM5G4+aG3bCh2A0biq5evbL+QKCKxq2ITsrB0VaPu4NVqX4QqTmFbD+ZRGRiNueScoiIyUDYXsSt7t942Hoxo9PHGD+cSt7ixVjf0xmHmdNZuHcmjSf/zWf9P2B036Y81KbW7TRRuct8tuoEKw/F8PXqj/HycMJx2TIembsHECx+tgOmJb+T8epreCz5ndWe8Xx//FseazyaBxsMsXT0u1ZMRCQv/XKERGsn3n+oGd0ae7P/fDT/F/Y+BpFBO/0kXunTEhv9v/2fp9JO8tG+DzGaDLzW5g1aeLW8/Jo0GCjctYvcn36mYN16MJnQuLqib9EC23t7YXv/ADTOdzzmlSoaFSkjt4g1R+JYcmwHBs/fsZO1+KTLR3htWEvGW5PQenjgNvdrcrdt5wOH5uyNK+ST4S24p6FXhWdVqo6M3CIe+GIbXY0JjP12Ep5/r2RBki0/7Ixm1mOhtPTQk9ixM/khdanxw888v+U5dBodX3afVe3nx6jsMvOKeOXnw0RcyuCzhmY6j+hDVkEO07dsY81eHU62GtrUc6NTA186N/DC3kZHUl4SH+6dwsWsCzzUYCjDgoaj1179czQlJFCwaTNFhw9TtG8/xnPnwNoa2/vuxfmjD9G6ud1uZFU0LMFgNPP5tmXszV5IYUZ9Ojg/wQhPA06vPo8pLo7U98czs8YJCi4NIDbBgW+fakegl4PF8iqV23dbo5i35Swz/phMUJ8upEycxOhv9tCnWQ3eHhhC5v99Qtbs2Uyb15siay0x2Zf4oONHNPNsbunoClBQZGLu218z8LfpeM2Yit2gQUDxXO5fh39NQsE50qL6oJfOdG7oRZdGXgR6W7Hy4kK2XNpMTQd/nmj8LI3dGiMEJGUVcCktDyieF0SvFRgiIsj7fQmFu3bhtX4dQnfbl/aromFJPx77lSVRP5If35mcxBaMbOXL8OVfkrZzE1M+boZrmpmouKexd3Xm+7HtcLRVnwqVqxUaTAycvp26CVG8tW4G3ju2cyxb8NW600x/uCX26ckk3tOFvU91YlHDZASC7rV68kLLFy0dXbmCOS+P1EdHc/b0Jf4Y/TZvP9MLZzsrdsft4stD0xFSSx2GcvCEK5l5BgBsrbQIu2hsa25Co88mP6UJufEdkCbry9t1ttPTp2kNWga64edmRw0XG2ys7uhesGsWDXV3WQV5OGQYsfnn2Ct20bpGCD+FxRN333hecPfkwcV/s3B0Le49NIs/xEvsikymd9Malo6sVDJrj8aTnltE/+2/4fTaa2jd3GjuBt8+1RYhBBkfziHfWsMfIfnYSBtstDY8GfKkpWMr/0NjZ4f7j4vYOWEKe9IlD0/dwMejO9ChVkdqOwXw6f7/IyLrO4b1HUxb14FEJuRxLikHvc4fO5uOnC5Yw2mxBTevC3T1GsU9/p3Izjew6nAsSw9c5Ne9FwAQAta/3r3MP4CqI40KlGfIY+LWF8g3FtDF8VXmb4wnxM+Zd8/8xZceu4itYcPLf7vT6qfFCE2lnOpEsRCzWTJy1g4056KYfmoJjstXMHNjJI90qkMN1+I7u805OSzc+QUrCopnRn6zzSTa1+hgydjKDcj8fPa9/B4f2zQl1cmTF3o3ZGi72hSaCvkuYgFrolcztMEwHm786H/eezYjklmHZ3IuM4p7/LowtsnTOFk7k1tg5EJqLpdSc0nIKOCxe+pcY8+lpk5PVQbRmdG8um0iDVyD6OY6nsnLThDi58yY1D/5sF443bekMLrOKDZ1GoyPi83la+6Vu9uWE4m8+Vs4L26ex6Dpb/FjtjMLtkYx94k2NKvlgszMJNWqiKc3PoXJbKJ9jQ680eYtS8dWbkKazaRs2MqnqS7sPJ3MyHb+PN+neM6NvXF7CPYIwdHKkeS8ZDxsPa66ktNoNrL0zBJ+P/0rGqGhvmsDGro1oqlnUxq7B2Ottb7ebkur6szcV50FOgfyXIsJRKQe47x5NVMeasqxSxks8hrEINGGRqdzSf9iBsu2nmLysqPEpedbOrJiYVJKFm4+jU9WEj1b1SKzQTCLd52nR7A3zWu7Urh5Cwlt22N35hJ+Dn7oNXrGNBlr6dhKKQiNBs/7uvPp8BY86F6E9w9zMcbGAtCuRnscrRzJKsxk4rYXmbznXeJyYi+/V6fRMbzhCL7o+iX3BfTBYC5ixdk/eG/3O4z8exjv7JpEVmFmmWdWRcMCuvl35/66D7Dy3J9oHE/wzqAmHDyfxs6U3jR4eQ5WDrZMXP4pZqOZt5ccwWgy33yjSrW192wKp5PzGXRkDS4vPM/XGyMxS8lzvRogjUYyP/oIPDw442nifNZ5hgYNx8O20g63rVyDViOY0NyFzoc3ktz/fmb9uI3tp5IAcLByZHjQCE6nnWLC5uf45dTPGEyGy+8NcA7gqaZjmdplOr/0+5332r9P38B+GMwGHKwcyzyrKhoWMjr4CZp4NGF2+EwaBhTy0ZBmnEvOYezRLD77vA+ZXtmM272YE7GZzN9y1tJxFQtauPUs7nkZ3FfHgVM6V9YciWNE+9rUcLUj7/clGE6fYe5rIUw7Mh0fe18G1VPTCldFNvd0xvPP5RQ5OLJjfySv/XKYt38PJ6/QRL86/ZnT8xva+bbnl1M/8cKW8ZxOO/XfbehsaOUdypNNnuKTzp+hEWX/J14VDQvRaXS82voNHKwc+OzA/9GugRM/jOuAj6MLkblxzB1Wl+DTe+l5MYwfdkRzMDrV0pEVCzh8Po0jMVk8cGQNrk+P5WxiNr4utjzauQ7m3FyyPp/KmQdaEi4vklWUxZiQp/5z85dSdeiDgvD/+y++KjzIiLDlbI5IYPQ3ezgdn4WbjRuvtn6d99q/j8FswCRNFsmoioYFuVi78Eroa8TnxDPnyCxqutqyYExHWjs/RIFtJq8+O5x2p3fTPj4Cl8wUS8dVLODnXdE4FeXSxyYb69ahDAz157cJnbC31pHz9VyMyUks7euKQNDUoxmtfdrcfKNKpaZxcsLrmzk8+VA7ZvbypdBo5qkF+9gfVfzBsZV3KF/3nEdj92CklHx+4FN2xu6goi5qUkXDwkI8mjCy0Si2x2xj3fm1WOk0vHffYOo5hqCpfZTPeo2m25ld2I95hMLY+Ar7xVAsLyYtj51nkul1fAuOY55gyb4LGIzmy3O52Pbpw5EpDxNjTEYiebLJGDWCbTUhhMDh8dG06tyMRY+3pFvWOerFnQGKx7nTaYpvscsuyiI2J5bPDnzCa9tf4XhKRLlnq3JFQwjRWwhxWghxVgjxhqXzlIWHGgylhVdL5h/7hujMcwgheLH1s2h0Rjy8tvFp5yfZ6FyP0V9tZdWhGEvHVSrI0n0X0UhJv/RTrPVozLTVpzgeW3w1jJQSc1Adfve9BEAP/55Xzc2gVB9O2ek8s+sH8ocPJfqdDxj+1U7eXXqE5KwCnKydmdZlOuObP09yfjJv7nydzw58QlpBWrnlqVJFQwihBWYDfYDGwAghRGPLprpzGqHhpVYv42jlyGcHPiHPkEctp9oMrD+Ifp7OBKZcYE7HRygoKOKLlRFcSs21dGSlnOUWGll58BLtow7gOag/P+46T8sAV5rXdqUo4jjJffuRc/Y0eo0evUZ/zRvAlOpBV7s2Xhs3YP/E48hF39M3YgNbIuIZNnMnfxy4hEZouDfgPr7pOY+RDUexL34vO2K2lVueKlU0gDbAWSnlOSllEfAr8ICFM5WJK/s3vj4yGyklo4Of4ImHPuDzhtA20JV4Fx8MRhOTf9yLyaxOU1Vnq8NjyTWY6XdyMxsadyM1p4gx3eoBkP3FFxgvXCTXSU9yfhKD6g3G3dbdwomV8qSxs8PlgynUXPILo9KPMv23STSyNfHZqhO8tPgQSVkFWOtsGN5wJLN7fE3/OveXX5Zy23L5qAlcuuLrmJJlVxFCjBVChAkhwpKTkyss3J0K8WjC8IYj2Razlb/PrQSgyFzE9t5ujHvAjUda+WDU6jmebuT3HZEWTquUF7NZsmTvRRqkXaRB03osPpZKq0A3Wga4YThxkoJ161n/Sje+OfcjVhorBtStFp+blFKwbtsWz79XETLrc2a+0JNX+jbiaGQCF+ctxJSWhsks8bH3RavR3nxjt6mqFY1SkVLOk1KGSilDPT2r1jAcQ4OG0danHQsi5nMkORyTNPNX1J8s3DODwVOe4rkaRQDM3hRFUlaBhdMq5WH76SQupuXRL3wNB3sOJS2niDFd6wKQ/dVXxNdxZblbNBEpx+hZuxfO1nc82Y5ShQiNBpse3dFotTzYqgbfX1iB7/SPiG/djscmL+PjxXuILRkuvTxUtaIRC/hf8bVfybJq45/+DT8Hfz7b/wlZRZkMDRrOccMFInwM9Nn9B63JwCQhesMOS8dVypiUkoXbz+FryKZj3iX6D+vGvCfb0CLADUNkJPmr/uavsc0uf5IcWG+QhRMrliR0Omovmo/X5o1oBw6kzpnDrD6VypAZ21hzJK5c9lnVisYBoL4QIlAIYQUMB/6ycKYyZ6e3Y1K7d5BIpoVN5b7avfGy8+avxxqSv2kj74Y6412YxZR9aZyNrFY18663PyqVU3FZDNz7B8YhI9Ho9TSt5QqAKT6BqHvqE+6YhkDQqWZnfOx9LZxYqQz0QUH4TvuM9756gR/cLjDINpMWtV3LZV9VqmhIKY3AeGAdcBL4XUp53LKpyoevvS9PNnmK0+mn2BG7nVGNHuaCPovDfRpgnvYZU/rWI8PKgUd/PMa5pGxLx1XKyMId5/AQBlpeOsaTxhB+2HHu8mvWnTvx59im2OnsMJgNDKqvhgtRrqb18qLOxOd49c3h+LjYlss+qlTRAJBSrpZSNpBS1pVSfmTpPOWpm393Grk1ZuHx72jl1YoAp0CiB7TEGBlJ3YRInjWexQyMm7+XIqNlhhRQys6Ri+kcPp/O/REbWNPncfKMZjoFFc8Zn7dkKSkXT5FryMEszYR6h1LPpb6FEyt3oypXNO4mGqFhXLNnyCnK4edTP/Fxp094fsBn2D38MLq69Rj+8kjuOR9GVpGZ5xaGqbvFq7ifd5/HSQetIrazyq0xvZvWoI6XA4azUaRPfBmrH5Zxj18XCkwFjGz0sKXjKncpVTQquUDnOvSr05810au5kHUeIQQXXxmObBGC1tOTt1s64Z2VxLFLGSxQo+FWWQkZ+ew4lcS9qSdY36ovRgRPdCm+wzv7y6842cSV+IfvZWXUX7Tzba+OMhSLUUWjChjV6BF87H2ZGvYZx1MieGfXW6zYu4DUMU9hN2IYkw/8iEaa+XbbOXaeTrJ0XOU2LA+LQUpou2UZ6+p1pFcTX/zd7YuPMlb/xaLHA5h65mtyjbmMaDjK0nGVu5gqGlWAnd6O11q/QWZRJksjl9DGpy0r0raQum0DuQu+Jei1Cby6YQ51dUW8u/Qo55JyLB1ZuQVFRjN/HYqhnS4b78wkRrTwZnTnkqOM6dPZ0tOLTL2BzKJMOtboRKBzoIUTK3czVTSqiLoudRkT8hQHE8Pwta9BvrmQzeM7kfPtd+ibN6NzPTfeXDIFvUbyzpJwCg2qY7yq2HwigfTcIu7dtQy3Dq15emArAr0cMCUmkrxlLevv9cbPwY9CUyHDgkZYOq5yl1NFowrpE9iPDjU68ve5lYR6t2ZjQA4ZzjqyP/0Ml48/RGM2Y8grJCopl683qWFGqopl+y/hZwOXTNb80e3hyxc0aL292fbN0xRqzaQVpNHGpy0BzgGWDavc9VTRqEKEEDzbfDxO1k7E5sRiFpJtE+4hf8WfmFNS8B//FE0vHEEL/LrnAvvOqombKrvIhGyOXcqge9wRfgsdyGlbT4QQmNLSyS3MZkvmAeq51CfPmMeQBkMtHVdRVNGoapysnJjQ4gXicmNp49OW0Q9+jK5ePUwxsTiMGcOY+H3oTAbsrLR8+GcE+UVGS0dWbmDloRj0GkFGTAK5VrY83aMBABmvvELekEeY1mU6SXmJNPNsRpBbQwunVRRVNKqkVt6h9Ansy5643ZwzxGO3bgW2A/ojdDrqvvQMDx5aSV6RieSsQhbvPG/puMp1FBnNrD0aT2syWN+oC10DnWjg60TRkSMk7N2M6N6Z8OTDZBZlMqTBMEvHVRRAFY0q6/HgJ/G292Hqgc94YsMTHL+wn5x587G+714e0ifjm5OCnZWWH3edIzEz39JxlWvYfiqJrHwDXLpAoc6ap/s2ASBr6jR+fDyQKSFRLDmzhIZujWji0dTCaRWlmCoaVZSNzobnW7xIWmEaIFl4aC4Z70+hYOVK3N96nee2LOA1m0uAYM5G1SleGa06HIOXXmIwmOjpJQj0cqDwQBhHYvZzor4tNZ38SS1IYVjQcDX3t1JpqKJRhYV4hDCgzgMUmgo5LRI5fV9jsj6filXLlrRoGkDTBVMZ3tKHdUfjiYjJsHRc5QoJGfnsi0qlR+Ix3opYxttjugGQ9fnn/PmgP+7WbpxNj6SeS31aerWycFpF+ZcqGlXco40fxcfOF43Q8NfAGhjPXyDv199wfOUVcgoM7As7i61ew7dboywdVbnC6vA4pASvI/uwGz4caxsrpJScGN6O835WtPBuRWJ+IkODhqmjDKVSUUWjirPW2TC+xQTM0ky0OYkTg1qQNeNL9PXq4tazK5rEBASwJzKFU3GZlo6rlNh0PAEviviy21Ok9yueSMmMmd+dz1DToSYnUk8Q4BRIW592Fk6qKFe7adEQQtgJId4RQswv+bq+EKJ/+UdTSqupZzO6+HVDILAfNAhzQgIFW7fiPPElRuxbQp7BjLVOw/fbz918Y0q5u5SaS1RSDhkmQYfciwQ0rkPBtm1kvPgyo2sPo41PO+JyYxkeNEIdZSiVTmmONL4HCoH2JV/HAh+WWyLltoxp8hQOegdWWp3EY/MGbHv3Rt+oEaGhQTSPPwlItp1MIipRTdhkadtPJQNQpNXzWDt/pJRkfv45hj17aebXml1xO6nrXJf2NTpYOKmi/FdpikZdKeVngAFASpkHqI8/lYyztTOjQ57gROpxXo+bg8lswnjxIo4TX2TUvqUUGiU6jWChOtqwuC0nEtBIM60ST9H0gR4UbtrMRscY5r7elDUxG0jKS+Thxo+qowylUipN0SgSQtgCEkAIUZfiIw+lkulZqxd1nOtwLjOKX78ZT1Kfvmh9fWncJpg+kTtpVcuJTccTiE3Ls3TUu1ZaTiERMZmYhYZHa0rQ6UieMY01/XzJ93Dij8ilNHYPVldMKZVWaYrGe8BawF8I8ROwCXitXFMpt0UIweT2U9AILct8Y8ktyCZn/gIcX5jAmG0LeTHzMBqN4Jc9Fywd9a6183TxqalH9i2h5bC+FKxZywaPBLIcNNRyrk16YTqPNFJHGUrlddOiIaXcAAwGRgO/AKFSyq3lG0u5XS42rjxUfwhGTMx/syU58+ajrVET6+7dKFr8I/4utvx1KIbMvCJLR70rbT2ZiFdBJg9p4tE3aEDWxbOs7+1DiFsIWy5toZ1ve4I9QiwdU1Gu67pFQwjR8p8HUBuIB+KAWiXLlEpqRKOROOgdOeVRRLSbiZy5c3GcMJ7CzBwupeZSZDSzbP8lS8e866RmF7I7MgWflBjshw4BYEtXd/JsBIXmInRCy9NNx1k4paLc2I2ONKaVPGYD+4B5wPyS57PLK5AQ4nMhxCkhxFEhxHIhhEvJ8gAhRL4QIrzkMbe8MlR1WqHlseDRCATLx4aQ++136AIC8G3agH5RuwD4de8FNVFTBZu98QwAobER2PTpS+6vv6KXWhq5NSIy4wyPBo/G3dbDwikV5cauWzSklN2klN0oPsJoKaUMlVK2AlpQfNltedkAhEgpmwJngDeveC1KStm85KE+kt1Az9q9eLTxaM64FnLpvTFonJxwnDCegbuXYi0kWfkG1hyJs3TMu0ahwcSmiAQ0ZhMP+OspWLOGjJdfpUuiCzHZMTRya0zvgD6WjqkoN1WajvAgKeWxf76QUkYAjcorkJRyvZTyn0kg9gJ+5bWv6kwrtAyoez+u1m78WPM8WFtj3aUL7vUDGBi1A0B1iFegPw/GUGg0Exx/Gsf7B5DwzUw2PtmSP90vkWPI4Zlmz6ERaoAGpfIrzW/pUSHEAiFE15LHfOBoeQcr8QSw5oqvA4UQh4UQ24QQna/3JiHEWCFEmBAiLDk5ufxTVlJ6jR6tRsPF7Itsmvkyme+8i+P45+i/43dsNZILKbmcjs+ydMxqz2A0X74bv2fiUYznzrG+KSxraWRN9N90qtlZTeOqVBmlKRqPA8eBF0oeJ0qW3TYhxEYhRMQ1Hg9csc4kwAj8VLIoHqglpWwBTAR+FkI4XWv7Usp5JafTQj09Pe8kapUmhODRRo8B8JN3NNk//oiucSOc/GvwZdh3WGk1/HUwxsIpq7/sAgOOOtCYTXRqVY+EHxewtacPNR38MJgNDG84wtIRFaXUSnPJbYGUcrqUclDJY7qUsuBOdiql7CmlDLnG408AIcRooD8wSkopS95TKKVMLXl+EIgCGtxJjrtBZ/8uuFq7kWpdxOGWrmTP+BKH557B/dAeOrtJ/g6PJbdATQlbntwcrBHZWcWnpuytWdfWhkI9JOUlco9fF/wda1k6oqKUWmkGLIwWQpz730d5BRJC9Kb45sH7S4Ys+We5pxBCW/K8DlAfUGNi3IRWaHm48SMA/DEigNwVK9A3bYrWzw+/gzsoMJj5ct0pC6esvrafSmLZvvNcMNvQTp+DYfzjbLu3Bn4OfpjMJoY3HGnpiIpyS0pzeioUaF3y6Ax8BSwux0yzAEdgw/9cWnsPxf0r4cBSYJyUMq0cc1Qb3fy742zlTJpVEYfaepIzew5Or7xM//UL0QvJmiNxFKjLb8uc0WRm+ppTLNp0GoB72tRjb/weTJiJz42nW63u1HSoaeGUinJrSnN6KvWKR6yUcgbQr7wCSSnrSSn9//fSWinlMillcMmyllLKleWVobrRaXS80vp1ajjU4O8hgZi0GmwGPoBVo4Z0v3QQg0kyf/NZS8esdlaHxxGfkY9dTgb+mQnYffAOXban0M63PQgY0XCUpSMqyi0rzempllc8QoUQ4wBdBWRTylAzz2aMDn6CBOt8wl7qjUavx/mNNxi67WdAsmT/RbLzDZaOWW0YTcVXTNX3sOWC1on22RdIcNeS0qExe+J20yegH152XpaOqSi3rDR//Kdd8dwIRANDyyeOUp687Xyw0ljxw/GFtDmSh2NwM7yaNaJJUhTHvOqxPOwSj3auY+mY1cK6Y/HEZ+TzgHUakRo36p3fxAdv1aN2zA9Yaa0YEqT+CylVU2mKxpNSyqs6nIUQgeWUp0IYDAZiYmIoKLiji8AqLRsbG/z8/NDr9Vct97bzRqvRkmvMZcnGaQxd2xznSZMY8MwkjvV+AV9XWwslrn7WH42nrpcDF49H4m0H4R1M6DR6orOiGR40AhdrF0tHVJTbUpqisRT43wEKlwJVdsD/mJgYHB0dCQgIqHZDUEspSU1NJSYmhsDAq2u7nd6OAXUe4Pczv7Khuzud315LgwkTaN8qELfcdP7ee462dT1wstVfZ+tKaU0d2ZJTqzbxTHwgXeP+Jux+Z2rae5NekMb9dQdaOp6i3LYbjXLbUAjxIOAshBh8xWM0YFNhCctBQUEB7u7u1a5gQPENfe7u7tc9ihpQ9370GiuMGvjzoVpkTZ2K65tv0D1qD3svZnP/tK0kZORXcOrqJT23CL1Ow6kNuzBqdeR3zcVWY0NsTgz96vTHwcrB0hEV5bbdqCM8iOIb7FyAAVc8WgJPlXuyclYdC8Y/btQ2Z2tn7q19LwB7WzpwPHIX5pQUBoTWAiEoMpr5bltURUWtdiJiMhgwbSu79p5mR64NHvbniXRKwdexJtZaa3WUoVR5Nxrl9k8p5eNAfynl41c8npdS7q7AjEoZG1T/Qfwda+Gsd+K3kbXI/ukn6k94ipCks9gaCvn7cByXUnMtHbNKWrwzGlu9lpo71hJeM5h2RluGNBhGdOY5egf0wdna2dIRFeWO3Oj01D9Tuo4UQnz1v48KyldpJCQkMHz4cOrWrUurVq3o27cv8+bNo3///hbL1LVrV8LCwm75fV52XszsPpuxzZ7hUg0r9j3TBY2TE/cFuZGrs0YjJAu2qqONWxWVmM3Wk0k82NqfsM0HKdLp6CpyyC7KQqvRMrDeYEtHVJQ7dqPTUydL/g0DDl7jcdeQUjJo0CC6du1KVFQUBw8e5P/+7/9ITEy0dLTbJoQgwCkAf0d/Fp9aTMbZE9z3WD/0JgO1s5NYfzSe88k5lo5ZpXy//Rx2VloGGS6yzScIzwaLudjblg0X1nNv7ftwt3W3dERFuWM3Oj21suTfRdd6VFxEy9uyZQt6vZ5x4/6d96lZs2Z07tyZnJwcHnroIRo2bMioUaMoGV+RKVOm0Lp1a0JCQhg7duzl5V27duX111+nTZs2NGjQgB07iue2WLhwIYMHD6Z3797Ur1+f11577fK+1q9fT/v27WnZsiVDhgwhJ6ds/pgvOfM7ibmJ5Bhy+OmL0ei2b6G9iyRBY8sAL4mdlbqHs7Sik3PYdDyBIW1rkT9/ASdagrDP4FjOKfQaPcOChls6oqKUiRudnlophPjreo+KDGlpERERtGp17SuMDx8+zIwZMzhx4gTnzp1j167i6VTHjx/PgQMHiIiIID8/n1WrVl1+j9FoZP/+/cyYMYP333//8vLw8HB+++03jh07xm+//calS5dISUnhww8/ZOPGjRw6dIjQ0FC++OKLMmnXkKChGMwG/B382dLNg4uzPqdvz2bk2DrSbNMyPB3UpbelZWel5YGWfgwJtGFzjhlb30P42dbheGoED9QbiKuNm6UjKkqZuNFHyakVlqIKa9OmDX5+xZMLNm/enPPnz9OpUye2bNnCZ599Rl5eHmlpaQQHBzNgwAAABg8uPrfdqlUrzp8/f3lbPXr0wNm5uKO0cePGXLhwgYyMDE6cOEHHjh0BKCoqon379mWS3d+xFh1rdiIs4QBGvYaVTY2MCduIk7YGW2z9KViwkrzgZjzSqUrfy1khvJ1teeP+YDI//YyNPT3Q6C5gY63FyeTEoHoPWjqeopSZ6xYNKeW2f54LIayAhoAETkspiyogW6URHBzM0qVLr/matbX15edarRaj0UhBQQHPPvssYWFh+Pv7M3ny5Kvum/jnPf+sf6NtSSnp1asXv/zyS1k3C4BhQcPZFbuTOs512dVR0mvW13SfsIDVRS3RhJ1kf7I9/VvUxNXeqlz2Xx3M33KWWu523Bviw+mt68l+yh53TT3OZkQypslY7PR2lo6oKGWmNAMW9qN4wqOvKB62/KwQok95B6tMunfvTmFhIfPmzbu87OjRo5f7I/7XPwXCw8ODnJyc6xac0mjXrh27du3i7NniUWhzc3M5c+bMbW/vf9V2CqBTzc4k5SWi0+hY0s2GLpcOUqTVUzP2LIUGE7/vVXOJX098Rj4Lt5/jeEwmOV/PZb+1J4WZAVhZF+Jp60mfgL6WjqgoZao082lMA7pJKbtKKbsA3YDp5RurchFCsHz5cjZu3EjdunUJDg7mzTffxMfH55rru7i48NRTTxESEsJ9991H69atb3vfnp6eLFy4kBEjRtC0aVPat2/PqVNlO2nS6ODH+ar7bEYFP8axJk4Yu3ni72bHscbtaRsbwdJ9F9TsftexeGc0QsCo0Bpkz/iSQ14dcDIGk5B/iZGNHkavVf1CSvUi/rmq57orCHFAStn6iq8FsP/KZZVZaGio/N97GU6ePEmjRo0slKhi3E4b8w35vLb9ZbINOXSweY3vtsTy+rqZfHrfBMb3asDDqm/jKinZhQyesZ0+zWrw7LkNfJWwnA3uI/AL2oervQ1fdZ+FtniySUWpiq45tERpjjTChBCrhRCjhRCPASuBA/+MRVWmERWLKTAWMH7zMwQ4B5JekErq7rcRQEzbrjRLPMP+yCRLR6x0ft59HqPJzKgmbuzZtJAD7Rywdj9NjjmJRxo9qgqGUi2VpmjYAIlAF6ArkAzYUjwOleVuh1bKlI3OhhCPpuyJ201Xh5bsbVhEY4fzbKnRjBfXz+YjefLmG7mLmM2S8Avp9Grii92ir1na3wOR54CtcwwNXINo69vO0hEVpVzc9O6tkvGnlLvAyIaj2BGzDY2bG46pWsyOfxMX/yyJ7brhOm8eyX0GUsvLCZ22NJ81qjeNRjB/TFvyMnNYsWEzSc2dMeQ4YK1N4bnmE6r1gJjK3a00V08FCiG+EEL8cbfe3He38Lb35r6A3my5tJkB/n1J9THh6HaCHZ0GcqJQz8i5+9hysuoOnVJW4jPyiU7OQasRGG2NrOrpin2aFXqHBB6sN5JAZ9X3o1RfpfnIuAI4D8yk+Eqqfx7lQggxWQgRK4QIL3n0veK1N4UQZ4UQp4UQ95VXhrvZ0KBh6DV6LuqzqJtlg4P3dnZmG2ngrKNmfhqLd0Zzs4snqruvN57hyfl7yTx5hoSLJ7DLLiLX2Yy1sTYjgx+ydDxFKVelGVyoQEpZ0aPaTpdSXnVHuhCiMTAcCAZqABuFEA2klKYKzlatudq48WTIU3jaeWLv3InXjn+MyXU35x99lvsXL+Nr28cIi06jdZ27c/C9M/FZrD+WwGOdAih86UWcUlOwHeGK2UnwgP9Y1fmtVHulOdL4UgjxnhCivRCi5T+Pck/2Xw8Av0opC6WU0cBZoI0FcpSJaw21XpY37d2J3oF9aOUdSsMGHelVqw+2nuGsdbOia+ppXE35LN4ZbemIFvPttigcbHQMjD3A0tpJrOnqTEwdHfnx93B/02BLx1OUcleaotGE4pn6PuHfU1PlPS7VeCHEUSHEd0II15JlNYFLV6wTU7KsyqkKQ61nFWby/p73aOoVgpXZmSMFv2A9bBB9D69hX1QqZ+KzLB2xwp1NzGbbySSGNPVix5ZZrO3jzd9tdJiz/Gnt2U3Nra7cFUpzemoIUKcsx5sSQmwErnU79STga+ADise5+oDiIvXELW5/LDAWoFatWjdcN+PdyRhOHL+Vzd+UvnEwLlMmX/f16w21/u677zJhwgQAkpOTuffee/n+++/LNFtp2esdSM5L5qdTi+me0pJ13lv4tk4OQ77dhrltBzwcrW++kWrmx53R2FlpuWfXYqbc64yNUYPJBGkXe9L3oSr5+UVRbllpjjQiKJ4nvMxIKXtKKUOu8fhTSpkopTRJKc3AfP49BRUL+F+xGb+SZdfa/jwpZaiUMtTT07Mso5eJ6w21PmXKFMLDw9m6dStubm6MHz/eAumKaTVangh5koTceNw7+lGY1Jh91sfI6d+aoUu+wFV393WGj+/VgPd7+POnaQe5djoKNCY8z7fGUe9G+3oelo6nKBWiNEcaLsApIcQBoLBkmZRSPlAegYQQvlLK+JIvB1FctAD+An4WQnxBcUd4fWD/ne7vRkcEliCl5OGHH2bixInXncOjorT0bkWodyjLzy+lXtFgLhrPsqyViWeXpvHronXE+Tfg5b7VeziWf5jMEk8nG5LqGdjd3hUkjFycyJxm7RnQyge9Tt27otwdSvOb/h7Ff7w/Br4ADgD1yjHTZ0KIY0KIoxQPjvgSgJTyOPA7cAJYCzxXVa+cCg4O5uDBa8+YO3nyZPz8/Hj88cpxT+VTTcdhNBuxanCanKQ2HHPKJPKeesQcPsnS/ReJvgumhI1MyOLBGdvZs24nsw7PBOCRVZmY3XpTZIY+zWpYOKGiVJybFo2SeTWyKB4yZCHQHZhbXoGklI9IKZtIKZtKKe+/4qgDKeVHUsq6UsogKeWa8spQ3q431PoHH3zAxo0b+eqrir7C+fp87X0Z2XAULWs2wJjaBKtcPcv7utN3wyJstILvt0VZOmK5klIy9e+T5Ofn89eBt4nNjWFMUVs6rL3A9uCu+LvbEeznbOmYilJhrnt6SgjRABhR8kgBfqN4VNxuFZSt2vpnqPUXX3yRTz/9FBsbGwICAsjLyyM2NpY2bYq7ce6//36mTJli4bTwYIMhABw9cohTWT2I9llLVLCO+0Uiv0dIHu9Sl0BPBwunLB/rjsVz5GI6LTy/57ifPU/WGUWbZ74mNbQD4Rlmnurmq4YMUe4qN+rTOAXsAPpLKc8CCCFeqpBUd4EaNWrw+++/WzpGqUkpcap5gNz0TOpZ+/PrCMmEHxfyV4c3+X7bOaY81NTSEctcboGRmetO419jF7HeOWjR0DzKiOn8efY9/hZcgt5N1akp5e5yo9NTg4F4YIsQYr4QogfXGV9dqf6EEOis07D33Y9rZANM0sQPfSX9a5pIzi7AaDJbOmKZW7jjHDm6CAq9DwEwqv5IrD/5Gl1wMBsLHGni70JNNzWVq3J3uW7RkFKukFIOp3hu8C3Ai4CXEOJrIcS9FZRPqUTGNX8ardBzwuMMTyzPIa6GDVnGb5n1WKtqOfKtf40sXGqvQ2OSBNnXpdeODEwxMSS/+CbnknPp3dTX0hEVpcKVpiM8V0r5s5RyAMX3RhwGXi/3ZEql42rjRhfPIQi7S5x/aBAP/hHPYc9ctkSt5ejFdGLT8iwdscxkFmay/NJX6HQ69DorXmj8LLlfzca6e3c2a33QaQU9Q6493a+iVGe39PFQSplecuNcj/IKpFRuT4UOxphbg7XWe2md70PtC3ksPvI9E37cy/wtZy0dr0yciM3gqT/fIqMgg1qOtXiy6dM4zPsVmZ2Nw1tvsv5YPB3qe+JsZ2XpqIpS4arfOQWlXDnYWNFINxxDTk2c3nuPgSviSdPl07rZRdYdi+d0FR+TSkrJR9vnUGB1nof+zODzNh/T06Y5uT/8iN3wYRy18iQlu1CdmlLuWqpoKLesb0gwKZF9SXCsT+jQF2h8IptY43qc7E3MXH+6Ss+3MX33r2Tb7sImW9B88BPo7B3I/fY7MBpxHP8c64/FY2+to0ODyjc8jaJUBFU0LESr1dK8efPLj/Pnz1s6Uql1qO+BjV7LimOHeLv+UboeMZBLAc2bnCDsXBp7zqZYOuJtWRO1nq3JixEFNhQ6SGgfijkzk9wfF2M7oD/mmv5sPZlIl0Ze2OjVvBnK3ak0Y08p5cDW1pbw8HBLx7gttlY6OgV5Enb2Ai5Bqazv70+HXRfZ3XELNfztWbzTlQ71q9Yn8X3xe/n66FcYC1zQ2WYwot4wgj1CyJ41G5mTg8Ozz7D3bAo5BUZ6qQ5w5S521xeN6WtOciYhu0y32cDHkZf63PpAfmPGjCEsLAyA2NhYxo8fz3vvvVem2cpKj2AfNkYkMMTnEVYY5tIozUiD0zmcDVrNI6Ghlo53S2JzYvni4FQcpSs51um0sG/IsOCHkQUF5Cz4Fut7OmMVEsL6JUdwsdPftbMWKgqo01MWk5+ff/nU1KBBgwBYsGAB4eHh/Pnnn3h4eDB69GjLhryB9vU9sLPSkhATSIcaHVnTz4f7/4qnRkIh0w9/wpGE0+QXGS0d86YKjQV8sv9jtEKH1taMt70Xr3SZjEZoyFuyFHNyMg7PPkteoZEdp5PoHuxTLe9JUZTSuuuPNG7niKAsXO/0VEFBAUOGDGHmzJnUrl274oOVko1eS6cgL7adSuLnXs9wPOkov4z055nZZ5n6VhMm7XiX+9xeY0IPyw7vfiNSSuYcmcOFrPM0PdWWHg+1I8CzHg5WjsiCArK//Ap9ixZYd+rI+mPxFBrM6tSUctdTH5kqmXHjxjF48GB69uxp6Sg31TPEh8w8A5FxJia2eZ0HUwNxz5JM+CUNna6QtYmziUnPtHTM6/rj7DK2XNpEUVJDUi+40tk1lEDnOgDkLv4JU3w8Tq+/hhCCDREJeDnZ0KyW6022qijVmyoalcjs2bPJzs7mjTfesHSUUmlb1x07ay2bjifQwqsFXUa8jjQacejYmadDJqKxTWTSto8xVcJpT3bEbGfR8e+xynbHyusUvQbaonN3A8Ccl0f2zFlYdeiATedOZOcb2Hs2hR7B3mg0avg15e6mikYlMnXqVI4dO3a5r2Pu3HKbtqRMWOu1dA7yYtvJJIwmM/r69dkx/h4m1TlMQE4BdaIbkU4EU/fMsXTUqxxPiWD6oWm4FblSYJeJW547A3o9dvn13G+/w5ySgtPrrwGw/XQSRpOkhzo1pSiqaFhKTs5/Z7yLjo7mzJkzhIeHEx4ezrhx4yyQ7Nb0CPYhK99AWHQaAL36voBzppGPI2cx6pf1mBNC2JW0lhVnl1s4abGY7Bg+2vcBzjiQKvLQGaz57P5p6DV6AMwZGWR/PRebnj2xDi3uj9l8PBFvZxuCa6rJlhRFFQ3ljrSt6469tY5NEQkAeDRrw8RTtSkyFbHwtcY8ufgQjdI9+C5iATtjd1g0a3pBOu/veReBwKwT6IA3m03Cy+Hfe0qyZ89BZmXh9NqrAOQUGNgflUK3xt5qsiVFQRUN5Q4Vn6LyZNupxMtzajR6bhJjv71EvDabfU978NQHW2lg9GT6wWmcSD1hkZx5hjw+2jeF9IJ0nvEagouNC5/3+pS2jZpdXscYG0fOt99h++CD6IMbA7DjdDIGk6RHsDo1pSigioZSBnqE+JCVb+TAuVQAdIGBtAq9n4d/jiXHwx7ZrRMpW5tAkQMf7Z1CbE5shebLLMzk7V1vEpkeyYCdNkz/JY8n/d+inkv9q9bLnjYNpMTp1ZcvL9t0vPiqKXVqSlGKqaKh3LG2dT2KT1EdT7y8zPHFF2h/OJd3V1lRd/rXtK9fg4TT/TGa4b1dbxOXE1ch2ZLzknlzx2tcyLyAS57gj4bW5Lh64OV59aWzhlOnyFuyFIfHR6Pz8wOKT03tO5tC98bqqilF+UelKxpCiN+EEOElj/NCiPCS5QFCiPwrXqvclxbdRax0GjoHebL9ilNUWh8fHJ4ei2HFStKOhnEsZAc1Ak6TdaYPeYY83tjxKtGZ0eWa62TqCV7Z9hIp+SnY5hvJ0EN6amc+GtUGHxfby+tJKcl45z2EgwMO48dfXr7zTPGpqW7B3uWaU1GqkkpXNKSUw6SUzaWUzYFlwB9XvBz1z2tSysp/adFdpFtjb7LyjRw8n3Z5mcOE8Whr1cLw9oc0c29Cof1+pMcePHY3RouGt3a+zuGkQ2WeRUrJ2vNrmLTzTYTQoMkvpMAsSI0aytMduhP6P2NH5f30M0W7d+P8ztto3f49Atl2MgkPR2ua+LmUeUZFqaoqXdH4hyi+VGUo8Iuls5SHf4ZGDwkJYciQIeTlVe2pUtvW88DWSsvWE/+eotLY2uLy4QeYIyMZvsPE+ObPY+0US1SzU4RukLjpnHlv9zt8fWQ2BcaCMsmRkp/CR/s+YE74LOq51CerKBNbnQOJ0Q/TvV5TRnYIuGp9U1w8mR9+hFWHDtiNGH55eaHBxN6zKXQO8lKnphTlCpW2aACdgUQpZeQVywKFEIeFENuEEJ2v90YhxFghRJgQIiw5Obn8k96Gf8aeioiIwMrKqtLfyHczNnotHep7sPVkEibzv5Mw2fTojk3fvmTNmEF30ZCPO3+Ki5Vgfac0Rs2PZIBXD9ZGr+H5LeOJSDl22/v/5+jiuU3jOJx0mJEFzXljnydDGwzny/7zmD6iJ2/dH3LVZbNSSjLeegsMBlw///Sq18Ki08gvMnFPw6o1xLuilDeLDFgohNgIXOsaxklSyj9Lno/g6qOMeKCWlDJVCNEKWCGECJZS/md+USnlPGAeQGho6A2nkZt/dB7RmedupxnXFehch6eaji31+p07d+bo0aPMnTv3cvHIzMwkICCALVu2lGm28tS1sTebjidy9GI6LQLcLi93eX8yiTt3kvb0MzRe8QcLBnzLlp0/kJ28hk5vrcXp69FsvLiRt3a+Qd/AfjwW/Di2Otsb7OlqGYUZzDz0JQcS9+Nv5UN2ZgI+3ywlu3YHbLsNx1HvSGid/x4t5P+1koING3F69x10AQFXvbbtZCJ21lpaBaph0BXlShYpGlLKG47GJ4TQAYOBy0OkSikLgcKS5weFEFFAAyCsHKOWO6PRyJo1a+jduzfjxo1j3LhxGAwGunfvzsSJEy0d75Z0qO+JlU7DlhOJVxUNbQ1fXL+cQdrjT5Dx9ju4Tv2cjm0eZ1CvOtR0jSfz9E94WLnRwKEuq6P/5ljKMSa1fYcaDjWuu688Qx5HU45wLPko2y5tJdeQg2e+nksk4JdtwPnZCcwwtGP/6tM0DXAn0NPhqveb09PJfPc99M2a4jDmyateM5klO04nX26Poij/qqxDo/cETkkpY/5ZIITwBNKklCYhRB2gPnDHhwi3ckRQlv6ZTwOKjzSefPLfP1wvvPAC3bt3Z8CAARbJdrvsrXW0revO1pNJvNi74VV9Abb39sLx+QlkfzUTq5YtcR45gncGN+XN30y09XkMu6RlnHA6CxpBbPYlXtg8nomhr9DEoykOVv/+wU/OS2LpmSVsvriJQnMheqFHCIEJM6aCQh5NrssDD3/I1wdS2LP7PK/3b/yfggGQ+cGHmNPT8fj5J4T26qlbj8dkkJ5bxD0Nvcrvm6UoVVRlLRrD+W8H+D3AFCGEATAD46SUaf95ZxVxvfk0Fi5cyIULF5g1a1bFhyoD3Rp7s+N0MidiMwnxd7nqNcdXXqboyBEyJr2Nrm4durVty7geDZi7KZIxzV/lySO/sS1uO/ubO5Brb+D/9n8EgECgkWBGIosX4J5m5NkfLlDv6df4PuAi3V3b0ta3PQVW9vzf2lOsDo/joTa1GNTa/z8ZC3fuIu+333EY/9zlO7+vtO1UEjqtoEN9j3L4DilK1VYpi4aUcvQ1li2j+BLcauvgwYNMnTqVHTt2oNFUzdMinYO80GsFG48n/KdoCK0WtzmzSb5/IKlPjMFr1V881jmQ88k5LAiPR9NtHI+3nszI3XvI2LGF8LSjXBhxD4fzzmDISIOcXKykluYpdoQW1qDRmEex6Xkv73r+21k9+fcjbD2ZyOh76jCma93/5JOFhWS8+RbagNo4vfjCf1+Xkh2nkmgV4IaDjb7Mvz+KUtVVyqJxt5o1axZpaWl069YNgNDQUBYsWGDhVLfG0VZP+/qebIpI4Pl7g/5zuarGxQX3Rd+TPOABUh8djedfK3h7YAgu9laE1nFDY2+Pba+e2PbqiW8p95mWU0h6bhF1vR15tmd9Hu4YQKPrDPuRPedrjOfO4f7zYoTtfzvbo5JyuJiax7B2lXfWREWxJFU0LORaQ6N///33FkhS9nqF+LD9VBJH/ucqqn/oAgNx+24BKcNGkPb8C7gvWsiLvRsCxZ/0X/vlMK0C3RjcutYNO6Ivpuby9+FYlofF4O1sww/j2lPTzY6a11nfGB1N9sxZ2A7oj02XLtdcZ8OxBLQaQbfG6i5wRbkWVTSUMtcpyBMbvZYNEQnXLBoA1m3a4Pzeu2ROepuc2XNwnFA8fEduoZECg5kZa0+zaEc0db0c8HOzo7aHPSM6BCClZOJPh0jIyCc6OReNgA4NPHmuZ4MbDl0upSTj7XcQej3Ok9+77jobI+JpFeiGm4P1nX8jFKUaUkVDKXO2Vjo6B3my+XgCE/s0RKe99tGC/WOPUrR/P1mffY5VaCus27fHwUbPzMdC2Xc2hTVH4ohJy2PryUT83OwY0SEAIQRSgr+7PX2a1aBPsxp4OtncNFPuoh8o3LoN5/cno/W59jDnp+KyiE3PZ/Q9de6o/YpSnamioZSLXk182RCRQFh0Gu3qXfsqJCEELp99iuFYBGnPjcdr3Vq0JZ3abet50PY675vxSKtrLr+ewrCDZE5+H+sePbB/4vHrrrchIgGdVtClkTo1pSjXUzUv0VEqvXb1PHCw0bH+WPwN19M4OOA2by7mzEzSJ7yANJnKNIcpOZm0p59GW8MXt69mIK5zVZrZLNl0PIG2dT1wslVXTSnK9aiioZQLK52GLg292HYyiQLDjQuBvlEjXD78kMIdO8j+amaZZTDGxpI66hHMGRm4z5+PxsXluusei8kgMbOAXk3UDH2KciOqaCjlpnezGuQWGtl15uaDRtoNH4bt4MFkT/uCgjIYb6tw716S+/TDePEi7vPnX/MmvittOBaPtU5D5yB1F7ii3IgqGqX00UcfERwcTNOmTWnevDn79u1jxowZVX5I8/LUMsANT0dr1h298SkqKOnf+ORjdA0bkjrmKQq2br2tfZrT08l4bzIpw0agcXHBc9Vf2HTvdsP3FBpMrD8WT5dGXthbq24+RbkRVTRKYc+ePaxatYpDhw5x9OhRNm7ciL+/vyoaN6HVCHo18WV3ZDKZeUU3XV9jb4/H77+ir1uP1MefJH/DxlLtRxYUULhnD1mffU5Cp87kfvc9dsOG4rnqL/T16t30/TvPJJOVb6Rfi+vd4aEoyj9U0SiF+Ph4PDw8sLYuvnbfw8ODpUuXEhcXR7du3S7fwf3MM88QGhpKcHAw7733770Aq1evpmHDhrRq1Yrnn3+e/v37A5Cbm8sTTzxBmzZtaNGiBX/++ed/d17F9W7qi9Ekr5o//Ea0bm54/PYL+kYNSXv8CdJffgVTQgJQfB+FOSsLc2YmprR08levIfWpp4kLDiHloaFkz5yFVYsWeK1fi+tnn6JxcirVPlcdjsXb2YZQNQy6otyUkPKG001UeaGhoTIs7OrR00+ePEmjRo1KvY2cnBw6depEXl4ePXv2ZNiwYXTp0oWAgADCwsLw8Ci+NDQtLQ03NzdMJhM9evTgq6++okGDBtSvX5/t27cTGBjIiBEjyM7OZtWqVbz11ls0btyYhx9+mIyMDNq0acPhw4ext7e/43bfahvLi5SSUXN242ij45sn25b6febsbLKnzyDn+4UIrRatry/GuFgoKLxqPY2HB7YD+mN9zz1Yt22Dxvnaw4dcT1JWAQO/2Majneswrkf9W3qvolRz17xbVp3ALQUHBwcOHjzIjh072LJlC8OGDeOTTz75z3q///478+bNw2g0Eh8fz4kTJzCbzdSpU4fAwEAARowYwbx58wBYv349f/31F1OnTgWgoKCAixcvVoo/9mVFCEHvpr7M2RhJXHoeNVztSvU+jaMjzu++g/1jj5I9cxYyOwebXj3ReHldHspc16A+1h07InS3/2u89kgcZgn9ml9/7g5FUf6likYpabVaunbtSteuXWnSpAmLFi266vXo6GimTp3KgQMHcHV1ZfTo0RQU3Hjeaykly5YtIygoqDyjW9y9TXz5elMka47E8WTXm/cxXElXuzauUz8vl1xSSv4Oj6N5bVf83e/86E5R7gaqT6MUTp8+TWTkv1OVh4eHU7t2bRwdHcnOzgYgKysLe3t7nJ2dSUxMZM2aNQAEBQVx7tw5zp8/D8Bvv/12eTv33XcfM2fO5J9ThIcPH66gFlUsHxdbWgW68Xd4HGZz5TkdejIuiwspufRtpo4yFKW01JFGKeTk5DBhwgQyMjLQ6XTUq1ePefPm8csvv9C7d29q1KjBli1baNGiBQ0bNsTf35+OHTsCxZMtzZkzh969e2Nvb0/r1q0vb/edd97hxRdfpGnTppjNZgIDA1m1apWlmlmu+jWvyft/HOPwhXRaBV57EMOKtu5oHFY6Dd2D1bAhilJaqiO8AuTk5ODg4ICUkueee4769evz0ksvles+K0tH+D8Kikz0m7qVLg29eHdwE0vHwWSWDJi2lab+LnwyvIWl4yhKZXTNjnB1eqoCzJ8/n+bNmxMcHExmZiZPP/20pSNVOBsrLT1DfNh8IpHcQqOl4xAWnUpaThH3Ni3tVE+KooAqGhXipZdeIjw8nBMnTvDTTz9hZ1e6K4iqm/4talJgMLEpIsHSUVh/NB57ax0d63vefGVFUS5TRUOpMCF+ztT2sGdVeKxFcxQYTGw5mUi3xt5Y67UWzaIoVY0qGkqFEULQv0VNjl7M4EJKrsVy7D6TTF6hiXubqFNTinKrLFY0hBBDhBDHhRBmIUTo/7z2phDirBDitBDiviuW9y5ZdlYI8UbFp1buVN9mNdBqBCsPxVgsw/KwGDwcrSvNVVyKUpVY8kgjAhgMbL9yoRCiMTAcCAZ6A3OEEFohhBaYDfQBGgMjStZVqhB3R2s6NvBk9ZE4jCZzhe8//EI6B86lMrJDAFrN9ecUVxTl2ixWNKSUJ6WUp6/x0gPAr1LKQillNHAWaFPyOCulPCelLAJ+LVm3yrl06RLdunWjcePGBAcH8+WXX1o6UoUa0LImaTlF7I5MqfB9z99yFjcHKwaH+lf4vhWlOqiMfRo1gUtXfB1Tsux6y/9DCDFWCBEmhAhLTr75BEAVTafTMW3aNE6cOMHevXuZPXs2J06csHSsCtO+ngcejtb8VcGnqA5Gp3EwOo1HOwViY6U6wBXldpTrHeFCiI3AtebPnCSlLLdxwKWU84B5UHxz343WnTZtGqdPX+uA5/YFBQXx8ssvX/d1X19ffH2LO2EdHR1p1KgRsbGxNG58d5xt02k19G1Wg592nycluxAPR+ty36eUkgVbzuLhaM1AdZShKLetXI80pJQ9pZQh13jcqGDEAlf+r/YrWXa95VXa+fPnOXz4MG3bln7Y8Oqgf8uamMySVYcr5ke492wKhy+kFx9lqMtsFeW2Vcaxp/4CfhZCfAHUAOoD+ym+pb2+ECKQ4mIxHBh5pzu70RFBecvJyeHBBx9kxowZOJVywqDqopa7PaF13FgRdolHOgWWa6e00WRmxtrT+LnZMUgdZSjKHbHkJbeDhBAxQHvgbyHEOgAp5XHgd+AEsBZ4TkppklIagfHAOuAk8HvJulWSwWDgwQcfZNSoUQwePNjScSziwda1SMgsYHdk+fY7/XHgEhdScnn+viD0usrYjacoVYfFjjSklMuB5dd57SPgo2ssXw2sLudo5U5KyZNPPkmjRo2YOHGipeNYTOcgTzwdrVm2/xKdg7zKZR+ZeUUs2HqW1nXc6RykhgxRlDulPnZZwK5du/jxxx/ZvHkzzZs3p3nz5qxeXeVr4S3TaTU80MqPvWdTiEnLK5d9zN10lpwCIy/0DkIIdV+GotypytinUe116tSJ6j4kfWk90MqP77efY3nYJSbcW7YzGG47mcjysEuMaF+bet6OZbptRblbqSMNxaI8nWzo0tCLlYdiKTSYymy7CRn5fPRnBA1rOPFMzwZltl1FudupoqFY3ODW/mTlG9h8IrFMtmc0mXln6VGMZsmHQ5phpTq/FaXMqP9NisW1CnSjlrsdy8Mu3XzlUvhuWxTHLmXw5oBg/NzuzrlLFKW8qKKhWJwQgoGh/hy9mMHZxOw72taRi+ks3H6Ovs1r0EsNfa4oZU4VDaVS6Ne8BlY6DSvu4Ggjt8DI5GXH8HGx5eU+lWd+dEWpTlTRUCoFZzsregT7sPpIHHm3MYe42Sz5v5XHSczMZ/LgJtjbqAsDFaU8qKJhAQUFBbRp04ZmzZoRHBzMe++9Z+lIlcKgUD/yCk2svMXxqKSUfLHmJBsjEhjXoz5Na7mWU0JFUVTRsABra2s2b97MkSNHCA8PZ+3atezdu9fSsSyuib8LoXXcmLPhDJEJpe/bmLspkqX7LzGqYwCPdAosx4SKoqhjeGDs2LHXXD5v3jzg+sOnv/zyywQFBbFy5UpWrlz5n/ddjxACBwcHoHgMKoPBoO5Wpvj78v6DTXls7h7e/C2chU+3w8FGf8P3bDqewKId0QwK9WN8rwbq+6go5UwdaViIyWSiefPmeHl50atXr7tuaPTrcXew5sMhzYjPyOeDFRGYzNe/c95oMvP1xkjqejvwSr/GqmAoSgUQ1X04i9DQUBkWFnbVspMnT9KoUeW4uiYjI4NBgwYxc+ZMQkJCymy7lamNt+OX3ef5ct1peoX48N7gJui0//18s2z/RT7/+yTTRrWkYwM1GKGilLFrfgpTRxoW5uLiQrdu3Vi7dq2lo1QqIzoE8GzP+myISOC1Xw5TUHT1ECN5hUa+3RZF89qudKjvYaGUinL3UUXDApKTk8nIyAAgPz+fDRs20LBhQ8uGqoQe7VyH1/s3Zs/ZFEbO2cWqw7EYTWaSswr4ZnMkaTlFPKf6MRSlQqmOcAuIj4/nsccew2QyYTabGTp0KP3797d0rEppUGt/arrZMXvDGT5cEcEXq0+SV3LU0TPEhyb+LpYNqCh3GVU0LKBp06YcPnzY0jGqjDZ13Wldpx3bTyWx80wydb0cCPFzoWGNu2uKXEWpDFTRUKoEIQRdGnnTpZG3paMoyl1N9WkoiqIopXbXFo3qfKlxdW6boiiWdVcWDRsbG1JTU6vlH1cpJampqdjY2Fg6iqIo1ZBF+jSEEEOAyUAjoI2UMqxkeS/gE8AKKAJelVJuLnltK+AL5Jds5l4pZdLt7N/Pz4+YmBiSk5PvpBmVlo2NDX5+fpaOoShKNWSpjvAIYDDwzf8sTwEGSCnjhBAhwDqg5hWvj/qnwNwJvV5PYKAa2E5RFOVWWaRoSClPAv+5KUtKeeV1qMcBWyGEtZSysALjKYqiKNdRmfs0HgQO/U/B+F4IES6EeEfc4DZgIcRYIUSYECKsup6CUhRFsYRyKxpCiI1CiIhrPB4oxXuDgU+Bp69YPEpK2QToXPJ45Hrvl1LOk1KGSilDPT3VQHaKoihlxaKj3JZ0br9yZT+FEMIP2Aw8LqXcdZ33jQZCpZTjS7GPZODCbUb0oLifpTqoLm2pLu0A1ZbKqrq05U7bkSKl7P2/CyvVHeFCCBfgb+CNKwuGEEIHuEgpU4QQeqA/sLE025RS3vahhhAiTEoZervvr0yqS1uqSztAtaWyqi5tKa92WKRPQwgxSAgRA7QH/hZCrCt5aTxQD3i3pO8iXAjhBVgD64QQR4FwIBaYb4HoiqIodzVLXT21HFh+jeUfAh9e522tyjWUoiiKclOV+eqpyuDGk31XLdWlLdWlHaDaUllVl7aUSzuq/XSviqIoStlRRxqKoihKqamioSiKopSaKhpXEEK4CSE2CCEiS/51vcG6TkKIGCHErIrMWFqlaYsQorkQYo8Q4rgQ4qgQYpglsl6LEKK3EOK0EOKsEOKNa7xuLYT4reT1fUKIAAvELJVStGWiEOJEyc9gkxCitiVylsbN2nLFeg8KIaQQolJeulqadgghhpb8XI4LIX6u6IylVYrfr1pCiC1CiMMlv2N972iHUkr1KHkAn1F8jwjAG8CnN1j3S+BnYJalc99uW4AGQP2S5zWAeIrvh7F0di0QBdSheMTjI0Dj/1nnWWBuyfPhwG+Wzn0HbekG2JU8f6Yqt6VkPUdgO7CX4ptwLZ79Nn4m9YHDgGvJ116Wzn0HbZkHPFPyvDFw/k72qY40rvYAsKjk+SJg4LVWEkK0AryB9RUT67bctC1SyjNSysiS53FAElAZxl1pA5yVUp6TUhYBv1Lcnitd2b6lQI8bjUdmQTdti5Ryi5Qyr+TLvUBlHde+ND8XgA8oHgaooCLD3YLStOMpYLaUMh1A3uY0DBWgNG2RgFPJc2cg7k52qIrG1byllPElzxMoLgxXEUJogGnAKxUZ7DbctC1XEkK0ofiTSlR5ByuFmsClK76O4eoh8q9aR0ppBDIB9wpJd2tK05YrPQmsKddEt++mbRFCtAT8pZR/V2SwW1San0kDoIEQYpcQYq8Q4j/DaVQSpWnLZODhkhuqVwMT7mSHlWoYkYoghNgI+FzjpUlXfiGllEKIa12P/CywWkoZY+kPtmXQln+24wv8CDwmpTSXbUqltIQQDwOhQBdLZ7kdJR+ovgBGWzhKWdBRfIqqK8VHftuFEE2klBmWDHWbRgALpZTThBDtgR+FECG3+3/9risaUsqe13tNCJEohPCVUsaX/CG91iFpe6CzEOJZwAGwEkLkSCmv2ylYXsqgLQghnCge72uSlHJvOUW9VbGA/xVf+5Usu9Y6MSVjkzkDqRUT75aUpi0IIXpSXOy7yMo7f8zN2uIIhABbSz5Q+QB/CSHul2UweVoZKs3PJAbYJ6U0ANFCiDMUF5EDFROx1ErTlieB3gBSyj1CCBuKBzO8rVNu6vTU1f4CHit5/hjw5/+uIKUcJaWsJaUMoPgU1Q+WKBilcNO2CCGsKB7O5Qcp5dIKzHYzB4D6QojAkozDKW7Pla5s30PAZlnS01fJ3LQtQogWFM9ieX8lPncON2mLlDJTSukhpQwo+f+xl+I2VaaCAaX7/VpB8VEGQggPik9XnavAjKVVmrZcBHoACCEaATbA7U80ZOne/8r0oPic+CYgkuJRdN1KlocCC66x/mgq79VTN20L8DBgoHgQyH8ezS2dvSRbX+AMxX0sk0qWTaH4jxAlv/hLgLPAfqCOpTPfQVs2AolX/Az+snTm223L/6y7lUp49VQpfyaC4lNtJ4BjwHBLZ76DtjQGdlF8ZVU4cO+d7E8NI6IoiqKUmjo9pSiKopSaKhqKoihKqamioSiKopSaKhqKoihKqamioSiKopSaKhqKUkaEEO5XzG2fIISILXmeI4SYY+l8ilIW1CW3ilIOhBCTgRwp5VRLZ1GUsqSONBSlnAkhugohVpU8nyyEWCSE2CGEuCCEGCyE+EwIcUwIsVYIoS9Zr5UQYpsQ4qAQYl3JUDCKYnGqaChKxasLdAfuBxYDW6SUTYB8oF9J4ZgJPCSlbAV8B3xkqbCKcqW7bsBCRakE1kgpDUKIYxRPorO2ZPkxIAAIonjgvw0lA/9pKZ4gS1EsThUNRal4hQBSSrMQwiD/7Vg0U/x/UgDHpZTtLRVQUa5HnZ5SlMrnNOBZMvcBQgi9ECLYwpkUBVBFQ1EqHVk8bedDwKdCiH9GJu1g0VCKUkJdcqsoiqKUmjrSUBRFUUpNFQ1FURSl1FTRUBRFUUpNFQ1FURSl1FTRUBRFUUpNFQ1FURSl1FTRUBRFUUrt/wEgzJ0yYUhQ2AAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Then we can use seaborn.lineplot to create the desired plot:\n",
    "ax = sns.lineplot(data=df_sync, x=\"Time\", y=\"Amplitude\", hue=\"Channel\", \n",
    "                  style=\"Stage\", palette=\"Set1\", ci=None, dashes=True)\n",
    "\n",
    "sns.despine()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*************\n",
    "\n",
    "## Example 2: Using a Raw object from MNE-Python\n",
    "\n",
    "This example demonstrates how to manipulate [MNE Raw object](https://mne-tools.github.io/stable/generated/mne.io.Raw.html#mne.io.Raw). The MNE package has several [functions](https://mne-tools.github.io/stable/python_reference.html#module-mne.io) to load the most standard EEG file formats (EDF, BrainVision, EEGLab, FieldTrip...).\n",
    "\n",
    "For the sake of this example, we'll load a PSG file encoded in the native MNE format (*.fif) using the [mne.io.read_raw_fif](https://martinos.org/mne/stable/generated/mne.io.read_raw_fif.html) function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load the raw object\n",
    "raw = mne.io.read_raw_fif('sub-02_mne_raw.fif', preload=True, verbose=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Chan = ['F3', 'F4', 'C3', 'C4', 'O1', 'O2', 'EOG1', 'EOG2', 'EMG1']\n",
      "Sampling frequency = 100.0\n",
      "Data shape = (9, 294000)\n"
     ]
    }
   ],
   "source": [
    "# Let's have a look at the data\n",
    "print('Chan =', raw.ch_names)\n",
    "print('Sampling frequency =', raw.info['sfreq'])\n",
    "print('Data shape =', raw._data.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Chan = ['F3', 'F4', 'C3', 'C4']\n"
     ]
    }
   ],
   "source": [
    "# Keep only the channels of interests\n",
    "raw_eeg = raw.copy().pick_types(eeg=True).drop_channels(['O1', 'O2'])\n",
    "print('Chan =', raw_eeg.ch_names)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "282 slow-waves detected.\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Start</th>\n",
       "      <th>NegPeak</th>\n",
       "      <th>MidCrossing</th>\n",
       "      <th>PosPeak</th>\n",
       "      <th>End</th>\n",
       "      <th>Duration</th>\n",
       "      <th>ValNegPeak</th>\n",
       "      <th>ValPosPeak</th>\n",
       "      <th>PTP</th>\n",
       "      <th>Slope</th>\n",
       "      <th>Frequency</th>\n",
       "      <th>Channel</th>\n",
       "      <th>IdxChannel</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.40</td>\n",
       "      <td>0.60</td>\n",
       "      <td>0.83</td>\n",
       "      <td>0.98</td>\n",
       "      <td>1.16</td>\n",
       "      <td>0.76</td>\n",
       "      <td>-51.950</td>\n",
       "      <td>24.293</td>\n",
       "      <td>76.243</td>\n",
       "      <td>331.493</td>\n",
       "      <td>1.316</td>\n",
       "      <td>F3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>15.30</td>\n",
       "      <td>15.77</td>\n",
       "      <td>16.01</td>\n",
       "      <td>16.26</td>\n",
       "      <td>16.54</td>\n",
       "      <td>1.24</td>\n",
       "      <td>-81.465</td>\n",
       "      <td>96.554</td>\n",
       "      <td>178.019</td>\n",
       "      <td>741.746</td>\n",
       "      <td>0.806</td>\n",
       "      <td>F3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>38.53</td>\n",
       "      <td>38.78</td>\n",
       "      <td>38.97</td>\n",
       "      <td>39.20</td>\n",
       "      <td>39.44</td>\n",
       "      <td>0.91</td>\n",
       "      <td>-64.282</td>\n",
       "      <td>80.737</td>\n",
       "      <td>145.019</td>\n",
       "      <td>763.255</td>\n",
       "      <td>1.099</td>\n",
       "      <td>F3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>137.70</td>\n",
       "      <td>137.99</td>\n",
       "      <td>138.29</td>\n",
       "      <td>138.49</td>\n",
       "      <td>139.21</td>\n",
       "      <td>1.51</td>\n",
       "      <td>-63.788</td>\n",
       "      <td>24.904</td>\n",
       "      <td>88.691</td>\n",
       "      <td>295.638</td>\n",
       "      <td>0.662</td>\n",
       "      <td>F3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>196.51</td>\n",
       "      <td>196.81</td>\n",
       "      <td>197.04</td>\n",
       "      <td>197.31</td>\n",
       "      <td>197.71</td>\n",
       "      <td>1.20</td>\n",
       "      <td>-46.628</td>\n",
       "      <td>51.581</td>\n",
       "      <td>98.209</td>\n",
       "      <td>426.996</td>\n",
       "      <td>0.833</td>\n",
       "      <td>F3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>277</th>\n",
       "      <td>2842.07</td>\n",
       "      <td>2842.45</td>\n",
       "      <td>2842.73</td>\n",
       "      <td>2842.94</td>\n",
       "      <td>2843.13</td>\n",
       "      <td>1.06</td>\n",
       "      <td>-65.999</td>\n",
       "      <td>46.460</td>\n",
       "      <td>112.459</td>\n",
       "      <td>401.638</td>\n",
       "      <td>0.943</td>\n",
       "      <td>C4</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>278</th>\n",
       "      <td>2843.13</td>\n",
       "      <td>2843.35</td>\n",
       "      <td>2843.54</td>\n",
       "      <td>2843.78</td>\n",
       "      <td>2844.01</td>\n",
       "      <td>0.88</td>\n",
       "      <td>-53.510</td>\n",
       "      <td>68.652</td>\n",
       "      <td>122.162</td>\n",
       "      <td>642.959</td>\n",
       "      <td>1.136</td>\n",
       "      <td>C4</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>279</th>\n",
       "      <td>2844.75</td>\n",
       "      <td>2845.03</td>\n",
       "      <td>2845.36</td>\n",
       "      <td>2845.89</td>\n",
       "      <td>2846.17</td>\n",
       "      <td>1.42</td>\n",
       "      <td>-124.029</td>\n",
       "      <td>99.849</td>\n",
       "      <td>223.878</td>\n",
       "      <td>678.417</td>\n",
       "      <td>0.704</td>\n",
       "      <td>C4</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>280</th>\n",
       "      <td>2846.17</td>\n",
       "      <td>2846.39</td>\n",
       "      <td>2846.64</td>\n",
       "      <td>2846.80</td>\n",
       "      <td>2847.00</td>\n",
       "      <td>0.83</td>\n",
       "      <td>-77.389</td>\n",
       "      <td>34.250</td>\n",
       "      <td>111.639</td>\n",
       "      <td>446.554</td>\n",
       "      <td>1.205</td>\n",
       "      <td>C4</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>281</th>\n",
       "      <td>2899.10</td>\n",
       "      <td>2899.53</td>\n",
       "      <td>2899.79</td>\n",
       "      <td>2900.04</td>\n",
       "      <td>2900.63</td>\n",
       "      <td>1.53</td>\n",
       "      <td>-62.844</td>\n",
       "      <td>55.791</td>\n",
       "      <td>118.635</td>\n",
       "      <td>456.289</td>\n",
       "      <td>0.654</td>\n",
       "      <td>C4</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>282 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Start  NegPeak  MidCrossing  PosPeak      End  Duration  ValNegPeak  \\\n",
       "0       0.40     0.60         0.83     0.98     1.16      0.76     -51.950   \n",
       "1      15.30    15.77        16.01    16.26    16.54      1.24     -81.465   \n",
       "2      38.53    38.78        38.97    39.20    39.44      0.91     -64.282   \n",
       "3     137.70   137.99       138.29   138.49   139.21      1.51     -63.788   \n",
       "4     196.51   196.81       197.04   197.31   197.71      1.20     -46.628   \n",
       "..       ...      ...          ...      ...      ...       ...         ...   \n",
       "277  2842.07  2842.45      2842.73  2842.94  2843.13      1.06     -65.999   \n",
       "278  2843.13  2843.35      2843.54  2843.78  2844.01      0.88     -53.510   \n",
       "279  2844.75  2845.03      2845.36  2845.89  2846.17      1.42    -124.029   \n",
       "280  2846.17  2846.39      2846.64  2846.80  2847.00      0.83     -77.389   \n",
       "281  2899.10  2899.53      2899.79  2900.04  2900.63      1.53     -62.844   \n",
       "\n",
       "     ValPosPeak      PTP    Slope  Frequency Channel  IdxChannel  \n",
       "0        24.293   76.243  331.493      1.316      F3           0  \n",
       "1        96.554  178.019  741.746      0.806      F3           0  \n",
       "2        80.737  145.019  763.255      1.099      F3           0  \n",
       "3        24.904   88.691  295.638      0.662      F3           0  \n",
       "4        51.581   98.209  426.996      0.833      F3           0  \n",
       "..          ...      ...      ...        ...     ...         ...  \n",
       "277      46.460  112.459  401.638      0.943      C4           3  \n",
       "278      68.652  122.162  642.959      1.136      C4           3  \n",
       "279      99.849  223.878  678.417      0.704      C4           3  \n",
       "280      34.250  111.639  446.554      1.205      C4           3  \n",
       "281      55.791  118.635  456.289      0.654      C4           3  \n",
       "\n",
       "[282 rows x 13 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Multi-channel slow-waves detection\n",
    "# Note that since we're using a MNE Raw object, there is no need\n",
    "# to manually specify the sampling frequency and channel names.\n",
    "# We also use a less conservative amplitude threshold\n",
    "sw = yasa.sw_detect(raw_eeg, amp_ptp=(50, 400))\n",
    "print(sw.summary().shape[0], 'slow-waves detected.')\n",
    "sw.summary().round(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Count</th>\n",
       "      <th>Duration</th>\n",
       "      <th>ValNegPeak</th>\n",
       "      <th>ValPosPeak</th>\n",
       "      <th>PTP</th>\n",
       "      <th>Slope</th>\n",
       "      <th>Frequency</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Channel</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>C3</th>\n",
       "      <td>48</td>\n",
       "      <td>1.100000</td>\n",
       "      <td>-65.586750</td>\n",
       "      <td>49.535053</td>\n",
       "      <td>115.121803</td>\n",
       "      <td>452.410610</td>\n",
       "      <td>0.950368</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C4</th>\n",
       "      <td>54</td>\n",
       "      <td>1.115370</td>\n",
       "      <td>-79.181103</td>\n",
       "      <td>57.808133</td>\n",
       "      <td>136.989236</td>\n",
       "      <td>548.546712</td>\n",
       "      <td>0.956062</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>F3</th>\n",
       "      <td>100</td>\n",
       "      <td>1.129200</td>\n",
       "      <td>-70.219874</td>\n",
       "      <td>53.856789</td>\n",
       "      <td>124.076663</td>\n",
       "      <td>486.470542</td>\n",
       "      <td>0.936939</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>F4</th>\n",
       "      <td>80</td>\n",
       "      <td>1.138375</td>\n",
       "      <td>-67.541603</td>\n",
       "      <td>50.583728</td>\n",
       "      <td>118.125331</td>\n",
       "      <td>457.817541</td>\n",
       "      <td>0.940776</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Count  Duration  ValNegPeak  ValPosPeak         PTP       Slope  \\\n",
       "Channel                                                                    \n",
       "C3          48  1.100000  -65.586750   49.535053  115.121803  452.410610   \n",
       "C4          54  1.115370  -79.181103   57.808133  136.989236  548.546712   \n",
       "F3         100  1.129200  -70.219874   53.856789  124.076663  486.470542   \n",
       "F4          80  1.138375  -67.541603   50.583728  118.125331  457.817541   \n",
       "\n",
       "         Frequency  \n",
       "Channel             \n",
       "C3        0.950368  \n",
       "C4        0.956062  \n",
       "F3        0.936939  \n",
       "F4        0.940776  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sw.summary(grp_chan=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x324 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot an average template of the detected slow-waves, centered around the start of the slow-wave\n",
    "# Definitely noiser than our previous plot since there are fewer detected slow-waves\n",
    "# in our second datset, because it's a 45 min nap and not a full-night recording!\n",
    "ax = sw.plot_average(center=\"Start\", time_before=0.2, time_after=1)\n",
    "ax.legend(frameon=False)\n",
    "sns.despine()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Visual inspection of the detected slow-waves\n",
    "%matplotlib widget\n",
    "# sw.plot_detection();"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}