Introduction_11_Representational_Similarity_Analysis.ipynb

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    "%matplotlib inline\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import scipy as sp\n",
    "import xarray as xr\n",
    "import seaborn as sns\n",
    "import cmlreaders as cml\n",
    "import cmldask.CMLDask as da\n",
    "import os\n",
    "import traceback\n",
    "from ptsa.data.filters import ResampleFilter, ButterworthFilter, MorletWaveletFilter\n",
    "from ptsa.data.timeseries import TimeSeries\n",
    "save_path = '/data7/rsa_class'"
   ]
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  {
   "cell_type": "markdown",
   "id": "8807a02d-0be8-4e35-86a3-3929904ddfae",
   "metadata": {},
   "source": [
    "# Computing Correlations Using xarray"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9ccf5f77-8e79-4dd1-bc93-dbf1d7d64208",
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   "source": [
    "First we are going to try to demonstrate how to compute a correlation by \"hand\" and then how to compute it efficiently using xarray."
   ]
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   "source": [
    "As a simple example, imagine we are looking at the brain while people look at states like in the Chipman and Shepard (1970) paper from lecture). Generate a dataset where we observe 10 neural features across 15 states"
   ]
  },
  {
   "cell_type": "code",
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   "source": [
    "A = np.diag(np.ones(15))  # a diagonal covariance matrix"
   ]
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    "A"
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   "source": [
    "Add in a couple true correlations across \"states\""
   ]
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    "A[1, 2] = A[2, 1] = .5\n",
    "A[6, 8] = A[6, 8] = .8\n",
    "A"
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     "text": [
      "/tmp/ipykernel_80346/3651426059.py:1: RuntimeWarning: covariance is not symmetric positive-semidefinite.\n",
      "  features = np.random.multivariate_normal(size=10, mean=np.zeros(15), cov=A).T\n"
     ]
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   "source": [
    "features = np.random.multivariate_normal(size=10, mean=np.zeros(15), cov=A).T"
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   "cell_type": "markdown",
   "id": "871a25f1-b861-43c4-a2ca-3e8b95341cca",
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   "source": [
    "Now we want to look at two sets of features and see how similar they are"
   ]
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   "cell_type": "code",
   "execution_count": 6,
   "id": "8b805f6c-136a-4e60-86a8-d004b1b11f65",
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    {
     "data": {
      "text/plain": [
       "(array([-0.42195266, -1.14316557,  0.13208253,  1.4317144 , -1.7296228 ,\n",
       "         0.08355146,  0.64419187, -0.04518374, -0.09451543, -0.12961003]),\n",
       " array([ 0.29693597, -0.66813372,  0.1977062 ,  1.60863947,  0.07052268,\n",
       "         0.01162864, -0.38585939,  1.10135484,  0.5557675 ,  0.08234905]))"
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     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "features[1], features[2]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "98ce8f20-bfb8-49a1-a050-70649acc70a6",
   "metadata": {},
   "source": [
    "Generally feature 1 is negative when feature 2 is negative and positive when feature 2 is positive so they seem pretty similar (this is because we made them similar when we generated the covariance matrix). This should be reflected when we compute their correlation. [Pearson's correlation](https://en.wikipedia.org/wiki/Pearson_correlation_coefficient) is the covariance of the two variables divided by the standard deviation of each multiplied together."
   ]
  },
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   "cell_type": "markdown",
   "id": "8484eb6e-632d-470e-a5f2-2c830201858f",
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   "source": [
    "Covariance is the expectation (aka expected value aka mean) of (feature 1 - mean(feature 1)) * (feature 2 - mean(feature 2). This measure is going to be high if feature 1 is usually above its mean when feature 2 is also above its mean. Lets compute it"
   ]
  },
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   "cell_type": "code",
   "execution_count": 7,
   "id": "3da50ff7-1daa-4c08-8e19-ef0ff0da1cbf",
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     "data": {
      "text/plain": [
       "(-0.1272509979917849, 0.287091124046894)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "np.mean(features[1]), np.mean(features[2])"
   ]
  },
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   "cell_type": "code",
   "execution_count": 8,
   "id": "ae79e619-05fc-4cf4-9866-d9b190001589",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-2.90129251e-03,  9.70426836e-01, -2.31805079e-02,  2.06024815e+00,\n",
       "        3.47023163e-01, -5.80681672e-02, -5.19142874e-01,  6.68243866e-02,\n",
       "        8.79527287e-03,  4.82993420e-04])"
      ]
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     "execution_count": 8,
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   "source": [
    "(features[1] - np.mean(features[1])) * (features[2] - np.mean(features[2]))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "082f725d-3d17-4ce7-9eb7-88cdcef398e6",
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   "source": [
    "Now we take the average of that value (but its a sample statistic so we use n-1 in the denominator, instead of n=10)"
   ]
  },
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   "cell_type": "code",
   "execution_count": 9,
   "id": "bd54b37d-0431-40b9-8ba9-4a65149cd01f",
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.3167231063336258"
      ]
     },
     "execution_count": 9,
     "metadata": {},
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   ],
   "source": [
    "feat_cov = (np.sum((features[1] - np.mean(features[1])) * \n",
    "        (features[2] - np.mean(features[2]))) / (10 - 1)) \n",
    "feat_cov "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9491d7d6-7e61-49ae-b724-8368247df197",
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   "source": [
    "We can check it against `numpy`'s version"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "6d249998-876d-49bb-a153-da393a93bdb4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.75905865, 0.31672311],\n",
       "       [0.31672311, 0.44664519]])"
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     "execution_count": 10,
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   "source": [
    "np.cov(features[1], features[2])"
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  {
   "cell_type": "markdown",
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   "source": [
    "Now just divide that by the sample standard deviations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "75a18f01-1a56-4be2-8b39-4dad88df1c0c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5439522178061897"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "feat_cov / (np.std(features[1], ddof=1) * np.std(features[2], ddof=1))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "decafb01-025b-48a2-b1c4-d5eee73dbdd1",
   "metadata": {},
   "source": [
    "And check that against numpy's version of correlation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "bf1c5329-9211-42f4-95ec-53d95b9690d5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1.        , 0.54395222],\n",
       "       [0.54395222, 1.        ]])"
      ]
     },
     "execution_count": 12,
     "metadata": {},
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   "source": [
    "np.corrcoef(features[1], features[2])"
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  },
  {
   "cell_type": "markdown",
   "id": "36b2282d-4bee-4d64-a324-51127a72813a",
   "metadata": {},
   "source": [
    "Now we want a way to compute these correlations for all of the states and keep track of all of the state info. Like we do with EEG, we'll store it using an xarray DataArray"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "0007f2c7-144a-4232-9e11-64029122ce13",
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   "source": [
    "states_da = xr.DataArray(data=features, \n",
    "             dims=[\"states\", \"features\"], \n",
    "             coords={'features': ['feat_' + str(i) for i in range(10)],\n",
    "                     'states': ['Minn.', 'Ore.', 'W.V.', 'Colo.', 'Ala.', 'Ill.',\n",
    "                               'Nev.', 'Nebr.', 'Okla.', 'Ida.', 'Fla.', 'La.', \n",
    "                                'S.C.', 'Mo.', 'Me.']})"
   ]
  },
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   "cell_type": "code",
   "execution_count": 14,
   "id": "15d49374-004a-4299-a997-2428fbccb609",
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       "}\n",
       "\n",
       ".xr-section-summary-in + label:before {\n",
       "  display: inline-block;\n",
       "  content: '►';\n",
       "  font-size: 11px;\n",
       "  width: 15px;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label:before {\n",
       "  color: var(--xr-disabled-color);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label:before {\n",
       "  content: '▼';\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label > span {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-section-summary,\n",
       ".xr-section-inline-details {\n",
       "  padding-top: 4px;\n",
       "  padding-bottom: 4px;\n",
       "}\n",
       "\n",
       ".xr-section-inline-details {\n",
       "  grid-column: 2 / -1;\n",
       "}\n",
       "\n",
       ".xr-section-details {\n",
       "  display: none;\n",
       "  grid-column: 1 / -1;\n",
       "  margin-bottom: 5px;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-array-wrap {\n",
       "  grid-column: 1 / -1;\n",
       "  display: grid;\n",
       "  grid-template-columns: 20px auto;\n",
       "}\n",
       "\n",
       ".xr-array-wrap > label {\n",
       "  grid-column: 1;\n",
       "  vertical-align: top;\n",
       "}\n",
       "\n",
       ".xr-preview {\n",
       "  color: var(--xr-font-color3);\n",
       "}\n",
       "\n",
       ".xr-array-preview,\n",
       ".xr-array-data {\n",
       "  padding: 0 5px !important;\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-array-data,\n",
       ".xr-array-in:checked ~ .xr-array-preview {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-array-in:checked ~ .xr-array-data,\n",
       ".xr-array-preview {\n",
       "  display: inline-block;\n",
       "}\n",
       "\n",
       ".xr-dim-list {\n",
       "  display: inline-block !important;\n",
       "  list-style: none;\n",
       "  padding: 0 !important;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list li {\n",
       "  display: inline-block;\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list:before {\n",
       "  content: '(';\n",
       "}\n",
       "\n",
       ".xr-dim-list:after {\n",
       "  content: ')';\n",
       "}\n",
       "\n",
       ".xr-dim-list li:not(:last-child):after {\n",
       "  content: ',';\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-has-index {\n",
       "  font-weight: bold;\n",
       "}\n",
       "\n",
       ".xr-var-list,\n",
       ".xr-var-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-var-item > div,\n",
       ".xr-var-item label,\n",
       ".xr-var-item > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-even);\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-var-item > .xr-var-name:hover span {\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-var-list > li:nth-child(odd) > div,\n",
       ".xr-var-list > li:nth-child(odd) > label,\n",
       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-odd);\n",
       "}\n",
       "\n",
       ".xr-var-name {\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-var-dims {\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-var-dtype {\n",
       "  grid-column: 3;\n",
       "  text-align: right;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-preview {\n",
       "  grid-column: 4;\n",
       "}\n",
       "\n",
       ".xr-index-preview {\n",
       "  grid-column: 2 / 5;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-name,\n",
       ".xr-var-dims,\n",
       ".xr-var-dtype,\n",
       ".xr-preview,\n",
       ".xr-attrs dt {\n",
       "  white-space: nowrap;\n",
       "  overflow: hidden;\n",
       "  text-overflow: ellipsis;\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-var-name:hover,\n",
       ".xr-var-dims:hover,\n",
       ".xr-var-dtype:hover,\n",
       ".xr-attrs dt:hover {\n",
       "  overflow: visible;\n",
       "  width: auto;\n",
       "  z-index: 1;\n",
       "}\n",
       "\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-data {\n",
       "  display: none;\n",
       "  background-color: var(--xr-background-color) !important;\n",
       "  padding-bottom: 5px !important;\n",
       "}\n",
       "\n",
       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
       ".xr-var-data-in:checked ~ .xr-var-data,\n",
       ".xr-index-data-in:checked ~ .xr-index-data {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       ".xr-var-data > table {\n",
       "  float: right;\n",
       "}\n",
       "\n",
       ".xr-var-name span,\n",
       ".xr-var-data,\n",
       ".xr-index-name div,\n",
       ".xr-index-data,\n",
       ".xr-attrs {\n",
       "  padding-left: 25px !important;\n",
       "}\n",
       "\n",
       ".xr-attrs,\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-data {\n",
       "  grid-column: 1 / -1;\n",
       "}\n",
       "\n",
       "dl.xr-attrs {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  display: grid;\n",
       "  grid-template-columns: 125px auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt,\n",
       ".xr-attrs dd {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  float: left;\n",
       "  padding-right: 10px;\n",
       "  width: auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt {\n",
       "  font-weight: normal;\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-attrs dt:hover span {\n",
       "  display: inline-block;\n",
       "  background: var(--xr-background-color);\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-attrs dd {\n",
       "  grid-column: 2;\n",
       "  white-space: pre-wrap;\n",
       "  word-break: break-all;\n",
       "}\n",
       "\n",
       ".xr-icon-database,\n",
       ".xr-icon-file-text2,\n",
       ".xr-no-icon {\n",
       "  display: inline-block;\n",
       "  vertical-align: middle;\n",
       "  width: 1em;\n",
       "  height: 1.5em !important;\n",
       "  stroke-width: 0;\n",
       "  stroke: currentColor;\n",
       "  fill: currentColor;\n",
       "}\n",
       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray (states: 15, features: 10)&gt;\n",
       "array([[-1.14843366, -2.17079084, -0.2282922 ,  0.08996026,  1.23422173,\n",
       "         1.58720084, -0.94213502,  0.30464815,  1.06261293, -0.2484342 ],\n",
       "       [-0.42195266, -1.14316557,  0.13208253,  1.4317144 , -1.7296228 ,\n",
       "         0.08355146,  0.64419187, -0.04518374, -0.09451543, -0.12961003],\n",
       "       [ 0.29693597, -0.66813372,  0.1977062 ,  1.60863947,  0.07052268,\n",
       "         0.01162864, -0.38585939,  1.10135484,  0.5557675 ,  0.08234905],\n",
       "       [-0.18326577, -0.0955466 , -0.84262116, -0.49176993, -0.63949582,\n",
       "        -0.46133198,  0.03356775,  0.52872575,  0.27394336, -1.02765781],\n",
       "       [-1.41135427,  1.0057875 ,  0.40880328, -1.64418017,  0.78715509,\n",
       "        -0.14690577, -1.64500873, -1.37068155, -0.04397012, -0.6956429 ],\n",
       "       [-0.34014389, -1.93289968,  1.17224547,  0.35545058, -1.11163452,\n",
       "         1.19745187,  0.68825367, -0.20230453,  0.18182986, -0.59927704],\n",
       "       [ 0.20378684, -0.42808622, -1.29626103,  1.29208375,  0.43886385,\n",
       "         1.30270035, -0.98606283,  0.49342844,  0.40246208, -1.33733643],\n",
       "       [ 0.52836015, -1.5259827 , -0.06982171, -0.72990648, -1.14151129,\n",
       "        -0.32481403,  0.34696061, -0.33095442, -1.60960574,  0.94646279],\n",
       "       [ 1.19256144, -0.377124  , -0.95017737,  1.75969085, -0.71013462,\n",
       "        -0.67387161,  0.29093025,  0.51307734,  0.42520984, -0.72738162],\n",
       "       [ 0.037174  , -0.7516586 ,  1.15635278, -0.41190199, -0.95758567,\n",
       "         1.29719018, -0.1946514 , -0.67871468,  1.81452862,  0.45184014],\n",
       "       [ 0.87606891, -0.20762068,  0.00656965, -0.12870202,  0.19201927,\n",
       "        -1.42863966,  0.59415821,  0.43620066, -0.11951924, -0.82813794],\n",
       "       [-0.05867057,  1.17234122, -1.05257566, -0.01616835,  0.13713519,\n",
       "         0.98341125, -1.44654495, -0.47338135, -0.11881636,  2.44550161],\n",
       "       [ 0.1695933 , -0.81398151,  0.64192629,  0.46759156,  0.32938111,\n",
       "        -0.45204878,  0.0229592 , -0.28498422,  0.48309163,  0.96450131],\n",
       "       [ 0.28496161, -0.96414636,  0.82759391,  0.89878348, -0.38713097,\n",
       "         0.79430369,  0.60849266, -1.229362  , -0.28736287, -1.09484773],\n",
       "       [ 1.19635412, -0.42688134,  0.53307788, -0.0482305 , -0.96137665,\n",
       "         0.97329867,  0.2210466 , -1.30863823, -0.14507083, -0.57076226]])\n",
       "Coordinates:\n",
       "  * features  (features) &lt;U6 &#x27;feat_0&#x27; &#x27;feat_1&#x27; &#x27;feat_2&#x27; ... &#x27;feat_8&#x27; &#x27;feat_9&#x27;\n",
       "  * states    (states) &lt;U5 &#x27;Minn.&#x27; &#x27;Ore.&#x27; &#x27;W.V.&#x27; &#x27;Colo.&#x27; ... &#x27;S.C.&#x27; &#x27;Mo.&#x27; &#x27;Me.&#x27;</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'></div><ul class='xr-dim-list'><li><span class='xr-has-index'>states</span>: 15</li><li><span class='xr-has-index'>features</span>: 10</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-d5dc5afc-943e-4224-83c7-e0b9fbf499bd' class='xr-array-in' type='checkbox' checked><label for='section-d5dc5afc-943e-4224-83c7-e0b9fbf499bd' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>-1.148 -2.171 -0.2283 0.08996 1.234 ... 0.221 -1.309 -0.1451 -0.5708</span></div><div class='xr-array-data'><pre>array([[-1.14843366, -2.17079084, -0.2282922 ,  0.08996026,  1.23422173,\n",
       "         1.58720084, -0.94213502,  0.30464815,  1.06261293, -0.2484342 ],\n",
       "       [-0.42195266, -1.14316557,  0.13208253,  1.4317144 , -1.7296228 ,\n",
       "         0.08355146,  0.64419187, -0.04518374, -0.09451543, -0.12961003],\n",
       "       [ 0.29693597, -0.66813372,  0.1977062 ,  1.60863947,  0.07052268,\n",
       "         0.01162864, -0.38585939,  1.10135484,  0.5557675 ,  0.08234905],\n",
       "       [-0.18326577, -0.0955466 , -0.84262116, -0.49176993, -0.63949582,\n",
       "        -0.46133198,  0.03356775,  0.52872575,  0.27394336, -1.02765781],\n",
       "       [-1.41135427,  1.0057875 ,  0.40880328, -1.64418017,  0.78715509,\n",
       "        -0.14690577, -1.64500873, -1.37068155, -0.04397012, -0.6956429 ],\n",
       "       [-0.34014389, -1.93289968,  1.17224547,  0.35545058, -1.11163452,\n",
       "         1.19745187,  0.68825367, -0.20230453,  0.18182986, -0.59927704],\n",
       "       [ 0.20378684, -0.42808622, -1.29626103,  1.29208375,  0.43886385,\n",
       "         1.30270035, -0.98606283,  0.49342844,  0.40246208, -1.33733643],\n",
       "       [ 0.52836015, -1.5259827 , -0.06982171, -0.72990648, -1.14151129,\n",
       "        -0.32481403,  0.34696061, -0.33095442, -1.60960574,  0.94646279],\n",
       "       [ 1.19256144, -0.377124  , -0.95017737,  1.75969085, -0.71013462,\n",
       "        -0.67387161,  0.29093025,  0.51307734,  0.42520984, -0.72738162],\n",
       "       [ 0.037174  , -0.7516586 ,  1.15635278, -0.41190199, -0.95758567,\n",
       "         1.29719018, -0.1946514 , -0.67871468,  1.81452862,  0.45184014],\n",
       "       [ 0.87606891, -0.20762068,  0.00656965, -0.12870202,  0.19201927,\n",
       "        -1.42863966,  0.59415821,  0.43620066, -0.11951924, -0.82813794],\n",
       "       [-0.05867057,  1.17234122, -1.05257566, -0.01616835,  0.13713519,\n",
       "         0.98341125, -1.44654495, -0.47338135, -0.11881636,  2.44550161],\n",
       "       [ 0.1695933 , -0.81398151,  0.64192629,  0.46759156,  0.32938111,\n",
       "        -0.45204878,  0.0229592 , -0.28498422,  0.48309163,  0.96450131],\n",
       "       [ 0.28496161, -0.96414636,  0.82759391,  0.89878348, -0.38713097,\n",
       "         0.79430369,  0.60849266, -1.229362  , -0.28736287, -1.09484773],\n",
       "       [ 1.19635412, -0.42688134,  0.53307788, -0.0482305 , -0.96137665,\n",
       "         0.97329867,  0.2210466 , -1.30863823, -0.14507083, -0.57076226]])</pre></div></div></li><li class='xr-section-item'><input id='section-04de8804-1647-4d17-a217-732f24f677c5' class='xr-section-summary-in' type='checkbox'  checked><label for='section-04de8804-1647-4d17-a217-732f24f677c5' class='xr-section-summary' >Coordinates: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>features</span></div><div class='xr-var-dims'>(features)</div><div class='xr-var-dtype'>&lt;U6</div><div class='xr-var-preview xr-preview'>&#x27;feat_0&#x27; &#x27;feat_1&#x27; ... &#x27;feat_9&#x27;</div><input id='attrs-9e7ba061-c8b6-4337-8740-992e4c499ea6' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-9e7ba061-c8b6-4337-8740-992e4c499ea6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-41e15510-702a-46a1-b670-2027d836c54a' class='xr-var-data-in' type='checkbox'><label for='data-41e15510-702a-46a1-b670-2027d836c54a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;feat_0&#x27;, &#x27;feat_1&#x27;, &#x27;feat_2&#x27;, &#x27;feat_3&#x27;, &#x27;feat_4&#x27;, &#x27;feat_5&#x27;, &#x27;feat_6&#x27;,\n",
       "       &#x27;feat_7&#x27;, &#x27;feat_8&#x27;, &#x27;feat_9&#x27;], dtype=&#x27;&lt;U6&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>states</span></div><div class='xr-var-dims'>(states)</div><div class='xr-var-dtype'>&lt;U5</div><div class='xr-var-preview xr-preview'>&#x27;Minn.&#x27; &#x27;Ore.&#x27; ... &#x27;Mo.&#x27; &#x27;Me.&#x27;</div><input id='attrs-ef7e9c34-de04-4707-8307-db8c07fc36d1' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-ef7e9c34-de04-4707-8307-db8c07fc36d1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5f2654a5-16c3-4c2a-9e1d-51d5fe9c5407' class='xr-var-data-in' type='checkbox'><label for='data-5f2654a5-16c3-4c2a-9e1d-51d5fe9c5407' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;Minn.&#x27;, &#x27;Ore.&#x27;, &#x27;W.V.&#x27;, &#x27;Colo.&#x27;, &#x27;Ala.&#x27;, &#x27;Ill.&#x27;, &#x27;Nev.&#x27;, &#x27;Nebr.&#x27;,\n",
       "       &#x27;Okla.&#x27;, &#x27;Ida.&#x27;, &#x27;Fla.&#x27;, &#x27;La.&#x27;, &#x27;S.C.&#x27;, &#x27;Mo.&#x27;, &#x27;Me.&#x27;], dtype=&#x27;&lt;U5&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-834e8b41-a767-4184-95f7-89c7b9d90593' class='xr-section-summary-in' type='checkbox'  ><label for='section-834e8b41-a767-4184-95f7-89c7b9d90593' class='xr-section-summary' >Indexes: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>features</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-410356e6-216a-4f75-b99d-d29aa740535f' class='xr-index-data-in' type='checkbox'/><label for='index-410356e6-216a-4f75-b99d-d29aa740535f' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([&#x27;feat_0&#x27;, &#x27;feat_1&#x27;, &#x27;feat_2&#x27;, &#x27;feat_3&#x27;, &#x27;feat_4&#x27;, &#x27;feat_5&#x27;, &#x27;feat_6&#x27;,\n",
       "       &#x27;feat_7&#x27;, &#x27;feat_8&#x27;, &#x27;feat_9&#x27;],\n",
       "      dtype=&#x27;object&#x27;, name=&#x27;features&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>states</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-1519d36f-742d-41f9-8d66-1fe34e0a8f4d' class='xr-index-data-in' type='checkbox'/><label for='index-1519d36f-742d-41f9-8d66-1fe34e0a8f4d' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([&#x27;Minn.&#x27;, &#x27;Ore.&#x27;, &#x27;W.V.&#x27;, &#x27;Colo.&#x27;, &#x27;Ala.&#x27;, &#x27;Ill.&#x27;, &#x27;Nev.&#x27;, &#x27;Nebr.&#x27;,\n",
       "       &#x27;Okla.&#x27;, &#x27;Ida.&#x27;, &#x27;Fla.&#x27;, &#x27;La.&#x27;, &#x27;S.C.&#x27;, &#x27;Mo.&#x27;, &#x27;Me.&#x27;],\n",
       "      dtype=&#x27;object&#x27;, name=&#x27;states&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-03420c47-9b7e-4330-b5c6-7dd54d46a70b' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-03420c47-9b7e-4330-b5c6-7dd54d46a70b' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
      ],
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       "<xarray.DataArray (states: 15, features: 10)>\n",
       "array([[-1.14843366, -2.17079084, -0.2282922 ,  0.08996026,  1.23422173,\n",
       "         1.58720084, -0.94213502,  0.30464815,  1.06261293, -0.2484342 ],\n",
       "       [-0.42195266, -1.14316557,  0.13208253,  1.4317144 , -1.7296228 ,\n",
       "         0.08355146,  0.64419187, -0.04518374, -0.09451543, -0.12961003],\n",
       "       [ 0.29693597, -0.66813372,  0.1977062 ,  1.60863947,  0.07052268,\n",
       "         0.01162864, -0.38585939,  1.10135484,  0.5557675 ,  0.08234905],\n",
       "       [-0.18326577, -0.0955466 , -0.84262116, -0.49176993, -0.63949582,\n",
       "        -0.46133198,  0.03356775,  0.52872575,  0.27394336, -1.02765781],\n",
       "       [-1.41135427,  1.0057875 ,  0.40880328, -1.64418017,  0.78715509,\n",
       "        -0.14690577, -1.64500873, -1.37068155, -0.04397012, -0.6956429 ],\n",
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       "         1.19745187,  0.68825367, -0.20230453,  0.18182986, -0.59927704],\n",
       "       [ 0.20378684, -0.42808622, -1.29626103,  1.29208375,  0.43886385,\n",
       "         1.30270035, -0.98606283,  0.49342844,  0.40246208, -1.33733643],\n",
       "       [ 0.52836015, -1.5259827 , -0.06982171, -0.72990648, -1.14151129,\n",
       "        -0.32481403,  0.34696061, -0.33095442, -1.60960574,  0.94646279],\n",
       "       [ 1.19256144, -0.377124  , -0.95017737,  1.75969085, -0.71013462,\n",
       "        -0.67387161,  0.29093025,  0.51307734,  0.42520984, -0.72738162],\n",
       "       [ 0.037174  , -0.7516586 ,  1.15635278, -0.41190199, -0.95758567,\n",
       "         1.29719018, -0.1946514 , -0.67871468,  1.81452862,  0.45184014],\n",
       "       [ 0.87606891, -0.20762068,  0.00656965, -0.12870202,  0.19201927,\n",
       "        -1.42863966,  0.59415821,  0.43620066, -0.11951924, -0.82813794],\n",
       "       [-0.05867057,  1.17234122, -1.05257566, -0.01616835,  0.13713519,\n",
       "         0.98341125, -1.44654495, -0.47338135, -0.11881636,  2.44550161],\n",
       "       [ 0.1695933 , -0.81398151,  0.64192629,  0.46759156,  0.32938111,\n",
       "        -0.45204878,  0.0229592 , -0.28498422,  0.48309163,  0.96450131],\n",
       "       [ 0.28496161, -0.96414636,  0.82759391,  0.89878348, -0.38713097,\n",
       "         0.79430369,  0.60849266, -1.229362  , -0.28736287, -1.09484773],\n",
       "       [ 1.19635412, -0.42688134,  0.53307788, -0.0482305 , -0.96137665,\n",
       "         0.97329867,  0.2210466 , -1.30863823, -0.14507083, -0.57076226]])\n",
       "Coordinates:\n",
       "  * features  (features) <U6 'feat_0' 'feat_1' 'feat_2' ... 'feat_8' 'feat_9'\n",
       "  * states    (states) <U5 'Minn.' 'Ore.' 'W.V.' 'Colo.' ... 'S.C.' 'Mo.' 'Me.'"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "states_da"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "18a127bc-e361-4713-bc9a-1ccbdf479911",
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   "source": [
    "Now we can use the xr.corr function to compute the correlation of features between states but its not so straightforward because the states have the same names in both -- just plugging it in like this leads to the wrong answer because it matches the array along its dimensions. This leads to just correlates each state with itself, which is going to  always be 1"
   ]
  },
  {
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   "id": "8c041b5f-c6c7-4185-a05d-e7260087730c",
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       "array([1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.])\n",
       "Coordinates:\n",
       "  * states   (states) &lt;U5 &#x27;Minn.&#x27; &#x27;Ore.&#x27; &#x27;W.V.&#x27; &#x27;Colo.&#x27; ... &#x27;S.C.&#x27; &#x27;Mo.&#x27; &#x27;Me.&#x27;</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'></div><ul class='xr-dim-list'><li><span class='xr-has-index'>states</span>: 15</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-ead8a4d9-fbc2-4a4b-846c-e748c1f4379a' class='xr-array-in' type='checkbox' checked><label for='section-ead8a4d9-fbc2-4a4b-846c-e748c1f4379a' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0</span></div><div class='xr-array-data'><pre>array([1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.])</pre></div></div></li><li class='xr-section-item'><input id='section-92c193ba-c55a-46c5-949c-53c165611bdb' class='xr-section-summary-in' type='checkbox'  checked><label for='section-92c193ba-c55a-46c5-949c-53c165611bdb' class='xr-section-summary' >Coordinates: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>states</span></div><div class='xr-var-dims'>(states)</div><div class='xr-var-dtype'>&lt;U5</div><div class='xr-var-preview xr-preview'>&#x27;Minn.&#x27; &#x27;Ore.&#x27; ... &#x27;Mo.&#x27; &#x27;Me.&#x27;</div><input id='attrs-055708ba-8589-4241-bc2e-0d7c9e39d834' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-055708ba-8589-4241-bc2e-0d7c9e39d834' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-447a5bf5-079e-4379-b9ca-2fe949ce147c' class='xr-var-data-in' type='checkbox'><label for='data-447a5bf5-079e-4379-b9ca-2fe949ce147c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;Minn.&#x27;, &#x27;Ore.&#x27;, &#x27;W.V.&#x27;, &#x27;Colo.&#x27;, &#x27;Ala.&#x27;, &#x27;Ill.&#x27;, &#x27;Nev.&#x27;, &#x27;Nebr.&#x27;,\n",
       "       &#x27;Okla.&#x27;, &#x27;Ida.&#x27;, &#x27;Fla.&#x27;, &#x27;La.&#x27;, &#x27;S.C.&#x27;, &#x27;Mo.&#x27;, &#x27;Me.&#x27;], dtype=&#x27;&lt;U5&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-d487185d-9a18-431c-adbe-6556384ed144' class='xr-section-summary-in' type='checkbox'  ><label for='section-d487185d-9a18-431c-adbe-6556384ed144' class='xr-section-summary' >Indexes: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>states</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-80c01374-e373-4f07-a7c5-60e3449e4dd2' class='xr-index-data-in' type='checkbox'/><label for='index-80c01374-e373-4f07-a7c5-60e3449e4dd2' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([&#x27;Minn.&#x27;, &#x27;Ore.&#x27;, &#x27;W.V.&#x27;, &#x27;Colo.&#x27;, &#x27;Ala.&#x27;, &#x27;Ill.&#x27;, &#x27;Nev.&#x27;, &#x27;Nebr.&#x27;,\n",
       "       &#x27;Okla.&#x27;, &#x27;Ida.&#x27;, &#x27;Fla.&#x27;, &#x27;La.&#x27;, &#x27;S.C.&#x27;, &#x27;Mo.&#x27;, &#x27;Me.&#x27;],\n",
       "      dtype=&#x27;object&#x27;, name=&#x27;states&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-4d709bb7-7337-4391-9edd-fbc80ab54912' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-4d709bb7-7337-4391-9edd-fbc80ab54912' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.DataArray (states: 15)>\n",
       "array([1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.])\n",
       "Coordinates:\n",
       "  * states   (states) <U5 'Minn.' 'Ore.' 'W.V.' 'Colo.' ... 'S.C.' 'Mo.' 'Me.'"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xr.corr(states_da, states_da, dim='features')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cd06ca9c-828a-4b9c-8501-de5138a71861",
   "metadata": {},
   "source": [
    "What we want is each state correlated with every other one. We need to rename the states variable to get the right answer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "c3edaa49-095d-4b41-b785-da60273419f9",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "states_da2 = states_da.rename({'states': 'states2'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "c55d3275-201e-4f19-bfd9-bd8b5758cf6a",
   "metadata": {},
   "outputs": [],
   "source": [
    "states_corr = xr.corr(states_da, states_da2, dim='features')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "7e53f2c9-d5cc-4650-a925-00be0eceadf9",
   "metadata": {
    "tags": []
   },
   "outputs": [
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray (states: 15, states2: 15)&gt;\n",
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       "         0.70270642,  0.11988758,  0.32194749,  0.53092095,  0.27405009,\n",
       "        -0.06846273, -0.29837119,  0.24861438,  0.55290039,  0.3083636 ],\n",
       "       [ 0.38643505,  0.54395222,  1.        ,  0.15943397, -0.52147548,\n",
       "         0.27804868,  0.54802347, -0.05533194,  0.65584101, -0.01827702,\n",
       "         0.118318  , -0.18986529,  0.3304217 ,  0.12582951, -0.18314454],\n",
       "       [-0.07976265,  0.04505798,  0.15943397,  1.        , -0.28096961,\n",
       "        -0.07873268,  0.30313415, -0.35616486,  0.46340679, -0.12017062,\n",
       "         0.44906319, -0.42544936, -0.53697489, -0.25667716, -0.20474754],\n",
       "       [ 0.09105109, -0.71879028, -0.52147548, -0.28096961,  1.        ,\n",
       "        -0.36757624, -0.14708579, -0.57070211, -0.7336755 ,  0.12368646,\n",
       "        -0.347708  ,  0.27980789, -0.17137721, -0.22435228, -0.15465711],\n",
       "       [ 0.42902949,  0.70270642,  0.27804868, -0.07873268, -0.36757624,\n",
       "         1.        ,  0.09943432,  0.32588167,  0.05757196,  0.63026963,\n",
       "        -0.18183588, -0.4693994 ,  0.24743514,  0.75142459,  0.54908603],\n",
       "       [ 0.54000149,  0.11988758,  0.54802347,  0.30313415, -0.14708579,\n",
       "         0.09943432,  1.        , -0.44594338,  0.47455692, -0.05466285,\n",
       "...\n",
       "         0.05757196,  0.47455692, -0.01884356,  1.        , -0.2248089 ,\n",
       "         0.48722261, -0.31236974,  0.00977628,  0.21579395,  0.12198557],\n",
       "       [ 0.39857437,  0.27405009, -0.01827702, -0.12017062,  0.12368646,\n",
       "         0.63026963, -0.05466285,  0.05484929, -0.2248089 ,  1.        ,\n",
       "        -0.45779029,  0.02705076,  0.31851429,  0.34406729,  0.51532152],\n",
       "       [-0.41862503, -0.06846273,  0.118318  ,  0.44906319, -0.347708  ,\n",
       "        -0.18183588, -0.1662455 ,  0.06662939,  0.48722261, -0.45779029,\n",
       "         1.        , -0.67659362,  0.02921328, -0.02658268, -0.08375413],\n",
       "       [ 0.00407398, -0.29837119, -0.18986529, -0.42544936,  0.27980789,\n",
       "        -0.4693994 ,  0.00321205,  0.06405049, -0.31236974,  0.02705076,\n",
       "        -0.67659362,  1.        ,  0.02097718, -0.48349462, -0.15823967],\n",
       "       [ 0.24416662,  0.24861438,  0.3304217 , -0.53697489, -0.17137721,\n",
       "         0.24743514, -0.32662798,  0.36732884,  0.00977628,  0.31851429,\n",
       "         0.02921328,  0.02097718,  1.        ,  0.13380128, -0.01197139],\n",
       "       [ 0.16153197,  0.55290039,  0.12582951, -0.25667716, -0.22435228,\n",
       "         0.75142459,  0.2110822 ,  0.1401289 ,  0.21579395,  0.34406729,\n",
       "        -0.02658268, -0.48349462,  0.13380128,  1.        ,  0.76312364],\n",
       "       [-0.1013232 ,  0.3083636 , -0.18314454, -0.20474754, -0.15465711,\n",
       "         0.54908603,  0.05137372,  0.32734426,  0.12198557,  0.51532152,\n",
       "        -0.08375413, -0.15823967, -0.01197139,  0.76312364,  1.        ]])\n",
       "Coordinates:\n",
       "  * states   (states) &lt;U5 &#x27;Minn.&#x27; &#x27;Ore.&#x27; &#x27;W.V.&#x27; &#x27;Colo.&#x27; ... &#x27;S.C.&#x27; &#x27;Mo.&#x27; &#x27;Me.&#x27;\n",
       "  * states2  (states2) &lt;U5 &#x27;Minn.&#x27; &#x27;Ore.&#x27; &#x27;W.V.&#x27; &#x27;Colo.&#x27; ... &#x27;S.C.&#x27; &#x27;Mo.&#x27; &#x27;Me.&#x27;</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'></div><ul class='xr-dim-list'><li><span class='xr-has-index'>states</span>: 15</li><li><span class='xr-has-index'>states2</span>: 15</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-9f0b61c4-3f0c-42da-a139-64093e2f1144' class='xr-array-in' type='checkbox' checked><label for='section-9f0b61c4-3f0c-42da-a139-64093e2f1144' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>1.0 0.03442 0.3864 -0.07976 0.09105 ... -0.1582 -0.01197 0.7631 1.0</span></div><div class='xr-array-data'><pre>array([[ 1.        ,  0.03441832,  0.38643505, -0.07976265,  0.09105109,\n",
       "         0.42902949,  0.54000149, -0.2001398 , -0.16359479,  0.39857437,\n",
       "        -0.41862503,  0.00407398,  0.24416662,  0.16153197, -0.1013232 ],\n",
       "       [ 0.03441832,  1.        ,  0.54395222,  0.04505798, -0.71879028,\n",
       "         0.70270642,  0.11988758,  0.32194749,  0.53092095,  0.27405009,\n",
       "        -0.06846273, -0.29837119,  0.24861438,  0.55290039,  0.3083636 ],\n",
       "       [ 0.38643505,  0.54395222,  1.        ,  0.15943397, -0.52147548,\n",
       "         0.27804868,  0.54802347, -0.05533194,  0.65584101, -0.01827702,\n",
       "         0.118318  , -0.18986529,  0.3304217 ,  0.12582951, -0.18314454],\n",
       "       [-0.07976265,  0.04505798,  0.15943397,  1.        , -0.28096961,\n",
       "        -0.07873268,  0.30313415, -0.35616486,  0.46340679, -0.12017062,\n",
       "         0.44906319, -0.42544936, -0.53697489, -0.25667716, -0.20474754],\n",
       "       [ 0.09105109, -0.71879028, -0.52147548, -0.28096961,  1.        ,\n",
       "        -0.36757624, -0.14708579, -0.57070211, -0.7336755 ,  0.12368646,\n",
       "        -0.347708  ,  0.27980789, -0.17137721, -0.22435228, -0.15465711],\n",
       "       [ 0.42902949,  0.70270642,  0.27804868, -0.07873268, -0.36757624,\n",
       "         1.        ,  0.09943432,  0.32588167,  0.05757196,  0.63026963,\n",
       "        -0.18183588, -0.4693994 ,  0.24743514,  0.75142459,  0.54908603],\n",
       "       [ 0.54000149,  0.11988758,  0.54802347,  0.30313415, -0.14708579,\n",
       "         0.09943432,  1.        , -0.44594338,  0.47455692, -0.05466285,\n",
       "...\n",
       "         0.05757196,  0.47455692, -0.01884356,  1.        , -0.2248089 ,\n",
       "         0.48722261, -0.31236974,  0.00977628,  0.21579395,  0.12198557],\n",
       "       [ 0.39857437,  0.27405009, -0.01827702, -0.12017062,  0.12368646,\n",
       "         0.63026963, -0.05466285,  0.05484929, -0.2248089 ,  1.        ,\n",
       "        -0.45779029,  0.02705076,  0.31851429,  0.34406729,  0.51532152],\n",
       "       [-0.41862503, -0.06846273,  0.118318  ,  0.44906319, -0.347708  ,\n",
       "        -0.18183588, -0.1662455 ,  0.06662939,  0.48722261, -0.45779029,\n",
       "         1.        , -0.67659362,  0.02921328, -0.02658268, -0.08375413],\n",
       "       [ 0.00407398, -0.29837119, -0.18986529, -0.42544936,  0.27980789,\n",
       "        -0.4693994 ,  0.00321205,  0.06405049, -0.31236974,  0.02705076,\n",
       "        -0.67659362,  1.        ,  0.02097718, -0.48349462, -0.15823967],\n",
       "       [ 0.24416662,  0.24861438,  0.3304217 , -0.53697489, -0.17137721,\n",
       "         0.24743514, -0.32662798,  0.36732884,  0.00977628,  0.31851429,\n",
       "         0.02921328,  0.02097718,  1.        ,  0.13380128, -0.01197139],\n",
       "       [ 0.16153197,  0.55290039,  0.12582951, -0.25667716, -0.22435228,\n",
       "         0.75142459,  0.2110822 ,  0.1401289 ,  0.21579395,  0.34406729,\n",
       "        -0.02658268, -0.48349462,  0.13380128,  1.        ,  0.76312364],\n",
       "       [-0.1013232 ,  0.3083636 , -0.18314454, -0.20474754, -0.15465711,\n",
       "         0.54908603,  0.05137372,  0.32734426,  0.12198557,  0.51532152,\n",
       "        -0.08375413, -0.15823967, -0.01197139,  0.76312364,  1.        ]])</pre></div></div></li><li class='xr-section-item'><input id='section-3c475fe3-5e63-436c-a1fd-53482d62a2aa' class='xr-section-summary-in' type='checkbox'  checked><label for='section-3c475fe3-5e63-436c-a1fd-53482d62a2aa' class='xr-section-summary' >Coordinates: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>states</span></div><div class='xr-var-dims'>(states)</div><div class='xr-var-dtype'>&lt;U5</div><div class='xr-var-preview xr-preview'>&#x27;Minn.&#x27; &#x27;Ore.&#x27; ... &#x27;Mo.&#x27; &#x27;Me.&#x27;</div><input id='attrs-e3096dff-580c-4c0b-bfe8-33e83ea43691' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-e3096dff-580c-4c0b-bfe8-33e83ea43691' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-11f89c42-015e-4daa-9066-cde47b8e224c' class='xr-var-data-in' type='checkbox'><label for='data-11f89c42-015e-4daa-9066-cde47b8e224c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;Minn.&#x27;, &#x27;Ore.&#x27;, &#x27;W.V.&#x27;, &#x27;Colo.&#x27;, &#x27;Ala.&#x27;, &#x27;Ill.&#x27;, &#x27;Nev.&#x27;, &#x27;Nebr.&#x27;,\n",
       "       &#x27;Okla.&#x27;, &#x27;Ida.&#x27;, &#x27;Fla.&#x27;, &#x27;La.&#x27;, &#x27;S.C.&#x27;, &#x27;Mo.&#x27;, &#x27;Me.&#x27;], dtype=&#x27;&lt;U5&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>states2</span></div><div class='xr-var-dims'>(states2)</div><div class='xr-var-dtype'>&lt;U5</div><div class='xr-var-preview xr-preview'>&#x27;Minn.&#x27; &#x27;Ore.&#x27; ... &#x27;Mo.&#x27; &#x27;Me.&#x27;</div><input id='attrs-3ea27be1-bc15-44f6-a01d-d3b0574ddc11' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-3ea27be1-bc15-44f6-a01d-d3b0574ddc11' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3153f2ea-fd2a-4aa5-a917-f458dfd09b09' class='xr-var-data-in' type='checkbox'><label for='data-3153f2ea-fd2a-4aa5-a917-f458dfd09b09' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;Minn.&#x27;, &#x27;Ore.&#x27;, &#x27;W.V.&#x27;, &#x27;Colo.&#x27;, &#x27;Ala.&#x27;, &#x27;Ill.&#x27;, &#x27;Nev.&#x27;, &#x27;Nebr.&#x27;,\n",
       "       &#x27;Okla.&#x27;, &#x27;Ida.&#x27;, &#x27;Fla.&#x27;, &#x27;La.&#x27;, &#x27;S.C.&#x27;, &#x27;Mo.&#x27;, &#x27;Me.&#x27;], dtype=&#x27;&lt;U5&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-3052b3eb-1b85-49c2-ba48-ab51e0a7ee30' class='xr-section-summary-in' type='checkbox'  ><label for='section-3052b3eb-1b85-49c2-ba48-ab51e0a7ee30' class='xr-section-summary' >Indexes: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>states</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-17bcdb0d-1204-44b7-a4f1-6b82ed5fb856' class='xr-index-data-in' type='checkbox'/><label for='index-17bcdb0d-1204-44b7-a4f1-6b82ed5fb856' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([&#x27;Minn.&#x27;, &#x27;Ore.&#x27;, &#x27;W.V.&#x27;, &#x27;Colo.&#x27;, &#x27;Ala.&#x27;, &#x27;Ill.&#x27;, &#x27;Nev.&#x27;, &#x27;Nebr.&#x27;,\n",
       "       &#x27;Okla.&#x27;, &#x27;Ida.&#x27;, &#x27;Fla.&#x27;, &#x27;La.&#x27;, &#x27;S.C.&#x27;, &#x27;Mo.&#x27;, &#x27;Me.&#x27;],\n",
       "      dtype=&#x27;object&#x27;, name=&#x27;states&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>states2</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-e6f6cdcb-48cb-4a22-8c04-540a3557532e' class='xr-index-data-in' type='checkbox'/><label for='index-e6f6cdcb-48cb-4a22-8c04-540a3557532e' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([&#x27;Minn.&#x27;, &#x27;Ore.&#x27;, &#x27;W.V.&#x27;, &#x27;Colo.&#x27;, &#x27;Ala.&#x27;, &#x27;Ill.&#x27;, &#x27;Nev.&#x27;, &#x27;Nebr.&#x27;,\n",
       "       &#x27;Okla.&#x27;, &#x27;Ida.&#x27;, &#x27;Fla.&#x27;, &#x27;La.&#x27;, &#x27;S.C.&#x27;, &#x27;Mo.&#x27;, &#x27;Me.&#x27;],\n",
       "      dtype=&#x27;object&#x27;, name=&#x27;states2&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-55e89c5d-0923-475c-aca4-dc097015bb83' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-55e89c5d-0923-475c-aca4-dc097015bb83' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.DataArray (states: 15, states2: 15)>\n",
       "array([[ 1.        ,  0.03441832,  0.38643505, -0.07976265,  0.09105109,\n",
       "         0.42902949,  0.54000149, -0.2001398 , -0.16359479,  0.39857437,\n",
       "        -0.41862503,  0.00407398,  0.24416662,  0.16153197, -0.1013232 ],\n",
       "       [ 0.03441832,  1.        ,  0.54395222,  0.04505798, -0.71879028,\n",
       "         0.70270642,  0.11988758,  0.32194749,  0.53092095,  0.27405009,\n",
       "        -0.06846273, -0.29837119,  0.24861438,  0.55290039,  0.3083636 ],\n",
       "       [ 0.38643505,  0.54395222,  1.        ,  0.15943397, -0.52147548,\n",
       "         0.27804868,  0.54802347, -0.05533194,  0.65584101, -0.01827702,\n",
       "         0.118318  , -0.18986529,  0.3304217 ,  0.12582951, -0.18314454],\n",
       "       [-0.07976265,  0.04505798,  0.15943397,  1.        , -0.28096961,\n",
       "        -0.07873268,  0.30313415, -0.35616486,  0.46340679, -0.12017062,\n",
       "         0.44906319, -0.42544936, -0.53697489, -0.25667716, -0.20474754],\n",
       "       [ 0.09105109, -0.71879028, -0.52147548, -0.28096961,  1.        ,\n",
       "        -0.36757624, -0.14708579, -0.57070211, -0.7336755 ,  0.12368646,\n",
       "        -0.347708  ,  0.27980789, -0.17137721, -0.22435228, -0.15465711],\n",
       "       [ 0.42902949,  0.70270642,  0.27804868, -0.07873268, -0.36757624,\n",
       "         1.        ,  0.09943432,  0.32588167,  0.05757196,  0.63026963,\n",
       "        -0.18183588, -0.4693994 ,  0.24743514,  0.75142459,  0.54908603],\n",
       "       [ 0.54000149,  0.11988758,  0.54802347,  0.30313415, -0.14708579,\n",
       "         0.09943432,  1.        , -0.44594338,  0.47455692, -0.05466285,\n",
       "...\n",
       "         0.05757196,  0.47455692, -0.01884356,  1.        , -0.2248089 ,\n",
       "         0.48722261, -0.31236974,  0.00977628,  0.21579395,  0.12198557],\n",
       "       [ 0.39857437,  0.27405009, -0.01827702, -0.12017062,  0.12368646,\n",
       "         0.63026963, -0.05466285,  0.05484929, -0.2248089 ,  1.        ,\n",
       "        -0.45779029,  0.02705076,  0.31851429,  0.34406729,  0.51532152],\n",
       "       [-0.41862503, -0.06846273,  0.118318  ,  0.44906319, -0.347708  ,\n",
       "        -0.18183588, -0.1662455 ,  0.06662939,  0.48722261, -0.45779029,\n",
       "         1.        , -0.67659362,  0.02921328, -0.02658268, -0.08375413],\n",
       "       [ 0.00407398, -0.29837119, -0.18986529, -0.42544936,  0.27980789,\n",
       "        -0.4693994 ,  0.00321205,  0.06405049, -0.31236974,  0.02705076,\n",
       "        -0.67659362,  1.        ,  0.02097718, -0.48349462, -0.15823967],\n",
       "       [ 0.24416662,  0.24861438,  0.3304217 , -0.53697489, -0.17137721,\n",
       "         0.24743514, -0.32662798,  0.36732884,  0.00977628,  0.31851429,\n",
       "         0.02921328,  0.02097718,  1.        ,  0.13380128, -0.01197139],\n",
       "       [ 0.16153197,  0.55290039,  0.12582951, -0.25667716, -0.22435228,\n",
       "         0.75142459,  0.2110822 ,  0.1401289 ,  0.21579395,  0.34406729,\n",
       "        -0.02658268, -0.48349462,  0.13380128,  1.        ,  0.76312364],\n",
       "       [-0.1013232 ,  0.3083636 , -0.18314454, -0.20474754, -0.15465711,\n",
       "         0.54908603,  0.05137372,  0.32734426,  0.12198557,  0.51532152,\n",
       "        -0.08375413, -0.15823967, -0.01197139,  0.76312364,  1.        ]])\n",
       "Coordinates:\n",
       "  * states   (states) <U5 'Minn.' 'Ore.' 'W.V.' 'Colo.' ... 'S.C.' 'Mo.' 'Me.'\n",
       "  * states2  (states2) <U5 'Minn.' 'Ore.' 'W.V.' 'Colo.' ... 'S.C.' 'Mo.' 'Me.'"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "states_corr"
   ]
  },
  {
   "cell_type": "code",
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   "id": "0fda7db3-d439-42d1-9acf-4c0a92bcbf8c",
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       "      <th>states</th>\n",
       "      <th>states2</th>\n",
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       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Minn.</td>\n",
       "      <td>Minn.</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Minn.</td>\n",
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       "      <td>0.034418</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Minn.</td>\n",
       "      <td>W.V.</td>\n",
       "      <td>0.386435</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Minn.</td>\n",
       "      <td>Colo.</td>\n",
       "      <td>-0.079763</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Minn.</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
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       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>220</th>\n",
       "      <td>Me.</td>\n",
       "      <td>Fla.</td>\n",
       "      <td>-0.083754</td>\n",
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       "    <tr>\n",
       "      <th>221</th>\n",
       "      <td>Me.</td>\n",
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       "    <tr>\n",
       "      <th>222</th>\n",
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       "    <tr>\n",
       "      <th>223</th>\n",
       "      <td>Me.</td>\n",
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       "    <tr>\n",
       "      <th>224</th>\n",
       "      <td>Me.</td>\n",
       "      <td>Me.</td>\n",
       "      <td>1.000000</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>225 rows × 3 columns</p>\n",
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      ],
      "text/plain": [
       "    states states2      corr\n",
       "0    Minn.   Minn.  1.000000\n",
       "1    Minn.    Ore.  0.034418\n",
       "2    Minn.    W.V.  0.386435\n",
       "3    Minn.   Colo. -0.079763\n",
       "4    Minn.    Ala.  0.091051\n",
       "..     ...     ...       ...\n",
       "220    Me.    Fla. -0.083754\n",
       "221    Me.     La. -0.158240\n",
       "222    Me.    S.C. -0.011971\n",
       "223    Me.     Mo.  0.763124\n",
       "224    Me.     Me.  1.000000\n",
       "\n",
       "[225 rows x 3 columns]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "states_corr_df = states_corr.to_dataframe('corr').reset_index()\n",
    "states_corr_df"
   ]
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   "id": "a852ac4e-ef86-4e3d-897a-109e50dd06d8",
   "metadata": {},
   "source": [
    "This is the same as we calculated before!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "ebb94bed-192a-4d4e-bc60-c7508f6cd366",
   "metadata": {
    "tags": []
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   "outputs": [
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       "   states states2      corr\n",
       "17   Ore.    W.V.  0.543952"
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   "id": "d69860de-aeaa-497c-a920-f32e0ed2e92e",
   "metadata": {},
   "source": [
    "# Computing correlations in EEG data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "4fd64a74-26ef-41df-9233-a41dd63a5ab9",
   "metadata": {},
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   "source": [
    "subs = ['R1045E', 'R1102P', 'R1108J', 'R1141T', 'R1144E', 'R1157C',\n",
    "       'R1192C', 'R1202M', 'R1226D', 'R1236J', 'R1269E', 'R1277J',\n",
    "       'R1291M', 'R1310J', 'R1328E', 'R1330D', 'R1337E', 'R1351M',\n",
    "       'R1354E', 'R1361C', 'R1375C', 'R1383J', 'R1389J', 'R1390M',\n",
    "       'R1395M', 'R1401J', 'R1403N', 'R1412M', 'R1465D', 'R1468J',\n",
    "       'R1474T', 'R1476J', 'R1477J', 'R1482J', 'R1486J', 'R1490T',\n",
    "       'R1497T', 'R1501J', 'R1515T', 'R1525J', 'R1527J', 'R1530J',\n",
    "       'R1536J', 'R1541T']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "7d5939f2-ba47-40c8-a08d-48342f11964a",
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   "source": [
    "ix = cml.get_data_index()\n",
    "exp = 'catFR1'\n",
    "exp_ix = ix.query('experiment == @exp and subject == @subs')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1d209a56-9cf0-4114-9c88-406d182aa9f4",
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    "tags": []
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   "source": [
    "## Preprocess data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6448531e-d8c9-4cb7-b77e-81b2cec2478f",
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   "source": [
    "We first load the data and preprocess into power at different frequency bands both during encoding (300ms to 1300 ms after the word appears on screen) and right before retrieval (900 to 100ms before vocalization). In order to make signals comparable across encoding and retrieval, we standardize both of them by data from during the 10s countdown periods that preceded each list. This means that all of the data is relative to the countdown period (e.g. there is .5 standard deviations more power in the 3 Hz band than the average of the countdown period)."
   ]
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  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "35cbf95b-42e0-41dc-8683-fad623b9e9b9",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/global/miniconda/py310_23.1.0-1/envs/workshop_311/lib/python3.11/site-packages/cmlreaders/readers/electrodes.py:237: MissingCoordinatesWarning: Could not load MNI coordinates\n",
      "  warnings.warn(cmlreaders.warnings.MissingCoordinatesWarning(\n"
     ]
    }
   ],
   "source": [
    "sub = 'R1045E'\n",
    "df = cml.get_data_index('r1', '/')\n",
    "\n",
    "enc_range_left_ms = 300\n",
    "enc_range_right_ms = 1300\n",
    "morlet_reps = 6\n",
    "freqs = np.logspace(np.log10(3), np.log10(180), 8)\n",
    "resameplerate = 500\n",
    "enc_buffer_time = (morlet_reps / 2) * (1000 / min(freqs))\n",
    "\n",
    "df_sub = df.query('subject == @sub and experiment == \"catFR1\"').iloc[:1]\n",
    "reader = cml.CMLReader(subject=sub, session=df_sub.session.item(), experiment=df_sub.experiment.item(),\n",
    "                   localization=df_sub.localization.item(), montage=df_sub.montage.item())\n",
    "pairs = reader.load('pairs')\n",
    "evs = reader.load('events')\n",
    "\n",
    "enc_evs = evs.query('type == \"WORD\" and eegoffset != -1')\n",
    "enc_evs = enc_evs[enc_evs['list'] > 0]\n",
    "enc_evs['category'] = enc_evs ['category'].str.lower()\n",
    "enc_evs = enc_evs.iloc[:25]\n",
    "\n",
    "eeg = reader.load_eeg(events=enc_evs, rel_start=enc_range_left_ms-enc_buffer_time, \\\n",
    "                      rel_stop=enc_range_right_ms+enc_buffer_time, scheme=pairs)\n",
    "\n",
    "eeg_ptsa = eeg.to_ptsa()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "cdd96fce-5d4a-454e-b334-4597883aee4b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPP total time wavelet loop:  1.5913777351379395\n"
     ]
    }
   ],
   "source": [
    "# centering signal within event\n",
    "# reduce edge effects / ringing in later processing steps:\n",
    "eeg_ptsa = eeg_ptsa.astype(float) - eeg_ptsa.mean('time')\n",
    "# filter out line noise at 60 Hz\n",
    "eeg_ptsa = ButterworthFilter(filt_type='stop', \n",
    "                        freq_range=[58, 62], \n",
    "                        order=4).filter(timeseries=eeg_ptsa)\n",
    "# compute spectral features!\n",
    "wf = MorletWaveletFilter(freqs=freqs, width=morlet_reps, output='power', complete=True)\n",
    "# freqs, events, elecs, and time\n",
    "powers = wf.filter(eeg_ptsa)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "ef7e01a4-6c73-4a27-b5ff-20d271048f06",
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   "outputs": [
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       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-var-list > li:nth-child(odd) > div,\n",
       ".xr-var-list > li:nth-child(odd) > label,\n",
       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-odd);\n",
       "}\n",
       "\n",
       ".xr-var-name {\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-var-dims {\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-var-dtype {\n",
       "  grid-column: 3;\n",
       "  text-align: right;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-preview {\n",
       "  grid-column: 4;\n",
       "}\n",
       "\n",
       ".xr-index-preview {\n",
       "  grid-column: 2 / 5;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-name,\n",
       ".xr-var-dims,\n",
       ".xr-var-dtype,\n",
       ".xr-preview,\n",
       ".xr-attrs dt {\n",
       "  white-space: nowrap;\n",
       "  overflow: hidden;\n",
       "  text-overflow: ellipsis;\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-var-name:hover,\n",
       ".xr-var-dims:hover,\n",
       ".xr-var-dtype:hover,\n",
       ".xr-attrs dt:hover {\n",
       "  overflow: visible;\n",
       "  width: auto;\n",
       "  z-index: 1;\n",
       "}\n",
       "\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-data {\n",
       "  display: none;\n",
       "  background-color: var(--xr-background-color) !important;\n",
       "  padding-bottom: 5px !important;\n",
       "}\n",
       "\n",
       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
       ".xr-var-data-in:checked ~ .xr-var-data,\n",
       ".xr-index-data-in:checked ~ .xr-index-data {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       ".xr-var-data > table {\n",
       "  float: right;\n",
       "}\n",
       "\n",
       ".xr-var-name span,\n",
       ".xr-var-data,\n",
       ".xr-index-name div,\n",
       ".xr-index-data,\n",
       ".xr-attrs {\n",
       "  padding-left: 25px !important;\n",
       "}\n",
       "\n",
       ".xr-attrs,\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-data {\n",
       "  grid-column: 1 / -1;\n",
       "}\n",
       "\n",
       "dl.xr-attrs {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  display: grid;\n",
       "  grid-template-columns: 125px auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt,\n",
       ".xr-attrs dd {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  float: left;\n",
       "  padding-right: 10px;\n",
       "  width: auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt {\n",
       "  font-weight: normal;\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-attrs dt:hover span {\n",
       "  display: inline-block;\n",
       "  background: var(--xr-background-color);\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-attrs dd {\n",
       "  grid-column: 2;\n",
       "  white-space: pre-wrap;\n",
       "  word-break: break-all;\n",
       "}\n",
       "\n",
       ".xr-icon-database,\n",
       ".xr-icon-file-text2,\n",
       ".xr-no-icon {\n",
       "  display: inline-block;\n",
       "  vertical-align: middle;\n",
       "  width: 1em;\n",
       "  height: 1.5em !important;\n",
       "  stroke-width: 0;\n",
       "  stroke: currentColor;\n",
       "  fill: currentColor;\n",
       "}\n",
       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.TimeSeries (frequency: 8, event: 25, channel: 109, time: 2996)&gt;\n",
       "array([[[[4.76698658, 4.76837155, 4.76983572, ..., 5.54551823,\n",
       "          5.54236658, 5.53920995],\n",
       "         [5.26733142, 5.2696319 , 5.2719315 , ..., 5.75266228,\n",
       "          5.75048357, 5.74829783],\n",
       "         [5.79720135, 5.7984041 , 5.79960466, ..., 5.66487594,\n",
       "          5.66164329, 5.65840377],\n",
       "         ...,\n",
       "         [7.42371459, 7.4252635 , 7.42680657, ..., 7.23797571,\n",
       "          7.23771718, 7.23744412],\n",
       "         [8.0730876 , 8.07455616, 8.07601977, ..., 7.66902121,\n",
       "          7.66974936, 7.67046042],\n",
       "         [6.95665514, 6.95914469, 6.9616686 , ..., 7.4421983 ,\n",
       "          7.44137383, 7.44054555]],\n",
       "\n",
       "        [[6.29680283, 6.29773813, 6.2986665 , ..., 5.8095292 ,\n",
       "          5.80688945, 5.80424107],\n",
       "         [6.25504632, 6.25616804, 6.25728279, ..., 5.04261047,\n",
       "          5.04198665, 5.04135961],\n",
       "         [6.24923723, 6.25056288, 6.25188073, ..., 5.62650455,\n",
       "          5.62530809, 5.62410542],\n",
       "...\n",
       "         [4.82416156, 4.80698482, 4.76908004, ..., 5.53752147,\n",
       "          5.59770832, 5.63049257],\n",
       "         [6.16943301, 6.15169855, 6.10301215, ..., 4.08423835,\n",
       "          4.21804311, 4.30355384],\n",
       "         [3.79268563, 3.83195721, 3.85911613, ..., 5.2938577 ,\n",
       "          5.41181234, 5.4865739 ]],\n",
       "\n",
       "        [[3.83426593, 3.89587473, 3.9406835 , ..., 3.67078452,\n",
       "          3.58820491, 3.48871817],\n",
       "         [3.86000907, 3.97933348, 4.07867495, ..., 4.01986538,\n",
       "          4.00345129, 3.95946936],\n",
       "         [3.71246538, 3.8065744 , 3.89274212, ..., 4.44216539,\n",
       "          4.35617906, 4.24257741],\n",
       "         ...,\n",
       "         [4.02072673, 4.07731805, 4.12568859, ..., 4.52433797,\n",
       "          4.60461783, 4.65976923],\n",
       "         [6.47338594, 6.45389213, 6.40024712, ..., 5.48008215,\n",
       "          5.57389463, 5.63347391],\n",
       "         [6.54323639, 6.5275412 , 6.47956407, ..., 6.51611073,\n",
       "          6.59356775, 6.63962062]]]])\n",
       "Coordinates: (12/31)\n",
       "  * event         (event) object MultiIndex\n",
       "  * answer        (event) int64 -999 -999 -999 -999 -999 ... -999 -999 -999 -999\n",
       "  * category      (event) object &#x27;officesupplies&#x27; ... &#x27;electronics&#x27;\n",
       "  * category_num  (event) int64 17 17 14 14 3 3 17 17 3 ... 6 15 15 6 6 23 23 7\n",
       "  * eegfile       (event) object &#x27;/protocols/r1/subjects/R1045E/experiments/c...\n",
       "  * eegoffset     (event) int64 491162 493597 495997 ... 636895 639329 710970\n",
       "    ...            ...\n",
       "  * test          (event) object (0, 0, 0) (0, 0, 0) ... (0, 0, 0) (0, 0, 0)\n",
       "  * type          (event) object &#x27;WORD&#x27; &#x27;WORD&#x27; &#x27;WORD&#x27; ... &#x27;WORD&#x27; &#x27;WORD&#x27; &#x27;WORD&#x27;\n",
       "  * channel       (channel) &lt;U16 &#x27;LAFS1-LAFS2&#x27; &#x27;LAFS2-LAFS3&#x27; ... &#x27;RPHGD5-RPHGD7&#x27;\n",
       "  * time          (time) float64 -700.0 -699.0 -698.0 ... 2.297e+03 2.298e+03\n",
       "    samplerate    float64 999.0\n",
       "  * frequency     (frequency) float64 3.0 5.384 9.664 ... 55.88 100.3 180.0</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.TimeSeries</div><div class='xr-array-name'></div><ul class='xr-dim-list'><li><span class='xr-has-index'>frequency</span>: 8</li><li><span class='xr-has-index'>event</span>: 25</li><li><span class='xr-has-index'>channel</span>: 109</li><li><span class='xr-has-index'>time</span>: 2996</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-9957800f-0deb-4141-a976-4716389562cb' class='xr-array-in' type='checkbox' checked><label for='section-9957800f-0deb-4141-a976-4716389562cb' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>4.767 4.768 4.77 4.771 4.773 4.775 ... 6.268 6.408 6.516 6.594 6.64</span></div><div class='xr-array-data'><pre>array([[[[4.76698658, 4.76837155, 4.76983572, ..., 5.54551823,\n",
       "          5.54236658, 5.53920995],\n",
       "         [5.26733142, 5.2696319 , 5.2719315 , ..., 5.75266228,\n",
       "          5.75048357, 5.74829783],\n",
       "         [5.79720135, 5.7984041 , 5.79960466, ..., 5.66487594,\n",
       "          5.66164329, 5.65840377],\n",
       "         ...,\n",
       "         [7.42371459, 7.4252635 , 7.42680657, ..., 7.23797571,\n",
       "          7.23771718, 7.23744412],\n",
       "         [8.0730876 , 8.07455616, 8.07601977, ..., 7.66902121,\n",
       "          7.66974936, 7.67046042],\n",
       "         [6.95665514, 6.95914469, 6.9616686 , ..., 7.4421983 ,\n",
       "          7.44137383, 7.44054555]],\n",
       "\n",
       "        [[6.29680283, 6.29773813, 6.2986665 , ..., 5.8095292 ,\n",
       "          5.80688945, 5.80424107],\n",
       "         [6.25504632, 6.25616804, 6.25728279, ..., 5.04261047,\n",
       "          5.04198665, 5.04135961],\n",
       "         [6.24923723, 6.25056288, 6.25188073, ..., 5.62650455,\n",
       "          5.62530809, 5.62410542],\n",
       "...\n",
       "         [4.82416156, 4.80698482, 4.76908004, ..., 5.53752147,\n",
       "          5.59770832, 5.63049257],\n",
       "         [6.16943301, 6.15169855, 6.10301215, ..., 4.08423835,\n",
       "          4.21804311, 4.30355384],\n",
       "         [3.79268563, 3.83195721, 3.85911613, ..., 5.2938577 ,\n",
       "          5.41181234, 5.4865739 ]],\n",
       "\n",
       "        [[3.83426593, 3.89587473, 3.9406835 , ..., 3.67078452,\n",
       "          3.58820491, 3.48871817],\n",
       "         [3.86000907, 3.97933348, 4.07867495, ..., 4.01986538,\n",
       "          4.00345129, 3.95946936],\n",
       "         [3.71246538, 3.8065744 , 3.89274212, ..., 4.44216539,\n",
       "          4.35617906, 4.24257741],\n",
       "         ...,\n",
       "         [4.02072673, 4.07731805, 4.12568859, ..., 4.52433797,\n",
       "          4.60461783, 4.65976923],\n",
       "         [6.47338594, 6.45389213, 6.40024712, ..., 5.48008215,\n",
       "          5.57389463, 5.63347391],\n",
       "         [6.54323639, 6.5275412 , 6.47956407, ..., 6.51611073,\n",
       "          6.59356775, 6.63962062]]]])</pre></div></div></li><li class='xr-section-item'><input id='section-cf3fe0f5-ef2a-4d41-83a9-9d55b26d2561' class='xr-section-summary-in' type='checkbox'  ><label for='section-cf3fe0f5-ef2a-4d41-83a9-9d55b26d2561' class='xr-section-summary' >Coordinates: <span>(31)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>event</span></div><div class='xr-var-dims'>(event)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>MultiIndex</div><input id='attrs-d64c13cc-335a-4aee-a297-ec38c9dccb38' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-d64c13cc-335a-4aee-a297-ec38c9dccb38' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-adc6be96-6fd3-4925-b591-7728887ce081' class='xr-var-data-in' type='checkbox'><label for='data-adc6be96-6fd3-4925-b591-7728887ce081' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([(-999, &#x27;officesupplies&#x27;, 17, &#x27;/protocols/r1/subjects/R1045E/experiments/catFR1/sessions/0/ephys/current_processed/noreref/R1045E_catFR1_0_01May15_1542&#x27;, 491162, &#x27;1.05&#x27;, &#x27;catFR1&#x27;, -999, 0, -999, &#x27;PAPER&#x27;, 207, 1, 0, 1, 1430510360829, &#x27;r1&#x27;, 0, -999, 1, 0, 0, (), &#x27;R1045E&#x27;, (0, 0, 0), &#x27;WORD&#x27;),\n",
       "       (-999, &#x27;officesupplies&#x27;, 17, &#x27;/protocols/r1/subjects/R1045E/experiments/catFR1/sessions/0/ephys/current_processed/noreref/R1045E_catFR1_0_01May15_1542&#x27;, 493597, &#x27;1.05&#x27;, &#x27;catFR1&#x27;, -999, 0, -999, &#x27;BINDER&#x27;, 211, 1, 0, 1, 1430510363264, &#x27;r1&#x27;, 0, -999, 2, 0, 0, (), &#x27;R1045E&#x27;, (0, 0, 0), &#x27;WORD&#x27;),\n",
       "       (-999, &#x27;kitchentools&#x27;, 14, &#x27;/protocols/r1/subjects/R1045E/experiments/catFR1/sessions/0/ephys/current_processed/noreref/R1045E_catFR1_0_01May15_1542&#x27;, 495997, &#x27;1.05&#x27;, &#x27;catFR1&#x27;, -999, 0, -999, &#x27;PEELER&#x27;, 176, 1, 0, 1, 1430510365664, &#x27;r1&#x27;, 0, -999, 3, 0, 0, (), &#x27;R1045E&#x27;, (0, 0, 0), &#x27;WORD&#x27;),\n",
       "       (-999, &#x27;kitchentools&#x27;, 14, &#x27;/protocols/r1/subjects/R1045E/experiments/catFR1/sessions/0/ephys/current_processed/noreref/R1045E_catFR1_0_01May15_1542&#x27;, 498580, &#x27;1.05&#x27;, &#x27;catFR1&#x27;, -999, 0, -999, &#x27;SPOON&#x27;, 169, 1, 0, 1, 1430510368248, &#x27;r1&#x27;, 0, -999, 4, 0, 0, (), &#x27;R1045E&#x27;, (0, 0, 0), &#x27;WORD&#x27;),\n",
       "       (-999, &#x27;bodyparts&#x27;, 3, &#x27;/protocols/r1/subjects/R1045E/experiments/catFR1/sessions/0/ephys/current_processed/noreref/R1045E_catFR1_0_01May15_1542&#x27;, 501196, &#x27;1.05&#x27;, &#x27;catFR1&#x27;, -999, 0, -999, &#x27;HAND&#x27;, 41, 1, 0, 1, 1430510370864, &#x27;r1&#x27;, 0, -999, 5, 0, 0, (), &#x27;R1045E&#x27;, (0, 0, 0), &#x27;WORD&#x27;),\n",
       "       (-999, &#x27;bodyparts&#x27;, 3, &#x27;/protocols/r1/subjects/R1045E/experiments/catFR1/sessions/0/ephys/current_processed/noreref/R1045E_catFR1_0_01May15_1542&#x27;, 503598, &#x27;1.05&#x27;, &#x27;catFR1&#x27;, -999, 0, -999, &#x27;ELBOW&#x27;, 47, 1, 0, 1, 1430510373266, &#x27;r1&#x27;, 1, 1375, 6, 0, 0, (), &#x27;R1045E&#x27;, (0, 0, 0), &#x27;WORD&#x27;),\n",
       "       (-999, &#x27;officesupplies&#x27;, 17, &#x27;/protocols/r1/subjects/R1045E/experiments/catFR1/sessions/0/ephys/current_processed/noreref/R1045E_catFR1_0_01May15_1542&#x27;, 506048, &#x27;1.05&#x27;, &#x27;catFR1&#x27;, -999, 0, -999, &#x27;GLUE&#x27;, 216, 1, 0, 1, 1430510375716, &#x27;r1&#x27;, 1, 12215, 7, 0, 0, (), &#x27;R1045E&#x27;, (0, 0, 0), &#x27;WORD&#x27;),\n",
       "       (-999, &#x27;officesupplies&#x27;, 17, &#x27;/protocols/r1/subjects/R1045E/experiments/catFR1/sessions/0/ephys/current_processed/noreref/R1045E_catFR1_0_01May15_1542&#x27;, 508565, &#x27;1.05&#x27;, &#x27;catFR1&#x27;, -999, 0, -999, &#x27;STAPLER&#x27;, 212, 1, 0, 1, 1430510378233, &#x27;r1&#x27;, 0, -999, 8, 0, 0, (), &#x27;R1045E&#x27;, (0, 0, 0), &#x27;WORD&#x27;),\n",
       "       (-999, &#x27;bodyparts&#x27;, 3, &#x27;/protocols/r1/subjects/R1045E/experiments/catFR1/sessions/0/ephys/current_processed/noreref/R1045E_catFR1_0_01May15_1542&#x27;, 511181, &#x27;1.05&#x27;, &#x27;catFR1&#x27;, -999, 0, -999, &#x27;FOOT&#x27;, 37, 1, 0, 1, 1430510380849, &#x27;r1&#x27;, 0, -999, 9, 0, 0, (), &#x27;R1045E&#x27;, (0, 0, 0), &#x27;WORD&#x27;),\n",
       "       (-999, &#x27;bodyparts&#x27;, 3, &#x27;/protocols/r1/subjects/R1045E/experiments/catFR1/sessions/0/ephys/current_processed/noreref/R1045E_catFR1_0_01May15_1542&#x27;, 513782, &#x27;1.05&#x27;, &#x27;catFR1&#x27;, -999, 0, -999, &#x27;NOSE&#x27;, 44, 1, 0, 1, 1430510383451, &#x27;r1&#x27;, 0, -999, 10, 0, 0, (), &#x27;R1045E&#x27;, (0, 0, 0), &#x27;WORD&#x27;),\n",
       "       (-999, &#x27;kitchentools&#x27;, 14, &#x27;/protocols/r1/subjects/R1045E/experiments/catFR1/sessions/0/ephys/current_processed/noreref/R1045E_catFR1_0_01May15_1542&#x27;, 516349, &#x27;1.05&#x27;, &#x27;catFR1&#x27;, -999, 0, -999, &#x27;KNIFE&#x27;, 170, 1, 0, 1, 1430510386018, &#x27;r1&#x27;, 0, -999, 11, 0, 0, (), &#x27;R1045E&#x27;, (0, 0, 0), &#x27;WORD&#x27;),\n",
       "       (-999, &#x27;kitchentools&#x27;, 14, &#x27;/protocols/r1/subjects/R1045E/experiments/catFR1/sessions/0/ephys/current_processed/noreref/R1045E_catFR1_0_01May15_1542&#x27;, 518766, &#x27;1.05&#x27;, &#x27;catFR1&#x27;, -999, 0, -999, &#x27;STRAINER&#x27;, 174, 1, 0, 1, 1430510388435, &#x27;r1&#x27;, 1, 7905, 12, 0, 0, (), &#x27;R1045E&#x27;, (0, 0, 0), &#x27;WORD&#x27;),\n",
       "       (-999, &#x27;landscapes&#x27;, 15, &#x27;/protocols/r1/subjects/R1045E/experiments/catFR1/sessions/0/ephys/current_processed/noreref/R1045E_catFR1_0_01May15_1542&#x27;, 611960, &#x27;1.05&#x27;, &#x27;catFR1&#x27;, -999, 0, -999, &#x27;HILLS&#x27;, 186, 2, 0, 1, 1430510481635, &#x27;r1&#x27;, 1, 20007, 1, 0, 0, (), &#x27;R1045E&#x27;, (0, 0, 0), &#x27;WORD&#x27;),\n",
       "       (-999, &#x27;landscapes&#x27;, 15, &#x27;/protocols/r1/subjects/R1045E/experiments/catFR1/sessions/0/ephys/current_processed/noreref/R1045E_catFR1_0_01May15_1542&#x27;, 614343, &#x27;1.05&#x27;, &#x27;catFR1&#x27;, -999, 0, -999, &#x27;BEACH&#x27;, 191, 2, 0, 1, 1430510484018, &#x27;r1&#x27;, 0, -999, 2, 0, 0, (), &#x27;R1045E&#x27;, (0, 0, 0), &#x27;WORD&#x27;),\n",
       "       (-999, &#x27;weather&#x27;, 23, &#x27;/protocols/r1/subjects/R1045E/experiments/catFR1/sessions/0/ephys/current_processed/noreref/R1045E_catFR1_0_01May15_1542&#x27;, 616960, &#x27;1.05&#x27;, &#x27;catFR1&#x27;, -999, 0, -999, &#x27;HUMIDITY&#x27;, 279, 2, 0, 1, 1430510486635, &#x27;r1&#x27;, 0, -999, 3, 0, 0, (), &#x27;R1045E&#x27;, (0, 0, 0), &#x27;WORD&#x27;),\n",
       "       (-999, &#x27;weather&#x27;, 23, &#x27;/protocols/r1/subjects/R1045E/experiments/catFR1/sessions/0/ephys/current_processed/noreref/R1045E_catFR1_0_01May15_1542&#x27;, 619410, &#x27;1.05&#x27;, &#x27;catFR1&#x27;, -999, 0, -999, &#x27;RAIN&#x27;, 277, 2, 0, 1, 1430510489086, &#x27;r1&#x27;, 0, -999, 4, 0, 0, (), &#x27;R1045E&#x27;, (0, 0, 0), &#x27;WORD&#x27;),\n",
       "       (-999, &#x27;desserts&#x27;, 6, &#x27;/protocols/r1/subjects/R1045E/experiments/catFR1/sessions/0/ephys/current_processed/noreref/R1045E_catFR1_0_01May15_1542&#x27;, 621927, &#x27;1.05&#x27;, &#x27;catFR1&#x27;, -999, 0, -999, &#x27;MOUSSE&#x27;, 83, 2, 0, 1, 1430510491603, &#x27;r1&#x27;, 1, 3252, 5, 0, 0, (), &#x27;R1045E&#x27;, (0, 0, 0), &#x27;WORD&#x27;),\n",
       "       (-999, &#x27;desserts&#x27;, 6, &#x27;/protocols/r1/subjects/R1045E/experiments/catFR1/sessions/0/ephys/current_processed/noreref/R1045E_catFR1_0_01May15_1542&#x27;, 624361, &#x27;1.05&#x27;, &#x27;catFR1&#x27;, -999, 0, -999, &#x27;CAKE&#x27;, 73, 2, 0, 1, 1430510494037, &#x27;r1&#x27;, 0, -999, 6, 0, 0, (), &#x27;R1045E&#x27;, (0, 0, 0), &#x27;WORD&#x27;),\n",
       "       (-999, &#x27;landscapes&#x27;, 15, &#x27;/protocols/r1/subjects/R1045E/experiments/catFR1/sessions/0/ephys/current_processed/noreref/R1045E_catFR1_0_01May15_1542&#x27;, 626995, &#x27;1.05&#x27;, &#x27;catFR1&#x27;, -999, 0, -999, &#x27;STREAM&#x27;, 182, 2, 0, 1, 1430510496671, &#x27;r1&#x27;, 0, -999, 7, 0, 0, (), &#x27;R1045E&#x27;, (0, 0, 0), &#x27;WORD&#x27;),\n",
       "       (-999, &#x27;landscapes&#x27;, 15, &#x27;/protocols/r1/subjects/R1045E/experiments/catFR1/sessions/0/ephys/current_processed/noreref/R1045E_catFR1_0_01May15_1542&#x27;, 629478, &#x27;1.05&#x27;, &#x27;catFR1&#x27;, -999, 0, -999, &#x27;ISLAND&#x27;, 192, 2, 0, 1, 1430510499154, &#x27;r1&#x27;, 0, -999, 8, 0, 0, (), &#x27;R1045E&#x27;, (0, 0, 0), &#x27;WORD&#x27;),\n",
       "       (-999, &#x27;desserts&#x27;, 6, &#x27;/protocols/r1/subjects/R1045E/experiments/catFR1/sessions/0/ephys/current_processed/noreref/R1045E_catFR1_0_01May15_1542&#x27;, 632062, &#x27;1.05&#x27;, &#x27;catFR1&#x27;, -999, 0, -999, &#x27;COOKIE&#x27;, 78, 2, 0, 1, 1430510501738, &#x27;r1&#x27;, 0, -999, 9, 0, 0, (), &#x27;R1045E&#x27;, (0, 0, 0), &#x27;WORD&#x27;),\n",
       "       (-999, &#x27;desserts&#x27;, 6, &#x27;/protocols/r1/subjects/R1045E/experiments/catFR1/sessions/0/ephys/current_processed/noreref/R1045E_catFR1_0_01May15_1542&#x27;, 634445, &#x27;1.05&#x27;, &#x27;catFR1&#x27;, -999, 0, -999, &#x27;BROWNIES&#x27;, 75, 2, 0, 1, 1430510504122, &#x27;r1&#x27;, 0, -999, 10, 0, 0, (), &#x27;R1045E&#x27;, (0, 0, 0), &#x27;WORD&#x27;),\n",
       "       (-999, &#x27;weather&#x27;, 23, &#x27;/protocols/r1/subjects/R1045E/experiments/catFR1/sessions/0/ephys/current_processed/noreref/R1045E_catFR1_0_01May15_1542&#x27;, 636895, &#x27;1.05&#x27;, &#x27;catFR1&#x27;, -999, 0, -999, &#x27;FOG&#x27;, 285, 2, 0, 1, 1430510506572, &#x27;r1&#x27;, 1, 5648, 11, 0, 0, (), &#x27;R1045E&#x27;, (0, 0, 0), &#x27;WORD&#x27;),\n",
       "       (-999, &#x27;weather&#x27;, 23, &#x27;/protocols/r1/subjects/R1045E/experiments/catFR1/sessions/0/ephys/current_processed/noreref/R1045E_catFR1_0_01May15_1542&#x27;, 639329, &#x27;1.05&#x27;, &#x27;catFR1&#x27;, -999, 0, -999, &#x27;CLOUD&#x27;, 281, 2, 0, 1, 1430510509006, &#x27;r1&#x27;, 1, 3940, 12, 0, 0, (), &#x27;R1045E&#x27;, (0, 0, 0), &#x27;WORD&#x27;),\n",
       "       (-999, &#x27;electronics&#x27;, 7, &#x27;/protocols/r1/subjects/R1045E/experiments/catFR1/sessions/0/ephys/current_processed/noreref/R1045E_catFR1_0_01May15_1542&#x27;, 710970, &#x27;1.05&#x27;, &#x27;catFR1&#x27;, -999, 0, -999, &#x27;MICROWAVE&#x27;, 96, 3, 0, 1, 1430510580652, &#x27;r1&#x27;, 1, 9012, 1, 0, 0, (), &#x27;R1045E&#x27;, (0, 0, 0), &#x27;WORD&#x27;)],\n",
       "      dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>answer</span></div><div class='xr-var-dims'>(event)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>-999 -999 -999 ... -999 -999 -999</div><input id='attrs-da24eda5-7dcb-401d-9dc6-1434daf6cd7f' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-da24eda5-7dcb-401d-9dc6-1434daf6cd7f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0dd55ae9-b4a1-4f40-849b-d8155dc7cf12' class='xr-var-data-in' type='checkbox'><label for='data-0dd55ae9-b4a1-4f40-849b-d8155dc7cf12' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([-999, -999, -999, -999, -999, -999, -999, -999, -999, -999, -999, -999,\n",
       "       -999, -999, -999, -999, -999, -999, -999, -999, -999, -999, -999, -999,\n",
       "       -999])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>category</span></div><div class='xr-var-dims'>(event)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>&#x27;officesupplies&#x27; ... &#x27;electronics&#x27;</div><input id='attrs-96202351-7ec2-46eb-8a3f-e7c9620d3f58' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-96202351-7ec2-46eb-8a3f-e7c9620d3f58' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-eeb5df6d-1821-4ccc-b459-064a66aefe36' class='xr-var-data-in' type='checkbox'><label for='data-eeb5df6d-1821-4ccc-b459-064a66aefe36' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;officesupplies&#x27;, &#x27;officesupplies&#x27;, &#x27;kitchentools&#x27;, &#x27;kitchentools&#x27;,\n",
       "       &#x27;bodyparts&#x27;, &#x27;bodyparts&#x27;, &#x27;officesupplies&#x27;, &#x27;officesupplies&#x27;,\n",
       "       &#x27;bodyparts&#x27;, &#x27;bodyparts&#x27;, &#x27;kitchentools&#x27;, &#x27;kitchentools&#x27;, &#x27;landscapes&#x27;,\n",
       "       &#x27;landscapes&#x27;, &#x27;weather&#x27;, &#x27;weather&#x27;, &#x27;desserts&#x27;, &#x27;desserts&#x27;,\n",
       "       &#x27;landscapes&#x27;, &#x27;landscapes&#x27;, &#x27;desserts&#x27;, &#x27;desserts&#x27;, &#x27;weather&#x27;,\n",
       "       &#x27;weather&#x27;, &#x27;electronics&#x27;], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>category_num</span></div><div class='xr-var-dims'>(event)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>17 17 14 14 3 3 ... 15 6 6 23 23 7</div><input id='attrs-ef9a8bee-f660-40d8-83f4-6f2db864b4e7' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-ef9a8bee-f660-40d8-83f4-6f2db864b4e7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5f5f9ed5-d28c-427f-8b53-f8bda166b450' class='xr-var-data-in' type='checkbox'><label for='data-5f5f9ed5-d28c-427f-8b53-f8bda166b450' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([17, 17, 14, 14,  3,  3, 17, 17,  3,  3, 14, 14, 15, 15, 23, 23,  6,  6,\n",
       "       15, 15,  6,  6, 23, 23,  7])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>eegfile</span></div><div class='xr-var-dims'>(event)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>&#x27;/protocols/r1/subjects/R1045E/e...</div><input id='attrs-2fc16f6e-90b4-4dc9-b78d-9fc477a0f1ff' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-2fc16f6e-90b4-4dc9-b78d-9fc477a0f1ff' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4d53ae80-3035-4e37-a9dc-dbec0d04905c' class='xr-var-data-in' type='checkbox'><label for='data-4d53ae80-3035-4e37-a9dc-dbec0d04905c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;/protocols/r1/subjects/R1045E/experiments/catFR1/sessions/0/ephys/current_processed/noreref/R1045E_catFR1_0_01May15_1542&#x27;,\n",
       "       &#x27;/protocols/r1/subjects/R1045E/experiments/catFR1/sessions/0/ephys/current_processed/noreref/R1045E_catFR1_0_01May15_1542&#x27;,\n",
       "       &#x27;/protocols/r1/subjects/R1045E/experiments/catFR1/sessions/0/ephys/current_processed/noreref/R1045E_catFR1_0_01May15_1542&#x27;,\n",
       "       &#x27;/protocols/r1/subjects/R1045E/experiments/catFR1/sessions/0/ephys/current_processed/noreref/R1045E_catFR1_0_01May15_1542&#x27;,\n",
       "       &#x27;/protocols/r1/subjects/R1045E/experiments/catFR1/sessions/0/ephys/current_processed/noreref/R1045E_catFR1_0_01May15_1542&#x27;,\n",
       "       &#x27;/protocols/r1/subjects/R1045E/experiments/catFR1/sessions/0/ephys/current_processed/noreref/R1045E_catFR1_0_01May15_1542&#x27;,\n",
       "       &#x27;/protocols/r1/subjects/R1045E/experiments/catFR1/sessions/0/ephys/current_processed/noreref/R1045E_catFR1_0_01May15_1542&#x27;,\n",
       "       &#x27;/protocols/r1/subjects/R1045E/experiments/catFR1/sessions/0/ephys/current_processed/noreref/R1045E_catFR1_0_01May15_1542&#x27;,\n",
       "       &#x27;/protocols/r1/subjects/R1045E/experiments/catFR1/sessions/0/ephys/current_processed/noreref/R1045E_catFR1_0_01May15_1542&#x27;,\n",
       "       &#x27;/protocols/r1/subjects/R1045E/experiments/catFR1/sessions/0/ephys/current_processed/noreref/R1045E_catFR1_0_01May15_1542&#x27;,\n",
       "       &#x27;/protocols/r1/subjects/R1045E/experiments/catFR1/sessions/0/ephys/current_processed/noreref/R1045E_catFR1_0_01May15_1542&#x27;,\n",
       "       &#x27;/protocols/r1/subjects/R1045E/experiments/catFR1/sessions/0/ephys/current_processed/noreref/R1045E_catFR1_0_01May15_1542&#x27;,\n",
       "       &#x27;/protocols/r1/subjects/R1045E/experiments/catFR1/sessions/0/ephys/current_processed/noreref/R1045E_catFR1_0_01May15_1542&#x27;,\n",
       "       &#x27;/protocols/r1/subjects/R1045E/experiments/catFR1/sessions/0/ephys/current_processed/noreref/R1045E_catFR1_0_01May15_1542&#x27;,\n",
       "       &#x27;/protocols/r1/subjects/R1045E/experiments/catFR1/sessions/0/ephys/current_processed/noreref/R1045E_catFR1_0_01May15_1542&#x27;,\n",
       "       &#x27;/protocols/r1/subjects/R1045E/experiments/catFR1/sessions/0/ephys/current_processed/noreref/R1045E_catFR1_0_01May15_1542&#x27;,\n",
       "       &#x27;/protocols/r1/subjects/R1045E/experiments/catFR1/sessions/0/ephys/current_processed/noreref/R1045E_catFR1_0_01May15_1542&#x27;,\n",
       "       &#x27;/protocols/r1/subjects/R1045E/experiments/catFR1/sessions/0/ephys/current_processed/noreref/R1045E_catFR1_0_01May15_1542&#x27;,\n",
       "       &#x27;/protocols/r1/subjects/R1045E/experiments/catFR1/sessions/0/ephys/current_processed/noreref/R1045E_catFR1_0_01May15_1542&#x27;,\n",
       "       &#x27;/protocols/r1/subjects/R1045E/experiments/catFR1/sessions/0/ephys/current_processed/noreref/R1045E_catFR1_0_01May15_1542&#x27;,\n",
       "       &#x27;/protocols/r1/subjects/R1045E/experiments/catFR1/sessions/0/ephys/current_processed/noreref/R1045E_catFR1_0_01May15_1542&#x27;,\n",
       "       &#x27;/protocols/r1/subjects/R1045E/experiments/catFR1/sessions/0/ephys/current_processed/noreref/R1045E_catFR1_0_01May15_1542&#x27;,\n",
       "       &#x27;/protocols/r1/subjects/R1045E/experiments/catFR1/sessions/0/ephys/current_processed/noreref/R1045E_catFR1_0_01May15_1542&#x27;,\n",
       "       &#x27;/protocols/r1/subjects/R1045E/experiments/catFR1/sessions/0/ephys/current_processed/noreref/R1045E_catFR1_0_01May15_1542&#x27;,\n",
       "       &#x27;/protocols/r1/subjects/R1045E/experiments/catFR1/sessions/0/ephys/current_processed/noreref/R1045E_catFR1_0_01May15_1542&#x27;],\n",
       "      dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>eegoffset</span></div><div class='xr-var-dims'>(event)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>491162 493597 ... 639329 710970</div><input id='attrs-3bfc49b3-5f11-4636-9568-d4e449629284' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-3bfc49b3-5f11-4636-9568-d4e449629284' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-db6cda77-6d17-4c12-a0c8-dc5b192678d6' class='xr-var-data-in' type='checkbox'><label for='data-db6cda77-6d17-4c12-a0c8-dc5b192678d6' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([491162, 493597, 495997, 498580, 501196, 503598, 506048, 508565, 511181,\n",
       "       513782, 516349, 518766, 611960, 614343, 616960, 619410, 621927, 624361,\n",
       "       626995, 629478, 632062, 634445, 636895, 639329, 710970])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>exp_version</span></div><div class='xr-var-dims'>(event)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>&#x27;1.05&#x27; &#x27;1.05&#x27; ... &#x27;1.05&#x27; &#x27;1.05&#x27;</div><input id='attrs-8ab17f07-7dc2-4b71-86cb-0e726d8a949b' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-8ab17f07-7dc2-4b71-86cb-0e726d8a949b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d110f98a-3906-4b94-bbca-1511cef873b3' class='xr-var-data-in' type='checkbox'><label for='data-d110f98a-3906-4b94-bbca-1511cef873b3' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;1.05&#x27;, &#x27;1.05&#x27;, &#x27;1.05&#x27;, &#x27;1.05&#x27;, &#x27;1.05&#x27;, &#x27;1.05&#x27;, &#x27;1.05&#x27;, &#x27;1.05&#x27;, &#x27;1.05&#x27;,\n",
       "       &#x27;1.05&#x27;, &#x27;1.05&#x27;, &#x27;1.05&#x27;, &#x27;1.05&#x27;, &#x27;1.05&#x27;, &#x27;1.05&#x27;, &#x27;1.05&#x27;, &#x27;1.05&#x27;, &#x27;1.05&#x27;,\n",
       "       &#x27;1.05&#x27;, &#x27;1.05&#x27;, &#x27;1.05&#x27;, &#x27;1.05&#x27;, &#x27;1.05&#x27;, &#x27;1.05&#x27;, &#x27;1.05&#x27;], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>experiment</span></div><div class='xr-var-dims'>(event)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>&#x27;catFR1&#x27; &#x27;catFR1&#x27; ... &#x27;catFR1&#x27;</div><input id='attrs-db9c9b40-7a5e-42f5-8fb8-40412c2420f1' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-db9c9b40-7a5e-42f5-8fb8-40412c2420f1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-88de49c5-aae5-4f33-a237-7798ee64f9da' class='xr-var-data-in' type='checkbox'><label for='data-88de49c5-aae5-4f33-a237-7798ee64f9da' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;catFR1&#x27;, &#x27;catFR1&#x27;, &#x27;catFR1&#x27;, &#x27;catFR1&#x27;, &#x27;catFR1&#x27;, &#x27;catFR1&#x27;, &#x27;catFR1&#x27;,\n",
       "       &#x27;catFR1&#x27;, &#x27;catFR1&#x27;, &#x27;catFR1&#x27;, &#x27;catFR1&#x27;, &#x27;catFR1&#x27;, &#x27;catFR1&#x27;, &#x27;catFR1&#x27;,\n",
       "       &#x27;catFR1&#x27;, &#x27;catFR1&#x27;, &#x27;catFR1&#x27;, &#x27;catFR1&#x27;, &#x27;catFR1&#x27;, &#x27;catFR1&#x27;, &#x27;catFR1&#x27;,\n",
       "       &#x27;catFR1&#x27;, &#x27;catFR1&#x27;, &#x27;catFR1&#x27;, &#x27;catFR1&#x27;], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>intrusion</span></div><div class='xr-var-dims'>(event)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>-999 -999 -999 ... -999 -999 -999</div><input id='attrs-72d0a86f-a6d3-4091-ae4f-2f52b8a52667' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-72d0a86f-a6d3-4091-ae4f-2f52b8a52667' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-58cd4a59-dce3-4376-9fe8-35e306117d64' class='xr-var-data-in' type='checkbox'><label for='data-58cd4a59-dce3-4376-9fe8-35e306117d64' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([-999, -999, -999, -999, -999, -999, -999, -999, -999, -999, -999, -999,\n",
       "       -999, -999, -999, -999, -999, -999, -999, -999, -999, -999, -999, -999,\n",
       "       -999])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>is_stim</span></div><div class='xr-var-dims'>(event)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 0 0 0 0 0 0 0 ... 0 0 0 0 0 0 0 0</div><input id='attrs-357af6de-81b5-4280-8397-f271e6592bd2' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-357af6de-81b5-4280-8397-f271e6592bd2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-044c0ae9-d659-4a7f-b83b-f1c4dcd7675e' class='xr-var-data-in' type='checkbox'><label for='data-044c0ae9-d659-4a7f-b83b-f1c4dcd7675e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>iscorrect</span></div><div class='xr-var-dims'>(event)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>-999 -999 -999 ... -999 -999 -999</div><input id='attrs-5c212dbc-879e-4839-b4cb-11fd7c40b819' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-5c212dbc-879e-4839-b4cb-11fd7c40b819' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-66bce5df-b095-4706-bed8-19f773ee852a' class='xr-var-data-in' type='checkbox'><label for='data-66bce5df-b095-4706-bed8-19f773ee852a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([-999, -999, -999, -999, -999, -999, -999, -999, -999, -999, -999, -999,\n",
       "       -999, -999, -999, -999, -999, -999, -999, -999, -999, -999, -999, -999,\n",
       "       -999])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>item_name</span></div><div class='xr-var-dims'>(event)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>&#x27;PAPER&#x27; &#x27;BINDER&#x27; ... &#x27;MICROWAVE&#x27;</div><input id='attrs-9090b112-6f79-4628-928f-769d90a7d600' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-9090b112-6f79-4628-928f-769d90a7d600' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b9fd19ba-4b0a-4b9e-abdd-08ab1bfc9eb5' class='xr-var-data-in' type='checkbox'><label for='data-b9fd19ba-4b0a-4b9e-abdd-08ab1bfc9eb5' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;PAPER&#x27;, &#x27;BINDER&#x27;, &#x27;PEELER&#x27;, &#x27;SPOON&#x27;, &#x27;HAND&#x27;, &#x27;ELBOW&#x27;, &#x27;GLUE&#x27;,\n",
       "       &#x27;STAPLER&#x27;, &#x27;FOOT&#x27;, &#x27;NOSE&#x27;, &#x27;KNIFE&#x27;, &#x27;STRAINER&#x27;, &#x27;HILLS&#x27;, &#x27;BEACH&#x27;,\n",
       "       &#x27;HUMIDITY&#x27;, &#x27;RAIN&#x27;, &#x27;MOUSSE&#x27;, &#x27;CAKE&#x27;, &#x27;STREAM&#x27;, &#x27;ISLAND&#x27;, &#x27;COOKIE&#x27;,\n",
       "       &#x27;BROWNIES&#x27;, &#x27;FOG&#x27;, &#x27;CLOUD&#x27;, &#x27;MICROWAVE&#x27;], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>item_num</span></div><div class='xr-var-dims'>(event)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>207 211 176 169 ... 75 285 281 96</div><input id='attrs-084a9168-fa99-41b2-a196-2b8bc8733c50' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-084a9168-fa99-41b2-a196-2b8bc8733c50' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-fc33ea5c-2651-4c75-a1a0-494b16ed203b' class='xr-var-data-in' type='checkbox'><label for='data-fc33ea5c-2651-4c75-a1a0-494b16ed203b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([207, 211, 176, 169,  41,  47, 216, 212,  37,  44, 170, 174, 186, 191,\n",
       "       279, 277,  83,  73, 182, 192,  78,  75, 285, 281,  96])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>list</span></div><div class='xr-var-dims'>(event)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>1 1 1 1 1 1 1 1 ... 2 2 2 2 2 2 2 3</div><input id='attrs-a7a0e41c-b3c8-40f4-9ede-a82dcc3377e1' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-a7a0e41c-b3c8-40f4-9ede-a82dcc3377e1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b448ec27-bbb0-41d9-8e10-7e195e6c8ebf' class='xr-var-data-in' type='checkbox'><label for='data-b448ec27-bbb0-41d9-8e10-7e195e6c8ebf' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
       "       3])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>montage</span></div><div class='xr-var-dims'>(event)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 0 0 0 0 0 0 0 ... 0 0 0 0 0 0 0 0</div><input id='attrs-ba4ebab3-a31d-48ed-b3a7-7ec4477d495c' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-ba4ebab3-a31d-48ed-b3a7-7ec4477d495c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3a63d11a-2d5a-455b-b19e-b10b24e6fbf0' class='xr-var-data-in' type='checkbox'><label for='data-3a63d11a-2d5a-455b-b19e-b10b24e6fbf0' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>msoffset</span></div><div class='xr-var-dims'>(event)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>1 1 1 1 1 1 1 1 ... 1 1 1 1 1 1 1 1</div><input id='attrs-ddfecf35-d3d0-4b50-9dbb-21136c49c673' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-ddfecf35-d3d0-4b50-9dbb-21136c49c673' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-045e2961-9f7b-4fa7-beb7-7952cbc6ab0d' class='xr-var-data-in' type='checkbox'><label for='data-045e2961-9f7b-4fa7-beb7-7952cbc6ab0d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>mstime</span></div><div class='xr-var-dims'>(event)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>1430510360829 ... 1430510580652</div><input id='attrs-345bdcb7-dc8a-46ef-8e00-7bea3811145b' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-345bdcb7-dc8a-46ef-8e00-7bea3811145b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9850f718-51ef-4219-a533-ec6b746ad561' class='xr-var-data-in' type='checkbox'><label for='data-9850f718-51ef-4219-a533-ec6b746ad561' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([1430510360829, 1430510363264, 1430510365664, 1430510368248,\n",
       "       1430510370864, 1430510373266, 1430510375716, 1430510378233,\n",
       "       1430510380849, 1430510383451, 1430510386018, 1430510388435,\n",
       "       1430510481635, 1430510484018, 1430510486635, 1430510489086,\n",
       "       1430510491603, 1430510494037, 1430510496671, 1430510499154,\n",
       "       1430510501738, 1430510504122, 1430510506572, 1430510509006,\n",
       "       1430510580652])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>protocol</span></div><div class='xr-var-dims'>(event)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>&#x27;r1&#x27; &#x27;r1&#x27; &#x27;r1&#x27; ... &#x27;r1&#x27; &#x27;r1&#x27; &#x27;r1&#x27;</div><input id='attrs-259e7fa4-306c-4c30-af8c-32ccad9f0c77' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-259e7fa4-306c-4c30-af8c-32ccad9f0c77' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f9001ef9-523b-4016-894c-d2c769324159' class='xr-var-data-in' type='checkbox'><label for='data-f9001ef9-523b-4016-894c-d2c769324159' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;r1&#x27;, &#x27;r1&#x27;, &#x27;r1&#x27;, &#x27;r1&#x27;, &#x27;r1&#x27;, &#x27;r1&#x27;, &#x27;r1&#x27;, &#x27;r1&#x27;, &#x27;r1&#x27;, &#x27;r1&#x27;, &#x27;r1&#x27;, &#x27;r1&#x27;,\n",
       "       &#x27;r1&#x27;, &#x27;r1&#x27;, &#x27;r1&#x27;, &#x27;r1&#x27;, &#x27;r1&#x27;, &#x27;r1&#x27;, &#x27;r1&#x27;, &#x27;r1&#x27;, &#x27;r1&#x27;, &#x27;r1&#x27;, &#x27;r1&#x27;, &#x27;r1&#x27;,\n",
       "       &#x27;r1&#x27;], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>recalled</span></div><div class='xr-var-dims'>(event)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 0 0 0 0 1 1 0 ... 0 0 0 0 0 1 1 1</div><input id='attrs-077f881c-360f-4c95-a387-8cedcead3bb4' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-077f881c-360f-4c95-a387-8cedcead3bb4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-58995991-a5ae-4a21-a0e2-59ed10da0e8c' class='xr-var-data-in' type='checkbox'><label for='data-58995991-a5ae-4a21-a0e2-59ed10da0e8c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 1,\n",
       "       1])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>rectime</span></div><div class='xr-var-dims'>(event)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>-999 -999 -999 ... 5648 3940 9012</div><input id='attrs-70e02b4b-eb81-47e4-8055-70bd2ca834c7' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-70e02b4b-eb81-47e4-8055-70bd2ca834c7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c1e011a6-e4be-449a-9da0-e58131264735' class='xr-var-data-in' type='checkbox'><label for='data-c1e011a6-e4be-449a-9da0-e58131264735' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([ -999,  -999,  -999,  -999,  -999,  1375, 12215,  -999,  -999,  -999,\n",
       "        -999,  7905, 20007,  -999,  -999,  -999,  3252,  -999,  -999,  -999,\n",
       "        -999,  -999,  5648,  3940,  9012])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>serialpos</span></div><div class='xr-var-dims'>(event)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>1 2 3 4 5 6 7 ... 7 8 9 10 11 12 1</div><input id='attrs-22124aaf-0ee2-436c-a460-6941031dd230' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-22124aaf-0ee2-436c-a460-6941031dd230' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-15dc5c14-530d-4eb9-8c2f-b1aea1d2e52d' class='xr-var-data-in' type='checkbox'><label for='data-15dc5c14-530d-4eb9-8c2f-b1aea1d2e52d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([ 1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12,  1,  2,  3,  4,  5,  6,\n",
       "        7,  8,  9, 10, 11, 12,  1])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>session</span></div><div class='xr-var-dims'>(event)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 0 0 0 0 0 0 0 ... 0 0 0 0 0 0 0 0</div><input id='attrs-7441621d-06b8-448a-8cdc-8f8c1b1d9ffc' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-7441621d-06b8-448a-8cdc-8f8c1b1d9ffc' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3dc98a35-740d-4d1f-b5fa-190453d59fd9' class='xr-var-data-in' type='checkbox'><label for='data-3dc98a35-740d-4d1f-b5fa-190453d59fd9' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>stim_list</span></div><div class='xr-var-dims'>(event)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 0 0 0 0 0 0 0 ... 0 0 0 0 0 0 0 0</div><input id='attrs-8e1bce52-a924-49e0-acfa-0a303741571c' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-8e1bce52-a924-49e0-acfa-0a303741571c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4d450a56-662f-41c4-89b8-07a02072dde0' class='xr-var-data-in' type='checkbox'><label for='data-4d450a56-662f-41c4-89b8-07a02072dde0' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>stim_params</span></div><div class='xr-var-dims'>(event)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>() () () () () ... () () () () ()</div><input id='attrs-e152f6a7-78bc-48eb-8c52-c0a1139a7a53' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-e152f6a7-78bc-48eb-8c52-c0a1139a7a53' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-42e3af8f-798d-462f-a16e-05ff210dbb0d' class='xr-var-data-in' type='checkbox'><label for='data-42e3af8f-798d-462f-a16e-05ff210dbb0d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([(), (), (), (), (), (), (), (), (), (), (), (), (), (), (), (), (), (),\n",
       "       (), (), (), (), (), (), ()], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>subject</span></div><div class='xr-var-dims'>(event)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>&#x27;R1045E&#x27; &#x27;R1045E&#x27; ... &#x27;R1045E&#x27;</div><input id='attrs-c4f6b6c3-7ee6-4d4b-b0c7-a711e7310a72' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-c4f6b6c3-7ee6-4d4b-b0c7-a711e7310a72' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3427b36c-7ec7-4885-a16c-ebff084d258a' class='xr-var-data-in' type='checkbox'><label for='data-3427b36c-7ec7-4885-a16c-ebff084d258a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;R1045E&#x27;, &#x27;R1045E&#x27;, &#x27;R1045E&#x27;, &#x27;R1045E&#x27;, &#x27;R1045E&#x27;, &#x27;R1045E&#x27;, &#x27;R1045E&#x27;,\n",
       "       &#x27;R1045E&#x27;, &#x27;R1045E&#x27;, &#x27;R1045E&#x27;, &#x27;R1045E&#x27;, &#x27;R1045E&#x27;, &#x27;R1045E&#x27;, &#x27;R1045E&#x27;,\n",
       "       &#x27;R1045E&#x27;, &#x27;R1045E&#x27;, &#x27;R1045E&#x27;, &#x27;R1045E&#x27;, &#x27;R1045E&#x27;, &#x27;R1045E&#x27;, &#x27;R1045E&#x27;,\n",
       "       &#x27;R1045E&#x27;, &#x27;R1045E&#x27;, &#x27;R1045E&#x27;, &#x27;R1045E&#x27;], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>test</span></div><div class='xr-var-dims'>(event)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>(0, 0, 0) (0, 0, 0) ... (0, 0, 0)</div><input id='attrs-c99f90a9-7502-4f9f-9add-72bec6f0f11d' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-c99f90a9-7502-4f9f-9add-72bec6f0f11d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ef7bb9ce-4c8d-420e-8917-ec0d7a9d5867' class='xr-var-data-in' type='checkbox'><label for='data-ef7bb9ce-4c8d-420e-8917-ec0d7a9d5867' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([(0, 0, 0), (0, 0, 0), (0, 0, 0), (0, 0, 0), (0, 0, 0), (0, 0, 0),\n",
       "       (0, 0, 0), (0, 0, 0), (0, 0, 0), (0, 0, 0), (0, 0, 0), (0, 0, 0),\n",
       "       (0, 0, 0), (0, 0, 0), (0, 0, 0), (0, 0, 0), (0, 0, 0), (0, 0, 0),\n",
       "       (0, 0, 0), (0, 0, 0), (0, 0, 0), (0, 0, 0), (0, 0, 0), (0, 0, 0),\n",
       "       (0, 0, 0)], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>type</span></div><div class='xr-var-dims'>(event)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>&#x27;WORD&#x27; &#x27;WORD&#x27; ... &#x27;WORD&#x27; &#x27;WORD&#x27;</div><input id='attrs-4b4de93c-9cdb-46cc-b049-05540a092e35' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-4b4de93c-9cdb-46cc-b049-05540a092e35' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-de88dbc0-4117-4642-b717-95a0d33b9b21' class='xr-var-data-in' type='checkbox'><label for='data-de88dbc0-4117-4642-b717-95a0d33b9b21' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;WORD&#x27;, &#x27;WORD&#x27;, &#x27;WORD&#x27;, &#x27;WORD&#x27;, &#x27;WORD&#x27;, &#x27;WORD&#x27;, &#x27;WORD&#x27;, &#x27;WORD&#x27;, &#x27;WORD&#x27;,\n",
       "       &#x27;WORD&#x27;, &#x27;WORD&#x27;, &#x27;WORD&#x27;, &#x27;WORD&#x27;, &#x27;WORD&#x27;, &#x27;WORD&#x27;, &#x27;WORD&#x27;, &#x27;WORD&#x27;, &#x27;WORD&#x27;,\n",
       "       &#x27;WORD&#x27;, &#x27;WORD&#x27;, &#x27;WORD&#x27;, &#x27;WORD&#x27;, &#x27;WORD&#x27;, &#x27;WORD&#x27;, &#x27;WORD&#x27;], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>channel</span></div><div class='xr-var-dims'>(channel)</div><div class='xr-var-dtype'>&lt;U16</div><div class='xr-var-preview xr-preview'>&#x27;LAFS1-LAFS2&#x27; ... &#x27;RPHGD5-RPHGD7&#x27;</div><input id='attrs-623f7d71-9369-4794-a8ae-f628e4d264df' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-623f7d71-9369-4794-a8ae-f628e4d264df' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-359fe3d6-509f-4da8-a60f-15579bf3184e' class='xr-var-data-in' type='checkbox'><label for='data-359fe3d6-509f-4da8-a60f-15579bf3184e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;LAFS1-LAFS2&#x27;, &#x27;LAFS2-LAFS3&#x27;, &#x27;LAFS3-LAFS4&#x27;, &#x27;LAFS4-LAFS5&#x27;,\n",
       "       &#x27;LAFS5-LAFS6&#x27;, &#x27;LAFS6-LAFS7&#x27;, &#x27;LAFS7-LAFS8&#x27;, &#x27;LIFS1-LIFS2&#x27;,\n",
       "       &#x27;LIFS2-LIFS3&#x27;, &#x27;LIFS3-LIFS4&#x27;, &#x27;LIFS4-LIFS5&#x27;, &#x27;LIFS5-LIFS6&#x27;,\n",
       "       &#x27;LIFS6-LIFS8&#x27;, &#x27;LIFS8-LIFS9&#x27;, &#x27;LIFS9-LIFS10&#x27;, &#x27;LATS1-LATS2&#x27;,\n",
       "       &#x27;LATS2-LATS3&#x27;, &#x27;LATS3-LATS4&#x27;, &#x27;LATS4-LATS5&#x27;, &#x27;LATS5-LATS6&#x27;,\n",
       "       &#x27;LATS6-LATS7&#x27;, &#x27;LATS7-LATS8&#x27;, &#x27;LPTS1-LPTS2&#x27;, &#x27;LPTS2-LPTS3&#x27;,\n",
       "       &#x27;LPTS3-LPTS4&#x27;, &#x27;LPTS4-LPTS5&#x27;, &#x27;LPTS5-LPTS6&#x27;, &#x27;LPTS6-LPTS7&#x27;,\n",
       "       &#x27;LPTS7-LPTS8&#x27;, &#x27;LPTS8-LPTS9&#x27;, &#x27;LPTS9-LPTS10&#x27;, &#x27;LAMYD1-LAMYD2&#x27;,\n",
       "       &#x27;LAMYD2-LAMYD3&#x27;, &#x27;LAMYD3-LAMYD4&#x27;, &#x27;LAMYD4-LAMYD5&#x27;, &#x27;LAMYD5-LAMYD6&#x27;,\n",
       "       &#x27;LAMYD6-LAMYD7&#x27;, &#x27;LAMYD7-LAMYD8&#x27;, &#x27;LAMYD8-LAMYD9&#x27;, &#x27;LAMYD9-LAMYD10&#x27;,\n",
       "       &#x27;LAHD1-LAHD2&#x27;, &#x27;LAHD2-LAHD3&#x27;, &#x27;LAHD3-LAHD4&#x27;, &#x27;LAHD4-LAHD5&#x27;,\n",
       "       &#x27;LAHD5-LAHD6&#x27;, &#x27;LAHD6-LAHD7&#x27;, &#x27;LAHD7-LAHD8&#x27;, &#x27;LAHD8-LAHD9&#x27;,\n",
       "       &#x27;LAHD9-LAHD10&#x27;, &#x27;LMHD1-LMHD2&#x27;, &#x27;LMHD2-LMHD3&#x27;, &#x27;LMHD3-LMHD4&#x27;,\n",
       "       &#x27;LMHD4-LMHD5&#x27;, &#x27;LMHD5-LMHD6&#x27;, &#x27;LMHD6-LMHD7&#x27;, &#x27;LMHD7-LMHD8&#x27;,\n",
       "       &#x27;LMHD8-LMHD9&#x27;, &#x27;LMHD9-LMHD10&#x27;, &#x27;LPHD1-LPHD2&#x27;, &#x27;LPHD2-LPHD3&#x27;,\n",
       "       &#x27;LPHD3-LPHD4&#x27;, &#x27;LPHD4-LPHD5&#x27;, &#x27;LPHD5-LPHD6&#x27;, &#x27;LPHD6-LPHD7&#x27;,\n",
       "       &#x27;LPHD7-LPHD8&#x27;, &#x27;LPHD8-LPHD9&#x27;, &#x27;LPHD9-LPHD10&#x27;, &#x27;LSTGID1-LSTGID2&#x27;,\n",
       "       &#x27;LSTGID2-LSTGID3&#x27;, &#x27;LSTGID3-LSTGID4&#x27;, &#x27;LSTGID4-LSTGID5&#x27;,\n",
       "       &#x27;LSTGID5-LSTGID6&#x27;, &#x27;LSTGID6-LSTGID7&#x27;, &#x27;LSTGID7-LSTGID8&#x27;,\n",
       "       &#x27;LSTGID8-LSTGID9&#x27;, &#x27;LSTGID9-LSTGID10&#x27;, &#x27;LPHGD1-LPHGD2&#x27;, &#x27;LPHGD2-LPHGD3&#x27;,\n",
       "       &#x27;LPHGD3-LPHGD4&#x27;, &#x27;LPHGD4-LPHGD5&#x27;, &#x27;LPHGD5-LPHGD6&#x27;, &#x27;LPHGD6-LPHGD7&#x27;,\n",
       "       &#x27;LPHGD7-LPHGD8&#x27;, &#x27;LPHGD8-LPHGD9&#x27;, &#x27;LPHGD9-LPHGD10&#x27;, &#x27;RAFS1-RAFS3&#x27;,\n",
       "       &#x27;RAFS3-RAFS5&#x27;, &#x27;RAFS5-RAFS7&#x27;, &#x27;RATS1-RATS3&#x27;, &#x27;RATS3-RATS5&#x27;,\n",
       "       &#x27;RATS5-RATS7&#x27;, &#x27;RPTS1-RPTS3&#x27;, &#x27;RPTS3-RPTS5&#x27;, &#x27;RPTS5-RPTS7&#x27;,\n",
       "       &#x27;RAMYD1-RAMYD3&#x27;, &#x27;RAMYD3-RAMYD5&#x27;, &#x27;RAMYD5-RAMYD7&#x27;, &#x27;RAHD1-RAHD3&#x27;,\n",
       "       &#x27;RAHD3-RAHD5&#x27;, &#x27;RAHD5-RAHD7&#x27;, &#x27;RPHD1-RPHD3&#x27;, &#x27;RPHD3-RPHD5&#x27;,\n",
       "       &#x27;RPHD5-RPHD7&#x27;, &#x27;RSTGID1-RSTGID3&#x27;, &#x27;RSTGID3-RSTGID5&#x27;, &#x27;RSTGID5-RSTGID7&#x27;,\n",
       "       &#x27;RPHGD1-RPHGD3&#x27;, &#x27;RPHGD3-RPHGD5&#x27;, &#x27;RPHGD5-RPHGD7&#x27;], dtype=&#x27;&lt;U16&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-700.0 -699.0 ... 2.298e+03</div><input id='attrs-1d130c3e-42a3-4f6d-aa1a-3eb8682225ed' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-1d130c3e-42a3-4f6d-aa1a-3eb8682225ed' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e02cfba8-7073-4483-8d8e-fd4dbb341486' class='xr-var-data-in' type='checkbox'><label for='data-e02cfba8-7073-4483-8d8e-fd4dbb341486' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([-700.      , -698.998999, -697.997998, ..., 2295.995996, 2296.996997,\n",
       "       2297.997998])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>samplerate</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>999.0</div><input id='attrs-b0630212-a4b8-4d5f-b291-f9158a130ed9' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-b0630212-a4b8-4d5f-b291-f9158a130ed9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-941f9112-00a7-458c-9526-b6ecbc2b25c6' class='xr-var-data-in' type='checkbox'><label for='data-941f9112-00a7-458c-9526-b6ecbc2b25c6' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array(999.)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>frequency</span></div><div class='xr-var-dims'>(frequency)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>3.0 5.384 9.664 ... 100.3 180.0</div><input id='attrs-d48b46e2-a2ef-4473-9c28-20a571f2bcf5' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-d48b46e2-a2ef-4473-9c28-20a571f2bcf5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5113dfd6-88cd-494b-b03a-bbf29b13e1a5' class='xr-var-data-in' type='checkbox'><label for='data-5113dfd6-88cd-494b-b03a-bbf29b13e1a5' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([  3.      ,   5.384469,   9.664168,  17.34547 ,  31.132047,  55.876512,\n",
       "       100.288445, 180.      ])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-094d0234-9665-4ca4-9ba9-ec96759e882a' class='xr-section-summary-in' type='checkbox'  ><label for='section-094d0234-9665-4ca4-9ba9-ec96759e882a' class='xr-section-summary' >Indexes: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>event<br>answer<br>category<br>category_num<br>eegfile<br>eegoffset<br>exp_version<br>experiment<br>intrusion<br>is_stim<br>iscorrect<br>item_name<br>item_num<br>list<br>montage<br>msoffset<br>mstime<br>protocol<br>recalled<br>rectime<br>serialpos<br>session<br>stim_list<br>stim_params<br>subject<br>test<br>type</div></div><div class='xr-index-preview'>PandasMultiIndex</div><div></div><input id='index-5ff71984-9488-4111-b2f6-8b730e7df656' class='xr-index-data-in' type='checkbox'/><label for='index-5ff71984-9488-4111-b2f6-8b730e7df656' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(MultiIndex([(-999, &#x27;officesupplies&#x27;, 17, ...),\n",
       "            (-999, &#x27;officesupplies&#x27;, 17, ...),\n",
       "            (-999,   &#x27;kitchentools&#x27;, 14, ...),\n",
       "            (-999,   &#x27;kitchentools&#x27;, 14, ...),\n",
       "            (-999,      &#x27;bodyparts&#x27;,  3, ...),\n",
       "            (-999,      &#x27;bodyparts&#x27;,  3, ...),\n",
       "            (-999, &#x27;officesupplies&#x27;, 17, ...),\n",
       "            (-999, &#x27;officesupplies&#x27;, 17, ...),\n",
       "            (-999,      &#x27;bodyparts&#x27;,  3, ...),\n",
       "            (-999,      &#x27;bodyparts&#x27;,  3, ...),\n",
       "            (-999,   &#x27;kitchentools&#x27;, 14, ...),\n",
       "            (-999,   &#x27;kitchentools&#x27;, 14, ...),\n",
       "            (-999,     &#x27;landscapes&#x27;, 15, ...),\n",
       "            (-999,     &#x27;landscapes&#x27;, 15, ...),\n",
       "            (-999,        &#x27;weather&#x27;, 23, ...),\n",
       "            (-999,        &#x27;weather&#x27;, 23, ...),\n",
       "            (-999,       &#x27;desserts&#x27;,  6, ...),\n",
       "            (-999,       &#x27;desserts&#x27;,  6, ...),\n",
       "            (-999,     &#x27;landscapes&#x27;, 15, ...),\n",
       "            (-999,     &#x27;landscapes&#x27;, 15, ...),\n",
       "            (-999,       &#x27;desserts&#x27;,  6, ...),\n",
       "            (-999,       &#x27;desserts&#x27;,  6, ...),\n",
       "            (-999,        &#x27;weather&#x27;, 23, ...),\n",
       "            (-999,        &#x27;weather&#x27;, 23, ...),\n",
       "            (-999,    &#x27;electronics&#x27;,  7, ...)],\n",
       "           name=&#x27;event&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>channel</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-c9be6e59-a1f0-4da1-a194-07540391345e' class='xr-index-data-in' type='checkbox'/><label for='index-c9be6e59-a1f0-4da1-a194-07540391345e' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([&#x27;LAFS1-LAFS2&#x27;, &#x27;LAFS2-LAFS3&#x27;, &#x27;LAFS3-LAFS4&#x27;, &#x27;LAFS4-LAFS5&#x27;,\n",
       "       &#x27;LAFS5-LAFS6&#x27;, &#x27;LAFS6-LAFS7&#x27;, &#x27;LAFS7-LAFS8&#x27;, &#x27;LIFS1-LIFS2&#x27;,\n",
       "       &#x27;LIFS2-LIFS3&#x27;, &#x27;LIFS3-LIFS4&#x27;,\n",
       "       ...\n",
       "       &#x27;RAHD5-RAHD7&#x27;, &#x27;RPHD1-RPHD3&#x27;, &#x27;RPHD3-RPHD5&#x27;, &#x27;RPHD5-RPHD7&#x27;,\n",
       "       &#x27;RSTGID1-RSTGID3&#x27;, &#x27;RSTGID3-RSTGID5&#x27;, &#x27;RSTGID5-RSTGID7&#x27;,\n",
       "       &#x27;RPHGD1-RPHGD3&#x27;, &#x27;RPHGD3-RPHGD5&#x27;, &#x27;RPHGD5-RPHGD7&#x27;],\n",
       "      dtype=&#x27;object&#x27;, name=&#x27;channel&#x27;, length=109))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-83bf94fe-8c86-4b86-bb84-1d80deed6f71' class='xr-index-data-in' type='checkbox'/><label for='index-83bf94fe-8c86-4b86-bb84-1d80deed6f71' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([            -700.0,  -698.998998998999,  -697.997997997998,\n",
       "        -696.996996996997,  -695.995995995996, -694.9949949949951,\n",
       "       -693.9939939939941, -692.9929929929931, -691.9919919919921,\n",
       "       -690.9909909909911,\n",
       "       ...\n",
       "       2288.9889889889455, 2289.9899899899465, 2290.9909909909475,\n",
       "       2291.9919919919485, 2292.9929929929494, 2293.9939939939504,\n",
       "       2294.9949949949514, 2295.9959959959524, 2296.9969969969534,\n",
       "       2297.9979979979544],\n",
       "      dtype=&#x27;float64&#x27;, name=&#x27;time&#x27;, length=2996))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>frequency</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-29f3d473-47cf-47cd-b94c-e1f54186cfc8' class='xr-index-data-in' type='checkbox'/><label for='index-29f3d473-47cf-47cd-b94c-e1f54186cfc8' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([               3.0,  5.384468764137864,  9.664167957325446,\n",
       "        17.34547016586696, 31.132047435798615,  55.87651232723863,\n",
       "       100.28844509166032, 179.99999999999997],\n",
       "      dtype=&#x27;float64&#x27;, name=&#x27;frequency&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-3e9189c0-c7ac-4c89-8a92-388bd60243c7' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-3e9189c0-c7ac-4c89-8a92-388bd60243c7' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.TimeSeries (frequency: 8, event: 25, channel: 109, time: 2996)>\n",
       "array([[[[4.76698658, 4.76837155, 4.76983572, ..., 5.54551823,\n",
       "          5.54236658, 5.53920995],\n",
       "         [5.26733142, 5.2696319 , 5.2719315 , ..., 5.75266228,\n",
       "          5.75048357, 5.74829783],\n",
       "         [5.79720135, 5.7984041 , 5.79960466, ..., 5.66487594,\n",
       "          5.66164329, 5.65840377],\n",
       "         ...,\n",
       "         [7.42371459, 7.4252635 , 7.42680657, ..., 7.23797571,\n",
       "          7.23771718, 7.23744412],\n",
       "         [8.0730876 , 8.07455616, 8.07601977, ..., 7.66902121,\n",
       "          7.66974936, 7.67046042],\n",
       "         [6.95665514, 6.95914469, 6.9616686 , ..., 7.4421983 ,\n",
       "          7.44137383, 7.44054555]],\n",
       "\n",
       "        [[6.29680283, 6.29773813, 6.2986665 , ..., 5.8095292 ,\n",
       "          5.80688945, 5.80424107],\n",
       "         [6.25504632, 6.25616804, 6.25728279, ..., 5.04261047,\n",
       "          5.04198665, 5.04135961],\n",
       "         [6.24923723, 6.25056288, 6.25188073, ..., 5.62650455,\n",
       "          5.62530809, 5.62410542],\n",
       "...\n",
       "         [4.82416156, 4.80698482, 4.76908004, ..., 5.53752147,\n",
       "          5.59770832, 5.63049257],\n",
       "         [6.16943301, 6.15169855, 6.10301215, ..., 4.08423835,\n",
       "          4.21804311, 4.30355384],\n",
       "         [3.79268563, 3.83195721, 3.85911613, ..., 5.2938577 ,\n",
       "          5.41181234, 5.4865739 ]],\n",
       "\n",
       "        [[3.83426593, 3.89587473, 3.9406835 , ..., 3.67078452,\n",
       "          3.58820491, 3.48871817],\n",
       "         [3.86000907, 3.97933348, 4.07867495, ..., 4.01986538,\n",
       "          4.00345129, 3.95946936],\n",
       "         [3.71246538, 3.8065744 , 3.89274212, ..., 4.44216539,\n",
       "          4.35617906, 4.24257741],\n",
       "         ...,\n",
       "         [4.02072673, 4.07731805, 4.12568859, ..., 4.52433797,\n",
       "          4.60461783, 4.65976923],\n",
       "         [6.47338594, 6.45389213, 6.40024712, ..., 5.48008215,\n",
       "          5.57389463, 5.63347391],\n",
       "         [6.54323639, 6.5275412 , 6.47956407, ..., 6.51611073,\n",
       "          6.59356775, 6.63962062]]]])\n",
       "Coordinates: (12/31)\n",
       "  * event         (event) object MultiIndex\n",
       "  * answer        (event) int64 -999 -999 -999 -999 -999 ... -999 -999 -999 -999\n",
       "  * category      (event) object 'officesupplies' ... 'electronics'\n",
       "  * category_num  (event) int64 17 17 14 14 3 3 17 17 3 ... 6 15 15 6 6 23 23 7\n",
       "  * eegfile       (event) object '/protocols/r1/subjects/R1045E/experiments/c...\n",
       "  * eegoffset     (event) int64 491162 493597 495997 ... 636895 639329 710970\n",
       "    ...            ...\n",
       "  * test          (event) object (0, 0, 0) (0, 0, 0) ... (0, 0, 0) (0, 0, 0)\n",
       "  * type          (event) object 'WORD' 'WORD' 'WORD' ... 'WORD' 'WORD' 'WORD'\n",
       "  * channel       (channel) <U16 'LAFS1-LAFS2' 'LAFS2-LAFS3' ... 'RPHGD5-RPHGD7'\n",
       "  * time          (time) float64 -700.0 -699.0 -698.0 ... 2.297e+03 2.298e+03\n",
       "    samplerate    float64 999.0\n",
       "  * frequency     (frequency) float64 3.0 5.384 9.664 ... 55.88 100.3 180.0"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "powers.data = np.log10(powers.data)\n",
    "powers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "3e5f3d9d-911b-429b-baf6-91b2dd31270b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "resample rate 500\n"
     ]
    }
   ],
   "source": [
    "print('resample rate', resameplerate)\n",
    "powers = ResampleFilter(resamplerate=resameplerate).filter(\n",
    "    timeseries=powers)\n",
    "powers = powers.remove_buffer(enc_buffer_time / 1000)\n",
    "\n",
    "# reshape as events x features\n",
    "powers = powers.stack(features=(\"channel\", \"frequency\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "7c990b30-aeda-49f0-927c-e4d98da09fc1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "FrozenList(['answer', 'category', 'category_num', 'eegfile', 'eegoffset', 'exp_version', 'experiment', 'intrusion', 'is_stim', 'iscorrect', 'item_name', 'item_num', 'list', 'montage', 'msoffset', 'mstime', 'protocol', 'recalled', 'rectime', 'serialpos', 'session', 'stim_list', 'stim_params', 'subject', 'test', 'type'])"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "powers.indexes['event'].names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "436d6030-193e-41cd-88de-621c1b1241f2",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/global/miniconda/py310_23.1.0-1/envs/workshop_311/lib/python3.11/site-packages/xarray/core/common.py:615: FutureWarning: Updating MultiIndexed coordinate 'event' would corrupt indices for other variables: ['answer', 'category', 'category_num', 'eegfile', 'eegoffset', 'exp_version', 'experiment', 'intrusion', 'is_stim', 'iscorrect', 'item_name', 'item_num', 'list', 'montage', 'msoffset', 'mstime', 'protocol', 'recalled', 'rectime', 'serialpos', 'session', 'stim_list', 'stim_params', 'subject', 'test', 'type']. This will raise an error in the future. Use `.drop_vars({'protocol', 'mstime', 'type', 'recalled', 'list', 'exp_version', 'eegfile', 'msoffset', 'intrusion', 'answer', 'eegoffset', 'test', 'montage', 'experiment', 'is_stim', 'serialpos', 'stim_params', 'category', 'event', 'item_name', 'rectime', 'iscorrect', 'stim_list', 'session', 'item_num', 'subject', 'category_num'})` before assigning new coordinate values.\n",
      "  data.coords.update(results)\n"
     ]
    }
   ],
   "source": [
    "# need to remove stim_params because of issues with converting to pandas DataFrame\n",
    "if 'stim_params' in powers.indexes['event'].names:\n",
    "    powers = powers.assign_coords(\n",
    "        {\"event\": powers.indexes['event'].droplevel('stim_params')}\n",
    "    )"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ed03f666-f460-4654-9cf2-220e5194c016",
   "metadata": {},
   "source": [
    "## Preprocess recall events"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "4e3023fe-e422-44d0-a41c-4e96c5652816",
   "metadata": {},
   "outputs": [],
   "source": [
    "rec_range_left_ms = -900\n",
    "rec_range_right_ms = -100\n",
    "rec_buffer_time = 760"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "b09780a7-e5be-4494-869b-0e37576611b2",
   "metadata": {},
   "outputs": [
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       "}\n",
       "\n",
       ".xr-index-preview {\n",
       "  grid-column: 2 / 5;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-name,\n",
       ".xr-var-dims,\n",
       ".xr-var-dtype,\n",
       ".xr-preview,\n",
       ".xr-attrs dt {\n",
       "  white-space: nowrap;\n",
       "  overflow: hidden;\n",
       "  text-overflow: ellipsis;\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-var-name:hover,\n",
       ".xr-var-dims:hover,\n",
       ".xr-var-dtype:hover,\n",
       ".xr-attrs dt:hover {\n",
       "  overflow: visible;\n",
       "  width: auto;\n",
       "  z-index: 1;\n",
       "}\n",
       "\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-data {\n",
       "  display: none;\n",
       "  background-color: var(--xr-background-color) !important;\n",
       "  padding-bottom: 5px !important;\n",
       "}\n",
       "\n",
       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
       ".xr-var-data-in:checked ~ .xr-var-data,\n",
       ".xr-index-data-in:checked ~ .xr-index-data {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       ".xr-var-data > table {\n",
       "  float: right;\n",
       "}\n",
       "\n",
       ".xr-var-name span,\n",
       ".xr-var-data,\n",
       ".xr-index-name div,\n",
       ".xr-index-data,\n",
       ".xr-attrs {\n",
       "  padding-left: 25px !important;\n",
       "}\n",
       "\n",
       ".xr-attrs,\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-data {\n",
       "  grid-column: 1 / -1;\n",
       "}\n",
       "\n",
       "dl.xr-attrs {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  display: grid;\n",
       "  grid-template-columns: 125px auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt,\n",
       ".xr-attrs dd {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  float: left;\n",
       "  padding-right: 10px;\n",
       "  width: auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt {\n",
       "  font-weight: normal;\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-attrs dt:hover span {\n",
       "  display: inline-block;\n",
       "  background: var(--xr-background-color);\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-attrs dd {\n",
       "  grid-column: 2;\n",
       "  white-space: pre-wrap;\n",
       "  word-break: break-all;\n",
       "}\n",
       "\n",
       ".xr-icon-database,\n",
       ".xr-icon-file-text2,\n",
       ".xr-no-icon {\n",
       "  display: inline-block;\n",
       "  vertical-align: middle;\n",
       "  width: 1em;\n",
       "  height: 1.5em !important;\n",
       "  stroke-width: 0;\n",
       "  stroke: currentColor;\n",
       "  fill: currentColor;\n",
       "}\n",
       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.TimeSeries (event: 25, channel: 109, time: 2996)&gt;\n",
       "array([[[ 9.07564493e+00,  8.91269333e+00,  7.78982271e+00, ...,\n",
       "         -6.73819109e-01, -1.87117464e+00, -7.41286306e-02],\n",
       "        [-2.41358269e-01, -1.06808596e+00,  5.58388921e-02, ...,\n",
       "          6.17264351e-01,  7.38114355e-01, -2.16292342e+00],\n",
       "        [-1.78423067e+00, -1.86807224e+00, -5.95247050e+00, ...,\n",
       "          2.94886808e+00,  8.09032297e-01, -2.80513706e-01],\n",
       "        ...,\n",
       "        [-2.24676236e+01, -2.20258621e+01, -2.66083894e+01, ...,\n",
       "          1.96299162e+01,  2.41579243e+01,  3.07893597e+01],\n",
       "        [-6.65766688e+00, -2.37185228e+00,  4.77603047e+00, ...,\n",
       "         -3.21688971e+01, -3.65799464e+01, -4.09612498e+01],\n",
       "        [-7.82027343e+00, -1.26559781e+01, -1.33940386e+01, ...,\n",
       "          5.05643902e+01,  5.60168139e+01,  5.63652462e+01]],\n",
       "\n",
       "       [[-3.38053776e+00, -5.53729369e+00,  1.32428359e+00, ...,\n",
       "         -1.59123144e+00, -2.78913482e+00, -3.07713927e+00],\n",
       "        [ 2.20039630e+00,  1.80903284e-01,  1.17953354e+00, ...,\n",
       "          5.36357579e+00,  2.50743427e+00,  1.62261419e+00],\n",
       "        [-6.26592442e+00, -2.32556354e+00, -5.39314791e+00, ...,\n",
       "          2.82679465e+00,  4.69374759e+00,  6.54390395e+00],\n",
       "...\n",
       "        [ 2.22690152e+01,  2.23792903e+01,  2.76467132e+01, ...,\n",
       "          4.30307265e+01,  4.16097071e+01,  4.80597664e+01],\n",
       "        [-8.78085138e+01, -9.15945719e+01, -9.34250001e+01, ...,\n",
       "          2.26051479e+00,  7.98647529e+00,  9.70564080e+00],\n",
       "        [-1.90823222e+00,  4.11886671e+00,  8.19484822e+00, ...,\n",
       "         -4.43647335e+01, -4.76717534e+01, -4.39332957e+01]],\n",
       "\n",
       "       [[ 3.46525556e+00,  2.67293505e+00, -1.84780437e-01, ...,\n",
       "          3.10392949e-03, -1.92384541e+00,  1.67094468e-01],\n",
       "        [-1.00597547e+00, -3.06064247e+00,  1.91805484e+00, ...,\n",
       "          5.23218025e+00,  3.23524652e+00,  3.22686822e+00],\n",
       "        [-6.20852043e+00, -2.36264060e+00, -7.50220507e+00, ...,\n",
       "         -4.33676943e+00,  5.13274023e-01, -1.60446468e+00],\n",
       "        ...,\n",
       "        [ 7.24180938e+00,  3.51565233e+00,  6.80312768e+00, ...,\n",
       "          1.12593874e+01,  1.41993560e+01,  1.62463436e+01],\n",
       "        [ 1.18972840e+02,  1.21403893e+02,  1.12696600e+02, ...,\n",
       "         -4.98330170e+01, -5.13540185e+01, -4.99439155e+01],\n",
       "        [-1.31323141e+02, -1.29884174e+02, -1.29432126e+02, ...,\n",
       "          1.47524206e+02,  1.47774399e+02,  1.50136671e+02]]])\n",
       "Coordinates: (12/30)\n",
       "  * event         (event) object MultiIndex\n",
       "  * answer        (event) int64 -999 -999 -999 -999 -999 ... -999 -999 -999 -999\n",
       "  * category      (event) object &#x27;officesupplies&#x27; ... &#x27;electronics&#x27;\n",
       "  * category_num  (event) int64 17 17 14 14 3 3 17 17 3 ... 6 15 15 6 6 23 23 7\n",
       "  * eegfile       (event) object &#x27;/protocols/r1/subjects/R1045E/experiments/c...\n",
       "  * eegoffset     (event) int64 491162 493597 495997 ... 636895 639329 710970\n",
       "    ...            ...\n",
       "  * subject       (event) object &#x27;R1045E&#x27; &#x27;R1045E&#x27; ... &#x27;R1045E&#x27; &#x27;R1045E&#x27;\n",
       "  * test          (event) object (0, 0, 0) (0, 0, 0) ... (0, 0, 0) (0, 0, 0)\n",
       "  * type          (event) object &#x27;WORD&#x27; &#x27;WORD&#x27; &#x27;WORD&#x27; ... &#x27;WORD&#x27; &#x27;WORD&#x27; &#x27;WORD&#x27;\n",
       "  * channel       (channel) &lt;U16 &#x27;LAFS1-LAFS2&#x27; &#x27;LAFS2-LAFS3&#x27; ... &#x27;RPHGD5-RPHGD7&#x27;\n",
       "  * time          (time) float64 -700.0 -699.0 -698.0 ... 2.297e+03 2.298e+03\n",
       "    samplerate    float64 999.0</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.TimeSeries</div><div class='xr-array-name'></div><ul class='xr-dim-list'><li><span class='xr-has-index'>event</span>: 25</li><li><span class='xr-has-index'>channel</span>: 109</li><li><span class='xr-has-index'>time</span>: 2996</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-b85c7616-1960-4edf-8f96-0d433eb8b388' class='xr-array-in' type='checkbox' checked><label for='section-b85c7616-1960-4edf-8f96-0d433eb8b388' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>9.076 8.913 7.79 11.72 7.727 7.797 ... 143.0 142.6 147.5 147.8 150.1</span></div><div class='xr-array-data'><pre>array([[[ 9.07564493e+00,  8.91269333e+00,  7.78982271e+00, ...,\n",
       "         -6.73819109e-01, -1.87117464e+00, -7.41286306e-02],\n",
       "        [-2.41358269e-01, -1.06808596e+00,  5.58388921e-02, ...,\n",
       "          6.17264351e-01,  7.38114355e-01, -2.16292342e+00],\n",
       "        [-1.78423067e+00, -1.86807224e+00, -5.95247050e+00, ...,\n",
       "          2.94886808e+00,  8.09032297e-01, -2.80513706e-01],\n",
       "        ...,\n",
       "        [-2.24676236e+01, -2.20258621e+01, -2.66083894e+01, ...,\n",
       "          1.96299162e+01,  2.41579243e+01,  3.07893597e+01],\n",
       "        [-6.65766688e+00, -2.37185228e+00,  4.77603047e+00, ...,\n",
       "         -3.21688971e+01, -3.65799464e+01, -4.09612498e+01],\n",
       "        [-7.82027343e+00, -1.26559781e+01, -1.33940386e+01, ...,\n",
       "          5.05643902e+01,  5.60168139e+01,  5.63652462e+01]],\n",
       "\n",
       "       [[-3.38053776e+00, -5.53729369e+00,  1.32428359e+00, ...,\n",
       "         -1.59123144e+00, -2.78913482e+00, -3.07713927e+00],\n",
       "        [ 2.20039630e+00,  1.80903284e-01,  1.17953354e+00, ...,\n",
       "          5.36357579e+00,  2.50743427e+00,  1.62261419e+00],\n",
       "        [-6.26592442e+00, -2.32556354e+00, -5.39314791e+00, ...,\n",
       "          2.82679465e+00,  4.69374759e+00,  6.54390395e+00],\n",
       "...\n",
       "        [ 2.22690152e+01,  2.23792903e+01,  2.76467132e+01, ...,\n",
       "          4.30307265e+01,  4.16097071e+01,  4.80597664e+01],\n",
       "        [-8.78085138e+01, -9.15945719e+01, -9.34250001e+01, ...,\n",
       "          2.26051479e+00,  7.98647529e+00,  9.70564080e+00],\n",
       "        [-1.90823222e+00,  4.11886671e+00,  8.19484822e+00, ...,\n",
       "         -4.43647335e+01, -4.76717534e+01, -4.39332957e+01]],\n",
       "\n",
       "       [[ 3.46525556e+00,  2.67293505e+00, -1.84780437e-01, ...,\n",
       "          3.10392949e-03, -1.92384541e+00,  1.67094468e-01],\n",
       "        [-1.00597547e+00, -3.06064247e+00,  1.91805484e+00, ...,\n",
       "          5.23218025e+00,  3.23524652e+00,  3.22686822e+00],\n",
       "        [-6.20852043e+00, -2.36264060e+00, -7.50220507e+00, ...,\n",
       "         -4.33676943e+00,  5.13274023e-01, -1.60446468e+00],\n",
       "        ...,\n",
       "        [ 7.24180938e+00,  3.51565233e+00,  6.80312768e+00, ...,\n",
       "          1.12593874e+01,  1.41993560e+01,  1.62463436e+01],\n",
       "        [ 1.18972840e+02,  1.21403893e+02,  1.12696600e+02, ...,\n",
       "         -4.98330170e+01, -5.13540185e+01, -4.99439155e+01],\n",
       "        [-1.31323141e+02, -1.29884174e+02, -1.29432126e+02, ...,\n",
       "          1.47524206e+02,  1.47774399e+02,  1.50136671e+02]]])</pre></div></div></li><li class='xr-section-item'><input id='section-6d276867-2fc5-4d41-af1f-d4732caef973' class='xr-section-summary-in' type='checkbox'  ><label for='section-6d276867-2fc5-4d41-af1f-d4732caef973' class='xr-section-summary' >Coordinates: <span>(30)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>event</span></div><div class='xr-var-dims'>(event)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>MultiIndex</div><input id='attrs-84109489-7401-441f-8bc3-6ba37548dfee' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-84109489-7401-441f-8bc3-6ba37548dfee' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2c7d2b83-4c60-4e32-98cd-cc0844920dcc' class='xr-var-data-in' type='checkbox'><label for='data-2c7d2b83-4c60-4e32-98cd-cc0844920dcc' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([(-999, &#x27;officesupplies&#x27;, 17, &#x27;/protocols/r1/subjects/R1045E/experiments/catFR1/sessions/0/ephys/current_processed/noreref/R1045E_catFR1_0_01May15_1542&#x27;, 491162, &#x27;1.05&#x27;, &#x27;catFR1&#x27;, -999, 0, -999, &#x27;PAPER&#x27;, 207, 1, 0, 1, 1430510360829, &#x27;r1&#x27;, 0, -999, 1, 0, 0, (), &#x27;R1045E&#x27;, (0, 0, 0), &#x27;WORD&#x27;),\n",
       "       (-999, &#x27;officesupplies&#x27;, 17, &#x27;/protocols/r1/subjects/R1045E/experiments/catFR1/sessions/0/ephys/current_processed/noreref/R1045E_catFR1_0_01May15_1542&#x27;, 493597, &#x27;1.05&#x27;, &#x27;catFR1&#x27;, -999, 0, -999, &#x27;BINDER&#x27;, 211, 1, 0, 1, 1430510363264, &#x27;r1&#x27;, 0, -999, 2, 0, 0, (), &#x27;R1045E&#x27;, (0, 0, 0), &#x27;WORD&#x27;),\n",
       "       (-999, &#x27;kitchentools&#x27;, 14, &#x27;/protocols/r1/subjects/R1045E/experiments/catFR1/sessions/0/ephys/current_processed/noreref/R1045E_catFR1_0_01May15_1542&#x27;, 495997, &#x27;1.05&#x27;, &#x27;catFR1&#x27;, -999, 0, -999, &#x27;PEELER&#x27;, 176, 1, 0, 1, 1430510365664, &#x27;r1&#x27;, 0, -999, 3, 0, 0, (), &#x27;R1045E&#x27;, (0, 0, 0), &#x27;WORD&#x27;),\n",
       "       (-999, &#x27;kitchentools&#x27;, 14, &#x27;/protocols/r1/subjects/R1045E/experiments/catFR1/sessions/0/ephys/current_processed/noreref/R1045E_catFR1_0_01May15_1542&#x27;, 498580, &#x27;1.05&#x27;, &#x27;catFR1&#x27;, -999, 0, -999, &#x27;SPOON&#x27;, 169, 1, 0, 1, 1430510368248, &#x27;r1&#x27;, 0, -999, 4, 0, 0, (), &#x27;R1045E&#x27;, (0, 0, 0), &#x27;WORD&#x27;),\n",
       "       (-999, &#x27;bodyparts&#x27;, 3, &#x27;/protocols/r1/subjects/R1045E/experiments/catFR1/sessions/0/ephys/current_processed/noreref/R1045E_catFR1_0_01May15_1542&#x27;, 501196, &#x27;1.05&#x27;, &#x27;catFR1&#x27;, -999, 0, -999, &#x27;HAND&#x27;, 41, 1, 0, 1, 1430510370864, &#x27;r1&#x27;, 0, -999, 5, 0, 0, (), &#x27;R1045E&#x27;, (0, 0, 0), &#x27;WORD&#x27;),\n",
       "       (-999, &#x27;bodyparts&#x27;, 3, &#x27;/protocols/r1/subjects/R1045E/experiments/catFR1/sessions/0/ephys/current_processed/noreref/R1045E_catFR1_0_01May15_1542&#x27;, 503598, &#x27;1.05&#x27;, &#x27;catFR1&#x27;, -999, 0, -999, &#x27;ELBOW&#x27;, 47, 1, 0, 1, 1430510373266, &#x27;r1&#x27;, 1, 1375, 6, 0, 0, (), &#x27;R1045E&#x27;, (0, 0, 0), &#x27;WORD&#x27;),\n",
       "       (-999, &#x27;officesupplies&#x27;, 17, &#x27;/protocols/r1/subjects/R1045E/experiments/catFR1/sessions/0/ephys/current_processed/noreref/R1045E_catFR1_0_01May15_1542&#x27;, 506048, &#x27;1.05&#x27;, &#x27;catFR1&#x27;, -999, 0, -999, &#x27;GLUE&#x27;, 216, 1, 0, 1, 1430510375716, &#x27;r1&#x27;, 1, 12215, 7, 0, 0, (), &#x27;R1045E&#x27;, (0, 0, 0), &#x27;WORD&#x27;),\n",
       "       (-999, &#x27;officesupplies&#x27;, 17, &#x27;/protocols/r1/subjects/R1045E/experiments/catFR1/sessions/0/ephys/current_processed/noreref/R1045E_catFR1_0_01May15_1542&#x27;, 508565, &#x27;1.05&#x27;, &#x27;catFR1&#x27;, -999, 0, -999, &#x27;STAPLER&#x27;, 212, 1, 0, 1, 1430510378233, &#x27;r1&#x27;, 0, -999, 8, 0, 0, (), &#x27;R1045E&#x27;, (0, 0, 0), &#x27;WORD&#x27;),\n",
       "       (-999, &#x27;bodyparts&#x27;, 3, &#x27;/protocols/r1/subjects/R1045E/experiments/catFR1/sessions/0/ephys/current_processed/noreref/R1045E_catFR1_0_01May15_1542&#x27;, 511181, &#x27;1.05&#x27;, &#x27;catFR1&#x27;, -999, 0, -999, &#x27;FOOT&#x27;, 37, 1, 0, 1, 1430510380849, &#x27;r1&#x27;, 0, -999, 9, 0, 0, (), &#x27;R1045E&#x27;, (0, 0, 0), &#x27;WORD&#x27;),\n",
       "       (-999, &#x27;bodyparts&#x27;, 3, &#x27;/protocols/r1/subjects/R1045E/experiments/catFR1/sessions/0/ephys/current_processed/noreref/R1045E_catFR1_0_01May15_1542&#x27;, 513782, &#x27;1.05&#x27;, &#x27;catFR1&#x27;, -999, 0, -999, &#x27;NOSE&#x27;, 44, 1, 0, 1, 1430510383451, &#x27;r1&#x27;, 0, -999, 10, 0, 0, (), &#x27;R1045E&#x27;, (0, 0, 0), &#x27;WORD&#x27;),\n",
       "       (-999, &#x27;kitchentools&#x27;, 14, &#x27;/protocols/r1/subjects/R1045E/experiments/catFR1/sessions/0/ephys/current_processed/noreref/R1045E_catFR1_0_01May15_1542&#x27;, 516349, &#x27;1.05&#x27;, &#x27;catFR1&#x27;, -999, 0, -999, &#x27;KNIFE&#x27;, 170, 1, 0, 1, 1430510386018, &#x27;r1&#x27;, 0, -999, 11, 0, 0, (), &#x27;R1045E&#x27;, (0, 0, 0), &#x27;WORD&#x27;),\n",
       "       (-999, &#x27;kitchentools&#x27;, 14, &#x27;/protocols/r1/subjects/R1045E/experiments/catFR1/sessions/0/ephys/current_processed/noreref/R1045E_catFR1_0_01May15_1542&#x27;, 518766, &#x27;1.05&#x27;, &#x27;catFR1&#x27;, -999, 0, -999, &#x27;STRAINER&#x27;, 174, 1, 0, 1, 1430510388435, &#x27;r1&#x27;, 1, 7905, 12, 0, 0, (), &#x27;R1045E&#x27;, (0, 0, 0), &#x27;WORD&#x27;),\n",
       "       (-999, &#x27;landscapes&#x27;, 15, &#x27;/protocols/r1/subjects/R1045E/experiments/catFR1/sessions/0/ephys/current_processed/noreref/R1045E_catFR1_0_01May15_1542&#x27;, 611960, &#x27;1.05&#x27;, &#x27;catFR1&#x27;, -999, 0, -999, &#x27;HILLS&#x27;, 186, 2, 0, 1, 1430510481635, &#x27;r1&#x27;, 1, 20007, 1, 0, 0, (), &#x27;R1045E&#x27;, (0, 0, 0), &#x27;WORD&#x27;),\n",
       "       (-999, &#x27;landscapes&#x27;, 15, &#x27;/protocols/r1/subjects/R1045E/experiments/catFR1/sessions/0/ephys/current_processed/noreref/R1045E_catFR1_0_01May15_1542&#x27;, 614343, &#x27;1.05&#x27;, &#x27;catFR1&#x27;, -999, 0, -999, &#x27;BEACH&#x27;, 191, 2, 0, 1, 1430510484018, &#x27;r1&#x27;, 0, -999, 2, 0, 0, (), &#x27;R1045E&#x27;, (0, 0, 0), &#x27;WORD&#x27;),\n",
       "       (-999, &#x27;weather&#x27;, 23, &#x27;/protocols/r1/subjects/R1045E/experiments/catFR1/sessions/0/ephys/current_processed/noreref/R1045E_catFR1_0_01May15_1542&#x27;, 616960, &#x27;1.05&#x27;, &#x27;catFR1&#x27;, -999, 0, -999, &#x27;HUMIDITY&#x27;, 279, 2, 0, 1, 1430510486635, &#x27;r1&#x27;, 0, -999, 3, 0, 0, (), &#x27;R1045E&#x27;, (0, 0, 0), &#x27;WORD&#x27;),\n",
       "       (-999, &#x27;weather&#x27;, 23, &#x27;/protocols/r1/subjects/R1045E/experiments/catFR1/sessions/0/ephys/current_processed/noreref/R1045E_catFR1_0_01May15_1542&#x27;, 619410, &#x27;1.05&#x27;, &#x27;catFR1&#x27;, -999, 0, -999, &#x27;RAIN&#x27;, 277, 2, 0, 1, 1430510489086, &#x27;r1&#x27;, 0, -999, 4, 0, 0, (), &#x27;R1045E&#x27;, (0, 0, 0), &#x27;WORD&#x27;),\n",
       "       (-999, &#x27;desserts&#x27;, 6, &#x27;/protocols/r1/subjects/R1045E/experiments/catFR1/sessions/0/ephys/current_processed/noreref/R1045E_catFR1_0_01May15_1542&#x27;, 621927, &#x27;1.05&#x27;, &#x27;catFR1&#x27;, -999, 0, -999, &#x27;MOUSSE&#x27;, 83, 2, 0, 1, 1430510491603, &#x27;r1&#x27;, 1, 3252, 5, 0, 0, (), &#x27;R1045E&#x27;, (0, 0, 0), &#x27;WORD&#x27;),\n",
       "       (-999, &#x27;desserts&#x27;, 6, &#x27;/protocols/r1/subjects/R1045E/experiments/catFR1/sessions/0/ephys/current_processed/noreref/R1045E_catFR1_0_01May15_1542&#x27;, 624361, &#x27;1.05&#x27;, &#x27;catFR1&#x27;, -999, 0, -999, &#x27;CAKE&#x27;, 73, 2, 0, 1, 1430510494037, &#x27;r1&#x27;, 0, -999, 6, 0, 0, (), &#x27;R1045E&#x27;, (0, 0, 0), &#x27;WORD&#x27;),\n",
       "       (-999, &#x27;landscapes&#x27;, 15, &#x27;/protocols/r1/subjects/R1045E/experiments/catFR1/sessions/0/ephys/current_processed/noreref/R1045E_catFR1_0_01May15_1542&#x27;, 626995, &#x27;1.05&#x27;, &#x27;catFR1&#x27;, -999, 0, -999, &#x27;STREAM&#x27;, 182, 2, 0, 1, 1430510496671, &#x27;r1&#x27;, 0, -999, 7, 0, 0, (), &#x27;R1045E&#x27;, (0, 0, 0), &#x27;WORD&#x27;),\n",
       "       (-999, &#x27;landscapes&#x27;, 15, &#x27;/protocols/r1/subjects/R1045E/experiments/catFR1/sessions/0/ephys/current_processed/noreref/R1045E_catFR1_0_01May15_1542&#x27;, 629478, &#x27;1.05&#x27;, &#x27;catFR1&#x27;, -999, 0, -999, &#x27;ISLAND&#x27;, 192, 2, 0, 1, 1430510499154, &#x27;r1&#x27;, 0, -999, 8, 0, 0, (), &#x27;R1045E&#x27;, (0, 0, 0), &#x27;WORD&#x27;),\n",
       "       (-999, &#x27;desserts&#x27;, 6, &#x27;/protocols/r1/subjects/R1045E/experiments/catFR1/sessions/0/ephys/current_processed/noreref/R1045E_catFR1_0_01May15_1542&#x27;, 632062, &#x27;1.05&#x27;, &#x27;catFR1&#x27;, -999, 0, -999, &#x27;COOKIE&#x27;, 78, 2, 0, 1, 1430510501738, &#x27;r1&#x27;, 0, -999, 9, 0, 0, (), &#x27;R1045E&#x27;, (0, 0, 0), &#x27;WORD&#x27;),\n",
       "       (-999, &#x27;desserts&#x27;, 6, &#x27;/protocols/r1/subjects/R1045E/experiments/catFR1/sessions/0/ephys/current_processed/noreref/R1045E_catFR1_0_01May15_1542&#x27;, 634445, &#x27;1.05&#x27;, &#x27;catFR1&#x27;, -999, 0, -999, &#x27;BROWNIES&#x27;, 75, 2, 0, 1, 1430510504122, &#x27;r1&#x27;, 0, -999, 10, 0, 0, (), &#x27;R1045E&#x27;, (0, 0, 0), &#x27;WORD&#x27;),\n",
       "       (-999, &#x27;weather&#x27;, 23, &#x27;/protocols/r1/subjects/R1045E/experiments/catFR1/sessions/0/ephys/current_processed/noreref/R1045E_catFR1_0_01May15_1542&#x27;, 636895, &#x27;1.05&#x27;, &#x27;catFR1&#x27;, -999, 0, -999, &#x27;FOG&#x27;, 285, 2, 0, 1, 1430510506572, &#x27;r1&#x27;, 1, 5648, 11, 0, 0, (), &#x27;R1045E&#x27;, (0, 0, 0), &#x27;WORD&#x27;),\n",
       "       (-999, &#x27;weather&#x27;, 23, &#x27;/protocols/r1/subjects/R1045E/experiments/catFR1/sessions/0/ephys/current_processed/noreref/R1045E_catFR1_0_01May15_1542&#x27;, 639329, &#x27;1.05&#x27;, &#x27;catFR1&#x27;, -999, 0, -999, &#x27;CLOUD&#x27;, 281, 2, 0, 1, 1430510509006, &#x27;r1&#x27;, 1, 3940, 12, 0, 0, (), &#x27;R1045E&#x27;, (0, 0, 0), &#x27;WORD&#x27;),\n",
       "       (-999, &#x27;electronics&#x27;, 7, &#x27;/protocols/r1/subjects/R1045E/experiments/catFR1/sessions/0/ephys/current_processed/noreref/R1045E_catFR1_0_01May15_1542&#x27;, 710970, &#x27;1.05&#x27;, &#x27;catFR1&#x27;, -999, 0, -999, &#x27;MICROWAVE&#x27;, 96, 3, 0, 1, 1430510580652, &#x27;r1&#x27;, 1, 9012, 1, 0, 0, (), &#x27;R1045E&#x27;, (0, 0, 0), &#x27;WORD&#x27;)],\n",
       "      dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>answer</span></div><div class='xr-var-dims'>(event)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>-999 -999 -999 ... -999 -999 -999</div><input id='attrs-aabe637a-01fc-4309-8f47-c84ff826ddd9' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-aabe637a-01fc-4309-8f47-c84ff826ddd9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f66d158a-8ae2-44f8-85d2-339f64582b74' class='xr-var-data-in' type='checkbox'><label for='data-f66d158a-8ae2-44f8-85d2-339f64582b74' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([-999, -999, -999, -999, -999, -999, -999, -999, -999, -999, -999, -999,\n",
       "       -999, -999, -999, -999, -999, -999, -999, -999, -999, -999, -999, -999,\n",
       "       -999])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>category</span></div><div class='xr-var-dims'>(event)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>&#x27;officesupplies&#x27; ... &#x27;electronics&#x27;</div><input id='attrs-a5a3f388-bad2-4fac-a37c-6d2cc101370a' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-a5a3f388-bad2-4fac-a37c-6d2cc101370a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-bc39bd18-c721-4442-9157-932900c677da' class='xr-var-data-in' type='checkbox'><label for='data-bc39bd18-c721-4442-9157-932900c677da' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;officesupplies&#x27;, &#x27;officesupplies&#x27;, &#x27;kitchentools&#x27;, &#x27;kitchentools&#x27;,\n",
       "       &#x27;bodyparts&#x27;, &#x27;bodyparts&#x27;, &#x27;officesupplies&#x27;, &#x27;officesupplies&#x27;,\n",
       "       &#x27;bodyparts&#x27;, &#x27;bodyparts&#x27;, &#x27;kitchentools&#x27;, &#x27;kitchentools&#x27;, &#x27;landscapes&#x27;,\n",
       "       &#x27;landscapes&#x27;, &#x27;weather&#x27;, &#x27;weather&#x27;, &#x27;desserts&#x27;, &#x27;desserts&#x27;,\n",
       "       &#x27;landscapes&#x27;, &#x27;landscapes&#x27;, &#x27;desserts&#x27;, &#x27;desserts&#x27;, &#x27;weather&#x27;,\n",
       "       &#x27;weather&#x27;, &#x27;electronics&#x27;], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>category_num</span></div><div class='xr-var-dims'>(event)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>17 17 14 14 3 3 ... 15 6 6 23 23 7</div><input id='attrs-62a11efc-37f4-4a3a-9e4c-a1ac1c239128' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-62a11efc-37f4-4a3a-9e4c-a1ac1c239128' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f2b13ead-7706-4edc-89c5-2bb881242b6d' class='xr-var-data-in' type='checkbox'><label for='data-f2b13ead-7706-4edc-89c5-2bb881242b6d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([17, 17, 14, 14,  3,  3, 17, 17,  3,  3, 14, 14, 15, 15, 23, 23,  6,  6,\n",
       "       15, 15,  6,  6, 23, 23,  7])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>eegfile</span></div><div class='xr-var-dims'>(event)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>&#x27;/protocols/r1/subjects/R1045E/e...</div><input id='attrs-b140552d-6cf3-44be-ae16-af66d5f09385' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-b140552d-6cf3-44be-ae16-af66d5f09385' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2c3d3535-060d-4855-83fa-7febbbc87ca1' class='xr-var-data-in' type='checkbox'><label for='data-2c3d3535-060d-4855-83fa-7febbbc87ca1' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;/protocols/r1/subjects/R1045E/experiments/catFR1/sessions/0/ephys/current_processed/noreref/R1045E_catFR1_0_01May15_1542&#x27;,\n",
       "       &#x27;/protocols/r1/subjects/R1045E/experiments/catFR1/sessions/0/ephys/current_processed/noreref/R1045E_catFR1_0_01May15_1542&#x27;,\n",
       "       &#x27;/protocols/r1/subjects/R1045E/experiments/catFR1/sessions/0/ephys/current_processed/noreref/R1045E_catFR1_0_01May15_1542&#x27;,\n",
       "       &#x27;/protocols/r1/subjects/R1045E/experiments/catFR1/sessions/0/ephys/current_processed/noreref/R1045E_catFR1_0_01May15_1542&#x27;,\n",
       "       &#x27;/protocols/r1/subjects/R1045E/experiments/catFR1/sessions/0/ephys/current_processed/noreref/R1045E_catFR1_0_01May15_1542&#x27;,\n",
       "       &#x27;/protocols/r1/subjects/R1045E/experiments/catFR1/sessions/0/ephys/current_processed/noreref/R1045E_catFR1_0_01May15_1542&#x27;,\n",
       "       &#x27;/protocols/r1/subjects/R1045E/experiments/catFR1/sessions/0/ephys/current_processed/noreref/R1045E_catFR1_0_01May15_1542&#x27;,\n",
       "       &#x27;/protocols/r1/subjects/R1045E/experiments/catFR1/sessions/0/ephys/current_processed/noreref/R1045E_catFR1_0_01May15_1542&#x27;,\n",
       "       &#x27;/protocols/r1/subjects/R1045E/experiments/catFR1/sessions/0/ephys/current_processed/noreref/R1045E_catFR1_0_01May15_1542&#x27;,\n",
       "       &#x27;/protocols/r1/subjects/R1045E/experiments/catFR1/sessions/0/ephys/current_processed/noreref/R1045E_catFR1_0_01May15_1542&#x27;,\n",
       "       &#x27;/protocols/r1/subjects/R1045E/experiments/catFR1/sessions/0/ephys/current_processed/noreref/R1045E_catFR1_0_01May15_1542&#x27;,\n",
       "       &#x27;/protocols/r1/subjects/R1045E/experiments/catFR1/sessions/0/ephys/current_processed/noreref/R1045E_catFR1_0_01May15_1542&#x27;,\n",
       "       &#x27;/protocols/r1/subjects/R1045E/experiments/catFR1/sessions/0/ephys/current_processed/noreref/R1045E_catFR1_0_01May15_1542&#x27;,\n",
       "       &#x27;/protocols/r1/subjects/R1045E/experiments/catFR1/sessions/0/ephys/current_processed/noreref/R1045E_catFR1_0_01May15_1542&#x27;,\n",
       "       &#x27;/protocols/r1/subjects/R1045E/experiments/catFR1/sessions/0/ephys/current_processed/noreref/R1045E_catFR1_0_01May15_1542&#x27;,\n",
       "       &#x27;/protocols/r1/subjects/R1045E/experiments/catFR1/sessions/0/ephys/current_processed/noreref/R1045E_catFR1_0_01May15_1542&#x27;,\n",
       "       &#x27;/protocols/r1/subjects/R1045E/experiments/catFR1/sessions/0/ephys/current_processed/noreref/R1045E_catFR1_0_01May15_1542&#x27;,\n",
       "       &#x27;/protocols/r1/subjects/R1045E/experiments/catFR1/sessions/0/ephys/current_processed/noreref/R1045E_catFR1_0_01May15_1542&#x27;,\n",
       "       &#x27;/protocols/r1/subjects/R1045E/experiments/catFR1/sessions/0/ephys/current_processed/noreref/R1045E_catFR1_0_01May15_1542&#x27;,\n",
       "       &#x27;/protocols/r1/subjects/R1045E/experiments/catFR1/sessions/0/ephys/current_processed/noreref/R1045E_catFR1_0_01May15_1542&#x27;,\n",
       "       &#x27;/protocols/r1/subjects/R1045E/experiments/catFR1/sessions/0/ephys/current_processed/noreref/R1045E_catFR1_0_01May15_1542&#x27;,\n",
       "       &#x27;/protocols/r1/subjects/R1045E/experiments/catFR1/sessions/0/ephys/current_processed/noreref/R1045E_catFR1_0_01May15_1542&#x27;,\n",
       "       &#x27;/protocols/r1/subjects/R1045E/experiments/catFR1/sessions/0/ephys/current_processed/noreref/R1045E_catFR1_0_01May15_1542&#x27;,\n",
       "       &#x27;/protocols/r1/subjects/R1045E/experiments/catFR1/sessions/0/ephys/current_processed/noreref/R1045E_catFR1_0_01May15_1542&#x27;,\n",
       "       &#x27;/protocols/r1/subjects/R1045E/experiments/catFR1/sessions/0/ephys/current_processed/noreref/R1045E_catFR1_0_01May15_1542&#x27;],\n",
       "      dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>eegoffset</span></div><div class='xr-var-dims'>(event)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>491162 493597 ... 639329 710970</div><input id='attrs-e2129931-cd6f-4aa9-9d6f-dc43dd4c7df2' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-e2129931-cd6f-4aa9-9d6f-dc43dd4c7df2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5d119ac9-9a60-4e3c-8ef0-944a171b3a78' class='xr-var-data-in' type='checkbox'><label for='data-5d119ac9-9a60-4e3c-8ef0-944a171b3a78' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([491162, 493597, 495997, 498580, 501196, 503598, 506048, 508565, 511181,\n",
       "       513782, 516349, 518766, 611960, 614343, 616960, 619410, 621927, 624361,\n",
       "       626995, 629478, 632062, 634445, 636895, 639329, 710970])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>exp_version</span></div><div class='xr-var-dims'>(event)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>&#x27;1.05&#x27; &#x27;1.05&#x27; ... &#x27;1.05&#x27; &#x27;1.05&#x27;</div><input id='attrs-c30e8204-8d99-4215-a02f-cf23a0f86cd7' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-c30e8204-8d99-4215-a02f-cf23a0f86cd7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4361d746-4a52-41cb-a6f7-f057636e56c2' class='xr-var-data-in' type='checkbox'><label for='data-4361d746-4a52-41cb-a6f7-f057636e56c2' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;1.05&#x27;, &#x27;1.05&#x27;, &#x27;1.05&#x27;, &#x27;1.05&#x27;, &#x27;1.05&#x27;, &#x27;1.05&#x27;, &#x27;1.05&#x27;, &#x27;1.05&#x27;, &#x27;1.05&#x27;,\n",
       "       &#x27;1.05&#x27;, &#x27;1.05&#x27;, &#x27;1.05&#x27;, &#x27;1.05&#x27;, &#x27;1.05&#x27;, &#x27;1.05&#x27;, &#x27;1.05&#x27;, &#x27;1.05&#x27;, &#x27;1.05&#x27;,\n",
       "       &#x27;1.05&#x27;, &#x27;1.05&#x27;, &#x27;1.05&#x27;, &#x27;1.05&#x27;, &#x27;1.05&#x27;, &#x27;1.05&#x27;, &#x27;1.05&#x27;], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>experiment</span></div><div class='xr-var-dims'>(event)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>&#x27;catFR1&#x27; &#x27;catFR1&#x27; ... &#x27;catFR1&#x27;</div><input id='attrs-7cdbf249-98db-4b85-add1-e8e597224815' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-7cdbf249-98db-4b85-add1-e8e597224815' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8f4d3af7-0838-41ab-8856-3a6f7e452752' class='xr-var-data-in' type='checkbox'><label for='data-8f4d3af7-0838-41ab-8856-3a6f7e452752' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;catFR1&#x27;, &#x27;catFR1&#x27;, &#x27;catFR1&#x27;, &#x27;catFR1&#x27;, &#x27;catFR1&#x27;, &#x27;catFR1&#x27;, &#x27;catFR1&#x27;,\n",
       "       &#x27;catFR1&#x27;, &#x27;catFR1&#x27;, &#x27;catFR1&#x27;, &#x27;catFR1&#x27;, &#x27;catFR1&#x27;, &#x27;catFR1&#x27;, &#x27;catFR1&#x27;,\n",
       "       &#x27;catFR1&#x27;, &#x27;catFR1&#x27;, &#x27;catFR1&#x27;, &#x27;catFR1&#x27;, &#x27;catFR1&#x27;, &#x27;catFR1&#x27;, &#x27;catFR1&#x27;,\n",
       "       &#x27;catFR1&#x27;, &#x27;catFR1&#x27;, &#x27;catFR1&#x27;, &#x27;catFR1&#x27;], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>intrusion</span></div><div class='xr-var-dims'>(event)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>-999 -999 -999 ... -999 -999 -999</div><input id='attrs-32c365d6-8521-4b57-a239-fd0ca0830228' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-32c365d6-8521-4b57-a239-fd0ca0830228' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c37bdafd-ff23-4aaf-b3bc-b9920074aab8' class='xr-var-data-in' type='checkbox'><label for='data-c37bdafd-ff23-4aaf-b3bc-b9920074aab8' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([-999, -999, -999, -999, -999, -999, -999, -999, -999, -999, -999, -999,\n",
       "       -999, -999, -999, -999, -999, -999, -999, -999, -999, -999, -999, -999,\n",
       "       -999])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>is_stim</span></div><div class='xr-var-dims'>(event)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 0 0 0 0 0 0 0 ... 0 0 0 0 0 0 0 0</div><input id='attrs-d434ad04-ae31-45e3-b761-980e561af0b7' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-d434ad04-ae31-45e3-b761-980e561af0b7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d0e2d365-38e4-4a32-a370-329867eb4afc' class='xr-var-data-in' type='checkbox'><label for='data-d0e2d365-38e4-4a32-a370-329867eb4afc' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>iscorrect</span></div><div class='xr-var-dims'>(event)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>-999 -999 -999 ... -999 -999 -999</div><input id='attrs-98701c33-34f8-4f04-bcb2-e8db8f539644' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-98701c33-34f8-4f04-bcb2-e8db8f539644' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2dcb8952-2976-43fa-b847-c917996eff5b' class='xr-var-data-in' type='checkbox'><label for='data-2dcb8952-2976-43fa-b847-c917996eff5b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([-999, -999, -999, -999, -999, -999, -999, -999, -999, -999, -999, -999,\n",
       "       -999, -999, -999, -999, -999, -999, -999, -999, -999, -999, -999, -999,\n",
       "       -999])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>item_name</span></div><div class='xr-var-dims'>(event)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>&#x27;PAPER&#x27; &#x27;BINDER&#x27; ... &#x27;MICROWAVE&#x27;</div><input id='attrs-3ec57dec-63c9-4d5c-8104-b75ddd2a1809' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-3ec57dec-63c9-4d5c-8104-b75ddd2a1809' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e5063f8c-0bf2-45ba-b494-5365dff3c0dd' class='xr-var-data-in' type='checkbox'><label for='data-e5063f8c-0bf2-45ba-b494-5365dff3c0dd' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;PAPER&#x27;, &#x27;BINDER&#x27;, &#x27;PEELER&#x27;, &#x27;SPOON&#x27;, &#x27;HAND&#x27;, &#x27;ELBOW&#x27;, &#x27;GLUE&#x27;,\n",
       "       &#x27;STAPLER&#x27;, &#x27;FOOT&#x27;, &#x27;NOSE&#x27;, &#x27;KNIFE&#x27;, &#x27;STRAINER&#x27;, &#x27;HILLS&#x27;, &#x27;BEACH&#x27;,\n",
       "       &#x27;HUMIDITY&#x27;, &#x27;RAIN&#x27;, &#x27;MOUSSE&#x27;, &#x27;CAKE&#x27;, &#x27;STREAM&#x27;, &#x27;ISLAND&#x27;, &#x27;COOKIE&#x27;,\n",
       "       &#x27;BROWNIES&#x27;, &#x27;FOG&#x27;, &#x27;CLOUD&#x27;, &#x27;MICROWAVE&#x27;], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>item_num</span></div><div class='xr-var-dims'>(event)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>207 211 176 169 ... 75 285 281 96</div><input id='attrs-5a11b886-bd5d-4d62-b82e-1ee73290b7bd' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-5a11b886-bd5d-4d62-b82e-1ee73290b7bd' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-edbd03d1-cb7d-49d8-9c93-92d6ec215b1e' class='xr-var-data-in' type='checkbox'><label for='data-edbd03d1-cb7d-49d8-9c93-92d6ec215b1e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([207, 211, 176, 169,  41,  47, 216, 212,  37,  44, 170, 174, 186, 191,\n",
       "       279, 277,  83,  73, 182, 192,  78,  75, 285, 281,  96])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>list</span></div><div class='xr-var-dims'>(event)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>1 1 1 1 1 1 1 1 ... 2 2 2 2 2 2 2 3</div><input id='attrs-f9fee633-a5dd-4368-8eb7-b776d9c57f1a' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-f9fee633-a5dd-4368-8eb7-b776d9c57f1a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-bb4f97b6-d104-4f48-a46c-99c8560c2ed0' class='xr-var-data-in' type='checkbox'><label for='data-bb4f97b6-d104-4f48-a46c-99c8560c2ed0' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
       "       3])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>montage</span></div><div class='xr-var-dims'>(event)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 0 0 0 0 0 0 0 ... 0 0 0 0 0 0 0 0</div><input id='attrs-435c4e0c-4a84-4f88-870e-becce5b0915c' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-435c4e0c-4a84-4f88-870e-becce5b0915c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-aff1374e-86f4-4893-a9cf-d16418cdb9c6' class='xr-var-data-in' type='checkbox'><label for='data-aff1374e-86f4-4893-a9cf-d16418cdb9c6' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>msoffset</span></div><div class='xr-var-dims'>(event)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>1 1 1 1 1 1 1 1 ... 1 1 1 1 1 1 1 1</div><input id='attrs-5023aea4-5190-405b-951d-32fffcd05180' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-5023aea4-5190-405b-951d-32fffcd05180' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-39c1388d-6974-46a6-bcd1-0e7665af62f9' class='xr-var-data-in' type='checkbox'><label for='data-39c1388d-6974-46a6-bcd1-0e7665af62f9' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>mstime</span></div><div class='xr-var-dims'>(event)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>1430510360829 ... 1430510580652</div><input id='attrs-ef51ba4e-7788-4e2b-a86a-259301bfbd5a' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-ef51ba4e-7788-4e2b-a86a-259301bfbd5a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b634bd1e-4c74-45b0-954d-f71319bda7ad' class='xr-var-data-in' type='checkbox'><label for='data-b634bd1e-4c74-45b0-954d-f71319bda7ad' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([1430510360829, 1430510363264, 1430510365664, 1430510368248,\n",
       "       1430510370864, 1430510373266, 1430510375716, 1430510378233,\n",
       "       1430510380849, 1430510383451, 1430510386018, 1430510388435,\n",
       "       1430510481635, 1430510484018, 1430510486635, 1430510489086,\n",
       "       1430510491603, 1430510494037, 1430510496671, 1430510499154,\n",
       "       1430510501738, 1430510504122, 1430510506572, 1430510509006,\n",
       "       1430510580652])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>protocol</span></div><div class='xr-var-dims'>(event)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>&#x27;r1&#x27; &#x27;r1&#x27; &#x27;r1&#x27; ... &#x27;r1&#x27; &#x27;r1&#x27; &#x27;r1&#x27;</div><input id='attrs-faa37e77-47e5-44d8-920a-f25bd2e8b9dd' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-faa37e77-47e5-44d8-920a-f25bd2e8b9dd' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7e945566-fff6-4a8b-a40d-8e0a0e8b4faf' class='xr-var-data-in' type='checkbox'><label for='data-7e945566-fff6-4a8b-a40d-8e0a0e8b4faf' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;r1&#x27;, &#x27;r1&#x27;, &#x27;r1&#x27;, &#x27;r1&#x27;, &#x27;r1&#x27;, &#x27;r1&#x27;, &#x27;r1&#x27;, &#x27;r1&#x27;, &#x27;r1&#x27;, &#x27;r1&#x27;, &#x27;r1&#x27;, &#x27;r1&#x27;,\n",
       "       &#x27;r1&#x27;, &#x27;r1&#x27;, &#x27;r1&#x27;, &#x27;r1&#x27;, &#x27;r1&#x27;, &#x27;r1&#x27;, &#x27;r1&#x27;, &#x27;r1&#x27;, &#x27;r1&#x27;, &#x27;r1&#x27;, &#x27;r1&#x27;, &#x27;r1&#x27;,\n",
       "       &#x27;r1&#x27;], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>recalled</span></div><div class='xr-var-dims'>(event)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 0 0 0 0 1 1 0 ... 0 0 0 0 0 1 1 1</div><input id='attrs-cac9546e-abc3-4ad3-8afe-b8c53a48874f' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-cac9546e-abc3-4ad3-8afe-b8c53a48874f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-165d0a33-400e-4665-99e9-6837ec6e54ae' class='xr-var-data-in' type='checkbox'><label for='data-165d0a33-400e-4665-99e9-6837ec6e54ae' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 1,\n",
       "       1])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>rectime</span></div><div class='xr-var-dims'>(event)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>-999 -999 -999 ... 5648 3940 9012</div><input id='attrs-788092fe-61b7-4edc-ab37-569d62aa861f' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-788092fe-61b7-4edc-ab37-569d62aa861f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-85240ade-6f2b-4b43-aaa9-30eb310bd136' class='xr-var-data-in' type='checkbox'><label for='data-85240ade-6f2b-4b43-aaa9-30eb310bd136' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([ -999,  -999,  -999,  -999,  -999,  1375, 12215,  -999,  -999,  -999,\n",
       "        -999,  7905, 20007,  -999,  -999,  -999,  3252,  -999,  -999,  -999,\n",
       "        -999,  -999,  5648,  3940,  9012])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>serialpos</span></div><div class='xr-var-dims'>(event)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>1 2 3 4 5 6 7 ... 7 8 9 10 11 12 1</div><input id='attrs-66e8be6b-e5df-4719-ac25-9846f62c9201' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-66e8be6b-e5df-4719-ac25-9846f62c9201' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8fdb5d94-f32e-443c-bf1b-5b71bf48b391' class='xr-var-data-in' type='checkbox'><label for='data-8fdb5d94-f32e-443c-bf1b-5b71bf48b391' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([ 1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12,  1,  2,  3,  4,  5,  6,\n",
       "        7,  8,  9, 10, 11, 12,  1])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>session</span></div><div class='xr-var-dims'>(event)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 0 0 0 0 0 0 0 ... 0 0 0 0 0 0 0 0</div><input id='attrs-08e43794-c958-45e8-96c9-0ebebba773aa' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-08e43794-c958-45e8-96c9-0ebebba773aa' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-dc3ea79a-adbb-436b-9287-4b09c2ce0370' class='xr-var-data-in' type='checkbox'><label for='data-dc3ea79a-adbb-436b-9287-4b09c2ce0370' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>stim_list</span></div><div class='xr-var-dims'>(event)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 0 0 0 0 0 0 0 ... 0 0 0 0 0 0 0 0</div><input id='attrs-f87bc47a-2021-4083-9165-914ce769d7c0' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-f87bc47a-2021-4083-9165-914ce769d7c0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2a9e4825-8dea-4202-a534-8b1219a9b4ab' class='xr-var-data-in' type='checkbox'><label for='data-2a9e4825-8dea-4202-a534-8b1219a9b4ab' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>stim_params</span></div><div class='xr-var-dims'>(event)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>() () () () () ... () () () () ()</div><input id='attrs-5c2c4dc8-2a51-4545-a422-2ad13abab928' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-5c2c4dc8-2a51-4545-a422-2ad13abab928' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ef54d4d1-6b90-453a-802a-7b75394009a5' class='xr-var-data-in' type='checkbox'><label for='data-ef54d4d1-6b90-453a-802a-7b75394009a5' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([(), (), (), (), (), (), (), (), (), (), (), (), (), (), (), (), (), (),\n",
       "       (), (), (), (), (), (), ()], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>subject</span></div><div class='xr-var-dims'>(event)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>&#x27;R1045E&#x27; &#x27;R1045E&#x27; ... &#x27;R1045E&#x27;</div><input id='attrs-ceb22234-3286-409d-b510-eebfa4577f94' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-ceb22234-3286-409d-b510-eebfa4577f94' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5b1ce287-b66c-47e6-ae28-091cd4b1a8e8' class='xr-var-data-in' type='checkbox'><label for='data-5b1ce287-b66c-47e6-ae28-091cd4b1a8e8' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;R1045E&#x27;, &#x27;R1045E&#x27;, &#x27;R1045E&#x27;, &#x27;R1045E&#x27;, &#x27;R1045E&#x27;, &#x27;R1045E&#x27;, &#x27;R1045E&#x27;,\n",
       "       &#x27;R1045E&#x27;, &#x27;R1045E&#x27;, &#x27;R1045E&#x27;, &#x27;R1045E&#x27;, &#x27;R1045E&#x27;, &#x27;R1045E&#x27;, &#x27;R1045E&#x27;,\n",
       "       &#x27;R1045E&#x27;, &#x27;R1045E&#x27;, &#x27;R1045E&#x27;, &#x27;R1045E&#x27;, &#x27;R1045E&#x27;, &#x27;R1045E&#x27;, &#x27;R1045E&#x27;,\n",
       "       &#x27;R1045E&#x27;, &#x27;R1045E&#x27;, &#x27;R1045E&#x27;, &#x27;R1045E&#x27;], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>test</span></div><div class='xr-var-dims'>(event)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>(0, 0, 0) (0, 0, 0) ... (0, 0, 0)</div><input id='attrs-2b136d5b-d093-4746-9e7a-ab1c8894c956' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-2b136d5b-d093-4746-9e7a-ab1c8894c956' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3f1151ef-d397-48c0-9ab4-b2a4cff30b21' class='xr-var-data-in' type='checkbox'><label for='data-3f1151ef-d397-48c0-9ab4-b2a4cff30b21' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([(0, 0, 0), (0, 0, 0), (0, 0, 0), (0, 0, 0), (0, 0, 0), (0, 0, 0),\n",
       "       (0, 0, 0), (0, 0, 0), (0, 0, 0), (0, 0, 0), (0, 0, 0), (0, 0, 0),\n",
       "       (0, 0, 0), (0, 0, 0), (0, 0, 0), (0, 0, 0), (0, 0, 0), (0, 0, 0),\n",
       "       (0, 0, 0), (0, 0, 0), (0, 0, 0), (0, 0, 0), (0, 0, 0), (0, 0, 0),\n",
       "       (0, 0, 0)], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>type</span></div><div class='xr-var-dims'>(event)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>&#x27;WORD&#x27; &#x27;WORD&#x27; ... &#x27;WORD&#x27; &#x27;WORD&#x27;</div><input id='attrs-0ee4beb4-daf9-42d0-ab16-c34b317f2df1' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-0ee4beb4-daf9-42d0-ab16-c34b317f2df1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ea86382c-5bb0-47b6-b767-8cd6f8acbc0d' class='xr-var-data-in' type='checkbox'><label for='data-ea86382c-5bb0-47b6-b767-8cd6f8acbc0d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;WORD&#x27;, &#x27;WORD&#x27;, &#x27;WORD&#x27;, &#x27;WORD&#x27;, &#x27;WORD&#x27;, &#x27;WORD&#x27;, &#x27;WORD&#x27;, &#x27;WORD&#x27;, &#x27;WORD&#x27;,\n",
       "       &#x27;WORD&#x27;, &#x27;WORD&#x27;, &#x27;WORD&#x27;, &#x27;WORD&#x27;, &#x27;WORD&#x27;, &#x27;WORD&#x27;, &#x27;WORD&#x27;, &#x27;WORD&#x27;, &#x27;WORD&#x27;,\n",
       "       &#x27;WORD&#x27;, &#x27;WORD&#x27;, &#x27;WORD&#x27;, &#x27;WORD&#x27;, &#x27;WORD&#x27;, &#x27;WORD&#x27;, &#x27;WORD&#x27;], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>channel</span></div><div class='xr-var-dims'>(channel)</div><div class='xr-var-dtype'>&lt;U16</div><div class='xr-var-preview xr-preview'>&#x27;LAFS1-LAFS2&#x27; ... &#x27;RPHGD5-RPHGD7&#x27;</div><input id='attrs-94c41862-6808-49db-ad45-d8ce8b253ce5' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-94c41862-6808-49db-ad45-d8ce8b253ce5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4d8e03e1-3063-43e6-a1e5-971dc8eb279f' class='xr-var-data-in' type='checkbox'><label for='data-4d8e03e1-3063-43e6-a1e5-971dc8eb279f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;LAFS1-LAFS2&#x27;, &#x27;LAFS2-LAFS3&#x27;, &#x27;LAFS3-LAFS4&#x27;, &#x27;LAFS4-LAFS5&#x27;,\n",
       "       &#x27;LAFS5-LAFS6&#x27;, &#x27;LAFS6-LAFS7&#x27;, &#x27;LAFS7-LAFS8&#x27;, &#x27;LIFS1-LIFS2&#x27;,\n",
       "       &#x27;LIFS2-LIFS3&#x27;, &#x27;LIFS3-LIFS4&#x27;, &#x27;LIFS4-LIFS5&#x27;, &#x27;LIFS5-LIFS6&#x27;,\n",
       "       &#x27;LIFS6-LIFS8&#x27;, &#x27;LIFS8-LIFS9&#x27;, &#x27;LIFS9-LIFS10&#x27;, &#x27;LATS1-LATS2&#x27;,\n",
       "       &#x27;LATS2-LATS3&#x27;, &#x27;LATS3-LATS4&#x27;, &#x27;LATS4-LATS5&#x27;, &#x27;LATS5-LATS6&#x27;,\n",
       "       &#x27;LATS6-LATS7&#x27;, &#x27;LATS7-LATS8&#x27;, &#x27;LPTS1-LPTS2&#x27;, &#x27;LPTS2-LPTS3&#x27;,\n",
       "       &#x27;LPTS3-LPTS4&#x27;, &#x27;LPTS4-LPTS5&#x27;, &#x27;LPTS5-LPTS6&#x27;, &#x27;LPTS6-LPTS7&#x27;,\n",
       "       &#x27;LPTS7-LPTS8&#x27;, &#x27;LPTS8-LPTS9&#x27;, &#x27;LPTS9-LPTS10&#x27;, &#x27;LAMYD1-LAMYD2&#x27;,\n",
       "       &#x27;LAMYD2-LAMYD3&#x27;, &#x27;LAMYD3-LAMYD4&#x27;, &#x27;LAMYD4-LAMYD5&#x27;, &#x27;LAMYD5-LAMYD6&#x27;,\n",
       "       &#x27;LAMYD6-LAMYD7&#x27;, &#x27;LAMYD7-LAMYD8&#x27;, &#x27;LAMYD8-LAMYD9&#x27;, &#x27;LAMYD9-LAMYD10&#x27;,\n",
       "       &#x27;LAHD1-LAHD2&#x27;, &#x27;LAHD2-LAHD3&#x27;, &#x27;LAHD3-LAHD4&#x27;, &#x27;LAHD4-LAHD5&#x27;,\n",
       "       &#x27;LAHD5-LAHD6&#x27;, &#x27;LAHD6-LAHD7&#x27;, &#x27;LAHD7-LAHD8&#x27;, &#x27;LAHD8-LAHD9&#x27;,\n",
       "       &#x27;LAHD9-LAHD10&#x27;, &#x27;LMHD1-LMHD2&#x27;, &#x27;LMHD2-LMHD3&#x27;, &#x27;LMHD3-LMHD4&#x27;,\n",
       "       &#x27;LMHD4-LMHD5&#x27;, &#x27;LMHD5-LMHD6&#x27;, &#x27;LMHD6-LMHD7&#x27;, &#x27;LMHD7-LMHD8&#x27;,\n",
       "       &#x27;LMHD8-LMHD9&#x27;, &#x27;LMHD9-LMHD10&#x27;, &#x27;LPHD1-LPHD2&#x27;, &#x27;LPHD2-LPHD3&#x27;,\n",
       "       &#x27;LPHD3-LPHD4&#x27;, &#x27;LPHD4-LPHD5&#x27;, &#x27;LPHD5-LPHD6&#x27;, &#x27;LPHD6-LPHD7&#x27;,\n",
       "       &#x27;LPHD7-LPHD8&#x27;, &#x27;LPHD8-LPHD9&#x27;, &#x27;LPHD9-LPHD10&#x27;, &#x27;LSTGID1-LSTGID2&#x27;,\n",
       "       &#x27;LSTGID2-LSTGID3&#x27;, &#x27;LSTGID3-LSTGID4&#x27;, &#x27;LSTGID4-LSTGID5&#x27;,\n",
       "       &#x27;LSTGID5-LSTGID6&#x27;, &#x27;LSTGID6-LSTGID7&#x27;, &#x27;LSTGID7-LSTGID8&#x27;,\n",
       "       &#x27;LSTGID8-LSTGID9&#x27;, &#x27;LSTGID9-LSTGID10&#x27;, &#x27;LPHGD1-LPHGD2&#x27;, &#x27;LPHGD2-LPHGD3&#x27;,\n",
       "       &#x27;LPHGD3-LPHGD4&#x27;, &#x27;LPHGD4-LPHGD5&#x27;, &#x27;LPHGD5-LPHGD6&#x27;, &#x27;LPHGD6-LPHGD7&#x27;,\n",
       "       &#x27;LPHGD7-LPHGD8&#x27;, &#x27;LPHGD8-LPHGD9&#x27;, &#x27;LPHGD9-LPHGD10&#x27;, &#x27;RAFS1-RAFS3&#x27;,\n",
       "       &#x27;RAFS3-RAFS5&#x27;, &#x27;RAFS5-RAFS7&#x27;, &#x27;RATS1-RATS3&#x27;, &#x27;RATS3-RATS5&#x27;,\n",
       "       &#x27;RATS5-RATS7&#x27;, &#x27;RPTS1-RPTS3&#x27;, &#x27;RPTS3-RPTS5&#x27;, &#x27;RPTS5-RPTS7&#x27;,\n",
       "       &#x27;RAMYD1-RAMYD3&#x27;, &#x27;RAMYD3-RAMYD5&#x27;, &#x27;RAMYD5-RAMYD7&#x27;, &#x27;RAHD1-RAHD3&#x27;,\n",
       "       &#x27;RAHD3-RAHD5&#x27;, &#x27;RAHD5-RAHD7&#x27;, &#x27;RPHD1-RPHD3&#x27;, &#x27;RPHD3-RPHD5&#x27;,\n",
       "       &#x27;RPHD5-RPHD7&#x27;, &#x27;RSTGID1-RSTGID3&#x27;, &#x27;RSTGID3-RSTGID5&#x27;, &#x27;RSTGID5-RSTGID7&#x27;,\n",
       "       &#x27;RPHGD1-RPHGD3&#x27;, &#x27;RPHGD3-RPHGD5&#x27;, &#x27;RPHGD5-RPHGD7&#x27;], dtype=&#x27;&lt;U16&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-700.0 -699.0 ... 2.298e+03</div><input id='attrs-5a9eccd8-a6f6-463c-98eb-da22861d8b61' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-5a9eccd8-a6f6-463c-98eb-da22861d8b61' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b6a14e25-82c9-44f8-b578-664fcc497984' class='xr-var-data-in' type='checkbox'><label for='data-b6a14e25-82c9-44f8-b578-664fcc497984' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([-700.      , -698.998999, -697.997998, ..., 2295.995996, 2296.996997,\n",
       "       2297.997998])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>samplerate</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>999.0</div><input id='attrs-f4ca2429-44ea-453f-8dc7-4a6abb2d1407' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-f4ca2429-44ea-453f-8dc7-4a6abb2d1407' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-514e0dc4-df0c-4771-ac65-431beecb0b54' class='xr-var-data-in' type='checkbox'><label for='data-514e0dc4-df0c-4771-ac65-431beecb0b54' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array(999.)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-2e590bd6-5519-4212-9852-393087e30ad0' class='xr-section-summary-in' type='checkbox'  ><label for='section-2e590bd6-5519-4212-9852-393087e30ad0' class='xr-section-summary' >Indexes: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>event<br>answer<br>category<br>category_num<br>eegfile<br>eegoffset<br>exp_version<br>experiment<br>intrusion<br>is_stim<br>iscorrect<br>item_name<br>item_num<br>list<br>montage<br>msoffset<br>mstime<br>protocol<br>recalled<br>rectime<br>serialpos<br>session<br>stim_list<br>stim_params<br>subject<br>test<br>type</div></div><div class='xr-index-preview'>PandasMultiIndex</div><div></div><input id='index-45952842-9cbd-4908-88a7-43b30bb44c99' class='xr-index-data-in' type='checkbox'/><label for='index-45952842-9cbd-4908-88a7-43b30bb44c99' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(MultiIndex([(-999, &#x27;officesupplies&#x27;, 17, ...),\n",
       "            (-999, &#x27;officesupplies&#x27;, 17, ...),\n",
       "            (-999,   &#x27;kitchentools&#x27;, 14, ...),\n",
       "            (-999,   &#x27;kitchentools&#x27;, 14, ...),\n",
       "            (-999,      &#x27;bodyparts&#x27;,  3, ...),\n",
       "            (-999,      &#x27;bodyparts&#x27;,  3, ...),\n",
       "            (-999, &#x27;officesupplies&#x27;, 17, ...),\n",
       "            (-999, &#x27;officesupplies&#x27;, 17, ...),\n",
       "            (-999,      &#x27;bodyparts&#x27;,  3, ...),\n",
       "            (-999,      &#x27;bodyparts&#x27;,  3, ...),\n",
       "            (-999,   &#x27;kitchentools&#x27;, 14, ...),\n",
       "            (-999,   &#x27;kitchentools&#x27;, 14, ...),\n",
       "            (-999,     &#x27;landscapes&#x27;, 15, ...),\n",
       "            (-999,     &#x27;landscapes&#x27;, 15, ...),\n",
       "            (-999,        &#x27;weather&#x27;, 23, ...),\n",
       "            (-999,        &#x27;weather&#x27;, 23, ...),\n",
       "            (-999,       &#x27;desserts&#x27;,  6, ...),\n",
       "            (-999,       &#x27;desserts&#x27;,  6, ...),\n",
       "            (-999,     &#x27;landscapes&#x27;, 15, ...),\n",
       "            (-999,     &#x27;landscapes&#x27;, 15, ...),\n",
       "            (-999,       &#x27;desserts&#x27;,  6, ...),\n",
       "            (-999,       &#x27;desserts&#x27;,  6, ...),\n",
       "            (-999,        &#x27;weather&#x27;, 23, ...),\n",
       "            (-999,        &#x27;weather&#x27;, 23, ...),\n",
       "            (-999,    &#x27;electronics&#x27;,  7, ...)],\n",
       "           name=&#x27;event&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>channel</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-804776a8-f344-4cb4-a4ce-eb576707d148' class='xr-index-data-in' type='checkbox'/><label for='index-804776a8-f344-4cb4-a4ce-eb576707d148' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([&#x27;LAFS1-LAFS2&#x27;, &#x27;LAFS2-LAFS3&#x27;, &#x27;LAFS3-LAFS4&#x27;, &#x27;LAFS4-LAFS5&#x27;,\n",
       "       &#x27;LAFS5-LAFS6&#x27;, &#x27;LAFS6-LAFS7&#x27;, &#x27;LAFS7-LAFS8&#x27;, &#x27;LIFS1-LIFS2&#x27;,\n",
       "       &#x27;LIFS2-LIFS3&#x27;, &#x27;LIFS3-LIFS4&#x27;,\n",
       "       ...\n",
       "       &#x27;RAHD5-RAHD7&#x27;, &#x27;RPHD1-RPHD3&#x27;, &#x27;RPHD3-RPHD5&#x27;, &#x27;RPHD5-RPHD7&#x27;,\n",
       "       &#x27;RSTGID1-RSTGID3&#x27;, &#x27;RSTGID3-RSTGID5&#x27;, &#x27;RSTGID5-RSTGID7&#x27;,\n",
       "       &#x27;RPHGD1-RPHGD3&#x27;, &#x27;RPHGD3-RPHGD5&#x27;, &#x27;RPHGD5-RPHGD7&#x27;],\n",
       "      dtype=&#x27;object&#x27;, name=&#x27;channel&#x27;, length=109))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-f8dcc246-8316-4985-97fd-194b9f5d2b88' class='xr-index-data-in' type='checkbox'/><label for='index-f8dcc246-8316-4985-97fd-194b9f5d2b88' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([            -700.0,  -698.998998998999,  -697.997997997998,\n",
       "        -696.996996996997,  -695.995995995996, -694.9949949949951,\n",
       "       -693.9939939939941, -692.9929929929931, -691.9919919919921,\n",
       "       -690.9909909909911,\n",
       "       ...\n",
       "       2288.9889889889455, 2289.9899899899465, 2290.9909909909475,\n",
       "       2291.9919919919485, 2292.9929929929494, 2293.9939939939504,\n",
       "       2294.9949949949514, 2295.9959959959524, 2296.9969969969534,\n",
       "       2297.9979979979544],\n",
       "      dtype=&#x27;float64&#x27;, name=&#x27;time&#x27;, length=2996))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-13b1f30c-264a-4d13-91dd-62ea66137f17' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-13b1f30c-264a-4d13-91dd-62ea66137f17' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.TimeSeries (event: 25, channel: 109, time: 2996)>\n",
       "array([[[ 9.07564493e+00,  8.91269333e+00,  7.78982271e+00, ...,\n",
       "         -6.73819109e-01, -1.87117464e+00, -7.41286306e-02],\n",
       "        [-2.41358269e-01, -1.06808596e+00,  5.58388921e-02, ...,\n",
       "          6.17264351e-01,  7.38114355e-01, -2.16292342e+00],\n",
       "        [-1.78423067e+00, -1.86807224e+00, -5.95247050e+00, ...,\n",
       "          2.94886808e+00,  8.09032297e-01, -2.80513706e-01],\n",
       "        ...,\n",
       "        [-2.24676236e+01, -2.20258621e+01, -2.66083894e+01, ...,\n",
       "          1.96299162e+01,  2.41579243e+01,  3.07893597e+01],\n",
       "        [-6.65766688e+00, -2.37185228e+00,  4.77603047e+00, ...,\n",
       "         -3.21688971e+01, -3.65799464e+01, -4.09612498e+01],\n",
       "        [-7.82027343e+00, -1.26559781e+01, -1.33940386e+01, ...,\n",
       "          5.05643902e+01,  5.60168139e+01,  5.63652462e+01]],\n",
       "\n",
       "       [[-3.38053776e+00, -5.53729369e+00,  1.32428359e+00, ...,\n",
       "         -1.59123144e+00, -2.78913482e+00, -3.07713927e+00],\n",
       "        [ 2.20039630e+00,  1.80903284e-01,  1.17953354e+00, ...,\n",
       "          5.36357579e+00,  2.50743427e+00,  1.62261419e+00],\n",
       "        [-6.26592442e+00, -2.32556354e+00, -5.39314791e+00, ...,\n",
       "          2.82679465e+00,  4.69374759e+00,  6.54390395e+00],\n",
       "...\n",
       "        [ 2.22690152e+01,  2.23792903e+01,  2.76467132e+01, ...,\n",
       "          4.30307265e+01,  4.16097071e+01,  4.80597664e+01],\n",
       "        [-8.78085138e+01, -9.15945719e+01, -9.34250001e+01, ...,\n",
       "          2.26051479e+00,  7.98647529e+00,  9.70564080e+00],\n",
       "        [-1.90823222e+00,  4.11886671e+00,  8.19484822e+00, ...,\n",
       "         -4.43647335e+01, -4.76717534e+01, -4.39332957e+01]],\n",
       "\n",
       "       [[ 3.46525556e+00,  2.67293505e+00, -1.84780437e-01, ...,\n",
       "          3.10392949e-03, -1.92384541e+00,  1.67094468e-01],\n",
       "        [-1.00597547e+00, -3.06064247e+00,  1.91805484e+00, ...,\n",
       "          5.23218025e+00,  3.23524652e+00,  3.22686822e+00],\n",
       "        [-6.20852043e+00, -2.36264060e+00, -7.50220507e+00, ...,\n",
       "         -4.33676943e+00,  5.13274023e-01, -1.60446468e+00],\n",
       "        ...,\n",
       "        [ 7.24180938e+00,  3.51565233e+00,  6.80312768e+00, ...,\n",
       "          1.12593874e+01,  1.41993560e+01,  1.62463436e+01],\n",
       "        [ 1.18972840e+02,  1.21403893e+02,  1.12696600e+02, ...,\n",
       "         -4.98330170e+01, -5.13540185e+01, -4.99439155e+01],\n",
       "        [-1.31323141e+02, -1.29884174e+02, -1.29432126e+02, ...,\n",
       "          1.47524206e+02,  1.47774399e+02,  1.50136671e+02]]])\n",
       "Coordinates: (12/30)\n",
       "  * event         (event) object MultiIndex\n",
       "  * answer        (event) int64 -999 -999 -999 -999 -999 ... -999 -999 -999 -999\n",
       "  * category      (event) object 'officesupplies' ... 'electronics'\n",
       "  * category_num  (event) int64 17 17 14 14 3 3 17 17 3 ... 6 15 15 6 6 23 23 7\n",
       "  * eegfile       (event) object '/protocols/r1/subjects/R1045E/experiments/c...\n",
       "  * eegoffset     (event) int64 491162 493597 495997 ... 636895 639329 710970\n",
       "    ...            ...\n",
       "  * subject       (event) object 'R1045E' 'R1045E' ... 'R1045E' 'R1045E'\n",
       "  * test          (event) object (0, 0, 0) (0, 0, 0) ... (0, 0, 0) (0, 0, 0)\n",
       "  * type          (event) object 'WORD' 'WORD' 'WORD' ... 'WORD' 'WORD' 'WORD'\n",
       "  * channel       (channel) <U16 'LAFS1-LAFS2' 'LAFS2-LAFS3' ... 'RPHGD5-RPHGD7'\n",
       "  * time          (time) float64 -700.0 -699.0 -698.0 ... 2.297e+03 2.298e+03\n",
       "    samplerate    float64 999.0"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "eeg_ptsa"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "707ab097-0bb2-47b8-b467-30ff4fff27d3",
   "metadata": {},
   "outputs": [],
   "source": [
    "rec_evs = evs.query('type == \"REC_WORD\" and eegoffset != -1')\n",
    "rec_evs = rec_evs[rec_evs['list'] > 0]\n",
    "rec_evs['category'] = rec_evs ['category'].str.lower()\n",
    "rec_evs = rec_evs.iloc[:25]\n",
    "# get inter-retreival times since previous recall\n",
    "rec_evs['pirt'] = rec_evs.groupby(['session', 'list'])['rectime'].diff().fillna(\n",
    "    rec_evs['rectime'])\n",
    "rec_evs['repeat'] = rec_evs.duplicated(subset=['session', 'list', 'item_name'])\n",
    "rec_evs['outpos'] = rec_evs.groupby(['subject', 'session', 'list']).cumcount()\n",
    "# only include recalls at least 1500 ms away and no repeats\n",
    "rec_evs = rec_evs.query('pirt > 1500 and repeat == 0')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b8daa9cf-699a-4fe7-a1fa-eb3797e9c00a",
   "metadata": {},
   "source": [
    "**Load EEG using PTSA just like above**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5c6a9b38-f104-427d-8ef5-68ab8dfd9e46",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "5e03dff0-c53a-417d-9baf-cdeee8d3b304",
   "metadata": {},
   "source": [
    "Because recalls might run into each other, in order to create a buffer, we need to use what's known as a mirror buffer. This ensures that future vocalizations don't screw up the buffer period"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "35ad656b-d582-4cbd-90e8-4d7ed911462a",
   "metadata": {},
   "outputs": [],
   "source": [
    "eeg_ptsa['time'] = eeg_ptsa['time'] / 1000 # PTSA time scale is in seconds instead of ms\n",
    "eeg_ptsa = eeg_ptsa.add_mirror_buffer(rec_buffer_time/1000)\n",
    "eeg_ptsa['time'] = eeg_ptsa['time'] * 1000"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c0b63af3-1233-49dc-96ae-fa2b2d37fdfd",
   "metadata": {},
   "source": [
    "**Preprocess data just like above**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2da4a7d3-eb9a-459c-aefe-aa1c15129f4e",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "f6e4c98a-70d2-492e-a1f6-f72365691ca5",
   "metadata": {},
   "source": [
    "## Preprocess countdown events"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "d3e72c43-a13b-40e1-968c-cd463b29b925",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_80346/1931903411.py:6: DtypeWarning: Columns (6,9,18,26) have mixed types. Specify dtype option on import or set low_memory=False.\n",
      "  countdown_events = pd.read_csv(save_path+'/'+exp+'_countdown_evs.csv')\n"
     ]
    }
   ],
   "source": [
    "countdown_rel_start = 0\n",
    "countdown_rel_stop = 10000\n",
    "countdown_resameplerate = 1\n",
    "\n",
    "row = df_sub.iloc[0]\n",
    "countdown_events = pd.read_csv(save_path+'/'+exp+'_countdown_evs.csv')\n",
    "countdown_evs = countdown_events.query('subject == @row.subject and session == @row.session')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "724a1b7d-b48f-40d1-b8f4-97ffd1a02bf4",
   "metadata": {},
   "source": [
    "**Preprocess data for countdown events just like above**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "638c943e-77b4-4bb2-9632-55f766dfe6c6",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "2d44e685-4708-4342-947d-e40837c20c52",
   "metadata": {
    "tags": []
   },
   "source": [
    "## Compute RSA"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1b5dca27-da86-4fff-a17e-7d5923b52e69",
   "metadata": {},
   "source": [
    "Now we have to compute the RSA. This basically involves loading the outputs of the `compute_features` function above and using the xarray `corr` function which will compute the correlation matrix between two data arrays and hold on to all the relevant information for us. There are two tricky things going on here. One is that we need to normalize the encoding and retrieval time features by the countdown features. we have a function called `normalize_features` to do that. We also need to change the names of the event information so that they don't match. If they do, the `corr` function will assume they are referring to the same informaiton and will not compute a correlation between them. In order to distinguish the item_name at retrieval and at encoding, we append the event `type` onto the name of each column. If we are computing the correlation between two events of the same `type` (e.g. each encoding event to other encoding events), we add a 2 on the end to distinguish them."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "47490b0e-5226-4d98-9d89-c913cc17fa1e",
   "metadata": {},
   "outputs": [],
   "source": [
    "experiment = \"catFR1\"\n",
    "encoding_type = \"WORD\"\n",
    "comparison_type = \"WORD\"\n",
    "countdown_normalize = 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "4d436f17-baa1-4696-826f-31230477b758",
   "metadata": {},
   "outputs": [],
   "source": [
    "def normalize_features(pows, save_path, countdown_normalize=True):\n",
    "    subject, session = pows.event.subject.values[0], pows.event.session.values[0]\n",
    "    if countdown_normalize:\n",
    "        countdown_fp = save_path + '/' + subject + '_' + str(session) + '_COUNTDOWN_START_feats.h5'\n",
    "        countdown_pows = TimeSeries.from_hdf(countdown_fp)\n",
    "        countdown_pows['samplerate'] = pows['samplerate']\n",
    "        pows = (pows - countdown_pows.mean(['event', 'time'])) / countdown_pows.std(['event', 'time']) \n",
    "    else:\n",
    "        pows = pows.reduce(func=sp.stats.zscore, dim='event', keep_attrs=True, ddof=1)\n",
    "    return pows\n",
    "\n",
    "def set_event_names(pows, period_type, col='event', copy=False):\n",
    "    if copy:\n",
    "        pows = pows.copy()\n",
    "    events_mi = pows[col].to_index()\n",
    "    events_mi = events_mi.rename(\n",
    "        [name+'_'+period_type for name in events_mi.names])\n",
    "    pows[col] = events_mi\n",
    "    pows = pows.rename({col: col+'_'+period_type})\n",
    "    return pows"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "95820540-edd4-4096-bfa1-0cb0984372e2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Processing subject: R1045E session: 0\n"
     ]
    }
   ],
   "source": [
    "print(\"Processing subject:\", row.subject, \"session:\", row.session)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "f99f5c73-0b66-4169-84db-0cfc22603bb5",
   "metadata": {},
   "outputs": [],
   "source": [
    "encoding_fp = save_path + '/' + row.subject + '_' + str(row.session) + '_' + encoding_type + '_feats.h5'\n",
    "encoding_pows = TimeSeries.from_hdf(encoding_fp)\n",
    "encoding_pows = normalize_features(encoding_pows, save_path, countdown_normalize=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "eb98fa6b-1fcb-475b-8116-58cb7c3f7e18",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/global/miniconda/py310_23.1.0-1/envs/workshop_311/lib/python3.11/site-packages/xarray/core/coordinates.py:41: FutureWarning: Updating MultiIndexed coordinate 'event' would corrupt indices for other variables: ['category', 'category_num', 'eegfile', 'eegoffset', 'exp_version', 'experiment', 'intrusion', 'is_stim', 'item_name', 'item_num', 'list', 'montage', 'msoffset', 'mstime', 'protocol', 'recalled', 'rectime', 'serialpos', 'session', 'stim_list', 'subject', 'type']. This will raise an error in the future. Use `.drop_vars({'protocol', 'mstime', 'type', 'recalled', 'list', 'exp_version', 'eegfile', 'msoffset', 'intrusion', 'eegoffset', 'montage', 'experiment', 'is_stim', 'serialpos', 'category', 'event', 'item_name', 'rectime', 'stim_list', 'session', 'item_num', 'subject', 'category_num'})` before assigning new coordinate values.\n",
      "  self.update({key: value})\n"
     ]
    }
   ],
   "source": [
    "#need to rename event index to keep track of things for correlation matrix\n",
    "comparison_pows = set_event_names(encoding_pows, encoding_type+'2', col='event', copy=True)\n",
    "encoding_pows = set_event_names(encoding_pows, encoding_type, col='event')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "9b020d28-dbcf-4b0f-b767-4eb01df2c4fa",
   "metadata": {},
   "outputs": [],
   "source": [
    "corr_arr = xr.corr(encoding_pows, comparison_pows, dim='features')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e6c1b362-88ef-4ebe-b624-d96d022e7f08",
   "metadata": {},
   "source": [
    "We now convert it to a dataframe for easy use later. We transform the correlation into `corr_z`, the [Fisher](https://academic.oup.com/biomet/article/10/4/507/203628) [$z$-transformed](https://en.wikipedia.org/wiki/Fisher_transformation) correlation between two encoding periods. We transform it because if we average together a bunch of correlations our result will be biased relative to the true average correlation. The transformation to some extent corrects for this bias (asymptotically). "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "317f455d-7f3b-49ae-93bd-650cb64c0998",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/global/miniconda/py310_23.1.0-1/envs/workshop_311/lib/python3.11/site-packages/pandas/core/arraylike.py:396: RuntimeWarning: divide by zero encountered in arctanh\n",
      "  result = getattr(ufunc, method)(*inputs, **kwargs)\n",
      "/usr/global/miniconda/py310_23.1.0-1/envs/workshop_311/lib/python3.11/site-packages/pandas/core/arraylike.py:396: RuntimeWarning: invalid value encountered in arctanh\n",
      "  result = getattr(ufunc, method)(*inputs, **kwargs)\n"
     ]
    }
   ],
   "source": [
    "corr_df = corr_arr.to_dataframe('corr').reset_index(drop=True)\n",
    "corr_df['corr_z'] = np.arctanh(corr_df['corr'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "35c71245-166d-4883-a774-c1831081376d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>samplerate</th>\n",
       "      <th>category_WORD</th>\n",
       "      <th>category_num_WORD</th>\n",
       "      <th>eegfile_WORD</th>\n",
       "      <th>eegoffset_WORD</th>\n",
       "      <th>exp_version_WORD</th>\n",
       "      <th>experiment_WORD</th>\n",
       "      <th>intrusion_WORD</th>\n",
       "      <th>is_stim_WORD</th>\n",
       "      <th>item_name_WORD</th>\n",
       "      <th>...</th>\n",
       "      <th>protocol_WORD2</th>\n",
       "      <th>recalled_WORD2</th>\n",
       "      <th>rectime_WORD2</th>\n",
       "      <th>serialpos_WORD2</th>\n",
       "      <th>session_WORD2</th>\n",
       "      <th>stim_list_WORD2</th>\n",
       "      <th>subject_WORD2</th>\n",
       "      <th>type_WORD2</th>\n",
       "      <th>corr</th>\n",
       "      <th>corr_z</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>500.0</td>\n",
       "      <td>officesupplies</td>\n",
       "      <td>17</td>\n",
       "      <td>/protocols/r1/subjects/R1045E/experiments/catF...</td>\n",
       "      <td>491162</td>\n",
       "      <td>1.05</td>\n",
       "      <td>catFR1</td>\n",
       "      <td>-999</td>\n",
       "      <td>0</td>\n",
       "      <td>PAPER</td>\n",
       "      <td>...</td>\n",
       "      <td>r1</td>\n",
       "      <td>0</td>\n",
       "      <td>-999</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>R1045E</td>\n",
       "      <td>WORD</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>18.714974</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>500.0</td>\n",
       "      <td>officesupplies</td>\n",
       "      <td>17</td>\n",
       "      <td>/protocols/r1/subjects/R1045E/experiments/catF...</td>\n",
       "      <td>491162</td>\n",
       "      <td>1.05</td>\n",
       "      <td>catFR1</td>\n",
       "      <td>-999</td>\n",
       "      <td>0</td>\n",
       "      <td>PAPER</td>\n",
       "      <td>...</td>\n",
       "      <td>r1</td>\n",
       "      <td>0</td>\n",
       "      <td>-999</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>R1045E</td>\n",
       "      <td>WORD</td>\n",
       "      <td>0.249906</td>\n",
       "      <td>0.255313</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>500.0</td>\n",
       "      <td>officesupplies</td>\n",
       "      <td>17</td>\n",
       "      <td>/protocols/r1/subjects/R1045E/experiments/catF...</td>\n",
       "      <td>491162</td>\n",
       "      <td>1.05</td>\n",
       "      <td>catFR1</td>\n",
       "      <td>-999</td>\n",
       "      <td>0</td>\n",
       "      <td>PAPER</td>\n",
       "      <td>...</td>\n",
       "      <td>r1</td>\n",
       "      <td>0</td>\n",
       "      <td>-999</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>R1045E</td>\n",
       "      <td>WORD</td>\n",
       "      <td>0.152489</td>\n",
       "      <td>0.153688</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>500.0</td>\n",
       "      <td>officesupplies</td>\n",
       "      <td>17</td>\n",
       "      <td>/protocols/r1/subjects/R1045E/experiments/catF...</td>\n",
       "      <td>491162</td>\n",
       "      <td>1.05</td>\n",
       "      <td>catFR1</td>\n",
       "      <td>-999</td>\n",
       "      <td>0</td>\n",
       "      <td>PAPER</td>\n",
       "      <td>...</td>\n",
       "      <td>r1</td>\n",
       "      <td>0</td>\n",
       "      <td>-999</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>R1045E</td>\n",
       "      <td>WORD</td>\n",
       "      <td>-0.034754</td>\n",
       "      <td>-0.034768</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>500.0</td>\n",
       "      <td>officesupplies</td>\n",
       "      <td>17</td>\n",
       "      <td>/protocols/r1/subjects/R1045E/experiments/catF...</td>\n",
       "      <td>491162</td>\n",
       "      <td>1.05</td>\n",
       "      <td>catFR1</td>\n",
       "      <td>-999</td>\n",
       "      <td>0</td>\n",
       "      <td>PAPER</td>\n",
       "      <td>...</td>\n",
       "      <td>r1</td>\n",
       "      <td>0</td>\n",
       "      <td>-999</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>R1045E</td>\n",
       "      <td>WORD</td>\n",
       "      <td>0.095259</td>\n",
       "      <td>0.095548</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>89995</th>\n",
       "      <td>500.0</td>\n",
       "      <td>oceananimals</td>\n",
       "      <td>16</td>\n",
       "      <td>/protocols/r1/subjects/R1045E/experiments/catF...</td>\n",
       "      <td>3021320</td>\n",
       "      <td>1.05</td>\n",
       "      <td>catFR1</td>\n",
       "      <td>-999</td>\n",
       "      <td>0</td>\n",
       "      <td>TURTLE</td>\n",
       "      <td>...</td>\n",
       "      <td>r1</td>\n",
       "      <td>0</td>\n",
       "      <td>-999</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>R1045E</td>\n",
       "      <td>WORD</td>\n",
       "      <td>0.097689</td>\n",
       "      <td>0.098001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>89996</th>\n",
       "      <td>500.0</td>\n",
       "      <td>oceananimals</td>\n",
       "      <td>16</td>\n",
       "      <td>/protocols/r1/subjects/R1045E/experiments/catF...</td>\n",
       "      <td>3021320</td>\n",
       "      <td>1.05</td>\n",
       "      <td>catFR1</td>\n",
       "      <td>-999</td>\n",
       "      <td>0</td>\n",
       "      <td>TURTLE</td>\n",
       "      <td>...</td>\n",
       "      <td>r1</td>\n",
       "      <td>0</td>\n",
       "      <td>-999</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>R1045E</td>\n",
       "      <td>WORD</td>\n",
       "      <td>0.077636</td>\n",
       "      <td>0.077793</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>89997</th>\n",
       "      <td>500.0</td>\n",
       "      <td>oceananimals</td>\n",
       "      <td>16</td>\n",
       "      <td>/protocols/r1/subjects/R1045E/experiments/catF...</td>\n",
       "      <td>3021320</td>\n",
       "      <td>1.05</td>\n",
       "      <td>catFR1</td>\n",
       "      <td>-999</td>\n",
       "      <td>0</td>\n",
       "      <td>TURTLE</td>\n",
       "      <td>...</td>\n",
       "      <td>r1</td>\n",
       "      <td>0</td>\n",
       "      <td>-999</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>R1045E</td>\n",
       "      <td>WORD</td>\n",
       "      <td>0.170163</td>\n",
       "      <td>0.171834</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>89998</th>\n",
       "      <td>500.0</td>\n",
       "      <td>oceananimals</td>\n",
       "      <td>16</td>\n",
       "      <td>/protocols/r1/subjects/R1045E/experiments/catF...</td>\n",
       "      <td>3021320</td>\n",
       "      <td>1.05</td>\n",
       "      <td>catFR1</td>\n",
       "      <td>-999</td>\n",
       "      <td>0</td>\n",
       "      <td>TURTLE</td>\n",
       "      <td>...</td>\n",
       "      <td>r1</td>\n",
       "      <td>0</td>\n",
       "      <td>-999</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>R1045E</td>\n",
       "      <td>WORD</td>\n",
       "      <td>0.246251</td>\n",
       "      <td>0.251418</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>89999</th>\n",
       "      <td>500.0</td>\n",
       "      <td>oceananimals</td>\n",
       "      <td>16</td>\n",
       "      <td>/protocols/r1/subjects/R1045E/experiments/catF...</td>\n",
       "      <td>3021320</td>\n",
       "      <td>1.05</td>\n",
       "      <td>catFR1</td>\n",
       "      <td>-999</td>\n",
       "      <td>0</td>\n",
       "      <td>TURTLE</td>\n",
       "      <td>...</td>\n",
       "      <td>r1</td>\n",
       "      <td>0</td>\n",
       "      <td>-999</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>R1045E</td>\n",
       "      <td>WORD</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>inf</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>90000 rows × 47 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       samplerate   category_WORD  category_num_WORD  \\\n",
       "0           500.0  officesupplies                 17   \n",
       "1           500.0  officesupplies                 17   \n",
       "2           500.0  officesupplies                 17   \n",
       "3           500.0  officesupplies                 17   \n",
       "4           500.0  officesupplies                 17   \n",
       "...           ...             ...                ...   \n",
       "89995       500.0    oceananimals                 16   \n",
       "89996       500.0    oceananimals                 16   \n",
       "89997       500.0    oceananimals                 16   \n",
       "89998       500.0    oceananimals                 16   \n",
       "89999       500.0    oceananimals                 16   \n",
       "\n",
       "                                            eegfile_WORD  eegoffset_WORD  \\\n",
       "0      /protocols/r1/subjects/R1045E/experiments/catF...          491162   \n",
       "1      /protocols/r1/subjects/R1045E/experiments/catF...          491162   \n",
       "2      /protocols/r1/subjects/R1045E/experiments/catF...          491162   \n",
       "3      /protocols/r1/subjects/R1045E/experiments/catF...          491162   \n",
       "4      /protocols/r1/subjects/R1045E/experiments/catF...          491162   \n",
       "...                                                  ...             ...   \n",
       "89995  /protocols/r1/subjects/R1045E/experiments/catF...         3021320   \n",
       "89996  /protocols/r1/subjects/R1045E/experiments/catF...         3021320   \n",
       "89997  /protocols/r1/subjects/R1045E/experiments/catF...         3021320   \n",
       "89998  /protocols/r1/subjects/R1045E/experiments/catF...         3021320   \n",
       "89999  /protocols/r1/subjects/R1045E/experiments/catF...         3021320   \n",
       "\n",
       "       exp_version_WORD experiment_WORD  intrusion_WORD  is_stim_WORD  \\\n",
       "0                  1.05          catFR1            -999             0   \n",
       "1                  1.05          catFR1            -999             0   \n",
       "2                  1.05          catFR1            -999             0   \n",
       "3                  1.05          catFR1            -999             0   \n",
       "4                  1.05          catFR1            -999             0   \n",
       "...                 ...             ...             ...           ...   \n",
       "89995              1.05          catFR1            -999             0   \n",
       "89996              1.05          catFR1            -999             0   \n",
       "89997              1.05          catFR1            -999             0   \n",
       "89998              1.05          catFR1            -999             0   \n",
       "89999              1.05          catFR1            -999             0   \n",
       "\n",
       "      item_name_WORD  ...  protocol_WORD2  recalled_WORD2  rectime_WORD2  \\\n",
       "0              PAPER  ...              r1               0           -999   \n",
       "1              PAPER  ...              r1               0           -999   \n",
       "2              PAPER  ...              r1               0           -999   \n",
       "3              PAPER  ...              r1               0           -999   \n",
       "4              PAPER  ...              r1               0           -999   \n",
       "...              ...  ...             ...             ...            ...   \n",
       "89995         TURTLE  ...              r1               0           -999   \n",
       "89996         TURTLE  ...              r1               0           -999   \n",
       "89997         TURTLE  ...              r1               0           -999   \n",
       "89998         TURTLE  ...              r1               0           -999   \n",
       "89999         TURTLE  ...              r1               0           -999   \n",
       "\n",
       "       serialpos_WORD2  session_WORD2 stim_list_WORD2  subject_WORD2  \\\n",
       "0                    1              0               0         R1045E   \n",
       "1                    2              0               0         R1045E   \n",
       "2                    3              0               0         R1045E   \n",
       "3                    4              0               0         R1045E   \n",
       "4                    5              0               0         R1045E   \n",
       "...                ...            ...             ...            ...   \n",
       "89995                8              0               0         R1045E   \n",
       "89996                9              0               0         R1045E   \n",
       "89997               10              0               0         R1045E   \n",
       "89998               11              0               0         R1045E   \n",
       "89999               12              0               0         R1045E   \n",
       "\n",
       "       type_WORD2      corr     corr_z  \n",
       "0            WORD  1.000000  18.714974  \n",
       "1            WORD  0.249906   0.255313  \n",
       "2            WORD  0.152489   0.153688  \n",
       "3            WORD -0.034754  -0.034768  \n",
       "4            WORD  0.095259   0.095548  \n",
       "...           ...       ...        ...  \n",
       "89995        WORD  0.097689   0.098001  \n",
       "89996        WORD  0.077636   0.077793  \n",
       "89997        WORD  0.170163   0.171834  \n",
       "89998        WORD  0.246251   0.251418  \n",
       "89999        WORD  1.000000        inf  \n",
       "\n",
       "[90000 rows x 47 columns]"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corr_df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "08d85962-1e41-41d0-a4c1-68e5226d9686",
   "metadata": {},
   "source": [
    "For the encoding-to-encoding RSA, we can cut things down a bit by enforcing that the `_WORD` events come before `_WORD2`. This is fine since the full correlation matrix is symmetric so we're just taking the lower triangle."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "d3a96fac-43d5-4d11-bc3a-7600973f475f",
   "metadata": {},
   "outputs": [],
   "source": [
    "corr_df = corr_df.query('((list_WORD2 > list_WORD) or ' +\n",
    "                              '((list_WORD2 == list_WORD) and ' +\n",
    "                              '(serialpos_WORD2 > serialpos_WORD)))'\n",
    "                             )"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "35d4cbd6-2c8f-432a-9c8c-d3cd7ce72437",
   "metadata": {},
   "source": [
    "Now we do the same thing comparing recall to encoding"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "2aa43722-1d51-4da2-9c90-88a2d6019511",
   "metadata": {},
   "outputs": [],
   "source": [
    "comparison_type = \"REC_WORD\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "d1d38897-77ad-42a3-a31a-50f3ca0e9220",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/global/miniconda/py310_23.1.0-1/envs/workshop_311/lib/python3.11/site-packages/xarray/core/coordinates.py:41: FutureWarning: Updating MultiIndexed coordinate 'event' would corrupt indices for other variables: ['category', 'category_num', 'eegfile', 'eegoffset', 'exp_version', 'experiment', 'intrusion', 'is_stim', 'item_name', 'item_num', 'list', 'montage', 'msoffset', 'mstime', 'outpos', 'pirt', 'protocol', 'recalled', 'rectime', 'repeat', 'serialpos', 'session', 'stim_list', 'subject', 'type']. This will raise an error in the future. Use `.drop_vars({'protocol', 'mstime', 'type', 'recalled', 'list', 'exp_version', 'eegfile', 'msoffset', 'intrusion', 'eegoffset', 'montage', 'experiment', 'is_stim', 'serialpos', 'category', 'pirt', 'event', 'item_name', 'rectime', 'repeat', 'stim_list', 'outpos', 'item_num', 'subject', 'category_num', 'session'})` before assigning new coordinate values.\n",
      "  self.update({key: value})\n"
     ]
    }
   ],
   "source": [
    "comparison_fp = save_path + '/' + row.subject + '_' + str(row.session) + '_' + comparison_type + '_feats.h5'\n",
    "comparison_pows = TimeSeries.from_hdf(comparison_fp)\n",
    "comparison_pows = normalize_features(comparison_pows, save_path, countdown_normalize=countdown_normalize)\n",
    "comparison_pows = set_event_names(comparison_pows, comparison_type, col='event')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "68b12631-2695-4283-9e35-621f54bba8cc",
   "metadata": {},
   "source": [
    "**Compute RSA comparing encoding to recall events just like above**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "30f35767-b273-43fc-b7ea-c49db945d7ab",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "033c9497-3086-4885-8c4e-1514848bca0e",
   "metadata": {},
   "source": [
    "# Analyzing RSA"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7ec1b1c2-e68d-456e-b232-39aec7a03b40",
   "metadata": {},
   "source": [
    "Having computed the correlation matrices, now we need to analyze it to see how it varies with, for instance, whether two items are from the same category. Fortunately for you, I've already computed the correlation matrices for several subjects so you don't need to wait forever or use tons of compute resources to do it."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f74709a8-51a3-4a8f-a06a-1d6aa7fbba7c",
   "metadata": {
    "tags": []
   },
   "source": [
    "# Across-list category similarity"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d4278e45-2921-4f85-ad0c-c5f183887a45",
   "metadata": {},
   "source": [
    "We'll start by looking at a classic result from [Haxby et al. (2001)](https://www.science.org/doi/10.1126/science.1063736). They presented subjects with images from eight different categories:"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "88980b80-7a1e-462b-a29c-1beeaf932313",
   "metadata": {},
   "source": [
    "<center>\n",
    "    <img src=\"figures/HaxbyEtal01_fig2.png\" width=400>\n",
    "</center>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ae935dde-9958-4207-b02f-62f1c2b93191",
   "metadata": {},
   "source": [
    "They then compared the neural representations, finding that the patterns in \"object-selective cortex\" were more similar when looking at two images from the same category than across categories!"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dfc96b6b-c00e-4fbe-b554-a4625a6d8548",
   "metadata": {},
   "source": [
    "<center>\n",
    "    <img src=\"figures/HaxbyEtal01_fig4.png\" width=400>\n",
    "</center>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "790368cf-49b9-4d3c-8d00-c10170ef9452",
   "metadata": {},
   "source": [
    "[Kriegeskorte et. al. (2008)](https://www.sciencedirect.com/science/article/pii/S0896627308009434) conducted a similar study using fMRI in humans and neural recordings in monkeys, showing that the categorical representation of images in Inferior Temporal (IT) cortex was similar in both:"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0129e1ba-1eaa-4591-a702-5eac7d1a5b23",
   "metadata": {},
   "source": [
    "<center>\n",
    "    <img src=\"figures/KrieEtal08_fig1.png\" width=800>\n",
    "</center>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "313f30f6-ca04-4ed6-91b5-ce6d85ddf611",
   "metadata": {},
   "source": [
    "Side note: you can actually analyze the original data from this study as it's freely available along with the [pyMVPA package](http://www.pymvpa.org/) (see [here](http://www.pymvpa.org/tutorial_classifiers.html) for an example showing you how to analyze this data). pyMVPA was one of the original python packages, developed by people from Jim Haxby's and Ken Norman's labs, for performing modern cognitive neuroscience analyses. The Haxby data is also available as part of [nilearn](https://nilearn.github.io/stable/decoding/decoding_intro.html?highlight=haxby), another python package for processing fMRI data.\n",
    "\n",
    "pyMVPA was to some extent superseded by [Brainiak](brainiak.org), developed by many labs at Princeton (including Ken Norman's). Brainiak has its own RSA tutorial [here](https://brainiak.org/tutorials/06-rsa/), using data from the Kriegeskorte study. In addition, Brainiak and nilearn both have a number of other tutorials which are worth looking at if you're interested in modern fMRI methods (some of which have yet to be applied to EEG!).\n",
    "\n",
    "That being said, if you look at these tutorials, you'll see that there's nothing fancy you need to do basic RSA with a correlation metric, just the simple correlation matrix computations from numpy or xarray, so no need to install those packages aside from them being an easy way to access these older datasets! "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9a541605-3d39-451d-9bda-a2353115c103",
   "metadata": {},
   "source": [
    "Because this class is focused on EEG, rather than getting into the details of fMRI, we'll try to look for similar effects in one of our intracranial EEG datasets. We have a collected data from a number of patients doing a categorized free recall task (first reported [here](https://psycnet.apa.org/record/2018-66881-001), although we have a lot more data collected now!). This task is the same as a standard verbal free recall task except that the words on each list come a smaller number of categories. In this particular version, it also has a special structure, where each list has three categories and the items were presented in pairs of same-category words. The structure looks like this:"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fee483bd-f207-4a65-a3c9-a012cc2411f8",
   "metadata": {},
   "source": [
    "<center>\n",
    "    <img src=\"figures/WeidEtal19_fig1_catFR.png\" width=400>\n",
    "</center>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "27bedbb3-2822-4e01-9fc4-0e63a0a10a7e",
   "metadata": {},
   "source": [
    "Within a session, subjects collected early on would see at most 300 words, 12 words from each of 25 categories. After collecting data from about 120 subjects, we decided to cut the sessions in half but added another category so subjects saw 312 words from 26 categories across two \"half sessions.\" Because the words on the lists come from a small set of categories, we can try to do a similar analysis on these data as they did in the fMRI papers mentioned above, investigating whether the representations of pairs of same category words are more similar than pairs from different categories. In most RSA analyses (like above) the focus is on regions of the brain that are known to respond to the type of stimuli in question. In intracranial EEG though, we don't get to choose where in the brain we are recording from and it's not consistent across patients. Therefore, in the analyses here, to simplify things for this class, we will just use all of the electrodes we get for each patient and look at their similarity. Perhaps surprisingly, as we'll see, this seems to work ok! "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "74737811-d7aa-437c-a38c-8216d6969572",
   "metadata": {},
   "source": [
    "In a related but slightly more complex analysis, [Kragel et al. (2021)](https://www.nature.com/articles/s41467-021-24393-1) showed that it was possible to decode the category of a word in this task from just electrodes within two specific brain regions (Posterior Medial, PM, and Anterior Temporal, AT):"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e8aca3f8-528f-4438-bce0-e648c705862a",
   "metadata": {},
   "source": [
    "<center>\n",
    "    <img src=\"figures/KragEtal21_fig2c.png\" width=400>\n",
    "</center>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9bd7ee9f-319d-43be-96a5-fa8ef6ecea29",
   "metadata": {},
   "source": [
    "In addition, using data from an uncategorized free recall task, [Manning et al. (2012)](https://www.jneurosci.org/content/32/26/8871.short) showed that similarity of neural activity seemed to be related to a measure of semantic similarity for some subjects:"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a9b34d48-ee9f-4ccf-99e7-a6f8f8dae4b7",
   "metadata": {},
   "source": [
    "<center>\n",
    "    <img src=\"figures/MannEtal12_fig3.png\" width=400>\n",
    "</center>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "32d47d79-b8d0-4c78-af75-4af78b0ffd58",
   "metadata": {},
   "source": [
    "## Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ec883b2e-196b-4772-a374-8307d73230dc",
   "metadata": {},
   "source": [
    "One tricky thing about doing this analysis (as we discussed in lecture) is that the EEG signal is really influenced by time, i.e. things that happen nearby also have similar patterns of activity. In the Haxby study, they handled this by only comparing representations across runs. Similarly, in this first analysis, we will only look at across list comparisons."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "9103add6-48fe-4b06-a423-4147e89f0af5",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_80346/2590305909.py:1: DtypeWarning: Columns (4,26,47,52) have mixed types. Specify dtype option on import or set low_memory=False.\n",
      "  raw_WORD_rsa_df = pd.read_csv(save_path + '/raw_WORD_rsa_df.csv')\n",
      "/tmp/ipykernel_80346/2590305909.py:2: DtypeWarning: Columns (4,26,50,55) have mixed types. Specify dtype option on import or set low_memory=False.\n",
      "  raw_REC_WORD_rsa_df = pd.read_csv(save_path + '/raw_REC_WORD_rsa_df.csv')\n"
     ]
    }
   ],
   "source": [
    "raw_WORD_rsa_df = pd.read_csv(save_path + '/raw_WORD_rsa_df.csv')\n",
    "raw_REC_WORD_rsa_df = pd.read_csv(save_path + '/raw_REC_WORD_rsa_df.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "50696b76-9c14-445d-b61a-e3f60fb6a126",
   "metadata": {},
   "outputs": [],
   "source": [
    "across_list_WORD_rsa_df = raw_WORD_rsa_df.query('(list_WORD2 > list_WORD)') \n",
    "within_list_WORD_rsa_df = raw_WORD_rsa_df.query('((list_WORD2 == list_WORD) and (serialpos_WORD2 > serialpos_WORD))')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "de126870-a097-447f-a824-c90849e110b1",
   "metadata": {},
   "source": [
    "Check out what information is contained in this dataframe. `corr_z` is going to be the relevant column for investigating similarity. \n",
    "Beyond that, the information contained in the `_WORD` columns refers to the word in the pair that occurred earlier in the session (on a prior list), `_WORD2` is the later word."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "a91f64e9-ff5f-4a2c-9cc5-6d73470edf79",
   "metadata": {},
   "outputs": [
    {
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      "text/plain": [
       "          category_WORD  category_num_WORD  \\\n",
       "11       officesupplies                 17   \n",
       "12       officesupplies                 17   \n",
       "13       officesupplies                 17   \n",
       "14       officesupplies                 17   \n",
       "15       officesupplies                 17   \n",
       "...                 ...                ...   \n",
       "3273835       bodyparts                  3   \n",
       "3273836       bodyparts                  3   \n",
       "3273837       bodyparts                  3   \n",
       "3273838       bodyparts                  3   \n",
       "3273839       bodyparts                  3   \n",
       "\n",
       "                                              eegfile_WORD  eegoffset_WORD  \\\n",
       "11       /protocols/r1/subjects/R1045E/experiments/catF...          491162   \n",
       "12       /protocols/r1/subjects/R1045E/experiments/catF...          491162   \n",
       "13       /protocols/r1/subjects/R1045E/experiments/catF...          491162   \n",
       "14       /protocols/r1/subjects/R1045E/experiments/catF...          491162   \n",
       "15       /protocols/r1/subjects/R1045E/experiments/catF...          491162   \n",
       "...                                                    ...             ...   \n",
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       "3273839  /protocols/r1/subjects/R1541T/experiments/catF...         1153986   \n",
       "\n",
       "        exp_version_WORD experiment_WORD  intrusion_WORD  is_stim_WORD  \\\n",
       "11                  1.05          catFR1            -999             0   \n",
       "12                  1.05          catFR1            -999             0   \n",
       "13                  1.05          catFR1            -999             0   \n",
       "14                  1.05          catFR1            -999             0   \n",
       "15                  1.05          catFR1            -999             0   \n",
       "...                  ...             ...             ...           ...   \n",
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       "3273837              NaN          catFR1            -999             0   \n",
       "3273838              NaN          catFR1            -999             0   \n",
       "3273839              NaN          catFR1            -999             0   \n",
       "\n",
       "        item_name_WORD  item_num_WORD  ...  phase_WORD  recog_resp_WORD  \\\n",
       "11               PAPER            207  ...         NaN              NaN   \n",
       "12               PAPER            207  ...         NaN              NaN   \n",
       "13               PAPER            207  ...         NaN              NaN   \n",
       "14               PAPER            207  ...         NaN              NaN   \n",
       "15               PAPER            207  ...         NaN              NaN   \n",
       "...                ...            ...  ...         ...              ...   \n",
       "3273835            ARM             40  ...    NON-STIM           -999.0   \n",
       "3273836            ARM             40  ...    NON-STIM           -999.0   \n",
       "3273837            ARM             40  ...    NON-STIM           -999.0   \n",
       "3273838            ARM             40  ...    NON-STIM           -999.0   \n",
       "3273839            ARM             40  ...    NON-STIM           -999.0   \n",
       "\n",
       "         recog_rt_WORD  recognized_WORD rejected_WORD  phase_WORD2  \\\n",
       "11                 NaN              NaN           NaN          NaN   \n",
       "12                 NaN              NaN           NaN          NaN   \n",
       "13                 NaN              NaN           NaN          NaN   \n",
       "14                 NaN              NaN           NaN          NaN   \n",
       "15                 NaN              NaN           NaN          NaN   \n",
       "...                ...              ...           ...          ...   \n",
       "3273835         -999.0           -999.0        -999.0     NON-STIM   \n",
       "3273836         -999.0           -999.0        -999.0     NON-STIM   \n",
       "3273837         -999.0           -999.0        -999.0     NON-STIM   \n",
       "3273838         -999.0           -999.0        -999.0     NON-STIM   \n",
       "3273839         -999.0           -999.0        -999.0     NON-STIM   \n",
       "\n",
       "         recog_resp_WORD2  recog_rt_WORD2  recognized_WORD2  rejected_WORD2  \n",
       "11                    NaN             NaN               NaN             NaN  \n",
       "12                    NaN             NaN               NaN             NaN  \n",
       "13                    NaN             NaN               NaN             NaN  \n",
       "14                    NaN             NaN               NaN             NaN  \n",
       "15                    NaN             NaN               NaN             NaN  \n",
       "...                   ...             ...               ...             ...  \n",
       "3273835            -999.0          -999.0            -999.0          -999.0  \n",
       "3273836            -999.0          -999.0            -999.0          -999.0  \n",
       "3273837            -999.0          -999.0            -999.0          -999.0  \n",
       "3273838            -999.0          -999.0            -999.0          -999.0  \n",
       "3273839            -999.0          -999.0            -999.0          -999.0  \n",
       "\n",
       "[3101184 rows x 57 columns]"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "across_list_WORD_rsa_df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e39f5da7-2925-4fe9-accf-8792dbf16682",
   "metadata": {},
   "source": [
    "In order to start looking at this we need to start averaging. We want to be careful about how we do this because the data isn't independent. There's a lot of structure since each word occurred on a list on a session for a subject. We'll first look only at comparisons between the same category on a specific pair of lists in a session and get the average representation there. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "71bf51b4-be59-4781-97f4-9f1e67b796d9",
   "metadata": {},
   "outputs": [],
   "source": [
    "across_list_WORD_rsa_list_df = across_list_WORD_rsa_df.groupby(\n",
    "    ['subject_WORD', 'session_WORD', 'list_WORD', 'list_WORD2', 'category_WORD', 'category_WORD2'], as_index=False\n",
    ").agg({'corr_z': 'mean'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "5fb57260-789d-4761-9802-1f1a2222edd0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>subject_WORD</th>\n",
       "      <th>session_WORD</th>\n",
       "      <th>list_WORD</th>\n",
       "      <th>list_WORD2</th>\n",
       "      <th>category_WORD</th>\n",
       "      <th>category_WORD2</th>\n",
       "      <th>corr_z</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>R1045E</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>bodyparts</td>\n",
       "      <td>desserts</td>\n",
       "      <td>0.067712</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>R1045E</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>bodyparts</td>\n",
       "      <td>landscapes</td>\n",
       "      <td>0.065518</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>R1045E</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>bodyparts</td>\n",
       "      <td>weather</td>\n",
       "      <td>0.068545</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>R1045E</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>kitchentools</td>\n",
       "      <td>desserts</td>\n",
       "      <td>0.013110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>R1045E</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>kitchentools</td>\n",
       "      <td>landscapes</td>\n",
       "      <td>0.030201</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>193819</th>\n",
       "      <td>R1541T</td>\n",
       "      <td>6</td>\n",
       "      <td>11</td>\n",
       "      <td>12</td>\n",
       "      <td>bodyparts</td>\n",
       "      <td>electronics</td>\n",
       "      <td>0.195061</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>193820</th>\n",
       "      <td>R1541T</td>\n",
       "      <td>6</td>\n",
       "      <td>11</td>\n",
       "      <td>12</td>\n",
       "      <td>bodyparts</td>\n",
       "      <td>weather</td>\n",
       "      <td>0.142105</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>193821</th>\n",
       "      <td>R1541T</td>\n",
       "      <td>6</td>\n",
       "      <td>11</td>\n",
       "      <td>12</td>\n",
       "      <td>tools</td>\n",
       "      <td>birds</td>\n",
       "      <td>0.148970</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>193822</th>\n",
       "      <td>R1541T</td>\n",
       "      <td>6</td>\n",
       "      <td>11</td>\n",
       "      <td>12</td>\n",
       "      <td>tools</td>\n",
       "      <td>electronics</td>\n",
       "      <td>0.182028</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>193823</th>\n",
       "      <td>R1541T</td>\n",
       "      <td>6</td>\n",
       "      <td>11</td>\n",
       "      <td>12</td>\n",
       "      <td>tools</td>\n",
       "      <td>weather</td>\n",
       "      <td>0.154833</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>193824 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       subject_WORD  session_WORD  list_WORD  list_WORD2 category_WORD  \\\n",
       "0            R1045E             0          1           2     bodyparts   \n",
       "1            R1045E             0          1           2     bodyparts   \n",
       "2            R1045E             0          1           2     bodyparts   \n",
       "3            R1045E             0          1           2  kitchentools   \n",
       "4            R1045E             0          1           2  kitchentools   \n",
       "...             ...           ...        ...         ...           ...   \n",
       "193819       R1541T             6         11          12     bodyparts   \n",
       "193820       R1541T             6         11          12     bodyparts   \n",
       "193821       R1541T             6         11          12         tools   \n",
       "193822       R1541T             6         11          12         tools   \n",
       "193823       R1541T             6         11          12         tools   \n",
       "\n",
       "       category_WORD2    corr_z  \n",
       "0            desserts  0.067712  \n",
       "1          landscapes  0.065518  \n",
       "2             weather  0.068545  \n",
       "3            desserts  0.013110  \n",
       "4          landscapes  0.030201  \n",
       "...               ...       ...  \n",
       "193819    electronics  0.195061  \n",
       "193820        weather  0.142105  \n",
       "193821          birds  0.148970  \n",
       "193822    electronics  0.182028  \n",
       "193823        weather  0.154833  \n",
       "\n",
       "[193824 rows x 7 columns]"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "across_list_WORD_rsa_list_df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "98f4544d-d2cf-4329-ad5d-136aa61195b8",
   "metadata": {},
   "source": [
    "If you haven't seen this code pattern yet, it's super useful and comes up all the time in data science. It's called a [split-apply-combine pattern](https://www.jstatsoft.org/article/view/v040i01) because we split up the data into chunks, run some code on each of them (here taking the mean correlation), and then recombine them again. You can see more about how this works in `pandas` [here](https://pandas.pydata.org/docs/user_guide/groupby.html). We then continue taking the average within session, and subject until we finally get to the average similarity between items from a pair of categories in our dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "d937133c-127a-4bd1-adf5-2217349c4251",
   "metadata": {},
   "outputs": [],
   "source": [
    "across_list_WORD_rsa_session_df = across_list_WORD_rsa_list_df.groupby(\n",
    "    ['subject_WORD', 'session_WORD', 'category_WORD', 'category_WORD2'], as_index=False\n",
    ").agg({'corr_z': 'mean'})\n",
    "across_list_WORD_rsa_subject_df = across_list_WORD_rsa_session_df.groupby(\n",
    "    ['subject_WORD', 'category_WORD', 'category_WORD2'], as_index=False\n",
    ").agg({'corr_z': 'mean'})\n",
    "across_list_WORD_rsa_category_df = across_list_WORD_rsa_session_df.groupby(\n",
    "    ['category_WORD', 'category_WORD2'], as_index=False\n",
    ").agg({'corr_z': 'mean'})"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4a2c9296-5cb2-461d-8358-0c9fcaf83ec8",
   "metadata": {},
   "source": [
    "As we mentioned before, some subjects saw 26 categories while some only saw 25. To make this analysis more straightforward, we'll remove the 26th category, \"fabrics\" from the analysis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "cbc3e0c7-d988-4bb2-9209-cf93e0e0aa8b",
   "metadata": {},
   "outputs": [],
   "source": [
    "across_list_WORD_rsa_category_df = across_list_WORD_rsa_category_df.query('(category_WORD != \"fabric\") and ' + \n",
    "                                                                            '(category_WORD2 != \"fabric\")')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e91af4aa-8bf6-40a7-a844-d0f187b0d6b4",
   "metadata": {},
   "source": [
    "Finally we'll reshape this data into a matrix so we can plot it like the Haxby/Kriegeskorte analyses above. Note that this matrix isn't symmetric -- the y-axis corresponds to the earlier word and the x-axis to the later word."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "b7c1d4b8-2bc5-4bf4-a23b-ec5df8764163",
   "metadata": {},
   "outputs": [],
   "source": [
    "across_list_WORD_rsa_category_mat = across_list_WORD_rsa_category_df.pivot_table(\n",
    "    index=['category_WORD'], columns=['category_WORD2'], \n",
    "    values='corr_z')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "6a06e8e4-829d-42d9-990e-491b1afd5395",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Text(0.5, 23.52222222222222, 'Later Item'),\n",
       " Text(50.72222222222221, 0.5, 'Earlier Item')]"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "g = sns.heatmap(across_list_WORD_rsa_category_mat)\n",
    "g.set(xlabel='Later Item', ylabel='Earlier Item')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3a484e12-77fe-4882-a8b1-9b33b4ed3a98",
   "metadata": {},
   "source": [
    "This doesn't look like much -- the diagonal structure isn't super apparent. One reason is that there is a lot of uncontrolled variance here. Some subjects' neural activity is more category driven than others so the average similarities vary a lot by subject. It also varies by session and list. Use the split apply combine approach to see what the variability in average similarity looks like across subjects. Use seaborn [displot](https://seaborn.pydata.org/generated/seaborn.displot.html) to investigate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "86a76dcf-5144-4565-9f80-5a0698aeecbf",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "a1992133-4c77-4101-bebd-e2530a3c2c95",
   "metadata": {},
   "source": [
    "What we are really interested in is, within a subject and a pair of lists, do same category words look more similar than across category? Therefore we'll subtract the average similarity within a pair of lists from the `corr_z` column. This gives us only the within-list-pair variance. This is often a [good strategy](https://www.tqmp.org/RegularArticles/vol01-1/p042/p042.pdf) for visualizing effects in studies like this where there is hierarchical structure in the design (subjects/sessions/lists). We'll store this in the `corr_z_list_adj` column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "f90dc878-43fb-4de2-8bf1-0872abd74297",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_80346/3905098452.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  across_list_WORD_rsa_df['corr_z_list_mean'] = across_list_WORD_rsa_df.groupby(\n",
      "/tmp/ipykernel_80346/3905098452.py:3: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  across_list_WORD_rsa_df['corr_z_list_adj'] = (across_list_WORD_rsa_df['corr_z'] -\n",
      "/tmp/ipykernel_80346/3905098452.py:5: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  within_list_WORD_rsa_df['corr_z_list_mean'] = within_list_WORD_rsa_df.groupby(\n",
      "/tmp/ipykernel_80346/3905098452.py:7: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  within_list_WORD_rsa_df['corr_z_list_adj'] = (within_list_WORD_rsa_df['corr_z'] -\n"
     ]
    }
   ],
   "source": [
    "across_list_WORD_rsa_df['corr_z_list_mean'] = across_list_WORD_rsa_df.groupby(\n",
    "    ['subject_WORD', 'session_WORD', 'list_WORD', 'list_WORD2'])['corr_z'].transform('mean')\n",
    "across_list_WORD_rsa_df['corr_z_list_adj'] = (across_list_WORD_rsa_df['corr_z'] - \n",
    "                                                     across_list_WORD_rsa_df['corr_z_list_mean'])\n",
    "within_list_WORD_rsa_df['corr_z_list_mean'] = within_list_WORD_rsa_df.groupby(\n",
    "    ['subject_WORD', 'session_WORD', 'list_WORD'])['corr_z'].transform('mean')\n",
    "within_list_WORD_rsa_df['corr_z_list_adj'] = (within_list_WORD_rsa_df['corr_z'] - \n",
    "                                                     within_list_WORD_rsa_df['corr_z_list_mean'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "171e2ebe-2612-4166-8782-b36f6495d9bf",
   "metadata": {},
   "outputs": [],
   "source": [
    "across_list_WORD_rsa_list_df = across_list_WORD_rsa_df.groupby(\n",
    "    ['subject_WORD', 'session_WORD', 'list_WORD', 'list_WORD2', 'category_WORD', 'category_WORD2'], as_index=False\n",
    ").agg({'corr_z_list_adj': 'mean'})\n",
    "across_list_WORD_rsa_session_df = across_list_WORD_rsa_list_df.groupby(\n",
    "    ['subject_WORD', 'session_WORD', 'category_WORD', 'category_WORD2'], as_index=False\n",
    ").agg({'corr_z_list_adj': 'mean'})\n",
    "across_list_WORD_rsa_subject_df = across_list_WORD_rsa_session_df.groupby(\n",
    "    ['subject_WORD', 'category_WORD', 'category_WORD2'], as_index=False\n",
    ").agg({'corr_z_list_adj': 'mean'})\n",
    "across_list_WORD_rsa_category_df = across_list_WORD_rsa_session_df.groupby(\n",
    "    ['category_WORD', 'category_WORD2'], as_index=False\n",
    ").agg({'corr_z_list_adj': 'mean'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "651f8317-cb40-4b20-8660-2d61fa2acd1c",
   "metadata": {},
   "outputs": [],
   "source": [
    "across_list_WORD_rsa_category_df = across_list_WORD_rsa_category_df.query(\n",
    "    '(category_WORD != \"fabric\") and ' + \n",
    "    '(category_WORD2 != \"fabric\")')\n",
    "across_list_WORD_rsa_category_mat = across_list_WORD_rsa_category_df.pivot_table(\n",
    "    index=['category_WORD'], columns=['category_WORD2'], \n",
    "    values='corr_z_list_adj')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "6ff60a94-04bc-4b1c-899c-e8562174ad45",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Text(0.5, 23.52222222222222, 'Later Item'),\n",
       " Text(50.72222222222221, 0.5, 'Earlier Item')]"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "g = sns.heatmap(across_list_WORD_rsa_category_mat)\n",
    "g.set(xlabel='Later Item', ylabel='Earlier Item')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "22b9246a-9e26-4b9d-9485-33437f58ed9f",
   "metadata": {},
   "source": [
    "This still doesn't look amazing, you can't really see a diagonal unless you really squint. It's challenging to see effects like this at some level because of the range of electrodes you get across patients. However, we can focus our analysis a little more. Let's see if same category comparisons are more similar than across in general."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "9e1b36ca-4438-413b-a627-94b83953cefb",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_80346/1890595422.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  across_list_WORD_rsa_df['same_category'] = across_list_WORD_rsa_df['category_WORD'] == across_list_WORD_rsa_df['category_WORD2']\n"
     ]
    }
   ],
   "source": [
    "across_list_WORD_rsa_df['same_category'] = across_list_WORD_rsa_df['category_WORD'] == across_list_WORD_rsa_df['category_WORD2']\n",
    "across_list_WORD_rsa_list_df = across_list_WORD_rsa_df.groupby(\n",
    "    ['subject_WORD', 'session_WORD', 'list_WORD', 'list_WORD2', 'same_category'], as_index=False\n",
    ").agg({'corr_z_list_adj': 'mean'})\n",
    "across_list_WORD_rsa_session_df = across_list_WORD_rsa_list_df.groupby(\n",
    "    ['subject_WORD', 'session_WORD', 'same_category'], as_index=False\n",
    ").agg({'corr_z_list_adj': 'mean'})\n",
    "across_list_WORD_rsa_subject_df = across_list_WORD_rsa_session_df.groupby(\n",
    "    ['subject_WORD', 'same_category'], as_index=False\n",
    ").agg({'corr_z_list_adj': 'mean'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "06c643a0-a52e-413c-8693-88b725e7dd0b",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/global/miniconda/py310_23.1.0-1/envs/workshop_311/lib/python3.11/site-packages/seaborn/axisgrid.py:118: UserWarning: The figure layout has changed to tight\n",
      "  self._figure.tight_layout(*args, **kwargs)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x2af084eba450>"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "g = sns.catplot(x='same_category', y='corr_z_list_adj', \n",
    "            data=across_list_WORD_rsa_subject_df, kind='point')\n",
    "g.set(xlabel='Same Category', ylabel='List-centered $z$-transformed Correlation')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1ab6d619-8fdc-49e9-8e52-f22e9f6c1cc5",
   "metadata": {},
   "source": [
    "Now we can see that there is in fact a difference, although it is somewhat small. In contrast, we would not have been able to see this without subtracting out the list mean."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "5c5ec8a0-0533-4fab-ae9d-cbbcc3e33e2c",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_80346/3445182721.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  across_list_WORD_rsa_df['same_category'] = across_list_WORD_rsa_df['category_WORD'] == across_list_WORD_rsa_df['category_WORD2']\n"
     ]
    }
   ],
   "source": [
    "across_list_WORD_rsa_df['same_category'] = across_list_WORD_rsa_df['category_WORD'] == across_list_WORD_rsa_df['category_WORD2']\n",
    "across_list_WORD_rsa_list_df = across_list_WORD_rsa_df.groupby(\n",
    "    ['subject_WORD', 'session_WORD', 'list_WORD', 'list_WORD2', 'same_category'], as_index=False\n",
    ").agg({'corr_z': 'mean'})\n",
    "across_list_WORD_rsa_session_df = across_list_WORD_rsa_list_df.groupby(\n",
    "    ['subject_WORD', 'session_WORD', 'same_category'], as_index=False\n",
    ").agg({'corr_z': 'mean'})\n",
    "across_list_WORD_rsa_subject_df = across_list_WORD_rsa_session_df.groupby(\n",
    "    ['subject_WORD', 'same_category'], as_index=False\n",
    ").agg({'corr_z': 'mean'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "f29acc81-47f5-4474-9c13-44c1b4c47fc2",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/global/miniconda/py310_23.1.0-1/envs/workshop_311/lib/python3.11/site-packages/seaborn/axisgrid.py:118: UserWarning: The figure layout has changed to tight\n",
      "  self._figure.tight_layout(*args, **kwargs)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x2af0dde54810>"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "g = sns.catplot(x='same_category', y='corr_z', \n",
    "            data=across_list_WORD_rsa_subject_df, kind='point')\n",
    "g.set(xlabel='Same Category', ylabel='$z$-transformed Correlation')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ba0f6dd0-3722-4742-8625-97852b073fa5",
   "metadata": {
    "tags": []
   },
   "source": [
    "# Serial position similarity"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "52850afe-ffd7-4796-91b3-0222153faf52",
   "metadata": {},
   "source": [
    "The next set of results we'll investigate are from [Manning et al. (2011)](https://www.pnas.org/doi/10.1073/pnas.1015174108). The first result that Manning showed was that neural activity seemed to drift such that the similarity of patterns of intracranial EEG decreased as a function of distance in the list."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "96d1c22a-80f7-48ec-82b5-1ce45e447164",
   "metadata": {},
   "source": [
    "<center>\n",
    "    <img src=\"figures/MannEtal11A.png\" width=400>\n",
    "</center>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a884f3f0-e9ca-421b-a54d-1060af57dabf",
   "metadata": {},
   "source": [
    "We'll again do this analysis within list. Try to follow what we did above but this time, instead of comparing two categories, we will compare two serial positions. The relevant columns are `serialpos_WORD` and `serialpos_WORD2`. Try to make a heatmap of all of the comparisons"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e2932ad3-241d-47f2-aa7e-b39f84d61e54",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "11faed97-b9d0-4222-9193-afbec6bcbdde",
   "metadata": {},
   "source": [
    "From this heatmap, we can clearly see how the similarity increases as a function of distance. We can replot this data as a function of distance to look a bit more like the Manning et al. plot. The following line will help you out:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "fc6c4718-2f6e-4627-967a-e513b97a0fe7",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_80346/4059334785.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  within_list_WORD_rsa_df['serialpos_dist'] = within_list_WORD_rsa_df['serialpos_WORD2'] - within_list_WORD_rsa_df['serialpos_WORD']\n"
     ]
    }
   ],
   "source": [
    "within_list_WORD_rsa_df['serialpos_dist'] = within_list_WORD_rsa_df['serialpos_WORD2'] - within_list_WORD_rsa_df['serialpos_WORD']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "470a182c-c661-40f3-8294-309b80278590",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "7e370a53-48bb-470d-a52c-6f37a83badf0",
   "metadata": {},
   "source": [
    "We'll also show that being from the same category influences similarity on top of the serial position distance within a list. It's crucial to account for both at the same time because of the category structure of the list. Use the split-apply-combine technique with both `serialpos_dist` and `same_category` to get the mean within each possible combination. Use the [catplot](https://seaborn.pydata.org/generated/seaborn.catplot.html) parameter `hue` to display both variables on the same plot."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "241ac7f6-5a67-482d-8e6a-c50a5148a6b7",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "workshop_311",
   "language": "python",
   "name": "workshop_311"
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  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
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   "file_extension": ".py",
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