Homework 4 2D Arrays (DataGen).ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Data Taken From: \n",
    "http://headit.ucsd.edu/studies/9d557882-a236-11e2-9420-0050563f2612\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python2.7/dist-packages/matplotlib/font_manager.py:273: UserWarning: Matplotlib is building the font cache using fc-list. This may take a moment.\n",
      "  warnings.warn('Matplotlib is building the font cache using fc-list. This may take a moment.')\n"
     ]
    }
   ],
   "source": [
    "import mne\n",
    "import os\n",
    "from os import path\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python2.7/dist-packages/ipykernel/pylab/config.py:66: DeprecationWarning: metadata {'config': True} was set from the constructor.  Metadata should be set using the .tag() method, e.g., Int().tag(key1='value1', key2='value2')\n",
      "  inline backend.\"\"\"\n",
      "/usr/local/lib/python2.7/dist-packages/ipykernel/pylab/config.py:71: DeprecationWarning: metadata {'config': True} was set from the constructor.  Metadata should be set using the .tag() method, e.g., Int().tag(key1='value1', key2='value2')\n",
      "  'retina', 'jpeg', 'svg', 'pdf'.\"\"\")\n",
      "/usr/local/lib/python2.7/dist-packages/ipykernel/pylab/config.py:85: DeprecationWarning: metadata {'config': True} was set from the constructor.  Metadata should be set using the .tag() method, e.g., Int().tag(key1='value1', key2='value2')\n",
      "  use `figure_formats` instead)\"\"\")\n",
      "/usr/local/lib/python2.7/dist-packages/ipykernel/pylab/config.py:95: DeprecationWarning: metadata {'config': True} was set from the constructor.  Metadata should be set using the .tag() method, e.g., Int().tag(key1='value1', key2='value2')\n",
      "  \"\"\"\n",
      "/usr/local/lib/python2.7/dist-packages/ipykernel/pylab/config.py:114: DeprecationWarning: metadata {'config': True} was set from the constructor.  Metadata should be set using the .tag() method, e.g., Int().tag(key1='value1', key2='value2')\n",
      "  \"\"\")\n",
      "/usr/local/lib/python2.7/dist-packages/ipykernel/pylab/config.py:44: DeprecationWarning: InlineBackend._config_changed is deprecated: use @observe and @unobserve instead.\n",
      "  def _config_changed(self, name, old, new):\n",
      "/usr/local/lib/python2.7/dist-packages/traitlets/traitlets.py:770: DeprecationWarning: A parent of InlineBackend._config_changed has adopted the new @observe(change) API\n",
      "  clsname, change_or_name), DeprecationWarning)\n",
      "/usr/local/lib/python2.7/dist-packages/IPython/core/formatters.py:98: DeprecationWarning: DisplayFormatter._formatters_default is deprecated: use @default decorator instead.\n",
      "  def _formatters_default(self):\n",
      "/usr/local/lib/python2.7/dist-packages/IPython/core/formatters.py:677: DeprecationWarning: PlainTextFormatter._deferred_printers_default is deprecated: use @default decorator instead.\n",
      "  def _deferred_printers_default(self):\n",
      "/usr/local/lib/python2.7/dist-packages/IPython/core/formatters.py:669: DeprecationWarning: PlainTextFormatter._singleton_printers_default is deprecated: use @default decorator instead.\n",
      "  def _singleton_printers_default(self):\n",
      "/usr/local/lib/python2.7/dist-packages/IPython/core/formatters.py:672: DeprecationWarning: PlainTextFormatter._type_printers_default is deprecated: use @default decorator instead.\n",
      "  def _type_printers_default(self):\n",
      "/usr/local/lib/python2.7/dist-packages/IPython/core/formatters.py:672: DeprecationWarning: PlainTextFormatter._type_printers_default is deprecated: use @default decorator instead.\n",
      "  def _type_printers_default(self):\n",
      "/usr/local/lib/python2.7/dist-packages/IPython/core/formatters.py:677: DeprecationWarning: PlainTextFormatter._deferred_printers_default is deprecated: use @default decorator instead.\n",
      "  def _deferred_printers_default(self):\n"
     ]
    }
   ],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Extracting edf Parameters from /home/nicholas/Downloads/eeg_recording_1 (1).bdf...\n",
      "Setting channel info structure...\n",
      "Creating Raw.info structure...\n",
      "Ready.\n"
     ]
    }
   ],
   "source": [
    "data_dir = path.join('home', 'nicholas', 'Downloads')\n",
    "bdf_filename = 'eeg_recording_1 (1).bdf'\n",
    "\n",
    "raw = mne.io.read_raw_edf('/' + path.join(data_dir, bdf_filename))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Get Events\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Reading 0 ... 954623  =      0.000 ...  3728.996 secs...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python2.7/dist-packages/scipy/special/__init__.py:627: RuntimeWarning: numpy.dtype size changed, may indicate binary incompatibility\n",
      "  from ._ufuncs import *\n",
      "/usr/local/lib/python2.7/dist-packages/scipy/special/_ellip_harm.py:7: RuntimeWarning: numpy.dtype size changed, may indicate binary incompatibility\n",
      "  from ._ellip_harm_2 import _ellipsoid, _ellipsoid_norm\n",
      "/usr/local/lib/python2.7/dist-packages/scipy/spatial/__init__.py:92: RuntimeWarning: numpy.dtype size changed, may indicate binary incompatibility\n",
      "  from .ckdtree import *\n",
      "/usr/local/lib/python2.7/dist-packages/scipy/spatial/__init__.py:93: RuntimeWarning: numpy.dtype size changed, may indicate binary incompatibility\n",
      "  from .qhull import *\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[done]\n",
      "Removing orphaned offset at the beginning of the file.\n",
      "4455 events found\n",
      "Events id: [    1     2    10    11    20    21    25   100   101   150   151   200\n",
      "   201   250   251  4096 65536]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python2.7/dist-packages/mne/viz/misc.py:453: UserWarning: event 1 missing from event_id will be ignored.\n",
      "  % this_event)\n",
      "/usr/local/lib/python2.7/dist-packages/mne/viz/misc.py:453: UserWarning: event 2 missing from event_id will be ignored.\n",
      "  % this_event)\n",
      "/usr/local/lib/python2.7/dist-packages/mne/viz/misc.py:453: UserWarning: event 10 missing from event_id will be ignored.\n",
      "  % this_event)\n",
      "/usr/local/lib/python2.7/dist-packages/mne/viz/misc.py:453: UserWarning: event 11 missing from event_id will be ignored.\n",
      "  % this_event)\n",
      "/usr/local/lib/python2.7/dist-packages/mne/viz/misc.py:453: UserWarning: event 20 missing from event_id will be ignored.\n",
      "  % this_event)\n",
      "/usr/local/lib/python2.7/dist-packages/mne/viz/misc.py:453: UserWarning: event 21 missing from event_id will be ignored.\n",
      "  % this_event)\n",
      "/usr/local/lib/python2.7/dist-packages/mne/viz/misc.py:453: UserWarning: event 25 missing from event_id will be ignored.\n",
      "  % this_event)\n",
      "/usr/local/lib/python2.7/dist-packages/mne/viz/misc.py:453: UserWarning: event 150 missing from event_id will be ignored.\n",
      "  % this_event)\n",
      "/usr/local/lib/python2.7/dist-packages/mne/viz/misc.py:453: UserWarning: event 151 missing from event_id will be ignored.\n",
      "  % this_event)\n",
      "/usr/local/lib/python2.7/dist-packages/mne/viz/misc.py:453: UserWarning: event 250 missing from event_id will be ignored.\n",
      "  % this_event)\n",
      "/usr/local/lib/python2.7/dist-packages/mne/viz/misc.py:453: UserWarning: event 251 missing from event_id will be ignored.\n",
      "  % this_event)\n",
      "/usr/local/lib/python2.7/dist-packages/mne/viz/misc.py:453: UserWarning: event 4096 missing from event_id will be ignored.\n",
      "  % this_event)\n",
      "/usr/local/lib/python2.7/dist-packages/mne/viz/misc.py:453: UserWarning: event 65536 missing from event_id will be ignored.\n",
      "  % this_event)\n",
      "/usr/local/lib/python2.7/dist-packages/mne/viz/misc.py:459: UserWarning: More events than colors available. You should pass a list of unique colors.\n",
      "  warnings.warn('More events than colors available. '\n",
      "/usr/local/lib/python2.7/dist-packages/IPython/core/formatters.py:92: DeprecationWarning: DisplayFormatter._ipython_display_formatter_default is deprecated: use @default decorator instead.\n",
      "  def _ipython_display_formatter_default(self):\n",
      "/usr/local/lib/python2.7/dist-packages/IPython/core/formatters.py:669: DeprecationWarning: PlainTextFormatter._singleton_printers_default is deprecated: use @default decorator instead.\n",
      "  def _singleton_printers_default(self):\n"
     ]
    },
    {
     "data": {
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6jwLAM888wwsvvFDv7XaXqxy35aPxocAwBy9Rxzwxv+iJMYHEJYRSymGnXFJS\nwtatWxk4cGADtEr3n//8h/j4eJo3b26dp2kau3btwmg0UlhYyPz5888r9+mnn2I2m8+La9iwYaxb\nt46TJ0+eV6YhOO24lVLHqn4edvS6dE0UQohLz1hmZNPRTRjLjDWvXA/lt2/fTq9evQgNDWXMmDGU\nVt3BzmL16tVceeWVhIeHc91117F7t/7lnzlz5jBq1Ci7dZ944gkmTZoEwKBBg/joo48AOHDgADfe\neCNNmzalWbNmjBs3zu4JW23btuXNN9+kR48ehIeHM2bMGEwmEyUlJSQmJpKTk2MdwVoe7PHjjz9y\n7bXXOnxe9qpVq+jWrRsRERHccMMN7N271+m2xo4di6nqLnuWuGJjY2nZsiULFizAy8uLA1V3T6xu\nzZo15/3joJSyG0VXV1hYyIsvvujwCWl+fn706tWLtWvXOi1/STm7M8vl/ELueCSEcBMXcOe0wtJC\n1eOfPZT3i96qxz97qMJSB3eQc+Fiy5tMJtW6dWs1b948ZTab1fLly5WPj4964YUXlFJKbdu2TTVr\n1kz9+uuvqrKyUi1evFi1adNGmUwmdfjwYdWkSRNVVFSklFKqoqJCxcTEqC1btiillEpISFALFixQ\nSim1b98+9cMPP6jy8nJ16tQpNXDgQDV58mRrO9q0aaP69u2rjh8/rvLz81V8fLx6//33lVJKpaSk\nqLi4uPPa/vDDD6v58+crpZSaOXOmGj9+vFJKqYyMDNWkSRP1448/KrPZrObMmaM6dOigysvLa9zW\nmjVrVExMjEpPT1dnz55V48aNU15eXmr//v0O99/VV1+tli9fbjdP0zTVokULFRMTo0aMGKEOHTpk\nt/zRRx9V8+bNU4cOHVJeXl6qoqLCbvnEiRPVU0895fK41SVn561Sqt6fxy2EEI1O6slU0nLTMFea\n2ZO7h7TctEtafvPmzZjNZiZOnIjBYGDEiBFcffXV1uUffPABDz/8ML1790bTNMaPH4+fnx+bN2+m\nVatWXHXVVaxcuRLQR8BNmjSxK2/Rvn17brzxRry9vYmMjGTy5MmsX7/ebp0nnniC6Oho66M/d+zY\n4bLtycnJJCYmnjf/3//+N0OHDuWGG27AYDDw9NNPc/bsWX7++ecat7Vs2TImTJhA586d8ff3Z+bM\nmS7bUFBQQHC1u/5t2LCBQ4cOsXfvXmJiYhg6dKh1BP7bb7/x888/8/jjjzutMzg4mIKCApfbvVRq\n1XFrmhZFcBDSAAAgAElEQVSuaVr3+mrMH50n5hc9MSaQuDxdt2bd6BrVFR8vH7pEdaFrVNdLWj4n\nJ4cWLVrYzWvdurX1/eHDh3nzzTeJiIggIiKC8PBwsrKyyMnJAWDs2LF89tlnAHz22WckJSU53M7J\nkycZO3YsLVu2JCwsjHHjxnHq1Cm7daKjo63vAwMDKSoqctru1NRUwsLCzmu7JSbbGDRNIy4ujuzs\nc8+RcratnJwcuwvh4uLiLJ+aOBQeHo7RaJ+iuO666/D29iYkJIR58+Zx6NAh0tPTUUrx6KOPMm/e\nPMuV3A7rNBqNhIWFOd3mpVRjx61pWoqmaSGapkUA24APNE27+OvrhRDiMhXsF8zGCRvZMGEDGyds\nJNjPyT3b66l8TEyMXYcGcOTIEev7uLg4nn/+efLy8sjLyyM/P5+ioiJGjx4NwKhRo0hJSSE7O5uV\nK1c67binTZuGl5cXaWlpFBQUsHTpUpcdoi1HF6Y5G20DxMbGcviw/eVRR48epWXLljVuKyYmhqys\nLOv0kSNHXF6t3r17dzIzM50ut8SolKKwsJDffvuN0aNHExMTQ58+fVBK0bJlS/73v/9Zy6Snp9Oj\nR48a23opuDPiDlVKFQLDgcVKqb6APL+tHlgeS+hJPDEmkLj+CIL9gunXsl+tO926KN+/f3+8vb15\n5513MJvNrFixgi1btliXP/DAA/zrX/+yzisuLiY5OZni4mIAmjZtysCBA5kwYQLt2rWjU6dODrdj\nNBoJCgoiODiY7OxshxdmORMdHc3p06ftLmZLTk7mlltucbj+HXfcwTfffMO6deswm8288cYb+Pv7\n079/jc+r4o477mDhwoXs3buXkpISZs+e7XL9xMREu0+P9uzZw86dO6msrKSoqIgnn3ySli1bEh8f\nT2hoKMeOHWPHjh3s3LmT5ORkALZt20bfvn0BKCsrY+vWrfzf//1fjW29FNzpuL01TYsB7gBW13N7\nhBDiD8/Hx4cVK1awcOFCIiMjWbZsGSNGjLAu79WrFx988AGPPfYYERERdOzYkUWLFtnVkZSUxI8/\n/sidd95pN992pDpjxgy2bt1qzSnbbqP6utV16tSJsWPH0q5dOyIiIjh27Bjp6elcc801Dtfv2LEj\nS5cu5bHHHiMqKopvvvmGr7/+Gm9v7xq3NXjwYCZOnMigQYPo2LGjtbP38/NzuP6wYcPIyMiwXul+\n4sQJRo8eTWhoKB06dODo0aOsXr0ag8EAQLNmzayvqKgoNE2jWbNm1ratWrWKQYMG2X29rEE5u2pN\nnbvyciT6vcn/UTXdDviypnL1+cJDrypft25dQzehznliTEpJXI0J8jzuS+Lf//63Gj169CXZVnp6\nuvL29j7vym9bH3zwgd0V8hejX79+Ki0trU7qcpez81Yp5da9yo8ppawXpCmlDkiOWwghhK3w8HAm\nT55cb/V/9dVXJCYmUlxczJQpU7j11lvx8nL+ofH9999fZ9vetGlTndVVF9x5Hvc2pdRVNc27lBrb\nPYaFEA3HU+5V/kc3ZMgQNm3ahLe3NwkJCbz33nt2V6F7Glf3Knc64tY0rT9wDRCladqTNotCAEPd\nNlEIIYRwbs2aNQ3dhMuGq4vTfIEg9M492OZViJ73FnXME79D64kxgcQlhGg4TkfcSqn1wHpN0z5W\ncm9yIYQQ4rLgTo67I/A00Aabjl4pdUO9tsx1myQ3JYRwi+S4RWN0QTluG8uAfwEfAhV12TAhhBBC\n1I47N2AxK6X+qZTaopTaannVe8v+gDwxv+iJMYHEJYRoOO503F9rmvaIpmkxmqZFWF713jIhhBCX\n3KJFixgwYECd1bd+/Xq7B4S4a9asWYwfP77O2uGupKQkVq1aVe/beffdd5k6deoFlXWn474beAb4\nGdha9frtgrYmXPLE+0R7YkwgcYmGM2vWLO6666563Yar24/WRX3Hjh1zqzOvy3ZMmDCBv/71ry7X\n2b17N7t27eLWW28F9H86DAYDISEhBAcHExISwpIlS6zr5+TkcPvttxMZGUmrVq14//337erbsWMH\nvXv3tj5WdefOndZlDzzwAJ988sl5T2NzR40dt1KqrYNXu1pvSQghhEerqHDvMqjk5GSGDBlSz605\nx/Lc7Zq8//77593bvUWLFhQWFmI0GiksLLT7FGDcuHG0b9+e3NxcVq9ezbRp06zPMy8vL+f222/n\nrrvuoqCggLvuuovbbrsNs9kM6PdZT0xMZPHixbWOx53HegZqmjZd07T5VdNXaJo2tNZbEjXyxPyi\nJ8YEEtcfgtEImzbpPxug/GuvvUbLli0JCQkhPj6edevWsXbtWl5++WW++OILgoODufLKKwH4+OOP\n6dKlCyEhIXTo0IH58+db67F8VP3WW28RHR1NixYt+Pjjj63L8/LyuPXWWwkNDaVfv37s37/frh2T\nJk2iVatWhIaGcvXVV/Pf//7XumzWrFmMGjWK8ePHExYWxqJFiygtLeWee+4hIiKCbt268euvv54X\nm+3jPx3FaVFWVsbdd99NSEgIf/rTn9i2bZt12d69exk0aBDh4eH86U9/4uuvv7YumzBhAo888gi3\n3HILwcHBLFiwgE8++YQ5c+YQEhLCbbfd5nCfr1mzhoEDB7pzeCguLiYlJcX6aNTu3bszcuRIPvro\nIwDWrVtHRUUFEydOxMfHh8cffxylFD/99JO1joEDB/LNN9+4tT07zm5irs7diP8L4FkgtWo6ENhR\nU7n6fOGhDwfwxAc8eGJMSklcjQkX8pCRwkKlevRQyttb/1lYWLuNXmT5jIwMFRcXp44fP66UUurw\n4cPqwIEDSimlZs6cqcaPH2+3fnJysjp48KBSSqkNGzaowMBAtX37dqWUUikpKcrb21vNnDlTmc1m\nlZycrAIDA1VBQYFSSqnRo0er0aNHq7Nnz6rU1FTVokULNWDAAGvdn3zyicrPz1cVFRXqrbfeUs2b\nN1dlZWXWtvj6+qpVq1YppZQ6e/asmjJlirr++utVQUGBysrKUt26dVNxcXHW+srLy1XTpk1VUVFR\njXEGBASob7/9VlVWVqrnnntO9evXz1pHhw4d1KuvvqrKy8vVTz/9pIKDg1VmZqZSSql77rlHhYWF\nqU2bNimllCotLVX33HOPeuGFF5zu8+LiYqVpmjp16pR1XkpKivLz81PNmzdX7dq1U5MnT1bFxcVK\nKaWMRqPy8vJSubm51vUfeOABddVVVymllJo7d65KTEy028awYcPUW2+9ZZ3etm2bioyMdNgeZ+et\nUsqtHHd7pdQcoLzqTC8B6jYBIgDPzC96YkwgcXm81FRISwOzGfbs0d9fwvIGgwGTyURqaipms5lW\nrVrRtm1bp+sPGTKENm3aADBgwABuuukmNm7caF3u6+vLCy+8gMFgYMiQIQQFBZGRkUFlZSUrVqzg\npZdewt/fn65du3L33Xfb1Z2UlERYWBheXl5MnjyZsrIyMjIyrMv79+/PsGHDAPD392fZsmVMnz6d\n0NBQWrRowcSJE+3q27BhAz179qRJkyY1xnnddddx8803o2ka48ePZ9euXYD+0A/Lw0a8vb0ZNGgQ\nQ4cO5bPPPrOWve222+jXrx/g/PGftgoKCtA0jeDgc89Pj4+PZ8eOHRw7doyffvqJrVu38tRTTwEQ\nFBTEtddey0svvURZWRnbtm3jyy+/pKSkBICioiJCQ0PtthESEoLR5hOY4OBgzpw5U2PbqnOn4zZp\nmhYAKABN09oDZbXekhBCNBbdukHXruDjA1266O8vYfn27dvz9ttvM3PmTKKjo0lKSrI+W9qRNWvW\n0L9/fyIjIwkPD2fNmjV2Fz1FRkbaPUkrMDCQoqIicnNzqaiooGXLltZlrVu3tqv7jTfeoEuXLoSH\nhxMeHk5hYaFd3dUvMsvJyXFZn+3H5DXFafv868DAQEpLS6msrHR4cVvr1q3Jzs522q6ahIWFAdh1\nrM2aNaNz587W+ufMmcOXX35pXf7JJ59w4MABWrVqxaOPPsr48eOtsQcFBVFYWGi3jTNnztj9Y2A0\nGs/r3N3hTsc9E/gWiNM07RPgR/SPzkUd88T8oifGBBKXxwsOho0bYcMG/afNH9tLUh4YM2YMGzdu\n5PBh/Y7TU6ZMAc6/0tpkMjFy5EieffZZcnNzyc/PZ8iQIZZ0gEtRUVEYDAaOHj1qnXfkyBHr+40b\nN/L666+zfPly8vPzyc/PJyQkxK7u6u2JjY21q8/SfgvbjttVnK5U34al3S1atHDarpquUA8MDKR9\n+/ZkZma6XM/2Qre4uDi+/vprTpw4waZNm8jNzaVPnz4AdO3a1foJgcWuXbvoavNPXHp6Oj169HC5\nPUfcuar8O2A4cA/wGdBbKZVS6y0JIURjEhwM/fpdUKd7seUzMzNZt24dJpMJX19fAgICrCPm6Oho\nDh06ZO08TSYTJpOJpk2b4uXlxZo1a/juu+/c2o6XlxcjRoxg5syZnD17lj179rBo0SLr8qKiInx8\nfIiMjMRkMvHiiy/ajUgdGTVqFK+88goFBQVkZWXx7rvvWpcdPHgQk8lEp06daozTEUvMffv2JTAw\nkDlz5mA2m0lJSWH16tWMHTvWadno6GgOHDjgsu2JiYnWq8JB/0fW8o/M0aNHmTp1Krfffrt1+d69\neykqKqK8vJylS5fy/fff8+ST+sM0ExISMBgMvPPOO5hMJv7+97/j5eXFDTecu1v4+vXrL+jqeneu\nKv8auAlIUUqtVkrV/ktnwi2emF/0xJhA4hL1q6ysjKlTpxIVFUVsbCy5ubm88sorgN4xKqWIjIyk\nd+/eBAUFMW/ePEaNGkVERASff/6506umLWxHn++88w5Go5GYmBjuvfde7r33Xuuym2++mZtvvpmO\nHTvStm1bAgMDa/wIesaMGdZc9eDBg+2+c159tO0qTlft9vHx4euvvyY5OZmmTZvy2GOPsWTJEq64\n4orz4rO47777SEtLIyIiguHDhzus/4EHHmDp0qXW6e3bt3PNNdcQFBTEddddR8+ePZk3b551+dq1\na2nXrh0RERHMnz+ftWvXEhkZaW3jV199xaJFiwgPD2fx4sX85z//wdtbv9N4aWkpycnJ511T4A53\nHjIyEBgN3AL8CnwOrFZKldZ6a3VEHg4ghHCXPGTk8nHLLbfw+OOPM3jw4IZuilPjxo3jjjvusN6E\npb68++67ZGVl8eqrrzpc7uohIzV23DaVGIAbgAeAwUqpkAts70Xz1F+4lJQUjxvxeGJMIHE1JtJx\nXz7eeOMNHn/8cbeu8v6ju9ing1F1Vfkw9JH3VcAi1yWEEEIIe08//XRDN8EjuPNR+b+BPuhXln8B\nrFdKuXf/uHoi/ykLIdwlI27RGLkacbvzdbAF6DdheVgpta42nbamaQs0TTuhadoum3nhmqZ9p2la\nhqZpazVNC62aH6Fp2k+aphk1Tfu7u9sQQggh/kicdtyapj0LoJRai/51MNtlL7tZ/0Lg5mrzpgI/\nKKU6AT8Bz1XNLwWmA0+5WbfH8cTv0HpiTCBxCSEajqsR9xib989VW+bWJYFKqf8C+dVm38a5HPki\n4PaqdUuUUj8jd2UTQgghnHJ1cZrm5L2j6dpoppQ6AaCUOq5pWrMLqcRYZmTdoXUcKjjEyPiRxIbE\n1rj+L9m/ANC3RV+C/c6/KYKxzEjqyVRah7bm8JnD1p/dmnVzuL6zei31uCpnW7akvIRAn0D69u/r\nVtw1xVG9bkCvv4VevzttcyWnMIfl6ctpFtiMpk2aumxHr/69+OHADzW2t3rbbY9Dt2bd3G63o7Lu\nrG9Zz91jl5CQYN0PbULbMKjtILfK1/Z8sZzne0/tpXNkZ+t2atp/dueVG+eJpT29+vdi09FNF3V+\n1MR2m12adnHrOAkhznHVcSsn7x1NX4wLqqvvB31JP50OwLPfP8uBiQecdt7GMiPXLLiG1NxUALpF\ndePn+362+0NhLDMyYOEAUk+m4mPwobyiHB+DD+ZKM12jurJxwkaHf1Sr17t23FoSP00kLTfNaTlH\nZZ21q7ZxOKsbID4yHoOXgb2n97psmys5hTm0+3s7yirOfTDibjtqis9SxvY4mCvNdI7U7xVcU7sd\nlXVnfcuxSk5KduvYOdoPXZp24fvx37ssX9vzxVhmtDvPLdvZfP9mt88RV/u9+rp1cX7UpPo2/Qx+\nVKiKetueEJ7IVcfdQ9O0QvTRdUDVe6qm/S9imyc0TYtWSp3QNK05cPJCKkn/MB30e8JT5l/G3GZz\nef3B14FzeTrL91EX/2cxe37dA2309ff8toclUUt4ZNQj1vXTTqaRlptGhaqg4nf9YfAVbfWfqVtS\nWRJpvz6AX3s/0k+lw0G93r1ee/nm92/YvWU3lZWV7GEPablplO4rtWuPZXvpp6r+IFeV33N4D2m3\nOl7f2fYc1V89Xsv6mVommqZh3m8m9WAqaUPT6Ney33n7y9X06t9XU7avqtNu63x/Wtq759c91n/N\nnLXXtv7Uk6nW/WfZ/3t+3QMaVLauZE/uHpasWkKXqC4O909abhoVByqooALa4tb6lv3xzdXf2E27\n2j//WPUPykLLrPt376G9fNPPdfnani9+7f3IOJ1hXZ+2kHE6w2k8lv1n3eduHB/b9mRqmahDisrK\nygs+P2qart6+sooyOEidbu/tt99mx44d1qdlidpZtGgRH374od3TxS7G+vXrGTdu3Hn3Frdo27Yt\nCxYssLsV6KVy3XXX8d57713Q/cJr4+mnn6ZDhw48/PDDdVKf2zdgueANaFob4Gul1J+qpl8D8pRS\nr2maNgUIV0pNtVn/bvT7oT/uok4V/068dSTiZ/CrsxF32sk0vA3emCvMeBu8qaisoEtUl1qPuPfk\n7nFazlFZgDb5bdj12q56H3FnnM5w2TZXajvi7j6lO4fCD7lcr3oZ2+NQUVlBp0j9vsY1tdtRWXfW\ntxwry4i7pmMHsPyb5YzbPs7hiNtZ+dqeLw0x4j677yzZkdkXfH7UxNGIu1JV1tv2wPO+DjZr1iz2\n79/P4sWL66X+RYsWsWDBAjZs2FAn9a1fv57x48fbPbzk2LFj9OnTh6NHjzZYx7169Wr+8Y9/kJyc\nbJ138OBBJk6cyPr16/H39+fee++13tnsvffe4+OPP2b37t0kJSXx0Ucf2dX3448/8thjj3H06FH6\n9u3LwoULadWqFQDHjx+nT58+HDhwwHrL05rUyZ3TLoSmaZ8CCUAkcAKYAXwFLAPigMPAHUqpgqr1\nDwLBgC9QANyklNrroF5VWFpIyqEUDhUcYkT8CLdy3FuytwDQp0Ufp3/I03LTaBXSiiOFR6w/u0Z1\ndfmHsnq9lnpclbMta8lFOmtXbeOoXjdgrR9wq22u5BTmsGLvCqICo2ga2NStdtTU3uplbI9D16iu\nbrfbUVl31res5+6xg3P7oXVoaxLaJLhVvrbni7HMSMqhFD3H3bSzdTuu1Oa8qt4euPjzoya224xv\nGu/WcboY0nHXzsV03BUVFRgMBrt5jjruBQsW8MsvvzB//vwG67iHDh3KnXfeaX0wSXl5OfHx8Tz+\n+OM89NBDeHl5kZmZSbdu+jU2X331FV5eXqxdu5azZ8/addynT5+mffv2fPTRRwwdOpTp06ezceNG\nNm3aZF3n5ptv5qGHHnJ6n/TqXHXcKKUa3UtvthBC1Kzq70Wt/44UlpernwsKVGF5+QVt92LLv/rq\nq6pFixYqODhYde7cWf3000/q22+/Vb6+vsrX11cFBQWpnj17KqWUWrhwoYqPj1fBwcGqffv26v33\n37fWk5KSolq2bKnefPNN1axZMxUbG6sWLlxoXX769Gk1bNgwFRISovr27ateeOEFNWDAAOvyJ554\nQsXFxamQkBDVu3dvtXHjRuuymTNnqpEjR6px48ap0NBQtWDBAnX27Fl19913q/DwcNW1a1f1+uuv\nq7i4OLvYhg8frlauXKmUUqpNmzbqxx9/VEopVVZWpp544gkVGxurWrRooSZNmqRMJpPbcQwdOlSF\nhISoPn36qOnTp6vrrrvO4b41mUwqICBAZWdnW+fNnz9fXX/99TUel+nTp6sJEybYzZs/f7669tpr\nrdPFxcUqICBAZWRkWOf97W9/U/fee2+N9Vs4O2+VUm7dgEVcIp74HVpPjAkkLk9nNJsZsH0712/f\nzoDt2zGazZe0fGZmJu+99x5bt26lsLCQtWvX0qZNG26++WamTZvG6NGjMRqNbN++HdAfWZmcnExh\nYSELFy5k8uTJ7Nixw1rf8ePHMRqN5OTk8OGHH/Loo49y5swZAB555BECAwM5ceIECxYsOO8j4D59\n+rBr1y7y8/NJSkpi1KhRmEwm6/JVq1Zxxx13UFBQQFJSEjNnzuTgwYMcPHiQtWvX2j0mFMBsNrNh\nwwZuuumm8+KePXs2W7ZsYdeuXezcuZMtW7Ywe/Zst+MIDg7m5MmTfPzxxyxatMjpM7h///13DAYD\nsbHnPqndvHkzrVu3JjExkaioKG644QZSU1Mdlq8uLS3NLk8eGBhIhw4dSEtLs86Lj49n586dbtVX\nE+m4hRCimtTiYtKKizEDe0pKSCsuvqTlDQYDJpOJ1NRUzGaz9TGZzgwZMsR6Md6AAQO46aab7C4u\n8/X15YUXXsBgMDBkyBCCgoLIyMigsrKSFStW8NJLL+Hv70/Xrl3Pe8xkUlISYWFheHl5MXnyZMrK\nysjIyLAu79+/P8OGDQPA39+fZcuWMX36dEJDQ2nRogUTJ060q2/Dhg307NmTwMDA8+L49NNPmTFj\nBpGRkURGRjJjxgyWLFnidhwvvvgifn5+xMfHu3xcZkFBAcHVnpOelZXFF198waRJkzh27BiJiYnc\ndtttmN34p6uoqIjQ0FC7eSEhIXbPLg8ODqagoKDGutwhHfdlxNOeygSeGRNIXJ6uW5MmdG3SBB9N\no0tgIF2bNLmk5du3b8/bb7/NzJkziY6OJikpiePHjztdf82aNfTv35/IyEjCw8NZs2YNp06dsi6P\njIzEy+vcn/vAwECKiorIzc2loqKCli1bWpe1bt3aru433niDLl26EB4eTnh4OIWFhXZ1V38+d05O\njsv6qj+Tu3pZywVdlrI5OTkXFIer54aHh4fbdaoAAQEBXHfdddx00014e3vz9NNPc/r0adLT053U\nck5QUBCFhYV2886cOWP3z4HRaCQsLKzGutwhHbcQQlQT7O3NxiuvZEPPnmy88kqC3bwSuK7KA4wZ\nM4aNGzdy+PBhAKZMmQJw3se/JpOJkSNH8uyzz5Kbm0t+fj5Dhgyx5PFdioqKwmAw2H1Vy/Yiso0b\nN/L666+zfPly8vPzyc/PJyQkxK7u6u2JjY21q8/SfgtXHXdsbKzd+ocPH7b7ONtVHN7e3mRlZVnn\nOfv6GUCHDh1QSnHs2DHrvO7duzv9aL0mXbt2tUtNFBcXs3//frp27Wqdl56eXmdfO5OO+zLiiflF\nT4wJJK4/gmBvb/qFhl5Qp3ux5TMzM1m3bh0mkwlfX18CAgKsI83o6GgOHTpk7TxNJhMmk4mmTZvi\n5eXFmjVr+O6779zajpeXFyNGjGDmzJmcPXuWPXv22OWki4qK8PHxITIyEpPJxIsvvnjeSLW6UaNG\n8corr1BQUEBWVhbvvvuuddnBgwcxmUx06tTJYdmxY8cye/ZsTp06xalTp3jppZcYP368W3EMHz7c\nGsfevXtdXnXv4+PDn//8Z9avX2+dN27cODZv3sxPP/1EZWUlc+fOJSoqivj4eEC/Yr60tJSKigrM\nZjNlZWVUVOj3mvjLX/5CWloaK1eupKysjFmzZtGzZ086duxorX/9+vUMGTKkxljcIR23EEJcZsrK\nypg6dSpRUVHExsaSm5vLK6+8Augdo1KKyMhIevfuTVBQEPPmzWPUqFFERETw+eefc9ttt7ms33Zk\n+c4772A0GomJieHee+/l3nvvtS67+eabufnmm+nYsSNt27YlMDDQ5UfQADNmzLDm5AcPHsxdd91l\nXeZotG3blunTp9O7d2+6d+9Ojx496N27N88//7zbcRQUFBATE8Pdd99NUlISfn5+Tss++OCDdp17\nx44dWbp0KQ899BARERF8/fXXrFq1yvq969mzZxMYGMhrr73GJ598QmBgIH/7298AaNq0KV9++SXT\npk0jIiKC3377jc8//9xa97Fjx0hPT+f22293ue/cVe83YKkPjfX7l0KIS8/TvsfdmN1yyy08/vjj\nDB7s1nOqLsrUqVM5ceIECxcudLrOgAEDePfddy/LO6c12A1Y6ov8wgkh3CUd9+XjjTfe4PHHH3c5\nEr5QGRkZmEwm/vSnP7FlyxZuueUWPvroI+sV742Nq45bPiq/jHhiftETYwKJS4gL8fTTT9dLpw36\nVdvDhw8nKCiIsWPH8swzzzTaTrsmF3bVhRBCCHEZ6d27N7///ntDN+OSkI/KhRAeTT4qF42RfFQu\nhBBCeAjpuC8jnphf9MSYQOISQjQcyXELIf6Q/P39T2iaFt3Q7RDCEX9//xPOlkmOWwjh0Vw+11iI\nRkg+KhdCCCEaEem4LyOemF/0xJhA4hJCNBzpuIUQQohGRHLcQgiPJjlu4WlkxC2EEEI0ItJxX0Y8\nMb/oiTGBxCWEaDjScQshhBCNiOS4hRAeTXLcwtPIiFsIIYRoRKTjvox4Yn7RE2MCiUsI0XCk4xZC\nCCEaEclxCyE8muS4haeREbcQQgjRiEjHfRnxxPyiJ8YEEpcQouFIxy2EEEI0IpLjFkJ4NMlxC08j\nI24hhBCiEZGO+zLiiflFT4wJJC4hRMORjlsIIYRoRCTHLYTwaJLjFp5GRtxCCCFEI1KvHbemaQs0\nTTuhadoum3nhmqZ9p2lahqZpazVNC7VZ9pymab9rmpauadpN9dm2y5En5hc9MSaQuIQQDae+R9wL\ngZurzZsK/KCU6gT8BDwHoGlaF+AOIB4YAvxD0zT5eEsIIYSwUe85bk3TWgNfK6W6V03vBQYqpU5o\nmgXHfuoAAA0rSURBVNYcSFFKddY0bSqglFKvVa23BpiplPrFQZ3WHLfRCKmp0Lo1HD587me3bhAc\n7Ho56Mss6wLk5MDq1TB0KMTGntumpR7bdY1G+KWqdX37Oq7PXTXVVZs4HLXV0fZSUyEyEr79Fpo1\ng6ZNXcfhqt7qy1zF46p+S5kuXeyPo6v9tm4d7N0LnTvDoEE173vb2FNSICEBTp+u3XGzjddZPI7K\n1Da+C91m9fOlehnQ99uhQzBypP257m5bfvkFSkrOzQsMrN3vQU1trO3vkDOS4xYeRylVry+gNbDL\nZjqv2vK8qp/vAEk28z8EhjupUymlVGGhUj16KGUwKOXvf+6nt7c+Pzvb+fJu3fSXZd3CQn19f3+l\nQP+Zna3stmO7bmGhXh70V3z8+fW5q6a6ahOHZV1X7bDE4+V1bps1xeFoH1Svz7YNzuJxtN8d7QM/\nv5r3ZWGhXrdt+7t0cb3vbWPXNL2MptXuuNnG6yyemo6xO/Fd6Dar/15UL9Otm1IdO9q3xXKuu6N6\nLO6cP87iqel3si5U/b2o97918pLXpXp5X/p/Fc5zQUP+e+65B4OhDbt2gVJhVFT0BBKoqABIITUV\nvvkmgbQ0qKhIqZp/bvmePeDllYDZDKmpKSxZAt7eCZSW6stLSyE5OYH774fFi1PYvRsqKxPYsweW\nLElBKUhPT6hqTQoZGefX98gj+nJL3jAhwfH04sV6e3QJZGToyy3be+utc9u3tN8ST3o6KJVCZSXs\n2ZPAN9/A7t3nptPSoLT0/O1Z6rO037LtzEz7+izl09IgLc1xfNX3z1tvWeJJsO4bSzzV22tbv+3+\nLCvTy1v2d5cu5+8/P7+EqrrPtT8jw/n6CQkJpKae2z96+/T2mM3O95ej47Vr1w6UmuQ0nurlLyS+\n6uUt+3/PHvvzo3p52+Ph6Hzfs0c/fy3xl5WlMHcuvP56gl2Ou+bz9Vw8lv3v7PxxFk9Fhf77Cfr+\n0TQwm/Xf37S0BPr1q/n3p/r022+/zY4dO2jTpg1CeKT6/s+A80fc6UB01fvmQHrV+6nAFJv1vgX6\nOqlTKWU/CrH81+7vr5SPz/mjz+rLLf/dW9Z1Z8Rtu66zUbLtOu46V9c6h3XVJg7Luq7aYTvacTZi\nql7e0T6oXp9tG87tm3V2dTra7/b7QFlHgTXty4sZcRsM9iPu2hy3wkKl2rdf5zIe58fY/fgctdud\nbVb/vahextWIe926dW61paYRtzv7w1UbZcQtL3k5f12KHHcb9Bz3n6qmX0P/ePw1TdOmAOFKqalV\nF6d9AvQFWgDfA1coBw2snuNOS4NWreDIkXM/u3Y9l2t1thz0ZZZ1Qc9xJydDYuL5Oe7q6xqNsGWL\n/r5PH8f1uaumumoTh6O2OtpeWhpERMB330FUlJ7jdhWHq3qrL3MVj6v6LWXi4+2Po6v9lpJyLsed\nkOBejtsS+4YNcP31kJdXu+NmG6+zeByVqW18F7rN6udL9TKg77dDh2DEiAvLcW/Zcn6Ouza/BzW1\nUXLcQjhWrx23pmmfon+eFgmcAGYAXwHLgDjgMHCHUqqgav3ngPuAcuAJpdR3Tup11J8LIcR5pOMW\nnqZevw6mlEpSSsX+//buNVauqgzj+P/BckklIJggKraoKIiiWOUSIfGCINHEYCKBSKT4yWs0kChq\nMH7AxPuFeIEYFQ0I4p1qFJDAB4NWQGmKhVbEEBG1qECDhmACrx/2qgzHApaesmdt/r9kp2uv2TOz\nnnNOz3vWrD2zq2rnqlpWVedW1Z1V9eqq2r+qjtlctNvxH62q/arqeQ9VtKdsiu+hnWImMJek8fjJ\naZIkdcTPKpc0ab5Urqlxxi1JUkcs3HNkiuuLU8wE5pI0Hgu3JEkdcY1b0qS5xq2pccYtSVJHLNxz\nZIrri1PMBOaSNB4LtyRJHXGNW9KkucatqXHGLUlSRyzcc2SK64tTzATmkjQeC7ckSR1xjVvSpLnG\nralxxi1JUkcs3HNkiuuLU8wE5pI0Hgu3JEkdcY1b0qS5xq2pccYtSVJHLNxzZIrri1PMBOaSNB4L\ntyRJHXGNW9KkucatqXHGLUlSRyzcc2SK64tTzATmkjQeC7ckSR1xjVvSpLnGralxxi1JUkcs3HNk\niuuLU8wE5pI0Hgu3JEkdcY1b0qS5xq2pccYtSVJHLNxzZIrri1PMBOaSNB4LtyRJHXGNW9Kkucat\nqXHGLUlSRyzcc2SK64tTzATmkjQeC/ccWbNmzdhDWHRTzATmkjQeC/ccueuuu8YewqKbYiYwl6Tx\nWLglSeqIhXuO3HLLLWMPYdFNMROYS9J4un072NhjkNQP3w6mKemycEuS9HjlS+WSJHXEwi1JUke6\nK9xJjk2yPsnvkpw+9ngAkuyT5Iok65Jcn+TdrX+PJJcl2ZDk0iS7z9znA0luSnJjkmNm+lckWdvy\nfW6mf6ck32r3+WWSZTO3rWzHb0hy8iJn2yHJb5KsmlCm3ZN8p41zXZLDes/VxriujeebbQzdZUry\n1SQbk6yd6Rs1R5J9k6xut12YZMm2ZJS2WVV1szH8ofF7YDmwI7AGOGAOxrU3cHBr7wpsAA4APg68\nr/WfDnystQ8ErgOWAPu2TJvPN/gVcEhr/wR4TWu/HfhSa58AfKu19wBuBnYHnrS5vYjZTgXOB1a1\n/Slk+jrwltZe0p6n21zt/8MfgJ3a/kXAyh4zAUcCBwNrZ/pGzdG+nse39tnAWxfz94eb29Zuow9g\nqwYLhwM/ndl/P3D62OPawjh/CLwaWA88pfXtDazf0riBnwKHtWNumOk/ETi7tS8BDmvtJwC3Lzym\n7Z8NnLBIOfYBfga8ggcKd++ZdgNu3kJ/t7la0Vnf/l0CrOr554/hD5HZwj1qDuBvwA6tfThwyWL8\nLLq5Pdqtt5fKnw7cOrP/p9Y3N5LsyzBjWM3wy2YjQFX9FdirHbYwx22t7+kMmTabzfff+1TVfcCm\nJHs+zGMths8C7wVm33rQe6ZnAn9Pcm5bAvhykqU956qqO4FPA39sj7mpqi7vOdMCe42VI8mTgTur\n6v6Zx3raIuWSHpXeCvdcS7Ir8F3gPVX1Tx5c8NjC/jY93SI+1v8+ePI6YGNVrXmE5+omU7MEWAF8\nsapWAP9imLn1/L16FsOSxnKGovLEJCfRcaZH8Fjn8D3gmiu9Fe7bgGUz+/u0vtG1E1a+C5xXVRe3\n7o1JntJu3xu4vfXfBjxj5u6bczxU/4Puk+QJwG5VdQfb72tyBPD6JH8ALgReleQ84K8dZ4JhxnRr\nVV3b9r/HUMh7/l69FLiqqu5os8gfAC/rPNOs0XJU1T+A3ZPssIXHkkbRW+G+BtgvyfIkOzGsS60a\neUybfY1hXe2smb5VwCmtvRK4eKb/xHaG6zOB/YCr28uAm5IcmiTAyQvus7K1jweuaO1LgaPbmdJ7\nAEe3vm1SVR+sqmVV9SyGr/MVVfVm4Ee9Zmq5NgK3Jnlu6zoKWEfH3yuGkyEPT7JLG8tRwA0dZwoP\nnuWOnePKduzC55fGMfYi+9ZuwLEMv6huAt4/9njamI4A7mM4y/064DdtnHsCl7fxXgY8aeY+H2A4\nC/ZG4JiZ/pcA17d8Z8307wx8u/WvBvadue2U1v874OTtkO/lPHByWveZgBcx/BG4Bvg+w5nEXedi\nOBdhHbAW+AbDuy66ywRcAPwZuJdhzf4tDCfdjZaD4byIX7X+i4AdF/v/mJvb1mx+5KkkSR3p7aVy\nSZIe1yzckiR1xMItSVJHLNySJHXEwi1JUkcs3JIkdcTCrceFJFcmWTH2OCRpW1m4JUnqiIVbo0my\nNMmPk1yXZG2S45N8KMnVbf+cmWOvTPKZJNckuSHJIUm+n2RDkjPbMcuT3Jjk/HbMt5PssoXnPTrJ\nL5Jcm+SidnUwknwsyW+TrEnyicfuKyFJ/z8Lt8Z0LMOFHF5cVS9kuFby56vq0La/tF2lbLN7q+oQ\n4ByGz4t+G3AQcEr7fGmA/YEvVNWBwN3AO2afsF2m8QzgqKp6KfBr4LR2acfjquoFVXUw8JHtFVqS\ntoWFW2O6nuHCDh9NcmRV3Q0clWR1krXAK4Hnzxy/auZ+11fV7VX1b+BmHrga1B+ranVrnw8cueA5\nDwcOBK5Kch3DBSiWAZuAe5J8JckbgHsWN6okLY4lYw9Aj19VdVM7Yey1wJlJrgDeCayoqj8n+TAw\n+1L3ve3f+2faMFyf+aF+lhd+GH+Ay6rqpIUHJjmU4cpaxwPvam1JmivOuDWaJE8F7qmqC4BPMVwX\nu4A7kuwKvPFRPOyyJIe19puAny+4fTVwRJJntzEsTfKcJE9kuOrUJcBpwAsfxXNL0nbnjFtjOgj4\nZJL7gX8DbweOA34L/AW4eubYh7uM3extG4B3JjmX4TKX58weU1V/T3IKcGGSnVv/GQzr4RfPnMx2\n6jbkkqTtxst6ajKSLAd+XFUHjT0WSdpefKlcU+NfopImzRm3JEkdccYtSVJHLNySJHXEwi1JUkcs\n3JIkdcTCLUlSRyzckiR15D+XrJfKfbkxcQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fafb620f4d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "events = mne.find_events(raw)\n",
    "event_id = {'standard/short': 200, 'standard/long': 201, 'deviant/short': 100, 'deviant/long': 101}\n",
    "\n",
    "_ = mne.viz.plot_events(events, event_id=event_id)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Pick out data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([  0,  50, 100, 150, 200, 250])"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "picks = mne.pick_types(raw.info, meg=False, eeg=True, eog=False)\n",
    "picks = picks[::50]\n",
    "picks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1320 matching events found\n",
      "Adding average EEG reference projection.\n",
      "Created an SSP operator (subspace dimension = 1)\n",
      "1 projection items activated\n"
     ]
    }
   ],
   "source": [
    "tmin, tmax = -0.2, 0.5\n",
    "baseline = (None, 0.0)\n",
    "\n",
    "epochs = mne.Epochs(raw, events=events, event_id=event_id, tmin=tmin,\n",
    "                    tmax=tmax, picks=picks)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Epochs  |  n_events : 120 (good & bad), tmin : -0.19921875 (s), tmax : 0.5 (s), baseline : (None, 0),\n",
       " 'deviant/long': 54, 'deviant/short': 66>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "epochs_dev = epochs['deviant']\n",
    "epochs_dev"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Epochs  |  n_events : 1200 (good & bad), tmin : -0.19921875 (s), tmax : 0.5 (s), baseline : (None, 0),\n",
       " 'standard/long': 610, 'standard/short': 590>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "epochs_std = epochs['standard']\n",
    "epochs_std"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Reading 5719 ... 5898  =     22.340 ...    23.039 secs...\n",
      "[done]\n",
      "Applying baseline correction ... (mode: mean)\n",
      "Reading 10130 ... 10309  =     39.570 ...    40.270 secs...\n",
      "[done]\n",
      "Applying baseline correction ... (mode: mean)\n",
      "Reading 11320 ... 11499  =     44.219 ...    44.918 secs...\n",
      "[done]\n",
      "Applying baseline correction ... (mode: mean)\n",
      "Reading 14885 ... 15064  =     58.145 ...    58.844 secs...\n",
      "[done]\n",
      "Applying baseline correction ... (mode: mean)\n",
      "Reading 19372 ... 19551  =     75.672 ...    76.371 secs...\n",
      "[done]\n",
      "Applying baseline correction ... (mode: mean)\n",
      "Reading 30216 ... 30395  =    118.031 ...   118.730 secs...\n",
      "[done]\n",
      "Applying baseline correction ... (mode: mean)\n",
      "Reading 44824 ... 45003  =    175.094 ...   175.793 secs...\n",
      "[done]\n",
      "Applying baseline correction ... (mode: mean)\n",
      "Reading 49234 ... 49413  =    192.320 ...   193.020 secs...\n",
      "[done]\n",
      "Applying baseline correction ... (mode: mean)\n",
      "Reading 50460 ... 50639  =    197.109 ...   197.809 secs...\n",
      "[done]\n",
      "Applying baseline correction ... (mode: mean)\n",
      "Reading 54060 ... 54239  =    211.172 ...   211.871 secs...\n",
      "[done]\n",
      "Applying baseline correction ... (mode: mean)\n",
      "Reading 58461 ... 58640  =    228.363 ...   229.062 secs...\n",
      "[done]\n",
      "Applying baseline correction ... (mode: mean)\n",
      "Reading 69167 ... 69346  =    270.184 ...   270.883 secs...\n",
      "[done]\n",
      "Applying baseline correction ... (mode: mean)\n",
      "Reading 84480 ... 84659  =    330.000 ...   330.699 secs...\n",
      "[done]\n",
      "Applying baseline correction ... (mode: mean)\n",
      "Reading 88909 ... 89088  =    347.301 ...   348.000 secs...\n",
      "[done]\n",
      "Applying baseline correction ... (mode: mean)\n",
      "Reading 90183 ... 90362  =    352.277 ...   352.977 secs...\n",
      "[done]\n",
      "Applying baseline correction ... (mode: mean)\n",
      "Reading 93745 ... 93924  =    366.191 ...   366.891 secs...\n",
      "[done]\n",
      "Applying baseline correction ... (mode: mean)\n",
      "Reading 98210 ... 98389  =    383.633 ...   384.332 secs...\n",
      "[done]\n",
      "Applying baseline correction ... (mode: mean)\n",
      "Reading 109144 ... 109323  =    426.344 ...   427.043 secs...\n",
      "[done]\n",
      "Applying baseline correction ... (mode: mean)\n",
      "Reading 129010 ... 129189  =    503.945 ...   504.645 secs...\n",
      "[done]\n",
      "Applying baseline correction ... (mode: mean)\n",
      "Reading 133495 ... 133674  =    521.465 ...   522.164 secs...\n",
      "[done]\n",
      "Applying baseline correction ... (mode: mean)\n",
      "Reading 134703 ... 134882  =    526.184 ...   526.883 secs...\n",
      "[done]\n",
      "Applying baseline correction ... (mode: mean)\n",
      "Reading 138221 ... 138400  =    539.926 ...   540.625 secs...\n",
      "[done]\n",
      "Applying baseline correction ... (mode: mean)\n",
      "Reading 142626 ... 142805  =    557.133 ...   557.832 secs...\n",
      "[done]\n",
      "Applying baseline correction ... (mode: mean)\n",
      "Reading 153543 ... 153722  =    599.777 ...   600.477 secs...\n",
      "[done]\n",
      "Applying baseline correction ... (mode: mean)\n",
      "Reading 190085 ... 190264  =    742.520 ...   743.219 secs...\n",
      "[done]\n",
      "Applying baseline correction ... (mode: mean)\n",
      "Reading 194493 ... 194672  =    759.738 ...   760.438 secs...\n",
      "[done]\n",
      "Applying baseline correction ... (mode: mean)\n",
      "Reading 195729 ... 195908  =    764.566 ...   765.266 secs...\n",
      "[done]\n",
      "Applying baseline correction ... (mode: mean)\n",
      "Reading 199329 ... 199508  =    778.629 ...   779.328 secs...\n",
      "[done]\n",
      "Applying baseline correction ... (mode: mean)\n",
      "Reading 203800 ... 203979  =    796.094 ...   796.793 secs...\n",
      "[done]\n",
      "Applying baseline correction ... (mode: mean)\n",
      "Reading 214721 ... 214900  =    838.754 ...   839.453 secs...\n",
      "[done]\n",
      "Applying baseline correction ... (mode: mean)\n",
      "Reading 231977 ... 232156  =    906.160 ...   906.859 secs...\n",
      "[done]\n",
      "Applying baseline correction ... (mode: mean)\n",
      "Reading 236521 ... 236700  =    923.910 ...   924.609 secs...\n",
      "[done]\n",
      "Applying baseline correction ... (mode: mean)\n",
      "Reading 237775 ... 237954  =    928.809 ...   929.508 secs...\n",
      "[done]\n",
      "Applying baseline correction ... (mode: mean)\n",
      "Reading 241386 ... 241565  =    942.914 ...   943.613 secs...\n",
      "[done]\n",
      "Applying baseline correction ... (mode: mean)\n",
      "Reading 245827 ... 246006  =    960.262 ...   960.961 secs...\n",
      "[done]\n",
      "Applying baseline correction ... (mode: mean)\n",
      "Reading 256715 ... 256894  =   1002.793 ...  1003.492 secs...\n",
      "[done]\n",
      "Applying baseline correction ... (mode: mean)\n",
      "Reading 272258 ... 272437  =   1063.508 ...  1064.207 secs...\n",
      "[done]\n",
      "Applying baseline correction ... (mode: mean)\n",
      "Reading 276742 ... 276921  =   1081.023 ...  1081.723 secs...\n",
      "[done]\n",
      "Applying baseline correction ... (mode: mean)\n",
      "Reading 278004 ... 278183  =   1085.953 ...  1086.652 secs...\n",
      "[done]\n",
      "Applying baseline correction ... (mode: mean)\n",
      "Reading 281558 ... 281737  =   1099.836 ...  1100.535 secs...\n",
      "[done]\n",
      "Applying baseline correction ... (mode: mean)\n",
      "Reading 285897 ... 286076  =   1116.785 ...  1117.484 secs...\n",
      "[done]\n",
      "Applying baseline correction ... (mode: mean)\n",
      "Reading 296836 ... 297015  =   1159.516 ...  1160.215 secs...\n",
      "[done]\n",
      "Applying baseline correction ... (mode: mean)\n",
      "Reading 306487 ... 306666  =   1197.215 ...  1197.914 secs...\n",
      "[done]\n",
      "Applying baseline correction ... (mode: mean)\n",
      "Reading 310956 ... 311135  =   1214.672 ...  1215.371 secs...\n",
      "[done]\n",
      "Applying baseline correction ... (mode: mean)\n",
      "Reading 312151 ... 312330  =   1219.340 ...  1220.039 secs...\n",
      "[done]\n",
      "Applying baseline correction ... (mode: mean)\n",
      "Reading 315839 ... 316018  =   1233.746 ...  1234.445 secs...\n",
      "[done]\n",
      "Applying baseline correction ... (mode: mean)\n",
      "Reading 320232 ... 320411  =   1250.906 ...  1251.605 secs...\n",
      "[done]\n",
      "Applying baseline correction ... (mode: mean)\n",
      "Reading 331092 ... 331271  =   1293.328 ...  1294.027 secs...\n",
      "[done]\n",
      "Applying baseline correction ... (mode: mean)\n",
      "Reading 354708 ... 354887  =   1385.578 ...  1386.277 secs...\n",
      "[done]\n",
      "Applying baseline correction ... (mode: mean)\n",
      "Reading 359086 ... 359265  =   1402.680 ...  1403.379 secs...\n",
      "[done]\n",
      "Applying baseline correction ... (mode: mean)\n",
      "Reading 360250 ... 360429  =   1407.227 ...  1407.926 secs...\n",
      "[done]\n",
      "Applying baseline correction ... (mode: mean)\n",
      "Reading 363823 ... 364002  =   1421.184 ...  1421.883 secs...\n",
      "[done]\n",
      "Applying baseline correction ... (mode: mean)\n",
      "Reading 368169 ... 368348  =   1438.160 ...  1438.859 secs...\n",
      "[done]\n",
      "Applying baseline correction ... (mode: mean)\n",
      "Reading 379185 ... 379364  =   1481.191 ...  1481.891 secs...\n",
      "[done]\n",
      "Applying baseline correction ... (mode: mean)\n",
      "Reading 394527 ... 394706  =   1541.121 ...  1541.820 secs...\n",
      "[done]\n",
      "Applying baseline correction ... (mode: mean)\n",
      "Reading 399038 ... 399217  =   1558.742 ...  1559.441 secs...\n",
      "[done]\n",
      "Applying baseline correction ... (mode: mean)\n",
      "Reading 400281 ... 400460  =   1563.598 ...  1564.297 secs...\n",
      "[done]\n",
      "Applying baseline correction ... (mode: mean)\n",
      "Reading 403902 ... 404081  =   1577.742 ...  1578.441 secs...\n",
      "[done]\n",
      "Applying baseline correction ... (mode: mean)\n",
      "Reading 408259 ... 408438  =   1594.762 ...  1595.461 secs...\n",
      "[done]\n",
      "Applying baseline correction ... (mode: mean)\n",
      "Reading 418878 ... 419057  =   1636.242 ...  1636.941 secs...\n",
      "[done]\n",
      "Applying baseline correction ... (mode: mean)\n",
      "Reading 443551 ... 443730  =   1732.621 ...  1733.320 secs...\n",
      "[done]\n",
      "Applying baseline correction ... (mode: mean)\n",
      "Reading 448049 ... 448228  =   1750.191 ...  1750.891 secs...\n",
      "[done]\n",
      "Applying baseline correction ... (mode: mean)\n",
      "Reading 449290 ... 449469  =   1755.039 ...  1755.738 secs...\n",
      "[done]\n",
      "Applying baseline correction ... (mode: mean)\n",
      "Reading 452951 ... 453130  =   1769.340 ...  1770.039 secs...\n",
      "[done]\n",
      "Applying baseline correction ... (mode: mean)\n",
      "Reading 457321 ... 457500  =   1786.410 ...  1787.109 secs...\n",
      "[done]\n",
      "Applying baseline correction ... (mode: mean)\n",
      "Reading 468086 ... 468265  =   1828.461 ...  1829.160 secs...\n",
      "[done]\n",
      "Applying baseline correction ... (mode: mean)\n",
      "Reading 491048 ... 491227  =   1918.156 ...  1918.855 secs...\n",
      "[done]\n",
      "Applying baseline correction ... (mode: mean)\n",
      "Reading 495410 ... 495589  =   1935.195 ...  1935.895 secs...\n",
      "[done]\n",
      "Applying baseline correction ... (mode: mean)\n",
      "Reading 496604 ... 496783  =   1939.859 ...  1940.559 secs...\n",
      "[done]\n",
      "Applying baseline correction ... (mode: mean)\n",
      "Reading 500284 ... 500463  =   1954.234 ...  1954.934 secs...\n",
      "[done]\n",
      "Applying baseline correction ... (mode: mean)\n",
      "Reading 504710 ... 504889  =   1971.523 ...  1972.223 secs...\n",
      "[done]\n",
      "Applying baseline correction ... (mode: mean)\n",
      "Reading 515602 ... 515781  =   2014.070 ...  2014.770 secs...\n",
      "[done]\n",
      "Applying baseline correction ... (mode: mean)\n",
      "Reading 541772 ... 541951  =   2116.297 ...  2116.996 secs...\n",
      "[done]\n",
      "Applying baseline correction ... (mode: mean)\n",
      "Reading 546211 ... 546390  =   2133.637 ...  2134.336 secs...\n",
      "[done]\n",
      "Applying baseline correction ... (mode: mean)\n",
      "Reading 547383 ... 547562  =   2138.215 ...  2138.914 secs...\n",
      "[done]\n",
      "Applying baseline correction ... (mode: mean)\n",
      "Reading 551034 ... 551213  =   2152.477 ...  2153.176 secs...\n",
      "[done]\n",
      "Applying baseline correction ... (mode: mean)\n",
      "Reading 555551 ... 555730  =   2170.121 ...  2170.820 secs...\n",
      "[done]\n",
      "Applying baseline correction ... (mode: mean)\n",
      "Reading 566412 ... 566591  =   2212.547 ...  2213.246 secs...\n",
      "[done]\n",
      "Applying baseline correction ... (mode: mean)\n",
      "Reading 601918 ... 602097  =   2351.242 ...  2351.941 secs...\n",
      "[done]\n",
      "Applying baseline correction ... (mode: mean)\n",
      "Reading 606454 ... 606633  =   2368.961 ...  2369.660 secs...\n",
      "[done]\n",
      "Applying baseline correction ... (mode: mean)\n",
      "Reading 607651 ... 607830  =   2373.637 ...  2374.336 secs...\n",
      "[done]\n",
      "Applying baseline correction ... (mode: mean)\n",
      "Reading 611280 ... 611459  =   2387.812 ...  2388.512 secs...\n",
      "[done]\n",
      "Applying baseline correction ... (mode: mean)\n",
      "Reading 615650 ... 615829  =   2404.883 ...  2405.582 secs...\n",
      "[done]\n",
      "Applying baseline correction ... (mode: mean)\n",
      "Reading 626449 ... 626628  =   2447.066 ...  2447.766 secs...\n",
      "[done]\n",
      "Applying baseline correction ... (mode: mean)\n",
      "Reading 650407 ... 650586  =   2540.652 ...  2541.352 secs...\n",
      "[done]\n",
      "Applying baseline correction ... (mode: mean)\n",
      "Reading 654827 ... 655006  =   2557.918 ...  2558.617 secs...\n",
      "[done]\n",
      "Applying baseline correction ... (mode: mean)\n",
      "Reading 656046 ... 656225  =   2562.680 ...  2563.379 secs...\n",
      "[done]\n",
      "Applying baseline correction ... (mode: mean)\n",
      "Reading 659637 ... 659816  =   2576.707 ...  2577.406 secs...\n",
      "[done]\n",
      "Applying baseline correction ... (mode: mean)\n",
      "Reading 664127 ... 664306  =   2594.246 ...  2594.945 secs...\n",
      "[done]\n",
      "Applying baseline correction ... (mode: mean)\n",
      "Reading 675146 ... 675325  =   2637.289 ...  2637.988 secs...\n",
      "[done]\n",
      "Applying baseline correction ... (mode: mean)\n",
      "Reading 699212 ... 699391  =   2731.297 ...  2731.996 secs...\n",
      "[done]\n",
      "Applying baseline correction ... (mode: mean)\n",
      "Reading 703615 ... 703794  =   2748.496 ...  2749.195 secs...\n",
      "[done]\n",
      "Applying baseline correction ... (mode: mean)\n",
      "Reading 704805 ... 704984  =   2753.145 ...  2753.844 secs...\n",
      "[done]\n",
      "Applying baseline correction ... (mode: mean)\n",
      "Reading 708402 ... 708581  =   2767.195 ...  2767.895 secs...\n",
      "[done]\n",
      "Applying baseline correction ... (mode: mean)\n",
      "Reading 712818 ... 712997  =   2784.445 ...  2785.145 secs...\n",
      "[done]\n",
      "Applying baseline correction ... (mode: mean)\n",
      "Reading 723775 ... 723954  =   2827.246 ...  2827.945 secs...\n",
      "[done]\n",
      "Applying baseline correction ... (mode: mean)\n",
      "Reading 747457 ... 747636  =   2919.754 ...  2920.453 secs...\n",
      "[done]\n",
      "Applying baseline correction ... (mode: mean)\n",
      "Reading 751883 ... 752062  =   2937.043 ...  2937.742 secs...\n",
      "[done]\n",
      "Applying baseline correction ... (mode: mean)\n",
      "Reading 753126 ... 753305  =   2941.898 ...  2942.598 secs...\n",
      "[done]\n",
      "Applying baseline correction ... (mode: mean)\n",
      "Reading 756755 ... 756934  =   2956.074 ...  2956.773 secs...\n",
      "[done]\n",
      "Applying baseline correction ... (mode: mean)\n",
      "Reading 761289 ... 761468  =   2973.785 ...  2974.484 secs...\n",
      "[done]\n",
      "Applying baseline correction ... (mode: mean)\n",
      "Reading 771995 ... 772174  =   3015.605 ...  3016.305 secs...\n",
      "[done]\n",
      "Applying baseline correction ... (mode: mean)\n",
      "Reading 781280 ... 781459  =   3051.875 ...  3052.574 secs...\n",
      "[done]\n",
      "Applying baseline correction ... (mode: mean)\n",
      "Reading 785689 ... 785868  =   3069.098 ...  3069.797 secs...\n",
      "[done]\n",
      "Applying baseline correction ... (mode: mean)\n",
      "Reading 786847 ... 787026  =   3073.621 ...  3074.320 secs...\n",
      "[done]\n",
      "Applying baseline correction ... (mode: mean)\n",
      "Reading 790453 ... 790632  =   3087.707 ...  3088.406 secs...\n",
      "[done]\n",
      "Applying baseline correction ... (mode: mean)\n",
      "Reading 794952 ... 795131  =   3105.281 ...  3105.980 secs...\n",
      "[done]\n",
      "Applying baseline correction ... (mode: mean)\n",
      "Reading 805765 ... 805944  =   3147.520 ...  3148.219 secs...\n",
      "[done]\n",
      "Applying baseline correction ... (mode: mean)\n",
      "Reading 842967 ... 843146  =   3292.840 ...  3293.539 secs...\n",
      "[done]\n",
      "Applying baseline correction ... (mode: mean)\n",
      "Reading 847360 ... 847539  =   3310.000 ...  3310.699 secs...\n",
      "[done]\n",
      "Applying baseline correction ... (mode: mean)\n",
      "Reading 848624 ... 848803  =   3314.938 ...  3315.637 secs...\n",
      "[done]\n",
      "Applying baseline correction ... (mode: mean)\n",
      "Reading 852298 ... 852477  =   3329.289 ...  3329.988 secs...\n",
      "[done]\n",
      "Applying baseline correction ... (mode: mean)\n",
      "Reading 856770 ... 856949  =   3346.758 ...  3347.457 secs...\n",
      "[done]\n",
      "Applying baseline correction ... (mode: mean)\n",
      "Reading 867639 ... 867818  =   3389.215 ...  3389.914 secs...\n",
      "[done]\n",
      "Applying baseline correction ... (mode: mean)\n",
      "Reading 899191 ... 899370  =   3512.465 ...  3513.164 secs...\n",
      "[done]\n",
      "Applying baseline correction ... (mode: mean)\n",
      "Reading 903622 ... 903801  =   3529.773 ...  3530.473 secs...\n",
      "[done]\n",
      "Applying baseline correction ... (mode: mean)\n",
      "Reading 904815 ... 904994  =   3534.434 ...  3535.133 secs...\n",
      "[done]\n",
      "Applying baseline correction ... (mode: mean)\n",
      "Reading 908505 ... 908684  =   3548.848 ...  3549.547 secs...\n",
      "[done]\n",
      "Applying baseline correction ... (mode: mean)\n",
      "Reading 912945 ... 913124  =   3566.191 ...  3566.891 secs...\n",
      "[done]\n",
      "Applying baseline correction ... (mode: mean)\n",
      "Reading 923692 ... 923871  =   3608.172 ...  3608.871 secs...\n",
      "[done]\n",
      "Applying baseline correction ... (mode: mean)\n"
     ]
    },
    {
     "ename": "IndexError",
     "evalue": "list index out of range",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mIndexError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-9-69c2088aabac>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mevoked_dev\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mepochs_dev\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0maverage\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mpicks\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m20\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m30\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      2\u001b[0m \u001b[0mevoked_std\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mepochs_std\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0maverage\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mpicks\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m20\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m30\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m/usr/local/lib/python2.7/dist-packages/mne/epochs.pyc\u001b[0m in \u001b[0;36maverage\u001b[1;34m(self, picks)\u001b[0m\n\u001b[0;32m    679\u001b[0m             \u001b[0mThe\u001b[0m \u001b[0maveraged\u001b[0m \u001b[0mepochs\u001b[0m\u001b[1;33m.\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    680\u001b[0m         \"\"\"\n\u001b[1;32m--> 681\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_compute_mean_or_stderr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mpicks\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'ave'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    682\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    683\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mstandard_error\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mpicks\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mNone\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m/usr/local/lib/python2.7/dist-packages/mne/epochs.pyc\u001b[0m in \u001b[0;36m_compute_mean_or_stderr\u001b[1;34m(self, picks, mode)\u001b[0m\n\u001b[0;32m    737\u001b[0m             \u001b[0mdata\u001b[0m \u001b[1;33m/=\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msqrt\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mn_events\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    738\u001b[0m         return self._evoked_from_epoch_data(data, self.info, picks, n_events,\n\u001b[1;32m--> 739\u001b[1;33m                                             _aspect_kind)\n\u001b[0m\u001b[0;32m    740\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    741\u001b[0m     def _evoked_from_epoch_data(self, data, info, picks, n_events,\n",
      "\u001b[1;32m/usr/local/lib/python2.7/dist-packages/mne/epochs.pyc\u001b[0m in \u001b[0;36m_evoked_from_epoch_data\u001b[1;34m(self, data, info, picks, n_events, aspect_kind)\u001b[0m\n\u001b[0;32m    754\u001b[0m             \u001b[0mpicks\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0m_pick_data_channels\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mevoked\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0minfo\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mexclude\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    755\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 756\u001b[1;33m         \u001b[0mch_names\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[0mevoked\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mch_names\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mp\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mp\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mpicks\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    757\u001b[0m         \u001b[0mevoked\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpick_channels\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mch_names\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    758\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mIndexError\u001b[0m: list index out of range"
     ]
    }
   ],
   "source": [
    "evoked_dev = epochs_dev.average(picks=[20, 30])\n",
    "evoked_std = epochs_std.average(picks=[20, 30])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "fig, axes = plt.subplots(nrows=1, ncols=2)\n",
    "_ = evoked_std.plot(axes=axes[0])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "_ = evoked_dev.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "dev = evoked_dev.copy()\n",
    "dev.detrend()\n",
    "dev.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "std = evoked_std.copy()\n",
    "std.detrend()\n",
    "std.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "epochs_dev."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "df = epochs_dev.to_data_frame()\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "data_dev = epochs_dev.get_data()\n",
    "data_std = epochs_std.get_data()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "data_dev.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "data_std.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "chnum = 4\n",
    "dd = data_dev[:, chnum, :]\n",
    "ss = data_std[:, chnum, :]\n",
    "plt.figure(chnum)\n",
    "plt.plot(np.mean(dd, axis=0))\n",
    "plt.plot(np.mean(ss, axis=0))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, the trials are in the **dd** and **ss** numpy arrays"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "times = evoked_dev.times\n",
    "times.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Okay, All the data I need for the homework is here.  Now to shuffle it up..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "dd.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "s_code = np.ones(ss.shape[0]) * 151\n",
    "d_code = np.ones(dd.shape[0]) * 251\n",
    "all_trials = np.vstack((dd, ss))\n",
    "all_codes = np.hstack((d_code, s_code))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "all_codes.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "all_trials.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "new_order = np.random.permutation(np.arange(all_trials.shape[0]))\n",
    "new_order"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "trials_shuffled = all_trials[new_order, :]\n",
    "codes_shuffled = all_codes[new_order]\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Randomly Un-baseline the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "new_offsets = np.random.randn(all_trials.shape[0])\n",
    "new_offsets * np.std(all_trials)\n",
    "\n",
    "trials_shuffled_offset = (trials_shuffled.T + new_offsets).T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "trials_shuffled_offset.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## ... and save it in a .mat file!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from scipy import io"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "\n",
    "home_data = {'eeg': trials_shuffled_offset,\n",
    "             'trial_labels': codes_shuffled,\n",
    "             'trial_time': times}\n",
    "\n",
    "save_dir = path.join('Homework Sample Data', 'Homework 4')\n",
    "# save_file = \n",
    "\n",
    "import pickle\n",
    "with open('eeg_data.p') as data_file:\n",
    "    pickle.dump(home_data, data_file)\n",
    "    \n",
    "io.savemat(path.join(save_dir, 'eeg_data.mat'), home_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "io.whosmat(path.join(save_dir, 'eeg_data.mat'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "loaded = io.loadmat(path.join(save_dir, 'eeg_data.mat'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "loaded"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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