FirstReadEDF.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pyedflib\n",
    "import numpy as np\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "import importlib"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "import scipy.signal as signal\n",
    "\n",
    "from scipy.signal import hilbert"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "import arrow"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "from datetime import datetime as dt\n",
    "from datetime import timedelta as td"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "from bs4 import BeautifulSoup"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "metadata": {},
   "outputs": [],
   "source": [
    "import taa_calc\n",
    "importlib.reload(taa_calc)\n",
    "from taa_calc import (\n",
    "    get_edf_signal_indices,\n",
    "    read_edf_signals,\n",
    "    read_edf_signals_file,\n",
    "    get_edf_frequencies,\n",
    "    resample_signals,\n",
    "    preprocess,\n",
    "    fund_freq,\n",
    "    avg_resp_period,\n",
    "    calc_windows,\n",
    "    calc_taa,\n",
    "    read_sleep_stages,\n",
    "    read_resp_events,\n",
    "    chart_taa,\n",
    "    compute_perc_taa,\n",
    "    read_files,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Test with 300004"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "metadata": {},
   "outputs": [],
   "source": [
    "edf_id = \"300004\"\n",
    "edf_id = \"300013\"\n",
    "edf_id = \"300066\" # freq 32 but seems incorrect\n",
    "# edf_id = \"300132\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "metadata": {},
   "outputs": [],
   "source": [
    "f = pyedflib.EdfReader(f'local-data/chat/polysomnography/edfs/baseline/chat-baseline-{edf_id}.edf')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open(f'./local-data/chat/polysomnography/annotations-events-nsrr/baseline//chat-baseline-{edf_id}-nsrr.xml') as fp:\n",
    "    annot = BeautifulSoup(fp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0"
      ]
     },
     "execution_count": 176,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "f.samplefrequency(51)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 177,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "35419"
      ]
     },
     "execution_count": 177,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "f.getFileDuration()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 178,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "datetime.datetime(1985, 1, 1, 20, 15, 33)"
      ]
     },
     "execution_count": 178,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "f.getStartdatetime()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 179,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Arrow [1985-01-01T20:15:33+00:00]>"
      ]
     },
     "execution_count": 179,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# The date information is nonsense, but the time is correct\n",
    "start_dt = arrow.get(f.getStartdatetime())\n",
    "start_dt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Arrow [1985-01-02T06:41:18+00:00]>"
      ]
     },
     "execution_count": 180,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "start_dt.shift(seconds=37545)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 181,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "datetime.time(20, 15, 33)"
      ]
     },
     "execution_count": 181,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "start_dt.time()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 182,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Uncomment to see the signal details\n",
    "#f.getSignalHeaders()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 183,
   "metadata": {},
   "outputs": [],
   "source": [
    "# This is how you cleanup the f object\n",
    "#f._close()\n",
    "#del f"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 184,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "38"
      ]
     },
     "execution_count": 184,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "n = f.signals_in_file\n",
    "n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 185,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "35419"
      ]
     },
     "execution_count": 185,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "annot_duration = int(float(annot.scoredevents.scoredevent.duration.text))\n",
    "annot_duration"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 186,
   "metadata": {},
   "outputs": [],
   "source": [
    "sigs = read_edf_signals(f, ['ABD', 'Chest'])\n",
    "f._close()\n",
    "#sigs = read_edf_signals_file('local-data/chat-baseline-300004.edf', ['ABD', 'Chest', 'SAO2'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 187,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "32"
      ]
     },
     "execution_count": 187,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "freqs = get_edf_frequencies(f, ['ABD', 'Chest'])\n",
    "assert freqs[0] == freqs[1]\n",
    "freq = freqs[0]\n",
    "freq"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 188,
   "metadata": {},
   "outputs": [],
   "source": [
    "abd, chest = preprocess(sigs[0], sigs[1], orig_freq=freq)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 190,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4.0"
      ]
     },
     "execution_count": 190,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "avg_resp_period(abd, chest)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 191,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "avg resp period 4.0 win 12.0 step size 1.0\n",
      "CPU times: user 19.9 s, sys: 267 ms, total: 20.1 s\n",
      "Wall time: 20.1 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "taa, taa_valid, taa_freq = calc_taa(abd, chest)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 192,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "35408 1.0 1957\n"
     ]
    }
   ],
   "source": [
    "print(len(taa), taa_freq, np.sum(taa_valid != 0))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 193,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1392\n"
     ]
    }
   ],
   "source": [
    "# Noisy abdomen signals\n",
    "print(np.sum(taa_valid & 1 != 0))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 194,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "812\n"
     ]
    }
   ],
   "source": [
    "# Noisy chest signals\n",
    "print(np.sum(taa_valid & 2 != 0))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 200,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1923\n"
     ]
    }
   ],
   "source": [
    "# abdomen or chest noisy signal\n",
    "print(np.sum((taa_valid & 3) != 0))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 201,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "147\n"
     ]
    }
   ],
   "source": [
    "# abdomen frequency out of range\n",
    "print(np.sum(taa_valid & 4 != 0))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 202,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "48\n"
     ]
    }
   ],
   "source": [
    "# chest frequency out of range\n",
    "print(np.sum(taa_valid & 8 != 0))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 203,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "191\n"
     ]
    }
   ],
   "source": [
    "# abdomen or chest frequency out of range\n",
    "print(np.sum((taa_valid & 12) != 0))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 204,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "191\n"
     ]
    }
   ],
   "source": [
    "# disparity between chest and abdominal frequencies\n",
    "print(np.sum(taa_valid & 16 != 0))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 205,
   "metadata": {},
   "outputs": [],
   "source": [
    "stages = read_sleep_stages(annot_duration, annot.scoredevents.find_all('scoredevent'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 206,
   "metadata": {},
   "outputs": [],
   "source": [
    "resp_events = read_resp_events(annot_duration, annot.scoredevents.find_all('scoredevent'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 207,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(35408, 35419, 35419)"
      ]
     },
     "execution_count": 207,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(taa), len(stages), len(resp_events)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 213,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 3, 3, 3, 3, 3, 3,\n",
       "       3, 3])"
      ]
     },
     "execution_count": 213,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.repeat(np.array([1, 2, 3]), 8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 216,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2266816, 2266816)"
      ]
     },
     "execution_count": 216,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "exp_stages = np.repeat(stages, 64)\n",
    "exp_events = np.repeat(resp_events, 64)\n",
    "len(exp_stages), len(exp_events)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 218,
   "metadata": {},
   "outputs": [],
   "source": [
    "sleep_stages = (stages != 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 220,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "22740"
      ]
     },
     "execution_count": 220,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.sum(sleep_stages)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "exp_stages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 217,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2266816, 2266816)"
      ]
     },
     "execution_count": 217,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(abd), len(chest)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def filter_arrays(arrays_to_filter, filters):\n",
    "    \"\"\"\n",
    "    Filter a list of arrays by a list of filters.\n",
    "    \n",
    "    All arrays and filters should be the same length.\n",
    "    \n",
    "    This will apply each filter, in order, to each array\n",
    "    and to each later filter\n",
    "    \"\"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([27631,   643,  1051,   229,  8728,   340,    28])"
      ]
     },
     "execution_count": 165,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.bincount(resp_events)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2, 7730000)"
      ]
     },
     "execution_count": 166,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sigs.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "x1 = sin( 0.1*arange(1024) )\n",
      "x2 = sin( 0.1*arange(1024) + 0.456)\n",
      "x1h = hilbert(x1)\n",
      "x2h = hilbert(x2)\n",
      "c = inner( x1h, conj(x2h) ) / sqrt( inner(x1h,conj(x1h)) * inner(x2h,conj(x2h)) )\n",
      "phase_diff = angle(c)\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# https://stackoverflow.com/questions/27545171/identifying-phase-shift-between-signals\n",
    "print('''\n",
    "x1 = sin( 0.1*arange(1024) )\n",
    "x2 = sin( 0.1*arange(1024) + 0.456)\n",
    "x1h = hilbert(x1)\n",
    "x2h = hilbert(x2)\n",
    "c = inner( x1h, conj(x2h) ) / sqrt( inner(x1h,conj(x1h)) * inner(x2h,conj(x2h)) )\n",
    "phase_diff = angle(c)\n",
    "''')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Phase angle: Method 1 - looks OK\n",
    "\n",
    "Issues:\n",
    "\n",
    "* Verify center-windowing\n",
    "* The TAA is calculated once for each window, we could also calculate for every second\n",
    "* Math from stack overflow - need to verify"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Phase angle: Method 2 - not correct yet"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x1152 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#chart_taa(start_time=11_530, window_len=60)\n",
    "# 13_560 looks weird. Jumps too high too early\n",
    "# 14_600 looks like TAA is ahead of the breathing problem\n",
    "# 14_900 looks TAA delayed again\n",
    "chart_taa(start_time=780, window_len=60,\n",
    "         abd=abd, chest=chest, taa=taa, taa_freq=taa_freq,\n",
    "         taa_valid=taa_valid, stages=stages, start_dt=start_dt)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## TODO\n",
    "\n",
    "* Discard times when awake \n",
    "* Calculate % of time over 30% out of sync\n",
    "* Figure out the breathing rate\n",
    "* Discard breathing rate out of range"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([163.25,  75.5 , 140.  , 145.  ,   0.  , 102.  ])"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Minutes in each sleep stage\n",
    "np.bincount(stages)/60"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10.429166666666667"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Study length in hours\n",
    "len(stages)/60/60"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2.720833333333333"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# hours awake\n",
    "sum(stages == 0)/60/60"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "462.5"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sleep_minutes = sum(stages != 0)/60\n",
    "sleep_minutes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "27750"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Filter taa by time asleep\n",
    "asleep_taa = taa[stages != 0]\n",
    "len(asleep_taa)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.2570810810810811"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Percent of time sleeping with taa > 30%\n",
    "sum(asleep_taa > 0.3) / len(asleep_taa)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(585.0, 'Normal'),\n",
       " (6.0, 'Hypopnea'),\n",
       " (2.0, 'Obstructive apnea'),\n",
       " (0.0, 'Central apnea'),\n",
       " (29.0, 'SpO2 desaturation'),\n",
       " (3.0, 'SpO2 artifact'),\n",
       " (0.0, 'Unsure')]"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Minutes in each respiratory event\n",
    "events = [\n",
    "    'Normal',\n",
    "    'Hypopnea',\n",
    "    'Obstructive apnea',\n",
    "    'Central apnea',\n",
    "    'SpO2 desaturation',\n",
    "    'SpO2 artifact',\n",
    "    'Unsure',\n",
    "    'Other',\n",
    "]\n",
    "list(zip(np.round(np.bincount(resp_events)/60,0), events))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "27247"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Filter taa by time asleep\n",
    "asleep_taa = taa[stages != 0]\n",
    "asleep_events = resp_events[stages != 0]\n",
    "result = asleep_taa[(asleep_events != 1) & (asleep_events != 2) & (asleep_events != 3)]\n",
    "len(result)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.25213785003853634"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Percent of time sleeping with taa > 30%\n",
    "sum(result > 0.3) / len(result)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 266,
   "metadata": {},
   "outputs": [],
   "source": [
    "f._close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 275,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "200"
      ]
     },
     "execution_count": 275,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "f = pyedflib.EdfReader('./local-data/chat/polysomnography/edfs/baseline/chat-baseline-300013.edf')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "signal_labels = ['ABD','Chest']\n",
    "edf_indices = get_edf_signal_indices(f, signal_labels)\n",
    "orig_freq = int(f.samplefrequency(edf_indices[0]))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[34, 33]"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "edf_indices"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "200.00000000000003"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "f.samplefrequency(23)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "200.00000000000003"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "f.samplefrequency(24)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "labels = f.getSignalLabels()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 273,
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('Event', 200.00000000000003),\n",
       " ('C3', 200.00000000000003),\n",
       " ('C4', 200.00000000000003),\n",
       " ('O1', 200.00000000000003),\n",
       " ('O2', 200.00000000000003),\n",
       " ('M1', 200.00000000000003),\n",
       " ('M2', 200.00000000000003),\n",
       " ('Cchin', 200.00000000000003),\n",
       " ('F3', 200.00000000000003),\n",
       " ('F4', 200.00000000000003),\n",
       " ('F7', 200.00000000000003),\n",
       " ('F8', 200.00000000000003),\n",
       " ('Fz', 200.00000000000003),\n",
       " ('Fp1', 200.00000000000003),\n",
       " ('Fp2', 200.00000000000003),\n",
       " ('Fpz', 200.00000000000003),\n",
       " ('P3', 200.00000000000003),\n",
       " ('P4', 200.00000000000003),\n",
       " ('ECG3', 200.00000000000003),\n",
       " ('T3', 200.00000000000003),\n",
       " ('T4', 200.00000000000003),\n",
       " ('T5', 200.00000000000003),\n",
       " ('T6', 200.00000000000003),\n",
       " ('E1', 200.00000000000003),\n",
       " ('E2', 200.00000000000003),\n",
       " ('Lchin', 200.00000000000003),\n",
       " ('Rchin', 200.00000000000003),\n",
       " ('ECG1', 200.00000000000003),\n",
       " ('ECG2', 200.00000000000003),\n",
       " ('Lleg1', 200.00000000000003),\n",
       " ('Lleg2', 200.00000000000003),\n",
       " ('Rleg1', 200.00000000000003),\n",
       " ('Rleg2', 200.00000000000003),\n",
       " ('Chest', 200.00000000000003),\n",
       " ('ABD', 200.00000000000003),\n",
       " ('Airflow', 200.00000000000003),\n",
       " ('Snore', 200.00000000000003),\n",
       " ('SUM', 200.00000000000003),\n",
       " ('CannulaFlow', 200.00000000000003),\n",
       " ('POSdc1', 200.00000000000003),\n",
       " ('BPOSdc2', 200.00000000000003),\n",
       " ('ORAL', 200.00000000000003),\n",
       " ('DC4', 200.00000000000003),\n",
       " ('ETCO2', 200.00000000000003),\n",
       " ('Cap', 200.00000000000003),\n",
       " ('CFLOW', 200.00000000000003),\n",
       " ('LEAK', 200.00000000000003),\n",
       " ('PAP', 200.00000000000003),\n",
       " ('SAO2nonin', 200.00000000000003),\n",
       " ('SAO2masimo', 200.00000000000003),\n",
       " ('Pulsemasimo', 200.00000000000003),\n",
       " ('DHR', 200.00000000000003)]"
      ]
     },
     "execution_count": 273,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(zip(labels, [f.samplefrequency(i) for i in range(len(labels))]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 226,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "baseline/chat-baseline-300004 sample frequency is 200.\n"
     ]
    },
    {
     "ename": "NameError",
     "evalue": "name 'nonevent_sleep' 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<timed exec>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n",
      "\u001b[0;32m<ipython-input-225-6058f8038350>\u001b[0m in \u001b[0;36mcompute_perc_taa\u001b[0;34m(filename, taa_cutoff, fileroot)\u001b[0m\n\u001b[1;32m     74\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     75\u001b[0m     \u001b[0;31m# Percent of time sleeping with taa over the cutoff\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 76\u001b[0;31m     \u001b[0mtime_in_async\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msum\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnonevent_sleep\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0mtaa_cutoff\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m/\u001b[0m \u001b[0masleep_non_event\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     77\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     78\u001b[0m     \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m'success'\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mNameError\u001b[0m: name 'nonevent_sleep' is not defined"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "results = compute_perc_taa()\n",
    "results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 232,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "baseline/chat-baseline-300001 sample frequency is 128.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "('baseline/chat-baseline-300001',\n",
       " 'success',\n",
       " 32258,\n",
       " 3608,\n",
       " 28650,\n",
       " 930,\n",
       " 13020,\n",
       " 8610,\n",
       " 6090,\n",
       " 28411,\n",
       " 28411,\n",
       " 0.01323431065432403,\n",
       " 0.042905916722396256,\n",
       " 0.02228010277709338,\n",
       " 0.01323431065432403,\n",
       " 0.009010594488050402,\n",
       " 0.006687550596599909,\n",
       " 0.006159586075815705)"
      ]
     },
     "execution_count": 232,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "compute_perc_taa('baseline/chat-baseline-300001')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [],
   "source": [
    "def read_files(files):\n",
    "    dtypes = np.dtype([\n",
    "        ('filename', np.object),\n",
    "        ('comment', np.object),\n",
    "        ('orig_freq', np.int64),\n",
    "        ('duration_sec', np.int64),\n",
    "        ('awake_seconds', np.int64),\n",
    "        ('sleep_seconds', np.int64),\n",
    "        ('stage_1_seconds', np.int64),\n",
    "        ('stage_2_seconds', np.int64),\n",
    "        ('stage_3_seconds', np.int64),\n",
    "        ('stage_5_seconds', np.int64),\n",
    "        ('hyp_apnea_seconds', np.int64),\n",
    "        ('asleep_non_event_seconds', np.int64),\n",
    "        ('time_in_async_30p', np.float64),\n",
    "        ('time_in_async_25p', np.float64),\n",
    "        ('time_in_async_50p', np.float64),\n",
    "        ('time_in_async_75p', np.float64),\n",
    "    ])\n",
    "    df = np.empty(len(files), dtypes)\n",
    "    for i, filename in enumerate(files):\n",
    "        print(f\"{i:3} processing {filename}\")\n",
    "        df[i] = compute_perc_taa(filename)\n",
    "    results_df = pd.DataFrame(df, index=files)\n",
    "    results_df = results_df.drop(columns='filename')\n",
    "    results_df.index.name = 'filename'\n",
    "    return results_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "_ = '''\n",
    "'''\n",
    "baseline_files = '''\n",
    "chat-baseline-300001.edf\n",
    "chat-baseline-300002.edf\n",
    "chat-baseline-300004.edf\n",
    "chat-baseline-300007.edf\n",
    "chat-baseline-300008.edf\n",
    "chat-baseline-300013.edf\n",
    "chat-baseline-300014.edf\n",
    "chat-baseline-300015.edf\n",
    "chat-baseline-300019.edf\n",
    "chat-baseline-300021.edf\n",
    "chat-baseline-300024.edf\n",
    "chat-baseline-300025.edf\n",
    "chat-baseline-300026.edf\n",
    "chat-baseline-300031.edf\n",
    "chat-baseline-300033.edf\n",
    "chat-baseline-300036.edf\n",
    "chat-baseline-300037.edf\n",
    "chat-baseline-300038.edf\n",
    "chat-baseline-300039.edf\n",
    "chat-baseline-300041.edf\n",
    "chat-baseline-300042.edf\n",
    "chat-baseline-300043.edf\n",
    "chat-baseline-300051.edf\n",
    "chat-baseline-300052.edf\n",
    "chat-baseline-300055.edf\n",
    "chat-baseline-300058.edf\n",
    "chat-baseline-300062.edf\n",
    "chat-baseline-300063.edf\n",
    "chat-baseline-300066.edf\n",
    "chat-baseline-300069.edf\n",
    "chat-baseline-300071.edf\n",
    "chat-baseline-300072.edf\n",
    "chat-baseline-300073.edf\n",
    "chat-baseline-300077.edf\n",
    "chat-baseline-300078.edf\n",
    "chat-baseline-300081.edf\n",
    "chat-baseline-300098.edf\n",
    "chat-baseline-300102.edf\n",
    "chat-baseline-300108.edf\n",
    "chat-baseline-300111.edf\n",
    "chat-baseline-300117.edf\n",
    "chat-baseline-300122.edf\n",
    "chat-baseline-300129.edf\n",
    "chat-baseline-300130.edf\n",
    "chat-baseline-300132.edf\n",
    "chat-baseline-300133.edf\n",
    "chat-baseline-300134.edf\n",
    "chat-baseline-300139.edf\n",
    "chat-baseline-300141.edf\n",
    "chat-baseline-300144.edf\n",
    "chat-baseline-300150.edf\n",
    "chat-baseline-300153.edf\n",
    "chat-baseline-300157.edf\n",
    "chat-baseline-300158.edf\n",
    "chat-baseline-300160.edf\n",
    "chat-baseline-300166.edf\n",
    "chat-baseline-300167.edf\n",
    "'''"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "import taa_calc\n",
    "importlib.reload(taa_calc)\n",
    "from taa_calc import (\n",
    "    get_edf_signal_indices,\n",
    "    read_edf_signals,\n",
    "    read_edf_signals_file,\n",
    "    resample_signals,\n",
    "    preprocess,\n",
    "    fund_freq,\n",
    "    avg_resp_period,\n",
    "    calc_windows,\n",
    "    calc_taa,\n",
    "    read_sleep_stages,\n",
    "    read_resp_events,\n",
    "    chart_taa,\n",
    "    compute_perc_taa,\n",
    "    read_files,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "from itertools import islice"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_freq(filename='baseline/chat-baseline-300004', taa_cutoff=0.3,\n",
    "                    fileroot='local-data/chat/polysomnography',):\n",
    "    EDF_DIR = 'edfs'\n",
    "    \n",
    "    edf_file = f'{fileroot}/{EDF_DIR}/{filename}.edf'\n",
    "    print(f\"Opening {edf_file}\")\n",
    "    \n",
    "    f = pyedflib.EdfReader(edf_file)\n",
    "    signal_labels = ['ABD','Chest']\n",
    "    edf_indices = get_edf_signal_indices(f, signal_labels)\n",
    "    orig_freq = int(f.samplefrequency(edf_indices[0]))\n",
    "    f._close()\n",
    "    return orig_freq\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Opening local-data/chat/polysomnography/edfs/baseline/chat-baseline-300001.edf\n",
      "128\n",
      "Opening local-data/chat/polysomnography/edfs/baseline/chat-baseline-300002.edf\n",
      "200\n",
      "Opening local-data/chat/polysomnography/edfs/baseline/chat-baseline-300004.edf\n",
      "200\n",
      "Opening local-data/chat/polysomnography/edfs/baseline/chat-baseline-300007.edf\n",
      "32\n",
      "Opening local-data/chat/polysomnography/edfs/baseline/chat-baseline-300008.edf\n",
      "32\n",
      "Opening local-data/chat/polysomnography/edfs/baseline/chat-baseline-300013.edf\n",
      "200\n",
      "Opening local-data/chat/polysomnography/edfs/baseline/chat-baseline-300014.edf\n",
      "200\n",
      "Opening local-data/chat/polysomnography/edfs/baseline/chat-baseline-300015.edf\n",
      "32\n",
      "Opening local-data/chat/polysomnography/edfs/baseline/chat-baseline-300019.edf\n",
      "200\n",
      "Opening local-data/chat/polysomnography/edfs/baseline/chat-baseline-300021.edf\n",
      "512\n",
      "Opening local-data/chat/polysomnography/edfs/baseline/chat-baseline-300024.edf\n",
      "256\n",
      "Opening local-data/chat/polysomnography/edfs/baseline/chat-baseline-300025.edf\n",
      "32\n",
      "Opening local-data/chat/polysomnography/edfs/baseline/chat-baseline-300026.edf\n",
      "200\n",
      "Opening local-data/chat/polysomnography/edfs/baseline/chat-baseline-300031.edf\n",
      "200\n",
      "Opening local-data/chat/polysomnography/edfs/baseline/chat-baseline-300033.edf\n",
      "32\n",
      "Opening local-data/chat/polysomnography/edfs/baseline/chat-baseline-300036.edf\n",
      "200\n",
      "Opening local-data/chat/polysomnography/edfs/baseline/chat-baseline-300037.edf\n",
      "200\n",
      "Opening local-data/chat/polysomnography/edfs/baseline/chat-baseline-300038.edf\n",
      "128\n",
      "Opening local-data/chat/polysomnography/edfs/baseline/chat-baseline-300039.edf\n",
      "200\n",
      "Opening local-data/chat/polysomnography/edfs/baseline/chat-baseline-300041.edf\n",
      "200\n",
      "Opening local-data/chat/polysomnography/edfs/baseline/chat-baseline-300042.edf\n",
      "200\n",
      "Opening local-data/chat/polysomnography/edfs/baseline/chat-baseline-300043.edf\n",
      "200\n",
      "Opening local-data/chat/polysomnography/edfs/baseline/chat-baseline-300051.edf\n",
      "25\n",
      "Opening local-data/chat/polysomnography/edfs/baseline/chat-baseline-300052.edf\n",
      "200\n",
      "Opening local-data/chat/polysomnography/edfs/baseline/chat-baseline-300055.edf\n",
      "200\n",
      "Opening local-data/chat/polysomnography/edfs/baseline/chat-baseline-300058.edf\n",
      "200\n",
      "Opening local-data/chat/polysomnography/edfs/baseline/chat-baseline-300062.edf\n",
      "200\n",
      "Opening local-data/chat/polysomnography/edfs/baseline/chat-baseline-300063.edf\n",
      "32\n",
      "Opening local-data/chat/polysomnography/edfs/baseline/chat-baseline-300066.edf\n",
      "32\n",
      "Opening local-data/chat/polysomnography/edfs/baseline/chat-baseline-300069.edf\n",
      "200\n",
      "Opening local-data/chat/polysomnography/edfs/baseline/chat-baseline-300071.edf\n",
      "200\n",
      "Opening local-data/chat/polysomnography/edfs/baseline/chat-baseline-300072.edf\n",
      "200\n",
      "Opening local-data/chat/polysomnography/edfs/baseline/chat-baseline-300073.edf\n",
      "200\n",
      "Opening local-data/chat/polysomnography/edfs/baseline/chat-baseline-300077.edf\n",
      "32\n",
      "Opening local-data/chat/polysomnography/edfs/baseline/chat-baseline-300078.edf\n",
      "200\n",
      "Opening local-data/chat/polysomnography/edfs/baseline/chat-baseline-300081.edf\n",
      "200\n",
      "Opening local-data/chat/polysomnography/edfs/baseline/chat-baseline-300098.edf\n",
      "200\n",
      "Opening local-data/chat/polysomnography/edfs/baseline/chat-baseline-300102.edf\n",
      "200\n",
      "Opening local-data/chat/polysomnography/edfs/baseline/chat-baseline-300108.edf\n",
      "200\n",
      "Opening local-data/chat/polysomnography/edfs/baseline/chat-baseline-300111.edf\n",
      "200\n",
      "Opening local-data/chat/polysomnography/edfs/baseline/chat-baseline-300117.edf\n",
      "128\n",
      "Opening local-data/chat/polysomnography/edfs/baseline/chat-baseline-300122.edf\n",
      "50\n",
      "Opening local-data/chat/polysomnography/edfs/baseline/chat-baseline-300129.edf\n",
      "200\n",
      "Opening local-data/chat/polysomnography/edfs/baseline/chat-baseline-300130.edf\n",
      "200\n",
      "Opening local-data/chat/polysomnography/edfs/baseline/chat-baseline-300132.edf\n",
      "200\n",
      "Opening local-data/chat/polysomnography/edfs/baseline/chat-baseline-300133.edf\n",
      "200\n",
      "Opening local-data/chat/polysomnography/edfs/baseline/chat-baseline-300134.edf\n",
      "200\n",
      "Opening local-data/chat/polysomnography/edfs/baseline/chat-baseline-300139.edf\n",
      "512\n",
      "Opening local-data/chat/polysomnography/edfs/baseline/chat-baseline-300141.edf\n",
      "128\n",
      "Opening local-data/chat/polysomnography/edfs/baseline/chat-baseline-300144.edf\n",
      "200\n",
      "Opening local-data/chat/polysomnography/edfs/baseline/chat-baseline-300150.edf\n",
      "200\n",
      "Opening local-data/chat/polysomnography/edfs/baseline/chat-baseline-300153.edf\n",
      "200\n",
      "Opening local-data/chat/polysomnography/edfs/baseline/chat-baseline-300157.edf\n",
      "200\n",
      "Opening local-data/chat/polysomnography/edfs/baseline/chat-baseline-300158.edf\n",
      "32\n",
      "Opening local-data/chat/polysomnography/edfs/baseline/chat-baseline-300160.edf\n",
      "32\n",
      "Opening local-data/chat/polysomnography/edfs/baseline/chat-baseline-300166.edf\n",
      "32\n",
      "Opening local-data/chat/polysomnography/edfs/baseline/chat-baseline-300167.edf\n",
      "200\n"
     ]
    }
   ],
   "source": [
    "files = [f'baseline/{fn.split(\".edf\")[0]}' for fn in baseline_files.strip().split('\\n')]\n",
    "for file in files:\n",
    "    print(get_freq(file))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  0 processing baseline/chat-baseline-300001\n",
      "baseline/chat-baseline-300001 sample frequency is 128.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/jbranam/files/study/iums/d590_dsip/aisdb/aisdb/taa_calc.py:361: UserWarning: No parser was explicitly specified, so I'm using the best available HTML parser for this system (\"html.parser\"). This usually isn't a problem, but if you run this code on another system, or in a different virtual environment, it may use a different parser and behave differently.\n",
      "\n",
      "The code that caused this warning is on line 361 of the file /Users/jbranam/files/study/iums/d590_dsip/aisdb/aisdb/taa_calc.py. To get rid of this warning, pass the additional argument 'features=\"html.parser\"' to the BeautifulSoup constructor.\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "avg resp period 4.0 win 12.0 step size 1.0\n"
     ]
    },
    {
     "ename": "AssertionError",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mAssertionError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-50-6bc69c3238ba>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0mfiles\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34mf'baseline/{fn.split(\".edf\")[0]}'\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mfn\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mbaseline_files\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstrip\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msplit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'\\n'\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[0mtest_df\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mread_files\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mislice\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfiles\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[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      3\u001b[0m \u001b[0mtest_df\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/files/study/iums/d590_dsip/aisdb/aisdb/taa_calc.py\u001b[0m in \u001b[0;36mread_files\u001b[0;34m(files)\u001b[0m\n\u001b[1;32m    480\u001b[0m     \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfilename\u001b[0m \u001b[0;32min\u001b[0m \u001b[0menumerate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfiles\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    481\u001b[0m         \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mf\"{i:3} processing {filename}\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 482\u001b[0;31m         \u001b[0mdf\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcompute_perc_taa\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilename\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    483\u001b[0m     \u001b[0mresults_df\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mDataFrame\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mindex\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mfiles\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    484\u001b[0m     \u001b[0mresults_df\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mresults_df\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdrop\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'filename'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/files/study/iums/d590_dsip/aisdb/aisdb/taa_calc.py\u001b[0m in \u001b[0;36mcompute_perc_taa\u001b[0;34m(filename, taa_cutoff, fileroot)\u001b[0m\n\u001b[1;32m    389\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    390\u001b[0m     \u001b[0;31m# Sanity check the parsing\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 391\u001b[0;31m     \u001b[0;32massert\u001b[0m \u001b[0mduration_sec\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtaa\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    392\u001b[0m     \u001b[0;32massert\u001b[0m \u001b[0mduration_sec\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstages\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    393\u001b[0m     \u001b[0;32massert\u001b[0m \u001b[0mduration_sec\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mresp_events\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mAssertionError\u001b[0m: "
     ]
    }
   ],
   "source": [
    "files = [f'baseline/{fn.split(\".edf\")[0]}' for fn in baseline_files.strip().split('\\n')]\n",
    "test_df = read_files(list(islice(files, 2)))\n",
    "test_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "results_df.head(4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  0 processing baseline/chat-baseline-300001\n",
      "baseline/chat-baseline-300001 sample frequency is 128.\n",
      "  1 processing baseline/chat-baseline-300002\n",
      "baseline/chat-baseline-300002 sample frequency is 200.\n",
      "  2 processing baseline/chat-baseline-300004\n",
      "baseline/chat-baseline-300004 sample frequency is 200.\n",
      "  3 processing baseline/chat-baseline-300007\n",
      "baseline/chat-baseline-300007 sample frequency is 32.\n",
      "  4 processing baseline/chat-baseline-300008\n",
      "baseline/chat-baseline-300008 sample frequency is 32.\n",
      "  5 processing baseline/chat-baseline-300013\n",
      "baseline/chat-baseline-300013 sample frequency is 200.\n",
      "  6 processing baseline/chat-baseline-300014\n",
      "baseline/chat-baseline-300014 sample frequency is 200.\n",
      "  7 processing baseline/chat-baseline-300015\n",
      "baseline/chat-baseline-300015 sample frequency is 32.\n",
      "  8 processing baseline/chat-baseline-300019\n",
      "baseline/chat-baseline-300019 sample frequency is 200.\n",
      "  9 processing baseline/chat-baseline-300021\n",
      "baseline/chat-baseline-300021 sample frequency is 512.\n",
      " 10 processing baseline/chat-baseline-300024\n",
      "baseline/chat-baseline-300024 sample frequency is 256.\n",
      " 11 processing baseline/chat-baseline-300025\n",
      "baseline/chat-baseline-300025 sample frequency is 32.\n",
      " 12 processing baseline/chat-baseline-300026\n",
      "baseline/chat-baseline-300026 sample frequency is 200.\n",
      "Event type['Periodic breathing', 'Periodic Breathing']\n",
      "Event type['Periodic breathing', 'Periodic Breathing']\n",
      "Event type['Periodic breathing', 'Periodic Breathing']\n",
      "Event type['Periodic breathing', 'Periodic Breathing']\n",
      " 13 processing baseline/chat-baseline-300031\n",
      "baseline/chat-baseline-300031 sample frequency is 200.\n",
      " 14 processing baseline/chat-baseline-300033\n",
      "baseline/chat-baseline-300033 sample frequency is 32.\n",
      " 15 processing baseline/chat-baseline-300036\n",
      "baseline/chat-baseline-300036 sample frequency is 200.\n",
      " 16 processing baseline/chat-baseline-300037\n",
      "baseline/chat-baseline-300037 sample frequency is 200.\n",
      " 17 processing baseline/chat-baseline-300038\n",
      "baseline/chat-baseline-300038 sample frequency is 128.\n",
      " 18 processing baseline/chat-baseline-300039\n",
      "baseline/chat-baseline-300039 sample frequency is 200.\n",
      " 19 processing baseline/chat-baseline-300041\n",
      "baseline/chat-baseline-300041 sample frequency is 200.\n",
      " 20 processing baseline/chat-baseline-300042\n",
      "baseline/chat-baseline-300042 sample frequency is 200.\n",
      " 21 processing baseline/chat-baseline-300043\n",
      "baseline/chat-baseline-300043 sample frequency is 200.\n",
      " 22 processing baseline/chat-baseline-300051\n",
      "baseline/chat-baseline-300051 sample frequency is 25.\n",
      " 23 processing baseline/chat-baseline-300052\n",
      "baseline/chat-baseline-300052 sample frequency is 200.\n",
      " 24 processing baseline/chat-baseline-300055\n",
      "baseline/chat-baseline-300055 sample frequency is 200.\n",
      " 25 processing baseline/chat-baseline-300058\n",
      "baseline/chat-baseline-300058 sample frequency is 200.\n",
      " 26 processing baseline/chat-baseline-300062\n",
      "baseline/chat-baseline-300062 sample frequency is 200.\n",
      " 27 processing baseline/chat-baseline-300063\n",
      "baseline/chat-baseline-300063 sample frequency is 32.\n",
      " 28 processing baseline/chat-baseline-300066\n",
      "baseline/chat-baseline-300066 sample frequency is 32.\n",
      " 29 processing baseline/chat-baseline-300069\n",
      "baseline/chat-baseline-300069 sample frequency is 200.\n",
      " 30 processing baseline/chat-baseline-300071\n",
      "baseline/chat-baseline-300071 sample frequency is 200.\n",
      " 31 processing baseline/chat-baseline-300072\n",
      "baseline/chat-baseline-300072 sample frequency is 200.\n",
      " 32 processing baseline/chat-baseline-300073\n",
      "baseline/chat-baseline-300073 sample frequency is 200.\n",
      " 33 processing baseline/chat-baseline-300077\n",
      "baseline/chat-baseline-300077 sample frequency is 32.\n",
      " 34 processing baseline/chat-baseline-300078\n",
      "baseline/chat-baseline-300078 sample frequency is 200.\n",
      " 35 processing baseline/chat-baseline-300081\n",
      "baseline/chat-baseline-300081 sample frequency is 200.\n",
      " 36 processing baseline/chat-baseline-300098\n",
      "baseline/chat-baseline-300098 sample frequency is 200.\n",
      " 37 processing baseline/chat-baseline-300102\n",
      "baseline/chat-baseline-300102 sample frequency is 200.\n",
      " 38 processing baseline/chat-baseline-300108\n",
      "baseline/chat-baseline-300108 sample frequency is 200.\n",
      " 39 processing baseline/chat-baseline-300111\n",
      "baseline/chat-baseline-300111 sample frequency is 200.\n",
      " 40 processing baseline/chat-baseline-300117\n",
      "baseline/chat-baseline-300117 sample frequency is 128.\n",
      " 41 processing baseline/chat-baseline-300122\n",
      "baseline/chat-baseline-300122 sample frequency is 50.\n",
      " 42 processing baseline/chat-baseline-300129\n",
      "baseline/chat-baseline-300129 sample frequency is 200.\n",
      " 43 processing baseline/chat-baseline-300130\n",
      "baseline/chat-baseline-300130 sample frequency is 200.\n",
      " 44 processing baseline/chat-baseline-300132\n",
      "baseline/chat-baseline-300132 sample frequency is 200.\n",
      " 45 processing baseline/chat-baseline-300133\n",
      "baseline/chat-baseline-300133 sample frequency is 200.\n",
      " 46 processing baseline/chat-baseline-300134\n",
      "baseline/chat-baseline-300134 sample frequency is 200.\n",
      " 47 processing baseline/chat-baseline-300139\n",
      "baseline/chat-baseline-300139 sample frequency is 512.\n",
      " 48 processing baseline/chat-baseline-300141\n",
      "baseline/chat-baseline-300141 sample frequency is 128.\n",
      " 49 processing baseline/chat-baseline-300144\n",
      "baseline/chat-baseline-300144 sample frequency is 200.\n",
      " 50 processing baseline/chat-baseline-300150\n",
      "baseline/chat-baseline-300150 sample frequency is 200.\n",
      " 51 processing baseline/chat-baseline-300153\n",
      "baseline/chat-baseline-300153 sample frequency is 200.\n",
      " 52 processing baseline/chat-baseline-300157\n",
      "baseline/chat-baseline-300157 sample frequency is 200.\n",
      " 53 processing baseline/chat-baseline-300158\n",
      "baseline/chat-baseline-300158 sample frequency is 32.\n",
      " 54 processing baseline/chat-baseline-300160\n",
      "baseline/chat-baseline-300160 sample frequency is 32.\n",
      " 55 processing baseline/chat-baseline-300166\n",
      "baseline/chat-baseline-300166 sample frequency is 32.\n",
      " 56 processing baseline/chat-baseline-300167\n",
      "baseline/chat-baseline-300167 sample frequency is 200.\n"
     ]
    }
   ],
   "source": [
    "files = [f'baseline/{fn.split(\".edf\")[0]}' for fn in baseline_files.strip().split('\\n')]\n",
    "#results_df = read_files(list(islice(files,10)))\n",
    "results_df = read_files(files)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "# results_df = results_df.rename(columns={'time_in_async':'time_in_async_30p'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "# results_df['hyp_apnea_seconds'] = results_df['sleep_seconds'] - results_df['asleep_non_event_seconds']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\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>comment</th>\n",
       "      <th>duration_sec</th>\n",
       "      <th>awake_seconds</th>\n",
       "      <th>sleep_seconds</th>\n",
       "      <th>stage_1_seconds</th>\n",
       "      <th>stage_2_seconds</th>\n",
       "      <th>stage_3_seconds</th>\n",
       "      <th>stage_5_seconds</th>\n",
       "      <th>hyp_apnea_seconds</th>\n",
       "      <th>asleep_non_event_seconds</th>\n",
       "      <th>time_in_async_30p</th>\n",
       "      <th>time_in_async_25p</th>\n",
       "      <th>time_in_async_50p</th>\n",
       "      <th>time_in_async_75p</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>filename</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>baseline/chat-baseline-300001</td>\n",
       "      <td>success</td>\n",
       "      <td>32258</td>\n",
       "      <td>3608</td>\n",
       "      <td>28650</td>\n",
       "      <td>930</td>\n",
       "      <td>13020</td>\n",
       "      <td>8610</td>\n",
       "      <td>6090</td>\n",
       "      <td>239</td>\n",
       "      <td>28411</td>\n",
       "      <td>0.0132</td>\n",
       "      <td>0.0223</td>\n",
       "      <td>0.0056</td>\n",
       "      <td>0.0022</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>baseline/chat-baseline-300002</td>\n",
       "      <td>success</td>\n",
       "      <td>45349</td>\n",
       "      <td>16219</td>\n",
       "      <td>29130</td>\n",
       "      <td>2460</td>\n",
       "      <td>16440</td>\n",
       "      <td>5490</td>\n",
       "      <td>4740</td>\n",
       "      <td>403</td>\n",
       "      <td>28727</td>\n",
       "      <td>0.0498</td>\n",
       "      <td>0.0825</td>\n",
       "      <td>0.0179</td>\n",
       "      <td>0.0076</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>baseline/chat-baseline-300004</td>\n",
       "      <td>success</td>\n",
       "      <td>37545</td>\n",
       "      <td>9795</td>\n",
       "      <td>27750</td>\n",
       "      <td>4530</td>\n",
       "      <td>8400</td>\n",
       "      <td>8700</td>\n",
       "      <td>6120</td>\n",
       "      <td>503</td>\n",
       "      <td>27247</td>\n",
       "      <td>0.2521</td>\n",
       "      <td>0.2821</td>\n",
       "      <td>0.1810</td>\n",
       "      <td>0.1196</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>baseline/chat-baseline-300007</td>\n",
       "      <td>success</td>\n",
       "      <td>35792</td>\n",
       "      <td>8042</td>\n",
       "      <td>27750</td>\n",
       "      <td>3540</td>\n",
       "      <td>13650</td>\n",
       "      <td>5370</td>\n",
       "      <td>5190</td>\n",
       "      <td>165</td>\n",
       "      <td>27585</td>\n",
       "      <td>0.0328</td>\n",
       "      <td>0.0440</td>\n",
       "      <td>0.0125</td>\n",
       "      <td>0.0051</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>baseline/chat-baseline-300008</td>\n",
       "      <td>success</td>\n",
       "      <td>32477</td>\n",
       "      <td>4517</td>\n",
       "      <td>27960</td>\n",
       "      <td>3840</td>\n",
       "      <td>8910</td>\n",
       "      <td>9900</td>\n",
       "      <td>5310</td>\n",
       "      <td>248</td>\n",
       "      <td>27712</td>\n",
       "      <td>0.3740</td>\n",
       "      <td>0.4175</td>\n",
       "      <td>0.1827</td>\n",
       "      <td>0.0795</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                               comment  duration_sec  awake_seconds  \\\n",
       "filename                                                              \n",
       "baseline/chat-baseline-300001  success         32258           3608   \n",
       "baseline/chat-baseline-300002  success         45349          16219   \n",
       "baseline/chat-baseline-300004  success         37545           9795   \n",
       "baseline/chat-baseline-300007  success         35792           8042   \n",
       "baseline/chat-baseline-300008  success         32477           4517   \n",
       "\n",
       "                               sleep_seconds  stage_1_seconds  \\\n",
       "filename                                                        \n",
       "baseline/chat-baseline-300001          28650              930   \n",
       "baseline/chat-baseline-300002          29130             2460   \n",
       "baseline/chat-baseline-300004          27750             4530   \n",
       "baseline/chat-baseline-300007          27750             3540   \n",
       "baseline/chat-baseline-300008          27960             3840   \n",
       "\n",
       "                               stage_2_seconds  stage_3_seconds  \\\n",
       "filename                                                          \n",
       "baseline/chat-baseline-300001            13020             8610   \n",
       "baseline/chat-baseline-300002            16440             5490   \n",
       "baseline/chat-baseline-300004             8400             8700   \n",
       "baseline/chat-baseline-300007            13650             5370   \n",
       "baseline/chat-baseline-300008             8910             9900   \n",
       "\n",
       "                               stage_5_seconds  hyp_apnea_seconds  \\\n",
       "filename                                                            \n",
       "baseline/chat-baseline-300001             6090                239   \n",
       "baseline/chat-baseline-300002             4740                403   \n",
       "baseline/chat-baseline-300004             6120                503   \n",
       "baseline/chat-baseline-300007             5190                165   \n",
       "baseline/chat-baseline-300008             5310                248   \n",
       "\n",
       "                               asleep_non_event_seconds  time_in_async_30p  \\\n",
       "filename                                                                     \n",
       "baseline/chat-baseline-300001                     28411             0.0132   \n",
       "baseline/chat-baseline-300002                     28727             0.0498   \n",
       "baseline/chat-baseline-300004                     27247             0.2521   \n",
       "baseline/chat-baseline-300007                     27585             0.0328   \n",
       "baseline/chat-baseline-300008                     27712             0.3740   \n",
       "\n",
       "                               time_in_async_25p  time_in_async_50p  \\\n",
       "filename                                                              \n",
       "baseline/chat-baseline-300001             0.0223             0.0056   \n",
       "baseline/chat-baseline-300002             0.0825             0.0179   \n",
       "baseline/chat-baseline-300004             0.2821             0.1810   \n",
       "baseline/chat-baseline-300007             0.0440             0.0125   \n",
       "baseline/chat-baseline-300008             0.4175             0.1827   \n",
       "\n",
       "                               time_in_async_75p  \n",
       "filename                                          \n",
       "baseline/chat-baseline-300001             0.0022  \n",
       "baseline/chat-baseline-300002             0.0076  \n",
       "baseline/chat-baseline-300004             0.1196  \n",
       "baseline/chat-baseline-300007             0.0051  \n",
       "baseline/chat-baseline-300008             0.0795  "
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>comment</th>\n",
       "      <th>duration_sec</th>\n",
       "      <th>awake_seconds</th>\n",
       "      <th>sleep_seconds</th>\n",
       "      <th>stage_1_seconds</th>\n",
       "      <th>stage_2_seconds</th>\n",
       "      <th>stage_3_seconds</th>\n",
       "      <th>stage_5_seconds</th>\n",
       "      <th>hyp_apnea_seconds</th>\n",
       "      <th>asleep_non_event_seconds</th>\n",
       "      <th>time_in_async_30p</th>\n",
       "      <th>time_in_async_25p</th>\n",
       "      <th>time_in_async_50p</th>\n",
       "      <th>time_in_async_75p</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>filename</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></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",
       "      <td>baseline/chat-baseline-300001</td>\n",
       "      <td>success</td>\n",
       "      <td>32258</td>\n",
       "      <td>3608</td>\n",
       "      <td>28650</td>\n",
       "      <td>930</td>\n",
       "      <td>13020</td>\n",
       "      <td>8610</td>\n",
       "      <td>6090</td>\n",
       "      <td>239</td>\n",
       "      <td>28411</td>\n",
       "      <td>0.0132</td>\n",
       "      <td>0.0223</td>\n",
       "      <td>0.0056</td>\n",
       "      <td>0.0022</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                               comment  duration_sec  awake_seconds  \\\n",
       "filename                                                              \n",
       "baseline/chat-baseline-300001  success         32258           3608   \n",
       "\n",
       "                               sleep_seconds  stage_1_seconds  \\\n",
       "filename                                                        \n",
       "baseline/chat-baseline-300001          28650              930   \n",
       "\n",
       "                               stage_2_seconds  stage_3_seconds  \\\n",
       "filename                                                          \n",
       "baseline/chat-baseline-300001            13020             8610   \n",
       "\n",
       "                               stage_5_seconds  hyp_apnea_seconds  \\\n",
       "filename                                                            \n",
       "baseline/chat-baseline-300001             6090                239   \n",
       "\n",
       "                               asleep_non_event_seconds  time_in_async_30p  \\\n",
       "filename                                                                     \n",
       "baseline/chat-baseline-300001                     28411             0.0132   \n",
       "\n",
       "                               time_in_async_25p  time_in_async_50p  \\\n",
       "filename                                                              \n",
       "baseline/chat-baseline-300001             0.0223             0.0056   \n",
       "\n",
       "                               time_in_async_75p  \n",
       "filename                                          \n",
       "baseline/chat-baseline-300001             0.0022  "
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results_df.head(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [],
   "source": [
    "date_str = '2019_10_30'\n",
    "#results_df.to_csv(f'exports/results_{date_str}.csv', header=True)\n",
    "#results_df.describe().to_csv(f'exports/results_stats_{date_str}.csv', header=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1.04e-322, 1.04e-322, 1.04e-322]])"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.empty((1,3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "comment                      object\n",
       "duration_sec                  int64\n",
       "awake_seconds                 int64\n",
       "sleep_seconds                 int64\n",
       "stage_1_seconds               int64\n",
       "stage_2_seconds               int64\n",
       "stage_3_seconds               int64\n",
       "stage_5_seconds               int64\n",
       "hyp_apnea_seconds             int64\n",
       "asleep_non_event_seconds      int64\n",
       "time_in_async_30p           float64\n",
       "time_in_async_25p           float64\n",
       "time_in_async_50p           float64\n",
       "time_in_async_75p           float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results_df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .dataframe tbody tr th {\n",
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       "\n",
       "    .dataframe thead th {\n",
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       "</style>\n",
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       "      <th>stage_2_seconds</th>\n",
       "      <th>stage_3_seconds</th>\n",
       "      <th>stage_5_seconds</th>\n",
       "      <th>hyp_apnea_seconds</th>\n",
       "      <th>asleep_non_event_seconds</th>\n",
       "      <th>time_in_async</th>\n",
       "      <th>time_in_async_20p</th>\n",
       "      <th>time_in_async_25p</th>\n",
       "      <th>time_in_async_30p</th>\n",
       "      <th>time_in_async_25p</th>\n",
       "      <th>time_in_async_40p</th>\n",
       "      <th>time_in_async_25p</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>baseline/chat-baseline-300004</td>\n",
       "      <td>baseline/chat-baseline-300004</td>\n",
       "      <td>37545</td>\n",
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       "      <td>0.19492054171101406</td>\n",
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       "    <tr>\n",
       "      <td>baseline/chat-baseline-300013</td>\n",
       "      <td>baseline/chat-baseline-300013</td>\n",
       "      <td>37446</td>\n",
       "      <td>7326</td>\n",
       "      <td>30120</td>\n",
       "      <td>1620</td>\n",
       "      <td>12450</td>\n",
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       "      <td>3990</td>\n",
       "      <td>29866</td>\n",
       "      <td>29866</td>\n",
       "      <td>0.08755775798566932</td>\n",
       "      <td>0.19011585080024107</td>\n",
       "      <td>0.1351704279113373</td>\n",
       "      <td>0.08755775798566932</td>\n",
       "      <td>0.05240072323042925</td>\n",
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       "      <td>0.013962365231366772</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                    filename duration_sec  \\\n",
       "baseline/chat-baseline-300004  baseline/chat-baseline-300004        37545   \n",
       "baseline/chat-baseline-300013  baseline/chat-baseline-300013        37446   \n",
       "\n",
       "                              awake_seconds sleep_seconds stage_1_seconds  \\\n",
       "baseline/chat-baseline-300004          9795         27750            4530   \n",
       "baseline/chat-baseline-300013          7326         30120            1620   \n",
       "\n",
       "                              stage_2_seconds stage_3_seconds stage_5_seconds  \\\n",
       "baseline/chat-baseline-300004            8400            8700            6120   \n",
       "baseline/chat-baseline-300013           12450           12060            3990   \n",
       "\n",
       "                              hyp_apnea_seconds asleep_non_event_seconds  \\\n",
       "baseline/chat-baseline-300004             27247                    27247   \n",
       "baseline/chat-baseline-300013             29866                    29866   \n",
       "\n",
       "                                     time_in_async    time_in_async_20p  \\\n",
       "baseline/chat-baseline-300004  0.25213785003853634  0.32150328476529527   \n",
       "baseline/chat-baseline-300013  0.08755775798566932  0.19011585080024107   \n",
       "\n",
       "                                time_in_async_25p    time_in_async_30p  \\\n",
       "baseline/chat-baseline-300004  0.2820861012221529  0.25213785003853634   \n",
       "baseline/chat-baseline-300013  0.1351704279113373  0.08755775798566932   \n",
       "\n",
       "                                 time_in_async_25p     time_in_async_40p  \\\n",
       "baseline/chat-baseline-300004   0.2293830513451022     0.210004771167468   \n",
       "baseline/chat-baseline-300013  0.05240072323042925  0.025915757048148396   \n",
       "\n",
       "                                  time_in_async_25p  \n",
       "baseline/chat-baseline-300004   0.19492054171101406  \n",
       "baseline/chat-baseline-300013  0.013962365231366772  "
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
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       "      <th></th>\n",
       "      <th>filename</th>\n",
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       "      <th>awake_seconds</th>\n",
       "      <th>sleep_seconds</th>\n",
       "      <th>stage_1_seconds</th>\n",
       "      <th>stage_2_seconds</th>\n",
       "      <th>stage_3_seconds</th>\n",
       "      <th>stage_5_seconds</th>\n",
       "      <th>hyp_apnea_seconds</th>\n",
       "      <th>asleep_non_event_seconds</th>\n",
       "      <th>time_in_async</th>\n",
       "      <th>time_in_async_20p</th>\n",
       "      <th>time_in_async_25p</th>\n",
       "      <th>time_in_async_30p</th>\n",
       "      <th>time_in_async_25p</th>\n",
       "      <th>time_in_async_40p</th>\n",
       "      <th>time_in_async_25p</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>baseline/chat-baseline-300004</td>\n",
       "      <td>baseline/chat-baseline-300004</td>\n",
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       "      <td>27750</td>\n",
       "      <td>4530</td>\n",
       "      <td>8400</td>\n",
       "      <td>8700</td>\n",
       "      <td>6120</td>\n",
       "      <td>27247</td>\n",
       "      <td>27247</td>\n",
       "      <td>0.25213785003853634</td>\n",
       "      <td>0.32150328476529527</td>\n",
       "      <td>0.2820861012221529</td>\n",
       "      <td>0.25213785003853634</td>\n",
       "      <td>0.2293830513451022</td>\n",
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       "      <td>0.19492054171101406</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>baseline/chat-baseline-300013</td>\n",
       "      <td>baseline/chat-baseline-300013</td>\n",
       "      <td>37446</td>\n",
       "      <td>7326</td>\n",
       "      <td>30120</td>\n",
       "      <td>1620</td>\n",
       "      <td>12450</td>\n",
       "      <td>12060</td>\n",
       "      <td>3990</td>\n",
       "      <td>29866</td>\n",
       "      <td>29866</td>\n",
       "      <td>0.08755775798566932</td>\n",
       "      <td>0.19011585080024107</td>\n",
       "      <td>0.1351704279113373</td>\n",
       "      <td>0.08755775798566932</td>\n",
       "      <td>0.05240072323042925</td>\n",
       "      <td>0.025915757048148396</td>\n",
       "      <td>0.013962365231366772</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                    filename duration_sec  \\\n",
       "baseline/chat-baseline-300004  baseline/chat-baseline-300004        37545   \n",
       "baseline/chat-baseline-300013  baseline/chat-baseline-300013        37446   \n",
       "\n",
       "                              awake_seconds sleep_seconds stage_1_seconds  \\\n",
       "baseline/chat-baseline-300004          9795         27750            4530   \n",
       "baseline/chat-baseline-300013          7326         30120            1620   \n",
       "\n",
       "                              stage_2_seconds stage_3_seconds stage_5_seconds  \\\n",
       "baseline/chat-baseline-300004            8400            8700            6120   \n",
       "baseline/chat-baseline-300013           12450           12060            3990   \n",
       "\n",
       "                              hyp_apnea_seconds asleep_non_event_seconds  \\\n",
       "baseline/chat-baseline-300004             27247                    27247   \n",
       "baseline/chat-baseline-300013             29866                    29866   \n",
       "\n",
       "                                     time_in_async    time_in_async_20p  \\\n",
       "baseline/chat-baseline-300004  0.25213785003853634  0.32150328476529527   \n",
       "baseline/chat-baseline-300013  0.08755775798566932  0.19011585080024107   \n",
       "\n",
       "                                time_in_async_25p    time_in_async_30p  \\\n",
       "baseline/chat-baseline-300004  0.2820861012221529  0.25213785003853634   \n",
       "baseline/chat-baseline-300013  0.1351704279113373  0.08755775798566932   \n",
       "\n",
       "                                 time_in_async_25p     time_in_async_40p  \\\n",
       "baseline/chat-baseline-300004   0.2293830513451022     0.210004771167468   \n",
       "baseline/chat-baseline-300013  0.05240072323042925  0.025915757048148396   \n",
       "\n",
       "                                  time_in_async_25p  \n",
       "baseline/chat-baseline-300004   0.19492054171101406  \n",
       "baseline/chat-baseline-300013  0.013962365231366772  "
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results_df_orig"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [],
   "source": [
    "import librosa\n",
    "abd_stft = librosa.stft(sigs[0], n_fft=512, win_length=512)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 2.2297444e+03+0.0000000e+00j,  2.2297444e+03+0.0000000e+00j,\n",
       "         2.2297444e+03+0.0000000e+00j,  2.2297444e+03+0.0000000e+00j,\n",
       "         2.2297444e+03+0.0000000e+00j],\n",
       "       [-1.1148722e+03+1.8103927e-06j, -1.1148722e+03+4.2418683e-06j,\n",
       "        -1.1148722e+03-7.2597636e-06j, -1.1148722e+03+4.8980955e-06j,\n",
       "        -1.1148722e+03+8.4729157e-07j],\n",
       "       [ 4.3148368e-05-1.1607954e-05j, -3.5863650e-05-2.5851639e-05j,\n",
       "         2.7103206e-06+4.4517958e-05j,  3.2103282e-05-3.1169391e-05j,\n",
       "        -4.3877033e-05-5.2358600e-06j],\n",
       "       [-4.5741639e-05+1.1831351e-05j,  3.9784747e-05+2.8701152e-05j,\n",
       "        -4.5752799e-06-4.8121641e-05j, -3.4906956e-05+3.2143424e-05j,\n",
       "         4.8744354e-05+5.8005276e-06j],\n",
       "       [ 3.3357603e-05-1.4721012e-05j, -1.3998673e-06+1.7351787e-05j,\n",
       "        -2.4638781e-05+1.3255505e-05j,  1.0010871e-05-3.3202803e-05j,\n",
       "         1.3037528e-05+5.3128415e-06j]], dtype=complex64)"
      ]
     },
     "execution_count": 120,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "abd_stft[:5,1000:1005]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[207,   6, 205, 205, 147],\n",
       "       [  6, 205, 202, 147, 205],\n",
       "       [205, 202,   7,   7,   7],\n",
       "       [  7,   7,   6, 256,   6],\n",
       "       [  4,   4,   4,   4,   4],\n",
       "       [  5,   5,   5,   5,   5],\n",
       "       [  3,   3,   3,   3,   3],\n",
       "       [  2,   1,   2,   2,   2],\n",
       "       [  1,   2,   1,   1,   1],\n",
       "       [  0,   0,   0,   0,   0]])"
      ]
     },
     "execution_count": 132,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.argsort(np.abs(abd_stft[:,3000:3005]), axis=0)[-10:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1 0.390625\n",
      "38 14.84375\n",
      "197 76.953125\n",
      "159 62.109375\n",
      "156 60.9375\n"
     ]
    }
   ],
   "source": [
    "fft_freq = librosa.fft_frequencies(sr=200, n_fft=512)\n",
    "print(f\"1 {fft_freq[1]}\")\n",
    "print(f\"38 {fft_freq[38]}\")\n",
    "print(f\"197 {fft_freq[197]}\")\n",
    "print(f\"159 {fft_freq[159]}\")\n",
    "print(f\"156 {fft_freq[156]}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x1614f7bd0>"
      ]
     },
     "execution_count": 122,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAABIQAAAFBCAYAAAAGz3SWAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjEsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy8QZhcZAAAT/UlEQVR4nO3da6xlZX3H8d+/DGK9whQ7wWFaqJ22wSaCmSCNpkHpBanpaGIoplVCaMYXmGJj06BvtC9MbOKlJW1JRqViY0WCGEhDbJGaWF+IDmqQi9YJgsx0AK9oNBkB/31x1uhWR+fc9uzDPJ9PcrLXetbaZz/nxcqafGettau7AwAAAMA4fmnREwAAAADg6BKEAAAAAAYjCAEAAAAMRhACAAAAGIwgBAAAADAYQQgAAABgMHMLQlV1flV9qar2VtUV8/ocAAAAAFamunv9f2nVcUn+N8kfJtmX5DNJXtXdd6/7hwEAAACwIvO6QujsJHu7+97u/kGSa5PsnNNnAQAAALACm+b0e7cmeWBmfV+SF/y8nU/efFyftu34OU0FAAAAYDy333Hw6939rMNtm1cQOqKq2pVkV5I8ecvTc/KVlyxqKgAAAADHnnOvvP/nbZpXENqfZNvM+qnT2I909+4ku5PkGbW5H3/x/81pKgAAAADMmtczhD6TZHtVnV5VT0pyUZKb5vRZAAAAAKzAXK4Q6u7Hqup1Sf4zyXFJru7uu+bxWQAAAACszNyeIdTdNye5eV6/HwAAAIDVmdctYwAAAABsUIIQAAAAwGAEIQAAAIDBCEIAAAAAgxGEAAAAAAYjCAEAAAAMRhACAAAAGIwgBAAAADAYQQgAAABgMIIQAAAAwGAEIQAAAIDBCEIAAAAAgxGEAAAAAAYjCAEAAAAMRhACAAAAGIwgBAAAADAYQQgAAABgMIIQAAAAwGAEIQAAAIDBCEIAAAAAgxGEAAAAAAYjCAEAAAAMRhACAAAAGIwgBAAAADAYQQgAAABgMIIQAAAAwGAEIQAAAIDBCEIAAAAAgxGEAAAAAAYjCAEAAAAMRhACAAAAGIwgBAAAADAYQQgAAABgMIIQAAAAwGAEIQAAAIDBCEIAAAAAgxGEAAAAAAYjCAEAAAAMRhACAAAAGIwgBAAAADAYQQgAAABgMIIQAAAAwGAEIQAAAIDBCEIAAAAAgxGEAAAAAAYjCAEAAAAMRhACAAAAGMymtby5qu5L8t0kjyd5rLt3VNXmJB9KclqS+5Jc2N3fWts0AQAAAFgv63GF0Iu7+8zu3jGtX5Hk1u7enuTWaR0AAACADWIet4ztTHLNtHxNkpfP4TMAAAAAWKW1BqFO8l9VdXtV7ZrGtnT3gWn5wSRbDvfGqtpVVXuqas+jObjGaQAAAACwXGt6hlCSF3X3/qr61SS3VNUXZzd2d1dVH+6N3b07ye4keUZtPuw+AAAAAKy/NV0h1N37p9eHk3wkydlJHqqqU5Jken14rZMEAAAAYP2sOghV1VOr6umHlpP8UZI7k9yU5OJpt4uT3LjWSQIAAACwftZyy9iWJB+pqkO/59+7+6NV9Zkk11XVpUnuT3Lh2qcJAAAAwHpZdRDq7nuTPO8w499Ict5aJgUAAADA/Mzja+cBAAAA2MAEIQAAAIDBCEIAAAAAgxGEAAAAAAYjCAEAAAAMRhACAAAAGIwgBAAAADAYQQgAAABgMIIQAAAAwGAEIQAAAIDBCEIAAAAAgxGEAAAAAAYjCAEAAAAMRhACAAAAGIwgBAAAADAYQQgAAABgMIIQAAAAwGAEIQAAAIDBCEIAAAAAgxGEAAAAAAYjCAEAAAAMRhACAAAAGIwgBAAAADAYQQgAAABgMIIQAAAAwGAEIQAAAIDBCEIAAAAAgxGEAAAAAAYjCAEAAAAMRhACAAAAGIwgBAAAADAYQQgAAABgMIIQAAAAwGAEIQAAAIDBCEIAAAAAgxGEAAAAAAYjCAEAAAAMRhACAAAAGIwgBAAAADAYQQgAAABgMIIQAAAAwGAEIQAAAIDBCEIAAAAAgxGEAAAAAAYjCAEAAAAMRhACAAAAGIwgBAAAADCYIwahqrq6qh6uqjtnxjZX1S1V9eXp9aRpvKrqyqraW1V3VNXz5zl5AAAAAFZuOVcIvS/J+T81dkWSW7t7e5Jbp/UkeWmS7dPPriRXrc80AQAAAFgvRwxC3f2JJN/8qeGdSa6Zlq9J8vKZ8ff3kk8lObGqTlmvyQIAAACwdqt9htCW7j4wLT+YZMu0vDXJAzP77ZvGfkZV7aqqPVW159EcXOU0AAAAAFipNT9Uurs7Sa/ifbu7e0d37zg+J6x1GgAAAAAs02qD0EOHbgWbXh+exvcn2Taz36nTGAAAAAAbxGqD0E1JLp6WL05y48z4a6ZvGzsnySMzt5YBAAAAsAFsOtIOVfXBJOcmObmq9iV5c5K3Jbmuqi5Ncn+SC6fdb05yQZK9Sb6f5JI5zBkAAACANThiEOruV/2cTecdZt9OctlaJwUAAADA/Kz5odIAAAAAPLEIQgAAAACDEYQAAAAABiMIAQAAAAxGEAIAAAAYjCAEAAAAMBhBCAAAAGAwghAAAADAYAQhAAAAgMEIQgAAAACDEYQAAAAABiMIAQAAAAxGEAIAAAAYjCAEAAAAMBhBCAAAAGAwghAAAADAYAQhAAAAgMEIQgAAAACDEYQAAAAABiMIAQAAAAxGEAIAAAAYjCAEAAAAMBhBCAAAAGAwghAAAADAYAQhAAAAgMEIQgAAAACDEYQAAAAABiMIAQAAAAxGEAIAAAAYjCAEAAAAMBhBCAAAAGAwghAAAADAYAQhAAAAgMEIQgAAAACDEYQAAAAABiMIAQAAAAxGEAIAAAAYjCAEAAAAMBhBCAAAAGAwghAAAADAYAQhAAAAgMEIQgAAAACDEYQAAAAABiMIAQAAAAxGEAIAAAAYjCAEAAAAMBhBCAAAAGAwRwxCVXV1VT1cVXfOjL2lqvZX1eennwtmtr2xqvZW1Zeq6o/nNXEAAAAAVmc5Vwi9L8n5hxl/V3efOf3cnCRVdUaSi5I8d3rPv1TVces1WQAAAADW7ohBqLs/keSby/x9O5Nc290Hu/srSfYmOXsN8wMAAABgna3lGUKvq6o7plvKTprGtiZ5YGaffdPYz6iqXVW1p6r2PJqDa5gGAAAAACux2iB0VZLnJDkzyYEk71jpL+ju3d29o7t3HJ8TVjkNAAAAAFZqVUGoux/q7se7+4dJ3p0f3xa2P8m2mV1PncYAAAAA2CBWFYSq6pSZ1VckOfQNZDcluaiqTqiq05NsT/LptU0RAAAAgPW06Ug7VNUHk5yb5OSq2pfkzUnOraozk3SS+5K8Nkm6+66qui7J3UkeS3JZdz8+n6kDAAAAsBrV3YueQ55Rm/sFdd6ipwEAAABwzPhYX397d+843La1fMsYAAAAAE9AghAAAADAYAQhAAAAgMEIQgAAAACDEYQAAAAABiMIAQAAAAxGEAIAAAAYjCAEAAAAMBhBCAAAAGAwghAAAADAYAQhAAAAgMEIQgAAAACDEYQAAAAABiMIAQAAAAxGEAIAAAAYjCAEAAAAMBhBCAAAAGAwghAAAADAYAQhAAAAgMEIQgAAAACDEYQAAAAABiMIAQAAAAxGEAIAAAAYjCAEAAAAMBhBCAAAAGAwghAAAADAYAQhAAAAgMEIQgAAAACDEYQAAAAABiMIAQAAAAxGEAIAAAAYjCAEAAAAMBhBCAAAAGAwghAAAADAYAQhAAAAgMEIQgAAAACDEYQAAAAABiMIAQAAAAxGEAIAAAAYjCAEAAAAMBhBCAAAAGAwghAAAADAYAQhAAAAgMEIQgAAAACDEYQAAAAABiMIAQAAAAzmiEGoqrZV1cer6u6ququqLp/GN1fVLVX15en1pGm8qurKqtpbVXdU1fPn/UcAAAAAsHzLuULosSRv6O4zkpyT5LKqOiPJFUlu7e7tSW6d1pPkpUm2Tz+7kly17rMGAAAAYNWOGIS6+0B3f3Za/m6Se5JsTbIzyTXTbtckefm0vDPJ+3vJp5KcWFWnrPvMAQAAAFiVFT1DqKpOS3JWktuSbOnuA9OmB5NsmZa3Jnlg5m37pjEAAAAANoBlB6GqelqSDyd5fXd/Z3Zbd3eSXskHV9WuqtpTVXsezcGVvBUAAACANVhWEKqq47MUgz7Q3TdMww8duhVsen14Gt+fZNvM20+dxn5Cd+/u7h3dveP4nLDa+QMAAACwQsv5lrFK8t4k93T3O2c23ZTk4mn54iQ3zoy/Zvq2sXOSPDJzaxkAAAAAC7ZpGfu8MMmrk3yhqj4/jb0pyduSXFdVlya5P8mF07abk1yQZG+S7ye5ZF1nDAAAAMCaHDEIdfcnk9TP2XzeYfbvJJetcV4AAAAAzMmKvmUMAAAAgCc+QQgAAABgMIIQAAAAwGAEIQAAAIDBCEIAAAAAgxGEAAAAAAYjCAEAAAAMRhACAAAAGIwgBAAAADAYQQgAAABgMIIQAAAAwGAEIQAAAIDBCEIAAAAAgxGEAAAAAAYjCAEAAAAMRhACAAAAGIwgBAAAADAYQQgAAABgMIIQAAAAwGAEIQAAAIDBCEIAAAAAgxGEAAAAAAYjCAEAAAAMRhACAAAAGIwgBAAAADAYQQgAAABgMIIQAAAAwGAEIQAAAIDBCEIAAAAAgxGEAAAAAAYjCAEAAAAMRhACAAAAGIwgBAAAADAYQQgAAABgMIIQAAAAwGAEIQAAAIDBCEIAAAAAgxGEAAAAAAYjCAEAAAAMRhACAAAAGIwgBAAAADAYQQgAAABgMIIQAAAAwGAEIQAAAIDBCEIAAAAAgxGEAAAAAAYjCAEAAAAM5ohBqKq2VdXHq+ruqrqrqi6fxt9SVfur6vPTzwUz73ljVe2tqi9V1R/P8w8AAAAAYGU2LWOfx5K8obs/W1VPT3J7Vd0ybXtXd799dueqOiPJRUmem+TZST5WVb/V3Y+v58QBAAAAWJ0jXiHU3Qe6+7PT8neT3JNk6y94y84k13b3we7+SpK9Sc5ej8kCAAAAsHYreoZQVZ2W5Kwkt01Dr6uqO6rq6qo6aRrbmuSBmbftyy8OSAAAAAAcRcsOQlX1tCQfTvL67v5OkquSPCfJmUkOJHnHSj64qnZV1Z6q2vNoDq7krQAAAACswbKCUFUdn6UY9IHuviFJuvuh7n68u3+Y5N358W1h+5Nsm3n7qdPYT+ju3d29o7t3HJ8T1vI3AAAAALACy/mWsUry3iT3dPc7Z8ZPmdntFUnunJZvSnJRVZ1QVacn2Z7k0+s3ZQAAAADWYjnfMvbCJK9O8oWq+vw09qYkr6qqM5N0kvuSvDZJuvuuqrouyd1Z+oayy3zDGAAAAMDGccQg1N2fTFKH2XTzL3jPW5O8dQ3zAgAAAGBOVvQtYwAAAAA88QlCAAAAAIOp7l70HFJVX0vyvSRfX/RcYEAnx7EHi+DYg8Vw7MHiOP7g6Pv17n7W4TZsiCCUJFW1p7t3LHoeMBrHHiyGYw8Ww7EHi+P4g43FLWMAAAAAgxGEAAAAAAazkYLQ7kVPAAbl2IPFcOzBYjj2YHEcf7CBbJhnCAEAAABwdGykK4QAAAAAOAoWHoSq6vyq+lJV7a2qKxY9HziWVNW2qvp4Vd1dVXdV1eXT+OaquqWqvjy9njSNV1VdOR2Pd1TV8xf7F8ATW1UdV1Wfq6r/mNZPr6rbpmPsQ1X1pGn8hGl977T9tEXOG57oqurEqrq+qr5YVfdU1e8598H8VdVfT//mvLOqPlhVT3bug41roUGoqo5L8s9JXprkjCSvqqozFjknOMY8luQN3X1GknOSXDYdY1ckubW7tye5dVpPlo7F7dPPriRXHf0pwzHl8iT3zKz/fZJ3dfdvJvlWkkun8UuTfGsaf9e0H7B6/5jko939O0mel6Xj0LkP5qiqtib5qyQ7uvt3kxyX5KI498GGtegrhM5Osre77+3uHyS5NsnOBc8JjhndfaC7PzstfzdL/yDemqXj7Jppt2uSvHxa3pnk/b3kU0lOrKpTjvK04ZhQVacm+ZMk75nWK8lLklw/7fLTx96hY/L6JOdN+wMrVFXPTPL7Sd6bJN39g+7+dpz74GjYlOSXq2pTkqckORDnPtiwFh2EtiZ5YGZ93zQGrLPpMtyzktyWZEt3H5g2PZhky7TsmIT18w9J/jbJD6f1X0ny7e5+bFqfPb5+dOxN2x+Z9gdW7vQkX0vyr9Mtm++pqqfGuQ/mqrv3J3l7kq9mKQQ9kuT2OPfBhrXoIAQcBVX1tCQfTvL67v7O7LZe+qpBXzcI66iqXpbk4e6+fdFzgQFtSvL8JFd191lJvpcf3x6WxLkP5mF6LtfOLEXZZyd5apLzFzop4BdadBDan2TbzPqp0xiwTqrq+CzFoA909w3T8EOHLoefXh+exh2TsD5emORPq+q+LN0O/ZIsPdPkxOky+uQnj68fHXvT9mcm+cbRnDAcQ/Yl2dfdt03r12cpEDn3wXz9QZKvdPfXuvvRJDdk6Xzo3Acb1KKD0GeSbJ+ePP+kLD107KYFzwmOGdN92O9Nck93v3Nm001JLp6WL05y48z4a6ZvXDknySMzl9cDy9Tdb+zuU7v7tCyd2/67u/88yceTvHLa7aePvUPH5Cun/V29AKvQ3Q8meaCqfnsaOi/J3XHug3n7apJzquop079BDx17zn2wQdWij7mquiBLz1k4LsnV3f3WhU4IjiFV9aIk/5PkC/nxc0zelKXnCF2X5NeS3J/kwu7+5nTy/qcsXd77/SSXdPeeoz5xOIZU1blJ/qa7X1ZVv5GlK4Y2J/lckr/o7oNV9eQk/5al53x9M8lF3X3vouYMT3RVdWaWHuj+pCT3JrkkS/8R6twHc1RVf5fkz7L0TbefS/KXWXpWkHMfbEALD0IAAAAAHF2LvmUMAAAAgKNMEAIAAAAYjCAEAAAAMBhBCAAAAGAwghAAAADAYAQhAAAAgMEIQgAAAACDEYQAAAAABvP/DJEFt8KgU7EAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20,10))\n",
    "plt.imshow(np.power(np.abs(abd_stft[:,1000:2000]),0.5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x177f3e610>"
      ]
     },
     "execution_count": 142,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import librosa.display\n",
    "librosa.display.specshow(np.abs(abd_stft[:,1000:1010]),sr=200)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 234,
   "metadata": {},
   "outputs": [],
   "source": [
    "abd, chest = preprocess(sigs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x13f14f290>]"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(14,6))\n",
    "# plt.plot(sigs[0,rng], 'y')\n",
    "start = 4_000\n",
    "end = start + 10\n",
    "#x = np.arange(start, end)\n",
    "#plt.ylim(-0.2, 0.2)\n",
    "plt.plot(np.linspace(start, end, (end-start)*64), abd[start*64:end*64], 'b')\n",
    "plt.plot(np.linspace(start, end, (end-start)*200), sigs[0,start*200:end*200], 'g', alpha=0.4)\n",
    "\n",
    "#plt.plot(np.linspace(start, end, (end-start)*64), chest[start*64:end*64], 'r')\n",
    "#plt.plot(np.linspace(start, end, (end-start)*200), sigs[1,start*200:end*200], 'y', alpha=0.4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 267,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1fdb22090>]"
      ]
     },
     "execution_count": 267,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(14,6))\n",
    "# plt.plot(sigs[0,rng], 'y')\n",
    "start = 41\n",
    "end = start + 20\n",
    "#x = np.arange(start, end)\n",
    "plt.ylim(-0.2, 0.2)\n",
    "plt.plot(np.linspace(start, end, (end-start)*64), abd[start*64:end*64], 'b')\n",
    "plt.plot(np.linspace(start, end, (end-start)*200), sigs[0,start*200:end*200], 'g', alpha=0.4)\n",
    "\n",
    "plt.plot(np.linspace(start, end, (end-start)*64), chest[start*64:end*64], 'r')\n",
    "plt.plot(np.linspace(start, end, (end-start)*200), sigs[1,start*200:end*200], 'y', alpha=0.4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 232,
   "metadata": {},
   "outputs": [],
   "source": [
    "# resample to 64Hz\n",
    "abd_64 = signal.resample(sigs[0], int(len(sigs[0])*64/200 ))\n",
    "chest_64 = signal.resample(sigs[0], int(len(sigs[1])*64/200 ))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 231,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x18fda0550>]"
      ]
     },
     "execution_count": 231,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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51+4FYsX07MzZJPtm0hQM0t295rifW4ghUkQfpdLUUhaX3AhATc2dvLzpxzQ2/RVATrxCjLmRKaIBUjz5OPxn0NFXQ39/+XE/nxDiKI3Ql2IAr8PLOcXnsLRoKUopslNOoicCHR0rj/u5hRgiRfQxSPbNwG5PIxrtxuOdyUCwmuDAXnrDvWZHE2LCGOmRq1R3Kh73NN5qgcZWaekQYiyNZE/0kOKUYlw2FwDp3gIi1jyZXChGlBTRx0ApxbfOfZVl8+4hN+c2lLLS2fkuvSEpooUYS1of/5JYQ0pSS7hg2vVE8LO9/pXjfj4hxNExjJHriT5QwBVAOafT0racpqYnR/z5xcQkRfQxSgksIDdlAVarj/TAQrq73qM31GF2LCEmDI1GjfDI1aTkSXg8U2lqf0/as4QYY3oE27MOlOxKJi3tEt7sLOat9dfQ2PjoiL+GmHikiD4OfqcfgOz0ZUSjfdQ0Pm9yIiEmFn0c2wQfit1qJ8k3k42tTby758A9o4QQo0VrjRrhz/O+0txpWK1usnP/lfV9RTy75rMYhqyqJY6PFNHHIeAMAFCWdwkORwaVNX9E6yg1XTU09TYRjoZNTihE4tJaj+hEpCFu91QA3q18imAkyM7WnfSF+0bs+YUQBxuNnuh9JTmTALBaXcwsuJEdnT1UNzwz4q8jJhYpoo9DijuFq2dezYLcE8nLuprO3gpWbvsZL+16iWe2P8Prla+bHVGIhKZH4aRrsWVgtfppb3+dx1d9kpe33cu6+nUj9vxCiEMbWifaYhn5nmilFEnOJDI8GSwpvQWr1UtV/SMj/jpiYpEi+jiluFNQSpGWfCqNxiTe3P4rDCM2At3c12xyOiESl0bDCE4sHKKUIhA4jUiki421L1Jb+3u6g00j9vxCiEMbqXXfD+eaWddw2fTL8DqT8PsXUt/yCpFIz6i8lpgYpIgeIaFomPSMK+kMttLU9Chaa2yW0fmHQAgxZORPuhdOuZCTp36N0tJfUFjwLaLRHvbU3s/T257muR3PyYRDIUbBSC9ZeSgWZcGiLHjsHvz+E+mPDFBZ9wh/2/Y3uge6R+U1RWKTInqELCtZhtczjdTU8+nsfJum5kexKPnfK8Ro2f+kax2x581Pyufs4rMBcLkKmJpzPhUNL7G3dQ0NPQ0EI8ERey0hxD6Gt/0e3QEop9WJx1NKxJLBmzvuoLGnjoc3PcyainsIBqtH9bVFYpEqb4Tk+nP59NxPc8vie0hJOYeO9hX0dr9vdiwhElpsdQ4rSqkRf277YF9mScHnsVhcVFf/lMrK79IgG7EIMeJiEwuHriyNfE/0vpRSeOxeOt2fYGvzdurr76ax8UFe3/BZtm27cVRfWyQWKaJHkN1qJy8pj4yMK7HZU2lu+4fZkYRIWLF1oqOjNmp1/ezruWnOTfhd2Uya9F0yM6/B0BE27/j68NJ6QoiRM9o90fvy2D0k+ReSmXUdPT0bcATfJWyfSkfHm3R0vD3qry8SgxTRo2BR/skEkk6hvWsTwWAN71S/Q313vdmxhEg4WhujdsJ12py4bC6cNid2eyrZ6ReSkX45K2p3snzr/47KawoxUY1FT/S+Ut2pAKQkn0V29qc5Z969pGV/iaqBZKqr/3vUX18kBimiR8Hc7LksmXIbYQ07qv/EluYtvLjrRbY0beGprU+ZHU+IhBA76UZHZTmsfQ3NbchLysPvX4jDkckb2/8Pw4iitcaQUWkhRoQeo55ogMUFiylNLQUgOXAKC4tvpTRtBvWWM/l7+ct0d8uyluLjSRE9SpK9pbhcpTy68e7hE+07e9+htb+Vlr4Ws+MJkRBGcyR6yFBvdFFyEUpZSE29iIGBWjbt+SOvlL/CPevuob2/fVQzCJHoYu1ZI79k5eE4rA6WFi1lWfEyrpp5FQCnTzqdwqxLaAp72FnxHwB0DXQRMSKjnkfEJymiR4nf6ScQOIVQqJHdld+mtvaPRKOx9Sif3vY0zb2yhrQQx2PopDvaRfT09OlcNOUiSlJLAPAnnYjLVcRf/vk1KlpWA/DE1idkiSwhjtNYjkQPKUopIsWdAsQK67OKzyc19ULKG/5Oc8vLPLr5UVbsWTFmeUR8kSJ6lCS7kvEnnUJm5rWcWbiE3t6N1NXfOzwhqSPYYXJCIeKf1qM3sXCIUoq8pDwAbp5zM5+eeyu5ubejlJ3a2t9TW/tH9uz5EU2dG0Y1hxCJbiwnFh5OpjeT1NSl9JDDBzu+htYGdd11puUR45sU0aPE7/BjUVZSUpZy5vzHyMz8JH29W2ls/AutrS9RWfuA2RGFSACjX0Tvy2lzxiYcOjLIyfkM4XALAwPVhMLNbKv40ZjlECLRaK0Hi+jRWbLySNksNrJ9ebS7Lubdum3U19+NUw2YlkeMb7Kl3iixWj7c/MFtd5OZdjbB4B46O2NL52zte5Yzp38ZhyPDrIhCxDWtY9t+j0X/5IGyfFlEdZTS0juxWOy0tL5AbcvztLS9RXrqkjHPI0S8G+0lK49GmjsNv28B4VATra0vsqfi3+iaPI+kpIVmRxPjjIxEj6KLp17MFTOuAMDn8JGV9SmKin5CfsFXaA1Bs2zaIMQxi23OMPo90YdyTvE5FKcUY7HYWVK4hLyMS9ndn8Tv37qNrmDnmOcRIhGMRXvWkZiXM49TC0/lwhN+yOTJPyCCm/c3fZpoNGx2NDHOSBE9inL9uaR70gHI8mahlGJq5olcOvMLBA0na6ufNjmhEPHNrJOuy+binOJzuHTapczImEGGL5e0tMvo7N3F/SvOoqptM1UdVTL3QYijMF6KaI/dQ1lmGXOy51CWcyrOlKt5pWozT6/7mkwgFvuRInqMnJB1AhZlYW72XErSppCfXMbmhnfMjiVEnDNnJHpIti8bgGXFy/jcqf/HjEmfY2/7Rn7/2hk8t/V+3qp6y7RsQsSTsVr3/Wi5bC78vvl4vDP5Z8XdPLNF5jOJD0kRPUYCrgCfmf8ZMr2ZAMzOO5vW3npq2zeZnEyI+BTriY6Mi5Eru9VOwBWgKOdqCgq+CTrC3r0/p69vi9nRhIgb42Ukel82iw2lFFmZ1wOK8j0/IxKRli0RI0W0SU7IvxKLgjV7HicUDQEQjARNTiVE/PiwJ3p8jVy53ZO5bvFTZHvTqKj8ITua1vP4lsdlZ0MhPoJGwzgsomMj5OBwZJCb9zl6B+r53fKzhpfjExObFNEmSUteSIrTx/qal/nT+z9lV8s2HtzwIDVdNQDUd9cPf3iFEIc23kauhq405SbPpnTydxiIBFlT8Ts6gh30hnpNTifEeDc+rizta072HJYWLeX62deT5C0jI/M6qtvWU7H3LrOjiXFAimiTKGUhO2UB3d1r2bPnB9y/chmGjlDXXUdddx3P73yeDY2yeYMQH83cnugDlWWWceWMK8nyZZGaVIbdPZPttU9i6AhVHRXsbt1hdkQhxqVYe1Z03F1ZclgdlKaW4nP4mJI2heTA6bhcRby77XtsbFgtV5gmOCmiTVQ66Rvk5H6GjIwrGBiopb3t70SMCP3hfiA2Gi2EOLQPT7rjp4hWSpHmSQPAaXXiDZxDX6iTxoa/8OCK07n/rU8Mt28JIfY33q4sHciiLCilyMy8mg2tLbyw8cfsbN1pdixhIimiTeRxZZHkP5FZhbfg98+nte1Fmru2D/dGS4+0EIen0eP6pOuyufB4ZuJ05tLVtQrQ9PVtZ1u9rNghxIFicxzG7+cZIGJEAHC7S2Ln7JYXWL35s3R0vGlyMmGW8ftunQDmZM1BoZidNZuGrnJ2VvwHq7d8nrbUmTRHsgn5ptMfOhe3w2t2VCHGJT3O2jn25bQ5UUqRk/NZQqFGnM58Kiv/i8qGp5mTf7bZ8YQYd/Q4WW3ncLz22Lk41Z1KNOtT2O0Z7G59j7Xrl3Ha4kr29vbxeuXr3DTnJlw2l8lpxViQkWgT2a12FuQuwGF1cNnMG/jkKX/BnXQ65V2dtLcvp2rvnbyw7vNmxxRi/BqHPZRDhk64pxZdSlryIhyODFJ8pdQ2v2JyMiHGJz2OP88A83Pmc27JuSwrXobV6qUg53rScr/Bps4I23f/NxsaNhCJdFHdttnsqGKMjN+vfBNMpjeTTO9VNIa87O3aS4Y7mZb6X7Fh77NcfmIQm1W+1Qqxr9jqNeN3JDrXn8vVM68mxZ3C1LSpOKwO/jqwmrq6e+jt3YbXO8PsiEKMG0ObrYznItpqsTI5eTIA18++HofVwZNbn2RX60k8tOEucnMUDQ338VZHFlPOLUcpGadMdPI3PM6UZZbhsrmYlFLK3Mm30R/u5qHV35etRoU4lHHcE62UIsWdAoDP4cNhdZCTdi4DhqKx8a8mpxNiHBrHn+cDDX2mHVYHqakXoI0wtbW/Resojd2VtLa+YHZEMQakiB5nCgIF3DTnJubnzKcg80JstgB7ml7mnb3voLWWyYZCDBrvEwsPJcVbiM09l717f0Fn1zqz4wgxbsQ+z+O7J/pQzph0Bk5nNoHAEpzOXOZM+wUhSzrV1T+VvR4mACmix7GAK4WkpFPo6dlMQ+sbPLz2h/z5g/vZ2ryVcDRsdjwhxoH4KqJ9Dh/pmTfwZquH37x2Id39jWZHEmLcGO890YeS4c1gadFSsrJu4IYlf6cs5zTcgWXUtr5HZ6esxJPopIgex9x2N4HAYgA2VfycdTt/SH3dXbxZtZK/bf+byemEMNfQOtEWS/ycdEtSS0hyZ+HPuI2O/iZeWPcFsyMJMY7E15fiISUpJVww5QJmZswk4AwQSFrMW21+3tr8ZXoGuqjvrqcj2EFrX6vZUcUIi7936wTjcGQxafJ/oY0B8p3d/LPiD9TX3Q25nzU7mhDjwPidWHgoLpuLq2deTU/oAp43trCp5gU+EazH7coxO5oQphlqe4jHdg6IzX8oDBQCsZFpi8VBZuY1rKi+l819N5GWduHwY29fcLtZMcUokJHocW5e9jxOmXQJXzzlJ1x54u/JzLiOnp71tLY8x3s175kdTwjTxGNPNMTWj07zpLGg9N8ZiIbZVnkHANtbtrOxcSPBSJDlu5fLzoZi4omzK0uH4nP4WFq0FL//RPz+BbS2Pk9Pzwa0bA+ekOLr7DMBnZh34n63U1KX0Rusoa3tFd6tmEKmN5PilGKT0glhtvgroodkJ8/D5zuBipp7mFX6Xd6siu161hPqYXf7btI96czNnmtySiFGX2y3wvG/7feR8jv8KKXIyrqB/mAltbW/x2ZLIiPzOqJGdHj7cBH/4v/dOsEY2iAz81qCwUoaGu5je+YCcv25sjuSmHCGeqLj9aTrd/pJSTmP9/b+L+uenYbTVYjNmsSbba8SCJxOa18rUSOK1WI1O6oQYyR+P8/78jv9AFitXoom/5Ce3k20t/2d+rq7uWNFiHmTb+Cc4nNMTilGgrRzxBmtNRaLg9zczwGKlZu/wv1r7zQ7lhAmid+Trsvmwu0uITPzWuz2NPr7y+nofJPm5qepq/sDu9p2sKpmldkxhRh18d4TfSC3zQ1Auicdi8VBkn8BBQVfw+OdRkPDn9m492mTE4qRIkV0nDk5/2QAbl34NeZM+QHaCFJV/Uver37N5GRCjC1DRwHibkmsfSmlSElZyjfOX8U3LtrB1Cn/R3b2LfT17aCh4QHW1yznjd3LGYgMmB1ViDEQf0vcHYpSipvn3Mwnpn+Ca2ZdA4DF4iAv90s4Xfk0Nj5MONxhckoxEqSIjjOzs2Zz+4Lb8dg9OF2TyMv7NyLRdp5f+zl6BtrNjifEmNHDRXT8jlydlHcSC3MX4nf6Sfekc8WMKwgETiEt/VK6u/7Jnj0/5OXVl7G9Qb4ki8Q11BMdz+1ZB3LanFiUhWRX8vAxi8VOdtZNGNEuKiq+ZWI6MVKkiI5jESOC213MlfP/l/7+Cpavu0l2SBIThpEARfTc7LnMz5k/fNtj9wCQnnYRkyb9Fzk5twJW1m79N3oHZORKJLZEaec40OKCxSzMXQiAyzWJQPLZlNfcRUPTC0SNqMnpxPGQIjqOZfuyAZhX/Hmm5X+S9XtfYNXWb2LIUjpiItARIL6L6AMN9VICuFwFJCUtIjv7Fra0VHDHa5fTF+o1MZ0QoyxBi+iyzDLyk/KHb6enX8rq7gJ++frVvIUACkoAACAASURBVLrjIROTieMlRXQcO63wNK6ccSUum4slM76L8pzEUxt+wXMbfsz2lu1mxxNiVBk6giKxiuihZa/y/Hn4HD4ApuacS3LKUjo6VvDMWyewfOsv6Qx2mhlTiBH14RXUxOiJPhSbJfbvlM/hIy9pMpk5X0QpO29u/gab61bSE+oxOaE4FlJExzGbxUaaJw2AGRkz+PLS55mWPou3t/83y3c9SWNPo8kJhRg9OgEmFh7KrfNu5YIpF7AobxE2i43zSs7j86c/Qk7uZ9jU3sfLH3ydFTv+ZHZMIUZMoq0TfSh+hx+FYnHBYsoyy7DbU8nL+yLRaA8PvnUxK3fdb3ZEcQykiE4gKZ5Mzpp7HxZlpaHhPjr621i+eznlbeXSdyUSjtbRhBuJhtiXY4uyUJJawq3zbsVutZPhzSQ37SyKJv8XyZ4C1u/6Ef39u82OKsSI0To+dyA9Unarnc8u+CyTkyeT6k4FwO0uprDwP7BaPeza9RV6e7eZnFIcrcR8t05gk9Lmkpl5A/X19/DU6ttweRdQ3joVi8XJeSXnMSl5ktkRhRgRmvifWHg0FuUtwqIstGYW8/iqK1m17nzqvf9OXvJMdrbu5NySc5mcPNnsmEIcE42RkF+KDyXgCnBi7olYLVbeq3mPgoJv0Lz3e/z6jWu59YxXyfHnmB1RHKHEf7dOMA6rg+sWfJ/73ymntX0ldKzCavWRmnoBdZkzpIgWCcMwIig1MU66ACWpJQBkeDPIzf0ib9b/nr7ot9kTOJm+vp0s73udzyx51OSUQhy92O6jsQnxidaedTjzcuahtcZtc7O7fTddXRfT3PwE71Xcz+Vz/9PseOIISTtHAspPymdh6TeZMuVO8vO/jNOVT3PzE2yvvMPsaEKMmERYJ/pY+Bw+pmYvJS33m1gsDtralhON9tDU+Bjt7W+YHU+IYxJr5ZhYn2elFFPSpuB3+klOOROHI5PVu37B+9t/zJ7WzWbHE0dAiugEtbhgMameLArST6Mg/yv4fHOpbV2BHvy2v6VpC7vbpadSxK+JWkQDnF96PosnX8Kisrv4/uUNLJv/F7rJ5PX1NxKJdJsdT4ijEptYOHE/z167F4uykZ19C1q5eHzd9/jDa0uIRmVJy/FuRIpopdT5SqkdSqlypdS3D3G/Uyn12OD97yulJg8eX6aUWquU2jT436UjkUfEeq6umXUNF5ReQElKCfMLLqIz2EFX1xoGQu08+u5VPL9FZviL+KWH14meGJd/92VRFhbkLuDyGVeQ5M5kVtZcJuXdznuNtWze9b3hxz2/43n5sizigh5u55h4RXSuPxcAr2cKM6f8D3l5/0ok0kF51a/2e9wHDR+wYs8KExKKwznuIlopZQV+B1wAzAQ+qZSaecDDbgPatdalwK+A/xk83gJcorWeDdwMyKrjI8zr8HJ28dmU5lyCoRXldY9xx4pb6evbTlfX+/s9ti/cR8SImJRUiKNjJOjqHMci25fNdfO+hsc7h+01f8EwIgQjQep76lm+e7nZ8YT4SFprNLEi2mKZeF+KM7wZnF10NueXno/X4cXnK8PrncWGil/ud2Xpn7X/ZGfrThOTigONxEj0SUC51nq31joEPApcdsBjLgMeGPz5SeBspZTSWq/XWtcNHt8CuJVSzhHIJA6QnVSK213K6j1P0N4eO6n29e0kFA0BEI6G+cvGv7Byz0ozYwpxxCZyO8ehBJwBUpNPpbWvhY6O1+gekLYOEUcm+JfiktQS8pPyKUmJTSBOS7uE9mAHNTV3HvRY2ZV4/BiJIjoP2LvP7ZrBY4d8jI5dg+0E0g54zJXAOq31wAhkEgdIdiWT5J/Lrva9RKM9LMxbQihUT0t37FLv7vbywf/Gbu9o2cH7Ne/L+tJiHJvYJ90DKaUoSD+TqqCHHdX3sKJyOe0dK+np2Ux1RyU7W3fS1t9mdkwhDqLR8qV40JzsOVxXdh0ZgdlYPYtZvePHbNz7zH6F84vrv3LQlWRhjnExsVApNYtYi8fnPuIxtyul1iil1jQ3N49duARhtVgpyToHgMmpszl1xncBqGx8lcaWv/PQ6/Pp6l6L3WrH0AYrq1ayoXEDTb1NZsYW4rCM4Z7oiX3S3Vdp2jR8vgU8v+NZNuz6Pk2ND1Nb+3/89tUTeH7Tr3ly65NmRxTiMCbWEncfJcmZRMAZIOS7grVdfv666hZaunYA0Nu3jRXbf8PaLbfvs126MMtIFNG1QME+t/MHjx3yMSp2xgsArYO384G/ATdprSsO9yJa67u01gu11gszMjJGIPbEc9606zllype5ZtH95Keficvm4J/VL/F/b32R/kgQW8f9dPZso6471mETjfbT1r0FQxvyYRXjjmHElsSaiD2Uh3NC1glMz7uEgWiYYHA32dm3kJf3Bez2NOrq/kBL64v8ef29NPQ0mB1ViP0k6g6kx8putWOz+cnN+xcMI8jraz9BNNpLS/PfAEV9x0Y6OqT90mwj8W5dDUxRShURK5avA64/4DHPEZs4uAq4Cnhda62VUsnAi8C3tdbvjEAW8RF8Dh9Xnfjr4duFKWWUt76JYQRJS7uYIucm3qn9HdszZhCNBtm79xf8s7mBFVXfZ0b2YpYWyeIpYjyRy7+HUpp5NuVNF5PiK+OMKTcSNaK8Uz2ThsaHaG15jo7210mPruKiBX8iNi9cCHMNTSycaOtEf5RFeYsoSCog15/LfZFOdjT+jr7WHxMJt5OVdQN9Ay+xdfdPmV02H4fVgcvmMjvyhHTc71atdUQp9SXgVcAK3Ke13qKU+hGwRmv9HHAv8JBSqhxoI1ZoA3wJKAW+p5QaWpfpXK219BCMgZzUk9jZvA6r1c+/nH4fHb27ebf5At7d/EVClhwGBmrZGnbgijyCzZ4hRbQYV6SH8tDKssrwOX9Jti+bJGcSAKtqVpGTfStnlN7IuxX3s7vmXjYHCkjOvBWAgkDBRz2lEKNKo0E+z/sJuAIEXAEAMpIXYhi30VV3F05nLoHAaTS1dbNtx3O81nAR2ujm60sfweudYXLqiWdE3q1a65eAlw449r19fg4CVx/iz/0E+MlIZBBHLytlCfBH0tPOJ+DOwGp1U1jwTWpq7sQV2U0g+0YMI0RT02MM9G00O64Q+zHkpHtIDquDqWlTDzqulGJRya20GTlsLP9/PLDmx0yapHE6c7l5zs04bbIwkjCPnmDbfh+NHF8OfeH5zJ7yIy6ZdiVBknh5Z5iO9uV0d69G6wgVld/jhLInDvqz5W3l+Bw+sn3ZJiRPfONiYqEwR1bqYgoKvkZKSmzCocvmwm5PpbDwW8yffieBwGkEks+gKLmY2oa/8lblyyYnFuJDWkekh/IouW1urBYrWVnXY7G4qW+4l0ikm75wn9nRxIQnX4oPpzilGACHq5i0pBnkJeVx64J/4fS5j1BaeicpKWezq/Yp+vv3UNlWzitb78YwYl9KXq98ned2PGdm/IQmRfQE5rV78XimMjRl0GaJ/eNltXqYlHkmABZl5eRZvyAa7eUfG79JeHBd6YgRkcmGwlTSznHkLpt2GYvyFqGUwqqs2GxJZGffTGiggaqqn1Df+qbZEcUEprWe0DsWfpyilCJOyjuJJYVLho9ZlIUzJp/FtPRpJCefRUWv4hdv3MKf376Sf3xwOxt230FPqMfE1BODvFsnMK/De9j7srxZXDHjCkLREBEjQkbmVTQ1Psr6XT/hxGk/5L719zEjfQZLJi057HMIMbrk8u+RyvJlkeXLAuCsorNo7GnE57iMx2zJ1NX9iQ+23MT03Cb6ImGsyorb7jY5sZhoZHWOjzY3e+5Bx/xOP2dOPpO2/jY2tiyke3C1Dpcjg3e3fQ+P7+SxjjnhyEj0BOa2xU6U09OnH3RfhjeDdE86uf5cCgOFXLfgZ/h9c1iz82e0dm0CYFvLtjHNK8S+DGnnOCY+h4+S1BICrgAuVyGZmdfSE+qio2MFD296mIc2PmR2RDHB7L/ZinwpPlq5/lxSU8/DZkvmc6f+jvnTf0Z1n+bV9Z+ht3cr6LDZEROWFNETmFKKW+bewmmFpw0fm5o2lVx/Lha1/1tjUvIkFk79FpV9Nl5a+2lp5RCm01qWxDoeLpuLm+fcTIp/DiHtoqXl2UM+LhgJ8l7Ne7J7qRhlhnwpPkbzsueRHSjjU2e8ydRJXyQnMJ2MzE9R3VFOTc2d7Cz/Co+8dR5/3/YnOXePMHm3TnAOq2O/22dOPvOwj12Yfxa7Gq5hQ90DZOm3SU6WVg5hHi07Fh43p81Jhi+HFvsJvLzjcVw5J6CUhdcrX6ckpYRJyZNYW7eWLc1bSHOnMSVtitmRRQKK9UTLHIdj5bQ5uWbWNcO3PXYPSUkn4vXOpr+/nN7eDayve4vo3n+Q645SNvmLJqZNLDISLY5Yti+bz5/6ezye6TQ1PUJv7xazI4kJTE66I2NK6hRwzqK6u5VgsIqBgXo2173GqxWvsnLPSvoj/QAMRAdMTioSmWy2MnJmpM+IrcRjdeHzlZGVdQNFRT/D45nKe5v/hYaGWMuWoQ2CkaDJaeObvFvFUXHbPUyZ/FW27/4xtXV/oLX9KtJSYiPSoWiIroEu0j3pJqcUE4EhPZQjojBQiM83G1A0NDxAKNQAaFyuIhqbitBGELs9iw2WHtLcaeT4c4b/bH13/X63hTgW+262YrHI5/l4OW1Ozis9j2e2PzN8zG7zMLP4O7TW/ZTdu79FVtanWFe/jnX167h21rW47W45fx8DGYkWR+262Tdz6YL7sFq9rN7+n+xu3w3APyr+wdPbnpbeSTE2ZDb/iHDb3VitXjze6YRC9QQCp5GVeR3Zrghdnavo7d1CS8vfWLfta9zz1nVEorE2mj0de3h+5/Nsa5YJxuL4yRJ3IyvgjO12uChvERdNuYirZl5Flj8P5VlMKFRPf/8uartqAVhdt5rlu5fz9Lan2di4EWPw72JfncFOqjqqxvR3iAfybhVHzWF1UJx+AoGkU/lH5UtUWJ7iiyd9ndru2AeyJ9QzvF2pEKNFdiwcOfNz5hOO3EI02oPLmU+SM4kFOT/ijT1vABCOdNLe9irt7a+xeefXmDP91/SEejB0mF21jzE9/XsoJWMy4ngMzXGQkeiR4LQ5uXXercP7PwCkulPR9kmE+qGjYyWaDACqOqoIG0EUNt6reQ+3zX3Q/IfHtzyORnP7gtvH9PcY7+TsI45JwBnA7z+R1tYX6eleQ3+kH0NHUVh4bMtjXD7lDFZV/4PizDMpyywzO65ISENFtNXkHPFvYe5CarpqaOptAgY3YrJ7hu932lO49dQHufft63l162+oC2cwOecKOtpXsqv5CbK8KSya8mWz4os4p7XGGFyGzWKRNcpHyr4FNECaOw27PZN1jcnkd6ygO3w+oe6V1La8QSjUgMXiwuWaRFd6FhxQROvBbdkiRuSg553I5P+EOCZOmxOnMwenq4Curn+yo/YZdld8DcMIYbG4+NmOXgB253+Zssxfm5xWJKKoEUWhZAR0hCwrXsYbe96grruOLF8Wuf5cFuYuxKIs5Cfl47K5yMy8jj17dvB2+d3sDWXQ2Rnb6fD5zb9lbtHncdqc+z1nOBpGow9aBUiIA2kjtu67xeIyO0rCyvXnYrVYCdpKeWDzi+Tll2DrehirtZjU1PMxjF66u9eyo+I7zC+8hDf2rKChp4HrZ18//Bytfa3DGzcdDa0Namp+Q07Op7HZEudKtZx9xDFLdiWT5D+JYHAPf33vFqzWZFJSzsHnm09GxtU4bEn0dsl2wmKU6KiMQo8gr8PLxVMv5pzic1iYuxClFPNz5jM3ey7pnnQ8dg9KWUhOWcpAsJq2tpcJhRpxuSbR319OdcvbQKx3siPYAcDDmx7mzx/82cTfSsSD2GYrQyPRzo95tDhWVouVRXmLcLunEol00tjwEMlOLyWTvkFGxifIyrqBjIyrae3ewraqeyhvKz9o6/Bndzw7/PmG2IICDT0NADT0NBCKhg752t3da6mo+AqtrS+M3i9oAimixTG7ZOol3LTw+4DCbkujoOCrZGRcwYIp3+Lfz3qQ04qvoq3rA7p6K8yOKhKQoaPSDz0KilOKD9psCcCiLKS6UwkknYzV6qGl5VksFje5uV9AWWxsq/ojAI9teYzHtzwOyLJ44shpHR6cKCw90aPJbXfjcU8DIBjcQ2n+ZylILgXg1IJTyUlfCvZiHlz9n7S1Laex8a/09Q/Od+rZTE3Nb+jsaxh+vneq3+G5Hc/R3NvMczueY8WeFYd83f7+cgCi0b5R/O3GnhTR4pi57W5ykssoLPw2hYXfZHb2Ij47/7NcOOVCXDYXJfm3AJrtVX8AoKarhhd3vkhdd92o5Glqepzq6l+MynOL8UhGosfa5dMv55ySCwgEYrucJgUW8YVF3yAzeTG765/htX+eRVvb30dkV7RotI/29teP+3nE+BfbbCWMxeJEKWV2nITmsXtwOLKwWv0oi4OZxd/g7KKzuWnOTczKnEWqOw0VuIJwuJXm5ifo6HiT+1aeTzjcRkPDffT2bqGq9g/Dzze0jvzq2vdpanqC7XsfOuTr9vfHBtMMI7HWpZYiWhwXpRSnFl/BJdOvZsmkJSilhv8RnJx+Isn+Mp7afA+728opbytna82TvLL6KqLR8Ec+7/aW7Yf9Rns49fX3UFf3x2P9VUScMaSdY8xZLVZSXCkkp5yNxzuTlORzsCgLs4u+SL/zJFbVldPc/BT9vR8QNaJobWAYoUMumfVxGhr+zIYNZxMKNY/CbyLGG0NHpJVjDLhtbpRSZGRcSXbWjfjcuSilcNlivejpnnQcrilMmvQdrj3t72Rnf5rKts1UVf0/DB3C7S5lT809DISahz/XWmvW7Pof2tuX09L8FNHowYXy0Ei0YfSP3S87BqSIFsdtYe5C8pPyDzrusrm4as7XiUQ62Vn3NP3hftraXmRLwyruWfUluga6GIgM0B8++ENV3lbOztad3LX2LvrCR3b5Z2Bgb8J9QMVH0FEsUkSPOa/Di92WTEH+l7nmhM8CMCv7FNIybyYj/zskewupb3qUP7z/U6qq/h9VVf/NXWv+gKENDG3QNdAFxJbC/Kg15T+8/Ns9+r+UMJVGo42wFNFjwG2PrX4SCJxCUtJJB90/JTW2KkdWYDaFqbMJBE4mEDiNaLSHjIyryMq6gd5wH79ZcRN3r72bqvYdNDY+QGfn23i9s9A6dp4/UKIW0dJQKEbVtPwb8Dm/Ql3TUzgDEUKhZiwWFxW1j/BXxyySXKl0h7r5/MIv0BnspKarhpLUElr7WolGgzQ3P8pKj4PZeeccslAforUmGNwr/whPIDISbQ6XzcXFUy8mw5OB3RrrXy1IKiDVnUp7fztLZnyP59d8hj17fgBKoY0I7e3/oDd0EzVdNbyz9x2unnk1j215jKLkIpaVLBt+7t7e7Xg801BKEQxWA4l30hWHNtTOIUbX0IhzsiuZ80rOO+j+FHcKl067lFR36vAAVmbW9QQCizm16GrWN6ynw3EaDS2v0Na1lmg0SDTaQ2rqBaSlX0JV5beprP0z2rOETG/m8PN+WEQnVjuHFNFiVFksNjLTzmFr/RO4u7tQFge5eV+gZu+vqKm5g3C4CYcjm7qpF7GleQc7Wz6gqWcuA9EBWlufo7NzFat3W9jbFzpokffugW6qOqsoyywjEunAMHqBo79sLOKTRiYWmiXXn7vfbaUUV8y4glA0xO723QQCS+jpWU9e3r/S1vYKrW0v0dy1nfZgGEMbbGzcCMC2hpXMzpxOW7CbbOteNm48nzlzXicl5SwGBmK7oyXaSVccLNYTHZHl7cbIp074FE6rE6vl0IMQ2b5sgOG5DRZl5fp5Xyfdk05VZxXRtGsx7AX09mzEboe87Gux2LLJT8qnxXcSj297mYKeXzDdtpqc7E8xM/9awuFGAKLRxPpSLGcgMepy0i9kd92T9PVupzT7HHIzzmCBt4bndz6PxzOFnp5N/G3tl/F6ZlJZ+XPqatJJSb2Q9vbXURY73d1rycz8JIY29ls14JXyV2gPtlOaWkpkYC8QG7XSWsvklAnAMMJYLLL+8HhhURZcNhcum4usrBvIzLqOkpQp7LT5qdyzmffWn01LOIUuyxSi0Ytoa/sHbW0vc3fn22RlXc8C67MA9PZuJCXlrOGR6EQ76YpD0zqMVUaix8S+Gyl9FKfNSa4/l1kZs8jwxnY3THYl09rfyuzCT7GseBkvl7/Myfknk+HJoCPYwa6mRbS3L6e6+n+pBjyNTSwJWmnph1x34l1ZkiJajDq/Ow+fbzY9PRuZmX8tZ0y9jKhxMduMu1HKgqfvedbvfQalnsfhyCYc6aK+/j5stgCZmddRV/cnens30RvqgUg9fm9seZ723krau9YTjFxDdLCIBghH+nDYvWb9umKMxC7/ShE93jisDpRSpLuzWJi7kMqOSnJzPk95z3oikU76+16jo3Ml2ohgs6fQ2fk2fv9JbOl+mV094Ej5gJycfsLh2O6JiXbSFQfTxHYsVEpGosebi6devN/t4pRiKtorSHImYbfauXTapcP3pbhT8HmK8XhnYFF2lMVFT886NtS9SXMH+G2QlWBXlqSIFqNOo0lL/wROZwEO9ywgNsv/7OJz6BroYmbaNexqmU0UN8WTvkp/qJPmlqcIBE7F456GzRagq2sVd/zjPLq63uPcOb/ihMJraWl+nNbOtbT3fBPHYBGtNdy7/i7m5pzMKQWnmPlri1FmGGEsSoro8cY62Kdus9iGdyr0+crw+coA8NDFzpoH8Him4/cvoLLyv6it/S17DY3NnsrW5o3M3edLsbRzTAyxiYWJs5NdopqcPJnFBYuHJyAeaGbGTCLGvwOxdaW7u/5JV9e7AGzrSyVjcGJxopAiWoy6cDSMy5mHy5m330SD0tTS4Z+XznuI7S07OLlgCW9Xv82ckv+guS+2tJXffyLt7csBUBYbb5ffTXlPiNbOdQC0dn1AKrGTbtCILZW0q22XFNEJLjYS7TY7hjiA1xG7ClScUozX4eWyaZehlMKiLDy97Wlm5i6lj6Thx0/NuYCOthfosc7GavUQDlUSDFZR2w/pDqhoryQtTVq0Ep3WEWnniANKKcoyyw57/6K8ReT6c3ml/BU8nikoZSXYv4N8X4DGsIeXqys4ee4YBh5lUkSLURc2YmtCLyteRlFK0SEfMyuzjKrOavKT8rll7i0Y2uDBDQ8CcN38/+RPb7xNWtqFGDpCa8tzNDc/CcQmPbR3b6GqdxsqBJs6Ic0TIsmZdMjXEYnD0GEslmSzY4gDJDmTuPGEG4eX0sryZQ3fd+MJN+K0OekL91HbVcuykmU49Hm8tbacGvtlNHdtpLP7fdbVvM669tifyVbryUivpDil2IxfR4yBfTdbEfFNKUVhoJDFBYtZV7+Oi4pPYaDnbRopoLmrD0N/9B4R8UaKaDHqTis8jfdr3qcwUHjYx6R70rlpzk37HZucPJnp6dMJOAOUlN6BRVmJRLpZZH+Fl+pX4/HOJDRQS0PnZna2VdI3uJx0qg4TcMplwURnGCFp5xinhgrowx0/rfC0fY6mcsGSbQA8sfY7bO54ju0Nbwzfq3V4uC1EJK5YES090YmiLLOMsswyqqsb2L37bVJck1Hdu9BGYhXRstmKGHXpnnQumnrRYZfTOZxzS86lMFCI0+Yc3lTj3CmXkZfzKewWSA6cjsOZx7aWLYTD7ajBSWaGETri2ccifhmyOUPCSfVNpS8Kte0bhv9uDSOEQlo5EllsYqHsWJiIUlLOBSDgKUYpR8KNREsRLca9fUehSlNLKSr6EUmpl+L1nUCqt4jQQD2RSDuz0yezICU2oiESn6zOkXhm554FQCgaxO6ItYFoHUYPtm6JxKUNWeIuEfl8cygp+SWFuZ9CKRvaCA+vP50IpIgW497Q2tBDPZFOZx4nT/kyFmXl4qmXonUUrSOUpM4g1w1WZchJdwKQIjrxZPoLSXalAOBwxCYhy5fixDfUE42SIjrRKKUoKPgqGUkzsFgcaB0yO9KIkp5oERdunXfrfhutnDn5TE6fdDrBvi3Dx5J902jpil3mF4lPlrhLTGneQtr627Hb0lEWmxTRE4RstpLYfA4f01OyWNe3DY1OmBYtGYkWccFmse1XRFuUBZvFhsczHTV4PNk3Y/DexLpcJA7NkNn8Cemk3AVkuWBZ0RJ8NieGDsnnOcEZ2kDrqEwsTHBOmzvhBrmkiBZxzWJxYrfHeieTBotow0isy0XiYHr4pCsj0YkmIzCLk/5/e3ceZ2dZ3///dd1nm3Nm3yczyWQlgSRAICGRTRCC4gauiGDFolLb2m+r37ba2u1nW6u1m239+qttLdqvqIiKoJRdxAVkCSEkIRtknUxmJrPPnP2+r+8fZ2aSkIUZZjnn3Of9fDzyyJwl53z05p77c3+uz3VddbC0YQ0XNZRjvWy+Q5JZ5nkpAN0U+5zjlPluZElJtBS9mvKFGBMgEhlbQk8TkXxvfBc7XXT9p7r6UhwnRnn5eRgTzVWidT77mpLo0hBwyrA2i+e5+Q5lxqgnWoreh1/3JQaGNxEI5Ja189vEBTmZ5yWxoJ5oH6qq2sDrXz8KjF10fTb8Kydzx26KA2rn8LXxmyTXSxAMhPIczcxQEi1Fr6F2PQ216yeqGZ6Gf31PlejS4DiR3BJ36on2NXfifFYS7WfOxF4OScAfuwqrnUN8w5gwYABNRPI7JdGlIRCI+q6HUk5m1c5REsZvkjwvkedIZo6SaPENYwyOE9XwbwlQEl0axiciqSfa39QTXRrGk+hsNp7nSGaOkmjxFceJYjURyfeO9UTroutnjpNb4k78zbNKokvB+PEdL4L4gZJo8ZVAwH/rUMrJVIkuDQGNLJWE8Uq0Jhb623jRw1U7h0hhyg0XaSKS33leEmvROtE+5zhRPE0s9D3dFJeGYEA90SIFLdcT27U9jgAAIABJREFUreFfv9NFtzQ4TgTrqSfa745VoqN5jkRmkxkbaVAlWqRA5XqiddH1OyXRpSG3Ooduiv1ufIk7o/PZ1wLqiRYpbI4T1bbfJcB1c5UMJdH+Nr7DmfibeqJLgzFanUOkoKlyVRomKtHasdDXckvcudpAyec0slQaAhM90ak8RzJzlESLrzhOGWgiku+N99TpoutvzliPrOuj4V852fjooc5nfzu2Oocq0SIFabwnWvxNlavSML45g+sqifYztXOUhvFKtPXRTbGSaPGV8Z5oTSz0t/GkShddfwsGc5Voa/0z/Csnm0iiAzqf/czR6hwihW18x0Lxt/FKtDHBPEcis8kx/rvoyslcL4kxAYwJ5DsUmUW539dGq3OIFKrxiYXqifY310tinBDGmHyHIrMoEMhVov00EUlO5nkp3RCXAMc4GCc0sbqSHyiJFl+ZWCdaSbSveV4Sx4QwKIn2s/GJhX6qXMnJrE1jdD77njEGx4R8NbKkJFp8xXHKMICnlg5fyw3/qhLtd2Z8Nr8mFvra+MiS+F+uEu2f81lJtPjKscqVkmg/87ykhn9LgNo5SoPnpXIjS7op9j3HhPCsKtEiBelYEq2Lrp95Y5Vo8bdgIAaoncPvPC+tm+ISYDAYE9YSdyKFaqJyZf1zksrJvPGJheqh9LXxdcC12Yq/5SYW6nz2O2MMxqid4yTGmGuNMTuNMXuMMZ8+xesRY8x3xl7/lTFm0XGv/dHY8zuNMW+aiXikdDlONNcTrUq0r7luUsO/JWBiZEnrRPuOtR6dnbfjukncsSRa/M+YEFkfrc4x7fETk1vY8cvANcAh4GljzD3W2u3Hve3DQL+1dpkx5kbgC8D7jDErgRuBVUAr8LAxZrm11p1uXFKaxhdzV0+0v6mdozQExpJoPw3/Ss7g4C/YufPXCQRiWC+FcYK6KS4Bxgn5qj1rJirR64E91tqXbW6Xi28D17/iPdcDXx/7+S7gapM7W64Hvm2tTVlr9wJ7xj5P5DXRxMLS4GpiYUkYvynO+mj4V3JGR18A4OWjz9OdGMHRTbHvGQyOCSuJfoU24OBxjw+NPXfK91hrs8AgUD/JfysyaY4TxRj1UPpdrhIdVg+lzwXGJhZatWf5RtbL8vyR5xkafoGjKXho/xPsHR7RyFKJyFWi1c4x54wxtwG3AbS3t+c5GilU4xMLtfW3v+XWlS3PdxgyyxwniDGObop9ZEvXFp45/Az1o8+Q8iCb6cfajCYWloDxzVZUiT5RB7DguMfzx5475XtMbgy2Guid5L8FwFr7VWvtOmvtusbGxhkIW/zo2MRC/5ykcjLrJXGMeij9LrckVkibJ/lIb7wXay3Do7uIZyGT7cPaLMaEsGinWb8zJuSrm+KZSKKfBs4yxiw2xoTJTRS85xXvuQe4Zezn9wCP2ty+zPcAN46t3rEYOAt4agZikhI13kPpqifa1zSbv3QYn1WuSkki8RKp1OETnutP9pN1hxhKDZHwAjhuP29sdljT0EZVpCpPkcpcGF8nWkvcHWesx/njwAPAi8Cd1tptxpjPGmOuG3vbfwL1xpg9wCeBT4/9223AncB24H7gt7Uyh0yHMzGbX0m0n42vzqHhX38zxmAcf01EKhXWemzefBU7d35k4rl4Yi8DyT7SqcOMumDKVhIxwwQZYWX9/DxGK3PFGeuJztVRi9+M9ERba+8D7nvFc3923M9J4L2n+bd/Dfz1TMQhoh0L/Wt4+FkOHfoSZ5/9X1riroQ4JqjzuUgMDPyUnV2/om3eTRzu/TndIweozRzF89J4XoLHnlhFV+piYuEmjqbAVC2jKvgC1qYnRhHFv8Y3WwE70cJT7IpmYqHIZAQCuZ5oVxdd3+nq+hZdXf/NokV/gWezuR0L1RPte7nhX53Phepo/Cjburdx+cLL2bHrE3x3z2aWLHEZGHiMvj44pyrO+cNPE08c4hc9CVLe46xf/HqysSpaa86nyfkBgJLoEpHrfQfPS+A4xZ9Ea9tv8ZVcW75W5yhGnpdh+/abGR5+7pSv/2TfT9k8AInEHtJerkKpdg5/MxgcJ0RuMFMK0c/2/4ydvTvZ3fULdh99DrD09T/M8PBzlJUt5MUh2HbobrYc/A5pG8FaFxN/lDctupB3r7qRSCD3OUqiS4Nxctdovyxzp0q0+IoxBscJa/i3CI2MbGZPxx2EI+1UVl5w0ut7+veQzeSS6F3DEK6JMr9KfZR+Nj78q4nChaMv0QdATVkNjnGIBCMA/OCFL9EzANHoWQwO/BRrXerqbsbaLP+z+15SqQ7KK85nXWMz4cS9lJevJhI5tjiXkmj/y222kks7/TLPQZVo8R3HhHXRLULd/T/npz3w0/2Pn/RaJjNANjMAQCKxm4EMtFe1UB7WWtF+p9U5Csf2nu3ctf0u7tp+F08eehLIbZ4CMDy8iUhZO2889y/YUOfylnmGG9b8IeWxFcTjO3HdEa5d/j7esPoLGBOmsnIDgUCMYLAOUBJdCnI3xWEs4LqqRIsUJOOEsVaV6GKSyqa4f3duZcw9fXtOeO3IyBH2dz987PHAdpIu1EVr5zRGyQ9jwnjecL7DEKBntGfi55f7X+aSBZcwkh6hJRplae1eFiz8LMuX3MTW5HexNsui+vMoi66A/ke5tMGwdvGHCIXquPjiQ4RC9QBEIvPJZvtwnEi+/mfJHBrvg1Y7h0iBCjhhPFWii8oL3S/QMbADgHS6h+FkH5VldWzq3MQzh59hYCBXnXacMu7b9zwA9dGGvMUrc8NgctsE66a4ILieSzLVgZsdYsjtZ/MLP2bbrp+xrAKqIjC/5X0ArFr1PRibr/CO1R/j7v6vsLjxUkKhXNU5HD62YVoksoDR0S2qRJeI8XlLSqJFClSucqWLbjEYTXbyg50PkHGTpNNHCIfnkU530j24mcqyq3jm8DMApNKHcZwIZdFFJOK7aSmD5ormPEcvc8ExIa3OkQdZL4vBEHACE88d7nuc/fs+O/H4nv4qAsGFtFY3sqDx/cRiywEw5lin6NKG83j3BZ+lqmr9Kb+nrCzXF60k2v9ym634qydaSbT4juNEVIkuAtZ6PPbkenYPNVJTeyUA1dWX0NPzPTr7n6Ot+iwOHvoSzU03k04dJhxupbWijerwDlqjEA6qH9rvcputhLA+ueAWk69v/joV4Qret/p9E88d6nmY+rIY797wTbb1d9OTtMRCMa4572Ycc/opVosW/elpXxufXKgk2v9yPdEBrAW/7KuniYXiO44J4WmJu4K35eDdHBo+xMjIcwz0PwLAhzd8DscYeoa2c6Dj34mPbic0eg9Re4RIpJULW86jNbefji66JcLR6hx54VqX/kQPO3Z8hD1HHuQXB35K7+AmVrVuZEXbO2itOQ9jDPOr5p8xgX41SqJLjYMFcptdFz9VosV3cne6/rjL9ZsHt/4VLWUOK5f8IT/a/h/0DRsCgQpscgsXNi2gsWo5NdEWeoZ3cSjzS4wTpDz9K9wADIVbqYy1M95Jp4uu/+WGf8NYm8Raq8115sj4yhv9/Q+ze+CHPLntAeobrsPzEixsficAK+pXsKdvD2ta1kzru6qrL6Oi4kJisRXTjlsKnzG59iBrM3mOZGYoiRbfySXRqlzlQzY7THf3d5g371aMcRgefpb+/odpb/8UQ6khfrLjX0mnu7kqM5/R0S1Eo8uorn4dFzn/TVPT5QA0Vi7myMCz1FckaKh/L9XhTVSlXyLTeAENFWfRPfZdSqJLxNjwL3hA4FXeLNM1kh5hS9cWstlh+vru5xeBejKZQ3R1fRPHidLe/BYAaqO1fOC8D0z7+6LRxaxb9+y0P0cKn8HA2KiFXyrRaucQ3zEmAKpEz6mMmyHjZnhq52f5+8c+SkfP/QD86LlP8Y2nPs1I/GX2920lne4CLE/u+htSqQ4qKs6nquoSmptvoaXlQwA0VCxjMJXgl72GyqqLWL3iS7TVbeC6Vb9JLLpw4juVRPufMQbDeOXKHxfdQpHI5MZ0rLUkM3EAktkke/v3Yq1Hb++9eF6atrbfYUHtSoJ2kIqK86nRqjgyDUbtHCKFzZgAnpLoOfW9F7/HcGqYroP/F4A9nT+mqe5qnuv8Fa4Lz+z9vwxlc0Px0egSTGoHIQduuuD3icWW0hD72MRnNVatAiAWW04oWE1rw0YWt7z1pO9UEl0ajDn+oqu1hGfCrt5dPLbvMd59zrv56eYP8Fz3y/zW1Y/xxKEn2N3xXfr6HySb6eOKpe/h3OUfpNy+ic1brqF1ySen1f8spW18YiEoiRYpWIYA1lMSPZeGUkMkEi/RHz+CMQ6dfY+zp/snuO4IAAeP/pS4raA+EuJDV36fHc9vwIYW0t6w4aTPWlD/OgCa6y7lPSvfM7GtMEAwWE0gUIHrjiiJLgEGw/iAqV8uuvmSyqbY07eHJZUVvHjoe0A9hwb38cSBR/G8NE/v/SYdw/10d3+bpqoVXL/+cyxf8Os4ThhYzDVXDKknXWaAkmiRgmZMAA8l0XMl4+YmiAwOPkFdJIwpfz27jj7C3vSXgVxluXvgKUZtPa3V59Baez6hVd8kGKw55ectano9f/CmR6ivuZRQ4MTKozGGSGQ+8fgOJdElwpiAr4Z/8+WJQ0+wq3cX9x/9Ou7oLylv/TybD96D56WJBh2efvnfsBbW1NdzwxueJBI+8fxUAi3TlZso7K+bYo3LiO+oJ3puPHnoSTZ1bqI/cZRkqoOzw5t518obWNR0FUMZS3//w4TDjSxqeRuHR0cYjO9nXk1uw4XGxndTW3v1aT+7pf6qkxLocZHIfEDtHKXAGP9NRMqXeCaOZzMc6X+WriQMDz/Fwd4nKA8GuOys3yYe30MisYezF//+SQm0yIyZWCfaH+ezKtHiO46WuJsTW7q2kE53UTH4D+zvG2BRo2HevI8yNBJk064wnpekvvISyssvmPg3bQ2XTft7c+vKBnCc0LQ/SwqfJhZOz13b76I8VE48Eyc++iKel8JxogwOPgHAuU1rWbnof/HortsJBCpY3HZrniMWv8pNFPbXTbGSaPEfJdGzLu3mlhDs6fk+B+IJLlz2v9m4+qPEYitYHuknFl1OhbuVd636IH3OUiJl7SwJH2Rl2/XT/u7a2qtJpQ5N+3OkOIwP/3qeP9aVnWvdwwcYHHycutqNVHg7iYWiVNReR3f3dwC4aOH/oqlyPu3tf4AxYarLavMcsfjZiROFi5+SaPEdxwS1OscsGEmPcGDwAOc0nMMvD/6SeHwXIyObaWi4nkuXf5xYbBGQWz/2pvNu5ciBzzC/6S0sCtVTe9GfYlMvEg5Nf5i4uflmmptvnvbnSLHwV+VqLiUyCQYGHqW398fE4ztYET3C6nlX0hfeQKL/Li6uc5nX+DbKgmXcvObjGGMIBTTCI7MjN1FYm62IFLTcna6X7zB850e7fsRQaohUNsWOnq1093yXYKiG2tqNzKuYd8J7Vyz6XZa0fYBwuBGApQs+ko+QxQccn/VQzqXB1CAjI5sJBquIx3cSicKy1nfydL8lUn4R1eU9lJfnlpSsjaoCLbNPlWiRgqeJhTPNWstQagiATYce5cCBf6IpcIhLzvtniK4/YRk6yP2iHE+gRabDcXKXKb9cdOfKlq4tPP7yvaRSHTQ2vhfHCdNStZ+z59/A0/3fYX7rR1m76nqtuiFzJjdR2F9zHJREi++onWNm9Y0c4OnO5wBIpQ5z8OAXWVYR5N2vu4/6+jfnOTrxPS1xd0bJbBJrLdFQlFQ2hWtdyoJlPHnoSUZGNlMdgoqKNYTDDVx8wa0EnSBXLb6KhlgDoRlorxKZCk0sFClwxjiqRE/T4eHDVEeq6Yn38NVHryCb6aGu7q309z+EMSGuXHcv9fWX5ztMKQF+u+jOtIdffhiD4a3L38odL9xBxsuwurYS103SFtjN0vZz6aleRddoF8Gxqv6yumV5jlpKVW7kw/HN+awkWnzHmCCeVU/0a5VIdfP9rf9JONxMKnWYVPIAwWA1Dam76LNR5i/4AxrGtuYWmW2OKtGnlfWyHBk5gpt4gcSClWS8DPH4Lu7Z/fc0lEU5pyZJa/Ofs779rWQ9/f8n+ZWbWAgQ9M35rCRafMeYAOBhrVW/3xQ8uvdRRtOjPLvzM4zGd7NkyV8zPPwMYPj1Kx4gnNlJ/MBLlEXaiIVi+Q5XSoXPeihnUvdoN6l0D/sP/DNbyrYA76e//1E8E2NV6xsp8zbT1HQTQSc4UYUWyZfx67ExAd+czzqrxHfMxEXXxRj9J/5KWS9L0MlV60fTo1RGKhlMDrKnbw/JVAfDIy8A0Nf3ACMjm4nFltNctZxYaA1lXV8HIHKa3QRFZpqjJPokrudijOHw8GGGhzfhWtjV9RhD0SWMjGymru4aLl/z31SEK/IdqsjJjCrRIgXLnDD8q//Ej5d209y++XbWta4jnomzvWc7l7dfTtdoFwD9fQ9hnDDlsRX09z+MtR61tddQFak6oaqvCr/MmYltv/2xruxM+Mbz35i4ER4efoZIpI2tIyO4g7cDloXNb1cCLQXLT5VoJ98BiMw0Va5OL5lN4lmXx3Z+mb54DwDPHbiTBzd9kKNH72F4+Cmqqy/hDSv/FGstYPjA2j8l4ARwjH5dyNxzTFDrRL9CxsuQyCYYTnQQcfdRVbWBurprWRjzeNtZ13Dj+R/Ld4giJ5noiVYlWqRwGSXRp5V208RHX6Tj8NcI2yFClVezdf+/kc70siT2Y3qtobZmIyuaL+OJ+rfSGgvRWrMi32FLCTP4a3OG6XK9YysPxUc3s7oaDlespbKshYtaIyxY8Hvqf5aCZgjgef4YWdKZJj6kJPp0UtkUqdQhAA523UNtFlKpTua1foQ3r/0EXc9/nXC4kXmV8/j0G+85qW1jTcsaqiJV+QhdSpQxQSXRxxlJjwCQyfTjJH5JW8taYrUXsr5tPXXRW/McncjpTVxPVIkWKVyOLrqnlXbTpNIdBAIxXC9NT89dhMPzaKi5hOrKlUSjSyfee6q+5/Vt6+cyXJHcuu+U5vmcdtMks0nSw48xOrqVhQv/mOH0MCMjmzly5Haawh5LF///NDRcm+9QRSbNTz3RSqLFd9TOcXppN0061UFZ2VIikVaGBx6gvuHtVEWq8x2ayKmV6Pkcz8S5c9udpLJJFox+Di9zkHh4AwkvRmfn7YTCDSxf/v/R0PC2fIcqMinjPdFGlWiRwpW7y9VsfshNJHyp7yVWNq7EGEMiM0I6fYSLF15DBxextOkKBr26iRaND5z3gTxHLHKiUttsJetl2dW7K1eBdtOMjr7Arv6DHIzDc5m/IhRsxPMSNDd9gGjZ/HyHKzJ1SqJFCpcq0cd8/8XvM5IewUk+xbJ51zE0uhtrXS6av5FrG2+ga7SLx/c/PtGmoU1UpNA4Y2u9l8r5/OzhZ3m+63miwShlwTKGRn9OV6aGyqrlDA0+gROI0Vx9NtHoIi01KUVFm62IFAH1ROdkvSwj6RESiZfYtO1vCSR/k6FEEyEHKirOIxQIMb9qPjede1O+QxU5g9JaJ3o0MwpAIpsgZkZpYgvVLTdz+dKb+PfH3ornpbhixecYdpbwuvmvy3O0Iq+FKtEihavEK9HxTDxXwUoNAdDX9xAHRmDb5v+iteEqQk6AWEzL1klxKIWRpQODB7h/z/0sr1/Onr49xOO76Om5iwp7gEsby3jdhn8gHG4iGl2G646wrPU91Mca8h22yJQc64lWJVqkYAWc0hr+PV5vvJfvvfg91rWuo7aslnS6Z2Lr7nh8F/u6H6Qh1objhPMdqsikHJvj4N/z+bnO5xiNv8j27BCpdCcdHf9CMFBDe9uvceF5v0843ARAW9vHAZfqspr8BiwyHUqiRQpYCVSuTmdr91YAjsaPgvXo73+I5jKH8nm3cujgP5FOH+HKdi1TJ8WjFCrRvYNPcujgPwG5/72hUBMLFnySpW2XUVFx7sT7zmpYxcv9L2szFSlKx3qig75pz9KZKL5jSnizldHMKJ51+eX2P2Z0ZDNhx+Vd536Q5kUf5j+GnqGn5y7m1a7Nd5gik+b3OQ7JbJLDfU9jnCDnt9/M3v6XaWi4jmCw6qSJvhuXbMRam6dIRWaKKtEiBcvvF90zSWQS9B69h+HhZ6muvpxF9Ws5e8XncJwyqqsvYTS+nYaGt+c7TJEp8PdmK53DncTjO3jz4ou5esPtPHP4GQaTgySyCeZXnbyEnVbkkGKXa9FK5TuMGaEkWnynFIZ/AVzPpXu0m/pYPRk3QzQYpavvcfr67qe6+jJaWj5Ae/1yAoEoANcsezu1qz9Ieaw+z5GLTJ7j+HOd6JH0CA/seYCu4X1k0h0sbvpNANa1rstzZCKz48TNVkbzHM3MUBItvuPXiUj9iX4AaqO1ADzf9TzPHH6GqkCKl7vux0s8S3+8i0ikjaam9wHgWW/i3y+rWzb3QYtMk8EBjK/OZ9dzueOFOwCIJ3ZSG4KG+o15jkpkdh0bRdESdyIFy6+V6O9u/y4At629DYC9fVvp6PgyO0e2AIZYbAUtLW9kWctb6UkMAnBW3Vn5CldkRuQuvP7ooRxKDbGlawu1kSgHDnyRqqoNJJMHWFVdQUWF5ipIidDqHCKFy+890U91PMWh3if51Yt/QmM4ScO897Gi7QaCoRr29O3h7MbzeGfjOfkOU2TG5EaXin82/+7e3Wzv2U5f38MkEntIJPawpi7G8parcbTihpQIrRMtUsD8Woke91znJvbv/1uME+R9lz1Ke+NlAKSyKYJOkMW1i/McocjM8stFdzg9jGdd+gceIRpdiuOUEWIbNTVvyHdoIrPuhJ5or/jPZ1ASLT5kjP82Wzm+t3lo6AlSqQ6uOfdzEwk0QCQY4fULX5+P8ERmTe7CW7xJdNbL0jncSVN5E8OpYaLZl8hm+mhuupFY7BxW1iZobb0t32GKzKHiPZ9fSUm0+I4fK9HJbBJrLVl3iKNHf0g0upj25uvzHZbIrDPGFGUPZdbLsm9gH32JPjYdforI6N0cHtpLldNPONxEefm5GOOwbOGtBAKxV/9AkSI3MbHQJ+1ZoCRafMiPSXRP/9O89NIf4LrDALS2/gbzq09eQ1bEj4qxnWNr91ae6ngKz8vQcfjLxEd3EIudzYLyFhYuuJl4oIbrV1xPNBTNd6gic6sIz+fTURItvmN8OLHwcM8Pcd1hGhvfQzS6jI+s/xPKw+X5DktkjhTfRXckPYJnMxw+/BXSiR20tHyI6uqLuWb525lXOS/f4YnMuYme6CI8n09HSbT4jt8q0du6t3Hf7vuIlM2nru4aAFWvpGQYiqudI+2mCQfC9Ma76Tz8Vaq9bWxc93cMhzewdt5anbsiRXQ+vxol0eI7jgn6arOVnx14nERyL9XVl3Lj6hvpS/ThGCffYYnMmWJp59jes52fH/g51624jm17/5UGu4V3rf8ybW2/le/QRPJuvCfamFBRnM+ToSRafOdYJbp4Jy4kkwf45VPriLX+FbWBONZLs6b1SqoiVVRFqvIdnsicKabNVvb07QHg0ZfuY3DoZ7xp1S1KoEVO4hTF+TwZSqLFd4q5J7o/0c+2nm3sO/gvPHeoh7Kjf0Nl5Tpqw7Bxxa35Dk8kL4wJ4HnFc1N8sOdB6oJZli/83XyHIlIwTlgnugivz6eiJFp8p5h7ou/ecTep7AgvH7qXQKCcZHIfrjvM/Ng8omVajUNKT7GsE739yK/Yc+QBysvPZXDw56yuXUll5QX5DkukAAUAi7UepshbE6cVvTGmzhjzkDFm99jftad53y1j79ltjLll7LmYMebHxpgdxphtxpjPTycWkXGOCQGFn0R3Dnfy1We/ynAqt2xd1suS8TIMDj2J6yZ403lfoDJSSSbTS23VmjxHK5JHBd4Tfe/Oe/nuM79HR8f/4cCBz5NKdbB64a/nOyyRgnKsJ7p4C12vNN1bgE8Dj1hrzwIeGXt8AmNMHfDnwAZgPfDnxyXbf2etPRu4ALjUGPPmacYjUjQn6BN7/o2hoaf4xcFfsL9vO9/7+RvYv/9v6D16L5Gydq46+7dY2fYeAOqr1+Y5WpH8yF14C7eH0lpLx9B+RkaeIxKZTyrdSSgQ4ez2j+Y7NJHC5IN5S+Om285xPXDl2M9fBx4DPvWK97wJeMha2wdgjHkIuNZa+y3gJwDW2rQxZhOg8WqZtvHhoUK96AL0xbt4ctff4roJ0ukufv7CdpLJ/USjywgEoqxu/xDGGBa2fpCd/fupqr4i3yGL5E0hr87xbOezjI5uxfNSNDa+h1CokYVV9YRD1fkOTaQgFUuhazKmm0Q3W2s7x34+AjSf4j1twMHjHh8ae26CMaYGeDvwpdN9kTHmNuA2gPb29mmELH5X6D2UO47u4L5t/4zrJigrW8hA349wraG19TYqKy/kIxd+ZOK9FWWNNDe9D2u0tqyULuMU5kSko/GjbOrcxNDQUwQClXxg7aepjzXmOyyRgjQ+sTDXE10iSbQx5mGg5RQvfeb4B9Zaa4yxUw3AGBMEvgX8s7X25dO9z1r7VeCrAOvWrZvy90iJMYU5/Jt20zy+/3GGhp4mEKjgo294GHfgW9y9v4PKygu5qPWiE9aAXl6/nCMjR1jTop5oKU25C29hns97+vbgugnKsy9w0wW/SWP5qepIIgLHeqIppUq0tXbj6V4zxnQZY+ZZazuNMfOA7lO8rYNjLR+Qa9l47LjHXwV2W2v/aVIRi7yKQl1XNp3p5z+f/T/gVDIy+jwrWt7IotplUPunXFe1m+qyaprKm074N6FAiKuXXJ2niEXyr1DP52+98C0GEkeww/eyvi5LS/NN+Q5JpEiUUBL9Ku4BbgE+P/b3D0/xngeAzx03mfCNwB8BGGP+CqgGPnKKfyfympkCrEQ/s/332PPyN4iUtWO9DE11x+5Pz6o/K4+RiRS2XE90Kt9hnODo8G4OHvwi7WWjLFj9+1RVbciPeJIGAAAan0lEQVR3SCJFIdeA4I8kerqrc3weuMYYsxvYOPYYY8w6Y8x/AIxNKPxL4OmxP5+11vYZY+aTawlZCWwyxmw2xiiZlmkrxOFf13P5yb5HCAZrWBrpJBSqJ1a+Mt9hiRSFXBJdODP5E5kEAwM/wbNJzlv13yxd+sVjQ9UickrHNltRJRoAa20vcNJYs7X2GY6rLltrvwZ87RXvOQTot47MjgKbzX9oYBd9ox0saX0fl53zeyT3PoYz9otERE6v0G6KM26GH+26h+HhZ6goP58L26/Pd0giRUZJtEjBKsR1Zbv6nwDgrWe/nwWNF3EkkWJtq9Z+FpmUArkp9qzHUx1PcfDoz3HdEW6+4BNUhCvyHZZIUfDjZitKosWX8r2urGc9BhK9RGw35eWr6Bp4GoB5dZcTcAJcsUjrPotMRiFNLPzG898g7aYZGn6KQCDGgmZVoUWmypoABiXRIgUsv5Xou7bfxd7OH3CO/TZLzrmb5w//kqpoG5FwXd5iEilW+b4phty8hrSbJpPtp5XnOWfFeykLqQotMlkTPdFj7RyeVzjzHF4rJdHiOwaT13WiE5kEA8kB4vHt/HQEno7/Mankfioqzs9LPCLFrFA2TxpKHqWz878YHn6KNzQZzl/yu3mNR6RoFcGuwpOlJFp8yeTxopvIJrDWIx7fjeNEScS3EzBwSftpl1wXkTPJ403xT/f9lHgmTnjk+wwNPck7V32YC8/6NLHYsrzEI1KsjvVE+2eJOyXR4jvGGGyeK9Gp1CE8L0Fz8wdYF7ofxzvKusXvzks8IsUsnz3RI+kRdvbuJJncz/79/0l19WWcf/Y/EotUznksIv7hn0r0dNeJFilIJg890VkvS3+in2Q2STy+E4DyinM556wvUFGxlvLyVXMaj4hf5KsnunO4E2s9mtLfIxisorHx3ZSHy+c8DhE/0DrRIkXAYPKyOcO9O++le7SLSxZcSiKxk3C4iVCwhnnzbmXevFvnNBYRP8ntQDr3k5C6R7tJxbfSzE4+ftk3iFS/Cceo9iQyLWrnECl0zpzO/HU9l4P9mzlw4G8oT76NeHw3lZUXzdn3i/hVPicWDqYGyYw8SlntQtpa3o/j6JIp8lodv060RUm0SEEyxsz58G9vopfhoV9hvQy7D/+AIHDRgjeypFXrQYtMhzEGiwNYrPUwc1gJ7ux/DpN6kfnz/14JtMiM8U9PtH4riD+ZAN4cnqBHR48yMrKJWPk5VNdcDclNXLPydwgGq+csBhG/Or6H0pjwrH1Pxs1gjCHoBHnk5Uc41H0vreEo8+Z9eNa+U6TU+KknWs1d4ktzObGwL9HH3qNPkM30UFFxARUV5/Le131bCbTIDJmri+4dL9zBd7d9F4CdPc8xPPwMi+e9V+eyyAwZn7ME5GWew0xTJVp8J3eSzn4Sba3l+SPP8quOZ+nt+zE1IaisuID5VfNZUL1gVr9bpFTkeqLnZvg35aZIuSm2dm9laOiX1IRcNiz/1Kx+p0ipsfinEq0kWnxnfF1Zz6Zn9XsODezgjsevIBiqx3PjrKg9j1+76Pc0e19kps1BJTrrZentvQ+Lyy+sx8DAz1nTeCGVFStn7TtFSk3u+qyeaJGCZoyD583uCbqv805cN45xwmQzAyxq+ShBTT4SmVFzddEdTY/S3/8IrjtCOn2ETKaHBfP+cNa+T6RU+aknWld88adZXp1jb/9eHtj5DSKRVhYt+jNG4zs5a8HHZu37RErZbF90s16WzoEtuO4IgUAlw0PPEAiU09Z4/ax8n0gpUxItUsAMZtYnFj720g9IJF6msfFdfPiCj7B/cD/zqxfO2veJlKoTe6JnZyLSvTvv5aUj9wEwv+036e27n1jsHGIRTSgUmUnj676DkmiRgjVbEwv3DezjwOABOnsfAQzntL2DgBNgSe2SGf8uERkzy5WrnngPyeRejBPk1ov+mB2972VT5yYqw5Wz8n0ipcoYox0LRQrZ+MTC2ThB733+z+jpf5IKe4D3n7ORted8YMa/Q0SOmaue6GRyH+Vli6iIVLOudR0XzrtQk4RFZoV/JhbqN4T4kjHOjG+2kkz3sv/wHSyNpbnunJtZueLLE9uYisjsmc0eStdz8axLMnWAtyy7euJ5JdAis0M90SKFboYnFnaPdnPoyLex1uX8FX/L2QtvmLHPFpHTm+0eyq7RLtKpDtZUZVjUeOWMf76IHDMXcxzmkpJo8Z2Znlh4aOgQ9+2+j46O/yIYrKat4coZ+VwRmSQzs8O/3aPdVIQrGI3v5Y7n/pls5ijtQaiqWj8jny8ip2eMAxhVokUKkTFmRts5OoY68LwUo/FtVFddSk1Z7Yx8roi8utnoib57x90A9B/5CkeHNrOuFhorGygrWzwjny8ip2aMwWIxJqgkWqRwBWZss5XOkU5GRrdivQwVlRcSCoRm5HNFZHJmqodydHQbL+3/Bzx7IdZL0zP0Au31l7J2yeuIxZZrjoPIHFESLVLAcsNFLtbaaV0YPetxNH6UcGoTgUAF5bHlMxekiEzS9JPontEevvnEH9KcuY+hcGZsGUyXRa3vZ9my356pQEXkDAxm7LqsJFqkIBnMxLqy4DF+AX4tBpODdHV/n1b3GWprr2dRjYZ7ReZS7nzOtXN43mufiLSzdyc9Q9s4mIRQ6H5C4UYioTo2Lv/1mQpVRCZJSbRIActNLMxddAOB155Eb9/3Ffr6/oeNqz/Imxd/gfpY/QxGKSKTYabRE22tZU/fHlKZIZLJA0TKFpBKHiST6eGq5bcQC8dmOlwROY1jPdEhXyTRWghTfGd8YiFMv4fy5c5vUVbWzrrV/0FLZYv6oUXmWO6i+9rP5554Dz/Z9xO2dDxIedBy5YpPEg63ANBY/+YZjVVEJkeVaJGCFsAyvSTa87L0Du+kvnIjAUfJs0i+TGdi4XBqGIBEYjcBY7hqxYdxLTy9//tUV66Z0ThF5Mxy60QriRYpaDMxmz8e385oJkN71TkzFZaITNGJmzNM7nxOu2mCTpDe4Zd4bvttJM0lxOO7qYkuIRis5A1nf4ya6g0sq1s2i5GLyKkcm1h4+jkOaTdNOBAGIJVNEc/EiYailAXL5irMSVESLb5z/ESk15pEDyQHGBh4krgLjdXnz2R4IjJVUzifdxzdweP7H6elooUXD95Bd/djREPPcm5VihXtHwGgLFjGxQsuntWQReRkASeAa13CZ6hEj6RHuOOFO7h0waUMHvoUD3f209JyC8vrl3PloivnNuBXoSRafMcYg5lGO4dnPe7cdiddXd8ibSPUV54980GKyKTk5jhMfmTpaPwoAEdGjjA6upVgqIZY0KM5kqat4epZjVVEzizkhMi4GWJnSKITmQQAmzqfJdn5KENDGRobb6AiXDGXoU6KkmjxJWMcsK8tiR6/CKdS+4lE2plX2TrT4YnIlEx9ZMnz0sTjO6mueT3ntb+XRn5Cbe01sxWgiExC0AmS8TJn7Il2rQvAYPwQg4kE1sLIyGYqw9fOZaiTotU5xKdeWztHT88P+MnTG8lmR0imDnHx/Ncxv2r+bAQoIpPgGAc7NhnpTD2UAJ6XYvPWGxkYeJx4fCfWZqkoX83qtmtZteo7BIOVcxGyiJxGKBAi62XPmERn3Nx5nk53MJDJzXEaHn5alWiRuWAYG/59DZXoOzf/Ay/3vEA0+q8siWVYM3/jLEUpIpMRMAE8m/v51c7n/9n5TfYOdGCcO4lFV9AcDXPJ4htor26fg0hF5NUEnSDxTPzMSfTYpkqpVAeehZqayxgYeJyISc9lqJOiSrT4k8lttjLZJPrw8GFG06McGNiGccKEsntZWQU1VetnOVAROZOAE8Ab+/nVzucdXU+PvdFldHQrbbXr2LDgUhyjS51IIQg6QTLumds5sl7u+YjtIRiqo7r6csCSGLx/DiOdHFWixXemOrHwyMgRfrTrR5Q5GbKZfhob300su4VgsIdYbPnsBywipxUwAVybK0W/2vmcyXQB0Nh0A91d36a6+tJZj09EJi/kjLdznHrHwlQ2xZauLQAsCHUzFJnPr134SZ7jO8RHn5vrcF+VkmjxnaATxBgHdxKV6ERiL5u23EI2cB3did0ARKNnsaThBtbMXzGx86GI5EfAmVw7RyKTIJ3uIhispqb6SiLhNhoarp+jKEVkMkKB0HETC1Mnvf7o3kfpS/Th2Sx1Zi83rfx9aqO1vH7D0wSD1XmI+MyURIvvhJwQ4JCdRBLd1/c/HB34GQOBKjwvhXGCRMoWEAhUUlGh9aFF8i1gAmAcvFc5nweSA6TTXYRCTRhjiMWWEw5G5zBSEXk1QSeYq0QHgrjuyEmvv9TzFKl0J6FwMw4u1ZW563AhJtCgJFp8KFeJDpxUie4a6eKHO3/Iu855Fw2xBjzr8bXn72R4EHAeJxiqpSyyEMcEqS4rzBNWpNQEnACGwElJ9OYjm3mq4yluXL6etI2yd7CXdLqLioo1tFe301TexHnN5+UxchF5paATxLMelgDWZsl6uT9lwTKstRztvZfh4WeprLwQUwnl5efmO+QzUhItvhMKhOAU7RwHhw4CsH9gPw2xBkbSI6RSh7EmgucO47rDvG7JTaxf+iYtaydSIAImABg8Tjyft/dsB+BHT76FHfF65s27labQCJcvuYILFl81sWWwiBSO3EgxpDxDXzLB3Tvupi/Rx21rbyOeiZNOHwFgeHgTpipELLYin+G+KiXR4jtBJ4jh5Er0+EU15eb6sHLDv51UVq7FZPaxtuIwFyx+B/NqFuYlbhE5WcAJYIzBtUG293awYGGWoBOkubyZwcRRdvZ3Ene7SaYO0lQGSxvXK4EWKVBBJ5d2/uxID72j/SyO9gGwb2AfXSOdpNNdVFVtIB7fSXn5YpyxpLtQKYkW3wk5oVNOLAw6Qay1dHb/gEzLSo4O78V1RwiH2whEl1IRvIv62tfnMXIReaXA2Jbf/ZkAe48eZNXQIRbVLMK1LplMNyNZAJeB/p9wVgyiUa2oI1KoQoFcUjyS9QAPz2ZIp7t58KUHSaePYm2WWGw5DQ3vZNWq9+Y32ElQEi2+k7vTdch6JybRrueSSnfw0pG/485UN91uAyEHLm6/ktaGa1hc/a8Eg1X5C1xEThJwckl02gawuLhebkvgjJshne4ae5eh0W6mIugQjS7JU6Qi8mrGK9G5vRw8hgafoKvrDpYs/Tzp9BECBkKhFkKh2qI4l5VEi++EAqFTTizMellSyQMMZmDgyKNUV19GZRA2LLyOsjL1QIsUovFKdNoLYB0P144l0V6G9Ni60PPrN7A68iRlZYtxHLVyiBSq8SQ6t5eDSybTA1gSid3Uhjyaa2Aw3JLfIKdASbT4jmMcHBMka6FrtIc0DzLiVZPIuiRTB0m6gHuI0cBWmiIxIpG2fIcsIqdxrBLtgPVIZpPsG9g3UYkOhupoqbsKRp/U5kgiBW78ptiYAFiPTHYAgER8D4tqa2mprOfc8z+MMSafYU6akmjxpVAgTMbC/Xt/RVfX71BecT6t8z5MKnmQYKiGbGaA0dGtlDetLpqTVaQUHatEO1jj8quDPyPjJgkGK8mkuwiHW2iuuwwnEaW8fFWeoxWRM5moRBsHa13c7CAAicRu3Fgzsaqzi2qJWW3HJr4UCoRJe+C6o3heivjoi1jrkUodpKL8fCKRVurCcH7L2nyHKiJnMFGJ9hzAo+PINzh48O+w1pJOdxEON1FRVs/atU/T3v6Z/AYrImc0fj5jcudzdqwSnUp1kEnuKvgl7V5JSbT4UigQIelCNptbPsd1hxkZ2YznJYlEFtBUs55LG6CxWrsSihSy8Up0ystVrrLpLtLpI4yObsPzkoRDzZQFyygvX0UoVJPnaEXkTE7oibYu2ezAREulY0eIxc7OZ3hTpiRafCnkhEl4kMn2TzzX1/cgAJHIAiqrNmBMhOrqy/IVoohMwnjlKmMNlmM9lH19PwIgHG4+NuNfRArasZ5oB89L4XkpKiouwBiHkEPRJdH6zSO+FAqWkXChLJNLoh0nQjK5FzBEIq2URWq5fO1wwS/kLlLqxi+6rg0Q8DJkJ3oo9wIQCimJFikWE+0cBAALQCjUSCSykKDZW3RJtCrR4kuRQBnWQnasEl1Zmet9DodbJpbAUgItUvjGL7rGOBPn8/j6seXBIKFQHa2VrXmLT0Qm7/iJhRPPhWqIxc4mEqyirGxxvkJ7TZREiy9FgmUAZLMDGCdIddX63PNlCwC0IodIkRivRGMCZDK9ALQ1XktTWZAr25bxG+s+RkW4Io8RishkHTufj0uiAzXU17+VtWt/gVNko0rTSqKNMXXGmIeMMbvH/q49zftuGXvPbmPMLad4/R5jzNbpxCJyvFAgl0Rb6xIMVDO/bgPBUA3lsZVjz9t8hicikzRRicbBdUcAqClfxnvO/yRL5/9aPkMTkSkaL2AZAhPPXbv8eta1baC5enW+wnrNppvyfxp4xFr7eWPMp8cef+r4Nxhj6oA/B9aRa4B51hhzj7W2f+z1dwEj04xD5ASRYHTi52CwmsqyOpYu+QLhQJi0m6atShusiBSDoBPEYE4Y/o1FWli69BN5jEpEpmP8fDZOmNaqZZwVLs9zRK/NdJPo64Erx37+OvAYr0iigTcBD1lr+wCMMQ8B1wLfMsZUAJ8EbgPunGYsIhMigWNJdCBQTTQY5cbVNxIJRBjNjFIdKZ7F3EVKXSQYAY7tdFYWrstvQCIyTbnzORSsJhaK5TmW1266SXSztbZz7OcjQPMp3tMGHDzu8aGx5wD+Evh7IP5qX2SMuY1csk17e/trjVdKRHisnQNylehoKEpVpAoYvyCLSLEIOsGJylUwWKNzWKTImbHe6ECgpqjnKL1qEm2MeRhoOcVLJ2wNZa21xphJN5oaY9YAS621nzDGLHq191trvwp8FWDdunVqaJUzCgfDY9uKegSD1ZQFy179H4lIQTIYGLvoBoM1hLSyjkhxG78pDhX3qPCrJtHW2o2ne80Y02WMmWet7TTGzAO6T/G2Do61fADMJ9f2cTGwzhizbyyOJmPMY9baKxGZpnAgnDtJx5LoYh4uEil1xhgMxyrRoYCSaJFiNnE+B4p7l9HpLnF3DzC+2sYtwA9P8Z4HgDcaY2rHVu94I/CAtfYr1tpWa+0i4DJglxJomSkhJzQx+zcQUCVapNiZ4yrRdVH1RIsUM2MCXNYA71p+Zb5DmZbpJtGfB64xxuwGNo49xhizzhjzHwBjEwr/Enh67M9nxycZiswW17oT24hGQrWaSChSxAwGJirRtcyrmJffgERkmhxqw1BfsSTfgUzLtCYWWmt7gatP8fwzwEeOe/w14Gtn+Jx9QPEtECgFq6m8ibAT4MJqePOFv0G0SJfPEZEcYwK0x6C6sp1ync8iRW18ZCkSKe7dRotraxiRSSoLlvGWtgrS6ThlkVMtGiMixcIYA8ZhSTlcfvY78h2OiEzDxiUb6e6vwjvyY2Kxc/IdzrQoiRbfMiZIONw8cccrIsWpPlqPIUDQgUhEGyWJFLMltUtYUrsEltyY71CmTUm0+JYxQUKhpnyHISLTdMWiKwgOLMQOPUE4XNzDvyLiH9OdWChSsHKVaE1AEil2QSfI/Or5RCILCAS00o6IFAZVosW35s//BJGIdrcU8YOFC/+E1tbfzncYIiITlESLb7W1/Va+QxCRGRIMVhMMaqlKESkcaucQEREREZkiJdEiIiIiIlOkJFpEREREZIqURIuIiIiITJGSaBERERGRKVISLSIiIiIyRUqiRURERESmSEm0iIiIiMgUKYkWEREREZkiJdEiIiIiIlOkJFpEREREZIqURIuIiIiITJGSaBERERGRKTLW2nzHMGXGmB5gfx6+ugE4mofvlbml41wadJz9T8e4NOg4l4Z8HeeF1trGU71QlEl0vhhjnrHWrst3HDK7dJxLg46z/+kYlwYd59JQiMdZ7RwiIiIiIlOkJFpEREREZIqURE/NV/MdgMwJHefSoOPsfzrGpUHHuTQU3HFWT7SIiIiIyBSpEi0iIiIiMkVKok/DGPM1Y0y3MWbrcc/VGWMeMsbsHvu7Np8xyvSd5ji/1xizzRjjGWMKaiawTN1pjvEXjTE7jDFbjDE/MMbU5DNGmb7THOe/HDvGm40xDxpjWvMZo0zfqY7zca/9b2OMNcY05CM2mTmnOZ//whjTMXY+bzbGvCWfMYKS6DO5Hbj2Fc99GnjEWnsW8MjYYylut3Pycd4KvAt4fM6jkdlwOycf44eA1dba84BdwB/NdVAy427n5OP8RWvtedbaNcCPgD+b86hkpt3OyccZY8wC4I3AgbkOSGbF7ZziOAP/aK1dM/bnvjmO6SRKok/DWvs40PeKp68Hvj7289eBd8xpUDLjTnWcrbUvWmt35ikkmWGnOcYPWmuzYw+fBObPeWAyo05znIeOe1gOaBJQkTvNtRngH4E/RMfYF85wnAuKkuipabbWdo79fARozmcwIjIjbgX+J99ByOwwxvy1MeYgcDOqRPuSMeZ6oMNa+3y+Y5FZ9/GxFq2vFUJLrZLo18jmljXRHa9IETPGfAbIAt/MdywyO6y1n7HWLiB3jD+e73hkZhljYsAfoxukUvAVYCmwBugE/j6/4SiJnqouY8w8gLG/u/Mcj4i8RsaYDwFvA262WuuzFHwTeHe+g5AZtxRYDDxvjNlHrjVrkzGmJa9RyYyz1nZZa11rrQf8O7A+3zEpiZ6ae4Bbxn6+BfhhHmMRkdfIGHMtuf7J66y18XzHI7PDGHPWcQ+vB3bkKxaZHdbaF6y1TdbaRdbaRcAh4EJr7ZE8hyYzbLyIOead5BYByCtttnIaxphvAVcCDUAX8OfA3cCdQDuwH7jBWlvwje9yeqc5zn3AvwCNwACw2Vr7pnzFKNNzmmP8R0AE6B1725PW2o/lJUCZEac5zm8BVgAeud/ZH7PWduQrRpm+Ux1na+1/Hvf6PmCdtfZoXgKUGXGa8/lKcq0cFtgH/MZx89TyQkm0iIiIiMgUqZ1DRERERGSKlESLiIiIiEyRkmgRERERkSlSEi0iIiIiMkVKokVEREREpkhJtIiIiIjIFCmJFhERERGZIiXRIiIiIiJT9P8A8O8kCmmzxpQAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,6))\n",
    "# plt.plot(sigs[0,rng], 'y')\n",
    "start, end = 10, 15\n",
    "#x = np.arange(start, end)\n",
    "plt.plot(np.linspace(start, end, (end-start)*64), abd_64[start*64:end*64], 'y')\n",
    "plt.plot(np.linspace(start, end, (end-start)*200), sigs[0,start*200:end*200], 'g', alpha=0.4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 220,
   "metadata": {},
   "outputs": [],
   "source": [
    "import scipy.signal as signal\n",
    "bands =   (0, 0.05, 0.1, 5, 10, 32)\n",
    "desired = (0, 0,    1,   1, 0, 0)\n",
    "# b_firwin = signal.firwin(numtaps=73, cutoff=5, fs=200, pass_zero=False)\n",
    "b_firwin = signal.firwin2(73, bands, desired, fs=64)\n",
    "b_firls = signal.firls(73, bands, desired, fs=64)\n",
    "#signal.firwin?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 221,
   "metadata": {},
   "outputs": [],
   "source": [
    "#freq, response = signal.freqz(b_firwin)\n",
    "y = signal.filtfilt(b_firwin, 1, abd_64)\n",
    "y2 = signal.filtfilt(b_firls, 1, abd_64)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 227,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x199965b90>]"
      ]
     },
     "execution_count": 227,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "rng = np.arange(300,1000)\n",
    "plt.figure(figsize=(12,6))\n",
    "# plt.plot(sigs[0,rng], 'y')\n",
    "plt.plot(abd_64[rng], 'y')\n",
    "plt.plot(y[rng], 'r')\n",
    "#plt.plot(y2[rng], 'g')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('Event', 0),\n",
       " ('C3', 1),\n",
       " ('C4', 2),\n",
       " ('O1', 3),\n",
       " ('O2', 4),\n",
       " ('M1', 5),\n",
       " ('M2', 6),\n",
       " ('Cchin', 7),\n",
       " ('F3', 8),\n",
       " ('F4', 9),\n",
       " ('F7', 10),\n",
       " ('F8', 11),\n",
       " ('Fz', 12),\n",
       " ('Fp1', 13),\n",
       " ('Fp2', 14),\n",
       " ('Fpz', 15),\n",
       " ('P3', 16),\n",
       " ('P4', 17),\n",
       " ('ECG3', 18),\n",
       " ('T3', 19),\n",
       " ('T4', 20),\n",
       " ('T5', 21),\n",
       " ('T6', 22),\n",
       " ('E1', 23),\n",
       " ('E2', 24),\n",
       " ('Lchin', 25),\n",
       " ('Rchin', 26),\n",
       " ('ECG2', 27),\n",
       " ('ECG1', 28),\n",
       " ('Lleg1', 29),\n",
       " ('Lleg2', 30),\n",
       " ('Rleg1', 31),\n",
       " ('Rleg2', 32),\n",
       " ('Chest', 33),\n",
       " ('ABD', 34),\n",
       " ('Airflow', 35),\n",
       " ('Snore', 36),\n",
       " ('CannulaFlow', 37),\n",
       " ('SUM', 38),\n",
       " ('Position', 39),\n",
       " ('DC2', 40),\n",
       " ('DC3', 41),\n",
       " ('DC4', 42),\n",
       " ('CPress', 43),\n",
       " ('CFlow', 44),\n",
       " ('Cap', 45),\n",
       " ('EtCO2', 46),\n",
       " ('EXTSAT', 47),\n",
       " ('Pleth', 48),\n",
       " ('SAO2', 49),\n",
       " ('PR', 50),\n",
       " ('DHR', 51)]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "signal_labels = f.getSignalLabels()\n",
    "list(zip(signal_labels, range(len(signal_labels))))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([7509000, 7509000, 7509000, 7509000, 7509000, 7509000, 7509000,\n",
       "       7509000, 7509000, 7509000, 7509000, 7509000, 7509000, 7509000,\n",
       "       7509000, 7509000, 7509000, 7509000, 7509000, 7509000, 7509000,\n",
       "       7509000, 7509000, 7509000, 7509000, 7509000, 7509000, 7509000,\n",
       "       7509000, 7509000, 7509000, 7509000, 7509000, 7509000, 7509000,\n",
       "       7509000, 7509000, 7509000, 7509000, 7509000, 7509000, 7509000,\n",
       "       7509000, 7509000, 7509000, 7509000, 7509000, 7509000, 7509000,\n",
       "       7509000, 7509000, 7509000])"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "f.getNSamples()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Learn about Welch's power spectral density estimation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "fs = 10e3\n",
    "N = 1e5\n",
    "amp = 2*np.sqrt(2)\n",
    "freq = 1234.0\n",
    "noise_power = 0.001 * fs / 2\n",
    "time = np.arange(N) / fs\n",
    "x = amp*np.sin(2*np.pi*freq*time)\n",
    "x += np.random.normal(scale=np.sqrt(noise_power), size=time.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x12f6b2690>]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "f, Pxx_den = signal.welch(x, fs, nperseg=1024)\n",
    "plt.semilogy(f, Pxx_den)\n",
    "plt.ylim([0.5e-3, 1])\n",
    "plt.xlabel('frequency [Hz]')\n",
    "plt.ylabel('PSD [V**2/Hz]')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(513,)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "f.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(513,)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Pxx_den.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x13ea71790>]"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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o+++FM0giAcRMDMXFwJ9/Aqmp4h72f//X9j75+Q2ve8vY02qF07ItTP1LrKyAtWubr3dza3gtU4ibIw1YC2LqRJyUJKwRjSa7y7Kqq4GtW4Hbb2+ogewKY8YIA9ZS2aVGox4pKU/j8GFPJCWtgZOTPy644Dj8/X/BjBnH4OIyDdnZHyM5+X7ExV2LmJgLkZKyFhpNNhTFGqmpzyAysus1w6mpwI8/ioZHN94oboh//WW54+0sYg7YAVBMRQ6NOHJE6G+arLojKArwwAMiYhUaKpbZ2fnA1XVWLxiwOgCAi0t5Ew9oY268UZzDL73Ug4q1w6+/CoNjzRrh7XV1BSoqRKr9rl1igHXDDWj1mM41jEbg0CFhgPv6ijSqtWuFcT5nTtOb+7lCZaU4L5YsEf/Vr78GUlJEiuGddwI33ywGvOnpwMyZYs7rv//uba0F69eLeba1WvG9p6YCv/8uOoSnpIharNdeA959Vxi4kZEiMnsuRgaMRuDJJwE/PzE1W1RUb2sk+beg14v/9ZgxItq6Y4dwUDVmxQox1jlwoHd0lJxbpKWJ7LIHHxTXzGHDxP34229FQ1ITRiMQECCuwUDTeVJ763735JPCaVnVxqyGGRliTJSV1fy/AIgmniZMEdh+/YQBe66MaXsVkufdY/r06Twf0OlIe3vy8cc1VKnAjIy3uyxr61YSIAMDu6fTp58KOTk53ZNzNrW12UxLW8fw8AlUqcCTJ+9iaWkgjUZDs22NRj1ra7NZXh7GwsLfWFOTVr+uuPgvHj7cn0FBziwu/qtTOqSmkm5u4vgAcvBg0sdHvJ4+nczM7O5Rdp+oqDmMibm0xXX33Uf26UNWVnZOZmUl6e5O3nJLw7L09LeoUoFqdVY3tO0czzzzOwFy0KAsXnJJSLP1KSkNv42DA5mb22OqtYpKJb7zadPI2FjSaGy63mgU/xlFIefNI9XqXlGzw4SHk+PHi+/Yw4N87DEyKIjMzyc3bhTHOnu2uDadK2i15OWXk9bW5BtvNNVNqyXXrSMdHcW11NqatLMTx3f55b2ns4n//lfosnQpWVbWsX3++EPs8/LLltWtK7z5ptDtrrvIAQPICy5o/p8wNykp5HffnRvXg8acOUOuWkUuWkRGRva2NucvRiP5yy/k2LHi3Jo7lzx8uOVttVpxD7/77p7VsTWMRjIigiwqEufpp5+KMVhJCRkf3/l7taRzPPooaWNDZjUaxiQni/Povfcalm3eLJa99JJ4//PPDWONP/7oWZ1NmD4/PLzl9UajGAc98UTrMj75pEFOSopY9v774n1Jifl1PlcBcJQt2IK9box25XG+GLAkOWUKeeWVZFCQC5OSHumynCuuIIcOJQ3N7cFO8c8/4lc/dKh7chpTU5PGkBBvqlQKo6IuZF7e9m7Jq63NYUTEZAYE2DI391saOzCCOn6cHDWKdHUlDxwgo6PFd6XRiIG7qyvp709WVXVLtW5z+PBAJiSsbLZcrRY37hUruib3qafE4D4jQ7yvqjpJlQrMzv6sG9p2jocf3kOAnDAhkbNmhTZbb7rw/v230PWpp3pMtRYJCBA3x/HjxWC1LbZtE7ovW2b5AX1bhIWJm/Qdd5Br14r3aWni8cYb4niGDBE39Jqa5vtv3y6O4+OPe1rzljEayXvuETp9913r28XHkzfdJM6ZzEzy9dfFPsnJPafr2ZjO46uvJvX6zu17662kkxNZWmoZ3brC0aPi/Ln1VvG7fPGF+I4DAiz3maGh4nsAyH79yIQEy31We+h05P794n+0fr1wCtrZkZ6ewvHTXefx/yIJCeT8+eL3HTOG3LOn/evnDTeQw4f3jH7t8eyzQncrqwZDovFDUYTzc80a8pFHxDnSm/eHrvLee+ScOcJQP1fQ6cS4bdmy5uv8/ITT0MSSJeL3uOAC8f7zzxt+o7buK5bE9PnbWxkOFxaK9R9+2LqMLVsa5JgcpKZg1qlT5tf5XEUasL3EihXkwIFkWNhoxsXd2iUZOTniAmryLnWHzEzxq//3v92XRZI6XSUjIiYxKMiNlZUnzCOUpFZbypiYBVSpwPj426jRFDRZX1srokt+fsKza2ND+vqSwcEty9u/X9xsHn7YbCp2GoNBS5VKYWpq89DLzp3id/nnn67JzsgQg+mnnxbvjUYjw8JG8dixK7qhcee4555dtLbWcfbsOE6aFMHy8gjqdBX16y++mJw4UbxesoT08hK/Y29gMIiovJ9fxyNnr74qfqNff7Woai1iNIqInWkwNXiwiEiePaBaurRtz6zRSF5yCTloUO9HYQsLyeuuE3q/8ELn9s3NFcbF6tWW0a09Tp4UDid/f7Kiov3tzyYsTBz3pk3m160rVFQIA2PgQLK4WCyrqSFdXMzzHRsMwuB//HFy5UrynXcajMQRI8h9+8T1YM6cnjcAAgLISZPEPaTxf+nii8nERDIvT3w3Pj7itaRj7N4tIkx9+4rxRkevNyZHp7mzxDpLZGSDg+qZZ8Q5Gx9P7thBbtggDIlXXhHX0759hZPDFGH+/ffzx5Ctqmo45z/4oLe1aSAmRuj0ww/N1914owhYkOJ79vIS21pbk9XVDfdKgHy764mPXcZobPj8999veZvoaLF+9+7W5fz2W4Mc0/m0f79439pY99+INGB7iXfeYZ0XezGjo+d1ScbHHwsZ3fFOGww61tSkUKutopOTSM3oLkajgSdO3ESVyqrT6b4dk69nWtrrVKmsqVJZMTp6PnNzv2Ne3i9cuDCLgPDWLlwoDKL2UtAefFAM/o8eNbuqHUKtTqdKBebmftNs3TXXiMFjZyM5jbn1VuGxNBkwp08/xYAAW+p0HbTQusmyZTvp5FTFBQtOctSoKKpU4OHDviwtDWJqqnAgPP98EYuL/+LevUYC5N69PaJaM0yeze+/7/g+Wi05enSDl7cnMUXDVq5sSFsrLhaDqe++E4+ORsp++aX3DHETRUXksGHCCH3//c4N9ioqYpiYuIZr1mTT2loYGT1JUZGIEPn4kOnpXZNhNJL9+5O3325e3bqqy9Kl4tqoUjVdt3y5SEXvjrNDpxNZSKbSAV/fhkHZzJmi9IMkv/pKLDtbB0tRUdFwTxgxgnzuOXLXLhEVT05uek6eOCHO1XMltdVEUpIYoL/2mogCLlhAXnutWNab2UaffSau97NmtZ/dcjaRkeI82LHDMrp1hIICkckyeHDHUzWrq0XK55AhQn9/f/Iv8w+LzM6+fU2dNucKpiiq6frQmOefFw4nna4hpXjxYvF85Ah5770io8PennzyyZ7Xvbq64TttLdPMdB+OiGhdzuHDDXIMBi0TElby4MF/2jV8/21IA7aXMF0cNm16hWFho7okY8ECEWXsCDpdOauq4lheHsbq6lMsLQ1kYuIDDAnxokoFBgW50d+/lFdd1SVV6jEajUxMXEOVCszMbMXFZCYqK48zNfUlhoWNokoFrljxKgHyiSdeZHV1x0evZWXCU3fllRZUtg1KS4OoUoHFxfubLM/PF57DZ57pnvzYWHGuvfiieF9WFkKVCt1O6e4oN964k56eJbz++nQOHpzA4OC+DAsbzc8+m0dPzxo6Ohr4009jqVKBsbH30tnZyP/8p0dUa8K2beLGNnt2c4dBVVUc8/N/Ynl5GPX66mb7mmpSoqN7SFmKG7KtLXnVVd1zcJjQakXEYPny7svqCkajiLza2rZeC9caWm0RDx/uT5UK/PnngbS11ddnHfQERqPw/tvaiihqd1iy5NxIlVy/XpzT69c3X/fjj2JdaPOKgA7zf/8nZGzY0JDWXlwsIvCNqakhnZ3J++/v+md1lKoqkfppZSWM2I5E0deuFUZZV50W5iY2VjgsTQNcd3fhEDDVwI8f3/NphkajGLADwinbFSNapxPnwQMPmF+/jqDViqiqgwMZFdW1/TdtEs5OW9tzP/X8ySeFc+aRR8Q4pKMZSZbmzjuFEdqSc/Obb8Q5lpbWUP9qGmt//LE496ZMEZlGK1f2uOrMz2/4Xy5f3nLdn2ks0ZaDJz29QU5h4W9UqcCffhpHQDj8/ldozYCVXYgtzMSJ4jktbSK02s7PgVpWJlpt33BD69vo9eVISnoQoaF+CAlxQ2TkRERHz0ZExFgcO3Yx8vI2wt39Mowe/QX69BkNb+/9SEjQdvGIBOnpryA39wsMHvwMBg9uof+3GXF29sewYa9h5sxTyM9PwpYtL+OOO3Jwww1fIi7uehgMtfXb1tQktzrXrJub6Ay3f7/oBNrTaDRihnZ7+yFNlm/fLqZpWbGie/InTQJuugn46COgvBxwdZ0NW1tvFBXt6Z7gDqJWK3Bw0MPDYxAMhgGYPv0YTp+OwpNP7oe9fQ7efXcufH0L4OOzAiUlX2P27Ej89Rd7RDcTX34ppjWZNUtML2NtDVRURCAjYz2OH78akZETcfLkLYiOno3gYBdERvojJuYSREdfiKiomZg+/SFYWxO7d/eMvsXFogPv4MHADz8IfdvCaNQgNfUFHD++GMnJj6Kk5B/hqWyErS1w/fWiU25vdML99FPgt9/EHHhz53Zu37S0l6HVFmDyZBWGDh0Bf38Vfvut5+bc+O47MdXPm2+23DWyM8yYIToWV1SYR7eusG8f8NxzomP10083X3/ppeL50KGuyT9xAli3DrjlFnHtdXQUy/v2beiqacLRUXyepbtLGwzA8uVATAzwyy/ifHRxaX+/Rx4Rzxs3Wla/jpCTA1x9NeDsDJw+Lf7HJSVAeDgQHy++w4IC4JJLerYL69q1wIYNojP+nj1i2qjOYmMjuqUfPmx+/TrCk0+KjrZffw1Mm9b5/W1tgbvuElNrDR8uusxmZppdzU5z9Ki4fsXENJ3Gbu9eYN48cZ8xGM6dDtChoeI8aGHCBgwfLp5TU8X37OIi5lP18RHHmZsL+Pqq4empaXHKGa0WiIuzXCffxp2Hs7IqW9wmK0ucK/36tS5n4EDxvGoVkJ8v5pNzdk4HIMYG//O0ZNWe64/zKQJrNArP6B13RFOlAnW6zrWt++GH9j3gJ07cQJXKmnFxNzM9/S3m5W1nUdEfPHNmCwsLf2lSh6jRFPLuu9+mohhabPLSEYqL91OlAhMS7u5QgyVzUFEhGrc4OoraxZqaBj3S098gSVZVJTAgwJaHDw9oNW22okJ4d++6q0fUbkJ6+ptUqUC9vqlbeto0cUzmwJRysmWLeJ+U9BADAuyp1Rab5wNaoKjoT4aFjeb8+T9x1Kg8PvigSDu8+mqhy7RpRoaFfcCjR2eytDSIpOiS/OCDjxIgs7MtploTcnJEndKVV4raW622hMePX0uVClSpwJAQL6anv8mKihgWFOxhaupLjI1dzOjo+Tx2bCGPHVvIgAB7Tp0azgkTzBAK7QArV4pUKVMkwGDQsazsCNPSXmNs7GImJt7P3NzvqNUWU69XMzZ2MVUqMCJiIgMD+1ClAmNiLqNandFE7u+/s1s1110lJkZ4+xcv7nyNWGXlCapUVkxMfJAkqddX8eGHXybQ0LzMkiQliYZDl13W/WZ6pEhXBboW5TEHqani3jR5ctuRsokT2aWMHa2WnDpVRFHOjraSIiUuO/sz5uf/WH8fMUUlWkobNBePPSY+46OPOr/vxReLetneJCWFHDdO3MeOHWt9u+PHRQSwpyKZpu7ajzzS+f92VVU8ExJWMSTEm1FRc/n007m0sur5NOiNG8UxrF1rHnlJSaKOfN683q2J/fHHpjXe9vYixXnBAvH+s89E5NvdvfcycxpTVNR6VgjZEJn8+mtxjVmwQCy/9lqRwt2vH3nNNZs4Y8ZfnDmz+Rf/2GMaAuSQIZXculVrdv2jo3X13/Xo0S13xrr5ZnL0aANralKaLK+tzWFGxnpmZr7L8vJIVlWRFRUZVKmsGRLSjyoV6OBg6JXU6N4CMoW495g/n5w+PZ8qFVhTc7pT+95yi6gZam3AVF4eTpUKTEt7vcMyP/nk1zqjuPPFY0ajgRERkxkaOpwGQ8904AkOJkeOFGfrokUiPcPEiRM3MTDQkTU1qYyPv63eGElLe61VeWvWiPSgYsvZdC2SmLiGwcGeTZaFhzekvZgDg0GkzVx3nXhfWXmMKhWYldWF0VoHqKqKY1CQM1UqcNasvZw0qRcJs10AACAASURBVKg+hQwQtVmtpb3u2fNlXZdAy7ce1WpLuXRpLG1stPz55yuZkvJcXadrO2ZkrO9wnXBpaSAffPCJukG2ZUck8fHiOzSlyFZVJTAsbHTdOa4wPHwcg4JcqVKBgYFODA8X6dk5OV+SJPV6NbOz/8ugIGcGBbkxL297vaFQWSkGNM8+a9FDaEJRkag17N+fzM2tYG7uRsbF3crExDWsqIhpsq3BoGNtbW6j9xpGR89jcLAHtdqGAcGff35AgPzmG8vnvd1wgxjgZZ01M5VOV069vvPeQFPK/48/mknBTpCfLwZ+bm4N0zO0xsqVpLd35wfg69aJ4/v555bXnz79dP31OitLtOI0NTbZtq1zn9VRTA1QHunihABvv81eazCkVpPbtpWzb99aurtrefBg6/df0/R1q1YJp6+l00IrK4XhMH686PwvdDCytDSQhYW/srIyluXlESwvD2dZ2REWFOxhcvJjPHHiJkZFza67hvVhfPxtPHx4ANevv5GW7oB9Nnv2iBTaBQsaar4rKqKZlfUxy8pCWVJygBpNgyfGaNRTrc5oEiBoiS+/ZK82/zNNA3fRRcJZtm2bSBtevFiUMCxcSJaVZbKm5jTvvVc4ebvSmM6c7N4tvrOgoKbL9Xo1i4r+ZE1NHm1sGtKeTU0ATbMFAORdd73CBQu2cuhQcUKWl5M//SSOzd29on67UaPM70E/dKiAADloUCLd3Fr2wkyeTM6bF06VSmFy8uM8duxyxsYuqr+nq1RgQIANNZo8pqW9RpVKYV7eNqpUYP/+NVy1yuxqn7NIA7YXWbuWdHTU88ABa5aWdrx1WG2t8LLee2/r2xw7diWDgz3bvYg2JiKivM4D3byZUHvk5/9UV1e5td1tjUZx4zx9WkxtEx8vvPLV1WIA1V5zhMZTbAwe3PxiRpJqdSYDA50YGjqCKhWYkvIcjx+/lsHBfalWN0z8qtfXsKIimhpNHo8dY5vd4SxFbOzVjIycWv/eaBSRDTc3894wHn1UeFhNEfajR2cwIsLf7NFyrbaIoaHDefhwf5aWBnPWrBRedJGer7zScBNpK8qvVlfTwaGKd9zxp1n1aknPzZsXUlEMXLZsC6OjL6ZKpTAgwJ5FRZ2fJO7AgS/rzh/Lhs5WrBCDiaIiYYCHhY1iSIg38/J+qDfijEYjKyqO8uTJ5YyImNhivXNNzen6QWJMzALm5HzBkpKDvOgiPWfMsOgh1JObK2qO7eyM3L17I4ODPeqafA1gYKBTvW4nTtxU//8VtdKLeebMJoaHj6trgNZ0ToSKini6u+fz5ptPWlT/5GQxCDTVl5Migh8Xd0vdQMOW8fF3sKhoL4uL/2Ze3nZmZr7L48evYXz8HczP/7FZ5oWp++cbb1hU9XoyMoSj7KKLRO2nnV3H5kg0RUU7M492dLRwkLQ0BQZJ6nQVDApyYVzcrTx+/Jo6J2QKdTphcJmjyWBLzJwppqMzGVmdxeR06MmpOTIzE7h+/R56eJTURY1OcsuWkVSpFIaGDmN8/O08cWIJo6Pn8/jxaxkZOZUqlTWjoy/mwYOxBMhvv7WsjiZnhakzql5fxfj4O+oH4i09AgMdGR4+ltHRFzM19eV641CjyeOBA1cRIJ955u8W55I3J0Yj+eSTWVQUA8ePj2ZExErm5HzJU6fuo0pl1UTnkBAvVlYe45kzmxgc7FlveJuchi2h04mGdfPnW/QwmlFTIzLNTPXI1dVnr09lYeEvzM3dyMDAPgwMdOKhQxl1DsGe1dWEXi/+o4C492nPCo6aMoyCgtw4YoSG/fuLbX//XazXakX0FSDffHMxb7rpAzo5CSE33dQwLgHIzz//kPfdt5PW1jqq1eYdG/38cxIBctasvQSazx+v15NOTgYuWfJh/bl1+PAAhoWN5rFjC1ldnciioj+oUoGFhb8zLu5mhoWNZk1NKlUqcPz4ovogxf8CrRmwNr2cwfw/wfTpgFptjYyMcfD3z+vwfiEhIpf+uutaXl9efhilpfsxfPg7sLHpQBFPHRMmuAIAjh/Ph8FQC2trhw7tRxqQkfEGHB1Ho1+/29rcdscO4KWXRH1Oa1hZiWN78klRC3d2rcO6dcA33wCPPw689lrL9TQODoMxevTnSEy8Fy4uF2DIkOdQW5uO6OjZCAvzg7PzVLi7z0d+/jbodAUAAFfXOZg1S4Uvv7THY4+1XGNhCTSaTDg4jKh//+23wF9/AR980LEarI5yxRWiDvbwYWDhQqB//3uQlLQGlZWRcHWdaZbPIIlTp+6GRpODqVOD6uX26SMegKjbNNW8tYSDQx/4+6cgOrovNJo82Nv7mkW3s0lLexmffPIk3NwM+OST5ejbdzk0mjwoijXs7Lw7Le+SS+6Gl1ch/vgjCw8/PAI2Nm5m1zkjA9i2TdTdublVIi7uJtTWpmPy5ENwd7+ofjtFUeDiMh3jxm1pVZaj4whMmRKM3NzPkJn5NpKS1gAAxo37EN988whKShT07Wv2Q6gnIkL8zysrDXjllXvQt+8muLldgyFDnoOr6xwYDBXIyvoAhYW7oNWegaJYw9NzMezsBiAn51OUlPwBB4eh8Pf/A56eVzeR7eIyHtOn70dw8FSQlvsvf/yxqM174AHxXpz/d6Gk5C8MHvwkjMZa5OV9j4KCH5rs5+g4Gnp9KQoKfoCVVR94el6Dfv2Wom/fRXBycsSAAUBysmV0NnHoEPDUU0B0tHg/aRLw/POi7tXUo8GEXl8FRbFpck+YPl08R0WJ/3R7aLWiBtDLC/jkk5a3yc//AQZDJQYNWgt7+0GIjJyAhIQ7MXny35gxow/Cw7twoO0QHS3OxU8/BezsGpar1alQq0/D1XUubGyc25Th7w94e4u+FKtWmV/HxpDAE0/E4aOPxsJoHIvx4+Px/vu/4dprxwH4CJWVkaiqOo7y8hBYWzvD1tYbtbWZsLFxxcCBD6GoaDesrKZj2LAybN3qhLvvtoye+fmi7vWmmwg/vw8REfEVtNoC6PWl8PN7GZ6eV6O2NgNWVn2gKFYArGBj4wYXl2mwsrJvJs/OzgeXXvoLBg/Ox5EjlcjMXA8/v+ctozyAt97Kw7vvDsI11+zAK6/8AY1mP5KSNkFRbDFgwP0YOPABVFUdh7W1I5KTH8LRo1MAAG5u89Gv3+0oKvoZSUn/QVmZCtbWLrC2doKTk3/dYwJsbPrg/vtFjXlcXPP/nCXQasU19+BB4JVXgJdfFuMtE7m5XyEp6QEABgCAvb0fNJoMeHs/hTFjfsTGjcDq1ZbX82zi48V/1Ntb6Gxr27CurCwEJSV/oH//e1BQ8CN8fRNx+LA/AGD2bLGNra045l27YjBz5j6kpExGdbUtKiuBX39tkGVnp8ayZVehpiYHBoMNMjJyMWbMALMdR3m5aGwwfHglwsOBM2eIYcMabk6hoUB1tRXGjo3ApEn7UVubgX79lsLGxrV+G1tbURxbUxOP2tp0ODgMg52dDwDA3b0SRUWeZtP3vKUlq/Zcf5xvEdiEBNZ5E+9iVlbHc0VNrcJbi87FxFzGkJB+zTz7HWHQoBouXLiF+fkd61Wv05XXe1Tz8trO7/roI9bVPopphL77jjx4UHSLW7eOfOstEQl45hlRKwmIFOGXXxa1vt98IyKTgEiB6kjg0GDQNIkwVlcnMi3tNUZE+FOlAo8dW8i8vO3MyHibQUFufOKJ1+q72PUUQUFuTEoSE9EeOCB+24ULzVNP15iz00N1unIGBvbhqVP3me0z8vK21nWgfq9+mb+/SLM0RWwuvbR9OY89Vkxb21omJb1pNt0aU1l5jBs2XGH2iPtttxXS3T2fCQmWKTB79lkRJUtMzKyLPlrxzJlOzPnTCkajkTU1aSwo2MXPPltAgPzpJ8ulQpvmSx02TMPNm+cwLGwUy8o63tJWrc5iQcHuFjtCm3j99V8IkPHxHZzvopOUlora18Z181lZnzRJfSVJvb6aJSWHWFoaxKqqOGo0YtJQo1HPkhIVExPXMCTEuy6C4Mri4n84d67oeGopwsNFpHXkSNEFOCFBRD9FaufvLCk5yIqKaJaUHGBc3M1UqawYEGDD6Oh5zMr6kDpdOaurOzcP+QsvNI2KnI3RaGREhD8jI6fWX7Pz8rZTpVIYFTWHjz9eQzs7888Rff/9onSktLRBj+TkJ+ojIEeO+DUp8TEaDczMfI8nTtzItLTXWVERTaPRyOuuE/PCWpqXXsqvK5v5m4cOlXS6A7lWW8rIyCm8++63qCjGZqnv5uKBB0QaZ2DgJqpUYFTUhUxIWMnS0u61373jDiO9vYupUtk3qxM0FwUF5XRxKeOcOf9QrRZ54UajnjU1aS1mtdXUpDAubinT0tbRYBCRPYNBy+TktQwM7MOQEJ/63gPiYcWEhLuZk1NIe/ueqUc2GIxcujS9roZ0CwsLf6XR2HDyFBX9WZfdsohlZSEsKvqDer2aKSkvUKUCX3kllYCo3+1pduwQ145jx8TvkJT0KIODPRkWNppHjvjVjXermZCwitdf/0392JFkk/FfWtqrVKnAZ555jkBD6cAbb7zN++57mps3v0KS3LnzZF0H4xbS+7rB++8HECDXrRPleipV0zkeb7+ddHWt4IEDI9rMMDh8eABPnryTwcGePHVKTNkQFOTKRYuie+QadK4AmULcexgMpLOzkTfe+AlTUp7v8H6zZolJsVuivDysW1PYXHGFkWPHxvLYscvb3ba29gwjIiZTpbJiauorbaaipqaKFLDFizs2d2BlpTBwL7tMpOiZ0juGDRPz8p2dQtJZjEYjdbryJstKS4P49dcXWLTW6mx0ujKqVGBGxjvMzSU9PUVzFEvVJl10UdP5ShMSVjIoyLnTTcRaorb2DIODPRgVNbfJjXHkSHFhNs1v9vjj7cv66SfWpbgtrh8QmAuj0cjo6PmcNi2IgwYZzDog3rSJdU0kJrOsrJNzwbRDbS3p5WXk1VfnMSSkH4OD3VlSctCsn0GSqamf0dGxgnff3Ync0E6g14tz0MtLzz17LmRIiHenewB0hKio4wTIt98+YnbZZMNc3qaGOZWVxxgQYM/Y2MWdTss3GHQsLv6H4eETGBzcl0uX1nDYMAsoXcfcuaSvr47R0f/H6OiLGRo6vFlapOkRHOzB5OS1PH36aUZGTqlLmfRhZeUxjh1LXn99+58XESGMmbamrigtDayr1f66yfKCgl0MCLDjhg3CuRge3s2Db0R1tZhypnGDmszM96hSgadO3cOCgj0MDu7L0NBhrK3NpVZbWt/g7ciRIVSplPpa8/vvf7duWotuTI7bCrm5onHNs89qCZBXXvkja2rameC8Daqq4rh16xgC4jw2N0lJwlm6alUaVSqFJ04sMVvKr8kR+tNPo3nixBKzyGxMdva3vOaa7VQUA4OCIs0m12g0sLo6mQUFu+uaKNrwyJFBXLasnC4ulq0v1evVvP/+HwmQq1evry/PCAsbxczM91hUtJchId6MiPBvVrev1ZbwyJEh3L59aF1fDvOf3+3xyitiHFhTQ2Znf06VCjxx4iaGhY2kSoV6J+6ZM1t4773P1Dl48hkVNYeBgQ7198n4+GU8cmQIP/zwHQLks88a61KjL2R6+lv1QZ/w8LK6+/gvzMtL4A8/fEC1uvs1sevW7SNA7t4dTYDctOkoSTIl5QUePryQjo41vPba/zIlpe0mFNHRF9WVBIAZGaKjVVjYaN566356eXVbzfMGacD2MvPmkRMmHGVCQscqr8vK2vZ6nzp1HwMD+3Sq9rUxDz9MOjnV8tAhhWp1eqvbqdUZDA0dwcDAPiwq2teu3OuuE9GKztRLmcjOFg0/YmIs37EvJeVdOjhU8t57e6Ybh+igCubl7eBVVwkj35Jz9JluBCYD2TQn7Nk1hF3hxIklDAiwZ3V10wMYMIBcvVoYLt9+23b9q4mMDHEVevTR+3nmzOZu69aYM2c2c8uWkXXGjVlFMytL6P3ww68xImKyWeuLv/oqjgC5YcNChoYOZ1WVZeo79Xo158w5wKFD0yzSTXzLFhEFePHF1QwKcmN5uRktkkYYDEZ6eZ3hVVd1vL9AR9HpRP39pZeKJlr5+Tt45MggHj48gBpNQZflVlWdZECAHVev3kMbG/PM73s2cXHiHH3wwceoUlnz6NFZjI+/nampL7Oo6E+Wl0ewpETFgoI9LCz8tVkmT1lZKI8cGcTQ0OG85RZtu4a20Sgakwwc2BDlbIkTJ25gcLBHi1H17OzP+eOPg8za2I4UXdlFJES8P3NmC1UqhXFxN9cbXOXl4QwMdKr7fQcyIMCGWVmf0Gg0UqPJY27uRiYnP8ZvvnmqrnPrGzQYulhM2wq33dbgxJ079xfm5e1vf6d2OH36aY4dG0Z/f/O39L3zTjUdHTXcvXsIjx6d0WamRGeJiGDd97yLKhXM6sQrKwvlggVbCZCPPZZmNrktUVERzeBgT3777a11tZeW+6w33/yaAHnzzbE0GPQ0GHTMz/+JkZHT6h1VQUHOrd5TNJpCJievpbd3Jq+91rJ9BUz8/rsY85Hk0qWisZTRqGdY2EgePTqTRqORen0Nq6sbQsI1NWn84IOLaW2t5+uv38QjRwZTpbJmRMQkGo1GRkZO47FjV3L3bvF9TJlSQSsrPZOTm06empvLukjpFi5b9kOdo6f7E6w+8cQeAmRyckWd/L2src3m1q0j6s+777//uN3syZMnV9T/bqZsyaio2bz33i20sjJ/9t65ijRge5m1a0l7ezWjohZ3aPvffmt6w22MXl/NoCBXnjx5Z5f1+ewzIX/nzoFMS3u1xW10ugqGh49nUJAby8raj26Y2uhbwtNrbvT6Gk6ZEsKJE3tmpndTQf6GDSk0ta23JP/8w/rUGVJEI8PCxjAq6sJuyTV1wUtPf6vZOnd38qGHOifPaCR9fIxcvPgXhodPMJv3Xq1OZ1CQKx977BMCou2+uRk7lrzsskyqVOh2upyJ7OzPecEF++jrm8WsrK8t3un7xReDCZBxceaNIpeUHODkyUH09U3jiRMrWF2dbFb5Z3PddeF0dy+gTtfFucFaYc8e8T/67383NUo1HdSsa3JXSEt7nWvX/odA887G5uDpp1OoKAYeOrS82TRKHaW0NIgqFfjEEwcJtB09+vNP1g3Mmq+rrk5mTMwlDAnxabNrvsiauIR9++bxjjvMZwwtWCAGxgYDmZn5PlUqhTExlzaLQpWVHWZ09DzGxCxgeXlYi7KqqkhrawNXrHiNx49fQ71e3eJ2nUWvJ93ctJw2LYzr1i3hqVOvmEWuTlfBRx55qW5Abb4Rb1ZWHu3s1Lz++k957NgVTbqDmwONRjQjXLtWy9DQoYyI8KfB0P2ooNFo5M6ddxIgn3xS0yPT25SUqHjokDXHjj3N6dMt84FHjpykra2ac+Ykt5i5plZnMj9/B6ur25994qqrDtHbO7dJhpUlqKxscNiQYoqqq682MifnqzqjreUW7UajkUeODOI//9gwMNCRWm0Rc3O/o0oFFhX9wcBAJyYlPcq4uF/q5Q8alNikizQpHJSKYuDq1R+zf/+UuqyHH7qdDXbvvbtoba2jXk9aWel5zz1bmJ+/k2PHhhMgHRwMHXJapqa+XH/fMTmAY2MX8fHH3yPQfiPUfwutGbBWbVfIdhxFUa5SFCVRUZTTiqI828J6e0VRfqxbH64oytBG656rW56oKMqV5tLpXGL6dECjcUBSUp8ObX/wIODg0FCc3piiol9gMFTA17frXSTGjBHPZWXLcObM1zAadc22ycp6BzU1JzFx4m64uc1pU55aLRrOjB0LPPpol9XqMaytHXHBBUBi4lCUlSVZ/PNqazOQlDQVL788FIsWAfffb9nPmzlTNG04ckS8VxQF/fuvRkXFYVRXn+q0PL2+Aikpz+LUqbvg6joXgwc/Ub+ushK45x6grAzw8OicXEUBZs5UkJR0MWpq4lFS8lendTsb0oCEhDsBGHH06GpMngz4+XVbbDMWLADCwweB7IecnE+7La+sLAgREesQHX057rrLB4MG3dNikxNzcv31MwAAv/yiEh5NM6DXl+PAgWcRGzsPDz/shYkTN6NPn5Fmkd0aCxc6oazMG6GhoWaV+803QP/+GowatRq+vqswfXo0Zs5MgovLlG7LHjLkafj5iVtwSkpVt+U1prY2Ezt36jB1ahTmzfsvHByGdEmOu/s8eHndCE/PLwGIJjSt8e67wMCBwG1n9fczGGoQH78ElZVH4em5CMOHr8eQIc2GCADEdWrMmC8xZsxRhIUVd0nns8nMFI2s7rwTSE9/ASkpj8PLawn8/f+AtXXTLnNubnMxdWoQpkw5AFfXWS3Kc3ICJk2yQlraKhQX78XJk0vN8t+JiipBebktrrtuJ+65ZyFGj36l2zIBwMbGBcuWTQIAbN0aYxaZAPD++4eh1Trg+efnYfLk/bC1NW9TGTs7YNo0ICLCFiNGvIfq6hPIzf2s23JLSv7Exo3z4eiow3PP2fVIE0cPj0swevRHWLDgY0RFKYiPN698gwFYtcoFLi5l2LWrX5PmRyYcHAajX7+l6NNndLvy5s51R2Fhf5w6Zd7r6dkEBze8Li0FEhMJD4/vkZR0H5ydp8Lb+6YW91MUBW5uF8HGRo9+/ZbC1tYTPj7LYG8/GCdOLIbRWA03tzkYPnwq+vQRDZVGjsyFnZ1XEzk2NoCHRxWyslxx5sxwAMDJk9NRUREGAC2OiztCZaUBjo61sLYGvLyqkJurR35+BJKSpsHOjti71wrW1u3LcXAY1uj10Dqd3eHsnAsAKCrqknr/GsxiwCqKYg3gMwCLAIwHcLuiKOPP2mw1gFKSIwF8AODtun3HA7gNwAQAVwH4vE7evwpTJ8cTJ/p3aPtDh4CLLhJG7Nnk5W2Eg8NQuLvP77I+Y8eK57KyO6HRZKOgYHuT9VptPrKyPoC3963w8FjQrrzXXgNSUpp3eDyXufTSCdDp7HHw4M9mlZucDFx/PTB8ODBrluj2uWTJFXjooSPw9lawcaPlOx+7uoqOmYcPNyzz9b0TimKDvLxvOyyHJM6c+Q7h4aOQlfU2+vW7Hf7+e2Fl1XCH3LlTdFQGAB+fzus6cyZw+rQbNJqRyMnp/gAlO/tDlJcHwcPjKxw54thqF+/usnAhUF2tIC/vZRQV7YFGk9NlWUajHklJ9+Pw4f/AYLDG8uUtjEAswLRpDnB3VyMkZACKin5tf4cOkJPzKQICRKfk225ru6uruVi8WAzK9u3LNZvM3Fxg3z7iyiu/g5PTEIwa9SlcXKY2M3q6ipWVHebMEW2No6J+MYtMQJxLv//+LNLTx2DFiiHtdtZtj2HDXsewYaIt8PHjLW8THS3uWY8+2nD91+urQBqRlHQ/qqvjMGHCLowduxFDhjwDK6vWJ0Do02c0Zs1yQWrqQKSn7+uW7gCwZYuIwSxatAeZmW+if/97MWHCj936HWfPBo4dGwQ/v3dQXPwbSksPdktHkti16zcAwLJl/8HAgfdDMeNNYubMJRg5Mhm//qqFwVDdbXlqdSG2bZuO2bMTMXPmJDNo2DKzZ4vu125uN8LDYyEyMt6AwaDusjyjUYOIiHdw4MAK3HWXlUW7r5/NgAEP4LbbtLC21uGbb8xreWzZYkRi4iA8//wODBjg2v4O7TB//jgAQEBAdLdltcXBRn+bf/4BNBoF/fsHY+jQVzF58j9oyxQYNOhxeHhcCT+/FwEAVlb2mDTpb/j4LIePz13w8roRjo5D0K9fIQBg0qSWZ9vo168WJ0+KAM3w4Qbk5IxAXt4BlJT8g5AQjy7dF6uqAGdnDQDA19eI4uJ+CA8PhNFogx9+ULCg/SE1gAajFQBsbcWMCTY2HnBxyQIAFJvHx3feYq4I7EwAp0mmktQC2AHg+rO2uR7A93WvdwFYoIgr9PUAdpDUkEwDcLpO3r+K0aMBJycNEhJGgTS0uW1BAXDiBHDZZc3X1dZmorT0IHx9V9a1pO8aAwYAzs5AVtYEODn5IzPzHZBGAIBWW4STJ28DqcWwYa+3Kys6WrTRX70aHf5jngvMnesOAAgJyYNWW9jmtqQR+fnbkZe3tc3fz2gErr0WCAoSN18nJ3ED1um0uOGGXQgJUbpk5HWFCy8EwsKEdxYQ0xN4el6DvLzN7XoWa2uzkZPzX8TEzENi4mo4Oo7EtGkRGDfue9jaNg2z7tzZ8Hro0M7rOXcuQCrIyXkFJSX7UFPTxtxL7aDTlSA9/TX07bsYISG3wWgEbryxy+La5JJLRJT7xInbQRqQm/tll2UVFu5CTc1JBAc/An//nplqARD6X3qpPWJjL8fp049Cr6/sljy9vgJZWe8jLGw1Jk0STpyeYPhwWwweXIDAQK8ue83P5s8/AaNRwfz5n2HUqM9hbd2x7JnOMGGC+KFPnUqERnPGLDKzsz/E3r1jYGVlxG23df9i4+Q0AePGTUCfPpWIjW352vfee2IqsPvuExkQ8fG3IiTEBYGBNsjP34yhQ9ehb9+OJ1dddtkckFbYt+/bdq/N7bFtGzBvnh463X/g6joXo0Z93ubAuCPMmSMGqeXlj8LW1ge5uV90S15x8R/488/RGD26EKNGtR8h6yyKYoUbbrDH8eMzcfLkzvZ3aIedOwOQn++HBx4w/3+iMbNmAbW1wIkTCvz8XoJOV4T8/M1dlpeW9iI2b74aOp0d1q7t2TiJoiiYOfM1zJlzAFu3Ajqd0SxyjUbgtdd0GDMmEsuWmScKPn26A6ysjIiIqOmWw6AtSGDfPsDHR2QvmKa5GTOmBkOHvtxuRN/V9QJMnvwXHB0bpiZ0chqLceO2YNy4TbCyEp60pUuF4XrTTS07Wnx97ZCdLf5zixZZw2i0QVxcNBIT1yA1dQhycjr33yaJqipr9OmjBwAMHNgHJSW+KCwUBmhHpiIz4ejYEIE1ObRsbNzh5JQJQBqw5jJgBwLIavQ+u25ZjawZ7wAAIABJREFUi9uQ1AMoB+DZwX2hKMp9iqIcVRTlaGFh925ovYGVFTBxYgkSE6ejtjajzW0PHBDPl1/efF1e3mYAhI/PXd3SR1FEGnFiooLBg59GTU08iov/BAAkJNyOioowjBr1ebvpJlVVIjWrXz+RQnY+MWQI4O2tR0LCFGRlta18ZuZbSEhYhlOnViAx8d5WU8YCAoDERBGJ3rZNRCVOnwa++mo5XnxxW6cuXt1l7lzx+zRO++vf/z7odAUoLGx9EJOd/SnCwoYiOfkBaDTZGD36K0ydGgxX1wuabVtcLLyo994LvP8+cNVVXdPTwQGIjb0BVlb2yMx8s/NC6sjK2gCDoRLDh6/H9u0Kxo0DpnQ/27NF3N2BGTOAoKC+8PRcjNzcL2Aw1HZaDklkZq6HWn0hIiK8cOutFlC2DS65xApnzgxCZiaQkvJUt2RlZW1AYaE1YmImWMxx0BqXX16N6Oh5OHPmkFnk/fWXHt7euZg2bQg8PbtwYncAJyfA09OAvLxByMx8u9vyamqSkJ7+EiIjl+PCCxX4mmlq5UGDHsTw4bGIiSlvti4nB/jxR2G8urkBublfoLDwJwwYcD8GD34CY8ZshJ/fS536vAsuEBkI8fHDERd3Xb1ztbMUFgInTwKzZu2HTleEUaM+aTP621Hm1FXUhIfboV+/W1BS8ke3nD8BAZsRHz8X995ruZDgrbcOhtFojd27T3cr5Zk0YuNGV3h4lGLpUsve0EwlVGFhgJvbPDg7T0dOzqdd0r+iIhxhYb9g9+7HceedIqjQ09jZeWPlSnsUFXlh586/zSLzyBEgLc0eN930UbulXh3F2RkYPVqNU6cm4uTJ22E0aswitzH79on/5tKl62BlZcAvv4jfdOpU8/4wb745EKdPA/Pnt+xsGTTIvf71okXiOSkJ2LfvAqxadRJbt3Yuw8BgqIRa7QgXF3HNGjjQHiUlA1FUJMyaAZ2YbtbefjB8fFZg7NhN9ctsbDzg6loAQBqwZquBtTQkvyI5g+QMb2/v3lanS0ybRqSkTEZ5+ck2t/v7b8DTE5g6telyksjL2wR390vh6Di02/qMHSuMrX79lsLefgiyst6GWp2C0tID8PN7CQMG3NPm/hUVwLJlQEICsHmzGNCfT4j6SxucPr0QOTmfQq1Ob3E7rbYAGRlvwstrCYYMeR55eRtRVNRyyt+334rvYcmSpstra1Ph4NBD4ag65s4Vz43TiPv2vRJ9+oyvi7g3HwSUl4fi9OlH0LfvVZg58xRmz07DgAH3thrt//lnEeFdswZYuxYdqus4GwcHYN484NAhZ/Tv/x/k5W3uUhRWqy1CdvYn6NfvNpSWTkRQkDg/LZmuvXAhEB4OuLk9CZ2uEPn5Wzsto6RkP6qrY5GSIgz3q682t5Ztc8kl4jkraz3OnPkSJSX7uyRHozmDrKz3cfz46yCVHjdgb7hhEGprnfDHH1HdlqXXAwcO6DFjxj4MHfqCGbRrHT8/a5SVzUVu7hfQaLqeAk0akZh4D0pLhyAhYQQWLTLfie/hsRAjR55CXJwDzr5s/PhjwzVAr69EevqrcHe/FKNGfYYRIzagf/+VnU6H9fUFBg0CcnPvR0VFWLMSl45iuvYNGfI2fH3vhovLtC7JOZsRIwAvLyA0FPD2vgVGY22X6/dra7Owa9cM2NgYsGKF5aKCM2Yo8PWtwqFD0+pr/LpCQkIIQkIuw+23n7F4udCQIaIsJTxcRKAGDFiD6uo4VFR0rjaTJFJSnsSmTW/D3t4G69dbSOEOcMcdl8LDoxQbNrhAo8nvtrytWwFHRw0uuSSkSTSyu8ya5YTU1EtRVPSrWXo8NKamBli5Ehg/vhyLFm2Ar28aamoUeHjkY8SIFiI33cDKSvxfW8OUEefu3uAwKSxciMTE5wAA0dFTOpUdo9XmoabGBc7O4prXvz9QWtoPWu1yKAo75VRUFCuMG7cZvr4NASsbG3e4uQnLVdbAmoccAI1dcYPqlrW4jaIoNgDcABR3cN9/BbNnu0Oj6YPY2NYvWqQwYBcubG4MlJeHoLY2pVvNmxozZgyQkQHU1tpi8OAnUF4egqSkBwAo8PFZ0ep+Wi3wxBOiYcfevcAnnwh9z0dmzgRSUwdCrfbE8eNXQK1Oa7ZNVtYGGI21GD78TQwd+iqcnCYiJeWJZqmKpaXA7t3AHXcAjo3Kq3S6Uuj1ZXB07FkDduhQMRAMazRWURQrDB78FKqrj6O0tKkH2GjUIDFxNeztB2H8+G3o02dMmwNPEvjiC2D8+ObOls6ycKGIFNvZPQcrK1tkZLzRaRnZ2R/CaKyBn9+L2LFDLLv99u7p1R4LFghj5/jx+XB2nors7A87HR3IzFwPe/tBCA29EN7elosYt8bEiaL5VkLCLejTZxxOnVoNna6s03LS018FqUNo6HIMGwZMslxpXIssWGALOzsd/vnHuUv6NyY8XI/ycgdcfHEW3NwuNJOGLePnBxT+P3v3HV91df4B/HPuvdk7IQkJIew9ZIMoigMBN+Kgzrrraq0/W22tbbW1rdWqbbW2OOoobkVUEAEB2UJkE/bO3nvnnt8fz/0mN8lNckNyV/J5v168ktz7/d4cIPne73PO8zwndxiAepw6deZ31hkZ/0Fx8XqcPPk6gDPLhmiNyeSPMWOsKCsLxqlTTZ/78ENptjN4MJCW9gJqa3MxcOCzna7hnDQJ2LevP4KDRyIt7Z9n9Brr12v4+1dj9OjjGDTor50ajz2lZBV282YgIuIc+PnFIi9v8Rm9Vnb2MqxYcSvmzKlwaXmJUsBVV/kjJWU2Tp58u/0TWvHmmxmor/fDgw92XbDUGqUkqDDew+LiFsBsDkNGxsIOvU5e3hIcOnQSq1dfjfvuU0hwrhWJSwQGKvz+99XYtesc/OIXG9s/oQ01NcDHHwMzZqxE795ju7RueuJEICcnBNXV1yMt7e/tlr51xLJlkh3xi1+8gcDASvTtexAAMGTI3i5bRXaWEVAmJcnCUXQ0UFn5M+zffxYAIDs7GRUV+51+vZqaLFRWhiIsTG7gExKkFOXEifMQF6ccNtjqCD+/KISEFMNs1lyB7aLX2QZgiFJqgFLKH9KU6Ytmx3wBwJhGuBbAalt75C8ALLB1KR4AYAiArV00Lq8yZYqkMKSktF6jtW8fkJkJXHJJy+eysv4LszkMsbHXtHzyDBiNnA4eBBIS7oSfXy8UFq5AdPQcBAY6Tg2qq5MGRS+8ILWF338P3H9/lwzHI6ZMkfrLmpplqK3Nw/btZ6OsbFfD81VVp5Ce/i/Ex9+I4OBhMJksGDjwL6iqOo6cnA+bvNb77wPV1cAddzT9HlVVxwDA7Suwzd/8DfHxN8LfPxGnTj3X5PHjx59ERcV+DB26EBZL+40gvv9e6p8ffLDzq5xGvfeWLfFITLwP2dnvOpxMaE1dXSnS019GbOx8hISMxHvvSf1UWzOvXcFIf/72W4U+fR5ARcU+lJRscvr84uLNKC7+DomJ/4cVK8yYPVtmjN3JZJK/x+bNFgwf/jZqarJw5MjDHXqNioqDyMx8HeHhD2PNmhDMm+f6RmXNBQcDM2ZUYPPmuS1+Nztq8eJUmEz1mD/ftcErIAHsqVP+iIu7BZmZr6O2tqDDr1FbW4Tjx3+DyMgLsWXLuYiPB846q2vHOX68XBN++KFxfCdOAFu3SqM6afz3PGJjr3VYbtBRkyYBhw4phITch9LS71Fe7vxNpGHNmgIMG7YVI0Y8Az+/rk3PPftsSTUsKDAjJuZK5OcvhdVa0+HXWbv2CAoLe+Pmm13f8OyKK/xRWRmKVasyUFfX8c7X9fXlWLEiGSNHnsSIEa7tkG6YNk0aI+bnAxZLKOLjb0Ju7oeorS106nyrtRbHjj2GbdvugdVqwj33uHjATnjood6YN+8H/POf12DhwjPv+bB8OVBQAMyc+SrCw7s28LvkErmGL178DKqrTyM/f2mXvfZHHwFxcRoDBz6H2NjrkJQkP4tjx1Z3uj69o4wdCkJtv35DhwI7d6KhU3ReXlKHAtiqquOoqAhDeLj8fhiTJSkpsujTWRZLFJQCYmJqYV9N+dhjwJ//3PnX9yVdcqtkq2l9EMA3APYD+EhrvU8p9bRSyugB+gaAGKXUEQCPAHjcdu4+AB8BSAWwHMADuiunerzI0KFAUFAVdu70a3WVZoVtUax5/WtdXRlycj5CbOz1MJtDumQ8xgrJzp2A2RyC0aM/R2zsDRg8+KVWz3nkEblo/uc/kjY8ufP3KR5ljD81dTTGj98Ik8kfO3bMwNGjv0R6+r+xe/dsKGVG//5PNZwTHT0XQUFDkZX1ZpPXeuMNWT2b0CxLzQjE7Avy3cX+zd9gMvkjKennKCr6Fvn5X0NrjfT0V3H69HNISLjH6Xq/l1+Wbse3tL5Y77Rx4+QNZN066S4IAJmZrzl9fmbm66ivL0bfvr9Eaqr8TN94Y+fH1Z7AQOkWvmqV/eqA882c0tP/CYslEllZ9yAvr2tXzTpi+nQpBairm4zk5MeRnf028vK+dPr806dfhMnkj9TU36CmxnWNs9pzzTXhSE8fgk2bvjvj19Ba45tvFEaM2ItBg1zfla5fP0mpCwp6FFZrZYd+7g2nTz+LurpCDBz4N6xcqXDJJV0/ETJpkswGpaQ0ptMZDdyuuw44ffp51NdXYsCAZ7ro+8nH9PSbAJg6nEZcXg7s2ROB8eNTER9/c5eMyZ5RorFlCxAbOx/19SXIzn63Q69RX1+B1aujYDJZccklrp/xueACIDCwHps2XYjc3I87fP6hQ8uwb99UXHaZCwbXiqm23Yy+l0bYSEi4B1ZrldPlGpmZb6Cy8hB27LgbQ4dKpoCnKQUsWjQU48dvwkMPJWHlynVn9DqLFgExMdWYPHlFlweww4bJdn8LFw5GaurVyMh4tUtet7wc+OorjVmzNsJqzUJ8/M1YsMCE+PiTuP32pC75Hh0xa1bjogwADBkiP2tWKzB6tEZeXiJKSpzferCi4iAqK0MRGSn36cm2HcyysroqgJVavdjYSmTbkjlzc4G//hX49a/l37en6LK3OK31Mq31UK31IK31M7bHfqu1/sL2eZXW+jqt9WCt9RSt9TG7c5+xnTdMa935vvleymQCRo0qxsGDQ1Fe7ngjsBUrgBEjWnYqy8v7FFZrORISuiZ9GJBf1NBQYIdta7iIiHMwatQHrTZuWrhQ0oUfeQReMYvZFWJiZGJh40YgJGQExo/fiMjImUhLexGHD9+H+vpyjB69pEn6r1ImxMZeg6KidQ2zwDt3ympk89VXAKislBlWd6/AAo01HcabvyEx8ScICRmNPXsuxfr1oTh8+H5ERc3GkCH/cOp1c3Lk5vXHP26cuewMi0W6Jq9bBwQGJtmaIr2K9PR/tdsYyWqtRVraS4iIOB/h4ZPx/vvyu+auZkhG+nNubgji429GTs5HTq2i1dWVIi/vc8TFLcDq1ZKd4ahxmzucY1to3LwZ6N//twgJGY3Dhx9yqgNlfX0VcnM/RK9e1+DLL8MRF9fY5Mbd5s1TUErj668HoaLi4Bm9xqlTm5GaOhKXXFLfqU7vzjJWAPLzRyIy8gKkp78Cq7XO6fPr6sqQnv4qYmOvRXb2OOTlAeed+Q5rrerTZywSEo5j167GDKIPP5QsluTkcmRmvo7Y2PlO7TPpDGPruZ07oxARcQ7y852fUAGADRuyUVdnwcyZ4S75f5w0Scp8Nm8GoqPnIDz8HBw58ghKS53feqSwcDW2br0IEyeWdHgP7TMRHAxceKEJ338/D6dPv9Thjt2LFx+D1WrG/Pnu60Y4ebJcz41MorCw8QgLm4yMjP+0W65htdbi1Kln4O9/ETZujHN7f4G2BAWF4bPPohAdnY/bb0/AsWMfdOj8khLgiy+ASy/dAYvF2iVZD809/7yUl3z99dMoKPgGlZXH2j+pHe+9B1RWKkye/Gv06fNTxMRcgfnz5+PUqSBMn+7muhNI7evnnze+Z9k397ruOoX6ej+kpWU5/XoVFftRWRnekEJsvzPDmezS0JzZHAYA6NWrvCGAPWK3iL9zZ+e/h6/wmSZO3cXEiSE4cmQc8vNbNkqpqgK++85x+nBm5n8RFDQE4eHTu2wsJpOkme1wYm/z774DHnhAVoj+2nWlRF5hxgxgwwaZcQsM7IsxY77AuecWYerU45g27QSioi5ocU509GUA6lFUJCs9b74p+x/edFPL16+o2IeAgL6wWMJc/DdpadKkpm/+BoslFOPGrcWAAX9CYuI9GDHifxg7dilMJufSwj74AKitle7DXeW88yQQzM8HBgz4I8zmMBw+/AAOHmy9mZjWGidP/hHV1aeQnPxLaC1ju/BCdFkH1vYYW0etWQMkJt4LraudWonJy/scVmsl4uNvxrp1wKhR0s3bEyZPlkmEjRtlhX7w4H+guvpku925ASA//wvU1RUhOvp2LFsms9ln0syrKyQkAFOn1mLDhnlnvK3RihU7YbWacemlI7p4dI4ZAezJk0CfPg/Z0vWcD9Zyct5DfX0xkpIebvg9NyauupLJFIChQ9OQmiorAEeOyKTdDTdId/y6uiIkJf2sy75fr17AgAGSehcTcwXKynaiqupU+yfarFhxEEpZMXu2a2ZTQkKk9n/NGmkwNHLk+7BYIrFjxwzs23dDq00B7R05sgYHD07GZZe5Z79kALjsMoW0tH44cKCqQ13Ha2sL8e23yYiJKcXkye67dQwNlT3N7d/DEhLusZVrtN3MKTf3E1RXp+HEiT+julp5VQALAP37j8B778UhI2MQfv3rkg51sf/sM7lnvOiiRQgNHdtlmXn2goIku2LNmlGoqQnF6dPPtX+SA4WFa3Ho0H3YtGkmnngiCyNHbsasWf0wePCLUEpBKRP8/T305teMUXoxYoT09wCAjIy209UzMl5Dbu5n0FqjoGALqqqCEWa73bOfmOraALYUWba4+sSJxud/6HwPQ5/BANbNJk8ORWVlGHbvbtmJeMMGuSA1D2ArK4+huPg7296vXZtmNH68zNhY29il4PhxYP58qSd8/33P3Zy6ynnnSQOmfXaL4mZzCIKC+rc6cx8WNhGAGWVlP6CqSjoBzpsHhxujl5fvRUiImzb2bCYkRFLFmwewAODnF4N+/X6FwYNfRHz8TR2qPVm0SC70XblfqbFqtHYtEBo6FtOmnURy8q+Rk7MIRUXrHZ6TlfU2Tp58GnFxNyI6ei4OH5Yba3emsI4fL7O4q1cDoaFnISxsilOrA9nZ/0NgYH+EhEzHxo2uWTVzVnCw/D022cp3o6IuQK9e83Hq1J9RVZXW5rlZWW8hICAJBw/ORGmp+7soNzd/vj8OH56AlJRlTgUR9rSux8aN1TCb63H22UHtn9AF7APYmJgrEBDQt0NdP/PyPrdNbp6NLVtkP9YRLoq9x4ypxqlTfVFUVNXQKO3aazXS0/+B0NCJXZ7GOGmSBLC9ekklkrOBvdYaGzeaMGjQMSQm9u/SMdm74gpZgc3KksnP8ePXIT7+JuTlLcHBg21nS2mtsXJlObQ2Yc6czm/t4ywj/Xf//meRnv53ZGcvcuq83NzV2LZtNi65pMLtdfrTpjWmdQKN5Rppaa1nDGmtkZb2AoKChmHdukkICfHsNbY1F1zgh1tuycSnn/4YmzY53whs0SJg4ECNAQPe6vLfO3vXXguUl5tw5MhzyMx8vcOrsOnpr2DXrguQnb0IH330I+Tm9sZvfrMPw4e/6ZYMl4669FLgH/+QlWJjQjkvD6ira7mFGABUVaXh0KF7sG/ffOTlLUFpqeTwGgGs/S17VwSwxkJIr15FyMqSZponbTtzBgcDu3a1cXI3430/Pd2c0a31hx+qW+yt9c03sop3/vlNz8nKehvtdQbuzHjKypqmINirqACuvlreOL780ve2ynGG8aa2rgNlKGZzEEJCRqC0dDs++UQC4DvvbHmc1VqH8vL9HgtggZZv/p11+LA0brm5i8vKpk6VmtpvbMkJSin06/cE/P0TcezY4w4DwtOnn0VY2BSMGPEulFJYbtvJwp21pGazbEWz2rb9aJ8+D6KiYj8KClqvhqiuzkJh4SrExd2E3bsVSkslE8CTzjlH/l9rbVmFgwY9B63rcfz4b1o9p7o6EwUF3yA+/hasWGGG2dzYkMtTjMmL9euvxuHDD3aoK3Rx8Sbs3n0WRo8uRkjXL2g4FB0tE00nTwImkwWJifehqGg1ysvb3m4NkM7hRUVrER09G0opbNkiKb2ummS86KIgWK1mLF16HIsWybUzNHQlKioOICnpZ10+wTppkkygVlYOQ1DQsFa3L2uuqGg7du8+C9Ond/3+lfbmzZMbyC9sLSsDA/th2LCFGDDgDygqWttmGntZ2S5s3z4aISG1DenS7tCvn2R7bN16FUJDJ+L48d86lbK+Zs1hlJZG46qrerlhlE2dfbakzO639dKxWELRp88DyM39qNVyrOLiDSgtTUFS0s+xbJnCrFlAgHv6TnXYX/6SCH//WvzhD6FO/V+kp8v+69demwmrtQwREa6LzGfOlGvUhg23QCkLTpx42ulzq6pO48iRnyM6+jJMnZqDjz++F7NmAT/60V0wmTrZjtdFzGbgoYekL4exY2dhYVyrTeRKSxvrs/btm4fKSlnFsC+tevppKVfrivd4kykYgAkxMQWorgaKi6Xxa0SE1OXv3u38a1VXo8W2aL6EAaybjRoFWCxWHD48qkWtzPLl8gNuf+OktRVZWW8jKmpWq52BO8NoYrS5lUychx4C9uyR2aghQ7r823uFfv2khfp6x4t8rQoNnYDi4p144QVpeHCRg34vFRX7oHU1QkO7uCVoB0ybJm/+B5zvQ9CmRYtkVrGrt6jx85N60uXLGy+qZnMw+vf/HUpKNiE//6smx1dWHkdFxQHEx9/YMJO7fLnUsAx0c7nxhRcCx45JKk9c3AIEBPTFqVPPtnp8Ts4HAKyIj7+pYeLE0wHs9OlAZWVjSUFQ0AAkJf0M2dnvoLTUcZ2BrN5Y0bv3bVixQm40w9tvYO1SgwZJdsC2bQ+ioGAp8vI+c/rczMzPsH//VMyY4b6UTqXkGmTMoick3AWTKQgnT/6p3XOLizfCaq1EVNQlKC+X2XdXpA8bLr54KIKDS/DYY4k4cEAapaWl/QN+fvGIi+v6onOjkdMPPwC9el2NoqK1TnWf3bBhMyorw3DRRf26fEz2Ro+Wn7dPP236eGzsdQDQ5p7K+flfYvfu8zB9uhUW9y3AApBV2HXrFGJinkZV1THk5n7U5vFaayxfHgaLpQ5z5rg/Bcv4mbbPJOrb91GYzSE4ceIph+ekpb0AiyUGubm34vRpuLXxVEclJCj8+McZ+Pbbufj++/+2e/yiRfIeefnlMmMbGem6ANbPTxYxvvwyGKGhjyA7+12UlGxz6lzjPXDo0H9h06ZAZGQ4nuj3VsYKbFFRLMrKHBeXVlWdACBlT4AZ0dGPAWhcgQWAJ5+UINMIiDtDKQWzORTR0bIJbFaWlF3FxEi23d69si+3M37/e2lqVtuxUnivwQDWzQICgJEj63D48HiUlTXeFKalyQ9e85WjoqK1qK4+2WV7vzY3apT84K9d2/K5JUuktvOJJzzXHdUdlJKVhHXrnJ+Nys0FPvnkFvz2ty9gxw65QDlKqyoqWgsALp0hbY+jN/8zZbUC774rs7Jd0VGvuTlzgNOnG2faAaB379sRFDQEx4//usledAUF8uYdHT0XgARfa9Z45mfVWHVcvRowmfyQlPRzFBevQ3Gx43/07Oz/ITR0IkJCRmDdOgm4k9zfgLEJo5HTJrtdgJKTfw2LJRpHj/5fi5VMrTWys99GePg0VFQMww8/OK7f94RrrgFSUvqgquoiHD78U6e2DNG6Hps3p6KqKgTnnuvvhlE2sg9g/f1j0afPg8jJeR/l5W3POhUWroRSFkRGzsQPP8iNiysD2NDQWFxxxedIT49AYiIwf/4RFBQsRWLiT5yun+8Io6N7SgoQGzsPWtc5tZ3HunWSxnfBBa6diFBKaoBXrUJDQxUACArqj8DA/igqWtPquUePrsOxY2Mxc6b7lwWvuEK2xNuyZQ6Cg0fi1Km/tpmpUFGRio0bz8OUKTmIiHDjQG2GDpVVQPuJdj+/GCQlPYzc3I9RVtZ02ami4jDy8pagT5/7sHy5lAJ4urShPY89NhgmE/DCC2VIS3sZWjtOmdIaePttmXCMjPwCQUGDERCQ6NKxPfSQZOp99dWT8PdPwIEDt7fIIGyuujoTmZmvo3fv2xAYmIwPPpAU18svd+lQu1RUFGA2a5SWDmm1fKGq6gTM5gj06/cEzj+/BuHh9wFo2dyys/u/2jObwxAVJRec7GzZSik6WiZuq6okS84ZK1bIfUdXjs2dGMB6wIQJfjh8eCJKShpXYI20yeY331lZb8FsjkCvXle5ZCwmk6Qs2wewOTnAtm2yr9Tw4cDvfueSb+1VZsyQGbJjTpR3bNokgf9vf3sx1q27FnfckdHqli25uZ8gOHiES1bPnTVkiFyIW1tl74hVq+Tf6K7W+yp1yuzZ8tFIBQYkIBww4BmUl+9tUq9VUPANAgMHIChIUgPWrZOL99y5rhlbW0aOBOLjJa0LABIS7obZHIH09L+3OLasbA/Kyn5A7963QGsZtzfUZiUmSiC1cWPjY35+kRgw4CkUFa1psQJeVrYd5eV7ER9/G9askRsrT3VRbu6aa2R/5wMHFqKmJgNpaX9r95zCwtXYuVNaUE7vul55TunXT1bvjRiib99fwGQKwrFjj7d5XkHBCoSHT4fFEtYwQWVsO+IqTz55HD/96YNYtSoPhYV/h1J+SEz8iUu+V2SkXL9SUoCwsMkICOjb7vYp1dXpSEnpjz59ilt083eFm26Sib0PmjWRjYy8AEVF6xwGIlVVadi8WTqPe+J3f/p0aXL32Wcm9O37C5SX70J/K/w9AAAgAElEQVRBwbJWj9+zZxOOHx+DK64IduMoGxl7mm9qtsV2UtIjMJvDcezY4w178GqtcezYr2AyBSEx8QEsXSqlUomujfE6LTlZ4frrga+/vhe7d/8KO3ac63AC64cfgNRU4JZbrCguXu+WyfFx4+Te9PXXAzFkyKuoqNiHrKy32jwnLe0FaF2L5ORfoaYG+OQTafDnrtKMrmAyAb16KdTUTENBwTKHW8tVVZ1AYGB/ALJDRZltrjTMhT07LZYwRERIAJuf3xjAGltjOpNGXFYm/W+alyz6EgawHjBpkkJRUSyOHk1veGz5clnRGjWq8bj6+grk5S1GXNz1MJtd11Bk5ky5eTp5Um6gLrxQ6qgOHgSeew5uT2/yBGfrYE+elItwRASwZUsGVq2y4A9/+BKOSr8KC79FcfEG9O7tYG8dNzKZ5Ka2syuwtbXAL38J28pL14ytueRkCQbtA1hA9lkMC5uCI0ceRmXlCVvt32pERV3SUHf39deyL6snLshKye/NqlVyM2uxhCIh4Q5bF8yMJsdmZf0XSvkhLu4m7N8vb0CeTh82nHOOBLD2izEJCfcgKGgYjh59tMm2GxkZC2EyBSIu7gZ8953cmLizlq8to0ZJ4LN8+UDExl6LU6eeQ01NdpvnZGf/D/v2zUTfvtotgY+9sWOljt5+FbZ//yeRn78EubmO6z5ranJRVrYd0dGy7L1li6SzdkWaWlv6978B8+b9C1brfcjMfB3x8TcjIMB1Lb+NRk5KmdC79x0oLFzRZnOu/Pzl2Lv3XJxzThcV/bdj5EhZKf5fs7g6IuIc1NUVOKyDzc39ELt3n4eAAKtH9lI3mWSSZ9kyIDT0RgQGDsSxY79uddXvyy8rAADz5nmuCcZ550lmTk5O42N+flHo1+9JFBR8jXXrAvDddwFYty4IeXmfIjn5Vygv741Nm7w7fdjez35mQXl5MHbvXoWKikPYvn1aw04HhpdektW9yy8/gLq6ApemD9u74QbJjjp16nKEhIxBRkbr+1VXV2cgI+PfiItbgKCggfjiC3mf6+q+Ge4QFwdUVIxDWNhk7Nt3LQoKVjZ5XgLYxlKFYluvJ1cGsGZzGMLDZT/uvLzGAHbECLlfdyaA3bpV7lWMzCtfxADWAxr3twtBfX0V6uvlxnf27KYdy/Lzl6G+vgxxcQtcOh4j/XHpUnmD2LdPZmf/8hffSvfojBEjZNuGtupgKyqkaUdtrfxbTZmSAIsl1GETieLizUhNXYCgoKHo0+c+F47cOWefLf+vJSVn/hrPPy81di+/7NpmGHPmyLZN9htyK2XCyJHvQet6pKZej+zs91FfX4rY2Gsajlm5Um5ygtzTPLaFOXPk5sqoIe3T5wFoXY+MjH83HFNfX4Hs7HcRE3MF/P17Nfy8ecMKLCBvZpmZTVOQTCY/DBr0HCorD9kaygE1NdnIzv4f4uJugp9fFL77Djj3XO9JRVJKbtC//RYICXkWVmtVm82oZLLwM+zfPxPTp3dtIyJnOErzT0p6BCEhY3H48IOoq2v5i1tYuAoAEBV1CbSWDAtXpg8bQkKGIyHhTuTmfgJAITn51y79fpMmyY1zdjaQkHAHAIWsrDdaPX737h+Ql9cH55/vvmDr5pslyLbvMxAeLneGxcUbmxxbU5ODtLSXsG/fpZg61eSxxkLXXivvaStW+NsyXHYjO/u9FsfV1ZVi9eohSE7Oa7JHprvNnCkfv2saz6Fv3//DmDHL0K/fb9G37yPo0+d+DBv2Bvr1ewLLl8tNuq8EsFOmSF+Sd96ZiokTtyMgIAH79l2H2tp8ALLQ8MEHwL33AoB0DXRXedLll8t1delShYSEu1FW9kOrJTJHjvwcWtejf//fo7ZW7iX79m3MsPIlsbFAXp4FY8d+g+Dg4di7d15DDbDWuskKLCDlZcZ5rmI2hyE0VCbG8/JkciA6Wu7Lhg+Xa1F7Nm6U/09XZ+y4EgNYDzjrLMBstuLgwXEoL9+NlBSgqKhl/VhOzgfw84tHZKRrl5RGjpRmFO++KzWEgPxwP/aYS7+tV1FKVsHWrnVcB6u1NB/YuVMaWg0dKsX0wcEjWwSw2dkfYOfO82E2R2DMmC9dsj9bR02bJn+Hbc71Xmhh40ZJJb/uOtdvUXPRRUBNTcuLcFDQIAwf/hZKS7fh4MHbERw8ClFRFwOQC3hqqmfTYYw3569tzYeDggYhJuYyZGT8p6FeKCPjP6itzUNS0sMAZMU/IUFWzryBUcKwrFk2YUzM5QgLm4JTp56B1VqDY8eegNY1SE7+JfLypH7f21KRfvxjqfN7//2BSEp6GJmZr7dYzTDk5X2BzMxIZGbGeGRGeswYqQ+zT5E0mfwwbJikQDsKvgsLV8BiiUZY2AScPi3NPNwRwALAkCEvY+jQ1zB+/AYEBw926feyb+QUGJiM6Og5yMx8o0k2gMFqrcWGDdLFdcYM901ELFgg3UvfsIurg4OHwWKJQUlJYwBbU5ONnTtnori4GgcODPfoxNWMGXKT/cknQFzc9QgNnYDjx59AbW1Rk+MyMlZjx46ZmDOnymGmkbtMmCBZHs0DWKUUYmLmYsCApzBw4J8xePALSEi4A0opLFsmE9OeWOU+Uw88IAsJmzcnY+TID1Fbm4e0NClFeeEFWT1/+GGpfw8MHIigoAFuGVevXrL4snIlEB9/M/z9e2Pv3itRXZ3Z5LjKyhPIzf0ESUk/Q3DwEPzhD/K7+8ILvrkFY1ycTEz7+UVh7Nhv4OfXCwcO/BhWay3q6gpRX1+Gjz++smHy0Qhge7mwWbfZHAaLJR+hoTK2wkLpZQNII8xvvpFrUVkbrR+MUjhf3lmEAawHBAVJI6dDhyaipGQrvv5aAij7LrZ1daUoKFiK2NhrO7Q/55lQCrj7bpn9/9nPpB5rgHuuiV7l4otlywZHWwr9618y8/mnPzVtBhESMqpJAFtVdRIHD96J8PCpmDhxK4KDPThlbWfKFPl4JmnES5fKv01yMvDqq107LkeMDIXt21s+Fxs7D8OGvYleva7BqFEfNnQfNm78zz3X9eNrTXy8jP1ru91z+vR5CLW1OcjJ+Qh1daU4depZREZeiMjIGdBabsbOOw8evTG0N3CgzOAubdYnRymF/v2fQlXVCezYMQNZWW8gKelhBAcPbUi797YAdvhwuab++99AcvJTCAoajNTUG1FTk9Pi2JycRTh48AoA7q9/BSTt6/zzW6bOh4dPRWLi/UhPfxklJVsbHtfaioKC5YiKuhhKmRt+r90VwJpMAUhMvAthYRNc/r3Gj5ffD2PyrU+f+1FTk2lbAW6qqGgtdu2aiIiImiblOK6WkCBlFa+91njTqJRCRMR0FBdvAADU1ZVh9+65qKo6ifLyVbBalUcDWItFust+9RVQXW3CkCH/QE1NJvbuvbpJg57ly3ejujoYV17pujRxZ/j5yfXdUcNJR6xWuZGfM8e3AqcbbpBg5OWXZT/0mJgrkJHxKo4fr8LChcAttwCJibUoKlqDqCj3Nh24+GK5h6iqisLYsctRW5uLnJwPmxyTkfEKAIXExPtx9KjcM916q6z4+6LY2Ma09YCA3hgy5J+oqEhFRsarqKg4hNOnh+CJJy7CAluiZF6eTEYGu7Bc3GIJQ319KXr1Ao4elcWJaNm9B3fcISuxd91lrNS3ZLVKxo4n3uu6EgNYD5k40Q+HDk1GcXEK/vc/SY+xn7HJy/scVmsV4uNb6Q7Uxe69V1YV6+tlNtlbbqjdyVh9an4TWVgI/OpXskLefFU6OHgkamuzG1J80tP/Bau1GiNGLIKfX7QbRu2cyEhJk+5oALtmjaRijh4tFzxjls+V4uOlHtxRAAsACQm3Y/ToTxES0niHunGj3OB4eqZ97lz5Ny4okK+jomYhOHg4Tp36E44efRS1tTkYOFC2RzlxQvbz85b6V8Nll0lg3Xz2NiZmDvr3/z0qKg4iLu5GDBggf4/vvpNJOWOlzJs88ABw6hTw9dchGDnyY9TW5mP//puadLOuqkpDfv7XOHLkZgQHS4aMJ1x6qaRuN+8gOXDgn+Dvn4jU1BsamrqUlGxBTU1WQ3O/LVuk/ttTY3elsDC5dhkZGdHRcxEUNMzWJKZpukxe3mLs3TsD06ebHXaFd6Wf/1zq3956q/Gx6OjZqKw8grKyXdi//2aUle3CqFEfY+vWsfDzk9IOT7r2Wvk9X7FCanaHD38LxcXf4eDBe6G1RkXFIaxcGQZ//1pccIHnm2HMnCmlMMYqV1tSUiSY8ERTv84IDJQFhSVL5NqVlPRz1NTk4f77JWX0d78DSkq+R319aUP9u7vMmiVZLWvXAqGhZyE4eGST/c7r68uRmfk6oqPnIyCgL556CvD3lxRiXxUXJ6VX1bY5nZiYyxEVdTFOnPg9CgtX4OTJkQCkf4HVKj+bru5DYDY3BrCHDsljRgA7ZoxsffmjH8mii3EvYm//frlWMYClMzJjhkJRUS/861+jcPSopLvZy85ehMDA/ggPd887XECArBz9+c/A4203vuy2Bg6Uxi/GpvSGRYuA0lK5CDcP7I0gqrw8FVZrLbKy3kJMzOUIDEx206idN22a3Og6u1VQZaWkTffvLzc4rr4o25swofUA1pGNG+UcT9W/GubOlTexlbY+D0opDBr0IioqDiAzcyESE+9DeLgUnXjL/q/NXXaZ1HmvWtXyuf79f4cZM4owcuSiho3o166V2ll/9+4845QrrpDaq5dfBsLCxmHIkJdRWLgKJ0/+seGYzMyFAKzYs2cipk71XNM6o06v+eq3xRKO0aMXo76+Atu3T0Nh4be2RmABiImRk7ZskQkEb6lB7mpGIyetpR4+KelhlJamNKxuApI+fPz4tzhxYiSmT/fMXqXTpgF//7tcAwDZD9ZkCkFKygTk5y/B4MEvIibmUixfLquJzbfacLcLLpAb349s28DGx9+I/v2fQnb22zh06CfYt28+tm2bi/POs7p0RclZRh2so2tTc0Zmm7ds7dURP7E19X72WSA4+Hy88spnWL58IP7whzokJwP5+V8AMCMy8gK3juucc2Sfb+PnJTLyAtte1JK2f/r0C8jODsSsWe9i+HApS3vwQclQ8FXGXrDGpIlSCoMHvwSrtQYnTvwOJSWNHf+OHpVafdcHsKGoq3McwAJyH3v//XIdap5yDzTuNODLDZwABrAec8UVgL9/Pf71r18gObmmIf0AAEpKUlBY+A169/5xQ4dVdxg4UIJXX86J76wbb5TGLydOyNdaS1rYxImSytZcSMgYAEBp6Q/Iz/8KtbU5SEhw0R4znTRtmsxIO7NVECBvnsePS9pwVJRrx9bc5MkyS5iR0f6xRr2sN1yMp06Vfyv7NOKYmDkYM+ZrDB26EIMHv9Tw+Jo1sqI9erQHBtqGc8+Vm5TmgZQjBQUy2+tt6cMGi0VuBletkq7qCQl3Ij7+Fpw48TSKizfbGur8A0FB12H37gCP/gwNGCArjY7+3cPDJ2PChO8REJCEXbsuRmbm60hMvAcWSwSqq2Wyx13pw54waZLU+BrXg969b4WfXxyOHXu8oXNubu6n2LNHJg491Zjk4YelBOUr245T/v5xGDHif4iMPB/Dhr2OpKSfIj1duoR6w97qfn7S02DJEpmwBIB+/Z5EYuL9yMxciJMnrTh5chjmzvVQp6lmpkwxtv9p/9hly+R4V9Yiukq/fsB990npUkSEwqefzsONN/4JCxa8Aqu1DtnZ/0NMzKXw83PvG3NAgDQs+/hj6TsRGXkerNZylJXtQF1dCU6ffh7btv0ROTn+OHRIsid8vZeKEXynpTU+FhIyCmPHLoW/fwK0bux0umuX3Du6ugTPbA6D1tWIiWnsGt48aJ4yRX6/v/++5fmbNsnx3tJ740wxgPWQ2FjgmWfKkJh4BM8889+G1Yuamhykpt4AP7+4hkYv5D533CGztv+2NY7dtk1uNlrb9zQwMAlBQUNRULAMaWkvISCgH6KjveDOxAFHnU5bc+SIrDgvWNDYpdqdfvQj+fjXv7Z/7Pbtsv+rp9PxAKm1mj0bDd0vDTExc5CYeHfDqqXWEsDOnAm3pzq2x89PVi2WLm36d3Bk/Xr5u3hrAAvI766/v9wMKqUwZMjLCAxMxp49l2P79mmwWiuRl/dX1Nd7PqXqyitlRTs/v+VzQUH9MWHCRvTt+yj69n0MAwc+C0Bumqqru38ACzSmEZvNwRg48FmUlGxCZuYb0Loep0//FYcPS4MCT5USzJ8vK/4vvtj4WGzs1Rg3bjUSEu4E0Pqe756yYIGkERuTbkopDB36CqZOPY6SEvkH95b9nU0mCbiXLZMOyq3JzZX3bl9LH7b30ktyzbrrLuDrrzUee2w9jh//FQ4cuA01NZlITGylwNHF7r5brjeffdbYATk/fykyM19DfX0J9u27BgMGAB9+CGzY4J6yI1cygrzmE/+Rkedj+vQMWK2zYTLJe/+OHRLADhzo2jGZzbJHT0xMTcNjzSdq/P2lD8TevS3P37hR3ut8vVTQy26depZHH43AqlVPoW/fB3HgwF3Yv/9WbN8+DTU1mRg9egkslghPD7HHSU6WTruvvCI3ka++Kp0Pb2yjFDk+/kYUFq5EcfE69O37CEwmz9cKOTJqlPxd2gtgq6qk6UJAAPC3v7lnbM0NHixvlC+/3PYqbF0d8PTTUjfkiUDbkblzJY1o587Wjzl2TOqbLnBvBpjTrr5attPZvLnt4777Tv7tjSZh3iguTn6n33pLbtQtlnCMHbsc4eFTERDQF2PGfImtW/vBZPL8Kv7118vP9OeOt36FxRKBQYOew6BBf2nYG9zdDZw8QTr3N+1M3rv3rYiMvAiHDz+E1NQFKCvbgePHr8fw4Z7LIrJYgIcekkmI1n7/ly+XVZ0xY9w6tFbNnCkT6u+/3/TxoKD+WL8+CDExcGtDrPbceKMEr8333bW3YoVMrPlyAGuxyCrsyy8Dc+YoDBv2Bvz9E5CT8x569/4xoqMvbf9FXOCss4CkJPk5DgjojZiYK3Dy5FM4evRRBAfPxsaNUZg5U65lY8d6ZIhdylhNPXrU8fP5+fLvMWyYZF7U1Lh+BdZikQA2Orqy4TFHmQajR7cMYE+dkgUKb9m6rzMYwHrYkCEvIybmcuTlfY6ionXw9++NsWO/QUREN74b8XJPPilvkLNmAe+8I3Wg4eGtH9+376Po0+en6Nfvd+jT50H3DbSDzGYJNNoKYEtKZBVoyxZJnU5MdN/4mnv4YWkqtnhx68e89JKsHDz9dNMaEE9qvp2OI8Z2Vd4SdDd35ZUSmH7wQdvHrVsngZOn9rJ01gMPyM+2cdMbHDwMY8cuw/jx3yE6+hKsXSs11G39nrvD+PEy4//hh+0fa9i8WW6g+vRx3bg8LThYgij7AFYpE0aN+hAhISORm/sJeve+Czt3Jnp8X8O775aJQvtVWENdndTHz5njPasfFosEhUuWyMSbvbVrJbvCm7JEzjlHSnpeeEH+PR1ZulQmrryxsdyZCghIxJQpqZg69RiGDXvTreVl9pSSn99Vq6RXwogRi5Cc/GtERJyLnTtfR2GhdFLuLoKCJKXb0UomIGVZvXpJYL97tzw2bpxrx2SswEZFNaYhOHrvGj1amkuV2G0jbtSPe0tWRWd40WWpZ7JYIjB69GKce24ezj77BCZM2ITISC/r6tLDjBoF/OMfcjGaNAl46qm2jzebQzBkyN8xYMDvG7Z18VbTpsnKQGVl08e3bpUbg4QEqQH+739lBtWTRoyQVJwVK1o/ZvVq+f/6xS/cN672ONpOp7k1a6SWa/hw942rI8LCZOP6jz5q/SaxuFhSpnxhJnfaNAkOX3mlZROzykqpE/KGNGilJH3+22/lxsMZW7Z079VXg30jJ4OfXwwmTkzBOefkISDgNeTmKo8HsJGRwO23y4pmZtMtMvH997Lnu7etDN5/vwQjr73W+NiJE/LH27JElAJ+8xupaXe0rVtdnawOzp3rXYF3VzCZAhAUNMBjwath9mwJilaskNXAgQOfwbhx6/HKK0kYPdo3G2e15eyzJe3WUQNM+wDW4K4ANjKycasARz8SI6VBMg4ebHzsvfekzMHbem+ciW72603UNR54QNINt2zpXk2tpk2TN/gffmh8rKpK6qCOH5cbr02bgNtu89wY7V10kawCtBZE7djRuG+sN5k7V1bGCgtbPqe1BN4zZ3rPKowjCxbI/net7bu4caPUyHpD4NcepaQb5t69jd2fDStWSE2Xt9x03XOPjNeZPZezsiTI6CkBbF6epMDZU8oEP7+YhmYlng5gAckesVplD0x7ixdLjbm3rX4MHSpBycsvN26ftXq1fDQ6/3qTq66S39ff/KblqvGqVXLdvfJKz4ytJ7jkEsnOuvHGxj1Sjx6VSf977/Xu97UzMXu2bHlnZE7Zy8uTOt9rrpFMkdtuc31GkhHARkeXtHncsGHy0Qhg9++XydGf/KR7/B8xgCVqRWBg9/gltzd9usxKG9u8AMA//ynB6zvvyA2MN9wAGi66SGZ67QNuQ1aW/HHUHdrTmm+nYy81VcbtrenDhksvlZXY1tKI162Tm3FfCZ4WLJAO0S+/3PTxjz+W9HNvWWnq21dqkF97rWWmRHNG0OYr/wedYTRmaq0EYutWuWZ7Q23poEFSevKf/8i1FZCJq08+keDVGydFn3pKgkGjcd4nn0jqpDfVvxqUkvetysqWXW7feENWxC6/3PG51Hnh4VKnX1LS2JRsg21HK2+5jnalG26Q6/K99zYG7AYjgB0yRPp1vPmm68dj1MCOHCkpHnfe6fi4QYOkdOyAbB+OV1+V5k533+36MboDA1iiHqRXL9kmxdiGIDcX+OMfZQ/Kiy/27NgcMYK8b79t+ZzRJMUbA1hH2+kYli2Tj97ShbQ1QUESSH36qTSmaG7tWgkqvGF/SGcEB8sb/eLFjat4paVS+zdvnnftofrgg7JFUfPGOs1t2SI1jBMmuGdcnjRunNw4GyuDzW3bJtcCb/l//O1v5ebx97+XrzdskLTw667z6LBaNXWqTPI8+6y8P6xcKV976yTu0KHAo48Cb7/duK/l0aMSWN16q3fuS92dTJgAREQ0Bq4bN8p73ogRnh2XKwQFSepterrs226Ud9TUSCmNsYVNRIR70taNFdjg4EJkZclEmSP+/lKGdeCAjPWdd+T64+p9at2lU//USqlopdRKpdRh28cWm1IppcYppTYrpfYppXYrpW6we+4tpdRxpdRO2x8XZ44T0fz5kkqZmio3V+XlwHPPeXpUjsXGSidDRxvX79ghH+1rT7yF2SxpVs230wEkgB07VmZ0vd2CBVKzt3x508fz8yVg8MZJj7Y8+KCshBmrsG+/LSmT99zj2XE1d/75svLV2o2JYcsWCeyCgtwzLk+yWGR1x9G1wCiL8KZu2H36yM/bu+/KVl9//rOs1Hi6t0Bb/vlPuebOny83vw96b09CAMATTzSujG3b1rhl1qOPenpk3Z/ZLEGsMZG8fn1jhld3dO65Um6SnS0TJFo3bnfm7oDQCGDr6koRHy//F60ZPlxSiLdvl2B73jw3DdINOvuj9jiAb7XWQwB8a/u6uQoAt2qtRwGYA+AlpZR9As0vtNbjbH/a2HiCiLrCggVSo/GjH8l+tz/5iXfPml58sdTlNk+n3LFD2tV7YzoeIGnEWVmyT6ehuFhmrC/1zA4IHTZrlnTzfPvtpo9/840E5pdd5plxnal+/YBrrwUWLpTsgxdflFVkbwp8AFn1uvtuSYs1Ols2V1cnN+09IX3YMGuWbEHVfE/G1FS5Pnjb/+Pjj0tjvMmTJRvjsce8O2OhVy/ZGuvJJ+VjUpKnR9S2kBCZjNq/X/7v16+XNMmEBE+PrGcYPRrYt09Wvg8e9L7a7q527rnAM89I+cz69fIeAngugK2vL2332GHDgMOH5fcZ8PxWcV2pswHsVQCMW5u3AVzd/ACt9SGt9WHb5xkAcgB0kwVsIt8TFycz1rt3SyfcP/7R0yNq20UXSZMdI03MsG2bd6dOGinC9mnEH3wggYevzIL6+QE33wx8+aXU+hiWLpU3bV/cpuKxxyR1ePBgCYS89ef/5ptlNemNNxw/v3evZE/0tAAWkJ8/e0ZdrFEn6y1iYqTxy803ywrs//2fp0fUvkGDZFsyX/ndvvJKCWDff1+CqFtv9fSIeo4xY+QadOedkiFx1VWeHpHr3X67TPQ8/7znAliTKQBKWZwKYIcPl/unRYvkPa93bzcM0E06G8DGa62NRvFZAOLbOlgpNQWAPwD7LYGfsaUWv6iUarV3l1LqHqVUilIqJdf4qSGiM/L881LT8f333ruCaTjvPHlztG+IdPKkdF/15i1c4uMlwDYC2Koq2bd27Fjvu9Fuy223yRYbRj1mVZWkQfvqNhUTJsgsel0d8Mtfek/34eaMzpbvviv/5s0ZEzrdaUa9PUOHSsmAsZ+vYeVKSdkdPNgz42rL0KGSwfD44775++ILhg6VzKJBgzw9kp7F2C7mu+8kbbt/f48Oxy2Cg2WXii+/bCyt6dXLvWNQSsFsDnN6BRYA9uyRFeTupN3LqVJqlVJqr4M/TeZatNYagINdkhpeJwHAuwBu11obVWG/AjAcwGQA0QAea+V0aK0Xaq0naa0nxXaXCmQiD/HzkxRib08RA4DQUKkJXLy4cR+2jz6Sj7Nne25czjC20zlxQmanDxyQlRhvbYziyNixEvT997/y7//FF1IXe9NNnh7ZmXv8cVk5ePZZT4+kbXfeKVuCfPVVy+c2bpStLPr1c/+4POnWWyW12uisWVcndbGXXOJbv1dEvm7CBCk/GjpU0s57ip/+VCbMnn9evk5Odv8YzOYw1NW1H8COHdv4uTduidUZ7QawWuuLtdajHfxZAiDbFpgaAWqOo9dQSoUDWArgCa31FrvXztSiGsB/AXhZBQsReYPrr2NBHZ4AABYlSURBVJc6jq1b5Yb+hRckqDVmF73VLbdI0DdggKwSvfmm79S/2rvnHqk5XrJEUm4HDpTUbnKtmTNldv/zz1s+t2GDzKj3tKDtxhslI+Pf/5avV6yQCRXu+0nkXmazNAfas8e7a7u7WnR0YyNAPz/Zbs7dnF2BDQ+XEoawMJlQ7046m9DyBYDbbJ/fBmBJ8wOUUv4AFgN4R2v9SbPnjOBXQepn93ZyPETUDd1wg6RUTpsmbx7Z2bKS6e2GDZOVy0sukbq922/39IjOzO23Sy3NvHlys/L88213PqSuYbHIfpZffSVp3IYjR4DTp2VLh56md2/JHnntNanLXrhQrg2+ODFE5OsCA3vmlkVXXSX9FJqXM7iLxeJcAAvIPcipU9L/pDtRWrea9dv+yUrFAPgIQDKAkwCu11oXKKUmAfiJ1voupdTNkNXVfXan/lhrvVMptRrS0EkB2Gk7p6y97ztp0iSdkpJyxuMmIt+zbZvU7UZHy+qrN9e/dkcnTsjK9znnyIQCuceSJbIf78qVjdsW/e1vUnN2/HjPqDtrLjVVamGHDm3cDux3v/P0qIiI3GPXrlmory/DhAmbPT0Ul1NK/aC1btFWrlMBrKcwgCUiop6gokLSiG+/HXjlFXls8mRZkd3Zgzeee+UVqUUbN06ayISGenpERETusXfvNaioOIQpU7p/4mprASx74hEREXmp4GDgiiuAd94B0tKkKVhKiuwT25M98IDUw2/dyuCViHoWZ2tguzOLpwdARERErfvTn2TbBqPrdnw897sEpEEJEVFPwwCWK7BERERebdAgaeSUlgYcOgS8+qpnOl8SEZHnGU2cfLEMtKtwBZaIiMjLXXihBLDV1VITS0REPZPZHAat62C1VsNsDvT0cDyCK7BEREQ+ICyMwSsRUU9nNksKTk9OI2YAS0RERERE5AMYwDKAJSIiIiIi8gkWCwNYBrBEREREREQ+wGyWvcPq68s9PBLPYQBLRERERETkA0ymIACA1Vrp4ZF4DgNYIiIiIiIiH2AEsPX1DGCJiIiIiIjIizWuwFZ4eCSewwCWiIiIiIjIB5jNTCFmAEtEREREROQDmELMAJaIiIiIiMgnsIkTA1giIiIiIiKfYDYHA2AAS0RERERERF5OKX8AigEsEREREREReTelFEymINbAEhERERERkfczmYK4AktERERERETez2xmAEtEREREREQ+QFKIKzw9DI/pdACrlIpWSq1USh22fYxq5bh6pdRO258v7B4foJT6Xil1RCn1oZLKZCIiIiIiImqGKcSd9ziAb7XWQwB8a/vakUqt9TjbnyvtHn8WwIta68EACgHc2QVjIiIiIiIi6nYYwHbeVQDetn3+NoCrnT1RKaUAXAjgkzM5n4iIiIiIqCdhDWznxWutM22fZwGIb+W4QKVUilJqi1LKCFJjABRpretsX6cB6OPoZKXUPbbzU3Jzc7tg2ERERERERL7FZAru0dvoWJw5SCm1CkBvB089Yf+F1lorpXQrL9NPa52ulBoIYLVSag+AYmcHqrVeCGAhAEyaNKm170FERERERNRt9fQUYqcCWK31xa09p5TKVkolaK0zlVIJAHJaeY1028djSqm1AMYD+BRApFLKYluFTQKQ3sG/AxERERERUY/AFOLO+wLAbbbPbwOwpPkBSqkopVSA7fNeAM4BkKq11gDWALi2rfOJiIiIiIiIK7BdEcD+BcAspdRhABfbvoZSapJS6nXbMSMApCildkEC1r9orVNtzz0G4BGl1BFITewbXTAmIiIiIiKibkf2ge25AaxTKcRt0VrnA7jIweMpAO6yfb4JwJhWzj8GYEpnx0FERERERNTdyQpshaeH4TFdsQJLREREREREbmA2B0HrWmhd7+mheAQDWCIiIiIiIh9hMgUBQI9NI2YAS0RERERE5CNMpmAA6LGNnBjAEhERERER+Qiz2Qhge2YdLANYIiIiIiIiH2GswNbXM4AlIiIiIiIiL9a4AssUYiIiIiIiIvJijU2cuAJLREREREREXow1sEREREREROQTWANLREREREREPoE1sEREREREROQTjBpYphATERERERGRV2MKMREREREREfkENnEiIiIiIiIin2AyBQLgCiwRERERERF5OaVMMJmC2MSJiIiIiIiIvJ/JFMQVWCIiIiIiIvJ+ZnMwa2CJiIiIiIjI+5lMwVyBJSIiIiIiIu8nK7Dlnh6GRzCAJSIiIiIi8iFmczjq6ko9PQyP6FQAq5SKVkqtVEodtn2McnDMBUqpnXZ/qpRSV9uee0spddzuuXGdGQ8REREREVF3Z7GEo76+xNPD8IjOrsA+DuBbrfUQAN/avm5Ca71Gaz1Oaz0OwIUAKgCssDvkF8bzWuudnRwPERERERFRtyYrsAxgz8RVAN62ff42gKvbOf5aAF9rrXtmxTEREREREVEnWSwRqK8v9vQwPKKzAWy81jrT9nkWgPh2jl8A4P1mjz2jlNqtlHpRKRXQ2olKqXuUUilKqZTc3NxODJmIiIiIiMh3cQW2DUqpVUqpvQ7+XGV/nNZaA9BtvE4CgDEAvrF7+FcAhgOYDCAawGOtna+1Xqi1nqS1nhQbG9vesImIiIiIiLoliyUcWtfAaq329FDcztLeAVrri1t7TimVrZRK0Fpn2gLUnDZe6noAi7XWtXavbazeViul/gvgUSfHTURERERE1COZzeEAgLq6Evj796zFvc6mEH8B4Dbb57cBWNLGsT9Cs/RhW9ALpZSC1M/u7eR4iIiIiIiIujWLJQIAemQn4s4GsH8BMEspdRjAxbavoZSapJR63ThIKdUfQF8A3zU7f5FSag+APQB6AfhjJ8dDRERERETUrVksxgpsz2vk1G4KcVu01vkALnLweAqAu+y+PgGgj4PjLuzM9yciIiIiIupp7FOIe5rOrsASERERERGRGzWmEPe8FVgGsERERERERD7ECGC5AktERERERERezWKJBADU1RV5eCTuxwCWiIiIiIjIh5jNxgosA1giIiIiIiLyYiaTBSZTCANYIiIiIiIi8n4WSyQDWCIiIiIiIvJ+DGCJiIiIiIjIJ5xpAHv69N+we/elsFprXDAq17N4egBERERERETUMRZLJGpqMjt8Xm7up9C6FiaTvwtG5XpcgSUiIiIiIvIxsgJb3KFz6upKUVKyFVFRs100KtdjAEtERERERORjziSFuLh4PYB6REVd4JpBuQEDWCIiIiIiIh9jBLBaa6fPKSxcDaX8ER4+3YUjcy0GsERERERERD7GYokEUI/6+nKnzykqWoPw8LNhNge5bmAuxgCWiIiIiIjIx0gAC6fTiGtrC1BWtgNRURe6clguxwCWiIiIiIjIx1gsEQCcD2CLitYB0IiMZABLREREREREbtTRFdiiotUwmYIRHj7FlcNyOQawREREREREPqbjAewaRESc67P7vxoYwBIREREREfmYjgSwNTU5KC/fi8hI390+x8AAloiIiIiIyMcYAWx9fXG7xxYVrQUAn97/1cAAloiIiIiIyMd0pIlTYeFqmM1hCA2d6OphuVynA1il1HVKqX1KKatSalIbx81RSh1USh1RSj1u9/gApdT3tsc/VEr5dlI2ERERERGRi5lM/jCZgp0KYKX+9TyYTBY3jMy1umIFdi+AawCsa+0ApZQZwCsA5gIYCeBHSqmRtqefBfCi1nowgEIAd3bBmIiIiIiIiLo1iyWi3QC2ujodlZWHfH7/V0OnA1it9X6t9cF2DpsC4IjW+pjWugbABwCuUkopABcC+MR23NsAru7smIiIiIiIiLo7CWBL2jymsHANAHSLBk6A+2pg+wA4bfd1mu2xGABFWuu6Zo+3oJS6RymVopRKyc3NdelgiYiIiIiIvJ3ZHI66urabOBUVrYbFEoXQ0LPcNCrXcioJWim1CkBvB089obVe0rVDckxrvRDAQgCYNGmSdsf3JCIiIiIi8lYWSzjq69tegS0u3oiIiPOgVPfo3+tUAKu1vriT3ycdQF+7r5Nsj+UDiFRKWWyrsMbjRERERERE1AazOQLV1ZmtPl9XV4bKysOIj7/ZjaNyLXeF4dsADLF1HPYHsADAF1prDWANgGttx90GwC0rukRERERERL5MVmBbTyEuL98DQHeb9GGga7bRmaeUSgNwNoClSqlvbI8nKqWWAYBtdfVBAN8A2A/gI631PttLPAbgEaXUEUhN7BudHRMREREREVF3JzWwracQl5XtAgCEho5z15BcrtMbAWmtFwNY7ODxDACX2n29DMAyB8cdg3QpJiIiIiIiIifJCmwptLY6rHEtK9sJiyUSAQF9HZztm7pHJS8REREREVEPY7FEANCory9z+Hx5+S6Eho6D7F7aPTCAJSIiIiIi8kFmczgAOEwj1roeZWW7ERLSfepfAQawREREREREPslikQDW0VY6lZVHYbVWdKv6V4ABLBERERERkU9qXIFt2Ym4pGQrACAsbLxbx+RqDGCJiIiIiIh8kNTAOl6BLSpaC4slCiEhY9w9LJdiAEtEREREROSD2qqBLSpag4iI8xx2J/Zl3etvQ0RERERE1EMYNbDNU4irqk6hquoYoqIu8MSwXIoBLBERERERkQ9qLYW4qGgtACAycqabR+R6DGCJiIiIiIh8kNkcCqBlCrHUv0Z3u/pXgAEsERERERGRT1LKDLM5tMUKbHHxekREzOh29a8AA1giIiIiIiKfZTaHN6mBranJQ2XlEURETPfgqFyHASwREREREZGPslgimqzAlpZ+DwAID5/mqSG5FANYIiIiIiIiHyUrsI0BbFnZLgBAaOgETw3JpRjAEhERERER+SiLpWkKcUXFfgQE9IXFEurBUbkOA1giIiIiIiIfZTaHN0khrqg4gODg4R4ckWsxgCUiIiIiIvJRfn5RqK0tAABorRnAEhERERERkXfy8+uFurp8aK1RW5uH+voyBAUN9vSwXIYBLBERERERkY/y8+sFretQV1eM6up0AEBAQB8Pj8p1GMASERERERH5KD+/XgCA2to81NRkAAD8/RnAEhERERERkZfx84sFIAFs4wpsoieH5FKdCmCVUtcppfYppaxKqUmtHNNXKbVGKZVqO/Znds/9XimVrpTaaftzaWfGQ0RERERE1JPYr8BKAKvg75/g2UG5kKWT5+8FcA2A/7RxTB2A/9Nab1dKhQH4QSm1Umudanv+Ra31850cBxERERERUY/TPIXYzy8OJpOfh0flOp0KYLXW+wFAKdXWMZkAMm2flyql9gPoAyC11ZOIiIiIiIioXc1XYLtzAyfAzTWwSqn+AMYD+N7u4QeVUruVUm8qpaLaOPcepVSKUiolNzfXxSMlIiIiIiLyfmZzGJTyR21tri2A7b71r4ATAaxSapVSaq+DP1d15BsppUIBfArgYa11ie3hVwEMAjAOskr7t9bO11ov1FpP0lpPio2N7ci3JiIiIiIi6paUUvDz69WQQtydOxADTqQQa60v7uw3UUr5QYLXRVrrz+xeO9vumNcAfNXZ70VERERERNST+Pn1QnV1Omprc5lC3FlKCmTfALBfa/1Cs+fs22PNgzSFIiIiIiIiIicFBCSitHSr7XMGsK1SSs1TSqUBOBvAUqXUN7bHE5VSy2yHnQPgFgAXOtgu569KqT1Kqd0ALgDw886Mh4iIiIiIqKcJCOiHurpCAEBgYD8Pj8a1OtuFeDGAxQ4ezwBwqe3zDQActinWWt/Sme9PRERERETU0wUGJjd8HhDQvQNYt3YhJiIiIiIioq4VGNjf7vO+nhuIGzCAJSIiIiIi8mFhYRMbPjeZAjw4EtfrVAoxEREREREReVZw8DAMGvQ8wsImeXooLscAloiIiIiIyMf17ft/nh6CWzCFmIiIiIiIiHwCA1giIiIiIiLyCQxgiYj+v727C7VsjOM4/v11xkTIeEsymBHRXDAmiWgaIxpMxoVElKTcuKBIuBHlwo2XCymNwYXXhmFyIdOY4mq8y8uQl0xGOMTk5YLwd7Ee7E7tkTqss+zvp057Pc/aF/+LX/s5/73Ws7YkSZIGwQZWkiRJkjQINrCSJEmSpEGwgZUkSZIkDYINrCRJkiRpEGxgJUmSJEmDkKrqu4Z/LMlXwPa+6/gbBwBf912E5iSzoXHMhsYxG9oV86FxzIbGGUI2Dq+qA2dODrKBHYIkr1TVCX3XobnHbGgcs6FxzIZ2xXxoHLOhcYacDW8hliRJkiQNgg2sJEmSJGkQbGD/Pff2XYDmLLOhccyGxjEb2hXzoXHMhsYZbDbcAytJkiRJGgSvwEqSJEmSBsEGVpIkSZI0CDawsyzJqiTvJ/kwyfV916N+JVmXZDrJ2yNz+yXZlOSD9rpvnzWqH0kOTbIlybtJ3klyVZs3HxMuye5JXkryZsvGzW1+cZKtbX15LMn8vmtVP5JMJXk9yTNtbDYEQJJPkryV5I0kr7Q51xWRZEGS9UneS7ItyclDzYYN7CxKMgXcDZwFLAEuSrKk36rUsweAVTPmrgc2V9VRwOY21uT5BbimqpYAJwFXts8L86GfgJVVdRywFFiV5CTgNuCOqjoS+Ba4vMca1a+rgG0jY7OhUadV1dKR3/h0XRHAXcCzVXUMcBzdZ8ggs2EDO7tOBD6sqo+r6mfgUWBNzzWpR1X1AvDNjOk1wIPt+EHgvP+0KM0JVfV5Vb3Wjr+nW0gOwXxMvOr80Ia7tb8CVgLr27zZmFBJFgLnAGvbOJgN7ZrryoRLsg+wHLgPoKp+rqqdDDQbNrCz6xDg05HxjjYnjTqoqj5vx18AB/VZjPqXZBFwPLAV8yH+vEX0DWAa2AR8BOysql/aW1xfJtedwHXAb228P2ZDfynguSSvJrmizbmuaDHwFXB/236wNsmeDDQbNrBSj6r7HSt/y2qCJdkLeAK4uqq+Gz1nPiZXVf1aVUuBhXR39xzTc0maA5KsBqar6tW+a9GcdWpVLaPbznZlkuWjJ11XJtY8YBlwT1UdD/zIjNuFh5QNG9jZ9Rlw6Mh4YZuTRn2Z5GCA9jrdcz3qSZLd6JrXh6rqyTZtPvSndovXFuBkYEGSee2U68tkOgU4N8kndNuUVtLtazMbAqCqPmuv08AGui/AXFe0A9hRVVvbeD1dQzvIbNjAzq6XgaPa0wDnAxcCG3uuSXPPRuDSdnwp8HSPtagnbd/afcC2qrp95JT5mHBJDkyyoB3vAZxBt0d6C3B+e5vZmEBVdUNVLayqRXT/YzxfVRdjNgQk2TPJ3n8cA2cCb+O6MvGq6gvg0yRHt6nTgXcZaDbSXS3WbElyNt3+lClgXVXd2nNJ6lGSR4AVwAHAl8BNwFPA48BhwHbggqqa+aAn/c8lORV4EXiLv/ay3Ui3D9Z8TLAkx9I9TGOK7ovmx6vqliRH0F112w94Hbikqn7qr1L1KckK4NqqWm02BNBysKEN5wEPV9WtSfbHdWXiJVlK9/C3+cDHwGW0NYaBZcMGVpIkSZI0CN5CLEmSJEkaBBtYSZIkSdIg2MBKkiRJkgbBBlaSJEmSNAg2sJIkSZKkQbCBlSRJkiQNgg2sJEmSJGkQfgcRBEjIOhCi0gAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1152x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "s = 3_624*64\n",
    "rng = np.arange(s,s+60*64)\n",
    "plt.figure(figsize=(16,4))\n",
    "#plt.plot(rng, abd[rng], 'y')\n",
    "plt.plot(np.arange(60*64)/64,abd[rng], 'y')\n",
    "plt.plot(np.arange(60*64)/64,chest[rng], 'b')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 304,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3.3333333333333335"
      ]
     },
     "execution_count": 304,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "1/0.3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 303,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.3"
      ]
     },
     "execution_count": 303,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "18/60"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 305,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.3125\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "f, Pxx_den = signal.welch(chest[rng], 64, nfft=1024)\n",
    "print(f[np.argmax(Pxx_den)])\n",
    "plt.semilogy(f, Pxx_den)\n",
    "#plt.ylim([0.5e-5, 1])\n",
    "plt.xlabel('frequency [Hz]')\n",
    "plt.ylabel('PSD [V**2/Hz]')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## number 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 308,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.125 , 0.1875, 0.25  , 0.3125, 0.375 , 0.4375, 0.5   , 0.5625])"
      ]
     },
     "execution_count": 308,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "f[2:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 306,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.31680387786647557"
      ]
     },
     "execution_count": 306,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.sum(Pxx_den)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 309,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.26294395076817106"
      ]
     },
     "execution_count": 309,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.sum(Pxx_den[2:10])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 311,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8299896849084498"
      ]
     },
     "execution_count": 311,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.sum(Pxx_den[2:10]) / np.sum(Pxx_den)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## number 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 312,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.3125\n"
     ]
    }
   ],
   "source": [
    "print(f[np.argmax(Pxx_den)])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Test 3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 313,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.25\n"
     ]
    }
   ],
   "source": [
    "f, Pxx_den = signal.welch(abd[rng], 64, nfft=1024)\n",
    "print(f[np.argmax(Pxx_den)])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 317,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.25"
      ]
     },
     "execution_count": 317,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(0.3125 - 0.25) / 0.25"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x13d39b550>]"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "freq = 64\n",
    "start_time = 5_600\n",
    "window_len=60\n",
    "\n",
    "plot_range = np.arange(start_time*freq, (start_time+window_len)*freq)\n",
    "\n",
    "time_range = np.empty_like(plot_range, dtype='datetime64[ms]')\n",
    "for i in range(len(plot_range)):\n",
    "    time_range[i] = start_dt.naive + td(milliseconds=plot_range[i]*1/freq*1000)\n",
    "\n",
    "plt.figure(figsize=(16,4))\n",
    "plt.title('ABD / Chest')\n",
    "plt.plot(time_range, abd[plot_range])\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 226,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.25\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "f, Pxx_den = signal.welch(abd[plot_range], 64, nfft=1024)\n",
    "print(f[np.argmax(Pxx_den)])\n",
    "plt.semilogy(f, Pxx_den)\n",
    "#plt.ylim([0.5e-3, 1])\n",
    "plt.xlabel('frequency [Hz]')\n",
    "plt.ylabel('PSD [V**2/Hz]')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 234,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ True,  True,  True,  True,  True,  True,  True,  True])"
      ]
     },
     "execution_count": 234,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.logical_and(f >= 0.12, f <= 0.585)[2:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 227,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.    ,  0.0625,  0.125 ,  0.1875,  0.25  ,  0.3125,  0.375 ,\n",
       "        0.4375,  0.5   ,  0.5625,  0.625 ,  0.6875,  0.75  ,  0.8125,\n",
       "        0.875 ,  0.9375,  1.    ,  1.0625,  1.125 ,  1.1875,  1.25  ,\n",
       "        1.3125,  1.375 ,  1.4375,  1.5   ,  1.5625,  1.625 ,  1.6875,\n",
       "        1.75  ,  1.8125,  1.875 ,  1.9375,  2.    ,  2.0625,  2.125 ,\n",
       "        2.1875,  2.25  ,  2.3125,  2.375 ,  2.4375,  2.5   ,  2.5625,\n",
       "        2.625 ,  2.6875,  2.75  ,  2.8125,  2.875 ,  2.9375,  3.    ,\n",
       "        3.0625,  3.125 ,  3.1875,  3.25  ,  3.3125,  3.375 ,  3.4375,\n",
       "        3.5   ,  3.5625,  3.625 ,  3.6875,  3.75  ,  3.8125,  3.875 ,\n",
       "        3.9375,  4.    ,  4.0625,  4.125 ,  4.1875,  4.25  ,  4.3125,\n",
       "        4.375 ,  4.4375,  4.5   ,  4.5625,  4.625 ,  4.6875,  4.75  ,\n",
       "        4.8125,  4.875 ,  4.9375,  5.    ,  5.0625,  5.125 ,  5.1875,\n",
       "        5.25  ,  5.3125,  5.375 ,  5.4375,  5.5   ,  5.5625,  5.625 ,\n",
       "        5.6875,  5.75  ,  5.8125,  5.875 ,  5.9375,  6.    ,  6.0625,\n",
       "        6.125 ,  6.1875,  6.25  ,  6.3125,  6.375 ,  6.4375,  6.5   ,\n",
       "        6.5625,  6.625 ,  6.6875,  6.75  ,  6.8125,  6.875 ,  6.9375,\n",
       "        7.    ,  7.0625,  7.125 ,  7.1875,  7.25  ,  7.3125,  7.375 ,\n",
       "        7.4375,  7.5   ,  7.5625,  7.625 ,  7.6875,  7.75  ,  7.8125,\n",
       "        7.875 ,  7.9375,  8.    ,  8.0625,  8.125 ,  8.1875,  8.25  ,\n",
       "        8.3125,  8.375 ,  8.4375,  8.5   ,  8.5625,  8.625 ,  8.6875,\n",
       "        8.75  ,  8.8125,  8.875 ,  8.9375,  9.    ,  9.0625,  9.125 ,\n",
       "        9.1875,  9.25  ,  9.3125,  9.375 ,  9.4375,  9.5   ,  9.5625,\n",
       "        9.625 ,  9.6875,  9.75  ,  9.8125,  9.875 ,  9.9375, 10.    ,\n",
       "       10.0625, 10.125 , 10.1875, 10.25  , 10.3125, 10.375 , 10.4375,\n",
       "       10.5   , 10.5625, 10.625 , 10.6875, 10.75  , 10.8125, 10.875 ,\n",
       "       10.9375, 11.    , 11.0625, 11.125 , 11.1875, 11.25  , 11.3125,\n",
       "       11.375 , 11.4375, 11.5   , 11.5625, 11.625 , 11.6875, 11.75  ,\n",
       "       11.8125, 11.875 , 11.9375, 12.    , 12.0625, 12.125 , 12.1875,\n",
       "       12.25  , 12.3125, 12.375 , 12.4375, 12.5   , 12.5625, 12.625 ,\n",
       "       12.6875, 12.75  , 12.8125, 12.875 , 12.9375, 13.    , 13.0625,\n",
       "       13.125 , 13.1875, 13.25  , 13.3125, 13.375 , 13.4375, 13.5   ,\n",
       "       13.5625, 13.625 , 13.6875, 13.75  , 13.8125, 13.875 , 13.9375,\n",
       "       14.    , 14.0625, 14.125 , 14.1875, 14.25  , 14.3125, 14.375 ,\n",
       "       14.4375, 14.5   , 14.5625, 14.625 , 14.6875, 14.75  , 14.8125,\n",
       "       14.875 , 14.9375, 15.    , 15.0625, 15.125 , 15.1875, 15.25  ,\n",
       "       15.3125, 15.375 , 15.4375, 15.5   , 15.5625, 15.625 , 15.6875,\n",
       "       15.75  , 15.8125, 15.875 , 15.9375, 16.    , 16.0625, 16.125 ,\n",
       "       16.1875, 16.25  , 16.3125, 16.375 , 16.4375, 16.5   , 16.5625,\n",
       "       16.625 , 16.6875, 16.75  , 16.8125, 16.875 , 16.9375, 17.    ,\n",
       "       17.0625, 17.125 , 17.1875, 17.25  , 17.3125, 17.375 , 17.4375,\n",
       "       17.5   , 17.5625, 17.625 , 17.6875, 17.75  , 17.8125, 17.875 ,\n",
       "       17.9375, 18.    , 18.0625, 18.125 , 18.1875, 18.25  , 18.3125,\n",
       "       18.375 , 18.4375, 18.5   , 18.5625, 18.625 , 18.6875, 18.75  ,\n",
       "       18.8125, 18.875 , 18.9375, 19.    , 19.0625, 19.125 , 19.1875,\n",
       "       19.25  , 19.3125, 19.375 , 19.4375, 19.5   , 19.5625, 19.625 ,\n",
       "       19.6875, 19.75  , 19.8125, 19.875 , 19.9375, 20.    , 20.0625,\n",
       "       20.125 , 20.1875, 20.25  , 20.3125, 20.375 , 20.4375, 20.5   ,\n",
       "       20.5625, 20.625 , 20.6875, 20.75  , 20.8125, 20.875 , 20.9375,\n",
       "       21.    , 21.0625, 21.125 , 21.1875, 21.25  , 21.3125, 21.375 ,\n",
       "       21.4375, 21.5   , 21.5625, 21.625 , 21.6875, 21.75  , 21.8125,\n",
       "       21.875 , 21.9375, 22.    , 22.0625, 22.125 , 22.1875, 22.25  ,\n",
       "       22.3125, 22.375 , 22.4375, 22.5   , 22.5625, 22.625 , 22.6875,\n",
       "       22.75  , 22.8125, 22.875 , 22.9375, 23.    , 23.0625, 23.125 ,\n",
       "       23.1875, 23.25  , 23.3125, 23.375 , 23.4375, 23.5   , 23.5625,\n",
       "       23.625 , 23.6875, 23.75  , 23.8125, 23.875 , 23.9375, 24.    ,\n",
       "       24.0625, 24.125 , 24.1875, 24.25  , 24.3125, 24.375 , 24.4375,\n",
       "       24.5   , 24.5625, 24.625 , 24.6875, 24.75  , 24.8125, 24.875 ,\n",
       "       24.9375, 25.    , 25.0625, 25.125 , 25.1875, 25.25  , 25.3125,\n",
       "       25.375 , 25.4375, 25.5   , 25.5625, 25.625 , 25.6875, 25.75  ,\n",
       "       25.8125, 25.875 , 25.9375, 26.    , 26.0625, 26.125 , 26.1875,\n",
       "       26.25  , 26.3125, 26.375 , 26.4375, 26.5   , 26.5625, 26.625 ,\n",
       "       26.6875, 26.75  , 26.8125, 26.875 , 26.9375, 27.    , 27.0625,\n",
       "       27.125 , 27.1875, 27.25  , 27.3125, 27.375 , 27.4375, 27.5   ,\n",
       "       27.5625, 27.625 , 27.6875, 27.75  , 27.8125, 27.875 , 27.9375,\n",
       "       28.    , 28.0625, 28.125 , 28.1875, 28.25  , 28.3125, 28.375 ,\n",
       "       28.4375, 28.5   , 28.5625, 28.625 , 28.6875, 28.75  , 28.8125,\n",
       "       28.875 , 28.9375, 29.    , 29.0625, 29.125 , 29.1875, 29.25  ,\n",
       "       29.3125, 29.375 , 29.4375, 29.5   , 29.5625, 29.625 , 29.6875,\n",
       "       29.75  , 29.8125, 29.875 , 29.9375, 30.    , 30.0625, 30.125 ,\n",
       "       30.1875, 30.25  , 30.3125, 30.375 , 30.4375, 30.5   , 30.5625,\n",
       "       30.625 , 30.6875, 30.75  , 30.8125, 30.875 , 30.9375, 31.    ,\n",
       "       31.0625, 31.125 , 31.1875, 31.25  , 31.3125, 31.375 , 31.4375,\n",
       "       31.5   , 31.5625, 31.625 , 31.6875, 31.75  , 31.8125, 31.875 ,\n",
       "       31.9375, 32.    ])"
      ]
     },
     "execution_count": 227,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "f"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.00309576, 0.02228785, 0.01059706, 0.00301506, 0.00156199])"
      ]
     },
     "execution_count": 135,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Pxx_den[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.25\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "f, Pxx_den = signal.welch(chest, 64)\n",
    "print(f[np.argmax(Pxx_den)])\n",
    "plt.semilogy(f, Pxx_den)\n",
    "#plt.ylim([0.5e-3, 1])\n",
    "plt.xlabel('frequency [Hz]')\n",
    "plt.ylabel('PSD [V**2/Hz]')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}