Experiment Master Analyze.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Neil T. Banerjee <br> HOME Experiment 1 Data Analysis <br> Begin 08.04.2020 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%matplotlib notebook\n",
    "import matplotlib.pyplot as plt\n",
    "from IPython.display import display\n",
    "import neurokit2 as nk\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "\n",
    "# Set matplotlib parameters for displaying graphs\n",
    "sns.set_style('darkgrid')\n",
    "plt.rcParams['figure.figsize'] = [10, 5]  # Bigger images\n",
    "plt.rcParams['font.size']= 14\n",
    "\n",
    "# import mpld3\n",
    "# mpld3.enable_notebook()\n",
    "\n",
    "pd.set_option('display.max_columns', None)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### BASELINE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "        RSP       EDA       ECG      TRIG\n",
      "0  7.755737 -0.259534  0.040741  8.486328\n",
      "1  7.756042 -0.261060  0.042114  8.489075\n",
      "2  7.755737 -0.262586  0.043030  8.496399\n",
      "3  7.755432 -0.258008  0.044403  8.493042\n",
      "4  7.756042 -0.259534  0.045776  8.482056\n"
     ]
    }
   ],
   "source": [
    "# Read in data directly from .acq file and rename dataframe columns\n",
    "# ACQ file has pre-named colums so you need to make them easier to deal with later in the script\n",
    "# subject numbers are 201-215\n",
    "# raw data is barely useful, in units of voltage (RSP), microsiemens (EDA), mV (ECG), and V (TRIG)\n",
    "\n",
    "data, sampling_rate = nk.read_acqknowledge('part201_baseline.acq')\n",
    "data = data.rename(columns={\"RSP, X, RSPEC-R\": \"RSP\", \"DTU100 - Trigger View, AMI / HLT - A11\": \"TRIG\",\n",
    "                            \"EDA, X, PPGED-R\": \"EDA\", \"ECG, X, RSPEC-R\": \"ECG\"})\n",
    "print(data.head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Process ECG, EDA, and RSP over the entire experiment\n",
    "# takes the unidimensional psychophys data and derives relevant features for each subject\n",
    "\n",
    "ecg_signals, info1 = nk.ecg_process(data[\"ECG\"], sampling_rate=2000)\n",
    "eda_signals, info2 = nk.eda_process(data[\"EDA\"], sampling_rate=2000)\n",
    "rsp_signals, info3 = nk.rsp_process(data[\"RSP\"], sampling_rate=2000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ECG_Rate_Mean</th>\n",
       "      <th>ECG_HRV_RMSSD</th>\n",
       "      <th>ECG_HRV_MeanNN</th>\n",
       "      <th>ECG_HRV_SDNN</th>\n",
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       "      <th>ECG_HRV_HTI</th>\n",
       "      <th>ECG_HRV_ULF</th>\n",
       "      <th>ECG_HRV_VLF</th>\n",
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       "      <th>ECG_HRV_HF</th>\n",
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       "      <th>ECG_HRV_LFn</th>\n",
       "      <th>ECG_HRV_HFn</th>\n",
       "      <th>ECG_HRV_LnHF</th>\n",
       "      <th>ECG_HRV_SD1</th>\n",
       "      <th>ECG_HRV_SD2</th>\n",
       "      <th>ECG_HRV_SD2SD1</th>\n",
       "      <th>ECG_HRV_CSI</th>\n",
       "      <th>ECG_HRV_CVI</th>\n",
       "      <th>ECG_HRV_CSI_Modified</th>\n",
       "      <th>ECG_HRV_SampEn</th>\n",
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       "      <th>0</th>\n",
       "      <td>65.124884</td>\n",
       "      <td>104.582055</td>\n",
       "      <td>1843.384615</td>\n",
       "      <td>137.126389</td>\n",
       "      <td>105.320444</td>\n",
       "      <td>0.074388</td>\n",
       "      <td>0.056734</td>\n",
       "      <td>1806.0</td>\n",
       "      <td>145.2948</td>\n",
       "      <td>0.080451</td>\n",
       "      <td>66.153846</td>\n",
       "      <td>84.615385</td>\n",
       "      <td>540.0</td>\n",
       "      <td>4.0625</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1210.133983</td>\n",
       "      <td>3815.098094</td>\n",
       "      <td>65.829854</td>\n",
       "      <td>0.021817</td>\n",
       "      <td>57.95392</td>\n",
       "      <td>0.749369</td>\n",
       "      <td>0.01293</td>\n",
       "      <td>4.187073</td>\n",
       "      <td>74.4728</td>\n",
       "      <td>179.056123</td>\n",
       "      <td>2.404316</td>\n",
       "      <td>2.404316</td>\n",
       "      <td>5.329107</td>\n",
       "      <td>1722.029811</td>\n",
       "      <td>1.722767</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      "text/plain": [
       "   ECG_Rate_Mean  ECG_HRV_RMSSD  ECG_HRV_MeanNN  ECG_HRV_SDNN  ECG_HRV_SDSD  \\\n",
       "0      65.124884     104.582055     1843.384615    137.126389    105.320444   \n",
       "\n",
       "   ECG_HRV_CVNN  ECG_HRV_CVSD  ECG_HRV_MedianNN  ECG_HRV_MadNN  ECG_HRV_MCVNN  \\\n",
       "0      0.074388      0.056734            1806.0       145.2948       0.080451   \n",
       "\n",
       "   ECG_HRV_pNN50  ECG_HRV_pNN20  ECG_HRV_TINN  ECG_HRV_HTI  ECG_HRV_ULF  \\\n",
       "0      66.153846      84.615385         540.0       4.0625          0.0   \n",
       "\n",
       "   ECG_HRV_VLF   ECG_HRV_LF  ECG_HRV_HF  ECG_HRV_VHF  ECG_HRV_LFHF  \\\n",
       "0  1210.133983  3815.098094   65.829854     0.021817      57.95392   \n",
       "\n",
       "   ECG_HRV_LFn  ECG_HRV_HFn  ECG_HRV_LnHF  ECG_HRV_SD1  ECG_HRV_SD2  \\\n",
       "0     0.749369      0.01293      4.187073      74.4728   179.056123   \n",
       "\n",
       "   ECG_HRV_SD2SD1  ECG_HRV_CSI  ECG_HRV_CVI  ECG_HRV_CSI_Modified  \\\n",
       "0        2.404316     2.404316     5.329107           1722.029811   \n",
       "\n",
       "   ECG_HRV_SampEn  \n",
       "0        1.722767  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Summary ECG signals over baseline\n",
    "\n",
    "display(nk.ecg_analyze(ecg_signals))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Tonic: -0.004968598877498141\n",
      "Phasic -0.00041282427459582666\n"
     ]
    }
   ],
   "source": [
    "# Summary EDA signals over baseline\n",
    "\n",
    "EDA_epochs_baseline = nk.epochs_create(eda_signals, events=[0], sampling_rate=2000, epochs_start= 0, epochs_end=60)\n",
    "\n",
    "baseTonic = (EDA_epochs_baseline['1']['EDA_Tonic'].mean())\n",
    "print('Tonic:',baseTonic)\n",
    "basePhasic = (EDA_epochs_baseline['1']['EDA_Phasic'].mean())\n",
    "print('Phasic',basePhasic)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "NeuroKit warning: complexity_dfa(): DFA with less than ten data points is unreliable.\n"
     ]
    },
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>RSP_Rate_Mean</th>\n",
       "      <th>RSP_Amplitude_Mean</th>\n",
       "      <th>RSP_RRV_SDBB</th>\n",
       "      <th>RSP_RRV_RMSSD</th>\n",
       "      <th>RSP_RRV_SDSD</th>\n",
       "      <th>RSP_RRV_VLF</th>\n",
       "      <th>RSP_RRV_LF</th>\n",
       "      <th>RSP_RRV_HF</th>\n",
       "      <th>RSP_RRV_LFHF</th>\n",
       "      <th>RSP_RRV_LFn</th>\n",
       "      <th>RSP_RRV_HFn</th>\n",
       "      <th>RSP_RRV_SD1</th>\n",
       "      <th>RSP_RRV_SD2</th>\n",
       "      <th>RSP_RRV_SD2SD1</th>\n",
       "      <th>RSP_RRV_ApEn</th>\n",
       "      <th>RSP_RRV_SampEn</th>\n",
       "      <th>RSP_RRV_DFA</th>\n",
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       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>9.749466</td>\n",
       "      <td>3.477905</td>\n",
       "      <td>5532.153103</td>\n",
       "      <td>5613.99243</td>\n",
       "      <td>6046.922909</td>\n",
       "      <td>7555.557029</td>\n",
       "      <td>75.199138</td>\n",
       "      <td>0.127033</td>\n",
       "      <td>591.967378</td>\n",
       "      <td>0.009855</td>\n",
       "      <td>0.000017</td>\n",
       "      <td>4275.820194</td>\n",
       "      <td>6551.854514</td>\n",
       "      <td>1.532304</td>\n",
       "      <td>0.182322</td>\n",
       "      <td>-inf</td>\n",
       "      <td>-0.423717</td>\n",
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       "   RSP_Rate_Mean  RSP_Amplitude_Mean  RSP_RRV_SDBB  RSP_RRV_RMSSD  \\\n",
       "0       9.749466            3.477905   5532.153103     5613.99243   \n",
       "\n",
       "   RSP_RRV_SDSD  RSP_RRV_VLF  RSP_RRV_LF  RSP_RRV_HF  RSP_RRV_LFHF  \\\n",
       "0   6046.922909  7555.557029   75.199138    0.127033    591.967378   \n",
       "\n",
       "   RSP_RRV_LFn  RSP_RRV_HFn  RSP_RRV_SD1  RSP_RRV_SD2  RSP_RRV_SD2SD1  \\\n",
       "0     0.009855     0.000017  4275.820194  6551.854514        1.532304   \n",
       "\n",
       "   RSP_RRV_ApEn  RSP_RRV_SampEn  RSP_RRV_DFA  \n",
       "0      0.182322            -inf    -0.423717  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Summary RSP Signals\n",
    "\n",
    "display(nk.rsp_analyze(rsp_signals))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### TRIGGER FINDER"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "        RSP       EDA       ECG      TRIG\n",
      "0  6.958313 -0.534233 -0.032654  8.494263\n",
      "1  6.957703 -0.535759 -0.032043  8.424683\n",
      "2  6.957703 -0.538811 -0.031281  8.443604\n",
      "3  6.957703 -0.534233 -0.031128  8.554993\n",
      "4  6.955261 -0.535759 -0.031128  8.601990\n"
     ]
    }
   ],
   "source": [
    "# Read in data directly from .acq file and rename dataframe columns\n",
    "# change the participant number in first line\n",
    "\n",
    "data, sampling_rate = nk.read_acqknowledge('part201_pilot.acq')\n",
    "data = data.rename(columns={\"RSP, X, RSPEC-R\": \"RSP\", \"DTU100 - Trigger View, AMI / HLT - A11\": \"TRIG\",\n",
    "                            \"EDA, X, PPGED-R\": \"EDA\", \"ECG, X, RSPEC-R\": \"ECG\"})\n",
    "print(data.head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[194598, 1010598, 1538598, 2146598, 2768598, 3480598, 3990598, 4584598, 5048598, 5814598, 6320598, 6796598]\n"
     ]
    }
   ],
   "source": [
    "# Using video timestamps, record min and sec for each trial start and trust feedback, insert into lists\n",
    "# Estimate the offset between video time and BioPac time and subtract in while loop\n",
    "# Printed values are the sample numbers where you should start to look for trigger onsets\n",
    "# Each sample is 0.0005 seconds, we only need .01 seconds resolution, so to the nearest 20 samples\n",
    "\n",
    "mins_201 = [1,8,13,18,23,29,33,38,42,48,53,56]\n",
    "secs_201 = [58,46,10,14,25,21,36,33,25,48,1,59]\n",
    "\n",
    "mins_202 = [1,10,15,20,25,29,34,44,49,53,58,63]\n",
    "secs_202 = [4,1,27,24,40,59,35,25,2,31,34,31]\n",
    "\n",
    "mins_203 = [2,8,15,19,25,29,34,44,49,53,58,63]\n",
    "secs_203 = [28,57,22,57,7,23,12,48,16,35,20,35]\n",
    "\n",
    "mins_204 = [1,12,16,20,25,29,36,40,44,48,52,56]\n",
    "secs_204 = [59,42,37,54,41,53,30,36,5,15,7,3]\n",
    "\n",
    "mins_205 = [2,10,17,22,28,34,40,46,51,56,62,68]\n",
    "secs_205 = [13,38,17,57,48,14,26,5,29,45,40,25]\n",
    "\n",
    "mins_206 = [4,12,17,22,27,32,36,41,46,51,55,60]\n",
    "secs_206 = [11,32,24,5,0,5,59,34,44,15,53,23]\n",
    "\n",
    "mins_207 = [2,8,13,17,22,27,32,36,41,45,49,54]\n",
    "secs_207 = [52,45,17,44,23,47,15,42,10,39,43,25]\n",
    "\n",
    "mins_208 = [2,10,17,22,27,32,36,41,45,50,55,60]\n",
    "secs_208 = [8,3,4,10,13,1,32,10,49,30,18,3]\n",
    "\n",
    "mins_209 = [2,7,12,16,20,25,29,36,39,43,47,52]\n",
    "secs_209 = [4,42,20,28,56,3,4,6,59,57,49,19]\n",
    "\n",
    "mins_210 = [2,9,15,19,23,28,32,36,40,43,47,52]\n",
    "secs_210 = [1,52,9,38,48,2,8,10,5,54,57,14]\n",
    "\n",
    "mins_211 = [2,8,12,16,19,23,26,30,34,37,41,45]\n",
    "secs_211 = [55,2,18,0,47,24,52,33,13,54,56,44]\n",
    "\n",
    "mins_212 = [1,9,13,18,22,27,32,36,41,45,49,54]\n",
    "secs_212 = [56,16,55,20,48,25,13,34,18,22,15,2]\n",
    "\n",
    "index = []\n",
    "\n",
    "i=0\n",
    "while i<12:\n",
    "    index.append((mins_201[i]*60+secs_201[i])*2000-41402)\n",
    "    i+=1\n",
    "    \n",
    "print(index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
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       "\n",
       "    this.imageObj.onunload = function() {\n",
       "        fig.ws.close();\n",
       "    }\n",
       "\n",
       "    this.ws.onmessage = this._make_on_message_function(this);\n",
       "\n",
       "    this.ondownload = ondownload;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_header = function() {\n",
       "    var titlebar = $(\n",
       "        '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
       "        'ui-helper-clearfix\"/>');\n",
       "    var titletext = $(\n",
       "        '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
       "        'text-align: center; padding: 3px;\"/>');\n",
       "    titlebar.append(titletext)\n",
       "    this.root.append(titlebar);\n",
       "    this.header = titletext[0];\n",
       "}\n",
       "\n",
       "\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_canvas = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var canvas_div = $('<div/>');\n",
       "\n",
       "    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
       "\n",
       "    function canvas_keyboard_event(event) {\n",
       "        return fig.key_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    canvas_div.keydown('key_press', canvas_keyboard_event);\n",
       "    canvas_div.keyup('key_release', canvas_keyboard_event);\n",
       "    this.canvas_div = canvas_div\n",
       "    this._canvas_extra_style(canvas_div)\n",
       "    this.root.append(canvas_div);\n",
       "\n",
       "    var canvas = $('<canvas/>');\n",
       "    canvas.addClass('mpl-canvas');\n",
       "    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
       "\n",
       "    this.canvas = canvas[0];\n",
       "    this.context = canvas[0].getContext(\"2d\");\n",
       "\n",
       "    var backingStore = this.context.backingStorePixelRatio ||\n",
       "\tthis.context.webkitBackingStorePixelRatio ||\n",
       "\tthis.context.mozBackingStorePixelRatio ||\n",
       "\tthis.context.msBackingStorePixelRatio ||\n",
       "\tthis.context.oBackingStorePixelRatio ||\n",
       "\tthis.context.backingStorePixelRatio || 1;\n",
       "\n",
       "    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
       "\n",
       "    var rubberband = $('<canvas/>');\n",
       "    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
       "\n",
       "    var pass_mouse_events = true;\n",
       "\n",
       "    canvas_div.resizable({\n",
       "        start: function(event, ui) {\n",
       "            pass_mouse_events = false;\n",
       "        },\n",
       "        resize: function(event, ui) {\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "        stop: function(event, ui) {\n",
       "            pass_mouse_events = true;\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "    });\n",
       "\n",
       "    function mouse_event_fn(event) {\n",
       "        if (pass_mouse_events)\n",
       "            return fig.mouse_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    rubberband.mousedown('button_press', mouse_event_fn);\n",
       "    rubberband.mouseup('button_release', mouse_event_fn);\n",
       "    // Throttle sequential mouse events to 1 every 20ms.\n",
       "    rubberband.mousemove('motion_notify', mouse_event_fn);\n",
       "\n",
       "    rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
       "    rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
       "\n",
       "    canvas_div.on(\"wheel\", function (event) {\n",
       "        event = event.originalEvent;\n",
       "        event['data'] = 'scroll'\n",
       "        if (event.deltaY < 0) {\n",
       "            event.step = 1;\n",
       "        } else {\n",
       "            event.step = -1;\n",
       "        }\n",
       "        mouse_event_fn(event);\n",
       "    });\n",
       "\n",
       "    canvas_div.append(canvas);\n",
       "    canvas_div.append(rubberband);\n",
       "\n",
       "    this.rubberband = rubberband;\n",
       "    this.rubberband_canvas = rubberband[0];\n",
       "    this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
       "    this.rubberband_context.strokeStyle = \"#000000\";\n",
       "\n",
       "    this._resize_canvas = function(width, height) {\n",
       "        // Keep the size of the canvas, canvas container, and rubber band\n",
       "        // canvas in synch.\n",
       "        canvas_div.css('width', width)\n",
       "        canvas_div.css('height', height)\n",
       "\n",
       "        canvas.attr('width', width * mpl.ratio);\n",
       "        canvas.attr('height', height * mpl.ratio);\n",
       "        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
       "\n",
       "        rubberband.attr('width', width);\n",
       "        rubberband.attr('height', height);\n",
       "    }\n",
       "\n",
       "    // Set the figure to an initial 600x600px, this will subsequently be updated\n",
       "    // upon first draw.\n",
       "    this._resize_canvas(600, 600);\n",
       "\n",
       "    // Disable right mouse context menu.\n",
       "    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
       "        return false;\n",
       "    });\n",
       "\n",
       "    function set_focus () {\n",
       "        canvas.focus();\n",
       "        canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    window.setTimeout(set_focus, 100);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>');\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items) {\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) {\n",
       "            // put a spacer in here.\n",
       "            continue;\n",
       "        }\n",
       "        var button = $('<button/>');\n",
       "        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
       "                        'ui-button-icon-only');\n",
       "        button.attr('role', 'button');\n",
       "        button.attr('aria-disabled', 'false');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "\n",
       "        var icon_img = $('<span/>');\n",
       "        icon_img.addClass('ui-button-icon-primary ui-icon');\n",
       "        icon_img.addClass(image);\n",
       "        icon_img.addClass('ui-corner-all');\n",
       "\n",
       "        var tooltip_span = $('<span/>');\n",
       "        tooltip_span.addClass('ui-button-text');\n",
       "        tooltip_span.html(tooltip);\n",
       "\n",
       "        button.append(icon_img);\n",
       "        button.append(tooltip_span);\n",
       "\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    var fmt_picker_span = $('<span/>');\n",
       "\n",
       "    var fmt_picker = $('<select/>');\n",
       "    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
       "    fmt_picker_span.append(fmt_picker);\n",
       "    nav_element.append(fmt_picker_span);\n",
       "    this.format_dropdown = fmt_picker[0];\n",
       "\n",
       "    for (var ind in mpl.extensions) {\n",
       "        var fmt = mpl.extensions[ind];\n",
       "        var option = $(\n",
       "            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
       "        fmt_picker.append(option);\n",
       "    }\n",
       "\n",
       "    // Add hover states to the ui-buttons\n",
       "    $( \".ui-button\" ).hover(\n",
       "        function() { $(this).addClass(\"ui-state-hover\");},\n",
       "        function() { $(this).removeClass(\"ui-state-hover\");}\n",
       "    );\n",
       "\n",
       "    var status_bar = $('<span class=\"mpl-message\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
       "    // which will in turn request a refresh of the image.\n",
       "    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_message = function(type, properties) {\n",
       "    properties['type'] = type;\n",
       "    properties['figure_id'] = this.id;\n",
       "    this.ws.send(JSON.stringify(properties));\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_draw_message = function() {\n",
       "    if (!this.waiting) {\n",
       "        this.waiting = true;\n",
       "        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
       "    }\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    var format_dropdown = fig.format_dropdown;\n",
       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
       "    fig.ondownload(fig, format);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
       "    var size = msg['size'];\n",
       "    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
       "        fig._resize_canvas(size[0], size[1]);\n",
       "        fig.send_message(\"refresh\", {});\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
       "    var x0 = msg['x0'] / mpl.ratio;\n",
       "    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
       "    var x1 = msg['x1'] / mpl.ratio;\n",
       "    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
       "    x0 = Math.floor(x0) + 0.5;\n",
       "    y0 = Math.floor(y0) + 0.5;\n",
       "    x1 = Math.floor(x1) + 0.5;\n",
       "    y1 = Math.floor(y1) + 0.5;\n",
       "    var min_x = Math.min(x0, x1);\n",
       "    var min_y = Math.min(y0, y1);\n",
       "    var width = Math.abs(x1 - x0);\n",
       "    var height = Math.abs(y1 - y0);\n",
       "\n",
       "    fig.rubberband_context.clearRect(\n",
       "        0, 0, fig.canvas.width / mpl.ratio, fig.canvas.height / mpl.ratio);\n",
       "\n",
       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
       "    // Updates the figure title.\n",
       "    fig.header.textContent = msg['label'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
       "    var cursor = msg['cursor'];\n",
       "    switch(cursor)\n",
       "    {\n",
       "    case 0:\n",
       "        cursor = 'pointer';\n",
       "        break;\n",
       "    case 1:\n",
       "        cursor = 'default';\n",
       "        break;\n",
       "    case 2:\n",
       "        cursor = 'crosshair';\n",
       "        break;\n",
       "    case 3:\n",
       "        cursor = 'move';\n",
       "        break;\n",
       "    }\n",
       "    fig.rubberband_canvas.style.cursor = cursor;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
       "    fig.message.textContent = msg['message'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
       "    // Request the server to send over a new figure.\n",
       "    fig.send_draw_message();\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
       "    fig.image_mode = msg['mode'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Called whenever the canvas gets updated.\n",
       "    this.send_message(\"ack\", {});\n",
       "}\n",
       "\n",
       "// A function to construct a web socket function for onmessage handling.\n",
       "// Called in the figure constructor.\n",
       "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
       "    return function socket_on_message(evt) {\n",
       "        if (evt.data instanceof Blob) {\n",
       "            /* FIXME: We get \"Resource interpreted as Image but\n",
       "             * transferred with MIME type text/plain:\" errors on\n",
       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
       "             * to be part of the websocket stream */\n",
       "            evt.data.type = \"image/png\";\n",
       "\n",
       "            /* Free the memory for the previous frames */\n",
       "            if (fig.imageObj.src) {\n",
       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
       "                    fig.imageObj.src);\n",
       "            }\n",
       "\n",
       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
       "                evt.data);\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
       "            fig.imageObj.src = evt.data;\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        var msg = JSON.parse(evt.data);\n",
       "        var msg_type = msg['type'];\n",
       "\n",
       "        // Call the  \"handle_{type}\" callback, which takes\n",
       "        // the figure and JSON message as its only arguments.\n",
       "        try {\n",
       "            var callback = fig[\"handle_\" + msg_type];\n",
       "        } catch (e) {\n",
       "            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        if (callback) {\n",
       "            try {\n",
       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
       "                callback(fig, msg);\n",
       "            } catch (e) {\n",
       "                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
       "            }\n",
       "        }\n",
       "    };\n",
       "}\n",
       "\n",
       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
       "mpl.findpos = function(e) {\n",
       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
       "    var targ;\n",
       "    if (!e)\n",
       "        e = window.event;\n",
       "    if (e.target)\n",
       "        targ = e.target;\n",
       "    else if (e.srcElement)\n",
       "        targ = e.srcElement;\n",
       "    if (targ.nodeType == 3) // defeat Safari bug\n",
       "        targ = targ.parentNode;\n",
       "\n",
       "    // jQuery normalizes the pageX and pageY\n",
       "    // pageX,Y are the mouse positions relative to the document\n",
       "    // offset() returns the position of the element relative to the document\n",
       "    var x = e.pageX - $(targ).offset().left;\n",
       "    var y = e.pageY - $(targ).offset().top;\n",
       "\n",
       "    return {\"x\": x, \"y\": y};\n",
       "};\n",
       "\n",
       "/*\n",
       " * return a copy of an object with only non-object keys\n",
       " * we need this to avoid circular references\n",
       " * http://stackoverflow.com/a/24161582/3208463\n",
       " */\n",
       "function simpleKeys (original) {\n",
       "  return Object.keys(original).reduce(function (obj, key) {\n",
       "    if (typeof original[key] !== 'object')\n",
       "        obj[key] = original[key]\n",
       "    return obj;\n",
       "  }, {});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.mouse_event = function(event, name) {\n",
       "    var canvas_pos = mpl.findpos(event)\n",
       "\n",
       "    if (name === 'button_press')\n",
       "    {\n",
       "        this.canvas.focus();\n",
       "        this.canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    var x = canvas_pos.x * mpl.ratio;\n",
       "    var y = canvas_pos.y * mpl.ratio;\n",
       "\n",
       "    this.send_message(name, {x: x, y: y, button: event.button,\n",
       "                             step: event.step,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "\n",
       "    /* This prevents the web browser from automatically changing to\n",
       "     * the text insertion cursor when the button is pressed.  We want\n",
       "     * to control all of the cursor setting manually through the\n",
       "     * 'cursor' event from matplotlib */\n",
       "    event.preventDefault();\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    // Handle any extra behaviour associated with a key event\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.key_event = function(event, name) {\n",
       "\n",
       "    // Prevent repeat events\n",
       "    if (name == 'key_press')\n",
       "    {\n",
       "        if (event.which === this._key)\n",
       "            return;\n",
       "        else\n",
       "            this._key = event.which;\n",
       "    }\n",
       "    if (name == 'key_release')\n",
       "        this._key = null;\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.which != 17)\n",
       "        value += \"ctrl+\";\n",
       "    if (event.altKey && event.which != 18)\n",
       "        value += \"alt+\";\n",
       "    if (event.shiftKey && event.which != 16)\n",
       "        value += \"shift+\";\n",
       "\n",
       "    value += 'k';\n",
       "    value += event.which.toString();\n",
       "\n",
       "    this._key_event_extra(event, name);\n",
       "\n",
       "    this.send_message(name, {key: value,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
       "    if (name == 'download') {\n",
       "        this.handle_save(this, null);\n",
       "    } else {\n",
       "        this.send_message(\"toolbar_button\", {name: name});\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
       "    this.message.textContent = tooltip;\n",
       "};\n",
       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
       "\n",
       "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
       "\n",
       "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
       "    // object with the appropriate methods. Currently this is a non binary\n",
       "    // socket, so there is still some room for performance tuning.\n",
       "    var ws = {};\n",
       "\n",
       "    ws.close = function() {\n",
       "        comm.close()\n",
       "    };\n",
       "    ws.send = function(m) {\n",
       "        //console.log('sending', m);\n",
       "        comm.send(m);\n",
       "    };\n",
       "    // Register the callback with on_msg.\n",
       "    comm.on_msg(function(msg) {\n",
       "        //console.log('receiving', msg['content']['data'], msg);\n",
       "        // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
       "        ws.onmessage(msg['content']['data'])\n",
       "    });\n",
       "    return ws;\n",
       "}\n",
       "\n",
       "mpl.mpl_figure_comm = function(comm, msg) {\n",
       "    // This is the function which gets called when the mpl process\n",
       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
       "\n",
       "    var id = msg.content.data.id;\n",
       "    // Get hold of the div created by the display call when the Comm\n",
       "    // socket was opened in Python.\n",
       "    var element = $(\"#\" + id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm)\n",
       "\n",
       "    function ondownload(figure, format) {\n",
       "        window.open(figure.imageObj.src);\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy,\n",
       "                           ondownload,\n",
       "                           element.get(0));\n",
       "\n",
       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
       "    // web socket which is closed, not our websocket->open comm proxy.\n",
       "    ws_proxy.onopen();\n",
       "\n",
       "    fig.parent_element = element.get(0);\n",
       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
       "    if (!fig.cell_info) {\n",
       "        console.error(\"Failed to find cell for figure\", id, fig);\n",
       "        return;\n",
       "    }\n",
       "\n",
       "    var output_index = fig.cell_info[2]\n",
       "    var cell = fig.cell_info[0];\n",
       "\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
       "    var width = fig.canvas.width/mpl.ratio\n",
       "    fig.root.unbind('remove')\n",
       "\n",
       "    // Update the output cell to use the data from the current canvas.\n",
       "    fig.push_to_output();\n",
       "    var dataURL = fig.canvas.toDataURL();\n",
       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
       "    // the notebook keyboard shortcuts fail.\n",
       "    IPython.keyboard_manager.enable()\n",
       "    $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
       "    fig.close_ws(fig, msg);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.close_ws = function(fig, msg){\n",
       "    fig.send_message('closing', msg);\n",
       "    // fig.ws.close()\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
       "    // Turn the data on the canvas into data in the output cell.\n",
       "    var width = this.canvas.width/mpl.ratio\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Tell IPython that the notebook contents must change.\n",
       "    IPython.notebook.set_dirty(true);\n",
       "    this.send_message(\"ack\", {});\n",
       "    var fig = this;\n",
       "    // Wait a second, then push the new image to the DOM so\n",
       "    // that it is saved nicely (might be nice to debounce this).\n",
       "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>');\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items){\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) { continue; };\n",
       "\n",
       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
       "    buttongrp.append(button);\n",
       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
       "    titlebar.prepend(buttongrp);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(el){\n",
       "    var fig = this\n",
       "    el.on(\"remove\", function(){\n",
       "\tfig.close_ws(fig, {});\n",
       "    });\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
       "    // this is important to make the div 'focusable\n",
       "    el.attr('tabindex', 0)\n",
       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
       "    // off when our div gets focus\n",
       "\n",
       "    // location in version 3\n",
       "    if (IPython.notebook.keyboard_manager) {\n",
       "        IPython.notebook.keyboard_manager.register_events(el);\n",
       "    }\n",
       "    else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\n",
       "    }\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    var manager = IPython.notebook.keyboard_manager;\n",
       "    if (!manager)\n",
       "        manager = IPython.keyboard_manager;\n",
       "\n",
       "    // Check for shift+enter\n",
       "    if (event.shiftKey && event.which == 13) {\n",
       "        this.canvas_div.blur();\n",
       "        // select the cell after this one\n",
       "        var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n",
       "        IPython.notebook.select(index + 1);\n",
       "    }\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    fig.ondownload(fig, null);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.find_output_cell = function(html_output) {\n",
       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
       "    // IPython event is triggered only after the cells have been serialised, which for\n",
       "    // our purposes (turning an active figure into a static one), is too late.\n",
       "    var cells = IPython.notebook.get_cells();\n",
       "    var ncells = cells.length;\n",
       "    for (var i=0; i<ncells; i++) {\n",
       "        var cell = cells[i];\n",
       "        if (cell.cell_type === 'code'){\n",
       "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
       "                var data = cell.output_area.outputs[j];\n",
       "                if (data.data) {\n",
       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
       "                    data = data.data;\n",
       "                }\n",
       "                if (data['text/html'] == html_output) {\n",
       "                    return [cell, data, j];\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    }\n",
       "}\n",
       "\n",
       "// Register the function which deals with the matplotlib target/channel.\n",
       "// The kernel may be null if the page has been refreshed.\n",
       "if (IPython.notebook.kernel != null) {\n",
       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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\" width=\"999.999985098839\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7ff6546b4cc0>"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['TRIG'].plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {
    "collapsed": true,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "###### Using the plot tool to identify trigger locations, input these into trigValues\n",
    "# compare index and start values here, and retain the ones that match loosely. These will be\n",
    "# the real start times where the trigger dips.\n",
    "\n",
    "# # print(str(index))\n",
    "# data['TRIG'].plot()\n",
    "\n",
    "# start = [] \n",
    "# for idx in range(0, len(data['TRIG'])) : \n",
    "#     if idx==0:\n",
    "#         pass\n",
    "#     elif data['TRIG'][idx] < 8.15 and data['TRIG'][idx]-data['TRIG'][idx-1]<-0.3: \n",
    "#         start.append(idx) \n",
    "# print(start)\n",
    "# # print(index)\n",
    "\n",
    "# feed = [] \n",
    "# for idx in range(0, len(data['TRIG'])) : \n",
    "#     if data['TRIG'][idx] > 9.5: \n",
    "#         feed.append(idx) \n",
    "\n",
    "# feed_half = []\n",
    "# i=0\n",
    "# while i<len(feed):\n",
    "#     if i%2==0 or i==0:\n",
    "#         feed_half.append(feed[i])\n",
    "#     i+=1\n",
    "\n",
    "\n",
    "# # print result \n",
    "# print(\"Feedback Triggers : \" + str(feed_half))\n",
    "\n",
    "start_times1 = [194698, 1005561, 1540609, 2147360, 2769599, 3481276, 3992184, 4585703, 5050115, 5815969, 6322160, 6797957]\n",
    "start_times2 = [194454, 1269507, 1922593, 2516873, 3148865, 3666784, 4218548, 5397350, 5952422, 6491108, 7097252, 7691496]\n",
    "start_times3 = [314593, 1092369, 1862621, 2414184, 3032840, 3545293, 4123413, 4676535, 5212411, 5729779, 6179051, 6690540]\n",
    "start_times4 = [265914, 1552433, 2022083, 2536588, 3110579, 3613764, 4408422, 4901834, 5318674, 5819387, 6282494, 6753798]\n",
    "start_times5 = [334600, 1344849, 2142669, 2821669, 3524315, 4173711, 4921500, 5597520, 6247026, 6878043, 7588660, 8279468]\n",
    "\n",
    "feedback_times1 = [889951, 1458686, 2041655, 2666775, 3366462, 3914176, 4488729, 4970775, 5554773, 6246421, 6707678, 7211068]\n",
    "feedback_times2 = [1125866, 1849636, 2428222, 3067840, 3605520, 4148242, 4810943, 5863661, 6424679, 7021668, 7591709, 8229150]\n",
    "feedback_times3 = [1013628, 1792001, 2346446, 2960952, 3479071, 4046890, 4609607, 5151855, 5662696, 6115721, 6585551, 7119120]\n",
    "feedback_times4 = [1412772, 1939638, 2449338, 2992715, 3506868, 4332269, 4824195, 5246587, 5735214, 6209677, 6647662, 7106437]\n",
    "feedback_times5 = [1261678, 2066838, 2754357, 3446248, 4096990, 4836504, 5521250, 6174368, 6803506, 7503325, 8170261, 8846106]\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### PILOTING TRIALS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "        RSP       EDA       ECG      TRIG\n",
      "0  6.958313 -0.534233 -0.032654  8.494263\n",
      "1  6.957703 -0.535759 -0.032043  8.424683\n",
      "2  6.957703 -0.538811 -0.031281  8.443604\n",
      "3  6.957703 -0.534233 -0.031128  8.554993\n",
      "4  6.955261 -0.535759 -0.031128  8.601990\n"
     ]
    }
   ],
   "source": [
    "# Read in data directly from .acq file and rename dataframe columns\n",
    "# Define the taskload list in order [Low, Low, Low, Med, Med, Med, High, High, High]\n",
    "# Define reliability list in order [0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1]\n",
    "# also need to change participant numbers here\n",
    "# rel = reliability, lock is lock-in time for voltage decision\n",
    "\n",
    "lowTask1 = ['1','4','8','12']\n",
    "medTask1 = ['3','6','7','10']\n",
    "highTask1 = ['2','5','9','11']\n",
    "rel1 = [1, 1, 0, 1, 1, 0, 1, 1, 1, 0, 1, 1]\n",
    "lock1 = [4, 4.73333333333333, 7.70000000000000, 4.36666666666667, 5.80000000000000, 5, 3.83333333333334, 4.40000000000000, 5.60000000000000, 3.60000000000000, 3.56666666666666, 3.86666666666667]\n",
    "\n",
    "lowTask2 = ['1','5','6','9']\n",
    "medTask2 = ['3','4','8','12']\n",
    "highTask2 = ['2','7','10','11']\n",
    "rel2 = [1, 1, 1, 0, 1, 0, 1, 1, 0, 1, 1, 1]\n",
    "lock2 = [4.60000000000000, 4.80000000000000, 3.53333333333333, 4.93333333333333, 3.53333333333333, 6.13333333333333, 4.20000000000000, 3.43333333333333, 6.63333333333333, 3.10000000000000, 3.36666666666667, 3.66666666666666]\n",
    "\n",
    "lowTask3 = ['3','5','7','10']\n",
    "medTask3 = ['1','2','4','12']\n",
    "highTask3 = ['6','8','9','11']\n",
    "rel3 = [1, 1, 1, 1, 0, 1, 0, 1, 1, 1, 1, 0]\n",
    "lock3 = [3.76666666666667, 4.20000000000000, 5.06666666666666, 4.86666666666667, 4.16666666666666, 4.46666666666667, 3.83333333333334, 4.76666666666667, 3.26666666666667, 3.93333333333333, 3.70000000000000, 3.33333333333334]\n",
    "\n",
    "lowTask4 = ['7','9','11','12']\n",
    "medTask4 = ['1','2','8','10']\n",
    "highTask4 = ['3','4','5','6']\n",
    "rel4 = [1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 0.0]\n",
    "lock4 = [3.76666666666667, 4.20000000000000, 5.06666666666666, 4.86666666666667, 4.16666666666666, 4.46666666666667, 3.83333333333334, 4.76666666666667, 3.26666666666667, 3.93333333333333, 3.70000000000000, 3.33333333333334]\n",
    "\n",
    "lowTask5 = ['2','6','7','10']\n",
    "medTask5 = ['3','4','5','8']\n",
    "highTask5 = ['1','9','11','12']\n",
    "rel5 = [1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 0.0]\n",
    "lock5 = [5.16666666666666, 4.10000000000000, 4.33333333333334, 5.66666666666666, 4.03333333333333, 3.73333333333333, 5.03333333333333, 8.33333333333334, 5.13333333333333, 5.76666666666667, 3.73333333333333, 6.06666666666666]\n",
    "\n",
    "data1, sampling_rate = nk.read_acqknowledge('part201_pilot.acq')\n",
    "data1 = data1.rename(columns={\"RSP, X, RSPEC-R\": \"RSP\", \"DTU100 - Trigger View, AMI / HLT - A11\": \"TRIG\",\n",
    "                            \"EDA, X, PPGED-R\": \"EDA\", \"ECG, X, RSPEC-R\": \"ECG\"})\n",
    "\n",
    "print(data1.head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Process ECG, EDA, and RSP over the entire experiment\n",
    "\n",
    "ecg_signals, info1 = nk.ecg_process(data1[\"ECG\"], sampling_rate=2000)\n",
    "eda_signals, info2 = nk.eda_process(data1[\"EDA\"], sampling_rate=2000)\n",
    "rsp_signals, info3 = nk.rsp_process(data1[\"RSP\"], sampling_rate=2000)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Electrocardiogram (Piloting)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ECG_Rate_Mean</th>\n",
       "      <th>ECG_HRV_RMSSD</th>\n",
       "      <th>ECG_HRV_MeanNN</th>\n",
       "      <th>ECG_HRV_SDNN</th>\n",
       "      <th>ECG_HRV_SDSD</th>\n",
       "      <th>ECG_HRV_CVNN</th>\n",
       "      <th>ECG_HRV_CVSD</th>\n",
       "      <th>ECG_HRV_MedianNN</th>\n",
       "      <th>ECG_HRV_MadNN</th>\n",
       "      <th>ECG_HRV_MCVNN</th>\n",
       "      <th>ECG_HRV_pNN50</th>\n",
       "      <th>ECG_HRV_pNN20</th>\n",
       "      <th>ECG_HRV_TINN</th>\n",
       "      <th>ECG_HRV_HTI</th>\n",
       "      <th>ECG_HRV_ULF</th>\n",
       "      <th>ECG_HRV_VLF</th>\n",
       "      <th>ECG_HRV_LF</th>\n",
       "      <th>ECG_HRV_HF</th>\n",
       "      <th>ECG_HRV_VHF</th>\n",
       "      <th>ECG_HRV_LFHF</th>\n",
       "      <th>ECG_HRV_LFn</th>\n",
       "      <th>ECG_HRV_HFn</th>\n",
       "      <th>ECG_HRV_LnHF</th>\n",
       "      <th>ECG_HRV_SD1</th>\n",
       "      <th>ECG_HRV_SD2</th>\n",
       "      <th>ECG_HRV_SD2SD1</th>\n",
       "      <th>ECG_HRV_CSI</th>\n",
       "      <th>ECG_HRV_CVI</th>\n",
       "      <th>ECG_HRV_CSI_Modified</th>\n",
       "      <th>ECG_HRV_SampEn</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>77.857078</td>\n",
       "      <td>105.738071</td>\n",
       "      <td>1543.062500</td>\n",
       "      <td>139.526054</td>\n",
       "      <td>106.576318</td>\n",
       "      <td>0.090422</td>\n",
       "      <td>0.068525</td>\n",
       "      <td>1535.0</td>\n",
       "      <td>106.0059</td>\n",
       "      <td>0.069059</td>\n",
       "      <td>53.125000</td>\n",
       "      <td>71.875000</td>\n",
       "      <td>639.0</td>\n",
       "      <td>3.764706</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5333.462388</td>\n",
       "      <td>9228.796666</td>\n",
       "      <td>1231.267596</td>\n",
       "      <td>5.234209</td>\n",
       "      <td>7.495362</td>\n",
       "      <td>0.584147</td>\n",
       "      <td>0.077934</td>\n",
       "      <td>7.115799</td>\n",
       "      <td>75.360837</td>\n",
       "      <td>182.361684</td>\n",
       "      <td>2.419847</td>\n",
       "      <td>2.419847</td>\n",
       "      <td>5.342199</td>\n",
       "      <td>1765.149392</td>\n",
       "      <td>1.981001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>79.659582</td>\n",
       "      <td>87.630518</td>\n",
       "      <td>1506.515152</td>\n",
       "      <td>85.360456</td>\n",
       "      <td>88.284252</td>\n",
       "      <td>0.056661</td>\n",
       "      <td>0.058168</td>\n",
       "      <td>1497.5</td>\n",
       "      <td>87.4734</td>\n",
       "      <td>0.058413</td>\n",
       "      <td>51.515152</td>\n",
       "      <td>74.242424</td>\n",
       "      <td>377.0</td>\n",
       "      <td>4.400000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>142.786616</td>\n",
       "      <td>1582.578951</td>\n",
       "      <td>1438.408459</td>\n",
       "      <td>4.696892</td>\n",
       "      <td>1.100229</td>\n",
       "      <td>0.499477</td>\n",
       "      <td>0.453976</td>\n",
       "      <td>7.271293</td>\n",
       "      <td>62.426393</td>\n",
       "      <td>103.323571</td>\n",
       "      <td>1.655126</td>\n",
       "      <td>1.655126</td>\n",
       "      <td>5.013688</td>\n",
       "      <td>684.054279</td>\n",
       "      <td>3.218876</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>77.563694</td>\n",
       "      <td>99.790495</td>\n",
       "      <td>1550.453125</td>\n",
       "      <td>96.453942</td>\n",
       "      <td>100.566012</td>\n",
       "      <td>0.062210</td>\n",
       "      <td>0.064362</td>\n",
       "      <td>1547.0</td>\n",
       "      <td>102.2994</td>\n",
       "      <td>0.066128</td>\n",
       "      <td>59.375000</td>\n",
       "      <td>84.375000</td>\n",
       "      <td>419.0</td>\n",
       "      <td>3.764706</td>\n",
       "      <td>0.0</td>\n",
       "      <td>364.959340</td>\n",
       "      <td>2753.317341</td>\n",
       "      <td>1289.623243</td>\n",
       "      <td>3.811351</td>\n",
       "      <td>2.134978</td>\n",
       "      <td>0.624093</td>\n",
       "      <td>0.292318</td>\n",
       "      <td>7.162105</td>\n",
       "      <td>71.110909</td>\n",
       "      <td>116.404314</td>\n",
       "      <td>1.636940</td>\n",
       "      <td>1.636940</td>\n",
       "      <td>5.122025</td>\n",
       "      <td>762.187657</td>\n",
       "      <td>2.302585</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>79.205197</td>\n",
       "      <td>79.393403</td>\n",
       "      <td>1519.646154</td>\n",
       "      <td>92.878185</td>\n",
       "      <td>79.870033</td>\n",
       "      <td>0.061118</td>\n",
       "      <td>0.052245</td>\n",
       "      <td>1520.0</td>\n",
       "      <td>88.9560</td>\n",
       "      <td>0.058524</td>\n",
       "      <td>55.384615</td>\n",
       "      <td>75.384615</td>\n",
       "      <td>508.0</td>\n",
       "      <td>4.333333</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1121.823308</td>\n",
       "      <td>1850.065495</td>\n",
       "      <td>1135.336940</td>\n",
       "      <td>2.084053</td>\n",
       "      <td>1.629530</td>\n",
       "      <td>0.450213</td>\n",
       "      <td>0.276284</td>\n",
       "      <td>7.034685</td>\n",
       "      <td>56.476642</td>\n",
       "      <td>118.587956</td>\n",
       "      <td>2.099770</td>\n",
       "      <td>2.099770</td>\n",
       "      <td>5.030029</td>\n",
       "      <td>996.029705</td>\n",
       "      <td>2.047693</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>82.033632</td>\n",
       "      <td>75.762098</td>\n",
       "      <td>1466.176471</td>\n",
       "      <td>108.834523</td>\n",
       "      <td>76.239022</td>\n",
       "      <td>0.074230</td>\n",
       "      <td>0.051673</td>\n",
       "      <td>1457.5</td>\n",
       "      <td>83.7669</td>\n",
       "      <td>0.057473</td>\n",
       "      <td>45.588235</td>\n",
       "      <td>76.470588</td>\n",
       "      <td>501.0</td>\n",
       "      <td>3.400000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>909.641142</td>\n",
       "      <td>2732.708582</td>\n",
       "      <td>923.095756</td>\n",
       "      <td>3.511751</td>\n",
       "      <td>2.960374</td>\n",
       "      <td>0.598103</td>\n",
       "      <td>0.202036</td>\n",
       "      <td>6.827733</td>\n",
       "      <td>53.909130</td>\n",
       "      <td>144.165574</td>\n",
       "      <td>2.674233</td>\n",
       "      <td>2.674233</td>\n",
       "      <td>5.094644</td>\n",
       "      <td>1542.129345</td>\n",
       "      <td>2.397895</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>77.492466</td>\n",
       "      <td>98.524027</td>\n",
       "      <td>1557.682540</td>\n",
       "      <td>119.833867</td>\n",
       "      <td>99.057223</td>\n",
       "      <td>0.076931</td>\n",
       "      <td>0.063250</td>\n",
       "      <td>1556.0</td>\n",
       "      <td>103.7820</td>\n",
       "      <td>0.066698</td>\n",
       "      <td>63.492063</td>\n",
       "      <td>80.952381</td>\n",
       "      <td>567.0</td>\n",
       "      <td>3.315789</td>\n",
       "      <td>0.0</td>\n",
       "      <td>267.526578</td>\n",
       "      <td>3165.795589</td>\n",
       "      <td>1071.106680</td>\n",
       "      <td>2.913167</td>\n",
       "      <td>2.955631</td>\n",
       "      <td>0.702364</td>\n",
       "      <td>0.237636</td>\n",
       "      <td>6.976448</td>\n",
       "      <td>70.044034</td>\n",
       "      <td>154.318322</td>\n",
       "      <td>2.203162</td>\n",
       "      <td>2.203162</td>\n",
       "      <td>5.237909</td>\n",
       "      <td>1359.952774</td>\n",
       "      <td>2.079442</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>74.169908</td>\n",
       "      <td>94.697765</td>\n",
       "      <td>1620.229508</td>\n",
       "      <td>98.338767</td>\n",
       "      <td>95.494550</td>\n",
       "      <td>0.060694</td>\n",
       "      <td>0.058447</td>\n",
       "      <td>1618.0</td>\n",
       "      <td>99.3342</td>\n",
       "      <td>0.061393</td>\n",
       "      <td>68.852459</td>\n",
       "      <td>93.442623</td>\n",
       "      <td>418.0</td>\n",
       "      <td>4.066667</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1013.535269</td>\n",
       "      <td>2666.973360</td>\n",
       "      <td>909.568144</td>\n",
       "      <td>2.160866</td>\n",
       "      <td>2.932131</td>\n",
       "      <td>0.580757</td>\n",
       "      <td>0.198066</td>\n",
       "      <td>6.812970</td>\n",
       "      <td>67.524844</td>\n",
       "      <td>121.578870</td>\n",
       "      <td>1.800506</td>\n",
       "      <td>1.800506</td>\n",
       "      <td>5.118442</td>\n",
       "      <td>875.613827</td>\n",
       "      <td>2.833213</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>72.056391</td>\n",
       "      <td>98.775608</td>\n",
       "      <td>1667.372881</td>\n",
       "      <td>94.683847</td>\n",
       "      <td>99.633867</td>\n",
       "      <td>0.056786</td>\n",
       "      <td>0.059240</td>\n",
       "      <td>1655.0</td>\n",
       "      <td>84.5082</td>\n",
       "      <td>0.051062</td>\n",
       "      <td>59.322034</td>\n",
       "      <td>83.050847</td>\n",
       "      <td>429.0</td>\n",
       "      <td>3.470588</td>\n",
       "      <td>0.0</td>\n",
       "      <td>258.470944</td>\n",
       "      <td>3521.735530</td>\n",
       "      <td>1120.518220</td>\n",
       "      <td>1.126599</td>\n",
       "      <td>3.142952</td>\n",
       "      <td>0.718450</td>\n",
       "      <td>0.228591</td>\n",
       "      <td>7.021547</td>\n",
       "      <td>70.451783</td>\n",
       "      <td>113.871016</td>\n",
       "      <td>1.616297</td>\n",
       "      <td>1.616297</td>\n",
       "      <td>5.108425</td>\n",
       "      <td>736.197592</td>\n",
       "      <td>1.481605</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>70.668294</td>\n",
       "      <td>135.695544</td>\n",
       "      <td>1697.775862</td>\n",
       "      <td>144.410240</td>\n",
       "      <td>136.858236</td>\n",
       "      <td>0.085058</td>\n",
       "      <td>0.079925</td>\n",
       "      <td>1684.0</td>\n",
       "      <td>148.2600</td>\n",
       "      <td>0.088040</td>\n",
       "      <td>74.137931</td>\n",
       "      <td>89.655172</td>\n",
       "      <td>795.0</td>\n",
       "      <td>3.411765</td>\n",
       "      <td>0.0</td>\n",
       "      <td>435.158881</td>\n",
       "      <td>6365.906888</td>\n",
       "      <td>1257.588512</td>\n",
       "      <td>1.435930</td>\n",
       "      <td>5.061995</td>\n",
       "      <td>0.789806</td>\n",
       "      <td>0.156027</td>\n",
       "      <td>7.136951</td>\n",
       "      <td>96.773387</td>\n",
       "      <td>179.843116</td>\n",
       "      <td>1.858394</td>\n",
       "      <td>1.858394</td>\n",
       "      <td>5.444770</td>\n",
       "      <td>1336.877727</td>\n",
       "      <td>1.757858</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>72.020382</td>\n",
       "      <td>157.050837</td>\n",
       "      <td>1671.016949</td>\n",
       "      <td>154.015280</td>\n",
       "      <td>158.413688</td>\n",
       "      <td>0.092169</td>\n",
       "      <td>0.093985</td>\n",
       "      <td>1686.0</td>\n",
       "      <td>109.7124</td>\n",
       "      <td>0.065073</td>\n",
       "      <td>61.016949</td>\n",
       "      <td>83.050847</td>\n",
       "      <td>749.0</td>\n",
       "      <td>3.933333</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4371.531620</td>\n",
       "      <td>15424.125739</td>\n",
       "      <td>6259.592693</td>\n",
       "      <td>88.162516</td>\n",
       "      <td>2.464078</td>\n",
       "      <td>0.589981</td>\n",
       "      <td>0.239433</td>\n",
       "      <td>8.741870</td>\n",
       "      <td>112.015393</td>\n",
       "      <td>186.799264</td>\n",
       "      <td>1.667621</td>\n",
       "      <td>1.667621</td>\n",
       "      <td>5.524773</td>\n",
       "      <td>1246.041780</td>\n",
       "      <td>2.047693</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>75.769024</td>\n",
       "      <td>79.385860</td>\n",
       "      <td>1589.000000</td>\n",
       "      <td>109.476100</td>\n",
       "      <td>80.037464</td>\n",
       "      <td>0.068896</td>\n",
       "      <td>0.049960</td>\n",
       "      <td>1597.5</td>\n",
       "      <td>139.3644</td>\n",
       "      <td>0.087239</td>\n",
       "      <td>51.612903</td>\n",
       "      <td>82.258065</td>\n",
       "      <td>493.0</td>\n",
       "      <td>4.133333</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2318.359258</td>\n",
       "      <td>2249.516743</td>\n",
       "      <td>1050.209091</td>\n",
       "      <td>1.459190</td>\n",
       "      <td>2.141970</td>\n",
       "      <td>0.400302</td>\n",
       "      <td>0.186885</td>\n",
       "      <td>6.956745</td>\n",
       "      <td>56.595033</td>\n",
       "      <td>144.107720</td>\n",
       "      <td>2.546296</td>\n",
       "      <td>2.546296</td>\n",
       "      <td>5.115586</td>\n",
       "      <td>1467.763773</td>\n",
       "      <td>1.504077</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>70.132867</td>\n",
       "      <td>98.359762</td>\n",
       "      <td>1719.596491</td>\n",
       "      <td>119.937314</td>\n",
       "      <td>98.987865</td>\n",
       "      <td>0.069747</td>\n",
       "      <td>0.057199</td>\n",
       "      <td>1689.0</td>\n",
       "      <td>103.7820</td>\n",
       "      <td>0.061446</td>\n",
       "      <td>57.894737</td>\n",
       "      <td>84.210526</td>\n",
       "      <td>575.0</td>\n",
       "      <td>3.800000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1268.404999</td>\n",
       "      <td>1693.847014</td>\n",
       "      <td>1050.122349</td>\n",
       "      <td>0.905065</td>\n",
       "      <td>1.613000</td>\n",
       "      <td>0.422061</td>\n",
       "      <td>0.261662</td>\n",
       "      <td>6.956662</td>\n",
       "      <td>69.994991</td>\n",
       "      <td>154.501197</td>\n",
       "      <td>2.207318</td>\n",
       "      <td>2.207318</td>\n",
       "      <td>5.238119</td>\n",
       "      <td>1364.133042</td>\n",
       "      <td>1.791759</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    ECG_Rate_Mean  ECG_HRV_RMSSD  ECG_HRV_MeanNN  ECG_HRV_SDNN  ECG_HRV_SDSD  \\\n",
       "1       77.857078     105.738071     1543.062500    139.526054    106.576318   \n",
       "2       79.659582      87.630518     1506.515152     85.360456     88.284252   \n",
       "3       77.563694      99.790495     1550.453125     96.453942    100.566012   \n",
       "4       79.205197      79.393403     1519.646154     92.878185     79.870033   \n",
       "5       82.033632      75.762098     1466.176471    108.834523     76.239022   \n",
       "6       77.492466      98.524027     1557.682540    119.833867     99.057223   \n",
       "7       74.169908      94.697765     1620.229508     98.338767     95.494550   \n",
       "8       72.056391      98.775608     1667.372881     94.683847     99.633867   \n",
       "9       70.668294     135.695544     1697.775862    144.410240    136.858236   \n",
       "10      72.020382     157.050837     1671.016949    154.015280    158.413688   \n",
       "11      75.769024      79.385860     1589.000000    109.476100     80.037464   \n",
       "12      70.132867      98.359762     1719.596491    119.937314     98.987865   \n",
       "\n",
       "    ECG_HRV_CVNN  ECG_HRV_CVSD  ECG_HRV_MedianNN  ECG_HRV_MadNN  \\\n",
       "1       0.090422      0.068525            1535.0       106.0059   \n",
       "2       0.056661      0.058168            1497.5        87.4734   \n",
       "3       0.062210      0.064362            1547.0       102.2994   \n",
       "4       0.061118      0.052245            1520.0        88.9560   \n",
       "5       0.074230      0.051673            1457.5        83.7669   \n",
       "6       0.076931      0.063250            1556.0       103.7820   \n",
       "7       0.060694      0.058447            1618.0        99.3342   \n",
       "8       0.056786      0.059240            1655.0        84.5082   \n",
       "9       0.085058      0.079925            1684.0       148.2600   \n",
       "10      0.092169      0.093985            1686.0       109.7124   \n",
       "11      0.068896      0.049960            1597.5       139.3644   \n",
       "12      0.069747      0.057199            1689.0       103.7820   \n",
       "\n",
       "    ECG_HRV_MCVNN  ECG_HRV_pNN50  ECG_HRV_pNN20  ECG_HRV_TINN  ECG_HRV_HTI  \\\n",
       "1        0.069059      53.125000      71.875000         639.0     3.764706   \n",
       "2        0.058413      51.515152      74.242424         377.0     4.400000   \n",
       "3        0.066128      59.375000      84.375000         419.0     3.764706   \n",
       "4        0.058524      55.384615      75.384615         508.0     4.333333   \n",
       "5        0.057473      45.588235      76.470588         501.0     3.400000   \n",
       "6        0.066698      63.492063      80.952381         567.0     3.315789   \n",
       "7        0.061393      68.852459      93.442623         418.0     4.066667   \n",
       "8        0.051062      59.322034      83.050847         429.0     3.470588   \n",
       "9        0.088040      74.137931      89.655172         795.0     3.411765   \n",
       "10       0.065073      61.016949      83.050847         749.0     3.933333   \n",
       "11       0.087239      51.612903      82.258065         493.0     4.133333   \n",
       "12       0.061446      57.894737      84.210526         575.0     3.800000   \n",
       "\n",
       "    ECG_HRV_ULF  ECG_HRV_VLF    ECG_HRV_LF   ECG_HRV_HF  ECG_HRV_VHF  \\\n",
       "1           0.0  5333.462388   9228.796666  1231.267596     5.234209   \n",
       "2           0.0   142.786616   1582.578951  1438.408459     4.696892   \n",
       "3           0.0   364.959340   2753.317341  1289.623243     3.811351   \n",
       "4           0.0  1121.823308   1850.065495  1135.336940     2.084053   \n",
       "5           0.0   909.641142   2732.708582   923.095756     3.511751   \n",
       "6           0.0   267.526578   3165.795589  1071.106680     2.913167   \n",
       "7           0.0  1013.535269   2666.973360   909.568144     2.160866   \n",
       "8           0.0   258.470944   3521.735530  1120.518220     1.126599   \n",
       "9           0.0   435.158881   6365.906888  1257.588512     1.435930   \n",
       "10          0.0  4371.531620  15424.125739  6259.592693    88.162516   \n",
       "11          0.0  2318.359258   2249.516743  1050.209091     1.459190   \n",
       "12          0.0  1268.404999   1693.847014  1050.122349     0.905065   \n",
       "\n",
       "    ECG_HRV_LFHF  ECG_HRV_LFn  ECG_HRV_HFn  ECG_HRV_LnHF  ECG_HRV_SD1  \\\n",
       "1       7.495362     0.584147     0.077934      7.115799    75.360837   \n",
       "2       1.100229     0.499477     0.453976      7.271293    62.426393   \n",
       "3       2.134978     0.624093     0.292318      7.162105    71.110909   \n",
       "4       1.629530     0.450213     0.276284      7.034685    56.476642   \n",
       "5       2.960374     0.598103     0.202036      6.827733    53.909130   \n",
       "6       2.955631     0.702364     0.237636      6.976448    70.044034   \n",
       "7       2.932131     0.580757     0.198066      6.812970    67.524844   \n",
       "8       3.142952     0.718450     0.228591      7.021547    70.451783   \n",
       "9       5.061995     0.789806     0.156027      7.136951    96.773387   \n",
       "10      2.464078     0.589981     0.239433      8.741870   112.015393   \n",
       "11      2.141970     0.400302     0.186885      6.956745    56.595033   \n",
       "12      1.613000     0.422061     0.261662      6.956662    69.994991   \n",
       "\n",
       "    ECG_HRV_SD2  ECG_HRV_SD2SD1  ECG_HRV_CSI  ECG_HRV_CVI  \\\n",
       "1    182.361684        2.419847     2.419847     5.342199   \n",
       "2    103.323571        1.655126     1.655126     5.013688   \n",
       "3    116.404314        1.636940     1.636940     5.122025   \n",
       "4    118.587956        2.099770     2.099770     5.030029   \n",
       "5    144.165574        2.674233     2.674233     5.094644   \n",
       "6    154.318322        2.203162     2.203162     5.237909   \n",
       "7    121.578870        1.800506     1.800506     5.118442   \n",
       "8    113.871016        1.616297     1.616297     5.108425   \n",
       "9    179.843116        1.858394     1.858394     5.444770   \n",
       "10   186.799264        1.667621     1.667621     5.524773   \n",
       "11   144.107720        2.546296     2.546296     5.115586   \n",
       "12   154.501197        2.207318     2.207318     5.238119   \n",
       "\n",
       "    ECG_HRV_CSI_Modified  ECG_HRV_SampEn  \n",
       "1            1765.149392        1.981001  \n",
       "2             684.054279        3.218876  \n",
       "3             762.187657        2.302585  \n",
       "4             996.029705        2.047693  \n",
       "5            1542.129345        2.397895  \n",
       "6            1359.952774        2.079442  \n",
       "7             875.613827        2.833213  \n",
       "8             736.197592        1.481605  \n",
       "9            1336.877727        1.757858  \n",
       "10           1246.041780        2.047693  \n",
       "11           1467.763773        1.504077  \n",
       "12           1364.133042        1.791759  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "75.83558420899374\n",
      "77.71472863579196\n",
      "73.60681600641178\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "ECG_epochs_piloting = nk.epochs_create(ecg_signals, events=start_times1, sampling_rate=2000, epochs_start= 0, epochs_end=50)\n",
    "\n",
    "display(nk.ecg_analyze(ECG_epochs_piloting))\n",
    "\n",
    "print((ECG_epochs_piloting[lowTask5[0]]['ECG_Rate'].mean()+\n",
    "         ECG_epochs_piloting[lowTask5[1]]['ECG_Rate'].mean()+\n",
    "         ECG_epochs_piloting[lowTask5[2]]['ECG_Rate'].mean()+\n",
    "         ECG_epochs_piloting[lowTask5[3]]['ECG_Rate'].mean())/4)\n",
    "\n",
    "print((ECG_epochs_piloting[medTask5[0]]['ECG_Rate'].mean()+\n",
    "         ECG_epochs_piloting[medTask5[1]]['ECG_Rate'].mean()+\n",
    "         ECG_epochs_piloting[medTask5[2]]['ECG_Rate'].mean()+\n",
    "         ECG_epochs_piloting[medTask5[3]]['ECG_Rate'].mean())/4)\n",
    "\n",
    "print((ECG_epochs_piloting[highTask5[0]]['ECG_Rate'].mean()+\n",
    "         ECG_epochs_piloting[highTask5[1]]['ECG_Rate'].mean()+\n",
    "         ECG_epochs_piloting[highTask5[2]]['ECG_Rate'].mean()+\n",
    "         ECG_epochs_piloting[highTask5[3]]['ECG_Rate'].mean())/4)\n",
    "\n",
    "print('\\n')\n",
    "\n",
    "# hrv = nk.ecg_analyze(ECG_epochs_piloting)['ECG_HRV_RMSSD']\n",
    "# print(hrv[lowTask5[0:4]].mean())\n",
    "# print(hrv[medTask5[0:4]].mean())\n",
    "# print(hrv[highTask5[0:4]].mean())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1     102.214628\n",
      "2     131.503169\n",
      "3     106.908120\n",
      "4      45.226590\n",
      "5      69.049479\n",
      "6      66.350288\n",
      "7     121.778263\n",
      "8      89.814624\n",
      "9     272.994015\n",
      "10    110.310524\n",
      "11    115.469404\n",
      "12     83.978062\n",
      "Name: ECG_HRV_RMSSD, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "# johnny can ignore this cell for now - just for demonstration\n",
    "\n",
    "ECG_epochs_piloting = nk.epochs_create(ecg_signals, events=start_times1, sampling_rate=2000, epochs_start= 0, epochs_end=50)\n",
    "print(nk.ecg_analyze(ECG_epochs_piloting)['ECG_HRV_RMSSD'])\n",
    "\n",
    "# print((ECG_epochs_piloting['1']['ECG_Rate'].mean()))\n",
    "# print((ECG_epochs_piloting['2']['ECG_Rate'].mean()))\n",
    "# print((ECG_epochs_piloting['3']['ECG_Rate'].mean()))\n",
    "# print((ECG_epochs_piloting['4']['ECG_Rate'].mean()))\n",
    "# print((ECG_epochs_piloting['5']['ECG_Rate'].mean()))\n",
    "# print((ECG_epochs_piloting['6']['ECG_Rate'].mean()))\n",
    "# print((ECG_epochs_piloting['7']['ECG_Rate'].mean()))\n",
    "# print((ECG_epochs_piloting['8']['ECG_Rate'].mean()))\n",
    "# print((ECG_epochs_piloting['9']['ECG_Rate'].mean()))\n",
    "# print((ECG_epochs_piloting['10']['ECG_Rate'].mean()))\n",
    "# print((ECG_epochs_piloting['11']['ECG_Rate'].mean()))\n",
    "# print((ECG_epochs_piloting['12']['ECG_Rate'].mean()))\n",
    "\n",
    "\n",
    "# print('\\n')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Electrodermal Analysis (Piloting)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Low Taskload Tonic EDA 1.1809057850725977\n",
      "Medium Taskload Tonic EDA -0.2648178751956727\n",
      "High Taskload Tonic EDA -3.4743051921368022\n",
      "\n",
      "Low Taskload Phasic EDA 0.00964643193993672\n",
      "Medium Taskload Phasic EDA -0.023516952434592984\n",
      "High Taskload Phasic EDA -0.0829535939938546 \n",
      "\n"
     ]
    }
   ],
   "source": [
    "EDA_epochs_piloting = nk.epochs_create(eda_signals, events=start_times1, sampling_rate=2000, epochs_start= 0, epochs_end=50)\n",
    "\n",
    "lowTonic = (EDA_epochs_piloting[lowTask5[0]]['EDA_Tonic'].mean()+EDA_epochs_piloting[lowTask5[1]]['EDA_Tonic'].mean()+EDA_epochs_piloting[lowTask5[2]]['EDA_Tonic'].mean()+EDA_epochs_piloting[lowTask5[3]]['EDA_Tonic'].mean())/4\n",
    "medTonic = (EDA_epochs_piloting[medTask5[0]]['EDA_Tonic'].mean()+EDA_epochs_piloting[medTask5[1]]['EDA_Tonic'].mean()+EDA_epochs_piloting[medTask5[2]]['EDA_Tonic'].mean()+EDA_epochs_piloting[medTask5[3]]['EDA_Tonic'].mean())/4\n",
    "highTonic = (EDA_epochs_piloting[highTask5[0]]['EDA_Tonic'].mean()+EDA_epochs_piloting[highTask5[1]]['EDA_Tonic'].mean()+EDA_epochs_piloting[highTask5[2]]['EDA_Tonic'].mean()+EDA_epochs_piloting[highTask5[3]]['EDA_Tonic'].mean())/4\n",
    "\n",
    "lowPhasic = (EDA_epochs_piloting[lowTask5[0]]['EDA_Phasic'].mean()+EDA_epochs_piloting[lowTask5[1]]['EDA_Phasic'].mean()+EDA_epochs_piloting[lowTask5[2]]['EDA_Phasic'].mean()+EDA_epochs_piloting[lowTask5[3]]['EDA_Phasic'].mean())/4\n",
    "medPhasic = (EDA_epochs_piloting[medTask5[0]]['EDA_Phasic'].mean()+EDA_epochs_piloting[medTask5[1]]['EDA_Phasic'].mean()+EDA_epochs_piloting[medTask5[2]]['EDA_Phasic'].mean()+EDA_epochs_piloting[medTask5[3]]['EDA_Phasic'].mean())/4\n",
    "highPhasic = (EDA_epochs_piloting[highTask5[0]]['EDA_Phasic'].mean()+EDA_epochs_piloting[highTask5[1]]['EDA_Phasic'].mean()+EDA_epochs_piloting[highTask5[2]]['EDA_Phasic'].mean()+EDA_epochs_piloting[highTask5[3]]['EDA_Phasic'].mean())/4\n",
    "\n",
    "print('\\nLow Taskload Tonic EDA', lowTonic)\n",
    "print('Medium Taskload Tonic EDA', medTonic)\n",
    "print('High Taskload Tonic EDA', highTonic)\n",
    "\n",
    "print('\\nLow Taskload Phasic EDA', lowPhasic)\n",
    "print('Medium Taskload Phasic EDA', medPhasic)\n",
    "print('High Taskload Phasic EDA', highPhasic, '\\n')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Respiration (Piloting)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Low Taskload Respiration Amplitude:  2.9371977719759403\n",
      "Medium Taskload Respiration Amplitude:  3.7274819950865044\n",
      "High Taskload Respiration Amplitude:  4.26633481432475\n",
      "\n",
      "Low Taskload Respiration Rate:  20.620910737073718\n",
      "Medium Taskload Respiration Rate:  23.58067984557877\n",
      "High Taskload Respiration Rate:  20.9470536222805 \n",
      "\n"
     ]
    }
   ],
   "source": [
    "RSP_epochs_piloting = nk.epochs_create(rsp_signals, events=start_times1, sampling_rate=2000, epochs_start= 0, epochs_end=50)\n",
    "\n",
    "lowAmp = (RSP_epochs_piloting[lowTask5[0]]['RSP_Amplitude'].mean()+RSP_epochs_piloting[lowTask5[1]]['RSP_Amplitude'].mean()+RSP_epochs_piloting[lowTask5[2]]['RSP_Amplitude'].mean()+RSP_epochs_piloting[lowTask5[3]]['RSP_Amplitude'].mean())/4\n",
    "medAmp = (RSP_epochs_piloting[medTask5[0]]['RSP_Amplitude'].mean()+RSP_epochs_piloting[medTask5[1]]['RSP_Amplitude'].mean()+RSP_epochs_piloting[medTask5[2]]['RSP_Amplitude'].mean()+RSP_epochs_piloting[medTask5[3]]['RSP_Amplitude'].mean())/4\n",
    "highAmp = (RSP_epochs_piloting[highTask5[0]]['RSP_Amplitude'].mean()+RSP_epochs_piloting[highTask5[1]]['RSP_Amplitude'].mean()+RSP_epochs_piloting[highTask5[2]]['RSP_Amplitude'].mean()+RSP_epochs_piloting[highTask5[3]]['RSP_Amplitude'].mean())/4\n",
    "\n",
    "print('\\nLow Taskload Respiration Amplitude: ', lowAmp)\n",
    "print('Medium Taskload Respiration Amplitude: ', medAmp)\n",
    "print('High Taskload Respiration Amplitude: ', highAmp)\n",
    "\n",
    "lowRate = (RSP_epochs_piloting[lowTask5[0]]['RSP_Rate'].mean()+RSP_epochs_piloting[lowTask5[1]]['RSP_Rate'].mean()+RSP_epochs_piloting[lowTask5[2]]['RSP_Rate'].mean()+RSP_epochs_piloting[lowTask5[3]]['RSP_Rate'].mean())/4\n",
    "medRate = (RSP_epochs_piloting[medTask5[0]]['RSP_Rate'].mean()+RSP_epochs_piloting[medTask5[1]]['RSP_Rate'].mean()+RSP_epochs_piloting[medTask5[2]]['RSP_Rate'].mean()+RSP_epochs_piloting[medTask5[3]]['RSP_Rate'].mean())/4\n",
    "highRate = (RSP_epochs_piloting[highTask5[0]]['RSP_Rate'].mean()+RSP_epochs_piloting[highTask5[1]]['RSP_Rate'].mean()+RSP_epochs_piloting[highTask5[2]]['RSP_Rate'].mean()+RSP_epochs_piloting[highTask5[3]]['RSP_Rate'].mean())/4\n",
    "\n",
    "print('\\nLow Taskload Respiration Rate: ', lowRate)\n",
    "print('Medium Taskload Respiration Rate: ', medRate)\n",
    "print('High Taskload Respiration Rate: ', highRate, '\\n')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### DECISION TRIALS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[302698, 1115027.6666666667, 1656009.0, 2256093.3333333335, 2881199.0, 3591276, 4099850.6666666665, 4694503.0, 5161315.0, 5923169.0, 6429293.333333333, 6905690.333333333]\n"
     ]
    }
   ],
   "source": [
    "# Make an array of trust decision times by adding lockin times to start time + 50 seconds\n",
    "# Everything from this point on is pretty experimental - proceed with caution\n",
    "\n",
    "decision_times = [0,0,0,0,0,0,0,0,0,0,0,0]\n",
    "i=0\n",
    "for time in start_times:\n",
    "    decision_times[i] = start_times1[i] + 50*2000 + lock1[i]*2000\n",
    "    i+=1\n",
    "\n",
    "print(decision_times)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Electrocardiogram (Decision)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "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>ECG_Rate_Mean</th>\n",
       "      <th>ECG_HRV_RMSSD</th>\n",
       "      <th>ECG_HRV_MeanNN</th>\n",
       "      <th>ECG_HRV_SDNN</th>\n",
       "      <th>ECG_HRV_SDSD</th>\n",
       "      <th>ECG_HRV_CVNN</th>\n",
       "      <th>ECG_HRV_CVSD</th>\n",
       "      <th>ECG_HRV_MedianNN</th>\n",
       "      <th>ECG_HRV_MadNN</th>\n",
       "      <th>ECG_HRV_MCVNN</th>\n",
       "      <th>ECG_HRV_pNN50</th>\n",
       "      <th>ECG_HRV_pNN20</th>\n",
       "      <th>ECG_HRV_TINN</th>\n",
       "      <th>ECG_HRV_HTI</th>\n",
       "      <th>ECG_HRV_ULF</th>\n",
       "      <th>ECG_HRV_VLF</th>\n",
       "      <th>ECG_HRV_LF</th>\n",
       "      <th>ECG_HRV_HF</th>\n",
       "      <th>ECG_HRV_VHF</th>\n",
       "      <th>ECG_HRV_LFHF</th>\n",
       "      <th>ECG_HRV_LFn</th>\n",
       "      <th>ECG_HRV_HFn</th>\n",
       "      <th>ECG_HRV_LnHF</th>\n",
       "      <th>ECG_HRV_SD1</th>\n",
       "      <th>ECG_HRV_SD2</th>\n",
       "      <th>ECG_HRV_SD2SD1</th>\n",
       "      <th>ECG_HRV_CSI</th>\n",
       "      <th>ECG_HRV_CVI</th>\n",
       "      <th>ECG_HRV_CSI_Modified</th>\n",
       "      <th>ECG_HRV_SampEn</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>71.462095</td>\n",
       "      <td>72.343562</td>\n",
       "      <td>1679.695652</td>\n",
       "      <td>85.617243</td>\n",
       "      <td>73.787002</td>\n",
       "      <td>0.050972</td>\n",
       "      <td>0.043069</td>\n",
       "      <td>1670.0</td>\n",
       "      <td>102.2994</td>\n",
       "      <td>0.061257</td>\n",
       "      <td>52.173913</td>\n",
       "      <td>69.565217</td>\n",
       "      <td>293.0</td>\n",
       "      <td>3.285714</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>753.876389</td>\n",
       "      <td>853.750478</td>\n",
       "      <td>1.045472</td>\n",
       "      <td>0.883017</td>\n",
       "      <td>0.468633</td>\n",
       "      <td>0.530717</td>\n",
       "      <td>6.749639</td>\n",
       "      <td>52.175289</td>\n",
       "      <td>109.262819</td>\n",
       "      <td>2.094149</td>\n",
       "      <td>2.094149</td>\n",
       "      <td>4.960057</td>\n",
       "      <td>915.250405</td>\n",
       "      <td>inf</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>73.677672</td>\n",
       "      <td>88.491955</td>\n",
       "      <td>1630.791667</td>\n",
       "      <td>101.957356</td>\n",
       "      <td>89.983947</td>\n",
       "      <td>0.062520</td>\n",
       "      <td>0.054263</td>\n",
       "      <td>1632.0</td>\n",
       "      <td>117.8667</td>\n",
       "      <td>0.072222</td>\n",
       "      <td>66.666667</td>\n",
       "      <td>87.500000</td>\n",
       "      <td>367.0</td>\n",
       "      <td>4.800000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1457.161396</td>\n",
       "      <td>954.489612</td>\n",
       "      <td>0.771335</td>\n",
       "      <td>1.526639</td>\n",
       "      <td>0.604024</td>\n",
       "      <td>0.395656</td>\n",
       "      <td>6.861177</td>\n",
       "      <td>63.628259</td>\n",
       "      <td>129.391073</td>\n",
       "      <td>2.033547</td>\n",
       "      <td>2.033547</td>\n",
       "      <td>5.119674</td>\n",
       "      <td>1052.491457</td>\n",
       "      <td>inf</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>70.548238</td>\n",
       "      <td>87.052529</td>\n",
       "      <td>1694.136364</td>\n",
       "      <td>71.585643</td>\n",
       "      <td>89.138736</td>\n",
       "      <td>0.042255</td>\n",
       "      <td>0.051385</td>\n",
       "      <td>1702.0</td>\n",
       "      <td>100.0755</td>\n",
       "      <td>0.058799</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>81.818182</td>\n",
       "      <td>255.0</td>\n",
       "      <td>4.400000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>700.703668</td>\n",
       "      <td>766.497395</td>\n",
       "      <td>0.729371</td>\n",
       "      <td>0.914163</td>\n",
       "      <td>0.477341</td>\n",
       "      <td>0.522162</td>\n",
       "      <td>6.641831</td>\n",
       "      <td>63.030605</td>\n",
       "      <td>79.222166</td>\n",
       "      <td>1.256884</td>\n",
       "      <td>1.256884</td>\n",
       "      <td>4.902518</td>\n",
       "      <td>398.292324</td>\n",
       "      <td>inf</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>82.995139</td>\n",
       "      <td>70.993803</td>\n",
       "      <td>1442.576923</td>\n",
       "      <td>94.270323</td>\n",
       "      <td>72.305486</td>\n",
       "      <td>0.065349</td>\n",
       "      <td>0.049213</td>\n",
       "      <td>1433.0</td>\n",
       "      <td>74.1300</td>\n",
       "      <td>0.051731</td>\n",
       "      <td>46.153846</td>\n",
       "      <td>65.384615</td>\n",
       "      <td>331.0</td>\n",
       "      <td>3.250000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2957.013208</td>\n",
       "      <td>646.523945</td>\n",
       "      <td>0.408439</td>\n",
       "      <td>4.573710</td>\n",
       "      <td>0.820493</td>\n",
       "      <td>0.179393</td>\n",
       "      <td>6.471610</td>\n",
       "      <td>51.127700</td>\n",
       "      <td>123.124920</td>\n",
       "      <td>2.408184</td>\n",
       "      <td>2.408184</td>\n",
       "      <td>5.003122</td>\n",
       "      <td>1186.029971</td>\n",
       "      <td>1.098612</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>83.460315</td>\n",
       "      <td>39.682878</td>\n",
       "      <td>1433.407407</td>\n",
       "      <td>50.914906</td>\n",
       "      <td>39.985324</td>\n",
       "      <td>0.035520</td>\n",
       "      <td>0.027684</td>\n",
       "      <td>1432.0</td>\n",
       "      <td>45.9606</td>\n",
       "      <td>0.032095</td>\n",
       "      <td>22.222222</td>\n",
       "      <td>48.148148</td>\n",
       "      <td>261.0</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>717.195293</td>\n",
       "      <td>519.096512</td>\n",
       "      <td>6.148365</td>\n",
       "      <td>1.381622</td>\n",
       "      <td>0.577247</td>\n",
       "      <td>0.417804</td>\n",
       "      <td>6.252090</td>\n",
       "      <td>28.273894</td>\n",
       "      <td>66.221161</td>\n",
       "      <td>2.342131</td>\n",
       "      <td>2.342131</td>\n",
       "      <td>4.476502</td>\n",
       "      <td>620.394518</td>\n",
       "      <td>1.791759</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>83.148286</td>\n",
       "      <td>33.264095</td>\n",
       "      <td>1444.111111</td>\n",
       "      <td>52.106646</td>\n",
       "      <td>33.921743</td>\n",
       "      <td>0.036082</td>\n",
       "      <td>0.023034</td>\n",
       "      <td>1433.0</td>\n",
       "      <td>53.3736</td>\n",
       "      <td>0.037246</td>\n",
       "      <td>14.814815</td>\n",
       "      <td>51.851852</td>\n",
       "      <td>193.0</td>\n",
       "      <td>3.857143</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>819.146469</td>\n",
       "      <td>76.652282</td>\n",
       "      <td>0.591176</td>\n",
       "      <td>10.686524</td>\n",
       "      <td>0.913828</td>\n",
       "      <td>0.085512</td>\n",
       "      <td>4.339279</td>\n",
       "      <td>23.986294</td>\n",
       "      <td>69.676846</td>\n",
       "      <td>2.904861</td>\n",
       "      <td>2.904861</td>\n",
       "      <td>4.427172</td>\n",
       "      <td>809.606150</td>\n",
       "      <td>inf</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>67.454556</td>\n",
       "      <td>91.736943</td>\n",
       "      <td>1764.181818</td>\n",
       "      <td>98.078024</td>\n",
       "      <td>92.570231</td>\n",
       "      <td>0.055594</td>\n",
       "      <td>0.052000</td>\n",
       "      <td>1753.5</td>\n",
       "      <td>116.3841</td>\n",
       "      <td>0.066372</td>\n",
       "      <td>54.545455</td>\n",
       "      <td>63.636364</td>\n",
       "      <td>370.0</td>\n",
       "      <td>3.666667</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1232.913509</td>\n",
       "      <td>567.915557</td>\n",
       "      <td>0.443476</td>\n",
       "      <td>2.170945</td>\n",
       "      <td>0.684468</td>\n",
       "      <td>0.315286</td>\n",
       "      <td>6.341973</td>\n",
       "      <td>65.457038</td>\n",
       "      <td>122.286441</td>\n",
       "      <td>1.868194</td>\n",
       "      <td>1.868194</td>\n",
       "      <td>5.107455</td>\n",
       "      <td>913.819145</td>\n",
       "      <td>1.791759</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>72.885351</td>\n",
       "      <td>64.847373</td>\n",
       "      <td>1654.652174</td>\n",
       "      <td>44.399640</td>\n",
       "      <td>66.360618</td>\n",
       "      <td>0.026833</td>\n",
       "      <td>0.039191</td>\n",
       "      <td>1652.0</td>\n",
       "      <td>40.0302</td>\n",
       "      <td>0.024231</td>\n",
       "      <td>47.826087</td>\n",
       "      <td>65.217391</td>\n",
       "      <td>181.0</td>\n",
       "      <td>2.555556</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>276.859856</td>\n",
       "      <td>301.029727</td>\n",
       "      <td>0.459125</td>\n",
       "      <td>0.919709</td>\n",
       "      <td>0.478707</td>\n",
       "      <td>0.520499</td>\n",
       "      <td>5.707209</td>\n",
       "      <td>46.924043</td>\n",
       "      <td>41.722779</td>\n",
       "      <td>0.889156</td>\n",
       "      <td>0.889156</td>\n",
       "      <td>4.495889</td>\n",
       "      <td>148.392186</td>\n",
       "      <td>inf</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>66.267315</td>\n",
       "      <td>189.108963</td>\n",
       "      <td>1797.142857</td>\n",
       "      <td>224.503293</td>\n",
       "      <td>190.332977</td>\n",
       "      <td>0.124922</td>\n",
       "      <td>0.105228</td>\n",
       "      <td>1859.0</td>\n",
       "      <td>131.9514</td>\n",
       "      <td>0.070980</td>\n",
       "      <td>61.904762</td>\n",
       "      <td>80.952381</td>\n",
       "      <td>817.0</td>\n",
       "      <td>1.909091</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>550.668600</td>\n",
       "      <td>1791.921028</td>\n",
       "      <td>0.252898</td>\n",
       "      <td>0.307306</td>\n",
       "      <td>0.235043</td>\n",
       "      <td>0.764849</td>\n",
       "      <td>7.491044</td>\n",
       "      <td>134.585739</td>\n",
       "      <td>287.558926</td>\n",
       "      <td>2.136623</td>\n",
       "      <td>2.136623</td>\n",
       "      <td>5.791846</td>\n",
       "      <td>2457.619564</td>\n",
       "      <td>2.197225</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>66.751648</td>\n",
       "      <td>102.188901</td>\n",
       "      <td>1796.863636</td>\n",
       "      <td>93.119634</td>\n",
       "      <td>104.712282</td>\n",
       "      <td>0.051823</td>\n",
       "      <td>0.056871</td>\n",
       "      <td>1798.5</td>\n",
       "      <td>90.4386</td>\n",
       "      <td>0.050286</td>\n",
       "      <td>68.181818</td>\n",
       "      <td>81.818182</td>\n",
       "      <td>339.0</td>\n",
       "      <td>3.666667</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>649.443955</td>\n",
       "      <td>607.387952</td>\n",
       "      <td>0.113876</td>\n",
       "      <td>1.069241</td>\n",
       "      <td>0.516684</td>\n",
       "      <td>0.483225</td>\n",
       "      <td>6.409168</td>\n",
       "      <td>74.042764</td>\n",
       "      <td>108.904552</td>\n",
       "      <td>1.470833</td>\n",
       "      <td>1.470833</td>\n",
       "      <td>5.110649</td>\n",
       "      <td>640.721703</td>\n",
       "      <td>-inf</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>67.594004</td>\n",
       "      <td>174.841500</td>\n",
       "      <td>1775.095238</td>\n",
       "      <td>182.702464</td>\n",
       "      <td>178.890874</td>\n",
       "      <td>0.102925</td>\n",
       "      <td>0.098497</td>\n",
       "      <td>1753.0</td>\n",
       "      <td>157.1556</td>\n",
       "      <td>0.089650</td>\n",
       "      <td>57.142857</td>\n",
       "      <td>66.666667</td>\n",
       "      <td>710.0</td>\n",
       "      <td>3.500000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2441.599890</td>\n",
       "      <td>1348.070100</td>\n",
       "      <td>1.549378</td>\n",
       "      <td>1.811182</td>\n",
       "      <td>0.644014</td>\n",
       "      <td>0.355577</td>\n",
       "      <td>7.206429</td>\n",
       "      <td>126.494950</td>\n",
       "      <td>225.298488</td>\n",
       "      <td>1.781087</td>\n",
       "      <td>1.781087</td>\n",
       "      <td>5.658951</td>\n",
       "      <td>1605.104665</td>\n",
       "      <td>inf</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>77.219797</td>\n",
       "      <td>50.260157</td>\n",
       "      <td>1552.520000</td>\n",
       "      <td>77.853559</td>\n",
       "      <td>51.060280</td>\n",
       "      <td>0.050147</td>\n",
       "      <td>0.032373</td>\n",
       "      <td>1532.0</td>\n",
       "      <td>87.4734</td>\n",
       "      <td>0.057098</td>\n",
       "      <td>44.000000</td>\n",
       "      <td>72.000000</td>\n",
       "      <td>255.0</td>\n",
       "      <td>4.166667</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>638.332818</td>\n",
       "      <td>357.752436</td>\n",
       "      <td>1.216417</td>\n",
       "      <td>1.784286</td>\n",
       "      <td>0.640060</td>\n",
       "      <td>0.358720</td>\n",
       "      <td>5.879841</td>\n",
       "      <td>36.105070</td>\n",
       "      <td>104.013351</td>\n",
       "      <td>2.880852</td>\n",
       "      <td>2.880852</td>\n",
       "      <td>4.778777</td>\n",
       "      <td>1198.588145</td>\n",
       "      <td>inf</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    ECG_Rate_Mean  ECG_HRV_RMSSD  ECG_HRV_MeanNN  ECG_HRV_SDNN  ECG_HRV_SDSD  \\\n",
       "1       71.462095      72.343562     1679.695652     85.617243     73.787002   \n",
       "2       73.677672      88.491955     1630.791667    101.957356     89.983947   \n",
       "3       70.548238      87.052529     1694.136364     71.585643     89.138736   \n",
       "4       82.995139      70.993803     1442.576923     94.270323     72.305486   \n",
       "5       83.460315      39.682878     1433.407407     50.914906     39.985324   \n",
       "6       83.148286      33.264095     1444.111111     52.106646     33.921743   \n",
       "7       67.454556      91.736943     1764.181818     98.078024     92.570231   \n",
       "8       72.885351      64.847373     1654.652174     44.399640     66.360618   \n",
       "9       66.267315     189.108963     1797.142857    224.503293    190.332977   \n",
       "10      66.751648     102.188901     1796.863636     93.119634    104.712282   \n",
       "11      67.594004     174.841500     1775.095238    182.702464    178.890874   \n",
       "12      77.219797      50.260157     1552.520000     77.853559     51.060280   \n",
       "\n",
       "    ECG_HRV_CVNN  ECG_HRV_CVSD  ECG_HRV_MedianNN  ECG_HRV_MadNN  \\\n",
       "1       0.050972      0.043069            1670.0       102.2994   \n",
       "2       0.062520      0.054263            1632.0       117.8667   \n",
       "3       0.042255      0.051385            1702.0       100.0755   \n",
       "4       0.065349      0.049213            1433.0        74.1300   \n",
       "5       0.035520      0.027684            1432.0        45.9606   \n",
       "6       0.036082      0.023034            1433.0        53.3736   \n",
       "7       0.055594      0.052000            1753.5       116.3841   \n",
       "8       0.026833      0.039191            1652.0        40.0302   \n",
       "9       0.124922      0.105228            1859.0       131.9514   \n",
       "10      0.051823      0.056871            1798.5        90.4386   \n",
       "11      0.102925      0.098497            1753.0       157.1556   \n",
       "12      0.050147      0.032373            1532.0        87.4734   \n",
       "\n",
       "    ECG_HRV_MCVNN  ECG_HRV_pNN50  ECG_HRV_pNN20  ECG_HRV_TINN  ECG_HRV_HTI  \\\n",
       "1        0.061257      52.173913      69.565217         293.0     3.285714   \n",
       "2        0.072222      66.666667      87.500000         367.0     4.800000   \n",
       "3        0.058799      50.000000      81.818182         255.0     4.400000   \n",
       "4        0.051731      46.153846      65.384615         331.0     3.250000   \n",
       "5        0.032095      22.222222      48.148148         261.0     3.000000   \n",
       "6        0.037246      14.814815      51.851852         193.0     3.857143   \n",
       "7        0.066372      54.545455      63.636364         370.0     3.666667   \n",
       "8        0.024231      47.826087      65.217391         181.0     2.555556   \n",
       "9        0.070980      61.904762      80.952381         817.0     1.909091   \n",
       "10       0.050286      68.181818      81.818182         339.0     3.666667   \n",
       "11       0.089650      57.142857      66.666667         710.0     3.500000   \n",
       "12       0.057098      44.000000      72.000000         255.0     4.166667   \n",
       "\n",
       "    ECG_HRV_ULF  ECG_HRV_VLF   ECG_HRV_LF   ECG_HRV_HF  ECG_HRV_VHF  \\\n",
       "1           0.0          0.0   753.876389   853.750478     1.045472   \n",
       "2           0.0          0.0  1457.161396   954.489612     0.771335   \n",
       "3           0.0          0.0   700.703668   766.497395     0.729371   \n",
       "4           0.0          0.0  2957.013208   646.523945     0.408439   \n",
       "5           0.0          0.0   717.195293   519.096512     6.148365   \n",
       "6           0.0          0.0   819.146469    76.652282     0.591176   \n",
       "7           0.0          0.0  1232.913509   567.915557     0.443476   \n",
       "8           0.0          0.0   276.859856   301.029727     0.459125   \n",
       "9           0.0          0.0   550.668600  1791.921028     0.252898   \n",
       "10          0.0          0.0   649.443955   607.387952     0.113876   \n",
       "11          0.0          0.0  2441.599890  1348.070100     1.549378   \n",
       "12          0.0          0.0   638.332818   357.752436     1.216417   \n",
       "\n",
       "    ECG_HRV_LFHF  ECG_HRV_LFn  ECG_HRV_HFn  ECG_HRV_LnHF  ECG_HRV_SD1  \\\n",
       "1       0.883017     0.468633     0.530717      6.749639    52.175289   \n",
       "2       1.526639     0.604024     0.395656      6.861177    63.628259   \n",
       "3       0.914163     0.477341     0.522162      6.641831    63.030605   \n",
       "4       4.573710     0.820493     0.179393      6.471610    51.127700   \n",
       "5       1.381622     0.577247     0.417804      6.252090    28.273894   \n",
       "6      10.686524     0.913828     0.085512      4.339279    23.986294   \n",
       "7       2.170945     0.684468     0.315286      6.341973    65.457038   \n",
       "8       0.919709     0.478707     0.520499      5.707209    46.924043   \n",
       "9       0.307306     0.235043     0.764849      7.491044   134.585739   \n",
       "10      1.069241     0.516684     0.483225      6.409168    74.042764   \n",
       "11      1.811182     0.644014     0.355577      7.206429   126.494950   \n",
       "12      1.784286     0.640060     0.358720      5.879841    36.105070   \n",
       "\n",
       "    ECG_HRV_SD2  ECG_HRV_SD2SD1  ECG_HRV_CSI  ECG_HRV_CVI  \\\n",
       "1    109.262819        2.094149     2.094149     4.960057   \n",
       "2    129.391073        2.033547     2.033547     5.119674   \n",
       "3     79.222166        1.256884     1.256884     4.902518   \n",
       "4    123.124920        2.408184     2.408184     5.003122   \n",
       "5     66.221161        2.342131     2.342131     4.476502   \n",
       "6     69.676846        2.904861     2.904861     4.427172   \n",
       "7    122.286441        1.868194     1.868194     5.107455   \n",
       "8     41.722779        0.889156     0.889156     4.495889   \n",
       "9    287.558926        2.136623     2.136623     5.791846   \n",
       "10   108.904552        1.470833     1.470833     5.110649   \n",
       "11   225.298488        1.781087     1.781087     5.658951   \n",
       "12   104.013351        2.880852     2.880852     4.778777   \n",
       "\n",
       "    ECG_HRV_CSI_Modified  ECG_HRV_SampEn  \n",
       "1             915.250405             inf  \n",
       "2            1052.491457             inf  \n",
       "3             398.292324             inf  \n",
       "4            1186.029971        1.098612  \n",
       "5             620.394518        1.791759  \n",
       "6             809.606150             inf  \n",
       "7             913.819145        1.791759  \n",
       "8             148.392186             inf  \n",
       "9            2457.619564        2.197225  \n",
       "10            640.721703            -inf  \n",
       "11           1605.104665             inf  \n",
       "12           1198.588145             inf  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ECG_epochs_decision = nk.epochs_create(ecg_signals, events=decision_times, sampling_rate=2000, epochs_start= -10, epochs_end=10)\n",
    "display(nk.ecg_analyze(ECG_epochs_decision))\n",
    "\n",
    "# Can look at the reliability of the previouso trial or the trust ratings based on how the analog gauge was behaving"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Electrodermal Analysis (Decision)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.009037407270984975\n",
      "0.014597018541543534\n",
      "-0.005639032724030261\n",
      "0.01848798558587196\n",
      "0.008749812220341113\n",
      "-0.0008508025992673645\n",
      "6.961129247821212e-05\n",
      "0.005926401283878683\n",
      "-0.004823681073774245\n",
      "0.007802192011370872\n",
      "0.029133189170355137\n",
      "-0.00508301204532469\n"
     ]
    }
   ],
   "source": [
    "EDA_epochs_decision = nk.epochs_create(eda_signals, events=decision_times, sampling_rate=2000, epochs_start= -10, epochs_end=10)\n",
    "print(EDA_epochs_decision['1']['EDA_Phasic'].mean())\n",
    "print(EDA_epochs_decision['2']['EDA_Phasic'].mean())\n",
    "print(EDA_epochs_decision['3']['EDA_Phasic'].mean())\n",
    "print(EDA_epochs_decision['4']['EDA_Phasic'].mean())\n",
    "print(EDA_epochs_decision['5']['EDA_Phasic'].mean())\n",
    "print(EDA_epochs_decision['6']['EDA_Phasic'].mean())\n",
    "print(EDA_epochs_decision['7']['EDA_Phasic'].mean())\n",
    "print(EDA_epochs_decision['8']['EDA_Phasic'].mean())\n",
    "print(EDA_epochs_decision['9']['EDA_Phasic'].mean())\n",
    "print(EDA_epochs_decision['10']['EDA_Phasic'].mean())\n",
    "print(EDA_epochs_decision['11']['EDA_Phasic'].mean())\n",
    "print(EDA_epochs_decision['12']['EDA_Phasic'].mean())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Respiration (Decision)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "25.67819914913322\n",
      "21.65552023395268\n",
      "21.61919545334184\n",
      "23.594918462063305\n",
      "20.34338884251946\n",
      "22.337636645434333\n",
      "23.71502894878224\n",
      "22.286145710206807\n",
      "13.549983322877935\n",
      "21.269580242170893\n",
      "20.664102723786254\n",
      "24.11111329404165\n",
      "\n",
      "2.0168252270601164\n",
      "1.9172639683765542\n",
      "1.8601406798310824\n",
      "2.1661668551761415\n",
      "1.6389825929297752\n",
      "1.9686865287790385\n",
      "1.8520935463335977\n",
      "1.982061277295609\n",
      "2.2424877567319355\n",
      "2.289809370628457\n",
      "3.064265833046421\n",
      "3.6103887075988053\n"
     ]
    }
   ],
   "source": [
    "RSP_epochs_decision = nk.epochs_create(rsp_signals, events=decision_times, sampling_rate=2000, epochs_start= -10, epochs_end=10)\n",
    "print(RSP_epochs_decision['1']['RSP_Rate'].mean())\n",
    "print(RSP_epochs_decision['2']['RSP_Rate'].mean())\n",
    "print(RSP_epochs_decision['3']['RSP_Rate'].mean())\n",
    "print(RSP_epochs_decision['4']['RSP_Rate'].mean())\n",
    "print(RSP_epochs_decision['5']['RSP_Rate'].mean())\n",
    "print(RSP_epochs_decision['6']['RSP_Rate'].mean())\n",
    "print(RSP_epochs_decision['7']['RSP_Rate'].mean())\n",
    "print(RSP_epochs_decision['8']['RSP_Rate'].mean())\n",
    "print(RSP_epochs_decision['9']['RSP_Rate'].mean())\n",
    "print(RSP_epochs_decision['10']['RSP_Rate'].mean())\n",
    "print(RSP_epochs_decision['11']['RSP_Rate'].mean())\n",
    "print(RSP_epochs_decision['12']['RSP_Rate'].mean())\n",
    "print()\n",
    "print(RSP_epochs_decision['1']['RSP_Amplitude'].mean())\n",
    "print(RSP_epochs_decision['2']['RSP_Amplitude'].mean())\n",
    "print(RSP_epochs_decision['3']['RSP_Amplitude'].mean())\n",
    "print(RSP_epochs_decision['4']['RSP_Amplitude'].mean())\n",
    "print(RSP_epochs_decision['5']['RSP_Amplitude'].mean())\n",
    "print(RSP_epochs_decision['6']['RSP_Amplitude'].mean())\n",
    "print(RSP_epochs_decision['7']['RSP_Amplitude'].mean())\n",
    "print(RSP_epochs_decision['8']['RSP_Amplitude'].mean())\n",
    "print(RSP_epochs_decision['9']['RSP_Amplitude'].mean())\n",
    "print(RSP_epochs_decision['10']['RSP_Amplitude'].mean())\n",
    "print(RSP_epochs_decision['11']['RSP_Amplitude'].mean())\n",
    "print(RSP_epochs_decision['12']['RSP_Amplitude'].mean())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### FEEDBACK TRIALS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "feedback_times = [889950, 1454440, 2041660, 2662550, 3366460, 3910000, 4488730, 4970770, 5550570, 6246420, 6707680, 7211070]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Electrocardiogram (Feedback)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ECG_Rate_Mean</th>\n",
       "      <th>ECG_HRV_RMSSD</th>\n",
       "      <th>ECG_HRV_MeanNN</th>\n",
       "      <th>ECG_HRV_SDNN</th>\n",
       "      <th>ECG_HRV_SDSD</th>\n",
       "      <th>ECG_HRV_CVNN</th>\n",
       "      <th>ECG_HRV_CVSD</th>\n",
       "      <th>ECG_HRV_MedianNN</th>\n",
       "      <th>ECG_HRV_MadNN</th>\n",
       "      <th>ECG_HRV_MCVNN</th>\n",
       "      <th>ECG_HRV_pNN50</th>\n",
       "      <th>ECG_HRV_pNN20</th>\n",
       "      <th>ECG_HRV_TINN</th>\n",
       "      <th>ECG_HRV_HTI</th>\n",
       "      <th>ECG_HRV_ULF</th>\n",
       "      <th>ECG_HRV_VLF</th>\n",
       "      <th>ECG_HRV_LF</th>\n",
       "      <th>ECG_HRV_HF</th>\n",
       "      <th>ECG_HRV_VHF</th>\n",
       "      <th>ECG_HRV_LFHF</th>\n",
       "      <th>ECG_HRV_LFn</th>\n",
       "      <th>ECG_HRV_HFn</th>\n",
       "      <th>ECG_HRV_LnHF</th>\n",
       "      <th>ECG_HRV_SD1</th>\n",
       "      <th>ECG_HRV_SD2</th>\n",
       "      <th>ECG_HRV_SD2SD1</th>\n",
       "      <th>ECG_HRV_CSI</th>\n",
       "      <th>ECG_HRV_CVI</th>\n",
       "      <th>ECG_HRV_CSI_Modified</th>\n",
       "      <th>ECG_HRV_SampEn</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>90.371979</td>\n",
       "      <td>77.798963</td>\n",
       "      <td>1328.000000</td>\n",
       "      <td>122.956148</td>\n",
       "      <td>78.317788</td>\n",
       "      <td>0.092587</td>\n",
       "      <td>0.058584</td>\n",
       "      <td>1331.0</td>\n",
       "      <td>164.5686</td>\n",
       "      <td>0.123643</td>\n",
       "      <td>51.724138</td>\n",
       "      <td>75.862069</td>\n",
       "      <td>443.0</td>\n",
       "      <td>4.142857</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>14212.952681</td>\n",
       "      <td>1233.814686</td>\n",
       "      <td>26.024343</td>\n",
       "      <td>11.519520</td>\n",
       "      <td>0.918577</td>\n",
       "      <td>0.079741</td>\n",
       "      <td>7.117866</td>\n",
       "      <td>55.379039</td>\n",
       "      <td>164.832007</td>\n",
       "      <td>2.976433</td>\n",
       "      <td>2.976433</td>\n",
       "      <td>5.164507</td>\n",
       "      <td>1962.445798</td>\n",
       "      <td>0.847298</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>75.338779</td>\n",
       "      <td>115.470117</td>\n",
       "      <td>1573.500000</td>\n",
       "      <td>149.130524</td>\n",
       "      <td>116.969473</td>\n",
       "      <td>0.094776</td>\n",
       "      <td>0.073384</td>\n",
       "      <td>1567.5</td>\n",
       "      <td>171.9816</td>\n",
       "      <td>0.109717</td>\n",
       "      <td>62.500000</td>\n",
       "      <td>79.166667</td>\n",
       "      <td>536.0</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1832.730193</td>\n",
       "      <td>1734.580968</td>\n",
       "      <td>2.856335</td>\n",
       "      <td>1.056584</td>\n",
       "      <td>0.513346</td>\n",
       "      <td>0.485854</td>\n",
       "      <td>7.458521</td>\n",
       "      <td>82.709908</td>\n",
       "      <td>194.007467</td>\n",
       "      <td>2.345638</td>\n",
       "      <td>2.345638</td>\n",
       "      <td>5.409496</td>\n",
       "      <td>1820.284811</td>\n",
       "      <td>inf</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>84.361217</td>\n",
       "      <td>94.075461</td>\n",
       "      <td>1421.000000</td>\n",
       "      <td>90.304613</td>\n",
       "      <td>95.567173</td>\n",
       "      <td>0.063550</td>\n",
       "      <td>0.066204</td>\n",
       "      <td>1400.0</td>\n",
       "      <td>68.1996</td>\n",
       "      <td>0.048714</td>\n",
       "      <td>48.148148</td>\n",
       "      <td>70.370370</td>\n",
       "      <td>406.0</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1963.070854</td>\n",
       "      <td>1956.785620</td>\n",
       "      <td>5.786550</td>\n",
       "      <td>1.003212</td>\n",
       "      <td>0.500064</td>\n",
       "      <td>0.498462</td>\n",
       "      <td>7.579058</td>\n",
       "      <td>67.576196</td>\n",
       "      <td>108.366525</td>\n",
       "      <td>1.603620</td>\n",
       "      <td>1.603620</td>\n",
       "      <td>5.068809</td>\n",
       "      <td>695.114818</td>\n",
       "      <td>inf</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>76.790863</td>\n",
       "      <td>91.149831</td>\n",
       "      <td>1577.480000</td>\n",
       "      <td>170.079423</td>\n",
       "      <td>91.761496</td>\n",
       "      <td>0.107817</td>\n",
       "      <td>0.057782</td>\n",
       "      <td>1578.0</td>\n",
       "      <td>225.3552</td>\n",
       "      <td>0.142811</td>\n",
       "      <td>52.000000</td>\n",
       "      <td>84.000000</td>\n",
       "      <td>588.0</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7021.819479</td>\n",
       "      <td>633.802874</td>\n",
       "      <td>1.529609</td>\n",
       "      <td>11.078870</td>\n",
       "      <td>0.917028</td>\n",
       "      <td>0.082773</td>\n",
       "      <td>6.451738</td>\n",
       "      <td>64.885176</td>\n",
       "      <td>231.611601</td>\n",
       "      <td>3.569561</td>\n",
       "      <td>3.569561</td>\n",
       "      <td>5.381026</td>\n",
       "      <td>3307.007076</td>\n",
       "      <td>inf</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>74.315801</td>\n",
       "      <td>102.796549</td>\n",
       "      <td>1618.416667</td>\n",
       "      <td>115.154307</td>\n",
       "      <td>104.367207</td>\n",
       "      <td>0.071152</td>\n",
       "      <td>0.063517</td>\n",
       "      <td>1611.5</td>\n",
       "      <td>134.9166</td>\n",
       "      <td>0.083721</td>\n",
       "      <td>70.833333</td>\n",
       "      <td>87.500000</td>\n",
       "      <td>384.0</td>\n",
       "      <td>4.800000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3380.089434</td>\n",
       "      <td>923.812582</td>\n",
       "      <td>0.432046</td>\n",
       "      <td>3.658848</td>\n",
       "      <td>0.785276</td>\n",
       "      <td>0.214624</td>\n",
       "      <td>6.828509</td>\n",
       "      <td>73.798760</td>\n",
       "      <td>145.171526</td>\n",
       "      <td>1.967127</td>\n",
       "      <td>1.967127</td>\n",
       "      <td>5.234050</td>\n",
       "      <td>1142.283268</td>\n",
       "      <td>inf</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>73.962247</td>\n",
       "      <td>252.098325</td>\n",
       "      <td>1633.000000</td>\n",
       "      <td>218.303339</td>\n",
       "      <td>257.631504</td>\n",
       "      <td>0.133682</td>\n",
       "      <td>0.154377</td>\n",
       "      <td>1601.5</td>\n",
       "      <td>160.1208</td>\n",
       "      <td>0.099982</td>\n",
       "      <td>62.500000</td>\n",
       "      <td>91.666667</td>\n",
       "      <td>1097.0</td>\n",
       "      <td>2.666667</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9864.157430</td>\n",
       "      <td>1217.042338</td>\n",
       "      <td>0.491991</td>\n",
       "      <td>8.105024</td>\n",
       "      <td>0.890131</td>\n",
       "      <td>0.109825</td>\n",
       "      <td>7.104179</td>\n",
       "      <td>182.172984</td>\n",
       "      <td>249.250275</td>\n",
       "      <td>1.368207</td>\n",
       "      <td>1.368207</td>\n",
       "      <td>5.861240</td>\n",
       "      <td>1364.103465</td>\n",
       "      <td>1.791759</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>68.596512</td>\n",
       "      <td>225.474632</td>\n",
       "      <td>1748.590909</td>\n",
       "      <td>267.359871</td>\n",
       "      <td>229.260436</td>\n",
       "      <td>0.152900</td>\n",
       "      <td>0.128946</td>\n",
       "      <td>1744.5</td>\n",
       "      <td>274.2810</td>\n",
       "      <td>0.157226</td>\n",
       "      <td>54.545455</td>\n",
       "      <td>81.818182</td>\n",
       "      <td>1113.0</td>\n",
       "      <td>3.142857</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2892.825829</td>\n",
       "      <td>1161.220543</td>\n",
       "      <td>0.588152</td>\n",
       "      <td>2.491194</td>\n",
       "      <td>0.713462</td>\n",
       "      <td>0.286393</td>\n",
       "      <td>7.057227</td>\n",
       "      <td>162.111609</td>\n",
       "      <td>341.588097</td>\n",
       "      <td>2.107117</td>\n",
       "      <td>2.107117</td>\n",
       "      <td>5.947437</td>\n",
       "      <td>2879.064086</td>\n",
       "      <td>1.098612</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>61.863554</td>\n",
       "      <td>166.673017</td>\n",
       "      <td>1945.000000</td>\n",
       "      <td>139.770489</td>\n",
       "      <td>171.164792</td>\n",
       "      <td>0.071861</td>\n",
       "      <td>0.085693</td>\n",
       "      <td>1947.0</td>\n",
       "      <td>117.1254</td>\n",
       "      <td>0.060157</td>\n",
       "      <td>75.000000</td>\n",
       "      <td>90.000000</td>\n",
       "      <td>560.0</td>\n",
       "      <td>3.333333</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2647.098869</td>\n",
       "      <td>2318.688204</td>\n",
       "      <td>0.083560</td>\n",
       "      <td>1.141636</td>\n",
       "      <td>0.533058</td>\n",
       "      <td>0.466925</td>\n",
       "      <td>7.748757</td>\n",
       "      <td>121.031785</td>\n",
       "      <td>156.278233</td>\n",
       "      <td>1.291216</td>\n",
       "      <td>1.291216</td>\n",
       "      <td>5.480918</td>\n",
       "      <td>807.156102</td>\n",
       "      <td>inf</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>73.309184</td>\n",
       "      <td>144.926627</td>\n",
       "      <td>1625.260870</td>\n",
       "      <td>181.209627</td>\n",
       "      <td>148.121133</td>\n",
       "      <td>0.111496</td>\n",
       "      <td>0.089171</td>\n",
       "      <td>1637.0</td>\n",
       "      <td>201.6336</td>\n",
       "      <td>0.123173</td>\n",
       "      <td>65.217391</td>\n",
       "      <td>86.956522</td>\n",
       "      <td>634.0</td>\n",
       "      <td>2.875000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6056.507967</td>\n",
       "      <td>698.246948</td>\n",
       "      <td>2.064104</td>\n",
       "      <td>8.673877</td>\n",
       "      <td>0.896355</td>\n",
       "      <td>0.103340</td>\n",
       "      <td>6.548573</td>\n",
       "      <td>104.737458</td>\n",
       "      <td>233.888697</td>\n",
       "      <td>2.233095</td>\n",
       "      <td>2.233095</td>\n",
       "      <td>5.593231</td>\n",
       "      <td>2089.182754</td>\n",
       "      <td>1.791759</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>69.952087</td>\n",
       "      <td>121.035307</td>\n",
       "      <td>1712.565217</td>\n",
       "      <td>153.645053</td>\n",
       "      <td>123.245084</td>\n",
       "      <td>0.089716</td>\n",
       "      <td>0.070675</td>\n",
       "      <td>1723.0</td>\n",
       "      <td>142.3296</td>\n",
       "      <td>0.082606</td>\n",
       "      <td>73.913043</td>\n",
       "      <td>91.304348</td>\n",
       "      <td>587.0</td>\n",
       "      <td>3.833333</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2578.523671</td>\n",
       "      <td>734.669688</td>\n",
       "      <td>0.495004</td>\n",
       "      <td>3.509773</td>\n",
       "      <td>0.778143</td>\n",
       "      <td>0.221708</td>\n",
       "      <td>6.599421</td>\n",
       "      <td>87.147434</td>\n",
       "      <td>199.045044</td>\n",
       "      <td>2.284003</td>\n",
       "      <td>2.284003</td>\n",
       "      <td>5.443326</td>\n",
       "      <td>1818.478292</td>\n",
       "      <td>1.098612</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>74.320976</td>\n",
       "      <td>91.781856</td>\n",
       "      <td>1586.347826</td>\n",
       "      <td>107.650364</td>\n",
       "      <td>93.195507</td>\n",
       "      <td>0.067861</td>\n",
       "      <td>0.057857</td>\n",
       "      <td>1570.0</td>\n",
       "      <td>106.7472</td>\n",
       "      <td>0.067992</td>\n",
       "      <td>43.478261</td>\n",
       "      <td>82.608696</td>\n",
       "      <td>369.0</td>\n",
       "      <td>3.833333</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2808.347698</td>\n",
       "      <td>1066.931407</td>\n",
       "      <td>2.803501</td>\n",
       "      <td>2.632173</td>\n",
       "      <td>0.724159</td>\n",
       "      <td>0.275118</td>\n",
       "      <td>6.972542</td>\n",
       "      <td>65.899175</td>\n",
       "      <td>137.238844</td>\n",
       "      <td>2.082558</td>\n",
       "      <td>2.082558</td>\n",
       "      <td>5.160477</td>\n",
       "      <td>1143.231320</td>\n",
       "      <td>-inf</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>68.101490</td>\n",
       "      <td>172.537242</td>\n",
       "      <td>1793.333333</td>\n",
       "      <td>233.879100</td>\n",
       "      <td>172.856070</td>\n",
       "      <td>0.130416</td>\n",
       "      <td>0.096210</td>\n",
       "      <td>1780.0</td>\n",
       "      <td>207.5640</td>\n",
       "      <td>0.116609</td>\n",
       "      <td>66.666667</td>\n",
       "      <td>85.714286</td>\n",
       "      <td>921.0</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7893.323966</td>\n",
       "      <td>1934.908167</td>\n",
       "      <td>0.592246</td>\n",
       "      <td>4.079431</td>\n",
       "      <td>0.803079</td>\n",
       "      <td>0.196861</td>\n",
       "      <td>7.567815</td>\n",
       "      <td>122.227700</td>\n",
       "      <td>307.342246</td>\n",
       "      <td>2.514506</td>\n",
       "      <td>2.514506</td>\n",
       "      <td>5.778912</td>\n",
       "      <td>3091.255305</td>\n",
       "      <td>inf</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    ECG_Rate_Mean  ECG_HRV_RMSSD  ECG_HRV_MeanNN  ECG_HRV_SDNN  ECG_HRV_SDSD  \\\n",
       "1       90.371979      77.798963     1328.000000    122.956148     78.317788   \n",
       "2       75.338779     115.470117     1573.500000    149.130524    116.969473   \n",
       "3       84.361217      94.075461     1421.000000     90.304613     95.567173   \n",
       "4       76.790863      91.149831     1577.480000    170.079423     91.761496   \n",
       "5       74.315801     102.796549     1618.416667    115.154307    104.367207   \n",
       "6       73.962247     252.098325     1633.000000    218.303339    257.631504   \n",
       "7       68.596512     225.474632     1748.590909    267.359871    229.260436   \n",
       "8       61.863554     166.673017     1945.000000    139.770489    171.164792   \n",
       "9       73.309184     144.926627     1625.260870    181.209627    148.121133   \n",
       "10      69.952087     121.035307     1712.565217    153.645053    123.245084   \n",
       "11      74.320976      91.781856     1586.347826    107.650364     93.195507   \n",
       "12      68.101490     172.537242     1793.333333    233.879100    172.856070   \n",
       "\n",
       "    ECG_HRV_CVNN  ECG_HRV_CVSD  ECG_HRV_MedianNN  ECG_HRV_MadNN  \\\n",
       "1       0.092587      0.058584            1331.0       164.5686   \n",
       "2       0.094776      0.073384            1567.5       171.9816   \n",
       "3       0.063550      0.066204            1400.0        68.1996   \n",
       "4       0.107817      0.057782            1578.0       225.3552   \n",
       "5       0.071152      0.063517            1611.5       134.9166   \n",
       "6       0.133682      0.154377            1601.5       160.1208   \n",
       "7       0.152900      0.128946            1744.5       274.2810   \n",
       "8       0.071861      0.085693            1947.0       117.1254   \n",
       "9       0.111496      0.089171            1637.0       201.6336   \n",
       "10      0.089716      0.070675            1723.0       142.3296   \n",
       "11      0.067861      0.057857            1570.0       106.7472   \n",
       "12      0.130416      0.096210            1780.0       207.5640   \n",
       "\n",
       "    ECG_HRV_MCVNN  ECG_HRV_pNN50  ECG_HRV_pNN20  ECG_HRV_TINN  ECG_HRV_HTI  \\\n",
       "1        0.123643      51.724138      75.862069         443.0     4.142857   \n",
       "2        0.109717      62.500000      79.166667         536.0     3.000000   \n",
       "3        0.048714      48.148148      70.370370         406.0     3.000000   \n",
       "4        0.142811      52.000000      84.000000         588.0     5.000000   \n",
       "5        0.083721      70.833333      87.500000         384.0     4.800000   \n",
       "6        0.099982      62.500000      91.666667        1097.0     2.666667   \n",
       "7        0.157226      54.545455      81.818182        1113.0     3.142857   \n",
       "8        0.060157      75.000000      90.000000         560.0     3.333333   \n",
       "9        0.123173      65.217391      86.956522         634.0     2.875000   \n",
       "10       0.082606      73.913043      91.304348         587.0     3.833333   \n",
       "11       0.067992      43.478261      82.608696         369.0     3.833333   \n",
       "12       0.116609      66.666667      85.714286         921.0     3.000000   \n",
       "\n",
       "    ECG_HRV_ULF  ECG_HRV_VLF    ECG_HRV_LF   ECG_HRV_HF  ECG_HRV_VHF  \\\n",
       "1           0.0          0.0  14212.952681  1233.814686    26.024343   \n",
       "2           0.0          0.0   1832.730193  1734.580968     2.856335   \n",
       "3           0.0          0.0   1963.070854  1956.785620     5.786550   \n",
       "4           0.0          0.0   7021.819479   633.802874     1.529609   \n",
       "5           0.0          0.0   3380.089434   923.812582     0.432046   \n",
       "6           0.0          0.0   9864.157430  1217.042338     0.491991   \n",
       "7           0.0          0.0   2892.825829  1161.220543     0.588152   \n",
       "8           0.0          0.0   2647.098869  2318.688204     0.083560   \n",
       "9           0.0          0.0   6056.507967   698.246948     2.064104   \n",
       "10          0.0          0.0   2578.523671   734.669688     0.495004   \n",
       "11          0.0          0.0   2808.347698  1066.931407     2.803501   \n",
       "12          0.0          0.0   7893.323966  1934.908167     0.592246   \n",
       "\n",
       "    ECG_HRV_LFHF  ECG_HRV_LFn  ECG_HRV_HFn  ECG_HRV_LnHF  ECG_HRV_SD1  \\\n",
       "1      11.519520     0.918577     0.079741      7.117866    55.379039   \n",
       "2       1.056584     0.513346     0.485854      7.458521    82.709908   \n",
       "3       1.003212     0.500064     0.498462      7.579058    67.576196   \n",
       "4      11.078870     0.917028     0.082773      6.451738    64.885176   \n",
       "5       3.658848     0.785276     0.214624      6.828509    73.798760   \n",
       "6       8.105024     0.890131     0.109825      7.104179   182.172984   \n",
       "7       2.491194     0.713462     0.286393      7.057227   162.111609   \n",
       "8       1.141636     0.533058     0.466925      7.748757   121.031785   \n",
       "9       8.673877     0.896355     0.103340      6.548573   104.737458   \n",
       "10      3.509773     0.778143     0.221708      6.599421    87.147434   \n",
       "11      2.632173     0.724159     0.275118      6.972542    65.899175   \n",
       "12      4.079431     0.803079     0.196861      7.567815   122.227700   \n",
       "\n",
       "    ECG_HRV_SD2  ECG_HRV_SD2SD1  ECG_HRV_CSI  ECG_HRV_CVI  \\\n",
       "1    164.832007        2.976433     2.976433     5.164507   \n",
       "2    194.007467        2.345638     2.345638     5.409496   \n",
       "3    108.366525        1.603620     1.603620     5.068809   \n",
       "4    231.611601        3.569561     3.569561     5.381026   \n",
       "5    145.171526        1.967127     1.967127     5.234050   \n",
       "6    249.250275        1.368207     1.368207     5.861240   \n",
       "7    341.588097        2.107117     2.107117     5.947437   \n",
       "8    156.278233        1.291216     1.291216     5.480918   \n",
       "9    233.888697        2.233095     2.233095     5.593231   \n",
       "10   199.045044        2.284003     2.284003     5.443326   \n",
       "11   137.238844        2.082558     2.082558     5.160477   \n",
       "12   307.342246        2.514506     2.514506     5.778912   \n",
       "\n",
       "    ECG_HRV_CSI_Modified  ECG_HRV_SampEn  \n",
       "1            1962.445798        0.847298  \n",
       "2            1820.284811             inf  \n",
       "3             695.114818             inf  \n",
       "4            3307.007076             inf  \n",
       "5            1142.283268             inf  \n",
       "6            1364.103465        1.791759  \n",
       "7            2879.064086        1.098612  \n",
       "8             807.156102             inf  \n",
       "9            2089.182754        1.791759  \n",
       "10           1818.478292        1.098612  \n",
       "11           1143.231320            -inf  \n",
       "12           3091.255305             inf  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ECG_epochs_feedback = nk.epochs_create(ecg_signals, events=feedback_times, sampling_rate=2000, epochs_start= -5, epochs_end=15)\n",
    "display(nk.ecg_analyze(ECG_epochs_feedback))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Electrodermal Analysis (Feedback)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.014377203129660614\n",
      "-0.09716354586737626\n",
      "-0.06584013225746116\n",
      "0.048655738926344824\n",
      "-0.005654629991411328\n",
      "0.03759126134332303\n",
      "-0.02077857176888849\n",
      "0.013875307541767891\n",
      "0.06987819802346754\n",
      "0.02268804411743658\n",
      "-0.1527461351184194\n",
      "-0.021894736584135495\n"
     ]
    }
   ],
   "source": [
    "EDA_epochs_feedback = nk.epochs_create(eda_signals, events=feedback_times, sampling_rate=2000, epochs_start= -5, epochs_end=15)\n",
    "print(EDA_epochs_feedback['1']['EDA_Phasic'].mean())\n",
    "print(EDA_epochs_feedback['2']['EDA_Phasic'].mean())\n",
    "print(EDA_epochs_feedback['3']['EDA_Phasic'].mean())\n",
    "print(EDA_epochs_feedback['4']['EDA_Phasic'].mean())\n",
    "print(EDA_epochs_feedback['5']['EDA_Phasic'].mean())\n",
    "print(EDA_epochs_feedback['6']['EDA_Phasic'].mean())\n",
    "print(EDA_epochs_feedback['7']['EDA_Phasic'].mean())\n",
    "print(EDA_epochs_feedback['8']['EDA_Phasic'].mean())\n",
    "print(EDA_epochs_feedback['9']['EDA_Phasic'].mean())\n",
    "print(EDA_epochs_feedback['10']['EDA_Phasic'].mean())\n",
    "print(EDA_epochs_feedback['11']['EDA_Phasic'].mean())\n",
    "print(EDA_epochs_feedback['12']['EDA_Phasic'].mean())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Respiration (Feedback)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "18.599501206489002\n",
      "20.274447704690857\n",
      "20.284657846427226\n",
      "18.59246785311726\n",
      "21.876864216987578\n",
      "10.505123680425411\n",
      "21.139070361803142\n",
      "21.076545159273003\n",
      "16.00914285344727\n",
      "19.50434131206495\n",
      "17.969257959647077\n",
      "21.520384778329163\n",
      "\n",
      "1.5095695156322784\n",
      "1.76224572126012\n",
      "1.3220507555278473\n",
      "2.059772950220466\n",
      "1.4316008258767632\n",
      "1.5989116653262463\n",
      "2.135844565243457\n",
      "0.8593674623840154\n",
      "1.6544182231685918\n",
      "1.6816285486078872\n",
      "1.747964209365681\n",
      "2.017370845329485\n"
     ]
    }
   ],
   "source": [
    "RSP_epochs_feedback = nk.epochs_create(rsp_signals, events=feedback_times, sampling_rate=2000, epochs_start= -5, epochs_end=15)\n",
    "print(RSP_epochs_feedback['1']['RSP_Rate'].mean())\n",
    "print(RSP_epochs_feedback['2']['RSP_Rate'].mean())\n",
    "print(RSP_epochs_feedback['3']['RSP_Rate'].mean())\n",
    "print(RSP_epochs_feedback['4']['RSP_Rate'].mean())\n",
    "print(RSP_epochs_feedback['5']['RSP_Rate'].mean())\n",
    "print(RSP_epochs_feedback['6']['RSP_Rate'].mean())\n",
    "print(RSP_epochs_feedback['7']['RSP_Rate'].mean())\n",
    "print(RSP_epochs_feedback['8']['RSP_Rate'].mean())\n",
    "print(RSP_epochs_feedback['9']['RSP_Rate'].mean())\n",
    "print(RSP_epochs_feedback['10']['RSP_Rate'].mean())\n",
    "print(RSP_epochs_feedback['11']['RSP_Rate'].mean())\n",
    "print(RSP_epochs_feedback['12']['RSP_Rate'].mean())\n",
    "print()\n",
    "print(RSP_epochs_feedback['1']['RSP_Amplitude'].mean())\n",
    "print(RSP_epochs_feedback['2']['RSP_Amplitude'].mean())\n",
    "print(RSP_epochs_feedback['3']['RSP_Amplitude'].mean())\n",
    "print(RSP_epochs_feedback['4']['RSP_Amplitude'].mean())\n",
    "print(RSP_epochs_feedback['5']['RSP_Amplitude'].mean())\n",
    "print(RSP_epochs_feedback['6']['RSP_Amplitude'].mean())\n",
    "print(RSP_epochs_feedback['7']['RSP_Amplitude'].mean())\n",
    "print(RSP_epochs_feedback['8']['RSP_Amplitude'].mean())\n",
    "print(RSP_epochs_feedback['9']['RSP_Amplitude'].mean())\n",
    "print(RSP_epochs_feedback['10']['RSP_Amplitude'].mean())\n",
    "print(RSP_epochs_feedback['11']['RSP_Amplitude'].mean())\n",
    "print(RSP_epochs_feedback['12']['RSP_Amplitude'].mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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