Homework 4 Answers.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Homework 4: 2D Array Analysis, Plotting, and SciPy\n",
    "\n",
    "In this homework exercise, we'll be analyzing electroencephalography (EEG) data. This data, taken from a study in which subjects listened to auditory tones of two different pitches: 200 Hz and 260 Hz.  There was a bit more to the study, but we'll be focusing only on one aspect of these tones: the 200 Hz tone was played much, much more often than the 260 Hz tone.  The hypothesis here is that we will pick up a different pattern of responses to the **Standard (200 Hz)** tones than the **Deviant (260 Hz)** tones.\n",
    "\n",
    "This data was collected by Berti et al at the Swartz Center at the University of California, San Diego.  I downloaded it from their public repository and processed it a bit for our exercise.  The original dataset, along with more information about the study, can be found at http://headit.ucsd.edu/studies/9d557882-a236-11e2-9420-0050563f2612.  We are working with one electrode of Subject 8's data."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Package Imports\n",
    "\n",
    "As you import new packages throughout this exercise, put the import statements here, at the top."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import os\n",
    "from os import path\n",
    "from scipy import io, signal, stats\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "np.set_printoptions(precision=2, suppress=True)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load the Data\n",
    "\n",
    "This time, all of the data variables are stored in a single file: a **.mat file** at **Homework Sample Data/Homework 4/eeg_data.mat**.  MAT files are standard file format that Matlab saves in, so after this exercise, you'll be able to confidently exchange data with your colleagues who use Matlab! Cool, right?\n",
    "\n",
    "The functions you need to load the data can be found in the **scipy.io** package.  I'll leave it to you to work out how to get the data.  In the end, though, you should have 3 variables:\n",
    "  - **eeg**: This is a 2D NumPy array that has all every single trial, from both conditions (Standard and Deviant). \n",
    "  - **time**: This is a 1D array of stimulus-offset time points.  It tells you how long before or after the stimulus each EEG voltage measurement took place.  \n",
    "  - **labels**: These are the condition labels for each trial.  There are only two values: one for the Standard trials, and one for the Deviant trials.  These labels are in the same order as the trials in the **eeg** matrix.  Unfortunately, you'll need to work out which number represents which trials (Hint: the rarer label is the Deviant condition)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['.gitattributes',\n",
       " 'source_code.zip',\n",
       " 'eeg_data.npy',\n",
       " 'time.npy',\n",
       " 'conditions.npy',\n",
       " 'eeg_data.mat']"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "load_dir = path.join(\"Homework Sample Data\", \"Homework 4\")\n",
    "os.listdir(load_dir)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['eeg', 'trial_time', '__version__', '__globals__', 'trial_labels', '__header__'])"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_file = 'eeg_data.mat'\n",
    "data = io.loadmat(path.join(load_dir, 'eeg_data.mat'))\n",
    "data.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1320, 180), (1, 180), (1, 1320), "
     ]
    }
   ],
   "source": [
    "eeg = data['eeg']\n",
    "time = data['trial_time']\n",
    "labels = data['trial_labels']\n",
    "\n",
    "def print_shapes(*args):\n",
    "    for mat in args:\n",
    "        print(mat.shape, end=', ')  \n",
    "\n",
    "print_shapes(eeg, time, labels)      "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1320, 180), (180,), (1320,), "
     ]
    }
   ],
   "source": [
    "time = time.flatten()\n",
    "labels = labels.flatten()\n",
    "print_shapes(eeg, time, labels)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plot the Mean Evoked Potential\n",
    "\n",
    "First, let's see what the overall data looks like.  Plot the mean EEG voltage over time, regardless of condition"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7fb3c3f005f8>]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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f0vTq447LOxIzy1tZSUhSF0njJc2SNE5S5xL1+kmaKekFSec3pb2kQZJmS5oh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\nRK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fb3c3ffef60>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "eeg_mean = np.mean(eeg, axis=0)\n",
    "plt.plot(time, eeg_mean)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Detrend the Data\n",
    "\n",
    "You should see a nice peak around 150 msecs post-stimulus!  But wait, there's also a constant increase in voltage throughout the trial.  Well, This really makes it hard to analyse, and could be because of artifacts in the data.  After long examination, we decide to simply detrend the data to flatten it out and make that peak easier to recognize.\n",
    "\n",
    "Now, NumPy doesn't have many great signal processing functions.  For that, we'll turn to **scipy.signal**!  They have a lot of great functions, and all of them work nicely with NumPy arrays--convenient, right?\n",
    "\n",
    "For full scipy docs: http://docs.scipy.org/doc/scipy/reference/\n",
    "\n",
    "For scipy's **detrend()** function, in its signal processing docs: http://docs.scipy.org/doc/scipy/reference/tutorial/signal.html#detrend\n",
    "\n",
    "Take some time to look around the docs, and come back to them regularly--you never know what nice feature you might have overlooked!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Detrend the Mean ERP signal, and plot the detrended version"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7fb3c3eb1da0>]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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z5rBqlQ0T1ma+bI8DYNw4W/16+HBPQLVF/fp2cXv16qgjyS9V+yJ1553wi1/A\nUUfZ0jpr1x5Y9pe/tBmPyRIQWP0lS/L3f+Ogg+xa3syZ+TlfTeVJqJZYv96WEvnNb6KOxOVa1652\nQ2Wx+Pprm1gwd64tGDpvni1h1LIl3HLL/mXnzrXZaH/7W3pt162b+3iTufBCuz/PJeZJqBZYtswW\nVvz5z/dfet7VDt26wZtvRh1FfuzYYZvHvfMO/POftioBwJFH2gy35cth4cJ95W+/He69t3Dv+xow\nAJ5//sA9iNw+noRquB07rMs/ZsyB3xJd7dC1a/EkobFjbVr100/bau9hBx9sU58rZwO//bb9FPJ9\nXy1a2M7FS5ZEHUnh8iRUw730kvV+rrwy6khcdenWrTiG48rLrVczYYJdT4nnuussIc+ebRMNLrus\n8K9/dulSfNf0qqLA//pcKjNmFPd6cMWgXTvbZfWTT2ymXG112222lluyHW8PPhh+9zsYNsxu5n3g\ngfzFl6kOHXxF9GS8J1TDFfuipMWgXj3bfiNHq6QUpC1bbHWBH/0oddlBg6BtW/j8c1ttvNB5EkrO\nk1ANVl4OX3yRemqqq/kuuMAucNdWjz1mX6YOOSR1WRFbIHTy5JqxOaMnoeRqwF+hA7tQe9ddthp2\npWeesSmg8bYhdrVL3761OwlNmQJDhqRf/uija84agJW75Nbg++Crla+YEFKoKyZ8/rl9mzrxRLv7\n+vnnbdy8c2fbb+ass6KO0FW3PXtsptX//Z/NtqpNKiqge3d4//3auypE8+a2l9VRR0UdSfXwFRNq\nubvvhnPPtTuv//3fbRvhu+6Ck07yBFQs6tSxTe5qY2/oySft3qDamoDAh+SS8SRU4N5/37Zg+PWv\n7flNN9lsqTFjfHWEYlNbrwvNnGnDyrWZJ6HEPAkVuF//Gq69dt8QjAhMnAizZtkMIVc8zjnHZpAV\n4IhxxnbssKV3zjsv6kiqlyehxDwJFbB16+yGvOHD9z9+2GG2cZcrLscdZwuaZvth9tRTtjBoIZg3\nz27mbNo06kiqlyehxDwJFajt2+36z7/+q62b5ZyIffnI5n6h5cvhhz+0Id5XXslZaBmbNctm/tV2\nnToVz9JLVeVJqACtXGmzherUObAX5IpbaWnmSUgVLr3UVhwYN86+4ES9sOZzz0G/ftHGkA8dO8IH\nH8BHH0UdSeHxJFSA7rnH1oKbOvXARRxdcSstzfy60Icf2s9VV8EVV0CjRtHtdbNihc2I++wz6N07\nmhjyqW7OJHoqAAAUZ0lEQVRdOOUUePXVqCMpPJ6ECszOnTZl9Qc/iDoSV4jatLFlfMrLq163ogJa\ntbLHIja9P4rVnVXtNoPTTrOFWRMtVlrb9OkDCxZEHUXh8SRUYJ5/3m5CTbaIoyteInDmmbbZW1WF\nkxDYkO/SpbmLLV2vvGLDgCNGQIMG+T9/VE491XtC8XgSKgB79tg30o0bbfmSK66IOiJXyHr33n9j\nt3Rt2LB/EjrxxNwkoVdeqdq1jgcegOuvL77lpiqT0J49UUdSWDwJRWzePFvK44orbPOyqVNtjxTn\nEjn11MySUGxPqEMHuxn688+zi+faa23x0S+/TF32449h2jQYOjS7c9ZEzZvDEUf43kKxPAlFbORI\nW/1g9WrYtMmm0NbW9aVcbvToYWsIhhezTUdsEqpXz4Z+s9kw79NPrYd1/PGWjJLZvdumhw8ZAs2a\nZX7OmqxPH9u23O3jSShCc+bYENz3vmfP69e3b6fOJdOggd3g+frrVasXm4TgwCG5jRurNvPu9det\njYkTbYPFbdsSl731VusJ/f73VYu7Nrn1VvjlL+HFF6OOpHBklYREpImIzBKR1SLynIjE3fdRRPqL\nyCoRWSMiw9OpLyIjRaRcRFaKSN/Q8Z4isixoa1zoeH0RmRLUmS8ix4Ree1ZEPhaR6dm831xZsgTG\njoWf/AT+8z8Lf3tiV3gyGZKLl4S6d4dly+zx/Pm2FNTf/nZg3Zdestl0t9xiiarSa69Br16WGE89\nNfGEiddft1mfTz5ZuxcqTeWUU+Dvf4fLL7eE7LLvCY0AXlDVjsBsYGRsARGpA9wH9AO6AleKSKdk\n9UWkCzAY6AwMAMaL7L2MOQG4TlU7AB1EpPJWt+uAraraHhgH3B0K427gqizfa058/rndH7F+vV0H\n+u53o47I1URVnZygmjgJzZ9vm8pdcol9KfrZz+z+nUo7d8INN9jrGzfaqu6VXnsNTj7ZHp91liWr\neP7rv+CnP/X73sBWvejSBRYvjjqSAqGqGf8Aq4CS4HELYFWcMn2AZ0PPRwDDk9UPlwmePwucGpRZ\nETo+BJgQPJ4JnBo8rgt8EBPH2cD0FO9Hq9vtt6tedVW1n8bVcitWqLZpk375jz9WbdTowOPbtql+\n85uqF12kOmWKHbv6atWbb1bdtcue33OPav/+qnv2qK5erVpSsu+19u1Vly+3x2Vlqr1772t7yRLV\nE05QHTvW6nzxRdXfZ201bJjqb38bdRS5E3x2ZpRHsh0Iaq6qm4NP700i0jxOmZbA+tDzCqDyHumS\nBPVbAvNDdTYEx74O6ofbahl7HlXdLSLbRKSpqm7N+N3l2IoV8Ne/2uQD57LRvr1NCPjqq/TutYnX\nCwJo3NhmaIaNGWM3k7ZrZ7O53n3XpmGL2DXLNm1szbfTT7fZdZ2CcY1TT7X10T77zFZjeOghW65m\n2jTrYaWzdXex6N79wN97sUqZhETkeaAkfAhQ4Gdxime7yHwuF6nP6C6EUaNG7X1cWlpKaY72EFaF\nYcPgjjtsh0znslGvnm3v8e67+5JAMomSUDxHHWXbK7z2ms3A693bJs1U+t737MvURx/ZpITKa5oN\nGkDPnpaw+va12w1mzIBu3ar89mq9E0+ECROijiJzZWVllGWzkm5IyiSkqhckek1ENotIiapuFpEW\nwJY4xTYA4Q2JWwXHADYlqL8BaB2nTqLj4Trvi0hd4LBMekHhJJRLjzxiM4d+9KNqad4VobZt4a23\n9iWh3bttplvPngeWrUoSqtSrV/zjV1xhX6bWrIHbb9//tXPPtYkNhx0GDRvavW/uQN262TT7Xbtq\n5rJFsV/QR48enXFb2U5MmA5cEzweCkyLU2YR0E5EjhWR+th1nMpZaonqTweGBDPe2gDtgIWqugn4\nRER6BxMVro6pU3kL3OXYRIcwIcPeUbb27LH7ge67z2fCudw5/nhLQpWmT7dJAv/7vweWzSQJJXLE\nEbYQ6rJlthhq2G23WUxXXmlDesW2KkK6Dj3U/j58j6Hsk9BY4AIRWQ2cB4wBEJGjROQfYNdngJuA\nWcCbwBRVXZmsvqquAKYCK4AZwI3BxS+AYcBEYA1QrqqV6wBPBJqJSDlwKza5gSCel4BHgXNF5D0R\nSdi7qw5z58Lhh9sYunO5cvzx8Pbb+54/+ij8+Mc29f+55/Yv+847uUtCyTRsCM88Y+f6zneq/3w1\nWVRr9xUa2ffZ7kREq+P38cMf2oXk2KEL57Lx1FN2k+jTT8MXX0DLlrB2ra2A8N3v2gSYpk1tyKdl\nS1vB2beELxy/+pVN4hg7NupIsiciqGpG/V5fMaGa7dgBTzxhwxPO5VJ4OO6ZZ2x2WrNmtufQpZfa\n3flgM9nat/cEVGi6d6/6qhe1kSeharBwIWwNpkQ88QSccIJvzeByr00bG2bbs8eG4sKrr991l/V8\nHnwQ/ud/Drx246J35pn2d1TVNQBrGx+OC8nFcFx5uV0cPuIImwl39922TEeOZno7t58WLawXdP75\nNl27cWjhrFWrbBWDr76ya0fFumhoITvjDJtp2Ldv6rKFLJvhOE9CIdkmod27bUmOyy+HkhL4zW/s\nfooTT8xhkM6FnH663QTati386U8Hvv7CC7Zq81135T82l9qdd9qtG/fcE3Uk2fEklCPZJqHf/c4W\naCwrgzo+0Ony4OqrbWWCJUvsGoOrWRYuhO9/3/4Or7rKnh96aNRRVZ1PTCgAa9bAr39tPR9PQC5f\n2ra13pAnoJqpVy/bR+yii6xHVIx7DXlPKCTTnpCqje0OGQI331wNgTmXwMaNtjJ7+/ZRR+IyddNN\nNoX+4IPtOt4DD0QdUdX5cFyOZJqEXnvNZiatWeO9IOdcZlavhvPOs21eatpKEz4cF7Enn7T7MjwB\nOecy1aGDLQJbbKso+MdmDjzxhG345ZxzmRKxa0PPPBN1JPnlSShLq1fDJ5/YcvfOOZeNc84pvn2G\nPAll6cknYdAgH4pzzmWvWzfbGLCY+EdnlqZOtetBzjmXrTZtbJuMTz+NOpL88SSUhVWrbI7/2WdH\nHYlzrjaoW9c2KVyxIupI8seTUBYeecTuDapbN+pInHO1Rbduth1HsfB9PjOkajtYPvJI1JE452qT\nrl2L67qQ94QyNDvYPLxXr2jjcM7VLsWWhLwnlIE1a2znyr/9rebd2eycK2zFNhzny/aEpLtsT7du\ncMsttm23c87lkiocdhisW2fbs9cEvmxPHn34oa3t9IMfRB2Jc642ErEhuWLpDXkSqqLly227bh+G\nc85Vl5NPhkWLoo4iPzwJVVFlEnLOuepy2mkwf37UUeSHJ6EqWr7ct+t2zlWvPn1gwYKoo8iPrJKQ\niDQRkVkislpEnhORxgnK9ReRVSKyRkSGp1NfREaKSLmIrBSRvqHjPUVkWdDWuNDx+iIyJagzX0SO\nCY53F5FXRGS5iCwRkcHZvOdly7wn5JyrXm3bwo4dUFERdSTVL9ue0AjgBVXtCMwGRsYWEJE6wH1A\nP6ArcKWIdEpWX0S6AIOBzsAAYLzI3qswE4DrVLUD0EFE+gXHrwO2qmp7YBxwd3D8S+B7qnpC0NY4\nETkskze7Z4/N3+/WLZPazjmXHhEbkiuG3lC2SWggMCl4PAkYFKdMb6BcVdep6i5gSlAvWf2LgSmq\n+rWqvguUA71FpAXQSFUrL9lNDtUJt/UYcB6Aqpar6lvB443AFuDITN7s22/DEUfA4YdnUts559JX\nLENy2Sah5qq6GUBVNwHN45RpCawPPa8IjgGUJKgfW2dDcKxlUD9eW3vrqOpuYJuI7DfLXkR6AwdV\nJqWq8kkJzrl8KZYklHLFBBF5HigJHwIU+Fmc4tne+ZrLO2f3m0QtIkdhPafvJas0atSovY9LS0sp\nLS3d+9yvBznn8uWUU2xF7UJUVlZGWVlZTtpKmYRU9YJEr4nIZhEpUdXNwVDZljjFNgDHhJ63Co4B\nbEpQfwPQOk6dRMfDdd4XkbrAYaq6NYizEfAPYGRoKC+ucBKKNW8eDBuWrLZzzuVGo0bwl79EHUV8\nsV/QR48enXFb2Q7HTQeuCR4PBabFKbMIaCcix4pIfWBIUC9Z/enAkGDGWxugHbAwGLL7RER6BxMV\nro6pMzR4fDk20QEROQh4Cpikqk9m+kZ37rR5+2edlWkLzjnnYmW7gOlYYKqIfB9Yh81oqxz6+rOq\nfktVd4vITcAsLOlNVNWVyeqr6goRmQqsAHYBN4YWdRsGPAg0AGao6szg+ETgIREpBz7Ckh1Bm2cA\nTUTkWmzI7xpVXVaVN7pwIXToAE2aVKWWc865ZHwB05BkC5jeeSd88gn85jd5Dso55wqcL2BaDT74\nAB57DB59FD7/HF58EUJDoM4553LAe0Ih4Z7QT38K//yn3Re0fr3dubxhAzSOuyaEc84Vr2x6Qr6p\nXQIvvwz33APnnAMPPwxz53oCcs65XPOeUEhlT2j7dmjWDLZsgUMPjToq55wrbH5NKMcWLbJNpTwB\nOedc9fIkFMe8eXDGGVFH4ZxztZ8noTheftmTkHPO5YMnoRj33w+vvAKnnx51JM45V/t5EoqxeDF8\n97tQUpK6rHPOuez47LiQZCsmOOeci89nxznnnKuRPAk555yLjCch55xzkfEk5JxzLjKehJxzzkXG\nk5BzzrnIeBJyzjkXGU9CzjnnIuNJyDnnXGQ8CTnnnIuMJyHnnHOR8STknHMuMlklIRFpIiKzRGS1\niDwnIo0TlOsvIqtEZI2IDE+nvoiMFJFyEVkpIn1Dx3uKyLKgrXGh4/VFZEpQZ76IHBMcP0ZEXhOR\n10VkuYjckM17ds45lzvZ9oRGAC+oakdgNjAytoCI1AHuA/oBXYErRaRTsvoi0gUYDHQGBgDjRaRy\nhdYJwHWq2gHoICL9guPXAVtVtT0wDrg7OL4R6KOqPYFTgREi0iLL9x2psrKyqENIi8eZWx5nbtWE\nOGtCjNnKNgkNBCYFjycBg+KU6Q2Uq+o6Vd0FTAnqJat/MTBFVb9W1XeBcqB3kDwaqeqioNzkUJ1w\nW48B5wGo6q7gvADfADJabryQ1JR/mB5nbnmcuVUT4qwJMWYr2yTUXFU3A6jqJqB5nDItgfWh5xXB\nMYCSBPVj62wIjrUM6sdra28dVd0NbBORpgAi0kpElgLrgLHBuZxzzkWsXqoCIvI8EN5nVAAFfhan\neLY7wuVyR7m9PR5VrQC6Bz2paSLymKp+kMNzOeecy4SqZvwDrMR6MwAtgJVxyvQBZoaejwCGJ6sf\nLhM8n4ldz9nvHMAQYEK4TPC4LrAlQcwTgW8neE39x3/8x3/8p+o/meaRlD2hFKYD1wBjgaHAtDhl\nFgHtRORYbJLAEODKFPWnAw+LyO+wYbZ2wEJVVRH5RER6B+1eDdwbqjMUeBW4HJvogIi0BD5S1a9E\npAlwBvDbeG8m0+1pnXPOZUaCHkBmle2ay1SgNXa9ZbCqbhORo4A/q+q3gnL9gd9j16AmquqYZPWD\n10ZiM952Abeo6qzgeC/gQaABMENVbwmOHww8BJwEfAQMUdV3ReR84B5gDzZE9wdVnZjxm3bOOZcz\nWSUh55xzLhtFvWJCOjfbBjPrZovIm8HNrj8uxDiDchNFZLOILMtzfHFvRo4pc29wI/ESEemRz/hC\nMSSNU0Q6isgrIvKViPykQGP8jogsDX5eFpETCjTOi4MYF4vIQhE5vRDjDJU7RUR2ici38xlf6Pyp\nfp9ni8i24Kb710Uk3sSwyOMMypQGf+9viMiLKRvNZmJCTf/BrkXdHjweDoyJU6YF0CN43BBYDXQq\ntDiD184AegDL8hhbHWAtcCxwELAk9veD3XD8TPD4VGBBBH/X6cTZDOgF3An8pEBj7AM0Dh73L+Df\n5SGhxycQZ9JSIcQZKvdP4B8kmLQUdZzA2cD0fMeWQZyNgTeBlsHzZqnaLeqeEGncbKuqm1R1SfD4\nc2xGX8vYctUsnZuCUdWXgY/zFVQg2c3IlQZiNxajqq8CjUWkhPxKGaeqfqiqrwFf5zm2SunEuEBV\nPwmeLiD//xYhvTi/DD1tiF2Tzbd0/m0C3Izd4L4ln8GFpBtn1BOn0onzO8DjqroB7P9UqkaLPQml\nc7PtXiJyHNbTeLXaI9tfleLMs2Q3IycqsyFOmeqWTpxRq2qMPwCerdaI4ksrThEZJCIrgaeB7+cp\ntrCUcYrI0cAgVZ1AdB/y6f69nxYMZz8TLG2Wb+nE2QFoKiIvisgiEfleqkaznaJd8HJ1s62INMS+\nLd0S9IhyKs83BbsaTkTOAa7FhmALkqo+BTwlImcAvwIuiDikeMZhQ9yVou5tJPIacIyqfikiA4Cn\nsA/8QlMP6AmcCxwKzBeR+aq6NlmFWk1VE/7DDy7il6jq5mA1hbjdcRGphyWgh1Q13r1QBRFnRDYA\nx4SetwqOxZZpnaJMdUsnzqilFaOInAg8APRX1XwPv0IVf5eq+rKItBWRpqq6tdqj2yedOE8GpoiI\nYNcEB4jILlWdnqcYIY04w198VfVZERlfoL/PCuBDVf0K+EpEXgK6Y9eS4ir24bjKm2Uh8c22AH8F\nVqjq7/MRVBzpxgn2TS6f3+b23owsIvWxm5Fj/wNPx24sRkT6ANsqhxfzKJ04w6L4RpwyRrEtSh4H\nvqeqb0UQI6QX5/Ghxz2B+nn+wIQ04lTVtsFPG+yL5o15TkBpxRm+hip2s74U4u8T+2w6Q0Tqisgh\n2ESklUlbjXK2RdQ/QFPgBWzG2yzg8OD4UcA/gsenA7uxmSCLgdexb6AFFWfw/H+B94EdwHvAtXmK\nr38QWzkwIjh2A3B9qMx92LehpUDPiP6+k8aJDYeuB7YBW4PfYcMCi/HP2M3Yrwf/HhcW6O/yduCN\nIM55wGmFGGdM2b8Swey4NH+fw4Lf52LgFYIlygotzuD5bdgMuWXAzana9JtVnXPORabYh+Occ85F\nyJOQc865yHgScs45FxlPQs455yLjScg551xkPAk555yLjCch55xzkfEk5JxzLjL/D0oWEEui3mgG\nAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fb3e0baea58>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(time, signal.detrend(eeg_mean))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Detrend all of the trials, individually, then plot that mean!\n",
    "\n",
    "Before you do it, though, think: Do you expect the result to be the same or different from removing the trend of the mean?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7fb3ab9cf390>]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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z5rBqlQ0T1ma+bI8DYNw4W/16+HBPQLVF/fp2cXv16qgjyS9V+yJ1553wi1/A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\nAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fb3c3e89240>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "eeg_d = signal.detrend(eeg, axis=1)\n",
    "plt.plot(time, eeg_d.mean(axis=0))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Filter Out Data Beyond certain Time Points\n",
    "Remove the EEG data occuring less than -150 msecs before the stimulus. Also remove all the data and greater than 400 msecs after the stimulus.  (We'll only work with the -150 - 400 msec time period from now on.)  Confirm it worked by plotting the mean response again."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1320, 141), (141,), (1320,), "
     ]
    }
   ],
   "source": [
    "time_mask = (time > -.150) & (time < .400)\n",
    "time_f = time[time_mask]\n",
    "eeg_df = eeg_d[:, time_mask]\n",
    "\n",
    "print_shapes(eeg_df, time_f, labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7fb3ab96bf98>]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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xGDRIq+RyxfbtYbHIY4/B7Nmwdi307w/z54f33cPfyy5d4LTTwrwwwM9/DkuX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\n/wcg/d7uZRJFtgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fb3ab9e1438>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(time_f, eeg_df.mean(axis=0))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Separate the Two Conditions from one another, and plot their mean responses\n",
    "\n",
    "### Which code represents which condition?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 151.  251.]\n"
     ]
    }
   ],
   "source": [
    "print(np.unique(labels))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "labels_i = labels.astype(int)\n",
    "labels_count = np.bincount(labels_i)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "151: 1200\n",
      "251: 120\n"
     ]
    }
   ],
   "source": [
    "for label in np.unique(labels_i):\n",
    "    print(\"{}: {}\".format(label, labels_count[label]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Create two 2D Matrices, one for each Condition (Standard and Deviant)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1200, 141), (120, 141), "
     ]
    }
   ],
   "source": [
    "eeg_stand = eeg_df[labels == 151, :]\n",
    "eeg_dev = eeg_df[labels == 251, :]\n",
    "\n",
    "print_shapes(eeg_stand, eeg_dev)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plot The two conditions' ERPs\n",
    "Make the two lines different colors, and label them in the axis!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7fb3ab8db1d0>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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JEZEDIjKwqrYi4iUim0TkqIh8LyKeZteWmvqKFpGxZuWDReQPU18vmpXfLyKH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\nAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fb3ab9774a8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "stand_mean = eeg_stand.mean(axis=0)\n",
    "dev_mean = eeg_dev.mean(axis=0)\n",
    "\n",
    "plt.plot(time_f, stand_mean, label='standard')\n",
    "plt.plot(time_f, dev_mean, label='deviant')\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plot some shaded error bars, to help us see whether where there may be significant differences between the two conditions' responses\n",
    "\n",
    "We'll just plot the **Standard Error of the Mean (SEM)**.  To calculate the SEM:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "  1. Calculate the Standard Deviation of the EEG values at each time point.  (hint: you'll end up with two 1D arrays of std values, one for each condition)\n",
    "  2. Divide the Standard Deviations by **the square root of one less the number of trials in the condition**.  \n",
    "  \n",
    "That's it!  Here's how the formula looks, in case my description was confusing.  In the formul below:\n",
    "  - **STD**: Standard Deviation\n",
    "  - **SEM**: Standard Error of the Mean\n",
    "  - **N**: The number of trials"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/latex": [
       "\n",
       "$$ SEM = \\frac{STD}{\\sqrt{N - 1}}$$"
      ],
      "text/plain": [
       "<IPython.core.display.Latex object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%latex\n",
    "\n",
    "$$ SEM = \\frac{STD}{\\sqrt{N - 1}}$$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "(Yes, that's right.  Latex formulas render properly in Jupyter Notebooks!)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Calculate the SEM of the EEG Data at each Time Pont for each Condition"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def calc_eeg_sem(mat):\n",
    "    std = np.std(mat, axis=0)\n",
    "    return std / np.sqrt(mat.shape[0] - 1)\n",
    "\n",
    "stand_sem = calc_eeg_sem(eeg_stand)\n",
    "dev_sem = calc_eeg_sem(eeg_dev)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###  Plot the ERP of each Condition again, this time with SEM shaded bars both above and below each condition's mean"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PolyCollection at 0x7fb3ab864ba8>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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gg564j65oG0X2chQKu8lBOGwIy+zZRloftxumTzfcbgCXXQY7dxpCBca6UHNz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\nIWSxWCyWrGGFkMVisViyxv8HkBlhjXncDXAAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fb3abbe62b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(time_f, stand_mean, label='standard')\n",
    "plt.plot(time_f, dev_mean, label='deviant')\n",
    "plt.legend()\n",
    "plt.fill_between(time_f, stand_mean - stand_sem, stand_mean + stand_sem, alpha=.2, color='b')\n",
    "plt.fill_between(time_f, dev_mean - dev_sem, dev_mean + dev_sem, alpha=.2, color='g')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Select the Peak, and Statistically Compare their Heights Between Conditions\n",
    "\n",
    "Let's simplify this analysis, and say that we're now just interested in finding out whether the EEG response around 140 Hz was significantly different between the two conditions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Get the Mean EEG Voltage between 130 and 150 msecs post-stimulus for each trial. \n",
    "Let's call this the **N140 Response**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1200,), (120,), "
     ]
    }
   ],
   "source": [
    "mask_n140 = (time_f > .130) & (time_f < .15)\n",
    "\n",
    "n140_stand = eeg_stand[:, mask_n140].mean(axis=1)\n",
    "n140_dev = eeg_dev[:, mask_n140].mean(axis=1)\n",
    "\n",
    "print_shapes(n140_stand, n140_dev)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[3.4738281141755711e-06, 4.2578202964607106e-06]"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "n140_stand_mean = n140_stand.mean(axis=0)\n",
    "n140_dev_mean = n140_dev.mean(axis=0)\n",
    "\n",
    "[n140_stand_mean, n140_dev_mean]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Filter out any outlier trials -- trials that are extremely different from the rest."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Make a histogram showing the distribution of N140s across all conditions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-0.000231596601421\n",
      "0.000376048241303\n"
     ]
    },
    {
     "data": {
      "image/png": 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cmuTHSa4ct/ZVqP+FSb6U5CtJvpjk+VNU+/kZvM/M3S4ap/aVrn/o8Sd3r5+3LFpgVY19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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fb3ab82f3c8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "n140_all = eeg_df[:, mask_n140].mean(axis=1)\n",
    "_ = plt.hist(n140_all, bins=300)\n",
    "print(n140_all.min())\n",
    "print(n140_all.max())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Remove outlier trials"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "n140_stand_f = n140_stand[(n140_stand > -.0001) & (n140_stand < .0001)]\n",
    "n140_dev_f = n140_dev[(n140_dev > -.0001) & (n140_dev < .0001)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Make a Bar Plot comparing the N140 Response between the two conditions.  \n",
    "\n",
    "Don't forget error bars, also showing the SEM!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7fb3abbc1128>"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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EjNRYnISg4bO8hBt+/4iI2Ewv63H9a4D6Rf29Slprnr0HyTOhTdm1kibZXleG\n1x4Zpq6h0utlfiRpW2Bn248NfjqJTZ35aNMN2GpsukQ5lozFNo1F+Z6PVq8D0DLgQEn7Aj8GTgRO\naslzLXAm8HlJRwJPlMDyaJuy1wKnAHOB2cA1tfTPSrqAamjtQOB225b0pKTppU0nAxfVyswGbgN+\nj2pSw0vYzk9lREQX9TQA2d4o6SxgCdVw3+W2V0iaUx32ZbYXSTpW0irgWeDUdmVL1XOBhZJOA1ZT\nzXzD9nJJC4HlwHrgDNsDw3NnAlcCOwCLbC8u6ZcDV0laCfyUKtBFRESPadPv54iIiC1nLE5CiC1E\n0l9J+p6k70i6U9JvSvpTSTt08T1+MJpZhZLeLOkr3WpP9DdJG8t3/XuS7pL057V7BTe3rtfWb2of\nQflzR1p2vOj1NaAYo8r1tmOBabY3lCCxPfAB4CrguS691WZ1sSVtY/uF0dQR0cazto8AkPSrwNXA\nzsB5m1uR7TuAO0bRlg8DHx9F+a1eekD969eAR21vACgz/34X+HXgZkn/DSDpEkm3S/qupI8MFC49\nm/Mk3VF6UFNL+m5leaTvSvo0tSlVkr4saVk59ke19Kcl/aOku4AjyxJMKyR9Gzh+C3wW0YdsPwr8\nMXAWVH/8SDpf0m1lWbD3lfSry4otlNdXSDq+3jsvowffLD8P35A0paTPlvQfkq4vS4d9oqR/HHh5\n6Y1dtYVPfeywna0PN+AVwF3AfcC/AG8q6d8Hdq3l26X8uw1wM3B4ef0DqkkeAP8buKzs/zPw12X/\nWGAjsFtLXTsA3x14H+AF4N1lf3vgh8Ary+vPA9c2/XllGx8b8NQgaY8BuwPvAz5c0iZQzZjdl2rV\nlCtL+nZUE5+2B9488N0EdgS2KftvBb5Y9mcDq8rx7YEHgclDtaXftvSA+pTtZ4EjqP4C/AmwQNLs\ncrg+Jn6ipDuogtWhZRvw5fLvHcB+Zf9NwGfKeywCHq/l/4Cku4Fbqe7FmlLSNwBfKvsHA9+3/f3y\n+jMjPMWIzXUUcHLpid8G7Eb1Hb0emCFpO6o1Jm+x/XxL2V2AL0r6LnABL/45+W/bz5Qyy6mCWpBr\nQH3N1Z9htwC3lB+c2fXjkvYDPgi81vZTkq6g6r0MGPgh3MjQ3yWVut4MvAX4LdvPS7q5VtdzpS0v\nKhPRa5JeCWy0/ZMyGeH9tr86SL6lwDHAe6iuG7X6GHCT7ePLvYs3147Vg1X9Z6Xvv+fpAfUpSVMl\nHVhLmkabFvvvAAABMElEQVQ1PPA01UVZyr/PAE+XBWJnMrxbgN8v7zGT6i9DgInA4yX4HAwcWW9O\nbf8+YF9J+5fXrTcuR4xG/Zrk7lSr5H+yJN0AnCHpZeX4FEkvL8cWUt2j+EZgMS81kU2rq5zaYVt+\nUVZf6VvpAfWvHYFPqnqW0gaqceo/Bt4LLJa0xvZby5DZCqoFW79RKz/UzLSPAldLOhH4JtX1HKh+\naP9E0r3A/cC3BqurBKg/BhZJehb4emlrRDfsIOlOqms864H5ti8ox/6Naij5ztIbeoRNq+YvoVpF\n/z9dJu60OB+YJ+mvgevavH/95+Yy4LuS7rD9hyM9oa1ZbkSNiIhGZAguIiIakQAUERGNSACKiIhG\nJABFREQjEoAiIqIRCUAREdGIBKCIiGhEAlBERDTi/wMMUWljJcKqFwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fb3ab740a20>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "n140_stand_mean = n140_stand_f.mean()\n",
    "n140_dev_mean = n140_dev_f.mean()\n",
    "\n",
    "n140_stand_sem = n140_stand_f.std() / np.sqrt(len(n140_stand_f) - 1)\n",
    "n140_dev_sem = n140_dev_f.std() / np.sqrt(len(n140_dev_f) - 1)\n",
    "\n",
    "fix, ax = plt.subplots()\n",
    "ax.bar([0, 1], [n140_stand_mean, n140_dev_mean], yerr=[n140_stand_sem, n140_dev_sem])\n",
    "ax.set_xticks([0.4, 1.4])\n",
    "ax.set_xticklabels(['Standard', 'Deviant'])\n",
    "ax.set_title('Mean N140 Response')\n",
    "ax.set_ylabel('Mean EEG Voltage')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Do a Statistical Test comparing N140 Between Responses to See if their means are different.\n",
    "\n",
    "Let's do a **Mann-Whitney U Test**.   It's a test that compares the two conditions without making any assumptions about how each condition is distributed.  \n",
    "\n",
    "Lots of good statistical tests can be found at **scipy.stats**, yet another subpackage of Scipy: http://docs.scipy.org/doc/scipy-0.17.0/reference/stats.html\n",
    "\n",
    "### Are the N140 Responses signficantly different between conditions?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MannwhitneyuResult(statistic=61126.0, pvalue=0.0073781136767421731)"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mw_result = stats.mannwhitneyu(n140_stand_f, n140_dev_f)\n",
    "mw_result"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Add the statistical values to your bar plot!\n",
    "\n",
    "Even if it isn't a significant difference, it's nice to know what the actual values are when we look at a plot, especially if there are only a few values to be made.  Put the U value and the P value on the bar plot you made earlier, in text!\n",
    "\n",
    "Intro on Text in Matplotlib: http://matplotlib.org/users/text_intro.html"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7fb3aa988860>"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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moIMOYuzYscyaNWuPfLNmzWLcuHEAnHLKKWzevJkNGzaUXX7mzJlcfPHFLWpra1D1ACTp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\nYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fb3ab7aae48>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fix, ax = plt.subplots()\n",
    "ax.bar([0, 1], [n140_stand_mean, n140_dev_mean], yerr=[n140_stand_sem, n140_dev_sem])\n",
    "ax.set_xticks([0.4, 1.4])\n",
    "ax.set_xticklabels(['Standard', 'Deviant'])\n",
    "ax.set_title('Mean N140 Response')\n",
    "ax.set_ylabel('Mean EEG Voltage')\n",
    "ax.set_ylim(0, .000007)\n",
    "ax.text(1.3, .000006, 'U = {:.2f}\\n p = {:.3f}'.format(mw_result.statistic, mw_result.pvalue))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Sandbox\n",
    "\n",
    "What other analysis steps do you think could/should be done on this data?  Try them out here!"
   ]
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