{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "# Reading EEG Data from events\n",
    "\n",
    "Each events array has an `eegfile` field, which points to the directory that contains the associated \n",
    "EEG files. To read this data, you use the EEGReader class. \n",
    "\n",
    "Two other important classes for loading EEG data are the TalReader class, which loads information about \n",
    "the electrodes in ECoG subjects, and the MonopolarToBipolarMapper, which performs a bipolar rereferencing \n",
    "on ECoG data.\n",
    "\n",
    "This notebook will go over loading EEG data from the three different CML formats."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "from ptsa.data.readers import BaseEventReader,EEGReader,CMLEventReader\n",
    "from ptsa.data.readers import TalReader\n",
    "from ptsa.data.filters import MonopolarToBipolarMapper\n",
    "from matplotlib import pyplot as plt\n",
    "import numpy as np\n",
    "rhino_root='/Volumes/rhino_root'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1. pyFR data (and old RAM data)\n",
    "First, we load the events, which are found on rhino in `/data/events/pyFR`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "events = BaseEventReader(filename=rhino_root+'/data/events/pyFR/BW001_events.mat',use_reref_eeg=False).read()\n",
    "words = events[events.type=='WORD']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, we use the TalReader class to get information about the subject's electrodes. \n",
    "\n",
    "Like the other readers, the TalReader class has a `read()` method that will return a record array;\n",
    "this one contains information about the location of each electrode. \n",
    "However, we're really only interested in two things: each electrode's number, and which electrodes \n",
    "are adjacent to each other.\n",
    "Therefore, TalReader has two convenience methods, `get_monopolar_channels` and `get_bipolar_pairs`, \n",
    "specifically for this purpose."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING: Could not retrieve the following objects:\n",
      "bpTalStruct\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array(['001', '002', '003', '004', '005', '006', '007', '008', '009',\n",
       "       '010', '011', '012', '013', '014', '015', '016', '017', '018',\n",
       "       '019', '020', '021', '022', '023', '024', '025', '026', '027',\n",
       "       '028', '029', '030', '031', '032', '033', '034', '035', '036',\n",
       "       '037', '038', '039', '040', '041', '042', '043', '044', '045',\n",
       "       '046', '047', '048', '049', '050', '051', '052', '053', '054',\n",
       "       '055', '056', '057', '058', '059', '060', '061', '062', '063', '064'], \n",
       "      dtype='|S3')"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tal_reader = TalReader(filename=rhino_root+'/data/eeg/BW001/tal/BW001_talLocs_database_bipol.mat')\n",
    "channels = tal_reader.get_monopolar_channels()\n",
    "channels"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note the format of the channel array: a numpy array of 3-character strings.\n",
    " These are literally the suffixes of binary files, telling the EEGReader which files to load.\n",
    "\n",
    "To load the EEG data into memory, we give the EEGReader the events and channels we want to load,\n",
    "and the time around each event that we are interested in.\n",
    "There is also a `buffer_time` parameter, that will add a buffer period to the timeseries we load at the beginning and the end.\n",
    "If you add a buffer period, you should remove it afterwards with `remove_buffer()`. \n",
    "\n",
    "All of these use time in seconds as their unit."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/leond/miniconda2/envs/ptsa/lib/python2.7/site-packages/ptsa/data/readers/ParamsReader.py:112: RuntimeWarning: Did not find \"gain\" in the params.txt file. Assuming gain=1.0\n",
      "  warnings.warn('Did not find \"gain\" in the params.txt file. Assuming gain=1.0', RuntimeWarning)\n",
      "/Users/leond/miniconda2/envs/ptsa/lib/python2.7/site-packages/ptsa/data/readers/BaseRawReader.py:66: RuntimeWarning: Could not find data format definition in the params file. Will read the file assuming data format is int16\n",
      "  ' data format is int16', RuntimeWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<xarray.TimeSeriesX (channels: 64, events: 444, time: 200)>\n",
      "array([[[ -93.,  -96.,  -87., ...,   -4.,   25.,   38.],\n",
      "        [-313., -296., -273., ...,  -99., -106.,  -90.],\n",
      "        [  86.,   76.,   91., ...,  218.,  214.,  192.],\n",
      "        ..., \n",
      "        [-291., -312., -287., ...,  -42.,  -24.,   -9.],\n",
      "        [ -90.,  -89.,  -84., ...,   42.,   70.,   53.],\n",
      "        [-123., -144., -135., ...,   -9.,  -27.,  -27.]],\n",
      "\n",
      "       [[  -2.,   -3.,   10., ...,  -21.,   -8.,   28.],\n",
      "        [-114., -107.,  -90., ...,  -40.,  -99., -158.],\n",
      "        [ 140.,  131.,  212., ...,  -28.,  -18.,  -23.],\n",
      "        ..., \n",
      "        [-160., -168., -129., ..., -130., -132., -136.],\n",
      "        [  75.,   80.,   99., ...,   14.,   48.,   18.],\n",
      "        [-186., -215., -203., ...,   23.,   11.,    7.]],\n",
      "\n",
      "       [[  45.,   38.,   38., ...,   69.,  135.,  210.],\n",
      "        [-261., -243., -218., ..., -125.,    2.,  129.],\n",
      "        [ 134.,  163.,  261., ...,   -5.,   -8.,  -15.],\n",
      "        ..., \n",
      "        [-247., -261., -226., ..., -127., -126., -110.],\n",
      "        [  74.,   66.,   48., ...,  -71., -146., -237.],\n",
      "        [ -59.,  -80.,  -87., ...,   54.,   52.,   41.]],\n",
      "\n",
      "       ..., \n",
      "       [[ -38.,  -54.,  -61., ...,   25.,   28.,   54.],\n",
      "        [ -73.,  -27.,   24., ...,    5.,  -27.,   10.],\n",
      "        [  29.,   23.,   29., ...,   23.,   18.,   -2.],\n",
      "        ..., \n",
      "        [ -39.,  -39.,  -14., ...,  -88.,  -81.,  -59.],\n",
      "        [  26.,   11.,    7., ...,   69.,  100.,   47.],\n",
      "        [ -38.,  -91., -127., ...,  115.,   91.,   91.]],\n",
      "\n",
      "       [[   0.,    8.,    8., ...,   62.,   91.,   73.],\n",
      "        [ -46.,  -51.,  -13., ...,   68.,   77.,  102.],\n",
      "        [  -9.,   13.,   35., ...,  -25.,  -26.,  -42.],\n",
      "        ..., \n",
      "        [ -48.,  -61.,  -42., ...,  -80.,  -88.,  -82.],\n",
      "        [ -25.,  -14.,    2., ...,   47.,   79.,   70.],\n",
      "        [ -30.,  -64., -104., ...,   45.,   28.,   13.]],\n",
      "\n",
      "       [[  -6.,  -30.,  -48., ...,   29.,   21.,    2.],\n",
      "        [ -29.,  -15.,   11., ...,   86.,   79.,   88.],\n",
      "        [ -53.,  -60.,  -54., ...,  -12.,   -7.,  -11.],\n",
      "        ..., \n",
      "        [   9.,  -27.,   14., ...,  -13.,  -16.,   11.],\n",
      "        [ -64.,  -55.,  -31., ...,   12.,   45.,   42.],\n",
      "        [  67.,   23.,  -13., ...,   47.,   13.,   -3.]]])\n",
      "Coordinates:\n",
      "  * channels    (channels) |S3 '001' '002' '003' '004' '005' '006' '007' ...\n",
      "    samplerate  float64 200.0\n",
      "  * events      (events) (numpy.record, [('subject', 'S256'), ('session', '<i8'), ('list', '<i8'), ('type', 'S256'), ('item', 'S256'), ('itemno', '<i8'), ('recalled', '<i8'), ('mstime', '<i8'), ('msoffset', '<i8'), ('rectime', '<i8'), ('intrusion', '<i8'), ('eegfile', 'S256'), ('eegoffset', '<i8'), ('serialpos', '<i8')]) ('BW001', 1, -999, 'SESS_START', 'X', -999, -999, 683395115, -999, -999, -999, '/Volumes/rhino_root/data/eeg/BW001/eeg.noreref/BW001_24Jul02_0001', 2841, 0) ...\n",
      "  * time        (time) float64 0.0 0.005 0.01 0.015 0.02 0.025 0.03 0.035 ...\n",
      "Attributes:\n",
      "    samplerate: 200.0\n",
      "    gain: 1.0\n"
     ]
    }
   ],
   "source": [
    "eeg = EEGReader(events=events,channels=channels,start_time=0.0,end_time=1.0).read()\n",
    "print(eeg)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The EEGReader returns a TimeSeriesX object, which is a subclass of xarray.DataArray.\n",
    "The DataArray class provides an interface for multi-dimensional arrays with labelled axes; the TimeSeriesX object adds some additional convenience methods.\n",
    "\n",
    "As an example of one might use a DataArray, let's plot the average voltage across all channels and events.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1162bfad0>]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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0yumpMDb02AvuCOThwnV21p2bicjt5/WkXWKpTMUNrEC8+l1OLUiCPioK+iWx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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x116a4a1d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(eeg.time,eeg.mean('channels').mean('events'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Coordinates:\n",
      "  * channels    (channels) |S3 '001' '002' '003' '004' '005' '006' '007' ...\n",
      "    samplerate  float64 200.0\n",
      "  * events      (events) (numpy.record, [('subject', 'S256'), ('session', '<i8'), ('list', '<i8'), ('type', 'S256'), ('item', 'S256'), ('itemno', '<i8'), ('recalled', '<i8'), ('mstime', '<i8'), ('msoffset', '<i8'), ('rectime', '<i8'), ('intrusion', '<i8'), ('eegfile', 'S256'), ('eegoffset', '<i8'), ('serialpos', '<i8')]) ('BW001', 1, -999, 'SESS_START', 'X', -999, -999, 683395115, -999, -999, -999, '/Volumes/rhino_root/data/eeg/BW001/eeg.noreref/BW001_24Jul02_0001', 2841, 0) ...\n",
      "  * time        (time) float64 0.0 0.005 0.01 0.015 0.02 0.025 0.03 0.035 ...\n",
      "\n",
      "\n",
      "OrderedDict([('samplerate', 200.0), ('gain', 1.0)])\n"
     ]
    }
   ],
   "source": [
    "print(eeg.coords)\n",
    "print \n",
    "print\n",
    "print(eeg.attrs)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\"Data Coordinates\" are marked with a star, and correspond to axes of our array. Other coordinates are not aligned to the data, but do persist under certain transformations. \n",
    "\n",
    "With ECoG data, it is usually more informative to look not at the voltage at each electrode, but at the difference across adjacent electrodes. \n",
    "The MonopolarToBipolarMapper class takes a list of which pairs are adjacent to each other, and returns a TimeSeriesX \n",
    "with the 'channels' dimension replaced by 'bipolar_pairs'."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<xarray.TimeSeriesX (bipolar_pairs: 72, events: 444, time: 200)>\n",
       "array([[[ -91.,  -93.,  -97., ...,   17.,   33.,   10.],\n",
       "        [-199., -189., -183., ...,  -59.,   -7.,   68.],\n",
       "        [ -54.,  -55., -121., ...,  246.,  232.,  215.],\n",
       "        ..., \n",
       "        [-131., -144., -158., ...,   88.,  108.,  127.],\n",
       "        [-165., -169., -183., ...,   28.,   22.,   35.],\n",
       "        [  63.,   71.,   68., ...,  -32.,  -38.,  -34.]],\n",
       "\n",
       "       [[ -47.,  -41.,  -28., ...,  -90., -143., -182.],\n",
       "        [ 147.,  136.,  128., ...,   85., -101., -287.],\n",
       "        [   6.,  -32.,  -49., ...,  -23.,  -10.,   -8.],\n",
       "        ..., \n",
       "        [  87.,   93.,   97., ...,   -3.,   -6.,  -26.],\n",
       "        [   1.,   14.,   51., ...,   85.,  194.,  255.],\n",
       "        [-127., -135., -116., ...,  -31.,  -41.,  -34.]],\n",
       "\n",
       "       [[ 103.,  103.,  104., ...,  255.,  449.,  625.],\n",
       "        [  43.,   42.,   50., ..., -384., -319., -269.],\n",
       "        [ 115.,  122.,  149., ...,  121.,  111.,  106.],\n",
       "        ..., \n",
       "        [  30.,   41.,   55., ...,   40.,   47.,   53.],\n",
       "        [  -5.,  -26.,  -70., ...,  120.,  -11.,  -86.],\n",
       "        [ -78.,  -73.,  -85., ..., -133.,  -66.,  -79.]],\n",
       "\n",
       "       ..., \n",
       "       [[  73.,   92.,  100., ...,   24.,   32.,   -2.],\n",
       "        [   1.,  -49.,  -91., ...,  140.,  170.,  131.],\n",
       "        [  -1.,   11.,   -1., ...,  -23.,   -4.,   13.],\n",
       "        ..., \n",
       "        [  39.,   29.,   40., ...,   37.,   38.,   39.],\n",
       "        [ -66.,  -61.,  -40., ...,  -39.,  -40.,   20.],\n",
       "        [  22.,   47.,   90., ...,  -59.,  -57.,  -48.]],\n",
       "\n",
       "       [[ -64.,  -72.,  -49., ...,   -4.,   -8.,   25.],\n",
       "        [ -55.,  -39.,  -65., ...,   41.,   21.,  -28.],\n",
       "        [ 153.,  142.,  134., ...,   85.,  116.,  156.],\n",
       "        ..., \n",
       "        [  -5.,   21.,   64., ...,  167.,  204.,  259.],\n",
       "        [  -2.,  -11.,  -13., ...,   36.,   41.,   34.],\n",
       "        [ -75.,  -82.,  -58., ...,    3.,  -20.,  -60.]],\n",
       "\n",
       "       [[ -20.,    3.,   38., ...,  -20.,   -9.,   11.],\n",
       "        [ -93.,  -97.,  -95., ...,   37.,   58.,   73.],\n",
       "        [  72.,   52.,   34., ...,  -72.,  -60.,  -44.],\n",
       "        ..., \n",
       "        [-111.,  -71.,  -59., ...,   46.,   46.,   25.],\n",
       "        [  12.,  -10.,   -9., ...,   31.,   38.,   33.],\n",
       "        [  19.,   35.,   66., ...,  -29.,    5.,   34.]]])\n",
       "Coordinates:\n",
       "  * bipolar_pairs  (bipolar_pairs) (numpy.record, [('ch0', 'S3'), ('ch1', 'S3')]) ('001', '002') ...\n",
       "    samplerate     float64 200.0\n",
       "  * events         (events) (numpy.record, [('subject', 'S256'), ('session', '<i8'), ('list', '<i8'), ('type', 'S256'), ('item', 'S256'), ('itemno', '<i8'), ('recalled', '<i8'), ('mstime', '<i8'), ('msoffset', '<i8'), ('rectime', '<i8'), ('intrusion', '<i8'), ('eegfile', 'S256'), ('eegoffset', '<i8'), ('serialpos', '<i8')]) ('BW001', 1, -999, 'SESS_START', 'X', -999, -999, 683395115, -999, -999, -999, '/Volumes/rhino_root/data/eeg/BW001/eeg.noreref/BW001_24Jul02_0001', 2841, 0) ...\n",
       "  * time           (time) float64 0.0 0.005 0.01 0.015 0.02 0.025 0.03 0.035 ...\n",
       "Attributes:\n",
       "    samplerate: 200.0\n",
       "    gain: 1.0"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bipolar_pairs = tal_reader.get_bipolar_pairs()\n",
    "bipolar_eeg= MonopolarToBipolarMapper(time_series=eeg,bipolar_pairs=bipolar_pairs).filter()\n",
    "bipolar_eeg"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x11596a7d0>]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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SKYFuh2mVC0j5jMpNYLkuIECZRDbjjWBS7Ts0qI6OdFuNzZUF1GpI80uOLFwMxDDtjWCo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\nTfm/q7iyn9JisWAAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1167dfa90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(bipolar_eeg.time,bipolar_eeg.mean('bipolar_pairs').mean('events'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can use the `sel` method of DataArrays to filter the timeseries under consideration; for example, we can only look at the events of type \"WORD\":"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1162587d0>]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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LSlx4NfGFExiejWJ3b5PmdinGlk5lJ352IojDl2aRzggtM6cShHIIAKAEyYig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\ne39BpO9BAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1167b9110>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(bipolar_eeg.time,bipolar_eeg.sel(events=bipolar_eeg.events.data['type']=='WORD').mean('bipolar_pairs').mean('events'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This data seems like it's heavily contaminated by 60 Hz line noise. We can use the `filtered` method to filter out the 60 Hz component of the signal:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x116b1cbd0>]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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f/t6rfOmJY0W9RjiWYMgfYU2zm+5WN1pPDyLJB601TxwcXNS/dSl5aLeR5XPH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\nRAAEQRBqFBEAQRCEGuX/AoMM4gKBarRBAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1158ac550>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(bipolar_eeg.time,bipolar_eeg.filtered([58.,62.]).mean('bipolar_pairs').mean('events'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2. RAM data (new)\n",
    "\n",
    "The new system for storing the RAM data stores the information about each subject's electrode montage in a file `pairs.json`,\n",
    "which we can find using a JsonIndexReader"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from ptsa.data.readers.IndexReader import JsonIndexReader\n",
    "jr = JsonIndexReader(rhino_root+'/protocols/r1.json')\n",
    "events = np.concatenate(\n",
    "    [BaseEventReader(filename=f).read() for f in jr.aggregate_values('task_events',subject='R1111M',experiment='FR1')]\n",
    ").view(np.recarray)\n",
    "words = events[events.type=='WORD']\n",
    "pairs_path = jr.get_value('pairs',subject='R1111M',experiment='FR1')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Once we have this path, we can create a TalReader object with it, and use that to load the list of channels and the list of bipolar pairs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Coordinates:\n",
       "  * channels    (channels) |S3 '001' '002' '003' '004' '005' '006' '007' ...\n",
       "    samplerate  float64 500.0\n",
       "  * events      (events) (numpy.record, [('session', '<i8'), ('eegoffset', '<i8'), ('protocol', 'S256'), ('intrusion', '<i8'), ('item_name', 'S256'), ('serialpos', '<i8'), ('rectime', '<i8'), ('stim_params', '<f8', (0,)), ('list', '<i8'), ('subject', 'S256'), ('eegfile', 'S256'), ('montage', 'S256'), ('experiment', 'S256'), ('recalled', '<i8'), ('msoffset', '<i8'), ('stim_list', '<i8'), ('item_num', '<i8'), ('mstime', '<i8'), ('is_stim', '<i8'), ('type', 'S256'), ('exp_version', 'S256')]) (2, 183362, 'r1', -999, 'JUICE', 1, -999, array([], dtype=float64), 1, 'R1111M', '/Volumes/rhino_root/protocols/r1/subjects/R1111M/experiments/FR1/sessions/2/ephys/current_processed/noreref/R1111M_FR1_2_26Jan16_1408', '0.0', 'FR1', 0, 1, 0, 138, 1453836066458, 0, 'WORD', '1.05') ...\n",
       "  * time        (time) float64 0.0 0.002 0.004 0.006 0.008 0.01 0.012 0.014 ..."
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tal_reader = TalReader(filename=pairs_path)\n",
    "channels = tal_reader.get_monopolar_channels()\n",
    "eeg = EEGReader(events=words,channels=channels,start_time=0.0,end_time=1.0).read()\n",
    "eeg.coords"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Coordinates:\n",
      "  * bipolar_pairs  (bipolar_pairs) (numpy.record, [('ch0', 'S3'), ('ch1', 'S3')]) ('001', '002') ...\n",
      "    samplerate     float64 500.0\n",
      "  * events         (events) (numpy.record, [('session', '<i8'), ('eegoffset', '<i8'), ('protocol', 'S256'), ('intrusion', '<i8'), ('item_name', 'S256'), ('serialpos', '<i8'), ('rectime', '<i8'), ('stim_params', '<f8', (0,)), ('list', '<i8'), ('subject', 'S256'), ('eegfile', 'S256'), ('montage', 'S256'), ('experiment', 'S256'), ('recalled', '<i8'), ('msoffset', '<i8'), ('stim_list', '<i8'), ('item_num', '<i8'), ('mstime', '<i8'), ('is_stim', '<i8'), ('type', 'S256'), ('exp_version', 'S256')]) (2, 183362, 'r1', -999, 'JUICE', 1, -999, array([], dtype=float64), 1, 'R1111M', '/Volumes/rhino_root/protocols/r1/subjects/R1111M/experiments/FR1/sessions/2/ephys/current_processed/noreref/R1111M_FR1_2_26Jan16_1408', '0.0', 'FR1', 0, 1, 0, 138, 1453836066458, 0, 'WORD', '1.05') ...\n",
      "  * time           (time) float64 0.0 0.002 0.004 0.006 0.008 0.01 0.012 ...\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1167ecd10>]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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xtnK+xa9cRkemirhyTbwDd3dxTswWcXy2hFduHwUAHJ0u4KH99ERCdAZy9bSY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\nQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1166564d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "bipolar_eeg= MonopolarToBipolarMapper(time_series=eeg,bipolar_pairs=tal_reader.get_bipolar_pairs()).filter()\n",
    "print(bipolar_eeg.coords)\n",
    "plt.plot(bipolar_eeg.time,bipolar_eeg.mean('bipolar_pairs').mean('events'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3. LTPFR/LTPFR2\n",
    "There are two main differences between reading scalp data and reading ECoG data. The first is that scalp data are usually rereferenced so that the data are relative to the average over all electrodes. This helps to eliminate artifacts due to muscle movement. To load the referenced data, we pass set the parameter `use_reref_eeg` to `True` when reading the events."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "events = BaseEventReader(filename=rhino_root+'/data/eeg/scalp/ltp/ltpFR2/behavioral/events/events_all_LTP093.mat',\n",
    "                         use_reref_eeg=True,common_root='data').read()\n",
    "words = events[events.type=='WORD']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The second difference between reading scalp EEG data and ECoG data is that the scalp experiments have a fixed set of electrodes. \n",
    "We therefore construct the array of channel labels ourselves, rather than reading it with a TalReader.\n",
    "\n",
    "The scalp EEG in the CML lab has 128 channels, but for purposes of speed we'll only load a few of them."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['020', '021', '022', '023', '024', '025', '026', '027', '028', '029'], \n",
       "      dtype='|S3')"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "channels = np.array(['{:03}'.format(x) for x in range(20,30)])\n",
    "channels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "eeg = EEGReader(events=words,channels=channels,start_time=0.0,end_time=1.0).read()\n",
    "plt.plot(eeg.mean('events').mean('channels'))"
   ]
  }
 ],
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  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
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 },
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